rev1:\\n date_tags = 'reversal1'\\n\\n # add date to metadata\\n flow.metadata.add_date(mouse, int(i), tags=[date_tags],\\n overwrite=overwrite, update=update)\\n\\n for k in run_nums:\\n # get correct run tags\\n # run_type_options = ['naive', 'learning', 'reversal1', 'reversal2']\\n first_run = True\\n if date_tags in ['learning_start', 'learning']:\\n run_tag1 = 'learning'\\n elif date_tags in ['reversal1_start', 'reversal1']:\\n run_tag1 = 'reversal1'\\n elif date_tags in ['naive']:\\n run_tag1 = 'naive'\\n\\n\\n # set run tags based on run number\\n if k in [1, 5]:\\n all_run_tags = ['hungry', run_tag1]\\n elif k in [2, 3, 4]:\\n all_run_tags = ['hungry', run_tag1]\\n elif k in [6, 7, 8, 9, 10, 11]:\\n all_run_tags = ['sated', run_tag1]\\n elif k in [7]:\\n all_run_tags = ['sated', run_tag1]\\n else:\\n continue\\n\\n # set run type based on run number\\n if k in [2, 3, 4, 7]:\\n run_type = 'training'\\n elif k in [1]:\\n run_type = 'running'\\n elif k in [1, 6, 8, 9, 10, 11]:\\n run_type = 'spontaneous'\\n else:\\n run_type = 'other'\\n\\n flow.metadata.add_run(mouse, int(i), int(k), run_type,\\n tags=all_run_tags,\\n overwrite=overwrite, update=update)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5797e18034c36d66ea0c02ef1671117c\",\n \"score\": \"0.5274835\",\n \"text\": \"def _full_srt_process(path1, lang1, path2, lang2, result_name=None):\\n # -- Check both files existence and read all content\\n pu.display_message('#1 Searching for files and '\\n 'scanning all lines in files ...')\\n lines_1 = get_file_content_safe(path1)\\n lines_2 = get_file_content_safe(path2)\\n pu.display_message('#1 ... Files found and all lines '\\n 'in both files were read!\\\\n')\\n\\n # -- Create dictionaries from all scanned lines in files\\n pu.display_message('#2 Creating dictionaries from scanned lines ...')\\n dict_1 = _create_dict(lines_1)\\n dict_2 = _create_dict(lines_2)\\n pu.display_message('#2 ... Created dictionaries from scanned lines!\\\\n')\\n\\n # -- Create xml file\\n # -- Create full filepath\\n pu.display_message(\\\"#3 Generating tmx file ...\\\")\\n if result_name is None:\\n xml_filename = str(time.time()) + '.tmx'\\n else:\\n xml_filename = result_name\\n full_file_path = get_output_file_name(xml_filename)\\n pu.display_message(\\\"File will be generated in: [\\\" + full_file_path + \\\"]\\\")\\n # -- Create root\\n xml_string = create_tmx_from_dicts(dict_1, lang1, dict_2, lang2)\\n try:\\n with open(full_file_path, mode='w') as result_file:\\n result_file.write(xml_string)\\n except Exception as e:\\n run_error(ERR_CODE_CREATING_XML, full_file_path, e)\\n return None\\n pu.display_message(\\\"#3 ... File \\\" + full_file_path + \\\" generated!\\\\n\\\")\\n return full_file_path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2c2c5e9c151ee5e4514ddc4d1b6e0ee\",\n \"score\": \"0.52708685\",\n \"text\": \"def extract_ts(indir, outdir):\\n\\n uuid = \\\"%x\\\" % int(time.time())\\n\\n x1_file = outdir + fp(\\\"data_x_size_\\\" + uuid + \\\".csv\\\")\\n x2_file = outdir + fp(\\\"data_x_dir_\\\" + uuid + \\\".csv\\\")\\n y_file = outdir + fp(\\\"data_y_\\\" + uuid + \\\".csv\\\")\\n log_file = outdir + fp(\\\"log_\\\" + uuid + \\\".txt\\\")\\n\\n with x1_file.open(mode='w') as x1f, x2_file.open(mode='w') as x2f, \\\\\\n y_file.open(mode='w') as yf, log_file.open(mode='w') as logf:\\n\\n label = 0\\n\\n for dir in indir.ls(order=SortOrder.ALPHA):\\n\\n label += 1\\n\\n log.info(\\\"Calling %s label %d\\\", dir.basename(), label)\\n logf.write(\\\"Label: %s -> %d\\\\n\\\" % (dir.basename(), label))\\n\\n for f in dir.find_files():\\n if f.is_file() and f.ext() not in [\\\"json\\\", \\\"csv\\\", \\\"txt\\\"]:\\n sizes, dirs = _extract_ts_file(f)\\n if sizes and dirs:\\n x1f.write(\\\",\\\".join(sizes) + \\\"\\\\n\\\")\\n x2f.write(\\\",\\\".join(dirs) + \\\"\\\\n\\\")\\n yf.write(str(label) + \\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0dc9e07aff5b486fb570508efe3bf9c\",\n \"score\": \"0.52641565\",\n \"text\": \"def main():\\n supportedFileTypes = ['jpg', 'jpeg', 'gif', 'png', 'mp4', 'avi', \\n 'ppm', 'mov', 'bmp', 'tiff']\\n for fn in os.listdir(os.getcwd()): #filename\\n if not os.path.isdir(fn) and fn.rsplit('.',1)[1] in supportedFileTypes:\\n try:\\n timeStamp = getEXIF(fn, '')['Exif.Image.DateTime']\\n except:\\n timeStamp = datetime.datetime.fromtimestamp(os.path.getctime(fn))\\n \\n target_folder = dateToString(timeStamp)\\n \\n if not os.path.exists(target_folder): #Make target folder as needed \\n os.mkdir(target_folder) \\n \\n target_dir = os.path.join(target_folder, os.path.basename(fn))\\n os.rename(os.path.join(os.getcwd(),fn), target_dir) # move file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d9661e97b499867433d760e1f64818e\",\n \"score\": \"0.5260936\",\n \"text\": \"def atsat_bright_temp_457(meta_path, outdir = False):\\n \\n OutList = []\\n metadata = grab_meta(meta_path)\\n spacecraft = getattr(metadata, \\\"SPACECRAFT_ID\\\")\\n\\n if \\\"4\\\" in spacecraft or \\\"5\\\" in spacecraft:\\n band_nums = [\\\"6\\\"]\\n elif \\\"7\\\" in spacecraft:\\n band_nums = [\\\"6_VCID_1\\\", \\\"6_VCID_2\\\"]\\n else:\\n print(\\\"Enter the MTL file corresponding to a Landsat 4, 5, or 7 dataset\\\")\\n\\n #These lists will be used to parse the meta data text file and locate relevant information\\n #metadata format was changed August 29, 2012. This tool can process either the new or old format\\n\\n f = open(meta_path)\\n MText = f.read()\\n\\n metadata = grab_meta(meta_path)\\n oldMeta = []\\n newMeta = []\\n \\n #the presence of a PRODUCT_CREATION_TIME category is used to identify old metadata\\n #if this is not present, the meta data is considered new.\\n #Band6length refers to the length of the Band 6 name string. In the new metadata this string is longer\\n if \\\"PRODUCT_CREATION_TIME\\\" in MText:\\n Meta = oldMeta\\n Band6length = 2\\n else:\\n Meta = newMeta\\n Band6length = 8\\n\\n #The tilename is located using the newMeta/oldMeta indixes and the date of capture is recorded\\n if Meta == newMeta:\\n TileName = getattr(metadata, \\\"LANDSAT_SCENE_ID\\\")\\n year = TileName[9:13]\\n jday = TileName[13:16]\\n date = getattr(metadata, \\\"DATE_ACQUIRED\\\")\\n \\n elif Meta == oldMeta:\\n TileName = getattr(metadata, \\\"BAND1_FILE_NAME\\\")\\n year = TileName[13:17]\\n jday = TileName[17:20]\\n date = getattr(metadata, \\\"ACQUISITION_DATE\\\")\\n\\n #the spacecraft from which the imagery was capture is identified\\n #this info determines the solar exoatmospheric irradiance (ESun) for each band\\n \\n #Calculating values for each band\\n for band_num in band_nums:\\n print(\\\"Processing Band {0}\\\".format(band_num))\\n pathname = meta_path.replace(\\\"MTL.txt\\\", \\\"B{0}.tif\\\".format(band_num))\\n Oraster = arcpy.Raster(pathname)\\n\\n #using the oldMeta/newMeta indixes to pull the min/max for radiance/Digital numbers\\n if Meta == newMeta:\\n LMax = getattr(metadata, \\\"RADIANCE_MAXIMUM_BAND_\\\" + band_num)\\n LMin = getattr(metadata, \\\"RADIANCE_MINIMUM_BAND_\\\" + band_num) \\n QCalMax = getattr(metadata, \\\"QUANTIZE_CAL_MAX_BAND_\\\" + band_num)\\n QCalMin = getattr(metadata, \\\"QUANTIZE_CAL_MIN_BAND_\\\" + band_num)\\n \\n elif Meta == oldMeta:\\n LMax = getattr(metadata, \\\"LMAX_BAND\\\" + band_num)\\n LMin = getattr(metadata, \\\"LMIN_BAND\\\" + band_num) \\n QCalMax = getattr(metadata, \\\"QCALMAX_BAND\\\" + band_num)\\n QCalMin = getattr(metadata, \\\"QCALMIN_BAND\\\" + band_num)\\n\\n Radraster = (((LMax - LMin)/(QCalMax-QCalMin)) * (Oraster - QCalMin)) + LMin\\n Oraster = 0\\n\\n #Calculating temperature for band 6 if present\\n if \\\"4\\\" in spacecraft or \\\"5\\\" in spacecraft:\\n Refraster = 1260.56/(arcpy.sa.Ln((607.76/Radraster) + 1.0))\\n BandPath = \\\"{0}\\\\\\\\{1}_B{2}_Temp.tif\\\".format(outdir,TileName,band_num)\\n \\n if \\\"7\\\" in spacecraft:\\n Refraster = 1282.71/(arcpy.sa.Ln((666.09/Radraster) + 1.0))\\n BandPath = \\\"{0}\\\\\\\\{1}_B{2}_Temp.tif\\\".format(outdir,TileName,band_num)\\n\\n Refraster.save(BandPath)\\n OutList.append(arcpy.Raster(BandPath))\\n\\n del Refraster,Radraster\\n\\n arcpy.AddMessage(\\\"Temperature Calculated for Band {0}\\\".format(band_num))\\n print(\\\"Temperature Calculated for Band {0}\\\".format(band_num))\\n \\n f.close()\\n return OutList\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa38f18df1ceaa7f995c39d5407842fc\",\n \"score\": \"0.5259163\",\n \"text\": \"def create_shot_dirs(pth, tp):\\n print(\\\"in create_shot_dirs()\\\")\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9094aa47a1a0026039e119b2d59a66ce\",\n \"score\": \"0.52511007\",\n \"text\": \"def set(info,s,audiodir,savedir, sourcedir):\\n segdir = savedir + '/segments'\\n textdir= savedir + '/text'\\n utt2spkdir=savedir + '/utt2spk'\\n wavdir=savedir + '/wav.scp'\\n files=[]\\n\\n info=info.split()\\n info[0]=info[0].split('/')\\n if s == 'pretrain':\\n info[0] = 'LRS2_' + info[0][0] + '_' + info[0][1] + 'p'\\n else:\\n info[0]='LRS2_' + info[0][0] + '_' + info[0][1] + 'm'\\n name = info[0]\\n name = name.split('_')\\n f = name[1] + '/' + name[2][:-1]\\n\\n textfile = sourcedir + f + '.txt'\\n mp3dir = audiodir + '/' + f + '.wav'\\n exist = os.path.exists(mp3dir)\\n if exist == 1:\\n with open(textfile) as filelists:\\n text = filelists.readlines()\\n text = text[0].split(':')[1]\\n splitname = f.split('/')\\n with contextlib.closing(wave.open(mp3dir, 'r')) as f:\\n frames = f.getnframes()\\n rate = f.getframerate()\\n duration = frames / float(rate)\\n starttime = round(0.00, 2)\\n endtime = round(duration, 2)\\n startint = int(starttime * 100)\\n endint = int(endtime * 100)\\n startstr = str(startint).zfill(7)\\n endstr = str(endint).zfill(7)\\n if s == 'pretrain':\\n title = 'LRS2_' + splitname[0] + '_' + splitname[1] + 'p_' + startstr + '_' + endstr\\n recoid = 'LRS2_' + splitname[0] + '_' + splitname[1] + 'p'\\n else:\\n title = 'LRS2_' + splitname[0] + '_' + splitname[1] + 'm_' + startstr + '_' + endstr\\n recoid = 'LRS2_' + splitname[0] + '_' + splitname[1] + 'm'\\n\\n if duration < 100:\\n with open(textdir, \\\"a\\\") as textprocess:\\n textprocess.writelines(title + '' + text)\\n textprocess.close()\\n\\n with open(segdir, \\\"a\\\") as seg:\\n seg.writelines(title + ' ' + recoid + ' ' + str('0') + ' ' + str(endtime) + '\\\\n')\\n seg.close()\\n with open(utt2spkdir, \\\"a\\\") as utt:\\n if s == 'pretrain':\\n utt.writelines(title + ' LRS2_' + splitname[0] + '_' + splitname[1] + 'p\\\\n')\\n else:\\n utt.writelines(title + ' LRS2_' + splitname[0] + '_' + splitname[1] + 'm\\\\n')\\n utt.close()\\n\\n with open(wavdir, \\\"a\\\") as wav:\\n wav.writelines(title + ' ' + mp3dir + '\\\\n')\\n wav.close()\\n\\n\\n\\n else:\\n pass\\n\\n else:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca1f72296acecb0f04f343498a7d911f\",\n \"score\": \"0.5244137\",\n \"text\": \"def load_txts(dir):\\n utterences = dict()\\n txts = []\\n dir = os.path.expanduser(dir)\\n for target in sorted(os.listdir(dir)):\\n d = os.path.join(dir, target)\\n if not os.path.isdir(d):\\n continue\\n\\n for root, _, fnames in sorted(os.walk(d)):\\n for fname in fnames:\\n if fname.endswith(\\\".txt\\\"):\\n with open(os.path.join(root, fname), \\\"r\\\") as f:\\n fname_no_ext = os.path.basename(fname).rsplit(\\\".\\\", 1)[0]\\n utterences[fname_no_ext] = f.readline()\\n #utterences += f.readlines()\\n #utterences = dict([tuple(u.strip().split(\\\" \\\", 1)) for u in utterences])\\n return utterences\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b79cc877feb75de5552deb2e65adbbb5\",\n \"score\": \"0.5233016\",\n \"text\": \"def import_tdms_dir(path=None, show=True):\\n path = load_dir(path)\\n names = os.listdir(path)\\n data_files = []\\n if show:\\n print(path)\\n i = 0\\n with tqdm(total=len(names), file=sys.stdout) as pbar:\\n for name in names:\\n pbar.set_description('Imported: %d' % (1 + i))\\n pbar.update(1)\\n sleep(0.01)\\n if name[-5:] == '.tdms':\\n stamp = name[:-5]\\n file_path = os.path.join(path, name)\\n data_file = _import_file(file_path)\\n data_file._import_meta(stamp)\\n data_file._set_temp()\\n data_files.append(data_file)\\n\\n data_files.sort(key=lambda d: int(str(d.date) + str(d.time)))\\n return data_files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1e4fbcd986465cb142a1dc952e547bd\",\n \"score\": \"0.5219041\",\n \"text\": \"def main(SOURCE, TARGET_TRAIN, TARGET_TEST, composers, tt_split, chordwise, sample, bptt):\\n \\n TARGET_TRAIN.mkdir(parents=True, exist_ok=True)\\n TARGET_TEST.mkdir(parents=True, exist_ok=True) \\n print(\\\"Clearing old files\\\")\\n shutil.rmtree(TARGET_TRAIN)\\n shutil.rmtree(TARGET_TEST)\\n\\n \\n print(\\\"Creating new dataset\\\")\\n for c in tqdm(composers):\\n TRAIN=TARGET_TRAIN/c\\n TRAIN.mkdir(parents=True, exist_ok=True)\\n TEST=TARGET_TEST/c\\n TEST.mkdir(parents=True, exist_ok=True) \\n \\n files=os.listdir(SOURCE/c)\\n for f in files:\\n if random.random()>sample:\\n continue\\n file=open(SOURCE/c/f)\\n text=(file.read()).split(\\\" \\\")[1:-1]\\n file.close()\\n\\n for i in range(len(text)//bptt):\\n dest=TEST if random.random()<tt_split else TRAIN\\n fname=str(i)+\\\"_\\\"+f\\n file=open(dest/fname, \\\"w\\\")\\n file.write(\\\" \\\".join(text[i*bptt:(i+1)*bptt]))\\n file.close()\\n \\n if chordwise:\\n print(\\\"Chordwise encoding: removing duration marks and expanding rests\\\")\\n remove_duration(TRAIN)\\n remove_duration(TEST)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ab57604f23118efc9364bff7b4aad00\",\n \"score\": \"0.52020735\",\n \"text\": \"def create_bash_scripts(fname_in, fname_tet, fname_rename):\\n\\n df = pd.read_csv(fname_in, sep=\\\"\\\\t\\\", parse_dates=[\\\"issue_date\\\"])\\n\\n with open(fname_tet, mode=\\\"w\\\") as f_tet, open(fname_rename, mode=\\\"w\\\") as f_rename:\\n # remove potential existing data of this newspaper\\n trg_dir = \\\"/\\\".join(df.loc[0, \\\"canonical_dir_tif\\\"].split(\\\"/\\\")[:2])\\n # os.system(\\\"rm -rf \\\" + trg_dir)\\n f_tet.write(\\\"rm -rf \\\" + trg_dir + \\\"\\\\n\\\")\\n\\n # Restrict to period for which scanned documents with OCR exists.\\n # Starting from 22 June 1999, documents are fully digital\\n for i, row in df[df.issue_date < \\\"1999-06-22\\\"].iterrows():\\n\\n mkdir = \\\"mkdir -p \\\" + row[\\\"canonical_dir_tif\\\"]\\n # os.system(mkdir + \\\"\\\\n\\\")\\n f_tet.write(mkdir + \\\"\\\\n\\\")\\n\\n # create tif files from pdf if document has a length of 1 at minimum\\n if row[\\\"page_count_full\\\"] > 0:\\n tet = \\\"tet --targetdir {} --image --lastpage {} {}\\\".format(\\n row[\\\"canonical_dir_tif\\\"], row[\\\"page_count_full\\\"], row[\\\"pdf_path\\\"]\\n )\\n # os.system(tet + \\\"\\\\n\\\")\\n f_tet.write(tet + \\\"\\\\n\\\")\\n\\n # rename the created files according to the canonical scheme after each day (change of directory)\\n stem = \\\"-\\\".join(row[\\\"canonical_dir_tif\\\"].split(\\\"/\\\")[1:])\\n try:\\n if row[\\\"canonical_dir_tif\\\"] != df.loc[i + 1, \\\"canonical_dir_tif\\\"]:\\n # os.system(rename.format(row[\\\"canonical_dir_tif\\\"], stem) + \\\"\\\\n\\\")\\n rename_issue = RENAME.format(row[\\\"canonical_dir_tif\\\"], stem)\\n f_rename.write(rename_issue + \\\"\\\\n\\\")\\n\\n except KeyError:\\n # do also the renaming after the last article\\n # os.system(rename.format(row[\\\"canonical_dir_tif\\\"], stem) + \\\"\\\\n\\\")\\n rename_issue = RENAME.format(row[\\\"canonical_dir_tif\\\"], stem) + \\\"\\\\n\\\"\\n f_rename.write(rename_issue + \\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3b8b8250f3329db4bf823a589d64c3e\",\n \"score\": \"0.5182959\",\n \"text\": \"def extendSALT2_temp0( salt2dir = 'models/SALT2/SALT2.Guy10_UV2IR', \\n salt2srcdir = 'models/SALT2/SALT2.Guy10_LAMOPEN', \\n tailsedfile = 'snsed/Hsiao07.extrap.dat',\\n wjoinblue = 2800, wjoinred = 8500 ,\\n wmin = MINWAVE, wmax = MAXWAVE ):\\n import shutil\\n sndataroot = os.environ['SNDATA_ROOT']\\n \\n salt2dir = os.path.join( sndataroot, salt2dir ) \\n salt2srcdir = os.path.join( sndataroot, salt2srcdir ) \\n \\n temp0fileIN = os.path.join( salt2srcdir, 'salt2_template_0.dat' ) \\n temp0fileOUT = os.path.join( salt2dir, 'salt2_template_0.dat' ) \\n temp0dat = getsed( sedfile=temp0fileIN ) \\n\\n tailsedfile = os.path.join( sndataroot, tailsedfile ) \\n\\n taildat = getsed( sedfile=tailsedfile ) \\n \\n dt,wt,ft = loadtxt( tailsedfile, unpack=True ) \\n taildays = unique( dt ) \\n\\n # build up modified template from day -20 to +50\\n outlines = []\\n for i in range( 71 ) : \\n thisday = i - 20\\n\\n # get the tail SED for this day\\n it = where( taildays == thisday )[0]\\n dt = taildat[0][it]\\n wt = taildat[1][it]\\n ft = taildat[2][it]\\n\\n #if thisday > 50 : \\n # d0new = dt\\n # w0new = wt\\n # f0new = ft * (bluescale+redscale)/2.\\n #else : \\n\\n # get the SALT2 template SED for this day\\n d0 = temp0dat[0][i]\\n w0 = temp0dat[1][i]\\n f0 = temp0dat[2][i]\\n print( 'splicing tail onto template for day : %i'%thisday )\\n\\n i0blue = argmin( abs(w0-wjoinblue) )\\n itblue = argmin( abs( wt-wjoinblue))\\n\\n i0red = argmin( abs(w0-wjoinred) )\\n itred = argmin( abs( wt-wjoinred))\\n\\n itmin = argmin( abs( wt-wmin))\\n itmax = argmin( abs( wt-wmax))\\n\\n bluescale = f0[i0blue]/ft[itblue] \\n redscale = f0[i0red]/ft[itred] \\n\\n d0new = dt.tolist()[itmin:itblue] + d0.tolist()[i0blue:i0red-1] + dt.tolist()[itred:itmax+1]\\n w0new = wt.tolist()[itmin:itblue] + w0.tolist()[i0blue:i0red-1] + wt.tolist()[itred:itmax+1]\\n f0new = (bluescale*ft).tolist()[itmin:itblue] + f0.tolist()[i0blue:i0red-1] + (redscale*ft).tolist()[itred:itmax+1]\\n\\n # plot it\\n clf()\\n plot( w0, f0, ls='-',color='b', lw=1, label='Input SALT2 Model')\\n plot( wt, (bluescale+redscale)/2. * ft, ls=':',color='r', lw=1, label='SED model')\\n plot( w0new, f0new, ls='--',color='k', lw=2, label='Extrapolated SALT2 model')\\n legend()\\n draw()\\n #raw_input('return to continue')\\n\\n # append to the list of output data lines\\n for j in range( len( d0new ) ) :\\n outlines.append( \\\"%6.2f %12i %12.7e\\\\n\\\"%(\\n d0new[j], w0new[j], f0new[j] ) )\\n\\n # write it out to the new template sed .dat file\\n fout = open( temp0fileOUT, 'w' ) \\n fout.writelines( outlines ) \\n fout.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a36632e4b0cea50ff67cb5c6d40cde9c\",\n \"score\": \"0.5155249\",\n \"text\": \"def setup_file_structure(self):\\n\\n# Create the run directory for this DA job, including I/O structure\\n\\n filtertime = self['time.start'].strftime('%Y%m%d')\\n\\n self['dir.exec'] = os.path.join(self['dir.da_run'], 'exec')\\n self['dir.input'] = os.path.join(self['dir.da_run'], 'input')\\n self['dir.output'] = os.path.join(self['dir.da_run'], 'output', filtertime)\\n self['dir.analysis'] = os.path.join(self['dir.da_run'], 'analysis')\\n self['dir.jobs'] = os.path.join(self['dir.da_run'], 'jobs')\\n self['dir.restart'] = os.path.join(self['dir.da_run'], 'restart')\\n\\n create_dirs(self['dir.da_run'])\\n create_dirs(os.path.join(self['dir.exec']))\\n create_dirs(os.path.join(self['dir.input']))\\n create_dirs(os.path.join(self['dir.output']))\\n create_dirs(os.path.join(self['dir.analysis']))\\n create_dirs(os.path.join(self['dir.jobs']))\\n create_dirs(os.path.join(self['dir.restart']))\\n\\n logging.info('Succesfully created the file structure for the assimilation job')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e199bcd696b376ac99e9fe95556583b\",\n \"score\": \"0.5153383\",\n \"text\": \"def main():\\n\\n\\tif len(sys.argv)<2:\\n\\t\\tprint \\\"Error in arguments. Required arguments:\\\"\\n\\t\\tprint \\\"\\\\t folderName/\\\"\\n\\t\\tsys.exit(-1)\\n\\n\\tfolderName = sys.argv[1]\\n\\textMuscle = \\\"GL\\\"\\n\\tflexMuscle = \\\"TA\\\"\\n\\n\\teesAmplitudes = [1,235,250,270]\\n\\teesLowAmplitudes = [\\\"%0.15_0_0\\\"] # change tp [\\\"0.15_0_0\\\"]\\n\\teesFrequency = 59\\n\\teesHighFrequency = 600\\n\\n\\tdelays = [2,16]\\n\\n\\tweights_1 = 0.018*np.arange(1,2.01,0.25) #in templateFrwSimHuman.txt: Iaf->Mn\\n\\tweights_2 = 0.011*np.arange(1,4.01,0.25) #in templateFrwSimHuman.txt: Iaf->IaInt\\n\\tweights_3 = 0.011*np.arange(0.5,3.01,0.25) #in templateFrwSimHuman.txt: IIf->IIExInt & IIf->IaInt\\n\\n\\tniceTrials = np.zeros([weights_1.size,weights_2.size,weights_3.size],dtype=bool)\\n\\n\\tiD, delay = 0,2\\n\\ti,eesAmplitude = 3,250\\n\\ti,eesAmplitude = 4,270\\n\\n\\tfor i1,w1 in enumerate(weights_1):\\n\\t\\tfor i2,w2 in enumerate(weights_2):\\n\\t\\t\\tfor i3,w3 in enumerate(weights_3):\\n\\n\\t\\t\\t\\tpattern = \\\"*\\\"+str(eesFrequency)+\\\"Hz_Delay_\\\"+str(delay)+\\\\\\n\\t\\t\\t\\t\\t\\\"ms_human_Tonic_w1_\\\"+str(w1)+\\\"_w2_\\\"+str(w2)+\\\"_w3_\\\"+str(w3)+\\\"_\\\"+str(eesAmplitude)+\\\"_sysHuman.p\\\"\\n\\t\\t\\t\\tmeanFr = load_results(pattern,\\\"../../results/\\\"+folderName)\\n\\t\\t\\t\\tif meanFr:\\n\\t\\t\\t\\t\\talternationMn,meanFrMn,_ = ppt.computeAlternationScore(meanFr[flexMuscle]['Mn'],meanFr[extMuscle]['Mn'])\\n\\n\\t\\t\\t\\t# (the meanfr could be lower)\\n\\t\\t\\t\\tif np.mean(meanFr[flexMuscle]['Mn'])>5 and np.mean(meanFr[extMuscle]['Mn'])>5 and alternationMn>0.7:\\n\\t\\t\\t\\t\\tniceTrials[i1,i2,i3] = True\\n\\t\\t\\t\\t\\tprint \\\"\\\\n\\\\n\\\\nmean fr:%d\\\\n\\\\nalternation:%f \\\"%(meanFrMn,alternationMn)\\n\\n\\t\\t\\t\\t\\t# pattern = \\\"*\\\"+str(eesFrequency)+\\\"Hz_Delay_\\\"+str(16)+\\\\\\n\\t\\t\\t\\t\\t# \\t\\\"ms_human_Tonic_w1_\\\"+str(w1)+\\\"_w2_\\\"+str(w2)+\\\"_w3_\\\"+str(w3)+\\\"_\\\"+str(eesAmplitude)+\\\"_sysHuman.p\\\"\\n\\t\\t\\t\\t\\t# meanFr = load_results(pattern,\\\"../../results/\\\"+folderName)\\n\\t\\t\\t\\t\\t# if meanFr:\\n\\t\\t\\t\\t\\t# \\talternationMn16,meanFrMn16,_ = ppt.computeAlternationScore(meanFr[flexMuscle]['Mn'],meanFr[extMuscle]['Mn'])\\n\\t\\t\\t\\t\\t# \\tif alternationMn16+0.05<alternationMn:\\n\\t\\t\\t\\t\\t# \\t\\tniceTrials[i1,i2,i3] = True\\n\\t\\t\\t\\t\\t# \\t\\tprint \\\"\\\\n\\\\n\\\\nmean fr:%d\\\\n\\\\nalternation:%f \\\"%(meanFrMn,alternationMn)\\n\\n\\n\\ti1s,i2s,i3s =np.nonzero(niceTrials)\\n\\n\\tfig,ax = plt.subplots(1,3,figsize=(16,9))\\n\\tprint i1s\\n\\tprint i2s\\n\\tprint i3s\\n\\tax[0].hist(i1s,range(weights_1.size+1),(0,weights_1.size))\\n\\tax[0].set_xticks(np.arange(0,weights_1.size)+0.5)\\n\\tax[0].set_xticklabels(weights_1)\\n\\tax[1].hist(i2s,range(weights_2.size+1),(0,weights_2.size))\\n\\tax[1].set_xticks(np.arange(0,weights_2.size)+0.5)\\n\\tax[1].set_xticklabels(weights_2)\\n\\tax[2].hist(i3s,range(weights_3.size+1),(0,weights_3.size))\\n\\tax[2].set_xticks(np.arange(0,weights_3.size)+0.5)\\n\\tax[2].set_xticklabels(weights_3)\\n\\tplt.show()\\n\\n\\tcount=0\\n\\tfor i1,i2,i3 in zip(i1s,i2s,i3s):\\n\\t\\tfig,ax = plt.subplots(len(eesAmplitudes)+len(eesLowAmplitudes),2,figsize=(16,9))\\n\\t\\tfig.suptitle(\\\"w1:%f w2:%f w3:%f amplitude:%d\\\"%(weights_1[i1],weights_2[i2],weights_3[i3],eesAmplitude),fontsize=14)\\n\\n\\t\\tfor i,delay in enumerate(delays):\\n\\n\\t\\t\\tfor j,eesAmplitude in enumerate(eesAmplitudes):\\n\\t\\t\\t\\tpatternTonic = \\\"*\\\"+str(eesFrequency)+\\\"Hz_Delay_\\\"+str(delay)+\\\\\\n\\t\\t\\t\\t\\t\\\"ms_human_Tonic_w1_\\\"+str(weights_1[i1])+\\\"_w2_\\\"+str(weights_2[i2])+\\\"_w3_\\\"+str(weights_3[i3])+\\\"_\\\"+str(eesAmplitude)+\\\"_sysHuman.p\\\"\\n\\t\\t\\t\\tmeanFr = load_results(patternTonic,\\\"../../results/\\\"+folderName)\\n\\t\\t\\t\\tif meanFr:\\n\\t\\t\\t\\t\\tax[j,i].plot(meanFr[extMuscle]['Mn'],'r')\\n\\t\\t\\t\\t\\tax[j,i].plot(meanFr[extMuscle]['IaInt'],'-.r')\\n\\t\\t\\t\\t\\tax[j,i].plot(meanFr[flexMuscle]['Mn'],'b')\\n\\t\\t\\t\\t\\tax[j,i].plot(meanFr[flexMuscle]['IaInt'],'-.b')\\n\\t\\t\\tfor j,eesLowAmplitude in enumerate(eesLowAmplitudes):\\n\\t\\t\\t\\tpatternHFLA = \\\"*\\\"+str(eesHighFrequency)+\\\"Hz_Delay_\\\"+str(delay)+\\\\\\n\\t\\t\\t\\t\\t\\\"ms_human_HFLA_\\\"+eesLowAmplitude+\\\"_w1_\\\"+str(weights_1[i1])+\\\"_w2_\\\"+str(weights_2[i2])+\\\"_w3_\\\"+str(weights_3[i3])+\\\"__sysHuman.p\\\"\\n\\t\\t\\t\\tmeanFr = load_results(patternHFLA,\\\"../../results/\\\"+folderName)\\n\\t\\t\\t\\tif meanFr:\\n\\t\\t\\t\\t\\tax[j+len(eesAmplitudes),i].plot(meanFr[extMuscle]['Mn'],'r')\\n\\t\\t\\t\\t\\tax[j+len(eesAmplitudes),i].plot(meanFr[extMuscle]['IaInt'],'-.r')\\n\\t\\t\\t\\t\\tax[j+len(eesAmplitudes),i].plot(meanFr[flexMuscle]['Mn'],'b')\\n\\t\\t\\t\\t\\tax[j+len(eesAmplitudes),i].plot(meanFr[flexMuscle]['IaInt'],'-.b')\\n\\t\\t\\t\\t\\t# print \\\"TODO: add statistics on plots\\\"\\n\\n\\t\\tplt.show(block=False)\\n\\t\\tcount+=1\\n\\t\\tif count==10:\\n\\t\\t\\traw_input(\\\"press a key to continue\\\")\\n\\t\\t\\tfor i in range(count):plt.close()\\n\\t\\t\\tcount=0\\n\\tplt.show()\\n\\n\\t\\t\\t\\t# fileName = \\\"Delay_\\\"+str(delay)+\\\"ms_human_w1_\\\"+str(w1)+\\\"_w2_\\\"+str(w2)+\\\"_w3_\\\"+str(w3)+\\\"_sysHuman.pdf\\\"\\n\\t\\t\\t\\t# plt.savefig(\\\"../../results/\\\"+folderName+fileName, format=\\\"pdf\\\",transparent=True)\\n\\t\\t\\t\\t# plt.show(block=False)\\n\\t\\t\\t\\t# plt.pause(1)\\n\\t\\t\\t\\t# plt.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93baf83458209a5ccf058e8773341d5f\",\n \"score\": \"0.51492274\",\n \"text\": \"def normalize_timestamps(sid):\\r\\n sub_paths = [ store.path(sid, submission.filename)\\r\\n for submission in g.source.submissions ]\\r\\n if len(sub_paths) > 1:\\r\\n args = [\\\"touch\\\"]\\r\\n args.extend(sub_paths[:-1])\\r\\n rc = subprocess.call(args)\\r\\n if rc != 0:\\r\\n app.logger.warning(\\\"Couldn't normalize submission timestamps (touch exited with %d)\\\" % rc)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f4f99650ff4dc20c57d7bd1f519fcc8\",\n \"score\": \"0.51348376\",\n \"text\": \"def main(f):\\n # Init\\n input_dir = './'\\n output_dir = './temp3'\\n snapidx = re.findall(r'[0-9][0-9][0-9]', f)\\n snapidx = int(snapidx[0])\\n # Init GasTrack class\\n gt = GasTrack(input_dir, output_dir)\\n gt1 = GasTrack(input_dir,output_dir)\\n gt2 = GasTrack(input_dir,output_dir)\\n # Init sub particle hdft files\\n ps = partsplit.PartSplit(input_dir, input_dir)\\n\\n # Gen split particle hdf5 files\\n h5name = (\\\"snap_%03d.hdf5\\\" % snapidx)\\n ps.get_single_file(h5name)\\n # Init save names\\n haloname = (\\\"halo_%03d_Projection_z_density_%f_%d_0.png\\\" %\\n (snapidx, gt.width[0], gt.buff_size[0]))\\n gasname = (\\\"snap_%03d_Projection_z_density_%f_%d_0.png\\\" %\\n (snapidx, gt.width[0], gt.buff_size[0]))\\n # get peaks\\n gt.save = False\\n width_old = gt.width\\n # cluster1\\n f_c1 = ('snap_%03d_c1.hdf5' % snapidx)\\n ds1 = gt.load_hdf5(f_c1)\\n halo_c1 = (\\\"halo_%03d_Projection_z_density_%f_%d_c1.png\\\" %\\n (snapidx, gt.width[0], gt.buff_size[0]))\\n halo_map_c1 = gt.get_halo_map(ds1, halo_c1)\\n peak1 = gt.locate_peak(halo_map_c1)\\n os.remove(os.path.join(gt.input_dir, f_c1))\\n # cluster2\\n f_c2 = ('snap_%03d_c2.hdf5' % snapidx)\\n ds2 = gt.load_hdf5(f_c2)\\n halo_c2 = (\\\"halo_%03d_Projection_z_density_%f_%d_c2.png\\\" %\\n (snapidx, gt.width[0], gt.buff_size[0]))\\n halo_map_c2 = gt.get_halo_map(ds2, halo_c2)\\n peak2 = gt.locate_peak(halo_map_c2)\\n os.remove(os.path.join(gt.input_dir, f_c2))\\n # Combine\\n peaklist = np.row_stack((peak1, peak2))\\n # get all images\\n gt.save = True\\n gt.width = width_old\\n figId = np.mod(snapidx, 2)\\n ds = gt.load_hdf5(f)\\n gt.get_halo_map(ds, figId, haloname)\\n gt.get_gas_map(ds, peaklist, figId, gasname)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a32f77a7071c8dc9c4512ef2c532f385\",\n \"score\": \"0.51268995\",\n \"text\": \"def find_obs(self, ti): \\n app_name_caps = self.app_name.upper() \\n valid_time = ti.getValidTime()\\n obs_dir = self.cg_dict['OBS_INPUT_DIR']\\n obs_template = self.cg_dict['OBS_INPUT_TEMPLATE']\\n # convert valid_time to unix time\\n valid_seconds = int(datetime.datetime.strptime(valid_time, \\\"%Y%m%d%H%M\\\").strftime(\\\"%s\\\"))\\n # get time of each file, compare to valid time, save best within range\\n closest_file = \\\"\\\"\\n closest_time = 9999999\\n\\n valid_range_lower = self.cg_dict['WINDOW_RANGE_BEG']\\n valid_range_upper = self.cg_dict['WINDOW_RANGE_END']\\n lower_limit = int(datetime.datetime.strptime(util.shift_time_seconds(valid_time, valid_range_lower),\\n \\\"%Y%m%d%H%M\\\").strftime(\\\"%s\\\"))\\n upper_limit = int(datetime.datetime.strptime(util.shift_time_seconds(valid_time, valid_range_upper),\\n \\\"%Y%m%d%H%M\\\").strftime(\\\"%s\\\"))\\n\\n for dirpath, dirnames, all_files in os.walk(obs_dir):\\n for filename in sorted(all_files):\\n fullpath = os.path.join(dirpath, filename)\\n f = fullpath.replace(obs_dir+\\\"/\\\", \\\"\\\")\\n # check depth of template to crop filepath\\n se = util.get_time_from_file(self.logger, f, obs_template)\\n if se is not None:\\n file_valid_time = se.getValidTime(\\\"%Y%m%d%H%M\\\")\\n if file_valid_time == '':\\n continue\\n file_valid_dt = datetime.datetime.strptime(file_valid_time, \\\"%Y%m%d%H%M\\\")\\n file_valid_seconds = int(file_valid_dt.strftime(\\\"%s\\\"))\\n if file_valid_seconds < lower_limit or file_valid_seconds > upper_limit:\\n continue\\n diff = abs(valid_seconds - file_valid_seconds)\\n if diff < closest_time:\\n closest_time = diff\\n closest_file = fullpath\\n\\n if closest_file != \\\"\\\":\\n return closest_file\\n else:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b142ffc4c45f655d8b8416c476c054b1\",\n \"score\": \"0.51218045\",\n \"text\": \"def create_timelapse_folder(self):\\n \\n os.mkdir(os.path.join(self.path, self.name))\\n os.mkdir(os.path.join(self.path, self.name, \\\"images\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b1fce6ecc46983246013713385cf8b2\",\n \"score\": \"0.5119813\",\n \"text\": \"def clean_files_a():\\n\\n file_name_path = paths.PATHS_OBJ_PATH\\n with open(file_name_path, 'rb') as f:\\n paths_loader = pickle.load(f)\\n # image_root = paths_loader.IMG_ROOT_PATH\\n train_root = paths_loader.TRAIN_DATA_PATH\\n label_root = paths_loader.LABEL_PATH\\n client_data_path = paths.CLIENT_DATA_PATH\\n seqs_name_path = osp.join(client_data_path, 'seqs_name_path.data')\\n with open(seqs_name_path, 'rb') as f:\\n seqs_name_dict = pickle.load(f)\\n seqs = seqs_name_dict['empty_seqs'] + seqs_name_dict['labeled_seqs']\\n\\n # Delete gt and labels directories\\n for seq in seqs:\\n gt_path = osp.join(train_root, f'{seq}', 'gt')\\n if osp.exists(gt_path):\\n shutil.rmtree(gt_path)\\n label_path = osp.join(label_root, f'train/{seq}/img1')\\n if osp.exists(label_path):\\n shutil.rmtree(label_path)\\n\\n # Clear image path directories\\n image_path_dir = osp.join(paths.ROOT_PATH, 'data')\\n for file in os.listdir(image_path_dir):\\n file_path = osp.join(paths.ROOT_PATH, 'data', file)\\n os.remove(file_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"041f333171cd0f02644766cfa3aedc2e\",\n \"score\": \"0.51156986\",\n \"text\": \"def test_getFilesStartTimeUnixTime(self):\\n self.dbu.addUnixTimeTable()\\n self.test_getFilesStartTime()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3116fb4569fc39fa4f2a1b26a2c67e33\",\n \"score\": \"0.51141065\",\n \"text\": \"def loadttls( pa, ttlfiles, compiler_setts={} ):\\n\\n for ttlfile in ttlfiles :\\n compiler = pa.qp(\\n pa, ISettings, 'tayra.ttlcompiler', settings=compiler_setts )\\n code = compiler.compilettl( file=ttlfile )\\n compiler.load( code, context={} )\\n pa.logdebug( \\\"Loaded template plugin %r ...\\\" % ttlfile )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0872c9465c5a8b99f25e43f32a2e4432\",\n \"score\": \"0.51130515\",\n \"text\": \"def lookforfiles():\\n os.chdir(\\\"/mesonet/data/smos\\\")\\n files = glob.glob(\\\"*.txt\\\")\\n for fn in files:\\n ts = datetime.datetime.strptime(fn, '%Y_%m_%d_%H%M.txt')\\n scursor.execute(\\\"\\\"\\\"\\n SELECT * from obtimes where valid = '%s-06'\\n \\\"\\\"\\\" % (ts.strftime(\\\"%Y-%m-%d %H:%M\\\"),))\\n row = scursor.fetchone()\\n if row is None:\\n # print \\\"INGEST FILE!\\\", file\\n consume(fn, ts)\\n scursor.execute(\\\"\\\"\\\"\\n INSERT into obtimes(valid) values ('%s-06')\\n \\\"\\\"\\\" % (ts.strftime(\\\"%Y-%m-%d %H:%M\\\"),))\\n SMOS.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbab62b7a49ab348d1938cc3a9d353b0\",\n \"score\": \"0.5112842\",\n \"text\": \"def _create_testrun_folder(self):\\n \\n timestamp = time.strftime(\\\"%y%m%d-%H%M%S\\\")\\n \\n #conductor executes as root.\\n dir1 = \\\"conductor\\\"\\n dir12 = os.path.join(dir1, \\\"%s_%s\\\" % (self.testrun.id, timestamp))\\n dir123 = os.path.join(dir12, \\\"%s\\\" % self.env)\\n\\n os.chdir(self.testrun.workdir)\\n\\n subprocess.call('rm -rf %s' % dir123, shell=True) #removes dir3 only\\n\\n #\\\"chmod -R\\\" only affects last dir in path so let's go through all dirs.\\n #chmod 777 changes all the rights it can depending on user privileges.\\n subprocess.call('mkdir -p %s' % dir123, shell=True)\\n subprocess.call('chmod 777 %s' % dir1, shell=True) \\n subprocess.call('chmod 777 %s' % dir12, shell=True)\\n subprocess.call('chmod 777 %s' % dir123, shell=True)\\n\\n pre_test_dir = os.path.join(dir123, \\\"pre\\\")\\n post_test_dir = os.path.join(dir123, \\\"post\\\")\\n\\n subprocess.call('mkdir -p ' +pre_test_dir, shell=True)\\n subprocess.call('chmod 777 '+pre_test_dir, shell=True)\\n subprocess.call('mkdir -p ' +post_test_dir, shell=True)\\n subprocess.call('chmod 777 '+post_test_dir, shell=True)\\n\\n self.testrun.base_dir = os.path.join(self.testrun.workdir, dir123)\\n self.testrun.pre_test_dir = os.path.join(self.testrun.workdir, \\n pre_test_dir)\\n self.testrun.post_test_dir = os.path.join(self.testrun.workdir, \\n post_test_dir)\\n\\n self.log.debug(\\\"Local folder for result files: %s\\\" \\\\\\n % self.testrun.base_dir)\\n self.log.debug(\\\"Folder for pre test files: %s\\\" \\\\\\n % self.testrun.pre_test_dir)\\n self.log.debug(\\\"Folder for post test files: %s\\\" \\\\\\n % self.testrun.post_test_dir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae6c7b772f78d8e36d37e273a0f45c0f\",\n \"score\": \"0.5110712\",\n \"text\": \"def fSetup_AppFiles():\\n#---------------------------------------------------#\\n\\n Settings.tsEXE = Settings.tsFolder + \\\"\\\\\\\\timeslip.exe\\\" \\n logging.debug(\\\"- Timeslips EXE: %s\\\" %Settings.tsEXE)\\n\\n Settings.tsimpEXE = Settings.tsFolder + \\\"\\\\\\\\tsimport.exe\\\" \\n logging.debug(\\\"- TSImport EXE: %s\\\" %Settings.tsimpEXE)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2beded9df2fdebe95711244a52db2466\",\n \"score\": \"0.5106142\",\n \"text\": \"def touch_oleans(path: Path) -> None:\\n now = datetime.now().timestamp()\\n for p in path.glob('**/*.olean'):\\n os.utime(str(p), (now, now))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55c1aca83b106690cb5ed846111a6917\",\n \"score\": \"0.51025915\",\n \"text\": \"def main():\\n parser = argparse.ArgumentParser(\\n description=\\\"Simulate the real-time depositing of Nanopore \\\"\\n \\\"reads into a given folder, conserving the order they \\\"\\n \\\"were processed during sequencing. If pass and fail \\\"\\n \\\"folders do not exist in output_dir they will be created \\\"\\n \\\"if detected in the file path for the fast5 file.\\\")\\n\\n parser.add_argument(\\n \\\"-i\\\", \\\"--input_dir\\\",\\n help=\\\"Directory where files are located.\\\",\\n type=str,\\n required=True)\\n\\n parser.add_argument(\\n \\\"--index\\\",\\n help=\\\"Provide a prebuilt index file to skip indexing. Be aware that \\\"\\n \\\"paths within an index file are relative to the current working \\\"\\n \\\"directory when they were built.\\\",\\n type=str)\\n\\n parser.add_argument(\\n \\\"-o\\\", \\\"--output\\\",\\n help=\\\"Directory to copy the files to. If not specified, will \\\"\\n \\\"generate the index file only.\\\",\\n type=str)\\n\\n parser.add_argument(\\n \\\"--scale\\\",\\n help=\\\"Amount to scale the timing by. i.e scale of 10 will \\\\\\n deposit the reads 10x fatser than they were generated.\\\\\\n (Default = 1.0)\\\",\\n type=check_positive,\\n default=1.0)\\n\\n parser.add_argument(\\n \\\"-d\\\", \\\"--dump_index\\\",\\n help=\\\"Path to save index as. Default is 'taeper_index.npy' in current \\\"\\n \\\"working directory. Note: Paths in the index are relative to the \\\"\\n \\\"current working directory.\\\",\\n default='taeper_index.npy',\\n type=str)\\n\\n parser.add_argument(\\n \\\"--no_index\\\",\\n help=\\\"Dont write the index list to file. This will mean it needs \\\"\\n \\\"regenerating for this dataset on each run.\\\",\\n action='store_true'\\n )\\n\\n parser.add_argument(\\n \\\"--log_level\\\",\\n help=\\\"Level of logging. 0 is none, 5 is for debugging. Default is 4 \\\"\\n \\\"which will report info, warnings, errors, and critical \\\"\\n \\\"information.\\\",\\n default=4,\\n type=int,\\n choices=range(6))\\n\\n parser.add_argument(\\n \\\"--no_progress_bar\\\",\\n help=\\\"Do not display progress bar.\\\",\\n action='store_true'\\n )\\n\\n args = parser.parse_args()\\n\\n # setup logging\\n log_level = LOGGING_LEVELS.get(args.log_level)\\n logging.basicConfig(level=log_level,\\n format='[%(asctime)s]:%(levelname)s:%(message)s',\\n datefmt='%m/%d/%Y %I:%M:%S %p')\\n\\n # it's business time\\n taeper.main(args)\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7542be08a6cd8aee5d56c4c127e201c\",\n \"score\": \"0.5100196\",\n \"text\": \"def get_large_audio_transcription(path_target):\\n audio_filename = path_target.split('.')[0]+\\\".\\\"+\\\"wav\\\"\\n print(\\\"file name : \\\",audio_filename)\\n #time.sleep(2)\\n os.system(f'cmd /c \\\" ffmpeg -i {path_target} -codec:a libmp3lame -qscale:a 2 {audio_filename}\\\"')\\n #os.system(f'cmd /c \\\" ffmpeg -i {path} -vn -ab 70 {audio_filename}\\\"')\\n\\n print('audio file is created...........')\\n path=audio_filename\\n\\n # open the audio file using pydub -ab 256\\n\\n sound = AudioSegment.from_wav(path) \\n # split audio sound where silence is 700 miliseconds or more and get chunks\\n start_end_time,chunks = silence_copy.split_on_silence(sound,\\n # experiment with this value for your target audio file\\n #by this we can adjust the size of each sentance length\\n min_silence_len = 500,\\n # adjust this per requirement\\n silence_thresh = sound.dBFS-11,\\n # keep the silence for 1 second, adjustable as well\\n keep_silence=1000,\\n )\\n #chunks=chunks_with_time[0]\\n #print(start_end_time)\\n folder_name = path_target.split('.')[0]\\n # create a directory to store the audio chunks\\n if not os.path.isdir(folder_name):\\n os.mkdir(folder_name)\\n whole_text = \\\"\\\"\\n # process each chunk \\n j=0 #for time from start_end_list\\n\\n\\n #** ............ **#\\n \\n ## start_end_subtile store the start and and time stamp and sentece related to that##\\n \\n ##** ..................... **#\\n\\n start_end_subtitle=[]\\n for i, audio_chunk in enumerate(chunks, start=1):\\n # export audio chunk and save it in\\n # the `folder_name` directory.\\n chunk_filename = os.path.join(folder_name, f\\\"chunk{i}.wav\\\")\\n #save the each audio chunk at chink_filename file\\n audio_chunk.export(chunk_filename, format=\\\"wav\\\")\\n # recognize the chunk\\n with sr.AudioFile(chunk_filename) as source:\\n audio_listened = r.record(source)\\n # try converting it to text\\n try:\\n text = r.recognize_google(audio_listened)\\n except sr.UnknownValueError as e:\\n pass\\n #print(\\\"Error:\\\", str(e))\\n else:\\n text = f\\\"{text.capitalize()}. \\\"\\n #print(chunk_filename, \\\":\\\", text,end=\\\" \\\")\\n #print(start_end_time[j])\\n text=text.strip()\\n if(len(text)>0):\\n start_end_time[j].append(text)\\n start_end_subtitle.append(start_end_time[j])\\n whole_text += text\\n j=j+1\\n # return the text for all chunks detected\\n ################################################################################\\n # now we have transcript and subtitle its time to filter out important key word ##\\n #################################################################################\\n final_start_end_subtitle=[]\\n obj=kwe.key_word_find(whole_text) ##by importing code of key_word_extraction\\n final_keyword=obj.get_top_n(10) ## get top x key_word\\n \\n ## select only thos subtitle that have keyword \\n for l in start_end_subtitle: \\n for word in final_keyword:\\n if word in l[2]:\\n print(word)\\n final_start_end_subtitle.append(l)\\n break\\n \\n delete_files_folder(audio_filename, folder_name)\\n return final_start_end_subtitle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"774670b98d88ccd1b81b990402423d4a\",\n \"score\": \"0.5099823\",\n \"text\": \"def eaf2rttm_CAS(path_to_eaf):\\n\\n sampling_freq = 1000.0\\n\\n print('\\\\n')\\n EAF = pmp.Elan.Eaf(path_to_eaf)\\n\\n participants = []\\n\\n for k in EAF.tiers.keys():\\n\\n if 'PARTICIPANT' in EAF.tiers[k][2].keys():\\n\\n if EAF.tiers[k][2]['PARTICIPANT'] not in participants:\\n\\n participants.append(EAF.tiers[k][2]['PARTICIPANT'])\\n\\n print('participants: {}'.format(participants))\\n\\n base = os.path.basename(path_to_eaf)\\n name = os.path.splitext(base)[0]\\n\\n print('parsing file: {}'.format(name))\\n\\n # get the begining, ending and transcription for each annotation of\\n # each tier\\n rttm = []\\n for participant in participants:\\n\\n for _, val in EAF.tiers[participant][0].items():\\n # Get timestamps\\n start = val[0]\\n end = val[1]\\n\\n t0 = EAF.timeslots[start] / sampling_freq\\n length = EAF.timeslots[end] / sampling_freq - t0\\n\\n # get transcription\\n transcript = val[2]\\n\\n rttm.append((name, t0, length, transcript, participant))\\n return rttm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d16583804715f501e1c8cee9d9d71d2c\",\n \"score\": \"0.50906867\",\n \"text\": \"def run_at_time_once(self, ti, v):\\n app_name_caps = self.app_name.upper() \\n valid_time = ti.getValidTime()\\n init_time = ti.getInitTime()\\n base_dir = self.cg_dict['OUTPUT_DIR']\\n if self.cg_dict['LOOP_BY_INIT']:\\n out_dir = os.path.join(base_dir,\\n init_time, self.app_name)\\n else:\\n out_dir = os.path.join(base_dir,\\n valid_time, self.app_name)\\n fcst_level = v.fcst_level\\n fcst_level_type = \\\"\\\"\\n if(fcst_level[0].isalpha()):\\n fcst_level_type = fcst_level[0]\\n fcst_level = fcst_level[1:]\\n obs_level = v.obs_level\\n obs_level_type = \\\"\\\"\\n if(obs_level[0].isalpha()):\\n obs_level_type = obs_level[0]\\n obs_level = obs_level[1:] \\n model_type = self.cg_dict['MODEL_TYPE']\\n obs_dir = self.cg_dict['OBS_INPUT_DIR']\\n obs_template = self.cg_dict['OBS_INPUT_TEMPLATE']\\n model_dir = self.cg_dict['FCST_INPUT_DIR']\\n config_dir = self.cg_dict['CONFIG_DIR']\\n\\n ymd_v = valid_time[0:8]\\n if not os.path.exists(out_dir):\\n os.makedirs(out_dir)\\n\\n # get model to compare\\n model_path = self.find_model(ti.lead, init_time, fcst_level)\\n\\n if model_path == \\\"\\\":\\n self.logger.error(\\\"ERROR: COULD NOT FIND FILE IN \\\"+model_dir+\\\" FOR \\\"+init_time+\\\" f\\\"+str(ti.lead))\\n return\\n self.add_input_file(model_path)\\n if self.cg_dict['OBS_EXACT_VALID_TIME']:\\n obsSts = sts.StringSub(self.logger,\\n obs_template,\\n valid=valid_time,\\n init=init_time,\\n level=str(obs_level.split('-')[0]).zfill(2))\\n obs_file = obsSts.doStringSub()\\n\\n obs_path = os.path.join(obs_dir, obs_file)\\n else:\\n obs_path = self.find_obs(ti)\\n\\n self.add_input_file(obs_path)\\n self.set_param_file(self.cg_dict['CONFIG_FILE'])\\n self.set_output_dir(out_dir)\\n\\n # set up environment variables for each run\\n # get fcst and obs thresh parameters\\n # verify they are the same size\\n fcst_cat_thresh = \\\"\\\"\\n fcst_threshs = []\\n if v.fcst_thresh != \\\"\\\":\\n fcst_threshs = v.fcst_thresh\\n fcst_cat_thresh = \\\"cat_thresh=[ \\\"\\n for fcst_thresh in fcst_threshs:\\n fcst_cat_thresh += \\\"gt\\\"+str(fcst_thresh)+\\\", \\\"\\n fcst_cat_thresh = fcst_cat_thresh[0:-2]+\\\" ];\\\"\\n \\n obs_cat_thresh = \\\"\\\"\\n obs_threshs = [] \\n if v.obs_thresh != \\\"\\\":\\n obs_threshs = v.obs_thresh\\n obs_cat_thresh = \\\"cat_thresh=[ \\\"\\n for obs_thresh in obs_threshs:\\n obs_cat_thresh += \\\"gt\\\"+str(obs_thresh)+\\\", \\\"\\n obs_cat_thresh = obs_cat_thresh[0:-2]+\\\" ];\\\"\\n\\n if len(fcst_threshs) != len(obs_threshs):\\n self.logger.error(\\\"Number of forecast and \\\"\\\\\\n \\\"observation thresholds must be the same\\\")\\n exit(1)\\n\\n # TODO: Allow NetCDF level with more than 2 dimensions i.e. (1,*,*)\\n # TODO: Need to check data type for PROB fcst? non PROB obs?\\n\\n fcst_field = \\\"\\\"\\n obs_field = \\\"\\\"\\n# TODO: change PROB mode to put all cat thresh values in 1 item \\n if self.cg_dict['FCST_IS_PROB']:\\n for fcst_thresh in fcst_threshs:\\n fcst_field += \\\"{ name=\\\\\\\"PROB\\\\\\\"; level=\\\\\\\"\\\"+fcst_level_type + \\\\\\n fcst_level.zfill(2) + \\\"\\\\\\\"; prob={ name=\\\\\\\"\\\" + \\\\\\n v.fcst_name + \\\\\\n \\\"\\\\\\\"; thresh_lo=\\\"+str(fcst_thresh)+\\\"; } },\\\"\\n for obs_thresh in obs_threshs:\\n obs_field += \\\"{ name=\\\\\\\"\\\"+v.obs_name+\\\"_\\\"+obs_level.zfill(2) + \\\\\\n \\\"\\\\\\\"; level=\\\\\\\"(*,*)\\\\\\\"; cat_thresh=[ gt\\\" + \\\\\\n str(obs_thresh)+\\\" ]; },\\\"\\n else:\\n obs_data_type = util.get_filetype(obs_path)\\n model_data_type = util.get_filetype(model_path)\\n if obs_data_type == \\\"NETCDF\\\":\\n\\n obs_field += \\\"{ name=\\\\\\\"\\\" + v.obs_name+\\\"_\\\" + obs_level.zfill(2) + \\\\\\n \\\"\\\\\\\"; level=\\\\\\\"(*,*)\\\\\\\"; \\\"\\n\\n else:\\n obs_field += \\\"{ name=\\\\\\\"\\\"+v.obs_name + \\\\\\n \\\"\\\\\\\"; level=\\\\\\\"[\\\"+obs_level_type + \\\\\\n obs_level.zfill(2)+\\\"]\\\\\\\"; \\\"\\n\\n if model_data_type == \\\"NETCDF\\\":\\n fcst_field += \\\"{ name=\\\\\\\"\\\"+v.fcst_name+\\\"_\\\"+fcst_level.zfill(2) + \\\\\\n \\\"\\\\\\\"; level=\\\\\\\"(*,*)\\\\\\\"; \\\"\\n else:\\n fcst_field += \\\"{ name=\\\\\\\"\\\"+v.fcst_name + \\\\\\n \\\"\\\\\\\"; level=\\\\\\\"[\\\"+fcst_level_type + \\\\\\n fcst_level.zfill(2)+\\\"]\\\\\\\"; \\\"\\n\\n fcst_field += fcst_cat_thresh+\\\" },\\\"\\n\\n obs_field += obs_cat_thresh+ \\\" },\\\"\\n\\n # remove last comma and } to be added back after extra options\\n fcst_field = fcst_field[0:-2]\\n obs_field = obs_field[0:-2]\\n\\n fcst_field += v.fcst_extra+\\\"}\\\"\\n obs_field += v.obs_extra+\\\"}\\\"\\n\\n ob_type = self.cg_dict[\\\"OB_TYPE\\\"]\\n input_base = self.cg_dict[\\\"INPUT_BASE\\\"]\\n\\n self.add_env_var(\\\"MODEL\\\", model_type)\\n self.add_env_var(\\\"FCST_VAR\\\", v.fcst_name)\\n self.add_env_var(\\\"OBS_VAR\\\", v.obs_name)\\n self.add_env_var(\\\"LEVEL\\\", v.fcst_level)\\n self.add_env_var(\\\"OBTYPE\\\", ob_type)\\n self.add_env_var(\\\"CONFIG_DIR\\\", config_dir)\\n self.add_env_var(\\\"FCST_FIELD\\\", fcst_field)\\n self.add_env_var(\\\"OBS_FIELD\\\", obs_field)\\n self.add_env_var(\\\"INPUT_BASE\\\", input_base)\\n self.add_env_var(\\\"MET_VALID_HHMM\\\", valid_time[4:8])\\n cmd = self.get_command()\\n\\n self.logger.debug(\\\"\\\")\\n self.logger.debug(\\\"ENVIRONMENT FOR NEXT COMMAND: \\\")\\n self.print_env_item(\\\"MODEL\\\")\\n self.print_env_item(\\\"FCST_VAR\\\")\\n self.print_env_item(\\\"OBS_VAR\\\")\\n self.print_env_item(\\\"LEVEL\\\")\\n self.print_env_item(\\\"OBTYPE\\\")\\n self.print_env_item(\\\"CONFIG_DIR\\\")\\n self.print_env_item(\\\"FCST_FIELD\\\")\\n self.print_env_item(\\\"OBS_FIELD\\\")\\n self.print_env_item(\\\"INPUT_BASE\\\")\\n self.print_env_item(\\\"MET_VALID_HHMM\\\") \\n self.logger.debug(\\\"\\\")\\n self.logger.debug(\\\"COPYABLE ENVIRONMENT FOR NEXT COMMAND: \\\")\\n self.print_env_copy([\\\"MODEL\\\", \\\"FCST_VAR\\\", \\\"OBS_VAR\\\",\\n \\\"LEVEL\\\", \\\"OBTYPE\\\", \\\"CONFIG_DIR\\\",\\n \\\"FCST_FIELD\\\", \\\"OBS_FIELD\\\",\\n \\\"INPUT_BASE\\\",\\n \\\"MET_VALID_HHMM\\\"])\\n self.logger.debug(\\\"\\\")\\n cmd = self.get_command()\\n if cmd is None:\\n self.logger.error(\\\"ERROR: \\\"+self.app_name+\\\\\\n \\\" could not generate command\\\")\\n return\\n self.logger.info(\\\"\\\")\\n self.build()\\n self.clear()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"423445f7df87092af53dd0bc0106535f\",\n \"score\": \"0.50904137\",\n \"text\": \"def eaf2rttm(path_to_eaf):\\n\\n # in EAF, timestamps are in milliseconds, convert them to seconds\\n # TODO read scale from header of EAF\\n sampling_freq = 1000.0\\n\\n print('\\\\n')\\n # read eaf file\\n EAF = pmp.Elan.Eaf(path_to_eaf)\\n\\n participants = []\\n\\n # gather all the talker's names\\n for k in EAF.tiers.keys():\\n\\n if 'PARTICIPANT' in EAF.tiers[k][2].keys():\\n\\n if EAF.tiers[k][2]['PARTICIPANT'] not in participants:\\n\\n participants.append(EAF.tiers[k][2]['PARTICIPANT'])\\n\\n print('participants: {}'.format(participants))\\n\\n base = os.path.basename(path_to_eaf)\\n name = os.path.splitext(base)[0]\\n\\n print('parsing file: {}'.format(name))\\n\\n # get the begining, ending and transcription for each annotation of\\n # each tier\\n rttm = []\\n for participant in participants:\\n if participant not in EAF.tiers:\\n continue\\n for _, val in EAF.tiers[participant][0].items():\\n # Get timestamps\\n start = val[0]\\n end = val[1]\\n\\n t0 = EAF.timeslots[start] / sampling_freq\\n length = EAF.timeslots[end] / sampling_freq - t0\\n\\n # get transcription\\n transcript = val[2]\\n\\n rttm.append((name, t0, length, transcript, participant))\\n\\n return rttm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c7fb42bcee3c1da07064aed174f9781\",\n \"score\": \"0.5069167\",\n \"text\": \"def THG_1560(sample,hero_int_time=2000,hero_avg_num=1,num_of_spec=5,alphas=np.arange(0,360,5),timesleep=0.5,log=''):\\r\\n main_path=r'D:\\\\Nonlinear_setup\\\\Experimental_data\\\\THG_1560\\\\%s'%sample \\r\\n os.makedirs(main_path)\\r\\n block_laser()\\r\\n total_len = len(alphas)\\r\\n total_time = total_len*hero_avg_num*num_of_spec*hero_int_time/1000. + total_len*(0.5+timesleep+1)\\r\\n start_time = time.time()\\r\\n\\r\\n init_line = 'Started THG_1560 (%s) on %s, expected to complete on %s\\\\nhero_int_time = %.2fms, hero_avg_num = %i, num_of_spec = %i, timesleep = %s\\\\nalphas = %s'%(sample,time.strftime(\\\"%d%b%Y %H:%M\\\", time.localtime()),time.strftime(\\\"%d%b%Y %H:%M\\\", time.localtime(time.time()+total_time)),hero_int_time,hero_avg_num,num_of_spec,timesleep,str(alphas))\\r\\n print(init_line)\\r\\n log_txt = [init_line,\\r\\n unicode(log)+u'\\\\n\\\\n']\\r\\n np.savetxt(os.path.join(main_path,'log.txt'),uniArray(log_txt),fmt='%s')\\r\\n \\r\\n np.save(os.path.join(main_path,'alphas'),alphas)\\r\\n _alphas_csv=open(os.path.join(main_path, \\\"alphas.csv\\\"), \\\"wb\\\") \\r\\n alphas_csv=csv.writer(_alphas_csv)\\r\\n alphas_csv.writerow(alphas)\\r\\n _alphas_csv.close()\\r\\n\\r\\n# np.save(os.path.join(main_path,'gammas'),gammas)\\r\\n# _gammas_csv=open(os.path.join(main_path, \\\"gammas.csv\\\"), \\\"wb\\\") \\r\\n# gammas_csv=csv.writer(_gammas_csv)\\r\\n# gammas_csv.writerow(gammas)\\r\\n# _gammas_csv.close()\\r\\n\\r\\n H.initialise_hero()\\r\\n H.hero_int_time(hero_int_time)\\r\\n H.hero_avg_num(hero_avg_num)\\r\\n\\r\\n def HOME_POL_LOOP():\\r\\n home_POL()\\r\\n# def HOME_QWP_LOOP():\\r\\n# home_QWP()\\r\\n# def HOME_ANA_LOOP():\\r\\n# home_ANA()\\r\\n print('Homing POL')\\r\\n POL_th = threading.Thread(target=HOME_POL_LOOP)\\r\\n# QWP_th = threading.Thread(target=HOME_QWP_LOOP)\\r\\n# ANA_th = threading.Thread(target=HOME_ANA_LOOP)\\r\\n POL_th.start()\\r\\n# QWP_th.start()\\r\\n# ANA_th.start()\\r\\n#\\r\\n BCKGND_SPEC = H.hero_spec()\\r\\n \\r\\n np.save(os.path.join(main_path,'BCKGND_SPEC'),BCKGND_SPEC)\\r\\n _BCKGND_SPEC_csv=open(os.path.join(main_path, \\\"BCKGND_SPEC.csv\\\"), \\\"wb\\\")\\r\\n BCKGND_SPEC_csv=csv.writer(_BCKGND_SPEC_csv)\\r\\n BCKGND_SPEC_csv.writerow(BCKGND_SPEC)\\r\\n _BCKGND_SPEC_csv.close()\\r\\n \\r\\n POL_th.join()\\r\\n# QWP_th.join()\\r\\n# ANA_th.join()\\r\\n\\r\\n def prepare_take_data():\\r\\n global specs\\r\\n specs = []\\r\\n\\r\\n def prepare_take_data_pm():\\r\\n global pm_ref\\r\\n pm_ref = []\\r\\n pma_wl(1560)\\r\\n\\r\\n def take_data():\\r\\n curr_spec = copy.copy(H.hero_spec())\\r\\n specs.append(curr_spec)\\r\\n\\r\\n def take_data_2():\\r\\n curr_power = pma_power()\\r\\n pm_ref.append(curr_power)\\r\\n\\r\\n def save_data(a):\\r\\n try:\\r\\n np.save(os.path.join(main_path,'SPECS_a%s'%(a)),np.array(specs))\\r\\n np.save(os.path.join(main_path,'PM_ref'),np.array(pm_ref))\\r\\n except:\\r\\n time.sleep(1)\\r\\n save_data(a)\\r\\n \\r\\n prepare_take_data_pm()\\r\\n \\r\\n prev_completed = ''\\r\\n _n = 0\\r\\n prints('\\\\n')\\r\\n for alpha in alphas:\\r\\n completed = 'alpha at %.1fdeg (%.2f percent)'%(alpha,_n*100./total_len)\\r\\n prints(completed,prev_completed)\\r\\n prev_completed = completed\\r\\n\\r\\n move_PQA(alpha)\\r\\n prepare_take_data()\\r\\n \\r\\n unblock_laser()\\r\\n plt.pause(0.5)\\r\\n for i in range(num_of_spec):\\r\\n take_data()\\r\\n take_data_2()\\r\\n block_laser()\\r\\n \\r\\n save_data(a='%i'%(alpha*100))\\r\\n\\r\\n plt.pause(timesleep)\\r\\n _n += 1\\r\\n\\r\\n block_laser()\\r\\n print 'Done! Time spent = %is'%(time.time()-start_time)\\r\\n play_sound(complete)\\r\\n\\r\\n H.hero_shutdown()\\r\\n try:\\r\\n anal_THG_1560(sample)\\r\\n plt.pause(1e-6)\\r\\n except:\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3838dc34ae9404ae4d8bec35cb8f294\",\n \"score\": \"0.5068991\",\n \"text\": \"def wta(infile= \\\"infile.txt\\\", outdir= \\\"output\\\",\\n seq= [0,...], add_silence= [0,...], fill_fit= False,\\n duer= 2000, rmold= False) :\\n longer, defects = 0, 0\\n\\n if type(seq)==list :\\n gtts, voices = _get_voices(seq)\\n\\n with open(infile,\\\"r\\\") as words_file :\\n words = words_file.read().replace('\\\\n',' ').split()\\n words = [''.join(filter(str.isalnum,x)) for x in words]\\n words = list(filter(len,words))\\n words = list(set(words))\\n log.debug(words)\\n log.info(\\\"no. of words = \\\"+str(len(words)))\\n err_free_words = []\\n\\n p_python = \\\"python3\\\"\\n p_pyttsx3_AFgen = \\\"generate_AFs.py\\\"\\n\\n language = 'en' #language assignment\\n for word in words :\\n try:\\n os.makedirs(os.path.join(outdir,word), exist_ok=True)\\n sequence = list(seq)\\n if gtts : #TODO add request_limit_exided exception\\n sequence.remove(0)\\n log.debug(\\\"calling gTTS(text=\\\"+str(word)+\\\", lang=\\\"+str(language)+\\\", slow=False)\\\")\\n audio = gTTS(text=word, lang=language, slow=False)\\n audio.save(os.path.join(outdir,word,\\\"gtts.mp3\\\"))\\n log.debug(os.path.join(outdir,word,\\\"gtts.mp3\\\")+\\\" saved\\\")\\n if len(voices) :\\n for voice in voices :\\n log.debug(\\\"calling \\\"+p_pyttsx3_AFgen+' '+word+' '+os.path.join(outdir,word,voice+\\\".mp3\\\")+' '+voice)\\n return_code = subprocess.call([p_python, p_pyttsx3_AFgen, word, os.path.join(outdir,word,voice+\\\".mp3\\\"), voice])\\n log.debug(\\\"return_code = \\\"+str(return_code))\\n if return_code != 0 : log.error(p_pyttsx3_AFgen+\\\" returned \\\"+str(return_code))\\n except FileExistsError:\\n pass\\n except :\\n log.error(\\\"form audio generating \\\"+str(sys.exc_info()))\\n else:\\n err_free_words.append(word)\\n\\n log.debug(err_free_words)\\n log.info(\\\"final no. of words \\\"+str(len(err_free_words)))\\n\\n if type(add_silence)==str and os.path.isdir(add_silence):\\n words = os.listdir(add_silence)\\n log.debug(str(words))\\n for word in words :\\n audios = os.listdir(os.path.join(add_silence,word))\\n for audio in audios :\\n try:\\n sound = As.from_mp3(os.path.join(add_silence,word,audio))\\n log.debug(\\\"adding silence to \\\"+os.path.join(add_silence,word,audio))\\n if len(sound)<duer:\\n remaining = duer - len(sound)\\n rem = remaining//2\\n if rem == remaining/2 :\\n sound = As.silent(duration=rem)+sound+As.silent(duration=rem)\\n else :\\n sound = As.silent(duration=rem+1)+sound+As.silent(duration=rem)\\n sound.export(os.path.join(add_silence,word,\\\"S\\\"+audio), format='mp3')\\n if rmold : os.remove(os.path.join(add_silence,word,audio))\\n else:\\n longer += 1\\n log.error(\\\"from add_silence\\\"+os.path.join(word,audio)+\\\" duration greater than \\\"+str(duer))\\n os.makedirs(os.path.join(add_silence,\\\"~LongerThan_duer\\\",word), exist_ok=True)\\n log.debug(\\\"moving \\\"+os.path.join(add_silence,word,audio)+' '+os.path.join(add_silence,\\\"~LongerThan_duer\\\",word))\\n shutil.move(os.path.join(add_silence,word,audio), os.path.join(add_silence,\\\"~LongerThan_duer\\\",word))\\n except:\\n defects += 1\\n log.error(\\\"form add_silence \\\"+str(sys.exc_info()))\\n os.makedirs(os.path.join(add_silence,\\\"~Defects\\\",word), exist_ok=True)\\n log.debug(\\\"moving \\\"+os.path.join(add_silence,word,audio)+' '+os.path.join(add_silence,\\\"~Defects\\\",word))\\n shutil.move(os.path.join(add_silence,word,audio), os.path.join(add_silence,\\\"~Defects\\\",word))\\n log.info(\\\"add_silence done \\\"+add_silence)\\n\\n elif type(add_silence)==list :\\n gtts, voices = _get_voices(add_silence)\\n audio_files = [voice+'.mp3' for voice in voices]\\n if gtts : audio_files.append(\\\"gtts.mp3\\\")\\n\\n if 'err_free_words' in locals().keys() :\\n words = err_free_words\\n else :\\n with open(infile,\\\"r\\\") as words_file :\\n words = words_file.read().replace('\\\\n',' ').split()\\n words = [''.join(filter(str.isalnum,x)) for x in words]\\n words = list(filter(len,words))\\n words = list(set(words))\\n\\n log.debug(str(words)+'\\\\n'+str(audio_files))\\n for word in words:\\n for audio_file in audio_files :\\n try:\\n sound=As.from_mp3(os.path.join(outdir,word,audio_file))\\n log.debug(\\\"adding silence to \\\"+os.path.join(outdir,word,audio_file))\\n if len(sound)<duer:\\n remaining = duer - len(sound)\\n rem = remaining//2\\n if rem == remaining/2 :\\n sound = As.silent(duration=rem)+sound+As.silent(duration=rem)\\n else :\\n sound = As.silent(duration=rem+1)+sound+As.silent(duration=rem)\\n sound.export(os.path.join(outdir,word,\\\"S\\\"+audio_file), format='mp3')\\n if rmold : os.remove(os.path.join(outdir,word,audio_file))\\n else:\\n longer += 1\\n log.error(\\\"from add_silence \\\"+os.path.join(word,audio_file)+\\\" duration greater than \\\"+str(duer))\\n os.makedirs(os.path.join(outdir,\\\"~LongerThan_duer\\\",word), exist_ok=True)\\n log.debug(\\\"moving \\\"+os.path.join(outdir,word,audio_file)+' '+os.path.join(outdir,\\\"~LongerThan_duer\\\",word))\\n shutil.move(os.path.join(outdir,word,audio_file), os.path.join(outdir,\\\"~LongerThan_duer\\\",word))\\n except:\\n defects += 1\\n log.error(\\\"form add_silence \\\"+os.path.join(word,audio_file)+' '+str(sys.exc_info()))\\n os.makedirs(os.path.join(outdir,\\\"~Defects\\\",word), exist_ok=True)\\n log.debug(\\\"moving \\\"+os.path.join(outdir,word,audio_file)+' '+os.path.join(outdir,\\\"~Defects\\\",word))\\n shutil.move(os.path.join(outdir,word,audio_file), os.path.join(outdir,\\\"~Defects\\\",word))\\n log.info(\\\"add_silence done \\\"+str(add_silence))\\n\\n if type(fill_fit)==str and os.path.isdir(fill_fit):\\n words = os.listdir(fill_fit)\\n log.debug(str(words))\\n for word in words :\\n audios = os.listdir(os.path.join(fill_fit,word))\\n for audio in audios :\\n try:\\n log.debug(\\\"fill_fitting \\\"+os.path.join(fill_fit,word,audio)+\\\" to \\\"+os.path.join(fill_fit,word,\\\"F\\\"+audio))\\n retrn = _change_duer(os.path.join(fill_fit,word,audio), os.path.join(fill_fit,word,\\\"F\\\"+audio), duer)\\n if retrn[0] == duer :\\n log.debug(\\\"change_duer returned with \\\"+str(retrn))\\n else :\\n log.error(\\\"change_duer not able to change dueration for this audio\\\")\\n raise Exception(\\\"change_duer not able to change dueration\\\")\\n except:\\n defects += 1\\n log.error(\\\"form fill_fitter \\\"+os.path.join(word,audio)+' '+str(sys.exc_info()))\\n os.makedirs(os.path.join(fill_fit,\\\"~Defects\\\",word), exist_ok=True)\\n log.debug(\\\"moving \\\"+os.path.join(fill_fit,word,audio)+' '+os.path.join(fill_fit,\\\"~Defects\\\",word))\\n shutil.move(os.path.join(fill_fit,word,audio), os.path.join(fill_fit,\\\"~Defects\\\",word))\\n\\n elif type(fill_fit)==list :\\n gtts, voices = _get_voices(fill_fit)\\n audio_files = [voice+'.mp3' for voice in voices]\\n if gtts : audio_files.append(\\\"gtts.mp3\\\")\\n\\n if 'err_free_words' in locals().keys() :\\n words = err_free_words\\n else :\\n with open(infile,\\\"r\\\") as words_file :\\n words = words_file.read().replace('\\\\n',' ').split()\\n words = [''.join(filter(str.isalnum,x)) for x in words]\\n words = list(filter(len,words))\\n words = list(set(words))\\n\\n log.debug(str(words)+'\\\\n'+str(audio_files))\\n for word in words:\\n for audio_file in audio_files :\\n try:\\n log.debug(\\\"fill_fitting \\\"+os.path.join(outdir,word,audio_file)+\\\" to \\\"+os.path.join(outdir,word,\\\"F\\\"+audio_file))\\n retrn = _change_duer(os.path.join(outdir,word,audio_file), os.path.join(outdir,word,\\\"F\\\"+audio_file), duer)\\n if retrn[0] == duer :\\n log.debug(\\\"change_duer returned with \\\"+str(retrn))\\n else :\\n log.error(\\\"change_duer not able to change dueration for this audio\\\")\\n raise Exception(\\\"change_duer not able to change dueration\\\")\\n except:\\n defects += 1\\n log.error(\\\"form fill_fitter \\\"+os.path.join(word,audio_file)+' '+str(sys.exc_info()))\\n os.makedirs(os.path.join(outdir,\\\"~Defects\\\",word), exist_ok=True)\\n log.debug(\\\"moving \\\"+os.path.join(outdir,word,audio_file)+' '+os.path.join(outdir,\\\"~Defects\\\",word))\\n shutil.move(os.path.join(outdir,word,audio_file), os.path.join(outdir,\\\"~Defects\\\",word))\\n\\n log.info(\\\"wta all done.\\\")\\n return err_free_words, longer, defects\",\n \"title\": \"\"\n },\n {\n \"docid\": \"490eb3304247c3c6fab886b26ead7e39\",\n \"score\": \"0.50504136\",\n \"text\": \"def genTypeTypePeriod2NumSize(inputFileName, outputTypeTimePeriod2FileSizeFileName, outputType2FileSizeFileName, baseTime):\\n lineNum = 0 \\n fileTotalSize = 0\\n \\\"\\\"\\\"\\n (key, value) in ftGapDetail is like(\\\"suffix of file, like .mrc\\\", [\\\"time gap result like \\\"xxx days, hh:mm:ss\\\"])\\n \\\"\\\"\\\"\\n ftGapDetail = {}\\n with open(inputFileName) as f:\\n for line in f: \\n lineNum = lineNum + 1\\n items = line.split('#+#') \\n fileLastAccessTime = items[1]\\n fname = items[3]\\n fileSize = items[-1]\\n if(fileSize[-1] == '\\\\n'):\\n fileSize = fileSize[:-1]\\n \\n fileSize = int(fileSize)\\n fileTotalSize = fileTotalSize + fileSize\\n \\n timeGap = getTimeGap(fileLastAccessTime, baseTime)\\n (name, ext) = os.path.splitext(fname)\\n \\n if(len(ext) > 0 and ext[-1] == '\\\\n'):\\n ext = ext[:-1]\\n if(ftGapDetail.has_key(ext)): \\n ftGapDetail[ext].append((timeGap[0], fileSize))\\n else:\\n ftGapDetail[ext] = [(timeGap[0], fileSize)]\\n \\n \\n if(lineNum % 100000 == 0):\\n logger.info(\\\"{0} lines have been finished processing\\\".format(lineNum))\\n \\n \\n logger.info(\\\"{0} lines have been finished processing\\\".format(lineNum))\\n \\n (ftGapNew, tmGapPeriod) = calculate_time_gap(ftGapDetail)\\n \\n \\n tmGapPeriodList = tmGapPeriod.iteritems()\\n sortedGapPeriodList = sorted(tmGapPeriodList, key=lambda x:x[0], reverse = True)\\n \\n f = open(outputTypeTimePeriod2FileSizeFileName, \\\"w\\\")\\n for item in sortedGapPeriodList:\\n f.write(\\\"{0}#{1}#{2}\\\\n\\\".format(item[0], item[1][0], sizeof_fmt(item[1][1])))\\n \\n f.write(\\\"Total File Size: {0}\\\\n\\\".format(sizeof_fmt(fileTotalSize)))\\n f.close()\\n\\n \\n f = open(outputType2FileSizeFileName, \\\"w\\\")\\n unSortedFTGapNew = []\\n for (k, v) in ftGapNew.iteritems():\\n item = (k, len(v), sum(vv[1] for vv in v))\\n unSortedFTGapNew.append(item)\\n \\n sortedFTGapNew = sorted(unSortedFTGapNew, key = lambda x: x[2], reverse = True)\\n \\n for item in sortedFTGapNew:\\n f.write(\\\"{0}#{1}#{2}\\\\n\\\".format(item[0], item[1], sizeof_fmt(item[2])))\\n\\n f.write(\\\"Total File Size: {0}\\\\n\\\".format(sizeof_fmt(fileTotalSize)))\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"028c23c8754adee79f59d2641f2466b9\",\n \"score\": \"0.50454193\",\n \"text\": \"def t_parse( myargs):\\n nargs = len(myargs)\\n#first argument is the averaging time in seconds\\n timearg = 1\\n namearg = 2\\n\\n iarg = 1\\n if nargs < 3:\\n print(\\\"T: Comput Tsys calibrated horn observations\\\")\\n print(\\\"Usage: T [-F <order>] [-L <velocity>] [-H <velocity>] <average_seconds> <files>\\\")\\n print(\\\"Where <average_seconds>: Number of seconds of observations to average.\\\")\\n print(\\\"-A optionally use pre-calculated hot and cold load files\\\")\\n print(\\\"-B optionally plot/keep the baseline subtratcted spectra\\\")\\n print(\\\"-C optionally flag the center of the band\\\")\\n print(\\\"-D optionally print extra debugging info\\\")\\n print(\\\"-F optionally do a polynomial baseline fit\\\")\\n print(\\\"-I optionally set Processor Index\\\")\\n print(\\\"-H optionally set the high velocity region for baseline fit\\\")\\n print(\\\"-K optionally keep average hot and cold load calibration files\\\")\\n print(\\\"-L optionally set the low velocity region for baseline fit\\\")\\n print(\\\"-N <number> optionally set the number of spectra to plot\\\")\\n print(\\\"-P write PNG and PDF files instead of showing plot\\\")\\n print(\\\"-Q optionally plot intensity versus freQuency, instead of velocity\\\")\\n print(\\\"-R optionally flag known RFI lines\\\")\\n print(\\\"-S <filename> optionally set summary file name\\\")\\n print(\\\"-U optionally update reference frequency for a different line\\\")\\n print(\\\" ie -U 1612.231, 1665.402, 1667.349, 1720.530 or 1420.40575\\\")\\n print(\\\"-W optionally write the calibrated Tsys files\\\")\\n print(\\\"-X optionally set Cold Load Temperature (Kelvins)\\\")\\n print(\\\"-Y optionally set Hot Load Temperature (Kelvins)\\\")\\n print(\\\"-Z <file tag> optionally add tag to PDF and PNG file names\\\")\\n print(\\\"-0 optionally plot zero intensity line(s)\\\")\\n print(\\\"-MINEL optionally set the lowest elevation allowed for calibration obs (default 60d)\\\")\\n print(\\\"Observation file list must include at least one hot load file\\\")\\n print(\\\"\\\")\\n print(\\\"Glen Langston - NSF March 5, 2021\\\")\\n exit()\\n\\n# for all arguments, read list and exit when no flag argument found\\n while iarg < nargs:\\n\\n # if hot and cold file names are provided\\n if myargv[iarg].upper() == '-A':\\n iarg = iarg+1\\n self.hotFileName = myargv[iarg]\\n iarg = iarg+1\\n self.coldFileName = myargv[iarg]\\n print((\\\"Calibrating with files: %s and %s\\\" %(hotFileName, coldFileName)))\\n elif myargv[iarg].upper() == '-B':\\n self.doBaseline = True\\n elif myargv[iarg].upper() == '-C':\\n self.flagCenter = True\\n elif myargv[iarg].upper() == '-D':\\n print( 'Adding Debug Printing')\\n self.doDebug = True\\n elif myargv[iarg].upper() == '-F':\\n iarg = iarg+1\\n self.fitOrder = int( myargv[iarg])\\n if self.fitOrder < 0:\\n self.itOrder = 0\\n elif fitOrder > 10:\\n self.fitOrder = 10\\n self.doPoly = True\\n if self.fitOrder == 0:\\n print(\\\"Fitting a constant baseline\\\")\\n elif self.fitOrder == 1:\\n print(\\\"Fitting a linear baseline\\\")\\n else:\\n print((\\\"Fitting a %d-nd order polynomical baseline\\\" % (fitOrder)))\\n elif myargv[iarg].upper() == '-G':\\n iarg = iarg+1\\n self.hotLoadFile = myargv[iarg]\\n iarg = iarg+1\\n self.coldLoadFile = myargv[iarg]\\n print(\\\"Gain calibration Using Hot Load Obs: %s and Cold Obs: %s\\\" \\\\\\n % (self.hotLoadFile, self.coldLoadFile))\\n elif myargv[iarg].upper() == '-H':\\n iarg = iarg+1\\n self.maxvel = np.float( myargv[iarg])\\n print(('Maximum (high) velocity for sum: %7.2f km/sec' % (self.maxvel)))\\n elif myargv[iarg].upper() == '-I':\\n iarg = iarg+1\\n self.cpuIndex = np.float( myargv[iarg])\\n print(('Telescope Index: %2d' % (self.cpuIndex)))\\n elif myargv[iarg].upper() == '-K':\\n self.doKeep = True\\n print('Keeping Average hot and cold load files')\\n elif myargv[iarg].upper() == '-L':\\n iarg = iarg+1\\n self.minvel = np.float( myargv[iarg])\\n print(('Minium (low) velocity for sum: %7.2f km/sec' % (self.minvel)))\\n elif myargv[iarg].upper() == '-N': # if number of spectra to plot\\n iarg = iarg+1\\n self.maxPlot = int(myargv[iarg])\\n if self.maxPlot < 1:\\n print(\\\"Not Plotting\\\")\\n else:\\n print((\\\"Plot will have a maximum of %d spectra\\\" % (self.maxPlot)))\\n elif myargv[iarg].upper() == '-P':\\n self.doPlotFile = True\\n iarg = iarg+1\\n self.plotFileDir = myargv[iarg].strip()\\n np = len(self.plotFileDir)\\n # must end with a slash\\n if self.plotFileDir[np-1] != '/':\\n self.plotFileDir = self.plotFileDir + \\\"/\\\"\\n print(\\\"Writing Plot Files to directory: %s\\\" % (self.plotFileDir))\\n elif myargv[iarg].upper() == '-Q':\\n self.plotFrequency = True\\n print(\\\"Plotting Intensity versus Frequency()\\\")\\n elif myargv[iarg].upper() == '-R':\\n self.flagRfi = True\\n print(\\\"Flagging known RFI lines\\\")\\n elif myargv[iarg].upper() == '-S': # if save file name provided\\n iarg = iarg+1\\n self.saveFile = myargv[iarg]\\n self.writeTsys = True\\n print( 'Saving integrated Intensities; File: %s' % ( self.saveFile))\\n elif myargv[iarg].upper() == '-T': # if plot title provided\\n iarg = iarg+1\\n self.myTitle = myargv[iarg]\\n print('Plot Title : %s' % (self.myTitle))\\n elif myargv[iarg].upper() == '-U': # if nU ref is provided (in MHz)n\\n iarg = iarg+1\\n self.nuRefFreq = float(myargv[iarg])\\n print(( 'Reference Frequency : %9.3f MHz' % (self.nuRefFreq)))\\n elif myargv[iarg].upper() == '-X': # if cold load temperature \\n iarg = iarg+1\\n self.tcold = float(myargv[iarg])\\n print(( 'Cold Load Reference Temperature: %9.3f Kelvins' % (self.tcold)))\\n elif myargv[iarg].upper() == '-Y': # if hot load\\n iarg = iarg+1\\n self.thot = float(myargv[iarg])\\n print(( 'Hot Load Reference Temperature: %9.3f Kelvins' % (self.thot)))\\n elif myargv[iarg].upper() == '-CEN': # if nU ref is provided (in MHz)n\\n iarg = iarg+1\\n self.nuRefFreq = float(myargv[iarg])\\n print(( 'Reference Frequency : %9.3f MHz' % (self.nuRefFreq)))\\n elif myargv[iarg].upper() == '-MINEL': # if min elevation \\n iarg = iarg + 1\\n lowel = float( myargv[iarg])\\n print(( \\\"Using elevations > %7.2f (d) for Cold load calculation\\\" % (self.lowel)))\\n elif myargv[iarg].upper() == '-W':\\n self.writeKelvin = True\\n elif myargv[iarg].upper() == '-Z': # label written files\\n iarg = iarg+1\\n self.fileTag = str(myargv[iarg])\\n print(( 'File tag: %s' % (self.fileTag)))\\n elif myargv[iarg].upper() == '-0':\\n self.doZero = True\\n print('Plotting zero intensity line')\\n else:\\n break\\n iarg = iarg + 1\\n timearg = iarg\\n namearg = iarg+1\\n# end for all arguments, read list and exit when no flag argument found\\n\\n try: \\n self.aveTimeSec = float(myargv[timearg])\\n except:\\n print(\\\"Error: Can not parse %s as a valid average time (seconds)\\\" % \\\\\\n myargv[timearg])\\n exit()\\n# end of while not reading file names\\n# rest of arguments are file names\\n if self.plotFrequency:\\n print( \\\"Plotting Intensity vs Frequency, Average time: %d (seconds)\\\" % (self.aveTimeSec))\\n else:\\n print( \\\"Plotting Intensity vs Velocity, Average time: %d (seconds)\\\" % (self.avetimesec))\\n self.avetime = datetime.timedelta(seconds=avetimesec) # now need average time in datetime format\\n\\n names = myargv[namearg:]\\n nFiles = len(names)\\n # check and see if any of the names are directories\\n count = 0\\n for iii in range(nFiles):\\n if os.path.isdir( names[iii]):\\n onlyfiles = [f for f in os.listdir(names[iii]) if os.path.isfile(join(names[iii], f))]\\n if count == 0:\\n onames = onlyfiles\\n count = len(onlyfiles)\\n else:\\n onames = onames.append(onlyfiles)\\n count = count + len(onlyfiles)\\n elif: os.path.isfile( names[iii]):\\n if count == 0:\\n onames = [ names[iii] ]\\n count = 1\\n else:\\n onames = onames.append( names[iii])\\n count = count + 1\\n print(\\\"Averaging and plotting %d files\\\" % (count))\\n \\n self.names = sorted(onames)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a69c1059cf55eff6a5b4cbd11974e3c\",\n \"score\": \"0.5027188\",\n \"text\": \"def run_foreach_tpoint(map_reads_output_master_dirpath, combined_gene_names_output_master_dirpath, combined_evalues_output_master_dirpath, combined_cdr3_seqs_output_dirpath):\\n if map_reads_output_master_dirpath[-1] != '/':\\n map_reads_output_master_dirpath += '/'\\n if combined_gene_names_output_master_dirpath[-1] != '/':\\n combined_gene_names_output_master_dirpath += '/'\\n if not os.path.exists(combined_gene_names_output_master_dirpath):\\n os.makedirs(combined_gene_names_output_master_dirpath)\\n if combined_evalues_output_master_dirpath[-1] != '/':\\n combined_evalues_output_master_dirpath += '/'\\n if not os.path.exists(combined_evalues_output_master_dirpath):\\n os.makedirs(combined_evalues_output_master_dirpath)\\n if combined_cdr3_seqs_output_dirpath[-1] != '/':\\n combined_cdr3_seqs_output_dirpath += '/'\\n if not os.path.exists(combined_cdr3_seqs_output_dirpath):\\n os.makedirs(combined_cdr3_seqs_output_dirpath)\\n\\n #this is a dictionary where each entry is a time point, and\\n #each definition is a list of the map_reads output directories\\n #that correspond to that timepoint\\n tpoint_dirpaths = {}\\n for i in os.listdir(map_reads_output_master_dirpath):\\n if i[0] == '.' or i == 'README':\\n continue\\n tpoint = i.split('_')[-2]\\n try:\\n tpoint_dirpaths[tpoint].append(map_reads_output_master_dirpath + i + '/')\\n except KeyError:\\n tpoint_dirpaths[tpoint] = [map_reads_output_master_dirpath + i + '/']\\n\\n for i in tpoint_dirpaths:\\n combined_gene_names_output_dirpath = combined_gene_names_output_master_dirpath + i + '/'\\n if not os.path.exists(combined_gene_names_output_dirpath):\\n os.makedirs(combined_gene_names_output_dirpath)\\n combined_evalues_output_dirpath = combined_evalues_output_master_dirpath + i + '/'\\n if not os.path.exists(combined_evalues_output_dirpath):\\n os.makedirs(combined_evalues_output_dirpath)\\n combine(tpoint_dirpaths[i], combined_gene_names_output_dirpath, combined_evalues_output_dirpath)\\n combined_cdr3_seqs_output_filepath = combined_cdr3_seqs_output_dirpath + i\\n pool_cdr3s(tpoint_dirpaths[i], combined_cdr3_seqs_output_filepath)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e94bcc207636479820a1f36247ad685b\",\n \"score\": \"0.5023674\",\n \"text\": \"def forecast(dstart, metdata):\\n FCTDIR = '/pub/forecast/'\\n ctimes = [0, 6, 12, 18]\\n times = []\\n metdirlist = []\\n metfilelist = []\\n if (metdata.lower() == 'gfs') or (metdata.lower() == 'gfs0p25'):\\n met = metdata.lower()\\n elif(metdata.lower() == 'nam'):\\n met = 'namsf'\\n elif(metdata.lower() == 'namak'):\\n met = 'namsf.AK'\\n elif(metdata.lower() == 'namhi'):\\n met = 'namsf.HI'\\n metnamefinal = 'No data found'\\n for hrs in ctimes:\\n times.append(datetime(dstart.year, dstart.month, dstart.day, hrs))\\n for dtm in times:\\n if (dtm <= dstart):\\n metime = dtm\\n metdir1 = FCTDIR + metime.strftime('%Y%m%d') + '/'\\n metfilename = 'hysplit.' + metime.strftime('t%Hz') + '.' + met + 'f'\\n if 'nam' in metdata.lower():\\n metfilename = 'hysplit.' + metime.strftime('t%Hz') + '.' + met\\n metname = metdir1 + metfilename\\n if not os.path.exists(metname):\\n metime = metime - td(hours=6)\\n metdir1 = FCTDIR + metime.strftime('%Y%m%d') + '/'\\n metfilename = 'hysplit.' + metime.strftime('t%Hz') + '.' + met + 'f'\\n if 'nam' in metdata.lower():\\n metfilename = 'hysplit.' + metime.strftime('t%Hz') + '.' + met\\n metfilelist.append(metfilename)\\n metdirlist.append(metdir1)\\n return metdirlist, metfilelist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32dd1b8b18f3f9d634afdef4b75c71ea\",\n \"score\": \"0.5021358\",\n \"text\": \"def convert_tdms_to_mp4():\\n videos_fld = os.path.join(paths.raw_data_folder, paths.raw_video_folder)\\n metadata_fld = os.path.join(paths.raw_data_folder, paths.raw_metadata_folder)\\n\\n tcvt = get_list_uncoverted_tdms_videos()\\n toconvert = [os.path.join(videos_fld, t) for t in tcvt]\\n # while True:\\n\\n for f in toconvert:\\n metadata = os.path.join(\\n metadata_fld, os.path.split(f)[1]\\n )\\n if not os.path.exists(metadata):\\n print(f'Metdata for {f} not found, skipping')\\n continue\\n # get metadata file\\n \\n \\n converter.convert(f, metadata)\\n\\n\\n # try:\\n # VideoConverter(os.path.join(videos_fld, f), extract_framesize=True)\\n # except:\\n # print(f\\\"Failed to convert {f} !!!!!!!!\\\")\\n # continue\\n\\n\\n # def get_list_not_tracked_videos(self):\\n # videos = [f.split('.')[0] for f in os.listdir(self.videos_fld) if 'tdms' not in f and \\\".\\\" in f and not \\\"Threat\\\" in f]\\n # poses = [f.split('_')[:-1] for f in os.listdir(self.pose_fld) if 'h5' in f]\\n\\n # not_tracked = []\\n # for f in videos:\\n # videoname = os.path.join(self.videos_fld, f+\\\".mp4\\\")\\n # if not os.path.isfile(videoname): videoname = os.path.join(self.videos_fld, f+\\\".avi\\\")\\n # if os.path.getsize(videoname) > 10000 and f.split('_') not in poses: not_tracked.append(f)\\n \\n # print('To track: ', not_tracked)\\n # print(len(not_tracked), ' files yet to track')\\n\\n # store = \\\"Utilities/file_io/files_to_track.yml\\\"\\n # with open(store, 'w') as out:\\n # yaml.dump([os.path.split(u)[-1]+\\\".mp4\\\" for u in not_tracked], out)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35ff00740bd56de0b343cd51f7cb9dfa\",\n \"score\": \"0.501751\",\n \"text\": \"def clean(self):\\r\\n now = time.time()\\r\\n for entry in os.listdir(os.path.join(self._path, 'tmp')):\\r\\n path = os.path.join(self._path, 'tmp', entry)\\r\\n if now - os.path.getatime(path) > 129600: # 60 * 60 * 36\\r\\n os.remove(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a5f2baf7284fd0e1293303d1cb94e00\",\n \"score\": \"0.5017088\",\n \"text\": \"def run():\\n\\n # path to the file\\n # config_path = \\\"/Volumes/Seagate_Expansion_Drive/taw_optimization_work_folder/ETRM_CONFIG_statewide.yml\\\"\\n # config_path = \\\"/Users/dcadol/Desktop/academic_docs_II/ameriflux_aoi_qgis/ETRM_CONFIG_ameriflux_aoi.yml\\\"\\n config_path = '/Users/dcadol/Desktop/academic_docs_II/ameriflux_aoi_qgis/ETRM_CONFIG_ameriflux_aoi_ceff_06.yml'\\n\\n # path to directory where output config will go:\\n output_path = \\\"/Volumes/Seagate_Blue/taw_optimization_work_folder\\\"\\n # starting TAW value\\n begin_taw = 25\\n # ending TAW value\\n end_taw = 2125\\n # grid search step size. Each ETRM run will increase the uniform TAW of the RZSW holding capacity by this many mm.\\n taw_step = 100\\n\\n # format and output a config file to generate ETRM outputs based on a changing TAW value.\\n taw_grid_search(config_path, output_path, begin_taw, end_taw, taw_step)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bac06b74d59b33861fb1f3ab455eb579\",\n \"score\": \"0.500733\",\n \"text\": \"def import_tdms_files(paths=[], show=True):\\n paths = load_files(paths)\\n data_files = []\\n if show:\\n print(paths)\\n i = 0\\n with tqdm(total=len(paths), file=sys.stdout) as pbar:\\n for p in paths:\\n pbar.set_description('Imported: %d' % (1 + i))\\n pbar.update(1)\\n sleep(0.01)\\n if p[-5:] == '.tdms':\\n stamp = os.path.basename(p)[:-5]\\n data_file = _import_file(p)\\n data_file._import_meta(stamp)\\n data_file._set_temp()\\n data_files.append(data_file)\\n data_files.sort(key=lambda d: int(str(d.date) + str(d.time)))\\n print('Imported file order:')\\n for i in range(len(data_files)):\\n pre = str(i) + ': ' + \\\\\\n ((len(str(len(data_files) - 1)) - len(str(i))) * ' ')\\n print(pre + str(data_files[i].stamp))\\n return data_files\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bee02b05013df343fc2bad1a2748b0eb\",\n \"score\": \"0.5002859\",\n \"text\": \"def pre_treatement(filelist, dir_='sample_data/OCS/'):\\r\\n\\r\\n sos = cheby2(4, 60, 10, output='sos', fs=1e3, btype='highpass')\\r\\n counter = 1\\r\\n conn = sqlite3.connect(dir_+'OCS_freq.db')\\r\\n c = conn.cursor()\\r\\n\\r\\n for f, phi01, phi02 in filelist:\\r\\n with open(path_join(dir_, f), 'r') as fh:\\r\\n hd = fh.readline().strip()\\r\\n hd_array = hd.split('|')\\r\\n if len(hd_array) == 14: # int64\\r\\n raw = np.loadtxt(path_join(dir_, f), skiprows=1, dtype='int64')\\r\\n total_avg = int(hd_array[6])\\r\\n vol = sig2vol(raw / total_avg) * 1e-3\\r\\n else:\\r\\n vol = np.loadtxt(path_join(dir_, f), skiprows=1)\\r\\n fmin = float(hd_array[0])\\r\\n bw = float(hd_array[1])\\r\\n fmax = fmin + bw\\r\\n len_ = int(hd_array[4])\\r\\n delay = int(hd_array[5])\\r\\n c.execute(\\\"SELECT species, mass FROM ocs WHERE freq >= {:g} AND freq <= {:g}\\\".format(fmin, fmax))\\r\\n try:\\r\\n species, mass = c.fetchone()\\r\\n except TypeError:\\r\\n print('cannot find record for ', counter, f)\\r\\n species = ''\\r\\n mass = 60\\r\\n\\r\\n if isinstance(phi01, type(None)): # bad FID, skip\\r\\n pass\\r\\n else:\\r\\n new_hd1 = 'fmin={:g}|fmax={:g}|bw={:g}|lo={:g}|len={:d}|delay={:d}|' \\\\\\r\\n 'species={:s}|mass={:g}|phi0={:g}'.format(\\r\\n fmin, fmax, bw, fmin, len_, delay, species, mass, phi01)\\r\\n fid1 = vol[:, 0] * 1e3\\r\\n fid1 -= np.average(fid1[-100:])\\r\\n y = sosfiltfilt(sos, fid1)\\r\\n np.savetxt(path_join(dir_, 'OCS_fid_{:d}.dat'.format(counter)),\\r\\n y, header=new_hd1, fmt='%9.6f')\\r\\n counter += 1\\r\\n\\r\\n if isinstance(phi02, type(None)): # bad FID, skip\\r\\n pass\\r\\n else:\\r\\n new_hd2 = 'fmin={:g}|fmax={:g}|bw={:g}|lo={:g}|len={:d}|delay={:d}|' \\\\\\r\\n 'species={:s}|mass={:g}|phi0={:g}'.format(\\r\\n fmin, fmax, bw, fmax, len_, delay, species, mass, phi02)\\r\\n fid2 = vol[:, 1] * 1e3\\r\\n fid2 -= np.average(fid2[-100:])\\r\\n y = sosfiltfilt(sos, fid2)\\r\\n np.savetxt(path_join(dir_, 'OCS_fid_{:d}.dat'.format(counter)),\\r\\n y, header=new_hd2, fmt='%9.6f')\\r\\n counter += 1\\r\\n\\r\\n conn.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcdda2549973dc31ebb54d0c4c415445\",\n \"score\": \"0.50026125\",\n \"text\": \"def logFileData_TS(path,file,pathTS):\\n \\n if not os.path.exists(path):\\n os.mkdir(path)\\n \\n if not os.path.exists(path+file):\\n header1 = '%-50s%5s%5s%5s%5s %15s %15s %15s %15s %15s %20s %20s %20s' \\\\\\n %('Species', '#C','#H','#N','#O', 'SCF(Hartrees)', 'ZPE(Hartrees)', 'H(Hartres)', 'G(Hartrees)', 'S(cal/mol-K)', 'Reduced mass(AMU)','Force const(mDyne/A)','Imag. freq.(cm-1)') + '\\\\n'\\n header2 = '%-50s%3s%3s%3s%3s %15s %15s %15s %15s %15s %20s %20s %20s' \\\\\\n %('-------', '-----','-----','-----','-----','-------------', '--------------', '---------- ', '------------','-------------','-----------------','--------------------', '-----------------') + '\\\\n' \\n \\n with open(path+file, 'w') as ofile:\\n ofile.writelines(header1+header2) \\n \\n fdata = open(os.path.join(path,path+file), \\\"r\\\").read()\\n \\n lines_to_write = []\\n flag_for_entropy = 0\\n counter_for_entropy = 0\\n \\n for file_name in os.listdir(pathTS):\\n if file_name.endswith('.log') and (file_name.split('.log')[0]+ ' ') not in fdata:\\n f = open(os.path.join(pathTS,file_name), \\\"r\\\")\\n lines = f.readlines()\\n \\n number_of_carbon = 0\\n number_of_hydrogen = 0\\n number_of_nitrogen = 0\\n number_of_oxygen = 0\\n \\n count_begin = 0\\n count_end = 0\\n \\n flag = 0\\n count = 0\\n \\n for line in lines:\\n # Read elemental composition\\n if line.startswith(' Redundant internal coordinates found in file.'):\\n count_begin = 1\\n if line.startswith(' Recover connectivity data from disk.'):\\n count_end = 1\\n if count_begin==1 and count_end==0 and line.startswith(' C'):\\n number_of_carbon = number_of_carbon + 1\\n if count_begin==1 and count_end==0 and line.startswith(' H'):\\n number_of_hydrogen = number_of_hydrogen + 1\\n if count_begin==1 and count_end==0 and line.startswith(' N'):\\n number_of_nitrogen = number_of_nitrogen + 1\\n if count_begin==1 and count_end==0 and line.startswith(' O'):\\n number_of_oxygen = number_of_oxygen + 1\\n \\n # Read SCF energy and ZPE\\n if line.startswith(' SCF Done:'):\\n scf = float((line.strip().split()[4]))\\n \\n if line.startswith(' Zero-point correction='):\\n zpe = float((line.strip().split()[2]))\\n \\n # Read enthalpy H, gibbs free energy G and entropy S\\n if line.startswith(' Sum of electronic and thermal Enthalpies='):\\n H = line.split('=')[1]\\n H = float(H)\\n if line.startswith(' Sum of electronic and thermal Free Energies='):\\n G = line.split('=')[1]\\n G = float(G)\\n flag_for_entropy = 1\\n if counter_for_entropy==4:\\n entropy = line[50:70]\\n S = (float(entropy))\\n if flag_for_entropy == 1:\\n counter_for_entropy = counter_for_entropy + 1\\n \\n # Read molar volume and radius\\n if line.startswith(' Frequencies --'):\\n if float((line.strip().split()[2])) < 0:\\n img_freq = float((line.strip().split()[2]))\\n flag = 1\\n \\n if flag == 1:\\n count = count + 1\\n \\n if count == 2:\\n mu = float((line.strip().split()[3]))\\n \\n if count == 3:\\n fc = float((line.strip().split()[3]))\\n \\n contents = '%-50s%-2s%3s%-2s%3s%-2s%3s%-2s%3s %15.6f %15.6f %15.6f %15.6f %15.3f %20.6f %20.6f %20.6f' \\\\\\n %(file_name.split('.log')[0], 'C',number_of_carbon,'H',number_of_hydrogen,'N',number_of_nitrogen,'O',number_of_oxygen, scf, zpe, H, G, S, mu, fc, img_freq)\\n lines_to_write.append(contents)\\n \\n flag_for_entropy = 0\\n counter_for_entropy = 0\\n \\n #lines_to_write = sorted(list(set(lines_to_write)),key=str.lower) \\n text = \\\"\\\\n\\\".join(lines_to_write) + \\\"\\\\n\\\"\\n with open(path+file, \\\"a\\\") as File:\\n File.writelines(text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26bf2fb46e0669489a946f131ea5aa05\",\n \"score\": \"0.5002591\",\n \"text\": \"def main():\\n start_time = time()\\n entries = read_file(IN_FILENAME)\\n with open(OUT_FILENAME, 'w') as out_file:\\n write_timestamp(out_file, START_MOMENT)\\n for test_id, target, prime in entries:\\n expression, count = find_expression(target, PRIMES, prime)\\n # Flush only if we are running out of time\\n write_result(out_file, test_id, count, expression, flush = time() - start_time > MAX_TIME - 15)\\n write_timestamp(out_file)\\n out_file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"926bf893703c3da78601220da564b6fa\",\n \"score\": \"0.497922\",\n \"text\": \"def files_with_timestamp(file, type):\\n with open(file, 'r', encoding=\\\"utf-8\\\") as src_file,\\\\\\n open(f'ts_{file}', 'w', encoding=\\\"utf-8\\\", newline=\\\"\\\") as out_file:\\n\\n if(type==\\\"ROOT\\\"):\\n fields = ['id', 'titulo', 'autor', 'fecha_ts', 'tema', 'mensaje']\\n fields_in= ['id', 'titulo', 'autor', 'fecha', 'tema', 'mensaje', 'current_time']\\n elif(type==\\\"CHILD\\\"):\\n fields = ['id', 'id_th', 'id_p', 'autor', 'fecha_ts', 'mensaje']\\n fields_in = ['id', 'id_th', 'id_p', 'autor', 'fecha', 'mensaje', 'current_time']\\n \\n reader = csv.DictReader(\\n src_file,\\n fieldnames=fields_in,\\n delimiter='\\\\t'\\n )\\n writer = csv.DictWriter(\\n out_file,\\n fieldnames=fields,\\n delimiter='\\\\t'\\n )\\n \\n # writer.writeheader()\\n\\n out_array = []\\n for row in reader:\\n temp = {}\\n for field in fields:\\n if field != 'fecha_ts':\\n temp[field] = row[field]\\n ts = str_2_timestamp(row['current_time'], row['fecha'])\\n temp['fecha_ts'] = ts\\n out_array.append(temp)\\n\\n print(f\\\"Escribiendo en el archivo ts_{file}\\\")\\n for line in out_array:\\n writer.writerow(line)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fac7162d05dfefda369ab67549d2b5c\",\n \"score\": \"0.49782237\",\n \"text\": \"def main():\\n parser = argparse.ArgumentParser(description=\\\"extract annotated segments\\\")\\n parser.add_argument('eaf', type=str,\\n help='''Path to the transcription of the wave file, '''\\n ''' in eaf format.''')\\n parser.add_argument('wav', type=str,\\n help='''Path to the wave file to treat''')\\n parser.add_argument('output', type=str)\\n parser.add_argument('-c', '--CAS', action='store_true',\\n help='''By default the script detects the segments'''\\n ''' using the \\\"on_off\\\" tier. For the CAS corpus,'''\\n ''' we should use the \\\"code\\\" tier.\\\\n'''\\n ''' Enable this option when treating the CAS corpus''')\\n args = parser.parse_args()\\n\\n output = args.output\\n print output\\n #if not os.path.isdir( os.path.join(output, 'treated')):\\n # os.makedirs(os.path.join(output, 'treated'))\\n #if not os.path.isdir( os.path.join(output, 'treated', 'talker_role')):\\n # os.makedirs(os.path.join(output, 'treated', 'talker_role'))\\n\\n if args.CAS:\\n # read transcriptions\\n complete_rttm = eaf2rttm_CAS(args.eaf)\\n\\n # extract annotated segments\\n on_offs = get_all_on_offs_CAS(args.eaf)\\n else:\\n # read transcriptions\\n complete_rttm = eaf2rttm(args.eaf)\\n\\n # extract annotated segments\\n on_offs = get_all_on_offs(args.eaf)\\n\\n # cut audio files according to on_off/code tier in eaf annotations\\n cut_audio(on_offs, args.wav, output)\\n\\n # store in dict the annotations to write in rttm format\\n extract_rttm = extract_from_rttm(on_offs, complete_rttm)\\n\\n # write one rttm file per on_off/tier segment\\n for key in extract_rttm:\\n base = os.path.basename(args.eaf)\\n\\n # get the name of the corpus by taking the name of the folder and removing \\\"raw\\\"\\n dir_name = os.path.split( os.path.dirname(args.eaf) )[-1].split('_')[-1]\\n\\n name = os.path.splitext(base)[0]\\n # check is initials of annotator are in eaf name\\n if '-' in name:\\n name = name.split('-')[0]\\n\\n # add 0's to have exactly 6 digits (i.e. 1 second is 000001 s)\\n str_on = str(int(key[0]))\\n str_off = str(int(key[1]))\\n\\n str_on = (6 - len(str_on)) * '0' + str_on\\n str_off = (6 - len(str_off)) * '0' + str_off\\n\\n rttm_path = '_'.join([dir_name, name,\\n str_on, str_off]) + '.rttm'\\n write_rttm(output, rttm_path, extract_rttm[key])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fce172932f6689175b5654ff617a347a\",\n \"score\": \"0.49742505\",\n \"text\": \"def main(inputDataset, outputFile):\\n start = time.time()\\n\\n with open(outputFile, 'w') as outFile:\\n for file in os.listdir(inputDataset):\\n if file.endswith(\\\".xml\\\"):\\n with open(inputDataset + \\\"/\\\" + file) as inputRunFile:\\n xml.sax.parse(inputRunFile, HyperpartisanNewsTFExtractor(outFile))\\n\\n\\n print(\\\"The vectors have been written to the output file.\\\")\\n\\n end = time.time()\\n totaltime = end-start\\n\\n writeFile = open(\\\"/scratch/anamikas/hyp/v2/tsvFileStats.txt\\\", \\\"a\\\")\\n writeFile.write(\\\"The time taken to run the script for article words counting and tf-extractor \\\" + str(totaltime))\\n writeFile.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49518e7f0d4122459c7e5c2c911b671f\",\n \"score\": \"0.49692667\",\n \"text\": \"def create_sarray_settings_file(evt_directory, cp, evt_path, priority):\\n\\n #return\\n # import required modules\\n\\n import sys\\n import os\\n import datetime\\n home_dir = os.path.expanduser(\\\"~\\\")\\n\\n # make sure evt_path is a directory and has loc_sync_done flag in it\\n if not os.path.isdir(evt_path + '/' + evt_directory):\\n sys.exit(\\\"ERROR: Filename prefix doesn't correspond to a directory in the evt path\\\")\\n\\n if not evt_directory.startswith('lux'):\\n sys.exit(\\\"ERROR: Input must be a valid filename prefix\\\")\\n\\n if not os.path.isfile( (evt_path + '/' + evt_directory + '/' + 'loc_sync_done') ):\\n \\tsys.exit(\\\"ERROR: LOC sync has not completed! Exiting...\\\")\\n\\n # Determine the lux10_blahblah from lux10_blahblah_ebblahblah\\n filename_prefix = re.match('^(lux10_[0-9]+T[0-9]+)', evt_directory).group() \\n print filename_prefix, cp, evt_path, priority\\n\\n # get list of evt files\\n #zipped_evt_files = glob.glob((evt_path + '/' + evt_directory + '/' + '*.evt.gz'))\\n #num_zipped_evt_files = len(zipped_evt_files)\\n #evt_files = glob.glob((evt_path + '/' + evt_directory + '/' + '*.evt'))\\n #num_evt_files = len(evt_files)\\n evt_files = glob.glob((evt_path + '/' + evt_directory + '/' + '*.evt*'))\\n num_evt_files = len(evt_files)\\n if num_evt_files == 0:\\n print \\\"WARNING: Found zero evt files in\\\", evt_directory\\n ## write LOC_DP_STARTED_FLAG\\n open(EVT_DIR + '/' + evt_directory + '/' + LOC_DP_STARTED_FLAG, 'w').close()\\n open(EVT_DIR + '/' + evt_directory + '/' + LOC_DP_ERROR_FLAG, 'w').close()\\n #return\\n\\n # calculate number of cores needed and walltime needed for this dataset\\n\\n # How many nodes available on Onsite Cluster.\\n total_number_of_cores_on_cluster = 0\\n total_number_of_cores_on_cluster += 0 #compute-0-0 **\\n total_number_of_cores_on_cluster += 0 #compute-0-1 **\\n total_number_of_cores_on_cluster += 4 #compute-0-2\\n total_number_of_cores_on_cluster += 0 #compute-0-3 **\\n total_number_of_cores_on_cluster += 4 #compute-0-4\\n total_number_of_cores_on_cluster += 4 #compute-0-5\\n total_number_of_cores_on_cluster += 0 #compute-0-6 **\\n total_number_of_cores_on_cluster += 4 #compute-0-7\\n total_number_of_cores_on_cluster += 0 #compute-0-8 **\\n total_number_of_cores_on_cluster += 0 #compute-0-9 **\\n total_number_of_cores_on_cluster += 4 #compute-0-10\\n total_number_of_cores_on_cluster += 4 #compute-0-11\\n total_number_of_cores_on_cluster += 0 #compute-0-12 **\\n total_number_of_cores_on_cluster += 4 #compute-0-13\\n total_number_of_cores_on_cluster += 4 #compute-0-14\\n total_number_of_cores_on_cluster += 4 #compute-0-15\\n total_number_of_cores_on_cluster += 0 #compute-0-16 **\\n total_number_of_cores_on_cluster += 4 #compute-0-17\\n all_available_nodes = 17\\n\\n if num_evt_files > total_number_of_cores_on_cluster:\\n num_cores = total_number_of_cores_on_cluster\\n \\n else:\\n num_cores = num_evt_files\\n\\n\\n print \\\"I have decided to use\\\", num_cores, \\\"cores to process these\\\", num_evt_files, \\\"evt files.\\\"\\n\\n proc_time_minutes = num_evt_files * 15 / num_cores\\n walltime_str = str(datetime.timedelta(minutes=proc_time_minutes))\\n\\n job_name = filename_prefix.split(\\\"T\\\")[-1] + \\\"_\\\" + evt_directory + \\\"_cp\\\" + str(cp)\\n \\n #print \\\"num_cores: \\\", num_cores, type(num_cores)\\n #print \\\"proc_time: \\\", proc_time_minutes\\n #print \\\"wall_time: \\\", walltime_str\\n\\n\\n #\\n # The submit_batch_job script is written below this point\\n #\\n\\n #path_to_auto_run = home_dir + '/LUXcode/Trunk/DataProcessing/ClusterDeployment/Oscar/auto_run_dp_framework_oscar.py'\\n\\n #path_to_auto_run = luxcode_path + '/Trunk/DataProcessing/ClusterDeployment/'\\n path_to_auto_run = dp_path + '/ClusterDeployment/'\\n path_to_auto_run += '/loc/auto_run_dp_framework_loc.py'\\n\\n # Make the directory where the logs will go.\\n os.mkdir(BASE_LOG_DIR + job_name)\\n\\n # Build name of output_file (qsub submission file)\\n output_file = BASE_LOG_DIR + job_name + \\\"/job_file.sh\\\"\\n\\n # rm old script and open new script\\n if os.path.isfile(output_file):\\n os.remove(output_file)\\n\\n of = open(output_file, 'w')\\n \\\"\\\"\\\"\\n \\\"\\\"\\\"\\n\\n #of.write(\\\"TEST\\\")\\n # write contents to file\\n of.write(\\\"\\\"\\\"#!/bin/bash\\n\\n# New job submission scheme for LOC queue.\\n# \\n#\\n# 20130128 - MW - Modified from submit_batch_job to run on loc\\n\\n################################################################\\n# YOU SHOULD NOT EDIT THIS FILE AT ALL!!! #\\n################################################################\\n\\n#!/bin/bash\\n#$ -cwd\\n#$ -j yes\\n#$ -N T%s\\n#$ -S /bin/bash\\n#$ -o /share/data/logs/dplogs/%s\\n#$ -t 1-%d\\n\\n# This file handles files:\\n# $file_index_stripped - $end_file\\n\\nsource /share/apps/environment.sh\\n\\necho \\\"SGE_TASK_ID: \\\" $SGE_TASK_ID\\necho \\\"SGE_TASK_FIRST: \\\" $SGE_TASK_FIRST\\necho \\\"SGE_TASK_LAST: \\\" $SGE_TASK_LAST\\necho \\\"SGE_STEP_SIZE: \\\" $SGE_STEP_SIZE\\necho \\\"JOB_ID: \\\" $JOB_ID\\necho \\\"ID: \\\" $ID\\n\\nexport ID=$((SLURM_TASKS_PER_NODE*(SLURM_ARRAYID-1)+SLURM_PROCID));\\necho \\\"Starting job $ID on $HOSTNAME (CPU $SLURM_PROCID)\\\";\\necho \\\"Starting job $SGE_TASK_ID on $HOSTNAME (JOB $JOB_ID)\\\";\\n\\n\\nexport EVT_DIRECTORY=%s\\nexport NUM_CORES=%d\\nexport AUTO_RUN_PATH=%s\\nexport CP=%d\\n\\necho `which python`\\necho \\\"/share/apps/python/pylux/bin/python \\\"\\n\\npython -u $AUTO_RUN_PATH $EVT_DIRECTORY $CP $SGE_TASK_ID $NUM_CORES $JOB_ID\\n\\n \\\"\\\"\\\" % (job_name, job_name, num_cores, evt_directory, num_cores, path_to_auto_run, cp))\\n of.close()\\n return output_file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"779e11e292ab366bdec339119a2aaae2\",\n \"score\": \"0.49656337\",\n \"text\": \"def test_file_function_time_with_domain(self):\\n\\n #Write file\\n import os, time, calendar\\n from anuga.config import time_format\\n from math import sin, pi\\n\\n finaltime = 1200\\n filename = 'test_file_function'\\n fid = open(filename + '.txt', 'w')\\n start = time.mktime(time.strptime('2000', '%Y'))\\n \\n #print 'start ',start\\n \\n dt = 60 #One minute intervals\\n t = 0.0\\n while t <= finaltime:\\n t_string = time.strftime(time_format, time.gmtime(t+start))\\n fid.write('%s, %f %f %f\\\\n' %(t_string, 2*t, t**2, sin(t*pi/600)))\\n t += dt\\n\\n fid.close()\\n\\n\\n #Convert ASCII file to NetCDF (Which is what we really like!)\\n timefile2netcdf(filename+'.txt')\\n\\n\\n\\n a = [0.0, 0.0]\\n b = [4.0, 0.0]\\n c = [0.0, 3.0]\\n\\n points = [a, b, c]\\n vertices = [[0,1,2]]\\n domain = Domain(points, vertices)\\n #print domain.starttime, start\\n\\t\\t\\n # Check that domain.starttime is updated if non-existing\\n F = file_function(filename + '.tms',\\n domain,\\n quantities = ['Attribute0', 'Attribute1', 'Attribute2']) \\n \\n \\n #print domain.starttime, start\\n assert num.allclose(domain.starttime, start)\\n\\n # Check that domain.starttime is updated if too early\\n domain.starttime = start - 1\\n F = file_function(filename + '.tms',\\n domain,\\n quantities = ['Attribute0', 'Attribute1', 'Attribute2'])\\n assert num.allclose(domain.starttime, start)\\n\\n # Check that domain.starttime isn't updated if later\\n domain.starttime = start + 1\\n F = file_function(filename + '.tms',\\n domain,\\n quantities = ['Attribute0', 'Attribute1', 'Attribute2'])\\n assert num.allclose(domain.starttime, start+1)\\n\\n domain.starttime = start\\n F = file_function(filename + '.tms',\\n domain,\\n quantities = ['Attribute0', 'Attribute1', 'Attribute2'],\\n use_cache=True)\\n \\n\\n #print F.precomputed_values\\n #print 'F(60)', F(60)\\n \\n #Now try interpolation\\n for i in range(20):\\n t = i*10\\n q = F(t)\\n\\n #Exact linear intpolation\\n assert num.allclose(q[0], 2*t)\\n if i%6 == 0:\\n assert num.allclose(q[1], t**2)\\n assert num.allclose(q[2], sin(t*pi/600))\\n\\n #Check non-exact\\n\\n t = 90 #Halfway between 60 and 120\\n q = F(t)\\n assert num.allclose( (120**2 + 60**2)/2, q[1] )\\n assert num.allclose( (sin(120*pi/600) + sin(60*pi/600))/2, q[2] )\\n\\n\\n t = 100 #Two thirds of the way between between 60 and 120\\n q = F(t)\\n assert num.allclose( 2*120**2/3 + 60**2/3, q[1] )\\n assert num.allclose( 2*sin(120*pi/600)/3 + sin(60*pi/600)/3, q[2] )\\n\\n os.remove(filename + '.tms')\\n os.remove(filename + '.txt')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"897f8a7684cfce4205a4c7f9f2162b00\",\n \"score\": \"0.49623284\",\n \"text\": \"def stitch_tiles(self):\\n \\n l = []\\n \\n for f in os.listdir(self.src_path):\\n l.append(os.path.join(self.src_path, f))\\n \\n vrt_path = os.path.join(self.vrt_path, 'mosaic.vrt')\\n HelperMethods().safeMakeDir(self.vrt_path)\\n vrt = gdal.BuildVRT(vrt_path, l)\\n \\n result = os.path.join(self.dest_path, 'mosaic.tif')\\n gdal.Translate(result, vrt, format=self.img_format)\\n \\n logging.info('------------Stitching Task Complete------------')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac9280cd98c78f852760d303e520a08e\",\n \"score\": \"0.49582306\",\n \"text\": \"def makeMovies():\\n from ROOT import TFile, TTree, MGTWaveform\\n import matplotlib.pyplot as plt\\n plt.style.use('./pltReports.mplstyle')\\n from matplotlib import animation\\n import pywt\\n\\n dsList = [0,1,2,3,4,\\\"5A\\\",\\\"5B\\\",\\\"5C\\\",6]\\n wfLimit = None\\n\\n # dsList = [0]\\n # wfLimit = 500\\n\\n for ds in dsList:\\n\\n outFile = \\\"./plots/final%d-wfs-DS%s.mp4\\\" % (pctTot, ds)\\n print(\\\"Saving WFs for DS-%s: %s\\\" % (ds, outFile))\\n\\n inFile = \\\"%s/bkg/cut/final%dt/final%dt_DS%s.root\\\" % (dsi.dataDir, pctTot, pctTot, ds)\\n tf = TFile(inFile)\\n tt = tf.Get(\\\"skimTree\\\")\\n\\n # make an entry list\\n tCut = \\\"trapENFCal >= 1 && trapENFCal <= 10\\\"\\n\\n # simple entry list\\n # n = tt.Draw(\\\"Entry$:Iteration$\\\",tCut,\\\"goff\\\")\\n # evt, itr = tt.GetV1(), tt.GetV2()\\n # evtList = [[int(evt[i]),int(itr[i])] for i in range(n)]\\n\\n # slightly fancy entry list, sorted by energy\\n n = tt.Draw(\\\"Entry$:Iteration$:trapENFCal\\\",tCut,\\\"goff\\\")\\n evt, itr, ene = tt.GetV1(), tt.GetV2(), tt.GetV3()\\n ene = [ene[i] for i in range(n)]\\n eHit = np.argsort(ene) # sort by ascending energy\\n # eHit = np.flip(np.argsort(ene), axis=0) # sort by descending energy\\n evtList = [[int(evt[i]),int(itr[i])] for i in eHit]\\n\\n # set up the figure, the axis, and the plot element we want to animate\\n fig = plt.figure(figsize=(10,6))\\n nFont = 14\\n a1 = plt.subplot(111)\\n a1.set_xlabel(\\\"Time (ns)\\\", ha='right', x=1, fontsize=nFont)\\n a1.set_ylabel(\\\"Voltage (ADC)\\\", ha='right', y=1, fontsize=nFont)\\n\\n p1, = a1.plot(np.ones(1), np.ones(1), c='b')\\n p2, = a1.plot(np.ones(1), np.ones(1), c='k',alpha=0.6)\\n\\n # initialization: plot the background of each frame\\n def init():\\n p1.set_data([],[])\\n return p1,\\n\\n # this is the loop over entries\\n def animate(i):\\n\\n iE = evtList[i][0]\\n iH = evtList[i][1]\\n tt.GetEntry(iE)\\n\\n run = tt.run\\n chan = tt.channel.at(iH)\\n hitE = tt.trapENFCal.at(iH)\\n fSlo = tt.fitSlo.at(iH)\\n rise = tt.riseNoise.at(iH)\\n\\n wf = tt.MGTWaveforms.at(iH)\\n truncLo, truncHi = 0, 2\\n if ds==6 or ds==2: truncLo = 4\\n signal = wl.processWaveform(wf,truncLo,truncHi)\\n\\n waveBLSub = signal.GetWaveBLSub()\\n waveTS = signal.GetTS()\\n p1.set_ydata(waveBLSub)\\n p1.set_xdata(waveTS)\\n\\n # wavelet denoised\\n wp = pywt.WaveletPacket(waveBLSub, 'db2', 'symmetric', maxlevel=4)\\n new_wp = pywt.WaveletPacket(data=None, wavelet='db2', mode='symmetric')\\n new_wp['aaa'] = wp['aaa'].data\\n waveDenoised = new_wp.reconstruct(update=False)\\n diff = len(waveDenoised) - len(waveBLSub)\\n if diff > 0: waveDenoised = waveDenoised[diff:]\\n p2.set_ydata(waveDenoised)\\n p2.set_xdata(waveTS)\\n\\n plt.title(\\\"Run %d Ch %d iE %d/%d trapENFCal %.1f fitSlo %.1f riseNoise %.1f\\\" % (run,chan,i,len(evtList),hitE,fSlo,rise), fontsize=nFont)\\n\\n # dynamically scale the axes\\n xmin, xmax = np.amin(waveTS), np.amax(waveTS)\\n a1.set_xlim([xmin,xmax])\\n\\n ymin, ymax = np.amin(waveDenoised), np.amax(waveDenoised)\\n yLo = -7 if ymin-abs(0.1 * ymin) < 7 else ymin-abs(0.1 * ymin)\\n yHi = ymax + abs(0.5+ymax)\\n a1.set_ylim([yLo,yHi])\\n\\n # print(\\\"%d / %d Run %d nCh %d chan %d trapE %.1f samp %d\\\" % (i,nList,run,nChans,chan,energy,wf.GetLength()))\\n if i % 500 == 0 and i != 0:\\n print(\\\"%d / %d entries saved (%.2f %% done).\\\" % (i,len(evtList),100*(float(i)/len(evtList))))\\n return p1,\\n\\n # make the movie\\n nWF = wfLimit if wfLimit is not None else len(evtList)\\n anim = animation.FuncAnimation(fig, animate, init_func=init, frames=nWF, interval=0, blit=False)\\n anim.save(outFile, fps=20)#, extra_args=['-vcodec', 'libx264'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fe954e113666ca2118fc6c0c4fb8388\",\n \"score\": \"0.49571162\",\n \"text\": \"def main_function(tqdm_enabled):\\n\\n\\targs = parse_cli_arguments()\\n\\tfilelist = sorted(list_files(args.path))\\n\\turl = Url()\\n\\turl.parse(args.url)\\n\\tsession = WttManager()\\n\\tsession = Session()\\n\\n\\tif args.auth == \\\"http\\\":\\n\\t\\tinitial_auth_http(args, session)\\n\\n\\tpool = mp.Pool(processes=args.threads)\\n\\twttmanager = Manager()\\n\\tsharedattacks = manager.list()\\n\\n\\tfor filename in filelist:\\n\\t\\tattacks = load_attacks(filename)\\n\\t\\tpbar = wttmanager.Bar()\\n\\t\\tif tqdm_enabled == 1:\\n\\t\\t\\tpbar.set(len(attacks),\\n\\t\\t\\t\\t\\tfilename,\\n\\t\\t\\t\\t\\tfilelist.index(filename))\\n\\t\\telse:\\n\\t\\t\\tprint(\\\"Processing file: %s\\\" % filename)\\n# DEBUG\\n#\\t\\tprocess_attack([attacks[0],url,session,args,sharedattacks,filename,pbar,tqdm_enabled])\\n\\t\\tpool.map( process_attack,\\n\\t\\t\\t\\t\\tzip(attacks,\\n\\t\\t\\t\\t\\t\\trepeat(url),\\n\\t\\t\\t\\t\\t\\trepeat(session),\\n\\t\\t\\t\\t\\t\\trepeat(args),\\n\\t\\t\\t\\t\\t\\trepeat(sharedattacks),\\n\\t\\t\\t\\t\\t\\trepeat(filename),\\n\\t\\t\\t\\t\\t\\trepeat(pbar),\\n\\t\\t\\t\\t\\t\\trepeat(tqdm_enabled)))\\n\\n\\tprint(\\\"\\\\n\\\" * len(filelist))\\n\\tprint(\\\"\\\\nGenerating report...\\\\t\\\")\\n\\tgenerate_report(sorted(sharedattacks, key=lambda k: k[\\\"id\\\"]), args)\\n\\tprint(\\\"ok\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29da6560de27105bd0c8256e6bd3f703\",\n \"score\": \"0.4957097\",\n \"text\": \"def run_mintemp():\\n base_path = \\\"/Volumes/SeagateExpansionDrive/ETRM_inputs/PRISM/Temp/Minimum\\\"\\n output_path = \\\"/Volumes/SeagateExpansionDrive/ETRM_inputs/new_inputs/Prism/Temp/Minimum_standard\\\"\\n resample_method = \\\"cubic\\\"\\n\\n folderwarp(base_path, output_path, resample_method)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6225b5d3eb48d9219ac4554fac7dd5d\",\n \"score\": \"0.49538043\",\n \"text\": \"def walk_300vw_3d(tts, path):\\n train, test = '', ''\\n for (dir_path, dir_names, file_names) in os.walk(path):\\n landmarks = []\\n for file_name in file_names:\\n # search for torch files\\n file_path = os.path.join(dir_path, file_name)\\n if not file_path.endswith('.t7'):\\n continue\\n\\n # load the points\\n points = torchfile.load(file_path)\\n\\n # search for image name\\n if os.path.isfile(file_path[:-2] + 'jpg'):\\n csv_list = [file_path[:-2] + 'jpg']\\n elif os.path.isfile(file_path[:-2] + 'png'):\\n csv_list = [file_path[:-2] + 'png']\\n else:\\n continue\\n\\n # add landmarks to our list\\n for p in points:\\n for n in p:\\n csv_list.append(str(n))\\n landmarks.append(f\\\"{','.join(csv_list)}\\\")\\n\\n # add landmarks from hole directory to train or path (no two files from one subset in different groups)\\n if len(landmarks) > 0:\\n if random.random() > tts:\\n test += '\\\\n'.join(landmarks)\\n test += '\\\\n'\\n else:\\n train += '\\\\n'.join(landmarks)\\n train += '\\\\n'\\n return train, test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f9d2b085755659addded4b0214720ab\",\n \"score\": \"0.49535906\",\n \"text\": \"def truncate(temporal_length=2000, clips=10):\\r\\n path = sys.path[0]\\r\\n path = '/'.join(path.split('/')[:-2] + ['features'])\\r\\n train_path = osp.join(path, f'train_mean_{temporal_length}')\\r\\n val_path = osp.join(path, f'val_mean_{temporal_length}')\\r\\n test_path = osp.join(path, f'test_mean_{temporal_length}')\\r\\n\\r\\n train_meta_path = osp.join(path, f'train_meta.json')\\r\\n val_meta_path = osp.join(path, f'val_meta.json')\\r\\n test_meta_path = osp.join(path, f'test_meta.json')\\r\\n\\r\\n subsets = list()\\r\\n subsets.append(dict(name='train', root=path, feature_path=train_path, meta_path=train_meta_path))\\r\\n subsets.append(dict(name='val', root=path, feature_path=val_path, meta_path=val_meta_path))\\r\\n subsets.append(dict(name='test', root=path, feature_path=test_path, meta_path=test_meta_path))\\r\\n\\r\\n length_per_clip = temporal_length // clips\\r\\n for subset in subsets:\\r\\n name, root, feature_path, meta_path = subset['name'], subset['root'], \\\\\\r\\n subset['feature_path'], subset['meta_path']\\r\\n new_meta_dict = dict()\\r\\n new_meta_name = osp.join(root, f'{name}_meta_{clips}.json')\\r\\n truncate_feature_dir = osp.join(root, f'{name}_mean_{temporal_length}_{clips}')\\r\\n if not osp.exists(truncate_feature_dir):\\r\\n os.makedirs(truncate_feature_dir)\\r\\n with open(meta_path, 'r', encoding='utf-8') as f:\\r\\n video_infos = json.load(f)\\r\\n for video_name in video_infos:\\r\\n video_info = video_infos[video_name]\\r\\n duration = video_info['duration_second']\\r\\n duration_per_clip = duration / clips\\r\\n duration_list = [(duration_per_clip * i, duration_per_clip * (i + 1)) for i in range(clips)]\\r\\n meta_dict = defaultdict(list)\\r\\n idx = 0\\r\\n video_info['annotations'] = sorted(video_info['annotations'], key=lambda x: x['segment'][0])\\r\\n video_infos[video_name] = video_info\\r\\n for anno in video_info['annotations']:\\r\\n start, end = anno['segment']\\r\\n while idx < clips and start > duration_list[idx][1]:\\r\\n idx += 1\\r\\n if idx == clips:\\r\\n continue\\r\\n temp_idx = idx\\r\\n while temp_idx < clips and end > duration_list[temp_idx][1]:\\r\\n temp_idx += 1\\r\\n if temp_idx == clips:\\r\\n end = duration_list[-1][1]\\r\\n temp_idx = clips - 1\\r\\n for i in range(idx, temp_idx):\\r\\n meta_dict[i].append(dict(segment=[start - duration_per_clip * i,\\r\\n duration_list[i][1] - duration_per_clip * i]))\\r\\n start = duration_list[i][1]\\r\\n meta_dict[temp_idx].append(dict(segment=[start - duration_per_clip * temp_idx,\\r\\n end - duration_per_clip * temp_idx]))\\r\\n feature_duration_list = [f'{length_per_clip * i}_{length_per_clip * (i + 1)}' for i in range(clips)]\\r\\n with open(osp.join(feature_path, video_name + '.pkl'), 'rb') as bf:\\r\\n features = pickle.load(bf, encoding='bytes')\\r\\n for i, feature_duration in enumerate(feature_duration_list):\\r\\n if meta_dict[i]:\\r\\n new_meta_dict[f'{video_name}_{feature_duration}'] = dict(duration_second=duration_per_clip,\\r\\n annotations=meta_dict[i])\\r\\n with open(osp.join(truncate_feature_dir, f'{video_name}_{feature_duration}.pkl'), 'wb') as bf:\\r\\n pickle.dump(features[length_per_clip * i: length_per_clip * (i + 1)], bf)\\r\\n with open(meta_path, 'w', encoding='utf-8') as f:\\r\\n json.dump(video_infos, f, ensure_ascii=False, indent=2)\\r\\n with open(new_meta_name, 'w', encoding='utf-8') as f:\\r\\n json.dump(new_meta_dict, f, ensure_ascii=False, indent=2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3c5e722da57cfddc2bcaff1de9e1eb9\",\n \"score\": \"0.49508253\",\n \"text\": \"def write_schedule(file_list,index,str_pattern,time_rewind=0):\\n\\n def get_next_file(list_sub,index_sub):\\n \\\"\\\"\\\"Get next file from list, looping the list around when it\\n runs out.\\n \\\"\\\"\\\"\\n\\n list_iter = (i for i in list_sub[index_sub:])\\n\\n while True:\\n try:\\n yield next(list_iter)\\n # Produce cycled list when generator runs out.\\n except StopIteration:\\n list_iter = itertools.cycle(list_sub)\\n\\n def get_prev_file(list_sub,index_sub):\\n \\\"\\\"\\\"Get previous file from list, looping the list around when it\\n runs out. This is done by slicing the list passed in, then\\n reversing the original and sliced lists.\\n \\\"\\\"\\\"\\n\\n list_sub_reverse = list_sub[:index_sub]\\n list_sub_reverse.reverse()\\n list_sub.reverse()\\n list_iter = (i for i in list_sub_reverse)\\n\\n while True:\\n try:\\n yield next(list_iter)\\n # Produce cycled list when generator runs out.\\n except StopIteration:\\n list_iter = itertools.cycle(list_sub)\\n\\n def create_entry(element,type=\\\"normal\\\",time=\\\"\\\"):\\n \\\"\\\"\\\"Return a dictionary for JSON based on information\\n parsed from string element in list.\\n If type is \\\"normal\\\", element should be a 2-element list\\n split by get_extra_info().\\n \\\"\\\"\\\"\\n\\n if type == \\\"normal\\\":\\n return {\\n \\\"type\\\":\\\"normal\\\",\\n \\\"name\\\":element[0][0],\\n \\\"time\\\":time,\\n \\\"extra_info\\\":element[1]\\n }\\n\\n elif type == \\\"extra\\\":\\n return {\\n \\\"type\\\":\\\"extra\\\",\\n \\\"name\\\":\\\"\\\",\\n \\\"time\\\":\\\"\\\",\\n \\\"extra_info\\\":element[1:]\\n }\\n\\n def process_filename(filename):\\n \\\"\\\"\\\"Process a filename for insertion. Returns either a\\n two-element list from get_extra_info() with a changed title\\n from alt_names, or None for entries to be passed over.\\n \\\"\\\"\\\"\\n\\n nonlocal duration, total_duration, alt_names\\n\\n filename = get_extra_info(filename)\\n\\n # Check file, and if entry cannot be found, skip the entry.\\n if not check_file(os.path.join(BASE_PATH,''.join(filename[0])),\\n no_exit=True):\\n\\n return None\\n\\n # Get length of video in seconds from ffprobe, plus\\n # ffmpeg padding.\\n duration += (get_length(os.path.join(BASE_PATH,''.join(filename[0])))\\n + VIDEO_PADDING)\\n total_duration += duration\\n\\n # Skip files matching SCHEDULE_EXCLUDE_FILE_PATTERN, but keep\\n # their durations.\\n if (str_pattern is not None and\\n filename[0][0].casefold().startswith(str_pattern)):\\n\\n return None\\n\\n # Read the alt_names dictionary. If filename has a matching\\n # key, replace the name with the value.\\n if alt_names is not None:\\n if filename[0][0] in alt_names:\\n if isinstance(alt_names[filename[0][0]],str):\\n filename[0] = (alt_names[filename[0][0]],filename[0][1])\\n else:\\n print(f\\\"\\\"\\\"Alternate name for {filename[0][0]} in\\n alt_names.json is not a valid string.\\\"\\\"\\\")\\n\\n return filename\\n\\n # Load alt_names.json.\\n try:\\n with open(\\\"alt_names.json\\\",\\\"r\\\") as alt_names_json:\\n alt_names = json.load(alt_names_json)\\n except:\\n alt_names = None\\n\\n # Get names and start times of upcoming videos.\\n\\n # For the first file in file_list, this is the current system time.\\n # Time is retrieved in UTC, to be converted to user's local time\\n # when they load the schedule in their browser.\\n next_time = datetime.datetime.utcnow()\\n\\n # Offset first program timing by elapsed_time read in the\\n # second line of play_index.txt.\\n duration = -time_rewind\\n total_duration = -time_rewind\\n\\n coming_up_next = deque()\\n\\n for filename in get_next_file(file_list,index):\\n\\n # Break when either limit is reached.\\n if (len([i for i in coming_up_next if i[\\\"type\\\"] == \\\"normal\\\"])\\n > SCHEDULE_MAX_VIDEOS or\\n total_duration > (SCHEDULE_UPCOMING_LENGTH * 60)):\\n\\n break\\n\\n # Skip comment lines.\\n if filename is None:\\n continue\\n\\n # Add extra lines and continue.\\n if filename.startswith(\\\":\\\"):\\n coming_up_next.append(create_entry(filename,type=\\\"extra\\\"))\\n continue\\n\\n processed_name = process_filename(filename)\\n\\n if processed_name is not None:\\n coming_up_next.append(create_entry(processed_name,\\n time=next_time.strftime(\\\"%Y-%m-%d %H:%M:%S\\\")))\\n else:\\n continue\\n\\n # Add length of current video to current time and use as\\n # starting time for next video.\\n next_time += datetime.timedelta(seconds=duration)\\n duration = 0\\n\\n # Get names and start times of previous videos.\\n # Process is similar to adding upcoming videos but some steps\\n # run in opposite order.\\n # If time_rewind is not 0 because of a resume, do not provide\\n # previous file information, as a stream may have restarted\\n # after an unexpected exit.\\n if SCHEDULE_PREVIOUS_MAX_VIDEOS > 0 and time_rewind == 0:\\n\\n prev_time = datetime.datetime.utcnow()\\n duration = 0\\n total_duration = 0\\n previous_files = deque()\\n\\n for filename in get_prev_file(file_list,index):\\n if (len([i for i in previous_files if i[\\\"type\\\"] == \\\"normal\\\"])\\n > SCHEDULE_PREVIOUS_MAX_VIDEOS or\\n total_duration > (SCHEDULE_PREVIOUS_LENGTH * 60)):\\n break\\n\\n if filename is None:\\n continue\\n\\n if filename.startswith(\\\":\\\"):\\n previous_files.appendleft(create_entry(filename,type=\\\"extra\\\"))\\n continue\\n\\n processed_name = process_filename(filename)\\n\\n # Subtract duration from current time before appending.\\n prev_time -= datetime.timedelta(seconds=duration)\\n\\n if processed_name is not None:\\n previous_files.appendleft(create_entry(processed_name,\\n time=prev_time.strftime(\\\"%Y-%m-%d %H:%M:%S\\\")))\\n else:\\n continue\\n\\n duration = 0\\n\\n else:\\n previous_files = []\\n\\n schedule_json_out = {\\n \\\"coming_up_next\\\":list(coming_up_next),\\n \\\"previous_files\\\":list(previous_files),\\n \\\"script_version\\\":SCRIPT_VERSION\\n }\\n\\n with open(SCHEDULE_PATH,\\\"w+\\\") as schedule_json:\\n schedule_json.write(json.dumps(schedule_json_out))\\n\\n # Upload JSON file to a publicly accessible location\\n # using pysftp or something similar if necessary here.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e32ee86deb267f5325a07729f0a985d\",\n \"score\": \"0.4948647\",\n \"text\": \"def compute_topicwords():\\n for d in os.listdir(__collection_root):\\n conf = open(__project_root + 'twords.conf', 'w')\\n configstring = \\\"stopFilePath = stoplist-smart-sys.txt\\\\n\\\\\\n performStemming = N\\\\n\\\\\\n backgroundCorpusFreqCounts = bgCounts-Giga.txt\\\\n\\\\\\n topicWordCutoff = 10.0\\\\n\\\"\\n configstring += \\\"inputDir = \\\"+ __collection_root + d + \\\"\\\\n\\\"\\n configstring += \\\"outputFile = \\\"+ __project_root + \\\"topicwords/\\\"+d+\\\".ts\\\"\\n conf.write(configstring)\\n conf.close()\\n # Run topicwords with that configfile\\n #os.system('java -Xmx1000m ' + __topicwordsbinary + ' ' + __project_root + 'twords.conf')\\n os.system('java -Xmx1000m TopicSignatures ' + __project_root + 'twords.conf')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93e229ab56c5ec7907e2a6b659d47824\",\n \"score\": \"0.49458197\",\n \"text\": \"def rotate(datadir,labeldir,workdir):\\n\\n pathS = datadir\\n pathL = labeldir\\n\\n if workdir in os.getcwd():\\n workdir = \\\".\\\"\\n\\n #skeleton directory\\n dirSkeleton = os.listdir( pathS )\\n\\n #label directory\\n dirLabel = os.listdir( pathL )\\n\\n theta_set=[]\\n\\n for theta in range(1,16):\\n theta_set.append(theta)\\n theta_set.append(theta+0.5)\\n\\n print(theta_set) \\n\\n\\n for angle_id in {\\\"Left\\\",\\\"Middle\\\",\\\"Right\\\"}:\\n if os.path.exists(workdir + '/actions'+angle_id)==False: os.mkdir(workdir + '/actions'+angle_id)\\n\\n for theta in theta_set:\\n if os.path.exists(workdir + '/actions'+angle_id+\\\"by\\\"+str(theta))==False: os.mkdir(workdir + '/actions'+angle_id+\\\"by\\\"+str(theta))\\n\\n\\n\\n #right middle and left action counters\\n count_actionsR=0\\n count_actionsM=0\\n count_actionsL=0\\n\\n #initialization of action file variables\\n action_file_original=\\\"\\\"\\n\\n\\n for data,label in zip(dirSkeleton,dirLabel):\\n\\n #ppp string to check the camera angle of each action \\n if \\\"L\\\" in label:\\n angle_id =\\\"Left\\\"\\n if \\\"M\\\" in label:\\n angle_id =\\\"Middle\\\"\\n if \\\"R\\\" in label:\\n angle_id =\\\"Right\\\" \\n\\n #single label path\\n path_single_label = pathL + '/' + label\\n\\n #label file\\n single_label_file = open(path_single_label)\\n\\n #line read\\n line = single_label_file.readline()\\n\\n while line!=\\\"\\\":\\n\\n #line values\\n values = line.split(',')\\n if len(values)<2:\\n break\\n\\n #get values\\n action = values[0]\\n starting_action_frame = values[1]\\n ending_action_frame = values[2]\\n\\n if \\\"Right\\\" in angle_id:\\n count_actionsR, pathset = create_directories(workdir,count_actionsR,action,angle_id,label,theta_set)\\n counter = count_actionsR\\n \\n if \\\"Middle\\\" in angle_id:\\n count_actionsM, pathset = create_directories(workdir,count_actionsM,action,angle_id,label,theta_set)\\n counter = count_actionsM\\n \\n if \\\"Left\\\" in angle_id:\\n count_actionsL, pathset = create_directories(workdir,count_actionsL,action,angle_id,label,theta_set)\\n counter = count_actionsL\\n \\n #data\\n pathdat = pathS + '/' + label\\n single_data_file = open(pathdat,\\\"r\\\")\\n\\n #write data\\n for num_of_frame,dataLine in enumerate(single_data_file):\\n #getting data according to frame instructions\\n if num_of_frame >= int(starting_action_frame)-1 and num_of_frame<=int(ending_action_frame)-1:\\n\\n values = dataLine.split(' ')\\n\\n #writes data to the original file\\n w_string_original=\\\"\\\"\\n\\n w_string_theta=\\\"\\\"\\n\\n\\n \\n array_for_rotations = np.zeros([1,150])\\n\\n for y in range(0,len(values)):\\n array_for_rotations[0,y] = Decimal(values[y])\\n\\n #ORIGINAL FILE \\n\\n path_original = workdir + '/actions'+angle_id+'/' +str(label[0:4])+\\\"_\\\"+str(counter)+\\\"_\\\"+\\\"action\\\"+\\\"_\\\"+action+\\\".csv\\\"\\n action_file_original = open(path_original,\\\"a\\\")\\n\\n for coordinates in range(0,150):\\n w_string_original += str(array_for_rotations[0,coordinates]) + ','\\n\\n\\n #write string in the action file\\n if(w_string_original!=\\\"\\\"): \\n action_file_original.write(w_string_original)\\n action_file_original.write(\\\"\\\\n\\\") \\n\\n #ROTATION BY theta DEGREES ABOUT THE Y AXIS\\n\\n for theta,path in zip(theta_set,pathset):\\n w_string_theta=rotation_by_angle(array_for_rotations,theta * np.pi/180)\\n \\n action_file = open(path,\\\"a\\\")\\n if(w_string_theta!=\\\"\\\"): \\n action_file.write(w_string_theta)\\n action_file.write(\\\"\\\\n\\\")\\n\\n\\n elif num_of_frame>int(ending_action_frame):\\n break\\n\\n\\n #read new line\\n line = single_label_file.readline()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35a5d9c3f86777c2bf62592de99c362a\",\n \"score\": \"0.4945094\",\n \"text\": \"def main():\\n parser = argparse.ArgumentParser()\\n parser.add_argument('--combined_name',\\n # default='2015_2016_tf')\\n default='2014_2015_tf')\\n parser.add_argument('--data_dir',\\n default='../../data/frequency/')\\n parser.add_argument('--tf_files', nargs='+',\\n default=None)\\n parser.add_argument('--min_tf', type=int, default=5)\\n args = parser.parse_args()\\n combined_name = args.combined_name\\n data_dir = args.data_dir\\n tf_files = args.tf_files\\n min_tf = args.min_tf\\n out_dir = data_dir\\n if(tf_files is None):\\n date_match_str = '201[0-9]-[0-9]{2}'\\n file_matcher = re.compile('%s.tsv'%(date_match_str))\\n date_matcher = re.compile(date_match_str)\\n combine_data_files(combined_name, data_dir, \\n file_matcher, date_matcher,\\n out_dir)\\n else:\\n # all_data = {}\\n all_data = []\\n for f in tf_files:\\n print('reading %s'%(f))\\n d = re.findall('201[0-9]-[01][0-9]', f)[0]\\n # tf = pd.read_csv(f, sep='\\\\t', index_col=0)\\n # dataframe takes too much space...just use a dictionary\\n tf = {}\\n with open(f, 'r') as tf_iter:\\n for i, l in enumerate(tf_iter):\\n if(i > 0):\\n w, count = l.split('\\\\t')\\n count = int(count)\\n tf[w] = count\\n print('got %d counts'%(len(tf)))\\n # get rid of non-alpha words\\n alpha_words = list(filter(lambda x: type(x) is str and x.isalpha(), tf.keys()))\\n tf = {k : tf[k] for k in alpha_words}\\n print('%d alpha words'%(len(alpha_words)))\\n # get rid of low tf words\\n tf = {k : v for k,v in tf.iteritems() if v > min_tf}\\n print('%d words at min tf %d'%(len(tf), min_tf))\\n tf = pd.DataFrame(tf, index=[d]).transpose()\\n print('got df %s'%(str(tf.shape)))\\n all_data.append(tf)\\n # date_str = re.findall('201[456]-[0-9]+', f)[0]\\n # all_data[date_str] = tf.ix[:, 0]\\n # all_data = all_data.join(tf, how='outer')\\n # print('full data has shape %s'%(str(all_data.shape)))\\n # all_data = pd.DataFrame(all_data)\\n # all_data = [pd.read_csv(f, sep='\\\\t', index_col=0) for f in tf_files]\\n all_data = pd.concat(all_data, axis=1)\\n print('full data has shape %s'%(str(all_data.shape)))\\n out_fname = os.path.join(out_dir, '%s.tsv'%(combined_name))\\n all_data.to_csv(out_fname, sep='\\\\t')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d86bf8e0e0b7277f4ab08d8fa107332\",\n \"score\": \"0.49431974\",\n \"text\": \"def saveNormalize(name, typ):\\n MetaLex.dicProject.createtemp()\\n if typ == u'text' :\\n if MetaLex.dicProject.inDir(name) :\\n with codecs.open(name, 'a', 'utf-8') as fil :\\n num = 1\\n for art in dicArticles :\\n for k, v in art.items() :\\n if k != u'article_1' :\\n fil.write('%10s : %s\\\\n' %(k, v))\\n else :\\n fil.write('\\\\n----- FILE: %s ---------------------------------------------------------------------------------\\\\n\\\\n' %num)\\n fil.write('%10s : %s\\\\n' %(k, v))\\n num += 1\\n message = name+u' is created and contain all text format data from html files > Saved in dicTemp folder' \\n MetaLex.dicLog.manageLog.writelog(message) \\n else :\\n message = name+u' is created and contain all text format data from html files > Saved in dicTemp folder' \\n MetaLex.dicLog.manageLog.writelog(message) \\n \\n if typ == u'pickle' : \\n if MetaLex.dicProject.inDir(name) and MetaLex.dicProject.filePickle(dicArticles, name) :\\n message = name+u' is created and contain pickle data object from html files > Saved in dicTemp folder' \\n MetaLex.dicLog.manageLog.writelog(message) \\n else :\\n message = name+u' is created and contain pickle data object from html files > Saved in dicTemp folder' \\n MetaLex.dicLog.manageLog.writelog(message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51b695d90f7276b0f12621990c1df775\",\n \"score\": \"0.49424013\",\n \"text\": \"def main(full_filename, verbose=0, classification=0, variance_test=1):\\n \\n #####################\\n ## SET SOME STUFF ###\\n #####################\\n \\n letters_as_numbers = {'a':10, 'b':11, 'c':12, 'd':13, \\n 'e':14, 'f':15, 'g':16, 'h':17, \\n 'i':18, 'j':19, 'k':20, 'l':21, \\n 'm':22, 'n':23}\\n \\n \\n #####################\\n ###### DO STUFF #####\\n #####################\\n \\n \\n # work out date and time from filename\\n filename = full_filename.split('/')[-1] # filename should be YYMDDHMM.trw\\n \\n year = int(f\\\"20{filename[:2]}\\\")\\n \\n month = filename[2]\\n if month in letters_as_numbers.keys():\\n month_num = letters_as_numbers[month]\\n if month_num > 12:\\n msg = f'Invalid month: {month}'\\n raise ValueError(msg)\\n else:\\n month = month_num\\n else:\\n month = int(month)\\n \\n day = int(f\\\"{filename[3:5]}\\\")\\n \\n hour = filename[5]\\n if hour in letters_as_numbers.keys():\\n hour = letters_as_numbers[hour]\\n else:\\n hour = int(hour)\\n \\n minutes = int(filename[6:8])\\n \\n # create datetime object\\n date_time_from_filename = dt.datetime(year, month, day, hour, minutes, tzinfo=dt.timezone.utc)\\n \\n time_in_minutes_since_start_of_day = date_time_from_filename.hour*60 + date_time_from_filename.minute\\n time_in_seconds_since_start_of_day = time_in_minutes_since_start_of_day*60\\n \\n \\n \\n #####################\\n ### READ THE FILE ###\\n #####################\\n \\n with open(full_filename, \\\"rb\\\") as f:\\n data = f.readlines() \\n \\n data = b''.join(data)\\n \\n ######################\\n # TRANSLATE THE FILE #\\n ######################\\n \\n # idl code lines 364 - 434\\n # first line\\n \\n # first two bytes are ignored\\n # third and fourth bytes might be heading_size, or might be ignored, if third and fourth bytes is > 20 then heading_size is fifth and sixth bytes\\n heading_size = int(f\\\"0b{decimalToBinary(data[3])}{decimalToBinary(data[2])}\\\",2)\\n if heading_size > 20:\\n heading_size = int(f\\\"0b{decimalToBinary(data[5])}{decimalToBinary(data[4])}\\\",2)\\n \\n # seventh byte is header type, 2 includes parameter table 3 doppler type file\\n header_type = data[6]\\n \\n # eigth byte is dummy\\n # ninth and tenth - version number. Kind of\\n version_no = int(f\\\"0b{decimalToBinary(data[9])}{decimalToBinary(data[8])}\\\",2)\\n # this is here. From IDL implementation\\n convert_version_no_uint = np.uint16(-1 * version_no)\\n \\n #print(version_no)\\n #print(np.uint16(-1 * version_no)) # this conversion is used in the idl code\\n \\n \\n # 11th and 12th bytes are ignored\\n # start date - 13th-16th bytes (well, 16th-13th), unix time\\n start_date_unix = int(f\\\"0b{decimalToBinary(data[15])}{decimalToBinary(data[14])}{decimalToBinary(data[13])}{decimalToBinary(data[12])}\\\",2)\\n start_date = dt.datetime.fromtimestamp(start_date_unix, dt.timezone.utc)\\n \\n # get day of year from this time stamp\\n day_of_year = start_date.timetuple().tm_yday\\n \\n # end date is 17th-20th bytes\\n end_date_unix = int(f\\\"0b{decimalToBinary(data[19])}{decimalToBinary(data[18])}{decimalToBinary(data[17])}{decimalToBinary(data[16])}\\\",2)\\n end_date = dt.datetime.fromtimestamp(end_date_unix, dt.timezone.utc)\\n \\n # 21st and 22nd bytes - update rate\\n update_rate = int(f\\\"0b{decimalToBinary(data[21])}{decimalToBinary(data[20])}\\\",2)\\n \\n # 23rd and 24th bytes - m_TrtParametersize\\n m_TrtParametersize = int(f\\\"0b{decimalToBinary(data[23])}{decimalToBinary(data[22])}\\\",2)\\n \\n # idl code line 468\\n version2_2a = 1 if m_TrtParametersize == 704 else 0\\n \\n # idl code lines 436 - 465\\n \\n # convert version_no to operational_software_version\\n if convert_version_no_uint == 5:\\n operational_software_version = '1.2'\\n elif convert_version_no_uint == 6:\\n operational_software_version = '2.0'\\n elif convert_version_no_uint == 7:\\n operational_software_version = '2.1'\\n elif convert_version_no_uint == 8:\\n operational_software_version = '2.2'\\n elif convert_version_no_uint == 10:\\n operational_software_version = '3.31'\\n elif convert_version_no_uint == 11:\\n operational_software_version = '5.34'\\n elif convert_version_no_uint == 13:\\n operational_software_version = '5.36'\\n elif convert_version_no_uint == 14:\\n operational_software_version = '5.37'\\n elif convert_version_no_uint == 16:\\n operational_software_version = '5.39'\\n elif convert_version_no_uint == 19:\\n operational_software_version = '5.43'\\n elif convert_version_no_uint == 21:\\n operational_software_version = '5.45' # note this is also compatible with 6.45\\n elif convert_version_no_uint == 22:\\n operational_software_version = '7.47'\\n elif convert_version_no_uint == 24:\\n operational_software_version = '7.49'\\n \\n \\n ############################\\n ## Come back and check if ##\\n # this is really necessary #\\n ############################\\n program_version_no = float(operational_software_version)\\n \\n \\n \\n \\n ###############################################\\n # Depending on which program version we have, #\\n ### the next bit of data could be anywhere ####\\n ############ IDL code line 534-548 ############\\n ###############################################\\n \\n if program_version_no in [1.2]:\\n read_pos = 98\\n elif program_version_no in [2.0,2.1,2.2]:\\n read_pos = 158\\n elif program_version_no in [3.31, 5.34]:\\n read_pos = 158+(4*12)\\n elif program_version_no in [5.36,5.37]:\\n read_pos = 158+(4*12)+(4*11)\\n elif program_version_no in [5.39]:\\n read_pos = 158+(4*12)+(4*22)\\n elif program_version_no in [5.43, 5.45, 7.47]:\\n read_pos = 26\\n elif program_version_no in [7.49]:\\n read_pos = 24\\n else:\\n msg = f\\\"Unexpected program version no: {program_version_no}\\\"\\n raise ValueError(msg)\\n \\n \\n ############################################\\n ########### Read the next bits, ############\\n ########### advance the read_pos ###########\\n ############################################\\n \\n north_correction = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\") # 32 bit float\\n read_pos += 4\\n \\n alt_correction = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\") # 32 bit float\\n read_pos += 4\\n \\n time_correction = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos += 2\\n\\n\\n # some more jumping, part to jump the colour structure information\\n \\n if program_version_no < 3.31:\\n read_pos += 8\\n \\n if program_version_no < 5.43:\\n colour = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos += 2\\n elif program_version_no == 5.43:\\n read_pos += 16\\n elif program_version_no in [5.45,7.47]:\\n read_pos += 52\\n elif program_version_no == 7.49:\\n read_pos += 54\\n else:\\n msg = f\\\"Unexpected program version no: {program_version_no}\\\"\\n raise ValueError(msg)\\n \\n \\n #########################\\n ### More reading data ###\\n #########################\\n \\n \\n # IDL lines 478 (program_version_no type), 599-600\\n if program_version_no < 3.31:\\n Processing_dur = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos += 2\\n else:\\n Processing_dur = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n Lag_between_processing = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos += 2\\n \\n \\n #########################\\n # Moving position again #\\n ### IDL lines 607-619 ###\\n #########################\\n \\n if program_version_no < 5.36:\\n byte_position = read_pos\\n print(\\\"WARNING: program version not recently tested\\\")\\n elif program_version_no == 5.36:\\n read_pos = 250\\n elif program_version_no == 5.43:\\n read_pos = 24+(8+4)*4+272+40\\n else:\\n read_pos = 24+(8+13)*4+272+40\\n \\n ########################################\\n # Read dir vector, antenna, elev stuff #\\n ########## IDL lines 625-681 ###########\\n ####### This is going to be long #######\\n ######### Do we need 4 and 5? ##########\\n ########################################\\n \\n if program_version_no >= 5.39 and program_version_no < 5.43:\\n no_radials = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos+=2\\n Dir_vector1 = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos+=2\\n Dir_vector2 = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos+=2\\n Dir_vector3 = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos+=2\\n Dir_vector4 = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos+=2\\n Dir_vector5 = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos+=2\\n \\n if program_version_no > 5.39:\\n read_pos += 4\\n \\n Dir_antenna1 = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos+=2\\n Dir_antenna2 = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos+=2\\n Dir_antenna3 = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos+=2\\n Dir_antenna4 = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos+=2\\n Dir_antenna5 = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos+=2\\n Angle_elev1 = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos+=2\\n Angle_elev2 = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos+=2\\n Angle_elev3 = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos+=2\\n Angle_elev4 = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos+=2\\n Angle_elev5 = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos+=2\\n \\n if program_version_no > 5.39:\\n read_pos += 2 #line 665-667\\n \\n elif program_version_no >= 5.43:\\n no_radials = int(f\\\"0b{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos+=4\\n Dir_vector1 = int(f\\\"0b{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos+=4\\n Dir_vector2 = int(f\\\"0b{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos+=4\\n Dir_vector3 = int(f\\\"0b{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos+=4\\n Dir_vector4 = int(f\\\"0b{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos+=4\\n Dir_vector5 = int(f\\\"0b{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos+=4\\n \\n read_pos += 4\\n \\n Dir_antenna1 = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos+=2\\n Dir_antenna2 = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos+=2\\n Dir_antenna3 = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos+=2\\n Dir_antenna4 = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos+=2\\n Dir_antenna5 = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos+=2\\n Angle_elev1 = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos+=2\\n Angle_elev2 = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos+=2\\n Angle_elev3 = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos+=2\\n Angle_elev4 = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos+=2\\n Angle_elev5 = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos+=2\\n \\n read_pos += 2 #line 665-667\\n \\n else:\\n Dir_vector1 = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos+=2\\n Dir_antenna1 = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos+=2\\n Angle_elev1 = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos+=2\\n Dir_vector2 = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos+=2\\n Dir_antenna2 = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos+=2\\n Angle_elev2 = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos+=2\\n Dir_vector3 = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos+=2\\n Dir_antenna3 = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos+=2\\n Angle_elev3 = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos+=2\\n\\n # Told you it would be long\\n \\n #####################################\\n # Once again, set the read position #\\n ######### IDL lines 705-721 #########\\n #####################################\\n \\n if program_version_no in [1.2]:\\n read_pos = 550\\n elif program_version_no in [2.0,2.1]:\\n read_pos = 554+60\\n elif program_version_no in [2.2]:\\n read_pos = 550+72\\n elif program_version_no in [3.31]:\\n read_pos = 554+72+(23*4)\\n elif program_version_no in [5.34, 5.36,5.37]:\\n read_pos = 554+72+(23*4)+(8*4)\\n elif program_version_no in [5.39]:\\n read_pos = 554+72+(23*4)+(8*4)+(4*111)\\n elif program_version_no in [5.43]:\\n read_pos = 24+(8+4)*4+272+40+48+48+20+16+808+2*2\\n elif program_version_no in [5.45]:\\n read_pos = 24+(8+13)*4+272+40+48+48+20+16+808+2*2\\n elif program_version_no in [7.47, 7.49]:\\n read_pos = 24+(8+13)*4+272+40+2156+48+20+16+808+2*2\\n else:\\n msg = f\\\"Unexpected program version no: {program_version_no}\\\"\\n raise ValueError(msg)\\n \\n ######################################################\\n ##### Okay, we're going to skip lines 723 - 742 ######\\n # for the moment, I don't think they add any value. ##\\n ############## Start agin from line 744 ##############\\n ######################################################\\n \\n read_pos = m_TrtParametersize + heading_size + 2 # there's a jump on line 746\\n \\n \\n if program_version_no < 5.43:\\n size_profile_info_rangegates = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos += 2\\n else:\\n read_pos += 2 # line 750\\n # signed\\n size_profile_info_rangegates = f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\"\\n size_profile_info_rangegates_sign = 1 if size_profile_info_rangegates[0] == '0' else -1\\n size_profile_info_rangegates = size_profile_info_rangegates_sign * int(size_profile_info_rangegates[1:],2)\\n read_pos += 4 \\n \\n ###################\\n # Line 767 onward #\\n # Well, line 790 ##\\n ###################\\n \\n # signed 32 bit\\n processing_type = f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\"\\n processing_type_sign = 1 if processing_type[0] == '0' else -1\\n processing_type = processing_type_sign * int(f\\\"{processing_type[1:]}\\\",2)\\n read_pos += 4\\n\\n # line 793\\n if program_version_no > 3.31:\\n # signed 32 bit\\n m_nProcessingFlags = f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\"\\n m_nProcessingFlags_sign = 1 if m_nProcessingFlags[0] == '0' else -1\\n m_nProcessingFlags = m_nProcessingFlags_sign * int(f\\\"{m_nProcessingFlags[1:]}\\\",2)\\n read_pos += 4\\n elif program_version_no > 1.2:\\n # unsigned 16 bit\\n m_nProcessingFlags = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2)\\n read_pos += 2\\n \\n # line 797\\n # signed 16 bit\\n mode_no = f\\\"{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\"\\n mode_no_sign = 1 if mode_no[0] == '0' else -1\\n mode_no = mode_no_sign * int(f\\\"{mode_no[1:]}\\\",2)\\n read_pos += 2\\n \\n # line 800\\n # signed 32 bit\\n profile_date = f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\"\\n profile_date_sign = 1 if profile_date[0] == '0' else -1\\n profile_date = profile_date_sign * int(f\\\"{profile_date[1:]}\\\",2)\\n read_pos += 4\\n\\n # line 802 & 803\\n # signed 32 bit\\n start_date_profile = f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\"\\n start_date_profile_sign = 1 if start_date_profile[0] == '0' else -1\\n start_date_profile = start_date_profile_sign * int(f\\\"{start_date_profile[1:]}\\\",2)\\n read_pos += 4\\n\\n end_date_profile = f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\"\\n end_date_profile_sign = 1 if end_date_profile[0] == '0' else -1\\n end_date_profile = end_date_profile_sign * int(f\\\"{end_date_profile[1:]}\\\",2)\\n read_pos += 4\\n\\n\\n # line 807\\n if program_version_no < 3.31:\\n # signed 16 bit\\n processing_duration_actual = f\\\"{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\"\\n processing_duration_actual_sign = 1 if processing_duration_actual[0] == '0' else -1\\n processing_duration_actual = processing_duration_actual_sign * int(f\\\"{processing_duration_actual[1:]}\\\",2)\\n read_pos += 2\\n else:\\n # 32 bit single precision float\\n processing_duration_actual = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n\\n \\n # line 808 \\n # signed 16\\n lag_between_processing_actual = f\\\"{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\"\\n lag_between_processing_actual_sign = 1 if lag_between_processing_actual[0] == '0' else -1\\n lag_between_processing_actual = lag_between_processing_actual_sign * int(f\\\"{lag_between_processing_actual[1:]}\\\",2)\\n read_pos += 2\\n \\n # line 823, unsigned 16 bit\\n no_heights = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2) \\n read_pos += 2\\n \\n # line 824, 32 bit single prec float\\n min_height = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n\\n # line 825, 32 bit single prec float\\n height_increment = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n\\n\\n # line 833\\n # aahhh, what is this a2? Best guess at the moment, float\\n a2 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n\\n \\n # line 893-899, unsigned 16\\n blocknumber = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2) \\n read_pos += 2\\n country = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2) \\n read_pos += 2\\n agency = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2) \\n read_pos += 2\\n station_no = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2) \\n read_pos += 2\\n station_type = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2) \\n read_pos += 2\\n instrument_type = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2) \\n read_pos += 2\\n antenna_type = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2) \\n read_pos += 4 # includes jump on line 900\\n \\n \\n # line 901 32 bit float\\n beamwidth = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n\\n # line 902 unsigned 32 bit\\n frequency = int(f\\\"0b{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2) \\n read_pos += 4\\n\\n #line 903-904 32 bit float\\n latitude_file = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n longitude_file = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n altitude_site = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n\\n # line 922-926 unsigned 16 bit\\n if program_version_no > 2.0:\\n time_difference = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2) \\n read_pos += 2\\n dlst = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2) \\n read_pos += 2\\n \\n \\n # lines 930 - 981\\n if program_version_no >= 2.0:\\n read_pos += 4\\n if program_version_no >= 3.1:\\n # signed 32\\n voltage1 = f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\"\\n voltage1_sign = 1 if voltage1[0] == '0' else -1\\n voltage1 = voltage1_sign * int(f\\\"{voltage1[1:]}\\\",2)\\n read_pos += 4\\n \\n voltage2 = f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\"\\n voltage2_sign = 1 if voltage2[0] == '0' else -1\\n voltage2 = voltage2_sign * int(f\\\"{voltage2[1:]}\\\",2)\\n read_pos += 4\\n \\n voltage3 = f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\"\\n voltage3_sign = 1 if voltage3[0] == '0' else -1\\n voltage3 = voltage3_sign * int(f\\\"{voltage3[1:]}\\\",2)\\n read_pos += 4\\n \\n voltage4 = f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\"\\n voltage4_sign = 1 if voltage4[0] == '0' else -1\\n voltage4 = voltage4_sign * int(f\\\"{voltage4[1:]}\\\",2)\\n read_pos += 4\\n \\n overheating = f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\"\\n overheating_sign = 1 if overheating[0] == '0' else -1\\n overheating = overheating_sign * int(f\\\"{overheating[1:]}\\\",2)\\n read_pos += 4\\n \\n preheating = f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\"\\n preheating_sign = 1 if preheating[0] == '0' else -1\\n preheating = preheating_sign * int(f\\\"{preheating[1:]}\\\",2)\\n read_pos += 4\\n \\n vswr = f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\"\\n vswr_sign = 1 if vswr[0] == '0' else -1\\n vswr = vswr_sign * int(f\\\"{vswr[1:]}\\\",2)\\n read_pos += 4\\n \\n if program_version_no > 5.43:\\n rain_detection = f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\"\\n rain_detection_sign = 1 if rain_detection[0] == '0' else -1\\n rain_detection = rain_detection_sign * int(f\\\"{rain_detection[1:]}\\\",2)\\n read_pos += 4\\n \\n attenuation = f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\"\\n attenuation_sign = 1 if attenuation[0] == '0' else -1\\n attenuation = attenuation_sign * int(f\\\"{attenuation[1:]}\\\",2)\\n read_pos += 4\\n \\n # 32 bit float\\n current = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n if program_version_no > 5.43:\\n # 32 bit float\\n shelter_temp = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n \\n # lines 985 - 1019 \\n if program_version_no < 5.34:\\n read_pos += 2 # line 1005\\n elif program_version_no < 5.43:\\n read_pos += 18 # lines 987 - 944 & 1005\\n else: # >= 5.43\\n # signed 32\\n sun_rise = f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\"\\n sun_rise_sign = 1 if sun_rise[0] == '0' else -1\\n sun_rise = sun_rise_sign * int(f\\\"{sun_rise[1:]}\\\",2)\\n read_pos += 4\\n \\n read_pos += 4 # lines 999-1000\\n \\n # signed 32\\n sun_set = f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\"\\n sun_set_sign = 1 if sun_set[0] == '0' else -1\\n sun_set = sun_set_sign * int(f\\\"{sun_set[1:]}\\\",2)\\n read_pos += 4\\n \\n read_pos += 18\\n \\n\\n # line 1021 signed 32\\n rain_junk = f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\"\\n rain_junk_sign = 1 if rain_junk[0] == '0' else -1\\n rain_junk = rain_junk_sign * int(f\\\"{rain_junk[1:]}\\\",2)\\n read_pos += 4 \\n \\n # from IDL, correct up to here for v1.2\\n current_position = read_pos # IDL checks current position, line 1027\\n\\n\\n # next up, various jumps from reading in CinfoSol (apparently)\\n # line 1036-1038\\n if program_version_no == 5.43:\\n read_pos += 2\\n\\n # lines 1040-1067 \\n if program_version_no >= 5.45:\\n read_pos += 38 # lines 1044-1046\\n for i in range(6): # this is here while testing, replace with read_pos += n\\n # 32 bit float\\n junk = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n #print(junk)\\n \\n if program_version_no >= 7.49:\\n for i in range(4): # see above about replacement\\n junk = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n #print(junk)\\n\\n else: \\n for i in range(6): # see above about replacement\\n junk = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n #print(junk)\\n\\n \\n # lines 1072-1091\\n # 32 bit floats\\n DBZ_coeff = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n\\n proc_gain = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n\\n pulse_length_metres = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n\\n boundary_layer_height = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n\\n if boundary_layer_height == 999999.:\\n boundary_layer_height=-1e20 \\n\\n # lines 1102-1108\\n if program_version_no >= 5.34: # who know why this appears a second time...\\n # signed 32 bits\\n pbl_time = f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\"\\n pbl_time_sign = 1 if pbl_time[0] == '0' else -1\\n pbl_time = pbl_time_sign * int(f\\\"{pbl_time[1:]}\\\",2)\\n read_pos += 4 \\n \\n read_pos += 4 # lines 1103+1104\\n \\n sun_rise = f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\"\\n sun_rise_sign = 1 if sun_rise[0] == '0' else -1\\n sun_rise = sun_rise_sign * int(f\\\"{sun_rise[1:]}\\\",2)\\n read_pos += 4 \\n \\n read_pos += 4 # lines 1103+1104\\n \\n sun_set = f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\"\\n sun_set_sign = 1 if sun_set[0] == '0' else -1\\n sun_set = sun_set_sign * int(f\\\"{sun_set[1:]}\\\",2)\\n read_pos += 4 \\n\\n\\n \\n # lines 1127-1133\\n speed = (74.9475/(500e-9)) *2\\n distance = ((3e8)*(500e-9))/2\\n pulse_length_time = (pulse_length_metres/speed)*2.0\\n pulse_length_time_ns = int(pulse_length_time * 1e9)\\n \\n \\n # line 1140 \\n ################################################\\n ################################################\\n ##### WHAT I AM ABOUT TO DO IS TERRIBLE!!! #####\\n ################################################\\n ################################################\\n if 'wp' in locals() or 'wp' in globals():\\n # assumed wp is dict\\n if wp['delay_correction'] == 1:\\n if pulse_length_time_ns == 500:\\n alt_correction = 102\\n elif pulse_length_time_ns == 599:\\n alt_correction = 122.1\\n elif pulse_length_time_ns == 999:\\n alt_correction = 214.5\\n elif pulse_length_time_ns == 1000:\\n alt_correction = 214.5\\n elif pulse_length_time_ns == 1568:\\n alt_correction = 250\\n elif pulse_length_time_ns == 1604:\\n alt_correction = 250\\n elif pulse_length_time_ns == 1700:\\n alt_correction = 250\\n elif pulse_length_time_ns == 2500:\\n alt_correction = 270\\n elif pulse_length_time_ns == 2499:\\n alt_correction = 270\\n else:\\n alt_correction = 250\\n \\n # line 1157-1167 \\n if pulse_length_time_ns == 599:\\n pulse_length_time_ns = 500\\n elif pulse_length_time_ns == 2499:\\n pulse_length_time_ns = 500\\n \\n \\n current_position2 = read_pos # IDL checks current position, line 1175 \\n \\n # linnes 1184-1192 junk not jump!\\n if program_version_no >= 5.36:\\n read_pos += 4\\n \\n # 32 bit floats\\n max_doppler_1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n max_doppler_2 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4 \\n \\n max_doppler_3 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4 \\n \\n max_doppler_4 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4 \\n \\n max_doppler_5 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4 \\n \\n if program_version_no >= 5.34:\\n bright_band = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4 \\n \\n \\n \\n # Loop over heights, starting at line 1248\\n # I think I need to define some arrays here first\\n altitude = np.zeros(no_heights)\\n u_east = np.zeros(no_heights)\\n v_north = np.zeros(no_heights)\\n w_vert = np.zeros(no_heights)\\n wp_winddir = np.zeros(no_heights)\\n wp_windspeed = np.zeros(no_heights)\\n Ascii_colour_info = np.zeros(no_heights)\\n radial_velocity_1 = np.zeros(no_heights)\\n radial_velocity_2 = np.zeros(no_heights)\\n radial_velocity_3 = np.zeros(no_heights)\\n width_1 = np.zeros(no_heights)\\n width_2 = np.zeros(no_heights)\\n width_3 = np.zeros(no_heights)\\n width_min = np.zeros(no_heights)\\n width_median = np.zeros(no_heights)\\n signal_1 = np.zeros(no_heights)\\n signal_2 = np.zeros(no_heights)\\n signal_3 = np.zeros(no_heights)\\n noise_1 = np.zeros(no_heights)\\n noise_2 = np.zeros(no_heights)\\n noise_3 = np.zeros(no_heights)\\n vel_sd_1 = np.zeros(no_heights)\\n vel_sd_2 = np.zeros(no_heights)\\n vel_sd_3 = np.zeros(no_heights)\\n sig_sd_1 = np.zeros(no_heights)\\n sig_sd_2 = np.zeros(no_heights)\\n sig_sd_3 = np.zeros(no_heights)\\n width_sd_1 = np.zeros(no_heights)\\n width_sd_2 = np.zeros(no_heights)\\n width_sd_3 = np.zeros(no_heights)\\n skew_1 = np.zeros(no_heights)\\n skew_2 = np.zeros(no_heights)\\n skew_3 = np.zeros(no_heights)\\n qualit_1 = np.zeros(no_heights)\\n qualit_2 = np.zeros(no_heights)\\n qualit_3 = np.zeros(no_heights)\\n popula_1 = np.zeros(no_heights)\\n popula_2 = np.zeros(no_heights)\\n popula_3 = np.zeros(no_heights)\\n absskew_1 = np.zeros(no_heights)\\n absskew_2 = np.zeros(no_heights)\\n absskew_3 = np.zeros(no_heights)\\n skew_sd_1 = np.zeros(no_heights)\\n skew_sd_2 = np.zeros(no_heights)\\n skew_sd_3 = np.zeros(no_heights)\\n absskew_sd_1 = np.zeros(no_heights)\\n absskew_sd_2 = np.zeros(no_heights)\\n absskew_sd_3 = np.zeros(no_heights)\\n noise_sd_1 = np.zeros(no_heights)\\n noise_sd_2 = np.zeros(no_heights)\\n noise_sd_3 = np.zeros(no_heights)\\n skewvel_1 = np.zeros(no_heights)\\n skewvel_2 = np.zeros(no_heights)\\n skewvel_3 = np.zeros(no_heights)\\n skewwidth_1 = np.zeros(no_heights)\\n skewwidth_2 = np.zeros(no_heights)\\n skewwidth_3 = np.zeros(no_heights)\\n skewsig_1 = np.zeros(no_heights)\\n skewsig_2 = np.zeros(no_heights)\\n skewsig_3 = np.zeros(no_heights)\\n skewskew_1 = np.zeros(no_heights)\\n skewskew_2 = np.zeros(no_heights)\\n skewskew_3 = np.zeros(no_heights)\\n skewabsskew_1 = np.zeros(no_heights)\\n skewabsskew_2 = np.zeros(no_heights)\\n skewabsskew_3 = np.zeros(no_heights)\\n skewnoise_1 = np.zeros(no_heights)\\n skewnoise_2 = np.zeros(no_heights)\\n skewnoise_3 = np.zeros(no_heights)\\n validation_1 = np.zeros(no_heights)\\n validation_2 = np.zeros(no_heights)\\n validation_3 = np.zeros(no_heights)\\n qc_flag_beam_1 = np.zeros(no_heights)\\n qc_flag_beam_2 = np.zeros(no_heights)\\n qc_flag_beam_3 = np.zeros(no_heights)\\n SNR_1 = np.zeros(no_heights)\\n SNR_2 = np.zeros(no_heights)\\n SNR_3 = np.zeros(no_heights)\\n SNR_min = np.zeros(no_heights)\\n overall_validation = np.zeros(no_heights)\\n qc_flag_wind = np.zeros(no_heights)\\n dshort = 0\\n m_fDuree_Measure_1 = np.zeros(no_heights)\\n m_fDuree_Measure_2 = np.zeros(no_heights)\\n m_fDuree_Measure_3 = np.zeros(no_heights)\\n consensus_1 = np.zeros(no_heights)\\n consensus_2 = np.zeros(no_heights)\\n consensus_3 = np.zeros(no_heights)\\n shear_width = np.zeros(no_heights)\\n turbulence_width = np.zeros(no_heights)\\n epsilon = np.zeros(no_heights)\\n \\n for k in range(no_heights):\\n mini_array = np.zeros(3)\\n if verbose:\\n print('###################################################')\\n \\n altitude[k] = height_increment * k + min_height\\n \\n # 32 bit floats\\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n u_east[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n v_north[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n w_vert[k] = d1\\n read_pos += 4\\n \\n # lines 1278-1284\\n # wind direction 'from', wind speed 'horizontal'\\n # AMOF standard states wind speed and direction should be 32bit floats\\n wp_winddir[k] = (180/np.pi) * np.arctan2(-u_east[k], -v_north[k])\\n wp_winddir[k] = np.float32(wp_winddir[k])\\n \\n wp_windspeed[k] = np.float32(((u_east[k] ** 2) + (v_north[k] ** 2)) ** 0.5)\\n\\n \\n if verbose:\\n print(f\\\"altitude: {altitude[k]}\\\")\\n print(f\\\"loop no: {k}\\\")\\n print(f\\\"east north vert: {u_east[k]} {v_north[k]} {w_vert[k]}\\\")\\n print(f\\\"wp_winddir wp_windspeed: {wp_winddir[k]} {wp_windspeed[k]}\\\")\\n \\n if classification == 1:\\n if wp_windspeed[k] >= 13.9:\\n print(f\\\"{day}/{month}/{year}\\\")\\n if wp_windspeed[k] >= 32.7:\\n print(\\\"HURRICANE FORCE\\\")\\n elif wp_windspeed[k] >= 28.5:\\n print(\\\"VIOLENT STORM\\\")\\n elif wp_windspeed[k] >= 24.5:\\n print(\\\"whole GALE, STORM\\\")\\n elif wp_windspeed[k] >= 20.8:\\n print(\\\"STRONG GALE\\\")\\n elif wp_windspeed[k] >= 17.2:\\n print(\\\"FRESH GALE\\\")\\n else:\\n print(\\\"NEAR GALE\\\")\\n \\n # line 1306-7\\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n Ascii_colour_info[k] = d1\\n read_pos += 4\\n \\n # line 1311 - 1363\\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n radial_velocity_1[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n radial_velocity_2[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n radial_velocity_3[k] = d1\\n read_pos += 4\\n \\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n width_1[k] = d1\\n mini_array[0] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n width_2[k] = d1\\n mini_array[1] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n width_3[k] = d1\\n mini_array[2] = d1\\n read_pos += 4\\n \\n width_min[k] = np.min(mini_array)\\n width_median[k] = np.median(mini_array)\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n signal_1[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n signal_2[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n signal_3[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n noise_1[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n noise_2[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n noise_3[k] = d1\\n read_pos += 4\\n \\n if verbose:\\n print(f\\\"radial velocity: {radial_velocity_1[k]} {radial_velocity_2[k]} {radial_velocity_3[k]}\\\")\\n print(f\\\"width: {width_1[k]} {width_2[k]} {width_3[k]}\\\")\\n print(f\\\"signal 1,2,3: {signal_1[k]} {signal_2[k]} {signal_3[k]}\\\")\\n print(f\\\"noise: {noise_1[k]} {noise_2[k]} {noise_3[k]}\\\")\\n \\n \\n # line 1373-1614\\n if not variance_test:\\n skip = 60 - 13 # no idea why this is a sum rather than a fixed number\\n if program_version_no >= 2.2:\\n for z in range(skip+1): # skip chunks\\n read_pos += 4 # could just do read_pos += 4*(skip+1) outside loop\\n else:\\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n vel_sd_1[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n vel_sd_2[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n vel_sd_3[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n sig_sd_1[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n sig_sd_2[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n sig_sd_3[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n width_sd_1[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n width_sd_2[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n width_sd_3[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n skew_1[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n skew_2[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n skew_3[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n qualit_1[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n qualit_2[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n qualit_3[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n popula_1[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n popula_2[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n popula_3[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n absskew_1[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n absskew_2[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n absskew_3[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n skew_sd_1[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n skew_sd_2[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n skew_sd_3[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n absskew_sd_1[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n absskew_sd_2[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n absskew_sd_3[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n noise_sd_1[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n noise_sd_2[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n noise_sd_3[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n skewvel_1[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n skewvel_2[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n skewvel_3[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n skewwidth_1[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n skewwidth_2[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n skewwidth_3[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n skewsig_1[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n skewsig_2[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n skewsig_3[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n skewskew_1[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n skewskew_2[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n skewskew_3[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n skewabsskew_1[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n skewabsskew_2[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n skewabsskew_3[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n skewnoise_1[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n skewnoise_2[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n skewnoise_3[k] = d1\\n read_pos += 4\\n \\n if verbose:\\n print(f'vel_sd123 : {vel_sd_1[k]} {vel_sd_2[k]} {vel_sd_3[k]}')\\n print(f'sig_sd123 : {sig_sd_1[k]} {sig_sd_2[k]} {sig_sd_3[k]}')\\n print(f'width_sd123 : {width_sd_1[k]} {width_sd_2[k]} {width_sd_3[k]}')\\n print(f'skew : {skew_1[k]} {skew_2[k]} {skew_3[k]}')\\n print(f'qualit : {qualit_1[k]} {qualit_2[k]} {qualit_3[k]}')\\n print(f'population : {popula_1[k]} {popula_2[k]} {popula_3[k]}')\\n print(f'absskew : {absskew_1[k]} {absskew_2[k]} {absskew_3[k]}')\\n print(f'skew_sd : {skew_sd_1[k]} {skew_sd_2[k]} {skew_sd_3[k]}') \\n print(f'absskew_sd : {absskew_sd_1[k]} {absskew_sd_2[k]} {absskew_3[k]}')\\n print(f'noise_sd : {noise_sd_1[k]} {noise_sd_2[k]} {noise_sd_3[k]}')\\n print(f'skewvel : {skewvel_1[k]} {skewvel_2[k]} {skewvel_3[k]}')\\n print(f'skewwidth : {skewwidth_1[k]} {skewwidth_2[k]} {skewwidth_3[k]}')\\n print(f'skewsig : {skewsig_1[k]} {skewsig_2[k]} {skewsig_3[k]}')\\n print(f'skewskew : {skewskew_1[k]} {skewskew_2[k]} {skewskew_3[k]}')\\n print(f'skewabsskew : {skewabsskew_1[k]} {skewabsskew_2[k]} {skewabsskew_3[k]}')\\n print(f'skewnoise : {skewnoise_1[k]} {skewnoise_2[k]} {skewnoise_3[k]}')\\n \\n \\n # line 1619 - 1651\\n if program_version_no < 100: # yes...\\n # signed 32 bits...\\n d2 = f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\"\\n d2_sign = 1 if d2[0] == '0' else -1\\n d2 = d2_sign * int(f\\\"{d2[1:]}\\\",2)\\n validation_1[k] = d2\\n read_pos += 4\\n \\n if d2 == 999999:\\n d2 = np.nan\\n # qc_flag_beam_1 (& 2 & 3) - from IDL, \\n # 0 no data, 1 good data, 2 bad\\n # should be 0 not used, 1 good, 2 bad, 3 no data\\n # in idl, this resulted in 1 or 0. so here, 1 or 3.\\n if d2 == 1:\\n qc_flag_beam_1[k] = 1\\n else:\\n qc_flag_beam_1[k] = 3\\n \\n d2 = f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\"\\n d2_sign = 1 if d2[0] == '0' else -1\\n d2 = d2_sign * int(f\\\"{d2[1:]}\\\",2)\\n validation_2[k] = d2\\n read_pos += 4\\n \\n if d2 == 999999:\\n d2 = np.nan\\n if d2 == 1:\\n qc_flag_beam_2[k] = 1\\n else:\\n qc_flag_beam_2[k] = 3\\n \\n d2 = f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\"\\n d2_sign = 1 if d2[0] == '0' else -1\\n d2 = d2_sign * int(f\\\"{d2[1:]}\\\",2)\\n validation_3[k] = d2\\n read_pos += 4\\n \\n if d2 == 999999:\\n d2 = np.nan\\n if d2 == 1:\\n qc_flag_beam_3[k] = 1\\n else:\\n qc_flag_beam_3[k] = 3\\n else:\\n # 32 bit floats...\\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n validation_1[k] = d1\\n read_pos += 4\\n \\n if d1 == 999999:\\n d1 = np.nan\\n # qc_flag_beam_1 (& 2 & 3) - from IDL, \\n # 0 no data, 1 good data, 2 bad\\n # should be 0 not used, 1 good, 2 bad, 3 no data\\n # in idl, this resulted in 1 or 0. so here, 1 or 3.\\n if d1 == 1:\\n qc_flag_beam_1[k] = 1\\n else:\\n qc_flag_beam_1[k] = 3\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n validation_2[k] = d1\\n read_pos += 4\\n \\n if d1 == 999999:\\n d1 = np.nan\\n if d2 == 1:\\n qc_flag_beam_2[k] = 1\\n else:\\n qc_flag_beam_2[k] = 3\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n validation_3[k] = d1\\n read_pos += 4\\n \\n if d1 == 999999:\\n d1 = np.nan\\n if d2 == 1:\\n qc_flag_beam_3[k] = 1\\n else:\\n qc_flag_beam_3[k] = 3\\n \\n # line 1653 - 1672\\n # reset this\\n mini_array = np.zeros(3)\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n SNR_1[k] = d1\\n mini_array[0] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n SNR_2[k] = d1\\n mini_array[1] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n if d1 == 999999:\\n d1 = np.nan\\n SNR_3[k] = d1\\n mini_array[2] = d1\\n read_pos += 4\\n \\n SNR_min[k] = np.min(mini_array)\\n \\n d2 = f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\"\\n d2_sign = 1 if d2[0] == '0' else -1\\n d2 = d2_sign * int(f\\\"{d2[1:]}\\\",2)\\n overall_validation[k] = d2\\n read_pos += 4\\n \\n # see same note as above re: qc_flag_beam_1,2,3\\n qc_flag_wind[k] = 1 if dshort == 1 else 3\\n \\n \\n if verbose:\\n print(f'validation: {validation_1[k]} {validation_2[k]} {validation_3[k]}')\\n print(f'qc flag: {qc_flag_beam_1[k]} {qc_flag_beam_2[k]} {qc_flag_beam_3[k]}')\\n print(f'overall validation: {overall_validation[k]}')\\n print(f'SNR: {SNR_1[k]} {SNR_2[k]} {SNR_3[k]}')\\n print(f'windspeed: {wp_windspeed[k]}')\\n \\n # line 1684\\n if program_version_no > 1.2:\\n if verbose:\\n print(f'byte point: {read_pos}')\\n if program_version_no > 3.0:\\n # 32 bit float\\n m_dureeTraitment = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n else:\\n # signed 16 bit\\n m_dureeTraitment = f\\\"{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\"\\n m_dureeTraitment_sign = 1 if m_dureeTraitment[0] == '0' else -1\\n m_dureeTraitment = m_dureeTraitment_sign * int(f\\\"{m_dureeTraitment[1:]}\\\",2)\\n read_pos += 2\\n \\n # signed 16\\n m_DecalageTraitment = f\\\"{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\"\\n m_DecalageTraitment_sign = 1 if m_DecalageTraitment[0] == '0' else -1\\n m_DecalageTraitment = m_DecalageTraitment_sign * int(f\\\"{m_DecalageTraitment[1:]}\\\",2)\\n read_pos += 2\\n \\n if program_version_no > 5.34:\\n # signed 16\\n dShort = f\\\"{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\"\\n dShort_sign = 1 if dShort[0] == '0' else -1\\n dShort = dShort_sign * int(f\\\"{dShort[1:]}\\\",2)\\n read_pos += 2\\n if verbose:\\n print(f'dShort: {dShort}')\\n \\n # line 1704, 32 bit float\\n m_fLargeurFen = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n if verbose:\\n print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')\\n print(f'm_dureeTraitment (consensus duration): {m_dureeTraitment}')\\n print(f'm_DecalageTraitment (update rate): {m_DecalageTraitment}')\\n print(f'm_fLargeurFen (window width): {m_fLargeurFen}')\\n \\n # from lines 1713-1716\\n if m_dureeTraitment > 120:\\n msg = f'Value m_dureeTraitment {m_dureeTraitment} is greater than 120'\\n raise ValueError(msg)\\n if m_DecalageTraitment < 0:\\n msg = f'Value m_DecalageTraitment {m_DecalageTraitment} is less than 0'\\n raise ValueError(msg)\\n elif m_DecalageTraitment > 60:\\n msg = f'Value m_DecalageTraitment {m_DecalageTraitment} is greater than 60'\\n raise ValueError(msg)\\n \\n # line 1722\\n if program_version_no > 2.1:\\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n m_fDuree_Measure_1[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n m_fDuree_Measure_2[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n m_fDuree_Measure_3[k] = d1\\n read_pos += 4\\n \\n if verbose:\\n print(f'dur1: {m_fDuree_Measure_1[k]}')\\n print(f'dur2: {m_fDuree_Measure_2[k]}')\\n print(f'dur3: {m_fDuree_Measure_3[k]}')\\n \\n # line 1738\\n if verbose:\\n print(f'loop k: {k}')\\n \\n # line 1740\\n if program_version_no > 5.34:\\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n consensus_1[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n consensus_2[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n consensus_3[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n shear_width[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n turbulence_width[k] = d1\\n read_pos += 4\\n \\n d1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n epsilon[k] = d1\\n read_pos += 4\\n \\n if verbose:\\n print(f'consensus: {consensus_1[k]} {consensus_2[k]} {consensus_3[k]}')\\n print(f'shear width: {shear_width[k]}')\\n print(f'turbulence width: {turbulence_width[k]}')\\n print(f'epsilon: {epsilon[k]}')\\n \\n # line 1769 \\n if program_version_no > 2.0:\\n # unsigned 16 bit\\n qc_override = int(f\\\"0b{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\",2) \\n read_pos += 2\\n if verbose:\\n print(f'qc override: {qc_override}')\\n read_pos += 2\\n \\n # line 1789\\n if program_version_no >= 5.45:\\n # 32bit floats\\n fivebeam_w14 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n fivebeam_w25 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n fivebeam_w = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n fivebeam_var_w14 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n fivebeam_var_w25 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n fivebeam_var_w = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n fivebeam_skew_w14 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n fivebeam_skew_w25 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n fivebeam_skew_w = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n fivebeam_pop_w14 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n fivebeam_pop_w25 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n fivebeam_pop_w = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n elif program_version_no >= 5.36:\\n # 32bit floats\\n fivebeam_w14 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n fivebeam_w25 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n fivebeam_var_w14 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n fivebeam_var_w25 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n fivebeam_skew_w14 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n fivebeam_skew_w25 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n fivebeam_pop_w14 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n fivebeam_pop_w25 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n \\n # line 1834 \\n if program_version_no >= 5.43:\\n # 32 bit floats\\n corrected_horiz_velocity_1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n corrected_horiz_velocity_2 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n corrected_horiz_velocity_3 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n corrected_horiz_velocityxW_1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n corrected_horiz_velocityxW_2 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n corrected_horiz_velocityxW_3 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n std_corrected_horiz_velocity_1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n std_corrected_horiz_velocity_2 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n std_corrected_horiz_velocity_3 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n std_corrected_horiz_velocityxW_1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n std_corrected_horiz_velocityxW_2 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n std_corrected_horiz_velocityxW_3 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n skew_corrected_horiz_velocity_1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n skew_corrected_horiz_velocity_2 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n skew_corrected_horiz_velocity_3 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n skew_corrected_horiz_velocityxW_1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n skew_corrected_horiz_velocityxW_2 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n skew_corrected_horiz_velocityxW_3 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n if verbose:\\n print(f'corrected horiz velocity: {corrected_horiz_velocity_1} {corrected_horiz_velocity_2} {corrected_horiz_velocity_3}')\\n print(f'corrected horiz velocity xW: {corrected_horiz_velocityxW_1} {corrected_horiz_velocityxW_2} {corrected_horiz_velocityxW_3}')\\n print(f'std cor horiz vel: {std_corrected_horiz_velocity_1} {std_corrected_horiz_velocity_2} {std_corrected_horiz_velocity_3}')\\n \\n # line 1862 \\n if program_version_no > 5.45:\\n # 32 bit float\\n display_colour1 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n display_colour2 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n display_colour3 = bin_to_float32(f\\\"{decimalToBinary(data[read_pos+3])}{decimalToBinary(data[read_pos+2])}{decimalToBinary(data[read_pos+1])}{decimalToBinary(data[read_pos])}\\\")\\n read_pos += 4\\n \\n if verbose:\\n print(f'display colour: {display_colour1} {display_colour2} {display_colour3}')\\n print('###################################################')\\n \\n \\n #print(bin_to_float32(f\\\"{decimalToBinary(data[91])}{decimalToBinary(data[90])}{decimalToBinary(data[89])}{decimalToBinary(data[88])}\\\"))\\n \\n \\n \\n \\n ############################\\n ##### Print everything #####\\n ############################\\n if verbose:\\n print(f\\\"heading size: {heading_size}\\\")\\n print(f\\\"header type: {header_type}\\\")\\n print(f\\\"version no from file: {version_no}\\\")\\n print(f\\\"version no converted: {convert_version_no_uint}\\\")\\n print(f\\\"start date unix: {start_date_unix}\\\")\\n print(f\\\"end date unix: {end_date_unix}\\\")\\n print(f\\\"start date: {start_date}\\\")\\n print(f\\\"end date: {end_date}\\\")\\n print(f\\\"date time from filename: {date_time_from_filename}\\\")\\n print(f\\\"update rate: {update_rate}\\\")\\n print(f\\\"m_TrtParametersize: {m_TrtParametersize}\\\")\\n print(f\\\"operational software version: {operational_software_version}\\\")\\n print(f\\\"program version number: {program_version_no}\\\")\\n print(f\\\"version2_2a: {version2_2a}\\\")\\n print(f\\\"north correction: {north_correction}\\\")\\n print(f\\\"alt correction: {alt_correction}\\\")\\n print(f\\\"time correction: {time_correction}\\\")\\n print(f\\\"processing duration: {Processing_dur}\\\")\\n print(f\\\"update rate (lag between processing): {Lag_between_processing}\\\")\\n print(f\\\"no radials: {no_radials}\\\")\\n print(f\\\"Dir vector 1: {Dir_vector1}\\\")\\n print(f\\\"Dir vector 2: {Dir_vector2}\\\")\\n print(f\\\"Dir vector 3: {Dir_vector3}\\\")\\n print(f\\\"Dir vector 4: {Dir_vector4}\\\")\\n print(f\\\"Dir vector 5: {Dir_vector5}\\\")\\n print(f\\\"Dir antenna 1: {Dir_antenna1}\\\")\\n print(f\\\"Dir antenna 2: {Dir_antenna2}\\\")\\n print(f\\\"Dir antenna 3: {Dir_antenna3}\\\")\\n print(f\\\"Dir antenna 4: {Dir_antenna4}\\\")\\n print(f\\\"Dir antenna 5: {Dir_antenna5}\\\")\\n print(f\\\"Angle elev 1: {Angle_elev1}\\\")\\n print(f\\\"Angle elev 2: {Angle_elev2}\\\")\\n print(f\\\"Angle elev 3: {Angle_elev3}\\\")\\n print(f\\\"Angle elev 4: {Angle_elev4}\\\")\\n print(f\\\"Angle elev 5: {Angle_elev5}\\\")\\n print(f\\\"size profile info rangegates: {size_profile_info_rangegates}\\\")\\n print(f\\\"processing type: {processing_type}\\\")\\n print(f\\\"m_nProcessingFlags: {m_nProcessingFlags}\\\")\\n print(f\\\"mode_no: {mode_no}\\\")\\n print(f\\\"profile date: {profile_date}\\\")\\n print(f\\\"start date profile: {start_date_profile}\\\")\\n print(f\\\"end date profile: {end_date_profile}\\\")\\n print(f\\\"processing duration actual: {processing_duration_actual}\\\")\\n print(f\\\"lag between processing actual: {lag_between_processing_actual}\\\")\\n print(f\\\"no heights: {no_heights}\\\")\\n print(f\\\"min height: {min_height}\\\")\\n print(f\\\"height increment: {height_increment}\\\")\\n print(f\\\"a2: {a2}\\\")\\n print(f\\\"blocknumber: {blocknumber}\\\")\\n print(f\\\"country: {country}\\\")\\n print(f\\\"agency: {agency}\\\")\\n print(f\\\"station no: {station_no}\\\")\\n print(f\\\"station type: {station_type}\\\")\\n print(f\\\"instrument type: {instrument_type}\\\")\\n print(f\\\"antenna type: {antenna_type}\\\")\\n print(f\\\"beamwidth: {beamwidth}\\\")\\n print(f\\\"frequency: {frequency}\\\")\\n print(f\\\"latitude file: {latitude_file}\\\")\\n print(f\\\"longitude file: {longitude_file}\\\")\\n print(f\\\"altitude site: {altitude_site}\\\")\\n print(f\\\"time difference: {time_difference}\\\")\\n print(f\\\"dlst: {dlst}\\\")\\n print(f\\\"voltage1: {voltage1}\\\")\\n print(f\\\"voltage2: {voltage2}\\\")\\n print(f\\\"voltage3: {voltage3}\\\")\\n print(f\\\"voltage4: {voltage4}\\\")\\n print(f\\\"overheating: {overheating}\\\")\\n print(f\\\"preheating: {preheating}\\\")\\n print(f\\\"vswr: {vswr}\\\")\\n print(f\\\"rain detection: {rain_detection}\\\")\\n print(f\\\"attenuation: {attenuation}\\\")\\n print(f\\\"current: {current}\\\")\\n print(f\\\"shelter temp: {shelter_temp}\\\")\\n print(f\\\"rain junk: {rain_junk}\\\")\\n print(f\\\"current position: {current_position}\\\")\\n print(f\\\"DBZ coeff: {DBZ_coeff}\\\")\\n print(f\\\"proc gain: {proc_gain}\\\")\\n print(f\\\"pulse length metres: {pulse_length_metres}\\\")\\n print(f\\\"boundary layer height: {boundary_layer_height}\\\")\\n print(f\\\"PBL time: {pbl_time}\\\")\\n print(f\\\"sun rise: {sun_rise}\\\")\\n print(f\\\"sun set: {sun_set}\\\")\\n print(f\\\"pulse_length_time_ns: {pulse_length_time_ns}\\\")\\n print(f\\\"current position2: {current_position2}\\\")\\n print(f\\\"max_doppler_1: {max_doppler_1}\\\")\\n print(f\\\"max_doppler_2: {max_doppler_2}\\\")\\n print(f\\\"max_doppler_3: {max_doppler_3}\\\")\\n print(f\\\"max_doppler_4: {max_doppler_4}\\\")\\n print(f\\\"max_doppler_5: {max_doppler_5}\\\")\\n print(f\\\"bright_band: {bright_band}\\\")\\n print(f\\\"wind speed: {wp_windspeed}\\\")\\n print(f\\\"wind direction: {wp_winddir}\\\")\\n \\n print(read_pos)\\n\\n print(\\\"Data successfully read in.\\\")\\n \\n # i'm sure more will join...\\n # keys should match name of variable in netcdf4 file\\n all_data = {'time': start_date_unix,\\n 'altitude': np.float32(altitude),\\n 'latitude': latitude_file,\\n 'longitude': longitude_file,\\n 'time_minutes_since_start_of_day': time_in_minutes_since_start_of_day,\\n 'size_of_gate': height_increment,\\n 'qc_flag_rain_detected': rain_detection+1, # +1 so 1, not 0, is good data\\n 'wind_speed': wp_windspeed,\\n 'wind_from_direction': wp_winddir,\\n 'eastward_wind': u_east,\\n 'northward_wind': v_north,\\n 'upward_air_velocity': w_vert,\\n 'signal_to_noise_ratio_of_beam_1': SNR_1,\\n 'signal_to_noise_ratio_of_beam_2': SNR_2,\\n 'signal_to_noise_ratio_of_beam_3': SNR_3,\\n 'signal_to_noise_ratio_minimum': SNR_min,\\n 'spectral_width_of_beam_1': width_1,\\n 'spectral_width_of_beam_2': width_2,\\n 'spectral_width_of_beam_3': width_3,\\n 'skew_of_beam_1': skew_1,\\n 'skew_of_beam_2': skew_2,\\n 'skew_of_beam_3': skew_3,\\n 'qc_flag_wind': qc_flag_wind,\\n 'qc_flag_beam_1': qc_flag_beam_1,\\n 'qc_flag_beam_2': qc_flag_beam_2,\\n 'qc_flag_beam_3': qc_flag_beam_3,\\n 'day_of_year': day_of_year,\\n 'year': year,\\n 'month': month,\\n 'day': day,\\n 'hour': hour,\\n 'minute': minutes,\\n 'second': np.float32(0.)\\n }\\n \\n all_attrs = {'platform_altitude': f'{altitude_site} m', # this should be deployment position above MSL, check with Emily what altitude_site is\\n 'geospatial_bounds': f'{latitude_file}N, {longitude_file}E',\\n 'instrument_software_version': operational_software_version,\\n 'averaging_interval' : f'{Processing_dur} minutes',\\n 'sampling_interval': f'{Lag_between_processing} minutes'\\n } \\n \\n return all_data, all_attrs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"457577bfcb4c6df426ff331052f02593\",\n \"score\": \"0.49406442\",\n \"text\": \"def reset_dir_timestamps(self, dirpath):\\n print(\\\"Resetting 'last loaded' timestamps for {0} in process {1}\\\".format(dirpath, os.getpid()))\\n for root, dirs, files in os.walk(dirpath):\\n for name in files:\\n self.reset_timestamp(os.path.join(root, name))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0baa1bf8e76550ee3d28a3bdb2a768d\",\n \"score\": \"0.4939383\",\n \"text\": \"def origin_tweet_time(windows, files, path):\\n\\n #windows = [None, [0, 60], [12796, 12850], [230, 300], [0, 100], [820, 900], [343180, 343280], [21650, 22e3], [0, 100],\\n # [0, 100], [9800, 9900], [0, 1500], [0, 3000], [7700, 8200], [300, 800], [100, 600], [1000, 1500]]\\n #path = 'cleaned_tweets/by_hashtag'\\n# files = [[tweet_file[0:-5], '{0}_retweets_cleaned'.format(tweet_file[0:-13])] for tweet_file in listdir(path)\\n# if not tweet_file.startswith(\\\".\\\") and not tweet_file.endswith('_retweets_cleaned.json')]\\n\\n origin_times = dict()\\n for i, hashtag_files in enumerate(files):\\n tweets = load_json('{0}/{1}'.format(path, hashtag_files[0]))\\n retweets = load_json('{0}/{1}'.format(path, hashtag_files[1]))\\n\\n tweet_timestamps = get_timestamps(tweets)\\n retweet_timestamps = np.array(get_timestamps(retweets))\\n\\n all_timestamps = np.hstack((tweet_timestamps, retweet_timestamps))\\n all_timestamps = np.sort(all_timestamps)\\n\\n origin = np.min(all_timestamps)\\n if windows[i] is not None:\\n origin_times[hashtag_files[0][:-8]] = tweet_timestamps[tweet_timestamps>=origin+windows[i][0]*60][0]\\n else:\\n origin_times[hashtag_files[0][:-8]] = origin\\n print hashtag_files[0]\\n\\n save_json('tweets_origin_time', origin_times)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d9c53f55e2bb29a080ee5085d740272\",\n \"score\": \"0.493838\",\n \"text\": \"def find_etls():\\n\\tfrom os import listdir\\n\\tfrom os.path import isfile, join, abspath\\n\\tfolderPath = abspath(\\\"path/to/etl/dir\\\")\\n\\tonlyfiles = [f for f in listdir(folderPath) if isfile(join(folderPath, f))]\\n\\tetl_dags = []\\n\\n\\tfor file in onlyfiles:\\t\\t\\t\\n\\t\\twith open(folderPath+'/'+file, 'r') as f:\\t\\t\\t\\t\\t\\t\\n\\t\\t\\tfirst_line = f.readline()\\n\\t\\t\\tif(first_line.startswith('--')):\\n\\t\\t\\t\\tnew_dictionary = clean_dict(first_line)\\n\\t\\t\\t\\tif new_dictionary:\\n\\t\\t\\t\\t\\tetl_dags.append((new_dictionary, file))\\t\\t\\n\\treturn etl_dags\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97d56c4da7b74bd60706cf6e42c5d2f6\",\n \"score\": \"0.4935976\",\n \"text\": \"def update(**kwargs):\\r\\n def calc_size(paths, sizes):\\r\\n \\\"\\\"\\\"calculates the real size of each path directly changing sizes list\\\"\\\"\\\"\\r\\n net_sizes = sizes[:]\\r\\n for index, path_outer in enumerate(paths):\\r\\n for path_inner in paths[index:]:\\r\\n if path_outer in path_inner:\\r\\n for hooked_index, path_inner_2 in enumerate(paths[index:]):\\r\\n if path_outer == path_inner_2:\\r\\n continue\\r\\n if path_outer in path_inner_2:\\r\\n sizes[index] += net_sizes[hooked_index+index]\\r\\n else:\\r\\n break\\r\\n break\\r\\n \\r\\n paths_txts = {\\\"H:/Anime_2Tera\\\": \\\"2_tera_animes_tree.txt\\\",\\r\\n \\\"H:/Movies_2Tera\\\": \\\"2_tera_movies_tree.txt\\\",\\r\\n \\\"H:/TV Series_2Tera\\\": \\\"2_tera_tv_tree.txt\\\",\\r\\n \\\"F:/Movies\\\": \\\"main_hard_movies_f_tree.txt\\\",\\r\\n \\\"F:/TV Series\\\": \\\"main hard_f_tv_series_tree.txt\\\",\\r\\n \\\"E:/Movies(2)\\\": \\\"main_hard_movies_e_tree.txt\\\",\\r\\n \\\"G:/Anime_1Tera\\\": \\\"1_tera_animes_tree.txt\\\",\\r\\n \\\"G:/Movies_1Tera\\\": \\\"1_tera_movies_tree.txt\\\",\\r\\n \\\"G:/TV Series_1Tera\\\": \\\"1_tera_tv_tree.txt\\\",\\r\\n \\\"E:/TV Series\\\": \\\"main hard_e_tv_series_tree.txt\\\", }\\r\\n for dir_path, txt in paths_txts.items():\\r\\n if not os.path.exists(dir_path):\\r\\n continue\\r\\n paths = []\\r\\n sizes = []\\r\\n txt_path = f\\\"{txt}\\\"#change this according to \\r\\n #current directory\\r\\n for root, dirs, files in os.walk(dir_path):\\r\\n paths.append(root)\\r\\n sizes.append(os.path.getsize(paths[-1]))\\r\\n for f in files:\\r\\n paths.append(f\\\"{root}{os.sep}{f}\\\")\\r\\n sizes.append(os.path.getsize(paths[-1]))\\r\\n calc_size(paths, sizes)\\r\\n with open(txt_path, \\\"w\\\",encoding=\\\"utf-8\\\") as file_object:#needs to be unicode\\r\\n #the default is ascii\\r\\n for path, size in zip(paths, sizes):\\r\\n file_object.write(f\\\"{path}|{size}\\\\n\\\")\\r\\n print(f\\\"Updated {dir_path}\\\")\\r\\n # if removed the .txt is going to be overwrriten even if path doesn't exist\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"092c4d410e75ab58ca2ea0bf742036f4\",\n \"score\": \"0.49356055\",\n \"text\": \"def watch(directory):\\n files = os.listdir(directory)\\n path = [os.path.join(directory, file) for file in files]\\n for file in path:\\n if good_extension(file):\\n filename = uniquify(file)\\n date_time_original = get_date_time_original(file)\\n insert_record(filename, date_time_original)\\n move(file, filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"823f00577f7e21cc47092c4b3c35d2d9\",\n \"score\": \"0.49261516\",\n \"text\": \"def main():\\n start_time = time.time()\\n input_path = 'data/'\\n output_path = 'master/'\\n redis_instance = setup_redis()\\n setup_directories(input_path, output_path)\\n ASN.aggregating_files.creating_files(input_path, output_path)\\n ASN.creating_asn_objects.creating_asns(output_path, input_path)\\n ASN.creating_asn_objects.stop_redis(redis_instance)\\n print(\\\"--- %s seconds ---\\\" % (time.time() - start_time))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9c7f01414643ce3a4951fafc65cbd71\",\n \"score\": \"0.49223283\",\n \"text\": \"def add_shot_templates(pth, tp):\\n print(\\\"in add_shot_templates()\\\")\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99a4633b5c30810c73a89529840f1b1d\",\n \"score\": \"0.49221954\",\n \"text\": \"def main():\\n\\n with open('imgnames.txt','r') as f:\\n imgnames = f.read().splitlines()\\n \\n imgtimes = []\\n for imgname in imgnames:\\n obj_img = Image.open(imgname)\\n imgtime = obj_img._getexif()[36867]\\n imgtime = int(imgtime[11:13])*3600 + int(imgtime[14:16])*60 \\\\\\n + int(imgtime[17:19])\\n imgtimes.append(imgtime)\\n \\n with open(\\\"imgtimes.txt\\\",\\\"w\\\") as f:\\n f.write('\\\\n'.join(str(imgtime) for imgtime in imgtimes))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19e61d5d2fdb00e548f818e607ee8b1b\",\n \"score\": \"0.49183664\",\n \"text\": \"def __timeshift_dirs(self):\\n for abs_path, mod in self.__dir_mods:\\n os.utime(abs_path, mod)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e23796a1a0287ae14c9f4fb330d4737\",\n \"score\": \"0.49150217\",\n \"text\": \"def copy_tiles(data_folder, target_folder, overwrite=False):\\n\\n subs = os.listdir(data_folder)\\n\\n if not isinstance(subs, list):\\n subs = [subs]\\n n_subs = len(subs)\\n\\n start = time.time()\\n\\n if overwrite and os.path.exists(target_folder):\\n print(f'Overwriting {target_folder}....')\\n shutil.rmtree(target_folder)\\n\\n if not os.path.exists(target_folder):\\n os.mkdir(target_folder)\\n\\n for i,sub in enumerate(subs):\\n intermediate1 = time.time()\\n print(f'Copying products from {sub}')\\n current = os.path.join(data_folder,sub)\\n files = os.listdir(current)\\n file_types = [f[-9:-4] for f in files]\\n\\n for file,ftype in zip(files,file_types):\\n target = os.path.join(target_folder,ftype)\\n if not os.path.exists(target):\\n print(f'MAKING {target}')\\n os.mkdir(target)\\n to_copy = os.path.join(current,file)\\n to_write = os.path.join(target,file)\\n if not os.path.exists(to_write):\\n shutil.copyfile(to_copy, to_write)\\n else:\\n print(f'{file} already present in target folder. Skipping....')\\n continue\\n\\n intermediate2 = time.time()\\n intermediate_elap = round(intermediate2-intermediate1, 2) # in seconds\\n running_time = round(intermediate2-start, 2)/60 # in minutes\\n frac_progress = (i+1)/n_subs\\n estimated_total_time = round(running_time*(1/frac_progress) - running_time, 2)\\n\\n print(f'Processing time: {intermediate_elap} seconds. Folder {i+1} of {n_subs} complete. Estimated time remaining: '\\n f'{estimated_total_time} minutes')\\n\\n final = time.time()\\n elap = round(final-start, 2)\\n print(f'FINISHED. Elapsed time: {elap/60} minutes')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e92adc8be8c4a62dffdbc06c12423141\",\n \"score\": \"0.491342\",\n \"text\": \"def targets(self):\\n printtime('Performing analysis with {} targets folder'.format(self.analysistype), self.start)\\n for sample in self.metadata:\\n # Initialise dictionaries\\n sample[self.analysistype].vtxhashes = dict()\\n sample[self.analysistype].hashcalls = dict()\\n try:\\n # Iterate through all the vtx genes found for each strain\\n for vtx, allelefile in sample[self.analysistype].targetfiles.items():\\n # Find the base name/path of the allele file\\n targetbase = allelefile.split('.')[0]\\n hashfile = '{}.mhs.gz'.format(targetbase)\\n # Define the hash call\\n hashcall = 'cd {} && mirabait -b {} -k 19 -K {}'.format(self.allelepath, allelefile, hashfile)\\n # Add the hash call to the dictionary\\n sample[self.analysistype].hashcalls[vtx] = hashcall\\n # Run the system call as required\\n if not os.path.isfile(hashfile):\\n call(hashcall, shell=True, stdout=self.devnull, stderr=self.devnull)\\n # Ensure that the hash file was successfully created\\n assert os.path.isfile(\\n hashfile), u'Hashfile could not be created for the combined target file {0!r:s}' \\\\\\n .format(allelefile)\\n # Add the hash filename/path to the dictionary\\n sample[self.analysistype].vtxhashes[vtx] = hashfile\\n except KeyError:\\n pass\\n # Bait the fastq files\\n self.mirabaiting()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"836344da2980040881281c67a365d907\",\n \"score\": \"0.48982254\",\n \"text\": \"def prepare_training():\\n if os.path.exists(config['temp_dir']):\\n shutil.rmtree(config['temp_dir'])\\n os.makedirs(config['temp_dir'])\\n training_starttime = datetime.now().isoformat()\\n return training_starttime\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e60f012f56835cd2a5edcf79680f0b9\",\n \"score\": \"0.48976588\",\n \"text\": \"def read_times() -> List:\\n sessions = []\\n session_files = [f for f in listdir(TIME_FOLDER) if isfile(join(TIME_FOLDER, f))]\\n for session_file in session_files:\\n with open(join(TIME_FOLDER, session_file), encoding='utf8') as parsed_file:\\n sessions.append(load(parsed_file))\\n return sessions\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16a5ac6e377d55a20a30348b955d9a2e\",\n \"score\": \"0.48968306\",\n \"text\": \"def main(ufiles, vfiles, tfiles, wfiles, biofiles, savedir=SAVEDIR):\\n for fname in ufiles:\\n print(fname)\\n build_unstaggered_U_file(fname, savedir=savedir)\\n for fname in vfiles:\\n print(fname)\\n build_unstaggered_V_file(fname, savedir=savedir)\\n move_other_files(tfiles,wfiles,biofiles)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0af1010b0ee9f35df4c9befc51569464\",\n \"score\": \"0.48960632\",\n \"text\": \"def on_the_fly(folder_path, base_filename, index, last_scan, d_in_pixel, Rotation_angle, tilt_angle, lamda,\\n x0, y0, PP, num_of_smpls_on_wafer, attribute1=[['scan#', 'Imax', 'Iave', 'Imax/Iave']],\\n attribute2=[['scan#', 'texture_sum']], attribute3=[['scan#', 'peak_num']],\\n attribute4=[['scan#', 'neighbor_distance']], attribute5 = [['scan#', 'SNR']]):\\n # initializing parameters # distance from sample to detector plane along beam direction in pixel space\\n Rot = (np.pi * 2 - Rotation_angle) / (2 * np.pi) * 360 # detector rotation\\n tilt = tilt_angle / (2 * np.pi) * 360 # detector tilt # wavelength\\n\\n # generate a folder to put processed files\\n save_path = os.path.join(folder_path, 'Processed')\\n if not os.path.exists(save_path):\\n os.makedirs(save_path)\\n\\n # generate a random series of numbers, in case restart the measurement from the middle, the new master file will not overwrite the previous one\\n master_index = str(int(random.random() * 100000000))\\n\\n while (index <= last_scan):\\n imageFilename = base_filename + file_index(index) + '.tif'\\n imageFullname = os.path.join(folder_path, imageFilename)\\n print(\\\"\\\\r\\\")\\n # wait until an image is created, and process the previous image, to avoid crashing\\n print 'waiting for image', imageFullname + ' to be created...'\\n print(\\\"\\\\r\\\")\\n while not os.path.exists(imageFullname):\\n time.sleep(1)\\n # print 'sleeping'\\n print 'processing image ' + imageFullname\\n print(\\\"\\\\r\\\")\\n\\n while (1):\\n try:\\n # data_reduction to generate Q-chi and 1D spectra, Q\\n Q, chi, cake, Qlist, IntAve = reduction.data_reduction(imageFullname, \\\\\\n d_in_pixel, Rot, tilt, lamda, x0, y0, PP)\\n # save Qchi as a plot *.png and *.mat\\n Qchi.save_Qchi(Q, chi, cake, imageFilename, save_path)\\n # save 1D spectra as a *.csv\\n oneDcsv.save_1Dcsv(Qlist, IntAve, imageFilename, save_path)\\n # extract maximum/average intensity from 1D spectra as attribute1\\n newRow1 = max_ave.extract_max_ave_intensity(IntAve, index)\\n attribute1.append(newRow1)\\n # save 1D texture spectra as a plot (*.png) and *.csv\\n Qlist_texture, texture = save_texture.save_texture_plot_csv(Q, chi, cake, imageFilename, save_path)\\n # extract texture square sum from the 1D texture spectra as attribute2\\n newRow2 = extract_texture.extract_texture_extent(Qlist_texture, texture, index)\\n attribute2.append(newRow2)\\n # extract composition information if the information is available\\n # extract the number of peaks in 1D spectra as attribute3\\n newRow3, peaks = peak_num.extract_peak_num(Qlist, IntAve, index)\\n attribute3.append(newRow3)\\n # # save 1D plot with detected peaks shown in the plot\\n oneDplot.save_1Dplot(Qlist, IntAve, peaks, imageFilename, save_path)\\n # extract neighbor distances as attribute4\\n newRow4 = neighbor.nearst_neighbor_distance(index, Qlist, IntAve, folder_path, save_path, base_filename,\\n num_of_smpls_on_wafer)\\n attribute4.append(newRow4)\\n # print attribute1, attribute2, attribute3, attribute4\\n\\n # extract signal-to-noise ratio\\n newRow5 = SNR.extract_SNR(index, IntAve)\\n attribute5.append(newRow5)\\n\\n # add features (floats) to master metadata\\n attributes = np.concatenate((attribute1, attribute2, attribute3, attribute4, attribute5), axis=1)\\n add_feature.add_feature_to_master(attributes, base_filename, folder_path, save_path, master_index, index)\\n\\n break\\n except (OSError, IOError):\\n # The image was being created but not complete yet\\n print 'waiting for image', imageFullname + ' to be ready...'\\n time.sleep(1)\\n index += 1 # next file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03b69f4b277f061d2bfd17a02836ce17\",\n \"score\": \"0.48959568\",\n \"text\": \"def fl_prep(sebgs):\\n\\n fl_nm = dl_dir + 'MLHD_' + sebgs + '.tar'\\n # make own new directory\\n dir_str = 'cd ' + gnr_dir + ' && mkdir ' + sebgs\\n os.system(dir_str)\\n\\n mv_str = 'mv ' + fl_nm + \\\" \\\" + gnr_dir + sebgs + \\\"/\\\"\\n os.system(mv_str)\\n\\n upk_str = 'cd ' + gnr_dir + sebgs + \\\" && tar -xvf MLHD_\\\" + sebgs + '.tar'\\n os.system(upk_str)\\n\\n rm_bs_str = 'cd ' + gnr_dir + sebgs + \\\" && rm MLHD_\\\" + sebgs + '.tar'\\n os.system(rm_bs_str)\\n\\n get_US_logs(gnr_dir + sebgs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"518b452934885837c7fe8719fe70aa63\",\n \"score\": \"0.48954552\",\n \"text\": \"def test_hook_timeline_file_write(set_up_smprofiler_config_path, out_dir):\\n hook = t_hook(out_dir=out_dir)\\n\\n for i in range(1, 11):\\n n = \\\"event\\\" + str(i)\\n hook.record_trace_events(\\n training_phase=\\\"MXNet_TimelineFileWriteTest\\\",\\n op_name=n,\\n step_num=i,\\n timestamp=time.time(),\\n )\\n\\n # need to explicitly close hook for the test here so that the JSON file is written and\\n # can be read back below.\\n # In training scripts, this is not necessary as _cleanup will take care of closing the trace file.\\n hook.close()\\n\\n files = []\\n for path in Path(out_dir + \\\"/\\\" + DEFAULT_PREFIX).rglob(\\\"*.json\\\"):\\n files.append(path)\\n\\n assert len(files) == 1\\n\\n with open(files[0]) as timeline_file:\\n events_dict = json.load(timeline_file)\\n\\n assert events_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfdf37ab9220731f4949f17a5d668e1a\",\n \"score\": \"0.4894056\",\n \"text\": \"def import_test_data():\\r\\n\\r\\n DATA_PATH = ['/mnt/plabNAS/Lorenzo/iSCAT/iSCAT Data/Lorenzo/2019/novembre/20/fliC-_PaQa_Gasket_0/',\\r\\n '/mnt/plabNAS/Lorenzo/iSCAT/iSCAT Data/Lorenzo/2020/janvier/16/fliC-_PaQa_solid_4/',\\r\\n '/mnt/plabNAS/Lorenzo/iSCAT/iSCAT Data/Lorenzo/2018/décembre/04/PilH-FliC-_Agar_Microchannel_0_Flo/',\\r\\n '/mnt/plabNAS/Lorenzo/iSCAT/iSCAT Data/Lorenzo/2020/janvier/17/pilH-fliC-_PaQa_solid_0/']\\r\\n\\r\\n DATA_PATH = DATA_PATH[-1]\\r\\n\\r\\n tirf_paths = glob.glob(DATA_PATH +'cam1/event[0-9]_tirf/*.tif')\\r\\n iscat_paths = glob.glob(DATA_PATH +'cam1/event[0-9]/*PreNbin*.tif')\\r\\n\\r\\n fps_iscat, fps_tirf = 100, 50\\r\\n\\r\\n for i, (iscat_path, tirf_path) in enumerate(zip(iscat_paths, tirf_paths)):\\r\\n\\r\\n iscat = tifffile.imread(iscat_path)[::int(fps_iscat /fps_tirf)]\\r\\n tirf = tifffile.imread(tirf_path)\\r\\n\\r\\n iscat = processing.image_correction(iscat)\\r\\n iscat = processing.enhance_contrast(iscat, 'stretching', percentile=(1, 99))\\r\\n iscat = processing.fft_filtering(iscat, 1, 13, True)\\r\\n iscat = processing.enhance_contrast(iscat, 'stretching', percentile=(3, 97))\\r\\n iscat = iscat.astype('float32')\\r\\n\\r\\n tirf = processing.coregister(tirf, 1.38)\\r\\n\\r\\n # For fluo tirf images\\r\\n # tirf = ndimage.median_filter(tirf, 5)\\r\\n # tirf = (tirf > .9 *skimage.filters.threshold_triangle(tirf.ravel())).astype('uint8')\\r\\n # tirf = ndimage.binary_opening(tirf, processing.structural_element('circle', (1,10,10))).astype('uint8')\\r\\n # tirf = predict_cell_detection(tirf)\\r\\n\\r\\n tirf = tirf.astype('float32')\\r\\n \\r\\n iscat -= iscat.min()\\r\\n iscat /= iscat.max()\\r\\n \\r\\n tirf -= tirf.min()\\r\\n tirf /= tirf.max()\\r\\n\\r\\n yield iscat, tirf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd4911ce1cdf8f43d4ebd6ef9c2da081\",\n \"score\": \"0.48887303\",\n \"text\": \"def test_short_mw_tt_full_lept(self):\\n\\n try:\\n shutil.rmtree(pjoin(MG5DIR,'TEST_MW_TT_prod'))\\n except Exception, error:\\n pass\\n \\n cmd = \\\"\\\"\\\"set automatic_html_opening False --no-save\\n set cluster_temp_path /tmp --no-save\\n generate p p > t t~, (t > w+ b, w+ > e+ ve), (t~ > w- b~, w- > e- ve~)\\n output madweight TEST_MW_TT_prod_full\\n launch\\n change_tf dbl_gauss_pt_jet\\n ./tests/input_files/mw_ttprod.lhco.gz\\n set nb_exp_events 2\\n set log_level debug\\n set nb_event_by_node 1\\n set mw_perm montecarlo F\\n \\\"\\\"\\\"\\n open('/tmp/mg5_cmd','w').write(cmd)\\n\\n if logging.getLogger('madgraph').level <= 20:\\n stdout=None\\n stderr=None\\n else:\\n devnull =open(os.devnull,'w')\\n stdout=devnull\\n stderr=devnull\\n\\n start = time.time()\\n print 'this mw test is expected to take 30s on two core. (MBP retina 2012) current time: %02dh%02d' % (time.localtime().tm_hour, time.localtime().tm_min) \\n subprocess.call([pjoin(MG5DIR,'bin','mg5'), \\n '/tmp/mg5_cmd'],\\n cwd=pjoin(MG5DIR),\\n stdout=stdout, stderr=stderr)\\n run_time = time.time() - start\\n print 'tt~ full takes %smin %is' % (run_time//60, run_time % 60)\\n data = open(pjoin(MG5DIR, 'TEST_MW_TT_prod_full', 'Events', 'fermi', 'weights.out')).read()\\n\\n solution = self.get_result(data)\\n expected = \\\"\\\"\\\"# Weight (un-normalize) for each card/event\\n# format: LHCO_event_number card_id value integration_error\\n24 1 7.13353274182e-22 3.48595541497e-24 \\n24 2 4.48106063562e-22 2.23501639194e-24 \\n28 1 1.22526200347e-23 5.8955444892e-26 \\n28 2 5.53271960779e-23 2.59251688524e-25 \\n\\\"\\\"\\\" \\n expected = self.get_result(expected)\\n for key, (value,error) in expected.items():\\n assert key in solution\\n value2, error2 = solution[key]\\n self.assertTrue(abs(value-value2) < 5* abs(error+error2))\\n self.assertTrue(abs(value-value2)/abs(value+value2) < 2*abs(value/error))\\n self.assertTrue(abs(error2)/abs(value2) < 0.02)\\n #try:\\n # shutil.rmtree(pjoin(MG5DIR,'TEST_MW_TT_prod_full'))\\n #except Exception, error:\\n # pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"196dcc71e7070aef394abb28a51f7eea\",\n \"score\": \"0.48879805\",\n \"text\": \"def sweep(path):\\n dont_kill_the_repository(path, False)\\n matchtable = {}\\n for entry in os.listdir(path):\\n fullpath = os.path.join(path, entry)\\n if not os.path.isdir(fullpath):\\n continue\\n match = level_regex.match(entry)\\n if match == None:\\n match = magetab_regex.match(entry)\\n if match == None:\\n continue\\n #now it's one of the types\\n myrevision = (int(match.group(\\\"revision\\\")),\\n int(match.group(\\\"build\\\")))\\n slay_the_weaker(matchtable, \\n os.path.join(path, match.group(\\\"key\\\")),\\n myrevision)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"633de5de1e36c9f2d96cf922dfd529b0\",\n \"score\": \"0.48870242\",\n \"text\": \"def budget_time_series(folder):\\n inputs = InputParams(folder)\\n ν = np.sqrt(inputs.PraS/inputs.RayS)\\n κT = ν/inputs.PraT\\n κS = ν/inputs.PraS\\n grid = Grid(folder)\\n uv = read_mean(folder, \\\"vxvy\\\")\\n uw = read_mean(folder, \\\"vxvz\\\")\\n v̄ = read_mean(folder, \\\"vybar\\\")\\n w̄ = read_mean(folder, \\\"vzbar\\\")\\n dv̄ = ddx(v̄, grid.xm)\\n dw̄ = ddx(w̄, grid.xm)\\n shear_prod = xmean(\\n -uv*dv̄ - uw*dw̄, grid.xc\\n )\\n eps_bar = xmean(ν*(dv̄**2 + dw̄**2), grid.xc)\\n epsilon = read_mean(folder, \\\"epsilon\\\")\\n eps_p = xmean(epsilon, grid.xc) - eps_bar\\n \\n Tbar = read_mean(folder, \\\"Tbar\\\")\\n qvT = read_mean(folder, \\\"vyT\\\")\\n qT_bar = xmean(v̄*Tbar, grid.xc)\\n qT_p = xmean(qvT,grid.xc) - qT_bar\\n \\n vr = refine(v̄, grid)\\n Sbar = read_mean(folder, \\\"Sbar\\\")\\n qvS = read_mean(folder, \\\"vyS\\\")\\n qS_bar = xmean(vr*Sbar, grid.xcr)\\n qS_p = xmean(qvS,grid.xcr) - qS_bar\\n \\n t = mean_time(folder)\\n \\n dTbar = ddx(Tbar, grid.xm, BClo=0.5, BCup=-0.5)\\n qT = read_mean(folder, \\\"vxT\\\")\\n buoy_prodT = xmean(-qT*dTbar, grid.xc)\\n chiT_bar = xmean(κT*dTbar**2, grid.xc)\\n chiT = read_mean(folder, \\\"chiT\\\")\\n chiT_p = xmean(chiT, grid.xc) - chiT_bar\\n qT = -0.5*κT*(dTbar[0,:] + dTbar[-1,:])\\n \\n dSbar = ddx(Sbar, grid.xmr, BClo=-0.5, BCup=0.5)\\n qS = read_mean(folder, \\\"vxS\\\")\\n buoy_prodS = xmean(-qS*dSbar, grid.xcr)\\n chiS_bar = xmean(κS*dSbar**2, grid.xcr)\\n chiS = read_mean(folder, \\\"chiS\\\")\\n chiS_p = xmean(chiS, grid.xcr) - chiS_bar\\n qS = 0.5*κS*(dSbar[0,:] + dSbar[-1,:])\\n \\n RaS = inputs.RayS*np.ones(len(t))\\n PrS = inputs.PraS*np.ones(len(t))\\n RaT = inputs.RayT*np.ones(len(t))\\n PrT = inputs.PraT*np.ones(len(t))\\n \\n df = DataFrame({\\n \\\"t\\\": t, \\\"shear_prod\\\": shear_prod,\\n \\\"eps_bar\\\": eps_bar, \\\"eps_p\\\": eps_p,\\n \\\"qT_bar\\\": qT_bar, \\\"qT_p\\\": qT_p,\\n \\\"qS_bar\\\": qS_bar, \\\"qS_p\\\": qS_p,\\n \\\"chiT_bar\\\": chiT_bar, \\\"chiT_p\\\": chiT_p,\\n \\\"chiS_bar\\\": chiS_bar, \\\"chiS_p\\\": chiS_p,\\n \\\"qT\\\": qT, \\\"qS\\\": qS,\\n \\\"buoy_prodT\\\": buoy_prodT, \\\"buoy_prodS\\\": buoy_prodS,\\n \\\"RaT\\\": RaT, \\\"PrT\\\": PrT, \\\"RaS\\\": RaS, \\\"PrS\\\": PrS\\n })\\n return df\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993268,"cells":{"query_id":{"kind":"string","value":"676fa315d8a1f7d5fa3ef30c0c723d9c"},"query":{"kind":"string","value":"pulls soup list from front part of mangapanda url and selector"},"positive_passages":{"kind":"list like","value":[{"docid":"84b78c04950d8c926831dec8d0a56e06","score":"0.79778224","text":"def pullSoup(front_url, selector):\n\t#add base url to front url to get complete url for page to be opened\n\tfull_url = \"http://www.mangapanda.com\" + front_url\n\t#open up page\n\tresPage = requests.get(full_url)\n\t#check for errors\n\tresPage.raise_for_status()\n\t#return list of results using beautifulsoup\n\treturn(bs4.BeautifulSoup(resPage.text,\"lxml\").select(selector))","title":""}],"string":"[\n {\n \"docid\": \"84b78c04950d8c926831dec8d0a56e06\",\n \"score\": \"0.79778224\",\n \"text\": \"def pullSoup(front_url, selector):\\n\\t#add base url to front url to get complete url for page to be opened\\n\\tfull_url = \\\"http://www.mangapanda.com\\\" + front_url\\n\\t#open up page\\n\\tresPage = requests.get(full_url)\\n\\t#check for errors\\n\\tresPage.raise_for_status()\\n\\t#return list of results using beautifulsoup\\n\\treturn(bs4.BeautifulSoup(resPage.text,\\\"lxml\\\").select(selector))\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"dc6c271d50837bf478ff6000fb86f793","score":"0.6672189","text":"def getChapters(manga_url):\n\t#gets the list of soup elements from url (first input) using the selector (second input)\n\tsoupList = pullSoup(manga_url,'#listing a')\n\t#reshape so that it is a list of front link+title elements\n\t#this won't change even after making changes above\n\tresultList = []\n\tfor result in soupList:\n\t\telement = [result.getText(), result.get(\"href\")]\n\t\tresultList.append(element)\n\t\n\t#reverse the list in descending order of chapters\n\tresultList.reverse()\n\treturn(resultList)","title":""},{"docid":"057960aa9d714362c39518339e71445a","score":"0.66142416","text":"def scrape_list(url,user_agent = user_agent):\n r = requests.get(url,headers = {'User-Agent': user_agent})\n if r.status_code != 200:\n raise ValueError(f\"Status code for {url} is {r.status_code}\")\n soup = BeautifulSoup(r.text,\"lxml\")\n\n links = soup.select(\".product_pod h3 a\")\n links = [urljoin(r.url,x[\"href\"]) for x in links]\n\n next_page = soup.select(\".next a\")\n next_page = urljoin(r.url,next_page[0][\"href\"]) if next_page else None\n \n return({\"links\":links,\"next\":next_page})","title":""},{"docid":"1d75a43fc7f6d298c8b7ea8a47b1b916","score":"0.6550639","text":"def mangaSearch(searchInput):\n\t#gets the list of soup elements from url (first input) using the selector (second input)\t\n\tsoupList = pullSoup(\"/search/?w=\"+searchInput,'.mangaresultitem a')\n\t#reshape so that it is a list of front link+title elements\n\t#this is identical to below but will change in the future once core fully functional\n\tresultList = []\n\tfor result in soupList:\n\t\telement = [result.getText(), result.get(\"href\")]\n\t\tresultList.append(element)\n\t\t\n\treturn(resultList)\n\t# get the core functionality out first, then build upon it.\n\t#next get images, tags and other stuff","title":""},{"docid":"db47013cd20cc0f35a05e6da1f36b4da","score":"0.6534393","text":"def parser_homeurl(html):\n\n soup = BeautifulSoup(html, \"html.parser\")\n for url in soup.select('a[class=\"movie-box\"]'):\n yield url['href']","title":""},{"docid":"f12e2c7d6a316d00c8139e9d7f4aaeb7","score":"0.633048","text":"def get_list(soup, selector):\n sel = soup.select(selector)\n items = (item.text for item in sel)\n return items","title":""},{"docid":"9eba24677c472b388ad32e9d960f8035","score":"0.62395185","text":"def parse_soup(self, soup):\n return soup.find_all('li', class_=\"serp-item\")","title":""},{"docid":"8b2b499ef7b24be0270e517818999234","score":"0.6233359","text":"def parse_search_page(search_doc):\n\n soup = BeautifulSoup(search_doc, \"html.parser\")\n ul = soup.find(\"ul\", class_=\"vT-srch-result-list-bid\")\n li = ul.find(\"li\")\n new_list = []\n while li:\n entry = parse_html_li_tag(li)\n new_list.append(entry)\n li = li.find_next(\"li\")\n print(\"{}|{}\".format(entry.link, entry.desc))\n\n return new_list","title":""},{"docid":"1247c942710c8d3b44a40d522bbd29e8","score":"0.62222606","text":"def collect_pages(soup):\n page = soup.find_all('div', class_=\"destination-info\")\n page_list = []\n for p in page:\n raw_data = str(p)\n page = re.search('href=(.*)/', raw_data).group()\n web_page = URL[:-1] + page[6:]\n page_list.append(web_page)\n return page_list","title":""},{"docid":"8addb4d643c580a4e6fec884d96cc1f5","score":"0.62148976","text":"def extract_links(html_page):\n\n # to find the <ul> tag that contains every job ad basic info\n jobs_list = html_page.find(\"ul\", class_=\"o-listView__list\")\n\n # iterate over every job recruiment to get it's link\n demo = list()\n for job_add in jobs_list.find_all(\"li\", class_=\"o-listView__item\"):\n link = job_add.find(\"a\", class_=\"c-jobListView__titleLink\")\n demo.append(link.get(\"href\", None))\n\n return demo","title":""},{"docid":"7af2678a2827e0f85792f54765b4883e","score":"0.6181112","text":"def listFD(url):\r\n page = requests.get(url).text\r\n soup = BeautifulSoup(page, 'html.parser')\r\n return [url + '/' + node.get('href') for node in soup.find_all('a')]","title":""},{"docid":"302bda9144dbc6b3298352cf156e2924","score":"0.61109954","text":"def get_bookmarks_list(html):\n\n first_dl_tag = html.find_all('dl')[0]\n\n return first_dl_tag","title":""},{"docid":"36b16f3e3c735b07567e07e57798f6fa","score":"0.61086905","text":"def parse_hotellist_page(html):\n soup = BeautifulSoup(html)\n # Extract hotel name, star rating and number of reviews\n hotel_boxes = soup.findAll('div', {'class' :'listing wrap reasoning_v5_wrap jfy_listing p13n_imperfect'})\n if not hotel_boxes:\n log.info(\"#################################### Option 2 ######################################\")\n hotel_boxes = soup.findAll('div', {'class' :'listing_info jfy'})\n if not hotel_boxes:\n log.info(\"#################################### Option 3 ######################################\")\n hotel_boxes = soup.findAll('div', {'class' :'listing easyClear p13n_imperfect'})\n\n for hotel_box in hotel_boxes:\n hotel_name = hotel_box.find(\"a\", {\"target\" : \"_blank\"}).find(text=True)\n log.info(\"Hotel name: %s\" % hotel_name.strip())\n #dict_hotels[hotel_name.strip()] = {}\n stars = hotel_box.find(\"img\", {\"class\" : \"sprite-ratings\"})\n if stars:\n log.info(\"Stars: %s\" % stars['alt'].split()[0])\n #dict_hotels[hotel_name.strip()][\"Stars\"] = stars['alt'].split()[0]\n\n num_reviews = hotel_box.find(\"span\", {'class': \"more\"}).findAll(text=True)\n if num_reviews:\n log.info(\"Number of reviews: %s \" % [x for x in num_reviews if \"review\" in x][0].strip())\n \n #find the url for the hotel, append it to a list of urls\n hotel_url = hotel_box.find(\"a\", {\"class\" : \"property_title\"})['href']\n ht_urls.append(hotel_url)\n\n #this didn't work :(\n #Get the url of the hotel page\n # hotel_pages = soup.findAll('div', {'class': 'qualilty wrap'})\n # print \"going to start parsing hotels\"\n # i = 0\n # for hotel_page in hotel_pages:\n # print \"parsing hotel #\", i\n # hotel_url = hotel_page.find('a', {'target': '_blank'}).find(text=True)\n # url = base_url + hotel_url\n # headers = { 'User-Agent' : user_agent }\n # time.sleep(2)\n # response = requests.get(url, headers=headers)\n # #html now holds the webpage for a specific website. \n # html = response.text.encode('utf-8')\n # parse_hotel_page(html) #will find the needed data and parse it. \n \n\n # Get next URL page if exists, otherwise exit\n div = soup.find(\"div\", {\"class\" : \"pagination paginationfillbtm\"})\n # check if this is the last page\n if div.find('span', {'class' : 'guiArw pageEndNext'}):\n log.info(\"We reached last page\")\n # print \"We reached last page\"\n sys.exit()\n # If not, return the url to the next page\n hrefs = div.findAll('a')\n for href in hrefs:\n cl = href['class'].strip()\n # print \"cl \", cl\n if cl == \"guiArw sprite-pageNext\":\n # print \"href \", href['href']\n return href['href']","title":""},{"docid":"8a4ccc03db85be796242fe8466d402e4","score":"0.6096425","text":"def parse(self, response):\n for url in response.css('ul.topicsList_main a::attr(\"href\")').re(r'/pickup/\\d+$'):\n yeild response.follow(url, self.parse_topics)","title":""},{"docid":"825107e71d7fbeffc38d30558e301c74","score":"0.59929407","text":"def parse_soup_page(soup):\n titles, authors, links = list(), list(), list()\n for div in soup.findAll('div'):\n if div.name == \"div\" and div.get('class') == \"gs_ri\":\n links.append(div.a['href'])\n div_pub = div.findAll('div')\n for d in div_pub:\n if d.name == 'div' and d.get('class') == 'gs_a':\n authors.append(d.text)\n titles.append(div.a.text)\n return titles, authors, links","title":""},{"docid":"805e451604422ba920a5a220181119b2","score":"0.5978821","text":"def scrapeLinks(selector):\n\n url = 'https://www.psp.cz/sqw/hp.sqw?k=1300'\n download_url = 'https://www.psp.cz'\n req = requests.get(url)\n soup = BeautifulSoup(req.content, 'html.parser')\n download_links = []\n\n for link in soup.select(selector):\n download_links.append(str(download_url + link.get('href').replace('..', '')))\n\n return download_links","title":""},{"docid":"f8de3911897d4cfa6223da93af963530","score":"0.5963124","text":"def parse_soup(self, soup):\n # find all li tags\n return soup.find_all('li', class_='repo-list-item')","title":""},{"docid":"b7e41b0f70e58c3c58619737d0ef80d0","score":"0.59541196","text":"def parse_the_next(self, content, url):\n\t\tout_url_list = []\n\t\ttry:\n\t\t\tparser = OnePageParser()\n\t\t\tparser.feed(content)\n\t\t\tfor i in range(len(parser.href_list)):\n\t\t\t\ttmp_href = parser.href_list[i]\n\t\t\t\treal_url = utils.joint_url(url, tmp_href)\n\t\t\t\t#把解析出来的url放到list中\n\t\t\t\tout_url_list.append(real_url)\n\t\texcept HTMLParser.HTMLParseError as error:\n\t\t\tlogging.error(\"Parsing url: %s failed. Details: %s \" % (url, error))\n\t\t\treturn None\n\t\treturn out_url_list","title":""},{"docid":"a74d33ec9e9107293f110e7572f1b298","score":"0.59488285","text":"def help_get_list_from_dropdown(url, attr_name):\n r = requests.get(url)\n raw_string = sub(r\"\\s+\", ' ', r.text)\n\n soup = BeautifulSoup(\n raw_string,\n from_encoding=\"utf-8\",\n parse_only=SoupStrainer(name=\"select\", attrs={\"name\" : attr_name})\n )\n\n del r, raw_string\n stack = []\n for option in soup(\"option\"):\n if option[\"value\"]:\n stack.append(option[\"value\"].strip().encode(\"utf-8\"))\n\n del soup\n return stack","title":""},{"docid":"3895a76e72a3b157dc2a066c7864e0a8","score":"0.59416556","text":"def getepisodelist(html, url):\n\t\n\tbase = re.search(\"(https?://[^/]+)\", url).group(1)\n\tcomicurl = re.search(\"comic_url = \\\"(.+?)\\\"\", html).group(1)\n\tms = re.findall(\"href=\\\"(/{}.+?)\\\" (?: class=\\\"color_red\\\")?>(.+?)</a></li>\".format(comicurl), html)\n\ts = []\n\tfor m in ms:\t\n\t\turl, title = m\n\t\te = comiccrawler.Episode()\n\t\te.title = title\n\t\t# safeprint(e.title)\n\t\te.firstpageurl = base + url\n\t\t# print(e.firstpageurl)\n\t\ts.append(e)\n\treturn s","title":""},{"docid":"7a015d0717dbed30a72b43470459ca68","score":"0.59404546","text":"def scrape(urls):\n\n url = random.choice(urls)\n #url = \"http://www.latimes.com\"\n print(\"reading: \" + url)\n\n front_page = newspaper.build(url)\n\n headlines = []\n\n for article in front_page.articles:\n headlines.append(article.url)\n\n return headlines, url","title":""},{"docid":"820dacae64bf11db865ed1289d375ab4","score":"0.59238493","text":"def parse(self, response):\n\n for href in response.xpath(\"//a[not(contains(@href, '#'))]/@href\"):\n # TODO remove dupliate urls\n url = response.urljoin(href.extract())\n\n print(url)\n\n #if url == 'https://www.astrazeneca.com/our-company.html':\n yield scrapy.Request(url, callback = self.parse_tags)\n # break","title":""},{"docid":"984fd01618fbe2ca3a94e8bb5d5904a3","score":"0.5899313","text":"def collect_links_from_main_page():\n law_pages = []\n response = requests.get(START_URL)\n html = BeautifulSoup(response.text, features=\"lxml\")\n\n for link in html.find_all('a'):\n if link.has_attr('href'):\n search = re.search('/index.php/en/library/albanian-legislation/category/[0-9a-zA-z-]+',\n link['href'])\n if search is not None:\n full_link = BASE_URL + link['href']\n if full_link == START_URL:\n continue\n law_pages.append(link['href'])\n return law_pages","title":""},{"docid":"c609cba9a95813913e3e38c70eecd0b9","score":"0.5887025","text":"def get_job_posts(target_url, search_term):\n\n # custom parameters\n unknown_string = 'UNKNOWN'\n\n # variables\n url_list = []\n page = requests.get(target_url)\n\n # Create a BeautifulSoup object\n big_soup = BeautifulSoup(page.text, 'html.parser')\n\n # Let's clean this mess up\n for script in big_soup([\"script\", \"style\"]): # remove all javascript and stylesheet code\n script.decompose()\n\n parent_search_section = big_soup.find_all('div', class_='mux-card mux-job-card')\n\n if parent_search_section is None:\n ScrapeHelper.print_error_string(big_soup.prettify())\n else:\n try:\n job_cards_master = parent_search_section[0]\n # job_cards = job_cards_master.find_all('section', class_='card-content')\n job_cards = job_cards_master.find_all(\n lambda tag: tag.name == 'section' and tag.get('class') == ['card-content'])\n for card in job_cards:\n try:\n company_div = card.find('div', class_='company')\n x1 = company_div.find('span', class_='name')\n if not x1 is None:\n company = x1.text.strip('\\n')\n else:\n company = unknown_string\n\n loc_div = card.find('div', class_='location')\n x2 = loc_div.find('span', class_='name')\n if not x2 is None:\n location = x2.text.strip('\\n')\n location = location.replace('\\r', '')\n location = location.replace('\\n', '')\n else:\n location = unknown_string\n\n sum_div = card.find('div', class_='summary')\n x3 = sum_div.find('header', class_='card-header')\n x0 = x3.find('a')\n links = x0.get('href')\n names = x0.get_text().strip('\\n')\n names = names.replace('\\r', '')\n\n # print(names + ' -- ' + location + ' -- ' + company + ' -- ' + links)\n url_list.append(\n JobPost(job_title=names, url=links, company=company, location=location, search_term=search_term,\n source=ScrapeHelper.MONSTER))\n except Exception as e:\n ScrapeHelper.print_error_string(str(e) + \" Monster Job Unavailable/Not Found: \" + search_term)\n except Exception as e:\n ScrapeHelper.print_error_string(str(e) + \" Monster Job Cards Not Found: \" + search_term)\n\n return url_list","title":""},{"docid":"abbd3140f47ab97a0d9641c2d66182b6","score":"0.58795094","text":"def getepisodelist(html, url):\n\t\n\tbase = re.search(\"(https?://[^/]+)\", url).group(1)\n\tms = re.findall(\"<a href='(.+?)'>(.+?)</a>\", html)\n\ts = []\n\tfor m in ms:\t\n\t\tu, title = m\n\t\te = comiccrawler.Episode()\n\t\te.title = title\n\t\te.firstpageurl = base + url\n\t\ts.append(e)\n\treturn s","title":""},{"docid":"7beb16e9b4e446771fc8f7ea2f6abab8","score":"0.58770764","text":"def parse_url(url):\n return parse_dom(flat(get(url).text))","title":""},{"docid":"e89d3ae285c5284200b7b1d616b054c2","score":"0.5871144","text":"def _loadList(self):\n\t\ttry:\n\t\t\tpastes = self._getData()\n\t\t\tsouppastes = BeautifulSoup(pastes)\n\t\t\tlinks = souppastes.findAll('a')\n\t\t\tfor link in links:\n\t\t\t\ttry:\n\t\t\t\t\tif link['href']:\n\t\t\t\t\t\tif link['href'].find(\"_id_\") != -1:\n\t\t\t\t\t\t\tself._list.append(link.string)\n\t\t\t\texcept Exception:\n\t\t\t\t\t# Handle programer 'href' errors \n\t\t\t\t\tpass\n\t\texcept Exception, e:\n\t\t\tprint ('[!] Error found: ',str(e))","title":""},{"docid":"e3221f7ddea40572ec9729aa9c0b75f0","score":"0.5867502","text":"def __parse_part(html):\r\n html = html.replace('\\t', ' '). \\\r\n replace('\\n', ' '). \\\r\n replace('\\r', ' '). \\\r\n replace('\\xa0', ''). \\\r\n replace('</a>', '')\r\n html = re.sub('[ ]{2,}|<a.+?>', ' ', html)\r\n parse_part_argument = re.compile('<div class=\"item column-1\" itemprop=\"blogPost\" itemscope '\r\n 'itemtype=\"https://schema\\.org/BlogPosting\">(.+?)<!-- end item -->')\r\n parts = re.findall(parse_part_argument, html)\r\n return parts","title":""},{"docid":"7d817279078779c3624e0dd52f2ce71c","score":"0.5850514","text":"def find_api_list(self):\n soup = html_code(self.url)\n body = soup.find('tr', class_='odd views-row-first').parent\n self.lists = body('tr')","title":""},{"docid":"7d631d1c25f599ea862e3bc6ecf7eb6e","score":"0.5833862","text":"def links_scrapping(self):\n html_page = urlopen(self.url)\n html_data = html_page.read().decode('utf-8')\n list_books_link = re.findall('(?<=<h3>).*?(?=</h3>)', html_data)\n for books in list_books_link:\n book_link = re.search('(?<=href=\").*?(?=\" )', books).group()\n self.links_list.append(self.links_beginning + book_link[9:])","title":""},{"docid":"252e5333755cd95568ab1f49309c59bd","score":"0.5810691","text":"def parse_for_filter(soup, terms=(\"li\", {\"class\": \"ads-ad\"})):\n\n # get a list of all the elements, which shall be filtered for\n specific = soup.find_all(terms[0], terms[1])\n\n # get list of information about the elements and save each in a dictionary\n entry_list = []\n for entry in specific:\n dic = {\n \"text\": entry.find('a').text,\n \"ad_link\": entry.find('div', {'class': 'ads-visurl'}).cite.text,\n \"html\": entry\n }\n entry_list.append(dic)\n\n return entry_list","title":""},{"docid":"2c47c81a50e4e0ad372b09fdf33a55b9","score":"0.57942796","text":"def list_scrape(lis, url_stem, elem = True):\n\n uht_list = []\n for l in lis:\n url = url_stem + l\n url_html_time = scrape(url)\n #sometimes we want to associate with the element with its tuple\n if elem:\n uht_list.append((url_html_time, l))\n return uht_list","title":""},{"docid":"20e363196a50370228a4de237ddc1ac0","score":"0.57906544","text":"def parse_items(self, response):\n self.logger.info('You are now crawling: %s', response.url)\n items = []\n hxs = Selector(response)\n # print response.url\n contents = hxs.xpath(\"//ul[@class='rows']/*\")\n for content in contents:\n item = ScrapyCraigslistItem()\n item [\"title\"] = content.xpath(\"//a[@class='result-title hdrlnk']/text()\").extract()[0]\n item [\"post_date\"] = content.xpath(\"//time[@class='result-date']/text()\").extract()[0]\n item [\"price\"] = content.xpath(\"//span[@class='result-price']/text()\").extract()[0]\n item [\"location\"] = content.xpath(\"//span[@class='result-hood']/text()\").extract()[0].strip()\n items.append(item)\n return items","title":""},{"docid":"525cee1076c12017fbe630c2da2df380","score":"0.576131","text":"def parse(self, response):\n params = {'channel': 'web', 'children': True}\n urls = response.xpath('//a[@aria-hidden=\"true\"]/@href').extract()\n for url in urls:\n departmentId = url.partition('-/N-')[2].split('?')[0].strip()\n if departmentId:\n params['url'] = url\n yield Request(self.find_cats_url + urlencode(params), self.get_seed_categories)","title":""},{"docid":"ac4e56a54045ddb5e99df14b9e2bb911","score":"0.57557905","text":"def parse_page(html):\n soup = BeautifulSoup(html, \"html.parser\")\n review_soups = soup.find_all(\"script\", type=\"application/ld+json\")\n\n description_list = []\n for soup in review_soups:\n text = soup.string\n # Decode the json into python dict\n js_dict = json.loads(text)\n if \"review\" in js_dict:\n review_list = js_dict[\"review\"]\n for i in range(len(review_list)):\n review_dict = review_list[i]\n description_list.append(review_dict[\"description\"])\n return description_list","title":""},{"docid":"83daa69d51638d9dc763e157e24f4992","score":"0.5744948","text":"def parse(self, soup):\n pass","title":""},{"docid":"17dc0b947cd866a6906287e68adbfae5","score":"0.57320696","text":"def parse_category(url):\n response = fetch(url)\n selector = HTML(response.text)\n last_page = selector.xpath('//div[@class=\"thisclass\"]/a[position()=last()]/@href')[0]\n total_num = int(last_page.rsplit(\"=\", 1)[-1])\n return [urljoin(url, f'?page={i}') for i in range(1, total_num + 1)]","title":""},{"docid":"9d780ceee97454bf735561c320d34b6c","score":"0.57314986","text":"def extract(url):\n pass","title":""},{"docid":"172e551aa8a278b2279bcda455b89a8c","score":"0.5711623","text":"def __get_html_parser(self, url: str):\r\n page = requests.get(url)\r\n soup = BeautifulSoup(page.content, 'html.parser')\r\n return soup","title":""},{"docid":"3b55c72078f0868bed2abd80e292bceb","score":"0.57085764","text":"def extract_games_from_page(page_num, category, stop_at=100): \n page = get_page_text(page_num, category)\n\n soup_parse = BeautifulSoup(page)\n\n #find the games \n rough_cut = soup_parse.find_all('div', attrs={\"style\":\"z-index:1000;\"})\n game_list = []\n for index, rough_entry in enumerate(rough_cut):\n game_list.append(extract_game_details_from_entry(rough_entry))\n if index + 1 == stop_at:\n break\n\n return game_list","title":""},{"docid":"9a4c0ee9ee022913ec1d2c5d7e3938b0","score":"0.57060045","text":"def parse_page(url, content):\n log.info(\"URL: %s\" % url)\n links = soup(content)\n links_in_page = []\n for link in links:\n if link.has_attr('href'):\n href = link['href']\n if href.startswith(\"/\"):\n href = \"%s%s\" % (url, href)\n if href.startswith('mailto') or href.startswith('tel:'):\n log.info(' skipping non web link: %s' % href)\n continue\n log.info(\" %s\" % href)\n links_in_page.append(href)\n return links_in_page","title":""},{"docid":"114628d71414019b460ba780c075915e","score":"0.56980443","text":"def parse_posts(list_of_posts, url):\n print(\"Parsing posts of \" + url)\n for i in range(len(list_of_posts) - 1):\n wait(By.XPATH, \"/html/body/div[1]/div[3]/div[1]/div/div/div[2]/div[2]/div/div[2]/div[2]/div/div/div[2]\" +\n \"/div/div[4]/div/div/div/div[3]/div[2]\", list_of_posts[i])\n click_links(list_of_posts[i])\n comment_divs = get_web_comment(list_of_posts[i])\n comments_list = get_list_of_comments(comment_divs)\n global_list_of_comments.extend(comments_list)\n # list_of_comments.extend(my_list)\n print(\"Parsing the \" + url + \" list is finished.\")\n # get_hrefs(my_list, name)","title":""},{"docid":"e46b6f799599b0300bb3db53b231bca4","score":"0.569607","text":"def extract_reviews(url, html_fetcher):\n dict_return = []\n #code, html = html_fethcher(url_next)\n page = html_fetcher(url)[1]\n reviews_list, url_next = parse_page(page)\n for x in reviews_list:\n dict_return.append(x)\n while url_next != None:\n page = html_fetcher(url_next)[1]\n reviews_list, url_next = parse_page(page)\n for x in reviews_list:\n dict_return.append(x)\n return dict_return\n\n\n #[YOUR CODE HERE]\n #code, html = html_fetcher(url) # function implemented in Q0 should work\n #[YOUR CODE HERE]","title":""},{"docid":"81a71a519b17700b3234fff46846801b","score":"0.56901556","text":"def __init__(self, url):\n\n expression = r'https://www.defense.gov/News/Contracts/Contract-View/Article/([^/\\\">]*)'\n pg = requests.get(url)\n\n it = re.finditer(expression, pg.text)\n\n results = []\n\n for i in it:\n results.append(i.group(1))\n self.results = results\n\n return","title":""},{"docid":"cd9c1fa532a9fa762f91ffc27566c258","score":"0.5681524","text":"def scrape(self, html):\n \n links = []\n \n #check headers for redirects\n redirects = filter(lambda x: x[0] == 'location', self.headers)\n links.extend([x[1] for x in redirects])\n \n #scrape response body\n soup = bs(html)\n metalinks = soup.findAll('meta', attrs={'http-equiv':True})\n for m in metalinks:\n index = str(m).find('url=')\n end = str(m).find('\"',index, len(str(m)))\n if index != -1:\n link = str(m)[index+4:end]\n links.append(link)\n \n attrs = ['background', 'cite', 'codebase', 'href', 'longdesc', 'src']\n \n for a in attrs:\n links.extend(\n map(lambda x: x[a], soup.findAll(**{a:True}))# **{} unzips dictionary to a=True\n )\n \n return links","title":""},{"docid":"50fa1a31ea712bcacd9c661d2ad7b370","score":"0.5667909","text":"def get_subwebpage(self):\n assert self.full\n content = None\n try:\n content = HTML(urllib.request.urlopen(self.url))\n print(\"*** Get: [{0}] ***\".format(self.url))\n except urllib.error.URLError as _: #look at it latter\n pass\n if content is None:\n return []\n return content.find_link()","title":""},{"docid":"b4fde6ee3575213099e70b54cabf9917","score":"0.566695","text":"def info_from_vagalume(html):\n\n soup = BeautifulSoup(html, 'html.parser')\n name = soup.select('#lyricContent h1')[0]\n author = soup.select('#lyricContent h2 a')[0]\n\n return info_from_html(html, '#lyricContent h1', '#lyricContent h2 a')\n return (name.get_text().strip(), author.get_text().strip())","title":""},{"docid":"c91cff1526869c6bb6044ec58c38d8aa","score":"0.56639546","text":"def parse_pagination(html) -> Tuple[List, str]:\n sel = Selector(text=html)\n jobs = sel.css(PAGER_SELECTOR).extract()\n next_page = sel.css('a[area-label=Next]::attr(href)').extract_first()\n\n return jobs, next_page","title":""},{"docid":"256bf62031f103288d4d96b5b7fcd696","score":"0.5659222","text":"def get_business_urls(url):\n base_url = \"http://www.yelp.com\"\n response = http.request('GET', url)\n soup = bs4.BeautifulSoup(response.data)\n links = []\n for a in soup.select('span.indexed-biz-name a[href^=/biz]'): #------- Retrieving the href from the div the link to the restaurants yelp page is found\n links.append(base_url + str(a.attrs.get('href')))\n return links","title":""},{"docid":"d9d90a8390bad9db5237e9f2e687b320","score":"0.5638547","text":"def get_info(self, url):\n self.driver.get(url)\n self.driver.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\n soup = BeautifulSoup(self.driver.page_source, \"html.parser\")\n return soup","title":""},{"docid":"7e635853ac1ddd921c9756c2668b26ae","score":"0.56261975","text":"def get_soup(url):\n r = requests.get(url)\n soup = BeautifulSoup(r.text, 'html.parser')\n return soup","title":""},{"docid":"b5a6906ebc77267a5ddd682621b2272f","score":"0.5623847","text":"def urls():\n return [\n 'http://www.generationbass.com/2014/01/27/'\n 'the-great-dj-terrivel-tarraxinha/',\n 'http://fontstruct.com/fontstructions/show/939228',\n 'http://fontstruct.com/fontstructions/show/939218',\n 'http://www.kfgo.com/news/articles/2014/jan/27/'\n 'pennsylvania-lawmaker-accused-of-ghost-employee-scam/',\n 'http://www.kfgo.com/news/articles/2014/jan/27/'\n 'fabulist-glass-may-not-practice-law-in-california-court/',\n 'http://www.kfgo.com/news/articles/2014/jan/27/'\n 'jurors-being-chosen-in-delaware-doctors-trial-over-'\n 'waterboarding-claims/',\n 'http://www.kfgo.com/news/articles/2014/jan/27/'\n 'fargo-will-not-prosecute-tv-reporter-for-secretly-entering-school/',\n 'http://www.kfgo.com/news/articles/2014/jan/27/'\n 'governor-daytons-bonding-proposal-devoid-of-funding-for-'\n 'moorhead-flood-mitigation/',\n 'http://www.kfgo.com/news/articles/2014/jan/27/'\n 'minn-touts-new-indoor-stadium-for-2018-super-bowl/'\n ]","title":""},{"docid":"e9634ab2b001c5fd62fecc2e629a4231","score":"0.56227475","text":"def web_crawl(urls):\n\n tags = ['h1',\"h2\",\"h3\",\"h4\",\"h5\",\"h6\",\"p\",\"li\"]\n global docids\n docids = []\n master_dict = {}\n\n for url in urls:\n docids.append(url)\n r = requests.get(url)\n s = BeautifulSoup(r.content, 'html.parser')\n\n #find text, get only text, append to array\n words = []\n for tag in tags:\n for text in s.find_all(tag):\n temp = text.get_text()\n words.append(temp)\n\n text = []\n for item in words:\n #Breaks down sentences into individual words\n text.append(word_tokenize(item))\n\n #flatten list of list to one list\n flattened_list = list(itertools.chain(*text))\n words = [word.lower() for word in flattened_list]\n\n #gets stop words in english and removes them\n stop_words = set(stopwords.words('english'))\n filtered_list = [w for w in words if not w in stop_words]\n\n #Removes alphanumeric\n flat_list = [re.sub('\\w*\\d\\w*', '', text) for text in filtered_list]\n\n #Removes punctuations\n table = str.maketrans('', '', string.punctuation)\n stripped = [w.translate(table) for w in flat_list]\n stripped = [word for word in stripped if len(word) > 1]\n master_dict = store_info(stripped, master_dict)\n\n return master_dict","title":""},{"docid":"d4d3bee3d23f0c20f3bba831925b6e61","score":"0.5621183","text":"def scrapper(self):\n parser = 'html.parser'\n self.url = f'{self.url}{self.n}'\n\n try:\n r = requests.get(self.url)\n while r.status_code < 400:\n resp = urllib.request.urlopen(self.url)\n soup = BeautifulSoup(resp, parser)\n for link in soup.find_all('a', href=True):\n if link not in self.urls:\n self.urls.append(link['href'])\n\n url_string = str(self.url)\n self.url = url_string.split('=', 1)[0]\n self.n += 1\n self.url = f'{self.url}={self.n}'\n except:\n self.ini_list()","title":""},{"docid":"57b080e3a9052945a8c475db26c33de0","score":"0.56170803","text":"def extract_link(url):\n\theaders = {\"Host\": \"www.zomato.com\",\n\t \"User-Agent\": \"Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:50.0) Gecko/20100101 Firefox/50.0\",\n\t \"Accept\": \"text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8\",\n\t \"Accept-Language\": \"en-US,en;q=0.5\",\n\t \"Accept-Encoding\": \"gzip, deflate, br\",\n\t \"Referer\": \"https://www.zomato.com/\",\n\t \"Connection\": \"keep-alive\"}\n\twhile(1):\n\t\ttry:\n\t\t\tr = requests.get(url, headers=headers)\n\t\texcept requests.exceptions.Timeout:\n\t\t\tcontinue\n\t\tbreak\n\tif r.status_code == 404:\n\t\treturn None\n\tdoc = r.content\n\tsoup = BeautifulSoup(doc,'html.parser')\n\twith open ('zomata_user_data', \"a\") as filew:\n\t\tfilew.write('\\n\\n##\\n\\n')\n\t\tptags = soup.find_all('title')\n\t\tstr = ptags[0].string\n\t\tre.sub('<title>','',str)\n\t\tre.sub('</title>','',str)\n\t\tstr = str.strip()\n\t\tstr = str[:-9]\n\t\tlst = str.split(',')\n\t\tfilew.write(lst[0].strip()+'\\n')\n\t\tif(len(lst)>1):\n\t\t\tfilew.write(lst[1].strip()+'\\n')\n\n\t\tptags = soup.find_all(\"a\", 'item user-tab user-tab-reviews cursor-pointer ')\n\t\tlis = ptags[0].find_all('div')\n\t\tstr = lis[0].string\n\t\tre.sub('<div class=\"ui label\">','',str)\n\t\tre.sub('</div>','',str)\n\t\tfilew.write('Reviews = '+str+'\\n')\n\n\t\tptags = soup.find_all(\"a\", 'item user-tab user-tab-blogs cursor-pointer ')\n\t\tif(ptags):\n\t\n\t\t\tlis = ptags[0].find_all('div')\t\n\t\t\tstr = lis[0].string\n\t\t\tre.sub('<div class=\"ui label\">','',str)\n\t\t\tre.sub('</div>','',str)\n\t\t\tfilew.write('Blog posts = '+str+'\\n')\n\n\t\n\n\t\tptags = soup.find_all(\"a\", 'item user-tab-follows user-tab cursor-pointer ')\n\t\tlis = ptags[0].find_all('div')\n\t\tstr = lis[0].string\n\t\tre.sub('<div class=\"ui label\">','',str)\n\t\tre.sub('</div>','',str)\n\t\tfilew.write('Followers = '+str+'\\n')\n\n\t\tptags = soup.find_all(\"a\", 'item user-tab user-tab-bookmarks cursor-pointer ')\n\t\tlis = ptags[0].find_all('div')\n\t\tstr = lis[0].string\n\t\tre.sub('<div class=\"ui label\">','',str)\n\t\tre.sub('</div>','',str)\n\t\tre.sub(' | Zomato','',str)\n\t\tfilew.write('Bookmarks = '+str+'\\n')\n\t\n\t\t#item user-tab user-tab-beenthere cursor-pointer \n\t\tptags = soup.find_all(\"a\", 'item user-tab user-tab-beenthere cursor-pointer ')\n\t\tlis = ptags[0].find_all('div')\n\t\tstr = lis[0].string\n\t\tre.sub('<div class=\"ui label\">','',str)\n\t\tre.sub('</div>','',str)\n\t\tfilew.write('Been there = '+str+'\\n')\n\t\n\t\tptags = soup.find_all(\"div\", 'user-stats_ranking')\n\t\tlis = ptags[0].find_all('span',{'data-icon' : 'ú'})\n\t\tstr = lis[0].string\n\t\t#print(str)\n\t\tfilew.write('Foodie level = '+str+'\\n')\n\t\t\n\t\tif(str != '13'):\n\t\t\tptags = soup.find_all(\"section\", 'user-cover ptop ta-center')\n\t\t\tlis = ptags[0].find_all('div','ui mini statistics')\n\t\t\tlcs = lis[0].find_all('div', {'class':\"label\"})\n\t\t\tstr = lcs[0].string\n\t\t\t#print(str)\n\t\t\tstr = str.strip()\n\t\t\tfilew.write(str+'\\n')\n\t\telse:\n\t\t\tfilew.write('0 points to level up\\n')\n\n\t\tlinkset = set()\n\t\trow = soup.find('div', 'row tac user-cover')\n\t\tlst = row.find('a',{'class':\"floating ui blue circular label tooltip_formatted verified-profile\"})\n\t\tif(lst):\n\t\t#\tprint (lst)\n\t\t\tfilew.write(\"Verified User\\n\")\n\t\telse:\n\t\t\tfilew.write('Not a verified user\\n')\n\n\n\t\tutag = soup.find('div','ui popup user-common-section user-expertise expertise_popup hidden')\n\t\tif(utag):\n\t\t\tptags = utag.find_all(\"li\", 'badge')\n\t\t\tneighbour = set()\n\t\t\tfor i in range(0,len(ptags)):\n\t\t\t\tstr = ptags[i].get_text()\n\t\t\t\tp = ''\n\t\t\t\tfor j in range(0,len(str)):\n\t\t\t\t\tif str[j] == ' ' and j+1<len(str) and str[j+1] == ' ':\n\t\t\t\t\t\tcontinue\n\t\t\t\t\tp = p + str[j]\n\t\n\t\t\t\tp = p.strip()\n\t\t\t\t#print(p)\n\t\t\t\tneighbour.add(p)\n\t\t\ttotal = repr(len(neighbour)) + ' Neighbourhoods'\n\t\t\tfilew.write(total+'\\n')\n\t\t\tfor i in neighbour:\n\t\t\t\tfilew.write(i+'\\n')\n\t\t\n\t\telse:\n\t\t\tfilew.write('0 Neighbourhoods\\n')","title":""},{"docid":"fc1b2666f6ed0d8e98f281b2caf059fe","score":"0.55853564","text":"def sportList(url, sportDataList):","title":""},{"docid":"d8566594a9fcdb53fc659762af04f815","score":"0.5584819","text":"def get_ads():\n response = requests.get('https://www.heyjobs.de/en/jobs-in-berlin')\n soup_obj = bs4.BeautifulSoup(response.text, 'html.parser')\n links = soup_obj.find(\n attrs={'id': 'job-search-container'}).find_all('a', recursive=True)\n ads = list()\n for link in links:\n if '/en-de/jobs/' in link.get('href'):\n uid = link.get('href').split('/', 3)[3]\n title = link.find(\"h2\", {\"class\": \"job-card-title\"}).string\n ads.append({'uid': uid, 'title': title})\n return ads","title":""},{"docid":"93f60717273ab5021eb3041ea9bcc66c","score":"0.55802876","text":"def parse_page(html):\n soup = BeautifulSoup(html,'html.parser')\n url_next = soup.find('link',rel='next')\n if url_next:\n url_next = url_next.get('href')\n else:\n url_next = None\n\n reviews = soup.find_all('div', itemprop=\"review\")\n reviews_list = []\n # HINT: print reviews to see what http tag to extract\n reviews_list = []\n reviews_dict = {}\n # HINT: print reviews to see what http tag to extract\n for r in reviews:\n reviews_dict['author'] = r.find('meta', itemprop = \"author\")['content']\n reviews_dict['rating'] = float(r.find('meta', itemprop = \"ratingValue\")['content'])\n reviews_dict['date'] = r.find('meta', itemprop = \"datePublished\")['content']\n reviews_dict['description'] = r.find('p', itemprop = \"description\").text\n reviews_list.append(reviews_dict)\n reviews_dict = {}\n return reviews_list, url_next","title":""},{"docid":"bb05a5309d3bb12b85873128855cc7bd","score":"0.55780655","text":"def parse_starters(self, response):\t\n\txs = HtmlXPathSelector(response)\n\tresources = xs.select('//ul[2]/li') + xs.select('//ul[1]/li')\n\t#rlist = []\n\tfor resource in resources:\n\t\tdescription = resource.select('text()').extract()\n\t\ttitle = resource.select('a/text()').extract()\n\t\tlink = resource.select('a/@href').extract()\n\t\t#path = str(response.url).replace(ROOT_DOMAIN, '')i\n\t\t#TODO: make this sensible to any lang\n\t\t#lang = 'es' if u'World/Español' in str(response.url) else 'en'\n\t\t#only allow outgoing links:\n\t\tif link and title:\n\t\t\t#self.log('%s info: %s, %s' % (response.url, title, link))\n\t\t\tf = lambda r, desc = description, name = title: self.parse_item(r,\n\t\t\t\t\t name = name[0],\n\t\t\t\t\t lang = find_lang(response.url), \n\t\t\t\t\t category =url2categorypath(response.url), \n\t\t\t\t\t desc = desc[0] if len(desc) else '')\n\t\t\t#rlist += [Request(link[0], callback= f),]\n\t\t\tyield Request(link[0], callback= f)\n\t\n\t#return rlist","title":""},{"docid":"fb77878dc54e340c2c4a00bd5e5e2d5e","score":"0.55772144","text":"def wiki_scraper(url):\n soup = BeautifulSoup(url, 'lxml')\n table = soup.find('table', {'class': 'wikitable sortable'})\n links = table.find_all(['b','a'])\n city_state = []\n\n for link in links:\n title = link.get('title')\n if title != None:\n city_state.append(title)\n return city_state","title":""},{"docid":"9a3dc45bc3e7dea174bf515c777b6507","score":"0.5573651","text":"def scrapper():\n responses = requests.get(HTML)\n soup = BeautifulSoup(responses.text, 'html.parser')\n\n items = {}\n\n item_htmls = soup.find_all(\"a\", href=True)\n\n for item in item_htmls:\n link = HOMEPAGE + item['href']\n\n if item.find_all(\"h3\", \"product-item-name\"):\n tag = item.find_all(\"h3\", \"product-item-name\")[0]\n items[tag.contents[0]] = link\n\n return items","title":""},{"docid":"3d739837bb1c97806ac4e8a6205399b6","score":"0.5566101","text":"def getlinks(phonename):\n\n links = []\n site = \"https://www.jumia.co.ke/smartphones/{}/?viewType=list\".format(phonename)\n links.append(site) # first link\n hdr = {'User-Agent': 'Mozilla/5.0'}\n req = Request(site, headers=hdr)\n page = urlopen(req)\n soup = BeautifulSoup(page, \"html.parser\")\n if soup.find(\"div\", {\"class\": \"pg-w -pvxl\"}): # if there are other pages\n tag_element = soup.find(\"div\", {\"class\": \"pg-w -pvxl\"})\n for lnk in tag_element.findAll(\"a\", {\"href\": re.compile(\"/smartphones/{}*\".format(phonename))}):\n links.append(\"https://www.jumia.co.ke\" + lnk[\"href\"])\n\n return links","title":""},{"docid":"ad640e7c464a7fb5999a39637a3c2e15","score":"0.55654657","text":"def parse_mirrors(self):\n\n html_content = request.urlopen(self._url)\n\n soup = BeautifulSoup(html_content, 'html.parser')\n\n list_of_objects = soup.find_all('td')\n\n list_of_objects = [html_object for html_object in list_of_objects if \"http\" in str(html_object) and\n 'a href' not in str(html_object)]\n\n self._list_of_mirrors = [str(html_object).split('/')[2] for html_object in list_of_objects]\n logger.debug(f'The mirrors we parsed are:\\n{self._list_of_mirrors}')\n\n return self._list_of_mirrors","title":""},{"docid":"ab93348ad915e50fba2fb8ddf9985f2b","score":"0.55590075","text":"def get_names(url):\n soup = make_soup(url)\n year_re = re.compile('\\d{4}')\n if ('steering' in url and 'sigsac' not in url):\n body = soup.find_all('tr')\n for i in body:\n info = ' '.join(i.text.split())\n name = ' '.join(info.split()[:2])\n org = ' '.join(info.split()[2:-1])\n org = re.sub(r'\\(.*\\)', '', org).lstrip()\n if (org[-1] == ','):\n org = org[:-1]\n location = info.split()[-1]\n print(name + ' (' + org + ') ' + location + '\\n')\n return None\n \n if ('shadowpc' in url):\n brs = soup.find_all('p', class_='', style='')\n for br in brs:\n info = ' '.join(str(br).split())\n if ('<br/>' in info):\n info = ' '.join(info.split())\n tag = re.sub('<br/> ', '\\n\\n', info).lstrip()\n tag = re.sub(r'<[^>]*>', '', tag).lstrip()\n print(tag)\n return None\n \n if ('awards' in url and 'sigsac' not in url):\n brs = soup.find_all('p', class_='', style='')\n for br in brs:\n info = ' '.join(str(br).split())\n if (re.findall('</strong>.*</p>', info) != []):\n tag = re.findall('</strong>.*</p>', info)[0]\n tag = re.sub(r'<[^>]*>', '', tag).lstrip()\n tag = re.sub(r'(,\\s)*(\\(*)\\d{4}(\\)*)', '', tag).lstrip()\n tag = re.sub(r'\\),\\s', ')), ', tag).lstrip()\n tag = ' '.join(tag.split())\n info = tag.replace(', and ', ', ')\n info = info.replace(' and ', ', ')\n if (') , ' in info):\n info = info.replace(') , ', '), ')\n info = info.split('), ')\n for i in info:\n if (i[-1] != ')' and '(' in i):\n i += ')'\n if ('(' in i):\n i = i[:i.find('(')].replace(', ', i[i.find('(')-1:] + '\\n\\n')\\\n + i[i.find('('):]\n else:\n i = i.replace(', ', '\\n\\n')\n print(i + '\\n')\n return None\n \n if ('sigsac' in url):\n r_pa_ar = '\\[[Paper, Artifact]*\\]'\n ps = soup.find_all('p', class_='', style='')\n if (len(ps) < 2 or '2017/posters' in url):\n ps = soup.find_all('li', class_='', style='')\n if (len(ps) < 2 or 'agenda' in url):\n ps = soup.find_all('div', id='authorshort')\n if (len(ps) < 2):\n ps = soup.find_all('td')\n for p in ps:\n if ('strong' in str(p)):\n ps.remove(p)\n if ('CCS2018/program/' in url):\n ps = soup.find_all('i', class_='', style='')\n for p in ps:\n if ('<b>' in str(p)):\n ps[ps.index(p)] = ''\n if (len(ps) < 2):\n ps = soup.find_all('div', class_='pcmember')\n if (len(ps) < 2):\n ps = soup.find_all('div', class_='', style='', id='')\n if ('CCS2017/awards' in url):\n ps = soup.find_all('b', class_='', style='')\n if ('2016/program-committee' in url):\n ps += soup.find_all('li', class_='', style='')\n if ('2016/awards' in url or '2016/accepted-papers' in url):\n ps = soup.find_all('em', class_='', style='')\n if ('2016/agenda' in url):\n ps = soup.find_all('span', class_='authors')\n count = 0\n for p in ps:\n if (p != ''):\n info = p.text\n else:\n info = ''\n name_list = []\n if ('2017/accepted-posters-demo' in url):\n r = '<br\\/>[\\w, \\s, \\(, \\), \\-]*<br\\/>'\n p = ' '.join(str(p).split())\n db = re.findall(r, p)\n for i in db:\n i = re.sub(r'<[^>]*>', '', i).lstrip()\n # print(i)\n name_list.append(i)\n info = 'None'\n for i in name_list:\n new_i = i.replace(', and ', ', ')\n new_i = new_i.replace(' and ', ', ')\n name_list[name_list.index(i)] = new_i\n for i in name_list:\n if ('), ' in i):\n new_i = i.replace('), ', ')), ')\n sub_list = new_i.split('), ')\n for sub in sub_list:\n sub_list[sub_list.index(sub)] = '\\n\\n' + \\\n sub[:sub.find('(')].replace(', ', \\\n sub[sub.find('(')-1:] + '\\n\\n') + \\\n sub[sub.find('('):]\n name_list[name_list.index(i)] =''.join(sub_list)\n else:\n name_list[name_list.index(i)] = i[:i.find('(')].\\\n replace(', ', i[i.find('(')-1:] + '\\n\\n') + \\\n i[i.find('('):]\n \n if ('CCS2018/cfposters/' in url):\n info = str(p)\n info = info.replace('<br/>', ')\\n')\n info = re.sub(r'<[^>]*>', '', info).lstrip()\n info = info.replace(', ', ' (')\n if (info[-1] != ')'):\n info += ')'\n if ('accepted/papers/' in url or '2016/awards' in url):\n if(count % 2 == 0):\n info = ''\n count += 1\n if ('2016/awards' in url or '2016/accepted-papers' in url or '2016/agenda' in url):\n if ('(Royal Holloway, University of London' in info):\n info += ')'\n info = info.replace(' and ', ', ')\n info = info.replace(') and ', '), ')\n info = info.replace(') ', '), ')\n info = info.replace(' (', ' (')\n info = info.replace('; ', ' (), ')\n info = info.replace(',\\xa0', ',')\n info = info.replace('\\xa0', '')\n info = info.replace(' ,', ', ')\n r = '\\([^\\)]*\\)'\n if (re.findall(r, info) == [] and info != '' and info != ' '):\n info += ' ()'\n if (';' in info):\n name_list = info.split(';')\n if ('),' in info):\n info = info.replace('),', ')),')\n name_list = info.split('),')\n if (':' in info):\n info = info[info.find(':')+2:]\n if ('2016/steering-committee' in url):\n info = info.replace('\\n', ', ')\n info = re.sub(r'\\([^\\)]*\\)', '', info).lstrip()\n r = '\\([^\\)]*\\)'\n if (',' in info and re.findall(r, info) == []):\n if ('CCS2017/awards' in url):\n info = info.replace(', ', '\\n\\n')\n else:\n if ('2016/organizing-committee' in url or '2016/program-committee' in url):\n tem_list = info.split('\\n')\n for i in tem_list:\n if ('' != i and ',' in i):\n name = i[:i.find(',')].strip()\n org = i[i.find(',')+2:i.rfind(',')].strip()\n loc = i[i.rfind(','):].strip()\n tem_info = name + ' (' + org + ') ' + loc + '\\n'\n info = info.replace(info[info.find(i):info.find(i)+len(i)], tem_info)\n else:\n info = info.split(',')\n name = info[0].strip()\n org = info[1].strip()\n loc = ' '.join(info[2:]).strip()\n info = name + ' (' + org + ') ' + loc\n if (',' in info and re.findall(r, info) != []):\n if (';' in info):\n name_list = info.split(';')\n for i in name_list:\n if (', and ' in i):\n new_i = i.replace(', and ', ', ')\n new_i = i.strip()\n name_list[name_list.index(i)] = new_i\n i = new_i\n if (',' in i):\n name_list[name_list.index(i)] = \\\n i[:i.find('(')].replace(', ', i[i.find('(')-1:] \\\n + '\\n\\n') + i[i.find('('):]\n if (', and ' in info):\n info = info.replace(', and ', ', ')\n info = info[:info.find('(')].replace(', ', \\\n info[info.find('(')-1:] + '\\n\\n') + info[info.find('('):]\n \n if (re.findall(year_re, info) != [] or 'poster' in info or 'Agenda' in info\\\n or 'Keynotes' in info or 'Accepted' in info or 'workshops' in url or \\\n 'Cornell Tech' in info or 'CCS2018/papers/' in url or \\\n 'Workshops' in info or 'Posters' in info or 'Awards' in url \\\n or 'Award' in info or 'Contact' in info or '@acm_ccs' in info \\\n or 'cookies' in info or 'Close' in info or 'Sandboxed' in info):\n info = ''\n if (info == ''):\n None\n elif (name_list == []):\n info = info.strip()\n if (re.findall(r_pa_ar, info) != []):\n for i in re.findall(r_pa_ar, info):\n info = info.replace(i, '')\n if (info == ''):\n None\n elif ('\\n' in info):\n if (info[-1:] != '\\n'):\n print(info + '\\n')\n else:\n print(info)\n else:\n print(info + '\\n')\n else:\n for name in name_list:\n name = name.strip()\n if (name != '' and name != '\\n'):\n if (re.findall(r_pa_ar, name) != []):\n for i in re.findall(r_pa_ar, name):\n name = name.replace(i, '')\n if ('(' in name and ',' in name[:name.find('(')]):\n name = name[:name.find('(')].replace(', ', \\\n name[name.find('(')-1:] + '\\n\\n') + name[name.find('('):]\n print(name + '\\n')\n return None\n\n if ('accepted' in url or 'program-papers' in url or 'program' == \\\n url[url.rfind('/')+1:url.rfind('.')] or 'program-posters' in url or 'program-shorttalks' in url):\n na = soup.find_all('p', class_='', style='')\n if ('program' not in url):\n for i in na:\n if (url[-3:] == 'php'):\n info = i.text.lstrip()\n info = info[:info.find(', ')]\n print(info + '\\n')\n else:\n info = i.text\n info = info.replace(', and ', ', ')\n info = info.replace(' and ', ', ')\n info = info.split('); ')\n for i in info:\n if (i[-1] != ')'):\n i += ')'\n i = i[:i.find('(')].replace(', ', i[i.find('(')-1:] + '\\n\\n')\\\n + i[i.find('('):]\n print(i + '\\n')\n \n if (url[-3:] == 'php' or 'program-papers' in url or 'program.html' in url\\\n or 'program-posters' in url or 'program-shorttalks' in url):\n # print(\"===================================\")\n if ('program-posters' in url or 'program-shorttalks' in url):\n brs = soup.find_all('p', class_='', style='')\n if (brs == []):\n brs = soup.find_all('div', class_='list-group-item')\n for br in brs:\n info = ' '.join(str(br).split())\n try:\n tag = re.findall('<br/>.*</p>', info)[0]\n except:\n tag = re.findall('<br/>.*</div>', info)[0]\n tag = re.sub(r'<[^>]*>', '', tag).lstrip()\n tag = ' '.join(tag.split())\n info = tag.replace(', and ', ', ')\n info = info.replace(' and ', ', ')\n if ('); ' in info):\n info = info.split('); ')\n elif ('), ' in info):\n info = info.split('), ')\n else:\n info = info.split('; ')\n if ('SP2016/program-posters' in url):\n i = 0\n while i < len(info):\n info[i] += ')'\n info[i] = info[i][:info[i].find(',')] + info[i].replace(info[i][:info[i].find(', ')+2], ' (')\n i+=1\n for i in info:\n if(i[-1] == '.'):\n i = i[:-1]\n if (i[-1] != ')' and '(' in i):\n i += ')'\n i = i[:i.find('(')].replace(', ', i[i.find('(')-1:] + '\\n\\n')\\\n + i[i.find('('):]\n print(i + '\\n')\n else:\n brs = soup.find_all('div', class_='list-group-item')\n for br in brs:\n info = ' '.join(str(br).split())\n if (re.findall('<br/>.*</div>', info) != []):\n tag = re.findall('<br/>.*</div>', info)[0]\n if (re.findall(r'(\\s*)\\w*\\:', tag)):\n tag = re.sub(r'<[^>]*>(\\s*)\\w*\\:', ' ', tag).lstrip()\n tag = tag.replace(', ', ' (')\n tag = tag.replace('<br/>', '), ')\n tag = re.sub(r'<[^>]*>', '', tag).lstrip()\n tag = ' '.join(tag.split())\n r = '\\([\\w,\\s,&]+(\\([^\\)]+\\),)[\\w,\\s]+\\)'\n if (re.findall(r, tag) != []):\n tem = re.findall(r, tag)\n for t in tem:\n index = tag.find(t)\n tag = tag.replace(t, t[:-1])\n r = '\\([\\w,\\s,&]+(\\([^\\)]+\\)\\sand\\s)[\\w,\\s,\\(,\\)]+\\)'\n if (re.findall(r, tag) != []):\n tem = re.findall(r, tag)\n for t in tem:\n tag = tag.replace(t, t.replace('and', '&'))\n if ('2019' not in url):\n if ('),' not in tag and '(' not in tag):\n info = ''\n else:\n if ('), and ' in tag):\n tag = tag.replace('), and ', '), ')\n if (') and ' in tag):\n tag = tag.replace(') and ', '),')\n if ('), ' in tag):\n info = tag.split('), ')\n if (') , ' in tag):\n info = tag.split(') , ')\n if ('); ' in tag):\n info = tag.split('); ')\n else:\n info = tag.split('),')\n else:\n if ('); ' in tag):\n info = tag.split('); ')\n else:\n info = tag.split('), ')\n for i in info:\n i = i.strip()\n if (i != ''):\n if (i[-1] == ','):\n i = i[:-1]\n if (i[-1] != ')' and '(' in i):\n i += ')'\n if (', and' in i):\n i = i.replace(', and', ',')\n i = i.replace(' and ', ', ')\n r = '\\([\\w,\\s,&]+(\\([^\\)]+\\)\\s&\\s)[\\w,\\s,\\(,\\)]+\\)'\n if (i.count('(') > 1 and re.findall(r, tag) == []):\n i = i.replace(i[i.find('('):i.find(')')+2], '')\n i = i[:i.find('(')].replace(', ', i[i.find('(')-1:] + '\\n\\n') + i[i.find('('):]\n if ('limited' in i or 'reproducibility' in i):\n None\n else:\n print(i + '\\n')\n # print('+++++++++++++++++++++++++++++++++++++++++')\n return None\n \n if ('2016/committee-organizing' in url):\n body = soup.select('tr')\n if (body == []):\n body = soup.find_all('div', class_='list-group-item')\n ct=0\n count=0\n for b in body:\n if ('Members' in b.text):\n ct=1\n elif (ct == 0):\n if (b.text != '\\n'):\n if ('2016/committee-organizing' in url):\n info = b.text.strip()\n info = info.split('\\n')\n info = info[1:]\n if (len(info) != 2):\n info = ' '.join(info)\n info = info.replace('C', '; C')\n info = info.split('; ')\n name = info[0].strip()\n org = info[1].strip()\n if (org.count(',') > 1):\n org = org.split(',')\n loca = org.pop().strip()\n org = ','.join(org).strip()\n print(name + ' (' + org + ') ' + loca + '\\n')\n else:\n print(name + ' (' + org + ')' + '\\n')\n else:\n info = re.sub(r'.*Chairs\\s', '', b.text)\n info = info.strip()\n info = info.split('\\n')\n name = info[0]\n org = info[1].strip()\n print(name + ' (' + org + ')' + '\\n')\n else:\n info = ' '.join(b.text.split())\n if (info == ''):\n count=0\n elif (count%2 == 0):\n print(info, end=' (')\n count+=1\n else:\n if ('(' in info):\n loca = info[info.find('(')+1:info.find(')')]\n print(info[:info.find('(')-1] + ') ' + loca + '\\n')\n else:\n print(info + ')\\n')\n count=0\n return None\n\n body = soup.select('td')\n count=0\n uni_re = re.compile('[Uu]niversity')\n for i in body:\n info = ' '.join(i.text.split())\n if ('Chairs' in info or 'Members' in info or 'Chair' in info or 'Organizers' in info\\\n or 'Treasurer' in info or 'TBD' in info or 'Admin' in info):\n count=0\n elif (info == ''):\n count=0\n elif (':' in info or ((re.findall(year_re, info) != []) and \\\n (re.findall(uni_re, info) == []))or 'deadline' in info \\\n or 'notification' in info or 'Poster' in info or 'registration' in info \\\n or 'period' in info or 'abstracts' in info or 'Rebuttal' in info \\\n or 'papers' in info or 'Papers' in info or 'Conference' in info \\\n or 'Notification' in info or len(i) != 1 or 'poster' in info \\\n or 'Submissions' in info or 'Student' in info or \\\n 'Registration' in info or 'ready' in info or \\\n 'Short' in info or 'Events' in info or \\\n 'resources' in info):\n None\n elif ('Workshop' in info or 'events' in info):\n break\n elif (count%2 == 0):\n print(info, end=' (')\n count+=1\n else:\n if (',' in info):\n info = info.replace(',', ')')\n if (info.count(')') > 1):\n print(info.replace(')', ',', info.count(')') - 1) + '\\n')\n else:\n print(info + '\\n')\n else:\n print(info + ')\\n')\n count=0","title":""},{"docid":"a3b3202f957e5a306cbf5683b2743dbd","score":"0.5551706","text":"def oxford_dictionary_of_education_scrape_URL(URL:str):\r\n anchor_tag_list = []\r\n\r\n print(f\"\\nFetching the URL: {URL} ------------------\")\r\n browser.get(URL)\r\n\r\n # Wait until the browser loads the URL properly\r\n print(\"\\nSleeping for 15 seconds ------------------\")\r\n time.sleep(15)\r\n \r\n Current_Page_Content = browser.page_source.encode('utf-8').strip()\r\n \r\n # Parsing the html page\r\n soup = BeautifulSoup(Current_Page_Content, 'html.parser')\r\n # print(soup.prettify())\r\n\r\n # Finding all h1 tags with class title\r\n all_h1_with_class_title = soup.find_all('h2' , {\"class\": \"itemTitle\"})\r\n\r\n for h1 in all_h1_with_class_title:\r\n # For each h1 tag get the content (i.e. text) of the anchor tag inside\r\n # anchor_tag = h1.find('a').contents[0]\r\n anchor_tag = h1.find('a').get_text()\r\n # Append the content to anchor_tag_list\r\n anchor_tag_list.append(anchor_tag)\r\n\r\n print(f\"\\nFinished parsing and storing Dictionary of education keywords for: {URL}--------------\")\r\n return anchor_tag_list","title":""},{"docid":"55a05a97a2131ac705cd64a8805f9449","score":"0.5550126","text":"def scrape_url(self, url):\n rec_ingredients = []\n print(\"Now scraping: \" + url)\n source = requests.get(url).text\n soup = BeautifulSoup(source, 'html.parser')\n ingredients = soup.findAll(\"li\", {\"class\": \"checkList__line\"})\n recipe_name_tag = soup.findAll(\"h1\", {\"id\": \"recipe-main-content\"})\n if recipe_name_tag != []:\n print(recipe_name_tag)\n print(\"************************* \" + str(self.rec_counter) + \" ************************\")\n print()\n recipe_name = recipe_name_tag[0].text\n else:\n return None, None\n for item in ingredients:\n if hasattr(item.label, \"title\"):\n rec_ingredients.append(item.label.get('title'))\n return recipe_name, rec_ingredients","title":""},{"docid":"a5ba081db48b26e3e6ac2b2992c3ebda","score":"0.55467707","text":"def brewsoup(url):\r\n try:\r\n res = requests.get(url)\r\n res.raise_for_status()\r\n except requests.exceptions.HTTPError as error:\r\n print(error)\r\n return None\r\n return bs4.BeautifulSoup(res.text, \"html.parser\")","title":""},{"docid":"7e0ec923d46601b597470a465fefb36a","score":"0.55443746","text":"def get_soup(url):\n page = requests.get(url)\n jsonResponse = page.json()\n html = jsonResponse['html']\n soup = BeautifulSoup(html, 'html.parser')\n return(soup)","title":""},{"docid":"e39c49ff75fdf6d94afa1f289aaa2d8d","score":"0.5537907","text":"def scrape(url):\n\n # Make soup.\n print 'Opening URL %s' % str(url)\n f = urllib2.urlopen(url)\n soup = BeautifulSoup(f, 'html.parser')\n \n # Get key tags.\n org = soup.find('div', id='adorg').get_text(strip=True).replace('\\n',' ')\n title, desc = soup.find('div', id='adtitle').get_text(strip=True).replace('\\n',' ').split('+')\n ov = soup.find('div', id='overview')\n\n # Navigate and extract content.\n content = []\n for tag in ov.children:\n if tag.name == 'p':\n content.append(tag.get_text(strip=True).replace('\\n',' '))\n elif tag.name == 'br':\n for tag2 in tag.children:\n if tag2.name == 'p':\n content.append(tag2.get_text(strip=True).replace('\\n',' '))\n\n org = org.encode('utf-8')\n title = title.encode('utf-8')\n desc = desc.encode('utf-8')\n content = [c.encode('utf-8') for c in content]\n\n return (org, title, desc, content)","title":""},{"docid":"527fc6e6b7cd5eb33917e8d83cd27684","score":"0.55357337","text":"def getMakes():\r\n \r\n url = 'http://www.carcomplaints.com/'\r\n html = request.urlopen(url)\r\n \r\n soup = BeautifulSoup(html)\r\n sections = soup.find_all('section', id=re.compile('makes'))\r\n \r\n make_list = []\r\n for section in range(len(sections)):\r\n for li in sections[section].find_all('li'):\r\n make_list.append(li.get_text())\r\n \r\n return make_list","title":""},{"docid":"15195f7146f6fd830578d57f3cec99d9","score":"0.5534482","text":"def urlparse(url):\n request = urllib2.Request(url)\n try:\n response = urllib2.urlopen(request)\n except URLError as e:\n if hasattr(e,'readon'):\n print 'We failed to reach a server.'\n print 'Reason:',e.reason\n elif hasattr(e,'code'):\n print 'The server couldn\\'t fullfill the request.'\n print 'Error code:', e.code\n soup = BeautifulSoup(response, 'html.parser')\n organized_html = soup.prettify()\n return organized_html","title":""},{"docid":"218ffc44ff0e03ac064900afad18c6a3","score":"0.553392","text":"def scrape(self):\n self.get_soup()\n self.get_title_by_soup()\n self.get_price_by_soup()\n self.get_availability_by_soup()\n self.get_ratings_by_soup()","title":""},{"docid":"b526458140857b59afb23f8129762c99","score":"0.55284685","text":"def get_link(soup):\n links = []\n re_nav = re.compile('^nav')\n if (soup.find_all(re_nav) != []):\n # Find all navagtion links in the webpage\n for nav in soup.find_all(re_nav):\n a = nav.find_all('a') #Find all 'a' tag\n for link in a:\n if (link not in links):\n links.append(link)\n else:\n for nav in soup.find_all(class_=re_nav):\n a = nav.find_all('a') #Find all 'a' tag\n for link in a:\n if (link not in links):\n links.append(link)\n \n return links","title":""},{"docid":"9dc9ebc112dc2bbe5bb759a051b4b52d","score":"0.5528023","text":"def parse(self, response):\n page = self._get_item(response)\n r = [page]\n if self.follow_links:\n r.extend(self._extract_requests(response))\n return r","title":""},{"docid":"6e87dc031386598c08d1286a81086906","score":"0.55184346","text":"def get_constant_soup(url):\n with open(TEST_FILE_NORMAL_LISTING, encoding='utf-8') as f:\n soup = BeautifulSoup(f.read())\n\n return soup","title":""},{"docid":"7af2405f3e45580012dc272d43c951f6","score":"0.55175906","text":"def get_links_from_site_reader(browser, url):\n\n browser.get(url)\n # can't grab links instantly\n time.sleep(3)\n inner_html = browser.page_source\n soup = BeautifulSoup(inner_html, \"html5lib\")\n # finding elements with links\n elements = soup.find_all('a', {'class': 'ms-listlink ms-draggable'})\n # add pre to make links absolute\n pre = \"https://reader.uni-mainz.de\"\n links = [pre + element.get(\"href\") for element in elements]\n filenames = [link.split('/')[-1].lower().replace(' ', '') for link in links]\n return links, filenames","title":""},{"docid":"3360ae98bda6d9c2f61c967dca061a77","score":"0.55173326","text":"def parsePage(html, infoList):\n try:\n price = re.findall(r'\"view_price\":\"(.*?)\"', html)\n num = len(price)\n title = re.findall(r'\"raw_title\":\"(.*?)\"', html)\n sales = re.findall(r'\"view_sales\":\"(\\d+)人付款\"', html)\n taobaoID = re.findall(r'\"nick\":\"(.*?)\"', html)[:num]\n location = re.findall(r'\"item_loc\":\"(.*?)\"', html)\n for i in range(num):\n infoList.append([title[i], price[i], sales[i], taobaoID[i], location[i]])\n return infoList\n except:\n return ''","title":""},{"docid":"db4442c291dbd2941652d868af0d7a37","score":"0.5516796","text":"def parse_results(raw_html):\n soup = BeautifulSoup(raw_html, 'html.parser')\n result_block = soup.find_all('div', attrs={'class': 'g'})\n for result in result_block:\n link = result.find('a', href=True)\n title = result.find('h3')\n if link and title:\n yield link['href']","title":""},{"docid":"c7fab8ce4adb1d90374795fe781350a3","score":"0.55156064","text":"def parser_one_page(url: str):\n html_doc = faker.faked_get(url)\n soup = BeautifulSoup(html_doc, \"html.parser\")\n poem_list = soup.find_all('strong')\n for li in poem_list:\n href = li.find('a')['href']\n poem_id = reg_int(href)\n parser_one_poem(poem_id)\n time.sleep(0.2)","title":""},{"docid":"e3b9ed36507f6dc04d138f05cc438b74","score":"0.55097544","text":"def parse_hotellist_page(html):\n h_list = [] \n page_info = []\n soup = BeautifulSoup(html)\n # Extract hotel name, star rating and number of reviews\n hotel_boxes = soup.findAll('div', {'class' :'listing wrap reasoning_v5_wrap jfy_listing p13n_imperfect'})\n if not hotel_boxes:\n hotel_boxes = soup.findAll('div', {'class' :'listing_info jfy'})\n if not hotel_boxes:\n hotel_boxes = soup.findAll('div', {'class' :'listing easyClear p13n_imperfect'})\n\n for hotel_box in hotel_boxes:\n #print info for each hotel\n h = hotel_box.find(\"a\", {\"target\" : \"_blank\"})\n hotel_url = base_url + h['href']\n time.sleep(2)\n heads = { 'User-Agent' : user_agent }\n resp = requests.get(hotel_url, headers=heads)\n hml = resp.text.encode('utf-8')\n soup2 = BeautifulSoup(hml) #get the soup for the hotel's page so we can scrape the info\n rating = soup2.findAll('span', {'class' : 'compositeCount'})\n hotel_name = hotel_box.find(\"a\", {\"target\" : \"_blank\"}).find(text=True)\n h_list.append(str(hotel_name.strip()))\n #traveler rating\n ratings = []\n for r in rating:\n ratings.append(r.find(text = True))\n\n\n #reviews\n review = soup2.findAll('div', {'class' : 'value'})\n reviews = []\n for r in review:\n reviews.append(r.find(text = True))\n\n #review summary \n sp = soup2.findAll('span', {'class' : 'rate sprite-rating_s rating_s'})\n summaries = [] \n count = 1 \n for item in sp:\n s = item.find('img', alt = True)\n rate = s['alt'].strip()[0:3]\n if rate[-1] == 'o':\n summaries.append(rate[0:1])\n else:\n summaries.append(rate) \n\n total = float(ratings[4].replace(',', '')) + float(ratings[3].replace(',', '')) + float(ratings[2].replace(',', '')) + float(ratings[1].replace(',', '')) + float(ratings[0].replace(',', ''))\n avg = 1*float(ratings[4].replace(',', '')) + 2*float(ratings[3].replace(',', '')) + 3*float(ratings[2].replace(',', '')) + 4*float(ratings[1].replace(',', '')) + 5*float(ratings[0].replace(',', ''))\n avg = avg/total \n\n m = MyStruct(float(ratings[0].replace(',', '')), float(ratings[1].replace(',', '')), float(ratings[2].replace(',', '')), float(ratings[3].replace(',', '')), \n float(ratings[4].replace(',', '')), float(reviews[0].replace(',', '')), \n float(reviews[1].replace(',', '')), float(reviews[2].replace(',', '')), float(reviews[3].replace(',', '')), float(summaries[0]), \n float(summaries[1]),float(summaries[2]), float(summaries[3]), float(summaries[4]), float(summaries[5]), avg)\n\n page_info.append(m)\n # Get next URL page if exists, otherwise exit\n div = soup.find(\"div\", {\"class\" : \"pgLinks\"})\n # check if this is the last page\n if div.find('span', {'class' : 'guiArw pageEndNext'}):\n #return the current info\n return u\"\", h_list, page_info\n # If not, return the url to the next page\n hrefs = div.findAll('a', href= True)\n for href in hrefs:\n c = href['class'].encode('ascii', 'ignore')\n if c == 'guiArw sprite-pageNext ':\n return href['href'], h_list, page_info","title":""},{"docid":"90f280cd4f4fd3b492f80dec6242bf93","score":"0.5507087","text":"def get_collections(parsed_html, substring):\n collection_hrefs = []\n for link in parsed_html.find_all('a'):\n if substring in str(link):\n collection_hrefs.append(link.get('href'))\n return collection_hrefs","title":""},{"docid":"0f3704ae405035bb85c35c84cd00b7a5","score":"0.55045646","text":"def postprocess_soup(self, soup, tag, class_):\n raw_base = soup.find_all(\"h3\", {\"class\": \"p-name\"})\n raw = soup.find_all(tag, {\"class\": class_})\n if len(raw) != len(raw_base):\n # Ugly hack to fill missing amenities with NaNs\n # Ensures that each field (list) are same length even if hotel doesn't have any amenities listed\n # NOTE: Might need to do for other fields as well\n if class_ == \"hmvt8258-amenities\":\n raw_tmp = soup.find_all(\"div\", {\"class\": \"additional-details resp-module\"})\n raw_list = []\n for content in raw_tmp:\n flag = False\n for subcont in content:\n if class_ in subcont:\n flag = True\n break\n if flag:\n raw_list.append(subcont)\n else:\n raw_list.append(None)\n else:\n raw_list = [content.text for content in raw]\n return raw_list","title":""},{"docid":"e572dfbcf16632244c11852b1f6b3299","score":"0.5502867","text":"def parse_query_page(text):\n parsed_text = BeautifulSoup(text, 'html5lib')\n posts = parsed_text.find(id='post-list-posts').find_all('li')\n parsed_posts = []\n for post in posts:\n parsed_post = {}\n\n # Parse ID and detail url\n detail_url = post.find(class_='plid').text[4:] # Remove (#pl )\n id_parttern = re.compile(r'http://konachan.net/post/show/(?P<id>\\d+)')\n id_ = id_parttern.search(detail_url).group('id')\n parsed_post['link'] = detail_url\n parsed_post['id'] = int(id_)\n\n # Parse score, tags and rating\n title_parttern = re.compile(\n r'Rating: (?P<rating>\\w+) Score: (?P<score>\\d+) '\n 'Tags: (?P<tags>[a-zA-z0-9_ ]+) User:'\n )\n title = post.find('a', class_='thumb').find('img')['title']\n matched_text = title_parttern.search(title)\n parsed_post['score'] = int(matched_text.group('score'))\n parsed_post['rating'] = matched_text.group('rating')\n parsed_post['tags'] = [\n tag.replace('_', ' ')\n for tag in matched_text.group('tags').split(' ')\n ]\n parsed_posts.append(parsed_post)\n\n return parsed_posts","title":""},{"docid":"51e23a463022607cca500b0d08a051f3","score":"0.55023044","text":"def find_link(self):\n subwebpages = []\n\n br_counter = 0\n last_time = \"\"\n try:\n while True:\n input_str = self.content.readline()\n expression = self.reader(input_str) # rader() read a line each time.\n for tag in expression:\n result = self.eval(tag)\n if isinstance(result, Webpage):\n subwebpages += [result]\n if input_str == last_time:\n br_counter += 1\n if input_str != last_time:\n br_counter = 0\n last_time = input_str\n if br_counter == 10:\n raise EOFError\n except EOFError as _:\n return subwebpages","title":""},{"docid":"6869949bb2744f975eb4ceb9ad6e0a56","score":"0.55014145","text":"def getSoup(url):\n # Set headers \n headers = requests.utils.default_headers()\n \n headers.update({ 'User-Agent' : 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_6) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/13.0.2 Safari/605.1.15'})\n while True:\n try:\n req = requests.get(url, headers)\n req.raise_for_status()\n break\n\n except requests.exceptions.HTTPError as HTTPError:\n if HTTPError.response.status_code == 403: \n print('\\n\\nFORBIDDEN.\\n\\n')\n exit()\n\n except requests.exceptions.ConnectionError:\n print('\\n\\nCONNECTION ERROR. Retrying in 30 seconds.\\n\\n')\n sleep(30)\n \n soup = BeautifulSoup(req.content, 'html.parser', parse_only=SoupStrainer('a'))\n\n timeout = randrange(7,10)\n sleep(timeout)\n\n return soup","title":""},{"docid":"cc2516e04e636a0e4c7239acabfc1e55","score":"0.5499542","text":"def parse_live_url(url):\n live_id_list = []\n browser = webdriver.Chrome()\n browser.get(url)\n browser.maximize_window()\n browser = scroll_down(browser, 20)\n time.sleep(2)\n a_elements = browser.find_elements_by_xpath(\"//ul[@class='pic-items js-list-ul']/li/a\")\n if a_elements is not None and len(a_elements) > 0:\n for a in a_elements:\n a_href = a.get_attribute(\"href\")\n live_id_list.append(a_href)\n print(a_href)\n else:\n print(\"a_elements is None or empty!!!\")\n browser.quit()\n return live_id_list","title":""},{"docid":"69e4f3ba4523d8acba1b6d4566ab10d7","score":"0.54971004","text":"def souper(site):\n headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36'}\n response = get(site, headers=headers)\n \n html = response.text\n soup = BeautifulSoup(html)\n \n return soup","title":""},{"docid":"328c3a097849a448defcd439a0aa5a9c","score":"0.54942805","text":"def ini_list(self):\n text = self.urls\n darbi = []\n for item in text:\n if 'htm' in item:\n darbi.append(item)\n darbi = sorted(darbi)\n\n prefix_ = 'https:'\n final_list = []\n for x in darbi:\n final_list.append(prefix_ + x)\n self.urls = final_list\n self.parser()","title":""},{"docid":"e531ad35d9832d29bd66afa1df4265a3","score":"0.54941046","text":"def scrapeData(self, url):\n response = requests.get(url)\n scraped = BeautifulSoup(response.text, \"html.parser\")\n return scraped","title":""},{"docid":"885653a9c6c39667d36f35750d41125e","score":"0.54940426","text":"def parse_html(cls, html):\n news_list = []\n soup = BeautifulSoup(html, 'html.parser')\n for a in soup.find_all(\"a\", class_=\"storylink\"):\n url = a.attrs['href']\n # self-hosted urls like \"item?id=20927552\"\n if not 'http' in url:\n url = cls.PARSE_URL + url\n title = a.text\n news_list.append({'url': url, 'title': title})\n return news_list","title":""},{"docid":"daa9d6001b046b3ae1f3a33ae97d2526","score":"0.54884267","text":"def get_sources(self):\n business_lst = []\n i = 0\n for url in self.url_list:\n print(url)\n self.browser.get(url)\n\n time.sleep(7)\n\n # Zoom in so that \"Search this area\" button will show up\n self.browser.find_element_by_id('widget-zoom-in').click()\n\n time.sleep(3)\n\n # Must hit \"Search this area\", otherwise Google defaults to local locations\n self.browser.find_element_by_class_name(\n 'widget-search-this-area-inner').click()\n\n time.sleep(5)\n\n htmlstring = self.browser.page_source\n soup = BeautifulSoup(htmlstring, \"html.parser\")\n results = soup.find_all(class_='section-result-content')\n\n for result in results:\n name = result.find(class_='section-result-title').text\n service = result.find(class_='section-result-details').text\n search_term = self.search_terms[i]\n\n # Default to the seach term if no service provided was found\n if service == '':\n service = self.search_terms[i]\n\n location = result.find(class_='section-result-location').text\n phone = result.find(\n class_='section-result-info section-result-phone-number').text\n\n # Get rid of phone numbers with the wrong extension. These may come from your ip address location!\n if phone.strip()[0:len(self.phone_extension)] != self.phone_extension:\n phone = ''\n\n business_lst.append(\n {'name': name, 'search_term': search_term, 'service': service, 'location': location, 'phone': phone})\n self.business_lst = business_lst\n i += 1\n return business_lst","title":""},{"docid":"0bad7ef5f24caeb637c0b06197fd6b5e","score":"0.5468703","text":"def read_html(list_of_adverts):\n\n\n def find_title(advert):\n \"\"\"\n Find the title from the job advert\n :param advert: the beatiful soup parsed version of an advert\n :return: a job title\n \"\"\"\n try:\n title = advert.find('h1').text\n if len(title) == 0:\n title = 'no_data'\n except:\n title = 'no_data'\n pass\n\n if title != 'no_data':\n title = re.sub(clean_lb, '', title)\n title = title.lower()\n\n return title\n\n\n def find_date(advert):\n \"\"\"\n Find the date on which the advert was placed\n :param advert: the beautiful soup parsed version of an advert\n :return: a date on which the advert was placed\n \"\"\"\n try:\n date = advert.find('td', text='Placed on:').find_next_sibling('td').text\n except:\n date = 'no_data'\n pass\n\n try:\n try_date = advert.find('th', text='Placed On:').find_next_sibling('td').text\n # Only replace the date if the previous date is no_data (i.e. don't overwrite\n # a valid date from the last 'try'\n if date == 'no_data':\n date = try_date\n except:\n pass\n\n return date\n\n\n def find_role(advert):\n \"\"\"\n Find the role (i.e. the job family) in the advert\n :param advert: the beautiful soup parsed version of an advert\n :return: the role from the advert\n \"\"\"\n try:\n role = advert.find('p', text='Type / Role:').find_next_sibling('p').text\n role = re.sub(clean_lb, '', role)\n except:\n role = 'no_data'\n pass\n\n try:\n try_role = advert.find('p', text='Type / Role:').find_next('a').text\n # Only replace the role if the previous role is zero (i.e. don't overwrite\n # a valid date from the last 'try'\n if role == 'no_data':\n role = try_role\n except:\n pass\n\n try:\n try_role = \\\n advert.find('b', text='Type / Role:').find_next('div', {'class': 'j-form-input ie-11-width'}).find_next(\n 'input').attrs['value']\n # Only replace the role if the previous role is zero (i.e. don't overwrite\n # a valid date from the last 'try'\n if role == 'no_data':\n role = try_role\n except:\n pass\n\n try:\n try_role = \\\n advert.find('p', text='Type / Role:').find_next('div', {'class': 'j-form-input ie-11-width'}).find_next(\n 'input').attrs['value']\n # Only replace the role if the previous role is zero (i.e. don't overwrite\n # a valid date from the last 'try'\n if role == 'no_data':\n role = try_role\n except:\n pass\n\n if role !='no_data':\n role = re.sub(clean_lb, '', role)\n role = role.lower()\n\n return role\n\n\n def find_organisation(advert):\n \"\"\"\n Find the organisation (i.e. the university where the job is based) in the advert\n :param advert: the beatiful soup parsed version of an advert\n :return: the organisation from the advert\n \"\"\"\n try:\n organisation = advert.find('h3').text.split('-',1)[0]\n except:\n organisation = 'no_data'\n pass\n\n if organisation !='no_data':\n organisation = re.sub(clean_lb, '', organisation)\n organisation = organisation.lower()\n\n return organisation\n\n def find_location(advert):\n \"\"\"\n Find the location (i.e. the city where the job is based) in the advert\n :param advert: the beatiful soup parsed version of an advert\n :return: the location from the advert\n \"\"\"\n try:\n location = advert.find('td', text='Location:').find_next_sibling('td').text\n except:\n location = 'no_data'\n pass\n\n try:\n try_location = advert.find('th', text='Location:').find_next_sibling('td').text\n if location == 'no_data':\n location = try_location\n except:\n pass\n\n if location !='no_data':\n location = re.sub(clean_lb, '', location)\n location = location.lower()\n location = location.strip()\n\n return location\n\n\n def find_salary(advert):\n \"\"\"\n Find the salary\n :param advert: the beautiful soup parsed version of an advert\n :return: a text field describing salary\n \"\"\"\n try:\n salary = advert.find('th', text='Salary:').find_next_sibling('td').text\n except:\n salary = 'no_data'\n pass\n\n # Remove carriage returns, tabs, multiple spaces and commas\n salary_string = salary.replace('\\n', ' ').replace('\\t', ' ').replace(' ', '').replace(',', '')\n # Find all the numbers in the salary string\n processing_salaries = re.findall(r'\\d+', salary_string)\n\n # Remove all the small numbers which relate to grades or hourly pay\n remove_list = []\n for current_value in processing_salaries:\n if int(current_value) < 10000:\n remove_list.append(current_value)\n\n processing_salaries = [x for x in processing_salaries if x not in remove_list]\n\n try:\n min_salary = min(processing_salaries)\n except:\n min_salary = ''\n\n try:\n max_salary = max(processing_salaries)\n except:\n max_salary = ''\n\n # Add original string to salaries data for checking purposes\n salaries = {\"salary_string\": salary_string, \"salary_min\": min_salary, \"salary_max\": max_salary}\n\n return salaries\n\n\n big_data_list = []\n\n # Setting up annoying text remover\n clean_lb = re.compile('\\n')\n\n # Set up a counter to print on screen and assure me that everything's working\n sanity_counter=0\n\n # Go through all the ads and extract the data I need\n for current_ad in list_of_adverts:\n sanity_counter+=1\n data = []\n filename = os.path.basename(current_ad)\n data.append(filename)\n\n # Check if the file is one of the job adverts (which have\n # a set patern of filename\n if re.match(r'\\w\\w\\w\\d\\d\\d', filename):\n\n with open(current_ad, \"r\") as f:\n contents = f.read()\n advert = BeautifulSoup(contents, 'lxml')\n\n #Extract info I want\n title = find_title(advert)\n date = find_date(advert)\n\n # Extract year directly from date variable (there's two forms of date, hence the if)\n if '-' in date:\n year = str(date)[:4]\n else:\n year = str(date)[-4:]\n\n role = find_role(advert)\n organisation = find_organisation(advert)\n location = find_location(advert)\n\n # Add the info to the data list\n data.append(title)\n data.append(date)\n data.append(year)\n data.append(role)\n data.append(organisation)\n data.append(location)\n\n # Add data to a list of lists which will later be transformed into a df\n big_data_list.append(data)\n\n # Not drowning but waving output for my sanity\n print('Processed ' + str(sanity_counter) + ' jobs', end='\\r')\n\n df = pd.DataFrame.from_records(big_data_list)\n df.columns = ['filename', 'job title', 'date', 'year', 'role', 'organisation', 'location']\n\n return df","title":""},{"docid":"e4992044e397f057400b4ea293ad8c84","score":"0.5465568","text":"def pull_news(num):\n\n headers = {\n \"user-agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_7_5)\",\n \"accept\": \"text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8\",\n \"accept-charset\": \"ISO-8859-1,utf-8;q=0.7,*;q=0.3\",\n \"accept-encoding\": \"gzip,deflate,sdch\",\n \"accept-language\": \"en-US,en;q=0.8\",\n }\n\n # Thanks to Luca Pattavina for giving me the right url\n if num <= 5:\n news_url = [\"http://www.disim.univaq.it/main/news.php?entrant=1\"]\n else:\n news_url = [\"http://www.disim.univaq.it/main/news.php?entrant=1\",\n \"http://www.disim.univaq.it/main/news.php?entrant=2\"]\n\n request = []\n bs_list = []\n news = []\n for i, url in enumerate(news_url):\n request.append(requests.get(url, headers=headers))\n bs_list.append(BeautifulSoup(request[i].text, \"html.parser\") \\\n .find_all(class_=\"post_item_list\"))\n descr_list = BeautifulSoup(request[i].text, \"html.parser\") \\\n .find_all(class_=\"post_description\")\n\n for j, single_news in enumerate(bs_list[i]):\n news.append({\n \"title\": single_news.h3.a.text,\n \"description\": descr_list[j].get_text().replace(\"\\n\", \" \"),\n \"link\": \"http://www.disim.univaq.it/main/\" + single_news.a.get('href')\n })\n\n return news","title":""},{"docid":"7d188ab20273cb1be440e5c3ddd77044","score":"0.54591626","text":"def extract_next_links(self, url_data):\n outputLinks = []\n root = html.fromstring(url_data['content'])\n new_Root = root.make_links_absolute(url_data['url'], resolve_base_href=True)\n #print(type(new_Root))\n\n parsed_uri = urlparse(url_data['url'])\n domain = '{uri.scheme}://{uri.netloc}'.format(uri=parsed_uri)\n \n \n \n tree = etree.ElementTree(root)\n r = tree.getroot()\n select = cssselect.CSSSelector(\"a\")\n links = [element.get('href') for element in select(r)]\n #currentPath = url_data['url'].strip('/')\n for link in links:\n if link is None:\n pass\n else:\n outputLinks.append(link)\n return outputLinks","title":""},{"docid":"ddf8cd01ec0a0d6045869fb995481bd6","score":"0.5457469","text":"def scrape_and_soup(self, target_url):\n\n # get the url contents for processing\n target_url_loc = urllib.urlopen( target_url )\n target_url_data = target_url_loc.read()\n target_url_loc.close()\n\n # prepare the soup that we will extract data from. \n target_url_soup = BeautifulSoup( target_url_data, \"lxml\" )\n\n return target_url_soup","title":""},{"docid":"444ad57a56ca73bf51db1209fdd767d0","score":"0.5452856","text":"def webCrawl(url):\r\n try:\r\n html = urllib2.urlopen(url).read() # Connect to the job posting\r\n except:\r\n return \"\"\r\n\r\n\r\n soup = BeautifulSoup(html, \"html.parser\")\r\n\r\n # Reference for this step: https://jessesw.com/Data-Science-Skills/\r\n for script in soup([\"script\", \"style\"]):\r\n script.extract() # Remove these two elements from the BS4 object to get clean text\r\n content = soup.getText().lower()\r\n return content","title":""},{"docid":"c52586f0cbf2b48096a1cb16df95686b","score":"0.54520947","text":"def decode_web_page() ->list:\n req = requests.get(\"https://www.nytimes.com/\")\n soup = BeautifulSoup(req.text,\"html.parser\")\n alist = []\n for item in soup.find_all(\"h2\",{\"class\":\"story-heading\"}):\n alist.append(item.text.strip())\n return alist","title":""},{"docid":"5a7e5d5affe8ff42daa10b4783ea61db","score":"0.5448262","text":"def parse_listing(self, response):\n ul_list = response.xpath('//*[@id=\"root\"]/article/div[3]/div[1]/section[1]/div/ul')[0]\n # #root > article > div.css-o19mot-Te > div.css-18jo4jf-Te > section.section-overview > div > ul\n\n l = ItemLoader(item=HouseListing(), response=response)\n l.add_value('listing_url', response.url)\n\n # #root > article > header > div.css-s3teq > div.css-13ywf6 > div.css-1vr19r7\n # l.add_css('listing_type', '#root > article > header > div.css-s3teq > div.css-13ywf6 > div.css-1vr19r7 > small::text')\n l.add_xpath('listing_type', '//*[@id=\"root\"]/article/section[1]/div[1]/ul/li[2]/a')\n l.add_css('title', '#root > article > header > div.css-1jiadof > div > div > h1::text')\n l.add_css('house_location', '#root > article > header > div.css-1jiadof > div > div > div > a::text')\n l.add_css('house_cost', '#root > article > header > div.css-s3teq > div.css-13ywf6 > div.css-1vr19r7::text')\n\n # Order of listing features (Propriedades as the website calls it) is not guaranteed. Must check the label name.\n l.add_value('effective_area', self.find_matching_field_value('area util', ul_list))\n l.add_value('total_area', self.find_matching_field_value('area bruta', ul_list))\n l.add_value('number_of_rooms', self.find_matching_field_value('tipologia', ul_list))\n l.add_value('number_of_bathrooms', self.find_matching_field_value('casas de banho', ul_list))\n l.add_value('energy_certificate', self.find_matching_field_value('certificado energetico', ul_list))\n l.add_value('house_condition', self.find_matching_field_value('condicao', ul_list))\n l.add_value('building_year', self.find_matching_field_value('ano de construcao', ul_list))\n\n l.add_xpath('listing_description', '//*[@id=\"root\"]/article/div[3]/div[1]/section[2]/div[1]')\n l.add_xpath('house_characteristics', '//*[@id=\"root\"]/article/div[3]/div[1]/section[3]/div/ul//text()')\n\n # Get Image urls\n imgs_div = response.xpath('//*[@id=\"root\"]/article/section[2]/div[1]/div/div[2]/div/div/div')[0]\n img_urls = []\n\n for i in imgs_div.xpath('//img[@width=114]'):\n img_urls.append(i.attrib['src'])\n\n l.add_value('img_urls', img_urls)\n\n return l.load_item()","title":""},{"docid":"70b359863a8b502b1508e74fa63a7127","score":"0.54476845","text":"def parse_urls(url, html):\n #print \"Searching directory: %s\" % url\n url_list = []\n # Parse the html we get from the site and then itterate over all 'a'\n # dom elements that have an href in them.\n soup = BeautifulSoup(html, \"lxml\")\n for anchor in soup.find_all('a', href=True):\n # Skip this anchor if it's one we should ignore\n if bad_anchor(anchor['href']):\n continue\n # build the full url and add it to the url bucket\n new_url = url + anchor['href']\n url_list.append(new_url)\n return url_list","title":""},{"docid":"9199838e0e60badc3dba871935830e22","score":"0.54433286","text":"def get_soup(url):\n # raw_page = urllib.request.urlopen(url).read()\n # soup = BeautifulSoup(raw_page, \"html.parser\")\n raw_page = requests.get(url)\n soup = BeautifulSoup(raw_page.text, \"lxml\")\n return soup","title":""},{"docid":"27cfb3bd42bb2e9cfce4ccb9852fa559","score":"0.5441952","text":"def parse(self, html):\n bs = BeautifulSoup(html, 'html.parser')\n houses_html = bs.find('ul', attrs={'class': 'listContent'})\n if houses_html:\n for house_html in houses_html:\n obj = House()\n obj.city = self.city\n for key, value in HTML_TXT_MAPPING.iteritems():\n self.parser.txtparser(obj, key, value, house_html)\n for key, value in HTML_HREF_MAPPING.iteritems():\n self.parser.hrefparser(obj, key, value, house_html)\n yield obj","title":""}],"string":"[\n {\n \"docid\": \"dc6c271d50837bf478ff6000fb86f793\",\n \"score\": \"0.6672189\",\n \"text\": \"def getChapters(manga_url):\\n\\t#gets the list of soup elements from url (first input) using the selector (second input)\\n\\tsoupList = pullSoup(manga_url,'#listing a')\\n\\t#reshape so that it is a list of front link+title elements\\n\\t#this won't change even after making changes above\\n\\tresultList = []\\n\\tfor result in soupList:\\n\\t\\telement = [result.getText(), result.get(\\\"href\\\")]\\n\\t\\tresultList.append(element)\\n\\t\\n\\t#reverse the list in descending order of chapters\\n\\tresultList.reverse()\\n\\treturn(resultList)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"057960aa9d714362c39518339e71445a\",\n \"score\": \"0.66142416\",\n \"text\": \"def scrape_list(url,user_agent = user_agent):\\n r = requests.get(url,headers = {'User-Agent': user_agent})\\n if r.status_code != 200:\\n raise ValueError(f\\\"Status code for {url} is {r.status_code}\\\")\\n soup = BeautifulSoup(r.text,\\\"lxml\\\")\\n\\n links = soup.select(\\\".product_pod h3 a\\\")\\n links = [urljoin(r.url,x[\\\"href\\\"]) for x in links]\\n\\n next_page = soup.select(\\\".next a\\\")\\n next_page = urljoin(r.url,next_page[0][\\\"href\\\"]) if next_page else None\\n \\n return({\\\"links\\\":links,\\\"next\\\":next_page})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d75a43fc7f6d298c8b7ea8a47b1b916\",\n \"score\": \"0.6550639\",\n \"text\": \"def mangaSearch(searchInput):\\n\\t#gets the list of soup elements from url (first input) using the selector (second input)\\t\\n\\tsoupList = pullSoup(\\\"/search/?w=\\\"+searchInput,'.mangaresultitem a')\\n\\t#reshape so that it is a list of front link+title elements\\n\\t#this is identical to below but will change in the future once core fully functional\\n\\tresultList = []\\n\\tfor result in soupList:\\n\\t\\telement = [result.getText(), result.get(\\\"href\\\")]\\n\\t\\tresultList.append(element)\\n\\t\\t\\n\\treturn(resultList)\\n\\t# get the core functionality out first, then build upon it.\\n\\t#next get images, tags and other stuff\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db47013cd20cc0f35a05e6da1f36b4da\",\n \"score\": \"0.6534393\",\n \"text\": \"def parser_homeurl(html):\\n\\n soup = BeautifulSoup(html, \\\"html.parser\\\")\\n for url in soup.select('a[class=\\\"movie-box\\\"]'):\\n yield url['href']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f12e2c7d6a316d00c8139e9d7f4aaeb7\",\n \"score\": \"0.633048\",\n \"text\": \"def get_list(soup, selector):\\n sel = soup.select(selector)\\n items = (item.text for item in sel)\\n return items\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9eba24677c472b388ad32e9d960f8035\",\n \"score\": \"0.62395185\",\n \"text\": \"def parse_soup(self, soup):\\n return soup.find_all('li', class_=\\\"serp-item\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b2b499ef7b24be0270e517818999234\",\n \"score\": \"0.6233359\",\n \"text\": \"def parse_search_page(search_doc):\\n\\n soup = BeautifulSoup(search_doc, \\\"html.parser\\\")\\n ul = soup.find(\\\"ul\\\", class_=\\\"vT-srch-result-list-bid\\\")\\n li = ul.find(\\\"li\\\")\\n new_list = []\\n while li:\\n entry = parse_html_li_tag(li)\\n new_list.append(entry)\\n li = li.find_next(\\\"li\\\")\\n print(\\\"{}|{}\\\".format(entry.link, entry.desc))\\n\\n return new_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1247c942710c8d3b44a40d522bbd29e8\",\n \"score\": \"0.62222606\",\n \"text\": \"def collect_pages(soup):\\n page = soup.find_all('div', class_=\\\"destination-info\\\")\\n page_list = []\\n for p in page:\\n raw_data = str(p)\\n page = re.search('href=(.*)/', raw_data).group()\\n web_page = URL[:-1] + page[6:]\\n page_list.append(web_page)\\n return page_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8addb4d643c580a4e6fec884d96cc1f5\",\n \"score\": \"0.62148976\",\n \"text\": \"def extract_links(html_page):\\n\\n # to find the <ul> tag that contains every job ad basic info\\n jobs_list = html_page.find(\\\"ul\\\", class_=\\\"o-listView__list\\\")\\n\\n # iterate over every job recruiment to get it's link\\n demo = list()\\n for job_add in jobs_list.find_all(\\\"li\\\", class_=\\\"o-listView__item\\\"):\\n link = job_add.find(\\\"a\\\", class_=\\\"c-jobListView__titleLink\\\")\\n demo.append(link.get(\\\"href\\\", None))\\n\\n return demo\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7af2678a2827e0f85792f54765b4883e\",\n \"score\": \"0.6181112\",\n \"text\": \"def listFD(url):\\r\\n page = requests.get(url).text\\r\\n soup = BeautifulSoup(page, 'html.parser')\\r\\n return [url + '/' + node.get('href') for node in soup.find_all('a')]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"302bda9144dbc6b3298352cf156e2924\",\n \"score\": \"0.61109954\",\n \"text\": \"def get_bookmarks_list(html):\\n\\n first_dl_tag = html.find_all('dl')[0]\\n\\n return first_dl_tag\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36b16f3e3c735b07567e07e57798f6fa\",\n \"score\": \"0.61086905\",\n \"text\": \"def parse_hotellist_page(html):\\n soup = BeautifulSoup(html)\\n # Extract hotel name, star rating and number of reviews\\n hotel_boxes = soup.findAll('div', {'class' :'listing wrap reasoning_v5_wrap jfy_listing p13n_imperfect'})\\n if not hotel_boxes:\\n log.info(\\\"#################################### Option 2 ######################################\\\")\\n hotel_boxes = soup.findAll('div', {'class' :'listing_info jfy'})\\n if not hotel_boxes:\\n log.info(\\\"#################################### Option 3 ######################################\\\")\\n hotel_boxes = soup.findAll('div', {'class' :'listing easyClear p13n_imperfect'})\\n\\n for hotel_box in hotel_boxes:\\n hotel_name = hotel_box.find(\\\"a\\\", {\\\"target\\\" : \\\"_blank\\\"}).find(text=True)\\n log.info(\\\"Hotel name: %s\\\" % hotel_name.strip())\\n #dict_hotels[hotel_name.strip()] = {}\\n stars = hotel_box.find(\\\"img\\\", {\\\"class\\\" : \\\"sprite-ratings\\\"})\\n if stars:\\n log.info(\\\"Stars: %s\\\" % stars['alt'].split()[0])\\n #dict_hotels[hotel_name.strip()][\\\"Stars\\\"] = stars['alt'].split()[0]\\n\\n num_reviews = hotel_box.find(\\\"span\\\", {'class': \\\"more\\\"}).findAll(text=True)\\n if num_reviews:\\n log.info(\\\"Number of reviews: %s \\\" % [x for x in num_reviews if \\\"review\\\" in x][0].strip())\\n \\n #find the url for the hotel, append it to a list of urls\\n hotel_url = hotel_box.find(\\\"a\\\", {\\\"class\\\" : \\\"property_title\\\"})['href']\\n ht_urls.append(hotel_url)\\n\\n #this didn't work :(\\n #Get the url of the hotel page\\n # hotel_pages = soup.findAll('div', {'class': 'qualilty wrap'})\\n # print \\\"going to start parsing hotels\\\"\\n # i = 0\\n # for hotel_page in hotel_pages:\\n # print \\\"parsing hotel #\\\", i\\n # hotel_url = hotel_page.find('a', {'target': '_blank'}).find(text=True)\\n # url = base_url + hotel_url\\n # headers = { 'User-Agent' : user_agent }\\n # time.sleep(2)\\n # response = requests.get(url, headers=headers)\\n # #html now holds the webpage for a specific website. \\n # html = response.text.encode('utf-8')\\n # parse_hotel_page(html) #will find the needed data and parse it. \\n \\n\\n # Get next URL page if exists, otherwise exit\\n div = soup.find(\\\"div\\\", {\\\"class\\\" : \\\"pagination paginationfillbtm\\\"})\\n # check if this is the last page\\n if div.find('span', {'class' : 'guiArw pageEndNext'}):\\n log.info(\\\"We reached last page\\\")\\n # print \\\"We reached last page\\\"\\n sys.exit()\\n # If not, return the url to the next page\\n hrefs = div.findAll('a')\\n for href in hrefs:\\n cl = href['class'].strip()\\n # print \\\"cl \\\", cl\\n if cl == \\\"guiArw sprite-pageNext\\\":\\n # print \\\"href \\\", href['href']\\n return href['href']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a4ccc03db85be796242fe8466d402e4\",\n \"score\": \"0.6096425\",\n \"text\": \"def parse(self, response):\\n for url in response.css('ul.topicsList_main a::attr(\\\"href\\\")').re(r'/pickup/\\\\d+$'):\\n yeild response.follow(url, self.parse_topics)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"825107e71d7fbeffc38d30558e301c74\",\n \"score\": \"0.59929407\",\n \"text\": \"def parse_soup_page(soup):\\n titles, authors, links = list(), list(), list()\\n for div in soup.findAll('div'):\\n if div.name == \\\"div\\\" and div.get('class') == \\\"gs_ri\\\":\\n links.append(div.a['href'])\\n div_pub = div.findAll('div')\\n for d in div_pub:\\n if d.name == 'div' and d.get('class') == 'gs_a':\\n authors.append(d.text)\\n titles.append(div.a.text)\\n return titles, authors, links\",\n \"title\": \"\"\n },\n {\n \"docid\": \"805e451604422ba920a5a220181119b2\",\n \"score\": \"0.5978821\",\n \"text\": \"def scrapeLinks(selector):\\n\\n url = 'https://www.psp.cz/sqw/hp.sqw?k=1300'\\n download_url = 'https://www.psp.cz'\\n req = requests.get(url)\\n soup = BeautifulSoup(req.content, 'html.parser')\\n download_links = []\\n\\n for link in soup.select(selector):\\n download_links.append(str(download_url + link.get('href').replace('..', '')))\\n\\n return download_links\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8de3911897d4cfa6223da93af963530\",\n \"score\": \"0.5963124\",\n \"text\": \"def parse_soup(self, soup):\\n # find all li tags\\n return soup.find_all('li', class_='repo-list-item')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7e41b0f70e58c3c58619737d0ef80d0\",\n \"score\": \"0.59541196\",\n \"text\": \"def parse_the_next(self, content, url):\\n\\t\\tout_url_list = []\\n\\t\\ttry:\\n\\t\\t\\tparser = OnePageParser()\\n\\t\\t\\tparser.feed(content)\\n\\t\\t\\tfor i in range(len(parser.href_list)):\\n\\t\\t\\t\\ttmp_href = parser.href_list[i]\\n\\t\\t\\t\\treal_url = utils.joint_url(url, tmp_href)\\n\\t\\t\\t\\t#把解析出来的url放到list中\\n\\t\\t\\t\\tout_url_list.append(real_url)\\n\\t\\texcept HTMLParser.HTMLParseError as error:\\n\\t\\t\\tlogging.error(\\\"Parsing url: %s failed. Details: %s \\\" % (url, error))\\n\\t\\t\\treturn None\\n\\t\\treturn out_url_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a74d33ec9e9107293f110e7572f1b298\",\n \"score\": \"0.59488285\",\n \"text\": \"def help_get_list_from_dropdown(url, attr_name):\\n r = requests.get(url)\\n raw_string = sub(r\\\"\\\\s+\\\", ' ', r.text)\\n\\n soup = BeautifulSoup(\\n raw_string,\\n from_encoding=\\\"utf-8\\\",\\n parse_only=SoupStrainer(name=\\\"select\\\", attrs={\\\"name\\\" : attr_name})\\n )\\n\\n del r, raw_string\\n stack = []\\n for option in soup(\\\"option\\\"):\\n if option[\\\"value\\\"]:\\n stack.append(option[\\\"value\\\"].strip().encode(\\\"utf-8\\\"))\\n\\n del soup\\n return stack\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3895a76e72a3b157dc2a066c7864e0a8\",\n \"score\": \"0.59416556\",\n \"text\": \"def getepisodelist(html, url):\\n\\t\\n\\tbase = re.search(\\\"(https?://[^/]+)\\\", url).group(1)\\n\\tcomicurl = re.search(\\\"comic_url = \\\\\\\"(.+?)\\\\\\\"\\\", html).group(1)\\n\\tms = re.findall(\\\"href=\\\\\\\"(/{}.+?)\\\\\\\" (?: class=\\\\\\\"color_red\\\\\\\")?>(.+?)</a></li>\\\".format(comicurl), html)\\n\\ts = []\\n\\tfor m in ms:\\t\\n\\t\\turl, title = m\\n\\t\\te = comiccrawler.Episode()\\n\\t\\te.title = title\\n\\t\\t# safeprint(e.title)\\n\\t\\te.firstpageurl = base + url\\n\\t\\t# print(e.firstpageurl)\\n\\t\\ts.append(e)\\n\\treturn s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a015d0717dbed30a72b43470459ca68\",\n \"score\": \"0.59404546\",\n \"text\": \"def scrape(urls):\\n\\n url = random.choice(urls)\\n #url = \\\"http://www.latimes.com\\\"\\n print(\\\"reading: \\\" + url)\\n\\n front_page = newspaper.build(url)\\n\\n headlines = []\\n\\n for article in front_page.articles:\\n headlines.append(article.url)\\n\\n return headlines, url\",\n \"title\": \"\"\n },\n {\n \"docid\": \"820dacae64bf11db865ed1289d375ab4\",\n \"score\": \"0.59238493\",\n \"text\": \"def parse(self, response):\\n\\n for href in response.xpath(\\\"//a[not(contains(@href, '#'))]/@href\\\"):\\n # TODO remove dupliate urls\\n url = response.urljoin(href.extract())\\n\\n print(url)\\n\\n #if url == 'https://www.astrazeneca.com/our-company.html':\\n yield scrapy.Request(url, callback = self.parse_tags)\\n # break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"984fd01618fbe2ca3a94e8bb5d5904a3\",\n \"score\": \"0.5899313\",\n \"text\": \"def collect_links_from_main_page():\\n law_pages = []\\n response = requests.get(START_URL)\\n html = BeautifulSoup(response.text, features=\\\"lxml\\\")\\n\\n for link in html.find_all('a'):\\n if link.has_attr('href'):\\n search = re.search('/index.php/en/library/albanian-legislation/category/[0-9a-zA-z-]+',\\n link['href'])\\n if search is not None:\\n full_link = BASE_URL + link['href']\\n if full_link == START_URL:\\n continue\\n law_pages.append(link['href'])\\n return law_pages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c609cba9a95813913e3e38c70eecd0b9\",\n \"score\": \"0.5887025\",\n \"text\": \"def get_job_posts(target_url, search_term):\\n\\n # custom parameters\\n unknown_string = 'UNKNOWN'\\n\\n # variables\\n url_list = []\\n page = requests.get(target_url)\\n\\n # Create a BeautifulSoup object\\n big_soup = BeautifulSoup(page.text, 'html.parser')\\n\\n # Let's clean this mess up\\n for script in big_soup([\\\"script\\\", \\\"style\\\"]): # remove all javascript and stylesheet code\\n script.decompose()\\n\\n parent_search_section = big_soup.find_all('div', class_='mux-card mux-job-card')\\n\\n if parent_search_section is None:\\n ScrapeHelper.print_error_string(big_soup.prettify())\\n else:\\n try:\\n job_cards_master = parent_search_section[0]\\n # job_cards = job_cards_master.find_all('section', class_='card-content')\\n job_cards = job_cards_master.find_all(\\n lambda tag: tag.name == 'section' and tag.get('class') == ['card-content'])\\n for card in job_cards:\\n try:\\n company_div = card.find('div', class_='company')\\n x1 = company_div.find('span', class_='name')\\n if not x1 is None:\\n company = x1.text.strip('\\\\n')\\n else:\\n company = unknown_string\\n\\n loc_div = card.find('div', class_='location')\\n x2 = loc_div.find('span', class_='name')\\n if not x2 is None:\\n location = x2.text.strip('\\\\n')\\n location = location.replace('\\\\r', '')\\n location = location.replace('\\\\n', '')\\n else:\\n location = unknown_string\\n\\n sum_div = card.find('div', class_='summary')\\n x3 = sum_div.find('header', class_='card-header')\\n x0 = x3.find('a')\\n links = x0.get('href')\\n names = x0.get_text().strip('\\\\n')\\n names = names.replace('\\\\r', '')\\n\\n # print(names + ' -- ' + location + ' -- ' + company + ' -- ' + links)\\n url_list.append(\\n JobPost(job_title=names, url=links, company=company, location=location, search_term=search_term,\\n source=ScrapeHelper.MONSTER))\\n except Exception as e:\\n ScrapeHelper.print_error_string(str(e) + \\\" Monster Job Unavailable/Not Found: \\\" + search_term)\\n except Exception as e:\\n ScrapeHelper.print_error_string(str(e) + \\\" Monster Job Cards Not Found: \\\" + search_term)\\n\\n return url_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"abbd3140f47ab97a0d9641c2d66182b6\",\n \"score\": \"0.58795094\",\n \"text\": \"def getepisodelist(html, url):\\n\\t\\n\\tbase = re.search(\\\"(https?://[^/]+)\\\", url).group(1)\\n\\tms = re.findall(\\\"<a href='(.+?)'>(.+?)</a>\\\", html)\\n\\ts = []\\n\\tfor m in ms:\\t\\n\\t\\tu, title = m\\n\\t\\te = comiccrawler.Episode()\\n\\t\\te.title = title\\n\\t\\te.firstpageurl = base + url\\n\\t\\ts.append(e)\\n\\treturn s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7beb16e9b4e446771fc8f7ea2f6abab8\",\n \"score\": \"0.58770764\",\n \"text\": \"def parse_url(url):\\n return parse_dom(flat(get(url).text))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e89d3ae285c5284200b7b1d616b054c2\",\n \"score\": \"0.5871144\",\n \"text\": \"def _loadList(self):\\n\\t\\ttry:\\n\\t\\t\\tpastes = self._getData()\\n\\t\\t\\tsouppastes = BeautifulSoup(pastes)\\n\\t\\t\\tlinks = souppastes.findAll('a')\\n\\t\\t\\tfor link in links:\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tif link['href']:\\n\\t\\t\\t\\t\\t\\tif link['href'].find(\\\"_id_\\\") != -1:\\n\\t\\t\\t\\t\\t\\t\\tself._list.append(link.string)\\n\\t\\t\\t\\texcept Exception:\\n\\t\\t\\t\\t\\t# Handle programer 'href' errors \\n\\t\\t\\t\\t\\tpass\\n\\t\\texcept Exception, e:\\n\\t\\t\\tprint ('[!] Error found: ',str(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3221f7ddea40572ec9729aa9c0b75f0\",\n \"score\": \"0.5867502\",\n \"text\": \"def __parse_part(html):\\r\\n html = html.replace('\\\\t', ' '). \\\\\\r\\n replace('\\\\n', ' '). \\\\\\r\\n replace('\\\\r', ' '). \\\\\\r\\n replace('\\\\xa0', ''). \\\\\\r\\n replace('</a>', '')\\r\\n html = re.sub('[ ]{2,}|<a.+?>', ' ', html)\\r\\n parse_part_argument = re.compile('<div class=\\\"item column-1\\\" itemprop=\\\"blogPost\\\" itemscope '\\r\\n 'itemtype=\\\"https://schema\\\\.org/BlogPosting\\\">(.+?)<!-- end item -->')\\r\\n parts = re.findall(parse_part_argument, html)\\r\\n return parts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d817279078779c3624e0dd52f2ce71c\",\n \"score\": \"0.5850514\",\n \"text\": \"def find_api_list(self):\\n soup = html_code(self.url)\\n body = soup.find('tr', class_='odd views-row-first').parent\\n self.lists = body('tr')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d631d1c25f599ea862e3bc6ecf7eb6e\",\n \"score\": \"0.5833862\",\n \"text\": \"def links_scrapping(self):\\n html_page = urlopen(self.url)\\n html_data = html_page.read().decode('utf-8')\\n list_books_link = re.findall('(?<=<h3>).*?(?=</h3>)', html_data)\\n for books in list_books_link:\\n book_link = re.search('(?<=href=\\\").*?(?=\\\" )', books).group()\\n self.links_list.append(self.links_beginning + book_link[9:])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"252e5333755cd95568ab1f49309c59bd\",\n \"score\": \"0.5810691\",\n \"text\": \"def parse_for_filter(soup, terms=(\\\"li\\\", {\\\"class\\\": \\\"ads-ad\\\"})):\\n\\n # get a list of all the elements, which shall be filtered for\\n specific = soup.find_all(terms[0], terms[1])\\n\\n # get list of information about the elements and save each in a dictionary\\n entry_list = []\\n for entry in specific:\\n dic = {\\n \\\"text\\\": entry.find('a').text,\\n \\\"ad_link\\\": entry.find('div', {'class': 'ads-visurl'}).cite.text,\\n \\\"html\\\": entry\\n }\\n entry_list.append(dic)\\n\\n return entry_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c47c81a50e4e0ad372b09fdf33a55b9\",\n \"score\": \"0.57942796\",\n \"text\": \"def list_scrape(lis, url_stem, elem = True):\\n\\n uht_list = []\\n for l in lis:\\n url = url_stem + l\\n url_html_time = scrape(url)\\n #sometimes we want to associate with the element with its tuple\\n if elem:\\n uht_list.append((url_html_time, l))\\n return uht_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20e363196a50370228a4de237ddc1ac0\",\n \"score\": \"0.57906544\",\n \"text\": \"def parse_items(self, response):\\n self.logger.info('You are now crawling: %s', response.url)\\n items = []\\n hxs = Selector(response)\\n # print response.url\\n contents = hxs.xpath(\\\"//ul[@class='rows']/*\\\")\\n for content in contents:\\n item = ScrapyCraigslistItem()\\n item [\\\"title\\\"] = content.xpath(\\\"//a[@class='result-title hdrlnk']/text()\\\").extract()[0]\\n item [\\\"post_date\\\"] = content.xpath(\\\"//time[@class='result-date']/text()\\\").extract()[0]\\n item [\\\"price\\\"] = content.xpath(\\\"//span[@class='result-price']/text()\\\").extract()[0]\\n item [\\\"location\\\"] = content.xpath(\\\"//span[@class='result-hood']/text()\\\").extract()[0].strip()\\n items.append(item)\\n return items\",\n \"title\": \"\"\n },\n {\n \"docid\": \"525cee1076c12017fbe630c2da2df380\",\n \"score\": \"0.576131\",\n \"text\": \"def parse(self, response):\\n params = {'channel': 'web', 'children': True}\\n urls = response.xpath('//a[@aria-hidden=\\\"true\\\"]/@href').extract()\\n for url in urls:\\n departmentId = url.partition('-/N-')[2].split('?')[0].strip()\\n if departmentId:\\n params['url'] = url\\n yield Request(self.find_cats_url + urlencode(params), self.get_seed_categories)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac4e56a54045ddb5e99df14b9e2bb911\",\n \"score\": \"0.57557905\",\n \"text\": \"def parse_page(html):\\n soup = BeautifulSoup(html, \\\"html.parser\\\")\\n review_soups = soup.find_all(\\\"script\\\", type=\\\"application/ld+json\\\")\\n\\n description_list = []\\n for soup in review_soups:\\n text = soup.string\\n # Decode the json into python dict\\n js_dict = json.loads(text)\\n if \\\"review\\\" in js_dict:\\n review_list = js_dict[\\\"review\\\"]\\n for i in range(len(review_list)):\\n review_dict = review_list[i]\\n description_list.append(review_dict[\\\"description\\\"])\\n return description_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83daa69d51638d9dc763e157e24f4992\",\n \"score\": \"0.5744948\",\n \"text\": \"def parse(self, soup):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17dc0b947cd866a6906287e68adbfae5\",\n \"score\": \"0.57320696\",\n \"text\": \"def parse_category(url):\\n response = fetch(url)\\n selector = HTML(response.text)\\n last_page = selector.xpath('//div[@class=\\\"thisclass\\\"]/a[position()=last()]/@href')[0]\\n total_num = int(last_page.rsplit(\\\"=\\\", 1)[-1])\\n return [urljoin(url, f'?page={i}') for i in range(1, total_num + 1)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d780ceee97454bf735561c320d34b6c\",\n \"score\": \"0.57314986\",\n \"text\": \"def extract(url):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"172e551aa8a278b2279bcda455b89a8c\",\n \"score\": \"0.5711623\",\n \"text\": \"def __get_html_parser(self, url: str):\\r\\n page = requests.get(url)\\r\\n soup = BeautifulSoup(page.content, 'html.parser')\\r\\n return soup\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b55c72078f0868bed2abd80e292bceb\",\n \"score\": \"0.57085764\",\n \"text\": \"def extract_games_from_page(page_num, category, stop_at=100): \\n page = get_page_text(page_num, category)\\n\\n soup_parse = BeautifulSoup(page)\\n\\n #find the games \\n rough_cut = soup_parse.find_all('div', attrs={\\\"style\\\":\\\"z-index:1000;\\\"})\\n game_list = []\\n for index, rough_entry in enumerate(rough_cut):\\n game_list.append(extract_game_details_from_entry(rough_entry))\\n if index + 1 == stop_at:\\n break\\n\\n return game_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a4c0ee9ee022913ec1d2c5d7e3938b0\",\n \"score\": \"0.57060045\",\n \"text\": \"def parse_page(url, content):\\n log.info(\\\"URL: %s\\\" % url)\\n links = soup(content)\\n links_in_page = []\\n for link in links:\\n if link.has_attr('href'):\\n href = link['href']\\n if href.startswith(\\\"/\\\"):\\n href = \\\"%s%s\\\" % (url, href)\\n if href.startswith('mailto') or href.startswith('tel:'):\\n log.info(' skipping non web link: %s' % href)\\n continue\\n log.info(\\\" %s\\\" % href)\\n links_in_page.append(href)\\n return links_in_page\",\n \"title\": \"\"\n },\n {\n \"docid\": \"114628d71414019b460ba780c075915e\",\n \"score\": \"0.56980443\",\n \"text\": \"def parse_posts(list_of_posts, url):\\n print(\\\"Parsing posts of \\\" + url)\\n for i in range(len(list_of_posts) - 1):\\n wait(By.XPATH, \\\"/html/body/div[1]/div[3]/div[1]/div/div/div[2]/div[2]/div/div[2]/div[2]/div/div/div[2]\\\" +\\n \\\"/div/div[4]/div/div/div/div[3]/div[2]\\\", list_of_posts[i])\\n click_links(list_of_posts[i])\\n comment_divs = get_web_comment(list_of_posts[i])\\n comments_list = get_list_of_comments(comment_divs)\\n global_list_of_comments.extend(comments_list)\\n # list_of_comments.extend(my_list)\\n print(\\\"Parsing the \\\" + url + \\\" list is finished.\\\")\\n # get_hrefs(my_list, name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e46b6f799599b0300bb3db53b231bca4\",\n \"score\": \"0.569607\",\n \"text\": \"def extract_reviews(url, html_fetcher):\\n dict_return = []\\n #code, html = html_fethcher(url_next)\\n page = html_fetcher(url)[1]\\n reviews_list, url_next = parse_page(page)\\n for x in reviews_list:\\n dict_return.append(x)\\n while url_next != None:\\n page = html_fetcher(url_next)[1]\\n reviews_list, url_next = parse_page(page)\\n for x in reviews_list:\\n dict_return.append(x)\\n return dict_return\\n\\n\\n #[YOUR CODE HERE]\\n #code, html = html_fetcher(url) # function implemented in Q0 should work\\n #[YOUR CODE HERE]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81a71a519b17700b3234fff46846801b\",\n \"score\": \"0.56901556\",\n \"text\": \"def __init__(self, url):\\n\\n expression = r'https://www.defense.gov/News/Contracts/Contract-View/Article/([^/\\\\\\\">]*)'\\n pg = requests.get(url)\\n\\n it = re.finditer(expression, pg.text)\\n\\n results = []\\n\\n for i in it:\\n results.append(i.group(1))\\n self.results = results\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd9c1fa532a9fa762f91ffc27566c258\",\n \"score\": \"0.5681524\",\n \"text\": \"def scrape(self, html):\\n \\n links = []\\n \\n #check headers for redirects\\n redirects = filter(lambda x: x[0] == 'location', self.headers)\\n links.extend([x[1] for x in redirects])\\n \\n #scrape response body\\n soup = bs(html)\\n metalinks = soup.findAll('meta', attrs={'http-equiv':True})\\n for m in metalinks:\\n index = str(m).find('url=')\\n end = str(m).find('\\\"',index, len(str(m)))\\n if index != -1:\\n link = str(m)[index+4:end]\\n links.append(link)\\n \\n attrs = ['background', 'cite', 'codebase', 'href', 'longdesc', 'src']\\n \\n for a in attrs:\\n links.extend(\\n map(lambda x: x[a], soup.findAll(**{a:True}))# **{} unzips dictionary to a=True\\n )\\n \\n return links\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50fa1a31ea712bcacd9c661d2ad7b370\",\n \"score\": \"0.5667909\",\n \"text\": \"def get_subwebpage(self):\\n assert self.full\\n content = None\\n try:\\n content = HTML(urllib.request.urlopen(self.url))\\n print(\\\"*** Get: [{0}] ***\\\".format(self.url))\\n except urllib.error.URLError as _: #look at it latter\\n pass\\n if content is None:\\n return []\\n return content.find_link()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4fde6ee3575213099e70b54cabf9917\",\n \"score\": \"0.566695\",\n \"text\": \"def info_from_vagalume(html):\\n\\n soup = BeautifulSoup(html, 'html.parser')\\n name = soup.select('#lyricContent h1')[0]\\n author = soup.select('#lyricContent h2 a')[0]\\n\\n return info_from_html(html, '#lyricContent h1', '#lyricContent h2 a')\\n return (name.get_text().strip(), author.get_text().strip())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c91cff1526869c6bb6044ec58c38d8aa\",\n \"score\": \"0.56639546\",\n \"text\": \"def parse_pagination(html) -> Tuple[List, str]:\\n sel = Selector(text=html)\\n jobs = sel.css(PAGER_SELECTOR).extract()\\n next_page = sel.css('a[area-label=Next]::attr(href)').extract_first()\\n\\n return jobs, next_page\",\n \"title\": \"\"\n },\n {\n \"docid\": \"256bf62031f103288d4d96b5b7fcd696\",\n \"score\": \"0.5659222\",\n \"text\": \"def get_business_urls(url):\\n base_url = \\\"http://www.yelp.com\\\"\\n response = http.request('GET', url)\\n soup = bs4.BeautifulSoup(response.data)\\n links = []\\n for a in soup.select('span.indexed-biz-name a[href^=/biz]'): #------- Retrieving the href from the div the link to the restaurants yelp page is found\\n links.append(base_url + str(a.attrs.get('href')))\\n return links\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9d90a8390bad9db5237e9f2e687b320\",\n \"score\": \"0.5638547\",\n \"text\": \"def get_info(self, url):\\n self.driver.get(url)\\n self.driver.execute_script(\\\"window.scrollTo(0, document.body.scrollHeight);\\\")\\n soup = BeautifulSoup(self.driver.page_source, \\\"html.parser\\\")\\n return soup\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e635853ac1ddd921c9756c2668b26ae\",\n \"score\": \"0.56261975\",\n \"text\": \"def get_soup(url):\\n r = requests.get(url)\\n soup = BeautifulSoup(r.text, 'html.parser')\\n return soup\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5a6906ebc77267a5ddd682621b2272f\",\n \"score\": \"0.5623847\",\n \"text\": \"def urls():\\n return [\\n 'http://www.generationbass.com/2014/01/27/'\\n 'the-great-dj-terrivel-tarraxinha/',\\n 'http://fontstruct.com/fontstructions/show/939228',\\n 'http://fontstruct.com/fontstructions/show/939218',\\n 'http://www.kfgo.com/news/articles/2014/jan/27/'\\n 'pennsylvania-lawmaker-accused-of-ghost-employee-scam/',\\n 'http://www.kfgo.com/news/articles/2014/jan/27/'\\n 'fabulist-glass-may-not-practice-law-in-california-court/',\\n 'http://www.kfgo.com/news/articles/2014/jan/27/'\\n 'jurors-being-chosen-in-delaware-doctors-trial-over-'\\n 'waterboarding-claims/',\\n 'http://www.kfgo.com/news/articles/2014/jan/27/'\\n 'fargo-will-not-prosecute-tv-reporter-for-secretly-entering-school/',\\n 'http://www.kfgo.com/news/articles/2014/jan/27/'\\n 'governor-daytons-bonding-proposal-devoid-of-funding-for-'\\n 'moorhead-flood-mitigation/',\\n 'http://www.kfgo.com/news/articles/2014/jan/27/'\\n 'minn-touts-new-indoor-stadium-for-2018-super-bowl/'\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9634ab2b001c5fd62fecc2e629a4231\",\n \"score\": \"0.56227475\",\n \"text\": \"def web_crawl(urls):\\n\\n tags = ['h1',\\\"h2\\\",\\\"h3\\\",\\\"h4\\\",\\\"h5\\\",\\\"h6\\\",\\\"p\\\",\\\"li\\\"]\\n global docids\\n docids = []\\n master_dict = {}\\n\\n for url in urls:\\n docids.append(url)\\n r = requests.get(url)\\n s = BeautifulSoup(r.content, 'html.parser')\\n\\n #find text, get only text, append to array\\n words = []\\n for tag in tags:\\n for text in s.find_all(tag):\\n temp = text.get_text()\\n words.append(temp)\\n\\n text = []\\n for item in words:\\n #Breaks down sentences into individual words\\n text.append(word_tokenize(item))\\n\\n #flatten list of list to one list\\n flattened_list = list(itertools.chain(*text))\\n words = [word.lower() for word in flattened_list]\\n\\n #gets stop words in english and removes them\\n stop_words = set(stopwords.words('english'))\\n filtered_list = [w for w in words if not w in stop_words]\\n\\n #Removes alphanumeric\\n flat_list = [re.sub('\\\\w*\\\\d\\\\w*', '', text) for text in filtered_list]\\n\\n #Removes punctuations\\n table = str.maketrans('', '', string.punctuation)\\n stripped = [w.translate(table) for w in flat_list]\\n stripped = [word for word in stripped if len(word) > 1]\\n master_dict = store_info(stripped, master_dict)\\n\\n return master_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4d3bee3d23f0c20f3bba831925b6e61\",\n \"score\": \"0.5621183\",\n \"text\": \"def scrapper(self):\\n parser = 'html.parser'\\n self.url = f'{self.url}{self.n}'\\n\\n try:\\n r = requests.get(self.url)\\n while r.status_code < 400:\\n resp = urllib.request.urlopen(self.url)\\n soup = BeautifulSoup(resp, parser)\\n for link in soup.find_all('a', href=True):\\n if link not in self.urls:\\n self.urls.append(link['href'])\\n\\n url_string = str(self.url)\\n self.url = url_string.split('=', 1)[0]\\n self.n += 1\\n self.url = f'{self.url}={self.n}'\\n except:\\n self.ini_list()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57b080e3a9052945a8c475db26c33de0\",\n \"score\": \"0.56170803\",\n \"text\": \"def extract_link(url):\\n\\theaders = {\\\"Host\\\": \\\"www.zomato.com\\\",\\n\\t \\\"User-Agent\\\": \\\"Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:50.0) Gecko/20100101 Firefox/50.0\\\",\\n\\t \\\"Accept\\\": \\\"text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8\\\",\\n\\t \\\"Accept-Language\\\": \\\"en-US,en;q=0.5\\\",\\n\\t \\\"Accept-Encoding\\\": \\\"gzip, deflate, br\\\",\\n\\t \\\"Referer\\\": \\\"https://www.zomato.com/\\\",\\n\\t \\\"Connection\\\": \\\"keep-alive\\\"}\\n\\twhile(1):\\n\\t\\ttry:\\n\\t\\t\\tr = requests.get(url, headers=headers)\\n\\t\\texcept requests.exceptions.Timeout:\\n\\t\\t\\tcontinue\\n\\t\\tbreak\\n\\tif r.status_code == 404:\\n\\t\\treturn None\\n\\tdoc = r.content\\n\\tsoup = BeautifulSoup(doc,'html.parser')\\n\\twith open ('zomata_user_data', \\\"a\\\") as filew:\\n\\t\\tfilew.write('\\\\n\\\\n##\\\\n\\\\n')\\n\\t\\tptags = soup.find_all('title')\\n\\t\\tstr = ptags[0].string\\n\\t\\tre.sub('<title>','',str)\\n\\t\\tre.sub('</title>','',str)\\n\\t\\tstr = str.strip()\\n\\t\\tstr = str[:-9]\\n\\t\\tlst = str.split(',')\\n\\t\\tfilew.write(lst[0].strip()+'\\\\n')\\n\\t\\tif(len(lst)>1):\\n\\t\\t\\tfilew.write(lst[1].strip()+'\\\\n')\\n\\n\\t\\tptags = soup.find_all(\\\"a\\\", 'item user-tab user-tab-reviews cursor-pointer ')\\n\\t\\tlis = ptags[0].find_all('div')\\n\\t\\tstr = lis[0].string\\n\\t\\tre.sub('<div class=\\\"ui label\\\">','',str)\\n\\t\\tre.sub('</div>','',str)\\n\\t\\tfilew.write('Reviews = '+str+'\\\\n')\\n\\n\\t\\tptags = soup.find_all(\\\"a\\\", 'item user-tab user-tab-blogs cursor-pointer ')\\n\\t\\tif(ptags):\\n\\t\\n\\t\\t\\tlis = ptags[0].find_all('div')\\t\\n\\t\\t\\tstr = lis[0].string\\n\\t\\t\\tre.sub('<div class=\\\"ui label\\\">','',str)\\n\\t\\t\\tre.sub('</div>','',str)\\n\\t\\t\\tfilew.write('Blog posts = '+str+'\\\\n')\\n\\n\\t\\n\\n\\t\\tptags = soup.find_all(\\\"a\\\", 'item user-tab-follows user-tab cursor-pointer ')\\n\\t\\tlis = ptags[0].find_all('div')\\n\\t\\tstr = lis[0].string\\n\\t\\tre.sub('<div class=\\\"ui label\\\">','',str)\\n\\t\\tre.sub('</div>','',str)\\n\\t\\tfilew.write('Followers = '+str+'\\\\n')\\n\\n\\t\\tptags = soup.find_all(\\\"a\\\", 'item user-tab user-tab-bookmarks cursor-pointer ')\\n\\t\\tlis = ptags[0].find_all('div')\\n\\t\\tstr = lis[0].string\\n\\t\\tre.sub('<div class=\\\"ui label\\\">','',str)\\n\\t\\tre.sub('</div>','',str)\\n\\t\\tre.sub(' | Zomato','',str)\\n\\t\\tfilew.write('Bookmarks = '+str+'\\\\n')\\n\\t\\n\\t\\t#item user-tab user-tab-beenthere cursor-pointer \\n\\t\\tptags = soup.find_all(\\\"a\\\", 'item user-tab user-tab-beenthere cursor-pointer ')\\n\\t\\tlis = ptags[0].find_all('div')\\n\\t\\tstr = lis[0].string\\n\\t\\tre.sub('<div class=\\\"ui label\\\">','',str)\\n\\t\\tre.sub('</div>','',str)\\n\\t\\tfilew.write('Been there = '+str+'\\\\n')\\n\\t\\n\\t\\tptags = soup.find_all(\\\"div\\\", 'user-stats_ranking')\\n\\t\\tlis = ptags[0].find_all('span',{'data-icon' : 'ú'})\\n\\t\\tstr = lis[0].string\\n\\t\\t#print(str)\\n\\t\\tfilew.write('Foodie level = '+str+'\\\\n')\\n\\t\\t\\n\\t\\tif(str != '13'):\\n\\t\\t\\tptags = soup.find_all(\\\"section\\\", 'user-cover ptop ta-center')\\n\\t\\t\\tlis = ptags[0].find_all('div','ui mini statistics')\\n\\t\\t\\tlcs = lis[0].find_all('div', {'class':\\\"label\\\"})\\n\\t\\t\\tstr = lcs[0].string\\n\\t\\t\\t#print(str)\\n\\t\\t\\tstr = str.strip()\\n\\t\\t\\tfilew.write(str+'\\\\n')\\n\\t\\telse:\\n\\t\\t\\tfilew.write('0 points to level up\\\\n')\\n\\n\\t\\tlinkset = set()\\n\\t\\trow = soup.find('div', 'row tac user-cover')\\n\\t\\tlst = row.find('a',{'class':\\\"floating ui blue circular label tooltip_formatted verified-profile\\\"})\\n\\t\\tif(lst):\\n\\t\\t#\\tprint (lst)\\n\\t\\t\\tfilew.write(\\\"Verified User\\\\n\\\")\\n\\t\\telse:\\n\\t\\t\\tfilew.write('Not a verified user\\\\n')\\n\\n\\n\\t\\tutag = soup.find('div','ui popup user-common-section user-expertise expertise_popup hidden')\\n\\t\\tif(utag):\\n\\t\\t\\tptags = utag.find_all(\\\"li\\\", 'badge')\\n\\t\\t\\tneighbour = set()\\n\\t\\t\\tfor i in range(0,len(ptags)):\\n\\t\\t\\t\\tstr = ptags[i].get_text()\\n\\t\\t\\t\\tp = ''\\n\\t\\t\\t\\tfor j in range(0,len(str)):\\n\\t\\t\\t\\t\\tif str[j] == ' ' and j+1<len(str) and str[j+1] == ' ':\\n\\t\\t\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\t\\tp = p + str[j]\\n\\t\\n\\t\\t\\t\\tp = p.strip()\\n\\t\\t\\t\\t#print(p)\\n\\t\\t\\t\\tneighbour.add(p)\\n\\t\\t\\ttotal = repr(len(neighbour)) + ' Neighbourhoods'\\n\\t\\t\\tfilew.write(total+'\\\\n')\\n\\t\\t\\tfor i in neighbour:\\n\\t\\t\\t\\tfilew.write(i+'\\\\n')\\n\\t\\t\\n\\t\\telse:\\n\\t\\t\\tfilew.write('0 Neighbourhoods\\\\n')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc1b2666f6ed0d8e98f281b2caf059fe\",\n \"score\": \"0.55853564\",\n \"text\": \"def sportList(url, sportDataList):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8566594a9fcdb53fc659762af04f815\",\n \"score\": \"0.5584819\",\n \"text\": \"def get_ads():\\n response = requests.get('https://www.heyjobs.de/en/jobs-in-berlin')\\n soup_obj = bs4.BeautifulSoup(response.text, 'html.parser')\\n links = soup_obj.find(\\n attrs={'id': 'job-search-container'}).find_all('a', recursive=True)\\n ads = list()\\n for link in links:\\n if '/en-de/jobs/' in link.get('href'):\\n uid = link.get('href').split('/', 3)[3]\\n title = link.find(\\\"h2\\\", {\\\"class\\\": \\\"job-card-title\\\"}).string\\n ads.append({'uid': uid, 'title': title})\\n return ads\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93f60717273ab5021eb3041ea9bcc66c\",\n \"score\": \"0.55802876\",\n \"text\": \"def parse_page(html):\\n soup = BeautifulSoup(html,'html.parser')\\n url_next = soup.find('link',rel='next')\\n if url_next:\\n url_next = url_next.get('href')\\n else:\\n url_next = None\\n\\n reviews = soup.find_all('div', itemprop=\\\"review\\\")\\n reviews_list = []\\n # HINT: print reviews to see what http tag to extract\\n reviews_list = []\\n reviews_dict = {}\\n # HINT: print reviews to see what http tag to extract\\n for r in reviews:\\n reviews_dict['author'] = r.find('meta', itemprop = \\\"author\\\")['content']\\n reviews_dict['rating'] = float(r.find('meta', itemprop = \\\"ratingValue\\\")['content'])\\n reviews_dict['date'] = r.find('meta', itemprop = \\\"datePublished\\\")['content']\\n reviews_dict['description'] = r.find('p', itemprop = \\\"description\\\").text\\n reviews_list.append(reviews_dict)\\n reviews_dict = {}\\n return reviews_list, url_next\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb05a5309d3bb12b85873128855cc7bd\",\n \"score\": \"0.55780655\",\n \"text\": \"def parse_starters(self, response):\\t\\n\\txs = HtmlXPathSelector(response)\\n\\tresources = xs.select('//ul[2]/li') + xs.select('//ul[1]/li')\\n\\t#rlist = []\\n\\tfor resource in resources:\\n\\t\\tdescription = resource.select('text()').extract()\\n\\t\\ttitle = resource.select('a/text()').extract()\\n\\t\\tlink = resource.select('a/@href').extract()\\n\\t\\t#path = str(response.url).replace(ROOT_DOMAIN, '')i\\n\\t\\t#TODO: make this sensible to any lang\\n\\t\\t#lang = 'es' if u'World/Español' in str(response.url) else 'en'\\n\\t\\t#only allow outgoing links:\\n\\t\\tif link and title:\\n\\t\\t\\t#self.log('%s info: %s, %s' % (response.url, title, link))\\n\\t\\t\\tf = lambda r, desc = description, name = title: self.parse_item(r,\\n\\t\\t\\t\\t\\t name = name[0],\\n\\t\\t\\t\\t\\t lang = find_lang(response.url), \\n\\t\\t\\t\\t\\t category =url2categorypath(response.url), \\n\\t\\t\\t\\t\\t desc = desc[0] if len(desc) else '')\\n\\t\\t\\t#rlist += [Request(link[0], callback= f),]\\n\\t\\t\\tyield Request(link[0], callback= f)\\n\\t\\n\\t#return rlist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb77878dc54e340c2c4a00bd5e5e2d5e\",\n \"score\": \"0.55772144\",\n \"text\": \"def wiki_scraper(url):\\n soup = BeautifulSoup(url, 'lxml')\\n table = soup.find('table', {'class': 'wikitable sortable'})\\n links = table.find_all(['b','a'])\\n city_state = []\\n\\n for link in links:\\n title = link.get('title')\\n if title != None:\\n city_state.append(title)\\n return city_state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a3dc45bc3e7dea174bf515c777b6507\",\n \"score\": \"0.5573651\",\n \"text\": \"def scrapper():\\n responses = requests.get(HTML)\\n soup = BeautifulSoup(responses.text, 'html.parser')\\n\\n items = {}\\n\\n item_htmls = soup.find_all(\\\"a\\\", href=True)\\n\\n for item in item_htmls:\\n link = HOMEPAGE + item['href']\\n\\n if item.find_all(\\\"h3\\\", \\\"product-item-name\\\"):\\n tag = item.find_all(\\\"h3\\\", \\\"product-item-name\\\")[0]\\n items[tag.contents[0]] = link\\n\\n return items\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d739837bb1c97806ac4e8a6205399b6\",\n \"score\": \"0.5566101\",\n \"text\": \"def getlinks(phonename):\\n\\n links = []\\n site = \\\"https://www.jumia.co.ke/smartphones/{}/?viewType=list\\\".format(phonename)\\n links.append(site) # first link\\n hdr = {'User-Agent': 'Mozilla/5.0'}\\n req = Request(site, headers=hdr)\\n page = urlopen(req)\\n soup = BeautifulSoup(page, \\\"html.parser\\\")\\n if soup.find(\\\"div\\\", {\\\"class\\\": \\\"pg-w -pvxl\\\"}): # if there are other pages\\n tag_element = soup.find(\\\"div\\\", {\\\"class\\\": \\\"pg-w -pvxl\\\"})\\n for lnk in tag_element.findAll(\\\"a\\\", {\\\"href\\\": re.compile(\\\"/smartphones/{}*\\\".format(phonename))}):\\n links.append(\\\"https://www.jumia.co.ke\\\" + lnk[\\\"href\\\"])\\n\\n return links\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad640e7c464a7fb5999a39637a3c2e15\",\n \"score\": \"0.55654657\",\n \"text\": \"def parse_mirrors(self):\\n\\n html_content = request.urlopen(self._url)\\n\\n soup = BeautifulSoup(html_content, 'html.parser')\\n\\n list_of_objects = soup.find_all('td')\\n\\n list_of_objects = [html_object for html_object in list_of_objects if \\\"http\\\" in str(html_object) and\\n 'a href' not in str(html_object)]\\n\\n self._list_of_mirrors = [str(html_object).split('/')[2] for html_object in list_of_objects]\\n logger.debug(f'The mirrors we parsed are:\\\\n{self._list_of_mirrors}')\\n\\n return self._list_of_mirrors\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab93348ad915e50fba2fb8ddf9985f2b\",\n \"score\": \"0.55590075\",\n \"text\": \"def get_names(url):\\n soup = make_soup(url)\\n year_re = re.compile('\\\\d{4}')\\n if ('steering' in url and 'sigsac' not in url):\\n body = soup.find_all('tr')\\n for i in body:\\n info = ' '.join(i.text.split())\\n name = ' '.join(info.split()[:2])\\n org = ' '.join(info.split()[2:-1])\\n org = re.sub(r'\\\\(.*\\\\)', '', org).lstrip()\\n if (org[-1] == ','):\\n org = org[:-1]\\n location = info.split()[-1]\\n print(name + ' (' + org + ') ' + location + '\\\\n')\\n return None\\n \\n if ('shadowpc' in url):\\n brs = soup.find_all('p', class_='', style='')\\n for br in brs:\\n info = ' '.join(str(br).split())\\n if ('<br/>' in info):\\n info = ' '.join(info.split())\\n tag = re.sub('<br/> ', '\\\\n\\\\n', info).lstrip()\\n tag = re.sub(r'<[^>]*>', '', tag).lstrip()\\n print(tag)\\n return None\\n \\n if ('awards' in url and 'sigsac' not in url):\\n brs = soup.find_all('p', class_='', style='')\\n for br in brs:\\n info = ' '.join(str(br).split())\\n if (re.findall('</strong>.*</p>', info) != []):\\n tag = re.findall('</strong>.*</p>', info)[0]\\n tag = re.sub(r'<[^>]*>', '', tag).lstrip()\\n tag = re.sub(r'(,\\\\s)*(\\\\(*)\\\\d{4}(\\\\)*)', '', tag).lstrip()\\n tag = re.sub(r'\\\\),\\\\s', ')), ', tag).lstrip()\\n tag = ' '.join(tag.split())\\n info = tag.replace(', and ', ', ')\\n info = info.replace(' and ', ', ')\\n if (') , ' in info):\\n info = info.replace(') , ', '), ')\\n info = info.split('), ')\\n for i in info:\\n if (i[-1] != ')' and '(' in i):\\n i += ')'\\n if ('(' in i):\\n i = i[:i.find('(')].replace(', ', i[i.find('(')-1:] + '\\\\n\\\\n')\\\\\\n + i[i.find('('):]\\n else:\\n i = i.replace(', ', '\\\\n\\\\n')\\n print(i + '\\\\n')\\n return None\\n \\n if ('sigsac' in url):\\n r_pa_ar = '\\\\[[Paper, Artifact]*\\\\]'\\n ps = soup.find_all('p', class_='', style='')\\n if (len(ps) < 2 or '2017/posters' in url):\\n ps = soup.find_all('li', class_='', style='')\\n if (len(ps) < 2 or 'agenda' in url):\\n ps = soup.find_all('div', id='authorshort')\\n if (len(ps) < 2):\\n ps = soup.find_all('td')\\n for p in ps:\\n if ('strong' in str(p)):\\n ps.remove(p)\\n if ('CCS2018/program/' in url):\\n ps = soup.find_all('i', class_='', style='')\\n for p in ps:\\n if ('<b>' in str(p)):\\n ps[ps.index(p)] = ''\\n if (len(ps) < 2):\\n ps = soup.find_all('div', class_='pcmember')\\n if (len(ps) < 2):\\n ps = soup.find_all('div', class_='', style='', id='')\\n if ('CCS2017/awards' in url):\\n ps = soup.find_all('b', class_='', style='')\\n if ('2016/program-committee' in url):\\n ps += soup.find_all('li', class_='', style='')\\n if ('2016/awards' in url or '2016/accepted-papers' in url):\\n ps = soup.find_all('em', class_='', style='')\\n if ('2016/agenda' in url):\\n ps = soup.find_all('span', class_='authors')\\n count = 0\\n for p in ps:\\n if (p != ''):\\n info = p.text\\n else:\\n info = ''\\n name_list = []\\n if ('2017/accepted-posters-demo' in url):\\n r = '<br\\\\/>[\\\\w, \\\\s, \\\\(, \\\\), \\\\-]*<br\\\\/>'\\n p = ' '.join(str(p).split())\\n db = re.findall(r, p)\\n for i in db:\\n i = re.sub(r'<[^>]*>', '', i).lstrip()\\n # print(i)\\n name_list.append(i)\\n info = 'None'\\n for i in name_list:\\n new_i = i.replace(', and ', ', ')\\n new_i = new_i.replace(' and ', ', ')\\n name_list[name_list.index(i)] = new_i\\n for i in name_list:\\n if ('), ' in i):\\n new_i = i.replace('), ', ')), ')\\n sub_list = new_i.split('), ')\\n for sub in sub_list:\\n sub_list[sub_list.index(sub)] = '\\\\n\\\\n' + \\\\\\n sub[:sub.find('(')].replace(', ', \\\\\\n sub[sub.find('(')-1:] + '\\\\n\\\\n') + \\\\\\n sub[sub.find('('):]\\n name_list[name_list.index(i)] =''.join(sub_list)\\n else:\\n name_list[name_list.index(i)] = i[:i.find('(')].\\\\\\n replace(', ', i[i.find('(')-1:] + '\\\\n\\\\n') + \\\\\\n i[i.find('('):]\\n \\n if ('CCS2018/cfposters/' in url):\\n info = str(p)\\n info = info.replace('<br/>', ')\\\\n')\\n info = re.sub(r'<[^>]*>', '', info).lstrip()\\n info = info.replace(', ', ' (')\\n if (info[-1] != ')'):\\n info += ')'\\n if ('accepted/papers/' in url or '2016/awards' in url):\\n if(count % 2 == 0):\\n info = ''\\n count += 1\\n if ('2016/awards' in url or '2016/accepted-papers' in url or '2016/agenda' in url):\\n if ('(Royal Holloway, University of London' in info):\\n info += ')'\\n info = info.replace(' and ', ', ')\\n info = info.replace(') and ', '), ')\\n info = info.replace(') ', '), ')\\n info = info.replace(' (', ' (')\\n info = info.replace('; ', ' (), ')\\n info = info.replace(',\\\\xa0', ',')\\n info = info.replace('\\\\xa0', '')\\n info = info.replace(' ,', ', ')\\n r = '\\\\([^\\\\)]*\\\\)'\\n if (re.findall(r, info) == [] and info != '' and info != ' '):\\n info += ' ()'\\n if (';' in info):\\n name_list = info.split(';')\\n if ('),' in info):\\n info = info.replace('),', ')),')\\n name_list = info.split('),')\\n if (':' in info):\\n info = info[info.find(':')+2:]\\n if ('2016/steering-committee' in url):\\n info = info.replace('\\\\n', ', ')\\n info = re.sub(r'\\\\([^\\\\)]*\\\\)', '', info).lstrip()\\n r = '\\\\([^\\\\)]*\\\\)'\\n if (',' in info and re.findall(r, info) == []):\\n if ('CCS2017/awards' in url):\\n info = info.replace(', ', '\\\\n\\\\n')\\n else:\\n if ('2016/organizing-committee' in url or '2016/program-committee' in url):\\n tem_list = info.split('\\\\n')\\n for i in tem_list:\\n if ('' != i and ',' in i):\\n name = i[:i.find(',')].strip()\\n org = i[i.find(',')+2:i.rfind(',')].strip()\\n loc = i[i.rfind(','):].strip()\\n tem_info = name + ' (' + org + ') ' + loc + '\\\\n'\\n info = info.replace(info[info.find(i):info.find(i)+len(i)], tem_info)\\n else:\\n info = info.split(',')\\n name = info[0].strip()\\n org = info[1].strip()\\n loc = ' '.join(info[2:]).strip()\\n info = name + ' (' + org + ') ' + loc\\n if (',' in info and re.findall(r, info) != []):\\n if (';' in info):\\n name_list = info.split(';')\\n for i in name_list:\\n if (', and ' in i):\\n new_i = i.replace(', and ', ', ')\\n new_i = i.strip()\\n name_list[name_list.index(i)] = new_i\\n i = new_i\\n if (',' in i):\\n name_list[name_list.index(i)] = \\\\\\n i[:i.find('(')].replace(', ', i[i.find('(')-1:] \\\\\\n + '\\\\n\\\\n') + i[i.find('('):]\\n if (', and ' in info):\\n info = info.replace(', and ', ', ')\\n info = info[:info.find('(')].replace(', ', \\\\\\n info[info.find('(')-1:] + '\\\\n\\\\n') + info[info.find('('):]\\n \\n if (re.findall(year_re, info) != [] or 'poster' in info or 'Agenda' in info\\\\\\n or 'Keynotes' in info or 'Accepted' in info or 'workshops' in url or \\\\\\n 'Cornell Tech' in info or 'CCS2018/papers/' in url or \\\\\\n 'Workshops' in info or 'Posters' in info or 'Awards' in url \\\\\\n or 'Award' in info or 'Contact' in info or '@acm_ccs' in info \\\\\\n or 'cookies' in info or 'Close' in info or 'Sandboxed' in info):\\n info = ''\\n if (info == ''):\\n None\\n elif (name_list == []):\\n info = info.strip()\\n if (re.findall(r_pa_ar, info) != []):\\n for i in re.findall(r_pa_ar, info):\\n info = info.replace(i, '')\\n if (info == ''):\\n None\\n elif ('\\\\n' in info):\\n if (info[-1:] != '\\\\n'):\\n print(info + '\\\\n')\\n else:\\n print(info)\\n else:\\n print(info + '\\\\n')\\n else:\\n for name in name_list:\\n name = name.strip()\\n if (name != '' and name != '\\\\n'):\\n if (re.findall(r_pa_ar, name) != []):\\n for i in re.findall(r_pa_ar, name):\\n name = name.replace(i, '')\\n if ('(' in name and ',' in name[:name.find('(')]):\\n name = name[:name.find('(')].replace(', ', \\\\\\n name[name.find('(')-1:] + '\\\\n\\\\n') + name[name.find('('):]\\n print(name + '\\\\n')\\n return None\\n\\n if ('accepted' in url or 'program-papers' in url or 'program' == \\\\\\n url[url.rfind('/')+1:url.rfind('.')] or 'program-posters' in url or 'program-shorttalks' in url):\\n na = soup.find_all('p', class_='', style='')\\n if ('program' not in url):\\n for i in na:\\n if (url[-3:] == 'php'):\\n info = i.text.lstrip()\\n info = info[:info.find(', ')]\\n print(info + '\\\\n')\\n else:\\n info = i.text\\n info = info.replace(', and ', ', ')\\n info = info.replace(' and ', ', ')\\n info = info.split('); ')\\n for i in info:\\n if (i[-1] != ')'):\\n i += ')'\\n i = i[:i.find('(')].replace(', ', i[i.find('(')-1:] + '\\\\n\\\\n')\\\\\\n + i[i.find('('):]\\n print(i + '\\\\n')\\n \\n if (url[-3:] == 'php' or 'program-papers' in url or 'program.html' in url\\\\\\n or 'program-posters' in url or 'program-shorttalks' in url):\\n # print(\\\"===================================\\\")\\n if ('program-posters' in url or 'program-shorttalks' in url):\\n brs = soup.find_all('p', class_='', style='')\\n if (brs == []):\\n brs = soup.find_all('div', class_='list-group-item')\\n for br in brs:\\n info = ' '.join(str(br).split())\\n try:\\n tag = re.findall('<br/>.*</p>', info)[0]\\n except:\\n tag = re.findall('<br/>.*</div>', info)[0]\\n tag = re.sub(r'<[^>]*>', '', tag).lstrip()\\n tag = ' '.join(tag.split())\\n info = tag.replace(', and ', ', ')\\n info = info.replace(' and ', ', ')\\n if ('); ' in info):\\n info = info.split('); ')\\n elif ('), ' in info):\\n info = info.split('), ')\\n else:\\n info = info.split('; ')\\n if ('SP2016/program-posters' in url):\\n i = 0\\n while i < len(info):\\n info[i] += ')'\\n info[i] = info[i][:info[i].find(',')] + info[i].replace(info[i][:info[i].find(', ')+2], ' (')\\n i+=1\\n for i in info:\\n if(i[-1] == '.'):\\n i = i[:-1]\\n if (i[-1] != ')' and '(' in i):\\n i += ')'\\n i = i[:i.find('(')].replace(', ', i[i.find('(')-1:] + '\\\\n\\\\n')\\\\\\n + i[i.find('('):]\\n print(i + '\\\\n')\\n else:\\n brs = soup.find_all('div', class_='list-group-item')\\n for br in brs:\\n info = ' '.join(str(br).split())\\n if (re.findall('<br/>.*</div>', info) != []):\\n tag = re.findall('<br/>.*</div>', info)[0]\\n if (re.findall(r'(\\\\s*)\\\\w*\\\\:', tag)):\\n tag = re.sub(r'<[^>]*>(\\\\s*)\\\\w*\\\\:', ' ', tag).lstrip()\\n tag = tag.replace(', ', ' (')\\n tag = tag.replace('<br/>', '), ')\\n tag = re.sub(r'<[^>]*>', '', tag).lstrip()\\n tag = ' '.join(tag.split())\\n r = '\\\\([\\\\w,\\\\s,&]+(\\\\([^\\\\)]+\\\\),)[\\\\w,\\\\s]+\\\\)'\\n if (re.findall(r, tag) != []):\\n tem = re.findall(r, tag)\\n for t in tem:\\n index = tag.find(t)\\n tag = tag.replace(t, t[:-1])\\n r = '\\\\([\\\\w,\\\\s,&]+(\\\\([^\\\\)]+\\\\)\\\\sand\\\\s)[\\\\w,\\\\s,\\\\(,\\\\)]+\\\\)'\\n if (re.findall(r, tag) != []):\\n tem = re.findall(r, tag)\\n for t in tem:\\n tag = tag.replace(t, t.replace('and', '&'))\\n if ('2019' not in url):\\n if ('),' not in tag and '(' not in tag):\\n info = ''\\n else:\\n if ('), and ' in tag):\\n tag = tag.replace('), and ', '), ')\\n if (') and ' in tag):\\n tag = tag.replace(') and ', '),')\\n if ('), ' in tag):\\n info = tag.split('), ')\\n if (') , ' in tag):\\n info = tag.split(') , ')\\n if ('); ' in tag):\\n info = tag.split('); ')\\n else:\\n info = tag.split('),')\\n else:\\n if ('); ' in tag):\\n info = tag.split('); ')\\n else:\\n info = tag.split('), ')\\n for i in info:\\n i = i.strip()\\n if (i != ''):\\n if (i[-1] == ','):\\n i = i[:-1]\\n if (i[-1] != ')' and '(' in i):\\n i += ')'\\n if (', and' in i):\\n i = i.replace(', and', ',')\\n i = i.replace(' and ', ', ')\\n r = '\\\\([\\\\w,\\\\s,&]+(\\\\([^\\\\)]+\\\\)\\\\s&\\\\s)[\\\\w,\\\\s,\\\\(,\\\\)]+\\\\)'\\n if (i.count('(') > 1 and re.findall(r, tag) == []):\\n i = i.replace(i[i.find('('):i.find(')')+2], '')\\n i = i[:i.find('(')].replace(', ', i[i.find('(')-1:] + '\\\\n\\\\n') + i[i.find('('):]\\n if ('limited' in i or 'reproducibility' in i):\\n None\\n else:\\n print(i + '\\\\n')\\n # print('+++++++++++++++++++++++++++++++++++++++++')\\n return None\\n \\n if ('2016/committee-organizing' in url):\\n body = soup.select('tr')\\n if (body == []):\\n body = soup.find_all('div', class_='list-group-item')\\n ct=0\\n count=0\\n for b in body:\\n if ('Members' in b.text):\\n ct=1\\n elif (ct == 0):\\n if (b.text != '\\\\n'):\\n if ('2016/committee-organizing' in url):\\n info = b.text.strip()\\n info = info.split('\\\\n')\\n info = info[1:]\\n if (len(info) != 2):\\n info = ' '.join(info)\\n info = info.replace('C', '; C')\\n info = info.split('; ')\\n name = info[0].strip()\\n org = info[1].strip()\\n if (org.count(',') > 1):\\n org = org.split(',')\\n loca = org.pop().strip()\\n org = ','.join(org).strip()\\n print(name + ' (' + org + ') ' + loca + '\\\\n')\\n else:\\n print(name + ' (' + org + ')' + '\\\\n')\\n else:\\n info = re.sub(r'.*Chairs\\\\s', '', b.text)\\n info = info.strip()\\n info = info.split('\\\\n')\\n name = info[0]\\n org = info[1].strip()\\n print(name + ' (' + org + ')' + '\\\\n')\\n else:\\n info = ' '.join(b.text.split())\\n if (info == ''):\\n count=0\\n elif (count%2 == 0):\\n print(info, end=' (')\\n count+=1\\n else:\\n if ('(' in info):\\n loca = info[info.find('(')+1:info.find(')')]\\n print(info[:info.find('(')-1] + ') ' + loca + '\\\\n')\\n else:\\n print(info + ')\\\\n')\\n count=0\\n return None\\n\\n body = soup.select('td')\\n count=0\\n uni_re = re.compile('[Uu]niversity')\\n for i in body:\\n info = ' '.join(i.text.split())\\n if ('Chairs' in info or 'Members' in info or 'Chair' in info or 'Organizers' in info\\\\\\n or 'Treasurer' in info or 'TBD' in info or 'Admin' in info):\\n count=0\\n elif (info == ''):\\n count=0\\n elif (':' in info or ((re.findall(year_re, info) != []) and \\\\\\n (re.findall(uni_re, info) == []))or 'deadline' in info \\\\\\n or 'notification' in info or 'Poster' in info or 'registration' in info \\\\\\n or 'period' in info or 'abstracts' in info or 'Rebuttal' in info \\\\\\n or 'papers' in info or 'Papers' in info or 'Conference' in info \\\\\\n or 'Notification' in info or len(i) != 1 or 'poster' in info \\\\\\n or 'Submissions' in info or 'Student' in info or \\\\\\n 'Registration' in info or 'ready' in info or \\\\\\n 'Short' in info or 'Events' in info or \\\\\\n 'resources' in info):\\n None\\n elif ('Workshop' in info or 'events' in info):\\n break\\n elif (count%2 == 0):\\n print(info, end=' (')\\n count+=1\\n else:\\n if (',' in info):\\n info = info.replace(',', ')')\\n if (info.count(')') > 1):\\n print(info.replace(')', ',', info.count(')') - 1) + '\\\\n')\\n else:\\n print(info + '\\\\n')\\n else:\\n print(info + ')\\\\n')\\n count=0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3b3202f957e5a306cbf5683b2743dbd\",\n \"score\": \"0.5551706\",\n \"text\": \"def oxford_dictionary_of_education_scrape_URL(URL:str):\\r\\n anchor_tag_list = []\\r\\n\\r\\n print(f\\\"\\\\nFetching the URL: {URL} ------------------\\\")\\r\\n browser.get(URL)\\r\\n\\r\\n # Wait until the browser loads the URL properly\\r\\n print(\\\"\\\\nSleeping for 15 seconds ------------------\\\")\\r\\n time.sleep(15)\\r\\n \\r\\n Current_Page_Content = browser.page_source.encode('utf-8').strip()\\r\\n \\r\\n # Parsing the html page\\r\\n soup = BeautifulSoup(Current_Page_Content, 'html.parser')\\r\\n # print(soup.prettify())\\r\\n\\r\\n # Finding all h1 tags with class title\\r\\n all_h1_with_class_title = soup.find_all('h2' , {\\\"class\\\": \\\"itemTitle\\\"})\\r\\n\\r\\n for h1 in all_h1_with_class_title:\\r\\n # For each h1 tag get the content (i.e. text) of the anchor tag inside\\r\\n # anchor_tag = h1.find('a').contents[0]\\r\\n anchor_tag = h1.find('a').get_text()\\r\\n # Append the content to anchor_tag_list\\r\\n anchor_tag_list.append(anchor_tag)\\r\\n\\r\\n print(f\\\"\\\\nFinished parsing and storing Dictionary of education keywords for: {URL}--------------\\\")\\r\\n return anchor_tag_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55a05a97a2131ac705cd64a8805f9449\",\n \"score\": \"0.5550126\",\n \"text\": \"def scrape_url(self, url):\\n rec_ingredients = []\\n print(\\\"Now scraping: \\\" + url)\\n source = requests.get(url).text\\n soup = BeautifulSoup(source, 'html.parser')\\n ingredients = soup.findAll(\\\"li\\\", {\\\"class\\\": \\\"checkList__line\\\"})\\n recipe_name_tag = soup.findAll(\\\"h1\\\", {\\\"id\\\": \\\"recipe-main-content\\\"})\\n if recipe_name_tag != []:\\n print(recipe_name_tag)\\n print(\\\"************************* \\\" + str(self.rec_counter) + \\\" ************************\\\")\\n print()\\n recipe_name = recipe_name_tag[0].text\\n else:\\n return None, None\\n for item in ingredients:\\n if hasattr(item.label, \\\"title\\\"):\\n rec_ingredients.append(item.label.get('title'))\\n return recipe_name, rec_ingredients\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5ba081db48b26e3e6ac2b2992c3ebda\",\n \"score\": \"0.55467707\",\n \"text\": \"def brewsoup(url):\\r\\n try:\\r\\n res = requests.get(url)\\r\\n res.raise_for_status()\\r\\n except requests.exceptions.HTTPError as error:\\r\\n print(error)\\r\\n return None\\r\\n return bs4.BeautifulSoup(res.text, \\\"html.parser\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e0ec923d46601b597470a465fefb36a\",\n \"score\": \"0.55443746\",\n \"text\": \"def get_soup(url):\\n page = requests.get(url)\\n jsonResponse = page.json()\\n html = jsonResponse['html']\\n soup = BeautifulSoup(html, 'html.parser')\\n return(soup)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e39c49ff75fdf6d94afa1f289aaa2d8d\",\n \"score\": \"0.5537907\",\n \"text\": \"def scrape(url):\\n\\n # Make soup.\\n print 'Opening URL %s' % str(url)\\n f = urllib2.urlopen(url)\\n soup = BeautifulSoup(f, 'html.parser')\\n \\n # Get key tags.\\n org = soup.find('div', id='adorg').get_text(strip=True).replace('\\\\n',' ')\\n title, desc = soup.find('div', id='adtitle').get_text(strip=True).replace('\\\\n',' ').split('+')\\n ov = soup.find('div', id='overview')\\n\\n # Navigate and extract content.\\n content = []\\n for tag in ov.children:\\n if tag.name == 'p':\\n content.append(tag.get_text(strip=True).replace('\\\\n',' '))\\n elif tag.name == 'br':\\n for tag2 in tag.children:\\n if tag2.name == 'p':\\n content.append(tag2.get_text(strip=True).replace('\\\\n',' '))\\n\\n org = org.encode('utf-8')\\n title = title.encode('utf-8')\\n desc = desc.encode('utf-8')\\n content = [c.encode('utf-8') for c in content]\\n\\n return (org, title, desc, content)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"527fc6e6b7cd5eb33917e8d83cd27684\",\n \"score\": \"0.55357337\",\n \"text\": \"def getMakes():\\r\\n \\r\\n url = 'http://www.carcomplaints.com/'\\r\\n html = request.urlopen(url)\\r\\n \\r\\n soup = BeautifulSoup(html)\\r\\n sections = soup.find_all('section', id=re.compile('makes'))\\r\\n \\r\\n make_list = []\\r\\n for section in range(len(sections)):\\r\\n for li in sections[section].find_all('li'):\\r\\n make_list.append(li.get_text())\\r\\n \\r\\n return make_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15195f7146f6fd830578d57f3cec99d9\",\n \"score\": \"0.5534482\",\n \"text\": \"def urlparse(url):\\n request = urllib2.Request(url)\\n try:\\n response = urllib2.urlopen(request)\\n except URLError as e:\\n if hasattr(e,'readon'):\\n print 'We failed to reach a server.'\\n print 'Reason:',e.reason\\n elif hasattr(e,'code'):\\n print 'The server couldn\\\\'t fullfill the request.'\\n print 'Error code:', e.code\\n soup = BeautifulSoup(response, 'html.parser')\\n organized_html = soup.prettify()\\n return organized_html\",\n \"title\": \"\"\n },\n {\n \"docid\": \"218ffc44ff0e03ac064900afad18c6a3\",\n \"score\": \"0.553392\",\n \"text\": \"def scrape(self):\\n self.get_soup()\\n self.get_title_by_soup()\\n self.get_price_by_soup()\\n self.get_availability_by_soup()\\n self.get_ratings_by_soup()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b526458140857b59afb23f8129762c99\",\n \"score\": \"0.55284685\",\n \"text\": \"def get_link(soup):\\n links = []\\n re_nav = re.compile('^nav')\\n if (soup.find_all(re_nav) != []):\\n # Find all navagtion links in the webpage\\n for nav in soup.find_all(re_nav):\\n a = nav.find_all('a') #Find all 'a' tag\\n for link in a:\\n if (link not in links):\\n links.append(link)\\n else:\\n for nav in soup.find_all(class_=re_nav):\\n a = nav.find_all('a') #Find all 'a' tag\\n for link in a:\\n if (link not in links):\\n links.append(link)\\n \\n return links\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9dc9ebc112dc2bbe5bb759a051b4b52d\",\n \"score\": \"0.5528023\",\n \"text\": \"def parse(self, response):\\n page = self._get_item(response)\\n r = [page]\\n if self.follow_links:\\n r.extend(self._extract_requests(response))\\n return r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e87dc031386598c08d1286a81086906\",\n \"score\": \"0.55184346\",\n \"text\": \"def get_constant_soup(url):\\n with open(TEST_FILE_NORMAL_LISTING, encoding='utf-8') as f:\\n soup = BeautifulSoup(f.read())\\n\\n return soup\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7af2405f3e45580012dc272d43c951f6\",\n \"score\": \"0.55175906\",\n \"text\": \"def get_links_from_site_reader(browser, url):\\n\\n browser.get(url)\\n # can't grab links instantly\\n time.sleep(3)\\n inner_html = browser.page_source\\n soup = BeautifulSoup(inner_html, \\\"html5lib\\\")\\n # finding elements with links\\n elements = soup.find_all('a', {'class': 'ms-listlink ms-draggable'})\\n # add pre to make links absolute\\n pre = \\\"https://reader.uni-mainz.de\\\"\\n links = [pre + element.get(\\\"href\\\") for element in elements]\\n filenames = [link.split('/')[-1].lower().replace(' ', '') for link in links]\\n return links, filenames\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3360ae98bda6d9c2f61c967dca061a77\",\n \"score\": \"0.55173326\",\n \"text\": \"def parsePage(html, infoList):\\n try:\\n price = re.findall(r'\\\"view_price\\\":\\\"(.*?)\\\"', html)\\n num = len(price)\\n title = re.findall(r'\\\"raw_title\\\":\\\"(.*?)\\\"', html)\\n sales = re.findall(r'\\\"view_sales\\\":\\\"(\\\\d+)人付款\\\"', html)\\n taobaoID = re.findall(r'\\\"nick\\\":\\\"(.*?)\\\"', html)[:num]\\n location = re.findall(r'\\\"item_loc\\\":\\\"(.*?)\\\"', html)\\n for i in range(num):\\n infoList.append([title[i], price[i], sales[i], taobaoID[i], location[i]])\\n return infoList\\n except:\\n return ''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db4442c291dbd2941652d868af0d7a37\",\n \"score\": \"0.5516796\",\n \"text\": \"def parse_results(raw_html):\\n soup = BeautifulSoup(raw_html, 'html.parser')\\n result_block = soup.find_all('div', attrs={'class': 'g'})\\n for result in result_block:\\n link = result.find('a', href=True)\\n title = result.find('h3')\\n if link and title:\\n yield link['href']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7fab8ce4adb1d90374795fe781350a3\",\n \"score\": \"0.55156064\",\n \"text\": \"def parser_one_page(url: str):\\n html_doc = faker.faked_get(url)\\n soup = BeautifulSoup(html_doc, \\\"html.parser\\\")\\n poem_list = soup.find_all('strong')\\n for li in poem_list:\\n href = li.find('a')['href']\\n poem_id = reg_int(href)\\n parser_one_poem(poem_id)\\n time.sleep(0.2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3b9ed36507f6dc04d138f05cc438b74\",\n \"score\": \"0.55097544\",\n \"text\": \"def parse_hotellist_page(html):\\n h_list = [] \\n page_info = []\\n soup = BeautifulSoup(html)\\n # Extract hotel name, star rating and number of reviews\\n hotel_boxes = soup.findAll('div', {'class' :'listing wrap reasoning_v5_wrap jfy_listing p13n_imperfect'})\\n if not hotel_boxes:\\n hotel_boxes = soup.findAll('div', {'class' :'listing_info jfy'})\\n if not hotel_boxes:\\n hotel_boxes = soup.findAll('div', {'class' :'listing easyClear p13n_imperfect'})\\n\\n for hotel_box in hotel_boxes:\\n #print info for each hotel\\n h = hotel_box.find(\\\"a\\\", {\\\"target\\\" : \\\"_blank\\\"})\\n hotel_url = base_url + h['href']\\n time.sleep(2)\\n heads = { 'User-Agent' : user_agent }\\n resp = requests.get(hotel_url, headers=heads)\\n hml = resp.text.encode('utf-8')\\n soup2 = BeautifulSoup(hml) #get the soup for the hotel's page so we can scrape the info\\n rating = soup2.findAll('span', {'class' : 'compositeCount'})\\n hotel_name = hotel_box.find(\\\"a\\\", {\\\"target\\\" : \\\"_blank\\\"}).find(text=True)\\n h_list.append(str(hotel_name.strip()))\\n #traveler rating\\n ratings = []\\n for r in rating:\\n ratings.append(r.find(text = True))\\n\\n\\n #reviews\\n review = soup2.findAll('div', {'class' : 'value'})\\n reviews = []\\n for r in review:\\n reviews.append(r.find(text = True))\\n\\n #review summary \\n sp = soup2.findAll('span', {'class' : 'rate sprite-rating_s rating_s'})\\n summaries = [] \\n count = 1 \\n for item in sp:\\n s = item.find('img', alt = True)\\n rate = s['alt'].strip()[0:3]\\n if rate[-1] == 'o':\\n summaries.append(rate[0:1])\\n else:\\n summaries.append(rate) \\n\\n total = float(ratings[4].replace(',', '')) + float(ratings[3].replace(',', '')) + float(ratings[2].replace(',', '')) + float(ratings[1].replace(',', '')) + float(ratings[0].replace(',', ''))\\n avg = 1*float(ratings[4].replace(',', '')) + 2*float(ratings[3].replace(',', '')) + 3*float(ratings[2].replace(',', '')) + 4*float(ratings[1].replace(',', '')) + 5*float(ratings[0].replace(',', ''))\\n avg = avg/total \\n\\n m = MyStruct(float(ratings[0].replace(',', '')), float(ratings[1].replace(',', '')), float(ratings[2].replace(',', '')), float(ratings[3].replace(',', '')), \\n float(ratings[4].replace(',', '')), float(reviews[0].replace(',', '')), \\n float(reviews[1].replace(',', '')), float(reviews[2].replace(',', '')), float(reviews[3].replace(',', '')), float(summaries[0]), \\n float(summaries[1]),float(summaries[2]), float(summaries[3]), float(summaries[4]), float(summaries[5]), avg)\\n\\n page_info.append(m)\\n # Get next URL page if exists, otherwise exit\\n div = soup.find(\\\"div\\\", {\\\"class\\\" : \\\"pgLinks\\\"})\\n # check if this is the last page\\n if div.find('span', {'class' : 'guiArw pageEndNext'}):\\n #return the current info\\n return u\\\"\\\", h_list, page_info\\n # If not, return the url to the next page\\n hrefs = div.findAll('a', href= True)\\n for href in hrefs:\\n c = href['class'].encode('ascii', 'ignore')\\n if c == 'guiArw sprite-pageNext ':\\n return href['href'], h_list, page_info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90f280cd4f4fd3b492f80dec6242bf93\",\n \"score\": \"0.5507087\",\n \"text\": \"def get_collections(parsed_html, substring):\\n collection_hrefs = []\\n for link in parsed_html.find_all('a'):\\n if substring in str(link):\\n collection_hrefs.append(link.get('href'))\\n return collection_hrefs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f3704ae405035bb85c35c84cd00b7a5\",\n \"score\": \"0.55045646\",\n \"text\": \"def postprocess_soup(self, soup, tag, class_):\\n raw_base = soup.find_all(\\\"h3\\\", {\\\"class\\\": \\\"p-name\\\"})\\n raw = soup.find_all(tag, {\\\"class\\\": class_})\\n if len(raw) != len(raw_base):\\n # Ugly hack to fill missing amenities with NaNs\\n # Ensures that each field (list) are same length even if hotel doesn't have any amenities listed\\n # NOTE: Might need to do for other fields as well\\n if class_ == \\\"hmvt8258-amenities\\\":\\n raw_tmp = soup.find_all(\\\"div\\\", {\\\"class\\\": \\\"additional-details resp-module\\\"})\\n raw_list = []\\n for content in raw_tmp:\\n flag = False\\n for subcont in content:\\n if class_ in subcont:\\n flag = True\\n break\\n if flag:\\n raw_list.append(subcont)\\n else:\\n raw_list.append(None)\\n else:\\n raw_list = [content.text for content in raw]\\n return raw_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e572dfbcf16632244c11852b1f6b3299\",\n \"score\": \"0.5502867\",\n \"text\": \"def parse_query_page(text):\\n parsed_text = BeautifulSoup(text, 'html5lib')\\n posts = parsed_text.find(id='post-list-posts').find_all('li')\\n parsed_posts = []\\n for post in posts:\\n parsed_post = {}\\n\\n # Parse ID and detail url\\n detail_url = post.find(class_='plid').text[4:] # Remove (#pl )\\n id_parttern = re.compile(r'http://konachan.net/post/show/(?P<id>\\\\d+)')\\n id_ = id_parttern.search(detail_url).group('id')\\n parsed_post['link'] = detail_url\\n parsed_post['id'] = int(id_)\\n\\n # Parse score, tags and rating\\n title_parttern = re.compile(\\n r'Rating: (?P<rating>\\\\w+) Score: (?P<score>\\\\d+) '\\n 'Tags: (?P<tags>[a-zA-z0-9_ ]+) User:'\\n )\\n title = post.find('a', class_='thumb').find('img')['title']\\n matched_text = title_parttern.search(title)\\n parsed_post['score'] = int(matched_text.group('score'))\\n parsed_post['rating'] = matched_text.group('rating')\\n parsed_post['tags'] = [\\n tag.replace('_', ' ')\\n for tag in matched_text.group('tags').split(' ')\\n ]\\n parsed_posts.append(parsed_post)\\n\\n return parsed_posts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51e23a463022607cca500b0d08a051f3\",\n \"score\": \"0.55023044\",\n \"text\": \"def find_link(self):\\n subwebpages = []\\n\\n br_counter = 0\\n last_time = \\\"\\\"\\n try:\\n while True:\\n input_str = self.content.readline()\\n expression = self.reader(input_str) # rader() read a line each time.\\n for tag in expression:\\n result = self.eval(tag)\\n if isinstance(result, Webpage):\\n subwebpages += [result]\\n if input_str == last_time:\\n br_counter += 1\\n if input_str != last_time:\\n br_counter = 0\\n last_time = input_str\\n if br_counter == 10:\\n raise EOFError\\n except EOFError as _:\\n return subwebpages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6869949bb2744f975eb4ceb9ad6e0a56\",\n \"score\": \"0.55014145\",\n \"text\": \"def getSoup(url):\\n # Set headers \\n headers = requests.utils.default_headers()\\n \\n headers.update({ 'User-Agent' : 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_6) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/13.0.2 Safari/605.1.15'})\\n while True:\\n try:\\n req = requests.get(url, headers)\\n req.raise_for_status()\\n break\\n\\n except requests.exceptions.HTTPError as HTTPError:\\n if HTTPError.response.status_code == 403: \\n print('\\\\n\\\\nFORBIDDEN.\\\\n\\\\n')\\n exit()\\n\\n except requests.exceptions.ConnectionError:\\n print('\\\\n\\\\nCONNECTION ERROR. Retrying in 30 seconds.\\\\n\\\\n')\\n sleep(30)\\n \\n soup = BeautifulSoup(req.content, 'html.parser', parse_only=SoupStrainer('a'))\\n\\n timeout = randrange(7,10)\\n sleep(timeout)\\n\\n return soup\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc2516e04e636a0e4c7239acabfc1e55\",\n \"score\": \"0.5499542\",\n \"text\": \"def parse_live_url(url):\\n live_id_list = []\\n browser = webdriver.Chrome()\\n browser.get(url)\\n browser.maximize_window()\\n browser = scroll_down(browser, 20)\\n time.sleep(2)\\n a_elements = browser.find_elements_by_xpath(\\\"//ul[@class='pic-items js-list-ul']/li/a\\\")\\n if a_elements is not None and len(a_elements) > 0:\\n for a in a_elements:\\n a_href = a.get_attribute(\\\"href\\\")\\n live_id_list.append(a_href)\\n print(a_href)\\n else:\\n print(\\\"a_elements is None or empty!!!\\\")\\n browser.quit()\\n return live_id_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69e4f3ba4523d8acba1b6d4566ab10d7\",\n \"score\": \"0.54971004\",\n \"text\": \"def souper(site):\\n headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36'}\\n response = get(site, headers=headers)\\n \\n html = response.text\\n soup = BeautifulSoup(html)\\n \\n return soup\",\n \"title\": \"\"\n },\n {\n \"docid\": \"328c3a097849a448defcd439a0aa5a9c\",\n \"score\": \"0.54942805\",\n \"text\": \"def ini_list(self):\\n text = self.urls\\n darbi = []\\n for item in text:\\n if 'htm' in item:\\n darbi.append(item)\\n darbi = sorted(darbi)\\n\\n prefix_ = 'https:'\\n final_list = []\\n for x in darbi:\\n final_list.append(prefix_ + x)\\n self.urls = final_list\\n self.parser()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e531ad35d9832d29bd66afa1df4265a3\",\n \"score\": \"0.54941046\",\n \"text\": \"def scrapeData(self, url):\\n response = requests.get(url)\\n scraped = BeautifulSoup(response.text, \\\"html.parser\\\")\\n return scraped\",\n \"title\": \"\"\n },\n {\n \"docid\": \"885653a9c6c39667d36f35750d41125e\",\n \"score\": \"0.54940426\",\n \"text\": \"def parse_html(cls, html):\\n news_list = []\\n soup = BeautifulSoup(html, 'html.parser')\\n for a in soup.find_all(\\\"a\\\", class_=\\\"storylink\\\"):\\n url = a.attrs['href']\\n # self-hosted urls like \\\"item?id=20927552\\\"\\n if not 'http' in url:\\n url = cls.PARSE_URL + url\\n title = a.text\\n news_list.append({'url': url, 'title': title})\\n return news_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"daa9d6001b046b3ae1f3a33ae97d2526\",\n \"score\": \"0.54884267\",\n \"text\": \"def get_sources(self):\\n business_lst = []\\n i = 0\\n for url in self.url_list:\\n print(url)\\n self.browser.get(url)\\n\\n time.sleep(7)\\n\\n # Zoom in so that \\\"Search this area\\\" button will show up\\n self.browser.find_element_by_id('widget-zoom-in').click()\\n\\n time.sleep(3)\\n\\n # Must hit \\\"Search this area\\\", otherwise Google defaults to local locations\\n self.browser.find_element_by_class_name(\\n 'widget-search-this-area-inner').click()\\n\\n time.sleep(5)\\n\\n htmlstring = self.browser.page_source\\n soup = BeautifulSoup(htmlstring, \\\"html.parser\\\")\\n results = soup.find_all(class_='section-result-content')\\n\\n for result in results:\\n name = result.find(class_='section-result-title').text\\n service = result.find(class_='section-result-details').text\\n search_term = self.search_terms[i]\\n\\n # Default to the seach term if no service provided was found\\n if service == '':\\n service = self.search_terms[i]\\n\\n location = result.find(class_='section-result-location').text\\n phone = result.find(\\n class_='section-result-info section-result-phone-number').text\\n\\n # Get rid of phone numbers with the wrong extension. These may come from your ip address location!\\n if phone.strip()[0:len(self.phone_extension)] != self.phone_extension:\\n phone = ''\\n\\n business_lst.append(\\n {'name': name, 'search_term': search_term, 'service': service, 'location': location, 'phone': phone})\\n self.business_lst = business_lst\\n i += 1\\n return business_lst\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bad7ef5f24caeb637c0b06197fd6b5e\",\n \"score\": \"0.5468703\",\n \"text\": \"def read_html(list_of_adverts):\\n\\n\\n def find_title(advert):\\n \\\"\\\"\\\"\\n Find the title from the job advert\\n :param advert: the beatiful soup parsed version of an advert\\n :return: a job title\\n \\\"\\\"\\\"\\n try:\\n title = advert.find('h1').text\\n if len(title) == 0:\\n title = 'no_data'\\n except:\\n title = 'no_data'\\n pass\\n\\n if title != 'no_data':\\n title = re.sub(clean_lb, '', title)\\n title = title.lower()\\n\\n return title\\n\\n\\n def find_date(advert):\\n \\\"\\\"\\\"\\n Find the date on which the advert was placed\\n :param advert: the beautiful soup parsed version of an advert\\n :return: a date on which the advert was placed\\n \\\"\\\"\\\"\\n try:\\n date = advert.find('td', text='Placed on:').find_next_sibling('td').text\\n except:\\n date = 'no_data'\\n pass\\n\\n try:\\n try_date = advert.find('th', text='Placed On:').find_next_sibling('td').text\\n # Only replace the date if the previous date is no_data (i.e. don't overwrite\\n # a valid date from the last 'try'\\n if date == 'no_data':\\n date = try_date\\n except:\\n pass\\n\\n return date\\n\\n\\n def find_role(advert):\\n \\\"\\\"\\\"\\n Find the role (i.e. the job family) in the advert\\n :param advert: the beautiful soup parsed version of an advert\\n :return: the role from the advert\\n \\\"\\\"\\\"\\n try:\\n role = advert.find('p', text='Type / Role:').find_next_sibling('p').text\\n role = re.sub(clean_lb, '', role)\\n except:\\n role = 'no_data'\\n pass\\n\\n try:\\n try_role = advert.find('p', text='Type / Role:').find_next('a').text\\n # Only replace the role if the previous role is zero (i.e. don't overwrite\\n # a valid date from the last 'try'\\n if role == 'no_data':\\n role = try_role\\n except:\\n pass\\n\\n try:\\n try_role = \\\\\\n advert.find('b', text='Type / Role:').find_next('div', {'class': 'j-form-input ie-11-width'}).find_next(\\n 'input').attrs['value']\\n # Only replace the role if the previous role is zero (i.e. don't overwrite\\n # a valid date from the last 'try'\\n if role == 'no_data':\\n role = try_role\\n except:\\n pass\\n\\n try:\\n try_role = \\\\\\n advert.find('p', text='Type / Role:').find_next('div', {'class': 'j-form-input ie-11-width'}).find_next(\\n 'input').attrs['value']\\n # Only replace the role if the previous role is zero (i.e. don't overwrite\\n # a valid date from the last 'try'\\n if role == 'no_data':\\n role = try_role\\n except:\\n pass\\n\\n if role !='no_data':\\n role = re.sub(clean_lb, '', role)\\n role = role.lower()\\n\\n return role\\n\\n\\n def find_organisation(advert):\\n \\\"\\\"\\\"\\n Find the organisation (i.e. the university where the job is based) in the advert\\n :param advert: the beatiful soup parsed version of an advert\\n :return: the organisation from the advert\\n \\\"\\\"\\\"\\n try:\\n organisation = advert.find('h3').text.split('-',1)[0]\\n except:\\n organisation = 'no_data'\\n pass\\n\\n if organisation !='no_data':\\n organisation = re.sub(clean_lb, '', organisation)\\n organisation = organisation.lower()\\n\\n return organisation\\n\\n def find_location(advert):\\n \\\"\\\"\\\"\\n Find the location (i.e. the city where the job is based) in the advert\\n :param advert: the beatiful soup parsed version of an advert\\n :return: the location from the advert\\n \\\"\\\"\\\"\\n try:\\n location = advert.find('td', text='Location:').find_next_sibling('td').text\\n except:\\n location = 'no_data'\\n pass\\n\\n try:\\n try_location = advert.find('th', text='Location:').find_next_sibling('td').text\\n if location == 'no_data':\\n location = try_location\\n except:\\n pass\\n\\n if location !='no_data':\\n location = re.sub(clean_lb, '', location)\\n location = location.lower()\\n location = location.strip()\\n\\n return location\\n\\n\\n def find_salary(advert):\\n \\\"\\\"\\\"\\n Find the salary\\n :param advert: the beautiful soup parsed version of an advert\\n :return: a text field describing salary\\n \\\"\\\"\\\"\\n try:\\n salary = advert.find('th', text='Salary:').find_next_sibling('td').text\\n except:\\n salary = 'no_data'\\n pass\\n\\n # Remove carriage returns, tabs, multiple spaces and commas\\n salary_string = salary.replace('\\\\n', ' ').replace('\\\\t', ' ').replace(' ', '').replace(',', '')\\n # Find all the numbers in the salary string\\n processing_salaries = re.findall(r'\\\\d+', salary_string)\\n\\n # Remove all the small numbers which relate to grades or hourly pay\\n remove_list = []\\n for current_value in processing_salaries:\\n if int(current_value) < 10000:\\n remove_list.append(current_value)\\n\\n processing_salaries = [x for x in processing_salaries if x not in remove_list]\\n\\n try:\\n min_salary = min(processing_salaries)\\n except:\\n min_salary = ''\\n\\n try:\\n max_salary = max(processing_salaries)\\n except:\\n max_salary = ''\\n\\n # Add original string to salaries data for checking purposes\\n salaries = {\\\"salary_string\\\": salary_string, \\\"salary_min\\\": min_salary, \\\"salary_max\\\": max_salary}\\n\\n return salaries\\n\\n\\n big_data_list = []\\n\\n # Setting up annoying text remover\\n clean_lb = re.compile('\\\\n')\\n\\n # Set up a counter to print on screen and assure me that everything's working\\n sanity_counter=0\\n\\n # Go through all the ads and extract the data I need\\n for current_ad in list_of_adverts:\\n sanity_counter+=1\\n data = []\\n filename = os.path.basename(current_ad)\\n data.append(filename)\\n\\n # Check if the file is one of the job adverts (which have\\n # a set patern of filename\\n if re.match(r'\\\\w\\\\w\\\\w\\\\d\\\\d\\\\d', filename):\\n\\n with open(current_ad, \\\"r\\\") as f:\\n contents = f.read()\\n advert = BeautifulSoup(contents, 'lxml')\\n\\n #Extract info I want\\n title = find_title(advert)\\n date = find_date(advert)\\n\\n # Extract year directly from date variable (there's two forms of date, hence the if)\\n if '-' in date:\\n year = str(date)[:4]\\n else:\\n year = str(date)[-4:]\\n\\n role = find_role(advert)\\n organisation = find_organisation(advert)\\n location = find_location(advert)\\n\\n # Add the info to the data list\\n data.append(title)\\n data.append(date)\\n data.append(year)\\n data.append(role)\\n data.append(organisation)\\n data.append(location)\\n\\n # Add data to a list of lists which will later be transformed into a df\\n big_data_list.append(data)\\n\\n # Not drowning but waving output for my sanity\\n print('Processed ' + str(sanity_counter) + ' jobs', end='\\\\r')\\n\\n df = pd.DataFrame.from_records(big_data_list)\\n df.columns = ['filename', 'job title', 'date', 'year', 'role', 'organisation', 'location']\\n\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4992044e397f057400b4ea293ad8c84\",\n \"score\": \"0.5465568\",\n \"text\": \"def pull_news(num):\\n\\n headers = {\\n \\\"user-agent\\\": \\\"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_7_5)\\\",\\n \\\"accept\\\": \\\"text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8\\\",\\n \\\"accept-charset\\\": \\\"ISO-8859-1,utf-8;q=0.7,*;q=0.3\\\",\\n \\\"accept-encoding\\\": \\\"gzip,deflate,sdch\\\",\\n \\\"accept-language\\\": \\\"en-US,en;q=0.8\\\",\\n }\\n\\n # Thanks to Luca Pattavina for giving me the right url\\n if num <= 5:\\n news_url = [\\\"http://www.disim.univaq.it/main/news.php?entrant=1\\\"]\\n else:\\n news_url = [\\\"http://www.disim.univaq.it/main/news.php?entrant=1\\\",\\n \\\"http://www.disim.univaq.it/main/news.php?entrant=2\\\"]\\n\\n request = []\\n bs_list = []\\n news = []\\n for i, url in enumerate(news_url):\\n request.append(requests.get(url, headers=headers))\\n bs_list.append(BeautifulSoup(request[i].text, \\\"html.parser\\\") \\\\\\n .find_all(class_=\\\"post_item_list\\\"))\\n descr_list = BeautifulSoup(request[i].text, \\\"html.parser\\\") \\\\\\n .find_all(class_=\\\"post_description\\\")\\n\\n for j, single_news in enumerate(bs_list[i]):\\n news.append({\\n \\\"title\\\": single_news.h3.a.text,\\n \\\"description\\\": descr_list[j].get_text().replace(\\\"\\\\n\\\", \\\" \\\"),\\n \\\"link\\\": \\\"http://www.disim.univaq.it/main/\\\" + single_news.a.get('href')\\n })\\n\\n return news\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d188ab20273cb1be440e5c3ddd77044\",\n \"score\": \"0.54591626\",\n \"text\": \"def extract_next_links(self, url_data):\\n outputLinks = []\\n root = html.fromstring(url_data['content'])\\n new_Root = root.make_links_absolute(url_data['url'], resolve_base_href=True)\\n #print(type(new_Root))\\n\\n parsed_uri = urlparse(url_data['url'])\\n domain = '{uri.scheme}://{uri.netloc}'.format(uri=parsed_uri)\\n \\n \\n \\n tree = etree.ElementTree(root)\\n r = tree.getroot()\\n select = cssselect.CSSSelector(\\\"a\\\")\\n links = [element.get('href') for element in select(r)]\\n #currentPath = url_data['url'].strip('/')\\n for link in links:\\n if link is None:\\n pass\\n else:\\n outputLinks.append(link)\\n return outputLinks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddf8cd01ec0a0d6045869fb995481bd6\",\n \"score\": \"0.5457469\",\n \"text\": \"def scrape_and_soup(self, target_url):\\n\\n # get the url contents for processing\\n target_url_loc = urllib.urlopen( target_url )\\n target_url_data = target_url_loc.read()\\n target_url_loc.close()\\n\\n # prepare the soup that we will extract data from. \\n target_url_soup = BeautifulSoup( target_url_data, \\\"lxml\\\" )\\n\\n return target_url_soup\",\n \"title\": \"\"\n },\n {\n \"docid\": \"444ad57a56ca73bf51db1209fdd767d0\",\n \"score\": \"0.5452856\",\n \"text\": \"def webCrawl(url):\\r\\n try:\\r\\n html = urllib2.urlopen(url).read() # Connect to the job posting\\r\\n except:\\r\\n return \\\"\\\"\\r\\n\\r\\n\\r\\n soup = BeautifulSoup(html, \\\"html.parser\\\")\\r\\n\\r\\n # Reference for this step: https://jessesw.com/Data-Science-Skills/\\r\\n for script in soup([\\\"script\\\", \\\"style\\\"]):\\r\\n script.extract() # Remove these two elements from the BS4 object to get clean text\\r\\n content = soup.getText().lower()\\r\\n return content\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c52586f0cbf2b48096a1cb16df95686b\",\n \"score\": \"0.54520947\",\n \"text\": \"def decode_web_page() ->list:\\n req = requests.get(\\\"https://www.nytimes.com/\\\")\\n soup = BeautifulSoup(req.text,\\\"html.parser\\\")\\n alist = []\\n for item in soup.find_all(\\\"h2\\\",{\\\"class\\\":\\\"story-heading\\\"}):\\n alist.append(item.text.strip())\\n return alist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a7e5d5affe8ff42daa10b4783ea61db\",\n \"score\": \"0.5448262\",\n \"text\": \"def parse_listing(self, response):\\n ul_list = response.xpath('//*[@id=\\\"root\\\"]/article/div[3]/div[1]/section[1]/div/ul')[0]\\n # #root > article > div.css-o19mot-Te > div.css-18jo4jf-Te > section.section-overview > div > ul\\n\\n l = ItemLoader(item=HouseListing(), response=response)\\n l.add_value('listing_url', response.url)\\n\\n # #root > article > header > div.css-s3teq > div.css-13ywf6 > div.css-1vr19r7\\n # l.add_css('listing_type', '#root > article > header > div.css-s3teq > div.css-13ywf6 > div.css-1vr19r7 > small::text')\\n l.add_xpath('listing_type', '//*[@id=\\\"root\\\"]/article/section[1]/div[1]/ul/li[2]/a')\\n l.add_css('title', '#root > article > header > div.css-1jiadof > div > div > h1::text')\\n l.add_css('house_location', '#root > article > header > div.css-1jiadof > div > div > div > a::text')\\n l.add_css('house_cost', '#root > article > header > div.css-s3teq > div.css-13ywf6 > div.css-1vr19r7::text')\\n\\n # Order of listing features (Propriedades as the website calls it) is not guaranteed. Must check the label name.\\n l.add_value('effective_area', self.find_matching_field_value('area util', ul_list))\\n l.add_value('total_area', self.find_matching_field_value('area bruta', ul_list))\\n l.add_value('number_of_rooms', self.find_matching_field_value('tipologia', ul_list))\\n l.add_value('number_of_bathrooms', self.find_matching_field_value('casas de banho', ul_list))\\n l.add_value('energy_certificate', self.find_matching_field_value('certificado energetico', ul_list))\\n l.add_value('house_condition', self.find_matching_field_value('condicao', ul_list))\\n l.add_value('building_year', self.find_matching_field_value('ano de construcao', ul_list))\\n\\n l.add_xpath('listing_description', '//*[@id=\\\"root\\\"]/article/div[3]/div[1]/section[2]/div[1]')\\n l.add_xpath('house_characteristics', '//*[@id=\\\"root\\\"]/article/div[3]/div[1]/section[3]/div/ul//text()')\\n\\n # Get Image urls\\n imgs_div = response.xpath('//*[@id=\\\"root\\\"]/article/section[2]/div[1]/div/div[2]/div/div/div')[0]\\n img_urls = []\\n\\n for i in imgs_div.xpath('//img[@width=114]'):\\n img_urls.append(i.attrib['src'])\\n\\n l.add_value('img_urls', img_urls)\\n\\n return l.load_item()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70b359863a8b502b1508e74fa63a7127\",\n \"score\": \"0.54476845\",\n \"text\": \"def parse_urls(url, html):\\n #print \\\"Searching directory: %s\\\" % url\\n url_list = []\\n # Parse the html we get from the site and then itterate over all 'a'\\n # dom elements that have an href in them.\\n soup = BeautifulSoup(html, \\\"lxml\\\")\\n for anchor in soup.find_all('a', href=True):\\n # Skip this anchor if it's one we should ignore\\n if bad_anchor(anchor['href']):\\n continue\\n # build the full url and add it to the url bucket\\n new_url = url + anchor['href']\\n url_list.append(new_url)\\n return url_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9199838e0e60badc3dba871935830e22\",\n \"score\": \"0.54433286\",\n \"text\": \"def get_soup(url):\\n # raw_page = urllib.request.urlopen(url).read()\\n # soup = BeautifulSoup(raw_page, \\\"html.parser\\\")\\n raw_page = requests.get(url)\\n soup = BeautifulSoup(raw_page.text, \\\"lxml\\\")\\n return soup\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27cfb3bd42bb2e9cfce4ccb9852fa559\",\n \"score\": \"0.5441952\",\n \"text\": \"def parse(self, html):\\n bs = BeautifulSoup(html, 'html.parser')\\n houses_html = bs.find('ul', attrs={'class': 'listContent'})\\n if houses_html:\\n for house_html in houses_html:\\n obj = House()\\n obj.city = self.city\\n for key, value in HTML_TXT_MAPPING.iteritems():\\n self.parser.txtparser(obj, key, value, house_html)\\n for key, value in HTML_HREF_MAPPING.iteritems():\\n self.parser.hrefparser(obj, key, value, house_html)\\n yield obj\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993269,"cells":{"query_id":{"kind":"string","value":"81fcd009c74629703007d9aca88f642a"},"query":{"kind":"string","value":"This method returns the function's output when the class is called."},"positive_passages":{"kind":"list like","value":[{"docid":"2644af6d3a882a635aa0db6234a6a4bf","score":"0.0","text":"def __call__(self, x):\n\n # Calculating the Ridge's function\n f = x[1:] ** 2\n\n return x[0] + np.sum(f) ** 0.5","title":""}],"string":"[\n {\n \"docid\": \"2644af6d3a882a635aa0db6234a6a4bf\",\n \"score\": \"0.0\",\n \"text\": \"def __call__(self, x):\\n\\n # Calculating the Ridge's function\\n f = x[1:] ** 2\\n\\n return x[0] + np.sum(f) ** 0.5\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"0a91a476fa8777f5c58ab3058d9b7111","score":"0.7639285","text":"def getOutput(self):\n pass","title":""},{"docid":"befa74be37040eadded7efa0be3a8454","score":"0.75097024","text":"def __call__(self):\n return self.out","title":""},{"docid":"0a908f2b91ef3471b050b19228fd5f7c","score":"0.74660754","text":"def __get_output__(self):\r\n return self._get_output_rots()","title":""},{"docid":"825b99ce0b421e9766b14610bc76a302","score":"0.7384274","text":"def get_output(self):\r\n return self.output","title":""},{"docid":"7d5ccbabfc67df374b50816733aee343","score":"0.7274843","text":"def __call__(self):\n return self.stdout","title":""},{"docid":"b3a0b1653bea55c0bb0d06edb4b91aa2","score":"0.72732216","text":"def output_fn(self):\n return self._output_fn","title":""},{"docid":"4fb14da024e3cc46406b0dc7b941a553","score":"0.7265855","text":"def output():","title":""},{"docid":"d2cd3ad276957a95caf5fc2473019c5f","score":"0.7220007","text":"def output(self):\n\n # This is simple...\n return _output.out","title":""},{"docid":"e40875e8b7510cea207ede5256d8641c","score":"0.71251494","text":"def __get_output(self):\n return self.__Output","title":""},{"docid":"ff7496abb32076f9e9db4aa40e9cf756","score":"0.7044952","text":"def output(self):\n \n pass","title":""},{"docid":"50eadfa6e28b8bf7db05aa517d4d17be","score":"0.7043953","text":"def output(self):\n pass","title":""},{"docid":"50eadfa6e28b8bf7db05aa517d4d17be","score":"0.7043953","text":"def output(self):\n pass","title":""},{"docid":"75fe0791953565356c5d81dc766ee044","score":"0.7009136","text":"def output(self):\n return self.output_","title":""},{"docid":"a460612ea061ce293b691dcf4fe3da5b","score":"0.6936306","text":"def getOutput(self):\n return self.output","title":""},{"docid":"295bda155e67f89c2693e948655677be","score":"0.69056034","text":"def Output(self):\n pass","title":""},{"docid":"03d395752453dbabe6de50520a712666","score":"0.6793182","text":"def output(self):\n if self.value is None:\n return '(None)'\n elif self.is_global:\n return '(Global)'\n\n return self.value.output()","title":""},{"docid":"5a908efcc6867a1e67ea5943291f207b","score":"0.6774565","text":"def result(self):\n return self.output","title":""},{"docid":"f852f8301fa8176185c498a60bb0223e","score":"0.67580354","text":"def output(self):\n return self._output","title":""},{"docid":"f852f8301fa8176185c498a60bb0223e","score":"0.67580354","text":"def output(self):\n return self._output","title":""},{"docid":"f852f8301fa8176185c498a60bb0223e","score":"0.67580354","text":"def output(self):\n return self._output","title":""},{"docid":"f852f8301fa8176185c498a60bb0223e","score":"0.67580354","text":"def output(self):\n return self._output","title":""},{"docid":"23a9c2fcc0968684115eb6d90a8fa677","score":"0.67572284","text":"def getOutput(self):\n\n return self.output","title":""},{"docid":"85afa4752a28e117f592fa4c2f0b1bdc","score":"0.6680761","text":"def get_output(self):\n self.output = self.perform_gate_logic()\n return self.output","title":""},{"docid":"60a5a3e7fb981d497c41055466edbf9f","score":"0.6679285","text":"def get_output(self):\n return self.input","title":""},{"docid":"221836ee7a590661d399f11aa55d96bc","score":"0.65552586","text":"def getOutput(self):\n #if self.actualActivation is None:\n # self.activate()\n return self.actualActivation","title":""},{"docid":"ba425252177c19dc6eba9f3af1c5cc78","score":"0.6479466","text":"def stdout(self):\n return self.output","title":""},{"docid":"7adedd9b35322fe677626cfe739fcc85","score":"0.6478236","text":"def __str__(self):\n return self.text_type(self._func())","title":""},{"docid":"13b6627a7b6ae9575d05636d35d8d9d6","score":"0.64482963","text":"def output(init,result):\n return","title":""},{"docid":"b65ab164d6e31960477b53f99193f652","score":"0.6427799","text":"def __repr__(self):\n # The pre defined __repr__ meaning does all the work. All I have to do\n #is provide a correct string, then the functions results will be\n #printed out in the shell without the print command.\n return '{'+ Comma.join(self._contents)+'}'","title":""},{"docid":"49f23725ca20d8254f8b02c9a2ac13b4","score":"0.6419616","text":"def get_sequence_output(self):\n\t\treturn self.sequence_output","title":""},{"docid":"91405ccb165c99e7322418b8be89c79d","score":"0.63865775","text":"def returning(self):\n return 'returned string'","title":""},{"docid":"a8f69b78fbb52afff7a29d90868b7b1c","score":"0.63181037","text":"def stdout(self):\n return self.output","title":""},{"docid":"a5fd6552bc1920132f24f8e190ba5900","score":"0.6309117","text":"def getReturnValue():","title":""},{"docid":"43f8333585c524aad73bd07ab5441ebc","score":"0.6268834","text":"def get_output(self):\r\n try:\r\n fileobj = open(self.output_filename, \"r\")\r\n output = fileobj.read()\r\n fileobj.close()\r\n return output\r\n except Exception:\r\n return \"\"","title":""},{"docid":"cd137a41cead99178cf85c3a73988af3","score":"0.6256576","text":"def result():","title":""},{"docid":"f7b36205855c8caf1564aaf8b95a168a","score":"0.62550265","text":"def function(self):\n return self._function","title":""},{"docid":"f7b36205855c8caf1564aaf8b95a168a","score":"0.62550265","text":"def function(self):\n return self._function","title":""},{"docid":"3cf51f23a79cf1620e4f6560b7c52bd6","score":"0.625249","text":"def _get_feature_processor_outputs(\n wrapped_func: Callable,\n) -> str:\n feature_processor_config: FeatureProcessorConfig = wrapped_func.feature_processor_config\n return feature_processor_config.output","title":""},{"docid":"92c911c3633b9df1fd8e5033ded1bece","score":"0.62496823","text":"def output(s):\n pass","title":""},{"docid":"bd09b77b9114cfcb240455bd58fd4fd6","score":"0.62465334","text":"def cmd_output(self, args):\n raise NotImplementedError","title":""},{"docid":"c70742577e0f952175de83c9e17e3c6f","score":"0.6244774","text":"def __repr__(self):\r\n return self.func.__doc__","title":""},{"docid":"b66d184bc1536466214cea5354467ccd","score":"0.62412506","text":"def __repr__(self):\n return f\"FunctionPipeline(steps={self.steps}, memorize={self.memorize})\"","title":""},{"docid":"62ff5f67dab75804e5e480e030e9991b","score":"0.6226825","text":"def __repr__(self):\r\n return self.func.__doc__","title":""},{"docid":"62ff5f67dab75804e5e480e030e9991b","score":"0.6226825","text":"def __repr__(self):\r\n return self.func.__doc__","title":""},{"docid":"1f7ff295282339dc0013d62380d450d8","score":"0.6225199","text":"def __repr__(self):\n return self.func.__doc__","title":""},{"docid":"ab340e1a4002c22517b59c43bf4d5157","score":"0.6221119","text":"def __repr__(self):\n return self.func.__doc__","title":""},{"docid":"ab340e1a4002c22517b59c43bf4d5157","score":"0.6221119","text":"def __repr__(self):\n return self.func.__doc__","title":""},{"docid":"ab340e1a4002c22517b59c43bf4d5157","score":"0.6221119","text":"def __repr__(self):\n return self.func.__doc__","title":""},{"docid":"ab340e1a4002c22517b59c43bf4d5157","score":"0.6221119","text":"def __repr__(self):\n return self.func.__doc__","title":""},{"docid":"ab340e1a4002c22517b59c43bf4d5157","score":"0.6221119","text":"def __repr__(self):\n return self.func.__doc__","title":""},{"docid":"ab340e1a4002c22517b59c43bf4d5157","score":"0.6221119","text":"def __repr__(self):\n return self.func.__doc__","title":""},{"docid":"ab340e1a4002c22517b59c43bf4d5157","score":"0.6221119","text":"def __repr__(self):\n return self.func.__doc__","title":""},{"docid":"ab340e1a4002c22517b59c43bf4d5157","score":"0.6221119","text":"def __repr__(self):\n return self.func.__doc__","title":""},{"docid":"f0a7c9e934c4ccef16209c1911ffc659","score":"0.6214474","text":"def get_sequence_output(self):\n return self.sequence_output","title":""},{"docid":"6bd54b8dd95c8feb7a452475eee733c7","score":"0.6212542","text":"def __repr__(self):\n return self.func.__doc__","title":""},{"docid":"626d3e38bddb6d2b1b65219d14b418d9","score":"0.6200559","text":"def get_output(self, state) -> list:\n ...","title":""},{"docid":"eedf7d4a86c7c1d62571266fb0f68975","score":"0.61909926","text":"def __repr__(self):\n return self.func.__doc__","title":""},{"docid":"eedf7d4a86c7c1d62571266fb0f68975","score":"0.61909926","text":"def __repr__(self):\n return self.func.__doc__","title":""},{"docid":"c9588259a578d934e3028a9961f7fa51","score":"0.61884207","text":"def get_outputs(self):\n raise NotImplementedError # abstract","title":""},{"docid":"aa5472fa18aea1cb7b9a414692cd25fb","score":"0.6182402","text":"def get_calls(self):\n txt = \"\"\n for func_name in self.func_calls:\n txt += func_name + '\\n'\n return txt","title":""},{"docid":"b3a95adb36868caba2488850eb65481d","score":"0.61783123","text":"def get_output(self):\n \n ### TODO: Extract and return the output results\n ### Note: You may need to update the function parameters. ###\n return self.infer_request.outputs[self.output_blob]","title":""},{"docid":"dfac0ad755735daf2e2671f9c3ed15e2","score":"0.61692464","text":"def function(self):\n\n return self._function","title":""},{"docid":"c5016fd7fef742480ca37f102482fc98","score":"0.61592484","text":"def outputs(self):\n return super().outputs","title":""},{"docid":"c5016fd7fef742480ca37f102482fc98","score":"0.61592484","text":"def outputs(self):\n return super().outputs","title":""},{"docid":"c5016fd7fef742480ca37f102482fc98","score":"0.61592484","text":"def outputs(self):\n return super().outputs","title":""},{"docid":"c5016fd7fef742480ca37f102482fc98","score":"0.61592484","text":"def outputs(self):\n return super().outputs","title":""},{"docid":"c5016fd7fef742480ca37f102482fc98","score":"0.61592484","text":"def outputs(self):\n return super().outputs","title":""},{"docid":"c5016fd7fef742480ca37f102482fc98","score":"0.61592484","text":"def outputs(self):\n return super().outputs","title":""},{"docid":"3bcd0c52ec49a49566d353ad8356bbe5","score":"0.6158951","text":"def outputs(self):\n raise NotImplementedError()","title":""},{"docid":"03c77d9c50c869a2177099db841c0ce9","score":"0.6144298","text":"def outputs(self):\n raise NotImplementedError # abstract","title":""},{"docid":"ef91bfc6d9ccac9fcf4aafe9e25a9d96","score":"0.613294","text":"def output(self, name):\n return self.desc.output(name)","title":""},{"docid":"b948d1afb089ce1b2c9625e5d8225216","score":"0.61069185","text":"def checker_output(self) -> str:\n return self.__checker_output","title":""},{"docid":"20d630e0664d0158ea0f7cc2f7352d07","score":"0.61059153","text":"def fn(self):\n return self._fn","title":""},{"docid":"a777137c34d6ebced02d978054c13aaa","score":"0.6077278","text":"def __repr__(self):\n\n return '{0}()'.format(self.__class__.__name__)","title":""},{"docid":"a658ebd173bdd77c364416f6721fb28d","score":"0.60722613","text":"def outputs(self) -> int:","title":""},{"docid":"7c85a222be48afd3d07fd0bd6f648531","score":"0.60576373","text":"def get_Func(self):\n pass","title":""},{"docid":"9aea1144ff6460b7c086a6a6347c9385","score":"0.60426986","text":"def result(self):\r\n\r\n raise NotImplementedError()","title":""},{"docid":"c9d9b22cf8739bc9c3b5b27578f4e9d0","score":"0.6042311","text":"def display_in_output():\n generator.generate_output()","title":""},{"docid":"04ddeb7208a7a5f0bc67d45958131aec","score":"0.6029557","text":"def output(self):\n return self._inout[1]","title":""},{"docid":"98d69087e4a9049f4c4b92ff33ed49a6","score":"0.6013824","text":"def _getfun(self):\n return self._fun","title":""},{"docid":"88e04a32b3025ddff409c432525911bb","score":"0.60049385","text":"def outputResults(x):\r\n pass","title":""},{"docid":"624db9ae226bdd2d78136da2bbdd9c4e","score":"0.5989291","text":"def print_results(self):\n print(f'\\nClass:\\t{self.__class__.__name__}')\n print(f'Result:\\t{self.run()}')","title":""},{"docid":"5b11fbff91d60586f0e7ce1af7b2a3dd","score":"0.59859663","text":"def fun ( self ) :\n return self.__fun","title":""},{"docid":"c59d5556592273c707fb07b6d08846d4","score":"0.59844196","text":"def __repr__(self) -> str:\r\n\t\treturn f'{self.__class__.__name__}()'","title":""},{"docid":"e80ea54f05cbdb99e037315a70358e5d","score":"0.5983163","text":"def get_working_output(self):\n raise NotImplementedError('Operation {:s} not supported for {:s} yet!'.format('get__working_output',\n self.__class__.__name__))","title":""},{"docid":"944c14c40b9bc55660cec7943d792856","score":"0.59831285","text":"def output_command(self):\n command = \"\"\n return command","title":""},{"docid":"b6bd3d8326e6980d4791a5fe5c3213c3","score":"0.5979335","text":"def function_name(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"function_name\")","title":""},{"docid":"b6bd3d8326e6980d4791a5fe5c3213c3","score":"0.5979335","text":"def function_name(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"function_name\")","title":""},{"docid":"90132938b72e309d29b72f8507ba9b01","score":"0.5973911","text":"def __str__(self):\n return f\"\"\"\n FunctionPipeline:\n steps: {self.steps}\n memorize: {self.memorize}\n results: {self.results if self.memorize else \"N/A\"}\n \"\"\"","title":""},{"docid":"7ceeb990ee4672a06fbeeba686cd7c64","score":"0.5955939","text":"def get_out(self):\n return self._out_info","title":""},{"docid":"e0512650c63b13df24613b4695f33b3d","score":"0.59537375","text":"def funcdef(self):\n return self._funcdef","title":""},{"docid":"39167801a73222901836f7b028584a65","score":"0.5943133","text":"def output(self):\n return '\\n'.join(self._output)","title":""},{"docid":"343ed855fe5563371fac500b00c5ce42","score":"0.5932036","text":"def this_one():\n print(\"this\")\n return \"this\"","title":""},{"docid":"944f3f653c779208c932c80df2c3e010","score":"0.5931061","text":"def output(self):\n if self._output is None:\n command = \"{} {}\".format(self.command, self.path)\n self.logger.debug(\"Running: '%s'\", command)\n outcome = subprocess.run(shlex.split(command),\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n universal_newlines=True,\n )\n self._output = outcome.stdout\n self.logger.debug(self._output)\n return self._output","title":""},{"docid":"ff9f5ffe2cce1e676114067ddb032b35","score":"0.5924344","text":"def __str__(self):\n if not self._cache:\n self._cache = self.text_type(self._func())\n return self._cache","title":""},{"docid":"ce5a7834acc1baa540e09a527014011b","score":"0.59230757","text":"def output(cls):\n\n ran_status = (cls.current_step, cls.total_steps)\n Log.info(\"\"\"Topic name: \"%s\" \"\"\" % cls.topic)\n Log.info(\"Steps ran successful %d out of %d\" % ran_status)\n Log.info(\"\"\"Last step error: \"%s\" \"\"\" % cls.error)\n Log.info(\"Runtime %ss\" % cls.runtime)\n Log.info(\"Build %s\" % cls.status.upper())","title":""},{"docid":"59ee8c4f2157f379c42af2a4ceef4779","score":"0.5921677","text":"def output(self):\n return self.recon","title":""},{"docid":"fdd647a65cfb7afa1361b5b1a1450867","score":"0.59163964","text":"def __repr__(self) -> str:\n return f'{self.__class__.__name__}()'","title":""},{"docid":"59d2c6a17128708b5d749959a0d284ab","score":"0.59152","text":"def test(self):\n\n # Also simple...\n return _output.test","title":""},{"docid":"843fd523e194ab0c75c9b98b25118e33","score":"0.5905128","text":"def print_output(self, func, *args):\n with self.output:\n func(*args)","title":""},{"docid":"496b0b73bc10aede6c69c183fe6e4ac0","score":"0.5900145","text":"def __str__(self):\n status = type(self).__name__ + \".\" + self.Name + \": \"\n status += \"(\" + str(self.Input0) + \",\" + str(self.Input1) + \") \"\n status += \"-> (\" + str(self.Output) + \")\"\n return status","title":""}],"string":"[\n {\n \"docid\": \"0a91a476fa8777f5c58ab3058d9b7111\",\n \"score\": \"0.7639285\",\n \"text\": \"def getOutput(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"befa74be37040eadded7efa0be3a8454\",\n \"score\": \"0.75097024\",\n \"text\": \"def __call__(self):\\n return self.out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a908f2b91ef3471b050b19228fd5f7c\",\n \"score\": \"0.74660754\",\n \"text\": \"def __get_output__(self):\\r\\n return self._get_output_rots()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"825b99ce0b421e9766b14610bc76a302\",\n \"score\": \"0.7384274\",\n \"text\": \"def get_output(self):\\r\\n return self.output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d5ccbabfc67df374b50816733aee343\",\n \"score\": \"0.7274843\",\n \"text\": \"def __call__(self):\\n return self.stdout\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3a0b1653bea55c0bb0d06edb4b91aa2\",\n \"score\": \"0.72732216\",\n \"text\": \"def output_fn(self):\\n return self._output_fn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fb14da024e3cc46406b0dc7b941a553\",\n \"score\": \"0.7265855\",\n \"text\": \"def output():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2cd3ad276957a95caf5fc2473019c5f\",\n \"score\": \"0.7220007\",\n \"text\": \"def output(self):\\n\\n # This is simple...\\n return _output.out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e40875e8b7510cea207ede5256d8641c\",\n \"score\": \"0.71251494\",\n \"text\": \"def __get_output(self):\\n return self.__Output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff7496abb32076f9e9db4aa40e9cf756\",\n \"score\": \"0.7044952\",\n \"text\": \"def output(self):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50eadfa6e28b8bf7db05aa517d4d17be\",\n \"score\": \"0.7043953\",\n \"text\": \"def output(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50eadfa6e28b8bf7db05aa517d4d17be\",\n \"score\": \"0.7043953\",\n \"text\": \"def output(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75fe0791953565356c5d81dc766ee044\",\n \"score\": \"0.7009136\",\n \"text\": \"def output(self):\\n return self.output_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a460612ea061ce293b691dcf4fe3da5b\",\n \"score\": \"0.6936306\",\n \"text\": \"def getOutput(self):\\n return self.output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"295bda155e67f89c2693e948655677be\",\n \"score\": \"0.69056034\",\n \"text\": \"def Output(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03d395752453dbabe6de50520a712666\",\n \"score\": \"0.6793182\",\n \"text\": \"def output(self):\\n if self.value is None:\\n return '(None)'\\n elif self.is_global:\\n return '(Global)'\\n\\n return self.value.output()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a908efcc6867a1e67ea5943291f207b\",\n \"score\": \"0.6774565\",\n \"text\": \"def result(self):\\n return self.output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f852f8301fa8176185c498a60bb0223e\",\n \"score\": \"0.67580354\",\n \"text\": \"def output(self):\\n return self._output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f852f8301fa8176185c498a60bb0223e\",\n \"score\": \"0.67580354\",\n \"text\": \"def output(self):\\n return self._output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f852f8301fa8176185c498a60bb0223e\",\n \"score\": \"0.67580354\",\n \"text\": \"def output(self):\\n return self._output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f852f8301fa8176185c498a60bb0223e\",\n \"score\": \"0.67580354\",\n \"text\": \"def output(self):\\n return self._output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23a9c2fcc0968684115eb6d90a8fa677\",\n \"score\": \"0.67572284\",\n \"text\": \"def getOutput(self):\\n\\n return self.output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85afa4752a28e117f592fa4c2f0b1bdc\",\n \"score\": \"0.6680761\",\n \"text\": \"def get_output(self):\\n self.output = self.perform_gate_logic()\\n return self.output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60a5a3e7fb981d497c41055466edbf9f\",\n \"score\": \"0.6679285\",\n \"text\": \"def get_output(self):\\n return self.input\",\n \"title\": \"\"\n },\n {\n \"docid\": \"221836ee7a590661d399f11aa55d96bc\",\n \"score\": \"0.65552586\",\n \"text\": \"def getOutput(self):\\n #if self.actualActivation is None:\\n # self.activate()\\n return self.actualActivation\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba425252177c19dc6eba9f3af1c5cc78\",\n \"score\": \"0.6479466\",\n \"text\": \"def stdout(self):\\n return self.output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7adedd9b35322fe677626cfe739fcc85\",\n \"score\": \"0.6478236\",\n \"text\": \"def __str__(self):\\n return self.text_type(self._func())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13b6627a7b6ae9575d05636d35d8d9d6\",\n \"score\": \"0.64482963\",\n \"text\": \"def output(init,result):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b65ab164d6e31960477b53f99193f652\",\n \"score\": \"0.6427799\",\n \"text\": \"def __repr__(self):\\n # The pre defined __repr__ meaning does all the work. All I have to do\\n #is provide a correct string, then the functions results will be\\n #printed out in the shell without the print command.\\n return '{'+ Comma.join(self._contents)+'}'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49f23725ca20d8254f8b02c9a2ac13b4\",\n \"score\": \"0.6419616\",\n \"text\": \"def get_sequence_output(self):\\n\\t\\treturn self.sequence_output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91405ccb165c99e7322418b8be89c79d\",\n \"score\": \"0.63865775\",\n \"text\": \"def returning(self):\\n return 'returned string'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8f69b78fbb52afff7a29d90868b7b1c\",\n \"score\": \"0.63181037\",\n \"text\": \"def stdout(self):\\n return self.output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5fd6552bc1920132f24f8e190ba5900\",\n \"score\": \"0.6309117\",\n \"text\": \"def getReturnValue():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43f8333585c524aad73bd07ab5441ebc\",\n \"score\": \"0.6268834\",\n \"text\": \"def get_output(self):\\r\\n try:\\r\\n fileobj = open(self.output_filename, \\\"r\\\")\\r\\n output = fileobj.read()\\r\\n fileobj.close()\\r\\n return output\\r\\n except Exception:\\r\\n return \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd137a41cead99178cf85c3a73988af3\",\n \"score\": \"0.6256576\",\n \"text\": \"def result():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7b36205855c8caf1564aaf8b95a168a\",\n \"score\": \"0.62550265\",\n \"text\": \"def function(self):\\n return self._function\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7b36205855c8caf1564aaf8b95a168a\",\n \"score\": \"0.62550265\",\n \"text\": \"def function(self):\\n return self._function\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cf51f23a79cf1620e4f6560b7c52bd6\",\n \"score\": \"0.625249\",\n \"text\": \"def _get_feature_processor_outputs(\\n wrapped_func: Callable,\\n) -> str:\\n feature_processor_config: FeatureProcessorConfig = wrapped_func.feature_processor_config\\n return feature_processor_config.output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92c911c3633b9df1fd8e5033ded1bece\",\n \"score\": \"0.62496823\",\n \"text\": \"def output(s):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd09b77b9114cfcb240455bd58fd4fd6\",\n \"score\": \"0.62465334\",\n \"text\": \"def cmd_output(self, args):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c70742577e0f952175de83c9e17e3c6f\",\n \"score\": \"0.6244774\",\n \"text\": \"def __repr__(self):\\r\\n return self.func.__doc__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b66d184bc1536466214cea5354467ccd\",\n \"score\": \"0.62412506\",\n \"text\": \"def __repr__(self):\\n return f\\\"FunctionPipeline(steps={self.steps}, memorize={self.memorize})\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62ff5f67dab75804e5e480e030e9991b\",\n \"score\": \"0.6226825\",\n \"text\": \"def __repr__(self):\\r\\n return self.func.__doc__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62ff5f67dab75804e5e480e030e9991b\",\n \"score\": \"0.6226825\",\n \"text\": \"def __repr__(self):\\r\\n return self.func.__doc__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f7ff295282339dc0013d62380d450d8\",\n \"score\": \"0.6225199\",\n \"text\": \"def __repr__(self):\\n return self.func.__doc__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab340e1a4002c22517b59c43bf4d5157\",\n \"score\": \"0.6221119\",\n \"text\": \"def __repr__(self):\\n return self.func.__doc__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab340e1a4002c22517b59c43bf4d5157\",\n \"score\": \"0.6221119\",\n \"text\": \"def __repr__(self):\\n return self.func.__doc__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab340e1a4002c22517b59c43bf4d5157\",\n \"score\": \"0.6221119\",\n \"text\": \"def __repr__(self):\\n return self.func.__doc__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab340e1a4002c22517b59c43bf4d5157\",\n \"score\": \"0.6221119\",\n \"text\": \"def __repr__(self):\\n return self.func.__doc__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab340e1a4002c22517b59c43bf4d5157\",\n \"score\": \"0.6221119\",\n \"text\": \"def __repr__(self):\\n return self.func.__doc__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab340e1a4002c22517b59c43bf4d5157\",\n \"score\": \"0.6221119\",\n \"text\": \"def __repr__(self):\\n return self.func.__doc__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab340e1a4002c22517b59c43bf4d5157\",\n \"score\": \"0.6221119\",\n \"text\": \"def __repr__(self):\\n return self.func.__doc__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab340e1a4002c22517b59c43bf4d5157\",\n \"score\": \"0.6221119\",\n \"text\": \"def __repr__(self):\\n return self.func.__doc__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0a7c9e934c4ccef16209c1911ffc659\",\n \"score\": \"0.6214474\",\n \"text\": \"def get_sequence_output(self):\\n return self.sequence_output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bd54b8dd95c8feb7a452475eee733c7\",\n \"score\": \"0.6212542\",\n \"text\": \"def __repr__(self):\\n return self.func.__doc__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"626d3e38bddb6d2b1b65219d14b418d9\",\n \"score\": \"0.6200559\",\n \"text\": \"def get_output(self, state) -> list:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eedf7d4a86c7c1d62571266fb0f68975\",\n \"score\": \"0.61909926\",\n \"text\": \"def __repr__(self):\\n return self.func.__doc__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eedf7d4a86c7c1d62571266fb0f68975\",\n \"score\": \"0.61909926\",\n \"text\": \"def __repr__(self):\\n return self.func.__doc__\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9588259a578d934e3028a9961f7fa51\",\n \"score\": \"0.61884207\",\n \"text\": \"def get_outputs(self):\\n raise NotImplementedError # abstract\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa5472fa18aea1cb7b9a414692cd25fb\",\n \"score\": \"0.6182402\",\n \"text\": \"def get_calls(self):\\n txt = \\\"\\\"\\n for func_name in self.func_calls:\\n txt += func_name + '\\\\n'\\n return txt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3a95adb36868caba2488850eb65481d\",\n \"score\": \"0.61783123\",\n \"text\": \"def get_output(self):\\n \\n ### TODO: Extract and return the output results\\n ### Note: You may need to update the function parameters. ###\\n return self.infer_request.outputs[self.output_blob]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfac0ad755735daf2e2671f9c3ed15e2\",\n \"score\": \"0.61692464\",\n \"text\": \"def function(self):\\n\\n return self._function\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5016fd7fef742480ca37f102482fc98\",\n \"score\": \"0.61592484\",\n \"text\": \"def outputs(self):\\n return super().outputs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5016fd7fef742480ca37f102482fc98\",\n \"score\": \"0.61592484\",\n \"text\": \"def outputs(self):\\n return super().outputs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5016fd7fef742480ca37f102482fc98\",\n \"score\": \"0.61592484\",\n \"text\": \"def outputs(self):\\n return super().outputs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5016fd7fef742480ca37f102482fc98\",\n \"score\": \"0.61592484\",\n \"text\": \"def outputs(self):\\n return super().outputs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5016fd7fef742480ca37f102482fc98\",\n \"score\": \"0.61592484\",\n \"text\": \"def outputs(self):\\n return super().outputs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5016fd7fef742480ca37f102482fc98\",\n \"score\": \"0.61592484\",\n \"text\": \"def outputs(self):\\n return super().outputs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bcd0c52ec49a49566d353ad8356bbe5\",\n \"score\": \"0.6158951\",\n \"text\": \"def outputs(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03c77d9c50c869a2177099db841c0ce9\",\n \"score\": \"0.6144298\",\n \"text\": \"def outputs(self):\\n raise NotImplementedError # abstract\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef91bfc6d9ccac9fcf4aafe9e25a9d96\",\n \"score\": \"0.613294\",\n \"text\": \"def output(self, name):\\n return self.desc.output(name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b948d1afb089ce1b2c9625e5d8225216\",\n \"score\": \"0.61069185\",\n \"text\": \"def checker_output(self) -> str:\\n return self.__checker_output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20d630e0664d0158ea0f7cc2f7352d07\",\n \"score\": \"0.61059153\",\n \"text\": \"def fn(self):\\n return self._fn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a777137c34d6ebced02d978054c13aaa\",\n \"score\": \"0.6077278\",\n \"text\": \"def __repr__(self):\\n\\n return '{0}()'.format(self.__class__.__name__)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a658ebd173bdd77c364416f6721fb28d\",\n \"score\": \"0.60722613\",\n \"text\": \"def outputs(self) -> int:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c85a222be48afd3d07fd0bd6f648531\",\n \"score\": \"0.60576373\",\n \"text\": \"def get_Func(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9aea1144ff6460b7c086a6a6347c9385\",\n \"score\": \"0.60426986\",\n \"text\": \"def result(self):\\r\\n\\r\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9d9b22cf8739bc9c3b5b27578f4e9d0\",\n \"score\": \"0.6042311\",\n \"text\": \"def display_in_output():\\n generator.generate_output()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04ddeb7208a7a5f0bc67d45958131aec\",\n \"score\": \"0.6029557\",\n \"text\": \"def output(self):\\n return self._inout[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98d69087e4a9049f4c4b92ff33ed49a6\",\n \"score\": \"0.6013824\",\n \"text\": \"def _getfun(self):\\n return self._fun\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88e04a32b3025ddff409c432525911bb\",\n \"score\": \"0.60049385\",\n \"text\": \"def outputResults(x):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"624db9ae226bdd2d78136da2bbdd9c4e\",\n \"score\": \"0.5989291\",\n \"text\": \"def print_results(self):\\n print(f'\\\\nClass:\\\\t{self.__class__.__name__}')\\n print(f'Result:\\\\t{self.run()}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b11fbff91d60586f0e7ce1af7b2a3dd\",\n \"score\": \"0.59859663\",\n \"text\": \"def fun ( self ) :\\n return self.__fun\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c59d5556592273c707fb07b6d08846d4\",\n \"score\": \"0.59844196\",\n \"text\": \"def __repr__(self) -> str:\\r\\n\\t\\treturn f'{self.__class__.__name__}()'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e80ea54f05cbdb99e037315a70358e5d\",\n \"score\": \"0.5983163\",\n \"text\": \"def get_working_output(self):\\n raise NotImplementedError('Operation {:s} not supported for {:s} yet!'.format('get__working_output',\\n self.__class__.__name__))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"944c14c40b9bc55660cec7943d792856\",\n \"score\": \"0.59831285\",\n \"text\": \"def output_command(self):\\n command = \\\"\\\"\\n return command\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6bd3d8326e6980d4791a5fe5c3213c3\",\n \"score\": \"0.5979335\",\n \"text\": \"def function_name(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"function_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6bd3d8326e6980d4791a5fe5c3213c3\",\n \"score\": \"0.5979335\",\n \"text\": \"def function_name(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"function_name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90132938b72e309d29b72f8507ba9b01\",\n \"score\": \"0.5973911\",\n \"text\": \"def __str__(self):\\n return f\\\"\\\"\\\"\\n FunctionPipeline:\\n steps: {self.steps}\\n memorize: {self.memorize}\\n results: {self.results if self.memorize else \\\"N/A\\\"}\\n \\\"\\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ceeb990ee4672a06fbeeba686cd7c64\",\n \"score\": \"0.5955939\",\n \"text\": \"def get_out(self):\\n return self._out_info\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0512650c63b13df24613b4695f33b3d\",\n \"score\": \"0.59537375\",\n \"text\": \"def funcdef(self):\\n return self._funcdef\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39167801a73222901836f7b028584a65\",\n \"score\": \"0.5943133\",\n \"text\": \"def output(self):\\n return '\\\\n'.join(self._output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"343ed855fe5563371fac500b00c5ce42\",\n \"score\": \"0.5932036\",\n \"text\": \"def this_one():\\n print(\\\"this\\\")\\n return \\\"this\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"944f3f653c779208c932c80df2c3e010\",\n \"score\": \"0.5931061\",\n \"text\": \"def output(self):\\n if self._output is None:\\n command = \\\"{} {}\\\".format(self.command, self.path)\\n self.logger.debug(\\\"Running: '%s'\\\", command)\\n outcome = subprocess.run(shlex.split(command),\\n stdout=subprocess.PIPE,\\n stderr=subprocess.PIPE,\\n universal_newlines=True,\\n )\\n self._output = outcome.stdout\\n self.logger.debug(self._output)\\n return self._output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff9f5ffe2cce1e676114067ddb032b35\",\n \"score\": \"0.5924344\",\n \"text\": \"def __str__(self):\\n if not self._cache:\\n self._cache = self.text_type(self._func())\\n return self._cache\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce5a7834acc1baa540e09a527014011b\",\n \"score\": \"0.59230757\",\n \"text\": \"def output(cls):\\n\\n ran_status = (cls.current_step, cls.total_steps)\\n Log.info(\\\"\\\"\\\"Topic name: \\\"%s\\\" \\\"\\\"\\\" % cls.topic)\\n Log.info(\\\"Steps ran successful %d out of %d\\\" % ran_status)\\n Log.info(\\\"\\\"\\\"Last step error: \\\"%s\\\" \\\"\\\"\\\" % cls.error)\\n Log.info(\\\"Runtime %ss\\\" % cls.runtime)\\n Log.info(\\\"Build %s\\\" % cls.status.upper())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59ee8c4f2157f379c42af2a4ceef4779\",\n \"score\": \"0.5921677\",\n \"text\": \"def output(self):\\n return self.recon\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdd647a65cfb7afa1361b5b1a1450867\",\n \"score\": \"0.59163964\",\n \"text\": \"def __repr__(self) -> str:\\n return f'{self.__class__.__name__}()'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59d2c6a17128708b5d749959a0d284ab\",\n \"score\": \"0.59152\",\n \"text\": \"def test(self):\\n\\n # Also simple...\\n return _output.test\",\n \"title\": \"\"\n },\n {\n \"docid\": \"843fd523e194ab0c75c9b98b25118e33\",\n \"score\": \"0.5905128\",\n \"text\": \"def print_output(self, func, *args):\\n with self.output:\\n func(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"496b0b73bc10aede6c69c183fe6e4ac0\",\n \"score\": \"0.5900145\",\n \"text\": \"def __str__(self):\\n status = type(self).__name__ + \\\".\\\" + self.Name + \\\": \\\"\\n status += \\\"(\\\" + str(self.Input0) + \\\",\\\" + str(self.Input1) + \\\") \\\"\\n status += \\\"-> (\\\" + str(self.Output) + \\\")\\\"\\n return status\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993270,"cells":{"query_id":{"kind":"string","value":"6df2705c11d30eb8209ecb0748477d75"},"query":{"kind":"string","value":"Function that returns the view page for a blog."},"positive_passages":{"kind":"list like","value":[{"docid":"2367d1a9b749a62cc21e21d01adfa256","score":"0.7342799","text":"def view_blog(blog_id):\n\n blog = Blog.query.filter_by(id=blog_id).first()\n comments = Comment.get_comments(blog_id)\n \n title = \"View Blog\"\n \n return render_template('view.html', title=title, blog=blog, comments=comments, id=blog_id)","title":""}],"string":"[\n {\n \"docid\": \"2367d1a9b749a62cc21e21d01adfa256\",\n \"score\": \"0.7342799\",\n \"text\": \"def view_blog(blog_id):\\n\\n blog = Blog.query.filter_by(id=blog_id).first()\\n comments = Comment.get_comments(blog_id)\\n \\n title = \\\"View Blog\\\"\\n \\n return render_template('view.html', title=title, blog=blog, comments=comments, id=blog_id)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"4d9aca7b2e1de6d48570604a588a6461","score":"0.7157125","text":"def blog_post(request, url):\n\n blog = get_object_or_404(Post, url=url)\n\n context = {\n \"blog\": blog,\n }\n\n return render(request, \"blog/blog_post.html\", context)","title":""},{"docid":"0a4d00d4803c69db2baa0861ad3235ce","score":"0.71384466","text":"def blog(request):\n posts = Post.objects.all()\n paginator = Paginator(posts, 3)\n page_number = request.GET.get('page')\n page_obj = paginator.get_page(page_number)\n\n context = {'posts': posts,\n 'page_obj': page_obj,\n 'on_blog_page': True,\n }\n return render(request, 'blog/blog.html', context)","title":""},{"docid":"a5ac2b47f2e432e6b64114cf394e0191","score":"0.7081953","text":"def blog(request, blog_id):\n\tblog = get_object_or_404(Blog, id=blog_id)\n\tif blog.public==False and check_blog_owner(blog.owner, request.user)!=Http404:\n\t\tposts = blog.post_set.order_by('-date_added')\n\t\tcontext = {'blog':blog, 'posts':posts}\n\t\treturn render(request, 'blogs/blog.html', context)\n\telif blog.public==True:\n\t\tposts = blog.post_set.order_by('-date_added')\n\t\tcontext = {'blog':blog, 'posts':posts}\n\t\treturn render(request, 'blogs/blog.html', context)\n\treturn Http404","title":""},{"docid":"dd549f9f3df885d0a7c239a8ab21f937","score":"0.7014336","text":"def blogpostdetail(request):\n return render(request, 'blogpost-detail.html')","title":""},{"docid":"7d251833ded86886af730775a085cc6f","score":"0.6806661","text":"def blog_index(request):\n return render(request,\"sciblog/index.html\",locals())","title":""},{"docid":"27018f1da7cb355a3f7f37b1385d1069","score":"0.67999965","text":"def get(self, post_id):\n user = self.user\n parent = user.key\n blog_key = Blog.get_by_id(int(post_id))\n post = blog_key\n\n if not post:\n self.render('error.html')\n return\n\n self.render(\"permalink.html\", blog=post)","title":""},{"docid":"3da32418e66967db9b746c4350f61a77","score":"0.673371","text":"def most_viewed(request):\n top_posts = BlogPost.objects.filter(\n published_date__lte=timezone.now()).order_by('-post_views')[:10]\n # add pagination to the blog page, we will display 3 posts per page\n paginator = Paginator(top_posts, 3) # 3 posts in each page\n page = request.GET.get('page')\n try:\n blog_posts = paginator.page(page)\n except PageNotAnInteger:\n # if the page is not an integer deliver the first page\n blog_posts = paginator.page(1)\n except EmptyPage:\n # if page is out of range deliver the last page of results\n blog_posts = paginator.page(paginator.num_pages)\n return render(request, 'gamersblog/blogposts/blogpostlist.html',\n {'page': page, 'blog_posts': blog_posts})","title":""},{"docid":"9e3631452fd704791f8220161027214b","score":"0.66507894","text":"def index(request):\n\treturn render(request, 'blogs/index.html')","title":""},{"docid":"e3ad8a4b6f66fa0c1c357f43cdb338b1","score":"0.6637677","text":"def blog(request, blog_id):\n blog = Blog.objects.get(id=blog_id)\n # Make sure the topic belongs to the current user.\n # if blog.owner != request.user:\n # raise Http404\n\n chapters = blog.text\n context = {'blog': blog, 'chapters': chapters,'image':''}\n return render(request, 'blog.html', context)","title":""},{"docid":"49c3fe0f5ed412c32163fbbaf462e87b","score":"0.6636706","text":"def blog_post(path):\n post = posts.get_or_404(f'{path}/{POST_FILENAME}')\n\n github_repo = None\n if 'github' in post.meta:\n try:\n github_repo = [r for r in get_github_user_repos() if r.full_name == post.meta.get('github')][0]\n except IndexError as e:\n print(f'The repository {post.meta.get(\"github\")} was not found in github_user_repos')\n raise e\n\n return render_template(\n 'blog-post.html',\n prev_and_next=posts.get_previous_and_next_posts(post),\n page=post,\n github_repo=github_repo\n )","title":""},{"docid":"5b5c6e2de3b82d592365c4aed0ca0cb6","score":"0.65678596","text":"def blog_home():\n page1_posts = posts.paginate_posts(1)\n return render_template(\n 'blog-home.html',\n pagination_nav=posts.get_pagination_nav_data(1),\n posts=page1_posts\n )","title":""},{"docid":"c50e8e1c6dff4468d4150265d4be57b7","score":"0.6518209","text":"def home(request):\n assert isinstance(request, HttpRequest)\n last_post = Blog.objects.first()\n return render(\n request,\n 'app/index.html',\n {\n 'title':'Главная',\n 'last_post': last_post,\n 'year':datetime.now().year,\n }\n )","title":""},{"docid":"39147b4c506607b62d8c9a7d7e27617b","score":"0.648939","text":"def blog_post():\n \n # retrieve the user id from the page arguments passed by the button\n # and then get the row and send it to the view\n blog_id = request.args(0)\n blog_post = db.blog_posts(blog_id)\n \n if blog_post is None or blog_post.hidden or blog_post.admin_status != 'Approved':\n session.flash = CENTER(B('Blog post id not available.'), _style='color: red')\n redirect(URL('blogs','blogs'))\n \n return dict(blog_post = blog_post)","title":""},{"docid":"d22078081a38296a96703fff63ff5507","score":"0.644632","text":"def blog(request):\n last = Blog.objects.order_by('-posted')[:3]\n assert isinstance(request, HttpRequest)\n posts = Blog.objects.all() # запрос на выбор всех статей блога из модели\n return render(\n request,\n 'blog/blog.html',\n {\n 'title':'Блог',\n 'last': last,\n 'posts': posts, # передача списка статей в шаблон веб-страницы\n 'year':datetime.now().year,\n }\n\n)","title":""},{"docid":"78a07ad929d7bededc05e2f10e51bbf9","score":"0.6445245","text":"def my_blog(request, slug):\n # Get the liveblog by slug\n blog = get_object_or_404(Blog, slug=slug)\n\n # Render it with the posts ordered in descending order.\n # If the user has JavaScript enabled, the template has JS that will\n # keep it updated.\n return render(request, \"posts/blog.html\", {\n \"blog\": blog,\n \"posts\": blog.posts.order_by(\"-created\"),\n})","title":""},{"docid":"19b854d5d62d889ac22a4d808394d92d","score":"0.6409632","text":"def _get_view(self):\r\n condition = None\r\n \r\n def blog(value, obj):\r\n return '<a href=\"%s\">%s</a>' % (value, value)\r\n \r\n fields_convert_map = {'blog':blog}\r\n \r\n view = functions.ListView(self.model, \r\n# pageno=page, \r\n# rows_per_page=rows,\r\n pagination=False,\r\n condition=condition,\r\n fields_convert_map=fields_convert_map,\r\n )\r\n return view","title":""},{"docid":"40f8ae388e89122251493d43d10f109e","score":"0.6372231","text":"def blog_post_detail(request, slug):\n post = get_object_or_404(BlogPost, slug=slug)\n post.post_views += 1 # increment the post views by 1, each time one is seen\n post.save()\n return render(request, 'gamersblog/blogposts/blogpostdetail.html', {'post': post})","title":""},{"docid":"776f63cf4b3d4bf23122ce90876feac8","score":"0.6341679","text":"def blog(request):\n assert isinstance(request, HttpRequest)\n posts = Blog.objects.all() # запрос на выбор всех статей блога из модели\n return render(\n request,\n 'app/blog.html',\n {\n 'title':'Блог',\n 'posts': posts,\n 'year':datetime.now().year,\n }\n )","title":""},{"docid":"47cfe02544b66f876a33f31b4817ef95","score":"0.63153785","text":"def blog_pagination(page):\n if page == 1:\n return redirect(url_for('blog_home'))\n if page < 1:\n abort(404)\n pagen_posts = posts.paginate_posts(page)\n if not pagen_posts:\n abort(404)\n return render_template(\n 'blog-home.html',\n pagination_nav=posts.get_pagination_nav_data(page),\n posts=pagen_posts\n )","title":""},{"docid":"4fe4d9a602b90d16487ab406cdebac01","score":"0.6282128","text":"def blog_post_list(request):\n object_list = BlogPost.objects.filter(\n published_date__lte=timezone.now()).order_by('published_date')\n # add pagination to the blog page, we will display 3 posts per page\n paginator = Paginator(object_list, 3) # 3 posts in each page\n page = request.GET.get('page')\n try:\n blog_posts = paginator.page(page)\n except PageNotAnInteger:\n # if the page is not an integer deliver the first page\n blog_posts = paginator.page(1)\n except EmptyPage:\n # if page is out of range deliver the last page of results\n blog_posts = paginator.page(paginator.num_pages)\n return render(request, 'gamersblog/blogposts/blogpostlist.html',\n {'page': page, 'blog_posts': blog_posts})","title":""},{"docid":"c1e2dbc9566c1771f916d9272fb0924b","score":"0.6238643","text":"def get(self):\n if self.user:\n user_key = self.user\n user_id = user_key.key.id()\n _user = User.by_id(int(user_id))\n _blogs = Blog.query_blogs().fetch()\n likes = Like.query().fetch()\n self.render('blog.html', blogs=_blogs, user=_user, likes=likes)\n else:\n _blogs = Blog.query_blogs().fetch()\n self.render('blog.html', blogs=_blogs, user=None, likes=None)","title":""},{"docid":"d9783ed2f6858650c3e995802d6c8afe","score":"0.62206197","text":"def post(post_id):\n post = Blogpost.query.filter_by(id=post_id).one()\n\n return render_template('post.html', post=post)","title":""},{"docid":"0ef47b95bcd7259646ffb36778709a6d","score":"0.6190501","text":"def get(self):\n show_comments = self.request.get(\"showComments\")\n if show_comments:\n if BlogEntry.get_by_id(int(show_comments), parent=fancy_blog):\n self.render_blog_page(expand_post=int(show_comments))\n else:\n self.handle_404()\n else:\n self.render_blog_page()","title":""},{"docid":"b801a49310f92bd45351f03993f09292","score":"0.61605465","text":"def edit():\n return render_template(\"editblog.html\",\n my_blog=mongo.db.blog.find())","title":""},{"docid":"4d82e2d76381c1bc6b246a0a9b054ba3","score":"0.61233556","text":"def index(request):\n # Get a list of liveblogs, ordered by the date of their most recent\n # post, descending (so ones with stuff happening are at the top)\n blogs = Blog.objects.annotate(\n max_created=Max(\"posts__created\")\n ).order_by(\"-max_created\")\n\n # Render that in the index template\n return render(request, \"posts/index.html\", {\n \"blogs\": blogs,\n })","title":""},{"docid":"a1344c1036a33f3f00a53ccecb20b132","score":"0.6093515","text":"def view_post(post_slug):\n try:\n deets = models.Post.select().where(models.Post.slug == post_slug).get()\n except models.DoesNotExist:\n abort(404)\n else:\n tags = deets.all_tags()\n return render_template('detail.html', deets=deets, tags=tags)","title":""},{"docid":"212601221d9b7056433d0e48633ec1d6","score":"0.6065636","text":"def index():\n # queries the db for all blog posts and displays the most recent first\n posts = Blogpost.query.order_by(Blogpost.date_posted.desc()).all()\n\n return render_template('index.html', posts=posts)","title":""},{"docid":"20440302fe89a9d399364d2089274a5e","score":"0.60421056","text":"def blog_list(request,):\n\n paginator = PageNumberPagination()\n paginator.page_size = 10\n blog_posts = Post.objects.all()\n paginated_results = paginator.paginate_queryset(blog_posts, request)\n serializer = BlogPostSerializer(paginated_results, many=True)\n return paginator.get_paginated_response(serializer.data)","title":""},{"docid":"b674c69e4730f08eeb26023e18e52aab","score":"0.6010444","text":"def get(self, _postID):\n blog_key = Blog.get_by_id(int(_postID))\n self.render('comment.html', blog=blog_key)","title":""},{"docid":"a3026fe0434106c38be97bc689de138a","score":"0.59919906","text":"def get_blog(id):\n if request.method == 'POST':\n error = None\n mark_left = '<'\n mark_right = '/>'\n content = request.form['content']\n if mark_left in content or mark_right in content:\n error = \"Input containing '<' or '/>' is not allowed.\"\n\n if error is None:\n db.session.add(Comment(blog_id=id,user_id=g.user.id,content=content))\n db.session.commit()\n return redirect(url_for('home.get_blog', id=id))\n\n flash(error)\n\n blog = Blog.query.filter_by(id=id).first()\n comments = Comment.query.filter(Comment.blog_id==id).all()\n return render_template('index_blog.html', blog=blog, comments=comments)","title":""},{"docid":"6ae928349c3d853c04e406ba81f2e99a","score":"0.5964416","text":"def get_absolute_url(self):\n return reverse('blog-detail', args=[str(self.id)])","title":""},{"docid":"23617bb46e54cc96593c0a0eb9c52574","score":"0.59584916","text":"def get(self):\r\n offset = int(self.request.get('start', 0))\r\n count = int(self.request.get('count', 20))\r\n posts = models.BlogPost.all().order('-published').fetch(count, offset)\r\n template_vals = {\r\n 'offset': offset,\r\n 'count': count,\r\n 'last_post': offset + len(posts) - 1,\r\n 'prev_offset': max(0, offset - count),\r\n 'next_offset': offset + count,\r\n 'posts': posts,\r\n }\r\n self.render_to_response('index.html', template_vals)","title":""},{"docid":"fd3f392cdfa79c61fe55cfe913a3018a","score":"0.5955088","text":"def blog_posts(blog_id):\n blog = Blog.from_mongo(blog_id)\n\n posts = blog.get_posts()\n\n return render_template('posts.html', posts=posts, blog_title=blog.title, blog_id=blog._id)","title":""},{"docid":"5ce8d537f4a97acff19e18b26a31f79f","score":"0.59389174","text":"def get_absolute_url(self):\n return reverse('blog:post_detail',\n args=[self.publish.year,\n self.publish.month,\n self.publish.day,\n self.slug])","title":""},{"docid":"793b7cda095b3b44e329aeeadbcab43c","score":"0.5934862","text":"def category_view(self, request, cat_slug):\n context = self.get_context(request)\n\n try:\n # Look for the blog category by its slug.\n category = BlogCategory.objects.get(slug=cat_slug)\n except Exception:\n # Blog category doesnt exist (ie /blog/category/missing-category/)\n # Redirect to self.url, return a 404.. that's up to you!\n category = None\n\n if category is None:\n # This is an additional check.\n # If the category is None, do something. Maybe default to a particular category.\n # Or redirect the user to /blog/ ¯\\_(ツ)_/¯\n pass\n\n context[\"posts\"] = BlogDetailPage.objects.live().public().filter(categories__in=[category])","title":""},{"docid":"211391d3fada7ea75223b1968539a920","score":"0.59243894","text":"def blog_details():\n \n # do we have a request for an existing blog post?\n blog_id = request.args(0)\n \n if blog_id is not None:\n record = db.blog_posts(blog_id)\n else:\n record = None\n \n if blog_id is not None and record is None:\n # avoid unknown blogs\n session.flash = B(CENTER('Invalid blog id'), _style='color:red;')\n redirect(URL('blogs','blogs'))\n \n elif record is None or record.user_id == auth.user.id or auth.has_membership('admin'):\n \n if record is None:\n buttons = [TAG.BUTTON('Submit', _type=\"submit\", _class=\"button btn btn-default\",\n _style='padding: 5px 15px 5px 15px;', _name='create')]\n readonly = False\n else:\n readonly = True if record.admin_status == 'Submitted' else False\n buttons = [TAG.BUTTON('Update and resubmit', _type=\"submit\",\n _class=\"button btn btn-default\",\n _style='padding: 5px 15px 5px 15px;', _name='update')]\n \n # provide a form to create or edit\n form = SQLFORM(db.blog_posts,\n readonly=readonly,\n record=record,\n buttons=buttons,\n fields=['thumbnail_figure','authors','title','content'], \n showid=False)\n \n if form.validate(onvalidation=validate_blog_post):\n \n req_keys = list(request.vars.keys())\n \n # get and add a comment to the history\n hist_str = '[{}] {} {}\\\\n -- {}\\\\n'\n new_history = hist_str.format(datetime.datetime.utcnow().strftime('%Y-%m-%dT%H:%MZ'),\n auth.user.first_name,\n auth.user.last_name,\n 'Post created' if blog_id is None else \"Post edited\")\n\n if 'update' in req_keys:\n id = record.update_record(admin_history = new_history + record.admin_history,\n **db.blog_posts._filter_fields(form.vars))\n id = id.id\n msg = CENTER(B('Blog post updated and resubmitted for approval.'),\n _style='color: green')\n\n elif 'create' in req_keys:\n id = db.blog_posts.insert(admin_history=new_history, \n **db.blog_posts._filter_fields(form.vars))\n \n msg = CENTER(B('Blog post created and submitted for approval.'),\n _style='color: green')\n else:\n pass\n \n # Email the link\n template_dict = {'name': auth.user.first_name, \n 'url': URL('blogs', 'blog_details', args=[id], scheme=True, host=True),\n 'submission_type': 'blog post'}\n \n safe_mailer(to=auth.user.email,\n subject='SAFE: blog post submitted',\n template = 'generic_submitted.html',\n template_dict = template_dict)\n \n session.flash = msg\n redirect(URL('blogs','blog_details', args=[id]))\n \n elif form.errors:\n response.flash = CENTER(B('Problems with the form, check below.'), _style='color: red')\n else:\n pass\n \n # package form into a panel\n if record is None:\n status = \"\"\n vis = \"\"\n else:\n status = DIV(approval_icons[record.admin_status], XML(' '),\n 'Status: ', XML(' '), record.admin_status, \n _class='col-sm-3',\n _style='padding: 5px 15px 5px 15px;background-color:lightgrey;color:black;')\n if record.hidden:\n vis = DIV('Hidden', _class='col-sm-1 col-sm-offset-1',\n _style='padding: 5px 15px 5px 15px;background-color:lightgrey;color:black;')\n else:\n vis = DIV('Visible', _class='col-sm-1 col-sm-offset-1',\n _style='padding: 5px 15px 5px 15px;background-color:lightgrey;color:black;')\n \n \n panel_header = DIV(H5('Blog post', _class='col-sm-7'), status, vis,\n _class='row', _style='margin:0px 0px')\n \n if readonly:\n content = XML(record.content)\n else:\n content = form.custom.widget.content\n \n form = FORM(form.custom.begin,\n DIV(DIV(panel_header, _class=\"panel-heading\"),\n DIV(LABEL('Thumbnail Figure:', _class=\"control-label col-sm-2\" ),\n DIV(form.custom.widget.thumbnail_figure, _class=\"col-sm-10\"),\n _class='row', _style='margin:10px 10px'),\n DIV(LABEL('Authors:', _class=\"control-label col-sm-2\" ),\n DIV(form.custom.widget.authors, _class=\"col-sm-10\"),\n _class='row', _style='margin:10px 10px'),\n DIV(LABEL('Title:', _class=\"control-label col-sm-2\" ),\n DIV(form.custom.widget.title, _class=\"col-sm-10\"),\n _class='row', _style='margin:10px 10px'),\n DIV(LABEL('Blog Content:', _class=\"control-label col-sm-2\" ),\n DIV(content, _class=\"col-sm-10\"),\n _class='row', _style='margin:10px 10px'),\n DIV(form.custom.submit, _class='panel-footer'),\n _class=\"panel panel-primary\"),\n form.custom.end)\n else: \n # security doesn't allow people editing other users blogs\n session.flash = CENTER(B('You do not have permission to edit this blog post.'), _style='color: red')\n redirect(URL('blogs','blog_post', args=blog_id))\n \n # admin history display\n if record is not None and record.admin_history is not None:\n admin_history = DIV(DIV(H5('Admin History', ), _class=\"panel-heading\"),\n DIV(XML(record.admin_history.replace('\\\\n', '<br />'),\n sanitize=True, permitted_tags=['br/']),\n _class = 'panel_body'),\n DIV(_class=\"panel-footer\"),\n _class='panel panel-primary')\n else:\n admin_history = DIV()\n \n ## ADMIN INTERFACE\n if record is not None and auth.has_membership('admin') and record.admin_status == 'Submitted':\n \n admin = admin_decision_form(['Resubmit','Approved'])\n \n if admin.process(formname='admin').accepted:\n \n # update record with decision\n admin_str = '[{}] {} {}\\\\n ** Decision: {}\\\\n ** Comments: {}\\\\n'\n new_history = admin_str.format(datetime.datetime.utcnow().strftime('%Y-%m-%dT%H:%MZ'),\n auth.user.first_name,\n auth.user.last_name,\n admin.vars.decision,\n admin.vars.comment) + record.admin_history\n \n record.update_record(admin_status = admin.vars.decision,\n admin_history = new_history)\n \n # pick an decision\n poster = record.user_id\n \n template_dict = {'name': poster.first_name, \n 'url': URL('blogs', 'blog_details', args=[blog_id], scheme=True, host=True),\n 'public_url': URL('blogs', 'blog_post', args=[blog_id], scheme=True, host=True),\n 'admin': auth.user.first_name + ' ' + auth.user.last_name,\n 'submission_type': 'blog post'}\n \n # pick an decision\n if admin.vars.decision == 'Approved':\n \n safe_mailer(to=poster.email,\n subject='SAFE: blog post approved',\n template = 'generic_approved.html',\n template_dict = template_dict)\n \n msg = CENTER(B('Blog approval emailed to poster at {}.'.format(poster.email)), _style='color: green')\n \n elif admin.vars.decision == 'Resubmit':\n\n safe_mailer(to=poster.email,\n subject='SAFE: blog post requires resubmission',\n template = 'generic_resubmit.html',\n template_dict = template_dict)\n \n msg = CENTER(B('Blog resubmission emailed to poster at {}.'.format(poster.email)), _style='color: green')\n \n else:\n pass\n \n redirect(URL('blogs','administer_blogs'))\n session.flash = msg\n \n elif admin.errors:\n response.flash = CENTER(B('Errors in form, please check and resubmit'), _style='color: red')\n else:\n pass\n else:\n admin = DIV()\n \n # pass components to the view\n return dict(form=form, admin_history=admin_history, admin=admin)","title":""},{"docid":"87d8ac83550e5618462ae678b816bbc5","score":"0.59204996","text":"def index():\n db = get_db()\n posts = db.execute(\n 'SELECT p.id, title, body, created, author_id, username'\n ' FROM post p JOIN user u ON p.author_id = u.id'\n ' ORDER BY created DESC'\n ).fetchall()\n return render_template('blog/index.html', posts=posts)","title":""},{"docid":"21e1a183829afa0ac2c157d9026a8e8e","score":"0.58921313","text":"def entryView(request, year, month, day, slug):\n date = datetime.date(int(year), int(month), int(day))\n entry = Entry.objects.get(slug=slug, pub_date__year=date.year,\n pub_date__month=date.month, pub_date__day=date.day)\n\n context = {'entry': entry}\n \n return render(request, 'blog/entry_detail.html', context)","title":""},{"docid":"cfd4b02ecebbda47b5c021d14d2e7cda","score":"0.5887412","text":"def get(self, _postID):\n if self.user:\n user = self.user\n parent = user.key\n blog_key = Blog.get_by_id(int(_postID))\n post = blog_key\n self.render('editpost.html', blog=post)","title":""},{"docid":"ef336aabfe3cfa90dc4961662817eb42","score":"0.5867479","text":"def get(self, year, month, day, slug):\n try:\n blog = Blog.find(year, month, day, slug)\n except Blog.DoesNotExist:\n abort(404, message='No such blog')\n except ValueError:\n abort(409, message='Multiple blogs found')\n return blog","title":""},{"docid":"86917dfe833533f4d3270b3ae3821881","score":"0.5861187","text":"def get_object(self, pk: int):\n try:\n return BlogArticle.objects.get(pk=pk)\n except BlogArticle.DoesNotExist:\n raise Http404","title":""},{"docid":"a53e72c1ecf9938fd90e9828132200db","score":"0.5810581","text":"def get(self, request, pk, blogger):\n possible_posts = PostQueryset.get_postdetail_by_user(request.user, pk, blogger)\n\n if len(possible_posts) == 0:\n return HttpResponseNotFound(\"El post solicitado no existe\")\n elif len(possible_posts) > 1:\n return HttpResponse(\"Multiples opciones\", status=300)\n\n post = possible_posts[0]\n context = {'post': post}\n return render(request, 'post/post_detail.html', context)","title":""},{"docid":"fd321764253c0b51e36f33d5e4cda3d4","score":"0.5791","text":"def all_blogs(request):\n blogs = Blog.objects.all()\n comments = Comment.objects.all()\n context = {\n 'blogs': blogs,\n 'comments': comments,\n 'on_blog_page': True,\n }\n\n return render(request, 'blogs/blogs.html', context)","title":""},{"docid":"ed1ef1fbf32a0af74148fc756dd305b6","score":"0.57875705","text":"def blog_guess_open(what):\n post_id = ''\n blog_index = -1\n if type(what) is str:\n\n for i, p in enumerate(vim.eval(\"VIMPRESS\")):\n if what.startswith(p[\"blog_url\"]):\n blog_index = i\n\n # User input a url contained in the profiles\n if blog_index != -1:\n guess_id = re.search(r\"\\S+?p=(\\d+)$\", what)\n\n # permantlinks\n if guess_id is None:\n\n # try again for /archives/%post_id%\n guess_id = re.search(r\"\\S+/archives/(\\d+)\", what)\n\n # fail, try get full link from headers\n if guess_id is None:\n headers = urllib.urlopen(what).headers.headers\n for link in headers:\n if link.startswith(\"Link:\"):\n post_id = re.search(r\"<\\S+?p=(\\d+)>\", link).group(1)\n\n else:\n post_id = guess_id.group(1)\n\n # full link with ID (http://blog.url/?p=ID)\n else:\n post_id = guess_id.group(1)\n\n # detected something ?\n assert post_id != '', \"Failed to get post/page id from '%s'.\" % what\n\n #switch view if needed.\n if blog_index != -1 and blog_index != g_data.conf_index:\n blog_config_switch(blog_index)\n\n # Uesr input something not a usabe url, try numberic\n else:\n try:\n post_id = str(int(what))\n except ValueError:\n raise VimPressException(\"Failed to get post/page id from '%s'.\" % what)\n\n blog_edit(\"post\", post_id)","title":""},{"docid":"8c8b55dfecfdab4f652e61c742db3730","score":"0.57394445","text":"def index(request):\n # pylint: disable=no-member\n latest = Post.objects.order_by('-pub_date')[:10]\n return render(request, 'index.html', {'posts': latest})","title":""},{"docid":"749733cfc7fe67d07cb07bf94469e6d5","score":"0.5732849","text":"def render_blog_page(self, posts=None, expand_post=0):\n if posts is None:\n posts = self.get_posts()\n\n self.render(\"blog_posts.html\",\n posts=posts, expand_post=expand_post,\n currUser=self.currUser, Comment=Comment, User=User,\n Like=Like, errors=self.errs)\n self.errs.clear()","title":""},{"docid":"0750cd741f316776e8e0a09feb077726","score":"0.5726915","text":"def bloggerlist(request):\n return render(request, 'blogger-list.html')","title":""},{"docid":"e22b20c2cefea6e71f7048b54dc190d7","score":"0.5721401","text":"def view_post(post_id):\n\n post = mongo.db.posts.find_one_or_404({\"_id\": ObjectId(post_id)})\n comments = mongo.db.comments.find({\"post_id\": post_id})\n\n context = {\n \"post\": post,\n \"comments\": comments\n }\n\n return render_template('view-post.html', **context)","title":""},{"docid":"c796e1179a68277261b7f73affa8121e","score":"0.57090974","text":"def posteo(request,slug):\n #post = Post.objects.get(slug=slug)\n post = get_object_or_404(Post,slug=slug)\n return render(request,'posts.html',{'post':post})","title":""},{"docid":"dbcd352cf622618687a5ef709ba59887","score":"0.57070285","text":"def view_advice(request):\n posts = Post.objects.filter(published_date__lte=timezone.now()).order_by('-published_date')\n return render(request, 'view_advice.html', {'posts': posts})","title":""},{"docid":"a06afb8098ebf3ea33fb99958f36c422","score":"0.5706511","text":"def get_absolute_url(self):\n\t\treturn reverse('blog:article', args=[str(self.id)])","title":""},{"docid":"27dc47716c327576ab912a8e47790ed3","score":"0.57050043","text":"def view(request, doc_id=None):\n # Get the document from the database.\n if doc_id is not None:\n try:\n doc = Document.objects.get(doc_id=doc_id)\n return render('index.html', {'doc':doc})\n except Document.ObjectNotFoundException:\n pass","title":""},{"docid":"5adf8224d1a8bec19c54d80af2905afb","score":"0.5701729","text":"def index(request):\n post_list = Post.objects.all()\n paginator = Paginator(post_list, PAGINATOR_PER_PAGE)\n page_number = request.GET.get('page')\n page = paginator.get_page(page_number)\n return render(request, 'index.html', {'page': page})","title":""},{"docid":"fdb77055f787ec755802ebc524cfe9d4","score":"0.56758744","text":"def article(request, slug):\n article = get_object_or_404(Article, slug=slug)\n return _render_contextually(request,\n 'blog/article.html',\n {'article': article,\n 'comment_form': CommentForm()})","title":""},{"docid":"42ba52f7dfa1874680324d6e0187992a","score":"0.56666684","text":"def post_detail(post_id):\n # TODO: change to use slug instead of id\n post = Post.query.get_or_404(post_id)\n return render_template('post_detail.html', post=post, title=post.title)","title":""},{"docid":"73c13ec6c88c9763ce7f1d6907e78ae0","score":"0.5666153","text":"def get_absolute_view_url(self):\n return ('books.views.view_entry', [self.id, '-' + slugify(self.title)])","title":""},{"docid":"fa3b1482d3bb6578837b6f4848b4a09c","score":"0.56246835","text":"def get_absolute_url(self):\r\n url_name = \"blog_post_detail\"\r\n kwargs = {\"slug\": self.slug}\r\n date_parts = (\"year\", \"month\", \"day\")\r\n if settings.BLOG_URLS_DATE_FORMAT in date_parts:\r\n url_name = \"blog_post_detail_%s\" % settings.BLOG_URLS_DATE_FORMAT\r\n for date_part in date_parts:\r\n date_value = str(getattr(self.publish_date, date_part))\r\n if len(date_value) == 1:\r\n date_value = \"0%s\" % date_value\r\n kwargs[date_part] = date_value\r\n if date_part == settings.BLOG_URLS_DATE_FORMAT:\r\n break\r\n return reverse(url_name, kwargs=kwargs)","title":""},{"docid":"e73d8679d8f086a62afffe78ee8beefb","score":"0.5613305","text":"def get_post(post_id):\n post = mongo.db.posts.find_one({\"_id\": ObjectId(post_id)})\n return render_template(\"get_post.html\", post=post)","title":""},{"docid":"0ccb5efebbefb0aec73116f9d02abbb5","score":"0.5605165","text":"def article_view(request, slug):\n # TODO: Tags in article view\n try:\n a = Article.objects.get(url=slug)\n except:\n # Article does not exist\n return HttpResponseRedirect(reverse('article_invalid'))\n\n path = os.path.join(os.path.dirname(__file__), a.location)\n with open(path, 'rU') as f:\n text_string = f.read()\n md = markdown.Markdown(extensions=['markdown.extensions.toc'])\n html = md.convert(text_string)\n s = md.toc\n\n # Rendering toc to include bootstrap classes\n toc = '<div class=\"toc\">\\n<ul>\\n'\n\n while 1:\n idx1 = s.find('<li>')\n idx2 = s.find('<li>', idx1 + 4)\n\n if idx2 == -1:\n toc += '<li class=\"list-group-item\">' + s[idx1 + 4:]\n break\n\n toc += '<li class=\"list-group-item\">' + s[idx1 + 4:idx2 - 1] + '\\n'\n s = s[idx2:]\n\n s = toc\n\n idx1 = s.find('<ul>')\n toc = s[:idx1]\n\n while 1:\n idx1 = s.find('<ul>')\n idx2 = s.find('<ul>', idx1 + 4)\n\n if idx2 == -1:\n toc += '<ul class=\"list-group\">' + s[idx1 + 4:]\n break\n\n toc += '<ul class=\"list-group\">' + s[idx1 + 4:idx2] + '\\n'\n s = s[idx2:]\n\n return render(request, 'wiki/article.html', {'title': a.title, 'text': html, 'toc': toc})","title":""},{"docid":"6840e83de15fdc911d1ff6c3bdac00f7","score":"0.5587149","text":"def details(request, slug):\n # get the right model\n context = RequestContext(request)\n # Get a Page model object from the request\n page = get_page_from_request(request, use_path=slug)\n if not page:\n return _handle_no_page(request, slug)\n \n current_language = get_language_from_request(request)\n \n # Check that the current page is available in the desired (current) language\n available_languages = page.get_languages()\n \n # We resolve an alternate language for the page if it's not available.\n # Since the \"old\" details view had an exception for the root page, it is\n # ported here. So no resolution if the slug is ''.\n if (current_language not in available_languages):\n if settings.CMS_LANGUAGE_FALLBACK:\n # If we didn't find the required page in the requested (current) \n # language, let's try to find a suitable fallback in the list of \n # fallback languages (CMS_LANGUAGE_CONF)\n for alt_lang in get_fallback_languages(current_language):\n if alt_lang in available_languages:\n alt_url = page.get_absolute_url(language=alt_lang, fallback=True)\n path = '/%s%s' % (alt_lang, alt_url)\n # In the case where the page is not available in the\n # preferred language, *redirect* to the fallback page. This\n # is a design decision (instead of rendering in place)).\n return HttpResponseRedirect(path)\n # There is a page object we can't find a proper language to render it \n _handle_no_page(request, slug)\n\n if apphook_pool.get_apphooks():\n # There are apphooks in the pool. Let's see if there is one for the\n # current page\n # since we always have a page at this point, applications_page_check is\n # pointless\n # page = applications_page_check(request, page, slug)\n # Check for apphooks! This time for real!\n app_urls = page.get_application_urls(current_language, False)\n if app_urls:\n app = apphook_pool.get_apphook(app_urls)\n pattern_list = []\n for urlpatterns in get_app_urls(app.urls):\n pattern_list += urlpatterns\n urlpatterns = patterns('', *pattern_list)\n try:\n view, args, kwargs = resolve('/', tuple(urlpatterns))\n return view(request, *args, **kwargs)\n except Resolver404:\n pass\n\n # Check if the page has a redirect url defined for this language. \n redirect_url = page.get_redirect(language=current_language)\n if redirect_url:\n if (settings.i18n_installed and redirect_url[0] == \"/\"\n and not redirect_url.startswith('/%s/' % current_language)):\n redirect_url = \"/%s/%s\" % (current_language, redirect_url.lstrip(\"/\"))\n # add language prefix to url\n return HttpResponseRedirect(redirect_url)\n \n # permission checks\n if page.login_required and not request.user.is_authenticated():\n if settings.i18n_installed:\n path = urlquote(\"/%s%s\" % (request.LANGUAGE_CODE, request.get_full_path()))\n else:\n path = urlquote(request.get_full_path())\n tup = settings.LOGIN_URL , \"next\", path\n return HttpResponseRedirect('%s?%s=%s' % tup)\n \n template_name = get_template_from_request(request, page, no_current_page=True)\n # fill the context \n context['lang'] = current_language\n context['current_page'] = page\n context['has_change_permissions'] = page.has_change_permission(request)\n context['has_view_permissions'] = page.has_view_permission(request)\n \n if not context['has_view_permissions']:\n return _handle_no_page(request, slug)\n \n return render_to_response(template_name, context)","title":""},{"docid":"0296c115cfd0fbedcc5792bb5c8c3fb3","score":"0.5580343","text":"def get(self, request, pk):\n blog = self.get_object(pk)\n serializer = BlogSerializer(blog)\n return Response(serializer.data)","title":""},{"docid":"a637148c77cc473adbee05bfd26e499e","score":"0.5547906","text":"def post(self):\n\n # check if user have login\n if not self.user:\n self.redirect('/login')\n else:\n # get parameters from header\n post_id = self.request.get(\"blog_id\", None)\n save = self.request.get(\"save\", None)\n cancel = self.request.get(\"cancel\", None)\n subject = self.request.get('subject', default_value=None)\n content = self.request.get('content', default_value=None)\n\n # check if user input just spaces to page\n if subject and content:\n subject = subject.strip()\n content = content.strip()\n\n # find post\n key = db.Key.from_path('Post', int(post_id), parent=blog_key())\n post = db.get(key)\n\n # if there is no any posts\n if not post:\n self.error(404)\n return\n\n # prepare parameters for rendering page\n param = dict(p=post, subject=post.subject, content=post.content)\n\n # check if user is owner of post\n if post.owner.key() != self.user.key():\n param[\"is_errors\"] = True\n param[\"error\"] = \"You cannot edit this post, you are not owner!\"\n\n self.render(\"edit-post.html\", **param)\n else:\n # handle user choice save or not\n if save or cancel:\n if save:\n if subject and content:\n post.subject = subject\n post.content = content\n post.save()\n\n self.redirect(\"/blog/{}\".format(post_id))\n else:\n param[\"edit_error\"] = \"subject and content, please!\"\n self.render(\"edit-post.html\", **param)\n else:\n self.redirect(\"/blog/{}\".format(post_id))\n else:\n self.render(\"edit-post.html\", **param)","title":""},{"docid":"bef0e8a6779e6dfcb6d9bb94d072894e","score":"0.5530161","text":"def get(self, request, slug):\n\t\ttry:\n\t\t\tpost = Post.objects.get(slug=str(slug), is_deleted=False)\n\t\t\tarticle = post.article\n\t\t\tis_user_article = False\n\t\t\tif request.user.is_authenticated():\n\t\t\t\tuser_prof = UserProfile.objects.get(user=request.user)\n\t\t\t\tcontributor = user_can_contribute(request.user)\n\t\t\t\tif user_prof.is_contributor == True:\n\t\t\t\t\tif contributor.id is post.contributor.id or user_prof.user.email == \"relevate@outlook.com\":\n\t\t\t\t\t\tis_user_article = True\n\t\t\t\telse:\n\t\t\t\t\tpost.views = post.views + 1\n\t\t\t\t\tpost.save()\n\t\t\t\treturn render(request, 'article_view.html',\n\t\t\t\t\t{\n\t\t\t\t\t\t'article':article,\n\t\t\t\t\t\t'is_user_article':is_user_article,\n\t\t\t\t\t\t'user_prof':user_prof,\n\t\t\t\t\t\t'post': post,\n\t\t\t\t\t\t'first_name':request.user.first_name,\n\t\t\t\t\t\t'last_name':request.user.last_name\n\t\t\t\t\t})\n\t\t\telse:\n\t\t\t\treturn render(request, 'article_view.html',\n\t\t\t\t\t\t\t {\n\t\t\t\t\t\t\t\t 'article': article,\n\t\t\t\t\t\t\t\t 'is_user_article': is_user_article,\n\t\t\t\t\t\t\t\t 'post': post\n\t\t\t\t\t\t\t })\n\t\texcept ObjectDoesNotExist:\n\t\t\treturn HttpResponseRedirect(reverse('contribution:all_posts'))","title":""},{"docid":"341b22eac2723be0c2640cd26e6f3566","score":"0.55256695","text":"def rss_xml_index(self, blog=False):\n blog_obj = request.env['blog.post']\n content = blog_obj._get_previous_blog_rss()\n if not content:\n return request.not_found()\n return request.make_response(\n content, [('Content-Type', 'application/xml;charset=utf-8')])","title":""},{"docid":"99bb60ce61449b3ca2c7e91742819e18","score":"0.5523938","text":"def detail_page(request, article_id):\n article = Article.objects.get(id=article_id)\n return render(request, 'article_detail_page.html', locals())","title":""},{"docid":"74642c13ee54e5f797ef8e8764d96f01","score":"0.5507638","text":"def post_detail(request, post_id):\n return render(request, 'post-detail.html', {\"post\": Post.objects.get(\n id=post_id)})","title":""},{"docid":"542430e80d3e7bd0b68cfcb504588b9d","score":"0.54808","text":"def view_blog_create(self):\n logged_in = authenticated_userid(self.request)\n form = Form(self.request, schema=BlogCreateSchema,\n state=State(request=self.request))\n blogname = get_param(self.request, 'blogname')\n text = get_param(self.request, 'text')\n image_url = get_param(self.request, 'image_url')\n\n if form.validate():\n blog = self.context.add(blogname, text,\n image_url, logged_in)\n return HTTPFound(location=resource_url(blog, self.request))\n\n _dict = {\n 'form': FormRenderer(form),\n 'message': self.message,\n 'blogname': blogname,\n 'text': text,\n 'image_url': image_url\n }\n return dict(self.base_dict.items() + _dict.items())","title":""},{"docid":"eaed33e32e1313c71f3205c4d71fcb3e","score":"0.54630744","text":"def main_page():\n cur1 = g.db.execute('SELECT published_date, title, author, content from POSTS ORDER BY published_date DESC')\n posts = [dict(pub_date=row[0], title=row[1],\n author=row[2], content=row[3]) for row in cur1.fetchall()]\n return render_template('mainpage.html', posts=posts)","title":""},{"docid":"bb92ab670741ac02867a5ba769dcd205","score":"0.5457308","text":"def article(request, author, year, month, day, slug):\n\n user = User.objects.get(username = author)\n blog = get_object_or_404(Blog, owner__username = author)\n article = get_object_or_404(Article,\n author__id = user.id,\n publication_date__year = int(year),\n publication_date__month = Article.month_number(month),\n publication_date__day = int(day),\n title_in_url = slug)\n\n if not (article.public or request.user.is_authenticated()):\n return handle_403(request)\n\n older_articles = Article.objects.filter(publication_date__lt = article.publication_date,\n author = user).order_by(\"-publication_date\")\n newer_articles = Article.objects.filter(publication_date__gt = article.publication_date,\n author = user).order_by(\"publication_date\")\n\n if not request.user.is_authenticated():\n older_articles = older_articles.filter(public = True)\n newer_articles = newer_articles.filter(public = True)\n\n try: previous_article = older_articles[0]\n except IndexError: previous_article = None\n\n try: next_article = newer_articles[0]\n except IndexError: next_article = None\n\n data = common_data(request)\n data.update({\"article\": article,\n \"previous_article\": previous_article,\n \"next_article\": next_article,\n \"active_blog\": blog,\n \"tags\": Tag.used_tags(user.id),\n \"recent_active_months\" : blog.last_active_months(),\n \"archive_qualifier\": \"/%s\" % user.username})\n\n # Substitute gallery links:\n def gallery_link(match):\n gallery = get_object_or_404(Gallery, title_slug = match.group(\"gallery_slug\"))\n context = RequestContext(request, dict = {\"gallery\": gallery,\n \"caption\": match.group(\"caption\") or gallery.description})\n return loader.render_to_string(\"gallery_fragment.html\", None, context)\n\n data[\"html_teaser\"] = GALLERY_MATCHER.sub(gallery_link, data[\"article\"].html_teaser)\n data[\"html_text\"] = GALLERY_MATCHER.sub(gallery_link, data[\"article\"].html)\n\n # Substitute photo links:\n def photo_link(match):\n photo = get_object_or_404(Photo, title_slug = match.group(\"photo_slug\"))\n size = match.group(\"size\") or \"display\"\n url = photo.__getattribute__(\"get_standalone_%s_url\" % size)()\n context = RequestContext(request, dict = {\"photo\": photo,\n \"url\": url,\n \"size\": size,\n \"caption\": match.group(\"caption\") or photo.caption})\n return loader.render_to_string(\"photo_fragment.html\", None, context)\n\n data[\"html_teaser\"] = PHOTO_MATCHER.sub(photo_link, data[\"html_teaser\"])\n data[\"html_text\"] = PHOTO_MATCHER.sub(photo_link, data[\"html_text\"])\n\n # Substitute youtube links:\n def youtube_link(match):\n youtube_id = match.group(\"youtube_id\")\n context = RequestContext(request, dict = {\"youtube_id\": youtube_id})\n return loader.render_to_string(\"youtube_fragment.html\", None, context)\n\n data[\"html_teaser\"] = YOUTUBE_MATCHER.sub(youtube_link, data[\"html_teaser\"])\n data[\"html_text\"] = YOUTUBE_MATCHER.sub(youtube_link, data[\"html_text\"])\n\n return render(\"blog_article.html\", request, data)","title":""},{"docid":"c1dadbec82c186de1b6b1b67c56b67b4","score":"0.545383","text":"def home_page():\n\n posts = Post.query.all()\n sorted_posts = sorted(posts, key=attrgetter('created_at'), reverse=True)\n\n return render_template('home.html', posts=sorted_posts)","title":""},{"docid":"6d1edb48bdfd44c8690ca1d2838419be","score":"0.54496306","text":"def blogpost(request, parametr):\n assert isinstance(request, HttpRequest)\n post_1 = Blog.objects.get(id=parametr) # запрос на выбор конкретной статьи по параметру\n comments = Comment.objects.filter(post=parametr) # запрос на выбор всех комментариев выбранной статьи\n if request.method == \"POST\":\n form = CommentForm(request.POST)\n if form.is_valid():\n comment_f = form.save(commit=False)\n comment_f.author = request.user # добавляем (т.к. этого поля нет в форме) в модель Комментария\n #(Comment) в поле автор авторизованного пользователя\n comment_f.date = datetime.now() # добавляем в модель Комментария (Comment) текущую дату\n comment_f.post = Blog.objects.get(id=parametr) # добавляем в модель Comment статью, для которой данный комментарий\n comment_f.save() # сохраняем изменения после добавления полей\n return redirect('blogpost', parametr=post_1.id) # переадресация на ту же страницу статьи после отправки комментария\n else:\n form = CommentForm() # создание формы для ввода комментария\n return render(\n request,\n 'app/blogpost.html',\n {\n 'post_1': post_1, # передача конкретной статьи в шаблон веб-страницы\n 'comments': comments, # передача всех комментов к данной статье в шаблон веб-страницы\n 'form': form, # передача формы добавления коммента в шаблон веб-страницы\n 'year':datetime.now().year,\n }\n )","title":""},{"docid":"18558f2288e3ea618b777e2311acb9d2","score":"0.54351205","text":"def get(current_user,self,blog_id):\n get_blog = self.db.get_single_blog(blog_id) \n if get_blog == None:\n return \"the blog doesn't exist\"\n \n return jsonify({\n \"status\" : 200,\n \"message\" : get_blog\n })","title":""},{"docid":"5961f121a617645aba32845b695882ac","score":"0.54345965","text":"def home(request):\n return render(request, 'blog/mapage.html')\n # return HttpResponse(\"\"\"\n # <h1>Bienvenue sur mon blog !</h1>\n # <p>Les crêpes bretonnes ça tue des mouettes en plein vol !</p>\n # \"\"\")","title":""},{"docid":"e513f6eb7d8ea9b95a4ff7541081a1ff","score":"0.54311866","text":"def document(request, document_slug):\n # This may change depending on how we decide to structure\n # the url and handle locales.\n doc = get_object_or_404(Document, title=document_slug.replace('+', ' '))\n return jingo.render(request, 'wiki/document.html',\n {'document': doc})","title":""},{"docid":"0501a83effc9cba28917936c9bdea1b2","score":"0.54190326","text":"def get(self, post_id):\n post = model.BlogPost.exists(post_id)\n if self.user:\n if post:\n if post.author != self.user.userName: # Cant modify someone elses post\n query_params = {'modifyError': \"Sorry - Cannot modify a post you did not author\"}\n self.redirect('/%s?%s' % (post_id, urllib.urlencode(query_params)))\n else:\n # display blogPost page with previous content for modification\n self.render('blogPost.html', title=\"Modify Blog Post\",\n subject=post.subject, content=post.content, id=post_id)\n else:\n query_params = {'message': \"That blog post does not exist\"}\n self.redirect('/?%s' % (urllib.urlencode(query_params)))\n else:\n query_params = {'loginError': \"You must be signed in to delete or modify a blog post\"}\n self.redirect('/signup?%s' % (urllib.urlencode(query_params)))","title":""},{"docid":"4762e619a0315283b14ce016d4c94964","score":"0.5418522","text":"def render(self, context):\n\n max_title = 20\n blogs = Blog.objects.all().order_by(\"-updated\")[:3]\n pages = Page.objects.all().order_by(\"-updated\")[:2]\n\n html = \"<ul id='latest'>\"\n\n for blog in blogs:\n extra_str = \"\"\n date = blog.updated.strftime(\"%Y/%m/%d\")\n\n if len(blog.title) > max_title:\n extra_str = \"...\"\n\n html = html + '<li><a href=\"/blog/%s\">%s</a> %s<span class=\"smalltxt\"> (%s)</span></li>' % \\\n (date, blog.title[:max_title], extra_str, date)\n\n # FIXME: Bad to hardcode these names here b/c now there are\n # dependencies here, views, and urls\n for page in pages:\n date = page.updated.strftime(\"%Y/%m/%d\")\n\n if page.name == \"Our Story\":\n link = \"story\"\n elif page.name == \"Gift Registry\":\n link = \"gifts\"\n else:\n link = \"home\"\n\n html = html + '<li><a href=\"/%s\">%s</a><span class=\"smalltxt\"> (%s)</span></li>' % (link, page.name[:max_title], date)\n html = html + \"</ul>\"\n return html","title":""},{"docid":"b30e934ef84c3bfdc2af15665bda590e","score":"0.54166925","text":"def index():\n page = request.args.get('page', 1, type=int)\n pagination = Post.query.order_by(Post.timestamp.desc()).paginate(page, per_page=10, error_out=False)\n posts = pagination.items\n return render_template(\n 'index.html',\n title='主页',\n posts=posts,\n pagination=pagination\n )","title":""},{"docid":"501d23437851c117fa94599b1774dabd","score":"0.5414214","text":"def post(post_id):\n the_post = get_post(post_id)\n if the_post is None:\n app.logger.error(\"no article by the id available\")\n sys.stderr.write(\"no article by the id available\")\n return render_template('404.html'), 404\n\n app.logger.info(\"article retrieved; title: \" + the_post.title)\n sys.stdout.write(\"article retrieved; title: \" + the_post.title)\n return render_template('post.html', post=the_post)","title":""},{"docid":"ebf3a5e87b88000ce54b55ddb35c1b47","score":"0.540924","text":"def pageViewActivity(request):\n return render(request, 'page_view_activity.html')","title":""},{"docid":"5d0bea90c2453a5cd49b3e25a1501530","score":"0.54091233","text":"def lire(request, id):\n # methode longue\n article = \"article\"\n try:\n article = Article.objects.get(id=id)\n except Article.DoesNOtExist:\n raise Http404\n\n return render(request, 'blog/lire.html', locals())","title":""},{"docid":"9ccfe0d8566af1032d175b9b4bb077f3","score":"0.54079276","text":"def author_detail(request, **kwargs):\n username = kwargs.pop('username')\n page = kwargs['page'] if 'page' in kwargs else None\n author = get_object_or_404(User, username=username)\n extra_context = {'author': author}\n filter = {}\n if ZINNIA_BLOG_ACTIVE:\n blog_slug = kwargs.pop('blog_slug')\n blog = get_object_or_404(Blog, slug = blog_slug)\n # We check if author is a blog author\n if author not in blog.authors.all():\n raise Http404('User not valid')\n filter = {'blog__slug': blog_slug}\n extra_context.update({'blog': blog, 'filters': filter})\n queryset = entries_published(author.entry_set).filter(**filter)\n return object_list(request, queryset=queryset, paginate_by=PAGINATION, \n page=page, extra_context=extra_context)","title":""},{"docid":"69fd092f578094316b0ab03bad7016bc","score":"0.5400128","text":"def fetch_blog_from_db(blog_id):\n with SQLite('application.db') as cur:\n cur.execute(f\"SELECT * FROM blogs where id=?\", [blog_id])\n result = cur.fetchone()\n if result is None:\n raise NotFoundError(f'Unable to find blog with id {blog_id}.')\n return result","title":""},{"docid":"4efeb00bc367fec86a622719f012995f","score":"0.5399736","text":"def home(request):\n ROOT_URL = settings.ROOT_BLOG_URL\n ROOT_URL = ROOT_URL.rstrip(\"/\")\n return render_to_response(\n \"home.html\", \n {\n 'ROOT_URL': ROOT_URL, \n 'heading':'Bob Flagg',\n 'subheading':'Mathematician and Data Scientist'\n }, \n context_instance=RequestContext(request)\n )","title":""},{"docid":"6db7e4d7f27c647e00fc55a049ec06df","score":"0.5398989","text":"def index():\n title = 'HOME'\n quotes = get_quotes()\n blogs = Blogs.query.all()\n return render_template('index.html', title=title, quotes=quotes, blogs=blogs)","title":""},{"docid":"57270f8f99ea6b0654efdd5a8b694f86","score":"0.5398321","text":"def about(request):\n args = dict()\n args['about'] = Pages.objects.filter(title='About us').first() or None\n return render(request, 'main/about.html', args)","title":""},{"docid":"6d3917c50c01c2582d344f790fc55607","score":"0.53938437","text":"def lire(request, id_article, slug):\r\n global_var.refresh()\r\n article = get_object_or_404(Article, id=id_article, slug=slug)\r\n print(article)\r\n return render(request, 'blog/post.html', {'article': article, 'global': global_var})","title":""},{"docid":"6cee0f5a5d1104fdfc3c9fab22b0ea0e","score":"0.53908306","text":"def page(path):\n page = pages.get_or_404(path)\n return render_template('page.html', page=page)","title":""},{"docid":"3db9c3378c98c85075ee7b055a8ceaea","score":"0.5384302","text":"def home():\n posts = [Post(bson=d) for d in mongo.db.posts.find()]\n return render_template('home.html', posts=posts)","title":""},{"docid":"68c3bd3fc72d6552ed059d0cc474347b","score":"0.5378881","text":"def home(request):\n return render(request, 'article_listing_page.html', locals())","title":""},{"docid":"270c94e49ce282f9487d2a5dd7865d8e","score":"0.5375665","text":"def display_post_detail(post_id):\n post = Post.query.get_or_404(post_id)\n return render_template('post_detail.html', post=post)","title":""},{"docid":"8c9c2fa8d528e9e20a7c25b09afaa2b0","score":"0.53541136","text":"def get(self, request: Request, pk: int):\n post = self.get_object(pk)\n return Response(serializers.BlogArticleGetSerializer(post).data)","title":""},{"docid":"49e017b76df21baedaa16a70b7f9b11c","score":"0.5345575","text":"def GetBlogFeed(self, uri=None):\n if not uri:\n uri = '/feeds/default/blogs'\n return self.Get(uri, converter=gdata.blogger.BlogFeedFromString)","title":""},{"docid":"53f500958dc643732e4c892807d3ef53","score":"0.53432244","text":"def get_pages(page=1, blog=None):\n\n if (page == 1):\n per_page = current_app.config['FLASKY_POSTS_PER_PAGE'] + 1\n print 'if per_page', per_page\n else:\n per_page = current_app.config['FLASKY_POSTS_PER_PAGE'] + 1\n print 'else per_page', per_page\n\n pages = Post.query.order_by(Post.timestamp.desc()).filter_by(is_blog=blog).paginate(page,\n per_page=per_page, \\\n error_out=False)\n\n posts = Post.query.filter_by(is_blog=blog)\n return pages","title":""},{"docid":"1c024dee8612a95591cb67d61c0eb864","score":"0.5338668","text":"def index():\n user_id = session.get(\"user_id\")\n messages = Message.query.filter_by(user_id = user_id).all()\n\n return render_template(\"blog/index.html\", messages=messages)","title":""},{"docid":"d8733b7dd704e247331e567b9093525d","score":"0.5329975","text":"def get(self, request):\n\t\tif request.user.is_authenticated:\n\t\t\tuser_prof = UserProfile.objects.get(user=request.user)\n\t\t\tif (user_prof.is_contributor):\n\t\t\t\treturn render(request, \"new_post.html\", {'user_prof': user_prof,})\n\t\t\telse:\n\t\t\t\treturn render(request, \"home.html\")\n\t\telse:\n\t\t\treturn render(request, \"home.html\")","title":""},{"docid":"5893a0c64728cc105a67d05650aba22b","score":"0.53247994","text":"def home_page_view(request):\n context = {\n 'categories': Category.objects.all()\n }\n return render(request, 'blog/home_page.html', context)","title":""},{"docid":"4104c84639f3c866186eeee8b893a3ea","score":"0.53239465","text":"def blog_archive():\n return render_template(\n 'blog-categories.html',\n categories=posts.posts_by_date(),\n title='Date',\n sort_type='date'\n )","title":""},{"docid":"01b539ae11e9b937286fc6428ad6315a","score":"0.5313553","text":"def get_all_blogs():\n blogs = Blog.query.order_by(Blog.created_at.desc()).all()\n return render_template('index.html', blogs=blogs)","title":""},{"docid":"a30cb943eb411471924fa58f0362f1a9","score":"0.53109276","text":"def post_list(request):\n #queryset to order the blog posts by published date\n posts = Post.objects.filter(published_date__lte=timezone.now()).order_by('published_date')\n #{} this is where we can store things the template will use \n return render(request, 'blog/post_list.html', {'posts' : posts})","title":""},{"docid":"efca8a22b9c3a4d7fcd80eb2c17f101f","score":"0.53051287","text":"def lire(request, id, slug):\n article = get_object_or_404(Article, id=id, slug=slug)\n return render(request, 'blog/lire.html', {'article': article})","title":""}],"string":"[\n {\n \"docid\": \"4d9aca7b2e1de6d48570604a588a6461\",\n \"score\": \"0.7157125\",\n \"text\": \"def blog_post(request, url):\\n\\n blog = get_object_or_404(Post, url=url)\\n\\n context = {\\n \\\"blog\\\": blog,\\n }\\n\\n return render(request, \\\"blog/blog_post.html\\\", context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a4d00d4803c69db2baa0861ad3235ce\",\n \"score\": \"0.71384466\",\n \"text\": \"def blog(request):\\n posts = Post.objects.all()\\n paginator = Paginator(posts, 3)\\n page_number = request.GET.get('page')\\n page_obj = paginator.get_page(page_number)\\n\\n context = {'posts': posts,\\n 'page_obj': page_obj,\\n 'on_blog_page': True,\\n }\\n return render(request, 'blog/blog.html', context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5ac2b47f2e432e6b64114cf394e0191\",\n \"score\": \"0.7081953\",\n \"text\": \"def blog(request, blog_id):\\n\\tblog = get_object_or_404(Blog, id=blog_id)\\n\\tif blog.public==False and check_blog_owner(blog.owner, request.user)!=Http404:\\n\\t\\tposts = blog.post_set.order_by('-date_added')\\n\\t\\tcontext = {'blog':blog, 'posts':posts}\\n\\t\\treturn render(request, 'blogs/blog.html', context)\\n\\telif blog.public==True:\\n\\t\\tposts = blog.post_set.order_by('-date_added')\\n\\t\\tcontext = {'blog':blog, 'posts':posts}\\n\\t\\treturn render(request, 'blogs/blog.html', context)\\n\\treturn Http404\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd549f9f3df885d0a7c239a8ab21f937\",\n \"score\": \"0.7014336\",\n \"text\": \"def blogpostdetail(request):\\n return render(request, 'blogpost-detail.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d251833ded86886af730775a085cc6f\",\n \"score\": \"0.6806661\",\n \"text\": \"def blog_index(request):\\n return render(request,\\\"sciblog/index.html\\\",locals())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27018f1da7cb355a3f7f37b1385d1069\",\n \"score\": \"0.67999965\",\n \"text\": \"def get(self, post_id):\\n user = self.user\\n parent = user.key\\n blog_key = Blog.get_by_id(int(post_id))\\n post = blog_key\\n\\n if not post:\\n self.render('error.html')\\n return\\n\\n self.render(\\\"permalink.html\\\", blog=post)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3da32418e66967db9b746c4350f61a77\",\n \"score\": \"0.673371\",\n \"text\": \"def most_viewed(request):\\n top_posts = BlogPost.objects.filter(\\n published_date__lte=timezone.now()).order_by('-post_views')[:10]\\n # add pagination to the blog page, we will display 3 posts per page\\n paginator = Paginator(top_posts, 3) # 3 posts in each page\\n page = request.GET.get('page')\\n try:\\n blog_posts = paginator.page(page)\\n except PageNotAnInteger:\\n # if the page is not an integer deliver the first page\\n blog_posts = paginator.page(1)\\n except EmptyPage:\\n # if page is out of range deliver the last page of results\\n blog_posts = paginator.page(paginator.num_pages)\\n return render(request, 'gamersblog/blogposts/blogpostlist.html',\\n {'page': page, 'blog_posts': blog_posts})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e3631452fd704791f8220161027214b\",\n \"score\": \"0.66507894\",\n \"text\": \"def index(request):\\n\\treturn render(request, 'blogs/index.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3ad8a4b6f66fa0c1c357f43cdb338b1\",\n \"score\": \"0.6637677\",\n \"text\": \"def blog(request, blog_id):\\n blog = Blog.objects.get(id=blog_id)\\n # Make sure the topic belongs to the current user.\\n # if blog.owner != request.user:\\n # raise Http404\\n\\n chapters = blog.text\\n context = {'blog': blog, 'chapters': chapters,'image':''}\\n return render(request, 'blog.html', context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49c3fe0f5ed412c32163fbbaf462e87b\",\n \"score\": \"0.6636706\",\n \"text\": \"def blog_post(path):\\n post = posts.get_or_404(f'{path}/{POST_FILENAME}')\\n\\n github_repo = None\\n if 'github' in post.meta:\\n try:\\n github_repo = [r for r in get_github_user_repos() if r.full_name == post.meta.get('github')][0]\\n except IndexError as e:\\n print(f'The repository {post.meta.get(\\\"github\\\")} was not found in github_user_repos')\\n raise e\\n\\n return render_template(\\n 'blog-post.html',\\n prev_and_next=posts.get_previous_and_next_posts(post),\\n page=post,\\n github_repo=github_repo\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b5c6e2de3b82d592365c4aed0ca0cb6\",\n \"score\": \"0.65678596\",\n \"text\": \"def blog_home():\\n page1_posts = posts.paginate_posts(1)\\n return render_template(\\n 'blog-home.html',\\n pagination_nav=posts.get_pagination_nav_data(1),\\n posts=page1_posts\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c50e8e1c6dff4468d4150265d4be57b7\",\n \"score\": \"0.6518209\",\n \"text\": \"def home(request):\\n assert isinstance(request, HttpRequest)\\n last_post = Blog.objects.first()\\n return render(\\n request,\\n 'app/index.html',\\n {\\n 'title':'Главная',\\n 'last_post': last_post,\\n 'year':datetime.now().year,\\n }\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39147b4c506607b62d8c9a7d7e27617b\",\n \"score\": \"0.648939\",\n \"text\": \"def blog_post():\\n \\n # retrieve the user id from the page arguments passed by the button\\n # and then get the row and send it to the view\\n blog_id = request.args(0)\\n blog_post = db.blog_posts(blog_id)\\n \\n if blog_post is None or blog_post.hidden or blog_post.admin_status != 'Approved':\\n session.flash = CENTER(B('Blog post id not available.'), _style='color: red')\\n redirect(URL('blogs','blogs'))\\n \\n return dict(blog_post = blog_post)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d22078081a38296a96703fff63ff5507\",\n \"score\": \"0.644632\",\n \"text\": \"def blog(request):\\n last = Blog.objects.order_by('-posted')[:3]\\n assert isinstance(request, HttpRequest)\\n posts = Blog.objects.all() # запрос на выбор всех статей блога из модели\\n return render(\\n request,\\n 'blog/blog.html',\\n {\\n 'title':'Блог',\\n 'last': last,\\n 'posts': posts, # передача списка статей в шаблон веб-страницы\\n 'year':datetime.now().year,\\n }\\n\\n)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78a07ad929d7bededc05e2f10e51bbf9\",\n \"score\": \"0.6445245\",\n \"text\": \"def my_blog(request, slug):\\n # Get the liveblog by slug\\n blog = get_object_or_404(Blog, slug=slug)\\n\\n # Render it with the posts ordered in descending order.\\n # If the user has JavaScript enabled, the template has JS that will\\n # keep it updated.\\n return render(request, \\\"posts/blog.html\\\", {\\n \\\"blog\\\": blog,\\n \\\"posts\\\": blog.posts.order_by(\\\"-created\\\"),\\n})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19b854d5d62d889ac22a4d808394d92d\",\n \"score\": \"0.6409632\",\n \"text\": \"def _get_view(self):\\r\\n condition = None\\r\\n \\r\\n def blog(value, obj):\\r\\n return '<a href=\\\"%s\\\">%s</a>' % (value, value)\\r\\n \\r\\n fields_convert_map = {'blog':blog}\\r\\n \\r\\n view = functions.ListView(self.model, \\r\\n# pageno=page, \\r\\n# rows_per_page=rows,\\r\\n pagination=False,\\r\\n condition=condition,\\r\\n fields_convert_map=fields_convert_map,\\r\\n )\\r\\n return view\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40f8ae388e89122251493d43d10f109e\",\n \"score\": \"0.6372231\",\n \"text\": \"def blog_post_detail(request, slug):\\n post = get_object_or_404(BlogPost, slug=slug)\\n post.post_views += 1 # increment the post views by 1, each time one is seen\\n post.save()\\n return render(request, 'gamersblog/blogposts/blogpostdetail.html', {'post': post})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"776f63cf4b3d4bf23122ce90876feac8\",\n \"score\": \"0.6341679\",\n \"text\": \"def blog(request):\\n assert isinstance(request, HttpRequest)\\n posts = Blog.objects.all() # запрос на выбор всех статей блога из модели\\n return render(\\n request,\\n 'app/blog.html',\\n {\\n 'title':'Блог',\\n 'posts': posts,\\n 'year':datetime.now().year,\\n }\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47cfe02544b66f876a33f31b4817ef95\",\n \"score\": \"0.63153785\",\n \"text\": \"def blog_pagination(page):\\n if page == 1:\\n return redirect(url_for('blog_home'))\\n if page < 1:\\n abort(404)\\n pagen_posts = posts.paginate_posts(page)\\n if not pagen_posts:\\n abort(404)\\n return render_template(\\n 'blog-home.html',\\n pagination_nav=posts.get_pagination_nav_data(page),\\n posts=pagen_posts\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fe4d9a602b90d16487ab406cdebac01\",\n \"score\": \"0.6282128\",\n \"text\": \"def blog_post_list(request):\\n object_list = BlogPost.objects.filter(\\n published_date__lte=timezone.now()).order_by('published_date')\\n # add pagination to the blog page, we will display 3 posts per page\\n paginator = Paginator(object_list, 3) # 3 posts in each page\\n page = request.GET.get('page')\\n try:\\n blog_posts = paginator.page(page)\\n except PageNotAnInteger:\\n # if the page is not an integer deliver the first page\\n blog_posts = paginator.page(1)\\n except EmptyPage:\\n # if page is out of range deliver the last page of results\\n blog_posts = paginator.page(paginator.num_pages)\\n return render(request, 'gamersblog/blogposts/blogpostlist.html',\\n {'page': page, 'blog_posts': blog_posts})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1e2dbc9566c1771f916d9272fb0924b\",\n \"score\": \"0.6238643\",\n \"text\": \"def get(self):\\n if self.user:\\n user_key = self.user\\n user_id = user_key.key.id()\\n _user = User.by_id(int(user_id))\\n _blogs = Blog.query_blogs().fetch()\\n likes = Like.query().fetch()\\n self.render('blog.html', blogs=_blogs, user=_user, likes=likes)\\n else:\\n _blogs = Blog.query_blogs().fetch()\\n self.render('blog.html', blogs=_blogs, user=None, likes=None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9783ed2f6858650c3e995802d6c8afe\",\n \"score\": \"0.62206197\",\n \"text\": \"def post(post_id):\\n post = Blogpost.query.filter_by(id=post_id).one()\\n\\n return render_template('post.html', post=post)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ef47b95bcd7259646ffb36778709a6d\",\n \"score\": \"0.6190501\",\n \"text\": \"def get(self):\\n show_comments = self.request.get(\\\"showComments\\\")\\n if show_comments:\\n if BlogEntry.get_by_id(int(show_comments), parent=fancy_blog):\\n self.render_blog_page(expand_post=int(show_comments))\\n else:\\n self.handle_404()\\n else:\\n self.render_blog_page()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b801a49310f92bd45351f03993f09292\",\n \"score\": \"0.61605465\",\n \"text\": \"def edit():\\n return render_template(\\\"editblog.html\\\",\\n my_blog=mongo.db.blog.find())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d82e2d76381c1bc6b246a0a9b054ba3\",\n \"score\": \"0.61233556\",\n \"text\": \"def index(request):\\n # Get a list of liveblogs, ordered by the date of their most recent\\n # post, descending (so ones with stuff happening are at the top)\\n blogs = Blog.objects.annotate(\\n max_created=Max(\\\"posts__created\\\")\\n ).order_by(\\\"-max_created\\\")\\n\\n # Render that in the index template\\n return render(request, \\\"posts/index.html\\\", {\\n \\\"blogs\\\": blogs,\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1344c1036a33f3f00a53ccecb20b132\",\n \"score\": \"0.6093515\",\n \"text\": \"def view_post(post_slug):\\n try:\\n deets = models.Post.select().where(models.Post.slug == post_slug).get()\\n except models.DoesNotExist:\\n abort(404)\\n else:\\n tags = deets.all_tags()\\n return render_template('detail.html', deets=deets, tags=tags)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"212601221d9b7056433d0e48633ec1d6\",\n \"score\": \"0.6065636\",\n \"text\": \"def index():\\n # queries the db for all blog posts and displays the most recent first\\n posts = Blogpost.query.order_by(Blogpost.date_posted.desc()).all()\\n\\n return render_template('index.html', posts=posts)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20440302fe89a9d399364d2089274a5e\",\n \"score\": \"0.60421056\",\n \"text\": \"def blog_list(request,):\\n\\n paginator = PageNumberPagination()\\n paginator.page_size = 10\\n blog_posts = Post.objects.all()\\n paginated_results = paginator.paginate_queryset(blog_posts, request)\\n serializer = BlogPostSerializer(paginated_results, many=True)\\n return paginator.get_paginated_response(serializer.data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b674c69e4730f08eeb26023e18e52aab\",\n \"score\": \"0.6010444\",\n \"text\": \"def get(self, _postID):\\n blog_key = Blog.get_by_id(int(_postID))\\n self.render('comment.html', blog=blog_key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3026fe0434106c38be97bc689de138a\",\n \"score\": \"0.59919906\",\n \"text\": \"def get_blog(id):\\n if request.method == 'POST':\\n error = None\\n mark_left = '<'\\n mark_right = '/>'\\n content = request.form['content']\\n if mark_left in content or mark_right in content:\\n error = \\\"Input containing '<' or '/>' is not allowed.\\\"\\n\\n if error is None:\\n db.session.add(Comment(blog_id=id,user_id=g.user.id,content=content))\\n db.session.commit()\\n return redirect(url_for('home.get_blog', id=id))\\n\\n flash(error)\\n\\n blog = Blog.query.filter_by(id=id).first()\\n comments = Comment.query.filter(Comment.blog_id==id).all()\\n return render_template('index_blog.html', blog=blog, comments=comments)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ae928349c3d853c04e406ba81f2e99a\",\n \"score\": \"0.5964416\",\n \"text\": \"def get_absolute_url(self):\\n return reverse('blog-detail', args=[str(self.id)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23617bb46e54cc96593c0a0eb9c52574\",\n \"score\": \"0.59584916\",\n \"text\": \"def get(self):\\r\\n offset = int(self.request.get('start', 0))\\r\\n count = int(self.request.get('count', 20))\\r\\n posts = models.BlogPost.all().order('-published').fetch(count, offset)\\r\\n template_vals = {\\r\\n 'offset': offset,\\r\\n 'count': count,\\r\\n 'last_post': offset + len(posts) - 1,\\r\\n 'prev_offset': max(0, offset - count),\\r\\n 'next_offset': offset + count,\\r\\n 'posts': posts,\\r\\n }\\r\\n self.render_to_response('index.html', template_vals)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd3f392cdfa79c61fe55cfe913a3018a\",\n \"score\": \"0.5955088\",\n \"text\": \"def blog_posts(blog_id):\\n blog = Blog.from_mongo(blog_id)\\n\\n posts = blog.get_posts()\\n\\n return render_template('posts.html', posts=posts, blog_title=blog.title, blog_id=blog._id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ce8d537f4a97acff19e18b26a31f79f\",\n \"score\": \"0.59389174\",\n \"text\": \"def get_absolute_url(self):\\n return reverse('blog:post_detail',\\n args=[self.publish.year,\\n self.publish.month,\\n self.publish.day,\\n self.slug])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"793b7cda095b3b44e329aeeadbcab43c\",\n \"score\": \"0.5934862\",\n \"text\": \"def category_view(self, request, cat_slug):\\n context = self.get_context(request)\\n\\n try:\\n # Look for the blog category by its slug.\\n category = BlogCategory.objects.get(slug=cat_slug)\\n except Exception:\\n # Blog category doesnt exist (ie /blog/category/missing-category/)\\n # Redirect to self.url, return a 404.. that's up to you!\\n category = None\\n\\n if category is None:\\n # This is an additional check.\\n # If the category is None, do something. Maybe default to a particular category.\\n # Or redirect the user to /blog/ ¯\\\\_(ツ)_/¯\\n pass\\n\\n context[\\\"posts\\\"] = BlogDetailPage.objects.live().public().filter(categories__in=[category])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"211391d3fada7ea75223b1968539a920\",\n \"score\": \"0.59243894\",\n \"text\": \"def blog_details():\\n \\n # do we have a request for an existing blog post?\\n blog_id = request.args(0)\\n \\n if blog_id is not None:\\n record = db.blog_posts(blog_id)\\n else:\\n record = None\\n \\n if blog_id is not None and record is None:\\n # avoid unknown blogs\\n session.flash = B(CENTER('Invalid blog id'), _style='color:red;')\\n redirect(URL('blogs','blogs'))\\n \\n elif record is None or record.user_id == auth.user.id or auth.has_membership('admin'):\\n \\n if record is None:\\n buttons = [TAG.BUTTON('Submit', _type=\\\"submit\\\", _class=\\\"button btn btn-default\\\",\\n _style='padding: 5px 15px 5px 15px;', _name='create')]\\n readonly = False\\n else:\\n readonly = True if record.admin_status == 'Submitted' else False\\n buttons = [TAG.BUTTON('Update and resubmit', _type=\\\"submit\\\",\\n _class=\\\"button btn btn-default\\\",\\n _style='padding: 5px 15px 5px 15px;', _name='update')]\\n \\n # provide a form to create or edit\\n form = SQLFORM(db.blog_posts,\\n readonly=readonly,\\n record=record,\\n buttons=buttons,\\n fields=['thumbnail_figure','authors','title','content'], \\n showid=False)\\n \\n if form.validate(onvalidation=validate_blog_post):\\n \\n req_keys = list(request.vars.keys())\\n \\n # get and add a comment to the history\\n hist_str = '[{}] {} {}\\\\\\\\n -- {}\\\\\\\\n'\\n new_history = hist_str.format(datetime.datetime.utcnow().strftime('%Y-%m-%dT%H:%MZ'),\\n auth.user.first_name,\\n auth.user.last_name,\\n 'Post created' if blog_id is None else \\\"Post edited\\\")\\n\\n if 'update' in req_keys:\\n id = record.update_record(admin_history = new_history + record.admin_history,\\n **db.blog_posts._filter_fields(form.vars))\\n id = id.id\\n msg = CENTER(B('Blog post updated and resubmitted for approval.'),\\n _style='color: green')\\n\\n elif 'create' in req_keys:\\n id = db.blog_posts.insert(admin_history=new_history, \\n **db.blog_posts._filter_fields(form.vars))\\n \\n msg = CENTER(B('Blog post created and submitted for approval.'),\\n _style='color: green')\\n else:\\n pass\\n \\n # Email the link\\n template_dict = {'name': auth.user.first_name, \\n 'url': URL('blogs', 'blog_details', args=[id], scheme=True, host=True),\\n 'submission_type': 'blog post'}\\n \\n safe_mailer(to=auth.user.email,\\n subject='SAFE: blog post submitted',\\n template = 'generic_submitted.html',\\n template_dict = template_dict)\\n \\n session.flash = msg\\n redirect(URL('blogs','blog_details', args=[id]))\\n \\n elif form.errors:\\n response.flash = CENTER(B('Problems with the form, check below.'), _style='color: red')\\n else:\\n pass\\n \\n # package form into a panel\\n if record is None:\\n status = \\\"\\\"\\n vis = \\\"\\\"\\n else:\\n status = DIV(approval_icons[record.admin_status], XML(' '),\\n 'Status: ', XML(' '), record.admin_status, \\n _class='col-sm-3',\\n _style='padding: 5px 15px 5px 15px;background-color:lightgrey;color:black;')\\n if record.hidden:\\n vis = DIV('Hidden', _class='col-sm-1 col-sm-offset-1',\\n _style='padding: 5px 15px 5px 15px;background-color:lightgrey;color:black;')\\n else:\\n vis = DIV('Visible', _class='col-sm-1 col-sm-offset-1',\\n _style='padding: 5px 15px 5px 15px;background-color:lightgrey;color:black;')\\n \\n \\n panel_header = DIV(H5('Blog post', _class='col-sm-7'), status, vis,\\n _class='row', _style='margin:0px 0px')\\n \\n if readonly:\\n content = XML(record.content)\\n else:\\n content = form.custom.widget.content\\n \\n form = FORM(form.custom.begin,\\n DIV(DIV(panel_header, _class=\\\"panel-heading\\\"),\\n DIV(LABEL('Thumbnail Figure:', _class=\\\"control-label col-sm-2\\\" ),\\n DIV(form.custom.widget.thumbnail_figure, _class=\\\"col-sm-10\\\"),\\n _class='row', _style='margin:10px 10px'),\\n DIV(LABEL('Authors:', _class=\\\"control-label col-sm-2\\\" ),\\n DIV(form.custom.widget.authors, _class=\\\"col-sm-10\\\"),\\n _class='row', _style='margin:10px 10px'),\\n DIV(LABEL('Title:', _class=\\\"control-label col-sm-2\\\" ),\\n DIV(form.custom.widget.title, _class=\\\"col-sm-10\\\"),\\n _class='row', _style='margin:10px 10px'),\\n DIV(LABEL('Blog Content:', _class=\\\"control-label col-sm-2\\\" ),\\n DIV(content, _class=\\\"col-sm-10\\\"),\\n _class='row', _style='margin:10px 10px'),\\n DIV(form.custom.submit, _class='panel-footer'),\\n _class=\\\"panel panel-primary\\\"),\\n form.custom.end)\\n else: \\n # security doesn't allow people editing other users blogs\\n session.flash = CENTER(B('You do not have permission to edit this blog post.'), _style='color: red')\\n redirect(URL('blogs','blog_post', args=blog_id))\\n \\n # admin history display\\n if record is not None and record.admin_history is not None:\\n admin_history = DIV(DIV(H5('Admin History', ), _class=\\\"panel-heading\\\"),\\n DIV(XML(record.admin_history.replace('\\\\\\\\n', '<br />'),\\n sanitize=True, permitted_tags=['br/']),\\n _class = 'panel_body'),\\n DIV(_class=\\\"panel-footer\\\"),\\n _class='panel panel-primary')\\n else:\\n admin_history = DIV()\\n \\n ## ADMIN INTERFACE\\n if record is not None and auth.has_membership('admin') and record.admin_status == 'Submitted':\\n \\n admin = admin_decision_form(['Resubmit','Approved'])\\n \\n if admin.process(formname='admin').accepted:\\n \\n # update record with decision\\n admin_str = '[{}] {} {}\\\\\\\\n ** Decision: {}\\\\\\\\n ** Comments: {}\\\\\\\\n'\\n new_history = admin_str.format(datetime.datetime.utcnow().strftime('%Y-%m-%dT%H:%MZ'),\\n auth.user.first_name,\\n auth.user.last_name,\\n admin.vars.decision,\\n admin.vars.comment) + record.admin_history\\n \\n record.update_record(admin_status = admin.vars.decision,\\n admin_history = new_history)\\n \\n # pick an decision\\n poster = record.user_id\\n \\n template_dict = {'name': poster.first_name, \\n 'url': URL('blogs', 'blog_details', args=[blog_id], scheme=True, host=True),\\n 'public_url': URL('blogs', 'blog_post', args=[blog_id], scheme=True, host=True),\\n 'admin': auth.user.first_name + ' ' + auth.user.last_name,\\n 'submission_type': 'blog post'}\\n \\n # pick an decision\\n if admin.vars.decision == 'Approved':\\n \\n safe_mailer(to=poster.email,\\n subject='SAFE: blog post approved',\\n template = 'generic_approved.html',\\n template_dict = template_dict)\\n \\n msg = CENTER(B('Blog approval emailed to poster at {}.'.format(poster.email)), _style='color: green')\\n \\n elif admin.vars.decision == 'Resubmit':\\n\\n safe_mailer(to=poster.email,\\n subject='SAFE: blog post requires resubmission',\\n template = 'generic_resubmit.html',\\n template_dict = template_dict)\\n \\n msg = CENTER(B('Blog resubmission emailed to poster at {}.'.format(poster.email)), _style='color: green')\\n \\n else:\\n pass\\n \\n redirect(URL('blogs','administer_blogs'))\\n session.flash = msg\\n \\n elif admin.errors:\\n response.flash = CENTER(B('Errors in form, please check and resubmit'), _style='color: red')\\n else:\\n pass\\n else:\\n admin = DIV()\\n \\n # pass components to the view\\n return dict(form=form, admin_history=admin_history, admin=admin)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87d8ac83550e5618462ae678b816bbc5\",\n \"score\": \"0.59204996\",\n \"text\": \"def index():\\n db = get_db()\\n posts = db.execute(\\n 'SELECT p.id, title, body, created, author_id, username'\\n ' FROM post p JOIN user u ON p.author_id = u.id'\\n ' ORDER BY created DESC'\\n ).fetchall()\\n return render_template('blog/index.html', posts=posts)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21e1a183829afa0ac2c157d9026a8e8e\",\n \"score\": \"0.58921313\",\n \"text\": \"def entryView(request, year, month, day, slug):\\n date = datetime.date(int(year), int(month), int(day))\\n entry = Entry.objects.get(slug=slug, pub_date__year=date.year,\\n pub_date__month=date.month, pub_date__day=date.day)\\n\\n context = {'entry': entry}\\n \\n return render(request, 'blog/entry_detail.html', context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cfd4b02ecebbda47b5c021d14d2e7cda\",\n \"score\": \"0.5887412\",\n \"text\": \"def get(self, _postID):\\n if self.user:\\n user = self.user\\n parent = user.key\\n blog_key = Blog.get_by_id(int(_postID))\\n post = blog_key\\n self.render('editpost.html', blog=post)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef336aabfe3cfa90dc4961662817eb42\",\n \"score\": \"0.5867479\",\n \"text\": \"def get(self, year, month, day, slug):\\n try:\\n blog = Blog.find(year, month, day, slug)\\n except Blog.DoesNotExist:\\n abort(404, message='No such blog')\\n except ValueError:\\n abort(409, message='Multiple blogs found')\\n return blog\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86917dfe833533f4d3270b3ae3821881\",\n \"score\": \"0.5861187\",\n \"text\": \"def get_object(self, pk: int):\\n try:\\n return BlogArticle.objects.get(pk=pk)\\n except BlogArticle.DoesNotExist:\\n raise Http404\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a53e72c1ecf9938fd90e9828132200db\",\n \"score\": \"0.5810581\",\n \"text\": \"def get(self, request, pk, blogger):\\n possible_posts = PostQueryset.get_postdetail_by_user(request.user, pk, blogger)\\n\\n if len(possible_posts) == 0:\\n return HttpResponseNotFound(\\\"El post solicitado no existe\\\")\\n elif len(possible_posts) > 1:\\n return HttpResponse(\\\"Multiples opciones\\\", status=300)\\n\\n post = possible_posts[0]\\n context = {'post': post}\\n return render(request, 'post/post_detail.html', context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd321764253c0b51e36f33d5e4cda3d4\",\n \"score\": \"0.5791\",\n \"text\": \"def all_blogs(request):\\n blogs = Blog.objects.all()\\n comments = Comment.objects.all()\\n context = {\\n 'blogs': blogs,\\n 'comments': comments,\\n 'on_blog_page': True,\\n }\\n\\n return render(request, 'blogs/blogs.html', context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed1ef1fbf32a0af74148fc756dd305b6\",\n \"score\": \"0.57875705\",\n \"text\": \"def blog_guess_open(what):\\n post_id = ''\\n blog_index = -1\\n if type(what) is str:\\n\\n for i, p in enumerate(vim.eval(\\\"VIMPRESS\\\")):\\n if what.startswith(p[\\\"blog_url\\\"]):\\n blog_index = i\\n\\n # User input a url contained in the profiles\\n if blog_index != -1:\\n guess_id = re.search(r\\\"\\\\S+?p=(\\\\d+)$\\\", what)\\n\\n # permantlinks\\n if guess_id is None:\\n\\n # try again for /archives/%post_id%\\n guess_id = re.search(r\\\"\\\\S+/archives/(\\\\d+)\\\", what)\\n\\n # fail, try get full link from headers\\n if guess_id is None:\\n headers = urllib.urlopen(what).headers.headers\\n for link in headers:\\n if link.startswith(\\\"Link:\\\"):\\n post_id = re.search(r\\\"<\\\\S+?p=(\\\\d+)>\\\", link).group(1)\\n\\n else:\\n post_id = guess_id.group(1)\\n\\n # full link with ID (http://blog.url/?p=ID)\\n else:\\n post_id = guess_id.group(1)\\n\\n # detected something ?\\n assert post_id != '', \\\"Failed to get post/page id from '%s'.\\\" % what\\n\\n #switch view if needed.\\n if blog_index != -1 and blog_index != g_data.conf_index:\\n blog_config_switch(blog_index)\\n\\n # Uesr input something not a usabe url, try numberic\\n else:\\n try:\\n post_id = str(int(what))\\n except ValueError:\\n raise VimPressException(\\\"Failed to get post/page id from '%s'.\\\" % what)\\n\\n blog_edit(\\\"post\\\", post_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c8b55dfecfdab4f652e61c742db3730\",\n \"score\": \"0.57394445\",\n \"text\": \"def index(request):\\n # pylint: disable=no-member\\n latest = Post.objects.order_by('-pub_date')[:10]\\n return render(request, 'index.html', {'posts': latest})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"749733cfc7fe67d07cb07bf94469e6d5\",\n \"score\": \"0.5732849\",\n \"text\": \"def render_blog_page(self, posts=None, expand_post=0):\\n if posts is None:\\n posts = self.get_posts()\\n\\n self.render(\\\"blog_posts.html\\\",\\n posts=posts, expand_post=expand_post,\\n currUser=self.currUser, Comment=Comment, User=User,\\n Like=Like, errors=self.errs)\\n self.errs.clear()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0750cd741f316776e8e0a09feb077726\",\n \"score\": \"0.5726915\",\n \"text\": \"def bloggerlist(request):\\n return render(request, 'blogger-list.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e22b20c2cefea6e71f7048b54dc190d7\",\n \"score\": \"0.5721401\",\n \"text\": \"def view_post(post_id):\\n\\n post = mongo.db.posts.find_one_or_404({\\\"_id\\\": ObjectId(post_id)})\\n comments = mongo.db.comments.find({\\\"post_id\\\": post_id})\\n\\n context = {\\n \\\"post\\\": post,\\n \\\"comments\\\": comments\\n }\\n\\n return render_template('view-post.html', **context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c796e1179a68277261b7f73affa8121e\",\n \"score\": \"0.57090974\",\n \"text\": \"def posteo(request,slug):\\n #post = Post.objects.get(slug=slug)\\n post = get_object_or_404(Post,slug=slug)\\n return render(request,'posts.html',{'post':post})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbcd352cf622618687a5ef709ba59887\",\n \"score\": \"0.57070285\",\n \"text\": \"def view_advice(request):\\n posts = Post.objects.filter(published_date__lte=timezone.now()).order_by('-published_date')\\n return render(request, 'view_advice.html', {'posts': posts})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a06afb8098ebf3ea33fb99958f36c422\",\n \"score\": \"0.5706511\",\n \"text\": \"def get_absolute_url(self):\\n\\t\\treturn reverse('blog:article', args=[str(self.id)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27dc47716c327576ab912a8e47790ed3\",\n \"score\": \"0.57050043\",\n \"text\": \"def view(request, doc_id=None):\\n # Get the document from the database.\\n if doc_id is not None:\\n try:\\n doc = Document.objects.get(doc_id=doc_id)\\n return render('index.html', {'doc':doc})\\n except Document.ObjectNotFoundException:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5adf8224d1a8bec19c54d80af2905afb\",\n \"score\": \"0.5701729\",\n \"text\": \"def index(request):\\n post_list = Post.objects.all()\\n paginator = Paginator(post_list, PAGINATOR_PER_PAGE)\\n page_number = request.GET.get('page')\\n page = paginator.get_page(page_number)\\n return render(request, 'index.html', {'page': page})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdb77055f787ec755802ebc524cfe9d4\",\n \"score\": \"0.56758744\",\n \"text\": \"def article(request, slug):\\n article = get_object_or_404(Article, slug=slug)\\n return _render_contextually(request,\\n 'blog/article.html',\\n {'article': article,\\n 'comment_form': CommentForm()})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42ba52f7dfa1874680324d6e0187992a\",\n \"score\": \"0.56666684\",\n \"text\": \"def post_detail(post_id):\\n # TODO: change to use slug instead of id\\n post = Post.query.get_or_404(post_id)\\n return render_template('post_detail.html', post=post, title=post.title)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73c13ec6c88c9763ce7f1d6907e78ae0\",\n \"score\": \"0.5666153\",\n \"text\": \"def get_absolute_view_url(self):\\n return ('books.views.view_entry', [self.id, '-' + slugify(self.title)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa3b1482d3bb6578837b6f4848b4a09c\",\n \"score\": \"0.56246835\",\n \"text\": \"def get_absolute_url(self):\\r\\n url_name = \\\"blog_post_detail\\\"\\r\\n kwargs = {\\\"slug\\\": self.slug}\\r\\n date_parts = (\\\"year\\\", \\\"month\\\", \\\"day\\\")\\r\\n if settings.BLOG_URLS_DATE_FORMAT in date_parts:\\r\\n url_name = \\\"blog_post_detail_%s\\\" % settings.BLOG_URLS_DATE_FORMAT\\r\\n for date_part in date_parts:\\r\\n date_value = str(getattr(self.publish_date, date_part))\\r\\n if len(date_value) == 1:\\r\\n date_value = \\\"0%s\\\" % date_value\\r\\n kwargs[date_part] = date_value\\r\\n if date_part == settings.BLOG_URLS_DATE_FORMAT:\\r\\n break\\r\\n return reverse(url_name, kwargs=kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e73d8679d8f086a62afffe78ee8beefb\",\n \"score\": \"0.5613305\",\n \"text\": \"def get_post(post_id):\\n post = mongo.db.posts.find_one({\\\"_id\\\": ObjectId(post_id)})\\n return render_template(\\\"get_post.html\\\", post=post)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ccb5efebbefb0aec73116f9d02abbb5\",\n \"score\": \"0.5605165\",\n \"text\": \"def article_view(request, slug):\\n # TODO: Tags in article view\\n try:\\n a = Article.objects.get(url=slug)\\n except:\\n # Article does not exist\\n return HttpResponseRedirect(reverse('article_invalid'))\\n\\n path = os.path.join(os.path.dirname(__file__), a.location)\\n with open(path, 'rU') as f:\\n text_string = f.read()\\n md = markdown.Markdown(extensions=['markdown.extensions.toc'])\\n html = md.convert(text_string)\\n s = md.toc\\n\\n # Rendering toc to include bootstrap classes\\n toc = '<div class=\\\"toc\\\">\\\\n<ul>\\\\n'\\n\\n while 1:\\n idx1 = s.find('<li>')\\n idx2 = s.find('<li>', idx1 + 4)\\n\\n if idx2 == -1:\\n toc += '<li class=\\\"list-group-item\\\">' + s[idx1 + 4:]\\n break\\n\\n toc += '<li class=\\\"list-group-item\\\">' + s[idx1 + 4:idx2 - 1] + '\\\\n'\\n s = s[idx2:]\\n\\n s = toc\\n\\n idx1 = s.find('<ul>')\\n toc = s[:idx1]\\n\\n while 1:\\n idx1 = s.find('<ul>')\\n idx2 = s.find('<ul>', idx1 + 4)\\n\\n if idx2 == -1:\\n toc += '<ul class=\\\"list-group\\\">' + s[idx1 + 4:]\\n break\\n\\n toc += '<ul class=\\\"list-group\\\">' + s[idx1 + 4:idx2] + '\\\\n'\\n s = s[idx2:]\\n\\n return render(request, 'wiki/article.html', {'title': a.title, 'text': html, 'toc': toc})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6840e83de15fdc911d1ff6c3bdac00f7\",\n \"score\": \"0.5587149\",\n \"text\": \"def details(request, slug):\\n # get the right model\\n context = RequestContext(request)\\n # Get a Page model object from the request\\n page = get_page_from_request(request, use_path=slug)\\n if not page:\\n return _handle_no_page(request, slug)\\n \\n current_language = get_language_from_request(request)\\n \\n # Check that the current page is available in the desired (current) language\\n available_languages = page.get_languages()\\n \\n # We resolve an alternate language for the page if it's not available.\\n # Since the \\\"old\\\" details view had an exception for the root page, it is\\n # ported here. So no resolution if the slug is ''.\\n if (current_language not in available_languages):\\n if settings.CMS_LANGUAGE_FALLBACK:\\n # If we didn't find the required page in the requested (current) \\n # language, let's try to find a suitable fallback in the list of \\n # fallback languages (CMS_LANGUAGE_CONF)\\n for alt_lang in get_fallback_languages(current_language):\\n if alt_lang in available_languages:\\n alt_url = page.get_absolute_url(language=alt_lang, fallback=True)\\n path = '/%s%s' % (alt_lang, alt_url)\\n # In the case where the page is not available in the\\n # preferred language, *redirect* to the fallback page. This\\n # is a design decision (instead of rendering in place)).\\n return HttpResponseRedirect(path)\\n # There is a page object we can't find a proper language to render it \\n _handle_no_page(request, slug)\\n\\n if apphook_pool.get_apphooks():\\n # There are apphooks in the pool. Let's see if there is one for the\\n # current page\\n # since we always have a page at this point, applications_page_check is\\n # pointless\\n # page = applications_page_check(request, page, slug)\\n # Check for apphooks! This time for real!\\n app_urls = page.get_application_urls(current_language, False)\\n if app_urls:\\n app = apphook_pool.get_apphook(app_urls)\\n pattern_list = []\\n for urlpatterns in get_app_urls(app.urls):\\n pattern_list += urlpatterns\\n urlpatterns = patterns('', *pattern_list)\\n try:\\n view, args, kwargs = resolve('/', tuple(urlpatterns))\\n return view(request, *args, **kwargs)\\n except Resolver404:\\n pass\\n\\n # Check if the page has a redirect url defined for this language. \\n redirect_url = page.get_redirect(language=current_language)\\n if redirect_url:\\n if (settings.i18n_installed and redirect_url[0] == \\\"/\\\"\\n and not redirect_url.startswith('/%s/' % current_language)):\\n redirect_url = \\\"/%s/%s\\\" % (current_language, redirect_url.lstrip(\\\"/\\\"))\\n # add language prefix to url\\n return HttpResponseRedirect(redirect_url)\\n \\n # permission checks\\n if page.login_required and not request.user.is_authenticated():\\n if settings.i18n_installed:\\n path = urlquote(\\\"/%s%s\\\" % (request.LANGUAGE_CODE, request.get_full_path()))\\n else:\\n path = urlquote(request.get_full_path())\\n tup = settings.LOGIN_URL , \\\"next\\\", path\\n return HttpResponseRedirect('%s?%s=%s' % tup)\\n \\n template_name = get_template_from_request(request, page, no_current_page=True)\\n # fill the context \\n context['lang'] = current_language\\n context['current_page'] = page\\n context['has_change_permissions'] = page.has_change_permission(request)\\n context['has_view_permissions'] = page.has_view_permission(request)\\n \\n if not context['has_view_permissions']:\\n return _handle_no_page(request, slug)\\n \\n return render_to_response(template_name, context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0296c115cfd0fbedcc5792bb5c8c3fb3\",\n \"score\": \"0.5580343\",\n \"text\": \"def get(self, request, pk):\\n blog = self.get_object(pk)\\n serializer = BlogSerializer(blog)\\n return Response(serializer.data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a637148c77cc473adbee05bfd26e499e\",\n \"score\": \"0.5547906\",\n \"text\": \"def post(self):\\n\\n # check if user have login\\n if not self.user:\\n self.redirect('/login')\\n else:\\n # get parameters from header\\n post_id = self.request.get(\\\"blog_id\\\", None)\\n save = self.request.get(\\\"save\\\", None)\\n cancel = self.request.get(\\\"cancel\\\", None)\\n subject = self.request.get('subject', default_value=None)\\n content = self.request.get('content', default_value=None)\\n\\n # check if user input just spaces to page\\n if subject and content:\\n subject = subject.strip()\\n content = content.strip()\\n\\n # find post\\n key = db.Key.from_path('Post', int(post_id), parent=blog_key())\\n post = db.get(key)\\n\\n # if there is no any posts\\n if not post:\\n self.error(404)\\n return\\n\\n # prepare parameters for rendering page\\n param = dict(p=post, subject=post.subject, content=post.content)\\n\\n # check if user is owner of post\\n if post.owner.key() != self.user.key():\\n param[\\\"is_errors\\\"] = True\\n param[\\\"error\\\"] = \\\"You cannot edit this post, you are not owner!\\\"\\n\\n self.render(\\\"edit-post.html\\\", **param)\\n else:\\n # handle user choice save or not\\n if save or cancel:\\n if save:\\n if subject and content:\\n post.subject = subject\\n post.content = content\\n post.save()\\n\\n self.redirect(\\\"/blog/{}\\\".format(post_id))\\n else:\\n param[\\\"edit_error\\\"] = \\\"subject and content, please!\\\"\\n self.render(\\\"edit-post.html\\\", **param)\\n else:\\n self.redirect(\\\"/blog/{}\\\".format(post_id))\\n else:\\n self.render(\\\"edit-post.html\\\", **param)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bef0e8a6779e6dfcb6d9bb94d072894e\",\n \"score\": \"0.5530161\",\n \"text\": \"def get(self, request, slug):\\n\\t\\ttry:\\n\\t\\t\\tpost = Post.objects.get(slug=str(slug), is_deleted=False)\\n\\t\\t\\tarticle = post.article\\n\\t\\t\\tis_user_article = False\\n\\t\\t\\tif request.user.is_authenticated():\\n\\t\\t\\t\\tuser_prof = UserProfile.objects.get(user=request.user)\\n\\t\\t\\t\\tcontributor = user_can_contribute(request.user)\\n\\t\\t\\t\\tif user_prof.is_contributor == True:\\n\\t\\t\\t\\t\\tif contributor.id is post.contributor.id or user_prof.user.email == \\\"relevate@outlook.com\\\":\\n\\t\\t\\t\\t\\t\\tis_user_article = True\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tpost.views = post.views + 1\\n\\t\\t\\t\\t\\tpost.save()\\n\\t\\t\\t\\treturn render(request, 'article_view.html',\\n\\t\\t\\t\\t\\t{\\n\\t\\t\\t\\t\\t\\t'article':article,\\n\\t\\t\\t\\t\\t\\t'is_user_article':is_user_article,\\n\\t\\t\\t\\t\\t\\t'user_prof':user_prof,\\n\\t\\t\\t\\t\\t\\t'post': post,\\n\\t\\t\\t\\t\\t\\t'first_name':request.user.first_name,\\n\\t\\t\\t\\t\\t\\t'last_name':request.user.last_name\\n\\t\\t\\t\\t\\t})\\n\\t\\t\\telse:\\n\\t\\t\\t\\treturn render(request, 'article_view.html',\\n\\t\\t\\t\\t\\t\\t\\t {\\n\\t\\t\\t\\t\\t\\t\\t\\t 'article': article,\\n\\t\\t\\t\\t\\t\\t\\t\\t 'is_user_article': is_user_article,\\n\\t\\t\\t\\t\\t\\t\\t\\t 'post': post\\n\\t\\t\\t\\t\\t\\t\\t })\\n\\t\\texcept ObjectDoesNotExist:\\n\\t\\t\\treturn HttpResponseRedirect(reverse('contribution:all_posts'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"341b22eac2723be0c2640cd26e6f3566\",\n \"score\": \"0.55256695\",\n \"text\": \"def rss_xml_index(self, blog=False):\\n blog_obj = request.env['blog.post']\\n content = blog_obj._get_previous_blog_rss()\\n if not content:\\n return request.not_found()\\n return request.make_response(\\n content, [('Content-Type', 'application/xml;charset=utf-8')])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99bb60ce61449b3ca2c7e91742819e18\",\n \"score\": \"0.5523938\",\n \"text\": \"def detail_page(request, article_id):\\n article = Article.objects.get(id=article_id)\\n return render(request, 'article_detail_page.html', locals())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74642c13ee54e5f797ef8e8764d96f01\",\n \"score\": \"0.5507638\",\n \"text\": \"def post_detail(request, post_id):\\n return render(request, 'post-detail.html', {\\\"post\\\": Post.objects.get(\\n id=post_id)})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"542430e80d3e7bd0b68cfcb504588b9d\",\n \"score\": \"0.54808\",\n \"text\": \"def view_blog_create(self):\\n logged_in = authenticated_userid(self.request)\\n form = Form(self.request, schema=BlogCreateSchema,\\n state=State(request=self.request))\\n blogname = get_param(self.request, 'blogname')\\n text = get_param(self.request, 'text')\\n image_url = get_param(self.request, 'image_url')\\n\\n if form.validate():\\n blog = self.context.add(blogname, text,\\n image_url, logged_in)\\n return HTTPFound(location=resource_url(blog, self.request))\\n\\n _dict = {\\n 'form': FormRenderer(form),\\n 'message': self.message,\\n 'blogname': blogname,\\n 'text': text,\\n 'image_url': image_url\\n }\\n return dict(self.base_dict.items() + _dict.items())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eaed33e32e1313c71f3205c4d71fcb3e\",\n \"score\": \"0.54630744\",\n \"text\": \"def main_page():\\n cur1 = g.db.execute('SELECT published_date, title, author, content from POSTS ORDER BY published_date DESC')\\n posts = [dict(pub_date=row[0], title=row[1],\\n author=row[2], content=row[3]) for row in cur1.fetchall()]\\n return render_template('mainpage.html', posts=posts)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb92ab670741ac02867a5ba769dcd205\",\n \"score\": \"0.5457308\",\n \"text\": \"def article(request, author, year, month, day, slug):\\n\\n user = User.objects.get(username = author)\\n blog = get_object_or_404(Blog, owner__username = author)\\n article = get_object_or_404(Article,\\n author__id = user.id,\\n publication_date__year = int(year),\\n publication_date__month = Article.month_number(month),\\n publication_date__day = int(day),\\n title_in_url = slug)\\n\\n if not (article.public or request.user.is_authenticated()):\\n return handle_403(request)\\n\\n older_articles = Article.objects.filter(publication_date__lt = article.publication_date,\\n author = user).order_by(\\\"-publication_date\\\")\\n newer_articles = Article.objects.filter(publication_date__gt = article.publication_date,\\n author = user).order_by(\\\"publication_date\\\")\\n\\n if not request.user.is_authenticated():\\n older_articles = older_articles.filter(public = True)\\n newer_articles = newer_articles.filter(public = True)\\n\\n try: previous_article = older_articles[0]\\n except IndexError: previous_article = None\\n\\n try: next_article = newer_articles[0]\\n except IndexError: next_article = None\\n\\n data = common_data(request)\\n data.update({\\\"article\\\": article,\\n \\\"previous_article\\\": previous_article,\\n \\\"next_article\\\": next_article,\\n \\\"active_blog\\\": blog,\\n \\\"tags\\\": Tag.used_tags(user.id),\\n \\\"recent_active_months\\\" : blog.last_active_months(),\\n \\\"archive_qualifier\\\": \\\"/%s\\\" % user.username})\\n\\n # Substitute gallery links:\\n def gallery_link(match):\\n gallery = get_object_or_404(Gallery, title_slug = match.group(\\\"gallery_slug\\\"))\\n context = RequestContext(request, dict = {\\\"gallery\\\": gallery,\\n \\\"caption\\\": match.group(\\\"caption\\\") or gallery.description})\\n return loader.render_to_string(\\\"gallery_fragment.html\\\", None, context)\\n\\n data[\\\"html_teaser\\\"] = GALLERY_MATCHER.sub(gallery_link, data[\\\"article\\\"].html_teaser)\\n data[\\\"html_text\\\"] = GALLERY_MATCHER.sub(gallery_link, data[\\\"article\\\"].html)\\n\\n # Substitute photo links:\\n def photo_link(match):\\n photo = get_object_or_404(Photo, title_slug = match.group(\\\"photo_slug\\\"))\\n size = match.group(\\\"size\\\") or \\\"display\\\"\\n url = photo.__getattribute__(\\\"get_standalone_%s_url\\\" % size)()\\n context = RequestContext(request, dict = {\\\"photo\\\": photo,\\n \\\"url\\\": url,\\n \\\"size\\\": size,\\n \\\"caption\\\": match.group(\\\"caption\\\") or photo.caption})\\n return loader.render_to_string(\\\"photo_fragment.html\\\", None, context)\\n\\n data[\\\"html_teaser\\\"] = PHOTO_MATCHER.sub(photo_link, data[\\\"html_teaser\\\"])\\n data[\\\"html_text\\\"] = PHOTO_MATCHER.sub(photo_link, data[\\\"html_text\\\"])\\n\\n # Substitute youtube links:\\n def youtube_link(match):\\n youtube_id = match.group(\\\"youtube_id\\\")\\n context = RequestContext(request, dict = {\\\"youtube_id\\\": youtube_id})\\n return loader.render_to_string(\\\"youtube_fragment.html\\\", None, context)\\n\\n data[\\\"html_teaser\\\"] = YOUTUBE_MATCHER.sub(youtube_link, data[\\\"html_teaser\\\"])\\n data[\\\"html_text\\\"] = YOUTUBE_MATCHER.sub(youtube_link, data[\\\"html_text\\\"])\\n\\n return render(\\\"blog_article.html\\\", request, data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1dadbec82c186de1b6b1b67c56b67b4\",\n \"score\": \"0.545383\",\n \"text\": \"def home_page():\\n\\n posts = Post.query.all()\\n sorted_posts = sorted(posts, key=attrgetter('created_at'), reverse=True)\\n\\n return render_template('home.html', posts=sorted_posts)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d1edb48bdfd44c8690ca1d2838419be\",\n \"score\": \"0.54496306\",\n \"text\": \"def blogpost(request, parametr):\\n assert isinstance(request, HttpRequest)\\n post_1 = Blog.objects.get(id=parametr) # запрос на выбор конкретной статьи по параметру\\n comments = Comment.objects.filter(post=parametr) # запрос на выбор всех комментариев выбранной статьи\\n if request.method == \\\"POST\\\":\\n form = CommentForm(request.POST)\\n if form.is_valid():\\n comment_f = form.save(commit=False)\\n comment_f.author = request.user # добавляем (т.к. этого поля нет в форме) в модель Комментария\\n #(Comment) в поле автор авторизованного пользователя\\n comment_f.date = datetime.now() # добавляем в модель Комментария (Comment) текущую дату\\n comment_f.post = Blog.objects.get(id=parametr) # добавляем в модель Comment статью, для которой данный комментарий\\n comment_f.save() # сохраняем изменения после добавления полей\\n return redirect('blogpost', parametr=post_1.id) # переадресация на ту же страницу статьи после отправки комментария\\n else:\\n form = CommentForm() # создание формы для ввода комментария\\n return render(\\n request,\\n 'app/blogpost.html',\\n {\\n 'post_1': post_1, # передача конкретной статьи в шаблон веб-страницы\\n 'comments': comments, # передача всех комментов к данной статье в шаблон веб-страницы\\n 'form': form, # передача формы добавления коммента в шаблон веб-страницы\\n 'year':datetime.now().year,\\n }\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18558f2288e3ea618b777e2311acb9d2\",\n \"score\": \"0.54351205\",\n \"text\": \"def get(current_user,self,blog_id):\\n get_blog = self.db.get_single_blog(blog_id) \\n if get_blog == None:\\n return \\\"the blog doesn't exist\\\"\\n \\n return jsonify({\\n \\\"status\\\" : 200,\\n \\\"message\\\" : get_blog\\n })\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5961f121a617645aba32845b695882ac\",\n \"score\": \"0.54345965\",\n \"text\": \"def home(request):\\n return render(request, 'blog/mapage.html')\\n # return HttpResponse(\\\"\\\"\\\"\\n # <h1>Bienvenue sur mon blog !</h1>\\n # <p>Les crêpes bretonnes ça tue des mouettes en plein vol !</p>\\n # \\\"\\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e513f6eb7d8ea9b95a4ff7541081a1ff\",\n \"score\": \"0.54311866\",\n \"text\": \"def document(request, document_slug):\\n # This may change depending on how we decide to structure\\n # the url and handle locales.\\n doc = get_object_or_404(Document, title=document_slug.replace('+', ' '))\\n return jingo.render(request, 'wiki/document.html',\\n {'document': doc})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0501a83effc9cba28917936c9bdea1b2\",\n \"score\": \"0.54190326\",\n \"text\": \"def get(self, post_id):\\n post = model.BlogPost.exists(post_id)\\n if self.user:\\n if post:\\n if post.author != self.user.userName: # Cant modify someone elses post\\n query_params = {'modifyError': \\\"Sorry - Cannot modify a post you did not author\\\"}\\n self.redirect('/%s?%s' % (post_id, urllib.urlencode(query_params)))\\n else:\\n # display blogPost page with previous content for modification\\n self.render('blogPost.html', title=\\\"Modify Blog Post\\\",\\n subject=post.subject, content=post.content, id=post_id)\\n else:\\n query_params = {'message': \\\"That blog post does not exist\\\"}\\n self.redirect('/?%s' % (urllib.urlencode(query_params)))\\n else:\\n query_params = {'loginError': \\\"You must be signed in to delete or modify a blog post\\\"}\\n self.redirect('/signup?%s' % (urllib.urlencode(query_params)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4762e619a0315283b14ce016d4c94964\",\n \"score\": \"0.5418522\",\n \"text\": \"def render(self, context):\\n\\n max_title = 20\\n blogs = Blog.objects.all().order_by(\\\"-updated\\\")[:3]\\n pages = Page.objects.all().order_by(\\\"-updated\\\")[:2]\\n\\n html = \\\"<ul id='latest'>\\\"\\n\\n for blog in blogs:\\n extra_str = \\\"\\\"\\n date = blog.updated.strftime(\\\"%Y/%m/%d\\\")\\n\\n if len(blog.title) > max_title:\\n extra_str = \\\"...\\\"\\n\\n html = html + '<li><a href=\\\"/blog/%s\\\">%s</a> %s<span class=\\\"smalltxt\\\"> (%s)</span></li>' % \\\\\\n (date, blog.title[:max_title], extra_str, date)\\n\\n # FIXME: Bad to hardcode these names here b/c now there are\\n # dependencies here, views, and urls\\n for page in pages:\\n date = page.updated.strftime(\\\"%Y/%m/%d\\\")\\n\\n if page.name == \\\"Our Story\\\":\\n link = \\\"story\\\"\\n elif page.name == \\\"Gift Registry\\\":\\n link = \\\"gifts\\\"\\n else:\\n link = \\\"home\\\"\\n\\n html = html + '<li><a href=\\\"/%s\\\">%s</a><span class=\\\"smalltxt\\\"> (%s)</span></li>' % (link, page.name[:max_title], date)\\n html = html + \\\"</ul>\\\"\\n return html\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b30e934ef84c3bfdc2af15665bda590e\",\n \"score\": \"0.54166925\",\n \"text\": \"def index():\\n page = request.args.get('page', 1, type=int)\\n pagination = Post.query.order_by(Post.timestamp.desc()).paginate(page, per_page=10, error_out=False)\\n posts = pagination.items\\n return render_template(\\n 'index.html',\\n title='主页',\\n posts=posts,\\n pagination=pagination\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"501d23437851c117fa94599b1774dabd\",\n \"score\": \"0.5414214\",\n \"text\": \"def post(post_id):\\n the_post = get_post(post_id)\\n if the_post is None:\\n app.logger.error(\\\"no article by the id available\\\")\\n sys.stderr.write(\\\"no article by the id available\\\")\\n return render_template('404.html'), 404\\n\\n app.logger.info(\\\"article retrieved; title: \\\" + the_post.title)\\n sys.stdout.write(\\\"article retrieved; title: \\\" + the_post.title)\\n return render_template('post.html', post=the_post)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebf3a5e87b88000ce54b55ddb35c1b47\",\n \"score\": \"0.540924\",\n \"text\": \"def pageViewActivity(request):\\n return render(request, 'page_view_activity.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d0bea90c2453a5cd49b3e25a1501530\",\n \"score\": \"0.54091233\",\n \"text\": \"def lire(request, id):\\n # methode longue\\n article = \\\"article\\\"\\n try:\\n article = Article.objects.get(id=id)\\n except Article.DoesNOtExist:\\n raise Http404\\n\\n return render(request, 'blog/lire.html', locals())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ccfe0d8566af1032d175b9b4bb077f3\",\n \"score\": \"0.54079276\",\n \"text\": \"def author_detail(request, **kwargs):\\n username = kwargs.pop('username')\\n page = kwargs['page'] if 'page' in kwargs else None\\n author = get_object_or_404(User, username=username)\\n extra_context = {'author': author}\\n filter = {}\\n if ZINNIA_BLOG_ACTIVE:\\n blog_slug = kwargs.pop('blog_slug')\\n blog = get_object_or_404(Blog, slug = blog_slug)\\n # We check if author is a blog author\\n if author not in blog.authors.all():\\n raise Http404('User not valid')\\n filter = {'blog__slug': blog_slug}\\n extra_context.update({'blog': blog, 'filters': filter})\\n queryset = entries_published(author.entry_set).filter(**filter)\\n return object_list(request, queryset=queryset, paginate_by=PAGINATION, \\n page=page, extra_context=extra_context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69fd092f578094316b0ab03bad7016bc\",\n \"score\": \"0.5400128\",\n \"text\": \"def fetch_blog_from_db(blog_id):\\n with SQLite('application.db') as cur:\\n cur.execute(f\\\"SELECT * FROM blogs where id=?\\\", [blog_id])\\n result = cur.fetchone()\\n if result is None:\\n raise NotFoundError(f'Unable to find blog with id {blog_id}.')\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4efeb00bc367fec86a622719f012995f\",\n \"score\": \"0.5399736\",\n \"text\": \"def home(request):\\n ROOT_URL = settings.ROOT_BLOG_URL\\n ROOT_URL = ROOT_URL.rstrip(\\\"/\\\")\\n return render_to_response(\\n \\\"home.html\\\", \\n {\\n 'ROOT_URL': ROOT_URL, \\n 'heading':'Bob Flagg',\\n 'subheading':'Mathematician and Data Scientist'\\n }, \\n context_instance=RequestContext(request)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6db7e4d7f27c647e00fc55a049ec06df\",\n \"score\": \"0.5398989\",\n \"text\": \"def index():\\n title = 'HOME'\\n quotes = get_quotes()\\n blogs = Blogs.query.all()\\n return render_template('index.html', title=title, quotes=quotes, blogs=blogs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57270f8f99ea6b0654efdd5a8b694f86\",\n \"score\": \"0.5398321\",\n \"text\": \"def about(request):\\n args = dict()\\n args['about'] = Pages.objects.filter(title='About us').first() or None\\n return render(request, 'main/about.html', args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d3917c50c01c2582d344f790fc55607\",\n \"score\": \"0.53938437\",\n \"text\": \"def lire(request, id_article, slug):\\r\\n global_var.refresh()\\r\\n article = get_object_or_404(Article, id=id_article, slug=slug)\\r\\n print(article)\\r\\n return render(request, 'blog/post.html', {'article': article, 'global': global_var})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cee0f5a5d1104fdfc3c9fab22b0ea0e\",\n \"score\": \"0.53908306\",\n \"text\": \"def page(path):\\n page = pages.get_or_404(path)\\n return render_template('page.html', page=page)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3db9c3378c98c85075ee7b055a8ceaea\",\n \"score\": \"0.5384302\",\n \"text\": \"def home():\\n posts = [Post(bson=d) for d in mongo.db.posts.find()]\\n return render_template('home.html', posts=posts)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68c3bd3fc72d6552ed059d0cc474347b\",\n \"score\": \"0.5378881\",\n \"text\": \"def home(request):\\n return render(request, 'article_listing_page.html', locals())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"270c94e49ce282f9487d2a5dd7865d8e\",\n \"score\": \"0.5375665\",\n \"text\": \"def display_post_detail(post_id):\\n post = Post.query.get_or_404(post_id)\\n return render_template('post_detail.html', post=post)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c9c2fa8d528e9e20a7c25b09afaa2b0\",\n \"score\": \"0.53541136\",\n \"text\": \"def get(self, request: Request, pk: int):\\n post = self.get_object(pk)\\n return Response(serializers.BlogArticleGetSerializer(post).data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49e017b76df21baedaa16a70b7f9b11c\",\n \"score\": \"0.5345575\",\n \"text\": \"def GetBlogFeed(self, uri=None):\\n if not uri:\\n uri = '/feeds/default/blogs'\\n return self.Get(uri, converter=gdata.blogger.BlogFeedFromString)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53f500958dc643732e4c892807d3ef53\",\n \"score\": \"0.53432244\",\n \"text\": \"def get_pages(page=1, blog=None):\\n\\n if (page == 1):\\n per_page = current_app.config['FLASKY_POSTS_PER_PAGE'] + 1\\n print 'if per_page', per_page\\n else:\\n per_page = current_app.config['FLASKY_POSTS_PER_PAGE'] + 1\\n print 'else per_page', per_page\\n\\n pages = Post.query.order_by(Post.timestamp.desc()).filter_by(is_blog=blog).paginate(page,\\n per_page=per_page, \\\\\\n error_out=False)\\n\\n posts = Post.query.filter_by(is_blog=blog)\\n return pages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c024dee8612a95591cb67d61c0eb864\",\n \"score\": \"0.5338668\",\n \"text\": \"def index():\\n user_id = session.get(\\\"user_id\\\")\\n messages = Message.query.filter_by(user_id = user_id).all()\\n\\n return render_template(\\\"blog/index.html\\\", messages=messages)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8733b7dd704e247331e567b9093525d\",\n \"score\": \"0.5329975\",\n \"text\": \"def get(self, request):\\n\\t\\tif request.user.is_authenticated:\\n\\t\\t\\tuser_prof = UserProfile.objects.get(user=request.user)\\n\\t\\t\\tif (user_prof.is_contributor):\\n\\t\\t\\t\\treturn render(request, \\\"new_post.html\\\", {'user_prof': user_prof,})\\n\\t\\t\\telse:\\n\\t\\t\\t\\treturn render(request, \\\"home.html\\\")\\n\\t\\telse:\\n\\t\\t\\treturn render(request, \\\"home.html\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5893a0c64728cc105a67d05650aba22b\",\n \"score\": \"0.53247994\",\n \"text\": \"def home_page_view(request):\\n context = {\\n 'categories': Category.objects.all()\\n }\\n return render(request, 'blog/home_page.html', context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4104c84639f3c866186eeee8b893a3ea\",\n \"score\": \"0.53239465\",\n \"text\": \"def blog_archive():\\n return render_template(\\n 'blog-categories.html',\\n categories=posts.posts_by_date(),\\n title='Date',\\n sort_type='date'\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01b539ae11e9b937286fc6428ad6315a\",\n \"score\": \"0.5313553\",\n \"text\": \"def get_all_blogs():\\n blogs = Blog.query.order_by(Blog.created_at.desc()).all()\\n return render_template('index.html', blogs=blogs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a30cb943eb411471924fa58f0362f1a9\",\n \"score\": \"0.53109276\",\n \"text\": \"def post_list(request):\\n #queryset to order the blog posts by published date\\n posts = Post.objects.filter(published_date__lte=timezone.now()).order_by('published_date')\\n #{} this is where we can store things the template will use \\n return render(request, 'blog/post_list.html', {'posts' : posts})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efca8a22b9c3a4d7fcd80eb2c17f101f\",\n \"score\": \"0.53051287\",\n \"text\": \"def lire(request, id, slug):\\n article = get_object_or_404(Article, id=id, slug=slug)\\n return render(request, 'blog/lire.html', {'article': article})\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993271,"cells":{"query_id":{"kind":"string","value":"be3b167740ad91196d1c07de6ce0dc5d"},"query":{"kind":"string","value":"Raw Github should be up to download stuff from it."},"positive_passages":{"kind":"list like","value":[{"docid":"05695d218b9fc640233c8404cbd1b1c2","score":"0.0","text":"def test_is_raw_gh_online(self):\n is_online = ONLINE_CHECKER.is_gh_raw_online()\n\n self.assertTrue(is_online)","title":""}],"string":"[\n {\n \"docid\": \"05695d218b9fc640233c8404cbd1b1c2\",\n \"score\": \"0.0\",\n \"text\": \"def test_is_raw_gh_online(self):\\n is_online = ONLINE_CHECKER.is_gh_raw_online()\\n\\n self.assertTrue(is_online)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"edab69dbea84f039aa1f2d2090f0d221","score":"0.67514336","text":"def test_repo_get_raw_file(self):\n pass","title":""},{"docid":"6bf6846ba20a0a34fbadfa4a08855a71","score":"0.6747231","text":"def test_raw(self):\n\t\thtml = http.page_text(\"https://raw.githubusercontent.com/shadowmoose/RedditDownloader/master/Dockerfile\")\n\t\tself.assertIn(\"python\", html, \"Downloaded invalid Raw data from url!\")","title":""},{"docid":"7acc52baf5e179ba380e8280a5d80470","score":"0.658578","text":"def test_repo_get_contents(self):\n pass","title":""},{"docid":"d90e84b54940d004663571b5e5364d0a","score":"0.6343138","text":"def pull(self, remote = 'origin'):","title":""},{"docid":"fcbc25304536d26a4954f9ab26e0fb3e","score":"0.6294206","text":"def download_github_xml(url):\n # before making the request, get it raw\n print(\"Downloading \", url.split(\"/\")[-1])\n # o = urlparse(url)\n # netloc = o.netloc\n file_name = ''\n \"\"\"\n if netloc.startswith('raw.'):\n #it's already raw, no change to make\n pass\n elif netloc.startswith('github.'):\n netloc = 'raw.' + netloc\n url = o.scheme + '://' + netloc + o.path\n \"\"\"\n url = url.replace('blob', 'raw')\n # print('using ', url)\n r = requests.get(url)\n try:\n r.raise_for_status()\n except:\n # catch exception\n pass\n else:\n # no exception occured, continue\n if r.status_code == 200:\n # split the file from the url\n file_name = url.split(\"/\")[-1]\n with open(file_name, 'wb') as fp:\n fp.write(r.text.encode('utf-8'))\n # return the file name\n return file_name","title":""},{"docid":"071af16b16dc6398286d952cf6bbc8a2","score":"0.6261239","text":"def test_github_loads(self):\n tester = app.test_client()\n response = tester.get('/github')\n self.assertTrue(b'GitHub Repos' in response.data)","title":""},{"docid":"4a3cead3179de979f9e6b4c4307eb404","score":"0.6209297","text":"def _download(self):\n self._system.download_file(\n \"https://github.com/LaurentGomila/SFML/tarball/\" + self._tar_name)","title":""},{"docid":"c7c7c76c9a61f1486414be833a307b29","score":"0.6121335","text":"def test_repo_get_pull_request(self):\n pass","title":""},{"docid":"fa281aece832e52c633776e959edbbe3","score":"0.60263526","text":"def fetch(self):\n try: \n Oprint.info('Start downloading repo to your project from {}'.format(self._args.get('<url>')), 'lmdo')\n spinner.start()\n\n tmp = tempfile.mkdtemp()\n self.git_clone(self._args.get('<url>'), tmp)\n copytree(tmp, './', ignore=shutil.ignore_patterns('*.git*'))\n shutil.rmtree(tmp)\n \n spinner.stop()\n Oprint.info('Complete downloading repo to your project from {}'.format(self._args.get('<url>')), 'lmdo')\n except Exception as e:\n spinner.stop()\n raise e","title":""},{"docid":"dd9ac9ed5ffacbb0ff74c9bb6dafb525","score":"0.6019963","text":"def test_repo_get(self):\n pass","title":""},{"docid":"09262e9da21441c4e2eb3fe484cac817","score":"0.6017732","text":"def test_github_sync_import(self):\n pass","title":""},{"docid":"951feec50562ab229527e80140cfd0b1","score":"0.6001563","text":"def test_github_sync_export(self):\n pass","title":""},{"docid":"f56a87db3e35f952c908e4ba41e43c21","score":"0.59602207","text":"def meta_git_repo(url):\n print('Currently unsupported')\n sys.exit(0)","title":""},{"docid":"e270256a67e1f91189b953edf66660a2","score":"0.59136623","text":"def test_repo_get_archive(self):\n pass","title":""},{"docid":"71a56bdba8879ab549d481ff91cbe5c9","score":"0.5911419","text":"def fetch_github():\n try:\n repositories = github.search_repositories(\n query=settings.SEARCH_QUERY, sort=\"stars\"\n )\n\n for item in repositories:\n data = dict(\n id=item.id,\n name=item.name,\n full_name=item.full_name,\n html_url=item.html_url,\n description=item.description,\n # TODO: falta colocar: data de ultimo commit/release\n # TODO: quant stars\n # TODO: quant de contribuidores\n )\n repo.upsert(data, [\"id\"])\n # Erros de Internet\n except ReadTimeout as e:\n logger.warning(e)\n except ConnectionError as e:\n logger.warning(e)\n # Erros no GitHub\n # Git except https://pygithub.readthedocs.io/en/latest/utilities.html\n except RateLimitExceededException as e:\n logger.warning(e)\n except UnknownObjectException as e:\n logger.warning(e)\n except BadCredentialsException as e:\n # Erro na configuração\n logger.critical(e)\n exit(1)\n except TwoFactorException as e:\n logger.critical(e)\n exit(1)\n # Erros do banco de dados\n # https://docs.sqlalchemy.org/en/13/core/exceptions.html\n except OperationalError as e:\n logger.critical(e)\n exit(1)\n # Erros na Configuração\n except AttributeError as e:\n logger.critical(e)\n exit(1)","title":""},{"docid":"bda845aab8b9f9e7e9c7306fd3a7fe09","score":"0.5901792","text":"def main():\n\n proj_response = requests.get(PROJECTS_URL)\n if proj_response.status_code != 200:\n print(\"Error reading projects: \", proj_response)\n return\n\n github_list = []\n projects = proj_response.json()\n for project in projects[\"data\"]:\n proj_link = requests.utils.requote_uri(\n \"https://brigade.cloud/static-api/v1\" + project[\"links\"][\"self\"]\n )\n try:\n proj_data = requests.get(proj_link).json()\n code_url = proj_data[\"data\"][\"attributes\"].get(\"code_url\", \"\")\n if not code_url.startswith(\"https://github.com/\"):\n print(\"Skipping \", proj_data[\"data\"][\"id\"], code_url)\n else:\n print(code_url)\n code_url = code_url.replace(\"https://github.com/\", \"\")\n github_list.append(code_url)\n\n except Exception as ex:\n print(ex)\n return\n\n github_urls = \" \".join(github_list)\n # now load the meltano.yml file\n meltano_filename = \"../meltano/cfp-pipeline/meltano.yml\"\n with open(meltano_filename) as f:\n doc = yaml.safe_load(f)\n\n github_extractor = None\n for e in doc[\"plugins\"][\"extractors\"]:\n if e[\"name\"] == \"tap-github\":\n github_extractor = e\n break\n if github_extractor is None:\n print(\"could not find tap-github in meltano.yml file\")\n return\n\n if github_urls != github_extractor[\"config\"][\"repository\"]:\n print(\"tap-github repositories is different, rewriting meltano.yml\")\n github_extractor[\"config\"][\"repository\"] = github_urls\n with open(meltano_filename, \"r\") as f:\n meltano_yaml = f.read()\n meltano_yaml = re.sub(\n \"repository:(.*)\\n?\", \"repository: \" + github_urls + \"\\n\", meltano_yaml\n )\n with open(meltano_filename, \"w\") as f:\n f.write(meltano_yaml)","title":""},{"docid":"9d70a3ae7ede88b0b4e5dde696f6fa7d","score":"0.58859545","text":"def test_fetch_tarball_and_keep():\n directory_name = fetch_open_samples(\n \"GW190424_180648\", read_file=False, outdir=\".\", unpack=True,\n catalog=\"GWTC-2\", download_kwargs={\"timeout\": 60}\n )\n assert os.path.isdir(\"./GW190424_180648\")\n assert os.path.isdir(directory_name)","title":""},{"docid":"11828590d1fdb924370298e0181ed022","score":"0.58739424","text":"def build_raw_github_url(state, datestring, raw_filename):\n tpl = \"https://raw.githubusercontent.com/openelections/openelections-data-{}/master/{}/{}\"\n return tpl.format(state.lower(), datestring, raw_filename)","title":""},{"docid":"3e27dfbad82764ac9dfa9467d1f59be3","score":"0.5857968","text":"def view_git_repo_retrieve(remote, do_base64_encode, client_ip, branches=False, chunk_size=0):\n\n model = Download()\n model.base64encode = do_base64_encode\n model.remote = remote\n model.file_name = ''\n model.client_ip = client_ip\n model.git_branches = branches\n \n try:\n output_file, file_name = git_repo_retrieve(remote, do_base64_encode, branches, chunk_size)\n model.file_name = file_name\n \n with FileCleaner(output_file, 'rb') as fp:\n\n response = HttpResponse(fp)\n response[\"Content-Disposition\"] = \"attachment; filename=%s\" % (file_name,)\n model.status = 'SUCCESS'\n return response\n except Exception, ex:\n model.status = 'FAILED'\n ex_str = traceback.format_exc()\n model.error = ex_str\n return HttpResponse(ex_str, status=400)\n finally:\n model.save()","title":""},{"docid":"2be1b0fe4964be8e111e25fee505da16","score":"0.58524805","text":"def pullExcelFromGithub():\n url = ('https://raw.githubusercontent.com/JamesHamiltonDev/craftDemo/master/files/')\n pathToExcelFile = Path(r\"C:\\Users\\james\\PycharmProjects\\craftDemo\\files\\hardware.xlsx\")\n fileNotFound = True\n while fileNotFound is True:\n print(\"Downloading file . . .\")\n\n#\n## URL requests to third party site intentionally delayed. Third party providers have a habit of\n## blocking IPs that bombard their servers with requests.\n#\n time.sleep(3)\n try:\n if pathToExcelFile.is_file():\n fileNotFound = False\n print('File downloaded')\n return fileNotFound\n else:\n\n urllib.request.urlretrieve('https://raw.githubusercontent.com/JamesHamiltonDev/craftDemo/master/files/hardware.xlsx')\n except:\n pass","title":""},{"docid":"304d62bcea07bc1cf8cd2331a7c515f3","score":"0.581636","text":"def test_downloading(self):\n #from os.path import join, abspath, dirname\n #deam_rel_root = dirname(dirname(abspath(__file__)))\n #repo_handler = RepositoryHandler(join(deam_rel_root, 'testing'))\n # repo_handler.execute(False)\n self.assertEqual(True)","title":""},{"docid":"54f92340af8662467372d5fb93849c8e","score":"0.5791929","text":"def github_link(self):\n if self.test_type == TestType.commit:\n test_type = 'commit'\n test_id = self.commit\n else:\n test_type = 'pull'\n test_id = self.pr_nr\n\n return \"{base}/{test_type}/{test_id}\".format(\n base=self.fork.github_url, test_type=test_type, test_id=test_id)","title":""},{"docid":"5f5f98bb40cc7995cf61b936b0b89633","score":"0.57814527","text":"def git_pull():\n run('git pull --all')","title":""},{"docid":"be204a99e1b241d1a8c24da46325033e","score":"0.57719195","text":"def az_pinkie_pull():","title":""},{"docid":"72d92f86165c12754b39b28b9cc0f67e","score":"0.5771329","text":"async def gh(self, ctx, repo: str):\n embed = get_embed()\n async with await self.bot.session.get(\n f\"https://api.github.com/repos/{repo}\"\n ) as r:\n if r.status == 200:\n r = await r.json()\n if r[\"description\"] == \"\":\n desc = \"No description provided.\"\n else:\n desc = r[\"description\"]\n stars = r[\"stargazers_count\"]\n forks = r[\"forks_count\"]\n cmd = f'git clone {r[\"clone_url\"]}'\n embed.title = f\"{repo} on GitHub\"\n embed.description = self.generate_description(desc, stars, forks, cmd)\n elif r.status == 404:\n embed.title = \"Oops...\"\n embed.description = \"That repository doesn't seem to exist, or is private. Are you sure you typed it correctly?\"\n await ctx.send(embed=embed)","title":""},{"docid":"9bfadf89c01b01f9e782779846a9b511","score":"0.57594305","text":"def download(self):\n print(f'git clone from {SYSEVR_URL}')\n clone_path = self._root / 'SySeVR.git'\n if not clone_path.exists():\n git_clone_file(SYSEVR_URL, str(clone_path))\n for file in clone_path.glob('**/*.zip'):\n with zipfile.ZipFile(str(file)) as z:\n z.extractall(str(self._root))\n else:\n print(f'{str(clone_path)} exist, download cancel')","title":""},{"docid":"81614effce5b3dad3ffbfa8c14c18afc","score":"0.5744819","text":"def inception_v3_download():","title":""},{"docid":"623097d3be5c02cb4bb18f74fc0c75af","score":"0.5719832","text":"def maybe_download(self):\n os.makedirs(self._rawdir, exist_ok=True)\n\n expected_checksums = self.md5 if self.md5 else [None] * len(self.data)\n\n for url, expected_md5 in zip(self.data, expected_checksums):\n tarball = os.path.join(self._rawdir, self._get_tarball_filename(url))\n\n download_file(\n url, tarball, extract_to=self._rawdir, expected_md5=expected_md5\n )","title":""},{"docid":"ad9080f64aed95877076751fcdd6c5f1","score":"0.5716542","text":"def test_repo_create_pull_request(self):\n pass","title":""},{"docid":"4b3bf745f46dec73bfb2ed2e6195c8d7","score":"0.5712749","text":"def test_issue_137(self):\n i = Issue(load('issue_137'))\n self.assertEqual(\n i.html_url,\n \"https://github.com/sigmavirus24/github3.py/pull/1\")\n self.assertEqual(i.repository, (\"sigmavirus24\", \"github3.py\"))","title":""},{"docid":"13a067e8fe103e5acac1c81151389e47","score":"0.57012063","text":"def test_full(self):\n github.input = lambda x: \"xmaayy\"\n data = get_hub(\"xmaayy\")\n assert data != []\n print(data[1].keys())\n repolist = [repo[\"name\"] for repo in data]\n assert repolist == [\n \"Alexandria\",\n \"cipher\",\n \"CUHackAPIO\",\n \"DarkBot\",\n \"megacrypt.js\",\n \"MGAN\",\n \"NoFace\",\n \"polybar\",\n \"SFMLinstall\",\n \"SubHelper\",\n \"UpStat\",\n \"Website\",\n \"xmaayy.github.io\",\n ]","title":""},{"docid":"332ec00e18fbcb8ca7a7901479531df2","score":"0.5698646","text":"def __init__(self, download_url):\n super(LibyalGitHubDownloadHelper, self).__init__(download_url)\n self._download_helper = None","title":""},{"docid":"356673990144dbfc6a8eb73cf077a103","score":"0.56726795","text":"def __init__(\n self,\n owner,\n repo,\n pr=None,\n branch=None,\n token=None,\n url=None,\n commit=None,\n ignore_paths=None,\n prefix=None,\n autofix=False,\n set_status=False,\n ):\n self.start = datetime.datetime.now()\n self.github = None\n self.stopped_early = False\n self.autofixed = False\n self.prefix = prefix\n self.autofix = autofix\n self.ignore_paths = set(ignore_paths or [])\n self.token = token\n self.set_status = set_status\n url = url or \"https://github.com\"\n print(\"url={}\".format(url))\n self.netloc = urlparse(url).netloc.strip()\n print(\"urlparse={}\".format(urlparse(url)))\n if not url or url == \"https://github.com\":\n self.github = github3.GitHub(token=token)\n else:\n self.github = github3.GitHubEnterprise(url, token=token)\n\n try:\n self.github_user = self.github.me().as_dict()\n except (TypeError, AttributeError):\n # github.py == 0.9.6\n self.github_user = self.github.user().to_json()\n\n self.username = \"\"\n self.email = \"\"\n try:\n self.username = self.github_user[\"login\"]\n for email in self.github.emails():\n try:\n email_obj = email.as_dict()\n except (TypeError, AttributeError):\n # github.py == 0.9.6\n email_obj = email.to_json()\n if email_obj[\"primary\"]:\n self.email = email_obj[\"email\"]\n except Exception: # NOQA\n traceback.print_exc()\n\n self.owner = owner\n self.repo = repo\n\n self.github_repo = self.github.repository(self.owner, self.repo)\n print(\"Branch: {0}\".format(branch))\n self.branch = branch\n self.pull_request_number = None\n if branch and not pr:\n for github_repo in [self.github_repo, self.github_repo.parent]:\n if pr:\n break\n\n if not github_repo:\n continue\n\n try:\n # github.py == 0.9.6\n pulls = github_repo.iter_pulls()\n except AttributeError:\n pulls = github_repo.pull_requests()\n\n for pull_request in pulls:\n print(\n \"Branch: {} - Pull Request Head Ref: {}\".format(\n branch, pull_request.head.ref\n )\n )\n if pull_request.head.ref == branch:\n pr = pull_request.number\n self.github_repo = github_repo\n break\n\n self.owner = self.github_repo.owner\n self.repo = self.github_repo.name\n\n # TODO: support non-PR runs\n try:\n pr = int(pr)\n except (ValueError, TypeError):\n print(\"{0} is not a valid pull request ID\".format(pr))\n self.github = None\n return\n\n print(\"PR ID: {0}\".format(pr))\n self.pull_request_number = pr\n self.pull_request = self.github.pull_request(self.owner, self.repo, pr)\n self.target_sha = self.pull_request.base.sha\n self.target_branch = self.pull_request.base.ref\n self.sha = self.pull_request.head.sha\n self.branch = self.pull_request.head.ref\n try:\n # github.py == 0.9.6\n try:\n git.fetch(self.pull_request.base.to_json()[\"repo\"][\"clone_url\"])\n except subprocess.CalledProcessError:\n git.fetch(self.pull_request.base.to_json()[\"repo\"][\"ssh_url\"])\n except AttributeError:\n # latest github.py\n try:\n git.fetch(self.pull_request.base.repository.as_dict()[\"clone_url\"])\n except subprocess.CalledProcessError:\n git.fetch(self.pull_request.base.repository.as_dict()[\"ssh_url\"])\n\n print(\"Target SHA: {0}\".format(self.target_sha))\n print(\"Target Branch: {0}\".format(self.target_branch))\n print(\"Head SHA: {0}\".format(self.sha))\n print(\"Head Branch: {0}\".format(self.branch))\n self.last_sha = commit or git.current_sha()\n print(\"Last SHA: {0}\".format(self.last_sha))\n self.diff = git.diff(self.target_sha, self.last_sha)\n self.patch = unidiff.PatchSet(self.diff.split(\"\\n\"))\n self.review_comments = list(self.pull_request.review_comments())\n self.last_update = time.time()\n self.messages_in_files = dict()\n self.filenames = set()\n\n try:\n try:\n pr_files = self.pull_request.files()\n except AttributeError:\n # github.py == 0.9.6\n pr_files = self.pull_request.iter_files()\n self.filenames = set(\n os.path.relpath(pr_file.filename).replace(\"\\\\\", \"/\").strip()\n for pr_file in pr_files\n )\n print(\"Files in PR: {}\".format(self.filenames))\n except Exception:\n traceback.print_exc()","title":""},{"docid":"fc0991cff9f43593fdf34badd17468b4","score":"0.56627953","text":"def git_pull(self):\n token = self.user_id.token\n clone_url = self.clone_url and self.clone_url.replace('https://', '')\n cmd = ['pull',\n GITHUB_CLONE.format(token=token,\n clone_url=clone_url)]\n self.git(cmd, cwd=1)","title":""},{"docid":"3eba1ece83836a2ebbcbe37a3e27446d","score":"0.56571054","text":"def downloadTheCode(self, whereToSaveCodePath, githubRelativeUrlList ):\n if not whereToSaveCodePath.endswith(\"/\"):\n whereToSaveCodePath = whereToSaveCodePath+\"/\"\n\n for relpath in githubRelativeUrlList :\n url = \"https://raw.github.com\" + (\"/\".join( [ part for part in relpath.split(\"/\") if part != \"blob\" ] ) )\n print \"Scraping file:\\n\", url\n opener = urllib2.build_opener()\n opener.addheaders = [('User-agent', 'Mozilla/5.0')]\n u = opener.open(url)\n meta = u.info()\n file_size = int(meta.getheaders(\"Content-Length\")[0])\n print \"Downloading: %s Bytes: %s\" % (url.split(\"/\")[-1], file_size)\n\n file_name = (whereToSaveCodePath+str(uuid.uuid4())+\"__\"+url.split(\"/\")[-1])\n f = open(file_name, 'w')\n\n file_size_dl = 0\n block_sz = 8192\n while True:\n buffer = u.read(block_sz)\n if not buffer:\n break\n\n file_size_dl += len(buffer)\n f.write(buffer)\n status = r\"%10d [%3.2f%%]\" % (file_size_dl, file_size_dl * 100. / file_size)\n status = status + chr(8)*(len(status)+1)\n print status,\n\n f.close()\n u.close()\n time.sleep(random.randint(1,3))","title":""},{"docid":"2cde82eafcb179b207714202ec49d552","score":"0.56204134","text":"async def git(self, ctx, *args):\n emb = Embed(title='GitHub · fiffu/arisa2',\n description=(f'Bugs and suggestions: create an issue!\\n'\n f'{GITHUB_LINK}/issues\\n\\n'\n f'Contributions: pull requests welcome!'\n f'```git clone {GITHUB_LINK}.git```'),\n #url=GITHUB_LINK,\n colour=0x1e2327)\n await ctx.send(embed=emb)","title":""},{"docid":"bfbd7136aac2ec47a5ba41559d3bfe9f","score":"0.5609883","text":"def get_data(github, selected_repos):\n org = \"custom-cards\"\n data = {}\n repos = []\n if selected_repos:\n repos.append(selected_repos)\n else:\n for repo in list(github.get_user(org).get_repos()):\n repos.append(repo.name)\n for repo in repos:\n try:\n repo = github.get_repo(org + \"/\" + repo)\n if repo.name not in BLACKLIST and not repo.archived:\n print(\"Generating json for:\", \"{}/{}\".format(org, repo.name))\n\n try:\n release = list(repo.get_releases())[0]\n except Exception: # pylint: disable=W0703\n release = None\n\n name = repo.name\n\n version = None\n try:\n if release and release.tag_name is not None:\n version = release.tag_name\n else:\n content = repo.get_file_contents(\"VERSION\")\n content = content.decoded_content.decode()\n version = content.split()[0]\n except Exception: # pylint: disable=W0703\n version = None\n\n if release:\n remote_location = REUSE_TAG.format(org, name, version, name)\n else:\n remote_location = REUSE.format(org, name, name)\n\n remote_location = remote_location + \".js\"\n testfile = requests.get(remote_location)\n\n if testfile.status_code != 200:\n remote_location = remote_location.split(name + \".js\")[0]\n remote_location = remote_location + \"dist/\" + name + \".js\"\n testfile = requests.get(remote_location)\n\n if testfile.status_code != 200:\n remote_location = remote_location.split(\"dist/\" + name + \".js\")[0]\n remote_location = remote_location + \"src/\" + name + \".js\"\n testfile = requests.get(remote_location)\n\n if testfile.status_code != 200:\n continue\n\n visit_repo = VISIT.format(org, name)\n\n try:\n changelog = list(repo.get_releases())[0].html_url\n if \"untagged\" in list(repo.get_releases())[0].name:\n changelog = None\n except Exception: # pylint: disable=W0703\n changelog = None\n\n if changelog is None:\n changelog = VISIT.format(org, name)\n\n data[name] = {}\n data[name][\"version\"] = version\n data[name][\"remote_location\"] = remote_location\n data[name][\"visit_repo\"] = visit_repo\n data[name][\"changelog\"] = changelog\n except Exception as error: # pylint: disable=W0703\n print(error)\n return data","title":""},{"docid":"9d3e835f9c0690ae58d623f74d18269a","score":"0.55903345","text":"def test_repo_get_release(self):\n pass","title":""},{"docid":"c071793bc97131fb3c3e5bfd11884bb5","score":"0.55789083","text":"def test_fetch_tarball_and_keep_single_file():\n file_name = fetch_open_samples(\n \"GW190424_180648\", read_file=False, outdir=\".\", unpack=True,\n path=\"GW190424_180648.h5\", catalog=\"GWTC-2\",\n download_kwargs={\"timeout\": 60}\n )\n assert os.path.isfile(\"./GW190424_180648.h5\")\n assert os.path.isfile(file_name)","title":""},{"docid":"223a5e8bf8fe79c3aba13891b1f902b6","score":"0.5575196","text":"def sync_repository(self):\n session = requests.Session()\n session.auth = (self.user_id.token, 'x-oauth-basic')\n res = session.get(GITHUB_REPO.format(name=self.name))\n res_commits = session.get(GITHUB_COMMIT.format(name=self.name))\n res_r = res.json()\n self.local_path = os.path.join(tools.config.filestore(self._cr.dbname),\n str(self.user_id.id),\n str(self.id))\n self.write({'url': res_r.get('html_url'),\n 'git_description': res_r.get('description'),\n 'clone_url': res_r.get('clone_url'),\n 'ssh_url': res_r.get('ssh_url'),\n 'last_json_answer': res_r,\n 'sha': res_commits.json()[0].get('sha')})","title":""},{"docid":"3e7b897ad48814b6fccfd4ce16d5d60f","score":"0.5573493","text":"def download_stdlib(token: str, org: str, repo: str, branch: str, folder: str) -> None:\n download_from_git(token, org, repo, branch, folder)","title":""},{"docid":"3984f681ff4e1237023e780d38f0945a","score":"0.5571107","text":"async def github():\n text = \"How tu veux voir mon repos Github pour me disséquer ? Pas de soucis ! Je suis un Bot, je ne ressens pas la douleur !\\n https://github.com/outout14/tuxbot-bot\"\n em = discord.Embed(title='Repos TuxBot-Bot', description=text, colour=0xE9D460)\n em.set_author(name='Outout', icon_url=\"https://avatars0.githubusercontent.com/u/14958554?v=3&s=400\")\n await bot.say(embed=em)","title":""},{"docid":"786a546dc897854f71de36b6a7a959df","score":"0.55704504","text":"def git_clone(self, url, target):\n pass","title":""},{"docid":"3d8f4e169bdf02609f35d0b1aa46c55c","score":"0.5567321","text":"async def github(self,ctx,person,repo):\n\t\tr = json.loads(requests.get(\"https://api.github.com/repos/ottomated/CrewLink/commits/master\").text)\n\n\t\tlast_pushed=r[\"commit\"][\"author\"][\"date\"]\n\t\tlast_pushed_general = last_pushed[:10]\n\t\tlast_pushed_time = last_pushed[11:-1]\n\t\tlast_pushed_date = datetime(int(last_pushed_general[0:4]),int(last_pushed_general[5:7]),int(last_pushed_general[8:]),int(last_pushed_time[:2]),int(last_pushed_time[3:5]),int(last_pushed_time[6:]))\n\t\tlast_pushed_date_pytz = last_pushed_date.replace(tzinfo=timezone(\"GMT\"))\n\t\tnow=datetime.now(pytz.timezone(\"GMT\"))\n\n\t\tself.log(\"git\",ctx.message.author)\n\t\tawait ctx.send(f\"Last Updated: *{now-last_pushed_date_pytz}*\")","title":""},{"docid":"dc1a11450e485c726e6d1c2f6a955338","score":"0.55661553","text":"def github_run(data):\n counter_talley = 0\n for key in data:\n data[key]['source'] = \"\"\n data[key]['url'] = \"\"\n data[key]['html'] = \"\"\n data[key]['commit_author'] = \"\"\n data[key]['commit_author_id'] = \"\"\n data[key]['commit_time'] = \"\"\n try:\n if data[key][\"new_link\"] == \"\":\n continue\n possible_commits = {'mention':[], 'first': [], 'second': [], 'third': [], 'oss_fuzz_error': [], 'sole_commit': []}\n for temp_link_value in data[key][\"new_link\"].split(\"*SEP*\"):\n username = '' # Specify GitHub username to use \n url = \"https://api.github.com/repos/\" + temp_link_value.split(\".com/\")[1]\n try:\n response = requests.get(url, auth=(username, TOKEN))\n json_data = response.json()\n count[0] += 1\n except:\n continue\n if \"commits\" not in json_data:\n continue\n for commit in json_data['commits']:\n ts = data[key]['state'].split(\"--\")\n lookup_url = commit['url']\n temp_response = requests.get(lookup_url, auth=(username, TOKEN))\n temp_json = temp_response.json()\n if count[0] > 4950:\n print(\"Resting the API...\")\n time.sleep(3600)\n print(\"Restarting the API...\")\n count[0] = 0\n count[0] += 1\n for f in temp_json['files']:\n if key in temp_json['commit']['message']:\n possible_commits['mention'].append(temp_json)\n if len(ts) > 0 and \"patch\" in f and ts[0].strip() in f['patch']:\n possible_commits['first'].append(temp_json)\n elif len(ts) > 1 and \"patch\" in f and ts[1].strip() in f['patch']:\n possible_commits['second'].append(temp_json)\n elif len(ts) > 2 and \"patch\" in f and ts[2].strip() in f['patch']:\n possible_commits['third'].append(temp_json)\n elif 'timeout' in data[key]['crash'].lower() and 'libFuzzer' in temp_json['commit']['message']:\n possible_commits['oss_fuzz_error'].append(temp_json)\n if len(json_data['commits']) == 1:\n possible_commits['sole_commit'].append(temp_json)\n most_likely = \"\"\n try:\n for subkey in possible_commits:\n for item in possible_commits[subkey]:\n if most_likely == \"\":\n most_likely = item\n data[key]['source'] = subkey\n if most_likely != \"\":\n data[key]['url'] = most_likely['url']\n data[key]['html'] = most_likely['html_url']\n data[key]['commit_author'] = most_likely['commit']['author']['name']\n data[key]['commit_author_id'] = most_likely['commit']['author']['email']\n data[key]['commit_time'] = most_likely['commit']['author']['date']\n counter_talley += 1\n \n except:\n continue\n except:\n continue","title":""},{"docid":"666ff38d9daf4f599d1a55ba600027a3","score":"0.55563235","text":"def test_repo_transfer(self):\n pass","title":""},{"docid":"796a1e437683d40df6288646a64cabed","score":"0.55539924","text":"def test_repo_get_hook(self):\n pass","title":""},{"docid":"e60f6286cf74387b6568fd695eaf37dc","score":"0.555213","text":"def get_data(url, user):\n langs = {\n '.c': 'C',\n '.cpp': 'C++',\n '.cs': 'C#',\n '.css': 'CSS',\n '.go': 'Go',\n '.html': 'HTML',\n '.java': 'Java',\n '.js': 'JavaScript',\n '.php': 'PHP',\n '.py': 'Python',\n '.rb': 'Ruby'\n }\n message = \"Okay\"\n api_url, repo, handler, contents = convert_github_html_url_to_api_url(url)\n\n if handler != user.username:\n raise Exception(\"You can't add a snippet of other people's code! 😓\")\n\n response = requests.get(api_url, auth=(\"friendwithnobenegits\", \"pixeldevtinder1\"))\n if response.status_code != 200:\n raise Exception(\"Repository is not public.\")\n file = base64.b64decode(response.json().get('content'))\n\n maxlines = 20\n startline = 0\n endline = startline+maxlines\n\n try:\n print url\n if '#' in url:\n lines = url[url.find(\"#\")+1:]\n if '-' in lines:\n endline = int(lines[lines.find('-')+2:])\n startline = int(lines[1:lines.find('-')]) - 1\n if startline >= endline:\n startline = endline - 20\n else:\n startline = int(lines[1:]) - 1\n endline = startline+maxlines\n except Exception, e:\n print e\n\n # limit lines of code\n file = \"\\n\".join(file.split('\\n')[startline:endline])\n if file == \"\":\n startline = 0\n endline = startline+maxlines\n file = \"\\n\".join(file.split('\\n')[0:endline])\n lines = 'L{}-L{}'.format(startline, endline)\n print lines\n print file\n\n language = contents.split(\"#\")[0].split(\".\")[-1]\n language = langs.get('.{}'.format(language), None)\n if not language:\n print \"Language \\\"{}\\\" does not exists. Query for repo language\".format(contents.split(\"#\")[0].split(\".\")[-1])\n r = requests.get('https://api.github.com/repos/{0}/{1}'.format(user.username, repo), auth=(\"friendwithnobenegits\", \"pixeldevtinder1\"))\n repository = r.json()\n language = repository.get('language')\n\n return {\n 'message': message,\n 'name': repo,\n 'filename' : response.json().get('name'),\n 'stars': response.json().get('size'),\n 'lines': lines,\n 'language': language,\n 'snippet': file\n }","title":""},{"docid":"e1d59df94c607a4953f784152366bc9e","score":"0.5545859","text":"def pull(self, repo, path):\n raise NotImplementedError()","title":""},{"docid":"1a7e6aebb6c2186ac2ff3fff38f9e085","score":"0.5542814","text":"async def git(ctx):\n if can_answer(ctx):\n msg = \"https://github.com/Eerolz/launchbot\"\n await ctx.send(msg)","title":""},{"docid":"b228900d589c97acb3931a309f09d019","score":"0.5526284","text":"def fetch_in_volume():\n top = \"%s/%s\" % (ssdroot, flag_subvol)\n dochdir(top)\n # First binutils (which is only git)\n do_fetch(\"git\", \"binutils\")\n dochdir(\"llvm\")\n # Next llvm stuff\n tofind = \".git\"\n if flag_scm_flavor == \"svn\":\n tofind = \".svn\"\n lines = u.docmdlines(\"find . -depth -name %s -print\" % tofind)\n for line in lines:\n do_fetch(flag_scm_flavor, line.strip())\n dochdir(top)","title":""},{"docid":"637384ef6d4276b2b669f9b8c6ffab13","score":"0.5525182","text":"def read_repo(self):\n return self._init_github(self._gh, self.team_name, self.repo_name)","title":""},{"docid":"dba66fdfe278dedc4e059f94e6db6607","score":"0.5520729","text":"def do_fetch(flavor, where):\n here = os.getcwd()\n dochdir(where)\n if flavor == \"git\":\n docmd(\"git fetch\")\n elif flavor == \"git-svn\":\n docmd(\"git fetch\")\n docmd(\"git svn rebase -l\")\n else:\n docmd(\"svn update\")\n dochdir(here)","title":""},{"docid":"4db1523d14d71ee41a1fca046bdd805e","score":"0.5513033","text":"async def pull(ctx):\n author = ctx.author\n if author.id in authorities:\n out = subprocess.Popen(['git', 'pull'], stdout=subprocess.PIPE, stderr=subprocess.STDOUT)\n stdout,stderr = out.communicate()\n stdout = stdout.decode(\"utf-8\")\n msg = '**Output: **{0}\\n'.format(stdout)\n if stderr:\n stderr = stderr.decode(\"utf-8\")\n msg += '**Error: **\\n{0}'.format(stderr)\n await ctx.send(msg)\n else:\n await ctx.send(\"You can't tell me what to do!\")","title":""},{"docid":"35031b31fa06bd2121c4d5fe8c0d7031","score":"0.5485385","text":"def download_data() -> None:\n if not os.path.exists(\"data\"):\n os.makedirs(\"data\")\n\n http = PoolManager()\n repo = http.request(\n \"GET\", \"https://github.com/yaringal/DropoutUncertaintyExps/archive/master.zip\"\n )\n\n with zipfile.ZipFile(io.BytesIO(repo.data)) as zip_ref:\n zip_ref.extractall(\"./data\")","title":""},{"docid":"cf5b34c159b3d4a06050cc6438e8a802","score":"0.547871","text":"def test_repo_get_contents_list(self):\n pass","title":""},{"docid":"acb45daa7198b4c685be51eff47e6aa1","score":"0.5477697","text":"def test_repo_get_git_hook(self):\n pass","title":""},{"docid":"f8f47e366954a018d755f02e0b12623f","score":"0.5466664","text":"async def source(self, ctx):\n await ctx.send(\"Check out https://github.com/maxx0911/idleapi.tio\")","title":""},{"docid":"b1a848d48a791cc4e1ce6091422af80b","score":"0.54599565","text":"def main():\n start = \"http://www.harkavagrant.com/archive.php\"\n pagelinks = getPageLinks(start)\n piclinks = getComicLinks(pagelinks)\n # one dir for harkavagrant-hosted images, and one dir for other ones\n checkdir(\"original\")\n checkdir(\"other\")\n retrieve(piclinks)","title":""},{"docid":"c0fb60a06dd1c9d68f3001603a2eea20","score":"0.5442824","text":"def infoAboutRepo():\n\tuser = raw_input('Enter the user name ')\n\turl = 'https://github.com/'+user\n\t# Check If username is invalid\n\ttry:\n\t\tsoup = BeautifulSoup(urllib2.urlopen(url).read(), \"html.parser\")\n\texcept Exception:\n\t\tprint 'User \"%s\" does not exist! Please try again.' %(user)\n\t\texit()\n\n\tpopularRepo = soup.find_all('span' , {'class': 'repo js-repo'})\n\tprint \"These are the some popular repo of user\",user\n\tfor repo in popularRepo:\n\t\tprint repo.string\n\n\trepo = raw_input('Enter the repository name : ')\n\turl = \"https://github.com/\"+user+'/'+repo\n\ttry:\n\t\turllib2.urlopen(url)\n\texcept urllib2.HTTPError, e:\n\t\tprint 'Sorry, there is no such repository named \"%s\" for user \"%s\"'%(repo, user)\n\t\texit()\n\n\n\tdef pulse(url):\n\t\t\"\"\"\n\t\t\"\"\"\n\t\turl += '/pulse/monthly'\n\t\tpage = urllib2.urlopen(url)\n\t\tsoup = BeautifulSoup(page.read(), \"html.parser\")\n\t\tdiv_all = soup.findAll('div',{'class':'section diffstat-summary'})\n\t\tif not div_all:\n\t\t\tprint 'No Recent activities in the repository.'\n\t\t\treturn\n\t\tprint '\\nThe whole information about the repository is as follows :\\n'\n\t\tfor each_div in div_all:\n\t\t print ' '.join(each_div.get_text().split())\n\n\tdef readme(url):\n\t\t\"\"\"\n\t\t\"\"\"\n\t\turl+= '/blob/master/README.md'\n\t\t# Check if ReadMe exists.\n\t\ttry:\n\t\t\tsoup = BeautifulSoup(urllib2.urlopen(url).read(), \"html.parser\")\n\t\t\tparagraphs = soup.find('article', {\"class\" : \"markdown-body entry-content\"}).get_text()\n\t\texcept Exception:\n\t\t\tprint 'ReadMe file for the repository doesn\\'t exist'\n\t\t\treturn\n\n\t\tprint '\\nREADME\\n'\n\t\tprint paragraphs\n\n\n\tdef watching(url):\n\t\t\"\"\"\n\t\t\"\"\"\n\t\t# TODO: watching not working as of now. Only giving 0 as Watcher...\n\t\tsoup = BeautifulSoup(urllib2.urlopen(url).read(), \"html.parser\")\n\t\twatch = soup.find('a' , {\"class\" : \"social-count js-social-count\"}).text\n\t\tprint 'Watchers: %s' %(watch.split()[0])\n\n\n\tdef statistics(url):\n\t\t\"\"\"\n\t\t\"\"\"\n\t\tsoup = BeautifulSoup(urllib2.urlopen(url).read(), \"html.parser\")\n\t\tultags_all= soup.find_all('ul', {'class' : 'numbers-summary'})\n\t\tif not ultags_all:\n\t\t\tprint 'No activities in the repository.'\n\t\t\treturn\n\t\tprint \"\\nUsers Activities in Repo\"\n\t\tfor ultag in ultags_all :\n\t\t\tfor litag in ultag.find_all('li'):\n\t\t\t\tif ' '.join(litag.text.split()) != \"Fetching contributors\":\n\t\t\t\t\tprint ' '.join(litag.text.split())\n\n\tstatistics(url)\n\tpulse(url)\n\treadme(url)\n\twatching(url)\n\t#more features to be added...","title":""},{"docid":"5f4f17f867800f561cccf097aff83e75","score":"0.5430694","text":"def test_repo_list_pull_requests(self):\n pass","title":""},{"docid":"48d2b4279fd11417a18150b29a80f807","score":"0.5426568","text":"async def on_message(self, message):\n\n gh_match = GITHUB_RE.search(message.content)\n\n if gh_match and not message.author.bot:\n for gh in GITHUB_RE.finditer(message.content):\n d = gh.groupdict()\n headers = {}\n if 'GITHUB_TOKEN' in os.environ:\n headers[\n 'Authorization'] = f'token {os.environ[\"GITHUB_TOKEN\"]}'\n pull_request = await fetch_http(\n self.session,\n f'https://api.github.com/repos/{d[\"owner\"]}/{d[\"repo\"]}/pulls/{d[\"pr\"]}',\n 'json',\n headers=headers,\n )\n\n body = pull_request[\"body\"]\n if len(body) > 512:\n body = body[:512] + \"...\"\n\n state = pull_request['state'].capitalize()\n if pull_request['draft']:\n state = 'Draft'\n if pull_request['merged']:\n state = 'Merged'\n\n embed = discord.Embed(\n title=f'{pull_request[\"title\"]} (#{pull_request[\"number\"]})',\n description=body,\n url=pull_request['html_url'],\n timestamp=datetime.datetime.fromisoformat(pull_request['created_at'][:-1]),\n color=COLORS[state],\n ).set_author(\n name=f'{d[\"owner\"]}/{d[\"repo\"]}',\n url=f'https://github.com/{d[\"owner\"]}/{d[\"repo\"]}',\n ).add_field(\n name=\"Status\",\n value=state,\n inline=True,\n ).add_field(\n name=\"Additions\",\n value=str(pull_request['additions']),\n inline=True,\n ).add_field(\n name=\"Deletions\",\n value=str(pull_request['deletions']),\n inline=True,\n ).add_field(\n name=\"Files Changed\",\n value=str(pull_request['changed_files']),\n inline=True,\n ).add_field(\n name=\"Commits\",\n value=str(pull_request['commits']),\n inline=True,\n ).set_footer(\n text=f'Pull request created by {pull_request[\"user\"][\"login\"]}',\n icon_url=pull_request['user']['avatar_url'],\n )\n\n if pull_request[\"merged\"]:\n embed = embed.add_field(\n name='Merged By',\n value=pull_request['merged_by']['login'],\n inline=True,\n )\n else:\n embed = embed.add_field(\n name='State',\n value='Mergeable' if pull_request['mergeable'] else 'Not Mergeable',\n inline=True,\n )\n\n await wait_for_deletion(message, self.bot, embed, True)\n\n await message.edit(suppress=True)","title":""},{"docid":"6305d052cb677ce2f40c4da1918ab0a0","score":"0.54137367","text":"async def git_loop(self):\n\t\twhile not self.bot.is_closed:\n\t\t\tif 'no-git-loop' in sys.argv:\n\t\t\t\treturn\n\t\t\ttry:\n\t\t\t\twith open('./cogs/github_info.json', 'r') as f:\n\t\t\t\t\tgit_json = json.load(f)\n\n\t\t\t\tfor server in git_json[\"repositories\"]:\n\t\t\t\t\tfor channel in git_json[\"repositories\"][server]:\n\t\t\t\t\t\tfor repo_owner in git_json[\"repositories\"][server][channel]:\n\t\t\t\t\t\t\tfor repo in git_json[\"repositories\"][server][channel][repo_owner]:\n\n\t\t\t\t\t\t\t\td = feedparser.parse('https://github.com/{}/{}/commits/master.atom'.format(repo_owner, repo))\n\t\t\t\t\t\t\t\tif 'bozo_exception' in d: #if rss url is invalid\n\t\t\t\t\t\t\t\t\tawait self.bot.send_message(discord.Object(channel), \"Error while retrieving data from URL: '{}'\".format(rss_url))\n\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\tlatest_commit = d[\"entries\"][0][\"link\"]\n\t\t\t\t\t\t\t\t\tfmt = \"{} @here\".format(latest_commit)\n\t\t\t\t\t\t\t\t\tif channel not in self.repo_data:\n\t\t\t\t\t\t\t\t\t\tself.repo_data[channel] = {}\n\t\t\t\t\t\t\t\t\t\tself.repo_data[channel][repo] = latest_commit\n\t\t\t\t\t\t\t\t\t\tawait self.bot.send_message(discord.Object(channel), fmt)\n\n\t\t\t\t\t\t\t\t\telif repo not in self.repo_data[channel]:\n\t\t\t\t\t\t\t\t\t\tself.repo_data[channel][repo] = latest_commit\n\t\t\t\t\t\t\t\t\t\tawait self.bot.send_message(discord.Object(channel), fmt)\n\n\t\t\t\t\t\t\t\t\telif self.repo_data[channel][repo] != latest_commit:\n\t\t\t\t\t\t\t\t\t\tself.repo_data[channel][repo] = latest_commit\n\t\t\t\t\t\t\t\t\t\tawait self.bot.send_message(discord.Object(channel), fmt)\n\n\t\t\texcept Exception as e:\n\t\t\t\tprint(\"LOOP_ERROR@git_loop! \" + self.return_traceback(*sys.exc_info()))\n\t\t\t\n\t\t\tawait asyncio.sleep(20)","title":""},{"docid":"bce603533827a83b02ff76bb56027928","score":"0.5412148","text":"def get_from_github(get, params={}):\n assert not get.startswith('/')\n url = 'https://api.github.com/' + get\n headers = {\n 'Accept': 'application/vnd.github.inertia-preview+json',\n }\n return get_json(url, headers=headers, params=params)","title":""},{"docid":"614c87c4cb503ce2e536064bfe68d578","score":"0.5409203","text":"def test_download_reference(self):\n pass","title":""},{"docid":"a42c77746238396b963471b8f202f9cb","score":"0.54017216","text":"def test_normal_style_repo_url(self, _, __):\n self.assertTrue(detect_repo.check_for_repo_name('fake-path', 'syzkaller'))","title":""},{"docid":"8357ce002caca3e5347a4153835317bc","score":"0.53984606","text":"def get_git_file_data(url, revision, file_path):\n if url.startswith('https://github.com/'):\n # Use github-specific URL API:\n data = urllib.request.urlopen(\n '%s/raw/%s/%s' % (url, revision, file_path))\n return data.read()\n if url.find('.googlesource.com') >= 0:\n url = '%s/+/%s/%s?format=TEXT' % (url, revision, file_path)\n data_file = urllib.request.urlopen(url)\n data = data_file.read()\n data = base64.b64decode(data)\n return data\n\n raise Exception('Unsupported URL type: ' + url)","title":""},{"docid":"d787b537638dc093726405446b0f13e2","score":"0.5389041","text":"def scrapeGitHubForCode(self, github_search_terms, num_pages_to_scrape, local_path_to_save_code_files ):\n search_terms = 'def+%28+%2C+%29+%3A+return+'+'+'.join(github_search_terms.split())\n code_page_links = []\n page_num = 1\n total_code_scraped = 0\n\n # Search for and gather up all the URLS first\n # using while loop with exceptions for persistence\n #\n while page_num <= num_pages_to_scrape:\n\n # more trying to avoid getting ip banned\n # and/or throttled by github (dis iz painful)\n try:\n url = \"https://github.com/search?l=python&type=Code&extension:py&p=\"+str(page_num)+\"&q=\"+search_terms\n print \"\\n\\n***********************\\nExecuting new GitHub python code search query:\\n\", url, \"\\n\"\n\n opener = urllib2.build_opener()\n opener.addheaders = [('User-agent', 'Mozilla/5.0')]\n response = opener.open(url)\n page = response.read()\n soup = BeautifulSoup(page)\n\n code_results_div = soup.findAll('div',attrs={'id':'code_search_results'})\n download_these_files_now = []\n for div in code_results_div:\n links = div.findAll('a')\n for link in links:\n href_loc = link.get('href')\n if( href_loc.endswith(\".py\") and link.text.endswith(\".py\") ):\n code_page_links.append( href_loc )\n download_these_files_now.append( href_loc )\n\n rel_code_paths = list(OrderedDict.fromkeys(download_these_files_now))\n\n # download the individual python files\n # oh my goodness github you are a pain\n # to scrape, stop rate limiting me....\n #\n count=0\n while count != len(rel_code_paths):\n try:\n print \"\\n\\nAttempting to fetch: \", rel_code_paths[count]\n self.downloadTheCode( local_path_to_save_code_files, [ rel_code_paths[count] ] )\n count = count +1\n except urllib2.HTTPError, errr:\n if( errr.code == 400 ):\n # someone must have used .py in a link that did not\n # contain a python file uh, why would they do that?\n # github server says this file doesn't exist...\n # anyway i guess they would know better than me.\n # anyway, skip this one for now, it doesn't exist\n count = count +1\n logging.error(errr)\n print \"Error Code: \", errr.code, \" File doesn't exist. Skipping this bogus URL...\"\n else:\n logging.error(errr)\n pausetime = random.randint(14,38)\n print \"Error Code: \", errr.code, \" PAUSING FOR \", str(pausetime), \" seconds. Rate limits?\"\n time.sleep(pausetime)\n\n page_num = page_num + 1\n total_code_scraped = total_code_scraped + len(rel_code_paths)\n print \"\\n\\n\\n*******\\nINFO: So far scraped \", total_code_scraped , \" URLS...\"\n response.close()\n opener.close()\n\n except urllib2.HTTPError, err:\n logging.error(err)\n pausetime = random.randint(11,33)\n print (\"PAUSING FOR \"+str(pausetime)+\" seconds. GIT HUB IS RATE LIMITING? \")\n time.sleep(pausetime)\n\n # keep looking natural ;) and avoid getting ip banned\n pausetime = random.randint(3,7)\n print (\"\\n\\n\\n*******\\nINFO: Pausing for \"+str(pausetime)+\" secs to avoid rate limits.\")\n time.sleep(pausetime)\n\n # in case we want to download again later, also for referring to original:\n save_paths_for_later = list(OrderedDict.fromkeys(code_page_links))\n self.saveCodeUrlList( local_path_to_save_code_files, save_paths_for_later, \"_\".join(github_search_terms.split()) )\n\n if not local_path_to_save_code_files.endswith(\"/\"):\n local_path_to_save_code_files = local_path_to_save_code_files+\"/\"\n\n merged_data_filename = \"ALL_\" + (\"_\".join(github_search_terms.split()).upper()) + \"_TRAINING_DATA.txt\"\n\n cmd = 'for i in {} ; do cat $i ; echo \"\" ; done > {} 2>&1'.format( local_path_to_save_code_files+\"*.py\", local_path_to_save_code_files+merged_data_filename )\n\n output = subprocess.check_output( '{} | tee /dev/stderr'.format( cmd ), shell = True )\n\n if os.path.exists(local_path_to_save_code_files+merged_data_filename):\n print \"\\n\\n********************************\\n\"\n print \"\\nSAVED MERGED CODE INTO ONE BIG FILE FOR FEATURE EXTRACTION:\\n\"\n print local_path_to_save_code_files+merged_data_filename\n print \"\\n\\n********************************\\n\\n\"\n\n print \"\\nScraping is completed for search term: \", github_search_terms, \"\\n\"","title":""},{"docid":"cb77115ec4ac4632a78736797829ef65","score":"0.5386815","text":"def update_data(dir):\n if not os.path.isdir(dir):\n r = check_output([\"git\", \"clone\", REPO_URL, dir])\n else:\n r = check_output([\"git\", \"pull\"], cwd=dir)\n print(r.decode(\"utf8\"))","title":""},{"docid":"b423124d6bcb78c70d066411690f480b","score":"0.537717","text":"def get_gitigore_file(headers: dict) -> str:\n headers[\"Accept\"] = \"application/vnd.github.v3.raw\"\n\n url = consts.GH_API_URL + \"/gitignore/templates/Python\"\n response = requests.get(url, headers=headers)\n if 200 <= response.status_code < 300: # Usually 200\n ignore_content = response.text\n\n else:\n print(f\"\\nAPI request failed when getting `.gitignore` file.\")\n server_message = json.loads(response.text)[\"message\"]\n print(f\"Error {response.status_code}: {response.reason} \"\n f\"({server_message})\")\n exit()\n\n return ignore_content","title":""},{"docid":"a8f761d51c0ac913a681b269f33e768c","score":"0.5351133","text":"def source_repository(self) -> str:","title":""},{"docid":"95e9eb1a1c7fa9296f6e96f1d67f2c08","score":"0.53419656","text":"def test_repo_get_release_attachment(self):\n pass","title":""},{"docid":"0503c6aac337762506bd7a071028ce27","score":"0.53389716","text":"def fetch(self):\n\n if self.repo_info != None:\n return\n\n self.repo_info = self.fetch_location(self.repo)","title":""},{"docid":"7c03fe0e14fb82873b9473f09118fa18","score":"0.53376156","text":"def test_unsafe_url(self):\n empty_package_response = {\n \"desc\":None,\n \"labels\":[],\n \"licenses\":[],\n \"website_url\":None,\n \"issue_tracker_url\":None,\n \"vcs_url\":None,\n \"github_repo\": None,\n \"maturity\":\"\"\n }\n responses.add(responses.GET, 'http://api.bintray.com/conan/foobar/conan/v1/ping')\n responses.add(responses.GET, 'http://api.bintray.com/conan/foobar/conan/v1/conans/dummy/0.1.0/foobar/stable/digest', json={\"conanmanifest.txt\":\"\"})\n responses.add(responses.GET, 'http://api.bintray.com/conan/foobar/conan', status=404)\n responses.add(responses.GET, 'http://api.bintray.com/conan/foobar/conan/v1/users/check_credentials')\n responses.add(responses.GET, 'http://api.bintray.com/conan/foobar/conan/v1/conans/dummy/0.1.0/foobar/stable', json={})\n responses.add(responses.POST, 'http://api.bintray.com/conan/foobar/conan/v1/conans/dummy/0.1.0/foobar/stable/upload_urls', json={})\n responses.add(responses.GET, \"http://api.bintray.com/licenses/oss_licenses\", json={})\n responses.add(responses.GET, 'http://api.bintray.com/packages/foobar/conan/dummy%3Afoobar', json=empty_package_response)\n with tools.environment_append({\"BINTRAY_API_URL\": \"http://api.bintray.com\"}):\n tools.save('conanfile.py', content=self.conanfile_complete)\n self.conan(['remote', 'add', 'unsafe', 'http://api.bintray.com/conan/foobar/conan'])\n self.conan(['export', '.', 'dummy/0.1.0@foobar/stable'])\n output = self.conan(['upload', '--remote=unsafe', 'dummy/0.1.0@foobar/stable'])\n self.assertIn(\"Uploading dummy/0.1.0@foobar/stable to remote 'unsafe'\", output)\n self.assertIn(\"Uploaded conan recipe 'dummy/0.1.0@foobar/stable' to 'unsafe': http://api.bintray.com/conan/foobar/conan\", output)\n self.assertIn(\"post_upload_recipe(): Reading package info from Bintray.\", output)\n self.assertIn(\"post_upload_recipe(): Inspecting recipe info.\", output)\n self.assertIn(\"post_upload_recipe(): Bintray is outdated. Updating Bintray package \" \\\n \"info: 'desc', 'github_repo', 'issue_tracker_url', 'labels', 'vcs_url'\" \\\n \", 'website_url'.\", output)\n self.assertIn('post_upload_recipe(): ERROR: Bad package URL: Only HTTPS is allowed, Bintray API uses Basic Auth', output)","title":""},{"docid":"97adb97a65e75edc7575b4de39e67fdb","score":"0.53337306","text":"def download(self):\n\n if self._check_raw_exists():\n return\n\n # download files\n try:\n os.makedirs(os.path.join(self.root, self.raw_folder))\n except OSError as e:\n if e.errno == errno.EEXIST:\n pass\n else:\n raise\n\n download_url(self.url,\n root=os.path.join(self.root, self.raw_folder),\n filename=self.filename,\n md5=None)\n if not self._check_raw_exists():\n raise RuntimeError(\"Unable to find downloaded file. Please try again.\")\n else:\n print(\"Download finished.\")","title":""},{"docid":"28a28e594ed1e0d384d9186fbf8891c0","score":"0.5331594","text":"def get_repo(config, repo_url, location, branch=\"master\", verbose=False):\n tarball_url = githuburl(\"{0}/tarball/{1}\".format(repo_url, branch))\n if verbose:\n sys.stderr.write(\"Fetching tutorial from \" + tarball_url + \"\\n\")\n try:\n url_obj = urllib2.urlopen(tarball_url)\n temp_obj = tempfile.TemporaryFile()\n shutil.copyfileobj(url_obj, temp_obj)\n extract_path = extract_tarfile(temp_obj, location)\n return extract_path\n except Exception, e:\n sys.stderr.write(\"Can't download files from github: %s\\n\" % str(e))\n if send_exc(config):\n sys.stderr.write(\"This error has been reported to system staff.\\n\")\n else:\n sys.stderr.write(\"Please report this error to your system staff.\\n\")","title":""},{"docid":"e8e68b8f32498043441ca9c61de06948","score":"0.5329416","text":"def __init__(self,\n name=None,\n source=None,\n version=None,\n localdir=None,\n repository_type=None,\n force_update=False,\n download=False,\n kconfig_extra={}):\n \n if source is None or len(source) == 0:\n raise InvalidRepositorySource(source)\n\n version_head = None\n\n # In good scenarios, we have already been given key information about\n # this repository\n if repository_type is None or name is None:\n # Let's passively determine the name and type first before we clone,\n # as this helps us determine where to place the repository when we\n # want to clone it.\n repository_type, name = self.passively_determine_type_and_name(source)\n\n already_downloaded = False\n\n if len(self._known_versions) == 0 or force_update or download:\n self.update_known_versions(source=source)\n \n # Determine how to set the version\n if version is not None:\n self._version = version\n elif BRANCH_MASTER in self._known_versions:\n self._version = BRANCH_MASTER\n elif BRANCH_STAGING in self._known_versions:\n self._version = BRANCH_STAGING\n else:\n raise NoSuchReferenceInRepo\n\n # If we cannot determine the type passively, we must clone the\n # repository into a temporary location and manually introspect\n # the files to determine its type.\n if repository_type is None or name is None:\n repository_type, name = self.intrusively_determine_type_and_name(source)\n\n # If we cannot determine the name and type passively we must\n # throw an error and prompt the user for more information to\n # address this roblem.\n if repository_type is None or name is None:\n raise NoTypeAndNameRepo\n \n # Check if we already have a repository saved locally with this remote\n # url set. If so, we do not need to make an outbound connection to\n # determine whether the repository is \"real\".\n if localdir is None:\n self._localdir = self.determine_localdir(repository_type=repository_type, name=name)\n else:\n self._localdir = localdir\n\n # Populate the repository information if we have made it this far, as it\n # means that the repository is valid and usable.\n self._name = name\n self._type = repository_type\n self._source = source\n self._kconfig_extra = kconfig_extra\n\n if download or force_update:\n self.update()\n \n if self._type == RepositoryType.ARCH:\n return\n elif self._type == RepositoryType.PLAT and self._source == UNIKRAFT_CORE:\n return\n \n self.__set_cache(source, self)","title":""},{"docid":"e62563f01d6e58d8e29e7f56ab4841f7","score":"0.53233135","text":"def fetch_from_github_extra(shapes_graph, client, oauth_token, filename):\n extra_shapes_repositories = []\n with open(root / '../' + filename, 'r') as f:\n for line in f:\n extra_shapes_repositories.append(line.rstrip('\\n').strip())\n\n for extra_repo in extra_shapes_repositories:\n data = client.execute(\n query=github_graphql_get_extra(extra_repo),\n headers={\"Authorization\": \"Bearer {}\".format(oauth_token)},\n )\n # print(json.dumps(data, indent=4))\n repo_json = data[\"data\"][\"repository\"]\n repo_url = repo_json[\"url\"]\n try:\n branch = repo_json['defaultBranchRef']['name']\n repo_description = repo_json[\"description\"]\n except Exception as e:\n print(e)\n print('🕊 No default_branch found for repo_url, using master')\n branch = 'master'\n repo_description = ''\n shapes_graph = clone_and_process_repo(shapes_graph, repo_url, branch, repo_description)\n return shapes_graph","title":""},{"docid":"07cc0a361b9359289f8003cd0a3836de","score":"0.53227985","text":"def download_earnings22(\n target_dir: Pathlike = \".\",\n force_download: Optional[bool] = False,\n url: Optional[str] = _DEFAULT_URL,\n) -> Path:\n logging.error(\n \"Downloading Earnings22 from github repository is not implemented. \"\n + f\"Please visit {_DEFAULT_URL} and download the files manually. Please \"\n + \"follow the instructions closely as you need to use git-lfs to download \"\n + \"some of the audio files.\"\n )","title":""},{"docid":"3720a6ab92bfe7ef85c0c4b84c256e27","score":"0.53227246","text":"async def _call_github(\n self,\n url: str,\n load_json: bool = True,\n method: str = \"GET\",\n body: Optional[dict] = None,\n params: Optional[Dict[str, str]] = None,\n media_type: str = \"application/vnd.github.v3+json\",\n has_pagination: bool = True,\n ) -> Union[dict, str]:\n headers = {\n \"Accept\": media_type,\n \"Authorization\": f\"token {self._config.access_token}\",\n }\n\n return await super()._call_provider(\n url,\n load_json=load_json,\n method=method,\n body=body,\n params=params,\n headers=headers,\n has_pagination=has_pagination,\n )","title":""},{"docid":"9b598c0325f17a5a941b97d509032d09","score":"0.53174305","text":"def DownLoadCode(self, handle, codeurl):\n self.logger.info(\"Now loading test codes! Please wait in patient...\")\n originalfolder = sys.path[0]\n self.logger.info(originalfolder)\n gitclone = handle\n gitclone.sendline(\"git clone \" + codeurl)\n index = 0\n # increment = 0\n while index != 1 or index != 4:\n index = gitclone.expect(['already exists',\n 'esolving deltas: 100%',\n 'eceiving objects',\n 'Already up-to-date',\n 'npacking objects: 100%', pexpect.EOF])\n\n filefolder = self.home + '/' + codeurl.split('/')[-1].split('.')[0]\n if index == 0:\n os.chdir(filefolder)\n os.system('git pull')\n os.chdir(originalfolder)\n self.loginfo.log('Download code success!')\n break\n elif index == 1 or index == 4:\n self.loginfo.log('Download code success!')\n gitclone.sendline(\"mkdir onos\")\n gitclone.prompt()\n gitclone.sendline(\"cp -rf \" + filefolder + \"/tools onos/\")\n gitclone.prompt()\n break\n elif index == 2:\n os.write(1, gitclone.before)\n sys.stdout.flush()\n else:\n self.loginfo.log('Download code failed!')\n self.loginfo.log('Information before' + gitclone.before)\n break\n gitclone.prompt()","title":""},{"docid":"403aae0adf5dd64177b9a0174478186c","score":"0.5309374","text":"def try_get_official(self, repo):\n official_repo = \"{repo}/{repo}\".format(repo=repo)\n log.info('Checking existence of %s', official_repo)\n r = self.get('https://{}/{}/releases.atom'.format(self.hostname, official_repo))\n # API requests are varied by cookie, we don't want serializer for cache fail because of that\n self.cookies.clear()\n if r.status_code == 200:\n self.feed_contents[official_repo] = r.text\n return official_repo\n return None","title":""},{"docid":"ad6c8de07bba926f048e2244ec6bed06","score":"0.5300511","text":"def get_from_bitbucket(url):\n\n # replace browse with raw in request url\n url = url.replace(\"/browse/\", \"/raw/\", 1)\n\n r = requests.get(url, auth=(user, pw))\n return r.json()","title":""},{"docid":"6bae0dda848e8ec6c56425cb522a4739","score":"0.5292668","text":"def download_workflow_static(self):\n\n # Download the centralised configs first\n if self.include_configs:\n log.info(\"Downloading centralised configs from GitHub\")\n self.download_configs()\n\n # Download the pipeline files for each selected revision\n log.info(\"Downloading workflow files from GitHub\")\n\n for item in zip(self.revision, self.wf_sha.values(), self.wf_download_url.values()):\n revision_dirname = self.download_wf_files(revision=item[0], wf_sha=item[1], download_url=item[2])\n\n if self.include_configs:\n try:\n self.wf_use_local_configs(revision_dirname)\n except FileNotFoundError as e:\n raise DownloadError(\"Error editing pipeline config file to use local configs!\") from e\n\n # Collect all required singularity images\n if self.container_system == \"singularity\":\n self.find_container_images(os.path.join(self.outdir, revision_dirname))\n\n try:\n self.get_singularity_images(current_revision=item[0])\n except OSError as e:\n raise DownloadError(f\"[red]{e}[/]\") from e\n\n # Compress into an archive\n if self.compress_type is not None:\n log.info(\"Compressing output into archive\")\n self.compress_download()","title":""},{"docid":"55a3878db263e82ed592fb69dbeb68bb","score":"0.5280596","text":"def convert_to_github(data):\n driver_loc = Service(\"/WebDrivers/chromedriver\") # configure with path to the webdriver for selenium to use\n prev = NONE\n links = {}\n with webdriver.Chrome(service=driver_loc) as driver:\n for key in data:\n temp_list = []\n links[key] = \"\"\n if data[key]['fixed'] == '':\n continue\n try:\n time.sleep(2)\n wait = WebDriverWait(driver, 10)\n driver.get(data[key][\"fixed\"])\n dom = wait.until(presence_of_element_located((By.CSS_SELECTOR, \"revisions-info\")))\n dom = dom.shadow_root\n found_links = dom.find_elements(By.CSS_SELECTOR, \"if-else\")\n # dom = dom.shadow_root\n for edom in found_links:\n link = edom.find_element(By.CSS_SELECTOR, \"a\")\n if link.get_attribute(\"href\").strip() and \"github\" in link.get_attribute(\"href\").strip().lower():\n temp_list.append(link.get_attribute(\"href\").strip())\n for item in temp_list:\n links[key] += item + \"*SEP*\"\n links[key] = links[key][:-5]\n except:\n continue\n return links","title":""},{"docid":"61d24ceac6726bcef27558b5c5c2b48c","score":"0.52788633","text":"def recursive_pull(URL, sha, workdir, depth):\n # some helpful locations\n final_location = workdir + 'final/'\n tarfile_path = workdir + 'current.tar.gz'\n # From the URL, determine which instance it is on\n if 'github.com' in URL:\n gh = github_external\n else:\n gh = github_internal\n owner, repo = parse_owner(URL)\n # This is going to be the most problematic section, you could get problems\n # because:\n # -The github instance you were talking to is unreachable/offline\n # -Mis spellings of the owner or repository\n # -The owner or repository do not exist\n # -They do exist, but the specified SHA does not\n try:\n repository = gh.repository(owner=owner, repository=repo)\n tarball = repository.archive(format=\"tarball\",\n path=tarfile_path, ref=sha)\n tar = tarfile.open(mode='r|gz', name=tarfile_path)\n tar.extractall(workdir)\n tar.close()\n except:\n raise NameError(\"Error pulling \" + URL + \" \" + sha + \"\\n\" +\n \"Ensure that the URL is properly formated, and that \" +\n \"the specified SHA exists.\")\n\n # move it into the correct place\n internal_name = workdir + owner + '-' + repo + '-' + sha[:7] + '/'\n os.rename(internal_name, final_location + depth)\n # Now get and parse the .submodules file\n modulefile = final_location + depth + '.gitmodules'\n if not os.path.exists(modulefile):\n # Then there is no submodules here\n return\n moduleparser = configparser.ConfigParser()\n moduleparser.read(modulefile)\n for submodule in moduleparser.sections():\n # determine the SHA for this submodule\n content = repository.contents(\n ref=sha,\n path=moduleparser[submodule]['path']\n )\n submodulesha = content.to_json()['sha']\n recursive_pull(\n moduleparser[submodule]['url'],\n submodulesha,\n workdir,\n depth + moduleparser[submodule]['path'] + '/'\n )","title":""},{"docid":"e26a108431909adf1ad92e6ad7a21dea","score":"0.5276766","text":"def _download_missing_files(self):\n import requests\n base_url = \"https://raw.githubusercontent.com/mozilla-releng/firefox-infra-changelog/oop/modules/\"\n for file in self._missing_files:\n raw_file = requests.get(base_url + file, allow_redirects=True).text\n f = open(os.path.abspath(os.path.join(self.path_level, \"modules\", file)), \"w\")\n f.write(raw_file)\n f.close()","title":""},{"docid":"d2d481ca8e1e4c7aac23c66e9172a42c","score":"0.5271279","text":"def fetch_all():\n for i in range(START,END +1):\n data = fetch_from_csv(\"out/new_\" + str(i) + \".csv\")\n github_run(data)\n write_commits(data, \"out/commit_link_\" + str(i) + \".csv\")","title":""},{"docid":"ae931c84ff3c1a56ccf92df3f9e0b254","score":"0.5268423","text":"def __init__(self, download_url):\n url_segments = download_url.split(u'/')\n if len(url_segments) < 5 or url_segments[2] != u'github.com':\n raise ValueError(u'Unsupported download URL.')\n\n super(GithubReleasesDownloadHelper, self).__init__(download_url)\n self._organization = url_segments[3]\n self._repository = url_segments[4]","title":""},{"docid":"274f036aa678aa107bb67ca6d0f71bcb","score":"0.52668333","text":"def github(request):\n\n return render(request, 'taric_books/github.html')","title":""},{"docid":"de521f195fe240c2db12ffccaa4425d6","score":"0.5263797","text":"def checkout_latest():\n import time\n env.release = time.strftime('%Y%m%d%H%M%S')\n run(\"cd %(path)s/repository; git pull origin master\" % env)\n run('cp -R %(path)s/repository %(path)s/releases/%(release)s; rm -rf %(path)s/releases/%(release)s/.git*' % env)","title":""},{"docid":"8cd9a43b02fd78a1512cc5446161c573","score":"0.5239792","text":"def test_github_targz_content(self):\n with temporary_directory() as github_mock_dir:\n archive_name = os.path.join(github_mock_dir, 'foo.tar.gz')\n self.setup_targz(archive_name, 'piecutter', 'master')\n with open(archive_name, 'r') as archive:\n response_mock = mock.MagicMock()\n response_mock.raw = archive\n response_mock.status_code = 200\n get_mock = mock.Mock(return_value=response_mock)\n with mock.patch('piecutter.loaders.github.requests.get',\n new=get_mock):\n with temporary_directory() as output_dir:\n loader = GithubLoader(output_dir)\n content = loader.github_targz_content('fake-url')\n self.assertTrue(archive is content)","title":""},{"docid":"fd3493c1c69e1d2790a5ddddbb5c5608","score":"0.52370584","text":"def _download_raw_dataset(metadata: dict) -> None:\n if os.path.exists(metadata['filename']):\n return\n\n print('Downloading raw dataset...')\n util.download_url(metadata['url'], metadata['filename'])\n\n print('Computing SHA-256...')\n sha256 = util.compute_sha256(metadata['filename'])\n if sha256 != metadata['sha256']:\n raise ValueError('Downloaded data file SHA-256 does not match that listed in metadata document.')","title":""},{"docid":"53a0cf135abf82b89e392a66b56a3f00","score":"0.52323395","text":"def fetch(self):\n command = \"git fetch origin\"\n\n logger_server.info(\"Fetch data from github[CMD:{cmd}]...\".format(cmd=command))\n\n self.cwd(self.git_path)\n\n fetch_content = self._run_shell_command(command=command)\n\n if _DEBUG:\n logger_server.debug(\"fetch_content:\" + fetch_content)\n\n return fetch_content","title":""},{"docid":"704e340cd323afb89a7cedb4f289974a","score":"0.5231682","text":"def test_remote_happy(zipprovider: Provider, url: URL) -> None:\n repository = zipprovider.provide(url)\n\n assert repository is not None\n\n with repository.get() as package:\n text = (package.tree / \"marker\").read_text()\n assert \"Lorem\" == text","title":""},{"docid":"277aae7b629b4b53b450c20ff6967f8e","score":"0.52268255","text":"def test_repo_edit_pull_request(self):\n pass","title":""},{"docid":"7e422ad477937f42bbe6f6876f551370","score":"0.52141625","text":"def build_github_url(state, generated_filename):\n tpl = \"https://raw.githubusercontent.com/openelections/openelections-data-{}/master/{}\"\n return tpl.format(state.lower(), generated_filename)","title":""},{"docid":"779707db3eee85bf155e7db02e28e722","score":"0.52118015","text":"def pull(self):\n command = \"git pull --rebase origin \" + self.branch\n\n logger_server.info(\"Pull data from github[CMD:{cmd}]...\".format(cmd=command))\n\n self.cwd(self.git_path)\n\n pull_content = self._run_shell_command(command=command)\n\n if _DEBUG:\n logger_server.debug(\"pull_content:\" + pull_content)\n\n return pull_content","title":""},{"docid":"d3b4bd7fbb11900f01b0567c24099a08","score":"0.52113914","text":"def download(self):\n pass","title":""},{"docid":"d3b4bd7fbb11900f01b0567c24099a08","score":"0.52113914","text":"def download(self):\n pass","title":""},{"docid":"d3b4bd7fbb11900f01b0567c24099a08","score":"0.52113914","text":"def download(self):\n pass","title":""}],"string":"[\n {\n \"docid\": \"edab69dbea84f039aa1f2d2090f0d221\",\n \"score\": \"0.67514336\",\n \"text\": \"def test_repo_get_raw_file(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bf6846ba20a0a34fbadfa4a08855a71\",\n \"score\": \"0.6747231\",\n \"text\": \"def test_raw(self):\\n\\t\\thtml = http.page_text(\\\"https://raw.githubusercontent.com/shadowmoose/RedditDownloader/master/Dockerfile\\\")\\n\\t\\tself.assertIn(\\\"python\\\", html, \\\"Downloaded invalid Raw data from url!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7acc52baf5e179ba380e8280a5d80470\",\n \"score\": \"0.658578\",\n \"text\": \"def test_repo_get_contents(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d90e84b54940d004663571b5e5364d0a\",\n \"score\": \"0.6343138\",\n \"text\": \"def pull(self, remote = 'origin'):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcbc25304536d26a4954f9ab26e0fb3e\",\n \"score\": \"0.6294206\",\n \"text\": \"def download_github_xml(url):\\n # before making the request, get it raw\\n print(\\\"Downloading \\\", url.split(\\\"/\\\")[-1])\\n # o = urlparse(url)\\n # netloc = o.netloc\\n file_name = ''\\n \\\"\\\"\\\"\\n if netloc.startswith('raw.'):\\n #it's already raw, no change to make\\n pass\\n elif netloc.startswith('github.'):\\n netloc = 'raw.' + netloc\\n url = o.scheme + '://' + netloc + o.path\\n \\\"\\\"\\\"\\n url = url.replace('blob', 'raw')\\n # print('using ', url)\\n r = requests.get(url)\\n try:\\n r.raise_for_status()\\n except:\\n # catch exception\\n pass\\n else:\\n # no exception occured, continue\\n if r.status_code == 200:\\n # split the file from the url\\n file_name = url.split(\\\"/\\\")[-1]\\n with open(file_name, 'wb') as fp:\\n fp.write(r.text.encode('utf-8'))\\n # return the file name\\n return file_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"071af16b16dc6398286d952cf6bbc8a2\",\n \"score\": \"0.6261239\",\n \"text\": \"def test_github_loads(self):\\n tester = app.test_client()\\n response = tester.get('/github')\\n self.assertTrue(b'GitHub Repos' in response.data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a3cead3179de979f9e6b4c4307eb404\",\n \"score\": \"0.6209297\",\n \"text\": \"def _download(self):\\n self._system.download_file(\\n \\\"https://github.com/LaurentGomila/SFML/tarball/\\\" + self._tar_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7c7c76c9a61f1486414be833a307b29\",\n \"score\": \"0.6121335\",\n \"text\": \"def test_repo_get_pull_request(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa281aece832e52c633776e959edbbe3\",\n \"score\": \"0.60263526\",\n \"text\": \"def fetch(self):\\n try: \\n Oprint.info('Start downloading repo to your project from {}'.format(self._args.get('<url>')), 'lmdo')\\n spinner.start()\\n\\n tmp = tempfile.mkdtemp()\\n self.git_clone(self._args.get('<url>'), tmp)\\n copytree(tmp, './', ignore=shutil.ignore_patterns('*.git*'))\\n shutil.rmtree(tmp)\\n \\n spinner.stop()\\n Oprint.info('Complete downloading repo to your project from {}'.format(self._args.get('<url>')), 'lmdo')\\n except Exception as e:\\n spinner.stop()\\n raise e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd9ac9ed5ffacbb0ff74c9bb6dafb525\",\n \"score\": \"0.6019963\",\n \"text\": \"def test_repo_get(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09262e9da21441c4e2eb3fe484cac817\",\n \"score\": \"0.6017732\",\n \"text\": \"def test_github_sync_import(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"951feec50562ab229527e80140cfd0b1\",\n \"score\": \"0.6001563\",\n \"text\": \"def test_github_sync_export(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f56a87db3e35f952c908e4ba41e43c21\",\n \"score\": \"0.59602207\",\n \"text\": \"def meta_git_repo(url):\\n print('Currently unsupported')\\n sys.exit(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e270256a67e1f91189b953edf66660a2\",\n \"score\": \"0.59136623\",\n \"text\": \"def test_repo_get_archive(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71a56bdba8879ab549d481ff91cbe5c9\",\n \"score\": \"0.5911419\",\n \"text\": \"def fetch_github():\\n try:\\n repositories = github.search_repositories(\\n query=settings.SEARCH_QUERY, sort=\\\"stars\\\"\\n )\\n\\n for item in repositories:\\n data = dict(\\n id=item.id,\\n name=item.name,\\n full_name=item.full_name,\\n html_url=item.html_url,\\n description=item.description,\\n # TODO: falta colocar: data de ultimo commit/release\\n # TODO: quant stars\\n # TODO: quant de contribuidores\\n )\\n repo.upsert(data, [\\\"id\\\"])\\n # Erros de Internet\\n except ReadTimeout as e:\\n logger.warning(e)\\n except ConnectionError as e:\\n logger.warning(e)\\n # Erros no GitHub\\n # Git except https://pygithub.readthedocs.io/en/latest/utilities.html\\n except RateLimitExceededException as e:\\n logger.warning(e)\\n except UnknownObjectException as e:\\n logger.warning(e)\\n except BadCredentialsException as e:\\n # Erro na configuração\\n logger.critical(e)\\n exit(1)\\n except TwoFactorException as e:\\n logger.critical(e)\\n exit(1)\\n # Erros do banco de dados\\n # https://docs.sqlalchemy.org/en/13/core/exceptions.html\\n except OperationalError as e:\\n logger.critical(e)\\n exit(1)\\n # Erros na Configuração\\n except AttributeError as e:\\n logger.critical(e)\\n exit(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bda845aab8b9f9e7e9c7306fd3a7fe09\",\n \"score\": \"0.5901792\",\n \"text\": \"def main():\\n\\n proj_response = requests.get(PROJECTS_URL)\\n if proj_response.status_code != 200:\\n print(\\\"Error reading projects: \\\", proj_response)\\n return\\n\\n github_list = []\\n projects = proj_response.json()\\n for project in projects[\\\"data\\\"]:\\n proj_link = requests.utils.requote_uri(\\n \\\"https://brigade.cloud/static-api/v1\\\" + project[\\\"links\\\"][\\\"self\\\"]\\n )\\n try:\\n proj_data = requests.get(proj_link).json()\\n code_url = proj_data[\\\"data\\\"][\\\"attributes\\\"].get(\\\"code_url\\\", \\\"\\\")\\n if not code_url.startswith(\\\"https://github.com/\\\"):\\n print(\\\"Skipping \\\", proj_data[\\\"data\\\"][\\\"id\\\"], code_url)\\n else:\\n print(code_url)\\n code_url = code_url.replace(\\\"https://github.com/\\\", \\\"\\\")\\n github_list.append(code_url)\\n\\n except Exception as ex:\\n print(ex)\\n return\\n\\n github_urls = \\\" \\\".join(github_list)\\n # now load the meltano.yml file\\n meltano_filename = \\\"../meltano/cfp-pipeline/meltano.yml\\\"\\n with open(meltano_filename) as f:\\n doc = yaml.safe_load(f)\\n\\n github_extractor = None\\n for e in doc[\\\"plugins\\\"][\\\"extractors\\\"]:\\n if e[\\\"name\\\"] == \\\"tap-github\\\":\\n github_extractor = e\\n break\\n if github_extractor is None:\\n print(\\\"could not find tap-github in meltano.yml file\\\")\\n return\\n\\n if github_urls != github_extractor[\\\"config\\\"][\\\"repository\\\"]:\\n print(\\\"tap-github repositories is different, rewriting meltano.yml\\\")\\n github_extractor[\\\"config\\\"][\\\"repository\\\"] = github_urls\\n with open(meltano_filename, \\\"r\\\") as f:\\n meltano_yaml = f.read()\\n meltano_yaml = re.sub(\\n \\\"repository:(.*)\\\\n?\\\", \\\"repository: \\\" + github_urls + \\\"\\\\n\\\", meltano_yaml\\n )\\n with open(meltano_filename, \\\"w\\\") as f:\\n f.write(meltano_yaml)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d70a3ae7ede88b0b4e5dde696f6fa7d\",\n \"score\": \"0.58859545\",\n \"text\": \"def test_fetch_tarball_and_keep():\\n directory_name = fetch_open_samples(\\n \\\"GW190424_180648\\\", read_file=False, outdir=\\\".\\\", unpack=True,\\n catalog=\\\"GWTC-2\\\", download_kwargs={\\\"timeout\\\": 60}\\n )\\n assert os.path.isdir(\\\"./GW190424_180648\\\")\\n assert os.path.isdir(directory_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11828590d1fdb924370298e0181ed022\",\n \"score\": \"0.58739424\",\n \"text\": \"def build_raw_github_url(state, datestring, raw_filename):\\n tpl = \\\"https://raw.githubusercontent.com/openelections/openelections-data-{}/master/{}/{}\\\"\\n return tpl.format(state.lower(), datestring, raw_filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e27dfbad82764ac9dfa9467d1f59be3\",\n \"score\": \"0.5857968\",\n \"text\": \"def view_git_repo_retrieve(remote, do_base64_encode, client_ip, branches=False, chunk_size=0):\\n\\n model = Download()\\n model.base64encode = do_base64_encode\\n model.remote = remote\\n model.file_name = ''\\n model.client_ip = client_ip\\n model.git_branches = branches\\n \\n try:\\n output_file, file_name = git_repo_retrieve(remote, do_base64_encode, branches, chunk_size)\\n model.file_name = file_name\\n \\n with FileCleaner(output_file, 'rb') as fp:\\n\\n response = HttpResponse(fp)\\n response[\\\"Content-Disposition\\\"] = \\\"attachment; filename=%s\\\" % (file_name,)\\n model.status = 'SUCCESS'\\n return response\\n except Exception, ex:\\n model.status = 'FAILED'\\n ex_str = traceback.format_exc()\\n model.error = ex_str\\n return HttpResponse(ex_str, status=400)\\n finally:\\n model.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2be1b0fe4964be8e111e25fee505da16\",\n \"score\": \"0.58524805\",\n \"text\": \"def pullExcelFromGithub():\\n url = ('https://raw.githubusercontent.com/JamesHamiltonDev/craftDemo/master/files/')\\n pathToExcelFile = Path(r\\\"C:\\\\Users\\\\james\\\\PycharmProjects\\\\craftDemo\\\\files\\\\hardware.xlsx\\\")\\n fileNotFound = True\\n while fileNotFound is True:\\n print(\\\"Downloading file . . .\\\")\\n\\n#\\n## URL requests to third party site intentionally delayed. Third party providers have a habit of\\n## blocking IPs that bombard their servers with requests.\\n#\\n time.sleep(3)\\n try:\\n if pathToExcelFile.is_file():\\n fileNotFound = False\\n print('File downloaded')\\n return fileNotFound\\n else:\\n\\n urllib.request.urlretrieve('https://raw.githubusercontent.com/JamesHamiltonDev/craftDemo/master/files/hardware.xlsx')\\n except:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"304d62bcea07bc1cf8cd2331a7c515f3\",\n \"score\": \"0.581636\",\n \"text\": \"def test_downloading(self):\\n #from os.path import join, abspath, dirname\\n #deam_rel_root = dirname(dirname(abspath(__file__)))\\n #repo_handler = RepositoryHandler(join(deam_rel_root, 'testing'))\\n # repo_handler.execute(False)\\n self.assertEqual(True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54f92340af8662467372d5fb93849c8e\",\n \"score\": \"0.5791929\",\n \"text\": \"def github_link(self):\\n if self.test_type == TestType.commit:\\n test_type = 'commit'\\n test_id = self.commit\\n else:\\n test_type = 'pull'\\n test_id = self.pr_nr\\n\\n return \\\"{base}/{test_type}/{test_id}\\\".format(\\n base=self.fork.github_url, test_type=test_type, test_id=test_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f5f98bb40cc7995cf61b936b0b89633\",\n \"score\": \"0.57814527\",\n \"text\": \"def git_pull():\\n run('git pull --all')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be204a99e1b241d1a8c24da46325033e\",\n \"score\": \"0.57719195\",\n \"text\": \"def az_pinkie_pull():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72d92f86165c12754b39b28b9cc0f67e\",\n \"score\": \"0.5771329\",\n \"text\": \"async def gh(self, ctx, repo: str):\\n embed = get_embed()\\n async with await self.bot.session.get(\\n f\\\"https://api.github.com/repos/{repo}\\\"\\n ) as r:\\n if r.status == 200:\\n r = await r.json()\\n if r[\\\"description\\\"] == \\\"\\\":\\n desc = \\\"No description provided.\\\"\\n else:\\n desc = r[\\\"description\\\"]\\n stars = r[\\\"stargazers_count\\\"]\\n forks = r[\\\"forks_count\\\"]\\n cmd = f'git clone {r[\\\"clone_url\\\"]}'\\n embed.title = f\\\"{repo} on GitHub\\\"\\n embed.description = self.generate_description(desc, stars, forks, cmd)\\n elif r.status == 404:\\n embed.title = \\\"Oops...\\\"\\n embed.description = \\\"That repository doesn't seem to exist, or is private. Are you sure you typed it correctly?\\\"\\n await ctx.send(embed=embed)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bfadf89c01b01f9e782779846a9b511\",\n \"score\": \"0.57594305\",\n \"text\": \"def download(self):\\n print(f'git clone from {SYSEVR_URL}')\\n clone_path = self._root / 'SySeVR.git'\\n if not clone_path.exists():\\n git_clone_file(SYSEVR_URL, str(clone_path))\\n for file in clone_path.glob('**/*.zip'):\\n with zipfile.ZipFile(str(file)) as z:\\n z.extractall(str(self._root))\\n else:\\n print(f'{str(clone_path)} exist, download cancel')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81614effce5b3dad3ffbfa8c14c18afc\",\n \"score\": \"0.5744819\",\n \"text\": \"def inception_v3_download():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"623097d3be5c02cb4bb18f74fc0c75af\",\n \"score\": \"0.5719832\",\n \"text\": \"def maybe_download(self):\\n os.makedirs(self._rawdir, exist_ok=True)\\n\\n expected_checksums = self.md5 if self.md5 else [None] * len(self.data)\\n\\n for url, expected_md5 in zip(self.data, expected_checksums):\\n tarball = os.path.join(self._rawdir, self._get_tarball_filename(url))\\n\\n download_file(\\n url, tarball, extract_to=self._rawdir, expected_md5=expected_md5\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad9080f64aed95877076751fcdd6c5f1\",\n \"score\": \"0.5716542\",\n \"text\": \"def test_repo_create_pull_request(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b3bf745f46dec73bfb2ed2e6195c8d7\",\n \"score\": \"0.5712749\",\n \"text\": \"def test_issue_137(self):\\n i = Issue(load('issue_137'))\\n self.assertEqual(\\n i.html_url,\\n \\\"https://github.com/sigmavirus24/github3.py/pull/1\\\")\\n self.assertEqual(i.repository, (\\\"sigmavirus24\\\", \\\"github3.py\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13a067e8fe103e5acac1c81151389e47\",\n \"score\": \"0.57012063\",\n \"text\": \"def test_full(self):\\n github.input = lambda x: \\\"xmaayy\\\"\\n data = get_hub(\\\"xmaayy\\\")\\n assert data != []\\n print(data[1].keys())\\n repolist = [repo[\\\"name\\\"] for repo in data]\\n assert repolist == [\\n \\\"Alexandria\\\",\\n \\\"cipher\\\",\\n \\\"CUHackAPIO\\\",\\n \\\"DarkBot\\\",\\n \\\"megacrypt.js\\\",\\n \\\"MGAN\\\",\\n \\\"NoFace\\\",\\n \\\"polybar\\\",\\n \\\"SFMLinstall\\\",\\n \\\"SubHelper\\\",\\n \\\"UpStat\\\",\\n \\\"Website\\\",\\n \\\"xmaayy.github.io\\\",\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"332ec00e18fbcb8ca7a7901479531df2\",\n \"score\": \"0.5698646\",\n \"text\": \"def __init__(self, download_url):\\n super(LibyalGitHubDownloadHelper, self).__init__(download_url)\\n self._download_helper = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"356673990144dbfc6a8eb73cf077a103\",\n \"score\": \"0.56726795\",\n \"text\": \"def __init__(\\n self,\\n owner,\\n repo,\\n pr=None,\\n branch=None,\\n token=None,\\n url=None,\\n commit=None,\\n ignore_paths=None,\\n prefix=None,\\n autofix=False,\\n set_status=False,\\n ):\\n self.start = datetime.datetime.now()\\n self.github = None\\n self.stopped_early = False\\n self.autofixed = False\\n self.prefix = prefix\\n self.autofix = autofix\\n self.ignore_paths = set(ignore_paths or [])\\n self.token = token\\n self.set_status = set_status\\n url = url or \\\"https://github.com\\\"\\n print(\\\"url={}\\\".format(url))\\n self.netloc = urlparse(url).netloc.strip()\\n print(\\\"urlparse={}\\\".format(urlparse(url)))\\n if not url or url == \\\"https://github.com\\\":\\n self.github = github3.GitHub(token=token)\\n else:\\n self.github = github3.GitHubEnterprise(url, token=token)\\n\\n try:\\n self.github_user = self.github.me().as_dict()\\n except (TypeError, AttributeError):\\n # github.py == 0.9.6\\n self.github_user = self.github.user().to_json()\\n\\n self.username = \\\"\\\"\\n self.email = \\\"\\\"\\n try:\\n self.username = self.github_user[\\\"login\\\"]\\n for email in self.github.emails():\\n try:\\n email_obj = email.as_dict()\\n except (TypeError, AttributeError):\\n # github.py == 0.9.6\\n email_obj = email.to_json()\\n if email_obj[\\\"primary\\\"]:\\n self.email = email_obj[\\\"email\\\"]\\n except Exception: # NOQA\\n traceback.print_exc()\\n\\n self.owner = owner\\n self.repo = repo\\n\\n self.github_repo = self.github.repository(self.owner, self.repo)\\n print(\\\"Branch: {0}\\\".format(branch))\\n self.branch = branch\\n self.pull_request_number = None\\n if branch and not pr:\\n for github_repo in [self.github_repo, self.github_repo.parent]:\\n if pr:\\n break\\n\\n if not github_repo:\\n continue\\n\\n try:\\n # github.py == 0.9.6\\n pulls = github_repo.iter_pulls()\\n except AttributeError:\\n pulls = github_repo.pull_requests()\\n\\n for pull_request in pulls:\\n print(\\n \\\"Branch: {} - Pull Request Head Ref: {}\\\".format(\\n branch, pull_request.head.ref\\n )\\n )\\n if pull_request.head.ref == branch:\\n pr = pull_request.number\\n self.github_repo = github_repo\\n break\\n\\n self.owner = self.github_repo.owner\\n self.repo = self.github_repo.name\\n\\n # TODO: support non-PR runs\\n try:\\n pr = int(pr)\\n except (ValueError, TypeError):\\n print(\\\"{0} is not a valid pull request ID\\\".format(pr))\\n self.github = None\\n return\\n\\n print(\\\"PR ID: {0}\\\".format(pr))\\n self.pull_request_number = pr\\n self.pull_request = self.github.pull_request(self.owner, self.repo, pr)\\n self.target_sha = self.pull_request.base.sha\\n self.target_branch = self.pull_request.base.ref\\n self.sha = self.pull_request.head.sha\\n self.branch = self.pull_request.head.ref\\n try:\\n # github.py == 0.9.6\\n try:\\n git.fetch(self.pull_request.base.to_json()[\\\"repo\\\"][\\\"clone_url\\\"])\\n except subprocess.CalledProcessError:\\n git.fetch(self.pull_request.base.to_json()[\\\"repo\\\"][\\\"ssh_url\\\"])\\n except AttributeError:\\n # latest github.py\\n try:\\n git.fetch(self.pull_request.base.repository.as_dict()[\\\"clone_url\\\"])\\n except subprocess.CalledProcessError:\\n git.fetch(self.pull_request.base.repository.as_dict()[\\\"ssh_url\\\"])\\n\\n print(\\\"Target SHA: {0}\\\".format(self.target_sha))\\n print(\\\"Target Branch: {0}\\\".format(self.target_branch))\\n print(\\\"Head SHA: {0}\\\".format(self.sha))\\n print(\\\"Head Branch: {0}\\\".format(self.branch))\\n self.last_sha = commit or git.current_sha()\\n print(\\\"Last SHA: {0}\\\".format(self.last_sha))\\n self.diff = git.diff(self.target_sha, self.last_sha)\\n self.patch = unidiff.PatchSet(self.diff.split(\\\"\\\\n\\\"))\\n self.review_comments = list(self.pull_request.review_comments())\\n self.last_update = time.time()\\n self.messages_in_files = dict()\\n self.filenames = set()\\n\\n try:\\n try:\\n pr_files = self.pull_request.files()\\n except AttributeError:\\n # github.py == 0.9.6\\n pr_files = self.pull_request.iter_files()\\n self.filenames = set(\\n os.path.relpath(pr_file.filename).replace(\\\"\\\\\\\\\\\", \\\"/\\\").strip()\\n for pr_file in pr_files\\n )\\n print(\\\"Files in PR: {}\\\".format(self.filenames))\\n except Exception:\\n traceback.print_exc()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc0991cff9f43593fdf34badd17468b4\",\n \"score\": \"0.56627953\",\n \"text\": \"def git_pull(self):\\n token = self.user_id.token\\n clone_url = self.clone_url and self.clone_url.replace('https://', '')\\n cmd = ['pull',\\n GITHUB_CLONE.format(token=token,\\n clone_url=clone_url)]\\n self.git(cmd, cwd=1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3eba1ece83836a2ebbcbe37a3e27446d\",\n \"score\": \"0.56571054\",\n \"text\": \"def downloadTheCode(self, whereToSaveCodePath, githubRelativeUrlList ):\\n if not whereToSaveCodePath.endswith(\\\"/\\\"):\\n whereToSaveCodePath = whereToSaveCodePath+\\\"/\\\"\\n\\n for relpath in githubRelativeUrlList :\\n url = \\\"https://raw.github.com\\\" + (\\\"/\\\".join( [ part for part in relpath.split(\\\"/\\\") if part != \\\"blob\\\" ] ) )\\n print \\\"Scraping file:\\\\n\\\", url\\n opener = urllib2.build_opener()\\n opener.addheaders = [('User-agent', 'Mozilla/5.0')]\\n u = opener.open(url)\\n meta = u.info()\\n file_size = int(meta.getheaders(\\\"Content-Length\\\")[0])\\n print \\\"Downloading: %s Bytes: %s\\\" % (url.split(\\\"/\\\")[-1], file_size)\\n\\n file_name = (whereToSaveCodePath+str(uuid.uuid4())+\\\"__\\\"+url.split(\\\"/\\\")[-1])\\n f = open(file_name, 'w')\\n\\n file_size_dl = 0\\n block_sz = 8192\\n while True:\\n buffer = u.read(block_sz)\\n if not buffer:\\n break\\n\\n file_size_dl += len(buffer)\\n f.write(buffer)\\n status = r\\\"%10d [%3.2f%%]\\\" % (file_size_dl, file_size_dl * 100. / file_size)\\n status = status + chr(8)*(len(status)+1)\\n print status,\\n\\n f.close()\\n u.close()\\n time.sleep(random.randint(1,3))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cde82eafcb179b207714202ec49d552\",\n \"score\": \"0.56204134\",\n \"text\": \"async def git(self, ctx, *args):\\n emb = Embed(title='GitHub · fiffu/arisa2',\\n description=(f'Bugs and suggestions: create an issue!\\\\n'\\n f'{GITHUB_LINK}/issues\\\\n\\\\n'\\n f'Contributions: pull requests welcome!'\\n f'```git clone {GITHUB_LINK}.git```'),\\n #url=GITHUB_LINK,\\n colour=0x1e2327)\\n await ctx.send(embed=emb)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfbd7136aac2ec47a5ba41559d3bfe9f\",\n \"score\": \"0.5609883\",\n \"text\": \"def get_data(github, selected_repos):\\n org = \\\"custom-cards\\\"\\n data = {}\\n repos = []\\n if selected_repos:\\n repos.append(selected_repos)\\n else:\\n for repo in list(github.get_user(org).get_repos()):\\n repos.append(repo.name)\\n for repo in repos:\\n try:\\n repo = github.get_repo(org + \\\"/\\\" + repo)\\n if repo.name not in BLACKLIST and not repo.archived:\\n print(\\\"Generating json for:\\\", \\\"{}/{}\\\".format(org, repo.name))\\n\\n try:\\n release = list(repo.get_releases())[0]\\n except Exception: # pylint: disable=W0703\\n release = None\\n\\n name = repo.name\\n\\n version = None\\n try:\\n if release and release.tag_name is not None:\\n version = release.tag_name\\n else:\\n content = repo.get_file_contents(\\\"VERSION\\\")\\n content = content.decoded_content.decode()\\n version = content.split()[0]\\n except Exception: # pylint: disable=W0703\\n version = None\\n\\n if release:\\n remote_location = REUSE_TAG.format(org, name, version, name)\\n else:\\n remote_location = REUSE.format(org, name, name)\\n\\n remote_location = remote_location + \\\".js\\\"\\n testfile = requests.get(remote_location)\\n\\n if testfile.status_code != 200:\\n remote_location = remote_location.split(name + \\\".js\\\")[0]\\n remote_location = remote_location + \\\"dist/\\\" + name + \\\".js\\\"\\n testfile = requests.get(remote_location)\\n\\n if testfile.status_code != 200:\\n remote_location = remote_location.split(\\\"dist/\\\" + name + \\\".js\\\")[0]\\n remote_location = remote_location + \\\"src/\\\" + name + \\\".js\\\"\\n testfile = requests.get(remote_location)\\n\\n if testfile.status_code != 200:\\n continue\\n\\n visit_repo = VISIT.format(org, name)\\n\\n try:\\n changelog = list(repo.get_releases())[0].html_url\\n if \\\"untagged\\\" in list(repo.get_releases())[0].name:\\n changelog = None\\n except Exception: # pylint: disable=W0703\\n changelog = None\\n\\n if changelog is None:\\n changelog = VISIT.format(org, name)\\n\\n data[name] = {}\\n data[name][\\\"version\\\"] = version\\n data[name][\\\"remote_location\\\"] = remote_location\\n data[name][\\\"visit_repo\\\"] = visit_repo\\n data[name][\\\"changelog\\\"] = changelog\\n except Exception as error: # pylint: disable=W0703\\n print(error)\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d3e835f9c0690ae58d623f74d18269a\",\n \"score\": \"0.55903345\",\n \"text\": \"def test_repo_get_release(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c071793bc97131fb3c3e5bfd11884bb5\",\n \"score\": \"0.55789083\",\n \"text\": \"def test_fetch_tarball_and_keep_single_file():\\n file_name = fetch_open_samples(\\n \\\"GW190424_180648\\\", read_file=False, outdir=\\\".\\\", unpack=True,\\n path=\\\"GW190424_180648.h5\\\", catalog=\\\"GWTC-2\\\",\\n download_kwargs={\\\"timeout\\\": 60}\\n )\\n assert os.path.isfile(\\\"./GW190424_180648.h5\\\")\\n assert os.path.isfile(file_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"223a5e8bf8fe79c3aba13891b1f902b6\",\n \"score\": \"0.5575196\",\n \"text\": \"def sync_repository(self):\\n session = requests.Session()\\n session.auth = (self.user_id.token, 'x-oauth-basic')\\n res = session.get(GITHUB_REPO.format(name=self.name))\\n res_commits = session.get(GITHUB_COMMIT.format(name=self.name))\\n res_r = res.json()\\n self.local_path = os.path.join(tools.config.filestore(self._cr.dbname),\\n str(self.user_id.id),\\n str(self.id))\\n self.write({'url': res_r.get('html_url'),\\n 'git_description': res_r.get('description'),\\n 'clone_url': res_r.get('clone_url'),\\n 'ssh_url': res_r.get('ssh_url'),\\n 'last_json_answer': res_r,\\n 'sha': res_commits.json()[0].get('sha')})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e7b897ad48814b6fccfd4ce16d5d60f\",\n \"score\": \"0.5573493\",\n \"text\": \"def download_stdlib(token: str, org: str, repo: str, branch: str, folder: str) -> None:\\n download_from_git(token, org, repo, branch, folder)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3984f681ff4e1237023e780d38f0945a\",\n \"score\": \"0.5571107\",\n \"text\": \"async def github():\\n text = \\\"How tu veux voir mon repos Github pour me disséquer ? Pas de soucis ! Je suis un Bot, je ne ressens pas la douleur !\\\\n https://github.com/outout14/tuxbot-bot\\\"\\n em = discord.Embed(title='Repos TuxBot-Bot', description=text, colour=0xE9D460)\\n em.set_author(name='Outout', icon_url=\\\"https://avatars0.githubusercontent.com/u/14958554?v=3&s=400\\\")\\n await bot.say(embed=em)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"786a546dc897854f71de36b6a7a959df\",\n \"score\": \"0.55704504\",\n \"text\": \"def git_clone(self, url, target):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d8f4e169bdf02609f35d0b1aa46c55c\",\n \"score\": \"0.5567321\",\n \"text\": \"async def github(self,ctx,person,repo):\\n\\t\\tr = json.loads(requests.get(\\\"https://api.github.com/repos/ottomated/CrewLink/commits/master\\\").text)\\n\\n\\t\\tlast_pushed=r[\\\"commit\\\"][\\\"author\\\"][\\\"date\\\"]\\n\\t\\tlast_pushed_general = last_pushed[:10]\\n\\t\\tlast_pushed_time = last_pushed[11:-1]\\n\\t\\tlast_pushed_date = datetime(int(last_pushed_general[0:4]),int(last_pushed_general[5:7]),int(last_pushed_general[8:]),int(last_pushed_time[:2]),int(last_pushed_time[3:5]),int(last_pushed_time[6:]))\\n\\t\\tlast_pushed_date_pytz = last_pushed_date.replace(tzinfo=timezone(\\\"GMT\\\"))\\n\\t\\tnow=datetime.now(pytz.timezone(\\\"GMT\\\"))\\n\\n\\t\\tself.log(\\\"git\\\",ctx.message.author)\\n\\t\\tawait ctx.send(f\\\"Last Updated: *{now-last_pushed_date_pytz}*\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc1a11450e485c726e6d1c2f6a955338\",\n \"score\": \"0.55661553\",\n \"text\": \"def github_run(data):\\n counter_talley = 0\\n for key in data:\\n data[key]['source'] = \\\"\\\"\\n data[key]['url'] = \\\"\\\"\\n data[key]['html'] = \\\"\\\"\\n data[key]['commit_author'] = \\\"\\\"\\n data[key]['commit_author_id'] = \\\"\\\"\\n data[key]['commit_time'] = \\\"\\\"\\n try:\\n if data[key][\\\"new_link\\\"] == \\\"\\\":\\n continue\\n possible_commits = {'mention':[], 'first': [], 'second': [], 'third': [], 'oss_fuzz_error': [], 'sole_commit': []}\\n for temp_link_value in data[key][\\\"new_link\\\"].split(\\\"*SEP*\\\"):\\n username = '' # Specify GitHub username to use \\n url = \\\"https://api.github.com/repos/\\\" + temp_link_value.split(\\\".com/\\\")[1]\\n try:\\n response = requests.get(url, auth=(username, TOKEN))\\n json_data = response.json()\\n count[0] += 1\\n except:\\n continue\\n if \\\"commits\\\" not in json_data:\\n continue\\n for commit in json_data['commits']:\\n ts = data[key]['state'].split(\\\"--\\\")\\n lookup_url = commit['url']\\n temp_response = requests.get(lookup_url, auth=(username, TOKEN))\\n temp_json = temp_response.json()\\n if count[0] > 4950:\\n print(\\\"Resting the API...\\\")\\n time.sleep(3600)\\n print(\\\"Restarting the API...\\\")\\n count[0] = 0\\n count[0] += 1\\n for f in temp_json['files']:\\n if key in temp_json['commit']['message']:\\n possible_commits['mention'].append(temp_json)\\n if len(ts) > 0 and \\\"patch\\\" in f and ts[0].strip() in f['patch']:\\n possible_commits['first'].append(temp_json)\\n elif len(ts) > 1 and \\\"patch\\\" in f and ts[1].strip() in f['patch']:\\n possible_commits['second'].append(temp_json)\\n elif len(ts) > 2 and \\\"patch\\\" in f and ts[2].strip() in f['patch']:\\n possible_commits['third'].append(temp_json)\\n elif 'timeout' in data[key]['crash'].lower() and 'libFuzzer' in temp_json['commit']['message']:\\n possible_commits['oss_fuzz_error'].append(temp_json)\\n if len(json_data['commits']) == 1:\\n possible_commits['sole_commit'].append(temp_json)\\n most_likely = \\\"\\\"\\n try:\\n for subkey in possible_commits:\\n for item in possible_commits[subkey]:\\n if most_likely == \\\"\\\":\\n most_likely = item\\n data[key]['source'] = subkey\\n if most_likely != \\\"\\\":\\n data[key]['url'] = most_likely['url']\\n data[key]['html'] = most_likely['html_url']\\n data[key]['commit_author'] = most_likely['commit']['author']['name']\\n data[key]['commit_author_id'] = most_likely['commit']['author']['email']\\n data[key]['commit_time'] = most_likely['commit']['author']['date']\\n counter_talley += 1\\n \\n except:\\n continue\\n except:\\n continue\",\n \"title\": \"\"\n },\n {\n \"docid\": \"666ff38d9daf4f599d1a55ba600027a3\",\n \"score\": \"0.55563235\",\n \"text\": \"def test_repo_transfer(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"796a1e437683d40df6288646a64cabed\",\n \"score\": \"0.55539924\",\n \"text\": \"def test_repo_get_hook(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e60f6286cf74387b6568fd695eaf37dc\",\n \"score\": \"0.555213\",\n \"text\": \"def get_data(url, user):\\n langs = {\\n '.c': 'C',\\n '.cpp': 'C++',\\n '.cs': 'C#',\\n '.css': 'CSS',\\n '.go': 'Go',\\n '.html': 'HTML',\\n '.java': 'Java',\\n '.js': 'JavaScript',\\n '.php': 'PHP',\\n '.py': 'Python',\\n '.rb': 'Ruby'\\n }\\n message = \\\"Okay\\\"\\n api_url, repo, handler, contents = convert_github_html_url_to_api_url(url)\\n\\n if handler != user.username:\\n raise Exception(\\\"You can't add a snippet of other people's code! 😓\\\")\\n\\n response = requests.get(api_url, auth=(\\\"friendwithnobenegits\\\", \\\"pixeldevtinder1\\\"))\\n if response.status_code != 200:\\n raise Exception(\\\"Repository is not public.\\\")\\n file = base64.b64decode(response.json().get('content'))\\n\\n maxlines = 20\\n startline = 0\\n endline = startline+maxlines\\n\\n try:\\n print url\\n if '#' in url:\\n lines = url[url.find(\\\"#\\\")+1:]\\n if '-' in lines:\\n endline = int(lines[lines.find('-')+2:])\\n startline = int(lines[1:lines.find('-')]) - 1\\n if startline >= endline:\\n startline = endline - 20\\n else:\\n startline = int(lines[1:]) - 1\\n endline = startline+maxlines\\n except Exception, e:\\n print e\\n\\n # limit lines of code\\n file = \\\"\\\\n\\\".join(file.split('\\\\n')[startline:endline])\\n if file == \\\"\\\":\\n startline = 0\\n endline = startline+maxlines\\n file = \\\"\\\\n\\\".join(file.split('\\\\n')[0:endline])\\n lines = 'L{}-L{}'.format(startline, endline)\\n print lines\\n print file\\n\\n language = contents.split(\\\"#\\\")[0].split(\\\".\\\")[-1]\\n language = langs.get('.{}'.format(language), None)\\n if not language:\\n print \\\"Language \\\\\\\"{}\\\\\\\" does not exists. Query for repo language\\\".format(contents.split(\\\"#\\\")[0].split(\\\".\\\")[-1])\\n r = requests.get('https://api.github.com/repos/{0}/{1}'.format(user.username, repo), auth=(\\\"friendwithnobenegits\\\", \\\"pixeldevtinder1\\\"))\\n repository = r.json()\\n language = repository.get('language')\\n\\n return {\\n 'message': message,\\n 'name': repo,\\n 'filename' : response.json().get('name'),\\n 'stars': response.json().get('size'),\\n 'lines': lines,\\n 'language': language,\\n 'snippet': file\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1d59df94c607a4953f784152366bc9e\",\n \"score\": \"0.5545859\",\n \"text\": \"def pull(self, repo, path):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a7e6aebb6c2186ac2ff3fff38f9e085\",\n \"score\": \"0.5542814\",\n \"text\": \"async def git(ctx):\\n if can_answer(ctx):\\n msg = \\\"https://github.com/Eerolz/launchbot\\\"\\n await ctx.send(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b228900d589c97acb3931a309f09d019\",\n \"score\": \"0.5526284\",\n \"text\": \"def fetch_in_volume():\\n top = \\\"%s/%s\\\" % (ssdroot, flag_subvol)\\n dochdir(top)\\n # First binutils (which is only git)\\n do_fetch(\\\"git\\\", \\\"binutils\\\")\\n dochdir(\\\"llvm\\\")\\n # Next llvm stuff\\n tofind = \\\".git\\\"\\n if flag_scm_flavor == \\\"svn\\\":\\n tofind = \\\".svn\\\"\\n lines = u.docmdlines(\\\"find . -depth -name %s -print\\\" % tofind)\\n for line in lines:\\n do_fetch(flag_scm_flavor, line.strip())\\n dochdir(top)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"637384ef6d4276b2b669f9b8c6ffab13\",\n \"score\": \"0.5525182\",\n \"text\": \"def read_repo(self):\\n return self._init_github(self._gh, self.team_name, self.repo_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dba66fdfe278dedc4e059f94e6db6607\",\n \"score\": \"0.5520729\",\n \"text\": \"def do_fetch(flavor, where):\\n here = os.getcwd()\\n dochdir(where)\\n if flavor == \\\"git\\\":\\n docmd(\\\"git fetch\\\")\\n elif flavor == \\\"git-svn\\\":\\n docmd(\\\"git fetch\\\")\\n docmd(\\\"git svn rebase -l\\\")\\n else:\\n docmd(\\\"svn update\\\")\\n dochdir(here)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4db1523d14d71ee41a1fca046bdd805e\",\n \"score\": \"0.5513033\",\n \"text\": \"async def pull(ctx):\\n author = ctx.author\\n if author.id in authorities:\\n out = subprocess.Popen(['git', 'pull'], stdout=subprocess.PIPE, stderr=subprocess.STDOUT)\\n stdout,stderr = out.communicate()\\n stdout = stdout.decode(\\\"utf-8\\\")\\n msg = '**Output: **{0}\\\\n'.format(stdout)\\n if stderr:\\n stderr = stderr.decode(\\\"utf-8\\\")\\n msg += '**Error: **\\\\n{0}'.format(stderr)\\n await ctx.send(msg)\\n else:\\n await ctx.send(\\\"You can't tell me what to do!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35031b31fa06bd2121c4d5fe8c0d7031\",\n \"score\": \"0.5485385\",\n \"text\": \"def download_data() -> None:\\n if not os.path.exists(\\\"data\\\"):\\n os.makedirs(\\\"data\\\")\\n\\n http = PoolManager()\\n repo = http.request(\\n \\\"GET\\\", \\\"https://github.com/yaringal/DropoutUncertaintyExps/archive/master.zip\\\"\\n )\\n\\n with zipfile.ZipFile(io.BytesIO(repo.data)) as zip_ref:\\n zip_ref.extractall(\\\"./data\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf5b34c159b3d4a06050cc6438e8a802\",\n \"score\": \"0.547871\",\n \"text\": \"def test_repo_get_contents_list(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acb45daa7198b4c685be51eff47e6aa1\",\n \"score\": \"0.5477697\",\n \"text\": \"def test_repo_get_git_hook(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8f47e366954a018d755f02e0b12623f\",\n \"score\": \"0.5466664\",\n \"text\": \"async def source(self, ctx):\\n await ctx.send(\\\"Check out https://github.com/maxx0911/idleapi.tio\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1a848d48a791cc4e1ce6091422af80b\",\n \"score\": \"0.54599565\",\n \"text\": \"def main():\\n start = \\\"http://www.harkavagrant.com/archive.php\\\"\\n pagelinks = getPageLinks(start)\\n piclinks = getComicLinks(pagelinks)\\n # one dir for harkavagrant-hosted images, and one dir for other ones\\n checkdir(\\\"original\\\")\\n checkdir(\\\"other\\\")\\n retrieve(piclinks)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0fb60a06dd1c9d68f3001603a2eea20\",\n \"score\": \"0.5442824\",\n \"text\": \"def infoAboutRepo():\\n\\tuser = raw_input('Enter the user name ')\\n\\turl = 'https://github.com/'+user\\n\\t# Check If username is invalid\\n\\ttry:\\n\\t\\tsoup = BeautifulSoup(urllib2.urlopen(url).read(), \\\"html.parser\\\")\\n\\texcept Exception:\\n\\t\\tprint 'User \\\"%s\\\" does not exist! Please try again.' %(user)\\n\\t\\texit()\\n\\n\\tpopularRepo = soup.find_all('span' , {'class': 'repo js-repo'})\\n\\tprint \\\"These are the some popular repo of user\\\",user\\n\\tfor repo in popularRepo:\\n\\t\\tprint repo.string\\n\\n\\trepo = raw_input('Enter the repository name : ')\\n\\turl = \\\"https://github.com/\\\"+user+'/'+repo\\n\\ttry:\\n\\t\\turllib2.urlopen(url)\\n\\texcept urllib2.HTTPError, e:\\n\\t\\tprint 'Sorry, there is no such repository named \\\"%s\\\" for user \\\"%s\\\"'%(repo, user)\\n\\t\\texit()\\n\\n\\n\\tdef pulse(url):\\n\\t\\t\\\"\\\"\\\"\\n\\t\\t\\\"\\\"\\\"\\n\\t\\turl += '/pulse/monthly'\\n\\t\\tpage = urllib2.urlopen(url)\\n\\t\\tsoup = BeautifulSoup(page.read(), \\\"html.parser\\\")\\n\\t\\tdiv_all = soup.findAll('div',{'class':'section diffstat-summary'})\\n\\t\\tif not div_all:\\n\\t\\t\\tprint 'No Recent activities in the repository.'\\n\\t\\t\\treturn\\n\\t\\tprint '\\\\nThe whole information about the repository is as follows :\\\\n'\\n\\t\\tfor each_div in div_all:\\n\\t\\t print ' '.join(each_div.get_text().split())\\n\\n\\tdef readme(url):\\n\\t\\t\\\"\\\"\\\"\\n\\t\\t\\\"\\\"\\\"\\n\\t\\turl+= '/blob/master/README.md'\\n\\t\\t# Check if ReadMe exists.\\n\\t\\ttry:\\n\\t\\t\\tsoup = BeautifulSoup(urllib2.urlopen(url).read(), \\\"html.parser\\\")\\n\\t\\t\\tparagraphs = soup.find('article', {\\\"class\\\" : \\\"markdown-body entry-content\\\"}).get_text()\\n\\t\\texcept Exception:\\n\\t\\t\\tprint 'ReadMe file for the repository doesn\\\\'t exist'\\n\\t\\t\\treturn\\n\\n\\t\\tprint '\\\\nREADME\\\\n'\\n\\t\\tprint paragraphs\\n\\n\\n\\tdef watching(url):\\n\\t\\t\\\"\\\"\\\"\\n\\t\\t\\\"\\\"\\\"\\n\\t\\t# TODO: watching not working as of now. Only giving 0 as Watcher...\\n\\t\\tsoup = BeautifulSoup(urllib2.urlopen(url).read(), \\\"html.parser\\\")\\n\\t\\twatch = soup.find('a' , {\\\"class\\\" : \\\"social-count js-social-count\\\"}).text\\n\\t\\tprint 'Watchers: %s' %(watch.split()[0])\\n\\n\\n\\tdef statistics(url):\\n\\t\\t\\\"\\\"\\\"\\n\\t\\t\\\"\\\"\\\"\\n\\t\\tsoup = BeautifulSoup(urllib2.urlopen(url).read(), \\\"html.parser\\\")\\n\\t\\tultags_all= soup.find_all('ul', {'class' : 'numbers-summary'})\\n\\t\\tif not ultags_all:\\n\\t\\t\\tprint 'No activities in the repository.'\\n\\t\\t\\treturn\\n\\t\\tprint \\\"\\\\nUsers Activities in Repo\\\"\\n\\t\\tfor ultag in ultags_all :\\n\\t\\t\\tfor litag in ultag.find_all('li'):\\n\\t\\t\\t\\tif ' '.join(litag.text.split()) != \\\"Fetching contributors\\\":\\n\\t\\t\\t\\t\\tprint ' '.join(litag.text.split())\\n\\n\\tstatistics(url)\\n\\tpulse(url)\\n\\treadme(url)\\n\\twatching(url)\\n\\t#more features to be added...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f4f17f867800f561cccf097aff83e75\",\n \"score\": \"0.5430694\",\n \"text\": \"def test_repo_list_pull_requests(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48d2b4279fd11417a18150b29a80f807\",\n \"score\": \"0.5426568\",\n \"text\": \"async def on_message(self, message):\\n\\n gh_match = GITHUB_RE.search(message.content)\\n\\n if gh_match and not message.author.bot:\\n for gh in GITHUB_RE.finditer(message.content):\\n d = gh.groupdict()\\n headers = {}\\n if 'GITHUB_TOKEN' in os.environ:\\n headers[\\n 'Authorization'] = f'token {os.environ[\\\"GITHUB_TOKEN\\\"]}'\\n pull_request = await fetch_http(\\n self.session,\\n f'https://api.github.com/repos/{d[\\\"owner\\\"]}/{d[\\\"repo\\\"]}/pulls/{d[\\\"pr\\\"]}',\\n 'json',\\n headers=headers,\\n )\\n\\n body = pull_request[\\\"body\\\"]\\n if len(body) > 512:\\n body = body[:512] + \\\"...\\\"\\n\\n state = pull_request['state'].capitalize()\\n if pull_request['draft']:\\n state = 'Draft'\\n if pull_request['merged']:\\n state = 'Merged'\\n\\n embed = discord.Embed(\\n title=f'{pull_request[\\\"title\\\"]} (#{pull_request[\\\"number\\\"]})',\\n description=body,\\n url=pull_request['html_url'],\\n timestamp=datetime.datetime.fromisoformat(pull_request['created_at'][:-1]),\\n color=COLORS[state],\\n ).set_author(\\n name=f'{d[\\\"owner\\\"]}/{d[\\\"repo\\\"]}',\\n url=f'https://github.com/{d[\\\"owner\\\"]}/{d[\\\"repo\\\"]}',\\n ).add_field(\\n name=\\\"Status\\\",\\n value=state,\\n inline=True,\\n ).add_field(\\n name=\\\"Additions\\\",\\n value=str(pull_request['additions']),\\n inline=True,\\n ).add_field(\\n name=\\\"Deletions\\\",\\n value=str(pull_request['deletions']),\\n inline=True,\\n ).add_field(\\n name=\\\"Files Changed\\\",\\n value=str(pull_request['changed_files']),\\n inline=True,\\n ).add_field(\\n name=\\\"Commits\\\",\\n value=str(pull_request['commits']),\\n inline=True,\\n ).set_footer(\\n text=f'Pull request created by {pull_request[\\\"user\\\"][\\\"login\\\"]}',\\n icon_url=pull_request['user']['avatar_url'],\\n )\\n\\n if pull_request[\\\"merged\\\"]:\\n embed = embed.add_field(\\n name='Merged By',\\n value=pull_request['merged_by']['login'],\\n inline=True,\\n )\\n else:\\n embed = embed.add_field(\\n name='State',\\n value='Mergeable' if pull_request['mergeable'] else 'Not Mergeable',\\n inline=True,\\n )\\n\\n await wait_for_deletion(message, self.bot, embed, True)\\n\\n await message.edit(suppress=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6305d052cb677ce2f40c4da1918ab0a0\",\n \"score\": \"0.54137367\",\n \"text\": \"async def git_loop(self):\\n\\t\\twhile not self.bot.is_closed:\\n\\t\\t\\tif 'no-git-loop' in sys.argv:\\n\\t\\t\\t\\treturn\\n\\t\\t\\ttry:\\n\\t\\t\\t\\twith open('./cogs/github_info.json', 'r') as f:\\n\\t\\t\\t\\t\\tgit_json = json.load(f)\\n\\n\\t\\t\\t\\tfor server in git_json[\\\"repositories\\\"]:\\n\\t\\t\\t\\t\\tfor channel in git_json[\\\"repositories\\\"][server]:\\n\\t\\t\\t\\t\\t\\tfor repo_owner in git_json[\\\"repositories\\\"][server][channel]:\\n\\t\\t\\t\\t\\t\\t\\tfor repo in git_json[\\\"repositories\\\"][server][channel][repo_owner]:\\n\\n\\t\\t\\t\\t\\t\\t\\t\\td = feedparser.parse('https://github.com/{}/{}/commits/master.atom'.format(repo_owner, repo))\\n\\t\\t\\t\\t\\t\\t\\t\\tif 'bozo_exception' in d: #if rss url is invalid\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tawait self.bot.send_message(discord.Object(channel), \\\"Error while retrieving data from URL: '{}'\\\".format(rss_url))\\n\\t\\t\\t\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tlatest_commit = d[\\\"entries\\\"][0][\\\"link\\\"]\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tfmt = \\\"{} @here\\\".format(latest_commit)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tif channel not in self.repo_data:\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tself.repo_data[channel] = {}\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tself.repo_data[channel][repo] = latest_commit\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tawait self.bot.send_message(discord.Object(channel), fmt)\\n\\n\\t\\t\\t\\t\\t\\t\\t\\t\\telif repo not in self.repo_data[channel]:\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tself.repo_data[channel][repo] = latest_commit\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tawait self.bot.send_message(discord.Object(channel), fmt)\\n\\n\\t\\t\\t\\t\\t\\t\\t\\t\\telif self.repo_data[channel][repo] != latest_commit:\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tself.repo_data[channel][repo] = latest_commit\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tawait self.bot.send_message(discord.Object(channel), fmt)\\n\\n\\t\\t\\texcept Exception as e:\\n\\t\\t\\t\\tprint(\\\"LOOP_ERROR@git_loop! \\\" + self.return_traceback(*sys.exc_info()))\\n\\t\\t\\t\\n\\t\\t\\tawait asyncio.sleep(20)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bce603533827a83b02ff76bb56027928\",\n \"score\": \"0.5412148\",\n \"text\": \"def get_from_github(get, params={}):\\n assert not get.startswith('/')\\n url = 'https://api.github.com/' + get\\n headers = {\\n 'Accept': 'application/vnd.github.inertia-preview+json',\\n }\\n return get_json(url, headers=headers, params=params)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"614c87c4cb503ce2e536064bfe68d578\",\n \"score\": \"0.5409203\",\n \"text\": \"def test_download_reference(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a42c77746238396b963471b8f202f9cb\",\n \"score\": \"0.54017216\",\n \"text\": \"def test_normal_style_repo_url(self, _, __):\\n self.assertTrue(detect_repo.check_for_repo_name('fake-path', 'syzkaller'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8357ce002caca3e5347a4153835317bc\",\n \"score\": \"0.53984606\",\n \"text\": \"def get_git_file_data(url, revision, file_path):\\n if url.startswith('https://github.com/'):\\n # Use github-specific URL API:\\n data = urllib.request.urlopen(\\n '%s/raw/%s/%s' % (url, revision, file_path))\\n return data.read()\\n if url.find('.googlesource.com') >= 0:\\n url = '%s/+/%s/%s?format=TEXT' % (url, revision, file_path)\\n data_file = urllib.request.urlopen(url)\\n data = data_file.read()\\n data = base64.b64decode(data)\\n return data\\n\\n raise Exception('Unsupported URL type: ' + url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d787b537638dc093726405446b0f13e2\",\n \"score\": \"0.5389041\",\n \"text\": \"def scrapeGitHubForCode(self, github_search_terms, num_pages_to_scrape, local_path_to_save_code_files ):\\n search_terms = 'def+%28+%2C+%29+%3A+return+'+'+'.join(github_search_terms.split())\\n code_page_links = []\\n page_num = 1\\n total_code_scraped = 0\\n\\n # Search for and gather up all the URLS first\\n # using while loop with exceptions for persistence\\n #\\n while page_num <= num_pages_to_scrape:\\n\\n # more trying to avoid getting ip banned\\n # and/or throttled by github (dis iz painful)\\n try:\\n url = \\\"https://github.com/search?l=python&type=Code&extension:py&p=\\\"+str(page_num)+\\\"&q=\\\"+search_terms\\n print \\\"\\\\n\\\\n***********************\\\\nExecuting new GitHub python code search query:\\\\n\\\", url, \\\"\\\\n\\\"\\n\\n opener = urllib2.build_opener()\\n opener.addheaders = [('User-agent', 'Mozilla/5.0')]\\n response = opener.open(url)\\n page = response.read()\\n soup = BeautifulSoup(page)\\n\\n code_results_div = soup.findAll('div',attrs={'id':'code_search_results'})\\n download_these_files_now = []\\n for div in code_results_div:\\n links = div.findAll('a')\\n for link in links:\\n href_loc = link.get('href')\\n if( href_loc.endswith(\\\".py\\\") and link.text.endswith(\\\".py\\\") ):\\n code_page_links.append( href_loc )\\n download_these_files_now.append( href_loc )\\n\\n rel_code_paths = list(OrderedDict.fromkeys(download_these_files_now))\\n\\n # download the individual python files\\n # oh my goodness github you are a pain\\n # to scrape, stop rate limiting me....\\n #\\n count=0\\n while count != len(rel_code_paths):\\n try:\\n print \\\"\\\\n\\\\nAttempting to fetch: \\\", rel_code_paths[count]\\n self.downloadTheCode( local_path_to_save_code_files, [ rel_code_paths[count] ] )\\n count = count +1\\n except urllib2.HTTPError, errr:\\n if( errr.code == 400 ):\\n # someone must have used .py in a link that did not\\n # contain a python file uh, why would they do that?\\n # github server says this file doesn't exist...\\n # anyway i guess they would know better than me.\\n # anyway, skip this one for now, it doesn't exist\\n count = count +1\\n logging.error(errr)\\n print \\\"Error Code: \\\", errr.code, \\\" File doesn't exist. Skipping this bogus URL...\\\"\\n else:\\n logging.error(errr)\\n pausetime = random.randint(14,38)\\n print \\\"Error Code: \\\", errr.code, \\\" PAUSING FOR \\\", str(pausetime), \\\" seconds. Rate limits?\\\"\\n time.sleep(pausetime)\\n\\n page_num = page_num + 1\\n total_code_scraped = total_code_scraped + len(rel_code_paths)\\n print \\\"\\\\n\\\\n\\\\n*******\\\\nINFO: So far scraped \\\", total_code_scraped , \\\" URLS...\\\"\\n response.close()\\n opener.close()\\n\\n except urllib2.HTTPError, err:\\n logging.error(err)\\n pausetime = random.randint(11,33)\\n print (\\\"PAUSING FOR \\\"+str(pausetime)+\\\" seconds. GIT HUB IS RATE LIMITING? \\\")\\n time.sleep(pausetime)\\n\\n # keep looking natural ;) and avoid getting ip banned\\n pausetime = random.randint(3,7)\\n print (\\\"\\\\n\\\\n\\\\n*******\\\\nINFO: Pausing for \\\"+str(pausetime)+\\\" secs to avoid rate limits.\\\")\\n time.sleep(pausetime)\\n\\n # in case we want to download again later, also for referring to original:\\n save_paths_for_later = list(OrderedDict.fromkeys(code_page_links))\\n self.saveCodeUrlList( local_path_to_save_code_files, save_paths_for_later, \\\"_\\\".join(github_search_terms.split()) )\\n\\n if not local_path_to_save_code_files.endswith(\\\"/\\\"):\\n local_path_to_save_code_files = local_path_to_save_code_files+\\\"/\\\"\\n\\n merged_data_filename = \\\"ALL_\\\" + (\\\"_\\\".join(github_search_terms.split()).upper()) + \\\"_TRAINING_DATA.txt\\\"\\n\\n cmd = 'for i in {} ; do cat $i ; echo \\\"\\\" ; done > {} 2>&1'.format( local_path_to_save_code_files+\\\"*.py\\\", local_path_to_save_code_files+merged_data_filename )\\n\\n output = subprocess.check_output( '{} | tee /dev/stderr'.format( cmd ), shell = True )\\n\\n if os.path.exists(local_path_to_save_code_files+merged_data_filename):\\n print \\\"\\\\n\\\\n********************************\\\\n\\\"\\n print \\\"\\\\nSAVED MERGED CODE INTO ONE BIG FILE FOR FEATURE EXTRACTION:\\\\n\\\"\\n print local_path_to_save_code_files+merged_data_filename\\n print \\\"\\\\n\\\\n********************************\\\\n\\\\n\\\"\\n\\n print \\\"\\\\nScraping is completed for search term: \\\", github_search_terms, \\\"\\\\n\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb77115ec4ac4632a78736797829ef65\",\n \"score\": \"0.5386815\",\n \"text\": \"def update_data(dir):\\n if not os.path.isdir(dir):\\n r = check_output([\\\"git\\\", \\\"clone\\\", REPO_URL, dir])\\n else:\\n r = check_output([\\\"git\\\", \\\"pull\\\"], cwd=dir)\\n print(r.decode(\\\"utf8\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b423124d6bcb78c70d066411690f480b\",\n \"score\": \"0.537717\",\n \"text\": \"def get_gitigore_file(headers: dict) -> str:\\n headers[\\\"Accept\\\"] = \\\"application/vnd.github.v3.raw\\\"\\n\\n url = consts.GH_API_URL + \\\"/gitignore/templates/Python\\\"\\n response = requests.get(url, headers=headers)\\n if 200 <= response.status_code < 300: # Usually 200\\n ignore_content = response.text\\n\\n else:\\n print(f\\\"\\\\nAPI request failed when getting `.gitignore` file.\\\")\\n server_message = json.loads(response.text)[\\\"message\\\"]\\n print(f\\\"Error {response.status_code}: {response.reason} \\\"\\n f\\\"({server_message})\\\")\\n exit()\\n\\n return ignore_content\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8f761d51c0ac913a681b269f33e768c\",\n \"score\": \"0.5351133\",\n \"text\": \"def source_repository(self) -> str:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95e9eb1a1c7fa9296f6e96f1d67f2c08\",\n \"score\": \"0.53419656\",\n \"text\": \"def test_repo_get_release_attachment(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0503c6aac337762506bd7a071028ce27\",\n \"score\": \"0.53389716\",\n \"text\": \"def fetch(self):\\n\\n if self.repo_info != None:\\n return\\n\\n self.repo_info = self.fetch_location(self.repo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c03fe0e14fb82873b9473f09118fa18\",\n \"score\": \"0.53376156\",\n \"text\": \"def test_unsafe_url(self):\\n empty_package_response = {\\n \\\"desc\\\":None,\\n \\\"labels\\\":[],\\n \\\"licenses\\\":[],\\n \\\"website_url\\\":None,\\n \\\"issue_tracker_url\\\":None,\\n \\\"vcs_url\\\":None,\\n \\\"github_repo\\\": None,\\n \\\"maturity\\\":\\\"\\\"\\n }\\n responses.add(responses.GET, 'http://api.bintray.com/conan/foobar/conan/v1/ping')\\n responses.add(responses.GET, 'http://api.bintray.com/conan/foobar/conan/v1/conans/dummy/0.1.0/foobar/stable/digest', json={\\\"conanmanifest.txt\\\":\\\"\\\"})\\n responses.add(responses.GET, 'http://api.bintray.com/conan/foobar/conan', status=404)\\n responses.add(responses.GET, 'http://api.bintray.com/conan/foobar/conan/v1/users/check_credentials')\\n responses.add(responses.GET, 'http://api.bintray.com/conan/foobar/conan/v1/conans/dummy/0.1.0/foobar/stable', json={})\\n responses.add(responses.POST, 'http://api.bintray.com/conan/foobar/conan/v1/conans/dummy/0.1.0/foobar/stable/upload_urls', json={})\\n responses.add(responses.GET, \\\"http://api.bintray.com/licenses/oss_licenses\\\", json={})\\n responses.add(responses.GET, 'http://api.bintray.com/packages/foobar/conan/dummy%3Afoobar', json=empty_package_response)\\n with tools.environment_append({\\\"BINTRAY_API_URL\\\": \\\"http://api.bintray.com\\\"}):\\n tools.save('conanfile.py', content=self.conanfile_complete)\\n self.conan(['remote', 'add', 'unsafe', 'http://api.bintray.com/conan/foobar/conan'])\\n self.conan(['export', '.', 'dummy/0.1.0@foobar/stable'])\\n output = self.conan(['upload', '--remote=unsafe', 'dummy/0.1.0@foobar/stable'])\\n self.assertIn(\\\"Uploading dummy/0.1.0@foobar/stable to remote 'unsafe'\\\", output)\\n self.assertIn(\\\"Uploaded conan recipe 'dummy/0.1.0@foobar/stable' to 'unsafe': http://api.bintray.com/conan/foobar/conan\\\", output)\\n self.assertIn(\\\"post_upload_recipe(): Reading package info from Bintray.\\\", output)\\n self.assertIn(\\\"post_upload_recipe(): Inspecting recipe info.\\\", output)\\n self.assertIn(\\\"post_upload_recipe(): Bintray is outdated. Updating Bintray package \\\" \\\\\\n \\\"info: 'desc', 'github_repo', 'issue_tracker_url', 'labels', 'vcs_url'\\\" \\\\\\n \\\", 'website_url'.\\\", output)\\n self.assertIn('post_upload_recipe(): ERROR: Bad package URL: Only HTTPS is allowed, Bintray API uses Basic Auth', output)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97adb97a65e75edc7575b4de39e67fdb\",\n \"score\": \"0.53337306\",\n \"text\": \"def download(self):\\n\\n if self._check_raw_exists():\\n return\\n\\n # download files\\n try:\\n os.makedirs(os.path.join(self.root, self.raw_folder))\\n except OSError as e:\\n if e.errno == errno.EEXIST:\\n pass\\n else:\\n raise\\n\\n download_url(self.url,\\n root=os.path.join(self.root, self.raw_folder),\\n filename=self.filename,\\n md5=None)\\n if not self._check_raw_exists():\\n raise RuntimeError(\\\"Unable to find downloaded file. Please try again.\\\")\\n else:\\n print(\\\"Download finished.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28a28e594ed1e0d384d9186fbf8891c0\",\n \"score\": \"0.5331594\",\n \"text\": \"def get_repo(config, repo_url, location, branch=\\\"master\\\", verbose=False):\\n tarball_url = githuburl(\\\"{0}/tarball/{1}\\\".format(repo_url, branch))\\n if verbose:\\n sys.stderr.write(\\\"Fetching tutorial from \\\" + tarball_url + \\\"\\\\n\\\")\\n try:\\n url_obj = urllib2.urlopen(tarball_url)\\n temp_obj = tempfile.TemporaryFile()\\n shutil.copyfileobj(url_obj, temp_obj)\\n extract_path = extract_tarfile(temp_obj, location)\\n return extract_path\\n except Exception, e:\\n sys.stderr.write(\\\"Can't download files from github: %s\\\\n\\\" % str(e))\\n if send_exc(config):\\n sys.stderr.write(\\\"This error has been reported to system staff.\\\\n\\\")\\n else:\\n sys.stderr.write(\\\"Please report this error to your system staff.\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8e68b8f32498043441ca9c61de06948\",\n \"score\": \"0.5329416\",\n \"text\": \"def __init__(self,\\n name=None,\\n source=None,\\n version=None,\\n localdir=None,\\n repository_type=None,\\n force_update=False,\\n download=False,\\n kconfig_extra={}):\\n \\n if source is None or len(source) == 0:\\n raise InvalidRepositorySource(source)\\n\\n version_head = None\\n\\n # In good scenarios, we have already been given key information about\\n # this repository\\n if repository_type is None or name is None:\\n # Let's passively determine the name and type first before we clone,\\n # as this helps us determine where to place the repository when we\\n # want to clone it.\\n repository_type, name = self.passively_determine_type_and_name(source)\\n\\n already_downloaded = False\\n\\n if len(self._known_versions) == 0 or force_update or download:\\n self.update_known_versions(source=source)\\n \\n # Determine how to set the version\\n if version is not None:\\n self._version = version\\n elif BRANCH_MASTER in self._known_versions:\\n self._version = BRANCH_MASTER\\n elif BRANCH_STAGING in self._known_versions:\\n self._version = BRANCH_STAGING\\n else:\\n raise NoSuchReferenceInRepo\\n\\n # If we cannot determine the type passively, we must clone the\\n # repository into a temporary location and manually introspect\\n # the files to determine its type.\\n if repository_type is None or name is None:\\n repository_type, name = self.intrusively_determine_type_and_name(source)\\n\\n # If we cannot determine the name and type passively we must\\n # throw an error and prompt the user for more information to\\n # address this roblem.\\n if repository_type is None or name is None:\\n raise NoTypeAndNameRepo\\n \\n # Check if we already have a repository saved locally with this remote\\n # url set. If so, we do not need to make an outbound connection to\\n # determine whether the repository is \\\"real\\\".\\n if localdir is None:\\n self._localdir = self.determine_localdir(repository_type=repository_type, name=name)\\n else:\\n self._localdir = localdir\\n\\n # Populate the repository information if we have made it this far, as it\\n # means that the repository is valid and usable.\\n self._name = name\\n self._type = repository_type\\n self._source = source\\n self._kconfig_extra = kconfig_extra\\n\\n if download or force_update:\\n self.update()\\n \\n if self._type == RepositoryType.ARCH:\\n return\\n elif self._type == RepositoryType.PLAT and self._source == UNIKRAFT_CORE:\\n return\\n \\n self.__set_cache(source, self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e62563f01d6e58d8e29e7f56ab4841f7\",\n \"score\": \"0.53233135\",\n \"text\": \"def fetch_from_github_extra(shapes_graph, client, oauth_token, filename):\\n extra_shapes_repositories = []\\n with open(root / '../' + filename, 'r') as f:\\n for line in f:\\n extra_shapes_repositories.append(line.rstrip('\\\\n').strip())\\n\\n for extra_repo in extra_shapes_repositories:\\n data = client.execute(\\n query=github_graphql_get_extra(extra_repo),\\n headers={\\\"Authorization\\\": \\\"Bearer {}\\\".format(oauth_token)},\\n )\\n # print(json.dumps(data, indent=4))\\n repo_json = data[\\\"data\\\"][\\\"repository\\\"]\\n repo_url = repo_json[\\\"url\\\"]\\n try:\\n branch = repo_json['defaultBranchRef']['name']\\n repo_description = repo_json[\\\"description\\\"]\\n except Exception as e:\\n print(e)\\n print('🕊 No default_branch found for repo_url, using master')\\n branch = 'master'\\n repo_description = ''\\n shapes_graph = clone_and_process_repo(shapes_graph, repo_url, branch, repo_description)\\n return shapes_graph\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07cc0a361b9359289f8003cd0a3836de\",\n \"score\": \"0.53227985\",\n \"text\": \"def download_earnings22(\\n target_dir: Pathlike = \\\".\\\",\\n force_download: Optional[bool] = False,\\n url: Optional[str] = _DEFAULT_URL,\\n) -> Path:\\n logging.error(\\n \\\"Downloading Earnings22 from github repository is not implemented. \\\"\\n + f\\\"Please visit {_DEFAULT_URL} and download the files manually. Please \\\"\\n + \\\"follow the instructions closely as you need to use git-lfs to download \\\"\\n + \\\"some of the audio files.\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3720a6ab92bfe7ef85c0c4b84c256e27\",\n \"score\": \"0.53227246\",\n \"text\": \"async def _call_github(\\n self,\\n url: str,\\n load_json: bool = True,\\n method: str = \\\"GET\\\",\\n body: Optional[dict] = None,\\n params: Optional[Dict[str, str]] = None,\\n media_type: str = \\\"application/vnd.github.v3+json\\\",\\n has_pagination: bool = True,\\n ) -> Union[dict, str]:\\n headers = {\\n \\\"Accept\\\": media_type,\\n \\\"Authorization\\\": f\\\"token {self._config.access_token}\\\",\\n }\\n\\n return await super()._call_provider(\\n url,\\n load_json=load_json,\\n method=method,\\n body=body,\\n params=params,\\n headers=headers,\\n has_pagination=has_pagination,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b598c0325f17a5a941b97d509032d09\",\n \"score\": \"0.53174305\",\n \"text\": \"def DownLoadCode(self, handle, codeurl):\\n self.logger.info(\\\"Now loading test codes! Please wait in patient...\\\")\\n originalfolder = sys.path[0]\\n self.logger.info(originalfolder)\\n gitclone = handle\\n gitclone.sendline(\\\"git clone \\\" + codeurl)\\n index = 0\\n # increment = 0\\n while index != 1 or index != 4:\\n index = gitclone.expect(['already exists',\\n 'esolving deltas: 100%',\\n 'eceiving objects',\\n 'Already up-to-date',\\n 'npacking objects: 100%', pexpect.EOF])\\n\\n filefolder = self.home + '/' + codeurl.split('/')[-1].split('.')[0]\\n if index == 0:\\n os.chdir(filefolder)\\n os.system('git pull')\\n os.chdir(originalfolder)\\n self.loginfo.log('Download code success!')\\n break\\n elif index == 1 or index == 4:\\n self.loginfo.log('Download code success!')\\n gitclone.sendline(\\\"mkdir onos\\\")\\n gitclone.prompt()\\n gitclone.sendline(\\\"cp -rf \\\" + filefolder + \\\"/tools onos/\\\")\\n gitclone.prompt()\\n break\\n elif index == 2:\\n os.write(1, gitclone.before)\\n sys.stdout.flush()\\n else:\\n self.loginfo.log('Download code failed!')\\n self.loginfo.log('Information before' + gitclone.before)\\n break\\n gitclone.prompt()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"403aae0adf5dd64177b9a0174478186c\",\n \"score\": \"0.5309374\",\n \"text\": \"def try_get_official(self, repo):\\n official_repo = \\\"{repo}/{repo}\\\".format(repo=repo)\\n log.info('Checking existence of %s', official_repo)\\n r = self.get('https://{}/{}/releases.atom'.format(self.hostname, official_repo))\\n # API requests are varied by cookie, we don't want serializer for cache fail because of that\\n self.cookies.clear()\\n if r.status_code == 200:\\n self.feed_contents[official_repo] = r.text\\n return official_repo\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad6c8de07bba926f048e2244ec6bed06\",\n \"score\": \"0.5300511\",\n \"text\": \"def get_from_bitbucket(url):\\n\\n # replace browse with raw in request url\\n url = url.replace(\\\"/browse/\\\", \\\"/raw/\\\", 1)\\n\\n r = requests.get(url, auth=(user, pw))\\n return r.json()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bae0dda848e8ec6c56425cb522a4739\",\n \"score\": \"0.5292668\",\n \"text\": \"def download_workflow_static(self):\\n\\n # Download the centralised configs first\\n if self.include_configs:\\n log.info(\\\"Downloading centralised configs from GitHub\\\")\\n self.download_configs()\\n\\n # Download the pipeline files for each selected revision\\n log.info(\\\"Downloading workflow files from GitHub\\\")\\n\\n for item in zip(self.revision, self.wf_sha.values(), self.wf_download_url.values()):\\n revision_dirname = self.download_wf_files(revision=item[0], wf_sha=item[1], download_url=item[2])\\n\\n if self.include_configs:\\n try:\\n self.wf_use_local_configs(revision_dirname)\\n except FileNotFoundError as e:\\n raise DownloadError(\\\"Error editing pipeline config file to use local configs!\\\") from e\\n\\n # Collect all required singularity images\\n if self.container_system == \\\"singularity\\\":\\n self.find_container_images(os.path.join(self.outdir, revision_dirname))\\n\\n try:\\n self.get_singularity_images(current_revision=item[0])\\n except OSError as e:\\n raise DownloadError(f\\\"[red]{e}[/]\\\") from e\\n\\n # Compress into an archive\\n if self.compress_type is not None:\\n log.info(\\\"Compressing output into archive\\\")\\n self.compress_download()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55a3878db263e82ed592fb69dbeb68bb\",\n \"score\": \"0.5280596\",\n \"text\": \"def convert_to_github(data):\\n driver_loc = Service(\\\"/WebDrivers/chromedriver\\\") # configure with path to the webdriver for selenium to use\\n prev = NONE\\n links = {}\\n with webdriver.Chrome(service=driver_loc) as driver:\\n for key in data:\\n temp_list = []\\n links[key] = \\\"\\\"\\n if data[key]['fixed'] == '':\\n continue\\n try:\\n time.sleep(2)\\n wait = WebDriverWait(driver, 10)\\n driver.get(data[key][\\\"fixed\\\"])\\n dom = wait.until(presence_of_element_located((By.CSS_SELECTOR, \\\"revisions-info\\\")))\\n dom = dom.shadow_root\\n found_links = dom.find_elements(By.CSS_SELECTOR, \\\"if-else\\\")\\n # dom = dom.shadow_root\\n for edom in found_links:\\n link = edom.find_element(By.CSS_SELECTOR, \\\"a\\\")\\n if link.get_attribute(\\\"href\\\").strip() and \\\"github\\\" in link.get_attribute(\\\"href\\\").strip().lower():\\n temp_list.append(link.get_attribute(\\\"href\\\").strip())\\n for item in temp_list:\\n links[key] += item + \\\"*SEP*\\\"\\n links[key] = links[key][:-5]\\n except:\\n continue\\n return links\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61d24ceac6726bcef27558b5c5c2b48c\",\n \"score\": \"0.52788633\",\n \"text\": \"def recursive_pull(URL, sha, workdir, depth):\\n # some helpful locations\\n final_location = workdir + 'final/'\\n tarfile_path = workdir + 'current.tar.gz'\\n # From the URL, determine which instance it is on\\n if 'github.com' in URL:\\n gh = github_external\\n else:\\n gh = github_internal\\n owner, repo = parse_owner(URL)\\n # This is going to be the most problematic section, you could get problems\\n # because:\\n # -The github instance you were talking to is unreachable/offline\\n # -Mis spellings of the owner or repository\\n # -The owner or repository do not exist\\n # -They do exist, but the specified SHA does not\\n try:\\n repository = gh.repository(owner=owner, repository=repo)\\n tarball = repository.archive(format=\\\"tarball\\\",\\n path=tarfile_path, ref=sha)\\n tar = tarfile.open(mode='r|gz', name=tarfile_path)\\n tar.extractall(workdir)\\n tar.close()\\n except:\\n raise NameError(\\\"Error pulling \\\" + URL + \\\" \\\" + sha + \\\"\\\\n\\\" +\\n \\\"Ensure that the URL is properly formated, and that \\\" +\\n \\\"the specified SHA exists.\\\")\\n\\n # move it into the correct place\\n internal_name = workdir + owner + '-' + repo + '-' + sha[:7] + '/'\\n os.rename(internal_name, final_location + depth)\\n # Now get and parse the .submodules file\\n modulefile = final_location + depth + '.gitmodules'\\n if not os.path.exists(modulefile):\\n # Then there is no submodules here\\n return\\n moduleparser = configparser.ConfigParser()\\n moduleparser.read(modulefile)\\n for submodule in moduleparser.sections():\\n # determine the SHA for this submodule\\n content = repository.contents(\\n ref=sha,\\n path=moduleparser[submodule]['path']\\n )\\n submodulesha = content.to_json()['sha']\\n recursive_pull(\\n moduleparser[submodule]['url'],\\n submodulesha,\\n workdir,\\n depth + moduleparser[submodule]['path'] + '/'\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e26a108431909adf1ad92e6ad7a21dea\",\n \"score\": \"0.5276766\",\n \"text\": \"def _download_missing_files(self):\\n import requests\\n base_url = \\\"https://raw.githubusercontent.com/mozilla-releng/firefox-infra-changelog/oop/modules/\\\"\\n for file in self._missing_files:\\n raw_file = requests.get(base_url + file, allow_redirects=True).text\\n f = open(os.path.abspath(os.path.join(self.path_level, \\\"modules\\\", file)), \\\"w\\\")\\n f.write(raw_file)\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2d481ca8e1e4c7aac23c66e9172a42c\",\n \"score\": \"0.5271279\",\n \"text\": \"def fetch_all():\\n for i in range(START,END +1):\\n data = fetch_from_csv(\\\"out/new_\\\" + str(i) + \\\".csv\\\")\\n github_run(data)\\n write_commits(data, \\\"out/commit_link_\\\" + str(i) + \\\".csv\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae931c84ff3c1a56ccf92df3f9e0b254\",\n \"score\": \"0.5268423\",\n \"text\": \"def __init__(self, download_url):\\n url_segments = download_url.split(u'/')\\n if len(url_segments) < 5 or url_segments[2] != u'github.com':\\n raise ValueError(u'Unsupported download URL.')\\n\\n super(GithubReleasesDownloadHelper, self).__init__(download_url)\\n self._organization = url_segments[3]\\n self._repository = url_segments[4]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"274f036aa678aa107bb67ca6d0f71bcb\",\n \"score\": \"0.52668333\",\n \"text\": \"def github(request):\\n\\n return render(request, 'taric_books/github.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de521f195fe240c2db12ffccaa4425d6\",\n \"score\": \"0.5263797\",\n \"text\": \"def checkout_latest():\\n import time\\n env.release = time.strftime('%Y%m%d%H%M%S')\\n run(\\\"cd %(path)s/repository; git pull origin master\\\" % env)\\n run('cp -R %(path)s/repository %(path)s/releases/%(release)s; rm -rf %(path)s/releases/%(release)s/.git*' % env)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cd9a43b02fd78a1512cc5446161c573\",\n \"score\": \"0.5239792\",\n \"text\": \"def test_github_targz_content(self):\\n with temporary_directory() as github_mock_dir:\\n archive_name = os.path.join(github_mock_dir, 'foo.tar.gz')\\n self.setup_targz(archive_name, 'piecutter', 'master')\\n with open(archive_name, 'r') as archive:\\n response_mock = mock.MagicMock()\\n response_mock.raw = archive\\n response_mock.status_code = 200\\n get_mock = mock.Mock(return_value=response_mock)\\n with mock.patch('piecutter.loaders.github.requests.get',\\n new=get_mock):\\n with temporary_directory() as output_dir:\\n loader = GithubLoader(output_dir)\\n content = loader.github_targz_content('fake-url')\\n self.assertTrue(archive is content)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd3493c1c69e1d2790a5ddddbb5c5608\",\n \"score\": \"0.52370584\",\n \"text\": \"def _download_raw_dataset(metadata: dict) -> None:\\n if os.path.exists(metadata['filename']):\\n return\\n\\n print('Downloading raw dataset...')\\n util.download_url(metadata['url'], metadata['filename'])\\n\\n print('Computing SHA-256...')\\n sha256 = util.compute_sha256(metadata['filename'])\\n if sha256 != metadata['sha256']:\\n raise ValueError('Downloaded data file SHA-256 does not match that listed in metadata document.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53a0cf135abf82b89e392a66b56a3f00\",\n \"score\": \"0.52323395\",\n \"text\": \"def fetch(self):\\n command = \\\"git fetch origin\\\"\\n\\n logger_server.info(\\\"Fetch data from github[CMD:{cmd}]...\\\".format(cmd=command))\\n\\n self.cwd(self.git_path)\\n\\n fetch_content = self._run_shell_command(command=command)\\n\\n if _DEBUG:\\n logger_server.debug(\\\"fetch_content:\\\" + fetch_content)\\n\\n return fetch_content\",\n \"title\": \"\"\n },\n {\n \"docid\": \"704e340cd323afb89a7cedb4f289974a\",\n \"score\": \"0.5231682\",\n \"text\": \"def test_remote_happy(zipprovider: Provider, url: URL) -> None:\\n repository = zipprovider.provide(url)\\n\\n assert repository is not None\\n\\n with repository.get() as package:\\n text = (package.tree / \\\"marker\\\").read_text()\\n assert \\\"Lorem\\\" == text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"277aae7b629b4b53b450c20ff6967f8e\",\n \"score\": \"0.52268255\",\n \"text\": \"def test_repo_edit_pull_request(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e422ad477937f42bbe6f6876f551370\",\n \"score\": \"0.52141625\",\n \"text\": \"def build_github_url(state, generated_filename):\\n tpl = \\\"https://raw.githubusercontent.com/openelections/openelections-data-{}/master/{}\\\"\\n return tpl.format(state.lower(), generated_filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"779707db3eee85bf155e7db02e28e722\",\n \"score\": \"0.52118015\",\n \"text\": \"def pull(self):\\n command = \\\"git pull --rebase origin \\\" + self.branch\\n\\n logger_server.info(\\\"Pull data from github[CMD:{cmd}]...\\\".format(cmd=command))\\n\\n self.cwd(self.git_path)\\n\\n pull_content = self._run_shell_command(command=command)\\n\\n if _DEBUG:\\n logger_server.debug(\\\"pull_content:\\\" + pull_content)\\n\\n return pull_content\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3b4bd7fbb11900f01b0567c24099a08\",\n \"score\": \"0.52113914\",\n \"text\": \"def download(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3b4bd7fbb11900f01b0567c24099a08\",\n \"score\": \"0.52113914\",\n \"text\": \"def download(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3b4bd7fbb11900f01b0567c24099a08\",\n \"score\": \"0.52113914\",\n \"text\": \"def download(self):\\n pass\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993272,"cells":{"query_id":{"kind":"string","value":"b20c42a9fbbc7c21fa416b7ee9fb2ad1"},"query":{"kind":"string","value":"List viewable contentish and folderish subobjects."},"positive_passages":{"kind":"list like","value":[{"docid":"0eba71dcef16c58921abf110ab3d2a89","score":"0.56333256","text":"def listFolderContents(self, contentFilter=None):\n fc_list = []\n for id, obj in self.contentItems(contentFilter):\n # validate() can either raise Unauthorized or return 0 to\n # mean unauthorized.\n try:\n if getSecurityManager().validate(self, self, id, obj):\n fc_list.append(obj)\n except zExceptions_Unauthorized: # Catch *all* Unauths!\n pass\n return fc_list","title":""}],"string":"[\n {\n \"docid\": \"0eba71dcef16c58921abf110ab3d2a89\",\n \"score\": \"0.56333256\",\n \"text\": \"def listFolderContents(self, contentFilter=None):\\n fc_list = []\\n for id, obj in self.contentItems(contentFilter):\\n # validate() can either raise Unauthorized or return 0 to\\n # mean unauthorized.\\n try:\\n if getSecurityManager().validate(self, self, id, obj):\\n fc_list.append(obj)\\n except zExceptions_Unauthorized: # Catch *all* Unauths!\\n pass\\n return fc_list\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"e8c114717b6787b3dbe00695cf0581b5","score":"0.6454088","text":"def children(self, obj):\n return obj.subfolders()","title":""},{"docid":"04aee46d33afd7c0f8c16422f831928d","score":"0.6329941","text":"def get_objs(request, my_item_container, allow_copy):\n main_obj = {}\n main_obj['item_container_id'] = my_item_container.id\n main_obj['name'] = my_item_container.item.name\n main_obj['url'] = get_site_url(my_item_container, 'index.html')\n main_obj['app_name'] = my_item_container.item.app.name\n main_obj['is_sortable'] = True\n main_obj['top_url'] = get_top_url(my_item_container)\n main_obj['paste_obj'] = get_paste_obj(request, my_item_container)\n main_obj['paste_app_name'] = get_paste_app_name(request)\n main_obj['title'] = my_item_container.item.title\n main_obj['last_modified'] = my_item_container.last_modified.\\\n strftime('%d.%m.%Y %H:%M')\n if request.GET.has_key('sort') :\n order = request.GET['sort']\n else :\n order = ''\n user_perms = UserEditPerms(request.user.username, request.path)\n if user_perms.perm_manage or user_perms.perm_manage_folderish:\n item_containers = get_folder_items(my_item_container, order)\n elif user_perms.perm_edit_own or user_perms.perm_manage_own:\n try:\n item_containers = get_folder_own_items(my_item_container.item, order, request.user.id)\n except:\n item_containers = []\n else:\n item_containers = []\n objs = []\n objs_linkable = []\n my_role = get_role_by_user_path(request.user, my_item_container.container.path)\n for item_container in item_containers:\n obj = get_single_obj(item_container, allow_copy, my_role)\n objs.append(obj)\n if item_container.item.app.is_linkable:\n objs_linkable.append(obj)\n return main_obj, objs, objs_linkable","title":""},{"docid":"ad849a938ed5f040350b85c91d5ee1a3","score":"0.6096485","text":"def subviews(self):\n return {}","title":""},{"docid":"cc0e988f27e6941ae612c0d33a1432e8","score":"0.6058188","text":"def contents(self, full_objects=True):\n cfilter = {'portal_type': ['Chapter', 'Glossary']}\n return self.context.getFolderContents(contentFilter = cfilter,\n full_objects = full_objects)","title":""},{"docid":"6fb9b3f3a648379178c49f54723dde4a","score":"0.59987134","text":"def get_all_objs(content, vimtype, folder=None, recurse=True):\n if not folder:\n folder = content.rootFolder\n\n obj = {}\n container = content.viewManager.CreateContainerView(folder, vimtype, recurse)\n for managed_object_ref in container.view:\n obj.update({managed_object_ref: managed_object_ref.name})\n return obj","title":""},{"docid":"d2a71876a34be52de2567b38b699e36d","score":"0.59455013","text":"def items(self, obj):\n return (\n obj.status_set.select_subclasses()\n .filter(\n privacy__in=[\"public\", \"unlisted\"],\n )\n .order_by(\"-published_date\")[:10]\n )","title":""},{"docid":"c98e4a6769e550927a70271a71aaa5e2","score":"0.5925703","text":"def list_used_views(self, specific_view=None):\n if not check_role(self):\n return \"You must have a manager role to run this script\"\n\n from Products.CMFCore.utils import getToolByName\n\n portal = getToolByName(self, \"portal_url\").getPortalObject()\n catalog = portal.portal_catalog\n types = portal.portal_types.objectIds()\n views = {}\n\n for t in types:\n brains = catalog(portal_type=t)\n for brain in brains:\n ob = brain.getObject()\n layout = ob.getLayout()\n if t not in views:\n views[t] = {}\n if not specific_view:\n if layout not in views[t]:\n views[t][layout] = 0\n views[t][layout] += 1\n elif specific_view and specific_view == layout:\n views[t][\"/\".join(ob.getPhysicalPath())] = ob.absolute_url()\n if not specific_view:\n out = [\"Used views on objects\\n---------------------\\n\"]\n for typ in views.keys():\n out.append(\n \"%s : %s\"\n % (\n typ,\n \", \".join(\n [\n \"%s (%s)\" % (lay, views[typ][lay])\n for lay in sorted(views[typ])\n ]\n ),\n )\n )\n else:\n out = [\n \"<html><b><u>Objects using the '%s' layout</u></b><br /><br />\"\n % specific_view\n ]\n for typ in views.keys():\n for path in sorted(views[typ]):\n out.append(\"%s : <a href='%s'>%s</a>\" % (typ, views[typ][path], path))\n out.append(\"<br />\")\n out.append(\"</html>\")\n return \"\\n\".join(out)","title":""},{"docid":"77d99bcb3cb45f997162b599fe1378d3","score":"0.58703405","text":"def getObjects(self) -> List[object]:\n ...","title":""},{"docid":"767819c553ed0cd44a8f346054bc4c3b","score":"0.58263755","text":"def list(self, request, *args, **kwargs):\n\n folder_id = self.kwargs['folderid']\n\n try:\n self.folder_obj = SystemFolder.objects.get(id=folder_id)\n except:\n return Response(dict(error=[\"invalid_folder_id\"], data=dict()),\n status=status.HTTP_400_BAD_REQUEST)\n\n response = generics.ListAPIView.list(\n self, request, *args, **kwargs).data\n # variable to retrieve pagination number\n minion_paginator = self.paginate_queryset(self.object_list)\n # set next and previous page numbers\n try:\n response.update({\"next_page_number\": minion_paginator.next_page_number()})\n except:\n response.update({\"next_page_number\": None})\n try:\n response.update({\"previous_page_number\": minion_paginator.previous_page_number()})\n except:\n response.update({\"previous_page_number\": None})\n try:\n response.update({\"total_page_number\": minion_paginator.paginator._get_num_pages()})\n except:\n response.update({\"total_page_number\": None})\n return Response(dict(data=response, error=[]), status=status.HTTP_200_OK)","title":""},{"docid":"2ca96b977a6965047979a11c1bdd125e","score":"0.5819711","text":"def restricted_object_list(*args, **kwargs):\r\n return list_detail.object_list(*args, **kwargs)","title":""},{"docid":"5c8cddd3f295e3f5ab6d7857f21e7b87","score":"0.57945967","text":"def objects(self):\r\n return self.queryset.all()","title":""},{"docid":"369d93fdb167dfd9f9a9ad9137464869","score":"0.5746928","text":"def list_folder(self, folder_path):\n pass","title":""},{"docid":"b744d6a00ce09531523dadabc54cf165","score":"0.574668","text":"def get_children(self, request=None, *args, **kwargs):\n return lfc.utils.get_content_objects(request, parent=self, **kwargs)","title":""},{"docid":"e684d60d9bad345e21145db568f20065","score":"0.5741314","text":"def objects(doc_cls, queryset):\n return queryset.filter(dashboardType=TestrunListDashboardTypeName)","title":""},{"docid":"8ba1c3ff85484e55dc4ba1f529c96529","score":"0.5716186","text":"def listResources(obj):","title":""},{"docid":"84aade1ab75dc4b3deebacb11981574c","score":"0.568571","text":"def get_children(self, request=None, *args, **kwargs):\n return lfc.utils.get_content_objects(request, parent=None, **kwargs)","title":""},{"docid":"049527c91cb895a957db1861633c2572","score":"0.56526285","text":"def getViews( self, onlyStatic = True ):\n viewsList = self.__createCatalog().getViews( onlyStatic )\n return S_OK( viewsList )","title":""},{"docid":"a88929f6c8f4398f8229dabea6578b51","score":"0.56384104","text":"def list(self, path=None, recursive=False, detailed=False, acl=False):\n\n if path is None:\n path = self.get_user_home()\n\n if self.is_dataobject(path):\n raise IrodsException(\"Cannot list an object, get it instead\")\n try:\n data = {}\n root = self.rpc.collections.get(path)\n\n for coll in root.subcollections:\n\n row = {}\n key = coll.name\n row[\"PID\"] = None\n row[\"name\"] = coll.name\n row[\"objects\"] = {}\n if recursive:\n row[\"objects\"] = self.list(\n coll.path, recursive, detailed, acl)\n row[\"path\"] = coll.path\n row[\"object_type\"] = \"collection\"\n if detailed:\n row[\"owner\"] = \"-\"\n if acl:\n acl = self.get_permissions(coll)\n row[\"acl\"] = acl[\"ACL\"]\n row[\"acl_inheritance\"] = acl[\"inheritance\"]\n\n data[key] = row\n\n for obj in root.data_objects:\n\n row = {}\n key = obj.name\n row[\"name\"] = obj.name\n row[\"path\"] = obj.path\n row[\"object_type\"] = \"dataobject\"\n row[\"PID\"] = None\n row[\"checksum\"] = None\n\n if detailed:\n row[\"owner\"] = obj.owner_name\n row[\"content_length\"] = obj.size\n row[\"created\"] = obj.create_time\n row[\"last_modified\"] = obj.modify_time\n if acl:\n acl = self.get_permissions(obj)\n row[\"acl\"] = acl[\"ACL\"]\n row[\"acl_inheritance\"] = acl[\"inheritance\"]\n\n data[key] = row\n\n return data\n except iexceptions.CollectionDoesNotExist as e:\n raise IrodsException(\"Collection not found: %s\" % path)\n\n # replicas = []\n # for line in lines:\n # replicas.append(re.split(\"\\s+\", line.strip()))\n # return replicas","title":""},{"docid":"5b4711f57cc6bf79a126e28f910cdae4","score":"0.56184864","text":"def findCacheables(\n ob,\n manager_id,\n require_assoc,\n subfolders,\n meta_types,\n rval,\n path\n):\n try:\n if meta_types:\n subobs = ob.objectValues(meta_types)\n else:\n subobs = ob.objectValues()\n sm = getSecurityManager()\n\n # Add to the list of cacheable objects.\n for subob in subobs:\n if not isCacheable(subob):\n continue\n associated = (subob.ZCacheable_getManagerId() == manager_id)\n if require_assoc and not associated:\n continue\n if not sm.checkPermission('Change cache settings', subob):\n continue\n subpath = path + (subob.getId(),)\n info = {\n 'sortkey': subpath,\n 'path': '/'.join(subpath),\n 'title': getattr(aq_base(subob), 'title', ''),\n 'icon': None,\n 'associated': associated,\n }\n rval.append(info)\n\n # Visit subfolders.\n if subfolders:\n if meta_types:\n subobs = ob.objectValues()\n for subob in subobs:\n subpath = path + (subob.getId(),)\n if hasattr(aq_base(subob), 'objectValues'):\n if sm.checkPermission(\n 'Access contents information', subob):\n findCacheables(\n subob, manager_id, require_assoc,\n subfolders, meta_types, rval, subpath)\n except Exception:\n # Ignore exceptions.\n import traceback\n traceback.print_exc()","title":""},{"docid":"3bc939fd4d607b938e4601162e124b29","score":"0.55942416","text":"def items(self, obj):\n return Version.objects.filter(project=obj, approved=True).order_by(\n '-datetime_created')[:5]","title":""},{"docid":"39b435ba6f0a103a02ee03eb0fdd189b","score":"0.55933243","text":"def document_list_folders(request,destination,location_id):\n # Get the document information\n folder_results = folder.objects.filter(\n is_deleted=False,\n )\n folder_results = get_object_from_destination(folder_results,destination,location_id)\n\n return HttpResponse(serializers.serialize('json',folder_results),content_type='application/json')","title":""},{"docid":"e297fdb151a38cc08187f64faefa64e1","score":"0.5593304","text":"def detail_list(self, request, cgs):\n return self._list_view(self.detail, request, cgs)","title":""},{"docid":"ff1d928035ac37e39775cf4e3c26cd85","score":"0.5558759","text":"async def list(self, session=None):\n logger.info(str(\"list \" + self.obj.pathname()))\n return await self.obj_access.list(session)","title":""},{"docid":"5e8ae5f87b4e425b07268358c5677523","score":"0.555531","text":"def get_objects(self):\n\t\treturn self.__objects","title":""},{"docid":"ac699b8cf00e7507f3e1827d1066b819","score":"0.5547583","text":"def list(self, request):\n\n a_viewset = [\n 'Uses actions (list, create, retrieve, update, partial_update)',\n 'automiatically maps to urls using routers',\n 'provide more functionaltiy with less code'\n\n ]\n\n return Response({'message':'hello a view set', 'a_viewset' : a_viewset})","title":""},{"docid":"a2e03f6aa56f2ddbff1c0dc889fcca96","score":"0.55472165","text":"def listGroups(obj):","title":""},{"docid":"8c146b9a047f14c4d5007bef77ff1a17","score":"0.55468124","text":"def getSubfolderList(self, folderPath: unicode) -> List[unicode]:\n ...","title":""},{"docid":"c90c7e7e0fb74fe2fa656746f555722a","score":"0.5532205","text":"def view(self) -> list:\n if self.access_level.value > 2:\n raise InvalidPermission()\n if len(self.cache) == 0:\n self.update_cache()\n return self.cache\n\n # return self.movie_database.view()","title":""},{"docid":"a7b9e61cf8cce98417d7271691b3b294","score":"0.5532121","text":"def list(self, request):\n \n request_author = WhoYouUser.objects.get(user=request.auth.user)\n filteredRequests = ContentViewRequest.objects.filter(Q(content__owner=request_author) | Q(requester=request_author))\n\n serializer = ContentViewRequestSerializer(\n filteredRequests, many=True, context={'request': request}\n )\n return Response(serializer.data)","title":""},{"docid":"341e8f1bb576e16ea7e1b41ef1630932","score":"0.5526323","text":"def list_objects_access(f):\n functools.wraps(f)\n\n def wrap(request, *args, **kwargs):\n if not django.conf.settings.PUBLIC_OBJECT_LIST:\n trusted(request)\n return f(request, *args, **kwargs)\n\n wrap.__doc__ = f.__doc__\n wrap.__name__ = f.__name__\n return wrap","title":""},{"docid":"2d011d1b6c4e520a69fce1b325c3f3e9","score":"0.55258477","text":"def _folder_contents(\n self,\n start=None,\n end=None,\n reverse=False,\n sort_column_name=None,\n filter_text=None,\n ):\n folder = self.context\n request = self.request\n system_catalog = find_catalog(folder, 'system')\n objectmap = find_objectmap(folder)\n\n path = system_catalog['path']\n allowed = system_catalog['allowed']\n\n if start is None:\n start = 0\n\n if end is None:\n end = start + 40\n\n q = ( path.eq(folder, depth=1, include_origin=False) &\n allowed.allows(request, 'sdi.view') )\n\n if filter_text:\n filter_text_globs = filter(None, filter_text.split())\n if filter_text_globs:\n text = system_catalog['text']\n for filter_glob in filter_text_globs:\n if not filter_glob.endswith('*'):\n filter_glob = filter_glob + '*' # glob (prefix) search\n q = q & text.eq(filter_glob)\n\n resultset = q.execute()\n # NB: must take snapshot of folder_length *before* limiting the length\n # of the resultset via any search\n folder_length = len(resultset)\n\n columns = self._columns()\n \n sort_info = self._sort_info(\n columns,\n sort_column_name=sort_column_name,\n )\n\n sorter = sort_info['sorter']\n sort_column_name = sort_info['column_name']\n\n if sorter is not None:\n resultset = sorter(folder, resultset, reverse=reverse, limit=end)\n\n ids = resultset.ids\n\n can_manage = bool(has_permission('sdi.manage-contents', folder,request))\n\n custom_columns = request.registry.content.metadata(\n folder,\n 'columns',\n _marker\n )\n\n buttons = self._buttons()\n\n records = []\n\n for oid in itertools.islice(ids, start, end):\n resource = objectmap.object_for(oid)\n name = getattr(resource, '__name__', '')\n # XXX CM: computation of deletable here should probably attached to\n # the delete button as an ``enabled_for`` instead of being treated\n # specially.\n deletable = getattr(resource, '__sdi_deletable__', None)\n if deletable is not None:\n if callable(deletable):\n deletable = deletable(resource, request)\n if deletable is None:\n deletable = can_manage\n deletable = bool(deletable) # cast return/attr value to bool\n icon = request.registry.content.metadata(resource, 'icon')\n if callable(icon):\n icon = icon(resource, request)\n url = request.sdiapi.mgmt_path(resource, '@@manage_main')\n record = dict(\n id=name,\n # Use the unique name, as an id. (A unique row id is needed\n # for slickgrid. In addition, we will pass back this same id\n # from the client, when a row is selected for an operation.)\n deletable=deletable,\n name=name,\n )\n columns = default_sdi_columns(folder, resource, request)\n if custom_columns is None:\n columns = []\n elif custom_columns is not _marker:\n columns = custom_columns(folder, resource, request, columns)\n for column in columns:\n # XXX CM: adding arbitrary keys to the record based on\n # configuration input is a bad idea here because we can't\n # guarantee a column name won't override the \"reserved\" names\n # (name, deletable, id) added to the record above. Ree?\n cname = column['name']\n record[cname] = column['value']\n # XXX CM: we should document the fact that each column can have\n # its own URL/icon if we keep this; although we should probably\n # not send them if the formatter doesn't want them. Need a\n # better formatter abstraction here, I think;\n # maybe server-side? Opinions, Ree?\n record[cname+'_icon'] = column.get('icon', icon)\n record[cname+'_url'] = column.get('url', url)\n disable = []\n for button_group in buttons:\n for button in button_group['buttons']:\n if 'enabled_for' not in button:\n continue\n condition = button['enabled_for']\n if not callable(condition):\n continue\n if not condition(folder, resource, request):\n disable.append(button['id'])\n record['disable'] = disable\n records.append(record)\n\n return {\n 'length':folder_length,\n 'records':records,\n 'sort_column_name':sort_column_name,\n 'columns':columns,\n }","title":""},{"docid":"93d5d5ba4cd8cc6aeb88eeacbe6db30f","score":"0.5519873","text":"def items(self, obj):\n return Comment.objects.filter(\n user=obj,\n privacy__in=[\"public\", \"unlisted\"],\n ).order_by(\"-published_date\")[:10]","title":""},{"docid":"57753ea48266fe68fb0797c0d5e904eb","score":"0.5517392","text":"def test_can_list_only_my_folders(self):\n\n self.login(username='one@snow.com')\n response = self.client.get('/api/v1/folder', format='json')\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(2, len(response.data)) # root and trash.\n self.logout()","title":""},{"docid":"f40e3aa5387a764afebe6df6e3793a92","score":"0.5515681","text":"def test_api__get_workspace_content__ok_200__get_all_root_and_folder_content(self):\n params = {\"parent_ids\": \"0,3\", \"show_archived\": 1, \"show_deleted\": 1, \"show_active\": 1}\n self.testapp.authorization = (\"Basic\", (\"bob@fsf.local\", \"foobarbaz\"))\n res = self.testapp.get(\"/api/v2/workspaces/2/contents\", status=200, params=params).json_body\n # TODO - G.M - 30-05-2018 - Check this test\n assert len(res) == 7\n assert [\n content\n for content in res\n if content[\"label\"] == \"Desserts\"\n and content[\"content_type\"] == \"folder\"\n and content[\"parent_id\"] is None\n and content[\"content_id\"] == 3\n ]\n assert [\n content\n for content in res\n if content[\"label\"] == \"Fruits Desserts\"\n and content[\"content_type\"] == \"folder\"\n and content[\"parent_id\"] == 3\n ]","title":""},{"docid":"4a6508b0f8404c7a7ec3a8c81eb481e0","score":"0.5506615","text":"def list(self, request):\n\n a_viewset = [\n 'Uses actions similar to APIViews',\n 'Give more functionality',\n 'Automatically maps to URL routers',\n ]\n\n return Response({'message':'Hello!!! viewset', 'a_viewset':a_viewset})","title":""},{"docid":"d10ffea26375e12c3ea5315c056495ba","score":"0.5506409","text":"def _getChildren(self, obj, no_nodes=0, no_leaves=0, mode='tree'):\n if not obj:\n return []\n\n if mode == 'tree':\n # we are called by a getTree so we use portal tree\n # we assume we want nodes only\n if not obj['nb_children']:\n return []\n if obj.has_key('children'): # only fake root has children key\n return [x for x in self._cps_tree\n if x['rpath'] in obj['children']]\n children_prefix = obj['rpath'] + '/'\n children_depth = obj['depth'] + 1\n return [x for x in self._cps_tree\n if ( x['depth'] == children_depth and\n x['rpath'].startswith(children_prefix) )]\n\n # for the listing use the ZODB\n lobj = obj\n if type(obj) is DictType:\n lobj = self.context.unrestrictedTraverse(self._getUid(obj))\n return ZODBNavigation._getChildren(self, lobj, no_nodes, no_leaves)","title":""},{"docid":"26c37714ee41cc2dafb1c5ad06e6a81b","score":"0.55052644","text":"def obj_get_list(self, request=None, **kwargs):\n filters = {}\n\n if hasattr(request, 'GET'):\n # Grab a mutable copy.\n filters = request.GET.copy()\n\n # Update with the provided kwargs.\n filters.update(kwargs)\n try:\n user = UserProfile.objects.filter(\n uuid=uuid_from_uri(filters['user']))\n sub_pk = sorted(chain(user[0].subscriptions(), user))\n im = Image.objects.filter(is_active=True, owner__in=sub_pk)\n wb = WordBox.objects.filter(is_active=True, owner__in=sub_pk)\n # sort reverse chronologically\n base_object_list = sorted(chain(im, wb),\n key=attrgetter('created_time'))[::-1]\n return self.apply_authorization_limits(request, base_object_list)\n except ValueError:\n raise BadRequest('Invalid resource lookup data provided'\n '(mismatched type).')","title":""},{"docid":"0140126b5ffd9ce7fa5682ba9c45259d","score":"0.5497397","text":"def filter_view_perms(self, user, object=None):\n return self.exclude(status=self.model.REMOVED).filter_perms(user, 'can_view', object)","title":""},{"docid":"f4846baf976d6295ca074258c2c54fac","score":"0.5494614","text":"def _getfolders(self):\r\n result = self._session.execute(\"up -show folders \\\"%s\\\" -u -f \\\"%%objectname\\\"\" % self, ObjectListResult(self._session))\r\n return result.output","title":""},{"docid":"e58d6dec1f52361bd1c254098ac13227","score":"0.54907656","text":"def test_sublist(self):\n url = 'http://blog/sublist/'\n request = self.factory.get(url)\n force_authenticate(request, user=self.user, token=self.token)\n response = SubList.as_view()(request)\n assert response.status_code == 200","title":""},{"docid":"015bb390236e5625241495c83491de96","score":"0.54742694","text":"def items(self, obj):\n return Review.objects.filter(\n user=obj,\n privacy__in=[\"public\", \"unlisted\"],\n ).order_by(\"-published_date\")[:10]","title":""},{"docid":"ede3f61f14006be2befee7ff24277026","score":"0.54669315","text":"def find_readable_descendants(self, auth):\n new_branches = []\n for node in self.nodes.filter(is_deleted=False):\n if node.can_view(auth):\n yield node\n else:\n new_branches.append(node)\n\n for bnode in new_branches:\n for node in bnode.find_readable_descendants(auth):\n yield node","title":""},{"docid":"c806cc40cf0918312b2cfc6d9e99cd10","score":"0.5455731","text":"def get_objects(self):\n raise NotImplementedError()","title":""},{"docid":"a493e47b0a0ed57d61a909402f06301d","score":"0.5453869","text":"def GetViews(pattern='.*'):\n return GetSubpictures(None, pattern)","title":""},{"docid":"08b4182c0d444c253cf9978eb258f717","score":"0.54491377","text":"def queryset(self, request):\n queryset = super(EntryAdmin, self).queryset(request)\n if request.user.has_perm('zinnia.can_view_all'):\n return queryset\n return request.user.entry_set.all()","title":""},{"docid":"a399e3fc1172be2b5a74706d49c5b450","score":"0.54450107","text":"def list_folders(service_instance):\n return utils_common.list_objects(service_instance, vim.Folder)","title":""},{"docid":"b91626723377d45ec332531754c87c33","score":"0.54406047","text":"def topic_list(request):\r\n return object_list(request, queryset=Topic.objects.published(),\r\n template_object_name='topic')","title":""},{"docid":"20e58724af23640e2e99b3d5f1823cc0","score":"0.5439843","text":"def test_api__get_workspace_content__ok_200__get_multiple_folder_content(self):\n self.testapp.authorization = (\"Basic\", (\"admin@admin.admin\", \"admin@admin.admin\"))\n params = {\"parent_id\": 1, \"label\": \"GenericCreatedContent\", \"content_type\": \"html-document\"}\n res = self.testapp.post_json(\"/api/v2/workspaces/1/contents\", params=params, status=200)\n content_id = res.json_body[\"content_id\"]\n params = {\"parent_ids\": \"1,2\", \"show_archived\": 1, \"show_deleted\": 1, \"show_active\": 1}\n res = self.testapp.get(\"/api/v2/workspaces/1/contents\", status=200, params=params).json_body\n # TODO - G.M - 30-05-2018 - Check this test\n assert len(res) == 2\n assert [\n content\n for content in res\n if content[\"label\"] == \"Current Menu\"\n and content[\"content_type\"] == \"html-document\"\n and content[\"parent_id\"] == 2\n and content[\"content_id\"] == 11\n ]\n assert [\n content\n for content in res\n if content[\"label\"] == \"GenericCreatedContent\"\n and content[\"content_type\"] == \"html-document\"\n and content[\"parent_id\"] == 1\n and content[\"content_id\"] == content_id\n ]","title":""},{"docid":"a0a95f28ed5f6cc193a446c96486e58e","score":"0.5439046","text":"def add_folder_contents_views(\r\n config,\r\n cls=None,\r\n name='contents',\r\n context=None,\r\n renderer='substanced.folder:templates/contents.pt', # do not abbreviate\r\n view_permission='sdi.view',\r\n manage_contents_permission='sdi.manage-contents',\r\n tab_title=_(\"Contents\"),\r\n tab_condition=True,\r\n tab_before=None,\r\n tab_after=None,\r\n tab_near=None,\r\n **predicates\r\n ):\r\n\r\n if cls is None:\r\n cls = FolderContents\r\n\r\n if context is None:\r\n context = IFolder\r\n \r\n add_fc_view = functools.partial(\r\n config.add_mgmt_view,\r\n view=cls,\r\n name=name,\r\n renderer=renderer,\r\n context=context,\r\n tab_condition=False,\r\n **predicates\r\n )\r\n\r\n add_fc_view(\r\n request_method='GET',\r\n permission=view_permission,\r\n tab_condition=tab_condition,\r\n tab_before=tab_before,\r\n tab_after=tab_after,\r\n tab_near=tab_near,\r\n tab_title=tab_title,\r\n xhr=False,\r\n attr='show',\r\n )\r\n add_fc_view(\r\n request_method='GET',\r\n permission=view_permission,\r\n xhr=True,\r\n renderer='json',\r\n attr='show_json',\r\n )\r\n add_fc_view(\r\n request_method='POST',\r\n request_param='form.delete',\r\n permission=manage_contents_permission,\r\n check_csrf=True,\r\n attr='delete',\r\n )\r\n add_fc_view(\r\n request_method='POST',\r\n request_param='form.duplicate',\r\n permission=manage_contents_permission,\r\n check_csrf=True,\r\n attr='duplicate',\r\n )\r\n add_fc_view(\r\n request_method='POST',\r\n request_param='form.rename',\r\n permission=manage_contents_permission,\r\n renderer='templates/rename.pt',\r\n check_csrf=True,\r\n attr='rename',\r\n )\r\n add_fc_view(\r\n request_method='POST',\r\n request_param='form.rename_finish',\r\n permission=manage_contents_permission,\r\n check_csrf=True,\r\n attr='rename_finish',\r\n )\r\n add_fc_view(\r\n request_method='POST',\r\n request_param='form.copy',\r\n permission=view_permission,\r\n check_csrf=True,\r\n attr='copy',\r\n )\r\n add_fc_view(\r\n request_method='POST',\r\n request_param='form.copy_finish',\r\n permission=manage_contents_permission,\r\n check_csrf=True,\r\n attr='copy_finish',\r\n )\r\n add_fc_view(\r\n request_method='POST',\r\n request_param='form.copy_finish_cancel',\r\n permission=view_permission,\r\n check_csrf=True,\r\n attr='copy_finish_cancel',\r\n )\r\n add_fc_view(\r\n request_method='POST',\r\n request_param='form.move',\r\n permission=view_permission,\r\n check_csrf=True,\r\n attr='move',\r\n )\r\n add_fc_view(\r\n request_method='POST',\r\n request_param='form.move_finish',\r\n permission=manage_contents_permission,\r\n check_csrf=True,\r\n attr='move_finish',\r\n )\r\n add_fc_view(\r\n request_method='POST',\r\n request_param='form.move_finish_cancel',\r\n permission=view_permission,\r\n check_csrf=True,\r\n attr='move_finish_cancel',\r\n )\r\n add_fc_view(\r\n request_method='POST',\r\n renderer='json',\r\n request_param='ajax.reorder',\r\n permission=manage_contents_permission,\r\n check_csrf=True,\r\n attr='reorder_rows',\r\n )","title":""},{"docid":"70273db52d9388f98e68ff2ef97d530e","score":"0.5429694","text":"def get_objects(self,model,page=1,frmt=\"list\"):\n if frmt not in self.FORMATS:\n raise InvalidFormat(\"The requested format is invalid\")\n klass = self.__get_class__(model)\n admin_klass = self.__get_admin_class__(model)\n list_display = admin_klass.list_display['cols']\n rows = []\n for obj in klass.objects: \n row = []\n for col in list_display:\n if hasattr(obj,col):\n func = getattr(obj,col)\n if callable(func):\n val = func()\n else:\n val = func\n # Check if value is of boolean type if yes then display icon else value\n if type(val).__name__ == 'bool':\n if val:\n row.append('<img src=\"/static/base/images/icon_accept.png\" alt=\"Yes\" />')\n else:\n row.append('<img src=\"/static/base/images/icon_cross.png\" alt=\"No\" />')\n else:\n row.append(val)\n if row:\n #Append Actions\n actions = [\"<a href='/admin/%s/%s/edit/' title='Edit %s'> <img src='/static/base/images/icon_edit.png' alt='Edit'\\\n class='mid_align'> </a>\" % (model,obj.id,model) ]\n actions.append(\"<a href='/admin/%s/%s/delete/' title='Delete %s'> <img src='/static/base/images/icon_delete.png' alt='Delete'\\\n class='mid_align'> </a>\" % (model,obj.id,model)) \n if admin_klass.list_display.has_key('actions'):\n for action in admin_klass.list_display['actions']:\n func = getattr(obj,action)\n if callable(func):\n val = func()\n if val:\n actions.append(val)\n row.append(\" | \".join(actions))\n if model == \"category\":\n if obj.parent:\n rows.append(((obj.id,obj.parent.id),row))\n else:\n rows.append(((obj.id,'None'),row))\n else:\n rows.append(row)\n title = 'List '+klass.meta.get('verbose_name_plural',model.capitalize()+\"s\")\n context = {'cols':self.__get_list_headers__(model),'objs':rows,'title':title,'model':model}\n context.update(self.__get_admin_includes__(model))\n return context","title":""},{"docid":"f0e1f036a8672887b406c5d5292f29ca","score":"0.5421653","text":"def objects(self) -> list:\r\n return self._objects","title":""},{"docid":"22069495f552da1b3a2633ff5bd453e7","score":"0.54177696","text":"def index_queryset(self, using=None):\n return self.get_model().objects.filter(Q(raccess__discoverable=True) |\n Q(raccess__public=True))","title":""},{"docid":"0ab8c4437267c411bc74c38ef1cab51a","score":"0.538767","text":"def detail_list(self, request, backupjobruns):\n return self._list_view(self.detail, request, backupjobruns)","title":""},{"docid":"0322170be6e30eb95ffe0751a2d4ef4c","score":"0.53702104","text":"def index():\n litems = []\n allitems = []\n viewmode = int(plugin.get_setting('viewmode'))\n if viewmode is None: viewmode = 500\n plugin.set_view_mode(viewmode)\n DOSTR8 = plugin.get_setting(key='dostr8')\n if not (DOSTR8 == True or DOSTR8 == 'true'): DOSTR8 = False\n else: DOSTR8 = True\n for sitename in getAPIURLS().keys():\n if sitename.find('_search') == -1:\n sicon = __imgsearch__.replace('search.', 'f{0}.'.format(sitename))\n spath = plugin.url_for(site, sitename=sitename, section='index', url='0')\n sitem = {'label': sitename.title(), 'icon': sicon, 'thumbnail': sicon, 'path': spath}\n sitem.setdefault(sitem.keys()[0])\n litems.append(sitem)\n allitems = sorted(litems, key=lambda litems: litems['label'])\n ifolder = __imgsearch__.replace('search.', 'folder.')\n itemallcats = {'label': 'Global Category List', 'path': plugin.url_for(allcats), 'icon': ifolder,\n 'thumbnail': ifolder}\n itemsearch = {'label': 'Search All Sites', 'path': plugin.url_for(search), 'icon': __imgsearch__,\n 'thumbnail': __imgsearch__}\n itemstream = {'label': 'Play Web URL', 'path': plugin.url_for(resolver), 'icon': 'DefaultFolder.png',\n 'thumbnail': 'DefaultFolder.png'}\n timg = __imgsearch__.replace('search.', 'ftumblr.')\n itemtumblr = {'label': 'Tumblr', 'icon': timg, 'thumbnail': timg, 'path': plugin.url_for(tumblrhome)}\n itemtumblr.setdefault(itemtumblr.keys()[0])\n itemallcats.setdefault(itemallcats.keys()[0])\n itemstream.setdefault(itemstream.keys()[0])\n itemsearch.setdefault(itemsearch.keys()[0])\n allitems.append(itemtumblr)\n allitems.append(itemallcats)\n allitems.append(itemstream)\n allitems.append(itemsearch)\n return finish(allitems)","title":""},{"docid":"b51756710402180e485008cca5b1766e","score":"0.5364568","text":"def list(self):\n results = self.model.objects.list()\n self.render_list(results)","title":""},{"docid":"e3d3fd29af556dfe6f06ee3051aee5b8","score":"0.5360611","text":"def get_queryset(self, request):\n if not request.user.has_perm('mp_blog.can_view_all'):\n queryset = self.model.objects.filter(\n authors__pk=request.user.pk,\n status=DRAFT\n )\n else:\n queryset = super(EntryAdmin, self).get_queryset(request)\n return queryset.prefetch_related('industries', 'types', 'topics',\n 'authors', 'sites')","title":""},{"docid":"a3956df2e523daf6f35024780cbc4d09","score":"0.53576565","text":"def hub_list(request):\n page_no = pagination.get_page_no(request.GET)\n object_list = Hub.active.all()\n page = pagination.get_page(object_list, page_no)\n featured_list = Hub.active.filter(is_featured=True)[:3]\n context = {\n 'page': page,\n 'featured_list': featured_list,\n }\n return TemplateResponse(request, 'hubs/object_list.html', context)","title":""},{"docid":"50d74847ddb2f5cc95a121e2a71f23fa","score":"0.5356083","text":"def list_models(self):","title":""},{"docid":"fece569771aafaa214b7badf3a4477d2","score":"0.53530556","text":"def __call__(self):\n\n # How many items is one one page\n limit = 30\n\n # Read the first index of the selected batch parameter as \n # HTTP GET request query parameter\n start = self.request.get(\"b_start\", 0)\n\n contentFilter = {}\n #contentFilter['portal_type'] = 'Folder'\n contentFilter['sort_on'] = 'getObjPositionInParent'\n contentFilter['sort_order'] = 'ascending'\n \n cur_path = '/'.join(self.context.getPhysicalPath())\n path = {}\n\n path['query'] = cur_path\n path['depth'] = 1\n #contentFilter['path'] = path\n\n self.contents = self.context.queryCatalog(contentFilter, show_all=1, show_inactive=False)\n\n self.contents = [b.getObject() for b in self.contents]\n\n batch = True\n if batch:\n from Products.CMFPlone import Batch\n b_start = self.context.REQUEST.get('b_start', 0)\n self.contents = Batch(self.contents, limit, int(b_start), orphan=0)\n\n # Return the rendered template with content listing info filled in\n return self.index()","title":""},{"docid":"e66b12773199a77dfa8c7d3938683272","score":"0.5352557","text":"def test_api__get_workspace_content__ok_200__get_only_archived_folder_content(self):\n params = {\"parent_ids\": 10, \"show_archived\": 1, \"show_deleted\": 0, \"show_active\": 0}\n self.testapp.authorization = (\"Basic\", (\"admin@admin.admin\", \"admin@admin.admin\"))\n res = self.testapp.get(\"/api/v2/workspaces/2/contents\", status=200, params=params).json_body\n assert len(res) == 1\n content = res[0]\n assert content[\"content_type\"] == \"html-document\"\n assert content[\"content_id\"] == 13\n assert content[\"is_archived\"] is True\n assert content[\"is_deleted\"] is False\n assert content[\"label\"].startswith(\"Fruit Salad\")\n assert content[\"parent_id\"] == 10\n assert content[\"show_in_ui\"] is True\n assert content[\"slug\"].startswith(\"fruit-salad\")\n assert content[\"status\"] == \"open\"\n assert set(content[\"sub_content_types\"]) == {\"comment\"}\n assert content[\"workspace_id\"] == 2\n assert content[\"modified\"]\n assert content[\"created\"]","title":""},{"docid":"5e04cfe14da2fc51105e5903424b6da3","score":"0.5348291","text":"def aggregate_views(scope):\n views = []\n if scope in ('file', 'f'):\n v = sublime.active_window().active_view().file_name()\n if not ignore_path(v):\n views.append(v)\n elif scope in ('open', 'o'):\n for v in sublime.active_window().views():\n name = v.file_name()\n if not ignore_path(name):\n views.append(name)\n else:\n for f in sublime.active_window().folders():\n for path, subdirs, files in os.walk(f):\n for name in files:\n p = os.path.join(path, name)\n if not ignore_path(p):\n views.append(p)\n return views","title":""},{"docid":"40e452937811971a15e58b4277b99153","score":"0.5341478","text":"def list_content(self) -> list:\n return self.zf.infolist()","title":""},{"docid":"4f9ae8e5bcafa25a44e3032b3228d6fb","score":"0.53361034","text":"def list_things(self):","title":""},{"docid":"e1c9237a866da999b42ef3fd6491c840","score":"0.533489","text":"def list_views(self, cls):\n if isinstance(cls, str):\n return [view for view in self._views if view.__class__.__name__ == cls]\n return [view for view in self._views if view.__class__ == cls]","title":""},{"docid":"b6676da487712c638ac043b1bf09a238","score":"0.53327924","text":"def objects():","title":""},{"docid":"4d15b7821593b2609c6ffcad8d352f66","score":"0.532594","text":"def __list__(self):\n raise NotImplementedError","title":""},{"docid":"eb2ffcd09031492abc384731dbedb852","score":"0.5325614","text":"def get_members(obj, ccm, parent_proj):\n if obj.get_type() == 'dir':\n objects = ccm.query(\"is_child_of('{0}', '{1}')\".format(\n obj.get_object_name(), parent_proj)).format('%objectname').run()\n else:\n # For projects only get the directory of the project\n objects = ccm.query(\n \"is_member_of('{0}') and type='dir' and name='{1}'\".format(\n obj.get_object_name(), obj.get_name())).format(\n '%objectname').run()\n return objects","title":""},{"docid":"a83bead2646da8dd92f0a948b0259a98","score":"0.5312527","text":"def get_objects(self):\n return []","title":""},{"docid":"6039cfa14625e2b3c654fe529e7b55f7","score":"0.5309652","text":"def test_api__get_workspace_content__ok_200__get_all_folder_content(self):\n params = {\n \"parent_ids\": 10, # TODO - G.M - 30-05-2018 - Find a real id\n \"show_archived\": 1,\n \"show_deleted\": 1,\n \"show_active\": 1,\n # 'content_type': 'any'\n }\n self.testapp.authorization = (\"Basic\", (\"admin@admin.admin\", \"admin@admin.admin\"))\n res = self.testapp.get(\"/api/v2/workspaces/2/contents\", status=200, params=params).json_body\n assert len(res) == 3\n content = res[0]\n assert content[\"content_type\"] == \"html-document\"\n assert content[\"content_id\"] == 14\n assert content[\"is_archived\"] is False\n assert content[\"is_deleted\"] is True\n assert content[\"label\"].startswith(\"Bad Fruit Salad\")\n assert content[\"parent_id\"] == 10\n assert content[\"show_in_ui\"] is True\n assert content[\"slug\"].startswith(\"bad-fruit-salad\")\n assert content[\"status\"] == \"open\"\n assert set(content[\"sub_content_types\"]) == {\"comment\"}\n assert content[\"workspace_id\"] == 2\n assert content[\"modified\"]\n assert content[\"created\"]\n content = res[1]\n assert content[\"content_type\"] == \"html-document\"\n assert content[\"content_id\"] == 13\n assert content[\"is_archived\"] is True\n assert content[\"is_deleted\"] is False\n assert content[\"label\"].startswith(\"Fruit Salad\")\n assert content[\"parent_id\"] == 10\n assert content[\"show_in_ui\"] is True\n assert content[\"slug\"].startswith(\"fruit-salad\")\n assert content[\"status\"] == \"open\"\n assert set(content[\"sub_content_types\"]) == {\"comment\"}\n assert content[\"workspace_id\"] == 2\n assert content[\"modified\"]\n assert content[\"created\"]\n content = res[2]\n assert content[\"content_type\"] == \"html-document\"\n assert content[\"content_id\"] == 12\n assert content[\"is_archived\"] is False\n assert content[\"is_deleted\"] is False\n assert content[\"label\"] == \"New Fruit Salad\"\n assert content[\"parent_id\"] == 10\n assert content[\"show_in_ui\"] is True\n assert content[\"slug\"] == \"new-fruit-salad\"\n assert content[\"status\"] == \"open\"\n assert set(content[\"sub_content_types\"]) == {\"comment\"}\n assert content[\"workspace_id\"] == 2\n assert content[\"modified\"]\n assert content[\"created\"]","title":""},{"docid":"c5b633166994af01248188bdc1d1e6e9","score":"0.5307412","text":"def list_objects(self, id, query=None):\n return self.make_request('list_objects', '/' + id + '/objects', schema=SpeckleObject, params=query)","title":""},{"docid":"243b2316502b7ce0f571bcbb2355a17e","score":"0.5306078","text":"def restricted_detail(*args, **kwargs):\r\n return list_detail.object_detail(*args, **kwargs)","title":""},{"docid":"23190fa09eb04ca72f967e6e2dcc5e6b","score":"0.53019345","text":"def visible_objects_and_duplis():\n\n\t\tfor obj in context.visible_objects:\n\t\t\tif obj.type == 'MESH':\n\t\t\t\tyield (obj, obj.matrix_world.copy())\n\n\t\t\t# if obj.dupli_type != 'NONE':\n\t\t\t# \tobj.dupli_list_create(scene)\n\t\t\t# \tfor dob in obj.dupli_list:\n\t\t\t# \t\tobj_dupli = dob.object\n\t\t\t# \t\tif obj_dupli.type == 'MESH':\n\t\t\t# \t\t\tyield (obj_dupli, dob.matrix.copy())\n\n\t\t\t# obj.dupli_list_clear()","title":""},{"docid":"cc7b330d4f1c31dcfd11a1e83a2bd6a4","score":"0.5298358","text":"def test_rest_list_content(self):\n with self.layer.get_browser() as browser:\n browser.login('author')\n # Working content\n self.assertEqual(\n browser.open(\n '/root/++rest++silva.core.references.items',\n query={'intid': str(get_content_id(self.root.index))}),\n 200)\n self.assertEqual(browser.content_type, 'application/json')\n self.assertIsInstance(browser.json, dict)\n self.assertDictContainsSubset(\n {'id': 'index', 'type': 'Mockup VersionedContent',\n 'path': 'index', 'title': 'Index'},\n browser.json)\n # Broken content\n self.assertEqual(\n browser.open(\n '/root/++rest++silva.core.references.items',\n query={'intid': '42'}),\n 200)\n self.assertEqual(browser.content_type, 'application/json')\n self.assertIsInstance(browser.json, dict)\n self.assertDictContainsSubset(\n {'id': 'broken', 'type': 'Broken',\n 'path': '', 'title': 'Missing content'},\n browser.json)","title":""},{"docid":"c29d0780ad31f6a2d086a5fc9d7759dc","score":"0.52946854","text":"def index_queryset(self, using=None):\n translation.activate(settings.LANGUAGE_CODE)\n return self.get_model().objects.filter(status=UrlNode.PUBLISHED).select_related()","title":""},{"docid":"022ae3dd9ca209a50532fc9127d97880","score":"0.5276151","text":"def obj_list(self):\n return self.__obj_list","title":""},{"docid":"eb87692748ef7a5a60b94523b077dab7","score":"0.52720463","text":"def explorer(path=u''):\n if current_user.is_anonymous and cmp(path[:6], u'public'):\n abort(403)\n path = path.rstrip(u'/')\n abs_path = os.path.join(current_app.data_path, u'files', path).rstrip(u'/')\n if os.path.isdir(abs_path):\n folders = {}\n objects = {}\n parent_url = (url_for(u'.explorer', path=path).rsplit(u'/', 1)[0] + u'/') \\\n if (path != (u'' if current_user.is_authenticated else u'public')) else None\n nav_tabs = _generate_urls(path)\n for name in os.listdir(abs_path):\n path_name = os.path.join(abs_path, name)\n if os.path.isdir(path_name):\n folders[name] = {\n u'link': url_for(u'.explorer', path=os.path.join(path, name)+u'/'),\n u'last_modify': time.strftime(u'%Y-%m-%d %H:%M:%S', time.localtime(os.path.getmtime(path_name))),\n }\n elif os.path.isfile(path_name):\n type_c = name.rsplit(u'.', 1)\n size = float(os.path.getsize(path_name))\n size_l = 0\n while size > 1000.0 and size_l < 4:\n size /= 1000.0\n size_l += 1\n objects[name] = {\n u'link': url_for(u'.explorer', path=os.path.join(path, name)),\n u'type': type_c[1] if len(type_c) == 2 else None,\n u'size': u'%.3f %s' % (size, [u'B', u'KB', u'MB', u'GB', u'TB'][size_l]) if size_l else u'%.0f B' % size,\n u'last_modify': time.strftime(u'%Y-%m-%d %H:%M:%S', time.localtime(os.path.getmtime(abs_path))),\n }\n # folders = sorted(folders.iteritems(), key=lambda k: k, reverse=False)\n # objects = sorted(objects.iteritems(), key=lambda k: k, reverse=False)\n #######################################################################\n # path : 相对于文件夹根目录的路径（根目录为空字符串）\n # nav_tabs : 当前路径对应的每一级路径名级链接\n # back_link : 上一级目录的链接（根目录的上一级为None）\n # folders : 文件夹列表\n # files : 文件列表\n #######################################################################\n return render_template(u'files/files_index.html', folders=folders, files=objects,\n path=path, nav_tabs=nav_tabs, back_link=parent_url)\n elif os.path.isfile(abs_path):\n return send_file(abs_path)\n else:\n abort(404)","title":""},{"docid":"ded46173a04e7d854a178173fbf79cc3","score":"0.5250477","text":"def list(self, request, *args, **kwargs):\n return super().list(request, *args, **kwargs)","title":""},{"docid":"ded46173a04e7d854a178173fbf79cc3","score":"0.5250477","text":"def list(self, request, *args, **kwargs):\n return super().list(request, *args, **kwargs)","title":""},{"docid":"ded46173a04e7d854a178173fbf79cc3","score":"0.5250477","text":"def list(self, request, *args, **kwargs):\n return super().list(request, *args, **kwargs)","title":""},{"docid":"3a3bbf45c2227181491f836e6b3cdc11","score":"0.52481556","text":"def list(self, request, *args, **kwargs):\n\n data = self.get_tree_structure_json()\n\n return Response(dict(results=data, error=[]), status=status.HTTP_200_OK)","title":""},{"docid":"3a3bbf45c2227181491f836e6b3cdc11","score":"0.52481556","text":"def list(self, request, *args, **kwargs):\n\n data = self.get_tree_structure_json()\n\n return Response(dict(results=data, error=[]), status=status.HTTP_200_OK)","title":""},{"docid":"9a7f75ba83f51efb6b21d45bcc87fde5","score":"0.5244862","text":"def test_rest_list(self):\n with self.layer.get_browser() as browser:\n browser.login('author')\n # Root\n self.assertEqual(\n browser.open(\n '/root/++rest++silva.core.references.items'),\n 200)\n self.assertEqual(browser.content_type, 'application/json')\n self.assertIsInstance(browser.json, list)\n self.assertEqual(len(browser.json), 2)\n self.assertDictContainsSubset(\n {'id': '.', 'type': 'Silva Root', 'path': '.'},\n browser.json[0])\n self.assertDictContainsSubset(\n {'id': 'folder', 'type': 'Silva Folder',\n 'path': 'folder', 'title': 'Folder'},\n browser.json[1])\n # Folder\n self.assertEqual(\n browser.open(\n '/root/folder/++rest++silva.core.references.items'),\n 200)\n self.assertEqual(browser.content_type, 'application/json')\n self.assertIsInstance(browser.json, list)\n self.assertEqual(len(browser.json), 1)\n self.assertDictContainsSubset(\n {'id': '.', 'type': 'Silva Folder',\n 'path': 'folder', 'title': 'Folder'},\n browser.json[0])\n # Object\n self.assertEqual(\n browser.open(\n '/root/index/++rest++silva.core.references.items'),\n 200)\n self.assertEqual(browser.content_type, 'application/json')\n self.assertIsInstance(browser.json, list)\n self.assertEqual(len(browser.json), 1)\n self.assertDictContainsSubset(\n {'id': '.', 'type': 'Mockup VersionedContent',\n 'path': 'index', 'title': 'Index'},\n browser.json[0])","title":""},{"docid":"4de7b1a34f0d812f1f7a9c3297d9fb55","score":"0.52443033","text":"def list_files(self):","title":""},{"docid":"b1829b68556e8ccdca0cfa7ead12d92b","score":"0.52420264","text":"def listdir(self):\n raise NotImplementedError","title":""},{"docid":"a085b2e62de5d75fa26a8ed5431bd4c5","score":"0.52271235","text":"def index_queryset(self, using=None):\n return self.get_model().objects.filter(published=True, private=False)","title":""},{"docid":"9aa4c0286ffe2112f5f74f84931c455f","score":"0.52258164","text":"def get_queryset(self):\n current_user = self.request.user\n queryset = File.objects.filter(owner=current_user)\n sub_file = self.request.query_params.get('sub_file', None)\n if sub_file is not None:\n queryset = queryset.filter(pk=sub_file)\n node = None\n if len(queryset):\n node = queryset[0]\n return node.get_sub_file()\n else:\n return node\n return queryset","title":""},{"docid":"db71564bd975a9972016f61f6dd4b62e","score":"0.52244407","text":"def get_resources(request):\n try:\n return HttpResponse(json.dumps(walkalldirs(request)), content_type='application/json')\n except Exception as e: # pragma: no cover\n return HttpResponseNotFound(str(e))","title":""},{"docid":"bbe06ff480fefdbbc41a91fd2e4d57a6","score":"0.52238226","text":"def _getChildBlurbs(self, request):\n return list(self.original.view(self.getRole()))","title":""},{"docid":"1da33380645810322246b367fb82fa7a","score":"0.5221357","text":"def get_queryset(self, request):\n url_subquery = PageUrl.objects.filter(\n language=OuterRef(\"language\"), page=OuterRef(\"page\")\n )\n # Collect locked status to handle the requirement that lock\n # on a draft version dictates the unpublish permission\n # on a published version\n draft_version_lock_subquery = VersionLock.objects.filter(\n version__content_type=OuterRef(\"content_type\"),\n version__object_id=OuterRef(\"object_id\"),\n version__state=DRAFT,\n ).order_by(\"-pk\")\n queryset = (\n super()\n .get_queryset(request)\n .filter(page__node__site=get_current_site(request))\n .annotate(_path=Subquery(url_subquery.values(\"path\")[:1]))\n )\n return queryset.select_related(\"page\").prefetch_related(\n Prefetch(\n \"versions\",\n queryset=Version.objects.annotate(\n # used by locking\n _draft_version_user_id=Subquery(\n draft_version_lock_subquery.values(\"created_by\")[:1]\n )\n )\n .select_related(\"created_by\", \"versionlock\")\n .prefetch_related(\"content\"),\n )\n )","title":""},{"docid":"4028d9bd83160692d56fcff0c87e2657","score":"0.52147985","text":"def _recursive_list(api, parent, base_folder=\"\"):\n out = []\n for f in api.files.query(parent=parent):\n if f.type == \"folder\":\n out += _recursive_list(api, f.id, os.path.join(base_folder, f.name))\n else:\n out.append((os.path.join(base_folder, f.name), f))\n return out","title":""},{"docid":"1b0537a2cff9c6cbf75c1fc091c87a66","score":"0.52134186","text":"def get_virt_objects(request, model, order_by, dc=None, include=(), **kwargs):\n dc = dc or request.dc\n qs = model.objects.filter(dc=dc)\n\n if request.user.is_admin(request, dc=dc):\n # noinspection PyUnresolvedReferences\n qf = ~Q(access__in=model.INVISIBLE)\n else:\n # noinspection PyUnresolvedReferences\n qf = (Q(access__in=(model.PUBLIC, model.DISABLED))) | (Q(owner=request.user.pk) & Q(access=model.PRIVATE))\n\n if include:\n # noinspection PyAugmentAssignment\n qf = qf | Q(pk__in=include)\n\n return qs.filter(qf).order_by(*order_by)","title":""},{"docid":"7e15275e84e2689a7297fbd55d20f37c","score":"0.5210278","text":"def listClipboardObjects( self, item=None, REQUEST=None, permission=CMFCorePermissions.ModifyPortalContent ):\n if item is None:\n uid = None\n linked = []\n else:\n uid = item.getUid()\n linked = map( lambda ob: ob['dest_uid'], self.searchResults( source_uid=uid ) ) + \\\n map( lambda ob: ob['source_uid'], self.searchResults( dest_uid=uid ) )\n\n oblist = listClipboardObjects( self, permission=permission, REQUEST=REQUEST )\n result = []\n\n for ob in oblist:\n if ob.implements('isDocument'):\n other = ob.getUid()\n if other and other != uid and other not in linked:\n result.append( ob )\n\n return result","title":""},{"docid":"87b5489415a148a33007e88cb0e92a77","score":"0.5206004","text":"def list (self, request, *args, **kwargs):\n return viewsets.ReadOnlyModelViewSet.list(self, request, *args, **kwargs)","title":""},{"docid":"f35ad96186f71f56f44ec12521bebcce","score":"0.51930195","text":"def list_permission(self, request):\n\t\tdata = self.get_serializer(self.get_queryset(), many=True).data\n\t\treturn Response(data, status.HTTP_200_OK)","title":""},{"docid":"f35ad96186f71f56f44ec12521bebcce","score":"0.51930195","text":"def list_permission(self, request):\n\t\tdata = self.get_serializer(self.get_queryset(), many=True).data\n\t\treturn Response(data, status.HTTP_200_OK)","title":""},{"docid":"0dd01401e46f426294c688bb72d64e59","score":"0.5185959","text":"def get_children_and_shared_documents(self, user):\n # Get documents that are direct children, or shared with but not owned by the current user\n documents = Document2.objects.filter(\n Q(parent_directory=self) |\n ((Q(document2permission__users=user) | Q(document2permission__groups__in=user.groups.all())) &\n ~Q(owner=user))\n )\n\n documents = documents.exclude(is_history=True).exclude(is_managed=True)\n\n # Excluding all trashed docs across users\n documents = documents.exclude(is_trashed=True)\n\n # Optimizing roll up for /home by checking only with directories instead of all documents\n # For all other directories roll up is done in _filter_documents()\n directories_all = Document2.objects.filter(type='directory').exclude(id=self.id)\n directories_inside_home = directories_all.filter(\n (Q(document2permission__users=user) | Q(document2permission__groups__in=user.groups.all())) &\n ~Q(owner=user)\n )\n documents = documents.exclude(parent_directory__in=directories_inside_home)\n\n return documents.defer('description', 'data', 'extra', 'search').distinct().order_by('-last_modified')","title":""},{"docid":"c7fa8597a17b8e9a57233543898d323c","score":"0.51758415","text":"def document_list_files(request,destination,location_id):\n document_permission_results = document_permission.objects.filter(\n is_deleted=False,\n ).values(\n 'document_key_id',\n 'document_key__document_description',\n 'document_key__document_url_location',\n 'document_key__document',\n 'document_key__whiteboard',\n 'folder',\n )\n\n # Limit to the required destination and location id\n document_permission_results = get_object_from_destination(\n document_permission_results,\n destination,\n location_id\n )\n\n # Send back json data\n json_results = json.dumps(list(document_permission_results), cls=DjangoJSONEncoder)\n\n return HttpResponse(json_results, content_type='application/json')\n\n\n # # Get the document information\n # document_results = document.objects.filter(\n # is_deleted=False,\n # document_key__in=document_permission_results.values('document_key')\n # )\n #\n # return HttpResponse(serializers.serialize('json',document_results),content_type='application/json')","title":""},{"docid":"cf6d3ba42bc5c58f21b882b53e4b0e20","score":"0.5175111","text":"def list(self, request, *args, **kwargs):\n return viewsets.ReadOnlyModelViewSet.list(self, request, *args, **kwargs)","title":""},{"docid":"cf6d3ba42bc5c58f21b882b53e4b0e20","score":"0.5175111","text":"def list(self, request, *args, **kwargs):\n return viewsets.ReadOnlyModelViewSet.list(self, request, *args, **kwargs)","title":""},{"docid":"745c58014e55c947e94f4b7e44fdb47b","score":"0.5173352","text":"def views(self):\n return self._views","title":""}],"string":"[\n {\n \"docid\": \"e8c114717b6787b3dbe00695cf0581b5\",\n \"score\": \"0.6454088\",\n \"text\": \"def children(self, obj):\\n return obj.subfolders()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04aee46d33afd7c0f8c16422f831928d\",\n \"score\": \"0.6329941\",\n \"text\": \"def get_objs(request, my_item_container, allow_copy):\\n main_obj = {}\\n main_obj['item_container_id'] = my_item_container.id\\n main_obj['name'] = my_item_container.item.name\\n main_obj['url'] = get_site_url(my_item_container, 'index.html')\\n main_obj['app_name'] = my_item_container.item.app.name\\n main_obj['is_sortable'] = True\\n main_obj['top_url'] = get_top_url(my_item_container)\\n main_obj['paste_obj'] = get_paste_obj(request, my_item_container)\\n main_obj['paste_app_name'] = get_paste_app_name(request)\\n main_obj['title'] = my_item_container.item.title\\n main_obj['last_modified'] = my_item_container.last_modified.\\\\\\n strftime('%d.%m.%Y %H:%M')\\n if request.GET.has_key('sort') :\\n order = request.GET['sort']\\n else :\\n order = ''\\n user_perms = UserEditPerms(request.user.username, request.path)\\n if user_perms.perm_manage or user_perms.perm_manage_folderish:\\n item_containers = get_folder_items(my_item_container, order)\\n elif user_perms.perm_edit_own or user_perms.perm_manage_own:\\n try:\\n item_containers = get_folder_own_items(my_item_container.item, order, request.user.id)\\n except:\\n item_containers = []\\n else:\\n item_containers = []\\n objs = []\\n objs_linkable = []\\n my_role = get_role_by_user_path(request.user, my_item_container.container.path)\\n for item_container in item_containers:\\n obj = get_single_obj(item_container, allow_copy, my_role)\\n objs.append(obj)\\n if item_container.item.app.is_linkable:\\n objs_linkable.append(obj)\\n return main_obj, objs, objs_linkable\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad849a938ed5f040350b85c91d5ee1a3\",\n \"score\": \"0.6096485\",\n \"text\": \"def subviews(self):\\n return {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc0e988f27e6941ae612c0d33a1432e8\",\n \"score\": \"0.6058188\",\n \"text\": \"def contents(self, full_objects=True):\\n cfilter = {'portal_type': ['Chapter', 'Glossary']}\\n return self.context.getFolderContents(contentFilter = cfilter,\\n full_objects = full_objects)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fb9b3f3a648379178c49f54723dde4a\",\n \"score\": \"0.59987134\",\n \"text\": \"def get_all_objs(content, vimtype, folder=None, recurse=True):\\n if not folder:\\n folder = content.rootFolder\\n\\n obj = {}\\n container = content.viewManager.CreateContainerView(folder, vimtype, recurse)\\n for managed_object_ref in container.view:\\n obj.update({managed_object_ref: managed_object_ref.name})\\n return obj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2a71876a34be52de2567b38b699e36d\",\n \"score\": \"0.59455013\",\n \"text\": \"def items(self, obj):\\n return (\\n obj.status_set.select_subclasses()\\n .filter(\\n privacy__in=[\\\"public\\\", \\\"unlisted\\\"],\\n )\\n .order_by(\\\"-published_date\\\")[:10]\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c98e4a6769e550927a70271a71aaa5e2\",\n \"score\": \"0.5925703\",\n \"text\": \"def list_used_views(self, specific_view=None):\\n if not check_role(self):\\n return \\\"You must have a manager role to run this script\\\"\\n\\n from Products.CMFCore.utils import getToolByName\\n\\n portal = getToolByName(self, \\\"portal_url\\\").getPortalObject()\\n catalog = portal.portal_catalog\\n types = portal.portal_types.objectIds()\\n views = {}\\n\\n for t in types:\\n brains = catalog(portal_type=t)\\n for brain in brains:\\n ob = brain.getObject()\\n layout = ob.getLayout()\\n if t not in views:\\n views[t] = {}\\n if not specific_view:\\n if layout not in views[t]:\\n views[t][layout] = 0\\n views[t][layout] += 1\\n elif specific_view and specific_view == layout:\\n views[t][\\\"/\\\".join(ob.getPhysicalPath())] = ob.absolute_url()\\n if not specific_view:\\n out = [\\\"Used views on objects\\\\n---------------------\\\\n\\\"]\\n for typ in views.keys():\\n out.append(\\n \\\"%s : %s\\\"\\n % (\\n typ,\\n \\\", \\\".join(\\n [\\n \\\"%s (%s)\\\" % (lay, views[typ][lay])\\n for lay in sorted(views[typ])\\n ]\\n ),\\n )\\n )\\n else:\\n out = [\\n \\\"<html><b><u>Objects using the '%s' layout</u></b><br /><br />\\\"\\n % specific_view\\n ]\\n for typ in views.keys():\\n for path in sorted(views[typ]):\\n out.append(\\\"%s : <a href='%s'>%s</a>\\\" % (typ, views[typ][path], path))\\n out.append(\\\"<br />\\\")\\n out.append(\\\"</html>\\\")\\n return \\\"\\\\n\\\".join(out)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77d99bcb3cb45f997162b599fe1378d3\",\n \"score\": \"0.58703405\",\n \"text\": \"def getObjects(self) -> List[object]:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"767819c553ed0cd44a8f346054bc4c3b\",\n \"score\": \"0.58263755\",\n \"text\": \"def list(self, request, *args, **kwargs):\\n\\n folder_id = self.kwargs['folderid']\\n\\n try:\\n self.folder_obj = SystemFolder.objects.get(id=folder_id)\\n except:\\n return Response(dict(error=[\\\"invalid_folder_id\\\"], data=dict()),\\n status=status.HTTP_400_BAD_REQUEST)\\n\\n response = generics.ListAPIView.list(\\n self, request, *args, **kwargs).data\\n # variable to retrieve pagination number\\n minion_paginator = self.paginate_queryset(self.object_list)\\n # set next and previous page numbers\\n try:\\n response.update({\\\"next_page_number\\\": minion_paginator.next_page_number()})\\n except:\\n response.update({\\\"next_page_number\\\": None})\\n try:\\n response.update({\\\"previous_page_number\\\": minion_paginator.previous_page_number()})\\n except:\\n response.update({\\\"previous_page_number\\\": None})\\n try:\\n response.update({\\\"total_page_number\\\": minion_paginator.paginator._get_num_pages()})\\n except:\\n response.update({\\\"total_page_number\\\": None})\\n return Response(dict(data=response, error=[]), status=status.HTTP_200_OK)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ca96b977a6965047979a11c1bdd125e\",\n \"score\": \"0.5819711\",\n \"text\": \"def restricted_object_list(*args, **kwargs):\\r\\n return list_detail.object_list(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c8cddd3f295e3f5ab6d7857f21e7b87\",\n \"score\": \"0.57945967\",\n \"text\": \"def objects(self):\\r\\n return self.queryset.all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"369d93fdb167dfd9f9a9ad9137464869\",\n \"score\": \"0.5746928\",\n \"text\": \"def list_folder(self, folder_path):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b744d6a00ce09531523dadabc54cf165\",\n \"score\": \"0.574668\",\n \"text\": \"def get_children(self, request=None, *args, **kwargs):\\n return lfc.utils.get_content_objects(request, parent=self, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e684d60d9bad345e21145db568f20065\",\n \"score\": \"0.5741314\",\n \"text\": \"def objects(doc_cls, queryset):\\n return queryset.filter(dashboardType=TestrunListDashboardTypeName)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ba1c3ff85484e55dc4ba1f529c96529\",\n \"score\": \"0.5716186\",\n \"text\": \"def listResources(obj):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84aade1ab75dc4b3deebacb11981574c\",\n \"score\": \"0.568571\",\n \"text\": \"def get_children(self, request=None, *args, **kwargs):\\n return lfc.utils.get_content_objects(request, parent=None, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"049527c91cb895a957db1861633c2572\",\n \"score\": \"0.56526285\",\n \"text\": \"def getViews( self, onlyStatic = True ):\\n viewsList = self.__createCatalog().getViews( onlyStatic )\\n return S_OK( viewsList )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a88929f6c8f4398f8229dabea6578b51\",\n \"score\": \"0.56384104\",\n \"text\": \"def list(self, path=None, recursive=False, detailed=False, acl=False):\\n\\n if path is None:\\n path = self.get_user_home()\\n\\n if self.is_dataobject(path):\\n raise IrodsException(\\\"Cannot list an object, get it instead\\\")\\n try:\\n data = {}\\n root = self.rpc.collections.get(path)\\n\\n for coll in root.subcollections:\\n\\n row = {}\\n key = coll.name\\n row[\\\"PID\\\"] = None\\n row[\\\"name\\\"] = coll.name\\n row[\\\"objects\\\"] = {}\\n if recursive:\\n row[\\\"objects\\\"] = self.list(\\n coll.path, recursive, detailed, acl)\\n row[\\\"path\\\"] = coll.path\\n row[\\\"object_type\\\"] = \\\"collection\\\"\\n if detailed:\\n row[\\\"owner\\\"] = \\\"-\\\"\\n if acl:\\n acl = self.get_permissions(coll)\\n row[\\\"acl\\\"] = acl[\\\"ACL\\\"]\\n row[\\\"acl_inheritance\\\"] = acl[\\\"inheritance\\\"]\\n\\n data[key] = row\\n\\n for obj in root.data_objects:\\n\\n row = {}\\n key = obj.name\\n row[\\\"name\\\"] = obj.name\\n row[\\\"path\\\"] = obj.path\\n row[\\\"object_type\\\"] = \\\"dataobject\\\"\\n row[\\\"PID\\\"] = None\\n row[\\\"checksum\\\"] = None\\n\\n if detailed:\\n row[\\\"owner\\\"] = obj.owner_name\\n row[\\\"content_length\\\"] = obj.size\\n row[\\\"created\\\"] = obj.create_time\\n row[\\\"last_modified\\\"] = obj.modify_time\\n if acl:\\n acl = self.get_permissions(obj)\\n row[\\\"acl\\\"] = acl[\\\"ACL\\\"]\\n row[\\\"acl_inheritance\\\"] = acl[\\\"inheritance\\\"]\\n\\n data[key] = row\\n\\n return data\\n except iexceptions.CollectionDoesNotExist as e:\\n raise IrodsException(\\\"Collection not found: %s\\\" % path)\\n\\n # replicas = []\\n # for line in lines:\\n # replicas.append(re.split(\\\"\\\\s+\\\", line.strip()))\\n # return replicas\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b4711f57cc6bf79a126e28f910cdae4\",\n \"score\": \"0.56184864\",\n \"text\": \"def findCacheables(\\n ob,\\n manager_id,\\n require_assoc,\\n subfolders,\\n meta_types,\\n rval,\\n path\\n):\\n try:\\n if meta_types:\\n subobs = ob.objectValues(meta_types)\\n else:\\n subobs = ob.objectValues()\\n sm = getSecurityManager()\\n\\n # Add to the list of cacheable objects.\\n for subob in subobs:\\n if not isCacheable(subob):\\n continue\\n associated = (subob.ZCacheable_getManagerId() == manager_id)\\n if require_assoc and not associated:\\n continue\\n if not sm.checkPermission('Change cache settings', subob):\\n continue\\n subpath = path + (subob.getId(),)\\n info = {\\n 'sortkey': subpath,\\n 'path': '/'.join(subpath),\\n 'title': getattr(aq_base(subob), 'title', ''),\\n 'icon': None,\\n 'associated': associated,\\n }\\n rval.append(info)\\n\\n # Visit subfolders.\\n if subfolders:\\n if meta_types:\\n subobs = ob.objectValues()\\n for subob in subobs:\\n subpath = path + (subob.getId(),)\\n if hasattr(aq_base(subob), 'objectValues'):\\n if sm.checkPermission(\\n 'Access contents information', subob):\\n findCacheables(\\n subob, manager_id, require_assoc,\\n subfolders, meta_types, rval, subpath)\\n except Exception:\\n # Ignore exceptions.\\n import traceback\\n traceback.print_exc()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bc939fd4d607b938e4601162e124b29\",\n \"score\": \"0.55942416\",\n \"text\": \"def items(self, obj):\\n return Version.objects.filter(project=obj, approved=True).order_by(\\n '-datetime_created')[:5]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39b435ba6f0a103a02ee03eb0fdd189b\",\n \"score\": \"0.55933243\",\n \"text\": \"def document_list_folders(request,destination,location_id):\\n # Get the document information\\n folder_results = folder.objects.filter(\\n is_deleted=False,\\n )\\n folder_results = get_object_from_destination(folder_results,destination,location_id)\\n\\n return HttpResponse(serializers.serialize('json',folder_results),content_type='application/json')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e297fdb151a38cc08187f64faefa64e1\",\n \"score\": \"0.5593304\",\n \"text\": \"def detail_list(self, request, cgs):\\n return self._list_view(self.detail, request, cgs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff1d928035ac37e39775cf4e3c26cd85\",\n \"score\": \"0.5558759\",\n \"text\": \"async def list(self, session=None):\\n logger.info(str(\\\"list \\\" + self.obj.pathname()))\\n return await self.obj_access.list(session)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e8ae5f87b4e425b07268358c5677523\",\n \"score\": \"0.555531\",\n \"text\": \"def get_objects(self):\\n\\t\\treturn self.__objects\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac699b8cf00e7507f3e1827d1066b819\",\n \"score\": \"0.5547583\",\n \"text\": \"def list(self, request):\\n\\n a_viewset = [\\n 'Uses actions (list, create, retrieve, update, partial_update)',\\n 'automiatically maps to urls using routers',\\n 'provide more functionaltiy with less code'\\n\\n ]\\n\\n return Response({'message':'hello a view set', 'a_viewset' : a_viewset})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2e03f6aa56f2ddbff1c0dc889fcca96\",\n \"score\": \"0.55472165\",\n \"text\": \"def listGroups(obj):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c146b9a047f14c4d5007bef77ff1a17\",\n \"score\": \"0.55468124\",\n \"text\": \"def getSubfolderList(self, folderPath: unicode) -> List[unicode]:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c90c7e7e0fb74fe2fa656746f555722a\",\n \"score\": \"0.5532205\",\n \"text\": \"def view(self) -> list:\\n if self.access_level.value > 2:\\n raise InvalidPermission()\\n if len(self.cache) == 0:\\n self.update_cache()\\n return self.cache\\n\\n # return self.movie_database.view()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7b9e61cf8cce98417d7271691b3b294\",\n \"score\": \"0.5532121\",\n \"text\": \"def list(self, request):\\n \\n request_author = WhoYouUser.objects.get(user=request.auth.user)\\n filteredRequests = ContentViewRequest.objects.filter(Q(content__owner=request_author) | Q(requester=request_author))\\n\\n serializer = ContentViewRequestSerializer(\\n filteredRequests, many=True, context={'request': request}\\n )\\n return Response(serializer.data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"341e8f1bb576e16ea7e1b41ef1630932\",\n \"score\": \"0.5526323\",\n \"text\": \"def list_objects_access(f):\\n functools.wraps(f)\\n\\n def wrap(request, *args, **kwargs):\\n if not django.conf.settings.PUBLIC_OBJECT_LIST:\\n trusted(request)\\n return f(request, *args, **kwargs)\\n\\n wrap.__doc__ = f.__doc__\\n wrap.__name__ = f.__name__\\n return wrap\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d011d1b6c4e520a69fce1b325c3f3e9\",\n \"score\": \"0.55258477\",\n \"text\": \"def _folder_contents(\\n self,\\n start=None,\\n end=None,\\n reverse=False,\\n sort_column_name=None,\\n filter_text=None,\\n ):\\n folder = self.context\\n request = self.request\\n system_catalog = find_catalog(folder, 'system')\\n objectmap = find_objectmap(folder)\\n\\n path = system_catalog['path']\\n allowed = system_catalog['allowed']\\n\\n if start is None:\\n start = 0\\n\\n if end is None:\\n end = start + 40\\n\\n q = ( path.eq(folder, depth=1, include_origin=False) &\\n allowed.allows(request, 'sdi.view') )\\n\\n if filter_text:\\n filter_text_globs = filter(None, filter_text.split())\\n if filter_text_globs:\\n text = system_catalog['text']\\n for filter_glob in filter_text_globs:\\n if not filter_glob.endswith('*'):\\n filter_glob = filter_glob + '*' # glob (prefix) search\\n q = q & text.eq(filter_glob)\\n\\n resultset = q.execute()\\n # NB: must take snapshot of folder_length *before* limiting the length\\n # of the resultset via any search\\n folder_length = len(resultset)\\n\\n columns = self._columns()\\n \\n sort_info = self._sort_info(\\n columns,\\n sort_column_name=sort_column_name,\\n )\\n\\n sorter = sort_info['sorter']\\n sort_column_name = sort_info['column_name']\\n\\n if sorter is not None:\\n resultset = sorter(folder, resultset, reverse=reverse, limit=end)\\n\\n ids = resultset.ids\\n\\n can_manage = bool(has_permission('sdi.manage-contents', folder,request))\\n\\n custom_columns = request.registry.content.metadata(\\n folder,\\n 'columns',\\n _marker\\n )\\n\\n buttons = self._buttons()\\n\\n records = []\\n\\n for oid in itertools.islice(ids, start, end):\\n resource = objectmap.object_for(oid)\\n name = getattr(resource, '__name__', '')\\n # XXX CM: computation of deletable here should probably attached to\\n # the delete button as an ``enabled_for`` instead of being treated\\n # specially.\\n deletable = getattr(resource, '__sdi_deletable__', None)\\n if deletable is not None:\\n if callable(deletable):\\n deletable = deletable(resource, request)\\n if deletable is None:\\n deletable = can_manage\\n deletable = bool(deletable) # cast return/attr value to bool\\n icon = request.registry.content.metadata(resource, 'icon')\\n if callable(icon):\\n icon = icon(resource, request)\\n url = request.sdiapi.mgmt_path(resource, '@@manage_main')\\n record = dict(\\n id=name,\\n # Use the unique name, as an id. (A unique row id is needed\\n # for slickgrid. In addition, we will pass back this same id\\n # from the client, when a row is selected for an operation.)\\n deletable=deletable,\\n name=name,\\n )\\n columns = default_sdi_columns(folder, resource, request)\\n if custom_columns is None:\\n columns = []\\n elif custom_columns is not _marker:\\n columns = custom_columns(folder, resource, request, columns)\\n for column in columns:\\n # XXX CM: adding arbitrary keys to the record based on\\n # configuration input is a bad idea here because we can't\\n # guarantee a column name won't override the \\\"reserved\\\" names\\n # (name, deletable, id) added to the record above. Ree?\\n cname = column['name']\\n record[cname] = column['value']\\n # XXX CM: we should document the fact that each column can have\\n # its own URL/icon if we keep this; although we should probably\\n # not send them if the formatter doesn't want them. Need a\\n # better formatter abstraction here, I think;\\n # maybe server-side? Opinions, Ree?\\n record[cname+'_icon'] = column.get('icon', icon)\\n record[cname+'_url'] = column.get('url', url)\\n disable = []\\n for button_group in buttons:\\n for button in button_group['buttons']:\\n if 'enabled_for' not in button:\\n continue\\n condition = button['enabled_for']\\n if not callable(condition):\\n continue\\n if not condition(folder, resource, request):\\n disable.append(button['id'])\\n record['disable'] = disable\\n records.append(record)\\n\\n return {\\n 'length':folder_length,\\n 'records':records,\\n 'sort_column_name':sort_column_name,\\n 'columns':columns,\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93d5d5ba4cd8cc6aeb88eeacbe6db30f\",\n \"score\": \"0.5519873\",\n \"text\": \"def items(self, obj):\\n return Comment.objects.filter(\\n user=obj,\\n privacy__in=[\\\"public\\\", \\\"unlisted\\\"],\\n ).order_by(\\\"-published_date\\\")[:10]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57753ea48266fe68fb0797c0d5e904eb\",\n \"score\": \"0.5517392\",\n \"text\": \"def test_can_list_only_my_folders(self):\\n\\n self.login(username='one@snow.com')\\n response = self.client.get('/api/v1/folder', format='json')\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\\n self.assertEqual(2, len(response.data)) # root and trash.\\n self.logout()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f40e3aa5387a764afebe6df6e3793a92\",\n \"score\": \"0.5515681\",\n \"text\": \"def test_api__get_workspace_content__ok_200__get_all_root_and_folder_content(self):\\n params = {\\\"parent_ids\\\": \\\"0,3\\\", \\\"show_archived\\\": 1, \\\"show_deleted\\\": 1, \\\"show_active\\\": 1}\\n self.testapp.authorization = (\\\"Basic\\\", (\\\"bob@fsf.local\\\", \\\"foobarbaz\\\"))\\n res = self.testapp.get(\\\"/api/v2/workspaces/2/contents\\\", status=200, params=params).json_body\\n # TODO - G.M - 30-05-2018 - Check this test\\n assert len(res) == 7\\n assert [\\n content\\n for content in res\\n if content[\\\"label\\\"] == \\\"Desserts\\\"\\n and content[\\\"content_type\\\"] == \\\"folder\\\"\\n and content[\\\"parent_id\\\"] is None\\n and content[\\\"content_id\\\"] == 3\\n ]\\n assert [\\n content\\n for content in res\\n if content[\\\"label\\\"] == \\\"Fruits Desserts\\\"\\n and content[\\\"content_type\\\"] == \\\"folder\\\"\\n and content[\\\"parent_id\\\"] == 3\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a6508b0f8404c7a7ec3a8c81eb481e0\",\n \"score\": \"0.5506615\",\n \"text\": \"def list(self, request):\\n\\n a_viewset = [\\n 'Uses actions similar to APIViews',\\n 'Give more functionality',\\n 'Automatically maps to URL routers',\\n ]\\n\\n return Response({'message':'Hello!!! viewset', 'a_viewset':a_viewset})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d10ffea26375e12c3ea5315c056495ba\",\n \"score\": \"0.5506409\",\n \"text\": \"def _getChildren(self, obj, no_nodes=0, no_leaves=0, mode='tree'):\\n if not obj:\\n return []\\n\\n if mode == 'tree':\\n # we are called by a getTree so we use portal tree\\n # we assume we want nodes only\\n if not obj['nb_children']:\\n return []\\n if obj.has_key('children'): # only fake root has children key\\n return [x for x in self._cps_tree\\n if x['rpath'] in obj['children']]\\n children_prefix = obj['rpath'] + '/'\\n children_depth = obj['depth'] + 1\\n return [x for x in self._cps_tree\\n if ( x['depth'] == children_depth and\\n x['rpath'].startswith(children_prefix) )]\\n\\n # for the listing use the ZODB\\n lobj = obj\\n if type(obj) is DictType:\\n lobj = self.context.unrestrictedTraverse(self._getUid(obj))\\n return ZODBNavigation._getChildren(self, lobj, no_nodes, no_leaves)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26c37714ee41cc2dafb1c5ad06e6a81b\",\n \"score\": \"0.55052644\",\n \"text\": \"def obj_get_list(self, request=None, **kwargs):\\n filters = {}\\n\\n if hasattr(request, 'GET'):\\n # Grab a mutable copy.\\n filters = request.GET.copy()\\n\\n # Update with the provided kwargs.\\n filters.update(kwargs)\\n try:\\n user = UserProfile.objects.filter(\\n uuid=uuid_from_uri(filters['user']))\\n sub_pk = sorted(chain(user[0].subscriptions(), user))\\n im = Image.objects.filter(is_active=True, owner__in=sub_pk)\\n wb = WordBox.objects.filter(is_active=True, owner__in=sub_pk)\\n # sort reverse chronologically\\n base_object_list = sorted(chain(im, wb),\\n key=attrgetter('created_time'))[::-1]\\n return self.apply_authorization_limits(request, base_object_list)\\n except ValueError:\\n raise BadRequest('Invalid resource lookup data provided'\\n '(mismatched type).')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0140126b5ffd9ce7fa5682ba9c45259d\",\n \"score\": \"0.5497397\",\n \"text\": \"def filter_view_perms(self, user, object=None):\\n return self.exclude(status=self.model.REMOVED).filter_perms(user, 'can_view', object)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4846baf976d6295ca074258c2c54fac\",\n \"score\": \"0.5494614\",\n \"text\": \"def _getfolders(self):\\r\\n result = self._session.execute(\\\"up -show folders \\\\\\\"%s\\\\\\\" -u -f \\\\\\\"%%objectname\\\\\\\"\\\" % self, ObjectListResult(self._session))\\r\\n return result.output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e58d6dec1f52361bd1c254098ac13227\",\n \"score\": \"0.54907656\",\n \"text\": \"def test_sublist(self):\\n url = 'http://blog/sublist/'\\n request = self.factory.get(url)\\n force_authenticate(request, user=self.user, token=self.token)\\n response = SubList.as_view()(request)\\n assert response.status_code == 200\",\n \"title\": \"\"\n },\n {\n \"docid\": \"015bb390236e5625241495c83491de96\",\n \"score\": \"0.54742694\",\n \"text\": \"def items(self, obj):\\n return Review.objects.filter(\\n user=obj,\\n privacy__in=[\\\"public\\\", \\\"unlisted\\\"],\\n ).order_by(\\\"-published_date\\\")[:10]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ede3f61f14006be2befee7ff24277026\",\n \"score\": \"0.54669315\",\n \"text\": \"def find_readable_descendants(self, auth):\\n new_branches = []\\n for node in self.nodes.filter(is_deleted=False):\\n if node.can_view(auth):\\n yield node\\n else:\\n new_branches.append(node)\\n\\n for bnode in new_branches:\\n for node in bnode.find_readable_descendants(auth):\\n yield node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c806cc40cf0918312b2cfc6d9e99cd10\",\n \"score\": \"0.5455731\",\n \"text\": \"def get_objects(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a493e47b0a0ed57d61a909402f06301d\",\n \"score\": \"0.5453869\",\n \"text\": \"def GetViews(pattern='.*'):\\n return GetSubpictures(None, pattern)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08b4182c0d444c253cf9978eb258f717\",\n \"score\": \"0.54491377\",\n \"text\": \"def queryset(self, request):\\n queryset = super(EntryAdmin, self).queryset(request)\\n if request.user.has_perm('zinnia.can_view_all'):\\n return queryset\\n return request.user.entry_set.all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a399e3fc1172be2b5a74706d49c5b450\",\n \"score\": \"0.54450107\",\n \"text\": \"def list_folders(service_instance):\\n return utils_common.list_objects(service_instance, vim.Folder)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b91626723377d45ec332531754c87c33\",\n \"score\": \"0.54406047\",\n \"text\": \"def topic_list(request):\\r\\n return object_list(request, queryset=Topic.objects.published(),\\r\\n template_object_name='topic')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20e58724af23640e2e99b3d5f1823cc0\",\n \"score\": \"0.5439843\",\n \"text\": \"def test_api__get_workspace_content__ok_200__get_multiple_folder_content(self):\\n self.testapp.authorization = (\\\"Basic\\\", (\\\"admin@admin.admin\\\", \\\"admin@admin.admin\\\"))\\n params = {\\\"parent_id\\\": 1, \\\"label\\\": \\\"GenericCreatedContent\\\", \\\"content_type\\\": \\\"html-document\\\"}\\n res = self.testapp.post_json(\\\"/api/v2/workspaces/1/contents\\\", params=params, status=200)\\n content_id = res.json_body[\\\"content_id\\\"]\\n params = {\\\"parent_ids\\\": \\\"1,2\\\", \\\"show_archived\\\": 1, \\\"show_deleted\\\": 1, \\\"show_active\\\": 1}\\n res = self.testapp.get(\\\"/api/v2/workspaces/1/contents\\\", status=200, params=params).json_body\\n # TODO - G.M - 30-05-2018 - Check this test\\n assert len(res) == 2\\n assert [\\n content\\n for content in res\\n if content[\\\"label\\\"] == \\\"Current Menu\\\"\\n and content[\\\"content_type\\\"] == \\\"html-document\\\"\\n and content[\\\"parent_id\\\"] == 2\\n and content[\\\"content_id\\\"] == 11\\n ]\\n assert [\\n content\\n for content in res\\n if content[\\\"label\\\"] == \\\"GenericCreatedContent\\\"\\n and content[\\\"content_type\\\"] == \\\"html-document\\\"\\n and content[\\\"parent_id\\\"] == 1\\n and content[\\\"content_id\\\"] == content_id\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0a95f28ed5f6cc193a446c96486e58e\",\n \"score\": \"0.5439046\",\n \"text\": \"def add_folder_contents_views(\\r\\n config,\\r\\n cls=None,\\r\\n name='contents',\\r\\n context=None,\\r\\n renderer='substanced.folder:templates/contents.pt', # do not abbreviate\\r\\n view_permission='sdi.view',\\r\\n manage_contents_permission='sdi.manage-contents',\\r\\n tab_title=_(\\\"Contents\\\"),\\r\\n tab_condition=True,\\r\\n tab_before=None,\\r\\n tab_after=None,\\r\\n tab_near=None,\\r\\n **predicates\\r\\n ):\\r\\n\\r\\n if cls is None:\\r\\n cls = FolderContents\\r\\n\\r\\n if context is None:\\r\\n context = IFolder\\r\\n \\r\\n add_fc_view = functools.partial(\\r\\n config.add_mgmt_view,\\r\\n view=cls,\\r\\n name=name,\\r\\n renderer=renderer,\\r\\n context=context,\\r\\n tab_condition=False,\\r\\n **predicates\\r\\n )\\r\\n\\r\\n add_fc_view(\\r\\n request_method='GET',\\r\\n permission=view_permission,\\r\\n tab_condition=tab_condition,\\r\\n tab_before=tab_before,\\r\\n tab_after=tab_after,\\r\\n tab_near=tab_near,\\r\\n tab_title=tab_title,\\r\\n xhr=False,\\r\\n attr='show',\\r\\n )\\r\\n add_fc_view(\\r\\n request_method='GET',\\r\\n permission=view_permission,\\r\\n xhr=True,\\r\\n renderer='json',\\r\\n attr='show_json',\\r\\n )\\r\\n add_fc_view(\\r\\n request_method='POST',\\r\\n request_param='form.delete',\\r\\n permission=manage_contents_permission,\\r\\n check_csrf=True,\\r\\n attr='delete',\\r\\n )\\r\\n add_fc_view(\\r\\n request_method='POST',\\r\\n request_param='form.duplicate',\\r\\n permission=manage_contents_permission,\\r\\n check_csrf=True,\\r\\n attr='duplicate',\\r\\n )\\r\\n add_fc_view(\\r\\n request_method='POST',\\r\\n request_param='form.rename',\\r\\n permission=manage_contents_permission,\\r\\n renderer='templates/rename.pt',\\r\\n check_csrf=True,\\r\\n attr='rename',\\r\\n )\\r\\n add_fc_view(\\r\\n request_method='POST',\\r\\n request_param='form.rename_finish',\\r\\n permission=manage_contents_permission,\\r\\n check_csrf=True,\\r\\n attr='rename_finish',\\r\\n )\\r\\n add_fc_view(\\r\\n request_method='POST',\\r\\n request_param='form.copy',\\r\\n permission=view_permission,\\r\\n check_csrf=True,\\r\\n attr='copy',\\r\\n )\\r\\n add_fc_view(\\r\\n request_method='POST',\\r\\n request_param='form.copy_finish',\\r\\n permission=manage_contents_permission,\\r\\n check_csrf=True,\\r\\n attr='copy_finish',\\r\\n )\\r\\n add_fc_view(\\r\\n request_method='POST',\\r\\n request_param='form.copy_finish_cancel',\\r\\n permission=view_permission,\\r\\n check_csrf=True,\\r\\n attr='copy_finish_cancel',\\r\\n )\\r\\n add_fc_view(\\r\\n request_method='POST',\\r\\n request_param='form.move',\\r\\n permission=view_permission,\\r\\n check_csrf=True,\\r\\n attr='move',\\r\\n )\\r\\n add_fc_view(\\r\\n request_method='POST',\\r\\n request_param='form.move_finish',\\r\\n permission=manage_contents_permission,\\r\\n check_csrf=True,\\r\\n attr='move_finish',\\r\\n )\\r\\n add_fc_view(\\r\\n request_method='POST',\\r\\n request_param='form.move_finish_cancel',\\r\\n permission=view_permission,\\r\\n check_csrf=True,\\r\\n attr='move_finish_cancel',\\r\\n )\\r\\n add_fc_view(\\r\\n request_method='POST',\\r\\n renderer='json',\\r\\n request_param='ajax.reorder',\\r\\n permission=manage_contents_permission,\\r\\n check_csrf=True,\\r\\n attr='reorder_rows',\\r\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70273db52d9388f98e68ff2ef97d530e\",\n \"score\": \"0.5429694\",\n \"text\": \"def get_objects(self,model,page=1,frmt=\\\"list\\\"):\\n if frmt not in self.FORMATS:\\n raise InvalidFormat(\\\"The requested format is invalid\\\")\\n klass = self.__get_class__(model)\\n admin_klass = self.__get_admin_class__(model)\\n list_display = admin_klass.list_display['cols']\\n rows = []\\n for obj in klass.objects: \\n row = []\\n for col in list_display:\\n if hasattr(obj,col):\\n func = getattr(obj,col)\\n if callable(func):\\n val = func()\\n else:\\n val = func\\n # Check if value is of boolean type if yes then display icon else value\\n if type(val).__name__ == 'bool':\\n if val:\\n row.append('<img src=\\\"/static/base/images/icon_accept.png\\\" alt=\\\"Yes\\\" />')\\n else:\\n row.append('<img src=\\\"/static/base/images/icon_cross.png\\\" alt=\\\"No\\\" />')\\n else:\\n row.append(val)\\n if row:\\n #Append Actions\\n actions = [\\\"<a href='/admin/%s/%s/edit/' title='Edit %s'> <img src='/static/base/images/icon_edit.png' alt='Edit'\\\\\\n class='mid_align'> </a>\\\" % (model,obj.id,model) ]\\n actions.append(\\\"<a href='/admin/%s/%s/delete/' title='Delete %s'> <img src='/static/base/images/icon_delete.png' alt='Delete'\\\\\\n class='mid_align'> </a>\\\" % (model,obj.id,model)) \\n if admin_klass.list_display.has_key('actions'):\\n for action in admin_klass.list_display['actions']:\\n func = getattr(obj,action)\\n if callable(func):\\n val = func()\\n if val:\\n actions.append(val)\\n row.append(\\\" | \\\".join(actions))\\n if model == \\\"category\\\":\\n if obj.parent:\\n rows.append(((obj.id,obj.parent.id),row))\\n else:\\n rows.append(((obj.id,'None'),row))\\n else:\\n rows.append(row)\\n title = 'List '+klass.meta.get('verbose_name_plural',model.capitalize()+\\\"s\\\")\\n context = {'cols':self.__get_list_headers__(model),'objs':rows,'title':title,'model':model}\\n context.update(self.__get_admin_includes__(model))\\n return context\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0e1f036a8672887b406c5d5292f29ca\",\n \"score\": \"0.5421653\",\n \"text\": \"def objects(self) -> list:\\r\\n return self._objects\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22069495f552da1b3a2633ff5bd453e7\",\n \"score\": \"0.54177696\",\n \"text\": \"def index_queryset(self, using=None):\\n return self.get_model().objects.filter(Q(raccess__discoverable=True) |\\n Q(raccess__public=True))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ab8c4437267c411bc74c38ef1cab51a\",\n \"score\": \"0.538767\",\n \"text\": \"def detail_list(self, request, backupjobruns):\\n return self._list_view(self.detail, request, backupjobruns)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0322170be6e30eb95ffe0751a2d4ef4c\",\n \"score\": \"0.53702104\",\n \"text\": \"def index():\\n litems = []\\n allitems = []\\n viewmode = int(plugin.get_setting('viewmode'))\\n if viewmode is None: viewmode = 500\\n plugin.set_view_mode(viewmode)\\n DOSTR8 = plugin.get_setting(key='dostr8')\\n if not (DOSTR8 == True or DOSTR8 == 'true'): DOSTR8 = False\\n else: DOSTR8 = True\\n for sitename in getAPIURLS().keys():\\n if sitename.find('_search') == -1:\\n sicon = __imgsearch__.replace('search.', 'f{0}.'.format(sitename))\\n spath = plugin.url_for(site, sitename=sitename, section='index', url='0')\\n sitem = {'label': sitename.title(), 'icon': sicon, 'thumbnail': sicon, 'path': spath}\\n sitem.setdefault(sitem.keys()[0])\\n litems.append(sitem)\\n allitems = sorted(litems, key=lambda litems: litems['label'])\\n ifolder = __imgsearch__.replace('search.', 'folder.')\\n itemallcats = {'label': 'Global Category List', 'path': plugin.url_for(allcats), 'icon': ifolder,\\n 'thumbnail': ifolder}\\n itemsearch = {'label': 'Search All Sites', 'path': plugin.url_for(search), 'icon': __imgsearch__,\\n 'thumbnail': __imgsearch__}\\n itemstream = {'label': 'Play Web URL', 'path': plugin.url_for(resolver), 'icon': 'DefaultFolder.png',\\n 'thumbnail': 'DefaultFolder.png'}\\n timg = __imgsearch__.replace('search.', 'ftumblr.')\\n itemtumblr = {'label': 'Tumblr', 'icon': timg, 'thumbnail': timg, 'path': plugin.url_for(tumblrhome)}\\n itemtumblr.setdefault(itemtumblr.keys()[0])\\n itemallcats.setdefault(itemallcats.keys()[0])\\n itemstream.setdefault(itemstream.keys()[0])\\n itemsearch.setdefault(itemsearch.keys()[0])\\n allitems.append(itemtumblr)\\n allitems.append(itemallcats)\\n allitems.append(itemstream)\\n allitems.append(itemsearch)\\n return finish(allitems)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b51756710402180e485008cca5b1766e\",\n \"score\": \"0.5364568\",\n \"text\": \"def list(self):\\n results = self.model.objects.list()\\n self.render_list(results)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3d3fd29af556dfe6f06ee3051aee5b8\",\n \"score\": \"0.5360611\",\n \"text\": \"def get_queryset(self, request):\\n if not request.user.has_perm('mp_blog.can_view_all'):\\n queryset = self.model.objects.filter(\\n authors__pk=request.user.pk,\\n status=DRAFT\\n )\\n else:\\n queryset = super(EntryAdmin, self).get_queryset(request)\\n return queryset.prefetch_related('industries', 'types', 'topics',\\n 'authors', 'sites')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3956df2e523daf6f35024780cbc4d09\",\n \"score\": \"0.53576565\",\n \"text\": \"def hub_list(request):\\n page_no = pagination.get_page_no(request.GET)\\n object_list = Hub.active.all()\\n page = pagination.get_page(object_list, page_no)\\n featured_list = Hub.active.filter(is_featured=True)[:3]\\n context = {\\n 'page': page,\\n 'featured_list': featured_list,\\n }\\n return TemplateResponse(request, 'hubs/object_list.html', context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50d74847ddb2f5cc95a121e2a71f23fa\",\n \"score\": \"0.5356083\",\n \"text\": \"def list_models(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fece569771aafaa214b7badf3a4477d2\",\n \"score\": \"0.53530556\",\n \"text\": \"def __call__(self):\\n\\n # How many items is one one page\\n limit = 30\\n\\n # Read the first index of the selected batch parameter as \\n # HTTP GET request query parameter\\n start = self.request.get(\\\"b_start\\\", 0)\\n\\n contentFilter = {}\\n #contentFilter['portal_type'] = 'Folder'\\n contentFilter['sort_on'] = 'getObjPositionInParent'\\n contentFilter['sort_order'] = 'ascending'\\n \\n cur_path = '/'.join(self.context.getPhysicalPath())\\n path = {}\\n\\n path['query'] = cur_path\\n path['depth'] = 1\\n #contentFilter['path'] = path\\n\\n self.contents = self.context.queryCatalog(contentFilter, show_all=1, show_inactive=False)\\n\\n self.contents = [b.getObject() for b in self.contents]\\n\\n batch = True\\n if batch:\\n from Products.CMFPlone import Batch\\n b_start = self.context.REQUEST.get('b_start', 0)\\n self.contents = Batch(self.contents, limit, int(b_start), orphan=0)\\n\\n # Return the rendered template with content listing info filled in\\n return self.index()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e66b12773199a77dfa8c7d3938683272\",\n \"score\": \"0.5352557\",\n \"text\": \"def test_api__get_workspace_content__ok_200__get_only_archived_folder_content(self):\\n params = {\\\"parent_ids\\\": 10, \\\"show_archived\\\": 1, \\\"show_deleted\\\": 0, \\\"show_active\\\": 0}\\n self.testapp.authorization = (\\\"Basic\\\", (\\\"admin@admin.admin\\\", \\\"admin@admin.admin\\\"))\\n res = self.testapp.get(\\\"/api/v2/workspaces/2/contents\\\", status=200, params=params).json_body\\n assert len(res) == 1\\n content = res[0]\\n assert content[\\\"content_type\\\"] == \\\"html-document\\\"\\n assert content[\\\"content_id\\\"] == 13\\n assert content[\\\"is_archived\\\"] is True\\n assert content[\\\"is_deleted\\\"] is False\\n assert content[\\\"label\\\"].startswith(\\\"Fruit Salad\\\")\\n assert content[\\\"parent_id\\\"] == 10\\n assert content[\\\"show_in_ui\\\"] is True\\n assert content[\\\"slug\\\"].startswith(\\\"fruit-salad\\\")\\n assert content[\\\"status\\\"] == \\\"open\\\"\\n assert set(content[\\\"sub_content_types\\\"]) == {\\\"comment\\\"}\\n assert content[\\\"workspace_id\\\"] == 2\\n assert content[\\\"modified\\\"]\\n assert content[\\\"created\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e04cfe14da2fc51105e5903424b6da3\",\n \"score\": \"0.5348291\",\n \"text\": \"def aggregate_views(scope):\\n views = []\\n if scope in ('file', 'f'):\\n v = sublime.active_window().active_view().file_name()\\n if not ignore_path(v):\\n views.append(v)\\n elif scope in ('open', 'o'):\\n for v in sublime.active_window().views():\\n name = v.file_name()\\n if not ignore_path(name):\\n views.append(name)\\n else:\\n for f in sublime.active_window().folders():\\n for path, subdirs, files in os.walk(f):\\n for name in files:\\n p = os.path.join(path, name)\\n if not ignore_path(p):\\n views.append(p)\\n return views\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40e452937811971a15e58b4277b99153\",\n \"score\": \"0.5341478\",\n \"text\": \"def list_content(self) -> list:\\n return self.zf.infolist()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f9ae8e5bcafa25a44e3032b3228d6fb\",\n \"score\": \"0.53361034\",\n \"text\": \"def list_things(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1c9237a866da999b42ef3fd6491c840\",\n \"score\": \"0.533489\",\n \"text\": \"def list_views(self, cls):\\n if isinstance(cls, str):\\n return [view for view in self._views if view.__class__.__name__ == cls]\\n return [view for view in self._views if view.__class__ == cls]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6676da487712c638ac043b1bf09a238\",\n \"score\": \"0.53327924\",\n \"text\": \"def objects():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d15b7821593b2609c6ffcad8d352f66\",\n \"score\": \"0.532594\",\n \"text\": \"def __list__(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb2ffcd09031492abc384731dbedb852\",\n \"score\": \"0.5325614\",\n \"text\": \"def get_members(obj, ccm, parent_proj):\\n if obj.get_type() == 'dir':\\n objects = ccm.query(\\\"is_child_of('{0}', '{1}')\\\".format(\\n obj.get_object_name(), parent_proj)).format('%objectname').run()\\n else:\\n # For projects only get the directory of the project\\n objects = ccm.query(\\n \\\"is_member_of('{0}') and type='dir' and name='{1}'\\\".format(\\n obj.get_object_name(), obj.get_name())).format(\\n '%objectname').run()\\n return objects\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a83bead2646da8dd92f0a948b0259a98\",\n \"score\": \"0.5312527\",\n \"text\": \"def get_objects(self):\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6039cfa14625e2b3c654fe529e7b55f7\",\n \"score\": \"0.5309652\",\n \"text\": \"def test_api__get_workspace_content__ok_200__get_all_folder_content(self):\\n params = {\\n \\\"parent_ids\\\": 10, # TODO - G.M - 30-05-2018 - Find a real id\\n \\\"show_archived\\\": 1,\\n \\\"show_deleted\\\": 1,\\n \\\"show_active\\\": 1,\\n # 'content_type': 'any'\\n }\\n self.testapp.authorization = (\\\"Basic\\\", (\\\"admin@admin.admin\\\", \\\"admin@admin.admin\\\"))\\n res = self.testapp.get(\\\"/api/v2/workspaces/2/contents\\\", status=200, params=params).json_body\\n assert len(res) == 3\\n content = res[0]\\n assert content[\\\"content_type\\\"] == \\\"html-document\\\"\\n assert content[\\\"content_id\\\"] == 14\\n assert content[\\\"is_archived\\\"] is False\\n assert content[\\\"is_deleted\\\"] is True\\n assert content[\\\"label\\\"].startswith(\\\"Bad Fruit Salad\\\")\\n assert content[\\\"parent_id\\\"] == 10\\n assert content[\\\"show_in_ui\\\"] is True\\n assert content[\\\"slug\\\"].startswith(\\\"bad-fruit-salad\\\")\\n assert content[\\\"status\\\"] == \\\"open\\\"\\n assert set(content[\\\"sub_content_types\\\"]) == {\\\"comment\\\"}\\n assert content[\\\"workspace_id\\\"] == 2\\n assert content[\\\"modified\\\"]\\n assert content[\\\"created\\\"]\\n content = res[1]\\n assert content[\\\"content_type\\\"] == \\\"html-document\\\"\\n assert content[\\\"content_id\\\"] == 13\\n assert content[\\\"is_archived\\\"] is True\\n assert content[\\\"is_deleted\\\"] is False\\n assert content[\\\"label\\\"].startswith(\\\"Fruit Salad\\\")\\n assert content[\\\"parent_id\\\"] == 10\\n assert content[\\\"show_in_ui\\\"] is True\\n assert content[\\\"slug\\\"].startswith(\\\"fruit-salad\\\")\\n assert content[\\\"status\\\"] == \\\"open\\\"\\n assert set(content[\\\"sub_content_types\\\"]) == {\\\"comment\\\"}\\n assert content[\\\"workspace_id\\\"] == 2\\n assert content[\\\"modified\\\"]\\n assert content[\\\"created\\\"]\\n content = res[2]\\n assert content[\\\"content_type\\\"] == \\\"html-document\\\"\\n assert content[\\\"content_id\\\"] == 12\\n assert content[\\\"is_archived\\\"] is False\\n assert content[\\\"is_deleted\\\"] is False\\n assert content[\\\"label\\\"] == \\\"New Fruit Salad\\\"\\n assert content[\\\"parent_id\\\"] == 10\\n assert content[\\\"show_in_ui\\\"] is True\\n assert content[\\\"slug\\\"] == \\\"new-fruit-salad\\\"\\n assert content[\\\"status\\\"] == \\\"open\\\"\\n assert set(content[\\\"sub_content_types\\\"]) == {\\\"comment\\\"}\\n assert content[\\\"workspace_id\\\"] == 2\\n assert content[\\\"modified\\\"]\\n assert content[\\\"created\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5b633166994af01248188bdc1d1e6e9\",\n \"score\": \"0.5307412\",\n \"text\": \"def list_objects(self, id, query=None):\\n return self.make_request('list_objects', '/' + id + '/objects', schema=SpeckleObject, params=query)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"243b2316502b7ce0f571bcbb2355a17e\",\n \"score\": \"0.5306078\",\n \"text\": \"def restricted_detail(*args, **kwargs):\\r\\n return list_detail.object_detail(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23190fa09eb04ca72f967e6e2dcc5e6b\",\n \"score\": \"0.53019345\",\n \"text\": \"def visible_objects_and_duplis():\\n\\n\\t\\tfor obj in context.visible_objects:\\n\\t\\t\\tif obj.type == 'MESH':\\n\\t\\t\\t\\tyield (obj, obj.matrix_world.copy())\\n\\n\\t\\t\\t# if obj.dupli_type != 'NONE':\\n\\t\\t\\t# \\tobj.dupli_list_create(scene)\\n\\t\\t\\t# \\tfor dob in obj.dupli_list:\\n\\t\\t\\t# \\t\\tobj_dupli = dob.object\\n\\t\\t\\t# \\t\\tif obj_dupli.type == 'MESH':\\n\\t\\t\\t# \\t\\t\\tyield (obj_dupli, dob.matrix.copy())\\n\\n\\t\\t\\t# obj.dupli_list_clear()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc7b330d4f1c31dcfd11a1e83a2bd6a4\",\n \"score\": \"0.5298358\",\n \"text\": \"def test_rest_list_content(self):\\n with self.layer.get_browser() as browser:\\n browser.login('author')\\n # Working content\\n self.assertEqual(\\n browser.open(\\n '/root/++rest++silva.core.references.items',\\n query={'intid': str(get_content_id(self.root.index))}),\\n 200)\\n self.assertEqual(browser.content_type, 'application/json')\\n self.assertIsInstance(browser.json, dict)\\n self.assertDictContainsSubset(\\n {'id': 'index', 'type': 'Mockup VersionedContent',\\n 'path': 'index', 'title': 'Index'},\\n browser.json)\\n # Broken content\\n self.assertEqual(\\n browser.open(\\n '/root/++rest++silva.core.references.items',\\n query={'intid': '42'}),\\n 200)\\n self.assertEqual(browser.content_type, 'application/json')\\n self.assertIsInstance(browser.json, dict)\\n self.assertDictContainsSubset(\\n {'id': 'broken', 'type': 'Broken',\\n 'path': '', 'title': 'Missing content'},\\n browser.json)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c29d0780ad31f6a2d086a5fc9d7759dc\",\n \"score\": \"0.52946854\",\n \"text\": \"def index_queryset(self, using=None):\\n translation.activate(settings.LANGUAGE_CODE)\\n return self.get_model().objects.filter(status=UrlNode.PUBLISHED).select_related()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"022ae3dd9ca209a50532fc9127d97880\",\n \"score\": \"0.5276151\",\n \"text\": \"def obj_list(self):\\n return self.__obj_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb87692748ef7a5a60b94523b077dab7\",\n \"score\": \"0.52720463\",\n \"text\": \"def explorer(path=u''):\\n if current_user.is_anonymous and cmp(path[:6], u'public'):\\n abort(403)\\n path = path.rstrip(u'/')\\n abs_path = os.path.join(current_app.data_path, u'files', path).rstrip(u'/')\\n if os.path.isdir(abs_path):\\n folders = {}\\n objects = {}\\n parent_url = (url_for(u'.explorer', path=path).rsplit(u'/', 1)[0] + u'/') \\\\\\n if (path != (u'' if current_user.is_authenticated else u'public')) else None\\n nav_tabs = _generate_urls(path)\\n for name in os.listdir(abs_path):\\n path_name = os.path.join(abs_path, name)\\n if os.path.isdir(path_name):\\n folders[name] = {\\n u'link': url_for(u'.explorer', path=os.path.join(path, name)+u'/'),\\n u'last_modify': time.strftime(u'%Y-%m-%d %H:%M:%S', time.localtime(os.path.getmtime(path_name))),\\n }\\n elif os.path.isfile(path_name):\\n type_c = name.rsplit(u'.', 1)\\n size = float(os.path.getsize(path_name))\\n size_l = 0\\n while size > 1000.0 and size_l < 4:\\n size /= 1000.0\\n size_l += 1\\n objects[name] = {\\n u'link': url_for(u'.explorer', path=os.path.join(path, name)),\\n u'type': type_c[1] if len(type_c) == 2 else None,\\n u'size': u'%.3f %s' % (size, [u'B', u'KB', u'MB', u'GB', u'TB'][size_l]) if size_l else u'%.0f B' % size,\\n u'last_modify': time.strftime(u'%Y-%m-%d %H:%M:%S', time.localtime(os.path.getmtime(abs_path))),\\n }\\n # folders = sorted(folders.iteritems(), key=lambda k: k, reverse=False)\\n # objects = sorted(objects.iteritems(), key=lambda k: k, reverse=False)\\n #######################################################################\\n # path : 相对于文件夹根目录的路径（根目录为空字符串）\\n # nav_tabs : 当前路径对应的每一级路径名级链接\\n # back_link : 上一级目录的链接（根目录的上一级为None）\\n # folders : 文件夹列表\\n # files : 文件列表\\n #######################################################################\\n return render_template(u'files/files_index.html', folders=folders, files=objects,\\n path=path, nav_tabs=nav_tabs, back_link=parent_url)\\n elif os.path.isfile(abs_path):\\n return send_file(abs_path)\\n else:\\n abort(404)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ded46173a04e7d854a178173fbf79cc3\",\n \"score\": \"0.5250477\",\n \"text\": \"def list(self, request, *args, **kwargs):\\n return super().list(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ded46173a04e7d854a178173fbf79cc3\",\n \"score\": \"0.5250477\",\n \"text\": \"def list(self, request, *args, **kwargs):\\n return super().list(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ded46173a04e7d854a178173fbf79cc3\",\n \"score\": \"0.5250477\",\n \"text\": \"def list(self, request, *args, **kwargs):\\n return super().list(request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a3bbf45c2227181491f836e6b3cdc11\",\n \"score\": \"0.52481556\",\n \"text\": \"def list(self, request, *args, **kwargs):\\n\\n data = self.get_tree_structure_json()\\n\\n return Response(dict(results=data, error=[]), status=status.HTTP_200_OK)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a3bbf45c2227181491f836e6b3cdc11\",\n \"score\": \"0.52481556\",\n \"text\": \"def list(self, request, *args, **kwargs):\\n\\n data = self.get_tree_structure_json()\\n\\n return Response(dict(results=data, error=[]), status=status.HTTP_200_OK)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a7f75ba83f51efb6b21d45bcc87fde5\",\n \"score\": \"0.5244862\",\n \"text\": \"def test_rest_list(self):\\n with self.layer.get_browser() as browser:\\n browser.login('author')\\n # Root\\n self.assertEqual(\\n browser.open(\\n '/root/++rest++silva.core.references.items'),\\n 200)\\n self.assertEqual(browser.content_type, 'application/json')\\n self.assertIsInstance(browser.json, list)\\n self.assertEqual(len(browser.json), 2)\\n self.assertDictContainsSubset(\\n {'id': '.', 'type': 'Silva Root', 'path': '.'},\\n browser.json[0])\\n self.assertDictContainsSubset(\\n {'id': 'folder', 'type': 'Silva Folder',\\n 'path': 'folder', 'title': 'Folder'},\\n browser.json[1])\\n # Folder\\n self.assertEqual(\\n browser.open(\\n '/root/folder/++rest++silva.core.references.items'),\\n 200)\\n self.assertEqual(browser.content_type, 'application/json')\\n self.assertIsInstance(browser.json, list)\\n self.assertEqual(len(browser.json), 1)\\n self.assertDictContainsSubset(\\n {'id': '.', 'type': 'Silva Folder',\\n 'path': 'folder', 'title': 'Folder'},\\n browser.json[0])\\n # Object\\n self.assertEqual(\\n browser.open(\\n '/root/index/++rest++silva.core.references.items'),\\n 200)\\n self.assertEqual(browser.content_type, 'application/json')\\n self.assertIsInstance(browser.json, list)\\n self.assertEqual(len(browser.json), 1)\\n self.assertDictContainsSubset(\\n {'id': '.', 'type': 'Mockup VersionedContent',\\n 'path': 'index', 'title': 'Index'},\\n browser.json[0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4de7b1a34f0d812f1f7a9c3297d9fb55\",\n \"score\": \"0.52443033\",\n \"text\": \"def list_files(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1829b68556e8ccdca0cfa7ead12d92b\",\n \"score\": \"0.52420264\",\n \"text\": \"def listdir(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a085b2e62de5d75fa26a8ed5431bd4c5\",\n \"score\": \"0.52271235\",\n \"text\": \"def index_queryset(self, using=None):\\n return self.get_model().objects.filter(published=True, private=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9aa4c0286ffe2112f5f74f84931c455f\",\n \"score\": \"0.52258164\",\n \"text\": \"def get_queryset(self):\\n current_user = self.request.user\\n queryset = File.objects.filter(owner=current_user)\\n sub_file = self.request.query_params.get('sub_file', None)\\n if sub_file is not None:\\n queryset = queryset.filter(pk=sub_file)\\n node = None\\n if len(queryset):\\n node = queryset[0]\\n return node.get_sub_file()\\n else:\\n return node\\n return queryset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db71564bd975a9972016f61f6dd4b62e\",\n \"score\": \"0.52244407\",\n \"text\": \"def get_resources(request):\\n try:\\n return HttpResponse(json.dumps(walkalldirs(request)), content_type='application/json')\\n except Exception as e: # pragma: no cover\\n return HttpResponseNotFound(str(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbe06ff480fefdbbc41a91fd2e4d57a6\",\n \"score\": \"0.52238226\",\n \"text\": \"def _getChildBlurbs(self, request):\\n return list(self.original.view(self.getRole()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1da33380645810322246b367fb82fa7a\",\n \"score\": \"0.5221357\",\n \"text\": \"def get_queryset(self, request):\\n url_subquery = PageUrl.objects.filter(\\n language=OuterRef(\\\"language\\\"), page=OuterRef(\\\"page\\\")\\n )\\n # Collect locked status to handle the requirement that lock\\n # on a draft version dictates the unpublish permission\\n # on a published version\\n draft_version_lock_subquery = VersionLock.objects.filter(\\n version__content_type=OuterRef(\\\"content_type\\\"),\\n version__object_id=OuterRef(\\\"object_id\\\"),\\n version__state=DRAFT,\\n ).order_by(\\\"-pk\\\")\\n queryset = (\\n super()\\n .get_queryset(request)\\n .filter(page__node__site=get_current_site(request))\\n .annotate(_path=Subquery(url_subquery.values(\\\"path\\\")[:1]))\\n )\\n return queryset.select_related(\\\"page\\\").prefetch_related(\\n Prefetch(\\n \\\"versions\\\",\\n queryset=Version.objects.annotate(\\n # used by locking\\n _draft_version_user_id=Subquery(\\n draft_version_lock_subquery.values(\\\"created_by\\\")[:1]\\n )\\n )\\n .select_related(\\\"created_by\\\", \\\"versionlock\\\")\\n .prefetch_related(\\\"content\\\"),\\n )\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4028d9bd83160692d56fcff0c87e2657\",\n \"score\": \"0.52147985\",\n \"text\": \"def _recursive_list(api, parent, base_folder=\\\"\\\"):\\n out = []\\n for f in api.files.query(parent=parent):\\n if f.type == \\\"folder\\\":\\n out += _recursive_list(api, f.id, os.path.join(base_folder, f.name))\\n else:\\n out.append((os.path.join(base_folder, f.name), f))\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b0537a2cff9c6cbf75c1fc091c87a66\",\n \"score\": \"0.52134186\",\n \"text\": \"def get_virt_objects(request, model, order_by, dc=None, include=(), **kwargs):\\n dc = dc or request.dc\\n qs = model.objects.filter(dc=dc)\\n\\n if request.user.is_admin(request, dc=dc):\\n # noinspection PyUnresolvedReferences\\n qf = ~Q(access__in=model.INVISIBLE)\\n else:\\n # noinspection PyUnresolvedReferences\\n qf = (Q(access__in=(model.PUBLIC, model.DISABLED))) | (Q(owner=request.user.pk) & Q(access=model.PRIVATE))\\n\\n if include:\\n # noinspection PyAugmentAssignment\\n qf = qf | Q(pk__in=include)\\n\\n return qs.filter(qf).order_by(*order_by)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e15275e84e2689a7297fbd55d20f37c\",\n \"score\": \"0.5210278\",\n \"text\": \"def listClipboardObjects( self, item=None, REQUEST=None, permission=CMFCorePermissions.ModifyPortalContent ):\\n if item is None:\\n uid = None\\n linked = []\\n else:\\n uid = item.getUid()\\n linked = map( lambda ob: ob['dest_uid'], self.searchResults( source_uid=uid ) ) + \\\\\\n map( lambda ob: ob['source_uid'], self.searchResults( dest_uid=uid ) )\\n\\n oblist = listClipboardObjects( self, permission=permission, REQUEST=REQUEST )\\n result = []\\n\\n for ob in oblist:\\n if ob.implements('isDocument'):\\n other = ob.getUid()\\n if other and other != uid and other not in linked:\\n result.append( ob )\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87b5489415a148a33007e88cb0e92a77\",\n \"score\": \"0.5206004\",\n \"text\": \"def list (self, request, *args, **kwargs):\\n return viewsets.ReadOnlyModelViewSet.list(self, request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f35ad96186f71f56f44ec12521bebcce\",\n \"score\": \"0.51930195\",\n \"text\": \"def list_permission(self, request):\\n\\t\\tdata = self.get_serializer(self.get_queryset(), many=True).data\\n\\t\\treturn Response(data, status.HTTP_200_OK)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f35ad96186f71f56f44ec12521bebcce\",\n \"score\": \"0.51930195\",\n \"text\": \"def list_permission(self, request):\\n\\t\\tdata = self.get_serializer(self.get_queryset(), many=True).data\\n\\t\\treturn Response(data, status.HTTP_200_OK)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0dd01401e46f426294c688bb72d64e59\",\n \"score\": \"0.5185959\",\n \"text\": \"def get_children_and_shared_documents(self, user):\\n # Get documents that are direct children, or shared with but not owned by the current user\\n documents = Document2.objects.filter(\\n Q(parent_directory=self) |\\n ((Q(document2permission__users=user) | Q(document2permission__groups__in=user.groups.all())) &\\n ~Q(owner=user))\\n )\\n\\n documents = documents.exclude(is_history=True).exclude(is_managed=True)\\n\\n # Excluding all trashed docs across users\\n documents = documents.exclude(is_trashed=True)\\n\\n # Optimizing roll up for /home by checking only with directories instead of all documents\\n # For all other directories roll up is done in _filter_documents()\\n directories_all = Document2.objects.filter(type='directory').exclude(id=self.id)\\n directories_inside_home = directories_all.filter(\\n (Q(document2permission__users=user) | Q(document2permission__groups__in=user.groups.all())) &\\n ~Q(owner=user)\\n )\\n documents = documents.exclude(parent_directory__in=directories_inside_home)\\n\\n return documents.defer('description', 'data', 'extra', 'search').distinct().order_by('-last_modified')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7fa8597a17b8e9a57233543898d323c\",\n \"score\": \"0.51758415\",\n \"text\": \"def document_list_files(request,destination,location_id):\\n document_permission_results = document_permission.objects.filter(\\n is_deleted=False,\\n ).values(\\n 'document_key_id',\\n 'document_key__document_description',\\n 'document_key__document_url_location',\\n 'document_key__document',\\n 'document_key__whiteboard',\\n 'folder',\\n )\\n\\n # Limit to the required destination and location id\\n document_permission_results = get_object_from_destination(\\n document_permission_results,\\n destination,\\n location_id\\n )\\n\\n # Send back json data\\n json_results = json.dumps(list(document_permission_results), cls=DjangoJSONEncoder)\\n\\n return HttpResponse(json_results, content_type='application/json')\\n\\n\\n # # Get the document information\\n # document_results = document.objects.filter(\\n # is_deleted=False,\\n # document_key__in=document_permission_results.values('document_key')\\n # )\\n #\\n # return HttpResponse(serializers.serialize('json',document_results),content_type='application/json')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf6d3ba42bc5c58f21b882b53e4b0e20\",\n \"score\": \"0.5175111\",\n \"text\": \"def list(self, request, *args, **kwargs):\\n return viewsets.ReadOnlyModelViewSet.list(self, request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf6d3ba42bc5c58f21b882b53e4b0e20\",\n \"score\": \"0.5175111\",\n \"text\": \"def list(self, request, *args, **kwargs):\\n return viewsets.ReadOnlyModelViewSet.list(self, request, *args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"745c58014e55c947e94f4b7e44fdb47b\",\n \"score\": \"0.5173352\",\n \"text\": \"def views(self):\\n return self._views\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993273,"cells":{"query_id":{"kind":"string","value":"381cb68d20b0a9c53d9d956a7e5dc3be"},"query":{"kind":"string","value":"Tests that updates are atomic; when part of an update is not allowed, the entire update does not happen. Try to make the bikeshed twice as large and change its color. Both will fail, since the useful operation blocks the entire change."},"positive_passages":{"kind":"list like","value":[{"docid":"11861073c42597eec5cec495d4aa107e","score":"0.0","text":"def test_partiallyUpdatableAttributes(self):\n self.headers.setRawHeaders(\"Content-Type\", [\"application/json\"])\n self.body = dict(disallowedUpdateState, **allowedUpdateState)\n return self._testUpdate(http.FORBIDDEN)","title":""}],"string":"[\n {\n \"docid\": \"11861073c42597eec5cec495d4aa107e\",\n \"score\": \"0.0\",\n \"text\": \"def test_partiallyUpdatableAttributes(self):\\n self.headers.setRawHeaders(\\\"Content-Type\\\", [\\\"application/json\\\"])\\n self.body = dict(disallowedUpdateState, **allowedUpdateState)\\n return self._testUpdate(http.FORBIDDEN)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"ba96f00006c63359a37bb8c6b94ea478","score":"0.5960921","text":"def _test_staledata_error_on_concurrent_object_update(self, model, dbid):\n lock = functools.partial(lockutils.lock, uuidutils.generate_uuid())\n self._blocked_on_lock = False\n\n def _lock_blocked_name_update():\n ctx = context.get_admin_context()\n with db_api.CONTEXT_WRITER.using(ctx):\n thing = ctx.session.query(model).filter_by(id=dbid).one()\n thing.bump_revision()\n thing.name = 'newname'\n self._blocked_on_lock = True\n with lock():\n return thing\n\n with lock():\n coro = eventlet.spawn(_lock_blocked_name_update)\n # wait for the coroutine to get blocked on the lock before\n # we proceed to update the record underneath it\n while not self._blocked_on_lock:\n eventlet.sleep(0)\n ctx = context.get_admin_context()\n with db_api.CONTEXT_WRITER.using(ctx):\n thing = ctx.session.query(model).filter_by(id=dbid).one()\n thing.bump_revision()\n thing.description = 'a description'\n revision_after_build = thing.revision_number\n\n with testtools.ExpectedException(orm.exc.StaleDataError):\n # the coroutine should have encountered a stale data error because\n # the status update thread would have bumped the revision number\n # while it was waiting to commit\n coro.wait()\n # another attempt should work fine\n thing = _lock_blocked_name_update()\n self.assertEqual('a description', thing.description)\n self.assertEqual('newname', thing.name)\n self.assertGreater(thing.revision_number, revision_after_build)","title":""},{"docid":"1c9f78eb9bc3e4c52363317a174c9c5c","score":"0.5868373","text":"def test_dcim_racks_partial_update(self):\n pass","title":""},{"docid":"a731c35891698c8297cbd04fe570182b","score":"0.58166724","text":"def test_dcim_cables_partial_update(self):\n pass","title":""},{"docid":"db5a0d2be5baa28d231ed48fe1f21cc8","score":"0.58038265","text":"def dummy_update(value):\n return False","title":""},{"docid":"224715caf31aba2c7c1b17851cf57845","score":"0.5787791","text":"def test_full_update_rule(self):\n pass","title":""},{"docid":"a56552ccc2bc04cbc9a0c580385bfdb0","score":"0.5767134","text":"def test_update_bre_global(self):\n pass","title":""},{"docid":"112dbb03ecf8860fa05f632262ac4a84","score":"0.57472086","text":"def test_update_escalation(self):\n pass","title":""},{"docid":"15db4aaf2df2db53e373a0fdf8cf633c","score":"0.5727039","text":"def test_update(self):\n s = Square(1, 0, 0, 1)\n self.assertEqual(str(s), \"[Square] (1) 0/0 - 1\")\n s.update(19)\n self.assertEqual(str(s), \"[Square] (19) 0/0 - 1\")\n s. update(800, 4, 2, 3)\n self.assertEqual(str(s), \"[Square] (800) 2/3 - 4\")","title":""},{"docid":"b83ff8071f857ec261f5fdc5191e10ff","score":"0.5726534","text":"def test_full_update_checker(self):\n pass","title":""},{"docid":"262a22e07b15cc5223e23d04d36e8a12","score":"0.56599796","text":"def test_update(self):\n # this is really tested graphically, no unit test here\n pass","title":""},{"docid":"1df2e45640c37060a90860cde57888de","score":"0.56587845","text":"def test_dcim_device_bays_partial_update(self):\n pass","title":""},{"docid":"548c1e897231bea9b6cd1ca347f98512","score":"0.5652845","text":"def test_full_update_layer(self):\n pass","title":""},{"docid":"104f2997c258ffe2cccbd1c122e49bc8","score":"0.5586589","text":"def test_full_update_ruleset(self):\n pass","title":""},{"docid":"cf7a88cd40706592b1e0add18d83981e","score":"0.55156994","text":"def test_update_compute_blade(self):\n pass","title":""},{"docid":"fc0896d1a19b161c8284dea730d9b013","score":"0.55109453","text":"def test_bulk_full_update_ifc_property(self):\n pass","title":""},{"docid":"4979fe959ecce0f4ded1ad4d921de9c8","score":"0.5508749","text":"def test_update_compute_board(self):\n pass","title":""},{"docid":"009c067d2dfc6962d64e1a084099f3f8","score":"0.54972327","text":"def test_full_update_checker_result(self):\n pass","title":""},{"docid":"0fb89253768519eabbb1f367b18670e5","score":"0.5473305","text":"def test_full_update_space(self):\n pass","title":""},{"docid":"1fa5aa91afa7151c7cb1d8b6ba42af11","score":"0.54495496","text":"def test_update(self):\n\n s6 = Square(4, 0, 0, 27)\n s7 = Square(32, 2, 4, 10)\n\n self.assertEqual(str(s6), \"[Square] (27) 0/0 - 4\")\n s6.update(1, 2, 3, 6)\n self.assertEqual(str(s6), \"[Square] (1) 3/6 - 2\")\n\n self.assertEqual(str(s7), \"[Square] (10) 2/4 - 32\")\n s7.update(size=7, id=89, y=1)\n self.assertEqual(str(s7), \"[Square] (89) 2/1 - 7\")","title":""},{"docid":"8fb72d13a13ab5790d440dc118fe22f4","score":"0.54491","text":"async def test_put_then_get_cached_properly(\n hass: HomeAssistant, hass_hue, hue_client\n) -> None:\n\n # Turn the bedroom light on first\n await hass_hue.services.async_call(\n light.DOMAIN,\n const.SERVICE_TURN_ON,\n {const.ATTR_ENTITY_ID: \"light.ceiling_lights\", light.ATTR_BRIGHTNESS: 153},\n blocking=True,\n )\n\n ceiling_lights = hass_hue.states.get(\"light.ceiling_lights\")\n assert ceiling_lights.state == STATE_ON\n assert ceiling_lights.attributes[light.ATTR_BRIGHTNESS] == 153\n\n # update light state through api\n await perform_put_light_state(\n hass_hue,\n hue_client,\n \"light.ceiling_lights\",\n True,\n hue=4369,\n saturation=127,\n brightness=254,\n )\n\n # Check that a Hue brightness level of 254 becomes 255 in HA realm.\n assert (\n hass.states.get(\"light.ceiling_lights\").attributes[light.ATTR_BRIGHTNESS] == 255\n )\n\n # Make sure that the GET response is the same as the PUT response within 2 seconds if the service call is successful and the state doesn't change.\n # We simulate a long latence for the actual setting of the entity by forcibly sitting different values directly.\n await hass_hue.services.async_call(\n light.DOMAIN,\n const.SERVICE_TURN_ON,\n {const.ATTR_ENTITY_ID: \"light.ceiling_lights\", light.ATTR_BRIGHTNESS: 153},\n blocking=True,\n )\n\n # go through api to get the state back, the value returned should match those set in the last PUT request.\n ceiling_json = await perform_get_light_state(\n hue_client, \"light.ceiling_lights\", HTTPStatus.OK\n )\n\n assert ceiling_json[\"state\"][HUE_API_STATE_HUE] == 4369\n assert ceiling_json[\"state\"][HUE_API_STATE_SAT] == 127\n assert ceiling_json[\"state\"][HUE_API_STATE_BRI] == 254\n\n # Make sure that the GET response does not use the cache if PUT response within 2 seconds if the service call is Unsuccessful and the state does not change.\n await hass_hue.services.async_call(\n light.DOMAIN,\n const.SERVICE_TURN_OFF,\n {const.ATTR_ENTITY_ID: \"light.ceiling_lights\"},\n blocking=True,\n )\n\n # go through api to get the state back\n ceiling_json = await perform_get_light_state(\n hue_client, \"light.ceiling_lights\", HTTPStatus.OK\n )\n\n # Now it should be the real value as the state of the entity has changed to OFF.\n assert ceiling_json[\"state\"][HUE_API_STATE_HUE] == 0\n assert ceiling_json[\"state\"][HUE_API_STATE_SAT] == 0\n assert ceiling_json[\"state\"][HUE_API_STATE_BRI] == 1\n\n # Ensure we read the actual value after exceeding the timeout time.\n\n # Turn the bedroom light back on first\n await hass_hue.services.async_call(\n light.DOMAIN,\n const.SERVICE_TURN_ON,\n {const.ATTR_ENTITY_ID: \"light.ceiling_lights\"},\n blocking=True,\n )\n\n # update light state through api\n await perform_put_light_state(\n hass_hue,\n hue_client,\n \"light.ceiling_lights\",\n True,\n hue=4369,\n saturation=127,\n brightness=254,\n )\n\n await hass_hue.services.async_call(\n light.DOMAIN,\n const.SERVICE_TURN_ON,\n {\n const.ATTR_ENTITY_ID: \"light.ceiling_lights\",\n light.ATTR_BRIGHTNESS: 127,\n light.ATTR_RGB_COLOR: (1, 2, 7),\n },\n blocking=True,\n )\n\n # go through api to get the state back, the value returned should match those set in the last PUT request.\n ceiling_json = await perform_get_light_state(\n hue_client, \"light.ceiling_lights\", HTTPStatus.OK\n )\n\n # With no wait, we must be reading what we set via the PUT call.\n assert ceiling_json[\"state\"][HUE_API_STATE_HUE] == 4369\n assert ceiling_json[\"state\"][HUE_API_STATE_SAT] == 127\n assert ceiling_json[\"state\"][HUE_API_STATE_BRI] == 254\n\n with patch.object(hue_api, \"STATE_CACHED_TIMEOUT\", 0.000001):\n await asyncio.sleep(0.000001)\n\n # go through api to get the state back, the value returned should now match the actual values.\n ceiling_json = await perform_get_light_state(\n hue_client, \"light.ceiling_lights\", HTTPStatus.OK\n )\n\n # Once we're after the cached duration, we should see the real value.\n assert ceiling_json[\"state\"][HUE_API_STATE_HUE] == 41869\n assert ceiling_json[\"state\"][HUE_API_STATE_SAT] == 217\n assert ceiling_json[\"state\"][HUE_API_STATE_BRI] == 127","title":""},{"docid":"a1c5c67514a6f388deceedd9eff90fac","score":"0.543819","text":"def test_full_update_property_set(self):\n pass","title":""},{"docid":"e0483d7edc27b86678b5835d6cfa249e","score":"0.54345864","text":"def test_full_update_element(self):\n pass","title":""},{"docid":"a1c889d04bb8ec80bef12307d9733727","score":"0.5427962","text":"async def test_async_update_locked_multiple_updates(\n self, hass, pywemo_device, wemo_entity\n ):\n await entity_test_helpers.test_async_update_locked_multiple_updates(\n hass,\n pywemo_device,\n wemo_entity,\n )","title":""},{"docid":"2eb25f59261041068933488639fea756","score":"0.5408724","text":"def test_update_mixin6(self):\n pass","title":""},{"docid":"025257c5fe610d6426b6dd095ff09a46","score":"0.54081184","text":"def test_update(self):\n p_arrs = generate_sample_dataset_2()\n cm = CellManager(arrays_to_bin=p_arrs, min_cell_size=2.,\n max_cell_size=2.)\n \n # move particle 3 in +ve y direction by one unit.\n y = p_arrs[0].get_carray('y')\n curr_y_2 = y.get(2)\n y[2] = curr_y_2 + 1.0\n # move particle 7 in -ve x direction by one unit\n x = p_arrs[0].get_carray('x')\n curr_x_6 = x.get(6)\n x[6] = curr_x_6 - 1.0\n # move particle 4 in -ve y direction by one unit\n curr_y_4 = y.get(4)\n y[4] = curr_y_4 - 1.0\n\n # 5 cells are formed\n self.assertEqual(len(cm.cells_dict), 5)\n # cell 0,0,0 has 3 particles since we have not updated\n self.assertEqual(cm.cells_dict[IntPoint()].py_get_number_of_particles(), 3)\n \n # because we changed the data, ask cm to update_status\n cm.py_update_status()\n cm.py_update()\n\n # no change should occur, as we have NOT set the dirty bit of the\n # parray.\n self.assertEqual(len(cm.cells_dict), 5)\n\n self.assertEqual(cm.cells_dict.has_key(IntPoint(0, 0, 0)), True)\n self.assertEqual(cm.cells_dict.has_key(IntPoint(1, 0, 0)), True)\n self.assertEqual(cm.cells_dict.has_key(IntPoint(1, -1, 0)), True)\n self.assertEqual(cm.cells_dict.has_key(IntPoint(-1, -1, 0)), True)\n self.assertEqual(cm.cells_dict.has_key(IntPoint(-1, 1, 0)), True)\n\n cell = cm.cells_dict[IntPoint(0, 0, 0)]\n self.assertEqual(cell.py_get_number_of_particles(), 3)\n cell = cm.cells_dict[IntPoint(1, 0, 0)]\n self.assertEqual(cell.py_get_number_of_particles(), 1)\n cell = cm.cells_dict[IntPoint(1, -1, 0)]\n self.assertEqual(cell.py_get_number_of_particles(), 1)\n cell = cm.cells_dict[IntPoint(-1, -1, 0)]\n self.assertEqual(cell.py_get_number_of_particles(), 1)\n cell = cm.cells_dict[IntPoint(-1, 1, 0)]\n self.assertEqual(cell.py_get_number_of_particles(), 1)\n\n # now set it to dirty and check.\n p_arrs[0].set_dirty(True)\n\n cm.py_update_status()\n cm.py_update()\n\n self.assertEqual(len(cm.cells_dict), 6)\n\n # make sure the proper cells have been created.\n self.assertEqual(cm.cells_dict.has_key(IntPoint(0, 0, 0)), True)\n\n # cell 1,-1,0 should have been deleted.\n self.assertEqual(cm.cells_dict.has_key(IntPoint(1, -1, 0)), False)\n\n # two new cells should have been created.\n self.assertEqual(cm.cells_dict.has_key(IntPoint(0, -1, 0)), True)\n self.assertEqual(cm.cells_dict.has_key(IntPoint(0, 1, 0)), True)\n\n cell = cm.cells_dict[IntPoint(0, -1, 0)]\n self.assertEqual(cell.py_get_number_of_particles(), 2)\n\n cell = cm.cells_dict[IntPoint(0, 1, 0)]\n self.assertEqual(cell.py_get_number_of_particles(), 1)","title":""},{"docid":"d278024caf69339e3035bc48d650da52","score":"0.5400628","text":"def test_update_square(self):\n s1 = Square(5)\n\n s1.update(10)\n self.assertEqual(s1.__str__(), \"[Square] (10) 0/0 - 5\")\n\n s1.update(1, 2)\n self.assertEqual(s1.__str__(), \"[Square] (1) 0/0 - 2\")\n\n s1.update(1, 2, 3)\n self.assertEqual(s1.__str__(), \"[Square] (1) 3/0 - 2\")\n\n s1.update(1, 2, 3, 4)\n self.assertEqual(s1.__str__(), \"[Square] (1) 3/4 - 2\")\n\n s1.update(x=12)\n self.assertEqual(s1.__str__(), \"[Square] (1) 12/4 - 2\")\n\n s1.update(size=7, y=1)\n self.assertEqual(s1.__str__(), \"[Square] (1) 12/1 - 7\")\n\n s1.update(size=7, id=89, y=1)\n self.assertEqual(s1.__str__(), \"[Square] (89) 12/1 - 7\")","title":""},{"docid":"1b5cd629ef8159575b85064b9609dac9","score":"0.53928655","text":"def test_update_checker(self):\n pass","title":""},{"docid":"aac088bda7eb6062365206cc56ec84d4","score":"0.53722274","text":"async def test_atomic_update(client: Client) -> None:\n post = await client.post.create({'title': 'My Post', 'published': False})\n assert post.title == 'My Post'\n assert post.views == 0\n\n updated = await client.post.update(\n where={'id': post.id}, data={'views': {'increment': 1}}\n )\n assert updated is not None\n assert updated.views == 1","title":""},{"docid":"64f656f9f007ed8a8f19610a640926a4","score":"0.5361777","text":"def test_update(benchmark):\n do_put()\n\n benchmark(db.update, [('4', (4, 'charlie', \"Charlie's data\"))], ['1'])\n assert ['2', '3', '4'] == db.keys()","title":""},{"docid":"44632cb8830a8fba32696e00162ccf87","score":"0.5349633","text":"def test_full_update_rule_component(self):\n pass","title":""},{"docid":"9cf7404ec81ba0a354cb7abf00f81a1e","score":"0.53271013","text":"def test_update_args_and_kwargs(self):\n test1 = Square(3, 3, 2, 10)\n self.assertEqual(test1.__str__(), \"[Square] (10) 3/2 - 3\")\n test1.update(69)\n self.assertEqual(test1.__str__(), \"[Square] (69) 3/2 - 3\")\n test1.update(69, 6)\n self.assertEqual(test1.__str__(), \"[Square] (69) 3/2 - 6\")\n test1.update(69, 6, 9)\n self.assertEqual(test1.__str__(), \"[Square] (69) 9/2 - 6\")\n test1.update(69, 6, 9, 6)\n self.assertEqual(test1.__str__(), \"[Square] (69) 9/6 - 6\")\n test1.update(69, 6, 9, 6, 9)\n self.assertEqual(test1.__str__(), \"[Square] (69) 9/6 - 6\")\n\n test2 = Square(1, 1, 1, 1)\n self.assertEqual(test2.__str__(), \"[Square] (1) 1/1 - 1\")\n test2.update(id=37)\n self.assertEqual(test2.__str__(), \"[Square] (37) 1/1 - 1\")\n test2.update(size=3)\n self.assertEqual(test2.__str__(), \"[Square] (37) 1/1 - 3\")\n test2.update(x=4, y=5)\n self.assertEqual(test2.__str__(), \"[Square] (37) 4/5 - 3\")\n test2.update(id=35, size=4, x=5, y=1)\n self.assertEqual(test2.__str__(), \"[Square] (35) 5/1 - 4\")\n\n test2.update(2, 3, x=5, y=1)\n self.assertEqual(test2.__str__(), \"[Square] (2) 5/1 - 3\")","title":""},{"docid":"1cee014b7bf9fbfcbe3ce5511d321dfa","score":"0.53046936","text":"def testChangingVotes(self):\n\n ballot_box = api.SantaBallotBox(self.santa_blockable1.key.id())\n with self.LoggedInUser() as user:\n ballot_box.Vote(True, user)\n ballot_box.Vote(False, user)\n ballot_box.Vote(True, user)\n\n blockable = self.santa_blockable1.key.get()\n\n self.assertEqual(blockable.score, 1)\n\n # SCORE_CHANGE x3\n self.assertBigQueryInsertions([TABLE.VOTE, TABLE.BINARY] * 3)","title":""},{"docid":"4d73e99f48aef4350c44e38c86cadbaf","score":"0.5299943","text":"def test_update_ref():","title":""},{"docid":"c48b34b068ee30003481793f155252a5","score":"0.52938837","text":"def test_bulk_update_ifc_property(self):\n pass","title":""},{"docid":"b8b8b254270395082cccc498cb759dba","score":"0.52938426","text":"def test_partialupdate_policy(self):\n pass","title":""},{"docid":"0399d9a7bc135fc0a0fbafeec73021c7","score":"0.52788883","text":"def test_integer_inplace_update(self):\n vm = Integer.value_manager(Integer(), 5, strategy=SaveOnChange)\n self.assertFalse(vm.changed)\n vm.value += 1\n self.assertTrue(vm.changed)","title":""},{"docid":"8467344a5c7c69807b3b01f6ccbb4a95","score":"0.5274749","text":"def test_update_run(self):\n pass","title":""},{"docid":"7b3cd8cb54abc1dcc90d8a3360339aed","score":"0.52715844","text":"def test_updated_barrier_ids_current_user(self):\n barrier1 = BarrierFactory(priority=\"LOW\")\n barrier2 = BarrierFactory(priority=\"MEDIUM\")\n barrier3 = BarrierFactory(priority=\"HIGH\")\n\n user = create_test_user(sso_user_id=self.sso_user_data_1[\"user_id\"])\n\n saved_search = SavedSearch.objects.create(\n user=user, name=\"Medium\", filters={\"priority\": [\"MEDIUM\"]}\n )\n saved_search.mark_as_seen()\n\n assert saved_search.updated_barrier_ids == []\n\n barrier2.summary = \"New summary\"\n barrier2.modified_by = user\n barrier2.save()\n\n saved_search = SavedSearch.objects.get(pk=saved_search.pk)\n assert barrier2.pk not in saved_search.updated_barrier_ids\n assert saved_search.updated_barrier_ids == []","title":""},{"docid":"01b7ee253705a37f61089c77b9f95ae4","score":"0.52656084","text":"def test_update_checker_result(self):\n pass","title":""},{"docid":"7ee1df1cb7cbfaa78bcac44e2f0ecdcd","score":"0.52647567","text":"def test_bulk_full_update_elements(self):\n pass","title":""},{"docid":"44f48c02abc734db3fb85d08f6ae92f4","score":"0.5245097","text":"def test_green(self):\n self.assertEqual(self.p1.green , 100 , self.green[self.idx1])\n self.assertEqual(self.p2.green , 84 , self.green[self.idx2])","title":""},{"docid":"d59ccf1bf6b4d2eee645c93b8540b03a","score":"0.5237491","text":"def test_green(self):\n g1 = [i+1 for i in self.FileObject.green]\n self.FileObject.green = g1\n g2 = self.FileObject.get_green()\n self.assertTrue((g1 == list(g2)))","title":""},{"docid":"2521e7ece6482d75660cedc293975616","score":"0.5236049","text":"def test_update_badge(self):\n badge = badge_api.create_badge(*self.badge_values)\n attrs = self.badge_attrs\n del attrs[1]\n kwargs = dict(zip(self.badge_attrs, [badge['uri']] + self.badge_values))\n del kwargs['author_uri']\n kwargs['title'] = 'A new title'\n badge_api.update_badge(**kwargs)\n badge2 = badge_api.get_badge(badge['uri'])\n self.assertNotEquals(badge, badge2)","title":""},{"docid":"1d8d93ee8c7a9499bebd502d998baad8","score":"0.523573","text":"def test_update_layer(self):\n pass","title":""},{"docid":"d33cb26bc69c2cf67327bb570a3abba7","score":"0.5230972","text":"def needs_update():","title":""},{"docid":"f2b8d6cbdce96d35c8c6f8aa71035ab4","score":"0.52241045","text":"def sane_after_update(self):\n assert self.W_ee.sane_after_update()\n assert self.W_oe.sane_after_update()\n # assert self.W_ie.sane_after_update()\n assert self.W_ei.sane_after_update()\n return True","title":""},{"docid":"eac5374390e82f08ebf85a6ca1bd293c","score":"0.52227384","text":"def hillClimb(self):\n #Pick random square, see if color change will improve it IF IT\"S MUTABLE\n #IF doesnt improve, rem from list and try diff square.\n #Generate list of mutable squares and choose a random one to improve\n pre_score= self.board.score()\n post_score= sys.maxint\n l= [(x,y) for y in range(self.board.size) for x in range(self.board.size) if self.board.mutable[x][y]]\n while len(l) > 0 and not self.board.isSolved():\n squ= random.choice(l)\n self.board.switchColor(squ)\n post_score= self.board.score()\n if post_score > pre_score:\n l.remove(squ)\n self.board.switchColor(squ)\n else:\n break\n if len(l) > 0 or (len(l) == 0 and (post_score < pre_score or self.board.isSolved())):\n return True\n else:\n return False","title":""},{"docid":"63db9f703f3a0dadae4ddc83ec88c966","score":"0.52160734","text":"def test_mutate_cell_taken():\n\n rv1, out1 = getstatusoutput(f'{prg} -b XXO...OOX --player X --cell 9')\n assert rv1 != 0\n assert re.search('--cell \"9\" already taken', out1)\n\n rv2, out2 = getstatusoutput(f'{prg} --board XXO...OOX --p O -c 1')\n assert rv2 != 0\n assert re.search('--cell \"1\" already taken', out2)","title":""},{"docid":"52d00b80461161fb6fddeba7b44e33c7","score":"0.52126414","text":"def test_Update(self):\n\n r = Rectangle(1, 2, 3, 4)\n arguments = (\n ('id', 'id'), ('width', 10), ('height', 20), ('x', 30), ('y', 40),\n ('extra', 0)\n )\n d = r.to_dictionary()\n for i in range(len(arguments)):\n args = arguments[:i + 1]\n if i < len(arguments) - 1:\n d.update(args)\n with self.subTest():\n r.update(*(val for _, val in args))\n self.assertEqual(r.to_dictionary(), d)\n r.update('new', width=5)\n d['id'] = 'new'\n with self.subTest():\n self.assertEqual(r.to_dictionary(), d)\n r.update('new', 1, 2, 3, 4)\n d = r.to_dictionary()\n for i in range(len(arguments)):\n args = arguments[:i + 1]\n if i < len(arguments) - 1:\n d.update(args)\n with self.subTest():\n r.update(**dict(args))\n self.assertEqual(r.to_dictionary(), d)","title":""},{"docid":"fafa78f0d8afa3363686620453eb50cb","score":"0.52073365","text":"def test_full_update_ifc_property(self):\n pass","title":""},{"docid":"7ac0ac6decd28e201c7d3736ac3ca5b5","score":"0.5196939","text":"def test_get_and_set(self):\r\n old = SOCO.treble\r\n self.assertIn(old, self.valid_values, NOT_IN_RANGE)\r\n # Values on the boundaries of the valid equivalence partition\r\n for value in [self.valid_values[0], self.valid_values[-1]]:\r\n SOCO.treble = value\r\n wait()\r\n self.assertEqual(SOCO.treble, value, NOT_EXP)\r\n SOCO.treble = old\r\n wait()","title":""},{"docid":"a044c953ab266c3bd0e919ca20ea99d2","score":"0.51767","text":"def test_updated_barrier_ids_other_user(self):\n barrier1 = BarrierFactory(priority=\"LOW\")\n barrier2 = BarrierFactory(priority=\"MEDIUM\")\n barrier3 = BarrierFactory(priority=\"HIGH\")\n\n user = create_test_user(sso_user_id=self.sso_user_data_1[\"user_id\"])\n\n saved_search = SavedSearch.objects.create(\n user=user, name=\"Medium\", filters={\"priority\": [\"MEDIUM\"]}\n )\n saved_search.mark_as_seen()\n\n assert saved_search.updated_barrier_ids == []\n\n barrier1.summary = \"New summary\"\n barrier1.save()\n\n barrier2.summary = \"New summary\"\n barrier2.save()\n\n saved_search = SavedSearch.objects.get(pk=saved_search.pk)\n assert saved_search.updated_barrier_ids == [barrier2.id]","title":""},{"docid":"223bb420c8a18c112f8d18672101399f","score":"0.51733667","text":"def test_single_update_reduces_cost_function(self):\n\n random_seed = 0\n random_state = check_random_state(random_seed)\n\n n_features = 25\n n_components = 6\n n_samples = 300\n\n X = random_state.uniform(size=(n_samples, n_features))\n S = random_state.uniform(size=(n_components, n_features))\n Gamma = right_stochastic_matrix(\n (n_samples, n_components), random_state=random_state)\n\n self.assertTrue(np.allclose(Gamma.sum(axis=1), 1, 1e-14))\n\n lags = [2, 7]\n n_lags = len(lags)\n\n order_weights = random_state.uniform(size=(n_lags,))\n order_weights /= order_weights.sum()\n\n transition_matrices = np.empty(\n (n_lags, n_components, n_components))\n for i in range(n_lags):\n transition_matrices[i] = left_stochastic_matrix(\n (n_components, n_components), random_state=random_state)\n\n model = MTDConvexCoding(n_components=n_components, epsilon_states=2.3,\n epsilon_weights=1.3, lags=lags)\n\n model.X = X\n model.Gamma = Gamma\n model.S = S\n model.order_weights = order_weights\n model.transition_matrices = transition_matrices\n\n model._initialize_workspace()\n\n initial_cost = model._evaluate_cost()\n\n error = model._update_parameters()\n\n self.assertEqual(error, 0)\n\n final_cost = model._evaluate_cost()\n\n self.assertTrue(final_cost <= initial_cost)","title":""},{"docid":"398834f5d41e016f5462dc49d533c8ab","score":"0.5167145","text":"def test_update_with_enabled_True(self):\n self._assert_update_raises_bad_request('True')","title":""},{"docid":"0438d9659e6523b5eda6c4286a81a242","score":"0.5162143","text":"def test_update_putaway(self):\n pass","title":""},{"docid":"7805c8b26a79f7b676ece19932e7d17b","score":"0.51602423","text":"def test_update():\n our_file = StringIO()\n progressbar = tqdm(total=2, file=our_file, miniters=1, mininterval=0)\n assert len(progressbar) == 2\n progressbar.update(2)\n our_file.seek(0)\n assert '| 2/2' in our_file.read()\n progressbar.desc = 'dynamically notify of 4 increments in total'\n progressbar.total = 4\n progressbar.update(-10) # should default to +1\n our_file.seek(0)\n assert '| 3/4 ' in our_file.read()\n our_file.seek(0)\n assert 'dynamically notify of 4 increments in total' in our_file.read()\n our_file.close()","title":""},{"docid":"3d3f9210f0582588c2424d6ef9f16a0a","score":"0.51565576","text":"def test_update_dog(self):\n pass","title":""},{"docid":"c4aa188a339c19e05cf253f55794cacf","score":"0.51525444","text":"def test_valid_update(self):\n self.assertEqual(python_to_couch({'update': 'true'}), {'update': 'true'})\n self.assertEqual(python_to_couch({'update': 'false'}), {'update': 'false'})\n self.assertEqual(python_to_couch({'update': 'lazy'}), {'update': 'lazy'})","title":""},{"docid":"fcafdc607a093dfd6c74d9b79a4c3745","score":"0.5148158","text":"def test_update_all_args(self):\n s1 = Square(10, 10, 10, 1)\n self.assertEqual(s1.__str__(), \"[Square] (1) 10/10 - 10\")\n s1.update(89, 2, 3, 4)\n self.assertEqual(s1.__str__(), \"[Square] (89) 3/4 - 2\")","title":""},{"docid":"8a7fa2d6fbda18780df4f9f2d22b686b","score":"0.514652","text":"def test_redo_update(self):\n ad_rep = AD_REP_FACTORY.create_ad_rep()\n self.assertEqual(ad_rep.consumer_points, 0)\n flyer = Flyer.objects.create(site_id=2, is_approved=True)\n flyer_recipients = CONSUMER_FACTORY.create_consumers(create_count=5)\n for consumer in flyer_recipients:\n AdRepConsumer.objects.create(ad_rep=ad_rep, consumer_id=consumer.id)\n BonusPoolFlyer.objects.create(flyer_id=flyer.id, calculate_status='2')\n UPDATE_CONSUMER_BONUS_POOL(flyer.id, [])\n ad_rep = AdRep.objects.get(id=ad_rep.id)\n self.assertEqual(ad_rep.consumer_points, 0)","title":""},{"docid":"5e6997eab1ba91b27a7b92f971c2f3ac","score":"0.5144648","text":"def possible_update_VaueErrors(self):\n test = Square(1)\n with self.assertRaises(ValueError):\n test.update(1, -4)\n\n with self.assertRaises(ValueError):\n test.update(height=-4)\n\n with self.assertRaises(ValueError):\n test.update(width=-3)\n\n with self.assertRaises(ValueError):\n test.update(1, 5, -1, 5)\n\n with self.assertRaises(ValueError):\n test.update(x=-1)\n\n with self.assertRaises(ValueError):\n test.update(1, 5, 1, -5)\n\n with self.assertRaises(ValueError):\n test.update(y=-5)","title":""},{"docid":"c6fe38c83b53bbd3a2708f23c2a1d711","score":"0.51401764","text":"def _safety_belt_update(self, safety_belt=''):\n\n if not self.isValidSafetyBelt(safety_belt):\n return 0\n self.updateSafetyBelt(safety_belt)\n return 1","title":""},{"docid":"6f940e8fd6a52df510ca25e9da89c9e0","score":"0.5134604","text":"def test_blue(self):\n b1 =[i+1 for i in self.FileObject.blue]\n self.FileObject.blue = b1\n b2 = self.FileObject.get_blue()\n self.assertTrue((b1 == list(b2)))","title":""},{"docid":"474163be59dfbaf3b92d7f0e90b13238","score":"0.5124191","text":"def test_operator_update_operator(self):\n pass","title":""},{"docid":"3d60d88e5ee7f70dacb87aac8dd755f7","score":"0.5123588","text":"def test_updated_barriers_since_notified_other_user(self):\n barrier1 = BarrierFactory(priority=\"LOW\")\n barrier2 = BarrierFactory(priority=\"MEDIUM\")\n barrier3 = BarrierFactory(priority=\"HIGH\")\n\n user = create_test_user(sso_user_id=self.sso_user_data_1[\"user_id\"])\n\n saved_search = SavedSearch.objects.create(\n user=user, name=\"Medium\", filters={\"priority\": [\"MEDIUM\"]}\n )\n saved_search.mark_as_notified()\n\n assert saved_search.updated_barriers_since_notified.exists() is False\n\n barrier1.summary = \"New summary\"\n barrier1.save()\n\n barrier2.summary = \"New summary\"\n barrier2.save()\n\n saved_search = SavedSearch.objects.get(pk=saved_search.pk)\n assert barrier2 in saved_search.updated_barriers_since_notified\n assert saved_search.updated_barriers_since_notified.count() == 1","title":""},{"docid":"7bb1ad3862e28fb900261cb3f7d5b3d4","score":"0.5114988","text":"def test_gotoGangChange_modify(self):\n queue = myGlobals.actorState.queues[sopActor.SLEW]\n # create something we can modify.\n msg = self._run_cmd('gotoGangChange alt=10', queue)\n msgNew = self._run_cmd('gotoGangChange alt=20', queue, empty=True)\n self.assertIsNone(msgNew)\n self.assertEqual(msg.cmdState.alt, 10)\n self._check_cmd(0,2,0,0,True,True)","title":""},{"docid":"714ef3ae6a8decfd999c6fa7b02ad188","score":"0.5114012","text":"def test_integer_update(self):\n vm = Integer.value_manager(Integer(), 5, strategy=SaveOnChange)\n self.assertFalse(vm.changed)\n vm.value = 4\n self.assertTrue(vm.changed)","title":""},{"docid":"c6cb0f347e543bab7fb5a4c95c58bd5a","score":"0.5104573","text":"def test_update(self):\n # Add a parameter.\n self._params.add(\"param\", 0)\n self.assertEqual(0, self._params.get_value(\"param\"))\n\n # Update the value.\n self._params.update(\"param\", 1)\n self.assertEqual(1, self._params.get_value(\"param\"))\n\n # Update again.\n self._params.update(\"param\", 2)\n self.assertEqual(2, self._params.get_value(\"param\"))","title":""},{"docid":"b18ff63afd7cb78bde6cebf4d2a86acb","score":"0.51041955","text":"def test_cells_update(self):\n p_arrs = generate_sample_dataset_2()\n cm = CellManager(arrays_to_bin=p_arrs, initialize=False)\n\n big_cell = Cell(IntPoint(0, 0, 0), cell_manager=cm,\n cell_size=2.0)\n \n cm.cells_dict[big_cell.id.py_copy()] = big_cell\n\n # add all particle indices to big_cell\n indx_arr = big_cell.index_lists[0]\n indx_arr.resize(7)\n indx_arr.set_data(numpy.arange(7, dtype=numpy.int))\n\n cm.py_update()\n\n # now we should have a total of 5 cells.\n self.assertEqual(len(cm.cells_dict), 5)\n\n self.assertEqual(cm.cells_dict.has_key(IntPoint(-1, 1, 0)), True)\n self.assertEqual(cm.cells_dict.has_key(IntPoint(-1, -1, 0)), True)\n self.assertEqual(cm.cells_dict.has_key(IntPoint(1, 0, 0)), True)\n self.assertEqual(cm.cells_dict.has_key(IntPoint(1, -1, 0)), True)\n self.assertEqual(cm.cells_dict.has_key(IntPoint(0, 0, 0)), True)\n\n # we do not test further as those test would have been performed by\n # tests for NonLeafCell.","title":""},{"docid":"6c94ea06d20b8a1a860e81ec18c779f2","score":"0.51038694","text":"def test_red(self):\n r1 = [i+1 for i in self.FileObject.red]\n self.FileObject.red = r1\n r2 = self.FileObject.get_red()\n self.assertTrue((r1 == list(r2)))","title":""},{"docid":"d5d807b0d9e838c401555fad925d924e","score":"0.51034784","text":"def test_bulk_update_elements(self):\n pass","title":""},{"docid":"6733bb2ece13e2f5f24339ac7729cd35","score":"0.510217","text":"def test_update_needed(self):\n self._test_significance(MEDIUM_SIGNIFICANCE, \"Update Needed\")","title":""},{"docid":"1bd02d6329a86aeb33e79461c08a3dda","score":"0.50995606","text":"def update():\n # TODO: Slalom between red and blue cones. The car should pass to the right of\n # each red cone and the left of each blue cone.\n\n SLALOM.run_slalom()","title":""},{"docid":"95295e643b506bca9967071105e913c4","score":"0.50979596","text":"def test_dcim_cables_update(self):\n pass","title":""},{"docid":"1e6549dd57b6d6349900ea40cdf8483b","score":"0.5088863","text":"def test_update_01(self):\n actual = model.Report(case='19-123456', date=now.date())\n other = model.Report(case='19-654321', date=now.date())\n with pytest.raises(ValueError):\n actual.update(other, True)","title":""},{"docid":"6fc18689778a472633b7ea270b131cde","score":"0.50887287","text":"def test_full_update_checkplan(self):\n pass","title":""},{"docid":"cabf34e261b96aece3de15ce7145f518","score":"0.5086508","text":"def update_wire(self, stress_range, crack_width, lambda_fatigue, \n lambda_fatigue_fretting_crack, lambda_fatigue_stress):\n if self.broken == True:\n return False\n else:\n self.fatigue = (self.fatigue \n - self.calc_fatigue_exp(\n stress_range, \n lambda_fatigue\n )\n - self.calc_fatigue_fretting(\n crack_width,\n stress_range, \n lambda_fatigue_fretting_crack, \n lambda_fatigue_stress\n ) \n )\n if self.fatigue < 0:\n self.broken = True\n return True\n else:\n return False","title":""},{"docid":"c3acd034978ae8d01e03a3d9f2706d62","score":"0.5085244","text":"def test_checkAndSetColor(self):\n # make explicitly sure that the tile is inactive, check functionality\n self.testTile.setInactive()\n self.testTile.checkAndSetColor()\n assert_equal(self.testTile.tileColor, (210, 210, 210) )\n # make explicitly sure that the tile is active, check functionality\n self.testTile.setActive()\n self.testTile.checkAndSetColor()\n assert_equal(self.testTile.tileColor, (190, 190, 250) )","title":""},{"docid":"b5e886dd26ea075c90ec2482b9cac284","score":"0.50835884","text":"def test_bulk_update(self):\n before_update_time = datetime.utcnow()\n\n self.session.query(self.character).update({\n # change everyone's name to Julia\n self.character.name: 'Julia',\n self.character.obj: {'favorite_book': 'Romeo & Juliet'}\n })\n\n self.session.commit()\n\n expected_stmt = (\"UPDATE characters SET \"\n \"name = ?, obj = ?, ts = ?\")\n args, kwargs = fake_cursor.execute.call_args\n stmt = args[0]\n args = args[1]\n self.assertEqual(expected_stmt, stmt)\n self.assertEqual('Julia', args[0])\n self.assertEqual({'favorite_book': 'Romeo & Juliet'}, args[1])\n dt = datetime.strptime(args[2], '%Y-%m-%dT%H:%M:%S.%fZ')\n self.assertIsInstance(dt, datetime)\n self.assertGreater(dt, before_update_time)","title":""},{"docid":"184fdf9efd0d648be7f79b6b20ce9bd3","score":"0.5081388","text":"def test_dcim_racks_update(self):\n pass","title":""},{"docid":"b0ae31ed0a7bbdb973b92acb43646ec4","score":"0.507621","text":"def test_dcim_device_bays_update(self):\n pass","title":""},{"docid":"0372b50c305c8785e4291c75497a9f34","score":"0.50719494","text":"def test_update_bad_value_board(self):\n\n #create a valid board\n good_col = [-1,1,1,-1,1,1]\n good_board = [good_col, good_col, good_col, good_col, good_col, good_col,\n good_col]\n\n self.model.board = good_board\n\n #create an invalid board\n bad_value = [2,2,2,2,2,2]\n bad_val_board = [bad_value, bad_value, bad_value, bad_value, bad_value,\n bad_value, bad_value]\n\n #test that the update board function is not setting an invalid board\n #with incorrect values\n self.model.update_board(bad_val_board)\n self.assertEqual(good_board, self.model.board)","title":""},{"docid":"934b6b045f1ab020cdca7a296e5c090d","score":"0.5070057","text":"def test_updated_barriers_since_notified_current_user(self):\n barrier1 = BarrierFactory(priority=\"LOW\")\n barrier2 = BarrierFactory(priority=\"MEDIUM\")\n barrier3 = BarrierFactory(priority=\"HIGH\")\n\n user = create_test_user(sso_user_id=self.sso_user_data_1[\"user_id\"])\n\n saved_search = SavedSearch.objects.create(\n user=user, name=\"Medium\", filters={\"priority\": [\"MEDIUM\"]}\n )\n saved_search.mark_as_notified()\n\n assert saved_search.updated_barriers_since_notified.exists() is False\n\n barrier2.summary = \"New summary\"\n barrier2.modified_by = user\n barrier2.save()\n\n saved_search = SavedSearch.objects.get(pk=saved_search.pk)\n assert barrier2 not in saved_search.updated_barriers_since_notified\n assert saved_search.updated_barriers_since_notified.exists() is False","title":""},{"docid":"7bb7fb18127aa3d244440e3b8772cef7","score":"0.5067463","text":"def test_update_space(self):\n pass","title":""},{"docid":"b9ada7048fd05f41af2be7abfe2bf336","score":"0.50621974","text":"def is_updating(self):\n return self.cur_iter % self.update_per_n_iteration == 0","title":""},{"docid":"817be66edcda8403358fc177a865d5f7","score":"0.5061357","text":"def test_update_rule(self):\n pass","title":""},{"docid":"b9f7ee6d5e1729bc443e9c2f431b5836","score":"0.50610447","text":"def do_updates(self, isCorrect):\n if isCorrect == True:\n self.score += 100\n elif isCorrect == False:\n self.score -= 75","title":""},{"docid":"70a91fd520ae05ee799b87f9dbb352a0","score":"0.505471","text":"def test_update(self):\n with self._tracer.start_as_current_span(\"rootSpan\"):\n self._collection.update_one(\n {\"name\": \"testName\"}, {\"$set\": {\"value\": \"someOtherValue\"}}\n )\n\n expected_db_statement = (\n \"update [SON([('q', {'name': 'testName'}), ('u', \"\n \"{'$set': {'value': 'someOtherValue'}}), ('multi', False), ('upsert', False)])]\"\n )\n self.validate_spans(expected_db_statement)","title":""},{"docid":"0f77f6aaec446803812602aea074600c","score":"0.5045329","text":"def testUpdateGameConsistency(self):\n # Create game with only one team and one game source.\n game = Game()\n self.CreateUser(2, 'bob').put()\n twt = self.CreateTweet(5, ('bob', 2), 'up 5-7')\n game.sources = [GameSource.FromTweet(twt, [5, 7])]\n\n game.teams = [Team(score_reporter_id=crawl_lists.UNKNOWN_SR_ID)]\n crawl_lists_handler = crawl_lists.CrawlListHandler()\n crawl_lists_handler._UpdateGameConsistency(game, {})\n self.assertEqual(2, len(game.teams))\n self.assertEqual(2, game.teams[0].twitter_id)\n\n # Update it again - no changes should have been made.\n crawl_lists_handler._UpdateGameConsistency(game, {})\n self.assertEqual(2, len(game.teams))\n self.assertEqual(2, game.teams[0].twitter_id)","title":""},{"docid":"0019079a687383ee1df961f75203294d","score":"0.5044909","text":"def test_update_context_state_calls():\n custom_integrators = get_all_custom_integrators()\n for integrator_name, integrator_class in custom_integrators:\n\n integrator = integrator_class()\n # NoseHooverChainVelocityVerletIntegrator will print a severe warning here,\n # because it is being initialized without a system. That's OK.\n\n num_force_update = 0\n for i in range(integrator.getNumComputations()):\n step_type, target, expr = integrator.getComputationStep(i)\n\n if step_type == 5:\n num_force_update += 1\n\n msg = \"Integrator '%s' has %d calls to addUpdateContextState(), while there should be only one.\" % (integrator_name, num_force_update)\n if hasattr(integrator, 'pretty_format'):\n msg += '\\n' + integrator.pretty_format(step_types_to_highlight=[5])\n assert num_force_update == 1, msg","title":""},{"docid":"eef403b6b60aa5bbf8706f3bb1e24643","score":"0.5044789","text":"def test_update_level(self):\n pass","title":""},{"docid":"c7c4c34598bf56d79a178aff344a3b9d","score":"0.5033966","text":"def test_update_ruleset(self):\n pass","title":""},{"docid":"52dd82aa0ead0f761aa3c79ba6679be6","score":"0.50254565","text":"def test_update_args(self):\n s1 = Square(10, 10, 10, 1)\n self.assertEqual(s1.__str__(), \"[Square] (1) 10/10 - 10\")\n s1.update(89, 2, 3, 4, 5)\n self.assertEqual(s1.__str__(), \"[Square] (89) 3/4 - 2\")","title":""},{"docid":"a4e66ba369aefeef2a1a32d02161693d","score":"0.50231946","text":"def update(self, colour, action):\n self.initial_state = self.result(self.initial_state, action)","title":""},{"docid":"746e2cd302e09df254d154c8c934ede9","score":"0.50164306","text":"def update_failed():","title":""},{"docid":"0faf7b40dc79b322fbb6831f9d3102ce","score":"0.50101733","text":"def test_update_large():\n np.random.seed(1)\n n_reads = 10_000_000\n n_snps = 100_000\n n_sfs = 40\n n_rgs = 20\n e = 0.001\n b = 0.001\n\n #psi = 1. / np.random.sample(n_snps)\n #psi /= np.max(psi)\n psi = np.random.sample(n_snps)\n #psi /= np.max(psi)\n\n true_tau = np.arange(0, 1, step = 1 / n_sfs)\n true_cont = np.arange(0, 1, step = 1 / n_rgs)\n\n SNP2SFS = np.random.randint(0, n_sfs, size=n_snps)\n #FLIPPED = np.zeros(n_snps, bool)\n\n\n G = np.random.binomial(2, true_tau[SNP2SFS], size = n_snps)\n A = np.random.binomial(1, psi, size = n_snps)\n\n #random assignments. In principle, a SNP might have repeat cats\n READ2RG = np.random.randint(0, n_rgs, size = n_reads)\n reps = np.random.multinomial(n_reads-n_snps, \n pvals=np.zeros(n_snps)+ 1. / n_snps) + 1\n READ2SNP = np.repeat(np.arange(n_snps, dtype=np.int), reps)\n\n p = true_cont[READ2RG] * psi[READ2SNP] + (1-true_cont[READ2RG]) * G[READ2SNP] / 2. \n p = e * (1.-p) + p * (1.-b)\n O = np.random.binomial(1, p, n_reads)\n\n FLIPPED = np.zeros(n_snps, bool)\n #FLIPPED = np.array(np.random.binomial(1, .4, size =n_snps), bool)\n #O[FLIPPED[READ2SNP]] = 1 - O[FLIPPED[READ2SNP]]\n\n\n np.set_printoptions(suppress=True,precision = 3)\n\n data = SlugReads(\n READS = O,\n psi = psi,\n READ2RG = READ2RG,\n READ2SNP = READ2SNP,\n FLIPPED = FLIPPED,\n SNP2SFS = SNP2SFS)\n pars = SlugParsSquare(\n cont = np.repeat(0.5, n_rgs),\n tau = np.repeat(.4, n_sfs),\n F = np.repeat(.5, n_sfs),\n e = 0.001,\n b = 0.001\n )\n #update_pars_reads(pars, data, data, \n # True, True, True)\n #print(f'eb= {pars.e}, {pars.b}')\n #print(f'C = {pars.cont}')\n #print(f'F = {pars.F}')\n #print(f'tau = {pars.tau}')\n #update_pars_reads(pars, data, data, \n # True, True, True)\n #print( f'll : {pars.prev_ll} -> {pars.ll}')\n controller = SlugController(update_eb=True, \n update_ftau=True, \n update_cont=True,\n n_iter=1000,\n ll_tol = 1e-4)\n #em(pars, data, controller)\n squarem(pars, data, controller)\n assert True","title":""},{"docid":"beb5e68d0cc2e7b50612f24714b296ec","score":"0.4999062","text":"def test_single(self):\r\n self.vector[buf_size / 2 - 1] = 1.\r\n self.vector[buf_size / 2] = -1.\r\n self.assertEqual(2./buf_size, zero_crossing_rate(self.vector))","title":""},{"docid":"77c9eee94f5dfa7a9399865addb84bf7","score":"0.49953946","text":"def update(self):\n if self.dirty < 1:\n # let's just try updating all of them\n pass\n #return\n self._update_background()\n self._update_text()\n self.dirty = 2 if self.dirty == 2 else 0","title":""},{"docid":"af9c1edb8500019749a256cdd21ac12d","score":"0.49953637","text":"def test_update(self):\n self.ftv.adb_shell_output = GET_PROPERTIES_OUTPUT1\n state = self.ftv.update()\n self.assertTupleEqual(state, STATE1)\n\n self.ftv.adb_shell_output = GET_PROPERTIES_OUTPUT2\n state = self.ftv.update()\n self.assertTupleEqual(state, STATE2)\n\n self.ftv.adb_shell_output = GET_PROPERTIES_OUTPUT3\n state = self.ftv.update()\n self.assertTupleEqual(state, STATE3)","title":""},{"docid":"cab0a3b30894f6a8df6381f87ff52a00","score":"0.49945658","text":"def test_get_and_set(self, soco):\r\n assert soco.treble in self.valid_values\r\n # Values on the boundaries of the valid equivalence partition\r\n for value in [self.valid_values[0], self.valid_values[-1]]:\r\n soco.treble = value\r\n wait()\r\n assert soco.treble == value","title":""},{"docid":"2bd1ff27153be31a1872d250c2245d33","score":"0.49932972","text":"def test_full_update_ifc(self):\n pass","title":""}],"string":"[\n {\n \"docid\": \"ba96f00006c63359a37bb8c6b94ea478\",\n \"score\": \"0.5960921\",\n \"text\": \"def _test_staledata_error_on_concurrent_object_update(self, model, dbid):\\n lock = functools.partial(lockutils.lock, uuidutils.generate_uuid())\\n self._blocked_on_lock = False\\n\\n def _lock_blocked_name_update():\\n ctx = context.get_admin_context()\\n with db_api.CONTEXT_WRITER.using(ctx):\\n thing = ctx.session.query(model).filter_by(id=dbid).one()\\n thing.bump_revision()\\n thing.name = 'newname'\\n self._blocked_on_lock = True\\n with lock():\\n return thing\\n\\n with lock():\\n coro = eventlet.spawn(_lock_blocked_name_update)\\n # wait for the coroutine to get blocked on the lock before\\n # we proceed to update the record underneath it\\n while not self._blocked_on_lock:\\n eventlet.sleep(0)\\n ctx = context.get_admin_context()\\n with db_api.CONTEXT_WRITER.using(ctx):\\n thing = ctx.session.query(model).filter_by(id=dbid).one()\\n thing.bump_revision()\\n thing.description = 'a description'\\n revision_after_build = thing.revision_number\\n\\n with testtools.ExpectedException(orm.exc.StaleDataError):\\n # the coroutine should have encountered a stale data error because\\n # the status update thread would have bumped the revision number\\n # while it was waiting to commit\\n coro.wait()\\n # another attempt should work fine\\n thing = _lock_blocked_name_update()\\n self.assertEqual('a description', thing.description)\\n self.assertEqual('newname', thing.name)\\n self.assertGreater(thing.revision_number, revision_after_build)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c9f78eb9bc3e4c52363317a174c9c5c\",\n \"score\": \"0.5868373\",\n \"text\": \"def test_dcim_racks_partial_update(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a731c35891698c8297cbd04fe570182b\",\n \"score\": \"0.58166724\",\n \"text\": \"def test_dcim_cables_partial_update(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db5a0d2be5baa28d231ed48fe1f21cc8\",\n \"score\": \"0.58038265\",\n \"text\": \"def dummy_update(value):\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"224715caf31aba2c7c1b17851cf57845\",\n \"score\": \"0.5787791\",\n \"text\": \"def test_full_update_rule(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a56552ccc2bc04cbc9a0c580385bfdb0\",\n \"score\": \"0.5767134\",\n \"text\": \"def test_update_bre_global(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"112dbb03ecf8860fa05f632262ac4a84\",\n \"score\": \"0.57472086\",\n \"text\": \"def test_update_escalation(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15db4aaf2df2db53e373a0fdf8cf633c\",\n \"score\": \"0.5727039\",\n \"text\": \"def test_update(self):\\n s = Square(1, 0, 0, 1)\\n self.assertEqual(str(s), \\\"[Square] (1) 0/0 - 1\\\")\\n s.update(19)\\n self.assertEqual(str(s), \\\"[Square] (19) 0/0 - 1\\\")\\n s. update(800, 4, 2, 3)\\n self.assertEqual(str(s), \\\"[Square] (800) 2/3 - 4\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b83ff8071f857ec261f5fdc5191e10ff\",\n \"score\": \"0.5726534\",\n \"text\": \"def test_full_update_checker(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"262a22e07b15cc5223e23d04d36e8a12\",\n \"score\": \"0.56599796\",\n \"text\": \"def test_update(self):\\n # this is really tested graphically, no unit test here\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1df2e45640c37060a90860cde57888de\",\n \"score\": \"0.56587845\",\n \"text\": \"def test_dcim_device_bays_partial_update(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"548c1e897231bea9b6cd1ca347f98512\",\n \"score\": \"0.5652845\",\n \"text\": \"def test_full_update_layer(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"104f2997c258ffe2cccbd1c122e49bc8\",\n \"score\": \"0.5586589\",\n \"text\": \"def test_full_update_ruleset(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf7a88cd40706592b1e0add18d83981e\",\n \"score\": \"0.55156994\",\n \"text\": \"def test_update_compute_blade(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc0896d1a19b161c8284dea730d9b013\",\n \"score\": \"0.55109453\",\n \"text\": \"def test_bulk_full_update_ifc_property(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4979fe959ecce0f4ded1ad4d921de9c8\",\n \"score\": \"0.5508749\",\n \"text\": \"def test_update_compute_board(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"009c067d2dfc6962d64e1a084099f3f8\",\n \"score\": \"0.54972327\",\n \"text\": \"def test_full_update_checker_result(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fb89253768519eabbb1f367b18670e5\",\n \"score\": \"0.5473305\",\n \"text\": \"def test_full_update_space(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fa5aa91afa7151c7cb1d8b6ba42af11\",\n \"score\": \"0.54495496\",\n \"text\": \"def test_update(self):\\n\\n s6 = Square(4, 0, 0, 27)\\n s7 = Square(32, 2, 4, 10)\\n\\n self.assertEqual(str(s6), \\\"[Square] (27) 0/0 - 4\\\")\\n s6.update(1, 2, 3, 6)\\n self.assertEqual(str(s6), \\\"[Square] (1) 3/6 - 2\\\")\\n\\n self.assertEqual(str(s7), \\\"[Square] (10) 2/4 - 32\\\")\\n s7.update(size=7, id=89, y=1)\\n self.assertEqual(str(s7), \\\"[Square] (89) 2/1 - 7\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fb72d13a13ab5790d440dc118fe22f4\",\n \"score\": \"0.54491\",\n \"text\": \"async def test_put_then_get_cached_properly(\\n hass: HomeAssistant, hass_hue, hue_client\\n) -> None:\\n\\n # Turn the bedroom light on first\\n await hass_hue.services.async_call(\\n light.DOMAIN,\\n const.SERVICE_TURN_ON,\\n {const.ATTR_ENTITY_ID: \\\"light.ceiling_lights\\\", light.ATTR_BRIGHTNESS: 153},\\n blocking=True,\\n )\\n\\n ceiling_lights = hass_hue.states.get(\\\"light.ceiling_lights\\\")\\n assert ceiling_lights.state == STATE_ON\\n assert ceiling_lights.attributes[light.ATTR_BRIGHTNESS] == 153\\n\\n # update light state through api\\n await perform_put_light_state(\\n hass_hue,\\n hue_client,\\n \\\"light.ceiling_lights\\\",\\n True,\\n hue=4369,\\n saturation=127,\\n brightness=254,\\n )\\n\\n # Check that a Hue brightness level of 254 becomes 255 in HA realm.\\n assert (\\n hass.states.get(\\\"light.ceiling_lights\\\").attributes[light.ATTR_BRIGHTNESS] == 255\\n )\\n\\n # Make sure that the GET response is the same as the PUT response within 2 seconds if the service call is successful and the state doesn't change.\\n # We simulate a long latence for the actual setting of the entity by forcibly sitting different values directly.\\n await hass_hue.services.async_call(\\n light.DOMAIN,\\n const.SERVICE_TURN_ON,\\n {const.ATTR_ENTITY_ID: \\\"light.ceiling_lights\\\", light.ATTR_BRIGHTNESS: 153},\\n blocking=True,\\n )\\n\\n # go through api to get the state back, the value returned should match those set in the last PUT request.\\n ceiling_json = await perform_get_light_state(\\n hue_client, \\\"light.ceiling_lights\\\", HTTPStatus.OK\\n )\\n\\n assert ceiling_json[\\\"state\\\"][HUE_API_STATE_HUE] == 4369\\n assert ceiling_json[\\\"state\\\"][HUE_API_STATE_SAT] == 127\\n assert ceiling_json[\\\"state\\\"][HUE_API_STATE_BRI] == 254\\n\\n # Make sure that the GET response does not use the cache if PUT response within 2 seconds if the service call is Unsuccessful and the state does not change.\\n await hass_hue.services.async_call(\\n light.DOMAIN,\\n const.SERVICE_TURN_OFF,\\n {const.ATTR_ENTITY_ID: \\\"light.ceiling_lights\\\"},\\n blocking=True,\\n )\\n\\n # go through api to get the state back\\n ceiling_json = await perform_get_light_state(\\n hue_client, \\\"light.ceiling_lights\\\", HTTPStatus.OK\\n )\\n\\n # Now it should be the real value as the state of the entity has changed to OFF.\\n assert ceiling_json[\\\"state\\\"][HUE_API_STATE_HUE] == 0\\n assert ceiling_json[\\\"state\\\"][HUE_API_STATE_SAT] == 0\\n assert ceiling_json[\\\"state\\\"][HUE_API_STATE_BRI] == 1\\n\\n # Ensure we read the actual value after exceeding the timeout time.\\n\\n # Turn the bedroom light back on first\\n await hass_hue.services.async_call(\\n light.DOMAIN,\\n const.SERVICE_TURN_ON,\\n {const.ATTR_ENTITY_ID: \\\"light.ceiling_lights\\\"},\\n blocking=True,\\n )\\n\\n # update light state through api\\n await perform_put_light_state(\\n hass_hue,\\n hue_client,\\n \\\"light.ceiling_lights\\\",\\n True,\\n hue=4369,\\n saturation=127,\\n brightness=254,\\n )\\n\\n await hass_hue.services.async_call(\\n light.DOMAIN,\\n const.SERVICE_TURN_ON,\\n {\\n const.ATTR_ENTITY_ID: \\\"light.ceiling_lights\\\",\\n light.ATTR_BRIGHTNESS: 127,\\n light.ATTR_RGB_COLOR: (1, 2, 7),\\n },\\n blocking=True,\\n )\\n\\n # go through api to get the state back, the value returned should match those set in the last PUT request.\\n ceiling_json = await perform_get_light_state(\\n hue_client, \\\"light.ceiling_lights\\\", HTTPStatus.OK\\n )\\n\\n # With no wait, we must be reading what we set via the PUT call.\\n assert ceiling_json[\\\"state\\\"][HUE_API_STATE_HUE] == 4369\\n assert ceiling_json[\\\"state\\\"][HUE_API_STATE_SAT] == 127\\n assert ceiling_json[\\\"state\\\"][HUE_API_STATE_BRI] == 254\\n\\n with patch.object(hue_api, \\\"STATE_CACHED_TIMEOUT\\\", 0.000001):\\n await asyncio.sleep(0.000001)\\n\\n # go through api to get the state back, the value returned should now match the actual values.\\n ceiling_json = await perform_get_light_state(\\n hue_client, \\\"light.ceiling_lights\\\", HTTPStatus.OK\\n )\\n\\n # Once we're after the cached duration, we should see the real value.\\n assert ceiling_json[\\\"state\\\"][HUE_API_STATE_HUE] == 41869\\n assert ceiling_json[\\\"state\\\"][HUE_API_STATE_SAT] == 217\\n assert ceiling_json[\\\"state\\\"][HUE_API_STATE_BRI] == 127\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1c5c67514a6f388deceedd9eff90fac\",\n \"score\": \"0.543819\",\n \"text\": \"def test_full_update_property_set(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0483d7edc27b86678b5835d6cfa249e\",\n \"score\": \"0.54345864\",\n \"text\": \"def test_full_update_element(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1c889d04bb8ec80bef12307d9733727\",\n \"score\": \"0.5427962\",\n \"text\": \"async def test_async_update_locked_multiple_updates(\\n self, hass, pywemo_device, wemo_entity\\n ):\\n await entity_test_helpers.test_async_update_locked_multiple_updates(\\n hass,\\n pywemo_device,\\n wemo_entity,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2eb25f59261041068933488639fea756\",\n \"score\": \"0.5408724\",\n \"text\": \"def test_update_mixin6(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"025257c5fe610d6426b6dd095ff09a46\",\n \"score\": \"0.54081184\",\n \"text\": \"def test_update(self):\\n p_arrs = generate_sample_dataset_2()\\n cm = CellManager(arrays_to_bin=p_arrs, min_cell_size=2.,\\n max_cell_size=2.)\\n \\n # move particle 3 in +ve y direction by one unit.\\n y = p_arrs[0].get_carray('y')\\n curr_y_2 = y.get(2)\\n y[2] = curr_y_2 + 1.0\\n # move particle 7 in -ve x direction by one unit\\n x = p_arrs[0].get_carray('x')\\n curr_x_6 = x.get(6)\\n x[6] = curr_x_6 - 1.0\\n # move particle 4 in -ve y direction by one unit\\n curr_y_4 = y.get(4)\\n y[4] = curr_y_4 - 1.0\\n\\n # 5 cells are formed\\n self.assertEqual(len(cm.cells_dict), 5)\\n # cell 0,0,0 has 3 particles since we have not updated\\n self.assertEqual(cm.cells_dict[IntPoint()].py_get_number_of_particles(), 3)\\n \\n # because we changed the data, ask cm to update_status\\n cm.py_update_status()\\n cm.py_update()\\n\\n # no change should occur, as we have NOT set the dirty bit of the\\n # parray.\\n self.assertEqual(len(cm.cells_dict), 5)\\n\\n self.assertEqual(cm.cells_dict.has_key(IntPoint(0, 0, 0)), True)\\n self.assertEqual(cm.cells_dict.has_key(IntPoint(1, 0, 0)), True)\\n self.assertEqual(cm.cells_dict.has_key(IntPoint(1, -1, 0)), True)\\n self.assertEqual(cm.cells_dict.has_key(IntPoint(-1, -1, 0)), True)\\n self.assertEqual(cm.cells_dict.has_key(IntPoint(-1, 1, 0)), True)\\n\\n cell = cm.cells_dict[IntPoint(0, 0, 0)]\\n self.assertEqual(cell.py_get_number_of_particles(), 3)\\n cell = cm.cells_dict[IntPoint(1, 0, 0)]\\n self.assertEqual(cell.py_get_number_of_particles(), 1)\\n cell = cm.cells_dict[IntPoint(1, -1, 0)]\\n self.assertEqual(cell.py_get_number_of_particles(), 1)\\n cell = cm.cells_dict[IntPoint(-1, -1, 0)]\\n self.assertEqual(cell.py_get_number_of_particles(), 1)\\n cell = cm.cells_dict[IntPoint(-1, 1, 0)]\\n self.assertEqual(cell.py_get_number_of_particles(), 1)\\n\\n # now set it to dirty and check.\\n p_arrs[0].set_dirty(True)\\n\\n cm.py_update_status()\\n cm.py_update()\\n\\n self.assertEqual(len(cm.cells_dict), 6)\\n\\n # make sure the proper cells have been created.\\n self.assertEqual(cm.cells_dict.has_key(IntPoint(0, 0, 0)), True)\\n\\n # cell 1,-1,0 should have been deleted.\\n self.assertEqual(cm.cells_dict.has_key(IntPoint(1, -1, 0)), False)\\n\\n # two new cells should have been created.\\n self.assertEqual(cm.cells_dict.has_key(IntPoint(0, -1, 0)), True)\\n self.assertEqual(cm.cells_dict.has_key(IntPoint(0, 1, 0)), True)\\n\\n cell = cm.cells_dict[IntPoint(0, -1, 0)]\\n self.assertEqual(cell.py_get_number_of_particles(), 2)\\n\\n cell = cm.cells_dict[IntPoint(0, 1, 0)]\\n self.assertEqual(cell.py_get_number_of_particles(), 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d278024caf69339e3035bc48d650da52\",\n \"score\": \"0.5400628\",\n \"text\": \"def test_update_square(self):\\n s1 = Square(5)\\n\\n s1.update(10)\\n self.assertEqual(s1.__str__(), \\\"[Square] (10) 0/0 - 5\\\")\\n\\n s1.update(1, 2)\\n self.assertEqual(s1.__str__(), \\\"[Square] (1) 0/0 - 2\\\")\\n\\n s1.update(1, 2, 3)\\n self.assertEqual(s1.__str__(), \\\"[Square] (1) 3/0 - 2\\\")\\n\\n s1.update(1, 2, 3, 4)\\n self.assertEqual(s1.__str__(), \\\"[Square] (1) 3/4 - 2\\\")\\n\\n s1.update(x=12)\\n self.assertEqual(s1.__str__(), \\\"[Square] (1) 12/4 - 2\\\")\\n\\n s1.update(size=7, y=1)\\n self.assertEqual(s1.__str__(), \\\"[Square] (1) 12/1 - 7\\\")\\n\\n s1.update(size=7, id=89, y=1)\\n self.assertEqual(s1.__str__(), \\\"[Square] (89) 12/1 - 7\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b5cd629ef8159575b85064b9609dac9\",\n \"score\": \"0.53928655\",\n \"text\": \"def test_update_checker(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aac088bda7eb6062365206cc56ec84d4\",\n \"score\": \"0.53722274\",\n \"text\": \"async def test_atomic_update(client: Client) -> None:\\n post = await client.post.create({'title': 'My Post', 'published': False})\\n assert post.title == 'My Post'\\n assert post.views == 0\\n\\n updated = await client.post.update(\\n where={'id': post.id}, data={'views': {'increment': 1}}\\n )\\n assert updated is not None\\n assert updated.views == 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64f656f9f007ed8a8f19610a640926a4\",\n \"score\": \"0.5361777\",\n \"text\": \"def test_update(benchmark):\\n do_put()\\n\\n benchmark(db.update, [('4', (4, 'charlie', \\\"Charlie's data\\\"))], ['1'])\\n assert ['2', '3', '4'] == db.keys()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44632cb8830a8fba32696e00162ccf87\",\n \"score\": \"0.5349633\",\n \"text\": \"def test_full_update_rule_component(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cf7404ec81ba0a354cb7abf00f81a1e\",\n \"score\": \"0.53271013\",\n \"text\": \"def test_update_args_and_kwargs(self):\\n test1 = Square(3, 3, 2, 10)\\n self.assertEqual(test1.__str__(), \\\"[Square] (10) 3/2 - 3\\\")\\n test1.update(69)\\n self.assertEqual(test1.__str__(), \\\"[Square] (69) 3/2 - 3\\\")\\n test1.update(69, 6)\\n self.assertEqual(test1.__str__(), \\\"[Square] (69) 3/2 - 6\\\")\\n test1.update(69, 6, 9)\\n self.assertEqual(test1.__str__(), \\\"[Square] (69) 9/2 - 6\\\")\\n test1.update(69, 6, 9, 6)\\n self.assertEqual(test1.__str__(), \\\"[Square] (69) 9/6 - 6\\\")\\n test1.update(69, 6, 9, 6, 9)\\n self.assertEqual(test1.__str__(), \\\"[Square] (69) 9/6 - 6\\\")\\n\\n test2 = Square(1, 1, 1, 1)\\n self.assertEqual(test2.__str__(), \\\"[Square] (1) 1/1 - 1\\\")\\n test2.update(id=37)\\n self.assertEqual(test2.__str__(), \\\"[Square] (37) 1/1 - 1\\\")\\n test2.update(size=3)\\n self.assertEqual(test2.__str__(), \\\"[Square] (37) 1/1 - 3\\\")\\n test2.update(x=4, y=5)\\n self.assertEqual(test2.__str__(), \\\"[Square] (37) 4/5 - 3\\\")\\n test2.update(id=35, size=4, x=5, y=1)\\n self.assertEqual(test2.__str__(), \\\"[Square] (35) 5/1 - 4\\\")\\n\\n test2.update(2, 3, x=5, y=1)\\n self.assertEqual(test2.__str__(), \\\"[Square] (2) 5/1 - 3\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cee014b7bf9fbfcbe3ce5511d321dfa\",\n \"score\": \"0.53046936\",\n \"text\": \"def testChangingVotes(self):\\n\\n ballot_box = api.SantaBallotBox(self.santa_blockable1.key.id())\\n with self.LoggedInUser() as user:\\n ballot_box.Vote(True, user)\\n ballot_box.Vote(False, user)\\n ballot_box.Vote(True, user)\\n\\n blockable = self.santa_blockable1.key.get()\\n\\n self.assertEqual(blockable.score, 1)\\n\\n # SCORE_CHANGE x3\\n self.assertBigQueryInsertions([TABLE.VOTE, TABLE.BINARY] * 3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d73e99f48aef4350c44e38c86cadbaf\",\n \"score\": \"0.5299943\",\n \"text\": \"def test_update_ref():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c48b34b068ee30003481793f155252a5\",\n \"score\": \"0.52938837\",\n \"text\": \"def test_bulk_update_ifc_property(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8b8b254270395082cccc498cb759dba\",\n \"score\": \"0.52938426\",\n \"text\": \"def test_partialupdate_policy(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0399d9a7bc135fc0a0fbafeec73021c7\",\n \"score\": \"0.52788883\",\n \"text\": \"def test_integer_inplace_update(self):\\n vm = Integer.value_manager(Integer(), 5, strategy=SaveOnChange)\\n self.assertFalse(vm.changed)\\n vm.value += 1\\n self.assertTrue(vm.changed)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8467344a5c7c69807b3b01f6ccbb4a95\",\n \"score\": \"0.5274749\",\n \"text\": \"def test_update_run(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b3cd8cb54abc1dcc90d8a3360339aed\",\n \"score\": \"0.52715844\",\n \"text\": \"def test_updated_barrier_ids_current_user(self):\\n barrier1 = BarrierFactory(priority=\\\"LOW\\\")\\n barrier2 = BarrierFactory(priority=\\\"MEDIUM\\\")\\n barrier3 = BarrierFactory(priority=\\\"HIGH\\\")\\n\\n user = create_test_user(sso_user_id=self.sso_user_data_1[\\\"user_id\\\"])\\n\\n saved_search = SavedSearch.objects.create(\\n user=user, name=\\\"Medium\\\", filters={\\\"priority\\\": [\\\"MEDIUM\\\"]}\\n )\\n saved_search.mark_as_seen()\\n\\n assert saved_search.updated_barrier_ids == []\\n\\n barrier2.summary = \\\"New summary\\\"\\n barrier2.modified_by = user\\n barrier2.save()\\n\\n saved_search = SavedSearch.objects.get(pk=saved_search.pk)\\n assert barrier2.pk not in saved_search.updated_barrier_ids\\n assert saved_search.updated_barrier_ids == []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01b7ee253705a37f61089c77b9f95ae4\",\n \"score\": \"0.52656084\",\n \"text\": \"def test_update_checker_result(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ee1df1cb7cbfaa78bcac44e2f0ecdcd\",\n \"score\": \"0.52647567\",\n \"text\": \"def test_bulk_full_update_elements(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44f48c02abc734db3fb85d08f6ae92f4\",\n \"score\": \"0.5245097\",\n \"text\": \"def test_green(self):\\n self.assertEqual(self.p1.green , 100 , self.green[self.idx1])\\n self.assertEqual(self.p2.green , 84 , self.green[self.idx2])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d59ccf1bf6b4d2eee645c93b8540b03a\",\n \"score\": \"0.5237491\",\n \"text\": \"def test_green(self):\\n g1 = [i+1 for i in self.FileObject.green]\\n self.FileObject.green = g1\\n g2 = self.FileObject.get_green()\\n self.assertTrue((g1 == list(g2)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2521e7ece6482d75660cedc293975616\",\n \"score\": \"0.5236049\",\n \"text\": \"def test_update_badge(self):\\n badge = badge_api.create_badge(*self.badge_values)\\n attrs = self.badge_attrs\\n del attrs[1]\\n kwargs = dict(zip(self.badge_attrs, [badge['uri']] + self.badge_values))\\n del kwargs['author_uri']\\n kwargs['title'] = 'A new title'\\n badge_api.update_badge(**kwargs)\\n badge2 = badge_api.get_badge(badge['uri'])\\n self.assertNotEquals(badge, badge2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d8d93ee8c7a9499bebd502d998baad8\",\n \"score\": \"0.523573\",\n \"text\": \"def test_update_layer(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d33cb26bc69c2cf67327bb570a3abba7\",\n \"score\": \"0.5230972\",\n \"text\": \"def needs_update():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2b8d6cbdce96d35c8c6f8aa71035ab4\",\n \"score\": \"0.52241045\",\n \"text\": \"def sane_after_update(self):\\n assert self.W_ee.sane_after_update()\\n assert self.W_oe.sane_after_update()\\n # assert self.W_ie.sane_after_update()\\n assert self.W_ei.sane_after_update()\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eac5374390e82f08ebf85a6ca1bd293c\",\n \"score\": \"0.52227384\",\n \"text\": \"def hillClimb(self):\\n #Pick random square, see if color change will improve it IF IT\\\"S MUTABLE\\n #IF doesnt improve, rem from list and try diff square.\\n #Generate list of mutable squares and choose a random one to improve\\n pre_score= self.board.score()\\n post_score= sys.maxint\\n l= [(x,y) for y in range(self.board.size) for x in range(self.board.size) if self.board.mutable[x][y]]\\n while len(l) > 0 and not self.board.isSolved():\\n squ= random.choice(l)\\n self.board.switchColor(squ)\\n post_score= self.board.score()\\n if post_score > pre_score:\\n l.remove(squ)\\n self.board.switchColor(squ)\\n else:\\n break\\n if len(l) > 0 or (len(l) == 0 and (post_score < pre_score or self.board.isSolved())):\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63db9f703f3a0dadae4ddc83ec88c966\",\n \"score\": \"0.52160734\",\n \"text\": \"def test_mutate_cell_taken():\\n\\n rv1, out1 = getstatusoutput(f'{prg} -b XXO...OOX --player X --cell 9')\\n assert rv1 != 0\\n assert re.search('--cell \\\"9\\\" already taken', out1)\\n\\n rv2, out2 = getstatusoutput(f'{prg} --board XXO...OOX --p O -c 1')\\n assert rv2 != 0\\n assert re.search('--cell \\\"1\\\" already taken', out2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52d00b80461161fb6fddeba7b44e33c7\",\n \"score\": \"0.52126414\",\n \"text\": \"def test_Update(self):\\n\\n r = Rectangle(1, 2, 3, 4)\\n arguments = (\\n ('id', 'id'), ('width', 10), ('height', 20), ('x', 30), ('y', 40),\\n ('extra', 0)\\n )\\n d = r.to_dictionary()\\n for i in range(len(arguments)):\\n args = arguments[:i + 1]\\n if i < len(arguments) - 1:\\n d.update(args)\\n with self.subTest():\\n r.update(*(val for _, val in args))\\n self.assertEqual(r.to_dictionary(), d)\\n r.update('new', width=5)\\n d['id'] = 'new'\\n with self.subTest():\\n self.assertEqual(r.to_dictionary(), d)\\n r.update('new', 1, 2, 3, 4)\\n d = r.to_dictionary()\\n for i in range(len(arguments)):\\n args = arguments[:i + 1]\\n if i < len(arguments) - 1:\\n d.update(args)\\n with self.subTest():\\n r.update(**dict(args))\\n self.assertEqual(r.to_dictionary(), d)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fafa78f0d8afa3363686620453eb50cb\",\n \"score\": \"0.52073365\",\n \"text\": \"def test_full_update_ifc_property(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ac0ac6decd28e201c7d3736ac3ca5b5\",\n \"score\": \"0.5196939\",\n \"text\": \"def test_get_and_set(self):\\r\\n old = SOCO.treble\\r\\n self.assertIn(old, self.valid_values, NOT_IN_RANGE)\\r\\n # Values on the boundaries of the valid equivalence partition\\r\\n for value in [self.valid_values[0], self.valid_values[-1]]:\\r\\n SOCO.treble = value\\r\\n wait()\\r\\n self.assertEqual(SOCO.treble, value, NOT_EXP)\\r\\n SOCO.treble = old\\r\\n wait()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a044c953ab266c3bd0e919ca20ea99d2\",\n \"score\": \"0.51767\",\n \"text\": \"def test_updated_barrier_ids_other_user(self):\\n barrier1 = BarrierFactory(priority=\\\"LOW\\\")\\n barrier2 = BarrierFactory(priority=\\\"MEDIUM\\\")\\n barrier3 = BarrierFactory(priority=\\\"HIGH\\\")\\n\\n user = create_test_user(sso_user_id=self.sso_user_data_1[\\\"user_id\\\"])\\n\\n saved_search = SavedSearch.objects.create(\\n user=user, name=\\\"Medium\\\", filters={\\\"priority\\\": [\\\"MEDIUM\\\"]}\\n )\\n saved_search.mark_as_seen()\\n\\n assert saved_search.updated_barrier_ids == []\\n\\n barrier1.summary = \\\"New summary\\\"\\n barrier1.save()\\n\\n barrier2.summary = \\\"New summary\\\"\\n barrier2.save()\\n\\n saved_search = SavedSearch.objects.get(pk=saved_search.pk)\\n assert saved_search.updated_barrier_ids == [barrier2.id]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"223bb420c8a18c112f8d18672101399f\",\n \"score\": \"0.51733667\",\n \"text\": \"def test_single_update_reduces_cost_function(self):\\n\\n random_seed = 0\\n random_state = check_random_state(random_seed)\\n\\n n_features = 25\\n n_components = 6\\n n_samples = 300\\n\\n X = random_state.uniform(size=(n_samples, n_features))\\n S = random_state.uniform(size=(n_components, n_features))\\n Gamma = right_stochastic_matrix(\\n (n_samples, n_components), random_state=random_state)\\n\\n self.assertTrue(np.allclose(Gamma.sum(axis=1), 1, 1e-14))\\n\\n lags = [2, 7]\\n n_lags = len(lags)\\n\\n order_weights = random_state.uniform(size=(n_lags,))\\n order_weights /= order_weights.sum()\\n\\n transition_matrices = np.empty(\\n (n_lags, n_components, n_components))\\n for i in range(n_lags):\\n transition_matrices[i] = left_stochastic_matrix(\\n (n_components, n_components), random_state=random_state)\\n\\n model = MTDConvexCoding(n_components=n_components, epsilon_states=2.3,\\n epsilon_weights=1.3, lags=lags)\\n\\n model.X = X\\n model.Gamma = Gamma\\n model.S = S\\n model.order_weights = order_weights\\n model.transition_matrices = transition_matrices\\n\\n model._initialize_workspace()\\n\\n initial_cost = model._evaluate_cost()\\n\\n error = model._update_parameters()\\n\\n self.assertEqual(error, 0)\\n\\n final_cost = model._evaluate_cost()\\n\\n self.assertTrue(final_cost <= initial_cost)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"398834f5d41e016f5462dc49d533c8ab\",\n \"score\": \"0.5167145\",\n \"text\": \"def test_update_with_enabled_True(self):\\n self._assert_update_raises_bad_request('True')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0438d9659e6523b5eda6c4286a81a242\",\n \"score\": \"0.5162143\",\n \"text\": \"def test_update_putaway(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7805c8b26a79f7b676ece19932e7d17b\",\n \"score\": \"0.51602423\",\n \"text\": \"def test_update():\\n our_file = StringIO()\\n progressbar = tqdm(total=2, file=our_file, miniters=1, mininterval=0)\\n assert len(progressbar) == 2\\n progressbar.update(2)\\n our_file.seek(0)\\n assert '| 2/2' in our_file.read()\\n progressbar.desc = 'dynamically notify of 4 increments in total'\\n progressbar.total = 4\\n progressbar.update(-10) # should default to +1\\n our_file.seek(0)\\n assert '| 3/4 ' in our_file.read()\\n our_file.seek(0)\\n assert 'dynamically notify of 4 increments in total' in our_file.read()\\n our_file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d3f9210f0582588c2424d6ef9f16a0a\",\n \"score\": \"0.51565576\",\n \"text\": \"def test_update_dog(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4aa188a339c19e05cf253f55794cacf\",\n \"score\": \"0.51525444\",\n \"text\": \"def test_valid_update(self):\\n self.assertEqual(python_to_couch({'update': 'true'}), {'update': 'true'})\\n self.assertEqual(python_to_couch({'update': 'false'}), {'update': 'false'})\\n self.assertEqual(python_to_couch({'update': 'lazy'}), {'update': 'lazy'})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcafdc607a093dfd6c74d9b79a4c3745\",\n \"score\": \"0.5148158\",\n \"text\": \"def test_update_all_args(self):\\n s1 = Square(10, 10, 10, 1)\\n self.assertEqual(s1.__str__(), \\\"[Square] (1) 10/10 - 10\\\")\\n s1.update(89, 2, 3, 4)\\n self.assertEqual(s1.__str__(), \\\"[Square] (89) 3/4 - 2\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a7fa2d6fbda18780df4f9f2d22b686b\",\n \"score\": \"0.514652\",\n \"text\": \"def test_redo_update(self):\\n ad_rep = AD_REP_FACTORY.create_ad_rep()\\n self.assertEqual(ad_rep.consumer_points, 0)\\n flyer = Flyer.objects.create(site_id=2, is_approved=True)\\n flyer_recipients = CONSUMER_FACTORY.create_consumers(create_count=5)\\n for consumer in flyer_recipients:\\n AdRepConsumer.objects.create(ad_rep=ad_rep, consumer_id=consumer.id)\\n BonusPoolFlyer.objects.create(flyer_id=flyer.id, calculate_status='2')\\n UPDATE_CONSUMER_BONUS_POOL(flyer.id, [])\\n ad_rep = AdRep.objects.get(id=ad_rep.id)\\n self.assertEqual(ad_rep.consumer_points, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e6997eab1ba91b27a7b92f971c2f3ac\",\n \"score\": \"0.5144648\",\n \"text\": \"def possible_update_VaueErrors(self):\\n test = Square(1)\\n with self.assertRaises(ValueError):\\n test.update(1, -4)\\n\\n with self.assertRaises(ValueError):\\n test.update(height=-4)\\n\\n with self.assertRaises(ValueError):\\n test.update(width=-3)\\n\\n with self.assertRaises(ValueError):\\n test.update(1, 5, -1, 5)\\n\\n with self.assertRaises(ValueError):\\n test.update(x=-1)\\n\\n with self.assertRaises(ValueError):\\n test.update(1, 5, 1, -5)\\n\\n with self.assertRaises(ValueError):\\n test.update(y=-5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6fe38c83b53bbd3a2708f23c2a1d711\",\n \"score\": \"0.51401764\",\n \"text\": \"def _safety_belt_update(self, safety_belt=''):\\n\\n if not self.isValidSafetyBelt(safety_belt):\\n return 0\\n self.updateSafetyBelt(safety_belt)\\n return 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f940e8fd6a52df510ca25e9da89c9e0\",\n \"score\": \"0.5134604\",\n \"text\": \"def test_blue(self):\\n b1 =[i+1 for i in self.FileObject.blue]\\n self.FileObject.blue = b1\\n b2 = self.FileObject.get_blue()\\n self.assertTrue((b1 == list(b2)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"474163be59dfbaf3b92d7f0e90b13238\",\n \"score\": \"0.5124191\",\n \"text\": \"def test_operator_update_operator(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d60d88e5ee7f70dacb87aac8dd755f7\",\n \"score\": \"0.5123588\",\n \"text\": \"def test_updated_barriers_since_notified_other_user(self):\\n barrier1 = BarrierFactory(priority=\\\"LOW\\\")\\n barrier2 = BarrierFactory(priority=\\\"MEDIUM\\\")\\n barrier3 = BarrierFactory(priority=\\\"HIGH\\\")\\n\\n user = create_test_user(sso_user_id=self.sso_user_data_1[\\\"user_id\\\"])\\n\\n saved_search = SavedSearch.objects.create(\\n user=user, name=\\\"Medium\\\", filters={\\\"priority\\\": [\\\"MEDIUM\\\"]}\\n )\\n saved_search.mark_as_notified()\\n\\n assert saved_search.updated_barriers_since_notified.exists() is False\\n\\n barrier1.summary = \\\"New summary\\\"\\n barrier1.save()\\n\\n barrier2.summary = \\\"New summary\\\"\\n barrier2.save()\\n\\n saved_search = SavedSearch.objects.get(pk=saved_search.pk)\\n assert barrier2 in saved_search.updated_barriers_since_notified\\n assert saved_search.updated_barriers_since_notified.count() == 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bb1ad3862e28fb900261cb3f7d5b3d4\",\n \"score\": \"0.5114988\",\n \"text\": \"def test_gotoGangChange_modify(self):\\n queue = myGlobals.actorState.queues[sopActor.SLEW]\\n # create something we can modify.\\n msg = self._run_cmd('gotoGangChange alt=10', queue)\\n msgNew = self._run_cmd('gotoGangChange alt=20', queue, empty=True)\\n self.assertIsNone(msgNew)\\n self.assertEqual(msg.cmdState.alt, 10)\\n self._check_cmd(0,2,0,0,True,True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"714ef3ae6a8decfd999c6fa7b02ad188\",\n \"score\": \"0.5114012\",\n \"text\": \"def test_integer_update(self):\\n vm = Integer.value_manager(Integer(), 5, strategy=SaveOnChange)\\n self.assertFalse(vm.changed)\\n vm.value = 4\\n self.assertTrue(vm.changed)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6cb0f347e543bab7fb5a4c95c58bd5a\",\n \"score\": \"0.5104573\",\n \"text\": \"def test_update(self):\\n # Add a parameter.\\n self._params.add(\\\"param\\\", 0)\\n self.assertEqual(0, self._params.get_value(\\\"param\\\"))\\n\\n # Update the value.\\n self._params.update(\\\"param\\\", 1)\\n self.assertEqual(1, self._params.get_value(\\\"param\\\"))\\n\\n # Update again.\\n self._params.update(\\\"param\\\", 2)\\n self.assertEqual(2, self._params.get_value(\\\"param\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b18ff63afd7cb78bde6cebf4d2a86acb\",\n \"score\": \"0.51041955\",\n \"text\": \"def test_cells_update(self):\\n p_arrs = generate_sample_dataset_2()\\n cm = CellManager(arrays_to_bin=p_arrs, initialize=False)\\n\\n big_cell = Cell(IntPoint(0, 0, 0), cell_manager=cm,\\n cell_size=2.0)\\n \\n cm.cells_dict[big_cell.id.py_copy()] = big_cell\\n\\n # add all particle indices to big_cell\\n indx_arr = big_cell.index_lists[0]\\n indx_arr.resize(7)\\n indx_arr.set_data(numpy.arange(7, dtype=numpy.int))\\n\\n cm.py_update()\\n\\n # now we should have a total of 5 cells.\\n self.assertEqual(len(cm.cells_dict), 5)\\n\\n self.assertEqual(cm.cells_dict.has_key(IntPoint(-1, 1, 0)), True)\\n self.assertEqual(cm.cells_dict.has_key(IntPoint(-1, -1, 0)), True)\\n self.assertEqual(cm.cells_dict.has_key(IntPoint(1, 0, 0)), True)\\n self.assertEqual(cm.cells_dict.has_key(IntPoint(1, -1, 0)), True)\\n self.assertEqual(cm.cells_dict.has_key(IntPoint(0, 0, 0)), True)\\n\\n # we do not test further as those test would have been performed by\\n # tests for NonLeafCell.\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c94ea06d20b8a1a860e81ec18c779f2\",\n \"score\": \"0.51038694\",\n \"text\": \"def test_red(self):\\n r1 = [i+1 for i in self.FileObject.red]\\n self.FileObject.red = r1\\n r2 = self.FileObject.get_red()\\n self.assertTrue((r1 == list(r2)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5d807b0d9e838c401555fad925d924e\",\n \"score\": \"0.51034784\",\n \"text\": \"def test_bulk_update_elements(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6733bb2ece13e2f5f24339ac7729cd35\",\n \"score\": \"0.510217\",\n \"text\": \"def test_update_needed(self):\\n self._test_significance(MEDIUM_SIGNIFICANCE, \\\"Update Needed\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bd02d6329a86aeb33e79461c08a3dda\",\n \"score\": \"0.50995606\",\n \"text\": \"def update():\\n # TODO: Slalom between red and blue cones. The car should pass to the right of\\n # each red cone and the left of each blue cone.\\n\\n SLALOM.run_slalom()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95295e643b506bca9967071105e913c4\",\n \"score\": \"0.50979596\",\n \"text\": \"def test_dcim_cables_update(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e6549dd57b6d6349900ea40cdf8483b\",\n \"score\": \"0.5088863\",\n \"text\": \"def test_update_01(self):\\n actual = model.Report(case='19-123456', date=now.date())\\n other = model.Report(case='19-654321', date=now.date())\\n with pytest.raises(ValueError):\\n actual.update(other, True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fc18689778a472633b7ea270b131cde\",\n \"score\": \"0.50887287\",\n \"text\": \"def test_full_update_checkplan(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cabf34e261b96aece3de15ce7145f518\",\n \"score\": \"0.5086508\",\n \"text\": \"def update_wire(self, stress_range, crack_width, lambda_fatigue, \\n lambda_fatigue_fretting_crack, lambda_fatigue_stress):\\n if self.broken == True:\\n return False\\n else:\\n self.fatigue = (self.fatigue \\n - self.calc_fatigue_exp(\\n stress_range, \\n lambda_fatigue\\n )\\n - self.calc_fatigue_fretting(\\n crack_width,\\n stress_range, \\n lambda_fatigue_fretting_crack, \\n lambda_fatigue_stress\\n ) \\n )\\n if self.fatigue < 0:\\n self.broken = True\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3acd034978ae8d01e03a3d9f2706d62\",\n \"score\": \"0.5085244\",\n \"text\": \"def test_checkAndSetColor(self):\\n # make explicitly sure that the tile is inactive, check functionality\\n self.testTile.setInactive()\\n self.testTile.checkAndSetColor()\\n assert_equal(self.testTile.tileColor, (210, 210, 210) )\\n # make explicitly sure that the tile is active, check functionality\\n self.testTile.setActive()\\n self.testTile.checkAndSetColor()\\n assert_equal(self.testTile.tileColor, (190, 190, 250) )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5e886dd26ea075c90ec2482b9cac284\",\n \"score\": \"0.50835884\",\n \"text\": \"def test_bulk_update(self):\\n before_update_time = datetime.utcnow()\\n\\n self.session.query(self.character).update({\\n # change everyone's name to Julia\\n self.character.name: 'Julia',\\n self.character.obj: {'favorite_book': 'Romeo & Juliet'}\\n })\\n\\n self.session.commit()\\n\\n expected_stmt = (\\\"UPDATE characters SET \\\"\\n \\\"name = ?, obj = ?, ts = ?\\\")\\n args, kwargs = fake_cursor.execute.call_args\\n stmt = args[0]\\n args = args[1]\\n self.assertEqual(expected_stmt, stmt)\\n self.assertEqual('Julia', args[0])\\n self.assertEqual({'favorite_book': 'Romeo & Juliet'}, args[1])\\n dt = datetime.strptime(args[2], '%Y-%m-%dT%H:%M:%S.%fZ')\\n self.assertIsInstance(dt, datetime)\\n self.assertGreater(dt, before_update_time)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"184fdf9efd0d648be7f79b6b20ce9bd3\",\n \"score\": \"0.5081388\",\n \"text\": \"def test_dcim_racks_update(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0ae31ed0a7bbdb973b92acb43646ec4\",\n \"score\": \"0.507621\",\n \"text\": \"def test_dcim_device_bays_update(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0372b50c305c8785e4291c75497a9f34\",\n \"score\": \"0.50719494\",\n \"text\": \"def test_update_bad_value_board(self):\\n\\n #create a valid board\\n good_col = [-1,1,1,-1,1,1]\\n good_board = [good_col, good_col, good_col, good_col, good_col, good_col,\\n good_col]\\n\\n self.model.board = good_board\\n\\n #create an invalid board\\n bad_value = [2,2,2,2,2,2]\\n bad_val_board = [bad_value, bad_value, bad_value, bad_value, bad_value,\\n bad_value, bad_value]\\n\\n #test that the update board function is not setting an invalid board\\n #with incorrect values\\n self.model.update_board(bad_val_board)\\n self.assertEqual(good_board, self.model.board)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"934b6b045f1ab020cdca7a296e5c090d\",\n \"score\": \"0.5070057\",\n \"text\": \"def test_updated_barriers_since_notified_current_user(self):\\n barrier1 = BarrierFactory(priority=\\\"LOW\\\")\\n barrier2 = BarrierFactory(priority=\\\"MEDIUM\\\")\\n barrier3 = BarrierFactory(priority=\\\"HIGH\\\")\\n\\n user = create_test_user(sso_user_id=self.sso_user_data_1[\\\"user_id\\\"])\\n\\n saved_search = SavedSearch.objects.create(\\n user=user, name=\\\"Medium\\\", filters={\\\"priority\\\": [\\\"MEDIUM\\\"]}\\n )\\n saved_search.mark_as_notified()\\n\\n assert saved_search.updated_barriers_since_notified.exists() is False\\n\\n barrier2.summary = \\\"New summary\\\"\\n barrier2.modified_by = user\\n barrier2.save()\\n\\n saved_search = SavedSearch.objects.get(pk=saved_search.pk)\\n assert barrier2 not in saved_search.updated_barriers_since_notified\\n assert saved_search.updated_barriers_since_notified.exists() is False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7bb7fb18127aa3d244440e3b8772cef7\",\n \"score\": \"0.5067463\",\n \"text\": \"def test_update_space(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9ada7048fd05f41af2be7abfe2bf336\",\n \"score\": \"0.50621974\",\n \"text\": \"def is_updating(self):\\n return self.cur_iter % self.update_per_n_iteration == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"817be66edcda8403358fc177a865d5f7\",\n \"score\": \"0.5061357\",\n \"text\": \"def test_update_rule(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9f7ee6d5e1729bc443e9c2f431b5836\",\n \"score\": \"0.50610447\",\n \"text\": \"def do_updates(self, isCorrect):\\n if isCorrect == True:\\n self.score += 100\\n elif isCorrect == False:\\n self.score -= 75\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70a91fd520ae05ee799b87f9dbb352a0\",\n \"score\": \"0.505471\",\n \"text\": \"def test_update(self):\\n with self._tracer.start_as_current_span(\\\"rootSpan\\\"):\\n self._collection.update_one(\\n {\\\"name\\\": \\\"testName\\\"}, {\\\"$set\\\": {\\\"value\\\": \\\"someOtherValue\\\"}}\\n )\\n\\n expected_db_statement = (\\n \\\"update [SON([('q', {'name': 'testName'}), ('u', \\\"\\n \\\"{'$set': {'value': 'someOtherValue'}}), ('multi', False), ('upsert', False)])]\\\"\\n )\\n self.validate_spans(expected_db_statement)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f77f6aaec446803812602aea074600c\",\n \"score\": \"0.5045329\",\n \"text\": \"def testUpdateGameConsistency(self):\\n # Create game with only one team and one game source.\\n game = Game()\\n self.CreateUser(2, 'bob').put()\\n twt = self.CreateTweet(5, ('bob', 2), 'up 5-7')\\n game.sources = [GameSource.FromTweet(twt, [5, 7])]\\n\\n game.teams = [Team(score_reporter_id=crawl_lists.UNKNOWN_SR_ID)]\\n crawl_lists_handler = crawl_lists.CrawlListHandler()\\n crawl_lists_handler._UpdateGameConsistency(game, {})\\n self.assertEqual(2, len(game.teams))\\n self.assertEqual(2, game.teams[0].twitter_id)\\n\\n # Update it again - no changes should have been made.\\n crawl_lists_handler._UpdateGameConsistency(game, {})\\n self.assertEqual(2, len(game.teams))\\n self.assertEqual(2, game.teams[0].twitter_id)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0019079a687383ee1df961f75203294d\",\n \"score\": \"0.5044909\",\n \"text\": \"def test_update_context_state_calls():\\n custom_integrators = get_all_custom_integrators()\\n for integrator_name, integrator_class in custom_integrators:\\n\\n integrator = integrator_class()\\n # NoseHooverChainVelocityVerletIntegrator will print a severe warning here,\\n # because it is being initialized without a system. That's OK.\\n\\n num_force_update = 0\\n for i in range(integrator.getNumComputations()):\\n step_type, target, expr = integrator.getComputationStep(i)\\n\\n if step_type == 5:\\n num_force_update += 1\\n\\n msg = \\\"Integrator '%s' has %d calls to addUpdateContextState(), while there should be only one.\\\" % (integrator_name, num_force_update)\\n if hasattr(integrator, 'pretty_format'):\\n msg += '\\\\n' + integrator.pretty_format(step_types_to_highlight=[5])\\n assert num_force_update == 1, msg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eef403b6b60aa5bbf8706f3bb1e24643\",\n \"score\": \"0.5044789\",\n \"text\": \"def test_update_level(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7c4c34598bf56d79a178aff344a3b9d\",\n \"score\": \"0.5033966\",\n \"text\": \"def test_update_ruleset(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52dd82aa0ead0f761aa3c79ba6679be6\",\n \"score\": \"0.50254565\",\n \"text\": \"def test_update_args(self):\\n s1 = Square(10, 10, 10, 1)\\n self.assertEqual(s1.__str__(), \\\"[Square] (1) 10/10 - 10\\\")\\n s1.update(89, 2, 3, 4, 5)\\n self.assertEqual(s1.__str__(), \\\"[Square] (89) 3/4 - 2\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4e66ba369aefeef2a1a32d02161693d\",\n \"score\": \"0.50231946\",\n \"text\": \"def update(self, colour, action):\\n self.initial_state = self.result(self.initial_state, action)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"746e2cd302e09df254d154c8c934ede9\",\n \"score\": \"0.50164306\",\n \"text\": \"def update_failed():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0faf7b40dc79b322fbb6831f9d3102ce\",\n \"score\": \"0.50101733\",\n \"text\": \"def test_update_large():\\n np.random.seed(1)\\n n_reads = 10_000_000\\n n_snps = 100_000\\n n_sfs = 40\\n n_rgs = 20\\n e = 0.001\\n b = 0.001\\n\\n #psi = 1. / np.random.sample(n_snps)\\n #psi /= np.max(psi)\\n psi = np.random.sample(n_snps)\\n #psi /= np.max(psi)\\n\\n true_tau = np.arange(0, 1, step = 1 / n_sfs)\\n true_cont = np.arange(0, 1, step = 1 / n_rgs)\\n\\n SNP2SFS = np.random.randint(0, n_sfs, size=n_snps)\\n #FLIPPED = np.zeros(n_snps, bool)\\n\\n\\n G = np.random.binomial(2, true_tau[SNP2SFS], size = n_snps)\\n A = np.random.binomial(1, psi, size = n_snps)\\n\\n #random assignments. In principle, a SNP might have repeat cats\\n READ2RG = np.random.randint(0, n_rgs, size = n_reads)\\n reps = np.random.multinomial(n_reads-n_snps, \\n pvals=np.zeros(n_snps)+ 1. / n_snps) + 1\\n READ2SNP = np.repeat(np.arange(n_snps, dtype=np.int), reps)\\n\\n p = true_cont[READ2RG] * psi[READ2SNP] + (1-true_cont[READ2RG]) * G[READ2SNP] / 2. \\n p = e * (1.-p) + p * (1.-b)\\n O = np.random.binomial(1, p, n_reads)\\n\\n FLIPPED = np.zeros(n_snps, bool)\\n #FLIPPED = np.array(np.random.binomial(1, .4, size =n_snps), bool)\\n #O[FLIPPED[READ2SNP]] = 1 - O[FLIPPED[READ2SNP]]\\n\\n\\n np.set_printoptions(suppress=True,precision = 3)\\n\\n data = SlugReads(\\n READS = O,\\n psi = psi,\\n READ2RG = READ2RG,\\n READ2SNP = READ2SNP,\\n FLIPPED = FLIPPED,\\n SNP2SFS = SNP2SFS)\\n pars = SlugParsSquare(\\n cont = np.repeat(0.5, n_rgs),\\n tau = np.repeat(.4, n_sfs),\\n F = np.repeat(.5, n_sfs),\\n e = 0.001,\\n b = 0.001\\n )\\n #update_pars_reads(pars, data, data, \\n # True, True, True)\\n #print(f'eb= {pars.e}, {pars.b}')\\n #print(f'C = {pars.cont}')\\n #print(f'F = {pars.F}')\\n #print(f'tau = {pars.tau}')\\n #update_pars_reads(pars, data, data, \\n # True, True, True)\\n #print( f'll : {pars.prev_ll} -> {pars.ll}')\\n controller = SlugController(update_eb=True, \\n update_ftau=True, \\n update_cont=True,\\n n_iter=1000,\\n ll_tol = 1e-4)\\n #em(pars, data, controller)\\n squarem(pars, data, controller)\\n assert True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"beb5e68d0cc2e7b50612f24714b296ec\",\n \"score\": \"0.4999062\",\n \"text\": \"def test_single(self):\\r\\n self.vector[buf_size / 2 - 1] = 1.\\r\\n self.vector[buf_size / 2] = -1.\\r\\n self.assertEqual(2./buf_size, zero_crossing_rate(self.vector))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77c9eee94f5dfa7a9399865addb84bf7\",\n \"score\": \"0.49953946\",\n \"text\": \"def update(self):\\n if self.dirty < 1:\\n # let's just try updating all of them\\n pass\\n #return\\n self._update_background()\\n self._update_text()\\n self.dirty = 2 if self.dirty == 2 else 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af9c1edb8500019749a256cdd21ac12d\",\n \"score\": \"0.49953637\",\n \"text\": \"def test_update(self):\\n self.ftv.adb_shell_output = GET_PROPERTIES_OUTPUT1\\n state = self.ftv.update()\\n self.assertTupleEqual(state, STATE1)\\n\\n self.ftv.adb_shell_output = GET_PROPERTIES_OUTPUT2\\n state = self.ftv.update()\\n self.assertTupleEqual(state, STATE2)\\n\\n self.ftv.adb_shell_output = GET_PROPERTIES_OUTPUT3\\n state = self.ftv.update()\\n self.assertTupleEqual(state, STATE3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cab0a3b30894f6a8df6381f87ff52a00\",\n \"score\": \"0.49945658\",\n \"text\": \"def test_get_and_set(self, soco):\\r\\n assert soco.treble in self.valid_values\\r\\n # Values on the boundaries of the valid equivalence partition\\r\\n for value in [self.valid_values[0], self.valid_values[-1]]:\\r\\n soco.treble = value\\r\\n wait()\\r\\n assert soco.treble == value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bd1ff27153be31a1872d250c2245d33\",\n \"score\": \"0.49932972\",\n \"text\": \"def test_full_update_ifc(self):\\n pass\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993274,"cells":{"query_id":{"kind":"string","value":"3a9a13fe2dfdd04567227bf7a5c59ed4"},"query":{"kind":"string","value":"Sets the launch_date_utc of this Roadster."},"positive_passages":{"kind":"list like","value":[{"docid":"fc4e976a06f01a0fa403acf4f1bc5702","score":"0.8859175","text":"def launch_date_utc(self, launch_date_utc):\n\n\n self._launch_date_utc = launch_date_utc","title":""}],"string":"[\n {\n \"docid\": \"fc4e976a06f01a0fa403acf4f1bc5702\",\n \"score\": \"0.8859175\",\n \"text\": \"def launch_date_utc(self, launch_date_utc):\\n\\n\\n self._launch_date_utc = launch_date_utc\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"4d38cc69dc6b63af9664399878087caa","score":"0.68396974","text":"def set_utc_date(self, date_utc):\n self.date_utc = utils.to_datetime(date_utc)\n self.date_localized = utils.to_datetime(date_utc, self._config['__tzinfo__'])","title":""},{"docid":"6ae675e5d069220b59838fb968364ba0","score":"0.66708827","text":"def launch_date_unix(self, launch_date_unix):\n\n\n self._launch_date_unix = launch_date_unix","title":""},{"docid":"1f600ac2db6c1cca65d913b75b5d43c6","score":"0.648278","text":"def updated_date_utc(self, updated_date_utc):\n\n self._updated_date_utc = updated_date_utc","title":""},{"docid":"1f600ac2db6c1cca65d913b75b5d43c6","score":"0.648278","text":"def updated_date_utc(self, updated_date_utc):\n\n self._updated_date_utc = updated_date_utc","title":""},{"docid":"3228553d11ed69d72ead73326a38e80c","score":"0.5707908","text":"def utc_time(self, utc_time):\n\n self._utc_time = utc_time","title":""},{"docid":"39acd47ae149aa7a80404a114dea7aa8","score":"0.56868947","text":"def set_milestone_start_date(self):\n\n url = '{zenhub_api_root}/p1/repositories/{repo_id}/milestones/{milestone_number}/start_date'.format(\n zenhub_api_root=ZENHUB_API_ROOT,\n repo_id=self.repo_id,\n milestone_number=self.milestone_number)\n # Gotta add the time offset.\n data = {'start_date': self.start_date + 'T08:00:00Z'}\n if not self.dry_run:\n r = requests.post(url, data=data, headers=self.zenhub_headers)\n return r","title":""},{"docid":"5d033a12a7261234fefa21fa68aae790","score":"0.5615806","text":"def start_time_utc(self, start_time):\n start_time = self._valitate_dt_str(start_time)\n \n if start_time == self.start_time_utc:\n return\n\n dt = datetime.datetime.strptime(start_time, self._date_time_fmt)\n self._start_time_utc = datetime.datetime.strftime(dt,\n self._date_time_fmt)\n \n # make a time series that the data can be indexed by\n if hasattr(self.ts, 'data'):\n self._set_dt_index(self._start_time_utc)","title":""},{"docid":"600cfa9f7bbb417c00ad83707b8dbb9c","score":"0.5577982","text":"def set_start_date(self):\r\n pass","title":""},{"docid":"a45af11b5c19ac08a24df19dc9fb9bea","score":"0.54950935","text":"def tour_date(self, tour_date):\n\n self._tour_date = tour_date","title":""},{"docid":"7e8a31ca9ce1d0cbc96c40c484854c78","score":"0.5451108","text":"def first_run_date(self, first_run_date):\n\n self._first_run_date = first_run_date","title":""},{"docid":"0491932bd995efdb29d99c71b2684b54","score":"0.5442097","text":"def start_date_time(self, start_date_time):\n\n self._start_date_time = start_date_time","title":""},{"docid":"7595da268c81fa8647182bc74fa2b9fc","score":"0.5437673","text":"def launch_time(self, launch_time):\n if launch_time is None:\n raise ValueError(\"Invalid value for `launch_time`, must not be `None`\")\n\n self._launch_time = launch_time","title":""},{"docid":"32352ef489cb9650ec66e34196150b73","score":"0.52915347","text":"def time_sycro(self):\n now_utc = datetime.utcnow()\n date = datetime(now_utc.year, now_utc.month, now_utc.day, now_utc.hour, now_utc.minute, now_utc.second,\n now_utc.microsecond)\n self.mount.UTCDate = date","title":""},{"docid":"09d41c784541adb43abf7efa874e93f2","score":"0.5261524","text":"def start_date_time(self, activity_start_date_time):\r\n self.__start_date_time = activity_start_date_time","title":""},{"docid":"a953f206b6306e319e940d967cc51d63","score":"0.5225833","text":"def schedule_date_start(self, schedule_date_start):\n\n self._schedule_date_start = schedule_date_start","title":""},{"docid":"1b42db322a0b3691a79dd73e5e6eca7c","score":"0.51798916","text":"def suspension_date(self, suspension_date):\n\n self._suspension_date = suspension_date","title":""},{"docid":"b1fb2d66317e1ddc73bec0781833946c","score":"0.516667","text":"def set_date(date):\n ret = _LIB.rtc_click_set_date(date)\n if ret < 0:\n raise Exception(\"rtc click set date failed\")","title":""},{"docid":"333c06ee4c94de7960fc1701dc9e8d31","score":"0.5165087","text":"def start_date(self, start_date):\n \n self._start_date = start_date","title":""},{"docid":"f76b298d564e42fb1601f9a6cf6aa15a","score":"0.5158881","text":"def _set_realdate(self, newdate):\n # convert to utc\n self.utcdt = newdate.astimezone(tz.tzutc())\n\n # get timezone name\n newtzname = newdate.tzname()\n if newtzname is not None:\n self.tzname = unicode(newtzname)\n else:\n self.tzname = None\n\n # set offset\n self.offsetseconds = self._calc_offset_seconds(newdate.utcoffset())","title":""},{"docid":"ff3dd0192b200c2eb48c83323e458242","score":"0.51573676","text":"def display_date(self, display_date):\n\n self._display_date = display_date","title":""},{"docid":"5a5d14bc170e8887eeb491b68dc3843e","score":"0.515649","text":"def from_utc(self, from_utc):\n if self.local_vars_configuration.client_side_validation and from_utc is None: # noqa: E501\n raise ValueError(\"Invalid value for `from_utc`, must not be `None`\") # noqa: E501\n\n self._from_utc = from_utc","title":""},{"docid":"23d3272cdd2f814ff9ab44b5bcd76675","score":"0.5144487","text":"def start_date(self, start_date):\n\n self._start_date = start_date","title":""},{"docid":"23d3272cdd2f814ff9ab44b5bcd76675","score":"0.5144487","text":"def start_date(self, start_date):\n\n self._start_date = start_date","title":""},{"docid":"23d3272cdd2f814ff9ab44b5bcd76675","score":"0.5144487","text":"def start_date(self, start_date):\n\n self._start_date = start_date","title":""},{"docid":"23d3272cdd2f814ff9ab44b5bcd76675","score":"0.5144487","text":"def start_date(self, start_date):\n\n self._start_date = start_date","title":""},{"docid":"63ce98790e819417c55bd1efe9424711","score":"0.5140137","text":"def setDate(self, date):\n self._date=str(date)","title":""},{"docid":"99d6b0b6982e44371de48ca5b8b45220","score":"0.5137187","text":"def utc_offset(self, utc_offset):\n\n self._utc_offset = utc_offset","title":""},{"docid":"108bc0bd225b7e2fb2292896eccb212a","score":"0.5136073","text":"def change_start_date(self, new_start_date):\r\n self.start_date_time = new_start_date","title":""},{"docid":"b46759c06803838d517eae7e54d14bdd","score":"0.51322097","text":"def set_start_end_date(self, starting_date, ending_date=datetime.datetime.now().strftime(\"%m/%d/%Y\")):\n self.data['st_date'] = starting_date\n self.data['end_date'] = ending_date","title":""},{"docid":"dae68880c8c0c841bcac83d124d95ead","score":"0.51225084","text":"def set_manual_date_machines():\n date = kl.next_date()\n dt.manual.update_variable('machines_schedule', date)","title":""},{"docid":"227be6e1b8fccd711e0a562a3cfd9e98","score":"0.5104547","text":"def set_date(self, new_date):\n self.date = new_date","title":""},{"docid":"2a97af72cfc7ad9ff4d5b8bc9623b5a9","score":"0.5102857","text":"def to_utc(self, to_utc):\n if self.local_vars_configuration.client_side_validation and to_utc is None: # noqa: E501\n raise ValueError(\"Invalid value for `to_utc`, must not be `None`\") # noqa: E501\n\n self._to_utc = to_utc","title":""},{"docid":"323709ec7cbd23ea3eebf087cf6c2960","score":"0.5098247","text":"def date_start(self, date_start):\n\n self._date_start = date_start","title":""},{"docid":"e5327d65db2dbc342cb68682824c6a7a","score":"0.5095275","text":"def cnfSetReleaseDate( date ):\n\n global _relDate\n\n _relDate = date","title":""},{"docid":"c614558608a6e93d835d89ad5b6f1e3d","score":"0.5092269","text":"def setDate(self, date=None, baseDate=None):\n if date is None:\n date = datetime.datetime.now()\n if date != self._date or baseDate != self._baseDate:\n self._date = date\n self._baseDate = baseDate\n self._timeRangeError = None\n try:\n for comp in self._stcomponents:\n comp.setDate(date, baseDate)\n except OutOfRangeError:\n self._timeRangeError = sys.exc_info()[1]","title":""},{"docid":"a4b94b7af6ccd80b00def508e325d18e","score":"0.5084017","text":"def set_datetime(self, datetime):\n raise NotImplementedError","title":""},{"docid":"a9114a136b9f499cef7e2d9f0347a5c0","score":"0.5069278","text":"def set_date(self, date=None, absolute_date=None, step=0):\n if date != None:\n year, month, day, hour, minute, sec = date\n self._initial_date = AbsoluteDate(year, month, day, hour, minute, sec, UTC)\n elif absolute_date != None and step != 0:\n self._extrap_Date = AbsoluteDate(absolute_date, step, UTC)\n else:\n # no argument given, use current date and time\n now = datetime.datetime.now()\n year, month, day, hour, minute, sec = now.year, now.month, now.day, now.hour, now.minute, float(now.second)\n self._initial_date = AbsoluteDate(year, month, day, hour, minute, sec, UTC)","title":""},{"docid":"ddf9e84a9ba06f61a28bf49393e34997","score":"0.5059197","text":"def start_datetime(self, start_datetime):\n\n self._start_datetime = start_datetime","title":""},{"docid":"564d701c95c56adf7eaad58ef379fac3","score":"0.5057921","text":"def log_last_started_datetime(self):\n self._obj.last_started_datetime = DateAccessor().today_with_timezone(\"UTC\")\n self._obj.save()","title":""},{"docid":"d7d0e1edf95060120eec689d0db4cca8","score":"0.5050143","text":"def schedule_date_last_start(self, schedule_date_last_start):\n\n self._schedule_date_last_start = schedule_date_last_start","title":""},{"docid":"bb4b2f4625ca0f363097316c54a09756","score":"0.5022434","text":"def update_date(self, update_date: datetime):\n\n self._update_date = update_date","title":""},{"docid":"9d6b0526e6c039447a92b791aae8f1f9","score":"0.50085044","text":"def start_time_utc(self):\n return self._start_time_utc","title":""},{"docid":"d9717d9df7a38392d2500374470d8cec","score":"0.5004531","text":"def sunset_utc(self, date, latitude, longitude):\n\n julianday = self._julianday(date.day, date.month, date.year)\n\n t = self._jday_to_jcentury(julianday)\n eqtime = self._eq_of_time(t)\n solarDec = self._sun_declination(t)\n\n try:\n hourangle = self._hour_angle_sunset(latitude, solarDec)\n except:\n raise AstralError('Sun remains below horizon on this day, at this location.')\n\n delta = longitude - degrees(hourangle)\n timeDiff = 4.0 * delta\n timeUTC = 720.0 + timeDiff - eqtime\n\n newt = self._jday_to_jcentury(self._jcentury_to_jday(t) + timeUTC / 1440.0)\n eqtime = self._eq_of_time(newt)\n solarDec = self._sun_declination(newt)\n hourangle = self._hour_angle_sunset(latitude, solarDec)\n delta = longitude - degrees(hourangle)\n timeDiff = 4 * delta\n timeUTC = 720 + timeDiff - eqtime\n\n timeUTC = timeUTC/60.0\n hour = int(timeUTC)\n minute = int((timeUTC - hour) * 60)\n second = int((((timeUTC - hour) * 60) - minute) * 60)\n\n sunset = datetime.datetime(date.year, date.month, date.day, hour, minute, second, tzinfo=pytz.utc)\n\n return sunset","title":""},{"docid":"818164986018ed05dc646432b175f1c2","score":"0.4984784","text":"def set_unapproved_version_publication_datetime(dt):","title":""},{"docid":"fdc7b7e7913fe9985b536886c211bc4a","score":"0.49830016","text":"def creation_date(self, creation_date):\n self._creation_date = creation_date","title":""},{"docid":"cd515fc3c4fffb14e0a34ba866cd2ff8","score":"0.49824524","text":"def set_last_run_time(self):\n self.update(\"last_run_time\", int(time()))","title":""},{"docid":"b9ec8553ff5f5453b822505f36341fb1","score":"0.49765602","text":"def creation_date(self, creation_date):\n\n self._creation_date = creation_date","title":""},{"docid":"484951b9cc52724c1d3fef08afb977b2","score":"0.49627826","text":"def population_date(self, population_date):\n\n self._population_date = population_date","title":""},{"docid":"0ea6a810e636304b04280fd1e1cd5269","score":"0.49568227","text":"def set_dates(self, start, end):\n self.dates = {\n \"start\": start,\n \"end\": end\n }","title":""},{"docid":"83457eb29b473837167405761e3c410b","score":"0.4949272","text":"def date(self, date):\n if self.local_vars_configuration.client_side_validation and date is None: # noqa: E501\n raise ValueError(\"Invalid value for `date`, must not be `None`\") # noqa: E501\n\n self._date = date","title":""},{"docid":"93f448447ca6007b8db172e3cb26c3b8","score":"0.49350345","text":"def trigger_date(self, trigger_date):\n\n self._trigger_date = trigger_date","title":""},{"docid":"0d59abbe58b93ed0da53ccae8933177d","score":"0.49284157","text":"def _date(self, _date):\n\n self.__date = _date","title":""},{"docid":"0d59abbe58b93ed0da53ccae8933177d","score":"0.49284157","text":"def _date(self, _date):\n\n self.__date = _date","title":""},{"docid":"7c08fbf3a1600b56f337d5eb73938b02","score":"0.4920489","text":"def set_start_date(self, date):\r\n _date = self._encode_date(date)\r\n\r\n if len(self._event.when) == 0:\r\n self._event.when.append(gdata.calendar.When(start_time=_date))\r\n else:\r\n self._event.when[0].start_time = _date","title":""},{"docid":"c3017738c2b09988f7ab368723cdc131","score":"0.4917911","text":"def setCreationDate(self, creation_date=None):\n created = creation_date\n if not created:\n created = DateTime()\n self.getField('creation_date').set(self, created)","title":""},{"docid":"b15bfa2cbd4b0660c8e2e0c207cc9f01","score":"0.49172977","text":"def arrival_date(self, arrival_date):\n\n self._arrival_date = arrival_date","title":""},{"docid":"ff5e8c738843e1f4935ba78f8ccd4301","score":"0.4911359","text":"def creation_date(self, creation_date: datetime):\n\n self._creation_date = creation_date","title":""},{"docid":"a680d448ee654fa4516963a68ef36476","score":"0.49105534","text":"def set_min_datetime(self, datetime):\n raise NotImplementedError","title":""},{"docid":"70c92b53806d1aa36b1066e83a02cc66","score":"0.49105427","text":"def last_updated_date(self, last_updated_date):\n\n self._last_updated_date = last_updated_date","title":""},{"docid":"ba247d872e62b280ded3f103defa1353","score":"0.49091884","text":"def availability_date(self, availability_date):\n\n self._availability_date = availability_date","title":""},{"docid":"1baf4056d7d25c7a2c32afd097edf485","score":"0.49025252","text":"def set_start_date(self, date):\n _date = self._encode_date(date)\n\n if len(self._event.when) == 0:\n self._event.when.append(gdata.calendar.When(start_time=_date))\n else:\n self._event.when[0].start_time = _date","title":""},{"docid":"eadea6f1e41668a987570c3d5d6e18f8","score":"0.48966068","text":"def set_end_date(self):\r\n pass","title":""},{"docid":"5a1eada5f66e300a366aa0714461ede4","score":"0.4893178","text":"def date(self, date):\n\n self._date = date","title":""},{"docid":"c4653a5d744eb4c11f59acf1194db442","score":"0.48917526","text":"def _date(self, _date: datetime):\n\n self.__date = _date","title":""},{"docid":"8cb9496da0143ae27b90866a8d9934cf","score":"0.489147","text":"def set_approved_version_publication_datetime(dt):","title":""},{"docid":"17ca34bb9ec76c8875225016b5c915bf","score":"0.4889597","text":"def set_date(self, date):\n if 'datetime.date' in str(type(date)):\n self._date = date\n return\n if date!=None:\n try:\n self._date = datetime.strptime(date, '%m/%d/%Y')\n except:\n pass\n if not hasattr(self, '_date'):\n self._date = None","title":""},{"docid":"44365548a82e381eec65c41162d4e604","score":"0.48813248","text":"def set_jump_date(self, date):\n self.jump_to_date = date if date > self.date else None","title":""},{"docid":"5f35160ce21cbf501fe9fa2cd3615708","score":"0.48720586","text":"def SetCTime(self, st_ctime):\n self.st_ctime = st_ctime","title":""},{"docid":"dd30a136cf2356bde56b156218a3f301","score":"0.4866796","text":"def settlement_date(self, settlement_date):\n\n self._settlement_date = settlement_date","title":""},{"docid":"c9609545dd629aab83748c06ba08eaaf","score":"0.48511738","text":"def set_manual_date_car(date):\n dt.manual.update_variable('car_schedule', date)","title":""},{"docid":"17ce062216ee241ad7d0b3d84f9e1bd6","score":"0.48348957","text":"def set_current_datetime(self):\n self.datetime.set_current_datetime()","title":""},{"docid":"fece05f72ddfd89e4070e85e8d44f9ab","score":"0.48306578","text":"def set_date(self, date, validate=True):\n self.dates.set_date(self, date, validate=validate)","title":""},{"docid":"fd2e688587cfeddbd2db566e1393e440","score":"0.48229864","text":"def set_time(self):\n target = self.obj.target\n return self.run.set_time(target)","title":""},{"docid":"dd429088f13765d503ffb994f5520774","score":"0.47831485","text":"def activity_date(self, activity_date):\n\n self._activity_date = activity_date","title":""},{"docid":"178dbac7d0d3db31007a265fc578bcbe","score":"0.47725418","text":"def set_start_time(self, start):\n # Implemented from template for osid.assessment.AssessmentOfferedForm.set_start_time_template\n if self.get_start_time_metadata().is_read_only():\n raise errors.NoAccess()\n if not self._is_valid_date_time(\n start,\n self.get_start_time_metadata()):\n raise errors.InvalidArgument()\n self._my_map['startTime'] = start","title":""},{"docid":"01f86828bdf2e0f49e73469fca42cf70","score":"0.4765881","text":"def _date(self, _date: List[str]):\n\n self.__date = _date","title":""},{"docid":"b14c8161b115c61aebd534a13e72caaa","score":"0.47394982","text":"def set_manual_date_temperature():\n date = kl.date_now()\n dt.manual.update_variable('temperature_schedule', date)","title":""},{"docid":"0470a17d427b3e8b16eea6c11b1dce38","score":"0.4738295","text":"def create_date(self, create_date):\n self._create_date = create_date","title":""},{"docid":"128dbcd9d44f9e8563965b4661fadb9f","score":"0.473537","text":"def create_date(self, create_date):\n \n self._create_date = create_date","title":""},{"docid":"1c42e6386c150f46158afb0a6dd7e933","score":"0.47339365","text":"def sun_utc(self, date, latitude, longitude):\n\n dawn = self.dawn_utc(date, latitude, longitude)\n sunrise = self.sunrise_utc(date, latitude, longitude)\n noon = self.solar_noon_utc(date, longitude)\n sunset = self.sunset_utc(date, latitude, longitude)\n dusk = self.dusk_utc(date, latitude, longitude)\n\n return {'dawn': dawn, 'sunrise': sunrise, 'noon': noon, 'sunset': sunset, 'dusk': dusk}","title":""},{"docid":"e9542675a2aa8373b6b4de6e68cbbad6","score":"0.47293547","text":"def created_at(self, value):\r\n self.logger.warn(\"Setting values on created_at will NOT update the remote Canvas instance.\")\r\n self._created_at = value","title":""},{"docid":"e9542675a2aa8373b6b4de6e68cbbad6","score":"0.47293547","text":"def created_at(self, value):\r\n self.logger.warn(\"Setting values on created_at will NOT update the remote Canvas instance.\")\r\n self._created_at = value","title":""},{"docid":"e9542675a2aa8373b6b4de6e68cbbad6","score":"0.47293547","text":"def created_at(self, value):\r\n self.logger.warn(\"Setting values on created_at will NOT update the remote Canvas instance.\")\r\n self._created_at = value","title":""},{"docid":"e9542675a2aa8373b6b4de6e68cbbad6","score":"0.47293547","text":"def created_at(self, value):\r\n self.logger.warn(\"Setting values on created_at will NOT update the remote Canvas instance.\")\r\n self._created_at = value","title":""},{"docid":"ade5188dec42c2d8d243e240befbfeae","score":"0.47248694","text":"def set_system_time(self):\n dt = self.read()\n\n second = str(dt.second)\n minute = str(dt.minute)\n hour = str(dt.hour)\n day = str(dt.day)\n month = str(dt.month)\n year = str(dt.year)\n subprocess.call(\"sudo date -s '{}-{}-{} {}:{}:{}' > /dev/null\".\n format(year, month, day, hour, minute, second),\n shell=True)","title":""},{"docid":"959ab88c28b7233747178e97ccae3ad6","score":"0.4700586","text":"def dawn_utc(self, date, latitude, longitude):\n\n julianday = self._julianday(date.day, date.month, date.year)\n\n if latitude > 89.8:\n latitude = 89.8\n\n if latitude < -89.8:\n latitude = -89.8\n\n t = self._jday_to_jcentury(julianday)\n eqtime = self._eq_of_time(t)\n solarDec = self._sun_declination(t)\n\n try:\n hourangle = self._hour_angle_sunrise(latitude, solarDec)\n except:\n raise AstralError('Sun remains below horizon on this day, at this location.')\n\n delta = longitude - degrees(hourangle)\n timeDiff = 4.0 * delta\n timeUTC = 720.0 + timeDiff - eqtime\n\n newt = self._jday_to_jcentury(self._jcentury_to_jday(t) + timeUTC / 1440.0)\n eqtime = self._eq_of_time(newt)\n solarDec = self._sun_declination(newt)\n hourangle = self._hour_angle_dawn(latitude, solarDec, self._depression)\n delta = longitude - degrees(hourangle)\n timeDiff = 4 * delta\n timeUTC = 720 + timeDiff - eqtime\n\n timeUTC = timeUTC/60.0\n hour = int(timeUTC)\n minute = int((timeUTC - hour) * 60)\n second = int((((timeUTC - hour) * 60) - minute) * 60)\n\n dawn = datetime.datetime(date.year, date.month, date.day, hour, minute, second, tzinfo=pytz.utc)\n\n return dawn","title":""},{"docid":"05c85fc99178bf181adca47a2dd0943e","score":"0.4700249","text":"def set_checkout(self, date=False):\n if date == False:\n self.checkout_date = datetime.datetime.now()\n else:\n self.checkout_date = date","title":""},{"docid":"7db7bc627576a98f9b4d381542145668","score":"0.469985","text":"def started_at(self, started_at):\n\n self._started_at = started_at","title":""},{"docid":"7db7bc627576a98f9b4d381542145668","score":"0.469985","text":"def started_at(self, started_at):\n\n self._started_at = started_at","title":""},{"docid":"3357120b9dff0b475845c10512ce1751","score":"0.46961823","text":"def void_date(self, void_date):\n self._void_date = void_date","title":""},{"docid":"66256ce1f55c78a485d8341c979d2728","score":"0.4695941","text":"def set_date(self, date):\n\n newdate = datetime.datetime.strptime(date, \"%Y-%m-%dT%H:%M:%S\")\n self.__get_century(date)\n self.__bus.write_byte_data(self.__rtcaddress,\n self.SECONDS,\n self.__dec_bcd(newdate.second))\n self.__bus.write_byte_data(self.__rtcaddress,\n self.MINUTES,\n self.__dec_bcd(newdate.minute))\n self.__bus.write_byte_data(self.__rtcaddress,\n self.HOURS,\n self.__dec_bcd(newdate.hour))\n self.__bus.write_byte_data(self.__rtcaddress,\n self.DAYOFWEEK,\n self.__dec_bcd(newdate.weekday()))\n self.__bus.write_byte_data(self.__rtcaddress,\n self.DAY,\n self.__dec_bcd(newdate.day))\n self.__bus.write_byte_data(self.__rtcaddress,\n self.MONTH,\n self.__dec_bcd(newdate.month))\n self.__bus.write_byte_data(self.__rtcaddress,\n self.YEAR,\n self.__dec_bcd(newdate.year -\n self.__century))\n return","title":""},{"docid":"bbaa7bb953b471241c2f76a7ad5a06f3","score":"0.4685464","text":"def merger_date(self, merger_date):\n\n self._merger_date = merger_date","title":""},{"docid":"7119ed00d0bc236ff2f7f326dfae00cf","score":"0.46846858","text":"def updated_date(self, updated_date):\n\n self._updated_date = updated_date","title":""},{"docid":"ba8c1521a830572b954dc2ea33403be2","score":"0.46799648","text":"def date_joined(self, date_joined: object):\n\n self._date_joined = date_joined","title":""},{"docid":"6fd11222b7fcffba7797ba0b9057640d","score":"0.46785975","text":"def event_date(self, event_date):\n\n self._event_date = event_date","title":""},{"docid":"6fd11222b7fcffba7797ba0b9057640d","score":"0.46785975","text":"def event_date(self, event_date):\n\n self._event_date = event_date","title":""},{"docid":"d7caf1e85f10e34526a89f7348f9083c","score":"0.46618196","text":"def build_time(self, build_time):\n\n self._build_time = build_time","title":""},{"docid":"6afa3aeb424db4de535caa4660930379","score":"0.4660389","text":"def setModifyTime(self, date: java.util.Date) -> None:\n ...","title":""},{"docid":"4f2328c1f83d2f744646f20400bef902","score":"0.4657822","text":"def __set_releasedate(self, value: str):\n if value:\n self.__releasedate = value\n else:\n raise ValueError(\"La fecha de estreno de la pelicula no puede estar vacia\")","title":""},{"docid":"2cc0ec69f1b03cfe2877a374558cff1f","score":"0.46425372","text":"def set_min_date(self, date):\n self.widget.PySetLowerDateLimit(date)","title":""}],"string":"[\n {\n \"docid\": \"4d38cc69dc6b63af9664399878087caa\",\n \"score\": \"0.68396974\",\n \"text\": \"def set_utc_date(self, date_utc):\\n self.date_utc = utils.to_datetime(date_utc)\\n self.date_localized = utils.to_datetime(date_utc, self._config['__tzinfo__'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ae675e5d069220b59838fb968364ba0\",\n \"score\": \"0.66708827\",\n \"text\": \"def launch_date_unix(self, launch_date_unix):\\n\\n\\n self._launch_date_unix = launch_date_unix\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f600ac2db6c1cca65d913b75b5d43c6\",\n \"score\": \"0.648278\",\n \"text\": \"def updated_date_utc(self, updated_date_utc):\\n\\n self._updated_date_utc = updated_date_utc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f600ac2db6c1cca65d913b75b5d43c6\",\n \"score\": \"0.648278\",\n \"text\": \"def updated_date_utc(self, updated_date_utc):\\n\\n self._updated_date_utc = updated_date_utc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3228553d11ed69d72ead73326a38e80c\",\n \"score\": \"0.5707908\",\n \"text\": \"def utc_time(self, utc_time):\\n\\n self._utc_time = utc_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39acd47ae149aa7a80404a114dea7aa8\",\n \"score\": \"0.56868947\",\n \"text\": \"def set_milestone_start_date(self):\\n\\n url = '{zenhub_api_root}/p1/repositories/{repo_id}/milestones/{milestone_number}/start_date'.format(\\n zenhub_api_root=ZENHUB_API_ROOT,\\n repo_id=self.repo_id,\\n milestone_number=self.milestone_number)\\n # Gotta add the time offset.\\n data = {'start_date': self.start_date + 'T08:00:00Z'}\\n if not self.dry_run:\\n r = requests.post(url, data=data, headers=self.zenhub_headers)\\n return r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d033a12a7261234fefa21fa68aae790\",\n \"score\": \"0.5615806\",\n \"text\": \"def start_time_utc(self, start_time):\\n start_time = self._valitate_dt_str(start_time)\\n \\n if start_time == self.start_time_utc:\\n return\\n\\n dt = datetime.datetime.strptime(start_time, self._date_time_fmt)\\n self._start_time_utc = datetime.datetime.strftime(dt,\\n self._date_time_fmt)\\n \\n # make a time series that the data can be indexed by\\n if hasattr(self.ts, 'data'):\\n self._set_dt_index(self._start_time_utc)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"600cfa9f7bbb417c00ad83707b8dbb9c\",\n \"score\": \"0.5577982\",\n \"text\": \"def set_start_date(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a45af11b5c19ac08a24df19dc9fb9bea\",\n \"score\": \"0.54950935\",\n \"text\": \"def tour_date(self, tour_date):\\n\\n self._tour_date = tour_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e8a31ca9ce1d0cbc96c40c484854c78\",\n \"score\": \"0.5451108\",\n \"text\": \"def first_run_date(self, first_run_date):\\n\\n self._first_run_date = first_run_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0491932bd995efdb29d99c71b2684b54\",\n \"score\": \"0.5442097\",\n \"text\": \"def start_date_time(self, start_date_time):\\n\\n self._start_date_time = start_date_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7595da268c81fa8647182bc74fa2b9fc\",\n \"score\": \"0.5437673\",\n \"text\": \"def launch_time(self, launch_time):\\n if launch_time is None:\\n raise ValueError(\\\"Invalid value for `launch_time`, must not be `None`\\\")\\n\\n self._launch_time = launch_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32352ef489cb9650ec66e34196150b73\",\n \"score\": \"0.52915347\",\n \"text\": \"def time_sycro(self):\\n now_utc = datetime.utcnow()\\n date = datetime(now_utc.year, now_utc.month, now_utc.day, now_utc.hour, now_utc.minute, now_utc.second,\\n now_utc.microsecond)\\n self.mount.UTCDate = date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09d41c784541adb43abf7efa874e93f2\",\n \"score\": \"0.5261524\",\n \"text\": \"def start_date_time(self, activity_start_date_time):\\r\\n self.__start_date_time = activity_start_date_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a953f206b6306e319e940d967cc51d63\",\n \"score\": \"0.5225833\",\n \"text\": \"def schedule_date_start(self, schedule_date_start):\\n\\n self._schedule_date_start = schedule_date_start\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b42db322a0b3691a79dd73e5e6eca7c\",\n \"score\": \"0.51798916\",\n \"text\": \"def suspension_date(self, suspension_date):\\n\\n self._suspension_date = suspension_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1fb2d66317e1ddc73bec0781833946c\",\n \"score\": \"0.516667\",\n \"text\": \"def set_date(date):\\n ret = _LIB.rtc_click_set_date(date)\\n if ret < 0:\\n raise Exception(\\\"rtc click set date failed\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"333c06ee4c94de7960fc1701dc9e8d31\",\n \"score\": \"0.5165087\",\n \"text\": \"def start_date(self, start_date):\\n \\n self._start_date = start_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f76b298d564e42fb1601f9a6cf6aa15a\",\n \"score\": \"0.5158881\",\n \"text\": \"def _set_realdate(self, newdate):\\n # convert to utc\\n self.utcdt = newdate.astimezone(tz.tzutc())\\n\\n # get timezone name\\n newtzname = newdate.tzname()\\n if newtzname is not None:\\n self.tzname = unicode(newtzname)\\n else:\\n self.tzname = None\\n\\n # set offset\\n self.offsetseconds = self._calc_offset_seconds(newdate.utcoffset())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff3dd0192b200c2eb48c83323e458242\",\n \"score\": \"0.51573676\",\n \"text\": \"def display_date(self, display_date):\\n\\n self._display_date = display_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a5d14bc170e8887eeb491b68dc3843e\",\n \"score\": \"0.515649\",\n \"text\": \"def from_utc(self, from_utc):\\n if self.local_vars_configuration.client_side_validation and from_utc is None: # noqa: E501\\n raise ValueError(\\\"Invalid value for `from_utc`, must not be `None`\\\") # noqa: E501\\n\\n self._from_utc = from_utc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23d3272cdd2f814ff9ab44b5bcd76675\",\n \"score\": \"0.5144487\",\n \"text\": \"def start_date(self, start_date):\\n\\n self._start_date = start_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23d3272cdd2f814ff9ab44b5bcd76675\",\n \"score\": \"0.5144487\",\n \"text\": \"def start_date(self, start_date):\\n\\n self._start_date = start_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23d3272cdd2f814ff9ab44b5bcd76675\",\n \"score\": \"0.5144487\",\n \"text\": \"def start_date(self, start_date):\\n\\n self._start_date = start_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23d3272cdd2f814ff9ab44b5bcd76675\",\n \"score\": \"0.5144487\",\n \"text\": \"def start_date(self, start_date):\\n\\n self._start_date = start_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63ce98790e819417c55bd1efe9424711\",\n \"score\": \"0.5140137\",\n \"text\": \"def setDate(self, date):\\n self._date=str(date)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99d6b0b6982e44371de48ca5b8b45220\",\n \"score\": \"0.5137187\",\n \"text\": \"def utc_offset(self, utc_offset):\\n\\n self._utc_offset = utc_offset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"108bc0bd225b7e2fb2292896eccb212a\",\n \"score\": \"0.5136073\",\n \"text\": \"def change_start_date(self, new_start_date):\\r\\n self.start_date_time = new_start_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b46759c06803838d517eae7e54d14bdd\",\n \"score\": \"0.51322097\",\n \"text\": \"def set_start_end_date(self, starting_date, ending_date=datetime.datetime.now().strftime(\\\"%m/%d/%Y\\\")):\\n self.data['st_date'] = starting_date\\n self.data['end_date'] = ending_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dae68880c8c0c841bcac83d124d95ead\",\n \"score\": \"0.51225084\",\n \"text\": \"def set_manual_date_machines():\\n date = kl.next_date()\\n dt.manual.update_variable('machines_schedule', date)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"227be6e1b8fccd711e0a562a3cfd9e98\",\n \"score\": \"0.5104547\",\n \"text\": \"def set_date(self, new_date):\\n self.date = new_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a97af72cfc7ad9ff4d5b8bc9623b5a9\",\n \"score\": \"0.5102857\",\n \"text\": \"def to_utc(self, to_utc):\\n if self.local_vars_configuration.client_side_validation and to_utc is None: # noqa: E501\\n raise ValueError(\\\"Invalid value for `to_utc`, must not be `None`\\\") # noqa: E501\\n\\n self._to_utc = to_utc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"323709ec7cbd23ea3eebf087cf6c2960\",\n \"score\": \"0.5098247\",\n \"text\": \"def date_start(self, date_start):\\n\\n self._date_start = date_start\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5327d65db2dbc342cb68682824c6a7a\",\n \"score\": \"0.5095275\",\n \"text\": \"def cnfSetReleaseDate( date ):\\n\\n global _relDate\\n\\n _relDate = date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c614558608a6e93d835d89ad5b6f1e3d\",\n \"score\": \"0.5092269\",\n \"text\": \"def setDate(self, date=None, baseDate=None):\\n if date is None:\\n date = datetime.datetime.now()\\n if date != self._date or baseDate != self._baseDate:\\n self._date = date\\n self._baseDate = baseDate\\n self._timeRangeError = None\\n try:\\n for comp in self._stcomponents:\\n comp.setDate(date, baseDate)\\n except OutOfRangeError:\\n self._timeRangeError = sys.exc_info()[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4b94b7af6ccd80b00def508e325d18e\",\n \"score\": \"0.5084017\",\n \"text\": \"def set_datetime(self, datetime):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9114a136b9f499cef7e2d9f0347a5c0\",\n \"score\": \"0.5069278\",\n \"text\": \"def set_date(self, date=None, absolute_date=None, step=0):\\n if date != None:\\n year, month, day, hour, minute, sec = date\\n self._initial_date = AbsoluteDate(year, month, day, hour, minute, sec, UTC)\\n elif absolute_date != None and step != 0:\\n self._extrap_Date = AbsoluteDate(absolute_date, step, UTC)\\n else:\\n # no argument given, use current date and time\\n now = datetime.datetime.now()\\n year, month, day, hour, minute, sec = now.year, now.month, now.day, now.hour, now.minute, float(now.second)\\n self._initial_date = AbsoluteDate(year, month, day, hour, minute, sec, UTC)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddf9e84a9ba06f61a28bf49393e34997\",\n \"score\": \"0.5059197\",\n \"text\": \"def start_datetime(self, start_datetime):\\n\\n self._start_datetime = start_datetime\",\n \"title\": \"\"\n },\n {\n \"docid\": \"564d701c95c56adf7eaad58ef379fac3\",\n \"score\": \"0.5057921\",\n \"text\": \"def log_last_started_datetime(self):\\n self._obj.last_started_datetime = DateAccessor().today_with_timezone(\\\"UTC\\\")\\n self._obj.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7d0e1edf95060120eec689d0db4cca8\",\n \"score\": \"0.5050143\",\n \"text\": \"def schedule_date_last_start(self, schedule_date_last_start):\\n\\n self._schedule_date_last_start = schedule_date_last_start\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb4b2f4625ca0f363097316c54a09756\",\n \"score\": \"0.5022434\",\n \"text\": \"def update_date(self, update_date: datetime):\\n\\n self._update_date = update_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d6b0526e6c039447a92b791aae8f1f9\",\n \"score\": \"0.50085044\",\n \"text\": \"def start_time_utc(self):\\n return self._start_time_utc\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9717d9df7a38392d2500374470d8cec\",\n \"score\": \"0.5004531\",\n \"text\": \"def sunset_utc(self, date, latitude, longitude):\\n\\n julianday = self._julianday(date.day, date.month, date.year)\\n\\n t = self._jday_to_jcentury(julianday)\\n eqtime = self._eq_of_time(t)\\n solarDec = self._sun_declination(t)\\n\\n try:\\n hourangle = self._hour_angle_sunset(latitude, solarDec)\\n except:\\n raise AstralError('Sun remains below horizon on this day, at this location.')\\n\\n delta = longitude - degrees(hourangle)\\n timeDiff = 4.0 * delta\\n timeUTC = 720.0 + timeDiff - eqtime\\n\\n newt = self._jday_to_jcentury(self._jcentury_to_jday(t) + timeUTC / 1440.0)\\n eqtime = self._eq_of_time(newt)\\n solarDec = self._sun_declination(newt)\\n hourangle = self._hour_angle_sunset(latitude, solarDec)\\n delta = longitude - degrees(hourangle)\\n timeDiff = 4 * delta\\n timeUTC = 720 + timeDiff - eqtime\\n\\n timeUTC = timeUTC/60.0\\n hour = int(timeUTC)\\n minute = int((timeUTC - hour) * 60)\\n second = int((((timeUTC - hour) * 60) - minute) * 60)\\n\\n sunset = datetime.datetime(date.year, date.month, date.day, hour, minute, second, tzinfo=pytz.utc)\\n\\n return sunset\",\n \"title\": \"\"\n },\n {\n \"docid\": \"818164986018ed05dc646432b175f1c2\",\n \"score\": \"0.4984784\",\n \"text\": \"def set_unapproved_version_publication_datetime(dt):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdc7b7e7913fe9985b536886c211bc4a\",\n \"score\": \"0.49830016\",\n \"text\": \"def creation_date(self, creation_date):\\n self._creation_date = creation_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd515fc3c4fffb14e0a34ba866cd2ff8\",\n \"score\": \"0.49824524\",\n \"text\": \"def set_last_run_time(self):\\n self.update(\\\"last_run_time\\\", int(time()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9ec8553ff5f5453b822505f36341fb1\",\n \"score\": \"0.49765602\",\n \"text\": \"def creation_date(self, creation_date):\\n\\n self._creation_date = creation_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"484951b9cc52724c1d3fef08afb977b2\",\n \"score\": \"0.49627826\",\n \"text\": \"def population_date(self, population_date):\\n\\n self._population_date = population_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ea6a810e636304b04280fd1e1cd5269\",\n \"score\": \"0.49568227\",\n \"text\": \"def set_dates(self, start, end):\\n self.dates = {\\n \\\"start\\\": start,\\n \\\"end\\\": end\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83457eb29b473837167405761e3c410b\",\n \"score\": \"0.4949272\",\n \"text\": \"def date(self, date):\\n if self.local_vars_configuration.client_side_validation and date is None: # noqa: E501\\n raise ValueError(\\\"Invalid value for `date`, must not be `None`\\\") # noqa: E501\\n\\n self._date = date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93f448447ca6007b8db172e3cb26c3b8\",\n \"score\": \"0.49350345\",\n \"text\": \"def trigger_date(self, trigger_date):\\n\\n self._trigger_date = trigger_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d59abbe58b93ed0da53ccae8933177d\",\n \"score\": \"0.49284157\",\n \"text\": \"def _date(self, _date):\\n\\n self.__date = _date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d59abbe58b93ed0da53ccae8933177d\",\n \"score\": \"0.49284157\",\n \"text\": \"def _date(self, _date):\\n\\n self.__date = _date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c08fbf3a1600b56f337d5eb73938b02\",\n \"score\": \"0.4920489\",\n \"text\": \"def set_start_date(self, date):\\r\\n _date = self._encode_date(date)\\r\\n\\r\\n if len(self._event.when) == 0:\\r\\n self._event.when.append(gdata.calendar.When(start_time=_date))\\r\\n else:\\r\\n self._event.when[0].start_time = _date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3017738c2b09988f7ab368723cdc131\",\n \"score\": \"0.4917911\",\n \"text\": \"def setCreationDate(self, creation_date=None):\\n created = creation_date\\n if not created:\\n created = DateTime()\\n self.getField('creation_date').set(self, created)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b15bfa2cbd4b0660c8e2e0c207cc9f01\",\n \"score\": \"0.49172977\",\n \"text\": \"def arrival_date(self, arrival_date):\\n\\n self._arrival_date = arrival_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff5e8c738843e1f4935ba78f8ccd4301\",\n \"score\": \"0.4911359\",\n \"text\": \"def creation_date(self, creation_date: datetime):\\n\\n self._creation_date = creation_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a680d448ee654fa4516963a68ef36476\",\n \"score\": \"0.49105534\",\n \"text\": \"def set_min_datetime(self, datetime):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70c92b53806d1aa36b1066e83a02cc66\",\n \"score\": \"0.49105427\",\n \"text\": \"def last_updated_date(self, last_updated_date):\\n\\n self._last_updated_date = last_updated_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba247d872e62b280ded3f103defa1353\",\n \"score\": \"0.49091884\",\n \"text\": \"def availability_date(self, availability_date):\\n\\n self._availability_date = availability_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1baf4056d7d25c7a2c32afd097edf485\",\n \"score\": \"0.49025252\",\n \"text\": \"def set_start_date(self, date):\\n _date = self._encode_date(date)\\n\\n if len(self._event.when) == 0:\\n self._event.when.append(gdata.calendar.When(start_time=_date))\\n else:\\n self._event.when[0].start_time = _date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eadea6f1e41668a987570c3d5d6e18f8\",\n \"score\": \"0.48966068\",\n \"text\": \"def set_end_date(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a1eada5f66e300a366aa0714461ede4\",\n \"score\": \"0.4893178\",\n \"text\": \"def date(self, date):\\n\\n self._date = date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4653a5d744eb4c11f59acf1194db442\",\n \"score\": \"0.48917526\",\n \"text\": \"def _date(self, _date: datetime):\\n\\n self.__date = _date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cb9496da0143ae27b90866a8d9934cf\",\n \"score\": \"0.489147\",\n \"text\": \"def set_approved_version_publication_datetime(dt):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17ca34bb9ec76c8875225016b5c915bf\",\n \"score\": \"0.4889597\",\n \"text\": \"def set_date(self, date):\\n if 'datetime.date' in str(type(date)):\\n self._date = date\\n return\\n if date!=None:\\n try:\\n self._date = datetime.strptime(date, '%m/%d/%Y')\\n except:\\n pass\\n if not hasattr(self, '_date'):\\n self._date = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44365548a82e381eec65c41162d4e604\",\n \"score\": \"0.48813248\",\n \"text\": \"def set_jump_date(self, date):\\n self.jump_to_date = date if date > self.date else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f35160ce21cbf501fe9fa2cd3615708\",\n \"score\": \"0.48720586\",\n \"text\": \"def SetCTime(self, st_ctime):\\n self.st_ctime = st_ctime\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd30a136cf2356bde56b156218a3f301\",\n \"score\": \"0.4866796\",\n \"text\": \"def settlement_date(self, settlement_date):\\n\\n self._settlement_date = settlement_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9609545dd629aab83748c06ba08eaaf\",\n \"score\": \"0.48511738\",\n \"text\": \"def set_manual_date_car(date):\\n dt.manual.update_variable('car_schedule', date)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17ce062216ee241ad7d0b3d84f9e1bd6\",\n \"score\": \"0.48348957\",\n \"text\": \"def set_current_datetime(self):\\n self.datetime.set_current_datetime()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fece05f72ddfd89e4070e85e8d44f9ab\",\n \"score\": \"0.48306578\",\n \"text\": \"def set_date(self, date, validate=True):\\n self.dates.set_date(self, date, validate=validate)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd2e688587cfeddbd2db566e1393e440\",\n \"score\": \"0.48229864\",\n \"text\": \"def set_time(self):\\n target = self.obj.target\\n return self.run.set_time(target)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd429088f13765d503ffb994f5520774\",\n \"score\": \"0.47831485\",\n \"text\": \"def activity_date(self, activity_date):\\n\\n self._activity_date = activity_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"178dbac7d0d3db31007a265fc578bcbe\",\n \"score\": \"0.47725418\",\n \"text\": \"def set_start_time(self, start):\\n # Implemented from template for osid.assessment.AssessmentOfferedForm.set_start_time_template\\n if self.get_start_time_metadata().is_read_only():\\n raise errors.NoAccess()\\n if not self._is_valid_date_time(\\n start,\\n self.get_start_time_metadata()):\\n raise errors.InvalidArgument()\\n self._my_map['startTime'] = start\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01f86828bdf2e0f49e73469fca42cf70\",\n \"score\": \"0.4765881\",\n \"text\": \"def _date(self, _date: List[str]):\\n\\n self.__date = _date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b14c8161b115c61aebd534a13e72caaa\",\n \"score\": \"0.47394982\",\n \"text\": \"def set_manual_date_temperature():\\n date = kl.date_now()\\n dt.manual.update_variable('temperature_schedule', date)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0470a17d427b3e8b16eea6c11b1dce38\",\n \"score\": \"0.4738295\",\n \"text\": \"def create_date(self, create_date):\\n self._create_date = create_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"128dbcd9d44f9e8563965b4661fadb9f\",\n \"score\": \"0.473537\",\n \"text\": \"def create_date(self, create_date):\\n \\n self._create_date = create_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c42e6386c150f46158afb0a6dd7e933\",\n \"score\": \"0.47339365\",\n \"text\": \"def sun_utc(self, date, latitude, longitude):\\n\\n dawn = self.dawn_utc(date, latitude, longitude)\\n sunrise = self.sunrise_utc(date, latitude, longitude)\\n noon = self.solar_noon_utc(date, longitude)\\n sunset = self.sunset_utc(date, latitude, longitude)\\n dusk = self.dusk_utc(date, latitude, longitude)\\n\\n return {'dawn': dawn, 'sunrise': sunrise, 'noon': noon, 'sunset': sunset, 'dusk': dusk}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9542675a2aa8373b6b4de6e68cbbad6\",\n \"score\": \"0.47293547\",\n \"text\": \"def created_at(self, value):\\r\\n self.logger.warn(\\\"Setting values on created_at will NOT update the remote Canvas instance.\\\")\\r\\n self._created_at = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9542675a2aa8373b6b4de6e68cbbad6\",\n \"score\": \"0.47293547\",\n \"text\": \"def created_at(self, value):\\r\\n self.logger.warn(\\\"Setting values on created_at will NOT update the remote Canvas instance.\\\")\\r\\n self._created_at = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9542675a2aa8373b6b4de6e68cbbad6\",\n \"score\": \"0.47293547\",\n \"text\": \"def created_at(self, value):\\r\\n self.logger.warn(\\\"Setting values on created_at will NOT update the remote Canvas instance.\\\")\\r\\n self._created_at = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9542675a2aa8373b6b4de6e68cbbad6\",\n \"score\": \"0.47293547\",\n \"text\": \"def created_at(self, value):\\r\\n self.logger.warn(\\\"Setting values on created_at will NOT update the remote Canvas instance.\\\")\\r\\n self._created_at = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ade5188dec42c2d8d243e240befbfeae\",\n \"score\": \"0.47248694\",\n \"text\": \"def set_system_time(self):\\n dt = self.read()\\n\\n second = str(dt.second)\\n minute = str(dt.minute)\\n hour = str(dt.hour)\\n day = str(dt.day)\\n month = str(dt.month)\\n year = str(dt.year)\\n subprocess.call(\\\"sudo date -s '{}-{}-{} {}:{}:{}' > /dev/null\\\".\\n format(year, month, day, hour, minute, second),\\n shell=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"959ab88c28b7233747178e97ccae3ad6\",\n \"score\": \"0.4700586\",\n \"text\": \"def dawn_utc(self, date, latitude, longitude):\\n\\n julianday = self._julianday(date.day, date.month, date.year)\\n\\n if latitude > 89.8:\\n latitude = 89.8\\n\\n if latitude < -89.8:\\n latitude = -89.8\\n\\n t = self._jday_to_jcentury(julianday)\\n eqtime = self._eq_of_time(t)\\n solarDec = self._sun_declination(t)\\n\\n try:\\n hourangle = self._hour_angle_sunrise(latitude, solarDec)\\n except:\\n raise AstralError('Sun remains below horizon on this day, at this location.')\\n\\n delta = longitude - degrees(hourangle)\\n timeDiff = 4.0 * delta\\n timeUTC = 720.0 + timeDiff - eqtime\\n\\n newt = self._jday_to_jcentury(self._jcentury_to_jday(t) + timeUTC / 1440.0)\\n eqtime = self._eq_of_time(newt)\\n solarDec = self._sun_declination(newt)\\n hourangle = self._hour_angle_dawn(latitude, solarDec, self._depression)\\n delta = longitude - degrees(hourangle)\\n timeDiff = 4 * delta\\n timeUTC = 720 + timeDiff - eqtime\\n\\n timeUTC = timeUTC/60.0\\n hour = int(timeUTC)\\n minute = int((timeUTC - hour) * 60)\\n second = int((((timeUTC - hour) * 60) - minute) * 60)\\n\\n dawn = datetime.datetime(date.year, date.month, date.day, hour, minute, second, tzinfo=pytz.utc)\\n\\n return dawn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05c85fc99178bf181adca47a2dd0943e\",\n \"score\": \"0.4700249\",\n \"text\": \"def set_checkout(self, date=False):\\n if date == False:\\n self.checkout_date = datetime.datetime.now()\\n else:\\n self.checkout_date = date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7db7bc627576a98f9b4d381542145668\",\n \"score\": \"0.469985\",\n \"text\": \"def started_at(self, started_at):\\n\\n self._started_at = started_at\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7db7bc627576a98f9b4d381542145668\",\n \"score\": \"0.469985\",\n \"text\": \"def started_at(self, started_at):\\n\\n self._started_at = started_at\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3357120b9dff0b475845c10512ce1751\",\n \"score\": \"0.46961823\",\n \"text\": \"def void_date(self, void_date):\\n self._void_date = void_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66256ce1f55c78a485d8341c979d2728\",\n \"score\": \"0.4695941\",\n \"text\": \"def set_date(self, date):\\n\\n newdate = datetime.datetime.strptime(date, \\\"%Y-%m-%dT%H:%M:%S\\\")\\n self.__get_century(date)\\n self.__bus.write_byte_data(self.__rtcaddress,\\n self.SECONDS,\\n self.__dec_bcd(newdate.second))\\n self.__bus.write_byte_data(self.__rtcaddress,\\n self.MINUTES,\\n self.__dec_bcd(newdate.minute))\\n self.__bus.write_byte_data(self.__rtcaddress,\\n self.HOURS,\\n self.__dec_bcd(newdate.hour))\\n self.__bus.write_byte_data(self.__rtcaddress,\\n self.DAYOFWEEK,\\n self.__dec_bcd(newdate.weekday()))\\n self.__bus.write_byte_data(self.__rtcaddress,\\n self.DAY,\\n self.__dec_bcd(newdate.day))\\n self.__bus.write_byte_data(self.__rtcaddress,\\n self.MONTH,\\n self.__dec_bcd(newdate.month))\\n self.__bus.write_byte_data(self.__rtcaddress,\\n self.YEAR,\\n self.__dec_bcd(newdate.year -\\n self.__century))\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbaa7bb953b471241c2f76a7ad5a06f3\",\n \"score\": \"0.4685464\",\n \"text\": \"def merger_date(self, merger_date):\\n\\n self._merger_date = merger_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7119ed00d0bc236ff2f7f326dfae00cf\",\n \"score\": \"0.46846858\",\n \"text\": \"def updated_date(self, updated_date):\\n\\n self._updated_date = updated_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba8c1521a830572b954dc2ea33403be2\",\n \"score\": \"0.46799648\",\n \"text\": \"def date_joined(self, date_joined: object):\\n\\n self._date_joined = date_joined\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fd11222b7fcffba7797ba0b9057640d\",\n \"score\": \"0.46785975\",\n \"text\": \"def event_date(self, event_date):\\n\\n self._event_date = event_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fd11222b7fcffba7797ba0b9057640d\",\n \"score\": \"0.46785975\",\n \"text\": \"def event_date(self, event_date):\\n\\n self._event_date = event_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7caf1e85f10e34526a89f7348f9083c\",\n \"score\": \"0.46618196\",\n \"text\": \"def build_time(self, build_time):\\n\\n self._build_time = build_time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6afa3aeb424db4de535caa4660930379\",\n \"score\": \"0.4660389\",\n \"text\": \"def setModifyTime(self, date: java.util.Date) -> None:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f2328c1f83d2f744646f20400bef902\",\n \"score\": \"0.4657822\",\n \"text\": \"def __set_releasedate(self, value: str):\\n if value:\\n self.__releasedate = value\\n else:\\n raise ValueError(\\\"La fecha de estreno de la pelicula no puede estar vacia\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cc0ec69f1b03cfe2877a374558cff1f\",\n \"score\": \"0.46425372\",\n \"text\": \"def set_min_date(self, date):\\n self.widget.PySetLowerDateLimit(date)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993275,"cells":{"query_id":{"kind":"string","value":"705c5590fb9aa8ff7593df29d2fe4f51"},"query":{"kind":"string","value":"Expose containers externally via secured routes"},"positive_passages":{"kind":"list like","value":[{"docid":"5ce9fcc8aa8ba80fc79d0c172d9fe9c4","score":"0.0","text":"def create(self, name: str, route_type: \"Types\" = Types.EDGE, **kwargs):\n cmd_args = [f\"--{k}={v}\" for k, v in kwargs.items()]\n self._client.do_action(\"create\", [\"route\", route_type.value, name, cmd_args])","title":""}],"string":"[\n {\n \"docid\": \"5ce9fcc8aa8ba80fc79d0c172d9fe9c4\",\n \"score\": \"0.0\",\n \"text\": \"def create(self, name: str, route_type: \\\"Types\\\" = Types.EDGE, **kwargs):\\n cmd_args = [f\\\"--{k}={v}\\\" for k, v in kwargs.items()]\\n self._client.do_action(\\\"create\\\", [\\\"route\\\", route_type.value, name, cmd_args])\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"352d9f89089989cc330798ccbba9a78e","score":"0.57136905","text":"def runpublic():\n app.run(host='0.0.0.0')","title":""},{"docid":"82e3d8128d7d5e40b4e4b8eb9e5c7c9c","score":"0.5393183","text":"def expose_endpoint(self, exec_endpoint: str, path: Optional[str] = None):\n ...","title":""},{"docid":"5ae041808f7752f54dba15ad576d038c","score":"0.53242624","text":"def run(url):\r\n url = url + \":2375/info\"\r\n req = requests.get(url)\r\n if \"Containers\" in req.text:\r\n print(req.text)\r\n return \"Docker Remote API unauth Vulnerable\"\r\n else:\r\n return False","title":""},{"docid":"85d2e68cca10c33a51e764e58adbeece","score":"0.5149998","text":"def test_app_networking_mode_with_defined_container_port(dcos_api_session: DcosApiSession,\n networking_mode: marathon.Network,\n host_port: int) -> None:\n app_definition, test_uuid = test_helpers.marathon_test_app(\n healthcheck_protocol=marathon.Healthcheck.MESOS_HTTP,\n container_type=marathon.Container.DOCKER,\n network=networking_mode,\n host_port=host_port)\n\n dcos_service_name = uuid.uuid4().hex\n\n app_definition['labels'] = {\n 'DCOS_SERVICE_NAME': dcos_service_name,\n 'DCOS_SERVICE_PORT_INDEX': '0',\n 'DCOS_SERVICE_SCHEME': 'http',\n }\n\n # Arbitrary buffer time, accounting for propagation/processing delay.\n buffer_time = 5\n\n # Cache refresh in Adminrouter takes 30 seconds at most.\n # CACHE_POLL_PERIOD=25s + valid=5s Nginx resolver DNS entry TTL\n # https://github.com/dcos/dcos/blob/cb9105ee537cc44cbe63cc7c53b3b01b764703a0/\n # packages/adminrouter/extra/src/includes/http/master.conf#L21\n adminrouter_default_refresh = 25 + 5 + buffer_time\n app_id = app_definition['id']\n app_instances = app_definition['instances']\n app_definition['constraints'] = [['hostname', 'UNIQUE']]\n\n # For the routing check to work, two conditions must be true:\n #\n # 1. The application must be deployed, so that `/ping` responds with 200.\n # 2. The Admin Router routing layer must not be using an outdated\n # version of the Nginx resolver cache.\n #\n # We therefore wait until these conditions have certainly been met.\n # We wait for the Admin Router cache refresh first so that there is\n # unlikely to be much double-waiting. That is, we do not want to be waiting\n # for the cache to refresh when it already refreshed while we were waiting\n # for the app to become healthy.\n with dcos_api_session.marathon.deploy_and_cleanup(app_definition, check_health=False):\n time.sleep(adminrouter_default_refresh)\n dcos_api_session.marathon.wait_for_app_deployment(\n app_id=app_id,\n app_instances=app_instances,\n check_health=False,\n ignore_failed_tasks=False,\n timeout=1200,\n )\n r = dcos_api_session.get('/service/' + dcos_service_name + '/ping')\n assert r.status_code == 200\n assert 'pong' in r.json()","title":""},{"docid":"ddecb71ea773dafaab3941c29e9070e8","score":"0.50935376","text":"def _register_api(app):\n\n app.add_url_rule('/container+item/',\n \"get_containers_and_items\", get_containers_and_items, methods=['GET'])\n\n app.add_url_rule('/container/',\n \"put_container\", put_container, methods=['PUT'])\n\n app.add_url_rule('/item/',\n \"put_item\", put_item, methods=['PUT', 'POST'])\n\n app.add_url_rule('/put_item/',\n \"put_item\", put_item, methods=['GET'])\n\n app.add_url_rule('/item/',\n \"del_item\", del_item, methods=['DELETE'])\n\n app.add_url_rule('/container/',\n \"del_container\", del_container, methods=['DELETE'])","title":""},{"docid":"32520ec95a901cf5b1f799c52e848526","score":"0.49791166","text":"def bind(self, app):\n def url(fmt):\n return '/v1' + fmt.format(\n id='{id:[A-Za-z0-9]{24}}',\n path='{path:.*}'\n )\n app.add_routes([\n # GET handlers for experiments\n web.get(url('/_query'), self.handle_query),\n web.get(url('/_get/{id}'), self.handle_get),\n web.get(url('/_tarball/{id}'), self.handle_tarball),\n\n # POST handlers for experiments\n web.post(url('/_heartbeat/{id}'), self.handle_heartbeat),\n web.post(url('/_delete/{id}'), self.handle_delete),\n web.post(url('/_query'), self.handle_query),\n web.post(url('/_create'), self.handle_create),\n web.post(url('/_update/{id}'), self.handle_update),\n web.post(url('/_update_fs_size/{id}'), self.handle_update_fs_size),\n web.post(url('/_set_finished/{id}'), self.handle_set_finished),\n web.post(url('/_kill/{id}'), self.handle_kill),\n\n # GET handlers for files\n web.get(url('/_listdir/{id}'), self.handle_listdir),\n web.get(url('/_listdir/{id}/{path}'), self.handle_listdir),\n web.get(url('/_listzip/{id}/{path}'), self.handle_listzip),\n web.get(url('/_getfile/{id}/{path}'), self.handle_getfile),\n web.get(url('/_getzipentry/{id}/{path}'), self.handle_getzipentry),\n ])","title":""},{"docid":"9d74095549bb8c03b17c6b1b25398b57","score":"0.49361095","text":"def export(container):","title":""},{"docid":"8770acf8901e8ec779c971542e7db90f","score":"0.49213618","text":"def start_server():\n global ctr, dkr\n env = docker.utils.kwargs_from_env(assert_hostname=False)\n dkr = docker.Client(timeout=60, **env)\n # docker run --rm -P --name smtserve apdavison/sumatra-server-v4\n ctr = dkr.create_container(image, command=None, hostname=None, user=None,\n detach=False, stdin_open=False, tty=False,\n ports=None, environment=None, dns=None, volumes=None,\n volumes_from=None, network_disabled=False, name=None,\n entrypoint=None, cpu_shares=None, working_dir=None)\n dkr.start(ctr, publish_all_ports=True)\n utils.env[\"url\"] = get_url()","title":""},{"docid":"7a648a6faa0ca17656c57823e67a7fba","score":"0.4895414","text":"def http_service(docker_ip, docker_services):\n\n # `port_for` takes a container port and returns the corresponding host port\n url = 'http://{}:{}'.format(docker_ip, 8000)\n return url","title":""},{"docid":"77ca1865a97f4fd2c734270277b1830b","score":"0.48360625","text":"def container_actions(container_name, action):\n dclient = docker.from_env()\n container = dclient.containers.get(container_name)\n print(action)\n if action == 'start':\n container.start()\n elif action == 'stop':\n container.stop()\n elif action == 'restart':\n container.restart()\n elif action == 'kill':\n container.kill()\n elif action == 'remove':\n container.remove(force=True)\n return redirect(url_for('apps.running_apps'))\n else:\n print('else')\n\n return render_template('apps/manage_app.html', container=container)","title":""},{"docid":"f027c1d4eeb25454e9f3b0fa29df8094","score":"0.48350552","text":"def server():\n app.run(host='0.0.0.0', port=8022, debug=True)","title":""},{"docid":"a27bf49734638e8395246e96bfdd3d7d","score":"0.48309353","text":"def main():\r\n app.run(debug=True, host=\"0.0.0.0\", port=8000)","title":""},{"docid":"b512a6f99295864eb197b27a49c14160","score":"0.48045236","text":"def add_routes(self, app: Sanic) -> Any:\n app.add_route(UserResource.as_view(), \"/\")","title":""},{"docid":"afa5ca0d919344330139ab794d152b72","score":"0.4798193","text":"def action(hostname=('h', hostname), port=('p', port),\r\n reloader=use_reloader, debugger=use_debugger,\r\n evalex=use_evalex, threaded=threaded, processes=processes):\r\n from werkzeug.serving import run_simple\r\n app = app_factory()\r\n run_simple(hostname, port, app, reloader, debugger, evalex,\r\n extra_files, 1, threaded, processes,\r\n static_files=static_files, ssl_context=ssl_context)","title":""},{"docid":"2b32ab9b51055c2f8d5610ca1542efa8","score":"0.47976813","text":"def expose(self, name, service, hostname):\n return self._client.do_action(\"expose\", [\"service\", service, f\"--hostname={hostname}\", f\"--name={name}\"])","title":""},{"docid":"73ba5158010c3d3ba8a2212a05d36cb5","score":"0.47793567","text":"def serve(ctx, server):\n\n if server == \"flask\":\n from pygeoapi.flask_app import serve as serve_flask\n ctx.forward(serve_flask)\n ctx.invoke(serve_flask)\n elif server == \"starlette\":\n from pygeoapi.starlette_app import serve as serve_starlette\n ctx.forward(serve_starlette)\n ctx.invoke(serve_starlette)\n elif server == \"django\":\n from pygeoapi.django_app import main as serve_django\n ctx.invoke(serve_django)\n else:\n raise click.ClickException('--flask/--starlette/--django is required')","title":""},{"docid":"0ce9f185a891a07cf1c3a0b917f41e30","score":"0.47516328","text":"def __init__(self):\n self.client = Client()\n self.filters = {\n 'event': ['start', 'restart', 'die', 'stop'],\n 'type': 'container'\n }\n self.host = Host('docker')","title":""},{"docid":"f85add23bfcc5c4086e9559c06f286d6","score":"0.47472745","text":"def www(args):\n from minc.view import app\n\n app.run(host=\"0.0.0.0\", port=5000)","title":""},{"docid":"d739e1c1571866028e26be9088108b52","score":"0.47459567","text":"def container_addr(self):\n return '%s:%d' % (self._container_host, self._container_opts.port)","title":""},{"docid":"51cdc29908f3d6ac752033a9d8e4f266","score":"0.474318","text":"def add_routes(self, app):\n app.add_endpoint('/open-gate', 'open-gate', self.handle_open_gate_request)\n app.add_endpoint('/close-gate', 'close-gate', self.handle_close_gate_request)\n app.add_endpoint('/gate-status', 'gate-state', self.handle_gate_status_request)\n app.add_endpoint('/reset-gate', 'reset-gate', self.handle_reset_gate_request)","title":""},{"docid":"cce7bb2dd74328d4b9d6345d25da6daa","score":"0.47316366","text":"def bind_health_routes(app: Flask):\n\n @app.route('/healthz')\n def health():\n logging.info(\"api: /healthz\")\n return \"I am healthy!\"\n\n @app.route('/testing')\n def testing():\n logging.info(\"api: /testing\")\n return \"Hi tez, how are you?\"","title":""},{"docid":"cde38b38f2edaf8991c51760da609d24","score":"0.4730625","text":"def container_monitor(self):","title":""},{"docid":"69d21d67f225044f20a40b6c498ef51d","score":"0.47006157","text":"def main():\n for container in EVENTS:\n try:\n container_stop = False\n if \"start\" in container[\"status\"]:\n try:\n container_event_id = container[\"Actor\"][\"ID\"]\n container_inspect = CLIENT.inspect_container(container_event_id)\n container_id = container_inspect[\"Id\"]\n container_name = container_inspect[\"Name\"].replace(\"/\", \"\")\n\n container_cap_drop = container_inspect[\"HostConfig\"][\"CapDrop\"]\n container_cap_add = container_inspect[\"HostConfig\"][\"CapAdd\"]\n container_privileged = container_inspect[\"HostConfig\"][\"Privileged\"]\n container_security_opt = container_inspect[\"HostConfig\"][\n \"SecurityOpt\"\n ]\n\n no_security_opt = \"%s: no security options has been set\" % (\n container_name\n )\n priv_not_allowed_log = \"%s: privileged set but not allowed\" % (\n container_name\n )\n priv_no_policy_log = \"%s: privileged set but no policy\" % (\n container_name\n )\n cap_drop_log = \"%s: all capabilities not dropped\" % (container_name)\n cap_add_all_log = \"%s: capability ALL has been set\" % (\n container_name\n )\n stop_container_log = \"%s: stopping container\" % (container_name)\n\n try:\n if CONF[\"debug\"]:\n print((\"container_name: \", container_name))\n print((\"containerStatus: \", container[\"status\"]))\n print((\"container_event_id: \", container_event_id))\n print(\n (\n \"container_inspect: \",\n CLIENT.inspect_container(container_event_id),\n )\n )\n print((\"containerID: \", container_inspect[\"Id\"]))\n print((\"container_cap_drop: \", container_cap_drop))\n print((\"container_cap_add: \", container_cap_add))\n print((\"container_security_opt: \", container_security_opt))\n print((\"container_privileged: \", container_privileged))\n print((\"container_stop: \", container_stop))\n\n except NameError:\n pass\n\n try:\n if container_privileged is not False:\n if not CONF[container_name][\"privileged\"]:\n syslog.syslog(priv_not_allowed_log)\n container_stop = True\n\n except KeyError:\n syslog.syslog(priv_no_policy_log)\n container_stop = True\n\n try:\n if container_security_opt is None:\n if CONF[container_name][\"security_opt_required\"]:\n syslog.syslog(no_security_opt)\n container_stop = True\n\n if CONF[\"debug\"]:\n print(\n (\n \"container_security_opt: \",\n container_security_opt,\n )\n )\n\n except KeyError:\n syslog.syslog(no_security_opt)\n container_stop = True\n\n try:\n if container_cap_drop is not None:\n for cap_drop in container_cap_drop:\n if (\n \"all\" not in cap_drop.lower()\n and not CONF[container_name][\"cap_drop_required\"]\n ):\n syslog.syslog(cap_drop_log)\n container_stop = True\n\n if CONF[\"debug\"]:\n print((\"cap_drop: \", cap_drop.lower()))\n print(\n (\n \"cap_dropRequired: \",\n CONF[container_name][\n \"cap_drop_required\"\n ],\n )\n )\n\n except KeyError:\n syslog.syslog(cap_drop_log)\n container_stop = True\n\n try:\n if container_cap_drop is None:\n if CONF[container_name][\"cap_drop_required\"]:\n syslog.syslog(cap_drop_log)\n container_stop = True\n\n except KeyError:\n syslog.syslog(cap_drop_log)\n container_stop = True\n\n if container_cap_add is not None:\n for cap_add in container_cap_add:\n if \"all\" in cap_add.lower():\n syslog.syslog(cap_add_all_log)\n container_stop = True\n\n if container_stop and container_stop is not False:\n client_stop = \"%s\" % (container_id)\n\n try:\n if CONF[\"debug\"]:\n print((\"container_stop: \", container_stop))\n print((\"CLIENT.stop sent to \", client_stop))\n\n except NameError:\n pass\n\n try:\n CLIENT.stop(client_stop)\n syslog.syslog(stop_container_log)\n\n except UnboundLocalError as exception:\n print(exception)\n\n except KeyError as exception:\n print(exception)\n\n except UnboundLocalError as exception:\n print(exception)\n\n except KeyError as exception:\n print(exception)\n\n except KeyError:\n pass","title":""},{"docid":"c39bc80bd02d1b51f7ac18401a58a15f","score":"0.46957305","text":"def test_get_containers(self):\n pass","title":""},{"docid":"d448652ada54131db16119aaead8210c","score":"0.46852827","text":"def web():\n app.run(host=\"0.0.0.0\", debug=debug, port=port)","title":""},{"docid":"11b9261ea2cb12bb0bb8ce0c4628ac89","score":"0.46846414","text":"def runserver():\n from cryptoparty import app\n app.run(host='0.0.0.0', debug=True)","title":""},{"docid":"f0592ee624fc7c7689279186904878c3","score":"0.46846122","text":"def build_security_service(app):\n manager = get_manager(app)\n user_datastore = SQLAlchemyUserDatastore(manager, User, Role)\n Security(app, user_datastore, register_form=ExtendedRegisterForm)\n\n @app.before_first_request\n def create_user():\n manager.create_all()\n user_datastore.find_or_create_role(name='admin', description='Administrator of PyBEL Web')\n user_datastore.find_or_create_role(name='scai', description='Users of PyBEL Web from Fraunhofer SCAI')\n manager.session.commit()\n\n @app.route('/wrap/logout')\n def logout():\n return redirect(url_for('security.logout'))\n\n @app.route('/wrap/login')\n def login():\n return redirect(url_for('security.login'))\n\n @app.route('/admin/user/<user>/add_role/<role>')\n @roles_required('admin')\n def add_user_role(user, role):\n user_datastore.add_role_to_user(user, role)\n user_datastore.commit()\n return jsonify({'status': 200})\n\n @app.route('/admin/user/<user>/remove_role/<role>')\n @roles_required('admin')\n def remove_user_role(user, role):\n user_datastore.remove_role_from_user(user, role)\n user_datastore.commit()\n return jsonify({'status': 200})\n\n @app.route('/admin/user/list')\n @roles_required('admin')\n def list_users():\n return jsonify(manager.session.query(User).all())\n\n @app.route('/users')\n @roles_required('admin')\n def view_users():\n return render_template('view_users.html', users=manager.session.query(User).all())\n\n @app.route('/api/user/<int:user_id>/delete')\n @roles_required('admin')\n def delete_user(user_id):\n \"\"\"Deletes a user\"\"\"\n u = User.query.get(user_id)\n user_datastore.delete_user(u)\n user_datastore.commit()\n return jsonify({'status': 200, 'action': 'deleted user', 'user': str(u)})\n\n log.info('built security service')\n\n return user_datastore","title":""},{"docid":"808738775f4a7be580c245cab22159be","score":"0.4675486","text":"def start(self):\n resp = yield self.docker('create_container',\n image=\"jupyter/singleuser\",\n environment=self.env,\n )\n self.container_id = container_id = resp['Id']\n self.log.info(\"Created container {}\".format(container_id))\n\n yield self.docker('start', container_id, port_bindings={8888: (self.container_ip,)})\n resp = yield self.docker('port', container_id, 8888)\n self.user.server.port = resp[0]['HostPort']","title":""},{"docid":"e7a5186d06b682f5106c900b52e7db18","score":"0.46738473","text":"def list_containers():\n response = requests.get(f'{Global.BASE_URL}containers/json')\n return response","title":""},{"docid":"65b445f25c83a9aa548808c726c5d781","score":"0.4653287","text":"def client_fwd_app(request):\n if \"cid\" not in request.cookies:\n return register_client(request)\n else:\n return client_pull(request)","title":""},{"docid":"5336074ec99910f3bf6c832915d4cc13","score":"0.46521938","text":"def init_firewall(self):","title":""},{"docid":"95ecac918c83793c757acb5ec0c7b066","score":"0.46464646","text":"def deploy_local_k8s(ctx, port_forward=\"true\"):\n ctx.run(minikube(\"start\"))\n ctx.run(skaffold(\"dev\", f\"--port-forward={port_forward}\"))","title":""},{"docid":"521a5502a17c12c2c5547938d565dff5","score":"0.46305144","text":"def __assign_public_ports(self, context, host_server):\n port_cfg = context.port_config\n context.host_server_id = host_server.id\n\n if self.util.is_local():\n for cfg in port_cfg:\n cfg[DOCKER_UNIT.PORTS_PUBLIC_PORT] = cfg[DOCKER_UNIT.PORTS_HOST_PORT]\n self.__update_virtual_environment_cfg(context)\n else:\n # configure ports on azure cloud service\n vm_adapter = self.__get_azure_vm_adapter(context)\n virtual_machine_name = host_server.vm_name\n cloud_service_name = host_server.public_dns.split('.')[0] # cloud_service_name.chinacloudapp.cn\n\n # filter the port config with \"public\" True\n self.log.debug(\"starting to assign azure ports...\")\n public_ports_cfg = filter(lambda p: DOCKER_UNIT.PORTS_PUBLIC in p, port_cfg)\n host_ports = [u[DOCKER_UNIT.PORTS_HOST_PORT] for u in public_ports_cfg]\n\n # get assigned ports of cloud service\n assigned_endpoints = vm_adapter.get_assigned_endpoints(cloud_service_name)\n if not assigned_endpoints:\n self.log.debug('fail to assign endpoints: %s' % cloud_service_name)\n self._on_virtual_environment_failed(context)\n return\n\n endpoints_to_assign = find_unassigned_endpoints(host_ports, assigned_endpoints)\n # duplication detection for public endpoint\n deployment_name = vm_adapter.get_deployment_name(cloud_service_name, DEPLOYMENT_SLOT)\n network_config = vm_adapter.get_virtual_machine_network_config(cloud_service_name,\n deployment_name,\n virtual_machine_name)\n new_network_config = add_endpoint_to_network_config(network_config, endpoints_to_assign, host_ports)\n try:\n result = vm_adapter.update_virtual_machine_network_config(cloud_service_name,\n deployment_name,\n virtual_machine_name,\n new_network_config)\n context.request_id = result.request_id\n except Exception as e:\n self.log.error(e)\n self.log.error('fail to assign endpoints: %s' % endpoints_to_assign)\n self._on_virtual_environment_failed(context)\n return\n\n updated_config = []\n for cfg in context.port_config:\n if cfg[DOCKER_UNIT.PORTS_HOST_PORT] in host_ports:\n index = host_ports.index(cfg[DOCKER_UNIT.PORTS_HOST_PORT])\n cfg[DOCKER_UNIT.PORTS_PUBLIC_PORT] = endpoints_to_assign[index]\n updated_config.append(cfg)\n context.port_config = updated_config\n\n # query azure to make sure the network config updated\n context.virtual_machine_name = virtual_machine_name\n context.cloud_service_name = cloud_service_name\n context.trial = 0\n self.query_network_config_status(context)","title":""},{"docid":"305b10e3872748d11f2add7dc21da91d","score":"0.46108752","text":"def __call__(self):\n # Add shm\n self.volumes.append({'/dev/shm': V1Volume(name=\"dshm\", empty_dir=V1EmptyDirVolumeSource(medium='Memory'))})\n\n self.op = kfp.dsl.ContainerOp(self.name, self.image, arguments=self.args, file_outputs = self.file_outputs)\n if self.node:\n self.op.add_affinity(V1Affinity(node_affinity=V1NodeAffinity(required_during_scheduling_ignored_during_execution=V1NodeSelector(node_selector_terms=[V1NodeSelectorTerm(match_expressions=[V1NodeSelectorRequirement(key='kubernetes.io/hostname', operator='In', values=[self.node])])]))))\n if self.ngpu:\n self.op.set_gpu_limit(self.ngpu)\n if len(self.volumes) > 0:\n for vol in self.volumes:\n self.op.add_pvolumes(vol)","title":""},{"docid":"4da3c25a3f23ca12d8f2e534bb9b2aac","score":"0.45957643","text":"def includeme(config: Configurator) -> None:\n config.router.add_route('index', '/')","title":""},{"docid":"05d93292fedf9e25c3627f01a1a203fa","score":"0.45898187","text":"def _router(self):\n raise NotImplementedError # pragma: no cover","title":""},{"docid":"d123eb3c4681bc18568c451778dbc0fc","score":"0.45857227","text":"def vpc_endpoint(myClusterProps, DWH_PORT, ec2):\n try:\n vpc = ec2.Vpc(id=myClusterProps['VpcId'])\n defaultSg = list(vpc.security_groups.all())[0]\n defaultSg.authorize_ingress(\n GroupName = defaultSg.group_name,\n CidrIp = '0.0.0.0/0',\n IpProtocol = 'TCP',\n FromPort = int(DWH_PORT),\n ToPort = int(DWH_PORT)\n )\n except Exception as e:\n print(e)","title":""},{"docid":"b7d664137021595105a7d838b4641328","score":"0.45815855","text":"def run():\n web_app.run(host='0.0.0.0', port=5000, debug=True)","title":""},{"docid":"a5514c33480903825a417fb488cbd428","score":"0.4572467","text":"def set_endpoints(self):\n try:\n # pylint: disable=W0612\n @app.route('/data')\n def get_data():\n self.has_new_update = False\n return self.handle_api_call(self.gekko_data)\n\n @app.route('/get_options')\n def get_options():\n return self.handle_api_call(self.options_dict)\n\n @app.route('/poll')\n def get_poll():\n return self.handle_api_call({'updates': self.has_new_update})\n\n @app.route('/<path:path>')\n def static_file(path):\n return app.send_static_file(path)\n\n @app.route('/')\n def redirect_to_index():\n newPath = 'http://localhost:' + str(self.port) + '/index.html'\n return redirect(newPath)\n\n @app.errorhandler(404)\n def endpoint_not_found(error):\n return jsonify(error=404, text='Endpoint not found')\n\n @app.errorhandler(Exception)\n def unhandled_error(e):\n return jsonify(error=500, text='Internal API Error')\n\n except AssertionError as e:\n # This try/except is because ipython keeps the endpoints in\n # memory between runs. This will simply not set the endpoints if they\n # are already there.\n pass\n\n except Exception as e:\n raise e","title":""},{"docid":"0aca90f5c39adda3edbb0732d460e32d","score":"0.4572295","text":"def test_access_public_container_object_without_using_creds(self):\n # update container metadata to make it publicly readable\n cont_headers = {'X-Container-Read': '.r:*,.rlistings'}\n resp_meta, body = (\n self.container_client.create_update_or_delete_container_metadata(\n self.container_name,\n create_update_metadata=cont_headers,\n create_update_metadata_prefix=''))\n self.assertHeaders(resp_meta, 'Container', 'POST')\n\n # create object\n object_name = data_utils.rand_name(name='Object')\n data = data_utils.random_bytes(size=len(object_name))\n resp, _ = self.object_client.create_object(self.container_name,\n object_name, data)\n self.assertHeaders(resp, 'Object', 'PUT')\n\n # list container metadata\n resp_meta, _ = self.container_client.list_container_metadata(\n self.container_name)\n self.assertHeaders(resp_meta, 'Container', 'HEAD')\n\n self.assertIn('x-container-read', resp_meta)\n self.assertEqual(resp_meta['x-container-read'], '.r:*,.rlistings')\n\n # trying to get object with empty headers as it is public readable\n self.object_client.auth_provider.set_alt_auth_data(\n request_part='headers',\n auth_data=None\n )\n resp, body = self.object_client.get_object(\n self.container_name, object_name)\n self.assertHeaders(resp, 'Object', 'GET')\n\n self.assertEqual(body, data)","title":""},{"docid":"08e4225123ea3c54f25adc9b301fb548","score":"0.45556995","text":"def run():\n app.run(host='0.0.0.0', port='8001')","title":""},{"docid":"a73c57c0da5b6da197d5c37faad5a91a","score":"0.45527732","text":"def register(self, app: FastAPI) -> None:\n self.router.add_api_route(\n name=\"Queryables\",\n path=\"/queryables\",\n methods=[\"GET\"],\n endpoint=self._create_endpoint(self.client.get_queryables, EmptyRequest),\n )\n self.router.add_api_route(\n name=\"Collection Queryables\",\n path=\"/collections/{collectionId}/queryables\",\n methods=[\"GET\"],\n endpoint=self._create_endpoint(self.client.get_queryables, CollectionUri),\n )\n app.include_router(self.router, tags=[\"Filter Extension\"])","title":""},{"docid":"6e26ee2d209513b1327c5821e4d7a734","score":"0.45511606","text":"def test_create_container_with_ports(self, cluster):\n return self._create_container()","title":""},{"docid":"6e4afc5fdd156e786e27c2b980aa8205","score":"0.4550273","text":"def _route(self):\n self._app.route('/', callback=self.show_index)\n self._app.route('/run', method='POST', callback=self.run_program)\n self._app.route('/static/<filename>', callback=self.static_files)","title":""},{"docid":"5a764432315fb0b722d598a63f379cbb","score":"0.45448127","text":"def vservers_view(request):\n get_vservers()\n return HTTPFound(location='/')","title":""},{"docid":"c0025e8b0c4218ea249779a6495c4541","score":"0.45445272","text":"def access_route(self):\r\n if 'HTTP_X_FORWARDED_FOR' in self.environ:\r\n addr = self.environ['HTTP_X_FORWARDED_FOR'].split(',')\r\n return self.list_storage_class([x.strip() for x in addr])\r\n elif 'REMOTE_ADDR' in self.environ:\r\n return self.list_storage_class([self.environ['REMOTE_ADDR']])\r\n return self.list_storage_class()","title":""},{"docid":"926a206104a8c56df9a3fed98b86ec1f","score":"0.4544434","text":"def route_to_app(app, redis):\n collectors = get_collectors(app_ref=app, redis_ref=redis)\n affiliates = get_affiliates()\n api = get_api()\n\n app.before_request(identify_user)\n\n urls = list()\n\n urls.append(register_endpoint(app, '/tag/pageview/<string:store_id>/<string:pagetype>', collectors.pageview))\n urls.append(register_endpoint(app, '/tag/add-to-cart/<string:store_id>', collectors.add_to_cart))\n urls.append(register_endpoint(app, '/tag/remove-from-cart/<string:store_id>', collectors.remove_from_cart))\n urls.append(register_endpoint(app, '/tag/purchase/<string:store_id>/<string:tr_id>/<int:total>', collectors.purchase))\n urls.append(register_endpoint(app, '/tag/get-affiliates/<string:store_id>', affiliates.get_affiliates))\n urls.append(register_endpoint(app, '/api/remarketing', api.get_remarketing, ['GET']))\n urls.append(register_endpoint(app, '/api/carts', api.get_open_carts, ['GET']))\n urls.append(register_endpoint(app, '/api/purchases', api.get_purchases, ['GET']))\n\n # Default 404 route that shows valid endpoints\n app.add_url_rule(\n rule='/<path:_>',\n endpoint='404_fallback',\n view_func=lambda _: ('Invalid path, try one of:\\n\\n' + '\\n'.join(urls), 404, {'Content-Type': 'text-plain'}),\n methods=['POST', 'GET']\n )","title":""},{"docid":"b8850d8931156a861cb5af3a7b1a092e","score":"0.45433876","text":"def expose(name=None):\n def _expose(func):\n def _decorator(*args, **kwargs):\n return func(*args, **kwargs)\n func.exposed = True\n func.exposed_name = name or func.__name__\n return functools.wraps(func)(_decorator)\n return _expose","title":""},{"docid":"40374c776736d90f5b5aeecb52fdf0d6","score":"0.4540795","text":"def configure_access(self, server, share_name):\n pass","title":""},{"docid":"7d728229e4319516e40ca8cf40c5f810","score":"0.45387807","text":"def use_service_secret(_service_secret):","title":""},{"docid":"3be69e809036d9faf6d6608cdb785aba","score":"0.45303947","text":"def test_org_apache_jackrabbit_oak_security_internal_security_provider_registrati(self):\n pass","title":""},{"docid":"1090d47e8fe89c93503665dea6c529df","score":"0.4520967","text":"def index():\n return api.formatted_routes(request.host_url)","title":""},{"docid":"6732f481ceaef4441320ff83232c4553","score":"0.45187342","text":"def test_api_service(self):\n\n port = 8088\n for swagger in self.service_swaggers:\n swagger = os.path.join(self.prefix, swagger)\n pid = os.fork()\n if not pid:\n name = swagger.split('/')[2]\n print(('Starting server for: {} at: {}'.format(name, swagger)))\n resolver = MockResolver(mock_all='all')\n api_extra_args ={'resolver': resolver}\n\n app = connexion.FlaskApp(name,\n swagger_json=False,\n swagger_ui=False)\n\n app.add_api(swagger + '/swagger.yaml',\n resolver_error=True,\n validate_responses=True,\n strict_validation=True,\n **api_extra_args)\n\n app.run(port=port)\n\n else:\n try:\n print('Wait for pinging server')\n # Let the api initialize\n time.sleep(2)\n try:\n t = urllib.request.urlopen('http://localhost:{}/'.format(port))\n t.close()\n except Exception as e:\n print(('Fetch error: {}'.format(e)))\n\n print('Killing child pid')\n os.kill(pid, signal.SIGKILL)\n print('Killed')\n\n time.sleep(1)\n except (KeyboardInterrupt, Exception) as e:\n os.kill(pid, signal.SIGKILL)\n raise\n\n port +=1","title":""},{"docid":"1d6559a0ae91b69bb128c3a2127be544","score":"0.4513166","text":"def create_app(debug):\n app = Flask(__name__)\n hostname = os.environ.get('DOCKER_MACHINE_IP', 'localhost')\n if not hostname:\n hostname = 'localhost'\n origins = ('^(https?://)?(www\\.)?({}|0|0.0.0.0|dimi-bat.local|'\n 'localhost|127.0.0.1)(\\.com)?:\\d{{1,5}}$').format(hostname),\n CORS(app,\n origins=origins,\n headers=(\n 'x-requested-with',\n 'content-type',\n 'accept',\n 'origin',\n 'authorization',\n 'x-csrftoken',\n 'withcredentials',\n 'cache-control',\n 'cookie',\n 'session-id',\n ),\n supports_credentials=True,\n )\n\n app.debug = debug\n\n app.register_blueprint(api_views, url_prefix='/api')\n return app","title":""},{"docid":"aa064f06cf894d70687171a9f695e92b","score":"0.44995013","text":"def create_endpoint(self, meta_container: str, coapPort: int, httpPort: int, grpcPort: int) -> None:\n\n # get the docker client\n client = docker.from_env()\n\n logging.debug('building endpoint container image')\n\n # build the reverse proxy image\n endpoint_image = client.images.build(tag='tinyfaas-reverse-proxy', path='./reverse-proxy/', rm=True)[0]\n\n logging.debug('removing old endpoint networks')\n\n # remove old endpoint-net networks\n for n in client.networks.list(names=['endpoint-net']):\n n.remove()\n\n logging.debug('creating endpoint network')\n\n # create new endpoint-net network\n self.endpoint_network = client.networks.create('endpoint-net', driver='bridge')\n\n # find out which ports to forward\n # if a port is set to zero, we just don't use it\n ports: typing.Dict[str, int] = {}\n if coapPort > 0:\n ports['6000/udp'] = coapPort\n\n if httpPort > 0:\n ports['7000/tcp'] = httpPort\n\n if grpcPort > 0:\n ports['8000/tcp'] = grpcPort\n\n logging.debug('creating endpoint container')\n\n # create the endpoint container\n self.endpoint_container = client.containers.run(endpoint_image, network=self.endpoint_network.name, ports=ports, detach=True, remove=True, name='tinyfaas-reverse-proxy')\n\n # getting IP address of the handler container by inspecting the network and converting CIDR to IPv4 address notation (very dirtily, removing the last 3 chars -> i.e. '/20', so let's hope we don't have a /8 subnet mask)\n self.endpoint_container_ipaddr = docker.APIClient().inspect_network(self.endpoint_network.id)['Containers'][self.endpoint_container.id]['IPv4Address'][:-3]\n logging.debug(f'endpoint container ip address: f{self.endpoint_container_ipaddr}')\n\n logging.debug('connecting meta_container to endpoint network')\n\n # add our container to the endpoint-net network\n self.endpoint_network.connect(meta_container)","title":""},{"docid":"a4b616d870211054c2c11890ea67c1af","score":"0.44970858","text":"def test_get_containers(self):\n response = self.client.open(\n '/v2/containers',\n method='GET')\n self.assert200(response,\n 'Response body is : ' + response.data.decode('utf-8'))","title":""},{"docid":"f9046fb7f95792a07bfc505c0cab8670","score":"0.44955412","text":"def serve(self, processes=1):\n super().serve(processes=processes)","title":""},{"docid":"ebb9fcbc8830db855166566bb760312d","score":"0.44842616","text":"def orchestration():","title":""},{"docid":"f133568797a70742dc007174d0833af0","score":"0.44829464","text":"def __call__(self, environ, start_response):\n try:\n for var in 'QUERY_STRING PATH_INFO'.split():\n if var not in environ:\n raise KnownUnknown('Missing \"%s\" environment variable' % var)\n \n query = dict(parse_qsl(environ['QUERY_STRING']))\n \n for param in 'size zoom'.split():\n if param not in query:\n raise KnownUnknown('Missing \"%s\" parameter' % param)\n \n layer = environ['PATH_INFO'].strip('/')\n layer = self.config.layers[layer]\n print layer\n provider = layer.provider\n \n if not hasattr(provider, 'renderStaticMap'):\n raise KnownUnknown('Layer \"%s\" provider %s has no renderStaticMap() method' % (layer.name(), provider.__class__))\n \n zoom = int(query['zoom'])\n w, h = [int(p) for p in query['size'].split('x')]\n layer.staticmap = StaticMaps(w, h, zoom)\n\n if 'markers' in query:\n for m in query['markers'].split('|'):\n if m.startswith('icon:'):\n icon = m[5:]\n elif len(m.split(','))==2:\n lat, lon = [float(p) for p in m.split(',')]\n layer.staticmap.addMarker(lat, lon, icon)\n \n if 'path' in query:\n params = [m.split(':') for m in query['path'].split('|') if m.find(':')>0]\n static_path = dict(params)\n static_path['points'] = [m.split(',') for m in query['path'].split('|') if m.find(',')>0]\n layer.staticmap.addPath(static_path)\n \n if layer.staticmap.path is None and layer.staticmap.marker is None:\n lat, lon = [float(p) for p in query['center'].split(',')]\n \n mimetype, content = getStaticMap(layer)\n headers = [('Content-Type', 'image/png')]\n \n if layer.allowed_origin:\n headers.append(('Access-Control-Allow-Origin', layer.allowed_origin))\n \n if layer.max_cache_age is not None:\n expires = datetime.utcnow() + timedelta(seconds=layer.max_cache_age)\n headers.append(('Expires', expires.strftime('%a %d %b %Y %H:%M:%S GMT')))\n headers.append(('Cache-Control', 'public, max-age=%d' % layer.max_cache_age))\n\n start_response('200 OK', headers)\n return content\n \n except KnownUnknown, e:\n start_response('400 Bad Request', [('Content-Type', 'text/plain')])\n return str(e)","title":""},{"docid":"610948eb6960331e1109add27e3497fc","score":"0.44827572","text":"def _create_container(self):\n data = {\n u\"name\": random_name(self),\n u\"image\": \"clusterhq/flask:latest\",\n u\"ports\": [{u\"internal\": 80, u\"external\": 8080}],\n u'restart_policy': {u'name': u'never'}\n }\n waiting_for_cluster = get_test_cluster(reactor, node_count=1)\n\n def create_container(cluster, data):\n data[u\"node_uuid\"] = cluster.nodes[0].uuid\n return cluster.create_container(data)\n\n d = waiting_for_cluster.addCallback(create_container, data)\n\n def check_result(result):\n cluster, response = result\n self.addCleanup(cluster.remove_container, data[u\"name\"])\n\n self.assertEqual(response, data)\n dl = verify_socket(cluster.nodes[0].address, 8080)\n dl.addCallback(lambda _: (cluster, response))\n return dl\n\n d.addCallback(check_result)\n return d","title":""},{"docid":"42894577a02369a541fc07d00030eede","score":"0.44812348","text":"def view_public_cluster(name):\n if request.method == 'GET':\n slate_user_id = session['slate_id']\n token_query = {'token': session['slate_token']}\n\n cluster_query = \"/v1alpha3/clusters/\"+name+\"?token=\"+token_query['token']\n allowed_groups_query = \"/v1alpha3/clusters/\"+name+\"/allowed_groups?token=\"+token_query['token']\n cluster_status_query = \"/v1alpha3/clusters/\"+name+\"/ping?token=\"+token_query['token']+\"&cache\"\n # Set up multiplex JSON\n multiplexJson = {cluster_query: {\"method\":\"GET\"},\n allowed_groups_query: {\"method\":\"GET\"},\n cluster_status_query: {\"method\":\"GET\"}}\n # POST request for multiplex return\n multiplex = requests.post(\n slate_api_endpoint + '/v1alpha3/multiplex', params=token_query, json=multiplexJson)\n multiplex = multiplex.json()\n # Parse post return for apps, clusters, and pub groups\n # allowed_groups_json = ast.literal_eval(multiplex[allowed_groups_query]['body'])\n allowed_groups_json = json.loads(multiplex[allowed_groups_query]['body'])\n allowed_groups = [item for item in allowed_groups_json['items']]\n\n # cluster = ast.literal_eval(multiplex[cluster_query]['body'])\n cluster = json.loads(multiplex[cluster_query]['body'])\n # Get owning group information for contact info\n owningGroupName = cluster['metadata']['owningGroup']\n owningGroup = requests.get(\n slate_api_endpoint + '/v1alpha3/groups/' + owningGroupName, params=token_query)\n owningGroup = owningGroup.json()\n owningGroupEmail = owningGroup['metadata']['email']\n # Get Cluster status from multiplex\n cluster_status = json.loads(multiplex[cluster_status_query]['body'])\n cluster_status = str(cluster_status['reachable'])\n\n return render_template('cluster_public_profile.html', cluster=cluster,\n owningGroupEmail=owningGroupEmail,\n allowed_groups=allowed_groups,\n cluster_status=cluster_status)","title":""},{"docid":"e7383995b283fd1f69dc6029f171eea5","score":"0.44788024","text":"def endpoint(self):","title":""},{"docid":"a20ace165ed11a5759b0546a49d9a93f","score":"0.44787738","text":"def web(manager, debug, port, host, secret_key):\n if not manager.is_populated():\n click.echo(f'{manager.module_name} has not yet been populated')\n sys.exit(0)\n\n app = manager.get_flask_admin_app(url='/', secret_key=secret_key)\n app.run(debug=debug, host=host, port=port)","title":""},{"docid":"31aa9f4e40b7e3e1db23cd823e470509","score":"0.4478268","text":"def iam():\n pass","title":""},{"docid":"b2ceb26004cba824a7c2a7855b033e2d","score":"0.44734558","text":"def run():\n app.run(host='0.0.0.0', debug=True, port=5000)\n # app.run()","title":""},{"docid":"751d86dd0a606ca7837da7569e50a3be","score":"0.44690892","text":"def add_file_routes(app,CreateCredentials,isLocal,serverPath,bcrypt):\n RouteUtils.CREATE_CREDENTIALS = CreateCredentials\n IS_LOCAL =isLocal\n SERVER_PATH = serverPath\n # Keys for the post route will correspond to the four types of files\n @app.route(\"/v1/submit_files/\", methods = [\"POST\"])\n @permissions_check\n def submit_files():\n fileManager = FileHandler(request,isLocal=IS_LOCAL, serverPath=SERVER_PATH)\n return RouteUtils.run_instance_function(fileManager, fileManager.submit, getUser=True,getCredentials=True)\n\n @app.route(\"/v1/finalize_job/\", methods = [\"POST\"])\n @permissions_check\n def finalize_submission():\n fileManager = FileHandler(request,isLocal=IS_LOCAL, serverPath=SERVER_PATH)\n return RouteUtils.run_instance_function(fileManager, fileManager.finalize)\n\n @app.route(\"/v1/check_status/\", methods = [\"POST\"])\n @permissions_check\n def check_status():\n fileManager = FileHandler(request,isLocal=IS_LOCAL, serverPath=SERVER_PATH)\n return RouteUtils.run_instance_function(fileManager, fileManager.getStatus)\n\n @app.route(\"/v1/submission_error_reports/\", methods = [\"POST\"])\n @permissions_check\n def submission_error_reports():\n fileManager = FileHandler(request,isLocal=IS_LOCAL, serverPath=SERVER_PATH)\n return RouteUtils.run_instance_function(fileManager, fileManager.getErrorReportURLsForSubmission)\n\n @app.route(\"/v1/error_metrics/\", methods = [\"POST\"])\n @permissions_check\n def submission_error_metrics():\n fileManager = FileHandler(request,isLocal=IS_LOCAL ,serverPath=SERVER_PATH)\n return RouteUtils.run_instance_function(fileManager, fileManager.getErrorMetrics)\n\n @app.route(\"/v1/local_upload/\", methods = [\"POST\"])\n @permissions_check\n def upload_local_file():\n fileManager = FileHandler(request,isLocal=IS_LOCAL, serverPath=SERVER_PATH)\n return RouteUtils.run_instance_function(fileManager, fileManager.uploadFile)\n\n @app.route(\"/v1/get_rss/\", methods = [\"GET\"])\n @permissions_check\n def get_rss():\n fileManager = FileHandler(request,isLocal=IS_LOCAL, serverPath=SERVER_PATH)\n return RouteUtils.run_instance_function(fileManager, fileManager.getRss)\n\n @app.route(\"/v1/list_submissions/\", methods = [\"GET\"])\n @permissions_check\n def list_submissions():\n \"\"\" List submission IDs associated with the current user \"\"\"\n filter_by = request.args.get('filter_by')\n filter_by = filter_by.lower() if filter_by is not None else filter_by\n accountManager = AccountHandler(request,bcrypt = bcrypt)\n\n if filter_by == 'agency':\n return RouteUtils.run_instance_function(accountManager, accountManager.listSubmissionsByCurrentUserAgency)\n return RouteUtils.run_instance_function(accountManager, accountManager.listSubmissionsByCurrentUser)","title":""},{"docid":"aa15ea3ada6e507d1653ae306e4272e6","score":"0.44678646","text":"def setContainer(self, *args):\n return _pilot.ServerNode_setContainer(self, *args)","title":""},{"docid":"0e7cc007546a9ae8854def936d049cca","score":"0.44571552","text":"def server_api():\n with tempfile.TemporaryDirectory() as tmp:\n tmp = os.path.join(tmp, \".prefect\")\n os.makedirs(tmp)\n with configuration.set_temporary_config({\n \"home_dir\": tmp,\n \"backend\": \"server\",\n \"cloud.api\": \"https:/localhost:4200\",\n \"cloud.auth_token\": \"secret_token\",\n \"cloud.use_local_secrets\": False}):\n yield","title":""},{"docid":"4ae7c8405a766b4b60d099589a882dae","score":"0.44526687","text":"def expose_endpoint(self, exec_endpoint: str, **kwargs):\n self._endpoints_mapping[exec_endpoint] = kwargs","title":""},{"docid":"4efec5b9d662b136a9a26696d9c75c30","score":"0.44486427","text":"def container_shell(name=\"3bot\", configdir=None):\n IT.MyEnv\n docker = container_get(name=name)\n IT.Tools.execute('ssh-keygen -f \"%s/.ssh/known_hosts\" -R \"[localhost]:9122\"' % IT.MyEnv.config[\"DIR_HOME\"])\n os.execv(\n shutil.which(\"ssh\"),\n [\"ssh\", \"root@localhost\", \"-A\", \"-t\", \"-oStrictHostKeyChecking=no\", \"-p\", str(docker.config.sshport)],\n )","title":""},{"docid":"5d3fa62eb9c3d61a26d93bbb9185b0bc","score":"0.44471252","text":"def deploy_private(self, group_name, pod_name, app_name, ncore,\n ncontainer, version, entrypoint, env, network_ids, ports=None,\n host_name=None, raw=False, image='', spec_ips=None, args=None):\n if raw and not image:\n raise EruException('raw and image must be set together.')\n\n if ports is None:\n ports = []\n if args is None:\n args = []\n\n url = '/api/deploy/private/{0}/{1}/{2}'.format(group_name, pod_name, app_name)\n data = {\n 'ncore': ncore,\n 'ncontainer': ncontainer,\n 'version': version,\n 'entrypoint': entrypoint,\n 'env': env,\n 'networks': network_ids,\n 'ports': ports,\n 'args': args,\n }\n if raw and image:\n data['raw'] = True\n data['image'] = image\n if host_name:\n data['hostname'] = host_name\n if spec_ips:\n data['spec_ips'] = spec_ips\n return self.post(url, data=data)","title":""},{"docid":"315f459b6d50de962ff1cad7e6605805","score":"0.44457242","text":"def containers(self, containers):\n\n self._containers = containers","title":""},{"docid":"7e794e62ec40806480dabc03550fdf74","score":"0.44436753","text":"def setup_privnet(self):\n self.setup(\n [\n \"http://127.0.0.1:30333\"\n ]\n )","title":""},{"docid":"211e9dbbd578095395ed1dae40832df0","score":"0.44420752","text":"def __init__(self) -> None:\n self._url_map = werkzeug_routing.Map([\n werkzeug_routing.Rule(\"/\", endpoint=self._OnRoot, methods=[\"GET\"]), # pytype: disable=wrong-arg-types\n werkzeug_routing.Rule(\n \"/search\", endpoint=self._OnSearch, methods=[\"POST\"]), # pytype: disable=wrong-arg-types\n werkzeug_routing.Rule(\n \"/query\", endpoint=self._OnQuery, methods=[\"POST\"]), # pytype: disable=wrong-arg-types\n werkzeug_routing.Rule(\n \"/annotations\", endpoint=self._OnAnnotations, methods=[\"POST\"]), # pytype: disable=wrong-arg-types\n ])","title":""},{"docid":"1231e67fbfcbd46dbfee1e241b98df9b","score":"0.44383287","text":"def apply(self, config, containers):\n entry_point = { 'type' : 'spark' }\n entry_point['ip'] = containers[0]['manage_ip']\n config_dirs = []\n\n new_config_dir = \"/tmp/\" + self._generate_config_dir(config.uuid)\n try:\n sh.mkdir('-p', new_config_dir)\n except:\n sys.stderr.write('could not create config dir ' + new_config_dir)\n\n # This file records all instances so that we can\n # generate the hosts file. \n entry_point['instances'] = []\n for server in containers:\n entry_point['instances'].append([server['data_ip'], server['host_name']])\n\n if not 'compute' in containers[0]:\n # This is being called as a compute service. \n slave_file = open(new_config_dir + '/slaves', 'w+')\n entry_point['master'] = containers[0]['host_name']\n entry_point['instances'] = []\n master = entry_point['master']\n for server in containers:\n if server != master:\n slave_file.write(\"%s\\n\" % server['host_name'])\n slave_file.close()\n else:\n # This is being called as a client service. \n # For the client, also include the host/IP of the compute service. \n compute = containers[0]['compute'][0]\n entry_point['master'] = compute['master']\n\n # Transfer the configuration. \n for c in containers:\n config_files = new_config_dir + '/*'\n config_dirs.append([c['container'],\n config_files, \n config.config_directory])\n\n return config_dirs, entry_point","title":""},{"docid":"ba99cfe668e6c98c082229278f989824","score":"0.44350347","text":"def hosts(self):","title":""},{"docid":"bd8fad84d31ff8476fdc72c6a397dc62","score":"0.44278106","text":"def client(crypto, steghide_password):\n\tsteg = steganography.StegHide()\n\tweb = WebHostAndScraper()\n\tprint(\"Running as client\")\n\ttarget_ip = input(\"Enter target IP: \")\n\ttarget_port = input(\"Enter target PORT: \")\n\tfile_name = input(\"Enter the name of the file you want to write: \")\n\timgarray = web.download_imgs(target_ip, target_port)\n\tfiles_array = steg.steg_read(imgarray, steghide_password)\n\tsteg.join_files(files_array, file_name)\n\tcrypto.decrypt_file(file_name)\n\texit(0)","title":""},{"docid":"c584d0c525552e4b56b06894a5070bfd","score":"0.44186455","text":"def Resources(self) -> HostProtectionResource:","title":""},{"docid":"6b977d6430c4fbecba0e6f1a7119c39e","score":"0.44137394","text":"def grant_access(self, username, mode='read'):\n if username == \"PUBLIC\":\n mode = 'read'\n current_acl = self.access_control(show_usernames=True)[mode]\n if username in current_acl:\n logger.info(\"User {} already has {} access to this container!\".format(username, mode))\n else:\n if username == \"PUBLIC\":\n new_acl = self.access_control(show_usernames=False)[\n mode] + ['.r:*', '.rlistings']\n else:\n name_map = {v: k for k, v in self.project.users.items()}\n user_id = name_map[username]\n new_acl = self.access_control(show_usernames=False)[\n mode] + [\"{}:{}\".format(self.project.id, user_id)]\n headers = {\"x-container-{}\".format(mode): \",\".join(new_acl)}\n response = self.project._connection.post_container(self.name, headers)\n self._metadata = None # needs to be refreshed\n logger.info(\"User {} has been granted {} access to this container.\".format(username, mode))","title":""},{"docid":"6ea5bae084623d04f9d181f3598f9e4d","score":"0.44031814","text":"def route(*args, **kwargs):\n ensure_client = kwargs.get('ensure_client', True)\n\n def wrapper(func):\n def client_or_redirect(*fargs, **fkwargs):\n if ensure_client:\n client = get_client()\n session = get_session()\n\n if client is None:\n session = get_session()\n if session.get('endpoint', None) is not None:\n # XXX we need to pass SSH too here\n connect_to_circus(session['endpoint'])\n else:\n return redirect(url('connect'))\n\n return func(*fargs, **fkwargs)\n return route_(*args, **kwargs)(client_or_redirect)\n return wrapper","title":""},{"docid":"f98f9e6d88cd1a6a2b23444563070569","score":"0.44021404","text":"def setup_cluster(request):\n pass","title":""},{"docid":"ad411ae9b1b20848e548d3a077ada8dc","score":"0.44007805","text":"def _create_container(self):\n return container.ContainerClient(self.config)","title":""},{"docid":"3a2677266d1acd0241b7da4d7ed78d52","score":"0.439879","text":"def create_routes(app):\n @app.route('/fashion', methods=[\"GET\"])\n @app.route('/fashion/<page>', methods=[\"GET\"])\n def get_fashion_tips(page=1):\n \"\"\"\n Gets a list of fashion items\n \"\"\"\n try:\n # validates the page\n validated_page = validate(page)\n # gets the list of fashion items for the page\n rows = get_fashion_items(validated_page, 20)\n #converts rows to fashion items\n fashion_items = convert_to_fashion_item(rows)\n # randomizes the page of items\n random_list = randomize_list(fashion_items)\n except ValueError:\n return \"BAD REQUEST\", codes.BAD_REQUEST\n\n resp = Response(json.dumps(random_list), status=codes.ok, mimetype='application/json')\n\n return resp\n \n @app.route('/fashion/image/<image_name>', methods=[\"GET\"])\n def get_image(image_name):\n \"\"\"\n Gets an image for a fashion item\n \"\"\"\n # gets the path of the image in the images folder\n image_path = os.path.join(os.path.dirname(IMAGES.__file__), image_name)\n # verifies that the image exists\n if not os.path.isfile(image_path):\n return None, codes.not_found\n # sends the image\n return send_file(image_path), codes.ok","title":""},{"docid":"a741cbf26e3face2d1adfb6c287da350","score":"0.43980458","text":"def main():\n\n in_dev = app.config.get('ENV') == 'development'\n server_name = app.config.get('SERVER_NAME')\n\n ssl_context = None\n if in_dev and server_name:\n host_name = server_name.split(':')[0]\n cert_file = './ssl/{}.pem'.format(host_name)\n pkey_file = './ssl/{}-key.pem'.format(host_name)\n\n if os.path.isfile(cert_file) and os.path.isfile(pkey_file):\n ssl_context = (cert_file, pkey_file)\n\n app.run(ssl_context=ssl_context)","title":""},{"docid":"b539a3073d8bdd397cb921dfae8db9d6","score":"0.43964863","text":"def server(crypto, steghide_password):\n\tsteg = steganography.StegHide()\n\tweb = WebHostAndScraper()\n\n\tprint(\"Running as Server\")\n\tfilename = input(\"What file do you want to serve?: \")\n\t# Encrypt the contents of the file before using steghide.\n\tfilename = crypto.encrypt_file(filename)\n\tpicture_list = steg.read_images()\n\t# array of image holding capacity\n\tfile_sizes = steg.steg_info(picture_list)\n\t# total room in pictures folder\n\ttotal_room = sum(file_sizes)\n\tencrypted_file_size = os.stat(filename).st_size\n\n\t# Ensure we have enough space in our pictures to hide the encrypted file\n\tif total_room < encrypted_file_size:\n\t\tprint(\"Not enough space\")\n\t\texit(1)\n\n\t# Now the file needs to be split and encoded into images\n\n\tsize_sum = 0\n\tindex = 0\n\t# This will figure out how many pictures we have to use.\n\twhile size_sum < encrypted_file_size:\n\t\tsize_sum += file_sizes[index]\n\t\tindex += 1\n\n\tsplit_array = file_sizes[:index]\n\tpicstoencode = picture_list[:index]\n\n\t# If we can't fit our encrypted file into one picture we must split the encrypted file.\n\tif len(split_array) > 1:\n\t\tsplit_files = steg.split_file(filename, split_array)\n\t\tsteg.steg_hide_many(split_files, picstoencode, steghide_password)\n\telse:\n\t\tsteg.steg_hide(filename, picstoencode[0], steghide_password)\n\n\t# the actual process of encoding the files into the images\n\tweb.generate_index(picstoencode)\n\tweb.start_http()\n\texit(0)","title":""},{"docid":"dc8ac694fccd190a5fbbec282ed41177","score":"0.43881267","text":"def run_server(config):\n if config.has_option(\"server\", \"host\"):\n host = config.get(\"server\", \"host\")\n else:\n host = \"localhost\"\n\n if config.has_option(\"server\", \"port\"):\n port = config.getint(\"server\", \"port\")\n else:\n port = 5000\n\n if config.has_option(\"server\", \"reload\"):\n use_reloader = config.getboolean(\"server\", \"reload\")\n else:\n use_reloader = False\n\n application = Slicer(config)\n werkzeug.serving.run_simple(host, port, application, use_reloader=use_reloader)","title":""},{"docid":"64c237b650f44ff9fe6c43ad26ae26f8","score":"0.43841392","text":"def main():\n parser = ArgumentParser()\n parser = cf.add_config_args(parser)\n args = parser.parse_args()\n deploy_container(args)","title":""},{"docid":"f2f5b459657dc9c869797545f423346a","score":"0.43813682","text":"def register_api_endpoints():\n api.add_resource(interviewbox_api.Login, '/api_login')\n api.add_resource(interviewbox_api.CreateUser, '/api_create_user')\n api.add_resource(interviewbox_api.GetUser, '/api_get_user')\n api.add_resource(interviewbox_api.GetProfile, '/api_get_profile')\n api.add_resource(interviewbox_api.SearchUser, '/api_search')\n api.add_resource(interviewbox_api.UpdateAvailability, '/api_availability')\n api.add_resource(interviewbox_api.Logout, '/api_logout')\n api.add_resource(interviewbox_api.EditUser, '/api_edit_user')\n api.add_resource(interviewbox_api.CreateInterview, '/api_create_interview')\n api.add_resource(interviewbox_api.GetUpcomingInterviews, '/api_upcoming_interviews')\n api.add_resource(interviewbox_api.GetPastInterviews, '/api_past_interviews')\n api.add_resource(interviewbox_api.AddFeedbackToInterview, '/api_add_interview_feedback')","title":""},{"docid":"8dca97263b2bbb76fc0a0907ebda6d4a","score":"0.4378803","text":"def _get_exposed_ports(debug_options):\n if not debug_options:\n return None\n\n if not debug_options.debug_ports:\n return None\n\n # container port : host port\n ports_map = {}\n for port in debug_options.debug_ports:\n ports_map[port] = port\n\n return ports_map","title":""},{"docid":"44ca99c4b6741903a6a4ce12f7ce60bd","score":"0.4378766","text":"def test_api_challenge_get_solves_visibility_private():\n app = create_ctfd()\n with app.app_context():\n gen_challenge(app.db)\n register_user(app)\n client = login_as_user(app)\n r = client.get(\"/api/v1/challenges/1/solves\")\n assert r.status_code == 200\n set_config(\"challenge_visibility\", \"public\")\n r = client.get(\"/api/v1/challenges/1/solves\")\n assert r.status_code == 200\n destroy_ctfd(app)","title":""},{"docid":"bad63b46d8ab893844477f3dbb816908","score":"0.43753925","text":"def _docker(self, method, *args, **kwargs):\n m = getattr(self.client, method)\n return m(*args, **kwargs)","title":""},{"docid":"82ec40ed292d55f2447c56e945499c5a","score":"0.4374429","text":"def exposed(func):\n func.exposed = True\n return func","title":""},{"docid":"fcd633ab6664484b2ae4eee9a08c5f2e","score":"0.43709645","text":"async def host(self):\r\n runner = web.AppRunner(self)\r\n await runner.setup()\r\n site = web.TCPSite(runner, '0.0.0.0', 5556)\r\n await site.start()","title":""},{"docid":"70abee0bd1f086f6cfd4667bca181974","score":"0.43700346","text":"def routers_in_debug(self):","title":""},{"docid":"73bb0b261299b9388e718b20de58ce20","score":"0.43662733","text":"def rack_url(docker_ip, docker_services) -> str: # type: ignore\n url = \"http://{}\".format(docker_ip)\n set_host(url)\n # TODO(lb): For some reason, check_services always returns false.\n # docker_services.wait_until_responsive(\n # timeout=240.0, pause=0.1, check=lambda: check_services()\n # )\n docker_services.wait_until_responsive(\n timeout=TIMEOUT, pause=PAUSE, check=lambda: check(url)\n )\n sleep(AFTER)\n return url","title":""},{"docid":"564b29d2e0da4084f2cc6e3a5671b03c","score":"0.43654513","text":"def get_docker_api(host_ip):\n\t\treturn docker.DockerClient(base_url='tcp://{}:2375'.format(host_ip))","title":""},{"docid":"ff17ef864822b65fdef4bdb4886954be","score":"0.43648928","text":"def test_rewrite_function(self):\n self.sh.rewrite(\n {\n 'CSP':{'default-src':['none']},\n 'HPKP':{'pins':[{'sha256':'test123'}]}\n }\n )\n @self.app.route('/')\n @self.sh.wrapper()\n def index(): return \"hi\"\n with self.app.test_client() as c:\n result = c.get('/')\n self.assertEquals(result.headers.get('Content-Security-Policy'),\"default-src 'none'\")\n self.assertEquals(result.headers.get('Public-Key-Pins'),\"pin-sha256=test123\")","title":""},{"docid":"f760ce2815af6f164db9b59acaef7876","score":"0.43640256","text":"def inspect_container(container_id):\n response = requests.get(f'{Global.BASE_URL}containers/{container_id}/json')\n return response","title":""},{"docid":"3a81e3c081571a2042fd5647081532e0","score":"0.43611273","text":"def __call__(self, req):\n return self._router","title":""},{"docid":"157140b1010dd5ca2c18921ca006e46c","score":"0.4359512","text":"def login(protocol, container):","title":""},{"docid":"95cd2327a03b35f02e9120618a5c3335","score":"0.43464574","text":"def create_app():\n container = ApplicationContainer()\n container.config.from_yaml('config.yml')\n container.config.giphy.api_key.from_env('GIPHY_API_KEY')\n\n app: web.Application = container.app()\n app.container = container\n\n app.add_routes([\n web.get('/', container.index_view.as_view()),\n ])\n\n return app","title":""}],"string":"[\n {\n \"docid\": \"352d9f89089989cc330798ccbba9a78e\",\n \"score\": \"0.57136905\",\n \"text\": \"def runpublic():\\n app.run(host='0.0.0.0')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82e3d8128d7d5e40b4e4b8eb9e5c7c9c\",\n \"score\": \"0.5393183\",\n \"text\": \"def expose_endpoint(self, exec_endpoint: str, path: Optional[str] = None):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ae041808f7752f54dba15ad576d038c\",\n \"score\": \"0.53242624\",\n \"text\": \"def run(url):\\r\\n url = url + \\\":2375/info\\\"\\r\\n req = requests.get(url)\\r\\n if \\\"Containers\\\" in req.text:\\r\\n print(req.text)\\r\\n return \\\"Docker Remote API unauth Vulnerable\\\"\\r\\n else:\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85d2e68cca10c33a51e764e58adbeece\",\n \"score\": \"0.5149998\",\n \"text\": \"def test_app_networking_mode_with_defined_container_port(dcos_api_session: DcosApiSession,\\n networking_mode: marathon.Network,\\n host_port: int) -> None:\\n app_definition, test_uuid = test_helpers.marathon_test_app(\\n healthcheck_protocol=marathon.Healthcheck.MESOS_HTTP,\\n container_type=marathon.Container.DOCKER,\\n network=networking_mode,\\n host_port=host_port)\\n\\n dcos_service_name = uuid.uuid4().hex\\n\\n app_definition['labels'] = {\\n 'DCOS_SERVICE_NAME': dcos_service_name,\\n 'DCOS_SERVICE_PORT_INDEX': '0',\\n 'DCOS_SERVICE_SCHEME': 'http',\\n }\\n\\n # Arbitrary buffer time, accounting for propagation/processing delay.\\n buffer_time = 5\\n\\n # Cache refresh in Adminrouter takes 30 seconds at most.\\n # CACHE_POLL_PERIOD=25s + valid=5s Nginx resolver DNS entry TTL\\n # https://github.com/dcos/dcos/blob/cb9105ee537cc44cbe63cc7c53b3b01b764703a0/\\n # packages/adminrouter/extra/src/includes/http/master.conf#L21\\n adminrouter_default_refresh = 25 + 5 + buffer_time\\n app_id = app_definition['id']\\n app_instances = app_definition['instances']\\n app_definition['constraints'] = [['hostname', 'UNIQUE']]\\n\\n # For the routing check to work, two conditions must be true:\\n #\\n # 1. The application must be deployed, so that `/ping` responds with 200.\\n # 2. The Admin Router routing layer must not be using an outdated\\n # version of the Nginx resolver cache.\\n #\\n # We therefore wait until these conditions have certainly been met.\\n # We wait for the Admin Router cache refresh first so that there is\\n # unlikely to be much double-waiting. That is, we do not want to be waiting\\n # for the cache to refresh when it already refreshed while we were waiting\\n # for the app to become healthy.\\n with dcos_api_session.marathon.deploy_and_cleanup(app_definition, check_health=False):\\n time.sleep(adminrouter_default_refresh)\\n dcos_api_session.marathon.wait_for_app_deployment(\\n app_id=app_id,\\n app_instances=app_instances,\\n check_health=False,\\n ignore_failed_tasks=False,\\n timeout=1200,\\n )\\n r = dcos_api_session.get('/service/' + dcos_service_name + '/ping')\\n assert r.status_code == 200\\n assert 'pong' in r.json()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddecb71ea773dafaab3941c29e9070e8\",\n \"score\": \"0.50935376\",\n \"text\": \"def _register_api(app):\\n\\n app.add_url_rule('/container+item/',\\n \\\"get_containers_and_items\\\", get_containers_and_items, methods=['GET'])\\n\\n app.add_url_rule('/container/',\\n \\\"put_container\\\", put_container, methods=['PUT'])\\n\\n app.add_url_rule('/item/',\\n \\\"put_item\\\", put_item, methods=['PUT', 'POST'])\\n\\n app.add_url_rule('/put_item/',\\n \\\"put_item\\\", put_item, methods=['GET'])\\n\\n app.add_url_rule('/item/',\\n \\\"del_item\\\", del_item, methods=['DELETE'])\\n\\n app.add_url_rule('/container/',\\n \\\"del_container\\\", del_container, methods=['DELETE'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32520ec95a901cf5b1f799c52e848526\",\n \"score\": \"0.49791166\",\n \"text\": \"def bind(self, app):\\n def url(fmt):\\n return '/v1' + fmt.format(\\n id='{id:[A-Za-z0-9]{24}}',\\n path='{path:.*}'\\n )\\n app.add_routes([\\n # GET handlers for experiments\\n web.get(url('/_query'), self.handle_query),\\n web.get(url('/_get/{id}'), self.handle_get),\\n web.get(url('/_tarball/{id}'), self.handle_tarball),\\n\\n # POST handlers for experiments\\n web.post(url('/_heartbeat/{id}'), self.handle_heartbeat),\\n web.post(url('/_delete/{id}'), self.handle_delete),\\n web.post(url('/_query'), self.handle_query),\\n web.post(url('/_create'), self.handle_create),\\n web.post(url('/_update/{id}'), self.handle_update),\\n web.post(url('/_update_fs_size/{id}'), self.handle_update_fs_size),\\n web.post(url('/_set_finished/{id}'), self.handle_set_finished),\\n web.post(url('/_kill/{id}'), self.handle_kill),\\n\\n # GET handlers for files\\n web.get(url('/_listdir/{id}'), self.handle_listdir),\\n web.get(url('/_listdir/{id}/{path}'), self.handle_listdir),\\n web.get(url('/_listzip/{id}/{path}'), self.handle_listzip),\\n web.get(url('/_getfile/{id}/{path}'), self.handle_getfile),\\n web.get(url('/_getzipentry/{id}/{path}'), self.handle_getzipentry),\\n ])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d74095549bb8c03b17c6b1b25398b57\",\n \"score\": \"0.49361095\",\n \"text\": \"def export(container):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8770acf8901e8ec779c971542e7db90f\",\n \"score\": \"0.49213618\",\n \"text\": \"def start_server():\\n global ctr, dkr\\n env = docker.utils.kwargs_from_env(assert_hostname=False)\\n dkr = docker.Client(timeout=60, **env)\\n # docker run --rm -P --name smtserve apdavison/sumatra-server-v4\\n ctr = dkr.create_container(image, command=None, hostname=None, user=None,\\n detach=False, stdin_open=False, tty=False,\\n ports=None, environment=None, dns=None, volumes=None,\\n volumes_from=None, network_disabled=False, name=None,\\n entrypoint=None, cpu_shares=None, working_dir=None)\\n dkr.start(ctr, publish_all_ports=True)\\n utils.env[\\\"url\\\"] = get_url()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a648a6faa0ca17656c57823e67a7fba\",\n \"score\": \"0.4895414\",\n \"text\": \"def http_service(docker_ip, docker_services):\\n\\n # `port_for` takes a container port and returns the corresponding host port\\n url = 'http://{}:{}'.format(docker_ip, 8000)\\n return url\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77ca1865a97f4fd2c734270277b1830b\",\n \"score\": \"0.48360625\",\n \"text\": \"def container_actions(container_name, action):\\n dclient = docker.from_env()\\n container = dclient.containers.get(container_name)\\n print(action)\\n if action == 'start':\\n container.start()\\n elif action == 'stop':\\n container.stop()\\n elif action == 'restart':\\n container.restart()\\n elif action == 'kill':\\n container.kill()\\n elif action == 'remove':\\n container.remove(force=True)\\n return redirect(url_for('apps.running_apps'))\\n else:\\n print('else')\\n\\n return render_template('apps/manage_app.html', container=container)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f027c1d4eeb25454e9f3b0fa29df8094\",\n \"score\": \"0.48350552\",\n \"text\": \"def server():\\n app.run(host='0.0.0.0', port=8022, debug=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a27bf49734638e8395246e96bfdd3d7d\",\n \"score\": \"0.48309353\",\n \"text\": \"def main():\\r\\n app.run(debug=True, host=\\\"0.0.0.0\\\", port=8000)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b512a6f99295864eb197b27a49c14160\",\n \"score\": \"0.48045236\",\n \"text\": \"def add_routes(self, app: Sanic) -> Any:\\n app.add_route(UserResource.as_view(), \\\"/\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afa5ca0d919344330139ab794d152b72\",\n \"score\": \"0.4798193\",\n \"text\": \"def action(hostname=('h', hostname), port=('p', port),\\r\\n reloader=use_reloader, debugger=use_debugger,\\r\\n evalex=use_evalex, threaded=threaded, processes=processes):\\r\\n from werkzeug.serving import run_simple\\r\\n app = app_factory()\\r\\n run_simple(hostname, port, app, reloader, debugger, evalex,\\r\\n extra_files, 1, threaded, processes,\\r\\n static_files=static_files, ssl_context=ssl_context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b32ab9b51055c2f8d5610ca1542efa8\",\n \"score\": \"0.47976813\",\n \"text\": \"def expose(self, name, service, hostname):\\n return self._client.do_action(\\\"expose\\\", [\\\"service\\\", service, f\\\"--hostname={hostname}\\\", f\\\"--name={name}\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73ba5158010c3d3ba8a2212a05d36cb5\",\n \"score\": \"0.47793567\",\n \"text\": \"def serve(ctx, server):\\n\\n if server == \\\"flask\\\":\\n from pygeoapi.flask_app import serve as serve_flask\\n ctx.forward(serve_flask)\\n ctx.invoke(serve_flask)\\n elif server == \\\"starlette\\\":\\n from pygeoapi.starlette_app import serve as serve_starlette\\n ctx.forward(serve_starlette)\\n ctx.invoke(serve_starlette)\\n elif server == \\\"django\\\":\\n from pygeoapi.django_app import main as serve_django\\n ctx.invoke(serve_django)\\n else:\\n raise click.ClickException('--flask/--starlette/--django is required')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ce9f185a891a07cf1c3a0b917f41e30\",\n \"score\": \"0.47516328\",\n \"text\": \"def __init__(self):\\n self.client = Client()\\n self.filters = {\\n 'event': ['start', 'restart', 'die', 'stop'],\\n 'type': 'container'\\n }\\n self.host = Host('docker')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f85add23bfcc5c4086e9559c06f286d6\",\n \"score\": \"0.47472745\",\n \"text\": \"def www(args):\\n from minc.view import app\\n\\n app.run(host=\\\"0.0.0.0\\\", port=5000)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d739e1c1571866028e26be9088108b52\",\n \"score\": \"0.47459567\",\n \"text\": \"def container_addr(self):\\n return '%s:%d' % (self._container_host, self._container_opts.port)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51cdc29908f3d6ac752033a9d8e4f266\",\n \"score\": \"0.474318\",\n \"text\": \"def add_routes(self, app):\\n app.add_endpoint('/open-gate', 'open-gate', self.handle_open_gate_request)\\n app.add_endpoint('/close-gate', 'close-gate', self.handle_close_gate_request)\\n app.add_endpoint('/gate-status', 'gate-state', self.handle_gate_status_request)\\n app.add_endpoint('/reset-gate', 'reset-gate', self.handle_reset_gate_request)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cce7bb2dd74328d4b9d6345d25da6daa\",\n \"score\": \"0.47316366\",\n \"text\": \"def bind_health_routes(app: Flask):\\n\\n @app.route('/healthz')\\n def health():\\n logging.info(\\\"api: /healthz\\\")\\n return \\\"I am healthy!\\\"\\n\\n @app.route('/testing')\\n def testing():\\n logging.info(\\\"api: /testing\\\")\\n return \\\"Hi tez, how are you?\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cde38b38f2edaf8991c51760da609d24\",\n \"score\": \"0.4730625\",\n \"text\": \"def container_monitor(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69d21d67f225044f20a40b6c498ef51d\",\n \"score\": \"0.47006157\",\n \"text\": \"def main():\\n for container in EVENTS:\\n try:\\n container_stop = False\\n if \\\"start\\\" in container[\\\"status\\\"]:\\n try:\\n container_event_id = container[\\\"Actor\\\"][\\\"ID\\\"]\\n container_inspect = CLIENT.inspect_container(container_event_id)\\n container_id = container_inspect[\\\"Id\\\"]\\n container_name = container_inspect[\\\"Name\\\"].replace(\\\"/\\\", \\\"\\\")\\n\\n container_cap_drop = container_inspect[\\\"HostConfig\\\"][\\\"CapDrop\\\"]\\n container_cap_add = container_inspect[\\\"HostConfig\\\"][\\\"CapAdd\\\"]\\n container_privileged = container_inspect[\\\"HostConfig\\\"][\\\"Privileged\\\"]\\n container_security_opt = container_inspect[\\\"HostConfig\\\"][\\n \\\"SecurityOpt\\\"\\n ]\\n\\n no_security_opt = \\\"%s: no security options has been set\\\" % (\\n container_name\\n )\\n priv_not_allowed_log = \\\"%s: privileged set but not allowed\\\" % (\\n container_name\\n )\\n priv_no_policy_log = \\\"%s: privileged set but no policy\\\" % (\\n container_name\\n )\\n cap_drop_log = \\\"%s: all capabilities not dropped\\\" % (container_name)\\n cap_add_all_log = \\\"%s: capability ALL has been set\\\" % (\\n container_name\\n )\\n stop_container_log = \\\"%s: stopping container\\\" % (container_name)\\n\\n try:\\n if CONF[\\\"debug\\\"]:\\n print((\\\"container_name: \\\", container_name))\\n print((\\\"containerStatus: \\\", container[\\\"status\\\"]))\\n print((\\\"container_event_id: \\\", container_event_id))\\n print(\\n (\\n \\\"container_inspect: \\\",\\n CLIENT.inspect_container(container_event_id),\\n )\\n )\\n print((\\\"containerID: \\\", container_inspect[\\\"Id\\\"]))\\n print((\\\"container_cap_drop: \\\", container_cap_drop))\\n print((\\\"container_cap_add: \\\", container_cap_add))\\n print((\\\"container_security_opt: \\\", container_security_opt))\\n print((\\\"container_privileged: \\\", container_privileged))\\n print((\\\"container_stop: \\\", container_stop))\\n\\n except NameError:\\n pass\\n\\n try:\\n if container_privileged is not False:\\n if not CONF[container_name][\\\"privileged\\\"]:\\n syslog.syslog(priv_not_allowed_log)\\n container_stop = True\\n\\n except KeyError:\\n syslog.syslog(priv_no_policy_log)\\n container_stop = True\\n\\n try:\\n if container_security_opt is None:\\n if CONF[container_name][\\\"security_opt_required\\\"]:\\n syslog.syslog(no_security_opt)\\n container_stop = True\\n\\n if CONF[\\\"debug\\\"]:\\n print(\\n (\\n \\\"container_security_opt: \\\",\\n container_security_opt,\\n )\\n )\\n\\n except KeyError:\\n syslog.syslog(no_security_opt)\\n container_stop = True\\n\\n try:\\n if container_cap_drop is not None:\\n for cap_drop in container_cap_drop:\\n if (\\n \\\"all\\\" not in cap_drop.lower()\\n and not CONF[container_name][\\\"cap_drop_required\\\"]\\n ):\\n syslog.syslog(cap_drop_log)\\n container_stop = True\\n\\n if CONF[\\\"debug\\\"]:\\n print((\\\"cap_drop: \\\", cap_drop.lower()))\\n print(\\n (\\n \\\"cap_dropRequired: \\\",\\n CONF[container_name][\\n \\\"cap_drop_required\\\"\\n ],\\n )\\n )\\n\\n except KeyError:\\n syslog.syslog(cap_drop_log)\\n container_stop = True\\n\\n try:\\n if container_cap_drop is None:\\n if CONF[container_name][\\\"cap_drop_required\\\"]:\\n syslog.syslog(cap_drop_log)\\n container_stop = True\\n\\n except KeyError:\\n syslog.syslog(cap_drop_log)\\n container_stop = True\\n\\n if container_cap_add is not None:\\n for cap_add in container_cap_add:\\n if \\\"all\\\" in cap_add.lower():\\n syslog.syslog(cap_add_all_log)\\n container_stop = True\\n\\n if container_stop and container_stop is not False:\\n client_stop = \\\"%s\\\" % (container_id)\\n\\n try:\\n if CONF[\\\"debug\\\"]:\\n print((\\\"container_stop: \\\", container_stop))\\n print((\\\"CLIENT.stop sent to \\\", client_stop))\\n\\n except NameError:\\n pass\\n\\n try:\\n CLIENT.stop(client_stop)\\n syslog.syslog(stop_container_log)\\n\\n except UnboundLocalError as exception:\\n print(exception)\\n\\n except KeyError as exception:\\n print(exception)\\n\\n except UnboundLocalError as exception:\\n print(exception)\\n\\n except KeyError as exception:\\n print(exception)\\n\\n except KeyError:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c39bc80bd02d1b51f7ac18401a58a15f\",\n \"score\": \"0.46957305\",\n \"text\": \"def test_get_containers(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d448652ada54131db16119aaead8210c\",\n \"score\": \"0.46852827\",\n \"text\": \"def web():\\n app.run(host=\\\"0.0.0.0\\\", debug=debug, port=port)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11b9261ea2cb12bb0bb8ce0c4628ac89\",\n \"score\": \"0.46846414\",\n \"text\": \"def runserver():\\n from cryptoparty import app\\n app.run(host='0.0.0.0', debug=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0592ee624fc7c7689279186904878c3\",\n \"score\": \"0.46846122\",\n \"text\": \"def build_security_service(app):\\n manager = get_manager(app)\\n user_datastore = SQLAlchemyUserDatastore(manager, User, Role)\\n Security(app, user_datastore, register_form=ExtendedRegisterForm)\\n\\n @app.before_first_request\\n def create_user():\\n manager.create_all()\\n user_datastore.find_or_create_role(name='admin', description='Administrator of PyBEL Web')\\n user_datastore.find_or_create_role(name='scai', description='Users of PyBEL Web from Fraunhofer SCAI')\\n manager.session.commit()\\n\\n @app.route('/wrap/logout')\\n def logout():\\n return redirect(url_for('security.logout'))\\n\\n @app.route('/wrap/login')\\n def login():\\n return redirect(url_for('security.login'))\\n\\n @app.route('/admin/user/<user>/add_role/<role>')\\n @roles_required('admin')\\n def add_user_role(user, role):\\n user_datastore.add_role_to_user(user, role)\\n user_datastore.commit()\\n return jsonify({'status': 200})\\n\\n @app.route('/admin/user/<user>/remove_role/<role>')\\n @roles_required('admin')\\n def remove_user_role(user, role):\\n user_datastore.remove_role_from_user(user, role)\\n user_datastore.commit()\\n return jsonify({'status': 200})\\n\\n @app.route('/admin/user/list')\\n @roles_required('admin')\\n def list_users():\\n return jsonify(manager.session.query(User).all())\\n\\n @app.route('/users')\\n @roles_required('admin')\\n def view_users():\\n return render_template('view_users.html', users=manager.session.query(User).all())\\n\\n @app.route('/api/user/<int:user_id>/delete')\\n @roles_required('admin')\\n def delete_user(user_id):\\n \\\"\\\"\\\"Deletes a user\\\"\\\"\\\"\\n u = User.query.get(user_id)\\n user_datastore.delete_user(u)\\n user_datastore.commit()\\n return jsonify({'status': 200, 'action': 'deleted user', 'user': str(u)})\\n\\n log.info('built security service')\\n\\n return user_datastore\",\n \"title\": \"\"\n },\n {\n \"docid\": \"808738775f4a7be580c245cab22159be\",\n \"score\": \"0.4675486\",\n \"text\": \"def start(self):\\n resp = yield self.docker('create_container',\\n image=\\\"jupyter/singleuser\\\",\\n environment=self.env,\\n )\\n self.container_id = container_id = resp['Id']\\n self.log.info(\\\"Created container {}\\\".format(container_id))\\n\\n yield self.docker('start', container_id, port_bindings={8888: (self.container_ip,)})\\n resp = yield self.docker('port', container_id, 8888)\\n self.user.server.port = resp[0]['HostPort']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7a5186d06b682f5106c900b52e7db18\",\n \"score\": \"0.46738473\",\n \"text\": \"def list_containers():\\n response = requests.get(f'{Global.BASE_URL}containers/json')\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65b445f25c83a9aa548808c726c5d781\",\n \"score\": \"0.4653287\",\n \"text\": \"def client_fwd_app(request):\\n if \\\"cid\\\" not in request.cookies:\\n return register_client(request)\\n else:\\n return client_pull(request)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5336074ec99910f3bf6c832915d4cc13\",\n \"score\": \"0.46521938\",\n \"text\": \"def init_firewall(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95ecac918c83793c757acb5ec0c7b066\",\n \"score\": \"0.46464646\",\n \"text\": \"def deploy_local_k8s(ctx, port_forward=\\\"true\\\"):\\n ctx.run(minikube(\\\"start\\\"))\\n ctx.run(skaffold(\\\"dev\\\", f\\\"--port-forward={port_forward}\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"521a5502a17c12c2c5547938d565dff5\",\n \"score\": \"0.46305144\",\n \"text\": \"def __assign_public_ports(self, context, host_server):\\n port_cfg = context.port_config\\n context.host_server_id = host_server.id\\n\\n if self.util.is_local():\\n for cfg in port_cfg:\\n cfg[DOCKER_UNIT.PORTS_PUBLIC_PORT] = cfg[DOCKER_UNIT.PORTS_HOST_PORT]\\n self.__update_virtual_environment_cfg(context)\\n else:\\n # configure ports on azure cloud service\\n vm_adapter = self.__get_azure_vm_adapter(context)\\n virtual_machine_name = host_server.vm_name\\n cloud_service_name = host_server.public_dns.split('.')[0] # cloud_service_name.chinacloudapp.cn\\n\\n # filter the port config with \\\"public\\\" True\\n self.log.debug(\\\"starting to assign azure ports...\\\")\\n public_ports_cfg = filter(lambda p: DOCKER_UNIT.PORTS_PUBLIC in p, port_cfg)\\n host_ports = [u[DOCKER_UNIT.PORTS_HOST_PORT] for u in public_ports_cfg]\\n\\n # get assigned ports of cloud service\\n assigned_endpoints = vm_adapter.get_assigned_endpoints(cloud_service_name)\\n if not assigned_endpoints:\\n self.log.debug('fail to assign endpoints: %s' % cloud_service_name)\\n self._on_virtual_environment_failed(context)\\n return\\n\\n endpoints_to_assign = find_unassigned_endpoints(host_ports, assigned_endpoints)\\n # duplication detection for public endpoint\\n deployment_name = vm_adapter.get_deployment_name(cloud_service_name, DEPLOYMENT_SLOT)\\n network_config = vm_adapter.get_virtual_machine_network_config(cloud_service_name,\\n deployment_name,\\n virtual_machine_name)\\n new_network_config = add_endpoint_to_network_config(network_config, endpoints_to_assign, host_ports)\\n try:\\n result = vm_adapter.update_virtual_machine_network_config(cloud_service_name,\\n deployment_name,\\n virtual_machine_name,\\n new_network_config)\\n context.request_id = result.request_id\\n except Exception as e:\\n self.log.error(e)\\n self.log.error('fail to assign endpoints: %s' % endpoints_to_assign)\\n self._on_virtual_environment_failed(context)\\n return\\n\\n updated_config = []\\n for cfg in context.port_config:\\n if cfg[DOCKER_UNIT.PORTS_HOST_PORT] in host_ports:\\n index = host_ports.index(cfg[DOCKER_UNIT.PORTS_HOST_PORT])\\n cfg[DOCKER_UNIT.PORTS_PUBLIC_PORT] = endpoints_to_assign[index]\\n updated_config.append(cfg)\\n context.port_config = updated_config\\n\\n # query azure to make sure the network config updated\\n context.virtual_machine_name = virtual_machine_name\\n context.cloud_service_name = cloud_service_name\\n context.trial = 0\\n self.query_network_config_status(context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"305b10e3872748d11f2add7dc21da91d\",\n \"score\": \"0.46108752\",\n \"text\": \"def __call__(self):\\n # Add shm\\n self.volumes.append({'/dev/shm': V1Volume(name=\\\"dshm\\\", empty_dir=V1EmptyDirVolumeSource(medium='Memory'))})\\n\\n self.op = kfp.dsl.ContainerOp(self.name, self.image, arguments=self.args, file_outputs = self.file_outputs)\\n if self.node:\\n self.op.add_affinity(V1Affinity(node_affinity=V1NodeAffinity(required_during_scheduling_ignored_during_execution=V1NodeSelector(node_selector_terms=[V1NodeSelectorTerm(match_expressions=[V1NodeSelectorRequirement(key='kubernetes.io/hostname', operator='In', values=[self.node])])]))))\\n if self.ngpu:\\n self.op.set_gpu_limit(self.ngpu)\\n if len(self.volumes) > 0:\\n for vol in self.volumes:\\n self.op.add_pvolumes(vol)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4da3c25a3f23ca12d8f2e534bb9b2aac\",\n \"score\": \"0.45957643\",\n \"text\": \"def includeme(config: Configurator) -> None:\\n config.router.add_route('index', '/')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05d93292fedf9e25c3627f01a1a203fa\",\n \"score\": \"0.45898187\",\n \"text\": \"def _router(self):\\n raise NotImplementedError # pragma: no cover\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d123eb3c4681bc18568c451778dbc0fc\",\n \"score\": \"0.45857227\",\n \"text\": \"def vpc_endpoint(myClusterProps, DWH_PORT, ec2):\\n try:\\n vpc = ec2.Vpc(id=myClusterProps['VpcId'])\\n defaultSg = list(vpc.security_groups.all())[0]\\n defaultSg.authorize_ingress(\\n GroupName = defaultSg.group_name,\\n CidrIp = '0.0.0.0/0',\\n IpProtocol = 'TCP',\\n FromPort = int(DWH_PORT),\\n ToPort = int(DWH_PORT)\\n )\\n except Exception as e:\\n print(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7d664137021595105a7d838b4641328\",\n \"score\": \"0.45815855\",\n \"text\": \"def run():\\n web_app.run(host='0.0.0.0', port=5000, debug=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5514c33480903825a417fb488cbd428\",\n \"score\": \"0.4572467\",\n \"text\": \"def set_endpoints(self):\\n try:\\n # pylint: disable=W0612\\n @app.route('/data')\\n def get_data():\\n self.has_new_update = False\\n return self.handle_api_call(self.gekko_data)\\n\\n @app.route('/get_options')\\n def get_options():\\n return self.handle_api_call(self.options_dict)\\n\\n @app.route('/poll')\\n def get_poll():\\n return self.handle_api_call({'updates': self.has_new_update})\\n\\n @app.route('/<path:path>')\\n def static_file(path):\\n return app.send_static_file(path)\\n\\n @app.route('/')\\n def redirect_to_index():\\n newPath = 'http://localhost:' + str(self.port) + '/index.html'\\n return redirect(newPath)\\n\\n @app.errorhandler(404)\\n def endpoint_not_found(error):\\n return jsonify(error=404, text='Endpoint not found')\\n\\n @app.errorhandler(Exception)\\n def unhandled_error(e):\\n return jsonify(error=500, text='Internal API Error')\\n\\n except AssertionError as e:\\n # This try/except is because ipython keeps the endpoints in\\n # memory between runs. This will simply not set the endpoints if they\\n # are already there.\\n pass\\n\\n except Exception as e:\\n raise e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0aca90f5c39adda3edbb0732d460e32d\",\n \"score\": \"0.4572295\",\n \"text\": \"def test_access_public_container_object_without_using_creds(self):\\n # update container metadata to make it publicly readable\\n cont_headers = {'X-Container-Read': '.r:*,.rlistings'}\\n resp_meta, body = (\\n self.container_client.create_update_or_delete_container_metadata(\\n self.container_name,\\n create_update_metadata=cont_headers,\\n create_update_metadata_prefix=''))\\n self.assertHeaders(resp_meta, 'Container', 'POST')\\n\\n # create object\\n object_name = data_utils.rand_name(name='Object')\\n data = data_utils.random_bytes(size=len(object_name))\\n resp, _ = self.object_client.create_object(self.container_name,\\n object_name, data)\\n self.assertHeaders(resp, 'Object', 'PUT')\\n\\n # list container metadata\\n resp_meta, _ = self.container_client.list_container_metadata(\\n self.container_name)\\n self.assertHeaders(resp_meta, 'Container', 'HEAD')\\n\\n self.assertIn('x-container-read', resp_meta)\\n self.assertEqual(resp_meta['x-container-read'], '.r:*,.rlistings')\\n\\n # trying to get object with empty headers as it is public readable\\n self.object_client.auth_provider.set_alt_auth_data(\\n request_part='headers',\\n auth_data=None\\n )\\n resp, body = self.object_client.get_object(\\n self.container_name, object_name)\\n self.assertHeaders(resp, 'Object', 'GET')\\n\\n self.assertEqual(body, data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08e4225123ea3c54f25adc9b301fb548\",\n \"score\": \"0.45556995\",\n \"text\": \"def run():\\n app.run(host='0.0.0.0', port='8001')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a73c57c0da5b6da197d5c37faad5a91a\",\n \"score\": \"0.45527732\",\n \"text\": \"def register(self, app: FastAPI) -> None:\\n self.router.add_api_route(\\n name=\\\"Queryables\\\",\\n path=\\\"/queryables\\\",\\n methods=[\\\"GET\\\"],\\n endpoint=self._create_endpoint(self.client.get_queryables, EmptyRequest),\\n )\\n self.router.add_api_route(\\n name=\\\"Collection Queryables\\\",\\n path=\\\"/collections/{collectionId}/queryables\\\",\\n methods=[\\\"GET\\\"],\\n endpoint=self._create_endpoint(self.client.get_queryables, CollectionUri),\\n )\\n app.include_router(self.router, tags=[\\\"Filter Extension\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e26ee2d209513b1327c5821e4d7a734\",\n \"score\": \"0.45511606\",\n \"text\": \"def test_create_container_with_ports(self, cluster):\\n return self._create_container()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e4afc5fdd156e786e27c2b980aa8205\",\n \"score\": \"0.4550273\",\n \"text\": \"def _route(self):\\n self._app.route('/', callback=self.show_index)\\n self._app.route('/run', method='POST', callback=self.run_program)\\n self._app.route('/static/<filename>', callback=self.static_files)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a764432315fb0b722d598a63f379cbb\",\n \"score\": \"0.45448127\",\n \"text\": \"def vservers_view(request):\\n get_vservers()\\n return HTTPFound(location='/')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0025e8b0c4218ea249779a6495c4541\",\n \"score\": \"0.45445272\",\n \"text\": \"def access_route(self):\\r\\n if 'HTTP_X_FORWARDED_FOR' in self.environ:\\r\\n addr = self.environ['HTTP_X_FORWARDED_FOR'].split(',')\\r\\n return self.list_storage_class([x.strip() for x in addr])\\r\\n elif 'REMOTE_ADDR' in self.environ:\\r\\n return self.list_storage_class([self.environ['REMOTE_ADDR']])\\r\\n return self.list_storage_class()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"926a206104a8c56df9a3fed98b86ec1f\",\n \"score\": \"0.4544434\",\n \"text\": \"def route_to_app(app, redis):\\n collectors = get_collectors(app_ref=app, redis_ref=redis)\\n affiliates = get_affiliates()\\n api = get_api()\\n\\n app.before_request(identify_user)\\n\\n urls = list()\\n\\n urls.append(register_endpoint(app, '/tag/pageview/<string:store_id>/<string:pagetype>', collectors.pageview))\\n urls.append(register_endpoint(app, '/tag/add-to-cart/<string:store_id>', collectors.add_to_cart))\\n urls.append(register_endpoint(app, '/tag/remove-from-cart/<string:store_id>', collectors.remove_from_cart))\\n urls.append(register_endpoint(app, '/tag/purchase/<string:store_id>/<string:tr_id>/<int:total>', collectors.purchase))\\n urls.append(register_endpoint(app, '/tag/get-affiliates/<string:store_id>', affiliates.get_affiliates))\\n urls.append(register_endpoint(app, '/api/remarketing', api.get_remarketing, ['GET']))\\n urls.append(register_endpoint(app, '/api/carts', api.get_open_carts, ['GET']))\\n urls.append(register_endpoint(app, '/api/purchases', api.get_purchases, ['GET']))\\n\\n # Default 404 route that shows valid endpoints\\n app.add_url_rule(\\n rule='/<path:_>',\\n endpoint='404_fallback',\\n view_func=lambda _: ('Invalid path, try one of:\\\\n\\\\n' + '\\\\n'.join(urls), 404, {'Content-Type': 'text-plain'}),\\n methods=['POST', 'GET']\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8850d8931156a861cb5af3a7b1a092e\",\n \"score\": \"0.45433876\",\n \"text\": \"def expose(name=None):\\n def _expose(func):\\n def _decorator(*args, **kwargs):\\n return func(*args, **kwargs)\\n func.exposed = True\\n func.exposed_name = name or func.__name__\\n return functools.wraps(func)(_decorator)\\n return _expose\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40374c776736d90f5b5aeecb52fdf0d6\",\n \"score\": \"0.4540795\",\n \"text\": \"def configure_access(self, server, share_name):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d728229e4319516e40ca8cf40c5f810\",\n \"score\": \"0.45387807\",\n \"text\": \"def use_service_secret(_service_secret):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3be69e809036d9faf6d6608cdb785aba\",\n \"score\": \"0.45303947\",\n \"text\": \"def test_org_apache_jackrabbit_oak_security_internal_security_provider_registrati(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1090d47e8fe89c93503665dea6c529df\",\n \"score\": \"0.4520967\",\n \"text\": \"def index():\\n return api.formatted_routes(request.host_url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6732f481ceaef4441320ff83232c4553\",\n \"score\": \"0.45187342\",\n \"text\": \"def test_api_service(self):\\n\\n port = 8088\\n for swagger in self.service_swaggers:\\n swagger = os.path.join(self.prefix, swagger)\\n pid = os.fork()\\n if not pid:\\n name = swagger.split('/')[2]\\n print(('Starting server for: {} at: {}'.format(name, swagger)))\\n resolver = MockResolver(mock_all='all')\\n api_extra_args ={'resolver': resolver}\\n\\n app = connexion.FlaskApp(name,\\n swagger_json=False,\\n swagger_ui=False)\\n\\n app.add_api(swagger + '/swagger.yaml',\\n resolver_error=True,\\n validate_responses=True,\\n strict_validation=True,\\n **api_extra_args)\\n\\n app.run(port=port)\\n\\n else:\\n try:\\n print('Wait for pinging server')\\n # Let the api initialize\\n time.sleep(2)\\n try:\\n t = urllib.request.urlopen('http://localhost:{}/'.format(port))\\n t.close()\\n except Exception as e:\\n print(('Fetch error: {}'.format(e)))\\n\\n print('Killing child pid')\\n os.kill(pid, signal.SIGKILL)\\n print('Killed')\\n\\n time.sleep(1)\\n except (KeyboardInterrupt, Exception) as e:\\n os.kill(pid, signal.SIGKILL)\\n raise\\n\\n port +=1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d6559a0ae91b69bb128c3a2127be544\",\n \"score\": \"0.4513166\",\n \"text\": \"def create_app(debug):\\n app = Flask(__name__)\\n hostname = os.environ.get('DOCKER_MACHINE_IP', 'localhost')\\n if not hostname:\\n hostname = 'localhost'\\n origins = ('^(https?://)?(www\\\\.)?({}|0|0.0.0.0|dimi-bat.local|'\\n 'localhost|127.0.0.1)(\\\\.com)?:\\\\d{{1,5}}$').format(hostname),\\n CORS(app,\\n origins=origins,\\n headers=(\\n 'x-requested-with',\\n 'content-type',\\n 'accept',\\n 'origin',\\n 'authorization',\\n 'x-csrftoken',\\n 'withcredentials',\\n 'cache-control',\\n 'cookie',\\n 'session-id',\\n ),\\n supports_credentials=True,\\n )\\n\\n app.debug = debug\\n\\n app.register_blueprint(api_views, url_prefix='/api')\\n return app\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa064f06cf894d70687171a9f695e92b\",\n \"score\": \"0.44995013\",\n \"text\": \"def create_endpoint(self, meta_container: str, coapPort: int, httpPort: int, grpcPort: int) -> None:\\n\\n # get the docker client\\n client = docker.from_env()\\n\\n logging.debug('building endpoint container image')\\n\\n # build the reverse proxy image\\n endpoint_image = client.images.build(tag='tinyfaas-reverse-proxy', path='./reverse-proxy/', rm=True)[0]\\n\\n logging.debug('removing old endpoint networks')\\n\\n # remove old endpoint-net networks\\n for n in client.networks.list(names=['endpoint-net']):\\n n.remove()\\n\\n logging.debug('creating endpoint network')\\n\\n # create new endpoint-net network\\n self.endpoint_network = client.networks.create('endpoint-net', driver='bridge')\\n\\n # find out which ports to forward\\n # if a port is set to zero, we just don't use it\\n ports: typing.Dict[str, int] = {}\\n if coapPort > 0:\\n ports['6000/udp'] = coapPort\\n\\n if httpPort > 0:\\n ports['7000/tcp'] = httpPort\\n\\n if grpcPort > 0:\\n ports['8000/tcp'] = grpcPort\\n\\n logging.debug('creating endpoint container')\\n\\n # create the endpoint container\\n self.endpoint_container = client.containers.run(endpoint_image, network=self.endpoint_network.name, ports=ports, detach=True, remove=True, name='tinyfaas-reverse-proxy')\\n\\n # getting IP address of the handler container by inspecting the network and converting CIDR to IPv4 address notation (very dirtily, removing the last 3 chars -> i.e. '/20', so let's hope we don't have a /8 subnet mask)\\n self.endpoint_container_ipaddr = docker.APIClient().inspect_network(self.endpoint_network.id)['Containers'][self.endpoint_container.id]['IPv4Address'][:-3]\\n logging.debug(f'endpoint container ip address: f{self.endpoint_container_ipaddr}')\\n\\n logging.debug('connecting meta_container to endpoint network')\\n\\n # add our container to the endpoint-net network\\n self.endpoint_network.connect(meta_container)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4b616d870211054c2c11890ea67c1af\",\n \"score\": \"0.44970858\",\n \"text\": \"def test_get_containers(self):\\n response = self.client.open(\\n '/v2/containers',\\n method='GET')\\n self.assert200(response,\\n 'Response body is : ' + response.data.decode('utf-8'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9046fb7f95792a07bfc505c0cab8670\",\n \"score\": \"0.44955412\",\n \"text\": \"def serve(self, processes=1):\\n super().serve(processes=processes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebb9fcbc8830db855166566bb760312d\",\n \"score\": \"0.44842616\",\n \"text\": \"def orchestration():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f133568797a70742dc007174d0833af0\",\n \"score\": \"0.44829464\",\n \"text\": \"def __call__(self, environ, start_response):\\n try:\\n for var in 'QUERY_STRING PATH_INFO'.split():\\n if var not in environ:\\n raise KnownUnknown('Missing \\\"%s\\\" environment variable' % var)\\n \\n query = dict(parse_qsl(environ['QUERY_STRING']))\\n \\n for param in 'size zoom'.split():\\n if param not in query:\\n raise KnownUnknown('Missing \\\"%s\\\" parameter' % param)\\n \\n layer = environ['PATH_INFO'].strip('/')\\n layer = self.config.layers[layer]\\n print layer\\n provider = layer.provider\\n \\n if not hasattr(provider, 'renderStaticMap'):\\n raise KnownUnknown('Layer \\\"%s\\\" provider %s has no renderStaticMap() method' % (layer.name(), provider.__class__))\\n \\n zoom = int(query['zoom'])\\n w, h = [int(p) for p in query['size'].split('x')]\\n layer.staticmap = StaticMaps(w, h, zoom)\\n\\n if 'markers' in query:\\n for m in query['markers'].split('|'):\\n if m.startswith('icon:'):\\n icon = m[5:]\\n elif len(m.split(','))==2:\\n lat, lon = [float(p) for p in m.split(',')]\\n layer.staticmap.addMarker(lat, lon, icon)\\n \\n if 'path' in query:\\n params = [m.split(':') for m in query['path'].split('|') if m.find(':')>0]\\n static_path = dict(params)\\n static_path['points'] = [m.split(',') for m in query['path'].split('|') if m.find(',')>0]\\n layer.staticmap.addPath(static_path)\\n \\n if layer.staticmap.path is None and layer.staticmap.marker is None:\\n lat, lon = [float(p) for p in query['center'].split(',')]\\n \\n mimetype, content = getStaticMap(layer)\\n headers = [('Content-Type', 'image/png')]\\n \\n if layer.allowed_origin:\\n headers.append(('Access-Control-Allow-Origin', layer.allowed_origin))\\n \\n if layer.max_cache_age is not None:\\n expires = datetime.utcnow() + timedelta(seconds=layer.max_cache_age)\\n headers.append(('Expires', expires.strftime('%a %d %b %Y %H:%M:%S GMT')))\\n headers.append(('Cache-Control', 'public, max-age=%d' % layer.max_cache_age))\\n\\n start_response('200 OK', headers)\\n return content\\n \\n except KnownUnknown, e:\\n start_response('400 Bad Request', [('Content-Type', 'text/plain')])\\n return str(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"610948eb6960331e1109add27e3497fc\",\n \"score\": \"0.44827572\",\n \"text\": \"def _create_container(self):\\n data = {\\n u\\\"name\\\": random_name(self),\\n u\\\"image\\\": \\\"clusterhq/flask:latest\\\",\\n u\\\"ports\\\": [{u\\\"internal\\\": 80, u\\\"external\\\": 8080}],\\n u'restart_policy': {u'name': u'never'}\\n }\\n waiting_for_cluster = get_test_cluster(reactor, node_count=1)\\n\\n def create_container(cluster, data):\\n data[u\\\"node_uuid\\\"] = cluster.nodes[0].uuid\\n return cluster.create_container(data)\\n\\n d = waiting_for_cluster.addCallback(create_container, data)\\n\\n def check_result(result):\\n cluster, response = result\\n self.addCleanup(cluster.remove_container, data[u\\\"name\\\"])\\n\\n self.assertEqual(response, data)\\n dl = verify_socket(cluster.nodes[0].address, 8080)\\n dl.addCallback(lambda _: (cluster, response))\\n return dl\\n\\n d.addCallback(check_result)\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42894577a02369a541fc07d00030eede\",\n \"score\": \"0.44812348\",\n \"text\": \"def view_public_cluster(name):\\n if request.method == 'GET':\\n slate_user_id = session['slate_id']\\n token_query = {'token': session['slate_token']}\\n\\n cluster_query = \\\"/v1alpha3/clusters/\\\"+name+\\\"?token=\\\"+token_query['token']\\n allowed_groups_query = \\\"/v1alpha3/clusters/\\\"+name+\\\"/allowed_groups?token=\\\"+token_query['token']\\n cluster_status_query = \\\"/v1alpha3/clusters/\\\"+name+\\\"/ping?token=\\\"+token_query['token']+\\\"&cache\\\"\\n # Set up multiplex JSON\\n multiplexJson = {cluster_query: {\\\"method\\\":\\\"GET\\\"},\\n allowed_groups_query: {\\\"method\\\":\\\"GET\\\"},\\n cluster_status_query: {\\\"method\\\":\\\"GET\\\"}}\\n # POST request for multiplex return\\n multiplex = requests.post(\\n slate_api_endpoint + '/v1alpha3/multiplex', params=token_query, json=multiplexJson)\\n multiplex = multiplex.json()\\n # Parse post return for apps, clusters, and pub groups\\n # allowed_groups_json = ast.literal_eval(multiplex[allowed_groups_query]['body'])\\n allowed_groups_json = json.loads(multiplex[allowed_groups_query]['body'])\\n allowed_groups = [item for item in allowed_groups_json['items']]\\n\\n # cluster = ast.literal_eval(multiplex[cluster_query]['body'])\\n cluster = json.loads(multiplex[cluster_query]['body'])\\n # Get owning group information for contact info\\n owningGroupName = cluster['metadata']['owningGroup']\\n owningGroup = requests.get(\\n slate_api_endpoint + '/v1alpha3/groups/' + owningGroupName, params=token_query)\\n owningGroup = owningGroup.json()\\n owningGroupEmail = owningGroup['metadata']['email']\\n # Get Cluster status from multiplex\\n cluster_status = json.loads(multiplex[cluster_status_query]['body'])\\n cluster_status = str(cluster_status['reachable'])\\n\\n return render_template('cluster_public_profile.html', cluster=cluster,\\n owningGroupEmail=owningGroupEmail,\\n allowed_groups=allowed_groups,\\n cluster_status=cluster_status)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7383995b283fd1f69dc6029f171eea5\",\n \"score\": \"0.44788024\",\n \"text\": \"def endpoint(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a20ace165ed11a5759b0546a49d9a93f\",\n \"score\": \"0.44787738\",\n \"text\": \"def web(manager, debug, port, host, secret_key):\\n if not manager.is_populated():\\n click.echo(f'{manager.module_name} has not yet been populated')\\n sys.exit(0)\\n\\n app = manager.get_flask_admin_app(url='/', secret_key=secret_key)\\n app.run(debug=debug, host=host, port=port)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31aa9f4e40b7e3e1db23cd823e470509\",\n \"score\": \"0.4478268\",\n \"text\": \"def iam():\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2ceb26004cba824a7c2a7855b033e2d\",\n \"score\": \"0.44734558\",\n \"text\": \"def run():\\n app.run(host='0.0.0.0', debug=True, port=5000)\\n # app.run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"751d86dd0a606ca7837da7569e50a3be\",\n \"score\": \"0.44690892\",\n \"text\": \"def add_file_routes(app,CreateCredentials,isLocal,serverPath,bcrypt):\\n RouteUtils.CREATE_CREDENTIALS = CreateCredentials\\n IS_LOCAL =isLocal\\n SERVER_PATH = serverPath\\n # Keys for the post route will correspond to the four types of files\\n @app.route(\\\"/v1/submit_files/\\\", methods = [\\\"POST\\\"])\\n @permissions_check\\n def submit_files():\\n fileManager = FileHandler(request,isLocal=IS_LOCAL, serverPath=SERVER_PATH)\\n return RouteUtils.run_instance_function(fileManager, fileManager.submit, getUser=True,getCredentials=True)\\n\\n @app.route(\\\"/v1/finalize_job/\\\", methods = [\\\"POST\\\"])\\n @permissions_check\\n def finalize_submission():\\n fileManager = FileHandler(request,isLocal=IS_LOCAL, serverPath=SERVER_PATH)\\n return RouteUtils.run_instance_function(fileManager, fileManager.finalize)\\n\\n @app.route(\\\"/v1/check_status/\\\", methods = [\\\"POST\\\"])\\n @permissions_check\\n def check_status():\\n fileManager = FileHandler(request,isLocal=IS_LOCAL, serverPath=SERVER_PATH)\\n return RouteUtils.run_instance_function(fileManager, fileManager.getStatus)\\n\\n @app.route(\\\"/v1/submission_error_reports/\\\", methods = [\\\"POST\\\"])\\n @permissions_check\\n def submission_error_reports():\\n fileManager = FileHandler(request,isLocal=IS_LOCAL, serverPath=SERVER_PATH)\\n return RouteUtils.run_instance_function(fileManager, fileManager.getErrorReportURLsForSubmission)\\n\\n @app.route(\\\"/v1/error_metrics/\\\", methods = [\\\"POST\\\"])\\n @permissions_check\\n def submission_error_metrics():\\n fileManager = FileHandler(request,isLocal=IS_LOCAL ,serverPath=SERVER_PATH)\\n return RouteUtils.run_instance_function(fileManager, fileManager.getErrorMetrics)\\n\\n @app.route(\\\"/v1/local_upload/\\\", methods = [\\\"POST\\\"])\\n @permissions_check\\n def upload_local_file():\\n fileManager = FileHandler(request,isLocal=IS_LOCAL, serverPath=SERVER_PATH)\\n return RouteUtils.run_instance_function(fileManager, fileManager.uploadFile)\\n\\n @app.route(\\\"/v1/get_rss/\\\", methods = [\\\"GET\\\"])\\n @permissions_check\\n def get_rss():\\n fileManager = FileHandler(request,isLocal=IS_LOCAL, serverPath=SERVER_PATH)\\n return RouteUtils.run_instance_function(fileManager, fileManager.getRss)\\n\\n @app.route(\\\"/v1/list_submissions/\\\", methods = [\\\"GET\\\"])\\n @permissions_check\\n def list_submissions():\\n \\\"\\\"\\\" List submission IDs associated with the current user \\\"\\\"\\\"\\n filter_by = request.args.get('filter_by')\\n filter_by = filter_by.lower() if filter_by is not None else filter_by\\n accountManager = AccountHandler(request,bcrypt = bcrypt)\\n\\n if filter_by == 'agency':\\n return RouteUtils.run_instance_function(accountManager, accountManager.listSubmissionsByCurrentUserAgency)\\n return RouteUtils.run_instance_function(accountManager, accountManager.listSubmissionsByCurrentUser)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa15ea3ada6e507d1653ae306e4272e6\",\n \"score\": \"0.44678646\",\n \"text\": \"def setContainer(self, *args):\\n return _pilot.ServerNode_setContainer(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e7cc007546a9ae8854def936d049cca\",\n \"score\": \"0.44571552\",\n \"text\": \"def server_api():\\n with tempfile.TemporaryDirectory() as tmp:\\n tmp = os.path.join(tmp, \\\".prefect\\\")\\n os.makedirs(tmp)\\n with configuration.set_temporary_config({\\n \\\"home_dir\\\": tmp,\\n \\\"backend\\\": \\\"server\\\",\\n \\\"cloud.api\\\": \\\"https:/localhost:4200\\\",\\n \\\"cloud.auth_token\\\": \\\"secret_token\\\",\\n \\\"cloud.use_local_secrets\\\": False}):\\n yield\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ae7c8405a766b4b60d099589a882dae\",\n \"score\": \"0.44526687\",\n \"text\": \"def expose_endpoint(self, exec_endpoint: str, **kwargs):\\n self._endpoints_mapping[exec_endpoint] = kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4efec5b9d662b136a9a26696d9c75c30\",\n \"score\": \"0.44486427\",\n \"text\": \"def container_shell(name=\\\"3bot\\\", configdir=None):\\n IT.MyEnv\\n docker = container_get(name=name)\\n IT.Tools.execute('ssh-keygen -f \\\"%s/.ssh/known_hosts\\\" -R \\\"[localhost]:9122\\\"' % IT.MyEnv.config[\\\"DIR_HOME\\\"])\\n os.execv(\\n shutil.which(\\\"ssh\\\"),\\n [\\\"ssh\\\", \\\"root@localhost\\\", \\\"-A\\\", \\\"-t\\\", \\\"-oStrictHostKeyChecking=no\\\", \\\"-p\\\", str(docker.config.sshport)],\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d3fa62eb9c3d61a26d93bbb9185b0bc\",\n \"score\": \"0.44471252\",\n \"text\": \"def deploy_private(self, group_name, pod_name, app_name, ncore,\\n ncontainer, version, entrypoint, env, network_ids, ports=None,\\n host_name=None, raw=False, image='', spec_ips=None, args=None):\\n if raw and not image:\\n raise EruException('raw and image must be set together.')\\n\\n if ports is None:\\n ports = []\\n if args is None:\\n args = []\\n\\n url = '/api/deploy/private/{0}/{1}/{2}'.format(group_name, pod_name, app_name)\\n data = {\\n 'ncore': ncore,\\n 'ncontainer': ncontainer,\\n 'version': version,\\n 'entrypoint': entrypoint,\\n 'env': env,\\n 'networks': network_ids,\\n 'ports': ports,\\n 'args': args,\\n }\\n if raw and image:\\n data['raw'] = True\\n data['image'] = image\\n if host_name:\\n data['hostname'] = host_name\\n if spec_ips:\\n data['spec_ips'] = spec_ips\\n return self.post(url, data=data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"315f459b6d50de962ff1cad7e6605805\",\n \"score\": \"0.44457242\",\n \"text\": \"def containers(self, containers):\\n\\n self._containers = containers\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e794e62ec40806480dabc03550fdf74\",\n \"score\": \"0.44436753\",\n \"text\": \"def setup_privnet(self):\\n self.setup(\\n [\\n \\\"http://127.0.0.1:30333\\\"\\n ]\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"211e9dbbd578095395ed1dae40832df0\",\n \"score\": \"0.44420752\",\n \"text\": \"def __init__(self) -> None:\\n self._url_map = werkzeug_routing.Map([\\n werkzeug_routing.Rule(\\\"/\\\", endpoint=self._OnRoot, methods=[\\\"GET\\\"]), # pytype: disable=wrong-arg-types\\n werkzeug_routing.Rule(\\n \\\"/search\\\", endpoint=self._OnSearch, methods=[\\\"POST\\\"]), # pytype: disable=wrong-arg-types\\n werkzeug_routing.Rule(\\n \\\"/query\\\", endpoint=self._OnQuery, methods=[\\\"POST\\\"]), # pytype: disable=wrong-arg-types\\n werkzeug_routing.Rule(\\n \\\"/annotations\\\", endpoint=self._OnAnnotations, methods=[\\\"POST\\\"]), # pytype: disable=wrong-arg-types\\n ])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1231e67fbfcbd46dbfee1e241b98df9b\",\n \"score\": \"0.44383287\",\n \"text\": \"def apply(self, config, containers):\\n entry_point = { 'type' : 'spark' }\\n entry_point['ip'] = containers[0]['manage_ip']\\n config_dirs = []\\n\\n new_config_dir = \\\"/tmp/\\\" + self._generate_config_dir(config.uuid)\\n try:\\n sh.mkdir('-p', new_config_dir)\\n except:\\n sys.stderr.write('could not create config dir ' + new_config_dir)\\n\\n # This file records all instances so that we can\\n # generate the hosts file. \\n entry_point['instances'] = []\\n for server in containers:\\n entry_point['instances'].append([server['data_ip'], server['host_name']])\\n\\n if not 'compute' in containers[0]:\\n # This is being called as a compute service. \\n slave_file = open(new_config_dir + '/slaves', 'w+')\\n entry_point['master'] = containers[0]['host_name']\\n entry_point['instances'] = []\\n master = entry_point['master']\\n for server in containers:\\n if server != master:\\n slave_file.write(\\\"%s\\\\n\\\" % server['host_name'])\\n slave_file.close()\\n else:\\n # This is being called as a client service. \\n # For the client, also include the host/IP of the compute service. \\n compute = containers[0]['compute'][0]\\n entry_point['master'] = compute['master']\\n\\n # Transfer the configuration. \\n for c in containers:\\n config_files = new_config_dir + '/*'\\n config_dirs.append([c['container'],\\n config_files, \\n config.config_directory])\\n\\n return config_dirs, entry_point\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba99cfe668e6c98c082229278f989824\",\n \"score\": \"0.44350347\",\n \"text\": \"def hosts(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd8fad84d31ff8476fdc72c6a397dc62\",\n \"score\": \"0.44278106\",\n \"text\": \"def client(crypto, steghide_password):\\n\\tsteg = steganography.StegHide()\\n\\tweb = WebHostAndScraper()\\n\\tprint(\\\"Running as client\\\")\\n\\ttarget_ip = input(\\\"Enter target IP: \\\")\\n\\ttarget_port = input(\\\"Enter target PORT: \\\")\\n\\tfile_name = input(\\\"Enter the name of the file you want to write: \\\")\\n\\timgarray = web.download_imgs(target_ip, target_port)\\n\\tfiles_array = steg.steg_read(imgarray, steghide_password)\\n\\tsteg.join_files(files_array, file_name)\\n\\tcrypto.decrypt_file(file_name)\\n\\texit(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c584d0c525552e4b56b06894a5070bfd\",\n \"score\": \"0.44186455\",\n \"text\": \"def Resources(self) -> HostProtectionResource:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b977d6430c4fbecba0e6f1a7119c39e\",\n \"score\": \"0.44137394\",\n \"text\": \"def grant_access(self, username, mode='read'):\\n if username == \\\"PUBLIC\\\":\\n mode = 'read'\\n current_acl = self.access_control(show_usernames=True)[mode]\\n if username in current_acl:\\n logger.info(\\\"User {} already has {} access to this container!\\\".format(username, mode))\\n else:\\n if username == \\\"PUBLIC\\\":\\n new_acl = self.access_control(show_usernames=False)[\\n mode] + ['.r:*', '.rlistings']\\n else:\\n name_map = {v: k for k, v in self.project.users.items()}\\n user_id = name_map[username]\\n new_acl = self.access_control(show_usernames=False)[\\n mode] + [\\\"{}:{}\\\".format(self.project.id, user_id)]\\n headers = {\\\"x-container-{}\\\".format(mode): \\\",\\\".join(new_acl)}\\n response = self.project._connection.post_container(self.name, headers)\\n self._metadata = None # needs to be refreshed\\n logger.info(\\\"User {} has been granted {} access to this container.\\\".format(username, mode))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ea5bae084623d04f9d181f3598f9e4d\",\n \"score\": \"0.44031814\",\n \"text\": \"def route(*args, **kwargs):\\n ensure_client = kwargs.get('ensure_client', True)\\n\\n def wrapper(func):\\n def client_or_redirect(*fargs, **fkwargs):\\n if ensure_client:\\n client = get_client()\\n session = get_session()\\n\\n if client is None:\\n session = get_session()\\n if session.get('endpoint', None) is not None:\\n # XXX we need to pass SSH too here\\n connect_to_circus(session['endpoint'])\\n else:\\n return redirect(url('connect'))\\n\\n return func(*fargs, **fkwargs)\\n return route_(*args, **kwargs)(client_or_redirect)\\n return wrapper\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f98f9e6d88cd1a6a2b23444563070569\",\n \"score\": \"0.44021404\",\n \"text\": \"def setup_cluster(request):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad411ae9b1b20848e548d3a077ada8dc\",\n \"score\": \"0.44007805\",\n \"text\": \"def _create_container(self):\\n return container.ContainerClient(self.config)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a2677266d1acd0241b7da4d7ed78d52\",\n \"score\": \"0.439879\",\n \"text\": \"def create_routes(app):\\n @app.route('/fashion', methods=[\\\"GET\\\"])\\n @app.route('/fashion/<page>', methods=[\\\"GET\\\"])\\n def get_fashion_tips(page=1):\\n \\\"\\\"\\\"\\n Gets a list of fashion items\\n \\\"\\\"\\\"\\n try:\\n # validates the page\\n validated_page = validate(page)\\n # gets the list of fashion items for the page\\n rows = get_fashion_items(validated_page, 20)\\n #converts rows to fashion items\\n fashion_items = convert_to_fashion_item(rows)\\n # randomizes the page of items\\n random_list = randomize_list(fashion_items)\\n except ValueError:\\n return \\\"BAD REQUEST\\\", codes.BAD_REQUEST\\n\\n resp = Response(json.dumps(random_list), status=codes.ok, mimetype='application/json')\\n\\n return resp\\n \\n @app.route('/fashion/image/<image_name>', methods=[\\\"GET\\\"])\\n def get_image(image_name):\\n \\\"\\\"\\\"\\n Gets an image for a fashion item\\n \\\"\\\"\\\"\\n # gets the path of the image in the images folder\\n image_path = os.path.join(os.path.dirname(IMAGES.__file__), image_name)\\n # verifies that the image exists\\n if not os.path.isfile(image_path):\\n return None, codes.not_found\\n # sends the image\\n return send_file(image_path), codes.ok\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a741cbf26e3face2d1adfb6c287da350\",\n \"score\": \"0.43980458\",\n \"text\": \"def main():\\n\\n in_dev = app.config.get('ENV') == 'development'\\n server_name = app.config.get('SERVER_NAME')\\n\\n ssl_context = None\\n if in_dev and server_name:\\n host_name = server_name.split(':')[0]\\n cert_file = './ssl/{}.pem'.format(host_name)\\n pkey_file = './ssl/{}-key.pem'.format(host_name)\\n\\n if os.path.isfile(cert_file) and os.path.isfile(pkey_file):\\n ssl_context = (cert_file, pkey_file)\\n\\n app.run(ssl_context=ssl_context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b539a3073d8bdd397cb921dfae8db9d6\",\n \"score\": \"0.43964863\",\n \"text\": \"def server(crypto, steghide_password):\\n\\tsteg = steganography.StegHide()\\n\\tweb = WebHostAndScraper()\\n\\n\\tprint(\\\"Running as Server\\\")\\n\\tfilename = input(\\\"What file do you want to serve?: \\\")\\n\\t# Encrypt the contents of the file before using steghide.\\n\\tfilename = crypto.encrypt_file(filename)\\n\\tpicture_list = steg.read_images()\\n\\t# array of image holding capacity\\n\\tfile_sizes = steg.steg_info(picture_list)\\n\\t# total room in pictures folder\\n\\ttotal_room = sum(file_sizes)\\n\\tencrypted_file_size = os.stat(filename).st_size\\n\\n\\t# Ensure we have enough space in our pictures to hide the encrypted file\\n\\tif total_room < encrypted_file_size:\\n\\t\\tprint(\\\"Not enough space\\\")\\n\\t\\texit(1)\\n\\n\\t# Now the file needs to be split and encoded into images\\n\\n\\tsize_sum = 0\\n\\tindex = 0\\n\\t# This will figure out how many pictures we have to use.\\n\\twhile size_sum < encrypted_file_size:\\n\\t\\tsize_sum += file_sizes[index]\\n\\t\\tindex += 1\\n\\n\\tsplit_array = file_sizes[:index]\\n\\tpicstoencode = picture_list[:index]\\n\\n\\t# If we can't fit our encrypted file into one picture we must split the encrypted file.\\n\\tif len(split_array) > 1:\\n\\t\\tsplit_files = steg.split_file(filename, split_array)\\n\\t\\tsteg.steg_hide_many(split_files, picstoencode, steghide_password)\\n\\telse:\\n\\t\\tsteg.steg_hide(filename, picstoencode[0], steghide_password)\\n\\n\\t# the actual process of encoding the files into the images\\n\\tweb.generate_index(picstoencode)\\n\\tweb.start_http()\\n\\texit(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc8ac694fccd190a5fbbec282ed41177\",\n \"score\": \"0.43881267\",\n \"text\": \"def run_server(config):\\n if config.has_option(\\\"server\\\", \\\"host\\\"):\\n host = config.get(\\\"server\\\", \\\"host\\\")\\n else:\\n host = \\\"localhost\\\"\\n\\n if config.has_option(\\\"server\\\", \\\"port\\\"):\\n port = config.getint(\\\"server\\\", \\\"port\\\")\\n else:\\n port = 5000\\n\\n if config.has_option(\\\"server\\\", \\\"reload\\\"):\\n use_reloader = config.getboolean(\\\"server\\\", \\\"reload\\\")\\n else:\\n use_reloader = False\\n\\n application = Slicer(config)\\n werkzeug.serving.run_simple(host, port, application, use_reloader=use_reloader)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64c237b650f44ff9fe6c43ad26ae26f8\",\n \"score\": \"0.43841392\",\n \"text\": \"def main():\\n parser = ArgumentParser()\\n parser = cf.add_config_args(parser)\\n args = parser.parse_args()\\n deploy_container(args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2f5b459657dc9c869797545f423346a\",\n \"score\": \"0.43813682\",\n \"text\": \"def register_api_endpoints():\\n api.add_resource(interviewbox_api.Login, '/api_login')\\n api.add_resource(interviewbox_api.CreateUser, '/api_create_user')\\n api.add_resource(interviewbox_api.GetUser, '/api_get_user')\\n api.add_resource(interviewbox_api.GetProfile, '/api_get_profile')\\n api.add_resource(interviewbox_api.SearchUser, '/api_search')\\n api.add_resource(interviewbox_api.UpdateAvailability, '/api_availability')\\n api.add_resource(interviewbox_api.Logout, '/api_logout')\\n api.add_resource(interviewbox_api.EditUser, '/api_edit_user')\\n api.add_resource(interviewbox_api.CreateInterview, '/api_create_interview')\\n api.add_resource(interviewbox_api.GetUpcomingInterviews, '/api_upcoming_interviews')\\n api.add_resource(interviewbox_api.GetPastInterviews, '/api_past_interviews')\\n api.add_resource(interviewbox_api.AddFeedbackToInterview, '/api_add_interview_feedback')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dca97263b2bbb76fc0a0907ebda6d4a\",\n \"score\": \"0.4378803\",\n \"text\": \"def _get_exposed_ports(debug_options):\\n if not debug_options:\\n return None\\n\\n if not debug_options.debug_ports:\\n return None\\n\\n # container port : host port\\n ports_map = {}\\n for port in debug_options.debug_ports:\\n ports_map[port] = port\\n\\n return ports_map\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44ca99c4b6741903a6a4ce12f7ce60bd\",\n \"score\": \"0.4378766\",\n \"text\": \"def test_api_challenge_get_solves_visibility_private():\\n app = create_ctfd()\\n with app.app_context():\\n gen_challenge(app.db)\\n register_user(app)\\n client = login_as_user(app)\\n r = client.get(\\\"/api/v1/challenges/1/solves\\\")\\n assert r.status_code == 200\\n set_config(\\\"challenge_visibility\\\", \\\"public\\\")\\n r = client.get(\\\"/api/v1/challenges/1/solves\\\")\\n assert r.status_code == 200\\n destroy_ctfd(app)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bad63b46d8ab893844477f3dbb816908\",\n \"score\": \"0.43753925\",\n \"text\": \"def _docker(self, method, *args, **kwargs):\\n m = getattr(self.client, method)\\n return m(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82ec40ed292d55f2447c56e945499c5a\",\n \"score\": \"0.4374429\",\n \"text\": \"def exposed(func):\\n func.exposed = True\\n return func\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcd633ab6664484b2ae4eee9a08c5f2e\",\n \"score\": \"0.43709645\",\n \"text\": \"async def host(self):\\r\\n runner = web.AppRunner(self)\\r\\n await runner.setup()\\r\\n site = web.TCPSite(runner, '0.0.0.0', 5556)\\r\\n await site.start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70abee0bd1f086f6cfd4667bca181974\",\n \"score\": \"0.43700346\",\n \"text\": \"def routers_in_debug(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73bb0b261299b9388e718b20de58ce20\",\n \"score\": \"0.43662733\",\n \"text\": \"def rack_url(docker_ip, docker_services) -> str: # type: ignore\\n url = \\\"http://{}\\\".format(docker_ip)\\n set_host(url)\\n # TODO(lb): For some reason, check_services always returns false.\\n # docker_services.wait_until_responsive(\\n # timeout=240.0, pause=0.1, check=lambda: check_services()\\n # )\\n docker_services.wait_until_responsive(\\n timeout=TIMEOUT, pause=PAUSE, check=lambda: check(url)\\n )\\n sleep(AFTER)\\n return url\",\n \"title\": \"\"\n },\n {\n \"docid\": \"564b29d2e0da4084f2cc6e3a5671b03c\",\n \"score\": \"0.43654513\",\n \"text\": \"def get_docker_api(host_ip):\\n\\t\\treturn docker.DockerClient(base_url='tcp://{}:2375'.format(host_ip))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff17ef864822b65fdef4bdb4886954be\",\n \"score\": \"0.43648928\",\n \"text\": \"def test_rewrite_function(self):\\n self.sh.rewrite(\\n {\\n 'CSP':{'default-src':['none']},\\n 'HPKP':{'pins':[{'sha256':'test123'}]}\\n }\\n )\\n @self.app.route('/')\\n @self.sh.wrapper()\\n def index(): return \\\"hi\\\"\\n with self.app.test_client() as c:\\n result = c.get('/')\\n self.assertEquals(result.headers.get('Content-Security-Policy'),\\\"default-src 'none'\\\")\\n self.assertEquals(result.headers.get('Public-Key-Pins'),\\\"pin-sha256=test123\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f760ce2815af6f164db9b59acaef7876\",\n \"score\": \"0.43640256\",\n \"text\": \"def inspect_container(container_id):\\n response = requests.get(f'{Global.BASE_URL}containers/{container_id}/json')\\n return response\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a81e3c081571a2042fd5647081532e0\",\n \"score\": \"0.43611273\",\n \"text\": \"def __call__(self, req):\\n return self._router\",\n \"title\": \"\"\n },\n {\n \"docid\": \"157140b1010dd5ca2c18921ca006e46c\",\n \"score\": \"0.4359512\",\n \"text\": \"def login(protocol, container):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95cd2327a03b35f02e9120618a5c3335\",\n \"score\": \"0.43464574\",\n \"text\": \"def create_app():\\n container = ApplicationContainer()\\n container.config.from_yaml('config.yml')\\n container.config.giphy.api_key.from_env('GIPHY_API_KEY')\\n\\n app: web.Application = container.app()\\n app.container = container\\n\\n app.add_routes([\\n web.get('/', container.index_view.as_view()),\\n ])\\n\\n return app\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993276,"cells":{"query_id":{"kind":"string","value":"c7f65ee6fe54c5b3c1cbe41880875758"},"query":{"kind":"string","value":"Parses the contour file name"},"positive_passages":{"kind":"list like","value":[{"docid":"326eb45f8cd05476a05715f7cff2d048","score":"0.69574046","text":"def parse_name(cls, fn_contour: str):\n\n matches = re.match('IM-[\\d]+-([\\d]+)-[a-z]contour-manual.txt', fn_contour)\n if matches is not None:\n slice_num = matches.groups()[0]\n slice_num = int(slice_num)\n return slice_num\n else:\n warn(f'unrecognized file:\\t{fn_contour}')\n return None","title":""}],"string":"[\n {\n \"docid\": \"326eb45f8cd05476a05715f7cff2d048\",\n \"score\": \"0.69574046\",\n \"text\": \"def parse_name(cls, fn_contour: str):\\n\\n matches = re.match('IM-[\\\\d]+-([\\\\d]+)-[a-z]contour-manual.txt', fn_contour)\\n if matches is not None:\\n slice_num = matches.groups()[0]\\n slice_num = int(slice_num)\\n return slice_num\\n else:\\n warn(f'unrecognized file:\\\\t{fn_contour}')\\n return None\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"0cb71a9653db83d5d3b061704811f9a0","score":"0.5983463","text":"def parse_calib_file_name(fname) :\n fnfields = os.path.splitext(fname) # '123-end.data' -> ('123-end', 'data') \n if len(fnfields) != 2 : return None # check that file name like '123-end.data' has splited extension\n if fnfields[1] != '.data': return None # check extension\n fnparts = fnfields[0].split('-') # '123-end' -> ('123', 'end')\n if len(fnparts) != 2 : return None # check that file name has two parts\n #print('XXX parts of the file name:', fnparts[0], fnparts[1], fnfields[1])\n return fnparts[0], fnparts[1], fnfields[1]","title":""},{"docid":"bf6c61be92994d04d75cb700880109e3","score":"0.5907608","text":"def _read_fnames(self, fname):\n with open(fname, 'r') as f:\n lines = f.readlines()\n\n # clip_name, img_path, la_path\n fnames = []\n for line in lines:\n im, la = line.strip().split(' ')\n clip = im.split('/')[-1].split('_')[0]\n fnames.append((clip, im, la)) # as order of batch-key\n return fnames","title":""},{"docid":"b9d7baaa032ca864f0bb5b53a0864f0c","score":"0.5840292","text":"def parse_file_name(self, file_name, dataset_info):\n if 'data' in file_name:\n self.set_data_name(file_name)\n elif re.match(r'\\d{6}', file_name):\n self.set_mc_name(file_name, dataset_info)\n else:\n _logger.debug(\"No dedicated parsing found. Assume MC and run simplified\")\n self.set_mc_name(file_name, dataset_info)","title":""},{"docid":"17cd49c62622a6e1f671f52c103954f6","score":"0.57834107","text":"def contour_load(self, filename):\n if filename:\n self.set(\"contour loadlevels {0:s}\".format(str(filename)))\n else:\n warnings.warn(\"No filename provided for contours\")","title":""},{"docid":"4979e4d87e5f1358f38085e643e21052","score":"0.56500024","text":"def parse(self, filename):","title":""},{"docid":"1796de5acfe7c9d5480ed1566354d9ac","score":"0.5643601","text":"def __process_filename(self, filename : str):\n for r in self.filename_regex:\n res = r.search(filename) # strip filename\n\n if res:\n break\n\n stripped_filename = filename.split(res.group(0))[0]\n\n out = {\"name\": self.dotremove_regex.sub(\" \", stripped_filename)}\n\n try:\n year = self.year_regex.findall(filename)[-1]\n out[\"year\"] = year\n except IndexError: \n out[\"year\"] = \"\"\n print(\"could not find year data\")\n\n if self.type == \"tv\":\n try: \n episode_data = self.episode_regex.findall(filename)[-1]\n\n out[\"season\"] = int(episode_data[1:3])\n out[\"episode\"] = int(episode_data[4:6])\n\n out[\"name\"] = out[\"name\"].split(episode_data)[0].rstrip()\n except IndexError:\n print(\"could not find episode data\")\n\n return out","title":""},{"docid":"46f25d8e44cd89029970b118e5b43e32","score":"0.56242645","text":"def extract_name(filename):\n\n name, _ = split_ext(filename)\n return name\n # n, ext = split_ext(filename)\n # if ext == 'cols':\n # return f'/{n}'\n # else:\n # return n\n # bname = os.path.basename(filename)\n # name = '.'.join(bname.split('.')[0:-1])\n # return name","title":""},{"docid":"80ca8dfb1cf37ff375eaae3afcda529c","score":"0.5601794","text":"def readSceneName(filePath):\n \n listName = []\n listNameFinal = []\n counter = len(filePath)-1\n counter02 = 1\n while counter >= 0 and counter02 <= 2:\n character = filePath[counter]\n listName.append(character)\n counter -= 1\n if character == '\"':\n counter02 += 1\n listName.reverse()\n for char in listName:\n if char != '\"':\n listNameFinal.append(char)\n\n name = \"\".join(listNameFinal)\n\n return name","title":""},{"docid":"1395cef16a5cfa7e0e1d53e015f197bc","score":"0.5583756","text":"def _parse_contour_file_(self, filename):\n coords_lst = []\n\n with open(filename, 'r') as infile:\n for line in infile:\n coords = line.strip().split()\n\n x_coord = float(coords[0])\n y_coord = float(coords[1])\n coords_lst.append((x_coord, y_coord))\n\n return coords_lst","title":""},{"docid":"ea2f2dbf00f56681e18239f3ccae0a60","score":"0.55354375","text":"def parse_contour_file(filename):\n\n coords_lst = []\n\n with open(filename, 'r') as infile:\n for line in infile:\n coords = line.strip().split()\n\n x_coord = float(coords[0])\n y_coord = float(coords[1])\n coords_lst.append((x_coord, y_coord))\n return coords_lst","title":""},{"docid":"91725eb7608635139edd18e5ac490b12","score":"0.54807264","text":"def ParseFileName(file_name):\n\n result = {}\n file_name = os.path.basename(file_name)\n #import pdb; pdb.set_trace()\n if config.global_regions:\n local_full_iregion_pattern = full_global_iregion_pattern\n local_full_pcregion_pattern = full_global_pcregion_pattern\n local_pp_iregion_pattern = pp_global_iregion_pattern\n local_pp_pcregion_pattern = pp_global_pcregion_pattern\n else:\n local_full_iregion_pattern = full_iregion_pattern\n local_full_pcregion_pattern = full_pcregion_pattern\n local_pp_iregion_pattern = pp_iregion_pattern\n local_pp_pcregion_pattern = pp_pcregion_pattern\n if re.search(local_full_iregion_pattern, file_name):\n result = ParseFullFileName(file_name, local_full_iregion_pattern)\n elif re.search(local_full_pcregion_pattern, file_name):\n result = ParseFullFileName(file_name, local_full_pcregion_pattern)\n elif re.search(local_pp_iregion_pattern, file_name):\n result = ParsePartialFileNameIregion(file_name)\n elif re.search(pp_pcregion_pattern, file_name):\n result = ParsePartialFileNamePCregion(file_name)\n else:\n msg.PrintAndExit('method util.ParseFileName() encountered a file name\\n' 'which does not meet requirements for parsing a region pinball name:\\n ' + \\\n file_name + '\\nFile name must conform to the region pinball naming convention.')\n\n return result","title":""},{"docid":"6a2099ba87ce7416fb29d368d8287f0e","score":"0.545208","text":"def filename(x):\n value = -1\n counter = -1\n name = x\n lenName = len(name)\n while abs(counter) < lenName:\n if name[counter] == \".\":\n value = counter\n break\n else:\n counter -= 1\n if abs(counter) == lenName:\n print \"there are no '.' in this file name\"\n return None\n #print counter\n return (name[:counter],name[lenName + counter :])\n # add one to the counter if you dont want to get the \".\"","title":""},{"docid":"e94f764d9c3abbe5b503be5e5891240d","score":"0.5443341","text":"def parse_name(cls, fn_dcm):\n\n matches = re.match('([\\d]+).dcm', fn_dcm)\n if matches is not None:\n slice_num = matches.groups()[0]\n slice_num = int(slice_num)\n return slice_num\n else:\n warn(f'unrecognized file:\\t{fn_dcm}')\n return None","title":""},{"docid":"9018a7f0df0c8246d9d59e779a1605e6","score":"0.5432766","text":"def parseFilename(cls, path):\n dirName, fileName = os.path.split(path)\n matches = re.search(cls.filenameRegex, fileName)\n if not matches:\n raise RuntimeError(\"Unable to parse filename: %s\" % (fileName,))\n return CalibIdentity.fromDict(matches.groupdict())","title":""},{"docid":"a0b5be8154f1b8b5e8c331feb5ea02e3","score":"0.53879416","text":"def _ExtractSourceName(self, gcno_summary, file_name):\n src_file_path = None\n for key in gcno_summary.functions:\n src_file_path = gcno_summary.functions[key].src_file_name\n src_parts = src_file_path.rsplit(\".\", 1)\n src_file_name = src_parts[0]\n src_extension = src_parts[1] if len(src_parts) > 1 else None\n if src_extension not in [\"c\", \"cpp\", \"cc\"]:\n logging.warn(\"Found unsupported file type: %s\", src_file_path)\n continue\n if src_file_name.endswith(file_name):\n logging.info(\"Coverage source file: %s\", src_file_path)\n break\n return src_file_path","title":""},{"docid":"edfd56103bb4d2a1e11f3215ff117a23","score":"0.53473526","text":"def get_imagename(path):\n dot = re.findall('[^\\/]*\\.', path)\n mesh = re.findall('[^\\/]*\\#', path)\n return dot[0][:-1] if len(mesh)==0 else mesh[0][:-1]","title":""},{"docid":"3c4b9ae7fb847a5f7738ad4d41fd317b","score":"0.53411204","text":"def _parse_filename(filename):\n sections = filename.split('.')\n return _casify(sections[0]), _casify(sections[1])","title":""},{"docid":"3aab172b3371b5b8f0627913775862cc","score":"0.53013694","text":"def decompose_newstyle_name(filename):\n path, parts, ext = _get_fields(filename)\n observatory = parts[0]\n serial = list_get(parts, 3, \"\")\n\n if ext == \".pmap\":\n assert len(parts) in [1,2], \"Invalid .pmap filename \" + repr(filename)\n instrument, filekind = \"\", \"\"\n serial = list_get(parts, 1, \"\")\n elif ext == \".imap\":\n assert len(parts) in [2,3], \"Invalid .imap filename \" + repr(filename)\n instrument = parts[1]\n filekind = \"\"\n serial = list_get(parts, 2, \"\")\n else:\n assert len(parts) in [3,4], \"Invalid filename \" + repr(filename)\n instrument = parts[1]\n filekind = parts[2]\n serial = list_get(parts, 3, \"\")\n\n assert instrument in INSTRUMENTS+[\"\"], \"Invalid instrument \" + \\\n repr(instrument) + \" in name \" + repr(filename)\n assert filekind in FILEKINDS+[\"\"], \"Invalid filekind \" + \\\n repr(filekind) + \" in name \" + repr(filename)\n assert re.match(\"\\d*\", serial), \"Invalid id field \" + \\\n repr(id) + \" in name \" + repr(filename)\n # extension may vary for upload temporary files.\n\n return path, observatory, instrument, filekind, serial, ext","title":""},{"docid":"3e88bfaa29e4110b3af096755d3319e5","score":"0.5297847","text":"def _parse_file(self, name):\n if '/' in name:\n bits = name.split('/')\n return (os.path.join(*bits[0:-1]), bits[-1])\n else:\n return None, name","title":""},{"docid":"cf38e15e55db9a0a13ea2f82e89f627d","score":"0.52879745","text":"def get_wind_comps(path, filename):\n with open(path + filename) as file:\n for i, line in enumerate(file):\n if i == 5:\n wind_comp1 = line.split()[-4][-1].lower()\n wind_comp2 = line.split()[-2][-1].lower()\n \n return wind_comp1, wind_comp2","title":""},{"docid":"8b6e8434ae3627ce4d57dd54870850e6","score":"0.52835125","text":"def deconstruct_file_name(name):\n return os.path.splitext(os.path.split(name)[1])[0].split('_')","title":""},{"docid":"d27ee7d665e0aad9bfbc7139aca17438","score":"0.52405745","text":"def parse_fname(base_path, fname):\n base_path += '/MergedReflectivityQC_'\n\n scan_re = re.compile(r'_(\\d{2}.\\d{2})_')\n match = scan_re.search(fname)\n\n if (match is not None):\n base_path += match.group(1)\n else:\n print('Error parsing filename')\n sys.exit(0)\n\n abs_path = join(base_path, fname)\n\n return abs_path","title":""},{"docid":"9f97faf54b07c1b691507c8dd61069cd","score":"0.52173734","text":"def filename_parse(filename):\n\n # Since SV-P is misspelled as SV-P_, catch that\n filename = filename.replace(\"SV-P_\", \"SV-P\")\n # Same with 6-31+G-d,p\n filename = filename.replace(\"6-31+G-d,p_\", \"6-31+G-d,p\")\n\n tokens = filename.split(\"/\")[-1].split(\"_\")\n mol = tokens[0]\n func = tokens[1]\n basis = tokens[2].split(\".\")[0]\n name = func + \"/\" + basis\n if func in ['rDCSD', 'df-lrmp2']:\n unrestricted = False\n #elif func in ['luCCSD', 'uCCSD', 'uDCSD']:\n elif func in ['uCCSD', 'uDCSD']:\n unrestricted = True\n else:\n unrestricted = (len(tokens) == 4)\n name = \"u-\" * unrestricted + name\n\n if func in ['rDCSD', 'uDCSD']:\n func = 'DCSD'\n\n return mol, func, basis, unrestricted, name","title":""},{"docid":"ff7843717cdf249fd542efad2cc82fcb","score":"0.51842237","text":"def generate_name_cvs_file(self,file_name = \"\"):\n get_time = time.strftime('%H%M%S%m%d%Y')\n if(file_name == \"\"):\n file_name = \"sudoku_\"+get_time+\".cvs\"\n else:\n file_name = name_of_file + \"_\"\n file_name += get_time+\".cvs\"\n return file_name","title":""},{"docid":"683d02585df16c194853d3e9686e15cb","score":"0.5158009","text":"def parse_file(self, filename): # [CO] this is where we would put the sequential calculation","title":""},{"docid":"4d0e6809f33d4f982ce02dbd6f6ee9b8","score":"0.5154683","text":"def separate(self, abs_filename):\n pass","title":""},{"docid":"2ccde7f47e2628b4d4086a1124843867","score":"0.5151052","text":"def __ConvertNameStile(self, name):\n l = name.split('_')\n fname = ''\n for i in l:\n fname += i[0].upper() + i[1:].lower()\n return fname","title":""},{"docid":"96a42f8ac02225159a5cccf21a9acbfa","score":"0.5131942","text":"def load_contact_points(path_name, file_name, dim):\n data = load_file(path_name+file_name)\n CP = data['CP']\n x = CP[:,0]\n y = CP[:,1]\n if dim == 2:\n return x, y\n else:\n z = CP[:,3]\n return x, y, z","title":""},{"docid":"0df570352d02ecea6e29ebd59d93b91f","score":"0.51295","text":"def _parse_filename(filename):\n filename_base, ext = os.path.splitext(filename)\n if '.' in filename_base:\n # Some images have double filename extensions.\n filename_base, ext = os.path.splitext(filename_base)\n if ext != \".jpg\":\n return None\n person_id, cam_seq, frame_idx, detection_idx = filename_base.split('_')\n return int(person_id), int(cam_seq[1]), filename_base, ext","title":""},{"docid":"ca0eeb41e98ec9eebd821ab3a4c44092","score":"0.5120978","text":"def parse_name(self, _string):\n\n path = support.normalize_path(_string)\n if os.path.isdir(path):\n # final part of directory\n return self._get_final_dir(path)\n elif os.path.isfile(path):\n # file name, without path or extension\n return self._get_file_no_ext(path)\n else:\n raise errors.SublpException(\n \"{0} does not know how to find project name for '{1}'.\",\n type(self).__name__, path\n )","title":""},{"docid":"af0ced4c2ead75cbedbf3686e64f17f3","score":"0.511001","text":"def parse(self, filename):\n grid = {}\n loc_of_door = {}\n loc_of_key = {}\n location = complex(0, 0)\n\n with open(filename) as f:\n for line in f:\n for char in line.strip():\n # print(char)\n grid[location] = char\n\n if char in string.ascii_uppercase:\n loc_of_door[char] = location\n if char in string.ascii_lowercase:\n loc_of_key[char] = location\n if char == \"@\":\n self.hero_locs.append(location)\n location += complex(1, 0)\n location += complex(0, 1)\n location = complex(0, location.imag)\n\n self.grid = grid\n self.loc_of_door = loc_of_door\n self.loc_of_key = loc_of_key\n self.door_of_loc = dict(map(reversed, self.loc_of_door.items()))\n self.all_keys = frozenset(loc_of_key.keys())","title":""},{"docid":"042045193cb7e3ba44d0ee31be7becbb","score":"0.5099243","text":"def file_name(self,name):\n return self._file[name]","title":""},{"docid":"54041cd59166ca8e8fe8977d81682df9","score":"0.5095792","text":"def get_cote(filename):\n full_cote = re.search(\"\\d+[a-z]*\",filename).group()\n cote_num = str(int(re.search(\"^\\d+\",full_cote).group()))\n cote_ext_match = re.search(\"[a-z]+$\",full_cote)\n if cote_ext_match:\n cote_ext = cote_ext_match.group()\n else:\n cote_ext = \"\"\n \n return cote_num+cote_ext","title":""},{"docid":"cd0aa12d222f47bc8cf2a1645386cef1","score":"0.50918114","text":"def ParseFullFileName(file_name, myfull_pattern):\n\n # Remove any directories from the name.\n #\n # import pdb; pdb.set_trace()\n file_name = os.path.basename(file_name)\n\n # Make sure the file name matches the full file name pattern.\n #\n # import pdb; pdb.set_trace()\n if not re.search(myfull_pattern, file_name):\n msg.PrintAndExit('method util.ParseFullFileName() encountered a file name\\n' 'which does not meet requirements for parsing a region pinball name:\\n ' + \\\n file_name + '\\nFile name must conform to the region pinball naming convention.')\n\n # Directory containing the fields which were successfull parsed.\n #\n result = {}\n\n # Define a lambda function to print a standard error msg.\n #\n err_msg = lambda fname, string: msg.PrintAndExit(\n 'method util.ParseFullFileName() encountered '\n 'an error parsing \\'' + string + '\\' in \\nfile name: ' + fname)\n\n # Get the first 'user' part of the file name and parse it. The example\n # given above produces this list:\n #\n # ['omnetpp.p10000-s10_57015']\n #\n # import pdb; pdb.set_trace()\n user_part = re.findall(user_pattern, file_name)\n field = user_part[0].split('.')\n tmp = field[1].split('_')\n result['program_name'] = field[0] # No error check for this field\n result['input_name'] = tmp[0] # No error check for this field\n\n # Old code to get the num_proc. Curently not used. MUST be updated to use it\n # again here... Also, code for PID must be modified if/when this code is used.\n #\n # Get the number of processes from the 1st 'user_part' string. It\n # contains a string like '.X.', where X is num_proc.\n #\n # import pdb; pdb.set_trace()\n # tmp = re.findall('[0-9]*\\.[0-9]+\\.', field[1])\n # if len(tmp) > 0:\n # num_proc = tmp[0].replace('.', '')\n # try:\n # num_proc = int(num_proc)\n # except ValueError:\n # err_msg(file_name, 'num_proc')\n # result['num_proc'] = num_proc\n\n # Get the PID\n #\n try:\n pid = int(tmp[1])\n except ValueError:\n err_msg(file_name, 'pid')\n result['pid'] = pid\n\n # Get the fields from the PinPlay part of the file name.\n #\n # import pdb; pdb.set_trace()\n if config.global_regions:\n local_pp_iregion_pattern = pp_global_iregion_pattern\n local_pp_pcregion_pattern = pp_global_pcregion_pattern\n else:\n local_pp_iregion_pattern = pp_iregion_pattern\n local_pp_pcregion_pattern = pp_pcregion_pattern\n if re.search(local_pp_iregion_pattern, file_name):\n partial_result = ParsePartialFileNameIregion(file_name)\n elif re.search(local_pp_pcregion_pattern, file_name):\n partial_result = ParsePartialFileNamePCregion(file_name)\n\n # Return the fields from both parts of the file name.\n #\n return dict(list(result.items()) + list(partial_result.items()))","title":""},{"docid":"f44becae6a6d1fb95e6d8fb73463b739","score":"0.50851387","text":"def analyze_filename(fname):\n m = re.match(\"^(?P<track>[0-9]+)[\\s-]+(?P<title>.+)\\.flac$\", fname)\n if m != None:\n return m.groupdict() ## return {'title', 'track'}\n m = re.match(\"^(?P<title>.*)\\.flac$\", fname)\n if m != None:\n d = m.groupdict()\n d.update({'track':''})\n return d\n return None","title":""},{"docid":"9d0694e6524d37faf130b255b2c5255b","score":"0.5079833","text":"def _get_filename(file):\n return file.name","title":""},{"docid":"73de3d7d98497c243039129caf176ed7","score":"0.5073598","text":"def _name(user_filename):\n # crude but good enough for now.\n m = re.search(r\"([^:/\\\\]+)\\.\\w+$\", user_filename)\n return m.group(1) if m else user_filename","title":""},{"docid":"88e5298c10d04ce651caef64d2602625","score":"0.50733334","text":"def fname_geo_and_nda_for_tname(tname):\n fname_geo, fname_nda = None, None\n if tname == '1':\n cdir = '/reg/d/psdm/MFX/mfx11116/calib'\n #fname_geo = '%s/%s' % (cdir, 'CsPad::CalibV1/MfxEndstation.0:Cspad.0/geometry/566-end.data')\n fname_geo = 'geo-cspad-mfx.txt'\n fname_nda = 'nda-mfx11116-r0625-e026505-MfxEndstation-0-Cspad-0-max.txt'\n #shape = (32,185,388)\n elif tname == '2':\n #fname_geo = '%s/%s' % (cdir, 'Jungfrau::CalibV1/MfxEndstation.0:Jungfrau.0/geometry/0-end.data')\n fname_geo = 'geo-jf-mfx.txt'\n fname_nda = 'nda-mfx11116-r0624-e005365-MfxEndstation-0-Jungfrau-0-max.txt'\n #shape = (2,512,1024)\n elif tname == '3':\n d = '/reg/g/psdm/detector/alignment/epix10ka2m/calib-xxx-epix10ka2m.1-2020-02-25'\n fname_geo = '%s/geometry-epix10ka2m-test.txt' % d\n fname_nda = '%s/nda-mfxc00118-r41-averaged.txt' % d\n #shape = (16,352,384) \n else:\n msg = 'Not-recognized test name: %s' % tname\n sys.exit('End of test %s' % tname)\n return fname_geo, fname_nda","title":""},{"docid":"8790b0d6a098575bb17225389a363473","score":"0.5071821","text":"def get_facename(filename):\n filename_without_extension = filename.split(\".\")[0]\n face_name = filename_without_extension.split(\"_\")[0]\n return face_name","title":""},{"docid":"375d5fd99f6d7c846e4a9cebe8615d31","score":"0.50610256","text":"def extract_structure_data_from_raystation_text_files(self, structureName):\r\n\r\n \r\n\r\n print 'Running extract_structure_data_from_raystation_text_files() ...'\r\n\r\n \r\n\r\n dataFile = open('RayStationExport/' + self.patientID + '/' + structureName + '.txt', 'r')\r\n\r\n dataReader = csv.reader(dataFile)\r\n\r\n \r\n\r\n for contourNumber, contour in enumerate(dataReader):\r\n\r\n contour = np.asarray(contour, dtype = np.float64)*10\r\n\r\n numContours = contourNumber + 1\r\n\r\n \r\n\r\n contourZCoords = np.zeros((numContours))\r\n\r\n numContourPoints = np.zeros((numContours))\r\n\r\n structure3d = []\r\n\r\n\r\n\r\n dataFile = open('RayStationExport/' + self.patientID + '/' + structureName + '.txt', 'r')\r\n\r\n dataReader = csv.reader(dataFile)\r\n\r\n\r\n\r\n for n, contour in enumerate(dataReader):\r\n\r\n # Convert from cm to mm when reading in contours\r\n\r\n contour = np.asarray(contour, dtype = np.float64)*10\r\n\r\n numContourPoints[n] = contour.size/3.0\r\n\r\n contourZCoords[n] = contour[2]\r\n\r\n structure3d = np.append(structure3d, contour)\r\n\r\n \r\n\r\n return (numContours, structure3d, contourZCoords, numContourPoints)","title":""},{"docid":"908d2fe75f53d38afd4a46fb09bc7592","score":"0.5053911","text":"def parse_filename(self):\n items = self.name.split('_')\n self.mod = items[0]\n keys = ['lagei', 'lagef', 'logz', 'dlogz']\n for key, val in zip(keys, items[1:]):\n if 'logz' in key:\n val = 0.02 * 10 ** (float(val) - 4.)\n self.__setattr__(key, float(val))","title":""},{"docid":"efa8f0f90f872180b2bb69e99b90e816","score":"0.5042871","text":"def extract_metadata(filename):\n \n a = re.compile(\"ra(.*)dec(.*)time(.*).dat\")\n filepath = filename.split('/')\n\n s_ra = float(a.search(filepath[-1]).group(1))\n s_dec = float(a.search(filepath[-1]).group(2))\n s_time = a.search(filepath[-1]).group(3)\n \n \n time = Time(s_time, format='isot', scale='utc')\n position = SkyCoord(s_ra*u.deg, s_dec*u.deg, frame=ICRS, obstime = time)\n return position, time","title":""},{"docid":"937c83258259b7423e5a7eed6282b686","score":"0.50391555","text":"def getfileName(file):\n\n # Split by backslash, get the last index\n file = file.rsplit('/', 1)[-1]\n # Remove )\" which is found in Pyfadix files\n file = re.sub('[\")]', '', file)\n return file","title":""},{"docid":"86cffa698f424c12fce859898faa9ffe","score":"0.50298417","text":"def _parse_name(self, name):\n\n return name.split(\"/\")","title":""},{"docid":"1ad38e75cdb445f5dafe9feb256d9084","score":"0.5022109","text":"def __init__(self, file_path: str) -> None:\n self.file_path = file_path\n parts: List[str] = file_path.split('/')\n self.mun_name = Mun.parse_mun_name(parts.pop(-1))\n self.state = parts.pop(-1).upper()","title":""},{"docid":"af8cde09ca8b17d1ad66da9ac0c49176","score":"0.5020684","text":"def _decode_file_name(filename: str) -> MissionFileNameInfo:\n\n\tfileList = filename.split(\".\")\n\n\t# Check if the file name ends with \".pbo\"\n\tif fileList[-1:][0].casefold() != \"pbo\":\n\t\traise Exception(\"Missions can only be submitted in .pbo format.\")\n\n\t# Check if there are any erroneuous periods in the file name\n\tif len(fileList) > 3:\n\t\traise Exception(\"File names can only have periods to denote the map and file extension.\")\n\tif len(fileList) < 3:\n\t\traise Exception(\"File name appears to be missing the map definition.\")\n\n\t# Get our map extension\n\tmapName = fileList[-2:][0].casefold()\n\n\t# Split up our map name\n\tnameList = fileList[0].split(\"_\")\n\n\t# Check if the mission is a test mission\n\tif nameList[0].casefold() == \"test\":\n\t\tdel nameList[0]\n\n\tif len(nameList) == 0:\n\t\traise Exception(\"File name appears to be missing the gametype and playercount\")\n\n\t# Check the mission type\n\tgameType = nameList[0].casefold()\n\tif not (gameType in VALID_GAMETYPES):\n\t\traise Exception(f\"`{gameType}` is not a valid mission type!\")\n\n\t# Grab the player count\n\ttry:\n\t\tplayerCount = int(nameList[1])\n\texcept:\n\t\traise Exception(\"I could not determine the player count in your mission.\")\n\n\tmissionInfo: MissionFileNameInfo = {\"gameType\": gameType, \"map\": mapName, \"playerCount\": playerCount}\n\treturn missionInfo","title":""},{"docid":"f714bdc79023b0b13787ae5ab5ed57c6","score":"0.50139105","text":"def buildCoeffsName(self):\n indx = self.name.rfind('.')\n return self.name[:indx]+'_coeffs'+self.chip+'.dat'","title":""},{"docid":"c6f155e16ea469d8a9307ecc25e3dd73","score":"0.50112516","text":"def FileChecker(self, name):\n if name[-4:] == \".csv\":\n self.fileName = name\n else:\n raise Exception(\"Must provide a .csv file\")","title":""},{"docid":"86cf71c0212f148fe3949f1b9ee42823","score":"0.50028986","text":"def get_bounds_file_name ( logger, bathyspace_dir, im_id ) :\n try :\n ascii_file_bounds = bathyspace_dir + \"\\\\\" + \"bounds_\" + str(im_id) + BathyExecutablesLib.FILE_EXT_ASCII\n return ascii_file_bounds\n except Exception, err:\n logger.critical( \"Get file name failed:ERROR: %s\\n\" % str(err))\n raise","title":""},{"docid":"c3161d211868c4fc4d334860d97c4557","score":"0.50023735","text":"def _parse_h36m_imgname(imgname):\n subj, rest = osp.basename(imgname).split('_', 1)\n action, rest = rest.split('.', 1)\n camera, rest = rest.split('_', 1)\n return subj, action, camera","title":""},{"docid":"e4caeb3e9c7706720c20eb9dc640e0b4","score":"0.49990866","text":"def get_mesh_filename(self):\n if Parallel.is_parallel():\n return libspud.get_option('/geometry/mesh::CoordinateMesh/from_file/file_name')+'_%d.msh'%Parallel.get_rank()\n #otherwise\n return libspud.get_option('/geometry/mesh::CoordinateMesh/from_file/file_name')+'.msh'","title":""},{"docid":"b5c9fa5e028a8d9a51c2d4cf10695612","score":"0.4990337","text":"def parse_filename(pathname):\n if base.SEPERATOR in pathname:\n cell, extra, extension = base.parse_filename(pathname)\n else:\n cell = None\n extra = pathname\n extension = ''\n\n if extension not in ('', 'lib', 'cell.lib', 'cell.json', 'wrap.lib', 'wrap.json'):\n raise ValueError('Not possible to extract corners from: {!r}'.format(extension))\n\n if not extra:\n extra = cell.name\n cell = None\n\n # FIXME: Hack?\n extra = extra.replace(\"lpflow_\",\"\")\n extra = extra.replace(\"udb_\",\"\")\n\n kw = {}\n kw['flags'] = []\n kw['volts'] = []\n kw['temps'] = []\n\n bits = extra.split(\"_\")\n random = []\n while len(bits) > 0:\n b = bits.pop(0)\n try:\n kw['corner'] = CornerType.parse(b)\n break\n except TypeError as e:\n random.append(b)\n\n while len(bits) > 0:\n b = bits.pop(0)\n\n if VOLTS_REGEX.match(b):\n assert b.endswith('V'), b\n kw['volts'].append(float(b[:-1].replace('p', '.')))\n elif TEMP_REGEX.match(b):\n assert b.endswith('C'), b\n kw['temps'].append(int(b[:-1].replace('n', '-')))\n elif CORNER_TYPE_REGEX.match(b):\n # FIXME: These are horrible hacks that should be removed.\n assert len(b) == 2, b\n assert b[0] == b[1], b\n assert 'corner' in kw, kw['corners']\n assert len(kw['corner']) == 2, kw['corners']\n assert kw['corner'][0] == kw['corner'][1], kw['corners']\n other_corner = CornerType.parse(b)\n assert len(other_corner) == 2, other_corner\n assert other_corner[0] == other_corner[1], other_corner\n kw['corner'][1] = other_corner[0]\n else:\n kw['flags'].append(CornerFlag.parse(b))\n\n for k, v in kw.items():\n kw[k] = tuple(v)\n\n if not kw['flags']:\n del kw['flags']\n\n if 'corner' not in kw:\n raise TypeError('Invalid corner value: '+extra)\n\n return Corner(**kw), random","title":""},{"docid":"324c550611fffd09d17bf11717e116d1","score":"0.49867225","text":"def get_file_name(input_file_name):\n\n input_file_name = input_file_name.split(\"\\\\\")\n input_file_name = input_file_name[-1]\n input_file_name = input_file_name.split(\"/\")\n file_name = input_file_name[-1]\n return (file_name)","title":""},{"docid":"76ef1de2da76f95f8effbd7bca5dff0f","score":"0.4985296","text":"def get_person_parsed_path(self, index):\n im_name = self.get_person_image_name(index)\n parse_name = im_name.replace(\".jpg\", \".png\")\n parsed_path = osp.join(self.data_path, \"image-parse\", parse_name)\n return parsed_path","title":""},{"docid":"e77856657081fd5c37d94c9f366cef56","score":"0.49828887","text":"def filename(file):\n\treturn os.path.basename(file)","title":""},{"docid":"76cb24f77cf095ab29661972ec14f45f","score":"0.49674705","text":"def getPoint(imgName):\r\n name = imgName.split('\\\\')[-1]\r\n msg = name.split('.')[0].split('_')\r\n if len(msg) == 4:\r\n return msg[-1]\r\n elif len(msg) == 5:\r\n return msg[-2]","title":""},{"docid":"7785b4b3a15caf48c2576f17050433a6","score":"0.4965356","text":"def get_unique_name(path):\n mesh = re.findall(r'.*GT\\d{3}', path)[0]\n return mesh","title":""},{"docid":"6f2f7c951263e35f1226f07991635418","score":"0.49644053","text":"def process_file_name(self, file_path_object):\n renamed = file_path_object.name.rstrip('recision.dat').lower()\n return renamed","title":""},{"docid":"b23bde3e540f157c2ecefd63c50ae49f","score":"0.4959844","text":"def extract_file_name(self):\n # The directory is generated from the path - filename.\n filename = os.path.basename(self.path)\n file_name = filename\n path = self.path.replace(filename, '')\n return file_name, path","title":""},{"docid":"c5f1ac46f23344af0732d199a87e605e","score":"0.49537307","text":"def open_file_name_dialog(self):\n\n options = QFileDialog.Options()\n options |= QFileDialog.DontUseNativeDialog\n file_path, _ = QFileDialog.getOpenFileName(self,\n \"Choose a data file\", \"\", \"CSV Files (*.csv)\", options=options)\n if file_path:\n # Storing the new data file path\n self.current_data_file = file_path\n # Extracting only the name of the file, without the whole path\n file_name = os.path.basename(file_path)\n print(file_name)\n\n # Displaying this into the input to provide visible feedback\n self.data_file_input.setText(file_name)","title":""},{"docid":"80248da38f25878095f66b61c96b26ff","score":"0.49516958","text":"def parse(self, filename):\n parsed = parse(self.conv_str, os.path.basename(filename))\n if parsed:\n return self.parse_attrs(parsed.named)\n else:\n print('parsing not successful for {}'.format(filename))\n return None","title":""},{"docid":"726878e4790efea6f469866e897df767","score":"0.49469915","text":"def get_file_namepath():\n\troot = tk.Tk()\n\troot.withdraw()\n\tfilepath = askopenfilename()\n\tname = filepath.split('/').pop()\n\tpath = filepath.replace(name, '')\n\treturn name, path, filepath","title":""},{"docid":"e221a4d158b535987aa05c2a84d3ee45","score":"0.49409786","text":"def sceneName(self):\n\n sFileName = mc.file(query = True, sn = True, shortName = True)\n\n if sFileName:\n sFileName = sFileName.rsplit(\".\", 1)[0]\n\n else:\n sFileName = \"untitled\"\n\n return sFileName","title":""},{"docid":"95335b9c19a58d773c75a4d63264f539","score":"0.49339423","text":"def _get_parts_from_file_name(file_name):\n simple_file_name = file_name.replace('.data', '')\n parts = simple_file_name.split(':')\n key = parts[0]\n floating_seconds = float(parts[1])\n serial_number = int(parts[2])\n return key, (floating_seconds, serial_number)","title":""},{"docid":"d5ed54533862d839eb4f9c358e70a694","score":"0.49332106","text":"def set_name(self, filepath):\n try: \n self.name = os.path.basename(filepath).split('.')[0]\n return self.name\n except:\n return None","title":""},{"docid":"134b8145676db784c8e100db9a6dbf22","score":"0.4928679","text":"def _parse_mesh_data(self, path) -> None:\n self.boundary = self.parse_mesh_file(os.path.join(path, 'boundary'), self.parse_boundary_content)\n self.points = self.parse_mesh_file(os.path.join(path, 'points'), self.parse_points_content)\n self.faces = self.parse_mesh_file(os.path.join(path, 'faces'), self.parse_faces_content)\n self.owner = self.parse_mesh_file(os.path.join(path, 'owner'), self.parse_owner_neighbour_content)\n self.neighbour = self.parse_mesh_file(os.path.join(path, 'neighbour'), self.parse_owner_neighbour_content)","title":""},{"docid":"5eb766fdf37fcec658b495bf25cf0738","score":"0.49274397","text":"def get_name(fname):\n with open(fname, encoding=\"utf-8\") as tmp:\n return tmp.readline()[1:].strip()","title":""},{"docid":"ba90a5ca8aab04f532f09785780ce885","score":"0.49256536","text":"def _parse_attr_name(self, name):\n fullpath = os.path.abspath(name)\n filename = os.path.split(fullpath)[1]\n parts = filename.split(\".\")\n if len(parts) != 4:\n log.error(\"Found filename %s with incorrect format, need 4 parts\" % filename)\n raise ValueError(\"Found filename %s with incorrect format, need 4 parts\" % filename)\n\n attr_name,type,cols,rows = parts\n if type not in str_to_dtype:\n log.error(\"Don't know how to interpret data type %s from %s\" % (type,filename))\n raise ValueError(\"Don't know how to interpret data type %s from %s\" % (type,filename))\n dtype = str_to_dtype[type]\n\n try:\n cols = int(cols)\n rows = int(rows)\n except ValueError:\n log.error(\"Columns and rows must be integers not (%s,%s)\" % (str(cols),str(rows)))\n raise ValueError(\"Columns and rows must be integers not (%s,%s)\" % (str(cols),str(rows)))\n\n return attr_name,dtype,cols,rows","title":""},{"docid":"f09a47a62fe5a334b6273f05af1d1e4f","score":"0.49212056","text":"def getFileGroup(fname):\n \n regexp = getFileRegExp(fname, match = False)\n \n base, _ = os.path.splitext(os.path.basename(fname))\n if regexp is not None:\n tokens = re.split(regexp,base)\n if tokens:\n return tokens[0]\n return None","title":""},{"docid":"dd307308366d9e78f7a85526ad938eb5","score":"0.49196035","text":"def locate_file(self, file_):\n return os.path.basename(file_)","title":""},{"docid":"3df5ecdf70d58ea86a0fc2087b1895e8","score":"0.49166444","text":"def parse_datafile_name(name: str) -> Optional[SO5FileLabel]:\n m = datafile_name_re.match(name)\n if (m):\n labels = [int(m.group(i)) for i in range(1, 6)]\n return labels[0], labels[1], labels[2], labels[3], labels[4]\n return None","title":""},{"docid":"66561df36eb7cca54890a0b6547bd0e1","score":"0.49155393","text":"def parse_channel(self, x):\n final_path = os.path.basename(x)\n if self.microscope == \"imagexpress\":\n return int(final_path.split(\"_\")[3][1])\n elif self.microscope == \"yokogawa\":\n plate, well, rest = final_path.split(\"_\")\n return int(rest.replace(\".tif\", \"\")[-2:])\n elif self.microscope == \"opera\":\n channel = int(final_path.replace(\".tif\", \"\")[16:])\n else:\n raise RuntimeError()","title":""},{"docid":"7b25b73261af3c6c1062f571d8f39670","score":"0.49089038","text":"def getNameFromFilename(cls, filename):\n return filename[0:3]","title":""},{"docid":"5c50ebd06e6758b758af9cea00fec425","score":"0.48974076","text":"def parse_filenames_from_dir(ddir, debug = False):\n flist = sorted(os.listdir(ddir))\n # remove files or dirs that do not end in '.nc'\n flist = [f for f in flist if '.nc' in f]\n # Get a unique sorted list of years\n step_list = sorted(list(set([a.split('.')[0] for a in flist])))\n # This assumes that all variables are given for the last time step.\n var_list = [b.split('.')[-2] for b in flist if step_list[-1] in b]\n return var_list","title":""},{"docid":"3671fb5a27bdcc22ac9bd2666a5f9d20","score":"0.48948908","text":"def parse_file_path(file_path):\n file_name = file_path.split('/')[-1]\n name_components = file_name.split('.')\n\n sample_name = name_components[0]\n result_type = name_components[1]\n file_type = name_components[2]\n\n return sample_name, result_type, file_type","title":""},{"docid":"ba43fe46d91b7fc18dfb73862cc30c1a","score":"0.4886662","text":"def determineFileType(fileName):\r\n\r\n if fileName.upper().endswith(\"STL\"):\r\n return \"STL\";\r\n \r\n if fileName.upper().endswith(\"STEP\"):\r\n return \"STEP\";\r\n \r\n #read the first 4 lines of the file, since we couldnt figure it out by file extension\r\n f = open(file,'r');\r\n text = \"\"; \r\n for i in range(5):\r\n text = text + f.readline();\r\n \r\n f.close();\r\n if 'FILE_DESCRIPTION' in text or 'STEP' in text:\r\n return \"STEP\";\r\n elif 'solid' in text: \r\n return \"STL\";\r\n else:\r\n return \"???\";","title":""},{"docid":"034f156c5683dfae571a3f7a1f206101","score":"0.488626","text":"def _get_name(self):\n name = super(CUDSFileSource, self)._get_name()\n return 'CUDS File: ' + name","title":""},{"docid":"8e8750df4dd44f83705eae957f43e22d","score":"0.48804036","text":"def getLabel(file_name):\n\n category = file_name.split('/')[-2]\n label = categories.get(category, categories['_background_noise_'])\n\n return label","title":""},{"docid":"13209ebd69d95720056c9c264edb6638","score":"0.48756275","text":"def from_filename(cls, filename):\n contour = []\n with open(filename) as fd:\n data = fd.read()\n for y, line in enumerate(data.splitlines(), start=1):\n for x, ch in enumerate(line, start=1):\n if ch == '#':\n contour.append((x, y))\n return ContourToPolygon(contour)","title":""},{"docid":"1efc39756479145be5e8bd8f2f27310f","score":"0.48750666","text":"def get_filename(self):\n if self.file_file:\n try:\n return re.search(r'([^\\/]+)$', self.file_file).groups()[0]\n except AttributeError:\n pass\n return None","title":""},{"docid":"cf14ad06e05a5d76668326e4ab307d86","score":"0.48743334","text":"def filename_parser(args):\n # Metadata data structure\n meta = {}\n args.jobcount = 0\n\n # Compile regular expression to remove image file extensions\n pattern = '\\.' + args.type + '$'\n ext = re.compile(pattern, re.IGNORECASE)\n\n # Walk through the input directory and find images that match input criteria\n for (dirpath, dirnames, filenames) in os.walk(args.dir):\n for filename in filenames:\n # Is filename and image?\n is_img = ext.search(filename)\n # If filename is an image, parse the metadata\n if is_img is not None:\n # Remove the file extension\n prefix = ext.sub('', filename)\n metadata = prefix.split(args.delimiter)\n\n # Image metadata\n img_meta = {'path': dirpath}\n img_pass = 1\n # For each of the type of metadata PlantCV keeps track of\n for field in args.valid_meta:\n # If the same metadata is found in the image filename, store the value\n if field in args.fields:\n meta_value = metadata[args.fields[field]]\n # If the metadata type has a user-provided restriction\n if field in args.imgtype:\n # If the input value does not match the image value, fail the image\n if meta_value != args.imgtype[field]:\n img_pass = 0\n img_meta[field] = meta_value\n # Or use the default value\n else:\n img_meta[field] = args.valid_meta[field]\n\n if args.dates and 'timestamp' in img_meta:\n in_date_range = check_date_range(args, img_meta['timestamp'])\n if in_date_range is False:\n img_pass = 0\n\n # If the image meets the user's criteria, store the metadata\n if img_pass == 1:\n meta[filename] = img_meta\n args.jobcount += 1\n\n return args, meta","title":""},{"docid":"3b84d0092a60a6d6a6929c4c245bbd55","score":"0.48704368","text":"def _get_file_characts(self) -> Dict[str, str]:\n return chrh.CharacteristicsHandler.handle_file_path(\n self.request.filename)","title":""},{"docid":"3336fd1ce2ca46fa67f53b7f258114df","score":"0.48564175","text":"def csdFileName(self):\n return self.csd","title":""},{"docid":"3336fd1ce2ca46fa67f53b7f258114df","score":"0.48564175","text":"def csdFileName(self):\n return self.csd","title":""},{"docid":"0ef1a820bef1a96bb0d94a17effe3ea9","score":"0.48556283","text":"def get_labels(filename, labelname):\n # print labelname\n labels = []\n lab = labelname + \"=\"\n with open(filename) as f:\n lines = f.readlines()\n for each in lines:\n if labelname in each:\n aux = each.replace(\" \", \"\")\n # print aux\n aux = aux[aux.index(labelname):]\n # print aux\n if \"\\n\" in aux:\n aux = aux.replace(\"\\n\", \"\")\n aux = aux.replace(lab, \"\")\n aux = aux.replace(\";\", \"\")\n # aux = aux.replace(\" \",\"\")\n if not aux[-1].isalnum(): # no idea why this happens :O\n aux = aux[:len(aux) - 1]\n if aux.endswith(\"ROUND\"):\n # print aux, \" se termina cu\"\n if aux not in labels and \"old\" not in aux:\n labels.append(aux)\n labels.append(\"ERR_ROUND\")\n return labels","title":""},{"docid":"e300b1c26238b9843d6e72c0129db574","score":"0.48531154","text":"def visitStr(self):\n import re\n v = re.split('_', os.path.basename(self.tracerRawdataLocation))[1]\n v = re.split('-Converted', v)[0]\n w = re.split('\\.', v)[0]\n if not w:\n return v.lower()\n else:\n return w.upper()","title":""},{"docid":"7f1302b11af8b49cbfa539dfcca7fb50","score":"0.48482966","text":"def get_file_name(self):\n return self.name + '.' + gs.FILE_TYPE[self.file_type][1]","title":""},{"docid":"650a0ef19da3f8841e845badf8872800","score":"0.4847801","text":"def process_file_names(data):\n return data.split('\\n')[:-1]","title":""},{"docid":"21fb2638fdc5ee9b2ec911eb6e011b3b","score":"0.4847756","text":"def readNameByReadFile(self, readFilePath):\n dir_name, file_name, read_name, ext, prog = self.splitFilePath(readFilePath)\n return read_name","title":""},{"docid":"d92f6d6259e955556a7e8e4a9cac61ef","score":"0.4847354","text":"def get_filename(file_path):\n return os.path.split(file_path)[1]","title":""},{"docid":"b06ee13d756a2cd6dfcce89744e7f5e3","score":"0.48459816","text":"def get_photo_names(file_name):\n # --- siehe dms.photos.views_add\n ext_pos = file_name.rfind('.')\n file_name_small = file_name[:ext_pos] + '_small' + file_name[ext_pos:]\n file_name_middle = file_name[:ext_pos] + '_middle' + file_name[ext_pos:]\n return file_name_small, file_name_middle","title":""},{"docid":"14512c642e95b4a76eeab2d746a6276f","score":"0.48362327","text":"def parse_file(self, name):\n with open(name, \"rb\") as fp:\n return self.parse(fp.read())","title":""},{"docid":"e814c52986d063fbb936230b63662973","score":"0.48355153","text":"def create_name(self):\n data_spliced = self.name.split(\"-\")\n self.folder = data_spliced[1]\n self.filename = data_spliced[2]","title":""},{"docid":"b830ced27ac6cfb3b3ed82677989a0e8","score":"0.4831577","text":"def character_statistics(file_name):\n return None","title":""},{"docid":"d5edffc36e804b5d4b23c98a95570bf2","score":"0.48310873","text":"def getFileNameNoChannels(path):\n\tgoodFile = False\n\tif path.endswith('_ch1.tif'):\n\t\tgoodFile = True\n\telif path.endswith('_ch2.tif'):\n\t\tgoodFile = True\n\telif path.endswith('_ch2.tif'):\n\t\tgoodFile = True\n\tif not goodFile:\n\t\treturn None\n\n\tbasePath, tmpExt = os.path.splitext(path)\n\tbasePath2, baseFileName = os.path.split(basePath)\n\n\tbaseFileName = baseFileName.replace('_ch1', '')\n\tbaseFileName = baseFileName.replace('_ch2', '')\n\tbaseFileName = baseFileName.replace('_ch3', '')\n\treturn baseFileName","title":""},{"docid":"eb44275b43af2dd836f4f9949a6beb75","score":"0.48287952","text":"def get_reference_name(filename):\r\n if filename.find('pegging.csv') != -1:\r\n return filename.replace('pegging.csv', 'pegging.reference.csv')\r\n return filename.replace('result', 'reference')","title":""},{"docid":"29ab393645558d995580eca5bbe1af8c","score":"0.48242596","text":"def filename(self):\r\n return posixpath.split(self)[1]","title":""},{"docid":"b85b12506d4ed68dacfd165bd0840ace","score":"0.48232743","text":"def ParsePartialFileNameIregion(file_name):\n\n # Remove any directories from the name.\n #\n # import pdb; pdb.set_trace()\n file_name = os.path.basename(file_name)\n\n # Make sure the file name matches the PinPlay pattern.\n #\n # import pdb; pdb.set_trace()\n if config.global_regions:\n local_pp_iregion_pattern = pp_global_iregion_pattern\n else:\n local_pp_iregion_pattern = pp_iregion_pattern\n if not re.search(local_pp_iregion_pattern, file_name):\n msg.PrintAndExit('method util.ParsePartialFileNameIregion() encountered a file name\\n' 'which does not meet requirements for parsing a region pinball name:\\n ' + \\\n file_name + '\\nFile name must conform to the region pinball naming convention.')\n\n # Directory containing the fields which were successfull parsed.\n #\n result = {}\n\n # Define a lambda function to print a standard error msg.\n #\n err_msg = lambda fname, string: msg.PrintAndExit(\n 'method util.ParsePartialFileNameIregion() encountered '\n 'an error parsing \\'' + string + '\\' in \\nfile name: ' + fname)\n\n # Get the file extension\n #\n # import pdb; pdb.set_trace()\n # pp_iregion_pattern = '_t([0-9])+r([0-9]+)_warmup([0-9]+)_prolog([0-9]+)_region([0-9]+)_epilog([0-9]+)_([0-9]+)_([0-1]-[0-9]+)'\n # group(1) tid, group(2) rid, group(3) warmup_length\n # group(4) prolog_length, group(5) sim_length, group(6) epilog_length, \n # group(7) traceno, group(8) weight\n # pp_global_iregion_pattern = '_globalr([0-9]+)_warmup([0-9]+)_prolog([0-9]+)_region([0-9]+)_epilog([0-9]+)_([0-9]+)_([0-1]-[0-9]+)'\n # group(1) rid, group(2) warmup_length\n # group(3) prolog_length, group(4) sim_length, group(5) epilog_length, \n # group(6) traceno, group(7) weight\n tmp = re.split(local_pp_iregion_pattern, file_name)\n if len(tmp) >= 2:\n file_ext = tmp[1]\n tmp = file_ext.split('.')\n if tmp[0].isdigit():\n file_ext = '.'.join(tmp[1:]) # Remove TID if it exists\n else:\n file_ext = '.'.join(tmp[0:])\n result['file_ext'] = file_ext\n\n # Get the PinPlay generated part of the file name.\n #\n # import pdb; pdb.set_trace()\n pp_part = re.search(local_pp_iregion_pattern, file_name)\n \n if pp_part:\n if config.global_regions:\n gr_offset = -1\n tid = -1\n else:\n gr_offset = 0\n tid = int(pp_part.group(1))\n result['tid'] = tid\n\n region_num = int(pp_part.group(gr_offset+2))\n result['region_num'] = region_num\n\n warmup = int(pp_part.group(gr_offset+3))\n result['warmup'] = warmup\n prolog = int(pp_part.group(gr_offset+4))\n result['prolog'] = prolog\n region = int(pp_part.group(gr_offset+5))\n result['region'] = region\n epilog = int(pp_part.group(gr_offset+6))\n result['epilog'] = epilog\n trace_num = int(pp_part.group(gr_offset+7))\n result['trace_num'] = trace_num\n\n # Final field contains the weight with '-' as the decimal point.\n # NOTE: string '1-00000' == 1.0\n #\n # import pdb ; pdb.set_trace()\n tmp = pp_part.group(gr_offset+8) # string\n if tmp.find('1-0') != -1:\n weight = 1.0\n else:\n pos = tmp.find('.')\n tmp = tmp[:pos] # Remove trailing '.' from the field\n try:\n weight = float(tmp.replace('-', '.'))\n except ValueError:\n err_msg(file_name, 'weight')\n result['weight'] = weight\n\n return result","title":""},{"docid":"2264b538c68f5e71c11cfc04716c8e4b","score":"0.48232102","text":"def ParsePartialFileNamePCregion(file_name):\n\n # Remove any directories from the name.\n #\n # import pdb; pdb.set_trace()\n if config.global_regions:\n local_pp_pcregion_pattern = pp_global_pcregion_pattern\n else:\n local_pp_pcregion_pattern = pp_pcregion_pattern\n file_name = os.path.basename(file_name)\n\n # Make sure the file name matches the PinPlay pattern.\n #\n # import pdb; pdb.set_trace()\n if not re.search(local_pp_pcregion_pattern, file_name):\n msg.PrintAndExit('method util.ParsePartialFileNamePCregion() encountered a file name\\n' 'which does not meet requirements for parsing a region pinball name:\\n ' + \\\n file_name + '\\nFile name must conform to the region pinball naming convention.')\n\n # Directory containing the fields which were successfull parsed.\n #\n result = {}\n\n # Define a lambda function to print a standard error msg.\n #\n err_msg = lambda fname, string: msg.PrintAndExit(\n 'method util.ParsePartialFileNamePCregion() encountered '\n 'an error parsing \\'' + string + '\\' in \\nfile name: ' + fname)\n\n # Get the file extension\n #\n # import pdb; pdb.set_trace()\n tmp = re.split(pp_pcregion_pattern, file_name)\n if len(tmp) >= 2:\n file_ext = tmp[1]\n tmp = file_ext.split('.')\n if tmp[0].isdigit():\n file_ext = '.'.join(tmp[1:]) # Remove TID if it exists\n else:\n file_ext = '.'.join(tmp[0:])\n result['file_ext'] = file_ext\n\n # Get the PinPlay generated part of the file name.\n #\n # import pdb; pdb.set_trace()\n pp_part = re.search(local_pp_pcregion_pattern, file_name)\n\n # Divide the pp_part into strings separated by '_' which contain the\n # fields of the file name. The example given above produces this\n # list:\n #\n #pp_pcregion_pattern = '_t([0-9])+r([0-9])+_warmupendPC(0x[0-9A-Fa-f]+)_warmupendPCCount([0-9]+)_warmuplength([0-9]+)_endPC(0x[0-9A-Fa-f]+)_endPCCount([0-9]+)_length([0-9]+)_multiplier([0-9]+-[0-9]+)_([0-9]+)_([0-1]-[0-9]+)'\n # group(1) tid, group(2) rid, group(3) warmupendPC, \n # group(4) warmupendPCCount, group(5) warmuplength, group(6) endPC, \n # group(7) endPCCount, group(8) length, group(9) multiplier,\n # group(10) traceno, group(11) weight\n #pp_global_pcregion_pattern = '_globalr([0-9]+)_warmupendPC(0x[0-9A-Fa-f]+)_warmupendPCCount([0-9]+)_warmuplength([0-9]+)_endPC(0x[0-9A-Fa-f]+)_endPCCount([0-9]+)_length([0-9]+)_multiplier([0-9]+-[0-9]+)_([0-9]+)_([0-1]-[0-9]+)'\n # group(1) rid, group(2) warmupendPC, \n # group(3) warmupendPCCount, group(4) warmuplength, group(5) endPC, \n # group(6) endPCCount, group(7) length, group(8) multiplier,\n # group(9) traceno, group(10) weight\n if pp_part:\n if config.global_regions:\n gr_offset = -1\n tid = -1\n else:\n gr_offset = 0\n tid = int(pp_part.group(1))\n result['tid'] = tid\n region_num = int(pp_part.group(gr_offset+2))\n result['region_num'] = region_num\n warmupendPC = pp_part.group(gr_offset+3) # string\n result['warmupendPC'] = warmupendPC\n warmupendPCCount = int(pp_part.group(gr_offset+4))\n result['warmupendPCCount'] = warmupendPCCount\n warmuplength = int(pp_part.group(gr_offset+5))\n result['warmuplength'] = warmuplength\n endPC = pp_part.group(gr_offset+6) # string\n result['endPC'] = endPC\n endPCCount = int(pp_part.group(gr_offset+7))\n result['endPCCount'] = endPCCount\n length = int(pp_part.group(gr_offset+8))\n result['length'] = length\n tmp = pp_part.group(gr_offset+9) # string\n multiplier = float(tmp.replace('-', '.'))\n result['multiplier'] = multiplier\n trace_num = int(pp_part.group(gr_offset+10))\n result['trace_num'] = trace_num\n\n # Final field contains the weight with '-' as the decimal point.\n # NOTE: string '1-00000' == 1.0\n #\n # import pdb ; pdb.set_trace()\n tmp = pp_part.group(gr_offset+11) # string\n if tmp.find('1-0') != -1:\n weight = 1.0\n else:\n pos = tmp.find('.')\n tmp = tmp[:pos] # Remove trailing '.' from the field\n try:\n weight = float(tmp.replace('-', '.'))\n except ValueError:\n err_msg(file_name, 'weight')\n result['weight'] = weight\n\n return result","title":""},{"docid":"48ab2e369209ecfa943b8e22426a5a20","score":"0.48180154","text":"def get_name(data, meta_path, title_counter):\n authors = \"\"\n try:\n title = data[\"stats\"][\"title\"]\n authors = data[\"stats\"][\"authors\"]\n except KeyError: # older type file\n title = splitext(basename(meta_path))[0]\n try:\n name = title.split(\"#] \")[1]\n title = splitext(name)[0]\n except IndexError: # no \"#] \" in filename\n pass\n if not title:\n try:\n name = meta_path.split(\"#] \")[1]\n title = splitext(name)[0]\n except IndexError: # no \"#] \" in filename\n title = _(\"NO TITLE FOUND\") + str(title_counter[0])\n title_counter[0] += 1\n name = title\n if authors:\n name = \"{} - {}\".format(authors, title)\n return name","title":""}],"string":"[\n {\n \"docid\": \"0cb71a9653db83d5d3b061704811f9a0\",\n \"score\": \"0.5983463\",\n \"text\": \"def parse_calib_file_name(fname) :\\n fnfields = os.path.splitext(fname) # '123-end.data' -> ('123-end', 'data') \\n if len(fnfields) != 2 : return None # check that file name like '123-end.data' has splited extension\\n if fnfields[1] != '.data': return None # check extension\\n fnparts = fnfields[0].split('-') # '123-end' -> ('123', 'end')\\n if len(fnparts) != 2 : return None # check that file name has two parts\\n #print('XXX parts of the file name:', fnparts[0], fnparts[1], fnfields[1])\\n return fnparts[0], fnparts[1], fnfields[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf6c61be92994d04d75cb700880109e3\",\n \"score\": \"0.5907608\",\n \"text\": \"def _read_fnames(self, fname):\\n with open(fname, 'r') as f:\\n lines = f.readlines()\\n\\n # clip_name, img_path, la_path\\n fnames = []\\n for line in lines:\\n im, la = line.strip().split(' ')\\n clip = im.split('/')[-1].split('_')[0]\\n fnames.append((clip, im, la)) # as order of batch-key\\n return fnames\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9d7baaa032ca864f0bb5b53a0864f0c\",\n \"score\": \"0.5840292\",\n \"text\": \"def parse_file_name(self, file_name, dataset_info):\\n if 'data' in file_name:\\n self.set_data_name(file_name)\\n elif re.match(r'\\\\d{6}', file_name):\\n self.set_mc_name(file_name, dataset_info)\\n else:\\n _logger.debug(\\\"No dedicated parsing found. Assume MC and run simplified\\\")\\n self.set_mc_name(file_name, dataset_info)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17cd49c62622a6e1f671f52c103954f6\",\n \"score\": \"0.57834107\",\n \"text\": \"def contour_load(self, filename):\\n if filename:\\n self.set(\\\"contour loadlevels {0:s}\\\".format(str(filename)))\\n else:\\n warnings.warn(\\\"No filename provided for contours\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4979e4d87e5f1358f38085e643e21052\",\n \"score\": \"0.56500024\",\n \"text\": \"def parse(self, filename):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1796de5acfe7c9d5480ed1566354d9ac\",\n \"score\": \"0.5643601\",\n \"text\": \"def __process_filename(self, filename : str):\\n for r in self.filename_regex:\\n res = r.search(filename) # strip filename\\n\\n if res:\\n break\\n\\n stripped_filename = filename.split(res.group(0))[0]\\n\\n out = {\\\"name\\\": self.dotremove_regex.sub(\\\" \\\", stripped_filename)}\\n\\n try:\\n year = self.year_regex.findall(filename)[-1]\\n out[\\\"year\\\"] = year\\n except IndexError: \\n out[\\\"year\\\"] = \\\"\\\"\\n print(\\\"could not find year data\\\")\\n\\n if self.type == \\\"tv\\\":\\n try: \\n episode_data = self.episode_regex.findall(filename)[-1]\\n\\n out[\\\"season\\\"] = int(episode_data[1:3])\\n out[\\\"episode\\\"] = int(episode_data[4:6])\\n\\n out[\\\"name\\\"] = out[\\\"name\\\"].split(episode_data)[0].rstrip()\\n except IndexError:\\n print(\\\"could not find episode data\\\")\\n\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46f25d8e44cd89029970b118e5b43e32\",\n \"score\": \"0.56242645\",\n \"text\": \"def extract_name(filename):\\n\\n name, _ = split_ext(filename)\\n return name\\n # n, ext = split_ext(filename)\\n # if ext == 'cols':\\n # return f'/{n}'\\n # else:\\n # return n\\n # bname = os.path.basename(filename)\\n # name = '.'.join(bname.split('.')[0:-1])\\n # return name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80ca8dfb1cf37ff375eaae3afcda529c\",\n \"score\": \"0.5601794\",\n \"text\": \"def readSceneName(filePath):\\n \\n listName = []\\n listNameFinal = []\\n counter = len(filePath)-1\\n counter02 = 1\\n while counter >= 0 and counter02 <= 2:\\n character = filePath[counter]\\n listName.append(character)\\n counter -= 1\\n if character == '\\\"':\\n counter02 += 1\\n listName.reverse()\\n for char in listName:\\n if char != '\\\"':\\n listNameFinal.append(char)\\n\\n name = \\\"\\\".join(listNameFinal)\\n\\n return name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1395cef16a5cfa7e0e1d53e015f197bc\",\n \"score\": \"0.5583756\",\n \"text\": \"def _parse_contour_file_(self, filename):\\n coords_lst = []\\n\\n with open(filename, 'r') as infile:\\n for line in infile:\\n coords = line.strip().split()\\n\\n x_coord = float(coords[0])\\n y_coord = float(coords[1])\\n coords_lst.append((x_coord, y_coord))\\n\\n return coords_lst\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea2f2dbf00f56681e18239f3ccae0a60\",\n \"score\": \"0.55354375\",\n \"text\": \"def parse_contour_file(filename):\\n\\n coords_lst = []\\n\\n with open(filename, 'r') as infile:\\n for line in infile:\\n coords = line.strip().split()\\n\\n x_coord = float(coords[0])\\n y_coord = float(coords[1])\\n coords_lst.append((x_coord, y_coord))\\n return coords_lst\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91725eb7608635139edd18e5ac490b12\",\n \"score\": \"0.54807264\",\n \"text\": \"def ParseFileName(file_name):\\n\\n result = {}\\n file_name = os.path.basename(file_name)\\n #import pdb; pdb.set_trace()\\n if config.global_regions:\\n local_full_iregion_pattern = full_global_iregion_pattern\\n local_full_pcregion_pattern = full_global_pcregion_pattern\\n local_pp_iregion_pattern = pp_global_iregion_pattern\\n local_pp_pcregion_pattern = pp_global_pcregion_pattern\\n else:\\n local_full_iregion_pattern = full_iregion_pattern\\n local_full_pcregion_pattern = full_pcregion_pattern\\n local_pp_iregion_pattern = pp_iregion_pattern\\n local_pp_pcregion_pattern = pp_pcregion_pattern\\n if re.search(local_full_iregion_pattern, file_name):\\n result = ParseFullFileName(file_name, local_full_iregion_pattern)\\n elif re.search(local_full_pcregion_pattern, file_name):\\n result = ParseFullFileName(file_name, local_full_pcregion_pattern)\\n elif re.search(local_pp_iregion_pattern, file_name):\\n result = ParsePartialFileNameIregion(file_name)\\n elif re.search(pp_pcregion_pattern, file_name):\\n result = ParsePartialFileNamePCregion(file_name)\\n else:\\n msg.PrintAndExit('method util.ParseFileName() encountered a file name\\\\n' 'which does not meet requirements for parsing a region pinball name:\\\\n ' + \\\\\\n file_name + '\\\\nFile name must conform to the region pinball naming convention.')\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a2099ba87ce7416fb29d368d8287f0e\",\n \"score\": \"0.545208\",\n \"text\": \"def filename(x):\\n value = -1\\n counter = -1\\n name = x\\n lenName = len(name)\\n while abs(counter) < lenName:\\n if name[counter] == \\\".\\\":\\n value = counter\\n break\\n else:\\n counter -= 1\\n if abs(counter) == lenName:\\n print \\\"there are no '.' in this file name\\\"\\n return None\\n #print counter\\n return (name[:counter],name[lenName + counter :])\\n # add one to the counter if you dont want to get the \\\".\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e94f764d9c3abbe5b503be5e5891240d\",\n \"score\": \"0.5443341\",\n \"text\": \"def parse_name(cls, fn_dcm):\\n\\n matches = re.match('([\\\\d]+).dcm', fn_dcm)\\n if matches is not None:\\n slice_num = matches.groups()[0]\\n slice_num = int(slice_num)\\n return slice_num\\n else:\\n warn(f'unrecognized file:\\\\t{fn_dcm}')\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9018a7f0df0c8246d9d59e779a1605e6\",\n \"score\": \"0.5432766\",\n \"text\": \"def parseFilename(cls, path):\\n dirName, fileName = os.path.split(path)\\n matches = re.search(cls.filenameRegex, fileName)\\n if not matches:\\n raise RuntimeError(\\\"Unable to parse filename: %s\\\" % (fileName,))\\n return CalibIdentity.fromDict(matches.groupdict())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0b5be8154f1b8b5e8c331feb5ea02e3\",\n \"score\": \"0.53879416\",\n \"text\": \"def _ExtractSourceName(self, gcno_summary, file_name):\\n src_file_path = None\\n for key in gcno_summary.functions:\\n src_file_path = gcno_summary.functions[key].src_file_name\\n src_parts = src_file_path.rsplit(\\\".\\\", 1)\\n src_file_name = src_parts[0]\\n src_extension = src_parts[1] if len(src_parts) > 1 else None\\n if src_extension not in [\\\"c\\\", \\\"cpp\\\", \\\"cc\\\"]:\\n logging.warn(\\\"Found unsupported file type: %s\\\", src_file_path)\\n continue\\n if src_file_name.endswith(file_name):\\n logging.info(\\\"Coverage source file: %s\\\", src_file_path)\\n break\\n return src_file_path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edfd56103bb4d2a1e11f3215ff117a23\",\n \"score\": \"0.53473526\",\n \"text\": \"def get_imagename(path):\\n dot = re.findall('[^\\\\/]*\\\\.', path)\\n mesh = re.findall('[^\\\\/]*\\\\#', path)\\n return dot[0][:-1] if len(mesh)==0 else mesh[0][:-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c4b9ae7fb847a5f7738ad4d41fd317b\",\n \"score\": \"0.53411204\",\n \"text\": \"def _parse_filename(filename):\\n sections = filename.split('.')\\n return _casify(sections[0]), _casify(sections[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3aab172b3371b5b8f0627913775862cc\",\n \"score\": \"0.53013694\",\n \"text\": \"def decompose_newstyle_name(filename):\\n path, parts, ext = _get_fields(filename)\\n observatory = parts[0]\\n serial = list_get(parts, 3, \\\"\\\")\\n\\n if ext == \\\".pmap\\\":\\n assert len(parts) in [1,2], \\\"Invalid .pmap filename \\\" + repr(filename)\\n instrument, filekind = \\\"\\\", \\\"\\\"\\n serial = list_get(parts, 1, \\\"\\\")\\n elif ext == \\\".imap\\\":\\n assert len(parts) in [2,3], \\\"Invalid .imap filename \\\" + repr(filename)\\n instrument = parts[1]\\n filekind = \\\"\\\"\\n serial = list_get(parts, 2, \\\"\\\")\\n else:\\n assert len(parts) in [3,4], \\\"Invalid filename \\\" + repr(filename)\\n instrument = parts[1]\\n filekind = parts[2]\\n serial = list_get(parts, 3, \\\"\\\")\\n\\n assert instrument in INSTRUMENTS+[\\\"\\\"], \\\"Invalid instrument \\\" + \\\\\\n repr(instrument) + \\\" in name \\\" + repr(filename)\\n assert filekind in FILEKINDS+[\\\"\\\"], \\\"Invalid filekind \\\" + \\\\\\n repr(filekind) + \\\" in name \\\" + repr(filename)\\n assert re.match(\\\"\\\\d*\\\", serial), \\\"Invalid id field \\\" + \\\\\\n repr(id) + \\\" in name \\\" + repr(filename)\\n # extension may vary for upload temporary files.\\n\\n return path, observatory, instrument, filekind, serial, ext\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e88bfaa29e4110b3af096755d3319e5\",\n \"score\": \"0.5297847\",\n \"text\": \"def _parse_file(self, name):\\n if '/' in name:\\n bits = name.split('/')\\n return (os.path.join(*bits[0:-1]), bits[-1])\\n else:\\n return None, name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf38e15e55db9a0a13ea2f82e89f627d\",\n \"score\": \"0.52879745\",\n \"text\": \"def get_wind_comps(path, filename):\\n with open(path + filename) as file:\\n for i, line in enumerate(file):\\n if i == 5:\\n wind_comp1 = line.split()[-4][-1].lower()\\n wind_comp2 = line.split()[-2][-1].lower()\\n \\n return wind_comp1, wind_comp2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b6e8434ae3627ce4d57dd54870850e6\",\n \"score\": \"0.52835125\",\n \"text\": \"def deconstruct_file_name(name):\\n return os.path.splitext(os.path.split(name)[1])[0].split('_')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d27ee7d665e0aad9bfbc7139aca17438\",\n \"score\": \"0.52405745\",\n \"text\": \"def parse_fname(base_path, fname):\\n base_path += '/MergedReflectivityQC_'\\n\\n scan_re = re.compile(r'_(\\\\d{2}.\\\\d{2})_')\\n match = scan_re.search(fname)\\n\\n if (match is not None):\\n base_path += match.group(1)\\n else:\\n print('Error parsing filename')\\n sys.exit(0)\\n\\n abs_path = join(base_path, fname)\\n\\n return abs_path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f97faf54b07c1b691507c8dd61069cd\",\n \"score\": \"0.52173734\",\n \"text\": \"def filename_parse(filename):\\n\\n # Since SV-P is misspelled as SV-P_, catch that\\n filename = filename.replace(\\\"SV-P_\\\", \\\"SV-P\\\")\\n # Same with 6-31+G-d,p\\n filename = filename.replace(\\\"6-31+G-d,p_\\\", \\\"6-31+G-d,p\\\")\\n\\n tokens = filename.split(\\\"/\\\")[-1].split(\\\"_\\\")\\n mol = tokens[0]\\n func = tokens[1]\\n basis = tokens[2].split(\\\".\\\")[0]\\n name = func + \\\"/\\\" + basis\\n if func in ['rDCSD', 'df-lrmp2']:\\n unrestricted = False\\n #elif func in ['luCCSD', 'uCCSD', 'uDCSD']:\\n elif func in ['uCCSD', 'uDCSD']:\\n unrestricted = True\\n else:\\n unrestricted = (len(tokens) == 4)\\n name = \\\"u-\\\" * unrestricted + name\\n\\n if func in ['rDCSD', 'uDCSD']:\\n func = 'DCSD'\\n\\n return mol, func, basis, unrestricted, name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff7843717cdf249fd542efad2cc82fcb\",\n \"score\": \"0.51842237\",\n \"text\": \"def generate_name_cvs_file(self,file_name = \\\"\\\"):\\n get_time = time.strftime('%H%M%S%m%d%Y')\\n if(file_name == \\\"\\\"):\\n file_name = \\\"sudoku_\\\"+get_time+\\\".cvs\\\"\\n else:\\n file_name = name_of_file + \\\"_\\\"\\n file_name += get_time+\\\".cvs\\\"\\n return file_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"683d02585df16c194853d3e9686e15cb\",\n \"score\": \"0.5158009\",\n \"text\": \"def parse_file(self, filename): # [CO] this is where we would put the sequential calculation\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d0e6809f33d4f982ce02dbd6f6ee9b8\",\n \"score\": \"0.5154683\",\n \"text\": \"def separate(self, abs_filename):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ccde7f47e2628b4d4086a1124843867\",\n \"score\": \"0.5151052\",\n \"text\": \"def __ConvertNameStile(self, name):\\n l = name.split('_')\\n fname = ''\\n for i in l:\\n fname += i[0].upper() + i[1:].lower()\\n return fname\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96a42f8ac02225159a5cccf21a9acbfa\",\n \"score\": \"0.5131942\",\n \"text\": \"def load_contact_points(path_name, file_name, dim):\\n data = load_file(path_name+file_name)\\n CP = data['CP']\\n x = CP[:,0]\\n y = CP[:,1]\\n if dim == 2:\\n return x, y\\n else:\\n z = CP[:,3]\\n return x, y, z\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0df570352d02ecea6e29ebd59d93b91f\",\n \"score\": \"0.51295\",\n \"text\": \"def _parse_filename(filename):\\n filename_base, ext = os.path.splitext(filename)\\n if '.' in filename_base:\\n # Some images have double filename extensions.\\n filename_base, ext = os.path.splitext(filename_base)\\n if ext != \\\".jpg\\\":\\n return None\\n person_id, cam_seq, frame_idx, detection_idx = filename_base.split('_')\\n return int(person_id), int(cam_seq[1]), filename_base, ext\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca0eeb41e98ec9eebd821ab3a4c44092\",\n \"score\": \"0.5120978\",\n \"text\": \"def parse_name(self, _string):\\n\\n path = support.normalize_path(_string)\\n if os.path.isdir(path):\\n # final part of directory\\n return self._get_final_dir(path)\\n elif os.path.isfile(path):\\n # file name, without path or extension\\n return self._get_file_no_ext(path)\\n else:\\n raise errors.SublpException(\\n \\\"{0} does not know how to find project name for '{1}'.\\\",\\n type(self).__name__, path\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af0ced4c2ead75cbedbf3686e64f17f3\",\n \"score\": \"0.511001\",\n \"text\": \"def parse(self, filename):\\n grid = {}\\n loc_of_door = {}\\n loc_of_key = {}\\n location = complex(0, 0)\\n\\n with open(filename) as f:\\n for line in f:\\n for char in line.strip():\\n # print(char)\\n grid[location] = char\\n\\n if char in string.ascii_uppercase:\\n loc_of_door[char] = location\\n if char in string.ascii_lowercase:\\n loc_of_key[char] = location\\n if char == \\\"@\\\":\\n self.hero_locs.append(location)\\n location += complex(1, 0)\\n location += complex(0, 1)\\n location = complex(0, location.imag)\\n\\n self.grid = grid\\n self.loc_of_door = loc_of_door\\n self.loc_of_key = loc_of_key\\n self.door_of_loc = dict(map(reversed, self.loc_of_door.items()))\\n self.all_keys = frozenset(loc_of_key.keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"042045193cb7e3ba44d0ee31be7becbb\",\n \"score\": \"0.5099243\",\n \"text\": \"def file_name(self,name):\\n return self._file[name]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54041cd59166ca8e8fe8977d81682df9\",\n \"score\": \"0.5095792\",\n \"text\": \"def get_cote(filename):\\n full_cote = re.search(\\\"\\\\d+[a-z]*\\\",filename).group()\\n cote_num = str(int(re.search(\\\"^\\\\d+\\\",full_cote).group()))\\n cote_ext_match = re.search(\\\"[a-z]+$\\\",full_cote)\\n if cote_ext_match:\\n cote_ext = cote_ext_match.group()\\n else:\\n cote_ext = \\\"\\\"\\n \\n return cote_num+cote_ext\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd0aa12d222f47bc8cf2a1645386cef1\",\n \"score\": \"0.50918114\",\n \"text\": \"def ParseFullFileName(file_name, myfull_pattern):\\n\\n # Remove any directories from the name.\\n #\\n # import pdb; pdb.set_trace()\\n file_name = os.path.basename(file_name)\\n\\n # Make sure the file name matches the full file name pattern.\\n #\\n # import pdb; pdb.set_trace()\\n if not re.search(myfull_pattern, file_name):\\n msg.PrintAndExit('method util.ParseFullFileName() encountered a file name\\\\n' 'which does not meet requirements for parsing a region pinball name:\\\\n ' + \\\\\\n file_name + '\\\\nFile name must conform to the region pinball naming convention.')\\n\\n # Directory containing the fields which were successfull parsed.\\n #\\n result = {}\\n\\n # Define a lambda function to print a standard error msg.\\n #\\n err_msg = lambda fname, string: msg.PrintAndExit(\\n 'method util.ParseFullFileName() encountered '\\n 'an error parsing \\\\'' + string + '\\\\' in \\\\nfile name: ' + fname)\\n\\n # Get the first 'user' part of the file name and parse it. The example\\n # given above produces this list:\\n #\\n # ['omnetpp.p10000-s10_57015']\\n #\\n # import pdb; pdb.set_trace()\\n user_part = re.findall(user_pattern, file_name)\\n field = user_part[0].split('.')\\n tmp = field[1].split('_')\\n result['program_name'] = field[0] # No error check for this field\\n result['input_name'] = tmp[0] # No error check for this field\\n\\n # Old code to get the num_proc. Curently not used. MUST be updated to use it\\n # again here... Also, code for PID must be modified if/when this code is used.\\n #\\n # Get the number of processes from the 1st 'user_part' string. It\\n # contains a string like '.X.', where X is num_proc.\\n #\\n # import pdb; pdb.set_trace()\\n # tmp = re.findall('[0-9]*\\\\.[0-9]+\\\\.', field[1])\\n # if len(tmp) > 0:\\n # num_proc = tmp[0].replace('.', '')\\n # try:\\n # num_proc = int(num_proc)\\n # except ValueError:\\n # err_msg(file_name, 'num_proc')\\n # result['num_proc'] = num_proc\\n\\n # Get the PID\\n #\\n try:\\n pid = int(tmp[1])\\n except ValueError:\\n err_msg(file_name, 'pid')\\n result['pid'] = pid\\n\\n # Get the fields from the PinPlay part of the file name.\\n #\\n # import pdb; pdb.set_trace()\\n if config.global_regions:\\n local_pp_iregion_pattern = pp_global_iregion_pattern\\n local_pp_pcregion_pattern = pp_global_pcregion_pattern\\n else:\\n local_pp_iregion_pattern = pp_iregion_pattern\\n local_pp_pcregion_pattern = pp_pcregion_pattern\\n if re.search(local_pp_iregion_pattern, file_name):\\n partial_result = ParsePartialFileNameIregion(file_name)\\n elif re.search(local_pp_pcregion_pattern, file_name):\\n partial_result = ParsePartialFileNamePCregion(file_name)\\n\\n # Return the fields from both parts of the file name.\\n #\\n return dict(list(result.items()) + list(partial_result.items()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f44becae6a6d1fb95e6d8fb73463b739\",\n \"score\": \"0.50851387\",\n \"text\": \"def analyze_filename(fname):\\n m = re.match(\\\"^(?P<track>[0-9]+)[\\\\s-]+(?P<title>.+)\\\\.flac$\\\", fname)\\n if m != None:\\n return m.groupdict() ## return {'title', 'track'}\\n m = re.match(\\\"^(?P<title>.*)\\\\.flac$\\\", fname)\\n if m != None:\\n d = m.groupdict()\\n d.update({'track':''})\\n return d\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d0694e6524d37faf130b255b2c5255b\",\n \"score\": \"0.5079833\",\n \"text\": \"def _get_filename(file):\\n return file.name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73de3d7d98497c243039129caf176ed7\",\n \"score\": \"0.5073598\",\n \"text\": \"def _name(user_filename):\\n # crude but good enough for now.\\n m = re.search(r\\\"([^:/\\\\\\\\]+)\\\\.\\\\w+$\\\", user_filename)\\n return m.group(1) if m else user_filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88e5298c10d04ce651caef64d2602625\",\n \"score\": \"0.50733334\",\n \"text\": \"def fname_geo_and_nda_for_tname(tname):\\n fname_geo, fname_nda = None, None\\n if tname == '1':\\n cdir = '/reg/d/psdm/MFX/mfx11116/calib'\\n #fname_geo = '%s/%s' % (cdir, 'CsPad::CalibV1/MfxEndstation.0:Cspad.0/geometry/566-end.data')\\n fname_geo = 'geo-cspad-mfx.txt'\\n fname_nda = 'nda-mfx11116-r0625-e026505-MfxEndstation-0-Cspad-0-max.txt'\\n #shape = (32,185,388)\\n elif tname == '2':\\n #fname_geo = '%s/%s' % (cdir, 'Jungfrau::CalibV1/MfxEndstation.0:Jungfrau.0/geometry/0-end.data')\\n fname_geo = 'geo-jf-mfx.txt'\\n fname_nda = 'nda-mfx11116-r0624-e005365-MfxEndstation-0-Jungfrau-0-max.txt'\\n #shape = (2,512,1024)\\n elif tname == '3':\\n d = '/reg/g/psdm/detector/alignment/epix10ka2m/calib-xxx-epix10ka2m.1-2020-02-25'\\n fname_geo = '%s/geometry-epix10ka2m-test.txt' % d\\n fname_nda = '%s/nda-mfxc00118-r41-averaged.txt' % d\\n #shape = (16,352,384) \\n else:\\n msg = 'Not-recognized test name: %s' % tname\\n sys.exit('End of test %s' % tname)\\n return fname_geo, fname_nda\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8790b0d6a098575bb17225389a363473\",\n \"score\": \"0.5071821\",\n \"text\": \"def get_facename(filename):\\n filename_without_extension = filename.split(\\\".\\\")[0]\\n face_name = filename_without_extension.split(\\\"_\\\")[0]\\n return face_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"375d5fd99f6d7c846e4a9cebe8615d31\",\n \"score\": \"0.50610256\",\n \"text\": \"def extract_structure_data_from_raystation_text_files(self, structureName):\\r\\n\\r\\n \\r\\n\\r\\n print 'Running extract_structure_data_from_raystation_text_files() ...'\\r\\n\\r\\n \\r\\n\\r\\n dataFile = open('RayStationExport/' + self.patientID + '/' + structureName + '.txt', 'r')\\r\\n\\r\\n dataReader = csv.reader(dataFile)\\r\\n\\r\\n \\r\\n\\r\\n for contourNumber, contour in enumerate(dataReader):\\r\\n\\r\\n contour = np.asarray(contour, dtype = np.float64)*10\\r\\n\\r\\n numContours = contourNumber + 1\\r\\n\\r\\n \\r\\n\\r\\n contourZCoords = np.zeros((numContours))\\r\\n\\r\\n numContourPoints = np.zeros((numContours))\\r\\n\\r\\n structure3d = []\\r\\n\\r\\n\\r\\n\\r\\n dataFile = open('RayStationExport/' + self.patientID + '/' + structureName + '.txt', 'r')\\r\\n\\r\\n dataReader = csv.reader(dataFile)\\r\\n\\r\\n\\r\\n\\r\\n for n, contour in enumerate(dataReader):\\r\\n\\r\\n # Convert from cm to mm when reading in contours\\r\\n\\r\\n contour = np.asarray(contour, dtype = np.float64)*10\\r\\n\\r\\n numContourPoints[n] = contour.size/3.0\\r\\n\\r\\n contourZCoords[n] = contour[2]\\r\\n\\r\\n structure3d = np.append(structure3d, contour)\\r\\n\\r\\n \\r\\n\\r\\n return (numContours, structure3d, contourZCoords, numContourPoints)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"908d2fe75f53d38afd4a46fb09bc7592\",\n \"score\": \"0.5053911\",\n \"text\": \"def parse_filename(self):\\n items = self.name.split('_')\\n self.mod = items[0]\\n keys = ['lagei', 'lagef', 'logz', 'dlogz']\\n for key, val in zip(keys, items[1:]):\\n if 'logz' in key:\\n val = 0.02 * 10 ** (float(val) - 4.)\\n self.__setattr__(key, float(val))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efa8f0f90f872180b2bb69e99b90e816\",\n \"score\": \"0.5042871\",\n \"text\": \"def extract_metadata(filename):\\n \\n a = re.compile(\\\"ra(.*)dec(.*)time(.*).dat\\\")\\n filepath = filename.split('/')\\n\\n s_ra = float(a.search(filepath[-1]).group(1))\\n s_dec = float(a.search(filepath[-1]).group(2))\\n s_time = a.search(filepath[-1]).group(3)\\n \\n \\n time = Time(s_time, format='isot', scale='utc')\\n position = SkyCoord(s_ra*u.deg, s_dec*u.deg, frame=ICRS, obstime = time)\\n return position, time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"937c83258259b7423e5a7eed6282b686\",\n \"score\": \"0.50391555\",\n \"text\": \"def getfileName(file):\\n\\n # Split by backslash, get the last index\\n file = file.rsplit('/', 1)[-1]\\n # Remove )\\\" which is found in Pyfadix files\\n file = re.sub('[\\\")]', '', file)\\n return file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86cffa698f424c12fce859898faa9ffe\",\n \"score\": \"0.50298417\",\n \"text\": \"def _parse_name(self, name):\\n\\n return name.split(\\\"/\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ad38e75cdb445f5dafe9feb256d9084\",\n \"score\": \"0.5022109\",\n \"text\": \"def __init__(self, file_path: str) -> None:\\n self.file_path = file_path\\n parts: List[str] = file_path.split('/')\\n self.mun_name = Mun.parse_mun_name(parts.pop(-1))\\n self.state = parts.pop(-1).upper()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af8cde09ca8b17d1ad66da9ac0c49176\",\n \"score\": \"0.5020684\",\n \"text\": \"def _decode_file_name(filename: str) -> MissionFileNameInfo:\\n\\n\\tfileList = filename.split(\\\".\\\")\\n\\n\\t# Check if the file name ends with \\\".pbo\\\"\\n\\tif fileList[-1:][0].casefold() != \\\"pbo\\\":\\n\\t\\traise Exception(\\\"Missions can only be submitted in .pbo format.\\\")\\n\\n\\t# Check if there are any erroneuous periods in the file name\\n\\tif len(fileList) > 3:\\n\\t\\traise Exception(\\\"File names can only have periods to denote the map and file extension.\\\")\\n\\tif len(fileList) < 3:\\n\\t\\traise Exception(\\\"File name appears to be missing the map definition.\\\")\\n\\n\\t# Get our map extension\\n\\tmapName = fileList[-2:][0].casefold()\\n\\n\\t# Split up our map name\\n\\tnameList = fileList[0].split(\\\"_\\\")\\n\\n\\t# Check if the mission is a test mission\\n\\tif nameList[0].casefold() == \\\"test\\\":\\n\\t\\tdel nameList[0]\\n\\n\\tif len(nameList) == 0:\\n\\t\\traise Exception(\\\"File name appears to be missing the gametype and playercount\\\")\\n\\n\\t# Check the mission type\\n\\tgameType = nameList[0].casefold()\\n\\tif not (gameType in VALID_GAMETYPES):\\n\\t\\traise Exception(f\\\"`{gameType}` is not a valid mission type!\\\")\\n\\n\\t# Grab the player count\\n\\ttry:\\n\\t\\tplayerCount = int(nameList[1])\\n\\texcept:\\n\\t\\traise Exception(\\\"I could not determine the player count in your mission.\\\")\\n\\n\\tmissionInfo: MissionFileNameInfo = {\\\"gameType\\\": gameType, \\\"map\\\": mapName, \\\"playerCount\\\": playerCount}\\n\\treturn missionInfo\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f714bdc79023b0b13787ae5ab5ed57c6\",\n \"score\": \"0.50139105\",\n \"text\": \"def buildCoeffsName(self):\\n indx = self.name.rfind('.')\\n return self.name[:indx]+'_coeffs'+self.chip+'.dat'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c6f155e16ea469d8a9307ecc25e3dd73\",\n \"score\": \"0.50112516\",\n \"text\": \"def FileChecker(self, name):\\n if name[-4:] == \\\".csv\\\":\\n self.fileName = name\\n else:\\n raise Exception(\\\"Must provide a .csv file\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"86cf71c0212f148fe3949f1b9ee42823\",\n \"score\": \"0.50028986\",\n \"text\": \"def get_bounds_file_name ( logger, bathyspace_dir, im_id ) :\\n try :\\n ascii_file_bounds = bathyspace_dir + \\\"\\\\\\\\\\\" + \\\"bounds_\\\" + str(im_id) + BathyExecutablesLib.FILE_EXT_ASCII\\n return ascii_file_bounds\\n except Exception, err:\\n logger.critical( \\\"Get file name failed:ERROR: %s\\\\n\\\" % str(err))\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3161d211868c4fc4d334860d97c4557\",\n \"score\": \"0.50023735\",\n \"text\": \"def _parse_h36m_imgname(imgname):\\n subj, rest = osp.basename(imgname).split('_', 1)\\n action, rest = rest.split('.', 1)\\n camera, rest = rest.split('_', 1)\\n return subj, action, camera\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4caeb3e9c7706720c20eb9dc640e0b4\",\n \"score\": \"0.49990866\",\n \"text\": \"def get_mesh_filename(self):\\n if Parallel.is_parallel():\\n return libspud.get_option('/geometry/mesh::CoordinateMesh/from_file/file_name')+'_%d.msh'%Parallel.get_rank()\\n #otherwise\\n return libspud.get_option('/geometry/mesh::CoordinateMesh/from_file/file_name')+'.msh'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5c9fa5e028a8d9a51c2d4cf10695612\",\n \"score\": \"0.4990337\",\n \"text\": \"def parse_filename(pathname):\\n if base.SEPERATOR in pathname:\\n cell, extra, extension = base.parse_filename(pathname)\\n else:\\n cell = None\\n extra = pathname\\n extension = ''\\n\\n if extension not in ('', 'lib', 'cell.lib', 'cell.json', 'wrap.lib', 'wrap.json'):\\n raise ValueError('Not possible to extract corners from: {!r}'.format(extension))\\n\\n if not extra:\\n extra = cell.name\\n cell = None\\n\\n # FIXME: Hack?\\n extra = extra.replace(\\\"lpflow_\\\",\\\"\\\")\\n extra = extra.replace(\\\"udb_\\\",\\\"\\\")\\n\\n kw = {}\\n kw['flags'] = []\\n kw['volts'] = []\\n kw['temps'] = []\\n\\n bits = extra.split(\\\"_\\\")\\n random = []\\n while len(bits) > 0:\\n b = bits.pop(0)\\n try:\\n kw['corner'] = CornerType.parse(b)\\n break\\n except TypeError as e:\\n random.append(b)\\n\\n while len(bits) > 0:\\n b = bits.pop(0)\\n\\n if VOLTS_REGEX.match(b):\\n assert b.endswith('V'), b\\n kw['volts'].append(float(b[:-1].replace('p', '.')))\\n elif TEMP_REGEX.match(b):\\n assert b.endswith('C'), b\\n kw['temps'].append(int(b[:-1].replace('n', '-')))\\n elif CORNER_TYPE_REGEX.match(b):\\n # FIXME: These are horrible hacks that should be removed.\\n assert len(b) == 2, b\\n assert b[0] == b[1], b\\n assert 'corner' in kw, kw['corners']\\n assert len(kw['corner']) == 2, kw['corners']\\n assert kw['corner'][0] == kw['corner'][1], kw['corners']\\n other_corner = CornerType.parse(b)\\n assert len(other_corner) == 2, other_corner\\n assert other_corner[0] == other_corner[1], other_corner\\n kw['corner'][1] = other_corner[0]\\n else:\\n kw['flags'].append(CornerFlag.parse(b))\\n\\n for k, v in kw.items():\\n kw[k] = tuple(v)\\n\\n if not kw['flags']:\\n del kw['flags']\\n\\n if 'corner' not in kw:\\n raise TypeError('Invalid corner value: '+extra)\\n\\n return Corner(**kw), random\",\n \"title\": \"\"\n },\n {\n \"docid\": \"324c550611fffd09d17bf11717e116d1\",\n \"score\": \"0.49867225\",\n \"text\": \"def get_file_name(input_file_name):\\n\\n input_file_name = input_file_name.split(\\\"\\\\\\\\\\\")\\n input_file_name = input_file_name[-1]\\n input_file_name = input_file_name.split(\\\"/\\\")\\n file_name = input_file_name[-1]\\n return (file_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76ef1de2da76f95f8effbd7bca5dff0f\",\n \"score\": \"0.4985296\",\n \"text\": \"def get_person_parsed_path(self, index):\\n im_name = self.get_person_image_name(index)\\n parse_name = im_name.replace(\\\".jpg\\\", \\\".png\\\")\\n parsed_path = osp.join(self.data_path, \\\"image-parse\\\", parse_name)\\n return parsed_path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e77856657081fd5c37d94c9f366cef56\",\n \"score\": \"0.49828887\",\n \"text\": \"def filename(file):\\n\\treturn os.path.basename(file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76cb24f77cf095ab29661972ec14f45f\",\n \"score\": \"0.49674705\",\n \"text\": \"def getPoint(imgName):\\r\\n name = imgName.split('\\\\\\\\')[-1]\\r\\n msg = name.split('.')[0].split('_')\\r\\n if len(msg) == 4:\\r\\n return msg[-1]\\r\\n elif len(msg) == 5:\\r\\n return msg[-2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7785b4b3a15caf48c2576f17050433a6\",\n \"score\": \"0.4965356\",\n \"text\": \"def get_unique_name(path):\\n mesh = re.findall(r'.*GT\\\\d{3}', path)[0]\\n return mesh\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f2f7c951263e35f1226f07991635418\",\n \"score\": \"0.49644053\",\n \"text\": \"def process_file_name(self, file_path_object):\\n renamed = file_path_object.name.rstrip('recision.dat').lower()\\n return renamed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b23bde3e540f157c2ecefd63c50ae49f\",\n \"score\": \"0.4959844\",\n \"text\": \"def extract_file_name(self):\\n # The directory is generated from the path - filename.\\n filename = os.path.basename(self.path)\\n file_name = filename\\n path = self.path.replace(filename, '')\\n return file_name, path\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5f1ac46f23344af0732d199a87e605e\",\n \"score\": \"0.49537307\",\n \"text\": \"def open_file_name_dialog(self):\\n\\n options = QFileDialog.Options()\\n options |= QFileDialog.DontUseNativeDialog\\n file_path, _ = QFileDialog.getOpenFileName(self,\\n \\\"Choose a data file\\\", \\\"\\\", \\\"CSV Files (*.csv)\\\", options=options)\\n if file_path:\\n # Storing the new data file path\\n self.current_data_file = file_path\\n # Extracting only the name of the file, without the whole path\\n file_name = os.path.basename(file_path)\\n print(file_name)\\n\\n # Displaying this into the input to provide visible feedback\\n self.data_file_input.setText(file_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80248da38f25878095f66b61c96b26ff\",\n \"score\": \"0.49516958\",\n \"text\": \"def parse(self, filename):\\n parsed = parse(self.conv_str, os.path.basename(filename))\\n if parsed:\\n return self.parse_attrs(parsed.named)\\n else:\\n print('parsing not successful for {}'.format(filename))\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"726878e4790efea6f469866e897df767\",\n \"score\": \"0.49469915\",\n \"text\": \"def get_file_namepath():\\n\\troot = tk.Tk()\\n\\troot.withdraw()\\n\\tfilepath = askopenfilename()\\n\\tname = filepath.split('/').pop()\\n\\tpath = filepath.replace(name, '')\\n\\treturn name, path, filepath\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e221a4d158b535987aa05c2a84d3ee45\",\n \"score\": \"0.49409786\",\n \"text\": \"def sceneName(self):\\n\\n sFileName = mc.file(query = True, sn = True, shortName = True)\\n\\n if sFileName:\\n sFileName = sFileName.rsplit(\\\".\\\", 1)[0]\\n\\n else:\\n sFileName = \\\"untitled\\\"\\n\\n return sFileName\",\n \"title\": \"\"\n },\n {\n \"docid\": \"95335b9c19a58d773c75a4d63264f539\",\n \"score\": \"0.49339423\",\n \"text\": \"def _get_parts_from_file_name(file_name):\\n simple_file_name = file_name.replace('.data', '')\\n parts = simple_file_name.split(':')\\n key = parts[0]\\n floating_seconds = float(parts[1])\\n serial_number = int(parts[2])\\n return key, (floating_seconds, serial_number)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5ed54533862d839eb4f9c358e70a694\",\n \"score\": \"0.49332106\",\n \"text\": \"def set_name(self, filepath):\\n try: \\n self.name = os.path.basename(filepath).split('.')[0]\\n return self.name\\n except:\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"134b8145676db784c8e100db9a6dbf22\",\n \"score\": \"0.4928679\",\n \"text\": \"def _parse_mesh_data(self, path) -> None:\\n self.boundary = self.parse_mesh_file(os.path.join(path, 'boundary'), self.parse_boundary_content)\\n self.points = self.parse_mesh_file(os.path.join(path, 'points'), self.parse_points_content)\\n self.faces = self.parse_mesh_file(os.path.join(path, 'faces'), self.parse_faces_content)\\n self.owner = self.parse_mesh_file(os.path.join(path, 'owner'), self.parse_owner_neighbour_content)\\n self.neighbour = self.parse_mesh_file(os.path.join(path, 'neighbour'), self.parse_owner_neighbour_content)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5eb766fdf37fcec658b495bf25cf0738\",\n \"score\": \"0.49274397\",\n \"text\": \"def get_name(fname):\\n with open(fname, encoding=\\\"utf-8\\\") as tmp:\\n return tmp.readline()[1:].strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba90a5ca8aab04f532f09785780ce885\",\n \"score\": \"0.49256536\",\n \"text\": \"def _parse_attr_name(self, name):\\n fullpath = os.path.abspath(name)\\n filename = os.path.split(fullpath)[1]\\n parts = filename.split(\\\".\\\")\\n if len(parts) != 4:\\n log.error(\\\"Found filename %s with incorrect format, need 4 parts\\\" % filename)\\n raise ValueError(\\\"Found filename %s with incorrect format, need 4 parts\\\" % filename)\\n\\n attr_name,type,cols,rows = parts\\n if type not in str_to_dtype:\\n log.error(\\\"Don't know how to interpret data type %s from %s\\\" % (type,filename))\\n raise ValueError(\\\"Don't know how to interpret data type %s from %s\\\" % (type,filename))\\n dtype = str_to_dtype[type]\\n\\n try:\\n cols = int(cols)\\n rows = int(rows)\\n except ValueError:\\n log.error(\\\"Columns and rows must be integers not (%s,%s)\\\" % (str(cols),str(rows)))\\n raise ValueError(\\\"Columns and rows must be integers not (%s,%s)\\\" % (str(cols),str(rows)))\\n\\n return attr_name,dtype,cols,rows\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f09a47a62fe5a334b6273f05af1d1e4f\",\n \"score\": \"0.49212056\",\n \"text\": \"def getFileGroup(fname):\\n \\n regexp = getFileRegExp(fname, match = False)\\n \\n base, _ = os.path.splitext(os.path.basename(fname))\\n if regexp is not None:\\n tokens = re.split(regexp,base)\\n if tokens:\\n return tokens[0]\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd307308366d9e78f7a85526ad938eb5\",\n \"score\": \"0.49196035\",\n \"text\": \"def locate_file(self, file_):\\n return os.path.basename(file_)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3df5ecdf70d58ea86a0fc2087b1895e8\",\n \"score\": \"0.49166444\",\n \"text\": \"def parse_datafile_name(name: str) -> Optional[SO5FileLabel]:\\n m = datafile_name_re.match(name)\\n if (m):\\n labels = [int(m.group(i)) for i in range(1, 6)]\\n return labels[0], labels[1], labels[2], labels[3], labels[4]\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66561df36eb7cca54890a0b6547bd0e1\",\n \"score\": \"0.49155393\",\n \"text\": \"def parse_channel(self, x):\\n final_path = os.path.basename(x)\\n if self.microscope == \\\"imagexpress\\\":\\n return int(final_path.split(\\\"_\\\")[3][1])\\n elif self.microscope == \\\"yokogawa\\\":\\n plate, well, rest = final_path.split(\\\"_\\\")\\n return int(rest.replace(\\\".tif\\\", \\\"\\\")[-2:])\\n elif self.microscope == \\\"opera\\\":\\n channel = int(final_path.replace(\\\".tif\\\", \\\"\\\")[16:])\\n else:\\n raise RuntimeError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b25b73261af3c6c1062f571d8f39670\",\n \"score\": \"0.49089038\",\n \"text\": \"def getNameFromFilename(cls, filename):\\n return filename[0:3]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c50ebd06e6758b758af9cea00fec425\",\n \"score\": \"0.48974076\",\n \"text\": \"def parse_filenames_from_dir(ddir, debug = False):\\n flist = sorted(os.listdir(ddir))\\n # remove files or dirs that do not end in '.nc'\\n flist = [f for f in flist if '.nc' in f]\\n # Get a unique sorted list of years\\n step_list = sorted(list(set([a.split('.')[0] for a in flist])))\\n # This assumes that all variables are given for the last time step.\\n var_list = [b.split('.')[-2] for b in flist if step_list[-1] in b]\\n return var_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3671fb5a27bdcc22ac9bd2666a5f9d20\",\n \"score\": \"0.48948908\",\n \"text\": \"def parse_file_path(file_path):\\n file_name = file_path.split('/')[-1]\\n name_components = file_name.split('.')\\n\\n sample_name = name_components[0]\\n result_type = name_components[1]\\n file_type = name_components[2]\\n\\n return sample_name, result_type, file_type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba43fe46d91b7fc18dfb73862cc30c1a\",\n \"score\": \"0.4886662\",\n \"text\": \"def determineFileType(fileName):\\r\\n\\r\\n if fileName.upper().endswith(\\\"STL\\\"):\\r\\n return \\\"STL\\\";\\r\\n \\r\\n if fileName.upper().endswith(\\\"STEP\\\"):\\r\\n return \\\"STEP\\\";\\r\\n \\r\\n #read the first 4 lines of the file, since we couldnt figure it out by file extension\\r\\n f = open(file,'r');\\r\\n text = \\\"\\\"; \\r\\n for i in range(5):\\r\\n text = text + f.readline();\\r\\n \\r\\n f.close();\\r\\n if 'FILE_DESCRIPTION' in text or 'STEP' in text:\\r\\n return \\\"STEP\\\";\\r\\n elif 'solid' in text: \\r\\n return \\\"STL\\\";\\r\\n else:\\r\\n return \\\"???\\\";\",\n \"title\": \"\"\n },\n {\n \"docid\": \"034f156c5683dfae571a3f7a1f206101\",\n \"score\": \"0.488626\",\n \"text\": \"def _get_name(self):\\n name = super(CUDSFileSource, self)._get_name()\\n return 'CUDS File: ' + name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e8750df4dd44f83705eae957f43e22d\",\n \"score\": \"0.48804036\",\n \"text\": \"def getLabel(file_name):\\n\\n category = file_name.split('/')[-2]\\n label = categories.get(category, categories['_background_noise_'])\\n\\n return label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13209ebd69d95720056c9c264edb6638\",\n \"score\": \"0.48756275\",\n \"text\": \"def from_filename(cls, filename):\\n contour = []\\n with open(filename) as fd:\\n data = fd.read()\\n for y, line in enumerate(data.splitlines(), start=1):\\n for x, ch in enumerate(line, start=1):\\n if ch == '#':\\n contour.append((x, y))\\n return ContourToPolygon(contour)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1efc39756479145be5e8bd8f2f27310f\",\n \"score\": \"0.48750666\",\n \"text\": \"def get_filename(self):\\n if self.file_file:\\n try:\\n return re.search(r'([^\\\\/]+)$', self.file_file).groups()[0]\\n except AttributeError:\\n pass\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf14ad06e05a5d76668326e4ab307d86\",\n \"score\": \"0.48743334\",\n \"text\": \"def filename_parser(args):\\n # Metadata data structure\\n meta = {}\\n args.jobcount = 0\\n\\n # Compile regular expression to remove image file extensions\\n pattern = '\\\\.' + args.type + '$'\\n ext = re.compile(pattern, re.IGNORECASE)\\n\\n # Walk through the input directory and find images that match input criteria\\n for (dirpath, dirnames, filenames) in os.walk(args.dir):\\n for filename in filenames:\\n # Is filename and image?\\n is_img = ext.search(filename)\\n # If filename is an image, parse the metadata\\n if is_img is not None:\\n # Remove the file extension\\n prefix = ext.sub('', filename)\\n metadata = prefix.split(args.delimiter)\\n\\n # Image metadata\\n img_meta = {'path': dirpath}\\n img_pass = 1\\n # For each of the type of metadata PlantCV keeps track of\\n for field in args.valid_meta:\\n # If the same metadata is found in the image filename, store the value\\n if field in args.fields:\\n meta_value = metadata[args.fields[field]]\\n # If the metadata type has a user-provided restriction\\n if field in args.imgtype:\\n # If the input value does not match the image value, fail the image\\n if meta_value != args.imgtype[field]:\\n img_pass = 0\\n img_meta[field] = meta_value\\n # Or use the default value\\n else:\\n img_meta[field] = args.valid_meta[field]\\n\\n if args.dates and 'timestamp' in img_meta:\\n in_date_range = check_date_range(args, img_meta['timestamp'])\\n if in_date_range is False:\\n img_pass = 0\\n\\n # If the image meets the user's criteria, store the metadata\\n if img_pass == 1:\\n meta[filename] = img_meta\\n args.jobcount += 1\\n\\n return args, meta\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b84d0092a60a6d6a6929c4c245bbd55\",\n \"score\": \"0.48704368\",\n \"text\": \"def _get_file_characts(self) -> Dict[str, str]:\\n return chrh.CharacteristicsHandler.handle_file_path(\\n self.request.filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3336fd1ce2ca46fa67f53b7f258114df\",\n \"score\": \"0.48564175\",\n \"text\": \"def csdFileName(self):\\n return self.csd\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3336fd1ce2ca46fa67f53b7f258114df\",\n \"score\": \"0.48564175\",\n \"text\": \"def csdFileName(self):\\n return self.csd\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ef1a820bef1a96bb0d94a17effe3ea9\",\n \"score\": \"0.48556283\",\n \"text\": \"def get_labels(filename, labelname):\\n # print labelname\\n labels = []\\n lab = labelname + \\\"=\\\"\\n with open(filename) as f:\\n lines = f.readlines()\\n for each in lines:\\n if labelname in each:\\n aux = each.replace(\\\" \\\", \\\"\\\")\\n # print aux\\n aux = aux[aux.index(labelname):]\\n # print aux\\n if \\\"\\\\n\\\" in aux:\\n aux = aux.replace(\\\"\\\\n\\\", \\\"\\\")\\n aux = aux.replace(lab, \\\"\\\")\\n aux = aux.replace(\\\";\\\", \\\"\\\")\\n # aux = aux.replace(\\\" \\\",\\\"\\\")\\n if not aux[-1].isalnum(): # no idea why this happens :O\\n aux = aux[:len(aux) - 1]\\n if aux.endswith(\\\"ROUND\\\"):\\n # print aux, \\\" se termina cu\\\"\\n if aux not in labels and \\\"old\\\" not in aux:\\n labels.append(aux)\\n labels.append(\\\"ERR_ROUND\\\")\\n return labels\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e300b1c26238b9843d6e72c0129db574\",\n \"score\": \"0.48531154\",\n \"text\": \"def visitStr(self):\\n import re\\n v = re.split('_', os.path.basename(self.tracerRawdataLocation))[1]\\n v = re.split('-Converted', v)[0]\\n w = re.split('\\\\.', v)[0]\\n if not w:\\n return v.lower()\\n else:\\n return w.upper()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f1302b11af8b49cbfa539dfcca7fb50\",\n \"score\": \"0.48482966\",\n \"text\": \"def get_file_name(self):\\n return self.name + '.' + gs.FILE_TYPE[self.file_type][1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"650a0ef19da3f8841e845badf8872800\",\n \"score\": \"0.4847801\",\n \"text\": \"def process_file_names(data):\\n return data.split('\\\\n')[:-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21fb2638fdc5ee9b2ec911eb6e011b3b\",\n \"score\": \"0.4847756\",\n \"text\": \"def readNameByReadFile(self, readFilePath):\\n dir_name, file_name, read_name, ext, prog = self.splitFilePath(readFilePath)\\n return read_name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d92f6d6259e955556a7e8e4a9cac61ef\",\n \"score\": \"0.4847354\",\n \"text\": \"def get_filename(file_path):\\n return os.path.split(file_path)[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b06ee13d756a2cd6dfcce89744e7f5e3\",\n \"score\": \"0.48459816\",\n \"text\": \"def get_photo_names(file_name):\\n # --- siehe dms.photos.views_add\\n ext_pos = file_name.rfind('.')\\n file_name_small = file_name[:ext_pos] + '_small' + file_name[ext_pos:]\\n file_name_middle = file_name[:ext_pos] + '_middle' + file_name[ext_pos:]\\n return file_name_small, file_name_middle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14512c642e95b4a76eeab2d746a6276f\",\n \"score\": \"0.48362327\",\n \"text\": \"def parse_file(self, name):\\n with open(name, \\\"rb\\\") as fp:\\n return self.parse(fp.read())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e814c52986d063fbb936230b63662973\",\n \"score\": \"0.48355153\",\n \"text\": \"def create_name(self):\\n data_spliced = self.name.split(\\\"-\\\")\\n self.folder = data_spliced[1]\\n self.filename = data_spliced[2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b830ced27ac6cfb3b3ed82677989a0e8\",\n \"score\": \"0.4831577\",\n \"text\": \"def character_statistics(file_name):\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5edffc36e804b5d4b23c98a95570bf2\",\n \"score\": \"0.48310873\",\n \"text\": \"def getFileNameNoChannels(path):\\n\\tgoodFile = False\\n\\tif path.endswith('_ch1.tif'):\\n\\t\\tgoodFile = True\\n\\telif path.endswith('_ch2.tif'):\\n\\t\\tgoodFile = True\\n\\telif path.endswith('_ch2.tif'):\\n\\t\\tgoodFile = True\\n\\tif not goodFile:\\n\\t\\treturn None\\n\\n\\tbasePath, tmpExt = os.path.splitext(path)\\n\\tbasePath2, baseFileName = os.path.split(basePath)\\n\\n\\tbaseFileName = baseFileName.replace('_ch1', '')\\n\\tbaseFileName = baseFileName.replace('_ch2', '')\\n\\tbaseFileName = baseFileName.replace('_ch3', '')\\n\\treturn baseFileName\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb44275b43af2dd836f4f9949a6beb75\",\n \"score\": \"0.48287952\",\n \"text\": \"def get_reference_name(filename):\\r\\n if filename.find('pegging.csv') != -1:\\r\\n return filename.replace('pegging.csv', 'pegging.reference.csv')\\r\\n return filename.replace('result', 'reference')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29ab393645558d995580eca5bbe1af8c\",\n \"score\": \"0.48242596\",\n \"text\": \"def filename(self):\\r\\n return posixpath.split(self)[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b85b12506d4ed68dacfd165bd0840ace\",\n \"score\": \"0.48232743\",\n \"text\": \"def ParsePartialFileNameIregion(file_name):\\n\\n # Remove any directories from the name.\\n #\\n # import pdb; pdb.set_trace()\\n file_name = os.path.basename(file_name)\\n\\n # Make sure the file name matches the PinPlay pattern.\\n #\\n # import pdb; pdb.set_trace()\\n if config.global_regions:\\n local_pp_iregion_pattern = pp_global_iregion_pattern\\n else:\\n local_pp_iregion_pattern = pp_iregion_pattern\\n if not re.search(local_pp_iregion_pattern, file_name):\\n msg.PrintAndExit('method util.ParsePartialFileNameIregion() encountered a file name\\\\n' 'which does not meet requirements for parsing a region pinball name:\\\\n ' + \\\\\\n file_name + '\\\\nFile name must conform to the region pinball naming convention.')\\n\\n # Directory containing the fields which were successfull parsed.\\n #\\n result = {}\\n\\n # Define a lambda function to print a standard error msg.\\n #\\n err_msg = lambda fname, string: msg.PrintAndExit(\\n 'method util.ParsePartialFileNameIregion() encountered '\\n 'an error parsing \\\\'' + string + '\\\\' in \\\\nfile name: ' + fname)\\n\\n # Get the file extension\\n #\\n # import pdb; pdb.set_trace()\\n # pp_iregion_pattern = '_t([0-9])+r([0-9]+)_warmup([0-9]+)_prolog([0-9]+)_region([0-9]+)_epilog([0-9]+)_([0-9]+)_([0-1]-[0-9]+)'\\n # group(1) tid, group(2) rid, group(3) warmup_length\\n # group(4) prolog_length, group(5) sim_length, group(6) epilog_length, \\n # group(7) traceno, group(8) weight\\n # pp_global_iregion_pattern = '_globalr([0-9]+)_warmup([0-9]+)_prolog([0-9]+)_region([0-9]+)_epilog([0-9]+)_([0-9]+)_([0-1]-[0-9]+)'\\n # group(1) rid, group(2) warmup_length\\n # group(3) prolog_length, group(4) sim_length, group(5) epilog_length, \\n # group(6) traceno, group(7) weight\\n tmp = re.split(local_pp_iregion_pattern, file_name)\\n if len(tmp) >= 2:\\n file_ext = tmp[1]\\n tmp = file_ext.split('.')\\n if tmp[0].isdigit():\\n file_ext = '.'.join(tmp[1:]) # Remove TID if it exists\\n else:\\n file_ext = '.'.join(tmp[0:])\\n result['file_ext'] = file_ext\\n\\n # Get the PinPlay generated part of the file name.\\n #\\n # import pdb; pdb.set_trace()\\n pp_part = re.search(local_pp_iregion_pattern, file_name)\\n \\n if pp_part:\\n if config.global_regions:\\n gr_offset = -1\\n tid = -1\\n else:\\n gr_offset = 0\\n tid = int(pp_part.group(1))\\n result['tid'] = tid\\n\\n region_num = int(pp_part.group(gr_offset+2))\\n result['region_num'] = region_num\\n\\n warmup = int(pp_part.group(gr_offset+3))\\n result['warmup'] = warmup\\n prolog = int(pp_part.group(gr_offset+4))\\n result['prolog'] = prolog\\n region = int(pp_part.group(gr_offset+5))\\n result['region'] = region\\n epilog = int(pp_part.group(gr_offset+6))\\n result['epilog'] = epilog\\n trace_num = int(pp_part.group(gr_offset+7))\\n result['trace_num'] = trace_num\\n\\n # Final field contains the weight with '-' as the decimal point.\\n # NOTE: string '1-00000' == 1.0\\n #\\n # import pdb ; pdb.set_trace()\\n tmp = pp_part.group(gr_offset+8) # string\\n if tmp.find('1-0') != -1:\\n weight = 1.0\\n else:\\n pos = tmp.find('.')\\n tmp = tmp[:pos] # Remove trailing '.' from the field\\n try:\\n weight = float(tmp.replace('-', '.'))\\n except ValueError:\\n err_msg(file_name, 'weight')\\n result['weight'] = weight\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2264b538c68f5e71c11cfc04716c8e4b\",\n \"score\": \"0.48232102\",\n \"text\": \"def ParsePartialFileNamePCregion(file_name):\\n\\n # Remove any directories from the name.\\n #\\n # import pdb; pdb.set_trace()\\n if config.global_regions:\\n local_pp_pcregion_pattern = pp_global_pcregion_pattern\\n else:\\n local_pp_pcregion_pattern = pp_pcregion_pattern\\n file_name = os.path.basename(file_name)\\n\\n # Make sure the file name matches the PinPlay pattern.\\n #\\n # import pdb; pdb.set_trace()\\n if not re.search(local_pp_pcregion_pattern, file_name):\\n msg.PrintAndExit('method util.ParsePartialFileNamePCregion() encountered a file name\\\\n' 'which does not meet requirements for parsing a region pinball name:\\\\n ' + \\\\\\n file_name + '\\\\nFile name must conform to the region pinball naming convention.')\\n\\n # Directory containing the fields which were successfull parsed.\\n #\\n result = {}\\n\\n # Define a lambda function to print a standard error msg.\\n #\\n err_msg = lambda fname, string: msg.PrintAndExit(\\n 'method util.ParsePartialFileNamePCregion() encountered '\\n 'an error parsing \\\\'' + string + '\\\\' in \\\\nfile name: ' + fname)\\n\\n # Get the file extension\\n #\\n # import pdb; pdb.set_trace()\\n tmp = re.split(pp_pcregion_pattern, file_name)\\n if len(tmp) >= 2:\\n file_ext = tmp[1]\\n tmp = file_ext.split('.')\\n if tmp[0].isdigit():\\n file_ext = '.'.join(tmp[1:]) # Remove TID if it exists\\n else:\\n file_ext = '.'.join(tmp[0:])\\n result['file_ext'] = file_ext\\n\\n # Get the PinPlay generated part of the file name.\\n #\\n # import pdb; pdb.set_trace()\\n pp_part = re.search(local_pp_pcregion_pattern, file_name)\\n\\n # Divide the pp_part into strings separated by '_' which contain the\\n # fields of the file name. The example given above produces this\\n # list:\\n #\\n #pp_pcregion_pattern = '_t([0-9])+r([0-9])+_warmupendPC(0x[0-9A-Fa-f]+)_warmupendPCCount([0-9]+)_warmuplength([0-9]+)_endPC(0x[0-9A-Fa-f]+)_endPCCount([0-9]+)_length([0-9]+)_multiplier([0-9]+-[0-9]+)_([0-9]+)_([0-1]-[0-9]+)'\\n # group(1) tid, group(2) rid, group(3) warmupendPC, \\n # group(4) warmupendPCCount, group(5) warmuplength, group(6) endPC, \\n # group(7) endPCCount, group(8) length, group(9) multiplier,\\n # group(10) traceno, group(11) weight\\n #pp_global_pcregion_pattern = '_globalr([0-9]+)_warmupendPC(0x[0-9A-Fa-f]+)_warmupendPCCount([0-9]+)_warmuplength([0-9]+)_endPC(0x[0-9A-Fa-f]+)_endPCCount([0-9]+)_length([0-9]+)_multiplier([0-9]+-[0-9]+)_([0-9]+)_([0-1]-[0-9]+)'\\n # group(1) rid, group(2) warmupendPC, \\n # group(3) warmupendPCCount, group(4) warmuplength, group(5) endPC, \\n # group(6) endPCCount, group(7) length, group(8) multiplier,\\n # group(9) traceno, group(10) weight\\n if pp_part:\\n if config.global_regions:\\n gr_offset = -1\\n tid = -1\\n else:\\n gr_offset = 0\\n tid = int(pp_part.group(1))\\n result['tid'] = tid\\n region_num = int(pp_part.group(gr_offset+2))\\n result['region_num'] = region_num\\n warmupendPC = pp_part.group(gr_offset+3) # string\\n result['warmupendPC'] = warmupendPC\\n warmupendPCCount = int(pp_part.group(gr_offset+4))\\n result['warmupendPCCount'] = warmupendPCCount\\n warmuplength = int(pp_part.group(gr_offset+5))\\n result['warmuplength'] = warmuplength\\n endPC = pp_part.group(gr_offset+6) # string\\n result['endPC'] = endPC\\n endPCCount = int(pp_part.group(gr_offset+7))\\n result['endPCCount'] = endPCCount\\n length = int(pp_part.group(gr_offset+8))\\n result['length'] = length\\n tmp = pp_part.group(gr_offset+9) # string\\n multiplier = float(tmp.replace('-', '.'))\\n result['multiplier'] = multiplier\\n trace_num = int(pp_part.group(gr_offset+10))\\n result['trace_num'] = trace_num\\n\\n # Final field contains the weight with '-' as the decimal point.\\n # NOTE: string '1-00000' == 1.0\\n #\\n # import pdb ; pdb.set_trace()\\n tmp = pp_part.group(gr_offset+11) # string\\n if tmp.find('1-0') != -1:\\n weight = 1.0\\n else:\\n pos = tmp.find('.')\\n tmp = tmp[:pos] # Remove trailing '.' from the field\\n try:\\n weight = float(tmp.replace('-', '.'))\\n except ValueError:\\n err_msg(file_name, 'weight')\\n result['weight'] = weight\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48ab2e369209ecfa943b8e22426a5a20\",\n \"score\": \"0.48180154\",\n \"text\": \"def get_name(data, meta_path, title_counter):\\n authors = \\\"\\\"\\n try:\\n title = data[\\\"stats\\\"][\\\"title\\\"]\\n authors = data[\\\"stats\\\"][\\\"authors\\\"]\\n except KeyError: # older type file\\n title = splitext(basename(meta_path))[0]\\n try:\\n name = title.split(\\\"#] \\\")[1]\\n title = splitext(name)[0]\\n except IndexError: # no \\\"#] \\\" in filename\\n pass\\n if not title:\\n try:\\n name = meta_path.split(\\\"#] \\\")[1]\\n title = splitext(name)[0]\\n except IndexError: # no \\\"#] \\\" in filename\\n title = _(\\\"NO TITLE FOUND\\\") + str(title_counter[0])\\n title_counter[0] += 1\\n name = title\\n if authors:\\n name = \\\"{} - {}\\\".format(authors, title)\\n return name\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993277,"cells":{"query_id":{"kind":"string","value":"79690304e21762f3fdc554260faa7dea"},"query":{"kind":"string","value":"Return all terms of a type (``s``, ``p``, or ``o``) in the store."},"positive_passages":{"kind":"list like","value":[{"docid":"b7cab220e8d1f97a27d2ca1e4037bc13","score":"0.7552455","text":"def all_terms(self, term_type):\n if term_type == 's':\n idx_label = 's:po'\n elif term_type == 'p':\n idx_label = 'p:so'\n elif term_type == 'o':\n idx_label = 'o:sp'\n else:\n raise ValueError('Term type must be \\'s\\', \\'p\\' or \\'o\\'.')\n\n with self.cur(idx_label) as cur:\n for key in cur.iternext_nodup():\n yield self._from_key(key)[0]","title":""}],"string":"[\n {\n \"docid\": \"b7cab220e8d1f97a27d2ca1e4037bc13\",\n \"score\": \"0.7552455\",\n \"text\": \"def all_terms(self, term_type):\\n if term_type == 's':\\n idx_label = 's:po'\\n elif term_type == 'p':\\n idx_label = 'p:so'\\n elif term_type == 'o':\\n idx_label = 'o:sp'\\n else:\\n raise ValueError('Term type must be \\\\'s\\\\', \\\\'p\\\\' or \\\\'o\\\\'.')\\n\\n with self.cur(idx_label) as cur:\\n for key in cur.iternext_nodup():\\n yield self._from_key(key)[0]\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"ea3a4c7357c27467aa72934cc79bc3c6","score":"0.6069078","text":"def terms(self):\n return []","title":""},{"docid":"228c8943f7f273ea3eb8a925309b356d","score":"0.6005916","text":"def terms(self) -> Set[Term]:","title":""},{"docid":"3bd79a1497c89900a3af8967273b5777","score":"0.594701","text":"def terms(self) -> Sequence[str]:\n return pulumi.get(self, \"terms\")","title":""},{"docid":"da6abc2551fa9990b2ab37843b5ba46b","score":"0.5945275","text":"def terms(self): # is this method necessary for this class?\n return [self.scalar], [self.base]","title":""},{"docid":"e8d034fe4b7d1ed1f5eae34289209416","score":"0.58156264","text":"def terms(self, order=None):\n return self._sorted(list(self.items()), order)","title":""},{"docid":"718068e32cf7fdf07dd8784dc4cfedab","score":"0.5797008","text":"def terms(self) -> Set[str]:\n return set([f['term'] for f in self.fields])","title":""},{"docid":"76162693147c2b35486722f71e77b446","score":"0.5778616","text":"def terms(self):\n return self._terms","title":""},{"docid":"7abb058014ae2864c14513608eb06dc6","score":"0.5634081","text":"def get_tokens(self, *types):\n return [t for t in self.tokens if t.type in types]","title":""},{"docid":"a5578f0bd4851d03cd68a5588e6f7748","score":"0.56092346","text":"def listterms(self):\n return list(self.items())","title":""},{"docid":"b2fd994f4575f4cf2d407811cebbfb37","score":"0.56085724","text":"def get_all(self):\n key = self.resource.config.type_var\n value = getattr(self.element_class,self.resource.config.type_var)\n return self.index.lookup(key,value)","title":""},{"docid":"1dd7da6796d37a72c08605345dfb58ff","score":"0.5580155","text":"def terms(self):\n\n return list(self._terms)","title":""},{"docid":"71a90e63d6a55d17ef36f09716d573d7","score":"0.5552349","text":"def types (self) :\r\n\t\treturn iter(self._type_name)","title":""},{"docid":"a3e8804c12873cede01e87e9cf20ffef","score":"0.5546789","text":"def terms(self):\n terms = []\n\n if type(self.recurrence) is Add:\n # We have multiple terms\n terms = self.recurrence.args\n else:\n # We do not have multiple terms, so the whole expression is one term\n terms.append(self.recurrence)\n\n return terms","title":""},{"docid":"6a600d3bfb0d74c66bc71ce20abfb79d","score":"0.55235326","text":"def terms(self, order=None):\n return dmp_list_terms(self.rep, self.lev, self.domain, order=order)","title":""},{"docid":"084bc1c32e81717841167a4b0d4bbc0f","score":"0.55140245","text":"def all_terms(f):\n return smp_all_terms(f.rep, f.lev, f.ord, f.dom)","title":""},{"docid":"6f4fe7f1f8355d6fcd964f7139250852","score":"0.5488184","text":"def get_values(self, *types):\n return tuple(t.value for t in self.tokens if t.type in types)","title":""},{"docid":"e98e60c0fddaffb3ba3a5ee40cd9965b","score":"0.5479323","text":"def get_type_names(self):\n return [v[1] for v in self.knowledge.values()]","title":""},{"docid":"0665fbd583c0c108f43190552de41c59","score":"0.54375684","text":"def items(self):\n\n return self._terms","title":""},{"docid":"19f676ae802d10a06f02ee024510ca80","score":"0.54201204","text":"def term_set(self):\n return {term.term for term in self.terms}","title":""},{"docid":"789aca40906cbc6fcca2ec224040f192","score":"0.54023355","text":"def terms(self):\n return self.properties.get(\"terms\",\n TaxonomyItemCollection(self.context, Term,\n ResourcePath(\"terms\", self.resource_path)))","title":""},{"docid":"6c525b4cee874baedc9309aba485a039","score":"0.5345627","text":"def vars(self, term):\n if term.type == clingo.SymbolType.Number:\n return set()\n if match(term, \"-\", 2) or match(term, \"+\", 2) or match(term, \"*\", 2) or match(term, \"..\", 2):\n return self.vars(term.arguments[0]).union(self.vars(term.arguments[1]))\n if match(term, \"-\", 1) or match(term, \"+\", 1):\n return self.vars(term.arguments[0])\n if term.type in (clingo.TheoryTermType.Symbol, clingo.TheoryTermType.Function, clingo.TheoryTermType.Tuple):\n return {term}\n return set()","title":""},{"docid":"d0065744831e47899e27d70a3815b179","score":"0.5334623","text":"def product_types(self):\n\n query = 'SELECT DISTINCT tyyppi FROM juomat;'\n cursor = self.connection.cursor()\n cursor.execute(query)\n rows = cursor.fetchall()\n\n return [row[0] for row in rows if row[0] != None]","title":""},{"docid":"a0727be46ea3eef008ffb2f58bc0bbe1","score":"0.53262806","text":"def all_meta_types(self):\n global metatypes \n metatypes = self.adt_meta_types\n if len(metatypes) > 0:\n f = lambda x: x['name'] in metatypes\n return filter(f, Products.meta_types) + self.meta_types\n else:\n return self.meta_types","title":""},{"docid":"edba00a6f6bb1f477c6335f2f12ea787","score":"0.5310193","text":"def types(self):\n r = requests.get(self.evaluator_url + 'types')\n r.raise_for_status()\n return r.json()","title":""},{"docid":"30353cd0550d0eefcf7b7ba9d8b436fd","score":"0.5256919","text":"def getXsdTerms (self):\n\t\txsd = self.getXsd(self.vocabUrl)\n\t\tvocabType = xsd.getEnumerationType(self.typeName)\n\t\tif not vocabType:\n\t\t\traise Exception, 'vocabType not found for \"%s\"' % self.typeName\n\t\treturn vocabType.getValues()","title":""},{"docid":"8b423f9baec5f1da0c172faed51e36ab","score":"0.5243929","text":"def types_vocab(context):\n context = getattr(context, 'context', context) or getSite()\n ptool = getToolByName(context, 'plone_utils', None)\n ttool = getToolByName(context, 'portal_types', None)\n if ptool is None or ttool is None:\n return None\n items = [ (ttool[t].Title(), t)\n for t in ptool.getUserFriendlyTypes() ]\n items.sort()\n items = [SimpleTerm(i[1], i[1], i[0]) for i in items]\n return SimpleVocabulary(items)","title":""},{"docid":"e48e91ffcebca46ac940302f28884ffa","score":"0.52369475","text":"def iterterms(self):\n return iter(self.items())","title":""},{"docid":"632de240cfe7498e88f9cd70b62bbf0f","score":"0.5233667","text":"def __call__(self, context=None):\n context = getSite()\n sm = getSiteManager(context)\n # Get all registered rating types\n categories = (c for n,c in sm.adapters.lookupAll((IDynamicType,),\n self.interface))\n terms = []\n for cat in categories:\n terms.append(SimpleTerm(value=cat, token=cat.name, title=_(cat.title)))\n return SimpleVocabulary(terms)","title":""},{"docid":"52aff986e23884ed435962f7ce5b4a10","score":"0.52202356","text":"def terms():\t\n\tkeywords = ['dissent','protest','sosvenezuela']\t\t\n\treturn keywords","title":""},{"docid":"b4ce88382f4102dd960843f2d679e1fa","score":"0.52161","text":"def query_terms(self) -> Sequence[str]:\n return pulumi.get(self, \"query_terms\")","title":""},{"docid":"b4ce88382f4102dd960843f2d679e1fa","score":"0.52161","text":"def query_terms(self) -> Sequence[str]:\n return pulumi.get(self, \"query_terms\")","title":""},{"docid":"b4ce88382f4102dd960843f2d679e1fa","score":"0.52161","text":"def query_terms(self) -> Sequence[str]:\n return pulumi.get(self, \"query_terms\")","title":""},{"docid":"fdc1ed5c0c1a63ca398b77a741469e6f","score":"0.52118754","text":"def terms(self):\n return self.terms_error + self.terms_regularization","title":""},{"docid":"b75cff8efb66ee1526cd60465e2af4d1","score":"0.5208267","text":"def get_all_store_objects(self):\n return self.get_store(key='*')","title":""},{"docid":"d4afa8b180ba91598a22905b6d71a314","score":"0.519935","text":"def terms(f):\n return smp_terms(f.rep, f.lev, f.ord, f.dom)","title":""},{"docid":"85e33d7a4f61deedf46286805d5fdd30","score":"0.51868534","text":"def all_terms(self):\n\n for s_name, s in self.sections.items():\n\n # Yield the section header\n if s.name != 'Root':\n yield s\n\n # Yield all of the rows for terms in the section\n for rterm in s:\n yield rterm\n for d in rterm.descendents:\n yield d","title":""},{"docid":"a8057d29ccf2667b5cbb79bdc27ce71e","score":"0.5186738","text":"def _get_triples_from_store(\n self, spo: tuple = (None, None, None)\n ) -> Iterable[tuple]:\n if not hasattr(self, \"sparqlstore\"):\n self._open_sparql_store()\n\n return self.sparql_store.triples(spo)","title":""},{"docid":"6a2241a00f8e6ddb33a66309792c4e7d","score":"0.5146953","text":"def product_subtypes(self):\n\n query = 'SELECT DISTINCT alatyyppi FROM juomat;'\n cursor = self.connection.cursor()\n cursor.execute(query)\n rows = cursor.fetchall()\n\n return [row[0] for row in rows if row[0] != None]","title":""},{"docid":"03250c0f8eb97fbf503745f122a4442b","score":"0.51414317","text":"def product_types(self):","title":""},{"docid":"17fa7d7b213d754c9d1df8bf94274bd5","score":"0.5111763","text":"def get_queryset(self):\n # get just the non-class paleocore terms, which get added to the context as terms\n paleocore_terms = Term.objects.filter(projects__name='pc').order_by('category', 'name')\n paleocore_terms = paleocore_terms.exclude(is_class=True)\n return paleocore_terms","title":""},{"docid":"632f3de1ed4c9c964d5e262b8ee95130","score":"0.5093937","text":"def get_relax_types_eos():\n return [item.value for item in RelaxType if 'cell' not in item.value and 'volume' not in item.value]","title":""},{"docid":"3d4afefef6acb8ce4bfb86da8967b0a0","score":"0.50901294","text":"def get_relax_types():\n return [entry.value for entry in RelaxType]","title":""},{"docid":"b3ade0d6e0a524750ad2e68771657bed","score":"0.5078263","text":"def collect(self, term, seen = None):\n for t in subterms(term, seen):\n t.type_constraints(self)","title":""},{"docid":"5de885ff0501b0371db8e429c3b06d62","score":"0.5063471","text":"def free_terms(self, var_only=False):\n result = set()\n # term case handled in subclass\n if isinstance(self.op, TypedExpr):\n result.update(self.op.free_terns(var_only=var_only))\n for a in self.args:\n result.update(a.free_terms(var_only=var_only))\n return result","title":""},{"docid":"a0790326cc33fe05f2a858282fa600c2","score":"0.5051801","text":"def term_frequencies(self):\n try:\n vocab = self.df['type'].value_counts().reset_index()\n vocab.columns = ['type', 'term_freq']\n vocab = vocab.sort_values(by='type', ascending=True)\n return vocab\n except BaseException:\n sys.exit('Could not get term frequencies.')","title":""},{"docid":"575b44e60911dee675c0fb3acbb55d46","score":"0.50133795","text":"def get_site_terms(self):\n return # osid.installation.SiteQueryInspector","title":""},{"docid":"c541cecce8b2fcae4fabd8a20527699d","score":"0.49980584","text":"def get_recipes_by_types(self, recipe_type):\n lst = self.recipe_list[recipe_type]\n return \", \".join(map(str, lst.keys()))","title":""},{"docid":"9865255d85dc1e6b5e96732bafe5f7b5","score":"0.49973938","text":"def monoms(self, order=None):\n return [ monom for monom, _ in self.terms(order) ]","title":""},{"docid":"50d9adf837bcbe6cfc0045c846b932ee","score":"0.4978654","text":"def get_terms(self):\n # self.fields['title'] = util.remove_punctuations(self.fields['title'])\n terms = util.remove_punctuations(self.fields['title']).split() + self.abstract\n normalized_terms = [util.normalize_token(term) for term in util.remove_stopwords(terms)]\n return normalized_terms","title":""},{"docid":"b5d037c1efa044e13c570b08866d0668","score":"0.4975402","text":"def getWordsForTypes(self):\n type_df = []\n for i in self.types:\n type_df.append(self.df[self.df['type'] == i])\n\n for i, t in enumerate(self.types):\n self.type_word_lst[t] = self.wordCounter(type_df[i]['tokens'])\n return self.type_word_lst","title":""},{"docid":"448e0c178ac24b663b446cbb42249466","score":"0.4964904","text":"def get_objects_NT_for_s_p(self, p, s):\n local_sprql = SPARQLWrapper(self.knowledge_base)\n\n o_list = []\n query_text = (\"\"\"\n SELECT DISTINCT ?o\n WHERE{\n <%s> <%s> ?o .\n } \"\"\" % (s, p))\n #FILTER (regex(?t, \"^http://dbpedia.org/\")) maybe removed\n # I figured out that a good filter for the type of the object has to be of \"^http://dbpedia.org/ontology\"\n # in oreder to get valuable results\n local_sprql.setQuery(query_text)\n local_sprql.setReturnFormat(JSON)\n results_inner = local_sprql.query().convert()\n for inner_res in results_inner[\"results\"][\"bindings\"]:\n # s = inner_res[\"s\"][\"value\"]\n o = inner_res[\"o\"][\"value\"]\n o_list.append(o)\n\n return o_list","title":""},{"docid":"952a9ce7963558932378d29a29e29ba3","score":"0.49600717","text":"def get_objects_WT_for_s_p(self, p, s):\n local_sprql = SPARQLWrapper(self.knowledge_base)\n pu = p.encode('utf-8')\n o_list = []\n query_text = (\"\"\"\n SELECT DISTINCT ?o\n WHERE{\n <%s> <%s> ?o .\n ?o a ?t .\n } \"\"\" % (s, pu))\n #FILTER (regex(?t, \"^http://dbpedia.org/\")) maybe removed\n # I figured out that a good filter for the type of the object has to be of \"^http://dbpedia.org/ontology\"\n # in oreder to get valuable results\n local_sprql.setQuery(query_text)\n local_sprql.setReturnFormat(JSON)\n results_inner = local_sprql.query().convert()\n for inner_res in results_inner[\"results\"][\"bindings\"]:\n # s = inner_res[\"s\"][\"value\"]\n o = inner_res[\"o\"][\"value\"]\n o_list.append(o)\n\n return o_list","title":""},{"docid":"6c9a6079d7e50ac8a1e1b1ec9338ab76","score":"0.4958613","text":"def terms(self): # pylint: disable=no-self-use\n raise TermsUndefinedError","title":""},{"docid":"9fced3e14397afe36e6a38979034e87d","score":"0.4953121","text":"def list_observations_types(mycur):\n \n print \"Current type available:\"\n type_query = \"select distinct TYPE from OBS_SET_TYPE\"\n mycur.arraysize = 1000 # get 1000 at a time when fetching\n mycur.execute(type_query)\n for item in mycur:\n print item[0]\n\n sys.exit(0)","title":""},{"docid":"b36773ab14104f1cf421c007ee49c2ce","score":"0.49442348","text":"def listVocTerms(self, field):\n context = self.real_context\n field = context.getField(field)\n keys = type(field.getRaw(context)) in (list, tuple) and field.getRaw(context) or [field.getRaw(context)]\n objs = [field.vocabulary.getAllVocTerms(context).get(key, None) for key in keys]\n return objs","title":""},{"docid":"644932f0ed4c586316de5fcee9a74777","score":"0.49316746","text":"def list_ostypes():\n return list_items(\"ostypes\", True, \"ID\")","title":""},{"docid":"5c32cb32cfb82aa0e43f4c231d98c7fb","score":"0.49254468","text":"def get_pseudopotential_type(cls, calc, **kwargs):\n types = []\n raw_types = cls._get_raw_pseudopotential_type(calc, **kwargs)\n for element in sorted(raw_types):\n if raw_types[element] is None or raw_types[element] in cls._pseudopotential_types:\n types.append(raw_types[element])\n else:\n types.append('other')\n return types","title":""},{"docid":"91bd72111d7920ff5a9031875c956ad5","score":"0.49219","text":"def get_all_types(request):\n controller = RuleTypeController()\n query_set = controller.get_all()\n response = {}\n response['types'] = list(query_set.values())\n return JsonResponse(response, safe=False)","title":""},{"docid":"fbd11edc8dfe7c8bd21543a403502e76","score":"0.4912332","text":"def possible_filter_terms(self) -> FrozenSet[FilterTerm]:","title":""},{"docid":"4dd55a45df103071d962fe6d3a584591","score":"0.49116492","text":"def get_url_terms(self):\n return # osid.search.terms.StringTerm","title":""},{"docid":"799892c9e6be47087d39f4e8518924fe","score":"0.49103883","text":"def types(self):\n return list(self.__items.keys())","title":""},{"docid":"d408e3d342983b0cc808c5b4936cdf44","score":"0.49080944","text":"def destructure(self) -> list[ir.ValueExpr]:\n return [self[field_name] for field_name in self.type().names]","title":""},{"docid":"2449ae1c5bda62b8011e9a993f207884","score":"0.49007508","text":"def typepi(self):\n typep = []\n b = 2\n while b < len(self.parserdata):\n typep.append(self.parserdata[b])\n b += 4\n return typep","title":""},{"docid":"339bf942c1bfe815de1d57e7cda425c6","score":"0.48991424","text":"def get_all():\n\n # This relies on access to the global dictionary `melon_types`\n\n return melon_types.values()","title":""},{"docid":"2d60760405e5531bc2647c25c12aecfc","score":"0.48896676","text":"def tf (self, verbose = False): \n\t\tif verbose is True:\n\t\t\tprint \"extracting term-frequency of term : \" + term.term().text() \n\t\tout = []\n\t\tfor term in self.query: # all the words repeat only once .. ( Assumption : even if they repeat more than once, ignore it)\n\t\t\tout.append(1)\n\t\treturn out","title":""},{"docid":"a88cb3b0df1814b76d6d8363ed09d07c","score":"0.48687857","text":"def get_terms(self, buffer: str) -> Iterator[str]:\n pass","title":""},{"docid":"6342b04ef43ed89eb9a21d2b6cf350e5","score":"0.48673657","text":"def terms(self, letter):\n return self.get_entries()[letter]","title":""},{"docid":"a77c07b7f147b807278a0101cf91455f","score":"0.4863108","text":"def _type_hints_to_formulas(self, vrs, action):\n r = list()\n for var in vrs:\n hints = self.vars[var]\n if hints['type'] == 'bool':\n continue\n assert hints['type'] == 'int', hints\n a, b = hints['dom']\n s = '({a} <= {var}) /\\ ({var} <= {b})'\n type_inv = s.format(a=a, b=b, var=var)\n r.append(type_inv)\n if not action:\n continue\n type_inv_primed = s.format(\n a=a, b=b, var=stx.prime(var))\n r.append(type_inv_primed)\n return stx.conj(r)","title":""},{"docid":"4cd56300d7f6ee00bd6fd4f43edb50d0","score":"0.4863034","text":"def percepts(self, type='ALL'):\n if type=='ALL':\n return self.nodes\n elif type=='CAT':\n return [per for per in self.nodes if isinstance(per, PERCEPT_CAT)]\n elif type=='TOKEN':\n return [per for per in self.nodes if isinstance(per, PERCEPT_TOKEN)]\n else:\n print 'invalid type'\n return None","title":""},{"docid":"f99ff18bfbb00664c8ea100cff79ada5","score":"0.48547268","text":"def get_pseudopotential_type_other_name(cls, calc, **kwargs): # pylint: disable=invalid-name\n types = []\n raw_types = cls._get_raw_pseudopotential_type(calc, **kwargs)\n for element in sorted(raw_types):\n if raw_types[element] is None or raw_types[element] in cls._pseudopotential_types:\n types.append(None)\n else:\n types.append(raw_types[element])\n return types","title":""},{"docid":"9f032c72ac4babedb37feec8c64719be","score":"0.485155","text":"def get_terms(db_session, text: str) -> List[str]:\n all_terms = db_session.query(Term).filter(Term.discoverable == True).all() # noqa\n phrases = build_term_vocab([t.text for t in all_terms])\n matcher = build_phrase_matcher(\"dispatch-terms\", phrases)\n extracted_terms = extract_terms_from_text(text, matcher)\n return extracted_terms","title":""},{"docid":"0cf29752143dce43b7e5da94859bcc04","score":"0.48405552","text":"def terms(self):\n for term in self.term_matrix[1:]:\n yield Polynomial(Polynomial.clean([self.term_matrix[0], term]), self.field_characteristic)","title":""},{"docid":"e3c610b62c0d721e4ee22eadc4a0bc1a","score":"0.48252597","text":"def ele_types(eles):\n return list(set([e[\"type\"] for e in eles]))","title":""},{"docid":"af205a0936ffea7415ee23424c751873","score":"0.4819703","text":"def list_ops(self, op_type='', op_name='', pass_name='', kernel_name=''):\n return filter(lambda op: op_type in op.type() and op_name in op.name() and pass_name in op.pass_name()\n and kernel_name in op.kernel_name(), self.ops_list)","title":""},{"docid":"3ce5b8d65f9d242c7d6da008d78c9781","score":"0.48187575","text":"def get_data_terms(self):\n return # osid.search.terms.BytesTerm","title":""},{"docid":"f77fbb824fcfc72ad0093bdfeb087e2f","score":"0.48010176","text":"def datatypes(self):\n return []","title":""},{"docid":"a2a4a9fc2ed798ba8e54e986d7cbbf05","score":"0.47997743","text":"def get_all_symbols(type, client):\n if type == \"S\":\n all_tickers = client.stock_symbols('US')\n elif type == \"C\":\n all_tickers = client.crypto_symbols('BINANCE')\n symbols = []\n for ticker in all_tickers:\n symbols.append(ticker[\"symbol\"])\n return symbols","title":""},{"docid":"10ee044b1683e80ab85c11bdb032d483","score":"0.47935882","text":"def get_types(self):\n return self.instanceTypes","title":""},{"docid":"37e55e0d6a744a2e860e6235f4ae11f7","score":"0.4788895","text":"def op_types(self):","title":""},{"docid":"b960f0eb4f094e8a180efc1a05e92d8c","score":"0.47835895","text":"def get_food_types(food, categories):\n\n retval = \"\"\n for c in categories:\n filter_category = c['type']\n food_item = c['filter'].upper()\n\n if filter_category == \"food\" and food[food_item] == 1:\n retval = retval + \" data-\" + food_item.lower()\n \n # Clear out the whitespaces on the ends\n return retval.strip()","title":""},{"docid":"e4db686cb5eb6f6787ee0d6d05e61d4d","score":"0.4781249","text":"def spatial_term(powers) :\n\n if term_type=='arr' :\n if powers[0]==0 : \n return eta if powers[1]==1 else pow(eta, powers[1])\n if powers[1]==0 : \n return xi if powers[0]==1 else pow(xi, powers[0])\n return (xi if powers[0]==1 else pow(xi, powers[0]))*(\n eta if powers[1]==1 else pow(eta, powers[1]))\n elif term_type=='coef' :\n if powers[0]==0 : \n return 'eta' if powers[1]==1 else 'eta%d'%powers[1]\n if powers[1]==0 : \n return 'xi' if powers[0]==1 else 'xi%d'%powers[0]\n return ('xi' if powers[0]==1 else 'xi'+str(powers[0]))+(\n 'eta' if powers[1]==1 else 'eta'+str(powers[1]))\n elif term_type=='expr' :\n if powers[0]==0 : return 'eta^%d'%powers[1]\n if powers[1]==0 : return 'xi^%d'%powers[0]\n return 'xi^%d*eta^%d'%tuple(powers)\n else : raise Error.Input(\"Unknown term type '%s' in \"\n \"MagnitudeFit.__linear_terms\"%term_type)","title":""},{"docid":"fca4d3402a2b4344f3ccfccfe3c4883b","score":"0.47734714","text":"def gens(self):\n return tuple(self(x) for x in self.free_module().gens())","title":""},{"docid":"15ee8bf93426b4def755a537287c1596","score":"0.47712448","text":"def get_terms(self, id: int) -> List[TermBase]:\n terms = []\n if self.terms != [] or self.terms_slc != []:\n for all_terms in [self.terms, self.terms_slc]:\n for term in all_terms:\n if isinstance(term, Term):\n if id in term.ids:\n terms.append(term)\n elif isinstance(term, SlcTerm):\n for subterm in term.terms:\n if id in subterm.ids:\n terms.append(term)\n break\n return terms\n else:\n raise Exception(\n \"There are currently no terms in this problem. \\\n Please download the problem on the client or add terms to the \\\n problem to perform this operation\"\n )","title":""},{"docid":"6996643db4a21c232573ebdbf45c8a1f","score":"0.47542888","text":"def get_types(self):\n endpoint = '/types'\n return self.make_request(self.BASE_URL + endpoint)","title":""},{"docid":"02e4b9bef0090f7cf830b060c5ff6782","score":"0.475413","text":"def compute_terms(self):\n terms_dict = {self.name: self.data}\n poly_terms = self.generate_poly_terms() # Replaced w decorators...\n terms_dict.update(poly_terms)\n return terms_dict","title":""},{"docid":"4145a8fb5ce5bc1fe1101a602696a45c","score":"0.47454032","text":"def ostype(value, arg):\n rlist = []\n for nco in value:\n if arg[0] == '!' and nco['ostype'] != arg[1:]:\n rlist.append(nco)\n elif nco['ostype'] == arg:\n rlist.append(nco)\n return rlist","title":""},{"docid":"18f085e98ad92fcb91c6110a9f5ef77f","score":"0.47428417","text":"def DefineTermAnyType(t: Kind):\n\n class _SIPO(Circuit):\n name = 'Term_{}t'.format(cleanName(str(t)))\n IO = ['I', In(t)]\n @classmethod\n def definition(cls):\n type_size_in_bits = GetCoreIRBackend().get_type(t).size\n type_to_bits = Dehydrate(t)\n term = Term(type_size_in_bits)\n wire(cls.I, type_to_bits.I)\n wire(type_to_bits.out, term.I)\n\n return _SIPO","title":""},{"docid":"aebbcae8c22710d5b47be4672e00551c","score":"0.47164845","text":"def to_terms(self) -> OrderedSet[Term]:\n return OrderedSet((Term([self]),))","title":""},{"docid":"e345d3029e080dc945486dfac672d6eb","score":"0.47120032","text":"def values() -> typing.List['SpsRunType']:\n ...","title":""},{"docid":"7942b1c6508c996996b45de0e0214a0d","score":"0.47115138","text":"def types(self):\n return self._types","title":""},{"docid":"7942b1c6508c996996b45de0e0214a0d","score":"0.47115138","text":"def types(self):\n return self._types","title":""},{"docid":"7942b1c6508c996996b45de0e0214a0d","score":"0.47115138","text":"def types(self):\n return self._types","title":""},{"docid":"480fcd6a4708aae7727ec48dba09b49b","score":"0.47099948","text":"def get_subtypes(self):\n return self.subtypes.all()","title":""},{"docid":"947c01e8f82d844a127c5e83b75a125c","score":"0.47054058","text":"def QueryForAllResults(type):","title":""},{"docid":"d87ffab2263621ce6b24e171addfc045","score":"0.469911","text":"def get_all(self):\n return self._store","title":""},{"docid":"e0e24de4de5471e441e704d9e2850543","score":"0.46979028","text":"def load_types(self):\n instance_types = set()\n for key in self:\n instance_type = key[1]\n instance_types.add(instance_type)\n\n return list(instance_types)","title":""},{"docid":"dcf030a2845b49261a143bc913b17092","score":"0.4683023","text":"def get_package_terms(self):\n return # osid.installation.PackageQueryInspector","title":""},{"docid":"dcf030a2845b49261a143bc913b17092","score":"0.4683023","text":"def get_package_terms(self):\n return # osid.installation.PackageQueryInspector","title":""},{"docid":"03de6ca1ff6a5e34089828f7576a2530","score":"0.46808532","text":"def all() -> List['TypeFilter']:\n return [e.value for e in list(AvailableTypeFilters)]","title":""},{"docid":"5ff46078cbc6ea87b7c01b6e8b5082e7","score":"0.46768472","text":"def narrowTypes(*args):\n return _coin.SoNodekitCatalog_narrowTypes(*args)","title":""}],"string":"[\n {\n \"docid\": \"ea3a4c7357c27467aa72934cc79bc3c6\",\n \"score\": \"0.6069078\",\n \"text\": \"def terms(self):\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"228c8943f7f273ea3eb8a925309b356d\",\n \"score\": \"0.6005916\",\n \"text\": \"def terms(self) -> Set[Term]:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bd79a1497c89900a3af8967273b5777\",\n \"score\": \"0.594701\",\n \"text\": \"def terms(self) -> Sequence[str]:\\n return pulumi.get(self, \\\"terms\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da6abc2551fa9990b2ab37843b5ba46b\",\n \"score\": \"0.5945275\",\n \"text\": \"def terms(self): # is this method necessary for this class?\\n return [self.scalar], [self.base]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8d034fe4b7d1ed1f5eae34289209416\",\n \"score\": \"0.58156264\",\n \"text\": \"def terms(self, order=None):\\n return self._sorted(list(self.items()), order)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"718068e32cf7fdf07dd8784dc4cfedab\",\n \"score\": \"0.5797008\",\n \"text\": \"def terms(self) -> Set[str]:\\n return set([f['term'] for f in self.fields])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76162693147c2b35486722f71e77b446\",\n \"score\": \"0.5778616\",\n \"text\": \"def terms(self):\\n return self._terms\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7abb058014ae2864c14513608eb06dc6\",\n \"score\": \"0.5634081\",\n \"text\": \"def get_tokens(self, *types):\\n return [t for t in self.tokens if t.type in types]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5578f0bd4851d03cd68a5588e6f7748\",\n \"score\": \"0.56092346\",\n \"text\": \"def listterms(self):\\n return list(self.items())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2fd994f4575f4cf2d407811cebbfb37\",\n \"score\": \"0.56085724\",\n \"text\": \"def get_all(self):\\n key = self.resource.config.type_var\\n value = getattr(self.element_class,self.resource.config.type_var)\\n return self.index.lookup(key,value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1dd7da6796d37a72c08605345dfb58ff\",\n \"score\": \"0.5580155\",\n \"text\": \"def terms(self):\\n\\n return list(self._terms)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71a90e63d6a55d17ef36f09716d573d7\",\n \"score\": \"0.5552349\",\n \"text\": \"def types (self) :\\r\\n\\t\\treturn iter(self._type_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3e8804c12873cede01e87e9cf20ffef\",\n \"score\": \"0.5546789\",\n \"text\": \"def terms(self):\\n terms = []\\n\\n if type(self.recurrence) is Add:\\n # We have multiple terms\\n terms = self.recurrence.args\\n else:\\n # We do not have multiple terms, so the whole expression is one term\\n terms.append(self.recurrence)\\n\\n return terms\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a600d3bfb0d74c66bc71ce20abfb79d\",\n \"score\": \"0.55235326\",\n \"text\": \"def terms(self, order=None):\\n return dmp_list_terms(self.rep, self.lev, self.domain, order=order)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"084bc1c32e81717841167a4b0d4bbc0f\",\n \"score\": \"0.55140245\",\n \"text\": \"def all_terms(f):\\n return smp_all_terms(f.rep, f.lev, f.ord, f.dom)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f4fe7f1f8355d6fcd964f7139250852\",\n \"score\": \"0.5488184\",\n \"text\": \"def get_values(self, *types):\\n return tuple(t.value for t in self.tokens if t.type in types)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e98e60c0fddaffb3ba3a5ee40cd9965b\",\n \"score\": \"0.5479323\",\n \"text\": \"def get_type_names(self):\\n return [v[1] for v in self.knowledge.values()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0665fbd583c0c108f43190552de41c59\",\n \"score\": \"0.54375684\",\n \"text\": \"def items(self):\\n\\n return self._terms\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19f676ae802d10a06f02ee024510ca80\",\n \"score\": \"0.54201204\",\n \"text\": \"def term_set(self):\\n return {term.term for term in self.terms}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"789aca40906cbc6fcca2ec224040f192\",\n \"score\": \"0.54023355\",\n \"text\": \"def terms(self):\\n return self.properties.get(\\\"terms\\\",\\n TaxonomyItemCollection(self.context, Term,\\n ResourcePath(\\\"terms\\\", self.resource_path)))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c525b4cee874baedc9309aba485a039\",\n \"score\": \"0.5345627\",\n \"text\": \"def vars(self, term):\\n if term.type == clingo.SymbolType.Number:\\n return set()\\n if match(term, \\\"-\\\", 2) or match(term, \\\"+\\\", 2) or match(term, \\\"*\\\", 2) or match(term, \\\"..\\\", 2):\\n return self.vars(term.arguments[0]).union(self.vars(term.arguments[1]))\\n if match(term, \\\"-\\\", 1) or match(term, \\\"+\\\", 1):\\n return self.vars(term.arguments[0])\\n if term.type in (clingo.TheoryTermType.Symbol, clingo.TheoryTermType.Function, clingo.TheoryTermType.Tuple):\\n return {term}\\n return set()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0065744831e47899e27d70a3815b179\",\n \"score\": \"0.5334623\",\n \"text\": \"def product_types(self):\\n\\n query = 'SELECT DISTINCT tyyppi FROM juomat;'\\n cursor = self.connection.cursor()\\n cursor.execute(query)\\n rows = cursor.fetchall()\\n\\n return [row[0] for row in rows if row[0] != None]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0727be46ea3eef008ffb2f58bc0bbe1\",\n \"score\": \"0.53262806\",\n \"text\": \"def all_meta_types(self):\\n global metatypes \\n metatypes = self.adt_meta_types\\n if len(metatypes) > 0:\\n f = lambda x: x['name'] in metatypes\\n return filter(f, Products.meta_types) + self.meta_types\\n else:\\n return self.meta_types\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edba00a6f6bb1f477c6335f2f12ea787\",\n \"score\": \"0.5310193\",\n \"text\": \"def types(self):\\n r = requests.get(self.evaluator_url + 'types')\\n r.raise_for_status()\\n return r.json()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30353cd0550d0eefcf7b7ba9d8b436fd\",\n \"score\": \"0.5256919\",\n \"text\": \"def getXsdTerms (self):\\n\\t\\txsd = self.getXsd(self.vocabUrl)\\n\\t\\tvocabType = xsd.getEnumerationType(self.typeName)\\n\\t\\tif not vocabType:\\n\\t\\t\\traise Exception, 'vocabType not found for \\\"%s\\\"' % self.typeName\\n\\t\\treturn vocabType.getValues()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b423f9baec5f1da0c172faed51e36ab\",\n \"score\": \"0.5243929\",\n \"text\": \"def types_vocab(context):\\n context = getattr(context, 'context', context) or getSite()\\n ptool = getToolByName(context, 'plone_utils', None)\\n ttool = getToolByName(context, 'portal_types', None)\\n if ptool is None or ttool is None:\\n return None\\n items = [ (ttool[t].Title(), t)\\n for t in ptool.getUserFriendlyTypes() ]\\n items.sort()\\n items = [SimpleTerm(i[1], i[1], i[0]) for i in items]\\n return SimpleVocabulary(items)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e48e91ffcebca46ac940302f28884ffa\",\n \"score\": \"0.52369475\",\n \"text\": \"def iterterms(self):\\n return iter(self.items())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"632de240cfe7498e88f9cd70b62bbf0f\",\n \"score\": \"0.5233667\",\n \"text\": \"def __call__(self, context=None):\\n context = getSite()\\n sm = getSiteManager(context)\\n # Get all registered rating types\\n categories = (c for n,c in sm.adapters.lookupAll((IDynamicType,),\\n self.interface))\\n terms = []\\n for cat in categories:\\n terms.append(SimpleTerm(value=cat, token=cat.name, title=_(cat.title)))\\n return SimpleVocabulary(terms)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52aff986e23884ed435962f7ce5b4a10\",\n \"score\": \"0.52202356\",\n \"text\": \"def terms():\\t\\n\\tkeywords = ['dissent','protest','sosvenezuela']\\t\\t\\n\\treturn keywords\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4ce88382f4102dd960843f2d679e1fa\",\n \"score\": \"0.52161\",\n \"text\": \"def query_terms(self) -> Sequence[str]:\\n return pulumi.get(self, \\\"query_terms\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4ce88382f4102dd960843f2d679e1fa\",\n \"score\": \"0.52161\",\n \"text\": \"def query_terms(self) -> Sequence[str]:\\n return pulumi.get(self, \\\"query_terms\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4ce88382f4102dd960843f2d679e1fa\",\n \"score\": \"0.52161\",\n \"text\": \"def query_terms(self) -> Sequence[str]:\\n return pulumi.get(self, \\\"query_terms\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdc1ed5c0c1a63ca398b77a741469e6f\",\n \"score\": \"0.52118754\",\n \"text\": \"def terms(self):\\n return self.terms_error + self.terms_regularization\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b75cff8efb66ee1526cd60465e2af4d1\",\n \"score\": \"0.5208267\",\n \"text\": \"def get_all_store_objects(self):\\n return self.get_store(key='*')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4afa8b180ba91598a22905b6d71a314\",\n \"score\": \"0.519935\",\n \"text\": \"def terms(f):\\n return smp_terms(f.rep, f.lev, f.ord, f.dom)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85e33d7a4f61deedf46286805d5fdd30\",\n \"score\": \"0.51868534\",\n \"text\": \"def all_terms(self):\\n\\n for s_name, s in self.sections.items():\\n\\n # Yield the section header\\n if s.name != 'Root':\\n yield s\\n\\n # Yield all of the rows for terms in the section\\n for rterm in s:\\n yield rterm\\n for d in rterm.descendents:\\n yield d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8057d29ccf2667b5cbb79bdc27ce71e\",\n \"score\": \"0.5186738\",\n \"text\": \"def _get_triples_from_store(\\n self, spo: tuple = (None, None, None)\\n ) -> Iterable[tuple]:\\n if not hasattr(self, \\\"sparqlstore\\\"):\\n self._open_sparql_store()\\n\\n return self.sparql_store.triples(spo)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a2241a00f8e6ddb33a66309792c4e7d\",\n \"score\": \"0.5146953\",\n \"text\": \"def product_subtypes(self):\\n\\n query = 'SELECT DISTINCT alatyyppi FROM juomat;'\\n cursor = self.connection.cursor()\\n cursor.execute(query)\\n rows = cursor.fetchall()\\n\\n return [row[0] for row in rows if row[0] != None]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03250c0f8eb97fbf503745f122a4442b\",\n \"score\": \"0.51414317\",\n \"text\": \"def product_types(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17fa7d7b213d754c9d1df8bf94274bd5\",\n \"score\": \"0.5111763\",\n \"text\": \"def get_queryset(self):\\n # get just the non-class paleocore terms, which get added to the context as terms\\n paleocore_terms = Term.objects.filter(projects__name='pc').order_by('category', 'name')\\n paleocore_terms = paleocore_terms.exclude(is_class=True)\\n return paleocore_terms\",\n \"title\": \"\"\n },\n {\n \"docid\": \"632f3de1ed4c9c964d5e262b8ee95130\",\n \"score\": \"0.5093937\",\n \"text\": \"def get_relax_types_eos():\\n return [item.value for item in RelaxType if 'cell' not in item.value and 'volume' not in item.value]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d4afefef6acb8ce4bfb86da8967b0a0\",\n \"score\": \"0.50901294\",\n \"text\": \"def get_relax_types():\\n return [entry.value for entry in RelaxType]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3ade0d6e0a524750ad2e68771657bed\",\n \"score\": \"0.5078263\",\n \"text\": \"def collect(self, term, seen = None):\\n for t in subterms(term, seen):\\n t.type_constraints(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5de885ff0501b0371db8e429c3b06d62\",\n \"score\": \"0.5063471\",\n \"text\": \"def free_terms(self, var_only=False):\\n result = set()\\n # term case handled in subclass\\n if isinstance(self.op, TypedExpr):\\n result.update(self.op.free_terns(var_only=var_only))\\n for a in self.args:\\n result.update(a.free_terms(var_only=var_only))\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0790326cc33fe05f2a858282fa600c2\",\n \"score\": \"0.5051801\",\n \"text\": \"def term_frequencies(self):\\n try:\\n vocab = self.df['type'].value_counts().reset_index()\\n vocab.columns = ['type', 'term_freq']\\n vocab = vocab.sort_values(by='type', ascending=True)\\n return vocab\\n except BaseException:\\n sys.exit('Could not get term frequencies.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"575b44e60911dee675c0fb3acbb55d46\",\n \"score\": \"0.50133795\",\n \"text\": \"def get_site_terms(self):\\n return # osid.installation.SiteQueryInspector\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c541cecce8b2fcae4fabd8a20527699d\",\n \"score\": \"0.49980584\",\n \"text\": \"def get_recipes_by_types(self, recipe_type):\\n lst = self.recipe_list[recipe_type]\\n return \\\", \\\".join(map(str, lst.keys()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9865255d85dc1e6b5e96732bafe5f7b5\",\n \"score\": \"0.49973938\",\n \"text\": \"def monoms(self, order=None):\\n return [ monom for monom, _ in self.terms(order) ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50d9adf837bcbe6cfc0045c846b932ee\",\n \"score\": \"0.4978654\",\n \"text\": \"def get_terms(self):\\n # self.fields['title'] = util.remove_punctuations(self.fields['title'])\\n terms = util.remove_punctuations(self.fields['title']).split() + self.abstract\\n normalized_terms = [util.normalize_token(term) for term in util.remove_stopwords(terms)]\\n return normalized_terms\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5d037c1efa044e13c570b08866d0668\",\n \"score\": \"0.4975402\",\n \"text\": \"def getWordsForTypes(self):\\n type_df = []\\n for i in self.types:\\n type_df.append(self.df[self.df['type'] == i])\\n\\n for i, t in enumerate(self.types):\\n self.type_word_lst[t] = self.wordCounter(type_df[i]['tokens'])\\n return self.type_word_lst\",\n \"title\": \"\"\n },\n {\n \"docid\": \"448e0c178ac24b663b446cbb42249466\",\n \"score\": \"0.4964904\",\n \"text\": \"def get_objects_NT_for_s_p(self, p, s):\\n local_sprql = SPARQLWrapper(self.knowledge_base)\\n\\n o_list = []\\n query_text = (\\\"\\\"\\\"\\n SELECT DISTINCT ?o\\n WHERE{\\n <%s> <%s> ?o .\\n } \\\"\\\"\\\" % (s, p))\\n #FILTER (regex(?t, \\\"^http://dbpedia.org/\\\")) maybe removed\\n # I figured out that a good filter for the type of the object has to be of \\\"^http://dbpedia.org/ontology\\\"\\n # in oreder to get valuable results\\n local_sprql.setQuery(query_text)\\n local_sprql.setReturnFormat(JSON)\\n results_inner = local_sprql.query().convert()\\n for inner_res in results_inner[\\\"results\\\"][\\\"bindings\\\"]:\\n # s = inner_res[\\\"s\\\"][\\\"value\\\"]\\n o = inner_res[\\\"o\\\"][\\\"value\\\"]\\n o_list.append(o)\\n\\n return o_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"952a9ce7963558932378d29a29e29ba3\",\n \"score\": \"0.49600717\",\n \"text\": \"def get_objects_WT_for_s_p(self, p, s):\\n local_sprql = SPARQLWrapper(self.knowledge_base)\\n pu = p.encode('utf-8')\\n o_list = []\\n query_text = (\\\"\\\"\\\"\\n SELECT DISTINCT ?o\\n WHERE{\\n <%s> <%s> ?o .\\n ?o a ?t .\\n } \\\"\\\"\\\" % (s, pu))\\n #FILTER (regex(?t, \\\"^http://dbpedia.org/\\\")) maybe removed\\n # I figured out that a good filter for the type of the object has to be of \\\"^http://dbpedia.org/ontology\\\"\\n # in oreder to get valuable results\\n local_sprql.setQuery(query_text)\\n local_sprql.setReturnFormat(JSON)\\n results_inner = local_sprql.query().convert()\\n for inner_res in results_inner[\\\"results\\\"][\\\"bindings\\\"]:\\n # s = inner_res[\\\"s\\\"][\\\"value\\\"]\\n o = inner_res[\\\"o\\\"][\\\"value\\\"]\\n o_list.append(o)\\n\\n return o_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c9a6079d7e50ac8a1e1b1ec9338ab76\",\n \"score\": \"0.4958613\",\n \"text\": \"def terms(self): # pylint: disable=no-self-use\\n raise TermsUndefinedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fced3e14397afe36e6a38979034e87d\",\n \"score\": \"0.4953121\",\n \"text\": \"def list_observations_types(mycur):\\n \\n print \\\"Current type available:\\\"\\n type_query = \\\"select distinct TYPE from OBS_SET_TYPE\\\"\\n mycur.arraysize = 1000 # get 1000 at a time when fetching\\n mycur.execute(type_query)\\n for item in mycur:\\n print item[0]\\n\\n sys.exit(0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b36773ab14104f1cf421c007ee49c2ce\",\n \"score\": \"0.49442348\",\n \"text\": \"def listVocTerms(self, field):\\n context = self.real_context\\n field = context.getField(field)\\n keys = type(field.getRaw(context)) in (list, tuple) and field.getRaw(context) or [field.getRaw(context)]\\n objs = [field.vocabulary.getAllVocTerms(context).get(key, None) for key in keys]\\n return objs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"644932f0ed4c586316de5fcee9a74777\",\n \"score\": \"0.49316746\",\n \"text\": \"def list_ostypes():\\n return list_items(\\\"ostypes\\\", True, \\\"ID\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c32cb32cfb82aa0e43f4c231d98c7fb\",\n \"score\": \"0.49254468\",\n \"text\": \"def get_pseudopotential_type(cls, calc, **kwargs):\\n types = []\\n raw_types = cls._get_raw_pseudopotential_type(calc, **kwargs)\\n for element in sorted(raw_types):\\n if raw_types[element] is None or raw_types[element] in cls._pseudopotential_types:\\n types.append(raw_types[element])\\n else:\\n types.append('other')\\n return types\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91bd72111d7920ff5a9031875c956ad5\",\n \"score\": \"0.49219\",\n \"text\": \"def get_all_types(request):\\n controller = RuleTypeController()\\n query_set = controller.get_all()\\n response = {}\\n response['types'] = list(query_set.values())\\n return JsonResponse(response, safe=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbd11edc8dfe7c8bd21543a403502e76\",\n \"score\": \"0.4912332\",\n \"text\": \"def possible_filter_terms(self) -> FrozenSet[FilterTerm]:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4dd55a45df103071d962fe6d3a584591\",\n \"score\": \"0.49116492\",\n \"text\": \"def get_url_terms(self):\\n return # osid.search.terms.StringTerm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"799892c9e6be47087d39f4e8518924fe\",\n \"score\": \"0.49103883\",\n \"text\": \"def types(self):\\n return list(self.__items.keys())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d408e3d342983b0cc808c5b4936cdf44\",\n \"score\": \"0.49080944\",\n \"text\": \"def destructure(self) -> list[ir.ValueExpr]:\\n return [self[field_name] for field_name in self.type().names]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2449ae1c5bda62b8011e9a993f207884\",\n \"score\": \"0.49007508\",\n \"text\": \"def typepi(self):\\n typep = []\\n b = 2\\n while b < len(self.parserdata):\\n typep.append(self.parserdata[b])\\n b += 4\\n return typep\",\n \"title\": \"\"\n },\n {\n \"docid\": \"339bf942c1bfe815de1d57e7cda425c6\",\n \"score\": \"0.48991424\",\n \"text\": \"def get_all():\\n\\n # This relies on access to the global dictionary `melon_types`\\n\\n return melon_types.values()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d60760405e5531bc2647c25c12aecfc\",\n \"score\": \"0.48896676\",\n \"text\": \"def tf (self, verbose = False): \\n\\t\\tif verbose is True:\\n\\t\\t\\tprint \\\"extracting term-frequency of term : \\\" + term.term().text() \\n\\t\\tout = []\\n\\t\\tfor term in self.query: # all the words repeat only once .. ( Assumption : even if they repeat more than once, ignore it)\\n\\t\\t\\tout.append(1)\\n\\t\\treturn out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a88cb3b0df1814b76d6d8363ed09d07c\",\n \"score\": \"0.48687857\",\n \"text\": \"def get_terms(self, buffer: str) -> Iterator[str]:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6342b04ef43ed89eb9a21d2b6cf350e5\",\n \"score\": \"0.48673657\",\n \"text\": \"def terms(self, letter):\\n return self.get_entries()[letter]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a77c07b7f147b807278a0101cf91455f\",\n \"score\": \"0.4863108\",\n \"text\": \"def _type_hints_to_formulas(self, vrs, action):\\n r = list()\\n for var in vrs:\\n hints = self.vars[var]\\n if hints['type'] == 'bool':\\n continue\\n assert hints['type'] == 'int', hints\\n a, b = hints['dom']\\n s = '({a} <= {var}) /\\\\ ({var} <= {b})'\\n type_inv = s.format(a=a, b=b, var=var)\\n r.append(type_inv)\\n if not action:\\n continue\\n type_inv_primed = s.format(\\n a=a, b=b, var=stx.prime(var))\\n r.append(type_inv_primed)\\n return stx.conj(r)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cd56300d7f6ee00bd6fd4f43edb50d0\",\n \"score\": \"0.4863034\",\n \"text\": \"def percepts(self, type='ALL'):\\n if type=='ALL':\\n return self.nodes\\n elif type=='CAT':\\n return [per for per in self.nodes if isinstance(per, PERCEPT_CAT)]\\n elif type=='TOKEN':\\n return [per for per in self.nodes if isinstance(per, PERCEPT_TOKEN)]\\n else:\\n print 'invalid type'\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f99ff18bfbb00664c8ea100cff79ada5\",\n \"score\": \"0.48547268\",\n \"text\": \"def get_pseudopotential_type_other_name(cls, calc, **kwargs): # pylint: disable=invalid-name\\n types = []\\n raw_types = cls._get_raw_pseudopotential_type(calc, **kwargs)\\n for element in sorted(raw_types):\\n if raw_types[element] is None or raw_types[element] in cls._pseudopotential_types:\\n types.append(None)\\n else:\\n types.append(raw_types[element])\\n return types\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f032c72ac4babedb37feec8c64719be\",\n \"score\": \"0.485155\",\n \"text\": \"def get_terms(db_session, text: str) -> List[str]:\\n all_terms = db_session.query(Term).filter(Term.discoverable == True).all() # noqa\\n phrases = build_term_vocab([t.text for t in all_terms])\\n matcher = build_phrase_matcher(\\\"dispatch-terms\\\", phrases)\\n extracted_terms = extract_terms_from_text(text, matcher)\\n return extracted_terms\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cf29752143dce43b7e5da94859bcc04\",\n \"score\": \"0.48405552\",\n \"text\": \"def terms(self):\\n for term in self.term_matrix[1:]:\\n yield Polynomial(Polynomial.clean([self.term_matrix[0], term]), self.field_characteristic)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3c610b62c0d721e4ee22eadc4a0bc1a\",\n \"score\": \"0.48252597\",\n \"text\": \"def ele_types(eles):\\n return list(set([e[\\\"type\\\"] for e in eles]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af205a0936ffea7415ee23424c751873\",\n \"score\": \"0.4819703\",\n \"text\": \"def list_ops(self, op_type='', op_name='', pass_name='', kernel_name=''):\\n return filter(lambda op: op_type in op.type() and op_name in op.name() and pass_name in op.pass_name()\\n and kernel_name in op.kernel_name(), self.ops_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ce5b8d65f9d242c7d6da008d78c9781\",\n \"score\": \"0.48187575\",\n \"text\": \"def get_data_terms(self):\\n return # osid.search.terms.BytesTerm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f77fbb824fcfc72ad0093bdfeb087e2f\",\n \"score\": \"0.48010176\",\n \"text\": \"def datatypes(self):\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2a4a9fc2ed798ba8e54e986d7cbbf05\",\n \"score\": \"0.47997743\",\n \"text\": \"def get_all_symbols(type, client):\\n if type == \\\"S\\\":\\n all_tickers = client.stock_symbols('US')\\n elif type == \\\"C\\\":\\n all_tickers = client.crypto_symbols('BINANCE')\\n symbols = []\\n for ticker in all_tickers:\\n symbols.append(ticker[\\\"symbol\\\"])\\n return symbols\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10ee044b1683e80ab85c11bdb032d483\",\n \"score\": \"0.47935882\",\n \"text\": \"def get_types(self):\\n return self.instanceTypes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37e55e0d6a744a2e860e6235f4ae11f7\",\n \"score\": \"0.4788895\",\n \"text\": \"def op_types(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b960f0eb4f094e8a180efc1a05e92d8c\",\n \"score\": \"0.47835895\",\n \"text\": \"def get_food_types(food, categories):\\n\\n retval = \\\"\\\"\\n for c in categories:\\n filter_category = c['type']\\n food_item = c['filter'].upper()\\n\\n if filter_category == \\\"food\\\" and food[food_item] == 1:\\n retval = retval + \\\" data-\\\" + food_item.lower()\\n \\n # Clear out the whitespaces on the ends\\n return retval.strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4db686cb5eb6f6787ee0d6d05e61d4d\",\n \"score\": \"0.4781249\",\n \"text\": \"def spatial_term(powers) :\\n\\n if term_type=='arr' :\\n if powers[0]==0 : \\n return eta if powers[1]==1 else pow(eta, powers[1])\\n if powers[1]==0 : \\n return xi if powers[0]==1 else pow(xi, powers[0])\\n return (xi if powers[0]==1 else pow(xi, powers[0]))*(\\n eta if powers[1]==1 else pow(eta, powers[1]))\\n elif term_type=='coef' :\\n if powers[0]==0 : \\n return 'eta' if powers[1]==1 else 'eta%d'%powers[1]\\n if powers[1]==0 : \\n return 'xi' if powers[0]==1 else 'xi%d'%powers[0]\\n return ('xi' if powers[0]==1 else 'xi'+str(powers[0]))+(\\n 'eta' if powers[1]==1 else 'eta'+str(powers[1]))\\n elif term_type=='expr' :\\n if powers[0]==0 : return 'eta^%d'%powers[1]\\n if powers[1]==0 : return 'xi^%d'%powers[0]\\n return 'xi^%d*eta^%d'%tuple(powers)\\n else : raise Error.Input(\\\"Unknown term type '%s' in \\\"\\n \\\"MagnitudeFit.__linear_terms\\\"%term_type)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fca4d3402a2b4344f3ccfccfe3c4883b\",\n \"score\": \"0.47734714\",\n \"text\": \"def gens(self):\\n return tuple(self(x) for x in self.free_module().gens())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15ee8bf93426b4def755a537287c1596\",\n \"score\": \"0.47712448\",\n \"text\": \"def get_terms(self, id: int) -> List[TermBase]:\\n terms = []\\n if self.terms != [] or self.terms_slc != []:\\n for all_terms in [self.terms, self.terms_slc]:\\n for term in all_terms:\\n if isinstance(term, Term):\\n if id in term.ids:\\n terms.append(term)\\n elif isinstance(term, SlcTerm):\\n for subterm in term.terms:\\n if id in subterm.ids:\\n terms.append(term)\\n break\\n return terms\\n else:\\n raise Exception(\\n \\\"There are currently no terms in this problem. \\\\\\n Please download the problem on the client or add terms to the \\\\\\n problem to perform this operation\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6996643db4a21c232573ebdbf45c8a1f\",\n \"score\": \"0.47542888\",\n \"text\": \"def get_types(self):\\n endpoint = '/types'\\n return self.make_request(self.BASE_URL + endpoint)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02e4b9bef0090f7cf830b060c5ff6782\",\n \"score\": \"0.475413\",\n \"text\": \"def compute_terms(self):\\n terms_dict = {self.name: self.data}\\n poly_terms = self.generate_poly_terms() # Replaced w decorators...\\n terms_dict.update(poly_terms)\\n return terms_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4145a8fb5ce5bc1fe1101a602696a45c\",\n \"score\": \"0.47454032\",\n \"text\": \"def ostype(value, arg):\\n rlist = []\\n for nco in value:\\n if arg[0] == '!' and nco['ostype'] != arg[1:]:\\n rlist.append(nco)\\n elif nco['ostype'] == arg:\\n rlist.append(nco)\\n return rlist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18f085e98ad92fcb91c6110a9f5ef77f\",\n \"score\": \"0.47428417\",\n \"text\": \"def DefineTermAnyType(t: Kind):\\n\\n class _SIPO(Circuit):\\n name = 'Term_{}t'.format(cleanName(str(t)))\\n IO = ['I', In(t)]\\n @classmethod\\n def definition(cls):\\n type_size_in_bits = GetCoreIRBackend().get_type(t).size\\n type_to_bits = Dehydrate(t)\\n term = Term(type_size_in_bits)\\n wire(cls.I, type_to_bits.I)\\n wire(type_to_bits.out, term.I)\\n\\n return _SIPO\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aebbcae8c22710d5b47be4672e00551c\",\n \"score\": \"0.47164845\",\n \"text\": \"def to_terms(self) -> OrderedSet[Term]:\\n return OrderedSet((Term([self]),))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e345d3029e080dc945486dfac672d6eb\",\n \"score\": \"0.47120032\",\n \"text\": \"def values() -> typing.List['SpsRunType']:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7942b1c6508c996996b45de0e0214a0d\",\n \"score\": \"0.47115138\",\n \"text\": \"def types(self):\\n return self._types\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7942b1c6508c996996b45de0e0214a0d\",\n \"score\": \"0.47115138\",\n \"text\": \"def types(self):\\n return self._types\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7942b1c6508c996996b45de0e0214a0d\",\n \"score\": \"0.47115138\",\n \"text\": \"def types(self):\\n return self._types\",\n \"title\": \"\"\n },\n {\n \"docid\": \"480fcd6a4708aae7727ec48dba09b49b\",\n \"score\": \"0.47099948\",\n \"text\": \"def get_subtypes(self):\\n return self.subtypes.all()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"947c01e8f82d844a127c5e83b75a125c\",\n \"score\": \"0.47054058\",\n \"text\": \"def QueryForAllResults(type):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d87ffab2263621ce6b24e171addfc045\",\n \"score\": \"0.469911\",\n \"text\": \"def get_all(self):\\n return self._store\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0e24de4de5471e441e704d9e2850543\",\n \"score\": \"0.46979028\",\n \"text\": \"def load_types(self):\\n instance_types = set()\\n for key in self:\\n instance_type = key[1]\\n instance_types.add(instance_type)\\n\\n return list(instance_types)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcf030a2845b49261a143bc913b17092\",\n \"score\": \"0.4683023\",\n \"text\": \"def get_package_terms(self):\\n return # osid.installation.PackageQueryInspector\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcf030a2845b49261a143bc913b17092\",\n \"score\": \"0.4683023\",\n \"text\": \"def get_package_terms(self):\\n return # osid.installation.PackageQueryInspector\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03de6ca1ff6a5e34089828f7576a2530\",\n \"score\": \"0.46808532\",\n \"text\": \"def all() -> List['TypeFilter']:\\n return [e.value for e in list(AvailableTypeFilters)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ff46078cbc6ea87b7c01b6e8b5082e7\",\n \"score\": \"0.46768472\",\n \"text\": \"def narrowTypes(*args):\\n return _coin.SoNodekitCatalog_narrowTypes(*args)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993278,"cells":{"query_id":{"kind":"string","value":"797140e4a92b3e65ae68a8b07ea7704a"},"query":{"kind":"string","value":"itkIntensityWindowingImageFilterID3ID3_Superclass_cast(itkLightObject obj) > itkIntensityWindowingImageFilterID3ID3_Superclass"},"positive_passages":{"kind":"list like","value":[{"docid":"b319a8b048c55a305bd49d2cfc34c65c","score":"0.88690996","text":"def itkIntensityWindowingImageFilterID3ID3_Superclass_cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterID3ID3_Superclass_cast(*args)","title":""}],"string":"[\n {\n \"docid\": \"b319a8b048c55a305bd49d2cfc34c65c\",\n \"score\": \"0.88690996\",\n \"text\": \"def itkIntensityWindowingImageFilterID3ID3_Superclass_cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterID3ID3_Superclass_cast(*args)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"f12da4b506e1d9a13378f3eff1c90259","score":"0.88566303","text":"def itkIntensityWindowingImageFilterIF3ID3_Superclass_cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIF3ID3_Superclass_cast(*args)","title":""},{"docid":"02cc02986cba2fcaaa3b3dfdcdd2bbea","score":"0.88355964","text":"def itkIntensityWindowingImageFilterID3IF3_Superclass_cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterID3IF3_Superclass_cast(*args)","title":""},{"docid":"c2f218adc742dc90ab5936ebebc1ce6f","score":"0.87867594","text":"def itkIntensityWindowingImageFilterIUL3ID3_Superclass_cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUL3ID3_Superclass_cast(*args)","title":""},{"docid":"e76c922a3af51d912c926f37e5c51af6","score":"0.8774636","text":"def itkMeanProjectionImageFilterIF3IF3_Superclass_cast(obj: 'itkLightObject') -> \"itkMeanProjectionImageFilterIF3IF3_Superclass *\":\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterIF3IF3_Superclass_cast(obj)","title":""},{"docid":"dd5a59badf16bfeddf1078f203277655","score":"0.8743591","text":"def itkIntensityWindowingImageFilterIUS3ID3_Superclass_cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUS3ID3_Superclass_cast(*args)","title":""},{"docid":"8f14b20241cd9b01221d3bb251752f43","score":"0.8724031","text":"def itkIntensityWindowingImageFilterID3IUL3_Superclass_cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterID3IUL3_Superclass_cast(*args)","title":""},{"docid":"4e88be34fb665dcc167485a5d359ee90","score":"0.8720527","text":"def itkIntensityWindowingImageFilterID3IUS3_Superclass_cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterID3IUS3_Superclass_cast(*args)","title":""},{"docid":"31098242784cb366c3672faf3d0df156","score":"0.8716019","text":"def itkMeanProjectionImageFilterIF3IF2_Superclass_cast(obj: 'itkLightObject') -> \"itkMeanProjectionImageFilterIF3IF2_Superclass *\":\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterIF3IF2_Superclass_cast(obj)","title":""},{"docid":"b7a3f7cf1dc31bdcda854573aeec3e4a","score":"0.8713104","text":"def itkIntensityWindowingImageFilterIF3IF3_Superclass_cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIF3IF3_Superclass_cast(*args)","title":""},{"docid":"3b1aca8f66b5511659d93d2386df89fe","score":"0.86960584","text":"def itkIntensityWindowingImageFilterIUC3ID3_Superclass_cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUC3ID3_Superclass_cast(*args)","title":""},{"docid":"9df25f8b0c98558b6dd45b80d9841d4a","score":"0.86811346","text":"def itkIntensityWindowingImageFilterIF3IUL3_Superclass_cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIF3IUL3_Superclass_cast(*args)","title":""},{"docid":"bde616cea1bdf708fbbc915b0c1ead8b","score":"0.868008","text":"def itkIntensityWindowingImageFilterIUL3IF3_Superclass_cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUL3IF3_Superclass_cast(*args)","title":""},{"docid":"13bca15afc9f4646fb2289c575c58730","score":"0.86733735","text":"def itkMeanProjectionImageFilterISS3ISS3_Superclass_cast(obj: 'itkLightObject') -> \"itkMeanProjectionImageFilterISS3ISS3_Superclass *\":\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterISS3ISS3_Superclass_cast(obj)","title":""},{"docid":"6ac459233f3b1ea5c8201c85a6ec45ff","score":"0.86472464","text":"def itkIntensityWindowingImageFilterIUL3IUL3_Superclass_cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUL3IUL3_Superclass_cast(*args)","title":""},{"docid":"8501f900d9d7704df2bba4f5e73356ce","score":"0.86244005","text":"def itkIntensityWindowingImageFilterID3IUC3_Superclass_cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterID3IUC3_Superclass_cast(*args)","title":""},{"docid":"32d46c6acd763de9b54b40057e036f0a","score":"0.86008805","text":"def itkIntensityWindowingImageFilterIF3IUS3_Superclass_cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIF3IUS3_Superclass_cast(*args)","title":""},{"docid":"7a58c6c207840737300dbb3c5852dc0d","score":"0.8599646","text":"def cast(obj: 'itkLightObject') -> \"itkMeanProjectionImageFilterIF3IF3_Superclass *\":\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterIF3IF3_Superclass_cast(obj)","title":""},{"docid":"bccdfa473d29c0a95d3dcae56971761e","score":"0.8582382","text":"def itkIntensityWindowingImageFilterIUS3IF3_Superclass_cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUS3IF3_Superclass_cast(*args)","title":""},{"docid":"f3e539db238e44847b187eafa6fd7348","score":"0.85718817","text":"def itkMeanProjectionImageFilterIUC3IUC3_Superclass_cast(obj: 'itkLightObject') -> \"itkMeanProjectionImageFilterIUC3IUC3_Superclass *\":\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterIUC3IUC3_Superclass_cast(obj)","title":""},{"docid":"386ed9b8a54e2bdf589725d84a502a36","score":"0.855263","text":"def cast(obj: 'itkLightObject') -> \"itkMeanProjectionImageFilterISS3ISS3_Superclass *\":\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterISS3ISS3_Superclass_cast(obj)","title":""},{"docid":"339fa6f6566d0620b10d20ebd3c4ca2d","score":"0.85466784","text":"def itkIntensityWindowingImageFilterIF3IUC3_Superclass_cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIF3IUC3_Superclass_cast(*args)","title":""},{"docid":"0ee1ca06715a3897e9389946c8b401c5","score":"0.8539758","text":"def itkIntensityWindowingImageFilterIUL3IUS3_Superclass_cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUL3IUS3_Superclass_cast(*args)","title":""},{"docid":"23854a06eec0c39a840a497cb0ff5705","score":"0.85355","text":"def cast(obj: 'itkLightObject') -> \"itkMeanProjectionImageFilterIF3IF2_Superclass *\":\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterIF3IF2_Superclass_cast(obj)","title":""},{"docid":"948ad3893bdc7c2e919747d4a57d7b3d","score":"0.8531539","text":"def itkIntensityWindowingImageFilterIUC3IF3_Superclass_cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUC3IF3_Superclass_cast(*args)","title":""},{"docid":"df06b0bc921f9e9e1a594d6c722e3bed","score":"0.85007036","text":"def cast(obj: 'itkLightObject') -> \"itkMeanProjectionImageFilterIUC3IUC3_Superclass *\":\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterIUC3IUC3_Superclass_cast(obj)","title":""},{"docid":"92df9edb55899ecbb5261eb4547f3a34","score":"0.84980434","text":"def itkIntensityWindowingImageFilterIUL3IUC3_Superclass_cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUL3IUC3_Superclass_cast(*args)","title":""},{"docid":"b4d58bcd77ac44f18f462f90c8bb2b71","score":"0.84835863","text":"def itkIntensityWindowingImageFilterIUC3IUL3_Superclass_cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUC3IUL3_Superclass_cast(*args)","title":""},{"docid":"2e0845ebd3f8d2c041290df751886d1e","score":"0.8449341","text":"def itkMeanProjectionImageFilterISS3ISS2_Superclass_cast(obj: 'itkLightObject') -> \"itkMeanProjectionImageFilterISS3ISS2_Superclass *\":\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterISS3ISS2_Superclass_cast(obj)","title":""},{"docid":"597cd7e48dd897866578f7395de019eb","score":"0.8440394","text":"def itkIntensityWindowingImageFilterIUS3IUL3_Superclass_cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUS3IUL3_Superclass_cast(*args)","title":""},{"docid":"199a6ecd685e80fddb0dd8348950ef24","score":"0.84171915","text":"def itkIntensityWindowingImageFilterIUC3IUC3_Superclass_cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUC3IUC3_Superclass_cast(*args)","title":""},{"docid":"3dc5ac4a56060d75b67435c95f155e31","score":"0.8391533","text":"def itkIntensityWindowingImageFilterIUC3IUS3_Superclass_cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUC3IUS3_Superclass_cast(*args)","title":""},{"docid":"123cf7d91041cea5cdbb75a31ae404d2","score":"0.83866054","text":"def itkMeanProjectionImageFilterIUC3IUC2_Superclass_cast(obj: 'itkLightObject') -> \"itkMeanProjectionImageFilterIUC3IUC2_Superclass *\":\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterIUC3IUC2_Superclass_cast(obj)","title":""},{"docid":"657d1380559f8246c3fd25a93de41556","score":"0.8379679","text":"def itkMeanProjectionImageFilterIUS3IUS3_Superclass_cast(obj: 'itkLightObject') -> \"itkMeanProjectionImageFilterIUS3IUS3_Superclass *\":\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterIUS3IUS3_Superclass_cast(obj)","title":""},{"docid":"24e055358ced781d15ce8ba06c68b913","score":"0.833398","text":"def itkIntensityWindowingImageFilterIUS3IUS3_Superclass_cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUS3IUS3_Superclass_cast(*args)","title":""},{"docid":"756d832756bbeacb4521a0df53a43bd3","score":"0.83326316","text":"def cast(obj: 'itkLightObject') -> \"itkMeanProjectionImageFilterISS3ISS2_Superclass *\":\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterISS3ISS2_Superclass_cast(obj)","title":""},{"docid":"52509a7ce4a94c66fcca6929a17700d0","score":"0.8329677","text":"def itkIntensityWindowingImageFilterIUS3IUC3_Superclass_cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUS3IUC3_Superclass_cast(*args)","title":""},{"docid":"bac1ff01e775dbb4627cb7b19da8a6b8","score":"0.8327011","text":"def cast(obj: 'itkLightObject') -> \"itkMeanProjectionImageFilterIUC3IUC2_Superclass *\":\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterIUC3IUC2_Superclass_cast(obj)","title":""},{"docid":"c0a67822a939158b645c1996510a1ab1","score":"0.8314562","text":"def itkBinaryThresholdImageFilterID3ID3_Superclass_cast(*args):\n return _itkBinaryThresholdImageFilterPython.itkBinaryThresholdImageFilterID3ID3_Superclass_cast(*args)","title":""},{"docid":"900f96476bda2cb38d7dfc6657170474","score":"0.83051103","text":"def cast(obj: 'itkLightObject') -> \"itkMeanProjectionImageFilterIUS3IUS3_Superclass *\":\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterIUS3IUS3_Superclass_cast(obj)","title":""},{"docid":"82c5fd61ee878656e0d6e6e55e15caad","score":"0.8302348","text":"def itkBinaryThresholdImageFilterID3IF3_Superclass_cast(*args):\n return _itkBinaryThresholdImageFilterPython.itkBinaryThresholdImageFilterID3IF3_Superclass_cast(*args)","title":""},{"docid":"2b0a19e702a39246116d1806f1043622","score":"0.821228","text":"def itkBinaryThresholdImageFilterID3IUL3_Superclass_cast(*args):\n return _itkBinaryThresholdImageFilterPython.itkBinaryThresholdImageFilterID3IUL3_Superclass_cast(*args)","title":""},{"docid":"f61c7c4a81088640b7f2ab78794fdc5c","score":"0.8198618","text":"def itkMeanProjectionImageFilterIUS3IUS2_Superclass_cast(obj: 'itkLightObject') -> \"itkMeanProjectionImageFilterIUS3IUS2_Superclass *\":\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterIUS3IUS2_Superclass_cast(obj)","title":""},{"docid":"079f05203de253788e2c4ade3e45eada","score":"0.81882155","text":"def itkExpImageFilterID3ID3_Superclass_cast(*args):\n return _itkExpImageFilterPython.itkExpImageFilterID3ID3_Superclass_cast(*args)","title":""},{"docid":"5a66a2bd592b39311d3b4ae0159f61ba","score":"0.81772196","text":"def itkBinaryThresholdImageFilterID3IUC3_Superclass_cast(*args):\n return _itkBinaryThresholdImageFilterPython.itkBinaryThresholdImageFilterID3IUC3_Superclass_cast(*args)","title":""},{"docid":"63f3f4d008945aca92f2f84ca90897e9","score":"0.8176214","text":"def itkBinaryThresholdImageFilterID3IUS3_Superclass_cast(*args):\n return _itkBinaryThresholdImageFilterPython.itkBinaryThresholdImageFilterID3IUS3_Superclass_cast(*args)","title":""},{"docid":"d482c2d45a6394ef82313ba581c2db2b","score":"0.8162989","text":"def itkExpImageFilterIF3IF3_Superclass_cast(*args):\n return _itkExpImageFilterPython.itkExpImageFilterIF3IF3_Superclass_cast(*args)","title":""},{"docid":"c7afe7ec8f7d19835346e1ce0accd1e9","score":"0.8158752","text":"def cast(obj: 'itkLightObject') -> \"itkMeanProjectionImageFilterIUS3IUS2_Superclass *\":\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterIUS3IUS2_Superclass_cast(obj)","title":""},{"docid":"3be036cbd31f2866b0cfad4e49fd0e9a","score":"0.8145145","text":"def itkBinaryThresholdImageFilterIF3IF3_Superclass_cast(*args):\n return _itkBinaryThresholdImageFilterPython.itkBinaryThresholdImageFilterIF3IF3_Superclass_cast(*args)","title":""},{"docid":"b60c6222f6e8f6cf00e99bcec37a27a6","score":"0.813541","text":"def cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterID3ID3_Superclass_cast(*args)","title":""},{"docid":"373ef52d9427ab119e664f4960607a70","score":"0.8133941","text":"def cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIF3ID3_Superclass_cast(*args)","title":""},{"docid":"5dec893ec6cd0fe86b08da7b4a166173","score":"0.8132761","text":"def cast(obj: 'itkLightObject') -> \"itkMeanProjectionImageFilterIF2IF2_Superclass *\":\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterIF2IF2_Superclass_cast(obj)","title":""},{"docid":"23362f64f1ad99c6a5fd247e058e6d38","score":"0.81281525","text":"def itkBinaryProjectionImageFilterID3ID3_Superclass_cast(*args):\n return _itkBinaryProjectionImageFilterPython.itkBinaryProjectionImageFilterID3ID3_Superclass_cast(*args)","title":""},{"docid":"7cd7e4e4b14823f81d5643783b2dbfe0","score":"0.8122367","text":"def itkMeanProjectionImageFilterIF2IF2_Superclass_cast(obj: 'itkLightObject') -> \"itkMeanProjectionImageFilterIF2IF2_Superclass *\":\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterIF2IF2_Superclass_cast(obj)","title":""},{"docid":"14763da0c6df1171b53863cc5fb27eb1","score":"0.8119492","text":"def cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterID3IF3_Superclass_cast(*args)","title":""},{"docid":"09c3160873492e7808051c97c7d5d5d4","score":"0.8098276","text":"def itkExpImageFilterIUL3IUL3_Superclass_cast(*args):\n return _itkExpImageFilterPython.itkExpImageFilterIUL3IUL3_Superclass_cast(*args)","title":""},{"docid":"b69dad3bed28edfa5dda0f26d3d5a1d9","score":"0.809521","text":"def itkBinaryThresholdImageFilterIF3IUL3_Superclass_cast(*args):\n return _itkBinaryThresholdImageFilterPython.itkBinaryThresholdImageFilterIF3IUL3_Superclass_cast(*args)","title":""},{"docid":"6b1c7272164aa738e64c9e802d39a3d4","score":"0.80732596","text":"def cast(obj: 'itkLightObject') -> \"itkMeanProjectionImageFilterIUC2IUC2_Superclass *\":\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterIUC2IUC2_Superclass_cast(obj)","title":""},{"docid":"c369fcc142ea41c8f71184515ebfffc0","score":"0.80711764","text":"def cast(obj: 'itkLightObject') -> \"itkMeanProjectionImageFilterISS2ISS2_Superclass *\":\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterISS2ISS2_Superclass_cast(obj)","title":""},{"docid":"f21a8821964bd3dabfd4ab601542c7f1","score":"0.8049341","text":"def itkBinaryThresholdImageFilterIUL3IUL3_Superclass_cast(*args):\n return _itkBinaryThresholdImageFilterPython.itkBinaryThresholdImageFilterIUL3IUL3_Superclass_cast(*args)","title":""},{"docid":"458cd271ce341fd8cc690f7719b8ff83","score":"0.8048721","text":"def cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUL3ID3_Superclass_cast(*args)","title":""},{"docid":"4b4b334ac52d78b484774dd4acbafdd7","score":"0.8046036","text":"def itkBinaryThresholdImageFilterIF3IUC3_Superclass_cast(*args):\n return _itkBinaryThresholdImageFilterPython.itkBinaryThresholdImageFilterIF3IUC3_Superclass_cast(*args)","title":""},{"docid":"02ad6fa9fb1999b86f5d79c16ce2a77e","score":"0.80359405","text":"def cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUS3ID3_Superclass_cast(*args)","title":""},{"docid":"b59e4e090217cfcf6abc75a2f1e632c1","score":"0.803484","text":"def cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUC3ID3_Superclass_cast(*args)","title":""},{"docid":"448ea10d4865183e402df59a765d4db9","score":"0.8029892","text":"def cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIF3IF3_Superclass_cast(*args)","title":""},{"docid":"f593cfa7a9cb141302f0015f84a96845","score":"0.80268764","text":"def itkBinaryProjectionImageFilterIF3IF3_Superclass_cast(*args):\n return _itkBinaryProjectionImageFilterPython.itkBinaryProjectionImageFilterIF3IF3_Superclass_cast(*args)","title":""},{"docid":"e31851ff63583dd39788a296dc2af94e","score":"0.8025101","text":"def itkMeanProjectionImageFilterIUC2IUC2_Superclass_cast(obj: 'itkLightObject') -> \"itkMeanProjectionImageFilterIUC2IUC2_Superclass *\":\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterIUC2IUC2_Superclass_cast(obj)","title":""},{"docid":"412931d31087f0530d8637e44d063961","score":"0.80215895","text":"def itkAtan2ImageFilterID3ID3ID3_Superclass_cast(*args):\n return _itkAtan2ImageFilterPython.itkAtan2ImageFilterID3ID3ID3_Superclass_cast(*args)","title":""},{"docid":"d2eefd9b42f1c35cedb996c4613fb934","score":"0.80181354","text":"def cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterID3IUS3_Superclass_cast(*args)","title":""},{"docid":"fe3948ed722abb52f80d7e426f52ed72","score":"0.801093","text":"def itkFastApproximateRankImageFilterID3ID3_Superclass_cast(*args):\n return _itkFastApproximateRankImageFilterPython.itkFastApproximateRankImageFilterID3ID3_Superclass_cast(*args)","title":""},{"docid":"42822aade6f255f678b30afe31543ca4","score":"0.80096585","text":"def cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterID3IUL3_Superclass_cast(*args)","title":""},{"docid":"0b572aa85235686a0716a3c81a905bad","score":"0.8001255","text":"def itkBinaryProjectionImageFilterIUL3IUL3_Superclass_cast(*args):\n return _itkBinaryProjectionImageFilterPython.itkBinaryProjectionImageFilterIUL3IUL3_Superclass_cast(*args)","title":""},{"docid":"bce376c309a2b5b6d071f08c3111eb3b","score":"0.80006874","text":"def cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterID3IUC3_Superclass_cast(*args)","title":""},{"docid":"c1b43dc6904897df1ef8e3afa1f2b06e","score":"0.7990471","text":"def itkBinaryThresholdImageFilterIF3IUS3_Superclass_cast(*args):\n return _itkBinaryThresholdImageFilterPython.itkBinaryThresholdImageFilterIF3IUS3_Superclass_cast(*args)","title":""},{"docid":"6565848d8f63418f06e4858747a000ff","score":"0.7974886","text":"def itkBinaryThresholdImageFilterIUL3IUC3_Superclass_cast(*args):\n return _itkBinaryThresholdImageFilterPython.itkBinaryThresholdImageFilterIUL3IUC3_Superclass_cast(*args)","title":""},{"docid":"7e664715ae07c2624d54baa79a4ae83d","score":"0.79699284","text":"def cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIF3IUS3_Superclass_cast(*args)","title":""},{"docid":"612b15147aceb3cd76eda7973c03a286","score":"0.79657155","text":"def cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIF3IUL3_Superclass_cast(*args)","title":""},{"docid":"92205a3f55b9a5911c13567c47ebed75","score":"0.7956797","text":"def itkExpImageFilterIUC3IUC3_Superclass_cast(*args):\n return _itkExpImageFilterPython.itkExpImageFilterIUC3IUC3_Superclass_cast(*args)","title":""},{"docid":"ba95ff97dbc5cfa82f9831b29c2723d4","score":"0.7956402","text":"def cast(obj: 'itkLightObject') -> \"itkMeanProjectionImageFilterIUS2IUS2_Superclass *\":\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterIUS2IUS2_Superclass_cast(obj)","title":""},{"docid":"d1267ea3848e6fced49f638be642a8a2","score":"0.79550743","text":"def cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUL3IF3_Superclass_cast(*args)","title":""},{"docid":"34938ff0debb0cc699e3a3d359182809","score":"0.7951793","text":"def itkBinaryThresholdImageFilterIUL3IUS3_Superclass_cast(*args):\n return _itkBinaryThresholdImageFilterPython.itkBinaryThresholdImageFilterIUL3IUS3_Superclass_cast(*args)","title":""},{"docid":"f6dea84b2f847baf735230d598af5297","score":"0.7946439","text":"def cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIF3IUC3_Superclass_cast(*args)","title":""},{"docid":"aeb4b346ce2f16968b2308a9bb9039de","score":"0.7937531","text":"def itkBinaryThresholdImageFilterIUC3IUC3_Superclass_cast(*args):\n return _itkBinaryThresholdImageFilterPython.itkBinaryThresholdImageFilterIUC3IUC3_Superclass_cast(*args)","title":""},{"docid":"6212902aa81aef56bda24f7274bc2c9d","score":"0.7928491","text":"def itkFastApproximateRankImageFilterIF3IF3_Superclass_cast(*args):\n return _itkFastApproximateRankImageFilterPython.itkFastApproximateRankImageFilterIF3IF3_Superclass_cast(*args)","title":""},{"docid":"ca86c49f545f814b3a7758c60e775522","score":"0.79193926","text":"def itkHistogramToEntropyImageFilterHFIF3_Superclass_cast(obj: 'itkLightObject') -> \"itkHistogramToEntropyImageFilterHFIF3_Superclass *\":\n return _itkHistogramToEntropyImageFilterPython.itkHistogramToEntropyImageFilterHFIF3_Superclass_cast(obj)","title":""},{"docid":"a250c987214885e1ba3b2947751364f8","score":"0.7917758","text":"def itkAtan2ImageFilterIF3IF3IF3_Superclass_cast(*args):\n return _itkAtan2ImageFilterPython.itkAtan2ImageFilterIF3IF3IF3_Superclass_cast(*args)","title":""},{"docid":"763d85e2459dc33987b55aaae952d23d","score":"0.79147696","text":"def cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUS3IF3_Superclass_cast(*args)","title":""},{"docid":"b963b1346e64b3aa4bde267016d6aa8e","score":"0.7912498","text":"def itkMeanProjectionImageFilterISS2ISS2_Superclass_cast(obj: 'itkLightObject') -> \"itkMeanProjectionImageFilterISS2ISS2_Superclass *\":\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterISS2ISS2_Superclass_cast(obj)","title":""},{"docid":"38a9a87d33bb2fe9eb5fa829ce09cf4e","score":"0.79064196","text":"def cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUL3IUL3_Superclass_cast(*args)","title":""},{"docid":"805d6abd86d66f630687d599a035982b","score":"0.7899451","text":"def cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUC3IF3_Superclass_cast(*args)","title":""},{"docid":"11b47591654af5daf9c6bf2af2bdd64e","score":"0.7897342","text":"def itkHistogramToEntropyImageFilterHDIF3_Superclass_cast(obj: 'itkLightObject') -> \"itkHistogramToEntropyImageFilterHDIF3_Superclass *\":\n return _itkHistogramToEntropyImageFilterPython.itkHistogramToEntropyImageFilterHDIF3_Superclass_cast(obj)","title":""},{"docid":"1fafcf0abc4cc2db8ca89d5fa97cf60d","score":"0.78815037","text":"def cast(obj: 'itkLightObject') -> \"itkHistogramToEntropyImageFilterHDIF3_Superclass *\":\n return _itkHistogramToEntropyImageFilterPython.itkHistogramToEntropyImageFilterHDIF3_Superclass_cast(obj)","title":""},{"docid":"72c58e81b4eab7ad7cfe8f7e15a02b3f","score":"0.7878861","text":"def itkVectorConnectedComponentImageFilterIVF33IUL3_Superclass_cast(*args):\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterIVF33IUL3_Superclass_cast(*args)","title":""},{"docid":"592ba4079871bcbc0f4fc8bc43f09679","score":"0.7870837","text":"def itkBinaryThresholdImageFilterIUS3IUC3_Superclass_cast(*args):\n return _itkBinaryThresholdImageFilterPython.itkBinaryThresholdImageFilterIUS3IUC3_Superclass_cast(*args)","title":""},{"docid":"465befdda4135daee90e1c7a32ca0d2b","score":"0.7865448","text":"def cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUL3IUS3_Superclass_cast(*args)","title":""},{"docid":"d7b859d7d294bed05a39dc8165c5e20e","score":"0.78585553","text":"def cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUL3IUC3_Superclass_cast(*args)","title":""},{"docid":"9c07d46513579b2e06b9fba779d7ec25","score":"0.7836825","text":"def itkMeanProjectionImageFilterIUS2IUS2_Superclass_cast(obj: 'itkLightObject') -> \"itkMeanProjectionImageFilterIUS2IUS2_Superclass *\":\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterIUS2IUS2_Superclass_cast(obj)","title":""},{"docid":"3bf3814197a8581e502f21b1a607f9df","score":"0.78347975","text":"def itkMaximumProjectionImageFilterID3ID3_Superclass_cast(*args):\n return _itkMaximumProjectionImageFilterPython.itkMaximumProjectionImageFilterID3ID3_Superclass_cast(*args)","title":""},{"docid":"10d29924e8be11df31e71931d18373c6","score":"0.7831498","text":"def cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUC3IUS3_Superclass_cast(*args)","title":""},{"docid":"d31aaf03aa052c618d636098a65a8c0c","score":"0.7825933","text":"def itkBinaryProjectionImageFilterIUC3IUC3_Superclass_cast(*args):\n return _itkBinaryProjectionImageFilterPython.itkBinaryProjectionImageFilterIUC3IUC3_Superclass_cast(*args)","title":""},{"docid":"3a5c5052472b6103f6a61a1966932d59","score":"0.7824537","text":"def cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUC3IUL3_Superclass_cast(*args)","title":""}],"string":"[\n {\n \"docid\": \"f12da4b506e1d9a13378f3eff1c90259\",\n \"score\": \"0.88566303\",\n \"text\": \"def itkIntensityWindowingImageFilterIF3ID3_Superclass_cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIF3ID3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02cc02986cba2fcaaa3b3dfdcdd2bbea\",\n \"score\": \"0.88355964\",\n \"text\": \"def itkIntensityWindowingImageFilterID3IF3_Superclass_cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterID3IF3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2f218adc742dc90ab5936ebebc1ce6f\",\n \"score\": \"0.87867594\",\n \"text\": \"def itkIntensityWindowingImageFilterIUL3ID3_Superclass_cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUL3ID3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e76c922a3af51d912c926f37e5c51af6\",\n \"score\": \"0.8774636\",\n \"text\": \"def itkMeanProjectionImageFilterIF3IF3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkMeanProjectionImageFilterIF3IF3_Superclass *\\\":\\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterIF3IF3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd5a59badf16bfeddf1078f203277655\",\n \"score\": \"0.8743591\",\n \"text\": \"def itkIntensityWindowingImageFilterIUS3ID3_Superclass_cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUS3ID3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f14b20241cd9b01221d3bb251752f43\",\n \"score\": \"0.8724031\",\n \"text\": \"def itkIntensityWindowingImageFilterID3IUL3_Superclass_cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterID3IUL3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e88be34fb665dcc167485a5d359ee90\",\n \"score\": \"0.8720527\",\n \"text\": \"def itkIntensityWindowingImageFilterID3IUS3_Superclass_cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterID3IUS3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31098242784cb366c3672faf3d0df156\",\n \"score\": \"0.8716019\",\n \"text\": \"def itkMeanProjectionImageFilterIF3IF2_Superclass_cast(obj: 'itkLightObject') -> \\\"itkMeanProjectionImageFilterIF3IF2_Superclass *\\\":\\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterIF3IF2_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7a3f7cf1dc31bdcda854573aeec3e4a\",\n \"score\": \"0.8713104\",\n \"text\": \"def itkIntensityWindowingImageFilterIF3IF3_Superclass_cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIF3IF3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b1aca8f66b5511659d93d2386df89fe\",\n \"score\": \"0.86960584\",\n \"text\": \"def itkIntensityWindowingImageFilterIUC3ID3_Superclass_cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUC3ID3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9df25f8b0c98558b6dd45b80d9841d4a\",\n \"score\": \"0.86811346\",\n \"text\": \"def itkIntensityWindowingImageFilterIF3IUL3_Superclass_cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIF3IUL3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bde616cea1bdf708fbbc915b0c1ead8b\",\n \"score\": \"0.868008\",\n \"text\": \"def itkIntensityWindowingImageFilterIUL3IF3_Superclass_cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUL3IF3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13bca15afc9f4646fb2289c575c58730\",\n \"score\": \"0.86733735\",\n \"text\": \"def itkMeanProjectionImageFilterISS3ISS3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkMeanProjectionImageFilterISS3ISS3_Superclass *\\\":\\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterISS3ISS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ac459233f3b1ea5c8201c85a6ec45ff\",\n \"score\": \"0.86472464\",\n \"text\": \"def itkIntensityWindowingImageFilterIUL3IUL3_Superclass_cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUL3IUL3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8501f900d9d7704df2bba4f5e73356ce\",\n \"score\": \"0.86244005\",\n \"text\": \"def itkIntensityWindowingImageFilterID3IUC3_Superclass_cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterID3IUC3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32d46c6acd763de9b54b40057e036f0a\",\n \"score\": \"0.86008805\",\n \"text\": \"def itkIntensityWindowingImageFilterIF3IUS3_Superclass_cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIF3IUS3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a58c6c207840737300dbb3c5852dc0d\",\n \"score\": \"0.8599646\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkMeanProjectionImageFilterIF3IF3_Superclass *\\\":\\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterIF3IF3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bccdfa473d29c0a95d3dcae56971761e\",\n \"score\": \"0.8582382\",\n \"text\": \"def itkIntensityWindowingImageFilterIUS3IF3_Superclass_cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUS3IF3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3e539db238e44847b187eafa6fd7348\",\n \"score\": \"0.85718817\",\n \"text\": \"def itkMeanProjectionImageFilterIUC3IUC3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkMeanProjectionImageFilterIUC3IUC3_Superclass *\\\":\\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterIUC3IUC3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"386ed9b8a54e2bdf589725d84a502a36\",\n \"score\": \"0.855263\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkMeanProjectionImageFilterISS3ISS3_Superclass *\\\":\\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterISS3ISS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"339fa6f6566d0620b10d20ebd3c4ca2d\",\n \"score\": \"0.85466784\",\n \"text\": \"def itkIntensityWindowingImageFilterIF3IUC3_Superclass_cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIF3IUC3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0ee1ca06715a3897e9389946c8b401c5\",\n \"score\": \"0.8539758\",\n \"text\": \"def itkIntensityWindowingImageFilterIUL3IUS3_Superclass_cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUL3IUS3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23854a06eec0c39a840a497cb0ff5705\",\n \"score\": \"0.85355\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkMeanProjectionImageFilterIF3IF2_Superclass *\\\":\\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterIF3IF2_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"948ad3893bdc7c2e919747d4a57d7b3d\",\n \"score\": \"0.8531539\",\n \"text\": \"def itkIntensityWindowingImageFilterIUC3IF3_Superclass_cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUC3IF3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df06b0bc921f9e9e1a594d6c722e3bed\",\n \"score\": \"0.85007036\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkMeanProjectionImageFilterIUC3IUC3_Superclass *\\\":\\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterIUC3IUC3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92df9edb55899ecbb5261eb4547f3a34\",\n \"score\": \"0.84980434\",\n \"text\": \"def itkIntensityWindowingImageFilterIUL3IUC3_Superclass_cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUL3IUC3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4d58bcd77ac44f18f462f90c8bb2b71\",\n \"score\": \"0.84835863\",\n \"text\": \"def itkIntensityWindowingImageFilterIUC3IUL3_Superclass_cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUC3IUL3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e0845ebd3f8d2c041290df751886d1e\",\n \"score\": \"0.8449341\",\n \"text\": \"def itkMeanProjectionImageFilterISS3ISS2_Superclass_cast(obj: 'itkLightObject') -> \\\"itkMeanProjectionImageFilterISS3ISS2_Superclass *\\\":\\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterISS3ISS2_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"597cd7e48dd897866578f7395de019eb\",\n \"score\": \"0.8440394\",\n \"text\": \"def itkIntensityWindowingImageFilterIUS3IUL3_Superclass_cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUS3IUL3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"199a6ecd685e80fddb0dd8348950ef24\",\n \"score\": \"0.84171915\",\n \"text\": \"def itkIntensityWindowingImageFilterIUC3IUC3_Superclass_cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUC3IUC3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3dc5ac4a56060d75b67435c95f155e31\",\n \"score\": \"0.8391533\",\n \"text\": \"def itkIntensityWindowingImageFilterIUC3IUS3_Superclass_cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUC3IUS3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"123cf7d91041cea5cdbb75a31ae404d2\",\n \"score\": \"0.83866054\",\n \"text\": \"def itkMeanProjectionImageFilterIUC3IUC2_Superclass_cast(obj: 'itkLightObject') -> \\\"itkMeanProjectionImageFilterIUC3IUC2_Superclass *\\\":\\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterIUC3IUC2_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"657d1380559f8246c3fd25a93de41556\",\n \"score\": \"0.8379679\",\n \"text\": \"def itkMeanProjectionImageFilterIUS3IUS3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkMeanProjectionImageFilterIUS3IUS3_Superclass *\\\":\\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterIUS3IUS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24e055358ced781d15ce8ba06c68b913\",\n \"score\": \"0.833398\",\n \"text\": \"def itkIntensityWindowingImageFilterIUS3IUS3_Superclass_cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUS3IUS3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"756d832756bbeacb4521a0df53a43bd3\",\n \"score\": \"0.83326316\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkMeanProjectionImageFilterISS3ISS2_Superclass *\\\":\\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterISS3ISS2_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52509a7ce4a94c66fcca6929a17700d0\",\n \"score\": \"0.8329677\",\n \"text\": \"def itkIntensityWindowingImageFilterIUS3IUC3_Superclass_cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUS3IUC3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bac1ff01e775dbb4627cb7b19da8a6b8\",\n \"score\": \"0.8327011\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkMeanProjectionImageFilterIUC3IUC2_Superclass *\\\":\\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterIUC3IUC2_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0a67822a939158b645c1996510a1ab1\",\n \"score\": \"0.8314562\",\n \"text\": \"def itkBinaryThresholdImageFilterID3ID3_Superclass_cast(*args):\\n return _itkBinaryThresholdImageFilterPython.itkBinaryThresholdImageFilterID3ID3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"900f96476bda2cb38d7dfc6657170474\",\n \"score\": \"0.83051103\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkMeanProjectionImageFilterIUS3IUS3_Superclass *\\\":\\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterIUS3IUS3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82c5fd61ee878656e0d6e6e55e15caad\",\n \"score\": \"0.8302348\",\n \"text\": \"def itkBinaryThresholdImageFilterID3IF3_Superclass_cast(*args):\\n return _itkBinaryThresholdImageFilterPython.itkBinaryThresholdImageFilterID3IF3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b0a19e702a39246116d1806f1043622\",\n \"score\": \"0.821228\",\n \"text\": \"def itkBinaryThresholdImageFilterID3IUL3_Superclass_cast(*args):\\n return _itkBinaryThresholdImageFilterPython.itkBinaryThresholdImageFilterID3IUL3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f61c7c4a81088640b7f2ab78794fdc5c\",\n \"score\": \"0.8198618\",\n \"text\": \"def itkMeanProjectionImageFilterIUS3IUS2_Superclass_cast(obj: 'itkLightObject') -> \\\"itkMeanProjectionImageFilterIUS3IUS2_Superclass *\\\":\\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterIUS3IUS2_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"079f05203de253788e2c4ade3e45eada\",\n \"score\": \"0.81882155\",\n \"text\": \"def itkExpImageFilterID3ID3_Superclass_cast(*args):\\n return _itkExpImageFilterPython.itkExpImageFilterID3ID3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a66a2bd592b39311d3b4ae0159f61ba\",\n \"score\": \"0.81772196\",\n \"text\": \"def itkBinaryThresholdImageFilterID3IUC3_Superclass_cast(*args):\\n return _itkBinaryThresholdImageFilterPython.itkBinaryThresholdImageFilterID3IUC3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63f3f4d008945aca92f2f84ca90897e9\",\n \"score\": \"0.8176214\",\n \"text\": \"def itkBinaryThresholdImageFilterID3IUS3_Superclass_cast(*args):\\n return _itkBinaryThresholdImageFilterPython.itkBinaryThresholdImageFilterID3IUS3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d482c2d45a6394ef82313ba581c2db2b\",\n \"score\": \"0.8162989\",\n \"text\": \"def itkExpImageFilterIF3IF3_Superclass_cast(*args):\\n return _itkExpImageFilterPython.itkExpImageFilterIF3IF3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7afe7ec8f7d19835346e1ce0accd1e9\",\n \"score\": \"0.8158752\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkMeanProjectionImageFilterIUS3IUS2_Superclass *\\\":\\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterIUS3IUS2_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3be036cbd31f2866b0cfad4e49fd0e9a\",\n \"score\": \"0.8145145\",\n \"text\": \"def itkBinaryThresholdImageFilterIF3IF3_Superclass_cast(*args):\\n return _itkBinaryThresholdImageFilterPython.itkBinaryThresholdImageFilterIF3IF3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b60c6222f6e8f6cf00e99bcec37a27a6\",\n \"score\": \"0.813541\",\n \"text\": \"def cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterID3ID3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"373ef52d9427ab119e664f4960607a70\",\n \"score\": \"0.8133941\",\n \"text\": \"def cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIF3ID3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dec893ec6cd0fe86b08da7b4a166173\",\n \"score\": \"0.8132761\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkMeanProjectionImageFilterIF2IF2_Superclass *\\\":\\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterIF2IF2_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23362f64f1ad99c6a5fd247e058e6d38\",\n \"score\": \"0.81281525\",\n \"text\": \"def itkBinaryProjectionImageFilterID3ID3_Superclass_cast(*args):\\n return _itkBinaryProjectionImageFilterPython.itkBinaryProjectionImageFilterID3ID3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cd7e4e4b14823f81d5643783b2dbfe0\",\n \"score\": \"0.8122367\",\n \"text\": \"def itkMeanProjectionImageFilterIF2IF2_Superclass_cast(obj: 'itkLightObject') -> \\\"itkMeanProjectionImageFilterIF2IF2_Superclass *\\\":\\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterIF2IF2_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14763da0c6df1171b53863cc5fb27eb1\",\n \"score\": \"0.8119492\",\n \"text\": \"def cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterID3IF3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09c3160873492e7808051c97c7d5d5d4\",\n \"score\": \"0.8098276\",\n \"text\": \"def itkExpImageFilterIUL3IUL3_Superclass_cast(*args):\\n return _itkExpImageFilterPython.itkExpImageFilterIUL3IUL3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b69dad3bed28edfa5dda0f26d3d5a1d9\",\n \"score\": \"0.809521\",\n \"text\": \"def itkBinaryThresholdImageFilterIF3IUL3_Superclass_cast(*args):\\n return _itkBinaryThresholdImageFilterPython.itkBinaryThresholdImageFilterIF3IUL3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b1c7272164aa738e64c9e802d39a3d4\",\n \"score\": \"0.80732596\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkMeanProjectionImageFilterIUC2IUC2_Superclass *\\\":\\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterIUC2IUC2_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c369fcc142ea41c8f71184515ebfffc0\",\n \"score\": \"0.80711764\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkMeanProjectionImageFilterISS2ISS2_Superclass *\\\":\\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterISS2ISS2_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f21a8821964bd3dabfd4ab601542c7f1\",\n \"score\": \"0.8049341\",\n \"text\": \"def itkBinaryThresholdImageFilterIUL3IUL3_Superclass_cast(*args):\\n return _itkBinaryThresholdImageFilterPython.itkBinaryThresholdImageFilterIUL3IUL3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"458cd271ce341fd8cc690f7719b8ff83\",\n \"score\": \"0.8048721\",\n \"text\": \"def cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUL3ID3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b4b334ac52d78b484774dd4acbafdd7\",\n \"score\": \"0.8046036\",\n \"text\": \"def itkBinaryThresholdImageFilterIF3IUC3_Superclass_cast(*args):\\n return _itkBinaryThresholdImageFilterPython.itkBinaryThresholdImageFilterIF3IUC3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02ad6fa9fb1999b86f5d79c16ce2a77e\",\n \"score\": \"0.80359405\",\n \"text\": \"def cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUS3ID3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b59e4e090217cfcf6abc75a2f1e632c1\",\n \"score\": \"0.803484\",\n \"text\": \"def cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUC3ID3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"448ea10d4865183e402df59a765d4db9\",\n \"score\": \"0.8029892\",\n \"text\": \"def cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIF3IF3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f593cfa7a9cb141302f0015f84a96845\",\n \"score\": \"0.80268764\",\n \"text\": \"def itkBinaryProjectionImageFilterIF3IF3_Superclass_cast(*args):\\n return _itkBinaryProjectionImageFilterPython.itkBinaryProjectionImageFilterIF3IF3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e31851ff63583dd39788a296dc2af94e\",\n \"score\": \"0.8025101\",\n \"text\": \"def itkMeanProjectionImageFilterIUC2IUC2_Superclass_cast(obj: 'itkLightObject') -> \\\"itkMeanProjectionImageFilterIUC2IUC2_Superclass *\\\":\\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterIUC2IUC2_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"412931d31087f0530d8637e44d063961\",\n \"score\": \"0.80215895\",\n \"text\": \"def itkAtan2ImageFilterID3ID3ID3_Superclass_cast(*args):\\n return _itkAtan2ImageFilterPython.itkAtan2ImageFilterID3ID3ID3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2eefd9b42f1c35cedb996c4613fb934\",\n \"score\": \"0.80181354\",\n \"text\": \"def cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterID3IUS3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe3948ed722abb52f80d7e426f52ed72\",\n \"score\": \"0.801093\",\n \"text\": \"def itkFastApproximateRankImageFilterID3ID3_Superclass_cast(*args):\\n return _itkFastApproximateRankImageFilterPython.itkFastApproximateRankImageFilterID3ID3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42822aade6f255f678b30afe31543ca4\",\n \"score\": \"0.80096585\",\n \"text\": \"def cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterID3IUL3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b572aa85235686a0716a3c81a905bad\",\n \"score\": \"0.8001255\",\n \"text\": \"def itkBinaryProjectionImageFilterIUL3IUL3_Superclass_cast(*args):\\n return _itkBinaryProjectionImageFilterPython.itkBinaryProjectionImageFilterIUL3IUL3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bce376c309a2b5b6d071f08c3111eb3b\",\n \"score\": \"0.80006874\",\n \"text\": \"def cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterID3IUC3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1b43dc6904897df1ef8e3afa1f2b06e\",\n \"score\": \"0.7990471\",\n \"text\": \"def itkBinaryThresholdImageFilterIF3IUS3_Superclass_cast(*args):\\n return _itkBinaryThresholdImageFilterPython.itkBinaryThresholdImageFilterIF3IUS3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6565848d8f63418f06e4858747a000ff\",\n \"score\": \"0.7974886\",\n \"text\": \"def itkBinaryThresholdImageFilterIUL3IUC3_Superclass_cast(*args):\\n return _itkBinaryThresholdImageFilterPython.itkBinaryThresholdImageFilterIUL3IUC3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e664715ae07c2624d54baa79a4ae83d\",\n \"score\": \"0.79699284\",\n \"text\": \"def cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIF3IUS3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"612b15147aceb3cd76eda7973c03a286\",\n \"score\": \"0.79657155\",\n \"text\": \"def cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIF3IUL3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92205a3f55b9a5911c13567c47ebed75\",\n \"score\": \"0.7956797\",\n \"text\": \"def itkExpImageFilterIUC3IUC3_Superclass_cast(*args):\\n return _itkExpImageFilterPython.itkExpImageFilterIUC3IUC3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba95ff97dbc5cfa82f9831b29c2723d4\",\n \"score\": \"0.7956402\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkMeanProjectionImageFilterIUS2IUS2_Superclass *\\\":\\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterIUS2IUS2_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1267ea3848e6fced49f638be642a8a2\",\n \"score\": \"0.79550743\",\n \"text\": \"def cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUL3IF3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34938ff0debb0cc699e3a3d359182809\",\n \"score\": \"0.7951793\",\n \"text\": \"def itkBinaryThresholdImageFilterIUL3IUS3_Superclass_cast(*args):\\n return _itkBinaryThresholdImageFilterPython.itkBinaryThresholdImageFilterIUL3IUS3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6dea84b2f847baf735230d598af5297\",\n \"score\": \"0.7946439\",\n \"text\": \"def cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIF3IUC3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aeb4b346ce2f16968b2308a9bb9039de\",\n \"score\": \"0.7937531\",\n \"text\": \"def itkBinaryThresholdImageFilterIUC3IUC3_Superclass_cast(*args):\\n return _itkBinaryThresholdImageFilterPython.itkBinaryThresholdImageFilterIUC3IUC3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6212902aa81aef56bda24f7274bc2c9d\",\n \"score\": \"0.7928491\",\n \"text\": \"def itkFastApproximateRankImageFilterIF3IF3_Superclass_cast(*args):\\n return _itkFastApproximateRankImageFilterPython.itkFastApproximateRankImageFilterIF3IF3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca86c49f545f814b3a7758c60e775522\",\n \"score\": \"0.79193926\",\n \"text\": \"def itkHistogramToEntropyImageFilterHFIF3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkHistogramToEntropyImageFilterHFIF3_Superclass *\\\":\\n return _itkHistogramToEntropyImageFilterPython.itkHistogramToEntropyImageFilterHFIF3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a250c987214885e1ba3b2947751364f8\",\n \"score\": \"0.7917758\",\n \"text\": \"def itkAtan2ImageFilterIF3IF3IF3_Superclass_cast(*args):\\n return _itkAtan2ImageFilterPython.itkAtan2ImageFilterIF3IF3IF3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"763d85e2459dc33987b55aaae952d23d\",\n \"score\": \"0.79147696\",\n \"text\": \"def cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUS3IF3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b963b1346e64b3aa4bde267016d6aa8e\",\n \"score\": \"0.7912498\",\n \"text\": \"def itkMeanProjectionImageFilterISS2ISS2_Superclass_cast(obj: 'itkLightObject') -> \\\"itkMeanProjectionImageFilterISS2ISS2_Superclass *\\\":\\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterISS2ISS2_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38a9a87d33bb2fe9eb5fa829ce09cf4e\",\n \"score\": \"0.79064196\",\n \"text\": \"def cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUL3IUL3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"805d6abd86d66f630687d599a035982b\",\n \"score\": \"0.7899451\",\n \"text\": \"def cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUC3IF3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11b47591654af5daf9c6bf2af2bdd64e\",\n \"score\": \"0.7897342\",\n \"text\": \"def itkHistogramToEntropyImageFilterHDIF3_Superclass_cast(obj: 'itkLightObject') -> \\\"itkHistogramToEntropyImageFilterHDIF3_Superclass *\\\":\\n return _itkHistogramToEntropyImageFilterPython.itkHistogramToEntropyImageFilterHDIF3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fafcf0abc4cc2db8ca89d5fa97cf60d\",\n \"score\": \"0.78815037\",\n \"text\": \"def cast(obj: 'itkLightObject') -> \\\"itkHistogramToEntropyImageFilterHDIF3_Superclass *\\\":\\n return _itkHistogramToEntropyImageFilterPython.itkHistogramToEntropyImageFilterHDIF3_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72c58e81b4eab7ad7cfe8f7e15a02b3f\",\n \"score\": \"0.7878861\",\n \"text\": \"def itkVectorConnectedComponentImageFilterIVF33IUL3_Superclass_cast(*args):\\n return _itkVectorConnectedComponentImageFilterPython.itkVectorConnectedComponentImageFilterIVF33IUL3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"592ba4079871bcbc0f4fc8bc43f09679\",\n \"score\": \"0.7870837\",\n \"text\": \"def itkBinaryThresholdImageFilterIUS3IUC3_Superclass_cast(*args):\\n return _itkBinaryThresholdImageFilterPython.itkBinaryThresholdImageFilterIUS3IUC3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"465befdda4135daee90e1c7a32ca0d2b\",\n \"score\": \"0.7865448\",\n \"text\": \"def cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUL3IUS3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7b859d7d294bed05a39dc8165c5e20e\",\n \"score\": \"0.78585553\",\n \"text\": \"def cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUL3IUC3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c07d46513579b2e06b9fba779d7ec25\",\n \"score\": \"0.7836825\",\n \"text\": \"def itkMeanProjectionImageFilterIUS2IUS2_Superclass_cast(obj: 'itkLightObject') -> \\\"itkMeanProjectionImageFilterIUS2IUS2_Superclass *\\\":\\n return _itkMeanProjectionImageFilterPython.itkMeanProjectionImageFilterIUS2IUS2_Superclass_cast(obj)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bf3814197a8581e502f21b1a607f9df\",\n \"score\": \"0.78347975\",\n \"text\": \"def itkMaximumProjectionImageFilterID3ID3_Superclass_cast(*args):\\n return _itkMaximumProjectionImageFilterPython.itkMaximumProjectionImageFilterID3ID3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10d29924e8be11df31e71931d18373c6\",\n \"score\": \"0.7831498\",\n \"text\": \"def cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUC3IUS3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d31aaf03aa052c618d636098a65a8c0c\",\n \"score\": \"0.7825933\",\n \"text\": \"def itkBinaryProjectionImageFilterIUC3IUC3_Superclass_cast(*args):\\n return _itkBinaryProjectionImageFilterPython.itkBinaryProjectionImageFilterIUC3IUC3_Superclass_cast(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a5c5052472b6103f6a61a1966932d59\",\n \"score\": \"0.7824537\",\n \"text\": \"def cast(*args):\\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIUC3IUL3_Superclass_cast(*args)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993279,"cells":{"query_id":{"kind":"string","value":"b0685b59a1a8c19bded0b78253b1a4e5"},"query":{"kind":"string","value":"NTSTATUS NtUnloadDriver( PUNICODE_STRING DriverServiceName )"},"positive_passages":{"kind":"list like","value":[{"docid":"d95d989b5badc51e4b44659759f8de6d","score":"0.8218995","text":"def ntdll_NtUnloadDriver(jitter):\n ret_ad, args = jitter.func_args_stdcall([\"DriverServiceName\"])\n raise RuntimeError('API not implemented')\n jitter.func_ret_stdcall(ret_ad, ret_value)","title":""}],"string":"[\n {\n \"docid\": \"d95d989b5badc51e4b44659759f8de6d\",\n \"score\": \"0.8218995\",\n \"text\": \"def ntdll_NtUnloadDriver(jitter):\\n ret_ad, args = jitter.func_args_stdcall([\\\"DriverServiceName\\\"])\\n raise RuntimeError('API not implemented')\\n jitter.func_ret_stdcall(ret_ad, ret_value)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"b6361d2362e8373d980c4b2cbddfac6c","score":"0.5760336","text":"def ntdll_NtLoadDriver(jitter):\n ret_ad, args = jitter.func_args_stdcall([\"DriverServiceName\"])\n raise RuntimeError('API not implemented')\n jitter.func_ret_stdcall(ret_ad, ret_value)","title":""},{"docid":"46152057e88c7b113362be207111d852","score":"0.54765874","text":"def unload_services(hass: HomeAssistant):\n hass.services.async_remove(DOMAIN, SERVICE_SET_TIMER_BASIC_SETTINGS)\n hass.services.async_remove(DOMAIN, SERVICE_UPDATE_SCHEDULE)\n hass.services.async_remove(DOMAIN, SERVICE_UPDATE_PROFILE)\n hass.services.async_remove(DOMAIN, SERVICE_SET_CHARGER_MAX_CURRENT)","title":""},{"docid":"5a7b8c1f8b2dacaff17dc989c98c6043","score":"0.5439787","text":"def unregister_service(self):\r\n pass","title":""},{"docid":"008bd0b6c704550a1299063428ee9963","score":"0.5305615","text":"def ntdll_LdrUnloadAlternateResourceModule(jitter):\n ret_ad, args = jitter.func_args_stdcall([\"BaseAddress\"])\n raise RuntimeError('API not implemented')\n jitter.func_ret_stdcall(ret_ad, ret_value)","title":""},{"docid":"3a95d4feac217b781522ff79e8a7517a","score":"0.52907413","text":"def unload_module(self, name):\n pass","title":""},{"docid":"2627bbc892d7c6c8e9dfe1c20c49f74c","score":"0.52203137","text":"def unregister_conductor(self, hostname):","title":""},{"docid":"d3e0b7b05e0abab977ef7b54106f1da6","score":"0.51830584","text":"def ntdll_LdrUnloadDll(jitter):\n ret_ad, args = jitter.func_args_stdcall([\"BaseAddress\"])\n raise RuntimeError('API not implemented')\n jitter.func_ret_stdcall(ret_ad, ret_value)","title":""},{"docid":"87c888ec77f236b92aaadf11ff987791","score":"0.5150221","text":"def ntdll_NtDeleteDriverEntry(jitter):\n ret_ad, args = jitter.func_args_stdcall([\"Id\"])\n raise RuntimeError('API not implemented')\n jitter.func_ret_stdcall(ret_ad, ret_value)","title":""},{"docid":"926a537602b832183fbca31d1c2039eb","score":"0.51186365","text":"def quit_driver(self):\n self.driver.quit()","title":""},{"docid":"39012b9bacead6e4b64d4528985375c4","score":"0.50936633","text":"def _stop_driver(self, force=False):\n\n if self._dvr_proc:\n self._dvr_proc.stop(force)\n self._dvr_proc = None\n self._dvr_client = None\n self._resource_schema = {}\n\n log.info('Instrument agent %s stopped its driver.', self._proc_name)","title":""},{"docid":"e54c1015fadf8a0fba7ea3ab0c2868e9","score":"0.50622284","text":"def unregister_service(self, service):\n pass","title":""},{"docid":"f604192ee325303a72c506c27b4460a8","score":"0.5052263","text":"def unload_plugin(self):\n pass","title":""},{"docid":"50cb58a9dabc699fbe67620ab28c2a1a","score":"0.5045084","text":"def softwareTearDown(software):","title":""},{"docid":"d798f1485ea980c49c87d8a760f14316","score":"0.50179195","text":"def shutdown(self):\n self.driver.close()\n return None","title":""},{"docid":"a85d0aa90a68dbcb5e1ce9c80e9cc351","score":"0.5003383","text":"def ntdll_DbgUnLoadImageSymbols(jitter):\n ret_ad, args = jitter.func_args_stdcall([\"Name\", \"Base\", \"ProcessId\"])\n raise RuntimeError('API not implemented')\n jitter.func_ret_stdcall(ret_ad, ret_value)","title":""},{"docid":"0cec094e2a97abe6cfdb116f63354437","score":"0.4969931","text":"def test_service_disable(modules, service_name):\n # enable service before test\n assert modules.service.enable(service_name)\n\n assert modules.service.disable(service_name)\n if salt.utils.platform.is_darwin():\n assert modules.service.disabled(service_name)\n else:\n assert service_name in modules.service.get_disabled()","title":""},{"docid":"80155468deb5b389f1ebf472889a28e0","score":"0.49285817","text":"def unbind_to(self, ifaces, drv):\n if conf.DPDK_VERSION == '17.11.3' or conf.DPDK_VERSION == '18.11.1':\n tools_dir = '/usertools/'\n else:\n tools_dir = '/tools/'\n\n run(['sudo', conf.DPDK_DIR + tools_dir + 'dpdk-devbind.py', '--unbind'] + ifaces)\n run(['sudo', conf.DPDK_DIR + tools_dir + 'dpdk-devbind.py', '--bind=' + drv] + ifaces)","title":""},{"docid":"1c0fa155be4be885962cf123aa5820ae","score":"0.49213973","text":"def release_driver(env: simpy.Environment, driver: Driver, driver_pool: simpy.FilterStore):\n if env.now % TIME_UNITS_PER_MIN == 0:\n yield env.timeout(1)\n driver.in_trip = False\n driver_pool.put(driver)","title":""},{"docid":"a1716a6a4c4dfa020e3e6f69348072f0","score":"0.49169055","text":"def quit(self):\n self.driver = None","title":""},{"docid":"0f21f1ffbfbc524cb318c7bc9e1186b8","score":"0.49048558","text":"def unload_procedure(self, name: str) -> None:\n HCkE(_hdevengine_lib.HCenUnloadProcedure(\n self._engine_ptr,\n ctypes.c_char_p(name.encode('utf8'))\n ))","title":""},{"docid":"c56b7d596491fb3456d8210565da42fd","score":"0.4900018","text":"def importDriver(driverName=None):\n MscContext = None\n MtpContext = None\n VendorSpecificContext = None\n MscContext2 = None\n MtpContext2 = None\n VendorSpecificContext2 = None\n\n # Load native drivers\n if driverName == 'native' or driverName is None:\n if sys.platform == 'win32':\n from ..usb.driver.windows.msc import MscContext\n from ..usb.driver.windows.wpd import MtpContext\n from ..usb.driver.windows.driverless import VendorSpecificContext\n elif sys.platform == 'darwin':\n from ..usb.driver.osx import isMscDriverAvailable\n if isMscDriverAvailable():\n from ..usb.driver.osx import MscContext\n else:\n print('Native driver not installed')\n else:\n print('No native drivers available')\n elif driverName == 'qemu':\n from ..usb.driver.generic.qemu import MscContext\n from ..usb.driver.generic.qemu import MtpContext\n elif driverName != 'libusb':\n raise Exception('Unknown driver')\n\n # Fallback to libusb\n if MscContext is None or (driverName is None and sys.platform == 'win32'):\n from ..usb.driver.generic.libusb import MscContext as MscContext2\n if MtpContext is None or (driverName is None and sys.platform == 'win32'):\n from ..usb.driver.generic.libusb import MtpContext as MtpContext2\n if (VendorSpecificContext is None and driverName != 'qemu') or (driverName is None and sys.platform == 'win32'):\n from ..usb.driver.generic.libusb import VendorSpecificContext as VendorSpecificContext2\n\n drivers = [context() for context in [MscContext, MtpContext, VendorSpecificContext, MscContext2, MtpContext2, VendorSpecificContext2] if context]\n print('Using drivers %s' % ', '.join(d.name for d in drivers))\n return UsbDriverList(*drivers)","title":""},{"docid":"33f809df3e9f35e89aa38a997a567c81","score":"0.4822499","text":"def nss_shutdown(): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"1517f5da5379d2657447ab4b8b8b129b","score":"0.48061725","text":"def quit_driver(self):\n try:\n self.driver.quit()\n except Exception as quit_error:\n message = \"Unable to quit the driver. Is it possible you already quit the driver?\\n\" \\\n \"{0}\".format(quit_error)\n raise DriverAttributeError(msg=message, stacktrace=traceback.format_exc())","title":""},{"docid":"0a7c7ecc3e4dc4a7bec780c826b7fb4d","score":"0.47975987","text":"def stop(self):\n super(QemuPciPassthrough, self).stop()\n\n # bind original driver to every interface\n for nic in self._host_nics:\n if nic['driver']:\n try:\n tasks.run_task(['sudo', '/usr/bin/python2',S.getValue('TOOLS')['bind-tool'], '--bind=' + nic['driver'], nic['pci']],\n self._logger, 'Binding NIC %s...' % nic['pci'], True)\n\n except subprocess.CalledProcessError:\n self._logger.error('Unable to bind NIC %s to driver %s', nic['pci'], nic['driver'])\n\n # unload vfio-pci driver\n _MODULE_MANAGER.remove_modules()","title":""},{"docid":"9e91e4c5e040b02da98b07c6dc76ed61","score":"0.4786137","text":"def __remove_service(smt_server_name):\n service_files = glob.glob('/etc/zypp/services.d/*')\n for service_file in service_files:\n service_cfg = get_config(service_file)\n for section in service_cfg.sections():\n url = service_cfg.get(section, 'url')\n if url:\n if (\n smt_server_name in url or\n 'plugin:/susecloud' in url or\n 'plugin:susecloud' in url\n ):\n logging.info('Removing service: %s'\n % os.path.basename(service_file))\n os.unlink(service_file)\n\n service_plugins = __get_service_plugins()\n for service_plugin in service_plugins:\n os.unlink(service_plugin)\n\n return 1","title":""},{"docid":"7833b0c209c83a96205e075f6e322d8a","score":"0.47768977","text":"def deregister(cls, service):\n key = '.'.join((service.group, service.name))\n\n if key in cls.core:\n del cls.core[key]","title":""},{"docid":"c0834a831595f84fff6b53d35b5fead5","score":"0.47527963","text":"def remove_service(service_name):","title":""},{"docid":"39ff8e25d5a76fc16b58d09f8aa54cce","score":"0.47508743","text":"def delete_service(self, service_name):","title":""},{"docid":"826d725f6c8df8a6162d2717ac5dea4e","score":"0.47441024","text":"async def unload(self, ctx, module: str) -> discord.Message:\n try:\n ctx.bot.unload_extension(module)\n except commands.errors.ExtensionNotLoaded:\n return await ctx.send(\"That module either isn't loaded or doesn't exist.\")\n return await ctx.send(f\"Unloaded `{module}`!\")","title":""},{"docid":"7822a2a5cb0e339665a5bee6cd983097","score":"0.47280425","text":"def _unbind_directory(self, _, svc, svc_ref):\n # pylint: disable=W0613\n # Remove the ConfigurationAdmin service\n self._controller = False","title":""},{"docid":"e46d293ddd57a08baa40ebc03819a40d","score":"0.47129226","text":"def tearDownRun():\n driver.quit()","title":""},{"docid":"dd75487acac09e702c37763984e32320","score":"0.47030613","text":"def die(self):\n self.__driver.close()","title":""},{"docid":"ab200cb5f77dcdfb6808e4dea1aabdfa","score":"0.46998447","text":"def close():\n try:\n global driver\n driver.close()\n driver = None\n return True\n except Exception as e:\n logger.warning(f\"Unable to close chrome driver instance: {e}\")\n return False","title":""},{"docid":"b62089b73fa390e5fe3bcae05df0f9e9","score":"0.46997446","text":"def _unregister_service(self) -> None:\n strategy = cast(AliceStrategy, self.context.strategy)\n description = strategy.get_unregister_service_description()\n oef_search_dialogues = cast(\n OefSearchDialogues, self.context.oef_search_dialogues\n )\n oef_search_msg, _ = oef_search_dialogues.create(\n counterparty=self.context.search_service_address,\n performative=OefSearchMessage.Performative.UNREGISTER_SERVICE,\n service_description=description,\n )\n self.context.outbox.put_message(message=oef_search_msg)\n self.context.logger.info(\"unregistering service from SOEF.\")","title":""},{"docid":"17f982ec36ea8c17d7faf047fb8a4c56","score":"0.46990088","text":"def unload_drive(self, slot=None, drive=0):\n try:\n if slot is None:\n sh.mtx('-f', self.changer, 'altres', 'unload')\n else:\n sh.mtx('-f', self.changer, 'altres', 'unload', slot)\n except sh.ErrorReturnCode_1:\n raise FailedToUnloadDrive()","title":""},{"docid":"bd2de95c3c5ee27fda57b7b250d40826","score":"0.4690692","text":"def unmount_obj(path, driver):\n\n dev = DockerMount.get_dev_at_mountpoint(path)\n\n # If there's a bind-mount over the directory, unbind it.\n if dev.rsplit('[', 1)[-1].strip(']') == '/rootfs' \\\n and driver == 'devicemapper':\n Mount.unmount_path(path)\n\n DockerMount(path).unmount()","title":""},{"docid":"dbccabe7fb6dd461fddaad7cee013ba1","score":"0.46881944","text":"def unhook(ptr: str):","title":""},{"docid":"4230cda2f12aa8b3f951d9b977d66bc0","score":"0.46770287","text":"def shutdown():\n\n import fsl.utils.settings as fslsettings\n\n # Clear the cached directory for loading/saving\n # files - when FSLeyes starts up, we want it to\n # default to the current directory.\n fslsettings.delete('loadSaveOverlayDir')","title":""},{"docid":"cba1748c45f0fd0163c08fdadcf3ee45","score":"0.46744576","text":"async def unload(ctx, extension):\n client.unload_extension(f'cogs.{extension}')\n print(f'Cog \"{extension}\" was unloaded')\n await ctx.send(f'Unloaded Cog \"{extension}\"')","title":""},{"docid":"54b38cd9891efe26132067f526d13e1f","score":"0.4637666","text":"def unloaded():\n global netManager\n\n if netManager is not None:\n netManager.shutdown()\n netManager = None","title":""},{"docid":"92beb2d05bacb43f4264d809d2eed8ef","score":"0.46225893","text":"def disconnect(self, driver):\n while self.device.mode != 'global':\n self.device.send('exit')\n\n self.device.send('exit', wait_for_string=driver.press_return_re)\n\n self.device.ctrl.send(chr(4))\n\n # self.device.ctrl.sendcontrol(']')\n # self.device.ctrl.sendline('quit')","title":""},{"docid":"402d716c7717290ae49f109fbf88c45a","score":"0.45956808","text":"async def uload(self, ctx, extension_name:str):\n prompt = await helpers.yes_no(ctx, ctx.bot)\n if not prompt:\n return\n ctx.bot.unload_extension(extension_name)\n settings.manager.setdefault(\"EXTENSIONS\", settings.DEFAULT_EXTENSIONS)\n try:\n settings.manager[\"EXTENSIONS\"].remove(extension_name)\n except ValueError:\n message = f\"Extension {extension_name} is already unloaded. :<\"\n else:\n message = f\"Extension {extension_name} unloaded.\"\n await ctx.send(message)\n logger.info(message)\n settings.save()","title":""},{"docid":"2a50f02f675edfb49d020ba2d5f5e993","score":"0.45934147","text":"def on_unload(data):\n print('{0} plugin v{1} unloaded'.format(__module_name__, __module_version__))","title":""},{"docid":"40fe044d7ade3277754a6e319c6e9659","score":"0.458138","text":"def unregister(protocol):","title":""},{"docid":"a6f49c60089155be5df8918f9ef6d8a6","score":"0.4575457","text":"def _HANDLE_driver_connection():\n handle = kernel32.CreateFileA(\n \"\\\\\\\\.\\\\HackSysExtremeVulnerableDriver\", # lpFileName\n 0xC0000000, # dwDesiredAccess\n 0, # dwShareMode\n None, # lpSecurityAttributes\n 0x3, # dwCreationDisposition\n 0, # dwFlagsAndAttributes\n None # hTemplateFile\n )\n\n return handle","title":""},{"docid":"4183fe8fa5c27fc81d5a2aa0419d4ca8","score":"0.45699832","text":"def unbind(self, svc, ref):\n\t\tprops = ref.get_properties()\n\t\tif props.get(\"service.imported\", False):\n\t\t\timport_str = \"from %s\" % props.get(\"service.imported.from\")\n\t\telse:\n\t\t\timport_str = \"local\"\n\t\t\t_logger.debug(\"%s> UnBound of %s (%s)\", self._name, ref, import_str)","title":""},{"docid":"6486087a29bbab4bf890afc4e4e10c3c","score":"0.45673695","text":"def close_www_connection(ser):\n commands = ['AT+SAPBR=0,1']\n handle_commands(ser, commands)","title":""},{"docid":"15450f676b4a3bb272e4b61034cc75f8","score":"0.45633394","text":"def close(self):\n close_driver(self.driver)","title":""},{"docid":"98a2cc373d0cd06e66b68c97e28112c7","score":"0.45627278","text":"def cmdunload(self, msg, args):\n self.log.debug(\"Unloading ShellExec Scripts\")\n if self.dynamic_plugin is not None:\n self._bot.remove_commands_from(self.dynamic_plugin)\n return (\"Done unloading commands.\")","title":""},{"docid":"5030cd008ba736b4f86b035fc2e7439e","score":"0.45607397","text":"def deregister(cls, plugin='None'):\n \n pass","title":""},{"docid":"a3b27a7e6dea69065d2a5f7d7242301a","score":"0.45455232","text":"def unsubscribe_from_save_mp4(node):\n rospy.wait_for_service('/racecar/save_mp4/unsubscribe_from_save_mp4')\n return ServiceProxyWrapper('/racecar/save_mp4/unsubscribe_from_save_mp4', Empty)","title":""},{"docid":"d058f57d2db24c5947a2428114d4fc95","score":"0.45446151","text":"def closeDriver(self, packet, client):\n Logger.log(self.classname, \"closeApplication\", \"Dostalem request do zakonczenia sesji drivera.\")\n self.closeApplication(packet, client)\n self.groupManager.delDriver(packet.id)\n p = Packet.packetFromContent(packet.id, Sources.SERVER, 0, Communicates.CONFIRM, \"\")\n Logger.log(self.classname, \"closeApplication\", \"Dostalem request do zakonczenia sesji drivera.\")","title":""},{"docid":"4661188e26365d862b6a86c571f8a462","score":"0.45385972","text":"def unload_module(self, module):\n self._modules.update({module: \"unload\"})","title":""},{"docid":"0e9f70009a253874b9f70377c319de41","score":"0.45368376","text":"def stopServices() :\n # stop services\n os.system(\"sudo systemctl stop hostapd\")\n os.system(\"sudo systemctl stop dnsmasq\")\n os.system(\"sudo systemctl stop dhcpcd\")\n os.system(\"sudo systemctl disable hostapd\")\n os.system(\"sudo systemctl disable dnsmasq\")\n os.system(\"sudo systemctl disable dhcpcd\")\n\n os.system(\"sudo systemctl unmask hostapd && sudo systemctl enable hostapd\")\n\n # start custom script\n os.system(\"sudo sh /usr/local/bin/wifistart\")","title":""},{"docid":"4a88f4857d34119073231729a7c7c193","score":"0.45255935","text":"def unregister(self, context=None):\n self.dbapi.unregister_service(self.hostname, type=self.type)","title":""},{"docid":"74c9d8fc3af5bf82aaa400b014eedd0d","score":"0.45176476","text":"def service_uninstall(self, service_name):\n self.service_stop(service_name)\n j.tools.path.get(\"/etc/init/%s.conf\" % service_name).remove_p()","title":""},{"docid":"22511fc03a22d54494aff32853a42b69","score":"0.45118758","text":"async def _unload(self, ctx: commands.Context, module: str):\n\n message = await ctx.send(\"unloading modules.{0}\".format(module))\n\n if (module == \"owner\"):\n await message.edit(content=\"modules.owner can not be unloaded\")\n return\n\n try:\n ctx.bot.unload_extension(\"modules.{0}\".format(module))\n except (commands.ExtensionNotLoaded) as e:\n await message.edit(content=\"modules.{0} was not loaded\".format(module))\n return\n \n await message.edit(content=\"done.\")","title":""},{"docid":"89afb9499546a9940eabead5cc78af2e","score":"0.45117706","text":"def FreeDlls(self):\n ctypes.windll.ole32.CoFreeUnusedLibraries()","title":""},{"docid":"c1197c62ba5beff9d9da15517f4effd2","score":"0.45086515","text":"def DPSS_teardownIDL(self, sessionHandle, onep_sender_id, elem_sender_id):\n pass","title":""},{"docid":"9d6c0c3990fcd2f7111e8e27144365fb","score":"0.44944075","text":"def unload_module(module):\n if isinstance(module, str):\n modulename = module\n elif hasattr(module, \"__module__\"):\n modulename = module.__module__\n else:\n modulename = module.__name__\n\n if modulename in sys.modules:\n del sys.modules[modulename]","title":""},{"docid":"91e1f128adf7b293302e766e06169ccb","score":"0.44937664","text":"def removeInstance(self, instName, serverName):\n if serverName in self.hardwareDict:\n return self.hardwareDict[serverName].remove(instName)\n else:\n print \"No hardware %s in hardware manager\" % serverName\n return False","title":""},{"docid":"1c88d741c81b4d2e7f22dfa3bf3115bc","score":"0.44919407","text":"def tearDown(self):\n self.driver.close()","title":""},{"docid":"1c88d741c81b4d2e7f22dfa3bf3115bc","score":"0.44919407","text":"def tearDown(self):\n self.driver.close()","title":""},{"docid":"2352feb911d2db69be28fbf91b9c3253","score":"0.44915333","text":"def unplug(self, device_name, network):\n self.driver.unplug(device_name, namespace=network.namespace)","title":""},{"docid":"4a498b8d968aeec644049d01e3e92413","score":"0.4487544","text":"def teardown_module():\n os.remove(db_path[len(\"sqlite:///\") :])","title":""},{"docid":"5ed45fe7fa394f1ddc986ad881610dff","score":"0.44808456","text":"def teardown_module():\n os.system(\"rm *.dat\")\n hblockchain.blockchain.clear()","title":""},{"docid":"8e641f90f6f1c0b8e9e7810da35a2bfb","score":"0.44742936","text":"def unload_extension(self, module_str: str):\n if BUILTINS_EXTS.get(module_str, False) is True:\n module_str = \"IPython.extensions.\" + module_str\n if module_str not in self.loaded:\n return \"not loaded\"\n\n if module_str in sys.modules:\n mod = sys.modules[module_str]\n if self._call_unload_ipython_extension(mod):\n self.loaded.discard(module_str)\n else:\n return \"no unload function\"","title":""},{"docid":"dbbceb371f9cfe5a40a8d311e85242f0","score":"0.44704387","text":"def tearDownModule():\n commontestsuite.commonTearDownModule(testsuite, testprocess)","title":""},{"docid":"a3ef1edea1a46bd6fe67436151e1b30b","score":"0.447019","text":"def tearDown(self):\n super(SSOBackendRegistryTests, self).tearDown()\n\n try:\n sso_backends.unregister(self.dummy_backend)\n except ItemLookupError:\n pass\n\n try:\n sso_backends.unregister(self.dummy_backend_with_urls)\n except ItemLookupError:\n pass","title":""},{"docid":"fe74fc8e9f32ef4e3fbc96c62c268424","score":"0.44684577","text":"def ntdll_LdrUnregisterDllNotification(jitter):\n ret_ad, args = jitter.func_args_stdcall([\"Cookie\"])\n raise RuntimeError('API not implemented')\n jitter.func_ret_stdcall(ret_ad, ret_value)","title":""},{"docid":"b332524fe651ae6f4b7e5af911481a8e","score":"0.4467572","text":"def close(self):\n if not self.driver:\n return\n\n self.driver.quit()\n self.driver = None","title":""},{"docid":"b332524fe651ae6f4b7e5af911481a8e","score":"0.4467572","text":"def close(self):\n if not self.driver:\n return\n\n self.driver.quit()\n self.driver = None","title":""},{"docid":"a8c75896f04992868e8a913141de01e1","score":"0.44590187","text":"def teardown_module(module):\n os.remove(dummy_script)","title":""},{"docid":"67fb7f10837a23638aaf974537e22901","score":"0.44521892","text":"def test_unregister_without_urls(self):\n sso_backends.register(self.dummy_backend)\n sso_backends.unregister(self.dummy_backend)","title":""},{"docid":"5ed3bc6f34c79d0bfdbd2afb6246061a","score":"0.44514525","text":"def deregisterServer(self, serverId, serverType):\n pass","title":""},{"docid":"7008d4119b133f76aedf7d2fc0137aa4","score":"0.44510606","text":"async def mp_unloadpool(ctx: Context) -> str:\n if ctx.args:\n return 'Invalid syntax: !mp unloadpool'\n\n if ctx.player is not ctx.match.host:\n return 'Only available to the host.'\n\n if not ctx.match.pool:\n return 'No mappool currently selected!'\n\n ctx.match.pool = None\n return 'Mappool unloaded.'","title":""},{"docid":"f9c0025b8b5f2b57b801e5a25011737c","score":"0.44510314","text":"def quit(self):\n driver.quit()","title":""},{"docid":"b15e061ad85736dd499411125dcb13b2","score":"0.44445112","text":"def unload(bot: lightbulb.BotApp):\n bot.remove_plugin(plugin)","title":""},{"docid":"ed81c1920d9003bd56bcaf9387b6dce3","score":"0.44320536","text":"def deregister():\n raise NotImplementedError","title":""},{"docid":"61310ed48210b73dc086cb76e837285a","score":"0.4409755","text":"def kill_services():\n proc = subprocess.Popen(['/usr/bin/killall', '-9', 'cfprefsd'],\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE)\n proc = subprocess.Popen(['/usr/bin/killall', '-9', 'locationd'],\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE)","title":""},{"docid":"d8030dd8bea97e90db9015b497f6e5ce","score":"0.44047114","text":"def close(self):\r\n self.driver.close()","title":""},{"docid":"c0486310205da28e8ba5ef6153697288","score":"0.44039074","text":"def unlock_bootloader(self):\n raise Exception(\"Not implemented!\")","title":""},{"docid":"14d0d6b36cf7e61125515f7ecc4db76a","score":"0.44002485","text":"def unregister(self, class_name, module):\n\t\tself.handlers.pop((class_name, module))","title":""},{"docid":"5f03adf97fe52fe1207c2068da417613","score":"0.43980128","text":"def stopAgent():\n logging.info(\"Shutting down the local server.\")\n db = pickledb.load(WRAPPER_DB_PATH, False)\n host = db.get('host')\n port = db.get('port')\n url = 'http://%s:%s' % (host, port)+\"/v2/api/wrapper/shutdown\"\n requests.post(url)\n db.rem('host')\n db.rem('port')\n db.dump()","title":""},{"docid":"c58408a38c0284ff4b7aedf847777bd2","score":"0.4396541","text":"def deactivate(self):\n\n self.dnsmasq.deactivate()\n self.scheme.deactivate()\n self.hostapd.deactivate()","title":""},{"docid":"848d5a41323abf7f3cc8c3317abd6f6d","score":"0.4390581","text":"def unload_plugin(plugin_name):\n\n if not is_plugin_loaded(plugin_name):\n return False\n\n return maya.cmds.unloadPlugin(plugin_name)","title":""},{"docid":"0e5a519bf3f4a9abe6258a30e032b30b","score":"0.4389734","text":"def close(ddpSession):\n del DDP.sessions[ddpSession.getSessionId()]","title":""},{"docid":"0f0cbde37ed2eab246d46857ad42dea7","score":"0.43883076","text":"def delete_updates():\n windir = os.path.expandvars('$windir')\n dirs = glob.glob(os.path.join(windir, '$NtUninstallKB*'))\n dirs += [os.path.expandvars('$windir\\\\SoftwareDistribution\\\\Download')]\n dirs += [os.path.expandvars('$windir\\\\ie7updates')]\n dirs += [os.path.expandvars('$windir\\\\ie8updates')]\n if not dirs:\n # if nothing to delete, then also do not restart service\n return\n\n import win32serviceutil\n wu_running = win32serviceutil.QueryServiceStatus('wuauserv')[1] == 4\n\n args = ['net', 'stop', 'wuauserv']\n\n def wu_service():\n General.run_external(args)\n return 0\n if wu_running:\n yield Command.Function(None, wu_service, \" \".join(args))\n\n for path1 in dirs:\n for path2 in FileUtilities.children_in_directory(path1, True):\n yield Command.Delete(path2)\n if os.path.exists(path1):\n yield Command.Delete(path1)\n\n args = ['net', 'start', 'wuauserv']\n if wu_running:\n yield Command.Function(None, wu_service, \" \".join(args))","title":""},{"docid":"5ebfca55f64235f2daf68161fe63943d","score":"0.43859544","text":"def unregister():\n\n from bpy.utils import unregister_class\n\n # remove OpenGL data\n pdt_pivot_point.PDT_OT_ModalDrawOperator.handle_remove(\n pdt_pivot_point.PDT_OT_ModalDrawOperator, bpy.context\n )\n window_manager = bpy.context.window_manager\n pdt_wm = \"pdt_run_opengl\"\n if pdt_wm in window_manager:\n del window_manager[pdt_wm]\n\n for cls in reversed(classes):\n unregister_class(cls)\n\n del Scene.pdt_pg","title":""},{"docid":"ed6da016492612cb63ee5cc09d59b5ba","score":"0.4384337","text":"def ntdll_NtUnloadKeyEx(jitter):\n ret_ad, args = jitter.func_args_stdcall([\"TargetKey\", \"Event\"])\n raise RuntimeError('API not implemented')\n jitter.func_ret_stdcall(ret_ad, ret_value)","title":""},{"docid":"6eec0b6f88ac4d65c9b3cc53fd2d8d46","score":"0.43776724","text":"def close(self):\n self.core.unload_all_devices()","title":""},{"docid":"dea2c4b05cdb33090ed4183e9ca3d6b6","score":"0.43693548","text":"def stop_pfcwd(duthosts, enum_rand_one_per_hwsku_frontend_hostname):\n duthost = duthosts[enum_rand_one_per_hwsku_frontend_hostname]\n logger.info(\"--- Stop Pfcwd --\")\n duthost.command(\"pfcwd stop\")","title":""},{"docid":"67474de1f77b7e7f579080352535e7f8","score":"0.43536806","text":"def unexport(self):\n self.connection.unregister_object(self.__dbus_info.reg_id)\n self.__dbus_info.reg_id = None\n self.__dbus_info.connection = None","title":""},{"docid":"6e3457f2ac7e32a5d58bd9859b0d78ef","score":"0.4349856","text":"def tearDownClass(cls):\r\n cls.driver.close()\r\n cls.driver.quit()\r\n print(\"'Create an Account' Test completed!\")","title":""},{"docid":"7e338fc52ee16ef7fba273c75bc7aebb","score":"0.43484804","text":"def _py_usb_close_func(self):\n return 0","title":""},{"docid":"e4f5beb5029b4b314dae4425f87286fd","score":"0.43483624","text":"async def stop_dynamic_service(\n app: web.Application,\n service_uuid: NodeIDStr,\n simcore_user_agent: str,\n *,\n save_state: bool = True,\n progress: ProgressBarData | None = None,\n) -> None:\n headers = {\n X_SIMCORE_USER_AGENT: simcore_user_agent,\n }\n settings: DirectorV2Settings = get_plugin_settings(app)\n\n async with AsyncExitStack() as stack:\n if progress:\n await stack.enter_async_context(progress)\n\n await request_director_v2(\n app,\n \"DELETE\",\n url=(settings.base_url / f\"dynamic_services/{service_uuid}\").update_query(\n can_save=\"true\" if save_state else \"false\",\n ),\n headers=headers,\n expected_status=web.HTTPNoContent,\n timeout=settings.DIRECTOR_V2_STOP_SERVICE_TIMEOUT,\n on_error={\n web.HTTPConflict.status_code: (\n ServiceWaitingForManualIntervention,\n {\"service_uuid\": service_uuid},\n )\n },\n )","title":""},{"docid":"e5330aebedffd222d861cb6295bdde58","score":"0.4336767","text":"def shutdown(\n self, cli_context: CliContext, service_name: tuple[str, ...] = None\n ) -> CompletedProcess:\n raise NotImplementedError","title":""},{"docid":"252a328ed3ada7357e6e69ac5e18c08e","score":"0.43333846","text":"def _unload( self ):\n\t\tlog.debug( 'Unregistering commands.' )\n\t\tvim.command( 'delcommand PyVimDevMode' )\n\t\tvim.command( 'delcommand PyVimReload' )\n\t\tvim.command( 'delcommand PyVimNormalMode' )","title":""},{"docid":"60feb488f3e8408953a11ea5a7a4cb10","score":"0.43312","text":"def unload():\n\n for player in players:\n save_player_data(database_path, player)","title":""},{"docid":"fb2d41b8932dad37d2015f1ee094e6a0","score":"0.43296602","text":"def uninstall_Windows():\n # First see if seattle appears as a value in the Windows startup registry key,\n # and remove it if it exists.\n # removed_from_registry is used later and thus must have a value in case the\n # try: block below raises an exception.\n removed_from_registry = False\n try:\n removed_from_registry = remove_seattle_from_win_startup_registry()\n except WindowsError:\n print \"The uninstaller does not have access to the Windows registry \" \\\n + \"startup keys. This means that seattle is likely not installed in \" \\\n + \"your Windows registry startup key, though you may want to \" \\\n + \"manually check the following registry keys and remove seattle \" \\\n + \"from those keys should it exist there: \"\n print \"HKEY_LOCAL_MACHINE\\Software\\Microsoft\\Windows\\CurrentVersion\\Run\"\n print \"HKEY_CURRENT_USER\\Software\\Microsoft\\Windows\\CurrentVersion\\Run\"\n # Distinguish the above-printed text from what will be printed later by\n # by printing a blank line.\n print\n servicelogger.log(\" uninstaller could not access the Windows registry \" \\\n + \"during this attempted uninstall.\")\n\n\n\n # Next, see if there is a link to the seattle starter script in the startup\n # folder and remove it if it is there.\n if not WIN_STARTUP_SCRIPT_PATH == None:\n removed_from_startup_folder = \\\n remove_seattle_from_win_startup_folder()\n\n\n\n # Check to see if uninstall actually removed seattle from the computer.\n if not removed_from_registry and not removed_from_startup_folder:\n raise SeattleNotInstalledError(\"Seattle could not be detected as \" \\\n + \"having been installed prior to \" \\\n + \"uninstall.\")\n elif removed_from_registry or removed_from_startup_folder:\n # Stop all instances of seattle from running before returning.\n stop_all_seattle_processes.main()\n return True","title":""}],"string":"[\n {\n \"docid\": \"b6361d2362e8373d980c4b2cbddfac6c\",\n \"score\": \"0.5760336\",\n \"text\": \"def ntdll_NtLoadDriver(jitter):\\n ret_ad, args = jitter.func_args_stdcall([\\\"DriverServiceName\\\"])\\n raise RuntimeError('API not implemented')\\n jitter.func_ret_stdcall(ret_ad, ret_value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46152057e88c7b113362be207111d852\",\n \"score\": \"0.54765874\",\n \"text\": \"def unload_services(hass: HomeAssistant):\\n hass.services.async_remove(DOMAIN, SERVICE_SET_TIMER_BASIC_SETTINGS)\\n hass.services.async_remove(DOMAIN, SERVICE_UPDATE_SCHEDULE)\\n hass.services.async_remove(DOMAIN, SERVICE_UPDATE_PROFILE)\\n hass.services.async_remove(DOMAIN, SERVICE_SET_CHARGER_MAX_CURRENT)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a7b8c1f8b2dacaff17dc989c98c6043\",\n \"score\": \"0.5439787\",\n \"text\": \"def unregister_service(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"008bd0b6c704550a1299063428ee9963\",\n \"score\": \"0.5305615\",\n \"text\": \"def ntdll_LdrUnloadAlternateResourceModule(jitter):\\n ret_ad, args = jitter.func_args_stdcall([\\\"BaseAddress\\\"])\\n raise RuntimeError('API not implemented')\\n jitter.func_ret_stdcall(ret_ad, ret_value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a95d4feac217b781522ff79e8a7517a\",\n \"score\": \"0.52907413\",\n \"text\": \"def unload_module(self, name):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2627bbc892d7c6c8e9dfe1c20c49f74c\",\n \"score\": \"0.52203137\",\n \"text\": \"def unregister_conductor(self, hostname):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3e0b7b05e0abab977ef7b54106f1da6\",\n \"score\": \"0.51830584\",\n \"text\": \"def ntdll_LdrUnloadDll(jitter):\\n ret_ad, args = jitter.func_args_stdcall([\\\"BaseAddress\\\"])\\n raise RuntimeError('API not implemented')\\n jitter.func_ret_stdcall(ret_ad, ret_value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87c888ec77f236b92aaadf11ff987791\",\n \"score\": \"0.5150221\",\n \"text\": \"def ntdll_NtDeleteDriverEntry(jitter):\\n ret_ad, args = jitter.func_args_stdcall([\\\"Id\\\"])\\n raise RuntimeError('API not implemented')\\n jitter.func_ret_stdcall(ret_ad, ret_value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"926a537602b832183fbca31d1c2039eb\",\n \"score\": \"0.51186365\",\n \"text\": \"def quit_driver(self):\\n self.driver.quit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39012b9bacead6e4b64d4528985375c4\",\n \"score\": \"0.50936633\",\n \"text\": \"def _stop_driver(self, force=False):\\n\\n if self._dvr_proc:\\n self._dvr_proc.stop(force)\\n self._dvr_proc = None\\n self._dvr_client = None\\n self._resource_schema = {}\\n\\n log.info('Instrument agent %s stopped its driver.', self._proc_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e54c1015fadf8a0fba7ea3ab0c2868e9\",\n \"score\": \"0.50622284\",\n \"text\": \"def unregister_service(self, service):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f604192ee325303a72c506c27b4460a8\",\n \"score\": \"0.5052263\",\n \"text\": \"def unload_plugin(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50cb58a9dabc699fbe67620ab28c2a1a\",\n \"score\": \"0.5045084\",\n \"text\": \"def softwareTearDown(software):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d798f1485ea980c49c87d8a760f14316\",\n \"score\": \"0.50179195\",\n \"text\": \"def shutdown(self):\\n self.driver.close()\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a85d0aa90a68dbcb5e1ce9c80e9cc351\",\n \"score\": \"0.5003383\",\n \"text\": \"def ntdll_DbgUnLoadImageSymbols(jitter):\\n ret_ad, args = jitter.func_args_stdcall([\\\"Name\\\", \\\"Base\\\", \\\"ProcessId\\\"])\\n raise RuntimeError('API not implemented')\\n jitter.func_ret_stdcall(ret_ad, ret_value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cec094e2a97abe6cfdb116f63354437\",\n \"score\": \"0.4969931\",\n \"text\": \"def test_service_disable(modules, service_name):\\n # enable service before test\\n assert modules.service.enable(service_name)\\n\\n assert modules.service.disable(service_name)\\n if salt.utils.platform.is_darwin():\\n assert modules.service.disabled(service_name)\\n else:\\n assert service_name in modules.service.get_disabled()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80155468deb5b389f1ebf472889a28e0\",\n \"score\": \"0.49285817\",\n \"text\": \"def unbind_to(self, ifaces, drv):\\n if conf.DPDK_VERSION == '17.11.3' or conf.DPDK_VERSION == '18.11.1':\\n tools_dir = '/usertools/'\\n else:\\n tools_dir = '/tools/'\\n\\n run(['sudo', conf.DPDK_DIR + tools_dir + 'dpdk-devbind.py', '--unbind'] + ifaces)\\n run(['sudo', conf.DPDK_DIR + tools_dir + 'dpdk-devbind.py', '--bind=' + drv] + ifaces)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c0fa155be4be885962cf123aa5820ae\",\n \"score\": \"0.49213973\",\n \"text\": \"def release_driver(env: simpy.Environment, driver: Driver, driver_pool: simpy.FilterStore):\\n if env.now % TIME_UNITS_PER_MIN == 0:\\n yield env.timeout(1)\\n driver.in_trip = False\\n driver_pool.put(driver)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1716a6a4c4dfa020e3e6f69348072f0\",\n \"score\": \"0.49169055\",\n \"text\": \"def quit(self):\\n self.driver = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f21f1ffbfbc524cb318c7bc9e1186b8\",\n \"score\": \"0.49048558\",\n \"text\": \"def unload_procedure(self, name: str) -> None:\\n HCkE(_hdevengine_lib.HCenUnloadProcedure(\\n self._engine_ptr,\\n ctypes.c_char_p(name.encode('utf8'))\\n ))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c56b7d596491fb3456d8210565da42fd\",\n \"score\": \"0.4900018\",\n \"text\": \"def importDriver(driverName=None):\\n MscContext = None\\n MtpContext = None\\n VendorSpecificContext = None\\n MscContext2 = None\\n MtpContext2 = None\\n VendorSpecificContext2 = None\\n\\n # Load native drivers\\n if driverName == 'native' or driverName is None:\\n if sys.platform == 'win32':\\n from ..usb.driver.windows.msc import MscContext\\n from ..usb.driver.windows.wpd import MtpContext\\n from ..usb.driver.windows.driverless import VendorSpecificContext\\n elif sys.platform == 'darwin':\\n from ..usb.driver.osx import isMscDriverAvailable\\n if isMscDriverAvailable():\\n from ..usb.driver.osx import MscContext\\n else:\\n print('Native driver not installed')\\n else:\\n print('No native drivers available')\\n elif driverName == 'qemu':\\n from ..usb.driver.generic.qemu import MscContext\\n from ..usb.driver.generic.qemu import MtpContext\\n elif driverName != 'libusb':\\n raise Exception('Unknown driver')\\n\\n # Fallback to libusb\\n if MscContext is None or (driverName is None and sys.platform == 'win32'):\\n from ..usb.driver.generic.libusb import MscContext as MscContext2\\n if MtpContext is None or (driverName is None and sys.platform == 'win32'):\\n from ..usb.driver.generic.libusb import MtpContext as MtpContext2\\n if (VendorSpecificContext is None and driverName != 'qemu') or (driverName is None and sys.platform == 'win32'):\\n from ..usb.driver.generic.libusb import VendorSpecificContext as VendorSpecificContext2\\n\\n drivers = [context() for context in [MscContext, MtpContext, VendorSpecificContext, MscContext2, MtpContext2, VendorSpecificContext2] if context]\\n print('Using drivers %s' % ', '.join(d.name for d in drivers))\\n return UsbDriverList(*drivers)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33f809df3e9f35e89aa38a997a567c81\",\n \"score\": \"0.4822499\",\n \"text\": \"def nss_shutdown(): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1517f5da5379d2657447ab4b8b8b129b\",\n \"score\": \"0.48061725\",\n \"text\": \"def quit_driver(self):\\n try:\\n self.driver.quit()\\n except Exception as quit_error:\\n message = \\\"Unable to quit the driver. Is it possible you already quit the driver?\\\\n\\\" \\\\\\n \\\"{0}\\\".format(quit_error)\\n raise DriverAttributeError(msg=message, stacktrace=traceback.format_exc())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a7c7ecc3e4dc4a7bec780c826b7fb4d\",\n \"score\": \"0.47975987\",\n \"text\": \"def stop(self):\\n super(QemuPciPassthrough, self).stop()\\n\\n # bind original driver to every interface\\n for nic in self._host_nics:\\n if nic['driver']:\\n try:\\n tasks.run_task(['sudo', '/usr/bin/python2',S.getValue('TOOLS')['bind-tool'], '--bind=' + nic['driver'], nic['pci']],\\n self._logger, 'Binding NIC %s...' % nic['pci'], True)\\n\\n except subprocess.CalledProcessError:\\n self._logger.error('Unable to bind NIC %s to driver %s', nic['pci'], nic['driver'])\\n\\n # unload vfio-pci driver\\n _MODULE_MANAGER.remove_modules()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e91e4c5e040b02da98b07c6dc76ed61\",\n \"score\": \"0.4786137\",\n \"text\": \"def __remove_service(smt_server_name):\\n service_files = glob.glob('/etc/zypp/services.d/*')\\n for service_file in service_files:\\n service_cfg = get_config(service_file)\\n for section in service_cfg.sections():\\n url = service_cfg.get(section, 'url')\\n if url:\\n if (\\n smt_server_name in url or\\n 'plugin:/susecloud' in url or\\n 'plugin:susecloud' in url\\n ):\\n logging.info('Removing service: %s'\\n % os.path.basename(service_file))\\n os.unlink(service_file)\\n\\n service_plugins = __get_service_plugins()\\n for service_plugin in service_plugins:\\n os.unlink(service_plugin)\\n\\n return 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7833b0c209c83a96205e075f6e322d8a\",\n \"score\": \"0.47768977\",\n \"text\": \"def deregister(cls, service):\\n key = '.'.join((service.group, service.name))\\n\\n if key in cls.core:\\n del cls.core[key]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0834a831595f84fff6b53d35b5fead5\",\n \"score\": \"0.47527963\",\n \"text\": \"def remove_service(service_name):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39ff8e25d5a76fc16b58d09f8aa54cce\",\n \"score\": \"0.47508743\",\n \"text\": \"def delete_service(self, service_name):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"826d725f6c8df8a6162d2717ac5dea4e\",\n \"score\": \"0.47441024\",\n \"text\": \"async def unload(self, ctx, module: str) -> discord.Message:\\n try:\\n ctx.bot.unload_extension(module)\\n except commands.errors.ExtensionNotLoaded:\\n return await ctx.send(\\\"That module either isn't loaded or doesn't exist.\\\")\\n return await ctx.send(f\\\"Unloaded `{module}`!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7822a2a5cb0e339665a5bee6cd983097\",\n \"score\": \"0.47280425\",\n \"text\": \"def _unbind_directory(self, _, svc, svc_ref):\\n # pylint: disable=W0613\\n # Remove the ConfigurationAdmin service\\n self._controller = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e46d293ddd57a08baa40ebc03819a40d\",\n \"score\": \"0.47129226\",\n \"text\": \"def tearDownRun():\\n driver.quit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd75487acac09e702c37763984e32320\",\n \"score\": \"0.47030613\",\n \"text\": \"def die(self):\\n self.__driver.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab200cb5f77dcdfb6808e4dea1aabdfa\",\n \"score\": \"0.46998447\",\n \"text\": \"def close():\\n try:\\n global driver\\n driver.close()\\n driver = None\\n return True\\n except Exception as e:\\n logger.warning(f\\\"Unable to close chrome driver instance: {e}\\\")\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b62089b73fa390e5fe3bcae05df0f9e9\",\n \"score\": \"0.46997446\",\n \"text\": \"def _unregister_service(self) -> None:\\n strategy = cast(AliceStrategy, self.context.strategy)\\n description = strategy.get_unregister_service_description()\\n oef_search_dialogues = cast(\\n OefSearchDialogues, self.context.oef_search_dialogues\\n )\\n oef_search_msg, _ = oef_search_dialogues.create(\\n counterparty=self.context.search_service_address,\\n performative=OefSearchMessage.Performative.UNREGISTER_SERVICE,\\n service_description=description,\\n )\\n self.context.outbox.put_message(message=oef_search_msg)\\n self.context.logger.info(\\\"unregistering service from SOEF.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17f982ec36ea8c17d7faf047fb8a4c56\",\n \"score\": \"0.46990088\",\n \"text\": \"def unload_drive(self, slot=None, drive=0):\\n try:\\n if slot is None:\\n sh.mtx('-f', self.changer, 'altres', 'unload')\\n else:\\n sh.mtx('-f', self.changer, 'altres', 'unload', slot)\\n except sh.ErrorReturnCode_1:\\n raise FailedToUnloadDrive()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd2de95c3c5ee27fda57b7b250d40826\",\n \"score\": \"0.4690692\",\n \"text\": \"def unmount_obj(path, driver):\\n\\n dev = DockerMount.get_dev_at_mountpoint(path)\\n\\n # If there's a bind-mount over the directory, unbind it.\\n if dev.rsplit('[', 1)[-1].strip(']') == '/rootfs' \\\\\\n and driver == 'devicemapper':\\n Mount.unmount_path(path)\\n\\n DockerMount(path).unmount()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbccabe7fb6dd461fddaad7cee013ba1\",\n \"score\": \"0.46881944\",\n \"text\": \"def unhook(ptr: str):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4230cda2f12aa8b3f951d9b977d66bc0\",\n \"score\": \"0.46770287\",\n \"text\": \"def shutdown():\\n\\n import fsl.utils.settings as fslsettings\\n\\n # Clear the cached directory for loading/saving\\n # files - when FSLeyes starts up, we want it to\\n # default to the current directory.\\n fslsettings.delete('loadSaveOverlayDir')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cba1748c45f0fd0163c08fdadcf3ee45\",\n \"score\": \"0.46744576\",\n \"text\": \"async def unload(ctx, extension):\\n client.unload_extension(f'cogs.{extension}')\\n print(f'Cog \\\"{extension}\\\" was unloaded')\\n await ctx.send(f'Unloaded Cog \\\"{extension}\\\"')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54b38cd9891efe26132067f526d13e1f\",\n \"score\": \"0.4637666\",\n \"text\": \"def unloaded():\\n global netManager\\n\\n if netManager is not None:\\n netManager.shutdown()\\n netManager = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92beb2d05bacb43f4264d809d2eed8ef\",\n \"score\": \"0.46225893\",\n \"text\": \"def disconnect(self, driver):\\n while self.device.mode != 'global':\\n self.device.send('exit')\\n\\n self.device.send('exit', wait_for_string=driver.press_return_re)\\n\\n self.device.ctrl.send(chr(4))\\n\\n # self.device.ctrl.sendcontrol(']')\\n # self.device.ctrl.sendline('quit')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"402d716c7717290ae49f109fbf88c45a\",\n \"score\": \"0.45956808\",\n \"text\": \"async def uload(self, ctx, extension_name:str):\\n prompt = await helpers.yes_no(ctx, ctx.bot)\\n if not prompt:\\n return\\n ctx.bot.unload_extension(extension_name)\\n settings.manager.setdefault(\\\"EXTENSIONS\\\", settings.DEFAULT_EXTENSIONS)\\n try:\\n settings.manager[\\\"EXTENSIONS\\\"].remove(extension_name)\\n except ValueError:\\n message = f\\\"Extension {extension_name} is already unloaded. :<\\\"\\n else:\\n message = f\\\"Extension {extension_name} unloaded.\\\"\\n await ctx.send(message)\\n logger.info(message)\\n settings.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a50f02f675edfb49d020ba2d5f5e993\",\n \"score\": \"0.45934147\",\n \"text\": \"def on_unload(data):\\n print('{0} plugin v{1} unloaded'.format(__module_name__, __module_version__))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40fe044d7ade3277754a6e319c6e9659\",\n \"score\": \"0.458138\",\n \"text\": \"def unregister(protocol):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6f49c60089155be5df8918f9ef6d8a6\",\n \"score\": \"0.4575457\",\n \"text\": \"def _HANDLE_driver_connection():\\n handle = kernel32.CreateFileA(\\n \\\"\\\\\\\\\\\\\\\\.\\\\\\\\HackSysExtremeVulnerableDriver\\\", # lpFileName\\n 0xC0000000, # dwDesiredAccess\\n 0, # dwShareMode\\n None, # lpSecurityAttributes\\n 0x3, # dwCreationDisposition\\n 0, # dwFlagsAndAttributes\\n None # hTemplateFile\\n )\\n\\n return handle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4183fe8fa5c27fc81d5a2aa0419d4ca8\",\n \"score\": \"0.45699832\",\n \"text\": \"def unbind(self, svc, ref):\\n\\t\\tprops = ref.get_properties()\\n\\t\\tif props.get(\\\"service.imported\\\", False):\\n\\t\\t\\timport_str = \\\"from %s\\\" % props.get(\\\"service.imported.from\\\")\\n\\t\\telse:\\n\\t\\t\\timport_str = \\\"local\\\"\\n\\t\\t\\t_logger.debug(\\\"%s> UnBound of %s (%s)\\\", self._name, ref, import_str)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6486087a29bbab4bf890afc4e4e10c3c\",\n \"score\": \"0.45673695\",\n \"text\": \"def close_www_connection(ser):\\n commands = ['AT+SAPBR=0,1']\\n handle_commands(ser, commands)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15450f676b4a3bb272e4b61034cc75f8\",\n \"score\": \"0.45633394\",\n \"text\": \"def close(self):\\n close_driver(self.driver)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98a2cc373d0cd06e66b68c97e28112c7\",\n \"score\": \"0.45627278\",\n \"text\": \"def cmdunload(self, msg, args):\\n self.log.debug(\\\"Unloading ShellExec Scripts\\\")\\n if self.dynamic_plugin is not None:\\n self._bot.remove_commands_from(self.dynamic_plugin)\\n return (\\\"Done unloading commands.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5030cd008ba736b4f86b035fc2e7439e\",\n \"score\": \"0.45607397\",\n \"text\": \"def deregister(cls, plugin='None'):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3b27a7e6dea69065d2a5f7d7242301a\",\n \"score\": \"0.45455232\",\n \"text\": \"def unsubscribe_from_save_mp4(node):\\n rospy.wait_for_service('/racecar/save_mp4/unsubscribe_from_save_mp4')\\n return ServiceProxyWrapper('/racecar/save_mp4/unsubscribe_from_save_mp4', Empty)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d058f57d2db24c5947a2428114d4fc95\",\n \"score\": \"0.45446151\",\n \"text\": \"def closeDriver(self, packet, client):\\n Logger.log(self.classname, \\\"closeApplication\\\", \\\"Dostalem request do zakonczenia sesji drivera.\\\")\\n self.closeApplication(packet, client)\\n self.groupManager.delDriver(packet.id)\\n p = Packet.packetFromContent(packet.id, Sources.SERVER, 0, Communicates.CONFIRM, \\\"\\\")\\n Logger.log(self.classname, \\\"closeApplication\\\", \\\"Dostalem request do zakonczenia sesji drivera.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4661188e26365d862b6a86c571f8a462\",\n \"score\": \"0.45385972\",\n \"text\": \"def unload_module(self, module):\\n self._modules.update({module: \\\"unload\\\"})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e9f70009a253874b9f70377c319de41\",\n \"score\": \"0.45368376\",\n \"text\": \"def stopServices() :\\n # stop services\\n os.system(\\\"sudo systemctl stop hostapd\\\")\\n os.system(\\\"sudo systemctl stop dnsmasq\\\")\\n os.system(\\\"sudo systemctl stop dhcpcd\\\")\\n os.system(\\\"sudo systemctl disable hostapd\\\")\\n os.system(\\\"sudo systemctl disable dnsmasq\\\")\\n os.system(\\\"sudo systemctl disable dhcpcd\\\")\\n\\n os.system(\\\"sudo systemctl unmask hostapd && sudo systemctl enable hostapd\\\")\\n\\n # start custom script\\n os.system(\\\"sudo sh /usr/local/bin/wifistart\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a88f4857d34119073231729a7c7c193\",\n \"score\": \"0.45255935\",\n \"text\": \"def unregister(self, context=None):\\n self.dbapi.unregister_service(self.hostname, type=self.type)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74c9d8fc3af5bf82aaa400b014eedd0d\",\n \"score\": \"0.45176476\",\n \"text\": \"def service_uninstall(self, service_name):\\n self.service_stop(service_name)\\n j.tools.path.get(\\\"/etc/init/%s.conf\\\" % service_name).remove_p()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22511fc03a22d54494aff32853a42b69\",\n \"score\": \"0.45118758\",\n \"text\": \"async def _unload(self, ctx: commands.Context, module: str):\\n\\n message = await ctx.send(\\\"unloading modules.{0}\\\".format(module))\\n\\n if (module == \\\"owner\\\"):\\n await message.edit(content=\\\"modules.owner can not be unloaded\\\")\\n return\\n\\n try:\\n ctx.bot.unload_extension(\\\"modules.{0}\\\".format(module))\\n except (commands.ExtensionNotLoaded) as e:\\n await message.edit(content=\\\"modules.{0} was not loaded\\\".format(module))\\n return\\n \\n await message.edit(content=\\\"done.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89afb9499546a9940eabead5cc78af2e\",\n \"score\": \"0.45117706\",\n \"text\": \"def FreeDlls(self):\\n ctypes.windll.ole32.CoFreeUnusedLibraries()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c1197c62ba5beff9d9da15517f4effd2\",\n \"score\": \"0.45086515\",\n \"text\": \"def DPSS_teardownIDL(self, sessionHandle, onep_sender_id, elem_sender_id):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d6c0c3990fcd2f7111e8e27144365fb\",\n \"score\": \"0.44944075\",\n \"text\": \"def unload_module(module):\\n if isinstance(module, str):\\n modulename = module\\n elif hasattr(module, \\\"__module__\\\"):\\n modulename = module.__module__\\n else:\\n modulename = module.__name__\\n\\n if modulename in sys.modules:\\n del sys.modules[modulename]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91e1f128adf7b293302e766e06169ccb\",\n \"score\": \"0.44937664\",\n \"text\": \"def removeInstance(self, instName, serverName):\\n if serverName in self.hardwareDict:\\n return self.hardwareDict[serverName].remove(instName)\\n else:\\n print \\\"No hardware %s in hardware manager\\\" % serverName\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c88d741c81b4d2e7f22dfa3bf3115bc\",\n \"score\": \"0.44919407\",\n \"text\": \"def tearDown(self):\\n self.driver.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c88d741c81b4d2e7f22dfa3bf3115bc\",\n \"score\": \"0.44919407\",\n \"text\": \"def tearDown(self):\\n self.driver.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2352feb911d2db69be28fbf91b9c3253\",\n \"score\": \"0.44915333\",\n \"text\": \"def unplug(self, device_name, network):\\n self.driver.unplug(device_name, namespace=network.namespace)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a498b8d968aeec644049d01e3e92413\",\n \"score\": \"0.4487544\",\n \"text\": \"def teardown_module():\\n os.remove(db_path[len(\\\"sqlite:///\\\") :])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ed45fe7fa394f1ddc986ad881610dff\",\n \"score\": \"0.44808456\",\n \"text\": \"def teardown_module():\\n os.system(\\\"rm *.dat\\\")\\n hblockchain.blockchain.clear()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e641f90f6f1c0b8e9e7810da35a2bfb\",\n \"score\": \"0.44742936\",\n \"text\": \"def unload_extension(self, module_str: str):\\n if BUILTINS_EXTS.get(module_str, False) is True:\\n module_str = \\\"IPython.extensions.\\\" + module_str\\n if module_str not in self.loaded:\\n return \\\"not loaded\\\"\\n\\n if module_str in sys.modules:\\n mod = sys.modules[module_str]\\n if self._call_unload_ipython_extension(mod):\\n self.loaded.discard(module_str)\\n else:\\n return \\\"no unload function\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbbceb371f9cfe5a40a8d311e85242f0\",\n \"score\": \"0.44704387\",\n \"text\": \"def tearDownModule():\\n commontestsuite.commonTearDownModule(testsuite, testprocess)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3ef1edea1a46bd6fe67436151e1b30b\",\n \"score\": \"0.447019\",\n \"text\": \"def tearDown(self):\\n super(SSOBackendRegistryTests, self).tearDown()\\n\\n try:\\n sso_backends.unregister(self.dummy_backend)\\n except ItemLookupError:\\n pass\\n\\n try:\\n sso_backends.unregister(self.dummy_backend_with_urls)\\n except ItemLookupError:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe74fc8e9f32ef4e3fbc96c62c268424\",\n \"score\": \"0.44684577\",\n \"text\": \"def ntdll_LdrUnregisterDllNotification(jitter):\\n ret_ad, args = jitter.func_args_stdcall([\\\"Cookie\\\"])\\n raise RuntimeError('API not implemented')\\n jitter.func_ret_stdcall(ret_ad, ret_value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b332524fe651ae6f4b7e5af911481a8e\",\n \"score\": \"0.4467572\",\n \"text\": \"def close(self):\\n if not self.driver:\\n return\\n\\n self.driver.quit()\\n self.driver = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b332524fe651ae6f4b7e5af911481a8e\",\n \"score\": \"0.4467572\",\n \"text\": \"def close(self):\\n if not self.driver:\\n return\\n\\n self.driver.quit()\\n self.driver = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8c75896f04992868e8a913141de01e1\",\n \"score\": \"0.44590187\",\n \"text\": \"def teardown_module(module):\\n os.remove(dummy_script)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67fb7f10837a23638aaf974537e22901\",\n \"score\": \"0.44521892\",\n \"text\": \"def test_unregister_without_urls(self):\\n sso_backends.register(self.dummy_backend)\\n sso_backends.unregister(self.dummy_backend)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ed3bc6f34c79d0bfdbd2afb6246061a\",\n \"score\": \"0.44514525\",\n \"text\": \"def deregisterServer(self, serverId, serverType):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7008d4119b133f76aedf7d2fc0137aa4\",\n \"score\": \"0.44510606\",\n \"text\": \"async def mp_unloadpool(ctx: Context) -> str:\\n if ctx.args:\\n return 'Invalid syntax: !mp unloadpool'\\n\\n if ctx.player is not ctx.match.host:\\n return 'Only available to the host.'\\n\\n if not ctx.match.pool:\\n return 'No mappool currently selected!'\\n\\n ctx.match.pool = None\\n return 'Mappool unloaded.'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9c0025b8b5f2b57b801e5a25011737c\",\n \"score\": \"0.44510314\",\n \"text\": \"def quit(self):\\n driver.quit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b15e061ad85736dd499411125dcb13b2\",\n \"score\": \"0.44445112\",\n \"text\": \"def unload(bot: lightbulb.BotApp):\\n bot.remove_plugin(plugin)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed81c1920d9003bd56bcaf9387b6dce3\",\n \"score\": \"0.44320536\",\n \"text\": \"def deregister():\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61310ed48210b73dc086cb76e837285a\",\n \"score\": \"0.4409755\",\n \"text\": \"def kill_services():\\n proc = subprocess.Popen(['/usr/bin/killall', '-9', 'cfprefsd'],\\n stdout=subprocess.PIPE,\\n stderr=subprocess.PIPE)\\n proc = subprocess.Popen(['/usr/bin/killall', '-9', 'locationd'],\\n stdout=subprocess.PIPE,\\n stderr=subprocess.PIPE)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8030dd8bea97e90db9015b497f6e5ce\",\n \"score\": \"0.44047114\",\n \"text\": \"def close(self):\\r\\n self.driver.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0486310205da28e8ba5ef6153697288\",\n \"score\": \"0.44039074\",\n \"text\": \"def unlock_bootloader(self):\\n raise Exception(\\\"Not implemented!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14d0d6b36cf7e61125515f7ecc4db76a\",\n \"score\": \"0.44002485\",\n \"text\": \"def unregister(self, class_name, module):\\n\\t\\tself.handlers.pop((class_name, module))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f03adf97fe52fe1207c2068da417613\",\n \"score\": \"0.43980128\",\n \"text\": \"def stopAgent():\\n logging.info(\\\"Shutting down the local server.\\\")\\n db = pickledb.load(WRAPPER_DB_PATH, False)\\n host = db.get('host')\\n port = db.get('port')\\n url = 'http://%s:%s' % (host, port)+\\\"/v2/api/wrapper/shutdown\\\"\\n requests.post(url)\\n db.rem('host')\\n db.rem('port')\\n db.dump()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c58408a38c0284ff4b7aedf847777bd2\",\n \"score\": \"0.4396541\",\n \"text\": \"def deactivate(self):\\n\\n self.dnsmasq.deactivate()\\n self.scheme.deactivate()\\n self.hostapd.deactivate()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"848d5a41323abf7f3cc8c3317abd6f6d\",\n \"score\": \"0.4390581\",\n \"text\": \"def unload_plugin(plugin_name):\\n\\n if not is_plugin_loaded(plugin_name):\\n return False\\n\\n return maya.cmds.unloadPlugin(plugin_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e5a519bf3f4a9abe6258a30e032b30b\",\n \"score\": \"0.4389734\",\n \"text\": \"def close(ddpSession):\\n del DDP.sessions[ddpSession.getSessionId()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f0cbde37ed2eab246d46857ad42dea7\",\n \"score\": \"0.43883076\",\n \"text\": \"def delete_updates():\\n windir = os.path.expandvars('$windir')\\n dirs = glob.glob(os.path.join(windir, '$NtUninstallKB*'))\\n dirs += [os.path.expandvars('$windir\\\\\\\\SoftwareDistribution\\\\\\\\Download')]\\n dirs += [os.path.expandvars('$windir\\\\\\\\ie7updates')]\\n dirs += [os.path.expandvars('$windir\\\\\\\\ie8updates')]\\n if not dirs:\\n # if nothing to delete, then also do not restart service\\n return\\n\\n import win32serviceutil\\n wu_running = win32serviceutil.QueryServiceStatus('wuauserv')[1] == 4\\n\\n args = ['net', 'stop', 'wuauserv']\\n\\n def wu_service():\\n General.run_external(args)\\n return 0\\n if wu_running:\\n yield Command.Function(None, wu_service, \\\" \\\".join(args))\\n\\n for path1 in dirs:\\n for path2 in FileUtilities.children_in_directory(path1, True):\\n yield Command.Delete(path2)\\n if os.path.exists(path1):\\n yield Command.Delete(path1)\\n\\n args = ['net', 'start', 'wuauserv']\\n if wu_running:\\n yield Command.Function(None, wu_service, \\\" \\\".join(args))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ebfca55f64235f2daf68161fe63943d\",\n \"score\": \"0.43859544\",\n \"text\": \"def unregister():\\n\\n from bpy.utils import unregister_class\\n\\n # remove OpenGL data\\n pdt_pivot_point.PDT_OT_ModalDrawOperator.handle_remove(\\n pdt_pivot_point.PDT_OT_ModalDrawOperator, bpy.context\\n )\\n window_manager = bpy.context.window_manager\\n pdt_wm = \\\"pdt_run_opengl\\\"\\n if pdt_wm in window_manager:\\n del window_manager[pdt_wm]\\n\\n for cls in reversed(classes):\\n unregister_class(cls)\\n\\n del Scene.pdt_pg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed6da016492612cb63ee5cc09d59b5ba\",\n \"score\": \"0.4384337\",\n \"text\": \"def ntdll_NtUnloadKeyEx(jitter):\\n ret_ad, args = jitter.func_args_stdcall([\\\"TargetKey\\\", \\\"Event\\\"])\\n raise RuntimeError('API not implemented')\\n jitter.func_ret_stdcall(ret_ad, ret_value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6eec0b6f88ac4d65c9b3cc53fd2d8d46\",\n \"score\": \"0.43776724\",\n \"text\": \"def close(self):\\n self.core.unload_all_devices()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dea2c4b05cdb33090ed4183e9ca3d6b6\",\n \"score\": \"0.43693548\",\n \"text\": \"def stop_pfcwd(duthosts, enum_rand_one_per_hwsku_frontend_hostname):\\n duthost = duthosts[enum_rand_one_per_hwsku_frontend_hostname]\\n logger.info(\\\"--- Stop Pfcwd --\\\")\\n duthost.command(\\\"pfcwd stop\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67474de1f77b7e7f579080352535e7f8\",\n \"score\": \"0.43536806\",\n \"text\": \"def unexport(self):\\n self.connection.unregister_object(self.__dbus_info.reg_id)\\n self.__dbus_info.reg_id = None\\n self.__dbus_info.connection = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e3457f2ac7e32a5d58bd9859b0d78ef\",\n \"score\": \"0.4349856\",\n \"text\": \"def tearDownClass(cls):\\r\\n cls.driver.close()\\r\\n cls.driver.quit()\\r\\n print(\\\"'Create an Account' Test completed!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e338fc52ee16ef7fba273c75bc7aebb\",\n \"score\": \"0.43484804\",\n \"text\": \"def _py_usb_close_func(self):\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4f5beb5029b4b314dae4425f87286fd\",\n \"score\": \"0.43483624\",\n \"text\": \"async def stop_dynamic_service(\\n app: web.Application,\\n service_uuid: NodeIDStr,\\n simcore_user_agent: str,\\n *,\\n save_state: bool = True,\\n progress: ProgressBarData | None = None,\\n) -> None:\\n headers = {\\n X_SIMCORE_USER_AGENT: simcore_user_agent,\\n }\\n settings: DirectorV2Settings = get_plugin_settings(app)\\n\\n async with AsyncExitStack() as stack:\\n if progress:\\n await stack.enter_async_context(progress)\\n\\n await request_director_v2(\\n app,\\n \\\"DELETE\\\",\\n url=(settings.base_url / f\\\"dynamic_services/{service_uuid}\\\").update_query(\\n can_save=\\\"true\\\" if save_state else \\\"false\\\",\\n ),\\n headers=headers,\\n expected_status=web.HTTPNoContent,\\n timeout=settings.DIRECTOR_V2_STOP_SERVICE_TIMEOUT,\\n on_error={\\n web.HTTPConflict.status_code: (\\n ServiceWaitingForManualIntervention,\\n {\\\"service_uuid\\\": service_uuid},\\n )\\n },\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5330aebedffd222d861cb6295bdde58\",\n \"score\": \"0.4336767\",\n \"text\": \"def shutdown(\\n self, cli_context: CliContext, service_name: tuple[str, ...] = None\\n ) -> CompletedProcess:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"252a328ed3ada7357e6e69ac5e18c08e\",\n \"score\": \"0.43333846\",\n \"text\": \"def _unload( self ):\\n\\t\\tlog.debug( 'Unregistering commands.' )\\n\\t\\tvim.command( 'delcommand PyVimDevMode' )\\n\\t\\tvim.command( 'delcommand PyVimReload' )\\n\\t\\tvim.command( 'delcommand PyVimNormalMode' )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60feb488f3e8408953a11ea5a7a4cb10\",\n \"score\": \"0.43312\",\n \"text\": \"def unload():\\n\\n for player in players:\\n save_player_data(database_path, player)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb2d41b8932dad37d2015f1ee094e6a0\",\n \"score\": \"0.43296602\",\n \"text\": \"def uninstall_Windows():\\n # First see if seattle appears as a value in the Windows startup registry key,\\n # and remove it if it exists.\\n # removed_from_registry is used later and thus must have a value in case the\\n # try: block below raises an exception.\\n removed_from_registry = False\\n try:\\n removed_from_registry = remove_seattle_from_win_startup_registry()\\n except WindowsError:\\n print \\\"The uninstaller does not have access to the Windows registry \\\" \\\\\\n + \\\"startup keys. This means that seattle is likely not installed in \\\" \\\\\\n + \\\"your Windows registry startup key, though you may want to \\\" \\\\\\n + \\\"manually check the following registry keys and remove seattle \\\" \\\\\\n + \\\"from those keys should it exist there: \\\"\\n print \\\"HKEY_LOCAL_MACHINE\\\\Software\\\\Microsoft\\\\Windows\\\\CurrentVersion\\\\Run\\\"\\n print \\\"HKEY_CURRENT_USER\\\\Software\\\\Microsoft\\\\Windows\\\\CurrentVersion\\\\Run\\\"\\n # Distinguish the above-printed text from what will be printed later by\\n # by printing a blank line.\\n print\\n servicelogger.log(\\\" uninstaller could not access the Windows registry \\\" \\\\\\n + \\\"during this attempted uninstall.\\\")\\n\\n\\n\\n # Next, see if there is a link to the seattle starter script in the startup\\n # folder and remove it if it is there.\\n if not WIN_STARTUP_SCRIPT_PATH == None:\\n removed_from_startup_folder = \\\\\\n remove_seattle_from_win_startup_folder()\\n\\n\\n\\n # Check to see if uninstall actually removed seattle from the computer.\\n if not removed_from_registry and not removed_from_startup_folder:\\n raise SeattleNotInstalledError(\\\"Seattle could not be detected as \\\" \\\\\\n + \\\"having been installed prior to \\\" \\\\\\n + \\\"uninstall.\\\")\\n elif removed_from_registry or removed_from_startup_folder:\\n # Stop all instances of seattle from running before returning.\\n stop_all_seattle_processes.main()\\n return True\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993280,"cells":{"query_id":{"kind":"string","value":"3ee24e0c0f2e5a6987f2da134b1c9cd3"},"query":{"kind":"string","value":"Forward message to a specific node."},"positive_passages":{"kind":"list like","value":[{"docid":"fbad94a39b5cad96916cd8b5c3a9d74b","score":"0.6388381","text":"def forward(message, socket):\n try:\n time.sleep(1)\n dest = message[MSG_FIELD.DESTINATION]\n worker = socket_handler.get(dest)\n if worker:\n content = message[MSG_FIELD.CONTENT]\n worker.send(json.dumps(content))\n except Exception:\n pass","title":""}],"string":"[\n {\n \"docid\": \"fbad94a39b5cad96916cd8b5c3a9d74b\",\n \"score\": \"0.6388381\",\n \"text\": \"def forward(message, socket):\\n try:\\n time.sleep(1)\\n dest = message[MSG_FIELD.DESTINATION]\\n worker = socket_handler.get(dest)\\n if worker:\\n content = message[MSG_FIELD.CONTENT]\\n worker.send(json.dumps(content))\\n except Exception:\\n pass\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"6bd3382d4ecd4e07e411a9bc93d58ae9","score":"0.67194664","text":"def send_message(self, request, node):\n\n request.set(\"src\", self.node)\n node.process_request(request)","title":""},{"docid":"fe13c38927d4d74f9291554d03f3cfb4","score":"0.66790444","text":"def forward(self, socket):\n if self.msg:\n socket.send( self.msg )\n return self.msg","title":""},{"docid":"00b796ea9c6f3f2460b3830b1a2580b1","score":"0.6654636","text":"def _forward_msg_to_client(self, message, destination):\n self._forwards[destination].forward_queue.put_nowait(message)","title":""},{"docid":"e0c47aae4039ae1499d725ec7b2fda39","score":"0.6576539","text":"def forward(\n self,\n msg: torch.FloatTensor,\n source: NodeIDs,\n target: NodeIDs,\n edge_type: Optional[RelationIDs] = None,\n num_nodes: Optional[int] = None,\n ) -> torch.FloatTensor:\n raise NotImplementedError","title":""},{"docid":"efadefea9cedbb7c20d1f0eec0634b02","score":"0.6568965","text":"def _msg_forward(self, request, components, msg):\n self.Ledger.handle_message(msg)\n return msg","title":""},{"docid":"0e4dafee9126bd139230fa3c9325851d","score":"0.6426186","text":"def messageNode(self, toNode, message, isRelay=False): \n messageId = sha.sha(\"%s%s%s\" % (time.time(), toNode or \"MB\", self.myName)).hexdigest()\n if isRelay:\n # This messsage is being relayed ad-hoc from somewhere else\n # We don't reconstruct the message at all, we just pass it on\n thisMessage = message\n header, messageEncap = tuple(message.split(';', 1))\n messageFrom, messageTo, messageId = tuple(header.split())\n else:\n thisMessage = \"%s %s %s;%s\" % (self.myName, toNode or \"MB\", messageId, message)\n\n if toNode in self.connectedNodes:\n # We have direct peer to this node\n #log(\"We have peer\")\n self.connectedNodes[toNode][0].write(self.constructMessage('M', thisMessage))\n else:\n log(\"No con-route to message peer, searching for new path\")\n if False: #toNode in self.knownNodes:\n nextHop = self.knownNodes[toNode]['nextHop']\n log(\"Nearest peer is \", nextHop)\n if nextHop in self.connectedNodes: \n # we must retry untill we get MACK - XXX\n log(\"Sending message to \", nextHop)\n try:\n self.connectedNodes[nextHop][0].write(self.constructMessage('M', thisMessage))\n # In any situation it's not our duty to relay an MACK on success, so we let\n # the receiver handle that. \n except:\n log(\"Peer\",nextHop,\"vanished while we were in the queue - requeuing\")\n # Don't queue for relays\n if isRelay:\n # send MACKF - relayed messages never get queued, and neither does our MACKF\n self.messageNode(messageFrom, \"MACKF-%s\" % (messageId))\n else:\n self.reQueue(messageId)\n else:\n # At this point we requeue - the peer exists, but I'm not connected to it yet.\n # I was connected, and I am the nearest peer - so I want to retry until something\n # goes horribly wrong. \n if not isRelay:\n log(\"Critical network organisation failure - peer exists but hop does not: Stacking message\", \n thisMessage, \"into queue\")\n self.reQueue(messageId)\n else:\n log(\"Critical network organisation failure - Sending MACKF for relayed message\")\n # Send MACKF\n self.messageNode(messageFrom, \"MACKF-%s\" % (messageId))\n else:\n if isRelay:\n log(\"Critical network organisation failure - peer not known at all: Sending MACKF\")\n # Send MACKF\n self.messageNode(messageFrom, \"MACKF-%s\" % (messageId))\n else:\n log(\"Message in queue is for node that does not exist at this time - requeueing\")\n self.reQueue(messageId)\n # At this point we give up\n \n # XXX Not implemented yet. \n if toNode == \"MB\":\n # This is a broadcast. In broadcast mode we will\n # send this message to all our peers. Peers will \n # then broadcast a \"MBACK:messageId\".\n # After kHold, peers relay this message to to any \n # open peer they have which has not given them an MBACK yet\n # Nodes store messageID in stack of size 20 and discard messages\n # already in this stack.\n log(self.knowNodes, thisMessage)","title":""},{"docid":"c8682c0bca8af49584a7b9e8cacff526","score":"0.6401066","text":"def forwardMsg(self, msg):\n msg['message']['path'].append(self.myName)\n nextIp = self.table.next(msg['message']['destination'])\n if not msg['source']:\n msg['source'] = self.myName\n dump = json.dumps(msg)\n return (dump.encode('utf-8'), nextIp)","title":""},{"docid":"c2fa81f0811b15a75219815dc65b985a","score":"0.6386781","text":"async def _forward_message_to_target_chat(\n from_chat_id: int,\n message_id: int,\n uow: AbstractUnitOfWork,\n):\n target_chat = await uow.target_chats.get_latest()\n if not target_chat:\n return\n \n forwarded_message_id = await uow.telegram_api.forward_message(\n from_chat_id=from_chat_id,\n to_chat_id=target_chat.chat_id,\n message_id=message_id,\n )\n forwarded_message = ForwardedMessage(\n forwarded_message_id=forwarded_message_id,\n target_chat_id=target_chat.chat_id,\n origin_chat_id=from_chat_id,\n )\n await uow.forwarded_messages.add(forwarded_message)\n await uow.commit()","title":""},{"docid":"330d2062a3c41a0fdb844b02d5e017e4","score":"0.6336163","text":"def propagate_message(self, message, sender):\n if self.leader_id == self.id():\n self.persist_message(message, sender, True)\n else:\n try:\n url = self.format_url(self.next_host, self.next_port,\n '/ring/message')\n requests.post(url, {'message': message, 'sender': sender})\n except requests.ConnectionError:\n # TODO: Handle error.\n pass","title":""},{"docid":"b2666b7f0c868f3befda77f1dd4aeab2","score":"0.63179684","text":"def forward_msg(self, message):\n if message.header.destination == message_types.BROADCAST_DESTINATION:\n for destination in set(self.forward_list()):\n self._forward_msg_to_client(message, destination)\n else:\n self._forward_msg_to_client(message, message.header.destination)","title":""},{"docid":"f3499cef8e30dba7807678aadbca8256","score":"0.63153917","text":"def process_send(self, msg):\n if msg.network.dest_id == self.node_data.id:\n # Loopback\n self.receive_buffer.put(msg)\n elif msg.network.dest_id == -1:\n ignore_list = [msg.network.src_id, ]\n if msg.link is not None:\n ignore_list.append(msg.link.src_id)\n self.broadcast(msg, ignore_list)\n else:\n path = djikstra(self.node_data.id, msg.network.dest_id, self.node_data.network, self.node_data.battery_table, self.args.battery_weight)\n if len(path) == 0:\n raise RuntimeError(\"Networking layer could not find a path to the destination\")\n dest = path[1]\n msg.link = LinkPacket(self.node_data.id, dest)\n super(NetworkingLayer, self).process_send(msg)","title":""},{"docid":"fcfaa9eed071c174e013706f9861049a","score":"0.62776047","text":"def forward(self):\n self._post(\"forward\")","title":""},{"docid":"f4cfdc94c57bf789ef230f4f97d6c573","score":"0.6230856","text":"def send(self, pid, msg):\n def doSend(instance):\n return self.handler.send(instance, pid, msg)\n return self._getNodeConnection(pid.nodeName.text).addCallback(doSend)","title":""},{"docid":"76617a183df7dd95d653354d8ebe42d8","score":"0.6212568","text":"def forward_message(self, msg: ReliableMessage) -> Optional[Content]:\n res = self.filter.check_forward(msg=msg)\n if res is not None:\n # forward is not allowed\n return res\n forward = ForwardContent.new(message=msg)\n receiver = self.facebook.identifier(string=msg.envelope.receiver)\n if self.send_content(content=forward, receiver=receiver):\n return ReceiptCommand.new(message='Message forwarded', envelope=msg.envelope)\n else:\n return TextContent.new(text='Failed to forward your message')","title":""},{"docid":"1cdd276b1a0fae29350a19b75b4f20eb","score":"0.618906","text":"def forward_messages(data: dict | None = None):\n connection.send_message(websocket_api.event_message(msg[\"id\"], data))","title":""},{"docid":"094c6dad051b4d5f4f6c0feee07f7df7","score":"0.61612946","text":"def write_forward_msg(self, msg: ForwardMsg) -> None:\n raise NotImplementedError","title":""},{"docid":"dd03e0bd70914d1887cdf2fa59e9dc57","score":"0.6097685","text":"def forwardMessage(self, instance, title, message, receiver):\n forward = self.sendMessage(title, message, receiver, None, None)\n if forward is not None:\n instance.forwarded = True\n instance.reindexObject()\n return forward\n return None","title":""},{"docid":"b1f941f1fba0799ce3782997ab70c4ae","score":"0.6079143","text":"def message(payload):\n\n router.handle_message(payload)","title":""},{"docid":"2a5bc29c2b15eb843d097422eebe4e61","score":"0.60484695","text":"def test_forward_message(self):\n pass","title":""},{"docid":"a793fa73caa9442c0e4a26cce7622d90","score":"0.5976433","text":"def forward(self, *args, **kwargs):\n pass","title":""},{"docid":"a793fa73caa9442c0e4a26cce7622d90","score":"0.5976433","text":"def forward(self, *args, **kwargs):\n pass","title":""},{"docid":"a793fa73caa9442c0e4a26cce7622d90","score":"0.5976433","text":"def forward(self, *args, **kwargs):\n pass","title":""},{"docid":"9d0cb418be274601faee32b64c2b98a4","score":"0.5964249","text":"def sendaction(self, nick, target, msg):\n self.write(\"-> <%s> %s %s\" % (target, nick, msg))","title":""},{"docid":"71437bf6fc0bc494ca5ded80f3c60665","score":"0.5957495","text":"def send_one_way(self, message):\n self.actor_inbox.put(message)","title":""},{"docid":"60cb3e619d809800d1d43ce6ace50c37","score":"0.5950372","text":"def send_message(datagram, address, relay_addr):","title":""},{"docid":"1bbd4ff754b6568194e196420a4a3e72","score":"0.5949214","text":"def handle_forward(left_entry_box, right_entry_box, mqtt_sender):\n print('Forward', [left_entry_box.get(), right_entry_box.get()])\n mqtt_sender.send_message(\"forward\", [left_entry_box.get(), right_entry_box.get()])","title":""},{"docid":"76d6ec0d87cd36f86850db7b796ca9b9","score":"0.5942444","text":"def handleOneNodeMsg(self, wrappedMsg) -> None:\n self.inBox.append(wrappedMsg)\n msg, frm = wrappedMsg\n logger.debug(\"Client {} got msg from node {}: {}\".\n format(self.clientId, frm, msg),\n extra={\"cli\": True})","title":""},{"docid":"5eb81587fc7be008b901e85e1cc6e080","score":"0.5927494","text":"async def forward(self, message):\n\n for republisher in self.republishers:\n self.debug('forward to %s', republisher.url)\n await republisher.forward(message)","title":""},{"docid":"6017a885cf60b7784860506d5fe70657","score":"0.5912421","text":"def send(self, params):\n if len(params) != 2:\n print '[!] ERROR: Expected 2 params, got %d' % len(params)\n return\n\n sender, receiver = params\n if not(sender in self.nodes and receiver in self.nodes):\n print '[!] ERROR: Invalid node(s) (%s, %s)' % (sender, receiver)\n return\n\n self.nodes[sender].send(self.nodes[receiver])","title":""},{"docid":"784e5d7e5151fc5720fc88d5cec6a9bc","score":"0.5883314","text":"async def send(self, msg):\n tasks = []\n nodes = await self.get_nodes()\n logger.debug('send() triggered to {0}'.format(nodes))\n for key, val in nodes:\n if key == '/nodes/{0}'.format(self.name):\n continue\n host, port = val.split(\":\")\n tasks.append(\n self.loop.create_task(\n self._send_single((host, int(port)), msg)))\n await asyncio.wait(tasks)","title":""},{"docid":"8521f0198f9c08da9485104b8664781c","score":"0.58778614","text":"def handle_forward(left_entry_box, right_entry_box, mqtt_sender):\n\n print('Forward:', left_entry_box.get(), right_entry_box.get())\n mqtt_sender.send_message('forward', [left_entry_box.get(), right_entry_box.get()])","title":""},{"docid":"78ac36616cc9ae92063ee80a930df5e7","score":"0.5872832","text":"def forward (port):\n print (\"forwarding to {}\".format(port))\n msg = of.ofp_packet_out()\n msg.actions.append(of.ofp_action_output(port = port))\n msg.data = packet_in\n msg.in_port = packet_in.in_port\n self.connection.send(msg)","title":""},{"docid":"c532c305eeb1e54260b3e764ae3e6f5f","score":"0.58530885","text":"def send(self, message):\n self.protocol.transmit(self, message)","title":""},{"docid":"c205a780d50e749fde6e6c9398dcc404","score":"0.58175534","text":"def forward(self):\n pass","title":""},{"docid":"9bbc4bbd283c42c75f7e6fd85f351e63","score":"0.57787097","text":"def send_message(self, message):","title":""},{"docid":"97b6f5fadc97176be5f2d89890d6a2f7","score":"0.5751659","text":"def forward(self):\n self.execute(Command.GO_FORWARD)","title":""},{"docid":"766c219ed2b72dcb376885e3baa13046","score":"0.574947","text":"def namedSend(self, nodeName, processName, msg):\n def doSend(instance):\n return self.handler.namedSend(\n instance, self.pid, Atom(processName), msg)\n return self._getNodeConnection(nodeName).addCallback(doSend)","title":""},{"docid":"64235079b5983e1adbc5db39be2dc03d","score":"0.573794","text":"def on_relay(self, from_key, to_key, direction, data):\r\n pass","title":""},{"docid":"22f606cd53ebb9286c70930dfdf5057d","score":"0.5713282","text":"def forward(self):\n encounters = self.encounters()\n for agent in encounters:\n for msg in self.received:\n # do not forward if the encountered agent already has the message\n if self.history[msg].get(agent, None):\n continue\n self.sendmsg(agent, msg)\n\n # receiver then knows sender has the message\n agent.history[msg][self] = 1\n # sender transfers its history with piggy backing\n for _ in self.history[msg]:\n agent.history[msg][_] = 1","title":""},{"docid":"5a69721d7c0a0cc6168eb822165ecad8","score":"0.5712194","text":"def push_msg(self, node, next_node, msg):\n if next_node is None:\n # if self.message_graph.node[node]['msg'] is None:\n self.message_graph.node[node]['msg'] = msg\n # else:\n # self.message_graph.node[node]['msg'] = self.message_graph.node[node]['msg'] * msg\n else:\n self.message_graph.edge[node][next_node]['msg'] = msg\n self.message_graph.edge[node][next_node]['ct'] += 1","title":""},{"docid":"8cf390dc8b347cb565b54058f13437a7","score":"0.57024586","text":"def send(self, payload: bytes, destination: ILNPAddress):\n logging.debug(\"Sending '%s' to %s\", payload, destination)\n self.__node.send_from_host(payload, destination)","title":""},{"docid":"9ebc11d2d05d17565ab009fa87162999","score":"0.567714","text":"def send(self, message):\n return","title":""},{"docid":"ca4ab715fdc1e58126a47639159688d0","score":"0.5676873","text":"def transmitONlink(self, source, dest, msg):\r\n with self.wire.request() as req:\r\n yield req\r\n # print(\"Time {}: Sending Msg {} from R{} to R{} origin {} count{}\".format(self.env.now, msg.msgNum,source, dest, msg.Rnum, msg.msgCount))\r\n yield self.env.timeout(self.cost)\r\n global Network\r\n self.env.process(Network[dest].router_queue(msg, -1, source))","title":""},{"docid":"263eab17ed32b04b40dd7e31ba8133e5","score":"0.5657532","text":"def forward(self) -> None:\n pass","title":""},{"docid":"151a88fc5cfa3e7e36577f2b9ce1c32f","score":"0.56552213","text":"def send_message(message):","title":""},{"docid":"8b4da0762f73679c3300472efd96c5ee","score":"0.5649222","text":"def send_message(self, event):\n \n n = messenger_pb2.Msg()\n n.name = self.username\n n.message = self.text\n n.friendname= self.user\n \n print(\"[{}] {}\".format(self.username, self.text))\n self.conn.SendMsg(n)","title":""},{"docid":"f6cee31a836b8d5c29dc9b9016ce8c84","score":"0.56479204","text":"def send_msg(self, msg):\n self.udp_socket.sendto(msg, (self.host, self.udp_port))","title":""},{"docid":"ca60472cda8015fb9750e12c65d662e1","score":"0.5645582","text":"def peer_unicast(self, peer, payload, target):\n if target is None:\n message = payload\n else:\n message = target.prefix() + '|' + payload\n\n self.message_id += 1\n self.peer_propagate(\n self._format_peer_line(self.peer_id, self.message_id, message, broadcast=False),\n include={peer})","title":""},{"docid":"bccdf47fca5391e326b2962d49220704","score":"0.5629855","text":"def _port_forward_handler(self, chan):\n local_server = 'localhost'\n logger.debug('Opening socket %s:%s'% (local_server, self.local_port))\n sock = socket.socket()\n try:\n sock.connect((local_server, self.local_port))\n except Exception, e:\n logger.error('Forwarding request to %s:%d failed: %r' % (local_server, self.local_port, e))\n return\n\n logger.debug('Connected! Tunnel open %r -> %r -> %r' % (chan.origin_addr,\n chan.getpeername(), (local_server, self.local_port)))\n while True:\n r, w, x = select.select([sock, chan], [], [])\n if sock in r:\n data = sock.recv(1024)\n if not len(data):\n break\n chan.send(data)\n if chan in r:\n data = chan.recv(1024)\n if not len(data):\n break\n sock.send(data)\n chan.close()\n sock.close()\n logger.debug('Tunnel closed from %r' % (chan.origin_addr,))","title":""},{"docid":"956b96f08d15dba7701f08d2b866bc0d","score":"0.5629723","text":"def send(self, msg):\n pass","title":""},{"docid":"45ce786f6d47ec05a40e1f9937a0ef71","score":"0.5620644","text":"def forward(\n self,\n x: torch.FloatTensor,\n source: NodeIDs,\n target: NodeIDs,\n edge_type: Optional[RelationIDs] = None,\n edge_weights: Optional[torch.FloatTensor] = None,\n ) -> torch.FloatTensor:\n # create messages\n messages = self.message_creator(x=x, source=source, target=target, edge_type=edge_type)\n\n # apply edge weights\n if edge_weights is not None:\n messages = messages * edge_weights.unsqueeze(dim=-1)\n\n # aggregate\n delta = self.message_aggregator(msg=messages, source=source, target=target, edge_type=edge_type, num_nodes=x.shape[0])\n del messages\n\n return self.node_updater(x=x, delta=delta)","title":""},{"docid":"268435d33fc9e21a1c4a800e6c586f8a","score":"0.5616221","text":"def send(self, message):\n\n self.connection.send(message)","title":""},{"docid":"fcc77e5531891e4388b87ac0ea444f82","score":"0.5613406","text":"def to_user(self, data):\n members = self.client_data['members']\n to_member = Member.find_member(data['to_user'], members)\n message = TaskExecutor.encode_message(data['message_settings'])\n to_member.request.sendall(message)","title":""},{"docid":"8751fdf59552dc8be8f182b3ac0cd753","score":"0.55955863","text":"async def send_message(self, message: 'Envelope') -> None:\n await self.linked.wait()\n await self.connection.send_message(await message.to_recon())","title":""},{"docid":"8751fdf59552dc8be8f182b3ac0cd753","score":"0.55955863","text":"async def send_message(self, message: 'Envelope') -> None:\n await self.linked.wait()\n await self.connection.send_message(await message.to_recon())","title":""},{"docid":"ffafb25c25ad27621d14d356d8984299","score":"0.55916184","text":"def forward(self, *args, **kwargs):","title":""},{"docid":"0be206dbbc59e51a6c8f118b68dc8229","score":"0.55860907","text":"def send(fromaddr, toaddrs, message):","title":""},{"docid":"0be206dbbc59e51a6c8f118b68dc8229","score":"0.55860907","text":"def send(fromaddr, toaddrs, message):","title":""},{"docid":"4745cc6ab1b98a393eb58cfdf26fe9cf","score":"0.5581391","text":"def send_msg(self, target, data):\n # Get IP Address to populate message sender field\n my_addr = NetworkUtilities.get_my_ip()\n\n # Create a new message object \n msg = Message(my_addr, target, data) \n msg = msg.get_json()\n \n # Create a new connection to the server\n s = socket(AF_INET, SOCK_STREAM)\n s.connect((self.host, self.port)) \n s.send(msg)\n\n # Get response from server\n response = s.recv(512)\n print \"Response from server : \" + response\n s.close()","title":""},{"docid":"eb945b004cf5506183fac51d05a832fd","score":"0.55490094","text":"def handle_forward(entry_box, mqtt_client):\n speed_string = entry_box.get()\n print('Sending the go_forward message with speed')\n mqtt_client.send_message('forward', [speed_string])","title":""},{"docid":"2dd16053389b14eddb4524e8751d4783","score":"0.5545081","text":"def send(self, message):\r\n if not self.has_left:\r\n self.interactor.print(message)","title":""},{"docid":"2b4958ced8d9d0b9dd6ecbcd31aef487","score":"0.55431163","text":"def _sendToEsp32 (self, message): \n\n self.port_serie.write(message.data)","title":""},{"docid":"284ef4e9780f78293b01c83c08ba87c6","score":"0.5540753","text":"def sendmsg(self, nick, msg):\n self.write(\"-> <%s> %s\" % (nick, msg))","title":""},{"docid":"a0c07f91a6a0a9b9b1046db2a2a8d425","score":"0.55397445","text":"def handle_forward(left_entry_box, right_entry_box, mqtt_sender):\n left = int(left_entry_box.get())\n right = int(right_entry_box.get())\n go(mqtt_sender, \"GO FORWARD\", left, right)","title":""},{"docid":"2624fad7ddef6f59cccfba072d10ed75","score":"0.55316746","text":"def send_message(self, message):\n if(not self._guard_network(\"send_message\")):\n return\n self._network.send_message(message)","title":""},{"docid":"874758b48bab9409a22af4c2ef575852","score":"0.5523492","text":"def send(self, to, msg):\n s.send('PRIVMSG %s :%s\\r\\n' % (to, msg))","title":""},{"docid":"de1768e142e6b2fcacca62e03e8f8f46","score":"0.5521996","text":"def forward(self, in_packet, in_sender=None):\n if in_sender == None: # FORWARD\n host_dst = self.forwardtable[in_packet.dst]\n # If Host? only forward packet\n if isinstance(self.ports[host_dst], Host):\n self.ports[host_dst].receive(in_packet)\n # If Switch? send my switch info as well\n else:\n self.ports[host_dst].receive(self, in_packet)\n else: # BROADCAST\n for allports in self.ports:\n if allports != in_sender:\n # If Host? Only send packet info\n if isinstance(allports, Host):\n allports.receive(in_packet)\n # If Switch? send my switch infor as well\n else:\n allports.receive(self, in_packet)","title":""},{"docid":"d52747460e8bab3eb98be7fe770a4cad","score":"0.55033475","text":"def send(self, message):\n print(message)","title":""},{"docid":"54421e9ae9aa1988e625e8deac128875","score":"0.5503187","text":"def send_message(addr, value):\n update_client()\n client.send_message(addr, value)","title":""},{"docid":"7d4b7c75e960c6cab051bac7c4e50c91","score":"0.5486629","text":"def _dispatch(self, msg):\n self.message_arrived.emit(\n KernelMessage(\n msg,\n from_here=self.from_here(msg),\n local_kernel=self.local_kernel))","title":""},{"docid":"64cf3b787834c176d54417f890078ff5","score":"0.54755294","text":"def node(self, node_id):\n ...","title":""},{"docid":"68bbdb7934448e884f97e9e7ffd24686","score":"0.5463401","text":"def send(self, message: Message):\n pass","title":""},{"docid":"97c0c8f69344b5a7d0cfd8d1693de025","score":"0.5459357","text":"async def send(self, client: 'Player', msg: str,\n to_self: bool = False) -> None:\n self.enqueue(\n packets.sendMessage(\n client = client.name,\n msg = msg,\n target = self.name,\n client_id = client.id\n ),\n immune = () if to_self else (client.id,)\n )","title":""},{"docid":"d1027c471aa99248e1747f49691bb446","score":"0.5437257","text":"def parse_forward(message: Message) -> ForwardMessage:\n return ForwardMessage(\n next=\"\",\n forwarded_msg=\"\",\n id=\"\", type=\"\")","title":""},{"docid":"db67fa824e9fff2339d0265394c287dd","score":"0.5425712","text":"def sendto(self, data, destination):\r\n self.socket.sendto(data, destination)","title":""},{"docid":"d900b3a2f2a11e2e09bb02dc2aa140cd","score":"0.54193234","text":"def forward(self, srcif, packet):\n route = self.get_route(srcif, packet[DEST])\n if route is None:\n self.send_error(srcif, packet)\n return True\n else:\n self.sockets[route[NTHP]].send(json.dumps(packet).encode(UTF8))\n return True","title":""},{"docid":"e91f4ebadfb313a8f735fa90af8506b5","score":"0.5415911","text":"def send_msg(self, msg):\n self.port.send_msg(self.encrypt_msg(msg))","title":""},{"docid":"b81a1dae306420e93bb23c5cd6be4693","score":"0.5392628","text":"def emit_message(self, message):\r\n try:\r\n nickname_color = self.nicknames[self.nickname]\r\n except KeyError:\r\n # Only accept messages if we've joined.\r\n return\r\n message = message[:settings.MAX_MESSAGE_LENGTH]\r\n # Handle IRC commands.\r\n if message.startswith(\"/\"):\r\n self.connection.send_raw(message.lstrip(\"/\"))\r\n return\r\n self.message_channel(message)\r\n self.namespace.emit(\"message\", self.nickname, message, nickname_color)","title":""},{"docid":"29c469b9f25c0ece36edb7ae7f93bc47","score":"0.5389225","text":"def sendto(self, data, destination):\r\n\r\n self.socket.sendto(data, destination)","title":""},{"docid":"737ad7dd9328d2eb79673294de170e61","score":"0.5381682","text":"def send_message(self, to_send):\n\t\tself.ser.write((to_send + \"\\n\").encode('utf-8'))","title":""},{"docid":"39b69c845c54a97ac2a1ae40b78b9279","score":"0.538161","text":"def send_message(self, channel, message):\n pass","title":""},{"docid":"70152b4d24bbf89d4f89af782b46a7f0","score":"0.53733426","text":"def __cmd_forward(self, args):\n from log.logger import Log\n\n channelName = args['channel']\n appName = args['app']\n commande = args['args']\n app = args['app']\n if args['channel'] and args['app'] and \\\n self.client.room.forward(channelName=channelName,\n appName=appName, commande=commande,\n uid=self.uid, app=app):\n Log().add(\"[+] La commande: %s a ete envoye a tous les utilisateurs du channel: %s\" %\n (args['args'], str(args['channel'])), 'yellow')\n return ('true')\n else:\n if self.client.room_name is None:\n Log().add(\"[!] Command error: la commande forward a echoue (le Client n'est dans aucun channel)\", 'red')\n else:\n Log().add(\"[!] Command error: la commande forward a echoue (Aucun autre client dans le salon)\", 'red')\n return ('false')","title":""},{"docid":"8d14c13041952cece7f21148e36913b7","score":"0.53670126","text":"def sendMsg(self):\n\n for i in range(len(self.neighbors)):\n self.neighbors[i].receiveMsg(self, self.out_msgs[i])","title":""},{"docid":"63f5199faad158d8ae31c33decb68600","score":"0.5363182","text":"def send(self, msg):\n\n # For now, just send the message.\n # This can be expanded later with a delay parameter\n # that delays the mesage, or filtering.\n\n self._send(msg)","title":""},{"docid":"1f3d6d44bc5e521c4fc0af04f8e952a6","score":"0.53578717","text":"def forward(self, *args, **kwargs):\n raise NotImplementedError(\"Missing implementation for forward\")","title":""},{"docid":"5077044e9945546b40127937b5a0b16d","score":"0.5352833","text":"def transmit(self, session, message):\n session.transmit(message)","title":""},{"docid":"ebf637a2521d9afbfc13ec8afc695af5","score":"0.5350246","text":"def send_message(self, message, target=None):\n # create URL for SMS Eagle\n url = self._config.get_value(\"SmsEagleForwarder\", \"url\",\n \"http://127.0.0.1/index.php/http_api/send_sms\")\n\n # check if a value for target is set\n if target is None:\n target = self._config.get_value(\"SmsEagleForwarder\", \"target\", \"+49123456789\")\n\n # setup URL parameters\n url_parameters = {\n \"login\" : self._config.get_value(\"SmsEagleForwarder\", \"user\", \"admin\"),\n \"pass\" : self._config.get_value(\"SmsEagleForwarder\", \"password\", \"admin\"),\n \"to\" : target,\n \"message\" : message\n }\n\n # log message\n self._logger.info(\"Send SMS to %s: %s\", target, message)\n\n # send HTTP GET request\n try:\n requests.get(url, params=url_parameters)\n except:\n self._logger.error(\"Error sending SMS. Problem in communication with SMS Eagle\")","title":""},{"docid":"858f2fddac9458707801671cdbfdbc9a","score":"0.5349071","text":"def send(self, message):\n raise NotImplementedError()","title":""},{"docid":"ce888455845ba836aa12bb6beda86d11","score":"0.53488225","text":"def transfer_token_request(self, requestor):\n\t\tif self.has_token():\n\t\t\tprint(\"You already have the token\")\n\t\telse :\n\t\t\tholder = self.get_holder()\n\t\t\tnumber = self.get_number()\n\t\t\tbody = \"REQUEST TOKEN from node \" + str(number)\n\n\t\t\tconnection = pika.BlockingConnection(pika.ConnectionParameters('localhost'))\n\t\t\tchannel = connection.channel()\n\t\t\tchannel.basic_publish(exchange='',\n \t\t\trouting_key=str(holder),\n \t\t\tbody=body)\n\t\t\tprint(\" [*] Sent \" + body + \" to queue \" + str(holder))\n\t\t\tconnection.close()\n\t\t\t\n\t\t\tself.add_elem_queue(requestor)\n\t\t\tself.asked = True\n\t\t\tprint(\" [*] Message send\")\n\t\t\tprint(\" [*] My queue :\" + str(self.queue))\n\t\t\tprint(\" [*] Have I asked ? : \" + str(self.asked))\n\t\t\tprint(\" [*] My holder : \" + str(self.holder))\n\t\t\tprint(\" [*] Am I using ? : \" + str(self.using))","title":""},{"docid":"54acf62f01507b0505f42c9c6cd92d92","score":"0.534842","text":"def forwarded_post_msg(update: Update, context: CallbackContext):\n info = get_message_info(update, context)\n forward_from_chat_id = update.message.forward_from_chat.id\n forward_from_id = update.message.forward_from_message_id\n\n if info['chat_id'] == config_map['meme']['channel_group_id'] and forward_from_chat_id == config_map['meme']['channel_id']:\n user_id = context.bot_data[f\"{forward_from_chat_id},{forward_from_id}\"]\n send_post_to(message=update.message, bot=info['bot'], destination=\"channel_group\", user_id=user_id)\n del context.bot_data[f\"{forward_from_chat_id},{forward_from_id}\"]","title":""},{"docid":"12dbad972ee0ed8c4d5157c0f7b0050a","score":"0.5346474","text":"def process(self, entity):\n\t\tself.send(entity)","title":""},{"docid":"17ff0829437ee5b44c856d8defc94318","score":"0.5339346","text":"def __send(self, msg):\n self.__queue.put(msg)","title":""},{"docid":"669ae98f36d264bb88d71fa4ed2506ec","score":"0.53368175","text":"def direct_msg(self, user, msg):\n\n self.connect()\n\n if self.twitter_api is not None:\n # Twitter doesn't like the same msg sent over and over, so add a timestamp\n msg = strftime(\"%Y-%m-%d %H:%M:%S: \") + msg\n\n self.logger.info(\"Sending twitter DM to %s: %s\", user, msg)\n try:\n self.twitter_api.send_direct_message(recipient_id=user, text=truncate(msg, 140))\n except tweepy.error.TweepError as ex:\n self.logger.error(\"Unable to send Tweet: %s\", ex)","title":""},{"docid":"ce8255c19e9b4fabc9bffb124827559a","score":"0.5322391","text":"def send_forward(mqtt_client, left_speed, right_speed):\n print(\"drive_forward\")\n mqtt_client.send_message(\"drive_forward\", [left_speed, right_speed])","title":""},{"docid":"04419975e34643f19f124a20c62dade0","score":"0.5322193","text":"def send_message(self, message):\n\t\t\n\t\tasyncio.new_event_loop().run_until_complete(self.connect_and_send(message))","title":""},{"docid":"43643ca907411afc404188769b030cf8","score":"0.5321782","text":"def perform(self, node, fullDeviceName=None):\n address = socket.gethostname().split(\".\")[0]\n client = RouterClient(address)\n\n # FIXME: need to convert fullDeviceName . to / for routing\n if fullDeviceName:\n self.log.debug(\"sending status request to %s/%s\", node, fullDeviceName)\n client.forwardMessage(Status(\"{0}/{1}\".format(node, fullDeviceName)))\n else:\n self.log.debug(\"sending status request to %s\", node)\n client.forwardMessage(Status(node))\n\n return True","title":""},{"docid":"7c35d9d0f591768f93e17c6efc99804e","score":"0.53206325","text":"def send_handler(state, client, id, message):\n\n dst, msg = message.split(\":\", 1)\n c = state.get_client_by_name(dst)\n c.send(msg)","title":""},{"docid":"b09481b1385385d2bc46cfe191c92b99","score":"0.5318344","text":"def sendto(self, message):\n if self.phase < 2:\n raise ValueError('Unexpected sendto while connection not initialized')\n self.lastsent = time.time()\n logging.info('sending at {}'.format(self.lastsent))\n self.sock.sendto(self.f.encrypt(message.encode('utf-8')), self.addr)","title":""},{"docid":"914bf1088f63577e646f108778edbc91","score":"0.53139675","text":"def forward(self):\n param_attr_init = fluid.initializer.Uniform(\n low=-1.0, high=1.0, seed=np.random.randint(100))\n embed_param_attrs = fluid.ParamAttr(\n name='Base_node_embed', initializer=param_attr_init)\n\n # node_embeddings\n base_node_embed = fl.embedding(\n input=fl.reshape(\n self.train_inputs, shape=[-1, 1]),\n size=[self.num_nodes, self.embedding_size],\n param_attr=embed_param_attrs)\n\n node_features = []\n for edge_type in self.edge_types:\n param_attr_init = fluid.initializer.Uniform(\n low=-1.0, high=1.0, seed=np.random.randint(100))\n embed_param_attrs = fluid.ParamAttr(\n name='%s_node_embed' % edge_type, initializer=param_attr_init)\n\n features = fl.embedding(\n input=self.gw[edge_type].node_feat['index'],\n size=[self.num_nodes, self.embedding_u_size],\n param_attr=embed_param_attrs)\n\n node_features.append(features)\n\n # mp_output: list of embedding(self.num_nodes, dim)\n mp_output = self.message_passing(self.gw, self.edge_types,\n node_features)\n\n # U : (num_type[m], num_nodes, dim[s])\n node_type_embed = fl.stack(mp_output, axis=0)\n\n # U : (num_nodes, num_type[m], dim[s])\n node_type_embed = fl.transpose(node_type_embed, perm=[1, 0, 2])\n\n #gather node_type_embed from train_inputs\n node_type_embed = fl.gather(node_type_embed, self.train_inputs)\n\n # M_r\n tn_initializer = fluid.initializer.TruncatedNormalInitializer(\n loc=0.0, scale=1.0 / math.sqrt(self.embedding_size))\n\n trans_weights = fl.create_parameter(\n shape=[\n self.edge_type_count, self.embedding_u_size,\n self.embedding_size // self.att_head\n ],\n default_initializer=tn_initializer,\n dtype='float32',\n name='trans_w')\n\n # W_r\n trans_weights_s1 = fl.create_parameter(\n shape=[self.edge_type_count, self.embedding_u_size, self.dim_a],\n default_initializer=tn_initializer,\n dtype='float32',\n name='trans_w_s1')\n\n # w_r\n trans_weights_s2 = fl.create_parameter(\n shape=[self.edge_type_count, self.dim_a, self.att_head],\n default_initializer=tn_initializer,\n dtype='float32',\n name='trans_w_s2')\n\n trans_w = fl.gather(trans_weights, self.train_types)\n trans_w_s1 = fl.gather(trans_weights_s1, self.train_types)\n trans_w_s2 = fl.gather(trans_weights_s2, self.train_types)\n\n attention = self.attention(node_type_embed, trans_w_s1, trans_w_s2)\n node_type_embed = fl.matmul(attention, node_type_embed)\n node_embed = base_node_embed + fl.reshape(\n fl.matmul(node_type_embed, trans_w), [-1, self.embedding_size])\n\n self.last_node_embed = fl.l2_normalize(node_embed, axis=1)\n\n nce_weight_initializer = fluid.initializer.TruncatedNormalInitializer(\n loc=0.0, scale=1.0 / math.sqrt(self.embedding_size))\n nce_weight_attrs = fluid.ParamAttr(\n name='nce_weight', initializer=nce_weight_initializer)\n\n weight_pos = fl.embedding(\n input=self.train_labels,\n size=[self.num_nodes, self.embedding_size],\n param_attr=nce_weight_attrs)\n weight_neg = fl.embedding(\n input=self.train_negs,\n size=[self.num_nodes, self.embedding_size],\n param_attr=nce_weight_attrs)\n tmp_node_embed = fl.unsqueeze(self.last_node_embed, axes=[1])\n pos_logits = fl.matmul(\n tmp_node_embed, weight_pos, transpose_y=True) # [B, 1, 1]\n\n neg_logits = fl.matmul(\n tmp_node_embed, weight_neg, transpose_y=True) # [B, 1, neg_num]\n\n pos_score = fl.squeeze(pos_logits, axes=[1])\n pos_score = fl.clip(pos_score, min=-10, max=10)\n pos_score = -1.0 * fl.logsigmoid(pos_score)\n\n neg_score = fl.squeeze(neg_logits, axes=[1])\n neg_score = fl.clip(neg_score, min=-10, max=10)\n neg_score = -1.0 * fl.logsigmoid(-1.0 * neg_score)\n\n neg_score = fl.reduce_sum(neg_score, dim=1, keep_dim=True)\n self.loss = fl.reduce_mean(pos_score + neg_score)","title":""},{"docid":"8fd0dcf50adea833e1a2ea0f6b47a554","score":"0.53064793","text":"def forward(self, *args, **kwargs):\n raise NotImplementedError","title":""}],"string":"[\n {\n \"docid\": \"6bd3382d4ecd4e07e411a9bc93d58ae9\",\n \"score\": \"0.67194664\",\n \"text\": \"def send_message(self, request, node):\\n\\n request.set(\\\"src\\\", self.node)\\n node.process_request(request)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe13c38927d4d74f9291554d03f3cfb4\",\n \"score\": \"0.66790444\",\n \"text\": \"def forward(self, socket):\\n if self.msg:\\n socket.send( self.msg )\\n return self.msg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00b796ea9c6f3f2460b3830b1a2580b1\",\n \"score\": \"0.6654636\",\n \"text\": \"def _forward_msg_to_client(self, message, destination):\\n self._forwards[destination].forward_queue.put_nowait(message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0c47aae4039ae1499d725ec7b2fda39\",\n \"score\": \"0.6576539\",\n \"text\": \"def forward(\\n self,\\n msg: torch.FloatTensor,\\n source: NodeIDs,\\n target: NodeIDs,\\n edge_type: Optional[RelationIDs] = None,\\n num_nodes: Optional[int] = None,\\n ) -> torch.FloatTensor:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"efadefea9cedbb7c20d1f0eec0634b02\",\n \"score\": \"0.6568965\",\n \"text\": \"def _msg_forward(self, request, components, msg):\\n self.Ledger.handle_message(msg)\\n return msg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e4dafee9126bd139230fa3c9325851d\",\n \"score\": \"0.6426186\",\n \"text\": \"def messageNode(self, toNode, message, isRelay=False): \\n messageId = sha.sha(\\\"%s%s%s\\\" % (time.time(), toNode or \\\"MB\\\", self.myName)).hexdigest()\\n if isRelay:\\n # This messsage is being relayed ad-hoc from somewhere else\\n # We don't reconstruct the message at all, we just pass it on\\n thisMessage = message\\n header, messageEncap = tuple(message.split(';', 1))\\n messageFrom, messageTo, messageId = tuple(header.split())\\n else:\\n thisMessage = \\\"%s %s %s;%s\\\" % (self.myName, toNode or \\\"MB\\\", messageId, message)\\n\\n if toNode in self.connectedNodes:\\n # We have direct peer to this node\\n #log(\\\"We have peer\\\")\\n self.connectedNodes[toNode][0].write(self.constructMessage('M', thisMessage))\\n else:\\n log(\\\"No con-route to message peer, searching for new path\\\")\\n if False: #toNode in self.knownNodes:\\n nextHop = self.knownNodes[toNode]['nextHop']\\n log(\\\"Nearest peer is \\\", nextHop)\\n if nextHop in self.connectedNodes: \\n # we must retry untill we get MACK - XXX\\n log(\\\"Sending message to \\\", nextHop)\\n try:\\n self.connectedNodes[nextHop][0].write(self.constructMessage('M', thisMessage))\\n # In any situation it's not our duty to relay an MACK on success, so we let\\n # the receiver handle that. \\n except:\\n log(\\\"Peer\\\",nextHop,\\\"vanished while we were in the queue - requeuing\\\")\\n # Don't queue for relays\\n if isRelay:\\n # send MACKF - relayed messages never get queued, and neither does our MACKF\\n self.messageNode(messageFrom, \\\"MACKF-%s\\\" % (messageId))\\n else:\\n self.reQueue(messageId)\\n else:\\n # At this point we requeue - the peer exists, but I'm not connected to it yet.\\n # I was connected, and I am the nearest peer - so I want to retry until something\\n # goes horribly wrong. \\n if not isRelay:\\n log(\\\"Critical network organisation failure - peer exists but hop does not: Stacking message\\\", \\n thisMessage, \\\"into queue\\\")\\n self.reQueue(messageId)\\n else:\\n log(\\\"Critical network organisation failure - Sending MACKF for relayed message\\\")\\n # Send MACKF\\n self.messageNode(messageFrom, \\\"MACKF-%s\\\" % (messageId))\\n else:\\n if isRelay:\\n log(\\\"Critical network organisation failure - peer not known at all: Sending MACKF\\\")\\n # Send MACKF\\n self.messageNode(messageFrom, \\\"MACKF-%s\\\" % (messageId))\\n else:\\n log(\\\"Message in queue is for node that does not exist at this time - requeueing\\\")\\n self.reQueue(messageId)\\n # At this point we give up\\n \\n # XXX Not implemented yet. \\n if toNode == \\\"MB\\\":\\n # This is a broadcast. In broadcast mode we will\\n # send this message to all our peers. Peers will \\n # then broadcast a \\\"MBACK:messageId\\\".\\n # After kHold, peers relay this message to to any \\n # open peer they have which has not given them an MBACK yet\\n # Nodes store messageID in stack of size 20 and discard messages\\n # already in this stack.\\n log(self.knowNodes, thisMessage)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8682c0bca8af49584a7b9e8cacff526\",\n \"score\": \"0.6401066\",\n \"text\": \"def forwardMsg(self, msg):\\n msg['message']['path'].append(self.myName)\\n nextIp = self.table.next(msg['message']['destination'])\\n if not msg['source']:\\n msg['source'] = self.myName\\n dump = json.dumps(msg)\\n return (dump.encode('utf-8'), nextIp)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2fa81f0811b15a75219815dc65b985a\",\n \"score\": \"0.6386781\",\n \"text\": \"async def _forward_message_to_target_chat(\\n from_chat_id: int,\\n message_id: int,\\n uow: AbstractUnitOfWork,\\n):\\n target_chat = await uow.target_chats.get_latest()\\n if not target_chat:\\n return\\n \\n forwarded_message_id = await uow.telegram_api.forward_message(\\n from_chat_id=from_chat_id,\\n to_chat_id=target_chat.chat_id,\\n message_id=message_id,\\n )\\n forwarded_message = ForwardedMessage(\\n forwarded_message_id=forwarded_message_id,\\n target_chat_id=target_chat.chat_id,\\n origin_chat_id=from_chat_id,\\n )\\n await uow.forwarded_messages.add(forwarded_message)\\n await uow.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"330d2062a3c41a0fdb844b02d5e017e4\",\n \"score\": \"0.6336163\",\n \"text\": \"def propagate_message(self, message, sender):\\n if self.leader_id == self.id():\\n self.persist_message(message, sender, True)\\n else:\\n try:\\n url = self.format_url(self.next_host, self.next_port,\\n '/ring/message')\\n requests.post(url, {'message': message, 'sender': sender})\\n except requests.ConnectionError:\\n # TODO: Handle error.\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2666b7f0c868f3befda77f1dd4aeab2\",\n \"score\": \"0.63179684\",\n \"text\": \"def forward_msg(self, message):\\n if message.header.destination == message_types.BROADCAST_DESTINATION:\\n for destination in set(self.forward_list()):\\n self._forward_msg_to_client(message, destination)\\n else:\\n self._forward_msg_to_client(message, message.header.destination)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3499cef8e30dba7807678aadbca8256\",\n \"score\": \"0.63153917\",\n \"text\": \"def process_send(self, msg):\\n if msg.network.dest_id == self.node_data.id:\\n # Loopback\\n self.receive_buffer.put(msg)\\n elif msg.network.dest_id == -1:\\n ignore_list = [msg.network.src_id, ]\\n if msg.link is not None:\\n ignore_list.append(msg.link.src_id)\\n self.broadcast(msg, ignore_list)\\n else:\\n path = djikstra(self.node_data.id, msg.network.dest_id, self.node_data.network, self.node_data.battery_table, self.args.battery_weight)\\n if len(path) == 0:\\n raise RuntimeError(\\\"Networking layer could not find a path to the destination\\\")\\n dest = path[1]\\n msg.link = LinkPacket(self.node_data.id, dest)\\n super(NetworkingLayer, self).process_send(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcfaa9eed071c174e013706f9861049a\",\n \"score\": \"0.62776047\",\n \"text\": \"def forward(self):\\n self._post(\\\"forward\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4cfdc94c57bf789ef230f4f97d6c573\",\n \"score\": \"0.6230856\",\n \"text\": \"def send(self, pid, msg):\\n def doSend(instance):\\n return self.handler.send(instance, pid, msg)\\n return self._getNodeConnection(pid.nodeName.text).addCallback(doSend)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76617a183df7dd95d653354d8ebe42d8\",\n \"score\": \"0.6212568\",\n \"text\": \"def forward_message(self, msg: ReliableMessage) -> Optional[Content]:\\n res = self.filter.check_forward(msg=msg)\\n if res is not None:\\n # forward is not allowed\\n return res\\n forward = ForwardContent.new(message=msg)\\n receiver = self.facebook.identifier(string=msg.envelope.receiver)\\n if self.send_content(content=forward, receiver=receiver):\\n return ReceiptCommand.new(message='Message forwarded', envelope=msg.envelope)\\n else:\\n return TextContent.new(text='Failed to forward your message')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1cdd276b1a0fae29350a19b75b4f20eb\",\n \"score\": \"0.618906\",\n \"text\": \"def forward_messages(data: dict | None = None):\\n connection.send_message(websocket_api.event_message(msg[\\\"id\\\"], data))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"094c6dad051b4d5f4f6c0feee07f7df7\",\n \"score\": \"0.61612946\",\n \"text\": \"def write_forward_msg(self, msg: ForwardMsg) -> None:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd03e0bd70914d1887cdf2fa59e9dc57\",\n \"score\": \"0.6097685\",\n \"text\": \"def forwardMessage(self, instance, title, message, receiver):\\n forward = self.sendMessage(title, message, receiver, None, None)\\n if forward is not None:\\n instance.forwarded = True\\n instance.reindexObject()\\n return forward\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1f941f1fba0799ce3782997ab70c4ae\",\n \"score\": \"0.6079143\",\n \"text\": \"def message(payload):\\n\\n router.handle_message(payload)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a5bc29c2b15eb843d097422eebe4e61\",\n \"score\": \"0.60484695\",\n \"text\": \"def test_forward_message(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a793fa73caa9442c0e4a26cce7622d90\",\n \"score\": \"0.5976433\",\n \"text\": \"def forward(self, *args, **kwargs):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a793fa73caa9442c0e4a26cce7622d90\",\n \"score\": \"0.5976433\",\n \"text\": \"def forward(self, *args, **kwargs):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a793fa73caa9442c0e4a26cce7622d90\",\n \"score\": \"0.5976433\",\n \"text\": \"def forward(self, *args, **kwargs):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d0cb418be274601faee32b64c2b98a4\",\n \"score\": \"0.5964249\",\n \"text\": \"def sendaction(self, nick, target, msg):\\n self.write(\\\"-> <%s> %s %s\\\" % (target, nick, msg))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71437bf6fc0bc494ca5ded80f3c60665\",\n \"score\": \"0.5957495\",\n \"text\": \"def send_one_way(self, message):\\n self.actor_inbox.put(message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60cb3e619d809800d1d43ce6ace50c37\",\n \"score\": \"0.5950372\",\n \"text\": \"def send_message(datagram, address, relay_addr):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bbd4ff754b6568194e196420a4a3e72\",\n \"score\": \"0.5949214\",\n \"text\": \"def handle_forward(left_entry_box, right_entry_box, mqtt_sender):\\n print('Forward', [left_entry_box.get(), right_entry_box.get()])\\n mqtt_sender.send_message(\\\"forward\\\", [left_entry_box.get(), right_entry_box.get()])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76d6ec0d87cd36f86850db7b796ca9b9\",\n \"score\": \"0.5942444\",\n \"text\": \"def handleOneNodeMsg(self, wrappedMsg) -> None:\\n self.inBox.append(wrappedMsg)\\n msg, frm = wrappedMsg\\n logger.debug(\\\"Client {} got msg from node {}: {}\\\".\\n format(self.clientId, frm, msg),\\n extra={\\\"cli\\\": True})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5eb81587fc7be008b901e85e1cc6e080\",\n \"score\": \"0.5927494\",\n \"text\": \"async def forward(self, message):\\n\\n for republisher in self.republishers:\\n self.debug('forward to %s', republisher.url)\\n await republisher.forward(message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6017a885cf60b7784860506d5fe70657\",\n \"score\": \"0.5912421\",\n \"text\": \"def send(self, params):\\n if len(params) != 2:\\n print '[!] ERROR: Expected 2 params, got %d' % len(params)\\n return\\n\\n sender, receiver = params\\n if not(sender in self.nodes and receiver in self.nodes):\\n print '[!] ERROR: Invalid node(s) (%s, %s)' % (sender, receiver)\\n return\\n\\n self.nodes[sender].send(self.nodes[receiver])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"784e5d7e5151fc5720fc88d5cec6a9bc\",\n \"score\": \"0.5883314\",\n \"text\": \"async def send(self, msg):\\n tasks = []\\n nodes = await self.get_nodes()\\n logger.debug('send() triggered to {0}'.format(nodes))\\n for key, val in nodes:\\n if key == '/nodes/{0}'.format(self.name):\\n continue\\n host, port = val.split(\\\":\\\")\\n tasks.append(\\n self.loop.create_task(\\n self._send_single((host, int(port)), msg)))\\n await asyncio.wait(tasks)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8521f0198f9c08da9485104b8664781c\",\n \"score\": \"0.58778614\",\n \"text\": \"def handle_forward(left_entry_box, right_entry_box, mqtt_sender):\\n\\n print('Forward:', left_entry_box.get(), right_entry_box.get())\\n mqtt_sender.send_message('forward', [left_entry_box.get(), right_entry_box.get()])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78ac36616cc9ae92063ee80a930df5e7\",\n \"score\": \"0.5872832\",\n \"text\": \"def forward (port):\\n print (\\\"forwarding to {}\\\".format(port))\\n msg = of.ofp_packet_out()\\n msg.actions.append(of.ofp_action_output(port = port))\\n msg.data = packet_in\\n msg.in_port = packet_in.in_port\\n self.connection.send(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c532c305eeb1e54260b3e764ae3e6f5f\",\n \"score\": \"0.58530885\",\n \"text\": \"def send(self, message):\\n self.protocol.transmit(self, message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c205a780d50e749fde6e6c9398dcc404\",\n \"score\": \"0.58175534\",\n \"text\": \"def forward(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bbc4bbd283c42c75f7e6fd85f351e63\",\n \"score\": \"0.57787097\",\n \"text\": \"def send_message(self, message):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97b6f5fadc97176be5f2d89890d6a2f7\",\n \"score\": \"0.5751659\",\n \"text\": \"def forward(self):\\n self.execute(Command.GO_FORWARD)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"766c219ed2b72dcb376885e3baa13046\",\n \"score\": \"0.574947\",\n \"text\": \"def namedSend(self, nodeName, processName, msg):\\n def doSend(instance):\\n return self.handler.namedSend(\\n instance, self.pid, Atom(processName), msg)\\n return self._getNodeConnection(nodeName).addCallback(doSend)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64235079b5983e1adbc5db39be2dc03d\",\n \"score\": \"0.573794\",\n \"text\": \"def on_relay(self, from_key, to_key, direction, data):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22f606cd53ebb9286c70930dfdf5057d\",\n \"score\": \"0.5713282\",\n \"text\": \"def forward(self):\\n encounters = self.encounters()\\n for agent in encounters:\\n for msg in self.received:\\n # do not forward if the encountered agent already has the message\\n if self.history[msg].get(agent, None):\\n continue\\n self.sendmsg(agent, msg)\\n\\n # receiver then knows sender has the message\\n agent.history[msg][self] = 1\\n # sender transfers its history with piggy backing\\n for _ in self.history[msg]:\\n agent.history[msg][_] = 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a69721d7c0a0cc6168eb822165ecad8\",\n \"score\": \"0.5712194\",\n \"text\": \"def push_msg(self, node, next_node, msg):\\n if next_node is None:\\n # if self.message_graph.node[node]['msg'] is None:\\n self.message_graph.node[node]['msg'] = msg\\n # else:\\n # self.message_graph.node[node]['msg'] = self.message_graph.node[node]['msg'] * msg\\n else:\\n self.message_graph.edge[node][next_node]['msg'] = msg\\n self.message_graph.edge[node][next_node]['ct'] += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8cf390dc8b347cb565b54058f13437a7\",\n \"score\": \"0.57024586\",\n \"text\": \"def send(self, payload: bytes, destination: ILNPAddress):\\n logging.debug(\\\"Sending '%s' to %s\\\", payload, destination)\\n self.__node.send_from_host(payload, destination)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ebc11d2d05d17565ab009fa87162999\",\n \"score\": \"0.567714\",\n \"text\": \"def send(self, message):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca4ab715fdc1e58126a47639159688d0\",\n \"score\": \"0.5676873\",\n \"text\": \"def transmitONlink(self, source, dest, msg):\\r\\n with self.wire.request() as req:\\r\\n yield req\\r\\n # print(\\\"Time {}: Sending Msg {} from R{} to R{} origin {} count{}\\\".format(self.env.now, msg.msgNum,source, dest, msg.Rnum, msg.msgCount))\\r\\n yield self.env.timeout(self.cost)\\r\\n global Network\\r\\n self.env.process(Network[dest].router_queue(msg, -1, source))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"263eab17ed32b04b40dd7e31ba8133e5\",\n \"score\": \"0.5657532\",\n \"text\": \"def forward(self) -> None:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"151a88fc5cfa3e7e36577f2b9ce1c32f\",\n \"score\": \"0.56552213\",\n \"text\": \"def send_message(message):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b4da0762f73679c3300472efd96c5ee\",\n \"score\": \"0.5649222\",\n \"text\": \"def send_message(self, event):\\n \\n n = messenger_pb2.Msg()\\n n.name = self.username\\n n.message = self.text\\n n.friendname= self.user\\n \\n print(\\\"[{}] {}\\\".format(self.username, self.text))\\n self.conn.SendMsg(n)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6cee31a836b8d5c29dc9b9016ce8c84\",\n \"score\": \"0.56479204\",\n \"text\": \"def send_msg(self, msg):\\n self.udp_socket.sendto(msg, (self.host, self.udp_port))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca60472cda8015fb9750e12c65d662e1\",\n \"score\": \"0.5645582\",\n \"text\": \"def peer_unicast(self, peer, payload, target):\\n if target is None:\\n message = payload\\n else:\\n message = target.prefix() + '|' + payload\\n\\n self.message_id += 1\\n self.peer_propagate(\\n self._format_peer_line(self.peer_id, self.message_id, message, broadcast=False),\\n include={peer})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bccdf47fca5391e326b2962d49220704\",\n \"score\": \"0.5629855\",\n \"text\": \"def _port_forward_handler(self, chan):\\n local_server = 'localhost'\\n logger.debug('Opening socket %s:%s'% (local_server, self.local_port))\\n sock = socket.socket()\\n try:\\n sock.connect((local_server, self.local_port))\\n except Exception, e:\\n logger.error('Forwarding request to %s:%d failed: %r' % (local_server, self.local_port, e))\\n return\\n\\n logger.debug('Connected! Tunnel open %r -> %r -> %r' % (chan.origin_addr,\\n chan.getpeername(), (local_server, self.local_port)))\\n while True:\\n r, w, x = select.select([sock, chan], [], [])\\n if sock in r:\\n data = sock.recv(1024)\\n if not len(data):\\n break\\n chan.send(data)\\n if chan in r:\\n data = chan.recv(1024)\\n if not len(data):\\n break\\n sock.send(data)\\n chan.close()\\n sock.close()\\n logger.debug('Tunnel closed from %r' % (chan.origin_addr,))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"956b96f08d15dba7701f08d2b866bc0d\",\n \"score\": \"0.5629723\",\n \"text\": \"def send(self, msg):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45ce786f6d47ec05a40e1f9937a0ef71\",\n \"score\": \"0.5620644\",\n \"text\": \"def forward(\\n self,\\n x: torch.FloatTensor,\\n source: NodeIDs,\\n target: NodeIDs,\\n edge_type: Optional[RelationIDs] = None,\\n edge_weights: Optional[torch.FloatTensor] = None,\\n ) -> torch.FloatTensor:\\n # create messages\\n messages = self.message_creator(x=x, source=source, target=target, edge_type=edge_type)\\n\\n # apply edge weights\\n if edge_weights is not None:\\n messages = messages * edge_weights.unsqueeze(dim=-1)\\n\\n # aggregate\\n delta = self.message_aggregator(msg=messages, source=source, target=target, edge_type=edge_type, num_nodes=x.shape[0])\\n del messages\\n\\n return self.node_updater(x=x, delta=delta)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"268435d33fc9e21a1c4a800e6c586f8a\",\n \"score\": \"0.5616221\",\n \"text\": \"def send(self, message):\\n\\n self.connection.send(message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcc77e5531891e4388b87ac0ea444f82\",\n \"score\": \"0.5613406\",\n \"text\": \"def to_user(self, data):\\n members = self.client_data['members']\\n to_member = Member.find_member(data['to_user'], members)\\n message = TaskExecutor.encode_message(data['message_settings'])\\n to_member.request.sendall(message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8751fdf59552dc8be8f182b3ac0cd753\",\n \"score\": \"0.55955863\",\n \"text\": \"async def send_message(self, message: 'Envelope') -> None:\\n await self.linked.wait()\\n await self.connection.send_message(await message.to_recon())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8751fdf59552dc8be8f182b3ac0cd753\",\n \"score\": \"0.55955863\",\n \"text\": \"async def send_message(self, message: 'Envelope') -> None:\\n await self.linked.wait()\\n await self.connection.send_message(await message.to_recon())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffafb25c25ad27621d14d356d8984299\",\n \"score\": \"0.55916184\",\n \"text\": \"def forward(self, *args, **kwargs):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0be206dbbc59e51a6c8f118b68dc8229\",\n \"score\": \"0.55860907\",\n \"text\": \"def send(fromaddr, toaddrs, message):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0be206dbbc59e51a6c8f118b68dc8229\",\n \"score\": \"0.55860907\",\n \"text\": \"def send(fromaddr, toaddrs, message):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4745cc6ab1b98a393eb58cfdf26fe9cf\",\n \"score\": \"0.5581391\",\n \"text\": \"def send_msg(self, target, data):\\n # Get IP Address to populate message sender field\\n my_addr = NetworkUtilities.get_my_ip()\\n\\n # Create a new message object \\n msg = Message(my_addr, target, data) \\n msg = msg.get_json()\\n \\n # Create a new connection to the server\\n s = socket(AF_INET, SOCK_STREAM)\\n s.connect((self.host, self.port)) \\n s.send(msg)\\n\\n # Get response from server\\n response = s.recv(512)\\n print \\\"Response from server : \\\" + response\\n s.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb945b004cf5506183fac51d05a832fd\",\n \"score\": \"0.55490094\",\n \"text\": \"def handle_forward(entry_box, mqtt_client):\\n speed_string = entry_box.get()\\n print('Sending the go_forward message with speed')\\n mqtt_client.send_message('forward', [speed_string])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2dd16053389b14eddb4524e8751d4783\",\n \"score\": \"0.5545081\",\n \"text\": \"def send(self, message):\\r\\n if not self.has_left:\\r\\n self.interactor.print(message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b4958ced8d9d0b9dd6ecbcd31aef487\",\n \"score\": \"0.55431163\",\n \"text\": \"def _sendToEsp32 (self, message): \\n\\n self.port_serie.write(message.data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"284ef4e9780f78293b01c83c08ba87c6\",\n \"score\": \"0.5540753\",\n \"text\": \"def sendmsg(self, nick, msg):\\n self.write(\\\"-> <%s> %s\\\" % (nick, msg))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0c07f91a6a0a9b9b1046db2a2a8d425\",\n \"score\": \"0.55397445\",\n \"text\": \"def handle_forward(left_entry_box, right_entry_box, mqtt_sender):\\n left = int(left_entry_box.get())\\n right = int(right_entry_box.get())\\n go(mqtt_sender, \\\"GO FORWARD\\\", left, right)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2624fad7ddef6f59cccfba072d10ed75\",\n \"score\": \"0.55316746\",\n \"text\": \"def send_message(self, message):\\n if(not self._guard_network(\\\"send_message\\\")):\\n return\\n self._network.send_message(message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"874758b48bab9409a22af4c2ef575852\",\n \"score\": \"0.5523492\",\n \"text\": \"def send(self, to, msg):\\n s.send('PRIVMSG %s :%s\\\\r\\\\n' % (to, msg))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de1768e142e6b2fcacca62e03e8f8f46\",\n \"score\": \"0.5521996\",\n \"text\": \"def forward(self, in_packet, in_sender=None):\\n if in_sender == None: # FORWARD\\n host_dst = self.forwardtable[in_packet.dst]\\n # If Host? only forward packet\\n if isinstance(self.ports[host_dst], Host):\\n self.ports[host_dst].receive(in_packet)\\n # If Switch? send my switch info as well\\n else:\\n self.ports[host_dst].receive(self, in_packet)\\n else: # BROADCAST\\n for allports in self.ports:\\n if allports != in_sender:\\n # If Host? Only send packet info\\n if isinstance(allports, Host):\\n allports.receive(in_packet)\\n # If Switch? send my switch infor as well\\n else:\\n allports.receive(self, in_packet)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d52747460e8bab3eb98be7fe770a4cad\",\n \"score\": \"0.55033475\",\n \"text\": \"def send(self, message):\\n print(message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54421e9ae9aa1988e625e8deac128875\",\n \"score\": \"0.5503187\",\n \"text\": \"def send_message(addr, value):\\n update_client()\\n client.send_message(addr, value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d4b7c75e960c6cab051bac7c4e50c91\",\n \"score\": \"0.5486629\",\n \"text\": \"def _dispatch(self, msg):\\n self.message_arrived.emit(\\n KernelMessage(\\n msg,\\n from_here=self.from_here(msg),\\n local_kernel=self.local_kernel))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64cf3b787834c176d54417f890078ff5\",\n \"score\": \"0.54755294\",\n \"text\": \"def node(self, node_id):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68bbdb7934448e884f97e9e7ffd24686\",\n \"score\": \"0.5463401\",\n \"text\": \"def send(self, message: Message):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97c0c8f69344b5a7d0cfd8d1693de025\",\n \"score\": \"0.5459357\",\n \"text\": \"async def send(self, client: 'Player', msg: str,\\n to_self: bool = False) -> None:\\n self.enqueue(\\n packets.sendMessage(\\n client = client.name,\\n msg = msg,\\n target = self.name,\\n client_id = client.id\\n ),\\n immune = () if to_self else (client.id,)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1027c471aa99248e1747f49691bb446\",\n \"score\": \"0.5437257\",\n \"text\": \"def parse_forward(message: Message) -> ForwardMessage:\\n return ForwardMessage(\\n next=\\\"\\\",\\n forwarded_msg=\\\"\\\",\\n id=\\\"\\\", type=\\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db67fa824e9fff2339d0265394c287dd\",\n \"score\": \"0.5425712\",\n \"text\": \"def sendto(self, data, destination):\\r\\n self.socket.sendto(data, destination)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d900b3a2f2a11e2e09bb02dc2aa140cd\",\n \"score\": \"0.54193234\",\n \"text\": \"def forward(self, srcif, packet):\\n route = self.get_route(srcif, packet[DEST])\\n if route is None:\\n self.send_error(srcif, packet)\\n return True\\n else:\\n self.sockets[route[NTHP]].send(json.dumps(packet).encode(UTF8))\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e91f4ebadfb313a8f735fa90af8506b5\",\n \"score\": \"0.5415911\",\n \"text\": \"def send_msg(self, msg):\\n self.port.send_msg(self.encrypt_msg(msg))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b81a1dae306420e93bb23c5cd6be4693\",\n \"score\": \"0.5392628\",\n \"text\": \"def emit_message(self, message):\\r\\n try:\\r\\n nickname_color = self.nicknames[self.nickname]\\r\\n except KeyError:\\r\\n # Only accept messages if we've joined.\\r\\n return\\r\\n message = message[:settings.MAX_MESSAGE_LENGTH]\\r\\n # Handle IRC commands.\\r\\n if message.startswith(\\\"/\\\"):\\r\\n self.connection.send_raw(message.lstrip(\\\"/\\\"))\\r\\n return\\r\\n self.message_channel(message)\\r\\n self.namespace.emit(\\\"message\\\", self.nickname, message, nickname_color)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29c469b9f25c0ece36edb7ae7f93bc47\",\n \"score\": \"0.5389225\",\n \"text\": \"def sendto(self, data, destination):\\r\\n\\r\\n self.socket.sendto(data, destination)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"737ad7dd9328d2eb79673294de170e61\",\n \"score\": \"0.5381682\",\n \"text\": \"def send_message(self, to_send):\\n\\t\\tself.ser.write((to_send + \\\"\\\\n\\\").encode('utf-8'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39b69c845c54a97ac2a1ae40b78b9279\",\n \"score\": \"0.538161\",\n \"text\": \"def send_message(self, channel, message):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70152b4d24bbf89d4f89af782b46a7f0\",\n \"score\": \"0.53733426\",\n \"text\": \"def __cmd_forward(self, args):\\n from log.logger import Log\\n\\n channelName = args['channel']\\n appName = args['app']\\n commande = args['args']\\n app = args['app']\\n if args['channel'] and args['app'] and \\\\\\n self.client.room.forward(channelName=channelName,\\n appName=appName, commande=commande,\\n uid=self.uid, app=app):\\n Log().add(\\\"[+] La commande: %s a ete envoye a tous les utilisateurs du channel: %s\\\" %\\n (args['args'], str(args['channel'])), 'yellow')\\n return ('true')\\n else:\\n if self.client.room_name is None:\\n Log().add(\\\"[!] Command error: la commande forward a echoue (le Client n'est dans aucun channel)\\\", 'red')\\n else:\\n Log().add(\\\"[!] Command error: la commande forward a echoue (Aucun autre client dans le salon)\\\", 'red')\\n return ('false')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d14c13041952cece7f21148e36913b7\",\n \"score\": \"0.53670126\",\n \"text\": \"def sendMsg(self):\\n\\n for i in range(len(self.neighbors)):\\n self.neighbors[i].receiveMsg(self, self.out_msgs[i])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63f5199faad158d8ae31c33decb68600\",\n \"score\": \"0.5363182\",\n \"text\": \"def send(self, msg):\\n\\n # For now, just send the message.\\n # This can be expanded later with a delay parameter\\n # that delays the mesage, or filtering.\\n\\n self._send(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f3d6d44bc5e521c4fc0af04f8e952a6\",\n \"score\": \"0.53578717\",\n \"text\": \"def forward(self, *args, **kwargs):\\n raise NotImplementedError(\\\"Missing implementation for forward\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5077044e9945546b40127937b5a0b16d\",\n \"score\": \"0.5352833\",\n \"text\": \"def transmit(self, session, message):\\n session.transmit(message)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebf637a2521d9afbfc13ec8afc695af5\",\n \"score\": \"0.5350246\",\n \"text\": \"def send_message(self, message, target=None):\\n # create URL for SMS Eagle\\n url = self._config.get_value(\\\"SmsEagleForwarder\\\", \\\"url\\\",\\n \\\"http://127.0.0.1/index.php/http_api/send_sms\\\")\\n\\n # check if a value for target is set\\n if target is None:\\n target = self._config.get_value(\\\"SmsEagleForwarder\\\", \\\"target\\\", \\\"+49123456789\\\")\\n\\n # setup URL parameters\\n url_parameters = {\\n \\\"login\\\" : self._config.get_value(\\\"SmsEagleForwarder\\\", \\\"user\\\", \\\"admin\\\"),\\n \\\"pass\\\" : self._config.get_value(\\\"SmsEagleForwarder\\\", \\\"password\\\", \\\"admin\\\"),\\n \\\"to\\\" : target,\\n \\\"message\\\" : message\\n }\\n\\n # log message\\n self._logger.info(\\\"Send SMS to %s: %s\\\", target, message)\\n\\n # send HTTP GET request\\n try:\\n requests.get(url, params=url_parameters)\\n except:\\n self._logger.error(\\\"Error sending SMS. Problem in communication with SMS Eagle\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"858f2fddac9458707801671cdbfdbc9a\",\n \"score\": \"0.5349071\",\n \"text\": \"def send(self, message):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce888455845ba836aa12bb6beda86d11\",\n \"score\": \"0.53488225\",\n \"text\": \"def transfer_token_request(self, requestor):\\n\\t\\tif self.has_token():\\n\\t\\t\\tprint(\\\"You already have the token\\\")\\n\\t\\telse :\\n\\t\\t\\tholder = self.get_holder()\\n\\t\\t\\tnumber = self.get_number()\\n\\t\\t\\tbody = \\\"REQUEST TOKEN from node \\\" + str(number)\\n\\n\\t\\t\\tconnection = pika.BlockingConnection(pika.ConnectionParameters('localhost'))\\n\\t\\t\\tchannel = connection.channel()\\n\\t\\t\\tchannel.basic_publish(exchange='',\\n \\t\\t\\trouting_key=str(holder),\\n \\t\\t\\tbody=body)\\n\\t\\t\\tprint(\\\" [*] Sent \\\" + body + \\\" to queue \\\" + str(holder))\\n\\t\\t\\tconnection.close()\\n\\t\\t\\t\\n\\t\\t\\tself.add_elem_queue(requestor)\\n\\t\\t\\tself.asked = True\\n\\t\\t\\tprint(\\\" [*] Message send\\\")\\n\\t\\t\\tprint(\\\" [*] My queue :\\\" + str(self.queue))\\n\\t\\t\\tprint(\\\" [*] Have I asked ? : \\\" + str(self.asked))\\n\\t\\t\\tprint(\\\" [*] My holder : \\\" + str(self.holder))\\n\\t\\t\\tprint(\\\" [*] Am I using ? : \\\" + str(self.using))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"54acf62f01507b0505f42c9c6cd92d92\",\n \"score\": \"0.534842\",\n \"text\": \"def forwarded_post_msg(update: Update, context: CallbackContext):\\n info = get_message_info(update, context)\\n forward_from_chat_id = update.message.forward_from_chat.id\\n forward_from_id = update.message.forward_from_message_id\\n\\n if info['chat_id'] == config_map['meme']['channel_group_id'] and forward_from_chat_id == config_map['meme']['channel_id']:\\n user_id = context.bot_data[f\\\"{forward_from_chat_id},{forward_from_id}\\\"]\\n send_post_to(message=update.message, bot=info['bot'], destination=\\\"channel_group\\\", user_id=user_id)\\n del context.bot_data[f\\\"{forward_from_chat_id},{forward_from_id}\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12dbad972ee0ed8c4d5157c0f7b0050a\",\n \"score\": \"0.5346474\",\n \"text\": \"def process(self, entity):\\n\\t\\tself.send(entity)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17ff0829437ee5b44c856d8defc94318\",\n \"score\": \"0.5339346\",\n \"text\": \"def __send(self, msg):\\n self.__queue.put(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"669ae98f36d264bb88d71fa4ed2506ec\",\n \"score\": \"0.53368175\",\n \"text\": \"def direct_msg(self, user, msg):\\n\\n self.connect()\\n\\n if self.twitter_api is not None:\\n # Twitter doesn't like the same msg sent over and over, so add a timestamp\\n msg = strftime(\\\"%Y-%m-%d %H:%M:%S: \\\") + msg\\n\\n self.logger.info(\\\"Sending twitter DM to %s: %s\\\", user, msg)\\n try:\\n self.twitter_api.send_direct_message(recipient_id=user, text=truncate(msg, 140))\\n except tweepy.error.TweepError as ex:\\n self.logger.error(\\\"Unable to send Tweet: %s\\\", ex)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce8255c19e9b4fabc9bffb124827559a\",\n \"score\": \"0.5322391\",\n \"text\": \"def send_forward(mqtt_client, left_speed, right_speed):\\n print(\\\"drive_forward\\\")\\n mqtt_client.send_message(\\\"drive_forward\\\", [left_speed, right_speed])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04419975e34643f19f124a20c62dade0\",\n \"score\": \"0.5322193\",\n \"text\": \"def send_message(self, message):\\n\\t\\t\\n\\t\\tasyncio.new_event_loop().run_until_complete(self.connect_and_send(message))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43643ca907411afc404188769b030cf8\",\n \"score\": \"0.5321782\",\n \"text\": \"def perform(self, node, fullDeviceName=None):\\n address = socket.gethostname().split(\\\".\\\")[0]\\n client = RouterClient(address)\\n\\n # FIXME: need to convert fullDeviceName . to / for routing\\n if fullDeviceName:\\n self.log.debug(\\\"sending status request to %s/%s\\\", node, fullDeviceName)\\n client.forwardMessage(Status(\\\"{0}/{1}\\\".format(node, fullDeviceName)))\\n else:\\n self.log.debug(\\\"sending status request to %s\\\", node)\\n client.forwardMessage(Status(node))\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c35d9d0f591768f93e17c6efc99804e\",\n \"score\": \"0.53206325\",\n \"text\": \"def send_handler(state, client, id, message):\\n\\n dst, msg = message.split(\\\":\\\", 1)\\n c = state.get_client_by_name(dst)\\n c.send(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b09481b1385385d2bc46cfe191c92b99\",\n \"score\": \"0.5318344\",\n \"text\": \"def sendto(self, message):\\n if self.phase < 2:\\n raise ValueError('Unexpected sendto while connection not initialized')\\n self.lastsent = time.time()\\n logging.info('sending at {}'.format(self.lastsent))\\n self.sock.sendto(self.f.encrypt(message.encode('utf-8')), self.addr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"914bf1088f63577e646f108778edbc91\",\n \"score\": \"0.53139675\",\n \"text\": \"def forward(self):\\n param_attr_init = fluid.initializer.Uniform(\\n low=-1.0, high=1.0, seed=np.random.randint(100))\\n embed_param_attrs = fluid.ParamAttr(\\n name='Base_node_embed', initializer=param_attr_init)\\n\\n # node_embeddings\\n base_node_embed = fl.embedding(\\n input=fl.reshape(\\n self.train_inputs, shape=[-1, 1]),\\n size=[self.num_nodes, self.embedding_size],\\n param_attr=embed_param_attrs)\\n\\n node_features = []\\n for edge_type in self.edge_types:\\n param_attr_init = fluid.initializer.Uniform(\\n low=-1.0, high=1.0, seed=np.random.randint(100))\\n embed_param_attrs = fluid.ParamAttr(\\n name='%s_node_embed' % edge_type, initializer=param_attr_init)\\n\\n features = fl.embedding(\\n input=self.gw[edge_type].node_feat['index'],\\n size=[self.num_nodes, self.embedding_u_size],\\n param_attr=embed_param_attrs)\\n\\n node_features.append(features)\\n\\n # mp_output: list of embedding(self.num_nodes, dim)\\n mp_output = self.message_passing(self.gw, self.edge_types,\\n node_features)\\n\\n # U : (num_type[m], num_nodes, dim[s])\\n node_type_embed = fl.stack(mp_output, axis=0)\\n\\n # U : (num_nodes, num_type[m], dim[s])\\n node_type_embed = fl.transpose(node_type_embed, perm=[1, 0, 2])\\n\\n #gather node_type_embed from train_inputs\\n node_type_embed = fl.gather(node_type_embed, self.train_inputs)\\n\\n # M_r\\n tn_initializer = fluid.initializer.TruncatedNormalInitializer(\\n loc=0.0, scale=1.0 / math.sqrt(self.embedding_size))\\n\\n trans_weights = fl.create_parameter(\\n shape=[\\n self.edge_type_count, self.embedding_u_size,\\n self.embedding_size // self.att_head\\n ],\\n default_initializer=tn_initializer,\\n dtype='float32',\\n name='trans_w')\\n\\n # W_r\\n trans_weights_s1 = fl.create_parameter(\\n shape=[self.edge_type_count, self.embedding_u_size, self.dim_a],\\n default_initializer=tn_initializer,\\n dtype='float32',\\n name='trans_w_s1')\\n\\n # w_r\\n trans_weights_s2 = fl.create_parameter(\\n shape=[self.edge_type_count, self.dim_a, self.att_head],\\n default_initializer=tn_initializer,\\n dtype='float32',\\n name='trans_w_s2')\\n\\n trans_w = fl.gather(trans_weights, self.train_types)\\n trans_w_s1 = fl.gather(trans_weights_s1, self.train_types)\\n trans_w_s2 = fl.gather(trans_weights_s2, self.train_types)\\n\\n attention = self.attention(node_type_embed, trans_w_s1, trans_w_s2)\\n node_type_embed = fl.matmul(attention, node_type_embed)\\n node_embed = base_node_embed + fl.reshape(\\n fl.matmul(node_type_embed, trans_w), [-1, self.embedding_size])\\n\\n self.last_node_embed = fl.l2_normalize(node_embed, axis=1)\\n\\n nce_weight_initializer = fluid.initializer.TruncatedNormalInitializer(\\n loc=0.0, scale=1.0 / math.sqrt(self.embedding_size))\\n nce_weight_attrs = fluid.ParamAttr(\\n name='nce_weight', initializer=nce_weight_initializer)\\n\\n weight_pos = fl.embedding(\\n input=self.train_labels,\\n size=[self.num_nodes, self.embedding_size],\\n param_attr=nce_weight_attrs)\\n weight_neg = fl.embedding(\\n input=self.train_negs,\\n size=[self.num_nodes, self.embedding_size],\\n param_attr=nce_weight_attrs)\\n tmp_node_embed = fl.unsqueeze(self.last_node_embed, axes=[1])\\n pos_logits = fl.matmul(\\n tmp_node_embed, weight_pos, transpose_y=True) # [B, 1, 1]\\n\\n neg_logits = fl.matmul(\\n tmp_node_embed, weight_neg, transpose_y=True) # [B, 1, neg_num]\\n\\n pos_score = fl.squeeze(pos_logits, axes=[1])\\n pos_score = fl.clip(pos_score, min=-10, max=10)\\n pos_score = -1.0 * fl.logsigmoid(pos_score)\\n\\n neg_score = fl.squeeze(neg_logits, axes=[1])\\n neg_score = fl.clip(neg_score, min=-10, max=10)\\n neg_score = -1.0 * fl.logsigmoid(-1.0 * neg_score)\\n\\n neg_score = fl.reduce_sum(neg_score, dim=1, keep_dim=True)\\n self.loss = fl.reduce_mean(pos_score + neg_score)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fd0dcf50adea833e1a2ea0f6b47a554\",\n \"score\": \"0.53064793\",\n \"text\": \"def forward(self, *args, **kwargs):\\n raise NotImplementedError\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993281,"cells":{"query_id":{"kind":"string","value":"8006ac81663c8ab8f5fef2bcd6b06a80"},"query":{"kind":"string","value":"fitness(target) will return the fitness value of permutation named \"target\". Higher scores are better and are equal to the total value (Benefit/ profit) of items in the permutation. If total_weight is higher than the capacity, return 0 because the permutation cannot be used."},"positive_passages":{"kind":"list like","value":[{"docid":"2e3e16c6025faf95d5530088c9e2daa3","score":"0.75124675","text":"def fitness(target):\r\n total_value = 0\r\n total_weight = 0\r\n index = 0\r\n for i in target: \r\n if index >= len(ITEMS):\r\n break\r\n if (i == 1):\r\n total_value += ITEMS[index].value\r\n total_weight += ITEMS[index].weight\r\n index += 1\r\n \r\n \r\n if total_weight > CAPACITY:\r\n #no\r\n return 0\r\n else:\r\n # yes\r\n return total_value\r\n #Spawn Population Individual by individual\r","title":""}],"string":"[\n {\n \"docid\": \"2e3e16c6025faf95d5530088c9e2daa3\",\n \"score\": \"0.75124675\",\n \"text\": \"def fitness(target):\\r\\n total_value = 0\\r\\n total_weight = 0\\r\\n index = 0\\r\\n for i in target: \\r\\n if index >= len(ITEMS):\\r\\n break\\r\\n if (i == 1):\\r\\n total_value += ITEMS[index].value\\r\\n total_weight += ITEMS[index].weight\\r\\n index += 1\\r\\n \\r\\n \\r\\n if total_weight > CAPACITY:\\r\\n #no\\r\\n return 0\\r\\n else:\\r\\n # yes\\r\\n return total_value\\r\\n #Spawn Population Individual by individual\\r\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"06ec5d66c638fd314e2e509c2bef6eb2","score":"0.64524364","text":"def fitness(self, target):\n target = list(target)\n counter = 0\n\n for i in range(len(self.dna)):\n if self.dna[i] == target[i]:\n counter += 1\n self.score = counter / len(target)\n if self.score == 1.0:\n print(\"DONE!\")\n return counter / len(target)","title":""},{"docid":"fb8cbdb859b0eb65135144a90079d6ab","score":"0.6447148","text":"def fitness(self):\n # https://stackoverflow.com/a/38759133\n weight: int = sum(list(map(lambda chromosome, item: chromosome * item, self.genotype,\n [item[0] for item in self.population.task.list_of_items])))\n size: int = sum(list(map(lambda chromosome, item: chromosome * item, self.genotype,\n [item[1] for item in self.population.task.list_of_items])))\n cost: int = sum(list(map(lambda chromosome, item: chromosome * item, self.genotype,\n [item[2] for item in self.population.task.list_of_items])))\n if weight <= self.population.task.w and size <= self.population.task.s:\n return cost\n else:\n return 0","title":""},{"docid":"6bc947d88730a7c527da4d06f7a9caa0","score":"0.64030707","text":"def fitness(self, chromosom):\n\n if capacity <sum(self.weights*chromosom):\n return 0\n return sum(self.costs*chromosom)","title":""},{"docid":"1ace06689db17867ca2596291fad203b","score":"0.6320283","text":"def fitness(self):\n if self.weight <= 0:\n return 0\n else:\n return self.q(+1, self.age, self.params[\"a_half\"], self.params[\"phi_age\"]) * self.q(\n -1, self.weight, self.params[\"w_half\"], self.params[\"phi_weight\"]\n )","title":""},{"docid":"faa7101b315fa8581c937d597691ce24","score":"0.6005293","text":"def weighted_choice(population):\n weight_total = sum((fitness[1] for fitness in population))\n random_pick = random.uniform(0, weight_total)\n for indiviudal, fitness in population:\n if random_pick < fitness:\n return indiviudal\n random_pick = random_pick - fitness\n return indiviudal","title":""},{"docid":"3393196e4028c5e9769c62608fa57458","score":"0.5978832","text":"def get_best_fitness(self):\n raise NotImplementedError","title":""},{"docid":"e4344504d80b88a493067f4f135bb493","score":"0.5957254","text":"def fitness(chromosome, pdvs, max_cap, output=False):\n\tcost = pdvs[chromosome[0]].get_visit_cost()\n\t# If just visiting the first pdv exceeds the max cap\n\tif cost > max_cap:\n\t\tif output:\n\t\t\treturn 0, cost, 0\n\t\treturn 0\n\tvalue = pdvs[chromosome[0]].get_value()\n\tfor i in range(len(chromosome)-1):\n\t\tcost_aux = cost + pdvs[chromosome[i]].get_travel_cost(pdvs[chromosome[i+1]].get_code()) +\\\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tpdvs[chromosome[i+1]].get_visit_cost()\n\t\tif cost_aux > max_cap:\n\t\t\tif output:\n\t\t\t\treturn value, cost, i\n\t\t\treturn value\n\t\telse:\n\t\t\tcost = cost_aux\n\t\t\tvalue += pdvs[chromosome[i]].get_value()\n\treturn value","title":""},{"docid":"0f1102c93b5c5994f9377357a9a68a70","score":"0.5911225","text":"def compute_fitness(population, target, beta):\n #store the fitness values of each cell\n tot = []\n #iterate through each cell\n for cell in population:\n \n #calculate the bp_distance of each chromosome using the cell's structure\n bp_distance_1 = bp_distance(cell.structure_1, target)\n #bp_distance_2 = bp_distance(cell.structure_2[-1], target)\n \n #use the bp_distances and the above fitness equation to calculate the fitness of each chromosome\n fitness_1 = math.exp(beta * float(bp_distance_1) / len(target))\n #fitness_2 = math.exp(beta * float(bp_distance_2) / len(target))\n \n #get the fitness of the whole cell by multiplying the fitnesses of each chromosome\n cell.fitness = fitness_1 #* fitness_2\n \n #store the bp_distance of each chromosome.\n cell.bp_distance_1 = bp_distance_1\n #cell.bp_distance_2 = bp_distance_2\n \n \n #add the cell's fitness value to the list of all fitness values (used for normalization later)\n tot.append(cell.fitness)\n \n #normalization factor is sum of all fitness values in population\n norm = np.sum(tot)\n #divide all fitness values by the normalization factor.\n for cell in population:\n cell.fitness = cell.fitness / norm\n \n return None","title":""},{"docid":"7e6d42ff546e0be467d7478248d405b6","score":"0.5870904","text":"def dp_make_weight(egg_weights, target_weight, memo = {}):\n\n # If one of our egg weights matches the target so we can skip the calculation\n if target_weight in egg_weights:\n return 1\n\n # Otherwise work from the ground up!\n for weight in range(1, target_weight + 1):\n if weight in egg_weights:\n # Catch here for an incorrectly (or not at all) initialized memo\n # dictionary. Because we're starting from the ground and working up,\n # if we don't have memo[1] then a KeyError will be thrown\n memo[weight] = 1\n continue # We can also skip the calculation\n\n # Start with the worst case of all weight 1 eggs\n neggs = weight\n\n for egg in egg_weights:\n if egg > weight:\n # Skip eggs that are heavier than the weight we're calculating\n continue\n \n # See if the number of eggs needed for the current weight - the \n # current egg weight is less than the currently calculated number of\n # eggs. If it is, then our new number of eggs is that number + 1, \n # equivalent to adding the current egg to the previously calculated\n # weight\n if memo[weight - egg] + 1 < neggs: \n neggs = memo[weight - egg] + 1\n \n # Store the current calculation for later use, either as a comparison\n # point or as the optimal solution for weight\n memo[weight] = neggs\n \n # Once we get here we've mapped all of the optimal egg distributions to our dictionary\n return memo[target_weight]","title":""},{"docid":"0f0986473a76c844f8151883c6f835f6","score":"0.5837258","text":"def fitness(self, p):\n \n # f = 1.0 - self.params[\"selection_strength\"] + \\\n # self.params[\"selection_strength\"] * p\n # assert f >= 0.0\n f = math.exp(self.params[\"selection_strength\"] * p)\n return f","title":""},{"docid":"e10e829bd4c663cff3c678a9963ff932","score":"0.58051306","text":"def dp_make_weight(egg_weights, target_weight, memo = {0:0}):\n if target_weight == 0:\n return memo[0]\n try:\n return memo[target_weight]\n except KeyError:\n result = []\n for egg in egg_weights:\n new_target_weight = target_weight - egg\n if new_target_weight >= 0:\n result.append(1 + dp_make_weight(egg_weights,\n new_target_weight))\n memo[target_weight] = min(result)\n return memo[target_weight]","title":""},{"docid":"39c3e4c539b362428f2bfd8b18092057","score":"0.57456654","text":"def target_distribution(q):\n weight = q ** 2 / q.sum(0)\n return (weight.T / weight.sum(1)).T","title":""},{"docid":"9f244fd3456b51b060a18cfab4914418","score":"0.57450634","text":"def fitness(self):\n return self.quantization()","title":""},{"docid":"cbb8e236d082b6a0f87014be1fecee38","score":"0.5710339","text":"def fitness(self):\n cost = []\n for machine_index in range(self.machines):\n machine_cost = 0\n for task_index, task_line in enumerate(self.solution):\n if task_line[machine_index] == 1:\n machine_cost += self.problem.cost_matrix[task_index][machine_index]\n cost.append(machine_cost)\n return max(cost)","title":""},{"docid":"19f802bd11896fe27c3bf77773bbd69b","score":"0.5706405","text":"def sample_best_permutation(key, coupling, cost, num_trials=10):\n vec_sample_permutation = jax.vmap(\n sample_permutation, in_axes=(0, None), out_axes=0)\n key = jax.random.split(key, num_trials)\n perms = vec_sample_permutation(key, coupling)\n\n # Pick the permutation with minimal ot cost\n ot = jnp.sum(perms * cost[jnp.newaxis, :, :], axis=(1, 2))\n min_idx = jnp.argmin(ot)\n out_perm = perms[min_idx]\n return out_perm","title":""},{"docid":"3d5349085bf0dad92fad15536da61234","score":"0.5654172","text":"def calculateFitness(individual):\n fitness = 0\n for i in range(len(individual)):\n if individual[i] == target[i]:\n fitness += 1\n\n return fitness","title":""},{"docid":"3c87708853a50e9ad9448eb6b768f8ad","score":"0.5603705","text":"def get_optimal_value(capacity, weights, values):\n value = 0.\n # Get a sorted list of [value/weight, value, weight]\n values = sorted([[i[0] / i[1]] + list(i) for i in zip(values, weights)], reverse=True)\n\n for i in values:\n if not capacity:\n return value\n a = min((i[2], capacity))\n value += a * i[0]\n capacity -= a\n\n return value","title":""},{"docid":"fbfac81f8da19defe9071cf3e180ef36","score":"0.5581835","text":"def findMax(items, weights, w):\n # key-value pairs where key is a tuple(size,total value)\n lookup = dict()\n maximum = 0 # maximum total weight\n \n def isPresent(size,k,total):\n nonlocal maximum\n # key = (size,total)\n key = (size,k)\n \n if key in lookup:\n return lookup[key]\n if size == 0:\n if k <= w:\n maximum = max(maximum, total)\n lookup[key] = total\n else:\n lookup[key] = 0\n return lookup[key]\n elif k > w:\n return 0\n maximum = max(maximum, total)\n # Either an item is included in the optimal subset or it's not.\n # We'll try both and cache the boolean result whether \n # the total weight <= weight capacity\n inc = isPresent(size-1, k+weights[size-1], total+items[size-1])\n exc = isPresent(size-1, k, total)\n lookup[key] = max(inc, exc)\n return lookup[key]\n \n _ = isPresent(len(items),0,0)\n return maximum","title":""},{"docid":"4303105d53d8d8a3592e440aba996467","score":"0.55802137","text":"def best_in_population(population):\n return max(population, key=fitness)","title":""},{"docid":"a47bac0435144c4a3261e884992eca7d","score":"0.55651927","text":"def p_accept(cur_fitness, candidate_fitness, Tem):\n return math.exp(-abs(candidate_fitness - cur_fitness) / Tem)","title":""},{"docid":"f142c5ba12bfc1bcc10993fb13e589bd","score":"0.5555438","text":"def ideal_price(self):\n return calc_price(self.target_weight)","title":""},{"docid":"679b0e5d721968eaa3ba43294edefcb4","score":"0.5522592","text":"def get_best_individual_fitness(self):\n return np.max(self.fitness)","title":""},{"docid":"68236df6fd83528349483cafd03d3889","score":"0.5517962","text":"def fitness(self):\n return self._fitness","title":""},{"docid":"8adb2f0f1a880319d99d010840ee62a3","score":"0.5502526","text":"def evalutate_fitness(individual):\n difference = abs(TARGET - decode(individual))\n fitness = 1000 - difference\n return fitness","title":""},{"docid":"0255177fed4a8a88a71bcc4b7bf250cd","score":"0.5472982","text":"def init_solution(weight_cost, max_weight):\n solution = []\n allowed_positions = list(range(len(weight_cost)))\n while len(allowed_positions) > 0:\n idx = random.randint(0, len(allowed_positions) - 1)\n selected_position = allowed_positions.pop(idx)\n if get_cost_and_weight_of_knapsack(solution + [selected_position], weight_cost)[1] <= max_weight:\n solution.append(selected_position)\n else:\n break\n return solution","title":""},{"docid":"372ba46fa42a482c30d551c81f5bdbe2","score":"0.54439515","text":"def candidate(self) -> Optional[Knapsack]:\n target_index: int = random.randint(1, self._total_importance)\n importance_accumulator: int = 0\n for knapsack in self._knapsacks: # type: Knapsack\n importance_accumulator += knapsack.importance()\n if importance_accumulator >= target_index:\n return knapsack\n return None","title":""},{"docid":"fba8b94a3b52d5372f24d799671f71b8","score":"0.5418524","text":"def simulate(solution, weight_cost, max_weight, init_temp, steps):\n temperature = init_temp\n\n best = solution\n best_cost = get_cost_and_weight_of_knapsack(solution, weight_cost)[0]\n\n current_sol = solution\n while True:\n current_cost = get_cost_and_weight_of_knapsack(best, weight_cost)[0]\n for i in range(0, steps):\n moves = moveto(current_sol, weight_cost, max_weight)\n idx = random.randint(0, len(moves) - 1)\n random_move = moves[idx]\n delta = get_cost_and_weight_of_knapsack(random_move, weight_cost)[0] - best_cost\n if delta > 0:\n best = random_move\n best_cost = get_cost_and_weight_of_knapsack(best, weight_cost)[0]\n current_sol = random_move\n else:\n if math.exp(delta / float(temperature)) > random.random():\n current_sol = random_move\n\n temperature *= ALPHA\n if current_cost >= best_cost or temperature <= 0:\n break\n return best_cost, best","title":""},{"docid":"4dd8b2ac05206e6918c75ce3e72e7d2d","score":"0.53962725","text":"def maxProfitMatching(weights, _flip=False): # minimum cost\n global U,V,S,T,Mu,Mv,lu,lv, minSlack, w\n \n w = np.array(weights)\n if _flip: \n w = -w # more profit is equivalent to less cost\n \n n = len(w)\n U = V = range(n)\n lu = [ max([w[u][v] for v in V]) for u in U] # start with trivial labels\n lv = [ 0 for v in V]\n Mu = {} # start with empty matching\n Mv = {}\n while len(Mu)<n:\n free = [u for u in V if u not in Mu] # choose free vertex u0\n u0 = free[0]\n S = {u0: True} # grow tree from u0 on\n T = {}\n minSlack = [[slack(u0,v), u0] for v in V]\n augment()\n \n # val. of matching is total edge weight \n val = sum(lu)+sum(lv)\n \n return (Mu, Mv, 0.0-val if _flip else val)","title":""},{"docid":"33e038d980f0eda5c107f004f92531c8","score":"0.53907925","text":"def best_reward(self, observation, sess, weighted=False):\n if weighted:\n return self.weighted_choice(observation, sess)[1]\n else:\n return self.best_choice(observation, sess)[1]","title":""},{"docid":"bd69c9445abd5f8da72cdcbeb2f81d2c","score":"0.53564787","text":"def cost(result, target):\n return - (result - target)**2","title":""},{"docid":"0e92f1431ba49ca0981e2a740058991f","score":"0.53341436","text":"def knapsack_max_value(knapsack_max_weight, items):\n\n # Initialize a lookup table to store the maximum value ($)\n memoization = [0] * knapsack_max_weight\n\n # Iterate down the given list\n for item in items:\n\n # The \"capcacity\" represents amount of remaining capacity (kg) of knapsack at a given moment.\n for capacity in range(knapsack_max_weight - 1, item.weight - 2, -1):\n not_pick_value = memoization[capacity]\n pick_value = item.value + (memoization[capacity - item.weight] if capacity - item.weight > 0 else 0)\n\n memoization[capacity] = max(pick_value, not_pick_value)\n\n return memoization[-1]","title":""},{"docid":"0dc55a6f5420b780db691b1203faa84b","score":"0.5330018","text":"def calculate_probabilities(self):\n max_fit = max(self.fitness)\n self.prob = (0.9 * (self.fitness / max_fit)) + 0.1\n return self","title":""},{"docid":"1b524a72147ff94fed6f1576ccb67063","score":"0.53253984","text":"def fitness(plan, goal, threshold, model_perf_func, **model_parameters):\n plan_perf = model_perf_func(plan, **model_parameters)\n total_workload = sum(plan)\n fitness_wo_load = 1 - (abs(goal - plan_perf))\n fitness_with_load = fitness_wo_load - WORKLOAD_FACTOR * total_workload\n approx_deviation = abs(100 - (plan_perf / goal) * 100)\n se_threshold = approx_deviation <= threshold\n return fitness_with_load, se_threshold","title":""},{"docid":"38601a08c0b79f044ceccb1700674274","score":"0.5321909","text":"def _getWeight(self, target_node):\n\t\treturn self.connectedTo[target_node]","title":""},{"docid":"70564e31666dd76dc963891c41a9a69a","score":"0.53175634","text":"def dp(length, target):\n if length <= 0 or target <= 0:\n return \"\" if length == target == 0 else None\n if min_cost * length > target or max_cost * length < target:\n return None\n for num, cost in sorted_num_cost:\n res = dp(length - 1, target - cost)\n if res is not None:\n return num + res","title":""},{"docid":"c461096adf5f6c93eb6677df113f5d48","score":"0.5312875","text":"def appetite_checker(eaten_weight, desired_weight, last_kill):\r\n if eaten_weight + last_kill > desired_weight:\r\n appetite_weight = desired_weight - eaten_weight\r\n return appetite_weight\r\n else:\r\n return last_kill","title":""},{"docid":"1eac694c1f3b3e35cdd3063e7ce47b33","score":"0.5311565","text":"def food_fitness(self):\n return self.food_ph","title":""},{"docid":"262bdede736bee51703bec370ef82b13","score":"0.5294326","text":"def gen_loss_weight(target):\n positive_num = torch.sum(target, 1)\n class_num = torch.FloatTensor([target.size(1)]).cuda(gpuID) if args.cuda else torch.Tensor([target.size(1)])\n negative_num = class_num - positive_num\n weight = torch.div(negative_num, positive_num)\n weight = weight.expand((target.size(0), target.size(1)))\n return torch.mul(weight, target)","title":""},{"docid":"6c883118597d4e4579e376997fcc5bea","score":"0.5290734","text":"def get_max_weight(self):\n if self.weights.size == 0:\n return 0\n return np.amax(np.abs(self.weights))","title":""},{"docid":"2b935f891e37f27743a39b07410fa3ad","score":"0.5277587","text":"def evaluate(self, ind, **kwargs):\n \n p, d = ind.phenotype, {'pred': pred, 'succ': succ}\n exec(p, d)\n\n # this is the program's output: a generator\n s = d['XXX_output_XXX']\n\n # Truncate the generator and convert to list\n s = list(truncate(len(self.target), s))\n \n # Set target\n t = self.target\n\n # various weightings of four aspects of our fitness. the formula is:\n # fitness = gamma * dist + (1 - gamma) * length\n # where dist = alpha * lev_dist(t, s) + (1 - alpha) * dtw_dist(t, s)\n # and length = beta * proglen(t) + (1 - beta) * compressibility(t)\n # but when any of alpha, beta and gamma is 0 or 1, we can save some\n # calculation:\n\n if self.gamma > 0.0:\n if self.alpha > 0.0:\n lev_dist_v = lev_dist(t, s)\n else:\n lev_dist_v = 0.0\n if self.alpha < 1.0:\n dtw_dist_v = dtw_dist(t, s)\n else:\n dtw_dist_v = 0.0\n dist_v = self.alpha * lev_dist_v + (1 - self.alpha) * dtw_dist_v\n else:\n dist_v = 0.0\n\n if self.gamma < 1.0:\n if self.beta > 0.0:\n proglen_v = proglen(p)\n else:\n proglen_v = 0.0\n if self.beta < 1.0:\n compressibility_v = compressibility(p)\n else:\n compressibility_v = 0.0\n length_v = self.beta * proglen_v + (1 - self.beta) * \\\n compressibility_v\n else:\n length_v = 0.0\n\n return self.gamma * dist_v + (1 - self.gamma) * length_v","title":""},{"docid":"3e887abcb7fd6ea41bd91ce4a89c8d4d","score":"0.52688587","text":"def maximize(self, positive_only):\n\t\tself.max_profit = 0\n\t\tself.max_solution = [0]*self.nb_items\n\t\tself.ids = list(range(self.nb_items))\n\n\t\t\"\"\"# easy case condition (optional):\n\t\t# the sum of weights is less or equal to capacity\n\t\tdone = False\n\t\tcumul_weights = 0\n\t\ti = 0\n\t\twhile cumul_weights < self.capacity:\n\t\t\tcumul_weights += self.profits[i]\n\t\t\ti += 1\n\t\tif i == self.length:\n\t\t\tself.max_profit = sum(self.profits)\n\t\t\tself.max_solution = [1]*self.len\n\t\t\tdone = True\n\t\t\treturn done\"\"\"\n\n\t\tself.Nzero_ids = []\n\t\tself.None_ids = []\n\t\tself.Nminus_ids = []\n\t\tself.Nplus_ids = self.ids\n\n\t\tif not positive_only:\n\t\t\t# 2nd condition: Non positive values in weight and profit\n\t\t\tself.Nzero_ids = []\n\t\t\tself.None_ids = []\n\t\t\tself.Nminus_ids = []\n\t\t\tself.Nplus_ids = []\n\t\t\tfor i in self.ids:\n\t\t\t\t# Group N0\n\t\t\t\tif self.weights[i] >= 0 and self.profits[i] <= 0:\n\t\t\t\t\tself.max_solution[i] = 0\n\t\t\t\t\tself.Nzero_ids.append(i)\n\t\t\t\t# Group N1\n\t\t\t\telif self.weights[i] <= 0 and self.profits[i] >= 0:\n\t\t\t\t\tself.max_solution[i] = 1\n\t\t\t\t\tself.max_profit += self.profits[i]\n\t\t\t\t\tself.capacity -= self.weights[i]\n\t\t\t\t\tself.None_ids.append(i)\n\t\t\t\t# Group N-\n\t\t\t\telif self.weights[i] < 0 and self.profits[i] < 0:\n\t\t\t\t\tself.max_profit += self.profits[i]\n\t\t\t\t\tself.capacity -= self.weights[i]\n\t\t\t\t\t# then change sign\n\t\t\t\t\tself.weights[i] *= -1\n\t\t\t\t\tself.profits[i] *= -1\n\n\t\t\t\t\tself.Nminus_ids.append(i)\n\t\t\t\t# Group N+\n\t\t\t\telse:\n\t\t\t\t\tself.Nplus_ids.append(i)\n\n\t\t\tself.ids = self.Nminus_ids + self.Nplus_ids\n\t\t\tself.len = len(self.ids)# self.len = len(self.ids) all time","title":""},{"docid":"eb1f21b4a0bee4b1c6e0957e7a3f6f77","score":"0.5267604","text":"def next(self):\n mean = np.nanmean(self.population_, axis=0) # column mean.\n rs = self.random_state\n\n teacher = np.argmin(self.fitness_) # select teacher\n\n # teaching phase\n for i in self.pidx:\n T_F = rs.randint(1, 3) # teaching factor is randomly 1 or 2.\n r_i = rs.rand(self.m) # multiplier also random.\n\n new_solution = self.population_[i] + (r_i * (teacher - (T_F * mean)))\n new_solution = np.minimum(np.maximum(new_solution, self.lower_bound), self.upper_bound)\n\n new_fitness = self.f(new_solution)\n\n if new_fitness < self.fitness_[i]:\n self.population_[i] = new_solution\n self.fitness_[i] = new_fitness\n\n # learning phase\n for i in self.pidx:\n\n j = rs.choice(self.pidx[:i] + self.pidx[(i + 1):], 1) # pick another random i!=j\n r_i = rs.rand(self.m)\n\n if self.fitness_[i] < self.fitness_[j]:\n new_solution = self.population_[i] + r_i * (self.population_[i] - self.population_[j]).flatten()\n else:\n new_solution = self.population_[i] + r_i * (self.population_[j] - self.population_[i]).flatten()\n\n new_solution = np.minimum(np.maximum(new_solution, self.lower_bound), self.upper_bound)\n new_fitness = self.f(new_solution)\n \n if new_fitness < self.fitness_[i]:\n self.population_[i] = new_solution\n self.fitness_[i] = new_fitness\n\n self.bestidx_ = np.argmin(self.fitness_) # update details\n self.bestcosts_.append(self.fitness_[self.bestidx_])","title":""},{"docid":"46fa6c9726b02db0e4e2d3d295dac601","score":"0.5267358","text":"def knapsack(items, capacity):\n if len(items) == 0 or capacity == 0:\n return 0\n \n first_item_weight = items[0][1]\n first_item_value = items[0][2]\n\n value_with = first_item_value + knapsack(items[1:], capacity-first_item_weight)\n value_without = knapsack(items[1:], capacity)\n\n if first_item_weight > capacity:\n return value_without\n\n return max(value_with, value_without)","title":""},{"docid":"9e00a6496dab526c98b8cf611571e9d4","score":"0.52667785","text":"def get_fitness(self):\n \n return self.fitness_current","title":""},{"docid":"877bad30ba8977b0ff064d6148551370","score":"0.52569634","text":"def get_fitness(self):\r\n return self.fitness","title":""},{"docid":"af6960acd11f5ceb213a679119341f91","score":"0.5253209","text":"def max_score(self):\n return self.weight","title":""},{"docid":"bf55c11f25376dff2cd0a03b2e4fb1f6","score":"0.5226057","text":"def calculate_weights(self, target):\n if len(target.shape) == 3:\n hist = np.sum(target, axis=(1, 2)) * 1.0 / target.sum()\n else:\n hist = np.sum(target, axis=(0, 2, 3)) * 1.0 / target.sum()\n if self.norm:\n hist = ((hist != 0) * self.upper_bound * (1 / hist)) + 1\n else:\n hist = ((hist != 0) * self.upper_bound * (1 - hist)) + 1\n return hist[:]","title":""},{"docid":"850a4529320b483dead301244b36fb76","score":"0.5221587","text":"def fitness(individual: IndividualType, fleet: Fleet, indices: IndicesType, starting_points: StartingPointsType,\n weights=(1.0, 1.0, 1.0, 1.0), penalization_constant=500000, allowed_charging_operations=2):\n\n # Decode\n routes = decode(individual, indices, starting_points, allowed_charging_operations=allowed_charging_operations)\n\n # Set routes\n fleet.set_routes_of_vehicles(routes)\n\n # Get optimization vector\n fleet.create_optimization_vector()\n\n # Cost\n costs = np.array(fleet.cost_function())\n\n # Check if the solution is feasible\n feasible, penalization = fleet.feasible()\n\n # penalization\n if not feasible:\n penalization += penalization_constant\n\n fit = np.dot(costs, np.asarray(weights)) + penalization\n\n return fit, feasible","title":""},{"docid":"45ed425676ce4349284d0cd8c8c89653","score":"0.5218327","text":"def get_cost_and_weight_of_knapsack(solution, weight_cost):\n cost, weight = 0, 0\n for item in solution:\n weight += weight_cost[item][0]\n cost += weight_cost[item][1]\n return cost, weight","title":""},{"docid":"38e0ac3bb862bbda041bb5faf243f653","score":"0.5199759","text":"def get_fitness(self, itr, all_paths):\n fitness = np.zeros(self.pop_size)\n for p in range(self.pop_size):\n rewards = all_paths[p][\"rewards\"]\n valid_rewards = rewards * all_paths[p][\"valids\"]\n path_rewards = np.sum(valid_rewards, -1)\n if self.f_F == \"max\":\n fitness[p] = np.max(path_rewards)\n else:\n fitness[p] = np.mean(path_rewards)\n return fitness","title":""},{"docid":"49f1c2da0a07de0aca79731d6940b5ef","score":"0.51916695","text":"def weight(weights):\n total = sum(weights)\n\n p = SEED.random()\n acc = 0\n for i, w in enumerate(weights):\n acc += w / total\n if p < acc:\n return i\n return len(weights) - 1","title":""},{"docid":"f650fb371db764576b6c177889746246","score":"0.51804346","text":"def dynamic_programming_solver(items, weights, values, capacity, n_items):\n\n rows, cols = (n_items, capacity+1)\n\n # create a two dimensional array for Memoization\n M = [[0 for i in range(cols)] for j in range(rows)]\n\n for row in range(rows):\n for col in range(cols):\n if weights[row] > col:\n M[row][col] = M[row - 1][col]\n else:\n x = max(M[row - 1][col], M[row - 1]\n [col - weights[row]] + values[row])\n M[row][col] = x\n\n i = rows - 1\n j = cols - 1\n while M[i][j] == M[i][j - 1]:\n j -= 1\n\n i = rows - 1\n best_items = []\n best_items_index = []\n\n while j > 0:\n while i > 0 and M[i][j] == M[i - 1][j]:\n i -= 1\n j = j - weights[i]\n if j >= 0:\n best_items.append(items[i])\n best_items_index.append(i)\n i -= 1\n\n cost = 0\n for i in best_items_index:\n cost += weights[i]\n\n cost = cost / 100\n max_value = M[rows - 1][cols - 1]/10000\n return cost, max_value, best_items","title":""},{"docid":"09b0bad7b42c617939dc329fbcb34eec","score":"0.51780933","text":"def heuristic(cap, values, weights, epsilon=0.2):\n n = len(values)\n vmax = max(values)\n\n m = epsilon * vmax / n\n values = [int(v // m) for v in values]\n\n return knapsack(cap, values, weights) * m","title":""},{"docid":"278a0d441a164aeb385f047042d34923","score":"0.5158766","text":"def get_optimal_knapsack_value(W, items):\n n = len(items)\n knapsacks = dict()\n for i in range(n, -1, -1):\n for j in range(W + 1):\n if i == n:\n knapsacks[(i, j)] = 0\n continue\n if j == 0:\n knapsacks[(i, j)] = 0\n continue\n weight, value = items[i]\n if weight <= j:\n knapsacks[(i, j)] = max(\n knapsacks[(i + 1, j)],\n knapsacks[(i + 1, j - weight)] + value)\n else:\n knapsacks[(i, j)] = knapsacks[(i + 1, j)]\n return knapsacks[(0, W)]","title":""},{"docid":"63e5d1cabe2557d79e2650518616dead","score":"0.5152632","text":"def knapsack(items, max_weight):\n if max_weight < 0:\n raise ValueError(f'max_weight cannot be < 0 (got {max_weight})')\n if not items or not max_weight:\n return 0\n x = [[0 for _ in range(max_weight + 1)] for _ in range(len(items) + 1)]\n for i, (value, weight) in enumerate(items, start=1):\n if weight < 0:\n raise ValueError(f'item weights cannot be < 0 (got {weight})')\n for w in range(max_weight + 1):\n if weight > w:\n x[i][w] = x[i-1][w]\n else:\n x[i][w] = max(x[i-1][w], x[i-1][w-weight] + value)\n return x[len(items)][max_weight]","title":""},{"docid":"50f044840c0e88e880ac23284b0cb4b4","score":"0.51509476","text":"def fittest(self, population, fit=None):\n if fit==None:\n fit = pop_fitness(population, self.costs, self.weights)\n max_fit = max(fit)\n return population[fit.index(max_fit)], max_fit","title":""},{"docid":"0644b80f3c83bdc648628dea41e5756f","score":"0.5150334","text":"def fitness(self):\n if self._fitness is None:\n clauses, passed = self._equation.check(self)\n self._fitness = clauses / self._equation.clauses\n self._valid = passed\n return self._fitness","title":""},{"docid":"4e969b467f182fcd49d65c74ba585a3e","score":"0.5146512","text":"def fitness_function(prediction, true_class=None):\n return -get_probability_for_class(prediction, true_class)","title":""},{"docid":"88e541536b40078699b0ce7ebfabf029","score":"0.51276875","text":"def get_best_individual(self):\n return self.pop[np.argmax(self.fitness), :]","title":""},{"docid":"8c864318ff5ab79fb014c69409fbc43d","score":"0.5124056","text":"def fitness(self, chromosom):\n #TODO \n return sum(chromosom)","title":""},{"docid":"450842f6d0b7bf8971b920c2234a54a3","score":"0.5116017","text":"def get_max_fitness(self):\n return max([individual.fitness() for individual in self.individuals])","title":""},{"docid":"afb2312c3f891bacce85a6be8e356f7f","score":"0.5115378","text":"def fitness_change(self):\r\n phi_age = self.parameters[\"phi_age\"]\r\n a_half = self.parameters[\"a_half\"]\r\n phi_weight = self.parameters[\"phi_weight\"]\r\n w_half = self.parameters[\"w_half\"]\r\n\r\n if self.weight > 0:\r\n self.fitness = ((1 /\r\n (1 + exp(phi_age *\r\n (self.age - a_half)))) *\r\n (1 / (1 + exp(-(phi_weight *\r\n (self.weight - w_half))))))\r\n else:\r\n self.fitness = 0","title":""},{"docid":"407488fe9366c3aec17c9e350dbd0967","score":"0.5108754","text":"def calculate_fitness(self):\n\t\tval = self.decode(self.kromosom)\n\t\tx = val[0]\n\t\ty = val[1]\n\t\tf= 3*(x**2) + 2*(y**2) - (4*x) + (y*1.0/2)\n\t\treturn 1 * 1.0 / (f + 0.01)","title":""},{"docid":"2158492bd0235a7a948b7b5bd379cdc5","score":"0.51000714","text":"def next_best(target_items):\n\n # track the set of all items already learnt\n ALREADY_LEARNT = set()\n\n # a dictionary mapping targets to the set of prerequisite items\n IN_TARGET = {}\n\n # a dictionary mapping targets to a set of items still not learnt\n MISSING_IN_TARGET = {}\n\n # a dictionary mapping items to a set of targets the items are needed for\n # and are missing from\n TARGETS_MISSING = collections.defaultdict(set)\n\n ## fill the dictionaries with initial data based on the input\n\n for target, items in target_items.items():\n IN_TARGET[target] = set(items)\n MISSING_IN_TARGET[target] = set(items)\n for item in items:\n TARGETS_MISSING[item].add(target)\n\n while True:\n\n # for each item, a score of how bad it is that it is missing\n MISSING_ITEMS = collections.defaultdict(int)\n\n for missing in MISSING_IN_TARGET.values():\n for item in missing:\n # if item is only missing item for a target, add 1/2 to its score\n # if item is 1 of 2 missing items for a target, add 1/4\n # if item is 1 of 3 missing items for a target, add 1/8\n # and so on...\n MISSING_ITEMS[item] += 1. / (2 ** len(missing))\n\n # stop if there are no missing items\n if not MISSING_ITEMS:\n break\n\n # otherwise the next item to learn is the one with the highest score\n next_item = sorted(MISSING_ITEMS, key=MISSING_ITEMS.get)[-1]\n\n # for each target missing that item, remove the item\n for target in TARGETS_MISSING[next_item]:\n MISSING_IN_TARGET[target].remove(next_item)\n\n # if the target is now missing no items...\n if len(MISSING_IN_TARGET[target]) == 0:\n\n # calculate what is new to learn for that target\n items_to_learn = IN_TARGET[target] - ALREADY_LEARNT\n\n yield target, items_to_learn\n\n # remove from missing in that target\n del MISSING_IN_TARGET[target]\n\n # add to items already learnt\n ALREADY_LEARNT.update(items_to_learn)\n\n # remove the item from all targets requiring it\n del TARGETS_MISSING[next_item]","title":""},{"docid":"46c5c5bd5c128fdea6ac2dc772869c7a","score":"0.50935864","text":"def knapsack(self, weight):\n # calculate max length of combinations: the worst case is when we use\n # only the item with the smallest weight\n max_length = weight / min(self.items).weight\n # keep the best combination so far\n best = Knapsack()\n for length in xrange(1, max_length + 1):\n # generate all possible multicombinations for a given length\n for combination in self._multicombinations(self.items, length):\n knapsack = Knapsack()\n for item in combination:\n knapsack.add(item)\n if knapsack.weight <= weight and knapsack > best:\n best = knapsack\n return best","title":""},{"docid":"5bfff401eed996cff2ebd36ccd22a7d9","score":"0.50868464","text":"def get_best_individual(population, weights, clauses):\r\n valid_individuals = []\r\n for individual in population:\r\n if is_valid(individual, clauses):\r\n valid_individuals.append(individual)\r\n if len(valid_individuals) == 0:\r\n return None\r\n weights_sums = [get_weights_sum(valid_individual, weights) for valid_individual in valid_individuals]\r\n best_sum = max(weights_sums)\r\n index = weights_sums.index(best_sum)\r\n best_individual = valid_individuals[index]\r\n return {'individual': best_individual, 'weights_sum': best_sum}","title":""},{"docid":"e9ba5d7e4ce899b36d459e59d3c9c45a","score":"0.508629","text":"def get_probabilities(self):\n weights = np.ones(shape=self.size, dtype=np.float32)\n\n if self.sampling_method == 'nonzero_reward':\n weights[self.rewards[:self.size] == 0] = self.smallest_weight\n elif self.sampling_method == 'positive_reward':\n weights[self.rewards[:self.size] <= 0] = self.smallest_weight\n else:\n raise ValueError(\"Unknown sampling_method given to ReplayMemory ({}).\".format(self.sampling_method))\n\n weights_sum = weights.sum()\n\n # It might be the cases where all the weights are zero.\n # In this extreme case, return a uniform sampling weights.\n if weights_sum > 0:\n return weights / weights_sum\n else:\n return np.ones(shape=self.size, dtype=np.float32) / self.size","title":""},{"docid":"0126debb4f8303efbe77dcfa51e1329d","score":"0.50831866","text":"def total_weight(self) -> float:\n pass","title":""},{"docid":"a7d464d104d2412631c88f23de231538","score":"0.50778776","text":"def get_cost(self):\n if self.boundaries == 0:\n return 0.0\n tc = self.transition_cost()\n assert self.logemissionsum >= 0\n assert tc >= 0\n return (self.logemissionsum + tc) * self.weight","title":""},{"docid":"cee3869e7059257f7ace650b0584948a","score":"0.50731385","text":"def ComputeFitness(results, Maximize):\n scoreMultiplier = 100000\n movementsMultiplier = 0\n snakelengthMultiplier = 0\n penaltiesMultiplier = -1000000\n rewardsMultiplier = 1000\n \n return np.sum(np.array(results) * np.array([scoreMultiplier,\n movementsMultiplier,\n snakelengthMultiplier,\n penaltiesMultiplier,\n rewardsMultiplier]\n )\n , axis=1\n )","title":""},{"docid":"d625155c86f32299d317588c94e5b3d4","score":"0.5069805","text":"def fitness_score(self):\n pass","title":""},{"docid":"804584bf95e252791e8dfd238b3189cd","score":"0.50668436","text":"def max_profit(self):\n if not self.is_optimized:\n raise UnoptimizedStrategyOptimizerException('Must run StrategyOptimizer.optimize() first')\n return -self._optimization_result.fun","title":""},{"docid":"dd59ef3a19ae75f8fdd1cd49b6bdffaa","score":"0.5060159","text":"def weight(self, *, attacker=None, move=None):\r\n cur_ability = self.ability(attacker=attacker, move=move)\r\n cur_weight = self.starting_weight\r\n if cur_ability == Ability.HEAVY_METAL:\r\n cur_weight *= 2\r\n if cur_ability == Ability.LIGHT_METAL:\r\n cur_weight //= 2\r\n cur_weight = max(1, cur_weight)\r\n cur_weight -= self.autotomize * 1000\r\n cur_weight = max(1, cur_weight)\r\n return cur_weight","title":""},{"docid":"d6c33b452300ce82d9496a9f78aa9e35","score":"0.50596005","text":"def fitness(self) -> float:\n self.multi_fitness = list()\n self._performance()\n for fitness_func in self._fitness_function:\n self.multi_fitness.append(fitness_func(self._maze_with_robot))\n self.my_fitness = self.multi_fitness[1]\n return self.my_fitness","title":""},{"docid":"0129229fb9242c939e09c0c2b4a8914e","score":"0.5057175","text":"def max_product(self, target_node: VariableNode) -> Tensor:\n\n # Record for storing optimal assignments for\n # each incoming variable node\n self.record[target_node] = {}\n\n # Initialize the outgoing message\n outgoing_msg = (torch.zeros(self.factor.shape) + self.factor).to(self.device)\n\n # Loop over incoming neighbors is the same as in sum-product\n for n in self.neighbors(target_node):\n\n # Get message\n edge = self.graph[n][self][\"object\"]\n incoming_msg = edge.get_message(n, self).to(self.device)\n\n # Reshape\n broadcast_shape = [1] * len(self.factor.shape)\n broadcast_shape[edge.factor_dim] = len(incoming_msg)\n\n # Sum\n outgoing_msg += incoming_msg.view(broadcast_shape)\n\n \"\"\"\n Determine the maximum value for each possible assignment to the\n target variable. The below is a fancy way to compute this that\n gets around torch.max's lack of support for max'ing over multiple\n dimensions simultaneously.\n \"\"\"\n target_dim = self.graph[self][target_node][\"object\"].factor_dim\n maxes = outgoing_msg.view(outgoing_msg.size(target_dim), -1).max(dim=-1)\n\n # If there *are* incoming variable nodes, we record the best assignment\n # for each one, for each possible value of the target variable node.\n if len(self.factor.shape) > 1:\n # The dimensions of the factor tensor associated with the\n # incoming variables\n non_target_dimensions = {\n i: dim for i, dim in enumerate(self.factor.shape) if i != target_dim\n }\n\n # The indices in the message tensor corresponding to the\n # maximum values\n max_indices = np.unravel_index(\n maxes.indices, tuple(non_target_dimensions.values())\n )\n\n # Associate each index with the appropriate incoming variable\n max_indices_by_factor_dim = dict(\n zip(non_target_dimensions.keys(), max_indices)\n )\n\n # Record the best possible assignment for each incoming variable\n for n in self.neighbors(target_node):\n factor_dim = self.graph[n][self][\"object\"].factor_dim\n self.record[target_node][n] = max_indices_by_factor_dim[factor_dim]\n\n # Return the max values\n return maxes.values","title":""},{"docid":"e8ce5513e2417e57ee7c42e4a796c75b","score":"0.505216","text":"def fitness(individual, weights, clauses, n_clauses, satisfied_clause_bonus, satisfied_formula_bonus):\r\n index, fitness = 0, 0\r\n\r\n for i in individual:\r\n if i==1:\r\n fitness += weights[index]\r\n index += 1\r\n\r\n satisfied_clauses = 0\r\n for clause in clauses:\r\n satisfied = False\r\n for var in clause:\r\n negation = False\r\n if var < 0:\r\n negation = True\r\n var *= (-1)\r\n index = var - 1\r\n value = individual[index]\r\n if (value == 1 and not negation) or (value == 0 and negation):\r\n satisfied = True\r\n if satisfied:\r\n satisfied_clauses += 1\r\n\r\n fitness += satisfied_clauses*satisfied_clause_bonus\r\n if satisfied_clauses == n_clauses:\r\n fitness += satisfied_formula_bonus\r\n\r\n return fitness","title":""},{"docid":"edb7fb7695ec9dae9c525f81bec40a8c","score":"0.505165","text":"def find_higest_food_collector(self):\n fitness = self.agents[0].food_collected\n fittest = self.agents[0]\n for agent in self.agents:\n if agent.food_collected > fitness:\n fittest = agent\n return fittest","title":""},{"docid":"edb7fb7695ec9dae9c525f81bec40a8c","score":"0.505165","text":"def find_higest_food_collector(self):\n fitness = self.agents[0].food_collected\n fittest = self.agents[0]\n for agent in self.agents:\n if agent.food_collected > fitness:\n fittest = agent\n return fittest","title":""},{"docid":"863ae6a99eafd52ab7c07bb36d1986cb","score":"0.50498414","text":"def best_of_gen(population):\n assert (len(population) > 0)\n return max(population)","title":""},{"docid":"863ae6a99eafd52ab7c07bb36d1986cb","score":"0.50498414","text":"def best_of_gen(population):\n assert (len(population) > 0)\n return max(population)","title":""},{"docid":"a43bc61bb35c50a0441b1a6897378c5c","score":"0.50483906","text":"def weighted_choice(items):\n weight_total = sum((item[1] for item in items))\n n = random.uniform(0, weight_total)\n for item, weight in items:\n if n < weight:\n return item\n n = n - weight\n return item","title":""},{"docid":"6e3565149527a238101583b9cc2f6182","score":"0.50421214","text":"def x_final(feasible, target, w, Delta):\n # Make sure there's at least one target\n assert np.any(target == 1)\n\n # Initialize array with known values\n x_final = feasible + Delta*target\n\n # TODO: make this a generator or something sensible\n q = tuple(i, j for i,j in target[target == 1])\n # Store calculated values (i, j): cost in this dict\n d = {}\n # Initialize an array to deal with noise\n destination_prob = np.zeros(x_final.shape)\n # Create arrays of x, y, ..., z indices for repeated use\n grid = np.indices(x_final)\n # Do DP. Work from target backwards, adding progressively more distant\n # points to the queue.\n # TODO: generalize to n dimensions\n for i, j in q:\n # For each possible control,\n for u_i, u_j in ((1, 0), (-1, 0), (0, 1), (0, -1)):\n # Check if it's farther from a target point (i.e. not already\n # calculated and not a candidate for a previous step)\n if (i + u_i, j + u_j) not in d: continue\n # calc probs of each possible actual next destination\n destination_prob[:] = w(i_grid, j_grid)\n # calc min expected value of risk\n # store minimum of optoins in d\n # add not-already-calculated neighbors to q\n pass","title":""},{"docid":"d1ff04a9f86a5ad1885d6a8a50fae5b8","score":"0.503544","text":"def optimize(self, threshold_weight):\r\n values = [None] * (threshold_weight + 1)\r\n values[0] = 0\r\n for w in xrange(1, threshold_weight + 1):\r\n best_so_far = None\r\n for item in self.items:\r\n cost = item.cost\r\n if item.weight <= w:\r\n cost += values[w - item.weight]\r\n # end: if\r\n if best_so_far is None or cost < best_so_far:\r\n best_so_far = cost\r\n # end: for item\r\n values[w] = best_so_far\r\n # end: for w\r\n if Global.DEBUG_LEVEL > 1:\r\n print \"-\"*20\r\n print \"Optimize table:\"\r\n print zip(range(len(values)), values)\r\n print \"-\"*20\r\n # end: DEBUG\r\n return values[threshold_weight]","title":""},{"docid":"72767fd2e4b2292e408a1180073fd6ca","score":"0.5027283","text":"def fitness(self, solution):\n return countHardConstraints(solution, self.data) + \\\n totalSoftConstraintsForTimetable(solution.getTimeTable(), self.data)","title":""},{"docid":"9adbed3af579a3cbf13ba2c5ad35bdf0","score":"0.50249964","text":"def wmape(actual: dict, target: dict, skus: dict):\n sum_all = 0\n sum_actual = 0\n\n for item in actual:\n if skus.get(item) is None:\n weight = 0\n # make a new entry\n else:\n if target.get(item) is None:\n weight = actual[item] * skus[item].unit_cost\n else:\n weight = (actual[item] - target[item]) * skus[item].unit_cost\n sum_all += weight\n sum_actual += actual[item] * skus[item].unit_cost\n if sum_actual == 0:\n\n final = 100\n\n else:\n final = sum_all / sum_actual\n return final","title":""},{"docid":"83089e544c6fdd9d0a8272f6a8965198","score":"0.50146395","text":"def traverse(program, desired_total_weight):\n # type: (Program, Optional[int]) -> int\n child_weights = {\n child: weight(programs[child])\n for child in program.children\n }\n counted_weights = Counter(child_weights.values())\n if len(counted_weights.values()) > 1:\n # Unbalanced children\n desired_weight = counted_weights.most_common(1)[0][0]\n odd_weight = counted_weights.most_common()[-1:][0][0]\n for child, child_weight in child_weights.items():\n if child_weight == odd_weight:\n return traverse(programs[child], desired_weight)\n\n else:\n # This node has all balanced children (or no children), therefore it is has the wrong weight\n return desired_total_weight - sum(child_weights.values())","title":""},{"docid":"05b9b5ec7b9d2cdeaa9ced0aee85a71e","score":"0.50134474","text":"def knapsack(value, size, capacity):\n #Sort value, size by decreasing efficiency\n n = len(value)\n a = [(value[i], size[i], i, value[i]/size[i]) for i in xrange(n)]\n dtype = [('value', float), ('size', float), ('index', int), ('ratio', float),]\n a = np.array(a, dtype=dtype)\n a = np.sort(a, order='ratio')\n a = a[::-1]\n sortedValues = [] \n sortedSize = []\n hashTable = []\n for i in xrange(n):\n sortedValues.append(a[i][0])\n sortedSize.append(a[i][1])\n hashTable.append(a[i][2])\n #Initialize the estimator in order to not clone the list \n estimator = Estimator(sortedValues, sortedSize)\n DP = {}\n #DP(i) = DP[weight] = [value, estimatedValue, items]\n #DP(i+1) = DP2 (in order to reduce memory cost)\n DP[0]= (0, estimator.bestRelaxedValue(0,capacity), [])\n bestSolution = (0, [0]*n)\n for i in xrange(n):\n DP2 = {}\n for s, v in DP.iteritems():\n for x in xrange(2):\n weight=s+x*sortedSize[i]\n #Cut invalid capacity\n if(weight<=capacity):\n newValue = v[0]+sortedValues[i]*x\n #Merge branches with same weight\n if (not weight in DP2 or newValue>DP2[weight][0]):\n if(weight in DP2):\n estimate = DP2[weight][1]-DP2[weight][0]+newValue\n else:\n estimate = estimator.bestRelaxedValue(i+1, capacity-weight)+newValue\n #Cut bad branches\n if(estimate>bestSolution[0]):\n DP2[weight] = (newValue, estimate, v[2]+[x])\n if(newValue>bestSolution[0]):\n bestSolution = (newValue, v[2]+[x]+[0]*(n-i-1))\n DP = DP2\n #Take the best\n final_items = np.zeros(n, 'int')\n for i in xrange(n):\n final_items[hashTable[i]]=bestSolution[1][i]\n #Retrive data\n return bestSolution[0], final_items","title":""},{"docid":"fc1959796697ee422e8f97f34e296830","score":"0.5002871","text":"def get_prob(paction, paction_set, plambda):\n soft = softmax(paction_set * np.ones(paction_set.shape) * plambda)\n return soft[np.where(paction_set == paction)][0]\n # return np.exp(plambda * paction) / (1e-4 + np.sum([np.exp(plambda * action) for action in paction_set]))","title":""},{"docid":"6f83903106edac1d8347e202004a3c62","score":"0.4999354","text":"def fit(self, mutation_rate, mutation_power, num_generations, n_players, roster_space, cap_limit, strategy, positions, ratings, salaries, verbose=True):\n self.initialize_population(n_players, roster_space, cap_limit, strategy, positions, ratings, salaries)\n for individual in self.population:\n individual.evaluation()\n \n self.sort_population()\n self.best_solution = self.population[0]\n self.solutions_list.append(self.best_solution.grade)\n if verbose:\n self.visualize_generation()\n \n for _ in tqdm(range(num_generations)):\n grade_sum = self.sum_grades()\n new_population = []\n \n for _ in range(0, self.pop_size, 2):\n parent1 = self.select_parent(grade_sum)\n parent2 = self.select_parent(grade_sum)\n \n children = self.population[parent1].crossover(self.population[parent2])\n \n new_population.append(children[0].mutation(mutation_rate, mutation_power))\n new_population.append(children[1].mutation(mutation_rate, mutation_power))\n \n self.population = list(new_population)\n for individual in self.population:\n individual.evaluation()\n self.sort_population()\n if verbose:\n self.visualize_generation()\n best = self.population[0]\n self.solutions_list.append(best.grade)\n self.best_individual(best)\n \n print('\\n\\nBest Solution: \\nG: {}\\nGrade: {}\\nCap Used: {}'.format(self.best_solution.generation, self.best_solution.grade, self.best_solution.cap_used))\n \n return self.best_solution.chromosome","title":""},{"docid":"91a02db8d97952d387a846ee22cc33a1","score":"0.49987155","text":"def fitness_func(self):\n\t\tfit = 0.0\n\t\tfor i in range(1, len(self.path)):\n\t\t\tfit += self.dist_func[self.dist_func_id](self.path[i - 1], self.path[i])\n\t\treturn fit","title":""},{"docid":"44d06c99889c4cee4e803810c78995e2","score":"0.49984458","text":"def __call__(self, population, target_population_size):\n if (len(population) % 2) > 0 or (target_population_size % 2) > 0:\n raise ValueError('Population must be of even length')\n\n half_pop_size = len(population) // 2\n if target_population_size > half_pop_size:\n raise ValueError('Target population size cannot be greater\\\n than the half of the population')\n\n offspring = population[half_pop_size:]\n population = population[:half_pop_size]\n\n for i in range(target_population_size // 2):\n parent_1 = population[i*2]\n parent_2 = population[i*2+1]\n child_1 = offspring[i*2]\n child_2 = offspring[i*2+1]\n\n dist_a = parent_1.distance(child_1) + parent_2.distance(child_2)\n dist_b = parent_1.distance(child_2) + parent_2.distance(child_1)\n if dist_a <= dist_b:\n population[i*2] = self._return_most_fit(child_1, parent_1)\n population[i*2+1] = self._return_most_fit(child_2, parent_2)\n else:\n population[i*2] = self._return_most_fit(child_2, parent_1)\n population[i*2+1] = self._return_most_fit(child_1, parent_2)\n\n return population","title":""},{"docid":"50bd3fdd8a86622aa80146c9e4a33b4b","score":"0.49978065","text":"def UCBWeight(self):\n return (self.value + \n self.heuristic/self.visits +\n sqrt(sqrt(self.parent.visits)/self.visits) * UCB_CONST)","title":""},{"docid":"75d95ae4fc2951d8da6d112d2700f3da","score":"0.49969524","text":"def knapsack(self, weight):\n if weight <= 0:\n # empty knapsack for empty set of items\n return Knapsack()\n # add an empty knapsack by default\n # it will be used if no items are added at the next step\n knapsacks = [Knapsack()]\n for item in self.items:\n # filter items which still can be put into the knapsack\n if item.weight <= weight:\n knapsack = self.knapsack(weight - item.weight).copy()\n knapsack.add(item)\n knapsacks.append(knapsack)\n return max(knapsacks)","title":""},{"docid":"4d7293e4aff695ac63ad295428bd2a9d","score":"0.49883604","text":"def getBestFitness(self):\n if self.__fitnessObject == None or self.__bestState == None:\n return None\n\n return self.__bestStateFitness","title":""},{"docid":"ed71439cfdf04880cb86125af3cfacaf","score":"0.4987931","text":"def sum_weight():\n return 1.0","title":""},{"docid":"3d505dda75b472bae626bb230bbe5754","score":"0.49828202","text":"def cost_function(self):\n hyp_minus_targets = self.hypothesis_targets()\n return ((1./(2.*self.nobs))*(hyp_minus_targets).T.dot(\n hyp_minus_targets) +\n self.regularizer_cost_function())","title":""},{"docid":"7526f854c31790fa895662fe2ae50b87","score":"0.49807456","text":"def calculatePercentCost(weight, bias,costfuncvalues,numiterations):\n\tif numiterations==0: #to handle initial case when starting algorithm\n\t\treturn 10\n\telse:\n\t\titerdiff=costfuncvalues[numiterations]-costfuncvalues[numiterations-1]\n\t\treturn math.fabs(iterdiff)*100/costfuncvalues[numiterations-1]","title":""},{"docid":"5a62ee78aacb41dce1130c4a55568892","score":"0.49804783","text":"def calculateCostFunction(weight,bias):\n\terrorvals=0.0\n\t#numvectors=featuredata.shape[0]\n\tfor i in range(numvectors):\n\t\tconfidence=1-outputdata[i]*(sum(featuredata[i]*weight)+bias)\n\t\terrorvals+=max(0,confidence)\n\treturn regparameter*errorvals+0.5*sum(weight**2)","title":""},{"docid":"cf3186beacb74c0503232eef515823ad","score":"0.4980308","text":"def fitness(self, individual):\r\n result_all = [] # all possible sets with a minimal number of banknotes\r\n for i in range(self.money_min, self.money_max + 1):\r\n q, s = MinSetGenerator(self.number_of_genes, individual, i).generate()\r\n # if exists another set with a minimal number of banknotes - return bad fitness value (100)\r\n if MinSetChecker(self.number_of_genes, individual, i, q).is_exist_another_solution():\r\n return 100.0\r\n else:\r\n result_all.append(s)\r\n result_unique = get_list_with_unique_values(result_all) # unique sets with a minimal number of banknotes\r\n probability = 0.0\r\n result_all_count = len(result_all)\r\n for item in result_unique:\r\n buff = result_all.count(item)\r\n up = buff * (buff - 1)\r\n down = result_all_count * (result_all_count - 1)\r\n probability += up / down\r\n probability = probability * 100\r\n return abs(self.target - probability)","title":""},{"docid":"65f2418cf6d64022145bd05f47659659","score":"0.49768674","text":"def cost_estimate(self):\n return 1.0","title":""}],"string":"[\n {\n \"docid\": \"06ec5d66c638fd314e2e509c2bef6eb2\",\n \"score\": \"0.64524364\",\n \"text\": \"def fitness(self, target):\\n target = list(target)\\n counter = 0\\n\\n for i in range(len(self.dna)):\\n if self.dna[i] == target[i]:\\n counter += 1\\n self.score = counter / len(target)\\n if self.score == 1.0:\\n print(\\\"DONE!\\\")\\n return counter / len(target)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb8cbdb859b0eb65135144a90079d6ab\",\n \"score\": \"0.6447148\",\n \"text\": \"def fitness(self):\\n # https://stackoverflow.com/a/38759133\\n weight: int = sum(list(map(lambda chromosome, item: chromosome * item, self.genotype,\\n [item[0] for item in self.population.task.list_of_items])))\\n size: int = sum(list(map(lambda chromosome, item: chromosome * item, self.genotype,\\n [item[1] for item in self.population.task.list_of_items])))\\n cost: int = sum(list(map(lambda chromosome, item: chromosome * item, self.genotype,\\n [item[2] for item in self.population.task.list_of_items])))\\n if weight <= self.population.task.w and size <= self.population.task.s:\\n return cost\\n else:\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bc947d88730a7c527da4d06f7a9caa0\",\n \"score\": \"0.64030707\",\n \"text\": \"def fitness(self, chromosom):\\n\\n if capacity <sum(self.weights*chromosom):\\n return 0\\n return sum(self.costs*chromosom)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ace06689db17867ca2596291fad203b\",\n \"score\": \"0.6320283\",\n \"text\": \"def fitness(self):\\n if self.weight <= 0:\\n return 0\\n else:\\n return self.q(+1, self.age, self.params[\\\"a_half\\\"], self.params[\\\"phi_age\\\"]) * self.q(\\n -1, self.weight, self.params[\\\"w_half\\\"], self.params[\\\"phi_weight\\\"]\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"faa7101b315fa8581c937d597691ce24\",\n \"score\": \"0.6005293\",\n \"text\": \"def weighted_choice(population):\\n weight_total = sum((fitness[1] for fitness in population))\\n random_pick = random.uniform(0, weight_total)\\n for indiviudal, fitness in population:\\n if random_pick < fitness:\\n return indiviudal\\n random_pick = random_pick - fitness\\n return indiviudal\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3393196e4028c5e9769c62608fa57458\",\n \"score\": \"0.5978832\",\n \"text\": \"def get_best_fitness(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4344504d80b88a493067f4f135bb493\",\n \"score\": \"0.5957254\",\n \"text\": \"def fitness(chromosome, pdvs, max_cap, output=False):\\n\\tcost = pdvs[chromosome[0]].get_visit_cost()\\n\\t# If just visiting the first pdv exceeds the max cap\\n\\tif cost > max_cap:\\n\\t\\tif output:\\n\\t\\t\\treturn 0, cost, 0\\n\\t\\treturn 0\\n\\tvalue = pdvs[chromosome[0]].get_value()\\n\\tfor i in range(len(chromosome)-1):\\n\\t\\tcost_aux = cost + pdvs[chromosome[i]].get_travel_cost(pdvs[chromosome[i+1]].get_code()) +\\\\\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tpdvs[chromosome[i+1]].get_visit_cost()\\n\\t\\tif cost_aux > max_cap:\\n\\t\\t\\tif output:\\n\\t\\t\\t\\treturn value, cost, i\\n\\t\\t\\treturn value\\n\\t\\telse:\\n\\t\\t\\tcost = cost_aux\\n\\t\\t\\tvalue += pdvs[chromosome[i]].get_value()\\n\\treturn value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f1102c93b5c5994f9377357a9a68a70\",\n \"score\": \"0.5911225\",\n \"text\": \"def compute_fitness(population, target, beta):\\n #store the fitness values of each cell\\n tot = []\\n #iterate through each cell\\n for cell in population:\\n \\n #calculate the bp_distance of each chromosome using the cell's structure\\n bp_distance_1 = bp_distance(cell.structure_1, target)\\n #bp_distance_2 = bp_distance(cell.structure_2[-1], target)\\n \\n #use the bp_distances and the above fitness equation to calculate the fitness of each chromosome\\n fitness_1 = math.exp(beta * float(bp_distance_1) / len(target))\\n #fitness_2 = math.exp(beta * float(bp_distance_2) / len(target))\\n \\n #get the fitness of the whole cell by multiplying the fitnesses of each chromosome\\n cell.fitness = fitness_1 #* fitness_2\\n \\n #store the bp_distance of each chromosome.\\n cell.bp_distance_1 = bp_distance_1\\n #cell.bp_distance_2 = bp_distance_2\\n \\n \\n #add the cell's fitness value to the list of all fitness values (used for normalization later)\\n tot.append(cell.fitness)\\n \\n #normalization factor is sum of all fitness values in population\\n norm = np.sum(tot)\\n #divide all fitness values by the normalization factor.\\n for cell in population:\\n cell.fitness = cell.fitness / norm\\n \\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e6d42ff546e0be467d7478248d405b6\",\n \"score\": \"0.5870904\",\n \"text\": \"def dp_make_weight(egg_weights, target_weight, memo = {}):\\n\\n # If one of our egg weights matches the target so we can skip the calculation\\n if target_weight in egg_weights:\\n return 1\\n\\n # Otherwise work from the ground up!\\n for weight in range(1, target_weight + 1):\\n if weight in egg_weights:\\n # Catch here for an incorrectly (or not at all) initialized memo\\n # dictionary. Because we're starting from the ground and working up,\\n # if we don't have memo[1] then a KeyError will be thrown\\n memo[weight] = 1\\n continue # We can also skip the calculation\\n\\n # Start with the worst case of all weight 1 eggs\\n neggs = weight\\n\\n for egg in egg_weights:\\n if egg > weight:\\n # Skip eggs that are heavier than the weight we're calculating\\n continue\\n \\n # See if the number of eggs needed for the current weight - the \\n # current egg weight is less than the currently calculated number of\\n # eggs. If it is, then our new number of eggs is that number + 1, \\n # equivalent to adding the current egg to the previously calculated\\n # weight\\n if memo[weight - egg] + 1 < neggs: \\n neggs = memo[weight - egg] + 1\\n \\n # Store the current calculation for later use, either as a comparison\\n # point or as the optimal solution for weight\\n memo[weight] = neggs\\n \\n # Once we get here we've mapped all of the optimal egg distributions to our dictionary\\n return memo[target_weight]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f0986473a76c844f8151883c6f835f6\",\n \"score\": \"0.5837258\",\n \"text\": \"def fitness(self, p):\\n \\n # f = 1.0 - self.params[\\\"selection_strength\\\"] + \\\\\\n # self.params[\\\"selection_strength\\\"] * p\\n # assert f >= 0.0\\n f = math.exp(self.params[\\\"selection_strength\\\"] * p)\\n return f\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e10e829bd4c663cff3c678a9963ff932\",\n \"score\": \"0.58051306\",\n \"text\": \"def dp_make_weight(egg_weights, target_weight, memo = {0:0}):\\n if target_weight == 0:\\n return memo[0]\\n try:\\n return memo[target_weight]\\n except KeyError:\\n result = []\\n for egg in egg_weights:\\n new_target_weight = target_weight - egg\\n if new_target_weight >= 0:\\n result.append(1 + dp_make_weight(egg_weights,\\n new_target_weight))\\n memo[target_weight] = min(result)\\n return memo[target_weight]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39c3e4c539b362428f2bfd8b18092057\",\n \"score\": \"0.57456654\",\n \"text\": \"def target_distribution(q):\\n weight = q ** 2 / q.sum(0)\\n return (weight.T / weight.sum(1)).T\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f244fd3456b51b060a18cfab4914418\",\n \"score\": \"0.57450634\",\n \"text\": \"def fitness(self):\\n return self.quantization()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbb8e236d082b6a0f87014be1fecee38\",\n \"score\": \"0.5710339\",\n \"text\": \"def fitness(self):\\n cost = []\\n for machine_index in range(self.machines):\\n machine_cost = 0\\n for task_index, task_line in enumerate(self.solution):\\n if task_line[machine_index] == 1:\\n machine_cost += self.problem.cost_matrix[task_index][machine_index]\\n cost.append(machine_cost)\\n return max(cost)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19f802bd11896fe27c3bf77773bbd69b\",\n \"score\": \"0.5706405\",\n \"text\": \"def sample_best_permutation(key, coupling, cost, num_trials=10):\\n vec_sample_permutation = jax.vmap(\\n sample_permutation, in_axes=(0, None), out_axes=0)\\n key = jax.random.split(key, num_trials)\\n perms = vec_sample_permutation(key, coupling)\\n\\n # Pick the permutation with minimal ot cost\\n ot = jnp.sum(perms * cost[jnp.newaxis, :, :], axis=(1, 2))\\n min_idx = jnp.argmin(ot)\\n out_perm = perms[min_idx]\\n return out_perm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d5349085bf0dad92fad15536da61234\",\n \"score\": \"0.5654172\",\n \"text\": \"def calculateFitness(individual):\\n fitness = 0\\n for i in range(len(individual)):\\n if individual[i] == target[i]:\\n fitness += 1\\n\\n return fitness\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c87708853a50e9ad9448eb6b768f8ad\",\n \"score\": \"0.5603705\",\n \"text\": \"def get_optimal_value(capacity, weights, values):\\n value = 0.\\n # Get a sorted list of [value/weight, value, weight]\\n values = sorted([[i[0] / i[1]] + list(i) for i in zip(values, weights)], reverse=True)\\n\\n for i in values:\\n if not capacity:\\n return value\\n a = min((i[2], capacity))\\n value += a * i[0]\\n capacity -= a\\n\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbfac81f8da19defe9071cf3e180ef36\",\n \"score\": \"0.5581835\",\n \"text\": \"def findMax(items, weights, w):\\n # key-value pairs where key is a tuple(size,total value)\\n lookup = dict()\\n maximum = 0 # maximum total weight\\n \\n def isPresent(size,k,total):\\n nonlocal maximum\\n # key = (size,total)\\n key = (size,k)\\n \\n if key in lookup:\\n return lookup[key]\\n if size == 0:\\n if k <= w:\\n maximum = max(maximum, total)\\n lookup[key] = total\\n else:\\n lookup[key] = 0\\n return lookup[key]\\n elif k > w:\\n return 0\\n maximum = max(maximum, total)\\n # Either an item is included in the optimal subset or it's not.\\n # We'll try both and cache the boolean result whether \\n # the total weight <= weight capacity\\n inc = isPresent(size-1, k+weights[size-1], total+items[size-1])\\n exc = isPresent(size-1, k, total)\\n lookup[key] = max(inc, exc)\\n return lookup[key]\\n \\n _ = isPresent(len(items),0,0)\\n return maximum\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4303105d53d8d8a3592e440aba996467\",\n \"score\": \"0.55802137\",\n \"text\": \"def best_in_population(population):\\n return max(population, key=fitness)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a47bac0435144c4a3261e884992eca7d\",\n \"score\": \"0.55651927\",\n \"text\": \"def p_accept(cur_fitness, candidate_fitness, Tem):\\n return math.exp(-abs(candidate_fitness - cur_fitness) / Tem)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f142c5ba12bfc1bcc10993fb13e589bd\",\n \"score\": \"0.5555438\",\n \"text\": \"def ideal_price(self):\\n return calc_price(self.target_weight)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"679b0e5d721968eaa3ba43294edefcb4\",\n \"score\": \"0.5522592\",\n \"text\": \"def get_best_individual_fitness(self):\\n return np.max(self.fitness)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"68236df6fd83528349483cafd03d3889\",\n \"score\": \"0.5517962\",\n \"text\": \"def fitness(self):\\n return self._fitness\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8adb2f0f1a880319d99d010840ee62a3\",\n \"score\": \"0.5502526\",\n \"text\": \"def evalutate_fitness(individual):\\n difference = abs(TARGET - decode(individual))\\n fitness = 1000 - difference\\n return fitness\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0255177fed4a8a88a71bcc4b7bf250cd\",\n \"score\": \"0.5472982\",\n \"text\": \"def init_solution(weight_cost, max_weight):\\n solution = []\\n allowed_positions = list(range(len(weight_cost)))\\n while len(allowed_positions) > 0:\\n idx = random.randint(0, len(allowed_positions) - 1)\\n selected_position = allowed_positions.pop(idx)\\n if get_cost_and_weight_of_knapsack(solution + [selected_position], weight_cost)[1] <= max_weight:\\n solution.append(selected_position)\\n else:\\n break\\n return solution\",\n \"title\": \"\"\n },\n {\n \"docid\": \"372ba46fa42a482c30d551c81f5bdbe2\",\n \"score\": \"0.54439515\",\n \"text\": \"def candidate(self) -> Optional[Knapsack]:\\n target_index: int = random.randint(1, self._total_importance)\\n importance_accumulator: int = 0\\n for knapsack in self._knapsacks: # type: Knapsack\\n importance_accumulator += knapsack.importance()\\n if importance_accumulator >= target_index:\\n return knapsack\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fba8b94a3b52d5372f24d799671f71b8\",\n \"score\": \"0.5418524\",\n \"text\": \"def simulate(solution, weight_cost, max_weight, init_temp, steps):\\n temperature = init_temp\\n\\n best = solution\\n best_cost = get_cost_and_weight_of_knapsack(solution, weight_cost)[0]\\n\\n current_sol = solution\\n while True:\\n current_cost = get_cost_and_weight_of_knapsack(best, weight_cost)[0]\\n for i in range(0, steps):\\n moves = moveto(current_sol, weight_cost, max_weight)\\n idx = random.randint(0, len(moves) - 1)\\n random_move = moves[idx]\\n delta = get_cost_and_weight_of_knapsack(random_move, weight_cost)[0] - best_cost\\n if delta > 0:\\n best = random_move\\n best_cost = get_cost_and_weight_of_knapsack(best, weight_cost)[0]\\n current_sol = random_move\\n else:\\n if math.exp(delta / float(temperature)) > random.random():\\n current_sol = random_move\\n\\n temperature *= ALPHA\\n if current_cost >= best_cost or temperature <= 0:\\n break\\n return best_cost, best\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4dd8b2ac05206e6918c75ce3e72e7d2d\",\n \"score\": \"0.53962725\",\n \"text\": \"def maxProfitMatching(weights, _flip=False): # minimum cost\\n global U,V,S,T,Mu,Mv,lu,lv, minSlack, w\\n \\n w = np.array(weights)\\n if _flip: \\n w = -w # more profit is equivalent to less cost\\n \\n n = len(w)\\n U = V = range(n)\\n lu = [ max([w[u][v] for v in V]) for u in U] # start with trivial labels\\n lv = [ 0 for v in V]\\n Mu = {} # start with empty matching\\n Mv = {}\\n while len(Mu)<n:\\n free = [u for u in V if u not in Mu] # choose free vertex u0\\n u0 = free[0]\\n S = {u0: True} # grow tree from u0 on\\n T = {}\\n minSlack = [[slack(u0,v), u0] for v in V]\\n augment()\\n \\n # val. of matching is total edge weight \\n val = sum(lu)+sum(lv)\\n \\n return (Mu, Mv, 0.0-val if _flip else val)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33e038d980f0eda5c107f004f92531c8\",\n \"score\": \"0.53907925\",\n \"text\": \"def best_reward(self, observation, sess, weighted=False):\\n if weighted:\\n return self.weighted_choice(observation, sess)[1]\\n else:\\n return self.best_choice(observation, sess)[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd69c9445abd5f8da72cdcbeb2f81d2c\",\n \"score\": \"0.53564787\",\n \"text\": \"def cost(result, target):\\n return - (result - target)**2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e92f1431ba49ca0981e2a740058991f\",\n \"score\": \"0.53341436\",\n \"text\": \"def knapsack_max_value(knapsack_max_weight, items):\\n\\n # Initialize a lookup table to store the maximum value ($)\\n memoization = [0] * knapsack_max_weight\\n\\n # Iterate down the given list\\n for item in items:\\n\\n # The \\\"capcacity\\\" represents amount of remaining capacity (kg) of knapsack at a given moment.\\n for capacity in range(knapsack_max_weight - 1, item.weight - 2, -1):\\n not_pick_value = memoization[capacity]\\n pick_value = item.value + (memoization[capacity - item.weight] if capacity - item.weight > 0 else 0)\\n\\n memoization[capacity] = max(pick_value, not_pick_value)\\n\\n return memoization[-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0dc55a6f5420b780db691b1203faa84b\",\n \"score\": \"0.5330018\",\n \"text\": \"def calculate_probabilities(self):\\n max_fit = max(self.fitness)\\n self.prob = (0.9 * (self.fitness / max_fit)) + 0.1\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b524a72147ff94fed6f1576ccb67063\",\n \"score\": \"0.53253984\",\n \"text\": \"def fitness(plan, goal, threshold, model_perf_func, **model_parameters):\\n plan_perf = model_perf_func(plan, **model_parameters)\\n total_workload = sum(plan)\\n fitness_wo_load = 1 - (abs(goal - plan_perf))\\n fitness_with_load = fitness_wo_load - WORKLOAD_FACTOR * total_workload\\n approx_deviation = abs(100 - (plan_perf / goal) * 100)\\n se_threshold = approx_deviation <= threshold\\n return fitness_with_load, se_threshold\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38601a08c0b79f044ceccb1700674274\",\n \"score\": \"0.5321909\",\n \"text\": \"def _getWeight(self, target_node):\\n\\t\\treturn self.connectedTo[target_node]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70564e31666dd76dc963891c41a9a69a\",\n \"score\": \"0.53175634\",\n \"text\": \"def dp(length, target):\\n if length <= 0 or target <= 0:\\n return \\\"\\\" if length == target == 0 else None\\n if min_cost * length > target or max_cost * length < target:\\n return None\\n for num, cost in sorted_num_cost:\\n res = dp(length - 1, target - cost)\\n if res is not None:\\n return num + res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c461096adf5f6c93eb6677df113f5d48\",\n \"score\": \"0.5312875\",\n \"text\": \"def appetite_checker(eaten_weight, desired_weight, last_kill):\\r\\n if eaten_weight + last_kill > desired_weight:\\r\\n appetite_weight = desired_weight - eaten_weight\\r\\n return appetite_weight\\r\\n else:\\r\\n return last_kill\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1eac694c1f3b3e35cdd3063e7ce47b33\",\n \"score\": \"0.5311565\",\n \"text\": \"def food_fitness(self):\\n return self.food_ph\",\n \"title\": \"\"\n },\n {\n \"docid\": \"262bdede736bee51703bec370ef82b13\",\n \"score\": \"0.5294326\",\n \"text\": \"def gen_loss_weight(target):\\n positive_num = torch.sum(target, 1)\\n class_num = torch.FloatTensor([target.size(1)]).cuda(gpuID) if args.cuda else torch.Tensor([target.size(1)])\\n negative_num = class_num - positive_num\\n weight = torch.div(negative_num, positive_num)\\n weight = weight.expand((target.size(0), target.size(1)))\\n return torch.mul(weight, target)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c883118597d4e4579e376997fcc5bea\",\n \"score\": \"0.5290734\",\n \"text\": \"def get_max_weight(self):\\n if self.weights.size == 0:\\n return 0\\n return np.amax(np.abs(self.weights))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b935f891e37f27743a39b07410fa3ad\",\n \"score\": \"0.5277587\",\n \"text\": \"def evaluate(self, ind, **kwargs):\\n \\n p, d = ind.phenotype, {'pred': pred, 'succ': succ}\\n exec(p, d)\\n\\n # this is the program's output: a generator\\n s = d['XXX_output_XXX']\\n\\n # Truncate the generator and convert to list\\n s = list(truncate(len(self.target), s))\\n \\n # Set target\\n t = self.target\\n\\n # various weightings of four aspects of our fitness. the formula is:\\n # fitness = gamma * dist + (1 - gamma) * length\\n # where dist = alpha * lev_dist(t, s) + (1 - alpha) * dtw_dist(t, s)\\n # and length = beta * proglen(t) + (1 - beta) * compressibility(t)\\n # but when any of alpha, beta and gamma is 0 or 1, we can save some\\n # calculation:\\n\\n if self.gamma > 0.0:\\n if self.alpha > 0.0:\\n lev_dist_v = lev_dist(t, s)\\n else:\\n lev_dist_v = 0.0\\n if self.alpha < 1.0:\\n dtw_dist_v = dtw_dist(t, s)\\n else:\\n dtw_dist_v = 0.0\\n dist_v = self.alpha * lev_dist_v + (1 - self.alpha) * dtw_dist_v\\n else:\\n dist_v = 0.0\\n\\n if self.gamma < 1.0:\\n if self.beta > 0.0:\\n proglen_v = proglen(p)\\n else:\\n proglen_v = 0.0\\n if self.beta < 1.0:\\n compressibility_v = compressibility(p)\\n else:\\n compressibility_v = 0.0\\n length_v = self.beta * proglen_v + (1 - self.beta) * \\\\\\n compressibility_v\\n else:\\n length_v = 0.0\\n\\n return self.gamma * dist_v + (1 - self.gamma) * length_v\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e887abcb7fd6ea41bd91ce4a89c8d4d\",\n \"score\": \"0.52688587\",\n \"text\": \"def maximize(self, positive_only):\\n\\t\\tself.max_profit = 0\\n\\t\\tself.max_solution = [0]*self.nb_items\\n\\t\\tself.ids = list(range(self.nb_items))\\n\\n\\t\\t\\\"\\\"\\\"# easy case condition (optional):\\n\\t\\t# the sum of weights is less or equal to capacity\\n\\t\\tdone = False\\n\\t\\tcumul_weights = 0\\n\\t\\ti = 0\\n\\t\\twhile cumul_weights < self.capacity:\\n\\t\\t\\tcumul_weights += self.profits[i]\\n\\t\\t\\ti += 1\\n\\t\\tif i == self.length:\\n\\t\\t\\tself.max_profit = sum(self.profits)\\n\\t\\t\\tself.max_solution = [1]*self.len\\n\\t\\t\\tdone = True\\n\\t\\t\\treturn done\\\"\\\"\\\"\\n\\n\\t\\tself.Nzero_ids = []\\n\\t\\tself.None_ids = []\\n\\t\\tself.Nminus_ids = []\\n\\t\\tself.Nplus_ids = self.ids\\n\\n\\t\\tif not positive_only:\\n\\t\\t\\t# 2nd condition: Non positive values in weight and profit\\n\\t\\t\\tself.Nzero_ids = []\\n\\t\\t\\tself.None_ids = []\\n\\t\\t\\tself.Nminus_ids = []\\n\\t\\t\\tself.Nplus_ids = []\\n\\t\\t\\tfor i in self.ids:\\n\\t\\t\\t\\t# Group N0\\n\\t\\t\\t\\tif self.weights[i] >= 0 and self.profits[i] <= 0:\\n\\t\\t\\t\\t\\tself.max_solution[i] = 0\\n\\t\\t\\t\\t\\tself.Nzero_ids.append(i)\\n\\t\\t\\t\\t# Group N1\\n\\t\\t\\t\\telif self.weights[i] <= 0 and self.profits[i] >= 0:\\n\\t\\t\\t\\t\\tself.max_solution[i] = 1\\n\\t\\t\\t\\t\\tself.max_profit += self.profits[i]\\n\\t\\t\\t\\t\\tself.capacity -= self.weights[i]\\n\\t\\t\\t\\t\\tself.None_ids.append(i)\\n\\t\\t\\t\\t# Group N-\\n\\t\\t\\t\\telif self.weights[i] < 0 and self.profits[i] < 0:\\n\\t\\t\\t\\t\\tself.max_profit += self.profits[i]\\n\\t\\t\\t\\t\\tself.capacity -= self.weights[i]\\n\\t\\t\\t\\t\\t# then change sign\\n\\t\\t\\t\\t\\tself.weights[i] *= -1\\n\\t\\t\\t\\t\\tself.profits[i] *= -1\\n\\n\\t\\t\\t\\t\\tself.Nminus_ids.append(i)\\n\\t\\t\\t\\t# Group N+\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tself.Nplus_ids.append(i)\\n\\n\\t\\t\\tself.ids = self.Nminus_ids + self.Nplus_ids\\n\\t\\t\\tself.len = len(self.ids)# self.len = len(self.ids) all time\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb1f21b4a0bee4b1c6e0957e7a3f6f77\",\n \"score\": \"0.5267604\",\n \"text\": \"def next(self):\\n mean = np.nanmean(self.population_, axis=0) # column mean.\\n rs = self.random_state\\n\\n teacher = np.argmin(self.fitness_) # select teacher\\n\\n # teaching phase\\n for i in self.pidx:\\n T_F = rs.randint(1, 3) # teaching factor is randomly 1 or 2.\\n r_i = rs.rand(self.m) # multiplier also random.\\n\\n new_solution = self.population_[i] + (r_i * (teacher - (T_F * mean)))\\n new_solution = np.minimum(np.maximum(new_solution, self.lower_bound), self.upper_bound)\\n\\n new_fitness = self.f(new_solution)\\n\\n if new_fitness < self.fitness_[i]:\\n self.population_[i] = new_solution\\n self.fitness_[i] = new_fitness\\n\\n # learning phase\\n for i in self.pidx:\\n\\n j = rs.choice(self.pidx[:i] + self.pidx[(i + 1):], 1) # pick another random i!=j\\n r_i = rs.rand(self.m)\\n\\n if self.fitness_[i] < self.fitness_[j]:\\n new_solution = self.population_[i] + r_i * (self.population_[i] - self.population_[j]).flatten()\\n else:\\n new_solution = self.population_[i] + r_i * (self.population_[j] - self.population_[i]).flatten()\\n\\n new_solution = np.minimum(np.maximum(new_solution, self.lower_bound), self.upper_bound)\\n new_fitness = self.f(new_solution)\\n \\n if new_fitness < self.fitness_[i]:\\n self.population_[i] = new_solution\\n self.fitness_[i] = new_fitness\\n\\n self.bestidx_ = np.argmin(self.fitness_) # update details\\n self.bestcosts_.append(self.fitness_[self.bestidx_])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46fa6c9726b02db0e4e2d3d295dac601\",\n \"score\": \"0.5267358\",\n \"text\": \"def knapsack(items, capacity):\\n if len(items) == 0 or capacity == 0:\\n return 0\\n \\n first_item_weight = items[0][1]\\n first_item_value = items[0][2]\\n\\n value_with = first_item_value + knapsack(items[1:], capacity-first_item_weight)\\n value_without = knapsack(items[1:], capacity)\\n\\n if first_item_weight > capacity:\\n return value_without\\n\\n return max(value_with, value_without)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e00a6496dab526c98b8cf611571e9d4\",\n \"score\": \"0.52667785\",\n \"text\": \"def get_fitness(self):\\n \\n return self.fitness_current\",\n \"title\": \"\"\n },\n {\n \"docid\": \"877bad30ba8977b0ff064d6148551370\",\n \"score\": \"0.52569634\",\n \"text\": \"def get_fitness(self):\\r\\n return self.fitness\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af6960acd11f5ceb213a679119341f91\",\n \"score\": \"0.5253209\",\n \"text\": \"def max_score(self):\\n return self.weight\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf55c11f25376dff2cd0a03b2e4fb1f6\",\n \"score\": \"0.5226057\",\n \"text\": \"def calculate_weights(self, target):\\n if len(target.shape) == 3:\\n hist = np.sum(target, axis=(1, 2)) * 1.0 / target.sum()\\n else:\\n hist = np.sum(target, axis=(0, 2, 3)) * 1.0 / target.sum()\\n if self.norm:\\n hist = ((hist != 0) * self.upper_bound * (1 / hist)) + 1\\n else:\\n hist = ((hist != 0) * self.upper_bound * (1 - hist)) + 1\\n return hist[:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"850a4529320b483dead301244b36fb76\",\n \"score\": \"0.5221587\",\n \"text\": \"def fitness(individual: IndividualType, fleet: Fleet, indices: IndicesType, starting_points: StartingPointsType,\\n weights=(1.0, 1.0, 1.0, 1.0), penalization_constant=500000, allowed_charging_operations=2):\\n\\n # Decode\\n routes = decode(individual, indices, starting_points, allowed_charging_operations=allowed_charging_operations)\\n\\n # Set routes\\n fleet.set_routes_of_vehicles(routes)\\n\\n # Get optimization vector\\n fleet.create_optimization_vector()\\n\\n # Cost\\n costs = np.array(fleet.cost_function())\\n\\n # Check if the solution is feasible\\n feasible, penalization = fleet.feasible()\\n\\n # penalization\\n if not feasible:\\n penalization += penalization_constant\\n\\n fit = np.dot(costs, np.asarray(weights)) + penalization\\n\\n return fit, feasible\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45ed425676ce4349284d0cd8c8c89653\",\n \"score\": \"0.5218327\",\n \"text\": \"def get_cost_and_weight_of_knapsack(solution, weight_cost):\\n cost, weight = 0, 0\\n for item in solution:\\n weight += weight_cost[item][0]\\n cost += weight_cost[item][1]\\n return cost, weight\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38e0ac3bb862bbda041bb5faf243f653\",\n \"score\": \"0.5199759\",\n \"text\": \"def get_fitness(self, itr, all_paths):\\n fitness = np.zeros(self.pop_size)\\n for p in range(self.pop_size):\\n rewards = all_paths[p][\\\"rewards\\\"]\\n valid_rewards = rewards * all_paths[p][\\\"valids\\\"]\\n path_rewards = np.sum(valid_rewards, -1)\\n if self.f_F == \\\"max\\\":\\n fitness[p] = np.max(path_rewards)\\n else:\\n fitness[p] = np.mean(path_rewards)\\n return fitness\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49f1c2da0a07de0aca79731d6940b5ef\",\n \"score\": \"0.51916695\",\n \"text\": \"def weight(weights):\\n total = sum(weights)\\n\\n p = SEED.random()\\n acc = 0\\n for i, w in enumerate(weights):\\n acc += w / total\\n if p < acc:\\n return i\\n return len(weights) - 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f650fb371db764576b6c177889746246\",\n \"score\": \"0.51804346\",\n \"text\": \"def dynamic_programming_solver(items, weights, values, capacity, n_items):\\n\\n rows, cols = (n_items, capacity+1)\\n\\n # create a two dimensional array for Memoization\\n M = [[0 for i in range(cols)] for j in range(rows)]\\n\\n for row in range(rows):\\n for col in range(cols):\\n if weights[row] > col:\\n M[row][col] = M[row - 1][col]\\n else:\\n x = max(M[row - 1][col], M[row - 1]\\n [col - weights[row]] + values[row])\\n M[row][col] = x\\n\\n i = rows - 1\\n j = cols - 1\\n while M[i][j] == M[i][j - 1]:\\n j -= 1\\n\\n i = rows - 1\\n best_items = []\\n best_items_index = []\\n\\n while j > 0:\\n while i > 0 and M[i][j] == M[i - 1][j]:\\n i -= 1\\n j = j - weights[i]\\n if j >= 0:\\n best_items.append(items[i])\\n best_items_index.append(i)\\n i -= 1\\n\\n cost = 0\\n for i in best_items_index:\\n cost += weights[i]\\n\\n cost = cost / 100\\n max_value = M[rows - 1][cols - 1]/10000\\n return cost, max_value, best_items\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09b0bad7b42c617939dc329fbcb34eec\",\n \"score\": \"0.51780933\",\n \"text\": \"def heuristic(cap, values, weights, epsilon=0.2):\\n n = len(values)\\n vmax = max(values)\\n\\n m = epsilon * vmax / n\\n values = [int(v // m) for v in values]\\n\\n return knapsack(cap, values, weights) * m\",\n \"title\": \"\"\n },\n {\n \"docid\": \"278a0d441a164aeb385f047042d34923\",\n \"score\": \"0.5158766\",\n \"text\": \"def get_optimal_knapsack_value(W, items):\\n n = len(items)\\n knapsacks = dict()\\n for i in range(n, -1, -1):\\n for j in range(W + 1):\\n if i == n:\\n knapsacks[(i, j)] = 0\\n continue\\n if j == 0:\\n knapsacks[(i, j)] = 0\\n continue\\n weight, value = items[i]\\n if weight <= j:\\n knapsacks[(i, j)] = max(\\n knapsacks[(i + 1, j)],\\n knapsacks[(i + 1, j - weight)] + value)\\n else:\\n knapsacks[(i, j)] = knapsacks[(i + 1, j)]\\n return knapsacks[(0, W)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63e5d1cabe2557d79e2650518616dead\",\n \"score\": \"0.5152632\",\n \"text\": \"def knapsack(items, max_weight):\\n if max_weight < 0:\\n raise ValueError(f'max_weight cannot be < 0 (got {max_weight})')\\n if not items or not max_weight:\\n return 0\\n x = [[0 for _ in range(max_weight + 1)] for _ in range(len(items) + 1)]\\n for i, (value, weight) in enumerate(items, start=1):\\n if weight < 0:\\n raise ValueError(f'item weights cannot be < 0 (got {weight})')\\n for w in range(max_weight + 1):\\n if weight > w:\\n x[i][w] = x[i-1][w]\\n else:\\n x[i][w] = max(x[i-1][w], x[i-1][w-weight] + value)\\n return x[len(items)][max_weight]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50f044840c0e88e880ac23284b0cb4b4\",\n \"score\": \"0.51509476\",\n \"text\": \"def fittest(self, population, fit=None):\\n if fit==None:\\n fit = pop_fitness(population, self.costs, self.weights)\\n max_fit = max(fit)\\n return population[fit.index(max_fit)], max_fit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0644b80f3c83bdc648628dea41e5756f\",\n \"score\": \"0.5150334\",\n \"text\": \"def fitness(self):\\n if self._fitness is None:\\n clauses, passed = self._equation.check(self)\\n self._fitness = clauses / self._equation.clauses\\n self._valid = passed\\n return self._fitness\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e969b467f182fcd49d65c74ba585a3e\",\n \"score\": \"0.5146512\",\n \"text\": \"def fitness_function(prediction, true_class=None):\\n return -get_probability_for_class(prediction, true_class)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88e541536b40078699b0ce7ebfabf029\",\n \"score\": \"0.51276875\",\n \"text\": \"def get_best_individual(self):\\n return self.pop[np.argmax(self.fitness), :]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c864318ff5ab79fb014c69409fbc43d\",\n \"score\": \"0.5124056\",\n \"text\": \"def fitness(self, chromosom):\\n #TODO \\n return sum(chromosom)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"450842f6d0b7bf8971b920c2234a54a3\",\n \"score\": \"0.5116017\",\n \"text\": \"def get_max_fitness(self):\\n return max([individual.fitness() for individual in self.individuals])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afb2312c3f891bacce85a6be8e356f7f\",\n \"score\": \"0.5115378\",\n \"text\": \"def fitness_change(self):\\r\\n phi_age = self.parameters[\\\"phi_age\\\"]\\r\\n a_half = self.parameters[\\\"a_half\\\"]\\r\\n phi_weight = self.parameters[\\\"phi_weight\\\"]\\r\\n w_half = self.parameters[\\\"w_half\\\"]\\r\\n\\r\\n if self.weight > 0:\\r\\n self.fitness = ((1 /\\r\\n (1 + exp(phi_age *\\r\\n (self.age - a_half)))) *\\r\\n (1 / (1 + exp(-(phi_weight *\\r\\n (self.weight - w_half))))))\\r\\n else:\\r\\n self.fitness = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"407488fe9366c3aec17c9e350dbd0967\",\n \"score\": \"0.5108754\",\n \"text\": \"def calculate_fitness(self):\\n\\t\\tval = self.decode(self.kromosom)\\n\\t\\tx = val[0]\\n\\t\\ty = val[1]\\n\\t\\tf= 3*(x**2) + 2*(y**2) - (4*x) + (y*1.0/2)\\n\\t\\treturn 1 * 1.0 / (f + 0.01)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2158492bd0235a7a948b7b5bd379cdc5\",\n \"score\": \"0.51000714\",\n \"text\": \"def next_best(target_items):\\n\\n # track the set of all items already learnt\\n ALREADY_LEARNT = set()\\n\\n # a dictionary mapping targets to the set of prerequisite items\\n IN_TARGET = {}\\n\\n # a dictionary mapping targets to a set of items still not learnt\\n MISSING_IN_TARGET = {}\\n\\n # a dictionary mapping items to a set of targets the items are needed for\\n # and are missing from\\n TARGETS_MISSING = collections.defaultdict(set)\\n\\n ## fill the dictionaries with initial data based on the input\\n\\n for target, items in target_items.items():\\n IN_TARGET[target] = set(items)\\n MISSING_IN_TARGET[target] = set(items)\\n for item in items:\\n TARGETS_MISSING[item].add(target)\\n\\n while True:\\n\\n # for each item, a score of how bad it is that it is missing\\n MISSING_ITEMS = collections.defaultdict(int)\\n\\n for missing in MISSING_IN_TARGET.values():\\n for item in missing:\\n # if item is only missing item for a target, add 1/2 to its score\\n # if item is 1 of 2 missing items for a target, add 1/4\\n # if item is 1 of 3 missing items for a target, add 1/8\\n # and so on...\\n MISSING_ITEMS[item] += 1. / (2 ** len(missing))\\n\\n # stop if there are no missing items\\n if not MISSING_ITEMS:\\n break\\n\\n # otherwise the next item to learn is the one with the highest score\\n next_item = sorted(MISSING_ITEMS, key=MISSING_ITEMS.get)[-1]\\n\\n # for each target missing that item, remove the item\\n for target in TARGETS_MISSING[next_item]:\\n MISSING_IN_TARGET[target].remove(next_item)\\n\\n # if the target is now missing no items...\\n if len(MISSING_IN_TARGET[target]) == 0:\\n\\n # calculate what is new to learn for that target\\n items_to_learn = IN_TARGET[target] - ALREADY_LEARNT\\n\\n yield target, items_to_learn\\n\\n # remove from missing in that target\\n del MISSING_IN_TARGET[target]\\n\\n # add to items already learnt\\n ALREADY_LEARNT.update(items_to_learn)\\n\\n # remove the item from all targets requiring it\\n del TARGETS_MISSING[next_item]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46c5c5bd5c128fdea6ac2dc772869c7a\",\n \"score\": \"0.50935864\",\n \"text\": \"def knapsack(self, weight):\\n # calculate max length of combinations: the worst case is when we use\\n # only the item with the smallest weight\\n max_length = weight / min(self.items).weight\\n # keep the best combination so far\\n best = Knapsack()\\n for length in xrange(1, max_length + 1):\\n # generate all possible multicombinations for a given length\\n for combination in self._multicombinations(self.items, length):\\n knapsack = Knapsack()\\n for item in combination:\\n knapsack.add(item)\\n if knapsack.weight <= weight and knapsack > best:\\n best = knapsack\\n return best\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bfff401eed996cff2ebd36ccd22a7d9\",\n \"score\": \"0.50868464\",\n \"text\": \"def get_best_individual(population, weights, clauses):\\r\\n valid_individuals = []\\r\\n for individual in population:\\r\\n if is_valid(individual, clauses):\\r\\n valid_individuals.append(individual)\\r\\n if len(valid_individuals) == 0:\\r\\n return None\\r\\n weights_sums = [get_weights_sum(valid_individual, weights) for valid_individual in valid_individuals]\\r\\n best_sum = max(weights_sums)\\r\\n index = weights_sums.index(best_sum)\\r\\n best_individual = valid_individuals[index]\\r\\n return {'individual': best_individual, 'weights_sum': best_sum}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9ba5d7e4ce899b36d459e59d3c9c45a\",\n \"score\": \"0.508629\",\n \"text\": \"def get_probabilities(self):\\n weights = np.ones(shape=self.size, dtype=np.float32)\\n\\n if self.sampling_method == 'nonzero_reward':\\n weights[self.rewards[:self.size] == 0] = self.smallest_weight\\n elif self.sampling_method == 'positive_reward':\\n weights[self.rewards[:self.size] <= 0] = self.smallest_weight\\n else:\\n raise ValueError(\\\"Unknown sampling_method given to ReplayMemory ({}).\\\".format(self.sampling_method))\\n\\n weights_sum = weights.sum()\\n\\n # It might be the cases where all the weights are zero.\\n # In this extreme case, return a uniform sampling weights.\\n if weights_sum > 0:\\n return weights / weights_sum\\n else:\\n return np.ones(shape=self.size, dtype=np.float32) / self.size\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0126debb4f8303efbe77dcfa51e1329d\",\n \"score\": \"0.50831866\",\n \"text\": \"def total_weight(self) -> float:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7d464d104d2412631c88f23de231538\",\n \"score\": \"0.50778776\",\n \"text\": \"def get_cost(self):\\n if self.boundaries == 0:\\n return 0.0\\n tc = self.transition_cost()\\n assert self.logemissionsum >= 0\\n assert tc >= 0\\n return (self.logemissionsum + tc) * self.weight\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cee3869e7059257f7ace650b0584948a\",\n \"score\": \"0.50731385\",\n \"text\": \"def ComputeFitness(results, Maximize):\\n scoreMultiplier = 100000\\n movementsMultiplier = 0\\n snakelengthMultiplier = 0\\n penaltiesMultiplier = -1000000\\n rewardsMultiplier = 1000\\n \\n return np.sum(np.array(results) * np.array([scoreMultiplier,\\n movementsMultiplier,\\n snakelengthMultiplier,\\n penaltiesMultiplier,\\n rewardsMultiplier]\\n )\\n , axis=1\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d625155c86f32299d317588c94e5b3d4\",\n \"score\": \"0.5069805\",\n \"text\": \"def fitness_score(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"804584bf95e252791e8dfd238b3189cd\",\n \"score\": \"0.50668436\",\n \"text\": \"def max_profit(self):\\n if not self.is_optimized:\\n raise UnoptimizedStrategyOptimizerException('Must run StrategyOptimizer.optimize() first')\\n return -self._optimization_result.fun\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd59ef3a19ae75f8fdd1cd49b6bdffaa\",\n \"score\": \"0.5060159\",\n \"text\": \"def weight(self, *, attacker=None, move=None):\\r\\n cur_ability = self.ability(attacker=attacker, move=move)\\r\\n cur_weight = self.starting_weight\\r\\n if cur_ability == Ability.HEAVY_METAL:\\r\\n cur_weight *= 2\\r\\n if cur_ability == Ability.LIGHT_METAL:\\r\\n cur_weight //= 2\\r\\n cur_weight = max(1, cur_weight)\\r\\n cur_weight -= self.autotomize * 1000\\r\\n cur_weight = max(1, cur_weight)\\r\\n return cur_weight\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6c33b452300ce82d9496a9f78aa9e35\",\n \"score\": \"0.50596005\",\n \"text\": \"def fitness(self) -> float:\\n self.multi_fitness = list()\\n self._performance()\\n for fitness_func in self._fitness_function:\\n self.multi_fitness.append(fitness_func(self._maze_with_robot))\\n self.my_fitness = self.multi_fitness[1]\\n return self.my_fitness\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0129229fb9242c939e09c0c2b4a8914e\",\n \"score\": \"0.5057175\",\n \"text\": \"def max_product(self, target_node: VariableNode) -> Tensor:\\n\\n # Record for storing optimal assignments for\\n # each incoming variable node\\n self.record[target_node] = {}\\n\\n # Initialize the outgoing message\\n outgoing_msg = (torch.zeros(self.factor.shape) + self.factor).to(self.device)\\n\\n # Loop over incoming neighbors is the same as in sum-product\\n for n in self.neighbors(target_node):\\n\\n # Get message\\n edge = self.graph[n][self][\\\"object\\\"]\\n incoming_msg = edge.get_message(n, self).to(self.device)\\n\\n # Reshape\\n broadcast_shape = [1] * len(self.factor.shape)\\n broadcast_shape[edge.factor_dim] = len(incoming_msg)\\n\\n # Sum\\n outgoing_msg += incoming_msg.view(broadcast_shape)\\n\\n \\\"\\\"\\\"\\n Determine the maximum value for each possible assignment to the\\n target variable. The below is a fancy way to compute this that\\n gets around torch.max's lack of support for max'ing over multiple\\n dimensions simultaneously.\\n \\\"\\\"\\\"\\n target_dim = self.graph[self][target_node][\\\"object\\\"].factor_dim\\n maxes = outgoing_msg.view(outgoing_msg.size(target_dim), -1).max(dim=-1)\\n\\n # If there *are* incoming variable nodes, we record the best assignment\\n # for each one, for each possible value of the target variable node.\\n if len(self.factor.shape) > 1:\\n # The dimensions of the factor tensor associated with the\\n # incoming variables\\n non_target_dimensions = {\\n i: dim for i, dim in enumerate(self.factor.shape) if i != target_dim\\n }\\n\\n # The indices in the message tensor corresponding to the\\n # maximum values\\n max_indices = np.unravel_index(\\n maxes.indices, tuple(non_target_dimensions.values())\\n )\\n\\n # Associate each index with the appropriate incoming variable\\n max_indices_by_factor_dim = dict(\\n zip(non_target_dimensions.keys(), max_indices)\\n )\\n\\n # Record the best possible assignment for each incoming variable\\n for n in self.neighbors(target_node):\\n factor_dim = self.graph[n][self][\\\"object\\\"].factor_dim\\n self.record[target_node][n] = max_indices_by_factor_dim[factor_dim]\\n\\n # Return the max values\\n return maxes.values\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8ce5513e2417e57ee7c42e4a796c75b\",\n \"score\": \"0.505216\",\n \"text\": \"def fitness(individual, weights, clauses, n_clauses, satisfied_clause_bonus, satisfied_formula_bonus):\\r\\n index, fitness = 0, 0\\r\\n\\r\\n for i in individual:\\r\\n if i==1:\\r\\n fitness += weights[index]\\r\\n index += 1\\r\\n\\r\\n satisfied_clauses = 0\\r\\n for clause in clauses:\\r\\n satisfied = False\\r\\n for var in clause:\\r\\n negation = False\\r\\n if var < 0:\\r\\n negation = True\\r\\n var *= (-1)\\r\\n index = var - 1\\r\\n value = individual[index]\\r\\n if (value == 1 and not negation) or (value == 0 and negation):\\r\\n satisfied = True\\r\\n if satisfied:\\r\\n satisfied_clauses += 1\\r\\n\\r\\n fitness += satisfied_clauses*satisfied_clause_bonus\\r\\n if satisfied_clauses == n_clauses:\\r\\n fitness += satisfied_formula_bonus\\r\\n\\r\\n return fitness\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edb7fb7695ec9dae9c525f81bec40a8c\",\n \"score\": \"0.505165\",\n \"text\": \"def find_higest_food_collector(self):\\n fitness = self.agents[0].food_collected\\n fittest = self.agents[0]\\n for agent in self.agents:\\n if agent.food_collected > fitness:\\n fittest = agent\\n return fittest\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edb7fb7695ec9dae9c525f81bec40a8c\",\n \"score\": \"0.505165\",\n \"text\": \"def find_higest_food_collector(self):\\n fitness = self.agents[0].food_collected\\n fittest = self.agents[0]\\n for agent in self.agents:\\n if agent.food_collected > fitness:\\n fittest = agent\\n return fittest\",\n \"title\": \"\"\n },\n {\n \"docid\": \"863ae6a99eafd52ab7c07bb36d1986cb\",\n \"score\": \"0.50498414\",\n \"text\": \"def best_of_gen(population):\\n assert (len(population) > 0)\\n return max(population)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"863ae6a99eafd52ab7c07bb36d1986cb\",\n \"score\": \"0.50498414\",\n \"text\": \"def best_of_gen(population):\\n assert (len(population) > 0)\\n return max(population)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a43bc61bb35c50a0441b1a6897378c5c\",\n \"score\": \"0.50483906\",\n \"text\": \"def weighted_choice(items):\\n weight_total = sum((item[1] for item in items))\\n n = random.uniform(0, weight_total)\\n for item, weight in items:\\n if n < weight:\\n return item\\n n = n - weight\\n return item\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e3565149527a238101583b9cc2f6182\",\n \"score\": \"0.50421214\",\n \"text\": \"def x_final(feasible, target, w, Delta):\\n # Make sure there's at least one target\\n assert np.any(target == 1)\\n\\n # Initialize array with known values\\n x_final = feasible + Delta*target\\n\\n # TODO: make this a generator or something sensible\\n q = tuple(i, j for i,j in target[target == 1])\\n # Store calculated values (i, j): cost in this dict\\n d = {}\\n # Initialize an array to deal with noise\\n destination_prob = np.zeros(x_final.shape)\\n # Create arrays of x, y, ..., z indices for repeated use\\n grid = np.indices(x_final)\\n # Do DP. Work from target backwards, adding progressively more distant\\n # points to the queue.\\n # TODO: generalize to n dimensions\\n for i, j in q:\\n # For each possible control,\\n for u_i, u_j in ((1, 0), (-1, 0), (0, 1), (0, -1)):\\n # Check if it's farther from a target point (i.e. not already\\n # calculated and not a candidate for a previous step)\\n if (i + u_i, j + u_j) not in d: continue\\n # calc probs of each possible actual next destination\\n destination_prob[:] = w(i_grid, j_grid)\\n # calc min expected value of risk\\n # store minimum of optoins in d\\n # add not-already-calculated neighbors to q\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1ff04a9f86a5ad1885d6a8a50fae5b8\",\n \"score\": \"0.503544\",\n \"text\": \"def optimize(self, threshold_weight):\\r\\n values = [None] * (threshold_weight + 1)\\r\\n values[0] = 0\\r\\n for w in xrange(1, threshold_weight + 1):\\r\\n best_so_far = None\\r\\n for item in self.items:\\r\\n cost = item.cost\\r\\n if item.weight <= w:\\r\\n cost += values[w - item.weight]\\r\\n # end: if\\r\\n if best_so_far is None or cost < best_so_far:\\r\\n best_so_far = cost\\r\\n # end: for item\\r\\n values[w] = best_so_far\\r\\n # end: for w\\r\\n if Global.DEBUG_LEVEL > 1:\\r\\n print \\\"-\\\"*20\\r\\n print \\\"Optimize table:\\\"\\r\\n print zip(range(len(values)), values)\\r\\n print \\\"-\\\"*20\\r\\n # end: DEBUG\\r\\n return values[threshold_weight]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72767fd2e4b2292e408a1180073fd6ca\",\n \"score\": \"0.5027283\",\n \"text\": \"def fitness(self, solution):\\n return countHardConstraints(solution, self.data) + \\\\\\n totalSoftConstraintsForTimetable(solution.getTimeTable(), self.data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9adbed3af579a3cbf13ba2c5ad35bdf0\",\n \"score\": \"0.50249964\",\n \"text\": \"def wmape(actual: dict, target: dict, skus: dict):\\n sum_all = 0\\n sum_actual = 0\\n\\n for item in actual:\\n if skus.get(item) is None:\\n weight = 0\\n # make a new entry\\n else:\\n if target.get(item) is None:\\n weight = actual[item] * skus[item].unit_cost\\n else:\\n weight = (actual[item] - target[item]) * skus[item].unit_cost\\n sum_all += weight\\n sum_actual += actual[item] * skus[item].unit_cost\\n if sum_actual == 0:\\n\\n final = 100\\n\\n else:\\n final = sum_all / sum_actual\\n return final\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83089e544c6fdd9d0a8272f6a8965198\",\n \"score\": \"0.50146395\",\n \"text\": \"def traverse(program, desired_total_weight):\\n # type: (Program, Optional[int]) -> int\\n child_weights = {\\n child: weight(programs[child])\\n for child in program.children\\n }\\n counted_weights = Counter(child_weights.values())\\n if len(counted_weights.values()) > 1:\\n # Unbalanced children\\n desired_weight = counted_weights.most_common(1)[0][0]\\n odd_weight = counted_weights.most_common()[-1:][0][0]\\n for child, child_weight in child_weights.items():\\n if child_weight == odd_weight:\\n return traverse(programs[child], desired_weight)\\n\\n else:\\n # This node has all balanced children (or no children), therefore it is has the wrong weight\\n return desired_total_weight - sum(child_weights.values())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05b9b5ec7b9d2cdeaa9ced0aee85a71e\",\n \"score\": \"0.50134474\",\n \"text\": \"def knapsack(value, size, capacity):\\n #Sort value, size by decreasing efficiency\\n n = len(value)\\n a = [(value[i], size[i], i, value[i]/size[i]) for i in xrange(n)]\\n dtype = [('value', float), ('size', float), ('index', int), ('ratio', float),]\\n a = np.array(a, dtype=dtype)\\n a = np.sort(a, order='ratio')\\n a = a[::-1]\\n sortedValues = [] \\n sortedSize = []\\n hashTable = []\\n for i in xrange(n):\\n sortedValues.append(a[i][0])\\n sortedSize.append(a[i][1])\\n hashTable.append(a[i][2])\\n #Initialize the estimator in order to not clone the list \\n estimator = Estimator(sortedValues, sortedSize)\\n DP = {}\\n #DP(i) = DP[weight] = [value, estimatedValue, items]\\n #DP(i+1) = DP2 (in order to reduce memory cost)\\n DP[0]= (0, estimator.bestRelaxedValue(0,capacity), [])\\n bestSolution = (0, [0]*n)\\n for i in xrange(n):\\n DP2 = {}\\n for s, v in DP.iteritems():\\n for x in xrange(2):\\n weight=s+x*sortedSize[i]\\n #Cut invalid capacity\\n if(weight<=capacity):\\n newValue = v[0]+sortedValues[i]*x\\n #Merge branches with same weight\\n if (not weight in DP2 or newValue>DP2[weight][0]):\\n if(weight in DP2):\\n estimate = DP2[weight][1]-DP2[weight][0]+newValue\\n else:\\n estimate = estimator.bestRelaxedValue(i+1, capacity-weight)+newValue\\n #Cut bad branches\\n if(estimate>bestSolution[0]):\\n DP2[weight] = (newValue, estimate, v[2]+[x])\\n if(newValue>bestSolution[0]):\\n bestSolution = (newValue, v[2]+[x]+[0]*(n-i-1))\\n DP = DP2\\n #Take the best\\n final_items = np.zeros(n, 'int')\\n for i in xrange(n):\\n final_items[hashTable[i]]=bestSolution[1][i]\\n #Retrive data\\n return bestSolution[0], final_items\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc1959796697ee422e8f97f34e296830\",\n \"score\": \"0.5002871\",\n \"text\": \"def get_prob(paction, paction_set, plambda):\\n soft = softmax(paction_set * np.ones(paction_set.shape) * plambda)\\n return soft[np.where(paction_set == paction)][0]\\n # return np.exp(plambda * paction) / (1e-4 + np.sum([np.exp(plambda * action) for action in paction_set]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f83903106edac1d8347e202004a3c62\",\n \"score\": \"0.4999354\",\n \"text\": \"def fit(self, mutation_rate, mutation_power, num_generations, n_players, roster_space, cap_limit, strategy, positions, ratings, salaries, verbose=True):\\n self.initialize_population(n_players, roster_space, cap_limit, strategy, positions, ratings, salaries)\\n for individual in self.population:\\n individual.evaluation()\\n \\n self.sort_population()\\n self.best_solution = self.population[0]\\n self.solutions_list.append(self.best_solution.grade)\\n if verbose:\\n self.visualize_generation()\\n \\n for _ in tqdm(range(num_generations)):\\n grade_sum = self.sum_grades()\\n new_population = []\\n \\n for _ in range(0, self.pop_size, 2):\\n parent1 = self.select_parent(grade_sum)\\n parent2 = self.select_parent(grade_sum)\\n \\n children = self.population[parent1].crossover(self.population[parent2])\\n \\n new_population.append(children[0].mutation(mutation_rate, mutation_power))\\n new_population.append(children[1].mutation(mutation_rate, mutation_power))\\n \\n self.population = list(new_population)\\n for individual in self.population:\\n individual.evaluation()\\n self.sort_population()\\n if verbose:\\n self.visualize_generation()\\n best = self.population[0]\\n self.solutions_list.append(best.grade)\\n self.best_individual(best)\\n \\n print('\\\\n\\\\nBest Solution: \\\\nG: {}\\\\nGrade: {}\\\\nCap Used: {}'.format(self.best_solution.generation, self.best_solution.grade, self.best_solution.cap_used))\\n \\n return self.best_solution.chromosome\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91a02db8d97952d387a846ee22cc33a1\",\n \"score\": \"0.49987155\",\n \"text\": \"def fitness_func(self):\\n\\t\\tfit = 0.0\\n\\t\\tfor i in range(1, len(self.path)):\\n\\t\\t\\tfit += self.dist_func[self.dist_func_id](self.path[i - 1], self.path[i])\\n\\t\\treturn fit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44d06c99889c4cee4e803810c78995e2\",\n \"score\": \"0.49984458\",\n \"text\": \"def __call__(self, population, target_population_size):\\n if (len(population) % 2) > 0 or (target_population_size % 2) > 0:\\n raise ValueError('Population must be of even length')\\n\\n half_pop_size = len(population) // 2\\n if target_population_size > half_pop_size:\\n raise ValueError('Target population size cannot be greater\\\\\\n than the half of the population')\\n\\n offspring = population[half_pop_size:]\\n population = population[:half_pop_size]\\n\\n for i in range(target_population_size // 2):\\n parent_1 = population[i*2]\\n parent_2 = population[i*2+1]\\n child_1 = offspring[i*2]\\n child_2 = offspring[i*2+1]\\n\\n dist_a = parent_1.distance(child_1) + parent_2.distance(child_2)\\n dist_b = parent_1.distance(child_2) + parent_2.distance(child_1)\\n if dist_a <= dist_b:\\n population[i*2] = self._return_most_fit(child_1, parent_1)\\n population[i*2+1] = self._return_most_fit(child_2, parent_2)\\n else:\\n population[i*2] = self._return_most_fit(child_2, parent_1)\\n population[i*2+1] = self._return_most_fit(child_1, parent_2)\\n\\n return population\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50bd3fdd8a86622aa80146c9e4a33b4b\",\n \"score\": \"0.49978065\",\n \"text\": \"def UCBWeight(self):\\n return (self.value + \\n self.heuristic/self.visits +\\n sqrt(sqrt(self.parent.visits)/self.visits) * UCB_CONST)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75d95ae4fc2951d8da6d112d2700f3da\",\n \"score\": \"0.49969524\",\n \"text\": \"def knapsack(self, weight):\\n if weight <= 0:\\n # empty knapsack for empty set of items\\n return Knapsack()\\n # add an empty knapsack by default\\n # it will be used if no items are added at the next step\\n knapsacks = [Knapsack()]\\n for item in self.items:\\n # filter items which still can be put into the knapsack\\n if item.weight <= weight:\\n knapsack = self.knapsack(weight - item.weight).copy()\\n knapsack.add(item)\\n knapsacks.append(knapsack)\\n return max(knapsacks)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d7293e4aff695ac63ad295428bd2a9d\",\n \"score\": \"0.49883604\",\n \"text\": \"def getBestFitness(self):\\n if self.__fitnessObject == None or self.__bestState == None:\\n return None\\n\\n return self.__bestStateFitness\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed71439cfdf04880cb86125af3cfacaf\",\n \"score\": \"0.4987931\",\n \"text\": \"def sum_weight():\\n return 1.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d505dda75b472bae626bb230bbe5754\",\n \"score\": \"0.49828202\",\n \"text\": \"def cost_function(self):\\n hyp_minus_targets = self.hypothesis_targets()\\n return ((1./(2.*self.nobs))*(hyp_minus_targets).T.dot(\\n hyp_minus_targets) +\\n self.regularizer_cost_function())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7526f854c31790fa895662fe2ae50b87\",\n \"score\": \"0.49807456\",\n \"text\": \"def calculatePercentCost(weight, bias,costfuncvalues,numiterations):\\n\\tif numiterations==0: #to handle initial case when starting algorithm\\n\\t\\treturn 10\\n\\telse:\\n\\t\\titerdiff=costfuncvalues[numiterations]-costfuncvalues[numiterations-1]\\n\\t\\treturn math.fabs(iterdiff)*100/costfuncvalues[numiterations-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a62ee78aacb41dce1130c4a55568892\",\n \"score\": \"0.49804783\",\n \"text\": \"def calculateCostFunction(weight,bias):\\n\\terrorvals=0.0\\n\\t#numvectors=featuredata.shape[0]\\n\\tfor i in range(numvectors):\\n\\t\\tconfidence=1-outputdata[i]*(sum(featuredata[i]*weight)+bias)\\n\\t\\terrorvals+=max(0,confidence)\\n\\treturn regparameter*errorvals+0.5*sum(weight**2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf3186beacb74c0503232eef515823ad\",\n \"score\": \"0.4980308\",\n \"text\": \"def fitness(self, individual):\\r\\n result_all = [] # all possible sets with a minimal number of banknotes\\r\\n for i in range(self.money_min, self.money_max + 1):\\r\\n q, s = MinSetGenerator(self.number_of_genes, individual, i).generate()\\r\\n # if exists another set with a minimal number of banknotes - return bad fitness value (100)\\r\\n if MinSetChecker(self.number_of_genes, individual, i, q).is_exist_another_solution():\\r\\n return 100.0\\r\\n else:\\r\\n result_all.append(s)\\r\\n result_unique = get_list_with_unique_values(result_all) # unique sets with a minimal number of banknotes\\r\\n probability = 0.0\\r\\n result_all_count = len(result_all)\\r\\n for item in result_unique:\\r\\n buff = result_all.count(item)\\r\\n up = buff * (buff - 1)\\r\\n down = result_all_count * (result_all_count - 1)\\r\\n probability += up / down\\r\\n probability = probability * 100\\r\\n return abs(self.target - probability)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65f2418cf6d64022145bd05f47659659\",\n \"score\": \"0.49768674\",\n \"text\": \"def cost_estimate(self):\\n return 1.0\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993282,"cells":{"query_id":{"kind":"string","value":"09357b88590bf7b1fa9eab0cd18851ed"},"query":{"kind":"string","value":"Extracts the name of the month from the given filename/path (assumes /path/to/file/name_yearmonth.txt)."},"positive_passages":{"kind":"list like","value":[{"docid":"cfee2b92a61ea058ac742355226f2e33","score":"0.82106656","text":"def getMonthName (filename):\n #\n m = re.search('-([0-9]+).txt', filename)\n if m != None:\n return m.group(1)\n else:\n return \"current\"","title":""}],"string":"[\n {\n \"docid\": \"cfee2b92a61ea058ac742355226f2e33\",\n \"score\": \"0.82106656\",\n \"text\": \"def getMonthName (filename):\\n #\\n m = re.search('-([0-9]+).txt', filename)\\n if m != None:\\n return m.group(1)\\n else:\\n return \\\"current\\\"\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"2893cf73a9bdea5d2db1fb4382594252","score":"0.80404526","text":"def get_month_info(filename):\n\n pass","title":""},{"docid":"e8e9e2b2121ec1bc1f0e5a3fbb8f6a2c","score":"0.6470807","text":"def getYear (filename):\n m = re.search('_([0-9]+)-', filename)\n if m != None:\n return m.group(1)\n else:\n return filename","title":""},{"docid":"04fcb38214469712d95e880ae2aa029e","score":"0.6431144","text":"def year(filename):\n\n year = filename.split('_')[1][0:4]\n return year","title":""},{"docid":"2ce42c3f59be37a16cd375e2bb22e20a","score":"0.6406164","text":"def lookup_month(name):\n return month_names[name.lower()]","title":""},{"docid":"a547bbcd9c2a9ff9df918dcec77cf62d","score":"0.63318014","text":"def get_current_month():\n today = datetime.date.today()\n # Append the .txt.gz extension to match the extension format of Pipermail's\n # extension format. %Y is the current year and %B the current month. \n current_month = today.strftime(\"%Y-%B\") + '.txt.gz'\n return current_month","title":""},{"docid":"3b2fc6ba877e15fa4ab4e09f2d2db62f","score":"0.62962073","text":"def get_monthname(month: int):\n return datetime.datetime.strptime(str(month), '%m').strftime('%B')","title":""},{"docid":"60ed2e14a58712e790b0b217b8329cdc","score":"0.6049052","text":"def month_name(number):\n months = (\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\")\n return months[number-1]","title":""},{"docid":"d303d3369129388ca8696c4c8bcaade0","score":"0.6016383","text":"def named_month(pMonthNumber):\n return date(1900, pMonthNumber, 1).strftime('%B')","title":""},{"docid":"de81d2ca973782b68b92ad7da0e1cdec","score":"0.59999484","text":"def _extract_date_string(path):\n date_extractor = r\"([1-2][0-9]{3}-[0-1][0-9]-[0-3][0-9])\"\n match = re.search(date_extractor, path)\n if match:\n return match.group(0)\n return None","title":""},{"docid":"25b85693d5a386d2b8be053ea588849f","score":"0.59927595","text":"def parse_month(month: Union[str, int]) -> str:\n\n\terror_text = f\"The given month ({month!r}) is not recognised.\"\n\n\ttry:\n\t\tmonth = int(month)\n\texcept ValueError:\n\t\ttry:\n\t\t\treturn months[month.capitalize()[:3]] # type: ignore\n\t\texcept KeyError:\n\t\t\traise ValueError(error_text)\n\n\t# Only get here if first try succeeded\n\tif 0 < month <= 12:\n\t\treturn list(months.values())[month - 1]\n\telse:\n\t\traise ValueError(error_text)","title":""},{"docid":"dcb5ee7eb5f4c82f0b74e0dce74e5193","score":"0.5976848","text":"def get_month_name(month, names): # noqa\n\n # make sure its integer\n try:\n moni = int(month)\n except ValueError:\n msg = 'input must be an integer value'\n raise ValueError(msg)\n\n # make sure it's a month index\n if moni > 12 or moni < 1:\n msg = 'input month index must lie in [1, 12]'\n raise ValueError(msg)\n\n # get month name\n if names == '3L':\n mname = DMON3L[moni]\n elif names == '1L':\n mname = DMON1L[moni]\n elif names == 'F':\n mname = DMON_FNAME[moni]\n else:\n msg = 'unrecognized month name type'\n raise ValueError(msg)\n\n return mname","title":""},{"docid":"bbf3db576ce7b07392a21c6eecbbc36f","score":"0.59678644","text":"def named_month(month_number):\n return date(1900, month_number, 1).strftime(\"%B\")","title":""},{"docid":"6e84d5e774b181a647e0850fba8a11f6","score":"0.5953965","text":"def get_month(month: int) -> str:\n\n # Define a tuple containing the names of all months\n months = (\n \"January\",\n \"February\",\n \"March\",\n \"April\",\n \"May\",\n \"June\",\n \"July\",\n \"August\",\n \"September\",\n \"October\",\n \"November\",\n \"December\",\n )\n\n # Convert float inputs to integers\n if isinstance(month, float) and float(month).is_integer():\n month = int(month)\n\n # Check if the input month is within the range of 1-12\n if isinstance(month, int) == False:\n # If the input month is not an integer, log an error message and return an error message\n logging.error(\"Invalid input: %s, integer is required\", month)\n return \"Invalid input, integer is required\"\n elif 1 <= month <= 12:\n # Return the month name corresponding to the input month number\n logging.info(\"Returning month name for month number: %s\", month)\n return months[month - 1]\n else:\n # If the input month is outside the range of 1-12, log an error message and return an error message\n logging.error(\n \"Invalid input: %s, month number should be between 1 and 12\", month\n )\n return \"Invalid month number\"","title":""},{"docid":"1efc57013c5b95dea796db5e01c626ab","score":"0.5851337","text":"def normalize_month(text: str) -> str:\n prefix = text[:3].lower()\n if prefix in MONTHS:\n return MONTHS[prefix]\n return text","title":""},{"docid":"c4e45f5d81652416d2f7e9e75c8af094","score":"0.5841506","text":"def get_oldest_year_month(self, y):\n return os.path.basename(min(iglob(os.path.join(octopus_data, 'archives', y, y+'-[0-9][0-9]'))))","title":""},{"docid":"46f25d8e44cd89029970b118e5b43e32","score":"0.5779264","text":"def extract_name(filename):\n\n name, _ = split_ext(filename)\n return name\n # n, ext = split_ext(filename)\n # if ext == 'cols':\n # return f'/{n}'\n # else:\n # return n\n # bname = os.path.basename(filename)\n # name = '.'.join(bname.split('.')[0:-1])\n # return name","title":""},{"docid":"55cbe05ccd8f77dabac8099bc05ae045","score":"0.57459986","text":"def get_date_as_month_year(date_string):\n date_list = date_string.split('-')\n year = date_list[0]\n month = date_list[1]\n\n if len(year) == 4 and len(month) == 2:\n return month, year","title":""},{"docid":"c9d5fd8a362cce7e726849dbbb0c8cd4","score":"0.57449263","text":"def us_month(cls, date_string):\r\n date_string = date_string.lower()\r\n\r\n month_backslash = re.compile(r'\\s+\\d{1,2}/', re.IGNORECASE)\r\n month_dash = re.compile(r'\\s+\\d{1,2}-', re.IGNORECASE)\r\n month_name = re.compile(cls.MONTHS)\r\n\r\n if re.search(r'\\d{1,2}/\\d{1,2}/\\d{2,4}', date_string):\r\n date_string = month_backslash.sub(' <month> ', date_string)\r\n\r\n if re.search(r'\\d{1,2}-\\d{1,2}-\\d{2,4}', date_string):\r\n date_string = month_dash.sub(' <month> ', date_string)\r\n\r\n if re.search('(' + cls.MONTHS + ')', date_string):\r\n date_string = month_name.sub(' <month> ', date_string)\r\n\r\n return date_string","title":""},{"docid":"b97e2bfe466ec2d446777c7e44ed43d3","score":"0.572343","text":"def get_month(time):\n return args.months[time.month-1]","title":""},{"docid":"98acf3fcba8aca7e84c50e8e207b3d7f","score":"0.57107705","text":"def get_filename(station, year, month):\r\n if month < 10:\r\n fname = 'Station_Data/{}/{}_{}_ComprehensiveFormat/{}_01_{}-0{}.csv'.format(station, station, year, station, year, str(month))\r\n else:\r\n fname = 'Station_Data/{}/{}_{}_ComprehensiveFormat/{}_01_{}-{}.csv'.format(station, station, year, station, year, str(month))\r\n if not path.exists(fname):\r\n if month < 10:\r\n fname = 'Station_Data/{}/{}_{}_ComprehensiveFormat/{}_05_{}-0{}.csv'.format(station, station, year, station, year, str(month))\r\n else:\r\n fname = 'Station_Data/{}/{}_{}_ComprehensiveFormat/{}_05_{}-{}.csv'.format(station, station, year, station, year, str(month))\r\n return fname","title":""},{"docid":"15618017b7fcb218da7cdb08d04f8288","score":"0.57079804","text":"def mosaic_date_parser(project_name):\n \n # The date is always a sequence of 6 numbers in the filename.\n seq = re.compile('[0-9]{6}')\n seq_match = seq.search(project_name)\n \n if seq_match:\n year = '20' + seq_match[0][-2:]\n if year == '2020':\n date = year + '-' + seq_match[0][2:4] + '-' + seq_match[0][:2]\n elif year == '2019':\n date = year + '-' + seq_match[0][:2] + '-' + seq_match[0][2:4]\n else:\n return None\n \n # Looks like Dec 6 was an exception to the format...\n if date=='2019-06-12':\n date = '2019-12-06'\n \n # Handle April 8 b scan case.\n if project_name[seq_match.end()]=='b':\n date = date + ' 12:00:00'\n \n return date","title":""},{"docid":"1fa23052f94e4abae57e8ce04858daec","score":"0.570112","text":"def file_time(filename):\n import datetime\n t = os.path.getmtime(filename)\n d = datetime.datetime.fromtimestamp(t)\n day = str(d.day)\n month = str(d.month)\n year = str(d.year)\n \n if len(day) < 2:\n day = '0' + day\n if len(month) < 2:\n month = '0' + month\n year = year[2:]\n \n return month+day+year","title":""},{"docid":"a9bc62e5795b8613605c6aecdabf4233","score":"0.5700694","text":"def get_symbol_from_path_file_name(path_file_name):\n # ./data/daily/AAPL_2021-01-23.csv\n # ./data/one_off_min/ZION_min_batch13_2021-01-23.csv\n str1 = path_file_name.split('/')[3]\n str2 = str1.split('_')[0]\n return str2","title":""},{"docid":"284c4b060913aaab5d752e2f37722be4","score":"0.56826293","text":"def extract_YYYYMMDD(filename):\n try:\n dt = datetime.datetime.strptime( \\\n \tre.search(\"[12][09][0-9][0-9][01][0-9][0-3][0-9]\", filename).group(), \"%Y%m%d\")\n except (ValueError, AttributeError): # there is not one\n return None\n if dt < datetime.datetime(1957, 10, 4, 19, 28, 34): # Sputnik 1 launch datetime\n dt = None\n # better not still be using this... present to help with random numbers combinations\n elif dt > datetime.datetime(2050, 1, 1):\n dt = None\n return dt","title":""},{"docid":"de864d09ec38c6f3784b603525431375","score":"0.56730074","text":"def parse_file(file_name):\n import re\n import datefinder\n with open(file_name, 'r', encoding='utf8') as f:\n file_string = f.read()\n\n d_iter = re.finditer(r\"<date.*\\n\", file_string) # [0]\n d_match = next(d_iter)\n d = next(datefinder.find_dates(d_match.group())).date()\n\n t = re.findall(r\"<title.*>\", file_string)[0]\n t = t.strip('<>').split('=')[1].strip('\"')\n\n s = file_string[d_match.end():]\n return (t, d, s)","title":""},{"docid":"be03f88e4d2aaeacc1abe114fbd0656f","score":"0.5666367","text":"def extract_month(cube, month):\n if month not in range(1, 13):\n raise ValueError('Please provide a month number between 1 and 12.')\n return cube.extract(iris.Constraint(month_number=month))","title":""},{"docid":"4f5ed7ba8128f1bfcbee8ac7e919d3dd","score":"0.56544363","text":"def extract_filename(path_filename):\n filename = ntpath.basename(path_filename)\n return filename","title":""},{"docid":"215477bc5ee04b5959ffcf326b436158","score":"0.5642729","text":"def filenameToDate(filename):\n \n date = filename.split('_')[1] # From 'M_20190610_01' take '20190610'\n date = '-'.join([date[:4], date[4:6], date[6:]]) # Transfrom to '2019-06-10'\n \n return date","title":""},{"docid":"f61b56d1065d7a6ac150eadeb9579e44","score":"0.56118983","text":"def oleaut32_VarMonthName(jitter):\n ret_ad, args = jitter.func_args_stdcall([\"iMonth\", \"fAbbrev\", \"dwFlags\", \"pbstrOut\"])\n raise RuntimeError('API not implemented')\n jitter.func_ret_stdcall(ret_ad, ret_value)","title":""},{"docid":"72c493338c5149c687f999f9aa231718","score":"0.55862534","text":"def get_month(month_no):\n\n months = [\n \"january\",\n \"february\",\n \"march\",\n \"april\",\n \"may\",\n \"june\",\n \"july\",\n \"august\",\n \"september\",\n \"october\",\n \"november\",\n \"december\"\n ]\n\n month = months[int(month_no) - 1]\n\n return month","title":""},{"docid":"bcf8f54fc715137262c5deef1c8bef3c","score":"0.5544727","text":"def file_name(path):\n return os.path.basename(path).split('.')[0]","title":""},{"docid":"d92f6d6259e955556a7e8e4a9cac61ef","score":"0.5536999","text":"def get_filename(file_path):\n return os.path.split(file_path)[1]","title":""},{"docid":"3dbbca9ddf5504abb6d7de47bb8fba74","score":"0.5535749","text":"def get_year_and_month(word):\n nlp = en_core_web_sm.load()\n doc = nlp(word)\n month = 0\n year = 0\n\n for i in range(len(doc)):\n if doc[i].lemma_ == \"year\":\n year = doc[i - 1]\n if doc[i].lemma_ == \"month\":\n month = doc[i - 1]\n return [int(str(year)), int(str(month))]","title":""},{"docid":"7048e37405499327ae1c15ef25830c0b","score":"0.55160594","text":"def file_name(path):\n import os\n\n return os.path.splitext(os.path.basename(path))[0]","title":""},{"docid":"8b6e8434ae3627ce4d57dd54870850e6","score":"0.5487039","text":"def deconstruct_file_name(name):\n return os.path.splitext(os.path.split(name)[1])[0].split('_')","title":""},{"docid":"f91646d1a9309c61dbdd15851b5b8bb2","score":"0.5447287","text":"def extract_names(filename):\n myFile = extract_from_file(filename)\n# print(myFile)\n year = extract_year(myFile)\n# print(year)\n durtyList = extract_all_names(myFile)\n cleanList = create_dictionary(durtyList)\n# print(cleanList)\n finalList = {year: \"\"}\n finalList.update(cleanList[0])\n finalList.update(cleanList[1])\n finalSortedList = sorted(finalList.items())\n# print (finalList)\n return finalSortedList","title":""},{"docid":"dde6f4a149e2630c7d8e23e432f9b50e","score":"0.5441289","text":"def get_file_name(filename):\n filename = filename.split('\\n')[0]\n filename = filename.replace('/', '')\n return filename","title":""},{"docid":"7147f86f325fce68663b0e72b83d7002","score":"0.5432966","text":"def translate_month(month):\r\n # Create array containing month names, return the correct moth name based on the month number found earlier\r\n months = ['January', 'February', 'March', 'April', 'May', \"June\", \"July\", \"August\", \"September\", \"October\",\r\n \"November\", \"December\"]\r\n if (month >= 1) and (month <= 12):\r\n return months[month - 1]\r\n else:\r\n return \"Error\"","title":""},{"docid":"af1794ff5ac89ea0ad801157645d88fc","score":"0.542772","text":"def get_hfname(station, year, month):\r\n if month < 10:\r\n fname = 'Station_Data/{}/{}_{}_HourFormat/{}_05_{}-0{}_hour.csv'.format(station, station, year, station, year, str(month))\r\n else:\r\n fname = 'Station_Data/{}/{}_{}_HourFormat/{}_05_{}-{}_hour.csv'.format(station, station, year, station, year, str(month))\r\n if not path.exists(fname):\r\n if month < 10:\r\n fname = 'Station_Data/{}/{}_{}_HourFormat/{}_01_{}-0{}_hour.csv'.format(station, station, year, station, year, str(month))\r\n else:\r\n fname = 'Station_Data/{}/{}_{}_HourFormat/{}_01_{}-{}_hour.csv'.format(station, station, year, station, year, str(month))\r\n return fname","title":""},{"docid":"418139cdcbf2cb531517f37aec29241d","score":"0.54263574","text":"def test_month_name(self):\n # T273573\n self.assertEqual(date.formats['MonthName']['hu']('január'), 1)\n self.assertEqual(date.formats['MonthName']['hu'](5), 'május')","title":""},{"docid":"3f30b9beb58be828927715a1c2137ca0","score":"0.5420096","text":"def subject_from_regeneron1_fname(fname):\n # return fname.split('_')[1].rstrip('P').rstrip('M').rstrip('D').rstrip('F')\n return fname.split('_')[1].replace('-', '.')","title":""},{"docid":"8a031d3ffc0f8bbef0e594afc91528e9","score":"0.5411086","text":"def get_symbol_from_file_name(file_name):\n # AAPL_2021-01-13.csv\n return file_name.split('_')[0]","title":""},{"docid":"3b28a0f99bfec6bb0736134ee00043c1","score":"0.5401249","text":"def FileNameFromPath(path):\n return os.path.split(path)[1]","title":""},{"docid":"96dc48f92568f229db3041514d208f17","score":"0.5395899","text":"def get_date_from_file_name(\n path2file: Path, # Path to the file\n date_pattern: str=None, # regex pattern for the date, if None, default date pattern is YY-MM-DD\n) -> dt.datetime:\n if date_pattern is None:\n date_pattern = r\"^(\\d\\d)-(\\d\\d)-(\\d\\d)*\"\n\n if not path2file.is_file() or path2file is None:\n return False\n p = re.compile(date_pattern)\n results = p.match(path2file.name)\n\n if results is not None:\n y, m, d = results.groups()\n if y is None or m is None or d is None:\n return False\n else:\n y = int(y)\n offset = (1900 if 50 < y <= 99 else 2000)\n y = y + offset\n date = dt.datetime(year=y, month=int(m), day=int(d))\n return date\n else:\n return False","title":""},{"docid":"01bd6e9c0a239d11826f461ef82d6f93","score":"0.5380744","text":"def parse(file_name):\n with open(file_name, \"r\") as f:\n match = re.match(r\"(\\d{6})-(\\d{6})\", f.readline())\n return (match.group(1), match.group(2))","title":""},{"docid":"de04bc59e3031c844f8a9166853c7c53","score":"0.5378534","text":"def get_name(path):\n name, _ = os.path.splitext(os.path.basename(path))\n\n return name","title":""},{"docid":"166a7283828bd5ff2dfeac195ba62e0e","score":"0.5374061","text":"def month_of_date(date_str):\n return _MONTHS_LIST[int(date_str.split('/')[1]) - 1]","title":""},{"docid":"f35135f7944f01a1f82b6a437a30fdf0","score":"0.5370653","text":"def parse_filename(filename, name=None):\n version = None\n for ext in ALL_EXTENSIONS:\n if filename.endswith(ext):\n trimmed = filename[:-len(ext)]\n parsed = split_filename(trimmed, name)\n if parsed is None:\n break\n else:\n parsed_name, version = parsed[:2]\n break\n if version is None:\n raise ValueError(\"Cannot parse package file '%s'\" % filename)\n if name is None:\n name = parsed_name\n return normalize_name(name), version","title":""},{"docid":"193c8f590ee4058388ae709e067adec8","score":"0.5341717","text":"def my_bir_month(m):\r\n cal = calendar.TextCalendar(3)\r\n cal.prmonth(2012, m)","title":""},{"docid":"af8cde09ca8b17d1ad66da9ac0c49176","score":"0.5339409","text":"def _decode_file_name(filename: str) -> MissionFileNameInfo:\n\n\tfileList = filename.split(\".\")\n\n\t# Check if the file name ends with \".pbo\"\n\tif fileList[-1:][0].casefold() != \"pbo\":\n\t\traise Exception(\"Missions can only be submitted in .pbo format.\")\n\n\t# Check if there are any erroneuous periods in the file name\n\tif len(fileList) > 3:\n\t\traise Exception(\"File names can only have periods to denote the map and file extension.\")\n\tif len(fileList) < 3:\n\t\traise Exception(\"File name appears to be missing the map definition.\")\n\n\t# Get our map extension\n\tmapName = fileList[-2:][0].casefold()\n\n\t# Split up our map name\n\tnameList = fileList[0].split(\"_\")\n\n\t# Check if the mission is a test mission\n\tif nameList[0].casefold() == \"test\":\n\t\tdel nameList[0]\n\n\tif len(nameList) == 0:\n\t\traise Exception(\"File name appears to be missing the gametype and playercount\")\n\n\t# Check the mission type\n\tgameType = nameList[0].casefold()\n\tif not (gameType in VALID_GAMETYPES):\n\t\traise Exception(f\"`{gameType}` is not a valid mission type!\")\n\n\t# Grab the player count\n\ttry:\n\t\tplayerCount = int(nameList[1])\n\texcept:\n\t\traise Exception(\"I could not determine the player count in your mission.\")\n\n\tmissionInfo: MissionFileNameInfo = {\"gameType\": gameType, \"map\": mapName, \"playerCount\": playerCount}\n\treturn missionInfo","title":""},{"docid":"300dcfa12e9103321cc588df2c6ece66","score":"0.53339964","text":"def getFileInfo(filename: str):\n date = filename[10:18]\n time = filename[-12:-4]\n folder = filename[19:27]\n filenumber = filename[28:31]\n\n return date, time, folder, filenumber","title":""},{"docid":"4bd8804647565d83dd7fb809524d5e91","score":"0.5324675","text":"def proper_name(file, timestamp):\n (folder, full_name) = os.path.split(file)\n (name, ext) = os.path.splitext(full_name)\n\n fix_hex = re.match(\"^MOV[0-9A-F]{3}$\", name)\n\n # first pull the date out\n # try to find embedded date\n existing_date = re.search(\"[-._ ]?(?P<Y>\\d{4})[-._ ]?(?P<m>\\d{2})[-._ ]?(?P<d>\\d{2})[-._ ]?(?P<H>\\d{2})[-._ ]?(?P<M>\\d{2})[-._ ]?(?P<S>\\d{2})[-._ ]?\", name)\n if existing_date:\n name = name.replace(existing_date.group(0), \"\").__str__()\n\n # check there's no dup date\n dup_match = re.search(\"[-._ ]?\\d\\d\\d\\d\\.\\d\\d\\.\\d\\d$\", name)\n if dup_match:\n name = name.replace(dup_match.group(0), \"\")\n\n name = \"_{0}\".format(name) if name else \"\"\n name = \"%s.%s.%s_%s-%s-%s%s\" % (\n existing_date.group(\"Y\"),\n existing_date.group(\"m\"),\n existing_date.group(\"d\"),\n existing_date.group(\"H\"),\n existing_date.group(\"M\"),\n existing_date.group(\"S\"),\n name\n )\n else:\n date_match = re.search(\"\\d\\d\\d\\d\\.\\d\\d\\.\\d\\d[_ ]\", name)\n if not date_match:\n name = apply_timestamp_convention(name, timestamp)\n elif not re.match(\"\\d\\d\\d\\d\\.\\d\\d\\.\\d\\d_\\d\\d-\\d\\d-\\d\\d\", name):\n name = name.replace(date_match.group(0), \"\")\n name = apply_timestamp_convention(name, timestamp)\n\n if fix_hex:\n # now unwrap hex number from JVC\n match = re.search(\"MOV([0-9A-F]{3})\", name)\n if match:\n number = int(match.group(1), 16)\n name = name.replace(match.group(0), \"MOV\" + (\"%03d\" % (number,)))\n\n return os.path.join(folder, name + ext)","title":""},{"docid":"7fd1a2acf23f03029ed8376542b44ed2","score":"0.52925444","text":"def get_month(string):\n if 'Apr' in string:\n month = '04'\n elif 'Mai' in string:\n month = '05'\n elif 'Jun' in string:\n month = '06'\n elif 'Jul' in string:\n month = '07'\n else:\n raise Exception('wrong month (not in April - July interval)')\n return month","title":""},{"docid":"e77856657081fd5c37d94c9f366cef56","score":"0.529037","text":"def filename(file):\n\treturn os.path.basename(file)","title":""},{"docid":"2d16aa02649b16914e4f72827831dc13","score":"0.5283355","text":"def read_month(fileGlob, reanalysis, variable):\n ds = xr.open_mfdataset(fileGlob, concat_dim='time', data_vars='different')\n ds.set_coords(['latitude','longitude'], inplace=True)\n ds.coords['time'] = util.make_time_coordinate(fileGlob)\n ds.load()\n return ds","title":""},{"docid":"4ba66840d5990e6f415dab5ffe1c7062","score":"0.52725536","text":"def modulename(path: str) -> str:\n return os.path.splitext(os.path.basename(path))[0]","title":""},{"docid":"fb162adfbd7d122a1ab675d194d57229","score":"0.5266075","text":"def read_climatology(fname,vname,nlat,nlon):\n \n infile = cdms2.open(fname)\n \n months = ['jan','feb','mar','apr','may','jun','jul','aug','sep','oct','nov','dec']\n monthly_climatology = numpy.zeros([len(months),nlat,nlon])\n for i in range(0,len(months)):\n data = infile(vname+'_'+months[i],order='yx')\n\tmonthly_climatology[i,:,:] = data\n \n infile.close()\n \n return monthly_climatology","title":""},{"docid":"7cf791c0adf750e70b8768558db1a4a5","score":"0.5265543","text":"def dateparser(filename):\n \n #Open the file with read only mode\n f= open(filename,\"r\")\n \n #Read the file line by line\n for i in range(10):\n dat = f.readline()\n \n dat = dat[5:-1]\n #dat = datetime.strptime(dat, '%Y/%m/%d').date()\n \n return dat","title":""},{"docid":"ac453f5b788165d512e5f4cdb05c64c6","score":"0.5254481","text":"def get_month_year(self):\n calendar_heading = self.find(By.CSS_SELECTOR,\n '.calendar-header-label')\n Assignment.scroll_to(self.driver, calendar_heading)\n calendar_date = calendar_heading.text\n month, year = calendar_date.split(' ')\n return self.get_month_number(month), int(year)","title":""},{"docid":"c17d05ce7016b5175a132693c698a2d1","score":"0.5250424","text":"def filenameToMeasureFilename(filename, \n home=os.getcwd()):\n \n date = filenameToDate(filename) # Transfrom to '2019-06-10'\n fits_filename = os.path.join(home, 'Mediciones', date, filename+'.txt')\n \n return fits_filename","title":""},{"docid":"10b6b5584687b82f38618b3886e5f4bf","score":"0.52503884","text":"def _get_date_from_filename(self, filename):\n date_string = filename[:-4]\n return datetime.strptime(date_string, '%Y-%m-%d')","title":""},{"docid":"48ab2e369209ecfa943b8e22426a5a20","score":"0.5241301","text":"def get_name(data, meta_path, title_counter):\n authors = \"\"\n try:\n title = data[\"stats\"][\"title\"]\n authors = data[\"stats\"][\"authors\"]\n except KeyError: # older type file\n title = splitext(basename(meta_path))[0]\n try:\n name = title.split(\"#] \")[1]\n title = splitext(name)[0]\n except IndexError: # no \"#] \" in filename\n pass\n if not title:\n try:\n name = meta_path.split(\"#] \")[1]\n title = splitext(name)[0]\n except IndexError: # no \"#] \" in filename\n title = _(\"NO TITLE FOUND\") + str(title_counter[0])\n title_counter[0] += 1\n name = title\n if authors:\n name = \"{} - {}\".format(authors, title)\n return name","title":""},{"docid":"2e2c5fea9acca82170ebee5378407ac5","score":"0.52329886","text":"def formatmonthname(year, themonth, width, withyear=True):\n s = month_names[themonth]\n if withyear:\n s = \"%s %r\" % (s, year)\n return s.center(width)","title":""},{"docid":"24dc9c85d3e828c2f4e96b0b8567bb18","score":"0.5231518","text":"def extract_date(ifile):\n\n global dt\n (idir, fil) = split_filename(ifile)\n\n # MCD43C1.A2008001.005.2008025105553.hdf\n # MCD43C3.A2008001.005.2008025111631.hdf\n if fil.startswith('MCD'):\n fsplit = fil.split('.')\n for s in fsplit:\n if s.startswith('A'):\n ss = s.replace(\"A\", \"\")\n dt = date_from_year_doy(int(ss[:-3]),\n int(ss[4:]))\n\n # global_emis_inf10_monthFilled_MYD11C3.A2008001.041.nc\n # global_emis_inf10_monthFilled_MYD11C3.A2008001.nc\n elif fil.startswith('global'):\n fbasen = os.path.splitext(fil)\n fsplit = fbasen[0].split('.')\n for s in fsplit:\n if s.startswith('A'):\n ss = s.replace(\"A\", \"\")\n dt = date_from_year_doy(int(ss[:-3]),\n int(ss[4:]))\n\n # NISE_SSMIF13_20080101.HDFEOS\n elif fil.startswith('NISE'):\n fbasen = os.path.splitext(fil)\n fsplit = fbasen[0].split('_')\n dt = datetime.datetime(int(fsplit[2][:-4]),\n int(fsplit[2][4:-2]),\n int(fsplit[2][6:]))\n\n # ERA_Interim_an_20080101_00+00.nc\n elif fil.startswith('ERA_Interim'):\n fbasen = os.path.splitext(fil)\n fsplit = fbasen[0].split('_')\n fstime = fsplit[4].split('+')\n dt = datetime.datetime(int(fsplit[3][:-4]),\n int(fsplit[3][4:-2]),\n int(fsplit[3][6:]),\n int(fstime[0]),\n int(fstime[1]))\n return dt","title":""},{"docid":"5eb766fdf37fcec658b495bf25cf0738","score":"0.5230452","text":"def get_name(fname):\n with open(fname, encoding=\"utf-8\") as tmp:\n return tmp.readline()[1:].strip()","title":""},{"docid":"706901595a8a1428cffd5140fc691060","score":"0.5230213","text":"def extract_year_month(init_time, logger):\n # pylint:disable=protected-access\n # Need to call sys.__getframe() to get the filename and method/func\n # for logging information.\n\n # Useful for logging\n cur_filename = sys._getframe().f_code.co_filename\n cur_function = sys._getframe().f_code.co_name\n\n # Match on anything that starts with 1 or 2 (for the century)\n # followed by 5 digits for the remainder of the YYYMM\n year_month = re.match(r'^((1|2)[0-9]{5})', init_time)\n if year_month:\n year_month = year_month.group(0)\n return year_month\n else:\n logger.warning(\"WARNING|\" + \"[\" + cur_filename + \":\" + cur_function +\n \"]\" + \" | Cannot extract YYYYMM from \"\n \"initialization time, unexpected format\")\n raise Warning(\"Cannot extract YYYYMM from initialization time,\"\n \" unexpected format\")","title":""},{"docid":"279dc8302b21eac28ba7b0f63b9e3278","score":"0.522959","text":"def get_file_name(file):\n return Path(file).stem","title":""},{"docid":"9d0694e6524d37faf130b255b2c5255b","score":"0.52256095","text":"def _get_filename(file):\n return file.name","title":""},{"docid":"cc9e8198c573fe64cd93008d4f840810","score":"0.52250177","text":"def get_month(self, month_nr):\n return self.months[month_nr - 1]","title":""},{"docid":"ab24fc242788cb980eb2aadbdd708888","score":"0.5219688","text":"def filename_from_datetime(dt: datetime.datetime, path: str) -> str:\n base = basename_from_datetime(dt)\n ext = os.path.splitext(path)[1]\n return base + ext.lower()","title":""},{"docid":"983c88b1acc99870c83d8bf3b3780550","score":"0.52050406","text":"def get_months(s):\n ret = s.split(\",\")\n if len(ret) == 1:\n if ret[0].lower() == \"system\":\n return get_system_months()\n if ret[0].lower() == \"number\":\n return get_number_months()\n if len(ret) != 12:\n raise PhorgError(\"We needs exactly 12 months\")\n return ret","title":""},{"docid":"f15ca7ef06ba401a72a0128390000bd6","score":"0.52002996","text":"def concat_last_date_2file_name(filename: str,\n path: str,\n log_file):\n \n last_date = extract_last_date(path, log_file)[:-1] #ignore new line\n filename_date = f\"{filename}_{last_date}.xlsx\"\n \n return filename_date","title":""},{"docid":"bed072a00cf744334c7b7eb23eb04d32","score":"0.5196681","text":"def getMonthHeader(self):\n return self.curr.getDate(1).strftime(\"%b\"), self.curr.year","title":""},{"docid":"5279f13c48285ada3f00fcd0d701cfc1","score":"0.519643","text":"def newname(fname):\n\n if is_skipdir(fname):\n return None\n\n dirname = os.path.dirname(fname)\n basename = os.path.basename(fname)\n ext = os.path.splitext(basename)[1]\n\n if ext in skip_exts:\n return None\n\n if is_skip_pat(basename):\n return None\n\n for p in mdy_pats:\n m = p.search(basename)\n if m:\n (month,day,year) = m.group(1,2,3)\n new_year = make_year_digit4(year)\n new_month = make_digit2(month)\n new_day = make_digit2(day)\n\n assert len(new_year)==4\n assert len(new_month)==2\n assert len(new_day)==2\n\n if not valid_year(new_year): return None\n if not valid_month(new_month): return None\n if not valid_day(new_day): return None\n\n # Look for a simple replacement, otherwise replace part-by-part\n fmt0 = f\"{month}.{day}.{year}\"\n fmt1 = f\"{month}{day}{year}\"\n iso = f\"{new_year}-{new_month}-{new_day}\"\n if fmt0 in basename:\n basename_new = basename.replace(fmt0,iso)\n elif fmt1 in basename:\n basename_new = basename.replace(fmt1,iso)\n else:\n basename_new = basename.replace(month,\"MONTH\",1).replace(day,\"DAY\",1).replace(year,\"YEAR\",1)\n basename_new = basename_new.replace(\"MONTHDAYYEAR\",\"MONTH-DAY-YEAR\")\n basename_new = basename_new.replace(\"MONTH.DAY.YEAR\",\"MONTH-DAY-YEAR\")\n basename_new = basename_new.replace(\"MONTH\",new_year,1).replace(\"DAY\",new_month,1).replace(\"YEAR\",new_day,1)\n return os.path.join(dirname,basename_new)\n\n # Look for a month and a year\n for myreg in [my_re1,my_re2]:\n m = myreg.search(basename)\n if m:\n month = MONTHS[m.group(1)]\n year = int(m.group(2))\n print(\"month=\",month,\"year=\",year)\n basename_new = basename.replace(m.group(1),\"MONTH\").replace(m.group(2),\"YEAR\")\n basename_new = basename_new.replace(\"MONTH YEAR\",\"YEAR-MONTH\")\n basename_new = basename_new.replace(\"MONTH.YEAR\",\"YEAR-MONTH\")\n basename_new = basename_new.replace(\"MONTH. YEAR\",\"YEAR-MONTH\")\n basename_new = basename_new.replace(\"MONTH, YEAR\",\"YEAR-MONTH\")\n basename_new = basename_new.replace(\"MONTHYEAR\",\"YEAR-MONTH\")\n basename_new = basename_new.replace(\"YEAR\",f\"{year:04}\").replace(\"MONTH\",f\"{month:02}\")\n return os.path.join(dirname,basename_new)\n return None","title":""},{"docid":"cc6c221ccf3437f5dfc5cfed51bd0859","score":"0.5194717","text":"def splitRPMFilename(filename):\n\n if filename[-4:] == '.rpm':\n filename = filename[:-4]\n\n archIndex = filename.rfind('.')\n arch = filename[archIndex + 1:]\n\n relIndex = filename[:archIndex].rfind('-')\n rel = filename[relIndex + 1:archIndex]\n\n verIndex = filename[:relIndex].rfind('-')\n ver = filename[verIndex + 1:relIndex]\n\n epochIndex = filename.find(':')\n if epochIndex == -1:\n epoch = ''\n else:\n epoch = filename[:epochIndex]\n\n name = filename[epochIndex + 1:verIndex]\n return name, ver, rel, epoch, arch","title":""},{"docid":"a10c0eb482bc627eeb255c24c6c8ec16","score":"0.5192341","text":"def get_month_n_year(string):\n all_months = ('january', 'february', 'march', 'april', 'may', 'june',\n 'july', 'august', 'september', 'october', 'november',\n 'december')\n month = [ind for ind, month in enumerate(all_months) if month in\n string.lower()]\n month[0] = month[0] + 1\n year = re.findall('\\d+', string)\n return month[0], int(year[0])","title":""},{"docid":"30ef47a38a016392dd6923f6aeebc1ed","score":"0.5191631","text":"def this_month():\n this_month_str = \"%4d_%02d\" % time.gmtime()[0:2]\n return this_month_str","title":""},{"docid":"e15a4af71d27401bd3c863af89085306","score":"0.51911485","text":"def get_monthtext(self, month, year, page_pos=None, anchor=\"south east\"):\n if page_pos is None:\n page_pos = [self.page_width-0.2, 0]\n monthtext = month + \" \" + str(year)\n ms = \" %%% get_monthtext\\n\"\n ms += r\" \\node at ({},{}) [anchor={},font=\\scshape,color=month!70!footerbackgroundcolor,scale=4,inner sep=0, outer sep=0] {{{}}};\".format(\n page_pos[0], page_pos[1], anchor, monthtext) + \"\\n\\n\"\n return ms","title":""},{"docid":"ddd5ab6a7d0c605fdb37fbe559237bb0","score":"0.5181049","text":"def get_filename_from_path(file_name_with_path):\n head, tail = os.path.split(file_name_with_path)\n return tail","title":""},{"docid":"09b53a9ed44b4585ea8f0aca91c6a6d4","score":"0.5178857","text":"def get_name(path):\n file_name = ospath.basename(path)\n return ospath.splitext(file_name)[0]","title":""},{"docid":"2843a9ca4a7b8f7b02d02c9af36bd1d6","score":"0.51713604","text":"def dateFromFilePath(file_path) -> datetime.date:\n\tbaseName = os.path.basename(file_path)\n\tname, ext = os.path.splitext(baseName)\n\ttry:\n\t\tif not EXTERNAL_DIR in file_path:\n\t\t\ttry:\n\t\t\t\tproduct, year, doy = name.split(\".\")\n\t\t\t\tdate = \".\".join([year, doy])\n\t\t\t\treturn parseDateString(date)\n\t\t\texcept (ValueError, BadInputError):\n\t\t\t\tproduct, date = name.split(\".\")[:2]\n\t\t\t\treturn parseDateString(date)\n\t\telse: # we do things differently for Ritvik's products\n\t\t\tproduct = os.path.dirname(file_path).split(\"/\")[-1]\n\t\t\tif product == \"chirps_gefs\":\n\t\t\t\tdateBlob = name.split(\"_\")[1] # gives something like 20171021\n\t\t\t\tyear = dateBlob[:4]\n\t\t\t\tmonth = dateBlob[4:6]\n\t\t\t\tday = dateBlob[6:]\n\t\t\t\treturn parseDateString(f\"{year}-{month}-{day}\")\n\t\t\telif product == \"esi_4wk\":\n\t\t\t\tdateBlob = name.split(\"_\")[-1]\n\t\t\t\tyear = dateBlob[:4]\n\t\t\t\tdoy = dateBlob[4:]\n\t\t\t\treturn parseDateString(f\"{year}.{doy}\")\n\t\t\telif product in [\"soil_moisture_as1\",\"soil_moisture_as2\"]:\n\t\t\t\tyear = name.split(\"_\")[-4]\n\t\t\t\tdoy = name.split(\"_\")[-3]\n\t\t\t\treturn parseDateString(f\"{year}.{doy}\")\n\texcept Exception as e:\n\t\tlog.error(f\"Failed to extract date from file name '{baseName}'\")\n\t\traise e","title":""},{"docid":"6074000a2d0efdacf2fab9886e3ede2b","score":"0.5167556","text":"def extract_version_from_filename(filename):\n filename = os.path.splitext(os.path.basename(filename))[0]\n if filename.endswith('.tar'):\n filename = os.path.splitext(filename)[0]\n return filename.split('-')[-1]","title":""},{"docid":"2e522c40edf88484bb5250e8ea23c5df","score":"0.5163665","text":"def get_file_name(path):\n base = os.path.basename(path)\n return os.path.splitext(base)[0]","title":""},{"docid":"3517e3f59fd71f2f2da1f92ed58c2371","score":"0.5161937","text":"def get_date(paragraph, filename):\n try:\n date = paragraph.split('From:')[0].split('Date:')[1].strip()\n if len(date) > 8:\n date = datetime.strptime(date, '%d/%m/%Y') \n # print(date)\n return date\n\n else:\n date = datetime.strptime(date, '%d/%m/%y') \n # print(date)\n return date\n \n except Exception as e:\n print('Error finding date for: ', filename)\n print(e)","title":""},{"docid":"937c83258259b7423e5a7eed6282b686","score":"0.5154342","text":"def getfileName(file):\n\n # Split by backslash, get the last index\n file = file.rsplit('/', 1)[-1]\n # Remove )\" which is found in Pyfadix files\n file = re.sub('[\")]', '', file)\n return file","title":""},{"docid":"aaf02bde958c92c0b2e5f0f3dda2cc9d","score":"0.51523423","text":"def get_datetime_from_file_name(file_name):\n try:\n for p1, p2 in DATETIME_PATTERNS:\n m = re.search(p1, file_name)\n if m:\n ts = []\n for c in m.group(0):\n if c not in ' -_.:':\n ts.append(c)\n return datetime.strptime(''.join(ts)[0:14], p2)\n except:\n pass","title":""},{"docid":"999bd86b4b6c8186fe20a8e4853f5d4b","score":"0.51447415","text":"def path_to_startdate(path):\n # The string before the first / should be fall|summer|spring + YYYY,\n # else this returns the empty string.\n s = str(path.split('/')[0])\n if s.startswith('fall'):\n return s[-4:] + '-09-01'\n elif s.startswith('spring'):\n return s[-4:] + '-01-01'\n elif s.startswith('summer'):\n return s[-4:] + '-06-01'\n else:\n return ''","title":""},{"docid":"0cb71a9653db83d5d3b061704811f9a0","score":"0.51402533","text":"def parse_calib_file_name(fname) :\n fnfields = os.path.splitext(fname) # '123-end.data' -> ('123-end', 'data') \n if len(fnfields) != 2 : return None # check that file name like '123-end.data' has splited extension\n if fnfields[1] != '.data': return None # check extension\n fnparts = fnfields[0].split('-') # '123-end' -> ('123', 'end')\n if len(fnparts) != 2 : return None # check that file name has two parts\n #print('XXX parts of the file name:', fnparts[0], fnparts[1], fnfields[1])\n return fnparts[0], fnparts[1], fnfields[1]","title":""},{"docid":"17dbbc81bce04d624fd91d862d3b3c7e","score":"0.5138658","text":"def get_month_number(self, month):\n months = {v: k for k, v in enumerate(calendar.month_name)}\n return months[month]","title":""},{"docid":"f8dcd0f5f36af4628bea0c58c66ce632","score":"0.5123069","text":"def _get_month_names_match(self):\n\n months_match = ''\n for month_name in self._months.keys():\n months_match = months_match + month_name + '|'\n\n months_match = r'\\b(' + months_match[0:-1] + r')\\b'\n\n return months_match","title":""},{"docid":"3ae71329304140b374a1e569b0e8f42b","score":"0.5120749","text":"def get_month_name(index):\n if index == 13:\n index = 1\n elif index == 14:\n index = 2\n elif index == 15:\n index = 3\n elif index == 0:\n index = 12\n if index == 1:\n monthname = 'Januari'\n elif index == 2:\n monthname = 'Februari'\n elif index == 3:\n monthname = 'Maret'\n elif index == 4:\n monthname = 'April'\n elif index == 5:\n monthname = 'Mei'\n elif index == 6:\n monthname = 'Juni'\n elif index == 7:\n monthname = 'Juli'\n elif index == 8:\n monthname = 'Agustus'\n elif index == 9:\n monthname = 'September'\n elif index == 10:\n monthname = 'Oktober'\n elif index == 11:\n monthname = 'November'\n else:\n monthname = 'Desember'\n return monthname","title":""},{"docid":"fcb35ab5c744821d7da8a38980e8b03c","score":"0.5119535","text":"def parse_filename(filepath):\n\n filename = os.path.split(filepath)[1]\n if len(filename) == 12: # new file format\n year = 2000 + int(filename[1:3])\n month = int(filename[3], 16)\n day = int(filename[4:6])\n hour = int(filename[6:8])\n minute = int(filename[9:11])\n sec = 6 * int(filename[11])\n result = datetime.datetime(year, month, day, hour, minute, sec)\n else:\n raise Exception(\"Filename length <> 12: {}\".format(len(filename)))\n\n return result","title":""},{"docid":"9159903c5e9165471407e452679a9e60","score":"0.51137316","text":"def get_filename(path):\n path = os.path.abspath(path)\n x = os.path.basename(path)\n return x","title":""},{"docid":"a392aa90c0d6f508ff2b51f82422e73e","score":"0.5111258","text":"def get_filename(path: str) -> str:\n return os.path.splitext(os.path.basename(path))[0]","title":""},{"docid":"f951bb7a6dcdd990550e45a9f4307728","score":"0.5106488","text":"def get_da_plan_mo_name(self, file_name):\n f = open(file_name)\n plan_text = f.read()\n plan = XML_utils.XML_utils().get_cmdata_elem_from_text(plan_text)\n print '*DEBUG* %s' % plan_text\n mo_names = XML_utils.XML_utils()._get_plan_mo_names(plan)\n return mo_names.pop()","title":""},{"docid":"b9620fb9f57b2992ab77219dccab3625","score":"0.51056486","text":"def monthly_schedule(self, month):\n response = requests.get\\\n (f'http://company.com/{self.last.title()}/{month.title()}')\n if response.ok:\n return response.text\n return 'Bad Response!'","title":""},{"docid":"179f88a29b6e5219e8c0583c354db90d","score":"0.51003516","text":"def find_basename_from_file(file_path):\n return os.path.basename(file_path).split(\".\")[0]","title":""},{"docid":"e2ffbfda311f05dab2dc513553887e78","score":"0.508476","text":"def get_file_name_file(file_name):\n return file_name.split(os.sep)[-1]","title":""},{"docid":"7610775fd2e4446b8f2b46e4bb061406","score":"0.50826496","text":"def _get_file_name_from_path(path):\n base_name = os.path.basename(path)\n return os.path.splitext(base_name)[0]","title":""},{"docid":"7b25b73261af3c6c1062f571d8f39670","score":"0.5070315","text":"def getNameFromFilename(cls, filename):\n return filename[0:3]","title":""}],"string":"[\n {\n \"docid\": \"2893cf73a9bdea5d2db1fb4382594252\",\n \"score\": \"0.80404526\",\n \"text\": \"def get_month_info(filename):\\n\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8e9e2b2121ec1bc1f0e5a3fbb8f6a2c\",\n \"score\": \"0.6470807\",\n \"text\": \"def getYear (filename):\\n m = re.search('_([0-9]+)-', filename)\\n if m != None:\\n return m.group(1)\\n else:\\n return filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04fcb38214469712d95e880ae2aa029e\",\n \"score\": \"0.6431144\",\n \"text\": \"def year(filename):\\n\\n year = filename.split('_')[1][0:4]\\n return year\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ce42c3f59be37a16cd375e2bb22e20a\",\n \"score\": \"0.6406164\",\n \"text\": \"def lookup_month(name):\\n return month_names[name.lower()]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a547bbcd9c2a9ff9df918dcec77cf62d\",\n \"score\": \"0.63318014\",\n \"text\": \"def get_current_month():\\n today = datetime.date.today()\\n # Append the .txt.gz extension to match the extension format of Pipermail's\\n # extension format. %Y is the current year and %B the current month. \\n current_month = today.strftime(\\\"%Y-%B\\\") + '.txt.gz'\\n return current_month\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b2fc6ba877e15fa4ab4e09f2d2db62f\",\n \"score\": \"0.62962073\",\n \"text\": \"def get_monthname(month: int):\\n return datetime.datetime.strptime(str(month), '%m').strftime('%B')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60ed2e14a58712e790b0b217b8329cdc\",\n \"score\": \"0.6049052\",\n \"text\": \"def month_name(number):\\n months = (\\\"January\\\",\\\"February\\\",\\\"March\\\",\\\"April\\\",\\\"May\\\",\\\"June\\\",\\\"July\\\",\\\"August\\\",\\\"September\\\",\\\"October\\\",\\\"November\\\",\\\"December\\\")\\n return months[number-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d303d3369129388ca8696c4c8bcaade0\",\n \"score\": \"0.6016383\",\n \"text\": \"def named_month(pMonthNumber):\\n return date(1900, pMonthNumber, 1).strftime('%B')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de81d2ca973782b68b92ad7da0e1cdec\",\n \"score\": \"0.59999484\",\n \"text\": \"def _extract_date_string(path):\\n date_extractor = r\\\"([1-2][0-9]{3}-[0-1][0-9]-[0-3][0-9])\\\"\\n match = re.search(date_extractor, path)\\n if match:\\n return match.group(0)\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"25b85693d5a386d2b8be053ea588849f\",\n \"score\": \"0.59927595\",\n \"text\": \"def parse_month(month: Union[str, int]) -> str:\\n\\n\\terror_text = f\\\"The given month ({month!r}) is not recognised.\\\"\\n\\n\\ttry:\\n\\t\\tmonth = int(month)\\n\\texcept ValueError:\\n\\t\\ttry:\\n\\t\\t\\treturn months[month.capitalize()[:3]] # type: ignore\\n\\t\\texcept KeyError:\\n\\t\\t\\traise ValueError(error_text)\\n\\n\\t# Only get here if first try succeeded\\n\\tif 0 < month <= 12:\\n\\t\\treturn list(months.values())[month - 1]\\n\\telse:\\n\\t\\traise ValueError(error_text)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcb5ee7eb5f4c82f0b74e0dce74e5193\",\n \"score\": \"0.5976848\",\n \"text\": \"def get_month_name(month, names): # noqa\\n\\n # make sure its integer\\n try:\\n moni = int(month)\\n except ValueError:\\n msg = 'input must be an integer value'\\n raise ValueError(msg)\\n\\n # make sure it's a month index\\n if moni > 12 or moni < 1:\\n msg = 'input month index must lie in [1, 12]'\\n raise ValueError(msg)\\n\\n # get month name\\n if names == '3L':\\n mname = DMON3L[moni]\\n elif names == '1L':\\n mname = DMON1L[moni]\\n elif names == 'F':\\n mname = DMON_FNAME[moni]\\n else:\\n msg = 'unrecognized month name type'\\n raise ValueError(msg)\\n\\n return mname\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbf3db576ce7b07392a21c6eecbbc36f\",\n \"score\": \"0.59678644\",\n \"text\": \"def named_month(month_number):\\n return date(1900, month_number, 1).strftime(\\\"%B\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e84d5e774b181a647e0850fba8a11f6\",\n \"score\": \"0.5953965\",\n \"text\": \"def get_month(month: int) -> str:\\n\\n # Define a tuple containing the names of all months\\n months = (\\n \\\"January\\\",\\n \\\"February\\\",\\n \\\"March\\\",\\n \\\"April\\\",\\n \\\"May\\\",\\n \\\"June\\\",\\n \\\"July\\\",\\n \\\"August\\\",\\n \\\"September\\\",\\n \\\"October\\\",\\n \\\"November\\\",\\n \\\"December\\\",\\n )\\n\\n # Convert float inputs to integers\\n if isinstance(month, float) and float(month).is_integer():\\n month = int(month)\\n\\n # Check if the input month is within the range of 1-12\\n if isinstance(month, int) == False:\\n # If the input month is not an integer, log an error message and return an error message\\n logging.error(\\\"Invalid input: %s, integer is required\\\", month)\\n return \\\"Invalid input, integer is required\\\"\\n elif 1 <= month <= 12:\\n # Return the month name corresponding to the input month number\\n logging.info(\\\"Returning month name for month number: %s\\\", month)\\n return months[month - 1]\\n else:\\n # If the input month is outside the range of 1-12, log an error message and return an error message\\n logging.error(\\n \\\"Invalid input: %s, month number should be between 1 and 12\\\", month\\n )\\n return \\\"Invalid month number\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1efc57013c5b95dea796db5e01c626ab\",\n \"score\": \"0.5851337\",\n \"text\": \"def normalize_month(text: str) -> str:\\n prefix = text[:3].lower()\\n if prefix in MONTHS:\\n return MONTHS[prefix]\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4e45f5d81652416d2f7e9e75c8af094\",\n \"score\": \"0.5841506\",\n \"text\": \"def get_oldest_year_month(self, y):\\n return os.path.basename(min(iglob(os.path.join(octopus_data, 'archives', y, y+'-[0-9][0-9]'))))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46f25d8e44cd89029970b118e5b43e32\",\n \"score\": \"0.5779264\",\n \"text\": \"def extract_name(filename):\\n\\n name, _ = split_ext(filename)\\n return name\\n # n, ext = split_ext(filename)\\n # if ext == 'cols':\\n # return f'/{n}'\\n # else:\\n # return n\\n # bname = os.path.basename(filename)\\n # name = '.'.join(bname.split('.')[0:-1])\\n # return name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55cbe05ccd8f77dabac8099bc05ae045\",\n \"score\": \"0.57459986\",\n \"text\": \"def get_date_as_month_year(date_string):\\n date_list = date_string.split('-')\\n year = date_list[0]\\n month = date_list[1]\\n\\n if len(year) == 4 and len(month) == 2:\\n return month, year\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9d5fd8a362cce7e726849dbbb0c8cd4\",\n \"score\": \"0.57449263\",\n \"text\": \"def us_month(cls, date_string):\\r\\n date_string = date_string.lower()\\r\\n\\r\\n month_backslash = re.compile(r'\\\\s+\\\\d{1,2}/', re.IGNORECASE)\\r\\n month_dash = re.compile(r'\\\\s+\\\\d{1,2}-', re.IGNORECASE)\\r\\n month_name = re.compile(cls.MONTHS)\\r\\n\\r\\n if re.search(r'\\\\d{1,2}/\\\\d{1,2}/\\\\d{2,4}', date_string):\\r\\n date_string = month_backslash.sub(' <month> ', date_string)\\r\\n\\r\\n if re.search(r'\\\\d{1,2}-\\\\d{1,2}-\\\\d{2,4}', date_string):\\r\\n date_string = month_dash.sub(' <month> ', date_string)\\r\\n\\r\\n if re.search('(' + cls.MONTHS + ')', date_string):\\r\\n date_string = month_name.sub(' <month> ', date_string)\\r\\n\\r\\n return date_string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b97e2bfe466ec2d446777c7e44ed43d3\",\n \"score\": \"0.572343\",\n \"text\": \"def get_month(time):\\n return args.months[time.month-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98acf3fcba8aca7e84c50e8e207b3d7f\",\n \"score\": \"0.57107705\",\n \"text\": \"def get_filename(station, year, month):\\r\\n if month < 10:\\r\\n fname = 'Station_Data/{}/{}_{}_ComprehensiveFormat/{}_01_{}-0{}.csv'.format(station, station, year, station, year, str(month))\\r\\n else:\\r\\n fname = 'Station_Data/{}/{}_{}_ComprehensiveFormat/{}_01_{}-{}.csv'.format(station, station, year, station, year, str(month))\\r\\n if not path.exists(fname):\\r\\n if month < 10:\\r\\n fname = 'Station_Data/{}/{}_{}_ComprehensiveFormat/{}_05_{}-0{}.csv'.format(station, station, year, station, year, str(month))\\r\\n else:\\r\\n fname = 'Station_Data/{}/{}_{}_ComprehensiveFormat/{}_05_{}-{}.csv'.format(station, station, year, station, year, str(month))\\r\\n return fname\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15618017b7fcb218da7cdb08d04f8288\",\n \"score\": \"0.57079804\",\n \"text\": \"def mosaic_date_parser(project_name):\\n \\n # The date is always a sequence of 6 numbers in the filename.\\n seq = re.compile('[0-9]{6}')\\n seq_match = seq.search(project_name)\\n \\n if seq_match:\\n year = '20' + seq_match[0][-2:]\\n if year == '2020':\\n date = year + '-' + seq_match[0][2:4] + '-' + seq_match[0][:2]\\n elif year == '2019':\\n date = year + '-' + seq_match[0][:2] + '-' + seq_match[0][2:4]\\n else:\\n return None\\n \\n # Looks like Dec 6 was an exception to the format...\\n if date=='2019-06-12':\\n date = '2019-12-06'\\n \\n # Handle April 8 b scan case.\\n if project_name[seq_match.end()]=='b':\\n date = date + ' 12:00:00'\\n \\n return date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fa23052f94e4abae57e8ce04858daec\",\n \"score\": \"0.570112\",\n \"text\": \"def file_time(filename):\\n import datetime\\n t = os.path.getmtime(filename)\\n d = datetime.datetime.fromtimestamp(t)\\n day = str(d.day)\\n month = str(d.month)\\n year = str(d.year)\\n \\n if len(day) < 2:\\n day = '0' + day\\n if len(month) < 2:\\n month = '0' + month\\n year = year[2:]\\n \\n return month+day+year\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9bc62e5795b8613605c6aecdabf4233\",\n \"score\": \"0.5700694\",\n \"text\": \"def get_symbol_from_path_file_name(path_file_name):\\n # ./data/daily/AAPL_2021-01-23.csv\\n # ./data/one_off_min/ZION_min_batch13_2021-01-23.csv\\n str1 = path_file_name.split('/')[3]\\n str2 = str1.split('_')[0]\\n return str2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"284c4b060913aaab5d752e2f37722be4\",\n \"score\": \"0.56826293\",\n \"text\": \"def extract_YYYYMMDD(filename):\\n try:\\n dt = datetime.datetime.strptime( \\\\\\n \\tre.search(\\\"[12][09][0-9][0-9][01][0-9][0-3][0-9]\\\", filename).group(), \\\"%Y%m%d\\\")\\n except (ValueError, AttributeError): # there is not one\\n return None\\n if dt < datetime.datetime(1957, 10, 4, 19, 28, 34): # Sputnik 1 launch datetime\\n dt = None\\n # better not still be using this... present to help with random numbers combinations\\n elif dt > datetime.datetime(2050, 1, 1):\\n dt = None\\n return dt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de864d09ec38c6f3784b603525431375\",\n \"score\": \"0.56730074\",\n \"text\": \"def parse_file(file_name):\\n import re\\n import datefinder\\n with open(file_name, 'r', encoding='utf8') as f:\\n file_string = f.read()\\n\\n d_iter = re.finditer(r\\\"<date.*\\\\n\\\", file_string) # [0]\\n d_match = next(d_iter)\\n d = next(datefinder.find_dates(d_match.group())).date()\\n\\n t = re.findall(r\\\"<title.*>\\\", file_string)[0]\\n t = t.strip('<>').split('=')[1].strip('\\\"')\\n\\n s = file_string[d_match.end():]\\n return (t, d, s)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be03f88e4d2aaeacc1abe114fbd0656f\",\n \"score\": \"0.5666367\",\n \"text\": \"def extract_month(cube, month):\\n if month not in range(1, 13):\\n raise ValueError('Please provide a month number between 1 and 12.')\\n return cube.extract(iris.Constraint(month_number=month))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f5ed7ba8128f1bfcbee8ac7e919d3dd\",\n \"score\": \"0.56544363\",\n \"text\": \"def extract_filename(path_filename):\\n filename = ntpath.basename(path_filename)\\n return filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"215477bc5ee04b5959ffcf326b436158\",\n \"score\": \"0.5642729\",\n \"text\": \"def filenameToDate(filename):\\n \\n date = filename.split('_')[1] # From 'M_20190610_01' take '20190610'\\n date = '-'.join([date[:4], date[4:6], date[6:]]) # Transfrom to '2019-06-10'\\n \\n return date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f61b56d1065d7a6ac150eadeb9579e44\",\n \"score\": \"0.56118983\",\n \"text\": \"def oleaut32_VarMonthName(jitter):\\n ret_ad, args = jitter.func_args_stdcall([\\\"iMonth\\\", \\\"fAbbrev\\\", \\\"dwFlags\\\", \\\"pbstrOut\\\"])\\n raise RuntimeError('API not implemented')\\n jitter.func_ret_stdcall(ret_ad, ret_value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72c493338c5149c687f999f9aa231718\",\n \"score\": \"0.55862534\",\n \"text\": \"def get_month(month_no):\\n\\n months = [\\n \\\"january\\\",\\n \\\"february\\\",\\n \\\"march\\\",\\n \\\"april\\\",\\n \\\"may\\\",\\n \\\"june\\\",\\n \\\"july\\\",\\n \\\"august\\\",\\n \\\"september\\\",\\n \\\"october\\\",\\n \\\"november\\\",\\n \\\"december\\\"\\n ]\\n\\n month = months[int(month_no) - 1]\\n\\n return month\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcf8f54fc715137262c5deef1c8bef3c\",\n \"score\": \"0.5544727\",\n \"text\": \"def file_name(path):\\n return os.path.basename(path).split('.')[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d92f6d6259e955556a7e8e4a9cac61ef\",\n \"score\": \"0.5536999\",\n \"text\": \"def get_filename(file_path):\\n return os.path.split(file_path)[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3dbbca9ddf5504abb6d7de47bb8fba74\",\n \"score\": \"0.5535749\",\n \"text\": \"def get_year_and_month(word):\\n nlp = en_core_web_sm.load()\\n doc = nlp(word)\\n month = 0\\n year = 0\\n\\n for i in range(len(doc)):\\n if doc[i].lemma_ == \\\"year\\\":\\n year = doc[i - 1]\\n if doc[i].lemma_ == \\\"month\\\":\\n month = doc[i - 1]\\n return [int(str(year)), int(str(month))]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7048e37405499327ae1c15ef25830c0b\",\n \"score\": \"0.55160594\",\n \"text\": \"def file_name(path):\\n import os\\n\\n return os.path.splitext(os.path.basename(path))[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b6e8434ae3627ce4d57dd54870850e6\",\n \"score\": \"0.5487039\",\n \"text\": \"def deconstruct_file_name(name):\\n return os.path.splitext(os.path.split(name)[1])[0].split('_')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f91646d1a9309c61dbdd15851b5b8bb2\",\n \"score\": \"0.5447287\",\n \"text\": \"def extract_names(filename):\\n myFile = extract_from_file(filename)\\n# print(myFile)\\n year = extract_year(myFile)\\n# print(year)\\n durtyList = extract_all_names(myFile)\\n cleanList = create_dictionary(durtyList)\\n# print(cleanList)\\n finalList = {year: \\\"\\\"}\\n finalList.update(cleanList[0])\\n finalList.update(cleanList[1])\\n finalSortedList = sorted(finalList.items())\\n# print (finalList)\\n return finalSortedList\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dde6f4a149e2630c7d8e23e432f9b50e\",\n \"score\": \"0.5441289\",\n \"text\": \"def get_file_name(filename):\\n filename = filename.split('\\\\n')[0]\\n filename = filename.replace('/', '')\\n return filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7147f86f325fce68663b0e72b83d7002\",\n \"score\": \"0.5432966\",\n \"text\": \"def translate_month(month):\\r\\n # Create array containing month names, return the correct moth name based on the month number found earlier\\r\\n months = ['January', 'February', 'March', 'April', 'May', \\\"June\\\", \\\"July\\\", \\\"August\\\", \\\"September\\\", \\\"October\\\",\\r\\n \\\"November\\\", \\\"December\\\"]\\r\\n if (month >= 1) and (month <= 12):\\r\\n return months[month - 1]\\r\\n else:\\r\\n return \\\"Error\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af1794ff5ac89ea0ad801157645d88fc\",\n \"score\": \"0.542772\",\n \"text\": \"def get_hfname(station, year, month):\\r\\n if month < 10:\\r\\n fname = 'Station_Data/{}/{}_{}_HourFormat/{}_05_{}-0{}_hour.csv'.format(station, station, year, station, year, str(month))\\r\\n else:\\r\\n fname = 'Station_Data/{}/{}_{}_HourFormat/{}_05_{}-{}_hour.csv'.format(station, station, year, station, year, str(month))\\r\\n if not path.exists(fname):\\r\\n if month < 10:\\r\\n fname = 'Station_Data/{}/{}_{}_HourFormat/{}_01_{}-0{}_hour.csv'.format(station, station, year, station, year, str(month))\\r\\n else:\\r\\n fname = 'Station_Data/{}/{}_{}_HourFormat/{}_01_{}-{}_hour.csv'.format(station, station, year, station, year, str(month))\\r\\n return fname\",\n \"title\": \"\"\n },\n {\n \"docid\": \"418139cdcbf2cb531517f37aec29241d\",\n \"score\": \"0.54263574\",\n \"text\": \"def test_month_name(self):\\n # T273573\\n self.assertEqual(date.formats['MonthName']['hu']('január'), 1)\\n self.assertEqual(date.formats['MonthName']['hu'](5), 'május')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f30b9beb58be828927715a1c2137ca0\",\n \"score\": \"0.5420096\",\n \"text\": \"def subject_from_regeneron1_fname(fname):\\n # return fname.split('_')[1].rstrip('P').rstrip('M').rstrip('D').rstrip('F')\\n return fname.split('_')[1].replace('-', '.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a031d3ffc0f8bbef0e594afc91528e9\",\n \"score\": \"0.5411086\",\n \"text\": \"def get_symbol_from_file_name(file_name):\\n # AAPL_2021-01-13.csv\\n return file_name.split('_')[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b28a0f99bfec6bb0736134ee00043c1\",\n \"score\": \"0.5401249\",\n \"text\": \"def FileNameFromPath(path):\\n return os.path.split(path)[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96dc48f92568f229db3041514d208f17\",\n \"score\": \"0.5395899\",\n \"text\": \"def get_date_from_file_name(\\n path2file: Path, # Path to the file\\n date_pattern: str=None, # regex pattern for the date, if None, default date pattern is YY-MM-DD\\n) -> dt.datetime:\\n if date_pattern is None:\\n date_pattern = r\\\"^(\\\\d\\\\d)-(\\\\d\\\\d)-(\\\\d\\\\d)*\\\"\\n\\n if not path2file.is_file() or path2file is None:\\n return False\\n p = re.compile(date_pattern)\\n results = p.match(path2file.name)\\n\\n if results is not None:\\n y, m, d = results.groups()\\n if y is None or m is None or d is None:\\n return False\\n else:\\n y = int(y)\\n offset = (1900 if 50 < y <= 99 else 2000)\\n y = y + offset\\n date = dt.datetime(year=y, month=int(m), day=int(d))\\n return date\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01bd6e9c0a239d11826f461ef82d6f93\",\n \"score\": \"0.5380744\",\n \"text\": \"def parse(file_name):\\n with open(file_name, \\\"r\\\") as f:\\n match = re.match(r\\\"(\\\\d{6})-(\\\\d{6})\\\", f.readline())\\n return (match.group(1), match.group(2))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de04bc59e3031c844f8a9166853c7c53\",\n \"score\": \"0.5378534\",\n \"text\": \"def get_name(path):\\n name, _ = os.path.splitext(os.path.basename(path))\\n\\n return name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"166a7283828bd5ff2dfeac195ba62e0e\",\n \"score\": \"0.5374061\",\n \"text\": \"def month_of_date(date_str):\\n return _MONTHS_LIST[int(date_str.split('/')[1]) - 1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f35135f7944f01a1f82b6a437a30fdf0\",\n \"score\": \"0.5370653\",\n \"text\": \"def parse_filename(filename, name=None):\\n version = None\\n for ext in ALL_EXTENSIONS:\\n if filename.endswith(ext):\\n trimmed = filename[:-len(ext)]\\n parsed = split_filename(trimmed, name)\\n if parsed is None:\\n break\\n else:\\n parsed_name, version = parsed[:2]\\n break\\n if version is None:\\n raise ValueError(\\\"Cannot parse package file '%s'\\\" % filename)\\n if name is None:\\n name = parsed_name\\n return normalize_name(name), version\",\n \"title\": \"\"\n },\n {\n \"docid\": \"193c8f590ee4058388ae709e067adec8\",\n \"score\": \"0.5341717\",\n \"text\": \"def my_bir_month(m):\\r\\n cal = calendar.TextCalendar(3)\\r\\n cal.prmonth(2012, m)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af8cde09ca8b17d1ad66da9ac0c49176\",\n \"score\": \"0.5339409\",\n \"text\": \"def _decode_file_name(filename: str) -> MissionFileNameInfo:\\n\\n\\tfileList = filename.split(\\\".\\\")\\n\\n\\t# Check if the file name ends with \\\".pbo\\\"\\n\\tif fileList[-1:][0].casefold() != \\\"pbo\\\":\\n\\t\\traise Exception(\\\"Missions can only be submitted in .pbo format.\\\")\\n\\n\\t# Check if there are any erroneuous periods in the file name\\n\\tif len(fileList) > 3:\\n\\t\\traise Exception(\\\"File names can only have periods to denote the map and file extension.\\\")\\n\\tif len(fileList) < 3:\\n\\t\\traise Exception(\\\"File name appears to be missing the map definition.\\\")\\n\\n\\t# Get our map extension\\n\\tmapName = fileList[-2:][0].casefold()\\n\\n\\t# Split up our map name\\n\\tnameList = fileList[0].split(\\\"_\\\")\\n\\n\\t# Check if the mission is a test mission\\n\\tif nameList[0].casefold() == \\\"test\\\":\\n\\t\\tdel nameList[0]\\n\\n\\tif len(nameList) == 0:\\n\\t\\traise Exception(\\\"File name appears to be missing the gametype and playercount\\\")\\n\\n\\t# Check the mission type\\n\\tgameType = nameList[0].casefold()\\n\\tif not (gameType in VALID_GAMETYPES):\\n\\t\\traise Exception(f\\\"`{gameType}` is not a valid mission type!\\\")\\n\\n\\t# Grab the player count\\n\\ttry:\\n\\t\\tplayerCount = int(nameList[1])\\n\\texcept:\\n\\t\\traise Exception(\\\"I could not determine the player count in your mission.\\\")\\n\\n\\tmissionInfo: MissionFileNameInfo = {\\\"gameType\\\": gameType, \\\"map\\\": mapName, \\\"playerCount\\\": playerCount}\\n\\treturn missionInfo\",\n \"title\": \"\"\n },\n {\n \"docid\": \"300dcfa12e9103321cc588df2c6ece66\",\n \"score\": \"0.53339964\",\n \"text\": \"def getFileInfo(filename: str):\\n date = filename[10:18]\\n time = filename[-12:-4]\\n folder = filename[19:27]\\n filenumber = filename[28:31]\\n\\n return date, time, folder, filenumber\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bd8804647565d83dd7fb809524d5e91\",\n \"score\": \"0.5324675\",\n \"text\": \"def proper_name(file, timestamp):\\n (folder, full_name) = os.path.split(file)\\n (name, ext) = os.path.splitext(full_name)\\n\\n fix_hex = re.match(\\\"^MOV[0-9A-F]{3}$\\\", name)\\n\\n # first pull the date out\\n # try to find embedded date\\n existing_date = re.search(\\\"[-._ ]?(?P<Y>\\\\d{4})[-._ ]?(?P<m>\\\\d{2})[-._ ]?(?P<d>\\\\d{2})[-._ ]?(?P<H>\\\\d{2})[-._ ]?(?P<M>\\\\d{2})[-._ ]?(?P<S>\\\\d{2})[-._ ]?\\\", name)\\n if existing_date:\\n name = name.replace(existing_date.group(0), \\\"\\\").__str__()\\n\\n # check there's no dup date\\n dup_match = re.search(\\\"[-._ ]?\\\\d\\\\d\\\\d\\\\d\\\\.\\\\d\\\\d\\\\.\\\\d\\\\d$\\\", name)\\n if dup_match:\\n name = name.replace(dup_match.group(0), \\\"\\\")\\n\\n name = \\\"_{0}\\\".format(name) if name else \\\"\\\"\\n name = \\\"%s.%s.%s_%s-%s-%s%s\\\" % (\\n existing_date.group(\\\"Y\\\"),\\n existing_date.group(\\\"m\\\"),\\n existing_date.group(\\\"d\\\"),\\n existing_date.group(\\\"H\\\"),\\n existing_date.group(\\\"M\\\"),\\n existing_date.group(\\\"S\\\"),\\n name\\n )\\n else:\\n date_match = re.search(\\\"\\\\d\\\\d\\\\d\\\\d\\\\.\\\\d\\\\d\\\\.\\\\d\\\\d[_ ]\\\", name)\\n if not date_match:\\n name = apply_timestamp_convention(name, timestamp)\\n elif not re.match(\\\"\\\\d\\\\d\\\\d\\\\d\\\\.\\\\d\\\\d\\\\.\\\\d\\\\d_\\\\d\\\\d-\\\\d\\\\d-\\\\d\\\\d\\\", name):\\n name = name.replace(date_match.group(0), \\\"\\\")\\n name = apply_timestamp_convention(name, timestamp)\\n\\n if fix_hex:\\n # now unwrap hex number from JVC\\n match = re.search(\\\"MOV([0-9A-F]{3})\\\", name)\\n if match:\\n number = int(match.group(1), 16)\\n name = name.replace(match.group(0), \\\"MOV\\\" + (\\\"%03d\\\" % (number,)))\\n\\n return os.path.join(folder, name + ext)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fd1a2acf23f03029ed8376542b44ed2\",\n \"score\": \"0.52925444\",\n \"text\": \"def get_month(string):\\n if 'Apr' in string:\\n month = '04'\\n elif 'Mai' in string:\\n month = '05'\\n elif 'Jun' in string:\\n month = '06'\\n elif 'Jul' in string:\\n month = '07'\\n else:\\n raise Exception('wrong month (not in April - July interval)')\\n return month\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e77856657081fd5c37d94c9f366cef56\",\n \"score\": \"0.529037\",\n \"text\": \"def filename(file):\\n\\treturn os.path.basename(file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d16aa02649b16914e4f72827831dc13\",\n \"score\": \"0.5283355\",\n \"text\": \"def read_month(fileGlob, reanalysis, variable):\\n ds = xr.open_mfdataset(fileGlob, concat_dim='time', data_vars='different')\\n ds.set_coords(['latitude','longitude'], inplace=True)\\n ds.coords['time'] = util.make_time_coordinate(fileGlob)\\n ds.load()\\n return ds\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ba66840d5990e6f415dab5ffe1c7062\",\n \"score\": \"0.52725536\",\n \"text\": \"def modulename(path: str) -> str:\\n return os.path.splitext(os.path.basename(path))[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb162adfbd7d122a1ab675d194d57229\",\n \"score\": \"0.5266075\",\n \"text\": \"def read_climatology(fname,vname,nlat,nlon):\\n \\n infile = cdms2.open(fname)\\n \\n months = ['jan','feb','mar','apr','may','jun','jul','aug','sep','oct','nov','dec']\\n monthly_climatology = numpy.zeros([len(months),nlat,nlon])\\n for i in range(0,len(months)):\\n data = infile(vname+'_'+months[i],order='yx')\\n\\tmonthly_climatology[i,:,:] = data\\n \\n infile.close()\\n \\n return monthly_climatology\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cf791c0adf750e70b8768558db1a4a5\",\n \"score\": \"0.5265543\",\n \"text\": \"def dateparser(filename):\\n \\n #Open the file with read only mode\\n f= open(filename,\\\"r\\\")\\n \\n #Read the file line by line\\n for i in range(10):\\n dat = f.readline()\\n \\n dat = dat[5:-1]\\n #dat = datetime.strptime(dat, '%Y/%m/%d').date()\\n \\n return dat\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac453f5b788165d512e5f4cdb05c64c6\",\n \"score\": \"0.5254481\",\n \"text\": \"def get_month_year(self):\\n calendar_heading = self.find(By.CSS_SELECTOR,\\n '.calendar-header-label')\\n Assignment.scroll_to(self.driver, calendar_heading)\\n calendar_date = calendar_heading.text\\n month, year = calendar_date.split(' ')\\n return self.get_month_number(month), int(year)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c17d05ce7016b5175a132693c698a2d1\",\n \"score\": \"0.5250424\",\n \"text\": \"def filenameToMeasureFilename(filename, \\n home=os.getcwd()):\\n \\n date = filenameToDate(filename) # Transfrom to '2019-06-10'\\n fits_filename = os.path.join(home, 'Mediciones', date, filename+'.txt')\\n \\n return fits_filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10b6b5584687b82f38618b3886e5f4bf\",\n \"score\": \"0.52503884\",\n \"text\": \"def _get_date_from_filename(self, filename):\\n date_string = filename[:-4]\\n return datetime.strptime(date_string, '%Y-%m-%d')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48ab2e369209ecfa943b8e22426a5a20\",\n \"score\": \"0.5241301\",\n \"text\": \"def get_name(data, meta_path, title_counter):\\n authors = \\\"\\\"\\n try:\\n title = data[\\\"stats\\\"][\\\"title\\\"]\\n authors = data[\\\"stats\\\"][\\\"authors\\\"]\\n except KeyError: # older type file\\n title = splitext(basename(meta_path))[0]\\n try:\\n name = title.split(\\\"#] \\\")[1]\\n title = splitext(name)[0]\\n except IndexError: # no \\\"#] \\\" in filename\\n pass\\n if not title:\\n try:\\n name = meta_path.split(\\\"#] \\\")[1]\\n title = splitext(name)[0]\\n except IndexError: # no \\\"#] \\\" in filename\\n title = _(\\\"NO TITLE FOUND\\\") + str(title_counter[0])\\n title_counter[0] += 1\\n name = title\\n if authors:\\n name = \\\"{} - {}\\\".format(authors, title)\\n return name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e2c5fea9acca82170ebee5378407ac5\",\n \"score\": \"0.52329886\",\n \"text\": \"def formatmonthname(year, themonth, width, withyear=True):\\n s = month_names[themonth]\\n if withyear:\\n s = \\\"%s %r\\\" % (s, year)\\n return s.center(width)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24dc9c85d3e828c2f4e96b0b8567bb18\",\n \"score\": \"0.5231518\",\n \"text\": \"def extract_date(ifile):\\n\\n global dt\\n (idir, fil) = split_filename(ifile)\\n\\n # MCD43C1.A2008001.005.2008025105553.hdf\\n # MCD43C3.A2008001.005.2008025111631.hdf\\n if fil.startswith('MCD'):\\n fsplit = fil.split('.')\\n for s in fsplit:\\n if s.startswith('A'):\\n ss = s.replace(\\\"A\\\", \\\"\\\")\\n dt = date_from_year_doy(int(ss[:-3]),\\n int(ss[4:]))\\n\\n # global_emis_inf10_monthFilled_MYD11C3.A2008001.041.nc\\n # global_emis_inf10_monthFilled_MYD11C3.A2008001.nc\\n elif fil.startswith('global'):\\n fbasen = os.path.splitext(fil)\\n fsplit = fbasen[0].split('.')\\n for s in fsplit:\\n if s.startswith('A'):\\n ss = s.replace(\\\"A\\\", \\\"\\\")\\n dt = date_from_year_doy(int(ss[:-3]),\\n int(ss[4:]))\\n\\n # NISE_SSMIF13_20080101.HDFEOS\\n elif fil.startswith('NISE'):\\n fbasen = os.path.splitext(fil)\\n fsplit = fbasen[0].split('_')\\n dt = datetime.datetime(int(fsplit[2][:-4]),\\n int(fsplit[2][4:-2]),\\n int(fsplit[2][6:]))\\n\\n # ERA_Interim_an_20080101_00+00.nc\\n elif fil.startswith('ERA_Interim'):\\n fbasen = os.path.splitext(fil)\\n fsplit = fbasen[0].split('_')\\n fstime = fsplit[4].split('+')\\n dt = datetime.datetime(int(fsplit[3][:-4]),\\n int(fsplit[3][4:-2]),\\n int(fsplit[3][6:]),\\n int(fstime[0]),\\n int(fstime[1]))\\n return dt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5eb766fdf37fcec658b495bf25cf0738\",\n \"score\": \"0.5230452\",\n \"text\": \"def get_name(fname):\\n with open(fname, encoding=\\\"utf-8\\\") as tmp:\\n return tmp.readline()[1:].strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"706901595a8a1428cffd5140fc691060\",\n \"score\": \"0.5230213\",\n \"text\": \"def extract_year_month(init_time, logger):\\n # pylint:disable=protected-access\\n # Need to call sys.__getframe() to get the filename and method/func\\n # for logging information.\\n\\n # Useful for logging\\n cur_filename = sys._getframe().f_code.co_filename\\n cur_function = sys._getframe().f_code.co_name\\n\\n # Match on anything that starts with 1 or 2 (for the century)\\n # followed by 5 digits for the remainder of the YYYMM\\n year_month = re.match(r'^((1|2)[0-9]{5})', init_time)\\n if year_month:\\n year_month = year_month.group(0)\\n return year_month\\n else:\\n logger.warning(\\\"WARNING|\\\" + \\\"[\\\" + cur_filename + \\\":\\\" + cur_function +\\n \\\"]\\\" + \\\" | Cannot extract YYYYMM from \\\"\\n \\\"initialization time, unexpected format\\\")\\n raise Warning(\\\"Cannot extract YYYYMM from initialization time,\\\"\\n \\\" unexpected format\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"279dc8302b21eac28ba7b0f63b9e3278\",\n \"score\": \"0.522959\",\n \"text\": \"def get_file_name(file):\\n return Path(file).stem\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d0694e6524d37faf130b255b2c5255b\",\n \"score\": \"0.52256095\",\n \"text\": \"def _get_filename(file):\\n return file.name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc9e8198c573fe64cd93008d4f840810\",\n \"score\": \"0.52250177\",\n \"text\": \"def get_month(self, month_nr):\\n return self.months[month_nr - 1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab24fc242788cb980eb2aadbdd708888\",\n \"score\": \"0.5219688\",\n \"text\": \"def filename_from_datetime(dt: datetime.datetime, path: str) -> str:\\n base = basename_from_datetime(dt)\\n ext = os.path.splitext(path)[1]\\n return base + ext.lower()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"983c88b1acc99870c83d8bf3b3780550\",\n \"score\": \"0.52050406\",\n \"text\": \"def get_months(s):\\n ret = s.split(\\\",\\\")\\n if len(ret) == 1:\\n if ret[0].lower() == \\\"system\\\":\\n return get_system_months()\\n if ret[0].lower() == \\\"number\\\":\\n return get_number_months()\\n if len(ret) != 12:\\n raise PhorgError(\\\"We needs exactly 12 months\\\")\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f15ca7ef06ba401a72a0128390000bd6\",\n \"score\": \"0.52002996\",\n \"text\": \"def concat_last_date_2file_name(filename: str,\\n path: str,\\n log_file):\\n \\n last_date = extract_last_date(path, log_file)[:-1] #ignore new line\\n filename_date = f\\\"{filename}_{last_date}.xlsx\\\"\\n \\n return filename_date\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bed072a00cf744334c7b7eb23eb04d32\",\n \"score\": \"0.5196681\",\n \"text\": \"def getMonthHeader(self):\\n return self.curr.getDate(1).strftime(\\\"%b\\\"), self.curr.year\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5279f13c48285ada3f00fcd0d701cfc1\",\n \"score\": \"0.519643\",\n \"text\": \"def newname(fname):\\n\\n if is_skipdir(fname):\\n return None\\n\\n dirname = os.path.dirname(fname)\\n basename = os.path.basename(fname)\\n ext = os.path.splitext(basename)[1]\\n\\n if ext in skip_exts:\\n return None\\n\\n if is_skip_pat(basename):\\n return None\\n\\n for p in mdy_pats:\\n m = p.search(basename)\\n if m:\\n (month,day,year) = m.group(1,2,3)\\n new_year = make_year_digit4(year)\\n new_month = make_digit2(month)\\n new_day = make_digit2(day)\\n\\n assert len(new_year)==4\\n assert len(new_month)==2\\n assert len(new_day)==2\\n\\n if not valid_year(new_year): return None\\n if not valid_month(new_month): return None\\n if not valid_day(new_day): return None\\n\\n # Look for a simple replacement, otherwise replace part-by-part\\n fmt0 = f\\\"{month}.{day}.{year}\\\"\\n fmt1 = f\\\"{month}{day}{year}\\\"\\n iso = f\\\"{new_year}-{new_month}-{new_day}\\\"\\n if fmt0 in basename:\\n basename_new = basename.replace(fmt0,iso)\\n elif fmt1 in basename:\\n basename_new = basename.replace(fmt1,iso)\\n else:\\n basename_new = basename.replace(month,\\\"MONTH\\\",1).replace(day,\\\"DAY\\\",1).replace(year,\\\"YEAR\\\",1)\\n basename_new = basename_new.replace(\\\"MONTHDAYYEAR\\\",\\\"MONTH-DAY-YEAR\\\")\\n basename_new = basename_new.replace(\\\"MONTH.DAY.YEAR\\\",\\\"MONTH-DAY-YEAR\\\")\\n basename_new = basename_new.replace(\\\"MONTH\\\",new_year,1).replace(\\\"DAY\\\",new_month,1).replace(\\\"YEAR\\\",new_day,1)\\n return os.path.join(dirname,basename_new)\\n\\n # Look for a month and a year\\n for myreg in [my_re1,my_re2]:\\n m = myreg.search(basename)\\n if m:\\n month = MONTHS[m.group(1)]\\n year = int(m.group(2))\\n print(\\\"month=\\\",month,\\\"year=\\\",year)\\n basename_new = basename.replace(m.group(1),\\\"MONTH\\\").replace(m.group(2),\\\"YEAR\\\")\\n basename_new = basename_new.replace(\\\"MONTH YEAR\\\",\\\"YEAR-MONTH\\\")\\n basename_new = basename_new.replace(\\\"MONTH.YEAR\\\",\\\"YEAR-MONTH\\\")\\n basename_new = basename_new.replace(\\\"MONTH. YEAR\\\",\\\"YEAR-MONTH\\\")\\n basename_new = basename_new.replace(\\\"MONTH, YEAR\\\",\\\"YEAR-MONTH\\\")\\n basename_new = basename_new.replace(\\\"MONTHYEAR\\\",\\\"YEAR-MONTH\\\")\\n basename_new = basename_new.replace(\\\"YEAR\\\",f\\\"{year:04}\\\").replace(\\\"MONTH\\\",f\\\"{month:02}\\\")\\n return os.path.join(dirname,basename_new)\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc6c221ccf3437f5dfc5cfed51bd0859\",\n \"score\": \"0.5194717\",\n \"text\": \"def splitRPMFilename(filename):\\n\\n if filename[-4:] == '.rpm':\\n filename = filename[:-4]\\n\\n archIndex = filename.rfind('.')\\n arch = filename[archIndex + 1:]\\n\\n relIndex = filename[:archIndex].rfind('-')\\n rel = filename[relIndex + 1:archIndex]\\n\\n verIndex = filename[:relIndex].rfind('-')\\n ver = filename[verIndex + 1:relIndex]\\n\\n epochIndex = filename.find(':')\\n if epochIndex == -1:\\n epoch = ''\\n else:\\n epoch = filename[:epochIndex]\\n\\n name = filename[epochIndex + 1:verIndex]\\n return name, ver, rel, epoch, arch\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a10c0eb482bc627eeb255c24c6c8ec16\",\n \"score\": \"0.5192341\",\n \"text\": \"def get_month_n_year(string):\\n all_months = ('january', 'february', 'march', 'april', 'may', 'june',\\n 'july', 'august', 'september', 'october', 'november',\\n 'december')\\n month = [ind for ind, month in enumerate(all_months) if month in\\n string.lower()]\\n month[0] = month[0] + 1\\n year = re.findall('\\\\d+', string)\\n return month[0], int(year[0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30ef47a38a016392dd6923f6aeebc1ed\",\n \"score\": \"0.5191631\",\n \"text\": \"def this_month():\\n this_month_str = \\\"%4d_%02d\\\" % time.gmtime()[0:2]\\n return this_month_str\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e15a4af71d27401bd3c863af89085306\",\n \"score\": \"0.51911485\",\n \"text\": \"def get_monthtext(self, month, year, page_pos=None, anchor=\\\"south east\\\"):\\n if page_pos is None:\\n page_pos = [self.page_width-0.2, 0]\\n monthtext = month + \\\" \\\" + str(year)\\n ms = \\\" %%% get_monthtext\\\\n\\\"\\n ms += r\\\" \\\\node at ({},{}) [anchor={},font=\\\\scshape,color=month!70!footerbackgroundcolor,scale=4,inner sep=0, outer sep=0] {{{}}};\\\".format(\\n page_pos[0], page_pos[1], anchor, monthtext) + \\\"\\\\n\\\\n\\\"\\n return ms\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ddd5ab6a7d0c605fdb37fbe559237bb0\",\n \"score\": \"0.5181049\",\n \"text\": \"def get_filename_from_path(file_name_with_path):\\n head, tail = os.path.split(file_name_with_path)\\n return tail\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09b53a9ed44b4585ea8f0aca91c6a6d4\",\n \"score\": \"0.5178857\",\n \"text\": \"def get_name(path):\\n file_name = ospath.basename(path)\\n return ospath.splitext(file_name)[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2843a9ca4a7b8f7b02d02c9af36bd1d6\",\n \"score\": \"0.51713604\",\n \"text\": \"def dateFromFilePath(file_path) -> datetime.date:\\n\\tbaseName = os.path.basename(file_path)\\n\\tname, ext = os.path.splitext(baseName)\\n\\ttry:\\n\\t\\tif not EXTERNAL_DIR in file_path:\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tproduct, year, doy = name.split(\\\".\\\")\\n\\t\\t\\t\\tdate = \\\".\\\".join([year, doy])\\n\\t\\t\\t\\treturn parseDateString(date)\\n\\t\\t\\texcept (ValueError, BadInputError):\\n\\t\\t\\t\\tproduct, date = name.split(\\\".\\\")[:2]\\n\\t\\t\\t\\treturn parseDateString(date)\\n\\t\\telse: # we do things differently for Ritvik's products\\n\\t\\t\\tproduct = os.path.dirname(file_path).split(\\\"/\\\")[-1]\\n\\t\\t\\tif product == \\\"chirps_gefs\\\":\\n\\t\\t\\t\\tdateBlob = name.split(\\\"_\\\")[1] # gives something like 20171021\\n\\t\\t\\t\\tyear = dateBlob[:4]\\n\\t\\t\\t\\tmonth = dateBlob[4:6]\\n\\t\\t\\t\\tday = dateBlob[6:]\\n\\t\\t\\t\\treturn parseDateString(f\\\"{year}-{month}-{day}\\\")\\n\\t\\t\\telif product == \\\"esi_4wk\\\":\\n\\t\\t\\t\\tdateBlob = name.split(\\\"_\\\")[-1]\\n\\t\\t\\t\\tyear = dateBlob[:4]\\n\\t\\t\\t\\tdoy = dateBlob[4:]\\n\\t\\t\\t\\treturn parseDateString(f\\\"{year}.{doy}\\\")\\n\\t\\t\\telif product in [\\\"soil_moisture_as1\\\",\\\"soil_moisture_as2\\\"]:\\n\\t\\t\\t\\tyear = name.split(\\\"_\\\")[-4]\\n\\t\\t\\t\\tdoy = name.split(\\\"_\\\")[-3]\\n\\t\\t\\t\\treturn parseDateString(f\\\"{year}.{doy}\\\")\\n\\texcept Exception as e:\\n\\t\\tlog.error(f\\\"Failed to extract date from file name '{baseName}'\\\")\\n\\t\\traise e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6074000a2d0efdacf2fab9886e3ede2b\",\n \"score\": \"0.5167556\",\n \"text\": \"def extract_version_from_filename(filename):\\n filename = os.path.splitext(os.path.basename(filename))[0]\\n if filename.endswith('.tar'):\\n filename = os.path.splitext(filename)[0]\\n return filename.split('-')[-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e522c40edf88484bb5250e8ea23c5df\",\n \"score\": \"0.5163665\",\n \"text\": \"def get_file_name(path):\\n base = os.path.basename(path)\\n return os.path.splitext(base)[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3517e3f59fd71f2f2da1f92ed58c2371\",\n \"score\": \"0.5161937\",\n \"text\": \"def get_date(paragraph, filename):\\n try:\\n date = paragraph.split('From:')[0].split('Date:')[1].strip()\\n if len(date) > 8:\\n date = datetime.strptime(date, '%d/%m/%Y') \\n # print(date)\\n return date\\n\\n else:\\n date = datetime.strptime(date, '%d/%m/%y') \\n # print(date)\\n return date\\n \\n except Exception as e:\\n print('Error finding date for: ', filename)\\n print(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"937c83258259b7423e5a7eed6282b686\",\n \"score\": \"0.5154342\",\n \"text\": \"def getfileName(file):\\n\\n # Split by backslash, get the last index\\n file = file.rsplit('/', 1)[-1]\\n # Remove )\\\" which is found in Pyfadix files\\n file = re.sub('[\\\")]', '', file)\\n return file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aaf02bde958c92c0b2e5f0f3dda2cc9d\",\n \"score\": \"0.51523423\",\n \"text\": \"def get_datetime_from_file_name(file_name):\\n try:\\n for p1, p2 in DATETIME_PATTERNS:\\n m = re.search(p1, file_name)\\n if m:\\n ts = []\\n for c in m.group(0):\\n if c not in ' -_.:':\\n ts.append(c)\\n return datetime.strptime(''.join(ts)[0:14], p2)\\n except:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"999bd86b4b6c8186fe20a8e4853f5d4b\",\n \"score\": \"0.51447415\",\n \"text\": \"def path_to_startdate(path):\\n # The string before the first / should be fall|summer|spring + YYYY,\\n # else this returns the empty string.\\n s = str(path.split('/')[0])\\n if s.startswith('fall'):\\n return s[-4:] + '-09-01'\\n elif s.startswith('spring'):\\n return s[-4:] + '-01-01'\\n elif s.startswith('summer'):\\n return s[-4:] + '-06-01'\\n else:\\n return ''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0cb71a9653db83d5d3b061704811f9a0\",\n \"score\": \"0.51402533\",\n \"text\": \"def parse_calib_file_name(fname) :\\n fnfields = os.path.splitext(fname) # '123-end.data' -> ('123-end', 'data') \\n if len(fnfields) != 2 : return None # check that file name like '123-end.data' has splited extension\\n if fnfields[1] != '.data': return None # check extension\\n fnparts = fnfields[0].split('-') # '123-end' -> ('123', 'end')\\n if len(fnparts) != 2 : return None # check that file name has two parts\\n #print('XXX parts of the file name:', fnparts[0], fnparts[1], fnfields[1])\\n return fnparts[0], fnparts[1], fnfields[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17dbbc81bce04d624fd91d862d3b3c7e\",\n \"score\": \"0.5138658\",\n \"text\": \"def get_month_number(self, month):\\n months = {v: k for k, v in enumerate(calendar.month_name)}\\n return months[month]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8dcd0f5f36af4628bea0c58c66ce632\",\n \"score\": \"0.5123069\",\n \"text\": \"def _get_month_names_match(self):\\n\\n months_match = ''\\n for month_name in self._months.keys():\\n months_match = months_match + month_name + '|'\\n\\n months_match = r'\\\\b(' + months_match[0:-1] + r')\\\\b'\\n\\n return months_match\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ae71329304140b374a1e569b0e8f42b\",\n \"score\": \"0.5120749\",\n \"text\": \"def get_month_name(index):\\n if index == 13:\\n index = 1\\n elif index == 14:\\n index = 2\\n elif index == 15:\\n index = 3\\n elif index == 0:\\n index = 12\\n if index == 1:\\n monthname = 'Januari'\\n elif index == 2:\\n monthname = 'Februari'\\n elif index == 3:\\n monthname = 'Maret'\\n elif index == 4:\\n monthname = 'April'\\n elif index == 5:\\n monthname = 'Mei'\\n elif index == 6:\\n monthname = 'Juni'\\n elif index == 7:\\n monthname = 'Juli'\\n elif index == 8:\\n monthname = 'Agustus'\\n elif index == 9:\\n monthname = 'September'\\n elif index == 10:\\n monthname = 'Oktober'\\n elif index == 11:\\n monthname = 'November'\\n else:\\n monthname = 'Desember'\\n return monthname\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcb35ab5c744821d7da8a38980e8b03c\",\n \"score\": \"0.5119535\",\n \"text\": \"def parse_filename(filepath):\\n\\n filename = os.path.split(filepath)[1]\\n if len(filename) == 12: # new file format\\n year = 2000 + int(filename[1:3])\\n month = int(filename[3], 16)\\n day = int(filename[4:6])\\n hour = int(filename[6:8])\\n minute = int(filename[9:11])\\n sec = 6 * int(filename[11])\\n result = datetime.datetime(year, month, day, hour, minute, sec)\\n else:\\n raise Exception(\\\"Filename length <> 12: {}\\\".format(len(filename)))\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9159903c5e9165471407e452679a9e60\",\n \"score\": \"0.51137316\",\n \"text\": \"def get_filename(path):\\n path = os.path.abspath(path)\\n x = os.path.basename(path)\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a392aa90c0d6f508ff2b51f82422e73e\",\n \"score\": \"0.5111258\",\n \"text\": \"def get_filename(path: str) -> str:\\n return os.path.splitext(os.path.basename(path))[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f951bb7a6dcdd990550e45a9f4307728\",\n \"score\": \"0.5106488\",\n \"text\": \"def get_da_plan_mo_name(self, file_name):\\n f = open(file_name)\\n plan_text = f.read()\\n plan = XML_utils.XML_utils().get_cmdata_elem_from_text(plan_text)\\n print '*DEBUG* %s' % plan_text\\n mo_names = XML_utils.XML_utils()._get_plan_mo_names(plan)\\n return mo_names.pop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9620fb9f57b2992ab77219dccab3625\",\n \"score\": \"0.51056486\",\n \"text\": \"def monthly_schedule(self, month):\\n response = requests.get\\\\\\n (f'http://company.com/{self.last.title()}/{month.title()}')\\n if response.ok:\\n return response.text\\n return 'Bad Response!'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"179f88a29b6e5219e8c0583c354db90d\",\n \"score\": \"0.51003516\",\n \"text\": \"def find_basename_from_file(file_path):\\n return os.path.basename(file_path).split(\\\".\\\")[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2ffbfda311f05dab2dc513553887e78\",\n \"score\": \"0.508476\",\n \"text\": \"def get_file_name_file(file_name):\\n return file_name.split(os.sep)[-1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7610775fd2e4446b8f2b46e4bb061406\",\n \"score\": \"0.50826496\",\n \"text\": \"def _get_file_name_from_path(path):\\n base_name = os.path.basename(path)\\n return os.path.splitext(base_name)[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b25b73261af3c6c1062f571d8f39670\",\n \"score\": \"0.5070315\",\n \"text\": \"def getNameFromFilename(cls, filename):\\n return filename[0:3]\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993283,"cells":{"query_id":{"kind":"string","value":"6f6d9a1e725ce0f4ef8522cd99bf1a2c"},"query":{"kind":"string","value":"State machine to pop data out of the PIFO upon request"},"positive_passages":{"kind":"list like","value":[{"docid":"5dd07dc1fdf8fabcca914cb97b2bd203","score":"0.6355643","text":"def pop_sm(self):\n while True:\n # wait to receive a read request\n req = yield self.r_in_pipe.get()\n # model read latency\n yield self.wait_sys_clks(self.read_latency)\n # try to read head element\n if len(self.items) > 0:\n data = self.items[0]\n self.items = self.items[1:]\n else:\n print >> sys.stderr, \"ERROR: PIFO pop_sm: attempted to read from empty PIFO\"\n data = None\n # write data back\n self.r_out_pipe.put(data)","title":""}],"string":"[\n {\n \"docid\": \"5dd07dc1fdf8fabcca914cb97b2bd203\",\n \"score\": \"0.6355643\",\n \"text\": \"def pop_sm(self):\\n while True:\\n # wait to receive a read request\\n req = yield self.r_in_pipe.get()\\n # model read latency\\n yield self.wait_sys_clks(self.read_latency)\\n # try to read head element\\n if len(self.items) > 0:\\n data = self.items[0]\\n self.items = self.items[1:]\\n else:\\n print >> sys.stderr, \\\"ERROR: PIFO pop_sm: attempted to read from empty PIFO\\\"\\n data = None\\n # write data back\\n self.r_out_pipe.put(data)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"e30a400a9997ad8e4203002e5c1eb6ec","score":"0.67517585","text":"def PopState(self):","title":""},{"docid":"dc63bf5591eac12daa4bf1eadab9c6df","score":"0.6186308","text":"def pop_state(self, *_):\n self.next_state = (StateOperations.POP, None)","title":""},{"docid":"cebba306e551552da79174bd8a777175","score":"0.61627305","text":"def PushState(self):","title":""},{"docid":"c93f4da28717bf488c3a3c538b6f4465","score":"0.593525","text":"def PopState(self):\n self.gstate = self.saved_state.pop()","title":""},{"docid":"2ff76d61d20cde8a93f1e71dc2f96af4","score":"0.5873438","text":"def pop_noop(self):\n\n basic_class.mylogger_record.info('command:<noop>') \n self.resp = self.pop3.noop()\n [basic_class.mylogger_record.debug(self.resp.decode())] \n #self.pop.quit() ","title":""},{"docid":"86c1847917035e845c1ff74de977f57b","score":"0.5775134","text":"def Pop():","title":""},{"docid":"ab3a31869e0a8127ec5499ce5cafa39d","score":"0.56789863","text":"def __getstate__(self):\n data = self.__dict__.copy()\n del data['_observation']\n del data['_action']\n del data['_returns']\n del data['_train_op']\n return data","title":""},{"docid":"be2e8b057510dfd46d24ad299b63bcec","score":"0.56524384","text":"def pop_sm(self):\n while True:\n # wait to receive a read request\n req = yield self.r_in_pipe.get()\n # model read latency\n yield self.wait_sys_clks(self.read_latency)\n # try to read head element\n if len(self.items) > 0:\n data = self.items[0]\n self.items = self.items[1:]\n self.bytes = self.bytes - data.get_bytes() # we assume data here is packet\n else:\n print >> sys.stderr, \"ERROR: FIFO pop_sm: attempted to read from empty FIFO\"\n data = None\n # write data back\n self.r_out_pipe.put(data)","title":""},{"docid":"f2bf66efeaca37028d6481c8365a1acb","score":"0.5614247","text":"def GetState(self):","title":""},{"docid":"f2bf66efeaca37028d6481c8365a1acb","score":"0.5614247","text":"def GetState(self):","title":""},{"docid":"2591ef6c5dc028d0a7440704701cd545","score":"0.5609776","text":"def _state(self):","title":""},{"docid":"5550e70e9e98fcc92bcf040a07457b9f","score":"0.55971557","text":"def _fetch(self):\n tag, fields = self.read_message()\n if tag == 0x70:\n self._responses.popleft().set_success(**fields[0])\n self._metadata.update(fields[0])\n elif tag == 0x71:\n self._responses[0].add_records(fields)\n elif tag == 0x7F:\n rs = self._responses.popleft()\n rs.set_failure(**fields[0])\n if rs.vital:\n self._wire.close()\n elif tag == 0x7E and not self._responses[0].vital:\n self._responses.popleft().set_ignored()\n else:\n self._wire.close()\n raise ProtocolError(\"Unexpected protocol message #%02X\", tag)","title":""},{"docid":"e97b4b8d7bac3ecc51d6119a27269e77","score":"0.5571678","text":"def DoFSM(self):\n pass","title":""},{"docid":"e97b4b8d7bac3ecc51d6119a27269e77","score":"0.5571678","text":"def DoFSM(self):\n pass","title":""},{"docid":"f6469b6918e48801d0b5b3c49dc54278","score":"0.55659395","text":"def _get_state(self):","title":""},{"docid":"4136dc2651477bb23e7107178b8108e5","score":"0.55530006","text":"def _process_remote_state(self):\n try:\n event = self._remote_events.pop(0)\n self._process_endpoint_event(event)\n except IndexError:\n LOG.debug(\"Endpoint %s: ignoring unexpected remote event\",\n self._name)","title":""},{"docid":"65d7e87068d2188e4660572ec0c9dde3","score":"0.5525905","text":"def __getstate__(self):\n state = self.__dict__.copy()\n state.pop(\"data\")\n if \"data_iter\" in state:\n state.pop(\"data_iter\")\n return state","title":""},{"docid":"0f1f77a938f21d4d3e01d208fe99b30c","score":"0.55120045","text":"def request(self) -> dapr.proto.common.v1.common_pb2.StateItem:","title":""},{"docid":"3366d3a22967d85cc0895ba118286d3c","score":"0.54983616","text":"def _process_local_state(self):\n try:\n event = self._local_events.pop(0)\n self._process_endpoint_event(event)\n except IndexError:\n LOG.debug(\"Endpoint %s: ignoring unexpected local event\",\n self._name)","title":""},{"docid":"37f48327e5033a6e78d38b662583b87d","score":"0.54916066","text":"def result(state, action):\n # copy the state\n child_state = deepcopy(state)\n # action[0] holds the peg from which to pop the top disc\n disc = child_state[action[0]].pop()\n # action[1] holds the peg to append the popped disc to\n child_state[action[1]].append(disc)\n return child_state","title":""},{"docid":"20eeff48e84df884ba5a7a558ebd6a6a","score":"0.54878384","text":"def _on_push(self, resp: dict, addr: Tuple[str, int]) -> None:\n self.history.message(HISTORY_PUSH, resp)\n self.last_push = time.monotonic()\n old_state = self.state.pilotResult if self.state else None\n new_state = resp[\"params\"]\n if old_state and states_match(old_state, new_state):\n return\n self.state = PilotParser(new_state)\n if self.push_callback:\n self.push_callback(self.state)","title":""},{"docid":"94bd6441ad2988672dd7912baf9cc42b","score":"0.5487515","text":"def __getstate__(self):\n\t\treturn None","title":""},{"docid":"1a738ce99ff644a447b64b7bd1e8d63c","score":"0.548465","text":"def pop_retr(self,which):\n \n self.which = which\n basic_class.mylogger_record.info('command:<retr '+str(self.which)+'>')\n try:\n self.resp,self.items,self.octets = self.pop3.retr(self.which)\n basic_class.mylogger_record.debug('the retr_rsp_data is:') \n [basic_class.mylogger_recordnf.debug(item.decode()) for item in self.items] \n except:\n pass\n #self.pop.quit() ","title":""},{"docid":"bbeb5b9f5d86d1c0cc33641caf75d565","score":"0.54574466","text":"def zero_state_command(self):\n self.phiiongun.State_Data_Packet()","title":""},{"docid":"5d8fb9b2c24706c7998649fb524d0151","score":"0.5445901","text":"def __getstate__(self):\n return self._data","title":""},{"docid":"fd19dc0e397aeca34a8ed471523870c6","score":"0.54341424","text":"def popitem(self):\n #return self.__state.popitem()\n return self.__session.state.popitem()","title":""},{"docid":"ed1ba2baebe62c94707836aa1d98fbfb","score":"0.542722","text":"def fsm_cb(data):\n global superviser, pilot\n # parse the new configuration\n pilot = data.data.split('_')[0]\n superviser = data.data.split('_')[1]","title":""},{"docid":"5552bb50bc34d76bb5cf962e9f253cc7","score":"0.54212123","text":"def POP(cpu):\n cpu.reg[cpu.OP_A] = cpu.ram_read(cpu.SP)\n cpu.SP += 1","title":""},{"docid":"fde5e95b8f00ae28390178e3fbc8081e","score":"0.54075074","text":"def pop(self, statemachine):\n assert(statemachine in self.uism)\n assert(len(self.uism) > 0)\n try:\n i = self.uism.index(statemachine)\n self.uism.pop(i) \n except ValueError:\n self.log.error('statemachine is not in self.uism')\n return\n except NameError:\n self.log.error('statemachine is not defined')\n return","title":""},{"docid":"26681ed466e197b50d705c91c78895c2","score":"0.54073954","text":"def firstObsDataAction(state, nextState, record, fields):\n global stack\n stack.pop() # close last element in obsContext\n stack.pop() # close obsContext\n stack.addPush(\"obsData\")\n return dataAction(state, nextState, record, fields, action=adesutility.ElementStack.addPush)","title":""},{"docid":"2a9abff7fcad4d1ecc364195f90f6803","score":"0.53953904","text":"def pull(self):","title":""},{"docid":"dd29ef753ed650c4f8d6206d1c9f3f16","score":"0.5386824","text":"def __getstate__(self) -> dict:\n self.close()\n time.sleep(0.1) # buffer time\n state = self.__dict__.copy()\n del state['rFp']\n del state['wFp']\n del state['Fp']\n return state","title":""},{"docid":"3be01d229557576a6520ebed2d04cbe3","score":"0.5386021","text":"def actions(self, state):","title":""},{"docid":"3be01d229557576a6520ebed2d04cbe3","score":"0.5386021","text":"def actions(self, state):","title":""},{"docid":"6fb3e869a3b7c76246e90c7c6c954100","score":"0.53856266","text":"def state(self):","title":""},{"docid":"6fb3e869a3b7c76246e90c7c6c954100","score":"0.53856266","text":"def state(self):","title":""},{"docid":"c2dec89c40847a1ff188f91abfa8c17c","score":"0.53799653","text":"def __getstate__():","title":""},{"docid":"fa2dc09e7ae2b96c0935d49e9d840e78","score":"0.5359047","text":"def handle_state_change(psp, context):\n pass","title":""},{"docid":"6b8bbeceaee48db0f84f13ffd14aa835","score":"0.5356365","text":"def popitem(self):\n raise InputConflict(\"Can't read and write in the same operation\")","title":""},{"docid":"29076877a1f2c9dc3f0626caac71380a","score":"0.53538644","text":"def pop(self):\n raise InputConflict(\n \"Can't read and write in the same operation\"\n )","title":""},{"docid":"fb033aeaf2143a215b5a372dd61dd144","score":"0.5330996","text":"def pop():","title":""},{"docid":"fb033aeaf2143a215b5a372dd61dd144","score":"0.5330996","text":"def pop():","title":""},{"docid":"1290071ba3caffd821d1c2be6ac8de93","score":"0.5323134","text":"def pre_inquiry (self):","title":""},{"docid":"131311db6c9835b2fee3b9ac8733f733","score":"0.53205574","text":"def state_cb(data):\n global state\n state = data.data","title":""},{"docid":"d9246a3bcc8165d9185e660198cae64f","score":"0.5319337","text":"def _received(self, msg):\n if self.state != msg['content']['data']['state']:\n self.state = msg[\"content\"][\"data\"][\"state\"]","title":""},{"docid":"a771406ff12acebb7eb72bcba05f40f2","score":"0.5312586","text":"def __getstate__(self):\n self.log.debug(\"G2P.__getstate__()\")\n odict = self.__dict__.copy() # copy the dict since we change it\n # Remove data that will require re-initialization upon reload.\n del odict['log']\n del odict['g2plib']\n if 'updater' in odict:\n del odict['updater']\n if 'pipe' in odict:\n del odict['pipe']\n return odict","title":""},{"docid":"e5e5fc678e91969202c6215b45e229a7","score":"0.53114057","text":"def _stateChanged(self):\n self._unstashInBox()\n if self.isPrimary is not None:\n # TODO handle suspicion exceptions here\n self.process3PhaseReqsQueue()\n # TODO handle suspicion exceptions here\n try:\n self.processPostElectionMsgs()\n except SuspiciousNode as ex:\n self.outBox.append(ex)\n self.discard(ex.msg, ex.reason, logger.warning)","title":""},{"docid":"a0b8d0e4de65322babdc47262878adcc","score":"0.5306768","text":"def __getstate__(self):\n state = self.__dict__.copy()\n state['vars'] = None\n state['fcns'] = None\n state['session'] = None\n return state","title":""},{"docid":"a53ff386f379913b487c8cb2560d227b","score":"0.52994955","text":"def pop_ih(self):\n if not self._ihs:\n return False\n\n # Pop it off.\n handler, prompt, state, cleanup = self._ihs.pop()\n\n # And call the cleanup function.\n if cleanup:\n try:\n cleanup(self)\n except Exception:\n if not state:\n state = handler.func_name\n log.exception(\"An error occurred while running the input \"\n \"handler cleanup function for the state %r on \"\n \"connection #%d.\" % (state, self._uid))\n\n return True","title":""},{"docid":"16bf07a61a773ca44e4394048e4a13e6","score":"0.52989006","text":"def __post_init__(self) -> None:\n parser = StateMessageParser(self.unparsed_response)\n\n self.state = parser.get_state()\n self.time_left = parser.get_time_left()\n self.time_on = parser.get_time_on()\n self.auto_shutdown = parser.get_auto_shutdown()\n self.power_consumption = parser.get_power_consumption()\n self.electric_current = watts_to_amps(self.power_consumption)","title":""},{"docid":"7048a8ccdd0a391da0b7f9c614b7116f","score":"0.5297024","text":"def result(self, state, action):","title":""},{"docid":"7048a8ccdd0a391da0b7f9c614b7116f","score":"0.5297024","text":"def result(self, state, action):","title":""},{"docid":"9b99382312e0a847ad2c18ec3e2be5fe","score":"0.52816844","text":"def __call__(self, state):\n raise NotImplementedError","title":""},{"docid":"268d4278052f4778dd5e52adbac7b3f5","score":"0.52707094","text":"def testGoProRequestState(self):\n self.mgr.goproManager = Mock()\n value = struct.pack('<II', app_packet.GOPRO_REQUEST_STATE, 0)\n trimValue = ''\n self.mgr.appMgr.client.recv.return_value = value\n handled = self.mgr.appMgr.parse()\n self.mgr.goproManager.handlePacket.assert_called_with(app_packet.GOPRO_REQUEST_STATE, trimValue)","title":""},{"docid":"ea4873a3a4e56b2170bd8273f68aa07b","score":"0.5270016","text":"def __getstate__(self):\n\n return None","title":""},{"docid":"4105d3d1b7b7f5f5512863e659943363","score":"0.5264336","text":"def loadState(self):\n pass","title":""},{"docid":"69b994abd771060e0f1caa858f80d07f","score":"0.52635896","text":"def fsm_back(self) -> None:\n if not self.__history:\n return\n self.request(self.__history.pop(), True)","title":""},{"docid":"bfc3a471cac8bfba13870b20b34e0ed7","score":"0.52436066","text":"def enter_state(self):\n\t\tpass","title":""},{"docid":"dae30527666df851ef6089805c28421e","score":"0.52421033","text":"def final(self, state):\r\n\r\n pass","title":""},{"docid":"c996e07581ea76bc50b77aac8aa54305","score":"0.52410156","text":"def pop_state(self):\n self._statestack.pop()","title":""},{"docid":"2ee47c2fdca4bce128cc688c70bbb8d8","score":"0.5238397","text":"def parse_state(self, data):\n self.parse_emission_state(data)\n self.parse_toggle_bit(data)\n self.parse_pressure_unit(data)","title":""},{"docid":"026041189eb1a6438e47ecd62c32c7aa","score":"0.5237256","text":"def __getstate__(self):\n ...","title":""},{"docid":"9d20ecef8eba23ea40cb7f032eda7886","score":"0.5236254","text":"def OnReceiveData(code):\n print(\"Query Success!\")\n RequestSessionEventHandler.query_state = 1","title":""},{"docid":"87dd2d4bf4d4ee24e1b017b30ce205fe","score":"0.5236237","text":"def pop(self, *args, **kwargs): # real signature unknown; restored from __doc__\n pass","title":""},{"docid":"039cffdbe132f43643d7ef410114dbce","score":"0.52210736","text":"def state(self, message):\n pass","title":""},{"docid":"37bfb2f8eb1830a6f91d1bbded6aaa94","score":"0.5216243","text":"def pop(self, *args, **kwargs): # real signature unknown\n pass","title":""},{"docid":"f92d44eeb16d04090325975865fc66a6","score":"0.5204015","text":"def decode_state(self, state):\n ...","title":""},{"docid":"71d97446b3d3888840903ad27a52386f","score":"0.5203083","text":"def __post_init__(self) -> None:\n parser = StateMessageParser(self.unparsed_response)\n\n self.direction = parser.get_shutter_direction()\n self.position = parser.get_shutter_position()","title":""},{"docid":"7cb350f4e82a642385d54d13cff2c840","score":"0.51962155","text":"def PushBack(provider):","title":""},{"docid":"742875850772bfd849f41229c221f12e","score":"0.5191453","text":"def get_input(self):\n if len(self.player.inq) > 0:\n # We've got something to work with!\n arg = self.player.inq[0].strip()\n del self.player.inq[0]\n self.next_state(arg)","title":""},{"docid":"84ae71c4e4728cde80a81e23154972f1","score":"0.51820445","text":"def get_action(self, state):","title":""},{"docid":"34d31bb96e43174e10b477ccc05e258a","score":"0.5179174","text":"def pop(self):","title":""},{"docid":"9216663eb862f8bfba71d878a481eca6","score":"0.516739","text":"def request(self, state):\n raise NotImplementedError(\"request function not implemented\")","title":""},{"docid":"0e5da8490bafe8794ddc8f437ccb7c56","score":"0.5161657","text":"def pop(self):\n self.register[self.ram_read(self.pc + 1)] = self.ram_read(self.register[7])\n self.register[7] += 1\n self.pc = self.pc + 2","title":""},{"docid":"30455fa5b84b0a3f8b2f3331177633ba","score":"0.5158398","text":"def structure_data(self, state):\n pass","title":""},{"docid":"e0b368bc735991ffb26e4e87378774da","score":"0.5157198","text":"def pull(self):\n try:\n self.history.pop()\n except IndexError:\n raise Exception(\"pull no thing\")","title":""},{"docid":"c05fa533abf76b624ce3cf5fddb60d4f","score":"0.51559144","text":"def _clearState(self):\n state = self.get('state')\n kind = self.get('kind')\n priority = self.get('priority')\n self.client.zrem('q:jobs:%s' % (state), self.job_id)\n self.client.zrem('q:jobs:%s:%s' % (kind, state), self.job_id)\n \n # as if we called set('state', None)\n self.client.hset('q:job:' + self.job_id, 'state', None)\n self.params['state'] = state","title":""},{"docid":"175844d73bf576822863d9a0664fc432","score":"0.51533574","text":"def popitem():","title":""},{"docid":"c8818bd04e5b1bb7267a3727228c2885","score":"0.5151481","text":"def pop(self, k, *args):\n #return self.__state.pop(k, *args)\n return self.__session.state.pop(k, *args)","title":""},{"docid":"0841419ec9ef3bccdc0c892ad61aa762","score":"0.51490796","text":"def pop_source(self, ):\n\t\tpass","title":""},{"docid":"fde8d3f26ad1de327461ddb61eb0e1c1","score":"0.51464427","text":"def event_m10_04_5020():\r\n \"\"\"State 0,1: Navigation mesh traffic is possible\"\"\"\r\n DeleteNavimeshAttribute(502000, 2)\r\n \"\"\"State 2: Finish\"\"\"\r\n EndMachine()\r\n Quit()","title":""},{"docid":"cb40b73f568ccc67ffc58ecca9c8abf8","score":"0.5145009","text":"def _save_state(self):\n self.push(self.PC >> 8)\n self.push(self.PC & 0XFF)\n self.push(self._pack_status_register(self.F))","title":""},{"docid":"afd2d146246499d5b39deedd21976f7b","score":"0.5140139","text":"def pop_apop(self,username,passwd):\n \n self.username = username\n self.passwd = passwd\n basic_class.mylogger_record.info('command:<apop '+self.username+' xxxx>') \n self.resp = self.pop3.apop(self.username,self.passwd) \n [basic_class.mylogger_record.debug(self.resp.decode())] \n #self.pop.quit() ","title":""},{"docid":"8d3181aaf3ab83f2ef1084a6687d9294","score":"0.513977","text":"def pop_list(self,which=None): \n \n self.which = which\n basic_class.mylogger_record.info('command:<list '+str(self.which)+'>')\n if self.which:\n self.resp = self.pop3.list(self.which) \n [basic_class.mylogger_record.debug('the list rsp is: '+self.resp.decode())] \n else: \n self.resp,self.items,self.octets = self.pop3.list(self.which)\n basic_class.mylogger_record.debug('the list_rsp_data is: ') \n [basic_class.mylogger_recordnf.debug(item.decode()) for item in self.items] \n #self.pop.quit() ","title":""},{"docid":"1e6bf28635d6b203dc5b14cb12129ef3","score":"0.5136416","text":"def _refresh_state(self) -> Dict:\n return self.connection.get(f'/{self._url_infix}/{self.id}/')","title":""},{"docid":"cf42919e198e9e67deee9ed6871ce2ad","score":"0.5134074","text":"def __init__(self, state):\n \n self.state = state\n self.info = {}","title":""},{"docid":"fab8912d833944f7ee780569cea7e7bf","score":"0.5131527","text":"def pop(self):\n raise Exception('TODO IMPLEMENT ME !')","title":""},{"docid":"e18fac6b3538ef99314edc35192bfefb","score":"0.5129666","text":"def read(self):\n return self.state","title":""},{"docid":"33f04b8061e9b939471d5037358d704b","score":"0.5127423","text":"def incoming(self, data):","title":""},{"docid":"2f01ef6070540b5f6c810b5d4fb8287c","score":"0.51105607","text":"def event_m10_04_3010():\r\n \"\"\"State 0,3: [Preset] Intrusion item usage determination_SubState\"\"\"\r\n call = event_m10_04_x59(z32=2)\r\n if call.Get() == 1:\r\n \"\"\"State 1: Finish\"\"\"\r\n EndMachine()\r\n Quit()\r\n elif call.Get() == 0:\r\n \"\"\"State 2: Rerun\"\"\"\r\n RestartMachine()\r\n Quit()","title":""},{"docid":"6b8ebc00fb4b2d705df25f625a34c4af","score":"0.5105347","text":"def popitem(self, ):\n\t\tpass","title":""},{"docid":"ffd0125f53ed97cb07f244e9f946548b","score":"0.50998384","text":"def test_pop(self):\n pass","title":""},{"docid":"8d0b3161b11cf24be5937e9b643dfe32","score":"0.50993586","text":"def go_cb(data):\n global current_state, shuttingdown, run_number, max_duration\n shuttingdown = False \n run_number+=1\n current_state = state_sequence[1]\n state_pub.publish(current_state)\n control_map_pub.publish(control_sequence['1']+\"_\"+supervision_sequence['1'])\n if len(state_sequence) > 2:\n if (run_number%evaluate_every)== 0:\n print(\"[fsm.py]: EVALUATE\")\n rospy.set_param('evaluate',True)\n else:\n rospy.set_param('evaluate',False)\n\n if rospy.has_param('max_duration'): \n max_duration=rospy.get_param('max_duration')\n # if rospy.has_param('max_duration') and rospy.has_param('evaluate') and rospy.get_param('evaluate'): \n # max_duration=rospy.get_param('max_duration')\n # print(\"[fsm] set max duration to {0}\".format(max_duration))\n # else: #only use max_duration during evaluation.\n # max_duration=-1\n\n if \"NN\" in [control_sequence['1'], supervision_sequence['1']] and start_nn_pub: start_nn_pub.publish(Empty())\n if \"BA\" in [control_sequence['1'], supervision_sequence['1']] and start_ba_pub: start_ba_pub.publish(Empty())\n if \"DH\" in [control_sequence['1'], supervision_sequence['1']] and start_dh_pub: start_dh_pub.publish(Empty())\n if save_images and run_number > 1 and len(data_location) != 0: update_data_location() # increment data location \n if save_images and start_createds_pub: start_createds_pub.publish(Empty())\n if start_gt_listener_pub: start_gt_listener_pub.publish(Empty())\n print(\"[fsm.py]:{0}: current state: {1}\".format(time.strftime(\"%Y-%m-%d_%I:%M:%S\"),current_state))","title":""},{"docid":"e5cd9798e1b1ac045cb6bf2dfec1a21c","score":"0.509885","text":"def _odom_cb(self, msg):\n self.curr_state = Odom_to_RobotState(msg)","title":""},{"docid":"5da76ecf94b743086cdf32e72ef72221","score":"0.5098747","text":"def __getstate__(self):\n return { \"interfaces\": self._interfaces,\n \"incoming\": self._incoming,\n \"clients\": self._clients,\n \"observers\": self._observers }","title":""},{"docid":"289128f51442577cf1c8189c9b749aae","score":"0.50984573","text":"def ProcessIdle(self):","title":""},{"docid":"ac5f7ffd6048c15111f770cbc783b3e4","score":"0.50946677","text":"def ice_postUnmarshal(self):\n pass # Currently unused","title":""},{"docid":"ac5f7ffd6048c15111f770cbc783b3e4","score":"0.50946677","text":"def ice_postUnmarshal(self):\n pass # Currently unused","title":""},{"docid":"ac5f7ffd6048c15111f770cbc783b3e4","score":"0.50946677","text":"def ice_postUnmarshal(self):\n pass # Currently unused","title":""},{"docid":"ac5f7ffd6048c15111f770cbc783b3e4","score":"0.50946677","text":"def ice_postUnmarshal(self):\n pass # Currently unused","title":""}],"string":"[\n {\n \"docid\": \"e30a400a9997ad8e4203002e5c1eb6ec\",\n \"score\": \"0.67517585\",\n \"text\": \"def PopState(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc63bf5591eac12daa4bf1eadab9c6df\",\n \"score\": \"0.6186308\",\n \"text\": \"def pop_state(self, *_):\\n self.next_state = (StateOperations.POP, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cebba306e551552da79174bd8a777175\",\n \"score\": \"0.61627305\",\n \"text\": \"def PushState(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c93f4da28717bf488c3a3c538b6f4465\",\n \"score\": \"0.593525\",\n \"text\": \"def PopState(self):\\n self.gstate = self.saved_state.pop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ff76d61d20cde8a93f1e71dc2f96af4\",\n \"score\": \"0.5873438\",\n \"text\": \"def pop_noop(self):\\n\\n basic_class.mylogger_record.info('command:<noop>') \\n self.resp = self.pop3.noop()\\n [basic_class.mylogger_record.debug(self.resp.decode())] \\n #self.pop.quit() \",\n \"title\": \"\"\n },\n {\n \"docid\": \"86c1847917035e845c1ff74de977f57b\",\n \"score\": \"0.5775134\",\n \"text\": \"def Pop():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab3a31869e0a8127ec5499ce5cafa39d\",\n \"score\": \"0.56789863\",\n \"text\": \"def __getstate__(self):\\n data = self.__dict__.copy()\\n del data['_observation']\\n del data['_action']\\n del data['_returns']\\n del data['_train_op']\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be2e8b057510dfd46d24ad299b63bcec\",\n \"score\": \"0.56524384\",\n \"text\": \"def pop_sm(self):\\n while True:\\n # wait to receive a read request\\n req = yield self.r_in_pipe.get()\\n # model read latency\\n yield self.wait_sys_clks(self.read_latency)\\n # try to read head element\\n if len(self.items) > 0:\\n data = self.items[0]\\n self.items = self.items[1:]\\n self.bytes = self.bytes - data.get_bytes() # we assume data here is packet\\n else:\\n print >> sys.stderr, \\\"ERROR: FIFO pop_sm: attempted to read from empty FIFO\\\"\\n data = None\\n # write data back\\n self.r_out_pipe.put(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2bf66efeaca37028d6481c8365a1acb\",\n \"score\": \"0.5614247\",\n \"text\": \"def GetState(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2bf66efeaca37028d6481c8365a1acb\",\n \"score\": \"0.5614247\",\n \"text\": \"def GetState(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2591ef6c5dc028d0a7440704701cd545\",\n \"score\": \"0.5609776\",\n \"text\": \"def _state(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5550e70e9e98fcc92bcf040a07457b9f\",\n \"score\": \"0.55971557\",\n \"text\": \"def _fetch(self):\\n tag, fields = self.read_message()\\n if tag == 0x70:\\n self._responses.popleft().set_success(**fields[0])\\n self._metadata.update(fields[0])\\n elif tag == 0x71:\\n self._responses[0].add_records(fields)\\n elif tag == 0x7F:\\n rs = self._responses.popleft()\\n rs.set_failure(**fields[0])\\n if rs.vital:\\n self._wire.close()\\n elif tag == 0x7E and not self._responses[0].vital:\\n self._responses.popleft().set_ignored()\\n else:\\n self._wire.close()\\n raise ProtocolError(\\\"Unexpected protocol message #%02X\\\", tag)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e97b4b8d7bac3ecc51d6119a27269e77\",\n \"score\": \"0.5571678\",\n \"text\": \"def DoFSM(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e97b4b8d7bac3ecc51d6119a27269e77\",\n \"score\": \"0.5571678\",\n \"text\": \"def DoFSM(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6469b6918e48801d0b5b3c49dc54278\",\n \"score\": \"0.55659395\",\n \"text\": \"def _get_state(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4136dc2651477bb23e7107178b8108e5\",\n \"score\": \"0.55530006\",\n \"text\": \"def _process_remote_state(self):\\n try:\\n event = self._remote_events.pop(0)\\n self._process_endpoint_event(event)\\n except IndexError:\\n LOG.debug(\\\"Endpoint %s: ignoring unexpected remote event\\\",\\n self._name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65d7e87068d2188e4660572ec0c9dde3\",\n \"score\": \"0.5525905\",\n \"text\": \"def __getstate__(self):\\n state = self.__dict__.copy()\\n state.pop(\\\"data\\\")\\n if \\\"data_iter\\\" in state:\\n state.pop(\\\"data_iter\\\")\\n return state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f1f77a938f21d4d3e01d208fe99b30c\",\n \"score\": \"0.55120045\",\n \"text\": \"def request(self) -> dapr.proto.common.v1.common_pb2.StateItem:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3366d3a22967d85cc0895ba118286d3c\",\n \"score\": \"0.54983616\",\n \"text\": \"def _process_local_state(self):\\n try:\\n event = self._local_events.pop(0)\\n self._process_endpoint_event(event)\\n except IndexError:\\n LOG.debug(\\\"Endpoint %s: ignoring unexpected local event\\\",\\n self._name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37f48327e5033a6e78d38b662583b87d\",\n \"score\": \"0.54916066\",\n \"text\": \"def result(state, action):\\n # copy the state\\n child_state = deepcopy(state)\\n # action[0] holds the peg from which to pop the top disc\\n disc = child_state[action[0]].pop()\\n # action[1] holds the peg to append the popped disc to\\n child_state[action[1]].append(disc)\\n return child_state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20eeff48e84df884ba5a7a558ebd6a6a\",\n \"score\": \"0.54878384\",\n \"text\": \"def _on_push(self, resp: dict, addr: Tuple[str, int]) -> None:\\n self.history.message(HISTORY_PUSH, resp)\\n self.last_push = time.monotonic()\\n old_state = self.state.pilotResult if self.state else None\\n new_state = resp[\\\"params\\\"]\\n if old_state and states_match(old_state, new_state):\\n return\\n self.state = PilotParser(new_state)\\n if self.push_callback:\\n self.push_callback(self.state)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94bd6441ad2988672dd7912baf9cc42b\",\n \"score\": \"0.5487515\",\n \"text\": \"def __getstate__(self):\\n\\t\\treturn None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a738ce99ff644a447b64b7bd1e8d63c\",\n \"score\": \"0.548465\",\n \"text\": \"def pop_retr(self,which):\\n \\n self.which = which\\n basic_class.mylogger_record.info('command:<retr '+str(self.which)+'>')\\n try:\\n self.resp,self.items,self.octets = self.pop3.retr(self.which)\\n basic_class.mylogger_record.debug('the retr_rsp_data is:') \\n [basic_class.mylogger_recordnf.debug(item.decode()) for item in self.items] \\n except:\\n pass\\n #self.pop.quit() \",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbeb5b9f5d86d1c0cc33641caf75d565\",\n \"score\": \"0.54574466\",\n \"text\": \"def zero_state_command(self):\\n self.phiiongun.State_Data_Packet()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d8fb9b2c24706c7998649fb524d0151\",\n \"score\": \"0.5445901\",\n \"text\": \"def __getstate__(self):\\n return self._data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd19dc0e397aeca34a8ed471523870c6\",\n \"score\": \"0.54341424\",\n \"text\": \"def popitem(self):\\n #return self.__state.popitem()\\n return self.__session.state.popitem()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed1ba2baebe62c94707836aa1d98fbfb\",\n \"score\": \"0.542722\",\n \"text\": \"def fsm_cb(data):\\n global superviser, pilot\\n # parse the new configuration\\n pilot = data.data.split('_')[0]\\n superviser = data.data.split('_')[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5552bb50bc34d76bb5cf962e9f253cc7\",\n \"score\": \"0.54212123\",\n \"text\": \"def POP(cpu):\\n cpu.reg[cpu.OP_A] = cpu.ram_read(cpu.SP)\\n cpu.SP += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fde5e95b8f00ae28390178e3fbc8081e\",\n \"score\": \"0.54075074\",\n \"text\": \"def pop(self, statemachine):\\n assert(statemachine in self.uism)\\n assert(len(self.uism) > 0)\\n try:\\n i = self.uism.index(statemachine)\\n self.uism.pop(i) \\n except ValueError:\\n self.log.error('statemachine is not in self.uism')\\n return\\n except NameError:\\n self.log.error('statemachine is not defined')\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26681ed466e197b50d705c91c78895c2\",\n \"score\": \"0.54073954\",\n \"text\": \"def firstObsDataAction(state, nextState, record, fields):\\n global stack\\n stack.pop() # close last element in obsContext\\n stack.pop() # close obsContext\\n stack.addPush(\\\"obsData\\\")\\n return dataAction(state, nextState, record, fields, action=adesutility.ElementStack.addPush)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a9abff7fcad4d1ecc364195f90f6803\",\n \"score\": \"0.53953904\",\n \"text\": \"def pull(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd29ef753ed650c4f8d6206d1c9f3f16\",\n \"score\": \"0.5386824\",\n \"text\": \"def __getstate__(self) -> dict:\\n self.close()\\n time.sleep(0.1) # buffer time\\n state = self.__dict__.copy()\\n del state['rFp']\\n del state['wFp']\\n del state['Fp']\\n return state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3be01d229557576a6520ebed2d04cbe3\",\n \"score\": \"0.5386021\",\n \"text\": \"def actions(self, state):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3be01d229557576a6520ebed2d04cbe3\",\n \"score\": \"0.5386021\",\n \"text\": \"def actions(self, state):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fb3e869a3b7c76246e90c7c6c954100\",\n \"score\": \"0.53856266\",\n \"text\": \"def state(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6fb3e869a3b7c76246e90c7c6c954100\",\n \"score\": \"0.53856266\",\n \"text\": \"def state(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c2dec89c40847a1ff188f91abfa8c17c\",\n \"score\": \"0.53799653\",\n \"text\": \"def __getstate__():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa2dc09e7ae2b96c0935d49e9d840e78\",\n \"score\": \"0.5359047\",\n \"text\": \"def handle_state_change(psp, context):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b8bbeceaee48db0f84f13ffd14aa835\",\n \"score\": \"0.5356365\",\n \"text\": \"def popitem(self):\\n raise InputConflict(\\\"Can't read and write in the same operation\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29076877a1f2c9dc3f0626caac71380a\",\n \"score\": \"0.53538644\",\n \"text\": \"def pop(self):\\n raise InputConflict(\\n \\\"Can't read and write in the same operation\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb033aeaf2143a215b5a372dd61dd144\",\n \"score\": \"0.5330996\",\n \"text\": \"def pop():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb033aeaf2143a215b5a372dd61dd144\",\n \"score\": \"0.5330996\",\n \"text\": \"def pop():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1290071ba3caffd821d1c2be6ac8de93\",\n \"score\": \"0.5323134\",\n \"text\": \"def pre_inquiry (self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"131311db6c9835b2fee3b9ac8733f733\",\n \"score\": \"0.53205574\",\n \"text\": \"def state_cb(data):\\n global state\\n state = data.data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9246a3bcc8165d9185e660198cae64f\",\n \"score\": \"0.5319337\",\n \"text\": \"def _received(self, msg):\\n if self.state != msg['content']['data']['state']:\\n self.state = msg[\\\"content\\\"][\\\"data\\\"][\\\"state\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a771406ff12acebb7eb72bcba05f40f2\",\n \"score\": \"0.5312586\",\n \"text\": \"def __getstate__(self):\\n self.log.debug(\\\"G2P.__getstate__()\\\")\\n odict = self.__dict__.copy() # copy the dict since we change it\\n # Remove data that will require re-initialization upon reload.\\n del odict['log']\\n del odict['g2plib']\\n if 'updater' in odict:\\n del odict['updater']\\n if 'pipe' in odict:\\n del odict['pipe']\\n return odict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5e5fc678e91969202c6215b45e229a7\",\n \"score\": \"0.53114057\",\n \"text\": \"def _stateChanged(self):\\n self._unstashInBox()\\n if self.isPrimary is not None:\\n # TODO handle suspicion exceptions here\\n self.process3PhaseReqsQueue()\\n # TODO handle suspicion exceptions here\\n try:\\n self.processPostElectionMsgs()\\n except SuspiciousNode as ex:\\n self.outBox.append(ex)\\n self.discard(ex.msg, ex.reason, logger.warning)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0b8d0e4de65322babdc47262878adcc\",\n \"score\": \"0.5306768\",\n \"text\": \"def __getstate__(self):\\n state = self.__dict__.copy()\\n state['vars'] = None\\n state['fcns'] = None\\n state['session'] = None\\n return state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a53ff386f379913b487c8cb2560d227b\",\n \"score\": \"0.52994955\",\n \"text\": \"def pop_ih(self):\\n if not self._ihs:\\n return False\\n\\n # Pop it off.\\n handler, prompt, state, cleanup = self._ihs.pop()\\n\\n # And call the cleanup function.\\n if cleanup:\\n try:\\n cleanup(self)\\n except Exception:\\n if not state:\\n state = handler.func_name\\n log.exception(\\\"An error occurred while running the input \\\"\\n \\\"handler cleanup function for the state %r on \\\"\\n \\\"connection #%d.\\\" % (state, self._uid))\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16bf07a61a773ca44e4394048e4a13e6\",\n \"score\": \"0.52989006\",\n \"text\": \"def __post_init__(self) -> None:\\n parser = StateMessageParser(self.unparsed_response)\\n\\n self.state = parser.get_state()\\n self.time_left = parser.get_time_left()\\n self.time_on = parser.get_time_on()\\n self.auto_shutdown = parser.get_auto_shutdown()\\n self.power_consumption = parser.get_power_consumption()\\n self.electric_current = watts_to_amps(self.power_consumption)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7048a8ccdd0a391da0b7f9c614b7116f\",\n \"score\": \"0.5297024\",\n \"text\": \"def result(self, state, action):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7048a8ccdd0a391da0b7f9c614b7116f\",\n \"score\": \"0.5297024\",\n \"text\": \"def result(self, state, action):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b99382312e0a847ad2c18ec3e2be5fe\",\n \"score\": \"0.52816844\",\n \"text\": \"def __call__(self, state):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"268d4278052f4778dd5e52adbac7b3f5\",\n \"score\": \"0.52707094\",\n \"text\": \"def testGoProRequestState(self):\\n self.mgr.goproManager = Mock()\\n value = struct.pack('<II', app_packet.GOPRO_REQUEST_STATE, 0)\\n trimValue = ''\\n self.mgr.appMgr.client.recv.return_value = value\\n handled = self.mgr.appMgr.parse()\\n self.mgr.goproManager.handlePacket.assert_called_with(app_packet.GOPRO_REQUEST_STATE, trimValue)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea4873a3a4e56b2170bd8273f68aa07b\",\n \"score\": \"0.5270016\",\n \"text\": \"def __getstate__(self):\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4105d3d1b7b7f5f5512863e659943363\",\n \"score\": \"0.5264336\",\n \"text\": \"def loadState(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69b994abd771060e0f1caa858f80d07f\",\n \"score\": \"0.52635896\",\n \"text\": \"def fsm_back(self) -> None:\\n if not self.__history:\\n return\\n self.request(self.__history.pop(), True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfc3a471cac8bfba13870b20b34e0ed7\",\n \"score\": \"0.52436066\",\n \"text\": \"def enter_state(self):\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dae30527666df851ef6089805c28421e\",\n \"score\": \"0.52421033\",\n \"text\": \"def final(self, state):\\r\\n\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c996e07581ea76bc50b77aac8aa54305\",\n \"score\": \"0.52410156\",\n \"text\": \"def pop_state(self):\\n self._statestack.pop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2ee47c2fdca4bce128cc688c70bbb8d8\",\n \"score\": \"0.5238397\",\n \"text\": \"def parse_state(self, data):\\n self.parse_emission_state(data)\\n self.parse_toggle_bit(data)\\n self.parse_pressure_unit(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"026041189eb1a6438e47ecd62c32c7aa\",\n \"score\": \"0.5237256\",\n \"text\": \"def __getstate__(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d20ecef8eba23ea40cb7f032eda7886\",\n \"score\": \"0.5236254\",\n \"text\": \"def OnReceiveData(code):\\n print(\\\"Query Success!\\\")\\n RequestSessionEventHandler.query_state = 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87dd2d4bf4d4ee24e1b017b30ce205fe\",\n \"score\": \"0.5236237\",\n \"text\": \"def pop(self, *args, **kwargs): # real signature unknown; restored from __doc__\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"039cffdbe132f43643d7ef410114dbce\",\n \"score\": \"0.52210736\",\n \"text\": \"def state(self, message):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37bfb2f8eb1830a6f91d1bbded6aaa94\",\n \"score\": \"0.5216243\",\n \"text\": \"def pop(self, *args, **kwargs): # real signature unknown\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f92d44eeb16d04090325975865fc66a6\",\n \"score\": \"0.5204015\",\n \"text\": \"def decode_state(self, state):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71d97446b3d3888840903ad27a52386f\",\n \"score\": \"0.5203083\",\n \"text\": \"def __post_init__(self) -> None:\\n parser = StateMessageParser(self.unparsed_response)\\n\\n self.direction = parser.get_shutter_direction()\\n self.position = parser.get_shutter_position()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cb350f4e82a642385d54d13cff2c840\",\n \"score\": \"0.51962155\",\n \"text\": \"def PushBack(provider):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"742875850772bfd849f41229c221f12e\",\n \"score\": \"0.5191453\",\n \"text\": \"def get_input(self):\\n if len(self.player.inq) > 0:\\n # We've got something to work with!\\n arg = self.player.inq[0].strip()\\n del self.player.inq[0]\\n self.next_state(arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84ae71c4e4728cde80a81e23154972f1\",\n \"score\": \"0.51820445\",\n \"text\": \"def get_action(self, state):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34d31bb96e43174e10b477ccc05e258a\",\n \"score\": \"0.5179174\",\n \"text\": \"def pop(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9216663eb862f8bfba71d878a481eca6\",\n \"score\": \"0.516739\",\n \"text\": \"def request(self, state):\\n raise NotImplementedError(\\\"request function not implemented\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e5da8490bafe8794ddc8f437ccb7c56\",\n \"score\": \"0.5161657\",\n \"text\": \"def pop(self):\\n self.register[self.ram_read(self.pc + 1)] = self.ram_read(self.register[7])\\n self.register[7] += 1\\n self.pc = self.pc + 2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30455fa5b84b0a3f8b2f3331177633ba\",\n \"score\": \"0.5158398\",\n \"text\": \"def structure_data(self, state):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0b368bc735991ffb26e4e87378774da\",\n \"score\": \"0.5157198\",\n \"text\": \"def pull(self):\\n try:\\n self.history.pop()\\n except IndexError:\\n raise Exception(\\\"pull no thing\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c05fa533abf76b624ce3cf5fddb60d4f\",\n \"score\": \"0.51559144\",\n \"text\": \"def _clearState(self):\\n state = self.get('state')\\n kind = self.get('kind')\\n priority = self.get('priority')\\n self.client.zrem('q:jobs:%s' % (state), self.job_id)\\n self.client.zrem('q:jobs:%s:%s' % (kind, state), self.job_id)\\n \\n # as if we called set('state', None)\\n self.client.hset('q:job:' + self.job_id, 'state', None)\\n self.params['state'] = state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"175844d73bf576822863d9a0664fc432\",\n \"score\": \"0.51533574\",\n \"text\": \"def popitem():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8818bd04e5b1bb7267a3727228c2885\",\n \"score\": \"0.5151481\",\n \"text\": \"def pop(self, k, *args):\\n #return self.__state.pop(k, *args)\\n return self.__session.state.pop(k, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0841419ec9ef3bccdc0c892ad61aa762\",\n \"score\": \"0.51490796\",\n \"text\": \"def pop_source(self, ):\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fde8d3f26ad1de327461ddb61eb0e1c1\",\n \"score\": \"0.51464427\",\n \"text\": \"def event_m10_04_5020():\\r\\n \\\"\\\"\\\"State 0,1: Navigation mesh traffic is possible\\\"\\\"\\\"\\r\\n DeleteNavimeshAttribute(502000, 2)\\r\\n \\\"\\\"\\\"State 2: Finish\\\"\\\"\\\"\\r\\n EndMachine()\\r\\n Quit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb40b73f568ccc67ffc58ecca9c8abf8\",\n \"score\": \"0.5145009\",\n \"text\": \"def _save_state(self):\\n self.push(self.PC >> 8)\\n self.push(self.PC & 0XFF)\\n self.push(self._pack_status_register(self.F))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afd2d146246499d5b39deedd21976f7b\",\n \"score\": \"0.5140139\",\n \"text\": \"def pop_apop(self,username,passwd):\\n \\n self.username = username\\n self.passwd = passwd\\n basic_class.mylogger_record.info('command:<apop '+self.username+' xxxx>') \\n self.resp = self.pop3.apop(self.username,self.passwd) \\n [basic_class.mylogger_record.debug(self.resp.decode())] \\n #self.pop.quit() \",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d3181aaf3ab83f2ef1084a6687d9294\",\n \"score\": \"0.513977\",\n \"text\": \"def pop_list(self,which=None): \\n \\n self.which = which\\n basic_class.mylogger_record.info('command:<list '+str(self.which)+'>')\\n if self.which:\\n self.resp = self.pop3.list(self.which) \\n [basic_class.mylogger_record.debug('the list rsp is: '+self.resp.decode())] \\n else: \\n self.resp,self.items,self.octets = self.pop3.list(self.which)\\n basic_class.mylogger_record.debug('the list_rsp_data is: ') \\n [basic_class.mylogger_recordnf.debug(item.decode()) for item in self.items] \\n #self.pop.quit() \",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e6bf28635d6b203dc5b14cb12129ef3\",\n \"score\": \"0.5136416\",\n \"text\": \"def _refresh_state(self) -> Dict:\\n return self.connection.get(f'/{self._url_infix}/{self.id}/')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf42919e198e9e67deee9ed6871ce2ad\",\n \"score\": \"0.5134074\",\n \"text\": \"def __init__(self, state):\\n \\n self.state = state\\n self.info = {}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fab8912d833944f7ee780569cea7e7bf\",\n \"score\": \"0.5131527\",\n \"text\": \"def pop(self):\\n raise Exception('TODO IMPLEMENT ME !')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e18fac6b3538ef99314edc35192bfefb\",\n \"score\": \"0.5129666\",\n \"text\": \"def read(self):\\n return self.state\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33f04b8061e9b939471d5037358d704b\",\n \"score\": \"0.5127423\",\n \"text\": \"def incoming(self, data):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f01ef6070540b5f6c810b5d4fb8287c\",\n \"score\": \"0.51105607\",\n \"text\": \"def event_m10_04_3010():\\r\\n \\\"\\\"\\\"State 0,3: [Preset] Intrusion item usage determination_SubState\\\"\\\"\\\"\\r\\n call = event_m10_04_x59(z32=2)\\r\\n if call.Get() == 1:\\r\\n \\\"\\\"\\\"State 1: Finish\\\"\\\"\\\"\\r\\n EndMachine()\\r\\n Quit()\\r\\n elif call.Get() == 0:\\r\\n \\\"\\\"\\\"State 2: Rerun\\\"\\\"\\\"\\r\\n RestartMachine()\\r\\n Quit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b8ebc00fb4b2d705df25f625a34c4af\",\n \"score\": \"0.5105347\",\n \"text\": \"def popitem(self, ):\\n\\t\\tpass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffd0125f53ed97cb07f244e9f946548b\",\n \"score\": \"0.50998384\",\n \"text\": \"def test_pop(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d0b3161b11cf24be5937e9b643dfe32\",\n \"score\": \"0.50993586\",\n \"text\": \"def go_cb(data):\\n global current_state, shuttingdown, run_number, max_duration\\n shuttingdown = False \\n run_number+=1\\n current_state = state_sequence[1]\\n state_pub.publish(current_state)\\n control_map_pub.publish(control_sequence['1']+\\\"_\\\"+supervision_sequence['1'])\\n if len(state_sequence) > 2:\\n if (run_number%evaluate_every)== 0:\\n print(\\\"[fsm.py]: EVALUATE\\\")\\n rospy.set_param('evaluate',True)\\n else:\\n rospy.set_param('evaluate',False)\\n\\n if rospy.has_param('max_duration'): \\n max_duration=rospy.get_param('max_duration')\\n # if rospy.has_param('max_duration') and rospy.has_param('evaluate') and rospy.get_param('evaluate'): \\n # max_duration=rospy.get_param('max_duration')\\n # print(\\\"[fsm] set max duration to {0}\\\".format(max_duration))\\n # else: #only use max_duration during evaluation.\\n # max_duration=-1\\n\\n if \\\"NN\\\" in [control_sequence['1'], supervision_sequence['1']] and start_nn_pub: start_nn_pub.publish(Empty())\\n if \\\"BA\\\" in [control_sequence['1'], supervision_sequence['1']] and start_ba_pub: start_ba_pub.publish(Empty())\\n if \\\"DH\\\" in [control_sequence['1'], supervision_sequence['1']] and start_dh_pub: start_dh_pub.publish(Empty())\\n if save_images and run_number > 1 and len(data_location) != 0: update_data_location() # increment data location \\n if save_images and start_createds_pub: start_createds_pub.publish(Empty())\\n if start_gt_listener_pub: start_gt_listener_pub.publish(Empty())\\n print(\\\"[fsm.py]:{0}: current state: {1}\\\".format(time.strftime(\\\"%Y-%m-%d_%I:%M:%S\\\"),current_state))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5cd9798e1b1ac045cb6bf2dfec1a21c\",\n \"score\": \"0.509885\",\n \"text\": \"def _odom_cb(self, msg):\\n self.curr_state = Odom_to_RobotState(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5da76ecf94b743086cdf32e72ef72221\",\n \"score\": \"0.5098747\",\n \"text\": \"def __getstate__(self):\\n return { \\\"interfaces\\\": self._interfaces,\\n \\\"incoming\\\": self._incoming,\\n \\\"clients\\\": self._clients,\\n \\\"observers\\\": self._observers }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"289128f51442577cf1c8189c9b749aae\",\n \"score\": \"0.50984573\",\n \"text\": \"def ProcessIdle(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac5f7ffd6048c15111f770cbc783b3e4\",\n \"score\": \"0.50946677\",\n \"text\": \"def ice_postUnmarshal(self):\\n pass # Currently unused\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac5f7ffd6048c15111f770cbc783b3e4\",\n \"score\": \"0.50946677\",\n \"text\": \"def ice_postUnmarshal(self):\\n pass # Currently unused\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac5f7ffd6048c15111f770cbc783b3e4\",\n \"score\": \"0.50946677\",\n \"text\": \"def ice_postUnmarshal(self):\\n pass # Currently unused\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac5f7ffd6048c15111f770cbc783b3e4\",\n \"score\": \"0.50946677\",\n \"text\": \"def ice_postUnmarshal(self):\\n pass # Currently unused\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993284,"cells":{"query_id":{"kind":"string","value":"b9cf588c58336fdc1b9c6583123cb0f6"},"query":{"kind":"string","value":"Get the servo's angle"},"positive_passages":{"kind":"list like","value":[{"docid":"0d1484fbf00fcb6d4ac7f5bebc16412c","score":"0.71814936","text":"def angle(self):\n return int(self.pos() * 180)","title":""}],"string":"[\n {\n \"docid\": \"0d1484fbf00fcb6d4ac7f5bebc16412c\",\n \"score\": \"0.71814936\",\n \"text\": \"def angle(self):\\n return int(self.pos() * 180)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"c8dd4df30d5c0ddd61b456737131d079","score":"0.8747807","text":"def getAngle(self):\n a = self.servo.duty()\n a -= 64\n a *= SERVO_ANGLE / (SERVO_MAX - SERVO_MIN)\n a -= (SERVO_ANGLE / 2)\n return a","title":""},{"docid":"8dee146df205737500fe028fb4a8c074","score":"0.8033039","text":"def getServoAngle(elevAngle):\n log = math.log10(elevAngle)\n servoAngle = 5 + elevAngle + 12*math.pow(log, 2.3)\n return servoAngle","title":""},{"docid":"53bf99000c9082a0cfa14357d51a5d33","score":"0.76313317","text":"def angle(self) -> float:\n return self.velocity.angle","title":""},{"docid":"74c8f58f4b4da63d74b846d9948bbdef","score":"0.71943146","text":"def angle(self) -> float:\n a = self.endpoints[0]\n b = self.endpoints[1]\n dy = b.y-a.y\n dx = b.x-a.x\n return math.atan(dy/dx)","title":""},{"docid":"7c687da767fba4973e0a91c5013bbd30","score":"0.71714395","text":"def getAngle(self):\n return self.vector.angle","title":""},{"docid":"de0e48079d82e5335eef447d5074332e","score":"0.71473926","text":"def get_angle(self):\n return self._get(\"/api/cmd/req-get-angle-pos\")","title":""},{"docid":"e5bf2d903133fb0afd8e2f764647a3a3","score":"0.71333236","text":"def getAngle(self):\n return self.angle","title":""},{"docid":"067c64d4ab9c00fa4f235f28834b16d4","score":"0.71271867","text":"def angle(self) -> Optional[float]:\n if self.fraction is None: # special case for disabled servos\n return None\n return self.actuation_range * self.fraction","title":""},{"docid":"e78e2b7943c117a2177d684524cb3b8c","score":"0.7093887","text":"def GetMotorAngles(self):\n motor_angles = self._AddSensorNoise(np.array(self._control_observation[0:self.num_motors]),\n self._observation_noise_stdev[0])\n return MapToMinusPiToPi(motor_angles)","title":""},{"docid":"454eb63aace10f7c40a9e1684dff08d2","score":"0.7077751","text":"def angle(self):\n return self._current_angle","title":""},{"docid":"a52159ca2ffd7e595053063cf8905220","score":"0.704842","text":"def get_angle(self):\n\n if self.falling:\n if self.y < 540:\n if self.vel * (self.time / 60) + G / 2 * (self.time / 60) ** 2 < 10 and self.alive:\n self.angle -= 3\n if self.angle < -30:\n self.angle = -30\n else:\n self.angle += 1 + 0.2 * self.time ** 0.56\n # angle = -110 + (self.time)**2/30\n if self.angle > 90:\n self.angle = 90\n if self.angle < -90:\n self.angle = -90\n else:\n return 0\n return self.angle","title":""},{"docid":"17fd8f860185e319c66f3091da27ee90","score":"0.70371073","text":"def angle(self) -> float:\n return self._angle","title":""},{"docid":"df6be67880229aecbec9a692b26f26f9","score":"0.70161736","text":"def angle(self):\n\t\td = math.atan2(self.y, self.x)\n\t\treturn d","title":""},{"docid":"4e110901048c2205d90aff53eae39d5b","score":"0.70157117","text":"def angle(self):\n return Vector.polar([self.x,self.y])[1]","title":""},{"docid":"4c183e65dd1180b46668fe4e9c2f0cc5","score":"0.69856966","text":"def getAngle(self):\n return self.__angle","title":""},{"docid":"f4b1d826d22dc9df809c3f877cc55df4","score":"0.6983973","text":"def getAngle(self):\n return self._angle","title":""},{"docid":"10923ff8695d48194cec6786f185f5ea","score":"0.6970461","text":"def getAngle(self):\n return Vector.polar(self.components)[1]","title":""},{"docid":"b5f6a04399eeffb1ef95c247e16113c0","score":"0.69413257","text":"def setAngle(self, angle, delay=True):\n a = self.servo.duty()\n if not -(SERVO_ANGLE / 2) <= angle <= (SERVO_ANGLE / 2):\n raise LookupError(\"Angle invalid: \"+str(angle)) \n d = angle + (SERVO_ANGLE / 2)\n d *= (SERVO_MAX - SERVO_MIN) / SERVO_ANGLE\n d = int(d + 64)\n self.servo.duty(d)\n if delay:\n time.sleep(0.1 * abs(angle - a) / 60)\n return d","title":""},{"docid":"39d11370a639b5b8f3106018f59354ab","score":"0.69327146","text":"def angle(self):\n return self._angle","title":""},{"docid":"39d11370a639b5b8f3106018f59354ab","score":"0.69327146","text":"def angle(self):\n return self._angle","title":""},{"docid":"e4ecf754634c16f8885590cccf5315a0","score":"0.69031966","text":"def getAngle(self):\n return Angle(self)","title":""},{"docid":"38da272190d33a773ac719295cca7e5f","score":"0.6845757","text":"def angle(self):\n if self._angle is not None:\n return self._angle\n [denominator, numerator] = (self.endpoints[1] - self.endpoints[0]).coordinates\n if abs(denominator) < 0.000001:\n # almost vertical line\n self._angle = pi/2\n else:\n angle = atan(numerator/denominator)\n if angle < 0:\n angle = pi + angle\n self._angle = angle\n return self._angle","title":""},{"docid":"48491240fc02a75b5b64ec19c6783baf","score":"0.68319154","text":"def get_laser_angle(point):\n return ((2 * math.pi * (point + 1) / 60.0) - math.pi ) - 30.0","title":""},{"docid":"329b51c20d1acd387174ec765e20aacf","score":"0.6705808","text":"def get_angle() -> float:\n global CURRENT_ANGLE\n CURRENT_ANGLE = -CURRENT_ANGLE\n return CURRENT_ANGLE","title":""},{"docid":"d74a059d6c39d1f6f709aabbfba19b8b","score":"0.664495","text":"def angle(self, value):\r\n #value est en pourcentage d'angle max[-100, 100]\r\n value = float(value)/100.\r\n value = minmax(value, -100., 100.)\r\n #angle commande\r\n angleCmd = self.__angmax*value\r\n diff = angleCmd - self.__angle\r\n # On ne peut pas depasser la vitesse max des servos\r\n if (abs(diff/DT) > self.__vitmax): \r\n diff = np.sign(diff)*self.__vitmax*DT\r\n self.__angle = self.__angle + diff","title":""},{"docid":"9c9bbb3dd5000cd0e5d6ab43ce24cb1e","score":"0.663126","text":"def getTiltAngle(self):\n\t\treturn self.__tiltNodeIN.getTiltAngle()","title":""},{"docid":"d287c66a3a72c65289c58afbe4c050c9","score":"0.6622105","text":"def set_angle(self, servo, angle):\n if angle > 180 or angle < 0:\n angle = 90\n byteone = int(254*angle/180)\n bud = [0xFF, servo, byteone]\n self.sc.write(bud)","title":""},{"docid":"e58a31850851730467790293cd8473bf","score":"0.6610297","text":"def current_angle(self):\n return self.angle.value","title":""},{"docid":"4dee1665e7067c6dd9643155e59b23c5","score":"0.6549726","text":"def involute_a(self):\n return math.tan(self.pressure_angle) - self.pressure_angle","title":""},{"docid":"5c190932e2a64ed453ce970146581d0e","score":"0.6524581","text":"def angle(self):\n # only for 2D vectors\n return math.atan2(self.y, self.x)","title":""},{"docid":"64bd96b2d75f7e2c0b935b5ae2c09a47","score":"0.64882964","text":"def angle(this):\n return math.atan2(this.y, this.x)","title":""},{"docid":"243ed3eb4e2336dfb3429867ebdab1e2","score":"0.6480937","text":"def getTiltAngle(self):\n\t\treturn self.__angle","title":""},{"docid":"7e3f05016ddfb26266a1a68fd8601d5f","score":"0.64471793","text":"def read_angle(self) -> int:\n return self._transfer(Sensor._ANGLE_READ_REQUEST) & Sensor._RESPONSE_MASK","title":""},{"docid":"12c5672bc0f175c85d7618742025610a","score":"0.643664","text":"def radians(self) -> float:\n return self._angle / 180.0 * math.pi","title":""},{"docid":"cd1285d02dd1a8b7f9d1580744e4915f","score":"0.64329773","text":"def angle_at_displacement(self, displacement):\n t = self.t_for(displacement)\n a = t + self.rotation\n a %= 2 * math.pi\n return a","title":""},{"docid":"3fd15322f26facc4424de2a79c3ea661","score":"0.6396762","text":"def get_theta(phase):\n return (phase % 1) * 2 * np.pi","title":""},{"docid":"7263e96bb83d051d68005bdec5fd7d59","score":"0.6372367","text":"def getBearing():\n return calc.bearing(getPose()['Pose']['Orientation'])","title":""},{"docid":"fa2e7c3bc2aa10208cdca58dae8923cb","score":"0.6338179","text":"def get_direction(self, angle):\n rad_angle = np.deg2rad((angle+90) % 360)\n return np.round(np.cos(rad_angle), 5), -np.round(np.sin(rad_angle), 5)","title":""},{"docid":"3c3495f70d0e815a09890a3b106ba7e9","score":"0.6331294","text":"def get_dist_from_angle(self,angle=None):\n if angle != None:\n self._present_angle = angle\n self.servo.setangle(self._present_angle)\n mean, std = self.uss.get_mean_distance(self.mean_measurements,self.us_sleep)\n return mean, std","title":""},{"docid":"a959d3cb2e0b6b94b09e89f96193aaaf","score":"0.632471","text":"def get_tilt():\n return tilt_servo.read()","title":""},{"docid":"3fcd7409e276a263b09db047206e5e64","score":"0.62961817","text":"def tip_pressure_angle(self):\n return math.acos(self.base_diameter / self.outside_diameter)","title":""},{"docid":"cbd01e35b74584f27cca5a2b96c001b8","score":"0.624891","text":"def orbitAngle(self):\n subject = self._get_subject()\n if subject is None:\n return None\n distance, worldVector = self.getVectTo(subject.point)[0:2]\n # Originally had a line like the following, but it caused a Blender\n # crash.\n # flatVector = (worldVector * distance).resized(2)\n # It's probably less efficient anyway, because it allocates a new\n # Vector object or two.\n worldVector *= distance\n worldVector.resize(2)\n xZero = Vector((worldVector.magnitude, 0))\n return xZero.angle_signed(worldVector, 0.0)","title":""},{"docid":"e0207c3f064f42c3d34181296ee5c884","score":"0.6238376","text":"def getAngle(self, sessionId=None, center=None):\n if center is None:\n center = self.getMeanCenter(sessionId)\n return 2*pi+atan2(center[1], center[0])","title":""},{"docid":"1fe7644d4c9faeef7271a61745160431","score":"0.62340295","text":"def get_relative_angle(self):\n x_obj, y_obj = self.get_object_pose()[0:2]\n x_robot, y_robot = self.get_pose()[0:2]\n\n \"\"\"Calculate relative angle\"\"\"\n relative_angle = atan2(y_robot - y_obj, x_robot - x_obj)\n\n return relative_angle","title":""},{"docid":"18c0ba437bc9845fe461b49023dfce4e","score":"0.6223402","text":"def pote_sweep():\n val = POTE.value / 65536\n SERVO.angle = 180 * val\n time.sleep(0.05)","title":""},{"docid":"5ce645057c22f32d76929ed4dea666b7","score":"0.6208178","text":"def find_angle(number_sides):\n return 180 - (((number_sides - 2)*180) / number_sides)","title":""},{"docid":"b05eb3d711f4c0647b429e382b3c00e2","score":"0.61783034","text":"def get_angle(self, relative_to=(1, 0)):\n v2_u = relative_to\n if relative_to != (1, 0):\n v2_u = LineSegment.get_unit_vector(relative_to[0], relative_to[1])\n # Not using the length since we are using both unit vectors\n angle = np.arccos(abs(np.dot(self.unit_vector, v2_u)))\n if np.isnan(angle):\n return 0.0\n return round(angle, self.angle_precision)","title":""},{"docid":"fcf0df18aac65871b37f5489c2e21380","score":"0.61775064","text":"def getAxisAngle(*args):\n return _coin.SbRotation_getAxisAngle(*args)","title":""},{"docid":"4a6eb7ff64a3b14a21222186c429bea5","score":"0.61583483","text":"def mod_angle(self):\n return self._mod_angle","title":""},{"docid":"7e6185dcf041762af8affec5d8294718","score":"0.614655","text":"def heading(self):\n return math.degrees(math.atan2(self.y, self.x))","title":""},{"docid":"067f0a391ddea15a803288c84a5ab012","score":"0.61436796","text":"def GetBackLightAngle(self):\n ...","title":""},{"docid":"2d2d5225b95d3c5947e0b04459d622b8","score":"0.6136827","text":"def shooting_angle(self) -> float:\n return self.__shooting_angle","title":""},{"docid":"924bf442f46c94b5167c08c98e614a70","score":"0.6136353","text":"def angle_at_index(self, idx):\n vect = self.vector_at_index(idx)\n x_vect = np.array([1, 0])\n dot = np.dot(vect, x_vect)\n det = np.linalg.det([x_vect, vect])\n track_ang = np.arctan2(det, dot)\n if track_ang < 0.:\n track_ang += 2 * np.pi\n return track_ang","title":""},{"docid":"5b76430532477e7b0f2675f14a10fa8d","score":"0.6120453","text":"def\t__convertAngle(self,value):\n\t\t# {{{\n\t\n\t\tau = self.__angleUnit\n\t\tif au == self.Unit_AngleDegrees:\n\t\t\treturn float(value)*math.pi/180.0\n\t\telif au == self.Unit_AngleRadians:\n\t\t\treturn float(value)\n\t\n\t\traise ParseException(\"convertAngle not reaching point\",0,Error_Internal)\n\t\t# }}}","title":""},{"docid":"d1b10402d315e030dd598138b47901cd","score":"0.6114449","text":"def PositionAngle(self, *args):\n return _stomp.AngularCoordinate_PositionAngle(self, *args)","title":""},{"docid":"a14094509ec6b1501ecb19eee59c8d2e","score":"0.6114236","text":"def _get_direction_change_angle() -> int:\n return config_parser.general_getter(\"PATH_FINDER\",\n \"DIRECTION_CHANGE_ANGLE\", DataType.INT)","title":""},{"docid":"45fbac1d0c7b060527924eb12cb2c56c","score":"0.6110663","text":"def _calculateAngleToGoal(self):\n [goalx, goaly] = self.goal\n [currx, curry, _] = self.coords # current\n\n diffx = (goalx-currx)\n diffy = (goaly-curry)\n theta = atan2(diffy, diffx)\n return theta * 180 / pi + 180","title":""},{"docid":"9c84cf854e75e95f2dc342fad633c7c8","score":"0.60991514","text":"def angleFromVector(x, y):\n deg = math.degrees((math.atan(float(y)/x)))\n if deg < 0:\n return 180 + deg\n else:\n return deg","title":""},{"docid":"7425a5cb7514d66ead31aae0cfa408aa","score":"0.60984176","text":"def get_angle(self, x, y, x2, y2):\n return (180 / math.pi) * math.atan((y2 - y) / (x2 - x))","title":""},{"docid":"6f45d2f859f521eefb795f579f93e2d7","score":"0.6091841","text":"def angle(y, x):\n return math.atan2(y, x)","title":""},{"docid":"dd6bb789ac7f97b7dfb3d1353e28786d","score":"0.6086512","text":"def calc_dist(self, point):\n var1 = math.pi * WHEEL_SPAN * abs(self.degree)\n if point == WHEEL:\n var1 *= 2\n return var1 / 360","title":""},{"docid":"0c77cfc460d7389f9dfb65e644999910","score":"0.6079058","text":"def _fix_angle(self, angle):\n theta = self.odometer.val.theta\n while angle < theta - np.pi:\n angle += 2*np.pi\n while angle > theta + np.pi:\n angle -= 2*np.pi\n return angle","title":""},{"docid":"5ecce51b62c4fcc5593d4dbcce7146c8","score":"0.60681546","text":"def involute_aa(self):\n return math.tan(self.tip_pressure_angle) - self.tip_pressure_angle","title":""},{"docid":"520f9b08db8e6cda6946538275ed59bd","score":"0.6066906","text":"def argument(self):\n arg_in_rad = math.acos(Vector(0,1)*self/self.norm())\n arg_in_deg = math.degrees(arg_in_rad)\n if self.values[0]<0: return 360 - arg_in_deg\n else: return arg_in_deg","title":""},{"docid":"c45471f27a1b2307a7ffc4e7fa869f97","score":"0.6065308","text":"def toRadians():\n return math.pi / 180","title":""},{"docid":"76f561b9d35c534379d2676d0ddf64c2","score":"0.6050384","text":"def degToServo(self, deg):\n\t\t''' Converts 0-360 degrees to 0-255 servo positions '''\n\t\t# remove any extra 360s or negatives\n\t\tdeg = deg % 360\n\n\t\t#convert if it is on the right hemisphere\n\t\tif deg < 180:\n\t\t\t#subtract from the 0 degree position\n\t\t\tposition = round(127 - deg * (255.0 / 360.0))\n\n\t\t#convert if it is on the left hemisphere\n\t\telse:\n\t\t\t#get the degree position going left from center\n\t\t\tdeg = 360 - deg\n\t\t\t# add from the 0 degree position\n\t\t\tposition = round(127 + deg * (255.0 / 360.0))\n\t\treturn int(position)","title":""},{"docid":"44c8a1cbcb6646cb089a673132499cf7","score":"0.6049875","text":"def getAxisAngle(*args):\n return _coin.SbDPRotation_getAxisAngle(*args)","title":""},{"docid":"fac1410262fda4b21b9688f9052f7faf","score":"0.6044217","text":"def getTiltAngle(self):\n\n # ToDo: get from header directly?\n if self.fileFormat == 'em':\n return self._tiltAngle / 1000.\n\n else:\n raise ValueError(\"Sorry can't get tilt angle for \" +\n self.fileFormat + \" file.\")","title":""},{"docid":"4baf42e3991582fdc6b763533bf32956","score":"0.6041992","text":"def voltage_to_degrees(voltage):\n deg = (voltage/5)*360\n\n if deg < 0:\n deg += 360\n\n return deg","title":""},{"docid":"da9ae343131ba312af93f905a2a4ee9c","score":"0.6035515","text":"def getRotAng(self):\n\n return self.rotSkyPos","title":""},{"docid":"2a9a997b8ff6e235973bb706401a00e3","score":"0.6035471","text":"def get_current_angle(avacs):\n while True:\n try:\n avacs['dev'].write(('A'+avacs['setpoint_str']+'\\r').encode())\n angle_raw = avacs['dev'].read(11).decode()\n angle = round(float(angle_raw.split(';')[2])/10, 1)\n break\n except ValueError:\n time.sleep(1)\n return angle","title":""},{"docid":"4ef8af1dba859ef3e4b4221fb82f633b","score":"0.6027569","text":"def backlash_angle(self):\n return 2 * self.backlash / self.pitch_diameter if self.pitch_diameter > 0 else 0","title":""},{"docid":"3747c32bfc9288a1b4a86345b2781881","score":"0.6021017","text":"def tooth_arc_angle(self):\n return 2 * math.pi / self.tooth_count if self.tooth_count > 0 else 0","title":""},{"docid":"5c3e609d271a4cf9b1a8e776e1736a6b","score":"0.60132426","text":"def angular_vel(self):\n return self.data.qvel[1:]","title":""},{"docid":"91e5562ee05ee4f7ed63bd2fd281a969","score":"0.60083926","text":"def angle(hour_hand: int, min_hand: int) -> float:\n\n hour_in_12 = hour_hand % HALF_DAY\n x = min_hand * MIN_ANGLE\n y = (hour_in_12 + min_hand / 60) * HOUR_ANGLE \n return abs(x - y) % PI","title":""},{"docid":"c866072f0a528026043d81305b76a95c","score":"0.6003988","text":"def getDegrees(self):\n return math.degrees(self.theta) , math.degrees(self.psi)","title":""},{"docid":"d781e48c65ab39f414cbde2fb02a87c2","score":"0.600247","text":"def get_angle_bearing(bearing):\n if bearing is RIGHT:\n return -45\n elif bearing is LEFT:\n return 45\n elif bearing is ASCEND or bearing is DESCEND:\n return 0","title":""},{"docid":"419292545850a0f3a6b7f70438a60061","score":"0.6000569","text":"def angle_unit(self):\n return self._angle_unit","title":""},{"docid":"af617a5152514a361816f3c98e0c7bca","score":"0.59991413","text":"def __get_random_angle():\n return random.gauss(0, 5), random.gauss(0, 5)","title":""},{"docid":"4e6082c771a73639c1207509d6613ac3","score":"0.59945285","text":"def get_vel(self):\n return 2 * math.pi * self.odrive_axis.encoder.vel_estimate / self.gear_ratio","title":""},{"docid":"85fe8101e8cd4e5e2b30bab06b8e24eb","score":"0.59860295","text":"def angle_to(x: int, y: int) -> int:\n #xt,yt = y,x\n #rad = math.atan2(yt,xt)\n rad = math.atan2(x,y)\n if rad < 0.0:\n rad = math.pi + (math.pi + rad)\n return rad","title":""},{"docid":"dadc942e8e065459886c155ed5baaab7","score":"0.5976682","text":"def angle(self, other: \"Vector\") -> float:\r\n assert(self.is_parallel(other))\r\n return acos((self.dot(other)) / (self.norm() * other.norm()))","title":""},{"docid":"e9363ad09626f07c14489496766935b7","score":"0.5966929","text":"def get_voltage(self):\n self._send_command('BV', expect_okay=False) # angles in degrees\n raw_output = self._readline()\n out_list = raw_output.split(',')\n assert out_list[0] == '$BV'\n\n return float(out_list[1]) / 1000.0","title":""},{"docid":"b0169bc19030223ad986c14ad39797d5","score":"0.59533596","text":"def get_relative_angle2(self):\n x_obj, y_obj = self.get_object_pose()[0:2]\n x_robot, y_robot = self.get_pose()[0:2]\n\n \"\"\"Calculate relative angle\"\"\"\n relative_angle = atan2(y_obj-y_robot, x_obj-x_robot)\n\n return relative_angle","title":""},{"docid":"9a48f7ee8fa26521bb5a056306e9bbd3","score":"0.5952816","text":"def get_reversed_angle(angle):\n return (180+angle)%360","title":""},{"docid":"0c3c2428241a3651bf7624cb1e388f7c","score":"0.59451574","text":"def test_mount_angle_to_servo_angle(self):\n\n\t\t# Clockwise servos:\n\t\tself.m.clockwise_servo = True\n\t\tclockwise_test_cases = [\n\t\t\t(0, 90),\n\t\t\t(90, 180),\n\t\t\t(270, 0)\n\t\t]\n\n\t\tfor case in clockwise_test_cases:\n\t\t\tself.assertEqual(\n\t\t\t\tself.m._mount_angle_to_servo_angle(case[0]),\n\t\t\t\tcase[1]\n\t\t)\n\n\t\t# Counterclockwise servos:\n\t\tself.m.clockwise_servo = False\n\t\tcounterclockwise_test_cases = [\n\t\t\t(0, 90),\n\t\t\t(90, 0),\n\t\t\t(270, 180)\n\t\t]\n\n\t\tfor case in counterclockwise_test_cases:\n\t\t\tself.assertEqual(\n\t\t\t\tself.m._mount_angle_to_servo_angle(case[0]),\n\t\t\t\tcase[1]\n\t\t)","title":""},{"docid":"0d21807ae848a7e99994e1de840fc854","score":"0.59354365","text":"def bearing(self):\n return math.atan2(self.y, self.x)","title":""},{"docid":"8be01afc6a8d22ed98103f80e0787d43","score":"0.5933811","text":"def getAngle(self, start, end):\n angle = int(math.atan2((start[1] - end[1]), (start[0] - end[0])) * 180 / math.pi)\n return angle","title":""},{"docid":"ff431b9edd61a94f84c38a52b15ea005","score":"0.59264255","text":"def get_pan():\n return pan_servo.read()","title":""},{"docid":"463f1d54af744deeed84f6ceaf7e2aba","score":"0.5925254","text":"def GetTrueMotorAngles(self):\n motor_angles = [\n self._pybullet_client.getJointState(self.quadruped, motor_id)[0]\n for motor_id in self._motor_id_list\n ]\n motor_angles = np.multiply(motor_angles, self._motor_direction)\n return motor_angles","title":""},{"docid":"563d0f0521add3815774466cbf050934","score":"0.59063137","text":"def _getBaseDrive(cls, joystick) -> float:\n\n yAxis = joystick.getRawAxis(JoystickManager.Y_AXIS_INDEX)\n return -yAxis / cls.DRIVE_SPD_DIVISOR","title":""},{"docid":"bb4792d881d5c02ba0a5b382aee65ba1","score":"0.5903208","text":"def get_bearing_for_angle(angle):\n assert angle >= -45\n assert angle <= 45\n if angle < 0:\n return RIGHT\n elif angle > 0:\n return LEFT\n else:\n return DESCEND","title":""},{"docid":"d5c9d11e78c259138f8694dc8a3625ff","score":"0.589991","text":"def norm(angle):\n return angle % 360","title":""},{"docid":"9af822411064bec46293cf749a6a13f9","score":"0.5896655","text":"def Angle(self, low, high, unit=None):\n if unit not in (None, 'radians', 'degrees'):\n raise RoombaSensorError('Invalid angle unit specified.')\n self.DecodeShort('angle', low, high)\n if unit == 'radians':\n self['angle'] = (2 * self['angle']) / 258\n if unit == 'degrees':\n self['angle'] #/= math.pi","title":""},{"docid":"b2c4f066206b58056084fdaffc6ad3bf","score":"0.5890955","text":"def __get_angle(self, iteration, bpoint):\n\t\tif iteration == 0:\n\t\t\treturn 0\n\t\telse:\n\t\t\tdet_angle = math.pi / 3 if bpoint < self.__bpoints - iteration else math.pi / 6\n\t\t\treturn det_angle / iteration","title":""},{"docid":"b7ade825a256b34813521cdd12fc02cb","score":"0.5889848","text":"def pos(self):\n return int((self.pwm.duty() - self.lower_duty) / (self.upper_duty - self.lower_duty) * 100) / 100","title":""},{"docid":"a6772ec48dab4fc75a61858f3e420e0d","score":"0.5878679","text":"def theta_rad(self):\r\n return self._theta","title":""},{"docid":"01ec38c7025a3a7fe819717a620be529","score":"0.5871762","text":"def get_heading_degrees(self) ->float:\r\n\t\treturn math.degrees(self.heading)","title":""},{"docid":"2605a62da1998e6908b61b58c42593eb","score":"0.5867632","text":"def angle_deg(self, other):\n return self.angle_rad(other) * (180/math.pi)","title":""},{"docid":"c98b8ca53d8f38f6b7b7460840a20e8f","score":"0.58657384","text":"def reference_angle(self):\n return self.parameters['reference_angle']","title":""}],"string":"[\n {\n \"docid\": \"c8dd4df30d5c0ddd61b456737131d079\",\n \"score\": \"0.8747807\",\n \"text\": \"def getAngle(self):\\n a = self.servo.duty()\\n a -= 64\\n a *= SERVO_ANGLE / (SERVO_MAX - SERVO_MIN)\\n a -= (SERVO_ANGLE / 2)\\n return a\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8dee146df205737500fe028fb4a8c074\",\n \"score\": \"0.8033039\",\n \"text\": \"def getServoAngle(elevAngle):\\n log = math.log10(elevAngle)\\n servoAngle = 5 + elevAngle + 12*math.pow(log, 2.3)\\n return servoAngle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"53bf99000c9082a0cfa14357d51a5d33\",\n \"score\": \"0.76313317\",\n \"text\": \"def angle(self) -> float:\\n return self.velocity.angle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74c8f58f4b4da63d74b846d9948bbdef\",\n \"score\": \"0.71943146\",\n \"text\": \"def angle(self) -> float:\\n a = self.endpoints[0]\\n b = self.endpoints[1]\\n dy = b.y-a.y\\n dx = b.x-a.x\\n return math.atan(dy/dx)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c687da767fba4973e0a91c5013bbd30\",\n \"score\": \"0.71714395\",\n \"text\": \"def getAngle(self):\\n return self.vector.angle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de0e48079d82e5335eef447d5074332e\",\n \"score\": \"0.71473926\",\n \"text\": \"def get_angle(self):\\n return self._get(\\\"/api/cmd/req-get-angle-pos\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5bf2d903133fb0afd8e2f764647a3a3\",\n \"score\": \"0.71333236\",\n \"text\": \"def getAngle(self):\\n return self.angle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"067c64d4ab9c00fa4f235f28834b16d4\",\n \"score\": \"0.71271867\",\n \"text\": \"def angle(self) -> Optional[float]:\\n if self.fraction is None: # special case for disabled servos\\n return None\\n return self.actuation_range * self.fraction\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e78e2b7943c117a2177d684524cb3b8c\",\n \"score\": \"0.7093887\",\n \"text\": \"def GetMotorAngles(self):\\n motor_angles = self._AddSensorNoise(np.array(self._control_observation[0:self.num_motors]),\\n self._observation_noise_stdev[0])\\n return MapToMinusPiToPi(motor_angles)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"454eb63aace10f7c40a9e1684dff08d2\",\n \"score\": \"0.7077751\",\n \"text\": \"def angle(self):\\n return self._current_angle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a52159ca2ffd7e595053063cf8905220\",\n \"score\": \"0.704842\",\n \"text\": \"def get_angle(self):\\n\\n if self.falling:\\n if self.y < 540:\\n if self.vel * (self.time / 60) + G / 2 * (self.time / 60) ** 2 < 10 and self.alive:\\n self.angle -= 3\\n if self.angle < -30:\\n self.angle = -30\\n else:\\n self.angle += 1 + 0.2 * self.time ** 0.56\\n # angle = -110 + (self.time)**2/30\\n if self.angle > 90:\\n self.angle = 90\\n if self.angle < -90:\\n self.angle = -90\\n else:\\n return 0\\n return self.angle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17fd8f860185e319c66f3091da27ee90\",\n \"score\": \"0.70371073\",\n \"text\": \"def angle(self) -> float:\\n return self._angle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df6be67880229aecbec9a692b26f26f9\",\n \"score\": \"0.70161736\",\n \"text\": \"def angle(self):\\n\\t\\td = math.atan2(self.y, self.x)\\n\\t\\treturn d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e110901048c2205d90aff53eae39d5b\",\n \"score\": \"0.70157117\",\n \"text\": \"def angle(self):\\n return Vector.polar([self.x,self.y])[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c183e65dd1180b46668fe4e9c2f0cc5\",\n \"score\": \"0.69856966\",\n \"text\": \"def getAngle(self):\\n return self.__angle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4b1d826d22dc9df809c3f877cc55df4\",\n \"score\": \"0.6983973\",\n \"text\": \"def getAngle(self):\\n return self._angle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10923ff8695d48194cec6786f185f5ea\",\n \"score\": \"0.6970461\",\n \"text\": \"def getAngle(self):\\n return Vector.polar(self.components)[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5f6a04399eeffb1ef95c247e16113c0\",\n \"score\": \"0.69413257\",\n \"text\": \"def setAngle(self, angle, delay=True):\\n a = self.servo.duty()\\n if not -(SERVO_ANGLE / 2) <= angle <= (SERVO_ANGLE / 2):\\n raise LookupError(\\\"Angle invalid: \\\"+str(angle)) \\n d = angle + (SERVO_ANGLE / 2)\\n d *= (SERVO_MAX - SERVO_MIN) / SERVO_ANGLE\\n d = int(d + 64)\\n self.servo.duty(d)\\n if delay:\\n time.sleep(0.1 * abs(angle - a) / 60)\\n return d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39d11370a639b5b8f3106018f59354ab\",\n \"score\": \"0.69327146\",\n \"text\": \"def angle(self):\\n return self._angle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39d11370a639b5b8f3106018f59354ab\",\n \"score\": \"0.69327146\",\n \"text\": \"def angle(self):\\n return self._angle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4ecf754634c16f8885590cccf5315a0\",\n \"score\": \"0.69031966\",\n \"text\": \"def getAngle(self):\\n return Angle(self)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38da272190d33a773ac719295cca7e5f\",\n \"score\": \"0.6845757\",\n \"text\": \"def angle(self):\\n if self._angle is not None:\\n return self._angle\\n [denominator, numerator] = (self.endpoints[1] - self.endpoints[0]).coordinates\\n if abs(denominator) < 0.000001:\\n # almost vertical line\\n self._angle = pi/2\\n else:\\n angle = atan(numerator/denominator)\\n if angle < 0:\\n angle = pi + angle\\n self._angle = angle\\n return self._angle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48491240fc02a75b5b64ec19c6783baf\",\n \"score\": \"0.68319154\",\n \"text\": \"def get_laser_angle(point):\\n return ((2 * math.pi * (point + 1) / 60.0) - math.pi ) - 30.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"329b51c20d1acd387174ec765e20aacf\",\n \"score\": \"0.6705808\",\n \"text\": \"def get_angle() -> float:\\n global CURRENT_ANGLE\\n CURRENT_ANGLE = -CURRENT_ANGLE\\n return CURRENT_ANGLE\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d74a059d6c39d1f6f709aabbfba19b8b\",\n \"score\": \"0.664495\",\n \"text\": \"def angle(self, value):\\r\\n #value est en pourcentage d'angle max[-100, 100]\\r\\n value = float(value)/100.\\r\\n value = minmax(value, -100., 100.)\\r\\n #angle commande\\r\\n angleCmd = self.__angmax*value\\r\\n diff = angleCmd - self.__angle\\r\\n # On ne peut pas depasser la vitesse max des servos\\r\\n if (abs(diff/DT) > self.__vitmax): \\r\\n diff = np.sign(diff)*self.__vitmax*DT\\r\\n self.__angle = self.__angle + diff\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c9bbb3dd5000cd0e5d6ab43ce24cb1e\",\n \"score\": \"0.663126\",\n \"text\": \"def getTiltAngle(self):\\n\\t\\treturn self.__tiltNodeIN.getTiltAngle()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d287c66a3a72c65289c58afbe4c050c9\",\n \"score\": \"0.6622105\",\n \"text\": \"def set_angle(self, servo, angle):\\n if angle > 180 or angle < 0:\\n angle = 90\\n byteone = int(254*angle/180)\\n bud = [0xFF, servo, byteone]\\n self.sc.write(bud)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e58a31850851730467790293cd8473bf\",\n \"score\": \"0.6610297\",\n \"text\": \"def current_angle(self):\\n return self.angle.value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4dee1665e7067c6dd9643155e59b23c5\",\n \"score\": \"0.6549726\",\n \"text\": \"def involute_a(self):\\n return math.tan(self.pressure_angle) - self.pressure_angle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c190932e2a64ed453ce970146581d0e\",\n \"score\": \"0.6524581\",\n \"text\": \"def angle(self):\\n # only for 2D vectors\\n return math.atan2(self.y, self.x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64bd96b2d75f7e2c0b935b5ae2c09a47\",\n \"score\": \"0.64882964\",\n \"text\": \"def angle(this):\\n return math.atan2(this.y, this.x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"243ed3eb4e2336dfb3429867ebdab1e2\",\n \"score\": \"0.6480937\",\n \"text\": \"def getTiltAngle(self):\\n\\t\\treturn self.__angle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e3f05016ddfb26266a1a68fd8601d5f\",\n \"score\": \"0.64471793\",\n \"text\": \"def read_angle(self) -> int:\\n return self._transfer(Sensor._ANGLE_READ_REQUEST) & Sensor._RESPONSE_MASK\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12c5672bc0f175c85d7618742025610a\",\n \"score\": \"0.643664\",\n \"text\": \"def radians(self) -> float:\\n return self._angle / 180.0 * math.pi\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd1285d02dd1a8b7f9d1580744e4915f\",\n \"score\": \"0.64329773\",\n \"text\": \"def angle_at_displacement(self, displacement):\\n t = self.t_for(displacement)\\n a = t + self.rotation\\n a %= 2 * math.pi\\n return a\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fd15322f26facc4424de2a79c3ea661\",\n \"score\": \"0.6396762\",\n \"text\": \"def get_theta(phase):\\n return (phase % 1) * 2 * np.pi\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7263e96bb83d051d68005bdec5fd7d59\",\n \"score\": \"0.6372367\",\n \"text\": \"def getBearing():\\n return calc.bearing(getPose()['Pose']['Orientation'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa2e7c3bc2aa10208cdca58dae8923cb\",\n \"score\": \"0.6338179\",\n \"text\": \"def get_direction(self, angle):\\n rad_angle = np.deg2rad((angle+90) % 360)\\n return np.round(np.cos(rad_angle), 5), -np.round(np.sin(rad_angle), 5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c3495f70d0e815a09890a3b106ba7e9\",\n \"score\": \"0.6331294\",\n \"text\": \"def get_dist_from_angle(self,angle=None):\\n if angle != None:\\n self._present_angle = angle\\n self.servo.setangle(self._present_angle)\\n mean, std = self.uss.get_mean_distance(self.mean_measurements,self.us_sleep)\\n return mean, std\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a959d3cb2e0b6b94b09e89f96193aaaf\",\n \"score\": \"0.632471\",\n \"text\": \"def get_tilt():\\n return tilt_servo.read()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fcd7409e276a263b09db047206e5e64\",\n \"score\": \"0.62961817\",\n \"text\": \"def tip_pressure_angle(self):\\n return math.acos(self.base_diameter / self.outside_diameter)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbd01e35b74584f27cca5a2b96c001b8\",\n \"score\": \"0.624891\",\n \"text\": \"def orbitAngle(self):\\n subject = self._get_subject()\\n if subject is None:\\n return None\\n distance, worldVector = self.getVectTo(subject.point)[0:2]\\n # Originally had a line like the following, but it caused a Blender\\n # crash.\\n # flatVector = (worldVector * distance).resized(2)\\n # It's probably less efficient anyway, because it allocates a new\\n # Vector object or two.\\n worldVector *= distance\\n worldVector.resize(2)\\n xZero = Vector((worldVector.magnitude, 0))\\n return xZero.angle_signed(worldVector, 0.0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0207c3f064f42c3d34181296ee5c884\",\n \"score\": \"0.6238376\",\n \"text\": \"def getAngle(self, sessionId=None, center=None):\\n if center is None:\\n center = self.getMeanCenter(sessionId)\\n return 2*pi+atan2(center[1], center[0])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fe7644d4c9faeef7271a61745160431\",\n \"score\": \"0.62340295\",\n \"text\": \"def get_relative_angle(self):\\n x_obj, y_obj = self.get_object_pose()[0:2]\\n x_robot, y_robot = self.get_pose()[0:2]\\n\\n \\\"\\\"\\\"Calculate relative angle\\\"\\\"\\\"\\n relative_angle = atan2(y_robot - y_obj, x_robot - x_obj)\\n\\n return relative_angle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18c0ba437bc9845fe461b49023dfce4e\",\n \"score\": \"0.6223402\",\n \"text\": \"def pote_sweep():\\n val = POTE.value / 65536\\n SERVO.angle = 180 * val\\n time.sleep(0.05)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ce645057c22f32d76929ed4dea666b7\",\n \"score\": \"0.6208178\",\n \"text\": \"def find_angle(number_sides):\\n return 180 - (((number_sides - 2)*180) / number_sides)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b05eb3d711f4c0647b429e382b3c00e2\",\n \"score\": \"0.61783034\",\n \"text\": \"def get_angle(self, relative_to=(1, 0)):\\n v2_u = relative_to\\n if relative_to != (1, 0):\\n v2_u = LineSegment.get_unit_vector(relative_to[0], relative_to[1])\\n # Not using the length since we are using both unit vectors\\n angle = np.arccos(abs(np.dot(self.unit_vector, v2_u)))\\n if np.isnan(angle):\\n return 0.0\\n return round(angle, self.angle_precision)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcf0df18aac65871b37f5489c2e21380\",\n \"score\": \"0.61775064\",\n \"text\": \"def getAxisAngle(*args):\\n return _coin.SbRotation_getAxisAngle(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a6eb7ff64a3b14a21222186c429bea5\",\n \"score\": \"0.61583483\",\n \"text\": \"def mod_angle(self):\\n return self._mod_angle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e6185dcf041762af8affec5d8294718\",\n \"score\": \"0.614655\",\n \"text\": \"def heading(self):\\n return math.degrees(math.atan2(self.y, self.x))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"067f0a391ddea15a803288c84a5ab012\",\n \"score\": \"0.61436796\",\n \"text\": \"def GetBackLightAngle(self):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d2d5225b95d3c5947e0b04459d622b8\",\n \"score\": \"0.6136827\",\n \"text\": \"def shooting_angle(self) -> float:\\n return self.__shooting_angle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"924bf442f46c94b5167c08c98e614a70\",\n \"score\": \"0.6136353\",\n \"text\": \"def angle_at_index(self, idx):\\n vect = self.vector_at_index(idx)\\n x_vect = np.array([1, 0])\\n dot = np.dot(vect, x_vect)\\n det = np.linalg.det([x_vect, vect])\\n track_ang = np.arctan2(det, dot)\\n if track_ang < 0.:\\n track_ang += 2 * np.pi\\n return track_ang\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b76430532477e7b0f2675f14a10fa8d\",\n \"score\": \"0.6120453\",\n \"text\": \"def\\t__convertAngle(self,value):\\n\\t\\t# {{{\\n\\t\\n\\t\\tau = self.__angleUnit\\n\\t\\tif au == self.Unit_AngleDegrees:\\n\\t\\t\\treturn float(value)*math.pi/180.0\\n\\t\\telif au == self.Unit_AngleRadians:\\n\\t\\t\\treturn float(value)\\n\\t\\n\\t\\traise ParseException(\\\"convertAngle not reaching point\\\",0,Error_Internal)\\n\\t\\t# }}}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1b10402d315e030dd598138b47901cd\",\n \"score\": \"0.6114449\",\n \"text\": \"def PositionAngle(self, *args):\\n return _stomp.AngularCoordinate_PositionAngle(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a14094509ec6b1501ecb19eee59c8d2e\",\n \"score\": \"0.6114236\",\n \"text\": \"def _get_direction_change_angle() -> int:\\n return config_parser.general_getter(\\\"PATH_FINDER\\\",\\n \\\"DIRECTION_CHANGE_ANGLE\\\", DataType.INT)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45fbac1d0c7b060527924eb12cb2c56c\",\n \"score\": \"0.6110663\",\n \"text\": \"def _calculateAngleToGoal(self):\\n [goalx, goaly] = self.goal\\n [currx, curry, _] = self.coords # current\\n\\n diffx = (goalx-currx)\\n diffy = (goaly-curry)\\n theta = atan2(diffy, diffx)\\n return theta * 180 / pi + 180\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c84cf854e75e95f2dc342fad633c7c8\",\n \"score\": \"0.60991514\",\n \"text\": \"def angleFromVector(x, y):\\n deg = math.degrees((math.atan(float(y)/x)))\\n if deg < 0:\\n return 180 + deg\\n else:\\n return deg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7425a5cb7514d66ead31aae0cfa408aa\",\n \"score\": \"0.60984176\",\n \"text\": \"def get_angle(self, x, y, x2, y2):\\n return (180 / math.pi) * math.atan((y2 - y) / (x2 - x))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f45d2f859f521eefb795f579f93e2d7\",\n \"score\": \"0.6091841\",\n \"text\": \"def angle(y, x):\\n return math.atan2(y, x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd6bb789ac7f97b7dfb3d1353e28786d\",\n \"score\": \"0.6086512\",\n \"text\": \"def calc_dist(self, point):\\n var1 = math.pi * WHEEL_SPAN * abs(self.degree)\\n if point == WHEEL:\\n var1 *= 2\\n return var1 / 360\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c77cfc460d7389f9dfb65e644999910\",\n \"score\": \"0.6079058\",\n \"text\": \"def _fix_angle(self, angle):\\n theta = self.odometer.val.theta\\n while angle < theta - np.pi:\\n angle += 2*np.pi\\n while angle > theta + np.pi:\\n angle -= 2*np.pi\\n return angle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ecce51b62c4fcc5593d4dbcce7146c8\",\n \"score\": \"0.60681546\",\n \"text\": \"def involute_aa(self):\\n return math.tan(self.tip_pressure_angle) - self.tip_pressure_angle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"520f9b08db8e6cda6946538275ed59bd\",\n \"score\": \"0.6066906\",\n \"text\": \"def argument(self):\\n arg_in_rad = math.acos(Vector(0,1)*self/self.norm())\\n arg_in_deg = math.degrees(arg_in_rad)\\n if self.values[0]<0: return 360 - arg_in_deg\\n else: return arg_in_deg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c45471f27a1b2307a7ffc4e7fa869f97\",\n \"score\": \"0.6065308\",\n \"text\": \"def toRadians():\\n return math.pi / 180\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76f561b9d35c534379d2676d0ddf64c2\",\n \"score\": \"0.6050384\",\n \"text\": \"def degToServo(self, deg):\\n\\t\\t''' Converts 0-360 degrees to 0-255 servo positions '''\\n\\t\\t# remove any extra 360s or negatives\\n\\t\\tdeg = deg % 360\\n\\n\\t\\t#convert if it is on the right hemisphere\\n\\t\\tif deg < 180:\\n\\t\\t\\t#subtract from the 0 degree position\\n\\t\\t\\tposition = round(127 - deg * (255.0 / 360.0))\\n\\n\\t\\t#convert if it is on the left hemisphere\\n\\t\\telse:\\n\\t\\t\\t#get the degree position going left from center\\n\\t\\t\\tdeg = 360 - deg\\n\\t\\t\\t# add from the 0 degree position\\n\\t\\t\\tposition = round(127 + deg * (255.0 / 360.0))\\n\\t\\treturn int(position)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44c8a1cbcb6646cb089a673132499cf7\",\n \"score\": \"0.6049875\",\n \"text\": \"def getAxisAngle(*args):\\n return _coin.SbDPRotation_getAxisAngle(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fac1410262fda4b21b9688f9052f7faf\",\n \"score\": \"0.6044217\",\n \"text\": \"def getTiltAngle(self):\\n\\n # ToDo: get from header directly?\\n if self.fileFormat == 'em':\\n return self._tiltAngle / 1000.\\n\\n else:\\n raise ValueError(\\\"Sorry can't get tilt angle for \\\" +\\n self.fileFormat + \\\" file.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4baf42e3991582fdc6b763533bf32956\",\n \"score\": \"0.6041992\",\n \"text\": \"def voltage_to_degrees(voltage):\\n deg = (voltage/5)*360\\n\\n if deg < 0:\\n deg += 360\\n\\n return deg\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da9ae343131ba312af93f905a2a4ee9c\",\n \"score\": \"0.6035515\",\n \"text\": \"def getRotAng(self):\\n\\n return self.rotSkyPos\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a9a997b8ff6e235973bb706401a00e3\",\n \"score\": \"0.6035471\",\n \"text\": \"def get_current_angle(avacs):\\n while True:\\n try:\\n avacs['dev'].write(('A'+avacs['setpoint_str']+'\\\\r').encode())\\n angle_raw = avacs['dev'].read(11).decode()\\n angle = round(float(angle_raw.split(';')[2])/10, 1)\\n break\\n except ValueError:\\n time.sleep(1)\\n return angle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ef8af1dba859ef3e4b4221fb82f633b\",\n \"score\": \"0.6027569\",\n \"text\": \"def backlash_angle(self):\\n return 2 * self.backlash / self.pitch_diameter if self.pitch_diameter > 0 else 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3747c32bfc9288a1b4a86345b2781881\",\n \"score\": \"0.6021017\",\n \"text\": \"def tooth_arc_angle(self):\\n return 2 * math.pi / self.tooth_count if self.tooth_count > 0 else 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c3e609d271a4cf9b1a8e776e1736a6b\",\n \"score\": \"0.60132426\",\n \"text\": \"def angular_vel(self):\\n return self.data.qvel[1:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91e5562ee05ee4f7ed63bd2fd281a969\",\n \"score\": \"0.60083926\",\n \"text\": \"def angle(hour_hand: int, min_hand: int) -> float:\\n\\n hour_in_12 = hour_hand % HALF_DAY\\n x = min_hand * MIN_ANGLE\\n y = (hour_in_12 + min_hand / 60) * HOUR_ANGLE \\n return abs(x - y) % PI\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c866072f0a528026043d81305b76a95c\",\n \"score\": \"0.6003988\",\n \"text\": \"def getDegrees(self):\\n return math.degrees(self.theta) , math.degrees(self.psi)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d781e48c65ab39f414cbde2fb02a87c2\",\n \"score\": \"0.600247\",\n \"text\": \"def get_angle_bearing(bearing):\\n if bearing is RIGHT:\\n return -45\\n elif bearing is LEFT:\\n return 45\\n elif bearing is ASCEND or bearing is DESCEND:\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"419292545850a0f3a6b7f70438a60061\",\n \"score\": \"0.6000569\",\n \"text\": \"def angle_unit(self):\\n return self._angle_unit\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af617a5152514a361816f3c98e0c7bca\",\n \"score\": \"0.59991413\",\n \"text\": \"def __get_random_angle():\\n return random.gauss(0, 5), random.gauss(0, 5)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e6082c771a73639c1207509d6613ac3\",\n \"score\": \"0.59945285\",\n \"text\": \"def get_vel(self):\\n return 2 * math.pi * self.odrive_axis.encoder.vel_estimate / self.gear_ratio\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85fe8101e8cd4e5e2b30bab06b8e24eb\",\n \"score\": \"0.59860295\",\n \"text\": \"def angle_to(x: int, y: int) -> int:\\n #xt,yt = y,x\\n #rad = math.atan2(yt,xt)\\n rad = math.atan2(x,y)\\n if rad < 0.0:\\n rad = math.pi + (math.pi + rad)\\n return rad\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dadc942e8e065459886c155ed5baaab7\",\n \"score\": \"0.5976682\",\n \"text\": \"def angle(self, other: \\\"Vector\\\") -> float:\\r\\n assert(self.is_parallel(other))\\r\\n return acos((self.dot(other)) / (self.norm() * other.norm()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9363ad09626f07c14489496766935b7\",\n \"score\": \"0.5966929\",\n \"text\": \"def get_voltage(self):\\n self._send_command('BV', expect_okay=False) # angles in degrees\\n raw_output = self._readline()\\n out_list = raw_output.split(',')\\n assert out_list[0] == '$BV'\\n\\n return float(out_list[1]) / 1000.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0169bc19030223ad986c14ad39797d5\",\n \"score\": \"0.59533596\",\n \"text\": \"def get_relative_angle2(self):\\n x_obj, y_obj = self.get_object_pose()[0:2]\\n x_robot, y_robot = self.get_pose()[0:2]\\n\\n \\\"\\\"\\\"Calculate relative angle\\\"\\\"\\\"\\n relative_angle = atan2(y_obj-y_robot, x_obj-x_robot)\\n\\n return relative_angle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a48f7ee8fa26521bb5a056306e9bbd3\",\n \"score\": \"0.5952816\",\n \"text\": \"def get_reversed_angle(angle):\\n return (180+angle)%360\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c3c2428241a3651bf7624cb1e388f7c\",\n \"score\": \"0.59451574\",\n \"text\": \"def test_mount_angle_to_servo_angle(self):\\n\\n\\t\\t# Clockwise servos:\\n\\t\\tself.m.clockwise_servo = True\\n\\t\\tclockwise_test_cases = [\\n\\t\\t\\t(0, 90),\\n\\t\\t\\t(90, 180),\\n\\t\\t\\t(270, 0)\\n\\t\\t]\\n\\n\\t\\tfor case in clockwise_test_cases:\\n\\t\\t\\tself.assertEqual(\\n\\t\\t\\t\\tself.m._mount_angle_to_servo_angle(case[0]),\\n\\t\\t\\t\\tcase[1]\\n\\t\\t)\\n\\n\\t\\t# Counterclockwise servos:\\n\\t\\tself.m.clockwise_servo = False\\n\\t\\tcounterclockwise_test_cases = [\\n\\t\\t\\t(0, 90),\\n\\t\\t\\t(90, 0),\\n\\t\\t\\t(270, 180)\\n\\t\\t]\\n\\n\\t\\tfor case in counterclockwise_test_cases:\\n\\t\\t\\tself.assertEqual(\\n\\t\\t\\t\\tself.m._mount_angle_to_servo_angle(case[0]),\\n\\t\\t\\t\\tcase[1]\\n\\t\\t)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d21807ae848a7e99994e1de840fc854\",\n \"score\": \"0.59354365\",\n \"text\": \"def bearing(self):\\n return math.atan2(self.y, self.x)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8be01afc6a8d22ed98103f80e0787d43\",\n \"score\": \"0.5933811\",\n \"text\": \"def getAngle(self, start, end):\\n angle = int(math.atan2((start[1] - end[1]), (start[0] - end[0])) * 180 / math.pi)\\n return angle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff431b9edd61a94f84c38a52b15ea005\",\n \"score\": \"0.59264255\",\n \"text\": \"def get_pan():\\n return pan_servo.read()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"463f1d54af744deeed84f6ceaf7e2aba\",\n \"score\": \"0.5925254\",\n \"text\": \"def GetTrueMotorAngles(self):\\n motor_angles = [\\n self._pybullet_client.getJointState(self.quadruped, motor_id)[0]\\n for motor_id in self._motor_id_list\\n ]\\n motor_angles = np.multiply(motor_angles, self._motor_direction)\\n return motor_angles\",\n \"title\": \"\"\n },\n {\n \"docid\": \"563d0f0521add3815774466cbf050934\",\n \"score\": \"0.59063137\",\n \"text\": \"def _getBaseDrive(cls, joystick) -> float:\\n\\n yAxis = joystick.getRawAxis(JoystickManager.Y_AXIS_INDEX)\\n return -yAxis / cls.DRIVE_SPD_DIVISOR\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb4792d881d5c02ba0a5b382aee65ba1\",\n \"score\": \"0.5903208\",\n \"text\": \"def get_bearing_for_angle(angle):\\n assert angle >= -45\\n assert angle <= 45\\n if angle < 0:\\n return RIGHT\\n elif angle > 0:\\n return LEFT\\n else:\\n return DESCEND\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5c9d11e78c259138f8694dc8a3625ff\",\n \"score\": \"0.589991\",\n \"text\": \"def norm(angle):\\n return angle % 360\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9af822411064bec46293cf749a6a13f9\",\n \"score\": \"0.5896655\",\n \"text\": \"def Angle(self, low, high, unit=None):\\n if unit not in (None, 'radians', 'degrees'):\\n raise RoombaSensorError('Invalid angle unit specified.')\\n self.DecodeShort('angle', low, high)\\n if unit == 'radians':\\n self['angle'] = (2 * self['angle']) / 258\\n if unit == 'degrees':\\n self['angle'] #/= math.pi\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2c4f066206b58056084fdaffc6ad3bf\",\n \"score\": \"0.5890955\",\n \"text\": \"def __get_angle(self, iteration, bpoint):\\n\\t\\tif iteration == 0:\\n\\t\\t\\treturn 0\\n\\t\\telse:\\n\\t\\t\\tdet_angle = math.pi / 3 if bpoint < self.__bpoints - iteration else math.pi / 6\\n\\t\\t\\treturn det_angle / iteration\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7ade825a256b34813521cdd12fc02cb\",\n \"score\": \"0.5889848\",\n \"text\": \"def pos(self):\\n return int((self.pwm.duty() - self.lower_duty) / (self.upper_duty - self.lower_duty) * 100) / 100\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6772ec48dab4fc75a61858f3e420e0d\",\n \"score\": \"0.5878679\",\n \"text\": \"def theta_rad(self):\\r\\n return self._theta\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01ec38c7025a3a7fe819717a620be529\",\n \"score\": \"0.5871762\",\n \"text\": \"def get_heading_degrees(self) ->float:\\r\\n\\t\\treturn math.degrees(self.heading)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2605a62da1998e6908b61b58c42593eb\",\n \"score\": \"0.5867632\",\n \"text\": \"def angle_deg(self, other):\\n return self.angle_rad(other) * (180/math.pi)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c98b8ca53d8f38f6b7b7460840a20e8f\",\n \"score\": \"0.58657384\",\n \"text\": \"def reference_angle(self):\\n return self.parameters['reference_angle']\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993285,"cells":{"query_id":{"kind":"string","value":"b4e4a98c0bb277e3fced207af52e5038"},"query":{"kind":"string","value":"returns the window of AA's around the Cterm of an epitope, given defined upstream and downstream window sizes and a row from a pandas df with ending position and full origin sequence of the epitope."},"positive_passages":{"kind":"list like","value":[{"docid":"591ed3df5d9c1747a1df54ec0071ae3b","score":"0.62344474","text":"def get_peptide_window(sequence, starting_position, ending_position, upstream=6,\n downstream=6, c_terminal=True):\n # set cleavage site index based on function flag\n if c_terminal:\n cleave_index = int(ending_position) - 1\n if not c_terminal:\n cleave_index = int(starting_position) - 1\n\n if cleave_index < 0 or (cleave_index - 1) >= len(sequence):\n return None\n\n # if upstream window does not hit boundary\n if (cleave_index - upstream) >= 0:\n upstream_seq = sequence[(cleave_index - upstream):cleave_index]\n # if peptide boundary is within upstream window\n if cleave_index - upstream < 0:\n # retrieve relevant sequence\n tmp_seq = sequence[0:cleave_index]\n # add empty AA's prior to seq start\n upstream_seq = abs(cleave_index - upstream) * \"*\" + tmp_seq\n # repeat above with downstream window\n if (cleave_index + 1 + downstream) < len(sequence):\n downstream_seq = sequence[(cleave_index + 1):(cleave_index +\n downstream + 1)]\n if (cleave_index + 1 + downstream) >= len(sequence):\n # handles issue where cleavage site was end of protein and\n # cleave_index + 1 was beyond sequence bounds\n if cleave_index == (len(sequence) - 1):\n downstream_seq = downstream * \"*\"\n else:\n tmp_seq = sequence[(cleave_index + 1):len(sequence)]\n downstream_seq = tmp_seq + (downstream + 1 + cleave_index -\n len(sequence)) * \"*\"\n # return up/down stream windows + cleavage site\n return upstream_seq + sequence[cleave_index] + downstream_seq","title":""}],"string":"[\n {\n \"docid\": \"591ed3df5d9c1747a1df54ec0071ae3b\",\n \"score\": \"0.62344474\",\n \"text\": \"def get_peptide_window(sequence, starting_position, ending_position, upstream=6,\\n downstream=6, c_terminal=True):\\n # set cleavage site index based on function flag\\n if c_terminal:\\n cleave_index = int(ending_position) - 1\\n if not c_terminal:\\n cleave_index = int(starting_position) - 1\\n\\n if cleave_index < 0 or (cleave_index - 1) >= len(sequence):\\n return None\\n\\n # if upstream window does not hit boundary\\n if (cleave_index - upstream) >= 0:\\n upstream_seq = sequence[(cleave_index - upstream):cleave_index]\\n # if peptide boundary is within upstream window\\n if cleave_index - upstream < 0:\\n # retrieve relevant sequence\\n tmp_seq = sequence[0:cleave_index]\\n # add empty AA's prior to seq start\\n upstream_seq = abs(cleave_index - upstream) * \\\"*\\\" + tmp_seq\\n # repeat above with downstream window\\n if (cleave_index + 1 + downstream) < len(sequence):\\n downstream_seq = sequence[(cleave_index + 1):(cleave_index +\\n downstream + 1)]\\n if (cleave_index + 1 + downstream) >= len(sequence):\\n # handles issue where cleavage site was end of protein and\\n # cleave_index + 1 was beyond sequence bounds\\n if cleave_index == (len(sequence) - 1):\\n downstream_seq = downstream * \\\"*\\\"\\n else:\\n tmp_seq = sequence[(cleave_index + 1):len(sequence)]\\n downstream_seq = tmp_seq + (downstream + 1 + cleave_index -\\n len(sequence)) * \\\"*\\\"\\n # return up/down stream windows + cleavage site\\n return upstream_seq + sequence[cleave_index] + downstream_seq\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"7b3624355498b38ef47c40a368055fa7","score":"0.5745897","text":"def _get_window(start, end):\n window = 1 - np.r_[hann(4)[:2], np.ones(np.abs(end - start) - 4), hann(4)[-2:]].T\n return window","title":""},{"docid":"3e6e2fb9cb52192f65c76b59f681e9e1","score":"0.57039005","text":"def window(self, df):\n df = df[df.lat > self.latmin]\n df = df[df.lat < self.latmax]\n df = df[df.lon > self.lonmin]\n df = df[df.lon < self.lonmax]\n df = df.reset_index()\n return df","title":""},{"docid":"e3f6340f21fdcce47c458932457933f8","score":"0.5591229","text":"def auto_window(self, taus, c):\n m = np.arange(len(taus)) < c * taus\n if np.any(m):\n return np.argmin(m)\n return len(taus) - 1","title":""},{"docid":"8d39c6e541ba18e71204d09cd3bdc43d","score":"0.55247456","text":"def window(df, latmin, latmax, lonmin, lonmax):\n df = df[df.lat > latmin]\n df = df[df.lat < latmax]\n df = df[df.lon > lonmin]\n df = df[df.lon < lonmax]\n return df","title":""},{"docid":"8aa43184e34bc91e728608003843fb0e","score":"0.5428853","text":"def windowmaking(dataset, window):\n padding = window//2\n window_list = []\n seq = parse_seq(dataset)\n for sequence in seq:\n sequence = ((padding*'0') + sequence + (padding*'0'))\n for residue in range(0, len(sequence)):\n if residue+(window) > len(sequence):\n break\n window_list.append(sequence[residue:residue+window])\n return window_list","title":""},{"docid":"bbaf9cb75155e9841e3dd059fb0bdd21","score":"0.5357955","text":"def overlap_window (self,window_function,dtime,dfreq,overlap_time=0,overlap_freq=0,dump=None,dumpfig=None):\n # if overlap_freq:\n # raise RuntimeError,\"frequency overlap not yet implemented\";\n # effective window size in time\n dtime1 = dtime + overlap_time*2;\n dfreq1 = dfreq + overlap_freq*2;\n # this is the output shape\n ntime1 = self.ntime/dtime;\n nfreq1 = self.nfreq/dfreq;\n # make a list of per-baseline output arrays \n result = [];\n # make resulting ANTENNA1/ANTENNA2 indices\n a1 = np.zeros(ntime1*self.nbl)\n a2 = np.zeros(ntime1*self.nbl)\n # make wl: array of wavelengths over each bin -- NF1,DF (where original shape was NF=NF1*DF)\n## wl = self.wavel.reshape((nfreq1,dfreq));\n # wl0: wavelength of centre channel of each frequency bin, shape NF1\n## if dfreq%2:\n## wl0 = self.wavel[dfreq/2::dfreq];\n## else:\n## wl0 = (self.wavel[dfreq/2-1::dfreq] + self.wavel[dfreq/2::dfreq])/2;\n # loop over each baseline\n freqs = self.channels[0,:];\n wavel = 3e8/freqs;\n for p in range(self.na):\n for q in range(p+1,self.na):\n input_index = (self.A0==p)&(self.A1==q)\n data1 = self.data[input_index].copy();\n uv1 = self.UVW[input_index,:2].copy();\n # prepare output array\n\t reswind = np.zeros((ntime1,150),dtype=float)\n resdata = np.zeros((ntime1,nfreq1,self.ncorr),self.data.dtype);\n # loop over all output timeslots\n for out_time in range(ntime1):\n # make timeslice corresponding to extended window\n in_time0 = self.time0+out_time*dtime-overlap_time;\n timeslice = slice(in_time0,in_time0+dtime1);\n # extract data subset over the window\n data = data1[timeslice];\n\t #loop over all output frequencies\n \t for out_freq in range(nfreq1):\n\t\tin_freq0 = self.freq0 + out_freq*dfreq - overlap_freq;\n\t\tfreqslice = slice(in_freq0, in_freq0 + dfreq1);\n\t\t#print \"freqslice.shape\",freqslice\n\t\tdata2 = data[:,freqslice,...];\n\t\t#print \"wavel shape******=\",wavel.shape\n\t\twavel1 = wavel[freqslice];\n\t\t#print\"wave1 shape\",wavel1.shape\n\t\t\n # reshape so that NF becomes NF1,DF, so that we can then reduce over the second axis\n # resulting shape is DT1,NF1,DF,NCORR\n# print timeslice,data.shape,(dtime1,nfreq1,dfreq,self.ncorr)\n\t \timport Pyxis\n\t \t#print \"data shape\",data.shape\n\t \tPyxis.info('shape dtime1%i, shape nfreq1%i, shape dfreq%i'%(dtime1,nfreq1,dfreq))\n ##data = data.reshape((dtime1,nfreq1,dfreq,self.ncorr))\n\t\t\n # get UV coordinates for the window, shape is DT1,2\n \tuv = uv1[timeslice];\n # get corresponding uv-center point (uv0), shape is 2 \n \tif dtime1%2: # odd dtime: we have a uv-bin at the centre, so start at n/2, then take every n-th uv point\n \t\tuv0 = uv[dtime1/2];\n \telse: # even dtime -- take the middle of the two centre bins\n \t\tuv0 = (uv[dtime1/2-1] + uv[dtime1/2])/2;#,:\n # take uv-distance component along time, shape is DT1\n \tuvd1 = np.sqrt(((uv-uv0[np.newaxis,:])**2).sum(1));\n # convert it to wavelengths at centre channel of each window, this will now be of shape DT1,NF1\n\t\tif dfreq1%2:\n\t\t\twl0 = wavel1[dfreq1/2];\n\t\telse:\n\t\t\twl0 = (wavel1[dfreq1/2 -1] + wavel1[dfreq/2])/2;\n\t\t#print \"uvd1.shape\",uvd1.shape\n\t\t#print \"wl0.shape\",wl0.shape\n \tuvd1 = uvd1[:,np.newaxis] / wl0;\n # now for the second component: ||uv0||/wl0 at each window is the centre channel uv-length,\n # the second component's distance is ||uv0||/wl - ||uv0||/wl0\n # first compute ||uv0||, this is scalar\n \tuv0r = np.sqrt((uv0**2).sum()); \n # work out second component of uv-distance, shape will be NF1,DF\n \tuvd2 = uv0r/wavel1 - uv0r/wl0;\n # evaluate windowing function\n # shapes are: uvd1 is DT1,NF1 and uvd2 is NF1,DF so insert new axes appropriately\n # wf shape is then DT1,NF1,DF\n\t\t#print \"uvd1.shape,uvd2.shape\",uvd1.shape,uvd2.shape\n \twf = window_function(uvd1[:,:],uvd2[np.newaxis,:]);\n\t\t#ham1 = signal.hamming(len(wf[:,0]))*np.max(wf[:,0])\n\t\t#ham2 = signal.hamming(len(wf[0,:]))*np.max(wf[0,:])\n\t\t#ham = ham1*ham2;\n\t\t#wf = wf*ham;\n \tif dump and out_time==0:\n \t\timport cPickle\n \t\tcPickle.dump((p,q,uvd1,uvd2,wf),dump);\n # sum of wf over whole window and each frequency bin, shape NF1\n \twfsum = wf.sum(1).sum(0);\n# if out_time == 0:\n# print p,q,wfsum;\n #if dumpfig and p==17 and q==26 and out_time==0:\n# fig = pylab.figure()\n# ax = fig.add_subplot(111, projection='3d')\n# ax.plot_wireframe(wf)\n #pylab.plot(wf[dtime1/2,0,:]);\n #pylab.plot(wf[:,0,dfreq/2]);\n #pylab.savefig(dumpfig);\n #print \"Saved figure\",dumpfig;\n #print uvd1,uvd2,wf;\n # apply wf to data (np.newaxis ensures every correlation is multiplied by the same wf),\n # then sum over whole window and each frequency bin, normalize, and assign to appropriate\n # timeslot in output\n\t \t#print wf[:,dfreq/2].shape\n\t \t#uvd1=uvd1[:,:,np.newaxis].reshape(uvd1.shape[0])\n\t \t#print uvd1.shape\n\t # if p==17 and q==26:\n\t #\tpylab.plot(wf[dtime1/2,0,:])\n\t #\tpylab.plot(uvd1,wf[:,0,dfreq/2])\n\t #\tpylab.show()\n\t # \treswind[out_time]=wf[:,0,dfreq/2]\n\t #\tresult.append((p,q,wf[:,0,dfreq/2]))\n\t\t#print \"##data2.shape\",data2.shape\n\t\t#print \"###wf.shape\",wf.shape\n\t\t#print \"###wsum.shape\",wfsum\n\t\t#print \"###resdata.shape\",resdata.shape\n\t\t#print \"###content .shape\",((data2*wf[...,np.newaxis]).sum(1).sum(0)).shape\n\t\tresdata[out_time,out_freq,...] = (data2*wf[...,np.newaxis]).sum(1).sum(0)/wfsum;\n #resdata[out_time,...] = (data*wf[...,np.newaxis]).sum(2).sum(0)/wfsum[...,np.newaxis];\n # return result\n result.append((p,q,resdata));\n return result;","title":""},{"docid":"f711f8c76df576dc503a3def0911300e","score":"0.5322091","text":"def draw_windows(df: pd.DataFrame, ax: plt.Axes) -> None:\n for row in df.itertuples():\n xy = (row.x - row.dx - 0.5, row.y - row.dy - 0.5)\n width = row.dx * 2 + 1\n height = row.dy * 2 + 1\n patch = patches.Rectangle(xy, width=width, height=height, linewidth=2, edgecolor=\"r\", fill=False)\n ax.add_patch(patch)\n return","title":""},{"docid":"b8585aa448227d1a42a476bbd45aaf77","score":"0.5313643","text":"def column_windows(session, column, windowsize):\n def int_for_range(start_id, end_id):\n if end_id:\n return and_(\n column>=start_id,\n column<end_id\n )\n else:\n return column>=start_id\n\n q = session.query(\n column,\n func.row_number().\\\n over(order_by=column).\\\n label('rownum')\n ).\\\n from_self(column)\n if windowsize > 1:\n q = q.filter(sqlalchemy.text(\"rownum %% %d=1\" % windowsize))\n\n intervals = [id for id, in q]\n\n while intervals:\n start = intervals.pop(0)\n if intervals:\n end = intervals[0]\n else:\n end = None\n yield int_for_range(start, end)","title":""},{"docid":"4104a84dd6d6c2027b745d15b5c2cb6f","score":"0.5306761","text":"def controlLoops(df):\n print('Making ctrls')\n dfs = []\n for i in range(10):\n df = df.copy()\n ran = (np.random.random(len(df)) + 0.1) * 1000000\n if i % 2 == 1:\n ran = -ran\n df[\"Start\"] = df[\"Start\"] + ran.astype(int)\n df[\"End\"] = df[\"End\"] + ran.astype(int)\n dfs.append(df)\n df = pd.concat(dfs)\n print('Made ctrl positions')\n return df","title":""},{"docid":"cc8c192cd512bfc37fbe6c6465778955","score":"0.52957165","text":"def column_windows(session, column, windowsize):\n def int_for_range(start_id, end_id):\n if end_id:\n return and_(\n column>=start_id,\n column<end_id\n )\n else:\n return column>=start_id\n\n q = session.query(column, func.row_number().over(order_by=column).label('rownum')).from_self(column)\n if windowsize > 1:\n q = q.filter(sqlalchemy.text(\"rownum %% %d=1\" % windowsize))\n\n intervals = [id for id, in q]\n\n while intervals:\n start = intervals.pop(0)\n if intervals:\n end = intervals[0]\n else:\n end = None\n yield int_for_range(start, end)","title":""},{"docid":"ad750b1dbb1c06a358ef7e38b98b09f6","score":"0.5291326","text":"def create_window(dataframe, ts_start, window_length):\n return dataframe[(dataframe['timestamp'] >= ts_start) & (dataframe['timestamp'] <= ts_start + window_length)]","title":""},{"docid":"a4ebb5cdf65e4cf19dc2e036973f5a70","score":"0.52901703","text":"def get_overbasepower_sections(df):\n\n # Find the switching actions, which stay constant for minimal_zerotime times\n #minimal_zerotime = 3\n #look_ahead = getattr(df, 'look_ahead', None)\n df = df > 15 \n tmp = df.astype(np.int).diff()\n overbasepower_sect_starts = (tmp == 1).values\n overbasepower_sect_ends = (tmp == -1).values\n #overbasepower_sect_starts = df[(tmp == 1).values].index\n if len(df) > 0:\n overbasepower_sect_starts[0] = df.iloc[0,0] # = np.append(df.index[0], overbasepower_sect_starts)\n overbasepower_sect_ends[-1] |= df.iloc[-1,0] #np.append(overbasepower_sect_ends, df.index[-1]) # FUCK THIS SHIT!!! Mal verliert er die Timezone\n overbasepower_sect_starts = pd.Series(df[overbasepower_sect_starts].index)\n overbasepower_sect_ends = pd.Series(df[overbasepower_sect_ends].index)\n return overbasepower_sect_starts, overbasepower_sect_ends\n\n #for i in range(2,minimal_zerotime):\n # tmp = df.astype(np.int).diff(i)\n # overbasepower_sect_starts *= tmp == 1\n # overbasepower_sect_ends *= tmp == 0\n #tmp = df.astype(np.int).diff(minimal_zerotime)\n #overbasepower_sect_starts *= tmp == 1\n #overbasepower_sect_ends *= tmp == -1\n #del tmp\n #overbasepower_sect_starts = list(df[overbasepower_sect_starts].dropna().index)\n #overbasepower_sect_ends = list(df[overbasepower_sect_ends.shift(-minimal_zerotime).fillna(False)].dropna().index)\n\n ## If this chunk starts or ends with an open-ended\n ## overbasepower section then the relevant TimeFrame needs to have\n ## a None as the start or end.\n #for i in range(minimal_zerotime):\n # if df.iloc[i, 0] == True:\n # overbasepower_sect_starts = [df.index[i]] + overbasepower_sect_starts\n # break\n\n #if df.iloc[-1,0] == True:\n # overbasepower_sect_ends += [None]\n #else:\n # # Only start new zerosection when long enough, need look_ahead\n # for i in range(1,minimal_zerotime+1):\n # if df.iloc[-i, 0] != False:\n # break\n\n # if i < (minimal_zerotime):\n # if look_ahead.head(minimal_zerotime-i).sum()[0] == 0:\n # overbasepower_sect_ends += [df.index[-i]] #, 0]]\n # else:\n # overbasepower_sect_ends += [None]\n\n\n ## Merge together ends and starts\n #assert len(overbasepower_sect_starts) == len(overbasepower_sect_ends)\n\n #if len(overbasepower_sect_ends) > len(overbasepower_sect_starts):\n # sections = [TimeFrame(start, end)\n #else:\n # sections = []\n #if len(overbasepower_sect_starts) == 0 == len(overbasepower_sect_ends):\n # return [TimeFrameGroup()]\n #else:\n # if overbasepower_sect_starts[0] > overbasepower_sect_ends[0]:\n # overbasepower_sect_starts.append(None)\n # if overbasepower_sect_ends[-1] < overbasepower_sect_starts[-1]:\n # overbasepower_sect_ends.append(None)\n #sections = [TimeFrame(start, end)\n # for start, end in zip(overbasepower_sect_starts, overbasepower_sect_ends)\n # if not (start == end and start is not None)]\n\n # Memory management\n #del overbasepower_sect_starts\n #del overbasepower_sect_ends\n #gc.collect()\n\n #return sections","title":""},{"docid":"84233a6cf92afab0bf3071df3ed4b728","score":"0.52877927","text":"def _get_window_sequence(self, delta_f, start_idx, length, b):\n fnow, dfnow = self.fb_dfb[b]\n fnext, dfnext = self.fb_dfb[b + 1]\n\n window_sequence = np.zeros(length)\n increase_start = np.clip(\n math.floor((fnow - dfnow) / delta_f) - start_idx + 1, 0, length\n )\n unity_start = np.clip(math.ceil(fnow / delta_f) - start_idx, 0, length)\n decrease_start = np.clip(\n math.floor((fnext - dfnext) / delta_f) - start_idx + 1, 0, length\n )\n decrease_stop = np.clip(math.ceil(fnext / delta_f) - start_idx, 0, length)\n\n window_sequence[unity_start:decrease_start] = 1.\n\n # this if statement avoids overflow caused by vanishing dfnow\n if increase_start < unity_start:\n frequencies = (np.arange(increase_start, unity_start) + start_idx) * delta_f\n window_sequence[increase_start:unity_start] = (\n 1. + np.cos(np.pi * (frequencies - fnow) / dfnow)\n ) / 2.\n\n if decrease_start < decrease_stop:\n frequencies = (np.arange(decrease_start, decrease_stop) + start_idx) * delta_f\n window_sequence[decrease_start:decrease_stop] = (\n 1. - np.cos(np.pi * (frequencies - fnext) / dfnext)\n ) / 2.\n\n return window_sequence","title":""},{"docid":"08dbc45de81d7a776a76dcbe2f802ca9","score":"0.52707666","text":"def agglutinate(df_node, dt_lag_min=pd.Timedelta('1 hour')):\n from operator import itemgetter\n from itertools import groupby\n pd.options.mode.chained_assignment = None # default='warn'\n\n # < STEP 1 >\n # Convert all close event intervals\n # I have to change only the key p-> m and proceed to next step\n null_index = df_node[df_node.isnull().any(axis=1)].index\n\n dt_lag = df_node.ts_n-df_node.te\n condition = df_node.key.isin(['p']) & (dt_lag < dt_lag_min)\n lag_index = df_node[condition].index\n lag_index = lag_index.difference(null_index)\n df_node.loc[lag_index, 'key'] = 'm'\n\n # < STEP 2 >\n # ============================================================\n # CAUTION: I am assuming that df_node has consecutive indexes\n # i.e., RangeIndex(start=0, stop=..., step=1), a\n # monotonic ordered set (maybe it is not that relevant)\n # ============================================================\n # get indices of rows whose intervals overlap or touch\n # (except last row which has NULL values)\n ovl_index = df_node[~df_node.key.isin(['p'])].index # operations: moFDs\n ovl_index = ovl_index.difference(null_index)\n\n for _, g in groupby(enumerate(ovl_index), lambda x: x[1]-x[0]):\n ovl_slice = map(itemgetter(1), g)\n # Complete list 'ovl_slice':\n # -Add index element of DataFrame 'df_node' that follows ovl_slice[-1]\n index_next_pos = df_node.index.get_loc(ovl_slice[-1])\n # Complete 'ovl_slice'\n ovl_slice.append(df_node.index[index_next_pos+1])\n ts_min = df_node.loc[ovl_slice, 'ts'].min()\n te_max = df_node.loc[ovl_slice, 'te'].max()\n df_node.loc[ovl_slice[-1], ['ts', 'te']] = [ts_min, te_max]\n df_node.drop(ovl_slice[:-1], inplace=True) # delete slice","title":""},{"docid":"d1fe487622fabedfcee8816bd46857dd","score":"0.52303755","text":"def create_windows_from_protein(protein_seq, **kwargs):\n # NOTE: last AA not made into window since c-terminal would be cleavage pos\n protein_windows = []\n for pos in range(len(protein_seq)-1):\n start_pos = pos + 1\n end_pos = pos + 2\n tmp_window = get_peptide_window(protein_seq,\n starting_position=start_pos,\n ending_position=end_pos,\n **kwargs)\n protein_windows.append(tmp_window)\n\n return protein_windows","title":""},{"docid":"013a4eee300831b9bc67547629aefd73","score":"0.5226974","text":"def window_iterator(seq, n=1, beg=\"<s>\", end=\"</s>\"):\n for i in range(len(seq)):\n l = max(0, i-n)\n r = min(len(seq), i+n+1)\n ret = seq[l:r]\n if i < n:\n ret = [beg,] * (n-i) + ret\n if i+n+1 > len(seq):\n ret = ret + [end,] * (i+n+1 - len(seq))\n yield ret","title":""},{"docid":"36998aa357918aa0bbaca854ee65de65","score":"0.52230245","text":"def calc_cov_windows(genomes, pairs, mappings, gc_correction, thresholds, threads):\n print('# calculating coverage over sliding windows', file=sys.stderr)\n sys.stderr.flush()\n # filter out any genome -> sample pairs not passing thresholds\n pairs = [(g, s) for g, s in pairs if s in genomes[g]['samples']]\n pool = Pool(threads)\n # calculate coverage windows for each pair\n for sample in \\\n pool.map(coverage_windows, \\\n [[g, genomes[g]['gc'], s, genomes[g]['samples'][s], gc_correction, thresholds]\\\n for g, s in pairs]):\n g, s, sample = sample\n genomes[g]['samples'][s] = sample\n pool.terminate()\n # filter out any genome -> sample pairs not passing thresholds\n pairs = [(g, s) for g, s in pairs if genomes[g]['samples'][s]['windows'] is not False]\n return genomes, pairs","title":""},{"docid":"740357a69681c901876b5303a43d159d","score":"0.52132034","text":"def assign_actual_windows(self, input_row: List, timestamp: int) -> List[W]:\n pass","title":""},{"docid":"fa2d3a9c2ee11297303ecb1995985650","score":"0.51899356","text":"def _alpha_window_in_bounds(alpha, window):\r\n IBef = np.where(alpha <= window[0])[0]\r\n if len(IBef) > 0:\r\n im = IBef[-1]\r\n else:\r\n im = 0\r\n IAft = np.where(alpha >= window[1])[0]\r\n if len(IAft) > 0:\r\n ip = IAft[0]\r\n else:\r\n ip = len(alpha) - 1\r\n window = [alpha[im], alpha[ip]]\r\n return window","title":""},{"docid":"d75cc5242df3c07550b48d7f3ddd4821","score":"0.51733464","text":"def _get_index_bounds(self, sc_id, i, ccWindow, ccOverlap):\n dt = timedelta(seconds=ccWindow/2)\n overlapW = timedelta(seconds=ccOverlap/20)\n # Find the correct center time from either AC6A or B.\n if sc_id.upper() == 'A':\n t0 = self.occurA.cat['dateTime'][i]\n else:\n t0 = self.occurB.cat['dateTime'][i]\n\n #### Get temporally-aligned indicies ######\n idtA = np.where( (self.occurA.data['dateTime'] >= t0-dt) \n & (self.occurA.data['dateTime'] < t0+dt)\n & (self.occurA.data['dos1rate'] != -1E31))[0]\n # The overlapW windens the index array to accomidate a CC lag.\n idtB = np.where( (self.occurB.data['dateTime'] >= t0-dt-overlapW) \n & (self.occurB.data['dateTime'] < t0+dt+overlapW)\n & (self.occurB.data['dos1rate'] != -1E31))[0]\n\n ####### Get spatially aligned indicies ######\n # If sc_id is 'A', then, the spatial indicies are the \n # same for AC6A, but shifted for AC6B. Since \n # lag = t_A - t_B\n # this implies that t_B_shifted = t_A - lag. If sc_id \n # is 'B', then everything is opposite, i.e. \n # t_A_shifted = t_B + lag\n if sc_id.upper() == 'A':\n idtA_shifted = idtA \n timeLag = timedelta(seconds=self.occurA.cat['Lag_In_Track'][i])\n idtB_shifted = np.where(\n (self.occurB.data['dateTime'] >= t0-dt+timeLag-overlapW) & \n (self.occurB.data['dateTime'] < t0+dt+timeLag+overlapW) &\n (self.occurB.data['dos1rate'] != -1E31)\n )[0]\n t_sA = t0\n t_sB = t0 + timeLag\n else:\n idtB_shifted = idtB \n timeLag = timedelta(seconds=self.occurB.cat['Lag_In_Track'][i])\n idtA_shifted = np.where(\n (self.occurA.data['dateTime'] >= t0-dt-timeLag-overlapW) & \n (self.occurA.data['dateTime'] < t0+dt-timeLag+overlapW) &\n (self.occurA.data['dos1rate'] != -1E31)\n )[0]\n t_sA = t0 - timeLag\n t_sB = t0\n return idtA, idtB, idtA_shifted, idtB_shifted, t0, t_sA, t_sB","title":""},{"docid":"301e5031fb2826c7efc8f7a4f0d92f89","score":"0.51693696","text":"def shift_video_w_random_offset(\n df_sensor,\n df_flow,\n vid_name,\n win_start_end,\n start_time,\n end_time,\n kde_num_offset,\n kde_max_offset,\n window_size_sec,\n window_criterion,\n fps,\n):\n df_dataset_vid = []\n info_dataset_vid = []\n cnt_windows = 0\n # add an offset to each window sensor-video pair\n for pair in win_start_end:\n start = pair[0] * 1000\n end = pair[1] * 1000 + 1000\n df_window_sensor = df_sensor[\n (df_sensor[\"time\"] >= pd.to_datetime(start, unit=\"ms\"))\n & (df_sensor[\"time\"] < pd.to_datetime(end, unit=\"ms\"))\n ]\n for i in range(kde_num_offset):\n # match video df\n offset = random.randint(-kde_max_offset, kde_max_offset)\n offset = (\n min(offset, end_time - end)\n if offset > 0\n else max(offset, start_time - start)\n )\n df_window_flow = df_flow[\n (df_flow[\"time\"] >= pd.to_datetime(start + offset, unit=\"ms\"))\n & (df_flow[\"time\"] < pd.to_datetime(end + offset, unit=\"ms\"))\n ]\n pd.options.mode.chained_assignment = None\n df_window_flow.loc[:, \"time\"] = df_window_flow.loc[\n :, \"time\"\n ] - pd.Timedelta(offset, unit=\"ms\")\n df_window = pd.merge_asof(\n df_window_sensor,\n df_window_flow,\n on=\"time\",\n tolerance=pd.Timedelta(\"29.969664ms\"),\n direction=\"nearest\",\n ).set_index(\"time\")\n df_window = df_window.dropna(how=\"any\")\n if len(df_window) > fps * window_size_sec * window_criterion:\n cnt_windows += 1\n df_dataset_vid.append(df_window)\n info_dataset_vid.append(\n [vid_name, start, end, offset]\n ) # video name, sensor start time, sensor end time, video offset\n return cnt_windows, df_dataset_vid, info_dataset_vid","title":""},{"docid":"64b34914322e43fce38024b0953a96a2","score":"0.51646316","text":"def _build_windows(self):\n\n # each window has horizon times subwindows\n w_episodes = []\n init_weights = []\n predictions = []\n for episode in self.episode_starts:\n ws = []\n preds = []\n assets = self.assets[episode - self.window_size: episode]\n weight = WeightOptimize(assets, np.cov(assets.T), self.risk_aversion).optimize_weights()\n for s in trange(self.horizon):\n w = self.data[episode - self.window_size + s : episode + s]\n ws.append(w)\n # TODO: Add model predictions\n try:\n w = self.scaler.transform(w)\n except:\n pass\n #preds.append([predictor.predict(w.reshape(1, w.shape[0], w.shape[1])) for predictor in self.predictors])\n\n w_episodes.append(ws)\n init_weights.append(weight)\n # prediction.append(preds)\n\n # w_episodes has the shape (nb_epsides, horizon, window_size, columns) 4D\n # init_weights has the shape (nb_episodes, columns) 2D\n # predictions has the shape (nb_episodes, horizon, num_predictors, columns) 3D\n return np.array(w_episodes), np.array(init_weights), np.array(predictions)","title":""},{"docid":"7262518cea401fb6bf5bf055010df100","score":"0.51635045","text":"def get_subwindow_tracking(im, pos, model_sz, original_sz, avgChans):\n if original_sz is None:\n original_sz = model_sz\n sz = original_sz\n im_sz = im.shape\n # make sure the size is not too small\n assert min(im_sz[:2]) > 2, \"the size is too small\"\n c = (np.array(sz) + 1) / 2\n \n # check out-of-bounds coordinates, and set them to black\n context_xmin = round(pos[1] - c[1])\n context_xmax = context_xmin + sz[1] - 1\n context_ymin = round(pos[0] - c[0])\n context_ymax = context_ymin + sz[0] - 1\n left_pad = max(0, int(-context_xmin))\n top_pad = max(0, int(-context_ymin))\n right_pad = max(0, int(context_xmax - im_sz[1] + 1))\n bottom_pad = max(0, int(context_ymax - im_sz[0] + 1))\n \n context_xmin = int(context_xmin + left_pad)\n context_xmax = int(context_xmax + left_pad)\n context_ymin = int(context_ymin + top_pad)\n context_ymax = int(context_ymax + top_pad)\n \n if top_pad or left_pad or bottom_pad or right_pad:\n b = np.pad(im[:,:,0], ((top_pad,bottom_pad),(left_pad,right_pad)), mode='constant', constant_values=avgChans[0])\n g = np.pad(im[:,:,1], ((top_pad,bottom_pad),(left_pad,right_pad)), mode='constant', constant_values=avgChans[1])\n r = np.pad(im[:,:,2], ((top_pad,bottom_pad),(left_pad,right_pad)), mode='constant', constant_values=avgChans[2]) \n im = cv2.merge((b,g,r))\n #imshow(im)\n \n im_patch_original = im[context_ymin:context_ymax+1, context_xmin:context_xmax+1, :]\n if not np.array_equal(model_sz, original_sz):\n im_patch = cv2.resize(im_patch_original, model_sz)\n else:\n im_patch = im_patch_original\n \n return im_patch, im_patch_original","title":""},{"docid":"3b3c9304c534130f9c3928880201eec7","score":"0.5135659","text":"def sliding_window(raw_data, para):\n log_size = raw_data.shape[0]\n label_data, time_data = raw_data.iloc[:, 1], raw_data.iloc[:, 0]\n logkey_data, deltaT_data = raw_data.iloc[:, 2], raw_data.iloc[:, 3]\n new_data = []\n start_end_index_pair = set()\n\n start_time = time_data[0]\n end_time = start_time + para[\"window_size\"]\n start_index = 0\n end_index = 0\n\n # get the first start, end index, end time\n for cur_time in time_data:\n if cur_time < end_time:\n end_index += 1\n else:\n break\n\n start_end_index_pair.add(tuple([start_index, end_index]))\n\n # move the start and end index until next sliding window\n num_session = 1\n while end_index < log_size:\n start_time = start_time + para['step_size']\n end_time = start_time + para[\"window_size\"]\n for i in range(start_index, log_size):\n if time_data[i] < start_time:\n i += 1\n else:\n break\n for j in range(end_index, log_size):\n if time_data[j] < end_time:\n j += 1\n else:\n break\n start_index = i\n end_index = j\n\n # when start_index == end_index, there is no value in the window\n if start_index != end_index:\n start_end_index_pair.add(tuple([start_index, end_index]))\n\n num_session += 1\n if num_session % 1000 == 0:\n print(\"process {} time window\".format(num_session), end='\\r')\n\n for (start_index, end_index) in start_end_index_pair:\n dt = deltaT_data[start_index: end_index].values\n dt[0] = 0\n new_data.append([\n time_data[start_index: end_index].values,\n max(label_data[start_index:end_index]),\n logkey_data[start_index: end_index].values,\n dt\n ])\n\n assert len(start_end_index_pair) == len(new_data)\n print('there are %d instances (sliding windows) in this dataset\\n' % len(start_end_index_pair))\n return pd.DataFrame(new_data, columns=raw_data.columns)","title":""},{"docid":"af04e69e11f540aa4daf7543018efa7f","score":"0.51164055","text":"def get_cis_ranges(phenotype_pos_df, chr_variant_dfs, window, verbose=True):\n # check phenotypes & calculate genotype ranges\n # get genotype indexes corresponding to cis-window of each phenotype\n if 'pos' in phenotype_pos_df:\n phenotype_pos_df = phenotype_pos_df.rename(columns={'pos':'start'})\n phenotype_pos_df['end'] = phenotype_pos_df['start']\n phenotype_pos_dict = phenotype_pos_df.to_dict(orient='index')\n\n drop_ids = []\n cis_ranges = {}\n n = len(phenotype_pos_df)\n for k, phenotype_id in enumerate(phenotype_pos_df.index, 1):\n if verbose and (k % 1000 == 0 or k == n):\n print(f'\\r * checking phenotypes: {k}/{n}', end='' if k != n else None)\n\n pos = phenotype_pos_dict[phenotype_id]\n chrom = pos['chr']\n m = len(chr_variant_dfs[chrom]['pos'].values)\n lb = bisect.bisect_left(chr_variant_dfs[chrom]['pos'].values, pos['start'] - window)\n ub = bisect.bisect_right(chr_variant_dfs[chrom]['pos'].values, pos['end'] + window)\n if lb != ub:\n r = chr_variant_dfs[chrom]['index'].values[[lb, ub - 1]]\n else:\n r = []\n\n if len(r) > 0:\n cis_ranges[phenotype_id] = r\n else:\n drop_ids.append(phenotype_id)\n\n return cis_ranges, drop_ids","title":""},{"docid":"15d390ca6a398ce17624bfe7e78443dc","score":"0.5102995","text":"def analyse_window(spatial_patterns,ts,maxchans,starttime,endtime,samplerate,data, grid_shape,\n x_peaks,x_troughs,eod_width,channels,eod_hights,cutwidth,window_size,window_dt,min_samples,\n max_clus,min_correlation=0.99,min_correlation_moving=0.75,coverage_factor=0.9,verbose=0,plot_level=0):\n\n # initialize storage arrays \n mean_eods = np.zeros((int(max_clus*(endtime-starttime)/window_dt), int(data.shape[0]*cutwidth*samplerate)))\n times = np.zeros(int(max_clus*(endtime-starttime)/window_dt))\n freqs = np.zeros(int(max_clus*(endtime-starttime)/window_dt))\n spiketimes = []\n counter_c = 0\n \n # sliding window\n for t in np.arange(starttime,endtime-window_size+window_dt,window_dt):\n\n # remember where analysis started for this timeblock\n prev_counter_c = counter_c\n\n # select values within window\n sl = (np.where((ts/samplerate+starttime>=t)&(ts/samplerate+starttime<t+window_size))[0]).astype('int')\n ctt = ts[sl]\n s_patterns = spatial_patterns[sl]\n\n if len(s_patterns)<min_samples:\n continue\n\n s_patterns[np.isnan(s_patterns)] = 0\n \n # cluster\n pc = OPTICS(min_samples=min_samples).fit(s_patterns).labels_\n\n # go through each cluster\n for cl in np.unique(pc[pc!=-1]):\n \n # get the most prominent maxchannel of this cluster and center snippets around its peak and trough\n all_mc, counts = np.unique(maxchans[sl][pc==cl],return_counts=True)\n maxchan = all_mc[np.argmax(counts)]\n mean_snip_p = []\n mean_snip_t = []\n\n for ct in ctt[pc==cl]:\n # check out surrounding peaks, troughs, and hights.\n eod_time = ct \n snip = (((x_peaks>(eod_time-eod_width*2)) & (x_peaks<(eod_time+eod_width*2)) & (x_troughs>(eod_time-eod_width*6)) & (x_troughs<(eod_time+eod_width*6))) | ((x_troughs>(eod_time-eod_width*2)) & (x_troughs<(eod_time+eod_width*2)) & (x_peaks>(eod_time-eod_width*6)) & (x_peaks<(eod_time+eod_width*6))))\n \n cp = x_peaks[snip]\n ct = x_troughs[snip]\n cc = channels[snip]\n ch = eod_hights[snip]\n\n if maxchan in cc:\n p_eods = np.zeros((data.shape[0],int(cutwidth*samplerate)))\n t_eods = np.zeros((data.shape[0],int(cutwidth*samplerate)))\n # center around biggest pt pair.\n p_eods = data[:, int(cp[cc==maxchan][np.argmax(ch[cc==maxchan])]-cutwidth*samplerate/2):int(cp[cc==maxchan][np.argmax(ch[cc==maxchan])]+cutwidth*samplerate/2)]\n p_eods[np.isnan(p_eods)] = 0 # set nan to zero as broken elctrodes give NaN\n t_eods = data[:, int(ct[cc==maxchan][np.argmax(ch[cc==maxchan])]-cutwidth*samplerate/2):int(ct[cc==maxchan][np.argmax(ch[cc==maxchan])]+cutwidth*samplerate/2)] \n t_eods[np.isnan(t_eods)] = 0\n mean_snip_p.append(p_eods.flatten())\n mean_snip_t.append(t_eods.flatten())\n\n mean_snip_p = np.vstack(mean_snip_p)\n mean_snip_t = np.vstack(mean_snip_t)\n \n # choose the centering which results in the most homogenously aligned snippets\n if np.mean(np.std(mean_snip_p,axis=0))<np.mean(np.std(mean_snip_t,axis=0)):\n mean_snip = np.mean(mean_snip_p,axis=0).reshape(data.shape[0],-1)\n else:\n mean_snip = np.mean(mean_snip_t,axis=0).reshape(data.shape[0],-1)\n\n # now add the cluster variables to the storage\n mean_eods[counter_c] = mean_snip.flatten()\n times[counter_c] = t\n spiketimes.append(ts[sl][pc==cl]/samplerate+starttime)\n freqs[counter_c] = np.median(1/np.diff(ts[sl][pc==cl]/samplerate))\n counter_c += 1\n \n # plot clusters\n if plot_level > 0:\n gs = gridspec.GridSpec(counter_c - prev_counter_c,3)\n fig = plt.figure()\n ax = fig.add_subplot(gs[:,0])\n for i,tp in enumerate(spiketimes[prev_counter_c:counter_c]):\n ax.plot(tp,-i*np.ones(len(tp)),'o')\n ax.set_xlim([t,t+window_size])\n ax.axis('off')\n\n s_patterns = np.std(mean_eods[prev_counter_c:counter_c].reshape(mean_eods[prev_counter_c:counter_c].shape[0],data.shape[0],-1),axis=2)**(1/3)\n for i,pattern in enumerate(s_patterns):\n ax = fig.add_subplot(gs[i,1])\n ax.imshow(pattern.reshape(grid_shape),vmin=np.min(s_patterns),vmax=np.max(s_patterns))\n ax.axis('off')\n ax.set_title(i)\n\n ax = fig.add_subplot(gs[:,2])\n if len(s_patterns)>1:\n pos=ax.imshow(np.corrcoef(s_patterns),vmin=0,vmax=1)\n fig.colorbar(pos, ax=ax)\n plt.title('t = %.2f'%t)\n if verbose>0:\n print('---------------------')\n \n # check out this analysis window and see if anything can be merged\n cur_freqs = freqs[prev_counter_c:counter_c]\n cur_mpe = mean_eods[prev_counter_c:counter_c]\n cur_tt = np.array(spiketimes[prev_counter_c:counter_c])\n\n if len(cur_mpe)>1:\n\n # first only merge stationary fish, correlation should be high.\n s_patterns = np.std(cur_mpe.reshape(cur_mpe.shape[0],data.shape[0],-1),axis=2)**(1/3)\n cors = np.corrcoef(s_patterns)\n np.fill_diagonal(cors,0)\n pops=[]\n\n if np.max(cors)>min_correlation:\n idxs = np.argwhere(cors>min_correlation)\n for i in idxs:\n \n x = i[0]\n y = i[1]\n \n if np.arange(prev_counter_c,counter_c)[y] not in pops and np.arange(prev_counter_c,counter_c)[x] not in pops:\n \n # append to whichever is more centered in the current timeblock\n center_d = t+window_size/2\n new_times = np.sort(np.append(cur_tt[x],cur_tt[y])).tolist()\n\n if np.abs(center_d - np.mean(cur_tt[x])/samplerate+starttime) < np.abs(center_d - np.mean(cur_tt[y])/samplerate+starttime):\n spiketimes[np.arange(prev_counter_c,counter_c)[x]] = new_times\n pops.append(np.arange(prev_counter_c,counter_c)[y])\n else:\n spiketimes[np.arange(prev_counter_c,counter_c)[y]] = new_times\n pops.append(np.arange(prev_counter_c,counter_c)[x])\n\n if verbose>0 or plot_level>0:\n print('merging cluster %i and %i with correlation of %.3f'%(x,y,cors[x,y]))\n\n if np.max(cors[cors<min_correlation])>min_correlation_moving:\n idxs = np.argwhere((cors>min_correlation_moving)&(cors<min_correlation)) #sort from high to low\n sort_idx = np.argsort(-cors[(cors>min_correlation_moving)&(cors<min_correlation)])\n idxs = idxs[sort_idx]\n for i in idxs:\n\n x = i[0]\n y = i[1]\n\n new_times = np.sort(np.append(cur_tt[x],cur_tt[y]))\n \n # only combine if the new coverage is (almost) equal to the seperate coverages combined.\n # I ignore the two largest ISIs, as they could be outliers.\n coverage_x = len(cur_tt[x])*np.mean(np.sort(np.diff(cur_tt[x]))[:-2])\n coverage_y = len(cur_tt[y])*np.mean(np.sort(np.diff(cur_tt[y]))[:-2])\n coverage_merge = len(new_times)*np.mean(np.sort(np.diff(new_times))[:-2])\n\n if coverage_merge < coverage_factor*(coverage_x + coverage_y):\n continue\n\n if np.arange(prev_counter_c,counter_c)[y] not in pops and np.arange(prev_counter_c,counter_c)[x] not in pops:\n \n # append to whichever is more centered in the current timeblock\n center_d = t+window_size/2\n\n new_times = np.sort(np.append(cur_tt[x],cur_tt[y])).tolist()\n \n if np.abs(center_d - np.mean(cur_tt[x])/samplerate+starttime) < np.abs(center_d - np.mean(cur_tt[y])/samplerate+starttime):\n spiketimes[np.arange(prev_counter_c,counter_c)[x]] = new_times\n pops.append(np.arange(prev_counter_c,counter_c)[y])\n else:\n spiketimes[np.arange(prev_counter_c,counter_c)[y]] = new_times\n pops.append(np.arange(prev_counter_c,counter_c)[x])\n\n if verbose>0 or plot_level>0:\n print('merging cluster %i and %i with correlation of %.3f and coverage scores of %.3f + %.3f > %.2f*%.3f'%(x,y,cors[x,y],coverage_x,coverage_y,coverage_factor,coverage_merge))\n\n # remove all duplicates\n for p in sorted(pops, reverse=True):\n spiketimes.pop(p)\n times = np.delete(times,p,0)\n mean_eods = np.delete(mean_eods,p,0)\n freqs = np.delete(freqs,p,0)\n counter_c -= 1\n \n if plot_level>0:\n plt.show()\n\n return mean_eods[:counter_c], times[:counter_c], spiketimes[:counter_c], freqs[:counter_c]","title":""},{"docid":"8ead51acc295fa26e734b2931bca8cca","score":"0.5092902","text":"def adaptive_windowing(X, B, neigborhood=1, add_start=False, add_end=False):\r\n len_X = X.shape[1]\r\n max_B = np.max(B)\r\n if max_B > len_X:\r\n print('Beat exceeds length of features sequence (b=%d, |X|=%d)'%(max_B,len_X))\r\n B = B[B<len_X]\r\n if add_start:\r\n if B[0] > 0:\r\n B = np.insert(B,0,0)\r\n if add_end:\r\n if B[-1] < len_X:\r\n B = np.append(B,len_X)\r\n X_adapt = np.zeros((X.shape[0], len(B)-1))\r\n B_s = np.zeros(len(B)-1).astype(int)\r\n B_t = np.zeros(len(B)-1).astype(int)\r\n for b in range(len(B)-1):\r\n s = B[b]\r\n t = B[b+1]\r\n reduce = np.floor((1 - neigborhood)*(t-s+1)/2).astype(int)\r\n s = s + reduce\r\n t = t - reduce\r\n if s==t: t = t+1\r\n X_slice = X[:,range(s,t)]\r\n a = np.sum(X_slice, axis=1)\r\n X_adapt[:,b] = np.mean(X_slice, axis=1)\r\n B_s[b] = s\r\n B_t[b] = t\r\n return X_adapt, B_s, B_t","title":""},{"docid":"db385dbd365f6a65779f9d3fd5d873b8","score":"0.5084934","text":"def window_data(df, window, feature_col_number, target_col_number):\n X = []\n y = []\n for i in range(len(df) - window - 1):\n features = df.iloc[i:(i + window), feature_col_number]\n target = df.iloc[(i + window), target_col_number]\n X.append(features)\n y.append(target)\n return np.array(X), np.array(y).reshape(-1, 1)","title":""},{"docid":"2799c3702fce7f2d5eb7d294733f3b5c","score":"0.5080753","text":"def leading_trends(window, lags, wdf, col_name):\n ma_df = wdf[[col_name]]\n # create rolling average\n roll_col = col_name + '_roll_' + str(window)\n ma_df.loc[:,roll_col] = ma_df.loc[:, col_name].rolling(window=window).mean()\n col_err = col_name + '_error'\n ma_df[col_err] = ma_df[col_name] - ma_df[roll_col]\n # get diff\n # lag columns \n ma_df = ma_df.assign(**{col_err+'_lag_'+str(lag_n): ma_df[col_err].shift(lag_n) for lag_n in lags}) \n return ma_df","title":""},{"docid":"2516c582e6a6b27b41e3750a2dbc0c62","score":"0.5080153","text":"def add_cn(df, cn_amp):\n\n # create GRange objects\n gr1 = GRange(df[['Chrom', 'Start', 'End']], 'dataframe_hasend')\n gr2 = GRange(cn_amp, 'dataframe_hasend')\n\n # # only use the mid point for the segments\n # op = []\n # for i in gr1.gr:\n # mid = int((i[1].start + i[1].stop)/2)\n # op.append((i[0], range(mid, mid+1), i[2]))\n # gr1.gr = op\n\n # extend binsize on both end of the cn_seg\n map_list = gr1.gmap(gr2, a_extend = 0, b_extend = 0, multi_hit = True)\n\n # output\n df['CN'] = [round(np.max([j[0] for j in i]))\n if (i is not None and i != []) else 0 for i in map_list]\n return df","title":""},{"docid":"8d7206a88724f7f9205a7db385e91f16","score":"0.50754505","text":"def input_windows(specgram):\n specgram = np.transpose(specgram)\n num_windows = specgram.shape[0]\n specgram = np.pad(specgram, ((window_size // 2, window_size // 2), (0, 0)), \"constant\")\n windows = []\n for i in range(num_windows):\n w = specgram[i:i+window_size,:]\n windows.append(w)\n return np.array(windows)","title":""},{"docid":"af4bed8b6d4e58554bc3e6c2476a805c","score":"0.50708544","text":"def get_windows(x, y, scanning_window=0):\n\n def list_populator(indexes):\n indexes = array(indexes)\n points = []\n\n dup_x = find(diff(new_x[indexes]) == 0)\n dup_y = find(diff(new_y[indexes]) == 0)\n\n index_equal = find(dup_x == dup_y)\n if len(index_equal) != 0:\n\n index_2_del = dup_x[index_equal]\n indexes = delete(indexes, index_2_del)\n\n index_on_window.append(indexes)\n [[points.append([new_x[i], new_y[i]]), points_total.append([new_x[i], new_y[i]])] for i in indexes]\n points_on_window.append(points)\n\n new_x, new_y, x_y_sorted = join_and_sort(x, y)\n\n window_end = new_x[0]\n points_on_window = []\n points_total = []\n index_on_window = []\n\n if scanning_window == 0:\n scanning_window = mean(abs(diff(new_x)))\n\n while window_end < new_x[len(new_x)-1] + scanning_window:\n\n window_start = window_end\n window_end = window_start + scanning_window\n\n indexes = list(where(logical_and(new_x >= window_start, new_x < window_end))[0])\n\n if len(indexes) != 0 and len(indexes) <= 2:\n list_populator(indexes)\n\n elif len(indexes) > 2:\n y_vec = new_y[indexes]\n max_y = find(y_vec == max(y_vec))[0]\n min_y = find(y_vec == min(y_vec))[0]\n indexes = array(indexes)\n indexes = list(indexes[[max_y, min_y]])\n list_populator(indexes)\n\n points_total = array(points_total)\n points_on_window = array(points_on_window)\n index_on_window = array(index_on_window)\n\n return scanning_window, points_total, points_on_window, index_on_window","title":""},{"docid":"76d71671302ee885326c0467e67b29f0","score":"0.50633043","text":"def moving_window(dframe, frame=4):\n\n for i in range(len(dframe) - frame - 1):\n yield dframe.iloc[:i + frame],\\\n dframe.iloc[i + frame:i + frame + 1]","title":""},{"docid":"5e06aeee3cff1ce6b9721629b3dc0551","score":"0.50575626","text":"def window_pipeline(\n genome_file,\n feat_file,\n window_size=50,\n num_windows=3,\n min_gene_size=200,\n max_gene_size=2000,\n sep=\"\\t\",\n out_file=False,\n by=None,\n manage_unk=True,\n annotate=True,\n):\n # 0. CONSISTENCY OF INPUT\n window_size = int(window_size)\n num_windows = int(num_windows)\n\n # 1. READ FILES\n # We expect to have the whole genome as the fist sequence in the multiFASTA\n # file and then some plasmids.\n # TODO: check if that works for a 1-seq FASTA file\n fna = pyfastx.Fasta(genome_file)\n genome = str(fna[0])\n # just avoid segmentation problems with the lower level API\n del fna\n df_feat = pd.read_csv(feat_file, sep=sep)\n genome_length = len(genome)\n\n # 2. STANDARIZE DATA\n df_feat = df_feat[\n df_feat.apply(\n lambda x: x.end - x.start < max_gene_size\n and x.end - x.start > min_gene_size,\n axis=1,\n )\n ]\n\n # 3. GET RANGES OF FEATURES\n # Test genes\n gene_ranges = get_feature_ranges(df_feat)\n if by is not None:\n gene_ranges = filter_ranges(gene_ranges, first=by[0], last=by[1])\n positive = extract_windows(gene_ranges, window_size, num_windows)\n # Test negative examples\n negative_ranges = get_negative_ranges(gene_ranges)\n negative = extract_windows(negative_ranges, window_size, num_windows)\n # Test partial genes\n partial_ranges = get_partial_ranges(\n gene_ranges, genome_length=genome_length\n )\n partial = extract_windows(partial_ranges, window_size, num_windows)\n\n # 4. BUILD THE PRE-PROCESSED DATAFRAME\n df_out = map_full_genomes(genome, positive, negative, partial)\n if manage_unk:\n df_out = remove_unk(df_out)\n if annotate:\n df_out = classify_genes(df_out, df_feat, feature=\"gene\")\n\n return df_out","title":""},{"docid":"b258585515469b96e88a8bd112b65035","score":"0.5054891","text":"def _get_adjusted_window(self, events, star_of_range):\n\n events.pop(star_of_range)\n return events","title":""},{"docid":"df1c684cff9daeb6e8cc96d8b9fc8536","score":"0.5053434","text":"def get_track_window(self, dataset, data, start, end):\n window = list()\n for record in data:\n fields = record.rstrip(\"\\n\\r\").split(\"\\t\")\n record_chrom = fields[dataset.metadata.chromCol-1]\n record_start = int(fields[dataset.metadata.startCol-1])\n record_end = int(fields[dataset.metadata.endCol-1])\n if record_start < end and record_end > start:\n window.append( (record_chrom, record_start, record_end) ) #Yes I did want to use a generator here, but it doesn't work downstream\n return window","title":""},{"docid":"8d8967b57f340396fac5022623bd2af0","score":"0.5050711","text":"def compute_sliding_windows_for_data(df_features, **kws):\n timestamps = df_features[\"ds\"]\n interval_delta = timestamps[1] - timestamps[0]\n kws.setdefault(\"interval_delta\", interval_delta)\n kws.setdefault(\"training_size\", 100)\n kws.setdefault(\"test_size\", 30)\n\n interval_delta = kws[\"interval_delta\"]\n test_size = kws[\"test_size\"]\n\n start = kws.pop(\"start\", timestamps.min())\n stop = kws.pop(\"stop\", timestamps.max())\n step_delta = kws.pop(\"step_delta\", interval_delta * int(test_size * 0.8))\n\n return compute_sliding_windows(start, stop, step_delta, **kws)","title":""},{"docid":"20ccce18b619ceeed9474e8740480dc8","score":"0.50326043","text":"def _window1(pred, annos):\n now = sorted(annos, key=lambda x: x.text_span())\n before = [None] + now[:-1] + [None]\n after = now[1:] + [None]\n for triplet in zip(before, now, after):\n if pred(triplet[1]):\n return triplet\n return None, None, None","title":""},{"docid":"55ecd16f651193f68938bc0c359cf9d4","score":"0.50110495","text":"def find_frames_when_cue_light_on(\n data_df: pd.DataFrame,\n cue_light_names: List[str],\n fps: int,\n prior_window_frames_cnt: int,\n post_window_frames_cnt: int,\n):\n\n light_on_dict = {}\n for cue_light in cue_light_names:\n light_on_dict[cue_light] = {}\n light_bouts = detect_bouts(data_df=data_df, target_lst=[cue_light], fps=fps)\n light_bouts[\"Start_pre_window\"] = (\n light_bouts[\"Start_frame\"] - prior_window_frames_cnt\n )\n light_bouts[\"Start_pre_window\"] = light_bouts[\"Start_pre_window\"].clip(lower=0)\n light_bouts[\"Start_post_window\"] = light_bouts[\"End_frame\"]\n light_bouts[\"End_pre_window\"] = light_bouts[\"Start_frame\"]\n light_bouts[\"End_post_window\"] = (\n light_bouts[\"End_frame\"] + post_window_frames_cnt\n )\n light_bouts[\"End_post_window\"] = light_bouts[\"End_post_window\"].clip(\n upper=len(data_df)\n )\n light_on_dict[cue_light][\"df\"] = light_bouts\n light_on_dict[cue_light][\"light_on_frames\"] = list(\n data_df.index[data_df[cue_light] == 1]\n )\n pre_window_frames, post_window_frames = [], []\n for i, r in light_bouts.iterrows():\n pre_window_frames.extend(\n (list(range(r[\"Start_pre_window\"], r[\"End_pre_window\"])))\n )\n post_window_frames.extend(\n (list(range(r[\"Start_post_window\"], r[\"End_post_window\"])))\n )\n light_on_dict[cue_light][\"pre_window_frames\"] = pre_window_frames\n light_on_dict[cue_light][\"post_window_frames\"] = post_window_frames\n\n return light_on_dict","title":""},{"docid":"b4322fe639e9bef500f7ea322ff6fdc3","score":"0.5005381","text":"def windows(input_values, length=2, overlap=0):\n results_array = []\n iterable = iter(input_values)\n window = list(islice(iterable, length))\n while len(window) == length:\n results_array.append(window)\n window = window[length - overlap:]\n window.extend(islice(iterable, length - overlap))\n if window:\n results_array.append(window)\n return results_array","title":""},{"docid":"606f1e9a3f2424f96f287ddad52706c9","score":"0.50032204","text":"def windowing(input):","title":""},{"docid":"23a73f1662574ac346de8851d9ece44d","score":"0.49898157","text":"def context_window(x, context_size):\n assert context_size % 2 == 1, \"context_size must be odd\"\n assert x.shape[1] > 2 * context_size + 1, \"Low size x\"\n assert x.ndim == 2, \"X dimension must be 2\"\n\n pad_number = context_size\n left_pad_idx = pad_number\n N = x.shape[1]\n dtype = x.dtype\n right_pad_idx = 1\n for i in range(N):\n if i - pad_number < 0:\n right = x[:, range(0, i + pad_number + 1)]\n left_pad = x[:, :left_pad_idx]\n window = np.concatenate([left_pad, right], axis=1)\n left_pad_idx -= 1\n elif i + pad_number >= N:\n left = x[:, range(i - pad_number, N)]\n right_pad = x[:, -right_pad_idx:]\n window = np.concatenate([left, right_pad], axis=1)\n right_pad_idx += 1\n else:\n indexes = list(range(i - pad_number, i)) + list(range(i, i + pad_number + 1))\n window = x[:, indexes]\n yield window.astype(dtype)","title":""},{"docid":"047803d7632d592555b387b8c3c8e8cd","score":"0.49888143","text":"def divide_windows(sequences, exon_file):\r\n sequence = sequences[0]\r\n exon_pos = []\r\n with open(exon_file) as exons:\r\n for line in exons:\r\n start, end = map(int, line.split())\r\n exon_pos.append((start, end))\r\n window_groups = {\"exon\": [], \"non-exon\": []}\r\n exon_indexes = []\r\n i = 0\r\n for base in sequence:\r\n if base != 5:\r\n i += 1\r\n exon_indexes.append(i)\r\n\r\n i = 0\r\n while i < len(sequence):\r\n window = [s[i:i + 100] for s in sequences]\r\n window_start = exon_indexes[i]\r\n window_end = exon_indexes[i+100] if len(exon_indexes) > i+100 else exon_indexes[len(exon_indexes)-1]\r\n exon = False\r\n for e_start, e_end in exon_pos:\r\n if e_start <= window_start <= e_end or e_start <= window_end <= e_end:\r\n window_groups[\"exon\"].append(tuple((i, window)))\r\n exon = True\r\n if not exon:\r\n window_groups[\"non-exon\"].append(tuple((i, window)))\r\n i += 100\r\n return window_groups","title":""},{"docid":"5acf9e8370e44dc03d1b0ae7920657dd","score":"0.49835402","text":"def count_sliding_window(top, step=10, window_size=(20,20)):\n c = 0\n # slide a window across the image\n for x in xrange(0, top.shape[0], step):\n if x + window_size[0] > top.shape[0]:\n x = top.shape[0] - window_size[0]\n for y in xrange(0, top.shape[1], step):\n if y + window_size[1] > top.shape[1]:\n y = top.shape[1] - window_size[1]\n c += 1\n return c","title":""},{"docid":"ee40945416d24cf2bf1582761d199297","score":"0.49833155","text":"def get_superpulse_window(df, dg, cut_str=[''], nwfs = 100, all = False, norm = True):\n waveforms = []\n energies = []\n for cut in cut_str:\n print(str(cut))\n\n if all==True:\n nwfs = len(df.query(str(cut)).copy())\n\n print(f'using all {nwfs} Waveforms passing cut')\n else:\n print(f'using first {nwfs} waveforms passing cut' )\n\n idx = df.query(str(cut)).copy().index[:nwfs]\n raw_store = lh5.Store()\n tb_name = 'ORSIS3302DecoderForEnergy/raw'\n lh5_dir = dg.lh5_dir\n raw_list = lh5_dir + dg.fileDB['raw_path'] + '/' + dg.fileDB['raw_file']\n raw_list = raw_list.tolist() # right now lh5.store.read_object() only works for lists, so need to convert the pandas object to a list first\n data_raw, nrows = raw_store.read_object(tb_name, raw_list)\n\n wfs_all = (data_raw['waveform']['values']).nda\n wfs = wfs_all[idx.values, :]\n # baseline subtraction\n bl_means = wfs[:,:3500].mean(axis=1)\n wf_blsub = (wfs.transpose() - bl_means).transpose()\n ts = np.arange(0, wf_blsub.shape[1]-1, 1)\n super_wf = np.mean(wf_blsub, axis=0)\n wf_max = np.amax(super_wf)\n if norm == True:\n superpulse = np.divide(super_wf, wf_max)\n else:\n superpulse = super_wf\n waveforms.append(superpulse)\n waveforms = np.asarray(waveforms)\n\n super_duper_wf = np.mean(wf_blsub, axis=0)\n wf_super_max = np.amax(super_duper_wf)\n if norm == True:\n super_duper_pulse = np.divide(super_duper_wf, wf_super_max)\n else:\n super_duper_pulse = super_duper_wf\n return(ts, waveforms, super_duper_pulse)","title":""},{"docid":"28f0fc8a5cb07a7425a8331619327f41","score":"0.49765944","text":"def overlap_window(self,w2):\n\t\tassert self.overlap(w2)# they have to overlap\n\t\treturn WindowBed(self.seq, max(self.start, w2.start), min(self.end, w2.end)) # from the biggest start to the smaller end","title":""},{"docid":"d81b812bfdf0a3210f240c56e4e2b723","score":"0.49751008","text":"def select_acc_intervals(df, ts_name, interval, window, incl_vect=False, incl_acc=False):\n\n ## the window must be larger tha a single epoch\n assert interval <= 60, \"Epoch larger than 1 minute.\"\n assert window >= interval, \"Window smaller than epoch.\"\n\n cols = df.columns\n selected_cols = ['axis1', 'axis2', 'axis3', 'steps'] # TODO: add eeAccumulator\n\n minp = df.select(F.min(ts_name).cast('long')).first()[0]\n\n if interval < window:\n\n df2 = df.withColumn('tmp', F.row_number().over(Window.orderBy(ts_name)) - 1)\n\n df2 = df2.withColumn('total_sec', F.col(ts_name).cast('long')).cache()\n df2 = df2.checkpoint()\n df2.count()\n\n for col in selected_cols:\n\n df2 = df2.withColumn(col, F.when(((F.col('total_sec') - minp) % window == 0),\n F.sum(col).over(Window.orderBy('total_sec')\n .rangeBetween(0, window - interval)\n )\n ).otherwise(0)\n )\n\n df2 = df2.withColumn('duration', F.col(ts_name).cast(IntegerType()) -\n F.lag(F.col(ts_name).cast(IntegerType()), 1, minp)\n .over(Window.orderBy(ts_name))\n ).drop('total_sec')\n\n df2 = df2.withColumn('tmp', (F.col('tmp') * F.col('duration')) % window).drop('duration').orderBy(ts_name)\n\n df2 = df2.filter(F.col('tmp') == 0).drop('tmp').orderBy(ts_name)\n\n else:\n\n df2 = df\n\n if incl_vect:\n\n df2 = df2.withColumn('vectMag', F.sqrt(F.col('axis1') ** 2 + F.col('axis2') ** 2 + F.col('axis3') ** 2))\n\n cols.insert(1, 'vectMag')\n\n df2 = df2.select(cols).orderBy(ts_name)\n\n if not incl_acc:\n\n df2 = df2.select(ts_name, cols[1])\n\n return df2","title":""},{"docid":"83c05bf61da8b626bc2b978ff03c6c07","score":"0.49729288","text":"def last_window(\n df,\n window_size: int = 10,\n input_dim: str = 'univariate',\n normalize_window: bool = False,\n):\n z = df.shape[0] - window_size\n shape_1 = df.shape[1] if df.ndim > 1 else 1\n if input_dim == 'univariate':\n cX = df.iloc[z : (z + window_size),]\n cX = (\n cX.reset_index(drop=True)\n .transpose()\n .set_index(np.repeat(z, (shape_1,)), append=True)\n )\n else:\n cX = df.iloc[z : (z + window_size),]\n cX = pd.DataFrame(cX.stack().reset_index(drop=True)).transpose()\n if normalize_window:\n cX = cX.div(cX.sum(axis=1), axis=0)\n\n return cX","title":""},{"docid":"34c774a15d7755048704357d1b3da24f","score":"0.49693456","text":"def calc_absr(df, window):\n ask, bid, volume, amount = df[['ap1', 'bp1', 'volume', 'turnover']].values.T\n\n vol = volume.copy().astype(float)\n np.place(vol, vol == 0, np.nan)\n avgcost = np.divide(amount, vol)\n spread = ask - bid\n\n # Seperate\n avgc_over_ask = avgcost[1:] > ask[:-1]\n avgc_below_bid = avgcost[1:] < bid[:-1]\n avgc_between = (~avgc_over_ask) & (~avgc_below_bid)\n vol_1 = vol[1:]\n\n # calculate active buyer\n actibuy = vol_1 * avgc_over_ask\n ab_ratios = np.divide((avgcost[1:] - bid[:-1]), spread[:-1])\n actibuy[avgc_between] = vol_1[avgc_between] * ab_ratios[avgc_between]\n\n # calculate active seller\n actisell = vol_1 * avgc_below_bid\n as_ratios = 1 - ab_ratios\n actisell[avgc_between] = vol_1[avgc_between] * as_ratios[avgc_between]\n\n actibuy_end = nbr.numba_ewma(actibuy, alpha=0.05)[0][-1]\n actisell_end = np.max([nbr.numba_ewma(actisell, alpha=0.05)[0][-1], 1])\n out = actibuy_end / actisell_end\n # active_volume = np.vstack([actibuy, actisell]).T.astype('float64')\n # active_volume = np.nan_to_num(active_volume)\n # active_volume_arr = np.full((len(vol), 2), np.nan)\n # active_volume_rolsum = np.sum(lut.sliding_window_view(active_volume.T, window), axis=2).T\n # active_volume_arr[window:] = active_volume_rolsum\n # out = np.log(np.divide(active_volume_arr[:, 0], active_volume_arr[:, 1], out=np.full(len(active_volume_arr), np.nan), where=active_volume_arr[:, 1]!=0))\n return out","title":""},{"docid":"8263e837e72dfd9ab3f72cfc1cd147ba","score":"0.49554327","text":"def get_windows(series, size, prefix = \"\", drop_t = False):\n frame = pd.DataFrame(series.copy())\n frame.columns = ['t']\n\n for shift_by in range(1, size+1):\n frame[(\"\" if (prefix==\"\") else prefix+\"_\") + \"t-\" + str(shift_by)] = frame['t'].shift(shift_by)\n\n frame.dropna(inplace = True)\n\n if drop_t:\n frame.drop(columns=['t'], inplace=True)\n\n return frame","title":""},{"docid":"a9da050ab87389b6af925813325a697c","score":"0.49479997","text":"def window (self,window_function,dtime,dfreq,dump=None,dumpfig=None):\n # this is the output shape\n ntime1 = self.ntime/dtime;\n nfreq1 = self.nfreq/dfreq;\n # make a list of per-baseline output arrays \n result = [];\n # make resulting ANTENNA1/ANTENNA2 indices\n a1 = np.zeros(ntime1*self.nbl)\n a2 = np.zeros(ntime1*self.nbl)\n # make wl: array of wavelengths over each bin -- shape x,x,NF1,DF (where original shape was NF=NF1*DF)\n # note that the x,x stands for a \"virtual\" time axis\n# wl = self.wavel.reshape((nfreq1,dfreq))[np.newaxis,np.newaxis,:,:];\n## wl = self.wavel.reshape((nfreq1,dfreq));\n # get frequency and wavelength (per channel) from the CHAN_FREQ\n freqs = self.channels[0,self.freq0:self.freq0+self.nfreq];\n wavel = 3e8/freqs;\n wl = wavel.reshape((nfreq1,dfreq));\n # wl0: wavelength of centre channel of each frequency bin, shape NF1\n if dfreq%2:\n wl0 = wavel[dfreq/2::dfreq];\n else:\n wl0 = (wavel[dfreq/2-1::dfreq] + wavel[dfreq/2::dfreq])/2;\n # loop over each baseline\n for p in range(self.na):\n for q in range(p+1,self.na):\n # extract full array for this baseline, apply subset in time\n input_index = (self.A0==p)&(self.A1==q)\n data = self.data[input_index].copy();\n\t data = data[self.time0:self.time0+self.ntime,self.freq0:self.freq0+self.nfreq,:];\n # reshape so that e.g. NT becomes NT1,DT, so that we can then reduce over the second axis\n # resulting shape is NT1,DT,NF1,DF,NCORR\n data = data.reshape((ntime1,dtime,nfreq1,dfreq,self.ncorr))\n # get UV coordinates (omit W!) over the entire (hi-res) time range\n uv = self.UVW[input_index].copy();\n uv = uv[self.time0:self.time0+self.ntime,:2];\n # get corresponding uv-center points (uv0) for the lo-res time range, at each time window \n # uv0 will have shape NT1,2\n if dtime%2: # odd dtime: we have a uv-bin at the centre, so start at n/2, then take every n-th uv point\n uv0 = uv[dtime/2::dtime,:];\n else: # even dtime -- take the middle of the two centre bins\n uv0 = (uv[dtime/2-1::dtime,:] + uv[dtime/2::dtime,:])/2;\n # uv-distance has two components: along the track (in time), and across (in freq)\n # compute uvd1, the first component\n # First convert to uv-distance from window centre, along time axis, this will be of shape NT1,DT\n uvd1 = np.sqrt(((uv.reshape((ntime1,dtime,2)) - uv0[:,np.newaxis,:])**2).sum(2))#2\n # and now convert to wavelength at centre freq: this will be of shape NT1,DT,NF1\n uvd1 = uvd1[:,:,np.newaxis] / wl0[np.newaxis,np.newaxis,:]\n # now for the second component: ||uv0||/wl0 at each window is the centre channel uv-length,\n # the second component's distance is ||uv0||/wl - ||uv0||/wl0\n # first compute ||uv0||, shape is NT1\n uv0r = np.sqrt((uv0**2).sum(1)); \n # work out second component of uv-distance, shape will be NT1,NF1,DF\n uvd2 = uv0r[:,np.newaxis,np.newaxis]/wl[np.newaxis,:,:] - uv0r[:,np.newaxis,np.newaxis]/wl0[np.newaxis,:,np.newaxis];\n # evaluate windowing function\n # shapes are: uvd1 is NT1,DT,DF and uvd2 is NT1,NF1,DF, so insert new axes appropriately\n # wf shape is then NT1,DT,NF1,DF\n\t # import scipy.signal\n #selepian = scipy.signal.slepian(dtime,.1)\n wf = window_function(uvd1[:,:,:,np.newaxis],uvd2[:,np.newaxis,:,:])#*selepian;\n if dump:\n import cPickle\n cPickle.dump((p,q,uvd1[0],uvd2[0],wf[0]),dump);\n if dumpfig and p==17 and q==26:\n #print uvd1[0],uvd2[0],wf[0];\n pylab.plot(wf[0,dtime/2,0,:])\n pylab.plot(wf[0,:,0,dfreq/2])\n pylab.savefig(dumpfig);\n #print \"Saved plot to\",dumpfig;\n # sum of wf over each DTxDF bin\n wfsum = wf.sum(3).sum(1);\n# print p,q,wfsum[0]\n # apply wf to data (np.newaxis ensures every correlation is multiplied by the same wf),\n # then sum over each DTxDF bin, and normalize\n data = (data*wf[...,np.newaxis]).sum(3).sum(1)/wfsum[...,np.newaxis];\n # return result\n result.append((p,q,data));\n return result;","title":""},{"docid":"545bccfdf6fa1ae18bdb018db0b233f7","score":"0.49458438","text":"def filter_data2(df, lookahead = 5):\r\n pd.options.mode.chained_assignment = None # default='warn'\r\n\r\n df[\"n_robots\"] = 0\r\n \r\n for i in range(len(df)-lookahead):\r\n for j in range(1, lookahead + 1):\r\n if df[\"start\"][j] >= df[\"start\"][i] and df[\"start\"][j] <= df[\"finish\"][i]:\r\n df[\"n_robots\"][i] += 1\r\n \r\n return df","title":""},{"docid":"01b453cc672804130574a42ccb50833a","score":"0.49395588","text":"def windowlized(df, cols, val_date, test_date, window, \n target_col='target', target_date_col='target_date', return_df=False):\n \n valid_date = df[df[target_col].isna() == False][target_date_col].min() - dt.timedelta(window)\n df_valid = pd.DataFrame(df[df[target_date_col] >= valid_date]).reset_index(drop=True)\n valid_target_date = df[df[target_col].isna() == False][target_date_col].max()\n df_valid = pd.DataFrame(df_valid[df_valid[target_date_col] <= valid_target_date]).reset_index(drop=True)\n\n X_org = df[cols].values\n scaler_X = MinMaxScaler()\n scaler_X.fit(X_org)\n\n y_org = df[[target_col]].values\n scaler_y = MinMaxScaler()\n scaler_y.fit(y_org)\n\n train_date = val_date - dt.timedelta(7*12)\n\n X_valid, y_valid = df_valid[cols].values, df_valid[[target_col]]\n X_valid = scaler_X.transform(X_valid)\n y_valid = scaler_y.transform(y_valid)\n\n train_idx = df_valid[df_valid[target_date_col] <= train_date].dropna().index.tolist()\n val_idx = df_valid[(df_valid[target_date_col] >= val_date)&(df_valid[target_date_col] < test_date)].index.tolist() \n test_idx = df_valid[df_valid[target_date_col] >= test_date].index.tolist() \n\n X_train, y_train = window_sliding(X_valid, y_valid, train_idx, window)\n X_val, y_val = window_sliding(X_valid, y_valid, val_idx, window)\n X_test, y_test = window_sliding(X_valid, y_valid, test_idx, window)\n\n if return_df == True:\n df_train = df_valid[df_valid.index.isin(train_idx)].reset_index(drop=True)\n df_val = df_valid[df_valid.index.isin(val_idx)].reset_index(drop=True)\n df_test = df_valid[df_valid.index.isin(test_idx)].reset_index(drop=True)\n\n return ((X_train, y_train), (X_val, y_val), (X_test, y_test), (scaler_X, scaler_y)), (df_train, df_val, df_test)\n else:\n return (X_train, y_train), (X_val, y_val), (X_test, y_test), (scaler_X, scaler_y)","title":""},{"docid":"dc94ca45944976c68d15caf532505cbb","score":"0.49330953","text":"def f(x):\n out = np.zeros(x.shape)\n np.put(out, indices.ravel(), 1)\n b, H, W, C = x.shape\n\n ow = W // 2\n oh = H // 2\n fh, fw = 2, 2\n\n # get first window\n start_idx = np.arange(fh)[:, None] * W * C + np.arange(fw) * C\n\n # Get the offsets along\n offset_idx = np.arange(ow) * C\n\n windows_along = start_idx[:, :, None] + offset_idx.ravel()[None, None, :][::2]\n windows_along = np.concatenate(np.split(windows_along, ow, axis=2), 1).squeeze()\n\n # Get the offsets down\n offset_idx = np.arange(oh) * C * W\n windows = windows_along[:, :, None] + offset_idx.ravel()[None, None, :][::2]\n windows = np.concatenate(np.split(windows, oh, axis=2), 0).squeeze()\n\n offset_idx = np.arange(oh) * C * W\n windows = windows[None, :, :] + offset_idx.ravel()[:, None, None]\n windows = np.concatenate(np.split(windows, oh, axis=0), 0).squeeze()\n\n offset_idx = np.arange(ow) * C\n windows = windows[:, :, :, None] + offset_idx.ravel()[None, None, None, :]\n\n return np.take(out, windows), ('max_pool df/dx', dims)","title":""},{"docid":"8588f1074776b2b53af3c278189f33f2","score":"0.49312168","text":"def atr(dataframe, n):\n df_ = dataframe.copy()\n df_['H-L'] = abs(df_['High'] - df_['Low'])\n df_['H-PC'] = abs(df_['High'] - df_['Adj Close'].shift(1))\n df_['L-PC'] = abs(df_['Low'] - df_['Adj Close'].shift(1))\n df_['TR'] = df_[['H-L', 'H-PC', 'L-PC']].max(axis=1, skipna=False)\n df_['ATR'] = df_['TR'].rolling(n).mean()\n # df['ATR'] = df['TR'].ewm(span=n,adjust=False,min_periods=n).mean()\n df2 = df_.drop(['H-L', 'H-PC', 'L-PC'], axis=1)\n return df2","title":""},{"docid":"6b497ed71de6fccfde36213c21b40ab6","score":"0.491446","text":"def new_sliding_window_array(t, ep_len, step):\n win_array = []\n index_start = 0\n\n while index_start < len(t) - ep_len - 1: # the last window starts ep_len seconds before the end of t\n # print(index_start)\n window_ok = 1 # window ok is 1 if it doesn't overlap with a time where the signal is cropped\n for i in range(ep_len - 1):\n if t[index_start + i] != (t[index_start + i + 1] - 1):\n window_ok = 0 # if window_ok is set to 0, it won't be added to the array of windows\n if window_ok:\n win_array.append((index_start, index_start+ep_len))\n # print((index_start, index_start+ep_len))\n index_start += step\n return win_array","title":""},{"docid":"4ca15cdd2fc64260044f05eda8574be7","score":"0.4912677","text":"def fitColumn(pos,slce,allTrace, winRng, reverse=False, plot=False, prnt=False, mxWidth=1,bright=False):\n\n #extract Ronchi information at given position\n y = slce[:,pos]\n nDips = allTrace.shape[0]\n trace =np.zeros(nDips)\n traceAmp = np.empty(nDips)\n traceAmp[:] = np.nan\n\n #compute 2nd derivative for identifying centre of bright or dark bands for tracing\n d2 = np.gradient(np.gradient(y))\n #d2 = y \n x = np.arange(len(y))\n winRng2 = int(winRng/2)\n \n #find the centroid of each band/dip\n for j in range(0,nDips):\n\n prevMeas = allTrace[j,:]\n\n #exclude bad measurements\n whr1 = np.where(prevMeas != 0)[0]\n whr2 = np.where(np.isfinite(prevMeas[whr1]))[0]\n\n if (reverse):\n prevFit = prevMeas[whr1[whr2[0]]]\n else:\n prevFit = prevMeas[whr1[whr2[-1]]]\n \n #find dip\n oldDip = np.round(prevFit).astype('int')\n xtmp = (np.arange(winRng)+ oldDip - winRng2).astype('int')\n xtmp = xtmp[np.where(xtmp < len(y))]\n \n #update centre position for better fitting, but only allow for 1-pixel shift\n ytmp = d2[xtmp]\n \n if (bright):\n try:\n dipPos = xtmp[np.nanargmin(ytmp[winRng2-1:winRng2+2])+winRng2-1]\n except(ValueError):\n dipPos=np.nan\n else:\n try:\n dipPos = xtmp[(np.nanargmax(ytmp[winRng2-1:winRng2+2])+winRng2-1).astype('int')]\n except(ValueError):\n dipPos = np.nan\n\n if (dipPos is not np.nan):\n xtmp = (np.arange(winRng)+dipPos - winRng2).astype('int') #can remove once know quality\n xtmp = xtmp[np.where(xtmp < len(y))]\n\n if (len(xtmp)>2):\n #fit Gaussian or parabola to region to determine line centre\n if (bright):\n yfit = np.nanmin(d2[xtmp])-d2[xtmp]\n yfit -= np.nanmin(yfit)\n else:\n yfit = d2[xtmp] - np.nanmin(d2[xtmp])\n \n if (len(xtmp)>2):\n trace[j],traceAmp[j] = getFit2(xtmp, yfit, plot=plot, mxWidth=mxWidth)\n \n else:\n trace[j] = np.nan\n\n if (prnt):\n print(trace[j], prevFit-trace[j])\n else:\n trace[j] = np.nan\n yfit = [np.nan]\n else:\n trace[j] = np.nan\n yfit = [np.nan]\n \n #avoid bad fits\n\n if (np.isnan(trace[j])):\n badFit = True\n else:\n if ((np.abs(trace[j]-prevFit)) > mxWidth/2.):\n badFit = True\n else:\n badFit = False\n \n if (badFit): \n\n #compute centre as weighted average instead\n try:\n if (len(xtmp)>2 and len(yfit)>2 and np.all(~np.isfinite(yfit[winRng2-1:winRng2+2]))):\n mxPos = np.nanargmax(yfit[winRng2-1:winRng2+2])\n #xtmp = xtmp[int(np.arange(int(winRng/2))-1+mxPos)]\n xtmp = xtmp[int(mxPos-winRng2):int(mxPos+winRng2+1)]\n ytmp = np.abs(d2[xtmp])\n c = np.nansum(xtmp*ytmp)/np.nansum(ytmp)\n else:\n c = np.nan\n except(ValueError):\n c = np.nan\n \n if (prnt):\n print(pos, j, trace[j], prevFit, prevFit-trace[j], c, prevFit-c)\n\n if (np.isnan(c)):\n trace[j] = np.nan\n elif (np.abs(c-prevFit) > mxWidth/2.):\n trace[j] = np.nan\n else:\n trace[j] = c\n \n if (plot):\n plt.figure()\n plt.plot(d2)\n plt.plot(xtmp, d2[xtmp], 'ro')\n plt.plot([trace[j],trace[j]], [np.min(d2),np.max(d2)],'--')\n plt.show() \n\n\n return trace, traceAmp","title":""},{"docid":"0832ce82d847dd97008363619ea1cb05","score":"0.49016023","text":"def _get_window(self):\n N = 2 * self.maxlag + 1\n window_even = utils.create_window(N, self.window_name)\n\n # 2d even window\n window2d = np.array(\n [\n window_even,\n ]\n * N\n )\n\n ## One-sided window with zero padding\n window = np.zeros(N)\n window[: self.maxlag + 1] = window_even[self.maxlag :]\n window[self.maxlag :] = 0\n\n # 2d window function to apply to bispectrum\n row = np.concatenate(([window[0]], np.zeros(2 * self.maxlag)))\n toep_matrix = toeplitz(window, row)\n toep_matrix += np.tril(toep_matrix, -1).transpose()\n window = toep_matrix[..., ::-1] * window2d * window2d.transpose()\n return window","title":""},{"docid":"aed91e2825943c9609e19645006ea8b9","score":"0.4892686","text":"def get_sliding_window_collate(self, stride, randomize):\n\n def sliding_window_collate(examples):\n windows = []\n for example in examples:\n c, q, y1, y2, id = example\n c_len = (c != self.padding_idx).sum()\n q_len = (q != self.padding_idx).sum()\n\n # We want to keep going so long as c_end = c_start + (block_size - q_len - 2)\n # has not been at least c_len for the first time, i.e. c_end < c_len + stride.\n # We also want to take at least one step.\n c_range = range(\n 0, max(1, c_len + q_len + 2 - self.block_size + stride), stride\n )\n if randomize:\n c_start = random.sample(c_range, k=1)[0]\n c_range = range(c_start, c_start + 1)\n\n for c_start in c_range:\n c_end = min(self.block_size - q_len - 2 + c_start, c_len)\n if y1 < c_start or y2 < c_start or y1 >= c_end or y2 >= c_end:\n y1 = -1\n y2 = -1\n else:\n y1 -= c_start\n y2 -= c_start\n windows.append((c[c_start:c_end], q[:q_len], y1, y2, c_start, id))\n\n # Collate windows\n max_len = max(len(window[0]) + len(window[1]) + 2 for window in windows)\n assert max_len <= self.block_size\n x = torch.full((len(windows), max_len), self.padding_idx, dtype=torch.long)\n y = torch.zeros(len(windows), 2, dtype=torch.long)\n c_padding_mask = torch.ones(len(windows), max_len, dtype=torch.bool)\n c_starts = torch.zeros(len(windows), dtype=torch.long)\n ids = torch.zeros(len(windows), dtype=torch.long)\n for i, window in enumerate(windows):\n c, q, y1, y2, c_start, id = window\n x[i, 0] = self.cls_idx\n x[i, 1 : 1 + len(c)] = c\n x[i, 1 + len(c)] = self.sep_idx\n x[i, 2 + len(c) : 2 + len(c) + len(q)] = q\n c_padding_mask[i][0 : 1 + len(c)] = False\n y[i, 0] = y1 + 1\n y[i, 1] = y2 + 1\n c_starts[i] = c_start\n ids[i] = id\n return x, y, c_padding_mask, c_starts, ids\n\n return sliding_window_collate","title":""},{"docid":"7745b282a1a13a7f18ed9fe09b6344ec","score":"0.48887002","text":"def compute_sliding_windows(\n start,\n stop,\n step_delta,\n interval_delta,\n training_size=25,\n test_size=5,\n):\n start = pd.Timestamp(str(start))\n stop = pd.Timestamp(str(stop))\n step_delta = pd.Timedelta(step_delta)\n interval_delta = pd.Timedelta(interval_delta)\n\n timestep = start\n test_stop = start\n windows = []\n while test_stop < stop:\n train_start = timestep\n train_stop = train_start + interval_delta * training_size\n test_start = train_stop\n test_stop = test_start + interval_delta * test_size\n\n windows.append(\n (\n (train_start, train_stop),\n (test_start, test_stop),\n )\n )\n timestep += step_delta\n return windows","title":""},{"docid":"7dcdb5a9233b5571de2469920b67438e","score":"0.4883226","text":"def eval_recon_rolling(\n df_in,\n roll_window=0.4,\n roll_offset=1,\n smooth=\"Lowess\",\n span=0.1,\n band_width=0.2,\n upto=\"Full\",\n):\n\n # Properties of df_in\n var_names = df_in.columns\n\n # Select portion of data where EWS are evaluated (e.g only up to bifurcation)\n if upto == \"Full\":\n df_pre = df_in.copy()\n else:\n df_pre = df_in.loc[:upto]\n\n # ------Data detrending--------\n\n # Compute the absolute size of the bandwidth if it is given as a proportion\n if 0 < band_width <= 1:\n bw_size = df_pre.shape[0] * band_width\n else:\n bw_size = band_width\n\n # Compute the Lowess span as a proportion if given as absolute\n if not 0 < span <= 1:\n span = span / df_pre.shape[0]\n else:\n span = span\n\n # Compute smoothed data and residuals\n if smooth == \"Gaussian\":\n # Loop through variables\n for var in var_names:\n smooth_data = gf(df_pre[var].values, sigma=bw_size, mode=\"reflect\")\n smooth_series = pd.Series(smooth_data, index=df_pre.index)\n residuals = df_pre[var].values - smooth_data\n resid_series = pd.Series(residuals, index=df_pre.index)\n # Add smoothed data and residuals to df_pre\n df_pre[var + \"_s\"] = smooth_series\n df_pre[var + \"_r\"] = resid_series\n\n if smooth == \"Lowess\":\n # Loop through variabless\n for var in var_names:\n smooth_data = lowess(df_pre[var].values, df_pre.index.values, frac=span)[\n :, 1\n ]\n smooth_series = pd.Series(smooth_data, index=df_pre.index)\n residuals = df_pre[var].values - smooth_data\n resid_series = pd.Series(residuals, index=df_pre.index)\n # Add smoothed data and residuals to df_pre\n df_pre[var + \"_s\"] = smooth_series\n df_pre[var + \"_r\"] = resid_series\n\n # Compute the rolling window size (integer value)\n rw_size = int(np.floor(roll_window * df_in.shape[0]))\n\n # Set up a rolling window\n\n # Number of components in the residual time-series\n num_comps = len(df_pre)\n # Rolling window offset (can make larger to save on computation time)\n roll_offset = int(roll_offset)\n\n # Initialise a list of dictionaries containing eval data\n list_evaldata = []\n\n # Loop through window locations shifted by roll_offset\n for k in np.arange(0, num_comps - (rw_size - 1), roll_offset):\n # Select subset of residuals contained in window\n df_window = df_pre[[var + \"_r\" for var in var_names]].iloc[k : k + rw_size]\n # Asisgn the time value for the metrics (right end point of window)\n t_point = df_pre.index[k + (rw_size - 1)]\n\n # Do eigenvalue reconstruction on residuals\n dic_eval_recon = helpers.eval_recon(df_window)\n # Add time component\n dic_eval_recon[\"time\"] = t_point\n # Add them to list\n list_evaldata.append(dic_eval_recon)\n\n # Create dataframe from list of dicts of eval data\n df_evaldata = pd.DataFrame(list_evaldata)\n df_evaldata.set_index(\"time\", inplace=True)\n\n # Create output dataframe that merges all useful info\n df_out = pd.concat(\n [\n df_in,\n df_pre[\n [var + \"_r\" for var in var_names] + [var + \"_s\" for var in var_names]\n ],\n df_evaldata,\n ],\n axis=1,\n )\n\n return df_out","title":""},{"docid":"b391fc305f190f0da54b4b8eba27996e","score":"0.48732123","text":"def get_visibility_windows(df_indices):\n\n window_indices = []\n window_start_index = df_indices[0]\n for x,y in zip(df_indices[::], df_indices[1::]):\n difference = y - x\n if difference > 1:\n window_end_index = x\n window_indices.append((window_start_index, window_end_index))\n window_start_index = y\n if y == df_indices[-1]:\n window_indices.append((window_start_index, y))\n else:\n continue\n\n return window_indices","title":""},{"docid":"8b9d4242e6e95fc9d1fa0adf81fcd447","score":"0.4837433","text":"def calculate_windows_to_L(alignment, taxa_order, patterns_of_interest, window_size, window_offset, verbose= False, alpha=0.01):\n\n # Separate the patterns of interest into their two terms\n terms1 = patterns_of_interest[0]\n terms2 = patterns_of_interest[1]\n\n sequence_list = []\n taxon_list = []\n\n with open(alignment) as f:\n\n # Create a list of each line in the file\n lines = f.readlines()\n\n # First line contains the number and length of the sequences\n first_line = lines[0].split()\n length_of_sequences = int(first_line[1])\n\n for line in lines[1:]:\n # Add each sequence to a list\n sequence = line.split()[1]\n sequence_list.append(sequence)\n\n # Add each taxon to a list\n taxon = line.split()[0]\n taxon_list.append(taxon)\n\n # The outgroup is the last taxa in taxa order\n outgroup = taxa_order[-1]\n\n i = 0\n num_windows = 0\n if window_size > length_of_sequences:\n num_windows = 1\n window_size = length_of_sequences\n else:\n # Determine the total number of windows needed\n while (i + window_size - 1 < length_of_sequences):\n i += window_offset\n num_windows += 1\n\n site_idx = 0\n windows_to_l = {}\n\n # Iterate over each window\n for window in range(num_windows):\n\n terms1_counts = defaultdict(int)\n terms2_counts = defaultdict(int)\n\n num_ignored = 0\n\n # Iterate over the indices in each window\n for window_idx in range(window_size):\n\n # Map each taxa to the base at a given site\n taxa_to_site = {}\n\n # Create a set of the bases at a given site to determine if the site is biallelic\n bases = set([])\n\n # Iterate over each sequence in the alignment\n for sequence, taxon in zip(sequence_list, taxon_list):\n # Map each taxon to the corresponding base at the site\n base = sequence[site_idx]\n taxa_to_site[taxon] = base\n bases.add(base)\n\n # Statistic can only be calculated where the nucleotides are known\n if \"-\" not in bases and len(bases) == 2:\n\n # Create the pattern that each site has\n site_pattern = []\n\n # The ancestral gene is always the same as the outgroup\n ancestral = taxa_to_site[outgroup]\n\n # Iterate over each taxon\n for taxon in taxa_order:\n nucleotide = taxa_to_site[taxon]\n\n # Determine if the correct derived/ancestral status of each nucleotide\n if nucleotide == ancestral:\n site_pattern.append(\"A\")\n else:\n site_pattern.append(\"B\")\n\n # Convert the site pattern to a string\n sites = pattern_string_generator([site_pattern])\n if sites != []:\n site_string = sites[0]\n\n # If the site string is a pattern of interest add to its count for one of the terms\n if site_string in terms1:\n terms1_counts[site_string] += 1\n\n elif site_string in terms2:\n terms2_counts[site_string] += 1\n\n\n elif \"-\" in bases or \"N\" in bases:\n num_ignored += 1\n\n # Increment the site index\n site_idx += 1\n\n terms1_total = sum(terms1_counts.values())\n terms2_total = sum(terms2_counts.values())\n\n numerator = terms1_total - terms2_total\n denominator = terms1_total + terms2_total\n\n if denominator != 0:\n l_stat = numerator / float(denominator)\n else:\n l_stat = 0\n\n # Verbose output\n if verbose:\n signif, left_counts, right_counts, chisq, pval = calculate_significance(terms1_total, terms2_total, verbose, alpha)\n # The line below can be changed to add more information to the windows to L mapping\n windows_to_l[window] = (l_stat, signif, num_ignored, chisq, pval)\n\n # Standard output\n else:\n signif = calculate_significance(terms1_total, terms2_total)\n windows_to_l[window] = (l_stat, signif)\n\n # Account for overlapping windows\n site_idx += (window_offset - window_size)\n\n return windows_to_l","title":""},{"docid":"ec3070700ddd5109fe9a00d3d7077419","score":"0.48335677","text":"def shape_context(points, nbins_r, nbins_theta, r_inner, r_outer, max_window_size, window=None):\n nbins = nbins_theta * nbins_r\n t_points = points.shape[0]\n\n descriptor = []\n\n r_array = cdist(points, points)\n am = r_array.argmax()\n max_points = [am // t_points, am % t_points]\n r_array_n = r_array // r_array.mean()\n r_bin_edges = np.logspace(np.log10(r_inner), np.log10(r_outer), nbins_r)\n r_array_q = np.zeros((t_points, t_points), dtype=int)\n\n for m in range(nbins_r):\n r_array_q += (r_array_n < r_bin_edges[m])\n\n fz = r_array_q > 0\n\n theta_array = cdist(points, points, lambda u, v: math.atan2((v[1] - u[1]), (v[0] - u[0])))\n norm_angle = theta_array[max_points[0], max_points[1]]\n theta_array = (theta_array - norm_angle * (np.ones((t_points, t_points)) - np.identity(t_points)))\n theta_array[np.abs(theta_array) < 1e-7] = 0\n\n theta_array_2 = theta_array + 2 * math.pi * (theta_array < 0)\n theta_array_q = (1 + np.floor(theta_array_2 // (2 * math.pi / nbins_theta))).astype(int)\n\n for i in range(t_points):\n sn = np.zeros((nbins_r, nbins_theta))\n x, y = points[i]\n\n if window is not None:\n local_x = np.where((points[:, 0] >= (x-window)) & (points[:, 0] <= (x+window)))\n local_y = np.where((points[:, 0] >= y-window) & (points[:, 0] <= y+window))\n local_points = np.intersect1d(local_x, local_y)\n for j in local_points:\n if (fz[i, j]):\n sn[r_array_q[i, j] - 1, theta_array_q[i, j] - 1] += 1\n else:\n for j in range(t_points):\n if (fz[i, j]):\n sn[r_array_q[i, j] - 1, theta_array_q[i, j] - 1] += 1\n # There need not be 500 points in the image, you can have less than 500 also.\n descriptor.append(sn.reshape(nbins))\n\n return np.array(descriptor)","title":""},{"docid":"1d2847c039a9316dd0a87089236b63ef","score":"0.4831764","text":"def c_window(n,n_cut):\n\tn_right = n[-1] - n_cut\n\t# n_left = n[0]+ n_cut \n\tn_r=n[ n[:] > n_right ] \n\t# n_l=n[ n[:] < n_left ] \n\ttheta_right=(n[-1]-n_r)/float(n[-1]-n_right-1) \n\t# theta_left=(n_l - n[0])/float(n_left-n[0]-1) \n\tW=np.ones(n.size)\n\tW[n[:] > n_right]= theta_right - 1/(2*np.pi)*np.sin(2*np.pi*theta_right)\n\t# W[n[:] < n_left]= theta_left - 1/(2*pi)*sin(2*pi*theta_left)\n\treturn W","title":""},{"docid":"1f4e138fddecc09c1a150eaedc9419a9","score":"0.48305053","text":"def _get_reading_window(width, height, data_bbox, eopatch_bbox):\n if eopatch_bbox.crs is not data_bbox.crs:\n eopatch_bbox = eopatch_bbox.transform(data_bbox.crs)\n\n # The following will be in the future moved to sentinelhub-py\n data_ul_x, data_lr_y = data_bbox.lower_left\n data_lr_x, data_ul_y = data_bbox.upper_right\n\n res_x = abs(data_ul_x - data_lr_x) / width\n res_y = abs(data_ul_y - data_lr_y) / height\n\n ul_x, lr_y = eopatch_bbox.lower_left\n lr_x, ul_y = eopatch_bbox.upper_right\n\n # If these coordinates wouldn't be rounded here, rasterio.io.DatasetReader.read would round\n # them in the same way\n top = round((data_ul_y - ul_y) / res_y)\n left = round((ul_x - data_ul_x) / res_x)\n bottom = round((data_ul_y - lr_y) / res_y)\n right = round((lr_x - data_ul_x) / res_x)\n\n return (top, bottom), (left, right)","title":""},{"docid":"571d92a2fb48fecf34fe9130c34df320","score":"0.48245016","text":"def window(inclusion_windows):\n def _window(mjd, *args):\n for wbegin,wend in inclusion_windows:\n if wbegin <= mjd <= wend:\n return True\n return False\n return _window","title":""},{"docid":"722044a8f80cbc85ce013c4cc645aa54","score":"0.48231345","text":"def admi(df, n, n_adx = 14):\n i = 0\n up_index = []\n do_index = []\n while i + 1 <= df.index[-1]:\n up_move = df.loc[i + 1, 'high'] - df.loc[i, 'high']\n do_move = df.loc[i, 'low'] - df.loc[i + 1, 'low']\n if up_move > do_move and up_move > 0:\n up_d = up_move\n else:\n up_d = 0\n up_index.append(up_d)\n if do_move > up_move and do_move > 0:\n do_d = do_move\n else:\n do_d = 0\n do_index.append(do_d)\n i = i + 1\n i = 0\n tr_l = [0]\n while i < df.index[-1]:\n tr = max(df.loc[i + 1, 'high'], df.loc[i, 'close']) - min(df.loc[i + 1, 'low'], df.loc[i, 'close'])\n tr_l.append(tr)\n i = i + 1\n tr_s = pd.Series(tr_l)\n atr = pd.Series(tr_s.ewm(span=n, min_periods=n).mean())\n up_index = pd.Series(up_index)\n do_index = pd.Series(do_index)\n pos_di = pd.Series(up_index.ewm(span=n, min_periods=n).mean() / atr)\n neg_di = pd.Series(do_index.ewm(span=n, min_periods=n).mean() / atr)\n adx = pd.Series((abs(pos_di - neg_di) / (pos_di + neg_di)).ewm(span=n_adx, min_periods=n_adx).mean(), name='adx_' + str(n) + '_' + str(n_adx))\n df = df.join(adx)\n return df","title":""},{"docid":"e9afddac00bd53075530173e0c690583","score":"0.48211673","text":"def ADX(df, n, n_ADX): # n = 14 (for buildinhg ATR in this function), n_ADX = 14 (for building final ADX in this func)\n \n df = df.reset_index()\n i = 0\n UpI = []\n DoI = []\n while i + 1 <= df.index[-1]:\n UpMove = df.at[i + 1, 'High'] - df.at[i, 'High']\n DoMove = df.at[i, 'Low'] - df.at[i + 1, 'Low']\n if UpMove > DoMove and UpMove > 0:\n UpI.append(UpMove)\n else:\n UpI.append(0)\n \n if DoMove > UpMove and DoMove > 0:\n DoI.append(DoMove)\n else:\n DoI.append(0)\n i = i + 1\n i = 0\n TR_l = [0]\n while i < df.index[-1]:\n TR = max(df.at[i + 1, 'High'], df.at[i, 'Close']) \\\n - min(df.at[i + 1, 'Low'], df.at[i, 'Close'])\n TR_l.append(TR)\n i = i + 1\n TR_s = pd.Series(TR_l)\n ATR = TR_s.ewm(span=n, min_periods=n).mean()\n UpI = pd.Series(UpI)\n DoI = pd.Series(DoI)\n PosDI = pd.Series(UpI.ewm(span=n, min_periods=n - 1).mean() / ATR)\n NegDI = pd.Series(DoI.ewm(span=n, min_periods=n - 1).mean() / ATR)\n ADX = pd.Series((abs(PosDI - NegDI) / (PosDI + NegDI)).ewm(span=n_ADX, min_periods=n_ADX - 1).mean(), \n name = 'ADX_' + str(n) + '_' + str(n_ADX))\n df = df.join(ADX).set_index(\"index\")\n return df['ADX_' + str(n) + '_' + str(n_ADX)]","title":""},{"docid":"98b5013779624b9f92514993c7e065fc","score":"0.48187187","text":"def sliding_window_calculate(self, window_size, window_jump = 1, operation = \"sum\", output = \"dict\",\n threshold = 1, seqs_to_exclude = []):\n if output == \"dict\":\n new_dic = {}\n elif output == \"annotation_set\":\n annotation_set = AnnotationSet()\n annotation_set.region = {}\n elif output == \"coords\":\n coords_list = []\n for seqid in self:\n if len(self[seqid]) > window_size and not seqid in seqs_to_exclude:\n if output == \"dict\":\n new_dic[seqid] = []\n window_in = False\n coords_list = []\n for position in range(len(self[seqid][:-1 * window_size]) / window_jump):\n window_start = position * window_jump\n if operation == \"set\":\n new_dic[seqid].append(set(self[seqid][window_start:window_start + window_size]))\n continue\n window_sum = numpy.sum(self[seqid][window_start:window_start + window_size])\n if operation == \"sum\":\n value = window_sum\n elif operation == \"average\":\n value = window_sum * 1.0 / window_size\n if output == \"dict\":\n new_dic[seqid].append(value)\n elif output == 'annotation_set':\n if value >= threshold:\n if window_in == False:\n window_in = True\n if len(coords_list) == 0:\n coords_list.append([1 + window_start])\n elif position > coords_list[-1][1]:\n coords_list.append([1 + window_start])\n else:\n if window_in == True:\n window_in = False\n if len(coords_list[-1]) == 1:\n coords_list[-1].append(window_start + window_size)\n else:\n coords_list[-1][1] = window_start + window_size\n elif output == \"coords\":\n if threshold[0] <= value <= thredshold[1]:\n coords_list.append([seqid, 1 + window_start, window_start + window_size])\n elif type(output) == file:\n output.write(seqid + '\\t' + str(value) + '\\n')\n if window_start % 10000000 == 0 and verbose:\n print(\"processed \" + seqid + \" to position \" + str(position))\n if output == \"annotation_set\":\n if len(coords_list) > 0:\n if len(coords_list[-1]) == 1:\n coords_list[-1].append(len(self[seqid]))\n for coords in coords_list:\n ID = seqid + \"-window\" + str(coords[0])\n annotation_set.region[ID] = BaseAnnotation(ID, seqid, tuple(coords), \"region\", annotation_set = annotation_set)\n if verbose:\n print(\"processed \" + seqid)\n if output == 'annotation_set':\n for region in annotation_set.region:\n if annotation_set.region[region].seqid == seqid:\n print(str(annotation_set.region[region].coords))\n if output == \"dict\":\n return new_dic\n elif output == \"annotation_set\":\n return copy.deepcopy(annotation_set)\n elif output == \"coords\":\n return coords_list","title":""},{"docid":"cadaf695107de24a04971a0ce318cfa3","score":"0.48182005","text":"def windowing(input):\n window = signal.hamming(len(input[1]),sym=0)\n windowed=np.zeros(input.shape)\n for i in range(len(input)):\n windowed[i]=np.multiply(input[i],window)\n #plt.pcolormesh(windowed)\n\n return windowed","title":""},{"docid":"e3258ab12fe12bdbee9bc80d45a3cbef","score":"0.48174053","text":"def get_NAV_windows(spacecraft,planet,times,threshold):\r\n interval = stypes.SPICEDOUBLE_CELL(2)\r\n sp.wninsd(times[0],times[1],interval)\r\n gam_times = stypes.SPICEDOUBLE_CELL(2000)\r\n sp.gfdist(planet,\"NONE\",spacecraft,\"<\",threshold,0.0,86400.0,1000,interval,gam_times)\r\n ngams = sp.wncard(gam_times)\r\n closest_approach = stypes.SPICEDOUBLE_CELL(2*ngams)\r\n sp.gfdist(planet,\"NONE\",spacecraft,\"LOCMIN\",threshold,0.0,86400.0,1000,gam_times, closest_approach)\r\n n_ca = sp.wncard(closest_approach)\r\n nav_windows = np.zeros([n_ca,3],dtype=\"float64\")\r\n for i in range(n_ca):\r\n ca=sp.wnfetd(closest_approach,i)\r\n nav_windows[i,:] = [ca[0]-(28*86400.0),ca[0],ca[0]+(7*86400.0)]\r\n return(nav_windows)","title":""},{"docid":"7a51756c5c3653a31688b97e57a54e55","score":"0.4815732","text":"def launch(ini, end, order):\n j = nbr_lanes*order + lane-1\n left_lane = envelope_data[:, j] # Upper envelope of the current lane\n thickness = rint(thickness_data[j]) # Thickness of the current lane\n\n #(ini, end) = cut_border_edge(left_lane, thickness) # First and last index of the lane\n\n # First research of the slits, with potential errors on X (x_pos is an array, y is a real)\n # Y coordinates are relative to the envelope (not absolute coordinates)\n x_pos, y = ThAr_global_center(left_lane, thickness, ini, end)\n\n # Y coordinates are set as absolute\n tab_pos = [[x, y + left_lane[rint(x)]] for x in x_pos if (ini <= x <= end)]\n #print(tab_pos)\n return tab_pos","title":""},{"docid":"8c5e27aa2f1943114ce789dbd18ef0da","score":"0.4813631","text":"def convolve_subarrays(df_bd):\n ALIGNMENT_KERNEL_EDGE_SIZE = 21 # should be odd\n half_size = ALIGNMENT_KERNEL_EDGE_SIZE // 2\n y, x = np.mgrid[0:ALIGNMENT_KERNEL_EDGE_SIZE, 0:ALIGNMENT_KERNEL_EDGE_SIZE]\n convolved_subarrays, new_shifts = [], []\n for ar, mis, sc in zip(df_bd['Subarray_first'], df_bd['Misalignment'],\n df_bd['Shift_current']):\n new_shift_x = sc[0] - mis[0]\n new_shift_y = sc[1] - mis[1]\n gaussian = Gaussian2D(1, half_size + new_shift_x, half_size + new_shift_y, 0.7, 0.7)\n kernel = gaussian(x, y)\n kernel /= np.sum(kernel)\n convolved_subarrays.append(convolve(ar, kernel))\n new_shifts.append((new_shift_x, new_shift_y))\n df_bd['Subarray_current'] = convolved_subarrays\n df_bd['Shift_current'] = new_shifts\n # plot_images('Convolved subarrays', df_bd['Filename'], convolved_subarrays)\n print('convolve_subarrays() done.')\n return df_bd","title":""},{"docid":"bfbe86d9ba2fadb4c3a8cef6216b3f6d","score":"0.4811607","text":"def position_handler(data: pd.DataFrame) -> Iterator[int]:\n\n curr_pos = 0\n for i in data.reset_index().index:\n if i == 0:\n curr_pos = 0\n yield 0\n elif data.iloc[i - 1, data.columns.get_loc('EntrySignal')] == 'LongEntry':\n curr_pos = 1\n yield 1\n elif np.logical_and(curr_pos == 1, data.iloc[i - 1, data.columns.get_loc('ExitSignal')] != 'LongExit'):\n curr_pos = 1\n yield 1\n elif data.iloc[i - 1, data.columns.get_loc('EntrySignal')] == 'ShortEntry':\n curr_pos = -1\n yield -1\n elif np.logical_and(curr_pos == -1, data.iloc[i - 1, data.columns.get_loc('ExitSignal')] != 'ShortExit'):\n curr_pos = -1\n yield -1\n else:\n curr_pos = 0\n yield 0","title":""},{"docid":"13a652fb269ba81a2760dc48d607b60d","score":"0.4802786","text":"def insert_windows(self, hull_df, u_f, ff_anteil):\n aw_row = hull_df.loc[0]\n aw_A = aw_row[\"Fläche\"]\n aw_Uwert = aw_row[\"U-Wert\"]\n aw_tfaktor = hull_df.loc[0,\"Temperatur-Korrekturfaktor\"]\n \n aw_opak_A = aw_A * (1-ff_anteil)\n fenster_A = aw_A * ff_anteil \n \n aw_opak = dict(zip(hull_df.columns,[\"AW (opak)\", aw_opak_A, aw_Uwert, aw_tfaktor]))\n fenster = dict(zip(hull_df.columns,[\"Fenster\", fenster_A, u_f, hull_df.loc[0,\"Temperatur-Korrekturfaktor\"]]))\n \n hull_df = hull_df.append(aw_opak, ignore_index = True)\n hull_df = hull_df.append(fenster, ignore_index = True)\n \n hull_df.drop(hull_df.index[0], inplace=True)\n \n return hull_df","title":""},{"docid":"ccf176b3a1b0ad671240f90035652110","score":"0.47999823","text":"def window_sliding(X, y, idxs, window):\n X_windows = []\n if y is not None:\n y_windows = []\n\n for i in idxs:\n X_t = X[i-window:i].copy()\n if y is not None:\n y_t = y[i].copy()\n X_windows.append(X_t)\n if y is not None:\n y_windows.append(y_t)\n \n X_windows = np.array(X_windows)\n if y is not None:\n y_windows = np.array(y_windows) \n return X_windows, y_windows","title":""},{"docid":"88c3653d8df3cf097eeb7aeea96362a5","score":"0.47957","text":"def overlapping_windows():\n n = 256\n window = numpy.hamming(n)\n\n thinkplot.preplot(num=5)\n start = 0\n for i in range(5):\n xs = numpy.arange(start, start+n)\n thinkplot.plot(xs, window)\n\n start += n/2\n\n thinkplot.save(root='windowing3',\n xlabel='time (s)',\n axis=[0, 800, 0, 1.05])","title":""},{"docid":"10b197233facacf45913c4cde9f1701b","score":"0.47813532","text":"def create_sliding_windows(dataframe, sliding_window_interval, window_length):\n ts_min = min(dataframe['timestamp'])\n ts_max = max(dataframe['timestamp'])\n ts_iter = ts_min\n windows = []\n while ts_iter <= ts_max:\n window = create_window(dataframe, ts_iter, window_length)\n windows.append(window)\n ts_iter += sliding_window_interval\n return windows","title":""},{"docid":"bdb6ff593080e51d22814b1aa217cf1b","score":"0.47678155","text":"def next(self):\n start_x = self.roi.left\n end_x = self.roi.right - self.window_size.width + 1\n step_length_x = np.round(self.window_size.width * (1.0 - self.window_overlap.horizontal)).astype(np.int)\n start_y = self.roi.top\n end_y = self.roi.bottom - self.window_size.height + 1\n step_length_y = np.round(self.window_size.height * (1.0 - self.window_overlap.vertical)).astype(np.int)\n\n for top in range(start_y, end_y + 1, step_length_y):\n bottom = top + self.window_size.height - 1\n for left in range(start_x, end_x + 1, step_length_x):\n right = left + self.window_size.width - 1\n yield SearchWindow(top=top, left=left, bottom=bottom, right=right)","title":""},{"docid":"97b9d55560ac6063e6c814b967f1da9f","score":"0.47486052","text":"def get_events(df: pd.DataFrame, tolerance: int = 1) -> List[List]:\n print(df)\n # get unique list of intervals\n set_intervals = list(set(get_intervals(df, tolerance)))\n # add max value as function excludes it\n set_intervals.append(max(df[\"timestamp\"].values))\n set_intervals.sort()\n\n clapperboard_sets = []\n # get clapperboards for each event or interval\n for i in range(0, len(set_intervals) - 1):\n\n start = max(0, set_intervals[i] - 1)\n end = set_intervals[i + 1]\n res = df[df[\"timestamp\"].between(start, end, inclusive=False)]\n clapperboard_sets.append(res[\"image\"].values)\n\n return clapperboard_sets","title":""},{"docid":"61019590d9678e7a3b67d3f34563161c","score":"0.47475535","text":"def calcLinkingWindow(times: Time, length: float) -> np.ndarray:\n _checkTime(times, \"times\")\n dt = times.mjd - times.mjd.min()\n window = np.floor(dt / length) + 1\n return window.astype(int)","title":""},{"docid":"46c60c966bb3ed5f244756ef0543d307","score":"0.4741053","text":"def sliding_window_corelations(a,b, window=30, startdate=None, enddate=None, resolution=\"d\"):\n resmap = {\"d\":\"days\", \"h\":\"hours\"}\n assert resolution in resmap.keys(), \"Only accepting resolution {} currently\".format(resmap.keys())\n\n if startdate is None:\n startdate = max(a.index.min(), b.index.min())\n if enddate is None:\n enddate = min(a.index.max(), b.index.max())\n\n corrs = pd.Series(index=pd.date_range(startdate, enddate, freq=resolution))\n\n delta = datetime.timedelta(**{resmap[resolution]:window})\n\n daterange_delta = enddate-startdate\n\n if resolution == \"h\":\n timesteps = int((enddate-startdate).total_seconds()/3600)\n elif resolution == \"d\":\n timesteps = daterange_delta.days\n\n for i in range(timesteps):\n offset = datetime.timedelta(**{resmap[resolution]:i})\n\n time_a = startdate + offset - delta\n time_b = startdate + offset\n\n corr = a[time_a:time_b].corr(b[time_a:time_b])\n corrs[time_b] = corr\n return corrs","title":""},{"docid":"07f8652f7d81d02874e8fc95e326d1e0","score":"0.47217298","text":"def get_overlap(env, width = 2, file_name = 'omeg5'):\n\n\tx = env.nb_temp * width\n\ty = x\n\tz = env.temp_size * 2 - 1\n\ttype_size = 8\n\ttab = np.empty(x * y * z)\n\tsize = 4096\n\ti = 0\n\tl = x * y * z * type_size\n\tn = 0\n\tfd = open(file_name, 'rb')\n\twhile l - n > size:\n\t\ts = fd.read(size)\n\t\ttab[n / type_size : (n + size) / type_size] = np.fromstring(s, dtype = np.float64)\n\t\ti += 1\n\t\tn += size\n\ts = fd.read(l - n)\n\ttab[i * size / type_size:] = np.fromstring(s, dtype = np.float64)\n\tfd.close()\n\ttab = tab.reshape(x, y, z)\n\n#### second overlap\n#\to = scipy.io.loadmat('../files/ALL_norm.overlap1')\n#\tover = o['c_overlap'].astype(np.float64)\n#\tover = over[:, :, ::-1]\n#\ttab[:382, :382, :] = over.copy()\n#\ttab = tab / np.sqrt(252 * 129)\n\treturn (tab)","title":""},{"docid":"cda78c743981232f353c921ee74e875b","score":"0.47197017","text":"def add_centered_windows(self, X, y, X_c, y_c, st, end, c_idx, current_label,\n max_perturbations=-1):\n SWL = self.win_len # For convenience\n gest_len, center = end-st, (st+end)/2\n X_c[c_idx:c_idx+SWL, :] = X[center - SWL/2:center - SWL/2 + SWL, :]\n y_c[c_idx:c_idx+SWL] = current_label\n c_idx = c_idx + SWL\n\n if current_label == 0:\n return c_idx\n\n g, shift_window = np.ceil(np.sqrt(gest_len)), 1\n num_windows = g if g % 2 else g-1\n\n while shift_window <= num_windows/2:\n for new_center in [center-shift_window, center+shift_window]:\n X_c[c_idx:c_idx+SWL,:] = X[new_center-SWL/2:new_center+SWL/2,:]\n y_c[c_idx:c_idx+SWL] = current_label\n c_idx = c_idx + SWL\n shift_window += 1\n return c_idx","title":""},{"docid":"965ee175863d370fafad04662d6a4e86","score":"0.47172076","text":"def coverage_windows(sample, window = 5000, slide = 100):\n g, gc, s, sample, gc_correction, thresholds = sample\n # calculate coverage windows for sample\n cov = sample['cov']\n del sample['cov']\n weights = np.ones(window)\n windows = [[], []] # x and y values for windows\n i = 0\n if len(cov) < len(weights):\n sample['windows'] = False\n sample['kept_windows'] = False\n sample['LTwindows'] = False\n sample['iRep'] = 'n/a'\n sample['GC bias'] = 'n/a'\n sample['fiRep'] = 'n/a'\n sample['fit'] = False\n return (g, s, sample)\n for c in signal.fftconvolve(cov, weights, 'valid').tolist()[0::slide]:\n windows[0].append(i)\n windows[1].append(c/window)\n i += slide\n # filter zero coverage windows\n sample['windows'] = windows\n if gc is not False:\n Fwindows, Fgc = filter_windows(windows, gc, mdif = thresholds['win_cov'])\n\n sample['GC'] = Fgc\n sample['Fwindows'] = Fwindows # only need this for plotting GC data\n else:\n Fwindows = filter_windows(windows, mdif = thresholds['win_cov'])\n\n total_windows = len(windows[0])\n total_Fwindows = len(Fwindows[0])\n sample['kept_windows'] = float(total_Fwindows)/float(total_windows)\n # calculate gc bias\n if gc is not False:\n fit, filtered, corrected_windows, Cfit = \\\n gc_bias(Fgc, Fwindows, thresholds['GC_min_r2'])\n fit, m, b, r2 = fit\n sample['GC fit'], sample['GC r2'] = fit, r2\n sample['GC bias'] = m * r2\n if gc_correction is True and corrected_windows is not False:\n sample['Cwindows'] = corrected_windows\n sample['Cfit'] = Cfit\n sample['OLTwindows'] = [Fwindows[0], log_trans(Fwindows[1])]\n Fwindows = corrected_windows\n # log transform\n sample['LTwindows'] = [Fwindows[0], log_trans(Fwindows[1])]\n return (g, s, sample)","title":""},{"docid":"567554793bcdd832b8c5b48cc2db4f51","score":"0.47136","text":"def compute_windows(numpy_image, patch_size, patch_overlap):\n\n if patch_overlap > 1:\n raise ValueError(\"Patch overlap {} must be between 0 - 1\".format(patch_overlap))\n\n # Generate overlapping sliding windows\n windows = slidingwindow.generate(numpy_image,\n slidingwindow.DimOrder.HeightWidthChannel,\n patch_size, patch_overlap)\n\n return (windows)","title":""},{"docid":"5666d12583b2d246ee86c5851615be97","score":"0.47134322","text":"def fft_window(self,ind_start: int = 0, ind_end: int = -1 ):\n # set window\n #extracting data interval to view\n # freq = self.natural_frequencies[ind_start:ind_end]\n # power = (self.fft_values[ind_start:ind_end])**2\n\n freq = self.natural_frequencies_example[ind_start:ind_end]\n power = (self.fft_values_example[ind_start:ind_end])**2\n\n\n # putting data into data frame\n np_data = [freq, power]\n np_data_trans = np.transpose(np_data)\n raw_data = pd.DataFrame(data=np_data_trans, columns=[\"Freq\", \"Power\"])\n #\n #identify max and min values in data\n max_value = raw_data.max()\n min_value = raw_data.min()\n print(\"Max values:\\n\" + str(max_value), \"\\nMin values:\\n\" + str(min_value))\n #\n #filter out values\n # threshold = power > 0.0001\n # you can set the thresholds here based on power or frequency and one of the threshold values needs to be an empty list\n threshold_value = 1\n threshold_power = [] # e.g. power < 100\n threshold_freq = freq == 1 # e.g. freq < 100\n #\n if len(threshold_power) > 0:\n filtered = pd.DataFrame(data=np_data_trans[threshold_power], columns=[\"F_Freq\", \"F_Power\"])\n filter_str = \"Power\"\n else:\n filtered = pd.DataFrame(data=np_data_trans[threshold_freq], columns=[\"F_Freq\", \"F_Power\"])\n y = filtered[\"F_Power\"]\n # removed_outliers = y.between(y.quantile(.05), y.quantile(.95))\n # outliers = pd.DataFrame(data=np_data_trans[:len(removed_outliers)], columns=[\"F_Freq\", \"F_Power\"])\n # # print(\"Outerliers:\" + str(len(removed_outliers)))\n filter_str = \"Frequency\"\n #\n # plt.plot(outliers)\n # plt.show()\n\n #\n #\n # print(threshold_freq,threshold_power)\n # filtered = pd.DataFrame(data=np_data_trans[threshold], columns=[\"F_Freq\", \"F_Power\"])\n # identify max and min values in data\n max_filt_value = raw_data.max()\n min_filt_value = raw_data.min()\n print(\"Filtered Max values:\\n\" + str(max_filt_value), \"\\nFiltered Min values:\\n\" + str(min_filt_value))\n\n # threshold = freq < 0.002\n # filtered = pd.DataFrame(data=np_data_trans[threshold], columns=[\"F_Freq\", \"F_Power\"])\n\n # print(filtered)\n #\n #\n #plot data\n ax1 = plt.subplot(2, 1, 1)\n # plt.figure(figsize=(5, 20))\n plt.title(\"Power spectrum: \" + str(len(freq)) + \" samples\")\n plt.plot(freq,power)\n # plt.xlabel('frequency [hz]')\n plt.ylabel('power')\n #\n ax2 = plt.subplot(2, 1, 2)\n # plt.figure(figsize=(1, 5))\n plt.title('Power spectrum: output based on filtered ' + filter_str + \" values\")\n plt.plot(filtered[\"F_Freq\"], filtered[\"F_Power\"])\n plt.ylabel(\"Power\")\n plt.xlabel(\"Frequency[Hz]\")\n #\n # ax3 = plt.subplot(3, 1, 3)\n\n # # plt.figure(figsize=(1, 5))\n # # ax3.margins(x=-0.25,y=-0.25)\n # plt.title('Filtered data based on threshold parameter:', loc='left')\n # plt.plot(freq[:np.sum(threshold)], power[:np.sum(threshold)])\n # plt.ylabel(\"Power\")\n # plt.xlabel(\"Frequency[Hz]\")\n\n plt.setp(ax1.get_xticklabels(), visible=True)\n plt.setp(ax2.get_xticklabels(), visible=True)\n plt.show()","title":""},{"docid":"49a89e8abf1c3b4848cbb34697d83d85","score":"0.47116864","text":"def retrieve_closest_indices(\n df,\n num_indices,\n forecast_length,\n window_size: int = 10,\n distance_metric: str = \"braycurtis\",\n stride_size: int = 1,\n start_index: int = None,\n include_differenced: bool = False,\n include_last: bool = True,\n verbose: int = 0,\n):\n array = df.to_numpy()\n index = df.index\n tlt_len = array.shape[0]\n combined_window_size = window_size + forecast_length\n # remove extra so last segment not included at all\n # have the last window end evenly\n max_steps = array.shape[0] - combined_window_size\n if not include_last:\n max_steps = max_steps - forecast_length\n if start_index is None:\n # handle massive stride size relative to data\n start_index = 0\n if stride_size * 6 < array.shape[0]:\n start_index = max_steps % stride_size\n if num_indices > (max_steps / stride_size):\n raise ValueError(\"num_validations/num_indices too high for this dataset\")\n window_idxs = window_id_maker(\n window_size=combined_window_size,\n start_index=start_index,\n max_steps=max_steps,\n stride_size=stride_size,\n skip_size=1,\n )\n # calculate distance between all points and last window of history\n if distance_metric == \"nan_euclidean\":\n from sklearn.metrics.pairwise import nan_euclidean_distances\n\n res = np.array(\n [\n nan_euclidean_distances(\n array[:, a][window_idxs[:, :window_size]],\n array[(tlt_len - window_size) : tlt_len, a].reshape(1, -1),\n )\n for a in range(array.shape[1])\n ]\n )\n if include_differenced:\n array_diff = np.diff(array, n=1, axis=0)\n array_diff = np.concatenate([array_diff[0:1], array_diff])\n res_diff = np.array(\n [\n nan_euclidean_distances(\n array_diff[:, a][window_idxs[:, :window_size]],\n array_diff[(tlt_len - window_size) : tlt_len, a].reshape(1, -1),\n )\n for a in range(array_diff.shape[1])\n ]\n )\n res = np.mean([res, res_diff], axis=0)\n else:\n from scipy.spatial.distance import cdist\n\n res = np.array(\n [\n cdist(\n array[:, a][window_idxs[:, :window_size]],\n array[(tlt_len - window_size) : tlt_len, a].reshape(1, -1),\n metric=distance_metric,\n )\n for a in range(array.shape[1])\n ]\n )\n if include_differenced:\n array_diff = np.diff(array, n=1, axis=0)\n array_diff = np.concatenate([array_diff[0:1], array_diff])\n res_diff = np.array(\n [\n cdist(\n array_diff[:, a][window_idxs[:, :window_size]],\n array_diff[(tlt_len - window_size) : tlt_len, a].reshape(1, -1),\n metric=distance_metric,\n )\n for a in range(array_diff.shape[1])\n ]\n )\n res = np.mean([res, res_diff], axis=0)\n # find the lowest distance historical windows\n res_sum = np.nansum(res, axis=0)\n num_top = num_indices\n # partial then full sort\n res_idx = np.argpartition(res_sum, num_top, axis=0)[0:num_top]\n res_idx = res_idx[np.argsort(res_sum[res_idx].flatten())]\n if verbose > 1:\n print(\n f\"similarity validation distance metrics: {res_sum[res_idx].flatten()} with last window: {res_sum[-1].item()}\"\n )\n select_index = index.to_numpy()[window_idxs[res_idx]]\n if select_index.ndim == 3:\n res_shape = select_index.shape\n select_index = select_index.reshape((res_shape[0], res_shape[2]))\n return select_index","title":""},{"docid":"2588c1695f6d2ff319e5e4df3335fee5","score":"0.47096744","text":"def estimate_local_offset(self,window_length=4, in_place=False):\n \n import pyacs.gts.lib.gts_estimators\n \n ### take the data\n clean_data = self.remove_outliers().data\n \n local_offsets_values = np.zeros((len(self.offsets_dates),7))\n local_offsets_values[:,0] = self.offsets_dates\n\n \n noffset = len(self.offsets_dates)\n \n list_index_t0_i = np.searchsorted(clean_data[:,0],local_offsets_values[:,0]) \n\n index_local = []\n for i in list_index_t0_i:\n index_local.append(range(i-window_length,i+window_length))\n \n index_local = np.hstack(index_local)\n index_local = np.delete(index_local,np.argwhere(index_local<0))\n index_local = np.delete(index_local,np.argwhere(index_local>=len(clean_data[:,0])))\n\n data_local = np.take(clean_data,index_local,axis=0)\n \n ndate = len(data_local[:,0])\n for k in range(1,4): \n A = np.ones((ndate,(1+noffset)), float)\n for i in range(ndate):\n for j in range(noffset):\n if data_local[i,0] <= local_offsets_values[j,0]: A[i,(1+j)] = 0.\n \n X,s_X = pyacs.gts.lib.gts_estimators.least_square(A,data_local[:,k])[0:2]\n if X is not None:\n local_offsets_values[:,k] = X[1:]\n local_offsets_values[:,3+k] = s_X[1:]\n else: local_offsets_values = None\n\n if in_place:\n self.offsets_values=local_offsets_values\n return(self)\n else:\n new_Gts=self.copy()\n new_Gts.offsets_values=local_offsets_values\n return(new_Gts)\n \n\n return local_offsets_values","title":""},{"docid":"39050a1aaecc7ce8c105f3ba45a57801","score":"0.4698258","text":"def pos_conv(window, a):\n return (window/2)*a","title":""},{"docid":"89946c7658ec7808df0b3caa9f9eba84","score":"0.46949366","text":"def get_coords(df):\n\n if 'start' in df.columns:\n return df\n df['start'] = df.pos.astype(int)\n df['end'] = ( df.pos + df.peptide.str.len() ).astype(int)\n return df","title":""},{"docid":"60721c34fa03a64ae66f0a07e73bb6fe","score":"0.46922645","text":"def get_window_start_positions(self, term_positions):","title":""},{"docid":"60721c34fa03a64ae66f0a07e73bb6fe","score":"0.46922645","text":"def get_window_start_positions(self, term_positions):","title":""},{"docid":"dfb6697653c764461ac63b5d61753b9b","score":"0.46884894","text":"def Vortex(df, n): # n = 14\n df = df.reset_index()\n i = 0\n TR = [0]\n while i < df.index[-1]:\n Range = max(df.at[i + 1, 'High'], df.at[i, 'Close']) \\\n - min(df.at[i + 1, 'Low'], df.at[i, 'Close'])\n TR.append(Range)\n i = i + 1\n i = 0\n VM = [0]\n while i < df.index[-1]:\n Range = abs(df.at[i + 1, 'High'] - df.at[i, 'Low']) \\\n - abs(df.at[i + 1, 'Low'] - df.at[i, 'High'])\n VM.append(Range)\n i = i + 1\n VI = pd.Series(pd.Series(VM).rolling(window=n).sum() / pd.Series(VM).rolling(window=n).sum(), \n name='Vortex_' + str(n))\n df = df.join(VI).set_index(\"index\")\n return df[\"Vortex_\" + str(n)]","title":""},{"docid":"9865f6926b01fbf5d26c240f6852c9e7","score":"0.46829778","text":"def _whctrs_twin(anchor):\n\n l = anchor[1] - anchor[0] + 1\n x_ctr = anchor[0] + 0.5 * (l - 1)\n return l, x_ctr","title":""},{"docid":"c7b35985b3660502003f2422d803644c","score":"0.4675449","text":"def count_windowed_increasing(measurements: Iterator[str]) -> int:\n return count_increasing(window_sum(measurements))","title":""},{"docid":"6bb957aecf87233a043e7d6670a00381","score":"0.46754074","text":"def rolling_func(func,data, window):\n rolling_mean = func(rolling_window(data, window), axis=1)\n \n # How much items in front and behind of item\n # are part of the rolling window for each element?\n n_front = window//2\n n_behind = window//2 - 1 + window%2\n \n # use different windows for endpoints where no complete window can be applied\n\n endpoints_front = np.zeros(n_front)\n for i in range(n_front):\n # window as large as possible but with maximal n_behind elements after current element inside window\n endpoints_front[i]=func(data[:1+n_behind+i])\n \n endpoints_behind = np.zeros(n_behind)\n for i in range(n_behind):\n # window as large as possible but with maximal n_front elements in front of current element inside window\n endpoints_behind[-(1+i)]=func(data[-(1+n_front+i):])\n \n # add endpoints\n return np.hstack([endpoints_front,rolling_mean,endpoints_behind])","title":""},{"docid":"eac087dc56dc9bd3c3da275e373fb8c3","score":"0.4673865","text":"def produce_easy_features(df):\n df['date'] = pd.to_datetime(df['date'], format='%Y%m%d')\n df['week_number'] = [date.isocalendar()[1] for date in list(pd.Series(df['date']))]\n teams = set(list(df['team']))\n df.sort_values(by=['date'], axis=0, inplace=True)\n dfs = []\n for team in teams:\n temp_df = df[df['team'] == team]\n temp_df['scored_cumulative'] = temp_df.scored.cumsum()\n temp_df['conceded_cumulative'] = temp_df.conceded.cumsum()\n temp_df['performance_cumulative'] = temp_df.result.cumsum()*2\n temp_df['away_perf_cumulative'] = temp_df.away_res.cumsum()*2\n temp_df['home_perf_cumulative'] = temp_df.home_res.cumsum()*2\n\n # for loop here to create rolling sums for n=1 to n=5\n\n for i in [1,2,3,4,5]:\n col_name_1 = 'avg_scored_past_' + str(i)\n col_name_2 = 'avg_conceded_past_' + str(i)\n col_name_3 = 'perf_past_' + str(i)\n temp_df[col_name_1] = temp_df['scored'].rolling(min_periods=0, window=i).mean()\n temp_df[col_name_2] = temp_df['conceded'].rolling(min_periods=0, window=i).mean()\n temp_df[col_name_3] = temp_df['result'].rolling(min_periods=0, window=i).sum()*2\n #temp_df['avg_scored_past_5'] = temp_df['scored'].rolling(min_periods=0, window=5).mean()\n #temp_df['avg_conceded_past_5'] = temp_df['conceded'].rolling(min_periods=0, window=5).mean()\n #temp_df['perf_past_5'] = temp_df['result'].rolling(min_periods=0, window=5).sum()*2\n #temp_df['perf_past_1'] = temp_df['result'].rolling(min_periods=0, window=1).sum()*2\n dfs.append(temp_df)\n return pd.concat(dfs, axis=0)","title":""},{"docid":"f90665438d7306ebf2a3b5bf29dfb738","score":"0.467324","text":"def accuracy_at_stg_change_trials(df, subj_unq, prev_w=10, nxt_w=10, conv_w=10):\n mat_perfs = {}\n for i_s, sbj in enumerate(subj_unq):\n acc = df.loc[df['subject_name'] == sbj, 'hithistory'].values\n if conv_w > 0:\n accur_conv = np.convolve(acc, np.ones((conv_w,))/conv_w,\n mode='same')\n else:\n accur_conv = acc\n stg = df.loc[df['subject_name'] == sbj, 'new_stage'].values\n # create the extremes (a 0 at the beggining and a 1 at the ending)\n stg_diff = np.diff(stg) # change of stage\n stg_chng = np.where(stg_diff != 0)[0] # index where stages change\n # We go over indexes where stage changes and plot chunks from ind_t-1\n # to ind_t\n for i_stg in range(len(stg_chng)):\n # color = stg_exp[stg_chng[i_stg-1]+1]-1\n stg_prev = stg[stg_chng[i_stg]] # get stage before the change\n stg_nxt = stg[stg_chng[i_stg]+1] # get stage after the change\n assert stg_prev != stg_nxt, 'stages are supposed to be different'\n key = str(stg_prev)+'-'+str(stg_nxt) # e.g. 1-2\n if key not in mat_perfs.keys():\n mat_perfs[key] = []\n # build chunk\n i_previo = max(0, stg_chng[i_stg]-prev_w)\n i_next = stg_chng[i_stg]+nxt_w\n chunk = -min(0, stg_chng[i_stg]-prev_w)*[np.nan] +\\\n accur_conv[i_previo:i_next].tolist() +\\\n max(0, i_next-len(acc))*[np.nan]\n # add chunk to the dictionary\n mat_perfs[key].append(chunk)\n # dictionary of the mean of the performance\n mat_mean_perfs = {}\n # dictionary of the standard deviation of the performace\n mat_std_perfs = {}\n # list of the number of samples of each change of stage\n number_samples = []\n for key in mat_perfs.keys():\n number_samples.append(len(mat_perfs[key])) # save number of samples\n assert np.std([len(p) for p in mat_perfs[key]]) == 0\n mat_mean_perfs[key] = np.nanmean(np.array(mat_perfs[key]), axis=0)\n sqrt_n_smpls = np.sqrt(np.array(mat_perfs[key]).shape[0])\n mat_std_perfs[key] =\\\n np.nanstd(np.array(mat_perfs[key]), axis=0)/sqrt_n_smpls\n return mat_mean_perfs, mat_std_perfs, number_samples","title":""}],"string":"[\n {\n \"docid\": \"7b3624355498b38ef47c40a368055fa7\",\n \"score\": \"0.5745897\",\n \"text\": \"def _get_window(start, end):\\n window = 1 - np.r_[hann(4)[:2], np.ones(np.abs(end - start) - 4), hann(4)[-2:]].T\\n return window\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e6e2fb9cb52192f65c76b59f681e9e1\",\n \"score\": \"0.57039005\",\n \"text\": \"def window(self, df):\\n df = df[df.lat > self.latmin]\\n df = df[df.lat < self.latmax]\\n df = df[df.lon > self.lonmin]\\n df = df[df.lon < self.lonmax]\\n df = df.reset_index()\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3f6340f21fdcce47c458932457933f8\",\n \"score\": \"0.5591229\",\n \"text\": \"def auto_window(self, taus, c):\\n m = np.arange(len(taus)) < c * taus\\n if np.any(m):\\n return np.argmin(m)\\n return len(taus) - 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d39c6e541ba18e71204d09cd3bdc43d\",\n \"score\": \"0.55247456\",\n \"text\": \"def window(df, latmin, latmax, lonmin, lonmax):\\n df = df[df.lat > latmin]\\n df = df[df.lat < latmax]\\n df = df[df.lon > lonmin]\\n df = df[df.lon < lonmax]\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8aa43184e34bc91e728608003843fb0e\",\n \"score\": \"0.5428853\",\n \"text\": \"def windowmaking(dataset, window):\\n padding = window//2\\n window_list = []\\n seq = parse_seq(dataset)\\n for sequence in seq:\\n sequence = ((padding*'0') + sequence + (padding*'0'))\\n for residue in range(0, len(sequence)):\\n if residue+(window) > len(sequence):\\n break\\n window_list.append(sequence[residue:residue+window])\\n return window_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbaf9cb75155e9841e3dd059fb0bdd21\",\n \"score\": \"0.5357955\",\n \"text\": \"def overlap_window (self,window_function,dtime,dfreq,overlap_time=0,overlap_freq=0,dump=None,dumpfig=None):\\n # if overlap_freq:\\n # raise RuntimeError,\\\"frequency overlap not yet implemented\\\";\\n # effective window size in time\\n dtime1 = dtime + overlap_time*2;\\n dfreq1 = dfreq + overlap_freq*2;\\n # this is the output shape\\n ntime1 = self.ntime/dtime;\\n nfreq1 = self.nfreq/dfreq;\\n # make a list of per-baseline output arrays \\n result = [];\\n # make resulting ANTENNA1/ANTENNA2 indices\\n a1 = np.zeros(ntime1*self.nbl)\\n a2 = np.zeros(ntime1*self.nbl)\\n # make wl: array of wavelengths over each bin -- NF1,DF (where original shape was NF=NF1*DF)\\n## wl = self.wavel.reshape((nfreq1,dfreq));\\n # wl0: wavelength of centre channel of each frequency bin, shape NF1\\n## if dfreq%2:\\n## wl0 = self.wavel[dfreq/2::dfreq];\\n## else:\\n## wl0 = (self.wavel[dfreq/2-1::dfreq] + self.wavel[dfreq/2::dfreq])/2;\\n # loop over each baseline\\n freqs = self.channels[0,:];\\n wavel = 3e8/freqs;\\n for p in range(self.na):\\n for q in range(p+1,self.na):\\n input_index = (self.A0==p)&(self.A1==q)\\n data1 = self.data[input_index].copy();\\n uv1 = self.UVW[input_index,:2].copy();\\n # prepare output array\\n\\t reswind = np.zeros((ntime1,150),dtype=float)\\n resdata = np.zeros((ntime1,nfreq1,self.ncorr),self.data.dtype);\\n # loop over all output timeslots\\n for out_time in range(ntime1):\\n # make timeslice corresponding to extended window\\n in_time0 = self.time0+out_time*dtime-overlap_time;\\n timeslice = slice(in_time0,in_time0+dtime1);\\n # extract data subset over the window\\n data = data1[timeslice];\\n\\t #loop over all output frequencies\\n \\t for out_freq in range(nfreq1):\\n\\t\\tin_freq0 = self.freq0 + out_freq*dfreq - overlap_freq;\\n\\t\\tfreqslice = slice(in_freq0, in_freq0 + dfreq1);\\n\\t\\t#print \\\"freqslice.shape\\\",freqslice\\n\\t\\tdata2 = data[:,freqslice,...];\\n\\t\\t#print \\\"wavel shape******=\\\",wavel.shape\\n\\t\\twavel1 = wavel[freqslice];\\n\\t\\t#print\\\"wave1 shape\\\",wavel1.shape\\n\\t\\t\\n # reshape so that NF becomes NF1,DF, so that we can then reduce over the second axis\\n # resulting shape is DT1,NF1,DF,NCORR\\n# print timeslice,data.shape,(dtime1,nfreq1,dfreq,self.ncorr)\\n\\t \\timport Pyxis\\n\\t \\t#print \\\"data shape\\\",data.shape\\n\\t \\tPyxis.info('shape dtime1%i, shape nfreq1%i, shape dfreq%i'%(dtime1,nfreq1,dfreq))\\n ##data = data.reshape((dtime1,nfreq1,dfreq,self.ncorr))\\n\\t\\t\\n # get UV coordinates for the window, shape is DT1,2\\n \\tuv = uv1[timeslice];\\n # get corresponding uv-center point (uv0), shape is 2 \\n \\tif dtime1%2: # odd dtime: we have a uv-bin at the centre, so start at n/2, then take every n-th uv point\\n \\t\\tuv0 = uv[dtime1/2];\\n \\telse: # even dtime -- take the middle of the two centre bins\\n \\t\\tuv0 = (uv[dtime1/2-1] + uv[dtime1/2])/2;#,:\\n # take uv-distance component along time, shape is DT1\\n \\tuvd1 = np.sqrt(((uv-uv0[np.newaxis,:])**2).sum(1));\\n # convert it to wavelengths at centre channel of each window, this will now be of shape DT1,NF1\\n\\t\\tif dfreq1%2:\\n\\t\\t\\twl0 = wavel1[dfreq1/2];\\n\\t\\telse:\\n\\t\\t\\twl0 = (wavel1[dfreq1/2 -1] + wavel1[dfreq/2])/2;\\n\\t\\t#print \\\"uvd1.shape\\\",uvd1.shape\\n\\t\\t#print \\\"wl0.shape\\\",wl0.shape\\n \\tuvd1 = uvd1[:,np.newaxis] / wl0;\\n # now for the second component: ||uv0||/wl0 at each window is the centre channel uv-length,\\n # the second component's distance is ||uv0||/wl - ||uv0||/wl0\\n # first compute ||uv0||, this is scalar\\n \\tuv0r = np.sqrt((uv0**2).sum()); \\n # work out second component of uv-distance, shape will be NF1,DF\\n \\tuvd2 = uv0r/wavel1 - uv0r/wl0;\\n # evaluate windowing function\\n # shapes are: uvd1 is DT1,NF1 and uvd2 is NF1,DF so insert new axes appropriately\\n # wf shape is then DT1,NF1,DF\\n\\t\\t#print \\\"uvd1.shape,uvd2.shape\\\",uvd1.shape,uvd2.shape\\n \\twf = window_function(uvd1[:,:],uvd2[np.newaxis,:]);\\n\\t\\t#ham1 = signal.hamming(len(wf[:,0]))*np.max(wf[:,0])\\n\\t\\t#ham2 = signal.hamming(len(wf[0,:]))*np.max(wf[0,:])\\n\\t\\t#ham = ham1*ham2;\\n\\t\\t#wf = wf*ham;\\n \\tif dump and out_time==0:\\n \\t\\timport cPickle\\n \\t\\tcPickle.dump((p,q,uvd1,uvd2,wf),dump);\\n # sum of wf over whole window and each frequency bin, shape NF1\\n \\twfsum = wf.sum(1).sum(0);\\n# if out_time == 0:\\n# print p,q,wfsum;\\n #if dumpfig and p==17 and q==26 and out_time==0:\\n# fig = pylab.figure()\\n# ax = fig.add_subplot(111, projection='3d')\\n# ax.plot_wireframe(wf)\\n #pylab.plot(wf[dtime1/2,0,:]);\\n #pylab.plot(wf[:,0,dfreq/2]);\\n #pylab.savefig(dumpfig);\\n #print \\\"Saved figure\\\",dumpfig;\\n #print uvd1,uvd2,wf;\\n # apply wf to data (np.newaxis ensures every correlation is multiplied by the same wf),\\n # then sum over whole window and each frequency bin, normalize, and assign to appropriate\\n # timeslot in output\\n\\t \\t#print wf[:,dfreq/2].shape\\n\\t \\t#uvd1=uvd1[:,:,np.newaxis].reshape(uvd1.shape[0])\\n\\t \\t#print uvd1.shape\\n\\t # if p==17 and q==26:\\n\\t #\\tpylab.plot(wf[dtime1/2,0,:])\\n\\t #\\tpylab.plot(uvd1,wf[:,0,dfreq/2])\\n\\t #\\tpylab.show()\\n\\t # \\treswind[out_time]=wf[:,0,dfreq/2]\\n\\t #\\tresult.append((p,q,wf[:,0,dfreq/2]))\\n\\t\\t#print \\\"##data2.shape\\\",data2.shape\\n\\t\\t#print \\\"###wf.shape\\\",wf.shape\\n\\t\\t#print \\\"###wsum.shape\\\",wfsum\\n\\t\\t#print \\\"###resdata.shape\\\",resdata.shape\\n\\t\\t#print \\\"###content .shape\\\",((data2*wf[...,np.newaxis]).sum(1).sum(0)).shape\\n\\t\\tresdata[out_time,out_freq,...] = (data2*wf[...,np.newaxis]).sum(1).sum(0)/wfsum;\\n #resdata[out_time,...] = (data*wf[...,np.newaxis]).sum(2).sum(0)/wfsum[...,np.newaxis];\\n # return result\\n result.append((p,q,resdata));\\n return result;\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f711f8c76df576dc503a3def0911300e\",\n \"score\": \"0.5322091\",\n \"text\": \"def draw_windows(df: pd.DataFrame, ax: plt.Axes) -> None:\\n for row in df.itertuples():\\n xy = (row.x - row.dx - 0.5, row.y - row.dy - 0.5)\\n width = row.dx * 2 + 1\\n height = row.dy * 2 + 1\\n patch = patches.Rectangle(xy, width=width, height=height, linewidth=2, edgecolor=\\\"r\\\", fill=False)\\n ax.add_patch(patch)\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8585aa448227d1a42a476bbd45aaf77\",\n \"score\": \"0.5313643\",\n \"text\": \"def column_windows(session, column, windowsize):\\n def int_for_range(start_id, end_id):\\n if end_id:\\n return and_(\\n column>=start_id,\\n column<end_id\\n )\\n else:\\n return column>=start_id\\n\\n q = session.query(\\n column,\\n func.row_number().\\\\\\n over(order_by=column).\\\\\\n label('rownum')\\n ).\\\\\\n from_self(column)\\n if windowsize > 1:\\n q = q.filter(sqlalchemy.text(\\\"rownum %% %d=1\\\" % windowsize))\\n\\n intervals = [id for id, in q]\\n\\n while intervals:\\n start = intervals.pop(0)\\n if intervals:\\n end = intervals[0]\\n else:\\n end = None\\n yield int_for_range(start, end)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4104a84dd6d6c2027b745d15b5c2cb6f\",\n \"score\": \"0.5306761\",\n \"text\": \"def controlLoops(df):\\n print('Making ctrls')\\n dfs = []\\n for i in range(10):\\n df = df.copy()\\n ran = (np.random.random(len(df)) + 0.1) * 1000000\\n if i % 2 == 1:\\n ran = -ran\\n df[\\\"Start\\\"] = df[\\\"Start\\\"] + ran.astype(int)\\n df[\\\"End\\\"] = df[\\\"End\\\"] + ran.astype(int)\\n dfs.append(df)\\n df = pd.concat(dfs)\\n print('Made ctrl positions')\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc8c192cd512bfc37fbe6c6465778955\",\n \"score\": \"0.52957165\",\n \"text\": \"def column_windows(session, column, windowsize):\\n def int_for_range(start_id, end_id):\\n if end_id:\\n return and_(\\n column>=start_id,\\n column<end_id\\n )\\n else:\\n return column>=start_id\\n\\n q = session.query(column, func.row_number().over(order_by=column).label('rownum')).from_self(column)\\n if windowsize > 1:\\n q = q.filter(sqlalchemy.text(\\\"rownum %% %d=1\\\" % windowsize))\\n\\n intervals = [id for id, in q]\\n\\n while intervals:\\n start = intervals.pop(0)\\n if intervals:\\n end = intervals[0]\\n else:\\n end = None\\n yield int_for_range(start, end)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad750b1dbb1c06a358ef7e38b98b09f6\",\n \"score\": \"0.5291326\",\n \"text\": \"def create_window(dataframe, ts_start, window_length):\\n return dataframe[(dataframe['timestamp'] >= ts_start) & (dataframe['timestamp'] <= ts_start + window_length)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4ebb5cdf65e4cf19dc2e036973f5a70\",\n \"score\": \"0.52901703\",\n \"text\": \"def get_overbasepower_sections(df):\\n\\n # Find the switching actions, which stay constant for minimal_zerotime times\\n #minimal_zerotime = 3\\n #look_ahead = getattr(df, 'look_ahead', None)\\n df = df > 15 \\n tmp = df.astype(np.int).diff()\\n overbasepower_sect_starts = (tmp == 1).values\\n overbasepower_sect_ends = (tmp == -1).values\\n #overbasepower_sect_starts = df[(tmp == 1).values].index\\n if len(df) > 0:\\n overbasepower_sect_starts[0] = df.iloc[0,0] # = np.append(df.index[0], overbasepower_sect_starts)\\n overbasepower_sect_ends[-1] |= df.iloc[-1,0] #np.append(overbasepower_sect_ends, df.index[-1]) # FUCK THIS SHIT!!! Mal verliert er die Timezone\\n overbasepower_sect_starts = pd.Series(df[overbasepower_sect_starts].index)\\n overbasepower_sect_ends = pd.Series(df[overbasepower_sect_ends].index)\\n return overbasepower_sect_starts, overbasepower_sect_ends\\n\\n #for i in range(2,minimal_zerotime):\\n # tmp = df.astype(np.int).diff(i)\\n # overbasepower_sect_starts *= tmp == 1\\n # overbasepower_sect_ends *= tmp == 0\\n #tmp = df.astype(np.int).diff(minimal_zerotime)\\n #overbasepower_sect_starts *= tmp == 1\\n #overbasepower_sect_ends *= tmp == -1\\n #del tmp\\n #overbasepower_sect_starts = list(df[overbasepower_sect_starts].dropna().index)\\n #overbasepower_sect_ends = list(df[overbasepower_sect_ends.shift(-minimal_zerotime).fillna(False)].dropna().index)\\n\\n ## If this chunk starts or ends with an open-ended\\n ## overbasepower section then the relevant TimeFrame needs to have\\n ## a None as the start or end.\\n #for i in range(minimal_zerotime):\\n # if df.iloc[i, 0] == True:\\n # overbasepower_sect_starts = [df.index[i]] + overbasepower_sect_starts\\n # break\\n\\n #if df.iloc[-1,0] == True:\\n # overbasepower_sect_ends += [None]\\n #else:\\n # # Only start new zerosection when long enough, need look_ahead\\n # for i in range(1,minimal_zerotime+1):\\n # if df.iloc[-i, 0] != False:\\n # break\\n\\n # if i < (minimal_zerotime):\\n # if look_ahead.head(minimal_zerotime-i).sum()[0] == 0:\\n # overbasepower_sect_ends += [df.index[-i]] #, 0]]\\n # else:\\n # overbasepower_sect_ends += [None]\\n\\n\\n ## Merge together ends and starts\\n #assert len(overbasepower_sect_starts) == len(overbasepower_sect_ends)\\n\\n #if len(overbasepower_sect_ends) > len(overbasepower_sect_starts):\\n # sections = [TimeFrame(start, end)\\n #else:\\n # sections = []\\n #if len(overbasepower_sect_starts) == 0 == len(overbasepower_sect_ends):\\n # return [TimeFrameGroup()]\\n #else:\\n # if overbasepower_sect_starts[0] > overbasepower_sect_ends[0]:\\n # overbasepower_sect_starts.append(None)\\n # if overbasepower_sect_ends[-1] < overbasepower_sect_starts[-1]:\\n # overbasepower_sect_ends.append(None)\\n #sections = [TimeFrame(start, end)\\n # for start, end in zip(overbasepower_sect_starts, overbasepower_sect_ends)\\n # if not (start == end and start is not None)]\\n\\n # Memory management\\n #del overbasepower_sect_starts\\n #del overbasepower_sect_ends\\n #gc.collect()\\n\\n #return sections\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84233a6cf92afab0bf3071df3ed4b728\",\n \"score\": \"0.52877927\",\n \"text\": \"def _get_window_sequence(self, delta_f, start_idx, length, b):\\n fnow, dfnow = self.fb_dfb[b]\\n fnext, dfnext = self.fb_dfb[b + 1]\\n\\n window_sequence = np.zeros(length)\\n increase_start = np.clip(\\n math.floor((fnow - dfnow) / delta_f) - start_idx + 1, 0, length\\n )\\n unity_start = np.clip(math.ceil(fnow / delta_f) - start_idx, 0, length)\\n decrease_start = np.clip(\\n math.floor((fnext - dfnext) / delta_f) - start_idx + 1, 0, length\\n )\\n decrease_stop = np.clip(math.ceil(fnext / delta_f) - start_idx, 0, length)\\n\\n window_sequence[unity_start:decrease_start] = 1.\\n\\n # this if statement avoids overflow caused by vanishing dfnow\\n if increase_start < unity_start:\\n frequencies = (np.arange(increase_start, unity_start) + start_idx) * delta_f\\n window_sequence[increase_start:unity_start] = (\\n 1. + np.cos(np.pi * (frequencies - fnow) / dfnow)\\n ) / 2.\\n\\n if decrease_start < decrease_stop:\\n frequencies = (np.arange(decrease_start, decrease_stop) + start_idx) * delta_f\\n window_sequence[decrease_start:decrease_stop] = (\\n 1. - np.cos(np.pi * (frequencies - fnext) / dfnext)\\n ) / 2.\\n\\n return window_sequence\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08dbc45de81d7a776a76dcbe2f802ca9\",\n \"score\": \"0.52707666\",\n \"text\": \"def agglutinate(df_node, dt_lag_min=pd.Timedelta('1 hour')):\\n from operator import itemgetter\\n from itertools import groupby\\n pd.options.mode.chained_assignment = None # default='warn'\\n\\n # < STEP 1 >\\n # Convert all close event intervals\\n # I have to change only the key p-> m and proceed to next step\\n null_index = df_node[df_node.isnull().any(axis=1)].index\\n\\n dt_lag = df_node.ts_n-df_node.te\\n condition = df_node.key.isin(['p']) & (dt_lag < dt_lag_min)\\n lag_index = df_node[condition].index\\n lag_index = lag_index.difference(null_index)\\n df_node.loc[lag_index, 'key'] = 'm'\\n\\n # < STEP 2 >\\n # ============================================================\\n # CAUTION: I am assuming that df_node has consecutive indexes\\n # i.e., RangeIndex(start=0, stop=..., step=1), a\\n # monotonic ordered set (maybe it is not that relevant)\\n # ============================================================\\n # get indices of rows whose intervals overlap or touch\\n # (except last row which has NULL values)\\n ovl_index = df_node[~df_node.key.isin(['p'])].index # operations: moFDs\\n ovl_index = ovl_index.difference(null_index)\\n\\n for _, g in groupby(enumerate(ovl_index), lambda x: x[1]-x[0]):\\n ovl_slice = map(itemgetter(1), g)\\n # Complete list 'ovl_slice':\\n # -Add index element of DataFrame 'df_node' that follows ovl_slice[-1]\\n index_next_pos = df_node.index.get_loc(ovl_slice[-1])\\n # Complete 'ovl_slice'\\n ovl_slice.append(df_node.index[index_next_pos+1])\\n ts_min = df_node.loc[ovl_slice, 'ts'].min()\\n te_max = df_node.loc[ovl_slice, 'te'].max()\\n df_node.loc[ovl_slice[-1], ['ts', 'te']] = [ts_min, te_max]\\n df_node.drop(ovl_slice[:-1], inplace=True) # delete slice\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1fe487622fabedfcee8816bd46857dd\",\n \"score\": \"0.52303755\",\n \"text\": \"def create_windows_from_protein(protein_seq, **kwargs):\\n # NOTE: last AA not made into window since c-terminal would be cleavage pos\\n protein_windows = []\\n for pos in range(len(protein_seq)-1):\\n start_pos = pos + 1\\n end_pos = pos + 2\\n tmp_window = get_peptide_window(protein_seq,\\n starting_position=start_pos,\\n ending_position=end_pos,\\n **kwargs)\\n protein_windows.append(tmp_window)\\n\\n return protein_windows\",\n \"title\": \"\"\n },\n {\n \"docid\": \"013a4eee300831b9bc67547629aefd73\",\n \"score\": \"0.5226974\",\n \"text\": \"def window_iterator(seq, n=1, beg=\\\"<s>\\\", end=\\\"</s>\\\"):\\n for i in range(len(seq)):\\n l = max(0, i-n)\\n r = min(len(seq), i+n+1)\\n ret = seq[l:r]\\n if i < n:\\n ret = [beg,] * (n-i) + ret\\n if i+n+1 > len(seq):\\n ret = ret + [end,] * (i+n+1 - len(seq))\\n yield ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36998aa357918aa0bbaca854ee65de65\",\n \"score\": \"0.52230245\",\n \"text\": \"def calc_cov_windows(genomes, pairs, mappings, gc_correction, thresholds, threads):\\n print('# calculating coverage over sliding windows', file=sys.stderr)\\n sys.stderr.flush()\\n # filter out any genome -> sample pairs not passing thresholds\\n pairs = [(g, s) for g, s in pairs if s in genomes[g]['samples']]\\n pool = Pool(threads)\\n # calculate coverage windows for each pair\\n for sample in \\\\\\n pool.map(coverage_windows, \\\\\\n [[g, genomes[g]['gc'], s, genomes[g]['samples'][s], gc_correction, thresholds]\\\\\\n for g, s in pairs]):\\n g, s, sample = sample\\n genomes[g]['samples'][s] = sample\\n pool.terminate()\\n # filter out any genome -> sample pairs not passing thresholds\\n pairs = [(g, s) for g, s in pairs if genomes[g]['samples'][s]['windows'] is not False]\\n return genomes, pairs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"740357a69681c901876b5303a43d159d\",\n \"score\": \"0.52132034\",\n \"text\": \"def assign_actual_windows(self, input_row: List, timestamp: int) -> List[W]:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa2d3a9c2ee11297303ecb1995985650\",\n \"score\": \"0.51899356\",\n \"text\": \"def _alpha_window_in_bounds(alpha, window):\\r\\n IBef = np.where(alpha <= window[0])[0]\\r\\n if len(IBef) > 0:\\r\\n im = IBef[-1]\\r\\n else:\\r\\n im = 0\\r\\n IAft = np.where(alpha >= window[1])[0]\\r\\n if len(IAft) > 0:\\r\\n ip = IAft[0]\\r\\n else:\\r\\n ip = len(alpha) - 1\\r\\n window = [alpha[im], alpha[ip]]\\r\\n return window\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d75cc5242df3c07550b48d7f3ddd4821\",\n \"score\": \"0.51733464\",\n \"text\": \"def _get_index_bounds(self, sc_id, i, ccWindow, ccOverlap):\\n dt = timedelta(seconds=ccWindow/2)\\n overlapW = timedelta(seconds=ccOverlap/20)\\n # Find the correct center time from either AC6A or B.\\n if sc_id.upper() == 'A':\\n t0 = self.occurA.cat['dateTime'][i]\\n else:\\n t0 = self.occurB.cat['dateTime'][i]\\n\\n #### Get temporally-aligned indicies ######\\n idtA = np.where( (self.occurA.data['dateTime'] >= t0-dt) \\n & (self.occurA.data['dateTime'] < t0+dt)\\n & (self.occurA.data['dos1rate'] != -1E31))[0]\\n # The overlapW windens the index array to accomidate a CC lag.\\n idtB = np.where( (self.occurB.data['dateTime'] >= t0-dt-overlapW) \\n & (self.occurB.data['dateTime'] < t0+dt+overlapW)\\n & (self.occurB.data['dos1rate'] != -1E31))[0]\\n\\n ####### Get spatially aligned indicies ######\\n # If sc_id is 'A', then, the spatial indicies are the \\n # same for AC6A, but shifted for AC6B. Since \\n # lag = t_A - t_B\\n # this implies that t_B_shifted = t_A - lag. If sc_id \\n # is 'B', then everything is opposite, i.e. \\n # t_A_shifted = t_B + lag\\n if sc_id.upper() == 'A':\\n idtA_shifted = idtA \\n timeLag = timedelta(seconds=self.occurA.cat['Lag_In_Track'][i])\\n idtB_shifted = np.where(\\n (self.occurB.data['dateTime'] >= t0-dt+timeLag-overlapW) & \\n (self.occurB.data['dateTime'] < t0+dt+timeLag+overlapW) &\\n (self.occurB.data['dos1rate'] != -1E31)\\n )[0]\\n t_sA = t0\\n t_sB = t0 + timeLag\\n else:\\n idtB_shifted = idtB \\n timeLag = timedelta(seconds=self.occurB.cat['Lag_In_Track'][i])\\n idtA_shifted = np.where(\\n (self.occurA.data['dateTime'] >= t0-dt-timeLag-overlapW) & \\n (self.occurA.data['dateTime'] < t0+dt-timeLag+overlapW) &\\n (self.occurA.data['dos1rate'] != -1E31)\\n )[0]\\n t_sA = t0 - timeLag\\n t_sB = t0\\n return idtA, idtB, idtA_shifted, idtB_shifted, t0, t_sA, t_sB\",\n \"title\": \"\"\n },\n {\n \"docid\": \"301e5031fb2826c7efc8f7a4f0d92f89\",\n \"score\": \"0.51693696\",\n \"text\": \"def shift_video_w_random_offset(\\n df_sensor,\\n df_flow,\\n vid_name,\\n win_start_end,\\n start_time,\\n end_time,\\n kde_num_offset,\\n kde_max_offset,\\n window_size_sec,\\n window_criterion,\\n fps,\\n):\\n df_dataset_vid = []\\n info_dataset_vid = []\\n cnt_windows = 0\\n # add an offset to each window sensor-video pair\\n for pair in win_start_end:\\n start = pair[0] * 1000\\n end = pair[1] * 1000 + 1000\\n df_window_sensor = df_sensor[\\n (df_sensor[\\\"time\\\"] >= pd.to_datetime(start, unit=\\\"ms\\\"))\\n & (df_sensor[\\\"time\\\"] < pd.to_datetime(end, unit=\\\"ms\\\"))\\n ]\\n for i in range(kde_num_offset):\\n # match video df\\n offset = random.randint(-kde_max_offset, kde_max_offset)\\n offset = (\\n min(offset, end_time - end)\\n if offset > 0\\n else max(offset, start_time - start)\\n )\\n df_window_flow = df_flow[\\n (df_flow[\\\"time\\\"] >= pd.to_datetime(start + offset, unit=\\\"ms\\\"))\\n & (df_flow[\\\"time\\\"] < pd.to_datetime(end + offset, unit=\\\"ms\\\"))\\n ]\\n pd.options.mode.chained_assignment = None\\n df_window_flow.loc[:, \\\"time\\\"] = df_window_flow.loc[\\n :, \\\"time\\\"\\n ] - pd.Timedelta(offset, unit=\\\"ms\\\")\\n df_window = pd.merge_asof(\\n df_window_sensor,\\n df_window_flow,\\n on=\\\"time\\\",\\n tolerance=pd.Timedelta(\\\"29.969664ms\\\"),\\n direction=\\\"nearest\\\",\\n ).set_index(\\\"time\\\")\\n df_window = df_window.dropna(how=\\\"any\\\")\\n if len(df_window) > fps * window_size_sec * window_criterion:\\n cnt_windows += 1\\n df_dataset_vid.append(df_window)\\n info_dataset_vid.append(\\n [vid_name, start, end, offset]\\n ) # video name, sensor start time, sensor end time, video offset\\n return cnt_windows, df_dataset_vid, info_dataset_vid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64b34914322e43fce38024b0953a96a2\",\n \"score\": \"0.51646316\",\n \"text\": \"def _build_windows(self):\\n\\n # each window has horizon times subwindows\\n w_episodes = []\\n init_weights = []\\n predictions = []\\n for episode in self.episode_starts:\\n ws = []\\n preds = []\\n assets = self.assets[episode - self.window_size: episode]\\n weight = WeightOptimize(assets, np.cov(assets.T), self.risk_aversion).optimize_weights()\\n for s in trange(self.horizon):\\n w = self.data[episode - self.window_size + s : episode + s]\\n ws.append(w)\\n # TODO: Add model predictions\\n try:\\n w = self.scaler.transform(w)\\n except:\\n pass\\n #preds.append([predictor.predict(w.reshape(1, w.shape[0], w.shape[1])) for predictor in self.predictors])\\n\\n w_episodes.append(ws)\\n init_weights.append(weight)\\n # prediction.append(preds)\\n\\n # w_episodes has the shape (nb_epsides, horizon, window_size, columns) 4D\\n # init_weights has the shape (nb_episodes, columns) 2D\\n # predictions has the shape (nb_episodes, horizon, num_predictors, columns) 3D\\n return np.array(w_episodes), np.array(init_weights), np.array(predictions)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7262518cea401fb6bf5bf055010df100\",\n \"score\": \"0.51635045\",\n \"text\": \"def get_subwindow_tracking(im, pos, model_sz, original_sz, avgChans):\\n if original_sz is None:\\n original_sz = model_sz\\n sz = original_sz\\n im_sz = im.shape\\n # make sure the size is not too small\\n assert min(im_sz[:2]) > 2, \\\"the size is too small\\\"\\n c = (np.array(sz) + 1) / 2\\n \\n # check out-of-bounds coordinates, and set them to black\\n context_xmin = round(pos[1] - c[1])\\n context_xmax = context_xmin + sz[1] - 1\\n context_ymin = round(pos[0] - c[0])\\n context_ymax = context_ymin + sz[0] - 1\\n left_pad = max(0, int(-context_xmin))\\n top_pad = max(0, int(-context_ymin))\\n right_pad = max(0, int(context_xmax - im_sz[1] + 1))\\n bottom_pad = max(0, int(context_ymax - im_sz[0] + 1))\\n \\n context_xmin = int(context_xmin + left_pad)\\n context_xmax = int(context_xmax + left_pad)\\n context_ymin = int(context_ymin + top_pad)\\n context_ymax = int(context_ymax + top_pad)\\n \\n if top_pad or left_pad or bottom_pad or right_pad:\\n b = np.pad(im[:,:,0], ((top_pad,bottom_pad),(left_pad,right_pad)), mode='constant', constant_values=avgChans[0])\\n g = np.pad(im[:,:,1], ((top_pad,bottom_pad),(left_pad,right_pad)), mode='constant', constant_values=avgChans[1])\\n r = np.pad(im[:,:,2], ((top_pad,bottom_pad),(left_pad,right_pad)), mode='constant', constant_values=avgChans[2]) \\n im = cv2.merge((b,g,r))\\n #imshow(im)\\n \\n im_patch_original = im[context_ymin:context_ymax+1, context_xmin:context_xmax+1, :]\\n if not np.array_equal(model_sz, original_sz):\\n im_patch = cv2.resize(im_patch_original, model_sz)\\n else:\\n im_patch = im_patch_original\\n \\n return im_patch, im_patch_original\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b3c9304c534130f9c3928880201eec7\",\n \"score\": \"0.5135659\",\n \"text\": \"def sliding_window(raw_data, para):\\n log_size = raw_data.shape[0]\\n label_data, time_data = raw_data.iloc[:, 1], raw_data.iloc[:, 0]\\n logkey_data, deltaT_data = raw_data.iloc[:, 2], raw_data.iloc[:, 3]\\n new_data = []\\n start_end_index_pair = set()\\n\\n start_time = time_data[0]\\n end_time = start_time + para[\\\"window_size\\\"]\\n start_index = 0\\n end_index = 0\\n\\n # get the first start, end index, end time\\n for cur_time in time_data:\\n if cur_time < end_time:\\n end_index += 1\\n else:\\n break\\n\\n start_end_index_pair.add(tuple([start_index, end_index]))\\n\\n # move the start and end index until next sliding window\\n num_session = 1\\n while end_index < log_size:\\n start_time = start_time + para['step_size']\\n end_time = start_time + para[\\\"window_size\\\"]\\n for i in range(start_index, log_size):\\n if time_data[i] < start_time:\\n i += 1\\n else:\\n break\\n for j in range(end_index, log_size):\\n if time_data[j] < end_time:\\n j += 1\\n else:\\n break\\n start_index = i\\n end_index = j\\n\\n # when start_index == end_index, there is no value in the window\\n if start_index != end_index:\\n start_end_index_pair.add(tuple([start_index, end_index]))\\n\\n num_session += 1\\n if num_session % 1000 == 0:\\n print(\\\"process {} time window\\\".format(num_session), end='\\\\r')\\n\\n for (start_index, end_index) in start_end_index_pair:\\n dt = deltaT_data[start_index: end_index].values\\n dt[0] = 0\\n new_data.append([\\n time_data[start_index: end_index].values,\\n max(label_data[start_index:end_index]),\\n logkey_data[start_index: end_index].values,\\n dt\\n ])\\n\\n assert len(start_end_index_pair) == len(new_data)\\n print('there are %d instances (sliding windows) in this dataset\\\\n' % len(start_end_index_pair))\\n return pd.DataFrame(new_data, columns=raw_data.columns)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af04e69e11f540aa4daf7543018efa7f\",\n \"score\": \"0.51164055\",\n \"text\": \"def get_cis_ranges(phenotype_pos_df, chr_variant_dfs, window, verbose=True):\\n # check phenotypes & calculate genotype ranges\\n # get genotype indexes corresponding to cis-window of each phenotype\\n if 'pos' in phenotype_pos_df:\\n phenotype_pos_df = phenotype_pos_df.rename(columns={'pos':'start'})\\n phenotype_pos_df['end'] = phenotype_pos_df['start']\\n phenotype_pos_dict = phenotype_pos_df.to_dict(orient='index')\\n\\n drop_ids = []\\n cis_ranges = {}\\n n = len(phenotype_pos_df)\\n for k, phenotype_id in enumerate(phenotype_pos_df.index, 1):\\n if verbose and (k % 1000 == 0 or k == n):\\n print(f'\\\\r * checking phenotypes: {k}/{n}', end='' if k != n else None)\\n\\n pos = phenotype_pos_dict[phenotype_id]\\n chrom = pos['chr']\\n m = len(chr_variant_dfs[chrom]['pos'].values)\\n lb = bisect.bisect_left(chr_variant_dfs[chrom]['pos'].values, pos['start'] - window)\\n ub = bisect.bisect_right(chr_variant_dfs[chrom]['pos'].values, pos['end'] + window)\\n if lb != ub:\\n r = chr_variant_dfs[chrom]['index'].values[[lb, ub - 1]]\\n else:\\n r = []\\n\\n if len(r) > 0:\\n cis_ranges[phenotype_id] = r\\n else:\\n drop_ids.append(phenotype_id)\\n\\n return cis_ranges, drop_ids\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15d390ca6a398ce17624bfe7e78443dc\",\n \"score\": \"0.5102995\",\n \"text\": \"def analyse_window(spatial_patterns,ts,maxchans,starttime,endtime,samplerate,data, grid_shape,\\n x_peaks,x_troughs,eod_width,channels,eod_hights,cutwidth,window_size,window_dt,min_samples,\\n max_clus,min_correlation=0.99,min_correlation_moving=0.75,coverage_factor=0.9,verbose=0,plot_level=0):\\n\\n # initialize storage arrays \\n mean_eods = np.zeros((int(max_clus*(endtime-starttime)/window_dt), int(data.shape[0]*cutwidth*samplerate)))\\n times = np.zeros(int(max_clus*(endtime-starttime)/window_dt))\\n freqs = np.zeros(int(max_clus*(endtime-starttime)/window_dt))\\n spiketimes = []\\n counter_c = 0\\n \\n # sliding window\\n for t in np.arange(starttime,endtime-window_size+window_dt,window_dt):\\n\\n # remember where analysis started for this timeblock\\n prev_counter_c = counter_c\\n\\n # select values within window\\n sl = (np.where((ts/samplerate+starttime>=t)&(ts/samplerate+starttime<t+window_size))[0]).astype('int')\\n ctt = ts[sl]\\n s_patterns = spatial_patterns[sl]\\n\\n if len(s_patterns)<min_samples:\\n continue\\n\\n s_patterns[np.isnan(s_patterns)] = 0\\n \\n # cluster\\n pc = OPTICS(min_samples=min_samples).fit(s_patterns).labels_\\n\\n # go through each cluster\\n for cl in np.unique(pc[pc!=-1]):\\n \\n # get the most prominent maxchannel of this cluster and center snippets around its peak and trough\\n all_mc, counts = np.unique(maxchans[sl][pc==cl],return_counts=True)\\n maxchan = all_mc[np.argmax(counts)]\\n mean_snip_p = []\\n mean_snip_t = []\\n\\n for ct in ctt[pc==cl]:\\n # check out surrounding peaks, troughs, and hights.\\n eod_time = ct \\n snip = (((x_peaks>(eod_time-eod_width*2)) & (x_peaks<(eod_time+eod_width*2)) & (x_troughs>(eod_time-eod_width*6)) & (x_troughs<(eod_time+eod_width*6))) | ((x_troughs>(eod_time-eod_width*2)) & (x_troughs<(eod_time+eod_width*2)) & (x_peaks>(eod_time-eod_width*6)) & (x_peaks<(eod_time+eod_width*6))))\\n \\n cp = x_peaks[snip]\\n ct = x_troughs[snip]\\n cc = channels[snip]\\n ch = eod_hights[snip]\\n\\n if maxchan in cc:\\n p_eods = np.zeros((data.shape[0],int(cutwidth*samplerate)))\\n t_eods = np.zeros((data.shape[0],int(cutwidth*samplerate)))\\n # center around biggest pt pair.\\n p_eods = data[:, int(cp[cc==maxchan][np.argmax(ch[cc==maxchan])]-cutwidth*samplerate/2):int(cp[cc==maxchan][np.argmax(ch[cc==maxchan])]+cutwidth*samplerate/2)]\\n p_eods[np.isnan(p_eods)] = 0 # set nan to zero as broken elctrodes give NaN\\n t_eods = data[:, int(ct[cc==maxchan][np.argmax(ch[cc==maxchan])]-cutwidth*samplerate/2):int(ct[cc==maxchan][np.argmax(ch[cc==maxchan])]+cutwidth*samplerate/2)] \\n t_eods[np.isnan(t_eods)] = 0\\n mean_snip_p.append(p_eods.flatten())\\n mean_snip_t.append(t_eods.flatten())\\n\\n mean_snip_p = np.vstack(mean_snip_p)\\n mean_snip_t = np.vstack(mean_snip_t)\\n \\n # choose the centering which results in the most homogenously aligned snippets\\n if np.mean(np.std(mean_snip_p,axis=0))<np.mean(np.std(mean_snip_t,axis=0)):\\n mean_snip = np.mean(mean_snip_p,axis=0).reshape(data.shape[0],-1)\\n else:\\n mean_snip = np.mean(mean_snip_t,axis=0).reshape(data.shape[0],-1)\\n\\n # now add the cluster variables to the storage\\n mean_eods[counter_c] = mean_snip.flatten()\\n times[counter_c] = t\\n spiketimes.append(ts[sl][pc==cl]/samplerate+starttime)\\n freqs[counter_c] = np.median(1/np.diff(ts[sl][pc==cl]/samplerate))\\n counter_c += 1\\n \\n # plot clusters\\n if plot_level > 0:\\n gs = gridspec.GridSpec(counter_c - prev_counter_c,3)\\n fig = plt.figure()\\n ax = fig.add_subplot(gs[:,0])\\n for i,tp in enumerate(spiketimes[prev_counter_c:counter_c]):\\n ax.plot(tp,-i*np.ones(len(tp)),'o')\\n ax.set_xlim([t,t+window_size])\\n ax.axis('off')\\n\\n s_patterns = np.std(mean_eods[prev_counter_c:counter_c].reshape(mean_eods[prev_counter_c:counter_c].shape[0],data.shape[0],-1),axis=2)**(1/3)\\n for i,pattern in enumerate(s_patterns):\\n ax = fig.add_subplot(gs[i,1])\\n ax.imshow(pattern.reshape(grid_shape),vmin=np.min(s_patterns),vmax=np.max(s_patterns))\\n ax.axis('off')\\n ax.set_title(i)\\n\\n ax = fig.add_subplot(gs[:,2])\\n if len(s_patterns)>1:\\n pos=ax.imshow(np.corrcoef(s_patterns),vmin=0,vmax=1)\\n fig.colorbar(pos, ax=ax)\\n plt.title('t = %.2f'%t)\\n if verbose>0:\\n print('---------------------')\\n \\n # check out this analysis window and see if anything can be merged\\n cur_freqs = freqs[prev_counter_c:counter_c]\\n cur_mpe = mean_eods[prev_counter_c:counter_c]\\n cur_tt = np.array(spiketimes[prev_counter_c:counter_c])\\n\\n if len(cur_mpe)>1:\\n\\n # first only merge stationary fish, correlation should be high.\\n s_patterns = np.std(cur_mpe.reshape(cur_mpe.shape[0],data.shape[0],-1),axis=2)**(1/3)\\n cors = np.corrcoef(s_patterns)\\n np.fill_diagonal(cors,0)\\n pops=[]\\n\\n if np.max(cors)>min_correlation:\\n idxs = np.argwhere(cors>min_correlation)\\n for i in idxs:\\n \\n x = i[0]\\n y = i[1]\\n \\n if np.arange(prev_counter_c,counter_c)[y] not in pops and np.arange(prev_counter_c,counter_c)[x] not in pops:\\n \\n # append to whichever is more centered in the current timeblock\\n center_d = t+window_size/2\\n new_times = np.sort(np.append(cur_tt[x],cur_tt[y])).tolist()\\n\\n if np.abs(center_d - np.mean(cur_tt[x])/samplerate+starttime) < np.abs(center_d - np.mean(cur_tt[y])/samplerate+starttime):\\n spiketimes[np.arange(prev_counter_c,counter_c)[x]] = new_times\\n pops.append(np.arange(prev_counter_c,counter_c)[y])\\n else:\\n spiketimes[np.arange(prev_counter_c,counter_c)[y]] = new_times\\n pops.append(np.arange(prev_counter_c,counter_c)[x])\\n\\n if verbose>0 or plot_level>0:\\n print('merging cluster %i and %i with correlation of %.3f'%(x,y,cors[x,y]))\\n\\n if np.max(cors[cors<min_correlation])>min_correlation_moving:\\n idxs = np.argwhere((cors>min_correlation_moving)&(cors<min_correlation)) #sort from high to low\\n sort_idx = np.argsort(-cors[(cors>min_correlation_moving)&(cors<min_correlation)])\\n idxs = idxs[sort_idx]\\n for i in idxs:\\n\\n x = i[0]\\n y = i[1]\\n\\n new_times = np.sort(np.append(cur_tt[x],cur_tt[y]))\\n \\n # only combine if the new coverage is (almost) equal to the seperate coverages combined.\\n # I ignore the two largest ISIs, as they could be outliers.\\n coverage_x = len(cur_tt[x])*np.mean(np.sort(np.diff(cur_tt[x]))[:-2])\\n coverage_y = len(cur_tt[y])*np.mean(np.sort(np.diff(cur_tt[y]))[:-2])\\n coverage_merge = len(new_times)*np.mean(np.sort(np.diff(new_times))[:-2])\\n\\n if coverage_merge < coverage_factor*(coverage_x + coverage_y):\\n continue\\n\\n if np.arange(prev_counter_c,counter_c)[y] not in pops and np.arange(prev_counter_c,counter_c)[x] not in pops:\\n \\n # append to whichever is more centered in the current timeblock\\n center_d = t+window_size/2\\n\\n new_times = np.sort(np.append(cur_tt[x],cur_tt[y])).tolist()\\n \\n if np.abs(center_d - np.mean(cur_tt[x])/samplerate+starttime) < np.abs(center_d - np.mean(cur_tt[y])/samplerate+starttime):\\n spiketimes[np.arange(prev_counter_c,counter_c)[x]] = new_times\\n pops.append(np.arange(prev_counter_c,counter_c)[y])\\n else:\\n spiketimes[np.arange(prev_counter_c,counter_c)[y]] = new_times\\n pops.append(np.arange(prev_counter_c,counter_c)[x])\\n\\n if verbose>0 or plot_level>0:\\n print('merging cluster %i and %i with correlation of %.3f and coverage scores of %.3f + %.3f > %.2f*%.3f'%(x,y,cors[x,y],coverage_x,coverage_y,coverage_factor,coverage_merge))\\n\\n # remove all duplicates\\n for p in sorted(pops, reverse=True):\\n spiketimes.pop(p)\\n times = np.delete(times,p,0)\\n mean_eods = np.delete(mean_eods,p,0)\\n freqs = np.delete(freqs,p,0)\\n counter_c -= 1\\n \\n if plot_level>0:\\n plt.show()\\n\\n return mean_eods[:counter_c], times[:counter_c], spiketimes[:counter_c], freqs[:counter_c]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ead51acc295fa26e734b2931bca8cca\",\n \"score\": \"0.5092902\",\n \"text\": \"def adaptive_windowing(X, B, neigborhood=1, add_start=False, add_end=False):\\r\\n len_X = X.shape[1]\\r\\n max_B = np.max(B)\\r\\n if max_B > len_X:\\r\\n print('Beat exceeds length of features sequence (b=%d, |X|=%d)'%(max_B,len_X))\\r\\n B = B[B<len_X]\\r\\n if add_start:\\r\\n if B[0] > 0:\\r\\n B = np.insert(B,0,0)\\r\\n if add_end:\\r\\n if B[-1] < len_X:\\r\\n B = np.append(B,len_X)\\r\\n X_adapt = np.zeros((X.shape[0], len(B)-1))\\r\\n B_s = np.zeros(len(B)-1).astype(int)\\r\\n B_t = np.zeros(len(B)-1).astype(int)\\r\\n for b in range(len(B)-1):\\r\\n s = B[b]\\r\\n t = B[b+1]\\r\\n reduce = np.floor((1 - neigborhood)*(t-s+1)/2).astype(int)\\r\\n s = s + reduce\\r\\n t = t - reduce\\r\\n if s==t: t = t+1\\r\\n X_slice = X[:,range(s,t)]\\r\\n a = np.sum(X_slice, axis=1)\\r\\n X_adapt[:,b] = np.mean(X_slice, axis=1)\\r\\n B_s[b] = s\\r\\n B_t[b] = t\\r\\n return X_adapt, B_s, B_t\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db385dbd365f6a65779f9d3fd5d873b8\",\n \"score\": \"0.5084934\",\n \"text\": \"def window_data(df, window, feature_col_number, target_col_number):\\n X = []\\n y = []\\n for i in range(len(df) - window - 1):\\n features = df.iloc[i:(i + window), feature_col_number]\\n target = df.iloc[(i + window), target_col_number]\\n X.append(features)\\n y.append(target)\\n return np.array(X), np.array(y).reshape(-1, 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2799c3702fce7f2d5eb7d294733f3b5c\",\n \"score\": \"0.5080753\",\n \"text\": \"def leading_trends(window, lags, wdf, col_name):\\n ma_df = wdf[[col_name]]\\n # create rolling average\\n roll_col = col_name + '_roll_' + str(window)\\n ma_df.loc[:,roll_col] = ma_df.loc[:, col_name].rolling(window=window).mean()\\n col_err = col_name + '_error'\\n ma_df[col_err] = ma_df[col_name] - ma_df[roll_col]\\n # get diff\\n # lag columns \\n ma_df = ma_df.assign(**{col_err+'_lag_'+str(lag_n): ma_df[col_err].shift(lag_n) for lag_n in lags}) \\n return ma_df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2516c582e6a6b27b41e3750a2dbc0c62\",\n \"score\": \"0.5080153\",\n \"text\": \"def add_cn(df, cn_amp):\\n\\n # create GRange objects\\n gr1 = GRange(df[['Chrom', 'Start', 'End']], 'dataframe_hasend')\\n gr2 = GRange(cn_amp, 'dataframe_hasend')\\n\\n # # only use the mid point for the segments\\n # op = []\\n # for i in gr1.gr:\\n # mid = int((i[1].start + i[1].stop)/2)\\n # op.append((i[0], range(mid, mid+1), i[2]))\\n # gr1.gr = op\\n\\n # extend binsize on both end of the cn_seg\\n map_list = gr1.gmap(gr2, a_extend = 0, b_extend = 0, multi_hit = True)\\n\\n # output\\n df['CN'] = [round(np.max([j[0] for j in i]))\\n if (i is not None and i != []) else 0 for i in map_list]\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d7206a88724f7f9205a7db385e91f16\",\n \"score\": \"0.50754505\",\n \"text\": \"def input_windows(specgram):\\n specgram = np.transpose(specgram)\\n num_windows = specgram.shape[0]\\n specgram = np.pad(specgram, ((window_size // 2, window_size // 2), (0, 0)), \\\"constant\\\")\\n windows = []\\n for i in range(num_windows):\\n w = specgram[i:i+window_size,:]\\n windows.append(w)\\n return np.array(windows)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af4bed8b6d4e58554bc3e6c2476a805c\",\n \"score\": \"0.50708544\",\n \"text\": \"def get_windows(x, y, scanning_window=0):\\n\\n def list_populator(indexes):\\n indexes = array(indexes)\\n points = []\\n\\n dup_x = find(diff(new_x[indexes]) == 0)\\n dup_y = find(diff(new_y[indexes]) == 0)\\n\\n index_equal = find(dup_x == dup_y)\\n if len(index_equal) != 0:\\n\\n index_2_del = dup_x[index_equal]\\n indexes = delete(indexes, index_2_del)\\n\\n index_on_window.append(indexes)\\n [[points.append([new_x[i], new_y[i]]), points_total.append([new_x[i], new_y[i]])] for i in indexes]\\n points_on_window.append(points)\\n\\n new_x, new_y, x_y_sorted = join_and_sort(x, y)\\n\\n window_end = new_x[0]\\n points_on_window = []\\n points_total = []\\n index_on_window = []\\n\\n if scanning_window == 0:\\n scanning_window = mean(abs(diff(new_x)))\\n\\n while window_end < new_x[len(new_x)-1] + scanning_window:\\n\\n window_start = window_end\\n window_end = window_start + scanning_window\\n\\n indexes = list(where(logical_and(new_x >= window_start, new_x < window_end))[0])\\n\\n if len(indexes) != 0 and len(indexes) <= 2:\\n list_populator(indexes)\\n\\n elif len(indexes) > 2:\\n y_vec = new_y[indexes]\\n max_y = find(y_vec == max(y_vec))[0]\\n min_y = find(y_vec == min(y_vec))[0]\\n indexes = array(indexes)\\n indexes = list(indexes[[max_y, min_y]])\\n list_populator(indexes)\\n\\n points_total = array(points_total)\\n points_on_window = array(points_on_window)\\n index_on_window = array(index_on_window)\\n\\n return scanning_window, points_total, points_on_window, index_on_window\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76d71671302ee885326c0467e67b29f0\",\n \"score\": \"0.50633043\",\n \"text\": \"def moving_window(dframe, frame=4):\\n\\n for i in range(len(dframe) - frame - 1):\\n yield dframe.iloc[:i + frame],\\\\\\n dframe.iloc[i + frame:i + frame + 1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e06aeee3cff1ce6b9721629b3dc0551\",\n \"score\": \"0.50575626\",\n \"text\": \"def window_pipeline(\\n genome_file,\\n feat_file,\\n window_size=50,\\n num_windows=3,\\n min_gene_size=200,\\n max_gene_size=2000,\\n sep=\\\"\\\\t\\\",\\n out_file=False,\\n by=None,\\n manage_unk=True,\\n annotate=True,\\n):\\n # 0. CONSISTENCY OF INPUT\\n window_size = int(window_size)\\n num_windows = int(num_windows)\\n\\n # 1. READ FILES\\n # We expect to have the whole genome as the fist sequence in the multiFASTA\\n # file and then some plasmids.\\n # TODO: check if that works for a 1-seq FASTA file\\n fna = pyfastx.Fasta(genome_file)\\n genome = str(fna[0])\\n # just avoid segmentation problems with the lower level API\\n del fna\\n df_feat = pd.read_csv(feat_file, sep=sep)\\n genome_length = len(genome)\\n\\n # 2. STANDARIZE DATA\\n df_feat = df_feat[\\n df_feat.apply(\\n lambda x: x.end - x.start < max_gene_size\\n and x.end - x.start > min_gene_size,\\n axis=1,\\n )\\n ]\\n\\n # 3. GET RANGES OF FEATURES\\n # Test genes\\n gene_ranges = get_feature_ranges(df_feat)\\n if by is not None:\\n gene_ranges = filter_ranges(gene_ranges, first=by[0], last=by[1])\\n positive = extract_windows(gene_ranges, window_size, num_windows)\\n # Test negative examples\\n negative_ranges = get_negative_ranges(gene_ranges)\\n negative = extract_windows(negative_ranges, window_size, num_windows)\\n # Test partial genes\\n partial_ranges = get_partial_ranges(\\n gene_ranges, genome_length=genome_length\\n )\\n partial = extract_windows(partial_ranges, window_size, num_windows)\\n\\n # 4. BUILD THE PRE-PROCESSED DATAFRAME\\n df_out = map_full_genomes(genome, positive, negative, partial)\\n if manage_unk:\\n df_out = remove_unk(df_out)\\n if annotate:\\n df_out = classify_genes(df_out, df_feat, feature=\\\"gene\\\")\\n\\n return df_out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b258585515469b96e88a8bd112b65035\",\n \"score\": \"0.5054891\",\n \"text\": \"def _get_adjusted_window(self, events, star_of_range):\\n\\n events.pop(star_of_range)\\n return events\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df1c684cff9daeb6e8cc96d8b9fc8536\",\n \"score\": \"0.5053434\",\n \"text\": \"def get_track_window(self, dataset, data, start, end):\\n window = list()\\n for record in data:\\n fields = record.rstrip(\\\"\\\\n\\\\r\\\").split(\\\"\\\\t\\\")\\n record_chrom = fields[dataset.metadata.chromCol-1]\\n record_start = int(fields[dataset.metadata.startCol-1])\\n record_end = int(fields[dataset.metadata.endCol-1])\\n if record_start < end and record_end > start:\\n window.append( (record_chrom, record_start, record_end) ) #Yes I did want to use a generator here, but it doesn't work downstream\\n return window\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d8967b57f340396fac5022623bd2af0\",\n \"score\": \"0.5050711\",\n \"text\": \"def compute_sliding_windows_for_data(df_features, **kws):\\n timestamps = df_features[\\\"ds\\\"]\\n interval_delta = timestamps[1] - timestamps[0]\\n kws.setdefault(\\\"interval_delta\\\", interval_delta)\\n kws.setdefault(\\\"training_size\\\", 100)\\n kws.setdefault(\\\"test_size\\\", 30)\\n\\n interval_delta = kws[\\\"interval_delta\\\"]\\n test_size = kws[\\\"test_size\\\"]\\n\\n start = kws.pop(\\\"start\\\", timestamps.min())\\n stop = kws.pop(\\\"stop\\\", timestamps.max())\\n step_delta = kws.pop(\\\"step_delta\\\", interval_delta * int(test_size * 0.8))\\n\\n return compute_sliding_windows(start, stop, step_delta, **kws)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20ccce18b619ceeed9474e8740480dc8\",\n \"score\": \"0.50326043\",\n \"text\": \"def _window1(pred, annos):\\n now = sorted(annos, key=lambda x: x.text_span())\\n before = [None] + now[:-1] + [None]\\n after = now[1:] + [None]\\n for triplet in zip(before, now, after):\\n if pred(triplet[1]):\\n return triplet\\n return None, None, None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55ecd16f651193f68938bc0c359cf9d4\",\n \"score\": \"0.50110495\",\n \"text\": \"def find_frames_when_cue_light_on(\\n data_df: pd.DataFrame,\\n cue_light_names: List[str],\\n fps: int,\\n prior_window_frames_cnt: int,\\n post_window_frames_cnt: int,\\n):\\n\\n light_on_dict = {}\\n for cue_light in cue_light_names:\\n light_on_dict[cue_light] = {}\\n light_bouts = detect_bouts(data_df=data_df, target_lst=[cue_light], fps=fps)\\n light_bouts[\\\"Start_pre_window\\\"] = (\\n light_bouts[\\\"Start_frame\\\"] - prior_window_frames_cnt\\n )\\n light_bouts[\\\"Start_pre_window\\\"] = light_bouts[\\\"Start_pre_window\\\"].clip(lower=0)\\n light_bouts[\\\"Start_post_window\\\"] = light_bouts[\\\"End_frame\\\"]\\n light_bouts[\\\"End_pre_window\\\"] = light_bouts[\\\"Start_frame\\\"]\\n light_bouts[\\\"End_post_window\\\"] = (\\n light_bouts[\\\"End_frame\\\"] + post_window_frames_cnt\\n )\\n light_bouts[\\\"End_post_window\\\"] = light_bouts[\\\"End_post_window\\\"].clip(\\n upper=len(data_df)\\n )\\n light_on_dict[cue_light][\\\"df\\\"] = light_bouts\\n light_on_dict[cue_light][\\\"light_on_frames\\\"] = list(\\n data_df.index[data_df[cue_light] == 1]\\n )\\n pre_window_frames, post_window_frames = [], []\\n for i, r in light_bouts.iterrows():\\n pre_window_frames.extend(\\n (list(range(r[\\\"Start_pre_window\\\"], r[\\\"End_pre_window\\\"])))\\n )\\n post_window_frames.extend(\\n (list(range(r[\\\"Start_post_window\\\"], r[\\\"End_post_window\\\"])))\\n )\\n light_on_dict[cue_light][\\\"pre_window_frames\\\"] = pre_window_frames\\n light_on_dict[cue_light][\\\"post_window_frames\\\"] = post_window_frames\\n\\n return light_on_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4322fe639e9bef500f7ea322ff6fdc3\",\n \"score\": \"0.5005381\",\n \"text\": \"def windows(input_values, length=2, overlap=0):\\n results_array = []\\n iterable = iter(input_values)\\n window = list(islice(iterable, length))\\n while len(window) == length:\\n results_array.append(window)\\n window = window[length - overlap:]\\n window.extend(islice(iterable, length - overlap))\\n if window:\\n results_array.append(window)\\n return results_array\",\n \"title\": \"\"\n },\n {\n \"docid\": \"606f1e9a3f2424f96f287ddad52706c9\",\n \"score\": \"0.50032204\",\n \"text\": \"def windowing(input):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23a73f1662574ac346de8851d9ece44d\",\n \"score\": \"0.49898157\",\n \"text\": \"def context_window(x, context_size):\\n assert context_size % 2 == 1, \\\"context_size must be odd\\\"\\n assert x.shape[1] > 2 * context_size + 1, \\\"Low size x\\\"\\n assert x.ndim == 2, \\\"X dimension must be 2\\\"\\n\\n pad_number = context_size\\n left_pad_idx = pad_number\\n N = x.shape[1]\\n dtype = x.dtype\\n right_pad_idx = 1\\n for i in range(N):\\n if i - pad_number < 0:\\n right = x[:, range(0, i + pad_number + 1)]\\n left_pad = x[:, :left_pad_idx]\\n window = np.concatenate([left_pad, right], axis=1)\\n left_pad_idx -= 1\\n elif i + pad_number >= N:\\n left = x[:, range(i - pad_number, N)]\\n right_pad = x[:, -right_pad_idx:]\\n window = np.concatenate([left, right_pad], axis=1)\\n right_pad_idx += 1\\n else:\\n indexes = list(range(i - pad_number, i)) + list(range(i, i + pad_number + 1))\\n window = x[:, indexes]\\n yield window.astype(dtype)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"047803d7632d592555b387b8c3c8e8cd\",\n \"score\": \"0.49888143\",\n \"text\": \"def divide_windows(sequences, exon_file):\\r\\n sequence = sequences[0]\\r\\n exon_pos = []\\r\\n with open(exon_file) as exons:\\r\\n for line in exons:\\r\\n start, end = map(int, line.split())\\r\\n exon_pos.append((start, end))\\r\\n window_groups = {\\\"exon\\\": [], \\\"non-exon\\\": []}\\r\\n exon_indexes = []\\r\\n i = 0\\r\\n for base in sequence:\\r\\n if base != 5:\\r\\n i += 1\\r\\n exon_indexes.append(i)\\r\\n\\r\\n i = 0\\r\\n while i < len(sequence):\\r\\n window = [s[i:i + 100] for s in sequences]\\r\\n window_start = exon_indexes[i]\\r\\n window_end = exon_indexes[i+100] if len(exon_indexes) > i+100 else exon_indexes[len(exon_indexes)-1]\\r\\n exon = False\\r\\n for e_start, e_end in exon_pos:\\r\\n if e_start <= window_start <= e_end or e_start <= window_end <= e_end:\\r\\n window_groups[\\\"exon\\\"].append(tuple((i, window)))\\r\\n exon = True\\r\\n if not exon:\\r\\n window_groups[\\\"non-exon\\\"].append(tuple((i, window)))\\r\\n i += 100\\r\\n return window_groups\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5acf9e8370e44dc03d1b0ae7920657dd\",\n \"score\": \"0.49835402\",\n \"text\": \"def count_sliding_window(top, step=10, window_size=(20,20)):\\n c = 0\\n # slide a window across the image\\n for x in xrange(0, top.shape[0], step):\\n if x + window_size[0] > top.shape[0]:\\n x = top.shape[0] - window_size[0]\\n for y in xrange(0, top.shape[1], step):\\n if y + window_size[1] > top.shape[1]:\\n y = top.shape[1] - window_size[1]\\n c += 1\\n return c\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee40945416d24cf2bf1582761d199297\",\n \"score\": \"0.49833155\",\n \"text\": \"def get_superpulse_window(df, dg, cut_str=[''], nwfs = 100, all = False, norm = True):\\n waveforms = []\\n energies = []\\n for cut in cut_str:\\n print(str(cut))\\n\\n if all==True:\\n nwfs = len(df.query(str(cut)).copy())\\n\\n print(f'using all {nwfs} Waveforms passing cut')\\n else:\\n print(f'using first {nwfs} waveforms passing cut' )\\n\\n idx = df.query(str(cut)).copy().index[:nwfs]\\n raw_store = lh5.Store()\\n tb_name = 'ORSIS3302DecoderForEnergy/raw'\\n lh5_dir = dg.lh5_dir\\n raw_list = lh5_dir + dg.fileDB['raw_path'] + '/' + dg.fileDB['raw_file']\\n raw_list = raw_list.tolist() # right now lh5.store.read_object() only works for lists, so need to convert the pandas object to a list first\\n data_raw, nrows = raw_store.read_object(tb_name, raw_list)\\n\\n wfs_all = (data_raw['waveform']['values']).nda\\n wfs = wfs_all[idx.values, :]\\n # baseline subtraction\\n bl_means = wfs[:,:3500].mean(axis=1)\\n wf_blsub = (wfs.transpose() - bl_means).transpose()\\n ts = np.arange(0, wf_blsub.shape[1]-1, 1)\\n super_wf = np.mean(wf_blsub, axis=0)\\n wf_max = np.amax(super_wf)\\n if norm == True:\\n superpulse = np.divide(super_wf, wf_max)\\n else:\\n superpulse = super_wf\\n waveforms.append(superpulse)\\n waveforms = np.asarray(waveforms)\\n\\n super_duper_wf = np.mean(wf_blsub, axis=0)\\n wf_super_max = np.amax(super_duper_wf)\\n if norm == True:\\n super_duper_pulse = np.divide(super_duper_wf, wf_super_max)\\n else:\\n super_duper_pulse = super_duper_wf\\n return(ts, waveforms, super_duper_pulse)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28f0fc8a5cb07a7425a8331619327f41\",\n \"score\": \"0.49765944\",\n \"text\": \"def overlap_window(self,w2):\\n\\t\\tassert self.overlap(w2)# they have to overlap\\n\\t\\treturn WindowBed(self.seq, max(self.start, w2.start), min(self.end, w2.end)) # from the biggest start to the smaller end\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d81b812bfdf0a3210f240c56e4e2b723\",\n \"score\": \"0.49751008\",\n \"text\": \"def select_acc_intervals(df, ts_name, interval, window, incl_vect=False, incl_acc=False):\\n\\n ## the window must be larger tha a single epoch\\n assert interval <= 60, \\\"Epoch larger than 1 minute.\\\"\\n assert window >= interval, \\\"Window smaller than epoch.\\\"\\n\\n cols = df.columns\\n selected_cols = ['axis1', 'axis2', 'axis3', 'steps'] # TODO: add eeAccumulator\\n\\n minp = df.select(F.min(ts_name).cast('long')).first()[0]\\n\\n if interval < window:\\n\\n df2 = df.withColumn('tmp', F.row_number().over(Window.orderBy(ts_name)) - 1)\\n\\n df2 = df2.withColumn('total_sec', F.col(ts_name).cast('long')).cache()\\n df2 = df2.checkpoint()\\n df2.count()\\n\\n for col in selected_cols:\\n\\n df2 = df2.withColumn(col, F.when(((F.col('total_sec') - minp) % window == 0),\\n F.sum(col).over(Window.orderBy('total_sec')\\n .rangeBetween(0, window - interval)\\n )\\n ).otherwise(0)\\n )\\n\\n df2 = df2.withColumn('duration', F.col(ts_name).cast(IntegerType()) -\\n F.lag(F.col(ts_name).cast(IntegerType()), 1, minp)\\n .over(Window.orderBy(ts_name))\\n ).drop('total_sec')\\n\\n df2 = df2.withColumn('tmp', (F.col('tmp') * F.col('duration')) % window).drop('duration').orderBy(ts_name)\\n\\n df2 = df2.filter(F.col('tmp') == 0).drop('tmp').orderBy(ts_name)\\n\\n else:\\n\\n df2 = df\\n\\n if incl_vect:\\n\\n df2 = df2.withColumn('vectMag', F.sqrt(F.col('axis1') ** 2 + F.col('axis2') ** 2 + F.col('axis3') ** 2))\\n\\n cols.insert(1, 'vectMag')\\n\\n df2 = df2.select(cols).orderBy(ts_name)\\n\\n if not incl_acc:\\n\\n df2 = df2.select(ts_name, cols[1])\\n\\n return df2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83c05bf61da8b626bc2b978ff03c6c07\",\n \"score\": \"0.49729288\",\n \"text\": \"def last_window(\\n df,\\n window_size: int = 10,\\n input_dim: str = 'univariate',\\n normalize_window: bool = False,\\n):\\n z = df.shape[0] - window_size\\n shape_1 = df.shape[1] if df.ndim > 1 else 1\\n if input_dim == 'univariate':\\n cX = df.iloc[z : (z + window_size),]\\n cX = (\\n cX.reset_index(drop=True)\\n .transpose()\\n .set_index(np.repeat(z, (shape_1,)), append=True)\\n )\\n else:\\n cX = df.iloc[z : (z + window_size),]\\n cX = pd.DataFrame(cX.stack().reset_index(drop=True)).transpose()\\n if normalize_window:\\n cX = cX.div(cX.sum(axis=1), axis=0)\\n\\n return cX\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34c774a15d7755048704357d1b3da24f\",\n \"score\": \"0.49693456\",\n \"text\": \"def calc_absr(df, window):\\n ask, bid, volume, amount = df[['ap1', 'bp1', 'volume', 'turnover']].values.T\\n\\n vol = volume.copy().astype(float)\\n np.place(vol, vol == 0, np.nan)\\n avgcost = np.divide(amount, vol)\\n spread = ask - bid\\n\\n # Seperate\\n avgc_over_ask = avgcost[1:] > ask[:-1]\\n avgc_below_bid = avgcost[1:] < bid[:-1]\\n avgc_between = (~avgc_over_ask) & (~avgc_below_bid)\\n vol_1 = vol[1:]\\n\\n # calculate active buyer\\n actibuy = vol_1 * avgc_over_ask\\n ab_ratios = np.divide((avgcost[1:] - bid[:-1]), spread[:-1])\\n actibuy[avgc_between] = vol_1[avgc_between] * ab_ratios[avgc_between]\\n\\n # calculate active seller\\n actisell = vol_1 * avgc_below_bid\\n as_ratios = 1 - ab_ratios\\n actisell[avgc_between] = vol_1[avgc_between] * as_ratios[avgc_between]\\n\\n actibuy_end = nbr.numba_ewma(actibuy, alpha=0.05)[0][-1]\\n actisell_end = np.max([nbr.numba_ewma(actisell, alpha=0.05)[0][-1], 1])\\n out = actibuy_end / actisell_end\\n # active_volume = np.vstack([actibuy, actisell]).T.astype('float64')\\n # active_volume = np.nan_to_num(active_volume)\\n # active_volume_arr = np.full((len(vol), 2), np.nan)\\n # active_volume_rolsum = np.sum(lut.sliding_window_view(active_volume.T, window), axis=2).T\\n # active_volume_arr[window:] = active_volume_rolsum\\n # out = np.log(np.divide(active_volume_arr[:, 0], active_volume_arr[:, 1], out=np.full(len(active_volume_arr), np.nan), where=active_volume_arr[:, 1]!=0))\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8263e837e72dfd9ab3f72cfc1cd147ba\",\n \"score\": \"0.49554327\",\n \"text\": \"def get_windows(series, size, prefix = \\\"\\\", drop_t = False):\\n frame = pd.DataFrame(series.copy())\\n frame.columns = ['t']\\n\\n for shift_by in range(1, size+1):\\n frame[(\\\"\\\" if (prefix==\\\"\\\") else prefix+\\\"_\\\") + \\\"t-\\\" + str(shift_by)] = frame['t'].shift(shift_by)\\n\\n frame.dropna(inplace = True)\\n\\n if drop_t:\\n frame.drop(columns=['t'], inplace=True)\\n\\n return frame\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a9da050ab87389b6af925813325a697c\",\n \"score\": \"0.49479997\",\n \"text\": \"def window (self,window_function,dtime,dfreq,dump=None,dumpfig=None):\\n # this is the output shape\\n ntime1 = self.ntime/dtime;\\n nfreq1 = self.nfreq/dfreq;\\n # make a list of per-baseline output arrays \\n result = [];\\n # make resulting ANTENNA1/ANTENNA2 indices\\n a1 = np.zeros(ntime1*self.nbl)\\n a2 = np.zeros(ntime1*self.nbl)\\n # make wl: array of wavelengths over each bin -- shape x,x,NF1,DF (where original shape was NF=NF1*DF)\\n # note that the x,x stands for a \\\"virtual\\\" time axis\\n# wl = self.wavel.reshape((nfreq1,dfreq))[np.newaxis,np.newaxis,:,:];\\n## wl = self.wavel.reshape((nfreq1,dfreq));\\n # get frequency and wavelength (per channel) from the CHAN_FREQ\\n freqs = self.channels[0,self.freq0:self.freq0+self.nfreq];\\n wavel = 3e8/freqs;\\n wl = wavel.reshape((nfreq1,dfreq));\\n # wl0: wavelength of centre channel of each frequency bin, shape NF1\\n if dfreq%2:\\n wl0 = wavel[dfreq/2::dfreq];\\n else:\\n wl0 = (wavel[dfreq/2-1::dfreq] + wavel[dfreq/2::dfreq])/2;\\n # loop over each baseline\\n for p in range(self.na):\\n for q in range(p+1,self.na):\\n # extract full array for this baseline, apply subset in time\\n input_index = (self.A0==p)&(self.A1==q)\\n data = self.data[input_index].copy();\\n\\t data = data[self.time0:self.time0+self.ntime,self.freq0:self.freq0+self.nfreq,:];\\n # reshape so that e.g. NT becomes NT1,DT, so that we can then reduce over the second axis\\n # resulting shape is NT1,DT,NF1,DF,NCORR\\n data = data.reshape((ntime1,dtime,nfreq1,dfreq,self.ncorr))\\n # get UV coordinates (omit W!) over the entire (hi-res) time range\\n uv = self.UVW[input_index].copy();\\n uv = uv[self.time0:self.time0+self.ntime,:2];\\n # get corresponding uv-center points (uv0) for the lo-res time range, at each time window \\n # uv0 will have shape NT1,2\\n if dtime%2: # odd dtime: we have a uv-bin at the centre, so start at n/2, then take every n-th uv point\\n uv0 = uv[dtime/2::dtime,:];\\n else: # even dtime -- take the middle of the two centre bins\\n uv0 = (uv[dtime/2-1::dtime,:] + uv[dtime/2::dtime,:])/2;\\n # uv-distance has two components: along the track (in time), and across (in freq)\\n # compute uvd1, the first component\\n # First convert to uv-distance from window centre, along time axis, this will be of shape NT1,DT\\n uvd1 = np.sqrt(((uv.reshape((ntime1,dtime,2)) - uv0[:,np.newaxis,:])**2).sum(2))#2\\n # and now convert to wavelength at centre freq: this will be of shape NT1,DT,NF1\\n uvd1 = uvd1[:,:,np.newaxis] / wl0[np.newaxis,np.newaxis,:]\\n # now for the second component: ||uv0||/wl0 at each window is the centre channel uv-length,\\n # the second component's distance is ||uv0||/wl - ||uv0||/wl0\\n # first compute ||uv0||, shape is NT1\\n uv0r = np.sqrt((uv0**2).sum(1)); \\n # work out second component of uv-distance, shape will be NT1,NF1,DF\\n uvd2 = uv0r[:,np.newaxis,np.newaxis]/wl[np.newaxis,:,:] - uv0r[:,np.newaxis,np.newaxis]/wl0[np.newaxis,:,np.newaxis];\\n # evaluate windowing function\\n # shapes are: uvd1 is NT1,DT,DF and uvd2 is NT1,NF1,DF, so insert new axes appropriately\\n # wf shape is then NT1,DT,NF1,DF\\n\\t # import scipy.signal\\n #selepian = scipy.signal.slepian(dtime,.1)\\n wf = window_function(uvd1[:,:,:,np.newaxis],uvd2[:,np.newaxis,:,:])#*selepian;\\n if dump:\\n import cPickle\\n cPickle.dump((p,q,uvd1[0],uvd2[0],wf[0]),dump);\\n if dumpfig and p==17 and q==26:\\n #print uvd1[0],uvd2[0],wf[0];\\n pylab.plot(wf[0,dtime/2,0,:])\\n pylab.plot(wf[0,:,0,dfreq/2])\\n pylab.savefig(dumpfig);\\n #print \\\"Saved plot to\\\",dumpfig;\\n # sum of wf over each DTxDF bin\\n wfsum = wf.sum(3).sum(1);\\n# print p,q,wfsum[0]\\n # apply wf to data (np.newaxis ensures every correlation is multiplied by the same wf),\\n # then sum over each DTxDF bin, and normalize\\n data = (data*wf[...,np.newaxis]).sum(3).sum(1)/wfsum[...,np.newaxis];\\n # return result\\n result.append((p,q,data));\\n return result;\",\n \"title\": \"\"\n },\n {\n \"docid\": \"545bccfdf6fa1ae18bdb018db0b233f7\",\n \"score\": \"0.49458438\",\n \"text\": \"def filter_data2(df, lookahead = 5):\\r\\n pd.options.mode.chained_assignment = None # default='warn'\\r\\n\\r\\n df[\\\"n_robots\\\"] = 0\\r\\n \\r\\n for i in range(len(df)-lookahead):\\r\\n for j in range(1, lookahead + 1):\\r\\n if df[\\\"start\\\"][j] >= df[\\\"start\\\"][i] and df[\\\"start\\\"][j] <= df[\\\"finish\\\"][i]:\\r\\n df[\\\"n_robots\\\"][i] += 1\\r\\n \\r\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01b453cc672804130574a42ccb50833a\",\n \"score\": \"0.49395588\",\n \"text\": \"def windowlized(df, cols, val_date, test_date, window, \\n target_col='target', target_date_col='target_date', return_df=False):\\n \\n valid_date = df[df[target_col].isna() == False][target_date_col].min() - dt.timedelta(window)\\n df_valid = pd.DataFrame(df[df[target_date_col] >= valid_date]).reset_index(drop=True)\\n valid_target_date = df[df[target_col].isna() == False][target_date_col].max()\\n df_valid = pd.DataFrame(df_valid[df_valid[target_date_col] <= valid_target_date]).reset_index(drop=True)\\n\\n X_org = df[cols].values\\n scaler_X = MinMaxScaler()\\n scaler_X.fit(X_org)\\n\\n y_org = df[[target_col]].values\\n scaler_y = MinMaxScaler()\\n scaler_y.fit(y_org)\\n\\n train_date = val_date - dt.timedelta(7*12)\\n\\n X_valid, y_valid = df_valid[cols].values, df_valid[[target_col]]\\n X_valid = scaler_X.transform(X_valid)\\n y_valid = scaler_y.transform(y_valid)\\n\\n train_idx = df_valid[df_valid[target_date_col] <= train_date].dropna().index.tolist()\\n val_idx = df_valid[(df_valid[target_date_col] >= val_date)&(df_valid[target_date_col] < test_date)].index.tolist() \\n test_idx = df_valid[df_valid[target_date_col] >= test_date].index.tolist() \\n\\n X_train, y_train = window_sliding(X_valid, y_valid, train_idx, window)\\n X_val, y_val = window_sliding(X_valid, y_valid, val_idx, window)\\n X_test, y_test = window_sliding(X_valid, y_valid, test_idx, window)\\n\\n if return_df == True:\\n df_train = df_valid[df_valid.index.isin(train_idx)].reset_index(drop=True)\\n df_val = df_valid[df_valid.index.isin(val_idx)].reset_index(drop=True)\\n df_test = df_valid[df_valid.index.isin(test_idx)].reset_index(drop=True)\\n\\n return ((X_train, y_train), (X_val, y_val), (X_test, y_test), (scaler_X, scaler_y)), (df_train, df_val, df_test)\\n else:\\n return (X_train, y_train), (X_val, y_val), (X_test, y_test), (scaler_X, scaler_y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc94ca45944976c68d15caf532505cbb\",\n \"score\": \"0.49330953\",\n \"text\": \"def f(x):\\n out = np.zeros(x.shape)\\n np.put(out, indices.ravel(), 1)\\n b, H, W, C = x.shape\\n\\n ow = W // 2\\n oh = H // 2\\n fh, fw = 2, 2\\n\\n # get first window\\n start_idx = np.arange(fh)[:, None] * W * C + np.arange(fw) * C\\n\\n # Get the offsets along\\n offset_idx = np.arange(ow) * C\\n\\n windows_along = start_idx[:, :, None] + offset_idx.ravel()[None, None, :][::2]\\n windows_along = np.concatenate(np.split(windows_along, ow, axis=2), 1).squeeze()\\n\\n # Get the offsets down\\n offset_idx = np.arange(oh) * C * W\\n windows = windows_along[:, :, None] + offset_idx.ravel()[None, None, :][::2]\\n windows = np.concatenate(np.split(windows, oh, axis=2), 0).squeeze()\\n\\n offset_idx = np.arange(oh) * C * W\\n windows = windows[None, :, :] + offset_idx.ravel()[:, None, None]\\n windows = np.concatenate(np.split(windows, oh, axis=0), 0).squeeze()\\n\\n offset_idx = np.arange(ow) * C\\n windows = windows[:, :, :, None] + offset_idx.ravel()[None, None, None, :]\\n\\n return np.take(out, windows), ('max_pool df/dx', dims)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8588f1074776b2b53af3c278189f33f2\",\n \"score\": \"0.49312168\",\n \"text\": \"def atr(dataframe, n):\\n df_ = dataframe.copy()\\n df_['H-L'] = abs(df_['High'] - df_['Low'])\\n df_['H-PC'] = abs(df_['High'] - df_['Adj Close'].shift(1))\\n df_['L-PC'] = abs(df_['Low'] - df_['Adj Close'].shift(1))\\n df_['TR'] = df_[['H-L', 'H-PC', 'L-PC']].max(axis=1, skipna=False)\\n df_['ATR'] = df_['TR'].rolling(n).mean()\\n # df['ATR'] = df['TR'].ewm(span=n,adjust=False,min_periods=n).mean()\\n df2 = df_.drop(['H-L', 'H-PC', 'L-PC'], axis=1)\\n return df2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b497ed71de6fccfde36213c21b40ab6\",\n \"score\": \"0.491446\",\n \"text\": \"def new_sliding_window_array(t, ep_len, step):\\n win_array = []\\n index_start = 0\\n\\n while index_start < len(t) - ep_len - 1: # the last window starts ep_len seconds before the end of t\\n # print(index_start)\\n window_ok = 1 # window ok is 1 if it doesn't overlap with a time where the signal is cropped\\n for i in range(ep_len - 1):\\n if t[index_start + i] != (t[index_start + i + 1] - 1):\\n window_ok = 0 # if window_ok is set to 0, it won't be added to the array of windows\\n if window_ok:\\n win_array.append((index_start, index_start+ep_len))\\n # print((index_start, index_start+ep_len))\\n index_start += step\\n return win_array\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ca15cdd2fc64260044f05eda8574be7\",\n \"score\": \"0.4912677\",\n \"text\": \"def fitColumn(pos,slce,allTrace, winRng, reverse=False, plot=False, prnt=False, mxWidth=1,bright=False):\\n\\n #extract Ronchi information at given position\\n y = slce[:,pos]\\n nDips = allTrace.shape[0]\\n trace =np.zeros(nDips)\\n traceAmp = np.empty(nDips)\\n traceAmp[:] = np.nan\\n\\n #compute 2nd derivative for identifying centre of bright or dark bands for tracing\\n d2 = np.gradient(np.gradient(y))\\n #d2 = y \\n x = np.arange(len(y))\\n winRng2 = int(winRng/2)\\n \\n #find the centroid of each band/dip\\n for j in range(0,nDips):\\n\\n prevMeas = allTrace[j,:]\\n\\n #exclude bad measurements\\n whr1 = np.where(prevMeas != 0)[0]\\n whr2 = np.where(np.isfinite(prevMeas[whr1]))[0]\\n\\n if (reverse):\\n prevFit = prevMeas[whr1[whr2[0]]]\\n else:\\n prevFit = prevMeas[whr1[whr2[-1]]]\\n \\n #find dip\\n oldDip = np.round(prevFit).astype('int')\\n xtmp = (np.arange(winRng)+ oldDip - winRng2).astype('int')\\n xtmp = xtmp[np.where(xtmp < len(y))]\\n \\n #update centre position for better fitting, but only allow for 1-pixel shift\\n ytmp = d2[xtmp]\\n \\n if (bright):\\n try:\\n dipPos = xtmp[np.nanargmin(ytmp[winRng2-1:winRng2+2])+winRng2-1]\\n except(ValueError):\\n dipPos=np.nan\\n else:\\n try:\\n dipPos = xtmp[(np.nanargmax(ytmp[winRng2-1:winRng2+2])+winRng2-1).astype('int')]\\n except(ValueError):\\n dipPos = np.nan\\n\\n if (dipPos is not np.nan):\\n xtmp = (np.arange(winRng)+dipPos - winRng2).astype('int') #can remove once know quality\\n xtmp = xtmp[np.where(xtmp < len(y))]\\n\\n if (len(xtmp)>2):\\n #fit Gaussian or parabola to region to determine line centre\\n if (bright):\\n yfit = np.nanmin(d2[xtmp])-d2[xtmp]\\n yfit -= np.nanmin(yfit)\\n else:\\n yfit = d2[xtmp] - np.nanmin(d2[xtmp])\\n \\n if (len(xtmp)>2):\\n trace[j],traceAmp[j] = getFit2(xtmp, yfit, plot=plot, mxWidth=mxWidth)\\n \\n else:\\n trace[j] = np.nan\\n\\n if (prnt):\\n print(trace[j], prevFit-trace[j])\\n else:\\n trace[j] = np.nan\\n yfit = [np.nan]\\n else:\\n trace[j] = np.nan\\n yfit = [np.nan]\\n \\n #avoid bad fits\\n\\n if (np.isnan(trace[j])):\\n badFit = True\\n else:\\n if ((np.abs(trace[j]-prevFit)) > mxWidth/2.):\\n badFit = True\\n else:\\n badFit = False\\n \\n if (badFit): \\n\\n #compute centre as weighted average instead\\n try:\\n if (len(xtmp)>2 and len(yfit)>2 and np.all(~np.isfinite(yfit[winRng2-1:winRng2+2]))):\\n mxPos = np.nanargmax(yfit[winRng2-1:winRng2+2])\\n #xtmp = xtmp[int(np.arange(int(winRng/2))-1+mxPos)]\\n xtmp = xtmp[int(mxPos-winRng2):int(mxPos+winRng2+1)]\\n ytmp = np.abs(d2[xtmp])\\n c = np.nansum(xtmp*ytmp)/np.nansum(ytmp)\\n else:\\n c = np.nan\\n except(ValueError):\\n c = np.nan\\n \\n if (prnt):\\n print(pos, j, trace[j], prevFit, prevFit-trace[j], c, prevFit-c)\\n\\n if (np.isnan(c)):\\n trace[j] = np.nan\\n elif (np.abs(c-prevFit) > mxWidth/2.):\\n trace[j] = np.nan\\n else:\\n trace[j] = c\\n \\n if (plot):\\n plt.figure()\\n plt.plot(d2)\\n plt.plot(xtmp, d2[xtmp], 'ro')\\n plt.plot([trace[j],trace[j]], [np.min(d2),np.max(d2)],'--')\\n plt.show() \\n\\n\\n return trace, traceAmp\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0832ce82d847dd97008363619ea1cb05\",\n \"score\": \"0.49016023\",\n \"text\": \"def _get_window(self):\\n N = 2 * self.maxlag + 1\\n window_even = utils.create_window(N, self.window_name)\\n\\n # 2d even window\\n window2d = np.array(\\n [\\n window_even,\\n ]\\n * N\\n )\\n\\n ## One-sided window with zero padding\\n window = np.zeros(N)\\n window[: self.maxlag + 1] = window_even[self.maxlag :]\\n window[self.maxlag :] = 0\\n\\n # 2d window function to apply to bispectrum\\n row = np.concatenate(([window[0]], np.zeros(2 * self.maxlag)))\\n toep_matrix = toeplitz(window, row)\\n toep_matrix += np.tril(toep_matrix, -1).transpose()\\n window = toep_matrix[..., ::-1] * window2d * window2d.transpose()\\n return window\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aed91e2825943c9609e19645006ea8b9\",\n \"score\": \"0.4892686\",\n \"text\": \"def get_sliding_window_collate(self, stride, randomize):\\n\\n def sliding_window_collate(examples):\\n windows = []\\n for example in examples:\\n c, q, y1, y2, id = example\\n c_len = (c != self.padding_idx).sum()\\n q_len = (q != self.padding_idx).sum()\\n\\n # We want to keep going so long as c_end = c_start + (block_size - q_len - 2)\\n # has not been at least c_len for the first time, i.e. c_end < c_len + stride.\\n # We also want to take at least one step.\\n c_range = range(\\n 0, max(1, c_len + q_len + 2 - self.block_size + stride), stride\\n )\\n if randomize:\\n c_start = random.sample(c_range, k=1)[0]\\n c_range = range(c_start, c_start + 1)\\n\\n for c_start in c_range:\\n c_end = min(self.block_size - q_len - 2 + c_start, c_len)\\n if y1 < c_start or y2 < c_start or y1 >= c_end or y2 >= c_end:\\n y1 = -1\\n y2 = -1\\n else:\\n y1 -= c_start\\n y2 -= c_start\\n windows.append((c[c_start:c_end], q[:q_len], y1, y2, c_start, id))\\n\\n # Collate windows\\n max_len = max(len(window[0]) + len(window[1]) + 2 for window in windows)\\n assert max_len <= self.block_size\\n x = torch.full((len(windows), max_len), self.padding_idx, dtype=torch.long)\\n y = torch.zeros(len(windows), 2, dtype=torch.long)\\n c_padding_mask = torch.ones(len(windows), max_len, dtype=torch.bool)\\n c_starts = torch.zeros(len(windows), dtype=torch.long)\\n ids = torch.zeros(len(windows), dtype=torch.long)\\n for i, window in enumerate(windows):\\n c, q, y1, y2, c_start, id = window\\n x[i, 0] = self.cls_idx\\n x[i, 1 : 1 + len(c)] = c\\n x[i, 1 + len(c)] = self.sep_idx\\n x[i, 2 + len(c) : 2 + len(c) + len(q)] = q\\n c_padding_mask[i][0 : 1 + len(c)] = False\\n y[i, 0] = y1 + 1\\n y[i, 1] = y2 + 1\\n c_starts[i] = c_start\\n ids[i] = id\\n return x, y, c_padding_mask, c_starts, ids\\n\\n return sliding_window_collate\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7745b282a1a13a7f18ed9fe09b6344ec\",\n \"score\": \"0.48887002\",\n \"text\": \"def compute_sliding_windows(\\n start,\\n stop,\\n step_delta,\\n interval_delta,\\n training_size=25,\\n test_size=5,\\n):\\n start = pd.Timestamp(str(start))\\n stop = pd.Timestamp(str(stop))\\n step_delta = pd.Timedelta(step_delta)\\n interval_delta = pd.Timedelta(interval_delta)\\n\\n timestep = start\\n test_stop = start\\n windows = []\\n while test_stop < stop:\\n train_start = timestep\\n train_stop = train_start + interval_delta * training_size\\n test_start = train_stop\\n test_stop = test_start + interval_delta * test_size\\n\\n windows.append(\\n (\\n (train_start, train_stop),\\n (test_start, test_stop),\\n )\\n )\\n timestep += step_delta\\n return windows\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7dcdb5a9233b5571de2469920b67438e\",\n \"score\": \"0.4883226\",\n \"text\": \"def eval_recon_rolling(\\n df_in,\\n roll_window=0.4,\\n roll_offset=1,\\n smooth=\\\"Lowess\\\",\\n span=0.1,\\n band_width=0.2,\\n upto=\\\"Full\\\",\\n):\\n\\n # Properties of df_in\\n var_names = df_in.columns\\n\\n # Select portion of data where EWS are evaluated (e.g only up to bifurcation)\\n if upto == \\\"Full\\\":\\n df_pre = df_in.copy()\\n else:\\n df_pre = df_in.loc[:upto]\\n\\n # ------Data detrending--------\\n\\n # Compute the absolute size of the bandwidth if it is given as a proportion\\n if 0 < band_width <= 1:\\n bw_size = df_pre.shape[0] * band_width\\n else:\\n bw_size = band_width\\n\\n # Compute the Lowess span as a proportion if given as absolute\\n if not 0 < span <= 1:\\n span = span / df_pre.shape[0]\\n else:\\n span = span\\n\\n # Compute smoothed data and residuals\\n if smooth == \\\"Gaussian\\\":\\n # Loop through variables\\n for var in var_names:\\n smooth_data = gf(df_pre[var].values, sigma=bw_size, mode=\\\"reflect\\\")\\n smooth_series = pd.Series(smooth_data, index=df_pre.index)\\n residuals = df_pre[var].values - smooth_data\\n resid_series = pd.Series(residuals, index=df_pre.index)\\n # Add smoothed data and residuals to df_pre\\n df_pre[var + \\\"_s\\\"] = smooth_series\\n df_pre[var + \\\"_r\\\"] = resid_series\\n\\n if smooth == \\\"Lowess\\\":\\n # Loop through variabless\\n for var in var_names:\\n smooth_data = lowess(df_pre[var].values, df_pre.index.values, frac=span)[\\n :, 1\\n ]\\n smooth_series = pd.Series(smooth_data, index=df_pre.index)\\n residuals = df_pre[var].values - smooth_data\\n resid_series = pd.Series(residuals, index=df_pre.index)\\n # Add smoothed data and residuals to df_pre\\n df_pre[var + \\\"_s\\\"] = smooth_series\\n df_pre[var + \\\"_r\\\"] = resid_series\\n\\n # Compute the rolling window size (integer value)\\n rw_size = int(np.floor(roll_window * df_in.shape[0]))\\n\\n # Set up a rolling window\\n\\n # Number of components in the residual time-series\\n num_comps = len(df_pre)\\n # Rolling window offset (can make larger to save on computation time)\\n roll_offset = int(roll_offset)\\n\\n # Initialise a list of dictionaries containing eval data\\n list_evaldata = []\\n\\n # Loop through window locations shifted by roll_offset\\n for k in np.arange(0, num_comps - (rw_size - 1), roll_offset):\\n # Select subset of residuals contained in window\\n df_window = df_pre[[var + \\\"_r\\\" for var in var_names]].iloc[k : k + rw_size]\\n # Asisgn the time value for the metrics (right end point of window)\\n t_point = df_pre.index[k + (rw_size - 1)]\\n\\n # Do eigenvalue reconstruction on residuals\\n dic_eval_recon = helpers.eval_recon(df_window)\\n # Add time component\\n dic_eval_recon[\\\"time\\\"] = t_point\\n # Add them to list\\n list_evaldata.append(dic_eval_recon)\\n\\n # Create dataframe from list of dicts of eval data\\n df_evaldata = pd.DataFrame(list_evaldata)\\n df_evaldata.set_index(\\\"time\\\", inplace=True)\\n\\n # Create output dataframe that merges all useful info\\n df_out = pd.concat(\\n [\\n df_in,\\n df_pre[\\n [var + \\\"_r\\\" for var in var_names] + [var + \\\"_s\\\" for var in var_names]\\n ],\\n df_evaldata,\\n ],\\n axis=1,\\n )\\n\\n return df_out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b391fc305f190f0da54b4b8eba27996e\",\n \"score\": \"0.48732123\",\n \"text\": \"def get_visibility_windows(df_indices):\\n\\n window_indices = []\\n window_start_index = df_indices[0]\\n for x,y in zip(df_indices[::], df_indices[1::]):\\n difference = y - x\\n if difference > 1:\\n window_end_index = x\\n window_indices.append((window_start_index, window_end_index))\\n window_start_index = y\\n if y == df_indices[-1]:\\n window_indices.append((window_start_index, y))\\n else:\\n continue\\n\\n return window_indices\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b9d4242e6e95fc9d1fa0adf81fcd447\",\n \"score\": \"0.4837433\",\n \"text\": \"def calculate_windows_to_L(alignment, taxa_order, patterns_of_interest, window_size, window_offset, verbose= False, alpha=0.01):\\n\\n # Separate the patterns of interest into their two terms\\n terms1 = patterns_of_interest[0]\\n terms2 = patterns_of_interest[1]\\n\\n sequence_list = []\\n taxon_list = []\\n\\n with open(alignment) as f:\\n\\n # Create a list of each line in the file\\n lines = f.readlines()\\n\\n # First line contains the number and length of the sequences\\n first_line = lines[0].split()\\n length_of_sequences = int(first_line[1])\\n\\n for line in lines[1:]:\\n # Add each sequence to a list\\n sequence = line.split()[1]\\n sequence_list.append(sequence)\\n\\n # Add each taxon to a list\\n taxon = line.split()[0]\\n taxon_list.append(taxon)\\n\\n # The outgroup is the last taxa in taxa order\\n outgroup = taxa_order[-1]\\n\\n i = 0\\n num_windows = 0\\n if window_size > length_of_sequences:\\n num_windows = 1\\n window_size = length_of_sequences\\n else:\\n # Determine the total number of windows needed\\n while (i + window_size - 1 < length_of_sequences):\\n i += window_offset\\n num_windows += 1\\n\\n site_idx = 0\\n windows_to_l = {}\\n\\n # Iterate over each window\\n for window in range(num_windows):\\n\\n terms1_counts = defaultdict(int)\\n terms2_counts = defaultdict(int)\\n\\n num_ignored = 0\\n\\n # Iterate over the indices in each window\\n for window_idx in range(window_size):\\n\\n # Map each taxa to the base at a given site\\n taxa_to_site = {}\\n\\n # Create a set of the bases at a given site to determine if the site is biallelic\\n bases = set([])\\n\\n # Iterate over each sequence in the alignment\\n for sequence, taxon in zip(sequence_list, taxon_list):\\n # Map each taxon to the corresponding base at the site\\n base = sequence[site_idx]\\n taxa_to_site[taxon] = base\\n bases.add(base)\\n\\n # Statistic can only be calculated where the nucleotides are known\\n if \\\"-\\\" not in bases and len(bases) == 2:\\n\\n # Create the pattern that each site has\\n site_pattern = []\\n\\n # The ancestral gene is always the same as the outgroup\\n ancestral = taxa_to_site[outgroup]\\n\\n # Iterate over each taxon\\n for taxon in taxa_order:\\n nucleotide = taxa_to_site[taxon]\\n\\n # Determine if the correct derived/ancestral status of each nucleotide\\n if nucleotide == ancestral:\\n site_pattern.append(\\\"A\\\")\\n else:\\n site_pattern.append(\\\"B\\\")\\n\\n # Convert the site pattern to a string\\n sites = pattern_string_generator([site_pattern])\\n if sites != []:\\n site_string = sites[0]\\n\\n # If the site string is a pattern of interest add to its count for one of the terms\\n if site_string in terms1:\\n terms1_counts[site_string] += 1\\n\\n elif site_string in terms2:\\n terms2_counts[site_string] += 1\\n\\n\\n elif \\\"-\\\" in bases or \\\"N\\\" in bases:\\n num_ignored += 1\\n\\n # Increment the site index\\n site_idx += 1\\n\\n terms1_total = sum(terms1_counts.values())\\n terms2_total = sum(terms2_counts.values())\\n\\n numerator = terms1_total - terms2_total\\n denominator = terms1_total + terms2_total\\n\\n if denominator != 0:\\n l_stat = numerator / float(denominator)\\n else:\\n l_stat = 0\\n\\n # Verbose output\\n if verbose:\\n signif, left_counts, right_counts, chisq, pval = calculate_significance(terms1_total, terms2_total, verbose, alpha)\\n # The line below can be changed to add more information to the windows to L mapping\\n windows_to_l[window] = (l_stat, signif, num_ignored, chisq, pval)\\n\\n # Standard output\\n else:\\n signif = calculate_significance(terms1_total, terms2_total)\\n windows_to_l[window] = (l_stat, signif)\\n\\n # Account for overlapping windows\\n site_idx += (window_offset - window_size)\\n\\n return windows_to_l\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec3070700ddd5109fe9a00d3d7077419\",\n \"score\": \"0.48335677\",\n \"text\": \"def shape_context(points, nbins_r, nbins_theta, r_inner, r_outer, max_window_size, window=None):\\n nbins = nbins_theta * nbins_r\\n t_points = points.shape[0]\\n\\n descriptor = []\\n\\n r_array = cdist(points, points)\\n am = r_array.argmax()\\n max_points = [am // t_points, am % t_points]\\n r_array_n = r_array // r_array.mean()\\n r_bin_edges = np.logspace(np.log10(r_inner), np.log10(r_outer), nbins_r)\\n r_array_q = np.zeros((t_points, t_points), dtype=int)\\n\\n for m in range(nbins_r):\\n r_array_q += (r_array_n < r_bin_edges[m])\\n\\n fz = r_array_q > 0\\n\\n theta_array = cdist(points, points, lambda u, v: math.atan2((v[1] - u[1]), (v[0] - u[0])))\\n norm_angle = theta_array[max_points[0], max_points[1]]\\n theta_array = (theta_array - norm_angle * (np.ones((t_points, t_points)) - np.identity(t_points)))\\n theta_array[np.abs(theta_array) < 1e-7] = 0\\n\\n theta_array_2 = theta_array + 2 * math.pi * (theta_array < 0)\\n theta_array_q = (1 + np.floor(theta_array_2 // (2 * math.pi / nbins_theta))).astype(int)\\n\\n for i in range(t_points):\\n sn = np.zeros((nbins_r, nbins_theta))\\n x, y = points[i]\\n\\n if window is not None:\\n local_x = np.where((points[:, 0] >= (x-window)) & (points[:, 0] <= (x+window)))\\n local_y = np.where((points[:, 0] >= y-window) & (points[:, 0] <= y+window))\\n local_points = np.intersect1d(local_x, local_y)\\n for j in local_points:\\n if (fz[i, j]):\\n sn[r_array_q[i, j] - 1, theta_array_q[i, j] - 1] += 1\\n else:\\n for j in range(t_points):\\n if (fz[i, j]):\\n sn[r_array_q[i, j] - 1, theta_array_q[i, j] - 1] += 1\\n # There need not be 500 points in the image, you can have less than 500 also.\\n descriptor.append(sn.reshape(nbins))\\n\\n return np.array(descriptor)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d2847c039a9316dd0a87089236b63ef\",\n \"score\": \"0.4831764\",\n \"text\": \"def c_window(n,n_cut):\\n\\tn_right = n[-1] - n_cut\\n\\t# n_left = n[0]+ n_cut \\n\\tn_r=n[ n[:] > n_right ] \\n\\t# n_l=n[ n[:] < n_left ] \\n\\ttheta_right=(n[-1]-n_r)/float(n[-1]-n_right-1) \\n\\t# theta_left=(n_l - n[0])/float(n_left-n[0]-1) \\n\\tW=np.ones(n.size)\\n\\tW[n[:] > n_right]= theta_right - 1/(2*np.pi)*np.sin(2*np.pi*theta_right)\\n\\t# W[n[:] < n_left]= theta_left - 1/(2*pi)*sin(2*pi*theta_left)\\n\\treturn W\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f4e138fddecc09c1a150eaedc9419a9\",\n \"score\": \"0.48305053\",\n \"text\": \"def _get_reading_window(width, height, data_bbox, eopatch_bbox):\\n if eopatch_bbox.crs is not data_bbox.crs:\\n eopatch_bbox = eopatch_bbox.transform(data_bbox.crs)\\n\\n # The following will be in the future moved to sentinelhub-py\\n data_ul_x, data_lr_y = data_bbox.lower_left\\n data_lr_x, data_ul_y = data_bbox.upper_right\\n\\n res_x = abs(data_ul_x - data_lr_x) / width\\n res_y = abs(data_ul_y - data_lr_y) / height\\n\\n ul_x, lr_y = eopatch_bbox.lower_left\\n lr_x, ul_y = eopatch_bbox.upper_right\\n\\n # If these coordinates wouldn't be rounded here, rasterio.io.DatasetReader.read would round\\n # them in the same way\\n top = round((data_ul_y - ul_y) / res_y)\\n left = round((ul_x - data_ul_x) / res_x)\\n bottom = round((data_ul_y - lr_y) / res_y)\\n right = round((lr_x - data_ul_x) / res_x)\\n\\n return (top, bottom), (left, right)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"571d92a2fb48fecf34fe9130c34df320\",\n \"score\": \"0.48245016\",\n \"text\": \"def window(inclusion_windows):\\n def _window(mjd, *args):\\n for wbegin,wend in inclusion_windows:\\n if wbegin <= mjd <= wend:\\n return True\\n return False\\n return _window\",\n \"title\": \"\"\n },\n {\n \"docid\": \"722044a8f80cbc85ce013c4cc645aa54\",\n \"score\": \"0.48231345\",\n \"text\": \"def admi(df, n, n_adx = 14):\\n i = 0\\n up_index = []\\n do_index = []\\n while i + 1 <= df.index[-1]:\\n up_move = df.loc[i + 1, 'high'] - df.loc[i, 'high']\\n do_move = df.loc[i, 'low'] - df.loc[i + 1, 'low']\\n if up_move > do_move and up_move > 0:\\n up_d = up_move\\n else:\\n up_d = 0\\n up_index.append(up_d)\\n if do_move > up_move and do_move > 0:\\n do_d = do_move\\n else:\\n do_d = 0\\n do_index.append(do_d)\\n i = i + 1\\n i = 0\\n tr_l = [0]\\n while i < df.index[-1]:\\n tr = max(df.loc[i + 1, 'high'], df.loc[i, 'close']) - min(df.loc[i + 1, 'low'], df.loc[i, 'close'])\\n tr_l.append(tr)\\n i = i + 1\\n tr_s = pd.Series(tr_l)\\n atr = pd.Series(tr_s.ewm(span=n, min_periods=n).mean())\\n up_index = pd.Series(up_index)\\n do_index = pd.Series(do_index)\\n pos_di = pd.Series(up_index.ewm(span=n, min_periods=n).mean() / atr)\\n neg_di = pd.Series(do_index.ewm(span=n, min_periods=n).mean() / atr)\\n adx = pd.Series((abs(pos_di - neg_di) / (pos_di + neg_di)).ewm(span=n_adx, min_periods=n_adx).mean(), name='adx_' + str(n) + '_' + str(n_adx))\\n df = df.join(adx)\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9afddac00bd53075530173e0c690583\",\n \"score\": \"0.48211673\",\n \"text\": \"def ADX(df, n, n_ADX): # n = 14 (for buildinhg ATR in this function), n_ADX = 14 (for building final ADX in this func)\\n \\n df = df.reset_index()\\n i = 0\\n UpI = []\\n DoI = []\\n while i + 1 <= df.index[-1]:\\n UpMove = df.at[i + 1, 'High'] - df.at[i, 'High']\\n DoMove = df.at[i, 'Low'] - df.at[i + 1, 'Low']\\n if UpMove > DoMove and UpMove > 0:\\n UpI.append(UpMove)\\n else:\\n UpI.append(0)\\n \\n if DoMove > UpMove and DoMove > 0:\\n DoI.append(DoMove)\\n else:\\n DoI.append(0)\\n i = i + 1\\n i = 0\\n TR_l = [0]\\n while i < df.index[-1]:\\n TR = max(df.at[i + 1, 'High'], df.at[i, 'Close']) \\\\\\n - min(df.at[i + 1, 'Low'], df.at[i, 'Close'])\\n TR_l.append(TR)\\n i = i + 1\\n TR_s = pd.Series(TR_l)\\n ATR = TR_s.ewm(span=n, min_periods=n).mean()\\n UpI = pd.Series(UpI)\\n DoI = pd.Series(DoI)\\n PosDI = pd.Series(UpI.ewm(span=n, min_periods=n - 1).mean() / ATR)\\n NegDI = pd.Series(DoI.ewm(span=n, min_periods=n - 1).mean() / ATR)\\n ADX = pd.Series((abs(PosDI - NegDI) / (PosDI + NegDI)).ewm(span=n_ADX, min_periods=n_ADX - 1).mean(), \\n name = 'ADX_' + str(n) + '_' + str(n_ADX))\\n df = df.join(ADX).set_index(\\\"index\\\")\\n return df['ADX_' + str(n) + '_' + str(n_ADX)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98b5013779624b9f92514993c7e065fc\",\n \"score\": \"0.48187187\",\n \"text\": \"def sliding_window_calculate(self, window_size, window_jump = 1, operation = \\\"sum\\\", output = \\\"dict\\\",\\n threshold = 1, seqs_to_exclude = []):\\n if output == \\\"dict\\\":\\n new_dic = {}\\n elif output == \\\"annotation_set\\\":\\n annotation_set = AnnotationSet()\\n annotation_set.region = {}\\n elif output == \\\"coords\\\":\\n coords_list = []\\n for seqid in self:\\n if len(self[seqid]) > window_size and not seqid in seqs_to_exclude:\\n if output == \\\"dict\\\":\\n new_dic[seqid] = []\\n window_in = False\\n coords_list = []\\n for position in range(len(self[seqid][:-1 * window_size]) / window_jump):\\n window_start = position * window_jump\\n if operation == \\\"set\\\":\\n new_dic[seqid].append(set(self[seqid][window_start:window_start + window_size]))\\n continue\\n window_sum = numpy.sum(self[seqid][window_start:window_start + window_size])\\n if operation == \\\"sum\\\":\\n value = window_sum\\n elif operation == \\\"average\\\":\\n value = window_sum * 1.0 / window_size\\n if output == \\\"dict\\\":\\n new_dic[seqid].append(value)\\n elif output == 'annotation_set':\\n if value >= threshold:\\n if window_in == False:\\n window_in = True\\n if len(coords_list) == 0:\\n coords_list.append([1 + window_start])\\n elif position > coords_list[-1][1]:\\n coords_list.append([1 + window_start])\\n else:\\n if window_in == True:\\n window_in = False\\n if len(coords_list[-1]) == 1:\\n coords_list[-1].append(window_start + window_size)\\n else:\\n coords_list[-1][1] = window_start + window_size\\n elif output == \\\"coords\\\":\\n if threshold[0] <= value <= thredshold[1]:\\n coords_list.append([seqid, 1 + window_start, window_start + window_size])\\n elif type(output) == file:\\n output.write(seqid + '\\\\t' + str(value) + '\\\\n')\\n if window_start % 10000000 == 0 and verbose:\\n print(\\\"processed \\\" + seqid + \\\" to position \\\" + str(position))\\n if output == \\\"annotation_set\\\":\\n if len(coords_list) > 0:\\n if len(coords_list[-1]) == 1:\\n coords_list[-1].append(len(self[seqid]))\\n for coords in coords_list:\\n ID = seqid + \\\"-window\\\" + str(coords[0])\\n annotation_set.region[ID] = BaseAnnotation(ID, seqid, tuple(coords), \\\"region\\\", annotation_set = annotation_set)\\n if verbose:\\n print(\\\"processed \\\" + seqid)\\n if output == 'annotation_set':\\n for region in annotation_set.region:\\n if annotation_set.region[region].seqid == seqid:\\n print(str(annotation_set.region[region].coords))\\n if output == \\\"dict\\\":\\n return new_dic\\n elif output == \\\"annotation_set\\\":\\n return copy.deepcopy(annotation_set)\\n elif output == \\\"coords\\\":\\n return coords_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cadaf695107de24a04971a0ce318cfa3\",\n \"score\": \"0.48182005\",\n \"text\": \"def windowing(input):\\n window = signal.hamming(len(input[1]),sym=0)\\n windowed=np.zeros(input.shape)\\n for i in range(len(input)):\\n windowed[i]=np.multiply(input[i],window)\\n #plt.pcolormesh(windowed)\\n\\n return windowed\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3258ab12fe12bdbee9bc80d45a3cbef\",\n \"score\": \"0.48174053\",\n \"text\": \"def get_NAV_windows(spacecraft,planet,times,threshold):\\r\\n interval = stypes.SPICEDOUBLE_CELL(2)\\r\\n sp.wninsd(times[0],times[1],interval)\\r\\n gam_times = stypes.SPICEDOUBLE_CELL(2000)\\r\\n sp.gfdist(planet,\\\"NONE\\\",spacecraft,\\\"<\\\",threshold,0.0,86400.0,1000,interval,gam_times)\\r\\n ngams = sp.wncard(gam_times)\\r\\n closest_approach = stypes.SPICEDOUBLE_CELL(2*ngams)\\r\\n sp.gfdist(planet,\\\"NONE\\\",spacecraft,\\\"LOCMIN\\\",threshold,0.0,86400.0,1000,gam_times, closest_approach)\\r\\n n_ca = sp.wncard(closest_approach)\\r\\n nav_windows = np.zeros([n_ca,3],dtype=\\\"float64\\\")\\r\\n for i in range(n_ca):\\r\\n ca=sp.wnfetd(closest_approach,i)\\r\\n nav_windows[i,:] = [ca[0]-(28*86400.0),ca[0],ca[0]+(7*86400.0)]\\r\\n return(nav_windows)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a51756c5c3653a31688b97e57a54e55\",\n \"score\": \"0.4815732\",\n \"text\": \"def launch(ini, end, order):\\n j = nbr_lanes*order + lane-1\\n left_lane = envelope_data[:, j] # Upper envelope of the current lane\\n thickness = rint(thickness_data[j]) # Thickness of the current lane\\n\\n #(ini, end) = cut_border_edge(left_lane, thickness) # First and last index of the lane\\n\\n # First research of the slits, with potential errors on X (x_pos is an array, y is a real)\\n # Y coordinates are relative to the envelope (not absolute coordinates)\\n x_pos, y = ThAr_global_center(left_lane, thickness, ini, end)\\n\\n # Y coordinates are set as absolute\\n tab_pos = [[x, y + left_lane[rint(x)]] for x in x_pos if (ini <= x <= end)]\\n #print(tab_pos)\\n return tab_pos\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c5e27aa2f1943114ce789dbd18ef0da\",\n \"score\": \"0.4813631\",\n \"text\": \"def convolve_subarrays(df_bd):\\n ALIGNMENT_KERNEL_EDGE_SIZE = 21 # should be odd\\n half_size = ALIGNMENT_KERNEL_EDGE_SIZE // 2\\n y, x = np.mgrid[0:ALIGNMENT_KERNEL_EDGE_SIZE, 0:ALIGNMENT_KERNEL_EDGE_SIZE]\\n convolved_subarrays, new_shifts = [], []\\n for ar, mis, sc in zip(df_bd['Subarray_first'], df_bd['Misalignment'],\\n df_bd['Shift_current']):\\n new_shift_x = sc[0] - mis[0]\\n new_shift_y = sc[1] - mis[1]\\n gaussian = Gaussian2D(1, half_size + new_shift_x, half_size + new_shift_y, 0.7, 0.7)\\n kernel = gaussian(x, y)\\n kernel /= np.sum(kernel)\\n convolved_subarrays.append(convolve(ar, kernel))\\n new_shifts.append((new_shift_x, new_shift_y))\\n df_bd['Subarray_current'] = convolved_subarrays\\n df_bd['Shift_current'] = new_shifts\\n # plot_images('Convolved subarrays', df_bd['Filename'], convolved_subarrays)\\n print('convolve_subarrays() done.')\\n return df_bd\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfbe86d9ba2fadb4c3a8cef6216b3f6d\",\n \"score\": \"0.4811607\",\n \"text\": \"def position_handler(data: pd.DataFrame) -> Iterator[int]:\\n\\n curr_pos = 0\\n for i in data.reset_index().index:\\n if i == 0:\\n curr_pos = 0\\n yield 0\\n elif data.iloc[i - 1, data.columns.get_loc('EntrySignal')] == 'LongEntry':\\n curr_pos = 1\\n yield 1\\n elif np.logical_and(curr_pos == 1, data.iloc[i - 1, data.columns.get_loc('ExitSignal')] != 'LongExit'):\\n curr_pos = 1\\n yield 1\\n elif data.iloc[i - 1, data.columns.get_loc('EntrySignal')] == 'ShortEntry':\\n curr_pos = -1\\n yield -1\\n elif np.logical_and(curr_pos == -1, data.iloc[i - 1, data.columns.get_loc('ExitSignal')] != 'ShortExit'):\\n curr_pos = -1\\n yield -1\\n else:\\n curr_pos = 0\\n yield 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13a652fb269ba81a2760dc48d607b60d\",\n \"score\": \"0.4802786\",\n \"text\": \"def insert_windows(self, hull_df, u_f, ff_anteil):\\n aw_row = hull_df.loc[0]\\n aw_A = aw_row[\\\"Fläche\\\"]\\n aw_Uwert = aw_row[\\\"U-Wert\\\"]\\n aw_tfaktor = hull_df.loc[0,\\\"Temperatur-Korrekturfaktor\\\"]\\n \\n aw_opak_A = aw_A * (1-ff_anteil)\\n fenster_A = aw_A * ff_anteil \\n \\n aw_opak = dict(zip(hull_df.columns,[\\\"AW (opak)\\\", aw_opak_A, aw_Uwert, aw_tfaktor]))\\n fenster = dict(zip(hull_df.columns,[\\\"Fenster\\\", fenster_A, u_f, hull_df.loc[0,\\\"Temperatur-Korrekturfaktor\\\"]]))\\n \\n hull_df = hull_df.append(aw_opak, ignore_index = True)\\n hull_df = hull_df.append(fenster, ignore_index = True)\\n \\n hull_df.drop(hull_df.index[0], inplace=True)\\n \\n return hull_df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccf176b3a1b0ad671240f90035652110\",\n \"score\": \"0.47999823\",\n \"text\": \"def window_sliding(X, y, idxs, window):\\n X_windows = []\\n if y is not None:\\n y_windows = []\\n\\n for i in idxs:\\n X_t = X[i-window:i].copy()\\n if y is not None:\\n y_t = y[i].copy()\\n X_windows.append(X_t)\\n if y is not None:\\n y_windows.append(y_t)\\n \\n X_windows = np.array(X_windows)\\n if y is not None:\\n y_windows = np.array(y_windows) \\n return X_windows, y_windows\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88c3653d8df3cf097eeb7aeea96362a5\",\n \"score\": \"0.47957\",\n \"text\": \"def overlapping_windows():\\n n = 256\\n window = numpy.hamming(n)\\n\\n thinkplot.preplot(num=5)\\n start = 0\\n for i in range(5):\\n xs = numpy.arange(start, start+n)\\n thinkplot.plot(xs, window)\\n\\n start += n/2\\n\\n thinkplot.save(root='windowing3',\\n xlabel='time (s)',\\n axis=[0, 800, 0, 1.05])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10b197233facacf45913c4cde9f1701b\",\n \"score\": \"0.47813532\",\n \"text\": \"def create_sliding_windows(dataframe, sliding_window_interval, window_length):\\n ts_min = min(dataframe['timestamp'])\\n ts_max = max(dataframe['timestamp'])\\n ts_iter = ts_min\\n windows = []\\n while ts_iter <= ts_max:\\n window = create_window(dataframe, ts_iter, window_length)\\n windows.append(window)\\n ts_iter += sliding_window_interval\\n return windows\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdb6ff593080e51d22814b1aa217cf1b\",\n \"score\": \"0.47678155\",\n \"text\": \"def next(self):\\n start_x = self.roi.left\\n end_x = self.roi.right - self.window_size.width + 1\\n step_length_x = np.round(self.window_size.width * (1.0 - self.window_overlap.horizontal)).astype(np.int)\\n start_y = self.roi.top\\n end_y = self.roi.bottom - self.window_size.height + 1\\n step_length_y = np.round(self.window_size.height * (1.0 - self.window_overlap.vertical)).astype(np.int)\\n\\n for top in range(start_y, end_y + 1, step_length_y):\\n bottom = top + self.window_size.height - 1\\n for left in range(start_x, end_x + 1, step_length_x):\\n right = left + self.window_size.width - 1\\n yield SearchWindow(top=top, left=left, bottom=bottom, right=right)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"97b9d55560ac6063e6c814b967f1da9f\",\n \"score\": \"0.47486052\",\n \"text\": \"def get_events(df: pd.DataFrame, tolerance: int = 1) -> List[List]:\\n print(df)\\n # get unique list of intervals\\n set_intervals = list(set(get_intervals(df, tolerance)))\\n # add max value as function excludes it\\n set_intervals.append(max(df[\\\"timestamp\\\"].values))\\n set_intervals.sort()\\n\\n clapperboard_sets = []\\n # get clapperboards for each event or interval\\n for i in range(0, len(set_intervals) - 1):\\n\\n start = max(0, set_intervals[i] - 1)\\n end = set_intervals[i + 1]\\n res = df[df[\\\"timestamp\\\"].between(start, end, inclusive=False)]\\n clapperboard_sets.append(res[\\\"image\\\"].values)\\n\\n return clapperboard_sets\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61019590d9678e7a3b67d3f34563161c\",\n \"score\": \"0.47475535\",\n \"text\": \"def calcLinkingWindow(times: Time, length: float) -> np.ndarray:\\n _checkTime(times, \\\"times\\\")\\n dt = times.mjd - times.mjd.min()\\n window = np.floor(dt / length) + 1\\n return window.astype(int)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46c60c966bb3ed5f244756ef0543d307\",\n \"score\": \"0.4741053\",\n \"text\": \"def sliding_window_corelations(a,b, window=30, startdate=None, enddate=None, resolution=\\\"d\\\"):\\n resmap = {\\\"d\\\":\\\"days\\\", \\\"h\\\":\\\"hours\\\"}\\n assert resolution in resmap.keys(), \\\"Only accepting resolution {} currently\\\".format(resmap.keys())\\n\\n if startdate is None:\\n startdate = max(a.index.min(), b.index.min())\\n if enddate is None:\\n enddate = min(a.index.max(), b.index.max())\\n\\n corrs = pd.Series(index=pd.date_range(startdate, enddate, freq=resolution))\\n\\n delta = datetime.timedelta(**{resmap[resolution]:window})\\n\\n daterange_delta = enddate-startdate\\n\\n if resolution == \\\"h\\\":\\n timesteps = int((enddate-startdate).total_seconds()/3600)\\n elif resolution == \\\"d\\\":\\n timesteps = daterange_delta.days\\n\\n for i in range(timesteps):\\n offset = datetime.timedelta(**{resmap[resolution]:i})\\n\\n time_a = startdate + offset - delta\\n time_b = startdate + offset\\n\\n corr = a[time_a:time_b].corr(b[time_a:time_b])\\n corrs[time_b] = corr\\n return corrs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07f8652f7d81d02874e8fc95e326d1e0\",\n \"score\": \"0.47217298\",\n \"text\": \"def get_overlap(env, width = 2, file_name = 'omeg5'):\\n\\n\\tx = env.nb_temp * width\\n\\ty = x\\n\\tz = env.temp_size * 2 - 1\\n\\ttype_size = 8\\n\\ttab = np.empty(x * y * z)\\n\\tsize = 4096\\n\\ti = 0\\n\\tl = x * y * z * type_size\\n\\tn = 0\\n\\tfd = open(file_name, 'rb')\\n\\twhile l - n > size:\\n\\t\\ts = fd.read(size)\\n\\t\\ttab[n / type_size : (n + size) / type_size] = np.fromstring(s, dtype = np.float64)\\n\\t\\ti += 1\\n\\t\\tn += size\\n\\ts = fd.read(l - n)\\n\\ttab[i * size / type_size:] = np.fromstring(s, dtype = np.float64)\\n\\tfd.close()\\n\\ttab = tab.reshape(x, y, z)\\n\\n#### second overlap\\n#\\to = scipy.io.loadmat('../files/ALL_norm.overlap1')\\n#\\tover = o['c_overlap'].astype(np.float64)\\n#\\tover = over[:, :, ::-1]\\n#\\ttab[:382, :382, :] = over.copy()\\n#\\ttab = tab / np.sqrt(252 * 129)\\n\\treturn (tab)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cda78c743981232f353c921ee74e875b\",\n \"score\": \"0.47197017\",\n \"text\": \"def add_centered_windows(self, X, y, X_c, y_c, st, end, c_idx, current_label,\\n max_perturbations=-1):\\n SWL = self.win_len # For convenience\\n gest_len, center = end-st, (st+end)/2\\n X_c[c_idx:c_idx+SWL, :] = X[center - SWL/2:center - SWL/2 + SWL, :]\\n y_c[c_idx:c_idx+SWL] = current_label\\n c_idx = c_idx + SWL\\n\\n if current_label == 0:\\n return c_idx\\n\\n g, shift_window = np.ceil(np.sqrt(gest_len)), 1\\n num_windows = g if g % 2 else g-1\\n\\n while shift_window <= num_windows/2:\\n for new_center in [center-shift_window, center+shift_window]:\\n X_c[c_idx:c_idx+SWL,:] = X[new_center-SWL/2:new_center+SWL/2,:]\\n y_c[c_idx:c_idx+SWL] = current_label\\n c_idx = c_idx + SWL\\n shift_window += 1\\n return c_idx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"965ee175863d370fafad04662d6a4e86\",\n \"score\": \"0.47172076\",\n \"text\": \"def coverage_windows(sample, window = 5000, slide = 100):\\n g, gc, s, sample, gc_correction, thresholds = sample\\n # calculate coverage windows for sample\\n cov = sample['cov']\\n del sample['cov']\\n weights = np.ones(window)\\n windows = [[], []] # x and y values for windows\\n i = 0\\n if len(cov) < len(weights):\\n sample['windows'] = False\\n sample['kept_windows'] = False\\n sample['LTwindows'] = False\\n sample['iRep'] = 'n/a'\\n sample['GC bias'] = 'n/a'\\n sample['fiRep'] = 'n/a'\\n sample['fit'] = False\\n return (g, s, sample)\\n for c in signal.fftconvolve(cov, weights, 'valid').tolist()[0::slide]:\\n windows[0].append(i)\\n windows[1].append(c/window)\\n i += slide\\n # filter zero coverage windows\\n sample['windows'] = windows\\n if gc is not False:\\n Fwindows, Fgc = filter_windows(windows, gc, mdif = thresholds['win_cov'])\\n\\n sample['GC'] = Fgc\\n sample['Fwindows'] = Fwindows # only need this for plotting GC data\\n else:\\n Fwindows = filter_windows(windows, mdif = thresholds['win_cov'])\\n\\n total_windows = len(windows[0])\\n total_Fwindows = len(Fwindows[0])\\n sample['kept_windows'] = float(total_Fwindows)/float(total_windows)\\n # calculate gc bias\\n if gc is not False:\\n fit, filtered, corrected_windows, Cfit = \\\\\\n gc_bias(Fgc, Fwindows, thresholds['GC_min_r2'])\\n fit, m, b, r2 = fit\\n sample['GC fit'], sample['GC r2'] = fit, r2\\n sample['GC bias'] = m * r2\\n if gc_correction is True and corrected_windows is not False:\\n sample['Cwindows'] = corrected_windows\\n sample['Cfit'] = Cfit\\n sample['OLTwindows'] = [Fwindows[0], log_trans(Fwindows[1])]\\n Fwindows = corrected_windows\\n # log transform\\n sample['LTwindows'] = [Fwindows[0], log_trans(Fwindows[1])]\\n return (g, s, sample)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"567554793bcdd832b8c5b48cc2db4f51\",\n \"score\": \"0.47136\",\n \"text\": \"def compute_windows(numpy_image, patch_size, patch_overlap):\\n\\n if patch_overlap > 1:\\n raise ValueError(\\\"Patch overlap {} must be between 0 - 1\\\".format(patch_overlap))\\n\\n # Generate overlapping sliding windows\\n windows = slidingwindow.generate(numpy_image,\\n slidingwindow.DimOrder.HeightWidthChannel,\\n patch_size, patch_overlap)\\n\\n return (windows)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5666d12583b2d246ee86c5851615be97\",\n \"score\": \"0.47134322\",\n \"text\": \"def fft_window(self,ind_start: int = 0, ind_end: int = -1 ):\\n # set window\\n #extracting data interval to view\\n # freq = self.natural_frequencies[ind_start:ind_end]\\n # power = (self.fft_values[ind_start:ind_end])**2\\n\\n freq = self.natural_frequencies_example[ind_start:ind_end]\\n power = (self.fft_values_example[ind_start:ind_end])**2\\n\\n\\n # putting data into data frame\\n np_data = [freq, power]\\n np_data_trans = np.transpose(np_data)\\n raw_data = pd.DataFrame(data=np_data_trans, columns=[\\\"Freq\\\", \\\"Power\\\"])\\n #\\n #identify max and min values in data\\n max_value = raw_data.max()\\n min_value = raw_data.min()\\n print(\\\"Max values:\\\\n\\\" + str(max_value), \\\"\\\\nMin values:\\\\n\\\" + str(min_value))\\n #\\n #filter out values\\n # threshold = power > 0.0001\\n # you can set the thresholds here based on power or frequency and one of the threshold values needs to be an empty list\\n threshold_value = 1\\n threshold_power = [] # e.g. power < 100\\n threshold_freq = freq == 1 # e.g. freq < 100\\n #\\n if len(threshold_power) > 0:\\n filtered = pd.DataFrame(data=np_data_trans[threshold_power], columns=[\\\"F_Freq\\\", \\\"F_Power\\\"])\\n filter_str = \\\"Power\\\"\\n else:\\n filtered = pd.DataFrame(data=np_data_trans[threshold_freq], columns=[\\\"F_Freq\\\", \\\"F_Power\\\"])\\n y = filtered[\\\"F_Power\\\"]\\n # removed_outliers = y.between(y.quantile(.05), y.quantile(.95))\\n # outliers = pd.DataFrame(data=np_data_trans[:len(removed_outliers)], columns=[\\\"F_Freq\\\", \\\"F_Power\\\"])\\n # # print(\\\"Outerliers:\\\" + str(len(removed_outliers)))\\n filter_str = \\\"Frequency\\\"\\n #\\n # plt.plot(outliers)\\n # plt.show()\\n\\n #\\n #\\n # print(threshold_freq,threshold_power)\\n # filtered = pd.DataFrame(data=np_data_trans[threshold], columns=[\\\"F_Freq\\\", \\\"F_Power\\\"])\\n # identify max and min values in data\\n max_filt_value = raw_data.max()\\n min_filt_value = raw_data.min()\\n print(\\\"Filtered Max values:\\\\n\\\" + str(max_filt_value), \\\"\\\\nFiltered Min values:\\\\n\\\" + str(min_filt_value))\\n\\n # threshold = freq < 0.002\\n # filtered = pd.DataFrame(data=np_data_trans[threshold], columns=[\\\"F_Freq\\\", \\\"F_Power\\\"])\\n\\n # print(filtered)\\n #\\n #\\n #plot data\\n ax1 = plt.subplot(2, 1, 1)\\n # plt.figure(figsize=(5, 20))\\n plt.title(\\\"Power spectrum: \\\" + str(len(freq)) + \\\" samples\\\")\\n plt.plot(freq,power)\\n # plt.xlabel('frequency [hz]')\\n plt.ylabel('power')\\n #\\n ax2 = plt.subplot(2, 1, 2)\\n # plt.figure(figsize=(1, 5))\\n plt.title('Power spectrum: output based on filtered ' + filter_str + \\\" values\\\")\\n plt.plot(filtered[\\\"F_Freq\\\"], filtered[\\\"F_Power\\\"])\\n plt.ylabel(\\\"Power\\\")\\n plt.xlabel(\\\"Frequency[Hz]\\\")\\n #\\n # ax3 = plt.subplot(3, 1, 3)\\n\\n # # plt.figure(figsize=(1, 5))\\n # # ax3.margins(x=-0.25,y=-0.25)\\n # plt.title('Filtered data based on threshold parameter:', loc='left')\\n # plt.plot(freq[:np.sum(threshold)], power[:np.sum(threshold)])\\n # plt.ylabel(\\\"Power\\\")\\n # plt.xlabel(\\\"Frequency[Hz]\\\")\\n\\n plt.setp(ax1.get_xticklabels(), visible=True)\\n plt.setp(ax2.get_xticklabels(), visible=True)\\n plt.show()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49a89e8abf1c3b4848cbb34697d83d85\",\n \"score\": \"0.47116864\",\n \"text\": \"def retrieve_closest_indices(\\n df,\\n num_indices,\\n forecast_length,\\n window_size: int = 10,\\n distance_metric: str = \\\"braycurtis\\\",\\n stride_size: int = 1,\\n start_index: int = None,\\n include_differenced: bool = False,\\n include_last: bool = True,\\n verbose: int = 0,\\n):\\n array = df.to_numpy()\\n index = df.index\\n tlt_len = array.shape[0]\\n combined_window_size = window_size + forecast_length\\n # remove extra so last segment not included at all\\n # have the last window end evenly\\n max_steps = array.shape[0] - combined_window_size\\n if not include_last:\\n max_steps = max_steps - forecast_length\\n if start_index is None:\\n # handle massive stride size relative to data\\n start_index = 0\\n if stride_size * 6 < array.shape[0]:\\n start_index = max_steps % stride_size\\n if num_indices > (max_steps / stride_size):\\n raise ValueError(\\\"num_validations/num_indices too high for this dataset\\\")\\n window_idxs = window_id_maker(\\n window_size=combined_window_size,\\n start_index=start_index,\\n max_steps=max_steps,\\n stride_size=stride_size,\\n skip_size=1,\\n )\\n # calculate distance between all points and last window of history\\n if distance_metric == \\\"nan_euclidean\\\":\\n from sklearn.metrics.pairwise import nan_euclidean_distances\\n\\n res = np.array(\\n [\\n nan_euclidean_distances(\\n array[:, a][window_idxs[:, :window_size]],\\n array[(tlt_len - window_size) : tlt_len, a].reshape(1, -1),\\n )\\n for a in range(array.shape[1])\\n ]\\n )\\n if include_differenced:\\n array_diff = np.diff(array, n=1, axis=0)\\n array_diff = np.concatenate([array_diff[0:1], array_diff])\\n res_diff = np.array(\\n [\\n nan_euclidean_distances(\\n array_diff[:, a][window_idxs[:, :window_size]],\\n array_diff[(tlt_len - window_size) : tlt_len, a].reshape(1, -1),\\n )\\n for a in range(array_diff.shape[1])\\n ]\\n )\\n res = np.mean([res, res_diff], axis=0)\\n else:\\n from scipy.spatial.distance import cdist\\n\\n res = np.array(\\n [\\n cdist(\\n array[:, a][window_idxs[:, :window_size]],\\n array[(tlt_len - window_size) : tlt_len, a].reshape(1, -1),\\n metric=distance_metric,\\n )\\n for a in range(array.shape[1])\\n ]\\n )\\n if include_differenced:\\n array_diff = np.diff(array, n=1, axis=0)\\n array_diff = np.concatenate([array_diff[0:1], array_diff])\\n res_diff = np.array(\\n [\\n cdist(\\n array_diff[:, a][window_idxs[:, :window_size]],\\n array_diff[(tlt_len - window_size) : tlt_len, a].reshape(1, -1),\\n metric=distance_metric,\\n )\\n for a in range(array_diff.shape[1])\\n ]\\n )\\n res = np.mean([res, res_diff], axis=0)\\n # find the lowest distance historical windows\\n res_sum = np.nansum(res, axis=0)\\n num_top = num_indices\\n # partial then full sort\\n res_idx = np.argpartition(res_sum, num_top, axis=0)[0:num_top]\\n res_idx = res_idx[np.argsort(res_sum[res_idx].flatten())]\\n if verbose > 1:\\n print(\\n f\\\"similarity validation distance metrics: {res_sum[res_idx].flatten()} with last window: {res_sum[-1].item()}\\\"\\n )\\n select_index = index.to_numpy()[window_idxs[res_idx]]\\n if select_index.ndim == 3:\\n res_shape = select_index.shape\\n select_index = select_index.reshape((res_shape[0], res_shape[2]))\\n return select_index\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2588c1695f6d2ff319e5e4df3335fee5\",\n \"score\": \"0.47096744\",\n \"text\": \"def estimate_local_offset(self,window_length=4, in_place=False):\\n \\n import pyacs.gts.lib.gts_estimators\\n \\n ### take the data\\n clean_data = self.remove_outliers().data\\n \\n local_offsets_values = np.zeros((len(self.offsets_dates),7))\\n local_offsets_values[:,0] = self.offsets_dates\\n\\n \\n noffset = len(self.offsets_dates)\\n \\n list_index_t0_i = np.searchsorted(clean_data[:,0],local_offsets_values[:,0]) \\n\\n index_local = []\\n for i in list_index_t0_i:\\n index_local.append(range(i-window_length,i+window_length))\\n \\n index_local = np.hstack(index_local)\\n index_local = np.delete(index_local,np.argwhere(index_local<0))\\n index_local = np.delete(index_local,np.argwhere(index_local>=len(clean_data[:,0])))\\n\\n data_local = np.take(clean_data,index_local,axis=0)\\n \\n ndate = len(data_local[:,0])\\n for k in range(1,4): \\n A = np.ones((ndate,(1+noffset)), float)\\n for i in range(ndate):\\n for j in range(noffset):\\n if data_local[i,0] <= local_offsets_values[j,0]: A[i,(1+j)] = 0.\\n \\n X,s_X = pyacs.gts.lib.gts_estimators.least_square(A,data_local[:,k])[0:2]\\n if X is not None:\\n local_offsets_values[:,k] = X[1:]\\n local_offsets_values[:,3+k] = s_X[1:]\\n else: local_offsets_values = None\\n\\n if in_place:\\n self.offsets_values=local_offsets_values\\n return(self)\\n else:\\n new_Gts=self.copy()\\n new_Gts.offsets_values=local_offsets_values\\n return(new_Gts)\\n \\n\\n return local_offsets_values\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39050a1aaecc7ce8c105f3ba45a57801\",\n \"score\": \"0.4698258\",\n \"text\": \"def pos_conv(window, a):\\n return (window/2)*a\",\n \"title\": \"\"\n },\n {\n \"docid\": \"89946c7658ec7808df0b3caa9f9eba84\",\n \"score\": \"0.46949366\",\n \"text\": \"def get_coords(df):\\n\\n if 'start' in df.columns:\\n return df\\n df['start'] = df.pos.astype(int)\\n df['end'] = ( df.pos + df.peptide.str.len() ).astype(int)\\n return df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60721c34fa03a64ae66f0a07e73bb6fe\",\n \"score\": \"0.46922645\",\n \"text\": \"def get_window_start_positions(self, term_positions):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60721c34fa03a64ae66f0a07e73bb6fe\",\n \"score\": \"0.46922645\",\n \"text\": \"def get_window_start_positions(self, term_positions):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfb6697653c764461ac63b5d61753b9b\",\n \"score\": \"0.46884894\",\n \"text\": \"def Vortex(df, n): # n = 14\\n df = df.reset_index()\\n i = 0\\n TR = [0]\\n while i < df.index[-1]:\\n Range = max(df.at[i + 1, 'High'], df.at[i, 'Close']) \\\\\\n - min(df.at[i + 1, 'Low'], df.at[i, 'Close'])\\n TR.append(Range)\\n i = i + 1\\n i = 0\\n VM = [0]\\n while i < df.index[-1]:\\n Range = abs(df.at[i + 1, 'High'] - df.at[i, 'Low']) \\\\\\n - abs(df.at[i + 1, 'Low'] - df.at[i, 'High'])\\n VM.append(Range)\\n i = i + 1\\n VI = pd.Series(pd.Series(VM).rolling(window=n).sum() / pd.Series(VM).rolling(window=n).sum(), \\n name='Vortex_' + str(n))\\n df = df.join(VI).set_index(\\\"index\\\")\\n return df[\\\"Vortex_\\\" + str(n)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9865f6926b01fbf5d26c240f6852c9e7\",\n \"score\": \"0.46829778\",\n \"text\": \"def _whctrs_twin(anchor):\\n\\n l = anchor[1] - anchor[0] + 1\\n x_ctr = anchor[0] + 0.5 * (l - 1)\\n return l, x_ctr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c7b35985b3660502003f2422d803644c\",\n \"score\": \"0.4675449\",\n \"text\": \"def count_windowed_increasing(measurements: Iterator[str]) -> int:\\n return count_increasing(window_sum(measurements))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bb957aecf87233a043e7d6670a00381\",\n \"score\": \"0.46754074\",\n \"text\": \"def rolling_func(func,data, window):\\n rolling_mean = func(rolling_window(data, window), axis=1)\\n \\n # How much items in front and behind of item\\n # are part of the rolling window for each element?\\n n_front = window//2\\n n_behind = window//2 - 1 + window%2\\n \\n # use different windows for endpoints where no complete window can be applied\\n\\n endpoints_front = np.zeros(n_front)\\n for i in range(n_front):\\n # window as large as possible but with maximal n_behind elements after current element inside window\\n endpoints_front[i]=func(data[:1+n_behind+i])\\n \\n endpoints_behind = np.zeros(n_behind)\\n for i in range(n_behind):\\n # window as large as possible but with maximal n_front elements in front of current element inside window\\n endpoints_behind[-(1+i)]=func(data[-(1+n_front+i):])\\n \\n # add endpoints\\n return np.hstack([endpoints_front,rolling_mean,endpoints_behind])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eac087dc56dc9bd3c3da275e373fb8c3\",\n \"score\": \"0.4673865\",\n \"text\": \"def produce_easy_features(df):\\n df['date'] = pd.to_datetime(df['date'], format='%Y%m%d')\\n df['week_number'] = [date.isocalendar()[1] for date in list(pd.Series(df['date']))]\\n teams = set(list(df['team']))\\n df.sort_values(by=['date'], axis=0, inplace=True)\\n dfs = []\\n for team in teams:\\n temp_df = df[df['team'] == team]\\n temp_df['scored_cumulative'] = temp_df.scored.cumsum()\\n temp_df['conceded_cumulative'] = temp_df.conceded.cumsum()\\n temp_df['performance_cumulative'] = temp_df.result.cumsum()*2\\n temp_df['away_perf_cumulative'] = temp_df.away_res.cumsum()*2\\n temp_df['home_perf_cumulative'] = temp_df.home_res.cumsum()*2\\n\\n # for loop here to create rolling sums for n=1 to n=5\\n\\n for i in [1,2,3,4,5]:\\n col_name_1 = 'avg_scored_past_' + str(i)\\n col_name_2 = 'avg_conceded_past_' + str(i)\\n col_name_3 = 'perf_past_' + str(i)\\n temp_df[col_name_1] = temp_df['scored'].rolling(min_periods=0, window=i).mean()\\n temp_df[col_name_2] = temp_df['conceded'].rolling(min_periods=0, window=i).mean()\\n temp_df[col_name_3] = temp_df['result'].rolling(min_periods=0, window=i).sum()*2\\n #temp_df['avg_scored_past_5'] = temp_df['scored'].rolling(min_periods=0, window=5).mean()\\n #temp_df['avg_conceded_past_5'] = temp_df['conceded'].rolling(min_periods=0, window=5).mean()\\n #temp_df['perf_past_5'] = temp_df['result'].rolling(min_periods=0, window=5).sum()*2\\n #temp_df['perf_past_1'] = temp_df['result'].rolling(min_periods=0, window=1).sum()*2\\n dfs.append(temp_df)\\n return pd.concat(dfs, axis=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f90665438d7306ebf2a3b5bf29dfb738\",\n \"score\": \"0.467324\",\n \"text\": \"def accuracy_at_stg_change_trials(df, subj_unq, prev_w=10, nxt_w=10, conv_w=10):\\n mat_perfs = {}\\n for i_s, sbj in enumerate(subj_unq):\\n acc = df.loc[df['subject_name'] == sbj, 'hithistory'].values\\n if conv_w > 0:\\n accur_conv = np.convolve(acc, np.ones((conv_w,))/conv_w,\\n mode='same')\\n else:\\n accur_conv = acc\\n stg = df.loc[df['subject_name'] == sbj, 'new_stage'].values\\n # create the extremes (a 0 at the beggining and a 1 at the ending)\\n stg_diff = np.diff(stg) # change of stage\\n stg_chng = np.where(stg_diff != 0)[0] # index where stages change\\n # We go over indexes where stage changes and plot chunks from ind_t-1\\n # to ind_t\\n for i_stg in range(len(stg_chng)):\\n # color = stg_exp[stg_chng[i_stg-1]+1]-1\\n stg_prev = stg[stg_chng[i_stg]] # get stage before the change\\n stg_nxt = stg[stg_chng[i_stg]+1] # get stage after the change\\n assert stg_prev != stg_nxt, 'stages are supposed to be different'\\n key = str(stg_prev)+'-'+str(stg_nxt) # e.g. 1-2\\n if key not in mat_perfs.keys():\\n mat_perfs[key] = []\\n # build chunk\\n i_previo = max(0, stg_chng[i_stg]-prev_w)\\n i_next = stg_chng[i_stg]+nxt_w\\n chunk = -min(0, stg_chng[i_stg]-prev_w)*[np.nan] +\\\\\\n accur_conv[i_previo:i_next].tolist() +\\\\\\n max(0, i_next-len(acc))*[np.nan]\\n # add chunk to the dictionary\\n mat_perfs[key].append(chunk)\\n # dictionary of the mean of the performance\\n mat_mean_perfs = {}\\n # dictionary of the standard deviation of the performace\\n mat_std_perfs = {}\\n # list of the number of samples of each change of stage\\n number_samples = []\\n for key in mat_perfs.keys():\\n number_samples.append(len(mat_perfs[key])) # save number of samples\\n assert np.std([len(p) for p in mat_perfs[key]]) == 0\\n mat_mean_perfs[key] = np.nanmean(np.array(mat_perfs[key]), axis=0)\\n sqrt_n_smpls = np.sqrt(np.array(mat_perfs[key]).shape[0])\\n mat_std_perfs[key] =\\\\\\n np.nanstd(np.array(mat_perfs[key]), axis=0)/sqrt_n_smpls\\n return mat_mean_perfs, mat_std_perfs, number_samples\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993286,"cells":{"query_id":{"kind":"string","value":"06aff972765844d6171c4bea536cf1d3"},"query":{"kind":"string","value":"Gets reference row. This is the row that contains the names of the intersecting columns, allowing fetching of cells in this row via passage of a reference string"},"positive_passages":{"kind":"list like","value":[{"docid":"16be2a035dc6ea6c68d226582881e787","score":"0.7425297","text":"def reference_row(self) -> 'Row':\n return self.sheet.reference_row","title":""}],"string":"[\n {\n \"docid\": \"16be2a035dc6ea6c68d226582881e787\",\n \"score\": \"0.7425297\",\n \"text\": \"def reference_row(self) -> 'Row':\\n return self.sheet.reference_row\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"17e80d7b4aa4487e8671cc28a483a5f8","score":"0.6681498","text":"def reference_row_index(self) -> int:\n return self._reference_row_index","title":""},{"docid":"43cff7c6a2da37b232d6ddf915a04635","score":"0.60901815","text":"def getRow(self, row):\r\n\t\treturn self.cells[row*self.size:row*self.size+self.size]","title":""},{"docid":"3efb1601d0de607527791b165e7ba92e","score":"0.60155094","text":"def name_cell_index(self) -> int:\n return self.sheet.reference_row_index","title":""},{"docid":"855b836f2426e2b52b44233ceef00f99","score":"0.5932592","text":"def get_row(self):\n return self.__row","title":""},{"docid":"8918d0e4da5ae088c7821b8e750dda19","score":"0.59267473","text":"def get_row(self):\n return self._row","title":""},{"docid":"67d731f197b35f1b8b8fbf7ff6421688","score":"0.588488","text":"def get_row(self, *args):\n return _vnl_matrixPython.vnl_matrixLD_get_row(self, *args)","title":""},{"docid":"91ad6f08516014ed83b9bea48761323c","score":"0.58738667","text":"def get_row(self):\n\n return self.row","title":""},{"docid":"595bafe88a1a47fd20b75bfad4d649d4","score":"0.5873565","text":"def reference(self):\n with TRN:\n sql = \"SELECT reference_id FROM qiita.{0} WHERE {1}=%s\".format(\n self._table, self._column_id)\n TRN.add(sql, [self.id])\n return TRN.execute_fetchlast()","title":""},{"docid":"9263bf3bef42ee2ac31fcf298fadf9d1","score":"0.5770706","text":"def get_by_name(self, name: int or float or str) -> 'Line':\n for cell in self.reference_line:\n if cell.value == name:\n if self._contents_type == LineSeries.COLUMNS_STR:\n return cell.column\n elif self._contents_type == LineSeries.ROWS_STR:\n return cell.row","title":""},{"docid":"60486d9becbce933b38aa902ec088351","score":"0.57576084","text":"def GetRow(self):","title":""},{"docid":"60486d9becbce933b38aa902ec088351","score":"0.57576084","text":"def GetRow(self):","title":""},{"docid":"60486d9becbce933b38aa902ec088351","score":"0.57576084","text":"def GetRow(self):","title":""},{"docid":"18cd0c554953f3d09e32d236c266c92d","score":"0.57569665","text":"def get_row(self):\n\n return self._row","title":""},{"docid":"18cd0c554953f3d09e32d236c266c92d","score":"0.57569665","text":"def get_row(self):\n\n return self._row","title":""},{"docid":"f348e3f669e199e76fcdf166f3370a73","score":"0.5747943","text":"def get_row_index_from_name(\n self,\n row_name: int or float or str\n ) -> int or None:\n for y, cell in enumerate(self.reference_column):\n if cell.value == row_name:\n return y","title":""},{"docid":"e0fe24054b1370ba4be02135d5e78728","score":"0.5743387","text":"def get_row(self, *args):\n return _vnl_matrixPython.vnl_matrixSC_get_row(self, *args)","title":""},{"docid":"0a963a16271d8edea9658f4ae5cda488","score":"0.5716616","text":"def get_row(self):","title":""},{"docid":"c3bc51241fbe8bf997bc80cea7ebc0a8","score":"0.57039917","text":"def get_row(self):\n return self._row_pos","title":""},{"docid":"afd25bd687039534aced3f7f7285e257","score":"0.5674896","text":"def get_cell_ref(cls, row_no, col_no, rowabs=False, colabs=False):\n buf = bytearray()\n if colabs:\n buf.append(ord(cls._fixed_ind))\n # Put the column letter into the buffer\n buf.extend(cls.get_col_ref(col_no))\n if rowabs:\n buf.append(ord(cls._fixed_ind))\n # bytearray default encoding is utf-8\n buf.extend(bytearray(str(row_no + 1), 'utf-8'))\n return buf.decode()","title":""},{"docid":"55ec38d3138244bbf4b515f97f57b2ef","score":"0.5641614","text":"def row(self):\n return self.__row","title":""},{"docid":"eab63a82d664717538c82598522ce4ea","score":"0.5638981","text":"def get_row(self, row):\n if not isinstance(row, int):\n row = self.get_col(0)._grid.table_data.index([row])\n temp = self._grid.table_data[row]\n temp2 = Tableist(1, self.ncols)\n for col, item in enumerate(temp):\n temp2[0][col] = item\n return temp2","title":""},{"docid":"6a90cc3927f9b2b8905361da67c33c19","score":"0.5628799","text":"def row(self):\n return self._row","title":""},{"docid":"3475c4d81090794996614c52b74bdfe5","score":"0.5627094","text":"def cref_row_no(cls, crs):\n numindex = cls.cref_num_index(crs)\n return int(crs[numindex:]) - 1 # pure python integer will not overflow","title":""},{"docid":"4dede537cfa53dfede894b7d55500d2a","score":"0.5620692","text":"def get_row(self, *args):\n return _vnl_matrixPython.vnl_matrixSL_get_row(self, *args)","title":""},{"docid":"41371732f085fa32c91cfc024eb40cbb","score":"0.5595464","text":"def X_REF(self):\n return self.data[[col for col in self.cols if col.startswith('REF')]]","title":""},{"docid":"5abf37dc3bc4d794d752f98d9ba3376b","score":"0.55809397","text":"def getRowWithColumns(self, tableName, row, columns):\r\n pass","title":""},{"docid":"c474bf35600f44b4242698b23f0e51e4","score":"0.55617553","text":"def get_row(self, *args):\n return _vnl_matrixPython.vnl_matrixD_get_row(self, *args)","title":""},{"docid":"dd0fc27bae095056c7bfaf71b22d67c0","score":"0.55575746","text":"def get_row(self, *args):\n return _vnl_matrixPython.vnl_matrixUI_get_row(self, *args)","title":""},{"docid":"862d6c1a61faff79a8129337f41df190","score":"0.5543766","text":"def get_selected_row(self) -> Optional[str]:\n if self._selected_row is not None and self.lines:\n return self.lines[self._selected_row]\n return None","title":""},{"docid":"31f7edd2c369638f020a6c4ca26eda15","score":"0.55340093","text":"def getRow(self, tableName, row):\r\n pass","title":""},{"docid":"5f92cc094ec3e1ef16d4c5a5173fe7a4","score":"0.55256516","text":"def find_row(self, point):\n\t\tns_tableview = self._ns_inner_view\n\t\treturn ns_tableview.rowAtPoint_(point)","title":""},{"docid":"4a9bfdb0820c186e91679e7c1d47e0e0","score":"0.55224717","text":"def get_row(self, *args):\n return _vnl_matrixPython.vnl_matrixSI_get_row(self, *args)","title":""},{"docid":"4a5ae8c9d14b3e3957ce041572427829","score":"0.55215347","text":"def getRow(self, tableName, row):\r\n self.send_getRow(tableName, row)\r\n return self.recv_getRow()","title":""},{"docid":"8f1333546dddb5c70865319ec3e17dbc","score":"0.55126405","text":"def get_row(self, *args):\n return _vnl_matrixPython.vnl_matrixF_get_row(self, *args)","title":""},{"docid":"f46c708aa67a202546f5cbb6b4b2bde3","score":"0.5503267","text":"def _get_exc_sl_ref_per_row(row):\n return rsh.get_exc_sl(\n alpha=alpha,\n beta=beta,\n # reference total Exc computed based of ref_value\n exc=row['ref_value'] - (row['Etot'] - row['Exc']),\n exx=row['Exx'],\n exx_lr=row['Exxlr'],\n enlc=row['Enlc'])","title":""},{"docid":"4ef026ca19ebed7ea9623668c8ae79df","score":"0.5452422","text":"def getRow(self, tableName, row):\n self.send_getRow(tableName, row)\n return self.recv_getRow()","title":""},{"docid":"f9fc5960394b5faed84ccfd736d914cf","score":"0.54458845","text":"def getRowWithColumns(self, tableName, row, columns):\n pass","title":""},{"docid":"36ebaebd84bf33eb010ee8ec335ca350","score":"0.54457474","text":"def getRowWithColumns(self, tableName, row, columns):\r\n self.send_getRowWithColumns(tableName, row, columns)\r\n return self.recv_getRowWithColumns()","title":""},{"docid":"63940184c6eea39f5d711a03b4f713b4","score":"0.5397386","text":"def get_row(self, *args):\n return _vnl_matrixPython.vnl_matrix_vcl_complexLD_get_row(self, *args)","title":""},{"docid":"d434adfb59708d8d656c310b5fc93865","score":"0.53879786","text":"def get_row(cls, chromosome, row, num_col):\r\n\t\tif row > (len(chromosome.dna) // num_col) - 1: raise ValueError(\"Chromosome doesn`t have this much rows.\")\r\n\t\treturn GAHelper.chunckdna(chromosome, num_col)[row]","title":""},{"docid":"92779120f18efe582ad7cd12d9a9d10c","score":"0.5371809","text":"def get_row_by_name(self, row_name: int or str or float) -> 'Row':\n y = self.get_row_index_from_name(row_name)\n return self.get_row_by_index(y) if y is not None else None","title":""},{"docid":"9e384efc2591dc2399605f3b2cbdf9fe","score":"0.53662145","text":"def get_row(self, idx):\n return self.table[idx][:]","title":""},{"docid":"2fcb3a1bc122773291a82bcd9439ece2","score":"0.53654224","text":"def row(self) -> int:\n return self.location.row","title":""},{"docid":"8e5d1311af496637a0abe9124ff0011d","score":"0.5365324","text":"def OwnerEventReference(self, row):\n ref = self.EventReference(row)\n if ref is not None:\n return ref\n eventTypeID = row.eventTypeID\n referenceID = row.referenceID\n if referenceID is None:\n return ['', '', '']\n et = self.cache.Index(const.cacheEventTypes, eventTypeID)\n eventGroupID = et.eventGroupID\n referenceColumn = et.referenceColumn\n if referenceColumn:\n if referenceColumn in ('dungeonID', 'instanceID'):\n if referenceColumn == 'dungeonID':\n dungeonID = referenceID\n elif 'dungeonID' in row.__columns__:\n dungeonID = row.dungeonID\n else:\n dungeonID = None\n if dungeonID in self.cache.Index(const.cacheDungeonDungeons):\n dungeonName = self.Link('/gd/dungeons.py', localization.GetByMessageID(self.cache.Index(const.cacheDungeonDungeons, dungeonID).dungeonNameID), {'action': 'Dungeon',\n 'dungeonID': dungeonID})\n else:\n dungeonName = self.FontRed('Dungeon not found')\n return [referenceID, 'Dungeon Instance', dungeonName]\n if referenceColumn == 'missionCountID':\n if 'missionID' in row.__columns__:\n missionName = self.MissionLink(row.missionID)\n else:\n missionName = self.FontRed('Mission not found')\n return [referenceID, 'Mission Instance', missionName]\n if referenceColumn == 'tutorialID':\n tutorialIx = sm.GetService('tutorialSvc').GetTutorialsIx()\n if referenceID in tutorialIx:\n tutorialName = localization.GetByMessageID(tutorialIx[referenceID].tutorialNameID)\n return [referenceID, 'Tutorial', self.Link('/gd/tutorials.py', tutorialName, {'action': 'Tutorial',\n 'tutorialID': referenceID})]\n else:\n return [referenceID, 'Tutorial', self.FontRed('Tutorial not found')]\n if referenceColumn == 'planetID':\n return [self.ItemID(referenceID), 'Planet', self.PlanetLink(referenceID)]\n if referenceColumn == 'launchID':\n return [self.Link('/gm/owner.py', referenceID, {'action': 'PlanetaryLaunch',\n 'launchID': referenceID}), 'Planetary Launch', '']\n if referenceColumn == 'offerID':\n if eventTypeID in (logConst.eventStoreGaveCredits, logConst.eventStoreTookCredits):\n rx = self.DB2.SQLBigInt('*', 'zevent.genericEvents', \"eventTable = 'O'\", '', 'eventID', row.eventID)\n refID = '%s %s' % ('Gave' if eventTypeID == logConst.eventStoreGaveCredits else 'Took', 'ISK' if rx[0].int_1 == const.creditsISK else 'AURUM')\n refType = '%10.2f' % (rx[0].money_1 or 0.0)\n if rx[0].bigint_1 is not None:\n refText = self.Link('/gm/accounting.py', 'Transaction ID: %s' % rx[0].bigint_1, {'action': 'TxDetail',\n 'transactionID': rx[0].bigint_1})\n else:\n refText = ''\n return [refID, refType, refText]\n return [referenceID, 'Offer', self.GetTooltip(href='/gd/store.py', ajax='/gm/store.py?action=OfferTooltip&offerID=%d' % referenceID, title='Offer Details', caption='Offer Details')]\n if referenceColumn == 'taleID':\n return [self.Link('/gm/tale.py', referenceID, {'taleID': referenceID}), 'Tale ID', self.Link('/gm/tale.py', 'Tale', {'taleID': referenceID})]\n if referenceColumn == 'killID':\n return [self.Link('/gm/character.py', referenceID, {'action': 'KillReport',\n 'killID': referenceID}), 'Kill Report', self.Link('/gm/character.py', 'Kill Report', {'action': 'KillReport',\n 'killID': referenceID})]\n if eventTypeID == logConst.eventCharacterPaused:\n return ['', 'User Account', 'The users account is not active, all training paused on all characters! (skills, RP)']\n if eventTypeID == logConst.eventCharacterResumed:\n return ['', 'User Account', 'The users account has been reactivated, all training resumed on all characters! (skills, RP)']\n if eventTypeID == logConst.eventResearchStopped:\n return [referenceID, 'Research points lost', '']\n if eventTypeID == logConst.eventResearchGMEdit:\n return [self.ItemID(referenceID), 'Character', self.CharacterLink(referenceID)]\n if eventTypeID == logConst.eventStoreOfferDeleted:\n return [referenceID, '', 'it was deleted, and lost for all time!']\n if eventGroupID == const.groupAlliance:\n if util.IsCorporation(referenceID):\n return [self.ItemID(referenceID), 'Corporation', self.CorporationLink(referenceID)]\n else:\n return [self.Link('/gm/logs.py', str(referenceID), {'action': 'OwnerEvent',\n 'eventID': referenceID}), 'Alliance Event', '']\n if eventTypeID in (logConst.eventItemReimburse,\n logConst.eventItemEdit,\n logConst.eventItemEditFrom,\n logConst.eventItemEditTo):\n referenceText = ''\n if 'uiItemID' in row.__columns__:\n if row.uiItemID:\n referenceText = self.ItemID(row.uiItemID)\n referenceText = self.SplitAdd(referenceText, self.TypeLink(row.uiTypeID))\n return [self.Link('/gm/logs.py', referenceID, {'action': 'ItemEvent',\n 'eventID': referenceID}), 'Item Event', referenceText]\n if eventTypeID == logConst.eventSecStatusGmRollback:\n return [self.Link('/gm/logs.py', str(referenceID), {'action': 'OwnerEvent',\n 'eventID': referenceID}), 'Original Event', '']\n refType = 'Unknown'\n if et.referenceTable and et.referenceColumn:\n refType = '%s.%s' % (et.referenceTable, et.referenceColumn)\n elif et.referenceTable:\n refType = et.referenceTable\n elif et.referenceColumn:\n refType = et.referenceColumn\n return [referenceID, refType, 'Unknown']","title":""},{"docid":"91ca6cdf36a55aac0c397916c180715f","score":"0.5362135","text":"def getRowWithColumns(self, tableName, row, columns):\n self.send_getRowWithColumns(tableName, row, columns)\n return self.recv_getRowWithColumns()","title":""},{"docid":"9a24887ad37e282993685f03046087c7","score":"0.5359364","text":"def get_ref(self):\n\n return [f[\"value\"] for f in self.fields if f[\"name\"] == \"Reference\"][0]","title":""},{"docid":"84e2e9aa35bfedcdfab386fcbe19b4bb","score":"0.534794","text":"def getrow(self, index, startcol, endcol):\n return self.df.iloc[index, startcol:endcol]","title":""},{"docid":"7ed90261f92d98864686dd3b33e752da","score":"0.53409266","text":"def getRow(self, tableName, row):\n pass","title":""},{"docid":"e8f7a3623f85a7e175993eba7103c0e3","score":"0.5315929","text":"def get_row(self, *args):\n return _vnl_matrixPython.vnl_matrixUL_get_row(self, *args)","title":""},{"docid":"fabbda55f6e8bfb35c2b1d92332f68e4","score":"0.5308592","text":"def termRows():\n return termRowsCols()[0]","title":""},{"docid":"6aadb04d4e40a0cf711b69e60584f845","score":"0.5308141","text":"def get_row(grid, rownum):\r\n return grid[rownum]","title":""},{"docid":"2e601b6a8fccfc6c28e4f8c4c57c473a","score":"0.5283108","text":"def jobRow(self):\n return self.__jobRow","title":""},{"docid":"3f603d1a10ecef5ef13ecc7c1ccc131c","score":"0.5270081","text":"def _get_table_row(file_handle):\n row = ''\n filename = ''\n while '</tr>' not in row and '</table>' not in row:\n line = file_handle.readline()\n name = re.search(r'href=\"(.*?)\"', line)\n row += line\n if name:\n filename = name.group(1)\n return row, filename","title":""},{"docid":"42de5f3ee63d52d8f5c3f2789d251060","score":"0.5252613","text":"def getRow(self, r):\n\n return self.matx[r]","title":""},{"docid":"9d2932ce06f362ea0c05267de1808f58","score":"0.52517027","text":"def get_row(self, row, pos1='a', pos2='i'):\n\n if row not in self._rows or pos1 not in self._columns or pos2 not in self._columns:\n return None\n\n return self._layout[int(row) - 1][ord(pos1) - ord('a'): ord(pos2) - ord('a') + 1]","title":""},{"docid":"9f1ee37b62cb5fa7ccdacb3b1f9749a0","score":"0.52511394","text":"def _source_cell(self):\n return self._uno_sheet.getCellByPosition(*self.position)","title":""},{"docid":"94f7a4e482565fc10ffaa9708c457ad9","score":"0.52166784","text":"def target_start_row(self):\n return self._target_start_row","title":""},{"docid":"240d858d78ad06cc3754b0e6213a1c60","score":"0.52110195","text":"def retriveRow(row, surround=5):\n # Account for the 1-based nature of R\n centre = row[\"centre\"] - 1\n seq = row[\"seq\"]\n len_ = row[\"len\"]\n\n start = max(centre - surround, 0)\n end = min(centre + surround + 1, len_)\n seqSlice = seq[start:end]\n\n nullChar = \"x\"\n\n # Test for boundary condition at end\n endOverlap = (centre + surround) - len_\n\n if endOverlap >= 0:\n seqSlice += nullChar * (endOverlap + 1)\n\n # Test for the boundary conditions at the start\n startOverlap = centre - surround\n if startOverlap <= 0:\n seqSlice = nullChar * (-startOverlap) + seqSlice\n\n return seqSlice","title":""},{"docid":"d6411ada4d9fac26ba46cbb83630a6ed","score":"0.5209201","text":"def factory(sheet: 'Sheet', index: int, reference_index: int) -> 'Row':\n # first try to find a pre-existing row with that sheet + index\n row_key = sheet, index\n try:\n row = Row._all[row_key]\n except KeyError:\n row = Office.get_row_class()(sheet, index, reference_index)\n return row","title":""},{"docid":"b71f6af4efecf274c46a07520488e8e3","score":"0.5198504","text":"def _find_row(self, matrix2d, row):\n return np.where((matrix2d == row).all(axis=1))[0]","title":""},{"docid":"e9c4e9c6e420032a1f58e1434783c731","score":"0.5156211","text":"def get_row(i):\n if i < len(TGAS):\n ind = i\n else:\n ind = np.where(TGAS.source_id==i)[0][0]\n return TGAS.iloc[ind]","title":""},{"docid":"290bc52c706505179260a4e32c77bfd6","score":"0.51452243","text":"def match_person_to_row(name: str, sample_personas_df: pd.DataFrame) -> pd.Series:\n first_name, last_name = name.split(\" \")[0], name.split(\" \")[1]\n name_match_condition = (\n sample_personas_df[\"person.firstName\"].str.strip() == first_name\n ) & (sample_personas_df[\"person.lastName\"].str.strip() == last_name)\n persona_row_match = sample_personas_df.loc[name_match_condition].iloc[0]\n return persona_row_match","title":""},{"docid":"9555e027dcf5dd1d8a00cee68308d836","score":"0.51399124","text":"def get_row(self, *args):\n return _vnl_matrixPython.vnl_matrixUC_get_row(self, *args)","title":""},{"docid":"e20be0779d45a1391b37d52522100a3c","score":"0.51265526","text":"def get_row_by_index(self, row_index: int or str) -> 'Row':\n return Row.factory(\n sheet=self,\n index=row_index,\n reference_index=self.reference_row_index\n )","title":""},{"docid":"4809c55cf8400667cbc9e771b9d303bb","score":"0.5099705","text":"def cell_ref_array(self):\n return self.__cell_ref_array","title":""},{"docid":"193f25a10e7b41f517785697ff169525","score":"0.5098722","text":"def row(self) -> int:\n return self._row","title":""},{"docid":"6188f855956e6c610b5d09109176bf80","score":"0.5097331","text":"def get_rows(self, resource_name):\n count = 0\n resource_name_row_start = 0\n resource_name_row_end = 0\n self.load_md()\n for line in self.all_lines:\n count += 1\n if line.startswith('| '+resource_name):\n resource_name_row_start = count\n\n for no in range(count, len(self.all_lines)):\n if self.all_lines[no].startswith('| **'):\n resource_name_row_end = no\n break\n\n return resource_name_row_start, resource_name_row_end","title":""},{"docid":"e4b01664dda33a8fdddb31e06067cc0b","score":"0.5084442","text":"def name_cell_index(self) -> int:\n return self.sheet.reference_column_index","title":""},{"docid":"0c79e22522e46fd9c3b22a664e2eb4e7","score":"0.5077693","text":"def get_row(self, *args):\n return _vnl_matrixPython.vnl_matrix_vcl_complexF_get_row(self, *args)","title":""},{"docid":"851c3b57e1b19823decf6c1d504a4454","score":"0.50762177","text":"def get_row(self, binary=False, columns=None, raw=None, prep_stmt=None):\n try:\n rows, eof = self.get_rows(count=1, binary=binary, columns=columns,\n raw=raw, prep_stmt=prep_stmt)\n if rows:\n return (rows[0], eof)\n return (None, eof)\n except IndexError:\n # No row available\n return (None, None)","title":""},{"docid":"d3a67f18dbe5451cbd827e8d1ee4330a","score":"0.5074132","text":"def match_property_to_row(address: str, sample_address_df: pd.DataFrame) -> pd.Series:\n address_line1 = address.split(\",\")[0].strip()\n address_match_condition = (\n sample_address_df[\"address.addressLine1\"].str.strip() == address_line1\n )\n address_row_match = sample_address_df.loc[address_match_condition].iloc[0]\n return address_row_match","title":""},{"docid":"e0a4836b8b11de1fb820dee433d5c98d","score":"0.50565726","text":"def get_cell(self):\n return self._cell","title":""},{"docid":"1eca122bb6c3bade91532821854d58e1","score":"0.5054133","text":"def get_cell_by_name(self, row, name):\n col = self.key[name]\n self.logger.debug(\"get cell by name %s, row = %d, col = %d\" % (name, row, col))\n return self.get_cell(row, self.key[name])","title":""},{"docid":"38fa906d4d54abb45d1d35ceb9abed0d","score":"0.50474244","text":"def get_row_index(self, candidate):\n return self.candidate_index[candidate.id]","title":""},{"docid":"fe4c3ce8d670b9756e1efb0f047c6096","score":"0.50449455","text":"def GetGridCursorRow(self):","title":""},{"docid":"1fac0c14ef21971f7b16ce51a6f4f2fc","score":"0.50388193","text":"def get_row(self, row_identifier: int or float or str) -> 'Row':\n if isinstance(row_identifier, str):\n return self.get_row_by_name(row_identifier)\n else:\n return self.get_row_by_index(row_identifier)","title":""},{"docid":"de1f19e1891ffcb7499b42cc8d249c1c","score":"0.50382864","text":"def _get_xref(self, grid):\n # Fetch a variable from the in file\n rows = self.infile.variables['ROW'][:]\n cols = self.infile.variables['COL'][:]\n self.stk_num = rows.shape[2]\n for stack in range(rows.shape[2]):\n row = int(rows[0][0][stack][0])-1\n col = int(cols[0][0][stack][0])-1\n if col in range(grid.NCOLS) and row in range(grid.NROWS): \n key = '%0.4d%0.4d' %(col,row)\n self.pt_xref.setdefault(key, [])\n self.pt_xref[key].append(stack)","title":""},{"docid":"ac87742a3c4515be3ba10e429ba17373","score":"0.50315046","text":"def return_original_row(x): # noqa: D401\n return x","title":""},{"docid":"1854bd2566f6fb59157b977f585a27f4","score":"0.50246227","text":"def get_reference(self):\n term = self.find_terminal()\n # \"pl160:\", term\n if self.find_terminal():\n # supposed to return None if the location is accessible\n return (None, None)\n endpart = self.parts[-1]\n reftuple = (endpart.struct, endpart.key)\n return reftuple","title":""},{"docid":"f7bbe173b977a5bfe1602272ac71c0a0","score":"0.5023135","text":"def get_row(self, row):\n if row < len(self.model_data):\n return [r for r in self.model_data.values()][row]\n return None","title":""},{"docid":"d1ce5942296fd2bc5ffe7366af3e92ef","score":"0.5013419","text":"def GetSpreadsheetRowMapping(self):","title":""},{"docid":"2755b078887798f5984e648617979b38","score":"0.5010136","text":"def reference_column_index(self) -> int:\n return self._reference_column_index","title":""},{"docid":"0b32f170881652c4fcde8579c93598fe","score":"0.50022304","text":"def test_get_row(self):\n row = self.wsb.getRow(0)\n assert len(row) == 6\n assert row[0].letter == \"F\"\n\n row = self.wsb.getRow(5)\n assert len(row) == 6\n assert row[3].letter == \"Q\"","title":""},{"docid":"2c8467a63083e6c460675e0a3e3a17ae","score":"0.50020003","text":"def __get_reference_in_subquery(self, current_column, target_i):\n if self.is_column_alias(current_column, target_i):\n # Si se trata de un alias, no lleva referencia de columna\n return '', current_column\n\n try:\n # Busca coincidencias en la subquery con el nombre de la columna actual\n new_reference = [e for e in self.__queries[target_i]['columns']['names'] if\n self._equal_columns(current_column, e)]\n return self._get_referenced_names(new_reference[0], target_i)\n except IndexError:\n # La columna puede no estar en la subquery, pero pertenecer a la subtabla\n pass\n\n # Si no es un alias y va sin referencia a tabla, esta haciendo referencia a una columna de la subquery que no\n # aparece en la consulta pero que deberia existir. Esto solo se permite si la subquery consulta una sola tabla\n table = self._get_unreferenced_table(target_i, current_column)\n\n try:\n return table, self.__mapping[table]['fields'][current_column]\n except KeyError as err:\n self._logger.warning(\"{}. La referencia a la columna '{}' de la tabla '{}' no se encuentra en los ficheros \"\n \"de mapping proporcionados ni en los generados.\"\n \" Se devuelve el nombre original\".format(err, current_column, table))\n return table, current_column","title":""},{"docid":"d37998abfbafe14b84a1a4f5bdb1acff","score":"0.49968097","text":"def table_row_start_i(self) -> int:\n if self._content_row_start is not None:\n return self._content_row_start\n else:\n return self.reference_row_index + 1","title":""},{"docid":"c248cf51a5e19d47b5f4b78a2badffe0","score":"0.4992496","text":"def get_name(self):\n\n return (self.parent.a[self.row_idx:],\n self.parent.b[self.col_idx:])","title":""},{"docid":"2e64e8aa250ddba2c7ff811078c7ae57","score":"0.49918708","text":"def rows(self) -> 'LineSeries':\n return LineSeries(reference_line=self.reference_column)","title":""},{"docid":"027a936ad5deea8e810f079078e075a9","score":"0.4989297","text":"def get_row_class() -> type:\n return Office.get_interface_class().Row","title":""},{"docid":"773855fb933855af4e4480c73fb1a17d","score":"0.49836728","text":"def get_cell(self, name):\n row, col = get_indices_from_name(name)\n return self.worksheet.row_values(row)[col]","title":""},{"docid":"725afffe9dd36c26bd7988c5d3530bdd","score":"0.49802655","text":"def match_business_to_row(business: str, sample_business_df: pd.DataFrame) -> pd.Series:\n business_name = business.strip()\n business_match_condition = [\n business_name in b for b in sample_business_df[\"names\"].apply(eval)\n ]\n business_row_match = sample_business_df.loc[business_match_condition].iloc[0]\n return business_row_match","title":""},{"docid":"716c77302c7dbbb5b90cd58b0d07ff2b","score":"0.49797878","text":"def get_row(self, *args):\n return _vnl_matrixPython.vnl_matrix_vcl_complexD_get_row(self, *args)","title":""},{"docid":"80c49b9ec72159032038eaa9ecf42bbd","score":"0.4966962","text":"def get_row(sheet, row):\r\n elements = []\r\n output = 1\r\n i = 0\r\n while output is not None:\r\n i += 1\r\n output = sheet.cell(row=row, column=i).value\r\n elements.append(output)\r\n\r\n elements = elements[:-1]\r\n return elements","title":""},{"docid":"0af8b8791de316f413872bc26ddd71b5","score":"0.49568832","text":"def get_start_cell(self):\n\n return self.cells[self.start_pos[0]][self.start_pos[1]]","title":""},{"docid":"e396433b0258d96995341d100028bfc6","score":"0.49527013","text":"def get_row(linear_index, matrix_columns):\n index = linear_index\n columns = matrix_columns\n return int(index/columns)","title":""},{"docid":"f50e28cce1af7983bfed0f8980b3a341","score":"0.49501008","text":"def get_sheet(\n self,\n sheet_name: str,\n row_ref_i: int=0,\n col_ref_i: int=0\n ) -> 'Sheet':\n raise NotImplementedError # todo: finish sorting these two methods out","title":""},{"docid":"a7efb118bf1a3f0fa1e6b4f0ce090f75","score":"0.49471548","text":"def __get_tbl_row(self, table, index, *fields):\n # If no fields passed get all fields in the table\n if fields is ():\n fields = table[1].keys()\n\n eOid, tFields = table\n oids = [\n (\".\".join((eOid, tFields[field][0])), index)\n for field in fields\n ]\n\n return self.get(oids)","title":""},{"docid":"7671485403e6c38fb2c4cbc4f538d94c","score":"0.49365413","text":"def ref(self):\n return self[\"Reference_Allele\"].value","title":""},{"docid":"6b82c0befe0e92b3c5c7027d14ed53d7","score":"0.49317604","text":"def __get_chr_names(self):\n return self.bam.references","title":""},{"docid":"b1ee2be4ff9dce17e52757818f1a69b1","score":"0.4931566","text":"def _reference_line(self) -> 'Line':\n raise NotImplementedError","title":""},{"docid":"5a94e655ed626236ab4a2cd96d52c7b1","score":"0.49296573","text":"def GetXYByRef(self, ref):\n for i in range(self.GetWidth()):\n for j in range(self.GetHeight()):\n if self[i, j]==ref:\n coord = Point(i, j)\n return coord","title":""}],"string":"[\n {\n \"docid\": \"17e80d7b4aa4487e8671cc28a483a5f8\",\n \"score\": \"0.6681498\",\n \"text\": \"def reference_row_index(self) -> int:\\n return self._reference_row_index\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43cff7c6a2da37b232d6ddf915a04635\",\n \"score\": \"0.60901815\",\n \"text\": \"def getRow(self, row):\\r\\n\\t\\treturn self.cells[row*self.size:row*self.size+self.size]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3efb1601d0de607527791b165e7ba92e\",\n \"score\": \"0.60155094\",\n \"text\": \"def name_cell_index(self) -> int:\\n return self.sheet.reference_row_index\",\n \"title\": \"\"\n },\n {\n \"docid\": \"855b836f2426e2b52b44233ceef00f99\",\n \"score\": \"0.5932592\",\n \"text\": \"def get_row(self):\\n return self.__row\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8918d0e4da5ae088c7821b8e750dda19\",\n \"score\": \"0.59267473\",\n \"text\": \"def get_row(self):\\n return self._row\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67d731f197b35f1b8b8fbf7ff6421688\",\n \"score\": \"0.588488\",\n \"text\": \"def get_row(self, *args):\\n return _vnl_matrixPython.vnl_matrixLD_get_row(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91ad6f08516014ed83b9bea48761323c\",\n \"score\": \"0.58738667\",\n \"text\": \"def get_row(self):\\n\\n return self.row\",\n \"title\": \"\"\n },\n {\n \"docid\": \"595bafe88a1a47fd20b75bfad4d649d4\",\n \"score\": \"0.5873565\",\n \"text\": \"def reference(self):\\n with TRN:\\n sql = \\\"SELECT reference_id FROM qiita.{0} WHERE {1}=%s\\\".format(\\n self._table, self._column_id)\\n TRN.add(sql, [self.id])\\n return TRN.execute_fetchlast()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9263bf3bef42ee2ac31fcf298fadf9d1\",\n \"score\": \"0.5770706\",\n \"text\": \"def get_by_name(self, name: int or float or str) -> 'Line':\\n for cell in self.reference_line:\\n if cell.value == name:\\n if self._contents_type == LineSeries.COLUMNS_STR:\\n return cell.column\\n elif self._contents_type == LineSeries.ROWS_STR:\\n return cell.row\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60486d9becbce933b38aa902ec088351\",\n \"score\": \"0.57576084\",\n \"text\": \"def GetRow(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60486d9becbce933b38aa902ec088351\",\n \"score\": \"0.57576084\",\n \"text\": \"def GetRow(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60486d9becbce933b38aa902ec088351\",\n \"score\": \"0.57576084\",\n \"text\": \"def GetRow(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18cd0c554953f3d09e32d236c266c92d\",\n \"score\": \"0.57569665\",\n \"text\": \"def get_row(self):\\n\\n return self._row\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18cd0c554953f3d09e32d236c266c92d\",\n \"score\": \"0.57569665\",\n \"text\": \"def get_row(self):\\n\\n return self._row\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f348e3f669e199e76fcdf166f3370a73\",\n \"score\": \"0.5747943\",\n \"text\": \"def get_row_index_from_name(\\n self,\\n row_name: int or float or str\\n ) -> int or None:\\n for y, cell in enumerate(self.reference_column):\\n if cell.value == row_name:\\n return y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0fe24054b1370ba4be02135d5e78728\",\n \"score\": \"0.5743387\",\n \"text\": \"def get_row(self, *args):\\n return _vnl_matrixPython.vnl_matrixSC_get_row(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a963a16271d8edea9658f4ae5cda488\",\n \"score\": \"0.5716616\",\n \"text\": \"def get_row(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3bc51241fbe8bf997bc80cea7ebc0a8\",\n \"score\": \"0.57039917\",\n \"text\": \"def get_row(self):\\n return self._row_pos\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afd25bd687039534aced3f7f7285e257\",\n \"score\": \"0.5674896\",\n \"text\": \"def get_cell_ref(cls, row_no, col_no, rowabs=False, colabs=False):\\n buf = bytearray()\\n if colabs:\\n buf.append(ord(cls._fixed_ind))\\n # Put the column letter into the buffer\\n buf.extend(cls.get_col_ref(col_no))\\n if rowabs:\\n buf.append(ord(cls._fixed_ind))\\n # bytearray default encoding is utf-8\\n buf.extend(bytearray(str(row_no + 1), 'utf-8'))\\n return buf.decode()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55ec38d3138244bbf4b515f97f57b2ef\",\n \"score\": \"0.5641614\",\n \"text\": \"def row(self):\\n return self.__row\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eab63a82d664717538c82598522ce4ea\",\n \"score\": \"0.5638981\",\n \"text\": \"def get_row(self, row):\\n if not isinstance(row, int):\\n row = self.get_col(0)._grid.table_data.index([row])\\n temp = self._grid.table_data[row]\\n temp2 = Tableist(1, self.ncols)\\n for col, item in enumerate(temp):\\n temp2[0][col] = item\\n return temp2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a90cc3927f9b2b8905361da67c33c19\",\n \"score\": \"0.5628799\",\n \"text\": \"def row(self):\\n return self._row\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3475c4d81090794996614c52b74bdfe5\",\n \"score\": \"0.5627094\",\n \"text\": \"def cref_row_no(cls, crs):\\n numindex = cls.cref_num_index(crs)\\n return int(crs[numindex:]) - 1 # pure python integer will not overflow\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4dede537cfa53dfede894b7d55500d2a\",\n \"score\": \"0.5620692\",\n \"text\": \"def get_row(self, *args):\\n return _vnl_matrixPython.vnl_matrixSL_get_row(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"41371732f085fa32c91cfc024eb40cbb\",\n \"score\": \"0.5595464\",\n \"text\": \"def X_REF(self):\\n return self.data[[col for col in self.cols if col.startswith('REF')]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5abf37dc3bc4d794d752f98d9ba3376b\",\n \"score\": \"0.55809397\",\n \"text\": \"def getRowWithColumns(self, tableName, row, columns):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c474bf35600f44b4242698b23f0e51e4\",\n \"score\": \"0.55617553\",\n \"text\": \"def get_row(self, *args):\\n return _vnl_matrixPython.vnl_matrixD_get_row(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd0fc27bae095056c7bfaf71b22d67c0\",\n \"score\": \"0.55575746\",\n \"text\": \"def get_row(self, *args):\\n return _vnl_matrixPython.vnl_matrixUI_get_row(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"862d6c1a61faff79a8129337f41df190\",\n \"score\": \"0.5543766\",\n \"text\": \"def get_selected_row(self) -> Optional[str]:\\n if self._selected_row is not None and self.lines:\\n return self.lines[self._selected_row]\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31f7edd2c369638f020a6c4ca26eda15\",\n \"score\": \"0.55340093\",\n \"text\": \"def getRow(self, tableName, row):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f92cc094ec3e1ef16d4c5a5173fe7a4\",\n \"score\": \"0.55256516\",\n \"text\": \"def find_row(self, point):\\n\\t\\tns_tableview = self._ns_inner_view\\n\\t\\treturn ns_tableview.rowAtPoint_(point)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a9bfdb0820c186e91679e7c1d47e0e0\",\n \"score\": \"0.55224717\",\n \"text\": \"def get_row(self, *args):\\n return _vnl_matrixPython.vnl_matrixSI_get_row(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a5ae8c9d14b3e3957ce041572427829\",\n \"score\": \"0.55215347\",\n \"text\": \"def getRow(self, tableName, row):\\r\\n self.send_getRow(tableName, row)\\r\\n return self.recv_getRow()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f1333546dddb5c70865319ec3e17dbc\",\n \"score\": \"0.55126405\",\n \"text\": \"def get_row(self, *args):\\n return _vnl_matrixPython.vnl_matrixF_get_row(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f46c708aa67a202546f5cbb6b4b2bde3\",\n \"score\": \"0.5503267\",\n \"text\": \"def _get_exc_sl_ref_per_row(row):\\n return rsh.get_exc_sl(\\n alpha=alpha,\\n beta=beta,\\n # reference total Exc computed based of ref_value\\n exc=row['ref_value'] - (row['Etot'] - row['Exc']),\\n exx=row['Exx'],\\n exx_lr=row['Exxlr'],\\n enlc=row['Enlc'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ef026ca19ebed7ea9623668c8ae79df\",\n \"score\": \"0.5452422\",\n \"text\": \"def getRow(self, tableName, row):\\n self.send_getRow(tableName, row)\\n return self.recv_getRow()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9fc5960394b5faed84ccfd736d914cf\",\n \"score\": \"0.54458845\",\n \"text\": \"def getRowWithColumns(self, tableName, row, columns):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36ebaebd84bf33eb010ee8ec335ca350\",\n \"score\": \"0.54457474\",\n \"text\": \"def getRowWithColumns(self, tableName, row, columns):\\r\\n self.send_getRowWithColumns(tableName, row, columns)\\r\\n return self.recv_getRowWithColumns()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63940184c6eea39f5d711a03b4f713b4\",\n \"score\": \"0.5397386\",\n \"text\": \"def get_row(self, *args):\\n return _vnl_matrixPython.vnl_matrix_vcl_complexLD_get_row(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d434adfb59708d8d656c310b5fc93865\",\n \"score\": \"0.53879786\",\n \"text\": \"def get_row(cls, chromosome, row, num_col):\\r\\n\\t\\tif row > (len(chromosome.dna) // num_col) - 1: raise ValueError(\\\"Chromosome doesn`t have this much rows.\\\")\\r\\n\\t\\treturn GAHelper.chunckdna(chromosome, num_col)[row]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92779120f18efe582ad7cd12d9a9d10c\",\n \"score\": \"0.5371809\",\n \"text\": \"def get_row_by_name(self, row_name: int or str or float) -> 'Row':\\n y = self.get_row_index_from_name(row_name)\\n return self.get_row_by_index(y) if y is not None else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e384efc2591dc2399605f3b2cbdf9fe\",\n \"score\": \"0.53662145\",\n \"text\": \"def get_row(self, idx):\\n return self.table[idx][:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fcb3a1bc122773291a82bcd9439ece2\",\n \"score\": \"0.53654224\",\n \"text\": \"def row(self) -> int:\\n return self.location.row\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e5d1311af496637a0abe9124ff0011d\",\n \"score\": \"0.5365324\",\n \"text\": \"def OwnerEventReference(self, row):\\n ref = self.EventReference(row)\\n if ref is not None:\\n return ref\\n eventTypeID = row.eventTypeID\\n referenceID = row.referenceID\\n if referenceID is None:\\n return ['', '', '']\\n et = self.cache.Index(const.cacheEventTypes, eventTypeID)\\n eventGroupID = et.eventGroupID\\n referenceColumn = et.referenceColumn\\n if referenceColumn:\\n if referenceColumn in ('dungeonID', 'instanceID'):\\n if referenceColumn == 'dungeonID':\\n dungeonID = referenceID\\n elif 'dungeonID' in row.__columns__:\\n dungeonID = row.dungeonID\\n else:\\n dungeonID = None\\n if dungeonID in self.cache.Index(const.cacheDungeonDungeons):\\n dungeonName = self.Link('/gd/dungeons.py', localization.GetByMessageID(self.cache.Index(const.cacheDungeonDungeons, dungeonID).dungeonNameID), {'action': 'Dungeon',\\n 'dungeonID': dungeonID})\\n else:\\n dungeonName = self.FontRed('Dungeon not found')\\n return [referenceID, 'Dungeon Instance', dungeonName]\\n if referenceColumn == 'missionCountID':\\n if 'missionID' in row.__columns__:\\n missionName = self.MissionLink(row.missionID)\\n else:\\n missionName = self.FontRed('Mission not found')\\n return [referenceID, 'Mission Instance', missionName]\\n if referenceColumn == 'tutorialID':\\n tutorialIx = sm.GetService('tutorialSvc').GetTutorialsIx()\\n if referenceID in tutorialIx:\\n tutorialName = localization.GetByMessageID(tutorialIx[referenceID].tutorialNameID)\\n return [referenceID, 'Tutorial', self.Link('/gd/tutorials.py', tutorialName, {'action': 'Tutorial',\\n 'tutorialID': referenceID})]\\n else:\\n return [referenceID, 'Tutorial', self.FontRed('Tutorial not found')]\\n if referenceColumn == 'planetID':\\n return [self.ItemID(referenceID), 'Planet', self.PlanetLink(referenceID)]\\n if referenceColumn == 'launchID':\\n return [self.Link('/gm/owner.py', referenceID, {'action': 'PlanetaryLaunch',\\n 'launchID': referenceID}), 'Planetary Launch', '']\\n if referenceColumn == 'offerID':\\n if eventTypeID in (logConst.eventStoreGaveCredits, logConst.eventStoreTookCredits):\\n rx = self.DB2.SQLBigInt('*', 'zevent.genericEvents', \\\"eventTable = 'O'\\\", '', 'eventID', row.eventID)\\n refID = '%s %s' % ('Gave' if eventTypeID == logConst.eventStoreGaveCredits else 'Took', 'ISK' if rx[0].int_1 == const.creditsISK else 'AURUM')\\n refType = '%10.2f' % (rx[0].money_1 or 0.0)\\n if rx[0].bigint_1 is not None:\\n refText = self.Link('/gm/accounting.py', 'Transaction ID: %s' % rx[0].bigint_1, {'action': 'TxDetail',\\n 'transactionID': rx[0].bigint_1})\\n else:\\n refText = ''\\n return [refID, refType, refText]\\n return [referenceID, 'Offer', self.GetTooltip(href='/gd/store.py', ajax='/gm/store.py?action=OfferTooltip&offerID=%d' % referenceID, title='Offer Details', caption='Offer Details')]\\n if referenceColumn == 'taleID':\\n return [self.Link('/gm/tale.py', referenceID, {'taleID': referenceID}), 'Tale ID', self.Link('/gm/tale.py', 'Tale', {'taleID': referenceID})]\\n if referenceColumn == 'killID':\\n return [self.Link('/gm/character.py', referenceID, {'action': 'KillReport',\\n 'killID': referenceID}), 'Kill Report', self.Link('/gm/character.py', 'Kill Report', {'action': 'KillReport',\\n 'killID': referenceID})]\\n if eventTypeID == logConst.eventCharacterPaused:\\n return ['', 'User Account', 'The users account is not active, all training paused on all characters! (skills, RP)']\\n if eventTypeID == logConst.eventCharacterResumed:\\n return ['', 'User Account', 'The users account has been reactivated, all training resumed on all characters! (skills, RP)']\\n if eventTypeID == logConst.eventResearchStopped:\\n return [referenceID, 'Research points lost', '']\\n if eventTypeID == logConst.eventResearchGMEdit:\\n return [self.ItemID(referenceID), 'Character', self.CharacterLink(referenceID)]\\n if eventTypeID == logConst.eventStoreOfferDeleted:\\n return [referenceID, '', 'it was deleted, and lost for all time!']\\n if eventGroupID == const.groupAlliance:\\n if util.IsCorporation(referenceID):\\n return [self.ItemID(referenceID), 'Corporation', self.CorporationLink(referenceID)]\\n else:\\n return [self.Link('/gm/logs.py', str(referenceID), {'action': 'OwnerEvent',\\n 'eventID': referenceID}), 'Alliance Event', '']\\n if eventTypeID in (logConst.eventItemReimburse,\\n logConst.eventItemEdit,\\n logConst.eventItemEditFrom,\\n logConst.eventItemEditTo):\\n referenceText = ''\\n if 'uiItemID' in row.__columns__:\\n if row.uiItemID:\\n referenceText = self.ItemID(row.uiItemID)\\n referenceText = self.SplitAdd(referenceText, self.TypeLink(row.uiTypeID))\\n return [self.Link('/gm/logs.py', referenceID, {'action': 'ItemEvent',\\n 'eventID': referenceID}), 'Item Event', referenceText]\\n if eventTypeID == logConst.eventSecStatusGmRollback:\\n return [self.Link('/gm/logs.py', str(referenceID), {'action': 'OwnerEvent',\\n 'eventID': referenceID}), 'Original Event', '']\\n refType = 'Unknown'\\n if et.referenceTable and et.referenceColumn:\\n refType = '%s.%s' % (et.referenceTable, et.referenceColumn)\\n elif et.referenceTable:\\n refType = et.referenceTable\\n elif et.referenceColumn:\\n refType = et.referenceColumn\\n return [referenceID, refType, 'Unknown']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91ca6cdf36a55aac0c397916c180715f\",\n \"score\": \"0.5362135\",\n \"text\": \"def getRowWithColumns(self, tableName, row, columns):\\n self.send_getRowWithColumns(tableName, row, columns)\\n return self.recv_getRowWithColumns()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a24887ad37e282993685f03046087c7\",\n \"score\": \"0.5359364\",\n \"text\": \"def get_ref(self):\\n\\n return [f[\\\"value\\\"] for f in self.fields if f[\\\"name\\\"] == \\\"Reference\\\"][0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84e2e9aa35bfedcdfab386fcbe19b4bb\",\n \"score\": \"0.534794\",\n \"text\": \"def getrow(self, index, startcol, endcol):\\n return self.df.iloc[index, startcol:endcol]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ed90261f92d98864686dd3b33e752da\",\n \"score\": \"0.53409266\",\n \"text\": \"def getRow(self, tableName, row):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8f7a3623f85a7e175993eba7103c0e3\",\n \"score\": \"0.5315929\",\n \"text\": \"def get_row(self, *args):\\n return _vnl_matrixPython.vnl_matrixUL_get_row(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fabbda55f6e8bfb35c2b1d92332f68e4\",\n \"score\": \"0.5308592\",\n \"text\": \"def termRows():\\n return termRowsCols()[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6aadb04d4e40a0cf711b69e60584f845\",\n \"score\": \"0.5308141\",\n \"text\": \"def get_row(grid, rownum):\\r\\n return grid[rownum]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e601b6a8fccfc6c28e4f8c4c57c473a\",\n \"score\": \"0.5283108\",\n \"text\": \"def jobRow(self):\\n return self.__jobRow\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f603d1a10ecef5ef13ecc7c1ccc131c\",\n \"score\": \"0.5270081\",\n \"text\": \"def _get_table_row(file_handle):\\n row = ''\\n filename = ''\\n while '</tr>' not in row and '</table>' not in row:\\n line = file_handle.readline()\\n name = re.search(r'href=\\\"(.*?)\\\"', line)\\n row += line\\n if name:\\n filename = name.group(1)\\n return row, filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42de5f3ee63d52d8f5c3f2789d251060\",\n \"score\": \"0.5252613\",\n \"text\": \"def getRow(self, r):\\n\\n return self.matx[r]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d2932ce06f362ea0c05267de1808f58\",\n \"score\": \"0.52517027\",\n \"text\": \"def get_row(self, row, pos1='a', pos2='i'):\\n\\n if row not in self._rows or pos1 not in self._columns or pos2 not in self._columns:\\n return None\\n\\n return self._layout[int(row) - 1][ord(pos1) - ord('a'): ord(pos2) - ord('a') + 1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f1ee37b62cb5fa7ccdacb3b1f9749a0\",\n \"score\": \"0.52511394\",\n \"text\": \"def _source_cell(self):\\n return self._uno_sheet.getCellByPosition(*self.position)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94f7a4e482565fc10ffaa9708c457ad9\",\n \"score\": \"0.52166784\",\n \"text\": \"def target_start_row(self):\\n return self._target_start_row\",\n \"title\": \"\"\n },\n {\n \"docid\": \"240d858d78ad06cc3754b0e6213a1c60\",\n \"score\": \"0.52110195\",\n \"text\": \"def retriveRow(row, surround=5):\\n # Account for the 1-based nature of R\\n centre = row[\\\"centre\\\"] - 1\\n seq = row[\\\"seq\\\"]\\n len_ = row[\\\"len\\\"]\\n\\n start = max(centre - surround, 0)\\n end = min(centre + surround + 1, len_)\\n seqSlice = seq[start:end]\\n\\n nullChar = \\\"x\\\"\\n\\n # Test for boundary condition at end\\n endOverlap = (centre + surround) - len_\\n\\n if endOverlap >= 0:\\n seqSlice += nullChar * (endOverlap + 1)\\n\\n # Test for the boundary conditions at the start\\n startOverlap = centre - surround\\n if startOverlap <= 0:\\n seqSlice = nullChar * (-startOverlap) + seqSlice\\n\\n return seqSlice\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6411ada4d9fac26ba46cbb83630a6ed\",\n \"score\": \"0.5209201\",\n \"text\": \"def factory(sheet: 'Sheet', index: int, reference_index: int) -> 'Row':\\n # first try to find a pre-existing row with that sheet + index\\n row_key = sheet, index\\n try:\\n row = Row._all[row_key]\\n except KeyError:\\n row = Office.get_row_class()(sheet, index, reference_index)\\n return row\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b71f6af4efecf274c46a07520488e8e3\",\n \"score\": \"0.5198504\",\n \"text\": \"def _find_row(self, matrix2d, row):\\n return np.where((matrix2d == row).all(axis=1))[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9c4e9c6e420032a1f58e1434783c731\",\n \"score\": \"0.5156211\",\n \"text\": \"def get_row(i):\\n if i < len(TGAS):\\n ind = i\\n else:\\n ind = np.where(TGAS.source_id==i)[0][0]\\n return TGAS.iloc[ind]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"290bc52c706505179260a4e32c77bfd6\",\n \"score\": \"0.51452243\",\n \"text\": \"def match_person_to_row(name: str, sample_personas_df: pd.DataFrame) -> pd.Series:\\n first_name, last_name = name.split(\\\" \\\")[0], name.split(\\\" \\\")[1]\\n name_match_condition = (\\n sample_personas_df[\\\"person.firstName\\\"].str.strip() == first_name\\n ) & (sample_personas_df[\\\"person.lastName\\\"].str.strip() == last_name)\\n persona_row_match = sample_personas_df.loc[name_match_condition].iloc[0]\\n return persona_row_match\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9555e027dcf5dd1d8a00cee68308d836\",\n \"score\": \"0.51399124\",\n \"text\": \"def get_row(self, *args):\\n return _vnl_matrixPython.vnl_matrixUC_get_row(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e20be0779d45a1391b37d52522100a3c\",\n \"score\": \"0.51265526\",\n \"text\": \"def get_row_by_index(self, row_index: int or str) -> 'Row':\\n return Row.factory(\\n sheet=self,\\n index=row_index,\\n reference_index=self.reference_row_index\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4809c55cf8400667cbc9e771b9d303bb\",\n \"score\": \"0.5099705\",\n \"text\": \"def cell_ref_array(self):\\n return self.__cell_ref_array\",\n \"title\": \"\"\n },\n {\n \"docid\": \"193f25a10e7b41f517785697ff169525\",\n \"score\": \"0.5098722\",\n \"text\": \"def row(self) -> int:\\n return self._row\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6188f855956e6c610b5d09109176bf80\",\n \"score\": \"0.5097331\",\n \"text\": \"def get_rows(self, resource_name):\\n count = 0\\n resource_name_row_start = 0\\n resource_name_row_end = 0\\n self.load_md()\\n for line in self.all_lines:\\n count += 1\\n if line.startswith('| '+resource_name):\\n resource_name_row_start = count\\n\\n for no in range(count, len(self.all_lines)):\\n if self.all_lines[no].startswith('| **'):\\n resource_name_row_end = no\\n break\\n\\n return resource_name_row_start, resource_name_row_end\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4b01664dda33a8fdddb31e06067cc0b\",\n \"score\": \"0.5084442\",\n \"text\": \"def name_cell_index(self) -> int:\\n return self.sheet.reference_column_index\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c79e22522e46fd9c3b22a664e2eb4e7\",\n \"score\": \"0.5077693\",\n \"text\": \"def get_row(self, *args):\\n return _vnl_matrixPython.vnl_matrix_vcl_complexF_get_row(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"851c3b57e1b19823decf6c1d504a4454\",\n \"score\": \"0.50762177\",\n \"text\": \"def get_row(self, binary=False, columns=None, raw=None, prep_stmt=None):\\n try:\\n rows, eof = self.get_rows(count=1, binary=binary, columns=columns,\\n raw=raw, prep_stmt=prep_stmt)\\n if rows:\\n return (rows[0], eof)\\n return (None, eof)\\n except IndexError:\\n # No row available\\n return (None, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3a67f18dbe5451cbd827e8d1ee4330a\",\n \"score\": \"0.5074132\",\n \"text\": \"def match_property_to_row(address: str, sample_address_df: pd.DataFrame) -> pd.Series:\\n address_line1 = address.split(\\\",\\\")[0].strip()\\n address_match_condition = (\\n sample_address_df[\\\"address.addressLine1\\\"].str.strip() == address_line1\\n )\\n address_row_match = sample_address_df.loc[address_match_condition].iloc[0]\\n return address_row_match\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0a4836b8b11de1fb820dee433d5c98d\",\n \"score\": \"0.50565726\",\n \"text\": \"def get_cell(self):\\n return self._cell\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1eca122bb6c3bade91532821854d58e1\",\n \"score\": \"0.5054133\",\n \"text\": \"def get_cell_by_name(self, row, name):\\n col = self.key[name]\\n self.logger.debug(\\\"get cell by name %s, row = %d, col = %d\\\" % (name, row, col))\\n return self.get_cell(row, self.key[name])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38fa906d4d54abb45d1d35ceb9abed0d\",\n \"score\": \"0.50474244\",\n \"text\": \"def get_row_index(self, candidate):\\n return self.candidate_index[candidate.id]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe4c3ce8d670b9756e1efb0f047c6096\",\n \"score\": \"0.50449455\",\n \"text\": \"def GetGridCursorRow(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fac0c14ef21971f7b16ce51a6f4f2fc\",\n \"score\": \"0.50388193\",\n \"text\": \"def get_row(self, row_identifier: int or float or str) -> 'Row':\\n if isinstance(row_identifier, str):\\n return self.get_row_by_name(row_identifier)\\n else:\\n return self.get_row_by_index(row_identifier)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de1f19e1891ffcb7499b42cc8d249c1c\",\n \"score\": \"0.50382864\",\n \"text\": \"def _get_xref(self, grid):\\n # Fetch a variable from the in file\\n rows = self.infile.variables['ROW'][:]\\n cols = self.infile.variables['COL'][:]\\n self.stk_num = rows.shape[2]\\n for stack in range(rows.shape[2]):\\n row = int(rows[0][0][stack][0])-1\\n col = int(cols[0][0][stack][0])-1\\n if col in range(grid.NCOLS) and row in range(grid.NROWS): \\n key = '%0.4d%0.4d' %(col,row)\\n self.pt_xref.setdefault(key, [])\\n self.pt_xref[key].append(stack)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac87742a3c4515be3ba10e429ba17373\",\n \"score\": \"0.50315046\",\n \"text\": \"def return_original_row(x): # noqa: D401\\n return x\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1854bd2566f6fb59157b977f585a27f4\",\n \"score\": \"0.50246227\",\n \"text\": \"def get_reference(self):\\n term = self.find_terminal()\\n # \\\"pl160:\\\", term\\n if self.find_terminal():\\n # supposed to return None if the location is accessible\\n return (None, None)\\n endpart = self.parts[-1]\\n reftuple = (endpart.struct, endpart.key)\\n return reftuple\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7bbe173b977a5bfe1602272ac71c0a0\",\n \"score\": \"0.5023135\",\n \"text\": \"def get_row(self, row):\\n if row < len(self.model_data):\\n return [r for r in self.model_data.values()][row]\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1ce5942296fd2bc5ffe7366af3e92ef\",\n \"score\": \"0.5013419\",\n \"text\": \"def GetSpreadsheetRowMapping(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2755b078887798f5984e648617979b38\",\n \"score\": \"0.5010136\",\n \"text\": \"def reference_column_index(self) -> int:\\n return self._reference_column_index\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b32f170881652c4fcde8579c93598fe\",\n \"score\": \"0.50022304\",\n \"text\": \"def test_get_row(self):\\n row = self.wsb.getRow(0)\\n assert len(row) == 6\\n assert row[0].letter == \\\"F\\\"\\n\\n row = self.wsb.getRow(5)\\n assert len(row) == 6\\n assert row[3].letter == \\\"Q\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c8467a63083e6c460675e0a3e3a17ae\",\n \"score\": \"0.50020003\",\n \"text\": \"def __get_reference_in_subquery(self, current_column, target_i):\\n if self.is_column_alias(current_column, target_i):\\n # Si se trata de un alias, no lleva referencia de columna\\n return '', current_column\\n\\n try:\\n # Busca coincidencias en la subquery con el nombre de la columna actual\\n new_reference = [e for e in self.__queries[target_i]['columns']['names'] if\\n self._equal_columns(current_column, e)]\\n return self._get_referenced_names(new_reference[0], target_i)\\n except IndexError:\\n # La columna puede no estar en la subquery, pero pertenecer a la subtabla\\n pass\\n\\n # Si no es un alias y va sin referencia a tabla, esta haciendo referencia a una columna de la subquery que no\\n # aparece en la consulta pero que deberia existir. Esto solo se permite si la subquery consulta una sola tabla\\n table = self._get_unreferenced_table(target_i, current_column)\\n\\n try:\\n return table, self.__mapping[table]['fields'][current_column]\\n except KeyError as err:\\n self._logger.warning(\\\"{}. La referencia a la columna '{}' de la tabla '{}' no se encuentra en los ficheros \\\"\\n \\\"de mapping proporcionados ni en los generados.\\\"\\n \\\" Se devuelve el nombre original\\\".format(err, current_column, table))\\n return table, current_column\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d37998abfbafe14b84a1a4f5bdb1acff\",\n \"score\": \"0.49968097\",\n \"text\": \"def table_row_start_i(self) -> int:\\n if self._content_row_start is not None:\\n return self._content_row_start\\n else:\\n return self.reference_row_index + 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c248cf51a5e19d47b5f4b78a2badffe0\",\n \"score\": \"0.4992496\",\n \"text\": \"def get_name(self):\\n\\n return (self.parent.a[self.row_idx:],\\n self.parent.b[self.col_idx:])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e64e8aa250ddba2c7ff811078c7ae57\",\n \"score\": \"0.49918708\",\n \"text\": \"def rows(self) -> 'LineSeries':\\n return LineSeries(reference_line=self.reference_column)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"027a936ad5deea8e810f079078e075a9\",\n \"score\": \"0.4989297\",\n \"text\": \"def get_row_class() -> type:\\n return Office.get_interface_class().Row\",\n \"title\": \"\"\n },\n {\n \"docid\": \"773855fb933855af4e4480c73fb1a17d\",\n \"score\": \"0.49836728\",\n \"text\": \"def get_cell(self, name):\\n row, col = get_indices_from_name(name)\\n return self.worksheet.row_values(row)[col]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"725afffe9dd36c26bd7988c5d3530bdd\",\n \"score\": \"0.49802655\",\n \"text\": \"def match_business_to_row(business: str, sample_business_df: pd.DataFrame) -> pd.Series:\\n business_name = business.strip()\\n business_match_condition = [\\n business_name in b for b in sample_business_df[\\\"names\\\"].apply(eval)\\n ]\\n business_row_match = sample_business_df.loc[business_match_condition].iloc[0]\\n return business_row_match\",\n \"title\": \"\"\n },\n {\n \"docid\": \"716c77302c7dbbb5b90cd58b0d07ff2b\",\n \"score\": \"0.49797878\",\n \"text\": \"def get_row(self, *args):\\n return _vnl_matrixPython.vnl_matrix_vcl_complexD_get_row(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80c49b9ec72159032038eaa9ecf42bbd\",\n \"score\": \"0.4966962\",\n \"text\": \"def get_row(sheet, row):\\r\\n elements = []\\r\\n output = 1\\r\\n i = 0\\r\\n while output is not None:\\r\\n i += 1\\r\\n output = sheet.cell(row=row, column=i).value\\r\\n elements.append(output)\\r\\n\\r\\n elements = elements[:-1]\\r\\n return elements\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0af8b8791de316f413872bc26ddd71b5\",\n \"score\": \"0.49568832\",\n \"text\": \"def get_start_cell(self):\\n\\n return self.cells[self.start_pos[0]][self.start_pos[1]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e396433b0258d96995341d100028bfc6\",\n \"score\": \"0.49527013\",\n \"text\": \"def get_row(linear_index, matrix_columns):\\n index = linear_index\\n columns = matrix_columns\\n return int(index/columns)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f50e28cce1af7983bfed0f8980b3a341\",\n \"score\": \"0.49501008\",\n \"text\": \"def get_sheet(\\n self,\\n sheet_name: str,\\n row_ref_i: int=0,\\n col_ref_i: int=0\\n ) -> 'Sheet':\\n raise NotImplementedError # todo: finish sorting these two methods out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7efb118bf1a3f0fa1e6b4f0ce090f75\",\n \"score\": \"0.49471548\",\n \"text\": \"def __get_tbl_row(self, table, index, *fields):\\n # If no fields passed get all fields in the table\\n if fields is ():\\n fields = table[1].keys()\\n\\n eOid, tFields = table\\n oids = [\\n (\\\".\\\".join((eOid, tFields[field][0])), index)\\n for field in fields\\n ]\\n\\n return self.get(oids)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7671485403e6c38fb2c4cbc4f538d94c\",\n \"score\": \"0.49365413\",\n \"text\": \"def ref(self):\\n return self[\\\"Reference_Allele\\\"].value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b82c0befe0e92b3c5c7027d14ed53d7\",\n \"score\": \"0.49317604\",\n \"text\": \"def __get_chr_names(self):\\n return self.bam.references\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1ee2be4ff9dce17e52757818f1a69b1\",\n \"score\": \"0.4931566\",\n \"text\": \"def _reference_line(self) -> 'Line':\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a94e655ed626236ab4a2cd96d52c7b1\",\n \"score\": \"0.49296573\",\n \"text\": \"def GetXYByRef(self, ref):\\n for i in range(self.GetWidth()):\\n for j in range(self.GetHeight()):\\n if self[i, j]==ref:\\n coord = Point(i, j)\\n return coord\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993287,"cells":{"query_id":{"kind":"string","value":"1df76c97edfb3f851e3fe52c7227b956"},"query":{"kind":"string","value":"Inline progress bar implementation."},"positive_passages":{"kind":"list like","value":[{"docid":"6be4c47277503cd9c3f51d77398d3cd5","score":"0.0","text":"def progress(count, total, suffix=''):\n\n bar_len = 60\n filled_len = int(round(bar_len * count / float(total)))\n percents = round(100.0 * count / float(total), 1)\n bar = '=' * filled_len + '-' * (bar_len - filled_len)\n sys.stdout.write('[%s] %s%s ...%s\\r' % (bar, percents, '%', suffix))\n sys.stdout.flush()","title":""}],"string":"[\n {\n \"docid\": \"6be4c47277503cd9c3f51d77398d3cd5\",\n \"score\": \"0.0\",\n \"text\": \"def progress(count, total, suffix=''):\\n\\n bar_len = 60\\n filled_len = int(round(bar_len * count / float(total)))\\n percents = round(100.0 * count / float(total), 1)\\n bar = '=' * filled_len + '-' * (bar_len - filled_len)\\n sys.stdout.write('[%s] %s%s ...%s\\\\r' % (bar, percents, '%', suffix))\\n sys.stdout.flush()\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"9933aeb88ac3e6df00a8f820b8f7ead5","score":"0.7358753","text":"def update_progress(self, progress):\n barLength = 20\n status = \"\"\n if isinstance(progress, int):\n progress = float(progress)\n\n block = int(round(barLength * progress))\n text = \"\\r[{}] {:0.2f}% {}\".format(\"#\" * block + \"-\" * (barLength - block), progress * 100, status)\n sys.stdout.write(text)\n sys.stdout.flush()","title":""},{"docid":"26de6746dc872f4acceefc60306d2b83","score":"0.7152523","text":"def do_progress():\n global __PROGRESS_BAR__\n __PROGRESS_BAR__=True","title":""},{"docid":"7ea6e2377efc8e105a77275c307746f1","score":"0.7141404","text":"def progress(self, count, total, status=''):\n bar_len = 60\n filled_len = int(round(bar_len * count / float(total)))\n\n percents = round(100.0 * count / float(total), 1)\n bar = '#' * filled_len + '.' * (bar_len - filled_len)\n\n sys.stdout.write('[%s] %s%s ...%s\\r' % (bar, percents, '%', status))\n sys.stdout.flush()","title":""},{"docid":"c051a44f60f6176d83687504056ba3b9","score":"0.71388406","text":"def progress(count, total, status=''):\n bar_len = 60\n filled_len = int(round(bar_len * count / float(total)))\n bar = '=' * filled_len + ' ' * (bar_len - filled_len)\n sys.stdout.write('[%s] %s %3d\\r\\n' % (bar, status, count))","title":""},{"docid":"f803df1f9db6b3cbfdf9bd475c09c84e","score":"0.709899","text":"def progress(curr, finish, width=10):\n ticks = ['▂','▃','▄','▅','▆','▇']\n finish = max(finish, 1)\n pct = max(curr, 0) / float(finish)\n bartip, barlen = math.modf(pct * width)\n bartip = ticks[int(len(ticks) * bartip)] if pct < 1.0 else ''\n bar = \"%s%s%s\" % (ticks[-1] * int(barlen), bartip, ' ' * (width - int(barlen) - 1))\n prog = \"%s%% [ %s ] %d/%d\" % (str(int(pct*100)).rjust(3), bar, curr, finish)\n return prog","title":""},{"docid":"a0766c7cae928bfa87ee4483b3b5645a","score":"0.70744014","text":"def _print_precaching_progress(i, total, bar_len=30):\n position = round((i + 1) / total * bar_len)\n pbar = \"[\" + \"█\" * position + \" \" * (bar_len - position) + \"]\"\n print(pbar, end=\"\\r\")","title":""},{"docid":"7d3105f06cda776357cf5067ecf06234","score":"0.70211333","text":"def __printProgressBar (iteration, total, prefix = 'Progress:', suffix = 'Complete', decimals = 1, length = 50, fill = '█'):\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n filledLength = int(length * iteration // total)\n bar = fill * filledLength + '-' * (length - filledLength)\n print('\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = '\\r')\n # Print New Line on Complete\n if iteration == total:\n print()","title":""},{"docid":"023f72babb1f72fa67c98f12b795898d","score":"0.7015529","text":"def progress_bar(text, fill_amount, prefix = '', length = 80): #pylint: disable=W0613\n filled_length = int(length * fill_amount)\n fill_char = '█' if sys.stdout.encoding.lower() == 'utf-8' else 'X'\n prog_bar = fill_char * filled_length + '-' * (length - filled_length)\n print('\\r%s |%s| %s' % (prefix, prog_bar, text), end = '\\r')","title":""},{"docid":"e006bf6d2bf332373c3e3f00544a3c20","score":"0.7003342","text":"def print_progress_bar (iteration, total, prefix='', suffix='', decimals=1, length=100, fill='█'):\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n filled_length = int((length * iteration) / total)\n bar = fill * filled_length + '--' * (length - filled_length)\n print('\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end=\" \")\n # Print New Line on Complete\n if iteration == total:\n print()","title":""},{"docid":"2972cf5db7f50c45e6325ec312877d4e","score":"0.6989833","text":"def print_progress_bar(iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█'):\n # percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n # filled_length = int(length * iteration // total)\n # bar = fill * filled_length + '-' * (length - filled_length)\n # print('\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = '\\r')\n # # Print New Line on Complete\n # if iteration == total:\n # print()\n pass","title":""},{"docid":"c11a2801230495fe1fd9e48df40d951c","score":"0.69718087","text":"def print_progress_bar(iteration, total, prefix='', suffix='', decimals=1, length=100, fill='█', print_end=\"\\r\"):\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n filled_length = int(length * iteration // total)\n bar = fill * filled_length + '-' * (length - filled_length)\n print('\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end=print_end)\n\n # Printing new line is on the caller","title":""},{"docid":"3b509e84f24311d9feebf37979d3b905","score":"0.6969002","text":"def progress_bar(value, endvalue, bar_length=20):\n percent = float(value) / endvalue\n arrow = '-' * int(round(percent * bar_length)-1) + '>'\n spaces = ' ' * (bar_length - len(arrow))\n sys.stdout.write(\"\\rPercent complete: [{0}] {1}%\".format(arrow + spaces, int(round(percent * 100))))\n sys.stdout.flush()","title":""},{"docid":"9cb4e477c417df94672824919a4a0721","score":"0.69630903","text":"def run(self):\n #\n # counter in progress bar symbols\n i = 0\n j = i % self._nb_prog_chars\n #\n # counter in bar\n k = 0\n sys.stdout.write('\\n')\n sys.stdout.flush()\n start_time = datetime.now()\n while True:\n now_time = datetime.now()\n delta_time = now_time - start_time\n hrs, rest = divmod(delta_time.seconds, 3600)\n mins, secs = divmod(rest, 60)\n pbar = ' ' \\\n + '%02d:%02d:%02d' % (hrs, mins, secs) \\\n + ' [' \\\n + ' '*k \\\n + self._prog_chars[j] \\\n + ' ' * (self._bar_len - k - self._prog_len) \\\n + ']'\n sys.stdout.write('\\r%s' % pbar)\n sys.stdout.flush()\n k += 1\n if k == self._cntr:\n k = 0\n i += 1\n j = i % self._nb_prog_chars\n time.sleep(self._prog_int)\n if self.stop_the_progress_bar:\n now_time = datetime.now()\n delta_time = now_time - start_time\n hrs, rest = divmod(delta_time.seconds, 3600)\n mins, secs = divmod(rest, 60)\n pbar = ' ' \\\n + '%02d:%02d:%02d' % (hrs, mins, secs) \\\n + ' [ ' \\\n + ' %s' % self._prog_chars[j] \\\n + ' done ]' \\\n + (self._bar_len - self._prog_len - 5)*' '\n sys.stdout.write('\\r%s\\n' % pbar)\n sys.stdout.flush()\n break","title":""},{"docid":"787d0c51dd83da59597791655e25e186","score":"0.6962559","text":"def print_progress_bar(\n iteration, total, prefix=\"\", suffix=\"\", decimals=1, length=100, fill=\"█\"\n):\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n filled_length = int(length * iteration // total)\n bar = fill * filled_length + \"-\" * (length - filled_length)\n print(\n \"\\r%s|%s| %s%% (%s/%s) %s\" % (prefix, bar, percent, iteration, total, suffix),\n end=\"\\r\",\n )\n # Print New Line on Complete\n if iteration == total:\n print(\"\\n\")","title":""},{"docid":"eeffd96e8e54de1882df3b25e5817e27","score":"0.69568115","text":"def print_progress_bar (iteration, total, prefix = \"\", suffix = \"\", decimals = 1, length = 100, fill = \"█\", printEnd = \"\\r\"):\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n filledLength = int(length * iteration // total)\n bar = fill * filledLength + \"-\" * (length - filledLength)\n print(\"\\r%s |%s| %s%% %s\" % (prefix, bar, percent, suffix), end = printEnd)\n if iteration == total: # Print New Line on Complete\n print()","title":""},{"docid":"bcdd3724bb213bfb86f6122b2592143d","score":"0.69555634","text":"def printProgress (iteration, total, prefix = '', suffix = '', decimals = 2, barLength = 100):\n total = 1 if total == 0 else total\n filledLength = int(round(barLength * iteration / float(total)))\n percents = round(100.00 * (iteration / float(total)), decimals)\n bar = '█' * filledLength + '-' * (barLength - filledLength)\n sys.stdout.write('\\r%s |%s| %s%s %s' % (prefix, bar, percents, '%', suffix)),\n sys.stdout.flush()\n if iteration == total:\n sys.stdout.write('\\n')\n sys.stdout.flush()","title":""},{"docid":"03e1f3840d2ce220b9fc4dbde4f4dda4","score":"0.69533545","text":"def _printProgressBar(self, iteration, total, \n prefix = '', suffix = '', \n decimals = 1, length = 100,\n fill='*'):\n# fill = '█'):\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n filledLength = int(length * iteration // total)\n bar = fill * filledLength + '-' * (length - filledLength)\n print('\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = '\\r')\n # Print New Line on Complete\n if iteration == total: \n print()","title":""},{"docid":"8194ac23d7fa2a0cefecb5a7f7b87c56","score":"0.69510275","text":"def _update_progressbar(self):\n \n if 0 >= self.N:\n return None\n\n global _counter, _counter_lock\n with _counter_lock:\n _counter.value += 1\n\n index = _counter.value\n \n increment = max(1, int(self.N/float(self.W)))\n \n #eta_minutes = ((time() - self.t_init) / index) * (self.N - index) / 60.0\n eta_minutes = 1.\n \n if index >= self.N:\n status = \"({0:.0f}s) \".format(time() - self.t_init)\n\n elif float(index)/self.N >= 0.05 \\\n and eta_minutes > 1: # MAGIC fraction for when we can predict ETA\n status = \"({0}/{1}; ~{2:.0f}m until finished)\".format(\n index, self.N, eta_minutes)\n\n else:\n status = \"({0}/{1}) \".format(index, self.N)\n\n sys.stdout.write(\n (\"\\r[{done: <\" + str(self.W) + \"}] {percent:3.0f}% {status}\").format(\n done=\"=\" * int(index/increment),\n percent=100. * index/self.N,\n status=status))\n sys.stdout.flush()\n\n if index >= self.N:\n sys.stdout.write(\"\\r\\n\")\n sys.stdout.flush()","title":""},{"docid":"9bc2dfeaae2f5ed7775b5cd90be5064d","score":"0.69504964","text":"def draw_progress_bar(index, total, start_time, barLen=45):\n percent = index / total\n pad = len(str(total))\n\n sys.stdout.write(\"\\r\")\n sys.stdout.write(\n \"Time elapsed: {} {} | {} / {} [{:<{}}] {:.0f}%\\t\".format(\n (datetime.now() - start_time).seconds, \"s\",\n str(index).rjust(pad),\n total,\n \"=\" * int(barLen * percent),\n barLen,\n percent * 100\n )\n )\n sys.stdout.flush()","title":""},{"docid":"ae6ba58c8a985d1fbd7f6444e7c0a17c","score":"0.69446844","text":"def drawProgressBar(percent, bar_length = 20):\n sys.stdout.write(\"\\r\")\n progress = \"\"\n for i in range(bar_length):\n if i < int(bar_length * percent):\n progress += \"=\"\n else:\n progress += \" \"\n sys.stdout.write(\"[ %s ] %.2f%%\" % (progress, percent*100))\n if percent == 1:\n\t\tsys.stdout.write(\"\\n\") \t\n sys.stdout.flush()","title":""},{"docid":"154da0996723b4bc014925c66006b0d6","score":"0.6937579","text":"def print_progress_bar(iteration, total, prefix='', suffix='', decimals=1, length=40, fill='█'):\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n filled_length = int(length * iteration // total)\n bar = fill * filled_length + '-' * (length - filled_length)\n print('\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end='')\n # Print New Line on Complete\n if iteration == total:\n print()","title":""},{"docid":"16e57e0c9947e831eb45ef068aaf6639","score":"0.6928108","text":"def printProgress (iteration, total, prefix = '', suffix = '', decimals = 1, barLength = 100):\n filledLength = int(round(barLength * iteration / float(total)))\n percents = round(100.00 * (iteration / float(total)), decimals)\n bar = '█' * filledLength + '-' * (barLength - filledLength)\n sys.stdout.write('\\r%s |%s| %s%s %s' % (prefix, bar, percents, '%', suffix)),\n sys.stdout.flush()\n if iteration == total:\n sys.stdout.write('\\n')\n sys.stdout.flush()","title":""},{"docid":"375598040ecfa5dfddce8769d5802278","score":"0.69279623","text":"def print_progress_bar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█'):\n if total > 0:\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n filledLength = int(length * iteration // total)\n bar = fill * filledLength + '-' * (length - filledLength)\n print('\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = '\\r')\n # Print New Line on Complete\n if iteration == total:\n print()","title":""},{"docid":"c5342d1cddbb97289c633ac0706e4d2a","score":"0.6919614","text":"def bar(self, i, message=\"\"):\n if i in self.ticks:\n b = int(np.ceil(((i+1) / self.nf) * self.length))\n sys.stdout.write(\"\\r[{0}{1}] {2}%\\t{3}\".format(\n \"=\"*b, \" \"*(self.length-b), int(100*((i+1) / self.nf)), message\n ))\n sys.stdout.flush()","title":""},{"docid":"dfbfa7774f9eafa0376b1fbe7a31fc9a","score":"0.6918811","text":"def print_progress_bar(iteration, total, prefix='', suffix='',\n decimals=1, length=100, fill='█'):\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n filledLength = int(length * iteration // total)\n bar = fill * filledLength + '-' * (length - filledLength)\n print('\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end='\\r')\n # Print New Line on Complete\n if iteration == total:\n print()","title":""},{"docid":"00c63809b9987da1906ee7e83343f206","score":"0.69084704","text":"def progressbar(self, dummy):\n return self._progressbar","title":""},{"docid":"988c7ea4cfd99a49ee10e7a0523c3d2e","score":"0.6907157","text":"def show_progressbar(status):\n progress = int(50*(status['downloaded_bytes']/status['total_bytes']))\n percent = ('{0:.1f}').format(progress*2)\n bar = '▓'*progress+'-'*(50 - progress)\n sys.stdout.write(Fore.YELLOW+'↳'+Style.RESET_ALL+' |{bar}| {percent}%\\r'.format(bar=bar, percent=percent))\n sys.stdout.flush()\n if status['status'] == 'finished':\n logging.info('')","title":""},{"docid":"533812d1a329919a6986704e51e9a966","score":"0.6904433","text":"def _update_progressbar(progress):\n try:\n rows, columns = os.popen('stty size', 'r').read().split()\n bar_length = int(columns) - 35\n if not bar_length > 1:\n bar_length = 20\n except:\n # Default value if determination of console size fails\n bar_length = 20\n block = int(round(bar_length * progress))\n text = \"\\rDownload Progress: [{0}] {1:.2f}%\".format(\n \"#\" * block + \"-\" * (bar_length - block), progress * 100)\n sys.stdout.write(text)\n sys.stdout.flush()","title":""},{"docid":"7cf92aacd8bb0a1a498fc1b5720894ce","score":"0.6902411","text":"def progress(count, total, status=''):\n\n bar_len = 30\n filled_len = int(round(bar_len * count / float(total)))\n\n percents = round(100.0 * count / float(total), 1)\n bar = '=' * (filled_len - 1) + '>' + '-' * (bar_len - filled_len)\n\n sys.stdout.write('[%s] %s%s - %s\\r' % (bar, percents, '%', status))\n sys.stdout.flush()","title":""},{"docid":"64a5b350d24c98d0657663b1dd88d409","score":"0.6899447","text":"def print_progress_bar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█', printEnd = \"\\r\"):\r\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\r\n filledLength = int(length * iteration // total)\r\n bar = fill * filledLength + '-' * (length - filledLength)\r\n print('\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = printEnd)\r\n # Print New Line on Complete\r\n if iteration == total: \r\n print()","title":""},{"docid":"a7d0292fbc2474667f25abc9890f880f","score":"0.6892232","text":"def printProgressBar(iteration, total, prefix='', suffix='', decimals=1, bar_length=100):\n str_format = \"{0:.\" + str(decimals) + \"f}\"\n percents = str_format.format(100 * (iteration / float(total)))\n filled_length = int(round(bar_length * iteration / float(total)))\n bar = '█' * filled_length + '-' * (bar_length - filled_length)\n\n sys.stdout.write('\\r%s %s %s%s %s' % (prefix, bar, percents, '%', suffix)),\n\n if iteration == total:\n sys.stdout.write('\\n')\n sys.stdout.flush()","title":""},{"docid":"85ba0f5c85ce8e3293c6ffb819626b30","score":"0.6877466","text":"def print_progress_bar(iteration, total, prefix='', suffix='', decimals=1, length=50, fill='=', forward_sample='>'):\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(int(100 * (iteration / float(total))))\n filled_length = int(length * iteration // total)\n forward_symbol = forward_sample if iteration < total else ''\n bar = fill * filled_length + forward_symbol + '-' * (length - filled_length)\n print('\\r %s |%s| %s%% %s' % (prefix, bar, percent, suffix), end='\\r')\n # Print New Line on Complete\n if iteration == total:\n print()\n print()","title":""},{"docid":"74b616ccb7f72cd77662fc8b834498f3","score":"0.686431","text":"def update_progressbar(progress):\n # Try to set the bar_length according to the console size\n try:\n rows, columns = os.popen('stty size', 'r').read().split()\n bar_length = int(columns) - 35\n if(not (bar_length > 1)):\n bar_length = 20\n except:\n # Default value if determination of console size fails\n bar_length = 20\n block = int(round(bar_length*progress))\n text = \"\\rDownload Progress: [{0}] {1:.2f}%\".format(\n \"#\"*block + \"-\"*(bar_length-block), progress*100)\n sys.stdout.write(text)\n sys.stdout.flush()","title":""},{"docid":"a35bb1079a556ca68c6e1a70a431b1a8","score":"0.68437374","text":"def _updateRunning(self):\n\n counter_width = math.floor(math.log10(self.progress_max)) + 1\n loader_width = self.line_length - 2 * counter_width - 2\n prog = round((self.progress / self.progress_max) * loader_width)\n\n line = (\"{:\" + str(loader_width) + \"} \" + \n \"{:\" + str(counter_width) + \"}/\" + \n \"{:\" + str(counter_width) + \"}\")\n \n self.print(\"\\b\" * self.line_length + line.format(\n \"█\" * prog + \"▒\" * (loader_width - prog),\n self.progress,\n self.progress_max\n ), sep=\"\", end=\"\")\n log_debug(self._logger, (\"Updating the running indicator, progress is \" + \n \"now '{}'/'{}'\").format(self.progress, self.progress_max))","title":""},{"docid":"7f04340b3e2e9702c594a838d09c67d5","score":"0.68402827","text":"def printProgressBar(iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█'): # █ = U-219\n \n percent = (\"{:0.\" + str(decimals) + \"f}\").format(100* (iteration/float(total)))\n filledlength = int(length * iteration // total)\n bar = fill * filledlength + \"-\" * (length - filledlength)\n print('\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = '\\r')\n # print New line after completition\n if iteration == total:\n print()","title":""},{"docid":"7f688f14660420c4a40fae16eab53be5","score":"0.6836656","text":"def printProgress (iteration, total, prefix = '', suffix = '', decimals = 2, barLength = 100):\r\n filledLength = int(round(barLength * iteration / float(total)))\r\n percents = round(100.00 * (iteration / float(total)), decimals)\r\n bar = '%' * filledLength + '-' * (barLength - filledLength)\r\n sys.stdout.write('\\r%s |%s| %s%s %s' % (prefix, bar, percents, '%', suffix)),\r\n sys.stdout.flush()\r\n if iteration == total:\r\n sys.stdout.write('\\n')\r\n sys.stdout.flush()","title":""},{"docid":"489613b683982885ecc2ae99e3853bc1","score":"0.68329877","text":"def printProgressBar (iteration=0, total=0, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█'):\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n filledLength = int(length * iteration // total)\n bar = fill * filledLength + '-' * (length - filledLength)\n print('\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = '\\r')\n # Print New Line on Complete\n if iteration == total:\n print()","title":""},{"docid":"e2987ea6ba46a97ec422a4fe937e401a","score":"0.68296653","text":"def _progress_handler(self):\n self.pbar.update(self.itercount)","title":""},{"docid":"5e0b9fbfb0d2d03372a34f0153b8b9bc","score":"0.68233275","text":"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█'):\r\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\r\n filledLength = int(length * iteration // total)\r\n bar = fill * filledLength + '-' * (length - filledLength)\r\n print('\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = '\\r')\r\n # Print New Line on Complete\r\n if iteration == total: \r\n print()","title":""},{"docid":"ff70138321450a2278002d2d7668cc93","score":"0.6815338","text":"def status_bar_printer(count, total, suffix):\n sys.stdout.write('\\r')\n progress = count / total * 20\n sys.stdout.write(\"[%-20s] %d%% - %i of %i %s\" % ('=' * int(progress), progress * 5, count, total, suffix))\n sys.stdout.flush()","title":""},{"docid":"96f8466f5fe4c00cdd1d0ba04b604452","score":"0.68068045","text":"def printProgressBar (iteration, total, prefix = 'Progress:', suffix = 'Complete', decimals = 1, length = 100, fill = '█', printEnd = \"\\r\"):\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n filledLength = int(length * iteration // total)\n bar = fill * filledLength + '-' * (length - filledLength)\n print('\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = printEnd)\n # Print New Line on Complete\n if iteration == total: \n print()","title":""},{"docid":"f17bb77f21973101f10e936752c8eb1b","score":"0.679994","text":"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 50, fill = '█'):\n import subprocess\n import time\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n filledLength = int(length * iteration // total)\n bar = fill * filledLength + '-' * (length - filledLength)\n out = '\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix)\n rows, columns = subprocess.check_output(['stty', 'size']).split()\n out += \" \" * abs(len(out) - int(columns))\n print(out, end = '\\r')\n # Print New Line on Complete\n if iteration == total: \n print()","title":""},{"docid":"e93de7560f148df97508428a3b7e6365","score":"0.67990166","text":"def print_progress_bar(iteration, total, prefix='', suffix='', decimals=1, length=25, fill='='):\n assert 0 <= iteration <= total\n\n iteration += 1\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n filledLength = int(length * iteration // total) + 1\n bar = fill * filledLength + '>' * min(length - filledLength, 1) + '.' * (length - filledLength - 1)\n\n print('\\r {} [{}] {}/{} ({}%) {}'.format(prefix, bar, iteration, total, percent, suffix), end='\\r')\n if iteration >= total: # Print new line on completion\n print(' ')\n sys.stdout.flush()","title":""},{"docid":"5482c1f70c4e0731b510ee118c824326","score":"0.67983526","text":"def printProgressBar(self, iteration, total, sec, elapsed, prefix='', suffix='', decimals=1, length=100, fill='█'):\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n filledLength = int(length * iteration // total)\n bar = fill * filledLength + '-' * (length - filledLength)\n return '{} |{}| {}% {} ({:.2f} sec / {:.2f} elapsed)'.format(prefix, bar, percent, suffix, sec, elapsed)","title":""},{"docid":"9cd6f15479afbb4022ec0dd446fe66c6","score":"0.67977643","text":"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 40, fill = '█'):\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n filledLength = int(length * iteration // total)\n bar = fill * filledLength + '-' * (length - filledLength)\n sys.stdout.write('\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix))\n sys.stdout.flush()\n # Print New Line on Complete\n if iteration == total:\n print()","title":""},{"docid":"9e817aa3da767faf1705969b8d872fc1","score":"0.6796534","text":"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█'):\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n filledLength = int(length * iteration // total)\n bar = fill * filledLength + '-' * (length - filledLength)\n print('\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = '\\r')\n # Print New Line on Complete\n if iteration == total:\n print()","title":""},{"docid":"9fbd2ea6a013abd7c96d1a1b16598567","score":"0.6795735","text":"def printProgressBar(iteration, total, prefix='', suffix=''):\n\tdecimals = 0\n\tbar_length = 40\n\tstr_format = \"{0:.\" + str(decimals) + \"f}\"\n\tpercents = str_format.format(100 * (iteration / float(total)))\n\tfilled_length = int(round(bar_length * iteration / float(total)))\n\tbar = '#' * filled_length + '.' * (bar_length - filled_length)\n\tsys.stdout.write('\\r\\033[44m\\033[37m%s [%s%s]\\033[0m |%s| %s' \\\n\t\t% (prefix, percents, '%', bar, suffix))\n\tsys.stdout.flush()\n\treturn","title":""},{"docid":"af008e2f20f1751015c172f1847eda56","score":"0.6794594","text":"def progress_bar(index: int, count: int, desc: str | None = None, bar_len: int = 30, newline: bool = False) -> None:\n end = \"\\r\" if not newline else \"\\r\\n\"\n filled_len = int(bar_len * index // count)\n bar = f\"{desc} \" if desc is not None else \"\"\n bar += \"[\" + \"=\" * filled_len + \" \" * (bar_len - filled_len) + \"]\"\n print(f\"{index}/{count} {bar}\", end=end)\n if index == count:\n print(\"\")","title":""},{"docid":"5a58a768dab110ef0d9496254769cbab","score":"0.6791022","text":"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█'):\n\tpercent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n\tfilledLength = int(length * iteration // total)\n\tbar = fill * filledLength + '-' * (length - filledLength)\n\tprint('\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = '\\r')\n\t# Print New Line on Complete\n\tif iteration == total: \n\t\tprint()","title":""},{"docid":"b9dded5c392279113c6edb1a6024bb59","score":"0.6789979","text":"def printProgressBar(iteration, total, prefix='', suffix='', decimals=1, length=100, fill='█', printEnd=\"\\r\"):\r\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\r\n filledLength = int(length * iteration // total)\r\n bar = fill * filledLength + '-' * (length - filledLength)\r\n print('\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end=printEnd)\r\n # Print New Line on Complete\r\n if iteration == total:\r\n print('\\n')\r\n return","title":""},{"docid":"6767e85929eb94e7be163b6246947c76","score":"0.67876405","text":"def printProgressBar(iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█'):\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n filledLength = int(length * iteration // total)\n bar = fill * filledLength + '-' * (length - filledLength)\n sys.stdout.flush()\n sys.stdout.write('\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix))\n # Print New Line on Complete\n if iteration == total: \n sys.stdout.flush()\n print()","title":""},{"docid":"7edb6eca9877daa8d98285b75270619e","score":"0.67875326","text":"def print_progress(iteration, total, prefix='', suffix='', decimals=1, bar_length=100):\n str_format = \"{0:.\" + str(decimals) + \"f}\"\n percents = str_format.format(100 * (iteration / float(total)))\n filled_length = int(round(bar_length * iteration / float(total)))\n bar = '█' * filled_length + '-' * (bar_length - filled_length)\n\n sys.stdout.write('\\r%s |%s| %s%s %s' % (prefix, bar, percents, '%', suffix)),\n\n if iteration == total:\n sys.stdout.write('\\n')\n sys.stdout.flush()","title":""},{"docid":"7edb6eca9877daa8d98285b75270619e","score":"0.67875326","text":"def print_progress(iteration, total, prefix='', suffix='', decimals=1, bar_length=100):\n str_format = \"{0:.\" + str(decimals) + \"f}\"\n percents = str_format.format(100 * (iteration / float(total)))\n filled_length = int(round(bar_length * iteration / float(total)))\n bar = '█' * filled_length + '-' * (bar_length - filled_length)\n\n sys.stdout.write('\\r%s |%s| %s%s %s' % (prefix, bar, percents, '%', suffix)),\n\n if iteration == total:\n sys.stdout.write('\\n')\n sys.stdout.flush()","title":""},{"docid":"7edb6eca9877daa8d98285b75270619e","score":"0.67875326","text":"def print_progress(iteration, total, prefix='', suffix='', decimals=1, bar_length=100):\n str_format = \"{0:.\" + str(decimals) + \"f}\"\n percents = str_format.format(100 * (iteration / float(total)))\n filled_length = int(round(bar_length * iteration / float(total)))\n bar = '█' * filled_length + '-' * (bar_length - filled_length)\n\n sys.stdout.write('\\r%s |%s| %s%s %s' % (prefix, bar, percents, '%', suffix)),\n\n if iteration == total:\n sys.stdout.write('\\n')\n sys.stdout.flush()","title":""},{"docid":"7edb6eca9877daa8d98285b75270619e","score":"0.67875326","text":"def print_progress(iteration, total, prefix='', suffix='', decimals=1, bar_length=100):\n str_format = \"{0:.\" + str(decimals) + \"f}\"\n percents = str_format.format(100 * (iteration / float(total)))\n filled_length = int(round(bar_length * iteration / float(total)))\n bar = '█' * filled_length + '-' * (bar_length - filled_length)\n\n sys.stdout.write('\\r%s |%s| %s%s %s' % (prefix, bar, percents, '%', suffix)),\n\n if iteration == total:\n sys.stdout.write('\\n')\n sys.stdout.flush()","title":""},{"docid":"7edb6eca9877daa8d98285b75270619e","score":"0.67875326","text":"def print_progress(iteration, total, prefix='', suffix='', decimals=1, bar_length=100):\n str_format = \"{0:.\" + str(decimals) + \"f}\"\n percents = str_format.format(100 * (iteration / float(total)))\n filled_length = int(round(bar_length * iteration / float(total)))\n bar = '█' * filled_length + '-' * (bar_length - filled_length)\n\n sys.stdout.write('\\r%s |%s| %s%s %s' % (prefix, bar, percents, '%', suffix)),\n\n if iteration == total:\n sys.stdout.write('\\n')\n sys.stdout.flush()","title":""},{"docid":"6e77bf33b645eefcdca4fd7df833ef4f","score":"0.6785296","text":"def print_progress_bar(iteration, total, prefix='', suffix='', decimals=1, length=100, fill='█', printEnd='\\r'):\n percent = ('{0:.' + str(decimals) + 'f}').format(100 * (iteration / float(total)))\n filledLength = int(length * iteration // total)\n bar = fill * filledLength + '-' * (length - filledLength)\n print(('\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix)), end=printEnd)\n if iteration == total:\n print()","title":""},{"docid":"b19400bc157ffe10a72f334e18185967","score":"0.67828906","text":"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█', printEnd = \"\\n\"):\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n filledLength = int(length * iteration // total)\n bar = fill * filledLength + '-' * (length - filledLength)\n print(f'\\r{prefix} |{bar}| {percent}% {suffix}', end = printEnd)\n # Print New Line on Complete\n # if iteration == total: \n # print()","title":""},{"docid":"ac401374f7a26ff02a3fac5afacc07c6","score":"0.67784303","text":"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█'):\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n filledLength = int(length * iteration // total)\n bar = fill * filledLength + '-' * (length - filledLength)\n print('\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = '\\r')\n # Print New Line on Complete\n if iteration == total: \n print()","title":""},{"docid":"ac401374f7a26ff02a3fac5afacc07c6","score":"0.67784303","text":"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█'):\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n filledLength = int(length * iteration // total)\n bar = fill * filledLength + '-' * (length - filledLength)\n print('\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = '\\r')\n # Print New Line on Complete\n if iteration == total: \n print()","title":""},{"docid":"ac401374f7a26ff02a3fac5afacc07c6","score":"0.67784303","text":"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█'):\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n filledLength = int(length * iteration // total)\n bar = fill * filledLength + '-' * (length - filledLength)\n print('\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = '\\r')\n # Print New Line on Complete\n if iteration == total: \n print()","title":""},{"docid":"ac401374f7a26ff02a3fac5afacc07c6","score":"0.67784303","text":"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█'):\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n filledLength = int(length * iteration // total)\n bar = fill * filledLength + '-' * (length - filledLength)\n print('\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = '\\r')\n # Print New Line on Complete\n if iteration == total: \n print()","title":""},{"docid":"ac401374f7a26ff02a3fac5afacc07c6","score":"0.67784303","text":"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█'):\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n filledLength = int(length * iteration // total)\n bar = fill * filledLength + '-' * (length - filledLength)\n print('\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = '\\r')\n # Print New Line on Complete\n if iteration == total: \n print()","title":""},{"docid":"db62698a74f59412652e18f73d57bf4b","score":"0.67761445","text":"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█', printend = \"\\r\"):\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n filledLength = int(length * iteration // total)\n bar = fill * filledLength + '-' * (length - filledLength)\n print('\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), printend)\n # Print New Line on Complete\n if iteration == total: \n print()","title":""},{"docid":"3ef0d92fa3fa387c3bda7043614d06af","score":"0.6775269","text":"def update_progress(self, job_title, progress):\r\n length = 100 # modify this to change the length\r\n block = int(round(length*progress))\r\n msg = \"\\r{0}: [{1}] {2}%\".format(job_title, \"#\"*block + \"-\"*(length-block), round(progress*100, 2))\r\n if progress >= 1: msg += \" DONE\\r\\n\"\r\n sys.stdout.write(msg)\r\n sys.stdout.flush()","title":""},{"docid":"28b90c933919e299a5998973a0518c27","score":"0.6772445","text":"def printProgressBar(iteration, total, prefix='', suffix='', decimals=1, length=100, fill='█'):\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n filledLength = int(length * iteration // total)\n bar = fill * filledLength + '-' * (length - filledLength)\n print('\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end='\\r')\n # Print New Line on Complete\n if iteration == total:\n print()","title":""},{"docid":"4acfece9aca502acded2871086a126fe","score":"0.6768273","text":"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 10, fill = '█', printEnd = \"\\r\"):\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n filledLength = int(length * iteration // total)\n bar = fill * filledLength + '-' * (length - filledLength)\n print(end=f'\\r{prefix} |{bar}| {percent}% {suffix}')\n # Print New Line on Complete\n if iteration >= total: \n print()","title":""},{"docid":"d2382008e635f8176fdee9f30ec707f7","score":"0.67678","text":"def progress(count, total, status=''):\n bar_len = 60\n filled_len = int(round(bar_len * count / float(total)))\n\n percents = round(100.0 * count / float(total), 1)\n bar = '=' * filled_len + '-' * (bar_len - filled_len)\n\n sys.stdout.write('\\r[%s] %s%s ...%s' % (bar, percents, '%', status))\n sys.stdout.flush()\n Client.clear()","title":""},{"docid":"c895ad7fbd05885376795ff676b3d787","score":"0.676626","text":"def _print_progress(iteration, total, prefix='', suffix='', decimals=1, length=100, fill='█', printEnd=\"\\r\"):\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n filledLength = int(length * iteration // total)\n bar = fill * filledLength + '-' * (length - filledLength)\n print('\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end=printEnd)\n # Print New Line on Complete\n if iteration == total:\n print()","title":""},{"docid":"966aa54761fe54d5b2e51bbbe5a89960","score":"0.67619616","text":"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█', printEnd = \"\\r\"):\r\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\r\n filledLength = int(length * iteration // total)\r\n bar = fill * filledLength + '-' * (length - filledLength)\r\n print('\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = printEnd)\r\n # Print New Line on Complete\r\n if iteration == total: \r\n print()","title":""},{"docid":"5398f0986a84278bc5a4a808a3b17cf1","score":"0.67591906","text":"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█', printEnd = \"\\r\"):\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n filledLength = int(length * iteration // total)\n bar = fill * filledLength + '-' * (length - filledLength)\n print('\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = printEnd)\n # Print New Line on Complete\n if iteration == total: \n print()","title":""},{"docid":"5398f0986a84278bc5a4a808a3b17cf1","score":"0.67591906","text":"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█', printEnd = \"\\r\"):\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n filledLength = int(length * iteration // total)\n bar = fill * filledLength + '-' * (length - filledLength)\n print('\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = printEnd)\n # Print New Line on Complete\n if iteration == total: \n print()","title":""},{"docid":"795cceb593c99c7998753844b1db9781","score":"0.6754499","text":"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█', printEnd = \"\\r\"):\n\tpercent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n\tfilledLength = int(length * iteration // total)\n\tbar = fill * filledLength + '-' * (length - filledLength)\n\tprint(f'\\r{prefix} |{bar}| {percent}% {suffix}', end = printEnd)\n\t# Print New Line on Complete\n\tif iteration == total: \n\t\tprint()","title":""},{"docid":"5465fe4dfc34f907268e1b2d3314cd22","score":"0.6754105","text":"def printProgressBar(iteration, total, prefix='', suffix='', decimals=2, length=80, fill='█', printEnd=\"\\r\"):\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n filledLength = int(length * iteration // total)\n bar = fill * filledLength + '-' * (length - filledLength)\n print(f'\\r{prefix} |{bar}| {percent}% {suffix}', end=printEnd)\n # Print New Line on Complete\n if iteration == total:\n print()","title":""},{"docid":"b005b4bfdec3aacf70e19c6936c00cd0","score":"0.6747428","text":"def print_progress(iteration, total, prefix=\"\", suffix=\"\", decimals=2, bar_length=100):\n filled_length = int(round(bar_length * iteration / float(total)))\n percents = round(100.00 * (iteration / float(total)), decimals)\n progress_bar = \"#\" * filled_length + \"-\" * (bar_length - filled_length)\n sys.stdout.write(\"\\r%s |%s| %s%s %s\" % (prefix, progress_bar, percents, \"%\", suffix))\n sys.stdout.flush()\n if iteration == total:\n sys.stdout.write(\"\\n\")\n sys.stdout.flush()","title":""},{"docid":"a6cfa54230c4ad0698ff1148d1b2c599","score":"0.67446744","text":"def printProgress (iteration, total, prefix = '', suffix = '', decimals = 2, barLength = 70):\n filledLength = int(round(barLength * iteration / float(total)))\n percents = round(100.00 * (iteration / float(total)), decimals)\n bar = '#' * filledLength + '-' * (barLength - filledLength)\n sys.stdout.write('%s [%s] %s%s %s\\r' % (prefix, bar, percents, '%', suffix)),\n sys.stdout.flush()\n if iteration == total:\n print(\"\\n\")","title":""},{"docid":"ff0b8d1d2180f14bc815e77e5fd64cc9","score":"0.67425686","text":"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█', printEnd = \"\\r\"):\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n filledLength = int(length * iteration // total)\n bar = fill * filledLength + '-' * (length - filledLength)\n print(f'\\r{prefix} |{bar}| {percent}% {suffix}', end = printEnd)\n # Print New Line on Complete\n if iteration == total: \n print()","title":""},{"docid":"ff0b8d1d2180f14bc815e77e5fd64cc9","score":"0.67425686","text":"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█', printEnd = \"\\r\"):\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n filledLength = int(length * iteration // total)\n bar = fill * filledLength + '-' * (length - filledLength)\n print(f'\\r{prefix} |{bar}| {percent}% {suffix}', end = printEnd)\n # Print New Line on Complete\n if iteration == total: \n print()","title":""},{"docid":"ff0b8d1d2180f14bc815e77e5fd64cc9","score":"0.67425686","text":"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█', printEnd = \"\\r\"):\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n filledLength = int(length * iteration // total)\n bar = fill * filledLength + '-' * (length - filledLength)\n print(f'\\r{prefix} |{bar}| {percent}% {suffix}', end = printEnd)\n # Print New Line on Complete\n if iteration == total: \n print()","title":""},{"docid":"ff0b8d1d2180f14bc815e77e5fd64cc9","score":"0.67425686","text":"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█', printEnd = \"\\r\"):\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n filledLength = int(length * iteration // total)\n bar = fill * filledLength + '-' * (length - filledLength)\n print(f'\\r{prefix} |{bar}| {percent}% {suffix}', end = printEnd)\n # Print New Line on Complete\n if iteration == total: \n print()","title":""},{"docid":"13b53e8d9f539263e86a6ec897863d74","score":"0.6742398","text":"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█'):\r\n\tpercent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\r\n\tfilledLength = int(length * iteration // total)\r\n\tbar = fill * filledLength + '-' * (length - filledLength)\r\n\tprint('\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = '\\r')\r\n\t# Print New Line on Complete\r\n\tif iteration == total: \r\n\t\tprint()","title":""},{"docid":"71eedbfc1e2ce394bb29ab7bd9ed31d4","score":"0.67376286","text":"def progress(self, count, total, start_time, status = 'Running'):\n\t\tbar_len = 60\n\t\tfilled_len = int(round(bar_len * count / float(total)))\n\n\t\tpercents = round(100.0 * count / float(total), 1)\n\t\tif count == total: bar = '=' * filled_len +'-' * (bar_len - filled_len)\n\t\telse: bar = '=' * filled_len + '>' +'-' * (bar_len - filled_len - 1)\n\t\tm, s = divmod(time.time() - start_time, 60)\n\t\th, m = divmod(m, 60)\n\n\t\tif count == total:\n\t\t\ttime.sleep(0.5)\n\t\t\tsys.stdout.write(\"\\033[K\")\n\t\t\tsys.stdout.write('|%s|%s%s|%d:%02d:%02d| %s\\r\\n' % \n\t\t\t\t\t\t\t(bar, percents, '%', h, m, s, status +\" OK\"))\n\t\telse:\n\t\t\tsys.stdout.write('|%s|%s%s|%d:%02d:%02d| %s\\r' % \n\t\t\t\t\t\t\t(bar, percents, '%', h, m, s, status))\n\t\tsys.stdout.flush()","title":""},{"docid":"1c9ae445744700ad213c4e0b560b2a86","score":"0.6735115","text":"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█', printEnd = \"\\r\"):\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n filledLength = int(length * iteration // total)\n bar = fill * filledLength + '-' * (length - filledLength)\n print('\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = printEnd)\n\n # Print New Line on Complete\n if iteration == total:\n print()","title":""},{"docid":"f4c9911a26dfeedc5aa817c32414400c","score":"0.673196","text":"def printProgressBar(iteration, total, prefix='', suffix='', decimals=1, length=100, fill='█', printEnd=\"\\r\"):\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 *\n (iteration / float(total)))\n filledLength = int(length * iteration // total)\n bar = fill * filledLength + '-' * (length - filledLength)\n print('\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end=printEnd)\n # Print New Line on Complete\n if iteration == total:\n print()","title":""},{"docid":"d4731635b3c83f0219ce294ac3b50b9b","score":"0.6731744","text":"def printProgressBar(iteration, total, prefix='', suffix='', decimals=2, length=80, fill='█', printEnd=\"\\r\"):\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n filledLength = int(length * iteration // total)\n bar = fill * filledLength + '-' * (length - filledLength)\n # print(f'\\r{prefix} |{bar}| {percent}% {suffix}', end=printEnd)\n sys.stdout.write(f'\\r{prefix} |{bar}| {percent}% {suffix}')\n\n # Print New Line on Complete\n if iteration == total:\n print()","title":""},{"docid":"de703f35ab13f5d1198c9692b99df2a1","score":"0.6718077","text":"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█'):\n # Source: https://stackoverflow.com/questions/3173320/text-progress-bar-in-the-console\n\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n filledLength = int(length * iteration // total)\n bar = fill * filledLength + '-' * (length - filledLength)\n print('\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = '\\r')\n \n # Print New Line on Complete\n if iteration == total: \n print()","title":""},{"docid":"5f467d1ce19b85863a20d97196b6877c","score":"0.6711195","text":"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1,\n length = 100, fill = '#'):\n val = 100 * (iteration / float(total))\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(val)\n filledLength = int(length * iteration // total)\n bar = fill * filledLength + '-' * (length - filledLength)\n print('\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = '\\r')\n # Print New Line on Complete\n if iteration == total:\n print()","title":""},{"docid":"cbb0420fa8d2068da87d72d84b0ace3a","score":"0.6707381","text":"def progress(percent, barlen=20, name=''):\n # percent float from 0 to 1.\n sys.stdout.write(\"\\r\") # removes the previous line\n sys.stdout.write(\"[{:<{}}] {:.0f}% {}\".format(\"=\" * int(barlen * percent), barlen, percent * 100, name))\n sys.stdout.flush()","title":""},{"docid":"bab30fd92522fc5d997f0a26d72eb38d","score":"0.67068875","text":"def progress(percent=0, width=30):\n left = width * percent // 100\n right = width - left\n print('\\r[', '#' * left, ' ' * right, ']',\n f' {percent:.0f}%',\n sep='', end='', flush=True)","title":""},{"docid":"59e6e56b488d769a055bb466b6015ea5","score":"0.6702873","text":"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '\\u2588', printEnd = \"\\r\"):\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * ((iteration+1) / float(total+1)))\n filledLength = int(length * (iteration+1) // (total+1))\n bar = fill * filledLength + '-' * (length - filledLength)\n print(f'\\r{prefix} |{bar}| {percent}% {suffix}', end = printEnd)\n # Print New Line on Complete\n if iteration == total: \n print()","title":""},{"docid":"e13f4635066faebae28cc317301cb485","score":"0.67002916","text":"def printProgressBar(\n iteration,\n total,\n prefix=\"\",\n suffix=\"\",\n decimals=1,\n length=100,\n fill=\"█\",\n printEnd=\"\\r\",\n):\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n filledLength = int(length * iteration // total)\n bar = fill * filledLength + \"-\" * (length - filledLength)\n print(\"\\r%s |%s| %s%% %s\" % (prefix, bar, percent, suffix), end=printEnd)\n # Print New Line on Complete\n if iteration == total:\n print()","title":""},{"docid":"4e3cfd193ca2c47cc42f7887289e442d","score":"0.6698633","text":"def progress(self):\n return tqdm(self, total=len(self))","title":""},{"docid":"17059ca955b9704cb881d4f7ec785389","score":"0.669806","text":"def print_progress(iteration, total, prefix='', suffix='', decimals=3, bar_length=100):\n format_str = \"{0:.\" + str(decimals) + \"f}\" # format the % done number string\n percents = format_str.format(100 * (iteration / float(total))) # calculate the % done\n filled_length = int(round(bar_length * iteration / float(total))) # calculate the filled bar length\n bar = '#' * filled_length + '-' * (bar_length - filled_length) # generate the bar string\n sys.stdout.write('\\r%s |%s| %s%s %s' % (prefix, bar, percents, '%', suffix)), # write out the bar\n sys.stdout.flush() # flush to stdout","title":""},{"docid":"3025366a9ece3bb1582421d7cedf66c1","score":"0.6679355","text":"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█', printEnd = \"\\r\"):\r\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\r\n filledLength = int(length * iteration // total)\r\n bar = fill * filledLength + '-' * (length - filledLength)\r\n print('\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = printEnd)\r\n # Imprime la nueva línea al terminar\r\n if iteration == total: \r\n print()","title":""},{"docid":"4d9d8b18f8e26d206e26ecada5972b65","score":"0.6675401","text":"def print_progress(iteration, total, prefix='', suffix='', decimals=1, length=100, fill='█'):\n bar_length = 60\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 *\n (iteration / float(total)))\n filledLength = int(length * iteration // total)\n bar = fill * filledLength + '-' * (length - filledLength)\n sys.stdout.write('\\r%s |%s| %s%s %s' %\n (prefix, bar, percent, '%', suffix)),\n\n if iteration == total:\n sys.stdout.write('\\n')\n sys.stdout.flush()","title":""},{"docid":"36addb26a9e28788f768316980b33100","score":"0.6673189","text":"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█', printEnd = \"\\r\", end=True):\n percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n filledLength = int(length * iteration // total)\n bar = fill * filledLength + '-' * (length - filledLength)\n print(f'\\r{prefix} |{bar}| {iteration}/{total} {suffix}', end = printEnd)\n \n if iteration == total and end: \n print()","title":""},{"docid":"35d552302691337e28273e8b587cbb2c","score":"0.66621214","text":"def progressbar(it, prefix=\"\", size=60, file=sys.stdout):\n count = len(it)\n def show(j):\n x = int(size*j/count)\n file.write(\"%s[%s%s] %i/%i\\r\" % (prefix, \"#\"*x, \".\"*(size-x), j, count))\n file.flush() \n show(0)\n for i, item in enumerate(it):\n yield item\n show(i+1)\n file.write(\"\\n\")\n file.flush()","title":""},{"docid":"474cceec93d797acef89833a7023a42e","score":"0.66588426","text":"def print_progress(iteration, total, prefix='', suffix='', decimals=1, bar_length=100):\n str_format = \"{0:.\" + str(decimals) + \"f}\"\n percents = str_format.format(100 * (iteration / float(total)))\n filled_length = int(round(bar_length * iteration / float(total)))\n bar = '=' * filled_length + '-' * (bar_length - filled_length)\n\n sys.stdout.write('\\r%s |%s| %s%s %s' % (prefix, bar, percents, '%', suffix)),\n\n if iteration == total:\n sys.stdout.write('\\n')\n sys.stdout.flush()","title":""},{"docid":"ae5da3f52cff09b679eb03bf8180655b","score":"0.66570103","text":"def print_progress(iteration, total, prefix='', suffix='', decimals=1, bar_length=100):\n format_str = \"{0:.\" + str(decimals) + \"f}\"\n if total == 0:\n total = 1\n percents = format_str.format(100 * (iteration / float(total)))\n filled_length = int(round(bar_length * iteration / float(total)))\n bar = '#' * filled_length + '-' * (bar_length - filled_length)\n sys.stdout.write('\\r%s (%s/%s) |%s| %s%s %s' % (prefix, iteration, total, bar, percents, '%', suffix)),\n sys.stdout.flush()\n if iteration == total:\n sys.stdout.write('\\n')\n sys.stdout.flush()","title":""},{"docid":"998bfc9e7deafa68a0f2272442df0bb6","score":"0.6656935","text":"def printProgress (iteration, total, prefix = '', suffix = '', decimals = 1, barLength = 100):\r\n formatStr = \"{0:.\" + str(decimals) + \"f}\"\r\n percents = formatStr.format(100 * (iteration / float(total)))\r\n filledLength = int(round(barLength * iteration / float(total)))\r\n bar = '?' * filledLength + '-' * (barLength - filledLength)\r\n sys.stdout.write('\\r%s |%s| %s%s %s' % (prefix, bar, percents, '%', suffix)),\r\n if iteration == total:\r\n sys.stdout.write('\\n')\r\n sys.stdout.flush()","title":""},{"docid":"c9546aac814e262df8b51158c1848cba","score":"0.66549736","text":"def progress_bar(it, prefix=\"\", size=40, verbose=True):\n if verbose is False:\n for i, item in enumerate(it):\n yield i, item\n else:\n count = len(it)\n\n def show(j):\n x = int(size * j / count)\n print(\n f\"{prefix}[{u'█'*x}{('.'*(size-x))}] {j}/{count}\",\n end=\"\\r\",\n file=sys.stdout,\n flush=True,\n )\n\n show(0)\n for i, item in enumerate(it):\n yield i, item\n show(i + 1)\n print(\"\\n\", flush=True, file=sys.stdout)","title":""}],"string":"[\n {\n \"docid\": \"9933aeb88ac3e6df00a8f820b8f7ead5\",\n \"score\": \"0.7358753\",\n \"text\": \"def update_progress(self, progress):\\n barLength = 20\\n status = \\\"\\\"\\n if isinstance(progress, int):\\n progress = float(progress)\\n\\n block = int(round(barLength * progress))\\n text = \\\"\\\\r[{}] {:0.2f}% {}\\\".format(\\\"#\\\" * block + \\\"-\\\" * (barLength - block), progress * 100, status)\\n sys.stdout.write(text)\\n sys.stdout.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26de6746dc872f4acceefc60306d2b83\",\n \"score\": \"0.7152523\",\n \"text\": \"def do_progress():\\n global __PROGRESS_BAR__\\n __PROGRESS_BAR__=True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ea6e2377efc8e105a77275c307746f1\",\n \"score\": \"0.7141404\",\n \"text\": \"def progress(self, count, total, status=''):\\n bar_len = 60\\n filled_len = int(round(bar_len * count / float(total)))\\n\\n percents = round(100.0 * count / float(total), 1)\\n bar = '#' * filled_len + '.' * (bar_len - filled_len)\\n\\n sys.stdout.write('[%s] %s%s ...%s\\\\r' % (bar, percents, '%', status))\\n sys.stdout.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c051a44f60f6176d83687504056ba3b9\",\n \"score\": \"0.71388406\",\n \"text\": \"def progress(count, total, status=''):\\n bar_len = 60\\n filled_len = int(round(bar_len * count / float(total)))\\n bar = '=' * filled_len + ' ' * (bar_len - filled_len)\\n sys.stdout.write('[%s] %s %3d\\\\r\\\\n' % (bar, status, count))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f803df1f9db6b3cbfdf9bd475c09c84e\",\n \"score\": \"0.709899\",\n \"text\": \"def progress(curr, finish, width=10):\\n ticks = ['▂','▃','▄','▅','▆','▇']\\n finish = max(finish, 1)\\n pct = max(curr, 0) / float(finish)\\n bartip, barlen = math.modf(pct * width)\\n bartip = ticks[int(len(ticks) * bartip)] if pct < 1.0 else ''\\n bar = \\\"%s%s%s\\\" % (ticks[-1] * int(barlen), bartip, ' ' * (width - int(barlen) - 1))\\n prog = \\\"%s%% [ %s ] %d/%d\\\" % (str(int(pct*100)).rjust(3), bar, curr, finish)\\n return prog\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0766c7cae928bfa87ee4483b3b5645a\",\n \"score\": \"0.70744014\",\n \"text\": \"def _print_precaching_progress(i, total, bar_len=30):\\n position = round((i + 1) / total * bar_len)\\n pbar = \\\"[\\\" + \\\"█\\\" * position + \\\" \\\" * (bar_len - position) + \\\"]\\\"\\n print(pbar, end=\\\"\\\\r\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d3105f06cda776357cf5067ecf06234\",\n \"score\": \"0.70211333\",\n \"text\": \"def __printProgressBar (iteration, total, prefix = 'Progress:', suffix = 'Complete', decimals = 1, length = 50, fill = '█'):\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n filledLength = int(length * iteration // total)\\n bar = fill * filledLength + '-' * (length - filledLength)\\n print('\\\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = '\\\\r')\\n # Print New Line on Complete\\n if iteration == total:\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"023f72babb1f72fa67c98f12b795898d\",\n \"score\": \"0.7015529\",\n \"text\": \"def progress_bar(text, fill_amount, prefix = '', length = 80): #pylint: disable=W0613\\n filled_length = int(length * fill_amount)\\n fill_char = '█' if sys.stdout.encoding.lower() == 'utf-8' else 'X'\\n prog_bar = fill_char * filled_length + '-' * (length - filled_length)\\n print('\\\\r%s |%s| %s' % (prefix, prog_bar, text), end = '\\\\r')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e006bf6d2bf332373c3e3f00544a3c20\",\n \"score\": \"0.7003342\",\n \"text\": \"def print_progress_bar (iteration, total, prefix='', suffix='', decimals=1, length=100, fill='█'):\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n filled_length = int((length * iteration) / total)\\n bar = fill * filled_length + '--' * (length - filled_length)\\n print('\\\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end=\\\" \\\")\\n # Print New Line on Complete\\n if iteration == total:\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2972cf5db7f50c45e6325ec312877d4e\",\n \"score\": \"0.6989833\",\n \"text\": \"def print_progress_bar(iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█'):\\n # percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n # filled_length = int(length * iteration // total)\\n # bar = fill * filled_length + '-' * (length - filled_length)\\n # print('\\\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = '\\\\r')\\n # # Print New Line on Complete\\n # if iteration == total:\\n # print()\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c11a2801230495fe1fd9e48df40d951c\",\n \"score\": \"0.69718087\",\n \"text\": \"def print_progress_bar(iteration, total, prefix='', suffix='', decimals=1, length=100, fill='█', print_end=\\\"\\\\r\\\"):\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n filled_length = int(length * iteration // total)\\n bar = fill * filled_length + '-' * (length - filled_length)\\n print('\\\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end=print_end)\\n\\n # Printing new line is on the caller\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b509e84f24311d9feebf37979d3b905\",\n \"score\": \"0.6969002\",\n \"text\": \"def progress_bar(value, endvalue, bar_length=20):\\n percent = float(value) / endvalue\\n arrow = '-' * int(round(percent * bar_length)-1) + '>'\\n spaces = ' ' * (bar_length - len(arrow))\\n sys.stdout.write(\\\"\\\\rPercent complete: [{0}] {1}%\\\".format(arrow + spaces, int(round(percent * 100))))\\n sys.stdout.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cb4e477c417df94672824919a4a0721\",\n \"score\": \"0.69630903\",\n \"text\": \"def run(self):\\n #\\n # counter in progress bar symbols\\n i = 0\\n j = i % self._nb_prog_chars\\n #\\n # counter in bar\\n k = 0\\n sys.stdout.write('\\\\n')\\n sys.stdout.flush()\\n start_time = datetime.now()\\n while True:\\n now_time = datetime.now()\\n delta_time = now_time - start_time\\n hrs, rest = divmod(delta_time.seconds, 3600)\\n mins, secs = divmod(rest, 60)\\n pbar = ' ' \\\\\\n + '%02d:%02d:%02d' % (hrs, mins, secs) \\\\\\n + ' [' \\\\\\n + ' '*k \\\\\\n + self._prog_chars[j] \\\\\\n + ' ' * (self._bar_len - k - self._prog_len) \\\\\\n + ']'\\n sys.stdout.write('\\\\r%s' % pbar)\\n sys.stdout.flush()\\n k += 1\\n if k == self._cntr:\\n k = 0\\n i += 1\\n j = i % self._nb_prog_chars\\n time.sleep(self._prog_int)\\n if self.stop_the_progress_bar:\\n now_time = datetime.now()\\n delta_time = now_time - start_time\\n hrs, rest = divmod(delta_time.seconds, 3600)\\n mins, secs = divmod(rest, 60)\\n pbar = ' ' \\\\\\n + '%02d:%02d:%02d' % (hrs, mins, secs) \\\\\\n + ' [ ' \\\\\\n + ' %s' % self._prog_chars[j] \\\\\\n + ' done ]' \\\\\\n + (self._bar_len - self._prog_len - 5)*' '\\n sys.stdout.write('\\\\r%s\\\\n' % pbar)\\n sys.stdout.flush()\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"787d0c51dd83da59597791655e25e186\",\n \"score\": \"0.6962559\",\n \"text\": \"def print_progress_bar(\\n iteration, total, prefix=\\\"\\\", suffix=\\\"\\\", decimals=1, length=100, fill=\\\"█\\\"\\n):\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n filled_length = int(length * iteration // total)\\n bar = fill * filled_length + \\\"-\\\" * (length - filled_length)\\n print(\\n \\\"\\\\r%s|%s| %s%% (%s/%s) %s\\\" % (prefix, bar, percent, iteration, total, suffix),\\n end=\\\"\\\\r\\\",\\n )\\n # Print New Line on Complete\\n if iteration == total:\\n print(\\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eeffd96e8e54de1882df3b25e5817e27\",\n \"score\": \"0.69568115\",\n \"text\": \"def print_progress_bar (iteration, total, prefix = \\\"\\\", suffix = \\\"\\\", decimals = 1, length = 100, fill = \\\"█\\\", printEnd = \\\"\\\\r\\\"):\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n filledLength = int(length * iteration // total)\\n bar = fill * filledLength + \\\"-\\\" * (length - filledLength)\\n print(\\\"\\\\r%s |%s| %s%% %s\\\" % (prefix, bar, percent, suffix), end = printEnd)\\n if iteration == total: # Print New Line on Complete\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcdd3724bb213bfb86f6122b2592143d\",\n \"score\": \"0.69555634\",\n \"text\": \"def printProgress (iteration, total, prefix = '', suffix = '', decimals = 2, barLength = 100):\\n total = 1 if total == 0 else total\\n filledLength = int(round(barLength * iteration / float(total)))\\n percents = round(100.00 * (iteration / float(total)), decimals)\\n bar = '█' * filledLength + '-' * (barLength - filledLength)\\n sys.stdout.write('\\\\r%s |%s| %s%s %s' % (prefix, bar, percents, '%', suffix)),\\n sys.stdout.flush()\\n if iteration == total:\\n sys.stdout.write('\\\\n')\\n sys.stdout.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03e1f3840d2ce220b9fc4dbde4f4dda4\",\n \"score\": \"0.69533545\",\n \"text\": \"def _printProgressBar(self, iteration, total, \\n prefix = '', suffix = '', \\n decimals = 1, length = 100,\\n fill='*'):\\n# fill = '█'):\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n filledLength = int(length * iteration // total)\\n bar = fill * filledLength + '-' * (length - filledLength)\\n print('\\\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = '\\\\r')\\n # Print New Line on Complete\\n if iteration == total: \\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8194ac23d7fa2a0cefecb5a7f7b87c56\",\n \"score\": \"0.69510275\",\n \"text\": \"def _update_progressbar(self):\\n \\n if 0 >= self.N:\\n return None\\n\\n global _counter, _counter_lock\\n with _counter_lock:\\n _counter.value += 1\\n\\n index = _counter.value\\n \\n increment = max(1, int(self.N/float(self.W)))\\n \\n #eta_minutes = ((time() - self.t_init) / index) * (self.N - index) / 60.0\\n eta_minutes = 1.\\n \\n if index >= self.N:\\n status = \\\"({0:.0f}s) \\\".format(time() - self.t_init)\\n\\n elif float(index)/self.N >= 0.05 \\\\\\n and eta_minutes > 1: # MAGIC fraction for when we can predict ETA\\n status = \\\"({0}/{1}; ~{2:.0f}m until finished)\\\".format(\\n index, self.N, eta_minutes)\\n\\n else:\\n status = \\\"({0}/{1}) \\\".format(index, self.N)\\n\\n sys.stdout.write(\\n (\\\"\\\\r[{done: <\\\" + str(self.W) + \\\"}] {percent:3.0f}% {status}\\\").format(\\n done=\\\"=\\\" * int(index/increment),\\n percent=100. * index/self.N,\\n status=status))\\n sys.stdout.flush()\\n\\n if index >= self.N:\\n sys.stdout.write(\\\"\\\\r\\\\n\\\")\\n sys.stdout.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bc2dfeaae2f5ed7775b5cd90be5064d\",\n \"score\": \"0.69504964\",\n \"text\": \"def draw_progress_bar(index, total, start_time, barLen=45):\\n percent = index / total\\n pad = len(str(total))\\n\\n sys.stdout.write(\\\"\\\\r\\\")\\n sys.stdout.write(\\n \\\"Time elapsed: {} {} | {} / {} [{:<{}}] {:.0f}%\\\\t\\\".format(\\n (datetime.now() - start_time).seconds, \\\"s\\\",\\n str(index).rjust(pad),\\n total,\\n \\\"=\\\" * int(barLen * percent),\\n barLen,\\n percent * 100\\n )\\n )\\n sys.stdout.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae6ba58c8a985d1fbd7f6444e7c0a17c\",\n \"score\": \"0.69446844\",\n \"text\": \"def drawProgressBar(percent, bar_length = 20):\\n sys.stdout.write(\\\"\\\\r\\\")\\n progress = \\\"\\\"\\n for i in range(bar_length):\\n if i < int(bar_length * percent):\\n progress += \\\"=\\\"\\n else:\\n progress += \\\" \\\"\\n sys.stdout.write(\\\"[ %s ] %.2f%%\\\" % (progress, percent*100))\\n if percent == 1:\\n\\t\\tsys.stdout.write(\\\"\\\\n\\\") \\t\\n sys.stdout.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"154da0996723b4bc014925c66006b0d6\",\n \"score\": \"0.6937579\",\n \"text\": \"def print_progress_bar(iteration, total, prefix='', suffix='', decimals=1, length=40, fill='█'):\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n filled_length = int(length * iteration // total)\\n bar = fill * filled_length + '-' * (length - filled_length)\\n print('\\\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end='')\\n # Print New Line on Complete\\n if iteration == total:\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16e57e0c9947e831eb45ef068aaf6639\",\n \"score\": \"0.6928108\",\n \"text\": \"def printProgress (iteration, total, prefix = '', suffix = '', decimals = 1, barLength = 100):\\n filledLength = int(round(barLength * iteration / float(total)))\\n percents = round(100.00 * (iteration / float(total)), decimals)\\n bar = '█' * filledLength + '-' * (barLength - filledLength)\\n sys.stdout.write('\\\\r%s |%s| %s%s %s' % (prefix, bar, percents, '%', suffix)),\\n sys.stdout.flush()\\n if iteration == total:\\n sys.stdout.write('\\\\n')\\n sys.stdout.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"375598040ecfa5dfddce8769d5802278\",\n \"score\": \"0.69279623\",\n \"text\": \"def print_progress_bar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█'):\\n if total > 0:\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n filledLength = int(length * iteration // total)\\n bar = fill * filledLength + '-' * (length - filledLength)\\n print('\\\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = '\\\\r')\\n # Print New Line on Complete\\n if iteration == total:\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5342d1cddbb97289c633ac0706e4d2a\",\n \"score\": \"0.6919614\",\n \"text\": \"def bar(self, i, message=\\\"\\\"):\\n if i in self.ticks:\\n b = int(np.ceil(((i+1) / self.nf) * self.length))\\n sys.stdout.write(\\\"\\\\r[{0}{1}] {2}%\\\\t{3}\\\".format(\\n \\\"=\\\"*b, \\\" \\\"*(self.length-b), int(100*((i+1) / self.nf)), message\\n ))\\n sys.stdout.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfbfa7774f9eafa0376b1fbe7a31fc9a\",\n \"score\": \"0.6918811\",\n \"text\": \"def print_progress_bar(iteration, total, prefix='', suffix='',\\n decimals=1, length=100, fill='█'):\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n filledLength = int(length * iteration // total)\\n bar = fill * filledLength + '-' * (length - filledLength)\\n print('\\\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end='\\\\r')\\n # Print New Line on Complete\\n if iteration == total:\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00c63809b9987da1906ee7e83343f206\",\n \"score\": \"0.69084704\",\n \"text\": \"def progressbar(self, dummy):\\n return self._progressbar\",\n \"title\": \"\"\n },\n {\n \"docid\": \"988c7ea4cfd99a49ee10e7a0523c3d2e\",\n \"score\": \"0.6907157\",\n \"text\": \"def show_progressbar(status):\\n progress = int(50*(status['downloaded_bytes']/status['total_bytes']))\\n percent = ('{0:.1f}').format(progress*2)\\n bar = '▓'*progress+'-'*(50 - progress)\\n sys.stdout.write(Fore.YELLOW+'↳'+Style.RESET_ALL+' |{bar}| {percent}%\\\\r'.format(bar=bar, percent=percent))\\n sys.stdout.flush()\\n if status['status'] == 'finished':\\n logging.info('')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"533812d1a329919a6986704e51e9a966\",\n \"score\": \"0.6904433\",\n \"text\": \"def _update_progressbar(progress):\\n try:\\n rows, columns = os.popen('stty size', 'r').read().split()\\n bar_length = int(columns) - 35\\n if not bar_length > 1:\\n bar_length = 20\\n except:\\n # Default value if determination of console size fails\\n bar_length = 20\\n block = int(round(bar_length * progress))\\n text = \\\"\\\\rDownload Progress: [{0}] {1:.2f}%\\\".format(\\n \\\"#\\\" * block + \\\"-\\\" * (bar_length - block), progress * 100)\\n sys.stdout.write(text)\\n sys.stdout.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cf92aacd8bb0a1a498fc1b5720894ce\",\n \"score\": \"0.6902411\",\n \"text\": \"def progress(count, total, status=''):\\n\\n bar_len = 30\\n filled_len = int(round(bar_len * count / float(total)))\\n\\n percents = round(100.0 * count / float(total), 1)\\n bar = '=' * (filled_len - 1) + '>' + '-' * (bar_len - filled_len)\\n\\n sys.stdout.write('[%s] %s%s - %s\\\\r' % (bar, percents, '%', status))\\n sys.stdout.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64a5b350d24c98d0657663b1dd88d409\",\n \"score\": \"0.6899447\",\n \"text\": \"def print_progress_bar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█', printEnd = \\\"\\\\r\\\"):\\r\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\r\\n filledLength = int(length * iteration // total)\\r\\n bar = fill * filledLength + '-' * (length - filledLength)\\r\\n print('\\\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = printEnd)\\r\\n # Print New Line on Complete\\r\\n if iteration == total: \\r\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7d0292fbc2474667f25abc9890f880f\",\n \"score\": \"0.6892232\",\n \"text\": \"def printProgressBar(iteration, total, prefix='', suffix='', decimals=1, bar_length=100):\\n str_format = \\\"{0:.\\\" + str(decimals) + \\\"f}\\\"\\n percents = str_format.format(100 * (iteration / float(total)))\\n filled_length = int(round(bar_length * iteration / float(total)))\\n bar = '█' * filled_length + '-' * (bar_length - filled_length)\\n\\n sys.stdout.write('\\\\r%s %s %s%s %s' % (prefix, bar, percents, '%', suffix)),\\n\\n if iteration == total:\\n sys.stdout.write('\\\\n')\\n sys.stdout.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85ba0f5c85ce8e3293c6ffb819626b30\",\n \"score\": \"0.6877466\",\n \"text\": \"def print_progress_bar(iteration, total, prefix='', suffix='', decimals=1, length=50, fill='=', forward_sample='>'):\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(int(100 * (iteration / float(total))))\\n filled_length = int(length * iteration // total)\\n forward_symbol = forward_sample if iteration < total else ''\\n bar = fill * filled_length + forward_symbol + '-' * (length - filled_length)\\n print('\\\\r %s |%s| %s%% %s' % (prefix, bar, percent, suffix), end='\\\\r')\\n # Print New Line on Complete\\n if iteration == total:\\n print()\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74b616ccb7f72cd77662fc8b834498f3\",\n \"score\": \"0.686431\",\n \"text\": \"def update_progressbar(progress):\\n # Try to set the bar_length according to the console size\\n try:\\n rows, columns = os.popen('stty size', 'r').read().split()\\n bar_length = int(columns) - 35\\n if(not (bar_length > 1)):\\n bar_length = 20\\n except:\\n # Default value if determination of console size fails\\n bar_length = 20\\n block = int(round(bar_length*progress))\\n text = \\\"\\\\rDownload Progress: [{0}] {1:.2f}%\\\".format(\\n \\\"#\\\"*block + \\\"-\\\"*(bar_length-block), progress*100)\\n sys.stdout.write(text)\\n sys.stdout.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a35bb1079a556ca68c6e1a70a431b1a8\",\n \"score\": \"0.68437374\",\n \"text\": \"def _updateRunning(self):\\n\\n counter_width = math.floor(math.log10(self.progress_max)) + 1\\n loader_width = self.line_length - 2 * counter_width - 2\\n prog = round((self.progress / self.progress_max) * loader_width)\\n\\n line = (\\\"{:\\\" + str(loader_width) + \\\"} \\\" + \\n \\\"{:\\\" + str(counter_width) + \\\"}/\\\" + \\n \\\"{:\\\" + str(counter_width) + \\\"}\\\")\\n \\n self.print(\\\"\\\\b\\\" * self.line_length + line.format(\\n \\\"█\\\" * prog + \\\"▒\\\" * (loader_width - prog),\\n self.progress,\\n self.progress_max\\n ), sep=\\\"\\\", end=\\\"\\\")\\n log_debug(self._logger, (\\\"Updating the running indicator, progress is \\\" + \\n \\\"now '{}'/'{}'\\\").format(self.progress, self.progress_max))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f04340b3e2e9702c594a838d09c67d5\",\n \"score\": \"0.68402827\",\n \"text\": \"def printProgressBar(iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█'): # █ = U-219\\n \\n percent = (\\\"{:0.\\\" + str(decimals) + \\\"f}\\\").format(100* (iteration/float(total)))\\n filledlength = int(length * iteration // total)\\n bar = fill * filledlength + \\\"-\\\" * (length - filledlength)\\n print('\\\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = '\\\\r')\\n # print New line after completition\\n if iteration == total:\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f688f14660420c4a40fae16eab53be5\",\n \"score\": \"0.6836656\",\n \"text\": \"def printProgress (iteration, total, prefix = '', suffix = '', decimals = 2, barLength = 100):\\r\\n filledLength = int(round(barLength * iteration / float(total)))\\r\\n percents = round(100.00 * (iteration / float(total)), decimals)\\r\\n bar = '%' * filledLength + '-' * (barLength - filledLength)\\r\\n sys.stdout.write('\\\\r%s |%s| %s%s %s' % (prefix, bar, percents, '%', suffix)),\\r\\n sys.stdout.flush()\\r\\n if iteration == total:\\r\\n sys.stdout.write('\\\\n')\\r\\n sys.stdout.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"489613b683982885ecc2ae99e3853bc1\",\n \"score\": \"0.68329877\",\n \"text\": \"def printProgressBar (iteration=0, total=0, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█'):\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n filledLength = int(length * iteration // total)\\n bar = fill * filledLength + '-' * (length - filledLength)\\n print('\\\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = '\\\\r')\\n # Print New Line on Complete\\n if iteration == total:\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2987ea6ba46a97ec422a4fe937e401a\",\n \"score\": \"0.68296653\",\n \"text\": \"def _progress_handler(self):\\n self.pbar.update(self.itercount)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e0b9fbfb0d2d03372a34f0153b8b9bc\",\n \"score\": \"0.68233275\",\n \"text\": \"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█'):\\r\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\r\\n filledLength = int(length * iteration // total)\\r\\n bar = fill * filledLength + '-' * (length - filledLength)\\r\\n print('\\\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = '\\\\r')\\r\\n # Print New Line on Complete\\r\\n if iteration == total: \\r\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff70138321450a2278002d2d7668cc93\",\n \"score\": \"0.6815338\",\n \"text\": \"def status_bar_printer(count, total, suffix):\\n sys.stdout.write('\\\\r')\\n progress = count / total * 20\\n sys.stdout.write(\\\"[%-20s] %d%% - %i of %i %s\\\" % ('=' * int(progress), progress * 5, count, total, suffix))\\n sys.stdout.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96f8466f5fe4c00cdd1d0ba04b604452\",\n \"score\": \"0.68068045\",\n \"text\": \"def printProgressBar (iteration, total, prefix = 'Progress:', suffix = 'Complete', decimals = 1, length = 100, fill = '█', printEnd = \\\"\\\\r\\\"):\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n filledLength = int(length * iteration // total)\\n bar = fill * filledLength + '-' * (length - filledLength)\\n print('\\\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = printEnd)\\n # Print New Line on Complete\\n if iteration == total: \\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f17bb77f21973101f10e936752c8eb1b\",\n \"score\": \"0.679994\",\n \"text\": \"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 50, fill = '█'):\\n import subprocess\\n import time\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n filledLength = int(length * iteration // total)\\n bar = fill * filledLength + '-' * (length - filledLength)\\n out = '\\\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix)\\n rows, columns = subprocess.check_output(['stty', 'size']).split()\\n out += \\\" \\\" * abs(len(out) - int(columns))\\n print(out, end = '\\\\r')\\n # Print New Line on Complete\\n if iteration == total: \\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e93de7560f148df97508428a3b7e6365\",\n \"score\": \"0.67990166\",\n \"text\": \"def print_progress_bar(iteration, total, prefix='', suffix='', decimals=1, length=25, fill='='):\\n assert 0 <= iteration <= total\\n\\n iteration += 1\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n filledLength = int(length * iteration // total) + 1\\n bar = fill * filledLength + '>' * min(length - filledLength, 1) + '.' * (length - filledLength - 1)\\n\\n print('\\\\r {} [{}] {}/{} ({}%) {}'.format(prefix, bar, iteration, total, percent, suffix), end='\\\\r')\\n if iteration >= total: # Print new line on completion\\n print(' ')\\n sys.stdout.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5482c1f70c4e0731b510ee118c824326\",\n \"score\": \"0.67983526\",\n \"text\": \"def printProgressBar(self, iteration, total, sec, elapsed, prefix='', suffix='', decimals=1, length=100, fill='█'):\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n filledLength = int(length * iteration // total)\\n bar = fill * filledLength + '-' * (length - filledLength)\\n return '{} |{}| {}% {} ({:.2f} sec / {:.2f} elapsed)'.format(prefix, bar, percent, suffix, sec, elapsed)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9cd6f15479afbb4022ec0dd446fe66c6\",\n \"score\": \"0.67977643\",\n \"text\": \"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 40, fill = '█'):\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n filledLength = int(length * iteration // total)\\n bar = fill * filledLength + '-' * (length - filledLength)\\n sys.stdout.write('\\\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix))\\n sys.stdout.flush()\\n # Print New Line on Complete\\n if iteration == total:\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e817aa3da767faf1705969b8d872fc1\",\n \"score\": \"0.6796534\",\n \"text\": \"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█'):\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n filledLength = int(length * iteration // total)\\n bar = fill * filledLength + '-' * (length - filledLength)\\n print('\\\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = '\\\\r')\\n # Print New Line on Complete\\n if iteration == total:\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fbd2ea6a013abd7c96d1a1b16598567\",\n \"score\": \"0.6795735\",\n \"text\": \"def printProgressBar(iteration, total, prefix='', suffix=''):\\n\\tdecimals = 0\\n\\tbar_length = 40\\n\\tstr_format = \\\"{0:.\\\" + str(decimals) + \\\"f}\\\"\\n\\tpercents = str_format.format(100 * (iteration / float(total)))\\n\\tfilled_length = int(round(bar_length * iteration / float(total)))\\n\\tbar = '#' * filled_length + '.' * (bar_length - filled_length)\\n\\tsys.stdout.write('\\\\r\\\\033[44m\\\\033[37m%s [%s%s]\\\\033[0m |%s| %s' \\\\\\n\\t\\t% (prefix, percents, '%', bar, suffix))\\n\\tsys.stdout.flush()\\n\\treturn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af008e2f20f1751015c172f1847eda56\",\n \"score\": \"0.6794594\",\n \"text\": \"def progress_bar(index: int, count: int, desc: str | None = None, bar_len: int = 30, newline: bool = False) -> None:\\n end = \\\"\\\\r\\\" if not newline else \\\"\\\\r\\\\n\\\"\\n filled_len = int(bar_len * index // count)\\n bar = f\\\"{desc} \\\" if desc is not None else \\\"\\\"\\n bar += \\\"[\\\" + \\\"=\\\" * filled_len + \\\" \\\" * (bar_len - filled_len) + \\\"]\\\"\\n print(f\\\"{index}/{count} {bar}\\\", end=end)\\n if index == count:\\n print(\\\"\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a58a768dab110ef0d9496254769cbab\",\n \"score\": \"0.6791022\",\n \"text\": \"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█'):\\n\\tpercent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n\\tfilledLength = int(length * iteration // total)\\n\\tbar = fill * filledLength + '-' * (length - filledLength)\\n\\tprint('\\\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = '\\\\r')\\n\\t# Print New Line on Complete\\n\\tif iteration == total: \\n\\t\\tprint()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9dded5c392279113c6edb1a6024bb59\",\n \"score\": \"0.6789979\",\n \"text\": \"def printProgressBar(iteration, total, prefix='', suffix='', decimals=1, length=100, fill='█', printEnd=\\\"\\\\r\\\"):\\r\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\r\\n filledLength = int(length * iteration // total)\\r\\n bar = fill * filledLength + '-' * (length - filledLength)\\r\\n print('\\\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end=printEnd)\\r\\n # Print New Line on Complete\\r\\n if iteration == total:\\r\\n print('\\\\n')\\r\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6767e85929eb94e7be163b6246947c76\",\n \"score\": \"0.67876405\",\n \"text\": \"def printProgressBar(iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█'):\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n filledLength = int(length * iteration // total)\\n bar = fill * filledLength + '-' * (length - filledLength)\\n sys.stdout.flush()\\n sys.stdout.write('\\\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix))\\n # Print New Line on Complete\\n if iteration == total: \\n sys.stdout.flush()\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7edb6eca9877daa8d98285b75270619e\",\n \"score\": \"0.67875326\",\n \"text\": \"def print_progress(iteration, total, prefix='', suffix='', decimals=1, bar_length=100):\\n str_format = \\\"{0:.\\\" + str(decimals) + \\\"f}\\\"\\n percents = str_format.format(100 * (iteration / float(total)))\\n filled_length = int(round(bar_length * iteration / float(total)))\\n bar = '█' * filled_length + '-' * (bar_length - filled_length)\\n\\n sys.stdout.write('\\\\r%s |%s| %s%s %s' % (prefix, bar, percents, '%', suffix)),\\n\\n if iteration == total:\\n sys.stdout.write('\\\\n')\\n sys.stdout.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7edb6eca9877daa8d98285b75270619e\",\n \"score\": \"0.67875326\",\n \"text\": \"def print_progress(iteration, total, prefix='', suffix='', decimals=1, bar_length=100):\\n str_format = \\\"{0:.\\\" + str(decimals) + \\\"f}\\\"\\n percents = str_format.format(100 * (iteration / float(total)))\\n filled_length = int(round(bar_length * iteration / float(total)))\\n bar = '█' * filled_length + '-' * (bar_length - filled_length)\\n\\n sys.stdout.write('\\\\r%s |%s| %s%s %s' % (prefix, bar, percents, '%', suffix)),\\n\\n if iteration == total:\\n sys.stdout.write('\\\\n')\\n sys.stdout.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7edb6eca9877daa8d98285b75270619e\",\n \"score\": \"0.67875326\",\n \"text\": \"def print_progress(iteration, total, prefix='', suffix='', decimals=1, bar_length=100):\\n str_format = \\\"{0:.\\\" + str(decimals) + \\\"f}\\\"\\n percents = str_format.format(100 * (iteration / float(total)))\\n filled_length = int(round(bar_length * iteration / float(total)))\\n bar = '█' * filled_length + '-' * (bar_length - filled_length)\\n\\n sys.stdout.write('\\\\r%s |%s| %s%s %s' % (prefix, bar, percents, '%', suffix)),\\n\\n if iteration == total:\\n sys.stdout.write('\\\\n')\\n sys.stdout.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7edb6eca9877daa8d98285b75270619e\",\n \"score\": \"0.67875326\",\n \"text\": \"def print_progress(iteration, total, prefix='', suffix='', decimals=1, bar_length=100):\\n str_format = \\\"{0:.\\\" + str(decimals) + \\\"f}\\\"\\n percents = str_format.format(100 * (iteration / float(total)))\\n filled_length = int(round(bar_length * iteration / float(total)))\\n bar = '█' * filled_length + '-' * (bar_length - filled_length)\\n\\n sys.stdout.write('\\\\r%s |%s| %s%s %s' % (prefix, bar, percents, '%', suffix)),\\n\\n if iteration == total:\\n sys.stdout.write('\\\\n')\\n sys.stdout.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7edb6eca9877daa8d98285b75270619e\",\n \"score\": \"0.67875326\",\n \"text\": \"def print_progress(iteration, total, prefix='', suffix='', decimals=1, bar_length=100):\\n str_format = \\\"{0:.\\\" + str(decimals) + \\\"f}\\\"\\n percents = str_format.format(100 * (iteration / float(total)))\\n filled_length = int(round(bar_length * iteration / float(total)))\\n bar = '█' * filled_length + '-' * (bar_length - filled_length)\\n\\n sys.stdout.write('\\\\r%s |%s| %s%s %s' % (prefix, bar, percents, '%', suffix)),\\n\\n if iteration == total:\\n sys.stdout.write('\\\\n')\\n sys.stdout.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e77bf33b645eefcdca4fd7df833ef4f\",\n \"score\": \"0.6785296\",\n \"text\": \"def print_progress_bar(iteration, total, prefix='', suffix='', decimals=1, length=100, fill='█', printEnd='\\\\r'):\\n percent = ('{0:.' + str(decimals) + 'f}').format(100 * (iteration / float(total)))\\n filledLength = int(length * iteration // total)\\n bar = fill * filledLength + '-' * (length - filledLength)\\n print(('\\\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix)), end=printEnd)\\n if iteration == total:\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b19400bc157ffe10a72f334e18185967\",\n \"score\": \"0.67828906\",\n \"text\": \"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█', printEnd = \\\"\\\\n\\\"):\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n filledLength = int(length * iteration // total)\\n bar = fill * filledLength + '-' * (length - filledLength)\\n print(f'\\\\r{prefix} |{bar}| {percent}% {suffix}', end = printEnd)\\n # Print New Line on Complete\\n # if iteration == total: \\n # print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac401374f7a26ff02a3fac5afacc07c6\",\n \"score\": \"0.67784303\",\n \"text\": \"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█'):\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n filledLength = int(length * iteration // total)\\n bar = fill * filledLength + '-' * (length - filledLength)\\n print('\\\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = '\\\\r')\\n # Print New Line on Complete\\n if iteration == total: \\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac401374f7a26ff02a3fac5afacc07c6\",\n \"score\": \"0.67784303\",\n \"text\": \"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█'):\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n filledLength = int(length * iteration // total)\\n bar = fill * filledLength + '-' * (length - filledLength)\\n print('\\\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = '\\\\r')\\n # Print New Line on Complete\\n if iteration == total: \\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac401374f7a26ff02a3fac5afacc07c6\",\n \"score\": \"0.67784303\",\n \"text\": \"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█'):\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n filledLength = int(length * iteration // total)\\n bar = fill * filledLength + '-' * (length - filledLength)\\n print('\\\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = '\\\\r')\\n # Print New Line on Complete\\n if iteration == total: \\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac401374f7a26ff02a3fac5afacc07c6\",\n \"score\": \"0.67784303\",\n \"text\": \"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█'):\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n filledLength = int(length * iteration // total)\\n bar = fill * filledLength + '-' * (length - filledLength)\\n print('\\\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = '\\\\r')\\n # Print New Line on Complete\\n if iteration == total: \\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac401374f7a26ff02a3fac5afacc07c6\",\n \"score\": \"0.67784303\",\n \"text\": \"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█'):\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n filledLength = int(length * iteration // total)\\n bar = fill * filledLength + '-' * (length - filledLength)\\n print('\\\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = '\\\\r')\\n # Print New Line on Complete\\n if iteration == total: \\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db62698a74f59412652e18f73d57bf4b\",\n \"score\": \"0.67761445\",\n \"text\": \"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█', printend = \\\"\\\\r\\\"):\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n filledLength = int(length * iteration // total)\\n bar = fill * filledLength + '-' * (length - filledLength)\\n print('\\\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), printend)\\n # Print New Line on Complete\\n if iteration == total: \\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ef0d92fa3fa387c3bda7043614d06af\",\n \"score\": \"0.6775269\",\n \"text\": \"def update_progress(self, job_title, progress):\\r\\n length = 100 # modify this to change the length\\r\\n block = int(round(length*progress))\\r\\n msg = \\\"\\\\r{0}: [{1}] {2}%\\\".format(job_title, \\\"#\\\"*block + \\\"-\\\"*(length-block), round(progress*100, 2))\\r\\n if progress >= 1: msg += \\\" DONE\\\\r\\\\n\\\"\\r\\n sys.stdout.write(msg)\\r\\n sys.stdout.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28b90c933919e299a5998973a0518c27\",\n \"score\": \"0.6772445\",\n \"text\": \"def printProgressBar(iteration, total, prefix='', suffix='', decimals=1, length=100, fill='█'):\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n filledLength = int(length * iteration // total)\\n bar = fill * filledLength + '-' * (length - filledLength)\\n print('\\\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end='\\\\r')\\n # Print New Line on Complete\\n if iteration == total:\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4acfece9aca502acded2871086a126fe\",\n \"score\": \"0.6768273\",\n \"text\": \"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 10, fill = '█', printEnd = \\\"\\\\r\\\"):\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n filledLength = int(length * iteration // total)\\n bar = fill * filledLength + '-' * (length - filledLength)\\n print(end=f'\\\\r{prefix} |{bar}| {percent}% {suffix}')\\n # Print New Line on Complete\\n if iteration >= total: \\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2382008e635f8176fdee9f30ec707f7\",\n \"score\": \"0.67678\",\n \"text\": \"def progress(count, total, status=''):\\n bar_len = 60\\n filled_len = int(round(bar_len * count / float(total)))\\n\\n percents = round(100.0 * count / float(total), 1)\\n bar = '=' * filled_len + '-' * (bar_len - filled_len)\\n\\n sys.stdout.write('\\\\r[%s] %s%s ...%s' % (bar, percents, '%', status))\\n sys.stdout.flush()\\n Client.clear()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c895ad7fbd05885376795ff676b3d787\",\n \"score\": \"0.676626\",\n \"text\": \"def _print_progress(iteration, total, prefix='', suffix='', decimals=1, length=100, fill='█', printEnd=\\\"\\\\r\\\"):\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n filledLength = int(length * iteration // total)\\n bar = fill * filledLength + '-' * (length - filledLength)\\n print('\\\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end=printEnd)\\n # Print New Line on Complete\\n if iteration == total:\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"966aa54761fe54d5b2e51bbbe5a89960\",\n \"score\": \"0.67619616\",\n \"text\": \"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█', printEnd = \\\"\\\\r\\\"):\\r\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\r\\n filledLength = int(length * iteration // total)\\r\\n bar = fill * filledLength + '-' * (length - filledLength)\\r\\n print('\\\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = printEnd)\\r\\n # Print New Line on Complete\\r\\n if iteration == total: \\r\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5398f0986a84278bc5a4a808a3b17cf1\",\n \"score\": \"0.67591906\",\n \"text\": \"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█', printEnd = \\\"\\\\r\\\"):\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n filledLength = int(length * iteration // total)\\n bar = fill * filledLength + '-' * (length - filledLength)\\n print('\\\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = printEnd)\\n # Print New Line on Complete\\n if iteration == total: \\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5398f0986a84278bc5a4a808a3b17cf1\",\n \"score\": \"0.67591906\",\n \"text\": \"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█', printEnd = \\\"\\\\r\\\"):\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n filledLength = int(length * iteration // total)\\n bar = fill * filledLength + '-' * (length - filledLength)\\n print('\\\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = printEnd)\\n # Print New Line on Complete\\n if iteration == total: \\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"795cceb593c99c7998753844b1db9781\",\n \"score\": \"0.6754499\",\n \"text\": \"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█', printEnd = \\\"\\\\r\\\"):\\n\\tpercent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n\\tfilledLength = int(length * iteration // total)\\n\\tbar = fill * filledLength + '-' * (length - filledLength)\\n\\tprint(f'\\\\r{prefix} |{bar}| {percent}% {suffix}', end = printEnd)\\n\\t# Print New Line on Complete\\n\\tif iteration == total: \\n\\t\\tprint()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5465fe4dfc34f907268e1b2d3314cd22\",\n \"score\": \"0.6754105\",\n \"text\": \"def printProgressBar(iteration, total, prefix='', suffix='', decimals=2, length=80, fill='█', printEnd=\\\"\\\\r\\\"):\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n filledLength = int(length * iteration // total)\\n bar = fill * filledLength + '-' * (length - filledLength)\\n print(f'\\\\r{prefix} |{bar}| {percent}% {suffix}', end=printEnd)\\n # Print New Line on Complete\\n if iteration == total:\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b005b4bfdec3aacf70e19c6936c00cd0\",\n \"score\": \"0.6747428\",\n \"text\": \"def print_progress(iteration, total, prefix=\\\"\\\", suffix=\\\"\\\", decimals=2, bar_length=100):\\n filled_length = int(round(bar_length * iteration / float(total)))\\n percents = round(100.00 * (iteration / float(total)), decimals)\\n progress_bar = \\\"#\\\" * filled_length + \\\"-\\\" * (bar_length - filled_length)\\n sys.stdout.write(\\\"\\\\r%s |%s| %s%s %s\\\" % (prefix, progress_bar, percents, \\\"%\\\", suffix))\\n sys.stdout.flush()\\n if iteration == total:\\n sys.stdout.write(\\\"\\\\n\\\")\\n sys.stdout.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6cfa54230c4ad0698ff1148d1b2c599\",\n \"score\": \"0.67446744\",\n \"text\": \"def printProgress (iteration, total, prefix = '', suffix = '', decimals = 2, barLength = 70):\\n filledLength = int(round(barLength * iteration / float(total)))\\n percents = round(100.00 * (iteration / float(total)), decimals)\\n bar = '#' * filledLength + '-' * (barLength - filledLength)\\n sys.stdout.write('%s [%s] %s%s %s\\\\r' % (prefix, bar, percents, '%', suffix)),\\n sys.stdout.flush()\\n if iteration == total:\\n print(\\\"\\\\n\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff0b8d1d2180f14bc815e77e5fd64cc9\",\n \"score\": \"0.67425686\",\n \"text\": \"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█', printEnd = \\\"\\\\r\\\"):\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n filledLength = int(length * iteration // total)\\n bar = fill * filledLength + '-' * (length - filledLength)\\n print(f'\\\\r{prefix} |{bar}| {percent}% {suffix}', end = printEnd)\\n # Print New Line on Complete\\n if iteration == total: \\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff0b8d1d2180f14bc815e77e5fd64cc9\",\n \"score\": \"0.67425686\",\n \"text\": \"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█', printEnd = \\\"\\\\r\\\"):\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n filledLength = int(length * iteration // total)\\n bar = fill * filledLength + '-' * (length - filledLength)\\n print(f'\\\\r{prefix} |{bar}| {percent}% {suffix}', end = printEnd)\\n # Print New Line on Complete\\n if iteration == total: \\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff0b8d1d2180f14bc815e77e5fd64cc9\",\n \"score\": \"0.67425686\",\n \"text\": \"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█', printEnd = \\\"\\\\r\\\"):\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n filledLength = int(length * iteration // total)\\n bar = fill * filledLength + '-' * (length - filledLength)\\n print(f'\\\\r{prefix} |{bar}| {percent}% {suffix}', end = printEnd)\\n # Print New Line on Complete\\n if iteration == total: \\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff0b8d1d2180f14bc815e77e5fd64cc9\",\n \"score\": \"0.67425686\",\n \"text\": \"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█', printEnd = \\\"\\\\r\\\"):\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n filledLength = int(length * iteration // total)\\n bar = fill * filledLength + '-' * (length - filledLength)\\n print(f'\\\\r{prefix} |{bar}| {percent}% {suffix}', end = printEnd)\\n # Print New Line on Complete\\n if iteration == total: \\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13b53e8d9f539263e86a6ec897863d74\",\n \"score\": \"0.6742398\",\n \"text\": \"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█'):\\r\\n\\tpercent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\r\\n\\tfilledLength = int(length * iteration // total)\\r\\n\\tbar = fill * filledLength + '-' * (length - filledLength)\\r\\n\\tprint('\\\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = '\\\\r')\\r\\n\\t# Print New Line on Complete\\r\\n\\tif iteration == total: \\r\\n\\t\\tprint()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71eedbfc1e2ce394bb29ab7bd9ed31d4\",\n \"score\": \"0.67376286\",\n \"text\": \"def progress(self, count, total, start_time, status = 'Running'):\\n\\t\\tbar_len = 60\\n\\t\\tfilled_len = int(round(bar_len * count / float(total)))\\n\\n\\t\\tpercents = round(100.0 * count / float(total), 1)\\n\\t\\tif count == total: bar = '=' * filled_len +'-' * (bar_len - filled_len)\\n\\t\\telse: bar = '=' * filled_len + '>' +'-' * (bar_len - filled_len - 1)\\n\\t\\tm, s = divmod(time.time() - start_time, 60)\\n\\t\\th, m = divmod(m, 60)\\n\\n\\t\\tif count == total:\\n\\t\\t\\ttime.sleep(0.5)\\n\\t\\t\\tsys.stdout.write(\\\"\\\\033[K\\\")\\n\\t\\t\\tsys.stdout.write('|%s|%s%s|%d:%02d:%02d| %s\\\\r\\\\n' % \\n\\t\\t\\t\\t\\t\\t\\t(bar, percents, '%', h, m, s, status +\\\" OK\\\"))\\n\\t\\telse:\\n\\t\\t\\tsys.stdout.write('|%s|%s%s|%d:%02d:%02d| %s\\\\r' % \\n\\t\\t\\t\\t\\t\\t\\t(bar, percents, '%', h, m, s, status))\\n\\t\\tsys.stdout.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c9ae445744700ad213c4e0b560b2a86\",\n \"score\": \"0.6735115\",\n \"text\": \"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█', printEnd = \\\"\\\\r\\\"):\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n filledLength = int(length * iteration // total)\\n bar = fill * filledLength + '-' * (length - filledLength)\\n print('\\\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = printEnd)\\n\\n # Print New Line on Complete\\n if iteration == total:\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4c9911a26dfeedc5aa817c32414400c\",\n \"score\": \"0.673196\",\n \"text\": \"def printProgressBar(iteration, total, prefix='', suffix='', decimals=1, length=100, fill='█', printEnd=\\\"\\\\r\\\"):\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 *\\n (iteration / float(total)))\\n filledLength = int(length * iteration // total)\\n bar = fill * filledLength + '-' * (length - filledLength)\\n print('\\\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end=printEnd)\\n # Print New Line on Complete\\n if iteration == total:\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4731635b3c83f0219ce294ac3b50b9b\",\n \"score\": \"0.6731744\",\n \"text\": \"def printProgressBar(iteration, total, prefix='', suffix='', decimals=2, length=80, fill='█', printEnd=\\\"\\\\r\\\"):\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n filledLength = int(length * iteration // total)\\n bar = fill * filledLength + '-' * (length - filledLength)\\n # print(f'\\\\r{prefix} |{bar}| {percent}% {suffix}', end=printEnd)\\n sys.stdout.write(f'\\\\r{prefix} |{bar}| {percent}% {suffix}')\\n\\n # Print New Line on Complete\\n if iteration == total:\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de703f35ab13f5d1198c9692b99df2a1\",\n \"score\": \"0.6718077\",\n \"text\": \"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█'):\\n # Source: https://stackoverflow.com/questions/3173320/text-progress-bar-in-the-console\\n\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n filledLength = int(length * iteration // total)\\n bar = fill * filledLength + '-' * (length - filledLength)\\n print('\\\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = '\\\\r')\\n \\n # Print New Line on Complete\\n if iteration == total: \\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5f467d1ce19b85863a20d97196b6877c\",\n \"score\": \"0.6711195\",\n \"text\": \"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1,\\n length = 100, fill = '#'):\\n val = 100 * (iteration / float(total))\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(val)\\n filledLength = int(length * iteration // total)\\n bar = fill * filledLength + '-' * (length - filledLength)\\n print('\\\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = '\\\\r')\\n # Print New Line on Complete\\n if iteration == total:\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbb0420fa8d2068da87d72d84b0ace3a\",\n \"score\": \"0.6707381\",\n \"text\": \"def progress(percent, barlen=20, name=''):\\n # percent float from 0 to 1.\\n sys.stdout.write(\\\"\\\\r\\\") # removes the previous line\\n sys.stdout.write(\\\"[{:<{}}] {:.0f}% {}\\\".format(\\\"=\\\" * int(barlen * percent), barlen, percent * 100, name))\\n sys.stdout.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bab30fd92522fc5d997f0a26d72eb38d\",\n \"score\": \"0.67068875\",\n \"text\": \"def progress(percent=0, width=30):\\n left = width * percent // 100\\n right = width - left\\n print('\\\\r[', '#' * left, ' ' * right, ']',\\n f' {percent:.0f}%',\\n sep='', end='', flush=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59e6e56b488d769a055bb466b6015ea5\",\n \"score\": \"0.6702873\",\n \"text\": \"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '\\\\u2588', printEnd = \\\"\\\\r\\\"):\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * ((iteration+1) / float(total+1)))\\n filledLength = int(length * (iteration+1) // (total+1))\\n bar = fill * filledLength + '-' * (length - filledLength)\\n print(f'\\\\r{prefix} |{bar}| {percent}% {suffix}', end = printEnd)\\n # Print New Line on Complete\\n if iteration == total: \\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e13f4635066faebae28cc317301cb485\",\n \"score\": \"0.67002916\",\n \"text\": \"def printProgressBar(\\n iteration,\\n total,\\n prefix=\\\"\\\",\\n suffix=\\\"\\\",\\n decimals=1,\\n length=100,\\n fill=\\\"█\\\",\\n printEnd=\\\"\\\\r\\\",\\n):\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n filledLength = int(length * iteration // total)\\n bar = fill * filledLength + \\\"-\\\" * (length - filledLength)\\n print(\\\"\\\\r%s |%s| %s%% %s\\\" % (prefix, bar, percent, suffix), end=printEnd)\\n # Print New Line on Complete\\n if iteration == total:\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4e3cfd193ca2c47cc42f7887289e442d\",\n \"score\": \"0.6698633\",\n \"text\": \"def progress(self):\\n return tqdm(self, total=len(self))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17059ca955b9704cb881d4f7ec785389\",\n \"score\": \"0.669806\",\n \"text\": \"def print_progress(iteration, total, prefix='', suffix='', decimals=3, bar_length=100):\\n format_str = \\\"{0:.\\\" + str(decimals) + \\\"f}\\\" # format the % done number string\\n percents = format_str.format(100 * (iteration / float(total))) # calculate the % done\\n filled_length = int(round(bar_length * iteration / float(total))) # calculate the filled bar length\\n bar = '#' * filled_length + '-' * (bar_length - filled_length) # generate the bar string\\n sys.stdout.write('\\\\r%s |%s| %s%s %s' % (prefix, bar, percents, '%', suffix)), # write out the bar\\n sys.stdout.flush() # flush to stdout\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3025366a9ece3bb1582421d7cedf66c1\",\n \"score\": \"0.6679355\",\n \"text\": \"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█', printEnd = \\\"\\\\r\\\"):\\r\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\r\\n filledLength = int(length * iteration // total)\\r\\n bar = fill * filledLength + '-' * (length - filledLength)\\r\\n print('\\\\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end = printEnd)\\r\\n # Imprime la nueva línea al terminar\\r\\n if iteration == total: \\r\\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d9d8b18f8e26d206e26ecada5972b65\",\n \"score\": \"0.6675401\",\n \"text\": \"def print_progress(iteration, total, prefix='', suffix='', decimals=1, length=100, fill='█'):\\n bar_length = 60\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 *\\n (iteration / float(total)))\\n filledLength = int(length * iteration // total)\\n bar = fill * filledLength + '-' * (length - filledLength)\\n sys.stdout.write('\\\\r%s |%s| %s%s %s' %\\n (prefix, bar, percent, '%', suffix)),\\n\\n if iteration == total:\\n sys.stdout.write('\\\\n')\\n sys.stdout.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36addb26a9e28788f768316980b33100\",\n \"score\": \"0.6673189\",\n \"text\": \"def printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█', printEnd = \\\"\\\\r\\\", end=True):\\n percent = (\\\"{0:.\\\" + str(decimals) + \\\"f}\\\").format(100 * (iteration / float(total)))\\n filledLength = int(length * iteration // total)\\n bar = fill * filledLength + '-' * (length - filledLength)\\n print(f'\\\\r{prefix} |{bar}| {iteration}/{total} {suffix}', end = printEnd)\\n \\n if iteration == total and end: \\n print()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35d552302691337e28273e8b587cbb2c\",\n \"score\": \"0.66621214\",\n \"text\": \"def progressbar(it, prefix=\\\"\\\", size=60, file=sys.stdout):\\n count = len(it)\\n def show(j):\\n x = int(size*j/count)\\n file.write(\\\"%s[%s%s] %i/%i\\\\r\\\" % (prefix, \\\"#\\\"*x, \\\".\\\"*(size-x), j, count))\\n file.flush() \\n show(0)\\n for i, item in enumerate(it):\\n yield item\\n show(i+1)\\n file.write(\\\"\\\\n\\\")\\n file.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"474cceec93d797acef89833a7023a42e\",\n \"score\": \"0.66588426\",\n \"text\": \"def print_progress(iteration, total, prefix='', suffix='', decimals=1, bar_length=100):\\n str_format = \\\"{0:.\\\" + str(decimals) + \\\"f}\\\"\\n percents = str_format.format(100 * (iteration / float(total)))\\n filled_length = int(round(bar_length * iteration / float(total)))\\n bar = '=' * filled_length + '-' * (bar_length - filled_length)\\n\\n sys.stdout.write('\\\\r%s |%s| %s%s %s' % (prefix, bar, percents, '%', suffix)),\\n\\n if iteration == total:\\n sys.stdout.write('\\\\n')\\n sys.stdout.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae5da3f52cff09b679eb03bf8180655b\",\n \"score\": \"0.66570103\",\n \"text\": \"def print_progress(iteration, total, prefix='', suffix='', decimals=1, bar_length=100):\\n format_str = \\\"{0:.\\\" + str(decimals) + \\\"f}\\\"\\n if total == 0:\\n total = 1\\n percents = format_str.format(100 * (iteration / float(total)))\\n filled_length = int(round(bar_length * iteration / float(total)))\\n bar = '#' * filled_length + '-' * (bar_length - filled_length)\\n sys.stdout.write('\\\\r%s (%s/%s) |%s| %s%s %s' % (prefix, iteration, total, bar, percents, '%', suffix)),\\n sys.stdout.flush()\\n if iteration == total:\\n sys.stdout.write('\\\\n')\\n sys.stdout.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"998bfc9e7deafa68a0f2272442df0bb6\",\n \"score\": \"0.6656935\",\n \"text\": \"def printProgress (iteration, total, prefix = '', suffix = '', decimals = 1, barLength = 100):\\r\\n formatStr = \\\"{0:.\\\" + str(decimals) + \\\"f}\\\"\\r\\n percents = formatStr.format(100 * (iteration / float(total)))\\r\\n filledLength = int(round(barLength * iteration / float(total)))\\r\\n bar = '?' * filledLength + '-' * (barLength - filledLength)\\r\\n sys.stdout.write('\\\\r%s |%s| %s%s %s' % (prefix, bar, percents, '%', suffix)),\\r\\n if iteration == total:\\r\\n sys.stdout.write('\\\\n')\\r\\n sys.stdout.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9546aac814e262df8b51158c1848cba\",\n \"score\": \"0.66549736\",\n \"text\": \"def progress_bar(it, prefix=\\\"\\\", size=40, verbose=True):\\n if verbose is False:\\n for i, item in enumerate(it):\\n yield i, item\\n else:\\n count = len(it)\\n\\n def show(j):\\n x = int(size * j / count)\\n print(\\n f\\\"{prefix}[{u'█'*x}{('.'*(size-x))}] {j}/{count}\\\",\\n end=\\\"\\\\r\\\",\\n file=sys.stdout,\\n flush=True,\\n )\\n\\n show(0)\\n for i, item in enumerate(it):\\n yield i, item\\n show(i + 1)\\n print(\\\"\\\\n\\\", flush=True, file=sys.stdout)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993288,"cells":{"query_id":{"kind":"string","value":"4710b2e29726e9d10bc8a2453f410619"},"query":{"kind":"string","value":"Initialize class data from input file."},"positive_passages":{"kind":"list like","value":[{"docid":"d48e294bf82fbfb6008b8dd04a824560","score":"0.0","text":"def init_from_file(filename, verbose=False):\n FuelPrice._data.clear()\n\n if verbose:\n omega_log.logwrite('\\nInitializing database from %s...' % filename)\n\n # don't forget to update the module docstring with changes here\n input_template_name = 'context_fuel_prices'\n input_template_version = 0.2\n input_template_columns = {'context_id', 'dollar_basis', 'case_id', 'fuel_id', 'calendar_year',\n 'retail_dollars_per_unit', 'pretax_dollars_per_unit'}\n\n template_errors = validate_template_version_info(filename, input_template_name, input_template_version,\n verbose=verbose)\n\n if not template_errors:\n # read in the data portion of the input file\n df = pd.read_csv(filename, skiprows=1)\n\n template_errors = validate_template_column_names(filename, input_template_columns, df.columns,\n verbose=verbose)\n\n if not template_errors:\n from context.new_vehicle_market import NewVehicleMarket\n from context.onroad_fuels import OnroadFuel\n\n # validate columns\n validation_dict = {'context_id': NewVehicleMarket.context_ids,\n 'case_id': NewVehicleMarket.context_case_ids,\n 'fuel_id': OnroadFuel.fuel_ids,\n }\n\n template_errors += validate_dataframe_columns(df, validation_dict, filename)\n\n if not template_errors:\n df = df.loc[(df['context_id'] == omega_globals.options.context_id) &\n (df['case_id'] == omega_globals.options.context_case_id), :]\n aeo_dollar_basis = df['dollar_basis'].mean()\n cols_to_convert = [col for col in df.columns if 'dollars_per_unit' in col]\n\n deflators = pd.read_csv(omega_globals.options.ip_deflators_file, skiprows=1, index_col=0).to_dict('index')\n\n adjustment_factor = deflators[omega_globals.options.analysis_dollar_basis]['price_deflator'] \\\n / deflators[aeo_dollar_basis]['price_deflator']\n\n for col in cols_to_convert:\n df[col] = df[col] * adjustment_factor\n\n df['dollar_basis'] = omega_globals.options.analysis_dollar_basis\n\n if not template_errors:\n FuelPrice._data = df.set_index(['context_id', 'case_id', 'fuel_id', 'calendar_year']).sort_index()\\\n .to_dict(orient='index')\n FuelPrice._data['min_calendar_year'] = df['calendar_year'].min()\n FuelPrice._data['max_calendar_year'] = df['calendar_year'].max()\n\n return template_errors","title":""}],"string":"[\n {\n \"docid\": \"d48e294bf82fbfb6008b8dd04a824560\",\n \"score\": \"0.0\",\n \"text\": \"def init_from_file(filename, verbose=False):\\n FuelPrice._data.clear()\\n\\n if verbose:\\n omega_log.logwrite('\\\\nInitializing database from %s...' % filename)\\n\\n # don't forget to update the module docstring with changes here\\n input_template_name = 'context_fuel_prices'\\n input_template_version = 0.2\\n input_template_columns = {'context_id', 'dollar_basis', 'case_id', 'fuel_id', 'calendar_year',\\n 'retail_dollars_per_unit', 'pretax_dollars_per_unit'}\\n\\n template_errors = validate_template_version_info(filename, input_template_name, input_template_version,\\n verbose=verbose)\\n\\n if not template_errors:\\n # read in the data portion of the input file\\n df = pd.read_csv(filename, skiprows=1)\\n\\n template_errors = validate_template_column_names(filename, input_template_columns, df.columns,\\n verbose=verbose)\\n\\n if not template_errors:\\n from context.new_vehicle_market import NewVehicleMarket\\n from context.onroad_fuels import OnroadFuel\\n\\n # validate columns\\n validation_dict = {'context_id': NewVehicleMarket.context_ids,\\n 'case_id': NewVehicleMarket.context_case_ids,\\n 'fuel_id': OnroadFuel.fuel_ids,\\n }\\n\\n template_errors += validate_dataframe_columns(df, validation_dict, filename)\\n\\n if not template_errors:\\n df = df.loc[(df['context_id'] == omega_globals.options.context_id) &\\n (df['case_id'] == omega_globals.options.context_case_id), :]\\n aeo_dollar_basis = df['dollar_basis'].mean()\\n cols_to_convert = [col for col in df.columns if 'dollars_per_unit' in col]\\n\\n deflators = pd.read_csv(omega_globals.options.ip_deflators_file, skiprows=1, index_col=0).to_dict('index')\\n\\n adjustment_factor = deflators[omega_globals.options.analysis_dollar_basis]['price_deflator'] \\\\\\n / deflators[aeo_dollar_basis]['price_deflator']\\n\\n for col in cols_to_convert:\\n df[col] = df[col] * adjustment_factor\\n\\n df['dollar_basis'] = omega_globals.options.analysis_dollar_basis\\n\\n if not template_errors:\\n FuelPrice._data = df.set_index(['context_id', 'case_id', 'fuel_id', 'calendar_year']).sort_index()\\\\\\n .to_dict(orient='index')\\n FuelPrice._data['min_calendar_year'] = df['calendar_year'].min()\\n FuelPrice._data['max_calendar_year'] = df['calendar_year'].max()\\n\\n return template_errors\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"21043b5751e1a501c0fcddd9d86c1228","score":"0.780452","text":"def __init__(self, input_file):\n self.data = []\n self.data_file = input_file #open(file_name, 'r')","title":""},{"docid":"6712e933ef0ba97c90073c986a49206d","score":"0.7547976","text":"def __init__(self, infile):\n super(Data7010, self).__init__(infile)\n self.read_data(infile)","title":""},{"docid":"17f31aec4c071eae095d5c8684349556","score":"0.75326604","text":"def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.data = self._read_data_from_file(self.filepath)","title":""},{"docid":"fd6f122cdf56d8f57fbbfca247576886","score":"0.7500667","text":"def __init__(self, infile):\n super(Data7041, self).__init__(infile)\n self.read_data(infile)","title":""},{"docid":"8a04a31d7f9dd9e2b7044e84f84d1d6c","score":"0.74850065","text":"def __init__(self, filename):\n # Attempt to load file\n try:\n self._data = self._read_file(filename)\n except FileNotFoundError:\n self._data = {}\n\n self._filename = filename","title":""},{"docid":"f8b9eb710887f28f3346e2aa844bf8d9","score":"0.74621165","text":"def __init__(self, infile):\n super(Data7004, self).__init__(infile)\n self.read_data(infile)","title":""},{"docid":"2dc513de8af59648a5789bc3fcfea2a3","score":"0.742669","text":"def __init__(self, infile):\n super(Data7007, self).__init__(infile)\n self.read_data(infile)","title":""},{"docid":"14ff6507270a7d364e9b030f5252e769","score":"0.7393236","text":"def __init__(self, filename):\n self._data = []\n self._filename = filename\n self._load()","title":""},{"docid":"7f4f58a5678bcaba3fb74b28db5291d8","score":"0.73915994","text":"def __init__(self, input_file_name):\n with open(input_file_name, \"r\") as input_file:\n for line in input_file:\n row = line.split()\n label = row[0]\n data = row[1:] # rest of row is data list\n\n attr = label_attr_map[label][0]\n datatypes = label_attr_map[label][1:]\n\n values = [(datatypes[i](data[i])) for i in range(len(datatypes))]\n self.__dict__[attr] = values if len(values) > 1 else values[0]\n return","title":""},{"docid":"e0c00c18331de1fb3ffc014aede1af5d","score":"0.7366259","text":"def __init__(self, data_file: Text) -> None:\n self.data_file = data_file\n self.data: Dict[Text, Any] = {}\n self.load()\n super().__init__()","title":""},{"docid":"d1e10b7f51adf3be2602bc04b4a92016","score":"0.7132197","text":"def __init__(self, input_data_path):\n self.idx = data_size\n\n self.data = {}\n self.loaded_data = load_data.load_folder(input_data_path)\n self.load_settings()\n self.get_params()\n self.sensors_count = len(list(self.loaded_data))\n\n self.prep_data()","title":""},{"docid":"04ef8e91b458959c1cf1999180f64ac1","score":"0.71135","text":"def __init__(self, file_name=CMR_path):\n try:\n self.data = np.loadtxt(file_name)\n except IOError:\n warning(\"Fail to open cmr file: {0:s}\".format(file_name))\n exit(1)","title":""},{"docid":"72a339ae2fee5587a4051260701f1b5e","score":"0.71126926","text":"def init_from_file(self, preprocessor_file: str):\n with open(preprocessor_file, 'rb') as f:\n self.tokenizer_obj = pickle.load(f)\n self.max_sequence_length = pickle.load(f)\n self.validation_split = pickle.load(f)\n self.vocab_size = pickle.load(f)\n self.labels_to_idx = pickle.load(f)","title":""},{"docid":"2359c99a1b161c13d7d144115ea259df","score":"0.71009153","text":"def __init__(self, data_file, input_file):\n\n self.full_data = np.loadtxt(data_file)\n self.length = len(self.full_data)\n self.input_data = np.loadtxt(input_file)\n\n self.i = self.full_data[:, 0]\n self.j = self.full_data[:, 1]\n self.k = self.full_data[:, 2]\n self.nx = self.full_data[:, 3]\n self.ny = self.full_data[:, 4]\n self.nz = self.full_data[:, 5]\n\n self.activity = self.input_data[0]\n self.body_force = self.input_data[1]\n self.shear_v = self.input_data[2]","title":""},{"docid":"a3daca46be63b03147e59a499121cf26","score":"0.70900553","text":"def __init__(self, data, file_path):\n super().__init__(data)\n self._apply_preprocessor()\n self._output_data(file_path)","title":""},{"docid":"f17f8480e652dcdbf5215c14880aa1ab","score":"0.70653886","text":"def __init__(self, filename):\n\n with open(filename, 'r') as f:\n self.data = json.load(f)","title":""},{"docid":"444205de563940c3849dd43f45f62b3a","score":"0.7033451","text":"def __init__(self, inputFile):\n\t\tself.inputFile = inputFile\n\t\tself.reset()","title":""},{"docid":"d65842168487143a490616101c50f21f","score":"0.7031135","text":"def __init__(self, filepath: str, data_columns: List[int]):\n self._records = list(\n _create_data_from_tsv(filepath, data_column_indices=data_columns)\n )\n self._sample_weights, self._class_weights = self._calculate_weights()","title":""},{"docid":"3a2f36d0010ec80a38211e57d87092a7","score":"0.7021572","text":"def __init__(self, dataset=None):\n filename = dataset if dataset is not None else 'default.txt'\n filepath = os.path.join(DATA_DIR, self.__class__.__name__, filename)\n\n with open(filepath, 'r') as f:\n self.data = f.read().splitlines()","title":""},{"docid":"122200bb91fbcf6312b89f9a243dcee0","score":"0.69908214","text":"def __init__(self):\n self.read_file()\n self.build_map()","title":""},{"docid":"6942181609d51e604f837bb7493cb94f","score":"0.69857746","text":"def __init__(self, filepath=None, old_cols=False):\n if filepath:\n pass\n else:\n filepath = input('Please give the absolute file location:')\n #filepath must yield a file\n if os.path.isfile(filepath):\n self.has_data=True\n print('found the file!')\n else:\n self.has_data = False\n print('filepath',filepath,'does not yield a file')\n return\n self.filepath, self.filename, self.file_suffix = filename_parse(filepath)\n rawDat = np.genfromtxt(filepath,dtype=np.float) \n # define arrray used for transposing dat file so cols are first index\n self.data = rawDat.T\n if old_cols:\n self.cols = self.old_cols\n print('file has imported')","title":""},{"docid":"b97089273c7aa4927b638bf46548030e","score":"0.69152766","text":"def __init__(self, path_to_file):\n self.path_to_file = path_to_file\n self.raw_data = self._get_raw_data()\n self.data = self._get_data()","title":""},{"docid":"8a2d1707611ffc5e4ce647d1a230cf42","score":"0.69040424","text":"def __init__(self,filename):\n self.filename = filename\n self.lines = self.readFile()\n self.moduleDict = self.getModuleDict()","title":""},{"docid":"6ca15936af3f168af837fe39478c4b54","score":"0.6886991","text":"def initialize(self, filename):\n pass","title":""},{"docid":"d180bd8f336fb6e3a0b871db3ef92e0e","score":"0.68832755","text":"def __init__(self, filename, data, labels):\n\n\t\tself.data = data\n\t\tself.labels = labels\n\t\tself.filename = filename\n\t\tself.parser = Parser()\n\t\tself.parser.parse_file(filename)\n\t\tself.pre_process()\n\t\tself.experiments = self.parser.get_all_combinations(self.parser.get_variables())","title":""},{"docid":"a005e97a0aba2f7e7db945cb4afa1c7d","score":"0.68703604","text":"def __init__(self, cfile='c.tmp'):\n\n setattr(self, 'inputfile', cfile)\n data = np.loadtxt(cfile)\n setattr(self, 'daynum', data[:, 0])\n setattr(self, 'res_P', data[:, 1])\n setattr(self, 'res', data[:, 2])\n setattr(self, 'uncertainty', data[:, 3])\n setattr(self, 'res_prefit', data[:, 4])\n setattr(self, 'orb_phase', data[:, 5])\n setattr(self, 'mjd', data[:, 6])\n setattr(self, 'ptype', data[:, 7])\n setattr(self, 'serial', data[:, 8])\n setattr(self, 'daynum_year', data[:, 9])\n setattr(self, 'freq', data[:, 10])\n setattr(self, 'timeofday', data[:, 11])\n setattr(self, 'serial2', data[:, 12])\n setattr(self, 'mjd2', data[:, 13])\n setattr(self, 'weight', data[:, 14])","title":""},{"docid":"c67e01f966925b84bf858ff886dd7778","score":"0.6865887","text":"def __init__(self, filename):\n \n with open(filename, 'r') as f:\n self.data = yaml.load(f)","title":""},{"docid":"014226a62f74bfc01eb2491ded153ad2","score":"0.6863758","text":"def __init__(self, path):\n\n self.common_pairs = None\n self.postcode_pairs = None\n self.readFiles(path)","title":""},{"docid":"1c4315c557756ef379c12c9025ca07d5","score":"0.68515944","text":"def __init__(self, fname, read = 0):\n\t\tself.usage = 'using class is a way to make your code clean'\n\t\tself.fname = fname\n\t\tself.file = open(fname, 'r')\n\n\t\t# Set up arrays to store data\n\t\tself.studentName = []\n\t\tself.coinDiameter = []\n\t\tself.coinDiameterUncertainty = []\n\t\tself.coinDiameterUnit = []\n\t\tself.coinHeight = []\n\t\tself.coinHeightUncertainty = []\n\t\tself.coinHeightUnit = []\n\t\tself.coinVolume = []\n\t\tself.coinVolumeUncertainty = []\n\t\tself.coinType = []\n\t\tself.coinVolumeUnit = []\n\t\tif (read == 1):\n\t\t\tself.readStudentData()","title":""},{"docid":"27b89d6ddb4d6c8167ee97d1a6808b73","score":"0.68157893","text":"def __init__(self, fname, **kwargs):\n self._fname = fname\n self._load_yml()\n self._process_files()","title":""},{"docid":"a697f69407a76cd6462aa1d7ea2c41ab","score":"0.6810576","text":"def __init__(self, data_file, title):\n self.title = title\n self.train_ids, self.test_ids, self.train_sequences, self.test_sequences = self.parse_data(data_file)\n self.classifier = self.train_module()\n self.dump_classifier()","title":""},{"docid":"72c953d00920f04cfd16a21ef8489be4","score":"0.6808054","text":"def __init__(self, filename):\n self.data = dict()\n self.get_infection_data(filename=filename)","title":""},{"docid":"2c7412ac7aae5338dfd62b25c3303fc4","score":"0.67895734","text":"def __init__(self, raw_input_data_dir: str):\n self.raw_input_data_dir = raw_input_data_dir","title":""},{"docid":"beae27abd41d0690c19f233f762458ea","score":"0.67834616","text":"def __init__(self, filename=None, f_start=None, f_stop=None,\n t_start=None, t_stop=None, load_data=True,\n header_dict=None, data_array=None):\n\n if filename:\n self.filename = filename\n if HAS_HDF5:\n if h5py.is_hdf5(filename):\n self.read_hdf5(filename, f_start, f_stop, t_start, t_stop, load_data)\n else:\n self.read_filterbank(filename, f_start, f_stop, t_start, t_stop, load_data)\n else:\n self.read_filterbank(filename, f_start, f_stop, t_start, t_stop, load_data)\n elif header_dict is not None and data_array is not None:\n self.gen_from_header(header_dict, data_array)\n else:\n pass","title":""},{"docid":"00456451e874d755e66236be75211367","score":"0.67800784","text":"def __init__(self, filename):\n self._data = []\n \n # load data\n with open(filename, \"r\", encoding=\"utf-8\") as map_file:\n raw = map_file.read()\n \n # parse data\n for line in raw.split(\"\\n\"):\n r = []\n for c in line.strip():\n r.append(c)\n self._data.append(r)","title":""},{"docid":"2da1a022e52100cef829d24dc5750db8","score":"0.675082","text":"def load_from_file(cls):\n file_instance = \"{}.json\".format(cls.__name__)\n #pending to complete this method","title":""},{"docid":"66c95d65fe6e855caaeda608c60cf5fc","score":"0.6722757","text":"def __init__(self, filename):\n\t\tself.rawdata = {}\n\n\t\tf = open(filename, 'r')\n\t\theader = f.readline().strip().split(',')\n\t\tfor line in f:\n\t\t\titems = line.strip().split(',')\n\n\t\t\tdate = re.match('(\\d\\d\\d\\d)(\\d\\d)(\\d\\d)', items[header.index('DATE')])\n\t\t\tyear = int(date.group(1))\n\t\t\tmonth = int(date.group(2))\n\t\t\tday = int(date.group(3))\n\n\t\t\tcity = items[header.index('CITY')]\n\t\t\ttemperature = float(items[header.index('TEMP')])\n\t\t\tif city not in self.rawdata:\n\t\t\t\tself.rawdata[city] = {}\n\t\t\tif year not in self.rawdata[city]:\n\t\t\t\tself.rawdata[city][year] = {}\n\t\t\tif month not in self.rawdata[city][year]:\n\t\t\t\tself.rawdata[city][year][month] = {}\n\t\t\tself.rawdata[city][year][month][day] = temperature\n\n\t\tf.close()","title":""},{"docid":"43b1d251e4831602e13a59a4857769d5","score":"0.66976804","text":"def __init__(self, filename):\n self.rawdata = {}\n\n f = open(filename, 'r')\n header = f.readline().strip().split(',')\n for line in f:\n items = line.strip().split(',')\n\n date = re.match('(\\d\\d\\d\\d)(\\d\\d)(\\d\\d)', items[header.index('DATE')])\n year = int(date.group(1))\n month = int(date.group(2))\n day = int(date.group(3))\n\n city = items[header.index('CITY')]\n temperature = float(items[header.index('TEMP')])\n if city not in self.rawdata:\n self.rawdata[city] = {}\n if year not in self.rawdata[city]:\n self.rawdata[city][year] = {}\n if month not in self.rawdata[city][year]:\n self.rawdata[city][year][month] = {}\n self.rawdata[city][year][month][day] = temperature\n\n f.close()","title":""},{"docid":"6db30b23677b95ff979ed9fcac5570e9","score":"0.66909236","text":"def init_from_file(self, input_filename):\n\n with h5py.File(input_filename, 'r') as f:\n\n self.varpi = f['varpi'].value\n self.params = f['params'].value\n self.kappa = f['main']['kappa'].value\n self.table = f['main']['table'].value\n \n if f['simulation']['kappa'].value is not h5py.Empty('f'):\n try:\n self.sim_kappa = f['simulation']['kappa'].value\n self.sim_table = f['simulation']['table'].value\n self.sim_alpha = f['simulation']['alpha'].value\n self.sim_B = f['simulation']['B'].value\n self.sim_D = f['simulation']['D'].value\n except:\n print(\"skipped simulation values\")","title":""},{"docid":"7286a656ee90f08cf4721d5533a665b7","score":"0.6687806","text":"def __init__(self, fname, **kwargs):\n pass","title":""},{"docid":"2d897144450299baee9fca36c1e1809c","score":"0.66846335","text":"def __init__(self, data_file, input_file):\n\n self.full_data = np.loadtxt(data_file)\n self.length = len(self.full_data)\n self.input_data = np.loadtxt(input_file)\n\n self.i = self.full_data[:, 0] # dividing data into its constituent parts\n self.j = self.full_data[:, 1]\n self.k = self.full_data[:, 2]\n self.Qxx = self.full_data[:, 3]\n self.Qxy = self.full_data[:, 4]\n self.Qxz = self.full_data[:, 5]\n self.Qyy = self.full_data[:, 6]\n self.Qyz = self.full_data[:, 7]\n self.vx = self.full_data[:, 8]\n self.vy = self.full_data[:, 9]\n self.vz = self.full_data[:, 10]\n self.qx = self.full_data[:, 11]\n self.qy = self.full_data[:, 12]\n self.qz = self.full_data[:, 13]\n self.q = self.full_data[:, 14]\n self.Stressyy = self.full_data[:, 15]\n self.Stressyz = self.full_data[:, 16]\n self.Stressxz = self.full_data[:, 17]\n\n self.activity = self.input_data[0]\n self.body_force = self.input_data[1]\n self.shear_v = self.input_data[2]","title":""},{"docid":"d6bd2eed49124893a1e1b3fd101353d8","score":"0.6684618","text":"def __init__(self, raw_data, *, data=None):\n self.__raw_data = raw_data\n if not data:\n self.__FACTOR_DATA_PATH = Path(__file__).parent.parent.parent.joinpath('data', 'processed', 'factor_data')\n else:\n self.__FACTOR_DATA_PATH = data","title":""},{"docid":"d2ff25d4572bafb87e2dd630b22f2c26","score":"0.6677816","text":"def __init__(self, filename):\n self.filename = filename\n self.items = []\n self.dict_keys = []\n self.readfile()","title":""},{"docid":"849b39f25c60bb0316ed2516cf6fc09f","score":"0.667488","text":"def __init__(self, filename):\n self.rawdata = {}\n\n f = open(filename, 'r')\n header = f.readline().strip().split(',')\n for line in f:\n items = line.strip().split(',')\n\n date = re.match('(\\d\\d\\d\\d)(\\d\\d)(\\d\\d)', items[header.index('DATE')])\n year = int(date.group(1))\n month = int(date.group(2))\n day = int(date.group(3))\n\n city = items[header.index('CITY')]\n temperature = float(items[header.index('TEMP')])\n if city not in self.rawdata:\n self.rawdata[city] = {}\n if year not in self.rawdata[city]:\n self.rawdata[city][year] = {}\n if month not in self.rawdata[city][year]:\n self.rawdata[city][year][month] = {}\n self.rawdata[city][year][month][day] = temperature\n \n f.close()","title":""},{"docid":"40a23b1f26dc53114a5d66d03fe6f11d","score":"0.6648202","text":"def __init__(self, file, keep_linebreaks=False, sequence_class=Sequence):\n\n file = open(file)\n self.fp = file\n self.sequence_class = sequence_class\n header = self.fp.readline().strip().split()\n self.nind = int(header[0])\n self.nloci = int(header[1])","title":""},{"docid":"f9bcf599869db31fefa3dcd32aea00ab","score":"0.66431195","text":"def __init__(self, filepath=None):\n if (filepath == None):\n self.phaseData = dict()\n self.comparison_data = dict()\n else:\n infile = open(filepath, 'r')\n self.phaseData, self.comparison_data = pickle.load(infile)","title":""},{"docid":"e4066e3eaba129c28931a3efb3d51eaa","score":"0.66429293","text":"def __init__(self, filename):\n self.version = \"\"\n self.date = \"\"\n self.vendors = {}\n self.contents = None\n self.readLocal(filename)\n self.parse()","title":""},{"docid":"94e2bf81b3a92aa994c81a2c495c35d4","score":"0.66389334","text":"def __init__(self, infile):\n super(Data7028, self).__init__(infile)\n self.fmt_descriptor = '<H3I'\n self.descriptor_sz = struct.calcsize(self.fmt_descriptor)\n self.read_data(infile)","title":""},{"docid":"3812e510cd7725e78984033f748e393a","score":"0.6633202","text":"def __init__(self, filename):\n self.dictIn={}\n self.dictOut={}\n self.dictInCosts={}\n self.dictOutCosts={}\n self.filename = filename\n self.nrVertices = 0\n self.nrEdges = 0\n self.loadFromFile()","title":""},{"docid":"fd34cb4cc8a4dcb994d4ee59a5a60371","score":"0.6618096","text":"def __init__(self, filename):\n self.__table = self.read_csv(filename)","title":""},{"docid":"b28adf8262c96b72d649e7b48358e1ca","score":"0.66170615","text":"def __init__(self, filepath: str, data_columns: List[int]):\n self._number_of_items = _get_tsv_file_length(filepath)\n self._iterator = _create_data_from_tsv(\n filepath, data_column_indices=data_columns\n )\n self._current_position = 0","title":""},{"docid":"aad58913618db288dd4f13c27ec6c835","score":"0.6612001","text":"def __init__(self, filename):\n super(Scanner, self).__init__()\n self.output = []\n self.bnf_file = None\n self.cursor = 0\n self.file_length = 0\n\n #initialization\n self._get_input(filename)\n self.initialize()","title":""},{"docid":"3682d8bf5f16fb9de2958e98fbdcfb03","score":"0.66020525","text":"def __init__(self, input_file, output_file):\n\t\tself.input_file = input_file\n\t\tself.output_file = output_file","title":""},{"docid":"241eda42552274b9facd16ce3ae6b54b","score":"0.65910405","text":"def __init__(self, **kwargs):\n self.__line = 0\n self.__error_count = 0\n self.__output = {}\n if 'data_file' in kwargs:\n self.data_file = kwargs['data_file']\n else:\n logging.error(\"Enter a data file path to read\")\n raise ValueError\n if 'output_file' in kwargs:\n self.output_file = kwargs['output_file']\n else:\n self.output_file = OUTPUT_FILE","title":""},{"docid":"5bf791b373c4e6a7e858aa28ce8c70b9","score":"0.65857047","text":"def __init__(self, file_name: str, data_dir: str, batch_size: int):\n super().__init__()\n\n # Save params\n self.file_name = file_name\n self.data_dir = data_dir\n self.batch_size = batch_size\n\n # Path to save/read the file is the data dir and the dataset's unique file name\n self.path_to_file = os.path.join(self.data_dir, self.get_target_file_name())\n\n self.prepare_data()\n self.setup()","title":""},{"docid":"b2460b8167f518e1208762fa3a4fec4c","score":"0.6585523","text":"def __init__(self, schemafile=None):\n self.data = {}\n self.loaded = False\n\n self.schemafile = schemafile","title":""},{"docid":"a63c96ece67b92625e7d11c86c668760","score":"0.6571061","text":"def __init__(self, data):\n\n super(AbstractBiometricData, self).__init__()\n\n # Read from string, otherwise store data\n if isinstance(data, str):\n self._data = N.loadtxt(data)\n else:\n self._data = data","title":""},{"docid":"49ff1e6ae2d7196ca73ebbdd3e3a0d6b","score":"0.6564938","text":"def __init__(self, **kwargs):\n self.data_file = kwargs[\"data_file\"]\n self.parent = None\n if \"parent\" in kwargs.keys():\n self.parent = kwargs[\"parent\"]\n if isfile(self.data_file):\n tree = ET.parse(kwargs[\"data_file\"])\n self.root = tree.getroot()\n else:\n self.root = self.initialize()","title":""},{"docid":"1a0ff927019f8417a7350f2b6ee1ee55","score":"0.6554327","text":"def __init__(self, file, **kwargs):\n if isinstance(file, str):\n f = open(file)\n else:\n f = file\n self.name = f.readline().replace(\"\\n\", \"\")\n ## Start reading the file in the tried POSCAR format\n self.scale = float(f.readline())\n self.trans = []\n for i in range(3):\n self.trans.append([float(n) for n in f.readline().split()])\n self.atoms = f.readline().split()\n self.nums = [int(n) for n in f.readline().split()]\n self.coords = []\n self.selective = False\n self.fix = None\n self.type = f.readline().split()[0]\n # Skip \"Selective dynamics\" if it's already a fixed file\n if self.type == \"Selective\":\n self.selective = True\n self.fix = []\n self.type = f.readline().split()[0]\n for i in range(sum(self.nums)):\n line = f.readline().split()\n if self.selective == False:\n self.coords.append([float(n) for n in line])\n else:\n self.coords.append([float(n) for n in line[0:3]])\n if len(line) == 6:\n self.fix.append([truth == \"T\" for truth in line[3:]])\n else:\n self.fix.append([True, True, True])\n f.close()\n # Get additional kwargs and store them\n for key in kwargs:\n setattr(self, key, kwargs[key])","title":""},{"docid":"98b0376c87d492ba3b31a744e5632b6e","score":"0.6552287","text":"def load(cls, file_path):","title":""},{"docid":"43ce94d1ee91f5872fcb0f93f4208772","score":"0.65506345","text":"def __init__(self, player_file_):\n self._scores = []\n self._filename = player_file_\n self.from_csv(self._filename)","title":""},{"docid":"592616c08a3f6894173a86ea689c0b3c","score":"0.65455705","text":"def __init__(self, path: str) -> None:\n\n self.__path = path\n with open(self.__path) as file:\n for line in file:\n key, value = line.rstrip().split(\"=\", maxsplit=1)\n self._validate_name(key)\n if value.isdigit():\n value = int(value) # type: ignore\n self.__dict__[key] = value","title":""},{"docid":"7ed416b23ac08831715bedc23def3de1","score":"0.65385574","text":"def setup_class(self):\n\n self.control = pd.read_csv(os.path.join(FILES_DIR, \"swanfile.txt\"), sep=\"\\t\")\n self.swanspec = read_swan(os.path.join(FILES_DIR, \"swanfile.spec\"))","title":""},{"docid":"f428c9a406597202772540e09da13ad1","score":"0.6527552","text":"def __init__(self, data):\n f = open(data, 'r')\n lines = f.readlines()[1:]\n stripped = [line.strip('\\n') for line in lines]\n parsed = [line.split(',') for line in stripped]\n self.parsed_array = np.asarray(parsed)\n f.close()","title":""},{"docid":"da2b93865de66cfa4cf96336892f4c27","score":"0.65130764","text":"def __init__(self,data=[],filepath=None,sequential=False):\n \n self.data = data\n self.filepath = \"\"\n self.source_headers = \"\"\n self.G = None\n self.transactions = {}\n self.items = {}\n self.num_transactions = 0\n self.df = None\n self.sequential = sequential\n \n self.item_counts = {}\n \n if filepath is not None:\n self.load_csv_data(filepath,sequential)","title":""},{"docid":"7c9b3564a76024da4ba5e2a56ae12245","score":"0.6509448","text":"def __init__(self, in_file):\n self.called_by = None\n self.functions = {}\n self._load_from_file(in_file)","title":""},{"docid":"d5f5008183d56f488046b87c3139293e","score":"0.65031356","text":"def __init__(self, filename):\n self._file = open(filename, 'rb')","title":""},{"docid":"af340c4843e6395613ebd4e2e35c2858","score":"0.6502869","text":"def __init__(self, file_path):\n self.file_path = file_path\n self.vcf_records = None\n self.genotype_dictionary = {}\n self.total_hom = 0\n self.total_het = 0\n self.total_hom_alt = 0","title":""},{"docid":"e5a4576fa20667c20fae870a487fa916","score":"0.6498094","text":"def __init__(self, input_file):\n\t\tsuper(LSF, self).__init__()\n\t\tself.input_file = input_file\n\t\tself.words = []\n\t\tself.load()","title":""},{"docid":"2bad7066bfc0cc34bf43705d82e7590b","score":"0.64935887","text":"def __init__(self, file):\n # TODO: set up the necessary instance variables\n self.file_name = file\n self.languages = {}\n self.domains = {}\n self.user_number = 0","title":""},{"docid":"6ecd4f76b6671a9ea9946cf9da2b8dd6","score":"0.64905685","text":"def __init__(self, filename):\n if os.path.exists(filename):\n # read in the input file namelist\n self.namelist = f90nml.read(filename)\n else:\n warnings.warn(\"Input file %s does not exist.\" % filename,\n RuntimeWarning)","title":""},{"docid":"64c59b3ab2bfe6a5e4f203f6ad738634","score":"0.64838517","text":"def __init__(self, filename):\n with open(filename, 'rb') as f:\n reader = csv.DictReader(f)\n self.rows = list(reader)","title":""},{"docid":"5d49a69f28082c8bfb7b3afd0ad0cb09","score":"0.6473477","text":"def __init__(self, directory = '', verbose = False, data_spec = []):\n self.directory = directory\n self.verbose = verbose\n self.data_spec = data_spec \n\n # Import data\n self.extract_data()","title":""},{"docid":"6de4d1de68f1f2935dda148dcc20fb99","score":"0.6466702","text":"def __init__(self, inputfile, outputfolder):\r\n check_file(inputfile)\r\n self.input = inputfile\r\n check_file(outputfolder)\r\n self.output = outputfolder","title":""},{"docid":"889f153f1632f984b3721f9b2a07f76e","score":"0.64665943","text":"def __init__(self, file: str):\n self._file = file","title":""},{"docid":"e7dc15759ca13383901fd01784d80fb0","score":"0.6463946","text":"def __init__(self, offset=0):\n self._load_classification_text_file(KaggleFakeNews.DATA_PATH, offset=offset)","title":""},{"docid":"bf8a6a802b20f95d68daa40f893238b2","score":"0.64625645","text":"def from_file(cls, filename):\r\n cn_class = cls() # initialize class\r\n cn_class.read_cn_file(filename)\r\n return cn_class # return class as it is a constructor\r","title":""},{"docid":"c12cd10e514ee7ce4def7dc911512a75","score":"0.64422065","text":"def __init__(self, path):\n self.path = Path(path)\n self.file_name = os.path.split(path)[1]\n self.header, self.data, self.units = self.read(path)","title":""},{"docid":"6b2f8f32428f5dbccbe5789ae0ed1fc2","score":"0.643671","text":"def __init__(self, infile):\n super(Data7058, self).__init__(infile)\n self.fmt_descriptor = '<H3I'\n self.descriptor_sz = struct.calcsize(self.fmt_descriptor)\n self.read_data(infile)","title":""},{"docid":"d0cc2a045dac3b0d0606f0d6ca31881f","score":"0.64359057","text":"def __init__(self, filename, package=None):\n\n super(SourceFile, self).__init__(filename)\n Coverable.__init__(self)\n FileTokenizer.__init__(self)\n self.package = package\n\n # parse results\n self.parsed = False\n self.imports = []\n self.globals = []\n self.classes = []\n self.functions = []\n\n # loading and parsing\n self.load_content()","title":""},{"docid":"27ca4199b13e55a39d1b09e9f28f8bd7","score":"0.64336854","text":"def __init__(self, filepath):\n self.filepath = filepath\n self.load_yaml()\n if self.load_failed():\n return\n self.ensure_dict_exists(self.data, 'paths')\n self.ensure_dict_exists(self.data, 'components')\n self.ensure_dict_exists(self.data['components'], 'schemas')\n self.ensure_dict_exists(self.data['components'], 'parameters')\n self.ensure_dict_exists(self.data['components'], 'responses')","title":""},{"docid":"27564f1afbd21004eebce0673d1e51fa","score":"0.6433581","text":"def __init__(self):\n self.file_train = []\n self.file_val = []\n self.file_test = []","title":""},{"docid":"e77d51e8ac579919dcfcddb8867895fe","score":"0.64099073","text":"def readInstance(self):\n file = open(self.fName, 'r')\n self.genSize = int(file.readline())\n self.data = {}\n for line in file:\n (id, x, y) = line.split()\n self.data[int(id)] = (int(x), int(y))\n file.close()","title":""},{"docid":"34ad27dfddf3cf770d0c453dd46656d9","score":"0.6404643","text":"def __init__(self, raw_input_data_csv: str):\n self.raw_input_data_csv = raw_input_data_csv","title":""},{"docid":"4c5e7272ec160551d3100a8aaa4fc0af","score":"0.6399774","text":"def __init__(self , filename) :\n BaseIO.__init__(self)\n self.filename = filename\n self.loader = Loader(filename)\n self.loader.load_file()\n self.header = self.loader.header","title":""},{"docid":"1d34968c03d8ed50ff398d2ca7b6a801","score":"0.6394196","text":"def __init__(self, data_name) -> None:\n super(LoadData, self).__init__()\n\n if data_name == 'adult':\n\n # Find the aif toolkit path.\n aif_path = os.path.abspath(aif360.__file__)\n aif_path = aif_path.replace('__init__.py', '') + 'data/raw/adult/'\n\n # Check if train, validation, and test data files are available.\n if not (os.path.isfile(aif_path + 'adult.data') and\n os.path.isfile(aif_path + 'adult.test') and\n os.path.isfile(aif_path + 'adult.names')):\n\n # Find the file directories.\n source = os.path.abspath(os.getcwd()) + \"/Data/Adult/\"\n get_files = os.listdir(source)\n\n # Copy the files to the aif toolkit.\n for f in get_files:\n shutil.move(source + f, aif_path)\n\n results = self.preprocessAdult()\n\n elif data_name == 'celeba':\n # Find the path of the correct data.\n path = os.path.abspath(os.getcwd()) + \"/Data/CelebA/\"\n\n # Check if the data is still zipped.\n if not (os.path.isdir(path + \"img_align_celeba/\")):\n zipped = path + \"img_align_celeba.zip\"\n\n # Open the zip file in READ mode.\n with ZipFile(zipped, 'r') as zip:\n # Extract all of the files.\n print('Extracting all the files for CelebA now.')\n zip.extractall(path=path)\n\n print('Done!')\n\n results = self.preprocessCelebA(path)\n\n elif data_name == 'civil':\n # Get the path to the data.\n path = os.path.abspath(os.getcwd()) + \"/Data/CivilComments/\"\n\n results = self.preprocessCivilComments(path)\n\n else:\n raise Exception('An invalid dataset name was chosen.')\n\n self.train = results[0]\n self.val = results[1]\n self.test = results[2]\n self.ratio = results[3]","title":""},{"docid":"c98949e5628394c51a94c88a3b70326b","score":"0.6394118","text":"def __init__(self):\n\n self.train = pd.read_csv(os.path.join(_DATA_DIR, _TRAIN))\n self.test = pd.read_csv(os.path.join(_DATA_DIR, _TEST))","title":""},{"docid":"08bb2e0319ce6c9786b96b5df359e251","score":"0.6382472","text":"def __init__(self) -> None:\n self._data = []\n self._load_data()","title":""},{"docid":"d5f3e79c4a688d4397784b1f821b8e64","score":"0.6374787","text":"def __init__(self, file,\n kwargs_Data={}, kwargs_ReadInData={}, kwargs_grid={}):\n # self.kwargs_ReadInData = kwargs_ReadInData\n self._kwargs_Data = kwargs_Data\n self._kwargs_ReadInData = kwargs_ReadInData\n self._kwargs_grid = kwargs_grid\n self._x = None\n self._y = None\n self._data = None\n self._X = None\n self._Y = None\n self._Data = None\n\n ReadInData.__init__(self, file, self._kwargs_ReadInData)\n self.format_raw_data()\n self.grid(**self._kwargs_grid)\n self.format_data()\n\n Plot.__init__(self, self.X(), self.Y())","title":""},{"docid":"ab523fc7a26ddd00ddb6c60b5616ef32","score":"0.63709337","text":"def __init__(self, filename=\"movie_data.txt\"):\n raise NotImplementedError(\"Problem 4 Incomplete\")","title":""},{"docid":"c9ae49b0756320b3ddbd5249b606b8e2","score":"0.6370287","text":"def __init__(self,fn):\n data=fromfile(fn,sep='\\n')\n data=data.reshape((len(data)/33,33)).T\n names=[ 'S%d%d' % (i,j) for i in range(1,5) for j in range(1,5)]\n self.f=data[0]\n self.t=f2t(self.f)\n self.f=t2f(self.t)\n self.names=names\n for i,n in enumerate(names):\n v=zeros(len(self.f),dtype=complex)\n v[1:]=db2c(data[i*2+1])*a2c(data[i*2+2])\n v[0]=1\n setattr(self,n,v)\n self.fn=fn[:-4]","title":""},{"docid":"c17f79e5eddf954d43f3bcb6aa0b5025","score":"0.63691825","text":"def __init__(self, dataset_file, categories_file,transform=None): \n self.transform = transform\n\n with open(categories_file) as f:\n self.categories = json.load(f)\n\n self.files, self.labels = self.__get_data(dataset_file=dataset_file)","title":""},{"docid":"00ea33fa6a3ed9a98c40a68420dfa2b4","score":"0.63642675","text":"def __init__(self, filePath):\n self.jsonFileName = filePath\n self.parsedData = self.parseJson()","title":""},{"docid":"00ea33fa6a3ed9a98c40a68420dfa2b4","score":"0.63642675","text":"def __init__(self, filePath):\n self.jsonFileName = filePath\n self.parsedData = self.parseJson()","title":""},{"docid":"180174206ffd53457b937ab377d22725","score":"0.6343072","text":"def __init__(self, filename):\n self.filename = filename\n self.__readFile = queue.Queue()\n self.datas = None\n self.__readCSV()","title":""},{"docid":"6f8606e9e9f8fcb5852fdce0a593052d","score":"0.63391465","text":"def my_init(self, fileobj):\n self._fileobj = fileobj","title":""},{"docid":"6f8606e9e9f8fcb5852fdce0a593052d","score":"0.63391465","text":"def my_init(self, fileobj):\n self._fileobj = fileobj","title":""},{"docid":"785cafb297da097eed6b53b187256c18","score":"0.6317455","text":"def __init__(self):\n\n try:\n with open(\n os.path.join(\n os.path.dirname(__file__),\n \"mtexposdata.json\"\n )\n ) as fp:\n self.data = json.load(fp)\n except FileNotFoundError:\n raise FileNotFoundError(\"File mtexposdata.json was not found!\")","title":""},{"docid":"7a5182a0190c01b81d5b101624cdc1b1","score":"0.63154566","text":"def __init__(self, filename):\n self.filename = filename\n if \"-atsymbol-\" in filename and \"-fullstop-\" in filename:\n self.email = \".\".join(filename.split(\".\")[:-1])\n else:\n self.email = None\n self.file = None\n self.num_splits = None\n self.prediction_arr = []\n self.score_arr = []\n self.sample_arr = []","title":""},{"docid":"c72a38e22831142d000590144901f04c","score":"0.63098115","text":"def fromFile(name, index = -1, data_in = \".data\", names_in = \".names\", class_name = \"class\"):\r\n file_data = name + data_in\r\n file_names = name + names_in\r\n \r\n #os.chdir()\r\n \r\n # parse .data file for data \r\n text = open(os.path.dirname(__file__) + '/../data/' + file_data, 'r').read().split('\\n')\r\n data = [[value.strip() for value in line.split(',')] for line in text if line]\r\n \r\n # parse .names file for attributes\r\n names = open(os.path.dirname(__file__) + '/../data/' + file_names, 'r').read().split('\\n')\r\n lines = [line for line in names if line and line[0] != \"|\" ] # filter\r\n \r\n class_att = Attribute(class_name, [s.strip() for s in lines.pop(0).split(',')])\r\n \r\n attributes = []\r\n \r\n for line in lines:\r\n n = line.index(\":\")\r\n att_name = line[:n] \r\n att = Attribute(att_name, [s.strip(' .') for s in line[n+1:].split(',')])\r\n attributes.append(att)\r\n \r\n attributes.append(class_att)\r\n \r\n col_data = list(zip(*data))\r\n col_data[-1], col_data[index] = col_data[index], col_data[-1]\r\n data = list(zip(*col_data))\r\n \r\n return Dataset(attributes, data)","title":""},{"docid":"a1e1bdba23154b104b165adcb3c20f08","score":"0.6308805","text":"def __init__(self, fileName, rowClass=None, typeMap=None, defaultColType=None, columns=None, columnNameMapper=None,\n ignoreExtraCols=False, inFh=None, allowEmpty=False, dialect=csv.excel_tab,\n encoding=None, errors=None):\n self.columns = []\n # external column name, before mapping with columnNameMapper\n if columnNameMapper is None:\n self.extColumns = self.columns\n else:\n self.extColumns = []\n self.colMap = {}\n self.fileName = fileName\n self.lineNum = 0\n self.rowClass = rowClass\n if rowClass is None:\n self.rowClass = TsvRow\n self.columnNameMapper = columnNameMapper\n self.colTypes = None\n self.ignoreExtraCols = ignoreExtraCols\n if inFh is not None:\n self.inFh = inFh\n else:\n self.inFh = fileOps.opengz(fileName, encoding=encoding, errors=errors)\n try:\n self.reader = csv.reader(self.inFh, dialect=dialect)\n if columns:\n self._setupColumns(columns)\n else:\n self._readHeader(allowEmpty)\n self._initColTypes(typeMap, defaultColType)\n except Exception:\n self.close()\n raise","title":""}],"string":"[\n {\n \"docid\": \"21043b5751e1a501c0fcddd9d86c1228\",\n \"score\": \"0.780452\",\n \"text\": \"def __init__(self, input_file):\\n self.data = []\\n self.data_file = input_file #open(file_name, 'r')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6712e933ef0ba97c90073c986a49206d\",\n \"score\": \"0.7547976\",\n \"text\": \"def __init__(self, infile):\\n super(Data7010, self).__init__(infile)\\n self.read_data(infile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17f31aec4c071eae095d5c8684349556\",\n \"score\": \"0.75326604\",\n \"text\": \"def __init__(self, *args, **kwargs):\\n super().__init__(*args, **kwargs)\\n self.data = self._read_data_from_file(self.filepath)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd6f122cdf56d8f57fbbfca247576886\",\n \"score\": \"0.7500667\",\n \"text\": \"def __init__(self, infile):\\n super(Data7041, self).__init__(infile)\\n self.read_data(infile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a04a31d7f9dd9e2b7044e84f84d1d6c\",\n \"score\": \"0.74850065\",\n \"text\": \"def __init__(self, filename):\\n # Attempt to load file\\n try:\\n self._data = self._read_file(filename)\\n except FileNotFoundError:\\n self._data = {}\\n\\n self._filename = filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8b9eb710887f28f3346e2aa844bf8d9\",\n \"score\": \"0.74621165\",\n \"text\": \"def __init__(self, infile):\\n super(Data7004, self).__init__(infile)\\n self.read_data(infile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2dc513de8af59648a5789bc3fcfea2a3\",\n \"score\": \"0.742669\",\n \"text\": \"def __init__(self, infile):\\n super(Data7007, self).__init__(infile)\\n self.read_data(infile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14ff6507270a7d364e9b030f5252e769\",\n \"score\": \"0.7393236\",\n \"text\": \"def __init__(self, filename):\\n self._data = []\\n self._filename = filename\\n self._load()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f4f58a5678bcaba3fb74b28db5291d8\",\n \"score\": \"0.73915994\",\n \"text\": \"def __init__(self, input_file_name):\\n with open(input_file_name, \\\"r\\\") as input_file:\\n for line in input_file:\\n row = line.split()\\n label = row[0]\\n data = row[1:] # rest of row is data list\\n\\n attr = label_attr_map[label][0]\\n datatypes = label_attr_map[label][1:]\\n\\n values = [(datatypes[i](data[i])) for i in range(len(datatypes))]\\n self.__dict__[attr] = values if len(values) > 1 else values[0]\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0c00c18331de1fb3ffc014aede1af5d\",\n \"score\": \"0.7366259\",\n \"text\": \"def __init__(self, data_file: Text) -> None:\\n self.data_file = data_file\\n self.data: Dict[Text, Any] = {}\\n self.load()\\n super().__init__()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1e10b7f51adf3be2602bc04b4a92016\",\n \"score\": \"0.7132197\",\n \"text\": \"def __init__(self, input_data_path):\\n self.idx = data_size\\n\\n self.data = {}\\n self.loaded_data = load_data.load_folder(input_data_path)\\n self.load_settings()\\n self.get_params()\\n self.sensors_count = len(list(self.loaded_data))\\n\\n self.prep_data()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"04ef8e91b458959c1cf1999180f64ac1\",\n \"score\": \"0.71135\",\n \"text\": \"def __init__(self, file_name=CMR_path):\\n try:\\n self.data = np.loadtxt(file_name)\\n except IOError:\\n warning(\\\"Fail to open cmr file: {0:s}\\\".format(file_name))\\n exit(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72a339ae2fee5587a4051260701f1b5e\",\n \"score\": \"0.71126926\",\n \"text\": \"def init_from_file(self, preprocessor_file: str):\\n with open(preprocessor_file, 'rb') as f:\\n self.tokenizer_obj = pickle.load(f)\\n self.max_sequence_length = pickle.load(f)\\n self.validation_split = pickle.load(f)\\n self.vocab_size = pickle.load(f)\\n self.labels_to_idx = pickle.load(f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2359c99a1b161c13d7d144115ea259df\",\n \"score\": \"0.71009153\",\n \"text\": \"def __init__(self, data_file, input_file):\\n\\n self.full_data = np.loadtxt(data_file)\\n self.length = len(self.full_data)\\n self.input_data = np.loadtxt(input_file)\\n\\n self.i = self.full_data[:, 0]\\n self.j = self.full_data[:, 1]\\n self.k = self.full_data[:, 2]\\n self.nx = self.full_data[:, 3]\\n self.ny = self.full_data[:, 4]\\n self.nz = self.full_data[:, 5]\\n\\n self.activity = self.input_data[0]\\n self.body_force = self.input_data[1]\\n self.shear_v = self.input_data[2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3daca46be63b03147e59a499121cf26\",\n \"score\": \"0.70900553\",\n \"text\": \"def __init__(self, data, file_path):\\n super().__init__(data)\\n self._apply_preprocessor()\\n self._output_data(file_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f17f8480e652dcdbf5215c14880aa1ab\",\n \"score\": \"0.70653886\",\n \"text\": \"def __init__(self, filename):\\n\\n with open(filename, 'r') as f:\\n self.data = json.load(f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"444205de563940c3849dd43f45f62b3a\",\n \"score\": \"0.7033451\",\n \"text\": \"def __init__(self, inputFile):\\n\\t\\tself.inputFile = inputFile\\n\\t\\tself.reset()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d65842168487143a490616101c50f21f\",\n \"score\": \"0.7031135\",\n \"text\": \"def __init__(self, filepath: str, data_columns: List[int]):\\n self._records = list(\\n _create_data_from_tsv(filepath, data_column_indices=data_columns)\\n )\\n self._sample_weights, self._class_weights = self._calculate_weights()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a2f36d0010ec80a38211e57d87092a7\",\n \"score\": \"0.7021572\",\n \"text\": \"def __init__(self, dataset=None):\\n filename = dataset if dataset is not None else 'default.txt'\\n filepath = os.path.join(DATA_DIR, self.__class__.__name__, filename)\\n\\n with open(filepath, 'r') as f:\\n self.data = f.read().splitlines()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"122200bb91fbcf6312b89f9a243dcee0\",\n \"score\": \"0.69908214\",\n \"text\": \"def __init__(self):\\n self.read_file()\\n self.build_map()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6942181609d51e604f837bb7493cb94f\",\n \"score\": \"0.69857746\",\n \"text\": \"def __init__(self, filepath=None, old_cols=False):\\n if filepath:\\n pass\\n else:\\n filepath = input('Please give the absolute file location:')\\n #filepath must yield a file\\n if os.path.isfile(filepath):\\n self.has_data=True\\n print('found the file!')\\n else:\\n self.has_data = False\\n print('filepath',filepath,'does not yield a file')\\n return\\n self.filepath, self.filename, self.file_suffix = filename_parse(filepath)\\n rawDat = np.genfromtxt(filepath,dtype=np.float) \\n # define arrray used for transposing dat file so cols are first index\\n self.data = rawDat.T\\n if old_cols:\\n self.cols = self.old_cols\\n print('file has imported')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b97089273c7aa4927b638bf46548030e\",\n \"score\": \"0.69152766\",\n \"text\": \"def __init__(self, path_to_file):\\n self.path_to_file = path_to_file\\n self.raw_data = self._get_raw_data()\\n self.data = self._get_data()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a2d1707611ffc5e4ce647d1a230cf42\",\n \"score\": \"0.69040424\",\n \"text\": \"def __init__(self,filename):\\n self.filename = filename\\n self.lines = self.readFile()\\n self.moduleDict = self.getModuleDict()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ca15936af3f168af837fe39478c4b54\",\n \"score\": \"0.6886991\",\n \"text\": \"def initialize(self, filename):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d180bd8f336fb6e3a0b871db3ef92e0e\",\n \"score\": \"0.68832755\",\n \"text\": \"def __init__(self, filename, data, labels):\\n\\n\\t\\tself.data = data\\n\\t\\tself.labels = labels\\n\\t\\tself.filename = filename\\n\\t\\tself.parser = Parser()\\n\\t\\tself.parser.parse_file(filename)\\n\\t\\tself.pre_process()\\n\\t\\tself.experiments = self.parser.get_all_combinations(self.parser.get_variables())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a005e97a0aba2f7e7db945cb4afa1c7d\",\n \"score\": \"0.68703604\",\n \"text\": \"def __init__(self, cfile='c.tmp'):\\n\\n setattr(self, 'inputfile', cfile)\\n data = np.loadtxt(cfile)\\n setattr(self, 'daynum', data[:, 0])\\n setattr(self, 'res_P', data[:, 1])\\n setattr(self, 'res', data[:, 2])\\n setattr(self, 'uncertainty', data[:, 3])\\n setattr(self, 'res_prefit', data[:, 4])\\n setattr(self, 'orb_phase', data[:, 5])\\n setattr(self, 'mjd', data[:, 6])\\n setattr(self, 'ptype', data[:, 7])\\n setattr(self, 'serial', data[:, 8])\\n setattr(self, 'daynum_year', data[:, 9])\\n setattr(self, 'freq', data[:, 10])\\n setattr(self, 'timeofday', data[:, 11])\\n setattr(self, 'serial2', data[:, 12])\\n setattr(self, 'mjd2', data[:, 13])\\n setattr(self, 'weight', data[:, 14])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c67e01f966925b84bf858ff886dd7778\",\n \"score\": \"0.6865887\",\n \"text\": \"def __init__(self, filename):\\n \\n with open(filename, 'r') as f:\\n self.data = yaml.load(f)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"014226a62f74bfc01eb2491ded153ad2\",\n \"score\": \"0.6863758\",\n \"text\": \"def __init__(self, path):\\n\\n self.common_pairs = None\\n self.postcode_pairs = None\\n self.readFiles(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c4315c557756ef379c12c9025ca07d5\",\n \"score\": \"0.68515944\",\n \"text\": \"def __init__(self, fname, read = 0):\\n\\t\\tself.usage = 'using class is a way to make your code clean'\\n\\t\\tself.fname = fname\\n\\t\\tself.file = open(fname, 'r')\\n\\n\\t\\t# Set up arrays to store data\\n\\t\\tself.studentName = []\\n\\t\\tself.coinDiameter = []\\n\\t\\tself.coinDiameterUncertainty = []\\n\\t\\tself.coinDiameterUnit = []\\n\\t\\tself.coinHeight = []\\n\\t\\tself.coinHeightUncertainty = []\\n\\t\\tself.coinHeightUnit = []\\n\\t\\tself.coinVolume = []\\n\\t\\tself.coinVolumeUncertainty = []\\n\\t\\tself.coinType = []\\n\\t\\tself.coinVolumeUnit = []\\n\\t\\tif (read == 1):\\n\\t\\t\\tself.readStudentData()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27b89d6ddb4d6c8167ee97d1a6808b73\",\n \"score\": \"0.68157893\",\n \"text\": \"def __init__(self, fname, **kwargs):\\n self._fname = fname\\n self._load_yml()\\n self._process_files()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a697f69407a76cd6462aa1d7ea2c41ab\",\n \"score\": \"0.6810576\",\n \"text\": \"def __init__(self, data_file, title):\\n self.title = title\\n self.train_ids, self.test_ids, self.train_sequences, self.test_sequences = self.parse_data(data_file)\\n self.classifier = self.train_module()\\n self.dump_classifier()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72c953d00920f04cfd16a21ef8489be4\",\n \"score\": \"0.6808054\",\n \"text\": \"def __init__(self, filename):\\n self.data = dict()\\n self.get_infection_data(filename=filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c7412ac7aae5338dfd62b25c3303fc4\",\n \"score\": \"0.67895734\",\n \"text\": \"def __init__(self, raw_input_data_dir: str):\\n self.raw_input_data_dir = raw_input_data_dir\",\n \"title\": \"\"\n },\n {\n \"docid\": \"beae27abd41d0690c19f233f762458ea\",\n \"score\": \"0.67834616\",\n \"text\": \"def __init__(self, filename=None, f_start=None, f_stop=None,\\n t_start=None, t_stop=None, load_data=True,\\n header_dict=None, data_array=None):\\n\\n if filename:\\n self.filename = filename\\n if HAS_HDF5:\\n if h5py.is_hdf5(filename):\\n self.read_hdf5(filename, f_start, f_stop, t_start, t_stop, load_data)\\n else:\\n self.read_filterbank(filename, f_start, f_stop, t_start, t_stop, load_data)\\n else:\\n self.read_filterbank(filename, f_start, f_stop, t_start, t_stop, load_data)\\n elif header_dict is not None and data_array is not None:\\n self.gen_from_header(header_dict, data_array)\\n else:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00456451e874d755e66236be75211367\",\n \"score\": \"0.67800784\",\n \"text\": \"def __init__(self, filename):\\n self._data = []\\n \\n # load data\\n with open(filename, \\\"r\\\", encoding=\\\"utf-8\\\") as map_file:\\n raw = map_file.read()\\n \\n # parse data\\n for line in raw.split(\\\"\\\\n\\\"):\\n r = []\\n for c in line.strip():\\n r.append(c)\\n self._data.append(r)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2da1a022e52100cef829d24dc5750db8\",\n \"score\": \"0.675082\",\n \"text\": \"def load_from_file(cls):\\n file_instance = \\\"{}.json\\\".format(cls.__name__)\\n #pending to complete this method\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66c95d65fe6e855caaeda608c60cf5fc\",\n \"score\": \"0.6722757\",\n \"text\": \"def __init__(self, filename):\\n\\t\\tself.rawdata = {}\\n\\n\\t\\tf = open(filename, 'r')\\n\\t\\theader = f.readline().strip().split(',')\\n\\t\\tfor line in f:\\n\\t\\t\\titems = line.strip().split(',')\\n\\n\\t\\t\\tdate = re.match('(\\\\d\\\\d\\\\d\\\\d)(\\\\d\\\\d)(\\\\d\\\\d)', items[header.index('DATE')])\\n\\t\\t\\tyear = int(date.group(1))\\n\\t\\t\\tmonth = int(date.group(2))\\n\\t\\t\\tday = int(date.group(3))\\n\\n\\t\\t\\tcity = items[header.index('CITY')]\\n\\t\\t\\ttemperature = float(items[header.index('TEMP')])\\n\\t\\t\\tif city not in self.rawdata:\\n\\t\\t\\t\\tself.rawdata[city] = {}\\n\\t\\t\\tif year not in self.rawdata[city]:\\n\\t\\t\\t\\tself.rawdata[city][year] = {}\\n\\t\\t\\tif month not in self.rawdata[city][year]:\\n\\t\\t\\t\\tself.rawdata[city][year][month] = {}\\n\\t\\t\\tself.rawdata[city][year][month][day] = temperature\\n\\n\\t\\tf.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43b1d251e4831602e13a59a4857769d5\",\n \"score\": \"0.66976804\",\n \"text\": \"def __init__(self, filename):\\n self.rawdata = {}\\n\\n f = open(filename, 'r')\\n header = f.readline().strip().split(',')\\n for line in f:\\n items = line.strip().split(',')\\n\\n date = re.match('(\\\\d\\\\d\\\\d\\\\d)(\\\\d\\\\d)(\\\\d\\\\d)', items[header.index('DATE')])\\n year = int(date.group(1))\\n month = int(date.group(2))\\n day = int(date.group(3))\\n\\n city = items[header.index('CITY')]\\n temperature = float(items[header.index('TEMP')])\\n if city not in self.rawdata:\\n self.rawdata[city] = {}\\n if year not in self.rawdata[city]:\\n self.rawdata[city][year] = {}\\n if month not in self.rawdata[city][year]:\\n self.rawdata[city][year][month] = {}\\n self.rawdata[city][year][month][day] = temperature\\n\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6db30b23677b95ff979ed9fcac5570e9\",\n \"score\": \"0.66909236\",\n \"text\": \"def init_from_file(self, input_filename):\\n\\n with h5py.File(input_filename, 'r') as f:\\n\\n self.varpi = f['varpi'].value\\n self.params = f['params'].value\\n self.kappa = f['main']['kappa'].value\\n self.table = f['main']['table'].value\\n \\n if f['simulation']['kappa'].value is not h5py.Empty('f'):\\n try:\\n self.sim_kappa = f['simulation']['kappa'].value\\n self.sim_table = f['simulation']['table'].value\\n self.sim_alpha = f['simulation']['alpha'].value\\n self.sim_B = f['simulation']['B'].value\\n self.sim_D = f['simulation']['D'].value\\n except:\\n print(\\\"skipped simulation values\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7286a656ee90f08cf4721d5533a665b7\",\n \"score\": \"0.6687806\",\n \"text\": \"def __init__(self, fname, **kwargs):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d897144450299baee9fca36c1e1809c\",\n \"score\": \"0.66846335\",\n \"text\": \"def __init__(self, data_file, input_file):\\n\\n self.full_data = np.loadtxt(data_file)\\n self.length = len(self.full_data)\\n self.input_data = np.loadtxt(input_file)\\n\\n self.i = self.full_data[:, 0] # dividing data into its constituent parts\\n self.j = self.full_data[:, 1]\\n self.k = self.full_data[:, 2]\\n self.Qxx = self.full_data[:, 3]\\n self.Qxy = self.full_data[:, 4]\\n self.Qxz = self.full_data[:, 5]\\n self.Qyy = self.full_data[:, 6]\\n self.Qyz = self.full_data[:, 7]\\n self.vx = self.full_data[:, 8]\\n self.vy = self.full_data[:, 9]\\n self.vz = self.full_data[:, 10]\\n self.qx = self.full_data[:, 11]\\n self.qy = self.full_data[:, 12]\\n self.qz = self.full_data[:, 13]\\n self.q = self.full_data[:, 14]\\n self.Stressyy = self.full_data[:, 15]\\n self.Stressyz = self.full_data[:, 16]\\n self.Stressxz = self.full_data[:, 17]\\n\\n self.activity = self.input_data[0]\\n self.body_force = self.input_data[1]\\n self.shear_v = self.input_data[2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6bd2eed49124893a1e1b3fd101353d8\",\n \"score\": \"0.6684618\",\n \"text\": \"def __init__(self, raw_data, *, data=None):\\n self.__raw_data = raw_data\\n if not data:\\n self.__FACTOR_DATA_PATH = Path(__file__).parent.parent.parent.joinpath('data', 'processed', 'factor_data')\\n else:\\n self.__FACTOR_DATA_PATH = data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2ff25d4572bafb87e2dd630b22f2c26\",\n \"score\": \"0.6677816\",\n \"text\": \"def __init__(self, filename):\\n self.filename = filename\\n self.items = []\\n self.dict_keys = []\\n self.readfile()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"849b39f25c60bb0316ed2516cf6fc09f\",\n \"score\": \"0.667488\",\n \"text\": \"def __init__(self, filename):\\n self.rawdata = {}\\n\\n f = open(filename, 'r')\\n header = f.readline().strip().split(',')\\n for line in f:\\n items = line.strip().split(',')\\n\\n date = re.match('(\\\\d\\\\d\\\\d\\\\d)(\\\\d\\\\d)(\\\\d\\\\d)', items[header.index('DATE')])\\n year = int(date.group(1))\\n month = int(date.group(2))\\n day = int(date.group(3))\\n\\n city = items[header.index('CITY')]\\n temperature = float(items[header.index('TEMP')])\\n if city not in self.rawdata:\\n self.rawdata[city] = {}\\n if year not in self.rawdata[city]:\\n self.rawdata[city][year] = {}\\n if month not in self.rawdata[city][year]:\\n self.rawdata[city][year][month] = {}\\n self.rawdata[city][year][month][day] = temperature\\n \\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40a23b1f26dc53114a5d66d03fe6f11d\",\n \"score\": \"0.6648202\",\n \"text\": \"def __init__(self, file, keep_linebreaks=False, sequence_class=Sequence):\\n\\n file = open(file)\\n self.fp = file\\n self.sequence_class = sequence_class\\n header = self.fp.readline().strip().split()\\n self.nind = int(header[0])\\n self.nloci = int(header[1])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f9bcf599869db31fefa3dcd32aea00ab\",\n \"score\": \"0.66431195\",\n \"text\": \"def __init__(self, filepath=None):\\n if (filepath == None):\\n self.phaseData = dict()\\n self.comparison_data = dict()\\n else:\\n infile = open(filepath, 'r')\\n self.phaseData, self.comparison_data = pickle.load(infile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4066e3eaba129c28931a3efb3d51eaa\",\n \"score\": \"0.66429293\",\n \"text\": \"def __init__(self, filename):\\n self.version = \\\"\\\"\\n self.date = \\\"\\\"\\n self.vendors = {}\\n self.contents = None\\n self.readLocal(filename)\\n self.parse()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94e2bf81b3a92aa994c81a2c495c35d4\",\n \"score\": \"0.66389334\",\n \"text\": \"def __init__(self, infile):\\n super(Data7028, self).__init__(infile)\\n self.fmt_descriptor = '<H3I'\\n self.descriptor_sz = struct.calcsize(self.fmt_descriptor)\\n self.read_data(infile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3812e510cd7725e78984033f748e393a\",\n \"score\": \"0.6633202\",\n \"text\": \"def __init__(self, filename):\\n self.dictIn={}\\n self.dictOut={}\\n self.dictInCosts={}\\n self.dictOutCosts={}\\n self.filename = filename\\n self.nrVertices = 0\\n self.nrEdges = 0\\n self.loadFromFile()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd34cb4cc8a4dcb994d4ee59a5a60371\",\n \"score\": \"0.6618096\",\n \"text\": \"def __init__(self, filename):\\n self.__table = self.read_csv(filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b28adf8262c96b72d649e7b48358e1ca\",\n \"score\": \"0.66170615\",\n \"text\": \"def __init__(self, filepath: str, data_columns: List[int]):\\n self._number_of_items = _get_tsv_file_length(filepath)\\n self._iterator = _create_data_from_tsv(\\n filepath, data_column_indices=data_columns\\n )\\n self._current_position = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aad58913618db288dd4f13c27ec6c835\",\n \"score\": \"0.6612001\",\n \"text\": \"def __init__(self, filename):\\n super(Scanner, self).__init__()\\n self.output = []\\n self.bnf_file = None\\n self.cursor = 0\\n self.file_length = 0\\n\\n #initialization\\n self._get_input(filename)\\n self.initialize()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3682d8bf5f16fb9de2958e98fbdcfb03\",\n \"score\": \"0.66020525\",\n \"text\": \"def __init__(self, input_file, output_file):\\n\\t\\tself.input_file = input_file\\n\\t\\tself.output_file = output_file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"241eda42552274b9facd16ce3ae6b54b\",\n \"score\": \"0.65910405\",\n \"text\": \"def __init__(self, **kwargs):\\n self.__line = 0\\n self.__error_count = 0\\n self.__output = {}\\n if 'data_file' in kwargs:\\n self.data_file = kwargs['data_file']\\n else:\\n logging.error(\\\"Enter a data file path to read\\\")\\n raise ValueError\\n if 'output_file' in kwargs:\\n self.output_file = kwargs['output_file']\\n else:\\n self.output_file = OUTPUT_FILE\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5bf791b373c4e6a7e858aa28ce8c70b9\",\n \"score\": \"0.65857047\",\n \"text\": \"def __init__(self, file_name: str, data_dir: str, batch_size: int):\\n super().__init__()\\n\\n # Save params\\n self.file_name = file_name\\n self.data_dir = data_dir\\n self.batch_size = batch_size\\n\\n # Path to save/read the file is the data dir and the dataset's unique file name\\n self.path_to_file = os.path.join(self.data_dir, self.get_target_file_name())\\n\\n self.prepare_data()\\n self.setup()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b2460b8167f518e1208762fa3a4fec4c\",\n \"score\": \"0.6585523\",\n \"text\": \"def __init__(self, schemafile=None):\\n self.data = {}\\n self.loaded = False\\n\\n self.schemafile = schemafile\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a63c96ece67b92625e7d11c86c668760\",\n \"score\": \"0.6571061\",\n \"text\": \"def __init__(self, data):\\n\\n super(AbstractBiometricData, self).__init__()\\n\\n # Read from string, otherwise store data\\n if isinstance(data, str):\\n self._data = N.loadtxt(data)\\n else:\\n self._data = data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49ff1e6ae2d7196ca73ebbdd3e3a0d6b\",\n \"score\": \"0.6564938\",\n \"text\": \"def __init__(self, **kwargs):\\n self.data_file = kwargs[\\\"data_file\\\"]\\n self.parent = None\\n if \\\"parent\\\" in kwargs.keys():\\n self.parent = kwargs[\\\"parent\\\"]\\n if isfile(self.data_file):\\n tree = ET.parse(kwargs[\\\"data_file\\\"])\\n self.root = tree.getroot()\\n else:\\n self.root = self.initialize()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a0ff927019f8417a7350f2b6ee1ee55\",\n \"score\": \"0.6554327\",\n \"text\": \"def __init__(self, file, **kwargs):\\n if isinstance(file, str):\\n f = open(file)\\n else:\\n f = file\\n self.name = f.readline().replace(\\\"\\\\n\\\", \\\"\\\")\\n ## Start reading the file in the tried POSCAR format\\n self.scale = float(f.readline())\\n self.trans = []\\n for i in range(3):\\n self.trans.append([float(n) for n in f.readline().split()])\\n self.atoms = f.readline().split()\\n self.nums = [int(n) for n in f.readline().split()]\\n self.coords = []\\n self.selective = False\\n self.fix = None\\n self.type = f.readline().split()[0]\\n # Skip \\\"Selective dynamics\\\" if it's already a fixed file\\n if self.type == \\\"Selective\\\":\\n self.selective = True\\n self.fix = []\\n self.type = f.readline().split()[0]\\n for i in range(sum(self.nums)):\\n line = f.readline().split()\\n if self.selective == False:\\n self.coords.append([float(n) for n in line])\\n else:\\n self.coords.append([float(n) for n in line[0:3]])\\n if len(line) == 6:\\n self.fix.append([truth == \\\"T\\\" for truth in line[3:]])\\n else:\\n self.fix.append([True, True, True])\\n f.close()\\n # Get additional kwargs and store them\\n for key in kwargs:\\n setattr(self, key, kwargs[key])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98b0376c87d492ba3b31a744e5632b6e\",\n \"score\": \"0.6552287\",\n \"text\": \"def load(cls, file_path):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43ce94d1ee91f5872fcb0f93f4208772\",\n \"score\": \"0.65506345\",\n \"text\": \"def __init__(self, player_file_):\\n self._scores = []\\n self._filename = player_file_\\n self.from_csv(self._filename)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"592616c08a3f6894173a86ea689c0b3c\",\n \"score\": \"0.65455705\",\n \"text\": \"def __init__(self, path: str) -> None:\\n\\n self.__path = path\\n with open(self.__path) as file:\\n for line in file:\\n key, value = line.rstrip().split(\\\"=\\\", maxsplit=1)\\n self._validate_name(key)\\n if value.isdigit():\\n value = int(value) # type: ignore\\n self.__dict__[key] = value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ed416b23ac08831715bedc23def3de1\",\n \"score\": \"0.65385574\",\n \"text\": \"def setup_class(self):\\n\\n self.control = pd.read_csv(os.path.join(FILES_DIR, \\\"swanfile.txt\\\"), sep=\\\"\\\\t\\\")\\n self.swanspec = read_swan(os.path.join(FILES_DIR, \\\"swanfile.spec\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f428c9a406597202772540e09da13ad1\",\n \"score\": \"0.6527552\",\n \"text\": \"def __init__(self, data):\\n f = open(data, 'r')\\n lines = f.readlines()[1:]\\n stripped = [line.strip('\\\\n') for line in lines]\\n parsed = [line.split(',') for line in stripped]\\n self.parsed_array = np.asarray(parsed)\\n f.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da2b93865de66cfa4cf96336892f4c27\",\n \"score\": \"0.65130764\",\n \"text\": \"def __init__(self,data=[],filepath=None,sequential=False):\\n \\n self.data = data\\n self.filepath = \\\"\\\"\\n self.source_headers = \\\"\\\"\\n self.G = None\\n self.transactions = {}\\n self.items = {}\\n self.num_transactions = 0\\n self.df = None\\n self.sequential = sequential\\n \\n self.item_counts = {}\\n \\n if filepath is not None:\\n self.load_csv_data(filepath,sequential)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7c9b3564a76024da4ba5e2a56ae12245\",\n \"score\": \"0.6509448\",\n \"text\": \"def __init__(self, in_file):\\n self.called_by = None\\n self.functions = {}\\n self._load_from_file(in_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5f5008183d56f488046b87c3139293e\",\n \"score\": \"0.65031356\",\n \"text\": \"def __init__(self, filename):\\n self._file = open(filename, 'rb')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af340c4843e6395613ebd4e2e35c2858\",\n \"score\": \"0.6502869\",\n \"text\": \"def __init__(self, file_path):\\n self.file_path = file_path\\n self.vcf_records = None\\n self.genotype_dictionary = {}\\n self.total_hom = 0\\n self.total_het = 0\\n self.total_hom_alt = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5a4576fa20667c20fae870a487fa916\",\n \"score\": \"0.6498094\",\n \"text\": \"def __init__(self, input_file):\\n\\t\\tsuper(LSF, self).__init__()\\n\\t\\tself.input_file = input_file\\n\\t\\tself.words = []\\n\\t\\tself.load()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2bad7066bfc0cc34bf43705d82e7590b\",\n \"score\": \"0.64935887\",\n \"text\": \"def __init__(self, file):\\n # TODO: set up the necessary instance variables\\n self.file_name = file\\n self.languages = {}\\n self.domains = {}\\n self.user_number = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ecd4f76b6671a9ea9946cf9da2b8dd6\",\n \"score\": \"0.64905685\",\n \"text\": \"def __init__(self, filename):\\n if os.path.exists(filename):\\n # read in the input file namelist\\n self.namelist = f90nml.read(filename)\\n else:\\n warnings.warn(\\\"Input file %s does not exist.\\\" % filename,\\n RuntimeWarning)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64c59b3ab2bfe6a5e4f203f6ad738634\",\n \"score\": \"0.64838517\",\n \"text\": \"def __init__(self, filename):\\n with open(filename, 'rb') as f:\\n reader = csv.DictReader(f)\\n self.rows = list(reader)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d49a69f28082c8bfb7b3afd0ad0cb09\",\n \"score\": \"0.6473477\",\n \"text\": \"def __init__(self, directory = '', verbose = False, data_spec = []):\\n self.directory = directory\\n self.verbose = verbose\\n self.data_spec = data_spec \\n\\n # Import data\\n self.extract_data()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6de4d1de68f1f2935dda148dcc20fb99\",\n \"score\": \"0.6466702\",\n \"text\": \"def __init__(self, inputfile, outputfolder):\\r\\n check_file(inputfile)\\r\\n self.input = inputfile\\r\\n check_file(outputfolder)\\r\\n self.output = outputfolder\",\n \"title\": \"\"\n },\n {\n \"docid\": \"889f153f1632f984b3721f9b2a07f76e\",\n \"score\": \"0.64665943\",\n \"text\": \"def __init__(self, file: str):\\n self._file = file\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7dc15759ca13383901fd01784d80fb0\",\n \"score\": \"0.6463946\",\n \"text\": \"def __init__(self, offset=0):\\n self._load_classification_text_file(KaggleFakeNews.DATA_PATH, offset=offset)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf8a6a802b20f95d68daa40f893238b2\",\n \"score\": \"0.64625645\",\n \"text\": \"def from_file(cls, filename):\\r\\n cn_class = cls() # initialize class\\r\\n cn_class.read_cn_file(filename)\\r\\n return cn_class # return class as it is a constructor\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c12cd10e514ee7ce4def7dc911512a75\",\n \"score\": \"0.64422065\",\n \"text\": \"def __init__(self, path):\\n self.path = Path(path)\\n self.file_name = os.path.split(path)[1]\\n self.header, self.data, self.units = self.read(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b2f8f32428f5dbccbe5789ae0ed1fc2\",\n \"score\": \"0.643671\",\n \"text\": \"def __init__(self, infile):\\n super(Data7058, self).__init__(infile)\\n self.fmt_descriptor = '<H3I'\\n self.descriptor_sz = struct.calcsize(self.fmt_descriptor)\\n self.read_data(infile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0cc2a045dac3b0d0606f0d6ca31881f\",\n \"score\": \"0.64359057\",\n \"text\": \"def __init__(self, filename, package=None):\\n\\n super(SourceFile, self).__init__(filename)\\n Coverable.__init__(self)\\n FileTokenizer.__init__(self)\\n self.package = package\\n\\n # parse results\\n self.parsed = False\\n self.imports = []\\n self.globals = []\\n self.classes = []\\n self.functions = []\\n\\n # loading and parsing\\n self.load_content()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27ca4199b13e55a39d1b09e9f28f8bd7\",\n \"score\": \"0.64336854\",\n \"text\": \"def __init__(self, filepath):\\n self.filepath = filepath\\n self.load_yaml()\\n if self.load_failed():\\n return\\n self.ensure_dict_exists(self.data, 'paths')\\n self.ensure_dict_exists(self.data, 'components')\\n self.ensure_dict_exists(self.data['components'], 'schemas')\\n self.ensure_dict_exists(self.data['components'], 'parameters')\\n self.ensure_dict_exists(self.data['components'], 'responses')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27564f1afbd21004eebce0673d1e51fa\",\n \"score\": \"0.6433581\",\n \"text\": \"def __init__(self):\\n self.file_train = []\\n self.file_val = []\\n self.file_test = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e77d51e8ac579919dcfcddb8867895fe\",\n \"score\": \"0.64099073\",\n \"text\": \"def readInstance(self):\\n file = open(self.fName, 'r')\\n self.genSize = int(file.readline())\\n self.data = {}\\n for line in file:\\n (id, x, y) = line.split()\\n self.data[int(id)] = (int(x), int(y))\\n file.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34ad27dfddf3cf770d0c453dd46656d9\",\n \"score\": \"0.6404643\",\n \"text\": \"def __init__(self, raw_input_data_csv: str):\\n self.raw_input_data_csv = raw_input_data_csv\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4c5e7272ec160551d3100a8aaa4fc0af\",\n \"score\": \"0.6399774\",\n \"text\": \"def __init__(self , filename) :\\n BaseIO.__init__(self)\\n self.filename = filename\\n self.loader = Loader(filename)\\n self.loader.load_file()\\n self.header = self.loader.header\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1d34968c03d8ed50ff398d2ca7b6a801\",\n \"score\": \"0.6394196\",\n \"text\": \"def __init__(self, data_name) -> None:\\n super(LoadData, self).__init__()\\n\\n if data_name == 'adult':\\n\\n # Find the aif toolkit path.\\n aif_path = os.path.abspath(aif360.__file__)\\n aif_path = aif_path.replace('__init__.py', '') + 'data/raw/adult/'\\n\\n # Check if train, validation, and test data files are available.\\n if not (os.path.isfile(aif_path + 'adult.data') and\\n os.path.isfile(aif_path + 'adult.test') and\\n os.path.isfile(aif_path + 'adult.names')):\\n\\n # Find the file directories.\\n source = os.path.abspath(os.getcwd()) + \\\"/Data/Adult/\\\"\\n get_files = os.listdir(source)\\n\\n # Copy the files to the aif toolkit.\\n for f in get_files:\\n shutil.move(source + f, aif_path)\\n\\n results = self.preprocessAdult()\\n\\n elif data_name == 'celeba':\\n # Find the path of the correct data.\\n path = os.path.abspath(os.getcwd()) + \\\"/Data/CelebA/\\\"\\n\\n # Check if the data is still zipped.\\n if not (os.path.isdir(path + \\\"img_align_celeba/\\\")):\\n zipped = path + \\\"img_align_celeba.zip\\\"\\n\\n # Open the zip file in READ mode.\\n with ZipFile(zipped, 'r') as zip:\\n # Extract all of the files.\\n print('Extracting all the files for CelebA now.')\\n zip.extractall(path=path)\\n\\n print('Done!')\\n\\n results = self.preprocessCelebA(path)\\n\\n elif data_name == 'civil':\\n # Get the path to the data.\\n path = os.path.abspath(os.getcwd()) + \\\"/Data/CivilComments/\\\"\\n\\n results = self.preprocessCivilComments(path)\\n\\n else:\\n raise Exception('An invalid dataset name was chosen.')\\n\\n self.train = results[0]\\n self.val = results[1]\\n self.test = results[2]\\n self.ratio = results[3]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c98949e5628394c51a94c88a3b70326b\",\n \"score\": \"0.6394118\",\n \"text\": \"def __init__(self):\\n\\n self.train = pd.read_csv(os.path.join(_DATA_DIR, _TRAIN))\\n self.test = pd.read_csv(os.path.join(_DATA_DIR, _TEST))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"08bb2e0319ce6c9786b96b5df359e251\",\n \"score\": \"0.6382472\",\n \"text\": \"def __init__(self) -> None:\\n self._data = []\\n self._load_data()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5f3e79c4a688d4397784b1f821b8e64\",\n \"score\": \"0.6374787\",\n \"text\": \"def __init__(self, file,\\n kwargs_Data={}, kwargs_ReadInData={}, kwargs_grid={}):\\n # self.kwargs_ReadInData = kwargs_ReadInData\\n self._kwargs_Data = kwargs_Data\\n self._kwargs_ReadInData = kwargs_ReadInData\\n self._kwargs_grid = kwargs_grid\\n self._x = None\\n self._y = None\\n self._data = None\\n self._X = None\\n self._Y = None\\n self._Data = None\\n\\n ReadInData.__init__(self, file, self._kwargs_ReadInData)\\n self.format_raw_data()\\n self.grid(**self._kwargs_grid)\\n self.format_data()\\n\\n Plot.__init__(self, self.X(), self.Y())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab523fc7a26ddd00ddb6c60b5616ef32\",\n \"score\": \"0.63709337\",\n \"text\": \"def __init__(self, filename=\\\"movie_data.txt\\\"):\\n raise NotImplementedError(\\\"Problem 4 Incomplete\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c9ae49b0756320b3ddbd5249b606b8e2\",\n \"score\": \"0.6370287\",\n \"text\": \"def __init__(self,fn):\\n data=fromfile(fn,sep='\\\\n')\\n data=data.reshape((len(data)/33,33)).T\\n names=[ 'S%d%d' % (i,j) for i in range(1,5) for j in range(1,5)]\\n self.f=data[0]\\n self.t=f2t(self.f)\\n self.f=t2f(self.t)\\n self.names=names\\n for i,n in enumerate(names):\\n v=zeros(len(self.f),dtype=complex)\\n v[1:]=db2c(data[i*2+1])*a2c(data[i*2+2])\\n v[0]=1\\n setattr(self,n,v)\\n self.fn=fn[:-4]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c17f79e5eddf954d43f3bcb6aa0b5025\",\n \"score\": \"0.63691825\",\n \"text\": \"def __init__(self, dataset_file, categories_file,transform=None): \\n self.transform = transform\\n\\n with open(categories_file) as f:\\n self.categories = json.load(f)\\n\\n self.files, self.labels = self.__get_data(dataset_file=dataset_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00ea33fa6a3ed9a98c40a68420dfa2b4\",\n \"score\": \"0.63642675\",\n \"text\": \"def __init__(self, filePath):\\n self.jsonFileName = filePath\\n self.parsedData = self.parseJson()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00ea33fa6a3ed9a98c40a68420dfa2b4\",\n \"score\": \"0.63642675\",\n \"text\": \"def __init__(self, filePath):\\n self.jsonFileName = filePath\\n self.parsedData = self.parseJson()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"180174206ffd53457b937ab377d22725\",\n \"score\": \"0.6343072\",\n \"text\": \"def __init__(self, filename):\\n self.filename = filename\\n self.__readFile = queue.Queue()\\n self.datas = None\\n self.__readCSV()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f8606e9e9f8fcb5852fdce0a593052d\",\n \"score\": \"0.63391465\",\n \"text\": \"def my_init(self, fileobj):\\n self._fileobj = fileobj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6f8606e9e9f8fcb5852fdce0a593052d\",\n \"score\": \"0.63391465\",\n \"text\": \"def my_init(self, fileobj):\\n self._fileobj = fileobj\",\n \"title\": \"\"\n },\n {\n \"docid\": \"785cafb297da097eed6b53b187256c18\",\n \"score\": \"0.6317455\",\n \"text\": \"def __init__(self):\\n\\n try:\\n with open(\\n os.path.join(\\n os.path.dirname(__file__),\\n \\\"mtexposdata.json\\\"\\n )\\n ) as fp:\\n self.data = json.load(fp)\\n except FileNotFoundError:\\n raise FileNotFoundError(\\\"File mtexposdata.json was not found!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a5182a0190c01b81d5b101624cdc1b1\",\n \"score\": \"0.63154566\",\n \"text\": \"def __init__(self, filename):\\n self.filename = filename\\n if \\\"-atsymbol-\\\" in filename and \\\"-fullstop-\\\" in filename:\\n self.email = \\\".\\\".join(filename.split(\\\".\\\")[:-1])\\n else:\\n self.email = None\\n self.file = None\\n self.num_splits = None\\n self.prediction_arr = []\\n self.score_arr = []\\n self.sample_arr = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c72a38e22831142d000590144901f04c\",\n \"score\": \"0.63098115\",\n \"text\": \"def fromFile(name, index = -1, data_in = \\\".data\\\", names_in = \\\".names\\\", class_name = \\\"class\\\"):\\r\\n file_data = name + data_in\\r\\n file_names = name + names_in\\r\\n \\r\\n #os.chdir()\\r\\n \\r\\n # parse .data file for data \\r\\n text = open(os.path.dirname(__file__) + '/../data/' + file_data, 'r').read().split('\\\\n')\\r\\n data = [[value.strip() for value in line.split(',')] for line in text if line]\\r\\n \\r\\n # parse .names file for attributes\\r\\n names = open(os.path.dirname(__file__) + '/../data/' + file_names, 'r').read().split('\\\\n')\\r\\n lines = [line for line in names if line and line[0] != \\\"|\\\" ] # filter\\r\\n \\r\\n class_att = Attribute(class_name, [s.strip() for s in lines.pop(0).split(',')])\\r\\n \\r\\n attributes = []\\r\\n \\r\\n for line in lines:\\r\\n n = line.index(\\\":\\\")\\r\\n att_name = line[:n] \\r\\n att = Attribute(att_name, [s.strip(' .') for s in line[n+1:].split(',')])\\r\\n attributes.append(att)\\r\\n \\r\\n attributes.append(class_att)\\r\\n \\r\\n col_data = list(zip(*data))\\r\\n col_data[-1], col_data[index] = col_data[index], col_data[-1]\\r\\n data = list(zip(*col_data))\\r\\n \\r\\n return Dataset(attributes, data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a1e1bdba23154b104b165adcb3c20f08\",\n \"score\": \"0.6308805\",\n \"text\": \"def __init__(self, fileName, rowClass=None, typeMap=None, defaultColType=None, columns=None, columnNameMapper=None,\\n ignoreExtraCols=False, inFh=None, allowEmpty=False, dialect=csv.excel_tab,\\n encoding=None, errors=None):\\n self.columns = []\\n # external column name, before mapping with columnNameMapper\\n if columnNameMapper is None:\\n self.extColumns = self.columns\\n else:\\n self.extColumns = []\\n self.colMap = {}\\n self.fileName = fileName\\n self.lineNum = 0\\n self.rowClass = rowClass\\n if rowClass is None:\\n self.rowClass = TsvRow\\n self.columnNameMapper = columnNameMapper\\n self.colTypes = None\\n self.ignoreExtraCols = ignoreExtraCols\\n if inFh is not None:\\n self.inFh = inFh\\n else:\\n self.inFh = fileOps.opengz(fileName, encoding=encoding, errors=errors)\\n try:\\n self.reader = csv.reader(self.inFh, dialect=dialect)\\n if columns:\\n self._setupColumns(columns)\\n else:\\n self._readHeader(allowEmpty)\\n self._initColTypes(typeMap, defaultColType)\\n except Exception:\\n self.close()\\n raise\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993289,"cells":{"query_id":{"kind":"string","value":"81be948beb5b7c253fda04cf4fa33cd4"},"query":{"kind":"string","value":"Set up the whirlwind AP with provided network info."},"positive_passages":{"kind":"list like","value":[{"docid":"23d3808e96ea08c1b69423e883cd8f32","score":"0.70799476","text":"def ap_setup(ap, network, bandwidth=80):\n log = logging.getLogger()\n bss_settings = []\n ssid = network[wutils.WifiEnums.SSID_KEY]\n if \"password\" in network.keys():\n password = network[\"password\"]\n security = hostapd_security.Security(\n security_mode=\"wpa\", password=password)\n else:\n security = hostapd_security.Security(security_mode=None, password=None)\n channel = network[\"channel\"]\n config = hostapd_ap_preset.create_ap_preset(\n channel=channel,\n ssid=ssid,\n security=security,\n bss_settings=bss_settings,\n vht_bandwidth=bandwidth,\n profile_name='whirlwind',\n iface_wlan_2g=ap.wlan_2g,\n iface_wlan_5g=ap.wlan_5g)\n config_bridge = ap.generate_bridge_configs(channel)\n brconfigs = bi.BridgeInterfaceConfigs(config_bridge[0], config_bridge[1],\n config_bridge[2])\n ap.bridge.startup(brconfigs)\n ap.start_ap(config)\n log.info(\"AP started on channel {} with SSID {}\".format(channel, ssid))\n return brconfigs","title":""}],"string":"[\n {\n \"docid\": \"23d3808e96ea08c1b69423e883cd8f32\",\n \"score\": \"0.70799476\",\n \"text\": \"def ap_setup(ap, network, bandwidth=80):\\n log = logging.getLogger()\\n bss_settings = []\\n ssid = network[wutils.WifiEnums.SSID_KEY]\\n if \\\"password\\\" in network.keys():\\n password = network[\\\"password\\\"]\\n security = hostapd_security.Security(\\n security_mode=\\\"wpa\\\", password=password)\\n else:\\n security = hostapd_security.Security(security_mode=None, password=None)\\n channel = network[\\\"channel\\\"]\\n config = hostapd_ap_preset.create_ap_preset(\\n channel=channel,\\n ssid=ssid,\\n security=security,\\n bss_settings=bss_settings,\\n vht_bandwidth=bandwidth,\\n profile_name='whirlwind',\\n iface_wlan_2g=ap.wlan_2g,\\n iface_wlan_5g=ap.wlan_5g)\\n config_bridge = ap.generate_bridge_configs(channel)\\n brconfigs = bi.BridgeInterfaceConfigs(config_bridge[0], config_bridge[1],\\n config_bridge[2])\\n ap.bridge.startup(brconfigs)\\n ap.start_ap(config)\\n log.info(\\\"AP started on channel {} with SSID {}\\\".format(channel, ssid))\\n return brconfigs\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"ba63a9cd6fd3b985f72e362db7c83127","score":"0.63868827","text":"def setup_network(cfg):\n display_msg(\"Init Network...\", msg2=\"Looking for WiFI\",\n msg3=cfg.cfg_json['WIFI_SSID'])\n\n # wait 2 seconds to see if the network comes up\n time.sleep(2)\n\n sta_if = network.WLAN(network.STA_IF)\n ap_if = network.WLAN(network.AP_IF)\n\n if ap_if.active():\n ap_if.active(False)\n\n if not sta_if.isconnected():\n display_msg(\"Connecting to WiFI..\")\n sta_if.active(True)\n sta_if.connect(cfg.cfg_json['WIFI_SSID'], cfg.cfg_json['WIFI_PASSWORD'])\n while not sta_if.isconnected():\n display_msg(\"Connecting to WiFI...\")\n display_msg(\"Connected to \", msg2=cfg.cfg_json['WIFI_SSID'])\n else:\n display_msg(\"Connected to \", msg2=cfg.cfg_json['WIFI_SSID'])","title":""},{"docid":"20f53b4c77b76c68b48680d6ce9ee749","score":"0.63112587","text":"def setup(self) -> None:\n # Create netns\n self.create()\n # Create links/interfaces + address them + assign to netns\n self.setup_links()\n # Create oob if selected\n self.create_oob()\n # Add any static routes\n self.route_add()\n\n LOG.info(f\"Finished setup of {self.name} namespace\")","title":""},{"docid":"f4f6414623003c3e6b229e8f925916e6","score":"0.6296718","text":"def setup_net(self):\n pass","title":""},{"docid":"6c5d0777f666403753908c98381f8d03","score":"0.6200128","text":"def setup_ap(ssid=config.SSID, channel=1):\n wlan = network.WLAN(\n mode=network.WLAN.AP,\n ssid=ssid,\n auth=(network.WLAN.WPA2, config.WLAN_PASSWORD),\n channel=channel,\n antenna=network.WLAN.INT_ANT)","title":""},{"docid":"e803f06e019cd9680159048f6cb21970","score":"0.61444616","text":"def startup(self):\n CoreNetwork.startup(self)\n if self.gretap:\n self.attach(self.gretap)","title":""},{"docid":"ac4edd2c96a2a41632eb6565dc0ba85a","score":"0.607519","text":"def _init_sniffer(self):\n self._sniffer_server.run('sudo modprobe -r iwlwifi')\n self._sniffer_server.run('sudo dmesg -C')\n self._sniffer_server.run('cat /dev/null | sudo tee /var/log/syslog')\n self._sniffer_server.run('sudo modprobe iwlwifi debug=0x1')\n # Wait for wifi config changes before trying to further configuration\n # e.g. setting monitor mode (which will fail if above is not complete)\n time.sleep(1)","title":""},{"docid":"ee9b6eb9265d80e46f7d1c985d31247b","score":"0.6048787","text":"def _configure_sniffer(self, network, chan, bw):\n\n self.log.debug('Connecting to network {}'.format(network['SSID']))\n\n if 'password' not in network:\n network['password'] = ''\n\n connect_command = 'networksetup -setairportnetwork en0 {} {}'.format(\n network['SSID'], network['password'])\n self._sniffer_server.run(connect_command)","title":""},{"docid":"48ba81120e8e2bcec01a10ea02a3ef5c","score":"0.60117435","text":"def __init__(self):\n\n # Setup the fields\n self._interfaces = {}\n self.ap_iface = \"\"\n self.jam_iface = \"\"\n\n # Create, add and check compatibility for each interface\n for interface in pyw.interfaces():\n try:\n self._interfaces[interface] = NetworkAdapter(interface)\n except pyric.error as e:\n pass","title":""},{"docid":"02753636ab30c315b4b1ea5b4984e7ac","score":"0.5844891","text":"def create(args):\n global ap_face, ap_ssid, ap_password\n ap_face = args.interface\n ap_ssid = args.ssid\n ap_password = args.password\n\n print('Giving %s a random Mac address...' % ap_face)\n assign_ap_mac()\n\n print(\"\\nConfiguring %s to ip %s...\" % (ap_face, AP_IP))\n assign_ap_ip()\n\n print(\"\\nStarting dnsmasq...\")\n start_dnsmasq()\n\n print('\\nStarting hostapd...')\n start_hostapd()","title":""},{"docid":"ec563bc92410d7cbde1ccaed1e86141d","score":"0.58421624","text":"def init_network(roads):","title":""},{"docid":"a607fa836c4d827a6247c952eb14f168","score":"0.58065313","text":"def setup(self):\n self.trajectory_model = TrajectoryGraphNet(self.config)\n self.winding_model = WindingGraphNet(self.config)\n self.trajectory_model.to(self.device)\n self.winding_model.to(self.device)","title":""},{"docid":"9da265c8c9d3b93f0f5bca6f715d4ef8","score":"0.5804919","text":"def wadlibrary_set_up(self):\n self.set_up_driver()\n self.set_up()","title":""},{"docid":"2e4db673d0a7fc5aa0d409200c148518","score":"0.58009803","text":"def setup(self):\n self._logger.debug('Setting up ' + str(self._tunnel_operation))\n if self._tunnel_operation == \"encapsulation\":\n self._setup_encap()\n else:\n if settings.getValue('VSWITCH').endswith('Vanilla'):\n self._setup_decap_vanilla()\n else:\n self._setup_decap()","title":""},{"docid":"f7113a9cb992bb14b8d766b1c229cc2f","score":"0.5674588","text":"def _setup_device_fw(self, restricted, web_forward, http_fwd_addr, http_fwd_port, https_fwd_addr, https_fwd_port, spoof_prevention, is_site_to_site):\n\n # allowed ports in restricted mode\n if restricted and not web_forward:\n allow_tcp_ports = [ ]\n allow_udp_ports = [ ]\n else:\n allow_tcp_ports = [ 53 ] # dns=53, wins=137\n allow_udp_ports = [ 53, 137 ]\n\n # collect all parameters\n params = {}\n params['dev'] = self.ppp_interface\n params['peeraddress'] = self.ppp_ipremote\n params['ownaddress'] = self.ppp_iplocal\n \n # add customization chain commands to params dict (if enabled)\n add_custom_chains = False\n\n t = {'raw': [('prerouting', 'prert', True),\n ('output', 'output', True)],\n 'nat': [('prerouting', 'prert', True),\n ('output', 'output', True),\n ('postrouting', 'postrt', True)],\n 'mangle': [('prerouting', 'prert', True),\n ('input', 'input', True),\n ('forward', 'forward', True),\n ('output', 'output', True),\n ('postrouting', 'postrt', True)],\n 'filter': [('input', 'input', True),\n ('forward', 'forward', True),\n ('output', 'output', True)]}\n\n for i in ['raw', 'nat', 'mangle', 'filter']:\n for j in t[i]:\n ident = 'custom_%s_%s' % (i, j[0])\n if j[1] and add_custom_chains:\n # e.g.: -A ppp_prert_l2tp12345-12345 -j raw_prerouting_ppp_cust\n params[ident] = '-A ppp_%s_%s -j %s_%s_ppp_cust' % (j[1], params['dev'], i, j[0])\n else:\n params[ident] = ''\n\n # check web forward\n w = ''\n if web_forward:\n # Own ppp address should be exempt so normal web UI can be used even when\n # web forwarding is on. This also prevents forwarded connections from being\n # reforwarded.\n w += ('-A ppp_prert_%(dev)s ! -d %(ownaddress)s -p tcp --dport 80 -j DNAT --to-destination ' % params) + '%s:%s-%s\\n' % (http_fwd_addr, http_fwd_port, http_fwd_port)\n w += ('-A ppp_prert_%(dev)s ! -d %(ownaddress)s -p tcp --dport 443 -j DNAT --to-destination ' % params) + '%s:%s-%s\\n' % (https_fwd_addr, https_fwd_port, https_fwd_port)\n params['portforward_nat'] = w\n\n # check spoof prevention\n s = ''\n if spoof_prevention:\n s = '-A ppp_prert_%s -s ! %s -j DROP' % (params['dev'], params['peeraddress'])\n params['spoof_raw_prerouting'] = s\n\n # check restrictions\n r = ''\n if restricted:\n for i in allow_tcp_ports:\n r += ('-A ppp_forward_%(dev)s -p tcp --dport ' % params) + str(i) + ' -j ACCEPT\\n'\n for i in allow_udp_ports:\n r += ('-A ppp_forward_%(dev)s -p udp --dport ' % params) + str(i) + ' -j ACCEPT\\n'\n r += '-A ppp_forward_%(dev)s -j REJECT --reject-with icmp-port-unreachable\\n' % params\n params['restricted_filter_forward'] = r\n\n r = ''\n if restricted:\n r += ('-A ppp_prert_%(dev)s -j MARK --set-mark ' % params) + ('%s' % constants.FWMARK_LICENSE_RESTRICTED) + '\\n'\n params['license_restricted_marking'] = r\n \n # mark site-to-site packets with a separate fwmark for routing purposes; note\n # that this mark is applies for *both* incoming and outgoing packets, unlike\n # the normal FWMARK_PPP.\n s = ''\n if is_site_to_site:\n s += ('-A ppp_prert_%(dev)s -j MARK --set-mark ' % params) + ('%s' % constants.FWMARK_PPP_S2S) + '\\n'\n s += ('-A ppp_postrt_%(dev)s -j MARK --set-mark ' % params) + ('%s' % constants.FWMARK_PPP_S2S) + '\\n'\n params['site_to_site_marking'] = s\n \n # create iptables-restore script\n tables = textwrap.dedent(\"\"\"\\\n *raw\n :ppp_prert_%(dev)s -\n :ppp_output_%(dev)s -\n -A raw_prerouting_ppp -i %(dev)s -j ppp_prert_%(dev)s\n -A raw_output_ppp -o %(dev)s -j ppp_output_%(dev)s\n\n %(spoof_raw_prerouting)s\n %(custom_raw_prerouting)s\n %(custom_raw_output)s\n COMMIT\n \n *nat\n :ppp_prert_%(dev)s -\n :ppp_output_%(dev)s -\n :ppp_postrt_%(dev)s -\n -A nat_prerouting_ppp -i %(dev)s -j ppp_prert_%(dev)s\n -A nat_output_ppp -o %(dev)s -j ppp_output_%(dev)s\n -A nat_postrouting_ppp -o %(dev)s -j ppp_postrt_%(dev)s\n\n %(portforward_nat)s\n %(custom_nat_prerouting)s\n %(custom_nat_output)s\n %(custom_nat_postrouting)s\n COMMIT\n \n *mangle\n :ppp_prert_%(dev)s -\n :ppp_input_%(dev)s -\n :ppp_forward_%(dev)s -\n :ppp_output_%(dev)s -\n :ppp_postrt_%(dev)s -\n -A mangle_prerouting_ppp -i %(dev)s -j ppp_prert_%(dev)s\n -A mangle_input_ppp -i %(dev)s -j ppp_input_%(dev)s\n -A mangle_forward_ppp -i %(dev)s -j ppp_forward_%(dev)s\n -A mangle_forward_ppp -o %(dev)s -j ppp_forward_%(dev)s\n -A mangle_output_ppp -o %(dev)s -j ppp_output_%(dev)s\n -A mangle_postrouting_ppp -o %(dev)s -j ppp_postrt_%(dev)s\n\n %(license_restricted_marking)s\n %(site_to_site_marking)s\n %(custom_mangle_prerouting)s\n %(custom_mangle_input)s\n %(custom_mangle_forward)s\n %(custom_mangle_output)s\n %(custom_mangle_postrouting)s\n COMMIT\n\n *filter\n :ppp_input_%(dev)s -\n :ppp_forward_%(dev)s -\n :ppp_output_%(dev)s -\n -A filter_input_ppp -i %(dev)s -j ppp_input_%(dev)s\n -A filter_forward_ppp -i %(dev)s -j ppp_forward_%(dev)s\n -A filter_forward_ppp -o %(dev)s -j ppp_forward_%(dev)s\n -A filter_output_ppp -o %(dev)s -j ppp_output_%(dev)s\n\n %(custom_filter_input)s\n %(restricted_filter_forward)s\n -A ppp_forward_%(dev)s -i %(dev)s -j filter_forward_ppp_firewall\n %(custom_filter_forward)s\n %(custom_filter_output)s\n COMMIT\n \"\"\")\n tables = tables % params\n\n # debug log of individual lines; this can be removed if\n # logger pre-step for multiline stuff is written\n if self.debug:\n self._log.debug('--- iptables-restore ---')\n for linenum, line in enumerate(tables.split('\\n')):\n t = line.strip()\n self._log.debug('%d: %s' % (linenum+1, t))\n\n iptables_lock = helpers.acquire_iptables_lock()\n # execution\n try:\n (retval, retout, reterr) = run_command([constants.CMD_IPTABLES_RESTORE, '-n'], stdin=tables.encode('ascii'), retval=runcommand.FAIL)\n except:\n helpers.release_iptables_lock(iptables_lock)\n raise\n\n helpers.release_iptables_lock(iptables_lock)\n\n self._log.debug('iptables-restore => %s\\n%s\\n%s' % (retval, retout, reterr))","title":""},{"docid":"477e6ae859db678adb77a30b08a8d9d5","score":"0.5652874","text":"def setup(self):\r\n # get tracking method\r\n self.tracking_method = self.cfg.tracking_method\r\n self.initialize_tracker()\r\n\r\n # initialize keypoint sampler\r\n self.kp_sampler = KeypointSampler(self.cfg)\r\n\r\n # Deep networks\r\n self.deep_models = DeepModel(self.cfg)\r\n self.deep_models.initialize_models()","title":""},{"docid":"fca7483fa39f4eb61fa00af72b66f0c4","score":"0.5636041","text":"def __init__(self, params):\n super(WAN, self).__init__(params)\n self.wan_ip = ip_address(params['wan_ip'])\n self.wan_networkgroup = params['wan_networkgroup']\n self.wan_type = params['wan_type']","title":""},{"docid":"e2420aab40b8df96a91bc3e02c19d2bc","score":"0.56013614","text":"def do_setup(self, context):\n self._client = NetAppApiClient(\n self.api_version, vfiler=self.configuration.netapp_nas_vfiler,\n configuration=self.configuration)\n self._setup_helpers()","title":""},{"docid":"9d48c348d5502cd7cb1698ae557b5be6","score":"0.55758965","text":"def setup_networking(ip, robot_id, wireless_only):\n tempdir = makeTempDir()\n try:\n scriptpath = '%s/install/script_wireless.sh' % NAO_HOME\n if not wireless_only:\n fillout_id('utwired', robot_id, tempdir)\n fillout_id('utwireless', robot_id, tempdir)\n copyFiles(ip, os.path.join(tempdir, 'utwire*'))\n copyFiles(ip, '../data/scripts/wpa_supplicant.conf',\n '~/wpa_supplicant.conf.tmp')\n runLocalScriptRemotely(ip, scriptpath, output=True)\n finally:\n removeTempDir(tempdir)","title":""},{"docid":"8d9e6ffd1ad0aca9a1917bc48177f013","score":"0.55627793","text":"def _configure_sniffer(self, network, chan, bw):\n\n self.log.debug('Connecting to network {}'.format(network['SSID']))\n self.set_monitor_mode(chan, bw)","title":""},{"docid":"146356eeadd23c922bef454c05dee93a","score":"0.5559324","text":"def setup(self):\n\n logger.info('Starting initiation process')\n self.enable_signaling_connection_urc()\n self.enable_network_registration()\n self.enable_psm_mode()\n self.enable_radio_functions()\n logger.info('Finished initiation process')","title":""},{"docid":"15c82202d44b84944c32068daf637dec","score":"0.5532712","text":"def setup(self):\n # if not system.restore_snapshot():\n # raise Exception\n \n self.tpd.navigate_to()\n mws.connect(\"third party data interface\")","title":""},{"docid":"79e5e8450f113f637e85597b7260c7dd","score":"0.55183464","text":"def setup_links(self) -> None:\n for _int_name, int_obj in self.interfaces.items():\n if not int_obj.exists():\n int_obj.create()\n\n int_obj.set_netns(self.name)\n int_obj.add_prefixes(self.name)\n int_obj.set_link_up(self.name)","title":""},{"docid":"4048083c486116f5a1739df2726f6a06","score":"0.5504765","text":"def setup(self):\n import lnetatmo\n try:\n self.homedata = lnetatmo.HomeData(self.auth)\n self.homestatus = lnetatmo.HomeStatus(self.auth, home=self.home)\n self.home_id = self.homedata.gethomeId(self.home)\n self.update()\n except TypeError:\n _LOGGER.error(\"ThermostatData::setup() got error.\")","title":""},{"docid":"d75604a691feb85358f03a4ad1b14765","score":"0.549387","text":"def setup(self):\n self._otaProject.initializeOtaProject()\n self._otaProject.setClientCredentialsForAwsIotEndpoint(self._otaAwsAgent.getAwsIotEndpoint())\n self._otaProject.setClientCredentialsForWifi(self._boardConfig['wifi_ssid'], self._boardConfig['wifi_password'], self._boardConfig['wifi_security'])\n self._otaProject.setClientCredentialForThingName(self._otaAwsAgent.getThingName())\n self._otaProject.setClientCredentialKeys(self._otaAwsAgent.getThingCertificate(), self._otaAwsAgent.getThingPrivateKey())\n self._otaProject.copyCodesignerCertificateToBootloader(self._otaAwsAgent.getCodeSignerCertificateFromArn(self._otaConfig['aws_signer_certificate_arn']))\n self._otaProject.setMqttLogsOn()\n self._otaProject.setFreeRtosConfigNetworkInterface(self._boardConfig.get('windows_network_interface', 0))\n if self._otaConfig.get('compile_codesigner_certificate', False):\n self._otaProject.setCodesignerCertificate(self._otaAwsAgent.getCodeSignerCertificateFromArn(self._otaConfig['aws_signer_certificate_arn']))\n transportation = self._otaConfig.get('transportation')\n if transportation == 'ble':\n self._otaProject.setBleConfig()\n if 'HTTP' in self._protocol:\n self._otaProject.setHTTPConfig()\n self._otaProject.setApplicationVersion(0, 9, 0)\n\n buildReturnCode = self._otaProject.buildProject()\n flashReturnCode = self._flashComm.flashAndRead()\n\n return buildReturnCode + flashReturnCode","title":""},{"docid":"6218a2b6d3f70f07d1c3b7df8d9697d0","score":"0.5459263","text":"def setupNetworks(self):\n networkConfig, status = self.loadNetworkInputs()\n if not status:\n return networkConfig, False\n resNET, statNET = self.getResponse(\"GET\", \"Network\")\n netResponse = []\n for network in networkConfig:\n found = False\n if network[\"VLANID\"] == \"\":\n continue\n if not statNET and \"No information found\" in resNET:\n found = False\n else:\n found = [nw[\"id\"] for nw in resNET if network[\"Name\"] == str(nw[\"name\"])] \n if not found:\n action = \"POST\"\n networkId = \"\"\n else:\n action = \"PUT\"\n networkId = found[0]\n resDN, statDN = self.defNetworkInfo(network, networkId, action)\n netResponse.append(str(resDN))\n if not statDN:\n return netResponse, False\n if not found: networkId = resDN[\"id\"]\n self.networkMap[network[\"Type\"]] = networkId\n return netResponse, True","title":""},{"docid":"134e7db3c5be733f3dd80e64a34f368d","score":"0.5444201","text":"def __init__(self, debug=False):\r\n self.api = zenApiLib.zenConnector(section = 'default', routerName = 'ImpactRouter')","title":""},{"docid":"400e36f3ba0b0d864e4b90f7ae0abd3a","score":"0.54329616","text":"def setup():\n if not wifi.is_enabled() or \\\n ask_confirmation('Wifi has already been enabled. Set it up again?'):\n wifi.setup()\n if not mqtt.is_enabled() or \\\n ask_confirmation('MQTT has already been enabled. Set it up again?'):\n mqtt.setup()\n hass_mqtt.setup()\n pins.setup()","title":""},{"docid":"44ac3d76b8e58e09ea5ce393fa9dc05c","score":"0.54133713","text":"def setup(self):\n self.setup_hostname()\n self.setup_monitoring()\n return self","title":""},{"docid":"9ce738d9e264de93212e4c6661422638","score":"0.54131854","text":"async def setup(self):\n if SETTINGS.atag_host:\n LOGGER.info(f\"Using configured ATAG ONE {SETTINGS.atag_host}\")\n else:\n LOGGER.info(f\"Discovering ATAG ONE\")\n await self.atag.async_host_search()\n\n await self.atag.async_update()\n self.device = DeviceAtagOne(self.atag, name=\"Atag One\", eventloop=self.eventloop)\n LOGGER.info(f\"Connected to ATAG_ONE device {self.atag.device} @ {self.atag.config.host}\")","title":""},{"docid":"bfc5d22dfe4f14c76f2589c3726e01a6","score":"0.53940713","text":"def setUpNetwork(self):\n\n # Set up the network structure\n self.W = lagrangeRL.tools.networks.feedForwardWtaReadout(\n self.layers,\n self.wtaStrength,\n offset=self.initWeightMean,\n noiseMagnitude=self.initWeightWidth)\n # Lagrange network\n self.simClass = lagrangeRL.network.lagrangeEligTfApprox()\n self.simClass.setLearningRate(1.)\n self.simClass.setTimeStep(self.timeStep)\n self.simClass.setTau(self.tau)\n self.simClass.setNudging(self.nudging)\n self.simClass.addMatrix(self.W)\n self.simClass.setTauEligibility(self.tauElig)\n self.simClass.saveTraces(False)\n wMaxFixed = np.zeros((self.N, self.N))\n wMaxFixed[-self.layers[-1]:, -self.layers[-1]:] = 1\n self.simClass.setFixedSynapseMask(wMaxFixed.astype(bool))","title":""},{"docid":"5b56d5d084dd4247d1764f4faf6ef3c8","score":"0.5392751","text":"def __init__(self, ryuapp, datapath, **kwargs):\n self.startime = time.time()\n self.debug = True\n self.priority = 10\n self.ryuapp = ryuapp\n self.datapath = datapath\n self.macTable = {}\n self.arpTable = {}\n # IP addresses 'owned' by the router for connected networks\n self.addresses = []\n # Packets that couldn't be sent because we need to ARP for them\n self.packetQueue = []\n # default server to send traffic to if lb is disabled and traffic for a VIP comes in\n self.defaultserver = '255.255.255.255'\n\n # we need to start with a clean slate and make sure all of the flows have been deleted from the datapath.\n self.deleteAllFlows()\n\n # we construct instances of the firewall, cache, and load balancer\n # even if disabled as they will need to be configured in case they are enabled in the future\n self.firewall = SDNFirewall(self,ryuapp,datapath)\n self.ecache = ExternalCache(self,ryuapp,datapath,\"255.255.255.255\")\n self.lb = SDNLoadBalancer(self,ryuapp,datapath)","title":""},{"docid":"12d184a373b1e64f3030e47c5f44e2d0","score":"0.5388348","text":"def initializeNetwork(self, network, *args, **kwargs):\n return network","title":""},{"docid":"e483ab832ba0e8983b5e4457193b2689","score":"0.53836095","text":"def setup(self):\n super(ShorewallMixin, self).setup()\n self._setup_firewall()","title":""},{"docid":"55c4169586ed909c0331b273d491a4b5","score":"0.53699404","text":"def _initialise_network(self, data):\n #if a pretrained model was supplied,\n #load in the grap\n self.num_layers = len(data['layers'])\n print('Total of layers in network = {}'.format(self.num_layers))\n print('Layer information')\n print(data['layers'])\n for ii in range(0, self.num_layers): # pylint: disable=invalid-name\n #setting parameters\n self.layer.append(self.get_layer(data[\"layers\"][ii]))","title":""},{"docid":"141de60a64b13d204d550bf0e0dc72a7","score":"0.535879","text":"def __init__(self, ap_name):\n self.ap_name = ap_name\n self.targetip = None\n self.targetipv6 = None\n self.targetport = None\n self.mac = None\n self.model = None\n self.opstate = None\n self.power_source = None\n self.serial = None\n self.stub = None\n self.vendor = ''","title":""},{"docid":"e9ea18992a97d698ff358fad408139cd","score":"0.53525954","text":"def __init__(self, username, key, wifi_manager, api_version='v2'):\n self.api_version = api_version\n self.url = 'https://io.adafruit.com/api'\n self.username = username\n self.key = key\n if wifi_manager:\n self.wifi = wifi_manager\n else:\n raise TypeError(\"This library requires a WiFiManager object.\")\n self.http_headers = [{bytes(\"X-AIO-KEY\", \"utf-8\"):bytes(self.key, \"utf-8\"),\n bytes(\"Content-Type\", \"utf-8\"):bytes('application/json', \"utf-8\")},\n {bytes(\"X-AIO-KEY\", \"utf-8\"):bytes(self.key, \"utf-8\")}]","title":""},{"docid":"76bf1f1b2afd5867848de27f010d5977","score":"0.52744865","text":"def set_network(self):\n ntwrk = dict()\n ntwrk[\"networking\"] = self.config.get('network', 'networking')\n ntwrk[\"port\"] = self.config.getint('network', 'port')\n ntwrk[\"buffer\"] = self.config.getint('network', 'buffer')\n\n if len(self.config.get('network', 'channels')) == 0:\n channels = [_ for _ in range(self.gpio_len)]\n else:\n channels = map(int, self.config.get('network', 'channels').split(\",\"))\n\n temp = defaultdict()\n for channel in range(len(channels)):\n temp[channels[channel]] = channel\n\n ntwrk[\"channels\"] = temp\n\n self.network = Section(ntwrk)","title":""},{"docid":"f63aa72588b6a43754c406d0ce642452","score":"0.52715534","text":"def connect(self):\n self.dsm = self._connect_to_deep_security()\n self.waf = self._connect_to_aws_waf()\n self.ec2 = self._connect_to_aws_ec2()\n self.elb = self._connect_to_aws_elb()\n self.cloudfront = self._connect_to_aws_cloudfront()","title":""},{"docid":"edcb1488683cbc0914cecbb45ecc9256","score":"0.5248705","text":"def setup(self):\n self._flow_mgr_url = 'http://localhost:8181/kytos/flow-manager/flows/%s'\n self._switches = dict()\n self.register_rest()\n self.instantiate_switches = Switches(self.controller.switches)","title":""},{"docid":"b73a554c4f866ce7c09e1d39f7bfaa85","score":"0.52458954","text":"async def wifi_connect(self, iface, ssid, psk=None, is_wep=False):\n netid = int(\n await run_cmd([\"sudo\", \"/sbin/wpa_cli\", \"-i\", iface, \"add_network\"])\n )\n if psk and not is_wep:\n wpacmd = [\n 'set_network %d ssid \"%s\"' % (netid, ssid),\n 'set_network %d psk \"%s\"' % (netid, psk),\n \"enable_network %d\" % netid,\n 3,\n \"quit\",\n ]\n elif psk and is_wep:\n wpacmd = [\n 'set_network %d ssid \"%s\"' % (netid, ssid),\n \"set_network %d key_mgmt NONE\" % (netid),\n \"set_network %d wep_key0 %s\" % (netid, psk),\n \"set_network %d auth_alg SHARED\" % (netid),\n \"enable_network %d\" % netid,\n 3,\n \"quit\",\n ]\n else:\n wpacmd = [\n 'set_network %d ssid \"%s\"' % (netid, ssid),\n \"set_network %d key_mgmt NONE\" % (netid),\n \"enable_network %d\" % netid,\n 3,\n \"quit\",\n ]\n\n resul = await run_inter_cmd([\"/sbin/wpa_cli\", \"-i\", iface], wpacmd)\n logging.debug(\"{}\".format(resul))\n return netid","title":""},{"docid":"80dba749ecd464765c5742721a118b7e","score":"0.5220037","text":"def main(options):\n if options.include_ssh:\n setup_passwordless_ssh(options.ip)\n if options.hostname:\n set_hostname(options.ip, options.hostname)\n if options.wireless_only:\n print('setting up networking')\n setup_networking(options.ip, options.id, options.wireless_only)\n if options.include_date:\n set_date_time(options.ip)\n print('complete')","title":""},{"docid":"de53776ebd29f16873eb407337ca6b62","score":"0.5217746","text":"def setup( cls ):\n klds = lsmod()\n kenv = quietRun( 'kenv net.inet.ip.fw.default_to_accept' ).strip()\n deny = True if kenv == '0' else False\n # if the default rule is deny all, change to allow all so hosts can\n # still pass traffic. Also reload ipfw so that it is reconfigured\n if deny:\n quietRun( 'kenv net.inet.ip.fw.default_to_accept=1' )\n if deny and 'ipfw.ko' in klds:\n rmmod( 'ipfw' )\n modprobe( 'ipfw' )\n if 'ipfw_nat.ko' not in klds:\n modprobe( 'ipfw_nat' )","title":""},{"docid":"6cc24d2d71884df4e94a573c82fd86ae","score":"0.52166283","text":"def generic_setup(self):\r\n run_setup()\r\n self.c = login()\r\n self.endpoint, self.schema = get_urls(self.obj_type)","title":""},{"docid":"5992568cacb8cf8f79f3e0747e436168","score":"0.5215557","text":"def prepare(self):\n super(Test200SmartFullIdevice046, self).prepare()\n\n self.logger.info('Preconditions:')\n self.logger.info('1. Open Micro/WIN V2.5;')\n self.logger.info('2. Plc configed as I-Device with fixed IP and station name, download;')","title":""},{"docid":"0748965064f46d8b4a2a339fd8591eab","score":"0.5190812","text":"def setup_method(self):\n # %% network setup\n fluid_list = ['Ar', 'N2', 'O2', 'CO2', 'CH4', 'H2O']\n self.nw = Network(\n fluids=fluid_list, p_unit='bar', T_unit='C', p_range=[0.5, 20])\n\n # %% components\n amb = Source('ambient')\n sf = Source('fuel')\n cc = CombustionChamber('combustion')\n cp = Compressor('compressor')\n gt = Turbine('turbine')\n fg = Sink('flue gas outlet')\n\n # %% connections\n amb_cp = Connection(amb, 'out1', cp, 'in1', label='ambient air flow')\n cp_cc = Connection(cp, 'out1', cc, 'in1')\n sf_cc = Connection(sf, 'out1', cc, 'in2')\n cc_gt = Connection(cc, 'out1', gt, 'in1')\n gt_fg = Connection(gt, 'out1', fg, 'in1')\n\n self.nw.add_conns(amb_cp, cp_cc, sf_cc, cc_gt, gt_fg)\n\n # %% component parameters\n cc.set_attr(lamb=3)\n cp.set_attr(eta_s=0.9, pr=15)\n gt.set_attr(eta_s=0.9)\n\n # %% connection parameters\n amb_cp.set_attr(\n T=20, p=1, m=100,\n fluid={'Ar': 0.0129, 'N2': 0.7553, 'H2O': 0, 'CH4': 0,\n 'CO2': 0.0004, 'O2': 0.2314})\n sf_cc.set_attr(\n T=20, fluid={'CO2': 0.04, 'Ar': 0, 'N2': 0,\n 'O2': 0, 'H2O': 0, 'CH4': 0.96})\n gt_fg.set_attr(p=1)\n\n # motor efficiency\n x = np.array(\n [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55,\n 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 1, 1.05, 1.1, 1.15,\n 1.2, 10])\n y = np.array(\n [0.01, 0.3148, 0.5346, 0.6843, 0.7835, 0.8477, 0.8885, 0.9145,\n 0.9318, 0.9443, 0.9546, 0.9638, 0.9724, 0.9806, 0.9878, 0.9938,\n 0.9982, 0.999, 0.9995, 0.9999, 1, 0.9977, 0.9947, 0.9909, 0.9853,\n 0.9644]) * 0.975\n self.motor_bus_based = CharLine(x=x, y=y)\n self.motor_comp_based = CharLine(x=x, y=1 / y)\n\n # generator efficiency\n x = np.array(\n [0.100, 0.345, 0.359, 0.383, 0.410, 0.432, 0.451, 0.504, 0.541,\n 0.600, 0.684, 0.805, 1.000, 1.700, 10])\n y = np.array(\n [0.976, 0.989, 0.990, 0.991, 0.992, 0.993, 0.994, 0.995, 0.996,\n 0.997, 0.998, 0.999, 1.000, 0.999, 0.99]) * 0.975\n self.generator = CharLine(x=x, y=y)\n\n power_bus_total = Bus('total power output')\n power_bus_total.add_comps(\n {'comp': cp, 'char': self.motor_bus_based, 'base': 'bus'},\n {'comp': gt, 'char': self.generator})\n\n thermal_input = Bus('thermal input')\n thermal_input.add_comps({'comp': cc})\n\n compressor_power_comp = Bus('compressor power input')\n compressor_power_comp.add_comps(\n {'comp': cp, 'char': self.motor_comp_based})\n\n compressor_power_bus = Bus('compressor power input bus based')\n compressor_power_bus.add_comps(\n {'comp': cp, 'char': self.motor_bus_based, 'base': 'bus'})\n\n self.nw.add_busses(\n power_bus_total, thermal_input, compressor_power_comp,\n compressor_power_bus)\n\n # %% solving\n self.nw.solve('design')\n self.nw.save('tmp')","title":""},{"docid":"d073f45b279108906c40509e557f23fe","score":"0.518442","text":"def connectWifi():\r\n ap_if = network.WLAN(network.AP_IF)\r\n ap_if.active(False) # disable access point\r\n\r\n wifi = network.WLAN(network.STA_IF)\r\n\r\n if not wifi.isconnected():\r\n\r\n print('connecting to ',config.ssid)\r\n\r\n #wifi.ifconfig(config.netconfig)\r\n wifi.active(True)\r\n wifi.connect(config.ssid, config.wifiPass)\r\n for retry in range(5):\r\n if wifi.isconnected():\r\n break\r\n else:\r\n print('Retrying #',retry)\r\n time.sleep(3)\r\n if not wifi.isconnected(): # could not connect\r\n print('Could not connect, rebooting')\r\n time.sleep(2)\r\n machine.reset()\r\n\r\n if wifi.isconnected(): # should be connected now\r\n print('network config:', wifi.ifconfig())\r\n\r\n else: # warn with led and reboot\r\n print('Connection failed!')\r\n time.sleep(5)\r\n print('Restarting')\r\n machine.reset()","title":""},{"docid":"503682902b6e8da398ccc68c830429f7","score":"0.5181567","text":"def wifi_test_device_init(ad):\n require_sl4a((ad,))\n ad.droid.wifiScannerToggleAlwaysAvailable(False)\n msg = \"Failed to turn off location service's scan.\"\n assert not ad.droid.wifiScannerIsAlwaysAvailable(), msg\n msg = \"Failed to turn WiFi on %s\" % ad.serial\n assert wifi_toggle_state(ad, True), msg\n reset_wifi(ad)\n msg = \"Failed to clear configured networks.\"\n assert not ad.droid.wifiGetConfiguredNetworks(), msg\n ad.droid.wifiEnableVerboseLogging(1)\n msg = \"Failed to enable WiFi verbose logging.\"\n assert ad.droid.wifiGetVerboseLoggingLevel() == 1, msg\n ad.droid.wifiScannerToggleAlwaysAvailable(False)\n # We don't verify the following settings since they are not critical.\n sync_device_time(ad)\n ad.droid.telephonyToggleDataConnection(False)\n # TODO(angli): need to verify the country code was actually set. No generic\n # way to check right now.\n ad.adb.shell(\"halutil -country %s\" % WifiEnums.CountryCode.US)","title":""},{"docid":"4f8dd882bbb0536b90e61313db59b22b","score":"0.51799047","text":"def __init__(__self__,\n resource_name: str,\n args: NetworkToNetworkInterconnectArgs,\n opts: Optional[pulumi.ResourceOptions] = None):\n ...","title":""},{"docid":"187d5a17b387f15d23514ed5b74abd7a","score":"0.517404","text":"def network_setup() -> Tuple[Any, Any]:\n\n # from https://gist.github.com/nguyendv/8cfd92fc8ed32ebb78e366f44c2daea6\n\n ec2 = u.get_ec2_resource()\n client = u.get_ec2_client()\n existing_vpcs = u.get_vpc_dict()\n zones = u.get_zones()\n\n # create VPC from scratch. Remove this if default VPC works well enough.\n create_non_default_vpc = False\n\n if create_non_default_vpc:\n vpc_name = u.get_vpc_name()\n if u.get_vpc_name() in existing_vpcs:\n print(\"Reusing VPC \" + vpc_name)\n vpc = existing_vpcs[vpc_name]\n subnets = list(vpc.subnets.all())\n assert len(subnets) == len(\n zones), \"Has %s subnets, but %s zones, something went wrong during resource creation, try delete_resources.py/create_resources.py\" % (\n len(subnets), len(zones))\n\n else:\n print(\"Creating VPC \" + vpc_name)\n vpc = ec2.create_vpc(CidrBlock='192.168.0.0/16')\n\n # enable DNS on the VPC\n response = vpc.modify_attribute(EnableDnsHostnames={\"Value\": True})\n assert u.is_good_response(response)\n response = vpc.modify_attribute(EnableDnsSupport={\"Value\": True})\n assert u.is_good_response(response)\n\n vpc.create_tags(Tags=u.create_name_tags(vpc_name))\n vpc.wait_until_available()\n\n gateways = u.get_gateway_dict(vpc)\n gateway_name = u.get_gateway_name()\n if gateway_name in gateways:\n print(\"Reusing gateways \" + gateway_name)\n else:\n print(\"Creating internet gateway \" + gateway_name)\n ig = ec2.create_internet_gateway()\n ig.attach_to_vpc(VpcId=vpc.id)\n ig.create_tags(Tags=u.create_name_tags(gateway_name))\n\n # check that attachment succeeded\n attach_state = u.extract_attr_for_match(ig.attachments, State=-1,\n VpcId=vpc.id)\n assert attach_state == 'available', \"vpc %s is in state %s\" % (vpc.id,\n attach_state)\n route_table = vpc.create_route_table()\n route_table_name = u.get_route_table_name()\n route_table.create_tags(Tags=u.create_name_tags(route_table_name))\n\n dest_cidr = '0.0.0.0/0'\n route_table.create_route(\n DestinationCidrBlock=dest_cidr,\n GatewayId=ig.id\n )\n # check success\n for route in route_table.routes:\n # result looks like this\n # ec2.Route(route_table_id='rtb-a8b438cf',\n # destination_cidr_block='0.0.0.0/0')\n if route.destination_cidr_block == dest_cidr:\n break\n else:\n # sometimes get\n # AssertionError: Route for 0.0.0.0/0 not found in [ec2.Route(route_table_id='rtb-cd9153b0', destination_cidr_block='192.168.0.0/16')]\n # TODO: add a wait/retry?\n assert False, \"Route for %s not found in %s\" % (dest_cidr,\n route_table.routes)\n\n assert len(zones) <= 16 # for cidr/20 to fit into cidr/16\n ip = 0\n for zone in zones:\n cidr_block = '192.168.%d.0/20' % (ip,)\n ip += 16\n print(\"Creating subnet %s in zone %s\" % (cidr_block, zone))\n subnet = vpc.create_subnet(CidrBlock=cidr_block,\n AvailabilityZone=zone)\n subnet.create_tags(Tags=[{'Key': 'Name', 'Value': f'{vpc_name}-subnet'},\n {'Key': 'Region', 'Value': zone}])\n response = client.modify_subnet_attribute(\n MapPublicIpOnLaunch={'Value': True},\n SubnetId=subnet.id\n )\n assert u.is_good_response(response)\n u.wait_until_available(subnet)\n assert subnet.map_public_ip_on_launch, \"Subnet doesn't enable public IP by default, why?\"\n\n route_table.associate_with_subnet(SubnetId=subnet.id)\n\n # Setup security group for non-default VPC\n # existing_security_groups = u.get_security_group_dict()\n # security_group_nd_name = u.get_security_group_nd_name()\n # if security_group_nd_name in existing_security_groups:\n # print(\"Reusing non-default security group \" + security_group_nd_name)\n # security_group_nd = existing_security_groups[security_group_nd_name]\n # assert security_group_nd.vpc_id == vpc.id, f\"Found non-default security group {security_group_nd} \" \\\n # f\"attached to {security_group_nd.vpc_id} but expected {vpc.id}\"\n # else:\n # security_group_nd = create_security_group(security_group_nd_name, vpc.id)\n\n # Setup things on default VPC for zone-agnostic launching\n vpc = u.get_default_vpc()\n if not vpc:\n util.log(f\"Creating default VPC for region {u.get_region()}\")\n client.create_default_vpc()\n vpc = u.get_default_vpc()\n assert vpc, \"Could not create default VPC?\"\n\n existing_security_groups = u.get_security_group_dict()\n security_group_name = u.get_security_group_name()\n if security_group_name in existing_security_groups:\n print(\"Reusing security group \" + security_group_name)\n security_group = existing_security_groups[security_group_name]\n assert security_group.vpc_id == vpc.id, f\"Found security group {security_group} \" \\\n f\"attached to {security_group.vpc_id} but expected {vpc.id}\"\n else:\n security_group = create_security_group(security_group_name, vpc.id)\n # Uncomment the following when setting up two VPC's\n # security_group = create_security_group(security_group_name, vpc.id, security_group_nd)\n\n return vpc, security_group","title":""},{"docid":"4687eca70b4e346691b29362f84ea3d9","score":"0.5170626","text":"def _initialize_wandb(args: Namespace) -> None:\n config = {'num_epochs': args.num_epochs,\n 'train_dl_kwargs': args.train_dl_kwargs,\n 'val_dl_kwargs': args.val_dl_kwargs,\n 'network_kwargs': args.network_kwargs}\n\n wandb.init(project=WANDB_PROJECT_NAME, config=config)","title":""},{"docid":"9f21df24ad76df24e0cf7d33a038132a","score":"0.5148415","text":"def setUp(self):\n self._s1ap_wrapper = s1ap_wrapper.TestWrapper()","title":""},{"docid":"9f21df24ad76df24e0cf7d33a038132a","score":"0.5148415","text":"def setUp(self):\n self._s1ap_wrapper = s1ap_wrapper.TestWrapper()","title":""},{"docid":"92c5d19aa7ae7066e8c20254d0bf0704","score":"0.5144262","text":"def _build_network(self) -> None:","title":""},{"docid":"bf94275e1d82ed3f17c9effd0836cdaf","score":"0.5133108","text":"def setup(self):\n server_names = json.loads(\n self.sensor_service.get_value('hosts', local=False)\n )\n self.servers = []\n for server in server_names:\n self.servers.append(\n json.loads(\n self.sensor_service.get_value(server, local=False)\n )\n )\n self.resource_ids = []\n for host in self.servers:\n self.resource_ids.append(host['pharos_id'])\n self.log = self.sensor_service.get_logger(name=self.__class__.__name__)\n self.dashboard = self.sensor_service.get_value(\n name='dashboard_url',\n local=False\n )\n self.log.info(\"connecting to dashboard at %s\", self.dashboard)\n # get token here for when dashboard supports it","title":""},{"docid":"84a1b8ce91aab498f92a2bb3ec44552f","score":"0.51242286","text":"def hotspot(args):\n global ap_face, ap_ssid, ap_password, out_face\n ap_face = args.interface\n ap_ssid = args.ssid\n ap_password = args.password\n out_face = args.share_interface\n\n print(\"\\nConfiguring %s to ip %s...\" % (ap_face, AP_IP))\n assign_ap_ip()\n\n print(\"\\nStarting dnsmasq...\")\n start_dnsmasq(hostspot=True)\n\n print(\"\\nStarting ip forward...\")\n start_ip_forward()\n\n print('\\nStarting hostapd...')\n start_hostapd()","title":""},{"docid":"5063696f29a862baee1299253a070921","score":"0.512415","text":"def configure_network(self):\n for network_switch_obj in self.my_parser.network_switches_list:\n sw_obj = switch.SwitchCfg(\n network_switch_obj.ip_address,\n network_switch_obj.username,\n network_switch_obj.password,\n network_switch_obj.tftp_server_ip,\n network_switch_obj.config_file_name)\n '''\n sw_obj.upload_switch_config_file_to_tftp_server()\n sw_obj.download_switch_config_file_to_switch()\n '''\n lista = []\n for vlan in network_switch_obj.vlans:\n vlan_dict = vlan.attrib\n vlan_dict['name'] = vlan.tag\n # print vlan_dict\n lista.append(vlan_dict)\n for i in lista:\n untag = i[\"UntaggedPorts\"]\n vlan_id = i[\"ID\"]\n tagged = i[\"TaggedPorts\"]\n # sw_obj.create_vlan(vlan_id)\n if vlan_id == 'None':\n log._info(\n \"There are no VLAN's defined to be created, Hence Tagging and Untagging of ports will be skipped\")\n log._info(\n \"Please define a VLAN to be created with tag %s so, ports could be tagged or untagged\" %\n vlan.tag)\n else:\n log._info(\"Creating VLAN %s\" % vlan_id)\n sw_obj.create_vlan(vlan_id)\n untagged_ports = untag.split(',')\n tagged_ports = tagged.split(',')\n\n if untagged_ports == 'None':\n log._info(\"There are no ports to be untagged\")\n else:\n for port in untagged_ports:\n log._info(\"untagging port %s to vlan %s\" %\n (port, vlan_id))\n sw_obj.untag_ports_to_vlan(vlan_id, port)\n\n if tagged_ports == 'None':\n log._info(\"There are no ports to be tagged\")\n else:\n for port in tagged_ports:\n log._info(\"tagging port %s to vlan %s\" %\n (port, vlan_id))\n sw_obj.tag_ports_to_vlan(vlan_id, port)\n user_list = []\n for user in network_switch_obj.users:\n user_dict = user.attrib\n user_dict['name'] = user.tag\n # print user_dict\n user_list.append(user_dict)\n for i in user_list:\n user_name = i[\"Username\"]\n user_pw = i[\"Password\"]\n sw_obj.configure_users(user_name, user_pw)\n\n port_list = []\n for port in network_switch_obj.ports:\n port_dict = port.attrib\n port_dict['name'] = port.tag\n port_list.append(port_dict)\n for i in port_list:\n port_nu = i['PortList']\n port_state = i['PortState']\n port_duplex = i['PortDuplex']\n port_speed = i['PortSpeed']\n # print port_number, port_state, port_duplex, port_speed\n if port_nu == 'None':\n log._warn(\n \"There are no ports mentioned for port attribute configuration under xml tag %s\" %\n port.tag)\n else:\n log._info(\n \"There ports mentioned to be configured with port attributes\")\n port_number = port_nu.split(',')\n for po in port_number:\n if port_state == 'None':\n log._info(\n \"port attribute state will not be configured as the value of it xml is set to None\")\n else:\n log._info(\n \"Port attribute state will be configured for port %s \" %\n po)\n sw_obj.configure_port_state(po, port_state)\n if port_duplex == 'None':\n log._info(\n \"port attribute duplex will not be configured as the value of it xml is set to None\")\n else:\n log._info(\n \"Port attribute duplex will be configured for port %s \" %\n po)\n sw_obj.configure_port_duplex_mode(po, port_duplex)\n if port_speed == 'None':\n log._info(\n \"port attribute speed will not be configured as the value of it xml is set to None\")\n else:\n log._info(\n \"Port attribute speed will be configured for port %s \" %\n po)\n sw_obj.configure_port_speed(po, port_speed)\n\n for san_switch_obj in self.my_parser.san_switches_list:\n # NOTE: expect should return 0 if 'successful' exists within the\n # output\n\n # here, check model and create object of corresponding class\n if 'SN6000B' in san_switch_obj.model:\n\n creds = san_switch_obj.ip_address + ';' + \\\n san_switch_obj.username + ';' + \\\n san_switch_obj.password + ';'\n sn_log = os.path.join(\n os.getcwd(), r\"logs\", r\"sn6000config.txt\")\n try:\n child = spawn(\n 'python san_switch.py model %s creds = %s' %\n (san_switch_obj.model, creds), logfile=file(\n sn_log, 'w'))\n # port zoning to go here later\n if os.name == 'nt':\n SENDLINE = child.direct_sendline\n else:\n SENDLINE = child.sendline\n # if there are zone configs present\n if san_switch_obj.zone_configs:\n for config in san_switch_obj.zone_configs:\n # create zones with added wwns\n # zone elements!\n zone_list = [zone for zone in config.find(\"Zones\")]\n for zone in zone_list:\n # compile the list of hosts to be added to zone\n host_list = [\n host for host in zone.find(\"Hosts\")]\n for host in host_list:\n # compile the list of wwns to add to host\n wwn_list = [wwn.attrib[\"wwn\"]\n for wwn in host.find('WWNs')]\n SENDLINE(\n 'my_san.create_alias(%s, %s)' %\n (host.attrib['alias'], wwn_list))\n # no need to send a y for creating alias\n # expect should return 0 if 'successful'\n # exists within the output\n if child.expect(\"successful\"):\n log._warn(\n \"Failed to create host %s with given WWNs or domain/port index list\" %\n (host.attrib['alias']))\n del wwn_list[:]\n SENDLINE('my_san.create_zone(%s,%s)' %\n (zone.attrib[\"alias\"], host_list))\n # no need to send a y for creating a zone\n if child.expect(\"successful\"):\n log._warn(\n \"Failed to create zone %s with given host list\" %\n (zone.attrib['alias']))\n del host_list[:]\n # compile the list of zones to be added to config\n SENDLINE('my_san.create_config(%s,%s)' %\n (config.attrib[\"name\"], zone_list))\n # no need to send a y for creating a zone\n if child.expect(\"successful\"):\n log._warn(\n \"Failed to create conig %s with given zone list\" %\n (config.attrib[\"name\"]))\n del zone_list[:]\n # here, use winpexpect to answer the decision prompts\n # from SN6000 switch\n if san_switch_obj.ports:\n for port in san_switch_obj.ports:\n port_num = port.tag.strip(\"PORTport\")\n SENDLINE('my_san.set_port_speed(%s,%s)' %\n (port_num, port.attrib[\"Speed\"]))\n # no need to send a y for creating a zone\n if child.expect(\"successful\"):\n log._warn(\n \"Failed to set port speed for port %s\" %\n (port_num))\n SENDLINE('my_san.set_port_fillword(%s, %s)' %\n (port_num, port.attrib[\"Fillword\"]))\n if child.expect(\"successful\"):\n log._warn(\n \"Failed to set port fillword for port %s\" %\n (port_num))\n SENDLINE('my_san.save_config()')\n child.expect(\"(yes, y, no, n):\")\n SENDLINE('yes')\n SENDLINE('my_san.enable_config(%s)' %\n (config.attrib[\"name\"]))\n child.expect(\"(yes, y, no, n):\")\n SENDLINE('yes')\n SENDLINE('my_san.terminate_connection()')\n child.terminate()\n if child.isalive():\n log._warn(\n \"CHILD PROCESS FAILED TO TERMINATE, PLEASE FILE BUG\")\n except Exception:\n log._warn(\n \"Failed to start child process for SAN switch configuration\")\n # elif 'some other model' in san_switch_obj.model:\n # configure that model\n\n else:\n log._warn(\"Model not supported by Config manager at this time\")","title":""},{"docid":"ed004ef95e2913fe1ece6249e5daa699","score":"0.5122543","text":"def _setup_decap(self):\n self._logger.debug('Setup using ' + str(self._vswitch_class))\n\n try:\n self._vswitch.start()\n bridge = settings.getValue('TUNNEL_INTEGRATION_BRIDGE')\n bridge_ext = settings.getValue('TUNNEL_EXTERNAL_BRIDGE')\n bridge_ext_ip = settings.getValue('TUNNEL_EXTERNAL_BRIDGE_IP')\n tgen_ip1 = settings.getValue('TRAFFICGEN_PORT1_IP')\n self._vswitch.add_switch(bridge)\n\n tasks.run_task(['sudo', 'ifconfig', bridge,\n settings.getValue('VTEP_IP1')],\n self._logger, 'Assign ' +\n settings.getValue('VTEP_IP1') + ' to ' + bridge, False)\n\n tunnel_type = self._traffic['tunnel_type']\n\n self._vswitch.add_switch(bridge_ext)\n self._vswitch.add_phy_port(bridge)\n (_, phy2_number) = self._vswitch.add_phy_port(bridge_ext)\n if tunnel_type == \"vxlan\":\n vxlan_vni = 'options:key=' + settings.getValue('VXLAN_VNI')\n (_, phy3_number) = self._vswitch.add_tunnel_port(bridge_ext,\n tgen_ip1,\n tunnel_type,\n params=[vxlan_vni])\n else:\n (_, phy3_number) = self._vswitch.add_tunnel_port(bridge_ext,\n tgen_ip1,\n tunnel_type)\n tasks.run_task(['sudo', 'ip', 'addr', 'add',\n bridge_ext_ip,\n 'dev', bridge_ext],\n self._logger, 'Assign ' +\n bridge_ext_ip\n + ' to ' + bridge_ext)\n\n tasks.run_task(['sudo', 'ip', 'link', 'set', 'dev', bridge_ext,\n 'up'],\n self._logger,\n 'Set ' + bridge_ext + ' status to up')\n\n self._vswitch.set_tunnel_arp(tgen_ip1,\n settings.getValue('TRAFFICGEN_PORT1_MAC'),\n bridge)\n # Test is unidirectional for now\n self._vswitch.del_flow(bridge_ext)\n flow1 = add_ports_to_flow(_FLOW_TEMPLATE, phy3_number,\n phy2_number)\n self._vswitch.add_flow(bridge_ext, flow1)\n\n except:\n self._vswitch.stop()\n raise","title":""},{"docid":"50ff59d27fba351bdd540e3ca986b8cf","score":"0.5120746","text":"def start_up(self):\n self.logger.message(self.my_name + ' Starting CATAP Hardware Interfaces',\n add_to_text_log=True,show_time_stamp=False)\n self.start_llrf_control()\n self.start_RF_protection()\n self.start_magnet_control()\n self.start_valve_control()\n self.start_mod_control()","title":""},{"docid":"af1ad7f6001a41e663332c6cdf1c358b","score":"0.51126975","text":"def setUpNetworkAndController(\n self, vlan: str = \"\",\n non_nat_arp_egress_port: Optional[str] = None,\n gw_mac_addr=\"ff:ff:ff:ff:ff:ff\",\n ):\n\n cls = self.__class__\n super(InOutNonNatTest, cls).setUpClass()\n egress.get_mobilityd_gw_info = mocked_get_mobilityd_gw_info\n egress.set_mobilityd_gw_info = mocked_set_mobilityd_gw_info\n\n warnings.simplefilter('ignore')\n cls.setup_uplink_br()\n\n if vlan != \"\":\n cls._setup_vlan_network(vlan)\n\n cls.service_manager = create_service_manager([])\n\n ingress_controller_reference: Future = Future()\n middle_controller_reference: Future = Future()\n egress_controller_reference: Future = Future()\n testing_controller_reference: Future = Future()\n\n if non_nat_arp_egress_port is None:\n non_nat_arp_egress_port = cls.DHCP_PORT\n\n patch_up_port_no = BridgeTools.get_ofport('patch-up')\n test_setup = TestSetup(\n apps=[\n PipelinedController.Ingress,\n PipelinedController.Middle,\n PipelinedController.Egress,\n PipelinedController.Testing,\n PipelinedController.StartupFlows,\n ],\n references={\n PipelinedController.Ingress:\n ingress_controller_reference,\n PipelinedController.Middle:\n middle_controller_reference,\n PipelinedController.Egress:\n egress_controller_reference,\n PipelinedController.Testing:\n testing_controller_reference,\n PipelinedController.StartupFlows:\n Future(),\n },\n config={\n 'setup_type': 'LTE',\n 'bridge_name': cls.BRIDGE,\n 'bridge_ip_address': cls.BRIDGE_IP,\n 'ovs_gtp_port_number': 32768,\n 'clean_restart': True,\n 'enable_nat': False,\n 'non_nat_gw_probe_frequency': 0.5,\n 'non_nat_arp_egress_port': non_nat_arp_egress_port,\n 'uplink_port': patch_up_port_no,\n 'uplink_gw_mac': gw_mac_addr,\n },\n mconfig=None,\n loop=None,\n service_manager=cls.service_manager,\n integ_test=False,\n )\n\n BridgeTools.create_bridge(cls.BRIDGE, cls.IFACE)\n subprocess.Popen([\"ifconfig\", cls.UPLINK_BR, \"192.168.128.41\"]).wait()\n cls.thread = start_ryu_app_thread(test_setup)\n cls.ingress_controller = ingress_controller_reference.result()\n cls.middle_controller = middle_controller_reference.result()\n cls.egress_controller = egress_controller_reference.result()\n\n cls.testing_controller = testing_controller_reference.result()","title":""},{"docid":"4ad056292c5880dcc5ddcfcfa77e1c2e","score":"0.5107478","text":"def create_wifi_profile(self, type, ssid, password, authentication, encryption):\n\n self.ssid = ssid\n\n self.root = XML.Element(\"WLANProfile\", attrib={'xmlns': 'http://www.microsoft.com/networking/WLAN/profile/v1'})\n XML.SubElement(self.root, \"name\").text = self.ssid\n self.ssid_config = XML.SubElement(self.root, \"SSIDConfig\")\n self.ssid_sub = XML.SubElement(self.ssid_config, \"SSID\")\n XML.SubElement(self.ssid_sub, \"name\").text = self.ssid\n XML.SubElement(self.root, \"connectionType\").text = 'ESS'\n XML.SubElement(self.root, \"connectionMode\").text = 'auto'\n self.msm = XML.SubElement(self.root, \"MSM\")\n self.security = XML.SubElement(self.msm, \"security\")\n\n self.network_type = type\n\n if self.network_type == 'SECURED':\n self.auth_encryption = XML.SubElement(self.security, \"authEncryption\")\n XML.SubElement(self.auth_encryption, \"authentication\").text = authentication\n XML.SubElement(self.auth_encryption, \"encryption\").text = encryption\n XML.SubElement(self.auth_encryption, \"useOneX\").text = 'false'\n\n self.shared_key = XML.SubElement(self.security, \"sharedKey\")\n XML.SubElement(self.shared_key, \"keyType\").text = 'passPhrase'\n XML.SubElement(self.shared_key, \"protected\").text = 'false'\n XML.SubElement(self.shared_key, \"keyMaterial\").text = password\n elif self.network_type == 'OPEN':\n self.auth_encryption = XML.SubElement(self.security, \"authEncryption\")\n XML.SubElement(self.auth_encryption, \"authentication\").text = 'open'\n XML.SubElement(self.auth_encryption, \"encryption\").text = 'none'\n XML.SubElement(self.auth_encryption, \"useOneX\").text = 'false'\n self.mac_random = XML.SubElement(self.root, \"MacRandomization\", attrib={'xmlns': 'http://www.microsoft.com/networking/WLAN/profile/v3'})\n XML.SubElement(self.mac_random, \"enableRandomization\").text = 'false'\n XML.SubElement(self.mac_random, \"randomizationSeed\").text = '10821901'\n else:\n LOGGER.error(f'A wrong input value for \"network_type\" parameter was delivered')\n try:\n tree = XML.ElementTree(self.root)\n tree.write(self.wifi_profile_filename)\n LOGGER.info(f'{self.wifi_profile_filename} was created successfully at {\"TBD: add location\"}')\n\n except:\n LOGGER.error(f'Was unable to create {self.wifi_profile_filename}')","title":""},{"docid":"11531cd18d781b04794b2edd4918d31c","score":"0.51074207","text":"def __init__(self, rinterface, rtinterface, protocol, tag):\n super().__init__(rinterface, rtinterface, protocol, tag)\n # Find the relevant group\n for group in rinterface.interface_config['subscribers'][protocol]:\n if tag == group['tag']:\n group_config = group\n\n # Take from HAG since we do not need their WiFi offload attributes\n self.test_session_handle = None\n self.isactive = False\n self.test_session_name = \"\"\n self.libname = \"\"\n self.tsname = \"\"\n self.access_interface = \"\"\n self.uplink_interface = \"\"\n self.control_interface = \"\"\n self.subscriber_group_indices = {}\n self.node_test_case_name = \"\"\n self.nodal_test_case_name = \"\"\n\n self.test_activity = group_config.get('test-activity', 'Capacity Test')\n self.session_count = group_config.get('session-count', '1')\n self.bearer_per_session = group_config.get('default-bearers', '1')\n self.dedicated_bearers = group_config.get('dedicated-bearers', '0')\n self.activation_rate = group_config.get('activation-rate', '1')\n self.deactivation_rate = group_config.get('deactivation-rate', '1')\n self.bearer_ipv6_addr_pool = group_config.get('bearer-ipv6-addr-pool', 'DB43:2019::0/64')\n self.bearer_ipv4_addr_pool = group_config.get('bearer-ipv4-addr-pool', '91.0.0.1')\n self.data_mtu = group_config.get('data-mtu', '1400')\n self.apn_name = group_config.get('apn-name', 'mx-upf')\n self.user_initiated_association = group_config.get('user-init-association', False)\n self.home_address_type = group_config.get('home-addr-type', '1') # 1 for ipv4, 2 for ipv6, 3 for both\n self.sut_loopback_ipv6 = group_config.get('sut-loopback-v6', 'FEED:D00D::1')\n self.sut_loopback_ip = group_config.get('sut-loopback', '100.0.0.1')\n self.use_loopback_access = group_config.get('use-loopback-access', False)\n self.use_loopback_control = group_config.get('use-loopback-control', False)\n self.use_static_address = group_config.get('use-static-addr', False)\n self.heartbeat_retries = group_config.get('heartbeat-retries', '3')\n self.heartbeat_interval = group_config.get('heartbeat-interval', '20')\n self.sx_node_retries = group_config.get('sx-node-retries', '3')\n self.sx_node_retry_interval = group_config.get('sx-node-retry-interval', '5')\n self.sx_session_retries = group_config.get('sx-session-retries', '3')\n self.sx_session_retry_interval = group_config.get('sx-session-retry-interval', '5')\n # Control node counts\n self.mme_control_node_count = group_config.get('mme-control-node-count', '1')\n self.sgw_control_node_count = group_config.get('sxab-control-node-count', '1')\n self.enodeb_user_node_count = group_config.get('enodeb-user-node-count', '1')\n\n import ipaddress\n\n self.user_base_address = group_config.get('user-base-address', '10.1.1.1/24')\n self.control_base_address = group_config.get('control-base-address', '20.2.2.1/24')\n self.uplink_base_address = group_config.get('uplink-base-address', '30.3.3.1/24')\n\n if 'user-v6-base-address' in group_config:\n self.user_v6_base_address = group_config.get('user-v6-base-address')\n if 'control-v6-base-address' in group_config:\n self.control_v6_base_address = group_config.get('control-v6-base-address')\n if 'uplink-v6-base-address' in group_config:\n self.uplink_v6_base_address = group_config.get('uplink-v6-base-address')\n\n # Logic for consolidating S1U + SGi. Need further granularity on individual interface\n # Get integer 'n' from rinterface.interface_link = 'accessn' ie. access0, access1, access2, etc.\n interface_index = [int(z) for z in list(rtinterface.interface_link) if z.isdigit()][0]\n access_interface_list = bbe.get_interfaces(rtinterface.device_id, interfaces=rinterface.interface_link)\n control_interface_list = bbe.get_interfaces(rtinterface.device_id, interfaces='control'+str(interface_index))\n uplink_interface_list = bbe.get_interfaces(rtinterface.device_id, interfaces='uplink'+str(interface_index))\n\n if len(access_interface_list) < 1 or len(uplink_interface_list) < 1:\n raise Exception('Must have minimum of 1 access and 1 uplink interface present to initialize a '\n 'CUPSSubscriber object!\\n')\n\n # Check Home Addr type to see if we need v6 uplink network, since decapsulated traffic will be IPv6\n if self.home_address_type == 2 or self.home_address_type == 3:\n self.uplink_v6_base_address = group_config.get('uplink-v6-base-address', 'FEED:BABE::1/64')\n\n # Assign interfaces to subscriber while we have them anyways. List will have 1 entry due to required\n # unique link names in params.yaml\n self.access_interface = access_interface_list[0].interface_pic\n if hasattr(self, 'user_v6_base_address'):\n self.access_v6_interface = self.access_interface + 'v6'\n self.uplink_interface = uplink_interface_list[0].interface_pic\n if hasattr(self, 'uplink_v6_base_address'):\n self.uplink_v6_interface = self.uplink_interface + 'v6'\n\n # Can generate IPs dynamically, since many required LS IPs do not matter to DUT. Let user pick their\n # desired base address and we will handle the rest. Next IP is retrieved from next() function applied\n # to appropriate IP iterator\n self.access_ip_generator = iter(ipaddress.IPv4Network(self.user_base_address, strict=False).hosts())\n if hasattr(self, 'user_v6_base_address'):\n self.access_ipv6_generator = iter(ipaddress.IPv6Network(self.user_v6_base_address, strict=False).hosts())\n if len(control_interface_list) == 0:\n t.log('No control interface found. Attaching control-base-network to the access RT port!\\n')\n self.control_ip_generator = self.access_ip_generator\n self.control_interface = self.access_interface\n if self.user_base_address != self.control_base_address:\n self.control_ip_generator = iter(ipaddress.IPv4Network(self.control_base_address, strict=False).hosts())\n self.control_interface = str(self.access_interface) + '_1'\n if hasattr(self, 'user_v6_base_address'):\n self.control_ipv6_generator = self.access_ipv6_generator\n self.control_v6_interface = self.access_v6_interface\n if not hasattr(self, 'control_v6_base_address'):\n self.control_v6_base_address = self.user_v6_base_address\n if (self.user_v6_base_address != self.control_v6_base_address):\n self.control_ipv6_generator = iter(ipaddress.IPv6Network(\n self.control_v6_base_address, strict=False).hosts())\n self.control_v6_interface = str(self.access_v6_interface) + '_1'\n else:\n self.control_ip_generator = iter(ipaddress.IPv4Network(self.control_base_address, strict=False).hosts())\n self.control_interface = control_interface_list[0].interface_pic\n if hasattr(self, 'control_v6_base_address'):\n self.control_ipv6_generator = iter(ipaddress.IPv6Network(\n self.control_v6_base_address, strict=False).hosts())\n self.control_v6_interface = self.control_interface + 'v6'\n self.uplink_ip_generator = iter(ipaddress.IPv4Network(self.uplink_base_address, strict=False).hosts())\n if hasattr(self, 'uplink_v6_base_address'):\n self.uplink_ipv6_generator = iter(ipaddress.IPv6Network(self.uplink_v6_base_address, strict=False).hosts())\n\n\n self.sgw_user_address = str(next(self.access_ip_generator)) # DUT local address for ANP\n self.access_gateway = self.sgw_user_address\n if self.use_loopback_access:\n self.sgw_user_address = self.sut_loopback_ip\n\n self.sxa_sxb_to_up_sgw_user_node = str(next(self.access_ip_generator))\n self.pgw_address = str(next(self.access_ip_generator))\n\n self.enodeb_user_node_start_ip = str(next(self.access_ip_generator))\n self.target_enodeb_user_node = str(next(self.access_ip_generator))\n\n if hasattr(self, 'user_v6_base_address'):\n self.sgw_v6_user_address = str(next(self.access_ipv6_generator)) # DUT local address for ANP\n self.v6_access_gateway = self.sgw_v6_user_address\n if self.use_loopback_access:\n self.sgw_v6_user_address = self.sut_loopback_ipv6\n\n self.sxa_sxb_to_up_sgw_v6_user_node = str(next(self.access_ipv6_generator))\n self.pgw_v6_address = str(next(self.access_ipv6_generator))\n\n self.enodeb_user_node_start_ipv6 = str(next(self.access_ipv6_generator))\n self.target_enodeb_user_node_v6 = str(next(self.access_ipv6_generator))\n\n # eNodeB User Node IPs should be consumed in sequence when multiple eNodeB in use\n if int(self.enodeb_user_node_count) > 1:\n self.enodeb_user_node_ip_list = []\n self.enodeb_user_node_ip_list.append(self.enodeb_user_node_start_ip)\n for count in range(self.enodeb_user_node_count - 1):\n self.enodeb_user_node_ip_list.append(str(next(self.access_ip_generator)))\n self.target_enodeb_user_node = str(next(self.access_ip_generator))\n self.target_enodeb_user_node_ip_list = []\n self.target_enodeb_user_node_ip_list.append(self.target_enodeb_user_node)\n for count in range(self.enodeb_user_node_count - 1):\n self.target_enodeb_user_node_ip_list.append(str(next(self.access_ip_generator)))\n if hasattr(self, 'user_v6_base_address'):\n self.enodeb_user_node_ipv6_list = []\n self.enodeb_user_node_ipv6_list.append(self.enodeb_user_node_start_ipv6)\n for count in range(self.enodeb_user_node_count - 1):\n self.enodeb_user_node_ipv6_list.append(str(next(self.access_ipv6_generator)))\n self.target_enodeb_user_node_v6 = str(next(self.access_ipv6_generator))\n self.target_enodeb_user_node_ipv6_list = []\n self.target_enodeb_user_node_ipv6_list.append(self.target_enodeb_user_node_v6)\n for count in range(self.enodeb_user_node_count - 1):\n self.target_enodeb_user_node_ipv6_list.append(str(next(self.access_ipv6_generator)))\n\n # self.sxa_sxb_to_up_sgw_user_node = str(next(self.access_ip_generator))\n # self.pgw_address = str(next(self.access_ip_generator))\n # self.target_enodeb_user_node = str(next(self.access_ip_generator))\n self.sxa_sxb_to_up_port = '8805'\n\n # Generate control IPs\n self.sxa_sxb_to_up_user_node = str(next(self.control_ip_generator)) # DUT local address for CPP\n if self.user_base_address == self.control_base_address:\n self.sxa_sxb_to_up_user_node = self.sgw_user_address\n self.control_gateway = self.sxa_sxb_to_up_user_node\n if self.use_loopback_control:\n self.sxa_sxb_to_up_user_node = self.sut_loopback_ip\n self.sgw_control_address = str(next(self.control_ip_generator))\n self.sgw_control_node_start_ip = self.sgw_control_address\n\n # SGW-C IPs should be consumed in sequence when multiple CPF in use\n if int(self.sgw_control_node_count) > 1:\n self.sgw_control_node_ip_list = []\n self.sgw_control_node_ip_list.append(self.sgw_control_address)\n for count in range(self.sgw_control_node_count - 1):\n self.sgw_control_node_ip_list.append(str(next(self.control_ip_generator)))\n\n self.mme_control_node_start_ip = str(next(self.control_ip_generator))\n self.sxa_sxb_control_node_start_ip = str(next(self.control_ip_generator))\n\n # Sxab control IPs should be consumed in sequence when multiple CPF in use\n if int(self.sgw_control_node_count) > 1:\n self.sxab_control_node_ip_list = []\n self.sxab_control_node_ip_list.append(self.sxa_sxb_control_node_start_ip)\n for count in range(self.sgw_control_node_count - 1):\n self.sxab_control_node_ip_list.append(str(next(self.control_ip_generator)))\n\n self.sxa_sxb_control_gtp_node_start_ip = str(next(self.control_ip_generator))\n self.sxa_sxb_to_up_gtp_node = self.sxa_sxb_to_up_user_node\n\n if hasattr(self, 'control_v6_base_address'):\n # Generate control IPs\n self.sxa_sxb_to_up_v6_user_node = str(next(self.control_ipv6_generator)) # DUT local address for CPP\n if self.user_v6_base_address == self.control_v6_base_address:\n self.sxa_sxb_to_up_v6_user_node = self.sgw_v6_user_address\n self.v6_control_gateway = self.sxa_sxb_to_up_v6_user_node\n if self.use_loopback_control:\n self.sxa_sxb_to_up_v6_user_node = self.sut_loopback_ipv6\n self.sgw_v6_control_address = str(next(self.control_ipv6_generator))\n self.sgw_control_node_start_ipv6 = self.sgw_v6_control_address\n\n # SGW-C IPs should be consumed in sequence when multiple CPF in use\n if int(self.sgw_control_node_count) > 1:\n self.sgw_control_node_ipv6_list = []\n self.sgw_control_node_ipv6_list.append(self.sgw_v6_control_address)\n for count in range(self.sgw_control_node_count - 1):\n self.sgw_control_node_ipv6_list.append(str(next(self.control_ipv6_generator)))\n\n self.mme_control_node_start_ipv6 = str(next(self.control_ipv6_generator))\n self.sxa_sxb_control_node_start_ipv6 = str(next(self.control_ipv6_generator))\n\n # Sxab control IPs should be consumed in sequence when multiple CPF in use\n if int(self.sgw_control_node_count) > 1:\n self.sxab_control_node_ipv6_list = []\n self.sxab_control_node_ipv6_list.append(self.sxa_sxb_control_node_start_ipv6)\n for count in range(self.sgw_control_node_count - 1):\n self.sxab_control_node_ipv6_list.append(str(next(self.control_ipv6_generator)))\n\n self.sxa_sxb_control_gtp_node_start_ipv6 = str(next(self.control_ipv6_generator))\n self.sxa_sxb_to_up_gtp_node_v6 = self.sxa_sxb_to_up_v6_user_node\n\n # Generate uplink IPs\n self.uplink_gateway = str(next(self.uplink_ip_generator))\n self.traffic_start_ip = str(next(self.uplink_ip_generator))\n if hasattr(self, 'uplink_v6_base_address'):\n self.v6_uplink_gateway = str(next(self.uplink_ipv6_generator))\n self.traffic_start_ipv6 = str(next(self.uplink_ipv6_generator))\n\n # SGW Nodal S11\n self.s11_gtpv2_version = group_config.get('gtpv2-version', '15.2.0')\n self.s11_total_apns = group_config.get('total-apns', '1')\n self.s11_start_imsi = group_config.get('start-imsi', '505024101215074')\n self.s11_start_imei = group_config.get('start-imei', '50502410121507')\n self.s11_radio_access_type = group_config.get('radio-access-type', 'eutran')\n self.s11_gtp_c_tunnel_id = group_config.get('start-gtp-c-tunnel-id', '1000000')\n self.s11_gtp_u_tunnel_id = group_config.get('start-gtp-u-tunnel-id', '1048576')\n self.s11_total_apns = group_config.get('total-apns', '1')\n self.s11_n3_requests = group_config.get('s11-n3-requests', '5')\n self.s11_t3_response_time = group_config.get('s11-t3-response-time', '20')\n\n # SGW Node S11\n self.node_start_imsi = group_config.get('start-imsi', '505024101215074')\n self.node_start_imei = group_config.get('start-imei', '50502410121507')\n self.node_radio_access_type = group_config.get('radio-access-type', 'eutran')\n self.node_gtp_c_tunnel_id = group_config.get('start-gtp-c-tunnel-id', '1000000')\n self.node_gtp_u_tunnel_id = group_config.get('start-gtp-u-tunnel-id', '1048576')\n self.node_start_s5_gtp_c_teid = group_config.get('start-s5-gtp-c-teid', '3000000')\n self.node_start_s5_gtp_u_teid = group_config.get('start-s5-gtp-u-teid', '4000000')\n self.node_start_fwd_gtp_u_teid = group_config.get('start-fwd-gtp-u-teid', '5000000')\n self.node_total_apns = group_config.get('total-apns', '1')\n self.node_gtpv2_version = group_config.get('gtpv2-version', '15.2.0')\n\n # VLANs\n if group_config.get('access-vlan-id') is not None:\n self.access_vlan_id = group_config.get('access-vlan-id')\n if group_config.get('control-vlan-id') is not None:\n self.control_vlan_id = group_config.get('control-vlan-id')\n if group_config.get('uplink-vlan-id') is not None:\n self.uplink_vlan_id = group_config.get('uplink-vlan-id')\n\n # Creates data structure allowing user to have different login groups within single Test Session\n if 'Command Mode' in self.test_activity and group_config.get('session-groups') is not None:\n count = 1\n for group in sorted(group_config['session-groups']):\n start_index = count\n stop_index = count + group_config['session-groups'][group] - 1\n self.subscriber_group_indices[group] = (start_index, int(stop_index))\n count += int(group_config['session-groups'][group])\n self.subscriber_group_indices['default'] = (1, self.session_count)","title":""},{"docid":"15e04e3020f2e10350d461cd6c5561ec","score":"0.510582","text":"def setup(self):\n super(BaseInstance, self).setup()\n self.instance.modify_attribute(\n \"blockDeviceMapping\", [\"%s=true\" % v[0] for v in self.volume_info]\n )\n self.create_users(self._get_users())\n self.setup_sudoers()\n # needed for SSH agent forwarding during replication setup:\n self.reset_authentication()\n self.create_deployer()\n self.update_deployer_keys()\n self.bind_app_directories(self.app_dirs, self.app_root)\n self.setup_swap() # after app (potentially secure) partition is created\n self.upgrade_packages()\n self.install_packages([\"ntp\"]) # keep date current","title":""},{"docid":"21eef862dda4da2b5f89baf24e81dd2b","score":"0.5099276","text":"def __init__(self, hass: HomeAssistant, entry: ConfigEntry) -> None:\n self.hass = hass\n self._entry = entry\n\n self._devices: dict[str, AsusWrtDevInfo] = {}\n self._connected_devices: int = 0\n self._connect_error: bool = False\n\n self._sensors_data_handler: AsusWrtSensorDataHandler | None = None\n self._sensors_coordinator: dict[str, Any] = {}\n\n self._on_close: list[Callable] = []\n\n self._options: dict[str, Any] = {\n CONF_DNSMASQ: DEFAULT_DNSMASQ,\n CONF_INTERFACE: DEFAULT_INTERFACE,\n CONF_REQUIRE_IP: True,\n }\n self._options.update(entry.options)\n\n self._api: AsusWrtBridge = AsusWrtBridge.get_bridge(\n self.hass, dict(self._entry.data), self._options\n )","title":""},{"docid":"0b5c6e975588199f5c14bbdc48f6e499","score":"0.5096234","text":"def network(ctx, network):\n ctx.obj['network'] = network\n ctx.obj['network_url'] = \"{}/networks/{}\".format(ctx.obj['chute_url'],\n network)","title":""},{"docid":"b3dddafccda2795f3bcc08a4c7d66088","score":"0.508461","text":"def init_for_next_scenario(self):\n self.reset()\n self.net_configurator = self.get_network_provisioner()\n\n event_system = singletons.event_system\n if singletons.simulation_manager.auto_stepping:\n if scenario_config.is_network_links_auto_ipv4() and self.net_configurator:\n log.info(\"adding network setup to EventSystem ...\")\n event_system.events.append(MyEventSystem.EVENT_NETWORK_SETUP)\n if scenario_config.is_connectivity_check_enabled():\n log.info(\"adding network check to EventSystem ...\")\n event_system.events.append(MyEventSystem.EVENT_NETWORK_CHECK)\n\n event_system.ready.set()\n log.info(\"%s ready ... \", event_system.__class__.__name__)\n\n if self.net_configurator:\n self.net_configurator = self.net_configurator(singletons.network_backend.get_interface_index)","title":""},{"docid":"b5b68faa49086f3622c90c03d07f7e02","score":"0.5074843","text":"def _construct_network(self, cfg):\n raise NotImplementedError()","title":""},{"docid":"3cfce461182cf26341680e20a133e46e","score":"0.5074374","text":"def assign_ap_ip():\n os.system('ifconfig ' + ap_face + ' ' + AP_IP + '/24 up')","title":""},{"docid":"947bbefb34f7ff5e7a854770d2b0bbe9","score":"0.50735915","text":"def setAP(controller, node, essid):\n\n #This UPI function stops the hostpad daemon, if present on node\n rvalue = controller.nodes(node).net.stop_hostapd()\n #This UPI function sets the node ip address on a specific interface\n rvalue = controller.nodes(node).net.set_ip_address(ap_wlan_interface, '192.168.3.' + node.ip.split('.')[3] )\n #This UPI function sets the hostapd configuration (starting from a generic file set interface, channel and SSID )\n rvalue = controller.nodes(node).net.set_hostapd_conf(ap_wlan_interface, './hostapd.conf', 6, essid)\n #This UPI function starts the hostapd daemon (it uses a specific hostapd.conf file present in the experiment directory\n rvalue = controller.nodes(node).net.start_hostapd('./hostapd.conf')\n #This UPI function sets the node power (value in dBm)\n rvalue = controller.nodes(node).radio.set_tx_power(15)\n #This UPI function sets the node modulation rate (value in Mbps)\n rvalue = controller.nodes(node).radio.set_modulation_rate(2)","title":""},{"docid":"79fe8593207b9ab6c1deab80ad4a49fe","score":"0.50699216","text":"def __init__(self, ssid, psk=\"\", manager=NMWiFiManager()):\n self.ssid = ssid\n self.ssid_encrypt = None\n self.psk = psk\n self.user = \"\"\n self.passw = \"\"\n self.cells = {}\n self.iface = None\n self.is_shared = False\n self.interfaces = {}\n self.wifimanager = manager\n self.do_persist = False","title":""},{"docid":"bfed88b1d0937ee3955355a3cbd77ab5","score":"0.5069002","text":"def __init__(self, request, IPAddr = None, user = None, password=None, verbose=False, version=None):\n \n self.request = request\n # ONTAP credentials\n self.IPAddr = IPAddr\n self.user = user\n self.password = password\n \n self.verbose = verbose\n # ONTAP version\n self.version = version\n \n self.ontap_server = None\n \n if self.IPAddr and self.user and self.password:\n # ONTAP server\n s = None\n # Version control\n if self.version == \"9.4\":\n from NetApp_9_4 import (NaServer, NaElement)\n s = NaServer(self.IPAddr, 1, 140)\n else:\n from NetApp_9_3 import (NaServer, NaElement)\n s = NaServer(self.IPAddr, 1, 130)\n \n s.set_server_type(\"FILER\")\n s.set_transport_type(\"HTTPS\")\n s.set_port(443)\n s.set_style(\"LOGIN\")\n s.set_admin_user(self.user, self.password)\n \n self.ontap_server = s\n \n # Our request must be initialized. This may throw errors;\n # however, we would rather not initialize a driver if there\n # is a faulty request\n if not request.initialized:\n request.initialize()\n\n # dictionary which matches outputs of requests to functions\n self.test_dictionary = {\n 'local_check_file_permissions' : self._local_check_file_permissions,\n 'local_search_for_pattern' : self._local_search_for_pattern,\n 'ontap_ssl_enabled' : self._ontap_ssl_enabled,\n 'ontap_vols_encrypted' : self._ontap_vols_encrypted,\n 'ontap_autosupport_disabled' : self._ontap_autosupport_disabled,\n 'ontap_password_authentication' : self._ontap_password_authentication,\n 'ontap_sha_hash_enabled' : self._ontap_sha_hash_enabled,\n 'ontap_password_policy' : self._ontap_password_policy\n }\n\n # end constructor ---------","title":""},{"docid":"9c652646422d9b82177710e6565335ba","score":"0.50678843","text":"def __init__(self, iface):\n Layer.__init__(self)\n self.arp_cache = {}\n self.hwaddr = ARP().hwsrc\n self.ip = get_if_addr(iface)","title":""},{"docid":"4764ce211549c03f10e524cc8068fccc","score":"0.5063888","text":"def _prepare_network_object(**kwargs):\n net = apis_utils.data_st.Network()\n name = kwargs.get(\"name\")\n description = kwargs.get(\"description\")\n stp = kwargs.get(\"stp\")\n data_center = kwargs.get(\"data_center\")\n vlan_id = kwargs.get(\"vlan_id\")\n usages = kwargs.get(\"usages\")\n mtu = kwargs.get(\"mtu\")\n profile_required = kwargs.get(\"profile_required\")\n qos_dict = kwargs.get(\"qos_dict\")\n dns = kwargs.get(\"dns\")\n external_network_provider_name = kwargs.get(\"external_provider_name\")\n\n if name:\n net.set_name(name)\n\n if description:\n net.set_description(description)\n\n if stp:\n net.set_stp(stp)\n\n if data_center:\n net.set_data_center(DC_API.find(data_center))\n\n if \"vlan_id\" in kwargs:\n net.set_vlan(apis_utils.data_st.Vlan(id=vlan_id))\n\n if usages is not None:\n if usages:\n net.set_usages(apis_utils.data_st.Usages(usage=usages.split(\",\")))\n else:\n net.set_usages(apis_utils.data_st.Usages())\n\n if mtu:\n net.set_mtu(mtu)\n\n if profile_required:\n net.set_profile_required(profile_required)\n\n if qos_dict:\n qos_obj = prepare_qos_on_net(qos_dict)\n net.set_qos(qos_obj)\n\n if dns is not None:\n dns_obj = prepare_network_dns_object(dns_servers=dns)\n net.set_dns_resolver_configuration(dns_obj)\n\n if external_network_provider_name:\n enp = OPENSTACK_NETWORK_PROVIDER_API.find(\n external_network_provider_name\n )\n net.set_external_provider(external_provider=enp)\n\n return net","title":""},{"docid":"bbb6d3e71dc09de75bbeac067c700e74","score":"0.50576925","text":"def connectionSetup(self, dispatcher):\n self.logger.debug(\"Create the connection to the mgr....\")\n # Create a connection to Hal driver mgr\n self.mgrConnection = HalTransport(HalTransport.HalTransportClientMgr,\n HalTransport.HalClientMode,\n disconnectHandlerCb=self.connectionDisconnectCb)\n\n # create the poller\n if self.poller is None:\n self.poller = dispatcher.get_poll()\n\n # register the mgr socket\n dispatcher.fd_register(self.mgrConnection.socket,\n zmq.POLLIN, self.provmgr_cb)\n dispatcher.fd_register(self.mgrConnection.monitor,\n zmq.POLLIN, self.provmgr_cb)","title":""},{"docid":"c47e8c274606c024c9511f79c55b633c","score":"0.50534624","text":"def Setup(propagator, setup, earth):","title":""},{"docid":"e175a334d4063f362dfcf28d8faeb8a9","score":"0.5051624","text":"def setup_topology():\n hosts = []\n switches = []\n links = []\n\n\n log.info('Adding controller')\n c0 = net.addController(name='c0'\n #, controller=RemoteController,\n #ip=CONTROLLER_IP,\n #port=CONTROLLER_PORT,\n )\n\n # Adding a Standalone switch\n #switches[0] = net.addSwitch('s21', cls=OVSKernelSwitch, failMode='standalone')\n\n # Connecting the standalone switch to the interface on the machine/VM\n #Intf('eth1', node=s21)\n\n for switch in range(1,8):\n sname = 's' + str(switch)\n log.info(\"Adding switch %s\" % sname)\n s = net.addSwitch(sname)#, cls=OVSKernelSwitch)\n switches.append(s)\n\n\t# Connecting to VM interface\n # Intf('eth2', node=switches[0])\n\n for host in range(1,15):\n hname = 'h' + str(host)\n log.info(\"Adding host %s\" % hname)\n h = net.addHost(hname) #, ip=IP_PREFIX+str(host), mac=MAC_PREFIX+str(host))\n hosts.append(h)\n\n manual_links = [['s1', 'h1', 100], ['s1', 'h2', 100], ['s1', 's2', 50 ],\n ['s1', 's3', 50 ], #['s2', 's3', 50 ], \n\t\t\t['s2', 's4', 50 ],\n ['s2', 's5', 50 ], ['s3', 's6', 50 ], ['s3', 's7', 50 ],\n ['s4', 'h3', 10 ], ['s4', 'h4', 10 ], ['s4', 'h5', 10 ],\n ['s5', 'h6', 10 ], ['s5', 'h7', 10 ], ['s5', 'h8', 10 ],\n ['s6', 'h9', 10 ], ['s6', 'h10',10 ], ['s6', 'h11',10 ],\n ['s7', 'h12',10 ], ['s7', 'h13',10 ], ['s7', 'h14',10 ],\n ]\n for link in manual_links:\n from_link = link[0]\n to_link = link[1]\n log.debug(\"Adding link from %s to %s\" % (from_link, to_link))\n bw = link[2]\n '''\n _bw = attributes.get('bw', 10) # in Mbps\n _delay = '%fms' % attributes.get('latency', 10)\n _jitter = '1ms'\n _loss = error_rate\n '''\n\t l = net.addLink(link[0], link[1])\n # l = net.addLink(net.get(from_link), net.get(to_link)\n #, cls=TCLink, bw=bw\n # , delay=_delay, jitter=_jitter, loss=_loss\n # )\n links.append(l)\n\n \t# Link for internet\n \t# l = net.addLink('h1', 's1')\n \t# links.append(l)\n\n '''\n #Simple minimal topolgy code\n #TODO: move to a different file\n sname = 's' + '1'\n log.info(\"Adding switch %s\" % sname)\n s = net.addSwitch(sname, cls=OVSKernelSwitch)\n\n # for host in range(1,2):\n # hname = 'h' + str(host)\n # log.info(\"Adding host %s\" % hname)\n h1 = net.addHost('h1')\n h = net.addHost('h2')\n\n l = net.addLink('s1', 'h1',\n cls=TCLink, bw=10\n # , delay=_delay, jitter=_jitter, loss=_loss\n )\n l1 = net.addLink('s1', 'h2',\n \tcls=TCLink, bw=10\n # , delay=10ms, jitter=1ms, loss=error_rate\n )\n '''\n\n # Build the network\n log.info('Building network')\n net.build()\n\n # Start the network\n log.info('Starting network')\n net.start()\n\n\t# info('*** Configure h1\\'s controller communication interface\\n')\n # hosts[0].cmd('ifconfig h1-eth1 hw ether 00:00:00:00:01:11')\n\n # info('*** Configure h1\\'s IP address\\n')\n # hosts[0].cmd('dhclient h1-eth1')\n\n \t#net.pingAll()\n \t#Starting the internet and stream traffic\n \tdf.setup_traffic_generators(net)\n\t#Popen(\"killall -9 top bwm-ng\", shell=True).wait()\n # Drop the user in to a CLI so user can run commands.\n CLI( net )\n\n # After the user exits the CLI, shutdown the network.\n log.info('Stopping network')\n net.stop()","title":""},{"docid":"36812e805f3e53a93b044c0489b60aae","score":"0.50488305","text":"def setup(self):\r\n # Retrieve configuration settings\r\n addr = self.classconfig['addr']\r\n password = self.classconfig['password']\r\n if not addr:\r\n raise Exception('No address specified')\r\n # Check if addr is a \"host:port\" string\r\n (host, colon, port) = addr.partition(\":\")\r\n try:\r\n if colon:\r\n # IPv4/IPv6\r\n self._m = manager.manager(host=host, port=int(port), password=password)\r\n else:\r\n # UNIX Socket\r\n self._m = manager.manager(path=addr, password=password)\r\n # Test connection\r\n self.getpid()\r\n except Exception, e:\r\n raise Exception(str(e))","title":""},{"docid":"2d43ba17e0ab557696760336f99057cb","score":"0.5044762","text":"def create_wifi_list(self):\r\n self.wifi_information.generating_wifi_list()","title":""},{"docid":"3a9960d8682dc7db95f9867cfb1b3047","score":"0.50412697","text":"def init_network():\r\n network = {}\r\n network['W1'] = np.array([[0.1, 0.3, 0.5], [0.2, 0.4, 0.6]])\r\n network['b1'] = np.array([0.1, 0.2, 0.3])\r\n network['W2'] = np.array([[0.1, 0.4], [0.2, 0.5], [0.3, 0.6]])\r\n network['b2'] = np.array([0.1, 0.2])\r\n network['W3'] = np.array([[0.1, 0.3], [0.2, 0.4]])\r\n network['b3'] = np.array([0.1, 0.2])\r\n\r\n return network","title":""},{"docid":"50d1b42a1b534cb0a4c5b5a27445479d","score":"0.50291187","text":"def _initialize(self):\n key = self.get_api_key()\n if not key:\n print \"You have to specify a valid API key.\"\n self.safe_options = False\n return\n self.url = \"%s%s\" % (self.server, self.url_path % key)","title":""},{"docid":"dd8f099dd5ac2c5ea2b42ee67840ca9d","score":"0.5028334","text":"def __init__(self):\n \n # uplink\n self.uplink, self.events = var.core\n self.protocol = self.uplink.protocol\n self.bursting = True\n var.protocol = self\n # what we're going to send for CAPAB\n self.capab_msg = 'QS EX IE KLN UNKLN TB EUID RSFNC EOPMOD SERVICES'\n # numeric\n self.numeric = var.c.get('uplink', 'SID')\n # description and server info\n self.desc = var.c.get('uplink', 'description')\n self.name = var.c.get('uplink', 'host')\n self.needs_sid = var.c.get('uplink', 'needs_sid')\n # database\n self.db = var.database","title":""},{"docid":"8ccea962f40497ab79abbc326c931f24","score":"0.50222325","text":"def setwifi(timeout=35):\n\n import gc\n from setwifi import setup_station\n from time import sleep_ms\n import usocket as socket\n import network\n # create access-point interface\n ap_if = network.WLAN(network.AP_IF)\n ap_if.active(True)\n # the AP has to be active to be able to configure it\n try:\n ap_if.config(authmode=network.AUTH_OPEN)\n except Exception as e:\n e_str = str(e)\n if \"can't set AP config\" in e_str:\n print(\"Ignoring exception: \".format(e_str))\n else:\n return e\n port = 80\n cfg_data = {}\n sta_if = network.WLAN(network.STA_IF)\n\n return_code = None\n setup_attempts = 0\n\n # the following constants are defined because the HTML strings need to match the python strings.\n SSID = \"SSID\"\n PASSWORD = \"password\"\n SUBMIT_VALUE = \"submit_value\"\n CANCEL = \"Cancel\"\n NETWORK_ID = \"Network ID\"\n \n MSG_ALREADY_CONNECTED = \"WiFi is already connected to a network - hit {} to leave it be.\"\n\n html_page = \"\"\"HTTP/1.1 200 OK\n Content-type:text/html\n Connection: close\\n\n <!DOCTYPE html><html>\n <head><title>{title}</title></head>\n <style>body {{background-color: lightblue}} h2 {{color: Red;}}</style>\n <div style=\\\"display: inline-block; text-align: center; color: Black; align: center; width: 100%\\\">\n <body><h2>{message}</h2><h3>{body}</h3></body>\n {form}\n </div>\n </html>\"\"\"\n \n ssid_form = \"\"\"<form method=\\\"POST\\\">\n {NETWORK_ID}:<br>\n <input type=\\\"text\\\" name=\\\"{SSID}\\\"><br>\n Password:<br>\n <input type=\\\"password\\\" name=\\\"{PASSWORD}\\\"><br><br>\n <input type=\\\"submit\\\" name =\\\"{SUBMIT_VALUE}\\\" style=\\\"background-color:#00FF80\\\">\n <input type=\\\"submit\\\" name =\\\"{SUBMIT_VALUE}\\\" value=\\\"{CANCEL}\\\" style=\\\"background-color:#D8D8D8\\\">\n </form>\"\"\"\n\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.settimeout(timeout)\n s.bind(('', port))\n s.listen(2)\n print('\\n-- Now Listening on: {} {}:{}...\\n'.format(ap_if.config('essid'), ap_if.ifconfig()[0], port))\n \n page_title = \"Configure WiFi\"\n\n try:\n while return_code is None:\n conn, addr = s.accept()\n method = None\n content_length = None\n page_message = \"\"\n if sta_if.isconnected():\n page_message = MSG_ALREADY_CONNECTED.format(CANCEL)\n page_body = \"Please enter your WiFi Info\"\n page_form = ssid_form.format(NETWORK_ID=NETWORK_ID, SSID=SSID, PASSWORD=PASSWORD, SUBMIT_VALUE=SUBMIT_VALUE, CANCEL=CANCEL)\n\n request = conn.recv(1024)\n\n lines = [d.strip() for d in request.decode('utf-8').split('\\r\\n')]\n method, raw_path, version = lines[0].split(' ')\n# path = raw_path.split('?', 1)[0]\n# print(\" request: {:5} {}\".format(method, path))\n headers = {k: v for k, v in\n (l.split(': ') for l in lines[1:-2])}\n# print(\" headers: {}\".format(headers))\n# host = headers['Host']\n# print(\" host: {}\".format(host))\n if 'Content-Length' in headers:\n content_length = headers['Content-Length']\n\n if method == 'POST':\n if content_length is None:\n print('Error: no content length in POST')\n page_message = \"Browser Error: no content length\"\n else:\n post_data = conn.recv(int(content_length))\n post_data = post_data.decode('UTF-8')\n# print('\\npost_data: {}'.format(post_data))\n for i, val in enumerate(post_data.split('&')):\n (k,v) = val.split('=')\n cfg_data[k] = v\n\n if SUBMIT_VALUE in cfg_data and cfg_data[SUBMIT_VALUE] == CANCEL:\n page_body=\"WiFi setup cancelled.\"\n page_message=\"\"\n page_form=\"\"\n return_code = \"cancelled: attempts: \" + str(setup_attempts)\n # could have added javascript validation, but since it can be disabled, its recommended the server do the validation\n elif SSID not in cfg_data or len(cfg_data[SSID]) < 2 or PASSWORD not in cfg_data or len(cfg_data[PASSWORD]) < 4:\n if SSID not in cfg_data or len(cfg_data[SSID]) < 2:\n page_message = \"Invalid {}: '{}'\".format(NETWORK_ID, cfg_data[SSID])\n else:\n page_message = \"Invalid {}: '{}'\".format(PASSWORD, cfg_data[PASSWORD])\n cfg_data = {}\n else:\n setup_ok, setup_message = setup_station(cfg_data[SSID], cfg_data[PASSWORD])\n setup_attempts += 1\n if setup_ok:\n page_body = setup_message\n page_form = \"\"\n return_code = \"done: connected\"\n else:\n page_message=setup_message\n cfg_data = {}\n\n response = html_page.format(title=page_title, message=page_message, body=page_body, form=page_form)\n conn.send(response)\n conn.close()\n gc.collect()\n\n except Exception as e:\n e_str = str(e)\n if 'ETIMEDOUT' in e_str:\n return_code = \"timeout\"\n else:\n return_code = \"exception: \".format(e_str)\n\n sleep_ms(2000)\n s.close()\n sleep_ms(2000)\n ap_if.active(False)\n return return_code","title":""},{"docid":"851e1d3550761d1bcc8da0394a68001b","score":"0.50136715","text":"def _setup_location(self):\n self.logger.debug('Setting up site details of observatory')\n\n try:\n config_site = self.config.get('location')\n\n name = config_site.get('name', 'Nameless Location')\n\n latitude = config_site.get('latitude')\n longitude = config_site.get('longitude')\n\n timezone = config_site.get('timezone')\n utc_offset = config_site.get('utc_offset')\n\n pressure = config_site.get('pressure', 0.680) * u.bar\n elevation = config_site.get('elevation', 0 * u.meter)\n horizon = config_site.get('horizon', 30 * u.degree)\n twilight_horizon = config_site.get(\n 'twilight_horizon', -18 * u.degree)\n\n self.location = {\n 'name': name,\n 'latitude': latitude,\n 'longitude': longitude,\n 'elevation': elevation,\n 'timezone': timezone,\n 'utc_offset': utc_offset,\n 'pressure': pressure,\n 'horizon': horizon,\n 'twilight_horizon': twilight_horizon,\n }\n self.logger.debug(\"Location: {}\".format(self.location))\n\n # Create an EarthLocation for the mount\n self.earth_location = EarthLocation(\n lat=latitude, lon=longitude, height=elevation)\n self.observer = Observer(\n location=self.earth_location, name=name, timezone=timezone)\n except Exception:\n raise error.PanError(msg='Bad site information')","title":""},{"docid":"8721c254fbad966fb914601742e3969d","score":"0.50121903","text":"def __init__(self, config):\n self.conf = dict(ip_addr = '192.168.0.20',\n username = 'anonymous',\n password = '',\n rootpath = '',\n init = True,\n rd_blocksize = 2048,\n debug = 0,\n )\n self.conf.update(config)\n if self.conf['init']: self.initialize()","title":""},{"docid":"02d13be8cb70f52658be80e21e447984","score":"0.5011904","text":"def start(self):\r\n self._log('Starting LoRaWAN nano forwarder with id: {}', self.id)\r\n\r\n # Define ID and Keys in LORAWAN mode.\r\n if self.mode != 1:\r\n self.lorawan = LoRa(mode=LoRa.LORAWAN)\r\n self.dev_addr = struct.unpack(\">l\", ubinascii.unhexlify('2601160C'))[0] #create an ABP authentication params\r\n self.nwk_swkey = ubinascii.unhexlify('5864350495E1123A5AD221DF700CCEC8')\r\n self.app_swkey = ubinascii.unhexlify('4B751B14B6C2C7BAB00B46EEA30E920D')\r\n for channel in range(0, 72): # remove all the channels\r\n self.lorawan.remove_channel(channel)\r\n for channel in range(0, 72): # set all channels to the same frequency (must be before sending the OTAA join request)\r\n self.lorawan.add_channel(channel, frequency=903900000, dr_min=0, dr_max=3)\r\n self.lorawan.join(activation=LoRa.ABP, auth=(self.dev_addr, self.nwk_swkey, self.app_swkey)) # join a network using ABP (Activation By Personalization)\r\n self.lorawan_sock = usocket.socket(usocket.AF_LORA, usocket.SOCK_RAW) # create a LoRa usocket\r\n self.lorawan_sock.setsockopt(usocket.SOL_LORA, usocket.SO_DR, 3) # set the LoRaWAN data rate\r\n self.lorawan_sock.setblocking(True) # make the usocket blocking\r\n\r\n #Saving LoRaWAN State in NVRAM\r\n if self.mode == 3:\r\n self.lorawan.nvram_save()\r\n\r\n # initialize the LoRa radio in LORA mode\r\n if self.mode != 2:\r\n self._log('Setting up the LoRa radio at {} Mhz using {}', self._freq_to_float(self.frequency), self.datarate)\r\n self.lora = LoRa(\r\n mode=LoRa.LORA,\r\n frequency=self.frequency,\r\n bandwidth=self.bw,\r\n sf=self.sf,\r\n preamble=8,\r\n coding_rate=LoRa.CODING_4_5,\r\n tx_iq=True\r\n )\r\n\r\n # create a raw LoRa socket\r\n self.lora_sock = usocket.socket(usocket.AF_LORA, usocket.SOCK_RAW)\r\n self.lora_sock.setblocking(False)\r\n self.lora_tx_done = False\r\n self.lora.callback(trigger=(LoRa.RX_PACKET_EVENT), handler=self._lora_cb)\r\n #self.lora.callback(trigger=(LoRa.RX_PACKET_EVENT), handler=self._lorawan_cb)\r\n\r\n #initialize application\r\n if self.mode != 1:\r\n self.app_alarm = Timer.Alarm(handler=lambda t: self._app(), s=7, periodic=True)\r\n self._log('LoRaWAN nano forwarder online')","title":""},{"docid":"faf22bb4716b2d4ef3fdd4396177416e","score":"0.5005024","text":"def setup_network_namespace(node, namespace_name, interface_name,\n ip_addr_list, prefix_length):\n Namespaces.create_namespace(node, namespace_name)\n\n cmd = f\"ip netns exec {namespace_name} ip link set {interface_name} up\"\n exec_cmd_no_error(node, cmd, sudo=True)\n\n for ip_addr in ip_addr_list:\n cmd = f\"ip netns exec {namespace_name} ip addr add \" \\\n f\"{ip_addr}/{prefix_length} dev {interface_name}\"\n exec_cmd_no_error(node, cmd, sudo=True)","title":""},{"docid":"7638ebf5926e24b9cfaa654007d96a02","score":"0.5004933","text":"def setup_method(self):\n self.hass = get_test_home_assistant()\n\n asyncio.run_coroutine_threadsafe(\n async_process_ha_core_config(\n self.hass, {\"internal_url\": \"http://example.local:8123\"}\n ),\n self.hass.loop,\n )\n\n self.url = \"https://translate.google.com/translate_tts\"\n self.url_param = {\n \"tl\": \"en\",\n \"q\": \"90%25%20of%20I%20person%20is%20on%20front%20of%20your%20door.\",\n \"tk\": 5,\n \"client\": \"tw-ob\",\n \"textlen\": 41,\n \"total\": 1,\n \"idx\": 0,\n \"ie\": \"UTF-8\",\n }","title":""},{"docid":"ec2686e7f05090ff20e636fe6ea91f4b","score":"0.49981785","text":"def setup(self):\n\t\t(self.packet, self.socket) = self.request\n\t\tself.replies = []","title":""},{"docid":"fb72ce26325aa5b41dda1ee15b810927","score":"0.49965006","text":"def setUp(self):\n self.net = Net('001')","title":""},{"docid":"ba1478ce753de4b36e28c662a07ad92a","score":"0.4991803","text":"def handle_setup(self) -> None:\n setup_xml = (\n '<?xml version=\"1.0\"?>'\n \"<root>\"\n \"<specVersion><major>1</major><minor>0</minor></specVersion>\"\n \"<device>\"\n \"<deviceType>urn:Belkin:device:controllee:1</deviceType>\"\n f\"<friendlyName>{self.name}</friendlyName>\"\n \"<manufacturer>Belkin International Inc.</manufacturer>\"\n \"<modelName>Emulated Socket</modelName>\"\n \"<modelNumber>3.1415</modelNumber>\"\n f\"<UDN>uuid:Socket-1_0-{self.serial}</UDN>\"\n \"<serviceList>\"\n \"<service>\"\n \"<serviceType>urn:Belkin:service:basicevent:1</serviceType>\"\n \"<serviceId>urn:Belkin:serviceId:basicevent1</serviceId>\"\n \"<controlURL>/upnp/control/basicevent1</controlURL>\"\n \"<eventSubURL>/upnp/event/basicevent1</eventSubURL>\"\n \"<SCPDURL>/eventservice.xml</SCPDURL>\"\n \"</service>\"\n \"<service>\"\n \"<serviceType>urn:Belkin:service:metainfo:1</serviceType>\"\n \"<serviceId>urn:Belkin:serviceId:metainfo1</serviceId>\"\n \"<controlURL>/upnp/control/metainfo1</controlURL>\"\n \"<eventSubURL>/upnp/event/metainfo1</eventSubURL>\"\n \"<SCPDURL>/metainfoservice.xml</SCPDURL>\"\n \"</service>\"\n \"</serviceList>\"\n \"</device>\"\n \"</root>\"\n )\n\n setup_response = self.add_http_headers(setup_xml)\n logger.debug(f\"Fauxmo response to setup request:\\n{setup_response}\")\n self.transport.write(setup_response.encode())\n self.transport.close()","title":""},{"docid":"fdef0e9e750d81f8022cd8e8ad9510c7","score":"0.49893913","text":"def _SetupVtiNetwork(cls, vti, is_add):\n if is_add:\n # Disable router solicitations to avoid occasional spurious packets\n # arriving on the underlying network; there are two possible behaviors\n # when that occurred: either only the RA packet is read, and when it\n # is echoed back to the VTI, it causes the test to fail by not receiving\n # the UDP_PAYLOAD; or, two packets may arrive on the underlying\n # network which fails the assertion that only one ESP packet is received.\n cls.SetSysctl(\n \"/proc/sys/net/ipv6/conf/%s/router_solicitations\" % vti.iface, 0)\n net_test.SetInterfaceUp(vti.iface)\n\n for version in [4, 6]:\n ifindex = net_test.GetInterfaceIndex(vti.iface)\n table = vti.netid\n\n # Set up routing rules.\n start, end = cls.UidRangeForNetid(vti.netid)\n cls.iproute.UidRangeRule(version, is_add, start, end, table,\n cls.PRIORITY_UID)\n cls.iproute.OifRule(version, is_add, vti.iface, table, cls.PRIORITY_OIF)\n cls.iproute.FwmarkRule(version, is_add, vti.netid, cls.NETID_FWMASK,\n table, cls.PRIORITY_FWMARK)\n\n # Configure IP addresses.\n if version == 4:\n addr = cls._MyIPv4Address(vti.netid)\n else:\n addr = cls.OnlinkPrefix(6, vti.netid) + \"1\"\n prefixlen = net_test.AddressLengthBits(version)\n vti.addrs[version] = addr\n if is_add:\n cls.iproute.AddAddress(addr, prefixlen, ifindex)\n cls.iproute.AddRoute(version, table, \"default\", 0, None, ifindex)\n else:\n cls.iproute.DelRoute(version, table, \"default\", 0, None, ifindex)\n cls.iproute.DelAddress(addr, prefixlen, ifindex)","title":""},{"docid":"0c17979c55cb1215310084853f57159e","score":"0.49857548","text":"def setup_method(self):\n self.hass = get_test_home_assistant()\n\n self.config = {rc.DOMAIN: {\"test_get\": {\"url\": \"http://example.com/\"}}}","title":""},{"docid":"d3fa339244fc21e7618d34958d242c2d","score":"0.4983651","text":"def __init__(self):\n # Call the constructor of the parent class\n super(self.__class__,self).__init__()\n\n self._yarpPorts = dict()\n self._component_ports = dict()\n\n # Get the Network attribute of yarp,\n # then call its init method\n # Strange that we should do all this,\n # probably because this file has the same name.\n # So this is the only way it seems to work\n self._yarp_module = sys.modules['yarp']\n #self._yarp_module.Network.init()\n self.yarp_object = self._yarp_module.Network()\n #yarp.Network.init()","title":""},{"docid":"80f201777e12b27373ff1f733e5e272b","score":"0.49818295","text":"def setUp(self):\n guid = self.__class__.__name__ + '-' + str(uuid.uuid4())\n stack_name = guid + '-stack'\n\n self.net_name = guid + '-net'\n self.subnet_name = guid + '-subnet'\n self.router_name = guid + '-router'\n\n env_values = {\n 'net_name': self.net_name,\n 'subnet_name': self.subnet_name,\n 'router_name': self.router_name,\n 'external_net_name': self.ext_net_name}\n\n heat_tmplt_path = pkg_resources.resource_filename(\n 'snaps.openstack.tests.heat', 'router_heat_template.yaml')\n self.stack_settings = StackConfig(\n name=stack_name, template_path=heat_tmplt_path,\n env_values=env_values)\n self.stack = None\n self.heat_client = heat_utils.heat_client(\n self.os_creds, self.os_session)\n self.neutron = neutron_utils.neutron_client(\n self.os_creds, self.os_session)","title":""},{"docid":"eb82d593a81d7660fdc7b2766020ad41","score":"0.49767727","text":"def __init__(__self__, *,\n network: pulumi.Input[str],\n description: Optional[pulumi.Input[str]] = None,\n name: Optional[pulumi.Input[str]] = None,\n project: Optional[pulumi.Input[str]] = None,\n region: Optional[pulumi.Input[str]] = None,\n stack_type: Optional[pulumi.Input[str]] = None,\n vpn_interfaces: Optional[pulumi.Input[Sequence[pulumi.Input['HaVpnGatewayVpnInterfaceArgs']]]] = None):\n pulumi.set(__self__, \"network\", network)\n if description is not None:\n pulumi.set(__self__, \"description\", description)\n if name is not None:\n pulumi.set(__self__, \"name\", name)\n if project is not None:\n pulumi.set(__self__, \"project\", project)\n if region is not None:\n pulumi.set(__self__, \"region\", region)\n if stack_type is not None:\n pulumi.set(__self__, \"stack_type\", stack_type)\n if vpn_interfaces is not None:\n pulumi.set(__self__, \"vpn_interfaces\", vpn_interfaces)","title":""},{"docid":"c65cd6a7b8b1c478e97eae693f78a2cd","score":"0.49697104","text":"def setup(self):\n context = zmq.Context()\n self._insocket = context.socket(zmq.ROUTER)\n self._outsocket = context.socket(zmq.DEALER)\n \n self._insocket.bind(self._in_address)\n self._outsocket.bind(self._out_address)\n \n self._loop = IOLoop.instance()","title":""},{"docid":"a0314e3203f8bea10f9fe0144d7788b3","score":"0.4961752","text":"def configure_wifi(self,\n ssid=None,\n psk=None,\n card_os='raspberry',\n host=None,\n country=None):\n\n country = country or 'US'\n\n card = SDCard(card_os=card_os, host_os=host)\n path = f\"{card.boot_volume}/wpa_supplicant.conf\"\n\n card_os = \"raspberry\" # needs to become a parameter based on tag\n\n # noinspection PyPep8Naming\n WifiClass = Wifi(card_os=card_os)\n\n if card_os == \"raspberry\":\n if psk:\n if os_is_mac():\n WifiClass.set(ssid=ssid, password=psk, country=country, location=path)\n else:\n WifiClass.set(ssid=ssid, password=psk, country=country, location=path, sudo=True)\n else:\n if os_is_mac():\n WifiClass.set(ssid=ssid, psk=False, country=country, location=path)\n else:\n WifiClass.set(ssid=ssid, psk=False, country=country, location=path, sudo=True)\n else:\n if os_is_mac():\n WifiClass.set(ssid=ssid, password=psk, country=country, location=path)\n else:\n WifiClass.set(ssid=ssid, password=psk, country=country, location=path, sudo=True)\n\n return \"\"","title":""},{"docid":"127b6fa2c88ee2c89b25ce762b2957fc","score":"0.49546182","text":"def setup_netconf_device(self):\n\n create_authgroup(self.device_name, self.username, self.password)\n create_device(self.device_name, self.ip_address,\n self.netconf_port, self.ned_id)\n fetch_host_keys(self.device_name)\n sync_from(self.device_name)","title":""},{"docid":"9c0ae528af0e37ff26338fc9218929e5","score":"0.4939803","text":"def init_communication(self):\n ctx = zmq.Context()\n self.pull_socket = ctx.socket(zmq.PULL)\n self.pull_socket.bind(f\"tcp://127.0.0.1:{self.comm_cfg.learner_logger_port}\")","title":""},{"docid":"69f56ad7a8b99ef7e89641ea794821b0","score":"0.493872","text":"def startup(self):\n if self.detectoldbridge():\n return\n\n CoreNetwork.startup(self)\n\n if self.hostid:\n addr = self.prefix.addr(self.hostid)\n else:\n addr = self.prefix.max_addr()\n\n logging.info(\"added control network bridge: %s %s\", self.brname, self.prefix)\n\n if self.assign_address:\n addrlist = [\"%s/%s\" % (addr, self.prefix.prefixlen)]\n self.addrconfig(addrlist=addrlist)\n logging.info(\"address %s\", addr)\n\n if self.updown_script:\n logging.info(\"interface %s updown script (%s startup) called\", self.brname, self.updown_script)\n utils.check_cmd([self.updown_script, self.brname, \"startup\"])\n\n if self.serverintf:\n # sets the interface as a port of the bridge\n utils.check_cmd([constants.BRCTL_BIN, \"addif\", self.brname, self.serverintf])\n\n # bring interface up\n utils.check_cmd([constants.IP_BIN, \"link\", \"set\", self.serverintf, \"up\"])","title":""},{"docid":"72a458849d73eaeb7f4f8a0b10cd8c36","score":"0.49371934","text":"def bootstrap(self):\n self.merge_ip_address_list()\n self.create_firewall_rules()\n self.populate_droplet_tags()","title":""}],"string":"[\n {\n \"docid\": \"ba63a9cd6fd3b985f72e362db7c83127\",\n \"score\": \"0.63868827\",\n \"text\": \"def setup_network(cfg):\\n display_msg(\\\"Init Network...\\\", msg2=\\\"Looking for WiFI\\\",\\n msg3=cfg.cfg_json['WIFI_SSID'])\\n\\n # wait 2 seconds to see if the network comes up\\n time.sleep(2)\\n\\n sta_if = network.WLAN(network.STA_IF)\\n ap_if = network.WLAN(network.AP_IF)\\n\\n if ap_if.active():\\n ap_if.active(False)\\n\\n if not sta_if.isconnected():\\n display_msg(\\\"Connecting to WiFI..\\\")\\n sta_if.active(True)\\n sta_if.connect(cfg.cfg_json['WIFI_SSID'], cfg.cfg_json['WIFI_PASSWORD'])\\n while not sta_if.isconnected():\\n display_msg(\\\"Connecting to WiFI...\\\")\\n display_msg(\\\"Connected to \\\", msg2=cfg.cfg_json['WIFI_SSID'])\\n else:\\n display_msg(\\\"Connected to \\\", msg2=cfg.cfg_json['WIFI_SSID'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20f53b4c77b76c68b48680d6ce9ee749\",\n \"score\": \"0.63112587\",\n \"text\": \"def setup(self) -> None:\\n # Create netns\\n self.create()\\n # Create links/interfaces + address them + assign to netns\\n self.setup_links()\\n # Create oob if selected\\n self.create_oob()\\n # Add any static routes\\n self.route_add()\\n\\n LOG.info(f\\\"Finished setup of {self.name} namespace\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4f6414623003c3e6b229e8f925916e6\",\n \"score\": \"0.6296718\",\n \"text\": \"def setup_net(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c5d0777f666403753908c98381f8d03\",\n \"score\": \"0.6200128\",\n \"text\": \"def setup_ap(ssid=config.SSID, channel=1):\\n wlan = network.WLAN(\\n mode=network.WLAN.AP,\\n ssid=ssid,\\n auth=(network.WLAN.WPA2, config.WLAN_PASSWORD),\\n channel=channel,\\n antenna=network.WLAN.INT_ANT)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e803f06e019cd9680159048f6cb21970\",\n \"score\": \"0.61444616\",\n \"text\": \"def startup(self):\\n CoreNetwork.startup(self)\\n if self.gretap:\\n self.attach(self.gretap)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac4edd2c96a2a41632eb6565dc0ba85a\",\n \"score\": \"0.607519\",\n \"text\": \"def _init_sniffer(self):\\n self._sniffer_server.run('sudo modprobe -r iwlwifi')\\n self._sniffer_server.run('sudo dmesg -C')\\n self._sniffer_server.run('cat /dev/null | sudo tee /var/log/syslog')\\n self._sniffer_server.run('sudo modprobe iwlwifi debug=0x1')\\n # Wait for wifi config changes before trying to further configuration\\n # e.g. setting monitor mode (which will fail if above is not complete)\\n time.sleep(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee9b6eb9265d80e46f7d1c985d31247b\",\n \"score\": \"0.6048787\",\n \"text\": \"def _configure_sniffer(self, network, chan, bw):\\n\\n self.log.debug('Connecting to network {}'.format(network['SSID']))\\n\\n if 'password' not in network:\\n network['password'] = ''\\n\\n connect_command = 'networksetup -setairportnetwork en0 {} {}'.format(\\n network['SSID'], network['password'])\\n self._sniffer_server.run(connect_command)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48ba81120e8e2bcec01a10ea02a3ef5c\",\n \"score\": \"0.60117435\",\n \"text\": \"def __init__(self):\\n\\n # Setup the fields\\n self._interfaces = {}\\n self.ap_iface = \\\"\\\"\\n self.jam_iface = \\\"\\\"\\n\\n # Create, add and check compatibility for each interface\\n for interface in pyw.interfaces():\\n try:\\n self._interfaces[interface] = NetworkAdapter(interface)\\n except pyric.error as e:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02753636ab30c315b4b1ea5b4984e7ac\",\n \"score\": \"0.5844891\",\n \"text\": \"def create(args):\\n global ap_face, ap_ssid, ap_password\\n ap_face = args.interface\\n ap_ssid = args.ssid\\n ap_password = args.password\\n\\n print('Giving %s a random Mac address...' % ap_face)\\n assign_ap_mac()\\n\\n print(\\\"\\\\nConfiguring %s to ip %s...\\\" % (ap_face, AP_IP))\\n assign_ap_ip()\\n\\n print(\\\"\\\\nStarting dnsmasq...\\\")\\n start_dnsmasq()\\n\\n print('\\\\nStarting hostapd...')\\n start_hostapd()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec563bc92410d7cbde1ccaed1e86141d\",\n \"score\": \"0.58421624\",\n \"text\": \"def init_network(roads):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a607fa836c4d827a6247c952eb14f168\",\n \"score\": \"0.58065313\",\n \"text\": \"def setup(self):\\n self.trajectory_model = TrajectoryGraphNet(self.config)\\n self.winding_model = WindingGraphNet(self.config)\\n self.trajectory_model.to(self.device)\\n self.winding_model.to(self.device)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9da265c8c9d3b93f0f5bca6f715d4ef8\",\n \"score\": \"0.5804919\",\n \"text\": \"def wadlibrary_set_up(self):\\n self.set_up_driver()\\n self.set_up()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e4db673d0a7fc5aa0d409200c148518\",\n \"score\": \"0.58009803\",\n \"text\": \"def setup(self):\\n self._logger.debug('Setting up ' + str(self._tunnel_operation))\\n if self._tunnel_operation == \\\"encapsulation\\\":\\n self._setup_encap()\\n else:\\n if settings.getValue('VSWITCH').endswith('Vanilla'):\\n self._setup_decap_vanilla()\\n else:\\n self._setup_decap()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7113a9cb992bb14b8d766b1c229cc2f\",\n \"score\": \"0.5674588\",\n \"text\": \"def _setup_device_fw(self, restricted, web_forward, http_fwd_addr, http_fwd_port, https_fwd_addr, https_fwd_port, spoof_prevention, is_site_to_site):\\n\\n # allowed ports in restricted mode\\n if restricted and not web_forward:\\n allow_tcp_ports = [ ]\\n allow_udp_ports = [ ]\\n else:\\n allow_tcp_ports = [ 53 ] # dns=53, wins=137\\n allow_udp_ports = [ 53, 137 ]\\n\\n # collect all parameters\\n params = {}\\n params['dev'] = self.ppp_interface\\n params['peeraddress'] = self.ppp_ipremote\\n params['ownaddress'] = self.ppp_iplocal\\n \\n # add customization chain commands to params dict (if enabled)\\n add_custom_chains = False\\n\\n t = {'raw': [('prerouting', 'prert', True),\\n ('output', 'output', True)],\\n 'nat': [('prerouting', 'prert', True),\\n ('output', 'output', True),\\n ('postrouting', 'postrt', True)],\\n 'mangle': [('prerouting', 'prert', True),\\n ('input', 'input', True),\\n ('forward', 'forward', True),\\n ('output', 'output', True),\\n ('postrouting', 'postrt', True)],\\n 'filter': [('input', 'input', True),\\n ('forward', 'forward', True),\\n ('output', 'output', True)]}\\n\\n for i in ['raw', 'nat', 'mangle', 'filter']:\\n for j in t[i]:\\n ident = 'custom_%s_%s' % (i, j[0])\\n if j[1] and add_custom_chains:\\n # e.g.: -A ppp_prert_l2tp12345-12345 -j raw_prerouting_ppp_cust\\n params[ident] = '-A ppp_%s_%s -j %s_%s_ppp_cust' % (j[1], params['dev'], i, j[0])\\n else:\\n params[ident] = ''\\n\\n # check web forward\\n w = ''\\n if web_forward:\\n # Own ppp address should be exempt so normal web UI can be used even when\\n # web forwarding is on. This also prevents forwarded connections from being\\n # reforwarded.\\n w += ('-A ppp_prert_%(dev)s ! -d %(ownaddress)s -p tcp --dport 80 -j DNAT --to-destination ' % params) + '%s:%s-%s\\\\n' % (http_fwd_addr, http_fwd_port, http_fwd_port)\\n w += ('-A ppp_prert_%(dev)s ! -d %(ownaddress)s -p tcp --dport 443 -j DNAT --to-destination ' % params) + '%s:%s-%s\\\\n' % (https_fwd_addr, https_fwd_port, https_fwd_port)\\n params['portforward_nat'] = w\\n\\n # check spoof prevention\\n s = ''\\n if spoof_prevention:\\n s = '-A ppp_prert_%s -s ! %s -j DROP' % (params['dev'], params['peeraddress'])\\n params['spoof_raw_prerouting'] = s\\n\\n # check restrictions\\n r = ''\\n if restricted:\\n for i in allow_tcp_ports:\\n r += ('-A ppp_forward_%(dev)s -p tcp --dport ' % params) + str(i) + ' -j ACCEPT\\\\n'\\n for i in allow_udp_ports:\\n r += ('-A ppp_forward_%(dev)s -p udp --dport ' % params) + str(i) + ' -j ACCEPT\\\\n'\\n r += '-A ppp_forward_%(dev)s -j REJECT --reject-with icmp-port-unreachable\\\\n' % params\\n params['restricted_filter_forward'] = r\\n\\n r = ''\\n if restricted:\\n r += ('-A ppp_prert_%(dev)s -j MARK --set-mark ' % params) + ('%s' % constants.FWMARK_LICENSE_RESTRICTED) + '\\\\n'\\n params['license_restricted_marking'] = r\\n \\n # mark site-to-site packets with a separate fwmark for routing purposes; note\\n # that this mark is applies for *both* incoming and outgoing packets, unlike\\n # the normal FWMARK_PPP.\\n s = ''\\n if is_site_to_site:\\n s += ('-A ppp_prert_%(dev)s -j MARK --set-mark ' % params) + ('%s' % constants.FWMARK_PPP_S2S) + '\\\\n'\\n s += ('-A ppp_postrt_%(dev)s -j MARK --set-mark ' % params) + ('%s' % constants.FWMARK_PPP_S2S) + '\\\\n'\\n params['site_to_site_marking'] = s\\n \\n # create iptables-restore script\\n tables = textwrap.dedent(\\\"\\\"\\\"\\\\\\n *raw\\n :ppp_prert_%(dev)s -\\n :ppp_output_%(dev)s -\\n -A raw_prerouting_ppp -i %(dev)s -j ppp_prert_%(dev)s\\n -A raw_output_ppp -o %(dev)s -j ppp_output_%(dev)s\\n\\n %(spoof_raw_prerouting)s\\n %(custom_raw_prerouting)s\\n %(custom_raw_output)s\\n COMMIT\\n \\n *nat\\n :ppp_prert_%(dev)s -\\n :ppp_output_%(dev)s -\\n :ppp_postrt_%(dev)s -\\n -A nat_prerouting_ppp -i %(dev)s -j ppp_prert_%(dev)s\\n -A nat_output_ppp -o %(dev)s -j ppp_output_%(dev)s\\n -A nat_postrouting_ppp -o %(dev)s -j ppp_postrt_%(dev)s\\n\\n %(portforward_nat)s\\n %(custom_nat_prerouting)s\\n %(custom_nat_output)s\\n %(custom_nat_postrouting)s\\n COMMIT\\n \\n *mangle\\n :ppp_prert_%(dev)s -\\n :ppp_input_%(dev)s -\\n :ppp_forward_%(dev)s -\\n :ppp_output_%(dev)s -\\n :ppp_postrt_%(dev)s -\\n -A mangle_prerouting_ppp -i %(dev)s -j ppp_prert_%(dev)s\\n -A mangle_input_ppp -i %(dev)s -j ppp_input_%(dev)s\\n -A mangle_forward_ppp -i %(dev)s -j ppp_forward_%(dev)s\\n -A mangle_forward_ppp -o %(dev)s -j ppp_forward_%(dev)s\\n -A mangle_output_ppp -o %(dev)s -j ppp_output_%(dev)s\\n -A mangle_postrouting_ppp -o %(dev)s -j ppp_postrt_%(dev)s\\n\\n %(license_restricted_marking)s\\n %(site_to_site_marking)s\\n %(custom_mangle_prerouting)s\\n %(custom_mangle_input)s\\n %(custom_mangle_forward)s\\n %(custom_mangle_output)s\\n %(custom_mangle_postrouting)s\\n COMMIT\\n\\n *filter\\n :ppp_input_%(dev)s -\\n :ppp_forward_%(dev)s -\\n :ppp_output_%(dev)s -\\n -A filter_input_ppp -i %(dev)s -j ppp_input_%(dev)s\\n -A filter_forward_ppp -i %(dev)s -j ppp_forward_%(dev)s\\n -A filter_forward_ppp -o %(dev)s -j ppp_forward_%(dev)s\\n -A filter_output_ppp -o %(dev)s -j ppp_output_%(dev)s\\n\\n %(custom_filter_input)s\\n %(restricted_filter_forward)s\\n -A ppp_forward_%(dev)s -i %(dev)s -j filter_forward_ppp_firewall\\n %(custom_filter_forward)s\\n %(custom_filter_output)s\\n COMMIT\\n \\\"\\\"\\\")\\n tables = tables % params\\n\\n # debug log of individual lines; this can be removed if\\n # logger pre-step for multiline stuff is written\\n if self.debug:\\n self._log.debug('--- iptables-restore ---')\\n for linenum, line in enumerate(tables.split('\\\\n')):\\n t = line.strip()\\n self._log.debug('%d: %s' % (linenum+1, t))\\n\\n iptables_lock = helpers.acquire_iptables_lock()\\n # execution\\n try:\\n (retval, retout, reterr) = run_command([constants.CMD_IPTABLES_RESTORE, '-n'], stdin=tables.encode('ascii'), retval=runcommand.FAIL)\\n except:\\n helpers.release_iptables_lock(iptables_lock)\\n raise\\n\\n helpers.release_iptables_lock(iptables_lock)\\n\\n self._log.debug('iptables-restore => %s\\\\n%s\\\\n%s' % (retval, retout, reterr))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"477e6ae859db678adb77a30b08a8d9d5\",\n \"score\": \"0.5652874\",\n \"text\": \"def setup(self):\\r\\n # get tracking method\\r\\n self.tracking_method = self.cfg.tracking_method\\r\\n self.initialize_tracker()\\r\\n\\r\\n # initialize keypoint sampler\\r\\n self.kp_sampler = KeypointSampler(self.cfg)\\r\\n\\r\\n # Deep networks\\r\\n self.deep_models = DeepModel(self.cfg)\\r\\n self.deep_models.initialize_models()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fca7483fa39f4eb61fa00af72b66f0c4\",\n \"score\": \"0.5636041\",\n \"text\": \"def __init__(self, params):\\n super(WAN, self).__init__(params)\\n self.wan_ip = ip_address(params['wan_ip'])\\n self.wan_networkgroup = params['wan_networkgroup']\\n self.wan_type = params['wan_type']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e2420aab40b8df96a91bc3e02c19d2bc\",\n \"score\": \"0.56013614\",\n \"text\": \"def do_setup(self, context):\\n self._client = NetAppApiClient(\\n self.api_version, vfiler=self.configuration.netapp_nas_vfiler,\\n configuration=self.configuration)\\n self._setup_helpers()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d48c348d5502cd7cb1698ae557b5be6\",\n \"score\": \"0.55758965\",\n \"text\": \"def setup_networking(ip, robot_id, wireless_only):\\n tempdir = makeTempDir()\\n try:\\n scriptpath = '%s/install/script_wireless.sh' % NAO_HOME\\n if not wireless_only:\\n fillout_id('utwired', robot_id, tempdir)\\n fillout_id('utwireless', robot_id, tempdir)\\n copyFiles(ip, os.path.join(tempdir, 'utwire*'))\\n copyFiles(ip, '../data/scripts/wpa_supplicant.conf',\\n '~/wpa_supplicant.conf.tmp')\\n runLocalScriptRemotely(ip, scriptpath, output=True)\\n finally:\\n removeTempDir(tempdir)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d9e6ffd1ad0aca9a1917bc48177f013\",\n \"score\": \"0.55627793\",\n \"text\": \"def _configure_sniffer(self, network, chan, bw):\\n\\n self.log.debug('Connecting to network {}'.format(network['SSID']))\\n self.set_monitor_mode(chan, bw)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"146356eeadd23c922bef454c05dee93a\",\n \"score\": \"0.5559324\",\n \"text\": \"def setup(self):\\n\\n logger.info('Starting initiation process')\\n self.enable_signaling_connection_urc()\\n self.enable_network_registration()\\n self.enable_psm_mode()\\n self.enable_radio_functions()\\n logger.info('Finished initiation process')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15c82202d44b84944c32068daf637dec\",\n \"score\": \"0.5532712\",\n \"text\": \"def setup(self):\\n # if not system.restore_snapshot():\\n # raise Exception\\n \\n self.tpd.navigate_to()\\n mws.connect(\\\"third party data interface\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79e5e8450f113f637e85597b7260c7dd\",\n \"score\": \"0.55183464\",\n \"text\": \"def setup_links(self) -> None:\\n for _int_name, int_obj in self.interfaces.items():\\n if not int_obj.exists():\\n int_obj.create()\\n\\n int_obj.set_netns(self.name)\\n int_obj.add_prefixes(self.name)\\n int_obj.set_link_up(self.name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4048083c486116f5a1739df2726f6a06\",\n \"score\": \"0.5504765\",\n \"text\": \"def setup(self):\\n import lnetatmo\\n try:\\n self.homedata = lnetatmo.HomeData(self.auth)\\n self.homestatus = lnetatmo.HomeStatus(self.auth, home=self.home)\\n self.home_id = self.homedata.gethomeId(self.home)\\n self.update()\\n except TypeError:\\n _LOGGER.error(\\\"ThermostatData::setup() got error.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d75604a691feb85358f03a4ad1b14765\",\n \"score\": \"0.549387\",\n \"text\": \"def setup(self):\\n self._otaProject.initializeOtaProject()\\n self._otaProject.setClientCredentialsForAwsIotEndpoint(self._otaAwsAgent.getAwsIotEndpoint())\\n self._otaProject.setClientCredentialsForWifi(self._boardConfig['wifi_ssid'], self._boardConfig['wifi_password'], self._boardConfig['wifi_security'])\\n self._otaProject.setClientCredentialForThingName(self._otaAwsAgent.getThingName())\\n self._otaProject.setClientCredentialKeys(self._otaAwsAgent.getThingCertificate(), self._otaAwsAgent.getThingPrivateKey())\\n self._otaProject.copyCodesignerCertificateToBootloader(self._otaAwsAgent.getCodeSignerCertificateFromArn(self._otaConfig['aws_signer_certificate_arn']))\\n self._otaProject.setMqttLogsOn()\\n self._otaProject.setFreeRtosConfigNetworkInterface(self._boardConfig.get('windows_network_interface', 0))\\n if self._otaConfig.get('compile_codesigner_certificate', False):\\n self._otaProject.setCodesignerCertificate(self._otaAwsAgent.getCodeSignerCertificateFromArn(self._otaConfig['aws_signer_certificate_arn']))\\n transportation = self._otaConfig.get('transportation')\\n if transportation == 'ble':\\n self._otaProject.setBleConfig()\\n if 'HTTP' in self._protocol:\\n self._otaProject.setHTTPConfig()\\n self._otaProject.setApplicationVersion(0, 9, 0)\\n\\n buildReturnCode = self._otaProject.buildProject()\\n flashReturnCode = self._flashComm.flashAndRead()\\n\\n return buildReturnCode + flashReturnCode\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6218a2b6d3f70f07d1c3b7df8d9697d0\",\n \"score\": \"0.5459263\",\n \"text\": \"def setupNetworks(self):\\n networkConfig, status = self.loadNetworkInputs()\\n if not status:\\n return networkConfig, False\\n resNET, statNET = self.getResponse(\\\"GET\\\", \\\"Network\\\")\\n netResponse = []\\n for network in networkConfig:\\n found = False\\n if network[\\\"VLANID\\\"] == \\\"\\\":\\n continue\\n if not statNET and \\\"No information found\\\" in resNET:\\n found = False\\n else:\\n found = [nw[\\\"id\\\"] for nw in resNET if network[\\\"Name\\\"] == str(nw[\\\"name\\\"])] \\n if not found:\\n action = \\\"POST\\\"\\n networkId = \\\"\\\"\\n else:\\n action = \\\"PUT\\\"\\n networkId = found[0]\\n resDN, statDN = self.defNetworkInfo(network, networkId, action)\\n netResponse.append(str(resDN))\\n if not statDN:\\n return netResponse, False\\n if not found: networkId = resDN[\\\"id\\\"]\\n self.networkMap[network[\\\"Type\\\"]] = networkId\\n return netResponse, True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"134e7db3c5be733f3dd80e64a34f368d\",\n \"score\": \"0.5444201\",\n \"text\": \"def __init__(self, debug=False):\\r\\n self.api = zenApiLib.zenConnector(section = 'default', routerName = 'ImpactRouter')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"400e36f3ba0b0d864e4b90f7ae0abd3a\",\n \"score\": \"0.54329616\",\n \"text\": \"def setup():\\n if not wifi.is_enabled() or \\\\\\n ask_confirmation('Wifi has already been enabled. Set it up again?'):\\n wifi.setup()\\n if not mqtt.is_enabled() or \\\\\\n ask_confirmation('MQTT has already been enabled. Set it up again?'):\\n mqtt.setup()\\n hass_mqtt.setup()\\n pins.setup()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44ac3d76b8e58e09ea5ce393fa9dc05c\",\n \"score\": \"0.54133713\",\n \"text\": \"def setup(self):\\n self.setup_hostname()\\n self.setup_monitoring()\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ce738d9e264de93212e4c6661422638\",\n \"score\": \"0.54131854\",\n \"text\": \"async def setup(self):\\n if SETTINGS.atag_host:\\n LOGGER.info(f\\\"Using configured ATAG ONE {SETTINGS.atag_host}\\\")\\n else:\\n LOGGER.info(f\\\"Discovering ATAG ONE\\\")\\n await self.atag.async_host_search()\\n\\n await self.atag.async_update()\\n self.device = DeviceAtagOne(self.atag, name=\\\"Atag One\\\", eventloop=self.eventloop)\\n LOGGER.info(f\\\"Connected to ATAG_ONE device {self.atag.device} @ {self.atag.config.host}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfc5d22dfe4f14c76f2589c3726e01a6\",\n \"score\": \"0.53940713\",\n \"text\": \"def setUpNetwork(self):\\n\\n # Set up the network structure\\n self.W = lagrangeRL.tools.networks.feedForwardWtaReadout(\\n self.layers,\\n self.wtaStrength,\\n offset=self.initWeightMean,\\n noiseMagnitude=self.initWeightWidth)\\n # Lagrange network\\n self.simClass = lagrangeRL.network.lagrangeEligTfApprox()\\n self.simClass.setLearningRate(1.)\\n self.simClass.setTimeStep(self.timeStep)\\n self.simClass.setTau(self.tau)\\n self.simClass.setNudging(self.nudging)\\n self.simClass.addMatrix(self.W)\\n self.simClass.setTauEligibility(self.tauElig)\\n self.simClass.saveTraces(False)\\n wMaxFixed = np.zeros((self.N, self.N))\\n wMaxFixed[-self.layers[-1]:, -self.layers[-1]:] = 1\\n self.simClass.setFixedSynapseMask(wMaxFixed.astype(bool))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b56d5d084dd4247d1764f4faf6ef3c8\",\n \"score\": \"0.5392751\",\n \"text\": \"def __init__(self, ryuapp, datapath, **kwargs):\\n self.startime = time.time()\\n self.debug = True\\n self.priority = 10\\n self.ryuapp = ryuapp\\n self.datapath = datapath\\n self.macTable = {}\\n self.arpTable = {}\\n # IP addresses 'owned' by the router for connected networks\\n self.addresses = []\\n # Packets that couldn't be sent because we need to ARP for them\\n self.packetQueue = []\\n # default server to send traffic to if lb is disabled and traffic for a VIP comes in\\n self.defaultserver = '255.255.255.255'\\n\\n # we need to start with a clean slate and make sure all of the flows have been deleted from the datapath.\\n self.deleteAllFlows()\\n\\n # we construct instances of the firewall, cache, and load balancer\\n # even if disabled as they will need to be configured in case they are enabled in the future\\n self.firewall = SDNFirewall(self,ryuapp,datapath)\\n self.ecache = ExternalCache(self,ryuapp,datapath,\\\"255.255.255.255\\\")\\n self.lb = SDNLoadBalancer(self,ryuapp,datapath)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12d184a373b1e64f3030e47c5f44e2d0\",\n \"score\": \"0.5388348\",\n \"text\": \"def initializeNetwork(self, network, *args, **kwargs):\\n return network\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e483ab832ba0e8983b5e4457193b2689\",\n \"score\": \"0.53836095\",\n \"text\": \"def setup(self):\\n super(ShorewallMixin, self).setup()\\n self._setup_firewall()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55c4169586ed909c0331b273d491a4b5\",\n \"score\": \"0.53699404\",\n \"text\": \"def _initialise_network(self, data):\\n #if a pretrained model was supplied,\\n #load in the grap\\n self.num_layers = len(data['layers'])\\n print('Total of layers in network = {}'.format(self.num_layers))\\n print('Layer information')\\n print(data['layers'])\\n for ii in range(0, self.num_layers): # pylint: disable=invalid-name\\n #setting parameters\\n self.layer.append(self.get_layer(data[\\\"layers\\\"][ii]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"141de60a64b13d204d550bf0e0dc72a7\",\n \"score\": \"0.535879\",\n \"text\": \"def __init__(self, ap_name):\\n self.ap_name = ap_name\\n self.targetip = None\\n self.targetipv6 = None\\n self.targetport = None\\n self.mac = None\\n self.model = None\\n self.opstate = None\\n self.power_source = None\\n self.serial = None\\n self.stub = None\\n self.vendor = ''\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e9ea18992a97d698ff358fad408139cd\",\n \"score\": \"0.53525954\",\n \"text\": \"def __init__(self, username, key, wifi_manager, api_version='v2'):\\n self.api_version = api_version\\n self.url = 'https://io.adafruit.com/api'\\n self.username = username\\n self.key = key\\n if wifi_manager:\\n self.wifi = wifi_manager\\n else:\\n raise TypeError(\\\"This library requires a WiFiManager object.\\\")\\n self.http_headers = [{bytes(\\\"X-AIO-KEY\\\", \\\"utf-8\\\"):bytes(self.key, \\\"utf-8\\\"),\\n bytes(\\\"Content-Type\\\", \\\"utf-8\\\"):bytes('application/json', \\\"utf-8\\\")},\\n {bytes(\\\"X-AIO-KEY\\\", \\\"utf-8\\\"):bytes(self.key, \\\"utf-8\\\")}]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76bf1f1b2afd5867848de27f010d5977\",\n \"score\": \"0.52744865\",\n \"text\": \"def set_network(self):\\n ntwrk = dict()\\n ntwrk[\\\"networking\\\"] = self.config.get('network', 'networking')\\n ntwrk[\\\"port\\\"] = self.config.getint('network', 'port')\\n ntwrk[\\\"buffer\\\"] = self.config.getint('network', 'buffer')\\n\\n if len(self.config.get('network', 'channels')) == 0:\\n channels = [_ for _ in range(self.gpio_len)]\\n else:\\n channels = map(int, self.config.get('network', 'channels').split(\\\",\\\"))\\n\\n temp = defaultdict()\\n for channel in range(len(channels)):\\n temp[channels[channel]] = channel\\n\\n ntwrk[\\\"channels\\\"] = temp\\n\\n self.network = Section(ntwrk)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f63aa72588b6a43754c406d0ce642452\",\n \"score\": \"0.52715534\",\n \"text\": \"def connect(self):\\n self.dsm = self._connect_to_deep_security()\\n self.waf = self._connect_to_aws_waf()\\n self.ec2 = self._connect_to_aws_ec2()\\n self.elb = self._connect_to_aws_elb()\\n self.cloudfront = self._connect_to_aws_cloudfront()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"edcb1488683cbc0914cecbb45ecc9256\",\n \"score\": \"0.5248705\",\n \"text\": \"def setup(self):\\n self._flow_mgr_url = 'http://localhost:8181/kytos/flow-manager/flows/%s'\\n self._switches = dict()\\n self.register_rest()\\n self.instantiate_switches = Switches(self.controller.switches)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b73a554c4f866ce7c09e1d39f7bfaa85\",\n \"score\": \"0.52458954\",\n \"text\": \"async def wifi_connect(self, iface, ssid, psk=None, is_wep=False):\\n netid = int(\\n await run_cmd([\\\"sudo\\\", \\\"/sbin/wpa_cli\\\", \\\"-i\\\", iface, \\\"add_network\\\"])\\n )\\n if psk and not is_wep:\\n wpacmd = [\\n 'set_network %d ssid \\\"%s\\\"' % (netid, ssid),\\n 'set_network %d psk \\\"%s\\\"' % (netid, psk),\\n \\\"enable_network %d\\\" % netid,\\n 3,\\n \\\"quit\\\",\\n ]\\n elif psk and is_wep:\\n wpacmd = [\\n 'set_network %d ssid \\\"%s\\\"' % (netid, ssid),\\n \\\"set_network %d key_mgmt NONE\\\" % (netid),\\n \\\"set_network %d wep_key0 %s\\\" % (netid, psk),\\n \\\"set_network %d auth_alg SHARED\\\" % (netid),\\n \\\"enable_network %d\\\" % netid,\\n 3,\\n \\\"quit\\\",\\n ]\\n else:\\n wpacmd = [\\n 'set_network %d ssid \\\"%s\\\"' % (netid, ssid),\\n \\\"set_network %d key_mgmt NONE\\\" % (netid),\\n \\\"enable_network %d\\\" % netid,\\n 3,\\n \\\"quit\\\",\\n ]\\n\\n resul = await run_inter_cmd([\\\"/sbin/wpa_cli\\\", \\\"-i\\\", iface], wpacmd)\\n logging.debug(\\\"{}\\\".format(resul))\\n return netid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80dba749ecd464765c5742721a118b7e\",\n \"score\": \"0.5220037\",\n \"text\": \"def main(options):\\n if options.include_ssh:\\n setup_passwordless_ssh(options.ip)\\n if options.hostname:\\n set_hostname(options.ip, options.hostname)\\n if options.wireless_only:\\n print('setting up networking')\\n setup_networking(options.ip, options.id, options.wireless_only)\\n if options.include_date:\\n set_date_time(options.ip)\\n print('complete')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de53776ebd29f16873eb407337ca6b62\",\n \"score\": \"0.5217746\",\n \"text\": \"def setup( cls ):\\n klds = lsmod()\\n kenv = quietRun( 'kenv net.inet.ip.fw.default_to_accept' ).strip()\\n deny = True if kenv == '0' else False\\n # if the default rule is deny all, change to allow all so hosts can\\n # still pass traffic. Also reload ipfw so that it is reconfigured\\n if deny:\\n quietRun( 'kenv net.inet.ip.fw.default_to_accept=1' )\\n if deny and 'ipfw.ko' in klds:\\n rmmod( 'ipfw' )\\n modprobe( 'ipfw' )\\n if 'ipfw_nat.ko' not in klds:\\n modprobe( 'ipfw_nat' )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6cc24d2d71884df4e94a573c82fd86ae\",\n \"score\": \"0.52166283\",\n \"text\": \"def generic_setup(self):\\r\\n run_setup()\\r\\n self.c = login()\\r\\n self.endpoint, self.schema = get_urls(self.obj_type)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5992568cacb8cf8f79f3e0747e436168\",\n \"score\": \"0.5215557\",\n \"text\": \"def prepare(self):\\n super(Test200SmartFullIdevice046, self).prepare()\\n\\n self.logger.info('Preconditions:')\\n self.logger.info('1. Open Micro/WIN V2.5;')\\n self.logger.info('2. Plc configed as I-Device with fixed IP and station name, download;')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0748965064f46d8b4a2a339fd8591eab\",\n \"score\": \"0.5190812\",\n \"text\": \"def setup_method(self):\\n # %% network setup\\n fluid_list = ['Ar', 'N2', 'O2', 'CO2', 'CH4', 'H2O']\\n self.nw = Network(\\n fluids=fluid_list, p_unit='bar', T_unit='C', p_range=[0.5, 20])\\n\\n # %% components\\n amb = Source('ambient')\\n sf = Source('fuel')\\n cc = CombustionChamber('combustion')\\n cp = Compressor('compressor')\\n gt = Turbine('turbine')\\n fg = Sink('flue gas outlet')\\n\\n # %% connections\\n amb_cp = Connection(amb, 'out1', cp, 'in1', label='ambient air flow')\\n cp_cc = Connection(cp, 'out1', cc, 'in1')\\n sf_cc = Connection(sf, 'out1', cc, 'in2')\\n cc_gt = Connection(cc, 'out1', gt, 'in1')\\n gt_fg = Connection(gt, 'out1', fg, 'in1')\\n\\n self.nw.add_conns(amb_cp, cp_cc, sf_cc, cc_gt, gt_fg)\\n\\n # %% component parameters\\n cc.set_attr(lamb=3)\\n cp.set_attr(eta_s=0.9, pr=15)\\n gt.set_attr(eta_s=0.9)\\n\\n # %% connection parameters\\n amb_cp.set_attr(\\n T=20, p=1, m=100,\\n fluid={'Ar': 0.0129, 'N2': 0.7553, 'H2O': 0, 'CH4': 0,\\n 'CO2': 0.0004, 'O2': 0.2314})\\n sf_cc.set_attr(\\n T=20, fluid={'CO2': 0.04, 'Ar': 0, 'N2': 0,\\n 'O2': 0, 'H2O': 0, 'CH4': 0.96})\\n gt_fg.set_attr(p=1)\\n\\n # motor efficiency\\n x = np.array(\\n [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55,\\n 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 1, 1.05, 1.1, 1.15,\\n 1.2, 10])\\n y = np.array(\\n [0.01, 0.3148, 0.5346, 0.6843, 0.7835, 0.8477, 0.8885, 0.9145,\\n 0.9318, 0.9443, 0.9546, 0.9638, 0.9724, 0.9806, 0.9878, 0.9938,\\n 0.9982, 0.999, 0.9995, 0.9999, 1, 0.9977, 0.9947, 0.9909, 0.9853,\\n 0.9644]) * 0.975\\n self.motor_bus_based = CharLine(x=x, y=y)\\n self.motor_comp_based = CharLine(x=x, y=1 / y)\\n\\n # generator efficiency\\n x = np.array(\\n [0.100, 0.345, 0.359, 0.383, 0.410, 0.432, 0.451, 0.504, 0.541,\\n 0.600, 0.684, 0.805, 1.000, 1.700, 10])\\n y = np.array(\\n [0.976, 0.989, 0.990, 0.991, 0.992, 0.993, 0.994, 0.995, 0.996,\\n 0.997, 0.998, 0.999, 1.000, 0.999, 0.99]) * 0.975\\n self.generator = CharLine(x=x, y=y)\\n\\n power_bus_total = Bus('total power output')\\n power_bus_total.add_comps(\\n {'comp': cp, 'char': self.motor_bus_based, 'base': 'bus'},\\n {'comp': gt, 'char': self.generator})\\n\\n thermal_input = Bus('thermal input')\\n thermal_input.add_comps({'comp': cc})\\n\\n compressor_power_comp = Bus('compressor power input')\\n compressor_power_comp.add_comps(\\n {'comp': cp, 'char': self.motor_comp_based})\\n\\n compressor_power_bus = Bus('compressor power input bus based')\\n compressor_power_bus.add_comps(\\n {'comp': cp, 'char': self.motor_bus_based, 'base': 'bus'})\\n\\n self.nw.add_busses(\\n power_bus_total, thermal_input, compressor_power_comp,\\n compressor_power_bus)\\n\\n # %% solving\\n self.nw.solve('design')\\n self.nw.save('tmp')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d073f45b279108906c40509e557f23fe\",\n \"score\": \"0.518442\",\n \"text\": \"def connectWifi():\\r\\n ap_if = network.WLAN(network.AP_IF)\\r\\n ap_if.active(False) # disable access point\\r\\n\\r\\n wifi = network.WLAN(network.STA_IF)\\r\\n\\r\\n if not wifi.isconnected():\\r\\n\\r\\n print('connecting to ',config.ssid)\\r\\n\\r\\n #wifi.ifconfig(config.netconfig)\\r\\n wifi.active(True)\\r\\n wifi.connect(config.ssid, config.wifiPass)\\r\\n for retry in range(5):\\r\\n if wifi.isconnected():\\r\\n break\\r\\n else:\\r\\n print('Retrying #',retry)\\r\\n time.sleep(3)\\r\\n if not wifi.isconnected(): # could not connect\\r\\n print('Could not connect, rebooting')\\r\\n time.sleep(2)\\r\\n machine.reset()\\r\\n\\r\\n if wifi.isconnected(): # should be connected now\\r\\n print('network config:', wifi.ifconfig())\\r\\n\\r\\n else: # warn with led and reboot\\r\\n print('Connection failed!')\\r\\n time.sleep(5)\\r\\n print('Restarting')\\r\\n machine.reset()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"503682902b6e8da398ccc68c830429f7\",\n \"score\": \"0.5181567\",\n \"text\": \"def wifi_test_device_init(ad):\\n require_sl4a((ad,))\\n ad.droid.wifiScannerToggleAlwaysAvailable(False)\\n msg = \\\"Failed to turn off location service's scan.\\\"\\n assert not ad.droid.wifiScannerIsAlwaysAvailable(), msg\\n msg = \\\"Failed to turn WiFi on %s\\\" % ad.serial\\n assert wifi_toggle_state(ad, True), msg\\n reset_wifi(ad)\\n msg = \\\"Failed to clear configured networks.\\\"\\n assert not ad.droid.wifiGetConfiguredNetworks(), msg\\n ad.droid.wifiEnableVerboseLogging(1)\\n msg = \\\"Failed to enable WiFi verbose logging.\\\"\\n assert ad.droid.wifiGetVerboseLoggingLevel() == 1, msg\\n ad.droid.wifiScannerToggleAlwaysAvailable(False)\\n # We don't verify the following settings since they are not critical.\\n sync_device_time(ad)\\n ad.droid.telephonyToggleDataConnection(False)\\n # TODO(angli): need to verify the country code was actually set. No generic\\n # way to check right now.\\n ad.adb.shell(\\\"halutil -country %s\\\" % WifiEnums.CountryCode.US)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f8dd882bbb0536b90e61313db59b22b\",\n \"score\": \"0.51799047\",\n \"text\": \"def __init__(__self__,\\n resource_name: str,\\n args: NetworkToNetworkInterconnectArgs,\\n opts: Optional[pulumi.ResourceOptions] = None):\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"187d5a17b387f15d23514ed5b74abd7a\",\n \"score\": \"0.517404\",\n \"text\": \"def network_setup() -> Tuple[Any, Any]:\\n\\n # from https://gist.github.com/nguyendv/8cfd92fc8ed32ebb78e366f44c2daea6\\n\\n ec2 = u.get_ec2_resource()\\n client = u.get_ec2_client()\\n existing_vpcs = u.get_vpc_dict()\\n zones = u.get_zones()\\n\\n # create VPC from scratch. Remove this if default VPC works well enough.\\n create_non_default_vpc = False\\n\\n if create_non_default_vpc:\\n vpc_name = u.get_vpc_name()\\n if u.get_vpc_name() in existing_vpcs:\\n print(\\\"Reusing VPC \\\" + vpc_name)\\n vpc = existing_vpcs[vpc_name]\\n subnets = list(vpc.subnets.all())\\n assert len(subnets) == len(\\n zones), \\\"Has %s subnets, but %s zones, something went wrong during resource creation, try delete_resources.py/create_resources.py\\\" % (\\n len(subnets), len(zones))\\n\\n else:\\n print(\\\"Creating VPC \\\" + vpc_name)\\n vpc = ec2.create_vpc(CidrBlock='192.168.0.0/16')\\n\\n # enable DNS on the VPC\\n response = vpc.modify_attribute(EnableDnsHostnames={\\\"Value\\\": True})\\n assert u.is_good_response(response)\\n response = vpc.modify_attribute(EnableDnsSupport={\\\"Value\\\": True})\\n assert u.is_good_response(response)\\n\\n vpc.create_tags(Tags=u.create_name_tags(vpc_name))\\n vpc.wait_until_available()\\n\\n gateways = u.get_gateway_dict(vpc)\\n gateway_name = u.get_gateway_name()\\n if gateway_name in gateways:\\n print(\\\"Reusing gateways \\\" + gateway_name)\\n else:\\n print(\\\"Creating internet gateway \\\" + gateway_name)\\n ig = ec2.create_internet_gateway()\\n ig.attach_to_vpc(VpcId=vpc.id)\\n ig.create_tags(Tags=u.create_name_tags(gateway_name))\\n\\n # check that attachment succeeded\\n attach_state = u.extract_attr_for_match(ig.attachments, State=-1,\\n VpcId=vpc.id)\\n assert attach_state == 'available', \\\"vpc %s is in state %s\\\" % (vpc.id,\\n attach_state)\\n route_table = vpc.create_route_table()\\n route_table_name = u.get_route_table_name()\\n route_table.create_tags(Tags=u.create_name_tags(route_table_name))\\n\\n dest_cidr = '0.0.0.0/0'\\n route_table.create_route(\\n DestinationCidrBlock=dest_cidr,\\n GatewayId=ig.id\\n )\\n # check success\\n for route in route_table.routes:\\n # result looks like this\\n # ec2.Route(route_table_id='rtb-a8b438cf',\\n # destination_cidr_block='0.0.0.0/0')\\n if route.destination_cidr_block == dest_cidr:\\n break\\n else:\\n # sometimes get\\n # AssertionError: Route for 0.0.0.0/0 not found in [ec2.Route(route_table_id='rtb-cd9153b0', destination_cidr_block='192.168.0.0/16')]\\n # TODO: add a wait/retry?\\n assert False, \\\"Route for %s not found in %s\\\" % (dest_cidr,\\n route_table.routes)\\n\\n assert len(zones) <= 16 # for cidr/20 to fit into cidr/16\\n ip = 0\\n for zone in zones:\\n cidr_block = '192.168.%d.0/20' % (ip,)\\n ip += 16\\n print(\\\"Creating subnet %s in zone %s\\\" % (cidr_block, zone))\\n subnet = vpc.create_subnet(CidrBlock=cidr_block,\\n AvailabilityZone=zone)\\n subnet.create_tags(Tags=[{'Key': 'Name', 'Value': f'{vpc_name}-subnet'},\\n {'Key': 'Region', 'Value': zone}])\\n response = client.modify_subnet_attribute(\\n MapPublicIpOnLaunch={'Value': True},\\n SubnetId=subnet.id\\n )\\n assert u.is_good_response(response)\\n u.wait_until_available(subnet)\\n assert subnet.map_public_ip_on_launch, \\\"Subnet doesn't enable public IP by default, why?\\\"\\n\\n route_table.associate_with_subnet(SubnetId=subnet.id)\\n\\n # Setup security group for non-default VPC\\n # existing_security_groups = u.get_security_group_dict()\\n # security_group_nd_name = u.get_security_group_nd_name()\\n # if security_group_nd_name in existing_security_groups:\\n # print(\\\"Reusing non-default security group \\\" + security_group_nd_name)\\n # security_group_nd = existing_security_groups[security_group_nd_name]\\n # assert security_group_nd.vpc_id == vpc.id, f\\\"Found non-default security group {security_group_nd} \\\" \\\\\\n # f\\\"attached to {security_group_nd.vpc_id} but expected {vpc.id}\\\"\\n # else:\\n # security_group_nd = create_security_group(security_group_nd_name, vpc.id)\\n\\n # Setup things on default VPC for zone-agnostic launching\\n vpc = u.get_default_vpc()\\n if not vpc:\\n util.log(f\\\"Creating default VPC for region {u.get_region()}\\\")\\n client.create_default_vpc()\\n vpc = u.get_default_vpc()\\n assert vpc, \\\"Could not create default VPC?\\\"\\n\\n existing_security_groups = u.get_security_group_dict()\\n security_group_name = u.get_security_group_name()\\n if security_group_name in existing_security_groups:\\n print(\\\"Reusing security group \\\" + security_group_name)\\n security_group = existing_security_groups[security_group_name]\\n assert security_group.vpc_id == vpc.id, f\\\"Found security group {security_group} \\\" \\\\\\n f\\\"attached to {security_group.vpc_id} but expected {vpc.id}\\\"\\n else:\\n security_group = create_security_group(security_group_name, vpc.id)\\n # Uncomment the following when setting up two VPC's\\n # security_group = create_security_group(security_group_name, vpc.id, security_group_nd)\\n\\n return vpc, security_group\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4687eca70b4e346691b29362f84ea3d9\",\n \"score\": \"0.5170626\",\n \"text\": \"def _initialize_wandb(args: Namespace) -> None:\\n config = {'num_epochs': args.num_epochs,\\n 'train_dl_kwargs': args.train_dl_kwargs,\\n 'val_dl_kwargs': args.val_dl_kwargs,\\n 'network_kwargs': args.network_kwargs}\\n\\n wandb.init(project=WANDB_PROJECT_NAME, config=config)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f21df24ad76df24e0cf7d33a038132a\",\n \"score\": \"0.5148415\",\n \"text\": \"def setUp(self):\\n self._s1ap_wrapper = s1ap_wrapper.TestWrapper()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f21df24ad76df24e0cf7d33a038132a\",\n \"score\": \"0.5148415\",\n \"text\": \"def setUp(self):\\n self._s1ap_wrapper = s1ap_wrapper.TestWrapper()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92c5d19aa7ae7066e8c20254d0bf0704\",\n \"score\": \"0.5144262\",\n \"text\": \"def _build_network(self) -> None:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf94275e1d82ed3f17c9effd0836cdaf\",\n \"score\": \"0.5133108\",\n \"text\": \"def setup(self):\\n server_names = json.loads(\\n self.sensor_service.get_value('hosts', local=False)\\n )\\n self.servers = []\\n for server in server_names:\\n self.servers.append(\\n json.loads(\\n self.sensor_service.get_value(server, local=False)\\n )\\n )\\n self.resource_ids = []\\n for host in self.servers:\\n self.resource_ids.append(host['pharos_id'])\\n self.log = self.sensor_service.get_logger(name=self.__class__.__name__)\\n self.dashboard = self.sensor_service.get_value(\\n name='dashboard_url',\\n local=False\\n )\\n self.log.info(\\\"connecting to dashboard at %s\\\", self.dashboard)\\n # get token here for when dashboard supports it\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84a1b8ce91aab498f92a2bb3ec44552f\",\n \"score\": \"0.51242286\",\n \"text\": \"def hotspot(args):\\n global ap_face, ap_ssid, ap_password, out_face\\n ap_face = args.interface\\n ap_ssid = args.ssid\\n ap_password = args.password\\n out_face = args.share_interface\\n\\n print(\\\"\\\\nConfiguring %s to ip %s...\\\" % (ap_face, AP_IP))\\n assign_ap_ip()\\n\\n print(\\\"\\\\nStarting dnsmasq...\\\")\\n start_dnsmasq(hostspot=True)\\n\\n print(\\\"\\\\nStarting ip forward...\\\")\\n start_ip_forward()\\n\\n print('\\\\nStarting hostapd...')\\n start_hostapd()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5063696f29a862baee1299253a070921\",\n \"score\": \"0.512415\",\n \"text\": \"def configure_network(self):\\n for network_switch_obj in self.my_parser.network_switches_list:\\n sw_obj = switch.SwitchCfg(\\n network_switch_obj.ip_address,\\n network_switch_obj.username,\\n network_switch_obj.password,\\n network_switch_obj.tftp_server_ip,\\n network_switch_obj.config_file_name)\\n '''\\n sw_obj.upload_switch_config_file_to_tftp_server()\\n sw_obj.download_switch_config_file_to_switch()\\n '''\\n lista = []\\n for vlan in network_switch_obj.vlans:\\n vlan_dict = vlan.attrib\\n vlan_dict['name'] = vlan.tag\\n # print vlan_dict\\n lista.append(vlan_dict)\\n for i in lista:\\n untag = i[\\\"UntaggedPorts\\\"]\\n vlan_id = i[\\\"ID\\\"]\\n tagged = i[\\\"TaggedPorts\\\"]\\n # sw_obj.create_vlan(vlan_id)\\n if vlan_id == 'None':\\n log._info(\\n \\\"There are no VLAN's defined to be created, Hence Tagging and Untagging of ports will be skipped\\\")\\n log._info(\\n \\\"Please define a VLAN to be created with tag %s so, ports could be tagged or untagged\\\" %\\n vlan.tag)\\n else:\\n log._info(\\\"Creating VLAN %s\\\" % vlan_id)\\n sw_obj.create_vlan(vlan_id)\\n untagged_ports = untag.split(',')\\n tagged_ports = tagged.split(',')\\n\\n if untagged_ports == 'None':\\n log._info(\\\"There are no ports to be untagged\\\")\\n else:\\n for port in untagged_ports:\\n log._info(\\\"untagging port %s to vlan %s\\\" %\\n (port, vlan_id))\\n sw_obj.untag_ports_to_vlan(vlan_id, port)\\n\\n if tagged_ports == 'None':\\n log._info(\\\"There are no ports to be tagged\\\")\\n else:\\n for port in tagged_ports:\\n log._info(\\\"tagging port %s to vlan %s\\\" %\\n (port, vlan_id))\\n sw_obj.tag_ports_to_vlan(vlan_id, port)\\n user_list = []\\n for user in network_switch_obj.users:\\n user_dict = user.attrib\\n user_dict['name'] = user.tag\\n # print user_dict\\n user_list.append(user_dict)\\n for i in user_list:\\n user_name = i[\\\"Username\\\"]\\n user_pw = i[\\\"Password\\\"]\\n sw_obj.configure_users(user_name, user_pw)\\n\\n port_list = []\\n for port in network_switch_obj.ports:\\n port_dict = port.attrib\\n port_dict['name'] = port.tag\\n port_list.append(port_dict)\\n for i in port_list:\\n port_nu = i['PortList']\\n port_state = i['PortState']\\n port_duplex = i['PortDuplex']\\n port_speed = i['PortSpeed']\\n # print port_number, port_state, port_duplex, port_speed\\n if port_nu == 'None':\\n log._warn(\\n \\\"There are no ports mentioned for port attribute configuration under xml tag %s\\\" %\\n port.tag)\\n else:\\n log._info(\\n \\\"There ports mentioned to be configured with port attributes\\\")\\n port_number = port_nu.split(',')\\n for po in port_number:\\n if port_state == 'None':\\n log._info(\\n \\\"port attribute state will not be configured as the value of it xml is set to None\\\")\\n else:\\n log._info(\\n \\\"Port attribute state will be configured for port %s \\\" %\\n po)\\n sw_obj.configure_port_state(po, port_state)\\n if port_duplex == 'None':\\n log._info(\\n \\\"port attribute duplex will not be configured as the value of it xml is set to None\\\")\\n else:\\n log._info(\\n \\\"Port attribute duplex will be configured for port %s \\\" %\\n po)\\n sw_obj.configure_port_duplex_mode(po, port_duplex)\\n if port_speed == 'None':\\n log._info(\\n \\\"port attribute speed will not be configured as the value of it xml is set to None\\\")\\n else:\\n log._info(\\n \\\"Port attribute speed will be configured for port %s \\\" %\\n po)\\n sw_obj.configure_port_speed(po, port_speed)\\n\\n for san_switch_obj in self.my_parser.san_switches_list:\\n # NOTE: expect should return 0 if 'successful' exists within the\\n # output\\n\\n # here, check model and create object of corresponding class\\n if 'SN6000B' in san_switch_obj.model:\\n\\n creds = san_switch_obj.ip_address + ';' + \\\\\\n san_switch_obj.username + ';' + \\\\\\n san_switch_obj.password + ';'\\n sn_log = os.path.join(\\n os.getcwd(), r\\\"logs\\\", r\\\"sn6000config.txt\\\")\\n try:\\n child = spawn(\\n 'python san_switch.py model %s creds = %s' %\\n (san_switch_obj.model, creds), logfile=file(\\n sn_log, 'w'))\\n # port zoning to go here later\\n if os.name == 'nt':\\n SENDLINE = child.direct_sendline\\n else:\\n SENDLINE = child.sendline\\n # if there are zone configs present\\n if san_switch_obj.zone_configs:\\n for config in san_switch_obj.zone_configs:\\n # create zones with added wwns\\n # zone elements!\\n zone_list = [zone for zone in config.find(\\\"Zones\\\")]\\n for zone in zone_list:\\n # compile the list of hosts to be added to zone\\n host_list = [\\n host for host in zone.find(\\\"Hosts\\\")]\\n for host in host_list:\\n # compile the list of wwns to add to host\\n wwn_list = [wwn.attrib[\\\"wwn\\\"]\\n for wwn in host.find('WWNs')]\\n SENDLINE(\\n 'my_san.create_alias(%s, %s)' %\\n (host.attrib['alias'], wwn_list))\\n # no need to send a y for creating alias\\n # expect should return 0 if 'successful'\\n # exists within the output\\n if child.expect(\\\"successful\\\"):\\n log._warn(\\n \\\"Failed to create host %s with given WWNs or domain/port index list\\\" %\\n (host.attrib['alias']))\\n del wwn_list[:]\\n SENDLINE('my_san.create_zone(%s,%s)' %\\n (zone.attrib[\\\"alias\\\"], host_list))\\n # no need to send a y for creating a zone\\n if child.expect(\\\"successful\\\"):\\n log._warn(\\n \\\"Failed to create zone %s with given host list\\\" %\\n (zone.attrib['alias']))\\n del host_list[:]\\n # compile the list of zones to be added to config\\n SENDLINE('my_san.create_config(%s,%s)' %\\n (config.attrib[\\\"name\\\"], zone_list))\\n # no need to send a y for creating a zone\\n if child.expect(\\\"successful\\\"):\\n log._warn(\\n \\\"Failed to create conig %s with given zone list\\\" %\\n (config.attrib[\\\"name\\\"]))\\n del zone_list[:]\\n # here, use winpexpect to answer the decision prompts\\n # from SN6000 switch\\n if san_switch_obj.ports:\\n for port in san_switch_obj.ports:\\n port_num = port.tag.strip(\\\"PORTport\\\")\\n SENDLINE('my_san.set_port_speed(%s,%s)' %\\n (port_num, port.attrib[\\\"Speed\\\"]))\\n # no need to send a y for creating a zone\\n if child.expect(\\\"successful\\\"):\\n log._warn(\\n \\\"Failed to set port speed for port %s\\\" %\\n (port_num))\\n SENDLINE('my_san.set_port_fillword(%s, %s)' %\\n (port_num, port.attrib[\\\"Fillword\\\"]))\\n if child.expect(\\\"successful\\\"):\\n log._warn(\\n \\\"Failed to set port fillword for port %s\\\" %\\n (port_num))\\n SENDLINE('my_san.save_config()')\\n child.expect(\\\"(yes, y, no, n):\\\")\\n SENDLINE('yes')\\n SENDLINE('my_san.enable_config(%s)' %\\n (config.attrib[\\\"name\\\"]))\\n child.expect(\\\"(yes, y, no, n):\\\")\\n SENDLINE('yes')\\n SENDLINE('my_san.terminate_connection()')\\n child.terminate()\\n if child.isalive():\\n log._warn(\\n \\\"CHILD PROCESS FAILED TO TERMINATE, PLEASE FILE BUG\\\")\\n except Exception:\\n log._warn(\\n \\\"Failed to start child process for SAN switch configuration\\\")\\n # elif 'some other model' in san_switch_obj.model:\\n # configure that model\\n\\n else:\\n log._warn(\\\"Model not supported by Config manager at this time\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed004ef95e2913fe1ece6249e5daa699\",\n \"score\": \"0.5122543\",\n \"text\": \"def _setup_decap(self):\\n self._logger.debug('Setup using ' + str(self._vswitch_class))\\n\\n try:\\n self._vswitch.start()\\n bridge = settings.getValue('TUNNEL_INTEGRATION_BRIDGE')\\n bridge_ext = settings.getValue('TUNNEL_EXTERNAL_BRIDGE')\\n bridge_ext_ip = settings.getValue('TUNNEL_EXTERNAL_BRIDGE_IP')\\n tgen_ip1 = settings.getValue('TRAFFICGEN_PORT1_IP')\\n self._vswitch.add_switch(bridge)\\n\\n tasks.run_task(['sudo', 'ifconfig', bridge,\\n settings.getValue('VTEP_IP1')],\\n self._logger, 'Assign ' +\\n settings.getValue('VTEP_IP1') + ' to ' + bridge, False)\\n\\n tunnel_type = self._traffic['tunnel_type']\\n\\n self._vswitch.add_switch(bridge_ext)\\n self._vswitch.add_phy_port(bridge)\\n (_, phy2_number) = self._vswitch.add_phy_port(bridge_ext)\\n if tunnel_type == \\\"vxlan\\\":\\n vxlan_vni = 'options:key=' + settings.getValue('VXLAN_VNI')\\n (_, phy3_number) = self._vswitch.add_tunnel_port(bridge_ext,\\n tgen_ip1,\\n tunnel_type,\\n params=[vxlan_vni])\\n else:\\n (_, phy3_number) = self._vswitch.add_tunnel_port(bridge_ext,\\n tgen_ip1,\\n tunnel_type)\\n tasks.run_task(['sudo', 'ip', 'addr', 'add',\\n bridge_ext_ip,\\n 'dev', bridge_ext],\\n self._logger, 'Assign ' +\\n bridge_ext_ip\\n + ' to ' + bridge_ext)\\n\\n tasks.run_task(['sudo', 'ip', 'link', 'set', 'dev', bridge_ext,\\n 'up'],\\n self._logger,\\n 'Set ' + bridge_ext + ' status to up')\\n\\n self._vswitch.set_tunnel_arp(tgen_ip1,\\n settings.getValue('TRAFFICGEN_PORT1_MAC'),\\n bridge)\\n # Test is unidirectional for now\\n self._vswitch.del_flow(bridge_ext)\\n flow1 = add_ports_to_flow(_FLOW_TEMPLATE, phy3_number,\\n phy2_number)\\n self._vswitch.add_flow(bridge_ext, flow1)\\n\\n except:\\n self._vswitch.stop()\\n raise\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50ff59d27fba351bdd540e3ca986b8cf\",\n \"score\": \"0.5120746\",\n \"text\": \"def start_up(self):\\n self.logger.message(self.my_name + ' Starting CATAP Hardware Interfaces',\\n add_to_text_log=True,show_time_stamp=False)\\n self.start_llrf_control()\\n self.start_RF_protection()\\n self.start_magnet_control()\\n self.start_valve_control()\\n self.start_mod_control()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af1ad7f6001a41e663332c6cdf1c358b\",\n \"score\": \"0.51126975\",\n \"text\": \"def setUpNetworkAndController(\\n self, vlan: str = \\\"\\\",\\n non_nat_arp_egress_port: Optional[str] = None,\\n gw_mac_addr=\\\"ff:ff:ff:ff:ff:ff\\\",\\n ):\\n\\n cls = self.__class__\\n super(InOutNonNatTest, cls).setUpClass()\\n egress.get_mobilityd_gw_info = mocked_get_mobilityd_gw_info\\n egress.set_mobilityd_gw_info = mocked_set_mobilityd_gw_info\\n\\n warnings.simplefilter('ignore')\\n cls.setup_uplink_br()\\n\\n if vlan != \\\"\\\":\\n cls._setup_vlan_network(vlan)\\n\\n cls.service_manager = create_service_manager([])\\n\\n ingress_controller_reference: Future = Future()\\n middle_controller_reference: Future = Future()\\n egress_controller_reference: Future = Future()\\n testing_controller_reference: Future = Future()\\n\\n if non_nat_arp_egress_port is None:\\n non_nat_arp_egress_port = cls.DHCP_PORT\\n\\n patch_up_port_no = BridgeTools.get_ofport('patch-up')\\n test_setup = TestSetup(\\n apps=[\\n PipelinedController.Ingress,\\n PipelinedController.Middle,\\n PipelinedController.Egress,\\n PipelinedController.Testing,\\n PipelinedController.StartupFlows,\\n ],\\n references={\\n PipelinedController.Ingress:\\n ingress_controller_reference,\\n PipelinedController.Middle:\\n middle_controller_reference,\\n PipelinedController.Egress:\\n egress_controller_reference,\\n PipelinedController.Testing:\\n testing_controller_reference,\\n PipelinedController.StartupFlows:\\n Future(),\\n },\\n config={\\n 'setup_type': 'LTE',\\n 'bridge_name': cls.BRIDGE,\\n 'bridge_ip_address': cls.BRIDGE_IP,\\n 'ovs_gtp_port_number': 32768,\\n 'clean_restart': True,\\n 'enable_nat': False,\\n 'non_nat_gw_probe_frequency': 0.5,\\n 'non_nat_arp_egress_port': non_nat_arp_egress_port,\\n 'uplink_port': patch_up_port_no,\\n 'uplink_gw_mac': gw_mac_addr,\\n },\\n mconfig=None,\\n loop=None,\\n service_manager=cls.service_manager,\\n integ_test=False,\\n )\\n\\n BridgeTools.create_bridge(cls.BRIDGE, cls.IFACE)\\n subprocess.Popen([\\\"ifconfig\\\", cls.UPLINK_BR, \\\"192.168.128.41\\\"]).wait()\\n cls.thread = start_ryu_app_thread(test_setup)\\n cls.ingress_controller = ingress_controller_reference.result()\\n cls.middle_controller = middle_controller_reference.result()\\n cls.egress_controller = egress_controller_reference.result()\\n\\n cls.testing_controller = testing_controller_reference.result()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ad056292c5880dcc5ddcfcfa77e1c2e\",\n \"score\": \"0.5107478\",\n \"text\": \"def create_wifi_profile(self, type, ssid, password, authentication, encryption):\\n\\n self.ssid = ssid\\n\\n self.root = XML.Element(\\\"WLANProfile\\\", attrib={'xmlns': 'http://www.microsoft.com/networking/WLAN/profile/v1'})\\n XML.SubElement(self.root, \\\"name\\\").text = self.ssid\\n self.ssid_config = XML.SubElement(self.root, \\\"SSIDConfig\\\")\\n self.ssid_sub = XML.SubElement(self.ssid_config, \\\"SSID\\\")\\n XML.SubElement(self.ssid_sub, \\\"name\\\").text = self.ssid\\n XML.SubElement(self.root, \\\"connectionType\\\").text = 'ESS'\\n XML.SubElement(self.root, \\\"connectionMode\\\").text = 'auto'\\n self.msm = XML.SubElement(self.root, \\\"MSM\\\")\\n self.security = XML.SubElement(self.msm, \\\"security\\\")\\n\\n self.network_type = type\\n\\n if self.network_type == 'SECURED':\\n self.auth_encryption = XML.SubElement(self.security, \\\"authEncryption\\\")\\n XML.SubElement(self.auth_encryption, \\\"authentication\\\").text = authentication\\n XML.SubElement(self.auth_encryption, \\\"encryption\\\").text = encryption\\n XML.SubElement(self.auth_encryption, \\\"useOneX\\\").text = 'false'\\n\\n self.shared_key = XML.SubElement(self.security, \\\"sharedKey\\\")\\n XML.SubElement(self.shared_key, \\\"keyType\\\").text = 'passPhrase'\\n XML.SubElement(self.shared_key, \\\"protected\\\").text = 'false'\\n XML.SubElement(self.shared_key, \\\"keyMaterial\\\").text = password\\n elif self.network_type == 'OPEN':\\n self.auth_encryption = XML.SubElement(self.security, \\\"authEncryption\\\")\\n XML.SubElement(self.auth_encryption, \\\"authentication\\\").text = 'open'\\n XML.SubElement(self.auth_encryption, \\\"encryption\\\").text = 'none'\\n XML.SubElement(self.auth_encryption, \\\"useOneX\\\").text = 'false'\\n self.mac_random = XML.SubElement(self.root, \\\"MacRandomization\\\", attrib={'xmlns': 'http://www.microsoft.com/networking/WLAN/profile/v3'})\\n XML.SubElement(self.mac_random, \\\"enableRandomization\\\").text = 'false'\\n XML.SubElement(self.mac_random, \\\"randomizationSeed\\\").text = '10821901'\\n else:\\n LOGGER.error(f'A wrong input value for \\\"network_type\\\" parameter was delivered')\\n try:\\n tree = XML.ElementTree(self.root)\\n tree.write(self.wifi_profile_filename)\\n LOGGER.info(f'{self.wifi_profile_filename} was created successfully at {\\\"TBD: add location\\\"}')\\n\\n except:\\n LOGGER.error(f'Was unable to create {self.wifi_profile_filename}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11531cd18d781b04794b2edd4918d31c\",\n \"score\": \"0.51074207\",\n \"text\": \"def __init__(self, rinterface, rtinterface, protocol, tag):\\n super().__init__(rinterface, rtinterface, protocol, tag)\\n # Find the relevant group\\n for group in rinterface.interface_config['subscribers'][protocol]:\\n if tag == group['tag']:\\n group_config = group\\n\\n # Take from HAG since we do not need their WiFi offload attributes\\n self.test_session_handle = None\\n self.isactive = False\\n self.test_session_name = \\\"\\\"\\n self.libname = \\\"\\\"\\n self.tsname = \\\"\\\"\\n self.access_interface = \\\"\\\"\\n self.uplink_interface = \\\"\\\"\\n self.control_interface = \\\"\\\"\\n self.subscriber_group_indices = {}\\n self.node_test_case_name = \\\"\\\"\\n self.nodal_test_case_name = \\\"\\\"\\n\\n self.test_activity = group_config.get('test-activity', 'Capacity Test')\\n self.session_count = group_config.get('session-count', '1')\\n self.bearer_per_session = group_config.get('default-bearers', '1')\\n self.dedicated_bearers = group_config.get('dedicated-bearers', '0')\\n self.activation_rate = group_config.get('activation-rate', '1')\\n self.deactivation_rate = group_config.get('deactivation-rate', '1')\\n self.bearer_ipv6_addr_pool = group_config.get('bearer-ipv6-addr-pool', 'DB43:2019::0/64')\\n self.bearer_ipv4_addr_pool = group_config.get('bearer-ipv4-addr-pool', '91.0.0.1')\\n self.data_mtu = group_config.get('data-mtu', '1400')\\n self.apn_name = group_config.get('apn-name', 'mx-upf')\\n self.user_initiated_association = group_config.get('user-init-association', False)\\n self.home_address_type = group_config.get('home-addr-type', '1') # 1 for ipv4, 2 for ipv6, 3 for both\\n self.sut_loopback_ipv6 = group_config.get('sut-loopback-v6', 'FEED:D00D::1')\\n self.sut_loopback_ip = group_config.get('sut-loopback', '100.0.0.1')\\n self.use_loopback_access = group_config.get('use-loopback-access', False)\\n self.use_loopback_control = group_config.get('use-loopback-control', False)\\n self.use_static_address = group_config.get('use-static-addr', False)\\n self.heartbeat_retries = group_config.get('heartbeat-retries', '3')\\n self.heartbeat_interval = group_config.get('heartbeat-interval', '20')\\n self.sx_node_retries = group_config.get('sx-node-retries', '3')\\n self.sx_node_retry_interval = group_config.get('sx-node-retry-interval', '5')\\n self.sx_session_retries = group_config.get('sx-session-retries', '3')\\n self.sx_session_retry_interval = group_config.get('sx-session-retry-interval', '5')\\n # Control node counts\\n self.mme_control_node_count = group_config.get('mme-control-node-count', '1')\\n self.sgw_control_node_count = group_config.get('sxab-control-node-count', '1')\\n self.enodeb_user_node_count = group_config.get('enodeb-user-node-count', '1')\\n\\n import ipaddress\\n\\n self.user_base_address = group_config.get('user-base-address', '10.1.1.1/24')\\n self.control_base_address = group_config.get('control-base-address', '20.2.2.1/24')\\n self.uplink_base_address = group_config.get('uplink-base-address', '30.3.3.1/24')\\n\\n if 'user-v6-base-address' in group_config:\\n self.user_v6_base_address = group_config.get('user-v6-base-address')\\n if 'control-v6-base-address' in group_config:\\n self.control_v6_base_address = group_config.get('control-v6-base-address')\\n if 'uplink-v6-base-address' in group_config:\\n self.uplink_v6_base_address = group_config.get('uplink-v6-base-address')\\n\\n # Logic for consolidating S1U + SGi. Need further granularity on individual interface\\n # Get integer 'n' from rinterface.interface_link = 'accessn' ie. access0, access1, access2, etc.\\n interface_index = [int(z) for z in list(rtinterface.interface_link) if z.isdigit()][0]\\n access_interface_list = bbe.get_interfaces(rtinterface.device_id, interfaces=rinterface.interface_link)\\n control_interface_list = bbe.get_interfaces(rtinterface.device_id, interfaces='control'+str(interface_index))\\n uplink_interface_list = bbe.get_interfaces(rtinterface.device_id, interfaces='uplink'+str(interface_index))\\n\\n if len(access_interface_list) < 1 or len(uplink_interface_list) < 1:\\n raise Exception('Must have minimum of 1 access and 1 uplink interface present to initialize a '\\n 'CUPSSubscriber object!\\\\n')\\n\\n # Check Home Addr type to see if we need v6 uplink network, since decapsulated traffic will be IPv6\\n if self.home_address_type == 2 or self.home_address_type == 3:\\n self.uplink_v6_base_address = group_config.get('uplink-v6-base-address', 'FEED:BABE::1/64')\\n\\n # Assign interfaces to subscriber while we have them anyways. List will have 1 entry due to required\\n # unique link names in params.yaml\\n self.access_interface = access_interface_list[0].interface_pic\\n if hasattr(self, 'user_v6_base_address'):\\n self.access_v6_interface = self.access_interface + 'v6'\\n self.uplink_interface = uplink_interface_list[0].interface_pic\\n if hasattr(self, 'uplink_v6_base_address'):\\n self.uplink_v6_interface = self.uplink_interface + 'v6'\\n\\n # Can generate IPs dynamically, since many required LS IPs do not matter to DUT. Let user pick their\\n # desired base address and we will handle the rest. Next IP is retrieved from next() function applied\\n # to appropriate IP iterator\\n self.access_ip_generator = iter(ipaddress.IPv4Network(self.user_base_address, strict=False).hosts())\\n if hasattr(self, 'user_v6_base_address'):\\n self.access_ipv6_generator = iter(ipaddress.IPv6Network(self.user_v6_base_address, strict=False).hosts())\\n if len(control_interface_list) == 0:\\n t.log('No control interface found. Attaching control-base-network to the access RT port!\\\\n')\\n self.control_ip_generator = self.access_ip_generator\\n self.control_interface = self.access_interface\\n if self.user_base_address != self.control_base_address:\\n self.control_ip_generator = iter(ipaddress.IPv4Network(self.control_base_address, strict=False).hosts())\\n self.control_interface = str(self.access_interface) + '_1'\\n if hasattr(self, 'user_v6_base_address'):\\n self.control_ipv6_generator = self.access_ipv6_generator\\n self.control_v6_interface = self.access_v6_interface\\n if not hasattr(self, 'control_v6_base_address'):\\n self.control_v6_base_address = self.user_v6_base_address\\n if (self.user_v6_base_address != self.control_v6_base_address):\\n self.control_ipv6_generator = iter(ipaddress.IPv6Network(\\n self.control_v6_base_address, strict=False).hosts())\\n self.control_v6_interface = str(self.access_v6_interface) + '_1'\\n else:\\n self.control_ip_generator = iter(ipaddress.IPv4Network(self.control_base_address, strict=False).hosts())\\n self.control_interface = control_interface_list[0].interface_pic\\n if hasattr(self, 'control_v6_base_address'):\\n self.control_ipv6_generator = iter(ipaddress.IPv6Network(\\n self.control_v6_base_address, strict=False).hosts())\\n self.control_v6_interface = self.control_interface + 'v6'\\n self.uplink_ip_generator = iter(ipaddress.IPv4Network(self.uplink_base_address, strict=False).hosts())\\n if hasattr(self, 'uplink_v6_base_address'):\\n self.uplink_ipv6_generator = iter(ipaddress.IPv6Network(self.uplink_v6_base_address, strict=False).hosts())\\n\\n\\n self.sgw_user_address = str(next(self.access_ip_generator)) # DUT local address for ANP\\n self.access_gateway = self.sgw_user_address\\n if self.use_loopback_access:\\n self.sgw_user_address = self.sut_loopback_ip\\n\\n self.sxa_sxb_to_up_sgw_user_node = str(next(self.access_ip_generator))\\n self.pgw_address = str(next(self.access_ip_generator))\\n\\n self.enodeb_user_node_start_ip = str(next(self.access_ip_generator))\\n self.target_enodeb_user_node = str(next(self.access_ip_generator))\\n\\n if hasattr(self, 'user_v6_base_address'):\\n self.sgw_v6_user_address = str(next(self.access_ipv6_generator)) # DUT local address for ANP\\n self.v6_access_gateway = self.sgw_v6_user_address\\n if self.use_loopback_access:\\n self.sgw_v6_user_address = self.sut_loopback_ipv6\\n\\n self.sxa_sxb_to_up_sgw_v6_user_node = str(next(self.access_ipv6_generator))\\n self.pgw_v6_address = str(next(self.access_ipv6_generator))\\n\\n self.enodeb_user_node_start_ipv6 = str(next(self.access_ipv6_generator))\\n self.target_enodeb_user_node_v6 = str(next(self.access_ipv6_generator))\\n\\n # eNodeB User Node IPs should be consumed in sequence when multiple eNodeB in use\\n if int(self.enodeb_user_node_count) > 1:\\n self.enodeb_user_node_ip_list = []\\n self.enodeb_user_node_ip_list.append(self.enodeb_user_node_start_ip)\\n for count in range(self.enodeb_user_node_count - 1):\\n self.enodeb_user_node_ip_list.append(str(next(self.access_ip_generator)))\\n self.target_enodeb_user_node = str(next(self.access_ip_generator))\\n self.target_enodeb_user_node_ip_list = []\\n self.target_enodeb_user_node_ip_list.append(self.target_enodeb_user_node)\\n for count in range(self.enodeb_user_node_count - 1):\\n self.target_enodeb_user_node_ip_list.append(str(next(self.access_ip_generator)))\\n if hasattr(self, 'user_v6_base_address'):\\n self.enodeb_user_node_ipv6_list = []\\n self.enodeb_user_node_ipv6_list.append(self.enodeb_user_node_start_ipv6)\\n for count in range(self.enodeb_user_node_count - 1):\\n self.enodeb_user_node_ipv6_list.append(str(next(self.access_ipv6_generator)))\\n self.target_enodeb_user_node_v6 = str(next(self.access_ipv6_generator))\\n self.target_enodeb_user_node_ipv6_list = []\\n self.target_enodeb_user_node_ipv6_list.append(self.target_enodeb_user_node_v6)\\n for count in range(self.enodeb_user_node_count - 1):\\n self.target_enodeb_user_node_ipv6_list.append(str(next(self.access_ipv6_generator)))\\n\\n # self.sxa_sxb_to_up_sgw_user_node = str(next(self.access_ip_generator))\\n # self.pgw_address = str(next(self.access_ip_generator))\\n # self.target_enodeb_user_node = str(next(self.access_ip_generator))\\n self.sxa_sxb_to_up_port = '8805'\\n\\n # Generate control IPs\\n self.sxa_sxb_to_up_user_node = str(next(self.control_ip_generator)) # DUT local address for CPP\\n if self.user_base_address == self.control_base_address:\\n self.sxa_sxb_to_up_user_node = self.sgw_user_address\\n self.control_gateway = self.sxa_sxb_to_up_user_node\\n if self.use_loopback_control:\\n self.sxa_sxb_to_up_user_node = self.sut_loopback_ip\\n self.sgw_control_address = str(next(self.control_ip_generator))\\n self.sgw_control_node_start_ip = self.sgw_control_address\\n\\n # SGW-C IPs should be consumed in sequence when multiple CPF in use\\n if int(self.sgw_control_node_count) > 1:\\n self.sgw_control_node_ip_list = []\\n self.sgw_control_node_ip_list.append(self.sgw_control_address)\\n for count in range(self.sgw_control_node_count - 1):\\n self.sgw_control_node_ip_list.append(str(next(self.control_ip_generator)))\\n\\n self.mme_control_node_start_ip = str(next(self.control_ip_generator))\\n self.sxa_sxb_control_node_start_ip = str(next(self.control_ip_generator))\\n\\n # Sxab control IPs should be consumed in sequence when multiple CPF in use\\n if int(self.sgw_control_node_count) > 1:\\n self.sxab_control_node_ip_list = []\\n self.sxab_control_node_ip_list.append(self.sxa_sxb_control_node_start_ip)\\n for count in range(self.sgw_control_node_count - 1):\\n self.sxab_control_node_ip_list.append(str(next(self.control_ip_generator)))\\n\\n self.sxa_sxb_control_gtp_node_start_ip = str(next(self.control_ip_generator))\\n self.sxa_sxb_to_up_gtp_node = self.sxa_sxb_to_up_user_node\\n\\n if hasattr(self, 'control_v6_base_address'):\\n # Generate control IPs\\n self.sxa_sxb_to_up_v6_user_node = str(next(self.control_ipv6_generator)) # DUT local address for CPP\\n if self.user_v6_base_address == self.control_v6_base_address:\\n self.sxa_sxb_to_up_v6_user_node = self.sgw_v6_user_address\\n self.v6_control_gateway = self.sxa_sxb_to_up_v6_user_node\\n if self.use_loopback_control:\\n self.sxa_sxb_to_up_v6_user_node = self.sut_loopback_ipv6\\n self.sgw_v6_control_address = str(next(self.control_ipv6_generator))\\n self.sgw_control_node_start_ipv6 = self.sgw_v6_control_address\\n\\n # SGW-C IPs should be consumed in sequence when multiple CPF in use\\n if int(self.sgw_control_node_count) > 1:\\n self.sgw_control_node_ipv6_list = []\\n self.sgw_control_node_ipv6_list.append(self.sgw_v6_control_address)\\n for count in range(self.sgw_control_node_count - 1):\\n self.sgw_control_node_ipv6_list.append(str(next(self.control_ipv6_generator)))\\n\\n self.mme_control_node_start_ipv6 = str(next(self.control_ipv6_generator))\\n self.sxa_sxb_control_node_start_ipv6 = str(next(self.control_ipv6_generator))\\n\\n # Sxab control IPs should be consumed in sequence when multiple CPF in use\\n if int(self.sgw_control_node_count) > 1:\\n self.sxab_control_node_ipv6_list = []\\n self.sxab_control_node_ipv6_list.append(self.sxa_sxb_control_node_start_ipv6)\\n for count in range(self.sgw_control_node_count - 1):\\n self.sxab_control_node_ipv6_list.append(str(next(self.control_ipv6_generator)))\\n\\n self.sxa_sxb_control_gtp_node_start_ipv6 = str(next(self.control_ipv6_generator))\\n self.sxa_sxb_to_up_gtp_node_v6 = self.sxa_sxb_to_up_v6_user_node\\n\\n # Generate uplink IPs\\n self.uplink_gateway = str(next(self.uplink_ip_generator))\\n self.traffic_start_ip = str(next(self.uplink_ip_generator))\\n if hasattr(self, 'uplink_v6_base_address'):\\n self.v6_uplink_gateway = str(next(self.uplink_ipv6_generator))\\n self.traffic_start_ipv6 = str(next(self.uplink_ipv6_generator))\\n\\n # SGW Nodal S11\\n self.s11_gtpv2_version = group_config.get('gtpv2-version', '15.2.0')\\n self.s11_total_apns = group_config.get('total-apns', '1')\\n self.s11_start_imsi = group_config.get('start-imsi', '505024101215074')\\n self.s11_start_imei = group_config.get('start-imei', '50502410121507')\\n self.s11_radio_access_type = group_config.get('radio-access-type', 'eutran')\\n self.s11_gtp_c_tunnel_id = group_config.get('start-gtp-c-tunnel-id', '1000000')\\n self.s11_gtp_u_tunnel_id = group_config.get('start-gtp-u-tunnel-id', '1048576')\\n self.s11_total_apns = group_config.get('total-apns', '1')\\n self.s11_n3_requests = group_config.get('s11-n3-requests', '5')\\n self.s11_t3_response_time = group_config.get('s11-t3-response-time', '20')\\n\\n # SGW Node S11\\n self.node_start_imsi = group_config.get('start-imsi', '505024101215074')\\n self.node_start_imei = group_config.get('start-imei', '50502410121507')\\n self.node_radio_access_type = group_config.get('radio-access-type', 'eutran')\\n self.node_gtp_c_tunnel_id = group_config.get('start-gtp-c-tunnel-id', '1000000')\\n self.node_gtp_u_tunnel_id = group_config.get('start-gtp-u-tunnel-id', '1048576')\\n self.node_start_s5_gtp_c_teid = group_config.get('start-s5-gtp-c-teid', '3000000')\\n self.node_start_s5_gtp_u_teid = group_config.get('start-s5-gtp-u-teid', '4000000')\\n self.node_start_fwd_gtp_u_teid = group_config.get('start-fwd-gtp-u-teid', '5000000')\\n self.node_total_apns = group_config.get('total-apns', '1')\\n self.node_gtpv2_version = group_config.get('gtpv2-version', '15.2.0')\\n\\n # VLANs\\n if group_config.get('access-vlan-id') is not None:\\n self.access_vlan_id = group_config.get('access-vlan-id')\\n if group_config.get('control-vlan-id') is not None:\\n self.control_vlan_id = group_config.get('control-vlan-id')\\n if group_config.get('uplink-vlan-id') is not None:\\n self.uplink_vlan_id = group_config.get('uplink-vlan-id')\\n\\n # Creates data structure allowing user to have different login groups within single Test Session\\n if 'Command Mode' in self.test_activity and group_config.get('session-groups') is not None:\\n count = 1\\n for group in sorted(group_config['session-groups']):\\n start_index = count\\n stop_index = count + group_config['session-groups'][group] - 1\\n self.subscriber_group_indices[group] = (start_index, int(stop_index))\\n count += int(group_config['session-groups'][group])\\n self.subscriber_group_indices['default'] = (1, self.session_count)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15e04e3020f2e10350d461cd6c5561ec\",\n \"score\": \"0.510582\",\n \"text\": \"def setup(self):\\n super(BaseInstance, self).setup()\\n self.instance.modify_attribute(\\n \\\"blockDeviceMapping\\\", [\\\"%s=true\\\" % v[0] for v in self.volume_info]\\n )\\n self.create_users(self._get_users())\\n self.setup_sudoers()\\n # needed for SSH agent forwarding during replication setup:\\n self.reset_authentication()\\n self.create_deployer()\\n self.update_deployer_keys()\\n self.bind_app_directories(self.app_dirs, self.app_root)\\n self.setup_swap() # after app (potentially secure) partition is created\\n self.upgrade_packages()\\n self.install_packages([\\\"ntp\\\"]) # keep date current\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21eef862dda4da2b5f89baf24e81dd2b\",\n \"score\": \"0.5099276\",\n \"text\": \"def __init__(self, hass: HomeAssistant, entry: ConfigEntry) -> None:\\n self.hass = hass\\n self._entry = entry\\n\\n self._devices: dict[str, AsusWrtDevInfo] = {}\\n self._connected_devices: int = 0\\n self._connect_error: bool = False\\n\\n self._sensors_data_handler: AsusWrtSensorDataHandler | None = None\\n self._sensors_coordinator: dict[str, Any] = {}\\n\\n self._on_close: list[Callable] = []\\n\\n self._options: dict[str, Any] = {\\n CONF_DNSMASQ: DEFAULT_DNSMASQ,\\n CONF_INTERFACE: DEFAULT_INTERFACE,\\n CONF_REQUIRE_IP: True,\\n }\\n self._options.update(entry.options)\\n\\n self._api: AsusWrtBridge = AsusWrtBridge.get_bridge(\\n self.hass, dict(self._entry.data), self._options\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b5c6e975588199f5c14bbdc48f6e499\",\n \"score\": \"0.5096234\",\n \"text\": \"def network(ctx, network):\\n ctx.obj['network'] = network\\n ctx.obj['network_url'] = \\\"{}/networks/{}\\\".format(ctx.obj['chute_url'],\\n network)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3dddafccda2795f3bcc08a4c7d66088\",\n \"score\": \"0.508461\",\n \"text\": \"def init_for_next_scenario(self):\\n self.reset()\\n self.net_configurator = self.get_network_provisioner()\\n\\n event_system = singletons.event_system\\n if singletons.simulation_manager.auto_stepping:\\n if scenario_config.is_network_links_auto_ipv4() and self.net_configurator:\\n log.info(\\\"adding network setup to EventSystem ...\\\")\\n event_system.events.append(MyEventSystem.EVENT_NETWORK_SETUP)\\n if scenario_config.is_connectivity_check_enabled():\\n log.info(\\\"adding network check to EventSystem ...\\\")\\n event_system.events.append(MyEventSystem.EVENT_NETWORK_CHECK)\\n\\n event_system.ready.set()\\n log.info(\\\"%s ready ... \\\", event_system.__class__.__name__)\\n\\n if self.net_configurator:\\n self.net_configurator = self.net_configurator(singletons.network_backend.get_interface_index)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5b68faa49086f3622c90c03d07f7e02\",\n \"score\": \"0.5074843\",\n \"text\": \"def _construct_network(self, cfg):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cfce461182cf26341680e20a133e46e\",\n \"score\": \"0.5074374\",\n \"text\": \"def assign_ap_ip():\\n os.system('ifconfig ' + ap_face + ' ' + AP_IP + '/24 up')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"947bbefb34f7ff5e7a854770d2b0bbe9\",\n \"score\": \"0.50735915\",\n \"text\": \"def setAP(controller, node, essid):\\n\\n #This UPI function stops the hostpad daemon, if present on node\\n rvalue = controller.nodes(node).net.stop_hostapd()\\n #This UPI function sets the node ip address on a specific interface\\n rvalue = controller.nodes(node).net.set_ip_address(ap_wlan_interface, '192.168.3.' + node.ip.split('.')[3] )\\n #This UPI function sets the hostapd configuration (starting from a generic file set interface, channel and SSID )\\n rvalue = controller.nodes(node).net.set_hostapd_conf(ap_wlan_interface, './hostapd.conf', 6, essid)\\n #This UPI function starts the hostapd daemon (it uses a specific hostapd.conf file present in the experiment directory\\n rvalue = controller.nodes(node).net.start_hostapd('./hostapd.conf')\\n #This UPI function sets the node power (value in dBm)\\n rvalue = controller.nodes(node).radio.set_tx_power(15)\\n #This UPI function sets the node modulation rate (value in Mbps)\\n rvalue = controller.nodes(node).radio.set_modulation_rate(2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79fe8593207b9ab6c1deab80ad4a49fe\",\n \"score\": \"0.50699216\",\n \"text\": \"def __init__(self, ssid, psk=\\\"\\\", manager=NMWiFiManager()):\\n self.ssid = ssid\\n self.ssid_encrypt = None\\n self.psk = psk\\n self.user = \\\"\\\"\\n self.passw = \\\"\\\"\\n self.cells = {}\\n self.iface = None\\n self.is_shared = False\\n self.interfaces = {}\\n self.wifimanager = manager\\n self.do_persist = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfed88b1d0937ee3955355a3cbd77ab5\",\n \"score\": \"0.5069002\",\n \"text\": \"def __init__(self, request, IPAddr = None, user = None, password=None, verbose=False, version=None):\\n \\n self.request = request\\n # ONTAP credentials\\n self.IPAddr = IPAddr\\n self.user = user\\n self.password = password\\n \\n self.verbose = verbose\\n # ONTAP version\\n self.version = version\\n \\n self.ontap_server = None\\n \\n if self.IPAddr and self.user and self.password:\\n # ONTAP server\\n s = None\\n # Version control\\n if self.version == \\\"9.4\\\":\\n from NetApp_9_4 import (NaServer, NaElement)\\n s = NaServer(self.IPAddr, 1, 140)\\n else:\\n from NetApp_9_3 import (NaServer, NaElement)\\n s = NaServer(self.IPAddr, 1, 130)\\n \\n s.set_server_type(\\\"FILER\\\")\\n s.set_transport_type(\\\"HTTPS\\\")\\n s.set_port(443)\\n s.set_style(\\\"LOGIN\\\")\\n s.set_admin_user(self.user, self.password)\\n \\n self.ontap_server = s\\n \\n # Our request must be initialized. This may throw errors;\\n # however, we would rather not initialize a driver if there\\n # is a faulty request\\n if not request.initialized:\\n request.initialize()\\n\\n # dictionary which matches outputs of requests to functions\\n self.test_dictionary = {\\n 'local_check_file_permissions' : self._local_check_file_permissions,\\n 'local_search_for_pattern' : self._local_search_for_pattern,\\n 'ontap_ssl_enabled' : self._ontap_ssl_enabled,\\n 'ontap_vols_encrypted' : self._ontap_vols_encrypted,\\n 'ontap_autosupport_disabled' : self._ontap_autosupport_disabled,\\n 'ontap_password_authentication' : self._ontap_password_authentication,\\n 'ontap_sha_hash_enabled' : self._ontap_sha_hash_enabled,\\n 'ontap_password_policy' : self._ontap_password_policy\\n }\\n\\n # end constructor ---------\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c652646422d9b82177710e6565335ba\",\n \"score\": \"0.50678843\",\n \"text\": \"def __init__(self, iface):\\n Layer.__init__(self)\\n self.arp_cache = {}\\n self.hwaddr = ARP().hwsrc\\n self.ip = get_if_addr(iface)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4764ce211549c03f10e524cc8068fccc\",\n \"score\": \"0.5063888\",\n \"text\": \"def _prepare_network_object(**kwargs):\\n net = apis_utils.data_st.Network()\\n name = kwargs.get(\\\"name\\\")\\n description = kwargs.get(\\\"description\\\")\\n stp = kwargs.get(\\\"stp\\\")\\n data_center = kwargs.get(\\\"data_center\\\")\\n vlan_id = kwargs.get(\\\"vlan_id\\\")\\n usages = kwargs.get(\\\"usages\\\")\\n mtu = kwargs.get(\\\"mtu\\\")\\n profile_required = kwargs.get(\\\"profile_required\\\")\\n qos_dict = kwargs.get(\\\"qos_dict\\\")\\n dns = kwargs.get(\\\"dns\\\")\\n external_network_provider_name = kwargs.get(\\\"external_provider_name\\\")\\n\\n if name:\\n net.set_name(name)\\n\\n if description:\\n net.set_description(description)\\n\\n if stp:\\n net.set_stp(stp)\\n\\n if data_center:\\n net.set_data_center(DC_API.find(data_center))\\n\\n if \\\"vlan_id\\\" in kwargs:\\n net.set_vlan(apis_utils.data_st.Vlan(id=vlan_id))\\n\\n if usages is not None:\\n if usages:\\n net.set_usages(apis_utils.data_st.Usages(usage=usages.split(\\\",\\\")))\\n else:\\n net.set_usages(apis_utils.data_st.Usages())\\n\\n if mtu:\\n net.set_mtu(mtu)\\n\\n if profile_required:\\n net.set_profile_required(profile_required)\\n\\n if qos_dict:\\n qos_obj = prepare_qos_on_net(qos_dict)\\n net.set_qos(qos_obj)\\n\\n if dns is not None:\\n dns_obj = prepare_network_dns_object(dns_servers=dns)\\n net.set_dns_resolver_configuration(dns_obj)\\n\\n if external_network_provider_name:\\n enp = OPENSTACK_NETWORK_PROVIDER_API.find(\\n external_network_provider_name\\n )\\n net.set_external_provider(external_provider=enp)\\n\\n return net\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bbb6d3e71dc09de75bbeac067c700e74\",\n \"score\": \"0.50576925\",\n \"text\": \"def connectionSetup(self, dispatcher):\\n self.logger.debug(\\\"Create the connection to the mgr....\\\")\\n # Create a connection to Hal driver mgr\\n self.mgrConnection = HalTransport(HalTransport.HalTransportClientMgr,\\n HalTransport.HalClientMode,\\n disconnectHandlerCb=self.connectionDisconnectCb)\\n\\n # create the poller\\n if self.poller is None:\\n self.poller = dispatcher.get_poll()\\n\\n # register the mgr socket\\n dispatcher.fd_register(self.mgrConnection.socket,\\n zmq.POLLIN, self.provmgr_cb)\\n dispatcher.fd_register(self.mgrConnection.monitor,\\n zmq.POLLIN, self.provmgr_cb)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c47e8c274606c024c9511f79c55b633c\",\n \"score\": \"0.50534624\",\n \"text\": \"def Setup(propagator, setup, earth):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e175a334d4063f362dfcf28d8faeb8a9\",\n \"score\": \"0.5051624\",\n \"text\": \"def setup_topology():\\n hosts = []\\n switches = []\\n links = []\\n\\n\\n log.info('Adding controller')\\n c0 = net.addController(name='c0'\\n #, controller=RemoteController,\\n #ip=CONTROLLER_IP,\\n #port=CONTROLLER_PORT,\\n )\\n\\n # Adding a Standalone switch\\n #switches[0] = net.addSwitch('s21', cls=OVSKernelSwitch, failMode='standalone')\\n\\n # Connecting the standalone switch to the interface on the machine/VM\\n #Intf('eth1', node=s21)\\n\\n for switch in range(1,8):\\n sname = 's' + str(switch)\\n log.info(\\\"Adding switch %s\\\" % sname)\\n s = net.addSwitch(sname)#, cls=OVSKernelSwitch)\\n switches.append(s)\\n\\n\\t# Connecting to VM interface\\n # Intf('eth2', node=switches[0])\\n\\n for host in range(1,15):\\n hname = 'h' + str(host)\\n log.info(\\\"Adding host %s\\\" % hname)\\n h = net.addHost(hname) #, ip=IP_PREFIX+str(host), mac=MAC_PREFIX+str(host))\\n hosts.append(h)\\n\\n manual_links = [['s1', 'h1', 100], ['s1', 'h2', 100], ['s1', 's2', 50 ],\\n ['s1', 's3', 50 ], #['s2', 's3', 50 ], \\n\\t\\t\\t['s2', 's4', 50 ],\\n ['s2', 's5', 50 ], ['s3', 's6', 50 ], ['s3', 's7', 50 ],\\n ['s4', 'h3', 10 ], ['s4', 'h4', 10 ], ['s4', 'h5', 10 ],\\n ['s5', 'h6', 10 ], ['s5', 'h7', 10 ], ['s5', 'h8', 10 ],\\n ['s6', 'h9', 10 ], ['s6', 'h10',10 ], ['s6', 'h11',10 ],\\n ['s7', 'h12',10 ], ['s7', 'h13',10 ], ['s7', 'h14',10 ],\\n ]\\n for link in manual_links:\\n from_link = link[0]\\n to_link = link[1]\\n log.debug(\\\"Adding link from %s to %s\\\" % (from_link, to_link))\\n bw = link[2]\\n '''\\n _bw = attributes.get('bw', 10) # in Mbps\\n _delay = '%fms' % attributes.get('latency', 10)\\n _jitter = '1ms'\\n _loss = error_rate\\n '''\\n\\t l = net.addLink(link[0], link[1])\\n # l = net.addLink(net.get(from_link), net.get(to_link)\\n #, cls=TCLink, bw=bw\\n # , delay=_delay, jitter=_jitter, loss=_loss\\n # )\\n links.append(l)\\n\\n \\t# Link for internet\\n \\t# l = net.addLink('h1', 's1')\\n \\t# links.append(l)\\n\\n '''\\n #Simple minimal topolgy code\\n #TODO: move to a different file\\n sname = 's' + '1'\\n log.info(\\\"Adding switch %s\\\" % sname)\\n s = net.addSwitch(sname, cls=OVSKernelSwitch)\\n\\n # for host in range(1,2):\\n # hname = 'h' + str(host)\\n # log.info(\\\"Adding host %s\\\" % hname)\\n h1 = net.addHost('h1')\\n h = net.addHost('h2')\\n\\n l = net.addLink('s1', 'h1',\\n cls=TCLink, bw=10\\n # , delay=_delay, jitter=_jitter, loss=_loss\\n )\\n l1 = net.addLink('s1', 'h2',\\n \\tcls=TCLink, bw=10\\n # , delay=10ms, jitter=1ms, loss=error_rate\\n )\\n '''\\n\\n # Build the network\\n log.info('Building network')\\n net.build()\\n\\n # Start the network\\n log.info('Starting network')\\n net.start()\\n\\n\\t# info('*** Configure h1\\\\'s controller communication interface\\\\n')\\n # hosts[0].cmd('ifconfig h1-eth1 hw ether 00:00:00:00:01:11')\\n\\n # info('*** Configure h1\\\\'s IP address\\\\n')\\n # hosts[0].cmd('dhclient h1-eth1')\\n\\n \\t#net.pingAll()\\n \\t#Starting the internet and stream traffic\\n \\tdf.setup_traffic_generators(net)\\n\\t#Popen(\\\"killall -9 top bwm-ng\\\", shell=True).wait()\\n # Drop the user in to a CLI so user can run commands.\\n CLI( net )\\n\\n # After the user exits the CLI, shutdown the network.\\n log.info('Stopping network')\\n net.stop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36812e805f3e53a93b044c0489b60aae\",\n \"score\": \"0.50488305\",\n \"text\": \"def setup(self):\\r\\n # Retrieve configuration settings\\r\\n addr = self.classconfig['addr']\\r\\n password = self.classconfig['password']\\r\\n if not addr:\\r\\n raise Exception('No address specified')\\r\\n # Check if addr is a \\\"host:port\\\" string\\r\\n (host, colon, port) = addr.partition(\\\":\\\")\\r\\n try:\\r\\n if colon:\\r\\n # IPv4/IPv6\\r\\n self._m = manager.manager(host=host, port=int(port), password=password)\\r\\n else:\\r\\n # UNIX Socket\\r\\n self._m = manager.manager(path=addr, password=password)\\r\\n # Test connection\\r\\n self.getpid()\\r\\n except Exception, e:\\r\\n raise Exception(str(e))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d43ba17e0ab557696760336f99057cb\",\n \"score\": \"0.5044762\",\n \"text\": \"def create_wifi_list(self):\\r\\n self.wifi_information.generating_wifi_list()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a9960d8682dc7db95f9867cfb1b3047\",\n \"score\": \"0.50412697\",\n \"text\": \"def init_network():\\r\\n network = {}\\r\\n network['W1'] = np.array([[0.1, 0.3, 0.5], [0.2, 0.4, 0.6]])\\r\\n network['b1'] = np.array([0.1, 0.2, 0.3])\\r\\n network['W2'] = np.array([[0.1, 0.4], [0.2, 0.5], [0.3, 0.6]])\\r\\n network['b2'] = np.array([0.1, 0.2])\\r\\n network['W3'] = np.array([[0.1, 0.3], [0.2, 0.4]])\\r\\n network['b3'] = np.array([0.1, 0.2])\\r\\n\\r\\n return network\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50d1b42a1b534cb0a4c5b5a27445479d\",\n \"score\": \"0.50291187\",\n \"text\": \"def _initialize(self):\\n key = self.get_api_key()\\n if not key:\\n print \\\"You have to specify a valid API key.\\\"\\n self.safe_options = False\\n return\\n self.url = \\\"%s%s\\\" % (self.server, self.url_path % key)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd8f099dd5ac2c5ea2b42ee67840ca9d\",\n \"score\": \"0.5028334\",\n \"text\": \"def __init__(self):\\n \\n # uplink\\n self.uplink, self.events = var.core\\n self.protocol = self.uplink.protocol\\n self.bursting = True\\n var.protocol = self\\n # what we're going to send for CAPAB\\n self.capab_msg = 'QS EX IE KLN UNKLN TB EUID RSFNC EOPMOD SERVICES'\\n # numeric\\n self.numeric = var.c.get('uplink', 'SID')\\n # description and server info\\n self.desc = var.c.get('uplink', 'description')\\n self.name = var.c.get('uplink', 'host')\\n self.needs_sid = var.c.get('uplink', 'needs_sid')\\n # database\\n self.db = var.database\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ccea962f40497ab79abbc326c931f24\",\n \"score\": \"0.50222325\",\n \"text\": \"def setwifi(timeout=35):\\n\\n import gc\\n from setwifi import setup_station\\n from time import sleep_ms\\n import usocket as socket\\n import network\\n # create access-point interface\\n ap_if = network.WLAN(network.AP_IF)\\n ap_if.active(True)\\n # the AP has to be active to be able to configure it\\n try:\\n ap_if.config(authmode=network.AUTH_OPEN)\\n except Exception as e:\\n e_str = str(e)\\n if \\\"can't set AP config\\\" in e_str:\\n print(\\\"Ignoring exception: \\\".format(e_str))\\n else:\\n return e\\n port = 80\\n cfg_data = {}\\n sta_if = network.WLAN(network.STA_IF)\\n\\n return_code = None\\n setup_attempts = 0\\n\\n # the following constants are defined because the HTML strings need to match the python strings.\\n SSID = \\\"SSID\\\"\\n PASSWORD = \\\"password\\\"\\n SUBMIT_VALUE = \\\"submit_value\\\"\\n CANCEL = \\\"Cancel\\\"\\n NETWORK_ID = \\\"Network ID\\\"\\n \\n MSG_ALREADY_CONNECTED = \\\"WiFi is already connected to a network - hit {} to leave it be.\\\"\\n\\n html_page = \\\"\\\"\\\"HTTP/1.1 200 OK\\n Content-type:text/html\\n Connection: close\\\\n\\n <!DOCTYPE html><html>\\n <head><title>{title}</title></head>\\n <style>body {{background-color: lightblue}} h2 {{color: Red;}}</style>\\n <div style=\\\\\\\"display: inline-block; text-align: center; color: Black; align: center; width: 100%\\\\\\\">\\n <body><h2>{message}</h2><h3>{body}</h3></body>\\n {form}\\n </div>\\n </html>\\\"\\\"\\\"\\n \\n ssid_form = \\\"\\\"\\\"<form method=\\\\\\\"POST\\\\\\\">\\n {NETWORK_ID}:<br>\\n <input type=\\\\\\\"text\\\\\\\" name=\\\\\\\"{SSID}\\\\\\\"><br>\\n Password:<br>\\n <input type=\\\\\\\"password\\\\\\\" name=\\\\\\\"{PASSWORD}\\\\\\\"><br><br>\\n <input type=\\\\\\\"submit\\\\\\\" name =\\\\\\\"{SUBMIT_VALUE}\\\\\\\" style=\\\\\\\"background-color:#00FF80\\\\\\\">\\n <input type=\\\\\\\"submit\\\\\\\" name =\\\\\\\"{SUBMIT_VALUE}\\\\\\\" value=\\\\\\\"{CANCEL}\\\\\\\" style=\\\\\\\"background-color:#D8D8D8\\\\\\\">\\n </form>\\\"\\\"\\\"\\n\\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n s.settimeout(timeout)\\n s.bind(('', port))\\n s.listen(2)\\n print('\\\\n-- Now Listening on: {} {}:{}...\\\\n'.format(ap_if.config('essid'), ap_if.ifconfig()[0], port))\\n \\n page_title = \\\"Configure WiFi\\\"\\n\\n try:\\n while return_code is None:\\n conn, addr = s.accept()\\n method = None\\n content_length = None\\n page_message = \\\"\\\"\\n if sta_if.isconnected():\\n page_message = MSG_ALREADY_CONNECTED.format(CANCEL)\\n page_body = \\\"Please enter your WiFi Info\\\"\\n page_form = ssid_form.format(NETWORK_ID=NETWORK_ID, SSID=SSID, PASSWORD=PASSWORD, SUBMIT_VALUE=SUBMIT_VALUE, CANCEL=CANCEL)\\n\\n request = conn.recv(1024)\\n\\n lines = [d.strip() for d in request.decode('utf-8').split('\\\\r\\\\n')]\\n method, raw_path, version = lines[0].split(' ')\\n# path = raw_path.split('?', 1)[0]\\n# print(\\\" request: {:5} {}\\\".format(method, path))\\n headers = {k: v for k, v in\\n (l.split(': ') for l in lines[1:-2])}\\n# print(\\\" headers: {}\\\".format(headers))\\n# host = headers['Host']\\n# print(\\\" host: {}\\\".format(host))\\n if 'Content-Length' in headers:\\n content_length = headers['Content-Length']\\n\\n if method == 'POST':\\n if content_length is None:\\n print('Error: no content length in POST')\\n page_message = \\\"Browser Error: no content length\\\"\\n else:\\n post_data = conn.recv(int(content_length))\\n post_data = post_data.decode('UTF-8')\\n# print('\\\\npost_data: {}'.format(post_data))\\n for i, val in enumerate(post_data.split('&')):\\n (k,v) = val.split('=')\\n cfg_data[k] = v\\n\\n if SUBMIT_VALUE in cfg_data and cfg_data[SUBMIT_VALUE] == CANCEL:\\n page_body=\\\"WiFi setup cancelled.\\\"\\n page_message=\\\"\\\"\\n page_form=\\\"\\\"\\n return_code = \\\"cancelled: attempts: \\\" + str(setup_attempts)\\n # could have added javascript validation, but since it can be disabled, its recommended the server do the validation\\n elif SSID not in cfg_data or len(cfg_data[SSID]) < 2 or PASSWORD not in cfg_data or len(cfg_data[PASSWORD]) < 4:\\n if SSID not in cfg_data or len(cfg_data[SSID]) < 2:\\n page_message = \\\"Invalid {}: '{}'\\\".format(NETWORK_ID, cfg_data[SSID])\\n else:\\n page_message = \\\"Invalid {}: '{}'\\\".format(PASSWORD, cfg_data[PASSWORD])\\n cfg_data = {}\\n else:\\n setup_ok, setup_message = setup_station(cfg_data[SSID], cfg_data[PASSWORD])\\n setup_attempts += 1\\n if setup_ok:\\n page_body = setup_message\\n page_form = \\\"\\\"\\n return_code = \\\"done: connected\\\"\\n else:\\n page_message=setup_message\\n cfg_data = {}\\n\\n response = html_page.format(title=page_title, message=page_message, body=page_body, form=page_form)\\n conn.send(response)\\n conn.close()\\n gc.collect()\\n\\n except Exception as e:\\n e_str = str(e)\\n if 'ETIMEDOUT' in e_str:\\n return_code = \\\"timeout\\\"\\n else:\\n return_code = \\\"exception: \\\".format(e_str)\\n\\n sleep_ms(2000)\\n s.close()\\n sleep_ms(2000)\\n ap_if.active(False)\\n return return_code\",\n \"title\": \"\"\n },\n {\n \"docid\": \"851e1d3550761d1bcc8da0394a68001b\",\n \"score\": \"0.50136715\",\n \"text\": \"def _setup_location(self):\\n self.logger.debug('Setting up site details of observatory')\\n\\n try:\\n config_site = self.config.get('location')\\n\\n name = config_site.get('name', 'Nameless Location')\\n\\n latitude = config_site.get('latitude')\\n longitude = config_site.get('longitude')\\n\\n timezone = config_site.get('timezone')\\n utc_offset = config_site.get('utc_offset')\\n\\n pressure = config_site.get('pressure', 0.680) * u.bar\\n elevation = config_site.get('elevation', 0 * u.meter)\\n horizon = config_site.get('horizon', 30 * u.degree)\\n twilight_horizon = config_site.get(\\n 'twilight_horizon', -18 * u.degree)\\n\\n self.location = {\\n 'name': name,\\n 'latitude': latitude,\\n 'longitude': longitude,\\n 'elevation': elevation,\\n 'timezone': timezone,\\n 'utc_offset': utc_offset,\\n 'pressure': pressure,\\n 'horizon': horizon,\\n 'twilight_horizon': twilight_horizon,\\n }\\n self.logger.debug(\\\"Location: {}\\\".format(self.location))\\n\\n # Create an EarthLocation for the mount\\n self.earth_location = EarthLocation(\\n lat=latitude, lon=longitude, height=elevation)\\n self.observer = Observer(\\n location=self.earth_location, name=name, timezone=timezone)\\n except Exception:\\n raise error.PanError(msg='Bad site information')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8721c254fbad966fb914601742e3969d\",\n \"score\": \"0.50121903\",\n \"text\": \"def __init__(self, config):\\n self.conf = dict(ip_addr = '192.168.0.20',\\n username = 'anonymous',\\n password = '',\\n rootpath = '',\\n init = True,\\n rd_blocksize = 2048,\\n debug = 0,\\n )\\n self.conf.update(config)\\n if self.conf['init']: self.initialize()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02d13be8cb70f52658be80e21e447984\",\n \"score\": \"0.5011904\",\n \"text\": \"def start(self):\\r\\n self._log('Starting LoRaWAN nano forwarder with id: {}', self.id)\\r\\n\\r\\n # Define ID and Keys in LORAWAN mode.\\r\\n if self.mode != 1:\\r\\n self.lorawan = LoRa(mode=LoRa.LORAWAN)\\r\\n self.dev_addr = struct.unpack(\\\">l\\\", ubinascii.unhexlify('2601160C'))[0] #create an ABP authentication params\\r\\n self.nwk_swkey = ubinascii.unhexlify('5864350495E1123A5AD221DF700CCEC8')\\r\\n self.app_swkey = ubinascii.unhexlify('4B751B14B6C2C7BAB00B46EEA30E920D')\\r\\n for channel in range(0, 72): # remove all the channels\\r\\n self.lorawan.remove_channel(channel)\\r\\n for channel in range(0, 72): # set all channels to the same frequency (must be before sending the OTAA join request)\\r\\n self.lorawan.add_channel(channel, frequency=903900000, dr_min=0, dr_max=3)\\r\\n self.lorawan.join(activation=LoRa.ABP, auth=(self.dev_addr, self.nwk_swkey, self.app_swkey)) # join a network using ABP (Activation By Personalization)\\r\\n self.lorawan_sock = usocket.socket(usocket.AF_LORA, usocket.SOCK_RAW) # create a LoRa usocket\\r\\n self.lorawan_sock.setsockopt(usocket.SOL_LORA, usocket.SO_DR, 3) # set the LoRaWAN data rate\\r\\n self.lorawan_sock.setblocking(True) # make the usocket blocking\\r\\n\\r\\n #Saving LoRaWAN State in NVRAM\\r\\n if self.mode == 3:\\r\\n self.lorawan.nvram_save()\\r\\n\\r\\n # initialize the LoRa radio in LORA mode\\r\\n if self.mode != 2:\\r\\n self._log('Setting up the LoRa radio at {} Mhz using {}', self._freq_to_float(self.frequency), self.datarate)\\r\\n self.lora = LoRa(\\r\\n mode=LoRa.LORA,\\r\\n frequency=self.frequency,\\r\\n bandwidth=self.bw,\\r\\n sf=self.sf,\\r\\n preamble=8,\\r\\n coding_rate=LoRa.CODING_4_5,\\r\\n tx_iq=True\\r\\n )\\r\\n\\r\\n # create a raw LoRa socket\\r\\n self.lora_sock = usocket.socket(usocket.AF_LORA, usocket.SOCK_RAW)\\r\\n self.lora_sock.setblocking(False)\\r\\n self.lora_tx_done = False\\r\\n self.lora.callback(trigger=(LoRa.RX_PACKET_EVENT), handler=self._lora_cb)\\r\\n #self.lora.callback(trigger=(LoRa.RX_PACKET_EVENT), handler=self._lorawan_cb)\\r\\n\\r\\n #initialize application\\r\\n if self.mode != 1:\\r\\n self.app_alarm = Timer.Alarm(handler=lambda t: self._app(), s=7, periodic=True)\\r\\n self._log('LoRaWAN nano forwarder online')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"faf22bb4716b2d4ef3fdd4396177416e\",\n \"score\": \"0.5005024\",\n \"text\": \"def setup_network_namespace(node, namespace_name, interface_name,\\n ip_addr_list, prefix_length):\\n Namespaces.create_namespace(node, namespace_name)\\n\\n cmd = f\\\"ip netns exec {namespace_name} ip link set {interface_name} up\\\"\\n exec_cmd_no_error(node, cmd, sudo=True)\\n\\n for ip_addr in ip_addr_list:\\n cmd = f\\\"ip netns exec {namespace_name} ip addr add \\\" \\\\\\n f\\\"{ip_addr}/{prefix_length} dev {interface_name}\\\"\\n exec_cmd_no_error(node, cmd, sudo=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7638ebf5926e24b9cfaa654007d96a02\",\n \"score\": \"0.5004933\",\n \"text\": \"def setup_method(self):\\n self.hass = get_test_home_assistant()\\n\\n asyncio.run_coroutine_threadsafe(\\n async_process_ha_core_config(\\n self.hass, {\\\"internal_url\\\": \\\"http://example.local:8123\\\"}\\n ),\\n self.hass.loop,\\n )\\n\\n self.url = \\\"https://translate.google.com/translate_tts\\\"\\n self.url_param = {\\n \\\"tl\\\": \\\"en\\\",\\n \\\"q\\\": \\\"90%25%20of%20I%20person%20is%20on%20front%20of%20your%20door.\\\",\\n \\\"tk\\\": 5,\\n \\\"client\\\": \\\"tw-ob\\\",\\n \\\"textlen\\\": 41,\\n \\\"total\\\": 1,\\n \\\"idx\\\": 0,\\n \\\"ie\\\": \\\"UTF-8\\\",\\n }\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec2686e7f05090ff20e636fe6ea91f4b\",\n \"score\": \"0.49981785\",\n \"text\": \"def setup(self):\\n\\t\\t(self.packet, self.socket) = self.request\\n\\t\\tself.replies = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb72ce26325aa5b41dda1ee15b810927\",\n \"score\": \"0.49965006\",\n \"text\": \"def setUp(self):\\n self.net = Net('001')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ba1478ce753de4b36e28c662a07ad92a\",\n \"score\": \"0.4991803\",\n \"text\": \"def handle_setup(self) -> None:\\n setup_xml = (\\n '<?xml version=\\\"1.0\\\"?>'\\n \\\"<root>\\\"\\n \\\"<specVersion><major>1</major><minor>0</minor></specVersion>\\\"\\n \\\"<device>\\\"\\n \\\"<deviceType>urn:Belkin:device:controllee:1</deviceType>\\\"\\n f\\\"<friendlyName>{self.name}</friendlyName>\\\"\\n \\\"<manufacturer>Belkin International Inc.</manufacturer>\\\"\\n \\\"<modelName>Emulated Socket</modelName>\\\"\\n \\\"<modelNumber>3.1415</modelNumber>\\\"\\n f\\\"<UDN>uuid:Socket-1_0-{self.serial}</UDN>\\\"\\n \\\"<serviceList>\\\"\\n \\\"<service>\\\"\\n \\\"<serviceType>urn:Belkin:service:basicevent:1</serviceType>\\\"\\n \\\"<serviceId>urn:Belkin:serviceId:basicevent1</serviceId>\\\"\\n \\\"<controlURL>/upnp/control/basicevent1</controlURL>\\\"\\n \\\"<eventSubURL>/upnp/event/basicevent1</eventSubURL>\\\"\\n \\\"<SCPDURL>/eventservice.xml</SCPDURL>\\\"\\n \\\"</service>\\\"\\n \\\"<service>\\\"\\n \\\"<serviceType>urn:Belkin:service:metainfo:1</serviceType>\\\"\\n \\\"<serviceId>urn:Belkin:serviceId:metainfo1</serviceId>\\\"\\n \\\"<controlURL>/upnp/control/metainfo1</controlURL>\\\"\\n \\\"<eventSubURL>/upnp/event/metainfo1</eventSubURL>\\\"\\n \\\"<SCPDURL>/metainfoservice.xml</SCPDURL>\\\"\\n \\\"</service>\\\"\\n \\\"</serviceList>\\\"\\n \\\"</device>\\\"\\n \\\"</root>\\\"\\n )\\n\\n setup_response = self.add_http_headers(setup_xml)\\n logger.debug(f\\\"Fauxmo response to setup request:\\\\n{setup_response}\\\")\\n self.transport.write(setup_response.encode())\\n self.transport.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdef0e9e750d81f8022cd8e8ad9510c7\",\n \"score\": \"0.49893913\",\n \"text\": \"def _SetupVtiNetwork(cls, vti, is_add):\\n if is_add:\\n # Disable router solicitations to avoid occasional spurious packets\\n # arriving on the underlying network; there are two possible behaviors\\n # when that occurred: either only the RA packet is read, and when it\\n # is echoed back to the VTI, it causes the test to fail by not receiving\\n # the UDP_PAYLOAD; or, two packets may arrive on the underlying\\n # network which fails the assertion that only one ESP packet is received.\\n cls.SetSysctl(\\n \\\"/proc/sys/net/ipv6/conf/%s/router_solicitations\\\" % vti.iface, 0)\\n net_test.SetInterfaceUp(vti.iface)\\n\\n for version in [4, 6]:\\n ifindex = net_test.GetInterfaceIndex(vti.iface)\\n table = vti.netid\\n\\n # Set up routing rules.\\n start, end = cls.UidRangeForNetid(vti.netid)\\n cls.iproute.UidRangeRule(version, is_add, start, end, table,\\n cls.PRIORITY_UID)\\n cls.iproute.OifRule(version, is_add, vti.iface, table, cls.PRIORITY_OIF)\\n cls.iproute.FwmarkRule(version, is_add, vti.netid, cls.NETID_FWMASK,\\n table, cls.PRIORITY_FWMARK)\\n\\n # Configure IP addresses.\\n if version == 4:\\n addr = cls._MyIPv4Address(vti.netid)\\n else:\\n addr = cls.OnlinkPrefix(6, vti.netid) + \\\"1\\\"\\n prefixlen = net_test.AddressLengthBits(version)\\n vti.addrs[version] = addr\\n if is_add:\\n cls.iproute.AddAddress(addr, prefixlen, ifindex)\\n cls.iproute.AddRoute(version, table, \\\"default\\\", 0, None, ifindex)\\n else:\\n cls.iproute.DelRoute(version, table, \\\"default\\\", 0, None, ifindex)\\n cls.iproute.DelAddress(addr, prefixlen, ifindex)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c17979c55cb1215310084853f57159e\",\n \"score\": \"0.49857548\",\n \"text\": \"def setup_method(self):\\n self.hass = get_test_home_assistant()\\n\\n self.config = {rc.DOMAIN: {\\\"test_get\\\": {\\\"url\\\": \\\"http://example.com/\\\"}}}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3fa339244fc21e7618d34958d242c2d\",\n \"score\": \"0.4983651\",\n \"text\": \"def __init__(self):\\n # Call the constructor of the parent class\\n super(self.__class__,self).__init__()\\n\\n self._yarpPorts = dict()\\n self._component_ports = dict()\\n\\n # Get the Network attribute of yarp,\\n # then call its init method\\n # Strange that we should do all this,\\n # probably because this file has the same name.\\n # So this is the only way it seems to work\\n self._yarp_module = sys.modules['yarp']\\n #self._yarp_module.Network.init()\\n self.yarp_object = self._yarp_module.Network()\\n #yarp.Network.init()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80f201777e12b27373ff1f733e5e272b\",\n \"score\": \"0.49818295\",\n \"text\": \"def setUp(self):\\n guid = self.__class__.__name__ + '-' + str(uuid.uuid4())\\n stack_name = guid + '-stack'\\n\\n self.net_name = guid + '-net'\\n self.subnet_name = guid + '-subnet'\\n self.router_name = guid + '-router'\\n\\n env_values = {\\n 'net_name': self.net_name,\\n 'subnet_name': self.subnet_name,\\n 'router_name': self.router_name,\\n 'external_net_name': self.ext_net_name}\\n\\n heat_tmplt_path = pkg_resources.resource_filename(\\n 'snaps.openstack.tests.heat', 'router_heat_template.yaml')\\n self.stack_settings = StackConfig(\\n name=stack_name, template_path=heat_tmplt_path,\\n env_values=env_values)\\n self.stack = None\\n self.heat_client = heat_utils.heat_client(\\n self.os_creds, self.os_session)\\n self.neutron = neutron_utils.neutron_client(\\n self.os_creds, self.os_session)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb82d593a81d7660fdc7b2766020ad41\",\n \"score\": \"0.49767727\",\n \"text\": \"def __init__(__self__, *,\\n network: pulumi.Input[str],\\n description: Optional[pulumi.Input[str]] = None,\\n name: Optional[pulumi.Input[str]] = None,\\n project: Optional[pulumi.Input[str]] = None,\\n region: Optional[pulumi.Input[str]] = None,\\n stack_type: Optional[pulumi.Input[str]] = None,\\n vpn_interfaces: Optional[pulumi.Input[Sequence[pulumi.Input['HaVpnGatewayVpnInterfaceArgs']]]] = None):\\n pulumi.set(__self__, \\\"network\\\", network)\\n if description is not None:\\n pulumi.set(__self__, \\\"description\\\", description)\\n if name is not None:\\n pulumi.set(__self__, \\\"name\\\", name)\\n if project is not None:\\n pulumi.set(__self__, \\\"project\\\", project)\\n if region is not None:\\n pulumi.set(__self__, \\\"region\\\", region)\\n if stack_type is not None:\\n pulumi.set(__self__, \\\"stack_type\\\", stack_type)\\n if vpn_interfaces is not None:\\n pulumi.set(__self__, \\\"vpn_interfaces\\\", vpn_interfaces)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c65cd6a7b8b1c478e97eae693f78a2cd\",\n \"score\": \"0.49697104\",\n \"text\": \"def setup(self):\\n context = zmq.Context()\\n self._insocket = context.socket(zmq.ROUTER)\\n self._outsocket = context.socket(zmq.DEALER)\\n \\n self._insocket.bind(self._in_address)\\n self._outsocket.bind(self._out_address)\\n \\n self._loop = IOLoop.instance()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0314e3203f8bea10f9fe0144d7788b3\",\n \"score\": \"0.4961752\",\n \"text\": \"def configure_wifi(self,\\n ssid=None,\\n psk=None,\\n card_os='raspberry',\\n host=None,\\n country=None):\\n\\n country = country or 'US'\\n\\n card = SDCard(card_os=card_os, host_os=host)\\n path = f\\\"{card.boot_volume}/wpa_supplicant.conf\\\"\\n\\n card_os = \\\"raspberry\\\" # needs to become a parameter based on tag\\n\\n # noinspection PyPep8Naming\\n WifiClass = Wifi(card_os=card_os)\\n\\n if card_os == \\\"raspberry\\\":\\n if psk:\\n if os_is_mac():\\n WifiClass.set(ssid=ssid, password=psk, country=country, location=path)\\n else:\\n WifiClass.set(ssid=ssid, password=psk, country=country, location=path, sudo=True)\\n else:\\n if os_is_mac():\\n WifiClass.set(ssid=ssid, psk=False, country=country, location=path)\\n else:\\n WifiClass.set(ssid=ssid, psk=False, country=country, location=path, sudo=True)\\n else:\\n if os_is_mac():\\n WifiClass.set(ssid=ssid, password=psk, country=country, location=path)\\n else:\\n WifiClass.set(ssid=ssid, password=psk, country=country, location=path, sudo=True)\\n\\n return \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"127b6fa2c88ee2c89b25ce762b2957fc\",\n \"score\": \"0.49546182\",\n \"text\": \"def setup_netconf_device(self):\\n\\n create_authgroup(self.device_name, self.username, self.password)\\n create_device(self.device_name, self.ip_address,\\n self.netconf_port, self.ned_id)\\n fetch_host_keys(self.device_name)\\n sync_from(self.device_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c0ae528af0e37ff26338fc9218929e5\",\n \"score\": \"0.4939803\",\n \"text\": \"def init_communication(self):\\n ctx = zmq.Context()\\n self.pull_socket = ctx.socket(zmq.PULL)\\n self.pull_socket.bind(f\\\"tcp://127.0.0.1:{self.comm_cfg.learner_logger_port}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69f56ad7a8b99ef7e89641ea794821b0\",\n \"score\": \"0.493872\",\n \"text\": \"def startup(self):\\n if self.detectoldbridge():\\n return\\n\\n CoreNetwork.startup(self)\\n\\n if self.hostid:\\n addr = self.prefix.addr(self.hostid)\\n else:\\n addr = self.prefix.max_addr()\\n\\n logging.info(\\\"added control network bridge: %s %s\\\", self.brname, self.prefix)\\n\\n if self.assign_address:\\n addrlist = [\\\"%s/%s\\\" % (addr, self.prefix.prefixlen)]\\n self.addrconfig(addrlist=addrlist)\\n logging.info(\\\"address %s\\\", addr)\\n\\n if self.updown_script:\\n logging.info(\\\"interface %s updown script (%s startup) called\\\", self.brname, self.updown_script)\\n utils.check_cmd([self.updown_script, self.brname, \\\"startup\\\"])\\n\\n if self.serverintf:\\n # sets the interface as a port of the bridge\\n utils.check_cmd([constants.BRCTL_BIN, \\\"addif\\\", self.brname, self.serverintf])\\n\\n # bring interface up\\n utils.check_cmd([constants.IP_BIN, \\\"link\\\", \\\"set\\\", self.serverintf, \\\"up\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72a458849d73eaeb7f4f8a0b10cd8c36\",\n \"score\": \"0.49371934\",\n \"text\": \"def bootstrap(self):\\n self.merge_ip_address_list()\\n self.create_firewall_rules()\\n self.populate_droplet_tags()\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993290,"cells":{"query_id":{"kind":"string","value":"ed849fdc0a449e95110b4776d88c6cf0"},"query":{"kind":"string","value":"Strips featured artists from the artist title according to Billboard Hot 100 formatting."},"positive_passages":{"kind":"list like","value":[{"docid":"014f37cfdd10332cf25d9d9b2e75247c","score":"0.5266041","text":"def stripFeat(s):\n if \" Featuring\" in s:\n return s[: s.index(\" Featuring\")]\n elif \" x \" in s:\n return s[: s.index(\" x \")]\n else:\n return s","title":""}],"string":"[\n {\n \"docid\": \"014f37cfdd10332cf25d9d9b2e75247c\",\n \"score\": \"0.5266041\",\n \"text\": \"def stripFeat(s):\\n if \\\" Featuring\\\" in s:\\n return s[: s.index(\\\" Featuring\\\")]\\n elif \\\" x \\\" in s:\\n return s[: s.index(\\\" x \\\")]\\n else:\\n return s\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"1b1b36d7b110cab664480b03386f60c9","score":"0.6408811","text":"def ft_in_title(item, drop_feat):\r\n artist = item.artist.strip()\r\n albumartist = item.albumartist.strip()\r\n\r\n # Check whether there is a featured artist on this track and the\r\n # artist field does not exactly match the album artist field. In\r\n # that case, we attempt to move the featured artist to the title.\r\n _, featured = split_on_feat(artist)\r\n if featured and albumartist != artist and albumartist:\r\n ui.print_(displayable_path(item.path))\r\n feat_part = None\r\n\r\n # Look for the album artist in the artist field. If it's not\r\n # present, give up.\r\n albumartist_split = artist.split(albumartist)\r\n if len(albumartist_split) <= 1:\r\n ui.print_('album artist not present in artist')\r\n\r\n # If the last element of the split (the right-hand side of the\r\n # album artist) is nonempty, then it probably contains the\r\n # featured artist.\r\n elif albumartist_split[-1] != '':\r\n # Extract the featured artist from the right-hand side.\r\n _, feat_part = split_on_feat(albumartist_split[-1])\r\n\r\n # Otherwise, if there's nothing on the right-hand side, look for a\r\n # featuring artist on the left-hand side.\r\n else:\r\n lhs, rhs = split_on_feat(albumartist_split[0])\r\n if rhs:\r\n feat_part = lhs\r\n\r\n # If we have a featuring artist, move it to the title.\r\n if feat_part:\r\n update_metadata(item, feat_part, drop_feat)\r\n else:\r\n ui.print_(u'no featuring artists found')\r\n\r\n ui.print_()","title":""},{"docid":"c25b4f5f1cc80f5bdac1175c49f22d1b","score":"0.63470244","text":"def sanitizeArtist(value):\n # https://www.w3schools.com/python/ref_string_rstrip.asp\n if value.lower() not in special_artists_name:\n split = value.split(\"/\")[0]\n clean = split.rstrip()\n return clean\n else:\n return value","title":""},{"docid":"235f7b905242a0f71c2bbfee2e5a4726","score":"0.6216853","text":"def strip_the( artistName ):\n # NOTE: This must be run BEFORE 'proper_dir_name' because it relies on there being a space after \"The\"\n if artistName and artistName[:4].lower() == \"the \": \n return artistName[4:]\n else:\n return artistName","title":""},{"docid":"c61904f80cf7780a7df3e95f1b52a8a4","score":"0.6113897","text":"def remove_artists(self):\n for a in self.artists:\n self.imgview.remove_artist(a)","title":""},{"docid":"1acf21c031891e04dc64045c8a0d087e","score":"0.5917704","text":"def remove_ft_artist_suffix(s):\r\n pattern = r\"(.*?) (&|\\b(and|feat(uring)?\\b))\"\r\n match = re.search(pattern, s, re.IGNORECASE)\r\n if match:\r\n s = match.group(1)\r\n return s","title":""},{"docid":"52b8a4973c3c3ac30f8f403639ab07e8","score":"0.5864724","text":"def clean_artist_title(data, artist_col='artist', title_col='title'):\n data[artist_col + '_clean'] = data[artist_col].apply(clean_value)\n data[title_col + '_clean'] = data[title_col].apply(clean_value)\n\n data_len = len(data)\n data.dropna(subset=['artist_clean', 'title_clean'], inplace=True)\n if len(data) < data_len:\n logger.warning(\n 'Removed rows where artist_clean or title_clean is empty.')\n\n return data","title":""},{"docid":"8841e2029cfb0132b2f8327b5845e058","score":"0.5811103","text":"def get_artist(self, artists):\r\n artist_id = None\r\n bits = []\r\n for artist in artists:\r\n if not artist_id:\r\n artist_id = artist['id']\r\n name = artist['name']\r\n # Strip disambiguation number.\r\n name = re.sub(r' \\(\\d+\\)$', '', name)\r\n # Move articles to the front.\r\n name = re.sub(r'(?i)^(.*?), (a|an|the)$', r'\\2 \\1', name)\r\n bits.append(name)\r\n if artist['join']:\r\n bits.append(artist['join'])\r\n artist = ' '.join(bits).replace(' ,', ',') or None\r\n return artist, artist_id","title":""},{"docid":"bd1a2387015f4e48b4fd60dab7133dce","score":"0.58102137","text":"def pop_ny(article_title):\n tlist = article_title.split(\" \")\n tout = \" \".join(tlist[0:len(tlist)-2])\n return tout","title":""},{"docid":"6362a6e62c3e797e0e5498663780c6ee","score":"0.57258356","text":"def _get_artist(self, artists):\r\n artist_id = None\r\n bits = []\r\n for artist in artists:\r\n if not artist_id:\r\n artist_id = artist[0]\r\n name = artist[1]\r\n # Strip disambiguation number.\r\n name = re.sub(r' \\(\\d+\\)$', '', name)\r\n # Move articles to the front.\r\n name = re.sub(r'^(.*?), (a|an|the)$', r'\\2 \\1', name, flags=re.I)\r\n bits.append(name)\r\n artist = ', '.join(bits).replace(' ,', ',') or None\r\n return artist, artist_id","title":""},{"docid":"f4c77966b7907cdfb5e0c90e2948a091","score":"0.5695241","text":"def get_artist_billing(self, summary):\n\n artists_blob = summary.select('.show_artists')[0].text\n stripped = artists_blob.strip()\n formatted = stripped.replace(' \\n', ',')\n\n return formatted","title":""},{"docid":"ce130c843b7f7dda75f7e3989114c114","score":"0.5583403","text":"def remove_title(name):\n titles = ['Mr ', 'Ms ', 'Mrs ', 'Miss ', 'Dr ', 'Professor ',\n 'Reverend ', 'Sir ', 'Dame ', 'Hon. ', 'Hon ']\n\n for title in titles:\n if name.startswith(title):\n name = name[len(title):]\n\n return name","title":""},{"docid":"3de241f42f508471fbb7e9cd7e178039","score":"0.5571916","text":"def clean_top_tracks(spotify, tracks_df): \n\n tracks_df['album_name'] = tracks_df['album'].map(lambda x:x['name'])\n tracks_df['album_art'] = tracks_df['album'].map(lambda x:x['images'][0]['url'] if len(x['images'])!=0 else 0)\n tracks_df['album_href'] = tracks_df['album'].map(lambda x:x['href'])\n tracks_df['album_release_date'] = tracks_df['album'].map(lambda x:x['release_date'])\n tracks_df['artist_names'] = tracks_df['artists'].map(lambda x: ', '.join([a['name'] for a in x]))\n drop_cols = ['album', 'available_markets', 'disc_number', 'external_ids', 'is_local', 'external_urls',\\\n 'track_number', 'duration_ms', 'episode', 'track', 'uri',\\\n 'preview_url', 'type',\t'album_name', 'album_href']\n tracks_df = tracks_df.drop(drop_cols, axis=1)\n\n return tracks_df","title":""},{"docid":"d982d478541da19ea1a3f3f2e3068ddf","score":"0.5554848","text":"def prepare_song_info_to_show(songs):\n for song in songs.list:\n new_title = song['title'].split('(')[0]\n # If name started with (\n if new_title == '':\n new_title = song['title'].split(')')[1]\n if len(new_title) > 30:\n new_title = new_title[:30] + '...'\n song['title'] = new_title\n\n if len(song['album']) > 30:\n song['album'] = song['album'][:30] + '...'\n # for show only\n year = ''\n position = ''\n for billboard in song['billboard'].values():\n year += billboard['year'] + ', '\n position += str(billboard['position']) + ', '\n song['year'] = year[:-2]\n song['position'] = position[:-2]","title":""},{"docid":"5d1f472804cadfb7b06ffbb6c4a08b0a","score":"0.55116487","text":"def artists_helper(self, artlist):\n ans = [x['name'] for x in artlist]\n return ', '.join(ans)","title":""},{"docid":"330a8bbeed30ff50134380e460584c4b","score":"0.5407476","text":"def test_getYTArtistTitle_GuessSongUnformatted(self):\n link = 'https://www.youtube.com/watch?v=Y98XgZ9vGoQ'\n res = getYTArtistTitle(link)\n self.assertEqual(res['artist'], None)\n self.assertEqual(res['title'], 'Tenderly (Remastered)')","title":""},{"docid":"226e4917f5642d3bc370fa43a41b59cf","score":"0.53778744","text":"def fix_title(title: str):\n if title.endswith(\", The\"):\n title = \"The \" + title[:-len(\", The\")]\n title, _ = extract_year(title)\n title = title.replace(\"&\", \"and\").replace(\" \", \" \")\n return title","title":""},{"docid":"42453034243112513eb5902c45b722ee","score":"0.5351818","text":"def remove_repeat_artists(originals, fetched):\n artists = [x.artist for x in originals]\n results = []\n for result in fetched:\n if result.track.artist in artists:\n results.append(lastfm.TrackSearchResult(result.track, 0))\n else:\n results.append(lastfm.TrackSearchResult(result.track, 100))\n artists.append(result.track.artist)\n return results","title":""},{"docid":"5749f4eef90a5ea2b2c30224eea6a041","score":"0.53269124","text":"def gNews_title_fix(title):\n dashOccurence = (len(title) - 1) - title[::-1].index('-')\n return title[0:dashOccurence]","title":""},{"docid":"66a6423d375409708c246585ae9214cf","score":"0.5308533","text":"def clean_title(self, title):\n title = re.sub(YEAR, \"\", title).lower()\n title = re.findall(r\"[\\w'\\(\\)\\[\\]]+\", title)\n title = filter(check_stop_words, title)\n if len(title) and title[-1] == 'the':\n title = [title[-1]] + title[:-1]\n return \" \".join(title).replace('\"', \"'\")","title":""},{"docid":"16f072f45db010a6f76e3c7d5dc6da71","score":"0.5294426","text":"def clean_up(markers):\n\n num_markers = 500\n return markers[-num_markers:]","title":""},{"docid":"db1dedd1ddaf193180239aa15c5a4a98","score":"0.5264326","text":"def clean_album_artist(path: str) -> None:\n files = files_in_dir(path)\n for f in files:\n match_info = re.search('^\\[\\'(.*)\\'\\]$', _get_unique_tag(f, 'albumartist'))\n if match_info is not None:\n info = EasyID3(f)\n info['albumartist'] = match_info.group(1)\n info.save()","title":""},{"docid":"bd9b6cd183e76cba7086092234a07d60","score":"0.525084","text":"def _remove_unannotated_items(items):\n \n return items[2:]","title":""},{"docid":"924cef5a70ba2200d6deb5832fb0149d","score":"0.52336174","text":"def remove_articles(correct,given):\n\n if correct.startswith('the ') and not given.startswith('the '):\n correct=correct[4:]\n if correct.startswith('an ') and not given.startswith('an '):\n correct=correct[3:]\n if correct.startswith('a ') and not given.startswith('a '):\n correct=correct[2:]\n return correct","title":""},{"docid":"88557ad92026ee14cbc86e34acf1531f","score":"0.52243763","text":"def format_name(artist: str, title: str) -> str:\n if artist is None or artist == \"\":\n return f\"{title}\"\n else:\n return f\"{artist} - {title}\"","title":""},{"docid":"3608b44b5745ca1f9881a50b67995ac8","score":"0.5219561","text":"def safe_artist_name( artistName ):\n artistName = strip_the( artistName )\n return safe_dir_name( artistName )","title":""},{"docid":"8562d63ca135c0c3d2b7e00194404dac","score":"0.51956576","text":"def showTopTagsForArtist (artist):\r\n print \"{0:20s}\".format(\"-------------------------------------------------------------\")\r\n print \"top tags for {0}\".format(artist)\r\n print \"{0:20s}\".format(\"-------------------------------------------------------------\")\r\n\r\n result = lastFM.getTopTagsForArtist(artist = artist)\r\n\r\n if result is not None:\r\n index = 0\r\n for i in range(len(result)/4):\r\n print \"%-20s --> [%s]\\t %-20s --> [%s]\\t %-20s --> [%s]\\t %-20s --> [%s]\" % (result[index][0], result[index][1], result[index+1][0], result[index+1][1], result[index+2][0], result[index+2][1], result[index+3][0], result[index+3][1])\r\n index = index + 4 \r\n print \"{0:20s}\".format(\"-------------------------------------------------------------\")","title":""},{"docid":"7f12ec829c88919c1b3a9d64628d2be3","score":"0.51750726","text":"def bb_artistname(hit):\n try:\n artist = hit.group(1)\n A = Artist.objects.get(handle=artist)\n\n return js.r2s('webview/t/artist.html', {'A' : A})\n\n T = loader.get_template('webview/t/artist.html')\n C = Context({'A' : A})\n return T.render(C)\n except:\n # This is normally thrown when the artist is invalid. Return the original result,\n # Only we add an icon to indicate an invalid artist.\n return u'<img src=\"%suser_error.png\" alt=\"artist\" border=\"0\" /> %s' % (STATIC, artist)","title":""},{"docid":"37bff1f865628dce083c3a07fe5024e7","score":"0.51554346","text":"def test_getYTArtistTitle_GuessSongFormatted(self):\n link = 'https://www.youtube.com/watch?v=QbSV0qvAji4'\n res = getYTArtistTitle(link)\n self.assertEqual(res['artist'], 'Lonely')\n self.assertEqual(res['title'], 'Bee and puppycat')","title":""},{"docid":"8907e1f93b1f990a69a83bfc295159a1","score":"0.5144493","text":"def for_fucks_sake(self, description):\n fucking_hell = self.strip_garbage(description)\n return self.strip_garbage(fucking_hell)","title":""},{"docid":"8142ec8ab74e1010fd3b71a750b00e88","score":"0.51261485","text":"def descr_strip(self, space, w_chars=None):","title":""},{"docid":"cc2ac350b479bcb46f058f134e7c0a81","score":"0.51084423","text":"def _convert_artist(self, artist):\n if not artist:\n return\n artist_kwargs = {}\n artist_kwargs['name'] = artist.name\n artist_kwargs['sortname'] = artist.albumartist_sort\n artist_kwargs['musicbrainz_id'] = artist.mb_albumartistid\n artist_kwargs['uri'] = uricompose('bigbeet',\n None,\n (\"artist:%s:%s\" % (artist.id,artist.mb_albumartistid)),\n None)\n return Artist(**artist_kwargs)","title":""},{"docid":"924ae08c81734ee0536a4afba2305ba3","score":"0.510724","text":"def clean_tweet(tweet):\n no_url = re.sub(r'http\\S+', '', tweet)\n no_hashtags = re.sub(r'#\\S+', '', no_url)\n no_users = re.sub(r'@\\S+', '', no_hashtags)\n no_amp = re.sub(r'&\\S+', '', no_users)\n\n\n result = ''.join([str(i).replace('\\n', ' ') for i in no_amp])\n\n return result","title":""},{"docid":"a14f1792fe9121ac087ace0573c3b03b","score":"0.5105907","text":"def clean_saved_tracks(spotify, tracks_df): \n\n tracks_df['album_name'] = tracks_df['album'].map(lambda x:x['name'])\n tracks_df['album_art'] = tracks_df['album'].map(lambda x:x['images'][0]['url'] if len(x['images'])!=0 else 0)\n tracks_df['album_href'] = tracks_df['album'].map(lambda x:x['href'])\n tracks_df['album_release_date'] = tracks_df['album'].map(lambda x:x['release_date'])\n tracks_df['artist_names'] = tracks_df['artists'].map(lambda x: ', '.join([a['name'] for a in x]))\n # '''\n # Uncomment Below to add genre attributes Note: took me about 70s to get 750 songs so expect ~10s for every 100 songs \n # '''\n tracks_df['genres'] =tracks_df['artists'].map(lambda x:spotify.artist(x[0][\"external_urls\"][\"spotify\"])[\"genres\"])\n drop_cols = ['artists', 'available_markets', 'disc_number', 'external_ids', 'is_local', 'external_urls',\\\n 'track_number', 'duration_ms', 'uri',\\\n 'preview_url', 'type','album','album_href']\n tracks_df = tracks_df.drop(drop_cols, axis=1)\n\n return tracks_df","title":""},{"docid":"f03d4b23c9190db4a1ed386995379ffd","score":"0.5100288","text":"def sanatize_title(self, track):\n return track.split(\"-\")[0].strip().lower()","title":""},{"docid":"11c67ab3742bd02e3deda76a235f174b","score":"0.5096808","text":"def shorten(tweet, maxlen=100):\n maxlen += random.randint(1,40) #length_variation \n #Get only full words\n markers = [\"≈\", \"¡\", \"¬\"]\n #markers = [\"≈\", \"¡\", \"¬\", \"!\", \"?\", \".\"]\n i = maxlen\n while tweet[i] not in markers:\n i -= 1\n tweet = tweet[:i]\n tweet = tweet.replace(\"¡\", \"\")\n tweet = tweet.replace(\"≈\", \"\")\n tweet = tweet.replace(\"¬\", \" \")\n return tweet","title":""},{"docid":"a751ed63b5d032afaa729bdebba9d316","score":"0.5082068","text":"def fix_title_the(): \n\n movies = Movie.query.all()\n \n for mov in movies:\n if mov.movie_title[0:4] == 'The ': \n mov.movie_title = mov.movie_title[4:] + \", The\" \n db.session.commit() \n\n elif mov.movie_title[0:2] == 'A ': \n mov.movie_title = mov.movie_title[2:] + \", A\"\n db.session.commit()","title":""},{"docid":"5ba735a7d3ccc3d820bdef7e499559e9","score":"0.5064646","text":"def artist(audio):\n try:\n return (audio['TPE1'].text[0])\n except Exception:\n return (\" \")","title":""},{"docid":"50666080d51e0e624f6e0ee48ecc6683","score":"0.5051492","text":"def CorrectArtistNames(artists, deadline = 10, maxFails = 20):\r\n correctedArtistDataLoader = Echonest_Correction_Loader(artists, deadline, maxFails)\r\n correctedArtistData = correctedArtistDataLoader.load()\r\n return correctedArtistData","title":""},{"docid":"ad4eb8f867be6b8be24b379bc940bb77","score":"0.5038134","text":"def CorrectArtistName(artist, deadline = 10, maxFails = 20):\r\n artistList = [artist]\r\n return CorrectArtistNames(artistList, deadline, maxFails)[0]","title":""},{"docid":"854f05a21d7a8825664f5a8db4c7e4e1","score":"0.502532","text":"def album_artist(audio):\n try:\n return(audio['TPE2'].text[0])\n except Exception:\n return(\" \")","title":""},{"docid":"bb3969da1ba778357941340557ce7fea","score":"0.50185984","text":"def update_metadata(item, feat_part, drop_feat):\r\n # In all cases, update the artist fields.\r\n ui.print_(u'artist: {0} -> {1}'.format(item.artist, item.albumartist))\r\n item.artist = item.albumartist\r\n if item.artist_sort:\r\n # Just strip the featured artist from the sort name.\r\n item.artist_sort, _ = split_on_feat(item.artist_sort)\r\n\r\n # Only update the title if it does not already contain a featured\r\n # artist and if we do not drop featuring information.\r\n if not drop_feat and not contains_feat(item.title):\r\n new_title = u\"{0} feat. {1}\".format(item.title, feat_part)\r\n ui.print_(u'title: {0} -> {1}'.format(item.title, new_title))\r\n item.title = new_title","title":""},{"docid":"62fed518e63ce5f1b73804de36244403","score":"0.5005102","text":"def __cut_game_title(game_title: str):\n\n if game_title is None or len(game_title) == 0:\n return None\n\n title = re.search(\"<title>(.*)<\\/title>\", game_title)\n # The position of start and end of this method may change if the website change\n cut_title = str(html.unescape(title.group(1)[12:-17]))\n return cut_title","title":""},{"docid":"f46c2390ebf5ea2bfec3c46524f518b1","score":"0.4972488","text":"def TruncateTitle(self, file_ending):\n filename_author = self.solr_metadata[\"author\"]\n if len(filename_author) > 75:\n filename_author = filename_author[:75] + \"+\"\n\n self.full_filename = \"_\".join([filename_author, self.solr_metadata[\"title\"], self.solr_metadata[\"id\"].replace(\"/\", \"\")])+file_ending\n self.max_length = 200\n if len(self.full_filename) > self.max_length:\n excise_length = len(self.full_filename) - self.max_length\n self.truncated_title = self.solr_metadata[\"title\"][:len(self.solr_metadata[\"title\"])-excise_length-2]\n self.truncated_title += \"+\"\n self.full_filename = \"_\".join([filename_author, self.truncated_title, self.solr_metadata[\"id\"].replace(\"/\", \"\")])+file_ending\n self._MakeFilename()\n return self.new_filename","title":""},{"docid":"60295b70191284b6083c9489d56df2e2","score":"0.49714646","text":"def normalize_title(title, body):\n if not (title.endswith(\"…\") and body.startswith(\"…\")):\n return title\n else:\n # Being paranoid in case \\r\\n is used.\n return title[:-1] + body[1:].partition(\"\\r\\n\")[0]","title":""},{"docid":"4f3b5d3c05390e78941effcca65934fa","score":"0.4971388","text":"def clean_up_artists(axis, artist_list):\n for artist in artist_list:\n try:\n # fist attempt: try to remove collection of contours for instance\n while artist.collections:\n for col in artist.collections:\n artist.collections.remove(col)\n try:\n axis.collections.remove(col)\n except ValueError:\n pass\n\n artist.collections = []\n axis.collections = []\n except AttributeError:\n pass\n\n # second attempt, try to remove the text\n try:\n artist.remove()\n except (AttributeError, ValueError):\n pass","title":""},{"docid":"6b5e24ee00397e83d61bc1c361aac250","score":"0.49676806","text":"def bb_artist(hit):\n try:\n artistid = hit.group(1)\n artist = Artist.objects.get(id=artistid)\n T = loader.get_template('webview/t/artist.html')\n C = Context({'A' : artist})\n mu = T.render(C)\n\n mu = js.r2s('webview/t/artist.html', {'A' : artist})\n\n return mu\n except:\n return u\"[artist]%s[/artist]\" % artistid","title":""},{"docid":"9634c891e2548a6418bce723d89edb0f","score":"0.49656492","text":"def _update_artist(self):\n for artist in self.artist_tuple:\n self.artist_seokey += \" \" + artist[\"seokey\"]","title":""},{"docid":"f4d69ed786a0fa119b61165abab5e2d6","score":"0.49399507","text":"def get_top_artists(sp: spotipy.Spotify, amount: int = 20) -> list:\n print('...getting top artists')\n artists_name = []\n artists_uri = []\n ranges = ['short_term', 'medium_term', 'long_term']\n for r in ranges:\n all_top_artist_data = sp.current_user_top_artists(limit=amount, time_range=r)\n top_artist_data = all_top_artist_data['items']\n for artist_data in top_artist_data:\n if artist_data['name'] not in artists_name:\n artists_name.append(artist_data['name'])\n artists_uri.append(artist_data['uri'])\n return artists_uri","title":""},{"docid":"58da5ccc6d6aab9070857011d5ca66c1","score":"0.49300402","text":"def remove_shorts(shows):\n def filterShorts(show):\n if show.medium and \"short\" in show.medium.lower():\n echo(f\"> Filtering: {show.get_title()}\")\n return False\n return True\n return list(filter(filterShorts,shows))","title":""},{"docid":"67480151ff3983c7f36ca92adcff3f9b","score":"0.4926339","text":"def scrobble(self, artist, title, album=None):\n\n self.lastfm.scrobble(\n artist=artist,\n title=title,\n album=album,\n timestamp=int(time.time()),\n )","title":""},{"docid":"952014952e3c93fcaaf0f3738e2b97b8","score":"0.48935834","text":"def remove_non_standard_formatting(cls, title, title_type):\n title = re.sub('[\\(\\[]?(bzw\\.?|oder)', '/', title)\n title = re.sub(' \\((\\D+|\\d{2}\\..+)\\)', '', title)\n title = re.sub('[\\)\\]]', '', title)\n title = re.sub(' \\D+$', '', title)\n if title_type == RegestTitleType.SIMPLE_ALTERNATIVES:\n title = re.sub('[\\(\\[]', '/ ', title)\n elif title_type == RegestTitleType.ELLIPTICAL_ALTERNATIVES:\n title = re.sub(' [\\(\\[]', '-', title)\n elif title_type == RegestTitleType.SIMPLE_ADDITIONS or \\\n title_type == RegestTitleType.ELLIPTICAL_ADDITIONS:\n title = re.sub('[\\(\\[]', '', title)\n return title","title":""},{"docid":"6ca9a8406ebf3784851ad8854c81a2cf","score":"0.48829016","text":"def remove_mentions(words):\n\n for word in words:\n if word.startswith(\"@\"):\n words.remove(word)\n return words","title":""},{"docid":"ca67a0b88ceba07aeeab38f4959625a9","score":"0.4875876","text":"def get_artist(self) -> str:\r\n return self.artist","title":""},{"docid":"70370d8d0d7f2940f8bb4fa4fd4ab9c5","score":"0.48590007","text":"def remove_punct(imdb_data_list):\n strippables = \"\".join(\n [chr(i) for i in range(sys.maxunicode) if category(chr(i)).startswith(\"P\")]\n )\n stripped_text = []\n\n for movie in reloaded_review_text:\n for sub_list in movie[\"review_info\"]:\n t = sub_list[\"review_text\"]\n t = t.replace(\"-\", \" \").replace(chr(8212), \" \")\n new_t = \"\".join(x for x in t if x not in strippables)\n sub_list[\"review_text\"] = new_t\n\n return reloaded_review_text","title":""},{"docid":"4fc109c907ed055cdbacca7a896a52e6","score":"0.4853355","text":"def test_parse_tracklist_disctitles(self):\n release = self._make_release_from_positions(['', '1-1', '1-2', '',\n '2-1'])\n # Track 1: Index track with medium title (Cd1)\n release.data['tracklist'][0]['title'] = 'MEDIUM TITLE CD1'\n # Track 4: Index track with medium title (Cd2)\n release.data['tracklist'][3]['title'] = 'MEDIUM TITLE CD2'\n\n d = DiscogsPlugin().get_album_info(release)\n self.assertEqual(d.mediums, 2)\n self.assertEqual(d.tracks[0].disctitle, 'MEDIUM TITLE CD1')\n self.assertEqual(d.tracks[1].disctitle, 'MEDIUM TITLE CD1')\n self.assertEqual(d.tracks[2].disctitle, 'MEDIUM TITLE CD2')\n self.assertEqual(len(d.tracks), 3)","title":""},{"docid":"b11864236a1a43cdb400483212130686","score":"0.48460004","text":"def get_lyrics(self, artist, title):\r\n # Remove featuring artists from search.\r\n pattern = u\"(.*) feat(uring|\\.)?\\s\\S+\"\r\n match = re.search(pattern, artist, re.IGNORECASE)\r\n if match:\r\n artist = match.group(0)\r\n\r\n for backend in self.backends:\r\n lyrics = backend(artist, title)\r\n if lyrics:\r\n if isinstance(lyrics, str):\r\n lyrics = lyrics.decode('utf8', 'ignore')\r\n log.debug(u'got lyrics from backend: {0}'.format(\r\n backend.__name__\r\n ))\r\n return lyrics.strip()","title":""},{"docid":"c776af6e593144a3f343ace646dc48c7","score":"0.48411","text":"async def artist(self, ctx):\n if not ctx.args:\n return await ctx.send('Please give me an artist to search for.')\n\n async with ctx.typing():\n data, releases = await self.info_getter.get_artist(ctx.suffix)\n\n # Construct embed from data\n # Any errors relating to getting valid data shouldn't happen here\n # If they do, please open an issue (you should regardless)\n\n description = discord.Embed.Empty\n\n if 'about' in data:\n description = data['about']\n\n embed = discord.Embed(title=data['name'], description=description)\n years = ', '.join(str(x) for x in sorted(y for y in data['years'] if y is not None))\n\n embed.set_thumbnail(url=data['profileImageUrl'].replace(' ', '%20'))\n embed.set_footer(text=f'Release years: {years}')\n\n if 'bookings' in data:\n embed.add_field(name='Bookings', value=data['bookings'].replace('Booking: ', ''), inline=False)\n\n if 'managementDetail' in data:\n embed.add_field(name='Management', value=data['managementDetail'].replace('Management: ', ''), inline=False)\n\n embed.add_field(name='Social Media', value=' '.join(f'**__[{mcutils.get_name(x)}]({x})__**' for x in\n data['urls']), inline=False)\n embed.add_field(name='Releases', value=len(releases), inline=False)\n\n await ctx.send(embed=embed)","title":""},{"docid":"14b1fcf0dbda7efff0f1a9cd325ee3e1","score":"0.48354152","text":"def main(artist: str, song: str):\n base_url = f'https://www.azlyrics.com/lyrics/{artist}/{song}.html'\n response = requests.get(f'{base_url}')\n soup = BeautifulSoup(response.text, \"lxml\")\n elements = soup.find_all(['a', 'b', 'h1', 'script',\n 'small', 'span', 'title']\n )\n hidden = soup.select('.hidden')\n for text in [*hidden, *elements]:\n text.decompose()\n return soup.get_text(strip=True, separator=\" \")","title":""},{"docid":"df807913def93dd51d529542ede7f809","score":"0.4833539","text":"def clean_imdb_title_basics(clean_titles=True, explode=False):\n # Initialize DataFrame\n title_basics_df = pd.read_csv(IMDB_TITLE_BASICS)\n\n # Drop rows without genres\n title_basics_df.dropna(subset=['genres'], inplace=True)\n\n # Remove punctuation and spaces from title names and make new column, 'cleaned_title', if specified\n if clean_titles:\n title_basics_df['cleaned_title'] = title_basics_df['primary_title'].map(remove_punctuation)\n # Explode DataFrame on 'genres' column if specified\n if explode:\n title_basics_df['genres'] = title_basics_df['genres'].str.strip().str.split(',')\n title_basics_df = title_basics_df.explode('genres')\n\n return title_basics_df","title":""},{"docid":"3ae4eddbc3e6df9d21f2add2bc764ff9","score":"0.48314184","text":"def sanitize_author(authors :list, author :str) -> str:\n # Find the best match in authors. Indeed if we search \"John Doe\", Google Scholar\n # returns \"J Doe\" and so the self.reshape_entries will drop the records.\n # In practice, the cutoff must be quite low, but not too low. Otherwise,\n # co-authors may be wrongly renamed! Empirically, 0.4 is a good tradeoff).\n l = difflib.get_close_matches(author, authors, n=1, cutoff=0.4)\n if len(l) != 1:\n # We are trying to rename a co-author without knowing is real name.\n return author\n else:\n # We are renaming an author with his/her real name.\n return l[0]","title":""},{"docid":"7504d95ecbc29dd36cbc62d70ccdc6cb","score":"0.4828488","text":"def Sufstrip(str1,lsuf):\n if not islstup(lsuf): # Should be str.\n lsuf = [lsuf]\n retstr = str(str1) # Copy.\n for sf in lsuf:\n if retstr.endswith(sf):\n retstr = retstr[:-len(sf)]\n return retstr","title":""},{"docid":"804e89aa70ed6c67bb7b88a6c8adef55","score":"0.48177645","text":"def clean(tweet):\n\n # clean hashtags, twitter names, web addresses, puncuation\n tweet = (re.sub(r\"#[\\w\\d]*|@[.]?[\\w\\d]*[\\'\\w*]*|https?:\\/\\/\\S+\\b|\\\n www\\.(\\w+\\.)+\\S*|[.,:;!?()$-/^]*\", \"\", tweet).lower())\n\n # strip repeated chars (extra vals)\n tweet = re.sub(r\"(.)\\1\\1{1,1}\", \"\", tweet)\n tweet = (re.sub(r\"($.)\\1{1,}\", \"\", tweet).split())\n\n tweet = [STEMMER.stem_word(x) for x in tweet if\n x not in STOPWORDS and len(x) > 1]\n return tweet","title":""},{"docid":"5814727c7fdbc321c570663f84524215","score":"0.48159862","text":"def _make_artist_url(self, artist):\n pass","title":""},{"docid":"5294a6a35300e8ed0a2263c701430ef8","score":"0.48156857","text":"def find_arist(self, artistname):\n # Fix the artist name\n artistname = artistname.replace('and', ' ')\n\n # Fix the title\n translation_table = dict.fromkeys(map(ord, ';/\"()&'), ' ')\n artistname = artistname.translate(translation_table)\n\n string = \"artist:\" + artistname\n results = self.api.search(q=string, type='artist', limit=1)\n items = results['artists']['items']\n\n if(len(items) == 0):\n return None\n else:\n return items[0]","title":""},{"docid":"81de00f986da2897c3dcc84d8943b517","score":"0.48143694","text":"def create_news_ticker(headlines):\n #ticker = \"\"\n #for headline in headlines:\n #if len(ticker) + len(headline)+1 > 140:\n #if headline and a space would put ticker over the line, truncate\n #the headline, add it, and break\n #letters_to_keep = 140 - len(ticker)\n #truncated_headline = headline[:letters_to_keep]\n #ticker += truncated_headline\n #print(\"final ticker is: \" + ticker)\n #break\n #else:\n #ticker += headline + \" \"\n #print(\"ticker is now: \" + ticker)\n\n #return ticker\n ticker = \"\"\n for headline in headlines:\n ticker += headline + \" \"\n if len(ticker) >= 140:\n ticker = ticker[:140]\n break\n\n return ticker","title":""},{"docid":"77e5b07735c79a1a92611d3cd63d786a","score":"0.4809427","text":"def remove_among_works(bio):\n tmp_bio = bio.split(' scientific works are')[0].split('published the following')[0].split(' most famous works are')[0].split(' most famous works include')[0].split(' scientific works include')[0].split(' publications are')[0].split(' publications include')[0]\n return '.'.join(tmp_bio.split('.')[:-1]) + '.'","title":""},{"docid":"a3ddbf8a2d037c198c1e65781cb33d86","score":"0.48028213","text":"def toptracks(self, mess, args):\n if not args:\n return 'give me a name of an artist.'\n\n self.send(mess.getFrom(), '/me is searching for to tracks by %s...' % args, message_type='groupchat')\n network = pylast.LastFMNetwork(api_key=API_KEY, api_secret=API_SECRET)\n artist = network.get_artist(args)\n if not artist:\n return 'Artist not found...'\n\n top_tracks = artist.get_top_tracks()[:10]\n ans = u''\n for tt in top_tracks:\n ans += tt.item.get_name() + u'\\n'\n\n return ans","title":""},{"docid":"a4da9c5bccc9e4c17f49e6e8782d0020","score":"0.47981495","text":"def get_item_artist(item_md):\n artist = ''\n if 'artist' in item_md:\n if isinstance(item_md['artist'], str):\n artist = item_md['artist']\n else:\n artist = item_md['artist'][0]\n elif 'creator' in item_md:\n if isinstance(item_md['creator'], str):\n artist = item_md['creator']\n else:\n artist = item_md['creator'][0]\n return artist","title":""},{"docid":"ee5511c162956d078fd6c9c7088e5e3e","score":"0.4795415","text":"def test_get_artist(self):\n a = beatport.Artists(id=69549)\n name = a.data[0]['name']\n self.assertEqual(name, 'Dyed Soundorom')","title":""},{"docid":"679f963dbfc14d59f579451dcc33a795","score":"0.47947708","text":"def cleaningChapiterTitle(chapiterTitleRaw):\n soup = BeautifulSoup(chapiterTitleRaw, 'html.parser')\n title = soup.findAll(\"a\")[-1].string\n \n return title","title":""},{"docid":"94b5dabe892ee634dccf854023462dcc","score":"0.47938457","text":"def remove_titles(name):\n ret = remove_title(name)\n while ret != name:\n name = ret\n ret = remove_title(name)\n\n return ret","title":""},{"docid":"ca8c1f812617d57ebbedc77e3fa2cd86","score":"0.47900856","text":"def remove_movies(shows):\n def filterMovies(show):\n if show.medium and show.medium.lower() in [\"movie\",]:\n echo(f\"> Filtering: {show.get_title()}\")\n return False\n return True\n return list(filter(filterMovies,shows))","title":""},{"docid":"4b9c5aca05746d38afe1e17c12237a20","score":"0.47888085","text":"def clean_entities(raw_entities):\n # remove \":\"\n cleaned_entities = [re.sub(r\"\\:\", \"\", entity) for entity in raw_entities]\n \n # Replace duplicates\n for index, entity in enumerate(cleaned_entities):\n \n # If entity in list, replace with \"Hillary Clinton\"\n if entity in [\"Hillary\", \"hillary\", \"Clinton\", \"clinton\", \"hillary clinton\", \n \"Hillary Rodham Clinton\", \"Hillary Clinton's\", \"Hillary Clinton’s\"]:\n cleaned_entities[index] = \"Hillary Clinton\"\n \n # If entity in list, replace with \"Donald Trump\"\n if entity in [\"trump\", \"Trump\", \"donald trump\", \"Donald J Trump\", \"Donald J. Trump\"]:\n cleaned_entities[index] = \"Donald Trump\"\n \n # If entity in list, replace with \"Barack Obama\"\n if entity in [\"obama\", \"Obama\", \"barack obama\"]:\n cleaned_entities[index] = \"Barack Obama\"\n \n return cleaned_entities","title":""},{"docid":"254f0f5b831438a7b50d499ed829e315","score":"0.4776454","text":"def simplifyTitle (text):\n text = text.lower()\n # remove ' inf ', ' sub ' - in MA such notation is used to denote subscripts etc.;\n text = text.replace(' inf ','')\n text = text.replace(' sup ','')\n \n #limiting title string to 150 chars, the maximum in Google Scholar\n text = text[:148]\n \n text = re.sub('[^a-zA-Z0-9\\n\\.]', ' ', text)\n \n \n text = text.replace(\".\", \"\")\n text = text.replace(\" \", \"\")\n text = \" \".join(text.split())\n \n text = ''.join(ch for ch, _ in itertools.groupby(text)) #remove all repeated letters, illiterate -> iliterate\n \n vowels = ('a', 'e', 'i', 'o', 'u')\n text = ''.join([l for l in text if l not in vowels]) #remove all vowels\n \n text = unicodedata.normalize('NFKD', text).encode('ASCII', 'ignore')\n \n text = str(text).encode('utf-8')\n return (str(text))","title":""},{"docid":"91334597e3b61728d5ca80b9876021ee","score":"0.477187","text":"def list_remover(text):\r\n removelist = ['AIS', 'APOE', 'HIV', 'Â', 'HER2-', '1000 Genomes',\r\n 'MYCN-amplification', 'Alzheimer', 'ASD', 'OCB', 'BD',\r\n 'Genetically', 'Homogenous', 'BRCA', 'ALL', 'Coronary',\r\n 'Amyotrophic', 'Large', 'anti-dsDNA', 'Up ', 'Biracial',\r\n 'Follicular', 'Hodgkin', 'Lymphoma', 'GI', 'Abstinent',\r\n 'Schizophrenia', 'ε', 'JAK', 'ADHD', 'Diabetes',\r\n 'Allogenic', 'BGPI', 'Ischemic', 'Chronic', 'Major',\r\n 'Diabetic', 'Microalbuminuria', 'Asthma', 'Individuals',\r\n 'At ', \"Barrett's\", 'Crohn', 'Bipolar', 'MMR', 'HBV', 'RA',\r\n 'Elated', 'Escitalpram', 'Irritable', 'Lymphoblastoid',\r\n 'ACPA', 'HCC', 'pPhe508del', 'Anti', 'B2GPI', 'Kashin Beck',\r\n '(LDL-cholesterol)', 'TPO', 'OCD', 'CCT', 'FTD', 'CAPOX B',\r\n 'LAC', 'LOAD', ' So ', 'MYCN-amplification', 'Yang', 'Tae',\r\n 'Eum', 'Non-abstinent', 'EBWL', 'Semantic', 'General',\r\n 'Cluster', 'Frontremporal', 'Frontotremporal',\r\n 'Frontotemporal', 'Graves', 'Attention', 'Autism', 'Liu',\r\n 'High', 'Low', 'HCV', 'Citalopram', 'Haemophilia', ' III ',\r\n ' II ', ' I ', 'NFT', 'Progressive', 'Ancestry', 'Parkinson',\r\n 'Lin', 'BMD', 'GBA', 'Traylor', 'Consortium', ' Torgerson',\r\n 'EVE', 'Germain', 'Boraska', 'Cases', 'HapMap', 'vWF', 'HDL',\r\n 'LDL', ' Mild', 'Cognitive', 'Impairment', 'Sarcoidosis',\r\n 'Yu Zhi', 'Lymphoma', 'Impairment', 'Type', 'Kuru',\r\n 'Frontemporal', 'Erasmus', 'Barrett', 'Lofgren', 'Hashimoto',\r\n 'Family', 'Multiple', 'Richardson', 'Metropolitan']\r\n for word in removelist:\r\n text = text.replace(word, '')\r\n return text","title":""},{"docid":"d2478a330f54e4c9309a948355138b4b","score":"0.47714573","text":"def filter_by_title(candidates):\n return [article for article in candidates if ':' not in article.title and not article.title.startswith('List')]","title":""},{"docid":"dcb0ab82218adb92bf31a890639b4331","score":"0.47662258","text":"def normalize_body_name(self, short_name: str) -> str:\n for affix in settings.CITY_AFFIXES:\n short_name = short_name.replace(affix, \"\")\n return re.sub(\" +\", \" \", short_name).strip()","title":""},{"docid":"fafd9eb573def156bae7773101dc8b20","score":"0.4764734","text":"def get_spotify_artists(auth_token):\n sp = spotipy.Spotify(auth=auth_token)\n artists = []\n top_artists = sp.current_user_top_artists(5, 0, 'long_term')\n try:\n for item in top_artists['items']:\n artists.append(\n [\n item['name'],\n item['images'][0]['url']\n ]\n )\n return artists\n except KeyError as e:\n print(\"key error: {}\".format(e))\n return \"\"","title":""},{"docid":"557c4f208b62f97a72f2cb7a3fd2ffec","score":"0.4761599","text":"def remove_email_address(article):\n match = re.findall(r'[\\w\\.-]+@[\\w\\.-]+', article)\n for x in match :\n \n article =article.replace(x, ' ')\n \n return article","title":""},{"docid":"2018bdf607d012493714818ef382bbb3","score":"0.47589558","text":"def _replace_symbols(self):\n for i in self.chart:\n i.title = re.sub(r'&', '&', i.title)\n i.artist = re.sub(r'&', '&', i.artist)","title":""},{"docid":"9f73ae783544b4d70d8cc16975d42d58","score":"0.47584704","text":"def get_artists(artists_raw):\n artists = []\n try:\n for artist in artists_raw:\n artists.append({\n 'id': artist.id,\n 'name': artist.name\n })\n except Exception as e:\n raise e\n return artists","title":""},{"docid":"13bd22fd76951ef68d8e5049f335a5cb","score":"0.4758381","text":"def __str__(self):\n return f\"{self.artist} - {self.title}\"","title":""},{"docid":"f2c187c534dd508b12c1c054ae200dc6","score":"0.47577068","text":"def remove_entity_mentions(self, string):\n entities = Entity.get_entities(string)\n ent_names = [e.name for e in entities]\n for i, ent in enumerate(entities):\n stem_name = \"\".join([self.stemmer.stem(t) for t in ent.clean_name().lower().split(\" \")])\n stem_original = \"\".join([self.stemmer.stem(t) for t in ent.original.lower().split(\" \")])\n if ent.original.islower() and ent.original.lower() not in ALL_PRONOUNS and stem_name != stem_original:\n the_before_entity = \"the \" + ent.to_entity_format() in string\n the_before_entity = the_before_entity or \"The \" + ent.to_entity_format() in string\n other_ent_names = ent_names[:i] + ent_names[i + 1:]\n if ent.name not in other_ent_names and the_before_entity:\n logger.debug(\"return empty string for %s\" % string)\n return \"\"\n string = string.replace(ent.to_entity_format(), ent.original)\n return string","title":""},{"docid":"a2a5ed38265f8213cc1fc3a4ac8d5153","score":"0.4752413","text":"def without_extra_phrases(self):\r\n # the last parenthesis is optional, because sometimes they are truncated\r\n name = re.sub(r'\\s*\\([^)]*\\)?\\s*$', '', self.name)\r\n name = re.sub(r'(?i)\\s* formerly.*$', '', name)\r\n name = re.sub(r'(?i)\\s*and its affiliates$', '', name)\r\n name = re.sub(r'\\bet al\\b', '', name)\r\n \r\n # in some datasets, the name of an organization is followed by a hyphen and an abbreviated name, or a specific\r\n # department or geographic subdivision; we want to remove this extraneous stuff without breaking names like\r\n # Wal-Mart or Williams-Sonoma\r\n\r\n # if there's a hyphen at least four characters in, proceed\r\n if \"-\" in name:\r\n hyphen_parts = name.rsplit(\"-\", 1)\r\n # if the part after the hyphen is shorter than the part before,\r\n # AND isn't either a number (often occurs in Union names) or a single letter (e.g., Tech-X),\r\n # AND the hyphen is preceded by either whitespace or at least four characters,\r\n # discard the hyphen and whatever follows\r\n if len(hyphen_parts[1]) < len(hyphen_parts[0]) and re.search(r'(\\w{4,}|\\s+)$', hyphen_parts[0]) and not re.match(r'^([a-zA-Z]|[0-9]+)$', hyphen_parts[1]):\r\n name = hyphen_parts[0].strip()\r\n\r\n return name","title":""},{"docid":"39444c0018cf43944a3e61bb93c0e193","score":"0.47514796","text":"def strip_nonteams(teams, strip_words):\n temp_teams = teams\n for word in strip_words:\n temp_teams = filter(lambda team: team[1] != word, temp_teams)\n return temp_teams","title":""},{"docid":"4bd48886b7f63af7a7de35f2a0483d07","score":"0.47436854","text":"def clean(title):\n title = re.sub(r\"^\\s+|\\s+$g\", '', title)\n # **NEW**\n title = re.sub(r\"\\s*\\*+\\s?\\S+\\s?\\*+$\", '', title)\n # [whatever]\n title = re.sub(r\"\\[[^\\]]+\\]$\", '', title)\n # (whatever version)\n title = re.sub(r\"(?i)\\s*\\([^\\)]*version\\)$\", '', title)\n # video extensions\n title = re.sub(r\"(?i)\\s*\\.(avi|wmv|mpg|mpeg|flv)$\", '', title)\n # (LYRIC VIDEO)\n title = re.sub(r\"(?i)(LYRIC VIDEO\\s*)?(lyric video\\s*)\", '', title)\n # (Official title Stream)\n title = re.sub(r\"(?i)(Official title Stream*)\", '', title)\n # (official)? (music)? video\n title = re.sub(r\"(?i)(of+icial\\s*)?(music\\s*)?video\", '', title)\n # (official)? (music)? audio\n title = re.sub(r\"(?i)\\s*(of+icial\\s*)?(music\\s*)?audio\", '', title)\n # (ALBUM title)\n title = re.sub(r\"(?i)\\s*(ALBUM title\\s*)?(album title\\s*)\", '', title)\n # (Cover Art)\n title = re.sub(r\"(?i)\\s*(COVER ART\\s*)?(Cover Art\\s*)\", '', title)\n # (official)\n title = re.sub(r\"(?i)\\s*\\(\\s*of+icial\\s*\\)\", '', title)\n # (1999)\n title = re.sub(r\"(?i)\\s*\\(\\s*[0-9]{4}\\s*\\)\", '', title)\n # HD (HQ)\n title = re.sub(r\"\\s+\\(\\s*(HD|HQ)\\s*\\)$\", '', title)\n # HD (HQ)\n title = re.sub(r\"\\s+(HD|HQ)\\s*$\", '', title)\n # video clip\n title = re.sub(r\"(?i)\\s*video\\s*clip\", '', title)\n # Full Album\n title = re.sub(r\"(?i)\\s*full\\s*album\", '', title)\n # live\n title = re.sub(r\"(?i)\\s+\\(?live\\)?$\", '', title)\n # Leftovers after e.g. (official video)\n title = re.sub(r\"\\(+\\s*\\)+\", '', title)\n # Remove featurings\n title = re.sub(r\"\\(feat\\. .*?\\)\", '', title)\n # 'title title'\n title = re.sub(r\"^(|.*\\s)'(.*)'(\\s.*|)$\", '\\2', title)\n # trim white chars, dash and quotes\n title = title.strip()\n title = title.strip(\"-\\\"'\")\n\n return title","title":""},{"docid":"3f67b4cf5248cbc9d9fddcba55e8f9ce","score":"0.4740522","text":"def word_to_artist(word, show_all=False):\n artists_url = [url for url in json.load(open(\"wiki_dump_101.json\")).keys()]\n sims = index[lsi[tfidf[dictionary.doc2bow([word])]]]\n if show_all:\n sims = {a:b for a, b in zip(artist_names, sims)}\n else:\n sims = {a:b for a, b in zip(artist_names, sims) if a in artists_url}\n sorted_sims = sorted(sims.items(), key=operator.itemgetter(1), reverse=True)\n # print \"Artists related to \" + str(len(sorted_sims)) + word\n # pp.pprint([[x[0].split('/')[-1], x[1]] for x in sorted_sims][:10])\n return [a[0] for a in [[x[0].split('/')[-1], x[1]] for x in sorted_sims][:5]]","title":""},{"docid":"30b223624b92c9354213d4d51b4ff856","score":"0.4735782","text":"def places_strip_enml(args):\n import re\n \n howmany = 0 if args.howmany <= 0 else args.howmany\n from places.models import Place\n\n # only records that have data in noteshtml\n placeslist = Place.objects.filter(noteshtml__isnull=False)\n #placeslist = Place.objects.all()\n if howmany > 0:\n\tplaceslist = placeslist[:howmany]\n\t\n for place in placeslist:\n\tif 'DOCTYPE en-note' in place.noteshtml:\n\t #reobj = re.compile(r\"^.*?<en-note.*?>\\s*(?P<html>.*?)\\s*<\\?xml.*?>\", re.DOTALL)\n\t reobj = re.compile(r\"^.*?<en-note.*?>[\\rn]*(?P<html>.*?)[\\\\rn]*</en-note>\", re.DOTALL)\n\t match = reobj.search(place.noteshtml)\n\t # cases where enml is empty\n\t if match:\n\t \n\t\thtml = match.group(\"html\").strip() \n\t\tplace.noteshtml = html\n\t\tplace.save()\n\t\tprint place.id;\n\t \n\t\tx = 0","title":""},{"docid":"c006e0a161ac19686461f9bbbad75bf7","score":"0.4732366","text":"def strip_marks(value):\n # value is appended afterwards so that like-accented values\n # will sort together.\n return u''.join([c for c in unicodedata.normalize('NFD', value)\n if unicodedata.category(c) != 'Mn']) + u\" \" + value","title":""},{"docid":"99474b848ab26eac2eea9392f5724ce8","score":"0.47313166","text":"def filter_tweet(tweet): \n letter_list = ['b','c','d','e','f','g','h','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z']\n d = enchant.Dict(\"en_US\")\n word_list = re.findall(r\"[a-zA-Z'#@]+\",tweet)\n new_list = []\n for word in word_list: \n word = word.lower()\n if word.find('#')==-1 and word.find('rt')==-1 and word.find('@')==-1 and word.find('http://')==-1 and d.check(word) == True and word not in letter_list: \n new_list.append(word) \n return ' '.join(new_list)","title":""},{"docid":"b65609017f73e374c0cc74aca52e8055","score":"0.4730864","text":"def remove_hashtags(tweet):\n return _HASHTAG_REGEX.sub('', tweet)","title":""},{"docid":"9c4fc8f652b348f4880c970bb040f4c8","score":"0.4724896","text":"def name_strip(name, blacklist=blacklist):\n result = re.sub(' \\(.*?\\)','', name)\n result = remove_multiple_strings(result, blacklist)\n result = result.split(',')[0]\n result = '\"' + result + '\"' + ' hong kong'\n return result","title":""},{"docid":"8e1ffa94992ba79d2bd3bb330c171f13","score":"0.47225985","text":"def select_artistname(data, artist):\n return data[data['artists'].apply(lambda x: artist in [a['name'] for a in x])]","title":""},{"docid":"3335578467ca2e9189c6441cca92be6a","score":"0.47201803","text":"def remove_usernames(post):\n return re.sub('@\\w+\\s', '', post)","title":""},{"docid":"3086107cdaeafd913400a5fb5ce95b19","score":"0.4713357","text":"def clean_tweet(self, tweet):\n return ' '.join(re.sub(\"(@[A-Za-z0-9]+)|([^0-9A-Za-z \\t])|(\\w+:\\/\\/\\S+)\", \" \", tweet).split())","title":""},{"docid":"31053a8a2feae179c07502c5a468131b","score":"0.4711042","text":"def load_artists():\n\n print \"Artists\"\n\n Artist.query.delete()\n\n for row in open(\"seed_data/u.artists\"):\n row = row.rstrip(\"\\n\").strip(chr(13))\n row = row.split(\"\\t\")\n # NOTE: look into range data types in Postgresql to make searching for years eaiser\n artist_id = row[0]\n primary_name = row[1]\n secondary_name = row[2] if row[2] else None\n birth_year = row[3] if row[3] else None\n death_year = row[4] if row[4] else None\n bio = row[5]\n image_url = row[6] if row[6] else None\n image_caption = row[7] if row[7] else None\n\n artist = Artist(artist_id=artist_id, primary_name=primary_name,\n secondary_name=secondary_name, birth_year=birth_year,\n death_year=death_year, bio=bio, image_url=image_url,\n image_caption=image_caption)\n\n db.session.add(artist)\n\n db.session.commit()","title":""},{"docid":"8fb85bc733448f874a6c2da6f818eb9f","score":"0.47063443","text":"def remove_page_artifacts(title, text):\n if title in text:\n index = text.find(title)\n return text[index:]\n return text","title":""},{"docid":"064fbbf3f729cffa9912a0dd1cc70d7c","score":"0.4698153","text":"def remove_emoji(self, review):\n emoji_pattern = re.compile(\"[\"\n u\"\\U0001F600-\\U0001F64F\" # emoticons\n u\"\\U0001F300-\\U0001F5FF\" # symbols & pictographs\n u\"\\U0001F680-\\U0001F6FF\" # transport & map symbols\n u\"\\U0001F1E0-\\U0001F1FF\" # flags (iOS)\n \"]+\", flags=re.UNICODE)\n n_review = emoji_pattern.sub(r'', review)\n emoji_pattern = re.compile(\n u\"(\\ud83d[\\ude00-\\ude4f])|\" # emoticons\n u\"(\\ud83c[\\udf00-\\uffff])|\" # symbols & pictographs (1 of 2)\n u\"(\\ud83d[\\u0000-\\uddff])|\" # symbols & pictographs (2 of 2)\n u\"(\\ud83d[\\ude80-\\udeff])|\" # transport & map symbols\n u\"(\\ud83c[\\udde0-\\uddff])|\"\n u\"([\\u2600-\\u27bf])\" # flags (iOS)\n \"+\", flags=re.UNICODE)\n return emoji_pattern.sub(r'', n_review)","title":""},{"docid":"07c5f35f0e9a0d77ec205e6620b96a40","score":"0.46946794","text":"def filte_title(self):\n #if word is None, it means no need to filte\n #set_trace()\n if self.word == None:\n return;\n\n for title in self.titles_urls.keys():\n if self.word not in title:\n del self.titles_urls[title]\n\n for title in self.titles_urls.keys():\n print title.encode('utf8'), self.titles_urls[title]","title":""},{"docid":"d721434ea1abd61356a70a56596807bb","score":"0.46859998","text":"def clean_authors(author):\n\n final_author_string = \"\"\n\n # cleaning\n # TODO: ev. do this more systematically with regex\n if \"(a cura di)\" in author:\n author = author.replace(\"(a cura di)\",\"\").strip()\n if \"a cura di\" in author:\n author = author.replace(\"a cura di\",\"\").strip()\n if \"<omonimi non identificati>\" in author:\n author = author.replace(\"<omonimi non identificati>\",\"\").strip()\n # split multiple authors\n if \"- \" in author:\n authors = author.split(\"- \")\n elif \",\" in author:\n authors = author.split(\",\")\n elif \" e \" in author:\n authors = author.split(\" e \")\n elif \" ed \" in author:\n authors = author.split(\" ed \")\n else:\n authors = [author]\n\n #print(authors)\n for a in authors:\n if len(a.strip()) == 0:\n continue\n a = a.strip()\n a = a.split(\".\")\n if len(a) > 1:\n surname = a[-1].strip()\n name = [x.strip() for x in a[:-1]]\n final_author_string += surname+\", \"+\" \".join([x+\".\" for x in name])\n else:\n a = a[0].split()\n final_author_string += a[-1]+\", \"+\" \".join(a[:-1])\n final_author_string += \" - \"\n final_author_string = final_author_string[:-3]\n\n return final_author_string","title":""}],"string":"[\n {\n \"docid\": \"1b1b36d7b110cab664480b03386f60c9\",\n \"score\": \"0.6408811\",\n \"text\": \"def ft_in_title(item, drop_feat):\\r\\n artist = item.artist.strip()\\r\\n albumartist = item.albumartist.strip()\\r\\n\\r\\n # Check whether there is a featured artist on this track and the\\r\\n # artist field does not exactly match the album artist field. In\\r\\n # that case, we attempt to move the featured artist to the title.\\r\\n _, featured = split_on_feat(artist)\\r\\n if featured and albumartist != artist and albumartist:\\r\\n ui.print_(displayable_path(item.path))\\r\\n feat_part = None\\r\\n\\r\\n # Look for the album artist in the artist field. If it's not\\r\\n # present, give up.\\r\\n albumartist_split = artist.split(albumartist)\\r\\n if len(albumartist_split) <= 1:\\r\\n ui.print_('album artist not present in artist')\\r\\n\\r\\n # If the last element of the split (the right-hand side of the\\r\\n # album artist) is nonempty, then it probably contains the\\r\\n # featured artist.\\r\\n elif albumartist_split[-1] != '':\\r\\n # Extract the featured artist from the right-hand side.\\r\\n _, feat_part = split_on_feat(albumartist_split[-1])\\r\\n\\r\\n # Otherwise, if there's nothing on the right-hand side, look for a\\r\\n # featuring artist on the left-hand side.\\r\\n else:\\r\\n lhs, rhs = split_on_feat(albumartist_split[0])\\r\\n if rhs:\\r\\n feat_part = lhs\\r\\n\\r\\n # If we have a featuring artist, move it to the title.\\r\\n if feat_part:\\r\\n update_metadata(item, feat_part, drop_feat)\\r\\n else:\\r\\n ui.print_(u'no featuring artists found')\\r\\n\\r\\n ui.print_()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c25b4f5f1cc80f5bdac1175c49f22d1b\",\n \"score\": \"0.63470244\",\n \"text\": \"def sanitizeArtist(value):\\n # https://www.w3schools.com/python/ref_string_rstrip.asp\\n if value.lower() not in special_artists_name:\\n split = value.split(\\\"/\\\")[0]\\n clean = split.rstrip()\\n return clean\\n else:\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"235f7b905242a0f71c2bbfee2e5a4726\",\n \"score\": \"0.6216853\",\n \"text\": \"def strip_the( artistName ):\\n # NOTE: This must be run BEFORE 'proper_dir_name' because it relies on there being a space after \\\"The\\\"\\n if artistName and artistName[:4].lower() == \\\"the \\\": \\n return artistName[4:]\\n else:\\n return artistName\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c61904f80cf7780a7df3e95f1b52a8a4\",\n \"score\": \"0.6113897\",\n \"text\": \"def remove_artists(self):\\n for a in self.artists:\\n self.imgview.remove_artist(a)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1acf21c031891e04dc64045c8a0d087e\",\n \"score\": \"0.5917704\",\n \"text\": \"def remove_ft_artist_suffix(s):\\r\\n pattern = r\\\"(.*?) (&|\\\\b(and|feat(uring)?\\\\b))\\\"\\r\\n match = re.search(pattern, s, re.IGNORECASE)\\r\\n if match:\\r\\n s = match.group(1)\\r\\n return s\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52b8a4973c3c3ac30f8f403639ab07e8\",\n \"score\": \"0.5864724\",\n \"text\": \"def clean_artist_title(data, artist_col='artist', title_col='title'):\\n data[artist_col + '_clean'] = data[artist_col].apply(clean_value)\\n data[title_col + '_clean'] = data[title_col].apply(clean_value)\\n\\n data_len = len(data)\\n data.dropna(subset=['artist_clean', 'title_clean'], inplace=True)\\n if len(data) < data_len:\\n logger.warning(\\n 'Removed rows where artist_clean or title_clean is empty.')\\n\\n return data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8841e2029cfb0132b2f8327b5845e058\",\n \"score\": \"0.5811103\",\n \"text\": \"def get_artist(self, artists):\\r\\n artist_id = None\\r\\n bits = []\\r\\n for artist in artists:\\r\\n if not artist_id:\\r\\n artist_id = artist['id']\\r\\n name = artist['name']\\r\\n # Strip disambiguation number.\\r\\n name = re.sub(r' \\\\(\\\\d+\\\\)$', '', name)\\r\\n # Move articles to the front.\\r\\n name = re.sub(r'(?i)^(.*?), (a|an|the)$', r'\\\\2 \\\\1', name)\\r\\n bits.append(name)\\r\\n if artist['join']:\\r\\n bits.append(artist['join'])\\r\\n artist = ' '.join(bits).replace(' ,', ',') or None\\r\\n return artist, artist_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd1a2387015f4e48b4fd60dab7133dce\",\n \"score\": \"0.58102137\",\n \"text\": \"def pop_ny(article_title):\\n tlist = article_title.split(\\\" \\\")\\n tout = \\\" \\\".join(tlist[0:len(tlist)-2])\\n return tout\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6362a6e62c3e797e0e5498663780c6ee\",\n \"score\": \"0.57258356\",\n \"text\": \"def _get_artist(self, artists):\\r\\n artist_id = None\\r\\n bits = []\\r\\n for artist in artists:\\r\\n if not artist_id:\\r\\n artist_id = artist[0]\\r\\n name = artist[1]\\r\\n # Strip disambiguation number.\\r\\n name = re.sub(r' \\\\(\\\\d+\\\\)$', '', name)\\r\\n # Move articles to the front.\\r\\n name = re.sub(r'^(.*?), (a|an|the)$', r'\\\\2 \\\\1', name, flags=re.I)\\r\\n bits.append(name)\\r\\n artist = ', '.join(bits).replace(' ,', ',') or None\\r\\n return artist, artist_id\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4c77966b7907cdfb5e0c90e2948a091\",\n \"score\": \"0.5695241\",\n \"text\": \"def get_artist_billing(self, summary):\\n\\n artists_blob = summary.select('.show_artists')[0].text\\n stripped = artists_blob.strip()\\n formatted = stripped.replace(' \\\\n', ',')\\n\\n return formatted\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce130c843b7f7dda75f7e3989114c114\",\n \"score\": \"0.5583403\",\n \"text\": \"def remove_title(name):\\n titles = ['Mr ', 'Ms ', 'Mrs ', 'Miss ', 'Dr ', 'Professor ',\\n 'Reverend ', 'Sir ', 'Dame ', 'Hon. ', 'Hon ']\\n\\n for title in titles:\\n if name.startswith(title):\\n name = name[len(title):]\\n\\n return name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3de241f42f508471fbb7e9cd7e178039\",\n \"score\": \"0.5571916\",\n \"text\": \"def clean_top_tracks(spotify, tracks_df): \\n\\n tracks_df['album_name'] = tracks_df['album'].map(lambda x:x['name'])\\n tracks_df['album_art'] = tracks_df['album'].map(lambda x:x['images'][0]['url'] if len(x['images'])!=0 else 0)\\n tracks_df['album_href'] = tracks_df['album'].map(lambda x:x['href'])\\n tracks_df['album_release_date'] = tracks_df['album'].map(lambda x:x['release_date'])\\n tracks_df['artist_names'] = tracks_df['artists'].map(lambda x: ', '.join([a['name'] for a in x]))\\n drop_cols = ['album', 'available_markets', 'disc_number', 'external_ids', 'is_local', 'external_urls',\\\\\\n 'track_number', 'duration_ms', 'episode', 'track', 'uri',\\\\\\n 'preview_url', 'type',\\t'album_name', 'album_href']\\n tracks_df = tracks_df.drop(drop_cols, axis=1)\\n\\n return tracks_df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d982d478541da19ea1a3f3f2e3068ddf\",\n \"score\": \"0.5554848\",\n \"text\": \"def prepare_song_info_to_show(songs):\\n for song in songs.list:\\n new_title = song['title'].split('(')[0]\\n # If name started with (\\n if new_title == '':\\n new_title = song['title'].split(')')[1]\\n if len(new_title) > 30:\\n new_title = new_title[:30] + '...'\\n song['title'] = new_title\\n\\n if len(song['album']) > 30:\\n song['album'] = song['album'][:30] + '...'\\n # for show only\\n year = ''\\n position = ''\\n for billboard in song['billboard'].values():\\n year += billboard['year'] + ', '\\n position += str(billboard['position']) + ', '\\n song['year'] = year[:-2]\\n song['position'] = position[:-2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d1f472804cadfb7b06ffbb6c4a08b0a\",\n \"score\": \"0.55116487\",\n \"text\": \"def artists_helper(self, artlist):\\n ans = [x['name'] for x in artlist]\\n return ', '.join(ans)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"330a8bbeed30ff50134380e460584c4b\",\n \"score\": \"0.5407476\",\n \"text\": \"def test_getYTArtistTitle_GuessSongUnformatted(self):\\n link = 'https://www.youtube.com/watch?v=Y98XgZ9vGoQ'\\n res = getYTArtistTitle(link)\\n self.assertEqual(res['artist'], None)\\n self.assertEqual(res['title'], 'Tenderly (Remastered)')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"226e4917f5642d3bc370fa43a41b59cf\",\n \"score\": \"0.53778744\",\n \"text\": \"def fix_title(title: str):\\n if title.endswith(\\\", The\\\"):\\n title = \\\"The \\\" + title[:-len(\\\", The\\\")]\\n title, _ = extract_year(title)\\n title = title.replace(\\\"&\\\", \\\"and\\\").replace(\\\" \\\", \\\" \\\")\\n return title\",\n \"title\": \"\"\n },\n {\n \"docid\": \"42453034243112513eb5902c45b722ee\",\n \"score\": \"0.5351818\",\n \"text\": \"def remove_repeat_artists(originals, fetched):\\n artists = [x.artist for x in originals]\\n results = []\\n for result in fetched:\\n if result.track.artist in artists:\\n results.append(lastfm.TrackSearchResult(result.track, 0))\\n else:\\n results.append(lastfm.TrackSearchResult(result.track, 100))\\n artists.append(result.track.artist)\\n return results\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5749f4eef90a5ea2b2c30224eea6a041\",\n \"score\": \"0.53269124\",\n \"text\": \"def gNews_title_fix(title):\\n dashOccurence = (len(title) - 1) - title[::-1].index('-')\\n return title[0:dashOccurence]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"66a6423d375409708c246585ae9214cf\",\n \"score\": \"0.5308533\",\n \"text\": \"def clean_title(self, title):\\n title = re.sub(YEAR, \\\"\\\", title).lower()\\n title = re.findall(r\\\"[\\\\w'\\\\(\\\\)\\\\[\\\\]]+\\\", title)\\n title = filter(check_stop_words, title)\\n if len(title) and title[-1] == 'the':\\n title = [title[-1]] + title[:-1]\\n return \\\" \\\".join(title).replace('\\\"', \\\"'\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16f072f45db010a6f76e3c7d5dc6da71\",\n \"score\": \"0.5294426\",\n \"text\": \"def clean_up(markers):\\n\\n num_markers = 500\\n return markers[-num_markers:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db1dedd1ddaf193180239aa15c5a4a98\",\n \"score\": \"0.5264326\",\n \"text\": \"def clean_album_artist(path: str) -> None:\\n files = files_in_dir(path)\\n for f in files:\\n match_info = re.search('^\\\\[\\\\'(.*)\\\\'\\\\]$', _get_unique_tag(f, 'albumartist'))\\n if match_info is not None:\\n info = EasyID3(f)\\n info['albumartist'] = match_info.group(1)\\n info.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd9b6cd183e76cba7086092234a07d60\",\n \"score\": \"0.525084\",\n \"text\": \"def _remove_unannotated_items(items):\\n \\n return items[2:]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"924cef5a70ba2200d6deb5832fb0149d\",\n \"score\": \"0.52336174\",\n \"text\": \"def remove_articles(correct,given):\\n\\n if correct.startswith('the ') and not given.startswith('the '):\\n correct=correct[4:]\\n if correct.startswith('an ') and not given.startswith('an '):\\n correct=correct[3:]\\n if correct.startswith('a ') and not given.startswith('a '):\\n correct=correct[2:]\\n return correct\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88557ad92026ee14cbc86e34acf1531f\",\n \"score\": \"0.52243763\",\n \"text\": \"def format_name(artist: str, title: str) -> str:\\n if artist is None or artist == \\\"\\\":\\n return f\\\"{title}\\\"\\n else:\\n return f\\\"{artist} - {title}\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3608b44b5745ca1f9881a50b67995ac8\",\n \"score\": \"0.5219561\",\n \"text\": \"def safe_artist_name( artistName ):\\n artistName = strip_the( artistName )\\n return safe_dir_name( artistName )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8562d63ca135c0c3d2b7e00194404dac\",\n \"score\": \"0.51956576\",\n \"text\": \"def showTopTagsForArtist (artist):\\r\\n print \\\"{0:20s}\\\".format(\\\"-------------------------------------------------------------\\\")\\r\\n print \\\"top tags for {0}\\\".format(artist)\\r\\n print \\\"{0:20s}\\\".format(\\\"-------------------------------------------------------------\\\")\\r\\n\\r\\n result = lastFM.getTopTagsForArtist(artist = artist)\\r\\n\\r\\n if result is not None:\\r\\n index = 0\\r\\n for i in range(len(result)/4):\\r\\n print \\\"%-20s --> [%s]\\\\t %-20s --> [%s]\\\\t %-20s --> [%s]\\\\t %-20s --> [%s]\\\" % (result[index][0], result[index][1], result[index+1][0], result[index+1][1], result[index+2][0], result[index+2][1], result[index+3][0], result[index+3][1])\\r\\n index = index + 4 \\r\\n print \\\"{0:20s}\\\".format(\\\"-------------------------------------------------------------\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7f12ec829c88919c1b3a9d64628d2be3\",\n \"score\": \"0.51750726\",\n \"text\": \"def bb_artistname(hit):\\n try:\\n artist = hit.group(1)\\n A = Artist.objects.get(handle=artist)\\n\\n return js.r2s('webview/t/artist.html', {'A' : A})\\n\\n T = loader.get_template('webview/t/artist.html')\\n C = Context({'A' : A})\\n return T.render(C)\\n except:\\n # This is normally thrown when the artist is invalid. Return the original result,\\n # Only we add an icon to indicate an invalid artist.\\n return u'<img src=\\\"%suser_error.png\\\" alt=\\\"artist\\\" border=\\\"0\\\" /> %s' % (STATIC, artist)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37bff1f865628dce083c3a07fe5024e7\",\n \"score\": \"0.51554346\",\n \"text\": \"def test_getYTArtistTitle_GuessSongFormatted(self):\\n link = 'https://www.youtube.com/watch?v=QbSV0qvAji4'\\n res = getYTArtistTitle(link)\\n self.assertEqual(res['artist'], 'Lonely')\\n self.assertEqual(res['title'], 'Bee and puppycat')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8907e1f93b1f990a69a83bfc295159a1\",\n \"score\": \"0.5144493\",\n \"text\": \"def for_fucks_sake(self, description):\\n fucking_hell = self.strip_garbage(description)\\n return self.strip_garbage(fucking_hell)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8142ec8ab74e1010fd3b71a750b00e88\",\n \"score\": \"0.51261485\",\n \"text\": \"def descr_strip(self, space, w_chars=None):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cc2ac350b479bcb46f058f134e7c0a81\",\n \"score\": \"0.51084423\",\n \"text\": \"def _convert_artist(self, artist):\\n if not artist:\\n return\\n artist_kwargs = {}\\n artist_kwargs['name'] = artist.name\\n artist_kwargs['sortname'] = artist.albumartist_sort\\n artist_kwargs['musicbrainz_id'] = artist.mb_albumartistid\\n artist_kwargs['uri'] = uricompose('bigbeet',\\n None,\\n (\\\"artist:%s:%s\\\" % (artist.id,artist.mb_albumartistid)),\\n None)\\n return Artist(**artist_kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"924ae08c81734ee0536a4afba2305ba3\",\n \"score\": \"0.510724\",\n \"text\": \"def clean_tweet(tweet):\\n no_url = re.sub(r'http\\\\S+', '', tweet)\\n no_hashtags = re.sub(r'#\\\\S+', '', no_url)\\n no_users = re.sub(r'@\\\\S+', '', no_hashtags)\\n no_amp = re.sub(r'&\\\\S+', '', no_users)\\n\\n\\n result = ''.join([str(i).replace('\\\\n', ' ') for i in no_amp])\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a14f1792fe9121ac087ace0573c3b03b\",\n \"score\": \"0.5105907\",\n \"text\": \"def clean_saved_tracks(spotify, tracks_df): \\n\\n tracks_df['album_name'] = tracks_df['album'].map(lambda x:x['name'])\\n tracks_df['album_art'] = tracks_df['album'].map(lambda x:x['images'][0]['url'] if len(x['images'])!=0 else 0)\\n tracks_df['album_href'] = tracks_df['album'].map(lambda x:x['href'])\\n tracks_df['album_release_date'] = tracks_df['album'].map(lambda x:x['release_date'])\\n tracks_df['artist_names'] = tracks_df['artists'].map(lambda x: ', '.join([a['name'] for a in x]))\\n # '''\\n # Uncomment Below to add genre attributes Note: took me about 70s to get 750 songs so expect ~10s for every 100 songs \\n # '''\\n tracks_df['genres'] =tracks_df['artists'].map(lambda x:spotify.artist(x[0][\\\"external_urls\\\"][\\\"spotify\\\"])[\\\"genres\\\"])\\n drop_cols = ['artists', 'available_markets', 'disc_number', 'external_ids', 'is_local', 'external_urls',\\\\\\n 'track_number', 'duration_ms', 'uri',\\\\\\n 'preview_url', 'type','album','album_href']\\n tracks_df = tracks_df.drop(drop_cols, axis=1)\\n\\n return tracks_df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f03d4b23c9190db4a1ed386995379ffd\",\n \"score\": \"0.5100288\",\n \"text\": \"def sanatize_title(self, track):\\n return track.split(\\\"-\\\")[0].strip().lower()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11c67ab3742bd02e3deda76a235f174b\",\n \"score\": \"0.5096808\",\n \"text\": \"def shorten(tweet, maxlen=100):\\n maxlen += random.randint(1,40) #length_variation \\n #Get only full words\\n markers = [\\\"≈\\\", \\\"¡\\\", \\\"¬\\\"]\\n #markers = [\\\"≈\\\", \\\"¡\\\", \\\"¬\\\", \\\"!\\\", \\\"?\\\", \\\".\\\"]\\n i = maxlen\\n while tweet[i] not in markers:\\n i -= 1\\n tweet = tweet[:i]\\n tweet = tweet.replace(\\\"¡\\\", \\\"\\\")\\n tweet = tweet.replace(\\\"≈\\\", \\\"\\\")\\n tweet = tweet.replace(\\\"¬\\\", \\\" \\\")\\n return tweet\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a751ed63b5d032afaa729bdebba9d316\",\n \"score\": \"0.5082068\",\n \"text\": \"def fix_title_the(): \\n\\n movies = Movie.query.all()\\n \\n for mov in movies:\\n if mov.movie_title[0:4] == 'The ': \\n mov.movie_title = mov.movie_title[4:] + \\\", The\\\" \\n db.session.commit() \\n\\n elif mov.movie_title[0:2] == 'A ': \\n mov.movie_title = mov.movie_title[2:] + \\\", A\\\"\\n db.session.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ba735a7d3ccc3d820bdef7e499559e9\",\n \"score\": \"0.5064646\",\n \"text\": \"def artist(audio):\\n try:\\n return (audio['TPE1'].text[0])\\n except Exception:\\n return (\\\" \\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50666080d51e0e624f6e0ee48ecc6683\",\n \"score\": \"0.5051492\",\n \"text\": \"def CorrectArtistNames(artists, deadline = 10, maxFails = 20):\\r\\n correctedArtistDataLoader = Echonest_Correction_Loader(artists, deadline, maxFails)\\r\\n correctedArtistData = correctedArtistDataLoader.load()\\r\\n return correctedArtistData\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad4eb8f867be6b8be24b379bc940bb77\",\n \"score\": \"0.5038134\",\n \"text\": \"def CorrectArtistName(artist, deadline = 10, maxFails = 20):\\r\\n artistList = [artist]\\r\\n return CorrectArtistNames(artistList, deadline, maxFails)[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"854f05a21d7a8825664f5a8db4c7e4e1\",\n \"score\": \"0.502532\",\n \"text\": \"def album_artist(audio):\\n try:\\n return(audio['TPE2'].text[0])\\n except Exception:\\n return(\\\" \\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb3969da1ba778357941340557ce7fea\",\n \"score\": \"0.50185984\",\n \"text\": \"def update_metadata(item, feat_part, drop_feat):\\r\\n # In all cases, update the artist fields.\\r\\n ui.print_(u'artist: {0} -> {1}'.format(item.artist, item.albumartist))\\r\\n item.artist = item.albumartist\\r\\n if item.artist_sort:\\r\\n # Just strip the featured artist from the sort name.\\r\\n item.artist_sort, _ = split_on_feat(item.artist_sort)\\r\\n\\r\\n # Only update the title if it does not already contain a featured\\r\\n # artist and if we do not drop featuring information.\\r\\n if not drop_feat and not contains_feat(item.title):\\r\\n new_title = u\\\"{0} feat. {1}\\\".format(item.title, feat_part)\\r\\n ui.print_(u'title: {0} -> {1}'.format(item.title, new_title))\\r\\n item.title = new_title\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62fed518e63ce5f1b73804de36244403\",\n \"score\": \"0.5005102\",\n \"text\": \"def __cut_game_title(game_title: str):\\n\\n if game_title is None or len(game_title) == 0:\\n return None\\n\\n title = re.search(\\\"<title>(.*)<\\\\/title>\\\", game_title)\\n # The position of start and end of this method may change if the website change\\n cut_title = str(html.unescape(title.group(1)[12:-17]))\\n return cut_title\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f46c2390ebf5ea2bfec3c46524f518b1\",\n \"score\": \"0.4972488\",\n \"text\": \"def TruncateTitle(self, file_ending):\\n filename_author = self.solr_metadata[\\\"author\\\"]\\n if len(filename_author) > 75:\\n filename_author = filename_author[:75] + \\\"+\\\"\\n\\n self.full_filename = \\\"_\\\".join([filename_author, self.solr_metadata[\\\"title\\\"], self.solr_metadata[\\\"id\\\"].replace(\\\"/\\\", \\\"\\\")])+file_ending\\n self.max_length = 200\\n if len(self.full_filename) > self.max_length:\\n excise_length = len(self.full_filename) - self.max_length\\n self.truncated_title = self.solr_metadata[\\\"title\\\"][:len(self.solr_metadata[\\\"title\\\"])-excise_length-2]\\n self.truncated_title += \\\"+\\\"\\n self.full_filename = \\\"_\\\".join([filename_author, self.truncated_title, self.solr_metadata[\\\"id\\\"].replace(\\\"/\\\", \\\"\\\")])+file_ending\\n self._MakeFilename()\\n return self.new_filename\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60295b70191284b6083c9489d56df2e2\",\n \"score\": \"0.49714646\",\n \"text\": \"def normalize_title(title, body):\\n if not (title.endswith(\\\"…\\\") and body.startswith(\\\"…\\\")):\\n return title\\n else:\\n # Being paranoid in case \\\\r\\\\n is used.\\n return title[:-1] + body[1:].partition(\\\"\\\\r\\\\n\\\")[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4f3b5d3c05390e78941effcca65934fa\",\n \"score\": \"0.4971388\",\n \"text\": \"def clean_up_artists(axis, artist_list):\\n for artist in artist_list:\\n try:\\n # fist attempt: try to remove collection of contours for instance\\n while artist.collections:\\n for col in artist.collections:\\n artist.collections.remove(col)\\n try:\\n axis.collections.remove(col)\\n except ValueError:\\n pass\\n\\n artist.collections = []\\n axis.collections = []\\n except AttributeError:\\n pass\\n\\n # second attempt, try to remove the text\\n try:\\n artist.remove()\\n except (AttributeError, ValueError):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b5e24ee00397e83d61bc1c361aac250\",\n \"score\": \"0.49676806\",\n \"text\": \"def bb_artist(hit):\\n try:\\n artistid = hit.group(1)\\n artist = Artist.objects.get(id=artistid)\\n T = loader.get_template('webview/t/artist.html')\\n C = Context({'A' : artist})\\n mu = T.render(C)\\n\\n mu = js.r2s('webview/t/artist.html', {'A' : artist})\\n\\n return mu\\n except:\\n return u\\\"[artist]%s[/artist]\\\" % artistid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9634c891e2548a6418bce723d89edb0f\",\n \"score\": \"0.49656492\",\n \"text\": \"def _update_artist(self):\\n for artist in self.artist_tuple:\\n self.artist_seokey += \\\" \\\" + artist[\\\"seokey\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4d69ed786a0fa119b61165abab5e2d6\",\n \"score\": \"0.49399507\",\n \"text\": \"def get_top_artists(sp: spotipy.Spotify, amount: int = 20) -> list:\\n print('...getting top artists')\\n artists_name = []\\n artists_uri = []\\n ranges = ['short_term', 'medium_term', 'long_term']\\n for r in ranges:\\n all_top_artist_data = sp.current_user_top_artists(limit=amount, time_range=r)\\n top_artist_data = all_top_artist_data['items']\\n for artist_data in top_artist_data:\\n if artist_data['name'] not in artists_name:\\n artists_name.append(artist_data['name'])\\n artists_uri.append(artist_data['uri'])\\n return artists_uri\",\n \"title\": \"\"\n },\n {\n \"docid\": \"58da5ccc6d6aab9070857011d5ca66c1\",\n \"score\": \"0.49300402\",\n \"text\": \"def remove_shorts(shows):\\n def filterShorts(show):\\n if show.medium and \\\"short\\\" in show.medium.lower():\\n echo(f\\\"> Filtering: {show.get_title()}\\\")\\n return False\\n return True\\n return list(filter(filterShorts,shows))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67480151ff3983c7f36ca92adcff3f9b\",\n \"score\": \"0.4926339\",\n \"text\": \"def scrobble(self, artist, title, album=None):\\n\\n self.lastfm.scrobble(\\n artist=artist,\\n title=title,\\n album=album,\\n timestamp=int(time.time()),\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"952014952e3c93fcaaf0f3738e2b97b8\",\n \"score\": \"0.48935834\",\n \"text\": \"def remove_non_standard_formatting(cls, title, title_type):\\n title = re.sub('[\\\\(\\\\[]?(bzw\\\\.?|oder)', '/', title)\\n title = re.sub(' \\\\((\\\\D+|\\\\d{2}\\\\..+)\\\\)', '', title)\\n title = re.sub('[\\\\)\\\\]]', '', title)\\n title = re.sub(' \\\\D+$', '', title)\\n if title_type == RegestTitleType.SIMPLE_ALTERNATIVES:\\n title = re.sub('[\\\\(\\\\[]', '/ ', title)\\n elif title_type == RegestTitleType.ELLIPTICAL_ALTERNATIVES:\\n title = re.sub(' [\\\\(\\\\[]', '-', title)\\n elif title_type == RegestTitleType.SIMPLE_ADDITIONS or \\\\\\n title_type == RegestTitleType.ELLIPTICAL_ADDITIONS:\\n title = re.sub('[\\\\(\\\\[]', '', title)\\n return title\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ca9a8406ebf3784851ad8854c81a2cf\",\n \"score\": \"0.48829016\",\n \"text\": \"def remove_mentions(words):\\n\\n for word in words:\\n if word.startswith(\\\"@\\\"):\\n words.remove(word)\\n return words\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca67a0b88ceba07aeeab38f4959625a9\",\n \"score\": \"0.4875876\",\n \"text\": \"def get_artist(self) -> str:\\r\\n return self.artist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70370d8d0d7f2940f8bb4fa4fd4ab9c5\",\n \"score\": \"0.48590007\",\n \"text\": \"def remove_punct(imdb_data_list):\\n strippables = \\\"\\\".join(\\n [chr(i) for i in range(sys.maxunicode) if category(chr(i)).startswith(\\\"P\\\")]\\n )\\n stripped_text = []\\n\\n for movie in reloaded_review_text:\\n for sub_list in movie[\\\"review_info\\\"]:\\n t = sub_list[\\\"review_text\\\"]\\n t = t.replace(\\\"-\\\", \\\" \\\").replace(chr(8212), \\\" \\\")\\n new_t = \\\"\\\".join(x for x in t if x not in strippables)\\n sub_list[\\\"review_text\\\"] = new_t\\n\\n return reloaded_review_text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fc109c907ed055cdbacca7a896a52e6\",\n \"score\": \"0.4853355\",\n \"text\": \"def test_parse_tracklist_disctitles(self):\\n release = self._make_release_from_positions(['', '1-1', '1-2', '',\\n '2-1'])\\n # Track 1: Index track with medium title (Cd1)\\n release.data['tracklist'][0]['title'] = 'MEDIUM TITLE CD1'\\n # Track 4: Index track with medium title (Cd2)\\n release.data['tracklist'][3]['title'] = 'MEDIUM TITLE CD2'\\n\\n d = DiscogsPlugin().get_album_info(release)\\n self.assertEqual(d.mediums, 2)\\n self.assertEqual(d.tracks[0].disctitle, 'MEDIUM TITLE CD1')\\n self.assertEqual(d.tracks[1].disctitle, 'MEDIUM TITLE CD1')\\n self.assertEqual(d.tracks[2].disctitle, 'MEDIUM TITLE CD2')\\n self.assertEqual(len(d.tracks), 3)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b11864236a1a43cdb400483212130686\",\n \"score\": \"0.48460004\",\n \"text\": \"def get_lyrics(self, artist, title):\\r\\n # Remove featuring artists from search.\\r\\n pattern = u\\\"(.*) feat(uring|\\\\.)?\\\\s\\\\S+\\\"\\r\\n match = re.search(pattern, artist, re.IGNORECASE)\\r\\n if match:\\r\\n artist = match.group(0)\\r\\n\\r\\n for backend in self.backends:\\r\\n lyrics = backend(artist, title)\\r\\n if lyrics:\\r\\n if isinstance(lyrics, str):\\r\\n lyrics = lyrics.decode('utf8', 'ignore')\\r\\n log.debug(u'got lyrics from backend: {0}'.format(\\r\\n backend.__name__\\r\\n ))\\r\\n return lyrics.strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c776af6e593144a3f343ace646dc48c7\",\n \"score\": \"0.48411\",\n \"text\": \"async def artist(self, ctx):\\n if not ctx.args:\\n return await ctx.send('Please give me an artist to search for.')\\n\\n async with ctx.typing():\\n data, releases = await self.info_getter.get_artist(ctx.suffix)\\n\\n # Construct embed from data\\n # Any errors relating to getting valid data shouldn't happen here\\n # If they do, please open an issue (you should regardless)\\n\\n description = discord.Embed.Empty\\n\\n if 'about' in data:\\n description = data['about']\\n\\n embed = discord.Embed(title=data['name'], description=description)\\n years = ', '.join(str(x) for x in sorted(y for y in data['years'] if y is not None))\\n\\n embed.set_thumbnail(url=data['profileImageUrl'].replace(' ', '%20'))\\n embed.set_footer(text=f'Release years: {years}')\\n\\n if 'bookings' in data:\\n embed.add_field(name='Bookings', value=data['bookings'].replace('Booking: ', ''), inline=False)\\n\\n if 'managementDetail' in data:\\n embed.add_field(name='Management', value=data['managementDetail'].replace('Management: ', ''), inline=False)\\n\\n embed.add_field(name='Social Media', value=' '.join(f'**__[{mcutils.get_name(x)}]({x})__**' for x in\\n data['urls']), inline=False)\\n embed.add_field(name='Releases', value=len(releases), inline=False)\\n\\n await ctx.send(embed=embed)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14b1fcf0dbda7efff0f1a9cd325ee3e1\",\n \"score\": \"0.48354152\",\n \"text\": \"def main(artist: str, song: str):\\n base_url = f'https://www.azlyrics.com/lyrics/{artist}/{song}.html'\\n response = requests.get(f'{base_url}')\\n soup = BeautifulSoup(response.text, \\\"lxml\\\")\\n elements = soup.find_all(['a', 'b', 'h1', 'script',\\n 'small', 'span', 'title']\\n )\\n hidden = soup.select('.hidden')\\n for text in [*hidden, *elements]:\\n text.decompose()\\n return soup.get_text(strip=True, separator=\\\" \\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df807913def93dd51d529542ede7f809\",\n \"score\": \"0.4833539\",\n \"text\": \"def clean_imdb_title_basics(clean_titles=True, explode=False):\\n # Initialize DataFrame\\n title_basics_df = pd.read_csv(IMDB_TITLE_BASICS)\\n\\n # Drop rows without genres\\n title_basics_df.dropna(subset=['genres'], inplace=True)\\n\\n # Remove punctuation and spaces from title names and make new column, 'cleaned_title', if specified\\n if clean_titles:\\n title_basics_df['cleaned_title'] = title_basics_df['primary_title'].map(remove_punctuation)\\n # Explode DataFrame on 'genres' column if specified\\n if explode:\\n title_basics_df['genres'] = title_basics_df['genres'].str.strip().str.split(',')\\n title_basics_df = title_basics_df.explode('genres')\\n\\n return title_basics_df\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3ae4eddbc3e6df9d21f2add2bc764ff9\",\n \"score\": \"0.48314184\",\n \"text\": \"def sanitize_author(authors :list, author :str) -> str:\\n # Find the best match in authors. Indeed if we search \\\"John Doe\\\", Google Scholar\\n # returns \\\"J Doe\\\" and so the self.reshape_entries will drop the records.\\n # In practice, the cutoff must be quite low, but not too low. Otherwise,\\n # co-authors may be wrongly renamed! Empirically, 0.4 is a good tradeoff).\\n l = difflib.get_close_matches(author, authors, n=1, cutoff=0.4)\\n if len(l) != 1:\\n # We are trying to rename a co-author without knowing is real name.\\n return author\\n else:\\n # We are renaming an author with his/her real name.\\n return l[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7504d95ecbc29dd36cbc62d70ccdc6cb\",\n \"score\": \"0.4828488\",\n \"text\": \"def Sufstrip(str1,lsuf):\\n if not islstup(lsuf): # Should be str.\\n lsuf = [lsuf]\\n retstr = str(str1) # Copy.\\n for sf in lsuf:\\n if retstr.endswith(sf):\\n retstr = retstr[:-len(sf)]\\n return retstr\",\n \"title\": \"\"\n },\n {\n \"docid\": \"804e89aa70ed6c67bb7b88a6c8adef55\",\n \"score\": \"0.48177645\",\n \"text\": \"def clean(tweet):\\n\\n # clean hashtags, twitter names, web addresses, puncuation\\n tweet = (re.sub(r\\\"#[\\\\w\\\\d]*|@[.]?[\\\\w\\\\d]*[\\\\'\\\\w*]*|https?:\\\\/\\\\/\\\\S+\\\\b|\\\\\\n www\\\\.(\\\\w+\\\\.)+\\\\S*|[.,:;!?()$-/^]*\\\", \\\"\\\", tweet).lower())\\n\\n # strip repeated chars (extra vals)\\n tweet = re.sub(r\\\"(.)\\\\1\\\\1{1,1}\\\", \\\"\\\", tweet)\\n tweet = (re.sub(r\\\"($.)\\\\1{1,}\\\", \\\"\\\", tweet).split())\\n\\n tweet = [STEMMER.stem_word(x) for x in tweet if\\n x not in STOPWORDS and len(x) > 1]\\n return tweet\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5814727c7fdbc321c570663f84524215\",\n \"score\": \"0.48159862\",\n \"text\": \"def _make_artist_url(self, artist):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5294a6a35300e8ed0a2263c701430ef8\",\n \"score\": \"0.48156857\",\n \"text\": \"def find_arist(self, artistname):\\n # Fix the artist name\\n artistname = artistname.replace('and', ' ')\\n\\n # Fix the title\\n translation_table = dict.fromkeys(map(ord, ';/\\\"()&'), ' ')\\n artistname = artistname.translate(translation_table)\\n\\n string = \\\"artist:\\\" + artistname\\n results = self.api.search(q=string, type='artist', limit=1)\\n items = results['artists']['items']\\n\\n if(len(items) == 0):\\n return None\\n else:\\n return items[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81de00f986da2897c3dcc84d8943b517\",\n \"score\": \"0.48143694\",\n \"text\": \"def create_news_ticker(headlines):\\n #ticker = \\\"\\\"\\n #for headline in headlines:\\n #if len(ticker) + len(headline)+1 > 140:\\n #if headline and a space would put ticker over the line, truncate\\n #the headline, add it, and break\\n #letters_to_keep = 140 - len(ticker)\\n #truncated_headline = headline[:letters_to_keep]\\n #ticker += truncated_headline\\n #print(\\\"final ticker is: \\\" + ticker)\\n #break\\n #else:\\n #ticker += headline + \\\" \\\"\\n #print(\\\"ticker is now: \\\" + ticker)\\n\\n #return ticker\\n ticker = \\\"\\\"\\n for headline in headlines:\\n ticker += headline + \\\" \\\"\\n if len(ticker) >= 140:\\n ticker = ticker[:140]\\n break\\n\\n return ticker\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77e5b07735c79a1a92611d3cd63d786a\",\n \"score\": \"0.4809427\",\n \"text\": \"def remove_among_works(bio):\\n tmp_bio = bio.split(' scientific works are')[0].split('published the following')[0].split(' most famous works are')[0].split(' most famous works include')[0].split(' scientific works include')[0].split(' publications are')[0].split(' publications include')[0]\\n return '.'.join(tmp_bio.split('.')[:-1]) + '.'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3ddbf8a2d037c198c1e65781cb33d86\",\n \"score\": \"0.48028213\",\n \"text\": \"def toptracks(self, mess, args):\\n if not args:\\n return 'give me a name of an artist.'\\n\\n self.send(mess.getFrom(), '/me is searching for to tracks by %s...' % args, message_type='groupchat')\\n network = pylast.LastFMNetwork(api_key=API_KEY, api_secret=API_SECRET)\\n artist = network.get_artist(args)\\n if not artist:\\n return 'Artist not found...'\\n\\n top_tracks = artist.get_top_tracks()[:10]\\n ans = u''\\n for tt in top_tracks:\\n ans += tt.item.get_name() + u'\\\\n'\\n\\n return ans\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4da9c5bccc9e4c17f49e6e8782d0020\",\n \"score\": \"0.47981495\",\n \"text\": \"def get_item_artist(item_md):\\n artist = ''\\n if 'artist' in item_md:\\n if isinstance(item_md['artist'], str):\\n artist = item_md['artist']\\n else:\\n artist = item_md['artist'][0]\\n elif 'creator' in item_md:\\n if isinstance(item_md['creator'], str):\\n artist = item_md['creator']\\n else:\\n artist = item_md['creator'][0]\\n return artist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee5511c162956d078fd6c9c7088e5e3e\",\n \"score\": \"0.4795415\",\n \"text\": \"def test_get_artist(self):\\n a = beatport.Artists(id=69549)\\n name = a.data[0]['name']\\n self.assertEqual(name, 'Dyed Soundorom')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"679f963dbfc14d59f579451dcc33a795\",\n \"score\": \"0.47947708\",\n \"text\": \"def cleaningChapiterTitle(chapiterTitleRaw):\\n soup = BeautifulSoup(chapiterTitleRaw, 'html.parser')\\n title = soup.findAll(\\\"a\\\")[-1].string\\n \\n return title\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94b5dabe892ee634dccf854023462dcc\",\n \"score\": \"0.47938457\",\n \"text\": \"def remove_titles(name):\\n ret = remove_title(name)\\n while ret != name:\\n name = ret\\n ret = remove_title(name)\\n\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca8c1f812617d57ebbedc77e3fa2cd86\",\n \"score\": \"0.47900856\",\n \"text\": \"def remove_movies(shows):\\n def filterMovies(show):\\n if show.medium and show.medium.lower() in [\\\"movie\\\",]:\\n echo(f\\\"> Filtering: {show.get_title()}\\\")\\n return False\\n return True\\n return list(filter(filterMovies,shows))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b9c5aca05746d38afe1e17c12237a20\",\n \"score\": \"0.47888085\",\n \"text\": \"def clean_entities(raw_entities):\\n # remove \\\":\\\"\\n cleaned_entities = [re.sub(r\\\"\\\\:\\\", \\\"\\\", entity) for entity in raw_entities]\\n \\n # Replace duplicates\\n for index, entity in enumerate(cleaned_entities):\\n \\n # If entity in list, replace with \\\"Hillary Clinton\\\"\\n if entity in [\\\"Hillary\\\", \\\"hillary\\\", \\\"Clinton\\\", \\\"clinton\\\", \\\"hillary clinton\\\", \\n \\\"Hillary Rodham Clinton\\\", \\\"Hillary Clinton's\\\", \\\"Hillary Clinton’s\\\"]:\\n cleaned_entities[index] = \\\"Hillary Clinton\\\"\\n \\n # If entity in list, replace with \\\"Donald Trump\\\"\\n if entity in [\\\"trump\\\", \\\"Trump\\\", \\\"donald trump\\\", \\\"Donald J Trump\\\", \\\"Donald J. Trump\\\"]:\\n cleaned_entities[index] = \\\"Donald Trump\\\"\\n \\n # If entity in list, replace with \\\"Barack Obama\\\"\\n if entity in [\\\"obama\\\", \\\"Obama\\\", \\\"barack obama\\\"]:\\n cleaned_entities[index] = \\\"Barack Obama\\\"\\n \\n return cleaned_entities\",\n \"title\": \"\"\n },\n {\n \"docid\": \"254f0f5b831438a7b50d499ed829e315\",\n \"score\": \"0.4776454\",\n \"text\": \"def simplifyTitle (text):\\n text = text.lower()\\n # remove ' inf ', ' sub ' - in MA such notation is used to denote subscripts etc.;\\n text = text.replace(' inf ','')\\n text = text.replace(' sup ','')\\n \\n #limiting title string to 150 chars, the maximum in Google Scholar\\n text = text[:148]\\n \\n text = re.sub('[^a-zA-Z0-9\\\\n\\\\.]', ' ', text)\\n \\n \\n text = text.replace(\\\".\\\", \\\"\\\")\\n text = text.replace(\\\" \\\", \\\"\\\")\\n text = \\\" \\\".join(text.split())\\n \\n text = ''.join(ch for ch, _ in itertools.groupby(text)) #remove all repeated letters, illiterate -> iliterate\\n \\n vowels = ('a', 'e', 'i', 'o', 'u')\\n text = ''.join([l for l in text if l not in vowels]) #remove all vowels\\n \\n text = unicodedata.normalize('NFKD', text).encode('ASCII', 'ignore')\\n \\n text = str(text).encode('utf-8')\\n return (str(text))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"91334597e3b61728d5ca80b9876021ee\",\n \"score\": \"0.477187\",\n \"text\": \"def list_remover(text):\\r\\n removelist = ['AIS', 'APOE', 'HIV', 'Â', 'HER2-', '1000 Genomes',\\r\\n 'MYCN-amplification', 'Alzheimer', 'ASD', 'OCB', 'BD',\\r\\n 'Genetically', 'Homogenous', 'BRCA', 'ALL', 'Coronary',\\r\\n 'Amyotrophic', 'Large', 'anti-dsDNA', 'Up ', 'Biracial',\\r\\n 'Follicular', 'Hodgkin', 'Lymphoma', 'GI', 'Abstinent',\\r\\n 'Schizophrenia', 'ε', 'JAK', 'ADHD', 'Diabetes',\\r\\n 'Allogenic', 'BGPI', 'Ischemic', 'Chronic', 'Major',\\r\\n 'Diabetic', 'Microalbuminuria', 'Asthma', 'Individuals',\\r\\n 'At ', \\\"Barrett's\\\", 'Crohn', 'Bipolar', 'MMR', 'HBV', 'RA',\\r\\n 'Elated', 'Escitalpram', 'Irritable', 'Lymphoblastoid',\\r\\n 'ACPA', 'HCC', 'pPhe508del', 'Anti', 'B2GPI', 'Kashin Beck',\\r\\n '(LDL-cholesterol)', 'TPO', 'OCD', 'CCT', 'FTD', 'CAPOX B',\\r\\n 'LAC', 'LOAD', ' So ', 'MYCN-amplification', 'Yang', 'Tae',\\r\\n 'Eum', 'Non-abstinent', 'EBWL', 'Semantic', 'General',\\r\\n 'Cluster', 'Frontremporal', 'Frontotremporal',\\r\\n 'Frontotemporal', 'Graves', 'Attention', 'Autism', 'Liu',\\r\\n 'High', 'Low', 'HCV', 'Citalopram', 'Haemophilia', ' III ',\\r\\n ' II ', ' I ', 'NFT', 'Progressive', 'Ancestry', 'Parkinson',\\r\\n 'Lin', 'BMD', 'GBA', 'Traylor', 'Consortium', ' Torgerson',\\r\\n 'EVE', 'Germain', 'Boraska', 'Cases', 'HapMap', 'vWF', 'HDL',\\r\\n 'LDL', ' Mild', 'Cognitive', 'Impairment', 'Sarcoidosis',\\r\\n 'Yu Zhi', 'Lymphoma', 'Impairment', 'Type', 'Kuru',\\r\\n 'Frontemporal', 'Erasmus', 'Barrett', 'Lofgren', 'Hashimoto',\\r\\n 'Family', 'Multiple', 'Richardson', 'Metropolitan']\\r\\n for word in removelist:\\r\\n text = text.replace(word, '')\\r\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2478a330f54e4c9309a948355138b4b\",\n \"score\": \"0.47714573\",\n \"text\": \"def filter_by_title(candidates):\\n return [article for article in candidates if ':' not in article.title and not article.title.startswith('List')]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dcb0ab82218adb92bf31a890639b4331\",\n \"score\": \"0.47662258\",\n \"text\": \"def normalize_body_name(self, short_name: str) -> str:\\n for affix in settings.CITY_AFFIXES:\\n short_name = short_name.replace(affix, \\\"\\\")\\n return re.sub(\\\" +\\\", \\\" \\\", short_name).strip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fafd9eb573def156bae7773101dc8b20\",\n \"score\": \"0.4764734\",\n \"text\": \"def get_spotify_artists(auth_token):\\n sp = spotipy.Spotify(auth=auth_token)\\n artists = []\\n top_artists = sp.current_user_top_artists(5, 0, 'long_term')\\n try:\\n for item in top_artists['items']:\\n artists.append(\\n [\\n item['name'],\\n item['images'][0]['url']\\n ]\\n )\\n return artists\\n except KeyError as e:\\n print(\\\"key error: {}\\\".format(e))\\n return \\\"\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"557c4f208b62f97a72f2cb7a3fd2ffec\",\n \"score\": \"0.4761599\",\n \"text\": \"def remove_email_address(article):\\n match = re.findall(r'[\\\\w\\\\.-]+@[\\\\w\\\\.-]+', article)\\n for x in match :\\n \\n article =article.replace(x, ' ')\\n \\n return article\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2018bdf607d012493714818ef382bbb3\",\n \"score\": \"0.47589558\",\n \"text\": \"def _replace_symbols(self):\\n for i in self.chart:\\n i.title = re.sub(r'&', '&', i.title)\\n i.artist = re.sub(r'&', '&', i.artist)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f73ae783544b4d70d8cc16975d42d58\",\n \"score\": \"0.47584704\",\n \"text\": \"def get_artists(artists_raw):\\n artists = []\\n try:\\n for artist in artists_raw:\\n artists.append({\\n 'id': artist.id,\\n 'name': artist.name\\n })\\n except Exception as e:\\n raise e\\n return artists\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13bd22fd76951ef68d8e5049f335a5cb\",\n \"score\": \"0.4758381\",\n \"text\": \"def __str__(self):\\n return f\\\"{self.artist} - {self.title}\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2c187c534dd508b12c1c054ae200dc6\",\n \"score\": \"0.47577068\",\n \"text\": \"def remove_entity_mentions(self, string):\\n entities = Entity.get_entities(string)\\n ent_names = [e.name for e in entities]\\n for i, ent in enumerate(entities):\\n stem_name = \\\"\\\".join([self.stemmer.stem(t) for t in ent.clean_name().lower().split(\\\" \\\")])\\n stem_original = \\\"\\\".join([self.stemmer.stem(t) for t in ent.original.lower().split(\\\" \\\")])\\n if ent.original.islower() and ent.original.lower() not in ALL_PRONOUNS and stem_name != stem_original:\\n the_before_entity = \\\"the \\\" + ent.to_entity_format() in string\\n the_before_entity = the_before_entity or \\\"The \\\" + ent.to_entity_format() in string\\n other_ent_names = ent_names[:i] + ent_names[i + 1:]\\n if ent.name not in other_ent_names and the_before_entity:\\n logger.debug(\\\"return empty string for %s\\\" % string)\\n return \\\"\\\"\\n string = string.replace(ent.to_entity_format(), ent.original)\\n return string\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a2a5ed38265f8213cc1fc3a4ac8d5153\",\n \"score\": \"0.4752413\",\n \"text\": \"def without_extra_phrases(self):\\r\\n # the last parenthesis is optional, because sometimes they are truncated\\r\\n name = re.sub(r'\\\\s*\\\\([^)]*\\\\)?\\\\s*$', '', self.name)\\r\\n name = re.sub(r'(?i)\\\\s* formerly.*$', '', name)\\r\\n name = re.sub(r'(?i)\\\\s*and its affiliates$', '', name)\\r\\n name = re.sub(r'\\\\bet al\\\\b', '', name)\\r\\n \\r\\n # in some datasets, the name of an organization is followed by a hyphen and an abbreviated name, or a specific\\r\\n # department or geographic subdivision; we want to remove this extraneous stuff without breaking names like\\r\\n # Wal-Mart or Williams-Sonoma\\r\\n\\r\\n # if there's a hyphen at least four characters in, proceed\\r\\n if \\\"-\\\" in name:\\r\\n hyphen_parts = name.rsplit(\\\"-\\\", 1)\\r\\n # if the part after the hyphen is shorter than the part before,\\r\\n # AND isn't either a number (often occurs in Union names) or a single letter (e.g., Tech-X),\\r\\n # AND the hyphen is preceded by either whitespace or at least four characters,\\r\\n # discard the hyphen and whatever follows\\r\\n if len(hyphen_parts[1]) < len(hyphen_parts[0]) and re.search(r'(\\\\w{4,}|\\\\s+)$', hyphen_parts[0]) and not re.match(r'^([a-zA-Z]|[0-9]+)$', hyphen_parts[1]):\\r\\n name = hyphen_parts[0].strip()\\r\\n\\r\\n return name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"39444c0018cf43944a3e61bb93c0e193\",\n \"score\": \"0.47514796\",\n \"text\": \"def strip_nonteams(teams, strip_words):\\n temp_teams = teams\\n for word in strip_words:\\n temp_teams = filter(lambda team: team[1] != word, temp_teams)\\n return temp_teams\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bd48886b7f63af7a7de35f2a0483d07\",\n \"score\": \"0.47436854\",\n \"text\": \"def clean(title):\\n title = re.sub(r\\\"^\\\\s+|\\\\s+$g\\\", '', title)\\n # **NEW**\\n title = re.sub(r\\\"\\\\s*\\\\*+\\\\s?\\\\S+\\\\s?\\\\*+$\\\", '', title)\\n # [whatever]\\n title = re.sub(r\\\"\\\\[[^\\\\]]+\\\\]$\\\", '', title)\\n # (whatever version)\\n title = re.sub(r\\\"(?i)\\\\s*\\\\([^\\\\)]*version\\\\)$\\\", '', title)\\n # video extensions\\n title = re.sub(r\\\"(?i)\\\\s*\\\\.(avi|wmv|mpg|mpeg|flv)$\\\", '', title)\\n # (LYRIC VIDEO)\\n title = re.sub(r\\\"(?i)(LYRIC VIDEO\\\\s*)?(lyric video\\\\s*)\\\", '', title)\\n # (Official title Stream)\\n title = re.sub(r\\\"(?i)(Official title Stream*)\\\", '', title)\\n # (official)? (music)? video\\n title = re.sub(r\\\"(?i)(of+icial\\\\s*)?(music\\\\s*)?video\\\", '', title)\\n # (official)? (music)? audio\\n title = re.sub(r\\\"(?i)\\\\s*(of+icial\\\\s*)?(music\\\\s*)?audio\\\", '', title)\\n # (ALBUM title)\\n title = re.sub(r\\\"(?i)\\\\s*(ALBUM title\\\\s*)?(album title\\\\s*)\\\", '', title)\\n # (Cover Art)\\n title = re.sub(r\\\"(?i)\\\\s*(COVER ART\\\\s*)?(Cover Art\\\\s*)\\\", '', title)\\n # (official)\\n title = re.sub(r\\\"(?i)\\\\s*\\\\(\\\\s*of+icial\\\\s*\\\\)\\\", '', title)\\n # (1999)\\n title = re.sub(r\\\"(?i)\\\\s*\\\\(\\\\s*[0-9]{4}\\\\s*\\\\)\\\", '', title)\\n # HD (HQ)\\n title = re.sub(r\\\"\\\\s+\\\\(\\\\s*(HD|HQ)\\\\s*\\\\)$\\\", '', title)\\n # HD (HQ)\\n title = re.sub(r\\\"\\\\s+(HD|HQ)\\\\s*$\\\", '', title)\\n # video clip\\n title = re.sub(r\\\"(?i)\\\\s*video\\\\s*clip\\\", '', title)\\n # Full Album\\n title = re.sub(r\\\"(?i)\\\\s*full\\\\s*album\\\", '', title)\\n # live\\n title = re.sub(r\\\"(?i)\\\\s+\\\\(?live\\\\)?$\\\", '', title)\\n # Leftovers after e.g. (official video)\\n title = re.sub(r\\\"\\\\(+\\\\s*\\\\)+\\\", '', title)\\n # Remove featurings\\n title = re.sub(r\\\"\\\\(feat\\\\. .*?\\\\)\\\", '', title)\\n # 'title title'\\n title = re.sub(r\\\"^(|.*\\\\s)'(.*)'(\\\\s.*|)$\\\", '\\\\2', title)\\n # trim white chars, dash and quotes\\n title = title.strip()\\n title = title.strip(\\\"-\\\\\\\"'\\\")\\n\\n return title\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f67b4cf5248cbc9d9fddcba55e8f9ce\",\n \"score\": \"0.4740522\",\n \"text\": \"def word_to_artist(word, show_all=False):\\n artists_url = [url for url in json.load(open(\\\"wiki_dump_101.json\\\")).keys()]\\n sims = index[lsi[tfidf[dictionary.doc2bow([word])]]]\\n if show_all:\\n sims = {a:b for a, b in zip(artist_names, sims)}\\n else:\\n sims = {a:b for a, b in zip(artist_names, sims) if a in artists_url}\\n sorted_sims = sorted(sims.items(), key=operator.itemgetter(1), reverse=True)\\n # print \\\"Artists related to \\\" + str(len(sorted_sims)) + word\\n # pp.pprint([[x[0].split('/')[-1], x[1]] for x in sorted_sims][:10])\\n return [a[0] for a in [[x[0].split('/')[-1], x[1]] for x in sorted_sims][:5]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"30b223624b92c9354213d4d51b4ff856\",\n \"score\": \"0.4735782\",\n \"text\": \"def places_strip_enml(args):\\n import re\\n \\n howmany = 0 if args.howmany <= 0 else args.howmany\\n from places.models import Place\\n\\n # only records that have data in noteshtml\\n placeslist = Place.objects.filter(noteshtml__isnull=False)\\n #placeslist = Place.objects.all()\\n if howmany > 0:\\n\\tplaceslist = placeslist[:howmany]\\n\\t\\n for place in placeslist:\\n\\tif 'DOCTYPE en-note' in place.noteshtml:\\n\\t #reobj = re.compile(r\\\"^.*?<en-note.*?>\\\\s*(?P<html>.*?)\\\\s*<\\\\?xml.*?>\\\", re.DOTALL)\\n\\t reobj = re.compile(r\\\"^.*?<en-note.*?>[\\\\rn]*(?P<html>.*?)[\\\\\\\\rn]*</en-note>\\\", re.DOTALL)\\n\\t match = reobj.search(place.noteshtml)\\n\\t # cases where enml is empty\\n\\t if match:\\n\\t \\n\\t\\thtml = match.group(\\\"html\\\").strip() \\n\\t\\tplace.noteshtml = html\\n\\t\\tplace.save()\\n\\t\\tprint place.id;\\n\\t \\n\\t\\tx = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c006e0a161ac19686461f9bbbad75bf7\",\n \"score\": \"0.4732366\",\n \"text\": \"def strip_marks(value):\\n # value is appended afterwards so that like-accented values\\n # will sort together.\\n return u''.join([c for c in unicodedata.normalize('NFD', value)\\n if unicodedata.category(c) != 'Mn']) + u\\\" \\\" + value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99474b848ab26eac2eea9392f5724ce8\",\n \"score\": \"0.47313166\",\n \"text\": \"def filter_tweet(tweet): \\n letter_list = ['b','c','d','e','f','g','h','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z']\\n d = enchant.Dict(\\\"en_US\\\")\\n word_list = re.findall(r\\\"[a-zA-Z'#@]+\\\",tweet)\\n new_list = []\\n for word in word_list: \\n word = word.lower()\\n if word.find('#')==-1 and word.find('rt')==-1 and word.find('@')==-1 and word.find('http://')==-1 and d.check(word) == True and word not in letter_list: \\n new_list.append(word) \\n return ' '.join(new_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b65609017f73e374c0cc74aca52e8055\",\n \"score\": \"0.4730864\",\n \"text\": \"def remove_hashtags(tweet):\\n return _HASHTAG_REGEX.sub('', tweet)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c4fc8f652b348f4880c970bb040f4c8\",\n \"score\": \"0.4724896\",\n \"text\": \"def name_strip(name, blacklist=blacklist):\\n result = re.sub(' \\\\(.*?\\\\)','', name)\\n result = remove_multiple_strings(result, blacklist)\\n result = result.split(',')[0]\\n result = '\\\"' + result + '\\\"' + ' hong kong'\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e1ffa94992ba79d2bd3bb330c171f13\",\n \"score\": \"0.47225985\",\n \"text\": \"def select_artistname(data, artist):\\n return data[data['artists'].apply(lambda x: artist in [a['name'] for a in x])]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3335578467ca2e9189c6441cca92be6a\",\n \"score\": \"0.47201803\",\n \"text\": \"def remove_usernames(post):\\n return re.sub('@\\\\w+\\\\s', '', post)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3086107cdaeafd913400a5fb5ce95b19\",\n \"score\": \"0.4713357\",\n \"text\": \"def clean_tweet(self, tweet):\\n return ' '.join(re.sub(\\\"(@[A-Za-z0-9]+)|([^0-9A-Za-z \\\\t])|(\\\\w+:\\\\/\\\\/\\\\S+)\\\", \\\" \\\", tweet).split())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31053a8a2feae179c07502c5a468131b\",\n \"score\": \"0.4711042\",\n \"text\": \"def load_artists():\\n\\n print \\\"Artists\\\"\\n\\n Artist.query.delete()\\n\\n for row in open(\\\"seed_data/u.artists\\\"):\\n row = row.rstrip(\\\"\\\\n\\\").strip(chr(13))\\n row = row.split(\\\"\\\\t\\\")\\n # NOTE: look into range data types in Postgresql to make searching for years eaiser\\n artist_id = row[0]\\n primary_name = row[1]\\n secondary_name = row[2] if row[2] else None\\n birth_year = row[3] if row[3] else None\\n death_year = row[4] if row[4] else None\\n bio = row[5]\\n image_url = row[6] if row[6] else None\\n image_caption = row[7] if row[7] else None\\n\\n artist = Artist(artist_id=artist_id, primary_name=primary_name,\\n secondary_name=secondary_name, birth_year=birth_year,\\n death_year=death_year, bio=bio, image_url=image_url,\\n image_caption=image_caption)\\n\\n db.session.add(artist)\\n\\n db.session.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8fb85bc733448f874a6c2da6f818eb9f\",\n \"score\": \"0.47063443\",\n \"text\": \"def remove_page_artifacts(title, text):\\n if title in text:\\n index = text.find(title)\\n return text[index:]\\n return text\",\n \"title\": \"\"\n },\n {\n \"docid\": \"064fbbf3f729cffa9912a0dd1cc70d7c\",\n \"score\": \"0.4698153\",\n \"text\": \"def remove_emoji(self, review):\\n emoji_pattern = re.compile(\\\"[\\\"\\n u\\\"\\\\U0001F600-\\\\U0001F64F\\\" # emoticons\\n u\\\"\\\\U0001F300-\\\\U0001F5FF\\\" # symbols & pictographs\\n u\\\"\\\\U0001F680-\\\\U0001F6FF\\\" # transport & map symbols\\n u\\\"\\\\U0001F1E0-\\\\U0001F1FF\\\" # flags (iOS)\\n \\\"]+\\\", flags=re.UNICODE)\\n n_review = emoji_pattern.sub(r'', review)\\n emoji_pattern = re.compile(\\n u\\\"(\\\\ud83d[\\\\ude00-\\\\ude4f])|\\\" # emoticons\\n u\\\"(\\\\ud83c[\\\\udf00-\\\\uffff])|\\\" # symbols & pictographs (1 of 2)\\n u\\\"(\\\\ud83d[\\\\u0000-\\\\uddff])|\\\" # symbols & pictographs (2 of 2)\\n u\\\"(\\\\ud83d[\\\\ude80-\\\\udeff])|\\\" # transport & map symbols\\n u\\\"(\\\\ud83c[\\\\udde0-\\\\uddff])|\\\"\\n u\\\"([\\\\u2600-\\\\u27bf])\\\" # flags (iOS)\\n \\\"+\\\", flags=re.UNICODE)\\n return emoji_pattern.sub(r'', n_review)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07c5f35f0e9a0d77ec205e6620b96a40\",\n \"score\": \"0.46946794\",\n \"text\": \"def filte_title(self):\\n #if word is None, it means no need to filte\\n #set_trace()\\n if self.word == None:\\n return;\\n\\n for title in self.titles_urls.keys():\\n if self.word not in title:\\n del self.titles_urls[title]\\n\\n for title in self.titles_urls.keys():\\n print title.encode('utf8'), self.titles_urls[title]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d721434ea1abd61356a70a56596807bb\",\n \"score\": \"0.46859998\",\n \"text\": \"def clean_authors(author):\\n\\n final_author_string = \\\"\\\"\\n\\n # cleaning\\n # TODO: ev. do this more systematically with regex\\n if \\\"(a cura di)\\\" in author:\\n author = author.replace(\\\"(a cura di)\\\",\\\"\\\").strip()\\n if \\\"a cura di\\\" in author:\\n author = author.replace(\\\"a cura di\\\",\\\"\\\").strip()\\n if \\\"<omonimi non identificati>\\\" in author:\\n author = author.replace(\\\"<omonimi non identificati>\\\",\\\"\\\").strip()\\n # split multiple authors\\n if \\\"- \\\" in author:\\n authors = author.split(\\\"- \\\")\\n elif \\\",\\\" in author:\\n authors = author.split(\\\",\\\")\\n elif \\\" e \\\" in author:\\n authors = author.split(\\\" e \\\")\\n elif \\\" ed \\\" in author:\\n authors = author.split(\\\" ed \\\")\\n else:\\n authors = [author]\\n\\n #print(authors)\\n for a in authors:\\n if len(a.strip()) == 0:\\n continue\\n a = a.strip()\\n a = a.split(\\\".\\\")\\n if len(a) > 1:\\n surname = a[-1].strip()\\n name = [x.strip() for x in a[:-1]]\\n final_author_string += surname+\\\", \\\"+\\\" \\\".join([x+\\\".\\\" for x in name])\\n else:\\n a = a[0].split()\\n final_author_string += a[-1]+\\\", \\\"+\\\" \\\".join(a[:-1])\\n final_author_string += \\\" - \\\"\\n final_author_string = final_author_string[:-3]\\n\\n return final_author_string\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993291,"cells":{"query_id":{"kind":"string","value":"86d422850502985ed56e7163dde3b78d"},"query":{"kind":"string","value":"Converts Pascal bounding box from (x, y, w, h) into (top, left, bottom, right) format, and switch x/y coordinates to make a converted array indexes consistent with Numpy."},"positive_passages":{"kind":"list like","value":[{"docid":"24d3fd752b23b30dc0ec8f6dc6236ef5","score":"0.63246226","text":"def from_voc(bbox):\n x, y, w, h = bbox\n new_box = [y, x, y + h - 1, x + w - 1]\n return np.array(new_box)","title":""}],"string":"[\n {\n \"docid\": \"24d3fd752b23b30dc0ec8f6dc6236ef5\",\n \"score\": \"0.63246226\",\n \"text\": \"def from_voc(bbox):\\n x, y, w, h = bbox\\n new_box = [y, x, y + h - 1, x + w - 1]\\n return np.array(new_box)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"e3ca31e55f39475243fc71f65d0b1a93","score":"0.65984535","text":"def bbox_xywh_to_xyxy(self, boxes):\n w, h = boxes[:, 2], boxes[:, 3]\n boxes[:, 0] -= w / 2.0 # x1 = center_x - width/2\n boxes[:, 1] -= h / 2.0 # y1 = center_y - height/2\n boxes[:, 2] = boxes[:, 0] + w # x2 = x1 + width\n boxes[:, 3] = boxes[:, 1] + h # y2 = y1 + height\n return boxes","title":""},{"docid":"6ecff0476f7fb5984c596845b9a521ec","score":"0.6362112","text":"def compute_bounding_box(homography, w, h):\n\n original_boundries = np.array([[0, 0], [w - 1, 0], [w, h], [0, h - 1]])\n transform = apply_homography(original_boundries, homography)\n x = transform[:, 0]\n y = transform[:, 1]\n x_right, y_right = np.ceil(np.max(x)), np.ceil(np.max(y))\n x_left, y_left = np.floor(np.min(x)), np.floor(np.min(y))\n right = [x_right, y_right]\n left = [x_left, y_left]\n return np.array([left, right]).astype(np.int)","title":""},{"docid":"8a54e47449c0df1f49922c6099d84ab8","score":"0.6134974","text":"def bbox_transform(bbox):\n cx, cy, w, h = bbox\n out_box = [[]]*4\n out_box[0] = cx-w/2\n out_box[1] = cy-h/2\n out_box[2] = cx+w/2\n out_box[3] = cy+h/2\n\n return out_box","title":""},{"docid":"443377b816034408d61c1e5bc8b5c99b","score":"0.6128214","text":"def _bbox2points(bbox):\n x, y, w, h = bbox\n xmin = int(round(x - (w / 2)))\n xmax = int(round(x + (w / 2)))\n ymin = int(round(y - (h / 2)))\n ymax = int(round(y + (h / 2)))\n return xmin, ymin, xmax, ymax","title":""},{"docid":"15b6360290e01f2148fc6aba5081edfc","score":"0.6121567","text":"def wh_to_xy(rects):\n newRects = {}\n for i in range(len(rects)):\n x = rects[i].x\n y = rects[i].y\n w = rects[i].w\n h = rects[i].h\n x0,y0 = rects[i].getPoint()\n x1,y1 = rects[i].getOppositePoint()\n box = [(x0, y0), (x0, y1), (x1, y1), (x1, y0)]\n newRects[i] = box\n \n return newRects","title":""},{"docid":"3199d1bc281bb89b47caf03122aa0bb6","score":"0.6102514","text":"def bbox2points(bbox):\n x, y, w, h = bbox\n xmin = int(round(x - (w / 2)))\n xmax = int(round(x + (w / 2)))\n ymin = int(round(y - (h / 2)))\n ymax = int(round(y + (h / 2)))\n return xmin, ymin, xmax, ymax","title":""},{"docid":"45f77ce20b37e6ebcaee0f3a340f9583","score":"0.60416144","text":"def calculateBoundingBoxes(label, x, y, w, h):\n X1 = x - (w / 2)\n Y1 = y - (h / 2)\n X2 = x + (w / 2)\n Y2 = y + (h / 2)\n\n return [label, round(Y1, 2), round(X1, 2), round(Y2, 2), round(X2, 2)]","title":""},{"docid":"1708ef768311058bcd1e775b504bbb61","score":"0.6016839","text":"def xywh_to_xyxy(boxes):\n return np.hstack((boxes[:, 0:2], boxes[:, 0:2] + boxes[:, 2:4] - 1))","title":""},{"docid":"1708ef768311058bcd1e775b504bbb61","score":"0.6016839","text":"def xywh_to_xyxy(boxes):\n return np.hstack((boxes[:, 0:2], boxes[:, 0:2] + boxes[:, 2:4] - 1))","title":""},{"docid":"ca00ecb25ff55da864920ef497c87e7e","score":"0.6009041","text":"def bbox2points(self, bbox):\n x, y, w, h = bbox\n xmin = int(round(x - (w / 2)))\n xmax = int(round(x + (w / 2)))\n ymin = int(round(y - (h / 2)))\n ymax = int(round(y + (h / 2)))\n return xmin, ymin, xmax, ymax","title":""},{"docid":"0a531e606c764261698e3bf887f16775","score":"0.60075676","text":"def box_2d_to_points(box):\n corners = [[0, 0, 0], [0, 1, 0], [1, 1, 0], [1, 0, 0]]\n corners.append(corners[0]) # Need to copy this so that the box is closed.\n return box.make_absolute(corners)[:, :2]","title":""},{"docid":"a8eb0848cf07f115fde988d95b88bf40","score":"0.59940386","text":"def bbox_transform_inv(bbox):\n xmin, ymin, xmax, ymax = bbox\n out_box = [[]]*4\n\n width = xmax - xmin + 1.0\n height = ymax - ymin + 1.0\n out_box[0] = xmin + 0.5*width\n out_box[1] = ymin + 0.5*height\n out_box[2] = width\n out_box[3] = height\n\n return out_box","title":""},{"docid":"aba473629d05054b7bf8e754f8b4a6f2","score":"0.5957452","text":"def bounds_to_box(left, right, bottom, top):\n box = [(left, bottom), (left, top), (right, top), (right, bottom)]\n box = Polygon(box)\n return box","title":""},{"docid":"20a2b9c2ddd4c6c980f1f068a9fa0fce","score":"0.59443575","text":"def bounding_boxes_pascal(\n pascal_file_path: str,\n) -> List[Dict]:\n\n boxes = []\n\n # load the contents of the annotations file into an ElementTree\n tree = ElementTree.parse(pascal_file_path)\n\n for obj in tree.iter(\"object\"):\n\n label = obj.find(\"name\").text\n bndbox = obj.find(\"bndbox\")\n bbox_min_x = int(float(bndbox.find(\"xmin\").text))\n bbox_min_y = int(float(bndbox.find(\"ymin\").text))\n bbox_max_x = int(float(bndbox.find(\"xmax\").text))\n bbox_max_y = int(float(bndbox.find(\"ymax\").text))\n\n # make sure we don't have wonky values with mins > maxs\n if (bbox_min_x >= bbox_max_x) or (bbox_min_y >= bbox_max_y):\n\n # report the issue via log message\n _logger.warning(\"Bounding box minimum(s) greater than maximum(s)\")\n\n # skip this box since it's not clear how to fix it\n continue\n\n # include this box in the list we'll return\n box = {\n \"label\": label,\n \"xmin\": bbox_min_x,\n \"ymin\": bbox_min_y,\n \"xmax\": bbox_max_x,\n \"ymax\": bbox_max_y,\n }\n boxes.append(box)\n\n return boxes","title":""},{"docid":"7557895a4b03c7f31bfe00765a55746d","score":"0.59068346","text":"def extract_bboxes(mask):\n boxes = np.zeros([4], dtype=np.int32)\n\n m = mask[:, :]\n # Bounding box.\n horizontal_indicies = np.where(np.any(m, axis=0))[0]\n # print('horizontal_indicies',horizontal_indicies)\n # print('horizontal_indicies_shape',horizontal_indicies.shape)\n vertical_indicies = np.where(np.any(m, axis=1))[0]\n if horizontal_indicies.shape[0]:\n x1, x2 = horizontal_indicies[[0, -1]]\n y1, y2 = vertical_indicies[[0, -1]]\n # x2 and y2 should not be part of the box. Increment by 1.\n # x2 += 1\n # y2 += 1\n y2 = y2 - y1\n x2 = x2 - x1\n else:\n # No mask for this instance. Might happen due to\n # resizing or cropping. Set bbox to zeros\n x1, x2, y1, y2 = 0, 0, 0, 0\n # boxes = np.array([y1, x1, y2, x2])\n # boxes = (int(y1), int(x1), int(y2), int(x2))\n boxes = (int(x1), int(y1), int(x2), int(y2))\n return boxes","title":""},{"docid":"f6904ad8656d435d93773532353be1e3","score":"0.5902869","text":"def rect_to_bounding_box(rect):\n x = rect.left()\n y = rect.top()\n w = rect.right() - x\n h = rect.bottom() - y\n\n return x, y, w, h","title":""},{"docid":"ff85d96c1e58927fd36c32ce2e313b76","score":"0.5882208","text":"def _get_xy_bounding_box(vertex, padding):\n min_ = np.floor(np.min(vertex[:, :2], axis=0) - padding).astype(np.int)\n max_ = np.ceil(np.max(vertex[:, :2], axis=0) + padding).astype(np.int)\n return min_, max_","title":""},{"docid":"7cbd1d8028ae98282da220c18db1ade4","score":"0.58702177","text":"def _preprocessing(\n self, path: str\n ) -> Tuple[np.array, float, int, int, int, np.array, int, int]:\n \n '''\n Post Processing about predict bounding box\n \n - scaling Bounding Box using offset\n \n '''\n \n if '.png' in path or '.jpg' in path or '.bmp' in path:\n image = cv2.imread(path)\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n imgo = image.copy()\n image = image[:, :, ::-1]\n h, w = image.shape[:2]\n \n image_height, image_width = image.shape[:2]\n image_size = 512 # b0 baseline\n \n if image_height > image_width:\n scale = image_size / image_height\n resized_height = image_size\n resized_width = int(image_width * scale)\n else:\n scale = image_size / image_width\n resized_height = int(image_height * scale)\n resized_width = image_size\n\n image = cv2.resize(image, (resized_width, resized_height))\n new_image = np.ones((image_size, image_size, 3), dtype=np.float32) * 128.\n offset_h = (image_size - resized_height) // 2\n offset_w = (image_size - resized_width) // 2\n new_image[offset_h:offset_h+resized_height, \n offset_w:offset_w+resized_width] = image.astype(np.float32)\n new_image /= 255.\n\n mean = [0.485, 0.456, 0.406]\n std = [0.229, 0.224, 0.225]\n \n for i in range(3):\n new_image[..., i] -= mean[i]\n new_image[..., i] /= std[i]\n \n return (new_image, scale, offset_h, offset_w, image_size, imgo, h, w)","title":""},{"docid":"0012793a49009bf3e70766d27a1dc80e","score":"0.5851479","text":"def bounding_box(self):\r\n\r\n return [(self.x + 10, self.y + 10),\r\n (self.x - 10, self.y + 10),\r\n (self.x - 10, self.y - 10),\r\n (self.x + 10, self.y - 10)]","title":""},{"docid":"ce6d97793fa92b258eaa439510c0c8da","score":"0.58498704","text":"def calculate_box_region(self, width, height):\n\n left = width * self.piece_width \n top = height * self.piece_height \n right = left + self.piece_width \n bottom = top + self.piece_height \n return left, top, right, bottom","title":""},{"docid":"4a09e78b98a887a3009183794aa753b7","score":"0.5847786","text":"def normalize_pts_within_bboxes(pts):\n b, _, _, h_pts, w_pts = pts.shape\n _pts_x = pts[:, :, 0, :, :] # (b, n, h_pts, w_pts)\n _pts_y = pts[:, :, 1, :, :] # (b, n, h_pts, w_pts)\n _bbox_left = torch.min(_pts_x, dim=1, keepdim=True)[0] # (b, 1, h_pts, w_pts)\n _bbox_right = torch.max(_pts_x, dim=1, keepdim=True)[0] # (b, 1, h_pts, w_pts)\n _bbox_bottom = torch.max(_pts_y, dim=1, keepdim=True)[0] # (b, 1, h_pts, w_pts)\n _bbox_up = torch.min(_pts_y, dim=1, keepdim=True)[0] # (b, 1, h_pts, w_pts)\n _bbox_w = _bbox_right - _bbox_left # (b, 1, h_pts, w_pts)\n _bbox_h = _bbox_bottom - _bbox_up # (b, 1, h_pts, w_pts)\n\n normalized_x = (_pts_x - _bbox_left) / (_bbox_w + 1e-6) # (b, n, h_pts, w_pts)\n normalized_y = (_pts_y - _bbox_up) / (_bbox_h + 1e-6) # (b, n, h_pts, w_pts)\n normalized_pts = torch.stack([normalized_x, normalized_y], dim=2) # (b, n, 2, h_pts, w_pts)\n return normalized_pts","title":""},{"docid":"63f7ff25b1b57c0df87dc6a5d741aed3","score":"0.5827113","text":"def convert_box(size, box):\n dw = 1. / (size[0])\n dh = 1. / (size[1])\n x = (box[0] + box[1]) / 2.0 - 1\n y = (box[2] + box[3]) / 2.0 - 1\n w = box[1] - box[0]\n h = box[3] - box[2]\n x = x * dw\n w = w * dw\n y = y * dh\n h = h * dh\n return x, y, w, h","title":""},{"docid":"e05c8b4df1183a3fdf48747fdb4cd0be","score":"0.5815482","text":"def bbox_poly2hbb_nd(boxes):\n n = boxes.shape[0]\n hbbs = mx.nd.zeros((n, 4))\n\n xs = boxes[:, : -1].reshape((n, 4, 2))[:, :, 0]\n ys = boxes[:, : -1].reshape((n, 4, 2))[:, :, 1]\n\n hbbs[:, 0] = mx.nd.min(xs, axis=1)\n hbbs[:, 1] = mx.nd.min(ys, axis=1)\n hbbs[:, 2] = mx.nd.max(xs, axis=1)\n hbbs[:, 3] = mx.nd.max(ys, axis=1)\n hbbs = mx.nd.concat(hbbs, mx.nd.expand_dims(boxes[:, -1], 1), dim=1)\n\n return hbbs","title":""},{"docid":"839a4dea8c71a1e0783a7cd4b731bae8","score":"0.57861143","text":"def deformable_transform_aabb_bkp(shape, transform, aabb):\n # Input bbox points\n x1, y1, x2, y2 = aabb.astype(int)\n\n # Create image with bbox\n tmp_img = np.zeros(shape)\n cv2.rectangle(tmp_img,(x1,y1),(x2,y2),(255,255,255),1)\n\n # scipy.ndimage.interpolation.map_coordinates:\n #\n # Map the input array to new coordinates by interpolation.\n #\n # The array of coordinates is used to find, for each point in the output,\n # the corresponding coordinates in the input. The value of the input at\n # those coordinates is determined by spline interpolation of the\n # requested order.\n #\n # The shape of the output is derived from that of the coordinate\n # array by dropping the first axis. The values of the array along\n # the first axis are the coordinates in the input array at which the\n # output value is found.\n distorted = map_coordinates(tmp_img, transform, order=1, mode='reflect').reshape(shape)\n\n A = np.nonzero(distorted>0)\n x1_ = min(A[1])\n y1_ = min(A[0])\n x2_ = max(A[1])\n y2_ = max(A[0])\n\n #-------------------------------------------\n # # Draw the findings\n # cv2.circle(distorted, (x1_,y1_), 3, (0,255,0), 1)\n # cv2.circle(distorted, (x2_,y2_), 3, (0,255,0), 1)\n # print(\"Top left: [\",x1,\",\",y1,\"] | Bottom right: [\",x2,\",\",y2,\"]\")\n # print(\"New top left: [\",x1_,\",\",y1_,\"] | New bottom right: [\",x2_,\",\",y2_,\"]\")\n #\n # print(\"Temp image shape: \", distorted.shape)\n # cv2.imshow(\"Temp Image\", distorted)\n # cv2.waitKey(0)\n #-------------------------------------------\n\n return [x1_, y1_, x2_, y2_]","title":""},{"docid":"f09c151bccd63b9a1d3b8cad174f37f6","score":"0.5774372","text":"def get_new_bbox(bbox, width, height):\n try:\n x1 = round(bbox[0]*100/float(width), 4)\n x2 = round(bbox[2]*100/float(width), 4)\n y1 = round(bbox[1]*100/float(height), 4)\n y2 = round(bbox[3]*100/float(height), 4)\n except IndexError:\n print(bbox)\n return [x1, y1, x2, y2]","title":""},{"docid":"6db329cb294d32f281b4e1d61d429a47","score":"0.5773582","text":"def points_to_bbox(p):\n llx = urx = p[0][0]\n lly = ury = p[0][1]\n for x in p[1:]:\n if x[0] < llx: llx = x[0]\n elif x[0] > urx: urx = x[0]\n if x[1] < lly: lly = x[1]\n elif x[1] > ury: ury = x[1]\n return (llx, lly, urx, ury)","title":""},{"docid":"38ec969a0d7e39c82e21b3e3d3af1a8b","score":"0.576676","text":"def rectangle_vertice(self, center, height, width):\n half_height = height / 2.\n half_width = width / 2.\n\n bias1 = np.ones(4) * half_width\n bias2 = np.ones(4) * half_height\n\n corner1 = np.empty([1, 3], dtype=np.float32)\n corner2 = np.empty([1, 3], dtype=np.float32)\n corner3 = np.empty([1, 3], dtype=np.float32)\n corner4 = np.empty([1, 3], dtype=np.float32)\n\n corner1[:, 0] = center[0] - bias1[0]\n corner1[:, 1] = center[1] - bias2[0]\n corner1[:, 2] = 0\n\n corner2[:, 0] = center[0] + bias1[1]\n corner2[:, 1] = center[1] - bias2[1]\n corner2[:, 2] = 0\n\n corner3[:, 0] = center[0] + bias1[2]\n corner3[:, 1] = center[1] + bias2[2]\n corner3[:, 2] = 0\n\n corner4[:, 0] = center[0] - bias1[3]\n corner4[:, 1] = center[1] + bias2[3]\n corner4[:, 2] = 0\n\n # Get vertices between each corner of the rectangle for border drawing\n vertices = np.concatenate(([[center[0], center[1], 0.]],\n [[center[0] - half_width, center[1], 0.]],\n corner1,\n [[center[0], center[1] - half_height, 0.]],\n corner2,\n [[center[0] + half_width, center[1], 0.]],\n corner3,\n [[center[0], center[1] + half_height, 0.]],\n corner4,\n [[center[0] - half_width, center[1], 0.]]))\n\n # vertices = np.array(output, dtype=np.float32)\n return vertices[1:, ..., :2]","title":""},{"docid":"5d350c85237587bcbe35bce711e4878b","score":"0.5723013","text":"def convert_kfx_to_bbox(x):\n w=max(0.0,x[2])\n h=max(0.0,x[3])\n return np.array([x[0]-w/2.,x[1]-h/2.,x[0]+w/2.,x[1]+h/2.]).reshape((1,4))","title":""},{"docid":"80f9cb443c948e55697882e61f1a2ab8","score":"0.5716591","text":"def dump_bounding_box(self):\n row, column = self.get_bounding_box_corner_pos()\n return row, column, self.get_bounding_box_frame()","title":""},{"docid":"2e33f4acacd1302c216ec354655e43e6","score":"0.56964046","text":"def boundingBox(self):\n return [[self.x_min,self.y_min],[self.x_min,self.y_max],[self.x_max,self.y_max],[self.x_max,self.y_min]]","title":""},{"docid":"90437aa9f8b8d1626272858cef4a18c0","score":"0.5674501","text":"def bbox2cv_bbox(self, bbox: iter) -> tuple:\n\n p_x = bbox[0] - bbox[2] // 2\n p_y = bbox[1] - bbox[3] // 2\n w = bbox[2]\n h = bbox[3]\n if p_x < 0:\n p_x = 0\n if p_y < 0:\n p_y = 0\n if p_x + w > 1280:\n w = 1280 - p_x\n if p_y + h > 720:\n h = 720 - p_y\n bbox = (p_x, p_y, w, h)\n return tuple(map(int, bbox))","title":""},{"docid":"959326f5fd24aedc6278c166727de80b","score":"0.5665393","text":"def point_form(bboxes):\n return np.concatenate((bboxes[:, :2] - bboxes[:, 2:]/2, # xmin, ymin\n bboxes[:, :2] + bboxes[:, 2:]/2), 1) # xmax, ymax","title":""},{"docid":"0c6d830b9806a1a7432e11b3f84086a9","score":"0.5663881","text":"def boxes_to_corners(box_xy, box_wh):\n box_mins = box_xy - (box_wh / 2.)\n box_maxes = box_xy + (box_wh / 2.)\n\n return np.concatenate([\n box_mins[..., 0:1], # x_min\n box_mins[..., 1:2], # y_min\n box_maxes[..., 0:1], # x_max\n box_maxes[..., 1:2] # y_max\n ], axis=4)","title":""},{"docid":"a010e42878dc3a7f2d14dcc3d99c2d3e","score":"0.5661924","text":"def _box_xyxy_to_xywh(boxes: Tensor) -> Tensor:\n x1, y1, x2, y2 = boxes.unbind(-1)\n w = x2 - x1 # x2 - x1\n h = y2 - y1 # y2 - y1\n boxes = torch.stack((x1, y1, w, h), dim=-1)\n return boxes","title":""},{"docid":"a224fadcaaa14797d383db440ed67f21","score":"0.565625","text":"def generate_bbox_interion(bbox, shape):\n shape = shape[1::-1]\n points = skimage.draw.polygon(bbox[::2], bbox[1::2], shape=shape)\n return np.vstack([points[0], points[1]]).T","title":""},{"docid":"ea24b15510a5a887ecb909eb3c05c11d","score":"0.5649105","text":"def bounding_box2D(pts):\n dim = len(pts[0]) # should be 2\n bb_min = [min([t[i] for t in pts]) for i in range(dim)]\n bb_max = [max([t[i] for t in pts]) for i in range(dim)]\n return bb_min[0], bb_min[1], bb_max[0] - bb_min[0], bb_max[1] - bb_min[1]","title":""},{"docid":"b253f0938ef010799ec2100629aee36a","score":"0.56353056","text":"def _box_cxcywh_to_xyxy(boxes: Tensor) -> Tensor:\n # We need to change all 4 of them so some temporary variable is needed.\n cx, cy, w, h = boxes.unbind(-1)\n x1 = cx - 0.5 * w\n y1 = cy - 0.5 * h\n x2 = cx + 0.5 * w\n y2 = cy + 0.5 * h\n\n boxes = torch.stack((x1, y1, x2, y2), dim=-1)\n\n return boxes","title":""},{"docid":"82a8a91c9faa861ffc7ef9554bb12b8b","score":"0.5632779","text":"def rect_to_bb(self, rect):\r\n\r\n x = rect.left()\r\n y = rect.top()\r\n w = rect.right() - x\r\n h = rect.bottom() - y\r\n\r\n return (x, y, w, h)","title":""},{"docid":"bdccbab0ddc1a0927fe2f9705c444711","score":"0.5630967","text":"def boundingBox(poly):\n min_x, min_y = N.infty, N.infty\n max_x, max_y = 0, 0\n for p in poly:\n min_x = min(min_x, p.x)\n max_x = max(max_x, p.x)\n min_y = min(min_y, p.y)\n max_y = max(max_y, p.y)\n return P(min_x, min_y), P(max_x, max_y)","title":""},{"docid":"5648212e5401d4609a076b6806c166e3","score":"0.56272864","text":"def extract_bboxes(mask):\n boxes = np.zeros([mask.shape[-1], 4], dtype=np.int32)\n for i in range(mask.shape[-1]):\n m = mask[:, :, i]\n # Bounding box.\n horizontal_indicies = np.where(np.any(m, axis=0))[0]\n vertical_indicies = np.where(np.any(m, axis=1))[0]\n if horizontal_indicies.shape[0]:\n x1, x2 = horizontal_indicies[[0, -1]]\n y1, y2 = vertical_indicies[[0, -1]]\n # x2 and y2 should not be part of the box. Increment by 1.\n x2 += 1\n y2 += 1\n else:\n # No mask for this instance. Might happen due to\n # resizing or cropping. Set bbox to zeros\n x1, x2, y1, y2 = 0, 0, 0, 0\n # boxes[i] = np.array([y1, x1, y2, x2])\n boxes[i] = np.array([x1, y1, x2, y2])\n return boxes.astype(np.int32)","title":""},{"docid":"5648212e5401d4609a076b6806c166e3","score":"0.56272864","text":"def extract_bboxes(mask):\n boxes = np.zeros([mask.shape[-1], 4], dtype=np.int32)\n for i in range(mask.shape[-1]):\n m = mask[:, :, i]\n # Bounding box.\n horizontal_indicies = np.where(np.any(m, axis=0))[0]\n vertical_indicies = np.where(np.any(m, axis=1))[0]\n if horizontal_indicies.shape[0]:\n x1, x2 = horizontal_indicies[[0, -1]]\n y1, y2 = vertical_indicies[[0, -1]]\n # x2 and y2 should not be part of the box. Increment by 1.\n x2 += 1\n y2 += 1\n else:\n # No mask for this instance. Might happen due to\n # resizing or cropping. Set bbox to zeros\n x1, x2, y1, y2 = 0, 0, 0, 0\n # boxes[i] = np.array([y1, x1, y2, x2])\n boxes[i] = np.array([x1, y1, x2, y2])\n return boxes.astype(np.int32)","title":""},{"docid":"cb4265c72f3b3c3616fa391530a0d651","score":"0.5626392","text":"def xyxy_to_xywh(boxes):\n return np.hstack((boxes[:, 0:2], boxes[:, 2:4] - boxes[:, 0:2] + 1))","title":""},{"docid":"5a7d033c6379d70c5c41ea27fb5d07eb","score":"0.5622092","text":"def _box_xywh_to_xyxy(boxes: Tensor) -> Tensor:\n x, y, w, h = boxes.unbind(-1)\n boxes = torch.stack([x, y, x + w, y + h], dim=-1)\n return boxes","title":""},{"docid":"75d41394ebdd05941aacd6eb95831a84","score":"0.5617597","text":"def retbox(bbox,format='xyxy'):\n if format =='xyxy':\n xmin, ymin, xmax, ymax = bbox\n elif format =='xywh':\n xmin, ymin, w, h = bbox\n xmax = xmin + w -1 \n ymax = ymin + h -1 \n \n box = np.array([[xmin, xmax, xmax, xmin, xmin],\n [ymin, ymin, ymax, ymax, ymin]])\n return box.T","title":""},{"docid":"0f70d7c18a7ed5788562abbd2e5c2dde","score":"0.5598006","text":"def convert_bbox_to_kfx(bbox):\n w = bbox[2]-bbox[0]\n h = bbox[3]-bbox[1]\n x = bbox[0]+w/2.\n y = bbox[1]+h/2.\n return np.array([x,y,w,h]).reshape((4,1))","title":""},{"docid":"0fa95bf28b9a53b4022bfa8776993756","score":"0.5589124","text":"def compute_crop_box(bound_box: dict):\n x_max, x_min, y_max, y_min = [int(v) for _, v in sorted(bound_box.items())]\n\n # compute half of the side of the scaled box to crop \n width = x_max - x_min\n height = y_max - y_min\n half_side = SCALE / 2 * max(width, height)\n\n # find the (x, y) center of the box\n x_center = (x_max + x_min) / 2\n y_center = (y_max + y_min) / 2\n \n left, top, right, bottom = (int(x_center - half_side),\n int(y_center - half_side),\n int(x_center + half_side),\n int(y_center + half_side))\n\n return((left, top, right, bottom))","title":""},{"docid":"e966ba4c0d046b569ed8f21fc25229b0","score":"0.5584946","text":"def BoundingBox( self ) :\n \n # loop over corners:\n for i in range(self.n) :\n # first?\n if i == 0 :\n west = self.corners[i].x\n east = self.corners[i].x\n south = self.corners[i].y\n north = self.corners[i].y\n else :\n west = min( west , self.corners[i].x )\n east = max( east , self.corners[i].x )\n south = min( south, self.corners[i].y )\n north = max( north, self.corners[i].y )\n #endif\n #endfor\n \n # ok\n return west,east,south,north","title":""},{"docid":"cf342c7d36548a9877af168643303047","score":"0.55840707","text":"def get_bound_box_points(self) -> Tuple[int, int, int, int]:\n return self.bound_box_coordinates[0], self.bound_box_coordinates[0] + self.width, \\\n self.bound_box_coordinates[1], self.bound_box_coordinates[1] + self.height","title":""},{"docid":"7a700179f12a672a0ad3bef350d5c138","score":"0.55793893","text":"def convert_to_square(self, bbox):\n square_bbox = bbox.copy()\n h = bbox[:, 3] - bbox[:, 1] + 1\n w = bbox[:, 2] - bbox[:, 0] + 1\n max_side = np.maximum(h,w)\n square_bbox[:, 0] = bbox[:, 0] + w*0.5 - max_side*0.5\n square_bbox[:, 1] = bbox[:, 1] + h*0.5 - max_side*0.5\n square_bbox[:, 2] = square_bbox[:, 0] + max_side - 1\n square_bbox[:, 3] = square_bbox[:, 1] + max_side - 1\n return square_bbox","title":""},{"docid":"af01040de718158487a01e02b20a3305","score":"0.55779827","text":"def to_bounding_box(self):\n left = self.x\n top = self.y\n right = self.x + self.w\n bottom = self.y + self.h\n retval = BoundingBox(left, top, right, bottom)\n logger.trace(\"Returning: %s\", retval)\n return retval","title":""},{"docid":"ec4474aa382cb1381f7272ec40ba90ab","score":"0.55569994","text":"def box(self):\n\t\treturn (self.x, self.y), (self.w, self.h), self.a","title":""},{"docid":"1ca374f14dbc79aaefc7b4cce9db0460","score":"0.5556784","text":"def get_square_bbox(ltrb):\n\n left = ltrb[:, 0]\n right = ltrb[:, 2]\n top = ltrb[:, 1]\n bottom = ltrb[:, 3]\n\n width, height = right - left, bottom - top\n\n if width > height:\n y_center = (ltrb[:, 3] + ltrb[:, 1]) // 2\n top = y_center - width // 2\n bottom = top + width\n else:\n x_center = (ltrb[:, 2] + ltrb[:, 0]) // 2\n left = x_center - height // 2\n right = left + height\n\n new_ltrb = torch.stack([left, top, right, bottom], dim=-1)\n return new_ltrb","title":""},{"docid":"d40fafa1249d1dad903ef229d7ebbf07","score":"0.5545522","text":"def crop_bounding_box(self):\n return (self.crop_x, self.crop_y,\n self.crop_x + self.crop_w, self.crop_y + self.crop_h)","title":""},{"docid":"9b19b3cc642cda51206ddf21d31fd341","score":"0.5544213","text":"def to_image_coords(self, boxes, height, width):\n box_coords = np.zeros_like(boxes)\n box_coords[:, 0] = boxes[:, 0] * height\n box_coords[:, 1] = boxes[:, 1] * width\n box_coords[:, 2] = boxes[:, 2] * height\n box_coords[:, 3] = boxes[:, 3] * width\n return box_coords","title":""},{"docid":"e35cd203421bf17a9ccae4fbb1f03dbe","score":"0.55390316","text":"def rect_to_xys(rect, image_shape):\n h, w = image_shape[0:2]\n def get_valid_x(x):\n if x < 0:\n return 0\n if x >= w:\n return w - 1\n return x\n \n def get_valid_y(y):\n if y < 0:\n return 0\n if y >= h:\n return h - 1\n return y\n \n rect = ((rect[0], rect[1]), (rect[2], rect[3]), rect[4])\n points = cv2.boxPoints(rect)\n points = np.int0(points)\n for i_xy, (x, y) in enumerate(points):\n x = get_valid_x(x)\n y = get_valid_y(y)\n points[i_xy, :] = [x, y]\n points = np.reshape(points, -1)\n return points","title":""},{"docid":"1be05ce906cfd03f0afeece9fc96cf81","score":"0.55379367","text":"def convert_z_to_bbox(z):\n w = np.sqrt(z[2] * z[3])\n h = z[2] / w\n x1 = z[0]-w/2.\n y1 = z[1]-h/2.\n x2 = z[0]+w/2.\n y2 = z[1]+h/2.\n if (x1 > x2) or (y1 > y2):\n print('convertion to bbox when wrong. w=%.3f h=%.3f' % (w, h))\n print([x1, y1, x2, y2])\n\n return np.array([x1, max(0, y1), min(x2, w-1), min(y2, h-1)])","title":""},{"docid":"660c584fdef8e26c4e4ef0110792a084","score":"0.5524904","text":"def _correct_boxes(\n self, box_xy, box_wh, input_shape, image_shape):\n box_yx = box_xy[..., ::-1]\n box_hw = box_wh[..., ::-1]\n input_shape = K.cast(input_shape, K.dtype(box_yx))\n image_shape = K.cast(image_shape, K.dtype(box_yx))\n new_shape = K.round(image_shape * K.min(input_shape / image_shape))\n offset = (input_shape - new_shape) / 2. / input_shape\n scale = input_shape / new_shape\n box_yx = (box_yx - offset) * scale\n box_hw *= scale\n\n box_mins = box_yx - (box_hw / 2.)\n box_maxes = box_yx + (box_hw / 2.)\n boxes = K.concatenate([\n box_mins[..., 0:1], # y_min\n box_mins[..., 1:2], # x_min\n box_maxes[..., 0:1], # y_max\n box_maxes[..., 1:2] # x_max\n ])\n\n # Scale boxes back to original image shape.\n boxes *= K.concatenate([image_shape, image_shape])\n return boxes","title":""},{"docid":"94aad998efd7ea0fd6043765c2c4d95c","score":"0.55237657","text":"def convert_bbox_to_z(bbox):\n w = bbox[2] - bbox[0]\n h = bbox[3] - bbox[1]\n x = bbox[0] + w/2.\n y = bbox[1] + h/2.\n s = w * h #scale is just area\n r = w / float(h)\n return np.array([x, y, s, r]).reshape((4, 1))","title":""},{"docid":"94aad998efd7ea0fd6043765c2c4d95c","score":"0.55237657","text":"def convert_bbox_to_z(bbox):\n w = bbox[2] - bbox[0]\n h = bbox[3] - bbox[1]\n x = bbox[0] + w/2.\n y = bbox[1] + h/2.\n s = w * h #scale is just area\n r = w / float(h)\n return np.array([x, y, s, r]).reshape((4, 1))","title":""},{"docid":"46c681675fe4983767f7f8166ff88bdf","score":"0.5519915","text":"def convert_bbox_to_z(bbox):\n w = bbox[2] - bbox[0]\n h = bbox[3] - bbox[1]\n x = bbox[0] + w / 2.\n y = bbox[1] + h / 2.\n s = w * h # scale is just area\n r = w / float(h)\n\n # 将数组 [x,y,s,r] 转为 4 行 1 列形式，即 [x,y,s,r].T\n return np.array([x, y, s, r]).reshape((4, 1))","title":""},{"docid":"afa8291ca5831b4ae12e0bfc7d3c3041","score":"0.55110615","text":"def _convert(self, pos):\n px = pos[0] + self.logical_size.width() / 2\n py = self.logical_size.height() / 2 - pos[1]\n return px, py","title":""},{"docid":"9190d2bd733a73b07b61e86c66a12c12","score":"0.55087054","text":"def pointgraph_from_rect(rect):\n x, y, w, h = rect\n return bounding_box((y, x), (y + h, x + w))","title":""},{"docid":"1be4136136f5b4695bd52770621b727a","score":"0.5507402","text":"def yolo_boxes_to_corners(box_xy, box_wh):\n box_mins = box_xy - (box_wh / 2.)\n box_maxes = box_xy + (box_wh / 2.)\n\n return K.concatenate([\n box_mins[..., 1:2], # y_min\n box_mins[..., 0:1], # x_min\n box_maxes[..., 1:2], # y_max\n box_maxes[..., 0:1] # x_max\n ])","title":""},{"docid":"3c523ed8e57b6d9f7de7e13f3c11927b","score":"0.5495114","text":"def make_bounding_box(s, centerx=0, centery=0):\n # result must be in the form: (left, bottom, right, top)\n return [ centerx - s/2, centery - s/2, centerx + s/2, centery + s/2 ]","title":""},{"docid":"989222bd50156609fffca24bf63aa3b0","score":"0.5471726","text":"def convert2relative(image, bbox):\n x, y, w, h = bbox\n height, width, _ = image.shape\n return x/width, y/height, w/width, h/height","title":""},{"docid":"267522d83b18e678d2cea49fc5e6071c","score":"0.5470764","text":"def convert_bbox_to_z(bbox):\n w = bbox[2] - bbox[0]\n h = bbox[3] - bbox[1]\n x = bbox[0] + w/2.\n y = bbox[1] + h/2.\n s = w * h #scale is just area\n r = w / float(h)\n return np.array([x, y, s, r]).reshape((4, 1))","title":""},{"docid":"47d4de2b5d45426a73ef9035e2015d51","score":"0.5465228","text":"def rectify_image(image, source_points, target_points, crop):\n\n # TODO: Compute the rectifying homography H that warps the source points to the target points.\n original_box = np.array([0,485,0,0,640,0,640,485]).reshape(4,2)\n H = compute_H(source_points,target_points)\n\n # TODO: Apply the homography to a rectangle of the bounding box of the of the image to find the warped bounding box in the rectified space.\n wbox = apply_homography(H,original_box)\n\n # Find the min_x and min_y values in the warped space to keep.\n if crop:\n \t# TODO: pick the second smallest values of x and y in the warped bounding box\n \txm=np.amin(wbox[:,0])\n \tym=np.amin(wbox[:,1])\n \ta=0\n \tb=0\n \txarr=np.empty([3,1])\n \tyarr=np.empty([3,1])\n \tfor i in range(4):\n \t\tif wbox[i,0]>xm:\n \t\t\txarr[a,0]=wbox[i,0]\n \t\t\ta=a+1\n \t\tif wbox[i,1]>ym:\n \t\t\tyarr[b,0]=wbox[i,1]\n \t\t\tb=b+1\n \tmin_x=np.amin(xarr[:,0])\n \tmin_y=np.amin(yarr[:,0])\n else:\n \t# TODO: Compute the min x and min y of the warped bounding box\n \tmin_x=np.amin(wbox[:,0])\n \tmin_y=np.amin(wbox[:,1])\n # TODO: Compute a translation matrix T such that min_x and min_y will go to zero\n T = np.array([[1,0,-min_x],[0,1,-min_y],[0,0,1]])\n # TODO: Compute the rectified bounding box by applying the translation matrix to the warped bounding box.\n rbox= np.empty([4, 3], dtype = 'float32')\n for i in range(4):\n \trbox[i,0]=wbox[i,0]-min_x\n \trbox[i,1]=wbox[i,1]-min_y\n \trbox[i,2]=1\n # TODO: Compute the inverse homography that maps the rectified bounding box to the original bounding box\n #original_box = np.array([0,485,0,0,640,0,640,485]).reshape(4,2)\n Hinv = compute_H(rbox[:,0:2],original_box)\n\n # Determine the shape of the output image\n if crop:\n # TODO: Determine the side of the final output image as the second highest X and Y values of the rectified bounding box\n xa=np.amax(rbox[:,0])\n ya=np.amax(rbox[:,1])\n am=0\n bm=0\n xar=np.empty([3,1])\n yar=np.empty([3,1])\n for i in range(4):\n if rbox[i,0]<xa:\n xar[am,0]=rbox[i,0]\n am=am+1\n if rbox[i,1]<ya:\n yar[bm,0]=rbox[i,1]\n bm=bm+1\n max_x=np.amax(xar[:,0])\n max_y=np.amax(yar[:,0])\n else:\n \t# TODO: Determine the side of the final output image as the maximum X and Y values of the rectified bounding box\n \tmax_x=np.amax(rbox[:,0])\n \tmax_y=np.amax(rbox[:,1])\n\n # TODO: Finally call warp_homography to rectify the image and return the result\n rectified_image = warp_homography(np.array(image),(max_y,max_x,3),Hinv)\n return rectified_image","title":""},{"docid":"d0c297357658d744603655d9fe6651a1","score":"0.5457607","text":"def bboard2pboard(bboard):\n return (bits2positions(bboard[0]), bits2positions(bboard[1]))","title":""},{"docid":"ade3c68dc62b03d316ad29878f641fea","score":"0.54562217","text":"def _crop_data(self, height, width, box, joints, boxp = 0.05):\n\t\tpadding = [[0,0],[0,0],[0,0]]\n\t\tj = np.copy(joints)\n\t\tif box[0:2] == [-1,-1]:\n\t\t\tj[joints == -1] = 1e5\n\t\t\tbox[0], box[1] = min(j[:,0]), min(j[:,1])\n\t\tcrop_box = [box[0] - int(boxp * (box[2]-box[0])), box[1] - int(boxp * (box[3]-box[1])), box[2] + int(boxp * (box[2]-box[0])), box[3] + int(boxp * (box[3]-box[1]))]\n\t\tif crop_box[0] < 0: crop_box[0] = 0\n\t\tif crop_box[1] < 0: crop_box[1] = 0\n\t\tif crop_box[2] > width -1: crop_box[2] = width -1\n\t\tif crop_box[3] > height -1: crop_box[3] = height -1\n\t\tnew_h = int(crop_box[3] - crop_box[1])\n\t\tnew_w = int(crop_box[2] - crop_box[0])\n\t\tcrop_box = [crop_box[0] + new_w //2, crop_box[1] + new_h //2, new_w, new_h]\n\t\tif new_h > new_w:\n\t\t\tbounds = (crop_box[0] - new_h //2, crop_box[0] + new_h //2)\n\t\t\tif bounds[0] < 0:\n\t\t\t\tpadding[1][0] = abs(bounds[0])\n\t\t\tif bounds[1] > width - 1:\n\t\t\t\tpadding[1][1] = abs(width - bounds[1])\n\t\telif new_h < new_w:\n\t\t\tbounds = (crop_box[1] - new_w //2, crop_box[1] + new_w //2)\n\t\t\tif bounds[0] < 0:\n\t\t\t\tpadding[0][0] = abs(bounds[0])\n\t\t\tif bounds[1] > width - 1:\n\t\t\t\tpadding[0][1] = abs(height - bounds[1])\n\t\tcrop_box[0] += padding[1][0]\n\t\tcrop_box[1] += padding[0][0]\n\t\treturn padding, crop_box","title":""},{"docid":"175409ca0d6fe3fd8c4d8f7bd6a6b2bd","score":"0.5454066","text":"def setup_rectangle( box_loc , width, height): \n xloc,yloc = box_loc\n low_left = (xloc - .5 * width, yloc -.5 * height)\n top_right = (xloc + .5 * width, yloc +.5 * height) \n return (low_left, top_right)","title":""},{"docid":"eaa00840966c9a66bcf702853c90b6db","score":"0.54535","text":"def parse_roi_box_from_landmark(pts):\n bbox = [min(pts[0, :]), min(pts[1, :]), max(pts[0, :]), max(pts[1, :])]\n center = [(bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2]\n radius = max(bbox[2] - bbox[0], bbox[3] - bbox[1]) / 2\n bbox = [center[0] - radius, center[1] - radius, center[0] + radius, center[1] + radius]\n\n llength = np.sqrt((bbox[2] - bbox[0]) ** 2 + (bbox[3] - bbox[1]) ** 2)\n\n \n center_x = (bbox[2] + bbox[0]) / 2\n center_y = (bbox[3] + bbox[1]) / 2\n\n llength = llength * 0.8\n\n roi_box = [0] * 4\n roi_box[0] = center_x - llength / 2\n roi_box[1] = center_y - llength / 2\n roi_box[2] = roi_box[0] + llength\n roi_box[3] = roi_box[1] + llength\n\n return np.array(roi_box)","title":""},{"docid":"d3ad40e7349eecbc8c6069c9a4c15421","score":"0.54351693","text":"def roi_bbox_V2(img):\n W = img.shape[0] - 1\n H = img.shape[1] - 1\n x1 = W\n y1 = H\n x2 = 0\n y2 = 0\n for x in range(0, W):\n for y in range(0, H):\n # print(x,y)\n if img[x, y]:\n if x < x1:\n x1 = x\n if y < y1:\n y1 = y\n if y > y2:\n y2 = y\n if x > x2:\n x2 = x\n return np.array([x1, y1, x2, y2])","title":""},{"docid":"c5b248d0a808f6bc6d777f47ce50f7ca","score":"0.54350454","text":"def get_bounding_box(self):\n\n rows = np.any(self.img, axis=1)\n cols = np.any(self.img, axis=0)\n left, right = np.where(rows)[0][[0, -1]]\n top, bottom = np.where(cols)[0][[0, -1]]\n return Panel(left, right, top, bottom)","title":""},{"docid":"e36f79ff157462a867e6930497f54abf","score":"0.54338324","text":"def mask_to_tight_box_numpy(mask):\n a = np.transpose(np.nonzero(mask))\n bbox = np.min(a[:, 1]), np.min(a[:, 0]), np.max(a[:, 1]), np.max(a[:, 0])\n return bbox # x_min, y_min, x_max, y_max","title":""},{"docid":"dd4e26ec81a2f2520e6f7f771a0b6c43","score":"0.5428883","text":"def _decode_boxes(self, raw_boxes, anchors):\n boxes = torch.zeros_like(raw_boxes)\n x_center = raw_boxes[..., 0] / self.x_scale * anchors[:, 2] + anchors[:, 0]\n y_center = raw_boxes[..., 1] / self.y_scale * anchors[:, 3] + anchors[:, 1]\n w = raw_boxes[..., 2] / self.w_scale * anchors[:, 2]\n h = raw_boxes[..., 3] / self.h_scale * anchors[:, 3]\n boxes[..., 0] = y_center - h / 2.0\n boxes[..., 1] = x_center - w / 2.0\n boxes[..., 2] = y_center + h / 2.0\n boxes[..., 3] = x_center + w / 2.0\n for k in range(6):\n offset = 4 + k * 2\n keypoint_x = raw_boxes[..., offset] / self.x_scale * anchors[:, 2] + anchors[:, 0]\n keypoint_y = raw_boxes[..., offset + 1] / self.y_scale * anchors[:, 3] + anchors[:, 1]\n boxes[..., offset] = keypoint_x\n boxes[..., offset + 1] = keypoint_y\n return boxes","title":""},{"docid":"93210406bb71b56373ea4c10b6887765","score":"0.5428714","text":"def bbox_to_pv(bbox_list):\n pv_3d = []\n for i in range(len(bbox_list[0])):\n p_1d = []\n v_1d = []\n pv_1d = []\n for t in range(len(bbox_list)):\n bbox = torch.squeeze(bbox_list[t][i], dim=0)\n # float\n b = list(map(lambda x: x.item(), bbox))\n # compute bbox center\n # xc = (b[0] + b[2]) / 2 - 960.0\n # c = abs(-(b[1] + b[3]) / 2 + 1080.0)\n xc = (b[0] + b[2]) / 2\n yc = (b[1] + b[3]) / 2\n # compute width, height\n w = abs(b[2] - b[0])\n h = abs(b[3] - b[1])\n p_1d.append([xc, yc, w, h])\n v_1d.append([0.0, 0.0, 0.0, 0.0])\n for t in range(1, len(bbox_list)):\n dx = abs(p_1d[t][0] - p_1d[t - 1][0])\n dy = abs(p_1d[t][1] - p_1d[t - 1][1])\n dw = abs(p_1d[t][2] - p_1d[t - 1][2])\n dh = abs(p_1d[t][3] - p_1d[t - 1][3])\n v_1d.append([dx, dy, dw, dh])\n for t in range(len(bbox_list)):\n pv_1d.append(torch.tensor(p_1d[t] + v_1d[t], dtype=torch.float32))\n pv_tensors_2d = torch.stack(pv_1d)\n pv_3d.append(pv_tensors_2d)\n # stack batch\n pv_tensors_3d = torch.stack(pv_3d)\n\n return pv_tensors_3d","title":""},{"docid":"ac70b0f793e1dbae8bf0cd11c9edfb6e","score":"0.5424306","text":"def get_bounding_box(prediction):\n x1 = prediction['x_min']\n x2 = prediction['x_max']\n y1 = prediction['y_min']\n y2 = prediction['y_max']\n return [x1, y1, x2, y2]","title":""},{"docid":"fa957139bba699b04d877301f3f600cc","score":"0.5423938","text":"def _translate_bboxes(self, results, offset, direction=\"horizontal\"):\n h, w, c = results[\"img_shape\"]\n for key in results.get(\"bbox_fields\", []):\n min_x, min_y, max_x, max_y = np.split(\n results[key], results[key].shape[-1], axis=-1\n )\n if direction == \"horizontal\":\n min_x = np.maximum(0, min_x + offset)\n max_x = np.minimum(w, max_x + offset)\n elif direction == \"vertical\":\n min_y = np.maximum(0, min_y + offset)\n max_y = np.minimum(h, max_y + offset)\n\n # the boxes translated outside of image will be filtered along with\n # the corresponding masks, by invoking ``_filter_invalid``.\n results[key] = np.concatenate([min_x, min_y, max_x, max_y], axis=-1)","title":""},{"docid":"2de4e41e5ab35d444a55e2336c6bdf47","score":"0.5407474","text":"def bounding_box(self, array_of_points):\n max_x = -float('Inf')\n max_y = -float('Inf')\n min_x = float('Inf')\n min_y = float('Inf')\n for each in array_of_points:\n if max_x < each[0]:\n max_x = each[0]\n if max_y < each[1]:\n max_y = each[1]\n if min_x > each[0]:\n min_x = each[0]\n if min_y > each[1]:\n min_y = each[1]\n return max_x, max_y, min_x, min_y","title":""},{"docid":"b53813bd8b29716fb9efcd20bc544a6e","score":"0.5407165","text":"def _correct_region_bounding_box(self, bounding_box):\n # Map coordinates from resized frame back to the original frame\n x = bounding_box.x * self._resize_scale_factor\n y = bounding_box.y * self._resize_scale_factor\n width = bounding_box.width * self._resize_scale_factor\n height = bounding_box.height * self._resize_scale_factor\n\n # Map ACS orientation info to MPF rotation info\n corrected_top_left, corrected_angle = self._orientation_correction(x, y)\n corrected_box = mpf_util.Rect.from_corner_and_size(corrected_top_left, (width, height))\n return corrected_box, mpf_util.normalize_angle(corrected_angle)","title":""},{"docid":"c16bbbb655363812e80940f575bc08db","score":"0.54016554","text":"def rect_to_xys(self,rect, image_shape):\n\n height, width = image_shape[0:2]\n\n def get_valid_x(x_coord):\n return np.clip(x_coord, 0, width - 1)\n\n def get_valid_y(y_coord):\n return np.clip(y_coord, 0, height - 1)\n\n rect = ((rect[0], rect[1]), (rect[2], rect[3]), rect[4])\n points = cv2.boxPoints(rect)\n points = np.int0(points)\n for i_xy, (x_coord, y_coord) in enumerate(points):\n x_coord = get_valid_x(x_coord)\n y_coord = get_valid_y(y_coord)\n points[i_xy, :] = [x_coord, y_coord]\n points = np.reshape(points, -1)\n return points","title":""},{"docid":"ebf71cbd3a52ae0cd9eec9d4682f734b","score":"0.5395743","text":"def convert_bbox_to_z(bbox):\n w = bbox[2] - bbox[0]\n h = bbox[3] - bbox[1]\n x = bbox[0] + w/2.\n y = bbox[1] + h/2.\n s = w * h # scale is just area\n r = w / float(h)\n return np.array([x, y, s, r]).reshape((4, 1))","title":""},{"docid":"35835d5561857c7d3626946ed0807f33","score":"0.5395644","text":"def xyxy2xywh(bbox):\n return [\n bbox[0],\n bbox[1],\n bbox[2] - bbox[0],\n bbox[3] - bbox[1],\n ]","title":""},{"docid":"3afd5d0de531ae1ed5a83e2a5f5f5e7b","score":"0.5394448","text":"def _flipBbox(self, bbox): \n temp_bbox = dict(bbox)\n new_bbox = {}\n new_bbox['maxy'] = temp_bbox['maxx']\n new_bbox['miny'] = temp_bbox['minx']\n new_bbox['maxx'] = temp_bbox['maxy']\n new_bbox['minx'] = temp_bbox['miny']\n\n return new_bbox","title":""},{"docid":"45d95fbdcca133cd7777d03c60956ace","score":"0.5389789","text":"def pad(self, bboxes, w, h):\n tmpw, tmph = bboxes[:, 2] - bboxes[:, 0] + 1, bboxes[:, 3] - bboxes[:, 1] + 1\n num_box = bboxes.shape[0]\n dx , dy= np.zeros((num_box, )), np.zeros((num_box, ))\n edx, edy = tmpw.copy()-1, tmph.copy()-1\n x, y, ex, ey = bboxes[:, 0], bboxes[:, 1], bboxes[:, 2], bboxes[:, 3]\n tmp_index = np.where(ex > w-1)\n edx[tmp_index] = tmpw[tmp_index] + w - 2 - ex[tmp_index]\n ex[tmp_index] = w - 1\n tmp_index = np.where(ey > h-1)\n edy[tmp_index] = tmph[tmp_index] + h - 2 - ey[tmp_index]\n ey[tmp_index] = h - 1\n tmp_index = np.where(x < 0)\n dx[tmp_index] = 0 - x[tmp_index]\n x[tmp_index] = 0\n tmp_index = np.where(y < 0)\n dy[tmp_index] = 0 - y[tmp_index]\n y[tmp_index] = 0\n return_list = [dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph]\n return_list = [item.astype(np.int32) for item in return_list]\n return return_list","title":""},{"docid":"c5e52ce3283b2baa8800b66a7bdd791d","score":"0.53877395","text":"def _get_available_bounding_box(self):\n width, height = self.get_material_width(),self.get_material_height()\n top, right, bottom, left = self.get_padding()\n return [left,width-right,bottom,height-top]","title":""},{"docid":"3baf2a1f10ecde8dcda0819e13a4bd78","score":"0.53839135","text":"def get_bounding_box(self):\n if not len(self._quadrilateral):\n raise ValueError('The `_quadrilateral` should not be empty list.')\n\n # each point is represented as (height, width)\n top_left, top_right, bottom_left, bottom_right = self.get()\n y_min = min(top_left[0], top_right[0])\n y_max = max(bottom_left[0], bottom_right[0])\n x_min = min(top_left[1], bottom_left[1])\n x_max = max(top_right[1], bottom_right[1])\n\n return [y_min, x_min, y_max, x_max]","title":""},{"docid":"a10f523ff5977de5ad0617c0ce46911e","score":"0.53772736","text":"def GetStandardRects(self, w,h, x_ps, y_ps):\n standard_rects = []\n totals_lbl = []\n digit_lbl = []\n for k, y_pos in enumerate(np.sort(y_ps)):\n for j, x_pos in enumerate(np.flip(np.sort(x_ps))):\n x = int(x_pos - w//2)\n y = int(y_pos - h//2)\n standard_rects.append((x, y, w, h ))\n totals_lbl.append(k)\n digit_lbl.append(j)\n\n return standard_rects, totals_lbl, digit_lbl","title":""},{"docid":"9a6c3313de3041b39510e212a28040d4","score":"0.53718203","text":"def box2coords(x, y, boxes):\n boxcoords = []\n for i, box in enumerate(boxes):\n b = box[:]\n if b[2] >= len(x): b[2] = len(x)-1\n if b[3] >= len(y): b[3] = len(y)-1\n boxcoords.append([x[b[0]], y[b[1]], x[b[2]], y[b[3]]])\n return boxcoords","title":""},{"docid":"60d1a6cc095ffd67361fdc5d4ae8700f","score":"0.53665525","text":"def to_image_coords(boxes, height, width):\n box_coords = np.zeros_like(boxes)\n box_coords[:, 0] = boxes[:, 0] * height\n box_coords[:, 1] = boxes[:, 1] * width\n box_coords[:, 2] = boxes[:, 2] * height\n box_coords[:, 3] = boxes[:, 3] * width\n\n return box_coords","title":""},{"docid":"60d1a6cc095ffd67361fdc5d4ae8700f","score":"0.53665525","text":"def to_image_coords(boxes, height, width):\n box_coords = np.zeros_like(boxes)\n box_coords[:, 0] = boxes[:, 0] * height\n box_coords[:, 1] = boxes[:, 1] * width\n box_coords[:, 2] = boxes[:, 2] * height\n box_coords[:, 3] = boxes[:, 3] * width\n\n return box_coords","title":""},{"docid":"404c9598930a2d16cd33afa500959241","score":"0.5365841","text":"def calc_bounding_box(image):\n\n thresh = threshold_li(image)\n thresh_image = np.where(image < thresh, 0, 1)\n column_maximums = np.max(thresh_image, axis=0)\n row_maximums = np.max(thresh_image, axis=1)\n x_start = np.argmax(column_maximums)\n x_end = len(column_maximums) - np.argmax(column_maximums[::-1]) - 1\n\n y_start = np.argmax(row_maximums)\n y_end = len(row_maximums) - np.argmax(row_maximums[::-1]) - 1\n\n return (x_start, x_end, y_start, y_end)","title":""},{"docid":"01b5b7f38e0c9d8db8f3e060706badb0","score":"0.5361575","text":"def rect_from_landmarks(landmarks):\n # Ignore the nose bridge and lip.\n arrays = (\n np.array(landmarks['chin']),\n np.array(landmarks['left_eyebrow']),\n np.array(landmarks['right_eyebrow']),\n np.array(landmarks['nose_tip']))\n coords = np.concatenate(arrays, axis=0)\n left, top = np.amin(coords, axis=0)\n right, bottom = np.amax(coords, axis=0)\n return (top, right, bottom, left)","title":""},{"docid":"b1fe9f734082c02d4f708348738d9d8e","score":"0.5342286","text":"def bbox_to_polygon(bbox):\n assert(len(bbox) == 4)\n lat_bl, long_bl, lat_ur, long_ur = bbox\n return [[lat_bl, long_bl], [lat_bl, long_ur],\n [lat_ur, long_ur], [lat_ur, long_bl]]","title":""},{"docid":"0f1d1ede3a17e1c0b3fc830d9bc6e4ec","score":"0.53255385","text":"def pad(total_boxes, w, h):\n tmpw = (total_boxes[:,2]-total_boxes[:,0]+1).astype(np.int32)\n tmph = (total_boxes[:,3]-total_boxes[:,1]+1).astype(np.int32)\n numbox = total_boxes.shape[0]\n\n dx = np.ones((numbox), dtype=np.int32)\n dy = np.ones((numbox), dtype=np.int32)\n edx = tmpw.copy().astype(np.int32)\n edy = tmph.copy().astype(np.int32)\n\n x = total_boxes[:,0].copy().astype(np.int32)\n y = total_boxes[:,1].copy().astype(np.int32)\n ex = total_boxes[:,2].copy().astype(np.int32)\n ey = total_boxes[:,3].copy().astype(np.int32)\n\n tmp = np.where(ex>w)\n edx.flat[tmp] = np.expand_dims(-ex[tmp]+w+tmpw[tmp],1)\n ex[tmp] = w\n \n tmp = np.where(ey>h)\n edy.flat[tmp] = np.expand_dims(-ey[tmp]+h+tmph[tmp],1)\n ey[tmp] = h\n\n tmp = np.where(x<1)\n dx.flat[tmp] = np.expand_dims(2-x[tmp],1)\n x[tmp] = 1\n\n tmp = np.where(y<1)\n dy.flat[tmp] = np.expand_dims(2-y[tmp],1)\n y[tmp] = 1\n \n return dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph","title":""},{"docid":"093b629ddf397af758bb287f64e28dd9","score":"0.5324469","text":"def make_bboxes(bbox_slices):\n minr = [sxy[0].start for sxy in bbox_slices]\n minc = [sxy[1].start for sxy in bbox_slices]\n maxr = [sxy[0].stop for sxy in bbox_slices]\n maxc = [sxy[1].stop for sxy in bbox_slices]\n\n bbox_rect = np.array(\n [[minr, minc], [maxr, minc], [maxr, maxc], [minr, maxc]]\n )\n bbox_rect = np.moveaxis(bbox_rect, 2, 0)\n return bbox_rect","title":""},{"docid":"021e12c55900c8c74e4775b4b02a4c0e","score":"0.5311388","text":"def _box_xyxy_to_cxcywh(boxes: Tensor) -> Tensor:\n x1, y1, x2, y2 = boxes.unbind(-1)\n cx = (x1 + x2) / 2\n cy = (y1 + y2) / 2\n w = x2 - x1\n h = y2 - y1\n\n boxes = torch.stack((cx, cy, w, h), dim=-1)\n\n return boxes","title":""},{"docid":"9c0c8b9749bdd7f7ec5bc831c28c60d0","score":"0.5306036","text":"def detection_center(bbox):\n center_x = (bbox[0] + bbox[2]) / 2.0 - 0.5\n center_y = (bbox[1] + bbox[3]) / 2.0 - 0.5\n return (center_x, center_y)","title":""},{"docid":"b1e5554888a14b8aa91caa254421898a","score":"0.5305808","text":"def _crop_bounding_box(self, bounding_box):\n\n bounding_box_ind = np.zeros(4, dtype='int64')\n \n XLAT_min = self.ds[self.lat_name].min(axis=2).mean(axis=0)\n XLAT_max = self.ds[self.lat_name].max(axis=2).mean(axis=0)\n XLONG_min = self.ds[self.lon_name].min(axis=1).mean(axis=0)\n XLONG_max = self.ds[self.lon_name].max(axis=1).mean(axis=0)\n bounding_box_ind[0] = np.argmin(\n np.abs(bounding_box[0] - np.squeeze(XLAT_min.values)))\n bounding_box_ind[2] = np.argmin(\n np.abs(bounding_box[2] - np.squeeze(XLAT_max.values)))\n bounding_box_ind[1] = np.argmin(\n np.abs(bounding_box[1] - np.squeeze(XLONG_min.values)))\n bounding_box_ind[3] = np.argmin(\n np.abs(bounding_box[3] - np.squeeze(XLONG_max.values)))\n input_dict = {}\n input_dict[self.lat_dim] = slice(bounding_box_ind[0], bounding_box_ind[2])\n input_dict[self.lon_dim] = slice(bounding_box_ind[1], bounding_box_ind[3])\n self.ds = self.ds.isel(input_dict)","title":""},{"docid":"797f97d594b765e7cf02444fc71fb0a6","score":"0.53050745","text":"def grid(ov, oh, s, h, w):\n g = np.zeros((h,w)) #nparray full of zeros\n p = np.zeros((h,w)) #nparray full of zeros\n nv = int(w/s +2) #+2 just to make sure\n nh = int(h/s+2)\n \n #calculate grid origin offset\n _hp = ov\n while _hp >= s:\n _hp = _hp - s\n\n #print _hp\n for _nv in xrange(nv): # for every vertical line\n #calculate horizontal position \n _pos = _nv * s + _hp\n #print _pos\n for _h in xrange(h): #for every height\n try:\n g[_h, int(_pos)] = (1-(_pos - int(_pos))) *255\n g[_h, int(_pos+1)] = (1-(int(_pos+1)-_pos)) *255\n p[_h, int(_pos)] += 1\n #p[_h, int(_pos)] += (1-(_pos - int(_pos))) *1\n #p[_h, int(_pos+1)] += (1-(int(_pos+1)-_pos)) *1\n except IndexError:\n pass\n \n _vp = oh\n while _vp >= s:\n _vp = _vp -s\n \n for _nh in xrange(nh):\n _pos = _nh * s + _vp\n for _v in xrange(w):\n try:\n g[int(_pos), _v] = (1-(_pos - int(_pos))) *255\n g[int(_pos+1), _v] = (1-(int(_pos+1)-_pos)) *255\n p[int(_pos), _v] += 1\n #p[int(_pos), _v] += (1-(_pos - int(_pos))) *1\n #p[int(_pos+1), _v] += (1-(int(_pos+1)-_pos)) *1\n except IndexError:\n pass\n \"\"\" \n points = []\n for _h in xrange(h):\n for _v in xrange(w):\n if p[_h, _v] <= 1:\n p[_h, _v] = 0\n else:\n p[_h, _v] = 255\n points.append((_h, _v))\n \"\"\" \n \n return g#,p, points #g=grid p=crossingpoints, points=list of crossingpoints","title":""}],"string":"[\n {\n \"docid\": \"e3ca31e55f39475243fc71f65d0b1a93\",\n \"score\": \"0.65984535\",\n \"text\": \"def bbox_xywh_to_xyxy(self, boxes):\\n w, h = boxes[:, 2], boxes[:, 3]\\n boxes[:, 0] -= w / 2.0 # x1 = center_x - width/2\\n boxes[:, 1] -= h / 2.0 # y1 = center_y - height/2\\n boxes[:, 2] = boxes[:, 0] + w # x2 = x1 + width\\n boxes[:, 3] = boxes[:, 1] + h # y2 = y1 + height\\n return boxes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6ecff0476f7fb5984c596845b9a521ec\",\n \"score\": \"0.6362112\",\n \"text\": \"def compute_bounding_box(homography, w, h):\\n\\n original_boundries = np.array([[0, 0], [w - 1, 0], [w, h], [0, h - 1]])\\n transform = apply_homography(original_boundries, homography)\\n x = transform[:, 0]\\n y = transform[:, 1]\\n x_right, y_right = np.ceil(np.max(x)), np.ceil(np.max(y))\\n x_left, y_left = np.floor(np.min(x)), np.floor(np.min(y))\\n right = [x_right, y_right]\\n left = [x_left, y_left]\\n return np.array([left, right]).astype(np.int)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a54e47449c0df1f49922c6099d84ab8\",\n \"score\": \"0.6134974\",\n \"text\": \"def bbox_transform(bbox):\\n cx, cy, w, h = bbox\\n out_box = [[]]*4\\n out_box[0] = cx-w/2\\n out_box[1] = cy-h/2\\n out_box[2] = cx+w/2\\n out_box[3] = cy+h/2\\n\\n return out_box\",\n \"title\": \"\"\n },\n {\n \"docid\": \"443377b816034408d61c1e5bc8b5c99b\",\n \"score\": \"0.6128214\",\n \"text\": \"def _bbox2points(bbox):\\n x, y, w, h = bbox\\n xmin = int(round(x - (w / 2)))\\n xmax = int(round(x + (w / 2)))\\n ymin = int(round(y - (h / 2)))\\n ymax = int(round(y + (h / 2)))\\n return xmin, ymin, xmax, ymax\",\n \"title\": \"\"\n },\n {\n \"docid\": \"15b6360290e01f2148fc6aba5081edfc\",\n \"score\": \"0.6121567\",\n \"text\": \"def wh_to_xy(rects):\\n newRects = {}\\n for i in range(len(rects)):\\n x = rects[i].x\\n y = rects[i].y\\n w = rects[i].w\\n h = rects[i].h\\n x0,y0 = rects[i].getPoint()\\n x1,y1 = rects[i].getOppositePoint()\\n box = [(x0, y0), (x0, y1), (x1, y1), (x1, y0)]\\n newRects[i] = box\\n \\n return newRects\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3199d1bc281bb89b47caf03122aa0bb6\",\n \"score\": \"0.6102514\",\n \"text\": \"def bbox2points(bbox):\\n x, y, w, h = bbox\\n xmin = int(round(x - (w / 2)))\\n xmax = int(round(x + (w / 2)))\\n ymin = int(round(y - (h / 2)))\\n ymax = int(round(y + (h / 2)))\\n return xmin, ymin, xmax, ymax\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45f77ce20b37e6ebcaee0f3a340f9583\",\n \"score\": \"0.60416144\",\n \"text\": \"def calculateBoundingBoxes(label, x, y, w, h):\\n X1 = x - (w / 2)\\n Y1 = y - (h / 2)\\n X2 = x + (w / 2)\\n Y2 = y + (h / 2)\\n\\n return [label, round(Y1, 2), round(X1, 2), round(Y2, 2), round(X2, 2)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1708ef768311058bcd1e775b504bbb61\",\n \"score\": \"0.6016839\",\n \"text\": \"def xywh_to_xyxy(boxes):\\n return np.hstack((boxes[:, 0:2], boxes[:, 0:2] + boxes[:, 2:4] - 1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1708ef768311058bcd1e775b504bbb61\",\n \"score\": \"0.6016839\",\n \"text\": \"def xywh_to_xyxy(boxes):\\n return np.hstack((boxes[:, 0:2], boxes[:, 0:2] + boxes[:, 2:4] - 1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca00ecb25ff55da864920ef497c87e7e\",\n \"score\": \"0.6009041\",\n \"text\": \"def bbox2points(self, bbox):\\n x, y, w, h = bbox\\n xmin = int(round(x - (w / 2)))\\n xmax = int(round(x + (w / 2)))\\n ymin = int(round(y - (h / 2)))\\n ymax = int(round(y + (h / 2)))\\n return xmin, ymin, xmax, ymax\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a531e606c764261698e3bf887f16775\",\n \"score\": \"0.60075676\",\n \"text\": \"def box_2d_to_points(box):\\n corners = [[0, 0, 0], [0, 1, 0], [1, 1, 0], [1, 0, 0]]\\n corners.append(corners[0]) # Need to copy this so that the box is closed.\\n return box.make_absolute(corners)[:, :2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8eb0848cf07f115fde988d95b88bf40\",\n \"score\": \"0.59940386\",\n \"text\": \"def bbox_transform_inv(bbox):\\n xmin, ymin, xmax, ymax = bbox\\n out_box = [[]]*4\\n\\n width = xmax - xmin + 1.0\\n height = ymax - ymin + 1.0\\n out_box[0] = xmin + 0.5*width\\n out_box[1] = ymin + 0.5*height\\n out_box[2] = width\\n out_box[3] = height\\n\\n return out_box\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aba473629d05054b7bf8e754f8b4a6f2\",\n \"score\": \"0.5957452\",\n \"text\": \"def bounds_to_box(left, right, bottom, top):\\n box = [(left, bottom), (left, top), (right, top), (right, bottom)]\\n box = Polygon(box)\\n return box\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20a2b9c2ddd4c6c980f1f068a9fa0fce\",\n \"score\": \"0.59443575\",\n \"text\": \"def bounding_boxes_pascal(\\n pascal_file_path: str,\\n) -> List[Dict]:\\n\\n boxes = []\\n\\n # load the contents of the annotations file into an ElementTree\\n tree = ElementTree.parse(pascal_file_path)\\n\\n for obj in tree.iter(\\\"object\\\"):\\n\\n label = obj.find(\\\"name\\\").text\\n bndbox = obj.find(\\\"bndbox\\\")\\n bbox_min_x = int(float(bndbox.find(\\\"xmin\\\").text))\\n bbox_min_y = int(float(bndbox.find(\\\"ymin\\\").text))\\n bbox_max_x = int(float(bndbox.find(\\\"xmax\\\").text))\\n bbox_max_y = int(float(bndbox.find(\\\"ymax\\\").text))\\n\\n # make sure we don't have wonky values with mins > maxs\\n if (bbox_min_x >= bbox_max_x) or (bbox_min_y >= bbox_max_y):\\n\\n # report the issue via log message\\n _logger.warning(\\\"Bounding box minimum(s) greater than maximum(s)\\\")\\n\\n # skip this box since it's not clear how to fix it\\n continue\\n\\n # include this box in the list we'll return\\n box = {\\n \\\"label\\\": label,\\n \\\"xmin\\\": bbox_min_x,\\n \\\"ymin\\\": bbox_min_y,\\n \\\"xmax\\\": bbox_max_x,\\n \\\"ymax\\\": bbox_max_y,\\n }\\n boxes.append(box)\\n\\n return boxes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7557895a4b03c7f31bfe00765a55746d\",\n \"score\": \"0.59068346\",\n \"text\": \"def extract_bboxes(mask):\\n boxes = np.zeros([4], dtype=np.int32)\\n\\n m = mask[:, :]\\n # Bounding box.\\n horizontal_indicies = np.where(np.any(m, axis=0))[0]\\n # print('horizontal_indicies',horizontal_indicies)\\n # print('horizontal_indicies_shape',horizontal_indicies.shape)\\n vertical_indicies = np.where(np.any(m, axis=1))[0]\\n if horizontal_indicies.shape[0]:\\n x1, x2 = horizontal_indicies[[0, -1]]\\n y1, y2 = vertical_indicies[[0, -1]]\\n # x2 and y2 should not be part of the box. Increment by 1.\\n # x2 += 1\\n # y2 += 1\\n y2 = y2 - y1\\n x2 = x2 - x1\\n else:\\n # No mask for this instance. Might happen due to\\n # resizing or cropping. Set bbox to zeros\\n x1, x2, y1, y2 = 0, 0, 0, 0\\n # boxes = np.array([y1, x1, y2, x2])\\n # boxes = (int(y1), int(x1), int(y2), int(x2))\\n boxes = (int(x1), int(y1), int(x2), int(y2))\\n return boxes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6904ad8656d435d93773532353be1e3\",\n \"score\": \"0.5902869\",\n \"text\": \"def rect_to_bounding_box(rect):\\n x = rect.left()\\n y = rect.top()\\n w = rect.right() - x\\n h = rect.bottom() - y\\n\\n return x, y, w, h\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff85d96c1e58927fd36c32ce2e313b76\",\n \"score\": \"0.5882208\",\n \"text\": \"def _get_xy_bounding_box(vertex, padding):\\n min_ = np.floor(np.min(vertex[:, :2], axis=0) - padding).astype(np.int)\\n max_ = np.ceil(np.max(vertex[:, :2], axis=0) + padding).astype(np.int)\\n return min_, max_\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cbd1d8028ae98282da220c18db1ade4\",\n \"score\": \"0.58702177\",\n \"text\": \"def _preprocessing(\\n self, path: str\\n ) -> Tuple[np.array, float, int, int, int, np.array, int, int]:\\n \\n '''\\n Post Processing about predict bounding box\\n \\n - scaling Bounding Box using offset\\n \\n '''\\n \\n if '.png' in path or '.jpg' in path or '.bmp' in path:\\n image = cv2.imread(path)\\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\\n imgo = image.copy()\\n image = image[:, :, ::-1]\\n h, w = image.shape[:2]\\n \\n image_height, image_width = image.shape[:2]\\n image_size = 512 # b0 baseline\\n \\n if image_height > image_width:\\n scale = image_size / image_height\\n resized_height = image_size\\n resized_width = int(image_width * scale)\\n else:\\n scale = image_size / image_width\\n resized_height = int(image_height * scale)\\n resized_width = image_size\\n\\n image = cv2.resize(image, (resized_width, resized_height))\\n new_image = np.ones((image_size, image_size, 3), dtype=np.float32) * 128.\\n offset_h = (image_size - resized_height) // 2\\n offset_w = (image_size - resized_width) // 2\\n new_image[offset_h:offset_h+resized_height, \\n offset_w:offset_w+resized_width] = image.astype(np.float32)\\n new_image /= 255.\\n\\n mean = [0.485, 0.456, 0.406]\\n std = [0.229, 0.224, 0.225]\\n \\n for i in range(3):\\n new_image[..., i] -= mean[i]\\n new_image[..., i] /= std[i]\\n \\n return (new_image, scale, offset_h, offset_w, image_size, imgo, h, w)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0012793a49009bf3e70766d27a1dc80e\",\n \"score\": \"0.5851479\",\n \"text\": \"def bounding_box(self):\\r\\n\\r\\n return [(self.x + 10, self.y + 10),\\r\\n (self.x - 10, self.y + 10),\\r\\n (self.x - 10, self.y - 10),\\r\\n (self.x + 10, self.y - 10)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce6d97793fa92b258eaa439510c0c8da\",\n \"score\": \"0.58498704\",\n \"text\": \"def calculate_box_region(self, width, height):\\n\\n left = width * self.piece_width \\n top = height * self.piece_height \\n right = left + self.piece_width \\n bottom = top + self.piece_height \\n return left, top, right, bottom\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4a09e78b98a887a3009183794aa753b7\",\n \"score\": \"0.5847786\",\n \"text\": \"def normalize_pts_within_bboxes(pts):\\n b, _, _, h_pts, w_pts = pts.shape\\n _pts_x = pts[:, :, 0, :, :] # (b, n, h_pts, w_pts)\\n _pts_y = pts[:, :, 1, :, :] # (b, n, h_pts, w_pts)\\n _bbox_left = torch.min(_pts_x, dim=1, keepdim=True)[0] # (b, 1, h_pts, w_pts)\\n _bbox_right = torch.max(_pts_x, dim=1, keepdim=True)[0] # (b, 1, h_pts, w_pts)\\n _bbox_bottom = torch.max(_pts_y, dim=1, keepdim=True)[0] # (b, 1, h_pts, w_pts)\\n _bbox_up = torch.min(_pts_y, dim=1, keepdim=True)[0] # (b, 1, h_pts, w_pts)\\n _bbox_w = _bbox_right - _bbox_left # (b, 1, h_pts, w_pts)\\n _bbox_h = _bbox_bottom - _bbox_up # (b, 1, h_pts, w_pts)\\n\\n normalized_x = (_pts_x - _bbox_left) / (_bbox_w + 1e-6) # (b, n, h_pts, w_pts)\\n normalized_y = (_pts_y - _bbox_up) / (_bbox_h + 1e-6) # (b, n, h_pts, w_pts)\\n normalized_pts = torch.stack([normalized_x, normalized_y], dim=2) # (b, n, 2, h_pts, w_pts)\\n return normalized_pts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63f7ff25b1b57c0df87dc6a5d741aed3\",\n \"score\": \"0.5827113\",\n \"text\": \"def convert_box(size, box):\\n dw = 1. / (size[0])\\n dh = 1. / (size[1])\\n x = (box[0] + box[1]) / 2.0 - 1\\n y = (box[2] + box[3]) / 2.0 - 1\\n w = box[1] - box[0]\\n h = box[3] - box[2]\\n x = x * dw\\n w = w * dw\\n y = y * dh\\n h = h * dh\\n return x, y, w, h\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e05c8b4df1183a3fdf48747fdb4cd0be\",\n \"score\": \"0.5815482\",\n \"text\": \"def bbox_poly2hbb_nd(boxes):\\n n = boxes.shape[0]\\n hbbs = mx.nd.zeros((n, 4))\\n\\n xs = boxes[:, : -1].reshape((n, 4, 2))[:, :, 0]\\n ys = boxes[:, : -1].reshape((n, 4, 2))[:, :, 1]\\n\\n hbbs[:, 0] = mx.nd.min(xs, axis=1)\\n hbbs[:, 1] = mx.nd.min(ys, axis=1)\\n hbbs[:, 2] = mx.nd.max(xs, axis=1)\\n hbbs[:, 3] = mx.nd.max(ys, axis=1)\\n hbbs = mx.nd.concat(hbbs, mx.nd.expand_dims(boxes[:, -1], 1), dim=1)\\n\\n return hbbs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"839a4dea8c71a1e0783a7cd4b731bae8\",\n \"score\": \"0.57861143\",\n \"text\": \"def deformable_transform_aabb_bkp(shape, transform, aabb):\\n # Input bbox points\\n x1, y1, x2, y2 = aabb.astype(int)\\n\\n # Create image with bbox\\n tmp_img = np.zeros(shape)\\n cv2.rectangle(tmp_img,(x1,y1),(x2,y2),(255,255,255),1)\\n\\n # scipy.ndimage.interpolation.map_coordinates:\\n #\\n # Map the input array to new coordinates by interpolation.\\n #\\n # The array of coordinates is used to find, for each point in the output,\\n # the corresponding coordinates in the input. The value of the input at\\n # those coordinates is determined by spline interpolation of the\\n # requested order.\\n #\\n # The shape of the output is derived from that of the coordinate\\n # array by dropping the first axis. The values of the array along\\n # the first axis are the coordinates in the input array at which the\\n # output value is found.\\n distorted = map_coordinates(tmp_img, transform, order=1, mode='reflect').reshape(shape)\\n\\n A = np.nonzero(distorted>0)\\n x1_ = min(A[1])\\n y1_ = min(A[0])\\n x2_ = max(A[1])\\n y2_ = max(A[0])\\n\\n #-------------------------------------------\\n # # Draw the findings\\n # cv2.circle(distorted, (x1_,y1_), 3, (0,255,0), 1)\\n # cv2.circle(distorted, (x2_,y2_), 3, (0,255,0), 1)\\n # print(\\\"Top left: [\\\",x1,\\\",\\\",y1,\\\"] | Bottom right: [\\\",x2,\\\",\\\",y2,\\\"]\\\")\\n # print(\\\"New top left: [\\\",x1_,\\\",\\\",y1_,\\\"] | New bottom right: [\\\",x2_,\\\",\\\",y2_,\\\"]\\\")\\n #\\n # print(\\\"Temp image shape: \\\", distorted.shape)\\n # cv2.imshow(\\\"Temp Image\\\", distorted)\\n # cv2.waitKey(0)\\n #-------------------------------------------\\n\\n return [x1_, y1_, x2_, y2_]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f09c151bccd63b9a1d3b8cad174f37f6\",\n \"score\": \"0.5774372\",\n \"text\": \"def get_new_bbox(bbox, width, height):\\n try:\\n x1 = round(bbox[0]*100/float(width), 4)\\n x2 = round(bbox[2]*100/float(width), 4)\\n y1 = round(bbox[1]*100/float(height), 4)\\n y2 = round(bbox[3]*100/float(height), 4)\\n except IndexError:\\n print(bbox)\\n return [x1, y1, x2, y2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6db329cb294d32f281b4e1d61d429a47\",\n \"score\": \"0.5773582\",\n \"text\": \"def points_to_bbox(p):\\n llx = urx = p[0][0]\\n lly = ury = p[0][1]\\n for x in p[1:]:\\n if x[0] < llx: llx = x[0]\\n elif x[0] > urx: urx = x[0]\\n if x[1] < lly: lly = x[1]\\n elif x[1] > ury: ury = x[1]\\n return (llx, lly, urx, ury)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38ec969a0d7e39c82e21b3e3d3af1a8b\",\n \"score\": \"0.576676\",\n \"text\": \"def rectangle_vertice(self, center, height, width):\\n half_height = height / 2.\\n half_width = width / 2.\\n\\n bias1 = np.ones(4) * half_width\\n bias2 = np.ones(4) * half_height\\n\\n corner1 = np.empty([1, 3], dtype=np.float32)\\n corner2 = np.empty([1, 3], dtype=np.float32)\\n corner3 = np.empty([1, 3], dtype=np.float32)\\n corner4 = np.empty([1, 3], dtype=np.float32)\\n\\n corner1[:, 0] = center[0] - bias1[0]\\n corner1[:, 1] = center[1] - bias2[0]\\n corner1[:, 2] = 0\\n\\n corner2[:, 0] = center[0] + bias1[1]\\n corner2[:, 1] = center[1] - bias2[1]\\n corner2[:, 2] = 0\\n\\n corner3[:, 0] = center[0] + bias1[2]\\n corner3[:, 1] = center[1] + bias2[2]\\n corner3[:, 2] = 0\\n\\n corner4[:, 0] = center[0] - bias1[3]\\n corner4[:, 1] = center[1] + bias2[3]\\n corner4[:, 2] = 0\\n\\n # Get vertices between each corner of the rectangle for border drawing\\n vertices = np.concatenate(([[center[0], center[1], 0.]],\\n [[center[0] - half_width, center[1], 0.]],\\n corner1,\\n [[center[0], center[1] - half_height, 0.]],\\n corner2,\\n [[center[0] + half_width, center[1], 0.]],\\n corner3,\\n [[center[0], center[1] + half_height, 0.]],\\n corner4,\\n [[center[0] - half_width, center[1], 0.]]))\\n\\n # vertices = np.array(output, dtype=np.float32)\\n return vertices[1:, ..., :2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d350c85237587bcbe35bce711e4878b\",\n \"score\": \"0.5723013\",\n \"text\": \"def convert_kfx_to_bbox(x):\\n w=max(0.0,x[2])\\n h=max(0.0,x[3])\\n return np.array([x[0]-w/2.,x[1]-h/2.,x[0]+w/2.,x[1]+h/2.]).reshape((1,4))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80f9cb443c948e55697882e61f1a2ab8\",\n \"score\": \"0.5716591\",\n \"text\": \"def dump_bounding_box(self):\\n row, column = self.get_bounding_box_corner_pos()\\n return row, column, self.get_bounding_box_frame()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e33f4acacd1302c216ec354655e43e6\",\n \"score\": \"0.56964046\",\n \"text\": \"def boundingBox(self):\\n return [[self.x_min,self.y_min],[self.x_min,self.y_max],[self.x_max,self.y_max],[self.x_max,self.y_min]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90437aa9f8b8d1626272858cef4a18c0\",\n \"score\": \"0.5674501\",\n \"text\": \"def bbox2cv_bbox(self, bbox: iter) -> tuple:\\n\\n p_x = bbox[0] - bbox[2] // 2\\n p_y = bbox[1] - bbox[3] // 2\\n w = bbox[2]\\n h = bbox[3]\\n if p_x < 0:\\n p_x = 0\\n if p_y < 0:\\n p_y = 0\\n if p_x + w > 1280:\\n w = 1280 - p_x\\n if p_y + h > 720:\\n h = 720 - p_y\\n bbox = (p_x, p_y, w, h)\\n return tuple(map(int, bbox))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"959326f5fd24aedc6278c166727de80b\",\n \"score\": \"0.5665393\",\n \"text\": \"def point_form(bboxes):\\n return np.concatenate((bboxes[:, :2] - bboxes[:, 2:]/2, # xmin, ymin\\n bboxes[:, :2] + bboxes[:, 2:]/2), 1) # xmax, ymax\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c6d830b9806a1a7432e11b3f84086a9\",\n \"score\": \"0.5663881\",\n \"text\": \"def boxes_to_corners(box_xy, box_wh):\\n box_mins = box_xy - (box_wh / 2.)\\n box_maxes = box_xy + (box_wh / 2.)\\n\\n return np.concatenate([\\n box_mins[..., 0:1], # x_min\\n box_mins[..., 1:2], # y_min\\n box_maxes[..., 0:1], # x_max\\n box_maxes[..., 1:2] # y_max\\n ], axis=4)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a010e42878dc3a7f2d14dcc3d99c2d3e\",\n \"score\": \"0.5661924\",\n \"text\": \"def _box_xyxy_to_xywh(boxes: Tensor) -> Tensor:\\n x1, y1, x2, y2 = boxes.unbind(-1)\\n w = x2 - x1 # x2 - x1\\n h = y2 - y1 # y2 - y1\\n boxes = torch.stack((x1, y1, w, h), dim=-1)\\n return boxes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a224fadcaaa14797d383db440ed67f21\",\n \"score\": \"0.565625\",\n \"text\": \"def generate_bbox_interion(bbox, shape):\\n shape = shape[1::-1]\\n points = skimage.draw.polygon(bbox[::2], bbox[1::2], shape=shape)\\n return np.vstack([points[0], points[1]]).T\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea24b15510a5a887ecb909eb3c05c11d\",\n \"score\": \"0.5649105\",\n \"text\": \"def bounding_box2D(pts):\\n dim = len(pts[0]) # should be 2\\n bb_min = [min([t[i] for t in pts]) for i in range(dim)]\\n bb_max = [max([t[i] for t in pts]) for i in range(dim)]\\n return bb_min[0], bb_min[1], bb_max[0] - bb_min[0], bb_max[1] - bb_min[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b253f0938ef010799ec2100629aee36a\",\n \"score\": \"0.56353056\",\n \"text\": \"def _box_cxcywh_to_xyxy(boxes: Tensor) -> Tensor:\\n # We need to change all 4 of them so some temporary variable is needed.\\n cx, cy, w, h = boxes.unbind(-1)\\n x1 = cx - 0.5 * w\\n y1 = cy - 0.5 * h\\n x2 = cx + 0.5 * w\\n y2 = cy + 0.5 * h\\n\\n boxes = torch.stack((x1, y1, x2, y2), dim=-1)\\n\\n return boxes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82a8a91c9faa861ffc7ef9554bb12b8b\",\n \"score\": \"0.5632779\",\n \"text\": \"def rect_to_bb(self, rect):\\r\\n\\r\\n x = rect.left()\\r\\n y = rect.top()\\r\\n w = rect.right() - x\\r\\n h = rect.bottom() - y\\r\\n\\r\\n return (x, y, w, h)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bdccbab0ddc1a0927fe2f9705c444711\",\n \"score\": \"0.5630967\",\n \"text\": \"def boundingBox(poly):\\n min_x, min_y = N.infty, N.infty\\n max_x, max_y = 0, 0\\n for p in poly:\\n min_x = min(min_x, p.x)\\n max_x = max(max_x, p.x)\\n min_y = min(min_y, p.y)\\n max_y = max(max_y, p.y)\\n return P(min_x, min_y), P(max_x, max_y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5648212e5401d4609a076b6806c166e3\",\n \"score\": \"0.56272864\",\n \"text\": \"def extract_bboxes(mask):\\n boxes = np.zeros([mask.shape[-1], 4], dtype=np.int32)\\n for i in range(mask.shape[-1]):\\n m = mask[:, :, i]\\n # Bounding box.\\n horizontal_indicies = np.where(np.any(m, axis=0))[0]\\n vertical_indicies = np.where(np.any(m, axis=1))[0]\\n if horizontal_indicies.shape[0]:\\n x1, x2 = horizontal_indicies[[0, -1]]\\n y1, y2 = vertical_indicies[[0, -1]]\\n # x2 and y2 should not be part of the box. Increment by 1.\\n x2 += 1\\n y2 += 1\\n else:\\n # No mask for this instance. Might happen due to\\n # resizing or cropping. Set bbox to zeros\\n x1, x2, y1, y2 = 0, 0, 0, 0\\n # boxes[i] = np.array([y1, x1, y2, x2])\\n boxes[i] = np.array([x1, y1, x2, y2])\\n return boxes.astype(np.int32)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5648212e5401d4609a076b6806c166e3\",\n \"score\": \"0.56272864\",\n \"text\": \"def extract_bboxes(mask):\\n boxes = np.zeros([mask.shape[-1], 4], dtype=np.int32)\\n for i in range(mask.shape[-1]):\\n m = mask[:, :, i]\\n # Bounding box.\\n horizontal_indicies = np.where(np.any(m, axis=0))[0]\\n vertical_indicies = np.where(np.any(m, axis=1))[0]\\n if horizontal_indicies.shape[0]:\\n x1, x2 = horizontal_indicies[[0, -1]]\\n y1, y2 = vertical_indicies[[0, -1]]\\n # x2 and y2 should not be part of the box. Increment by 1.\\n x2 += 1\\n y2 += 1\\n else:\\n # No mask for this instance. Might happen due to\\n # resizing or cropping. Set bbox to zeros\\n x1, x2, y1, y2 = 0, 0, 0, 0\\n # boxes[i] = np.array([y1, x1, y2, x2])\\n boxes[i] = np.array([x1, y1, x2, y2])\\n return boxes.astype(np.int32)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb4265c72f3b3c3616fa391530a0d651\",\n \"score\": \"0.5626392\",\n \"text\": \"def xyxy_to_xywh(boxes):\\n return np.hstack((boxes[:, 0:2], boxes[:, 2:4] - boxes[:, 0:2] + 1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a7d033c6379d70c5c41ea27fb5d07eb\",\n \"score\": \"0.5622092\",\n \"text\": \"def _box_xywh_to_xyxy(boxes: Tensor) -> Tensor:\\n x, y, w, h = boxes.unbind(-1)\\n boxes = torch.stack([x, y, x + w, y + h], dim=-1)\\n return boxes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75d41394ebdd05941aacd6eb95831a84\",\n \"score\": \"0.5617597\",\n \"text\": \"def retbox(bbox,format='xyxy'):\\n if format =='xyxy':\\n xmin, ymin, xmax, ymax = bbox\\n elif format =='xywh':\\n xmin, ymin, w, h = bbox\\n xmax = xmin + w -1 \\n ymax = ymin + h -1 \\n \\n box = np.array([[xmin, xmax, xmax, xmin, xmin],\\n [ymin, ymin, ymax, ymax, ymin]])\\n return box.T\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f70d7c18a7ed5788562abbd2e5c2dde\",\n \"score\": \"0.5598006\",\n \"text\": \"def convert_bbox_to_kfx(bbox):\\n w = bbox[2]-bbox[0]\\n h = bbox[3]-bbox[1]\\n x = bbox[0]+w/2.\\n y = bbox[1]+h/2.\\n return np.array([x,y,w,h]).reshape((4,1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0fa95bf28b9a53b4022bfa8776993756\",\n \"score\": \"0.5589124\",\n \"text\": \"def compute_crop_box(bound_box: dict):\\n x_max, x_min, y_max, y_min = [int(v) for _, v in sorted(bound_box.items())]\\n\\n # compute half of the side of the scaled box to crop \\n width = x_max - x_min\\n height = y_max - y_min\\n half_side = SCALE / 2 * max(width, height)\\n\\n # find the (x, y) center of the box\\n x_center = (x_max + x_min) / 2\\n y_center = (y_max + y_min) / 2\\n \\n left, top, right, bottom = (int(x_center - half_side),\\n int(y_center - half_side),\\n int(x_center + half_side),\\n int(y_center + half_side))\\n\\n return((left, top, right, bottom))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e966ba4c0d046b569ed8f21fc25229b0\",\n \"score\": \"0.5584946\",\n \"text\": \"def BoundingBox( self ) :\\n \\n # loop over corners:\\n for i in range(self.n) :\\n # first?\\n if i == 0 :\\n west = self.corners[i].x\\n east = self.corners[i].x\\n south = self.corners[i].y\\n north = self.corners[i].y\\n else :\\n west = min( west , self.corners[i].x )\\n east = max( east , self.corners[i].x )\\n south = min( south, self.corners[i].y )\\n north = max( north, self.corners[i].y )\\n #endif\\n #endfor\\n \\n # ok\\n return west,east,south,north\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf342c7d36548a9877af168643303047\",\n \"score\": \"0.55840707\",\n \"text\": \"def get_bound_box_points(self) -> Tuple[int, int, int, int]:\\n return self.bound_box_coordinates[0], self.bound_box_coordinates[0] + self.width, \\\\\\n self.bound_box_coordinates[1], self.bound_box_coordinates[1] + self.height\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a700179f12a672a0ad3bef350d5c138\",\n \"score\": \"0.55793893\",\n \"text\": \"def convert_to_square(self, bbox):\\n square_bbox = bbox.copy()\\n h = bbox[:, 3] - bbox[:, 1] + 1\\n w = bbox[:, 2] - bbox[:, 0] + 1\\n max_side = np.maximum(h,w)\\n square_bbox[:, 0] = bbox[:, 0] + w*0.5 - max_side*0.5\\n square_bbox[:, 1] = bbox[:, 1] + h*0.5 - max_side*0.5\\n square_bbox[:, 2] = square_bbox[:, 0] + max_side - 1\\n square_bbox[:, 3] = square_bbox[:, 1] + max_side - 1\\n return square_bbox\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af01040de718158487a01e02b20a3305\",\n \"score\": \"0.55779827\",\n \"text\": \"def to_bounding_box(self):\\n left = self.x\\n top = self.y\\n right = self.x + self.w\\n bottom = self.y + self.h\\n retval = BoundingBox(left, top, right, bottom)\\n logger.trace(\\\"Returning: %s\\\", retval)\\n return retval\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ec4474aa382cb1381f7272ec40ba90ab\",\n \"score\": \"0.55569994\",\n \"text\": \"def box(self):\\n\\t\\treturn (self.x, self.y), (self.w, self.h), self.a\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ca374f14dbc79aaefc7b4cce9db0460\",\n \"score\": \"0.5556784\",\n \"text\": \"def get_square_bbox(ltrb):\\n\\n left = ltrb[:, 0]\\n right = ltrb[:, 2]\\n top = ltrb[:, 1]\\n bottom = ltrb[:, 3]\\n\\n width, height = right - left, bottom - top\\n\\n if width > height:\\n y_center = (ltrb[:, 3] + ltrb[:, 1]) // 2\\n top = y_center - width // 2\\n bottom = top + width\\n else:\\n x_center = (ltrb[:, 2] + ltrb[:, 0]) // 2\\n left = x_center - height // 2\\n right = left + height\\n\\n new_ltrb = torch.stack([left, top, right, bottom], dim=-1)\\n return new_ltrb\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d40fafa1249d1dad903ef229d7ebbf07\",\n \"score\": \"0.5545522\",\n \"text\": \"def crop_bounding_box(self):\\n return (self.crop_x, self.crop_y,\\n self.crop_x + self.crop_w, self.crop_y + self.crop_h)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b19b3cc642cda51206ddf21d31fd341\",\n \"score\": \"0.5544213\",\n \"text\": \"def to_image_coords(self, boxes, height, width):\\n box_coords = np.zeros_like(boxes)\\n box_coords[:, 0] = boxes[:, 0] * height\\n box_coords[:, 1] = boxes[:, 1] * width\\n box_coords[:, 2] = boxes[:, 2] * height\\n box_coords[:, 3] = boxes[:, 3] * width\\n return box_coords\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e35cd203421bf17a9ccae4fbb1f03dbe\",\n \"score\": \"0.55390316\",\n \"text\": \"def rect_to_xys(rect, image_shape):\\n h, w = image_shape[0:2]\\n def get_valid_x(x):\\n if x < 0:\\n return 0\\n if x >= w:\\n return w - 1\\n return x\\n \\n def get_valid_y(y):\\n if y < 0:\\n return 0\\n if y >= h:\\n return h - 1\\n return y\\n \\n rect = ((rect[0], rect[1]), (rect[2], rect[3]), rect[4])\\n points = cv2.boxPoints(rect)\\n points = np.int0(points)\\n for i_xy, (x, y) in enumerate(points):\\n x = get_valid_x(x)\\n y = get_valid_y(y)\\n points[i_xy, :] = [x, y]\\n points = np.reshape(points, -1)\\n return points\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1be05ce906cfd03f0afeece9fc96cf81\",\n \"score\": \"0.55379367\",\n \"text\": \"def convert_z_to_bbox(z):\\n w = np.sqrt(z[2] * z[3])\\n h = z[2] / w\\n x1 = z[0]-w/2.\\n y1 = z[1]-h/2.\\n x2 = z[0]+w/2.\\n y2 = z[1]+h/2.\\n if (x1 > x2) or (y1 > y2):\\n print('convertion to bbox when wrong. w=%.3f h=%.3f' % (w, h))\\n print([x1, y1, x2, y2])\\n\\n return np.array([x1, max(0, y1), min(x2, w-1), min(y2, h-1)])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"660c584fdef8e26c4e4ef0110792a084\",\n \"score\": \"0.5524904\",\n \"text\": \"def _correct_boxes(\\n self, box_xy, box_wh, input_shape, image_shape):\\n box_yx = box_xy[..., ::-1]\\n box_hw = box_wh[..., ::-1]\\n input_shape = K.cast(input_shape, K.dtype(box_yx))\\n image_shape = K.cast(image_shape, K.dtype(box_yx))\\n new_shape = K.round(image_shape * K.min(input_shape / image_shape))\\n offset = (input_shape - new_shape) / 2. / input_shape\\n scale = input_shape / new_shape\\n box_yx = (box_yx - offset) * scale\\n box_hw *= scale\\n\\n box_mins = box_yx - (box_hw / 2.)\\n box_maxes = box_yx + (box_hw / 2.)\\n boxes = K.concatenate([\\n box_mins[..., 0:1], # y_min\\n box_mins[..., 1:2], # x_min\\n box_maxes[..., 0:1], # y_max\\n box_maxes[..., 1:2] # x_max\\n ])\\n\\n # Scale boxes back to original image shape.\\n boxes *= K.concatenate([image_shape, image_shape])\\n return boxes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94aad998efd7ea0fd6043765c2c4d95c\",\n \"score\": \"0.55237657\",\n \"text\": \"def convert_bbox_to_z(bbox):\\n w = bbox[2] - bbox[0]\\n h = bbox[3] - bbox[1]\\n x = bbox[0] + w/2.\\n y = bbox[1] + h/2.\\n s = w * h #scale is just area\\n r = w / float(h)\\n return np.array([x, y, s, r]).reshape((4, 1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94aad998efd7ea0fd6043765c2c4d95c\",\n \"score\": \"0.55237657\",\n \"text\": \"def convert_bbox_to_z(bbox):\\n w = bbox[2] - bbox[0]\\n h = bbox[3] - bbox[1]\\n x = bbox[0] + w/2.\\n y = bbox[1] + h/2.\\n s = w * h #scale is just area\\n r = w / float(h)\\n return np.array([x, y, s, r]).reshape((4, 1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46c681675fe4983767f7f8166ff88bdf\",\n \"score\": \"0.5519915\",\n \"text\": \"def convert_bbox_to_z(bbox):\\n w = bbox[2] - bbox[0]\\n h = bbox[3] - bbox[1]\\n x = bbox[0] + w / 2.\\n y = bbox[1] + h / 2.\\n s = w * h # scale is just area\\n r = w / float(h)\\n\\n # 将数组 [x,y,s,r] 转为 4 行 1 列形式，即 [x,y,s,r].T\\n return np.array([x, y, s, r]).reshape((4, 1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"afa8291ca5831b4ae12e0bfc7d3c3041\",\n \"score\": \"0.55110615\",\n \"text\": \"def _convert(self, pos):\\n px = pos[0] + self.logical_size.width() / 2\\n py = self.logical_size.height() / 2 - pos[1]\\n return px, py\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9190d2bd733a73b07b61e86c66a12c12\",\n \"score\": \"0.55087054\",\n \"text\": \"def pointgraph_from_rect(rect):\\n x, y, w, h = rect\\n return bounding_box((y, x), (y + h, x + w))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1be4136136f5b4695bd52770621b727a\",\n \"score\": \"0.5507402\",\n \"text\": \"def yolo_boxes_to_corners(box_xy, box_wh):\\n box_mins = box_xy - (box_wh / 2.)\\n box_maxes = box_xy + (box_wh / 2.)\\n\\n return K.concatenate([\\n box_mins[..., 1:2], # y_min\\n box_mins[..., 0:1], # x_min\\n box_maxes[..., 1:2], # y_max\\n box_maxes[..., 0:1] # x_max\\n ])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c523ed8e57b6d9f7de7e13f3c11927b\",\n \"score\": \"0.5495114\",\n \"text\": \"def make_bounding_box(s, centerx=0, centery=0):\\n # result must be in the form: (left, bottom, right, top)\\n return [ centerx - s/2, centery - s/2, centerx + s/2, centery + s/2 ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"989222bd50156609fffca24bf63aa3b0\",\n \"score\": \"0.5471726\",\n \"text\": \"def convert2relative(image, bbox):\\n x, y, w, h = bbox\\n height, width, _ = image.shape\\n return x/width, y/height, w/width, h/height\",\n \"title\": \"\"\n },\n {\n \"docid\": \"267522d83b18e678d2cea49fc5e6071c\",\n \"score\": \"0.5470764\",\n \"text\": \"def convert_bbox_to_z(bbox):\\n w = bbox[2] - bbox[0]\\n h = bbox[3] - bbox[1]\\n x = bbox[0] + w/2.\\n y = bbox[1] + h/2.\\n s = w * h #scale is just area\\n r = w / float(h)\\n return np.array([x, y, s, r]).reshape((4, 1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"47d4de2b5d45426a73ef9035e2015d51\",\n \"score\": \"0.5465228\",\n \"text\": \"def rectify_image(image, source_points, target_points, crop):\\n\\n # TODO: Compute the rectifying homography H that warps the source points to the target points.\\n original_box = np.array([0,485,0,0,640,0,640,485]).reshape(4,2)\\n H = compute_H(source_points,target_points)\\n\\n # TODO: Apply the homography to a rectangle of the bounding box of the of the image to find the warped bounding box in the rectified space.\\n wbox = apply_homography(H,original_box)\\n\\n # Find the min_x and min_y values in the warped space to keep.\\n if crop:\\n \\t# TODO: pick the second smallest values of x and y in the warped bounding box\\n \\txm=np.amin(wbox[:,0])\\n \\tym=np.amin(wbox[:,1])\\n \\ta=0\\n \\tb=0\\n \\txarr=np.empty([3,1])\\n \\tyarr=np.empty([3,1])\\n \\tfor i in range(4):\\n \\t\\tif wbox[i,0]>xm:\\n \\t\\t\\txarr[a,0]=wbox[i,0]\\n \\t\\t\\ta=a+1\\n \\t\\tif wbox[i,1]>ym:\\n \\t\\t\\tyarr[b,0]=wbox[i,1]\\n \\t\\t\\tb=b+1\\n \\tmin_x=np.amin(xarr[:,0])\\n \\tmin_y=np.amin(yarr[:,0])\\n else:\\n \\t# TODO: Compute the min x and min y of the warped bounding box\\n \\tmin_x=np.amin(wbox[:,0])\\n \\tmin_y=np.amin(wbox[:,1])\\n # TODO: Compute a translation matrix T such that min_x and min_y will go to zero\\n T = np.array([[1,0,-min_x],[0,1,-min_y],[0,0,1]])\\n # TODO: Compute the rectified bounding box by applying the translation matrix to the warped bounding box.\\n rbox= np.empty([4, 3], dtype = 'float32')\\n for i in range(4):\\n \\trbox[i,0]=wbox[i,0]-min_x\\n \\trbox[i,1]=wbox[i,1]-min_y\\n \\trbox[i,2]=1\\n # TODO: Compute the inverse homography that maps the rectified bounding box to the original bounding box\\n #original_box = np.array([0,485,0,0,640,0,640,485]).reshape(4,2)\\n Hinv = compute_H(rbox[:,0:2],original_box)\\n\\n # Determine the shape of the output image\\n if crop:\\n # TODO: Determine the side of the final output image as the second highest X and Y values of the rectified bounding box\\n xa=np.amax(rbox[:,0])\\n ya=np.amax(rbox[:,1])\\n am=0\\n bm=0\\n xar=np.empty([3,1])\\n yar=np.empty([3,1])\\n for i in range(4):\\n if rbox[i,0]<xa:\\n xar[am,0]=rbox[i,0]\\n am=am+1\\n if rbox[i,1]<ya:\\n yar[bm,0]=rbox[i,1]\\n bm=bm+1\\n max_x=np.amax(xar[:,0])\\n max_y=np.amax(yar[:,0])\\n else:\\n \\t# TODO: Determine the side of the final output image as the maximum X and Y values of the rectified bounding box\\n \\tmax_x=np.amax(rbox[:,0])\\n \\tmax_y=np.amax(rbox[:,1])\\n\\n # TODO: Finally call warp_homography to rectify the image and return the result\\n rectified_image = warp_homography(np.array(image),(max_y,max_x,3),Hinv)\\n return rectified_image\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0c297357658d744603655d9fe6651a1\",\n \"score\": \"0.5457607\",\n \"text\": \"def bboard2pboard(bboard):\\n return (bits2positions(bboard[0]), bits2positions(bboard[1]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ade3c68dc62b03d316ad29878f641fea\",\n \"score\": \"0.54562217\",\n \"text\": \"def _crop_data(self, height, width, box, joints, boxp = 0.05):\\n\\t\\tpadding = [[0,0],[0,0],[0,0]]\\n\\t\\tj = np.copy(joints)\\n\\t\\tif box[0:2] == [-1,-1]:\\n\\t\\t\\tj[joints == -1] = 1e5\\n\\t\\t\\tbox[0], box[1] = min(j[:,0]), min(j[:,1])\\n\\t\\tcrop_box = [box[0] - int(boxp * (box[2]-box[0])), box[1] - int(boxp * (box[3]-box[1])), box[2] + int(boxp * (box[2]-box[0])), box[3] + int(boxp * (box[3]-box[1]))]\\n\\t\\tif crop_box[0] < 0: crop_box[0] = 0\\n\\t\\tif crop_box[1] < 0: crop_box[1] = 0\\n\\t\\tif crop_box[2] > width -1: crop_box[2] = width -1\\n\\t\\tif crop_box[3] > height -1: crop_box[3] = height -1\\n\\t\\tnew_h = int(crop_box[3] - crop_box[1])\\n\\t\\tnew_w = int(crop_box[2] - crop_box[0])\\n\\t\\tcrop_box = [crop_box[0] + new_w //2, crop_box[1] + new_h //2, new_w, new_h]\\n\\t\\tif new_h > new_w:\\n\\t\\t\\tbounds = (crop_box[0] - new_h //2, crop_box[0] + new_h //2)\\n\\t\\t\\tif bounds[0] < 0:\\n\\t\\t\\t\\tpadding[1][0] = abs(bounds[0])\\n\\t\\t\\tif bounds[1] > width - 1:\\n\\t\\t\\t\\tpadding[1][1] = abs(width - bounds[1])\\n\\t\\telif new_h < new_w:\\n\\t\\t\\tbounds = (crop_box[1] - new_w //2, crop_box[1] + new_w //2)\\n\\t\\t\\tif bounds[0] < 0:\\n\\t\\t\\t\\tpadding[0][0] = abs(bounds[0])\\n\\t\\t\\tif bounds[1] > width - 1:\\n\\t\\t\\t\\tpadding[0][1] = abs(height - bounds[1])\\n\\t\\tcrop_box[0] += padding[1][0]\\n\\t\\tcrop_box[1] += padding[0][0]\\n\\t\\treturn padding, crop_box\",\n \"title\": \"\"\n },\n {\n \"docid\": \"175409ca0d6fe3fd8c4d8f7bd6a6b2bd\",\n \"score\": \"0.5454066\",\n \"text\": \"def setup_rectangle( box_loc , width, height): \\n xloc,yloc = box_loc\\n low_left = (xloc - .5 * width, yloc -.5 * height)\\n top_right = (xloc + .5 * width, yloc +.5 * height) \\n return (low_left, top_right)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eaa00840966c9a66bcf702853c90b6db\",\n \"score\": \"0.54535\",\n \"text\": \"def parse_roi_box_from_landmark(pts):\\n bbox = [min(pts[0, :]), min(pts[1, :]), max(pts[0, :]), max(pts[1, :])]\\n center = [(bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2]\\n radius = max(bbox[2] - bbox[0], bbox[3] - bbox[1]) / 2\\n bbox = [center[0] - radius, center[1] - radius, center[0] + radius, center[1] + radius]\\n\\n llength = np.sqrt((bbox[2] - bbox[0]) ** 2 + (bbox[3] - bbox[1]) ** 2)\\n\\n \\n center_x = (bbox[2] + bbox[0]) / 2\\n center_y = (bbox[3] + bbox[1]) / 2\\n\\n llength = llength * 0.8\\n\\n roi_box = [0] * 4\\n roi_box[0] = center_x - llength / 2\\n roi_box[1] = center_y - llength / 2\\n roi_box[2] = roi_box[0] + llength\\n roi_box[3] = roi_box[1] + llength\\n\\n return np.array(roi_box)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3ad40e7349eecbc8c6069c9a4c15421\",\n \"score\": \"0.54351693\",\n \"text\": \"def roi_bbox_V2(img):\\n W = img.shape[0] - 1\\n H = img.shape[1] - 1\\n x1 = W\\n y1 = H\\n x2 = 0\\n y2 = 0\\n for x in range(0, W):\\n for y in range(0, H):\\n # print(x,y)\\n if img[x, y]:\\n if x < x1:\\n x1 = x\\n if y < y1:\\n y1 = y\\n if y > y2:\\n y2 = y\\n if x > x2:\\n x2 = x\\n return np.array([x1, y1, x2, y2])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5b248d0a808f6bc6d777f47ce50f7ca\",\n \"score\": \"0.54350454\",\n \"text\": \"def get_bounding_box(self):\\n\\n rows = np.any(self.img, axis=1)\\n cols = np.any(self.img, axis=0)\\n left, right = np.where(rows)[0][[0, -1]]\\n top, bottom = np.where(cols)[0][[0, -1]]\\n return Panel(left, right, top, bottom)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e36f79ff157462a867e6930497f54abf\",\n \"score\": \"0.54338324\",\n \"text\": \"def mask_to_tight_box_numpy(mask):\\n a = np.transpose(np.nonzero(mask))\\n bbox = np.min(a[:, 1]), np.min(a[:, 0]), np.max(a[:, 1]), np.max(a[:, 0])\\n return bbox # x_min, y_min, x_max, y_max\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd4e26ec81a2f2520e6f7f771a0b6c43\",\n \"score\": \"0.5428883\",\n \"text\": \"def _decode_boxes(self, raw_boxes, anchors):\\n boxes = torch.zeros_like(raw_boxes)\\n x_center = raw_boxes[..., 0] / self.x_scale * anchors[:, 2] + anchors[:, 0]\\n y_center = raw_boxes[..., 1] / self.y_scale * anchors[:, 3] + anchors[:, 1]\\n w = raw_boxes[..., 2] / self.w_scale * anchors[:, 2]\\n h = raw_boxes[..., 3] / self.h_scale * anchors[:, 3]\\n boxes[..., 0] = y_center - h / 2.0\\n boxes[..., 1] = x_center - w / 2.0\\n boxes[..., 2] = y_center + h / 2.0\\n boxes[..., 3] = x_center + w / 2.0\\n for k in range(6):\\n offset = 4 + k * 2\\n keypoint_x = raw_boxes[..., offset] / self.x_scale * anchors[:, 2] + anchors[:, 0]\\n keypoint_y = raw_boxes[..., offset + 1] / self.y_scale * anchors[:, 3] + anchors[:, 1]\\n boxes[..., offset] = keypoint_x\\n boxes[..., offset + 1] = keypoint_y\\n return boxes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93210406bb71b56373ea4c10b6887765\",\n \"score\": \"0.5428714\",\n \"text\": \"def bbox_to_pv(bbox_list):\\n pv_3d = []\\n for i in range(len(bbox_list[0])):\\n p_1d = []\\n v_1d = []\\n pv_1d = []\\n for t in range(len(bbox_list)):\\n bbox = torch.squeeze(bbox_list[t][i], dim=0)\\n # float\\n b = list(map(lambda x: x.item(), bbox))\\n # compute bbox center\\n # xc = (b[0] + b[2]) / 2 - 960.0\\n # c = abs(-(b[1] + b[3]) / 2 + 1080.0)\\n xc = (b[0] + b[2]) / 2\\n yc = (b[1] + b[3]) / 2\\n # compute width, height\\n w = abs(b[2] - b[0])\\n h = abs(b[3] - b[1])\\n p_1d.append([xc, yc, w, h])\\n v_1d.append([0.0, 0.0, 0.0, 0.0])\\n for t in range(1, len(bbox_list)):\\n dx = abs(p_1d[t][0] - p_1d[t - 1][0])\\n dy = abs(p_1d[t][1] - p_1d[t - 1][1])\\n dw = abs(p_1d[t][2] - p_1d[t - 1][2])\\n dh = abs(p_1d[t][3] - p_1d[t - 1][3])\\n v_1d.append([dx, dy, dw, dh])\\n for t in range(len(bbox_list)):\\n pv_1d.append(torch.tensor(p_1d[t] + v_1d[t], dtype=torch.float32))\\n pv_tensors_2d = torch.stack(pv_1d)\\n pv_3d.append(pv_tensors_2d)\\n # stack batch\\n pv_tensors_3d = torch.stack(pv_3d)\\n\\n return pv_tensors_3d\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac70b0f793e1dbae8bf0cd11c9edfb6e\",\n \"score\": \"0.5424306\",\n \"text\": \"def get_bounding_box(prediction):\\n x1 = prediction['x_min']\\n x2 = prediction['x_max']\\n y1 = prediction['y_min']\\n y2 = prediction['y_max']\\n return [x1, y1, x2, y2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa957139bba699b04d877301f3f600cc\",\n \"score\": \"0.5423938\",\n \"text\": \"def _translate_bboxes(self, results, offset, direction=\\\"horizontal\\\"):\\n h, w, c = results[\\\"img_shape\\\"]\\n for key in results.get(\\\"bbox_fields\\\", []):\\n min_x, min_y, max_x, max_y = np.split(\\n results[key], results[key].shape[-1], axis=-1\\n )\\n if direction == \\\"horizontal\\\":\\n min_x = np.maximum(0, min_x + offset)\\n max_x = np.minimum(w, max_x + offset)\\n elif direction == \\\"vertical\\\":\\n min_y = np.maximum(0, min_y + offset)\\n max_y = np.minimum(h, max_y + offset)\\n\\n # the boxes translated outside of image will be filtered along with\\n # the corresponding masks, by invoking ``_filter_invalid``.\\n results[key] = np.concatenate([min_x, min_y, max_x, max_y], axis=-1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2de4e41e5ab35d444a55e2336c6bdf47\",\n \"score\": \"0.5407474\",\n \"text\": \"def bounding_box(self, array_of_points):\\n max_x = -float('Inf')\\n max_y = -float('Inf')\\n min_x = float('Inf')\\n min_y = float('Inf')\\n for each in array_of_points:\\n if max_x < each[0]:\\n max_x = each[0]\\n if max_y < each[1]:\\n max_y = each[1]\\n if min_x > each[0]:\\n min_x = each[0]\\n if min_y > each[1]:\\n min_y = each[1]\\n return max_x, max_y, min_x, min_y\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b53813bd8b29716fb9efcd20bc544a6e\",\n \"score\": \"0.5407165\",\n \"text\": \"def _correct_region_bounding_box(self, bounding_box):\\n # Map coordinates from resized frame back to the original frame\\n x = bounding_box.x * self._resize_scale_factor\\n y = bounding_box.y * self._resize_scale_factor\\n width = bounding_box.width * self._resize_scale_factor\\n height = bounding_box.height * self._resize_scale_factor\\n\\n # Map ACS orientation info to MPF rotation info\\n corrected_top_left, corrected_angle = self._orientation_correction(x, y)\\n corrected_box = mpf_util.Rect.from_corner_and_size(corrected_top_left, (width, height))\\n return corrected_box, mpf_util.normalize_angle(corrected_angle)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c16bbbb655363812e80940f575bc08db\",\n \"score\": \"0.54016554\",\n \"text\": \"def rect_to_xys(self,rect, image_shape):\\n\\n height, width = image_shape[0:2]\\n\\n def get_valid_x(x_coord):\\n return np.clip(x_coord, 0, width - 1)\\n\\n def get_valid_y(y_coord):\\n return np.clip(y_coord, 0, height - 1)\\n\\n rect = ((rect[0], rect[1]), (rect[2], rect[3]), rect[4])\\n points = cv2.boxPoints(rect)\\n points = np.int0(points)\\n for i_xy, (x_coord, y_coord) in enumerate(points):\\n x_coord = get_valid_x(x_coord)\\n y_coord = get_valid_y(y_coord)\\n points[i_xy, :] = [x_coord, y_coord]\\n points = np.reshape(points, -1)\\n return points\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ebf71cbd3a52ae0cd9eec9d4682f734b\",\n \"score\": \"0.5395743\",\n \"text\": \"def convert_bbox_to_z(bbox):\\n w = bbox[2] - bbox[0]\\n h = bbox[3] - bbox[1]\\n x = bbox[0] + w/2.\\n y = bbox[1] + h/2.\\n s = w * h # scale is just area\\n r = w / float(h)\\n return np.array([x, y, s, r]).reshape((4, 1))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35835d5561857c7d3626946ed0807f33\",\n \"score\": \"0.5395644\",\n \"text\": \"def xyxy2xywh(bbox):\\n return [\\n bbox[0],\\n bbox[1],\\n bbox[2] - bbox[0],\\n bbox[3] - bbox[1],\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3afd5d0de531ae1ed5a83e2a5f5f5e7b\",\n \"score\": \"0.5394448\",\n \"text\": \"def _flipBbox(self, bbox): \\n temp_bbox = dict(bbox)\\n new_bbox = {}\\n new_bbox['maxy'] = temp_bbox['maxx']\\n new_bbox['miny'] = temp_bbox['minx']\\n new_bbox['maxx'] = temp_bbox['maxy']\\n new_bbox['minx'] = temp_bbox['miny']\\n\\n return new_bbox\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45d95fbdcca133cd7777d03c60956ace\",\n \"score\": \"0.5389789\",\n \"text\": \"def pad(self, bboxes, w, h):\\n tmpw, tmph = bboxes[:, 2] - bboxes[:, 0] + 1, bboxes[:, 3] - bboxes[:, 1] + 1\\n num_box = bboxes.shape[0]\\n dx , dy= np.zeros((num_box, )), np.zeros((num_box, ))\\n edx, edy = tmpw.copy()-1, tmph.copy()-1\\n x, y, ex, ey = bboxes[:, 0], bboxes[:, 1], bboxes[:, 2], bboxes[:, 3]\\n tmp_index = np.where(ex > w-1)\\n edx[tmp_index] = tmpw[tmp_index] + w - 2 - ex[tmp_index]\\n ex[tmp_index] = w - 1\\n tmp_index = np.where(ey > h-1)\\n edy[tmp_index] = tmph[tmp_index] + h - 2 - ey[tmp_index]\\n ey[tmp_index] = h - 1\\n tmp_index = np.where(x < 0)\\n dx[tmp_index] = 0 - x[tmp_index]\\n x[tmp_index] = 0\\n tmp_index = np.where(y < 0)\\n dy[tmp_index] = 0 - y[tmp_index]\\n y[tmp_index] = 0\\n return_list = [dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph]\\n return_list = [item.astype(np.int32) for item in return_list]\\n return return_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5e52ce3283b2baa8800b66a7bdd791d\",\n \"score\": \"0.53877395\",\n \"text\": \"def _get_available_bounding_box(self):\\n width, height = self.get_material_width(),self.get_material_height()\\n top, right, bottom, left = self.get_padding()\\n return [left,width-right,bottom,height-top]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3baf2a1f10ecde8dcda0819e13a4bd78\",\n \"score\": \"0.53839135\",\n \"text\": \"def get_bounding_box(self):\\n if not len(self._quadrilateral):\\n raise ValueError('The `_quadrilateral` should not be empty list.')\\n\\n # each point is represented as (height, width)\\n top_left, top_right, bottom_left, bottom_right = self.get()\\n y_min = min(top_left[0], top_right[0])\\n y_max = max(bottom_left[0], bottom_right[0])\\n x_min = min(top_left[1], bottom_left[1])\\n x_max = max(top_right[1], bottom_right[1])\\n\\n return [y_min, x_min, y_max, x_max]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a10f523ff5977de5ad0617c0ce46911e\",\n \"score\": \"0.53772736\",\n \"text\": \"def GetStandardRects(self, w,h, x_ps, y_ps):\\n standard_rects = []\\n totals_lbl = []\\n digit_lbl = []\\n for k, y_pos in enumerate(np.sort(y_ps)):\\n for j, x_pos in enumerate(np.flip(np.sort(x_ps))):\\n x = int(x_pos - w//2)\\n y = int(y_pos - h//2)\\n standard_rects.append((x, y, w, h ))\\n totals_lbl.append(k)\\n digit_lbl.append(j)\\n\\n return standard_rects, totals_lbl, digit_lbl\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a6c3313de3041b39510e212a28040d4\",\n \"score\": \"0.53718203\",\n \"text\": \"def box2coords(x, y, boxes):\\n boxcoords = []\\n for i, box in enumerate(boxes):\\n b = box[:]\\n if b[2] >= len(x): b[2] = len(x)-1\\n if b[3] >= len(y): b[3] = len(y)-1\\n boxcoords.append([x[b[0]], y[b[1]], x[b[2]], y[b[3]]])\\n return boxcoords\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60d1a6cc095ffd67361fdc5d4ae8700f\",\n \"score\": \"0.53665525\",\n \"text\": \"def to_image_coords(boxes, height, width):\\n box_coords = np.zeros_like(boxes)\\n box_coords[:, 0] = boxes[:, 0] * height\\n box_coords[:, 1] = boxes[:, 1] * width\\n box_coords[:, 2] = boxes[:, 2] * height\\n box_coords[:, 3] = boxes[:, 3] * width\\n\\n return box_coords\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60d1a6cc095ffd67361fdc5d4ae8700f\",\n \"score\": \"0.53665525\",\n \"text\": \"def to_image_coords(boxes, height, width):\\n box_coords = np.zeros_like(boxes)\\n box_coords[:, 0] = boxes[:, 0] * height\\n box_coords[:, 1] = boxes[:, 1] * width\\n box_coords[:, 2] = boxes[:, 2] * height\\n box_coords[:, 3] = boxes[:, 3] * width\\n\\n return box_coords\",\n \"title\": \"\"\n },\n {\n \"docid\": \"404c9598930a2d16cd33afa500959241\",\n \"score\": \"0.5365841\",\n \"text\": \"def calc_bounding_box(image):\\n\\n thresh = threshold_li(image)\\n thresh_image = np.where(image < thresh, 0, 1)\\n column_maximums = np.max(thresh_image, axis=0)\\n row_maximums = np.max(thresh_image, axis=1)\\n x_start = np.argmax(column_maximums)\\n x_end = len(column_maximums) - np.argmax(column_maximums[::-1]) - 1\\n\\n y_start = np.argmax(row_maximums)\\n y_end = len(row_maximums) - np.argmax(row_maximums[::-1]) - 1\\n\\n return (x_start, x_end, y_start, y_end)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01b5b7f38e0c9d8db8f3e060706badb0\",\n \"score\": \"0.5361575\",\n \"text\": \"def rect_from_landmarks(landmarks):\\n # Ignore the nose bridge and lip.\\n arrays = (\\n np.array(landmarks['chin']),\\n np.array(landmarks['left_eyebrow']),\\n np.array(landmarks['right_eyebrow']),\\n np.array(landmarks['nose_tip']))\\n coords = np.concatenate(arrays, axis=0)\\n left, top = np.amin(coords, axis=0)\\n right, bottom = np.amax(coords, axis=0)\\n return (top, right, bottom, left)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1fe9f734082c02d4f708348738d9d8e\",\n \"score\": \"0.5342286\",\n \"text\": \"def bbox_to_polygon(bbox):\\n assert(len(bbox) == 4)\\n lat_bl, long_bl, lat_ur, long_ur = bbox\\n return [[lat_bl, long_bl], [lat_bl, long_ur],\\n [lat_ur, long_ur], [lat_ur, long_bl]]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f1d1ede3a17e1c0b3fc830d9bc6e4ec\",\n \"score\": \"0.53255385\",\n \"text\": \"def pad(total_boxes, w, h):\\n tmpw = (total_boxes[:,2]-total_boxes[:,0]+1).astype(np.int32)\\n tmph = (total_boxes[:,3]-total_boxes[:,1]+1).astype(np.int32)\\n numbox = total_boxes.shape[0]\\n\\n dx = np.ones((numbox), dtype=np.int32)\\n dy = np.ones((numbox), dtype=np.int32)\\n edx = tmpw.copy().astype(np.int32)\\n edy = tmph.copy().astype(np.int32)\\n\\n x = total_boxes[:,0].copy().astype(np.int32)\\n y = total_boxes[:,1].copy().astype(np.int32)\\n ex = total_boxes[:,2].copy().astype(np.int32)\\n ey = total_boxes[:,3].copy().astype(np.int32)\\n\\n tmp = np.where(ex>w)\\n edx.flat[tmp] = np.expand_dims(-ex[tmp]+w+tmpw[tmp],1)\\n ex[tmp] = w\\n \\n tmp = np.where(ey>h)\\n edy.flat[tmp] = np.expand_dims(-ey[tmp]+h+tmph[tmp],1)\\n ey[tmp] = h\\n\\n tmp = np.where(x<1)\\n dx.flat[tmp] = np.expand_dims(2-x[tmp],1)\\n x[tmp] = 1\\n\\n tmp = np.where(y<1)\\n dy.flat[tmp] = np.expand_dims(2-y[tmp],1)\\n y[tmp] = 1\\n \\n return dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph\",\n \"title\": \"\"\n },\n {\n \"docid\": \"093b629ddf397af758bb287f64e28dd9\",\n \"score\": \"0.5324469\",\n \"text\": \"def make_bboxes(bbox_slices):\\n minr = [sxy[0].start for sxy in bbox_slices]\\n minc = [sxy[1].start for sxy in bbox_slices]\\n maxr = [sxy[0].stop for sxy in bbox_slices]\\n maxc = [sxy[1].stop for sxy in bbox_slices]\\n\\n bbox_rect = np.array(\\n [[minr, minc], [maxr, minc], [maxr, maxc], [minr, maxc]]\\n )\\n bbox_rect = np.moveaxis(bbox_rect, 2, 0)\\n return bbox_rect\",\n \"title\": \"\"\n },\n {\n \"docid\": \"021e12c55900c8c74e4775b4b02a4c0e\",\n \"score\": \"0.5311388\",\n \"text\": \"def _box_xyxy_to_cxcywh(boxes: Tensor) -> Tensor:\\n x1, y1, x2, y2 = boxes.unbind(-1)\\n cx = (x1 + x2) / 2\\n cy = (y1 + y2) / 2\\n w = x2 - x1\\n h = y2 - y1\\n\\n boxes = torch.stack((cx, cy, w, h), dim=-1)\\n\\n return boxes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c0c8b9749bdd7f7ec5bc831c28c60d0\",\n \"score\": \"0.5306036\",\n \"text\": \"def detection_center(bbox):\\n center_x = (bbox[0] + bbox[2]) / 2.0 - 0.5\\n center_y = (bbox[1] + bbox[3]) / 2.0 - 0.5\\n return (center_x, center_y)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b1e5554888a14b8aa91caa254421898a\",\n \"score\": \"0.5305808\",\n \"text\": \"def _crop_bounding_box(self, bounding_box):\\n\\n bounding_box_ind = np.zeros(4, dtype='int64')\\n \\n XLAT_min = self.ds[self.lat_name].min(axis=2).mean(axis=0)\\n XLAT_max = self.ds[self.lat_name].max(axis=2).mean(axis=0)\\n XLONG_min = self.ds[self.lon_name].min(axis=1).mean(axis=0)\\n XLONG_max = self.ds[self.lon_name].max(axis=1).mean(axis=0)\\n bounding_box_ind[0] = np.argmin(\\n np.abs(bounding_box[0] - np.squeeze(XLAT_min.values)))\\n bounding_box_ind[2] = np.argmin(\\n np.abs(bounding_box[2] - np.squeeze(XLAT_max.values)))\\n bounding_box_ind[1] = np.argmin(\\n np.abs(bounding_box[1] - np.squeeze(XLONG_min.values)))\\n bounding_box_ind[3] = np.argmin(\\n np.abs(bounding_box[3] - np.squeeze(XLONG_max.values)))\\n input_dict = {}\\n input_dict[self.lat_dim] = slice(bounding_box_ind[0], bounding_box_ind[2])\\n input_dict[self.lon_dim] = slice(bounding_box_ind[1], bounding_box_ind[3])\\n self.ds = self.ds.isel(input_dict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"797f97d594b765e7cf02444fc71fb0a6\",\n \"score\": \"0.53050745\",\n \"text\": \"def grid(ov, oh, s, h, w):\\n g = np.zeros((h,w)) #nparray full of zeros\\n p = np.zeros((h,w)) #nparray full of zeros\\n nv = int(w/s +2) #+2 just to make sure\\n nh = int(h/s+2)\\n \\n #calculate grid origin offset\\n _hp = ov\\n while _hp >= s:\\n _hp = _hp - s\\n\\n #print _hp\\n for _nv in xrange(nv): # for every vertical line\\n #calculate horizontal position \\n _pos = _nv * s + _hp\\n #print _pos\\n for _h in xrange(h): #for every height\\n try:\\n g[_h, int(_pos)] = (1-(_pos - int(_pos))) *255\\n g[_h, int(_pos+1)] = (1-(int(_pos+1)-_pos)) *255\\n p[_h, int(_pos)] += 1\\n #p[_h, int(_pos)] += (1-(_pos - int(_pos))) *1\\n #p[_h, int(_pos+1)] += (1-(int(_pos+1)-_pos)) *1\\n except IndexError:\\n pass\\n \\n _vp = oh\\n while _vp >= s:\\n _vp = _vp -s\\n \\n for _nh in xrange(nh):\\n _pos = _nh * s + _vp\\n for _v in xrange(w):\\n try:\\n g[int(_pos), _v] = (1-(_pos - int(_pos))) *255\\n g[int(_pos+1), _v] = (1-(int(_pos+1)-_pos)) *255\\n p[int(_pos), _v] += 1\\n #p[int(_pos), _v] += (1-(_pos - int(_pos))) *1\\n #p[int(_pos+1), _v] += (1-(int(_pos+1)-_pos)) *1\\n except IndexError:\\n pass\\n \\\"\\\"\\\" \\n points = []\\n for _h in xrange(h):\\n for _v in xrange(w):\\n if p[_h, _v] <= 1:\\n p[_h, _v] = 0\\n else:\\n p[_h, _v] = 255\\n points.append((_h, _v))\\n \\\"\\\"\\\" \\n \\n return g#,p, points #g=grid p=crossingpoints, points=list of crossingpoints\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993292,"cells":{"query_id":{"kind":"string","value":"8ac1622dac757385eda28bbed5586a8a"},"query":{"kind":"string","value":"Take the required arguments and publish the given command."},"positive_passages":{"kind":"list like","value":[{"docid":"5a3433795b72113360cfeb450d179318","score":"0.5254576","text":"def pub(\n self,\n tgt,\n fun,\n arg=(),\n tgt_type=\"glob\",\n ret=\"\",\n jid=\"\",\n timeout=5,\n listen=False,\n **kwargs\n ):\n # Make sure the publisher is running by checking the unix socket\n if self.opts.get(\"ipc_mode\", \"\") != \"tcp\" and not os.path.exists(\n os.path.join(self.opts[\"sock_dir\"], \"publish_pull.ipc\")\n ):\n log.error(\n \"Unable to connect to the salt master publisher at %s\",\n self.opts[\"sock_dir\"],\n )\n raise SaltClientError\n\n payload_kwargs = self._prep_pub(\n tgt, fun, arg, tgt_type, ret, jid, timeout, **kwargs\n )\n\n master_uri = \"tcp://{}:{}\".format(\n salt.utils.network.ip_bracket(self.opts[\"interface\"]),\n str(self.opts[\"ret_port\"]),\n )\n\n with salt.channel.client.ReqChannel.factory(\n self.opts, crypt=\"clear\", master_uri=master_uri\n ) as channel:\n try:\n # Ensure that the event subscriber is connected.\n # If not, we won't get a response, so error out\n if listen and not self.event.connect_pub(timeout=timeout):\n raise SaltReqTimeoutError()\n payload = channel.send(payload_kwargs, timeout=timeout)\n except SaltReqTimeoutError as err:\n log.error(err)\n raise SaltReqTimeoutError(\n \"Salt request timed out. The master is not responding. You \"\n \"may need to run your command with `--async` in order to \"\n \"bypass the congested event bus. With `--async`, the CLI tool \"\n \"will print the job id (jid) and exit immediately without \"\n \"listening for responses. You can then use \"\n \"`salt-run jobs.lookup_jid` to look up the results of the job \"\n \"in the job cache later.\"\n )\n\n if not payload:\n # The master key could have changed out from under us! Regen\n # and try again if the key has changed\n key = self.__read_master_key()\n if key == self.key:\n return payload\n self.key = key\n payload_kwargs[\"key\"] = self.key\n payload = channel.send(payload_kwargs)\n\n error = payload.pop(\"error\", None)\n if error is not None:\n if isinstance(error, dict):\n err_name = error.get(\"name\", \"\")\n err_msg = error.get(\"message\", \"\")\n if err_name == \"AuthenticationError\":\n raise AuthenticationError(err_msg)\n elif err_name == \"AuthorizationError\":\n raise AuthorizationError(err_msg)\n\n raise PublishError(error)\n\n if not payload:\n return payload\n\n return {\"jid\": payload[\"load\"][\"jid\"], \"minions\": payload[\"load\"][\"minions\"]}","title":""}],"string":"[\n {\n \"docid\": \"5a3433795b72113360cfeb450d179318\",\n \"score\": \"0.5254576\",\n \"text\": \"def pub(\\n self,\\n tgt,\\n fun,\\n arg=(),\\n tgt_type=\\\"glob\\\",\\n ret=\\\"\\\",\\n jid=\\\"\\\",\\n timeout=5,\\n listen=False,\\n **kwargs\\n ):\\n # Make sure the publisher is running by checking the unix socket\\n if self.opts.get(\\\"ipc_mode\\\", \\\"\\\") != \\\"tcp\\\" and not os.path.exists(\\n os.path.join(self.opts[\\\"sock_dir\\\"], \\\"publish_pull.ipc\\\")\\n ):\\n log.error(\\n \\\"Unable to connect to the salt master publisher at %s\\\",\\n self.opts[\\\"sock_dir\\\"],\\n )\\n raise SaltClientError\\n\\n payload_kwargs = self._prep_pub(\\n tgt, fun, arg, tgt_type, ret, jid, timeout, **kwargs\\n )\\n\\n master_uri = \\\"tcp://{}:{}\\\".format(\\n salt.utils.network.ip_bracket(self.opts[\\\"interface\\\"]),\\n str(self.opts[\\\"ret_port\\\"]),\\n )\\n\\n with salt.channel.client.ReqChannel.factory(\\n self.opts, crypt=\\\"clear\\\", master_uri=master_uri\\n ) as channel:\\n try:\\n # Ensure that the event subscriber is connected.\\n # If not, we won't get a response, so error out\\n if listen and not self.event.connect_pub(timeout=timeout):\\n raise SaltReqTimeoutError()\\n payload = channel.send(payload_kwargs, timeout=timeout)\\n except SaltReqTimeoutError as err:\\n log.error(err)\\n raise SaltReqTimeoutError(\\n \\\"Salt request timed out. The master is not responding. You \\\"\\n \\\"may need to run your command with `--async` in order to \\\"\\n \\\"bypass the congested event bus. With `--async`, the CLI tool \\\"\\n \\\"will print the job id (jid) and exit immediately without \\\"\\n \\\"listening for responses. You can then use \\\"\\n \\\"`salt-run jobs.lookup_jid` to look up the results of the job \\\"\\n \\\"in the job cache later.\\\"\\n )\\n\\n if not payload:\\n # The master key could have changed out from under us! Regen\\n # and try again if the key has changed\\n key = self.__read_master_key()\\n if key == self.key:\\n return payload\\n self.key = key\\n payload_kwargs[\\\"key\\\"] = self.key\\n payload = channel.send(payload_kwargs)\\n\\n error = payload.pop(\\\"error\\\", None)\\n if error is not None:\\n if isinstance(error, dict):\\n err_name = error.get(\\\"name\\\", \\\"\\\")\\n err_msg = error.get(\\\"message\\\", \\\"\\\")\\n if err_name == \\\"AuthenticationError\\\":\\n raise AuthenticationError(err_msg)\\n elif err_name == \\\"AuthorizationError\\\":\\n raise AuthorizationError(err_msg)\\n\\n raise PublishError(error)\\n\\n if not payload:\\n return payload\\n\\n return {\\\"jid\\\": payload[\\\"load\\\"][\\\"jid\\\"], \\\"minions\\\": payload[\\\"load\\\"][\\\"minions\\\"]}\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"50a2eb172754fe6beac7e71b4cda7d1c","score":"0.688723","text":"def _publish_task(publish_args: str = '') -> DoitTask:\n return debug_task([\n Interactive('poetry run nox --session build_dist build_check'),\n f'poetry publish {publish_args}',\n ])","title":""},{"docid":"3266c59c83240514b6ca0b136d52b8b4","score":"0.66889817","text":"def doPublish(args):\n if sys.stdin.isatty():\n # treat it as a text file?\n pass\n\n filename = args[0]\n title = description = \"\"\n\n if len(args) > 1:\n title = args[1]\n\n if len(args) > 2:\n description = args[2]\n\n for thing in [filename, title, description]:\n print(f\"{thing}\")","title":""},{"docid":"6c3c57c321b6321a29376c7378b14589","score":"0.6463009","text":"def publish(self, info, *args, **kwds):\n raise NotImplementedError()","title":""},{"docid":"a6094edfc67e5726f3a8af602c6153c0","score":"0.6462211","text":"def handle(self, *args, **options):\n self._handle_publish(options, DiscoursePublisher)","title":""},{"docid":"f52366e548db5301717ddeacd1b061fe","score":"0.63315666","text":"def run_publish(args):\n home = Path(args.channel).absolute()\n channel = json.loads(home.joinpath(\"channel.json\").read_text())\n now = datetime.utcnow().strftime(r\"%a, %d %b %Y %H:%M:%S +0000\")\n episodes = [\n json.loads(line)\n for line in home.joinpath(\"episodes.json\").read_text().splitlines()\n ]\n env = Environment(\n loader=FileSystemLoader(home.as_posix()),\n autoescape=select_autoescape(['html', 'xml', 'jinja'])\n )\n template = env.get_template(\"feed_template.xml.jinja\")\n feed = template.render(channel=channel, episodes=episodes, now=now)\n feed_path = home.joinpath(\"latest_feed.xml\")\n feed_path.write_text(feed)\n\n if not args.dry_run:\n print(\"Publishing to IPNS. This can take 1-5 minutes.\")\n file_hash = (\n subprocess\n .check_output([\"ipfs\", \"add\", \"-Q\", feed_path.as_posix()])\n .decode()\n .strip()\n )\n subprocess.check_call(\n [\"ipfs\", \"name\", \"publish\", \"--key\", home.name, file_hash]\n )","title":""},{"docid":"5ffe75a6973324a4628111d4ecb075ce","score":"0.62642044","text":"def command(self, args):\n if 'create_release' in args.command:\n self.create_release()\n\n elif 'close_release' in args.command:\n self.close_release()","title":""},{"docid":"acb87e86f74a48cc6e0648729ad8062c","score":"0.6243611","text":"def gpg_publish(args):\n\n mirror = None\n if args.directory:\n mirror = spack.mirror.Mirror(args.directory, args.directory)\n elif args.mirror_name:\n mirror = spack.mirror.MirrorCollection().lookup(args.mirror_name)\n elif args.mirror_url:\n mirror = spack.mirror.Mirror(args.mirror_url, args.mirror_url)\n\n spack.binary_distribution.push_keys(\n mirror, keys=args.keys, regenerate_index=args.rebuild_index\n )","title":""},{"docid":"3c1f925581eea3a1fd87350abb11c740","score":"0.6205315","text":"def command(self, args):\n pass","title":""},{"docid":"734790c12f8b4e2f0baf2596cd5c248b","score":"0.61964345","text":"def cli_du_publisher(reader_name='mpe', parser=None, parser_name='rst',\n writer=None, writer_name='pseudoxml', description=''):\n\n # XXX: how far does inline customization go? parser = Parser(inliner=Inliner())\n reader_class = comp.get_reader_class(reader_name)\n parser_class = None\n if not parser:\n parser_class = comp.get_parser_class(parser_name)\n parser = parser_class()\n if not writer:\n writer_class = comp.get_writer_class(writer_name)\n writer = writer_class()\n\n publish_cmdline(\n parser=parser,\n parser_name=parser_name,\n reader=reader_class(parser),\n reader_name=reader_name,\n writer=writer,\n writer_name=writer_name,\n description=description)","title":""},{"docid":"1f375072b9ab95583274dd6f8ea661dc","score":"0.61568415","text":"def add_publish_qc_command(cmdparser):\n default_use_hierarchy = (\"yes\" if __settings.qc_web_server.use_hierarchy\n else \"no\")\n default_exclude_zips = (\"yes\" if __settings.qc_web_server.exclude_zip_files\n else \"no\")\n p = cmdparser.add_command('publish_qc',help=\"Copy QC reports to publication area\",\n usage=\"%prog publish_qc [OPTIONS] [ANALYSIS_DIR]\",\n description=\"Copy QC reports from ANALYSIS_DIR to local \"\n \"or remote directory (e.g. web server). By default existing \"\n \"QC reports will be copied without further checking; if no \"\n \"report is found then QC results will be verified and a \"\n \"report generated first.\")\n p.add_option('--projects',action='store',\n dest='project_pattern',default=None,\n help=\"simple wildcard-based pattern specifying a subset of projects \"\n \"and samples to publish the QC for. PROJECT_PATTERN can specify a \"\n \"single project, or a set of projects.\")\n p.add_option('--qc_dir',action='store',\n dest='qc_dir',default=None,\n help=\"specify target directory to copy QC reports to. QC_DIR can \"\n \"be a local directory, or a remote location in the form \"\n \"'[[user@]host:]directory'. Overrides the default settings.\")\n p.add_option('--use-hierarchy',type='choice',choices=[\"yes\",\"no\"],\n dest='use_hierarchy',default=default_use_hierarchy,\n help=\"use YEAR/PLATFORM hierarchy under QC_DIR; can be \"\n \"'yes' or 'no' (default: %s)\" % default_use_hierarchy)\n p.add_option('--exclude-zip-files',type='choice',choices=[\"yes\",\"no\"],\n dest='exclude_zip_files',default=default_exclude_zips,\n help=\"exclude ZIP archives from publication; can be 'yes' \"\n \"or 'no' (default: %s)\" % default_exclude_zips)\n p.add_option('--ignore-missing-qc',action='store_true',\n dest='ignore_missing_qc',default=False,\n help=\"skip projects where QC results are missing or can't be verified, \"\n \"or where reports can't be generated.\")\n p.add_option('--regenerate-reports',action='store_true',\n dest='regenerate_reports',default=False,\n help=\"attempt to regenerate existing QC reports\")\n p.add_option('--force',action='store_true',\n dest='force',default=False,\n help=\"force generation of QC reports for all projects even \"\n \"if verification has failed\")\n p.add_option('--legacy',action='store_true',\n dest='legacy_mode',default=False,\n help=\"legacy mode: include links to MultiQC reports in the \"\n \"top-level index page\")\n add_debug_option(p)","title":""},{"docid":"c61eba8f029a5efc0e5d1903cf8ebbb5","score":"0.61412495","text":"def publish(build_entry: dict, args: dict):\n skip_publishing = not build_entry.get('publish', True)\n for tag in build_entry['tags']:\n if args['dry_run']:\n print('[DRY-RUN]: Skipped pushing {}'.format(tag))\n elif skip_publishing:\n print('[SKIPPED]: Publishing not allowed for {}'.format(tag))\n else:\n print('[PUSHING]:', tag)\n os.system('docker push {}'.format(tag))","title":""},{"docid":"83619ca504179538a708d7dc37077b0b","score":"0.60522413","text":"def test_publish_positive(self, *mocks):\n result = self.runner.invoke(\n cli, [*CLI_LOG_OPTION, \"publish\", \"--local\"], standalone_mode=False,\n )\n self.assertEqual(result.exit_code, 0)\n result = self.runner.invoke(\n cli, [*CLI_LOG_OPTION, \"publish\", \"--remote\"], standalone_mode=False,\n )\n self.assertEqual(result.exit_code, 0)\n result = self.runner.invoke(\n cli, [*CLI_LOG_OPTION, \"publish\"], standalone_mode=False,\n )\n self.assertEqual(result.exit_code, 0)","title":""},{"docid":"fbccd92eea532fbf6e09162f587b6ba8","score":"0.6010531","text":"def register_command(subparser):\n parser = subparser.add_parser('serve', help='Serve the contents of the publishers output files')\n parser.set_defaults(func=_main)\n\n parser.add_argument('--port', '-p', type=int, help='Port that the server will be listening on',\n default=8080)","title":""},{"docid":"28e25027ec7f6199a8fa1176be1b9832","score":"0.60084635","text":"def test_publish_positive(self, *mocks):\n self.set_config(\"agent.description\", \"some-description\")\n self.run_cli_command(\"publish\", cwd=self._get_cwd())","title":""},{"docid":"ffae76038f71877599b76acd42426826","score":"0.6003352","text":"def busdriver(self, msg, cmd):\n self.publish(msg.command.lower(),\n dumps([msg.args,\n msg.prefix[0],\n msg.trailing, {\"prefix\": msg.prefix}]))","title":""},{"docid":"3fec088fcc99217d2a9194b8675f1ecd","score":"0.59700555","text":"def task_publish() -> DoitTask:\n return _publish_task()","title":""},{"docid":"bea9a992aee0a395063b8e1f92f9f367","score":"0.5967572","text":"def publish(self, msg: str) -> None:\n ...","title":""},{"docid":"e278d5b550d395c3b5197538c1e8d98b","score":"0.5917355","text":"def bus_command(self, msg, cmd):\n match = COMMAND_RE.match(msg.trailing)\n if match:\n cmd_name = match.groups()[1]\n cmd_args = msg.trailing[len(cmd_name) + 1:].strip()\n self.publish(\"command_{}\".format(cmd_name),\n dumps([msg.args,\n msg.prefix[0],\n cmd_args,\n {\"prefix\": msg.prefix}]))","title":""},{"docid":"611b96a1c1d435bbbd7a367fc993af7c","score":"0.5818544","text":"def Publisher(object):\n def __init__(self, channel_key):\n self.channel_key = str(channel_key)\n\n def publish(**kwargs):\n publish(self.channel_key, **kwargs)","title":""},{"docid":"f988ddb46bf8a4733704ef5bf96592d7","score":"0.5810508","text":"def publish(\n server: Murmur.Server,\n user: Murmur.User,\n msg: Murmur.TextMessage\n):\n # Wrap original message in a more context aware TextMessage\n wrapped = TextMessage(\n user,\n server,\n msg.sessions,\n msg.channels,\n msg.trees,\n msg.text\n )\n\n for command in command_subscribers:\n match = command['prog'].search(msg.text)\n if match:\n command['func'](wrapped, **match.groupdict())\n return","title":""},{"docid":"806995e838b5d3f63aac68159d8fd749","score":"0.5796559","text":"def publish(self, message):\n pass","title":""},{"docid":"49b525d73a87d9e73c30077d08cc98b4","score":"0.57887083","text":"def prepare_publish(cmd, client, resource_group_name, resource_name, sln_name, proj_file_path, code_dir=None, # pylint:disable=too-many-statements\n version='v3'):\n\n # The prepare-publish process for v3 bots and v4 bots differ, so if the user specifies a v4 version, end the command\n # and inform user of az bot publish.\n if version == 'v4':\n raise CLIError('\\'az bot prepare-publish\\' is only for v3 bots. Please use \\'az bot publish\\' to prepare and '\n 'publish a v4 bot.')\n\n logger.warning('WARNING: `az bot prepare-publish` is in maintenance mode for v3 bots as support for creating v3 '\n 'SDK bots via `az bot create` will be discontinued on August 1st, 2019. We encourage developers '\n 'move to creating and deploying v4 bots.\\n\\nFor more information on creating and deploying v4 bots, '\n 'please visit https://aka.ms/create-and-deploy-v4-bot\\n\\nFor more information on v3 bot '\n 'creation deprecation, please visit this blog post: '\n 'https://blog.botframework.com/2019/06/07/v3-bot-broadcast-message/')\n\n bot = client.bots.get(\n resource_group_name=resource_group_name,\n resource_name=resource_name\n )\n if bot.kind == 'bot':\n raise CLIError('Prepare Publish is not supported for registration only bots.')\n\n if not code_dir:\n code_dir = os.getcwd()\n logger.warning('Parameter --code-dir not provided, defaulting to current working directory, %s. For more '\n 'information, run \\'az bot prepare-publish -h\\'', code_dir)\n\n code_dir = code_dir.strip()\n if not os.path.isdir(code_dir):\n raise CLIError('Please supply a valid directory path containing your source code. '\n 'Path {0} does not exist.'.format(code_dir))\n\n os.chdir(code_dir)\n\n # Ensure that the directory does not contain appropriate post deploy scripts folder\n if 'PostDeployScripts' in os.listdir(code_dir):\n raise CLIError('Post deploy azure scripts are already in Place.')\n\n # Download bot source\n download_path = download_app(cmd, client, resource_group_name, resource_name)\n\n shutil.copytree(os.path.join(download_path['downloadPath'], 'PostDeployScripts'), 'PostDeployScripts')\n\n # If javascript, we need these files there for Azure WebApps to start\n if os.path.exists(os.path.join('PostDeployScripts', 'publish.js.template')):\n\n logger.info('Detected language javascript.')\n\n shutil.copy(os.path.join(download_path['downloadPath'], 'iisnode.yml'), 'iisnode.yml')\n shutil.copy(os.path.join(download_path['downloadPath'], 'publish.js'), 'publish.js')\n shutil.copy(os.path.join(download_path['downloadPath'], 'web.config'), 'web.config')\n\n # If C#, we need other set of files for the WebApp to start including build.cmd\n else:\n logger.info('Detected language CSharp.')\n solution_path = None\n csproj_path = None\n old_namev4 = 'AspNetCore-EchoBot-With-State'\n old_namev3 = 'Microsoft.Bot.Sample.SimpleEchoBot'\n shutil.copy(os.path.join(download_path['downloadPath'], 'build.cmd'), 'build.cmd')\n shutil.copy(os.path.join(download_path['downloadPath'], '.deployment'), '.deployment')\n\n # \"deploy.cmd.template\" does not exist for v4 bots. If the next section of code fails due to deploy.cmd.template\n # not being found, it is most likely due to trying to call prepare-publish on a v4 bot.\n # Inform the user of the potential problem and raise the error to exit the process.\n try:\n shutil.copyfile(os.path.join(download_path['downloadPath'], 'PostDeployScripts', 'deploy.cmd.template'),\n 'deploy.cmd')\n except OSError as error: # FileNotFoundError introduced in Python 3\n logger.error('\"deploy.cmd.template\" not found. This may be due to calling \\'az bot prepare-publish\\' on a '\n 'v4 bot. To prepare and publish a v4 bot, please instead use \\'az bot publish\\'.')\n raise CLIError(error)\n\n # Find solution and project name\n for root, _, files in os.walk(os.curdir):\n if solution_path and csproj_path:\n break\n for fileName in files:\n if solution_path and csproj_path:\n break\n if fileName == sln_name:\n solution_path = os.path.relpath(os.path.join(root, fileName))\n if fileName == proj_file_path:\n csproj_path = os.path.relpath(os.path.join(root, fileName))\n\n # Read deploy script contents\n with open('deploy.cmd') as f:\n content = f.read()\n\n logger.info('Visual studio solution detected: %s.', solution_path)\n logger.info('Visual studio project detected: %s.', csproj_path)\n\n # Using the deploy.cmd as a template, adapt it to use our solution and csproj\n with open('deploy.cmd', 'w') as f:\n content = content.replace(old_namev3 + '.sln', solution_path)\n content = content.replace(old_namev3 + '.csproj', csproj_path)\n content = content.replace(old_namev4 + '.sln', solution_path)\n content = content.replace(old_namev4 + '.csproj', csproj_path)\n f.write(content)\n\n shutil.rmtree(download_path['downloadPath'])\n\n logger.info('Bot prepare publish completed successfully.')","title":""},{"docid":"c340129e365cc49068be28636acc05b2","score":"0.5710041","text":"def publish_vehicle_command(self, command, **params) -> None:\n msg = VehicleCommand()\n msg.command = command\n msg.param1 = params.get(\"param1\", 0.0)\n msg.param2 = params.get(\"param2\", 0.0)\n msg.param3 = params.get(\"param3\", 0.0)\n msg.param4 = params.get(\"param4\", 0.0)\n msg.param5 = params.get(\"param5\", 0.0)\n msg.param6 = params.get(\"param6\", 0.0)\n msg.param7 = params.get(\"param7\", 0.0)\n msg.target_system = 1\n msg.target_component = 1\n msg.source_system = 1\n msg.source_component = 1\n msg.from_external = True\n msg.timestamp = int(self.get_clock().now().nanoseconds / 1000)\n self.vehicle_command_publisher.publish(msg)","title":""},{"docid":"14bd5a73c204ed62a5e0db718ae2f1c0","score":"0.56659275","text":"def publish(cls, nuget_package, address = 'default'):\n ret = NO_ERROR\n if address is 'default':\n ret = NugetUtility.nuget_cli('push', nuget_package, '-Source', 'https://www.nuget.org/')\n else:\n ret = NugetUtility.nuget_cli('push', nuget_package, '-Source', address)\n return ret","title":""},{"docid":"d99dfe248af761c7baec0fd332dc8af0","score":"0.56579053","text":"def run(command:'Command to execute in `plugin.action` format.',\n server:'URI to the remote SuRVoS API Server'=None,\n *args:'Extra keyword arguments in the form of `key=value` '\n 'required by plugin/commands. Please refer to SuRVoS API.'):\n plugin, command = command.split('.')\n args = [k.split('=') for k in args]\n params = {k: v for k, v in args}\n result = run_command(plugin, command, uri=server, **params)[0]\n if type(result) == dict:\n result = format_yaml(result)\n logger.info(result)","title":""},{"docid":"24ee256576d27ce9cac7c9935a4d8efc","score":"0.5645572","text":"def publish(self, notifications, **kwargs):\n raise NotImplementedError","title":""},{"docid":"96aa22af67ec48eb550f9e85ecd2732f","score":"0.5624073","text":"def send_redis_command(command, **kwargs):\n command_dictionary = copy(kwargs)\n command_dictionary['command'] = command\n encoded_command_dictionary = json.dumps(command_dictionary)\n REDIS.publish('comp.command', encoded_command_dictionary)","title":""},{"docid":"19082437a37ca2e890195ba70d635b79","score":"0.5605631","text":"def do_pocs_command(self, cmd):\n try:\n self.cmd_publisher.send_message('POCS-CMD', cmd)\n except AttributeError:\n print_info('Messaging not started')","title":""},{"docid":"9b67ed6d8f3e640a5117863982d95a7d","score":"0.5594868","text":"def publish():\n try:\n upload_dist_commands = [cmd + ['register', 'upload']\n for cmd in dist_commands]\n list(map(subprocess.check_call, upload_dist_commands))\n except:\n print(\"Unable to upload the dist files. Ask in IRC for help access 57\"\n \"or assistance.\")\n raise SystemExit(4)\n print('Distributions have been uploaded.')\n print('Please ask in IRC for others to help you test '\n 'CherryPy {NEXT_VERSION}.'\n .format(**globals()))\n print(\"Please confirm that the distro installs properly \"\n \"with `easy_install CherryPy=={NEXT_VERSION}`.\".format(**globals()))","title":""},{"docid":"40b67c563f77e9d30e6d6611287eec90","score":"0.55342305","text":"def zmq_publish_command(command, zmq_addr):\n (pid, fd) = pty.fork()\n if not pid:\n # child (slave)\n rc = os.system(command)\n sys.exit(rc)\n else:\n # parent (master)\n ctx = zmq.Context()\n sock = ctx.socket(zmq.PUB)\n sock.bind(zmq_addr)\n done = False\n \n # this allows clients to connect/reconnect, otherwise they'll always miss the first message\n time.sleep(0.5)\n\n while not done:\n data = os.read(fd, 8192)\n if data:\n sock.send(\"%s %s\" % (OUTPUT_TOPIC, data))\n else:\n done = True\n sock.send(\"%s \" % (OUTPUT_TOPIC)) # no data: signals end of output\n sock.close()\n ctx.term()","title":""},{"docid":"8ce9c1779290ef1587dd841970f2526e","score":"0.553045","text":"def publish(self):\n self.client.call(self.METHODS['publish'], self.id, True)","title":""},{"docid":"a248c3b7296b57ea692fbaf956bbed4e","score":"0.5527572","text":"def publish_for(self, instance, *event_args, **event_keys):\n\n key = self.__key\n publisher = getattr(instance, key, None)\n if publisher is not None:\n publisher.publish(*event_args, **event_keys)","title":""},{"docid":"c101dae251359523ef468c66708c798b","score":"0.55182755","text":"def publish(self, subject, msg, reply=None):\n if msg is None:\n msg = ''\n\n if reply is None:\n command = 'PUB %s %d' % (subject, len(msg))\n else:\n command = 'PUB %s %s %d' % (subject, reply, len(msg))\n\n self._send(command)\n self._send(msg)","title":""},{"docid":"d14e986f05e8829ecd5a3c9f3dc2b5b4","score":"0.5508381","text":"def publish():\n version = get_version(1)\n\n # Push to GitHub.\n with chdir(BASE):\n subprocess.check_call(['git', 'push'])\n subprocess.check_call(['git', 'push', '--tags'])\n\n # Upload to PyPI.\n path = os.path.join(BASE, 'dist', f'beets-{version}.tar.gz')\n subprocess.check_call(['twine', 'upload', path])","title":""},{"docid":"7e7b5d3743db87186a827c71d6bfbf55","score":"0.55079514","text":"def execute(self, command, *params, **kwargs):\n params = [x for x in params if x is not None]\n if \"trailing\" in kwargs:\n params = list(params)\n if kwargs[\"trailing\"] is not None:\n params.append(\":%s\" % kwargs[\"trailing\"])\n cmd_line = \"%s %s\\r\\n\" % (command.upper(), \" \".join(params))\n self.push(bytes(cmd_line, 'UTF-8', errors='ignore'))","title":""},{"docid":"92d3b86b6c9e70abad6982402c588731","score":"0.5500408","text":"def wrapper(self, command, args):\n command += \" \"\n for op, value in args.items():\n command += op + \" \" + value + \" \"\n sp.run(command, shell=True)","title":""},{"docid":"19a6c070c578e96a97959bb7582d5310","score":"0.5498583","text":"async def main():\n await runner(NewsPublisher.publish)","title":""},{"docid":"45a1e44f840fd4877b28b98bc9790980","score":"0.549816","text":"def on_publish(unused_client, unused_userdata, unused_mid):\n print('on_publish')","title":""},{"docid":"0e45903b6094be849eb821fe0a616105","score":"0.5487647","text":"def main():\n parser = argparse.ArgumentParser()\n parser.add_argument('--configFile', help='Publisher configuration file', default='PublisherConfig.py')\n parser.add_argument('--taskname', help='taskname', required=True)\n parser.add_argument('--verbose', help='Verbose mode, print dictionaries', action='store_true')\n parser.add_argument('--dry', help='Dry run mode, no changes done in DBS', action='store_true')\n parser.add_argument('--console', help='Console mode, send logging to stdout', action='store_true')\n args = parser.parse_args()\n configFile = os.path.abspath(args.configFile)\n taskname = args.taskname\n verbose = args.verbose\n dryRun = args.dry\n console = args.console\n modeMsg = \" in DRY RUN mode\" if dryRun else \"\"\n config = loadConfigurationFile(configFile)\n if dryRun:\n config.TaskPublisher.dryRun = True\n\n if verbose:\n print(f\"Will run {modeMsg} with:\\nconfigFile: {configFile}\\ntaskname : {taskname}\\n\")\n\n summaryFile = publishInDBS3(config, taskname, verbose, console)\n print(f\"Completed with result in {summaryFile}\")","title":""},{"docid":"a682dd2fcf80911a26ba3e587098dfb7","score":"0.54809767","text":"def publish_official():\n mssqlcli_dist_dir = os.listdir(utility.MSSQLCLI_DIST_DIRECTORY)\n # Run twine action for mssql-cli.\n # Only authorized users with credentials will be able to upload this package.\n # Credentials will be stored in a .pypirc file.\n for file_dist in mssqlcli_dist_dir:\n utility.exec_command(\n 'twine upload {}'.format(file_dist),\n utility.MSSQLCLI_DIST_DIRECTORY)","title":""},{"docid":"b005c53c7258f3a01656c855ae49d89b","score":"0.5442617","text":"def _prep_pub(self, tgt, fun, arg, tgt_type, ret, jid, timeout, **kwargs):\n if tgt_type == \"nodegroup\":\n tgt = self._resolve_nodegroup(tgt)\n tgt_type = \"compound\"\n\n if tgt_type == \"compound\":\n # Resolve all nodegroups, so that the minions don't have to.\n new_tgt = list()\n log.debug(\"compound resolution: original tgt: %s\", tgt)\n\n if isinstance(tgt, str):\n tgt = tgt.split()\n\n for word in tgt:\n if word.startswith(\"N@\") and len(word) > 2:\n resolved = self._resolve_nodegroup(word[2:])\n new_tgt.extend(resolved)\n else:\n new_tgt.append(word)\n\n log.debug(\"compound resolution: new_tgt: %s\", new_tgt)\n tgt = \" \".join(new_tgt)\n\n # Convert a range expression to a list of nodes and change expression\n # form to list\n if tgt_type == \"range\" and HAS_RANGE:\n tgt = self._convert_range_to_list(tgt)\n tgt_type = \"list\"\n\n # If an external job cache is specified add it to the ret list\n if self.opts.get(\"ext_job_cache\"):\n if ret:\n ret += \",{}\".format(self.opts[\"ext_job_cache\"])\n else:\n ret = self.opts[\"ext_job_cache\"]\n\n # format the payload - make a function that does this in the payload\n # module\n\n # Generate the standard keyword args to feed to format_payload\n payload_kwargs = {\n \"cmd\": \"publish\",\n \"tgt\": tgt,\n \"fun\": fun,\n \"arg\": arg,\n \"key\": self.key,\n \"tgt_type\": tgt_type,\n \"ret\": ret,\n \"jid\": jid,\n }\n\n # if kwargs are passed, pack them.\n if kwargs:\n payload_kwargs[\"kwargs\"] = kwargs\n\n payload_kwargs[\"user\"] = self.salt_user\n\n # If we're a syndication master, pass the timeout\n if self.opts[\"order_masters\"]:\n payload_kwargs[\"to\"] = timeout\n\n return payload_kwargs","title":""},{"docid":"10a51e109aac3d40ca7bea4d24e7fab4","score":"0.5413644","text":"def publish(**msg):\n result = None\n try:\n source_data = msg[\"source_data\"]\n out_dir = msg[\"out_dir\"]\n shutil.copy2(out_dir + source_data, out_dir + \"NGW/\" + source_data)\n except TypeError:\n pass\n return result","title":""},{"docid":"b38b984475a7606251ab1367ba3b4832","score":"0.54101676","text":"def publish(self, *,\n pub_path: str,\n recursive: bool = True,\n **kwargs: typing.Any) -> None:\n raise NotImplementedError","title":""},{"docid":"0df97e5a9f6ecdceacad8f9b514dc655","score":"0.53999346","text":"def do_pocs_message(self, cmd):\n try:\n self.msg_publisher.send_message('POCS-SHELL', cmd)\n except AttributeError:\n print_info('Messaging not started')","title":""},{"docid":"5b5612091852192ecf3a76508b03ca31","score":"0.5399926","text":"def process_command(command):\n if len(sys.argv) < 2:\n print_help()\n return\n command = sys.argv[1].lower()\n if command == 'help':\n if (validate_cli_arguments(3)):\n print_command_help(sys.argv[2])\n elif command == 'getdoc':\n if (validate_cli_arguments(4)):\n get_doc_file(sys.argv[2], sys.argv[3])\n elif command == 'putdoc':\n if (validate_cli_arguments(6)):\n put_document(sys.argv[2],sys.argv[3],sys.argv[4],sys.argv[5])\n elif command == 'listcollections':\n list_collections()\n elif command == 'listversions':\n if (len(sys.argv) ==3):\n list_collection_version(sys.argv[2],None)\n elif (len(sys.argv)==4 and sys.argv[3]=='documents'):\n list_collection_version(sys.argv[2],sys.argv[3]) \n else:\n print_help()\n elif command == 'showcollection':\n if (validate_cli_arguments(3)):\n show_collection(sys.argv[2])\n elif command == 'listen':\n from bitmessage.bitmessage_listener import get_collections\n get_collections()\n elif command == 'install':\n if (validate_cli_arguments(3)):\n install_dependencies(sys.argv[2])\n elif command == 'putcollection':\n if (validate_cli_arguments(7)):\n put_collection(sys.argv[2], \\\n sys.argv[3], sys.argv[4], \\\n sys.argv[5], sys.argv[6])\n elif command == 'publishcollection':\n if (validate_cli_arguments(4)):\n publish_collection(sys.argv[2], sys.argv[3])\n else:\n print_help()\n elif command == 'webapp':\n webapp.run(debug = config.WEBAPP_DEBUG, port = config.WEBAPP_PORT)\n elif command == 'uploader':\n from frontend.uploader import fjUploaderGUI\n fjUploaderGUI.run()\n elif command == 'keepalive':\n from bitmessage.bitmessage_keepalive import find_old_collections\n find_old_collections(3)\n else:\n print_help()","title":""},{"docid":"b9bdd42e4d536f8e3eb15aab44f45c59","score":"0.53911906","text":"def main(arguments):\n full_push = FullPush(arguments)\n return full_push.run()","title":""},{"docid":"fb40e2d44956335eee8f405867d7e3ae","score":"0.53827316","text":"def run():\n args = parse()\n\n build_results = [\n build(build_id, spec, args)\n for spec in BUILDS\n for build_id in spec['ids']\n if not args['ids'] or build_id in args['ids']\n ]\n\n if not args['publish']:\n return\n\n for entry in build_results:\n publish(entry, args)","title":""},{"docid":"6827da356d393cff7dc119a6e7545a01","score":"0.5379408","text":"def task_publish_test_pypi() -> DoitTask:\n return _publish_task('--repository testpypi')","title":""},{"docid":"7cfc7c41023d3fae35d580a7cdc9e6d9","score":"0.5375293","text":"def main():\n cli = PublishCLI()\n opts = cli.process_options(abort_on_missing_template=False)\n args = cli.arguments\n suite = Suite(workdir=opts['image_dir'], suites=opts['suites'])\n\n build_opts = ['no_cache', 'rm']\n build_opts = get_build_opts(build_opts, args)\n\n for ctx in suite.process():\n fmt = ctx.copy()\n fmt['path'] = dockerfile_path(suite=fmt['suite'], variant=fmt['variant'])\n\n fmt['variant'] = '-{}'.format(fmt['variant']) if fmt['variant'] else ''\n fmt['tag'] = TAG.format(**fmt)\n fmt['options'] = build_opts\n\n # Run docker build\n shell_out(BUILD.format(**fmt))\n\n # Tag latestest if suite.yml contains latest option\n if fmt['suite'] + fmt['variant'] == suite.latest:\n shell_out(TAG_LATEST.format(**fmt))\n if not args['no_push']:\n shell_out(PUSH.format(tag='{registry}{image}:latest'.format(**fmt)))\n\n # Push image\n if not args['no_push']:\n shell_out(PUSH.format(**fmt))","title":""},{"docid":"b43ded2165a93cc31e6f8297119d6d0b","score":"0.5372495","text":"def publisher():\n rospy.init_node('robotiq_simple_controller')\n pub = rospy.Publisher('command', CModelCommand, queue_size=3)\n command = CModelCommand()\n while not rospy.is_shutdown():\n s = askForCommand(command)\n if s == 'x':\n break\n command = genCommand(s, command)\n pub.publish(command)\n rospy.sleep(0.1)","title":""},{"docid":"eaa2d3c4c177bfc405513e9c1883a8d9","score":"0.5368741","text":"def invoke(command, args):\n SUBCOMMANDS[command](*args) # pylint: disable=W0142","title":""},{"docid":"dde7a1ff94a13e386c2222f089b7c82d","score":"0.5357089","text":"def submit_command() -> Response:\n\n command_name = request.args.get(\"command_name\", \"\")\n command_args = request.args.get(\"command_args\", [])\n command_kwargs = request.args.get(\"command_kwargs\", {})\n\n write_command(command_name, command_args, command_kwargs, fifo=STDIN_NAMED_PIPE_PATH)\n\n return make_response(\"\", 200)","title":""},{"docid":"fcef6193084138bd0047375cfe9b0ae7","score":"0.5344957","text":"def command(self, cmd, *args):\n c = \"!\" + cmd\n if args:\n c += \" \" + json.dumps(args)\n self.sendui(c)","title":""},{"docid":"c3b150610ddc880621106e60389da45d","score":"0.53440905","text":"def publish(args, pub_type, extensions):\n def _publish(release):\n build_path = os.path.join(args.build_dir, release)\n filename = release.split(os.path.sep)[-1]\n release_dir = os.path.join(args.pub, pub_type)\n release_path = os.path.join(release_dir, filename)\n os.renames(build_path, release_path)\n\n # Latest/symlink handler\n release_abspath = os.path.abspath(release_path)\n latest_abspath = release_abspath.replace(TSTAMP, 'latest')\n\n if os.path.islink(latest_abspath):\n os.unlink(latest_abspath)\n os.symlink(release_abspath, latest_abspath)\n\n return release_path\n\n published = []\n for extension in extensions:\n release = glob(\"%s/odoo_*.%s\" % (args.build_dir, extension))\n if release:\n published.append(_publish(release[0]))\n return published","title":""},{"docid":"0a40248f89b1c98c06a5832bedae0613","score":"0.5329816","text":"def publish(context):\n with context.cd(str(THIS_DIR)):\n print(\"Running pdsite\")\n context.run('pdsite build')","title":""},{"docid":"796436e100728a3090576403809a0bdc","score":"0.5323295","text":"def command(self, command, arguments=None, zone=None):\n try:\n iscp_message = command_to_iscp(command, arguments, zone)\n except ValueError as error:\n self.log.error(f\"Invalid message. {error}\")\n return\n\n self.log.debug(\"> %s\", command)\n try:\n self.transport.write(command_to_packet(iscp_message))\n except:\n self.log.warning(\"No transport found, unable to send command\")","title":""},{"docid":"5005ee223b10a4560f4b8e86dae8db9f","score":"0.5320653","text":"def publish(self):\n raise NotImplementedError","title":""},{"docid":"340a8596bdb3349b2c826e96940ca87d","score":"0.5312219","text":"def publish(c):\n c.run('pelican -s publishconf.py')\n c.run(\n 'rsync --delete --exclude \".DS_Store\" -pthrvz -c '\n '{} {production}:{dest_path}'.format(\n CONFIG['deploy_path'].rstrip('/') + '/',\n **CONFIG))","title":""},{"docid":"1472c057298a3c8340897ccb5572c488","score":"0.53053653","text":"def handle(self, *args, **options):\n\t\tfor DesignPost_id in args:\n\t\t\ttry:\n\t\t\t\tarticle = DesignPost.objects.get(pk=int(DesignPost_id))\n\t\t\t\tif article.status == 'published':\n\t\t\t\t\tself.stdout.write('This article was already published')\n\t\t\t\telse:\n\t\t\t\t\tarticle.published_on = timezone.now()\n\t\t\t\t\tarticle.status = 'published'\n\t\t\t\t\tarticle.save()\n\t\t\t\t\tself.stdout.write('Successfully published this article')\n\t\t\texcept DesignPost.DoesNotExist:\n\t\t\t\tlogger.info(\"The article requested does not exist\")\n\t\t\t\t\n\t\t\t\traise CommandError ('That article does not exist')","title":""},{"docid":"6082661fc42c6555eb03ca8aa48a5443","score":"0.5300951","text":"def cli(ctx, service, publish, ignore_untracked, key, show_doi):\n services = {\n 'zenodo': Zenodo,\n 'figshare': Figshare\n }\n environment_variables = {\n 'zenodo': 'POPPER_ZENODO_API_TOKEN',\n 'figshare': 'POPPER_FIGSHARE_API_TOKEN'\n }\n\n if service not in services:\n pu.fail(\"The service {} is not supported. See popper archive \"\n \"--help for more info.\".format(service))\n\n if not key:\n try:\n key = os.environ[environment_variables[service]]\n except KeyError:\n key = get_access_token(service)\n\n if show_doi and publish:\n pu.fail(\"--show-doi can not be given along with --publish\")\n\n archive = services[service](key)\n\n pu.info('Looking for an existing record for the project.')\n archive.fetch_depositions()\n\n if show_doi:\n if archive.record_exists():\n pu.info('Found record for project: {}'.format(archive.record_id))\n archive.show_doi()\n else:\n pu.info('No record for project exists.')\n sys.exit(0)\n\n if not archive.record_exists():\n pu.info('No record exists yet; creating one for project.')\n archive.create_new_deposition()\n pu.info('Record ID: {}'.format(archive.record_id))\n else:\n pu.info('Record exists: {}'.format(archive.record_id))\n\n if archive.is_last_deposition_published():\n pu.info('A published version exists, creating new version.')\n archive.create_new_version()\n else:\n pu.info('An unpublished version exists, updating it.')\n\n archive.delete_previous_file()\n\n archive.ignore_untracked = ignore_untracked\n\n pu.info('Uploading files.')\n archive.upload_snapshot()\n\n if publish:\n pu.info('Publishing record and obtaining DOI.')\n archive.publish_snapshot()\n archive.show_doi()\n\n pu.info(\"Done!\")","title":""},{"docid":"17db9995de40094d36987d68f410db12","score":"0.5299374","text":"def publish(build):\n build.packages.install(\"wheel\")\n build.executables.run([\n \"python\", \"setup.py\",\n \"sdist\", \"upload\"\n ])","title":""},{"docid":"94d0f20cbdcc2daaccc6c64e1958d455","score":"0.5291367","text":"def publish(self, message):\n self.__ensure_started()\n\n message_def = self.messages.get_by_cls(type(message))\n context = OutgoingContext(message, message_def)\n self.outgoing_pipeline.execute(context)","title":""},{"docid":"5d49101dd9afafe8d7148b0c7e1b9abd","score":"0.5289644","text":"def publish(gui, debug, paths):\n if debug:\n os.environ['PYPE_DEBUG'] = '3'\n PypeLauncher().publish(gui, list(paths))","title":""},{"docid":"99911b20befdcfc9b4309a11cc71f7ed","score":"0.5282902","text":"def main():\n writer = Writer()\n writer.translator_class = HTMLTranslator\n reader = Reader()\n publish_cmdline(reader=reader, writer=writer)","title":""},{"docid":"9e9793192338081c02722429bb5842df","score":"0.5278152","text":"async def push(self, ctx):\r\n if ctx.invoked_subcommand is not None:\r\n return\r\n\r\n await ctx.invoke(self.push_info)","title":""},{"docid":"2a790e707323d617dbfc8ac02a2064b5","score":"0.52700514","text":"def add(self, *args, **kwargs):\n self.out.append(command(*args, **kwargs))","title":""},{"docid":"8c325eb470b3fde32ce7c6d2e44da38a","score":"0.5266555","text":"def cli(**kwargs):","title":""},{"docid":"b69cbeed85436e5b0c2cff0695c455a4","score":"0.52621144","text":"def publish(self, request, *args, **kwargs):\n publication = self.get_object()\n return Response({'is_published': publication.publish()})","title":""},{"docid":"e17d005c7eaf3b87bcfad074c7d61f17","score":"0.5259506","text":"def cmd(self, *args):\n return self.__generate_cmd(*args, shell=True)","title":""},{"docid":"beaffdc9d8dda1c00bb533820937f9c1","score":"0.5258657","text":"def publish():\n local('git push origin master')\n local('git push origin master:gh-pages')","title":""},{"docid":"18652c8b6250cdcfd488e9de11c25b14","score":"0.5254601","text":"def send_command(self, command, *args, **kwargs):\n if args:\n args = list(args)\n for i in xrange(len(args)):\n arg = args[i]\n if not isinstance(arg, basestring):\n args[i] = str(arg)\n\n if not args[-1].startswith(':'):\n args[-1] = ':%s' % args[-1]\n\n out = '%s %s\\r\\n' % (command, ' '.join(args))\n else:\n out = '%s\\r\\n' % command\n\n if '_prefix' in kwargs and kwargs['_prefix']:\n out = '%s %s' % (kwargs['_prefix'], out)\n elif self.prefix:\n out = '%s %s' % (self.prefix, out)\n\n # Send it.\n log.debug('\\x1B[0;32m>> %s\\x1B[0m' % out.rstrip())\n\n self.write(out.encode(self.encoding))","title":""},{"docid":"a741e3626a055fad14cbedb8062365ee","score":"0.52511275","text":"def publish(ctx, test=False):\n clean(ctx)\n if test:\n run('python setup.py register -r test sdist bdist_wheel', echo=True)\n run('twine upload dist/* -r test', echo=True)\n else:\n run('python setup.py register sdist bdist_wheel', echo=True)\n run('twine upload dist/*', echo=True)","title":""},{"docid":"c644e2cb7c6a54bd83274476765163b2","score":"0.5250997","text":"def publish():\n gh_pages()","title":""},{"docid":"88348612b5f1c2c4e156d1ce23959435","score":"0.52472234","text":"def publish(self, message: Any) -> Publisher:\n # print(f\"=>TopicHandle.publish('{message}')\")\n publisher: Optional[Publisher] = self.publisher\n assert publisher, \"Publisher not set yet.\"\n message_type: Any = self.message_type\n assert isinstance(message, message_type), f\"Message is not of type {message_type}\"\n # print(f\"Topic('{self.ros_path}): Published: {message}'\")\n publisher.publish(message)\n self.publisher_count += 1\n # print(f\"<=TopicHandle.publish('{message}')\")","title":""},{"docid":"07c732ce801e12cf935dd3afbe01dcd0","score":"0.5234806","text":"def setup_publish(subargv):\n\n parser = optparse.OptionParser(usage=\"git-lockup setup-publish\")\n (options, subargs) = parser.parse_args(subargv)\n\n assert os.path.isdir(\".git\")\n tool = \".git/git-lockup\"\n me = read_myself()\n with open(tool, \"wb\") as f:\n f.write(me)\n make_executable(tool)\n # this makes a keypair, updates .git/config, and adds a post-commit hook\n setup_publish_config(True, \"master\")\n # Then we create ./setup-lockup for clients to use. We must insert a copy\n # of ourselves.\n setup_lockup = base64.b64decode(setup_lockup_b64)\n setup_lockup = setup_lockup.replace(\"GIT_LOCKUP_B64\", base64.b64encode(me))\n with open(\"setup-lockup\", \"wb\") as f:\n f.write(setup_lockup)\n make_executable(\"setup-lockup\")\n git_add(\"setup-lockup\")\n # along with an lockup.config with the branch->key information from\n # .git/config\n branches = get_config_verifykeys()\n with open(\"lockup.config\", \"wb\") as f:\n f.write(\"# -*- mode: conf; coding: utf-8 -*-\\n\")\n f.write(\"[branches]\\n\")\n for branch in sorted(branches):\n shortbranch = remove_prefix(branch, \"refs/heads/\", True)\n keys = \" \".join([key for key in branches[branch]])\n f.write(\"%s = %s\\n\" % (shortbranch, keys))\n git_add(\"lockup.config\")\n\n # then maybe execute TOOL subscribe, not sure yet","title":""},{"docid":"87d5eec4cabc1aa91c9c87c5aaf03f63","score":"0.52105814","text":"def publishSingle(self, key, value):\n\n self.publisher.publish(self.name, {key: value})","title":""},{"docid":"5246dc5bcabc20a9c1af785c6e4332bd","score":"0.5209397","text":"def publish(self, msg, *args, **kwargs):\n pub_hdrs = self._get_publish_headers(msg, kwargs)\n super(StreamPublisher, self).publish(msg, to_name=self._send_name, headers=pub_hdrs)","title":""},{"docid":"b972bac0f01cc2f826bc9d931fd006d2","score":"0.5208493","text":"def command(self):\n raise NotImplementedError(\"Container task must specify command\")","title":""},{"docid":"0f87cd7f698b3345546a73824191bda0","score":"0.52067506","text":"def main(argv):\n options = parse_args(argv)\n log.info(\"options: \" + str(options))\n\n processor = NewsAggregationProcessor(options)\n processor.process()","title":""},{"docid":"b020d4f9b14a25997144969cf947e575","score":"0.52040535","text":"def publish(self):\n return","title":""},{"docid":"ace6efe385c29d7d1d7d115a62afe0fa","score":"0.5203939","text":"def commandOne(self, repo, *args, **kwargs):\n pass","title":""},{"docid":"74e57fa39dfdb7e4bf190b281d9b60d9","score":"0.51922435","text":"def _command(self, *args):\n return self._client.command(*args)","title":""},{"docid":"a264cb232a5f2bbd4ac9e277495bd0e8","score":"0.51839775","text":"def publish(send_json_path, get_json_path, gui):\n\n log.info(\"avalon.session is: \\n{}\".format(ppl.SESSION))\n log.info(\"PUBLISH_PATH: \\n{}\".format(os.environ[\"PUBLISH_PATH\"]))\n\n pype_start = os.path.join(os.getenv('PYPE_SETUP_ROOT'),\n \"app\", \"pype-start.py\")\n\n args = [pype_start,\n \"--root\", os.environ['AVALON_PROJECTS'], \"--publish-gui\",\n \"-pp\", os.environ[\"PUBLISH_PATH\"],\n \"-d\", \"rqst_json_data_path\", send_json_path,\n \"-d\", \"post_json_data_path\", get_json_path\n ]\n\n log.debug(args)\n log.info(\"_aport.api Variable `AVALON_PROJECTS` had changed to `{0}`.\".format(\n os.environ['AVALON_PROJECTS']))\n forward([\n sys.executable, \"-u\"\n ] + args,\n # cwd=cwd\n )\n\n return {\"get_json_path\": get_json_path}","title":""},{"docid":"ffe0cbd9975e83a31454a881918d98f3","score":"0.5180994","text":"async def publish(self, topic, data):\n raise NotImplementedError","title":""},{"docid":"9bb74d4915ae6a71d8d130df048bab37","score":"0.5177897","text":"def execute(args):\n pass","title":""},{"docid":"a4d1868f85eabcf3659186c1a9b00561","score":"0.51732165","text":"def execute(self, container, command):\n raise NotImplementedError()","title":""},{"docid":"df3f346acfcc4f3c8383dff99a1d2078","score":"0.51593494","text":"def _svnwrite(self, cmd, *args):\n l = ['svn %s' % cmd]\n args = ['\"%s\"' % self._escape(item) for item in args]\n l.extend(args)\n l.append('\"%s\"' % self._encodedurl())\n # fixing the locale because we can't otherwise parse\n string = \" \".join(l)\n if DEBUG:\n print(\"execing %s\" % string)\n out = self._svncmdexecauth(string)\n return out","title":""},{"docid":"ed31c8765358eba325d808accabbe2ef","score":"0.51477003","text":"def redis_publish(mgr, params):\n if params.get('channel') and params.get('message'):\n serv_redis.redis.publish(params['channel'], params['message'])","title":""},{"docid":"d49ec9ba59ec5126b459b69af4eadc55","score":"0.5140094","text":"def command(self):\n \n pass","title":""},{"docid":"8f28ff2adeaf5f3a3386f9dfad53aac8","score":"0.5139412","text":"def push(args):\n p = OptionParser(push.__doc__)\n opts, args = p.parse_args(args)\n\n if len(args) != 1:\n sys.exit(not p.print_help())\n\n s, = args\n prefix = get_prefix()\n version, partID, unitigID = get_ID(s)\n\n cmd = \"tigStore\"\n cmd += \" -g ../{0}.gkpStore -t ../{0}.tigStore\".format(prefix)\n cmd += \" {0} -up {1} -R {2}\".format(version, partID, s)\n\n sh(cmd)","title":""},{"docid":"8a141bf4b3dc73e3f830536841bcc6a2","score":"0.51390314","text":"def __call__(self, *args, **kwargs):\n template, args, kwargs = self.pre(self.command_template, args, kwargs)\n command = template.format(*args, **kwargs)\n argv = command.split(\" \")\n stdout = check_output(argv, universal_newlines=True)\n return self.post(stdout)","title":""},{"docid":"4b468aa8d93e337a282a0fbf039c0851","score":"0.51280856","text":"def cli(**params):\n pass","title":""},{"docid":"7de5cadb3d3ef15190f43ba238c9cbcc","score":"0.5102143","text":"async def handle(self, command: str, args: str, message: m.Message) -> None:\n room = await self.master.room(room_id=message.roomid)\n roomref = room.to_roomref2(True)\n\n msg = await usage.get_message(self.master, roomref)\n await self.master.ddp.send_message(message.roomid, msg)","title":""},{"docid":"d45c4725075f2b86f13e400542deda46","score":"0.5099334","text":"def command(self,*args):\n return self.comp.command(*args)","title":""},{"docid":"3da45045a8a2595029c0b74b0644d129","score":"0.5097723","text":"def do_deploy(self, args):\r\n DeployPasta().cmdloop()","title":""},{"docid":"f0d9982ebb8425f75f6aa9ae7dbe1bb0","score":"0.5095803","text":"def publish(self, topic, **kwargs):\r\n pub = PublisherProtocol(topic, **kwargs)\r\n self._make_connection(pub)","title":""},{"docid":"fcaef1b87354a793362ffaadb36f05e5","score":"0.5087014","text":"def populate(parser: argparse.ArgumentParser):\n parser.add_argument(\"--push\", action=\"store_true\")","title":""},{"docid":"a77af1b822bc962e67e4beb28aee687e","score":"0.5081749","text":"def set_publish_sample_args(args):\n public_sample = {}\n if args.public_sample:\n public_sample = {\"private\": False}\n return public_sample","title":""},{"docid":"79a8b38c27c2ac5bd5ae19a9750a9377","score":"0.5078941","text":"def publish():\n repo = Repo(Path(__file__).resolve().parent)\n git_cli = repo.git\n\n for branch in BRANCHES.values():\n git_cli.push('origin', branch, tags=True)","title":""},{"docid":"e03be34f2b62840dad50fba5353dfd2a","score":"0.5075389","text":"def event_publish(self, cmd):\n for sub in self.subscribers:\n sub.event_receive(cmd)","title":""}],"string":"[\n {\n \"docid\": \"50a2eb172754fe6beac7e71b4cda7d1c\",\n \"score\": \"0.688723\",\n \"text\": \"def _publish_task(publish_args: str = '') -> DoitTask:\\n return debug_task([\\n Interactive('poetry run nox --session build_dist build_check'),\\n f'poetry publish {publish_args}',\\n ])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3266c59c83240514b6ca0b136d52b8b4\",\n \"score\": \"0.66889817\",\n \"text\": \"def doPublish(args):\\n if sys.stdin.isatty():\\n # treat it as a text file?\\n pass\\n\\n filename = args[0]\\n title = description = \\\"\\\"\\n\\n if len(args) > 1:\\n title = args[1]\\n\\n if len(args) > 2:\\n description = args[2]\\n\\n for thing in [filename, title, description]:\\n print(f\\\"{thing}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c3c57c321b6321a29376c7378b14589\",\n \"score\": \"0.6463009\",\n \"text\": \"def publish(self, info, *args, **kwds):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6094edfc67e5726f3a8af602c6153c0\",\n \"score\": \"0.6462211\",\n \"text\": \"def handle(self, *args, **options):\\n self._handle_publish(options, DiscoursePublisher)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f52366e548db5301717ddeacd1b061fe\",\n \"score\": \"0.63315666\",\n \"text\": \"def run_publish(args):\\n home = Path(args.channel).absolute()\\n channel = json.loads(home.joinpath(\\\"channel.json\\\").read_text())\\n now = datetime.utcnow().strftime(r\\\"%a, %d %b %Y %H:%M:%S +0000\\\")\\n episodes = [\\n json.loads(line)\\n for line in home.joinpath(\\\"episodes.json\\\").read_text().splitlines()\\n ]\\n env = Environment(\\n loader=FileSystemLoader(home.as_posix()),\\n autoescape=select_autoescape(['html', 'xml', 'jinja'])\\n )\\n template = env.get_template(\\\"feed_template.xml.jinja\\\")\\n feed = template.render(channel=channel, episodes=episodes, now=now)\\n feed_path = home.joinpath(\\\"latest_feed.xml\\\")\\n feed_path.write_text(feed)\\n\\n if not args.dry_run:\\n print(\\\"Publishing to IPNS. This can take 1-5 minutes.\\\")\\n file_hash = (\\n subprocess\\n .check_output([\\\"ipfs\\\", \\\"add\\\", \\\"-Q\\\", feed_path.as_posix()])\\n .decode()\\n .strip()\\n )\\n subprocess.check_call(\\n [\\\"ipfs\\\", \\\"name\\\", \\\"publish\\\", \\\"--key\\\", home.name, file_hash]\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ffe75a6973324a4628111d4ecb075ce\",\n \"score\": \"0.62642044\",\n \"text\": \"def command(self, args):\\n if 'create_release' in args.command:\\n self.create_release()\\n\\n elif 'close_release' in args.command:\\n self.close_release()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acb87e86f74a48cc6e0648729ad8062c\",\n \"score\": \"0.6243611\",\n \"text\": \"def gpg_publish(args):\\n\\n mirror = None\\n if args.directory:\\n mirror = spack.mirror.Mirror(args.directory, args.directory)\\n elif args.mirror_name:\\n mirror = spack.mirror.MirrorCollection().lookup(args.mirror_name)\\n elif args.mirror_url:\\n mirror = spack.mirror.Mirror(args.mirror_url, args.mirror_url)\\n\\n spack.binary_distribution.push_keys(\\n mirror, keys=args.keys, regenerate_index=args.rebuild_index\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c1f925581eea3a1fd87350abb11c740\",\n \"score\": \"0.6205315\",\n \"text\": \"def command(self, args):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"734790c12f8b4e2f0baf2596cd5c248b\",\n \"score\": \"0.61964345\",\n \"text\": \"def cli_du_publisher(reader_name='mpe', parser=None, parser_name='rst',\\n writer=None, writer_name='pseudoxml', description=''):\\n\\n # XXX: how far does inline customization go? parser = Parser(inliner=Inliner())\\n reader_class = comp.get_reader_class(reader_name)\\n parser_class = None\\n if not parser:\\n parser_class = comp.get_parser_class(parser_name)\\n parser = parser_class()\\n if not writer:\\n writer_class = comp.get_writer_class(writer_name)\\n writer = writer_class()\\n\\n publish_cmdline(\\n parser=parser,\\n parser_name=parser_name,\\n reader=reader_class(parser),\\n reader_name=reader_name,\\n writer=writer,\\n writer_name=writer_name,\\n description=description)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f375072b9ab95583274dd6f8ea661dc\",\n \"score\": \"0.61568415\",\n \"text\": \"def add_publish_qc_command(cmdparser):\\n default_use_hierarchy = (\\\"yes\\\" if __settings.qc_web_server.use_hierarchy\\n else \\\"no\\\")\\n default_exclude_zips = (\\\"yes\\\" if __settings.qc_web_server.exclude_zip_files\\n else \\\"no\\\")\\n p = cmdparser.add_command('publish_qc',help=\\\"Copy QC reports to publication area\\\",\\n usage=\\\"%prog publish_qc [OPTIONS] [ANALYSIS_DIR]\\\",\\n description=\\\"Copy QC reports from ANALYSIS_DIR to local \\\"\\n \\\"or remote directory (e.g. web server). By default existing \\\"\\n \\\"QC reports will be copied without further checking; if no \\\"\\n \\\"report is found then QC results will be verified and a \\\"\\n \\\"report generated first.\\\")\\n p.add_option('--projects',action='store',\\n dest='project_pattern',default=None,\\n help=\\\"simple wildcard-based pattern specifying a subset of projects \\\"\\n \\\"and samples to publish the QC for. PROJECT_PATTERN can specify a \\\"\\n \\\"single project, or a set of projects.\\\")\\n p.add_option('--qc_dir',action='store',\\n dest='qc_dir',default=None,\\n help=\\\"specify target directory to copy QC reports to. QC_DIR can \\\"\\n \\\"be a local directory, or a remote location in the form \\\"\\n \\\"'[[user@]host:]directory'. Overrides the default settings.\\\")\\n p.add_option('--use-hierarchy',type='choice',choices=[\\\"yes\\\",\\\"no\\\"],\\n dest='use_hierarchy',default=default_use_hierarchy,\\n help=\\\"use YEAR/PLATFORM hierarchy under QC_DIR; can be \\\"\\n \\\"'yes' or 'no' (default: %s)\\\" % default_use_hierarchy)\\n p.add_option('--exclude-zip-files',type='choice',choices=[\\\"yes\\\",\\\"no\\\"],\\n dest='exclude_zip_files',default=default_exclude_zips,\\n help=\\\"exclude ZIP archives from publication; can be 'yes' \\\"\\n \\\"or 'no' (default: %s)\\\" % default_exclude_zips)\\n p.add_option('--ignore-missing-qc',action='store_true',\\n dest='ignore_missing_qc',default=False,\\n help=\\\"skip projects where QC results are missing or can't be verified, \\\"\\n \\\"or where reports can't be generated.\\\")\\n p.add_option('--regenerate-reports',action='store_true',\\n dest='regenerate_reports',default=False,\\n help=\\\"attempt to regenerate existing QC reports\\\")\\n p.add_option('--force',action='store_true',\\n dest='force',default=False,\\n help=\\\"force generation of QC reports for all projects even \\\"\\n \\\"if verification has failed\\\")\\n p.add_option('--legacy',action='store_true',\\n dest='legacy_mode',default=False,\\n help=\\\"legacy mode: include links to MultiQC reports in the \\\"\\n \\\"top-level index page\\\")\\n add_debug_option(p)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c61eba8f029a5efc0e5d1903cf8ebbb5\",\n \"score\": \"0.61412495\",\n \"text\": \"def publish(build_entry: dict, args: dict):\\n skip_publishing = not build_entry.get('publish', True)\\n for tag in build_entry['tags']:\\n if args['dry_run']:\\n print('[DRY-RUN]: Skipped pushing {}'.format(tag))\\n elif skip_publishing:\\n print('[SKIPPED]: Publishing not allowed for {}'.format(tag))\\n else:\\n print('[PUSHING]:', tag)\\n os.system('docker push {}'.format(tag))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"83619ca504179538a708d7dc37077b0b\",\n \"score\": \"0.60522413\",\n \"text\": \"def test_publish_positive(self, *mocks):\\n result = self.runner.invoke(\\n cli, [*CLI_LOG_OPTION, \\\"publish\\\", \\\"--local\\\"], standalone_mode=False,\\n )\\n self.assertEqual(result.exit_code, 0)\\n result = self.runner.invoke(\\n cli, [*CLI_LOG_OPTION, \\\"publish\\\", \\\"--remote\\\"], standalone_mode=False,\\n )\\n self.assertEqual(result.exit_code, 0)\\n result = self.runner.invoke(\\n cli, [*CLI_LOG_OPTION, \\\"publish\\\"], standalone_mode=False,\\n )\\n self.assertEqual(result.exit_code, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbccd92eea532fbf6e09162f587b6ba8\",\n \"score\": \"0.6010531\",\n \"text\": \"def register_command(subparser):\\n parser = subparser.add_parser('serve', help='Serve the contents of the publishers output files')\\n parser.set_defaults(func=_main)\\n\\n parser.add_argument('--port', '-p', type=int, help='Port that the server will be listening on',\\n default=8080)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28e25027ec7f6199a8fa1176be1b9832\",\n \"score\": \"0.60084635\",\n \"text\": \"def test_publish_positive(self, *mocks):\\n self.set_config(\\\"agent.description\\\", \\\"some-description\\\")\\n self.run_cli_command(\\\"publish\\\", cwd=self._get_cwd())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffae76038f71877599b76acd42426826\",\n \"score\": \"0.6003352\",\n \"text\": \"def busdriver(self, msg, cmd):\\n self.publish(msg.command.lower(),\\n dumps([msg.args,\\n msg.prefix[0],\\n msg.trailing, {\\\"prefix\\\": msg.prefix}]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fec088fcc99217d2a9194b8675f1ecd\",\n \"score\": \"0.59700555\",\n \"text\": \"def task_publish() -> DoitTask:\\n return _publish_task()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bea9a992aee0a395063b8e1f92f9f367\",\n \"score\": \"0.5967572\",\n \"text\": \"def publish(self, msg: str) -> None:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e278d5b550d395c3b5197538c1e8d98b\",\n \"score\": \"0.5917355\",\n \"text\": \"def bus_command(self, msg, cmd):\\n match = COMMAND_RE.match(msg.trailing)\\n if match:\\n cmd_name = match.groups()[1]\\n cmd_args = msg.trailing[len(cmd_name) + 1:].strip()\\n self.publish(\\\"command_{}\\\".format(cmd_name),\\n dumps([msg.args,\\n msg.prefix[0],\\n cmd_args,\\n {\\\"prefix\\\": msg.prefix}]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"611b96a1c1d435bbbd7a367fc993af7c\",\n \"score\": \"0.5818544\",\n \"text\": \"def Publisher(object):\\n def __init__(self, channel_key):\\n self.channel_key = str(channel_key)\\n\\n def publish(**kwargs):\\n publish(self.channel_key, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f988ddb46bf8a4733704ef5bf96592d7\",\n \"score\": \"0.5810508\",\n \"text\": \"def publish(\\n server: Murmur.Server,\\n user: Murmur.User,\\n msg: Murmur.TextMessage\\n):\\n # Wrap original message in a more context aware TextMessage\\n wrapped = TextMessage(\\n user,\\n server,\\n msg.sessions,\\n msg.channels,\\n msg.trees,\\n msg.text\\n )\\n\\n for command in command_subscribers:\\n match = command['prog'].search(msg.text)\\n if match:\\n command['func'](wrapped, **match.groupdict())\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"806995e838b5d3f63aac68159d8fd749\",\n \"score\": \"0.5796559\",\n \"text\": \"def publish(self, message):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49b525d73a87d9e73c30077d08cc98b4\",\n \"score\": \"0.57887083\",\n \"text\": \"def prepare_publish(cmd, client, resource_group_name, resource_name, sln_name, proj_file_path, code_dir=None, # pylint:disable=too-many-statements\\n version='v3'):\\n\\n # The prepare-publish process for v3 bots and v4 bots differ, so if the user specifies a v4 version, end the command\\n # and inform user of az bot publish.\\n if version == 'v4':\\n raise CLIError('\\\\'az bot prepare-publish\\\\' is only for v3 bots. Please use \\\\'az bot publish\\\\' to prepare and '\\n 'publish a v4 bot.')\\n\\n logger.warning('WARNING: `az bot prepare-publish` is in maintenance mode for v3 bots as support for creating v3 '\\n 'SDK bots via `az bot create` will be discontinued on August 1st, 2019. We encourage developers '\\n 'move to creating and deploying v4 bots.\\\\n\\\\nFor more information on creating and deploying v4 bots, '\\n 'please visit https://aka.ms/create-and-deploy-v4-bot\\\\n\\\\nFor more information on v3 bot '\\n 'creation deprecation, please visit this blog post: '\\n 'https://blog.botframework.com/2019/06/07/v3-bot-broadcast-message/')\\n\\n bot = client.bots.get(\\n resource_group_name=resource_group_name,\\n resource_name=resource_name\\n )\\n if bot.kind == 'bot':\\n raise CLIError('Prepare Publish is not supported for registration only bots.')\\n\\n if not code_dir:\\n code_dir = os.getcwd()\\n logger.warning('Parameter --code-dir not provided, defaulting to current working directory, %s. For more '\\n 'information, run \\\\'az bot prepare-publish -h\\\\'', code_dir)\\n\\n code_dir = code_dir.strip()\\n if not os.path.isdir(code_dir):\\n raise CLIError('Please supply a valid directory path containing your source code. '\\n 'Path {0} does not exist.'.format(code_dir))\\n\\n os.chdir(code_dir)\\n\\n # Ensure that the directory does not contain appropriate post deploy scripts folder\\n if 'PostDeployScripts' in os.listdir(code_dir):\\n raise CLIError('Post deploy azure scripts are already in Place.')\\n\\n # Download bot source\\n download_path = download_app(cmd, client, resource_group_name, resource_name)\\n\\n shutil.copytree(os.path.join(download_path['downloadPath'], 'PostDeployScripts'), 'PostDeployScripts')\\n\\n # If javascript, we need these files there for Azure WebApps to start\\n if os.path.exists(os.path.join('PostDeployScripts', 'publish.js.template')):\\n\\n logger.info('Detected language javascript.')\\n\\n shutil.copy(os.path.join(download_path['downloadPath'], 'iisnode.yml'), 'iisnode.yml')\\n shutil.copy(os.path.join(download_path['downloadPath'], 'publish.js'), 'publish.js')\\n shutil.copy(os.path.join(download_path['downloadPath'], 'web.config'), 'web.config')\\n\\n # If C#, we need other set of files for the WebApp to start including build.cmd\\n else:\\n logger.info('Detected language CSharp.')\\n solution_path = None\\n csproj_path = None\\n old_namev4 = 'AspNetCore-EchoBot-With-State'\\n old_namev3 = 'Microsoft.Bot.Sample.SimpleEchoBot'\\n shutil.copy(os.path.join(download_path['downloadPath'], 'build.cmd'), 'build.cmd')\\n shutil.copy(os.path.join(download_path['downloadPath'], '.deployment'), '.deployment')\\n\\n # \\\"deploy.cmd.template\\\" does not exist for v4 bots. If the next section of code fails due to deploy.cmd.template\\n # not being found, it is most likely due to trying to call prepare-publish on a v4 bot.\\n # Inform the user of the potential problem and raise the error to exit the process.\\n try:\\n shutil.copyfile(os.path.join(download_path['downloadPath'], 'PostDeployScripts', 'deploy.cmd.template'),\\n 'deploy.cmd')\\n except OSError as error: # FileNotFoundError introduced in Python 3\\n logger.error('\\\"deploy.cmd.template\\\" not found. This may be due to calling \\\\'az bot prepare-publish\\\\' on a '\\n 'v4 bot. To prepare and publish a v4 bot, please instead use \\\\'az bot publish\\\\'.')\\n raise CLIError(error)\\n\\n # Find solution and project name\\n for root, _, files in os.walk(os.curdir):\\n if solution_path and csproj_path:\\n break\\n for fileName in files:\\n if solution_path and csproj_path:\\n break\\n if fileName == sln_name:\\n solution_path = os.path.relpath(os.path.join(root, fileName))\\n if fileName == proj_file_path:\\n csproj_path = os.path.relpath(os.path.join(root, fileName))\\n\\n # Read deploy script contents\\n with open('deploy.cmd') as f:\\n content = f.read()\\n\\n logger.info('Visual studio solution detected: %s.', solution_path)\\n logger.info('Visual studio project detected: %s.', csproj_path)\\n\\n # Using the deploy.cmd as a template, adapt it to use our solution and csproj\\n with open('deploy.cmd', 'w') as f:\\n content = content.replace(old_namev3 + '.sln', solution_path)\\n content = content.replace(old_namev3 + '.csproj', csproj_path)\\n content = content.replace(old_namev4 + '.sln', solution_path)\\n content = content.replace(old_namev4 + '.csproj', csproj_path)\\n f.write(content)\\n\\n shutil.rmtree(download_path['downloadPath'])\\n\\n logger.info('Bot prepare publish completed successfully.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c340129e365cc49068be28636acc05b2\",\n \"score\": \"0.5710041\",\n \"text\": \"def publish_vehicle_command(self, command, **params) -> None:\\n msg = VehicleCommand()\\n msg.command = command\\n msg.param1 = params.get(\\\"param1\\\", 0.0)\\n msg.param2 = params.get(\\\"param2\\\", 0.0)\\n msg.param3 = params.get(\\\"param3\\\", 0.0)\\n msg.param4 = params.get(\\\"param4\\\", 0.0)\\n msg.param5 = params.get(\\\"param5\\\", 0.0)\\n msg.param6 = params.get(\\\"param6\\\", 0.0)\\n msg.param7 = params.get(\\\"param7\\\", 0.0)\\n msg.target_system = 1\\n msg.target_component = 1\\n msg.source_system = 1\\n msg.source_component = 1\\n msg.from_external = True\\n msg.timestamp = int(self.get_clock().now().nanoseconds / 1000)\\n self.vehicle_command_publisher.publish(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14bd5a73c204ed62a5e0db718ae2f1c0\",\n \"score\": \"0.56659275\",\n \"text\": \"def publish(cls, nuget_package, address = 'default'):\\n ret = NO_ERROR\\n if address is 'default':\\n ret = NugetUtility.nuget_cli('push', nuget_package, '-Source', 'https://www.nuget.org/')\\n else:\\n ret = NugetUtility.nuget_cli('push', nuget_package, '-Source', address)\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d99dfe248af761c7baec0fd332dc8af0\",\n \"score\": \"0.56579053\",\n \"text\": \"def run(command:'Command to execute in `plugin.action` format.',\\n server:'URI to the remote SuRVoS API Server'=None,\\n *args:'Extra keyword arguments in the form of `key=value` '\\n 'required by plugin/commands. Please refer to SuRVoS API.'):\\n plugin, command = command.split('.')\\n args = [k.split('=') for k in args]\\n params = {k: v for k, v in args}\\n result = run_command(plugin, command, uri=server, **params)[0]\\n if type(result) == dict:\\n result = format_yaml(result)\\n logger.info(result)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"24ee256576d27ce9cac7c9935a4d8efc\",\n \"score\": \"0.5645572\",\n \"text\": \"def publish(self, notifications, **kwargs):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96aa22af67ec48eb550f9e85ecd2732f\",\n \"score\": \"0.5624073\",\n \"text\": \"def send_redis_command(command, **kwargs):\\n command_dictionary = copy(kwargs)\\n command_dictionary['command'] = command\\n encoded_command_dictionary = json.dumps(command_dictionary)\\n REDIS.publish('comp.command', encoded_command_dictionary)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19082437a37ca2e890195ba70d635b79\",\n \"score\": \"0.5605631\",\n \"text\": \"def do_pocs_command(self, cmd):\\n try:\\n self.cmd_publisher.send_message('POCS-CMD', cmd)\\n except AttributeError:\\n print_info('Messaging not started')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b67ed6d8f3e640a5117863982d95a7d\",\n \"score\": \"0.5594868\",\n \"text\": \"def publish():\\n try:\\n upload_dist_commands = [cmd + ['register', 'upload']\\n for cmd in dist_commands]\\n list(map(subprocess.check_call, upload_dist_commands))\\n except:\\n print(\\\"Unable to upload the dist files. Ask in IRC for help access 57\\\"\\n \\\"or assistance.\\\")\\n raise SystemExit(4)\\n print('Distributions have been uploaded.')\\n print('Please ask in IRC for others to help you test '\\n 'CherryPy {NEXT_VERSION}.'\\n .format(**globals()))\\n print(\\\"Please confirm that the distro installs properly \\\"\\n \\\"with `easy_install CherryPy=={NEXT_VERSION}`.\\\".format(**globals()))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"40b67c563f77e9d30e6d6611287eec90\",\n \"score\": \"0.55342305\",\n \"text\": \"def zmq_publish_command(command, zmq_addr):\\n (pid, fd) = pty.fork()\\n if not pid:\\n # child (slave)\\n rc = os.system(command)\\n sys.exit(rc)\\n else:\\n # parent (master)\\n ctx = zmq.Context()\\n sock = ctx.socket(zmq.PUB)\\n sock.bind(zmq_addr)\\n done = False\\n \\n # this allows clients to connect/reconnect, otherwise they'll always miss the first message\\n time.sleep(0.5)\\n\\n while not done:\\n data = os.read(fd, 8192)\\n if data:\\n sock.send(\\\"%s %s\\\" % (OUTPUT_TOPIC, data))\\n else:\\n done = True\\n sock.send(\\\"%s \\\" % (OUTPUT_TOPIC)) # no data: signals end of output\\n sock.close()\\n ctx.term()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ce9c1779290ef1587dd841970f2526e\",\n \"score\": \"0.553045\",\n \"text\": \"def publish(self):\\n self.client.call(self.METHODS['publish'], self.id, True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a248c3b7296b57ea692fbaf956bbed4e\",\n \"score\": \"0.5527572\",\n \"text\": \"def publish_for(self, instance, *event_args, **event_keys):\\n\\n key = self.__key\\n publisher = getattr(instance, key, None)\\n if publisher is not None:\\n publisher.publish(*event_args, **event_keys)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c101dae251359523ef468c66708c798b\",\n \"score\": \"0.55182755\",\n \"text\": \"def publish(self, subject, msg, reply=None):\\n if msg is None:\\n msg = ''\\n\\n if reply is None:\\n command = 'PUB %s %d' % (subject, len(msg))\\n else:\\n command = 'PUB %s %s %d' % (subject, reply, len(msg))\\n\\n self._send(command)\\n self._send(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d14e986f05e8829ecd5a3c9f3dc2b5b4\",\n \"score\": \"0.5508381\",\n \"text\": \"def publish():\\n version = get_version(1)\\n\\n # Push to GitHub.\\n with chdir(BASE):\\n subprocess.check_call(['git', 'push'])\\n subprocess.check_call(['git', 'push', '--tags'])\\n\\n # Upload to PyPI.\\n path = os.path.join(BASE, 'dist', f'beets-{version}.tar.gz')\\n subprocess.check_call(['twine', 'upload', path])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e7b5d3743db87186a827c71d6bfbf55\",\n \"score\": \"0.55079514\",\n \"text\": \"def execute(self, command, *params, **kwargs):\\n params = [x for x in params if x is not None]\\n if \\\"trailing\\\" in kwargs:\\n params = list(params)\\n if kwargs[\\\"trailing\\\"] is not None:\\n params.append(\\\":%s\\\" % kwargs[\\\"trailing\\\"])\\n cmd_line = \\\"%s %s\\\\r\\\\n\\\" % (command.upper(), \\\" \\\".join(params))\\n self.push(bytes(cmd_line, 'UTF-8', errors='ignore'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92d3b86b6c9e70abad6982402c588731\",\n \"score\": \"0.5500408\",\n \"text\": \"def wrapper(self, command, args):\\n command += \\\" \\\"\\n for op, value in args.items():\\n command += op + \\\" \\\" + value + \\\" \\\"\\n sp.run(command, shell=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"19a6c070c578e96a97959bb7582d5310\",\n \"score\": \"0.5498583\",\n \"text\": \"async def main():\\n await runner(NewsPublisher.publish)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45a1e44f840fd4877b28b98bc9790980\",\n \"score\": \"0.549816\",\n \"text\": \"def on_publish(unused_client, unused_userdata, unused_mid):\\n print('on_publish')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e45903b6094be849eb821fe0a616105\",\n \"score\": \"0.5487647\",\n \"text\": \"def main():\\n parser = argparse.ArgumentParser()\\n parser.add_argument('--configFile', help='Publisher configuration file', default='PublisherConfig.py')\\n parser.add_argument('--taskname', help='taskname', required=True)\\n parser.add_argument('--verbose', help='Verbose mode, print dictionaries', action='store_true')\\n parser.add_argument('--dry', help='Dry run mode, no changes done in DBS', action='store_true')\\n parser.add_argument('--console', help='Console mode, send logging to stdout', action='store_true')\\n args = parser.parse_args()\\n configFile = os.path.abspath(args.configFile)\\n taskname = args.taskname\\n verbose = args.verbose\\n dryRun = args.dry\\n console = args.console\\n modeMsg = \\\" in DRY RUN mode\\\" if dryRun else \\\"\\\"\\n config = loadConfigurationFile(configFile)\\n if dryRun:\\n config.TaskPublisher.dryRun = True\\n\\n if verbose:\\n print(f\\\"Will run {modeMsg} with:\\\\nconfigFile: {configFile}\\\\ntaskname : {taskname}\\\\n\\\")\\n\\n summaryFile = publishInDBS3(config, taskname, verbose, console)\\n print(f\\\"Completed with result in {summaryFile}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a682dd2fcf80911a26ba3e587098dfb7\",\n \"score\": \"0.54809767\",\n \"text\": \"def publish_official():\\n mssqlcli_dist_dir = os.listdir(utility.MSSQLCLI_DIST_DIRECTORY)\\n # Run twine action for mssql-cli.\\n # Only authorized users with credentials will be able to upload this package.\\n # Credentials will be stored in a .pypirc file.\\n for file_dist in mssqlcli_dist_dir:\\n utility.exec_command(\\n 'twine upload {}'.format(file_dist),\\n utility.MSSQLCLI_DIST_DIRECTORY)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b005c53c7258f3a01656c855ae49d89b\",\n \"score\": \"0.5442617\",\n \"text\": \"def _prep_pub(self, tgt, fun, arg, tgt_type, ret, jid, timeout, **kwargs):\\n if tgt_type == \\\"nodegroup\\\":\\n tgt = self._resolve_nodegroup(tgt)\\n tgt_type = \\\"compound\\\"\\n\\n if tgt_type == \\\"compound\\\":\\n # Resolve all nodegroups, so that the minions don't have to.\\n new_tgt = list()\\n log.debug(\\\"compound resolution: original tgt: %s\\\", tgt)\\n\\n if isinstance(tgt, str):\\n tgt = tgt.split()\\n\\n for word in tgt:\\n if word.startswith(\\\"N@\\\") and len(word) > 2:\\n resolved = self._resolve_nodegroup(word[2:])\\n new_tgt.extend(resolved)\\n else:\\n new_tgt.append(word)\\n\\n log.debug(\\\"compound resolution: new_tgt: %s\\\", new_tgt)\\n tgt = \\\" \\\".join(new_tgt)\\n\\n # Convert a range expression to a list of nodes and change expression\\n # form to list\\n if tgt_type == \\\"range\\\" and HAS_RANGE:\\n tgt = self._convert_range_to_list(tgt)\\n tgt_type = \\\"list\\\"\\n\\n # If an external job cache is specified add it to the ret list\\n if self.opts.get(\\\"ext_job_cache\\\"):\\n if ret:\\n ret += \\\",{}\\\".format(self.opts[\\\"ext_job_cache\\\"])\\n else:\\n ret = self.opts[\\\"ext_job_cache\\\"]\\n\\n # format the payload - make a function that does this in the payload\\n # module\\n\\n # Generate the standard keyword args to feed to format_payload\\n payload_kwargs = {\\n \\\"cmd\\\": \\\"publish\\\",\\n \\\"tgt\\\": tgt,\\n \\\"fun\\\": fun,\\n \\\"arg\\\": arg,\\n \\\"key\\\": self.key,\\n \\\"tgt_type\\\": tgt_type,\\n \\\"ret\\\": ret,\\n \\\"jid\\\": jid,\\n }\\n\\n # if kwargs are passed, pack them.\\n if kwargs:\\n payload_kwargs[\\\"kwargs\\\"] = kwargs\\n\\n payload_kwargs[\\\"user\\\"] = self.salt_user\\n\\n # If we're a syndication master, pass the timeout\\n if self.opts[\\\"order_masters\\\"]:\\n payload_kwargs[\\\"to\\\"] = timeout\\n\\n return payload_kwargs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10a51e109aac3d40ca7bea4d24e7fab4\",\n \"score\": \"0.5413644\",\n \"text\": \"def publish(**msg):\\n result = None\\n try:\\n source_data = msg[\\\"source_data\\\"]\\n out_dir = msg[\\\"out_dir\\\"]\\n shutil.copy2(out_dir + source_data, out_dir + \\\"NGW/\\\" + source_data)\\n except TypeError:\\n pass\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b38b984475a7606251ab1367ba3b4832\",\n \"score\": \"0.54101676\",\n \"text\": \"def publish(self, *,\\n pub_path: str,\\n recursive: bool = True,\\n **kwargs: typing.Any) -> None:\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0df97e5a9f6ecdceacad8f9b514dc655\",\n \"score\": \"0.53999346\",\n \"text\": \"def do_pocs_message(self, cmd):\\n try:\\n self.msg_publisher.send_message('POCS-SHELL', cmd)\\n except AttributeError:\\n print_info('Messaging not started')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5b5612091852192ecf3a76508b03ca31\",\n \"score\": \"0.5399926\",\n \"text\": \"def process_command(command):\\n if len(sys.argv) < 2:\\n print_help()\\n return\\n command = sys.argv[1].lower()\\n if command == 'help':\\n if (validate_cli_arguments(3)):\\n print_command_help(sys.argv[2])\\n elif command == 'getdoc':\\n if (validate_cli_arguments(4)):\\n get_doc_file(sys.argv[2], sys.argv[3])\\n elif command == 'putdoc':\\n if (validate_cli_arguments(6)):\\n put_document(sys.argv[2],sys.argv[3],sys.argv[4],sys.argv[5])\\n elif command == 'listcollections':\\n list_collections()\\n elif command == 'listversions':\\n if (len(sys.argv) ==3):\\n list_collection_version(sys.argv[2],None)\\n elif (len(sys.argv)==4 and sys.argv[3]=='documents'):\\n list_collection_version(sys.argv[2],sys.argv[3]) \\n else:\\n print_help()\\n elif command == 'showcollection':\\n if (validate_cli_arguments(3)):\\n show_collection(sys.argv[2])\\n elif command == 'listen':\\n from bitmessage.bitmessage_listener import get_collections\\n get_collections()\\n elif command == 'install':\\n if (validate_cli_arguments(3)):\\n install_dependencies(sys.argv[2])\\n elif command == 'putcollection':\\n if (validate_cli_arguments(7)):\\n put_collection(sys.argv[2], \\\\\\n sys.argv[3], sys.argv[4], \\\\\\n sys.argv[5], sys.argv[6])\\n elif command == 'publishcollection':\\n if (validate_cli_arguments(4)):\\n publish_collection(sys.argv[2], sys.argv[3])\\n else:\\n print_help()\\n elif command == 'webapp':\\n webapp.run(debug = config.WEBAPP_DEBUG, port = config.WEBAPP_PORT)\\n elif command == 'uploader':\\n from frontend.uploader import fjUploaderGUI\\n fjUploaderGUI.run()\\n elif command == 'keepalive':\\n from bitmessage.bitmessage_keepalive import find_old_collections\\n find_old_collections(3)\\n else:\\n print_help()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9bdd42e4d536f8e3eb15aab44f45c59\",\n \"score\": \"0.53911906\",\n \"text\": \"def main(arguments):\\n full_push = FullPush(arguments)\\n return full_push.run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb40e2d44956335eee8f405867d7e3ae\",\n \"score\": \"0.53827316\",\n \"text\": \"def run():\\n args = parse()\\n\\n build_results = [\\n build(build_id, spec, args)\\n for spec in BUILDS\\n for build_id in spec['ids']\\n if not args['ids'] or build_id in args['ids']\\n ]\\n\\n if not args['publish']:\\n return\\n\\n for entry in build_results:\\n publish(entry, args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6827da356d393cff7dc119a6e7545a01\",\n \"score\": \"0.5379408\",\n \"text\": \"def task_publish_test_pypi() -> DoitTask:\\n return _publish_task('--repository testpypi')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7cfc7c41023d3fae35d580a7cdc9e6d9\",\n \"score\": \"0.5375293\",\n \"text\": \"def main():\\n cli = PublishCLI()\\n opts = cli.process_options(abort_on_missing_template=False)\\n args = cli.arguments\\n suite = Suite(workdir=opts['image_dir'], suites=opts['suites'])\\n\\n build_opts = ['no_cache', 'rm']\\n build_opts = get_build_opts(build_opts, args)\\n\\n for ctx in suite.process():\\n fmt = ctx.copy()\\n fmt['path'] = dockerfile_path(suite=fmt['suite'], variant=fmt['variant'])\\n\\n fmt['variant'] = '-{}'.format(fmt['variant']) if fmt['variant'] else ''\\n fmt['tag'] = TAG.format(**fmt)\\n fmt['options'] = build_opts\\n\\n # Run docker build\\n shell_out(BUILD.format(**fmt))\\n\\n # Tag latestest if suite.yml contains latest option\\n if fmt['suite'] + fmt['variant'] == suite.latest:\\n shell_out(TAG_LATEST.format(**fmt))\\n if not args['no_push']:\\n shell_out(PUSH.format(tag='{registry}{image}:latest'.format(**fmt)))\\n\\n # Push image\\n if not args['no_push']:\\n shell_out(PUSH.format(**fmt))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b43ded2165a93cc31e6f8297119d6d0b\",\n \"score\": \"0.5372495\",\n \"text\": \"def publisher():\\n rospy.init_node('robotiq_simple_controller')\\n pub = rospy.Publisher('command', CModelCommand, queue_size=3)\\n command = CModelCommand()\\n while not rospy.is_shutdown():\\n s = askForCommand(command)\\n if s == 'x':\\n break\\n command = genCommand(s, command)\\n pub.publish(command)\\n rospy.sleep(0.1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eaa2d3c4c177bfc405513e9c1883a8d9\",\n \"score\": \"0.5368741\",\n \"text\": \"def invoke(command, args):\\n SUBCOMMANDS[command](*args) # pylint: disable=W0142\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dde7a1ff94a13e386c2222f089b7c82d\",\n \"score\": \"0.5357089\",\n \"text\": \"def submit_command() -> Response:\\n\\n command_name = request.args.get(\\\"command_name\\\", \\\"\\\")\\n command_args = request.args.get(\\\"command_args\\\", [])\\n command_kwargs = request.args.get(\\\"command_kwargs\\\", {})\\n\\n write_command(command_name, command_args, command_kwargs, fifo=STDIN_NAMED_PIPE_PATH)\\n\\n return make_response(\\\"\\\", 200)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcef6193084138bd0047375cfe9b0ae7\",\n \"score\": \"0.5344957\",\n \"text\": \"def command(self, cmd, *args):\\n c = \\\"!\\\" + cmd\\n if args:\\n c += \\\" \\\" + json.dumps(args)\\n self.sendui(c)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3b150610ddc880621106e60389da45d\",\n \"score\": \"0.53440905\",\n \"text\": \"def publish(args, pub_type, extensions):\\n def _publish(release):\\n build_path = os.path.join(args.build_dir, release)\\n filename = release.split(os.path.sep)[-1]\\n release_dir = os.path.join(args.pub, pub_type)\\n release_path = os.path.join(release_dir, filename)\\n os.renames(build_path, release_path)\\n\\n # Latest/symlink handler\\n release_abspath = os.path.abspath(release_path)\\n latest_abspath = release_abspath.replace(TSTAMP, 'latest')\\n\\n if os.path.islink(latest_abspath):\\n os.unlink(latest_abspath)\\n os.symlink(release_abspath, latest_abspath)\\n\\n return release_path\\n\\n published = []\\n for extension in extensions:\\n release = glob(\\\"%s/odoo_*.%s\\\" % (args.build_dir, extension))\\n if release:\\n published.append(_publish(release[0]))\\n return published\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a40248f89b1c98c06a5832bedae0613\",\n \"score\": \"0.5329816\",\n \"text\": \"def publish(context):\\n with context.cd(str(THIS_DIR)):\\n print(\\\"Running pdsite\\\")\\n context.run('pdsite build')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"796436e100728a3090576403809a0bdc\",\n \"score\": \"0.5323295\",\n \"text\": \"def command(self, command, arguments=None, zone=None):\\n try:\\n iscp_message = command_to_iscp(command, arguments, zone)\\n except ValueError as error:\\n self.log.error(f\\\"Invalid message. {error}\\\")\\n return\\n\\n self.log.debug(\\\"> %s\\\", command)\\n try:\\n self.transport.write(command_to_packet(iscp_message))\\n except:\\n self.log.warning(\\\"No transport found, unable to send command\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5005ee223b10a4560f4b8e86dae8db9f\",\n \"score\": \"0.5320653\",\n \"text\": \"def publish(self):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"340a8596bdb3349b2c826e96940ca87d\",\n \"score\": \"0.5312219\",\n \"text\": \"def publish(c):\\n c.run('pelican -s publishconf.py')\\n c.run(\\n 'rsync --delete --exclude \\\".DS_Store\\\" -pthrvz -c '\\n '{} {production}:{dest_path}'.format(\\n CONFIG['deploy_path'].rstrip('/') + '/',\\n **CONFIG))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1472c057298a3c8340897ccb5572c488\",\n \"score\": \"0.53053653\",\n \"text\": \"def handle(self, *args, **options):\\n\\t\\tfor DesignPost_id in args:\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tarticle = DesignPost.objects.get(pk=int(DesignPost_id))\\n\\t\\t\\t\\tif article.status == 'published':\\n\\t\\t\\t\\t\\tself.stdout.write('This article was already published')\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tarticle.published_on = timezone.now()\\n\\t\\t\\t\\t\\tarticle.status = 'published'\\n\\t\\t\\t\\t\\tarticle.save()\\n\\t\\t\\t\\t\\tself.stdout.write('Successfully published this article')\\n\\t\\t\\texcept DesignPost.DoesNotExist:\\n\\t\\t\\t\\tlogger.info(\\\"The article requested does not exist\\\")\\n\\t\\t\\t\\t\\n\\t\\t\\t\\traise CommandError ('That article does not exist')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6082661fc42c6555eb03ca8aa48a5443\",\n \"score\": \"0.5300951\",\n \"text\": \"def cli(ctx, service, publish, ignore_untracked, key, show_doi):\\n services = {\\n 'zenodo': Zenodo,\\n 'figshare': Figshare\\n }\\n environment_variables = {\\n 'zenodo': 'POPPER_ZENODO_API_TOKEN',\\n 'figshare': 'POPPER_FIGSHARE_API_TOKEN'\\n }\\n\\n if service not in services:\\n pu.fail(\\\"The service {} is not supported. See popper archive \\\"\\n \\\"--help for more info.\\\".format(service))\\n\\n if not key:\\n try:\\n key = os.environ[environment_variables[service]]\\n except KeyError:\\n key = get_access_token(service)\\n\\n if show_doi and publish:\\n pu.fail(\\\"--show-doi can not be given along with --publish\\\")\\n\\n archive = services[service](key)\\n\\n pu.info('Looking for an existing record for the project.')\\n archive.fetch_depositions()\\n\\n if show_doi:\\n if archive.record_exists():\\n pu.info('Found record for project: {}'.format(archive.record_id))\\n archive.show_doi()\\n else:\\n pu.info('No record for project exists.')\\n sys.exit(0)\\n\\n if not archive.record_exists():\\n pu.info('No record exists yet; creating one for project.')\\n archive.create_new_deposition()\\n pu.info('Record ID: {}'.format(archive.record_id))\\n else:\\n pu.info('Record exists: {}'.format(archive.record_id))\\n\\n if archive.is_last_deposition_published():\\n pu.info('A published version exists, creating new version.')\\n archive.create_new_version()\\n else:\\n pu.info('An unpublished version exists, updating it.')\\n\\n archive.delete_previous_file()\\n\\n archive.ignore_untracked = ignore_untracked\\n\\n pu.info('Uploading files.')\\n archive.upload_snapshot()\\n\\n if publish:\\n pu.info('Publishing record and obtaining DOI.')\\n archive.publish_snapshot()\\n archive.show_doi()\\n\\n pu.info(\\\"Done!\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"17db9995de40094d36987d68f410db12\",\n \"score\": \"0.5299374\",\n \"text\": \"def publish(build):\\n build.packages.install(\\\"wheel\\\")\\n build.executables.run([\\n \\\"python\\\", \\\"setup.py\\\",\\n \\\"sdist\\\", \\\"upload\\\"\\n ])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"94d0f20cbdcc2daaccc6c64e1958d455\",\n \"score\": \"0.5291367\",\n \"text\": \"def publish(self, message):\\n self.__ensure_started()\\n\\n message_def = self.messages.get_by_cls(type(message))\\n context = OutgoingContext(message, message_def)\\n self.outgoing_pipeline.execute(context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d49101dd9afafe8d7148b0c7e1b9abd\",\n \"score\": \"0.5289644\",\n \"text\": \"def publish(gui, debug, paths):\\n if debug:\\n os.environ['PYPE_DEBUG'] = '3'\\n PypeLauncher().publish(gui, list(paths))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99911b20befdcfc9b4309a11cc71f7ed\",\n \"score\": \"0.5282902\",\n \"text\": \"def main():\\n writer = Writer()\\n writer.translator_class = HTMLTranslator\\n reader = Reader()\\n publish_cmdline(reader=reader, writer=writer)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9e9793192338081c02722429bb5842df\",\n \"score\": \"0.5278152\",\n \"text\": \"async def push(self, ctx):\\r\\n if ctx.invoked_subcommand is not None:\\r\\n return\\r\\n\\r\\n await ctx.invoke(self.push_info)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2a790e707323d617dbfc8ac02a2064b5\",\n \"score\": \"0.52700514\",\n \"text\": \"def add(self, *args, **kwargs):\\n self.out.append(command(*args, **kwargs))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c325eb470b3fde32ce7c6d2e44da38a\",\n \"score\": \"0.5266555\",\n \"text\": \"def cli(**kwargs):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b69cbeed85436e5b0c2cff0695c455a4\",\n \"score\": \"0.52621144\",\n \"text\": \"def publish(self, request, *args, **kwargs):\\n publication = self.get_object()\\n return Response({'is_published': publication.publish()})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e17d005c7eaf3b87bcfad074c7d61f17\",\n \"score\": \"0.5259506\",\n \"text\": \"def cmd(self, *args):\\n return self.__generate_cmd(*args, shell=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"beaffdc9d8dda1c00bb533820937f9c1\",\n \"score\": \"0.5258657\",\n \"text\": \"def publish():\\n local('git push origin master')\\n local('git push origin master:gh-pages')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18652c8b6250cdcfd488e9de11c25b14\",\n \"score\": \"0.5254601\",\n \"text\": \"def send_command(self, command, *args, **kwargs):\\n if args:\\n args = list(args)\\n for i in xrange(len(args)):\\n arg = args[i]\\n if not isinstance(arg, basestring):\\n args[i] = str(arg)\\n\\n if not args[-1].startswith(':'):\\n args[-1] = ':%s' % args[-1]\\n\\n out = '%s %s\\\\r\\\\n' % (command, ' '.join(args))\\n else:\\n out = '%s\\\\r\\\\n' % command\\n\\n if '_prefix' in kwargs and kwargs['_prefix']:\\n out = '%s %s' % (kwargs['_prefix'], out)\\n elif self.prefix:\\n out = '%s %s' % (self.prefix, out)\\n\\n # Send it.\\n log.debug('\\\\x1B[0;32m>> %s\\\\x1B[0m' % out.rstrip())\\n\\n self.write(out.encode(self.encoding))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a741e3626a055fad14cbedb8062365ee\",\n \"score\": \"0.52511275\",\n \"text\": \"def publish(ctx, test=False):\\n clean(ctx)\\n if test:\\n run('python setup.py register -r test sdist bdist_wheel', echo=True)\\n run('twine upload dist/* -r test', echo=True)\\n else:\\n run('python setup.py register sdist bdist_wheel', echo=True)\\n run('twine upload dist/*', echo=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c644e2cb7c6a54bd83274476765163b2\",\n \"score\": \"0.5250997\",\n \"text\": \"def publish():\\n gh_pages()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88348612b5f1c2c4e156d1ce23959435\",\n \"score\": \"0.52472234\",\n \"text\": \"def publish(self, message: Any) -> Publisher:\\n # print(f\\\"=>TopicHandle.publish('{message}')\\\")\\n publisher: Optional[Publisher] = self.publisher\\n assert publisher, \\\"Publisher not set yet.\\\"\\n message_type: Any = self.message_type\\n assert isinstance(message, message_type), f\\\"Message is not of type {message_type}\\\"\\n # print(f\\\"Topic('{self.ros_path}): Published: {message}'\\\")\\n publisher.publish(message)\\n self.publisher_count += 1\\n # print(f\\\"<=TopicHandle.publish('{message}')\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07c732ce801e12cf935dd3afbe01dcd0\",\n \"score\": \"0.5234806\",\n \"text\": \"def setup_publish(subargv):\\n\\n parser = optparse.OptionParser(usage=\\\"git-lockup setup-publish\\\")\\n (options, subargs) = parser.parse_args(subargv)\\n\\n assert os.path.isdir(\\\".git\\\")\\n tool = \\\".git/git-lockup\\\"\\n me = read_myself()\\n with open(tool, \\\"wb\\\") as f:\\n f.write(me)\\n make_executable(tool)\\n # this makes a keypair, updates .git/config, and adds a post-commit hook\\n setup_publish_config(True, \\\"master\\\")\\n # Then we create ./setup-lockup for clients to use. We must insert a copy\\n # of ourselves.\\n setup_lockup = base64.b64decode(setup_lockup_b64)\\n setup_lockup = setup_lockup.replace(\\\"GIT_LOCKUP_B64\\\", base64.b64encode(me))\\n with open(\\\"setup-lockup\\\", \\\"wb\\\") as f:\\n f.write(setup_lockup)\\n make_executable(\\\"setup-lockup\\\")\\n git_add(\\\"setup-lockup\\\")\\n # along with an lockup.config with the branch->key information from\\n # .git/config\\n branches = get_config_verifykeys()\\n with open(\\\"lockup.config\\\", \\\"wb\\\") as f:\\n f.write(\\\"# -*- mode: conf; coding: utf-8 -*-\\\\n\\\")\\n f.write(\\\"[branches]\\\\n\\\")\\n for branch in sorted(branches):\\n shortbranch = remove_prefix(branch, \\\"refs/heads/\\\", True)\\n keys = \\\" \\\".join([key for key in branches[branch]])\\n f.write(\\\"%s = %s\\\\n\\\" % (shortbranch, keys))\\n git_add(\\\"lockup.config\\\")\\n\\n # then maybe execute TOOL subscribe, not sure yet\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87d5eec4cabc1aa91c9c87c5aaf03f63\",\n \"score\": \"0.52105814\",\n \"text\": \"def publishSingle(self, key, value):\\n\\n self.publisher.publish(self.name, {key: value})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5246dc5bcabc20a9c1af785c6e4332bd\",\n \"score\": \"0.5209397\",\n \"text\": \"def publish(self, msg, *args, **kwargs):\\n pub_hdrs = self._get_publish_headers(msg, kwargs)\\n super(StreamPublisher, self).publish(msg, to_name=self._send_name, headers=pub_hdrs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b972bac0f01cc2f826bc9d931fd006d2\",\n \"score\": \"0.5208493\",\n \"text\": \"def command(self):\\n raise NotImplementedError(\\\"Container task must specify command\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0f87cd7f698b3345546a73824191bda0\",\n \"score\": \"0.52067506\",\n \"text\": \"def main(argv):\\n options = parse_args(argv)\\n log.info(\\\"options: \\\" + str(options))\\n\\n processor = NewsAggregationProcessor(options)\\n processor.process()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b020d4f9b14a25997144969cf947e575\",\n \"score\": \"0.52040535\",\n \"text\": \"def publish(self):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ace6efe385c29d7d1d7d115a62afe0fa\",\n \"score\": \"0.5203939\",\n \"text\": \"def commandOne(self, repo, *args, **kwargs):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74e57fa39dfdb7e4bf190b281d9b60d9\",\n \"score\": \"0.51922435\",\n \"text\": \"def _command(self, *args):\\n return self._client.command(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a264cb232a5f2bbd4ac9e277495bd0e8\",\n \"score\": \"0.51839775\",\n \"text\": \"def publish(send_json_path, get_json_path, gui):\\n\\n log.info(\\\"avalon.session is: \\\\n{}\\\".format(ppl.SESSION))\\n log.info(\\\"PUBLISH_PATH: \\\\n{}\\\".format(os.environ[\\\"PUBLISH_PATH\\\"]))\\n\\n pype_start = os.path.join(os.getenv('PYPE_SETUP_ROOT'),\\n \\\"app\\\", \\\"pype-start.py\\\")\\n\\n args = [pype_start,\\n \\\"--root\\\", os.environ['AVALON_PROJECTS'], \\\"--publish-gui\\\",\\n \\\"-pp\\\", os.environ[\\\"PUBLISH_PATH\\\"],\\n \\\"-d\\\", \\\"rqst_json_data_path\\\", send_json_path,\\n \\\"-d\\\", \\\"post_json_data_path\\\", get_json_path\\n ]\\n\\n log.debug(args)\\n log.info(\\\"_aport.api Variable `AVALON_PROJECTS` had changed to `{0}`.\\\".format(\\n os.environ['AVALON_PROJECTS']))\\n forward([\\n sys.executable, \\\"-u\\\"\\n ] + args,\\n # cwd=cwd\\n )\\n\\n return {\\\"get_json_path\\\": get_json_path}\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffe0cbd9975e83a31454a881918d98f3\",\n \"score\": \"0.5180994\",\n \"text\": \"async def publish(self, topic, data):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bb74d4915ae6a71d8d130df048bab37\",\n \"score\": \"0.5177897\",\n \"text\": \"def execute(args):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4d1868f85eabcf3659186c1a9b00561\",\n \"score\": \"0.51732165\",\n \"text\": \"def execute(self, container, command):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"df3f346acfcc4f3c8383dff99a1d2078\",\n \"score\": \"0.51593494\",\n \"text\": \"def _svnwrite(self, cmd, *args):\\n l = ['svn %s' % cmd]\\n args = ['\\\"%s\\\"' % self._escape(item) for item in args]\\n l.extend(args)\\n l.append('\\\"%s\\\"' % self._encodedurl())\\n # fixing the locale because we can't otherwise parse\\n string = \\\" \\\".join(l)\\n if DEBUG:\\n print(\\\"execing %s\\\" % string)\\n out = self._svncmdexecauth(string)\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ed31c8765358eba325d808accabbe2ef\",\n \"score\": \"0.51477003\",\n \"text\": \"def redis_publish(mgr, params):\\n if params.get('channel') and params.get('message'):\\n serv_redis.redis.publish(params['channel'], params['message'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d49ec9ba59ec5126b459b69af4eadc55\",\n \"score\": \"0.5140094\",\n \"text\": \"def command(self):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f28ff2adeaf5f3a3386f9dfad53aac8\",\n \"score\": \"0.5139412\",\n \"text\": \"def push(args):\\n p = OptionParser(push.__doc__)\\n opts, args = p.parse_args(args)\\n\\n if len(args) != 1:\\n sys.exit(not p.print_help())\\n\\n s, = args\\n prefix = get_prefix()\\n version, partID, unitigID = get_ID(s)\\n\\n cmd = \\\"tigStore\\\"\\n cmd += \\\" -g ../{0}.gkpStore -t ../{0}.tigStore\\\".format(prefix)\\n cmd += \\\" {0} -up {1} -R {2}\\\".format(version, partID, s)\\n\\n sh(cmd)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a141bf4b3dc73e3f830536841bcc6a2\",\n \"score\": \"0.51390314\",\n \"text\": \"def __call__(self, *args, **kwargs):\\n template, args, kwargs = self.pre(self.command_template, args, kwargs)\\n command = template.format(*args, **kwargs)\\n argv = command.split(\\\" \\\")\\n stdout = check_output(argv, universal_newlines=True)\\n return self.post(stdout)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b468aa8d93e337a282a0fbf039c0851\",\n \"score\": \"0.51280856\",\n \"text\": \"def cli(**params):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7de5cadb3d3ef15190f43ba238c9cbcc\",\n \"score\": \"0.5102143\",\n \"text\": \"async def handle(self, command: str, args: str, message: m.Message) -> None:\\n room = await self.master.room(room_id=message.roomid)\\n roomref = room.to_roomref2(True)\\n\\n msg = await usage.get_message(self.master, roomref)\\n await self.master.ddp.send_message(message.roomid, msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d45c4725075f2b86f13e400542deda46\",\n \"score\": \"0.5099334\",\n \"text\": \"def command(self,*args):\\n return self.comp.command(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3da45045a8a2595029c0b74b0644d129\",\n \"score\": \"0.5097723\",\n \"text\": \"def do_deploy(self, args):\\r\\n DeployPasta().cmdloop()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f0d9982ebb8425f75f6aa9ae7dbe1bb0\",\n \"score\": \"0.5095803\",\n \"text\": \"def publish(self, topic, **kwargs):\\r\\n pub = PublisherProtocol(topic, **kwargs)\\r\\n self._make_connection(pub)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcaef1b87354a793362ffaadb36f05e5\",\n \"score\": \"0.5087014\",\n \"text\": \"def populate(parser: argparse.ArgumentParser):\\n parser.add_argument(\\\"--push\\\", action=\\\"store_true\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a77af1b822bc962e67e4beb28aee687e\",\n \"score\": \"0.5081749\",\n \"text\": \"def set_publish_sample_args(args):\\n public_sample = {}\\n if args.public_sample:\\n public_sample = {\\\"private\\\": False}\\n return public_sample\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79a8b38c27c2ac5bd5ae19a9750a9377\",\n \"score\": \"0.5078941\",\n \"text\": \"def publish():\\n repo = Repo(Path(__file__).resolve().parent)\\n git_cli = repo.git\\n\\n for branch in BRANCHES.values():\\n git_cli.push('origin', branch, tags=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e03be34f2b62840dad50fba5353dfd2a\",\n \"score\": \"0.5075389\",\n \"text\": \"def event_publish(self, cmd):\\n for sub in self.subscribers:\\n sub.event_receive(cmd)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993293,"cells":{"query_id":{"kind":"string","value":"d113d1c69349f622deed5b357a9170df"},"query":{"kind":"string","value":"Checks that the value conforms to the LanguageID"},"positive_passages":{"kind":"list like","value":[{"docid":"f66b4f0de21fc3bd0a8ee041d82e2728","score":"0.6371621","text":"def checkLanguageID(lang):\n ret = libxml2mod.xmlCheckLanguageID(lang)\n return ret","title":""}],"string":"[\n {\n \"docid\": \"f66b4f0de21fc3bd0a8ee041d82e2728\",\n \"score\": \"0.6371621\",\n \"text\": \"def checkLanguageID(lang):\\n ret = libxml2mod.xmlCheckLanguageID(lang)\\n return ret\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"9589b0955ee39d7c15634647a6924963","score":"0.6502951","text":"def validate_lang(lang):\n if lang in LANGUAGE_OPTIONS.keys():\n return True","title":""},{"docid":"1a3cba627ecaba3ecbd81c3dc38e4493","score":"0.64360905","text":"def validate_language(language):\n\n try:\n lang_code = language_dict[language]\n except KeyError:\n lang_code = None\n return lang_code","title":""},{"docid":"b3b3941857cbaf47cbe8dc774d70abf9","score":"0.62319374","text":"def validate(self,value):\r\n return type(value) is self.datatype","title":""},{"docid":"2f1e3e73a948a910998fdc673308adc8","score":"0.6140694","text":"def clean_lang(self):\n lang = self.cleaned_data.get('lang', None)\n if not lang in self.greetings:\n raise forms.ValidationError(\n \"We couldn't find the language you selected {}\"\n \" Please select another\".format(lang)\n )\n return lang","title":""},{"docid":"8518985fa49c2fc3c643be416ef4ec44","score":"0.6126314","text":"def is_valid_language_code(code):\n try:\n iso639.languages.get(part3=code)\n return True\n except KeyError:\n return False","title":""},{"docid":"96aa1a126c625b8f28cedb1808e08c58","score":"0.59482896","text":"def is_valid(self, value):\r\n pass","title":""},{"docid":"3287b2a2ac59cb53a8946dcf92771627","score":"0.5937368","text":"def test_good_values_for_validate_locale_code(good_value):\n bcvalidators.validate_locale_code(good_value)","title":""},{"docid":"3f9a41bcc8309c2597f10d5e34574f84","score":"0.588551","text":"def check_value(self, value):","title":""},{"docid":"7870a1dd290d398e5f4173c7eeea5118","score":"0.5885384","text":"def validate_translated_text(language_json):\n allowed_codes = {code for code, name in settings.LANGUAGES}\n for code, translated in language_json.items():\n if code not in allowed_codes:\n raise ValidationError(f'Unrecognized language code: {code}')\n if not isinstance(translated, str):\n raise ValidationError(f'Translation for {code} must be a string')","title":""},{"docid":"bc9677781233b7e1d5e921c6552ae455","score":"0.5739789","text":"def validate(self, value):\n return True","title":""},{"docid":"01f269bdb643d2afb0ef7d74d102e62e","score":"0.57237697","text":"def test_language_conversion(self):\n\n self.assertEqual(self.converter.language, LANGUAGE_CODE,\n 'Check the language conversion.')","title":""},{"docid":"fd8f9e87abba6ec75767cc2f68a00085","score":"0.57209945","text":"def can_handle_language(cls, language: Hashable) -> bool:\n\n # if language_list is set to `None` it means: support all languages\n if language is None or cls.language_list is None:\n return True\n\n return language in cls.language_list","title":""},{"docid":"fbfca20125e87f09bdba4feab6f3e461","score":"0.5706629","text":"def _validate(self, value):\n return True","title":""},{"docid":"b4b85f1b9dcd4ee433138cbfeec9c40d","score":"0.5615167","text":"def is_forced(self, lang):\r\n return False","title":""},{"docid":"278bd647e32419ba68bcece262b04c6c","score":"0.56020755","text":"def is_id_lang_available(self, seq_id: Optional[int], lang_code: str) -> bool:\r\n if not seq_id:\r\n return True\r\n\r\n if seq_id > self.get_next_seq_id(increase=False):\r\n return False\r\n\r\n return self.find_one({self._seq_id_key: seq_id, self._lang_code_key: lang_code}) is None","title":""},{"docid":"22a86984c3c4cbf45b2c50571361d207","score":"0.55923426","text":"def is_valid_language(self, file):\n if not self.languages or get_file_type(file[\"path\"]) in self.languages:\n return True\n return False","title":""},{"docid":"22a86984c3c4cbf45b2c50571361d207","score":"0.55923426","text":"def is_valid_language(self, file):\n if not self.languages or get_file_type(file[\"path\"]) in self.languages:\n return True\n return False","title":""},{"docid":"664eb3f3a34d0b8562d04421d5245f51","score":"0.5580878","text":"def validate(self, value):\n\n return True","title":""},{"docid":"3cb33fb5b3816c409c4932dde0e0689b","score":"0.5571379","text":"def checkType(self, value):\n pass","title":""},{"docid":"13ee78bd587f0748094da57a6e0940b6","score":"0.55425566","text":"def setLangId(self, value):\n return self._set(langId=value)","title":""},{"docid":"4b2fa795ebff874d987ae92f3c29a63b","score":"0.55303246","text":"def validate(self, value):\r\n return value","title":""},{"docid":"6437bccf71f3da47f29ce421a8bc8fe7","score":"0.55287796","text":"def test_languages(self):\n for i, item in enumerate(self._letters_proto.item):\n for code in item.language:\n # The language code should be in ISO 639 format and consists of\n # two letters for ISO 639-1 languages and three letters otherwise.\n self.assertLess(1, len(code))\n self.assertGreater(4, len(code))\n self.assertTrue(code.islower(), f'Line {i}: Language code should be '\n 'lower-case')\n if len(code) == 3:\n lang = pycountry.languages.get(alpha_3=code)\n self.assertTrue(lang, f'Failed to find language for code {code}')\n if hasattr(lang, 'alpha_2'):\n self.fail(f'Letter {i}: Please use two-letter code `{lang.alpha_2}`'\n f' instead of `{lang.alpha_3}` for {lang.name}')\n else:\n lang = pycountry.languages.get(alpha_2=code)\n self.assertTrue(lang, f'Failed to find language for code {code}')","title":""},{"docid":"d277129b3e456e3b02d4162aca46668d","score":"0.5484473","text":"def _validate(cls, pid_value):\n blop = re.compile('^[-\\w]+$')\n if not bool(blop.match(pid_value)):\n raise ValidationError(\n 'The ID should contain only letters with numbers or dashes.',\n field_name='id',\n )","title":""},{"docid":"e8f741ee7ca2e21be7a400eb78d51fae","score":"0.54337245","text":"def compare_language(language):\n if language in module.availableLanguages:\n return True\n else:\n return False","title":""},{"docid":"fd146acf44f900aa2f55552be42826c8","score":"0.5428892","text":"def test_langid(basic_multilingual):\n english_text = \"This is an English sentence.\"\n french_text = \"C'est une phrase française.\"\n docs = [english_text, french_text]\n\n docs = [Document([], text=text) for text in docs]\n basic_multilingual(docs)\n predictions = [doc.lang for doc in docs]\n assert predictions == [\"en\", \"fr\"]","title":""},{"docid":"65ec4c87df5aa28f8371fdcdb3afb270","score":"0.54190266","text":"def validate_typeID(self, type_ID):\n if type(type_ID) == str:\n for letter in type_ID:\n if not letter.isalpha() and not letter.isdigit():\n return False\n return True\n return False","title":""},{"docid":"f1c7877bc9407bba4e7006580c50504a","score":"0.541637","text":"def validate(self, value, obj=None):\n return True","title":""},{"docid":"e922987ab65649df43d07f808ae6dfc9","score":"0.5401962","text":"def _check_value(self, value):\n raise NotImplementedError","title":""},{"docid":"d473085956b891ded63d75a81c1929df","score":"0.538351","text":"def validate(self, converted_value, context):\n pass","title":""},{"docid":"4208e3e2d8b1c4797e008b634b941c9c","score":"0.53601676","text":"def validate(self, value):\n return value","title":""},{"docid":"9d8da689777288e7a4cff98fda48bfd3","score":"0.53235143","text":"def is_valid(self, value) -> 'True|str':\n if self.base_type is not None and not isinstance(value, self.base_type):\n return f'Value {value} is not type of {self.base_type}.'\n return True","title":""},{"docid":"c019a4b003bd975f742273612792b28f","score":"0.5322706","text":"def validate(self, value, clean=True):\n pass","title":""},{"docid":"c019a4b003bd975f742273612792b28f","score":"0.5322706","text":"def validate(self, value, clean=True):\n pass","title":""},{"docid":"f059d565f6bb1315b21feecbe3ad311b","score":"0.53138876","text":"def assertSessionLangEquals(self, value, request):\r\n self.assertEquals(\r\n value,\r\n request.session.get('django_language', UNSET)\r\n )","title":""},{"docid":"2216800c4133290054ee1ed21439495d","score":"0.5310737","text":"def validate_pluginid(value):\n valid = string.ascii_letters + string.digits + '.'\n return all(c in valid for c in value)","title":""},{"docid":"f391ce4d62159f796f6bc04014cfc28c","score":"0.53039813","text":"def validate_identifier(self, identifier):\n pass","title":""},{"docid":"3c42da23fbf462bcef160c124f9c671f","score":"0.52901685","text":"def _validate(self, instance, value):","title":""},{"docid":"62a9f1ea9d27a28fe02c4dd537f2838d","score":"0.5272782","text":"def validate(self, instance, value):","title":""},{"docid":"62a9f1ea9d27a28fe02c4dd537f2838d","score":"0.5272782","text":"def validate(self, instance, value):","title":""},{"docid":"a45d60735e642aa3757aed9ac90d0de3","score":"0.5271566","text":"def validate_vocabulary(value):\n if queryUtility(IVocabularyFactory, value) is None:\n raise Invalid(_(u\"Not a vocabulary: %s\") % value)\n return True","title":""},{"docid":"4965d46f1fe65cdfec4a87d97a0397f1","score":"0.52561307","text":"def testLanguage(self):\n if self.language in tools.LANGUAGES:\n self.assertEqual(\n self.language,\n self.config.language\n )\n else:\n self.assertNotEqual(\n self.language,\n self.config.language\n )\n self.assertEqual(\n tools.LANGUAGE_DEFAULT,\n self.config.language\n )","title":""},{"docid":"d44d346741eb8976cc415e09caf701ed","score":"0.5250673","text":"def test_bad_values_for_validate_locale_code(bad_value):\n with pytest.raises(ValidationError):\n bcvalidators.validate_locale_code(bad_value)","title":""},{"docid":"305a7bd0f9963bff044738064ffe31e4","score":"0.52450514","text":"def validateFormat(self, submittedValue):\n adapt = self.getSettings()\n return adapt.validate(submittedValue)","title":""},{"docid":"31a8053b3125116702866af40b361484","score":"0.52255183","text":"def validate(self, value):\n return self._validate(value, self.name)","title":""},{"docid":"dbe16511403b7e7ec3e0264340491003","score":"0.5223267","text":"def validate(cls, value):\n return cls.properties.validate(value)","title":""},{"docid":"84afe3098bd8e56154adb05d51fdbd20","score":"0.5214491","text":"def _is_valid_code(self, code):\r\n return code in COUNTRY_CODES","title":""},{"docid":"977e58ed7d309ede8586ee8319cee637","score":"0.5196979","text":"def test_language_id_correspondence():\n with open('bible/languages.json', 'r') as languages_file:\n languages = json.load(languages_file)\n for language in languages:\n case.assertTrue(\n os.path.exists(os.path.join(\n 'bible', 'bible-{}.json'.format(language['id']))),\n 'bible-{}.json does not exist'.format(language['id']))","title":""},{"docid":"36de2e20206727218f376eaf6f2bd705","score":"0.51888824","text":"def test_invalid_numeric_code_length_format(self, cred, language):\n resp = requests.get(verify_url.format('json', cred[0], cred[1],\n 'TestApp', test_number), params={'lg': language})\n assert resp.status_code == 200\n assert resp.headers['Content-Type'] == 'application/json'\n assert resp.json()['status'] == '3'\n assert resp.json()['error_text'] == invalid_value_msg.format('lg')","title":""},{"docid":"0e23c914889c1ccd76f845175b0e5c48","score":"0.5188024","text":"def _is_valid(self, value):\n\n # Entities have an istypeof method that can perform more sophisticated\n # type checking.\n if hasattr(self._type, \"istypeof\"):\n return self._type.istypeof(value)\n else:\n return isinstance(value, self._type)","title":""},{"docid":"847bb3d2e1abc67e93e68ac52b1ef0c3","score":"0.51798224","text":"def requires_matching_languages(self):\n return self._requires_matching_languages","title":""},{"docid":"ef5a495d2f6f675e88e76072f885954e","score":"0.5174038","text":"def check(self, value):\n raise NotImplementedError","title":""},{"docid":"121c895662484b822756ab121b4c4607","score":"0.5173988","text":"def verify_post_data ( ):\n # check every field is present\n try:\n request.json[ 'source_lang' ]\n request.json[ 'target_lang' ]\n request.json[ 'text' ]\n\n TranslatorApp.verify_rpc_value ( request.json )\n\n except KeyError: # All the values are not present\n # 400 Bad Request\n abort ( 400, \"All mandatory fields are not provided\" )\n except ValueError as err:\n # 422 Unprocessable Entity\n abort ( 422, \"Unprocessable value: {0}\".format ( err.args ) )\n except BadRequest:\n # 400 Bad Request\n abort ( 400, \"Provided values are having malformed syntax\" )","title":""},{"docid":"a0c0a63481be805751c242b06838790d","score":"0.5173922","text":"def is_valid_value(self, value):\n raise NotImplementedError(\"subclass must implement is_valid_value()\")","title":""},{"docid":"170ed83db83961e84d7de9926c45b64b","score":"0.5164426","text":"def check_id(self, id):","title":""},{"docid":"087928aab6b16e0e5567e9206790a2b4","score":"0.51560616","text":"def isValid(t_id):\n\tstr_id=str(t_id).strip()\n\treturn str_id.isdigit()","title":""},{"docid":"1af742c22fb560f9d9361bcc2168236b","score":"0.51279116","text":"def validate(self, value):\n\n if value is not None:\n\n if (isinstance(value, basestring)\n and not value.isdigit()):\n raise ValidationError(self.get_error_message(value))\n\n try:\n int(value)\n except ValueError:\n raise ValidationError(self.get_error_message(value))\n\n if self.choices and value not in self.choices:\n raise ValidationError('not a valid choice')\n\n return True","title":""},{"docid":"1c58808748b3f36a0311dff4251b4b72","score":"0.5127448","text":"def _check_value(self):\n value = str(self._value_field.toPlainText())\n if value=='': return True\n ACCEPTABLES_CHARS = ('1', '2', '3', '4', '5', '6', '7', '8', '9', '0',\n '.', ',', ';', ' ', '\\n', '-')\n\n for char in value:\n if not char in ACCEPTABLES_CHARS:\n return False\n if Variable.is_acceptable_arg(value):\n rows, columns = np.matrix(value).shape\n return 1 <= rows <= 4 and 1 <= columns <= 4\n else:\n return False","title":""},{"docid":"967e48ff6018f3b1137a7969471432b1","score":"0.5126403","text":"def _validate_value(self, val):\r\n if type(val) in (int, long, float, str, unicode, ):\r\n return val\r\n if isinstance(val, tuple) or isinstance(val, frozenset):\r\n for i in val:\r\n self._validate_value(i)\r\n return val\r\n raise TypeError(\r\n \"Only number/strings and tuples/frozensets allowed here.\",\r\n )","title":""},{"docid":"aef3b222ab5819b77922b18d16abb020","score":"0.51218873","text":"def is_valid_value(self, value):\n return value in self.values","title":""},{"docid":"deb131dfb391cb22af30ca26bd722d56","score":"0.5120042","text":"def _check_valid_value(self, value):\n if self._possible_values is None: # validation not defined (profile)\n return\n if value in self._possible_values:\n return\n if value is not None and \"ANY\" in self._possible_values:\n return\n msg = (\"'%s' is not a valid 'options.%s' value.\\nPossible values are %s\"\n % (value, self._name, self._possible_values))\n raise ConanException(msg)","title":""},{"docid":"b8257cb9ec3ea99322ecde63549e4814","score":"0.51170355","text":"def test_get_language(self):\n with translation.override(\"fr\"):\n # Despite being\n # Initialize form in other language.\n x = SimpleModel(shared=\"SHARED\", tr_title=\"TRANS\", _current_language=\"nl\")\n self.assertEqual(x.get_current_language(), \"nl\")\n x.save()\n\n x2 = SimpleModel.objects.language(\"nl\").get(pk=x.pk)\n self.assertEqual(x2.get_current_language(), \"nl\")\n self.assertEqual(x2.shared, \"SHARED\")\n self.assertEqual(x2.tr_title, \"TRANS\")","title":""},{"docid":"d4b7b8bfed685567f42a8608d9906408","score":"0.5111149","text":"def validate(self, word):\n\n return self.valid_word(word)","title":""},{"docid":"d4b7b8bfed685567f42a8608d9906408","score":"0.5111149","text":"def validate(self, word):\n\n return self.valid_word(word)","title":""},{"docid":"430e36d3558a3c87ddee0aadde9e6f1d","score":"0.51085025","text":"def _lang_id(dic: Dictionary, lang: str):\r\n idx = dic.index(lang)\r\n assert idx != dic.unk_index, \"cannot find language ID for lang {}\".format(lang)\r\n return idx","title":""},{"docid":"71aebaccbbd639c4b64fd4c6b97eb21d","score":"0.51036966","text":"def _has_language_changed(self, pid, lid, modified_at):\n if not self._has_project_changed(pid, modified_at):\n return False\n\n # look up the existing language entry\n cache_key = pid\n if cache_key in self.ts_languages_cache:\n ts_languages = self.ts_languages_cache[cache_key]\n else:\n ts_languages = self.get_url('https://cdn.door43.org/v2/ts/{0}/languages.json'.format(pid), True)\n if ts_languages:\n self.ts_languages_cache[cache_key] = ts_languages\n\n if not ts_languages:\n return False\n try:\n languages = json.loads(ts_languages)\n except:\n return False\n\n # check if the resource has been modified\n for lang in languages:\n if lang['language']['slug'] != lid:\n continue\n if 'long_date_modified' in lang['language']:\n return date_is_older(lang['language']['long_date_modified'], modified_at)\n else:\n # backwards compatibility\n return date_is_older(lang['language']['date_modified'], make_legacy_date(modified_at))","title":""},{"docid":"f66a162f59ab94b6c53d6a6e7fb96dff","score":"0.50945497","text":"def validate(self, value):\r\n if isinstance(value, self.Quoter):\r\n value = value.value\r\n return bool(value)","title":""},{"docid":"a524fe47ec2c0ec1d9a42bc370077abf","score":"0.50909066","text":"def is_valid(self, name, value, returnError = False):\n convert = self.expected[name]\n if convert == str:\n # only worry about int, list, etc;\n return True\n try:\n val = ast.literal_eval(value)\n '''\n Safely evaluate an expression node or a Unicode or Latin-1 encoded string containing a Python literal or container display.\n The string or node provided may only consist of the following\n Python literal structures: strings, numbers, tuples, lists, dicts, booleans, and None.\n '''\n except Exception, e:\n if returnError:\n return e\n else:\n print \"Tried %s(%s), but failed.\" % (convert.__name__, value)\n print e\n return False\n else:\n return type(val) == convert","title":""},{"docid":"f273407b8aae3bd88b8391b287d66894","score":"0.50871986","text":"def test_match_valid_data_val(self):\n f = lws.valid_data_val\n schema_val = ('some text', str, 'text')\n assert f(schema_val, 'text') is True\n schema_val = ('some number', float, 7.00)\n assert f(schema_val, 7) is False\n assert f(schema_val, 7.00) is True\n schema_val = ('True', bool, True)\n assert f(schema_val, True) is True\n assert f(schema_val, False) is False\n schema_val = ('even', int, lambda x: x % 2 == 0)\n assert f(schema_val, 2) is True\n assert f(schema_val, 257) is False","title":""},{"docid":"28c87254716ee9933d51816d8e0774d0","score":"0.5079756","text":"def is_valid_value(self, value):\n return value in self.categories","title":""},{"docid":"d080c13bf8680569fd962d843538c0d9","score":"0.5060185","text":"def _check_data_type(self, key: str, value: Any):\n allowedDataType = from_dot_notation(\n field=\".\".join([*self.parents, key]), obj=self.definition\n ).get(\"allowedDataType\", None)\n if allowedDataType is not None and not isinstance(value, allowedDataType):\n raise Exception(\n f\"Value '{value}' is not of the correct type. The allowed data type is: {allowedDataType.__name__}\"\n )","title":""},{"docid":"920a0eb338c35f5b59f40fbc692e66ea","score":"0.5053528","text":"def validate(self, value):\n if super().validate(value):\n return (value is None) or (isinstance(value, int) and self._validate_value(value))\n else:\n return False","title":""},{"docid":"4686ed167d3d89f1ce3624cea7ea3f99","score":"0.5050219","text":"def _assert_valid_value_and_cast(self, value):\n raise NotImplementedError()","title":""},{"docid":"e4a284d947c4d45b0a7d6dfc15c23941","score":"0.5041744","text":"def test_good_values_for_validate_guid(good_value):\n bcvalidators.validate_guid(good_value)","title":""},{"docid":"e021fa49f540e59236d94872b49d74c7","score":"0.5026196","text":"def test_is_valid_label_value_invalid_input():\n # test length violations\n assert not is_valid_label_value(value=f\"{'v' * 64}\") # value too long\n # test first character violations (not alphanum)\n assert not is_valid_label_value(value=\"-\")\n assert not is_valid_label_value(value=\"-a\")\n assert not is_valid_label_value(value=\".b\")\n assert not is_valid_label_value(value=\" c\")\n # test last character violations (not alphanum)\n assert not is_valid_label_value(value=\"a-\")\n assert not is_valid_label_value(value=\"b.\")\n assert not is_valid_label_value(value=\"c \")\n assert not is_valid_label_value(value=\"sw33T#\")\n # test middle characters violations\n assert not is_valid_label_value(value=\"a$$a\")\n assert not is_valid_label_value(value=\"b b\")","title":""},{"docid":"48756dbda98f6aaac01473792af9e590","score":"0.501369","text":"def _should_use_varname_value(self, value):\n if isinstance(value, KnownValue):\n return type(value.val) in six.integer_types\n elif (\n type(value) is TypedValue\n ): # Only replace exactly TypedValue(int), not subtypes\n return value.typ in six.integer_types\n else:\n return value is UNRESOLVED_VALUE","title":""},{"docid":"ef02e921da72310af19fef66fb0be6f4","score":"0.50122756","text":"def _validate_intermediary_institution_56D(self, val):\n return val","title":""},{"docid":"7160007545a4e4e231c34cdba411d11f","score":"0.500963","text":"def detect_language(self):\n if not self.clean:\n self._text_clean()\n if not self.clean:\n return\n self.payload = \"q={}\".format(self.text)\n resp = requests.request('POST', self.url_language, data=self.payload.encode('utf-8'),\n headers=self.translate_headers)\n try:\n self.language = json.loads(resp.text)['data']['detections'][0][0]['language']\n except KeyError:\n return","title":""},{"docid":"6352d8c445ff17e556cf954ccc904670","score":"0.5006873","text":"def is_id_valid(id_code: str) -> bool:\n if is_valid_gender_number(int(id_code[0:1])):\n if is_valid_year_number(int(id_code[1:3])):\n if is_valid_month_number(int(id_code[3:5])):\n if is_valid_day_number(int(id_code[0:1]), int(id_code[1:3]), int(id_code[3:5]), int(id_code[5:7])):\n if is_valid_birth_number(int(float(id_code[7:10]))):\n if is_valid_control_number(id_code):\n return True\n else:\n return False\n else:\n return False\n\n else:\n return False\n else:\n return False\n else:\n return False","title":""},{"docid":"02cfbfa5d016f8644e72c28b6c31a1d1","score":"0.50064754","text":"def test_langs(self):\n self._api.Init(lang=\"eng\")\n lang = self._api.GetInitLanguagesAsString()\n self.assertEqual(lang, \"eng\")\n langs = self._api.GetLoadedLanguages()\n self.assertEqual(langs, [\"eng\"])\n self.assertIn(\"eng\", self._api.GetAvailableLanguages())","title":""},{"docid":"eda13641d98745a14f5c782a8af94929","score":"0.4983522","text":"def IsLanguageTag(cls, string):\n return string.lower() in cls._LANGUAGE_PER_TAG_LOWER_CASE","title":""},{"docid":"029a0fc2644e6136895f96dee19aa16c","score":"0.49773318","text":"def validate_value(self, value: valueType) -> bool:\n if value is None:\n raise Exception\n return True","title":""},{"docid":"9f38ff18263fdd6b83cdfd09947f6520","score":"0.49747372","text":"def GetLangId(self):\n return self._code['lang_id']","title":""},{"docid":"559a5ed3bcb55ff1b8aaa581ce509721","score":"0.49741483","text":"async def check_lang(lang: str) -> str:\n\tlines = await get_cht([':list']).splitlines()\n\tif len(lines) == 1: # error\n\t\treturn lines[0]\n\t# get lang from lines:\n\tlangs = set()\n\tfor line in lines:\n\t\tind = line.find('/')\n\t\tif ind != -1:\n\t\t\tif lang == line[:ind]:\n\t\t\t\treturn ''\n\t\t\tlangs.add(line[:ind])\n\treturn INVALID_LANG.format(lang, '\\t'.join(langs))","title":""},{"docid":"ab34bb7def9b8f49066c0a0b571407d2","score":"0.49716765","text":"def test_is_valid_label_value_valid_input():\n # test valid label values\n assert is_valid_label_value(value=None)\n assert is_valid_label_value(value=\"\")\n assert is_valid_label_value(value=\"l0L\")\n assert is_valid_label_value(value=\"L-l\")\n assert is_valid_label_value(value=\"L.L\")\n assert is_valid_label_value(value=\"l_4\")\n assert is_valid_label_value(value=\"4-you\")\n assert is_valid_label_value(value=\"You.2\")","title":""},{"docid":"310afd8779dea47149177941ea3fe3bd","score":"0.49710947","text":"def is_valid(self, value) -> 'True | str':\n if not value in self.options:\n return f'The value \"{value}\" must one from \"{self.options}\".'\n return True","title":""},{"docid":"8d82e00483c42e7887fc3118cb2636fc","score":"0.49572963","text":"def test_max_int_string(self):\n val = DwcaValidator(yaml.load(self.yaml_value, Loader=yaml.FullLoader),\n error_handler=WhipErrorHandler)\n document = {'age_3': 'vijf'} # provide error on type mismatch\n val.validate(document)\n self.assertEqual(val.errors,\n {'age_3': [\"value 'vijf' is not numeric\"]},\n msg=\"alert on datatype mismatch for max \"\n \"evaluation fails\")","title":""},{"docid":"178af60c39de7f36c5b185f73d4acafa","score":"0.49482682","text":"def __checkInput(self, var):\n try:\n int(var)\n\n except:\n return False\n\n else:\n return True","title":""},{"docid":"a62441a1a827c3f35b41c9ebb8835d4a","score":"0.49473572","text":"def is_of_type(cls, value) -> bool:\n # UTF8 = 'utf-8'\n # UTF16 = 'utf-16'\n # UTF32 = 'utf-32'\n # ASCII = 'ascii'\n # BINARY = 'binary'\n # OCTAL = 'octal'\n # HEXADECIMAL = 'hexadecimal'\n # CP1252 = 'cp1252'\n # WINDOWS1252 = 'windows-1252'\n # UNICODEESCAPE = 'unicode-escape'\n\n v = None\n if cls == cls.UTF8 or cls == cls.UTF16 or cls == cls.UTF32 or cls == cls.UNICODEESCAPE:\n try:\n v = bytes(value)\n except:\n return False\n\n if cls == cls.ASCII:\n try:\n v = ascii(value)\n except:\n return False\n\n if cls == cls.BINARY:\n try:\n v = bin(value)\n except:\n return False\n\n if cls == cls.OCTAL:\n try:\n v = oct(value)\n except:\n return False\n\n if cls == cls.HEXADECIMAL:\n try:\n v = hex(value)\n except:\n return False\n\n if cls == cls.WINDOWS1252 or cls == cls.CP1252:\n try:\n v = str(value)\n except:\n return False\n return True","title":""},{"docid":"b03e5fa513cebd5753e8cc15470f05e0","score":"0.4942652","text":"def is_valid_option(cls, id_):\n return id_ in cls.CHOICES","title":""},{"docid":"80bc1b321723105a4815e71ac5d57b4e","score":"0.49419478","text":"def validate(self, incoming_city, lang, chat_id):\n validated = locate(incoming_city)\n if lang is 'ru':\n text = 'К сожалению город не найден, попробуйте еще раз.'\n btn_caption_yes = 'Да'\n btn_caption_no = 'Нет'\n else:\n text = 'Sorry, i can not find this city. Please try again.'\n btn_caption_yes = 'Yes'\n btn_caption_no = 'No'\n if validated:\n buttons = [[btn_caption_yes, btn_caption_no]]\n callbacks = [['city_ok|%s' % chat_id, 'city_false|%s' % chat_id]]\n kb = KeyboardBuilder()\n keyboard = kb.get_inline_keyboard(keyboard=buttons, callbacks=callbacks)\n self.sender.send_message(chat_id=chat_id, text=str(validated), reply_markup=keyboard)\n return True\n else:\n self.sender.send_message(chat_id=chat_id, text=text)\n return False","title":""},{"docid":"b6919ed5eceedc1763f2574cd00a90ff","score":"0.49379274","text":"def is_id_valid(id_code: str) -> bool:\n if id_code.isdigit():\n if len(str(id_code)) == 11:\n id_code = str(id_code)\n gender_number = int(id_code[0:1])\n day = int(id_code[5:7])\n month = int(id_code[3:5])\n year = id_code[1:3]\n birth_number = id_code[7:10]\n if is_valid_gender_number(gender_number) \\\n and is_valid_year_number(int(year)) \\\n and is_valid_month_number(int(month)) \\\n and is_valid_day_number(gender_number, int(year), int(month), int(day)) \\\n and is_valid_birth_number(int(birth_number)) \\\n and is_valid_control_number(str(id_code)):\n return True\n return False\n return False\n return False","title":""},{"docid":"c409f7cf7d806ca7ef2d5ff92c7ec499","score":"0.49340713","text":"def job_lang_check(lang):\n lang = lang\n def job_check(form, field):\n \"\"\"\n this is to check if job properties are well edited:\n job title and content should not be empty if it is published\n\n Arguments:\n - `from`:\n - `field`:\n \"\"\"\n data = field.data\n published = getattr(form, 'publish_'+lang)\n if published.data:\n if len(data) == 0:\n raise validators.ValidationError('field should not be empty if you choose to publish it')\n return job_check","title":""},{"docid":"a4a1a42548e6ae477115d1979ba9e22c","score":"0.4933797","text":"def test_lang_subset_unlikely_language(en_multilingual):\n sentences = [\"你好\" * 200]\n docs = [Document([], text=text) for text in sentences]\n en_multilingual(docs)\n assert [doc.lang for doc in docs] == [\"en\"]\n\n processor = en_multilingual.processors['langid']\n model = processor._model\n text_tensor = processor._text_to_tensor(sentences)\n en_idx = model.tag_to_idx['en']\n predictions = model(text_tensor)\n assert predictions[0, en_idx] < 0, \"If this test fails, then regardless of how unlikely it was, the model is predicting the input string is possibly English. Update the test by picking a different combination of languages & input\"","title":""},{"docid":"513a8555cb2e6619eda2468c23645ccd","score":"0.4928916","text":"def validate(self, key, val):\n return True","title":""},{"docid":"513a8555cb2e6619eda2468c23645ccd","score":"0.4928916","text":"def validate(self, key, val):\n return True","title":""},{"docid":"3fa3145b083d38bbf6103b9c8eeb80dc","score":"0.49263585","text":"def _check_value_type(self, value):\n if value is not None and self.value_type is not None:\n valid = isinstance(value, self.value_type)\n if not valid:\n return False\n return True","title":""},{"docid":"6c5b6093925ffb7a1a65462ff0d8bb30","score":"0.49246195","text":"def validate(self, text):\n raise NotImplementedError()","title":""},{"docid":"59a89243237f8be270ac2b0eba483042","score":"0.49206722","text":"def _validate_lookup_type(_lookup_type, _retrieval_value='id'):\n _accepted_lookup_values = {\n 'id': ('email', 'username'),\n 'username': ('id', 'email'),\n 'email': ('id', 'username')\n }\n if _retrieval_value == \"id\":\n if _lookup_type not in _accepted_lookup_values.get(_retrieval_value):\n raise errors.exceptions.InvalidLookupTypeError()\n else:\n _warn_msg = f\"The lookup type {_lookup_type} is not recognized. The function will \" + \\\n \"default to treating the lookup value as\"\n if _retrieval_value == \"email\" and _lookup_type not in _accepted_lookup_values.get(_retrieval_value):\n eprint(f\"{_warn_msg} a User ID.\")\n _lookup_type = \"id\"\n elif _retrieval_value == \"username\" and _lookup_type not in _accepted_lookup_values.get(_retrieval_value):\n eprint(f\"{_warn_msg} an email address.\")\n _lookup_type = \"email\"\n elif _lookup_type not in _accepted_lookup_values:\n raise errors.exceptions.InvalidLookupTypeError()\n return _lookup_type","title":""},{"docid":"80b12246dad4cba2d3dcbd815343f246","score":"0.49176446","text":"def has_language(lang):\n kn = _get_keyboard_names()\n return kn.has_language(lang)","title":""},{"docid":"c09825c5c128209b41d8a4d3cb74c539","score":"0.49036103","text":"def language(self, target):\n self._check_target(target)\n return target.language or self._default_language","title":""}],"string":"[\n {\n \"docid\": \"9589b0955ee39d7c15634647a6924963\",\n \"score\": \"0.6502951\",\n \"text\": \"def validate_lang(lang):\\n if lang in LANGUAGE_OPTIONS.keys():\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a3cba627ecaba3ecbd81c3dc38e4493\",\n \"score\": \"0.64360905\",\n \"text\": \"def validate_language(language):\\n\\n try:\\n lang_code = language_dict[language]\\n except KeyError:\\n lang_code = None\\n return lang_code\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3b3941857cbaf47cbe8dc774d70abf9\",\n \"score\": \"0.62319374\",\n \"text\": \"def validate(self,value):\\r\\n return type(value) is self.datatype\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f1e3e73a948a910998fdc673308adc8\",\n \"score\": \"0.6140694\",\n \"text\": \"def clean_lang(self):\\n lang = self.cleaned_data.get('lang', None)\\n if not lang in self.greetings:\\n raise forms.ValidationError(\\n \\\"We couldn't find the language you selected {}\\\"\\n \\\" Please select another\\\".format(lang)\\n )\\n return lang\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8518985fa49c2fc3c643be416ef4ec44\",\n \"score\": \"0.6126314\",\n \"text\": \"def is_valid_language_code(code):\\n try:\\n iso639.languages.get(part3=code)\\n return True\\n except KeyError:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96aa1a126c625b8f28cedb1808e08c58\",\n \"score\": \"0.59482896\",\n \"text\": \"def is_valid(self, value):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3287b2a2ac59cb53a8946dcf92771627\",\n \"score\": \"0.5937368\",\n \"text\": \"def test_good_values_for_validate_locale_code(good_value):\\n bcvalidators.validate_locale_code(good_value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3f9a41bcc8309c2597f10d5e34574f84\",\n \"score\": \"0.588551\",\n \"text\": \"def check_value(self, value):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7870a1dd290d398e5f4173c7eeea5118\",\n \"score\": \"0.5885384\",\n \"text\": \"def validate_translated_text(language_json):\\n allowed_codes = {code for code, name in settings.LANGUAGES}\\n for code, translated in language_json.items():\\n if code not in allowed_codes:\\n raise ValidationError(f'Unrecognized language code: {code}')\\n if not isinstance(translated, str):\\n raise ValidationError(f'Translation for {code} must be a string')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc9677781233b7e1d5e921c6552ae455\",\n \"score\": \"0.5739789\",\n \"text\": \"def validate(self, value):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"01f269bdb643d2afb0ef7d74d102e62e\",\n \"score\": \"0.57237697\",\n \"text\": \"def test_language_conversion(self):\\n\\n self.assertEqual(self.converter.language, LANGUAGE_CODE,\\n 'Check the language conversion.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd8f9e87abba6ec75767cc2f68a00085\",\n \"score\": \"0.57209945\",\n \"text\": \"def can_handle_language(cls, language: Hashable) -> bool:\\n\\n # if language_list is set to `None` it means: support all languages\\n if language is None or cls.language_list is None:\\n return True\\n\\n return language in cls.language_list\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbfca20125e87f09bdba4feab6f3e461\",\n \"score\": \"0.5706629\",\n \"text\": \"def _validate(self, value):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4b85f1b9dcd4ee433138cbfeec9c40d\",\n \"score\": \"0.5615167\",\n \"text\": \"def is_forced(self, lang):\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"278bd647e32419ba68bcece262b04c6c\",\n \"score\": \"0.56020755\",\n \"text\": \"def is_id_lang_available(self, seq_id: Optional[int], lang_code: str) -> bool:\\r\\n if not seq_id:\\r\\n return True\\r\\n\\r\\n if seq_id > self.get_next_seq_id(increase=False):\\r\\n return False\\r\\n\\r\\n return self.find_one({self._seq_id_key: seq_id, self._lang_code_key: lang_code}) is None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22a86984c3c4cbf45b2c50571361d207\",\n \"score\": \"0.55923426\",\n \"text\": \"def is_valid_language(self, file):\\n if not self.languages or get_file_type(file[\\\"path\\\"]) in self.languages:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"22a86984c3c4cbf45b2c50571361d207\",\n \"score\": \"0.55923426\",\n \"text\": \"def is_valid_language(self, file):\\n if not self.languages or get_file_type(file[\\\"path\\\"]) in self.languages:\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"664eb3f3a34d0b8562d04421d5245f51\",\n \"score\": \"0.5580878\",\n \"text\": \"def validate(self, value):\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3cb33fb5b3816c409c4932dde0e0689b\",\n \"score\": \"0.5571379\",\n \"text\": \"def checkType(self, value):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13ee78bd587f0748094da57a6e0940b6\",\n \"score\": \"0.55425566\",\n \"text\": \"def setLangId(self, value):\\n return self._set(langId=value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4b2fa795ebff874d987ae92f3c29a63b\",\n \"score\": \"0.55303246\",\n \"text\": \"def validate(self, value):\\r\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6437bccf71f3da47f29ce421a8bc8fe7\",\n \"score\": \"0.55287796\",\n \"text\": \"def test_languages(self):\\n for i, item in enumerate(self._letters_proto.item):\\n for code in item.language:\\n # The language code should be in ISO 639 format and consists of\\n # two letters for ISO 639-1 languages and three letters otherwise.\\n self.assertLess(1, len(code))\\n self.assertGreater(4, len(code))\\n self.assertTrue(code.islower(), f'Line {i}: Language code should be '\\n 'lower-case')\\n if len(code) == 3:\\n lang = pycountry.languages.get(alpha_3=code)\\n self.assertTrue(lang, f'Failed to find language for code {code}')\\n if hasattr(lang, 'alpha_2'):\\n self.fail(f'Letter {i}: Please use two-letter code `{lang.alpha_2}`'\\n f' instead of `{lang.alpha_3}` for {lang.name}')\\n else:\\n lang = pycountry.languages.get(alpha_2=code)\\n self.assertTrue(lang, f'Failed to find language for code {code}')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d277129b3e456e3b02d4162aca46668d\",\n \"score\": \"0.5484473\",\n \"text\": \"def _validate(cls, pid_value):\\n blop = re.compile('^[-\\\\w]+$')\\n if not bool(blop.match(pid_value)):\\n raise ValidationError(\\n 'The ID should contain only letters with numbers or dashes.',\\n field_name='id',\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8f741ee7ca2e21be7a400eb78d51fae\",\n \"score\": \"0.54337245\",\n \"text\": \"def compare_language(language):\\n if language in module.availableLanguages:\\n return True\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd146acf44f900aa2f55552be42826c8\",\n \"score\": \"0.5428892\",\n \"text\": \"def test_langid(basic_multilingual):\\n english_text = \\\"This is an English sentence.\\\"\\n french_text = \\\"C'est une phrase française.\\\"\\n docs = [english_text, french_text]\\n\\n docs = [Document([], text=text) for text in docs]\\n basic_multilingual(docs)\\n predictions = [doc.lang for doc in docs]\\n assert predictions == [\\\"en\\\", \\\"fr\\\"]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65ec4c87df5aa28f8371fdcdb3afb270\",\n \"score\": \"0.54190266\",\n \"text\": \"def validate_typeID(self, type_ID):\\n if type(type_ID) == str:\\n for letter in type_ID:\\n if not letter.isalpha() and not letter.isdigit():\\n return False\\n return True\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1c7877bc9407bba4e7006580c50504a\",\n \"score\": \"0.541637\",\n \"text\": \"def validate(self, value, obj=None):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e922987ab65649df43d07f808ae6dfc9\",\n \"score\": \"0.5401962\",\n \"text\": \"def _check_value(self, value):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d473085956b891ded63d75a81c1929df\",\n \"score\": \"0.538351\",\n \"text\": \"def validate(self, converted_value, context):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4208e3e2d8b1c4797e008b634b941c9c\",\n \"score\": \"0.53601676\",\n \"text\": \"def validate(self, value):\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d8da689777288e7a4cff98fda48bfd3\",\n \"score\": \"0.53235143\",\n \"text\": \"def is_valid(self, value) -> 'True|str':\\n if self.base_type is not None and not isinstance(value, self.base_type):\\n return f'Value {value} is not type of {self.base_type}.'\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c019a4b003bd975f742273612792b28f\",\n \"score\": \"0.5322706\",\n \"text\": \"def validate(self, value, clean=True):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c019a4b003bd975f742273612792b28f\",\n \"score\": \"0.5322706\",\n \"text\": \"def validate(self, value, clean=True):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f059d565f6bb1315b21feecbe3ad311b\",\n \"score\": \"0.53138876\",\n \"text\": \"def assertSessionLangEquals(self, value, request):\\r\\n self.assertEquals(\\r\\n value,\\r\\n request.session.get('django_language', UNSET)\\r\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2216800c4133290054ee1ed21439495d\",\n \"score\": \"0.5310737\",\n \"text\": \"def validate_pluginid(value):\\n valid = string.ascii_letters + string.digits + '.'\\n return all(c in valid for c in value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f391ce4d62159f796f6bc04014cfc28c\",\n \"score\": \"0.53039813\",\n \"text\": \"def validate_identifier(self, identifier):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c42da23fbf462bcef160c124f9c671f\",\n \"score\": \"0.52901685\",\n \"text\": \"def _validate(self, instance, value):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62a9f1ea9d27a28fe02c4dd537f2838d\",\n \"score\": \"0.5272782\",\n \"text\": \"def validate(self, instance, value):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"62a9f1ea9d27a28fe02c4dd537f2838d\",\n \"score\": \"0.5272782\",\n \"text\": \"def validate(self, instance, value):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a45d60735e642aa3757aed9ac90d0de3\",\n \"score\": \"0.5271566\",\n \"text\": \"def validate_vocabulary(value):\\n if queryUtility(IVocabularyFactory, value) is None:\\n raise Invalid(_(u\\\"Not a vocabulary: %s\\\") % value)\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4965d46f1fe65cdfec4a87d97a0397f1\",\n \"score\": \"0.52561307\",\n \"text\": \"def testLanguage(self):\\n if self.language in tools.LANGUAGES:\\n self.assertEqual(\\n self.language,\\n self.config.language\\n )\\n else:\\n self.assertNotEqual(\\n self.language,\\n self.config.language\\n )\\n self.assertEqual(\\n tools.LANGUAGE_DEFAULT,\\n self.config.language\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d44d346741eb8976cc415e09caf701ed\",\n \"score\": \"0.5250673\",\n \"text\": \"def test_bad_values_for_validate_locale_code(bad_value):\\n with pytest.raises(ValidationError):\\n bcvalidators.validate_locale_code(bad_value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"305a7bd0f9963bff044738064ffe31e4\",\n \"score\": \"0.52450514\",\n \"text\": \"def validateFormat(self, submittedValue):\\n adapt = self.getSettings()\\n return adapt.validate(submittedValue)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31a8053b3125116702866af40b361484\",\n \"score\": \"0.52255183\",\n \"text\": \"def validate(self, value):\\n return self._validate(value, self.name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dbe16511403b7e7ec3e0264340491003\",\n \"score\": \"0.5223267\",\n \"text\": \"def validate(cls, value):\\n return cls.properties.validate(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"84afe3098bd8e56154adb05d51fdbd20\",\n \"score\": \"0.5214491\",\n \"text\": \"def _is_valid_code(self, code):\\r\\n return code in COUNTRY_CODES\",\n \"title\": \"\"\n },\n {\n \"docid\": \"977e58ed7d309ede8586ee8319cee637\",\n \"score\": \"0.5196979\",\n \"text\": \"def test_language_id_correspondence():\\n with open('bible/languages.json', 'r') as languages_file:\\n languages = json.load(languages_file)\\n for language in languages:\\n case.assertTrue(\\n os.path.exists(os.path.join(\\n 'bible', 'bible-{}.json'.format(language['id']))),\\n 'bible-{}.json does not exist'.format(language['id']))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"36de2e20206727218f376eaf6f2bd705\",\n \"score\": \"0.51888824\",\n \"text\": \"def test_invalid_numeric_code_length_format(self, cred, language):\\n resp = requests.get(verify_url.format('json', cred[0], cred[1],\\n 'TestApp', test_number), params={'lg': language})\\n assert resp.status_code == 200\\n assert resp.headers['Content-Type'] == 'application/json'\\n assert resp.json()['status'] == '3'\\n assert resp.json()['error_text'] == invalid_value_msg.format('lg')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e23c914889c1ccd76f845175b0e5c48\",\n \"score\": \"0.5188024\",\n \"text\": \"def _is_valid(self, value):\\n\\n # Entities have an istypeof method that can perform more sophisticated\\n # type checking.\\n if hasattr(self._type, \\\"istypeof\\\"):\\n return self._type.istypeof(value)\\n else:\\n return isinstance(value, self._type)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"847bb3d2e1abc67e93e68ac52b1ef0c3\",\n \"score\": \"0.51798224\",\n \"text\": \"def requires_matching_languages(self):\\n return self._requires_matching_languages\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef5a495d2f6f675e88e76072f885954e\",\n \"score\": \"0.5174038\",\n \"text\": \"def check(self, value):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"121c895662484b822756ab121b4c4607\",\n \"score\": \"0.5173988\",\n \"text\": \"def verify_post_data ( ):\\n # check every field is present\\n try:\\n request.json[ 'source_lang' ]\\n request.json[ 'target_lang' ]\\n request.json[ 'text' ]\\n\\n TranslatorApp.verify_rpc_value ( request.json )\\n\\n except KeyError: # All the values are not present\\n # 400 Bad Request\\n abort ( 400, \\\"All mandatory fields are not provided\\\" )\\n except ValueError as err:\\n # 422 Unprocessable Entity\\n abort ( 422, \\\"Unprocessable value: {0}\\\".format ( err.args ) )\\n except BadRequest:\\n # 400 Bad Request\\n abort ( 400, \\\"Provided values are having malformed syntax\\\" )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0c0a63481be805751c242b06838790d\",\n \"score\": \"0.5173922\",\n \"text\": \"def is_valid_value(self, value):\\n raise NotImplementedError(\\\"subclass must implement is_valid_value()\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"170ed83db83961e84d7de9926c45b64b\",\n \"score\": \"0.5164426\",\n \"text\": \"def check_id(self, id):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"087928aab6b16e0e5567e9206790a2b4\",\n \"score\": \"0.51560616\",\n \"text\": \"def isValid(t_id):\\n\\tstr_id=str(t_id).strip()\\n\\treturn str_id.isdigit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1af742c22fb560f9d9361bcc2168236b\",\n \"score\": \"0.51279116\",\n \"text\": \"def validate(self, value):\\n\\n if value is not None:\\n\\n if (isinstance(value, basestring)\\n and not value.isdigit()):\\n raise ValidationError(self.get_error_message(value))\\n\\n try:\\n int(value)\\n except ValueError:\\n raise ValidationError(self.get_error_message(value))\\n\\n if self.choices and value not in self.choices:\\n raise ValidationError('not a valid choice')\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1c58808748b3f36a0311dff4251b4b72\",\n \"score\": \"0.5127448\",\n \"text\": \"def _check_value(self):\\n value = str(self._value_field.toPlainText())\\n if value=='': return True\\n ACCEPTABLES_CHARS = ('1', '2', '3', '4', '5', '6', '7', '8', '9', '0',\\n '.', ',', ';', ' ', '\\\\n', '-')\\n\\n for char in value:\\n if not char in ACCEPTABLES_CHARS:\\n return False\\n if Variable.is_acceptable_arg(value):\\n rows, columns = np.matrix(value).shape\\n return 1 <= rows <= 4 and 1 <= columns <= 4\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"967e48ff6018f3b1137a7969471432b1\",\n \"score\": \"0.5126403\",\n \"text\": \"def _validate_value(self, val):\\r\\n if type(val) in (int, long, float, str, unicode, ):\\r\\n return val\\r\\n if isinstance(val, tuple) or isinstance(val, frozenset):\\r\\n for i in val:\\r\\n self._validate_value(i)\\r\\n return val\\r\\n raise TypeError(\\r\\n \\\"Only number/strings and tuples/frozensets allowed here.\\\",\\r\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aef3b222ab5819b77922b18d16abb020\",\n \"score\": \"0.51218873\",\n \"text\": \"def is_valid_value(self, value):\\n return value in self.values\",\n \"title\": \"\"\n },\n {\n \"docid\": \"deb131dfb391cb22af30ca26bd722d56\",\n \"score\": \"0.5120042\",\n \"text\": \"def _check_valid_value(self, value):\\n if self._possible_values is None: # validation not defined (profile)\\n return\\n if value in self._possible_values:\\n return\\n if value is not None and \\\"ANY\\\" in self._possible_values:\\n return\\n msg = (\\\"'%s' is not a valid 'options.%s' value.\\\\nPossible values are %s\\\"\\n % (value, self._name, self._possible_values))\\n raise ConanException(msg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8257cb9ec3ea99322ecde63549e4814\",\n \"score\": \"0.51170355\",\n \"text\": \"def test_get_language(self):\\n with translation.override(\\\"fr\\\"):\\n # Despite being\\n # Initialize form in other language.\\n x = SimpleModel(shared=\\\"SHARED\\\", tr_title=\\\"TRANS\\\", _current_language=\\\"nl\\\")\\n self.assertEqual(x.get_current_language(), \\\"nl\\\")\\n x.save()\\n\\n x2 = SimpleModel.objects.language(\\\"nl\\\").get(pk=x.pk)\\n self.assertEqual(x2.get_current_language(), \\\"nl\\\")\\n self.assertEqual(x2.shared, \\\"SHARED\\\")\\n self.assertEqual(x2.tr_title, \\\"TRANS\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4b7b8bfed685567f42a8608d9906408\",\n \"score\": \"0.5111149\",\n \"text\": \"def validate(self, word):\\n\\n return self.valid_word(word)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d4b7b8bfed685567f42a8608d9906408\",\n \"score\": \"0.5111149\",\n \"text\": \"def validate(self, word):\\n\\n return self.valid_word(word)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"430e36d3558a3c87ddee0aadde9e6f1d\",\n \"score\": \"0.51085025\",\n \"text\": \"def _lang_id(dic: Dictionary, lang: str):\\r\\n idx = dic.index(lang)\\r\\n assert idx != dic.unk_index, \\\"cannot find language ID for lang {}\\\".format(lang)\\r\\n return idx\",\n \"title\": \"\"\n },\n {\n \"docid\": \"71aebaccbbd639c4b64fd4c6b97eb21d\",\n \"score\": \"0.51036966\",\n \"text\": \"def _has_language_changed(self, pid, lid, modified_at):\\n if not self._has_project_changed(pid, modified_at):\\n return False\\n\\n # look up the existing language entry\\n cache_key = pid\\n if cache_key in self.ts_languages_cache:\\n ts_languages = self.ts_languages_cache[cache_key]\\n else:\\n ts_languages = self.get_url('https://cdn.door43.org/v2/ts/{0}/languages.json'.format(pid), True)\\n if ts_languages:\\n self.ts_languages_cache[cache_key] = ts_languages\\n\\n if not ts_languages:\\n return False\\n try:\\n languages = json.loads(ts_languages)\\n except:\\n return False\\n\\n # check if the resource has been modified\\n for lang in languages:\\n if lang['language']['slug'] != lid:\\n continue\\n if 'long_date_modified' in lang['language']:\\n return date_is_older(lang['language']['long_date_modified'], modified_at)\\n else:\\n # backwards compatibility\\n return date_is_older(lang['language']['date_modified'], make_legacy_date(modified_at))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f66a162f59ab94b6c53d6a6e7fb96dff\",\n \"score\": \"0.50945497\",\n \"text\": \"def validate(self, value):\\r\\n if isinstance(value, self.Quoter):\\r\\n value = value.value\\r\\n return bool(value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a524fe47ec2c0ec1d9a42bc370077abf\",\n \"score\": \"0.50909066\",\n \"text\": \"def is_valid(self, name, value, returnError = False):\\n convert = self.expected[name]\\n if convert == str:\\n # only worry about int, list, etc;\\n return True\\n try:\\n val = ast.literal_eval(value)\\n '''\\n Safely evaluate an expression node or a Unicode or Latin-1 encoded string containing a Python literal or container display.\\n The string or node provided may only consist of the following\\n Python literal structures: strings, numbers, tuples, lists, dicts, booleans, and None.\\n '''\\n except Exception, e:\\n if returnError:\\n return e\\n else:\\n print \\\"Tried %s(%s), but failed.\\\" % (convert.__name__, value)\\n print e\\n return False\\n else:\\n return type(val) == convert\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f273407b8aae3bd88b8391b287d66894\",\n \"score\": \"0.50871986\",\n \"text\": \"def test_match_valid_data_val(self):\\n f = lws.valid_data_val\\n schema_val = ('some text', str, 'text')\\n assert f(schema_val, 'text') is True\\n schema_val = ('some number', float, 7.00)\\n assert f(schema_val, 7) is False\\n assert f(schema_val, 7.00) is True\\n schema_val = ('True', bool, True)\\n assert f(schema_val, True) is True\\n assert f(schema_val, False) is False\\n schema_val = ('even', int, lambda x: x % 2 == 0)\\n assert f(schema_val, 2) is True\\n assert f(schema_val, 257) is False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28c87254716ee9933d51816d8e0774d0\",\n \"score\": \"0.5079756\",\n \"text\": \"def is_valid_value(self, value):\\n return value in self.categories\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d080c13bf8680569fd962d843538c0d9\",\n \"score\": \"0.5060185\",\n \"text\": \"def _check_data_type(self, key: str, value: Any):\\n allowedDataType = from_dot_notation(\\n field=\\\".\\\".join([*self.parents, key]), obj=self.definition\\n ).get(\\\"allowedDataType\\\", None)\\n if allowedDataType is not None and not isinstance(value, allowedDataType):\\n raise Exception(\\n f\\\"Value '{value}' is not of the correct type. The allowed data type is: {allowedDataType.__name__}\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"920a0eb338c35f5b59f40fbc692e66ea\",\n \"score\": \"0.5053528\",\n \"text\": \"def validate(self, value):\\n if super().validate(value):\\n return (value is None) or (isinstance(value, int) and self._validate_value(value))\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4686ed167d3d89f1ce3624cea7ea3f99\",\n \"score\": \"0.5050219\",\n \"text\": \"def _assert_valid_value_and_cast(self, value):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4a284d947c4d45b0a7d6dfc15c23941\",\n \"score\": \"0.5041744\",\n \"text\": \"def test_good_values_for_validate_guid(good_value):\\n bcvalidators.validate_guid(good_value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e021fa49f540e59236d94872b49d74c7\",\n \"score\": \"0.5026196\",\n \"text\": \"def test_is_valid_label_value_invalid_input():\\n # test length violations\\n assert not is_valid_label_value(value=f\\\"{'v' * 64}\\\") # value too long\\n # test first character violations (not alphanum)\\n assert not is_valid_label_value(value=\\\"-\\\")\\n assert not is_valid_label_value(value=\\\"-a\\\")\\n assert not is_valid_label_value(value=\\\".b\\\")\\n assert not is_valid_label_value(value=\\\" c\\\")\\n # test last character violations (not alphanum)\\n assert not is_valid_label_value(value=\\\"a-\\\")\\n assert not is_valid_label_value(value=\\\"b.\\\")\\n assert not is_valid_label_value(value=\\\"c \\\")\\n assert not is_valid_label_value(value=\\\"sw33T#\\\")\\n # test middle characters violations\\n assert not is_valid_label_value(value=\\\"a$$a\\\")\\n assert not is_valid_label_value(value=\\\"b b\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"48756dbda98f6aaac01473792af9e590\",\n \"score\": \"0.501369\",\n \"text\": \"def _should_use_varname_value(self, value):\\n if isinstance(value, KnownValue):\\n return type(value.val) in six.integer_types\\n elif (\\n type(value) is TypedValue\\n ): # Only replace exactly TypedValue(int), not subtypes\\n return value.typ in six.integer_types\\n else:\\n return value is UNRESOLVED_VALUE\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ef02e921da72310af19fef66fb0be6f4\",\n \"score\": \"0.50122756\",\n \"text\": \"def _validate_intermediary_institution_56D(self, val):\\n return val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7160007545a4e4e231c34cdba411d11f\",\n \"score\": \"0.500963\",\n \"text\": \"def detect_language(self):\\n if not self.clean:\\n self._text_clean()\\n if not self.clean:\\n return\\n self.payload = \\\"q={}\\\".format(self.text)\\n resp = requests.request('POST', self.url_language, data=self.payload.encode('utf-8'),\\n headers=self.translate_headers)\\n try:\\n self.language = json.loads(resp.text)['data']['detections'][0][0]['language']\\n except KeyError:\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6352d8c445ff17e556cf954ccc904670\",\n \"score\": \"0.5006873\",\n \"text\": \"def is_id_valid(id_code: str) -> bool:\\n if is_valid_gender_number(int(id_code[0:1])):\\n if is_valid_year_number(int(id_code[1:3])):\\n if is_valid_month_number(int(id_code[3:5])):\\n if is_valid_day_number(int(id_code[0:1]), int(id_code[1:3]), int(id_code[3:5]), int(id_code[5:7])):\\n if is_valid_birth_number(int(float(id_code[7:10]))):\\n if is_valid_control_number(id_code):\\n return True\\n else:\\n return False\\n else:\\n return False\\n\\n else:\\n return False\\n else:\\n return False\\n else:\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"02cfbfa5d016f8644e72c28b6c31a1d1\",\n \"score\": \"0.50064754\",\n \"text\": \"def test_langs(self):\\n self._api.Init(lang=\\\"eng\\\")\\n lang = self._api.GetInitLanguagesAsString()\\n self.assertEqual(lang, \\\"eng\\\")\\n langs = self._api.GetLoadedLanguages()\\n self.assertEqual(langs, [\\\"eng\\\"])\\n self.assertIn(\\\"eng\\\", self._api.GetAvailableLanguages())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eda13641d98745a14f5c782a8af94929\",\n \"score\": \"0.4983522\",\n \"text\": \"def IsLanguageTag(cls, string):\\n return string.lower() in cls._LANGUAGE_PER_TAG_LOWER_CASE\",\n \"title\": \"\"\n },\n {\n \"docid\": \"029a0fc2644e6136895f96dee19aa16c\",\n \"score\": \"0.49773318\",\n \"text\": \"def validate_value(self, value: valueType) -> bool:\\n if value is None:\\n raise Exception\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9f38ff18263fdd6b83cdfd09947f6520\",\n \"score\": \"0.49747372\",\n \"text\": \"def GetLangId(self):\\n return self._code['lang_id']\",\n \"title\": \"\"\n },\n {\n \"docid\": \"559a5ed3bcb55ff1b8aaa581ce509721\",\n \"score\": \"0.49741483\",\n \"text\": \"async def check_lang(lang: str) -> str:\\n\\tlines = await get_cht([':list']).splitlines()\\n\\tif len(lines) == 1: # error\\n\\t\\treturn lines[0]\\n\\t# get lang from lines:\\n\\tlangs = set()\\n\\tfor line in lines:\\n\\t\\tind = line.find('/')\\n\\t\\tif ind != -1:\\n\\t\\t\\tif lang == line[:ind]:\\n\\t\\t\\t\\treturn ''\\n\\t\\t\\tlangs.add(line[:ind])\\n\\treturn INVALID_LANG.format(lang, '\\\\t'.join(langs))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab34bb7def9b8f49066c0a0b571407d2\",\n \"score\": \"0.49716765\",\n \"text\": \"def test_is_valid_label_value_valid_input():\\n # test valid label values\\n assert is_valid_label_value(value=None)\\n assert is_valid_label_value(value=\\\"\\\")\\n assert is_valid_label_value(value=\\\"l0L\\\")\\n assert is_valid_label_value(value=\\\"L-l\\\")\\n assert is_valid_label_value(value=\\\"L.L\\\")\\n assert is_valid_label_value(value=\\\"l_4\\\")\\n assert is_valid_label_value(value=\\\"4-you\\\")\\n assert is_valid_label_value(value=\\\"You.2\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"310afd8779dea47149177941ea3fe3bd\",\n \"score\": \"0.49710947\",\n \"text\": \"def is_valid(self, value) -> 'True | str':\\n if not value in self.options:\\n return f'The value \\\"{value}\\\" must one from \\\"{self.options}\\\".'\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d82e00483c42e7887fc3118cb2636fc\",\n \"score\": \"0.49572963\",\n \"text\": \"def test_max_int_string(self):\\n val = DwcaValidator(yaml.load(self.yaml_value, Loader=yaml.FullLoader),\\n error_handler=WhipErrorHandler)\\n document = {'age_3': 'vijf'} # provide error on type mismatch\\n val.validate(document)\\n self.assertEqual(val.errors,\\n {'age_3': [\\\"value 'vijf' is not numeric\\\"]},\\n msg=\\\"alert on datatype mismatch for max \\\"\\n \\\"evaluation fails\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"178af60c39de7f36c5b185f73d4acafa\",\n \"score\": \"0.49482682\",\n \"text\": \"def __checkInput(self, var):\\n try:\\n int(var)\\n\\n except:\\n return False\\n\\n else:\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a62441a1a827c3f35b41c9ebb8835d4a\",\n \"score\": \"0.49473572\",\n \"text\": \"def is_of_type(cls, value) -> bool:\\n # UTF8 = 'utf-8'\\n # UTF16 = 'utf-16'\\n # UTF32 = 'utf-32'\\n # ASCII = 'ascii'\\n # BINARY = 'binary'\\n # OCTAL = 'octal'\\n # HEXADECIMAL = 'hexadecimal'\\n # CP1252 = 'cp1252'\\n # WINDOWS1252 = 'windows-1252'\\n # UNICODEESCAPE = 'unicode-escape'\\n\\n v = None\\n if cls == cls.UTF8 or cls == cls.UTF16 or cls == cls.UTF32 or cls == cls.UNICODEESCAPE:\\n try:\\n v = bytes(value)\\n except:\\n return False\\n\\n if cls == cls.ASCII:\\n try:\\n v = ascii(value)\\n except:\\n return False\\n\\n if cls == cls.BINARY:\\n try:\\n v = bin(value)\\n except:\\n return False\\n\\n if cls == cls.OCTAL:\\n try:\\n v = oct(value)\\n except:\\n return False\\n\\n if cls == cls.HEXADECIMAL:\\n try:\\n v = hex(value)\\n except:\\n return False\\n\\n if cls == cls.WINDOWS1252 or cls == cls.CP1252:\\n try:\\n v = str(value)\\n except:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b03e5fa513cebd5753e8cc15470f05e0\",\n \"score\": \"0.4942652\",\n \"text\": \"def is_valid_option(cls, id_):\\n return id_ in cls.CHOICES\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80bc1b321723105a4815e71ac5d57b4e\",\n \"score\": \"0.49419478\",\n \"text\": \"def validate(self, incoming_city, lang, chat_id):\\n validated = locate(incoming_city)\\n if lang is 'ru':\\n text = 'К сожалению город не найден, попробуйте еще раз.'\\n btn_caption_yes = 'Да'\\n btn_caption_no = 'Нет'\\n else:\\n text = 'Sorry, i can not find this city. Please try again.'\\n btn_caption_yes = 'Yes'\\n btn_caption_no = 'No'\\n if validated:\\n buttons = [[btn_caption_yes, btn_caption_no]]\\n callbacks = [['city_ok|%s' % chat_id, 'city_false|%s' % chat_id]]\\n kb = KeyboardBuilder()\\n keyboard = kb.get_inline_keyboard(keyboard=buttons, callbacks=callbacks)\\n self.sender.send_message(chat_id=chat_id, text=str(validated), reply_markup=keyboard)\\n return True\\n else:\\n self.sender.send_message(chat_id=chat_id, text=text)\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b6919ed5eceedc1763f2574cd00a90ff\",\n \"score\": \"0.49379274\",\n \"text\": \"def is_id_valid(id_code: str) -> bool:\\n if id_code.isdigit():\\n if len(str(id_code)) == 11:\\n id_code = str(id_code)\\n gender_number = int(id_code[0:1])\\n day = int(id_code[5:7])\\n month = int(id_code[3:5])\\n year = id_code[1:3]\\n birth_number = id_code[7:10]\\n if is_valid_gender_number(gender_number) \\\\\\n and is_valid_year_number(int(year)) \\\\\\n and is_valid_month_number(int(month)) \\\\\\n and is_valid_day_number(gender_number, int(year), int(month), int(day)) \\\\\\n and is_valid_birth_number(int(birth_number)) \\\\\\n and is_valid_control_number(str(id_code)):\\n return True\\n return False\\n return False\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c409f7cf7d806ca7ef2d5ff92c7ec499\",\n \"score\": \"0.49340713\",\n \"text\": \"def job_lang_check(lang):\\n lang = lang\\n def job_check(form, field):\\n \\\"\\\"\\\"\\n this is to check if job properties are well edited:\\n job title and content should not be empty if it is published\\n\\n Arguments:\\n - `from`:\\n - `field`:\\n \\\"\\\"\\\"\\n data = field.data\\n published = getattr(form, 'publish_'+lang)\\n if published.data:\\n if len(data) == 0:\\n raise validators.ValidationError('field should not be empty if you choose to publish it')\\n return job_check\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4a1a42548e6ae477115d1979ba9e22c\",\n \"score\": \"0.4933797\",\n \"text\": \"def test_lang_subset_unlikely_language(en_multilingual):\\n sentences = [\\\"你好\\\" * 200]\\n docs = [Document([], text=text) for text in sentences]\\n en_multilingual(docs)\\n assert [doc.lang for doc in docs] == [\\\"en\\\"]\\n\\n processor = en_multilingual.processors['langid']\\n model = processor._model\\n text_tensor = processor._text_to_tensor(sentences)\\n en_idx = model.tag_to_idx['en']\\n predictions = model(text_tensor)\\n assert predictions[0, en_idx] < 0, \\\"If this test fails, then regardless of how unlikely it was, the model is predicting the input string is possibly English. Update the test by picking a different combination of languages & input\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"513a8555cb2e6619eda2468c23645ccd\",\n \"score\": \"0.4928916\",\n \"text\": \"def validate(self, key, val):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"513a8555cb2e6619eda2468c23645ccd\",\n \"score\": \"0.4928916\",\n \"text\": \"def validate(self, key, val):\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3fa3145b083d38bbf6103b9c8eeb80dc\",\n \"score\": \"0.49263585\",\n \"text\": \"def _check_value_type(self, value):\\n if value is not None and self.value_type is not None:\\n valid = isinstance(value, self.value_type)\\n if not valid:\\n return False\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c5b6093925ffb7a1a65462ff0d8bb30\",\n \"score\": \"0.49246195\",\n \"text\": \"def validate(self, text):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"59a89243237f8be270ac2b0eba483042\",\n \"score\": \"0.49206722\",\n \"text\": \"def _validate_lookup_type(_lookup_type, _retrieval_value='id'):\\n _accepted_lookup_values = {\\n 'id': ('email', 'username'),\\n 'username': ('id', 'email'),\\n 'email': ('id', 'username')\\n }\\n if _retrieval_value == \\\"id\\\":\\n if _lookup_type not in _accepted_lookup_values.get(_retrieval_value):\\n raise errors.exceptions.InvalidLookupTypeError()\\n else:\\n _warn_msg = f\\\"The lookup type {_lookup_type} is not recognized. The function will \\\" + \\\\\\n \\\"default to treating the lookup value as\\\"\\n if _retrieval_value == \\\"email\\\" and _lookup_type not in _accepted_lookup_values.get(_retrieval_value):\\n eprint(f\\\"{_warn_msg} a User ID.\\\")\\n _lookup_type = \\\"id\\\"\\n elif _retrieval_value == \\\"username\\\" and _lookup_type not in _accepted_lookup_values.get(_retrieval_value):\\n eprint(f\\\"{_warn_msg} an email address.\\\")\\n _lookup_type = \\\"email\\\"\\n elif _lookup_type not in _accepted_lookup_values:\\n raise errors.exceptions.InvalidLookupTypeError()\\n return _lookup_type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"80b12246dad4cba2d3dcbd815343f246\",\n \"score\": \"0.49176446\",\n \"text\": \"def has_language(lang):\\n kn = _get_keyboard_names()\\n return kn.has_language(lang)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c09825c5c128209b41d8a4d3cb74c539\",\n \"score\": \"0.49036103\",\n \"text\": \"def language(self, target):\\n self._check_target(target)\\n return target.language or self._default_language\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993294,"cells":{"query_id":{"kind":"string","value":"4e42cacc035c23b0856ffd1be1d2e98c"},"query":{"kind":"string","value":"Upgrades alpha beta values of the parent node."},"positive_passages":{"kind":"list like","value":[{"docid":"0a798d6e7655c15903d7fbeb1a1f259e","score":"0.7142382","text":"def updateAB(self, root, nodeNEW):\n if root.max == 1 and root.beta > nodeNEW.score:\n root.beta = nodeNEW.score\n elif root.max == 0 and root.alpha < nodeNEW.score:\n root.alpha = nodeNEW.score\n else:\n return","title":""}],"string":"[\n {\n \"docid\": \"0a798d6e7655c15903d7fbeb1a1f259e\",\n \"score\": \"0.7142382\",\n \"text\": \"def updateAB(self, root, nodeNEW):\\n if root.max == 1 and root.beta > nodeNEW.score:\\n root.beta = nodeNEW.score\\n elif root.max == 0 and root.alpha < nodeNEW.score:\\n root.alpha = nodeNEW.score\\n else:\\n return\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"22c9877fad8bf4bb48f144db7fb7bb9c","score":"0.73812336","text":"def update_before(self):\n if self.node_type == MAX_NODE:\n # get the beta from parent\n if self.parent:\n self.beta = self.parent.beta\n else:\n # get the alpha from parent\n if self.parent:\n self.alpha = self.parent.alpha","title":""},{"docid":"8385368578e6d7300f82307aa5dfe221","score":"0.62888014","text":"def compute_beta(self, alpha, child_alpha, scale,\n unary_potentials, pw_forward,\n traversal, parents, child_cnt, mask):\n n_vertices, n_states = unary_potentials.shape\n # build BFS traversal over the tree\n beta = np.ones((n_vertices, n_states))\n # propagate messages from root to the leaves\n for node in traversal:\n crnt_beta = beta[node]\n crnt_scale = scale[node]\n # compute the unnormalized potential at the current node\n prnt = parents[node]\n # simply assign the scale factor to the root node\n if prnt >= 0:\n # if the parent has more than one children, we need to remove\n # the alpha score that from this node\n if child_cnt[prnt] > 1:\n crnt_beta *= alpha[prnt]\n # yes, we multiply with the parent scale\n crnt_beta *= scale[prnt]\n crnt_beta /= child_alpha[node]\n else:\n crnt_beta *= unary_potentials[prnt]\n crnt_beta *= beta[prnt]\n crnt_beta[:] = np.dot(pw_forward[node], crnt_beta)\n crnt_beta /= crnt_scale\n crnt_beta *= mask[node]\n return beta","title":""},{"docid":"0a7b3a0e4cd1d632d33ad56d61a22642","score":"0.6217684","text":"def alphabeta(\n self,\n node, depth,\n alpha=-np.inf, beta=np.inf,\n maximizing_player=True,\n ):\n\n if depth == 0:\n value = self.state_value(node)\n else:\n if maximizing_player:\n value = - np.inf\n for move in node.moves(node.current_color):\n new_node = node.simulate_move(*move)\n new_value = self.alphabeta(new_node, depth - 1, alpha, beta, False)\n value = max(value, new_value)\n alpha = max(alpha, value)\n if alpha >= beta:\n break\n else:\n value = np.inf\n for move in node.moves(node.current_color):\n new_node = node.simulate_move(*move)\n new_value = self.alphabeta(new_node, depth - 1, alpha, beta, True)\n value = min(value, new_value)\n beta = min(beta, value)\n if alpha >= beta:\n break\n return value","title":""},{"docid":"aa1e542b592df63109b786e41fc691d5","score":"0.6164043","text":"def update_after(self):\n\n if self.node_type == MAX_NODE:\n max_val = MIN_VAL\n best_move = None\n for child in self.children:\n child_val = child.get_eval()\n\n # # max node, update alpha\n # if self.alpha < child_val:\n # self.alpha = child_val\n\n if max_val < child_val:\n max_val = child_val\n best_move = child.position\n\n # # update the parent's beta\n # if self.parent and self.parent.beta > self.alpha:\n # self.parent.beta = self.alpha\n\n self.value = max_val\n self.best_move = best_move\n else:\n min_val = MAX_VAL\n min_move = None\n for child in self.children:\n child_val = child.get_eval()\n\n # # min node, update beta\n # if self.beta > child_val:\n # self.beta = child_val\n\n if min_val > child_val:\n min_val = child_val\n min_move = child.position\n\n # # update the parent's alpha\n # if self.parent and self.parent.alpha < self.beta:\n # self.parent.alpha = self.beta\n\n self.value = min_val\n self.best_move = min_move","title":""},{"docid":"b5cf30b7694afea40551bdb74a82ac91","score":"0.6109212","text":"def setBeta(self, value) -> None:\n ...","title":""},{"docid":"3019c1dadbf0e58a10d1960976cea072","score":"0.5733292","text":"def update_beta(self):\n\t\tself.beta[0] = np.log(self.beta[1]) + np.expand_dims(np.mean(digamma(self.b[0]) - np.log(self.b[1]), axis=0), axis=0).repeat(self.P, axis=0)\n\t\tbeta_1 = self.beta[0, 0, :]\n\n\t\tfor k in range(self.K):\n\t\t\tbeta_1[k] = psi_inverse(2., self.beta[0, 0, k])\n\n\t\tself.beta[0] = np.expand_dims(beta_1, axis=0).repeat(self.P, axis=0)\n\t\tself.beta[1] = self.beta[0] / np.mean(self.b[0] / self.b[1], axis=0)","title":""},{"docid":"6c16f78a5d3029e0fe07e3f6278ebcce","score":"0.55616415","text":"def alphabeta(self,depth,currBoardIdx,alpha,beta,isMax):\n #YOUR CODE HERE\n self.expandedNodes += 1\n if depth == self.maxDepth:\n return self.evaluatePredifined(currBoardIdx)\n # If either player wins, return best score\n\n start_row, start_col = self.globalIdx[currBoardIdx]\n\n # If this is maxPlayer\n if isMax:\n bestValue = -inf\n # Traverse all cells\n for row in range(3):\n for col in range(3):\n if self.board[row + start_row][col + start_col] == '_':\n self.board[row + start_row][col + start_col] = self.maxPlayer\n if self.evaluatePredifined(currBoardIdx) == self.winnerMaxUtility or self.evaluatePredifined(currBoardIdx) == self.winnerMinUtility or not self.checkMovesLeft():\n self.board[row + start_row][col + start_col] = '_'\n return self.evaluatePredifined(currBoardIdx)\n next_board_index = 3*row + col\n isMax = not isMax\n bestValue = max(bestValue, self.alphabeta(depth + 1, next_board_index, alpha, beta, isMax))\n # Undo the move\n if bestValue > beta:\n self.board[row + start_row][col + start_col] = '_'\n return bestValue\n else:\n alpha = bestValue\n self.board[row + start_row][col + start_col] = '_'\n return bestValue\n else:\n bestValue = inf\n # Traverse all cells\n for row in range(3):\n for col in range(3):\n if self.board[row + start_row][col + start_col] == '_':\n self.board[row + start_row][col + start_col] = self.minPlayer\n if self.evaluatePredifined(currBoardIdx) == self.winnerMaxUtility or self.evaluatePredifined(currBoardIdx) == self.winnerMinUtility or not self.checkMovesLeft():\n self.board[row + start_row][col + start_col] = '_'\n return self.evaluatePredifined(currBoardIdx)\n next_board_index = 3*row + col\n isMax = not isMax\n bestValue = min(bestValue, self.alphabeta(depth + 1, next_board_index, alpha, beta, isMax))\n if bestValue < alpha:\n self.board[row + start_row][col + start_col] = '_'\n return bestValue\n else:\n beta = bestValue\n # Undo the move\n self.board[row + start_row][col + start_col] = '_'\n return bestValue\n return bestValue","title":""},{"docid":"2c54b5328098c77879483b5f579a9c68","score":"0.5548042","text":"def set_alpha(self, alpha):\n self._resistor_1.set_r((1 - alpha) * self.r)\n self._resistor_2.set_r(alpha * self.r)","title":""},{"docid":"de5470c0469c128bb16d77da0b3fd3b4","score":"0.54757196","text":"def alphaBeta(self, state, depth):\n def min_value(state, depth, alpha, beta):\n if state.terminal_test():\n return state.utility(self.player_id)\n if depth <= 0: \n return self.score(state)\n\n v = float(\"inf\")\n for a in state.actions():\n v = min(v, max_value(state.result(a), depth-1, alpha, beta))\n beta = min(beta, v)\n if v < alpha: \n return v\n return v\n\n def max_value(state, depth, alpha, beta):\n if state.terminal_test(): \n return state.utility(self.player_id)\n if depth <= 0: \n return self.score(state)\n\n v = float(\"-inf\")\n for a in state.actions():\n v = max(v, min_value(state.result(a), depth-1, alpha, beta))\n alpha = max(alpha, v)\n if v > beta:\n return beta\n return v\n\n # Alpha: Worst-case lower bound score that the MAX player could attain\t\n # Beta: Worst-case upper bound score that the MIN player could attain\t\n alpha = float(\"-inf\")\n beta = float(\"inf\")\n bestScore = float(\"-inf\")\n bestAction = None\n\n for a in state.actions():\t\n v = min_value(state.result(a), depth-1, alpha, beta)\t\n alpha = max(v, alpha)\n if v > bestScore:\n bestScore = v\n bestAction = a\n\n if bestAction is None: \n bestAction = random.choice(state.actions())\n return bestAction","title":""},{"docid":"e8d35deae188d312533f0f355cb1edf3","score":"0.54375505","text":"def set_alpha(node, alpha=0.1):\n for child_node in node.findall(\".//*[@rgba]\"):\n rgba_orig = string_to_array(child_node.get(\"rgba\"))\n child_node.set(\"rgba\", array_to_string(list(rgba_orig[0:3]) + [alpha]))","title":""},{"docid":"4cec5aed5b6993251d6c7541d45ca9a2","score":"0.5409931","text":"def update_value(self):\n\n self.value = self.l_child.value + self.r_child.value","title":""},{"docid":"92a51f1711317b16db644233f7e207e2","score":"0.54042333","text":"def alphabeta(self, game, depth, alpha=float(\"-inf\"), beta=float(\"inf\"), maximizing_player=True):\n if self.time_left() < self.TIMER_THRESHOLD:\n raise Timeout()\n\n # check if terminal node\n # if that is the case use the utility function\n # initialize v to neg infinity (max case)\n #\n\n margin = \"\"\n if DEBUG:\n for x in range(1, 1+self.search_depth-depth):\n margin += \" \"\n debug(margin+ \"============================================================\")\n debug(margin+ \"abp called with depth: %d alpha: %f beta: %f\" %(depth, alpha, beta))\n debug(margin+ \"============================================================\")\n\n debug(\"\\n\"+margin+\"abp depth: %d max: %s\" % (depth, str(maximizing_player)))\n debug(margin + \"self.search_depth:\"+str(self.search_depth)+\" depth: \"+ str(depth))\n\n legal_moves = game.get_legal_moves()\n\n debug(margin + \"legal moves length:\"+str(len(legal_moves)))\n\n v = (float(\"-inf\") if maximizing_player else float(\"+inf\"), (1,1))\n\n # terminal state?\n if not legal_moves:\n # return the utility of the terminal node\n debug(margin + \"terminal state, no legal moves left (%d) or depth (%d) > search_depth (%d)\" % (len(legal_moves), depth, self.search_depth))\n\n v = (game.utility(self), (-1,-1))\n debug(margin + \"A returning score: %s next_move: %s\" % (v[0], v[1]))\n\n elif depth == 0:\n v = (self.score(game, self), (-1, -1))\n debug(margin + \"B returning score: %s next_move: %s\" % (v[0], v[1]))\n\n else:\n debug(margin + \"non-terminal state, depth: %d %d legal moves.\" % (depth, len(legal_moves)))\n # iterate over the moves / children and look for\n # the min/max value\n\n debug(margin + \"is Maximizing:\", maximizing_player)\n\n for move in legal_moves:\n debug(margin + \"legal move \", move)\n new_game = game.forecast_move(move)\n new_v = (self.alphabeta(new_game, depth - 1, alpha, beta, not maximizing_player)[0], move)\n\n evaluated_score, _ = new_v\n debug(margin + \"evaluated_score:\", evaluated_score, \"score:\", v[0])\n debug()\n\n # --- MAX\n if maximizing_player:\n if evaluated_score >= beta:\n debug(margin + \"pruning\")\n #continue # MK: pruning as min would not pick this move\n return new_v\n\n if evaluated_score > alpha:\n debug(margin + \"alpha updated\")\n alpha = evaluated_score\n\n if evaluated_score > v[0]:\n v = new_v\n\n\n # --- MIN\n else:\n if evaluated_score <= alpha:\n debug(margin + \"pruning\")\n #continue # MK: pruning here as well\n return new_v\n\n if evaluated_score < beta:\n debug(margin + \"beta updated\")\n beta = evaluated_score\n\n if evaluated_score < v[0]:\n v = new_v\n\n #best = max if maximizing_player else min\n #v = best(v, new_v)\n\n debug(margin + \"C returning score: %s next_move: %s\" % (v[0], v[1]))\n return v","title":""},{"docid":"488d161dfd89161e26907c9c0b4d5dcf","score":"0.5389023","text":"def beta(self, beta):\n\n self._beta = beta","title":""},{"docid":"3b903409a9a3f8d9daaa27449d08a4da","score":"0.53837115","text":"def update_balance(self, node: AVLTreeNode) -> None:\n # TODO: Fix this method\n if node.balance > 1 or node.balance < -1:\n self.rebalance(node)\n return\n if node.parent:\n if node.is_right_child():\n node.parent.balance += 1\n elif node.is_left_child():\n node.parent.balance -= 1\n\n if node.parent.balance != 0:\n self.update_balance(node.parent)","title":""},{"docid":"ab7226b0750f6330c1676c4dde1b1fba","score":"0.5321984","text":"def inverseBeta1(self,k):\n k = k.chase()\n # Sequestered immutable part\n l2v = {l.chase(): set(self.closedChildren(l.chase()))\n for l in self.children(k) }\n eprint(\"Inverse beta\",k,\"which has\",len(l2v),\"members\",\n \"and a total of\",sum(len(v) for v in l2v.values() ),\"closed children\")\n for l,vs in l2v.items():\n eprint(\"\\tProcessing a expression w/ closed children =\",len(vs))\n for v in vs:\n l = l.chase()\n v = v.chase()\n \n for b in set(self.possibleBodies(v,l)):\n b = self.abstractClass(b)\n yield self.applyClass(b,v)","title":""},{"docid":"dfe830aa71bac09fe2087c530e87528c","score":"0.53061706","text":"def update_attributes(self):\n\n self.child_cell.targetVolume = self.parent_cell.targetVolume\n self.child_cell.lambdaVolume = self.parent_cell.lambdaVolume\n self.child_cell.type = self.parent_cell.type","title":""},{"docid":"2b42ce6e45646d541aa3258f14f64e7f","score":"0.5280276","text":"def set_alpha(self, alpha=1.0):\n self.alpha=alpha","title":""},{"docid":"26a826d1bc661dbc6290b66eac3d5147","score":"0.5272612","text":"def update_alpha(self):\n\t\tself.alpha[0] = np.log(self.alpha[1]) + np.expand_dims(np.mean(digamma(self.a[0]) - np.log(self.a[1]), axis=0), axis=0).repeat(self.N, axis=0)\n\t\talpha_1 = self.alpha[0, 0, :]\n\t\t\n\t\tfor k in range(self.K):\n\t\t\talpha_1[k] = psi_inverse(2., self.alpha[0, 0, k])\n\n\t\tself.alpha[0] = np.expand_dims(alpha_1, axis=0).repeat(self.N, axis=0)\n\t\tself.alpha[1] = self.alpha[0] / np.mean(self.a[0] / self.a[1], axis=0)","title":""},{"docid":"cbb42280a7bd9ddead2a711cdbdd1aa5","score":"0.5260954","text":"def set_beta(): \n function = LegacyFunctionSpecification() \n function.addParameter('beta', dtype='d', direction=function.IN)\n function.result_type = 'i'\n return function","title":""},{"docid":"aec8cb6b96df1d853ae28c9b3869369d","score":"0.5249142","text":"def update_recursive(self, leaf_value, gamma):\n # If it is not root, this node's parent should be updated first.\n if self._parent:\n self._parent.update_recursive(gamma * leaf_value, gamma)\n self.update(leaf_value)","title":""},{"docid":"6aa88e67e42c51f5a0611af76d5cbb55","score":"0.5245262","text":"def alphabeta(self, game, depth, alpha=float(\"-inf\"), beta=float(\"inf\"), maximizing_player=True):\n if self.time_left() < self.TIMER_THRESHOLD:\n raise Timeout()\n\n #if depth is 0, it is a leaf in the tree, and we just return the score of our player\n #note that I also return the location of the player that made the last move\n #which can be the location of our player or the opponent depending on who has played last\n if (depth == 0):\n score=self.score(game,self)\n m=game.get_player_location(game.inactive_player)\n return score,m\n \n\n legal_moves=game.get_legal_moves() \n \n #if there is no moves possible, return our score and -1,-1 as location \n if not legal_moves:\n return self.score(game,self),(-1,-1)\n \n scores=[] \n for m in legal_moves:\n #we alternate maximizing and minimizing levels, that's why maximizing is negated\n child_score,child_move=self.alphabeta(game.forecast_move(m), depth-1,alpha,beta, not maximizing_player)\n scores.append([child_score,m])\n \n if maximizing_player:\n \n if child_score>alpha:\n #we're maximizing, so this is a new lower bound\n alpha=child_score\n if child_score<alpha:\n #now that I write a comment here, I wonder if something special\n #should happen here...\n #unit tests fails if we break\n pass \n if child_score==alpha:\n #worst move possible, but it happens... Nothing special to do\n #units tests fails if we break\n pass \n \n if child_score<beta:\n #just not the best move possible.\n #continue to search to see is there is a better one in this branch\n pass\n if child_score==beta:\n #that's the bests possible score, we can stop here\n break\n if child_score>beta:\n #now that I write a comment here, I'm not sure why this happens\n #and why I should break here.\n #probably because the node above is a minimizing node and this is \n #the opponent turn, so we don't really want his score to be so high?\n #Yes. That's must be it. Another branch has a lower score for us.\n #So the opponent will chose this other branch, not this one.\n #So no need to continue searching.\n break\n \n\n \n #if minimizing level\n if not maximizing_player:\n \n if child_score<beta:\n #we're minimizing, so this is a new upper bound\n beta=child_score\n if child_score>beta:\n #now that I write a comment here, I'm not sure why this happens\n #and maybe something special should happen here...\n ###! unit tests doesnt fail if we break\n pass\n if child_score==beta:\n #just not a good move from our player point of view\n #unit tests fails if we break\n pass\n \n if child_score==alpha:\n #ok, that's a bad score, no need to search the other branches, even if\n #another have a better score, this is the one the opponent will chose\n break\n if child_score>alpha:\n #just not the worst move, lets see the other nodes...\n pass\n if child_score<alpha:\n #this move is so bad, that it's even lower than another branch we already search\n break\n \n best_score,best_move=best_score_move(scores,maximizing_player) \n return best_score,best_move","title":""},{"docid":"5323131a4b42a98ee5da3ca743c3f403","score":"0.5183604","text":"def update(self, X, Y, eta):\r\n nabla_b = [np.zeros(b.shape) for b in self.biases]\r\n nabla_w = [np.zeros(w.shape) for w in self.weights]\r\n for x,y in zip(X,Y):\r\n delta_nabla_b, delta_nabla_w = self.backPropagation(X,Y)\r\n nabla_b = [nb + dnb for nb,dnb in zip(nabla_b, delta_nabla_b)]\r\n nabla_w = [nw + dnw for nw,dnw in zip(nabla_w, delta_nabla_w)]\r\n \t\r\n self.weights = [w + (eta*nw) for w,nw in zip(self.weights, nabla_w)]\r\n self.biases = [b + (eta*nb) for b,nb in zip(self.biases, nabla_b)]","title":""},{"docid":"c77648c04f1a7dc1e982368b7aeedd7b","score":"0.51786363","text":"def beta(self):\n\t\treturn self.__beta","title":""},{"docid":"4fd3572fdac9b2275244f983ad137685","score":"0.51718736","text":"def alphabeta_designed(self,depth,currBoardIdx,alpha,beta,isDesigned):\n #YOUR CODE HERE\n self.expandedNodes += 1\n if depth == self.maxDepth:\n return self.evaluateDesigned(currBoardIdx)\n # If either player wins, return best score\n\n start_row, start_col = self.globalIdx[currBoardIdx]\n\n # If this is maxPlayer\n if isDesigned:\n bestValue = -inf\n # Traverse all cells\n for row in range(3):\n for col in range(3):\n if self.board[row + start_row][col + start_col] == '_':\n self.board[row + start_row][col + start_col] = self.minPlayer\n next_board_index = 3*row + col\n bestValue = min(bestValue, self.alphabeta_designed(depth + 1, next_board_index, alpha, beta, not isDesigned))\n # Undo the move\n if bestValue < beta:\n self.board[row + start_row][col + start_col] = '_'\n return bestValue\n else:\n alpha = bestValue\n self.board[row + start_row][col + start_col] = '_'\n return bestValue\n else:\n bestValue = inf\n # Traverse all cells\n for row in range(3):\n for col in range(3):\n if self.board[row + start_row][col + start_col] == '_':\n self.board[row + start_row][col + start_col] = self.maxPlayer\n next_board_index = 3*row + col\n isMax = not isMax\n bestValue = max(bestValue, self.alphabeta_designed(depth + 1, next_board_index, alpha, beta, not isDesigned))\n if bestValue > alpha:\n self.board[row + start_row][col + start_col] = '_'\n return bestValue\n else:\n beta = bestValue\n # Undo the move\n self.board[row + start_row][col + start_col] = '_'\n return bestValue\n return bestValue","title":""},{"docid":"c0341abab5e3683e4dc0174c5ba52c7d","score":"0.5140774","text":"def _set_alpha(self, alpha):\n self._alpha = alpha","title":""},{"docid":"1df762f7b61b9900d1e76d4092f42ddd","score":"0.51264906","text":"def alphabeta_alt(self, game, depth, alpha=float(\"-inf\"), beta=float(\"inf\"), maximizing_player=True, first=True):\n if self.time_left() < self.TIMER_THRESHOLD:\n raise Timeout()\n\n current_location = game.get_player_location(game.active_player)\n\n if depth == 0:\n return self.score(game, self), current_location\n\n moves = dict()\n\n # Empirical data shows that after around move 18, optimal ordering of branches is not worth the effort (gives worse results)\n if game.move_count < 18:\n if current_location in self.ab_trees:\n legal_moves = (k[0] for k in self.ab_trees[current_location])\n else:\n legal_moves = game.get_legal_moves()\n else:\n legal_moves = game.get_legal_moves()\n\n if maximizing_player:\n v_move = (float(\"-inf\"), (-1, -1))\n for move in legal_moves:\n new_game = game.forecast_move(move)\n v, _ = self.alphabeta(new_game, depth - 1, alpha, beta, False)\n moves[move] = v\n v_move = max([v_move, (v, move)], key=itemgetter(0))\n alpha = max([alpha, v_move[0]])\n if beta <= alpha:\n break\n\n if game.move_count < 18:\n self.ab_trees[current_location] = sorted(moves.items(), key=lambda x: x[1],\n reverse=True)\n\n return v_move\n\n else:\n v_move = (float(\"inf\"), (-1, -1))\n for move in legal_moves:\n new_game = game.forecast_move(move)\n v, _ = self.alphabeta(new_game, depth - 1, alpha, beta, True)\n moves[move] = v\n v_move = min([v_move, (v, move)], key=itemgetter(0))\n beta = min([beta, v_move[0]])\n if beta <= alpha:\n break\n\n if game.move_count < 18:\n self.ab_trees[current_location] = sorted(moves.items(), key=lambda x: x[1],\n reverse=False)\n\n return v_move","title":""},{"docid":"d88e8cccf03b749dba93db0b5a94dd3b","score":"0.51251227","text":"def adjust_beta(self, kl):\n if kl > self.args.kl_targ * self.nu:\n self.beta = np.minimum(35, self.omega * self.beta)\n elif kl < self.args.kl_targ / self.nu:\n self.beta = np.maximum(1 / 35, self.beta / self.omega)","title":""},{"docid":"90a17e8e39b6452d9d41ce6c4e6f2d08","score":"0.51177466","text":"def update_alpha(\n alpha,\n pc,\n pcj,\n epsilon,\n C,\n gene_len,\n xi_alpha_0,\n iteration,\n burn,\n accept_mh3,\n accept_mh3_postburnin,\n reject_mh3,\n reject_mh3_postburnin,\n):\n\n # f) alpha\n l_adir, alpha_proposal = calculate_l_adir(\n alpha, xi_alpha_0, iteration, pc, pcj, epsilon, gene_len, C\n )\n ## Metropolis-Hastings step\n accept_mh3, reject_mh3, accept_mh3_postburnin, reject_mh3_postburnin, quantity = MH(\n l_adir,\n accept_mh3,\n reject_mh3,\n accept_mh3_postburnin,\n reject_mh3_postburnin,\n alpha_proposal,\n alpha[iteration - 1, :],\n iteration,\n burn,\n )\n alpha[iteration, :] = quantity\n return alpha, accept_mh3, reject_mh3, accept_mh3_postburnin, reject_mh3_postburnin","title":""},{"docid":"875f89c73cd69625118dffe646ff3182","score":"0.51125747","text":"def test_update_speed_should_be_update_beta_v(self):\n #As above\n self.evacuee.optical_density_at_position = 1\n\n self.evacuee.beta_v = 0\n self.evacuee.update_speed()\n\n expected_beta_v = 0.00000001\n self.assertEqual(self.evacuee.beta_v, expected_beta_v)","title":""},{"docid":"0925bb15ddd5c57be9c023b20d8cc523","score":"0.51113397","text":"def build_update(self,alpha=0.01,beta=0.0,X=None,y=None):\n\t\tJ = self.J_Theta\n\t\tThetabs = self.GRU_model.__get_state__()['params']\n\n\t\tif X is not None:\n\t\t\tself.X = X\n\t\telse:\n\t\t\tX = self.X\n\t\tif y is not None:\n\t\t\tself.y = y\n\t\telse:\n\t\t\ty = self.y\n\t\t\n\t\tself.updateExpression, self.gradDescent_step = build_gradDescent_step(J, Thetabs,X,y,alpha,beta)","title":""},{"docid":"6bbf8534bf4201858c40f54a130b0c84","score":"0.51055807","text":"def alphabeta(self, game, depth, alpha=float(\"-inf\"), beta=float(\"inf\"), maximizing_player=True):\n if self.time_left() < self.TIMER_THRESHOLD:\n raise Timeout()\n\n best_move = [-1, -1]\n\n #if this is the last iteration, return\n if depth == 0:\n return self.score(game, self), [-1, -1]\n\n # find our possible moves from board state\n moves = game.get_legal_moves(game.active_player)\n\n # initialize minmax depending on active player\n if maximizing_player == True:\n minmax_score = float(\"-inf\")\n new_alpha = alpha\n else:\n minmax_score = float(\"inf\")\n new_beta = beta\n\n # for each legal move, consider new board state\n for move in moves:\n next_game = game.forecast_move(move)\n\n if maximizing_player:\n score, _ = self.alphabeta(next_game, depth-1, new_alpha, beta, not maximizing_player)\n if score >= minmax_score:\n minmax_score = score\n new_alpha = minmax_score\n best_move = move\n # the parent node (a minimizing node) will\n # not pick this node or any of its children\n if minmax_score >= beta:\n return minmax_score, best_move\n\n if not maximizing_player:\n score, _ = self.alphabeta(next_game, depth-1, alpha, new_beta, not maximizing_player)\n if score <= minmax_score:\n minmax_score = score\n new_beta = minmax_score\n best_move = move\n # the parent node (a maximizing node) will\n # not pick this node or any of its children\n if minmax_score <= alpha:\n return minmax_score, best_move\n\n return minmax_score, best_move","title":""},{"docid":"c91153fc2092b3fc287a88aa4fcbb034","score":"0.5092365","text":"def alphabeta(self, game, depth, alpha=float(\"-inf\"), beta=float(\"inf\")):\n # Needed to pass function signature/interface test\n return self.alphabeta_helper(game, depth, alpha, beta)[1]","title":""},{"docid":"102ecf462870f702362b891e917068c0","score":"0.5090338","text":"def setAlpha(self, alpha=...) -> None:\n ...","title":""},{"docid":"79d7bf584c4a6b6ae7b891c228f8c6bf","score":"0.5083531","text":"def alphabeta_with_ordering(self, game,initial_depth,depth,ordering_depth,best_scores_dict,parent_moves,alpha=float(\"-inf\"), beta=float(\"inf\"), maximizing_player=True):\n if self.time_left() < self.TIMER_THRESHOLD:\n raise Timeout()\n\n #if depth is 0, it is a leaf in the tree, and we just return the score of our player\n #note that I also return the location of the player that made the last move\n #which can be the location of our player or the opponent depending on who has played last\n if (depth == 0):\n score=self.score(game,self)\n m=game.get_player_location(game.inactive_player)\n return score,m\n \n pass\n \n #if we've already explores this depth and move in a previous iteration of\n #iterative search, we'll use the list of moves we have recorded\n #this list of moves is ordered by the previously evaluated scores\n #which should allow more 'alphabeta' pruning\n #it also avoids a call to get_legal_moves\n if (initial_depth - depth)<=ordering_depth:\n if( parent_moves in best_scores_dict ):\n legal_moves=[]\n for score,move in best_scores_dict[parent_moves]:\n legal_moves.append(move)\n if initial_depth==3 and depth==2:\n pass\n #print(\"YO\")\n \n else:\n legal_moves=game.get_legal_moves() \n if initial_depth==3 and depth==2:\n pass\n #print(\"YA\")\n else:\n legal_moves=game.get_legal_moves()\n if initial_depth==3 and depth==2:\n pass\n #print(\"YU\")\n \n #if there is no moves possible, return our score and -1,-1 as location \n if not legal_moves:\n return self.score(game,self),(-1,-1)\n \n scores=[]\n \n for m in legal_moves:\n #we alternate maximizing and minimizing levels, that's why maximizing is negated\n child_score,child_move=self.alphabeta_with_ordering(game.forecast_move(m),initial_depth,depth-1,ordering_depth,best_scores_dict,parent_moves+'-'+str(m),alpha,beta, not maximizing_player)\n\n \n #add score to scores already sorted\n i=0\n x=0\n if maximizing_player:\n for f_score,f_m in scores:\n if child_score>=f_score:\n x=i\n break\n else:\n x=i+1\n i=i+1\n else:\n for f_score,f_m in scores:\n if child_score<=f_score:\n x=i\n break\n else:\n x=i+1\n i=i+1\n \n scores.insert(x,[child_score,m])\n pass\n\n #scores.append([child_score,m])\n \n if maximizing_player:\n \n if child_score<beta:\n #just not the best move possible.\n #continue to search to see is there is a better one in this branch\n pass\n if child_score==beta:\n #that's the bests possible score, we can stop here\n break\n if child_score>beta:\n #now that I write a comment here, I'm not sure why this happens\n #and why I should break here.\n #probably because the node above is a minimizing node and this is \n #the opponent turn, so we don't really want his score to be so high?\n #Yes. That's must be it. Another branch has a lower score for us.\n #So the opponent will chose this other branch, not this one.\n #So no need to continue searching.\n break\n \n if child_score>alpha:\n #we're maximizing, so this is a new lower bound\n alpha=child_score\n if child_score<alpha:\n #now that I write a comment here, I wonder if something special\n #should happen here...\n #unit tests fails if we break\n pass \n if child_score==alpha:\n #worst move possible, but it happens... Nothing special to do\n #units tests fails if we break\n pass\n \n #if minimizing level\n if not maximizing_player:\n \n if child_score<beta:\n #we're minimizing, so this is a new upper bound\n beta=child_score\n if child_score>beta:\n #now that I write a comment here, I'm not sure why this happens\n #and maybe something special should happen here...\n ###! unit tests doesnt fail if we break\n pass\n if child_score==beta:\n #just not a good move from our player point of view\n #unit tests fails if we break\n pass\n \n if child_score==alpha:\n #ok, that's a bad score, no need to search the other branches, even if\n #another have a better score, this is the one the opponent will chose\n break\n if child_score>alpha:\n #just not the worst move, lets see the other nodes...\n pass\n if child_score<alpha:\n #this move is so bad, that it's even lower than another branch we already search\n break\n \n #best_score,best_move=best_score_move(scores,maximizing_player)\n #scores is sorted now. The first element is the best.\n best_score,best_move=scores[0]\n if (initial_depth - depth)<=ordering_depth:\n best_scores_dict[parent_moves]=scores\n \n return best_score,best_move","title":""},{"docid":"12c1624ad16bbe9a879d45d321b872bc","score":"0.5063983","text":"def setAlpha(self, value) -> None:\n ...","title":""},{"docid":"458ef121c9e3e59b75b925de7e5edd79","score":"0.50569916","text":"def get_beta_final(alpha, beta):\n if beta == alpha:\n return 0\n elif beta == 10:\n return 10\n elif beta == -10:\n return -10\n elif alpha == -10:\n return 10 - (10 - beta) * (10 - alpha) / 10\n else:\n return 10 * (10 + beta) / (10 + alpha) - 10","title":""},{"docid":"c4343d5717dfb3027cfda5e05b7cb418","score":"0.50481105","text":"def update_admissible(self,beta:float = 2.0):\n delta = 1/self.sampled[:,None]+np.sqrt(self.p*(1-self.p)/self.sampled[:,None])\n p_LB = np.fmax(self.p-delta,0)\n p_UB = np.fmin(self.p+delta,1)\n for l in np.arange(self.n_class):\n po_UB = np.delete(p_UB,l,axis = 1)\n self.A[:,l] = np.all(p_LB[:,l][:,None]>beta*po_UB-beta+1,axis = 1)\n # self.A[:,l] = p_LB[:,l]>1/3 #For 2 classes case.\n e_tilde = 1-self.p\n e_tilde[~self.A] = 1\n self.e_tilde = e_tilde\n self.p_LB = p_LB","title":""},{"docid":"d9984168093e0c544385e773f329f60a","score":"0.50352514","text":"def negamax_alpha_beta_TT_2(self, state, alpha, beta, depth, tt):\n\n known_node = None if (tt is None) else tt.retrieve_node(state)\n\n # Check if node exists\n if known_node:\n if known_node['depth'] >= depth:\n # The current state has been visited at least at the current depth\n if known_node['val_type'] == EXACT:\n return AIMove(known_node['state'], known_node[\"score\"])\n\n if known_node['val_type'] == LOWERBOUND and known_node['score'] > alpha:\n alpha = known_node['score']\n\n elif known_node[\"val_type\"] == UPPERBOUND and known_node[\"score\"] < beta:\n beta = known_node[\"score\"]\n\n if alpha >= beta:\n return AIMove(known_node[\"state\"],known_node[\"score\"])\n\n if state.terminal_test() or depth <= 0:\n\n val = self.utility(state) if depth<=0 else state.utility(self.player_id)\n\n if val <= alpha: val_type = LOWERBOUND\n elif val >= beta: val_type = UPPERBOUND\n else: val_type = EXACT\n\n tt.store_node(state, val, val_type, depth)\n\n return AIMove(state,val)\n\n best_score = float(\"-inf\")\n\n for action in state.actions():\n val = max(best_score, -self.negamax_alpha_beta_TT_2(state.result(action),\n -beta,\n -alpha,\n depth - 1,\n tt).score)\n\n alpha = max(alpha, val)\n\n if val > best_score:\n best_score = val\n best_move = action\n if best_score > alpha:\n alpha = best_score\n\n if best_score <= alpha:\n val_type = LOWERBOUND\n elif best_score >= beta:\n val_type = UPPERBOUND\n else: val_type = EXACT\n\n tt.store_node(state, val, val_type, depth)\n\n print(val_type, best_score)\n return AIMove(best_move,best_score)","title":""},{"docid":"34cccc4a83444c195cdd959c41e6850d","score":"0.503351","text":"def update_naive(self, x, a, x_, r, beta, id=None):\n V_x = self.joint_action_dist(x_)\n print('standard')\n # TODO: vectorize/cythonize\n for iv, v in enumerate(V_x):\n for i, atom in enumerate(self.atoms[1:]):\n V = self.Q[x.y, x.x, a].V[i]\n yC = self.Q[x.y, x.x, a].yc[i]\n\n # learning rates\n lr_v = beta * self.atom_p[iv] # p mirrors magnitude (for log-spaced)\n # lr_yc = beta * atom_p[iv] / atom # /atom for using the same beta when estimating cvar (not yc)\n lr_yc = beta * self.atom_p[iv]\n\n if self.Q[x.y, x.x, a].V[i] >= r + gamma * v:\n update = lr_v * (1 - 1 / atom)\n else:\n update = lr_v\n\n # UPDATE VaR\n self.Q[x.y, x.x, a].V[i] += update\n\n # UPDATE CVaR\n yCn = (1 - lr_yc) * yC + lr_yc * (atom*V + min(0, r+gamma*v - V))\n self.Q[x.y, x.x, a].yc[i] = yCn","title":""},{"docid":"e06b4c39b6e0769cce668b762e4a35c4","score":"0.503326","text":"def _update_scale(self,\n child: ConvBlockGene,\n new_scale: int,\n self_scale: int):\n child.set(spatial_scale=new_scale)\n\n # Update n kernels\n d_kernels = 2 ** (new_scale - self_scale)\n self_kernels = self.hyperparam('n_kernels')\n child.set(n_kernels=d_kernels * self_kernels)\n\n # Update name\n child.name = f'decode block {new_scale}'","title":""},{"docid":"e1a02fb366270348ca191b99f5b0251b","score":"0.50325537","text":"def beta(self, a, b, size=None):\n return cont(&legacy_beta, self._aug_state, size, self.lock, 2,\n a, 'a', CONS_POSITIVE,\n b, 'b', CONS_POSITIVE,\n 0.0, '', CONS_NONE, None)","title":""},{"docid":"8b71c43cfec5c2219b9823d8f477b90e","score":"0.5031596","text":"def alphabeta(self, game, depth, alpha=float(\"-inf\"), beta=float(\"inf\"), maximizing_player=True):\n if self.time_left() < self.TIMER_THRESHOLD:\n raise Timeout()\n\n current_location = game.get_player_location(game.active_player)\n\n if depth == 0:\n return self.score(game, self), current_location\n\n if current_location in self.ab_trees:\n legal_moves = (k[0] for k in self.ab_trees[current_location])\n else:\n legal_moves = game.get_legal_moves()\n\n moves = {}\n\n if maximizing_player:\n v_move = (float(\"-inf\"), (-1, -1))\n for move in legal_moves:\n new_game = game.forecast_move(move)\n v, _ = self.alphabeta(new_game, depth - 1, alpha, beta, False)\n moves[move] = v\n v_move = max([v_move, (v, move)], key=itemgetter(0))\n alpha = max([alpha, v_move[0]])\n if beta <= alpha:\n break\n\n self.ab_trees[current_location] = sorted(moves.items(), key=lambda x: x[1], reverse=True)\n\n return v_move\n\n else:\n v_move = (float(\"inf\"), (-1, -1))\n for move in legal_moves:\n new_game = game.forecast_move(move)\n v, _ = self.alphabeta(new_game, depth - 1, alpha, beta, True)\n moves[move] = v\n v_move = min([v_move, (v, move)], key=itemgetter(0))\n beta = min([beta, v_move[0]])\n if beta <= alpha:\n break\n\n self.ab_trees[current_location] = sorted(moves.items(), key=lambda x: x[1], reverse=False)\n\n return v_move","title":""},{"docid":"a3818ae8c323ad3f5b3d25a3cdab7a9b","score":"0.5029998","text":"def _get_alpha(beta: pd.Series, gamma: pd.Series) -> pd.Series:\n den = 3 - 2 * 2 ** .5\n alpha = (2 ** .5 - 1) * (beta ** .5) / den\n alpha -= (gamma / den) ** .5\n alpha[alpha < 0] = 0 # Set negative alphas to 0 (see p.727 of paper)\n return alpha","title":""},{"docid":"b8d22f16130df8454918a0a63774cef3","score":"0.5020915","text":"def getBeta(self) -> retval:\n ...","title":""},{"docid":"fc78ef6e7e8535594a51c93e3d29bca0","score":"0.5012561","text":"def update_alpha(self, gammat, rho):\n N = float(len(gammat))\n logphat = sum(dirichlet_expectation(gamma) for gamma in gammat) / N\n dalpha = np.copy(self.alpha)\n gradf = N * (psi(np.sum(self.alpha)) - psi(self.alpha) + logphat)\n\n c = N * polygamma(1, np.sum(self.alpha))\n q = -N * polygamma(1, self.alpha)\n\n b = np.sum(gradf / q) / ( 1 / c + np.sum(1 / q))\n\n dalpha = -(gradf - b) / q\n\n if all(rho() * dalpha + self.alpha > 0):\n self.alpha += rho() * dalpha\n else:\n print(\"Warning: updated alpha not positive.\")\n print(\"optimized alpha %s\" % list(self.alpha))\n\n return self.alpha","title":""},{"docid":"fc9e81ce69e2025993ed840419c5907d","score":"0.50087684","text":"def merge_node(cls, node1, node2, alpha = 1):\n\n data = np.vstack((node1.data, node2.data))\n\n nk = data.shape[0]\n dk = alpha * gamma(nk) + node1.dk * node2.dk\n pi_k = alpha * gamma(nk) / dk\n\n return cls(data, alpha, dk, pi_k)","title":""},{"docid":"61cc528cd156467e5588e13f212d132d","score":"0.49963313","text":"def __init__(self, alpha: float):\n self.alpha = alpha\n self._value = None","title":""},{"docid":"8f12a71a62c8bc68b082918a107f1208","score":"0.49918205","text":"def get_alpha_beta_move(self, board):\n alpha = float(\"-inf\")\n beta = float(\"inf\")\n v_max = float(\"-inf\")\n self.board = board\n possible_moves = self.get_possible_moves()\n col_to_return = np.random.choice(possible_moves)\n for move in possible_moves:\n self.update_board(move, self.player_number)\n v = self.get_alpha_beta_min_value(self.depth, alpha, beta)\n self.revert_move(move)\n if v > v_max:\n v_max = v\n col_to_return = move\n return col_to_return","title":""},{"docid":"2fea7f59b01f117b3afc392af33683cd","score":"0.49911717","text":"def mutate(self, probability=(1.0 / (float(MAX_DEPTH) * 2.0 + 1.0))):\n self.__mutate_aux(probability, self.root)","title":""},{"docid":"d8c6f023bb40c0cbf6eea60e2a1eac98","score":"0.49874762","text":"def compute_alpha(self, unary_potentials, pw_forward,\n traversal, parents, mask):\n n_vertices, n_states = unary_potentials.shape\n alpha = np.ones((n_vertices, n_states))\n child_alpha = np.zeros((n_vertices, n_states))\n scale = np.zeros(n_vertices)\n # messages from leaves to the root\n for node in traversal[::-1]:\n # compute the unnormalized potential at the current node\n alpha[node] *= unary_potentials[node]\n alpha[node] *= mask[node]\n scale[node] = np.sum(alpha[node]) or 1.\n alpha[node] /= scale[node]\n parent = parents[node]\n if parent >= 0:\n # estimate the belief\n child_alpha[node] = np.dot(alpha[node], pw_forward[node])\n # propagate belief to the parent\n alpha[parent] *= child_alpha[node]\n Z = np.sum(np.log(scale))\n return alpha, child_alpha, scale, Z","title":""},{"docid":"9586453fed2fbd2e4cfb9fa101e34430","score":"0.49796933","text":"def weight_adjustment(self, alpha=1.1):\n\n # weighted_edges = defaultdict(list)\n weighted_edges = {}\n\n # find the sum of all first positions\n norm = sum([s.length for s in self.sentences])\n\n # Topical boosting\n for variants in self.topics:\n\n # skip one candidate topics\n if len(variants) == 1:\n continue\n\n # get the offsets\n offsets = [self.candidates[v].offsets[0] for v in variants]\n\n # get the first occurring variant\n first = variants[offsets.index(min(offsets))]\n\n # find the nodes to which it connects -- Python 2/3 compatible\n # for start, end in self.graph.edges_iter(first):\n for start, end in self.graph.edges(first):\n\n boosters = []\n for v in variants:\n if v != first and self.graph.has_edge(v, end):\n boosters.append(self.graph[v][end]['weight'])\n\n if boosters:\n weighted_edges[(start, end)] = np.sum(boosters)\n\n # update edge weights -- Python 2/3 compatible\n # for nodes, boosters in weighted_edges.iteritems():\n for nodes, boosters in weighted_edges.items():\n node_i, node_j = nodes\n position_i = 1.0 / (1 + self.candidates[node_i].offsets[0])\n position_i = math.exp(position_i)\n self.graph[node_j][node_i]['weight'] += (boosters * alpha * position_i)","title":""},{"docid":"ce40896e64fa7ecfc0a089d42368b789","score":"0.4979133","text":"def replaceChild(self, *args):\n return _coin.SoVRMLParent_replaceChild(self, *args)","title":""},{"docid":"5665d9b8e9e0628e665e6761579ca070","score":"0.4974242","text":"def backPropogation(self, leafNode, value):\n node = leafNode\n while node != None:\n node.witnessValue(value)\n node = node.parent","title":""},{"docid":"ab8494aae034e88bfb889d4d59a74c0a","score":"0.4948806","text":"def update_pheromone(self):\n # ants lay down pheromone\n for i in range(0, self.num_ants):\n self.sigma[ self.ants[i][0] ][ self.ants[i][1] ] += self.eta \n # attenuate pheromone\n self.sigma = np.multiply(1 - self.kappa, self.sigma)","title":""},{"docid":"719fbd7f817380c8e46d303a0b67d7cf","score":"0.4944604","text":"def assign_scalar(self, alpha):\n self.assign(alpha)\n return self","title":""},{"docid":"c4881af69cf2545b87ee134af8af91bf","score":"0.49437675","text":"def SetBeta(self, beta: 'double') -> \"void\":\n return _itkSigmoidImageFilterPython.itkSigmoidImageFilterIUS3IUS3_SetBeta(self, beta)","title":""},{"docid":"f2d0277283f40ec9899752fce204747b","score":"0.4938041","text":"def alpha_beta(self, state, depth = 2):\n alpha = float(\"-inf\")\n beta = float(\"inf\")\n best_score = float(\"-inf\")\n best_move = None\n\n for action in state.actions():\n val = self.min_value(state.result(action), alpha, beta, depth - 1)\n alpha = max(alpha, val)\n if val > best_score:\n best_score = val\n best_move = action\n return AIMove(best_move, best_score)","title":""},{"docid":"0625ca2754e305e447cb5660437cd39b","score":"0.4934739","text":"def SetBeta(self, _arg: 'double const') -> \"void\":\n return _itkHessianToObjectnessMeasureImageFilterPython.itkHessianToObjectnessMeasureImageFilterISSRTD33IUS3_SetBeta(self, _arg)","title":""},{"docid":"b9395c333b6c405c42459197ca136930","score":"0.49226695","text":"def _update_versions(self, root):\n nodes = self._get_versioned_nodes(root)\n\n for node in nodes:\n attribs = node.attrib\n attribs[common.TAG_CYBOX_MAJOR] = '2'\n attribs[common.TAG_CYBOX_MINOR] = '1'\n\n with utils.ignored(KeyError):\n del attribs[common.TAG_CYBOX_UPDATE]","title":""},{"docid":"4166e9ecb9709af93f9e9751dcb58133","score":"0.49222142","text":"def alphabeta(self, game, depth, alpha=float(\"-inf\"), beta=float(\"inf\"), maximizing_player=True):\n # Raise an exception if we've run out of time without an answer\n if self.time_left() < self.TIMER_THRESHOLD:\n raise Timeout()\n\n # Evaluate the score of the current board for potential use\n # Keep track of our best move choice.\n if maximizing_player:\n best_score = float(\"-inf\")\n else:\n best_score = float(\"inf\")\n best_move = (-1,-1)\n current_score = best_score\n current_move = best_move\n \n # Get the set of legal moves for the current player\n legal_moves = game.get_legal_moves()\n \n # If, they're aren't any legal moves, then we're at an endgame\n if len(legal_moves) == 0:\n return best_score, best_move\n \n # If we've reached our max depth, evaluate the game board as per\n # the current player\n if depth == 0:\n return self.score(game, self), (-1,-1) \n\n # If the depth is 1, evaluate all children\n if depth == 1:\n for current_move in legal_moves:\n game_copy = game.forecast_move(current_move) # Get the counter to increment\n current_score = self.score(game_copy, self)\n # Update our best choice, if necessary\n if maximizing_player and current_score > best_score:\n best_score = current_score\n best_move = current_move\n elif not maximizing_player and current_score < best_score:\n best_score = current_score\n best_move = current_move\n # Shortcut if we've found the best score possible\n if best_score == float(\"inf\") or best_score == float(\"-inf\"):\n break\n \n # Decide if we need to perform a cut-off.\n # If we're a maximizing player:\n # - If the current move taken from the minimizing child is\n # higher than anything we've seen so far on the path back to the root,\n # we don't need to proceed further.\n # If we're a minimizing player:\n # - If the current move taken from the maximizing child is\n # lower than anything we've seen so far on the path back to the root,\n # we don't need to proceed further.\n if maximizing_player:\n alpha = max(alpha, current_score)\n if alpha >= beta:\n # Cutoff\n break\n else:\n beta = min(beta, current_score)\n if beta <= alpha:\n # Cutoff\n break\n \n # We're at our max depth, return a result\n return best_score, best_move\n \n # Iterate over every legal move in depth-search fashion if there's still depth left to go\n for current_move in legal_moves:\n # Copy the game state as if the move occurred and make a recursive call\n game_copy = game.forecast_move(current_move) # Get the counter to increment\n next_max_player = True\n if maximizing_player == True:\n next_max_player = False\n current_score, junk_move = self.alphabeta(game_copy, depth-1, alpha, beta, next_max_player)\n\n # Update our best choice, if necessary\n if maximizing_player and current_score > best_score:\n best_score = current_score\n best_move = current_move\n elif not maximizing_player and current_score < best_score:\n best_score = current_score\n best_move = current_move\n # Shortcut if we've found the best score possible\n if best_score == float(\"inf\") or best_score == float(\"-inf\"):\n break\n \n # Decide if we need to perform a cut-off.\n # If we're a maximizing player:\n # - If the current move taken from the minimizing child is\n # higher than anything we've seen so far on the path back to the root,\n # we don't need to proceed further.\n # If we're a minimizing player:\n # - If the current move taken from the maximizing child is\n # lower than anything we've seen so far on the path back to the root,\n # we don't need to proceed further.\n if maximizing_player:\n alpha = max(alpha, current_score)\n if alpha >= beta:\n # Cutoff\n break\n else:\n beta = min(beta, current_score)\n if beta <= alpha:\n # Cutoff\n break\n \n # Return the best score and move found\n return best_score, best_move","title":""},{"docid":"8d9fcf8a2d34142a7a17290e44e89fff","score":"0.49209356","text":"def beta_mu():","title":""},{"docid":"7209aa9a8cb05198b2ae23bcd90da5c7","score":"0.48967513","text":"def alpha_beta(asp):\n start_state = asp.get_start_state()\n\n action = None\n result = float('-inf')\n alpha = float('-inf')\n beta = float('inf')\n for a in asp.get_available_actions(start_state):\n value = alpha_beta_min(asp, asp.transition(start_state, a), alpha, beta)\n if value > result:\n result = value\n action = a\n if value >= beta:\n return action\n alpha = max(alpha, value)\n return action","title":""},{"docid":"8bfaaac31ca0eab6c3f160763cf1c3fc","score":"0.48904154","text":"def normalize_betas(self):\n\tbetas = deepcopy(self.betas)\n\tweights = np.array([p.w for p in betas])\n\tweights = weights/np.sum(weights)\n\n\tmod_beta = []\n\tfor i, p in enumerate(betas):\n\t p.w = weights[i]\n\t mod_beta.append(p)\n\n\tself.betas = mod_beta","title":""},{"docid":"8e8a72f7f5abc83081e5e2434bc0eeb2","score":"0.48811206","text":"def alphabeta_original(self, game, depth, alpha=float(\"-inf\"), beta=float(\"inf\"), maximizing_player=True):\n if self.time_left() < self.TIMER_THRESHOLD:\n raise Timeout()\n\n if depth == 0:\n return self.score(game, self), game.get_player_location(game.active_player)\n\n moves = dict()\n\n if maximizing_player:\n for move in game.get_legal_moves():\n new_game = game.forecast_move(move)\n v, _ = self.alphabeta(new_game, depth - 1, alpha, beta, False)\n moves[move] = v\n if v >= beta:\n return v, move\n alpha = max([alpha, v])\n\n if len(moves) > 0:\n value = max(moves, key=moves.get)\n return moves[value], value\n else:\n return float(\"-inf\"), (-1, -1)\n else:\n for move in game.get_legal_moves():\n new_game = game.forecast_move(move)\n v, _ = self.alphabeta(new_game, depth - 1, alpha, beta, True)\n moves[move] = v\n if v <= alpha:\n return v, move\n beta = min([beta, v])\n\n if len(moves) > 0:\n value = min(moves, key=moves.get)\n return moves[value], value\n else:\n return float(\"inf\"), (-1, -1)","title":""},{"docid":"8e8a72f7f5abc83081e5e2434bc0eeb2","score":"0.48811206","text":"def alphabeta_original(self, game, depth, alpha=float(\"-inf\"), beta=float(\"inf\"), maximizing_player=True):\n if self.time_left() < self.TIMER_THRESHOLD:\n raise Timeout()\n\n if depth == 0:\n return self.score(game, self), game.get_player_location(game.active_player)\n\n moves = dict()\n\n if maximizing_player:\n for move in game.get_legal_moves():\n new_game = game.forecast_move(move)\n v, _ = self.alphabeta(new_game, depth - 1, alpha, beta, False)\n moves[move] = v\n if v >= beta:\n return v, move\n alpha = max([alpha, v])\n\n if len(moves) > 0:\n value = max(moves, key=moves.get)\n return moves[value], value\n else:\n return float(\"-inf\"), (-1, -1)\n else:\n for move in game.get_legal_moves():\n new_game = game.forecast_move(move)\n v, _ = self.alphabeta(new_game, depth - 1, alpha, beta, True)\n moves[move] = v\n if v <= alpha:\n return v, move\n beta = min([beta, v])\n\n if len(moves) > 0:\n value = min(moves, key=moves.get)\n return moves[value], value\n else:\n return float(\"inf\"), (-1, -1)","title":""},{"docid":"a97207045e0374ed45b85d27f88e46c7","score":"0.4866564","text":"def negamax_alpha_beta_TT(self, state, alpha, beta, depth, tt):\n\n known_node = None if (tt is None) else tt.retrieve_node(state)\n\n # Check if node exists\n if known_node:\n if known_node['depth'] >= depth:\n # The current state has been visited at least at the current depth\n\n if known_node['beta'] <= alpha or known_node['beta'] == known_node['alpha']:\n return AIMove(known_node[\"state\"],known_node['beta'])\n\n if known_node['alpha'] >= beta:\n return AIMove(known_node[\"state\"],known_node['alpha'])\n\n # Maximum depth or terminal test\n if state.terminal_test() or depth <= 0:\n\n val = state.utility(self.player_id) if state.terminal_test() else self.utility(state)\n\n # Save alpha and beta as the same\n tt.store_node(state, val, val, val, depth)\n\n return AIMove(state,val)\n\n val = float(\"-inf\")\n a = alpha\n\n for action in state.actions():\n val = max(val, -self.negamax_alpha_beta_TT(state.result(action),\n -beta,\n -a,\n depth - 1,\n tt).score)\n a = max(a, val)\n best_move = action\n\n if val < beta:\n break\n\n if val <= alpha:\n tt.store_node(state, val, alpha, val, depth)\n elif val >= beta:\n tt.store_node(state, val, val, beta, depth)\n else: tt.store_node(state, val, val, val, depth)\n\n tt.store_node(state, val, val, beta, depth)\n\n return AIMove(best_move, val)","title":""},{"docid":"734f6f380e9179fa30e0215436e7992f","score":"0.48652628","text":"def alphabeta(self, game, depth, alpha=float(\"-inf\"), beta=float(\"inf\"), maximizing_player=True, first=True):\n if self.time_left() < self.TIMER_THRESHOLD:\n raise Timeout()\n\n current_location = game.get_player_location(game.active_player)\n\n if depth == 0:\n return self.score(game, self), current_location\n\n moves = dict()\n\n # Empirical data shows that after around move 18, optimal ordering of branches is not worth the effort (gives worse results)\n if game.move_count < 18:\n if current_location in self.ab_trees:\n legal_moves = (k[0] for k in self.ab_trees[current_location])\n else:\n legal_moves = game.get_legal_moves()\n else:\n legal_moves = game.get_legal_moves()\n\n if maximizing_player:\n for move in legal_moves:\n new_game = game.forecast_move(move)\n v, _ = self.alphabeta(new_game, depth - 1, alpha, beta, False)\n moves[move] = v\n # print(new_game.to_string())\n # print(depth, move, moves[move])\n if v >= beta:\n return v, move\n alpha = max([alpha, v])\n\n if game.move_count < 18:\n self.ab_trees[current_location] = sorted(moves.items(), key=lambda x: x[1],\n reverse=True)\n\n if len(moves) > 0:\n value = max(moves, key=moves.get)\n return moves[value], value\n else:\n return float(\"-inf\"), (-1, -1)\n else:\n for move in legal_moves:\n new_game = game.forecast_move(move)\n v, _ = self.alphabeta(new_game, depth - 1, alpha, beta, True)\n moves[move] = v\n # print(new_game.to_string())\n # print(depth, move, moves[move])\n if v <= alpha:\n return v, move\n beta = min([beta, v])\n\n if game.move_count < 18:\n self.ab_trees[current_location] = sorted(moves.items(), key=lambda x: x[1],\n reverse=False)\n\n if len(moves) > 0:\n value = min(moves, key=moves.get)\n return moves[value], value\n else:\n return float(\"inf\"), (-1, -1)","title":""},{"docid":"67339a284b03a9772112d547108b7a24","score":"0.4861774","text":"def _alpha_beta_search(game: MancalaEnv, alpha=-np.inf, beta=np.inf, depth=5):\n if depth == 0 or game.is_game_over():\n return game.get_player_utility()\n\n if game.side_to_move == Side.SOUTH:\n v = -np.inf\n for (_, new_s) in game.next_states():\n v = max(v, AlphaBeta._alpha_beta_search(new_s, alpha, beta, depth - 1))\n alpha = max(alpha, v)\n # if beta <= alpha:\n # break\n else:\n v = np.inf\n for (_, new_s) in game.next_states():\n v = min(v, AlphaBeta._alpha_beta_search(new_s, alpha, beta, depth - 1))\n beta = min(beta, v)\n # if beta <= alpha:\n # break\n return v","title":""},{"docid":"a7111cfd3f9a39df74eacee8a794e626","score":"0.48613393","text":"def update(self, leaf):\n\n parent = leaf.parent\n parent.update_value()\n parent = parent.parent\n while parent is not None:\n parent.update_value()\n parent = parent.parent","title":""},{"docid":"979abc3509e60d0c5379c88d34e610a8","score":"0.48586494","text":"def test_days_early_for_beta(self):\n assert self.course_outline.days_early_for_beta is None\n new_outline = attr.evolve(\n self.course_outline,\n days_early_for_beta=5\n )\n assert new_outline is not None\n assert new_outline != self.course_outline\n assert new_outline.days_early_for_beta == 5\n\n with pytest.raises(ValueError) as error:\n attr.evolve(self.course_outline, days_early_for_beta=-1)\n assert error.match(\n \"Provided value -1 for days_early_for_beta is invalid. The value must be positive or zero. \"\n \"A positive value will shift back the starting date for Beta users by that many days.\"\n )","title":""},{"docid":"b44a05eb5d372af8717acf8a0bcd6d6e","score":"0.4852976","text":"def inverseBeta_(e):\n for v in set(closedChildren(e)):\n for b in possibleBodies(v,e):\n yield Application(Abstraction(b),v)","title":""},{"docid":"5c893d67ec9775af98876ef51acdf705","score":"0.4842121","text":"def update_balance_factor(self):\n self._updateBalanceFactor(self.root)","title":""},{"docid":"6a13391bef97b27db04839ad7060c185","score":"0.4841156","text":"def update_attributes(self):\n\n parent_cell = self.mitosisSteppable.parentCell\n child_cell = self.mitosisSteppable.childCell\n compartment_list_child = self.inventory.getClusterCells(child_cell.clusterId)\n compartment_list_parent = self.inventory.getClusterCells(parent_cell.clusterId)\n # compartments in the parent and child clusters arel listed\n # in the same order so attribute changes require simple iteration through compartment list\n for i in range(compartment_list_child.size()):\n compartment_list_child[i].type = compartment_list_parent[i].type","title":""},{"docid":"bfede0d93036ea8f3cb0cfde4b5ccd8b","score":"0.4835326","text":"def SetBeta(self, _arg: 'double const') -> \"void\":\n return _itkHessianToObjectnessMeasureImageFilterPython.itkHessianToObjectnessMeasureImageFilterISSRTD33ISS3_SetBeta(self, _arg)","title":""},{"docid":"ee6038610c469a30024222f172998ef3","score":"0.4834767","text":"def update(self, alpha, omega, value):\n alpha += self.size\n omega += self.size + 1\n for i in reversed(range(1, alpha.bit_length())):\n self.push(alpha >> i)\n for i in reversed(range(1, omega.bit_length())):\n self.push(omega >> i)\n while alpha < omega:\n if alpha & 1:\n self.data[alpha] = value\n self.lazy[alpha] = value\n alpha += 1\n if omega & 1:\n omega -= 1\n self.data[omega] = value\n self.lazy[omega] = value\n alpha >>= 1\n omega >>= 1","title":""},{"docid":"a41862f489559c2b1e23b538d3373a86","score":"0.4831494","text":"def __init__(self, alpha):\n self.alpha = alpha","title":""},{"docid":"9af070a7dd52a8f90025d7c57713139a","score":"0.48251846","text":"def _update_versions(self, root):\n nodes = self._get_versioned_nodes(root)\n for node in nodes:\n name = utils.get_localname(node)\n\n if name == \"Indicator\":\n node.attrib['version'] = '2.2'\n else:\n node.attrib['version'] = '1.2'","title":""},{"docid":"773216ad35e234e1b944bf4f257e799f","score":"0.48198372","text":"def alphabeta(self,\n game,\n depth,\n alpha=float(\"-inf\"),\n beta=float(\"inf\"),\n maximizing_player=True):\n if self.time_left() < self.TIMER_THRESHOLD:\n raise Timeout()\n\n # leveraged help at: http://neverstopbuilding.com/minimax\n scores = []\n\n # determine which player we are\n if maximizing_player:\n whoami = game.active_player\n else:\n whoami = game.inactive_player\n\n # determine the moves of the other (active!) player\n legal_moves = game.get_legal_moves(game.active_player)\n\n # if no legal moves available, return the score immediately as someone\n # has thus won\n if not legal_moves:\n return [self.score(game, whoami), (-1, -1)]\n\n # determine the depth from `depth` and compute out the tree\n for legal_move in legal_moves:\n\n # if alpha ever greater than beta, prune and continue\n if alpha >= beta:\n continue\n\n forecasted_game = game.forecast_move(legal_move)\n\n if depth > 1: # if we need to recur\n score, _ = self.alphabeta(\n forecasted_game,\n depth - 1,\n alpha=alpha,\n beta=beta,\n maximizing_player=not maximizing_player)\n # set alpha if the returned score is greater than the current\n # alpha value\n if maximizing_player and score > alpha:\n alpha = score\n elif not maximizing_player and score < beta: # likewise w/beta\n beta = score\n\n scores.append([score, legal_move])\n else: # we need to determine a score for the forecasted game board\n score = self.score(forecasted_game, whoami)\n\n # same as above here\n if maximizing_player and score > alpha:\n alpha = score\n elif not maximizing_player and score < beta:\n beta = score\n\n scores.append([score, legal_move])\n\n # score the tree using minimax\n if maximizing_player:\n score_and_move = max(scores, key=lambda x: x[0])\n else: # minimizing player\n score_and_move = min(scores, key=lambda x: x[0])\n\n # return the score and the tuple(int,int) for position\n return score_and_move","title":""},{"docid":"4d87b96bd0344cdb3c32403829acbddc","score":"0.48189998","text":"def beta(self):\n return self._beta","title":""},{"docid":"c0c094de46f495c6af8d5bba298411f3","score":"0.4817924","text":"def update_cell(self, adj, cell):\n adj.g = cell.g + 1\n adj.h = self.get_cost(adj)\n adj.parent = cell\n adj.f = adj.h + adj.g","title":""},{"docid":"37aed025c2a217148f02730e9427cb63","score":"0.48133835","text":"def update_nodes(self):\n\n # get current level and problem description\n L = self.level\n P = L.prob\n\n # only if the level has been touched before\n assert L.status.unlocked\n\n # get number of collocation nodes for easier access\n M = self.coll.num_nodes\n\n # initialize integral terms with zeros, will add stuff later\n integral = [P.dtype_u(P.init, val=0.0) for l in range(M)]\n\n # gather all terms which are known already (e.g. from the previous iteration)\n # this corresponds to SF(u^k) - SdF(u^k) + tau (note: have integrals in pos and vel!)\n for m in range(M):\n for j in range(M + 1):\n # build RHS from f-terms (containing the E field) and the B field\n f = P.build_f(L.f[j], L.u[j], L.time + L.dt * self.coll.nodes[j - 1])\n # add SQF(u^k) - SxF(u^k) for the position\n integral[m].pos += L.dt * (L.dt * (self.SQ[m + 1, j] - self.Sx[m + 1, j]) * f)\n # add SF(u^k) - STF(u^k) for the velocity\n integral[m].vel += L.dt * (self.S[m + 1, j] - self.ST[m + 1, j]) * f\n # add tau if associated\n if L.tau[m] is not None:\n integral[m] += L.tau[m]\n # tau is 0-to-node, need to change it to node-to-node here\n if m > 0:\n integral[m] -= L.tau[m - 1]\n\n # do the sweep\n for m in range(0, M):\n # build rhs, consisting of the known values from above and new values from previous nodes (at k+1)\n tmp = P.dtype_u(integral[m])\n for j in range(m + 1):\n # build RHS from f-terms (containing the E field) and the B field\n f = P.build_f(L.f[j], L.u[j], L.time + L.dt * self.coll.nodes[j - 1])\n # add SxF(u^{k+1})\n tmp.pos += L.dt * (L.dt * self.Sx[m + 1, j] * f)\n # add pos at previous node + dt*v0\n tmp.pos += L.u[m].pos + L.dt * self.coll.delta_m[m] * L.u[0].vel\n # set new position, is explicit\n L.u[m + 1].pos = tmp.pos\n\n # get E field with new positions and compute mean\n L.f[m + 1] = P.eval_f(L.u[m + 1], L.time + L.dt * self.coll.nodes[m])\n\n ck = tmp.vel\n\n # do the boris scheme\n L.u[m + 1].vel = P.boris_solver(ck, L.dt * self.coll.delta_m[m], L.f[m], L.f[m + 1], L.u[m])\n\n # indicate presence of new values at this level\n L.status.updated = True\n\n return None","title":""},{"docid":"6a4124f9ee70ccbb4b5f04c623efd7ae","score":"0.48017776","text":"def SetBeta(self, _arg: 'double const') -> \"void\":\n return _itkHessianToObjectnessMeasureImageFilterPython.itkHessianToObjectnessMeasureImageFilterISSRTD33IF3_SetBeta(self, _arg)","title":""},{"docid":"9c09581737d3799fc93e1dc18e5ba4c4","score":"0.47999048","text":"def replaceChild(self, *args):\n return _coin.SoVRMLSwitch_replaceChild(self, *args)","title":""},{"docid":"691cd90f8349a677f6ca3b669d5a4688","score":"0.47912887","text":"def SetBeta(self, _arg: 'double const') -> \"void\":\n return _itkHessianToObjectnessMeasureImageFilterPython.itkHessianToObjectnessMeasureImageFilterISSRTD33IUC3_SetBeta(self, _arg)","title":""},{"docid":"9d7bdfe6a24b008d3f8c566d5a4c14d5","score":"0.47864673","text":"def get_beta(self):\n return self.__beta","title":""},{"docid":"008ff38e61cc5866d348fd014568c8b7","score":"0.47855422","text":"def SetBeta(self, beta: 'double') -> \"void\":\n return _itkSigmoidImageFilterPython.itkSigmoidImageFilterIUS2IUS2_SetBeta(self, beta)","title":""},{"docid":"5fad051ed84e714676876821551fec35","score":"0.47777498","text":"def minimax_alphabeta(board_state, depth, MaxPlayer, alpha, beta, prev_move=0, top_tree=False):\n board_copy = copy.deepcopy(board_state)\n if prev_move != 0:\n board_copy = combine_single_move(board_copy, prev_move[0], prev_move[1], prev_move[2], prev_move[3])\n\n if depth == 0 or ai_score(board_copy) == float(\"-infinity\") or ai_score(board_copy) == float(\"infinity\"):\n #print(\"Leaf Node Board: Depth: \" + str(depth) + str(board_copy))\n return ai_score(board_copy)\n board_copy = copy.deepcopy(board_state)\n if MaxPlayer:\n best_val = float(\"-infinity\")\n moves = possible_moves(board_copy, 0)\n # sort moves by ones who kill pieces first\n moves = sorted(moves, key=lambda x: x[1], reverse=True)\n if top_tree:\n best_move = []\n old_piece = []\n\n for move in moves:\n clean_move = move[0]\n #print(\"Max Depth \" + str(depth) + \" Board: \" + str(board_copy))\n val = minimax_alphabeta(copy.deepcopy(board_copy), depth - 1, False, alpha, beta, clean_move)\n #print(\"Score of \" + str(val))\n if val > best_val:\n best_move = clean_move\n if len(move) == 3:\n # this contains values about the piece that died in this process\n old_piece = move[2]\n\n best_val = max(best_val, val)\n alpha = max(best_val, alpha)\n\n if beta <= alpha:\n break\n if top_tree:\n return [[best_move, old_piece], best_val]\n else:\n return best_val\n else:\n # min player\n best_val = float(\"infinity\")\n moves = possible_moves(board_copy, 1)\n # sort moves by ones who kill pieces first\n moves = sorted(moves, key=lambda x: x[1], reverse=True)\n for move in moves:\n move = move[0]\n move_board = copy.deepcopy(board_copy)\n move_board = combine_moves(move_board, move[0], move[1], move[2], move[3], prev_move[0], prev_move[1], prev_move[2], prev_move[3])\n val = minimax_alphabeta(move_board, depth - 1, True, alpha, beta)\n #print(\"Min Depth \" + str(depth) + \" Board: \" + str(move_board))\n #print(\"Score of \" + str(val))\n\n best_val = min(best_val, val)\n beta = min(best_val, beta)\n if beta <= alpha:\n break\n return best_val","title":""},{"docid":"38cf9b8e4c2ff320b0e2c45fc60e3ac7","score":"0.47708696","text":"def reset(self, beta):\n # self.data = np.random.choice([-1, 1], size=(self.size, self.size))\n self.beta = beta\n self.decision = np.exp(- beta * np.array([-8, -4, 0, 4, 8]))","title":""},{"docid":"69093d6224fa978ab6ae3e0dc7af054a","score":"0.47701553","text":"def update(self, idx, p):\n\n change = p - self.tree[idx]\n self.tree[idx] = p\n self._propagate(idx, change)","title":""},{"docid":"8035c6bf53ee333f36f25d44dbd591ec","score":"0.47660863","text":"def update_lambda_beta(lambda_beta, beta_o, Nks):\n for k in range(K):\n lambda_beta[k] = beta_o + Nks[k]\n return lambda_beta","title":""},{"docid":"be395e3fe5d0dbae495f9c915f83731b","score":"0.475627","text":"def alphabeta(self, game, depth, alpha=float(\"-inf\"), beta=float(\"inf\"), maximizing_player=True):\n if self.time_left() < self.TIMER_THRESHOLD:\n raise Timeout()\n\n if game.is_winner(self):\n return float('+inf'), game.get_legal_moves()\n\n legal_moves = game.get_legal_moves()\n\n # check if we reached the required depth or no more moves (terminal node)\n if (depth == 0) or (not legal_moves):\n game_score = self.score(game, self)\n return (game_score, (-1, -1))\n\n global gDepth\n good_choice = ()\n if maximizing_player:\n best_score = alpha\n good_choice = (alpha, (-1, -1))\n for move in legal_moves:\n game_branch = game.forecast_move(move)\n gDepth = depth\n branch_score, _ = self.alphabeta(game_branch, depth -1, alpha, beta, (not maximizing_player))\n if branch_score > best_score:\n good_choice = (branch_score, move)\n best_score = branch_score\n if best_score >= beta:\n break\n alpha = max(alpha, best_score)\n\n else:\n best_score = beta\n good_choice = (beta, (-1, -1))\n for move in legal_moves:\n game_branch = game.forecast_move(move)\n branch_score, _ = self.alphabeta(game_branch, depth -1, alpha, beta, (not maximizing_player))\n if branch_score < best_score:\n good_choice = (branch_score, move)\n best_score = branch_score\n if best_score <= alpha:\n good_choice = (best_score, move)\n break\n beta = min(beta, best_score)\n\n return good_choice;","title":""},{"docid":"2e31617c4b4b2bdc54b041b8b020882a","score":"0.47539702","text":"def abel_inversion(self):\n self.func_prime()\n self.F_construct()\n self.r=self.newy","title":""},{"docid":"99c756b0869c887dd8ebd3e45d4258f7","score":"0.4742597","text":"def assign_generalized_bc(self, loc, alpha, beta, gamma):\n if loc.lower() == \"ilo\":\n idx = 0\n elif loc.lower() == \"ihi\":\n idx = 1\n else:\n error(\"assign_bc: loc must be one of ilo or ihi, got {0}\".format(loc))\n\n # determine if boundary already has an assigned bc\n if self.bcs[idx][1] is not None:\n error(\"attempting to assign multiple boundary conditions to same node\")\n\n if abs(beta) < EPS:\n beta = 1.e-80\n\n # algorithm requires negative on left side boundary\n if idx == 0:\n beta = -beta\n\n if abs(alpha - 1.) < EPS:\n self.constrained = True\n\n self.bcs[idx] = [self.boundary_nodes[idx], alpha, beta, gamma]","title":""},{"docid":"598b65a25fafbbbd7ffee420c8134ff9","score":"0.47409594","text":"def update_current_output_positions( self, t_boost, inv_gamma, inv_beta ):\n # Some shorcuts for further calculation's purposes\n t_lab = self.t_lab\n t_boost_prev = t_boost - self.dt\n\n # This implements the Lorentz transformation formulas,\n # for a snapshot having a fixed t_lab\n self.current_z_boost = (t_lab*inv_gamma - t_boost)*c*inv_beta\n self.prev_z_boost = (t_lab*inv_gamma - t_boost_prev)*c*inv_beta\n self.current_z_lab = (t_lab - t_boost*inv_gamma)*c*inv_beta\n self.prev_z_lab = (t_lab - t_boost_prev*inv_gamma)*c*inv_beta","title":""},{"docid":"598b65a25fafbbbd7ffee420c8134ff9","score":"0.47409594","text":"def update_current_output_positions( self, t_boost, inv_gamma, inv_beta ):\n # Some shorcuts for further calculation's purposes\n t_lab = self.t_lab\n t_boost_prev = t_boost - self.dt\n\n # This implements the Lorentz transformation formulas,\n # for a snapshot having a fixed t_lab\n self.current_z_boost = (t_lab*inv_gamma - t_boost)*c*inv_beta\n self.prev_z_boost = (t_lab*inv_gamma - t_boost_prev)*c*inv_beta\n self.current_z_lab = (t_lab - t_boost*inv_gamma)*c*inv_beta\n self.prev_z_lab = (t_lab - t_boost_prev*inv_gamma)*c*inv_beta","title":""},{"docid":"72fbf308e92428f8206ee4b947835cbf","score":"0.47384992","text":"def reduce_eta(self):\n self.eta = self.eta * self.eta_decay_factor","title":""},{"docid":"d914fe7f48134b8c1023530034e212d2","score":"0.47324023","text":"def update(self):\n self.weight_mom[self.index] = self.sub_weight_mom\n self.weight[self.index] = self.sub_weight","title":""},{"docid":"6af45e5acfd1dfc9af642aa3cf8b018a","score":"0.47240648","text":"def SetBeta(self, _arg: 'double const') -> \"void\":\n return _itkHessianToObjectnessMeasureImageFilterPython.itkHessianToObjectnessMeasureImageFilterISSRTD22IUS2_SetBeta(self, _arg)","title":""},{"docid":"a3f38b3082446c2ded9f395fad0eda0c","score":"0.47213665","text":"def inc_bl_adj(self):\n self.bl_adj += 1","title":""},{"docid":"6e012bc525675527d684baf0b41d7ced","score":"0.47185087","text":"def forward(self):\r\n\r\n self.value = np.dot(self.x_node.value, self.w_node.value) + self.b_node.value","title":""}],"string":"[\n {\n \"docid\": \"22c9877fad8bf4bb48f144db7fb7bb9c\",\n \"score\": \"0.73812336\",\n \"text\": \"def update_before(self):\\n if self.node_type == MAX_NODE:\\n # get the beta from parent\\n if self.parent:\\n self.beta = self.parent.beta\\n else:\\n # get the alpha from parent\\n if self.parent:\\n self.alpha = self.parent.alpha\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8385368578e6d7300f82307aa5dfe221\",\n \"score\": \"0.62888014\",\n \"text\": \"def compute_beta(self, alpha, child_alpha, scale,\\n unary_potentials, pw_forward,\\n traversal, parents, child_cnt, mask):\\n n_vertices, n_states = unary_potentials.shape\\n # build BFS traversal over the tree\\n beta = np.ones((n_vertices, n_states))\\n # propagate messages from root to the leaves\\n for node in traversal:\\n crnt_beta = beta[node]\\n crnt_scale = scale[node]\\n # compute the unnormalized potential at the current node\\n prnt = parents[node]\\n # simply assign the scale factor to the root node\\n if prnt >= 0:\\n # if the parent has more than one children, we need to remove\\n # the alpha score that from this node\\n if child_cnt[prnt] > 1:\\n crnt_beta *= alpha[prnt]\\n # yes, we multiply with the parent scale\\n crnt_beta *= scale[prnt]\\n crnt_beta /= child_alpha[node]\\n else:\\n crnt_beta *= unary_potentials[prnt]\\n crnt_beta *= beta[prnt]\\n crnt_beta[:] = np.dot(pw_forward[node], crnt_beta)\\n crnt_beta /= crnt_scale\\n crnt_beta *= mask[node]\\n return beta\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a7b3a0e4cd1d632d33ad56d61a22642\",\n \"score\": \"0.6217684\",\n \"text\": \"def alphabeta(\\n self,\\n node, depth,\\n alpha=-np.inf, beta=np.inf,\\n maximizing_player=True,\\n ):\\n\\n if depth == 0:\\n value = self.state_value(node)\\n else:\\n if maximizing_player:\\n value = - np.inf\\n for move in node.moves(node.current_color):\\n new_node = node.simulate_move(*move)\\n new_value = self.alphabeta(new_node, depth - 1, alpha, beta, False)\\n value = max(value, new_value)\\n alpha = max(alpha, value)\\n if alpha >= beta:\\n break\\n else:\\n value = np.inf\\n for move in node.moves(node.current_color):\\n new_node = node.simulate_move(*move)\\n new_value = self.alphabeta(new_node, depth - 1, alpha, beta, True)\\n value = min(value, new_value)\\n beta = min(beta, value)\\n if alpha >= beta:\\n break\\n return value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa1e542b592df63109b786e41fc691d5\",\n \"score\": \"0.6164043\",\n \"text\": \"def update_after(self):\\n\\n if self.node_type == MAX_NODE:\\n max_val = MIN_VAL\\n best_move = None\\n for child in self.children:\\n child_val = child.get_eval()\\n\\n # # max node, update alpha\\n # if self.alpha < child_val:\\n # self.alpha = child_val\\n\\n if max_val < child_val:\\n max_val = child_val\\n best_move = child.position\\n\\n # # update the parent's beta\\n # if self.parent and self.parent.beta > self.alpha:\\n # self.parent.beta = self.alpha\\n\\n self.value = max_val\\n self.best_move = best_move\\n else:\\n min_val = MAX_VAL\\n min_move = None\\n for child in self.children:\\n child_val = child.get_eval()\\n\\n # # min node, update beta\\n # if self.beta > child_val:\\n # self.beta = child_val\\n\\n if min_val > child_val:\\n min_val = child_val\\n min_move = child.position\\n\\n # # update the parent's alpha\\n # if self.parent and self.parent.alpha < self.beta:\\n # self.parent.alpha = self.beta\\n\\n self.value = min_val\\n self.best_move = min_move\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b5cf30b7694afea40551bdb74a82ac91\",\n \"score\": \"0.6109212\",\n \"text\": \"def setBeta(self, value) -> None:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3019c1dadbf0e58a10d1960976cea072\",\n \"score\": \"0.5733292\",\n \"text\": \"def update_beta(self):\\n\\t\\tself.beta[0] = np.log(self.beta[1]) + np.expand_dims(np.mean(digamma(self.b[0]) - np.log(self.b[1]), axis=0), axis=0).repeat(self.P, axis=0)\\n\\t\\tbeta_1 = self.beta[0, 0, :]\\n\\n\\t\\tfor k in range(self.K):\\n\\t\\t\\tbeta_1[k] = psi_inverse(2., self.beta[0, 0, k])\\n\\n\\t\\tself.beta[0] = np.expand_dims(beta_1, axis=0).repeat(self.P, axis=0)\\n\\t\\tself.beta[1] = self.beta[0] / np.mean(self.b[0] / self.b[1], axis=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c16f78a5d3029e0fe07e3f6278ebcce\",\n \"score\": \"0.55616415\",\n \"text\": \"def alphabeta(self,depth,currBoardIdx,alpha,beta,isMax):\\n #YOUR CODE HERE\\n self.expandedNodes += 1\\n if depth == self.maxDepth:\\n return self.evaluatePredifined(currBoardIdx)\\n # If either player wins, return best score\\n\\n start_row, start_col = self.globalIdx[currBoardIdx]\\n\\n # If this is maxPlayer\\n if isMax:\\n bestValue = -inf\\n # Traverse all cells\\n for row in range(3):\\n for col in range(3):\\n if self.board[row + start_row][col + start_col] == '_':\\n self.board[row + start_row][col + start_col] = self.maxPlayer\\n if self.evaluatePredifined(currBoardIdx) == self.winnerMaxUtility or self.evaluatePredifined(currBoardIdx) == self.winnerMinUtility or not self.checkMovesLeft():\\n self.board[row + start_row][col + start_col] = '_'\\n return self.evaluatePredifined(currBoardIdx)\\n next_board_index = 3*row + col\\n isMax = not isMax\\n bestValue = max(bestValue, self.alphabeta(depth + 1, next_board_index, alpha, beta, isMax))\\n # Undo the move\\n if bestValue > beta:\\n self.board[row + start_row][col + start_col] = '_'\\n return bestValue\\n else:\\n alpha = bestValue\\n self.board[row + start_row][col + start_col] = '_'\\n return bestValue\\n else:\\n bestValue = inf\\n # Traverse all cells\\n for row in range(3):\\n for col in range(3):\\n if self.board[row + start_row][col + start_col] == '_':\\n self.board[row + start_row][col + start_col] = self.minPlayer\\n if self.evaluatePredifined(currBoardIdx) == self.winnerMaxUtility or self.evaluatePredifined(currBoardIdx) == self.winnerMinUtility or not self.checkMovesLeft():\\n self.board[row + start_row][col + start_col] = '_'\\n return self.evaluatePredifined(currBoardIdx)\\n next_board_index = 3*row + col\\n isMax = not isMax\\n bestValue = min(bestValue, self.alphabeta(depth + 1, next_board_index, alpha, beta, isMax))\\n if bestValue < alpha:\\n self.board[row + start_row][col + start_col] = '_'\\n return bestValue\\n else:\\n beta = bestValue\\n # Undo the move\\n self.board[row + start_row][col + start_col] = '_'\\n return bestValue\\n return bestValue\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2c54b5328098c77879483b5f579a9c68\",\n \"score\": \"0.5548042\",\n \"text\": \"def set_alpha(self, alpha):\\n self._resistor_1.set_r((1 - alpha) * self.r)\\n self._resistor_2.set_r(alpha * self.r)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de5470c0469c128bb16d77da0b3fd3b4\",\n \"score\": \"0.54757196\",\n \"text\": \"def alphaBeta(self, state, depth):\\n def min_value(state, depth, alpha, beta):\\n if state.terminal_test():\\n return state.utility(self.player_id)\\n if depth <= 0: \\n return self.score(state)\\n\\n v = float(\\\"inf\\\")\\n for a in state.actions():\\n v = min(v, max_value(state.result(a), depth-1, alpha, beta))\\n beta = min(beta, v)\\n if v < alpha: \\n return v\\n return v\\n\\n def max_value(state, depth, alpha, beta):\\n if state.terminal_test(): \\n return state.utility(self.player_id)\\n if depth <= 0: \\n return self.score(state)\\n\\n v = float(\\\"-inf\\\")\\n for a in state.actions():\\n v = max(v, min_value(state.result(a), depth-1, alpha, beta))\\n alpha = max(alpha, v)\\n if v > beta:\\n return beta\\n return v\\n\\n # Alpha: Worst-case lower bound score that the MAX player could attain\\t\\n # Beta: Worst-case upper bound score that the MIN player could attain\\t\\n alpha = float(\\\"-inf\\\")\\n beta = float(\\\"inf\\\")\\n bestScore = float(\\\"-inf\\\")\\n bestAction = None\\n\\n for a in state.actions():\\t\\n v = min_value(state.result(a), depth-1, alpha, beta)\\t\\n alpha = max(v, alpha)\\n if v > bestScore:\\n bestScore = v\\n bestAction = a\\n\\n if bestAction is None: \\n bestAction = random.choice(state.actions())\\n return bestAction\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8d35deae188d312533f0f355cb1edf3\",\n \"score\": \"0.54375505\",\n \"text\": \"def set_alpha(node, alpha=0.1):\\n for child_node in node.findall(\\\".//*[@rgba]\\\"):\\n rgba_orig = string_to_array(child_node.get(\\\"rgba\\\"))\\n child_node.set(\\\"rgba\\\", array_to_string(list(rgba_orig[0:3]) + [alpha]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4cec5aed5b6993251d6c7541d45ca9a2\",\n \"score\": \"0.5409931\",\n \"text\": \"def update_value(self):\\n\\n self.value = self.l_child.value + self.r_child.value\",\n \"title\": \"\"\n },\n {\n \"docid\": \"92a51f1711317b16db644233f7e207e2\",\n \"score\": \"0.54042333\",\n \"text\": \"def alphabeta(self, game, depth, alpha=float(\\\"-inf\\\"), beta=float(\\\"inf\\\"), maximizing_player=True):\\n if self.time_left() < self.TIMER_THRESHOLD:\\n raise Timeout()\\n\\n # check if terminal node\\n # if that is the case use the utility function\\n # initialize v to neg infinity (max case)\\n #\\n\\n margin = \\\"\\\"\\n if DEBUG:\\n for x in range(1, 1+self.search_depth-depth):\\n margin += \\\" \\\"\\n debug(margin+ \\\"============================================================\\\")\\n debug(margin+ \\\"abp called with depth: %d alpha: %f beta: %f\\\" %(depth, alpha, beta))\\n debug(margin+ \\\"============================================================\\\")\\n\\n debug(\\\"\\\\n\\\"+margin+\\\"abp depth: %d max: %s\\\" % (depth, str(maximizing_player)))\\n debug(margin + \\\"self.search_depth:\\\"+str(self.search_depth)+\\\" depth: \\\"+ str(depth))\\n\\n legal_moves = game.get_legal_moves()\\n\\n debug(margin + \\\"legal moves length:\\\"+str(len(legal_moves)))\\n\\n v = (float(\\\"-inf\\\") if maximizing_player else float(\\\"+inf\\\"), (1,1))\\n\\n # terminal state?\\n if not legal_moves:\\n # return the utility of the terminal node\\n debug(margin + \\\"terminal state, no legal moves left (%d) or depth (%d) > search_depth (%d)\\\" % (len(legal_moves), depth, self.search_depth))\\n\\n v = (game.utility(self), (-1,-1))\\n debug(margin + \\\"A returning score: %s next_move: %s\\\" % (v[0], v[1]))\\n\\n elif depth == 0:\\n v = (self.score(game, self), (-1, -1))\\n debug(margin + \\\"B returning score: %s next_move: %s\\\" % (v[0], v[1]))\\n\\n else:\\n debug(margin + \\\"non-terminal state, depth: %d %d legal moves.\\\" % (depth, len(legal_moves)))\\n # iterate over the moves / children and look for\\n # the min/max value\\n\\n debug(margin + \\\"is Maximizing:\\\", maximizing_player)\\n\\n for move in legal_moves:\\n debug(margin + \\\"legal move \\\", move)\\n new_game = game.forecast_move(move)\\n new_v = (self.alphabeta(new_game, depth - 1, alpha, beta, not maximizing_player)[0], move)\\n\\n evaluated_score, _ = new_v\\n debug(margin + \\\"evaluated_score:\\\", evaluated_score, \\\"score:\\\", v[0])\\n debug()\\n\\n # --- MAX\\n if maximizing_player:\\n if evaluated_score >= beta:\\n debug(margin + \\\"pruning\\\")\\n #continue # MK: pruning as min would not pick this move\\n return new_v\\n\\n if evaluated_score > alpha:\\n debug(margin + \\\"alpha updated\\\")\\n alpha = evaluated_score\\n\\n if evaluated_score > v[0]:\\n v = new_v\\n\\n\\n # --- MIN\\n else:\\n if evaluated_score <= alpha:\\n debug(margin + \\\"pruning\\\")\\n #continue # MK: pruning here as well\\n return new_v\\n\\n if evaluated_score < beta:\\n debug(margin + \\\"beta updated\\\")\\n beta = evaluated_score\\n\\n if evaluated_score < v[0]:\\n v = new_v\\n\\n #best = max if maximizing_player else min\\n #v = best(v, new_v)\\n\\n debug(margin + \\\"C returning score: %s next_move: %s\\\" % (v[0], v[1]))\\n return v\",\n \"title\": \"\"\n },\n {\n \"docid\": \"488d161dfd89161e26907c9c0b4d5dcf\",\n \"score\": \"0.5389023\",\n \"text\": \"def beta(self, beta):\\n\\n self._beta = beta\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3b903409a9a3f8d9daaa27449d08a4da\",\n \"score\": \"0.53837115\",\n \"text\": \"def update_balance(self, node: AVLTreeNode) -> None:\\n # TODO: Fix this method\\n if node.balance > 1 or node.balance < -1:\\n self.rebalance(node)\\n return\\n if node.parent:\\n if node.is_right_child():\\n node.parent.balance += 1\\n elif node.is_left_child():\\n node.parent.balance -= 1\\n\\n if node.parent.balance != 0:\\n self.update_balance(node.parent)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab7226b0750f6330c1676c4dde1b1fba\",\n \"score\": \"0.5321984\",\n \"text\": \"def inverseBeta1(self,k):\\n k = k.chase()\\n # Sequestered immutable part\\n l2v = {l.chase(): set(self.closedChildren(l.chase()))\\n for l in self.children(k) }\\n eprint(\\\"Inverse beta\\\",k,\\\"which has\\\",len(l2v),\\\"members\\\",\\n \\\"and a total of\\\",sum(len(v) for v in l2v.values() ),\\\"closed children\\\")\\n for l,vs in l2v.items():\\n eprint(\\\"\\\\tProcessing a expression w/ closed children =\\\",len(vs))\\n for v in vs:\\n l = l.chase()\\n v = v.chase()\\n \\n for b in set(self.possibleBodies(v,l)):\\n b = self.abstractClass(b)\\n yield self.applyClass(b,v)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dfe830aa71bac09fe2087c530e87528c\",\n \"score\": \"0.53061706\",\n \"text\": \"def update_attributes(self):\\n\\n self.child_cell.targetVolume = self.parent_cell.targetVolume\\n self.child_cell.lambdaVolume = self.parent_cell.lambdaVolume\\n self.child_cell.type = self.parent_cell.type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b42ce6e45646d541aa3258f14f64e7f\",\n \"score\": \"0.5280276\",\n \"text\": \"def set_alpha(self, alpha=1.0):\\n self.alpha=alpha\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26a826d1bc661dbc6290b66eac3d5147\",\n \"score\": \"0.5272612\",\n \"text\": \"def update_alpha(self):\\n\\t\\tself.alpha[0] = np.log(self.alpha[1]) + np.expand_dims(np.mean(digamma(self.a[0]) - np.log(self.a[1]), axis=0), axis=0).repeat(self.N, axis=0)\\n\\t\\talpha_1 = self.alpha[0, 0, :]\\n\\t\\t\\n\\t\\tfor k in range(self.K):\\n\\t\\t\\talpha_1[k] = psi_inverse(2., self.alpha[0, 0, k])\\n\\n\\t\\tself.alpha[0] = np.expand_dims(alpha_1, axis=0).repeat(self.N, axis=0)\\n\\t\\tself.alpha[1] = self.alpha[0] / np.mean(self.a[0] / self.a[1], axis=0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbb42280a7bd9ddead2a711cdbdd1aa5\",\n \"score\": \"0.5260954\",\n \"text\": \"def set_beta(): \\n function = LegacyFunctionSpecification() \\n function.addParameter('beta', dtype='d', direction=function.IN)\\n function.result_type = 'i'\\n return function\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aec8cb6b96df1d853ae28c9b3869369d\",\n \"score\": \"0.5249142\",\n \"text\": \"def update_recursive(self, leaf_value, gamma):\\n # If it is not root, this node's parent should be updated first.\\n if self._parent:\\n self._parent.update_recursive(gamma * leaf_value, gamma)\\n self.update(leaf_value)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6aa88e67e42c51f5a0611af76d5cbb55\",\n \"score\": \"0.5245262\",\n \"text\": \"def alphabeta(self, game, depth, alpha=float(\\\"-inf\\\"), beta=float(\\\"inf\\\"), maximizing_player=True):\\n if self.time_left() < self.TIMER_THRESHOLD:\\n raise Timeout()\\n\\n #if depth is 0, it is a leaf in the tree, and we just return the score of our player\\n #note that I also return the location of the player that made the last move\\n #which can be the location of our player or the opponent depending on who has played last\\n if (depth == 0):\\n score=self.score(game,self)\\n m=game.get_player_location(game.inactive_player)\\n return score,m\\n \\n\\n legal_moves=game.get_legal_moves() \\n \\n #if there is no moves possible, return our score and -1,-1 as location \\n if not legal_moves:\\n return self.score(game,self),(-1,-1)\\n \\n scores=[] \\n for m in legal_moves:\\n #we alternate maximizing and minimizing levels, that's why maximizing is negated\\n child_score,child_move=self.alphabeta(game.forecast_move(m), depth-1,alpha,beta, not maximizing_player)\\n scores.append([child_score,m])\\n \\n if maximizing_player:\\n \\n if child_score>alpha:\\n #we're maximizing, so this is a new lower bound\\n alpha=child_score\\n if child_score<alpha:\\n #now that I write a comment here, I wonder if something special\\n #should happen here...\\n #unit tests fails if we break\\n pass \\n if child_score==alpha:\\n #worst move possible, but it happens... Nothing special to do\\n #units tests fails if we break\\n pass \\n \\n if child_score<beta:\\n #just not the best move possible.\\n #continue to search to see is there is a better one in this branch\\n pass\\n if child_score==beta:\\n #that's the bests possible score, we can stop here\\n break\\n if child_score>beta:\\n #now that I write a comment here, I'm not sure why this happens\\n #and why I should break here.\\n #probably because the node above is a minimizing node and this is \\n #the opponent turn, so we don't really want his score to be so high?\\n #Yes. That's must be it. Another branch has a lower score for us.\\n #So the opponent will chose this other branch, not this one.\\n #So no need to continue searching.\\n break\\n \\n\\n \\n #if minimizing level\\n if not maximizing_player:\\n \\n if child_score<beta:\\n #we're minimizing, so this is a new upper bound\\n beta=child_score\\n if child_score>beta:\\n #now that I write a comment here, I'm not sure why this happens\\n #and maybe something special should happen here...\\n ###! unit tests doesnt fail if we break\\n pass\\n if child_score==beta:\\n #just not a good move from our player point of view\\n #unit tests fails if we break\\n pass\\n \\n if child_score==alpha:\\n #ok, that's a bad score, no need to search the other branches, even if\\n #another have a better score, this is the one the opponent will chose\\n break\\n if child_score>alpha:\\n #just not the worst move, lets see the other nodes...\\n pass\\n if child_score<alpha:\\n #this move is so bad, that it's even lower than another branch we already search\\n break\\n \\n best_score,best_move=best_score_move(scores,maximizing_player) \\n return best_score,best_move\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5323131a4b42a98ee5da3ca743c3f403\",\n \"score\": \"0.5183604\",\n \"text\": \"def update(self, X, Y, eta):\\r\\n nabla_b = [np.zeros(b.shape) for b in self.biases]\\r\\n nabla_w = [np.zeros(w.shape) for w in self.weights]\\r\\n for x,y in zip(X,Y):\\r\\n delta_nabla_b, delta_nabla_w = self.backPropagation(X,Y)\\r\\n nabla_b = [nb + dnb for nb,dnb in zip(nabla_b, delta_nabla_b)]\\r\\n nabla_w = [nw + dnw for nw,dnw in zip(nabla_w, delta_nabla_w)]\\r\\n \\t\\r\\n self.weights = [w + (eta*nw) for w,nw in zip(self.weights, nabla_w)]\\r\\n self.biases = [b + (eta*nb) for b,nb in zip(self.biases, nabla_b)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c77648c04f1a7dc1e982368b7aeedd7b\",\n \"score\": \"0.51786363\",\n \"text\": \"def beta(self):\\n\\t\\treturn self.__beta\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4fd3572fdac9b2275244f983ad137685\",\n \"score\": \"0.51718736\",\n \"text\": \"def alphabeta_designed(self,depth,currBoardIdx,alpha,beta,isDesigned):\\n #YOUR CODE HERE\\n self.expandedNodes += 1\\n if depth == self.maxDepth:\\n return self.evaluateDesigned(currBoardIdx)\\n # If either player wins, return best score\\n\\n start_row, start_col = self.globalIdx[currBoardIdx]\\n\\n # If this is maxPlayer\\n if isDesigned:\\n bestValue = -inf\\n # Traverse all cells\\n for row in range(3):\\n for col in range(3):\\n if self.board[row + start_row][col + start_col] == '_':\\n self.board[row + start_row][col + start_col] = self.minPlayer\\n next_board_index = 3*row + col\\n bestValue = min(bestValue, self.alphabeta_designed(depth + 1, next_board_index, alpha, beta, not isDesigned))\\n # Undo the move\\n if bestValue < beta:\\n self.board[row + start_row][col + start_col] = '_'\\n return bestValue\\n else:\\n alpha = bestValue\\n self.board[row + start_row][col + start_col] = '_'\\n return bestValue\\n else:\\n bestValue = inf\\n # Traverse all cells\\n for row in range(3):\\n for col in range(3):\\n if self.board[row + start_row][col + start_col] == '_':\\n self.board[row + start_row][col + start_col] = self.maxPlayer\\n next_board_index = 3*row + col\\n isMax = not isMax\\n bestValue = max(bestValue, self.alphabeta_designed(depth + 1, next_board_index, alpha, beta, not isDesigned))\\n if bestValue > alpha:\\n self.board[row + start_row][col + start_col] = '_'\\n return bestValue\\n else:\\n beta = bestValue\\n # Undo the move\\n self.board[row + start_row][col + start_col] = '_'\\n return bestValue\\n return bestValue\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0341abab5e3683e4dc0174c5ba52c7d\",\n \"score\": \"0.5140774\",\n \"text\": \"def _set_alpha(self, alpha):\\n self._alpha = alpha\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1df762f7b61b9900d1e76d4092f42ddd\",\n \"score\": \"0.51264906\",\n \"text\": \"def alphabeta_alt(self, game, depth, alpha=float(\\\"-inf\\\"), beta=float(\\\"inf\\\"), maximizing_player=True, first=True):\\n if self.time_left() < self.TIMER_THRESHOLD:\\n raise Timeout()\\n\\n current_location = game.get_player_location(game.active_player)\\n\\n if depth == 0:\\n return self.score(game, self), current_location\\n\\n moves = dict()\\n\\n # Empirical data shows that after around move 18, optimal ordering of branches is not worth the effort (gives worse results)\\n if game.move_count < 18:\\n if current_location in self.ab_trees:\\n legal_moves = (k[0] for k in self.ab_trees[current_location])\\n else:\\n legal_moves = game.get_legal_moves()\\n else:\\n legal_moves = game.get_legal_moves()\\n\\n if maximizing_player:\\n v_move = (float(\\\"-inf\\\"), (-1, -1))\\n for move in legal_moves:\\n new_game = game.forecast_move(move)\\n v, _ = self.alphabeta(new_game, depth - 1, alpha, beta, False)\\n moves[move] = v\\n v_move = max([v_move, (v, move)], key=itemgetter(0))\\n alpha = max([alpha, v_move[0]])\\n if beta <= alpha:\\n break\\n\\n if game.move_count < 18:\\n self.ab_trees[current_location] = sorted(moves.items(), key=lambda x: x[1],\\n reverse=True)\\n\\n return v_move\\n\\n else:\\n v_move = (float(\\\"inf\\\"), (-1, -1))\\n for move in legal_moves:\\n new_game = game.forecast_move(move)\\n v, _ = self.alphabeta(new_game, depth - 1, alpha, beta, True)\\n moves[move] = v\\n v_move = min([v_move, (v, move)], key=itemgetter(0))\\n beta = min([beta, v_move[0]])\\n if beta <= alpha:\\n break\\n\\n if game.move_count < 18:\\n self.ab_trees[current_location] = sorted(moves.items(), key=lambda x: x[1],\\n reverse=False)\\n\\n return v_move\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d88e8cccf03b749dba93db0b5a94dd3b\",\n \"score\": \"0.51251227\",\n \"text\": \"def adjust_beta(self, kl):\\n if kl > self.args.kl_targ * self.nu:\\n self.beta = np.minimum(35, self.omega * self.beta)\\n elif kl < self.args.kl_targ / self.nu:\\n self.beta = np.maximum(1 / 35, self.beta / self.omega)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90a17e8e39b6452d9d41ce6c4e6f2d08\",\n \"score\": \"0.51177466\",\n \"text\": \"def update_alpha(\\n alpha,\\n pc,\\n pcj,\\n epsilon,\\n C,\\n gene_len,\\n xi_alpha_0,\\n iteration,\\n burn,\\n accept_mh3,\\n accept_mh3_postburnin,\\n reject_mh3,\\n reject_mh3_postburnin,\\n):\\n\\n # f) alpha\\n l_adir, alpha_proposal = calculate_l_adir(\\n alpha, xi_alpha_0, iteration, pc, pcj, epsilon, gene_len, C\\n )\\n ## Metropolis-Hastings step\\n accept_mh3, reject_mh3, accept_mh3_postburnin, reject_mh3_postburnin, quantity = MH(\\n l_adir,\\n accept_mh3,\\n reject_mh3,\\n accept_mh3_postburnin,\\n reject_mh3_postburnin,\\n alpha_proposal,\\n alpha[iteration - 1, :],\\n iteration,\\n burn,\\n )\\n alpha[iteration, :] = quantity\\n return alpha, accept_mh3, reject_mh3, accept_mh3_postburnin, reject_mh3_postburnin\",\n \"title\": \"\"\n },\n {\n \"docid\": \"875f89c73cd69625118dffe646ff3182\",\n \"score\": \"0.51125747\",\n \"text\": \"def test_update_speed_should_be_update_beta_v(self):\\n #As above\\n self.evacuee.optical_density_at_position = 1\\n\\n self.evacuee.beta_v = 0\\n self.evacuee.update_speed()\\n\\n expected_beta_v = 0.00000001\\n self.assertEqual(self.evacuee.beta_v, expected_beta_v)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0925bb15ddd5c57be9c023b20d8cc523\",\n \"score\": \"0.51113397\",\n \"text\": \"def build_update(self,alpha=0.01,beta=0.0,X=None,y=None):\\n\\t\\tJ = self.J_Theta\\n\\t\\tThetabs = self.GRU_model.__get_state__()['params']\\n\\n\\t\\tif X is not None:\\n\\t\\t\\tself.X = X\\n\\t\\telse:\\n\\t\\t\\tX = self.X\\n\\t\\tif y is not None:\\n\\t\\t\\tself.y = y\\n\\t\\telse:\\n\\t\\t\\ty = self.y\\n\\t\\t\\n\\t\\tself.updateExpression, self.gradDescent_step = build_gradDescent_step(J, Thetabs,X,y,alpha,beta)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bbf8534bf4201858c40f54a130b0c84\",\n \"score\": \"0.51055807\",\n \"text\": \"def alphabeta(self, game, depth, alpha=float(\\\"-inf\\\"), beta=float(\\\"inf\\\"), maximizing_player=True):\\n if self.time_left() < self.TIMER_THRESHOLD:\\n raise Timeout()\\n\\n best_move = [-1, -1]\\n\\n #if this is the last iteration, return\\n if depth == 0:\\n return self.score(game, self), [-1, -1]\\n\\n # find our possible moves from board state\\n moves = game.get_legal_moves(game.active_player)\\n\\n # initialize minmax depending on active player\\n if maximizing_player == True:\\n minmax_score = float(\\\"-inf\\\")\\n new_alpha = alpha\\n else:\\n minmax_score = float(\\\"inf\\\")\\n new_beta = beta\\n\\n # for each legal move, consider new board state\\n for move in moves:\\n next_game = game.forecast_move(move)\\n\\n if maximizing_player:\\n score, _ = self.alphabeta(next_game, depth-1, new_alpha, beta, not maximizing_player)\\n if score >= minmax_score:\\n minmax_score = score\\n new_alpha = minmax_score\\n best_move = move\\n # the parent node (a minimizing node) will\\n # not pick this node or any of its children\\n if minmax_score >= beta:\\n return minmax_score, best_move\\n\\n if not maximizing_player:\\n score, _ = self.alphabeta(next_game, depth-1, alpha, new_beta, not maximizing_player)\\n if score <= minmax_score:\\n minmax_score = score\\n new_beta = minmax_score\\n best_move = move\\n # the parent node (a maximizing node) will\\n # not pick this node or any of its children\\n if minmax_score <= alpha:\\n return minmax_score, best_move\\n\\n return minmax_score, best_move\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c91153fc2092b3fc287a88aa4fcbb034\",\n \"score\": \"0.5092365\",\n \"text\": \"def alphabeta(self, game, depth, alpha=float(\\\"-inf\\\"), beta=float(\\\"inf\\\")):\\n # Needed to pass function signature/interface test\\n return self.alphabeta_helper(game, depth, alpha, beta)[1]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"102ecf462870f702362b891e917068c0\",\n \"score\": \"0.5090338\",\n \"text\": \"def setAlpha(self, alpha=...) -> None:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"79d7bf584c4a6b6ae7b891c228f8c6bf\",\n \"score\": \"0.5083531\",\n \"text\": \"def alphabeta_with_ordering(self, game,initial_depth,depth,ordering_depth,best_scores_dict,parent_moves,alpha=float(\\\"-inf\\\"), beta=float(\\\"inf\\\"), maximizing_player=True):\\n if self.time_left() < self.TIMER_THRESHOLD:\\n raise Timeout()\\n\\n #if depth is 0, it is a leaf in the tree, and we just return the score of our player\\n #note that I also return the location of the player that made the last move\\n #which can be the location of our player or the opponent depending on who has played last\\n if (depth == 0):\\n score=self.score(game,self)\\n m=game.get_player_location(game.inactive_player)\\n return score,m\\n \\n pass\\n \\n #if we've already explores this depth and move in a previous iteration of\\n #iterative search, we'll use the list of moves we have recorded\\n #this list of moves is ordered by the previously evaluated scores\\n #which should allow more 'alphabeta' pruning\\n #it also avoids a call to get_legal_moves\\n if (initial_depth - depth)<=ordering_depth:\\n if( parent_moves in best_scores_dict ):\\n legal_moves=[]\\n for score,move in best_scores_dict[parent_moves]:\\n legal_moves.append(move)\\n if initial_depth==3 and depth==2:\\n pass\\n #print(\\\"YO\\\")\\n \\n else:\\n legal_moves=game.get_legal_moves() \\n if initial_depth==3 and depth==2:\\n pass\\n #print(\\\"YA\\\")\\n else:\\n legal_moves=game.get_legal_moves()\\n if initial_depth==3 and depth==2:\\n pass\\n #print(\\\"YU\\\")\\n \\n #if there is no moves possible, return our score and -1,-1 as location \\n if not legal_moves:\\n return self.score(game,self),(-1,-1)\\n \\n scores=[]\\n \\n for m in legal_moves:\\n #we alternate maximizing and minimizing levels, that's why maximizing is negated\\n child_score,child_move=self.alphabeta_with_ordering(game.forecast_move(m),initial_depth,depth-1,ordering_depth,best_scores_dict,parent_moves+'-'+str(m),alpha,beta, not maximizing_player)\\n\\n \\n #add score to scores already sorted\\n i=0\\n x=0\\n if maximizing_player:\\n for f_score,f_m in scores:\\n if child_score>=f_score:\\n x=i\\n break\\n else:\\n x=i+1\\n i=i+1\\n else:\\n for f_score,f_m in scores:\\n if child_score<=f_score:\\n x=i\\n break\\n else:\\n x=i+1\\n i=i+1\\n \\n scores.insert(x,[child_score,m])\\n pass\\n\\n #scores.append([child_score,m])\\n \\n if maximizing_player:\\n \\n if child_score<beta:\\n #just not the best move possible.\\n #continue to search to see is there is a better one in this branch\\n pass\\n if child_score==beta:\\n #that's the bests possible score, we can stop here\\n break\\n if child_score>beta:\\n #now that I write a comment here, I'm not sure why this happens\\n #and why I should break here.\\n #probably because the node above is a minimizing node and this is \\n #the opponent turn, so we don't really want his score to be so high?\\n #Yes. That's must be it. Another branch has a lower score for us.\\n #So the opponent will chose this other branch, not this one.\\n #So no need to continue searching.\\n break\\n \\n if child_score>alpha:\\n #we're maximizing, so this is a new lower bound\\n alpha=child_score\\n if child_score<alpha:\\n #now that I write a comment here, I wonder if something special\\n #should happen here...\\n #unit tests fails if we break\\n pass \\n if child_score==alpha:\\n #worst move possible, but it happens... Nothing special to do\\n #units tests fails if we break\\n pass\\n \\n #if minimizing level\\n if not maximizing_player:\\n \\n if child_score<beta:\\n #we're minimizing, so this is a new upper bound\\n beta=child_score\\n if child_score>beta:\\n #now that I write a comment here, I'm not sure why this happens\\n #and maybe something special should happen here...\\n ###! unit tests doesnt fail if we break\\n pass\\n if child_score==beta:\\n #just not a good move from our player point of view\\n #unit tests fails if we break\\n pass\\n \\n if child_score==alpha:\\n #ok, that's a bad score, no need to search the other branches, even if\\n #another have a better score, this is the one the opponent will chose\\n break\\n if child_score>alpha:\\n #just not the worst move, lets see the other nodes...\\n pass\\n if child_score<alpha:\\n #this move is so bad, that it's even lower than another branch we already search\\n break\\n \\n #best_score,best_move=best_score_move(scores,maximizing_player)\\n #scores is sorted now. The first element is the best.\\n best_score,best_move=scores[0]\\n if (initial_depth - depth)<=ordering_depth:\\n best_scores_dict[parent_moves]=scores\\n \\n return best_score,best_move\",\n \"title\": \"\"\n },\n {\n \"docid\": \"12c1624ad16bbe9a879d45d321b872bc\",\n \"score\": \"0.5063983\",\n \"text\": \"def setAlpha(self, value) -> None:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"458ef121c9e3e59b75b925de7e5edd79\",\n \"score\": \"0.50569916\",\n \"text\": \"def get_beta_final(alpha, beta):\\n if beta == alpha:\\n return 0\\n elif beta == 10:\\n return 10\\n elif beta == -10:\\n return -10\\n elif alpha == -10:\\n return 10 - (10 - beta) * (10 - alpha) / 10\\n else:\\n return 10 * (10 + beta) / (10 + alpha) - 10\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4343d5717dfb3027cfda5e05b7cb418\",\n \"score\": \"0.50481105\",\n \"text\": \"def update_admissible(self,beta:float = 2.0):\\n delta = 1/self.sampled[:,None]+np.sqrt(self.p*(1-self.p)/self.sampled[:,None])\\n p_LB = np.fmax(self.p-delta,0)\\n p_UB = np.fmin(self.p+delta,1)\\n for l in np.arange(self.n_class):\\n po_UB = np.delete(p_UB,l,axis = 1)\\n self.A[:,l] = np.all(p_LB[:,l][:,None]>beta*po_UB-beta+1,axis = 1)\\n # self.A[:,l] = p_LB[:,l]>1/3 #For 2 classes case.\\n e_tilde = 1-self.p\\n e_tilde[~self.A] = 1\\n self.e_tilde = e_tilde\\n self.p_LB = p_LB\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9984168093e0c544385e773f329f60a\",\n \"score\": \"0.50352514\",\n \"text\": \"def negamax_alpha_beta_TT_2(self, state, alpha, beta, depth, tt):\\n\\n known_node = None if (tt is None) else tt.retrieve_node(state)\\n\\n # Check if node exists\\n if known_node:\\n if known_node['depth'] >= depth:\\n # The current state has been visited at least at the current depth\\n if known_node['val_type'] == EXACT:\\n return AIMove(known_node['state'], known_node[\\\"score\\\"])\\n\\n if known_node['val_type'] == LOWERBOUND and known_node['score'] > alpha:\\n alpha = known_node['score']\\n\\n elif known_node[\\\"val_type\\\"] == UPPERBOUND and known_node[\\\"score\\\"] < beta:\\n beta = known_node[\\\"score\\\"]\\n\\n if alpha >= beta:\\n return AIMove(known_node[\\\"state\\\"],known_node[\\\"score\\\"])\\n\\n if state.terminal_test() or depth <= 0:\\n\\n val = self.utility(state) if depth<=0 else state.utility(self.player_id)\\n\\n if val <= alpha: val_type = LOWERBOUND\\n elif val >= beta: val_type = UPPERBOUND\\n else: val_type = EXACT\\n\\n tt.store_node(state, val, val_type, depth)\\n\\n return AIMove(state,val)\\n\\n best_score = float(\\\"-inf\\\")\\n\\n for action in state.actions():\\n val = max(best_score, -self.negamax_alpha_beta_TT_2(state.result(action),\\n -beta,\\n -alpha,\\n depth - 1,\\n tt).score)\\n\\n alpha = max(alpha, val)\\n\\n if val > best_score:\\n best_score = val\\n best_move = action\\n if best_score > alpha:\\n alpha = best_score\\n\\n if best_score <= alpha:\\n val_type = LOWERBOUND\\n elif best_score >= beta:\\n val_type = UPPERBOUND\\n else: val_type = EXACT\\n\\n tt.store_node(state, val, val_type, depth)\\n\\n print(val_type, best_score)\\n return AIMove(best_move,best_score)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"34cccc4a83444c195cdd959c41e6850d\",\n \"score\": \"0.503351\",\n \"text\": \"def update_naive(self, x, a, x_, r, beta, id=None):\\n V_x = self.joint_action_dist(x_)\\n print('standard')\\n # TODO: vectorize/cythonize\\n for iv, v in enumerate(V_x):\\n for i, atom in enumerate(self.atoms[1:]):\\n V = self.Q[x.y, x.x, a].V[i]\\n yC = self.Q[x.y, x.x, a].yc[i]\\n\\n # learning rates\\n lr_v = beta * self.atom_p[iv] # p mirrors magnitude (for log-spaced)\\n # lr_yc = beta * atom_p[iv] / atom # /atom for using the same beta when estimating cvar (not yc)\\n lr_yc = beta * self.atom_p[iv]\\n\\n if self.Q[x.y, x.x, a].V[i] >= r + gamma * v:\\n update = lr_v * (1 - 1 / atom)\\n else:\\n update = lr_v\\n\\n # UPDATE VaR\\n self.Q[x.y, x.x, a].V[i] += update\\n\\n # UPDATE CVaR\\n yCn = (1 - lr_yc) * yC + lr_yc * (atom*V + min(0, r+gamma*v - V))\\n self.Q[x.y, x.x, a].yc[i] = yCn\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e06b4c39b6e0769cce668b762e4a35c4\",\n \"score\": \"0.503326\",\n \"text\": \"def _update_scale(self,\\n child: ConvBlockGene,\\n new_scale: int,\\n self_scale: int):\\n child.set(spatial_scale=new_scale)\\n\\n # Update n kernels\\n d_kernels = 2 ** (new_scale - self_scale)\\n self_kernels = self.hyperparam('n_kernels')\\n child.set(n_kernels=d_kernels * self_kernels)\\n\\n # Update name\\n child.name = f'decode block {new_scale}'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1a02fb366270348ca191b99f5b0251b\",\n \"score\": \"0.50325537\",\n \"text\": \"def beta(self, a, b, size=None):\\n return cont(&legacy_beta, self._aug_state, size, self.lock, 2,\\n a, 'a', CONS_POSITIVE,\\n b, 'b', CONS_POSITIVE,\\n 0.0, '', CONS_NONE, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8b71c43cfec5c2219b9823d8f477b90e\",\n \"score\": \"0.5031596\",\n \"text\": \"def alphabeta(self, game, depth, alpha=float(\\\"-inf\\\"), beta=float(\\\"inf\\\"), maximizing_player=True):\\n if self.time_left() < self.TIMER_THRESHOLD:\\n raise Timeout()\\n\\n current_location = game.get_player_location(game.active_player)\\n\\n if depth == 0:\\n return self.score(game, self), current_location\\n\\n if current_location in self.ab_trees:\\n legal_moves = (k[0] for k in self.ab_trees[current_location])\\n else:\\n legal_moves = game.get_legal_moves()\\n\\n moves = {}\\n\\n if maximizing_player:\\n v_move = (float(\\\"-inf\\\"), (-1, -1))\\n for move in legal_moves:\\n new_game = game.forecast_move(move)\\n v, _ = self.alphabeta(new_game, depth - 1, alpha, beta, False)\\n moves[move] = v\\n v_move = max([v_move, (v, move)], key=itemgetter(0))\\n alpha = max([alpha, v_move[0]])\\n if beta <= alpha:\\n break\\n\\n self.ab_trees[current_location] = sorted(moves.items(), key=lambda x: x[1], reverse=True)\\n\\n return v_move\\n\\n else:\\n v_move = (float(\\\"inf\\\"), (-1, -1))\\n for move in legal_moves:\\n new_game = game.forecast_move(move)\\n v, _ = self.alphabeta(new_game, depth - 1, alpha, beta, True)\\n moves[move] = v\\n v_move = min([v_move, (v, move)], key=itemgetter(0))\\n beta = min([beta, v_move[0]])\\n if beta <= alpha:\\n break\\n\\n self.ab_trees[current_location] = sorted(moves.items(), key=lambda x: x[1], reverse=False)\\n\\n return v_move\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3818ae8c323ad3f5b3d25a3cdab7a9b\",\n \"score\": \"0.5029998\",\n \"text\": \"def _get_alpha(beta: pd.Series, gamma: pd.Series) -> pd.Series:\\n den = 3 - 2 * 2 ** .5\\n alpha = (2 ** .5 - 1) * (beta ** .5) / den\\n alpha -= (gamma / den) ** .5\\n alpha[alpha < 0] = 0 # Set negative alphas to 0 (see p.727 of paper)\\n return alpha\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b8d22f16130df8454918a0a63774cef3\",\n \"score\": \"0.5020915\",\n \"text\": \"def getBeta(self) -> retval:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc78ef6e7e8535594a51c93e3d29bca0\",\n \"score\": \"0.5012561\",\n \"text\": \"def update_alpha(self, gammat, rho):\\n N = float(len(gammat))\\n logphat = sum(dirichlet_expectation(gamma) for gamma in gammat) / N\\n dalpha = np.copy(self.alpha)\\n gradf = N * (psi(np.sum(self.alpha)) - psi(self.alpha) + logphat)\\n\\n c = N * polygamma(1, np.sum(self.alpha))\\n q = -N * polygamma(1, self.alpha)\\n\\n b = np.sum(gradf / q) / ( 1 / c + np.sum(1 / q))\\n\\n dalpha = -(gradf - b) / q\\n\\n if all(rho() * dalpha + self.alpha > 0):\\n self.alpha += rho() * dalpha\\n else:\\n print(\\\"Warning: updated alpha not positive.\\\")\\n print(\\\"optimized alpha %s\\\" % list(self.alpha))\\n\\n return self.alpha\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fc9e81ce69e2025993ed840419c5907d\",\n \"score\": \"0.50087684\",\n \"text\": \"def merge_node(cls, node1, node2, alpha = 1):\\n\\n data = np.vstack((node1.data, node2.data))\\n\\n nk = data.shape[0]\\n dk = alpha * gamma(nk) + node1.dk * node2.dk\\n pi_k = alpha * gamma(nk) / dk\\n\\n return cls(data, alpha, dk, pi_k)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61cc528cd156467e5588e13f212d132d\",\n \"score\": \"0.49963313\",\n \"text\": \"def __init__(self, alpha: float):\\n self.alpha = alpha\\n self._value = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f12a71a62c8bc68b082918a107f1208\",\n \"score\": \"0.49918205\",\n \"text\": \"def get_alpha_beta_move(self, board):\\n alpha = float(\\\"-inf\\\")\\n beta = float(\\\"inf\\\")\\n v_max = float(\\\"-inf\\\")\\n self.board = board\\n possible_moves = self.get_possible_moves()\\n col_to_return = np.random.choice(possible_moves)\\n for move in possible_moves:\\n self.update_board(move, self.player_number)\\n v = self.get_alpha_beta_min_value(self.depth, alpha, beta)\\n self.revert_move(move)\\n if v > v_max:\\n v_max = v\\n col_to_return = move\\n return col_to_return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fea7f59b01f117b3afc392af33683cd\",\n \"score\": \"0.49911717\",\n \"text\": \"def mutate(self, probability=(1.0 / (float(MAX_DEPTH) * 2.0 + 1.0))):\\n self.__mutate_aux(probability, self.root)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8c6f023bb40c0cbf6eea60e2a1eac98\",\n \"score\": \"0.49874762\",\n \"text\": \"def compute_alpha(self, unary_potentials, pw_forward,\\n traversal, parents, mask):\\n n_vertices, n_states = unary_potentials.shape\\n alpha = np.ones((n_vertices, n_states))\\n child_alpha = np.zeros((n_vertices, n_states))\\n scale = np.zeros(n_vertices)\\n # messages from leaves to the root\\n for node in traversal[::-1]:\\n # compute the unnormalized potential at the current node\\n alpha[node] *= unary_potentials[node]\\n alpha[node] *= mask[node]\\n scale[node] = np.sum(alpha[node]) or 1.\\n alpha[node] /= scale[node]\\n parent = parents[node]\\n if parent >= 0:\\n # estimate the belief\\n child_alpha[node] = np.dot(alpha[node], pw_forward[node])\\n # propagate belief to the parent\\n alpha[parent] *= child_alpha[node]\\n Z = np.sum(np.log(scale))\\n return alpha, child_alpha, scale, Z\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9586453fed2fbd2e4cfb9fa101e34430\",\n \"score\": \"0.49796933\",\n \"text\": \"def weight_adjustment(self, alpha=1.1):\\n\\n # weighted_edges = defaultdict(list)\\n weighted_edges = {}\\n\\n # find the sum of all first positions\\n norm = sum([s.length for s in self.sentences])\\n\\n # Topical boosting\\n for variants in self.topics:\\n\\n # skip one candidate topics\\n if len(variants) == 1:\\n continue\\n\\n # get the offsets\\n offsets = [self.candidates[v].offsets[0] for v in variants]\\n\\n # get the first occurring variant\\n first = variants[offsets.index(min(offsets))]\\n\\n # find the nodes to which it connects -- Python 2/3 compatible\\n # for start, end in self.graph.edges_iter(first):\\n for start, end in self.graph.edges(first):\\n\\n boosters = []\\n for v in variants:\\n if v != first and self.graph.has_edge(v, end):\\n boosters.append(self.graph[v][end]['weight'])\\n\\n if boosters:\\n weighted_edges[(start, end)] = np.sum(boosters)\\n\\n # update edge weights -- Python 2/3 compatible\\n # for nodes, boosters in weighted_edges.iteritems():\\n for nodes, boosters in weighted_edges.items():\\n node_i, node_j = nodes\\n position_i = 1.0 / (1 + self.candidates[node_i].offsets[0])\\n position_i = math.exp(position_i)\\n self.graph[node_j][node_i]['weight'] += (boosters * alpha * position_i)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce40896e64fa7ecfc0a089d42368b789\",\n \"score\": \"0.4979133\",\n \"text\": \"def replaceChild(self, *args):\\n return _coin.SoVRMLParent_replaceChild(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5665d9b8e9e0628e665e6761579ca070\",\n \"score\": \"0.4974242\",\n \"text\": \"def backPropogation(self, leafNode, value):\\n node = leafNode\\n while node != None:\\n node.witnessValue(value)\\n node = node.parent\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab8494aae034e88bfb889d4d59a74c0a\",\n \"score\": \"0.4948806\",\n \"text\": \"def update_pheromone(self):\\n # ants lay down pheromone\\n for i in range(0, self.num_ants):\\n self.sigma[ self.ants[i][0] ][ self.ants[i][1] ] += self.eta \\n # attenuate pheromone\\n self.sigma = np.multiply(1 - self.kappa, self.sigma)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"719fbd7f817380c8e46d303a0b67d7cf\",\n \"score\": \"0.4944604\",\n \"text\": \"def assign_scalar(self, alpha):\\n self.assign(alpha)\\n return self\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4881af69cf2545b87ee134af8af91bf\",\n \"score\": \"0.49437675\",\n \"text\": \"def SetBeta(self, beta: 'double') -> \\\"void\\\":\\n return _itkSigmoidImageFilterPython.itkSigmoidImageFilterIUS3IUS3_SetBeta(self, beta)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2d0277283f40ec9899752fce204747b\",\n \"score\": \"0.4938041\",\n \"text\": \"def alpha_beta(self, state, depth = 2):\\n alpha = float(\\\"-inf\\\")\\n beta = float(\\\"inf\\\")\\n best_score = float(\\\"-inf\\\")\\n best_move = None\\n\\n for action in state.actions():\\n val = self.min_value(state.result(action), alpha, beta, depth - 1)\\n alpha = max(alpha, val)\\n if val > best_score:\\n best_score = val\\n best_move = action\\n return AIMove(best_move, best_score)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0625ca2754e305e447cb5660437cd39b\",\n \"score\": \"0.4934739\",\n \"text\": \"def SetBeta(self, _arg: 'double const') -> \\\"void\\\":\\n return _itkHessianToObjectnessMeasureImageFilterPython.itkHessianToObjectnessMeasureImageFilterISSRTD33IUS3_SetBeta(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b9395c333b6c405c42459197ca136930\",\n \"score\": \"0.49226695\",\n \"text\": \"def _update_versions(self, root):\\n nodes = self._get_versioned_nodes(root)\\n\\n for node in nodes:\\n attribs = node.attrib\\n attribs[common.TAG_CYBOX_MAJOR] = '2'\\n attribs[common.TAG_CYBOX_MINOR] = '1'\\n\\n with utils.ignored(KeyError):\\n del attribs[common.TAG_CYBOX_UPDATE]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4166e9ecb9709af93f9e9751dcb58133\",\n \"score\": \"0.49222142\",\n \"text\": \"def alphabeta(self, game, depth, alpha=float(\\\"-inf\\\"), beta=float(\\\"inf\\\"), maximizing_player=True):\\n # Raise an exception if we've run out of time without an answer\\n if self.time_left() < self.TIMER_THRESHOLD:\\n raise Timeout()\\n\\n # Evaluate the score of the current board for potential use\\n # Keep track of our best move choice.\\n if maximizing_player:\\n best_score = float(\\\"-inf\\\")\\n else:\\n best_score = float(\\\"inf\\\")\\n best_move = (-1,-1)\\n current_score = best_score\\n current_move = best_move\\n \\n # Get the set of legal moves for the current player\\n legal_moves = game.get_legal_moves()\\n \\n # If, they're aren't any legal moves, then we're at an endgame\\n if len(legal_moves) == 0:\\n return best_score, best_move\\n \\n # If we've reached our max depth, evaluate the game board as per\\n # the current player\\n if depth == 0:\\n return self.score(game, self), (-1,-1) \\n\\n # If the depth is 1, evaluate all children\\n if depth == 1:\\n for current_move in legal_moves:\\n game_copy = game.forecast_move(current_move) # Get the counter to increment\\n current_score = self.score(game_copy, self)\\n # Update our best choice, if necessary\\n if maximizing_player and current_score > best_score:\\n best_score = current_score\\n best_move = current_move\\n elif not maximizing_player and current_score < best_score:\\n best_score = current_score\\n best_move = current_move\\n # Shortcut if we've found the best score possible\\n if best_score == float(\\\"inf\\\") or best_score == float(\\\"-inf\\\"):\\n break\\n \\n # Decide if we need to perform a cut-off.\\n # If we're a maximizing player:\\n # - If the current move taken from the minimizing child is\\n # higher than anything we've seen so far on the path back to the root,\\n # we don't need to proceed further.\\n # If we're a minimizing player:\\n # - If the current move taken from the maximizing child is\\n # lower than anything we've seen so far on the path back to the root,\\n # we don't need to proceed further.\\n if maximizing_player:\\n alpha = max(alpha, current_score)\\n if alpha >= beta:\\n # Cutoff\\n break\\n else:\\n beta = min(beta, current_score)\\n if beta <= alpha:\\n # Cutoff\\n break\\n \\n # We're at our max depth, return a result\\n return best_score, best_move\\n \\n # Iterate over every legal move in depth-search fashion if there's still depth left to go\\n for current_move in legal_moves:\\n # Copy the game state as if the move occurred and make a recursive call\\n game_copy = game.forecast_move(current_move) # Get the counter to increment\\n next_max_player = True\\n if maximizing_player == True:\\n next_max_player = False\\n current_score, junk_move = self.alphabeta(game_copy, depth-1, alpha, beta, next_max_player)\\n\\n # Update our best choice, if necessary\\n if maximizing_player and current_score > best_score:\\n best_score = current_score\\n best_move = current_move\\n elif not maximizing_player and current_score < best_score:\\n best_score = current_score\\n best_move = current_move\\n # Shortcut if we've found the best score possible\\n if best_score == float(\\\"inf\\\") or best_score == float(\\\"-inf\\\"):\\n break\\n \\n # Decide if we need to perform a cut-off.\\n # If we're a maximizing player:\\n # - If the current move taken from the minimizing child is\\n # higher than anything we've seen so far on the path back to the root,\\n # we don't need to proceed further.\\n # If we're a minimizing player:\\n # - If the current move taken from the maximizing child is\\n # lower than anything we've seen so far on the path back to the root,\\n # we don't need to proceed further.\\n if maximizing_player:\\n alpha = max(alpha, current_score)\\n if alpha >= beta:\\n # Cutoff\\n break\\n else:\\n beta = min(beta, current_score)\\n if beta <= alpha:\\n # Cutoff\\n break\\n \\n # Return the best score and move found\\n return best_score, best_move\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d9fcf8a2d34142a7a17290e44e89fff\",\n \"score\": \"0.49209356\",\n \"text\": \"def beta_mu():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7209aa9a8cb05198b2ae23bcd90da5c7\",\n \"score\": \"0.48967513\",\n \"text\": \"def alpha_beta(asp):\\n start_state = asp.get_start_state()\\n\\n action = None\\n result = float('-inf')\\n alpha = float('-inf')\\n beta = float('inf')\\n for a in asp.get_available_actions(start_state):\\n value = alpha_beta_min(asp, asp.transition(start_state, a), alpha, beta)\\n if value > result:\\n result = value\\n action = a\\n if value >= beta:\\n return action\\n alpha = max(alpha, value)\\n return action\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8bfaaac31ca0eab6c3f160763cf1c3fc\",\n \"score\": \"0.48904154\",\n \"text\": \"def normalize_betas(self):\\n\\tbetas = deepcopy(self.betas)\\n\\tweights = np.array([p.w for p in betas])\\n\\tweights = weights/np.sum(weights)\\n\\n\\tmod_beta = []\\n\\tfor i, p in enumerate(betas):\\n\\t p.w = weights[i]\\n\\t mod_beta.append(p)\\n\\n\\tself.betas = mod_beta\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e8a72f7f5abc83081e5e2434bc0eeb2\",\n \"score\": \"0.48811206\",\n \"text\": \"def alphabeta_original(self, game, depth, alpha=float(\\\"-inf\\\"), beta=float(\\\"inf\\\"), maximizing_player=True):\\n if self.time_left() < self.TIMER_THRESHOLD:\\n raise Timeout()\\n\\n if depth == 0:\\n return self.score(game, self), game.get_player_location(game.active_player)\\n\\n moves = dict()\\n\\n if maximizing_player:\\n for move in game.get_legal_moves():\\n new_game = game.forecast_move(move)\\n v, _ = self.alphabeta(new_game, depth - 1, alpha, beta, False)\\n moves[move] = v\\n if v >= beta:\\n return v, move\\n alpha = max([alpha, v])\\n\\n if len(moves) > 0:\\n value = max(moves, key=moves.get)\\n return moves[value], value\\n else:\\n return float(\\\"-inf\\\"), (-1, -1)\\n else:\\n for move in game.get_legal_moves():\\n new_game = game.forecast_move(move)\\n v, _ = self.alphabeta(new_game, depth - 1, alpha, beta, True)\\n moves[move] = v\\n if v <= alpha:\\n return v, move\\n beta = min([beta, v])\\n\\n if len(moves) > 0:\\n value = min(moves, key=moves.get)\\n return moves[value], value\\n else:\\n return float(\\\"inf\\\"), (-1, -1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e8a72f7f5abc83081e5e2434bc0eeb2\",\n \"score\": \"0.48811206\",\n \"text\": \"def alphabeta_original(self, game, depth, alpha=float(\\\"-inf\\\"), beta=float(\\\"inf\\\"), maximizing_player=True):\\n if self.time_left() < self.TIMER_THRESHOLD:\\n raise Timeout()\\n\\n if depth == 0:\\n return self.score(game, self), game.get_player_location(game.active_player)\\n\\n moves = dict()\\n\\n if maximizing_player:\\n for move in game.get_legal_moves():\\n new_game = game.forecast_move(move)\\n v, _ = self.alphabeta(new_game, depth - 1, alpha, beta, False)\\n moves[move] = v\\n if v >= beta:\\n return v, move\\n alpha = max([alpha, v])\\n\\n if len(moves) > 0:\\n value = max(moves, key=moves.get)\\n return moves[value], value\\n else:\\n return float(\\\"-inf\\\"), (-1, -1)\\n else:\\n for move in game.get_legal_moves():\\n new_game = game.forecast_move(move)\\n v, _ = self.alphabeta(new_game, depth - 1, alpha, beta, True)\\n moves[move] = v\\n if v <= alpha:\\n return v, move\\n beta = min([beta, v])\\n\\n if len(moves) > 0:\\n value = min(moves, key=moves.get)\\n return moves[value], value\\n else:\\n return float(\\\"inf\\\"), (-1, -1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a97207045e0374ed45b85d27f88e46c7\",\n \"score\": \"0.4866564\",\n \"text\": \"def negamax_alpha_beta_TT(self, state, alpha, beta, depth, tt):\\n\\n known_node = None if (tt is None) else tt.retrieve_node(state)\\n\\n # Check if node exists\\n if known_node:\\n if known_node['depth'] >= depth:\\n # The current state has been visited at least at the current depth\\n\\n if known_node['beta'] <= alpha or known_node['beta'] == known_node['alpha']:\\n return AIMove(known_node[\\\"state\\\"],known_node['beta'])\\n\\n if known_node['alpha'] >= beta:\\n return AIMove(known_node[\\\"state\\\"],known_node['alpha'])\\n\\n # Maximum depth or terminal test\\n if state.terminal_test() or depth <= 0:\\n\\n val = state.utility(self.player_id) if state.terminal_test() else self.utility(state)\\n\\n # Save alpha and beta as the same\\n tt.store_node(state, val, val, val, depth)\\n\\n return AIMove(state,val)\\n\\n val = float(\\\"-inf\\\")\\n a = alpha\\n\\n for action in state.actions():\\n val = max(val, -self.negamax_alpha_beta_TT(state.result(action),\\n -beta,\\n -a,\\n depth - 1,\\n tt).score)\\n a = max(a, val)\\n best_move = action\\n\\n if val < beta:\\n break\\n\\n if val <= alpha:\\n tt.store_node(state, val, alpha, val, depth)\\n elif val >= beta:\\n tt.store_node(state, val, val, beta, depth)\\n else: tt.store_node(state, val, val, val, depth)\\n\\n tt.store_node(state, val, val, beta, depth)\\n\\n return AIMove(best_move, val)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"734f6f380e9179fa30e0215436e7992f\",\n \"score\": \"0.48652628\",\n \"text\": \"def alphabeta(self, game, depth, alpha=float(\\\"-inf\\\"), beta=float(\\\"inf\\\"), maximizing_player=True, first=True):\\n if self.time_left() < self.TIMER_THRESHOLD:\\n raise Timeout()\\n\\n current_location = game.get_player_location(game.active_player)\\n\\n if depth == 0:\\n return self.score(game, self), current_location\\n\\n moves = dict()\\n\\n # Empirical data shows that after around move 18, optimal ordering of branches is not worth the effort (gives worse results)\\n if game.move_count < 18:\\n if current_location in self.ab_trees:\\n legal_moves = (k[0] for k in self.ab_trees[current_location])\\n else:\\n legal_moves = game.get_legal_moves()\\n else:\\n legal_moves = game.get_legal_moves()\\n\\n if maximizing_player:\\n for move in legal_moves:\\n new_game = game.forecast_move(move)\\n v, _ = self.alphabeta(new_game, depth - 1, alpha, beta, False)\\n moves[move] = v\\n # print(new_game.to_string())\\n # print(depth, move, moves[move])\\n if v >= beta:\\n return v, move\\n alpha = max([alpha, v])\\n\\n if game.move_count < 18:\\n self.ab_trees[current_location] = sorted(moves.items(), key=lambda x: x[1],\\n reverse=True)\\n\\n if len(moves) > 0:\\n value = max(moves, key=moves.get)\\n return moves[value], value\\n else:\\n return float(\\\"-inf\\\"), (-1, -1)\\n else:\\n for move in legal_moves:\\n new_game = game.forecast_move(move)\\n v, _ = self.alphabeta(new_game, depth - 1, alpha, beta, True)\\n moves[move] = v\\n # print(new_game.to_string())\\n # print(depth, move, moves[move])\\n if v <= alpha:\\n return v, move\\n beta = min([beta, v])\\n\\n if game.move_count < 18:\\n self.ab_trees[current_location] = sorted(moves.items(), key=lambda x: x[1],\\n reverse=False)\\n\\n if len(moves) > 0:\\n value = min(moves, key=moves.get)\\n return moves[value], value\\n else:\\n return float(\\\"inf\\\"), (-1, -1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67339a284b03a9772112d547108b7a24\",\n \"score\": \"0.4861774\",\n \"text\": \"def _alpha_beta_search(game: MancalaEnv, alpha=-np.inf, beta=np.inf, depth=5):\\n if depth == 0 or game.is_game_over():\\n return game.get_player_utility()\\n\\n if game.side_to_move == Side.SOUTH:\\n v = -np.inf\\n for (_, new_s) in game.next_states():\\n v = max(v, AlphaBeta._alpha_beta_search(new_s, alpha, beta, depth - 1))\\n alpha = max(alpha, v)\\n # if beta <= alpha:\\n # break\\n else:\\n v = np.inf\\n for (_, new_s) in game.next_states():\\n v = min(v, AlphaBeta._alpha_beta_search(new_s, alpha, beta, depth - 1))\\n beta = min(beta, v)\\n # if beta <= alpha:\\n # break\\n return v\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a7111cfd3f9a39df74eacee8a794e626\",\n \"score\": \"0.48613393\",\n \"text\": \"def update(self, leaf):\\n\\n parent = leaf.parent\\n parent.update_value()\\n parent = parent.parent\\n while parent is not None:\\n parent.update_value()\\n parent = parent.parent\",\n \"title\": \"\"\n },\n {\n \"docid\": \"979abc3509e60d0c5379c88d34e610a8\",\n \"score\": \"0.48586494\",\n \"text\": \"def test_days_early_for_beta(self):\\n assert self.course_outline.days_early_for_beta is None\\n new_outline = attr.evolve(\\n self.course_outline,\\n days_early_for_beta=5\\n )\\n assert new_outline is not None\\n assert new_outline != self.course_outline\\n assert new_outline.days_early_for_beta == 5\\n\\n with pytest.raises(ValueError) as error:\\n attr.evolve(self.course_outline, days_early_for_beta=-1)\\n assert error.match(\\n \\\"Provided value -1 for days_early_for_beta is invalid. The value must be positive or zero. \\\"\\n \\\"A positive value will shift back the starting date for Beta users by that many days.\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b44a05eb5d372af8717acf8a0bcd6d6e\",\n \"score\": \"0.4852976\",\n \"text\": \"def inverseBeta_(e):\\n for v in set(closedChildren(e)):\\n for b in possibleBodies(v,e):\\n yield Application(Abstraction(b),v)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c893d67ec9775af98876ef51acdf705\",\n \"score\": \"0.4842121\",\n \"text\": \"def update_balance_factor(self):\\n self._updateBalanceFactor(self.root)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a13391bef97b27db04839ad7060c185\",\n \"score\": \"0.4841156\",\n \"text\": \"def update_attributes(self):\\n\\n parent_cell = self.mitosisSteppable.parentCell\\n child_cell = self.mitosisSteppable.childCell\\n compartment_list_child = self.inventory.getClusterCells(child_cell.clusterId)\\n compartment_list_parent = self.inventory.getClusterCells(parent_cell.clusterId)\\n # compartments in the parent and child clusters arel listed\\n # in the same order so attribute changes require simple iteration through compartment list\\n for i in range(compartment_list_child.size()):\\n compartment_list_child[i].type = compartment_list_parent[i].type\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfede0d93036ea8f3cb0cfde4b5ccd8b\",\n \"score\": \"0.4835326\",\n \"text\": \"def SetBeta(self, _arg: 'double const') -> \\\"void\\\":\\n return _itkHessianToObjectnessMeasureImageFilterPython.itkHessianToObjectnessMeasureImageFilterISSRTD33ISS3_SetBeta(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee6038610c469a30024222f172998ef3\",\n \"score\": \"0.4834767\",\n \"text\": \"def update(self, alpha, omega, value):\\n alpha += self.size\\n omega += self.size + 1\\n for i in reversed(range(1, alpha.bit_length())):\\n self.push(alpha >> i)\\n for i in reversed(range(1, omega.bit_length())):\\n self.push(omega >> i)\\n while alpha < omega:\\n if alpha & 1:\\n self.data[alpha] = value\\n self.lazy[alpha] = value\\n alpha += 1\\n if omega & 1:\\n omega -= 1\\n self.data[omega] = value\\n self.lazy[omega] = value\\n alpha >>= 1\\n omega >>= 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a41862f489559c2b1e23b538d3373a86\",\n \"score\": \"0.4831494\",\n \"text\": \"def __init__(self, alpha):\\n self.alpha = alpha\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9af070a7dd52a8f90025d7c57713139a\",\n \"score\": \"0.48251846\",\n \"text\": \"def _update_versions(self, root):\\n nodes = self._get_versioned_nodes(root)\\n for node in nodes:\\n name = utils.get_localname(node)\\n\\n if name == \\\"Indicator\\\":\\n node.attrib['version'] = '2.2'\\n else:\\n node.attrib['version'] = '1.2'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"773216ad35e234e1b944bf4f257e799f\",\n \"score\": \"0.48198372\",\n \"text\": \"def alphabeta(self,\\n game,\\n depth,\\n alpha=float(\\\"-inf\\\"),\\n beta=float(\\\"inf\\\"),\\n maximizing_player=True):\\n if self.time_left() < self.TIMER_THRESHOLD:\\n raise Timeout()\\n\\n # leveraged help at: http://neverstopbuilding.com/minimax\\n scores = []\\n\\n # determine which player we are\\n if maximizing_player:\\n whoami = game.active_player\\n else:\\n whoami = game.inactive_player\\n\\n # determine the moves of the other (active!) player\\n legal_moves = game.get_legal_moves(game.active_player)\\n\\n # if no legal moves available, return the score immediately as someone\\n # has thus won\\n if not legal_moves:\\n return [self.score(game, whoami), (-1, -1)]\\n\\n # determine the depth from `depth` and compute out the tree\\n for legal_move in legal_moves:\\n\\n # if alpha ever greater than beta, prune and continue\\n if alpha >= beta:\\n continue\\n\\n forecasted_game = game.forecast_move(legal_move)\\n\\n if depth > 1: # if we need to recur\\n score, _ = self.alphabeta(\\n forecasted_game,\\n depth - 1,\\n alpha=alpha,\\n beta=beta,\\n maximizing_player=not maximizing_player)\\n # set alpha if the returned score is greater than the current\\n # alpha value\\n if maximizing_player and score > alpha:\\n alpha = score\\n elif not maximizing_player and score < beta: # likewise w/beta\\n beta = score\\n\\n scores.append([score, legal_move])\\n else: # we need to determine a score for the forecasted game board\\n score = self.score(forecasted_game, whoami)\\n\\n # same as above here\\n if maximizing_player and score > alpha:\\n alpha = score\\n elif not maximizing_player and score < beta:\\n beta = score\\n\\n scores.append([score, legal_move])\\n\\n # score the tree using minimax\\n if maximizing_player:\\n score_and_move = max(scores, key=lambda x: x[0])\\n else: # minimizing player\\n score_and_move = min(scores, key=lambda x: x[0])\\n\\n # return the score and the tuple(int,int) for position\\n return score_and_move\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d87b96bd0344cdb3c32403829acbddc\",\n \"score\": \"0.48189998\",\n \"text\": \"def beta(self):\\n return self._beta\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c0c094de46f495c6af8d5bba298411f3\",\n \"score\": \"0.4817924\",\n \"text\": \"def update_cell(self, adj, cell):\\n adj.g = cell.g + 1\\n adj.h = self.get_cost(adj)\\n adj.parent = cell\\n adj.f = adj.h + adj.g\",\n \"title\": \"\"\n },\n {\n \"docid\": \"37aed025c2a217148f02730e9427cb63\",\n \"score\": \"0.48133835\",\n \"text\": \"def update_nodes(self):\\n\\n # get current level and problem description\\n L = self.level\\n P = L.prob\\n\\n # only if the level has been touched before\\n assert L.status.unlocked\\n\\n # get number of collocation nodes for easier access\\n M = self.coll.num_nodes\\n\\n # initialize integral terms with zeros, will add stuff later\\n integral = [P.dtype_u(P.init, val=0.0) for l in range(M)]\\n\\n # gather all terms which are known already (e.g. from the previous iteration)\\n # this corresponds to SF(u^k) - SdF(u^k) + tau (note: have integrals in pos and vel!)\\n for m in range(M):\\n for j in range(M + 1):\\n # build RHS from f-terms (containing the E field) and the B field\\n f = P.build_f(L.f[j], L.u[j], L.time + L.dt * self.coll.nodes[j - 1])\\n # add SQF(u^k) - SxF(u^k) for the position\\n integral[m].pos += L.dt * (L.dt * (self.SQ[m + 1, j] - self.Sx[m + 1, j]) * f)\\n # add SF(u^k) - STF(u^k) for the velocity\\n integral[m].vel += L.dt * (self.S[m + 1, j] - self.ST[m + 1, j]) * f\\n # add tau if associated\\n if L.tau[m] is not None:\\n integral[m] += L.tau[m]\\n # tau is 0-to-node, need to change it to node-to-node here\\n if m > 0:\\n integral[m] -= L.tau[m - 1]\\n\\n # do the sweep\\n for m in range(0, M):\\n # build rhs, consisting of the known values from above and new values from previous nodes (at k+1)\\n tmp = P.dtype_u(integral[m])\\n for j in range(m + 1):\\n # build RHS from f-terms (containing the E field) and the B field\\n f = P.build_f(L.f[j], L.u[j], L.time + L.dt * self.coll.nodes[j - 1])\\n # add SxF(u^{k+1})\\n tmp.pos += L.dt * (L.dt * self.Sx[m + 1, j] * f)\\n # add pos at previous node + dt*v0\\n tmp.pos += L.u[m].pos + L.dt * self.coll.delta_m[m] * L.u[0].vel\\n # set new position, is explicit\\n L.u[m + 1].pos = tmp.pos\\n\\n # get E field with new positions and compute mean\\n L.f[m + 1] = P.eval_f(L.u[m + 1], L.time + L.dt * self.coll.nodes[m])\\n\\n ck = tmp.vel\\n\\n # do the boris scheme\\n L.u[m + 1].vel = P.boris_solver(ck, L.dt * self.coll.delta_m[m], L.f[m], L.f[m + 1], L.u[m])\\n\\n # indicate presence of new values at this level\\n L.status.updated = True\\n\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a4124f9ee70ccbb4b5f04c623efd7ae\",\n \"score\": \"0.48017776\",\n \"text\": \"def SetBeta(self, _arg: 'double const') -> \\\"void\\\":\\n return _itkHessianToObjectnessMeasureImageFilterPython.itkHessianToObjectnessMeasureImageFilterISSRTD33IF3_SetBeta(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c09581737d3799fc93e1dc18e5ba4c4\",\n \"score\": \"0.47999048\",\n \"text\": \"def replaceChild(self, *args):\\n return _coin.SoVRMLSwitch_replaceChild(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"691cd90f8349a677f6ca3b669d5a4688\",\n \"score\": \"0.47912887\",\n \"text\": \"def SetBeta(self, _arg: 'double const') -> \\\"void\\\":\\n return _itkHessianToObjectnessMeasureImageFilterPython.itkHessianToObjectnessMeasureImageFilterISSRTD33IUC3_SetBeta(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d7bdfe6a24b008d3f8c566d5a4c14d5\",\n \"score\": \"0.47864673\",\n \"text\": \"def get_beta(self):\\n return self.__beta\",\n \"title\": \"\"\n },\n {\n \"docid\": \"008ff38e61cc5866d348fd014568c8b7\",\n \"score\": \"0.47855422\",\n \"text\": \"def SetBeta(self, beta: 'double') -> \\\"void\\\":\\n return _itkSigmoidImageFilterPython.itkSigmoidImageFilterIUS2IUS2_SetBeta(self, beta)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5fad051ed84e714676876821551fec35\",\n \"score\": \"0.47777498\",\n \"text\": \"def minimax_alphabeta(board_state, depth, MaxPlayer, alpha, beta, prev_move=0, top_tree=False):\\n board_copy = copy.deepcopy(board_state)\\n if prev_move != 0:\\n board_copy = combine_single_move(board_copy, prev_move[0], prev_move[1], prev_move[2], prev_move[3])\\n\\n if depth == 0 or ai_score(board_copy) == float(\\\"-infinity\\\") or ai_score(board_copy) == float(\\\"infinity\\\"):\\n #print(\\\"Leaf Node Board: Depth: \\\" + str(depth) + str(board_copy))\\n return ai_score(board_copy)\\n board_copy = copy.deepcopy(board_state)\\n if MaxPlayer:\\n best_val = float(\\\"-infinity\\\")\\n moves = possible_moves(board_copy, 0)\\n # sort moves by ones who kill pieces first\\n moves = sorted(moves, key=lambda x: x[1], reverse=True)\\n if top_tree:\\n best_move = []\\n old_piece = []\\n\\n for move in moves:\\n clean_move = move[0]\\n #print(\\\"Max Depth \\\" + str(depth) + \\\" Board: \\\" + str(board_copy))\\n val = minimax_alphabeta(copy.deepcopy(board_copy), depth - 1, False, alpha, beta, clean_move)\\n #print(\\\"Score of \\\" + str(val))\\n if val > best_val:\\n best_move = clean_move\\n if len(move) == 3:\\n # this contains values about the piece that died in this process\\n old_piece = move[2]\\n\\n best_val = max(best_val, val)\\n alpha = max(best_val, alpha)\\n\\n if beta <= alpha:\\n break\\n if top_tree:\\n return [[best_move, old_piece], best_val]\\n else:\\n return best_val\\n else:\\n # min player\\n best_val = float(\\\"infinity\\\")\\n moves = possible_moves(board_copy, 1)\\n # sort moves by ones who kill pieces first\\n moves = sorted(moves, key=lambda x: x[1], reverse=True)\\n for move in moves:\\n move = move[0]\\n move_board = copy.deepcopy(board_copy)\\n move_board = combine_moves(move_board, move[0], move[1], move[2], move[3], prev_move[0], prev_move[1], prev_move[2], prev_move[3])\\n val = minimax_alphabeta(move_board, depth - 1, True, alpha, beta)\\n #print(\\\"Min Depth \\\" + str(depth) + \\\" Board: \\\" + str(move_board))\\n #print(\\\"Score of \\\" + str(val))\\n\\n best_val = min(best_val, val)\\n beta = min(best_val, beta)\\n if beta <= alpha:\\n break\\n return best_val\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38cf9b8e4c2ff320b0e2c45fc60e3ac7\",\n \"score\": \"0.47708696\",\n \"text\": \"def reset(self, beta):\\n # self.data = np.random.choice([-1, 1], size=(self.size, self.size))\\n self.beta = beta\\n self.decision = np.exp(- beta * np.array([-8, -4, 0, 4, 8]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69093d6224fa978ab6ae3e0dc7af054a\",\n \"score\": \"0.47701553\",\n \"text\": \"def update(self, idx, p):\\n\\n change = p - self.tree[idx]\\n self.tree[idx] = p\\n self._propagate(idx, change)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8035c6bf53ee333f36f25d44dbd591ec\",\n \"score\": \"0.47660863\",\n \"text\": \"def update_lambda_beta(lambda_beta, beta_o, Nks):\\n for k in range(K):\\n lambda_beta[k] = beta_o + Nks[k]\\n return lambda_beta\",\n \"title\": \"\"\n },\n {\n \"docid\": \"be395e3fe5d0dbae495f9c915f83731b\",\n \"score\": \"0.475627\",\n \"text\": \"def alphabeta(self, game, depth, alpha=float(\\\"-inf\\\"), beta=float(\\\"inf\\\"), maximizing_player=True):\\n if self.time_left() < self.TIMER_THRESHOLD:\\n raise Timeout()\\n\\n if game.is_winner(self):\\n return float('+inf'), game.get_legal_moves()\\n\\n legal_moves = game.get_legal_moves()\\n\\n # check if we reached the required depth or no more moves (terminal node)\\n if (depth == 0) or (not legal_moves):\\n game_score = self.score(game, self)\\n return (game_score, (-1, -1))\\n\\n global gDepth\\n good_choice = ()\\n if maximizing_player:\\n best_score = alpha\\n good_choice = (alpha, (-1, -1))\\n for move in legal_moves:\\n game_branch = game.forecast_move(move)\\n gDepth = depth\\n branch_score, _ = self.alphabeta(game_branch, depth -1, alpha, beta, (not maximizing_player))\\n if branch_score > best_score:\\n good_choice = (branch_score, move)\\n best_score = branch_score\\n if best_score >= beta:\\n break\\n alpha = max(alpha, best_score)\\n\\n else:\\n best_score = beta\\n good_choice = (beta, (-1, -1))\\n for move in legal_moves:\\n game_branch = game.forecast_move(move)\\n branch_score, _ = self.alphabeta(game_branch, depth -1, alpha, beta, (not maximizing_player))\\n if branch_score < best_score:\\n good_choice = (branch_score, move)\\n best_score = branch_score\\n if best_score <= alpha:\\n good_choice = (best_score, move)\\n break\\n beta = min(beta, best_score)\\n\\n return good_choice;\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e31617c4b4b2bdc54b041b8b020882a\",\n \"score\": \"0.47539702\",\n \"text\": \"def abel_inversion(self):\\n self.func_prime()\\n self.F_construct()\\n self.r=self.newy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"99c756b0869c887dd8ebd3e45d4258f7\",\n \"score\": \"0.4742597\",\n \"text\": \"def assign_generalized_bc(self, loc, alpha, beta, gamma):\\n if loc.lower() == \\\"ilo\\\":\\n idx = 0\\n elif loc.lower() == \\\"ihi\\\":\\n idx = 1\\n else:\\n error(\\\"assign_bc: loc must be one of ilo or ihi, got {0}\\\".format(loc))\\n\\n # determine if boundary already has an assigned bc\\n if self.bcs[idx][1] is not None:\\n error(\\\"attempting to assign multiple boundary conditions to same node\\\")\\n\\n if abs(beta) < EPS:\\n beta = 1.e-80\\n\\n # algorithm requires negative on left side boundary\\n if idx == 0:\\n beta = -beta\\n\\n if abs(alpha - 1.) < EPS:\\n self.constrained = True\\n\\n self.bcs[idx] = [self.boundary_nodes[idx], alpha, beta, gamma]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"598b65a25fafbbbd7ffee420c8134ff9\",\n \"score\": \"0.47409594\",\n \"text\": \"def update_current_output_positions( self, t_boost, inv_gamma, inv_beta ):\\n # Some shorcuts for further calculation's purposes\\n t_lab = self.t_lab\\n t_boost_prev = t_boost - self.dt\\n\\n # This implements the Lorentz transformation formulas,\\n # for a snapshot having a fixed t_lab\\n self.current_z_boost = (t_lab*inv_gamma - t_boost)*c*inv_beta\\n self.prev_z_boost = (t_lab*inv_gamma - t_boost_prev)*c*inv_beta\\n self.current_z_lab = (t_lab - t_boost*inv_gamma)*c*inv_beta\\n self.prev_z_lab = (t_lab - t_boost_prev*inv_gamma)*c*inv_beta\",\n \"title\": \"\"\n },\n {\n \"docid\": \"598b65a25fafbbbd7ffee420c8134ff9\",\n \"score\": \"0.47409594\",\n \"text\": \"def update_current_output_positions( self, t_boost, inv_gamma, inv_beta ):\\n # Some shorcuts for further calculation's purposes\\n t_lab = self.t_lab\\n t_boost_prev = t_boost - self.dt\\n\\n # This implements the Lorentz transformation formulas,\\n # for a snapshot having a fixed t_lab\\n self.current_z_boost = (t_lab*inv_gamma - t_boost)*c*inv_beta\\n self.prev_z_boost = (t_lab*inv_gamma - t_boost_prev)*c*inv_beta\\n self.current_z_lab = (t_lab - t_boost*inv_gamma)*c*inv_beta\\n self.prev_z_lab = (t_lab - t_boost_prev*inv_gamma)*c*inv_beta\",\n \"title\": \"\"\n },\n {\n \"docid\": \"72fbf308e92428f8206ee4b947835cbf\",\n \"score\": \"0.47384992\",\n \"text\": \"def reduce_eta(self):\\n self.eta = self.eta * self.eta_decay_factor\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d914fe7f48134b8c1023530034e212d2\",\n \"score\": \"0.47324023\",\n \"text\": \"def update(self):\\n self.weight_mom[self.index] = self.sub_weight_mom\\n self.weight[self.index] = self.sub_weight\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6af45e5acfd1dfc9af642aa3cf8b018a\",\n \"score\": \"0.47240648\",\n \"text\": \"def SetBeta(self, _arg: 'double const') -> \\\"void\\\":\\n return _itkHessianToObjectnessMeasureImageFilterPython.itkHessianToObjectnessMeasureImageFilterISSRTD22IUS2_SetBeta(self, _arg)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3f38b3082446c2ded9f395fad0eda0c\",\n \"score\": \"0.47213665\",\n \"text\": \"def inc_bl_adj(self):\\n self.bl_adj += 1\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e012bc525675527d684baf0b41d7ced\",\n \"score\": \"0.47185087\",\n \"text\": \"def forward(self):\\r\\n\\r\\n self.value = np.dot(self.x_node.value, self.w_node.value) + self.b_node.value\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993295,"cells":{"query_id":{"kind":"string","value":"fb5b30afd0e5c179978936780110729e"},"query":{"kind":"string","value":"Upload muscles and the neurons that connect to them"},"positive_passages":{"kind":"list like","value":[{"docid":"6f3054becbc9fe63dc97044698ece539","score":"0.67196697","text":"def upload_muscles():\n try:\n with open(CELL_NAMES_SOURCE) as csvfile:\n csvreader = csv.reader(csvfile)\n\n ev = P.Evidence(key=\"wormbase\", title=\"C. elegans Cell List - WormBase.csv\")\n w = WORM\n for num, line in enumerate(csvreader):\n if num < 4: # skip rows with no data\n continue\n\n if line[7] or line[8] or line[9] == '1': # muscle's marked in these columns\n muscle_name = normalize_cell_name(line[0]).upper()\n m = P.Muscle(name=muscle_name)\n w.muscle(m)\n ev.asserts(w)\n #second step, get the relationships between them and add them to the graph\n print (\"uploaded muscles\")\n except Exception:\n traceback.print_exc()","title":""}],"string":"[\n {\n \"docid\": \"6f3054becbc9fe63dc97044698ece539\",\n \"score\": \"0.67196697\",\n \"text\": \"def upload_muscles():\\n try:\\n with open(CELL_NAMES_SOURCE) as csvfile:\\n csvreader = csv.reader(csvfile)\\n\\n ev = P.Evidence(key=\\\"wormbase\\\", title=\\\"C. elegans Cell List - WormBase.csv\\\")\\n w = WORM\\n for num, line in enumerate(csvreader):\\n if num < 4: # skip rows with no data\\n continue\\n\\n if line[7] or line[8] or line[9] == '1': # muscle's marked in these columns\\n muscle_name = normalize_cell_name(line[0]).upper()\\n m = P.Muscle(name=muscle_name)\\n w.muscle(m)\\n ev.asserts(w)\\n #second step, get the relationships between them and add them to the graph\\n print (\\\"uploaded muscles\\\")\\n except Exception:\\n traceback.print_exc()\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"aad469f7cdda8f21594f47b702bbf18b","score":"0.63252246","text":"def upload_tracks():\n auto_upload_tracks()","title":""},{"docid":"061181d642add61974fefd7bcd56de38","score":"0.6084998","text":"def upload(self):","title":""},{"docid":"061181d642add61974fefd7bcd56de38","score":"0.6084998","text":"def upload(self):","title":""},{"docid":"f1f20cfc1e871657e3fe0275cd15c1a3","score":"0.59851444","text":"def db_add_muscle_synapses(clear):\n graph = graph_p2()\n if clear:\n statement = \"MATCH (n:Neuron)-[r]-(m:Muscle) DELETE r\"\n graph.cypher.run(statement)\n\n conns = preproc.muscle_conns_df()\n transx = graph.cypher.begin()\n statement = \"MATCH (n:Neuron {name:{neuron}}), (m:Muscle {name:{muscle}}) CREATE (n)-[s:Synapse {name:n.name+'->'+m.name, type:'NMJ', weight:{w}}]->(m)\"\n for idx, row in conns.iterrows():\n transx.append(statement, {\"neuron\":row['Neuron'], \"muscle\":row['Muscle'], \"w\":row[\"Weight\"]})\n transx.commit()\n db_set_node_degrees()","title":""},{"docid":"6e362d5bfcf0df6e377b86c3c20ad610","score":"0.59452313","text":"def upload(request):\n if(request.method == 'POST'):\n form = FileForm(request.POST, request.FILES)\n if form.is_valid():\n\n n_scenarios = 0\n for _, _, filenames in os.walk(\"scenarios/\"):\n n_scenarios = len(filenames)\n if n_scenarios > 15:\n shutil.rmtree(\"scenarios/\")\n\n fs = FileSystemStorage(\"scenarios/\")\n manager.clear_paths()\n for key in request.FILES.keys():\n global_file = request.FILES[key]\n filename = fs.save(global_file.name, global_file)\n manager.add_path(os.path.join(fs.location, filename))\n for path in manager.get_paths():\n if path is not None:\n track_stream = load.get_track_streams_from_prp(path)\n manager.add_track_stream(track_stream)\n station_utils.sync_stations(*manager.get_track_streams())\n\n return(HttpResponse('POST ok !'))\n else:\n return(HttpResponse('POST failed'))","title":""},{"docid":"77841ecd494416db38447ca66e48ecd0","score":"0.54216546","text":"def test_1(self):\n \n inputFile = os.path.join(rmgpy.getPath(),'tools','data','flux','input.py')\n chemFile = os.path.join(rmgpy.getPath(),'tools','data','flux','chemkin','chem.inp')\n dictFile = os.path.join(rmgpy.getPath(),'tools','data','flux','chemkin','species_dictionary.txt')\n \n with open(inputFile) as nf, open(chemFile) as cf, open(dictFile) as df:\n response = self.client.post('/tools/flux/', {'InputFile': nf, 'ChemkinFile': cf, 'DictionaryFile': df, \n 'MaxNodes': 50, 'MaxEdges': 50, 'TimeStep': 1.25, \n 'ConcentrationTolerance': 1e-6, 'SpeciesRateTolerance': 1e-6})\n \n self.assertEqual(response.status_code, 200)\n \n folder = os.path.join(settings.MEDIA_ROOT,'rmg','tools','flux')\n \n # Check if inputs were correctly uploaded\n pyInput = os.path.join(folder,'input.py')\n chemInput = os.path.join(folder,'chem.inp')\n dictInput = os.path.join(folder,'species_dictionary.txt')\n \n self.assertTrue(os.path.isfile(pyInput),'RMG input file was not uploaded')\n self.assertTrue(os.path.isfile(chemInput),'Chemkin file was not uploaded')\n self.assertTrue(os.path.isfile(dictInput),'Dictionary file was not uploaded')\n \n # Check if outputs were correctly generated\n videoOutput = os.path.join(folder,'1','flux_diagram.avi')\n species = os.path.join(folder,'species')\n \n self.assertTrue(os.path.isfile(videoOutput),'Video output was not generated')\n self.assertTrue(os.path.isdir(species),'Species directory was not generated')\n \n shutil.rmtree(folder)","title":""},{"docid":"bc637eb6c350ab98bf7119ef7124487a","score":"0.5369428","text":"def test_upload_essentials(self, server, runtime, tmpfolder):\n path = tmpfolder / uuid.uuid4().hex\n data = self.generate_byte_data()\n with open(path, 'w') as model:\n print(data, file=model)\n runtime.prepare_client()\n server.accept_client(server.serversocket, None)\n server.send_message(Message(MessageType.OK))\n runtime.upload_essentials(path)","title":""},{"docid":"5dccff926af1d6dc5136b9f1f6e8f428","score":"0.53643095","text":"def upload(subject_set, images, zooniverse_project):\n new_subjects = []\n\n print(\"Uploading {} images\".format(len(images)))\n for filename, metadata in images.items():\n subject = Subject()\n\n subject.links.project = zooniverse_project\n subject.add_location(filename)\n\n subject.metadata.update(metadata)\n\n #Trigger upload\n subject.save()\n new_subjects.append(subject)\n subject_set.add(new_subjects)","title":""},{"docid":"8c146882a9c0046a75d192981acedee3","score":"0.53142595","text":"def db_add_muscles(clear):\n graph = graph_p2()\n if clear:\n statement = \"MATCH (n:Muscle) DELETE n\"\n graph.cypher.run(statement)\n\n muscles = preproc.muscles_df()\n transx = graph.cypher.begin()\n statement = \"CREATE (n:Muscle {props})\"\n for name, params in muscles.iterrows():\n params = params.to_dict()\n params['name'] = name\n transx.append(statement, {\"props\":params})\n transx.commit()","title":""},{"docid":"76c5a42973980edfb057c54280e54f61","score":"0.52692986","text":"async def upload_and_save(\n self,\n nlu: File,\n domain: File,\n stories: File,\n config: File,\n rules: File,\n http_action: File,\n bot: Text,\n user: Text,\n overwrite: bool = True,\n ):\n training_file_loc = await Utility.save_training_files(nlu, domain, config, stories, rules, http_action)\n await self.save_from_path(training_file_loc['root'], bot, overwrite, user)\n Utility.delete_directory(training_file_loc['root'])","title":""},{"docid":"77f795619f696d418d91b0f667ca5944","score":"0.522585","text":"def tool_nomad_upload(files, token, name, legacy, dry):\n from .scripts.nomad_upload import nomad_upload\n\n nomad_upload(files, token, legacy, dry, name=name","title":""},{"docid":"db278a3d67405d5949247222da8f37de","score":"0.5213027","text":"def main():\n answers = [ os.path.join(ANSWERS_DIR, f) for f in os.listdir(ANSWERS_DIR)]\n for answer in answers:\n print('uploading %s' % answer)\n upload(answer, folder='answers')\n print('deleting %s', answer)\n os.remove(answer)","title":""},{"docid":"692a927f69624502db9f40e773a3ca0c","score":"0.5205346","text":"def upload(self):\n # Setup\n for sequencer_number in self._used_sequencers_numbers:\n target = self.device.sequencers[sequencer_number]\n self._set_device_parameter(target, \"sync_en\", value=True)\n self._set_device_parameter(target, \"marker_ovr_en\", value=True) # Default after reboot = False\n self._set_device_parameter(target, \"marker_ovr_value\", value=15) # Default after reboot = 0\n\n for sequencer_number in self._unused_sequencers_numbers:\n target = self.device.sequencers[sequencer_number]\n self._set_device_parameter(target, \"sync_en\", value=False)\n self._set_device_parameter(target, \"marker_ovr_en\", value=True) # Default after reboot = False\n self._set_device_parameter(target, \"marker_ovr_value\", value=0) # Default after reboot = 0\n if sequencer_number >= 2: # Never disconnect default sequencers\n self._set_device_parameter(target, \"channel_map_path0_out0_en\", value=False)\n self._set_device_parameter(target, \"channel_map_path0_out2_en\", value=False)\n self._set_device_parameter(target, \"channel_map_path1_out1_en\", value=False)\n self._set_device_parameter(target, \"channel_map_path1_out3_en\", value=False)\n\n # Upload waveforms and program\n qblox_dict = {}\n sequencer: Sequencer\n for port in self._output_ports_keys:\n for sequencer in self._sequencers[port]:\n # Add sequence program and waveforms to single dictionary\n qblox_dict[sequencer] = {\n \"waveforms\": sequencer.waveforms,\n \"weights\": sequencer.weights,\n \"acquisitions\": sequencer.acquisitions,\n \"program\": sequencer.program,\n }\n\n # Upload dictionary to the device sequencers\n self.device.sequencers[sequencer.number].sequence(qblox_dict[sequencer])\n\n # DEBUG: QCM RF Save sequence to file\n if self._debug_folder != \"\":\n filename = self._debug_folder + f\"Z_{self.name}_sequencer{sequencer.number}_sequence.json\"\n with open(filename, \"w\", encoding=\"utf-8\") as file:\n json.dump(qblox_dict[sequencer], file, indent=4)\n file.write(sequencer.program)\n\n # Arm sequencers\n for sequencer_number in self._used_sequencers_numbers:\n self.device.arm_sequencer(sequencer_number)\n\n # DEBUG: QCM RF Print Readable Snapshot\n # print(self.name)\n # self.device.print_readable_snapshot(update=True)\n\n # DEBUG: QCM RF Save Readable Snapshot\n from qibolab.instruments.qblox.debug import print_readable_snapshot\n\n if self._debug_folder != \"\":\n filename = self._debug_folder + f\"Z_{self.name}_snapshot.json\"\n with open(filename, \"w\", encoding=\"utf-8\") as file:\n print_readable_snapshot(self.device, file, update=True)","title":""},{"docid":"0d2e0f5c74e2c53ff9d010b6816b3571","score":"0.52015185","text":"def publish():\n\n\tupload()","title":""},{"docid":"0d2e0f5c74e2c53ff9d010b6816b3571","score":"0.52015185","text":"def publish():\n\n\tupload()","title":""},{"docid":"5d7becc91c8dd0b3550d90869d76c063","score":"0.5196205","text":"async def ftp_upload(self):\r\n for source, target in zip(self._client_path, self._server_path):\r\n ext = os.path.splitext(source)\r\n if ext in ['txt', 'html', 'htm']:\r\n with open(source, 'rb') as f:\r\n self._ftp.storlines('STOR ' + target, f)\r\n else:\r\n with open(source, 'rb') as f:\r\n self._ftp.storbinary(\r\n 'STOR ' + target, f, 1024)\r\n await asyncio.sleep(1.0)","title":""},{"docid":"21f521e133f33233fa828e490d7bd092","score":"0.51678973","text":"def upload_waveforms(self, sequence_names, sequence_channels, sequence_items, reload=True):\n raise NotImplementedError","title":""},{"docid":"10f5aa2d8ebbfd6a96daa18411cf619e","score":"0.5163708","text":"def upload_parts(self, parts):\n files = self.make_filelist(self.make_dirlist(parts))\n self.upload_logs(files)","title":""},{"docid":"c81e8ff971589ba3750fc454cc87f78e","score":"0.51607674","text":"def import_wiki():\n print('Importing Node wiki data into mongo...')\n db = mongoi.NodeDb()\n train_data = scrape_file(DATA_DIR + FILE_NAMES[6])\n test_data = scrape_file(DATA_DIR + FILE_NAMES[7])\n for doc in train_data:\n doc['gold_label'] = LABEL[doc['gold_label']]\n db.wiki_train.add(doc)\n for doc in test_data:\n doc['gold_label'] = LABEL[doc['gold_label']]\n db.wiki_test.add(doc)\n print('Completed successfully.')","title":""},{"docid":"788f22e835e1175a58d279f399461a63","score":"0.5154003","text":"def animals(bot, update):\n # WORKING\n chat_id = update.message.chat.id\n user_id = update.message.from_user.id\n BotActions.common_process(chat_id, user_id)\n BotActions.incrementa_animales(user_id, chat_id)\n file_name = BotActions.random_file_name(f'{BotActions.pytel_path}/Animals')\n bot.send_photo(chat_id=chat_id, photo=open(file_name, 'rb'))","title":""},{"docid":"fee6a202fb47352aec299693fbfdb2e3","score":"0.5152346","text":"def start(self):\n\n CloudConvert.upload(self.fromfile, self.toformat, self.url)","title":""},{"docid":"9866448efed04157b44cc3f4c2ca84ae","score":"0.5142552","text":"async def importmusic(self, ctx):\n if ctx.message.author.id not in self.owner_list:\n embed = discord.Embed(title='{}:'.format(ctx.message.author.name),\n description='You may not use this command :angry: only admins!',\n colour=0xf20006)\n a = await self.bot.say(embed=embed)\n await self.bot.add_reaction(a, self.emojiUnicode['warning'])\n return\n\n\n if not self.database_online:\n embed = discord.Embed(title='{}:'.format(ctx.message.author.name),\n description='Could not connect to database.',\n colour=0xf20006)\n a = await self.bot.say(embed=embed)\n await self.bot.add_reaction(a, self.emojiUnicode['error'])\n return\n\n\n try:\n with open(self.database_import_musicque, 'r') as file:\n reader = csv.reader(file, delimiter=',')\n for row in reader:\n b = re.search(r'^(.*)', str(row)).group()\n b = b.replace('[', '')\n b = b.replace('\"(', '')\n b = b.replace(',)\"', '')\n row = b.replace(']', '')\n try:\n self.cur.execute(\"INSERT INTO botzilla.musicque(url) VALUES({});\".format(row))\n self.cur.execute(\"ROLLBACK;\")\n except Exception as e:\n pass\n\n embed = discord.Embed(title='{}:'.format(ctx.message.author.name),\n description='Done!',\n colour=0xf20006)\n a = await self.bot.say(embed=embed)\n await self.bot.add_reaction(a, self.emojiUnicode['succes'])\n\n except Exception as e:\n embed = discord.Embed(title='{}:'.format('Error'),\n description='```Python\\n{}```'.format(e.args),\n colour=0xf20006)\n a = await self.bot.say(embed=embed)\n await self.bot.add_reaction(a, self.emojiUnicode['error'])","title":""},{"docid":"fbd52f19e045f40d4c493d105539dc85","score":"0.51331276","text":"def upload_genotypes(genotypes_by_chr_dir, ):\n from schema.connection import session, metadata # is session necessary?\n logging.basicConfig(level=logging.DEBUG)\n print metadata\n# metadata.bind = 'sqlite:///:memory:'\n metadata.bind.echo = True\n \n genotypes_by_chr_dir = '/home/gioby/Data/HGDP/Genotypes_by_chr'\n for filename in glob.glob(genotypes_by_chr_dir + '/*.geno'):\n print filename\n parsers.genotypes_parser(open(filename, 'r'))\n session.commit()\n\n# rosenberg_file = open(rosenberg_path, 'r')\n# parsers.rosenberg_parser(rosenberg_file)\n\n print 'upload of Genotypes to database %s completed' % metadata","title":""},{"docid":"ae9e4bd3621461073b18a2de9f0a35c5","score":"0.51181287","text":"def create_folders_server(cobertura=True, transicao=True, RGB=True):","title":""},{"docid":"4d724a9e891471bb9f0019eb36e24459","score":"0.51133394","text":"def upload_datasets(force=False):\n for dataset in datasets:\n datasource = Datasource(f\"{dataset.name}_train\")\n print(datasource.name)\n datasource.upload(force=force)","title":""},{"docid":"477cc456a5407809fa1c30b34d08821d","score":"0.5080628","text":"def store(self, mutagen_file, pictures):\n mutagen_file.clear_pictures()\n for pic in pictures:\n mutagen_file.add_picture(pic)","title":""},{"docid":"13d1ba28d69785f81e4ba89c46ee22e9","score":"0.50717145","text":"def upload(ctx):\n ctx.obj.run()","title":""},{"docid":"6b0d7f30dd2b91474a35ed11d6150573","score":"0.5071457","text":"async def test_upload_study(self, async_upload_study):\n dicom_path = Path(__file__) \\\n .parents[2] \\\n .joinpath('dicoms', 'anonymize')\n study = await async_upload_study(dicom_path)\n study_uid = '1.2.840.10008.1.142353.149743743.367518058.1727281618' # NOQA:E501\n assert study.image_count == 6\n assert study.study_uid == study_uid","title":""},{"docid":"637838ac7c932b7654d116f818363047","score":"0.50705326","text":"def send(session: nox.Session):\r\n session.install('twine')\r\n session.run('twine', 'upload', 'dist/*.whl')","title":""},{"docid":"29c572571109cf3e8aeb1f3d01a8c56e","score":"0.5057477","text":"async def nmb_of_mamacos():\n chan_to_send = None\n for chan in client.guilds[0].text_channels:\n if chan.name == 'plebe':\n chan_to_send = chan\n break\n actual_voice_channel = None\n\n while True:\n msg = \"\"\n\n nmb_of_chan = client.guilds[0].voice_channels\n nmb_of_chan.sort(reverse=True, key=lambda chan: len(chan.members))\n\n for chan in client.guilds[0].voice_channels:\n msg += chan.name + \" tem \" + str(len(chan.members)) + \" mamacos.\\n\"\n\n logging.info(msg)\n\n if len(nmb_of_chan[0].members) > 0:\n\n logging.info(nmb_of_chan[0].name)\n vc = await nmb_of_chan[0].connect()\n choosen_audio = meme_songs.songs[random.randint(\n 0, len(meme_songs.songs) - 1)]\n logging.info(choosen_audio)\n audio_s = discord.FFmpegPCMAudio(choosen_audio)\n vc.play(audio_s)\n\n while vc.is_playing():\n await asyncio.sleep(1)\n # disconnect after the player has finished\n vc.stop()\n await vc.disconnect()\n\n await asyncio.sleep(1800)","title":""},{"docid":"d7a155281978e14756091634c65472c7","score":"0.505276","text":"def upload_landsat(scene):\n upload_landsat.upload(scene)","title":""},{"docid":"c54c4adb2ba8caf7c64f48dae0ff23a3","score":"0.50513345","text":"async def nogas(self, ctx: KurisuContext):\n await self._meme(ctx, \"https://nintendohomebrew.com/assets/img/nhmemes/nogas.mp4\")","title":""},{"docid":"a74c0d512c810f39b0ec6262f14d22b3","score":"0.5045693","text":"def main():\n\n con = Connection()\n db = con.audiodb\n\n if use_proxy == True:\n install_proxy()\n\n words = open(filename).read().split()\n for wordname in words:\n if wordname.isalpha() == False:\n continue\n wordfilename = wordname.lower()+'.mp3'\n wordrecord = wordname.lower()+'_mp3'\n num = db.audiodb.find({\"file_name\":wordrecord}).count()\n if num > 0:\n print(\"Already got \"+\"\\\"\"+wordname+\"\\\"\")\n continue\n try:\n mp3file = get_data_from_req(\n \"https://ssl.gstatic.com/dictionary/static/sounds/de/0/\"+wordfilename)\n if not is_file_valid(mp3file):\n continue\n bin = bson.Binary(mp3file.read())\n db.audiodb.save({\"file_name\":wordrecord, \"mp3\":bin})\n print(\"Done!\")\n except Exception as e:\n print(e)","title":""},{"docid":"fb6c1cb309b9e882391021032e033a77","score":"0.5038316","text":"async def serotonin(self, ctx: KurisuContext):\n await self._meme(ctx, \"\",\n image_link=\"https://album.eiphax.tech/uploads/big/2549ac8b197ae68080041d3966a887e8.png\")","title":""},{"docid":"dd81fc0456a8c2df87c13129dd8a4622","score":"0.5033467","text":"def upload_piece():\n if request.method == 'POST':\n\n if 'username' not in request.form:\n return 'username not defined'\n\n if request.form['username'] == '':\n return 'username is empty'\n\n username = request.form['username']\n if db.pieces.find_one({'username': username}):\n return 'username taken.'\n\n document = {\n 'username': username,\n 'tag': None,\n 'model': None,\n 'texture': None\n }\n\n file_keys = ['tag', 'texture', 'model']\n if not all(x in request.files for x in file_keys):\n return 'file missing in request'\n\n for key, file in request.files.items():\n if file.filename == '':\n return 'the following file is missing: ' + key\n\n if not allowed_file(file.filename):\n return 'the following file is not in an allowed format ' + key\n\n if file and allowed_file(file.filename):\n _, extension = os.path.splitext(file.filename)\n new_filename = username + '_' + key + extension\n\n file.save(\n os.path.join(app.config['UPLOAD_FOLDER'], new_filename)\n )\n\n document[key] = {\n 'name': new_filename,\n 'url': url_for(\n 'uploaded_file',\n filename=new_filename,\n _external=True\n )\n }\n\n db.pieces.insert_one(document)\n return redirect(url_for('tags'))\n\n return render_template('upload.html')","title":""},{"docid":"a8043030e7276190ef6521f70b053f17","score":"0.49841464","text":"def upload_public(request):\n\n LOGGER.debug('upload starts ' + str(request.FILES.keys()))\n LOGGER.debug('before')\n\n chan = request.channel_name\n\n r = {}\n msg = {}\n\n values = request.REQUEST.get('values' , '')\n channel_str = request.REQUEST.get('channel', '')\n\n if values:\n # ********** create methodo ************* #\n s = Session.objects.using('megavideo').get(pk = values)\n session = s.get_decoded()\n # ********** end ************************ #\n r = session.get('values_form', '')\n try:\n del(session['values_form'])\n except:\n pass\n else:\n r = {}\n\n LOGGER.debug('values_rec: ' + str(r))\n\n if 'chan_id' in r:\n chan_id = r['chan_id']\n channel = Channel.objects.using('megavideo').get(id = chan_id)\n else:\n if channel_str != '':\n channel = Channel.objects.using('megavideo').get(name = channel_str)\n else:\n channel = Channel.objects.using('megavideo').get(name = chan)\n chan_id = channel.id\n\n LOGGER.debug('receive post')\n\n if 'Filedata' in request.FILES:\n LOGGER.debug('insider')\n\n file_uploaded = request.FILES['Filedata']\n\n # ver\n if 'name' in r and r['name'] == '':\n r['name'] = file_uploaded.name\n\n conf = settings.MEGAVIDEO_CONF\n tmp = tempfile.mktemp('_' + conf['tv_name'])\n tmp = conf['tmp'] + os.path.basename(tmp)\n fd = open(tmp, 'wb')\n\n LOGGER.debug('opened')\n\n for chunk in file_uploaded.chunks():\n fd.write(chunk)\n fd.close()\n\n LOGGER.debug('insider3')\n\n LOGGER.debug('channel: ' + str(channel))\n\n job = Job()\n job.channel = channel\n job.ip = request.META['REMOTE_ADDR']\n job.status = 'A'\n job.original_name = file_uploaded.name\n job.message = 'inserting upload/vflow interface'\n job.save(using='megavideo')\n\n LOGGER.debug('insider4')\n\n try:\n acall = AsyncCall(conf['dispatch'], conf['tv_name'], channel_id = chan_id)\n\n kargs = {\n 'metadatas' :-1, #create metadatas from scratch\n 'transcode' : True,\n 'keep_video' : True,\n 'chan_id' : chan_id,\n 'metas': r\n }\n\n if 'publish' in settings.VFLOW_REC:\n kargs['publish'] = settings.VFLOW_REC['publish']\n\n if 'cat_id' in r:\n kargs['cat_id'] = r['cat_id']\n\n LOGGER.debug('Push video async ' + str(kargs))\n\n LOGGER.debug('insider5')\n\n acall.set_job(job)\n acall.set_insert(tmp, **kargs)\n acall.call()\n\n LOGGER.debug('after calling')\n\n except StandardError, e:\n LOGGER.debug('error : ')\n LOGGER.debug(str(e))\n\n\n if 'redirect' in request.REQUEST:\n return HttpResponseRedirect(request.channel_url + 'manager/jobs/%d/' % (job.id))\n #return HttpResponse( str(job.id) )\n msg = {'error' : 0, 'job' : job.id}\n return HttpResponse(json.dumps(msg), mimetype = 'application/json')\n\n msg = {'error' : 1, 'job' : 0}\n\n return HttpResponse(json.dumps(msg), mimetype = 'application/json')","title":""},{"docid":"1571f1a843d6866d462a497945ae1b34","score":"0.4970229","text":"def upload(self, database):\n database.games.insert_many([g.as_dict() for g in self.parsed_games])","title":""},{"docid":"b34b91db2ff4ee1088196517e702745a","score":"0.4951148","text":"def file_upload(data):\n # emiting received file and user data back to the users current room\n emit('file received', data, room=data['room'])","title":""},{"docid":"0646c2f6e02b73518c9201f1a29e8e54","score":"0.49506062","text":"def atrophyMuscles(self):\n for dna_file in os.listdir(\"./population\"):\n if dna_file.endswith(\".vxa\"):\n print dna_file\n\n tree = ET.ElementTree(file=\"./population/\" + dna_file)\n root = tree.getroot()\n layers = root.find('VXC').find('Structure').find('Data').findall('Layer')\n\n \"\"\"Calculates probability based on amount of fat tissue\n \"\"\"\n dna = \"\"\n for layer in layers:\n dna += str(layer.text).strip()\n count_soft = dna.count('1')\n probability = (count_soft / 1000) * 0.5 # or 0.0005 * count_soft\n\n dna_list = \"\"\n for layer in layers:\n dna_list = list(layer.text.strip())\n position_to_check = 0\n for index in range(len(dna_list)):\n tissue = dna_list[index]\n if tissue == '3' or tissue == '4':\n if random.random() <= probability:\n dna_list[index] = '2'\n print \"Atrophied a muscle at index \" + str(index) + \" to \" + str(dna_list[index]) + \".\"\n else:\n print \"Muscle at index \" + str(index) + \" unchanged.\"\n pass\n\n else:\n continue\n\n layer.text = self.concat(dna_list)\n tree.write(\"./population/\" + dna_file)","title":""},{"docid":"addbeeaeeeded8b0f30128730d56692a","score":"0.49474025","text":"def upload_links(self, links):\n for link in links:\n self.client.chat_postMessage(channel='#general',\n text=link)","title":""},{"docid":"0d723e12b2b49128467dd37e96bcb6e2","score":"0.49461845","text":"def upload():\n\n\trun('twine upload dist/*')","title":""},{"docid":"0d723e12b2b49128467dd37e96bcb6e2","score":"0.49461845","text":"def upload():\n\n\trun('twine upload dist/*')","title":""},{"docid":"3a2defd4f537c019ed64372edc3bec42","score":"0.49407345","text":"def post(self):\n self.parse_paras()\n self.upload_files()\n\n self.convert_model()\n self.inference()","title":""},{"docid":"4799ed92d4977c91eff0e189c7e38dc2","score":"0.49329093","text":"def load_neurons(self, path, synapses):\n # return if not synapses\n if not len(synapses):\n return synapses\n ####\n # Get neurons from loaded synapses\n ####\n\n # All unqiue source nodes and their keys\n all_src, src_keys = np.unique(synapses.T[0], True)\n # All unique target nodes and their keys\n all_tgt, tgt_keys = np.unique(synapses.T[1], True)\n # Find keys for neurons that are never targets\n only_src = list(set(all_src) - set(all_tgt))\n src_dict = dict(zip(all_src, src_keys))\n src_keys = map(src_dict.get, only_src)\n # Get all neuron lists from synapse targets, sources\n neuron_tgt = np.delete(synapses[tgt_keys], 0, 1)\n neuron_src = np.delete(synapses[src_keys], 1, 1)\n # Get full neuron list from source and target\n neurons = np.r_[neuron_src, neuron_tgt]\n\n # Get file fields\n k_files = self.RUNTIME.DB.FILE\n # Get keywords for input file\n k_file = k_files.SOMA.VALUE\n k_file = os.path.join(path, k_file)\n\n if os.path.exists(k_file):\n msg = \"Loading neuron centers from {}\"\n log.info(msg.format(k_file))\n # Load the csv\n with open(k_file, 'r') as jf:\n # Keep a list of synapseless soma\n new_neurons = []\n # Add each new center point to database\n for soma in json.load(jf):\n # Make a numpy uint32 coorinate array\n k_soma = ['neuron_id', 'z', 'y', 'x']\n new_soma = np.uint32(map(soma.get, k_soma))\n soma_id = new_soma[0]\n # Find the correct ID\n neuron_ids = neurons.T[0]\n # If the soma has a synapse\n if soma_id in neuron_ids:\n # Insert into the correct ID\n soma_index = np.argwhere(neuron_ids == soma_id)[0][0]\n neurons[soma_index] = new_soma\n else:\n # Add new synapseless neuron\n new_neurons.append(new_soma)\n\n # Add new neurons to full neurons list\n if len(new_neurons):\n neurons = np.r_[neurons, new_neurons]\n\n # Add neurons to database\n self.add_neurons(path, neurons)\n return neurons","title":""},{"docid":"ecd01600483ca7aa118691363caccb2a","score":"0.49142516","text":"def createSongFile():","title":""},{"docid":"11fe13704089c535b2dca2c0afaad4df","score":"0.49095154","text":"def handle_upload(sock):\n dir_path = proto.read_field_str(sock)\n api_key = proto.read_field_str(sock)\n alg_id = proto.read_field_str(sock)\n if alg_id == '':\n alg_id = None\n try:\n gym.upload(dir_path, api_key=api_key, algorithm_id=alg_id)\n proto.write_field_str(sock, '')\n except gym.error.Error as exc:\n proto.write_field_str(sock, str(exc))\n sock.flush()","title":""},{"docid":"460cf264475fd61bcf35abfc80b5b12d","score":"0.49056706","text":"def upload(self, artifacts: List[Artifact]):\n pass","title":""},{"docid":"e978ba769406dacfa9026b7c6f2fad6c","score":"0.4897588","text":"def upload(self, data, files):\n print 'Uploading ...'\n\n try:\n r = requests.post(SKETCHFAB_API_URL, data=data, files=files, verify=False)\n except requests.exceptions.RequestException as e:\n print \"An error occured: {}\".format(e)\n return\n\n result = r.json()\n\n if r.status_code != requests.codes.created:\n print \"Upload failed with error: {}\".format(result)\n return\n\n model_uid = result['uid']\n model_url = SKETCHFAB_MODEL_URL + model_uid\n print \"Upload successful. Your model is being processed.\"\n print \"Once the processing is done, the model will be available at: {}\".format(model_url)\n\n return model_uid","title":""},{"docid":"8d3772638dd1e69ffe503c10cf9beb5e","score":"0.48903462","text":"def upload(request):\n\n # used to generate random unique id\n \n\n\n # settings for the file upload\n # you can define other parameters here\n # and check validity late in the code\n context = RequestContext(request)\n options = {\n # the maximum file size (must be in bytes)\n \"maxfilesize\": 2 * 2 ** 100, #\n # the minimum file size (must be in bytes)\n \"minfilesize\": 1 * 2 ** 10, # 1 Kb\n # the file types which are going to be allowed for upload\n # must be a mimetype\n \"acceptedformats\": (\n 'audio/basic',\n 'audio/L24',\n 'audio/mp3',\n 'application/octet-stream',\n 'audio/mp4',\n 'audio/mpeg',\n 'audio/ogg',\n 'audio/vorbis',\n 'audio/x-ms-wma',\n 'audio/x-ms-wax',\n 'audio/vnd.rn-realaudio',\n 'audio/vnd.wave',\n 'audio/webm'\n )\n }\n\n\n # POST request\n # meaning user has triggered an upload action\n if request.method == 'POST':\n # figure out the path where files will be uploaded to\n # PROJECT_ROOT is from the settings file\n temp_path = os.path.join(settings.MEDIA_ROOT, \"uploaded_music\")\n\n # if 'f' query parameter is not specified\n # file is being uploaded\n if not (\"f\" in request.GET.keys()): # upload file\n\n # make sure some files have been uploaded\n if not request.FILES:\n return HttpResponseBadRequest('Must upload a file')\n\n # make sure unique id is specified - VERY IMPORTANT\n # this is necessary because of the following:\n # we want users to upload to a unique directory\n # however the uploader will make independent requests to the server\n # to upload each file, so there has to be a method for all these files\n # to be recognized as a single batch of files\n # a unique id for each session will do the job\n \n\n # update the temporary path by creating a sub-folder within\n # the upload folder with the uid name\n\n # get the uploaded file\n file = request.FILES[u'files[]']\n\n # initialize the error\n # If error occurs, this will have the string error message so\n # uploader can display the appropriate message\n error = False\n\n # check against options for errors\n\n # file size\n if file.size > options[\"maxfilesize\"]:\n error = \"maxFileSize\"\n if file.size < options[\"minfilesize\"]:\n error = \"minFileSize\"\n # allowed file type\n if file.content_type not in options[\"acceptedformats\"]:\n error = \"acceptFileTypes\"\n\n\n # the response data which will be returned to the uploader as json\n response_data = {\n \"name\": file.name,\n \"size\": file.size,\n \"type\": file.content_type\n }\n\n # if there was an error, add error message to response_data and return\n if error:\n # append error message\n response_data[\"error\"] = error\n # generate json\n response_data = simplejson.dumps([response_data])\n # return response to uploader with error\n # so it can display error message\n return HttpResponse(response_data, mimetype='application/json')\n\n\n # make temporary dir if not exists already\n if not os.path.exists(temp_path):\n os.makedirs(temp_path)\n\n # get the absolute path of where the uploaded file will be saved\n # all add some random data to the filename in order to avoid conflicts\n # when user tries to upload two files with same filename\n filename = os.path.join(temp_path, str(file.name))\n # open the file handler with write binary mode\n \n\n destination = open(filename, \"wb+\")\n # save file data into the disk\n # use the chunk method in case the file is too big\n # in order not to clutter the system memory\n for chunk in file.chunks():\n destination.write(chunk)\n # close the file\n destination.close()\n\n\n print filename\n\n\n #use this for EchoNest\n #music_response = music_identify(filename)\n\n #use this for Acoustid + fpcalc + chromaprint\n music_response = aidmatch(filename)\n\n print music_response\n\n\n\n\n #################################################\n ####### ACOUSTID PARSING\n #################################################\n song_list=[]\n for elt in music_response:\n song_list.append(elt[1])\n\n song_list = list(set(song_list))\n\n song_list = song_list[:3]\n\n\n song_artist_name = music_response[0][0]\n song_name = music_response[0][1]\n artist_link = 'http://musicbrainz.org/recording/' + music_response[0][2]\n\n #################################################\n ####### END ACOUSTID PARSING\n #################################################\n\n\n\n\n\n \"\"\"\n # ##############################################\n ####### ECHONEST PARSING\n #################################################\n response_data = json.loads(music_response) \n print response_data\n \n song_name = response_data['response']['songs'][0]['title']\n print song_name\n song_artist_name = response_data['response']['songs'][0]['artist_name']\n print song_artist_name\n song_artist_id = response_data['response']['songs'][0]['artist_id']\n print song_artist_id\n song_id = response_data['response']['songs'][0]['id']\n print song_id\n\n # ##############################################\n ####### END ECHONEST PARSING\n #################################################\n \"\"\"\n\n\n #extract genre from file\n #if not found, then query Echonest\n from hsaudiotag import mpeg\n audio = mpeg.Mpeg(filename)\n artist_genre=\"\"\n song_genre=\"\"\n import string\n metadata_genre = string.lower(str(audio.tag.genre))\n if(metadata_genre in glist):\n #\n #artist_genre = metadata_genre + \" meta\"\n\n song_genre = metadata_genre.lower().strip()\n else: \n # use this for Acoustid + pygn\n song_genre = get_gracenote_genre(song_artist_name, song_name).lower().strip()\n\n # use this for EchoNest\n #artist_genre = genre_query(song_artist_id)\n # use this for EchoNest\n #print artist_genre \n\n # use this for Acoustid + pygn\n print song_genre \n if song_genre == \"urban\":\n song_genre = \"hip-hop\"\n\n\n\n results = []\n user_list = []\n send_user_list = []\n if request.user and str(request.user) != \"AnonymousUser\":\n print \"1\"\n print type(request.user)\n print type(str(request.user))\n print str(request.user)\n print \"1 end\"\n all_user = User.objects.all().values_list('username', flat=True) \n user = User.objects.get(username=request.user)\n try:\n userprofile = Profile.objects.get(user=user)\n if userprofile.genre:\n if not song_genre in userprofile.genre:\n userprofile.genre.append(song_genre)\n else:\n print \"No genre in profile\"\n if not song_genre in userprofile.genre:\n userprofile.genre.append(song_genre)\n userprofile.save()\n lst_genre = userprofile.genre\n for one_user in all_user:\n #print one_user\n for genre in lst_genre:\n #print genre\n two_user = User.objects.get(username=request.user)\n #print \"1\"\n one_user_profile = Profile.objects.get(user=two_user)\n #print \"2\"\n if genre in one_user_profile.genre:\n if one_user not in user_list:\n user_list.append(one_user)\n else:\n print \"Some crazy error\"\n for send_user in user_list:\n sed_user = User.objects.get(username=send_user)\n sed_user_profile = Profile.objects.get(user=sed_user)\n send_user_list.append({\n \"user\":sed_user,\n \"profile\":sed_user_profile\n })\n print user_list\n print userprofile.genre\n except:\n print \"userprofile exception\"\n userprofile = None\n else:\n print \"user not logged in\"\n\n \n\n # use for Acoustid\n results.append({\n 'artist':song_artist_name,\n 'title':song_list,\n 'artist_link':artist_link,\n 'song_genre':song_genre\n })\n\n\n \"\"\"\n # use for EchoNest\n results.append({\n 'artist':song_artist_name,\n 'title':song_name,\n 'artist_id':song_artist_id,\n 'song_id':song_id,\n 'artist_genre':artist_genre\n })\n \"\"\"\n\n # QUIRK HERE\n # in jQuey uploader, when it falls back to uploading using iFrames\n # the response content type has to be text/html\n # if json will be send, error will occur\n # if iframe is sending the request, it's headers are a little different compared\n # to the jQuery ajax request\n # they have different set of HTTP_ACCEPT values\n # so if the text/html is present, file was uploaded using jFrame because\n # that value is not in the set when uploaded by XHR\n if \"text/html\" in request.META[\"HTTP_ACCEPT\"]:\n response_type = \"text/html\"\n\n # return the data to the uploading plugin\n return render_to_response('userena/music_search.html',{'result_list':results, 'send_user_list':send_user_list, 'request_user':str(request.user) },context)\n\n\n else: # file has to be deleted\n\n # get the file path by getting it from the query (e.g. '?f=filename.here')\n filepath = os.path.join(temp_path, request.GET[\"f\"])\n\n # make sure file exists\n # if not return error\n if not os.path.isfile(filepath):\n return HttpResponseBadRequest(\"File does not exist\")\n\n # delete the file\n # this step might not be a secure method so extra\n # security precautions might have to be taken\n os.remove(filepath)\n\n # generate true json result\n # in this case is it a json True value\n # if true is not returned, the file will not be removed from the upload queue\n response_data = simplejson.dumps(True)\n\n # return the result data\n # here it always has to be json\n return HttpResponse(response_data, mimetype=\"application/json\")\n\n else: #GET\n # load the template\n return render_to_response('userena/upload.html',{},context)","title":""},{"docid":"98faf087a00f6ffc6f6a9b2dfe41c81c","score":"0.48885685","text":"def uploadresult(testroot):","title":""},{"docid":"1f3fc851ed7187c29699ea453d676310","score":"0.48696217","text":"def upload_all(self):\n self.upload_list(self.jobs)","title":""},{"docid":"837dade2bab8dbc206936e32fb468da1","score":"0.48632708","text":"def upload_files(cls, files, **kwargs):\n cls.REMOTE_MANAGER.upload_files(files, **kwargs)","title":""},{"docid":"c849df977028b9e043e781a7c4fd397c","score":"0.48606706","text":"def on_btn_upload(self, event):\n if not self.check_for_uncombined_files():\n return\n outstring=\"upload_magic.py\"\n print(\"-I- running python script:\\n %s\"%(outstring))\n wait = wx.BusyInfo(\"Please wait, working...\")\n wx.SafeYield()\n success_responses = ['200', 200, '201', 201, True]\n if 'measurements' in self.contribution.tables:\n self.contribution.tables['measurements'].add_measurement_names()\n upload_file, val_response, dummy1, dummy2 = ipmag.upload_magic(concat=False, input_dir_path=self.WD, dir_path=self.WD)\n del wait\n if val_response == \"no 3.0 files found, upload file not created\":\n pw.simple_warning(\"No 3.0 files were found in your directory, so no upload could be created!\")\n return\n status = val_response['status']\n if not status:\n pw.simple_warning(\"Oops, something went wrong with validating on the server.\\n{}\\nTry again later or submit a bug report.\".format(val_response['warnings']))\n return\n validation_errors = val_response['validation']\n if (not validation_errors['warnings']) and (not validation_errors['errors']):\n text = \"You are ready to upload!\\n{} was generated in {}\".format(os.path.split(upload_file)[1], self.WD)\n dlg = pw.ChooseOne(self, \"Go to MagIC for uploading\", \"Not ready yet\", text, \"Saved\")\n\n dlg.Centre()\n result = dlg.ShowModal()\n if result == wx.ID_OK:\n dlg.Destroy()\n if result == wx.ID_YES:\n pw.on_database_upload(None)\n return\n\n # there were problems, so display validation\n text = \"There were some problems with the creation of your upload file.\\nSee Terminal/message window for details\"\n dlg = wx.MessageDialog(self, caption=\"Error\", message=text, style=wx.OK)\n dlg.Centre()\n result = dlg.ShowModal()\n\n # TODO: get the error-y business formatted into a dict of lists of dicts\n from programs import magic_gui\n self.Disable()\n self.Hide()\n self.magic_gui_frame = magic_gui.MainFrame(self.WD,\n dmodel=self.data_model,\n title=\"Validations\",\n contribution=self.contribution,\n errors=validation_errors['errors'])\n self.magic_gui_frame.Centre()\n self.magic_gui_frame.Show()\n self.magic_gui_frame.highlight_problems()\n # bind that button to quitting magic gui and re-enabling Pmag GUI\n self.magic_gui_frame.Bind(wx.EVT_BUTTON, self.on_end_validation, self.magic_gui_frame.return_btn)\n # do binding so that closing/quitting re-opens the main frame\n self.magic_gui_frame.Bind(wx.EVT_CLOSE, self.on_end_validation)\n # this makes it work with only the validation window open\n self.magic_gui_frame.Bind(wx.EVT_MENU,\n lambda event: self.menubar.on_quit(event, self.magic_gui_frame),\n self.magic_gui_frame.menubar.file_quit)\n # this makes it work if an additional grid is open\n self.Bind(wx.EVT_MENU,\n lambda event: self.menubar.on_quit(event, self.magic_gui_frame),\n self.magic_gui_frame.menubar.file_quit)","title":""},{"docid":"70d1368f3f6f361d4c0bab5c77e7fc7e","score":"0.48543638","text":"def gather_audio_files():","title":""},{"docid":"c4a8bc45ae6535179fb547c92d7c961d","score":"0.48456827","text":"def upload(self):\n\n # FIXME wrap this in a try except\n\n self._create()\n self._update()\n for filename in self._file_list:\n self._upload(filename)\n self._publish()\n\n # and in the except, delete the partial upload as it is broken\n # - particularly to catch Ctrl-C","title":""},{"docid":"7391228c8c4255b79843ba3233b0ef79","score":"0.48414555","text":"def upload(self, results):\n raise NotImplementedError(\"Uploaders should implement .upload.\")","title":""},{"docid":"bb644156b3718948ba8a65178d5b2ba8","score":"0.48384976","text":"def import_from_music_ontology():\n \n file = open('instruments.txt')\n search_terms = file.readlines()\n results = []\n for search_term in search_terms:\n payload = {'query': search_term, 'limit': '1', 'fmt':'json'}\n query = 'http://musicbrainz.org/ws/2/instrument?'\n try:\n r = requests.get(query, params=payload).json()\n except:\n pass\n try:\n url = 'musicbrainz.org/instrument/' + r['instruments'][0]['id']\n results.append((r['instruments'][0]['name'], url))\n print((r['instruments'][0]['name'], url))\n except:\n print('empty: ', search_term)\n pass\n sleep(5)\n file.close()\n with open('instrumentLinks.txt', 'w') as fp:\n fp.write('\\n'.join('%s %s' % x for x in results))","title":""},{"docid":"f7aa01ae264961660ec977587c70aa33","score":"0.48352036","text":"def upload(self,\n filepaths,\n enable_matching=False,\n enable_transcoding=True,\n transcode_quality='320k',\n include_album_art=True,\n ):\n\n if self.uploader_id is None or self.uploader_name is None:\n raise NotLoggedIn(\"Not authenticated as an upload device;\"\n \" run Api.login(...perform_upload_auth=True...)\"\n \" first.\")\n\n # TODO there is way too much code in this function.\n\n # To return.\n uploaded = {}\n matched = {}\n not_uploaded = {}\n\n # Gather local information on the files.\n local_info = {} # {clientid: (path, Track)}\n for path in filepaths:\n try:\n track = musicmanager.UploadMetadata.fill_track_info(path)\n except BaseException as e:\n self.logger.warning(\"problem gathering local info of '%r'\", path)\n\n user_err_msg = str(e)\n\n if 'Non-ASCII strings must be converted to unicode' in str(e):\n # This is a protobuf-specific error; they require either ascii or unicode.\n # To keep behavior consistent, make no effort to guess - require users\n # to decode first.\n user_err_msg = (\"nonascii bytestrings must be decoded to unicode\"\n \" (error: '%s')\" % user_err_msg)\n\n not_uploaded[path] = user_err_msg\n else:\n local_info[track.client_id] = (path, track)\n\n if not local_info:\n return uploaded, matched, not_uploaded\n\n # TODO allow metadata faking\n\n # Upload metadata; the server tells us what to do next.\n res = self._make_call(musicmanager.UploadMetadata,\n [t for (path, t) in local_info.values()],\n self.uploader_id)\n\n # TODO checking for proper contents should be handled in verification\n md_res = res.metadata_response\n\n responses = [r for r in md_res.track_sample_response]\n sample_requests = [req for req in md_res.signed_challenge_info]\n\n # Send scan and match samples if requested.\n for sample_request in sample_requests:\n path, track = local_info[sample_request.challenge_info.client_track_id]\n\n bogus_sample = None\n if not enable_matching:\n bogus_sample = b'' # just send empty bytes\n\n album_art_image = None\n if include_album_art:\n if include_album_art is True: # Load embedded album art.\n song = mutagen.File(path)\n\n if isinstance(song, (mutagen.mp3.MP3)) and song.tags is not None:\n # Search through all the APIC frames to find the cover (type 3).\n for pic in song.tags.getall('APIC'):\n if pic.type == 3:\n album_art_image = pic.data\n elif isinstance(song, mutagen.flac.FLAC):\n # Search through all the picture frames to find the cover (type 3).\n for pic in song.pictures:\n if pic.type == 3:\n album_art_image = pic.data\n break\n elif isinstance(song, mutagen.mp4.MP4):\n if 'covr' in song:\n album_art_image = song['covr'][0]\n elif isinstance(song, mutagen.asf.ASF):\n if 'WM/Picture' in song:\n # Search through all the WM/Picture frames to find the cover (type 3).\n for pic in song['WM/Picture']:\n data = bytes(pic)\n if data[0] == 3:\n # Parse out the image data according to the WM_PICTURE spec:\n # 1 byte type + 4 bytes data length + null-terminated mime +\n # null-terminated description + data\n pos = 5\n while data[pos:pos + 2] != b\"\\x00\\x00\":\n pos += 2\n pos += 2\n while data[pos:pos + 2] != b\"\\x00\\x00\":\n pos += 2\n\n album_art_image = data[pos + 2:]\n break\n elif isinstance(song, mutagen.oggvorbis.OggVorbis):\n if 'metadata_block_picture' in song:\n # Search through all the picture frames to find the cover (type 3).\n for pic in song['metadata_block_picture']:\n # Mutagen does not parse out the picture fields to attributes\n # like with FLAC, so we use the FLAC Picture class to do so.\n # Picture blocks are base64 encoded in Ogg Vorbis.\n picture = mutagen.flac.Picture(base64.b64decode(pic))\n\n if picture.type == 3:\n album_art_image = picture.data\n break\n else: # Load external album art.\n try:\n with open(include_album_art, 'rb') as f:\n album_art_image = f.read()\n except OSError:\n self.logger.warning(\n \"Image file: %r cannot be read. Uploading %r without album art.\",\n include_album_art, path\n )\n\n try:\n res = self._make_call(\n MyProvideSample,\n path,\n sample_request,\n track,\n self.uploader_id,\n bogus_sample,\n album_art_image\n )\n\n except (IOError, ValueError) as e:\n self.logger.warning(\"couldn't create scan and match sample for '%r': %s\", path, str(e))\n not_uploaded[path] = str(e)\n else:\n responses.extend(res.sample_response.track_sample_response)\n\n # Read sample responses and prep upload requests.\n to_upload = {} # {serverid: (path, Track, do_not_rematch?)}\n for sample_res in responses:\n path, track = local_info[sample_res.client_track_id]\n\n if sample_res.response_code == upload_pb2.TrackSampleResponse.MATCHED:\n self.logger.info(\"matched '%r' to sid %s\", path, sample_res.server_track_id)\n\n matched[path] = sample_res.server_track_id\n\n if not enable_matching:\n self.logger.error(\"'%r' was matched without matching enabled\", path)\n\n elif sample_res.response_code == upload_pb2.TrackSampleResponse.UPLOAD_REQUESTED:\n to_upload[sample_res.server_track_id] = (path, track, False)\n\n else:\n # there was a problem\n # report the symbolic name of the response code enum for debugging\n enum_desc = upload_pb2._TRACKSAMPLERESPONSE.enum_types[0]\n res_name = enum_desc.values_by_number[sample_res.response_code].name\n\n err_msg = \"TrackSampleResponse code %s: %s\" % (sample_res.response_code, res_name)\n\n if res_name == 'ALREADY_EXISTS':\n # include the sid, too\n # this shouldn't be relied on externally, but I use it in\n # tests - being surrounded by parens is how it's matched\n err_msg += \"(%s)\" % sample_res.server_track_id\n\n self.logger.warning(\"upload of '%r' rejected: %s\", path, err_msg)\n\n if res_name == 'ALREADY_EXISTS':\n matched[path] = sample_res.server_track_id\n else:\n not_uploaded[path] = err_msg\n\n # Send upload requests.\n if to_upload:\n # TODO reordering requests could avoid wasting time waiting for reup sync\n self._make_call(musicmanager.UpdateUploadState, 'start', self.uploader_id)\n\n for server_id, (path, track, do_not_rematch) in to_upload.items():\n # It can take a few tries to get an session.\n should_retry = True\n attempts = 0\n\n while should_retry and attempts < 10:\n session = self._make_call(musicmanager.GetUploadSession,\n self.uploader_id, len(uploaded),\n track, path, server_id, do_not_rematch)\n attempts += 1\n\n got_session, error_details = \\\n musicmanager.GetUploadSession.process_session(session)\n\n if got_session:\n self.logger.info(\"got an upload session for '%r'\", path)\n break\n\n should_retry, reason, error_code = error_details\n self.logger.debug(\"problem getting upload session: %s\\ncode=%s retrying=%s\",\n reason, error_code, should_retry)\n\n if error_code == 200 and do_not_rematch:\n # reupload requests need to wait on a server sync\n # 200 == already uploaded, so force a retry in this case\n should_retry = True\n\n time.sleep(6) # wait before retrying\n else:\n err_msg = \"GetUploadSession error %s: %s\" % (error_code, reason)\n\n self.logger.warning(\"giving up on upload session for '%r': %s\", path, err_msg)\n not_uploaded[path] = err_msg\n\n continue # to next upload\n\n # got a session, do the upload\n # this terribly inconsistent naming isn't my fault: Google--\n session = session['sessionStatus']\n external = session['externalFieldTransfers'][0]\n\n session_url = external['putInfo']['url']\n content_type = external.get('content_type', 'audio/mpeg')\n\n if track.original_content_type != locker_pb2.Track.MP3:\n if enable_transcoding:\n try:\n self.logger.info(\"transcoding '%r' to mp3\", path)\n contents = utils.transcode_to_mp3(path, quality=transcode_quality)\n except (IOError, ValueError) as e:\n self.logger.warning(\"error transcoding %r: %s\", path, e)\n not_uploaded[path] = \"transcoding error: %s\" % e\n continue\n else:\n not_uploaded[path] = \"transcoding disabled\"\n continue\n else:\n with open(path, 'rb') as f:\n contents = f.read()\n\n upload_response = self._make_call(musicmanager.UploadFile,\n session_url, content_type, contents)\n\n success = upload_response.get('sessionStatus', {}).get('state')\n if success:\n uploaded[path] = server_id\n else:\n # 404 == already uploaded? serverside check on clientid?\n self.logger.debug(\"could not finalize upload of '%r'. response: %s\",\n path, upload_response)\n not_uploaded[path] = 'could not finalize upload; details in log'\n\n self._make_call(musicmanager.UpdateUploadState, 'stopped', self.uploader_id)\n\n return uploaded, matched, not_uploaded","title":""},{"docid":"6b7c67e7e2bed5a87afd69ffb5455d3b","score":"0.4830331","text":"def upload_crew(session: Session, data: Iterable, movie_id: int) -> Iterator:\n\n for item in data:\n crew_member_data = prepare_actor_or_crew_member_data(item)\n crew_member = models.CrewMember.get_or_create(session, **crew_member_data)\n yield crew_member\n crew_data = prepare_crew_data(item, crew_member, movie_id)\n crew = models.Crew.create(**crew_data)\n yield crew","title":""},{"docid":"98c60413414ae253b42bdd12833d23a1","score":"0.48278242","text":"def uploadChanges(self):\n self.asicConf.setModified(1);\n self.difConf.setModified(1);\n self.dccConf.setModified(0)\n try:\n self.setup.recursiveUpload();\n except Exception, e:\n print e.getMessage()","title":""},{"docid":"98c60413414ae253b42bdd12833d23a1","score":"0.48278242","text":"def uploadChanges(self):\n self.asicConf.setModified(1);\n self.difConf.setModified(1);\n self.dccConf.setModified(0)\n try:\n self.setup.recursiveUpload();\n except Exception, e:\n print e.getMessage()","title":""},{"docid":"3d9b02882049f6c987292d48f8fb45f3","score":"0.48141667","text":"def upload(self):\n for root, dirs, files in os.walk(self.__PATH__):\n for filename in files:\n if not filename.startswith(\".\"):\n local_p = os.path.join(root, filename)\n keyname = re.sub(r'files\\/', '', local_p)\n self.__upload_file(local_p, keyname)","title":""},{"docid":"6bd678432fbd4be949cf5d5a9effeee4","score":"0.4813621","text":"def getServerUpload(self, file):\n\n x = self.makeSHAFile( file)\n x['op'] = 'upload'\n\n self.socket.send_json(x)\n #print(x)\n\n resp = self.socket.recv_json()\n if(resp['resp']):\n \"\"\"\n res['resp'] = True\n res['info'] = \"se guardo el archivo empiece la descarga\"\n #res['hashfile'] = datajson['hashfile']\n res['trozos'] = self.UpdateFilesDist(datajson)\n \"\"\"\n print(resp['info'])\n #print(\"elementos enviados \", len(x['trozos']),\" :\\n\", x['trozos'])\n #print(\"elementos enviados \", len(resp['trozos']),\" :\\n\",resp['trozos'])\n \n #enviar todos los elementos\n self.uploadAllFiles( resp,file)\n\n else:\n print(resp['info'])","title":""},{"docid":"45d0c63c96f2489f443fd669512ebf1e","score":"0.48120642","text":"def test_1(self):\n \n chemFile = os.path.join(os.path.dirname(rmgweb.__file__),'tests','files','kinetics','chem.inp')\n dictFile = os.path.join(os.path.dirname(rmgweb.__file__),'tests','files','kinetics','species_dictionary.txt')\n \n with open(chemFile) as cf, open(dictFile) as df:\n response = self.client.post('/tools/java_kinetics_library/', {'ChemkinFile': cf, 'DictionaryFile': df})\n \n self.assertEqual(response.status_code, 200)\n \n folder = os.path.join(settings.MEDIA_ROOT,'rmg','tools','chemkin')\n \n # Check if inputs were correctly uploaded\n chemInput = os.path.join(folder,'chemkin','chem.inp')\n dictInput = os.path.join(folder,'RMG_Dictionary.txt')\n \n self.assertTrue(os.path.isfile(chemInput),'Chemkin file was not uploaded')\n self.assertTrue(os.path.isfile(dictInput),'Dictionary file was not uploaded')\n \n # Check if outputs were correctly generated\n reactions = os.path.join(folder,'reactions.txt')\n pdep = os.path.join(folder,'pdepreactions.txt')\n species = os.path.join(folder,'species.txt')\n \n self.assertTrue(os.path.isfile(reactions),'Reactions file was not generated')\n self.assertTrue(os.path.isfile(pdep),'Pdep reactions file was not generated')\n self.assertTrue(os.path.isfile(species),'Species file was not generated')\n \n shutil.rmtree(folder)","title":""},{"docid":"f8da8002fdab768f3acb602de878d1dd","score":"0.48069826","text":"def load_mus_tracks(self, subsets=None, split=None):\n\n if subsets is not None:\n if isinstance(subsets, str):\n subsets = [subsets]\n else:\n subsets = ['train', 'test']\n\n if subsets != ['train'] and split is not None:\n raise RuntimeError(\"Subset has to set to `train` when split is used\")\n\n tracks = []\n for subset in subsets: \n subset_folder = op.join(self.root, subset)\n\n for _, folders, files in os.walk(subset_folder):\n if self.is_wav:\n # parse pcm tracks and sort by name\n for track_name in sorted(folders):\n if subset == 'train':\n if split == 'train' and track_name in self.setup['validation_tracks']:\n continue\n elif split == 'valid' and track_name not in self.setup['validation_tracks']:\n continue\n\n track_folder = op.join(subset_folder, track_name)\n # create new mus track\n track = MultiTrack(\n name=track_name,\n path=op.join(\n track_folder,\n self.setup['mixture']\n ),\n subset=subset,\n is_wav=self.is_wav,\n stem_id=self.setup['stem_ids']['mixture'],\n sample_rate=self.sample_rate\n )\n\n # add sources to track\n sources = {}\n for src, source_file in list(\n self.setup['sources'].items()\n ):\n # create source object\n abs_path = op.join(\n track_folder,\n source_file\n )\n if os.path.exists(abs_path):\n sources[src] = Source(\n track,\n name=src,\n path=abs_path,\n stem_id=self.setup['stem_ids'][src],\n sample_rate=self.sample_rate\n )\n track.sources = sources\n track.targets = self.create_targets(track)\n\n # add track to list of tracks\n tracks.append(track)\n else:\n # parse stem files\n for track_name in sorted(files):\n if not track_name.endswith('.stem.mp4'):\n continue\n if subset == 'train':\n if split == 'train' and track_name.split('.stem.mp4')[0] in self.setup['validation_tracks']:\n continue\n elif split == 'valid' and track_name.split('.stem.mp4')[0] not in self.setup['validation_tracks']:\n continue\n\n # create new mus track\n track = MultiTrack(\n name=track_name.split('.stem.mp4')[0],\n path=op.join(subset_folder, track_name),\n subset=subset,\n stem_id=self.setup['stem_ids']['mixture'],\n is_wav=self.is_wav,\n sample_rate=self.sample_rate\n )\n # add sources to track\n sources = {}\n for src, source_file in list(\n self.setup['sources'].items()\n ):\n # create source object\n abs_path = op.join(\n subset_folder,\n track_name\n )\n if os.path.exists(abs_path):\n sources[src] = Source(\n track,\n name=src,\n path=abs_path,\n stem_id=self.setup['stem_ids'][src],\n sample_rate=self.sample_rate\n )\n track.sources = sources\n\n # add targets to track\n track.targets = self.create_targets(track)\n tracks.append(track)\n\n return tracks","title":""},{"docid":"43b9c1b3d6449000eb5a7e2d837d2066","score":"0.4806059","text":"def _upload_new_documents(self):\n \n self.document_file.flush()\n self.document_file.seek(0)\n self.corpus.upload_csv(self.document_file, 'documents')\n self.document_file.close()\n self.document_file = tempfile.TemporaryFile()\n self.document_writer = csv.writer(self.document_file)\n\n self.occurrence_file.flush()\n self.occurrence_file.seek(0)\n self.corpus.upload_csv(self.occurrence_file, 'phrase_occurrences')\n self.occurrence_file.close()\n self.occurrence_file = tempfile.TemporaryFile()","title":""},{"docid":"564c5dc2f6ef8f4e8ced986f79659f53","score":"0.48036733","text":"def upload(self, file):\r\n\t\traise NotImplementedError()","title":""},{"docid":"310fbcbac9a8f784784dc5d432f5ed82","score":"0.4803459","text":"def send_data_files():\r\n filelist = files.split(',')\r\n filenamelist = filenames.split(',')\r\n pathname = os.path.dirname(sys.argv[0])\r\n gi = get_galaxy_instance(url, key)\r\n new_hist_name = assayname\r\n gi.histories.create_history(name=new_hist_name)\r\n historyid = get_history_id(url, key)\r\n for nr in range(len(filelist)):\r\n newfile = os.path.abspath(pathname) + \"/filestore/tmp/\" + filenamelist[nr]\r\n call([\"cp \" + filelist[nr] + \" \" + newfile], shell=True)\r\n gi.tools.upload_file(newfile, historyid)\r\n call([\"rm \" + newfile], shell=True)","title":""},{"docid":"f7c66fc20e07561c548eb29f9bfbadff","score":"0.48026258","text":"def UploadFile(self):\n logging.debug('SimianAuthCliClient.UploadFile %s', self.config['uploadfile'])\n for file_path in self.config['uploadfile']:\n file_type = self.config['uploadfiletype']\n try:\n self.client.UploadFile(file_path, file_type)\n except client.SimianServerError, e:\n logging.exception('Error uploading log: %s', file_path)","title":""},{"docid":"7633b0af5e0df182c09403ca27f598a6","score":"0.4796921","text":"def initSongs():\n global songs\n mp3_filenames = os.listdir(\"MP3s\")\n \n for mp3_filename in mp3_filenames:\n mp3_path = \"MP3s/\" + mp3_filename\n wav_filename = os.path.splitext(mp3_filename)[0] + \".wav\" # wav filename is the same as mp3 except the extension\n wav_path = \"Wavs/\" + wav_filename\n \n # construct and run ffmpeg command\n input_mp3 = Input(mp3_path)\n output_wav = Output(wav_path, AudioCodec(\"pcm_mulaw\"))\n opt_dict = dict([(\"-ar\", \"8000\"), (\"-ac\", \"1\"), (\"-ab\", \"64000\")]) # sampling rate 8000 Hz, 1 audio channel (mono), bitrate 64kbits/s\n opt = options.Option(opt_dict)\n ffmpeg_command = FFmpeg(\"ffmpeg\", input_mp3, opt, output_wav)\n ffmpeg_command.run()\n print \"Playlist Server: File created: '\" + wav_filename + \"'\"\n \n mp3header = eyeD3.Mp3AudioFile(mp3_path)\n length = str(mp3header.getPlayTime())\n tag = eyeD3.Tag()\n tag.link(mp3_path)\n song = Song(length, tag.getArtist(), tag.getTitle(), wav_path)\n songs.append(song)","title":""},{"docid":"f04a5eb3b6057a9673c500b017a50712","score":"0.479319","text":"def upload_instant(name, filepath):\n\n # Create session and get cookies\n session = requests.session()\n r = session.get(LOGIN_URL)\n\n soup = BeautifulSoup(r.text, \"html.parser\")\n token = soup.find(\"input\", {'name': 'csrfmiddlewaretoken'}).get('value')\n\n if not token:\n raise InvalidPageErrorException\n\n data = {\n \"csrfmiddlewaretoken\": token,\n \"login\": os.environ[\"MYINSTANTS_USERNAME\"],\n \"password\": os.environ[\"MYINSTANTS_PASSWORD\"],\n \"next\": \"/new/\",\n }\n\n # Login\n r = session.post(LOGIN_URL, data=data)\n\n if r.status_code != 200:\n raise LoginErrorException\n\n # Get upload token\n soup = BeautifulSoup(r.text, \"html.parser\")\n token = soup.find(\"input\", {'name': 'csrfmiddlewaretoken'}).get('value')\n\n if not token:\n raise InvalidPageErrorException\n\n filename = os.path.basename(filepath)\n\n multipart_data = MultipartEncoder(\n fields={\n \"csrfmiddlewaretoken\": token,\n \"name\": name,\n \"sound\": (\n filename,\n open(filepath, \"rb\"),\n \"audio/mpeg\",\n ),\n \"image\": (\"\", None, \"\"),\n \"color\": \"00FF00\",\n \"category\": \"\",\n \"description\": \"\",\n \"tags\": \"\",\n \"accept_terms\": \"on\",\n }\n )\n\n # Upload sound\n response = session.post(\n UPLOAD_URL,\n data=multipart_data,\n headers={\"Content-Type\": multipart_data.content_type, \"Referer\": UPLOAD_URL},\n )\n\n soup = BeautifulSoup(response.text, \"html.parser\")\n for ul_obj in soup.find_all(\"ul\", class_=\"errorlist\"):\n if ul_obj.text.lower().find(\"instant with this name already exists.\") >= 0:\n raise NameAlreadyExistsException\n if ul_obj.text.lower().find(\"please keep filesize under 300.0 kb\") >= 0:\n raise FileSizeException\n if response.status_code != 200:\n raise HTTPErrorException\n\n soup = BeautifulSoup(response.text, \"html.parser\")\n last_uploaded_element = soup.find_all(\"a\", class_=\"instant-link\", text=name)\n if not last_uploaded_element:\n return response.url\n\n # Return sound url\n url = last_uploaded_element[-1].get(\"href\")\n return urljoin(response.url, url)","title":""},{"docid":"9692d8c6244b40427fbc3e61b6ce6b36","score":"0.47920832","text":"def connectionMade(self):","title":""},{"docid":"33bb011500a2bd4b971fd8b1e619b380","score":"0.4785044","text":"async def playall ():\n global song\n global player\n global construction\n global midiCounter\n global midifile\n mf = MIDIFile(1)\n midiCounter = 0\n await bot.say(\"player ready\")\n for part in construction:\n await bot.say(str(part) + \"...\")\n if part in song:\n playChordList(song[part],isTierce,mf)\n midifile = mf\n path = os.path.abspath(\"output.mid\")\n with open(\"output.mid\", 'wb') as outf:\n mf.writeFile(outf)\n\t#fs = FluidSynth()\n #with open(path, 'rb+') as inf: \n # with open(\"output.wav\", 'wb+') as wavf: \n # fs.midi_to_audio(inf, wavf)\n \n #path = str(os.getcwd()) + str(os.sep)\n #pathMidi = path + \"output.mid\"\n #pathWav = path + \"out.wav\"\n \n #await bot.say(\"midi Path : \" + pathMidi)\n #await bot.say(\"wav path : \" + pathWav)\n \n #fs = FluidSynth()\n #fs.midi_to_audio(pathMidi, pathWav)\n \n await bot.say(\"song played\")","title":""},{"docid":"9d65a3be09ab1b5bc035f02ea2ce4d2d","score":"0.47824302","text":"def handle_upload(corpus, f, fileattrs):\n\t# logger.info(fileattrs)\n\n\t# dest_folder = os.path.join(settings.UPLOAD_DIR, fileattrs['qquuid'])\n\t# dest = os.path.join(dest_folder, fileattrs['qqfilename'])\n\tqqtotalparts = int(fileattrs['qqtotalparts'] or 1)\n\tqqpartindex = int(fileattrs['qqpartindex'] or 0)\n\n\t# # Chunked\n\t# if qqtotalparts > 1:\n\t# \tdest_folder = os.path.join(settings.CHUNKS_DIR, fileattrs['qquuid'])\n\t# \tdest = os.path.join(dest_folder, fileattrs['qqfilename'], str(qqpartindex))\n\t# \t# logger.info('Chunked upload received')\n\n\tf.seek(0)\n\tmd5 = hashlib.md5(f.read()).hexdigest()\n\tf.seek(0)\n\tqquuid = fileattrs['qquuid']\n\tkey = {'size': f.size, 'md5': md5}\n\tfile_record = {'_id': ObjectId(qquuid), 'status': 0, 'nwords': 0}\n\tfile_record.update(key)\n\told_record = settings.DBC.common.files.find_one_and_update(key, {'$setOnInsert': file_record}, upsert=True)\n\tif not old_record:\n\t\tpaper_dest = paper_path(qquuid)\n\t\tutils.save_upload(f, paper_dest)\n\t\textractedResult = utils.extracted(paper_dest) # extract\n\t\tif extractedResult:\n\t\t\twith open(extracted_path(qquuid), 'w') as fout:\n\t\t\t\tfout.write(extractedResult)\n\t\t\trefinedResult = refine(extractedResult) # refine\n\t\t\tif refinedResult:\n\t\t\t\twith open(refined_path(qquuid), 'w') as fout:\n\t\t\t\t\tfout.write(refinedResult)\n\t\t\t\tstatus = 2\n\t\t\telse:\n\t\t\t\tstatus = -2\n\t\telse:\n\t\t\tstatus = -1\n\t\tsettings.DBC.common.files.find_one_and_update(file_record, {'$set': {'nwords': len(extractedResult.split()), 'status': status}})\n\n\t# logger.info('Upload saved: %s' % dest)\n\n\t# # If the last chunk has been sent, combine the parts.\n\t# if qqtotalparts > 1 and qqpartindex == (qqtotalparts - 1):\n\n\t# \t# logger.info('Combining chunks: %s' % os.path.dirname(dest))\n\t# \tutils.combine_chunks(qqtotalparts,\n\t# \t\tfileattrs['qqtotalfilesize'],\n\t# \t\tsource_folder=os.path.dirname(dest),\n\t# \t\tdest=os.path.join(settings.UPLOAD_DIR, fileattrs['qquuid'], fileattrs['qqfilename']))\n\t# \t# logger.info('Combined: %s' % dest)\n\n\t# \tshutil.rmtree(os.path.dirname(os.path.dirname(dest)))\n\n\t# # Upload done.\n\t# if qqpartindex == (qqtotalparts - 1):\n\t# \tpass\n\n\tfile_id = old_record and old_record['_id'] or ObjectId(qquuid)\n\ttitle = os.path.splitext(fileattrs['qqfilename'])[0]\n\tupload_record = {'file': file_id, 'corpus': corpus.pk, 'title': title}\n\tsettings.DBC.common.uploads.insert_one(upload_record)","title":""},{"docid":"ff918911b6fc26265fa731ec68a0916f","score":"0.47816557","text":"def upload(self):\n logger.info('Starting upload for {} file(s)'.format(len(self.__upload_queue)))\n while self.__upload_queue > []:\n file = self.__upload_queue.pop()\n if not self.client.check(os.path.dirname(file['remote'])):\n Util.webdav_mkdirs(os.path.dirname(file['remote']), '', self.client)\n logger.debug(f'Create missing directory {os.path.dirname(file[\"remote\"])}')\n logger.info('Uploading {local} to {remote}'.format(**file))\n self.client.upload_sync(remote_path=file['remote'], local_path=file['local'])\n logger.info('Finished uploading all files.')","title":""},{"docid":"a5d54a1bfe7a4490937b25ccd148fb94","score":"0.4781377","text":"def Test4(self):\r\n \r\n f= open('outputHighQuality.wav' ,\"rb\")\r\n myurl = 'http://localhost:5555/upload'\r\n header = { 'content_type': 'audio/wav' } \r\n x = requests.request('POST', url=myurl , data = f , headers=header ) \r\n print (x.text)\r\n print (x.status_code)\r\n if x.status_code == 200:\r\n self.music_id = x.content\r\n self.set_Logs(x.text)\r\n self.AssertOK()\r\n \r\n else:\r\n self.set_Logs(x.text)\r\n self.AssertKO()","title":""},{"docid":"e10183170a1a2f1a3873feaec6eb948a","score":"0.47785318","text":"def db_add_synapses(clear):\n graph = graph_p2()\n if clear:\n graph.delete_all()\n\n conns = preproc.conns_df()\n transx = graph.cypher.begin()\n statement = \"MATCH (n1:Neuron {name:{nm1}}), (n2:Neuron {name:{nm2}}) CREATE (n1)-[s:Synapse {name:n1.name+'->'+n2.name, type:{tp}, weight:{w}}]->(n2)\"\n for idx, row in conns.iterrows():\n transx.append(statement, {\"nm1\":row['Neuron1'], \"nm2\":row['Neuron2'], \"tp\":row[\"Type\"], \"w\":row[\"Nbr\"]})\n transx.commit()\n db_set_node_degrees()","title":""},{"docid":"3e6ce975c6e85d10af398ac56d3497c3","score":"0.477623","text":"def test_training_upload(self):\n\n logo_file = StringIO.StringIO(\n 'GIF87a\\x01\\x00\\x01\\x00\\x80\\x01\\x00\\x00\\x00\\x00ccc,\\x00'\n '\\x00\\x00\\x00\\x01\\x00\\x01\\x00\\x00\\x02\\x02D\\x01\\x00;')\n logo = SimpleUploadedFile(\n 'logo_test_file.gif', logo_file.read(), 'image/gif')\n manual_file = StringIO.StringIO(\n 'GIF87a\\x01\\x00\\x01\\x00\\x80\\x01\\x00\\x00\\x00\\x00ccc,\\x00'\n '\\x00\\x00\\x00\\x01\\x00\\x01\\x00\\x00\\x02\\x02D\\x01\\x00;')\n manual = SimpleUploadedFile(\n 'logo_test_file.gif', manual_file.read(), 'image/gif')\n\n c = Client()\n c.login(username='admin', password='admin')\n c.post(\n reverse('training_upload'),\n data={\n 'title': 'Uploaded Training',\n 'abstract': 'Abstract for uploaded training',\n 'publication_date': get_random_date(),\n 'logo': logo,\n 'manual': manual,\n },\n follow=True)\n\n self.assertEquals(Training.objects.all().count(), 1)\n\n training = Training.objects.all()[0]\n c = Client()\n response = c.get(reverse('training_detail', args=(str(training.id),)))\n self.assertEquals(response.status_code, 200)","title":""},{"docid":"e20802935133faa59138993d72ee8985","score":"0.4773659","text":"def test_can_retrieve_connected_user_post_media_video(self):\n user = make_user()\n connected_user = make_user()\n\n circle = make_circle(creator=connected_user)\n\n connected_user.connect_with_user_with_id(user_id=user.pk, circles_ids=[circle.pk])\n user.confirm_connection_with_user_with_id(user_id=connected_user.pk)\n\n headers = make_authentication_headers_for_user(user=user)\n\n test_video = get_test_video()\n\n with open(test_video['path'], 'rb') as file:\n file = File(file)\n post = connected_user.create_encircled_post(video=file, circles_ids=[circle.pk])\n\n get_worker('high', worker_class=SimpleWorker).work(burst=True)\n\n url = self._get_url(post=post)\n\n response = self.client.get(url, **headers, format='multipart')\n\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n response_media = json.loads(response.content)\n\n post.refresh_from_db()\n\n post_media = post.get_media().all()\n\n self._compare_response_media_with_post_media(post_media=post_media, response_media=response_media)","title":""},{"docid":"db3612149e54b27992ea91325c7d7d43","score":"0.47723678","text":"def _octopus(self):\n return self._serene.octopii.upload(self.octo_local)","title":""},{"docid":"e39b219aa6f437f228af45ae214bdc8e","score":"0.4769472","text":"def upload_instant(name, filepath):\n\n # Create session and get cookies\n session = requests.session()\n response = session.get(\n LOGIN_URL,\n headers={\n \"User-Agent\": generate_user_agent(),\n },\n )\n\n sel = parsel.Selector(response.text)\n token = sel.css(\"input[name=csrfmiddlewaretoken]::attr(value)\").get()\n\n if not token:\n raise InvalidPageErrorException\n\n data = {\n \"csrfmiddlewaretoken\": token,\n \"login\": os.environ[\"MYINSTANTS_USERNAME\"],\n \"password\": os.environ[\"MYINSTANTS_PASSWORD\"],\n \"next\": \"/en/new/\",\n }\n\n # Login\n response = session.post(\n LOGIN_URL,\n data=data,\n headers={\n \"User-Agent\": generate_user_agent(),\n },\n )\n\n if response.status_code != 200:\n raise LoginErrorException\n\n # Get upload token\n sel = parsel.Selector(response.text)\n token = sel.css(\"input[name=csrfmiddlewaretoken]::attr(value)\").get()\n\n if not token:\n raise InvalidPageErrorException\n\n filename = os.path.basename(filepath)\n\n multipart_data = MultipartEncoder(\n fields={\n \"csrfmiddlewaretoken\": token,\n \"name\": name,\n \"sound\": (\n filename,\n open(filepath, \"rb\"),\n \"audio/mpeg\",\n ),\n \"image\": (\"\", None, \"\"),\n \"color\": \"00FF00\",\n \"category\": \"\",\n \"description\": \"\",\n \"tags\": \"\",\n \"accept_terms\": \"on\",\n }\n )\n\n # Upload sound\n response = session.post(\n UPLOAD_URL,\n data=multipart_data,\n headers={\n \"Content-Type\": multipart_data.content_type,\n \"Referer\": UPLOAD_URL,\n \"User-Agent\": generate_user_agent(),\n },\n )\n\n sel = parsel.Selector(response.text)\n errors = \"\\n\".join(sel.css(\"ul.errorlist\").getall())\n if \"instant with this name already exists.\" in errors:\n raise NameAlreadyExistsException\n if \"please keep filesize under 300.0 kb\" in errors:\n raise FileSizeException\n if response.status_code != 200:\n raise HTTPErrorException\n\n last_uploaded_element = sel.xpath(\n \"//a[contains(@class, 'instant-link') and text()=$name]/@href\", name=name\n ).get()\n if not last_uploaded_element:\n return response.url\n\n # Return sound url\n return urljoin(response.url, last_uploaded_element)","title":""},{"docid":"912925461c0949f00263b2f33bad982b","score":"0.47686628","text":"def db_add_neurons(clear):\n graph = graph_p2()\n if clear:\n graph.delete_all()\n\n neurons_df = preproc.neurons_df()\n transx = graph.cypher.begin()\n statement = \"CREATE (n:Neuron {props})\"\n for name, params in neurons_df.iterrows():\n params = params.to_dict()\n params['name'] = name\n transx.append(statement, {\"props\":params})\n transx.commit()","title":""},{"docid":"23afe0b7b1275049340ec33cbc265083","score":"0.4757897","text":"def upload(args):\n log(\"Preparing to upload images...\")\n d = localfs.filepath(paths[\"datasetLocal\"], paths[\"datasetName\"])\n if localfs.isempty(d):\n raise RuntimeError(\"Missing images in dataset folder \"+d)\n def validate_filenames(names):\n for n in names:\n if \" \" in n:\n raise ValueError(\"Image file names cannot contain spaces. [{}]\".format(n)) # TODO add non-alphanumeric character checking\n validate_filenames(localfs.ls(d))\n log(\"Uploading. Please wait...\")\n spell.upload(paths[\"datasetLocal\"], paths[\"datasetRemoteDir\"])\n log(\"Upload complete.\")","title":""},{"docid":"9eb79306cdb1f527f21ed41ee87ba4b6","score":"0.47536415","text":"def scrape_and_write_medias(self):\n self.start_driver()\n if self.login():\n logging.info('HULU MOVIE SEARCH')\n movie_genre_pages = self.get_genre_pages(mtype='movie')\n movies = self.get_medias(movie_genre_pages)\n self.lookup_and_write_medias(medias=movies, mtype='movie')\n\n logging.info('HULU SHOW SEARCH')\n show_genre_pages = self.get_genre_pages(mtype='show')\n shows = self.get_medias(show_genre_pages)\n self.lookup_and_write_medias(medias=shows, mtype='show')\n else:\n logging.error('HULU login failed')\n\n self.stop_driver()\n flaskapp.remove_old_sources('hulu') # remove sources not just updated\n flaskapp.remove_hulu_addon_media() # remove overlap of sho and hulu","title":""},{"docid":"96f3131bc1e37339ca9c0072afd4b3da","score":"0.47527525","text":"def download(self):\n if self._check_exists():\n return\n\n # Create folders\n try:\n os.makedirs(os.path.join(self.root, self.raw_folder))\n os.makedirs(os.path.join(self.root, self.processed_folder))\n except OSError as e:\n if e.errno == errno.EEXIST:\n pass\n else:\n raise\n\n for url in self.urls:\n filename = url.rpartition('/')[2]\n download_url(url, root=os.path.join(self.root, self.raw_folder),\n filename=filename, md5=None)\n\n # process and save as torch files\n print('Processing raw data..')\n\n training_set = self._read_raw_image_file('binarized_mnist_train.amat')\n val_set = self._read_raw_image_file('binarized_mnist_valid.amat')\n test_set = self._read_raw_image_file('binarized_mnist_test.amat')\n\n processed_dir = os.path.join(self.root, self.processed_folder)\n with open(os.path.join(processed_dir, self.training_file), 'wb') as f:\n torch.save(training_set, f)\n with open(os.path.join(processed_dir, self.val_file), 'wb') as f:\n torch.save(val_set, f)\n with open(os.path.join(processed_dir, self.test_file), 'wb') as f:\n torch.save(test_set, f)\n\n print('Completed data download.')","title":""},{"docid":"7fba11591dd4c48a39e74fca4ea8c822","score":"0.474788","text":"def Test5(self):\r\n \r\n f= open('outputLowQuality.wav' ,\"rb\")\r\n myurl = 'http://localhost:5555/upload'\r\n header = { 'content_type': 'audio/wav' } \r\n x = requests.request('POST', url=myurl , data = f , headers=header ) \r\n print (x.text)\r\n print (x.status_code)\r\n if x.status_code == 200:\r\n self.music_id = x.content\r\n self.set_Logs(x.text)\r\n self.AssertOK()\r\n \r\n else:\r\n self.set_Logs(x.text)\r\n self.AssertKO()","title":""},{"docid":"664a13dd5393e34662516970b385c609","score":"0.4745173","text":"def update(self):\r\n# pwstate\r\n responsea = requests.request(\"POST\", url, data=PowerStatus, headers=headers)\r\n treed = ET.fromstring(responsea.content)\r\n for power_name in treed.findall('.//Power_Control/Power'):\r\n self._pwstate = power_name.text\r\n\r\n if self._pwstate == \"On\":\r\n self._state = STATE_IDLE\r\n else:\r\n self._state = STATE_OFF\r\n\r\n # volumestate\r\n responseb = requests.request(\"POST\", url, data=Status, headers=headers)\r\n treee = ET.fromstring(responseb.content)\r\n for vol_number in treee.findall('.//Volume/Lvl'):\r\n self._volume = vol_number.text\r\n\r\n\r\n # titel\r\n responsec = requests.request(\"POST\", url, data=PlayStatus, headers=headers)\r\n treef = ET.fromstring(responsec.content)\r\n for music_title in treef.findall('.//Meta_Info/Song'):\r\n self._media_title = music_title.text\r\n\r\n # album\r\n responsed = requests.request(\"POST\", url, data=PlayStatus, headers=headers)\r\n treeg = ET.fromstring(responsed.content)\r\n for album_name in treeg.findall('.//Meta_Info/Album'):\r\n self._media_album_name = album_name.text\r\n\r\n # Kunstler\r\n responsee = requests.request(\"POST\", url, data=PlayStatus, headers=headers)\r\n treeh = ET.fromstring(responsee.content)\r\n for artist_name in treeh.findall('.//Meta_Info/Artist'):\r\n self._media_artist = artist_name.text\r\n\r\n\r\n # mutestate\r\n responsef = requests.request(\"POST\", url, data=Status, headers=headers)\r\n treei = ET.fromstring(responsef.content)\r\n for volm_name in treei.findall('.//Volume/Mute'):\r\n resultf = volm_name.text\r\n if resultf == \"On\":\r\n self._muted = True\r\n if resultf == \"Off\":\r\n self._muted = False","title":""},{"docid":"563b56b48be7da15cf12e49d04b278a4","score":"0.47445157","text":"def perform_transfer_tracks(self, playlist_name, songs=[]):\n def check_for_playlist(playlist):\n \"\"\"\n This function checks if playlist is already created.\n Parameters:\n playlist: List of strings.\n Returns:\n playlist_id or None\n \"\"\"\n found_p_id = None\n current_user_playlists = self.get_playlist_info()\n for data_dic in current_user_playlists:\n for p_name, playlist_id in data_dic.items():\n if p_name == playlist_name:\n found_p_id = playlist_id\n return found_p_id\n uris = []\n # Check for playlist\n playlist_id = check_for_playlist(playlist_name)\n if not playlist_id:\n # If no id found, then create a new playlist.\n playlist_id = self.create_playlist(\n playlist_name,\n description='playlist created by rythmize.'\n )\n # Move uris into playlist. \n track_uris = self.get_tracks_uri(self.get_playlist_tracks(playlist_id))\n for song in songs:\n track_name, artist = song['track'], song['artist']\n if track_name and artist:\n uri = self.get_track_uri(track_name, artist)\n if uri and uri not in track_uris:\n # Add uri to uri's\n uris.append(uri)\n endpoint = f\"https://api.spotify.com/v1/playlists/{playlist_id}/tracks\"\n headers = self.get_resource_header()\n request_data = json.dumps(uris)\n response = requests.post(endpoint, headers=headers, data=request_data)\n if response.status_code in range(200, 299):\n # Valid response, return response\n return response.json()\n return None","title":""},{"docid":"5791bc7f51869c9a13aba0209ab9bff9","score":"0.47419113","text":"def ds_create_link_medias_w_handles(self, uniq_id: int, media_refs: list):\n doing = \"?\"\n try:\n for resu in media_refs:\n r_attr = {\"order\": resu.media_order}\n if resu.crop:\n r_attr[\"left\"] = resu.crop[0]\n r_attr[\"upper\"] = resu.crop[1]\n r_attr[\"right\"] = resu.crop[2]\n r_attr[\"lower\"] = resu.crop[3]\n doing = f\"(src:{uniq_id}) -[{r_attr}]-> Media {resu.media_handle}\"\n # print(doing)\n result = self.tx.run(\n CypherObjectWHandle.link_media,\n root_id=uniq_id,\n handle=resu.media_handle,\n r_attr=r_attr,\n )\n media_uid = result.single()[0] # for media object\n\n for handle in resu.note_handles:\n doing = f\"{media_uid}->Note {handle}\"\n self.tx.run(\n CypherObjectWHandle.link_note, root_id=media_uid, handle=handle\n )\n\n for handle in resu.citation_handles:\n doing = f\"{media_uid}->Citation {handle}\"\n self.tx.run(\n CypherObjectWHandle.link_citation,\n root_id=media_uid,\n handle=handle,\n )\n\n except Exception as err:\n logger.error(\n f\"Neo4jUpdateService.create_link_medias_by_handles {doing}: {err}\"\n )","title":""},{"docid":"27f7b9b644f8bc524d91fc92a8f8fab5","score":"0.47408184","text":"async def send_ucube_to_channel(self, channel_info: TextChannel, message_text, embed_list, media_files, club_name):\n ...\n try:\n channel: discord.TextChannel = self.bot.get_channel(channel_info.id)\n if not channel:\n # fetch channel instead (assuming discord.py cache did not load)\n channel: discord.TextChannel = await self.bot.fetch_channel(channel_info.id)\n except Exception as e:\n # remove the channel from future updates as it cannot be found.\n print(f\"{e} - Removing Text Channel {channel_info.id} from cache for {club_name} since it could not \"\n f\"be processed/found.\")\n return await self.delete_channel(channel_info.id, club_name.lower())\n\n msg_list: List[discord.Message] = []\n file_list = []\n\n try:\n mention_role = f\"<@&{channel_info.role_id}>\" if channel_info.role_id else None\n\n for count, embed in enumerate(embed_list, 1):\n msg_list.append(await channel.send(mention_role if count == 1 else None, embed=embed))\n\n if message_text or media_files:\n # Since an embed already exists, any individual content will not load\n # as an embed -> Make it it's own message.\n if media_files:\n # a list of file locations\n for photo_location in media_files:\n file_list.append(discord.File(photo_location))\n\n msg_list.append(await channel.send(message_text if message_text else None, files=file_list or None))\n print(f\"UCube Post for {club_name} sent to {channel_info.id}.\")\n except discord.Forbidden as e:\n # no permission to post\n print(f\"{e} (discord.Forbidden) - UCube Post Failed to {channel_info.id} for {club_name}\")\n\n # remove the channel from future updates as we do not want it to clog our rate-limits.\n return await self.delete_channel(channel_info.id, club_name.lower())\n except Exception as e:\n print(f\"{e} (Exception) - UCube Post Failed to {channel_info.id} for {club_name}\")\n return\n\n if not channel.is_news():\n return\n\n for msg in msg_list:\n try:\n await msg.publish()\n except Exception as e:\n print(f\"Failed to publish Message ID: {msg.id} for Channel ID: {channel_info.id} - {e}\")","title":""},{"docid":"0daeaa48b2e2ae305f3953bb8ca3fef2","score":"0.4738126","text":"async def add_torrent_magnet(magnet,message):\n client = await get_client()\n try:\n ctor = len(client.torrents_info())\n \n ext_hash = Hash_Fetch.get_hash_magnet(magnet)\n ext_res = client.torrents_info(torrent_hashes=ext_hash)\n if len(ext_res) > 0:\n await message.edit(\"This torrent is alreaded in the leech list.\")\n return False\n # hot fix for the below issue\n savepath = os.path.join(os.getcwd(), \"Downloads\", str(time.time()).replace(\".\",\"\"))\n op = client.torrents_add(magnet, save_path=savepath)\n \n \n # TODO uncomment the below line and remove the above fix when fixed https://github.com/qbittorrent/qBittorrent/issues/13572\n # op = client.torrents_add(magnet)\n\n # torrents_add method dosent return anything so have to work around\n if op.lower() == \"ok.\":\n st = datetime.now()\n \n ext_res = client.torrents_info(torrent_hashes=ext_hash)\n if len(ext_res) > 0:\n torlog.info(\"Got torrent info from ext hash.\")\n return ext_res[0]\n\n while True:\n if (datetime.now() - st).seconds >= 10:\n torlog.warning(\"The provided torrent was not added and it was timed out. magnet was:- {}\".format(magnet))\n await message.edit(\"The torrent was not added due to an error.\")\n return False\n ctor_new = client.torrents_info()\n if len(ctor_new) > ctor:\n # https://t.me/c/1439207386/2977 below line is for this\n torlog.info(ctor_new)\n torlog.info(magnet)\n return ctor_new[0]\n\n else:\n await message.edit(\"This is an unsupported/invalid link.\")\n except qba.UnsupportedMediaType415Error as e:\n #will not be used ever ;)\n torlog.error(\"Unsupported file was detected in the magnet here\")\n await message.edit(\"This is an unsupported/invalid link.\")\n return False\n except Exception as e:\n torlog.error(\"{}\\n{}\".format(e,traceback.format_exc()))\n await message.edit(\"Error occured check logs.\")\n return False","title":""},{"docid":"5824f0805f76f8b8b11fcc67c89a4abf","score":"0.47361273","text":"async def upload_read_files(sample_provider, read_files):\n for file in read_files:\n await sample_provider.upload(file)","title":""},{"docid":"ab12c986180eeda5de8aebd8b25204ec","score":"0.47333428","text":"def uploads(self, uploads):\n\n self._uploads = uploads","title":""},{"docid":"b0dff0c0ccd96ff1c15ebc31a5565316","score":"0.47325784","text":"def process_json(filenames, training, ambiguous, special):\n\n # We only want dialogs in German: K, L, M, N, G, W\n\n sid_to_uid = defaultdict(list)\n uid_to_sid = {}\n uid_to_text = {}\n uid_to_wav = {}\n \n for filename in filenames: \n with open(filename, 'r') as infile:\n content = json.loads(infile.read())\n \n # Get meta info\n uid_parts = content[\"name\"].split('_')\n uid = '_'.join([uid_parts[2], uid_parts[0], uid_parts[1]])\n wavfile = os.path.abspath(os.path.join(\n os.path.dirname(filename), \n content[\"annotates\"]\n ))\n\n # Add to the dict for wav\n uid_to_wav[uid] = wavfile\n\n # Get speaker ID\n sid = assocs(content[\"levels\"][0][\"items\"][0][\"labels\"])[\"SPN\"]\n\n # Add to the dict for spk2utt\n sid_to_uid[sid].append(uid)\n\n # Add to the dict for utt2spk\n uid_to_sid[uid] = sid\n\n # Get tokens\n tokens = []\n for item in content[\"levels\"][1][\"items\"]:\n labels = assocs(item[\"labels\"])\n \n try:\n tokens.append(labels[\"ORT\"])\n except KeyError:\n continue\n\n # Add to the dict for text\n uid_to_text[uid] = ' '.join(tokens)\n\n # Now we can write the wav.scp file\n dir_name = \"\" \n if training:\n dir_name = \"train\"\n else:\n dir_name = \"test\"\n if ambiguous:\n dir_name += \"_ambiguous\"\n else:\n dir_name += \"_unambiguous\"\n with open('data/%s/wav.scp' % dir_name, 'w') as outfile:\n for key, value in sorted(uid_to_wav.items(), key=itemgetter(0)):\n outfile.write(\"%s cat %s |\\n\" % (key, value))\n\n # Now we can write the text\n with open('data/%s/text' % dir_name, 'w') as outfile:\n for key, value in sorted(uid_to_text.items(), key=itemgetter(0)):\n if ambiguous:\n value = convert_ambiguous(value, special)\n else:\n value = convert_unambiguous(value, special)\n outfile.write(\"%s %s\\n\" % (key, value))\n\n # We also write the raw text\n with open('data/%s/raw_text' % dir_name, 'w') as outfile:\n for key, value in sorted(uid_to_text.items(), key=itemgetter(0)):\n if ambiguous:\n value = convert_ambiguous(value, special)\n else:\n value = convert_unambiguous(value, special)\n outfile.write(\"%s\\n\" % (value))\n\n # Now we can write utt2spk\n with open('data/%s/utt2spk' % dir_name, 'w') as utt2spk:\n for key, value in sorted(uid_to_sid.items(), key=itemgetter(0)):\n utt2spk.write(\"%s %s\\n\" % (key, value))\n\n # Now we can update spk2utt\n with open('data/%s/spk2utt' % dir_name, 'w') as spk2utt:\n for key, values in sorted(sid_to_uid.items(), key=itemgetter(0)):\n values = sorted(values)\n spk2utt.write(\"%s %s\\n\" % (key, ' '.join(values)))\n\n return sid_to_uid.keys()","title":""},{"docid":"8048116db2e876fbf609a639b2ded22b","score":"0.47302797","text":"def add_list(self, tracks):","title":""},{"docid":"a20357ead2d05f9c803d6365c3465dcc","score":"0.47293723","text":"def handle_upload(cls,f, attrs):\n \n if attrs['folderId'] == \"root\":\n folderId = 0\n else :\n folderId = attrs['folderId']\n\n chunked = False\n dest_folder = os.path.join(cls.UPLOAD_DIRECTORY, attrs['qquuid'])\n dest = os.path.join(dest_folder, attrs['qqfilename'])\n\n # Chunked\n if 'qqtotalparts' in attrs and int(attrs['qqtotalparts']) > 1:\n chunked = True\n dest_folder = os.path.join(cls.CHUNKS_DIRECTORY, attrs['qquuid'])\n dest = os.path.join(dest_folder, attrs['qqfilename'], str(attrs['qqpartindex']))\n cls.save_upload(f, dest)\n else:\n # Generate repo file in database before saving actual file\n\n chars = \"abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMOPQRTUVWXYZ0123456789\"\n fileName = \"\".join(random.sample(chars, 6))\n \n while RepoFileModel.exists(fileName):\n fileName = \"\".join(random.sample(chars, 6))\n\n ogName = attrs['qqfilename']\n p = ogName.split(\".\")\n\n newFile = RepoFileModel(\n fileName, p[0], p[1], folderId, attrs['qquuid'])\n print(newFile.json())\n newFile.save()\n\n dest = os.path.join(cls.UPLOAD_DIRECTORY, fileName + \".\" + p[1])\n cls.save_upload(f, dest)\n\n ImageManager().createThumbnails(newFile)\n # ^^^ Generate repo file in database before saving actual file\n\n \n\n if chunked and (int(attrs['qqtotalparts']) - 1 == int(attrs['qqpartindex'])):\n\n chars = \"abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMOPQRTUVWXYZ0123456789\"\n fileName = \"\".join(random.sample(chars, 6))\n\n while RepoFileModel.exists(fileName):\n fileName = \"\".join(random.sample(chars, 6))\n\n ogName = attrs['qqfilename']\n p = ogName.split(\".\")\n\n newFile = RepoFileModel(fileName, p[0], p[1],folderId, attrs['qquuid'])\n newFile.save()\n\n \n\n dst = os.path.join(cls.UPLOAD_DIRECTORY,fileName + \".\" + p[1])\n\n cls.combine_chunks(attrs['qqtotalparts'],\n attrs['qqtotalfilesize'],\n source_folder=os.path.dirname(dest),\n dest=dst)\n\n shutil.rmtree(os.path.dirname(os.path.dirname(dest)))\n\n ImageManager().createThumbnails(newFile)","title":""},{"docid":"46fb022f1babb64e8a789d3458fb7d61","score":"0.47291568","text":"async def get_music(self, ctx):\n if ctx.message.author.id not in self.owner_list:\n embed = discord.Embed(title='{}:'.format(ctx.message.author.name),\n description='You may not use this command :angry: only admins!',\n colour=0xf20006)\n a = await self.bot.say(embed=embed)\n await self.bot.add_reaction(a, self.emojiUnicode['warning'])\n return\n\n if not self.database_online:\n embed = discord.Embed(title='{}:'.format(ctx.message.author.name),\n description='Could not connect to database.',\n colour=0xf20006)\n a = await self.bot.say(embed=embed)\n await self.bot.add_reaction(a, self.emojiUnicode['error'])\n return\n\n data_channels = []\n for server in self.bot.servers:\n for channel in server.channels:\n if 'music' in str(channel).lower():\n channel_type = str(channel.type)\n print(channel_type)\n data = [int(channel.id), str(channel.name), re.sub('\\W+', '', str(server.name)), str(channel.type)]\n data_channels.append(data)\n\n\n self.cur.execute('ROLLBACK;')\n for items in data_channels:\n try:\n self.cur.execute(\n 'INSERT INTO botzilla.music (ID, channel_name, server_name, type_channel) VALUES ({}, \\'{}\\', \\'{}\\', \\'{}\\');'.format(\n items[0], items[1], items[2], items[3]\n ))\n self.cur.execute(\"ROLLBACK;\")\n except Exception as e:\n print('Error gathering info music channels:\\n```Python\\n{}```'.format(e.args))\n continue\n\n embed = discord.Embed(title='{}:'.format(ctx.message.author.name),\n description='Done with gathering music channel info!',\n colour=0xf20006)\n a = await self.bot.say(embed=embed)\n await self.bot.add_reaction(a, self.emojiUnicode['succes'])","title":""},{"docid":"755c56749ad929682b58cbb8df88ffe8","score":"0.47253004","text":"def _upload_dataset(self, dataset):\n for file_path in dataset.local_files:\n s3_url = self.upload_manager.upload_file(dataset.doi_upload_key,\n file_path)\n dataset.file_s3_urls[os.path.basename(file_path)] = s3_url\n\n self.upload_manager.upload_json_obj(\n dataset.doi_upload_key,\n 'original_pure{}_metadata.json'.format(self.api_version),\n dataset.original_metadata\n )\n self.upload_manager.upload_json_obj(\n dataset.doi_upload_key,\n 'metadata.json',\n dataset.rdss_canonical_metadata\n )","title":""},{"docid":"10e6ccf9452739363792c358a298b901","score":"0.4723323","text":"def upload_files(self):\n\n while True:\n # Look for files in the twitter storage directory and shove them into a list\n tmp_files = []\n for f in os.listdir(self.configs[\"grabber_storage_directory\"]):\n if os.path.isfile(os.path.join(self.configs[\"grabber_storage_directory\"],f)):\n tmp_files.append(f)\n # Make sure we only are uploading files that are finished and were created by a Grabber\n # using our config information\n files = []\n for filename in tmp_files:\n if self.configs[\"tweet_file_prefix\"] in filename:\n files.append(filename)\n\n\n # If we don't actually have any files to upload wait a bit and flip back to the beginning\n if len(files) == 0:\n if self.configs['verbosity'] and not(self.configs['quiet']):\n print(\"no files to upload, waiting for 10 seconds.\")\n time.sleep(10)\n continue\n\n # Pick the oldest file we can find for upload\n files.sort()\n filename = os.path.join(self.configs[\"grabber_storage_directory\"],files[0])\n to_upload = open( filename, \"rb\")\n\n # Setup our variables for the upcoming loop\n max_retries = self.configs[\"upload_retries\"]\n on_try = 0\n result = None\n if self.configs['verbosity'] and not(self.configs['quiet']):\n print(\"Upload attempting to push file %s\" % filename)\n\n # Attempt to upload the file until we hit max_retries\n while on_try < max_retries:\n if self.configs['verbosity'] and not(self.configs['quiet']):\n print(\"Upload try %s\" % on_try)\n try:\n result = self.do_upload(self.configs[\"upload_url\"], to_upload)\n break\n except requests.exceptions.RequestException as e:\n if not (self.log_pipe == None):\n self.log_pipe.send(['error','Upload failed, %s %s' % (type(e), str(e)) ])\n on_try += 1\n\n if not (result == None):\n # If we got our file up, send a message to the upload log, otherwise write to the error log\n if result.status_code == 201:\n if not (self.log_pipe == None):\n self.log_pipe.send(['log', '%s %s' % ( result.status_code, json.dumps(result.text) )])\n # If an upload is successful this is the filehash\n if self.configs['verbosity'] and not(self.configs['quiet']):\n print(result.text)\n \n # If we manage to successfully upload the file then close the handle and delete the file\n if self.configs['verbosity'] and not(self.configs['quiet']):\n print(\"Upload succeeded, deleting %s\" % filename)\n to_upload.close()\n os.remove(filename)\n else:\n if not (self.log_pipe == None):\n self.log_pipe.send(['error', '%s %s' % ( result.status_code, json.dumps(result.text) )])\n else:\n # If we hit max_retries bail on the upload process\n if not (self.log_pipe == None):\n self.log_pipe.send(['error','Upload failed %s times, killing uploader' % max_retries])\n sys.exit(1)","title":""},{"docid":"29ecab63f1f98b83dbbea707ef035b15","score":"0.47210714","text":"def run(self):\n class CamHandler(FTPHandler):\n\n def on_file_received(self, file_):\n\n date = datetime.now()\n\n path = os.path.join(save_folder, date.strftime(\"%m-%d-%Y\"))\n os.makedirs(path, exist_ok=True)\n fn = os.path.join(path, date.isoformat().replace(\":\", \",\") + \".jpg\")\n os.rename(file_, fn)\n\n if self.username not in last_files:\n last_files.update({self.username : None})\n\n print(fn, self.username)\n\n try:\n\n if collect_images:\n process_train(fn, self.username)\n else:\n process(fn, self.username)\n\n except Exception as e:\n print(e)\n\n def on_incomplete_file_received(self, file_):\n os.remove(file_)\n\n handler = CamHandler\n handler.authorizer = self.auth\n\n server = FTPServer((self.host, self.port), handler)\n server.serve_forever()","title":""}],"string":"[\n {\n \"docid\": \"aad469f7cdda8f21594f47b702bbf18b\",\n \"score\": \"0.63252246\",\n \"text\": \"def upload_tracks():\\n auto_upload_tracks()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"061181d642add61974fefd7bcd56de38\",\n \"score\": \"0.6084998\",\n \"text\": \"def upload(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"061181d642add61974fefd7bcd56de38\",\n \"score\": \"0.6084998\",\n \"text\": \"def upload(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1f20cfc1e871657e3fe0275cd15c1a3\",\n \"score\": \"0.59851444\",\n \"text\": \"def db_add_muscle_synapses(clear):\\n graph = graph_p2()\\n if clear:\\n statement = \\\"MATCH (n:Neuron)-[r]-(m:Muscle) DELETE r\\\"\\n graph.cypher.run(statement)\\n\\n conns = preproc.muscle_conns_df()\\n transx = graph.cypher.begin()\\n statement = \\\"MATCH (n:Neuron {name:{neuron}}), (m:Muscle {name:{muscle}}) CREATE (n)-[s:Synapse {name:n.name+'->'+m.name, type:'NMJ', weight:{w}}]->(m)\\\"\\n for idx, row in conns.iterrows():\\n transx.append(statement, {\\\"neuron\\\":row['Neuron'], \\\"muscle\\\":row['Muscle'], \\\"w\\\":row[\\\"Weight\\\"]})\\n transx.commit()\\n db_set_node_degrees()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6e362d5bfcf0df6e377b86c3c20ad610\",\n \"score\": \"0.59452313\",\n \"text\": \"def upload(request):\\n if(request.method == 'POST'):\\n form = FileForm(request.POST, request.FILES)\\n if form.is_valid():\\n\\n n_scenarios = 0\\n for _, _, filenames in os.walk(\\\"scenarios/\\\"):\\n n_scenarios = len(filenames)\\n if n_scenarios > 15:\\n shutil.rmtree(\\\"scenarios/\\\")\\n\\n fs = FileSystemStorage(\\\"scenarios/\\\")\\n manager.clear_paths()\\n for key in request.FILES.keys():\\n global_file = request.FILES[key]\\n filename = fs.save(global_file.name, global_file)\\n manager.add_path(os.path.join(fs.location, filename))\\n for path in manager.get_paths():\\n if path is not None:\\n track_stream = load.get_track_streams_from_prp(path)\\n manager.add_track_stream(track_stream)\\n station_utils.sync_stations(*manager.get_track_streams())\\n\\n return(HttpResponse('POST ok !'))\\n else:\\n return(HttpResponse('POST failed'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77841ecd494416db38447ca66e48ecd0\",\n \"score\": \"0.54216546\",\n \"text\": \"def test_1(self):\\n \\n inputFile = os.path.join(rmgpy.getPath(),'tools','data','flux','input.py')\\n chemFile = os.path.join(rmgpy.getPath(),'tools','data','flux','chemkin','chem.inp')\\n dictFile = os.path.join(rmgpy.getPath(),'tools','data','flux','chemkin','species_dictionary.txt')\\n \\n with open(inputFile) as nf, open(chemFile) as cf, open(dictFile) as df:\\n response = self.client.post('/tools/flux/', {'InputFile': nf, 'ChemkinFile': cf, 'DictionaryFile': df, \\n 'MaxNodes': 50, 'MaxEdges': 50, 'TimeStep': 1.25, \\n 'ConcentrationTolerance': 1e-6, 'SpeciesRateTolerance': 1e-6})\\n \\n self.assertEqual(response.status_code, 200)\\n \\n folder = os.path.join(settings.MEDIA_ROOT,'rmg','tools','flux')\\n \\n # Check if inputs were correctly uploaded\\n pyInput = os.path.join(folder,'input.py')\\n chemInput = os.path.join(folder,'chem.inp')\\n dictInput = os.path.join(folder,'species_dictionary.txt')\\n \\n self.assertTrue(os.path.isfile(pyInput),'RMG input file was not uploaded')\\n self.assertTrue(os.path.isfile(chemInput),'Chemkin file was not uploaded')\\n self.assertTrue(os.path.isfile(dictInput),'Dictionary file was not uploaded')\\n \\n # Check if outputs were correctly generated\\n videoOutput = os.path.join(folder,'1','flux_diagram.avi')\\n species = os.path.join(folder,'species')\\n \\n self.assertTrue(os.path.isfile(videoOutput),'Video output was not generated')\\n self.assertTrue(os.path.isdir(species),'Species directory was not generated')\\n \\n shutil.rmtree(folder)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bc637eb6c350ab98bf7119ef7124487a\",\n \"score\": \"0.5369428\",\n \"text\": \"def test_upload_essentials(self, server, runtime, tmpfolder):\\n path = tmpfolder / uuid.uuid4().hex\\n data = self.generate_byte_data()\\n with open(path, 'w') as model:\\n print(data, file=model)\\n runtime.prepare_client()\\n server.accept_client(server.serversocket, None)\\n server.send_message(Message(MessageType.OK))\\n runtime.upload_essentials(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5dccff926af1d6dc5136b9f1f6e8f428\",\n \"score\": \"0.53643095\",\n \"text\": \"def upload(subject_set, images, zooniverse_project):\\n new_subjects = []\\n\\n print(\\\"Uploading {} images\\\".format(len(images)))\\n for filename, metadata in images.items():\\n subject = Subject()\\n\\n subject.links.project = zooniverse_project\\n subject.add_location(filename)\\n\\n subject.metadata.update(metadata)\\n\\n #Trigger upload\\n subject.save()\\n new_subjects.append(subject)\\n subject_set.add(new_subjects)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8c146882a9c0046a75d192981acedee3\",\n \"score\": \"0.53142595\",\n \"text\": \"def db_add_muscles(clear):\\n graph = graph_p2()\\n if clear:\\n statement = \\\"MATCH (n:Muscle) DELETE n\\\"\\n graph.cypher.run(statement)\\n\\n muscles = preproc.muscles_df()\\n transx = graph.cypher.begin()\\n statement = \\\"CREATE (n:Muscle {props})\\\"\\n for name, params in muscles.iterrows():\\n params = params.to_dict()\\n params['name'] = name\\n transx.append(statement, {\\\"props\\\":params})\\n transx.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"76c5a42973980edfb057c54280e54f61\",\n \"score\": \"0.52692986\",\n \"text\": \"async def upload_and_save(\\n self,\\n nlu: File,\\n domain: File,\\n stories: File,\\n config: File,\\n rules: File,\\n http_action: File,\\n bot: Text,\\n user: Text,\\n overwrite: bool = True,\\n ):\\n training_file_loc = await Utility.save_training_files(nlu, domain, config, stories, rules, http_action)\\n await self.save_from_path(training_file_loc['root'], bot, overwrite, user)\\n Utility.delete_directory(training_file_loc['root'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"77f795619f696d418d91b0f667ca5944\",\n \"score\": \"0.522585\",\n \"text\": \"def tool_nomad_upload(files, token, name, legacy, dry):\\n from .scripts.nomad_upload import nomad_upload\\n\\n nomad_upload(files, token, legacy, dry, name=name\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db278a3d67405d5949247222da8f37de\",\n \"score\": \"0.5213027\",\n \"text\": \"def main():\\n answers = [ os.path.join(ANSWERS_DIR, f) for f in os.listdir(ANSWERS_DIR)]\\n for answer in answers:\\n print('uploading %s' % answer)\\n upload(answer, folder='answers')\\n print('deleting %s', answer)\\n os.remove(answer)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"692a927f69624502db9f40e773a3ca0c\",\n \"score\": \"0.5205346\",\n \"text\": \"def upload(self):\\n # Setup\\n for sequencer_number in self._used_sequencers_numbers:\\n target = self.device.sequencers[sequencer_number]\\n self._set_device_parameter(target, \\\"sync_en\\\", value=True)\\n self._set_device_parameter(target, \\\"marker_ovr_en\\\", value=True) # Default after reboot = False\\n self._set_device_parameter(target, \\\"marker_ovr_value\\\", value=15) # Default after reboot = 0\\n\\n for sequencer_number in self._unused_sequencers_numbers:\\n target = self.device.sequencers[sequencer_number]\\n self._set_device_parameter(target, \\\"sync_en\\\", value=False)\\n self._set_device_parameter(target, \\\"marker_ovr_en\\\", value=True) # Default after reboot = False\\n self._set_device_parameter(target, \\\"marker_ovr_value\\\", value=0) # Default after reboot = 0\\n if sequencer_number >= 2: # Never disconnect default sequencers\\n self._set_device_parameter(target, \\\"channel_map_path0_out0_en\\\", value=False)\\n self._set_device_parameter(target, \\\"channel_map_path0_out2_en\\\", value=False)\\n self._set_device_parameter(target, \\\"channel_map_path1_out1_en\\\", value=False)\\n self._set_device_parameter(target, \\\"channel_map_path1_out3_en\\\", value=False)\\n\\n # Upload waveforms and program\\n qblox_dict = {}\\n sequencer: Sequencer\\n for port in self._output_ports_keys:\\n for sequencer in self._sequencers[port]:\\n # Add sequence program and waveforms to single dictionary\\n qblox_dict[sequencer] = {\\n \\\"waveforms\\\": sequencer.waveforms,\\n \\\"weights\\\": sequencer.weights,\\n \\\"acquisitions\\\": sequencer.acquisitions,\\n \\\"program\\\": sequencer.program,\\n }\\n\\n # Upload dictionary to the device sequencers\\n self.device.sequencers[sequencer.number].sequence(qblox_dict[sequencer])\\n\\n # DEBUG: QCM RF Save sequence to file\\n if self._debug_folder != \\\"\\\":\\n filename = self._debug_folder + f\\\"Z_{self.name}_sequencer{sequencer.number}_sequence.json\\\"\\n with open(filename, \\\"w\\\", encoding=\\\"utf-8\\\") as file:\\n json.dump(qblox_dict[sequencer], file, indent=4)\\n file.write(sequencer.program)\\n\\n # Arm sequencers\\n for sequencer_number in self._used_sequencers_numbers:\\n self.device.arm_sequencer(sequencer_number)\\n\\n # DEBUG: QCM RF Print Readable Snapshot\\n # print(self.name)\\n # self.device.print_readable_snapshot(update=True)\\n\\n # DEBUG: QCM RF Save Readable Snapshot\\n from qibolab.instruments.qblox.debug import print_readable_snapshot\\n\\n if self._debug_folder != \\\"\\\":\\n filename = self._debug_folder + f\\\"Z_{self.name}_snapshot.json\\\"\\n with open(filename, \\\"w\\\", encoding=\\\"utf-8\\\") as file:\\n print_readable_snapshot(self.device, file, update=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d2e0f5c74e2c53ff9d010b6816b3571\",\n \"score\": \"0.52015185\",\n \"text\": \"def publish():\\n\\n\\tupload()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d2e0f5c74e2c53ff9d010b6816b3571\",\n \"score\": \"0.52015185\",\n \"text\": \"def publish():\\n\\n\\tupload()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d7becc91c8dd0b3550d90869d76c063\",\n \"score\": \"0.5196205\",\n \"text\": \"async def ftp_upload(self):\\r\\n for source, target in zip(self._client_path, self._server_path):\\r\\n ext = os.path.splitext(source)\\r\\n if ext in ['txt', 'html', 'htm']:\\r\\n with open(source, 'rb') as f:\\r\\n self._ftp.storlines('STOR ' + target, f)\\r\\n else:\\r\\n with open(source, 'rb') as f:\\r\\n self._ftp.storbinary(\\r\\n 'STOR ' + target, f, 1024)\\r\\n await asyncio.sleep(1.0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"21f521e133f33233fa828e490d7bd092\",\n \"score\": \"0.51678973\",\n \"text\": \"def upload_waveforms(self, sequence_names, sequence_channels, sequence_items, reload=True):\\n raise NotImplementedError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10f5aa2d8ebbfd6a96daa18411cf619e\",\n \"score\": \"0.5163708\",\n \"text\": \"def upload_parts(self, parts):\\n files = self.make_filelist(self.make_dirlist(parts))\\n self.upload_logs(files)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c81e8ff971589ba3750fc454cc87f78e\",\n \"score\": \"0.51607674\",\n \"text\": \"def import_wiki():\\n print('Importing Node wiki data into mongo...')\\n db = mongoi.NodeDb()\\n train_data = scrape_file(DATA_DIR + FILE_NAMES[6])\\n test_data = scrape_file(DATA_DIR + FILE_NAMES[7])\\n for doc in train_data:\\n doc['gold_label'] = LABEL[doc['gold_label']]\\n db.wiki_train.add(doc)\\n for doc in test_data:\\n doc['gold_label'] = LABEL[doc['gold_label']]\\n db.wiki_test.add(doc)\\n print('Completed successfully.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"788f22e835e1175a58d279f399461a63\",\n \"score\": \"0.5154003\",\n \"text\": \"def animals(bot, update):\\n # WORKING\\n chat_id = update.message.chat.id\\n user_id = update.message.from_user.id\\n BotActions.common_process(chat_id, user_id)\\n BotActions.incrementa_animales(user_id, chat_id)\\n file_name = BotActions.random_file_name(f'{BotActions.pytel_path}/Animals')\\n bot.send_photo(chat_id=chat_id, photo=open(file_name, 'rb'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fee6a202fb47352aec299693fbfdb2e3\",\n \"score\": \"0.5152346\",\n \"text\": \"def start(self):\\n\\n CloudConvert.upload(self.fromfile, self.toformat, self.url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9866448efed04157b44cc3f4c2ca84ae\",\n \"score\": \"0.5142552\",\n \"text\": \"async def importmusic(self, ctx):\\n if ctx.message.author.id not in self.owner_list:\\n embed = discord.Embed(title='{}:'.format(ctx.message.author.name),\\n description='You may not use this command :angry: only admins!',\\n colour=0xf20006)\\n a = await self.bot.say(embed=embed)\\n await self.bot.add_reaction(a, self.emojiUnicode['warning'])\\n return\\n\\n\\n if not self.database_online:\\n embed = discord.Embed(title='{}:'.format(ctx.message.author.name),\\n description='Could not connect to database.',\\n colour=0xf20006)\\n a = await self.bot.say(embed=embed)\\n await self.bot.add_reaction(a, self.emojiUnicode['error'])\\n return\\n\\n\\n try:\\n with open(self.database_import_musicque, 'r') as file:\\n reader = csv.reader(file, delimiter=',')\\n for row in reader:\\n b = re.search(r'^(.*)', str(row)).group()\\n b = b.replace('[', '')\\n b = b.replace('\\\"(', '')\\n b = b.replace(',)\\\"', '')\\n row = b.replace(']', '')\\n try:\\n self.cur.execute(\\\"INSERT INTO botzilla.musicque(url) VALUES({});\\\".format(row))\\n self.cur.execute(\\\"ROLLBACK;\\\")\\n except Exception as e:\\n pass\\n\\n embed = discord.Embed(title='{}:'.format(ctx.message.author.name),\\n description='Done!',\\n colour=0xf20006)\\n a = await self.bot.say(embed=embed)\\n await self.bot.add_reaction(a, self.emojiUnicode['succes'])\\n\\n except Exception as e:\\n embed = discord.Embed(title='{}:'.format('Error'),\\n description='```Python\\\\n{}```'.format(e.args),\\n colour=0xf20006)\\n a = await self.bot.say(embed=embed)\\n await self.bot.add_reaction(a, self.emojiUnicode['error'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fbd52f19e045f40d4c493d105539dc85\",\n \"score\": \"0.51331276\",\n \"text\": \"def upload_genotypes(genotypes_by_chr_dir, ):\\n from schema.connection import session, metadata # is session necessary?\\n logging.basicConfig(level=logging.DEBUG)\\n print metadata\\n# metadata.bind = 'sqlite:///:memory:'\\n metadata.bind.echo = True\\n \\n genotypes_by_chr_dir = '/home/gioby/Data/HGDP/Genotypes_by_chr'\\n for filename in glob.glob(genotypes_by_chr_dir + '/*.geno'):\\n print filename\\n parsers.genotypes_parser(open(filename, 'r'))\\n session.commit()\\n\\n# rosenberg_file = open(rosenberg_path, 'r')\\n# parsers.rosenberg_parser(rosenberg_file)\\n\\n print 'upload of Genotypes to database %s completed' % metadata\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae9e4bd3621461073b18a2de9f0a35c5\",\n \"score\": \"0.51181287\",\n \"text\": \"def create_folders_server(cobertura=True, transicao=True, RGB=True):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4d724a9e891471bb9f0019eb36e24459\",\n \"score\": \"0.51133394\",\n \"text\": \"def upload_datasets(force=False):\\n for dataset in datasets:\\n datasource = Datasource(f\\\"{dataset.name}_train\\\")\\n print(datasource.name)\\n datasource.upload(force=force)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"477cc456a5407809fa1c30b34d08821d\",\n \"score\": \"0.5080628\",\n \"text\": \"def store(self, mutagen_file, pictures):\\n mutagen_file.clear_pictures()\\n for pic in pictures:\\n mutagen_file.add_picture(pic)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"13d1ba28d69785f81e4ba89c46ee22e9\",\n \"score\": \"0.50717145\",\n \"text\": \"def upload(ctx):\\n ctx.obj.run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b0d7f30dd2b91474a35ed11d6150573\",\n \"score\": \"0.5071457\",\n \"text\": \"async def test_upload_study(self, async_upload_study):\\n dicom_path = Path(__file__) \\\\\\n .parents[2] \\\\\\n .joinpath('dicoms', 'anonymize')\\n study = await async_upload_study(dicom_path)\\n study_uid = '1.2.840.10008.1.142353.149743743.367518058.1727281618' # NOQA:E501\\n assert study.image_count == 6\\n assert study.study_uid == study_uid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"637838ac7c932b7654d116f818363047\",\n \"score\": \"0.50705326\",\n \"text\": \"def send(session: nox.Session):\\r\\n session.install('twine')\\r\\n session.run('twine', 'upload', 'dist/*.whl')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29c572571109cf3e8aeb1f3d01a8c56e\",\n \"score\": \"0.5057477\",\n \"text\": \"async def nmb_of_mamacos():\\n chan_to_send = None\\n for chan in client.guilds[0].text_channels:\\n if chan.name == 'plebe':\\n chan_to_send = chan\\n break\\n actual_voice_channel = None\\n\\n while True:\\n msg = \\\"\\\"\\n\\n nmb_of_chan = client.guilds[0].voice_channels\\n nmb_of_chan.sort(reverse=True, key=lambda chan: len(chan.members))\\n\\n for chan in client.guilds[0].voice_channels:\\n msg += chan.name + \\\" tem \\\" + str(len(chan.members)) + \\\" mamacos.\\\\n\\\"\\n\\n logging.info(msg)\\n\\n if len(nmb_of_chan[0].members) > 0:\\n\\n logging.info(nmb_of_chan[0].name)\\n vc = await nmb_of_chan[0].connect()\\n choosen_audio = meme_songs.songs[random.randint(\\n 0, len(meme_songs.songs) - 1)]\\n logging.info(choosen_audio)\\n audio_s = discord.FFmpegPCMAudio(choosen_audio)\\n vc.play(audio_s)\\n\\n while vc.is_playing():\\n await asyncio.sleep(1)\\n # disconnect after the player has finished\\n vc.stop()\\n await vc.disconnect()\\n\\n await asyncio.sleep(1800)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d7a155281978e14756091634c65472c7\",\n \"score\": \"0.505276\",\n \"text\": \"def upload_landsat(scene):\\n upload_landsat.upload(scene)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c54c4adb2ba8caf7c64f48dae0ff23a3\",\n \"score\": \"0.50513345\",\n \"text\": \"async def nogas(self, ctx: KurisuContext):\\n await self._meme(ctx, \\\"https://nintendohomebrew.com/assets/img/nhmemes/nogas.mp4\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a74c0d512c810f39b0ec6262f14d22b3\",\n \"score\": \"0.5045693\",\n \"text\": \"def main():\\n\\n con = Connection()\\n db = con.audiodb\\n\\n if use_proxy == True:\\n install_proxy()\\n\\n words = open(filename).read().split()\\n for wordname in words:\\n if wordname.isalpha() == False:\\n continue\\n wordfilename = wordname.lower()+'.mp3'\\n wordrecord = wordname.lower()+'_mp3'\\n num = db.audiodb.find({\\\"file_name\\\":wordrecord}).count()\\n if num > 0:\\n print(\\\"Already got \\\"+\\\"\\\\\\\"\\\"+wordname+\\\"\\\\\\\"\\\")\\n continue\\n try:\\n mp3file = get_data_from_req(\\n \\\"https://ssl.gstatic.com/dictionary/static/sounds/de/0/\\\"+wordfilename)\\n if not is_file_valid(mp3file):\\n continue\\n bin = bson.Binary(mp3file.read())\\n db.audiodb.save({\\\"file_name\\\":wordrecord, \\\"mp3\\\":bin})\\n print(\\\"Done!\\\")\\n except Exception as e:\\n print(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb6c1cb309b9e882391021032e033a77\",\n \"score\": \"0.5038316\",\n \"text\": \"async def serotonin(self, ctx: KurisuContext):\\n await self._meme(ctx, \\\"\\\",\\n image_link=\\\"https://album.eiphax.tech/uploads/big/2549ac8b197ae68080041d3966a887e8.png\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd81fc0456a8c2df87c13129dd8a4622\",\n \"score\": \"0.5033467\",\n \"text\": \"def upload_piece():\\n if request.method == 'POST':\\n\\n if 'username' not in request.form:\\n return 'username not defined'\\n\\n if request.form['username'] == '':\\n return 'username is empty'\\n\\n username = request.form['username']\\n if db.pieces.find_one({'username': username}):\\n return 'username taken.'\\n\\n document = {\\n 'username': username,\\n 'tag': None,\\n 'model': None,\\n 'texture': None\\n }\\n\\n file_keys = ['tag', 'texture', 'model']\\n if not all(x in request.files for x in file_keys):\\n return 'file missing in request'\\n\\n for key, file in request.files.items():\\n if file.filename == '':\\n return 'the following file is missing: ' + key\\n\\n if not allowed_file(file.filename):\\n return 'the following file is not in an allowed format ' + key\\n\\n if file and allowed_file(file.filename):\\n _, extension = os.path.splitext(file.filename)\\n new_filename = username + '_' + key + extension\\n\\n file.save(\\n os.path.join(app.config['UPLOAD_FOLDER'], new_filename)\\n )\\n\\n document[key] = {\\n 'name': new_filename,\\n 'url': url_for(\\n 'uploaded_file',\\n filename=new_filename,\\n _external=True\\n )\\n }\\n\\n db.pieces.insert_one(document)\\n return redirect(url_for('tags'))\\n\\n return render_template('upload.html')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8043030e7276190ef6521f70b053f17\",\n \"score\": \"0.49841464\",\n \"text\": \"def upload_public(request):\\n\\n LOGGER.debug('upload starts ' + str(request.FILES.keys()))\\n LOGGER.debug('before')\\n\\n chan = request.channel_name\\n\\n r = {}\\n msg = {}\\n\\n values = request.REQUEST.get('values' , '')\\n channel_str = request.REQUEST.get('channel', '')\\n\\n if values:\\n # ********** create methodo ************* #\\n s = Session.objects.using('megavideo').get(pk = values)\\n session = s.get_decoded()\\n # ********** end ************************ #\\n r = session.get('values_form', '')\\n try:\\n del(session['values_form'])\\n except:\\n pass\\n else:\\n r = {}\\n\\n LOGGER.debug('values_rec: ' + str(r))\\n\\n if 'chan_id' in r:\\n chan_id = r['chan_id']\\n channel = Channel.objects.using('megavideo').get(id = chan_id)\\n else:\\n if channel_str != '':\\n channel = Channel.objects.using('megavideo').get(name = channel_str)\\n else:\\n channel = Channel.objects.using('megavideo').get(name = chan)\\n chan_id = channel.id\\n\\n LOGGER.debug('receive post')\\n\\n if 'Filedata' in request.FILES:\\n LOGGER.debug('insider')\\n\\n file_uploaded = request.FILES['Filedata']\\n\\n # ver\\n if 'name' in r and r['name'] == '':\\n r['name'] = file_uploaded.name\\n\\n conf = settings.MEGAVIDEO_CONF\\n tmp = tempfile.mktemp('_' + conf['tv_name'])\\n tmp = conf['tmp'] + os.path.basename(tmp)\\n fd = open(tmp, 'wb')\\n\\n LOGGER.debug('opened')\\n\\n for chunk in file_uploaded.chunks():\\n fd.write(chunk)\\n fd.close()\\n\\n LOGGER.debug('insider3')\\n\\n LOGGER.debug('channel: ' + str(channel))\\n\\n job = Job()\\n job.channel = channel\\n job.ip = request.META['REMOTE_ADDR']\\n job.status = 'A'\\n job.original_name = file_uploaded.name\\n job.message = 'inserting upload/vflow interface'\\n job.save(using='megavideo')\\n\\n LOGGER.debug('insider4')\\n\\n try:\\n acall = AsyncCall(conf['dispatch'], conf['tv_name'], channel_id = chan_id)\\n\\n kargs = {\\n 'metadatas' :-1, #create metadatas from scratch\\n 'transcode' : True,\\n 'keep_video' : True,\\n 'chan_id' : chan_id,\\n 'metas': r\\n }\\n\\n if 'publish' in settings.VFLOW_REC:\\n kargs['publish'] = settings.VFLOW_REC['publish']\\n\\n if 'cat_id' in r:\\n kargs['cat_id'] = r['cat_id']\\n\\n LOGGER.debug('Push video async ' + str(kargs))\\n\\n LOGGER.debug('insider5')\\n\\n acall.set_job(job)\\n acall.set_insert(tmp, **kargs)\\n acall.call()\\n\\n LOGGER.debug('after calling')\\n\\n except StandardError, e:\\n LOGGER.debug('error : ')\\n LOGGER.debug(str(e))\\n\\n\\n if 'redirect' in request.REQUEST:\\n return HttpResponseRedirect(request.channel_url + 'manager/jobs/%d/' % (job.id))\\n #return HttpResponse( str(job.id) )\\n msg = {'error' : 0, 'job' : job.id}\\n return HttpResponse(json.dumps(msg), mimetype = 'application/json')\\n\\n msg = {'error' : 1, 'job' : 0}\\n\\n return HttpResponse(json.dumps(msg), mimetype = 'application/json')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1571f1a843d6866d462a497945ae1b34\",\n \"score\": \"0.4970229\",\n \"text\": \"def upload(self, database):\\n database.games.insert_many([g.as_dict() for g in self.parsed_games])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b34b91db2ff4ee1088196517e702745a\",\n \"score\": \"0.4951148\",\n \"text\": \"def file_upload(data):\\n # emiting received file and user data back to the users current room\\n emit('file received', data, room=data['room'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0646c2f6e02b73518c9201f1a29e8e54\",\n \"score\": \"0.49506062\",\n \"text\": \"def atrophyMuscles(self):\\n for dna_file in os.listdir(\\\"./population\\\"):\\n if dna_file.endswith(\\\".vxa\\\"):\\n print dna_file\\n\\n tree = ET.ElementTree(file=\\\"./population/\\\" + dna_file)\\n root = tree.getroot()\\n layers = root.find('VXC').find('Structure').find('Data').findall('Layer')\\n\\n \\\"\\\"\\\"Calculates probability based on amount of fat tissue\\n \\\"\\\"\\\"\\n dna = \\\"\\\"\\n for layer in layers:\\n dna += str(layer.text).strip()\\n count_soft = dna.count('1')\\n probability = (count_soft / 1000) * 0.5 # or 0.0005 * count_soft\\n\\n dna_list = \\\"\\\"\\n for layer in layers:\\n dna_list = list(layer.text.strip())\\n position_to_check = 0\\n for index in range(len(dna_list)):\\n tissue = dna_list[index]\\n if tissue == '3' or tissue == '4':\\n if random.random() <= probability:\\n dna_list[index] = '2'\\n print \\\"Atrophied a muscle at index \\\" + str(index) + \\\" to \\\" + str(dna_list[index]) + \\\".\\\"\\n else:\\n print \\\"Muscle at index \\\" + str(index) + \\\" unchanged.\\\"\\n pass\\n\\n else:\\n continue\\n\\n layer.text = self.concat(dna_list)\\n tree.write(\\\"./population/\\\" + dna_file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"addbeeaeeeded8b0f30128730d56692a\",\n \"score\": \"0.49474025\",\n \"text\": \"def upload_links(self, links):\\n for link in links:\\n self.client.chat_postMessage(channel='#general',\\n text=link)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d723e12b2b49128467dd37e96bcb6e2\",\n \"score\": \"0.49461845\",\n \"text\": \"def upload():\\n\\n\\trun('twine upload dist/*')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0d723e12b2b49128467dd37e96bcb6e2\",\n \"score\": \"0.49461845\",\n \"text\": \"def upload():\\n\\n\\trun('twine upload dist/*')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3a2defd4f537c019ed64372edc3bec42\",\n \"score\": \"0.49407345\",\n \"text\": \"def post(self):\\n self.parse_paras()\\n self.upload_files()\\n\\n self.convert_model()\\n self.inference()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4799ed92d4977c91eff0e189c7e38dc2\",\n \"score\": \"0.49329093\",\n \"text\": \"def load_neurons(self, path, synapses):\\n # return if not synapses\\n if not len(synapses):\\n return synapses\\n ####\\n # Get neurons from loaded synapses\\n ####\\n\\n # All unqiue source nodes and their keys\\n all_src, src_keys = np.unique(synapses.T[0], True)\\n # All unique target nodes and their keys\\n all_tgt, tgt_keys = np.unique(synapses.T[1], True)\\n # Find keys for neurons that are never targets\\n only_src = list(set(all_src) - set(all_tgt))\\n src_dict = dict(zip(all_src, src_keys))\\n src_keys = map(src_dict.get, only_src)\\n # Get all neuron lists from synapse targets, sources\\n neuron_tgt = np.delete(synapses[tgt_keys], 0, 1)\\n neuron_src = np.delete(synapses[src_keys], 1, 1)\\n # Get full neuron list from source and target\\n neurons = np.r_[neuron_src, neuron_tgt]\\n\\n # Get file fields\\n k_files = self.RUNTIME.DB.FILE\\n # Get keywords for input file\\n k_file = k_files.SOMA.VALUE\\n k_file = os.path.join(path, k_file)\\n\\n if os.path.exists(k_file):\\n msg = \\\"Loading neuron centers from {}\\\"\\n log.info(msg.format(k_file))\\n # Load the csv\\n with open(k_file, 'r') as jf:\\n # Keep a list of synapseless soma\\n new_neurons = []\\n # Add each new center point to database\\n for soma in json.load(jf):\\n # Make a numpy uint32 coorinate array\\n k_soma = ['neuron_id', 'z', 'y', 'x']\\n new_soma = np.uint32(map(soma.get, k_soma))\\n soma_id = new_soma[0]\\n # Find the correct ID\\n neuron_ids = neurons.T[0]\\n # If the soma has a synapse\\n if soma_id in neuron_ids:\\n # Insert into the correct ID\\n soma_index = np.argwhere(neuron_ids == soma_id)[0][0]\\n neurons[soma_index] = new_soma\\n else:\\n # Add new synapseless neuron\\n new_neurons.append(new_soma)\\n\\n # Add new neurons to full neurons list\\n if len(new_neurons):\\n neurons = np.r_[neurons, new_neurons]\\n\\n # Add neurons to database\\n self.add_neurons(path, neurons)\\n return neurons\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecd01600483ca7aa118691363caccb2a\",\n \"score\": \"0.49142516\",\n \"text\": \"def createSongFile():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"11fe13704089c535b2dca2c0afaad4df\",\n \"score\": \"0.49095154\",\n \"text\": \"def handle_upload(sock):\\n dir_path = proto.read_field_str(sock)\\n api_key = proto.read_field_str(sock)\\n alg_id = proto.read_field_str(sock)\\n if alg_id == '':\\n alg_id = None\\n try:\\n gym.upload(dir_path, api_key=api_key, algorithm_id=alg_id)\\n proto.write_field_str(sock, '')\\n except gym.error.Error as exc:\\n proto.write_field_str(sock, str(exc))\\n sock.flush()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"460cf264475fd61bcf35abfc80b5b12d\",\n \"score\": \"0.49056706\",\n \"text\": \"def upload(self, artifacts: List[Artifact]):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e978ba769406dacfa9026b7c6f2fad6c\",\n \"score\": \"0.4897588\",\n \"text\": \"def upload(self, data, files):\\n print 'Uploading ...'\\n\\n try:\\n r = requests.post(SKETCHFAB_API_URL, data=data, files=files, verify=False)\\n except requests.exceptions.RequestException as e:\\n print \\\"An error occured: {}\\\".format(e)\\n return\\n\\n result = r.json()\\n\\n if r.status_code != requests.codes.created:\\n print \\\"Upload failed with error: {}\\\".format(result)\\n return\\n\\n model_uid = result['uid']\\n model_url = SKETCHFAB_MODEL_URL + model_uid\\n print \\\"Upload successful. Your model is being processed.\\\"\\n print \\\"Once the processing is done, the model will be available at: {}\\\".format(model_url)\\n\\n return model_uid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d3772638dd1e69ffe503c10cf9beb5e\",\n \"score\": \"0.48903462\",\n \"text\": \"def upload(request):\\n\\n # used to generate random unique id\\n \\n\\n\\n # settings for the file upload\\n # you can define other parameters here\\n # and check validity late in the code\\n context = RequestContext(request)\\n options = {\\n # the maximum file size (must be in bytes)\\n \\\"maxfilesize\\\": 2 * 2 ** 100, #\\n # the minimum file size (must be in bytes)\\n \\\"minfilesize\\\": 1 * 2 ** 10, # 1 Kb\\n # the file types which are going to be allowed for upload\\n # must be a mimetype\\n \\\"acceptedformats\\\": (\\n 'audio/basic',\\n 'audio/L24',\\n 'audio/mp3',\\n 'application/octet-stream',\\n 'audio/mp4',\\n 'audio/mpeg',\\n 'audio/ogg',\\n 'audio/vorbis',\\n 'audio/x-ms-wma',\\n 'audio/x-ms-wax',\\n 'audio/vnd.rn-realaudio',\\n 'audio/vnd.wave',\\n 'audio/webm'\\n )\\n }\\n\\n\\n # POST request\\n # meaning user has triggered an upload action\\n if request.method == 'POST':\\n # figure out the path where files will be uploaded to\\n # PROJECT_ROOT is from the settings file\\n temp_path = os.path.join(settings.MEDIA_ROOT, \\\"uploaded_music\\\")\\n\\n # if 'f' query parameter is not specified\\n # file is being uploaded\\n if not (\\\"f\\\" in request.GET.keys()): # upload file\\n\\n # make sure some files have been uploaded\\n if not request.FILES:\\n return HttpResponseBadRequest('Must upload a file')\\n\\n # make sure unique id is specified - VERY IMPORTANT\\n # this is necessary because of the following:\\n # we want users to upload to a unique directory\\n # however the uploader will make independent requests to the server\\n # to upload each file, so there has to be a method for all these files\\n # to be recognized as a single batch of files\\n # a unique id for each session will do the job\\n \\n\\n # update the temporary path by creating a sub-folder within\\n # the upload folder with the uid name\\n\\n # get the uploaded file\\n file = request.FILES[u'files[]']\\n\\n # initialize the error\\n # If error occurs, this will have the string error message so\\n # uploader can display the appropriate message\\n error = False\\n\\n # check against options for errors\\n\\n # file size\\n if file.size > options[\\\"maxfilesize\\\"]:\\n error = \\\"maxFileSize\\\"\\n if file.size < options[\\\"minfilesize\\\"]:\\n error = \\\"minFileSize\\\"\\n # allowed file type\\n if file.content_type not in options[\\\"acceptedformats\\\"]:\\n error = \\\"acceptFileTypes\\\"\\n\\n\\n # the response data which will be returned to the uploader as json\\n response_data = {\\n \\\"name\\\": file.name,\\n \\\"size\\\": file.size,\\n \\\"type\\\": file.content_type\\n }\\n\\n # if there was an error, add error message to response_data and return\\n if error:\\n # append error message\\n response_data[\\\"error\\\"] = error\\n # generate json\\n response_data = simplejson.dumps([response_data])\\n # return response to uploader with error\\n # so it can display error message\\n return HttpResponse(response_data, mimetype='application/json')\\n\\n\\n # make temporary dir if not exists already\\n if not os.path.exists(temp_path):\\n os.makedirs(temp_path)\\n\\n # get the absolute path of where the uploaded file will be saved\\n # all add some random data to the filename in order to avoid conflicts\\n # when user tries to upload two files with same filename\\n filename = os.path.join(temp_path, str(file.name))\\n # open the file handler with write binary mode\\n \\n\\n destination = open(filename, \\\"wb+\\\")\\n # save file data into the disk\\n # use the chunk method in case the file is too big\\n # in order not to clutter the system memory\\n for chunk in file.chunks():\\n destination.write(chunk)\\n # close the file\\n destination.close()\\n\\n\\n print filename\\n\\n\\n #use this for EchoNest\\n #music_response = music_identify(filename)\\n\\n #use this for Acoustid + fpcalc + chromaprint\\n music_response = aidmatch(filename)\\n\\n print music_response\\n\\n\\n\\n\\n #################################################\\n ####### ACOUSTID PARSING\\n #################################################\\n song_list=[]\\n for elt in music_response:\\n song_list.append(elt[1])\\n\\n song_list = list(set(song_list))\\n\\n song_list = song_list[:3]\\n\\n\\n song_artist_name = music_response[0][0]\\n song_name = music_response[0][1]\\n artist_link = 'http://musicbrainz.org/recording/' + music_response[0][2]\\n\\n #################################################\\n ####### END ACOUSTID PARSING\\n #################################################\\n\\n\\n\\n\\n\\n \\\"\\\"\\\"\\n # ##############################################\\n ####### ECHONEST PARSING\\n #################################################\\n response_data = json.loads(music_response) \\n print response_data\\n \\n song_name = response_data['response']['songs'][0]['title']\\n print song_name\\n song_artist_name = response_data['response']['songs'][0]['artist_name']\\n print song_artist_name\\n song_artist_id = response_data['response']['songs'][0]['artist_id']\\n print song_artist_id\\n song_id = response_data['response']['songs'][0]['id']\\n print song_id\\n\\n # ##############################################\\n ####### END ECHONEST PARSING\\n #################################################\\n \\\"\\\"\\\"\\n\\n\\n #extract genre from file\\n #if not found, then query Echonest\\n from hsaudiotag import mpeg\\n audio = mpeg.Mpeg(filename)\\n artist_genre=\\\"\\\"\\n song_genre=\\\"\\\"\\n import string\\n metadata_genre = string.lower(str(audio.tag.genre))\\n if(metadata_genre in glist):\\n #\\n #artist_genre = metadata_genre + \\\" meta\\\"\\n\\n song_genre = metadata_genre.lower().strip()\\n else: \\n # use this for Acoustid + pygn\\n song_genre = get_gracenote_genre(song_artist_name, song_name).lower().strip()\\n\\n # use this for EchoNest\\n #artist_genre = genre_query(song_artist_id)\\n # use this for EchoNest\\n #print artist_genre \\n\\n # use this for Acoustid + pygn\\n print song_genre \\n if song_genre == \\\"urban\\\":\\n song_genre = \\\"hip-hop\\\"\\n\\n\\n\\n results = []\\n user_list = []\\n send_user_list = []\\n if request.user and str(request.user) != \\\"AnonymousUser\\\":\\n print \\\"1\\\"\\n print type(request.user)\\n print type(str(request.user))\\n print str(request.user)\\n print \\\"1 end\\\"\\n all_user = User.objects.all().values_list('username', flat=True) \\n user = User.objects.get(username=request.user)\\n try:\\n userprofile = Profile.objects.get(user=user)\\n if userprofile.genre:\\n if not song_genre in userprofile.genre:\\n userprofile.genre.append(song_genre)\\n else:\\n print \\\"No genre in profile\\\"\\n if not song_genre in userprofile.genre:\\n userprofile.genre.append(song_genre)\\n userprofile.save()\\n lst_genre = userprofile.genre\\n for one_user in all_user:\\n #print one_user\\n for genre in lst_genre:\\n #print genre\\n two_user = User.objects.get(username=request.user)\\n #print \\\"1\\\"\\n one_user_profile = Profile.objects.get(user=two_user)\\n #print \\\"2\\\"\\n if genre in one_user_profile.genre:\\n if one_user not in user_list:\\n user_list.append(one_user)\\n else:\\n print \\\"Some crazy error\\\"\\n for send_user in user_list:\\n sed_user = User.objects.get(username=send_user)\\n sed_user_profile = Profile.objects.get(user=sed_user)\\n send_user_list.append({\\n \\\"user\\\":sed_user,\\n \\\"profile\\\":sed_user_profile\\n })\\n print user_list\\n print userprofile.genre\\n except:\\n print \\\"userprofile exception\\\"\\n userprofile = None\\n else:\\n print \\\"user not logged in\\\"\\n\\n \\n\\n # use for Acoustid\\n results.append({\\n 'artist':song_artist_name,\\n 'title':song_list,\\n 'artist_link':artist_link,\\n 'song_genre':song_genre\\n })\\n\\n\\n \\\"\\\"\\\"\\n # use for EchoNest\\n results.append({\\n 'artist':song_artist_name,\\n 'title':song_name,\\n 'artist_id':song_artist_id,\\n 'song_id':song_id,\\n 'artist_genre':artist_genre\\n })\\n \\\"\\\"\\\"\\n\\n # QUIRK HERE\\n # in jQuey uploader, when it falls back to uploading using iFrames\\n # the response content type has to be text/html\\n # if json will be send, error will occur\\n # if iframe is sending the request, it's headers are a little different compared\\n # to the jQuery ajax request\\n # they have different set of HTTP_ACCEPT values\\n # so if the text/html is present, file was uploaded using jFrame because\\n # that value is not in the set when uploaded by XHR\\n if \\\"text/html\\\" in request.META[\\\"HTTP_ACCEPT\\\"]:\\n response_type = \\\"text/html\\\"\\n\\n # return the data to the uploading plugin\\n return render_to_response('userena/music_search.html',{'result_list':results, 'send_user_list':send_user_list, 'request_user':str(request.user) },context)\\n\\n\\n else: # file has to be deleted\\n\\n # get the file path by getting it from the query (e.g. '?f=filename.here')\\n filepath = os.path.join(temp_path, request.GET[\\\"f\\\"])\\n\\n # make sure file exists\\n # if not return error\\n if not os.path.isfile(filepath):\\n return HttpResponseBadRequest(\\\"File does not exist\\\")\\n\\n # delete the file\\n # this step might not be a secure method so extra\\n # security precautions might have to be taken\\n os.remove(filepath)\\n\\n # generate true json result\\n # in this case is it a json True value\\n # if true is not returned, the file will not be removed from the upload queue\\n response_data = simplejson.dumps(True)\\n\\n # return the result data\\n # here it always has to be json\\n return HttpResponse(response_data, mimetype=\\\"application/json\\\")\\n\\n else: #GET\\n # load the template\\n return render_to_response('userena/upload.html',{},context)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98faf087a00f6ffc6f6a9b2dfe41c81c\",\n \"score\": \"0.48885685\",\n \"text\": \"def uploadresult(testroot):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1f3fc851ed7187c29699ea453d676310\",\n \"score\": \"0.48696217\",\n \"text\": \"def upload_all(self):\\n self.upload_list(self.jobs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"837dade2bab8dbc206936e32fb468da1\",\n \"score\": \"0.48632708\",\n \"text\": \"def upload_files(cls, files, **kwargs):\\n cls.REMOTE_MANAGER.upload_files(files, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c849df977028b9e043e781a7c4fd397c\",\n \"score\": \"0.48606706\",\n \"text\": \"def on_btn_upload(self, event):\\n if not self.check_for_uncombined_files():\\n return\\n outstring=\\\"upload_magic.py\\\"\\n print(\\\"-I- running python script:\\\\n %s\\\"%(outstring))\\n wait = wx.BusyInfo(\\\"Please wait, working...\\\")\\n wx.SafeYield()\\n success_responses = ['200', 200, '201', 201, True]\\n if 'measurements' in self.contribution.tables:\\n self.contribution.tables['measurements'].add_measurement_names()\\n upload_file, val_response, dummy1, dummy2 = ipmag.upload_magic(concat=False, input_dir_path=self.WD, dir_path=self.WD)\\n del wait\\n if val_response == \\\"no 3.0 files found, upload file not created\\\":\\n pw.simple_warning(\\\"No 3.0 files were found in your directory, so no upload could be created!\\\")\\n return\\n status = val_response['status']\\n if not status:\\n pw.simple_warning(\\\"Oops, something went wrong with validating on the server.\\\\n{}\\\\nTry again later or submit a bug report.\\\".format(val_response['warnings']))\\n return\\n validation_errors = val_response['validation']\\n if (not validation_errors['warnings']) and (not validation_errors['errors']):\\n text = \\\"You are ready to upload!\\\\n{} was generated in {}\\\".format(os.path.split(upload_file)[1], self.WD)\\n dlg = pw.ChooseOne(self, \\\"Go to MagIC for uploading\\\", \\\"Not ready yet\\\", text, \\\"Saved\\\")\\n\\n dlg.Centre()\\n result = dlg.ShowModal()\\n if result == wx.ID_OK:\\n dlg.Destroy()\\n if result == wx.ID_YES:\\n pw.on_database_upload(None)\\n return\\n\\n # there were problems, so display validation\\n text = \\\"There were some problems with the creation of your upload file.\\\\nSee Terminal/message window for details\\\"\\n dlg = wx.MessageDialog(self, caption=\\\"Error\\\", message=text, style=wx.OK)\\n dlg.Centre()\\n result = dlg.ShowModal()\\n\\n # TODO: get the error-y business formatted into a dict of lists of dicts\\n from programs import magic_gui\\n self.Disable()\\n self.Hide()\\n self.magic_gui_frame = magic_gui.MainFrame(self.WD,\\n dmodel=self.data_model,\\n title=\\\"Validations\\\",\\n contribution=self.contribution,\\n errors=validation_errors['errors'])\\n self.magic_gui_frame.Centre()\\n self.magic_gui_frame.Show()\\n self.magic_gui_frame.highlight_problems()\\n # bind that button to quitting magic gui and re-enabling Pmag GUI\\n self.magic_gui_frame.Bind(wx.EVT_BUTTON, self.on_end_validation, self.magic_gui_frame.return_btn)\\n # do binding so that closing/quitting re-opens the main frame\\n self.magic_gui_frame.Bind(wx.EVT_CLOSE, self.on_end_validation)\\n # this makes it work with only the validation window open\\n self.magic_gui_frame.Bind(wx.EVT_MENU,\\n lambda event: self.menubar.on_quit(event, self.magic_gui_frame),\\n self.magic_gui_frame.menubar.file_quit)\\n # this makes it work if an additional grid is open\\n self.Bind(wx.EVT_MENU,\\n lambda event: self.menubar.on_quit(event, self.magic_gui_frame),\\n self.magic_gui_frame.menubar.file_quit)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70d1368f3f6f361d4c0bab5c77e7fc7e\",\n \"score\": \"0.48543638\",\n \"text\": \"def gather_audio_files():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c4a8bc45ae6535179fb547c92d7c961d\",\n \"score\": \"0.48456827\",\n \"text\": \"def upload(self):\\n\\n # FIXME wrap this in a try except\\n\\n self._create()\\n self._update()\\n for filename in self._file_list:\\n self._upload(filename)\\n self._publish()\\n\\n # and in the except, delete the partial upload as it is broken\\n # - particularly to catch Ctrl-C\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7391228c8c4255b79843ba3233b0ef79\",\n \"score\": \"0.48414555\",\n \"text\": \"def upload(self, results):\\n raise NotImplementedError(\\\"Uploaders should implement .upload.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bb644156b3718948ba8a65178d5b2ba8\",\n \"score\": \"0.48384976\",\n \"text\": \"def import_from_music_ontology():\\n \\n file = open('instruments.txt')\\n search_terms = file.readlines()\\n results = []\\n for search_term in search_terms:\\n payload = {'query': search_term, 'limit': '1', 'fmt':'json'}\\n query = 'http://musicbrainz.org/ws/2/instrument?'\\n try:\\n r = requests.get(query, params=payload).json()\\n except:\\n pass\\n try:\\n url = 'musicbrainz.org/instrument/' + r['instruments'][0]['id']\\n results.append((r['instruments'][0]['name'], url))\\n print((r['instruments'][0]['name'], url))\\n except:\\n print('empty: ', search_term)\\n pass\\n sleep(5)\\n file.close()\\n with open('instrumentLinks.txt', 'w') as fp:\\n fp.write('\\\\n'.join('%s %s' % x for x in results))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7aa01ae264961660ec977587c70aa33\",\n \"score\": \"0.48352036\",\n \"text\": \"def upload(self,\\n filepaths,\\n enable_matching=False,\\n enable_transcoding=True,\\n transcode_quality='320k',\\n include_album_art=True,\\n ):\\n\\n if self.uploader_id is None or self.uploader_name is None:\\n raise NotLoggedIn(\\\"Not authenticated as an upload device;\\\"\\n \\\" run Api.login(...perform_upload_auth=True...)\\\"\\n \\\" first.\\\")\\n\\n # TODO there is way too much code in this function.\\n\\n # To return.\\n uploaded = {}\\n matched = {}\\n not_uploaded = {}\\n\\n # Gather local information on the files.\\n local_info = {} # {clientid: (path, Track)}\\n for path in filepaths:\\n try:\\n track = musicmanager.UploadMetadata.fill_track_info(path)\\n except BaseException as e:\\n self.logger.warning(\\\"problem gathering local info of '%r'\\\", path)\\n\\n user_err_msg = str(e)\\n\\n if 'Non-ASCII strings must be converted to unicode' in str(e):\\n # This is a protobuf-specific error; they require either ascii or unicode.\\n # To keep behavior consistent, make no effort to guess - require users\\n # to decode first.\\n user_err_msg = (\\\"nonascii bytestrings must be decoded to unicode\\\"\\n \\\" (error: '%s')\\\" % user_err_msg)\\n\\n not_uploaded[path] = user_err_msg\\n else:\\n local_info[track.client_id] = (path, track)\\n\\n if not local_info:\\n return uploaded, matched, not_uploaded\\n\\n # TODO allow metadata faking\\n\\n # Upload metadata; the server tells us what to do next.\\n res = self._make_call(musicmanager.UploadMetadata,\\n [t for (path, t) in local_info.values()],\\n self.uploader_id)\\n\\n # TODO checking for proper contents should be handled in verification\\n md_res = res.metadata_response\\n\\n responses = [r for r in md_res.track_sample_response]\\n sample_requests = [req for req in md_res.signed_challenge_info]\\n\\n # Send scan and match samples if requested.\\n for sample_request in sample_requests:\\n path, track = local_info[sample_request.challenge_info.client_track_id]\\n\\n bogus_sample = None\\n if not enable_matching:\\n bogus_sample = b'' # just send empty bytes\\n\\n album_art_image = None\\n if include_album_art:\\n if include_album_art is True: # Load embedded album art.\\n song = mutagen.File(path)\\n\\n if isinstance(song, (mutagen.mp3.MP3)) and song.tags is not None:\\n # Search through all the APIC frames to find the cover (type 3).\\n for pic in song.tags.getall('APIC'):\\n if pic.type == 3:\\n album_art_image = pic.data\\n elif isinstance(song, mutagen.flac.FLAC):\\n # Search through all the picture frames to find the cover (type 3).\\n for pic in song.pictures:\\n if pic.type == 3:\\n album_art_image = pic.data\\n break\\n elif isinstance(song, mutagen.mp4.MP4):\\n if 'covr' in song:\\n album_art_image = song['covr'][0]\\n elif isinstance(song, mutagen.asf.ASF):\\n if 'WM/Picture' in song:\\n # Search through all the WM/Picture frames to find the cover (type 3).\\n for pic in song['WM/Picture']:\\n data = bytes(pic)\\n if data[0] == 3:\\n # Parse out the image data according to the WM_PICTURE spec:\\n # 1 byte type + 4 bytes data length + null-terminated mime +\\n # null-terminated description + data\\n pos = 5\\n while data[pos:pos + 2] != b\\\"\\\\x00\\\\x00\\\":\\n pos += 2\\n pos += 2\\n while data[pos:pos + 2] != b\\\"\\\\x00\\\\x00\\\":\\n pos += 2\\n\\n album_art_image = data[pos + 2:]\\n break\\n elif isinstance(song, mutagen.oggvorbis.OggVorbis):\\n if 'metadata_block_picture' in song:\\n # Search through all the picture frames to find the cover (type 3).\\n for pic in song['metadata_block_picture']:\\n # Mutagen does not parse out the picture fields to attributes\\n # like with FLAC, so we use the FLAC Picture class to do so.\\n # Picture blocks are base64 encoded in Ogg Vorbis.\\n picture = mutagen.flac.Picture(base64.b64decode(pic))\\n\\n if picture.type == 3:\\n album_art_image = picture.data\\n break\\n else: # Load external album art.\\n try:\\n with open(include_album_art, 'rb') as f:\\n album_art_image = f.read()\\n except OSError:\\n self.logger.warning(\\n \\\"Image file: %r cannot be read. Uploading %r without album art.\\\",\\n include_album_art, path\\n )\\n\\n try:\\n res = self._make_call(\\n MyProvideSample,\\n path,\\n sample_request,\\n track,\\n self.uploader_id,\\n bogus_sample,\\n album_art_image\\n )\\n\\n except (IOError, ValueError) as e:\\n self.logger.warning(\\\"couldn't create scan and match sample for '%r': %s\\\", path, str(e))\\n not_uploaded[path] = str(e)\\n else:\\n responses.extend(res.sample_response.track_sample_response)\\n\\n # Read sample responses and prep upload requests.\\n to_upload = {} # {serverid: (path, Track, do_not_rematch?)}\\n for sample_res in responses:\\n path, track = local_info[sample_res.client_track_id]\\n\\n if sample_res.response_code == upload_pb2.TrackSampleResponse.MATCHED:\\n self.logger.info(\\\"matched '%r' to sid %s\\\", path, sample_res.server_track_id)\\n\\n matched[path] = sample_res.server_track_id\\n\\n if not enable_matching:\\n self.logger.error(\\\"'%r' was matched without matching enabled\\\", path)\\n\\n elif sample_res.response_code == upload_pb2.TrackSampleResponse.UPLOAD_REQUESTED:\\n to_upload[sample_res.server_track_id] = (path, track, False)\\n\\n else:\\n # there was a problem\\n # report the symbolic name of the response code enum for debugging\\n enum_desc = upload_pb2._TRACKSAMPLERESPONSE.enum_types[0]\\n res_name = enum_desc.values_by_number[sample_res.response_code].name\\n\\n err_msg = \\\"TrackSampleResponse code %s: %s\\\" % (sample_res.response_code, res_name)\\n\\n if res_name == 'ALREADY_EXISTS':\\n # include the sid, too\\n # this shouldn't be relied on externally, but I use it in\\n # tests - being surrounded by parens is how it's matched\\n err_msg += \\\"(%s)\\\" % sample_res.server_track_id\\n\\n self.logger.warning(\\\"upload of '%r' rejected: %s\\\", path, err_msg)\\n\\n if res_name == 'ALREADY_EXISTS':\\n matched[path] = sample_res.server_track_id\\n else:\\n not_uploaded[path] = err_msg\\n\\n # Send upload requests.\\n if to_upload:\\n # TODO reordering requests could avoid wasting time waiting for reup sync\\n self._make_call(musicmanager.UpdateUploadState, 'start', self.uploader_id)\\n\\n for server_id, (path, track, do_not_rematch) in to_upload.items():\\n # It can take a few tries to get an session.\\n should_retry = True\\n attempts = 0\\n\\n while should_retry and attempts < 10:\\n session = self._make_call(musicmanager.GetUploadSession,\\n self.uploader_id, len(uploaded),\\n track, path, server_id, do_not_rematch)\\n attempts += 1\\n\\n got_session, error_details = \\\\\\n musicmanager.GetUploadSession.process_session(session)\\n\\n if got_session:\\n self.logger.info(\\\"got an upload session for '%r'\\\", path)\\n break\\n\\n should_retry, reason, error_code = error_details\\n self.logger.debug(\\\"problem getting upload session: %s\\\\ncode=%s retrying=%s\\\",\\n reason, error_code, should_retry)\\n\\n if error_code == 200 and do_not_rematch:\\n # reupload requests need to wait on a server sync\\n # 200 == already uploaded, so force a retry in this case\\n should_retry = True\\n\\n time.sleep(6) # wait before retrying\\n else:\\n err_msg = \\\"GetUploadSession error %s: %s\\\" % (error_code, reason)\\n\\n self.logger.warning(\\\"giving up on upload session for '%r': %s\\\", path, err_msg)\\n not_uploaded[path] = err_msg\\n\\n continue # to next upload\\n\\n # got a session, do the upload\\n # this terribly inconsistent naming isn't my fault: Google--\\n session = session['sessionStatus']\\n external = session['externalFieldTransfers'][0]\\n\\n session_url = external['putInfo']['url']\\n content_type = external.get('content_type', 'audio/mpeg')\\n\\n if track.original_content_type != locker_pb2.Track.MP3:\\n if enable_transcoding:\\n try:\\n self.logger.info(\\\"transcoding '%r' to mp3\\\", path)\\n contents = utils.transcode_to_mp3(path, quality=transcode_quality)\\n except (IOError, ValueError) as e:\\n self.logger.warning(\\\"error transcoding %r: %s\\\", path, e)\\n not_uploaded[path] = \\\"transcoding error: %s\\\" % e\\n continue\\n else:\\n not_uploaded[path] = \\\"transcoding disabled\\\"\\n continue\\n else:\\n with open(path, 'rb') as f:\\n contents = f.read()\\n\\n upload_response = self._make_call(musicmanager.UploadFile,\\n session_url, content_type, contents)\\n\\n success = upload_response.get('sessionStatus', {}).get('state')\\n if success:\\n uploaded[path] = server_id\\n else:\\n # 404 == already uploaded? serverside check on clientid?\\n self.logger.debug(\\\"could not finalize upload of '%r'. response: %s\\\",\\n path, upload_response)\\n not_uploaded[path] = 'could not finalize upload; details in log'\\n\\n self._make_call(musicmanager.UpdateUploadState, 'stopped', self.uploader_id)\\n\\n return uploaded, matched, not_uploaded\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b7c67e7e2bed5a87afd69ffb5455d3b\",\n \"score\": \"0.4830331\",\n \"text\": \"def upload_crew(session: Session, data: Iterable, movie_id: int) -> Iterator:\\n\\n for item in data:\\n crew_member_data = prepare_actor_or_crew_member_data(item)\\n crew_member = models.CrewMember.get_or_create(session, **crew_member_data)\\n yield crew_member\\n crew_data = prepare_crew_data(item, crew_member, movie_id)\\n crew = models.Crew.create(**crew_data)\\n yield crew\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98c60413414ae253b42bdd12833d23a1\",\n \"score\": \"0.48278242\",\n \"text\": \"def uploadChanges(self):\\n self.asicConf.setModified(1);\\n self.difConf.setModified(1);\\n self.dccConf.setModified(0)\\n try:\\n self.setup.recursiveUpload();\\n except Exception, e:\\n print e.getMessage()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98c60413414ae253b42bdd12833d23a1\",\n \"score\": \"0.48278242\",\n \"text\": \"def uploadChanges(self):\\n self.asicConf.setModified(1);\\n self.difConf.setModified(1);\\n self.dccConf.setModified(0)\\n try:\\n self.setup.recursiveUpload();\\n except Exception, e:\\n print e.getMessage()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d9b02882049f6c987292d48f8fb45f3\",\n \"score\": \"0.48141667\",\n \"text\": \"def upload(self):\\n for root, dirs, files in os.walk(self.__PATH__):\\n for filename in files:\\n if not filename.startswith(\\\".\\\"):\\n local_p = os.path.join(root, filename)\\n keyname = re.sub(r'files\\\\/', '', local_p)\\n self.__upload_file(local_p, keyname)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6bd678432fbd4be949cf5d5a9effeee4\",\n \"score\": \"0.4813621\",\n \"text\": \"def getServerUpload(self, file):\\n\\n x = self.makeSHAFile( file)\\n x['op'] = 'upload'\\n\\n self.socket.send_json(x)\\n #print(x)\\n\\n resp = self.socket.recv_json()\\n if(resp['resp']):\\n \\\"\\\"\\\"\\n res['resp'] = True\\n res['info'] = \\\"se guardo el archivo empiece la descarga\\\"\\n #res['hashfile'] = datajson['hashfile']\\n res['trozos'] = self.UpdateFilesDist(datajson)\\n \\\"\\\"\\\"\\n print(resp['info'])\\n #print(\\\"elementos enviados \\\", len(x['trozos']),\\\" :\\\\n\\\", x['trozos'])\\n #print(\\\"elementos enviados \\\", len(resp['trozos']),\\\" :\\\\n\\\",resp['trozos'])\\n \\n #enviar todos los elementos\\n self.uploadAllFiles( resp,file)\\n\\n else:\\n print(resp['info'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"45d0c63c96f2489f443fd669512ebf1e\",\n \"score\": \"0.48120642\",\n \"text\": \"def test_1(self):\\n \\n chemFile = os.path.join(os.path.dirname(rmgweb.__file__),'tests','files','kinetics','chem.inp')\\n dictFile = os.path.join(os.path.dirname(rmgweb.__file__),'tests','files','kinetics','species_dictionary.txt')\\n \\n with open(chemFile) as cf, open(dictFile) as df:\\n response = self.client.post('/tools/java_kinetics_library/', {'ChemkinFile': cf, 'DictionaryFile': df})\\n \\n self.assertEqual(response.status_code, 200)\\n \\n folder = os.path.join(settings.MEDIA_ROOT,'rmg','tools','chemkin')\\n \\n # Check if inputs were correctly uploaded\\n chemInput = os.path.join(folder,'chemkin','chem.inp')\\n dictInput = os.path.join(folder,'RMG_Dictionary.txt')\\n \\n self.assertTrue(os.path.isfile(chemInput),'Chemkin file was not uploaded')\\n self.assertTrue(os.path.isfile(dictInput),'Dictionary file was not uploaded')\\n \\n # Check if outputs were correctly generated\\n reactions = os.path.join(folder,'reactions.txt')\\n pdep = os.path.join(folder,'pdepreactions.txt')\\n species = os.path.join(folder,'species.txt')\\n \\n self.assertTrue(os.path.isfile(reactions),'Reactions file was not generated')\\n self.assertTrue(os.path.isfile(pdep),'Pdep reactions file was not generated')\\n self.assertTrue(os.path.isfile(species),'Species file was not generated')\\n \\n shutil.rmtree(folder)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f8da8002fdab768f3acb602de878d1dd\",\n \"score\": \"0.48069826\",\n \"text\": \"def load_mus_tracks(self, subsets=None, split=None):\\n\\n if subsets is not None:\\n if isinstance(subsets, str):\\n subsets = [subsets]\\n else:\\n subsets = ['train', 'test']\\n\\n if subsets != ['train'] and split is not None:\\n raise RuntimeError(\\\"Subset has to set to `train` when split is used\\\")\\n\\n tracks = []\\n for subset in subsets: \\n subset_folder = op.join(self.root, subset)\\n\\n for _, folders, files in os.walk(subset_folder):\\n if self.is_wav:\\n # parse pcm tracks and sort by name\\n for track_name in sorted(folders):\\n if subset == 'train':\\n if split == 'train' and track_name in self.setup['validation_tracks']:\\n continue\\n elif split == 'valid' and track_name not in self.setup['validation_tracks']:\\n continue\\n\\n track_folder = op.join(subset_folder, track_name)\\n # create new mus track\\n track = MultiTrack(\\n name=track_name,\\n path=op.join(\\n track_folder,\\n self.setup['mixture']\\n ),\\n subset=subset,\\n is_wav=self.is_wav,\\n stem_id=self.setup['stem_ids']['mixture'],\\n sample_rate=self.sample_rate\\n )\\n\\n # add sources to track\\n sources = {}\\n for src, source_file in list(\\n self.setup['sources'].items()\\n ):\\n # create source object\\n abs_path = op.join(\\n track_folder,\\n source_file\\n )\\n if os.path.exists(abs_path):\\n sources[src] = Source(\\n track,\\n name=src,\\n path=abs_path,\\n stem_id=self.setup['stem_ids'][src],\\n sample_rate=self.sample_rate\\n )\\n track.sources = sources\\n track.targets = self.create_targets(track)\\n\\n # add track to list of tracks\\n tracks.append(track)\\n else:\\n # parse stem files\\n for track_name in sorted(files):\\n if not track_name.endswith('.stem.mp4'):\\n continue\\n if subset == 'train':\\n if split == 'train' and track_name.split('.stem.mp4')[0] in self.setup['validation_tracks']:\\n continue\\n elif split == 'valid' and track_name.split('.stem.mp4')[0] not in self.setup['validation_tracks']:\\n continue\\n\\n # create new mus track\\n track = MultiTrack(\\n name=track_name.split('.stem.mp4')[0],\\n path=op.join(subset_folder, track_name),\\n subset=subset,\\n stem_id=self.setup['stem_ids']['mixture'],\\n is_wav=self.is_wav,\\n sample_rate=self.sample_rate\\n )\\n # add sources to track\\n sources = {}\\n for src, source_file in list(\\n self.setup['sources'].items()\\n ):\\n # create source object\\n abs_path = op.join(\\n subset_folder,\\n track_name\\n )\\n if os.path.exists(abs_path):\\n sources[src] = Source(\\n track,\\n name=src,\\n path=abs_path,\\n stem_id=self.setup['stem_ids'][src],\\n sample_rate=self.sample_rate\\n )\\n track.sources = sources\\n\\n # add targets to track\\n track.targets = self.create_targets(track)\\n tracks.append(track)\\n\\n return tracks\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43b9c1b3d6449000eb5a7e2d837d2066\",\n \"score\": \"0.4806059\",\n \"text\": \"def _upload_new_documents(self):\\n \\n self.document_file.flush()\\n self.document_file.seek(0)\\n self.corpus.upload_csv(self.document_file, 'documents')\\n self.document_file.close()\\n self.document_file = tempfile.TemporaryFile()\\n self.document_writer = csv.writer(self.document_file)\\n\\n self.occurrence_file.flush()\\n self.occurrence_file.seek(0)\\n self.corpus.upload_csv(self.occurrence_file, 'phrase_occurrences')\\n self.occurrence_file.close()\\n self.occurrence_file = tempfile.TemporaryFile()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"564c5dc2f6ef8f4e8ced986f79659f53\",\n \"score\": \"0.48036733\",\n \"text\": \"def upload(self, file):\\r\\n\\t\\traise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"310fbcbac9a8f784784dc5d432f5ed82\",\n \"score\": \"0.4803459\",\n \"text\": \"def send_data_files():\\r\\n filelist = files.split(',')\\r\\n filenamelist = filenames.split(',')\\r\\n pathname = os.path.dirname(sys.argv[0])\\r\\n gi = get_galaxy_instance(url, key)\\r\\n new_hist_name = assayname\\r\\n gi.histories.create_history(name=new_hist_name)\\r\\n historyid = get_history_id(url, key)\\r\\n for nr in range(len(filelist)):\\r\\n newfile = os.path.abspath(pathname) + \\\"/filestore/tmp/\\\" + filenamelist[nr]\\r\\n call([\\\"cp \\\" + filelist[nr] + \\\" \\\" + newfile], shell=True)\\r\\n gi.tools.upload_file(newfile, historyid)\\r\\n call([\\\"rm \\\" + newfile], shell=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f7c66fc20e07561c548eb29f9bfbadff\",\n \"score\": \"0.48026258\",\n \"text\": \"def UploadFile(self):\\n logging.debug('SimianAuthCliClient.UploadFile %s', self.config['uploadfile'])\\n for file_path in self.config['uploadfile']:\\n file_type = self.config['uploadfiletype']\\n try:\\n self.client.UploadFile(file_path, file_type)\\n except client.SimianServerError, e:\\n logging.exception('Error uploading log: %s', file_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7633b0af5e0df182c09403ca27f598a6\",\n \"score\": \"0.4796921\",\n \"text\": \"def initSongs():\\n global songs\\n mp3_filenames = os.listdir(\\\"MP3s\\\")\\n \\n for mp3_filename in mp3_filenames:\\n mp3_path = \\\"MP3s/\\\" + mp3_filename\\n wav_filename = os.path.splitext(mp3_filename)[0] + \\\".wav\\\" # wav filename is the same as mp3 except the extension\\n wav_path = \\\"Wavs/\\\" + wav_filename\\n \\n # construct and run ffmpeg command\\n input_mp3 = Input(mp3_path)\\n output_wav = Output(wav_path, AudioCodec(\\\"pcm_mulaw\\\"))\\n opt_dict = dict([(\\\"-ar\\\", \\\"8000\\\"), (\\\"-ac\\\", \\\"1\\\"), (\\\"-ab\\\", \\\"64000\\\")]) # sampling rate 8000 Hz, 1 audio channel (mono), bitrate 64kbits/s\\n opt = options.Option(opt_dict)\\n ffmpeg_command = FFmpeg(\\\"ffmpeg\\\", input_mp3, opt, output_wav)\\n ffmpeg_command.run()\\n print \\\"Playlist Server: File created: '\\\" + wav_filename + \\\"'\\\"\\n \\n mp3header = eyeD3.Mp3AudioFile(mp3_path)\\n length = str(mp3header.getPlayTime())\\n tag = eyeD3.Tag()\\n tag.link(mp3_path)\\n song = Song(length, tag.getArtist(), tag.getTitle(), wav_path)\\n songs.append(song)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f04a5eb3b6057a9673c500b017a50712\",\n \"score\": \"0.479319\",\n \"text\": \"def upload_instant(name, filepath):\\n\\n # Create session and get cookies\\n session = requests.session()\\n r = session.get(LOGIN_URL)\\n\\n soup = BeautifulSoup(r.text, \\\"html.parser\\\")\\n token = soup.find(\\\"input\\\", {'name': 'csrfmiddlewaretoken'}).get('value')\\n\\n if not token:\\n raise InvalidPageErrorException\\n\\n data = {\\n \\\"csrfmiddlewaretoken\\\": token,\\n \\\"login\\\": os.environ[\\\"MYINSTANTS_USERNAME\\\"],\\n \\\"password\\\": os.environ[\\\"MYINSTANTS_PASSWORD\\\"],\\n \\\"next\\\": \\\"/new/\\\",\\n }\\n\\n # Login\\n r = session.post(LOGIN_URL, data=data)\\n\\n if r.status_code != 200:\\n raise LoginErrorException\\n\\n # Get upload token\\n soup = BeautifulSoup(r.text, \\\"html.parser\\\")\\n token = soup.find(\\\"input\\\", {'name': 'csrfmiddlewaretoken'}).get('value')\\n\\n if not token:\\n raise InvalidPageErrorException\\n\\n filename = os.path.basename(filepath)\\n\\n multipart_data = MultipartEncoder(\\n fields={\\n \\\"csrfmiddlewaretoken\\\": token,\\n \\\"name\\\": name,\\n \\\"sound\\\": (\\n filename,\\n open(filepath, \\\"rb\\\"),\\n \\\"audio/mpeg\\\",\\n ),\\n \\\"image\\\": (\\\"\\\", None, \\\"\\\"),\\n \\\"color\\\": \\\"00FF00\\\",\\n \\\"category\\\": \\\"\\\",\\n \\\"description\\\": \\\"\\\",\\n \\\"tags\\\": \\\"\\\",\\n \\\"accept_terms\\\": \\\"on\\\",\\n }\\n )\\n\\n # Upload sound\\n response = session.post(\\n UPLOAD_URL,\\n data=multipart_data,\\n headers={\\\"Content-Type\\\": multipart_data.content_type, \\\"Referer\\\": UPLOAD_URL},\\n )\\n\\n soup = BeautifulSoup(response.text, \\\"html.parser\\\")\\n for ul_obj in soup.find_all(\\\"ul\\\", class_=\\\"errorlist\\\"):\\n if ul_obj.text.lower().find(\\\"instant with this name already exists.\\\") >= 0:\\n raise NameAlreadyExistsException\\n if ul_obj.text.lower().find(\\\"please keep filesize under 300.0 kb\\\") >= 0:\\n raise FileSizeException\\n if response.status_code != 200:\\n raise HTTPErrorException\\n\\n soup = BeautifulSoup(response.text, \\\"html.parser\\\")\\n last_uploaded_element = soup.find_all(\\\"a\\\", class_=\\\"instant-link\\\", text=name)\\n if not last_uploaded_element:\\n return response.url\\n\\n # Return sound url\\n url = last_uploaded_element[-1].get(\\\"href\\\")\\n return urljoin(response.url, url)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9692d8c6244b40427fbc3e61b6ce6b36\",\n \"score\": \"0.47920832\",\n \"text\": \"def connectionMade(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"33bb011500a2bd4b971fd8b1e619b380\",\n \"score\": \"0.4785044\",\n \"text\": \"async def playall ():\\n global song\\n global player\\n global construction\\n global midiCounter\\n global midifile\\n mf = MIDIFile(1)\\n midiCounter = 0\\n await bot.say(\\\"player ready\\\")\\n for part in construction:\\n await bot.say(str(part) + \\\"...\\\")\\n if part in song:\\n playChordList(song[part],isTierce,mf)\\n midifile = mf\\n path = os.path.abspath(\\\"output.mid\\\")\\n with open(\\\"output.mid\\\", 'wb') as outf:\\n mf.writeFile(outf)\\n\\t#fs = FluidSynth()\\n #with open(path, 'rb+') as inf: \\n # with open(\\\"output.wav\\\", 'wb+') as wavf: \\n # fs.midi_to_audio(inf, wavf)\\n \\n #path = str(os.getcwd()) + str(os.sep)\\n #pathMidi = path + \\\"output.mid\\\"\\n #pathWav = path + \\\"out.wav\\\"\\n \\n #await bot.say(\\\"midi Path : \\\" + pathMidi)\\n #await bot.say(\\\"wav path : \\\" + pathWav)\\n \\n #fs = FluidSynth()\\n #fs.midi_to_audio(pathMidi, pathWav)\\n \\n await bot.say(\\\"song played\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9d65a3be09ab1b5bc035f02ea2ce4d2d\",\n \"score\": \"0.47824302\",\n \"text\": \"def handle_upload(corpus, f, fileattrs):\\n\\t# logger.info(fileattrs)\\n\\n\\t# dest_folder = os.path.join(settings.UPLOAD_DIR, fileattrs['qquuid'])\\n\\t# dest = os.path.join(dest_folder, fileattrs['qqfilename'])\\n\\tqqtotalparts = int(fileattrs['qqtotalparts'] or 1)\\n\\tqqpartindex = int(fileattrs['qqpartindex'] or 0)\\n\\n\\t# # Chunked\\n\\t# if qqtotalparts > 1:\\n\\t# \\tdest_folder = os.path.join(settings.CHUNKS_DIR, fileattrs['qquuid'])\\n\\t# \\tdest = os.path.join(dest_folder, fileattrs['qqfilename'], str(qqpartindex))\\n\\t# \\t# logger.info('Chunked upload received')\\n\\n\\tf.seek(0)\\n\\tmd5 = hashlib.md5(f.read()).hexdigest()\\n\\tf.seek(0)\\n\\tqquuid = fileattrs['qquuid']\\n\\tkey = {'size': f.size, 'md5': md5}\\n\\tfile_record = {'_id': ObjectId(qquuid), 'status': 0, 'nwords': 0}\\n\\tfile_record.update(key)\\n\\told_record = settings.DBC.common.files.find_one_and_update(key, {'$setOnInsert': file_record}, upsert=True)\\n\\tif not old_record:\\n\\t\\tpaper_dest = paper_path(qquuid)\\n\\t\\tutils.save_upload(f, paper_dest)\\n\\t\\textractedResult = utils.extracted(paper_dest) # extract\\n\\t\\tif extractedResult:\\n\\t\\t\\twith open(extracted_path(qquuid), 'w') as fout:\\n\\t\\t\\t\\tfout.write(extractedResult)\\n\\t\\t\\trefinedResult = refine(extractedResult) # refine\\n\\t\\t\\tif refinedResult:\\n\\t\\t\\t\\twith open(refined_path(qquuid), 'w') as fout:\\n\\t\\t\\t\\t\\tfout.write(refinedResult)\\n\\t\\t\\t\\tstatus = 2\\n\\t\\t\\telse:\\n\\t\\t\\t\\tstatus = -2\\n\\t\\telse:\\n\\t\\t\\tstatus = -1\\n\\t\\tsettings.DBC.common.files.find_one_and_update(file_record, {'$set': {'nwords': len(extractedResult.split()), 'status': status}})\\n\\n\\t# logger.info('Upload saved: %s' % dest)\\n\\n\\t# # If the last chunk has been sent, combine the parts.\\n\\t# if qqtotalparts > 1 and qqpartindex == (qqtotalparts - 1):\\n\\n\\t# \\t# logger.info('Combining chunks: %s' % os.path.dirname(dest))\\n\\t# \\tutils.combine_chunks(qqtotalparts,\\n\\t# \\t\\tfileattrs['qqtotalfilesize'],\\n\\t# \\t\\tsource_folder=os.path.dirname(dest),\\n\\t# \\t\\tdest=os.path.join(settings.UPLOAD_DIR, fileattrs['qquuid'], fileattrs['qqfilename']))\\n\\t# \\t# logger.info('Combined: %s' % dest)\\n\\n\\t# \\tshutil.rmtree(os.path.dirname(os.path.dirname(dest)))\\n\\n\\t# # Upload done.\\n\\t# if qqpartindex == (qqtotalparts - 1):\\n\\t# \\tpass\\n\\n\\tfile_id = old_record and old_record['_id'] or ObjectId(qquuid)\\n\\ttitle = os.path.splitext(fileattrs['qqfilename'])[0]\\n\\tupload_record = {'file': file_id, 'corpus': corpus.pk, 'title': title}\\n\\tsettings.DBC.common.uploads.insert_one(upload_record)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ff918911b6fc26265fa731ec68a0916f\",\n \"score\": \"0.47816557\",\n \"text\": \"def upload(self):\\n logger.info('Starting upload for {} file(s)'.format(len(self.__upload_queue)))\\n while self.__upload_queue > []:\\n file = self.__upload_queue.pop()\\n if not self.client.check(os.path.dirname(file['remote'])):\\n Util.webdav_mkdirs(os.path.dirname(file['remote']), '', self.client)\\n logger.debug(f'Create missing directory {os.path.dirname(file[\\\"remote\\\"])}')\\n logger.info('Uploading {local} to {remote}'.format(**file))\\n self.client.upload_sync(remote_path=file['remote'], local_path=file['local'])\\n logger.info('Finished uploading all files.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a5d54a1bfe7a4490937b25ccd148fb94\",\n \"score\": \"0.4781377\",\n \"text\": \"def Test4(self):\\r\\n \\r\\n f= open('outputHighQuality.wav' ,\\\"rb\\\")\\r\\n myurl = 'http://localhost:5555/upload'\\r\\n header = { 'content_type': 'audio/wav' } \\r\\n x = requests.request('POST', url=myurl , data = f , headers=header ) \\r\\n print (x.text)\\r\\n print (x.status_code)\\r\\n if x.status_code == 200:\\r\\n self.music_id = x.content\\r\\n self.set_Logs(x.text)\\r\\n self.AssertOK()\\r\\n \\r\\n else:\\r\\n self.set_Logs(x.text)\\r\\n self.AssertKO()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e10183170a1a2f1a3873feaec6eb948a\",\n \"score\": \"0.47785318\",\n \"text\": \"def db_add_synapses(clear):\\n graph = graph_p2()\\n if clear:\\n graph.delete_all()\\n\\n conns = preproc.conns_df()\\n transx = graph.cypher.begin()\\n statement = \\\"MATCH (n1:Neuron {name:{nm1}}), (n2:Neuron {name:{nm2}}) CREATE (n1)-[s:Synapse {name:n1.name+'->'+n2.name, type:{tp}, weight:{w}}]->(n2)\\\"\\n for idx, row in conns.iterrows():\\n transx.append(statement, {\\\"nm1\\\":row['Neuron1'], \\\"nm2\\\":row['Neuron2'], \\\"tp\\\":row[\\\"Type\\\"], \\\"w\\\":row[\\\"Nbr\\\"]})\\n transx.commit()\\n db_set_node_degrees()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e6ce975c6e85d10af398ac56d3497c3\",\n \"score\": \"0.477623\",\n \"text\": \"def test_training_upload(self):\\n\\n logo_file = StringIO.StringIO(\\n 'GIF87a\\\\x01\\\\x00\\\\x01\\\\x00\\\\x80\\\\x01\\\\x00\\\\x00\\\\x00\\\\x00ccc,\\\\x00'\\n '\\\\x00\\\\x00\\\\x00\\\\x01\\\\x00\\\\x01\\\\x00\\\\x00\\\\x02\\\\x02D\\\\x01\\\\x00;')\\n logo = SimpleUploadedFile(\\n 'logo_test_file.gif', logo_file.read(), 'image/gif')\\n manual_file = StringIO.StringIO(\\n 'GIF87a\\\\x01\\\\x00\\\\x01\\\\x00\\\\x80\\\\x01\\\\x00\\\\x00\\\\x00\\\\x00ccc,\\\\x00'\\n '\\\\x00\\\\x00\\\\x00\\\\x01\\\\x00\\\\x01\\\\x00\\\\x00\\\\x02\\\\x02D\\\\x01\\\\x00;')\\n manual = SimpleUploadedFile(\\n 'logo_test_file.gif', manual_file.read(), 'image/gif')\\n\\n c = Client()\\n c.login(username='admin', password='admin')\\n c.post(\\n reverse('training_upload'),\\n data={\\n 'title': 'Uploaded Training',\\n 'abstract': 'Abstract for uploaded training',\\n 'publication_date': get_random_date(),\\n 'logo': logo,\\n 'manual': manual,\\n },\\n follow=True)\\n\\n self.assertEquals(Training.objects.all().count(), 1)\\n\\n training = Training.objects.all()[0]\\n c = Client()\\n response = c.get(reverse('training_detail', args=(str(training.id),)))\\n self.assertEquals(response.status_code, 200)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e20802935133faa59138993d72ee8985\",\n \"score\": \"0.4773659\",\n \"text\": \"def test_can_retrieve_connected_user_post_media_video(self):\\n user = make_user()\\n connected_user = make_user()\\n\\n circle = make_circle(creator=connected_user)\\n\\n connected_user.connect_with_user_with_id(user_id=user.pk, circles_ids=[circle.pk])\\n user.confirm_connection_with_user_with_id(user_id=connected_user.pk)\\n\\n headers = make_authentication_headers_for_user(user=user)\\n\\n test_video = get_test_video()\\n\\n with open(test_video['path'], 'rb') as file:\\n file = File(file)\\n post = connected_user.create_encircled_post(video=file, circles_ids=[circle.pk])\\n\\n get_worker('high', worker_class=SimpleWorker).work(burst=True)\\n\\n url = self._get_url(post=post)\\n\\n response = self.client.get(url, **headers, format='multipart')\\n\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\\n\\n response_media = json.loads(response.content)\\n\\n post.refresh_from_db()\\n\\n post_media = post.get_media().all()\\n\\n self._compare_response_media_with_post_media(post_media=post_media, response_media=response_media)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"db3612149e54b27992ea91325c7d7d43\",\n \"score\": \"0.47723678\",\n \"text\": \"def _octopus(self):\\n return self._serene.octopii.upload(self.octo_local)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e39b219aa6f437f228af45ae214bdc8e\",\n \"score\": \"0.4769472\",\n \"text\": \"def upload_instant(name, filepath):\\n\\n # Create session and get cookies\\n session = requests.session()\\n response = session.get(\\n LOGIN_URL,\\n headers={\\n \\\"User-Agent\\\": generate_user_agent(),\\n },\\n )\\n\\n sel = parsel.Selector(response.text)\\n token = sel.css(\\\"input[name=csrfmiddlewaretoken]::attr(value)\\\").get()\\n\\n if not token:\\n raise InvalidPageErrorException\\n\\n data = {\\n \\\"csrfmiddlewaretoken\\\": token,\\n \\\"login\\\": os.environ[\\\"MYINSTANTS_USERNAME\\\"],\\n \\\"password\\\": os.environ[\\\"MYINSTANTS_PASSWORD\\\"],\\n \\\"next\\\": \\\"/en/new/\\\",\\n }\\n\\n # Login\\n response = session.post(\\n LOGIN_URL,\\n data=data,\\n headers={\\n \\\"User-Agent\\\": generate_user_agent(),\\n },\\n )\\n\\n if response.status_code != 200:\\n raise LoginErrorException\\n\\n # Get upload token\\n sel = parsel.Selector(response.text)\\n token = sel.css(\\\"input[name=csrfmiddlewaretoken]::attr(value)\\\").get()\\n\\n if not token:\\n raise InvalidPageErrorException\\n\\n filename = os.path.basename(filepath)\\n\\n multipart_data = MultipartEncoder(\\n fields={\\n \\\"csrfmiddlewaretoken\\\": token,\\n \\\"name\\\": name,\\n \\\"sound\\\": (\\n filename,\\n open(filepath, \\\"rb\\\"),\\n \\\"audio/mpeg\\\",\\n ),\\n \\\"image\\\": (\\\"\\\", None, \\\"\\\"),\\n \\\"color\\\": \\\"00FF00\\\",\\n \\\"category\\\": \\\"\\\",\\n \\\"description\\\": \\\"\\\",\\n \\\"tags\\\": \\\"\\\",\\n \\\"accept_terms\\\": \\\"on\\\",\\n }\\n )\\n\\n # Upload sound\\n response = session.post(\\n UPLOAD_URL,\\n data=multipart_data,\\n headers={\\n \\\"Content-Type\\\": multipart_data.content_type,\\n \\\"Referer\\\": UPLOAD_URL,\\n \\\"User-Agent\\\": generate_user_agent(),\\n },\\n )\\n\\n sel = parsel.Selector(response.text)\\n errors = \\\"\\\\n\\\".join(sel.css(\\\"ul.errorlist\\\").getall())\\n if \\\"instant with this name already exists.\\\" in errors:\\n raise NameAlreadyExistsException\\n if \\\"please keep filesize under 300.0 kb\\\" in errors:\\n raise FileSizeException\\n if response.status_code != 200:\\n raise HTTPErrorException\\n\\n last_uploaded_element = sel.xpath(\\n \\\"//a[contains(@class, 'instant-link') and text()=$name]/@href\\\", name=name\\n ).get()\\n if not last_uploaded_element:\\n return response.url\\n\\n # Return sound url\\n return urljoin(response.url, last_uploaded_element)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"912925461c0949f00263b2f33bad982b\",\n \"score\": \"0.47686628\",\n \"text\": \"def db_add_neurons(clear):\\n graph = graph_p2()\\n if clear:\\n graph.delete_all()\\n\\n neurons_df = preproc.neurons_df()\\n transx = graph.cypher.begin()\\n statement = \\\"CREATE (n:Neuron {props})\\\"\\n for name, params in neurons_df.iterrows():\\n params = params.to_dict()\\n params['name'] = name\\n transx.append(statement, {\\\"props\\\":params})\\n transx.commit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"23afe0b7b1275049340ec33cbc265083\",\n \"score\": \"0.4757897\",\n \"text\": \"def upload(args):\\n log(\\\"Preparing to upload images...\\\")\\n d = localfs.filepath(paths[\\\"datasetLocal\\\"], paths[\\\"datasetName\\\"])\\n if localfs.isempty(d):\\n raise RuntimeError(\\\"Missing images in dataset folder \\\"+d)\\n def validate_filenames(names):\\n for n in names:\\n if \\\" \\\" in n:\\n raise ValueError(\\\"Image file names cannot contain spaces. [{}]\\\".format(n)) # TODO add non-alphanumeric character checking\\n validate_filenames(localfs.ls(d))\\n log(\\\"Uploading. Please wait...\\\")\\n spell.upload(paths[\\\"datasetLocal\\\"], paths[\\\"datasetRemoteDir\\\"])\\n log(\\\"Upload complete.\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9eb79306cdb1f527f21ed41ee87ba4b6\",\n \"score\": \"0.47536415\",\n \"text\": \"def scrape_and_write_medias(self):\\n self.start_driver()\\n if self.login():\\n logging.info('HULU MOVIE SEARCH')\\n movie_genre_pages = self.get_genre_pages(mtype='movie')\\n movies = self.get_medias(movie_genre_pages)\\n self.lookup_and_write_medias(medias=movies, mtype='movie')\\n\\n logging.info('HULU SHOW SEARCH')\\n show_genre_pages = self.get_genre_pages(mtype='show')\\n shows = self.get_medias(show_genre_pages)\\n self.lookup_and_write_medias(medias=shows, mtype='show')\\n else:\\n logging.error('HULU login failed')\\n\\n self.stop_driver()\\n flaskapp.remove_old_sources('hulu') # remove sources not just updated\\n flaskapp.remove_hulu_addon_media() # remove overlap of sho and hulu\",\n \"title\": \"\"\n },\n {\n \"docid\": \"96f3131bc1e37339ca9c0072afd4b3da\",\n \"score\": \"0.47527525\",\n \"text\": \"def download(self):\\n if self._check_exists():\\n return\\n\\n # Create folders\\n try:\\n os.makedirs(os.path.join(self.root, self.raw_folder))\\n os.makedirs(os.path.join(self.root, self.processed_folder))\\n except OSError as e:\\n if e.errno == errno.EEXIST:\\n pass\\n else:\\n raise\\n\\n for url in self.urls:\\n filename = url.rpartition('/')[2]\\n download_url(url, root=os.path.join(self.root, self.raw_folder),\\n filename=filename, md5=None)\\n\\n # process and save as torch files\\n print('Processing raw data..')\\n\\n training_set = self._read_raw_image_file('binarized_mnist_train.amat')\\n val_set = self._read_raw_image_file('binarized_mnist_valid.amat')\\n test_set = self._read_raw_image_file('binarized_mnist_test.amat')\\n\\n processed_dir = os.path.join(self.root, self.processed_folder)\\n with open(os.path.join(processed_dir, self.training_file), 'wb') as f:\\n torch.save(training_set, f)\\n with open(os.path.join(processed_dir, self.val_file), 'wb') as f:\\n torch.save(val_set, f)\\n with open(os.path.join(processed_dir, self.test_file), 'wb') as f:\\n torch.save(test_set, f)\\n\\n print('Completed data download.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7fba11591dd4c48a39e74fca4ea8c822\",\n \"score\": \"0.474788\",\n \"text\": \"def Test5(self):\\r\\n \\r\\n f= open('outputLowQuality.wav' ,\\\"rb\\\")\\r\\n myurl = 'http://localhost:5555/upload'\\r\\n header = { 'content_type': 'audio/wav' } \\r\\n x = requests.request('POST', url=myurl , data = f , headers=header ) \\r\\n print (x.text)\\r\\n print (x.status_code)\\r\\n if x.status_code == 200:\\r\\n self.music_id = x.content\\r\\n self.set_Logs(x.text)\\r\\n self.AssertOK()\\r\\n \\r\\n else:\\r\\n self.set_Logs(x.text)\\r\\n self.AssertKO()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"664a13dd5393e34662516970b385c609\",\n \"score\": \"0.4745173\",\n \"text\": \"def update(self):\\r\\n# pwstate\\r\\n responsea = requests.request(\\\"POST\\\", url, data=PowerStatus, headers=headers)\\r\\n treed = ET.fromstring(responsea.content)\\r\\n for power_name in treed.findall('.//Power_Control/Power'):\\r\\n self._pwstate = power_name.text\\r\\n\\r\\n if self._pwstate == \\\"On\\\":\\r\\n self._state = STATE_IDLE\\r\\n else:\\r\\n self._state = STATE_OFF\\r\\n\\r\\n # volumestate\\r\\n responseb = requests.request(\\\"POST\\\", url, data=Status, headers=headers)\\r\\n treee = ET.fromstring(responseb.content)\\r\\n for vol_number in treee.findall('.//Volume/Lvl'):\\r\\n self._volume = vol_number.text\\r\\n\\r\\n\\r\\n # titel\\r\\n responsec = requests.request(\\\"POST\\\", url, data=PlayStatus, headers=headers)\\r\\n treef = ET.fromstring(responsec.content)\\r\\n for music_title in treef.findall('.//Meta_Info/Song'):\\r\\n self._media_title = music_title.text\\r\\n\\r\\n # album\\r\\n responsed = requests.request(\\\"POST\\\", url, data=PlayStatus, headers=headers)\\r\\n treeg = ET.fromstring(responsed.content)\\r\\n for album_name in treeg.findall('.//Meta_Info/Album'):\\r\\n self._media_album_name = album_name.text\\r\\n\\r\\n # Kunstler\\r\\n responsee = requests.request(\\\"POST\\\", url, data=PlayStatus, headers=headers)\\r\\n treeh = ET.fromstring(responsee.content)\\r\\n for artist_name in treeh.findall('.//Meta_Info/Artist'):\\r\\n self._media_artist = artist_name.text\\r\\n\\r\\n\\r\\n # mutestate\\r\\n responsef = requests.request(\\\"POST\\\", url, data=Status, headers=headers)\\r\\n treei = ET.fromstring(responsef.content)\\r\\n for volm_name in treei.findall('.//Volume/Mute'):\\r\\n resultf = volm_name.text\\r\\n if resultf == \\\"On\\\":\\r\\n self._muted = True\\r\\n if resultf == \\\"Off\\\":\\r\\n self._muted = False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"563b56b48be7da15cf12e49d04b278a4\",\n \"score\": \"0.47445157\",\n \"text\": \"def perform_transfer_tracks(self, playlist_name, songs=[]):\\n def check_for_playlist(playlist):\\n \\\"\\\"\\\"\\n This function checks if playlist is already created.\\n Parameters:\\n playlist: List of strings.\\n Returns:\\n playlist_id or None\\n \\\"\\\"\\\"\\n found_p_id = None\\n current_user_playlists = self.get_playlist_info()\\n for data_dic in current_user_playlists:\\n for p_name, playlist_id in data_dic.items():\\n if p_name == playlist_name:\\n found_p_id = playlist_id\\n return found_p_id\\n uris = []\\n # Check for playlist\\n playlist_id = check_for_playlist(playlist_name)\\n if not playlist_id:\\n # If no id found, then create a new playlist.\\n playlist_id = self.create_playlist(\\n playlist_name,\\n description='playlist created by rythmize.'\\n )\\n # Move uris into playlist. \\n track_uris = self.get_tracks_uri(self.get_playlist_tracks(playlist_id))\\n for song in songs:\\n track_name, artist = song['track'], song['artist']\\n if track_name and artist:\\n uri = self.get_track_uri(track_name, artist)\\n if uri and uri not in track_uris:\\n # Add uri to uri's\\n uris.append(uri)\\n endpoint = f\\\"https://api.spotify.com/v1/playlists/{playlist_id}/tracks\\\"\\n headers = self.get_resource_header()\\n request_data = json.dumps(uris)\\n response = requests.post(endpoint, headers=headers, data=request_data)\\n if response.status_code in range(200, 299):\\n # Valid response, return response\\n return response.json()\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5791bc7f51869c9a13aba0209ab9bff9\",\n \"score\": \"0.47419113\",\n \"text\": \"def ds_create_link_medias_w_handles(self, uniq_id: int, media_refs: list):\\n doing = \\\"?\\\"\\n try:\\n for resu in media_refs:\\n r_attr = {\\\"order\\\": resu.media_order}\\n if resu.crop:\\n r_attr[\\\"left\\\"] = resu.crop[0]\\n r_attr[\\\"upper\\\"] = resu.crop[1]\\n r_attr[\\\"right\\\"] = resu.crop[2]\\n r_attr[\\\"lower\\\"] = resu.crop[3]\\n doing = f\\\"(src:{uniq_id}) -[{r_attr}]-> Media {resu.media_handle}\\\"\\n # print(doing)\\n result = self.tx.run(\\n CypherObjectWHandle.link_media,\\n root_id=uniq_id,\\n handle=resu.media_handle,\\n r_attr=r_attr,\\n )\\n media_uid = result.single()[0] # for media object\\n\\n for handle in resu.note_handles:\\n doing = f\\\"{media_uid}->Note {handle}\\\"\\n self.tx.run(\\n CypherObjectWHandle.link_note, root_id=media_uid, handle=handle\\n )\\n\\n for handle in resu.citation_handles:\\n doing = f\\\"{media_uid}->Citation {handle}\\\"\\n self.tx.run(\\n CypherObjectWHandle.link_citation,\\n root_id=media_uid,\\n handle=handle,\\n )\\n\\n except Exception as err:\\n logger.error(\\n f\\\"Neo4jUpdateService.create_link_medias_by_handles {doing}: {err}\\\"\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27f7b9b644f8bc524d91fc92a8f8fab5\",\n \"score\": \"0.47408184\",\n \"text\": \"async def send_ucube_to_channel(self, channel_info: TextChannel, message_text, embed_list, media_files, club_name):\\n ...\\n try:\\n channel: discord.TextChannel = self.bot.get_channel(channel_info.id)\\n if not channel:\\n # fetch channel instead (assuming discord.py cache did not load)\\n channel: discord.TextChannel = await self.bot.fetch_channel(channel_info.id)\\n except Exception as e:\\n # remove the channel from future updates as it cannot be found.\\n print(f\\\"{e} - Removing Text Channel {channel_info.id} from cache for {club_name} since it could not \\\"\\n f\\\"be processed/found.\\\")\\n return await self.delete_channel(channel_info.id, club_name.lower())\\n\\n msg_list: List[discord.Message] = []\\n file_list = []\\n\\n try:\\n mention_role = f\\\"<@&{channel_info.role_id}>\\\" if channel_info.role_id else None\\n\\n for count, embed in enumerate(embed_list, 1):\\n msg_list.append(await channel.send(mention_role if count == 1 else None, embed=embed))\\n\\n if message_text or media_files:\\n # Since an embed already exists, any individual content will not load\\n # as an embed -> Make it it's own message.\\n if media_files:\\n # a list of file locations\\n for photo_location in media_files:\\n file_list.append(discord.File(photo_location))\\n\\n msg_list.append(await channel.send(message_text if message_text else None, files=file_list or None))\\n print(f\\\"UCube Post for {club_name} sent to {channel_info.id}.\\\")\\n except discord.Forbidden as e:\\n # no permission to post\\n print(f\\\"{e} (discord.Forbidden) - UCube Post Failed to {channel_info.id} for {club_name}\\\")\\n\\n # remove the channel from future updates as we do not want it to clog our rate-limits.\\n return await self.delete_channel(channel_info.id, club_name.lower())\\n except Exception as e:\\n print(f\\\"{e} (Exception) - UCube Post Failed to {channel_info.id} for {club_name}\\\")\\n return\\n\\n if not channel.is_news():\\n return\\n\\n for msg in msg_list:\\n try:\\n await msg.publish()\\n except Exception as e:\\n print(f\\\"Failed to publish Message ID: {msg.id} for Channel ID: {channel_info.id} - {e}\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0daeaa48b2e2ae305f3953bb8ca3fef2\",\n \"score\": \"0.4738126\",\n \"text\": \"async def add_torrent_magnet(magnet,message):\\n client = await get_client()\\n try:\\n ctor = len(client.torrents_info())\\n \\n ext_hash = Hash_Fetch.get_hash_magnet(magnet)\\n ext_res = client.torrents_info(torrent_hashes=ext_hash)\\n if len(ext_res) > 0:\\n await message.edit(\\\"This torrent is alreaded in the leech list.\\\")\\n return False\\n # hot fix for the below issue\\n savepath = os.path.join(os.getcwd(), \\\"Downloads\\\", str(time.time()).replace(\\\".\\\",\\\"\\\"))\\n op = client.torrents_add(magnet, save_path=savepath)\\n \\n \\n # TODO uncomment the below line and remove the above fix when fixed https://github.com/qbittorrent/qBittorrent/issues/13572\\n # op = client.torrents_add(magnet)\\n\\n # torrents_add method dosent return anything so have to work around\\n if op.lower() == \\\"ok.\\\":\\n st = datetime.now()\\n \\n ext_res = client.torrents_info(torrent_hashes=ext_hash)\\n if len(ext_res) > 0:\\n torlog.info(\\\"Got torrent info from ext hash.\\\")\\n return ext_res[0]\\n\\n while True:\\n if (datetime.now() - st).seconds >= 10:\\n torlog.warning(\\\"The provided torrent was not added and it was timed out. magnet was:- {}\\\".format(magnet))\\n await message.edit(\\\"The torrent was not added due to an error.\\\")\\n return False\\n ctor_new = client.torrents_info()\\n if len(ctor_new) > ctor:\\n # https://t.me/c/1439207386/2977 below line is for this\\n torlog.info(ctor_new)\\n torlog.info(magnet)\\n return ctor_new[0]\\n\\n else:\\n await message.edit(\\\"This is an unsupported/invalid link.\\\")\\n except qba.UnsupportedMediaType415Error as e:\\n #will not be used ever ;)\\n torlog.error(\\\"Unsupported file was detected in the magnet here\\\")\\n await message.edit(\\\"This is an unsupported/invalid link.\\\")\\n return False\\n except Exception as e:\\n torlog.error(\\\"{}\\\\n{}\\\".format(e,traceback.format_exc()))\\n await message.edit(\\\"Error occured check logs.\\\")\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5824f0805f76f8b8b11fcc67c89a4abf\",\n \"score\": \"0.47361273\",\n \"text\": \"async def upload_read_files(sample_provider, read_files):\\n for file in read_files:\\n await sample_provider.upload(file)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab12c986180eeda5de8aebd8b25204ec\",\n \"score\": \"0.47333428\",\n \"text\": \"def uploads(self, uploads):\\n\\n self._uploads = uploads\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b0dff0c0ccd96ff1c15ebc31a5565316\",\n \"score\": \"0.47325784\",\n \"text\": \"def process_json(filenames, training, ambiguous, special):\\n\\n # We only want dialogs in German: K, L, M, N, G, W\\n\\n sid_to_uid = defaultdict(list)\\n uid_to_sid = {}\\n uid_to_text = {}\\n uid_to_wav = {}\\n \\n for filename in filenames: \\n with open(filename, 'r') as infile:\\n content = json.loads(infile.read())\\n \\n # Get meta info\\n uid_parts = content[\\\"name\\\"].split('_')\\n uid = '_'.join([uid_parts[2], uid_parts[0], uid_parts[1]])\\n wavfile = os.path.abspath(os.path.join(\\n os.path.dirname(filename), \\n content[\\\"annotates\\\"]\\n ))\\n\\n # Add to the dict for wav\\n uid_to_wav[uid] = wavfile\\n\\n # Get speaker ID\\n sid = assocs(content[\\\"levels\\\"][0][\\\"items\\\"][0][\\\"labels\\\"])[\\\"SPN\\\"]\\n\\n # Add to the dict for spk2utt\\n sid_to_uid[sid].append(uid)\\n\\n # Add to the dict for utt2spk\\n uid_to_sid[uid] = sid\\n\\n # Get tokens\\n tokens = []\\n for item in content[\\\"levels\\\"][1][\\\"items\\\"]:\\n labels = assocs(item[\\\"labels\\\"])\\n \\n try:\\n tokens.append(labels[\\\"ORT\\\"])\\n except KeyError:\\n continue\\n\\n # Add to the dict for text\\n uid_to_text[uid] = ' '.join(tokens)\\n\\n # Now we can write the wav.scp file\\n dir_name = \\\"\\\" \\n if training:\\n dir_name = \\\"train\\\"\\n else:\\n dir_name = \\\"test\\\"\\n if ambiguous:\\n dir_name += \\\"_ambiguous\\\"\\n else:\\n dir_name += \\\"_unambiguous\\\"\\n with open('data/%s/wav.scp' % dir_name, 'w') as outfile:\\n for key, value in sorted(uid_to_wav.items(), key=itemgetter(0)):\\n outfile.write(\\\"%s cat %s |\\\\n\\\" % (key, value))\\n\\n # Now we can write the text\\n with open('data/%s/text' % dir_name, 'w') as outfile:\\n for key, value in sorted(uid_to_text.items(), key=itemgetter(0)):\\n if ambiguous:\\n value = convert_ambiguous(value, special)\\n else:\\n value = convert_unambiguous(value, special)\\n outfile.write(\\\"%s %s\\\\n\\\" % (key, value))\\n\\n # We also write the raw text\\n with open('data/%s/raw_text' % dir_name, 'w') as outfile:\\n for key, value in sorted(uid_to_text.items(), key=itemgetter(0)):\\n if ambiguous:\\n value = convert_ambiguous(value, special)\\n else:\\n value = convert_unambiguous(value, special)\\n outfile.write(\\\"%s\\\\n\\\" % (value))\\n\\n # Now we can write utt2spk\\n with open('data/%s/utt2spk' % dir_name, 'w') as utt2spk:\\n for key, value in sorted(uid_to_sid.items(), key=itemgetter(0)):\\n utt2spk.write(\\\"%s %s\\\\n\\\" % (key, value))\\n\\n # Now we can update spk2utt\\n with open('data/%s/spk2utt' % dir_name, 'w') as spk2utt:\\n for key, values in sorted(sid_to_uid.items(), key=itemgetter(0)):\\n values = sorted(values)\\n spk2utt.write(\\\"%s %s\\\\n\\\" % (key, ' '.join(values)))\\n\\n return sid_to_uid.keys()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8048116db2e876fbf609a639b2ded22b\",\n \"score\": \"0.47302797\",\n \"text\": \"def add_list(self, tracks):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a20357ead2d05f9c803d6365c3465dcc\",\n \"score\": \"0.47293723\",\n \"text\": \"def handle_upload(cls,f, attrs):\\n \\n if attrs['folderId'] == \\\"root\\\":\\n folderId = 0\\n else :\\n folderId = attrs['folderId']\\n\\n chunked = False\\n dest_folder = os.path.join(cls.UPLOAD_DIRECTORY, attrs['qquuid'])\\n dest = os.path.join(dest_folder, attrs['qqfilename'])\\n\\n # Chunked\\n if 'qqtotalparts' in attrs and int(attrs['qqtotalparts']) > 1:\\n chunked = True\\n dest_folder = os.path.join(cls.CHUNKS_DIRECTORY, attrs['qquuid'])\\n dest = os.path.join(dest_folder, attrs['qqfilename'], str(attrs['qqpartindex']))\\n cls.save_upload(f, dest)\\n else:\\n # Generate repo file in database before saving actual file\\n\\n chars = \\\"abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMOPQRTUVWXYZ0123456789\\\"\\n fileName = \\\"\\\".join(random.sample(chars, 6))\\n \\n while RepoFileModel.exists(fileName):\\n fileName = \\\"\\\".join(random.sample(chars, 6))\\n\\n ogName = attrs['qqfilename']\\n p = ogName.split(\\\".\\\")\\n\\n newFile = RepoFileModel(\\n fileName, p[0], p[1], folderId, attrs['qquuid'])\\n print(newFile.json())\\n newFile.save()\\n\\n dest = os.path.join(cls.UPLOAD_DIRECTORY, fileName + \\\".\\\" + p[1])\\n cls.save_upload(f, dest)\\n\\n ImageManager().createThumbnails(newFile)\\n # ^^^ Generate repo file in database before saving actual file\\n\\n \\n\\n if chunked and (int(attrs['qqtotalparts']) - 1 == int(attrs['qqpartindex'])):\\n\\n chars = \\\"abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMOPQRTUVWXYZ0123456789\\\"\\n fileName = \\\"\\\".join(random.sample(chars, 6))\\n\\n while RepoFileModel.exists(fileName):\\n fileName = \\\"\\\".join(random.sample(chars, 6))\\n\\n ogName = attrs['qqfilename']\\n p = ogName.split(\\\".\\\")\\n\\n newFile = RepoFileModel(fileName, p[0], p[1],folderId, attrs['qquuid'])\\n newFile.save()\\n\\n \\n\\n dst = os.path.join(cls.UPLOAD_DIRECTORY,fileName + \\\".\\\" + p[1])\\n\\n cls.combine_chunks(attrs['qqtotalparts'],\\n attrs['qqtotalfilesize'],\\n source_folder=os.path.dirname(dest),\\n dest=dst)\\n\\n shutil.rmtree(os.path.dirname(os.path.dirname(dest)))\\n\\n ImageManager().createThumbnails(newFile)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46fb022f1babb64e8a789d3458fb7d61\",\n \"score\": \"0.47291568\",\n \"text\": \"async def get_music(self, ctx):\\n if ctx.message.author.id not in self.owner_list:\\n embed = discord.Embed(title='{}:'.format(ctx.message.author.name),\\n description='You may not use this command :angry: only admins!',\\n colour=0xf20006)\\n a = await self.bot.say(embed=embed)\\n await self.bot.add_reaction(a, self.emojiUnicode['warning'])\\n return\\n\\n if not self.database_online:\\n embed = discord.Embed(title='{}:'.format(ctx.message.author.name),\\n description='Could not connect to database.',\\n colour=0xf20006)\\n a = await self.bot.say(embed=embed)\\n await self.bot.add_reaction(a, self.emojiUnicode['error'])\\n return\\n\\n data_channels = []\\n for server in self.bot.servers:\\n for channel in server.channels:\\n if 'music' in str(channel).lower():\\n channel_type = str(channel.type)\\n print(channel_type)\\n data = [int(channel.id), str(channel.name), re.sub('\\\\W+', '', str(server.name)), str(channel.type)]\\n data_channels.append(data)\\n\\n\\n self.cur.execute('ROLLBACK;')\\n for items in data_channels:\\n try:\\n self.cur.execute(\\n 'INSERT INTO botzilla.music (ID, channel_name, server_name, type_channel) VALUES ({}, \\\\'{}\\\\', \\\\'{}\\\\', \\\\'{}\\\\');'.format(\\n items[0], items[1], items[2], items[3]\\n ))\\n self.cur.execute(\\\"ROLLBACK;\\\")\\n except Exception as e:\\n print('Error gathering info music channels:\\\\n```Python\\\\n{}```'.format(e.args))\\n continue\\n\\n embed = discord.Embed(title='{}:'.format(ctx.message.author.name),\\n description='Done with gathering music channel info!',\\n colour=0xf20006)\\n a = await self.bot.say(embed=embed)\\n await self.bot.add_reaction(a, self.emojiUnicode['succes'])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"755c56749ad929682b58cbb8df88ffe8\",\n \"score\": \"0.47253004\",\n \"text\": \"def _upload_dataset(self, dataset):\\n for file_path in dataset.local_files:\\n s3_url = self.upload_manager.upload_file(dataset.doi_upload_key,\\n file_path)\\n dataset.file_s3_urls[os.path.basename(file_path)] = s3_url\\n\\n self.upload_manager.upload_json_obj(\\n dataset.doi_upload_key,\\n 'original_pure{}_metadata.json'.format(self.api_version),\\n dataset.original_metadata\\n )\\n self.upload_manager.upload_json_obj(\\n dataset.doi_upload_key,\\n 'metadata.json',\\n dataset.rdss_canonical_metadata\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"10e6ccf9452739363792c358a298b901\",\n \"score\": \"0.4723323\",\n \"text\": \"def upload_files(self):\\n\\n while True:\\n # Look for files in the twitter storage directory and shove them into a list\\n tmp_files = []\\n for f in os.listdir(self.configs[\\\"grabber_storage_directory\\\"]):\\n if os.path.isfile(os.path.join(self.configs[\\\"grabber_storage_directory\\\"],f)):\\n tmp_files.append(f)\\n # Make sure we only are uploading files that are finished and were created by a Grabber\\n # using our config information\\n files = []\\n for filename in tmp_files:\\n if self.configs[\\\"tweet_file_prefix\\\"] in filename:\\n files.append(filename)\\n\\n\\n # If we don't actually have any files to upload wait a bit and flip back to the beginning\\n if len(files) == 0:\\n if self.configs['verbosity'] and not(self.configs['quiet']):\\n print(\\\"no files to upload, waiting for 10 seconds.\\\")\\n time.sleep(10)\\n continue\\n\\n # Pick the oldest file we can find for upload\\n files.sort()\\n filename = os.path.join(self.configs[\\\"grabber_storage_directory\\\"],files[0])\\n to_upload = open( filename, \\\"rb\\\")\\n\\n # Setup our variables for the upcoming loop\\n max_retries = self.configs[\\\"upload_retries\\\"]\\n on_try = 0\\n result = None\\n if self.configs['verbosity'] and not(self.configs['quiet']):\\n print(\\\"Upload attempting to push file %s\\\" % filename)\\n\\n # Attempt to upload the file until we hit max_retries\\n while on_try < max_retries:\\n if self.configs['verbosity'] and not(self.configs['quiet']):\\n print(\\\"Upload try %s\\\" % on_try)\\n try:\\n result = self.do_upload(self.configs[\\\"upload_url\\\"], to_upload)\\n break\\n except requests.exceptions.RequestException as e:\\n if not (self.log_pipe == None):\\n self.log_pipe.send(['error','Upload failed, %s %s' % (type(e), str(e)) ])\\n on_try += 1\\n\\n if not (result == None):\\n # If we got our file up, send a message to the upload log, otherwise write to the error log\\n if result.status_code == 201:\\n if not (self.log_pipe == None):\\n self.log_pipe.send(['log', '%s %s' % ( result.status_code, json.dumps(result.text) )])\\n # If an upload is successful this is the filehash\\n if self.configs['verbosity'] and not(self.configs['quiet']):\\n print(result.text)\\n \\n # If we manage to successfully upload the file then close the handle and delete the file\\n if self.configs['verbosity'] and not(self.configs['quiet']):\\n print(\\\"Upload succeeded, deleting %s\\\" % filename)\\n to_upload.close()\\n os.remove(filename)\\n else:\\n if not (self.log_pipe == None):\\n self.log_pipe.send(['error', '%s %s' % ( result.status_code, json.dumps(result.text) )])\\n else:\\n # If we hit max_retries bail on the upload process\\n if not (self.log_pipe == None):\\n self.log_pipe.send(['error','Upload failed %s times, killing uploader' % max_retries])\\n sys.exit(1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29ecab63f1f98b83dbbea707ef035b15\",\n \"score\": \"0.47210714\",\n \"text\": \"def run(self):\\n class CamHandler(FTPHandler):\\n\\n def on_file_received(self, file_):\\n\\n date = datetime.now()\\n\\n path = os.path.join(save_folder, date.strftime(\\\"%m-%d-%Y\\\"))\\n os.makedirs(path, exist_ok=True)\\n fn = os.path.join(path, date.isoformat().replace(\\\":\\\", \\\",\\\") + \\\".jpg\\\")\\n os.rename(file_, fn)\\n\\n if self.username not in last_files:\\n last_files.update({self.username : None})\\n\\n print(fn, self.username)\\n\\n try:\\n\\n if collect_images:\\n process_train(fn, self.username)\\n else:\\n process(fn, self.username)\\n\\n except Exception as e:\\n print(e)\\n\\n def on_incomplete_file_received(self, file_):\\n os.remove(file_)\\n\\n handler = CamHandler\\n handler.authorizer = self.auth\\n\\n server = FTPServer((self.host, self.port), handler)\\n server.serve_forever()\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993296,"cells":{"query_id":{"kind":"string","value":"65fbb237ba48a42183e928e84ee4eb93"},"query":{"kind":"string","value":"returns some edges, randomly selected"},"positive_passages":{"kind":"list like","value":[{"docid":"0f4b0aa3bdb80a832900079ee0262c18","score":"0.7515471","text":"def random_edges(how_many,only_alive=True): # FIXME: more elegant than parametrized?? Boh!\n pool = None\n if only_alive:\n pool = alive_edges()\n else:\n pool = edges()\n\n how_many = min(how_many,len(pool))\n\n ret=random.sample(pool,how_many)\n return ret","title":""}],"string":"[\n {\n \"docid\": \"0f4b0aa3bdb80a832900079ee0262c18\",\n \"score\": \"0.7515471\",\n \"text\": \"def random_edges(how_many,only_alive=True): # FIXME: more elegant than parametrized?? Boh!\\n pool = None\\n if only_alive:\\n pool = alive_edges()\\n else:\\n pool = edges()\\n\\n how_many = min(how_many,len(pool))\\n\\n ret=random.sample(pool,how_many)\\n return ret\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"2c464bb991f2dfc77bb0900780b7b411","score":"0.78476745","text":"def pick_random_edge(self):\r\n return random.choice(self.get_edges())","title":""},{"docid":"372f819474a5905adfa5a9d50320c43c","score":"0.7839612","text":"def random_edge(self):\n pass","title":""},{"docid":"bcbd09927b415081e25c3eebecf32a55","score":"0.7696494","text":"def make_random_edge(self):\n random_edge = tuple(random.sample(self.nodes, 2))\n return random_edge","title":""},{"docid":"894bde9e3167f2ea8520d27fa7abd0ce","score":"0.753902","text":"def re(self):\n return self.G.edge(*nth(self.G.iter_edges(), randint(self.G.num_edges())))","title":""},{"docid":"464726094dfe7aa04692b769def0c215","score":"0.7304091","text":"def generate_edges(self):\r\n random.seed(10)\r\n for i in range(self.size):\r\n for j in range(i + 1, self.size):\r\n if random.randrange(0, 100) <= self.connectedness * 100:\r\n yield [i, j]","title":""},{"docid":"a0cdb8725f343005d9cbfddffeca19bb","score":"0.6980972","text":"def random_edges(num_edges):\n if num_edges > 0:\n e = []\n # Calculates the required number of vertices.\n num_vertices = calc_min_number_of_vertices(num_edges)\n\n # Creates a list with vertices.\n # Each vertex is as many times member of the list as edges with that vertex are possible.\n vertices = []\n for i in range(0, num_vertices):\n for j in range(0, num_vertices - 1):\n vertices.append(i+1)\n\n # Creates as many edges as required.\n for i in range(0, num_edges):\n\n # Get a random vertex.\n a = random_el(vertices)\n\n # Get the index of another random vertex.\n # Loops are not allowed, vertex b must be different to vertex a.\n b_i = random_ind(vertices, a)\n\n # Add the edge (a, b) to the list\n # if the edge is not already member of the list.\n duplicate = True\n while duplicate:\n duplicate = False\n for edge in e:\n if a in edge and vertices[b_i] in edge:\n duplicate = True\n b_i = random_ind(vertices, a)\n\n b = vertices.pop(b_i)\n e.append((a,b))\n\n return e\n else:\n return []","title":""},{"docid":"b4726b0e130b06cd203fd17c83eed350","score":"0.6948543","text":"def random_edge(self):\n sample = random.sample(self.edges, 1)\n\n return sample[0] if len(sample) != 0 else None","title":""},{"docid":"78d5dadee3e537b9a0c1a0f00536f6c7","score":"0.6904613","text":"def generate_edges(size, connectedness):\n assert connectedness <= 1\n random.seed(10)\n for i in range(size):\n for j in range(i + 1, size):\n if random.randrange(0, 100) <= connectedness * 100:\n w = random.randint(1, 20)\n yield [i, j, w]","title":""},{"docid":"1521c0aeb104be54fad81b9ce5042b19","score":"0.6851834","text":"def get_random_edge(G):\n \n # List of edges and usage statistics \n edges = [(u, v) for u, v in G.edges()]\n usage_stat = [G[u][v]['usage_stat'] for u, v in G.edges()]\n \n # Select random edge based on statistics\n return random.choices(edges, weights=usage_stat)[0]","title":""},{"docid":"27aaa0341d4a663e66f9a4d7815ee11e","score":"0.6818581","text":"def rnd_edge_filtration(self):\n G = self.graph\n nodes = list(G.nodes)\n edges = list(G.edges)\n np.random.shuffle(edges)\n A = np.full((len(nodes), len(nodes)), np.inf)\n rel_timeline = {}\n count = 1\n for edge in edges:\n i, j = nodes.index(edge[0]), nodes.index(edge[1])\n # make sure corresponding nodes are introduced prior to introducing edge\n for ind in [i, j]:\n if A[ind, ind] == np.inf:\n A[ind, ind] = count\n count += 1\n A[i, j] = count\n A[j, i] = count\n rel_timeline[count] = edge\n count += 1\n return A, count, rel_timeline","title":""},{"docid":"44d3660612c080e2caaead60f32f910e","score":"0.6795535","text":"def random_graph(cls, size):\n print(cls, size)\n graph = [cls() for index in range(size)]\n for node in graph:\n num_edges = random.randrange(size//2)\n for neighbor in random.sample(graph, num_edges):\n if neighbor != node:\n node.connect(neighbor)\n return graph, lambda v1, v2: v1.weight(v2)","title":""},{"docid":"b7b682642358dff4fdd55fabc801244d","score":"0.6720819","text":"def rand_tree_edges(size):\n ret = []\n for u in range(1, size):\n v = randint(0, u-1)\n ret.append((u, v))\n return ret","title":""},{"docid":"f229dcd0d1a7a2a6ea10b0492bdcb1f7","score":"0.6704576","text":"def edge_sample(self, size=1):\n return sample(list(self.edges()), size)","title":""},{"docid":"2fc0381fc4bb48476697937a42204bc4","score":"0.67016083","text":"def _get_graph_with_random_edges(graph, number_seed_edges, random_state):\n log.debug('making new graph with %d random edges', number_seed_edges)\n\n result = BELGraph()\n\n for u, v, key in _get_random_u_v_k_triples(graph, number_seed_edges, random_state):\n result.add_edge(u, v, key=key, attr_dict=graph[u][v][key])\n\n return result","title":""},{"docid":"ea62dbe608bfc408942ac92cf073b29a","score":"0.6671015","text":"def AddRandomEdges(graph, num_edges_tried):\n pass","title":""},{"docid":"097ea130ed2cb795a9df44302a2d0eea","score":"0.6632942","text":"def getRandEdge(adjGrid, dim):\n loc = getRandLoc(dim)\n # we need a location that has an edge from it\n while len(adjGrid[loc]) is 0:\n loc = genRandLoc(dim)\n loc2 = adjGrid[loc][np.random.randint(0,len(adjGrid[loc]))]\n return [loc, loc2]","title":""},{"docid":"6b7cb1a1cfd66ff2488ccaa8cb097079","score":"0.6625906","text":"def adj():\n return [random.randrange(3), random.randrange(6), random.randrange(2)]","title":""},{"docid":"745dd5f0b35e894a0cb2646e4b5dbd9f","score":"0.65565914","text":"def SampleNegativeEdges(graph, num_edges):\n random_negatives = set()\n nodes = list(graph.nodes())\n while len(random_negatives) < num_edges:\n i1 = random.randint(0, len(nodes) - 1)\n i2 = random.randint(0, len(nodes) - 1)\n if i1 == i2:\n continue\n if i1 > i2:\n i1, i2 = i2, i1\n n1 = nodes[i1]\n n2 = nodes[i2]\n if graph.has_edge(n1, n2):\n continue\n random_negatives.add((n1, n2))\n\n return random_negatives","title":""},{"docid":"d9b0065eebe641dc474dabbc9c517807","score":"0.65495986","text":"def create_edges(vertices):\n\n colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k', 'lightgray', 'rosybrown', 'orange', 'lightgreen', 'lightblue', 'lightpink']\n cindex = 0\n for vertex in vertices:\n\n \"\"\"\n Building vertices connected to a given vertex:\n - select a random number of possible vertices from the whole set\n - from this selection, select only vertices closer to it than distance_max\n \"\"\"\n # vertex.out_vertices = random.sample(vertices, np.random.randint(0, len(vertices)))\n\n vertex.out_vertices = [v for v in random.sample(vertices, np.random.randint(0, np.sqrt(len(vertices)))) if vertex.dist(v) < distance_max]\n\n out = \",\".join([\"{}\".format(v.id) for v in vertex.out_vertices])\n\n # print(\"create_edges> id={} out={}\".format(vertex.id, out))\n\n plt.scatter(vertex.x, vertex.y, c=colors[cindex], s=1)\n\n for v in vertex.out_vertices:\n plt.plot([vertex.x, v.x], [vertex.y, v.y], colors[cindex])\n\n cindex += 1\n if cindex >= len(colors):\n cindex = 0","title":""},{"docid":"108fbfd1fc5a361e641c9152352476c8","score":"0.6512314","text":"def createRandomGraph():\n g = {}\n n = random.sample([0,1,2,3,4,5,6,7,8,9], 7)\n for i in n:\n g[i] =[]\n edges = random.randint(10,20)\n count = 0\n while count < edges:\n a = random.choice(n)\n b = random.choice(n)\n if b not in g[a] and a != b:\n g[a].append(b)\n count +=1\n return g","title":""},{"docid":"b3518a77571dcb37e8daa6b1919ec7df","score":"0.6482092","text":"def add_random_edge(self, total_edges):\n while len(self.edges) < total_edges:\n self.add_edge(self.make_random_edge())","title":""},{"docid":"4db108591e60765028f47f65d28ca405","score":"0.63894844","text":"def adj33():\n return [random.randrange(3), random.randrange(6), random.randrange(2)]","title":""},{"docid":"fd507cd32b5fb1b2f6e1dd3440cbc7fd","score":"0.63875794","text":"def random_graph_descriptors():\n graph = nx.dense_gnm_random_graph(11, 50, seed=1337)\n line_graph = nx.line_graph(graph)\n\n node_laplacian = np.array(nx.normalized_laplacian_matrix(graph).todense())\n edge_laplacian = np.array(nx.normalized_laplacian_matrix(line_graph).todense())\n incidence = np.array(nx.incidence_matrix(graph).todense())\n\n return node_laplacian, edge_laplacian, incidence","title":""},{"docid":"7d5ab18866bbbd9503986b059338ff52","score":"0.63832897","text":"def random_reverse_reachable_set(graph):\n n = len(graph[1]) - 1\n start = random.choice(range(n))\n stack = [start]\n visited = set()\n while stack:\n curr = stack.pop()\n if curr not in visited:\n visited.add(curr)\n\n # We are getting the value offsets from the second array here\n value_start = graph[1][curr]\n value_end = graph[1][curr + 1]\n\n # Using the offsets we just extracted, get head of outgoing edges\n edges = graph[2][value_start:value_end]\n\n # Do the same with the values of the edges\n probabilities = graph[0][value_start:value_end]\n\n for i in range(len(edges)):\n if random.random() < probabilities[i]:\n stack.append(edges[i])\n return visited","title":""},{"docid":"7d24fffac7b12c4004f6039df72f2076","score":"0.6377901","text":"def edges(self) -> typing.List:","title":""},{"docid":"0688b644e03cb287e9cb85752d85180a","score":"0.6369845","text":"def select_edges(self):\n guids = compas_rhino.select_lines()\n edges = [self.artist.guid_edge[guid] for guid in guids if guid in self.artist.guid_edge]\n return edges","title":""},{"docid":"1261bc937fa4a77f7cf2b83fe2800b40","score":"0.63674444","text":"def edges(self):\n return self.__generate_edges()","title":""},{"docid":"1261bc937fa4a77f7cf2b83fe2800b40","score":"0.63674444","text":"def edges(self):\n return self.__generate_edges()","title":""},{"docid":"1261bc937fa4a77f7cf2b83fe2800b40","score":"0.63674444","text":"def edges(self):\n return self.__generate_edges()","title":""},{"docid":"1261bc937fa4a77f7cf2b83fe2800b40","score":"0.63674444","text":"def edges(self):\n return self.__generate_edges()","title":""},{"docid":"340e8c6f32b95d33543704d2c8bf336f","score":"0.635858","text":"def alive_edges():\n return [ e for e in edges() if get_weight(e) > 0.0 ]","title":""},{"docid":"1fb0a011b19a3c2a70facc8722890180","score":"0.63278514","text":"def directedEdges(self):","title":""},{"docid":"82723a6c4ef2a35a854f092d65015b95","score":"0.6298398","text":"def remove_random_edges(G, target_user, portion=10):\n neighbors = set(G.neighbors(target_user))\n removed_neighbors = sample(neighbors, k=portion)\n remaining_neighbors = neighbors.difference(set(removed_neighbors))\n remaining_neighbors = list(remaining_neighbors)\n\n for neighbor in removed_neighbors:\n G.remove_edge(target_user, neighbor)\n\n return removed_neighbors, remaining_neighbors","title":""},{"docid":"280c75b4731e6d76a450088b422857a6","score":"0.62839603","text":"def edges(self):\n return self._generate_edges()","title":""},{"docid":"280c75b4731e6d76a450088b422857a6","score":"0.62839603","text":"def edges(self):\n return self._generate_edges()","title":""},{"docid":"089db9c2cac3e498b3d43d8e3b1652d4","score":"0.6280948","text":"def initRandomGraph(ctor, n, m):\n g = ctor(n)\n addedEdges = 0\n while addedEdges < m:\n x = random.randrange(0, n)\n y = random.randrange(0, n)\n if not g.isEdge(x, y):\n g.addEdge(x, y)\n addedEdges += 1\n return g","title":""},{"docid":"604fa7f42080e6a32eab942b62bf4f03","score":"0.6275545","text":"def genRandomGraph(self,n,p):\n graph={}\n for i in range(n):\n graph[i] = []\n for i in range(n):\n for j in range(i+1,n):\n #edge i-j\n r = random.random();\n if r <= p:\n graph[i].append(j)\n #edge j-i\n r = random.random();\n if r <= p:\n graph[j].append(i)\n return graph","title":""},{"docid":"f4ed14dc5792c071eb7e72a6c2b27cbe","score":"0.6262201","text":"def randomGraph(size, connectedness):\n graph = [Node() for _ in range(size)]\n for node in graph:\n max_edges = size * connectedness\n if max_edges > 0:\n num_edges = random.randrange(int(size * connectedness))\n else:\n num_edges = 0\n node.adj.update(random.sample(graph, num_edges))\n random.shuffle(graph)\n return graph","title":""},{"docid":"b069e140ea60e0cd667f5fa84175ebcd","score":"0.6261221","text":"def list_edges(self):\n return list(self._generate_edge())","title":""},{"docid":"fcbc2c6731ea0b89a5af381be660b43b","score":"0.62581587","text":"def rand_graph(n, m):\n g = {v: [] for v in range(n)}\n i = 0\n while i < m:\n x = randint(0, n)\n y = randint(0, n)\n if x > y:\n x, y = y, x\n if x != y and y not in g[x]:\n g[x].append(y)\n i += 1\n r_graph = ExtendedGraph(FUN.edge_list_from_dict(g))\n while r_graph.order() < n:\n r_graph.add_vertex()\n return r_graph","title":""},{"docid":"2794e6346c5eff1c29eb4da4852fafe3","score":"0.6249172","text":"def undirectedEdges(self):","title":""},{"docid":"0495c7ca07d161e1af5705825d669b9c","score":"0.6248403","text":"def random_select(self, neighbours):\n\n current_node = random.choice(neighbours).node\n current_node.select_random_attributes()\n return current_node","title":""},{"docid":"f2fde3789f5019a104aef57616df804f","score":"0.6242146","text":"def _make_erdos(n, e):\r\n edges = [(i,j) for j in range(n) for i in range(n) if i != j]\r\n edges = [edges[i] for i in np.random.choice(len(edges), e, replace=False)]\r\n g = {i:list(set([j for k,j in edges if k==i])) for i in range(n)}\r\n return g","title":""},{"docid":"d8414c3ad2b68634a675f2a4a08754f0","score":"0.6241641","text":"def test_edges(self):\n edges_tuple = (0, 3), (1, 2), (2, 3), (2, 4)\n stuff_to_find = set(edges_tuple)\n stuff_calculated = g_01.edges()\n self.assertSetEqual(stuff_to_find, stuff_calculated,\n f\"error: edges(), {stuff_to_find} is not expected\")","title":""},{"docid":"6da3d02be3396e7f2c2ec1f19e175597","score":"0.6231202","text":"def random_graph(n, m):\n G = Digraph()\n for v in range(n):\n G.add_vertex(v)\n\n max_num_edges = n * (n - 1)\n if m > max_num_edges:\n raise ValueError(\"For {} vertices, you wanted {} edges, but can only have a maximum of {}\".format(n, m, max_num_edges))\n\n while G.num_edges() < m:\n G.add_edge(random.sample(range(n), 2))\n\n return G","title":""},{"docid":"9ed7f0241725645b1537385c902d799f","score":"0.61847633","text":"def test_edges(self):\n edge_list = self.g.edges()\n self.assertEqual(42, len(edge_list))\n # p1 p3 and p3 p1 are valid edges\n t1 = ('p1', 'p3')\n self.assertTrue(t1 in edge_list)\n t2 = ('p3', 'p1')\n self.assertTrue(t2 in edge_list)\n made_up = ('z1', 'q123')\n self.assertFalse(made_up in edge_list)","title":""},{"docid":"5d3735886a47a68cac4553dcd3eec3c3","score":"0.6178505","text":"def choose_edge(size, degrees):\n prob = map(lambda x: x / float(sum(degrees)), degrees)\n return np.random.choice(len(degrees), size=size, p=prob)","title":""},{"docid":"4bd24ee0afec3141887b0bf2a8821523","score":"0.6172141","text":"def findEdge(self, intree, outtree):\n e1 = random.choice(list(intree))\n e2 = self.searchOnGraph(outtree, random.choice(list(intree)), 1)[0]\n return e1, e2","title":""},{"docid":"e7e8f2ba41af81f8ca31ef97a9583500","score":"0.6136359","text":"def select_nodes(network, max_neighbors):\n degree_to_nodes = {}\n for node, degree in network.degree():\n if degree in degree_to_nodes:\n degree_to_nodes[degree].append(node)\n else:\n degree_to_nodes[degree] = [node]\n\n selected_nodes = []\n for degree, nodes in sorted(degree_to_nodes.items(), key=lambda x: x[0]):\n if degree > max_neighbors:\n break\n\n selected_nodes.append(np.random.choice(nodes))\n\n return selected_nodes","title":""},{"docid":"444b58e7de5995a8be5ba97f3c57c8d4","score":"0.6117529","text":"def randomDAG(nodes, white_edges=(), black_edges=(), prob=.5):\n \n #shuffle the nodes\n N.random.shuffle(nodes)\n \n #add the white edges\n net = Network(nodes, white_edges)\n \n for e in itertools.combinations(nodes, 2):\n if N.random.randn() < prob:\n net.add_edge(*e)\n \n return net","title":""},{"docid":"2f76eb340b47cb7f773a2208ccaabea7","score":"0.6113278","text":"def bidirectedEdges(self):","title":""},{"docid":"c3b986fed0306cba065899a72ed192d5","score":"0.61092746","text":"def __generate_edges(self):\n edges = []\n for vertex in self.__graph_dict:\n for neighbour in self.__graph_dict[vertex]:\n if {neighbour, vertex} not in edges:\n edges.append({vertex, neighbour}) #povezuje dva cvora (cvor i link)\n return edges","title":""},{"docid":"bba920d7c0f547bc59e86b3076b1c0e7","score":"0.61051416","text":"def getRandomNeighbour(site, exceptSite, graph):\n\n # Convert sites to a tuples (tuples can be used as hash keys but not arrays)\n site = tuple(site)\n\n # Get all the neighbours to site\n # neighbours is as [(i, j+1), ...]\n neighbours = list(graph[site].keys())\n\n if exceptSite is not None:\n neighbours.remove(tuple(exceptSite))\n\n neighbour = random.choice(neighbours)\n\n # Since neighbour will be changed a lot, use a list\n return list(neighbour)","title":""},{"docid":"5cd7d47352f5f43106db35a5749500ca","score":"0.61008394","text":"def generate_graph(n):\n \n #graph is represented by an adjacency list\n graph = {} #dictionnary = {vertex i : [list of neighbours]}\n \n for i in range(n):\n # Each vertex\n # has more than one neighbour and less than n-1 neighbour\n nb_neighbours = rd.randint(1,n-1) \n \n # we forbid a vertex beeing its own neighbour\n possible_neighbours = [j for j in range(n) if j!=i]\n \n # list of neighbours\n # defined by taking nb_neighbours out of the whole set of neighbours \n neighbours = rd.sample(possible_neighbours, nb_neighbours)\n graph[i] = neighbours\n \n return graph","title":""},{"docid":"e4bae7e965f961198aeda1d77aae2a19","score":"0.6099499","text":"def edges(self):\n return self._mst","title":""},{"docid":"ab3bd80821b407f7ad8b6090cb57ee41","score":"0.60875195","text":"def random_subgraph(graph, number_of_nodes):\n all_nodes = set(graph.nodes())\n subgraph_nodes = random.sample(all_nodes, number_of_nodes)\n\n return graph.subgraph(subgraph_nodes)","title":""},{"docid":"678454c9298de035ff737197789ba5b7","score":"0.6082616","text":"def random_dag(nodes, edges):\n G = nx.DiGraph()\n for i in range(nodes):\n G.add_node(i)\n while edges > 0:\n a = randint(0, nodes-1)\n b = a\n while b == a:\n b = randint(0, nodes-1)\n G.add_edge(a, b)\n if nx.is_directed_acyclic_graph(G):\n edges -= 1\n else:\n # we closed a loop!\n G.remove_edge(a, b)\n\n first_node = nodes\n last_node = nodes + 1\n G.add_node(first_node)\n G.add_node(last_node)\n\n for node, in_edge in dict(G.in_degree()).items():\n if in_edge == 0:\n if node == first_node or node == last_node:\n continue\n G.add_edge(first_node, node)\n\n for node, out_edge in dict(G.out_degree()).items():\n if out_edge == 0:\n if node == first_node or node == last_node:\n continue\n G.add_edge(node, last_node)\n\n return G","title":""},{"docid":"1b3c2bbcfb8c66bd901a9bf376e79b0e","score":"0.6056807","text":"def select_elites():\n elites = []\n while len(elites) < E: # Keep choosing elites until there are E of them\n new_elites = popD[max(popD)] # These are the binary lists with the best fitness\n # If adding all the elites with this fitness would be too many, then discard the surplus at random\n while len(new_elites) > (E - len(elites)):\n new_elites.remove(random.sample(new_elites, 1)[0])\n elites.extend(new_elites)\n popD.pop(max(popD), None) # Remove the key with the value just added from popD{}\n return elites","title":""},{"docid":"5cd1a917d916e11b0da39d213952a153","score":"0.60468584","text":"def random_graph(num_edges):\n if num_edges <= 0:\n raise ValueError(\"The argument must be a positive integer.\")\n e = random_edges(num_edges)\n return Graph(e)","title":""},{"docid":"c930a982dc9652dd8b74c44792ce0afd","score":"0.60441923","text":"def _get_random_u_v_k_triples(graph, number_seed_edges, random_state):\n universe_edges = [\n (u, v)\n for u in graph\n for v in graph.edge[u]\n ]\n random_state.shuffle(universe_edges)\n\n for u, v in universe_edges[:number_seed_edges]:\n keys = list(graph[u][v])\n key = random_state.choice(keys)\n yield u, v, key","title":""},{"docid":"81a275e0b255ea63e6a24039cdd04e2a","score":"0.6038839","text":"def get_random_neighbors(self):\n x = randint(0, 8)\n y1 = randint(0, 8)\n y2 = randint(0, 8)\n while y1 == y2 or (x, y1) in self.read_only_tiles or (x, y2) in self.read_only_tiles:\n x = randint(0, 8)\n y1 = randint(0, 8)\n y2 = randint(0, 8)\n\n return (x, y1), (x, y2)","title":""},{"docid":"171125957197731e58cc48698ac4b28d","score":"0.6024078","text":"def edges(graph):\n return gr.edges(graph)","title":""},{"docid":"6780daecfb21e00f4f8b82aa3ca439b4","score":"0.6018661","text":"def add_random_edges(win_graph: nx.DiGraph, candidates: set) -> nx.DiGraph:\n to_add = candidates.difference(set(win_graph.nodes))\n win_graph = win_graph.copy()\n candidates = set(win_graph.nodes).union(to_add)\n edge_list = []\n for u in candidates:\n for v in candidates:\n if u != v:\n edge_list.append((u, v))\n\n random.shuffle(edge_list)\n\n # Existing edges don't have to be checked\n for edge in win_graph.edges:\n edge_list.remove(edge)\n\n for c1, c2 in edge_list:\n try:\n win_graph.add_edge(c1, c2)\n nx.find_cycle(win_graph)\n win_graph.remove_edge(c1, c2)\n except nx.NetworkXNoCycle:\n pass\n\n return win_graph","title":""},{"docid":"d090ddd13d2316696b3c0bba4b296aa2","score":"0.6015056","text":"def random_neighbor(G, node):\n neighbors = list(G.neighbors(node))\n randomly_selected_neighbor = choice(neighbors)\n return randomly_selected_neighbor","title":""},{"docid":"ce8e838dbcb9d4e9523ac2105ff7d8f3","score":"0.6010255","text":"def add_edges(graph, n_max_connections = 5) :\r\n for i in range(1, graph.number_of_nodes()):\r\n for n in range(1, n_max_connections):\r\n ni = np.random.randint(1, graph.number_of_nodes())\r\n if i != ni : cluster_graph.add_edge(i,ni)\r\n return graph","title":""},{"docid":"1a42fbb0a60dd6a1ce3f8fbd553a8f29","score":"0.600649","text":"def geo_graph(m,n,d):\n # Begin with a grid:\n res_graph = nx.grid_2d_graph(m,n,True)\n\n # Next connect each node to random nodes:\n for x in res_graph.nodes_iter():\n neis = random.sample(res_graph.nodes(),d)\n # We dont want to connect to ourselves:\n try:\n neis.remove(x)\n except ValueError:\n pass\n\n # Add the additional edges:\n for nei in neis:\n if not res_graph.has_edge(x,nei):\n res_graph.add_edge(x,nei)\n\n return res_graph","title":""},{"docid":"98794bbc9674cb345fe605faacae86e1","score":"0.59923995","text":"def get_edges(self):\n return","title":""},{"docid":"8641406bc41e89cd21d52d5e35d24524","score":"0.599198","text":"def random_graph(V=80, E=50, connected=False):\n graph = Graph(V)\n for _ in range(E):\n graph.add_edge(randrange(0, V), randrange(0, V))\n if not connected:\n return graph\n\n component = max([graph.connected_component(x) for x in range(len(graph.vertices))])\n while len(component) < len(graph.vertices):\n for x in range(len(graph.vertices)):\n if x not in component:\n graph.add_edge(x, choice(list(component)))\n break\n\n component = graph.connected_component(component.pop())\n\n return graph","title":""},{"docid":"c94bf4f3386041446bbf7c4bdf875bd5","score":"0.59897614","text":"def generateRandomSelection(self): \n rand1 = r.random()\n rand2 = r.random() \n \n if rand2 < rand1: #if the nr of rand2 is bigger swap them\n temp = rand2\n rand2 = rand1\n rand1 = temp\n #selectionPoints = self.storePointSelection(rand1, rand2) // CHECK IF I NEED TO DELETE THAT\n selectionPoints = u.getPointSelection (rand1, rand2, self.usablePointSet)\n return selectionPoints","title":""},{"docid":"50e629025d059d2c8cc5306774a91a3e","score":"0.5976913","text":"def __generate_edges(self):\n edges = []\n for v in self.__graph:\n for vi in self.__graph[v]:\n if v < vi:\n edges.append((v, vi))\n return edges","title":""},{"docid":"09da137052d1f627751cfb1e44a63128","score":"0.59740263","text":"def getEdges(self, filter=None) -> typing.List:","title":""},{"docid":"a40115d9f0b21f71c6fcdfac0ae39ee5","score":"0.5968571","text":"def sample_negative_pairs(graph, size):\n log(f\"Sampling {size} negative pairs\")\n negative_edges = []\n nodes = list(graph.nodes())\n while len(negative_edges) < size:\n u = np.random.randint(low=0, high=len(nodes), size=1)[0]\n v = np.random.randint(low=0, high=len(nodes), size=1)[0]\n if u != v and not graph.has_edge(u, v):\n negative_edges.append((u, v))\n log(f\"{len(negative_edges)}/{size}\", cr=True)\n log()\n return torch.tensor(negative_edges, dtype=torch.long)","title":""},{"docid":"d97b03dc3e837ba2cfc3bdb94f2708af","score":"0.5967824","text":"def randomNode(self):\r\n availableNodes = []\r\n for xNode in self.nodeList:\r\n for node in xNode:\r\n if node.wall == False:\r\n availableNodes.append(node)\r\n\r\n return random.choice(availableNodes)","title":""},{"docid":"e55848b13a615a619a0a12716021fb3d","score":"0.5940899","text":"def sample_negative_nodes(graph, dist, source, target, size=1):\n negatives = []\n while len(negatives) < size:\n rnd_idx = np.random.randint(0, len(dist), size)[0]\n rnd_node = dist[rnd_idx]\n if source != rnd_node != target and not graph.has_edge(source, rnd_node) and \\\n not graph.has_edge(target, rnd_node):\n negatives.append(rnd_node)\n return torch.tensor(negatives, dtype=torch.long)","title":""},{"docid":"230209c5387f9db718f1994651e8a091","score":"0.59373766","text":"def randomSelection(self):\r\n indA = self.matingPool[random.randint(0, self.popSize - 1)]\r\n indB = self.matingPool[random.randint(0, self.popSize - 1)]\r\n return [indA, indB]","title":""},{"docid":"d831db1dfc1a32f0050dadef6ec94ba1","score":"0.59341425","text":"def randomize(self):\n\n # shortcuts for quick references\n l = self.data.keys() # make a list of the vertices\n\n f = random.choice(l) # pick a random first place\n visited = list() # initialize an empty visited list\n visited.append(f) # add the first city to the list\n\n # while the number of visited nodes is less\n # than the number of entries in the list\n while len(visited) < len(l):\n n = random.choice(l) # select a random choice\n\n # If the node hasn't been visited\n if n not in visited:\n visited.append(n) # Append the visited list\n\n # Add the final connection between the\n # last node visited and the first node\n visited.append(f)\n self.graph.add_path(visited)","title":""},{"docid":"7e372bbaf11c6dbec4ba931455483f1a","score":"0.59324497","text":"def adj31():\n return [random.randrange(2), random.randrange(6), random.randrange(2)]","title":""},{"docid":"e61456c03e4e49928d70d76ac3406f32","score":"0.5930999","text":"def get_edges(self):\r\n return self.edges","title":""},{"docid":"8a0aed6adad808ea66d37484d781b05c","score":"0.5926841","text":"def randomGraph(node_count, color_count, neighbor_count):\n graph = Graph()\n\n # randomly make the nodes and color them.\n for i in range(0, node_count - 1):\n graph.add_node(Node(randint(0, color_count - 1), graph))\n\n # randomly make neighbors.\n for node in graph.nodes():\n # get all nodes of graph and store it in an array\n other_nodes = graph.nodes()\n # remove current node from that array\n other_nodes.remove(node)\n # randomly choose some nodes from that array\n neighbors = random.sample(other_nodes, randint(0, neighbor_count - 1))\n # loop on the second array and make them neighbors\n graph.make_neighbor(node, neighbors)\n\n return graph","title":""},{"docid":"7b9f4289628af0d97eda279ed6a5797a","score":"0.592557","text":"def random_dag(self):\n # add nodes, labeled 0...nodes:\n \n for i in range(self.n_nodes):\n self.randDAG.add_node(self.nodes[i])\n\n child_parent = {}\n shuffled_nodes = self.nodes[:]\n random.shuffle(shuffled_nodes)\n \n # to avoid infinit loop, need to have better solution\n round = 1000\n while self.n_edges > 0 and round > 0:\n round -= 1\n\n a = random.choice(self.nodes) \n b = random.choice(self.nodes)\n while a == b or self.randDAG.has_edge(a, b):\n b = random.choice(self.nodes)\n \n self.randDAG.add_edge(a, b)\n if nx.is_directed_acyclic_graph(self.randDAG):\n self.n_edges -= 1\n parent = child_parent.get(b)\n if parent is None:\n parent = [a]\n else:\n parent.append(a)\n child_parent[b] = parent\n # print(a,\"-> \", b)\n else:\n # we closed a loop!\n self.randDAG.remove_edge(a, b)\n \n return self.randDAG, child_parent","title":""},{"docid":"60cac2fd2a901f4eed428b25f1a64432","score":"0.592433","text":"def make_random_graph(num_nodes, prob):\n # initialize empty graph\n random_graph = {}\n for vertex in range(num_nodes): random_graph[vertex] = []\n # consider each vertex\n for vertex in range(num_nodes):\n # consider each neighbour with greater value\n for neighbour in range(vertex + 1, num_nodes):\n random_number = random.random()\n # add edge from vertex to neighbour with probability prob\n if random_number < prob:\n # maybe this method for set union is deprecated in python 3\n random_graph[vertex] = set(random_graph[vertex]) | set([neighbour])\n random_graph[neighbour] = set(random_graph[neighbour]) | set([vertex])\n return random_graph","title":""},{"docid":"ee13a7adf5fbd10f193d72fef6a5bfe3","score":"0.5921974","text":"def make_random_graph(num_nodes, prob):\n #initialize empty graph\n random_graph = {}\n #consider each vertex\n for i in range(num_nodes):\n random_graph[i] = []\n\n for vertex in range(num_nodes):\n for neighbour in range(vertex+1, num_nodes):\n random_number = random.random()\n if random_number < prob:\n random_graph[vertex] += [neighbour]\n random_graph[neighbour] += [vertex] \n #add vertex with list of out_ neighbours\n\n return random_graph","title":""},{"docid":"8df0bbf613be9c439d2bfaf5e9ae4b2e","score":"0.5920063","text":"def generate_edges(self):\n e = []\n for v in self._graph_dict:\n for neigh in self._graph_dict[v]:\n if {neigh,v} not in e:\n e.append({v,neigh})\n return e","title":""},{"docid":"e3b421642ebdc12755eb262a5df8f2c7","score":"0.5919125","text":"def init(graph):\n #sol = TP1.approx_steiner(graph, terms)\n return [round(rd.random()) == 1 for _ in range(len(graph.edges))]","title":""},{"docid":"87fce0055a69ec379af8cf8b9ef7ffa3","score":"0.59188426","text":"def generate_random_model__regular_digraph(m, out_d_birth, out_d_migration):\n\t\n\tnotdone = True\n\twhile notdone:\n\t\tbG = networkx.DiGraph()\n\t\tmG = networkx.DiGraph()\n\t\tfor node in range(m):\n\t\t\tfor k in range(out_d_birth):\n\t\t\t\talter = randint(0, m)\n\t\t\t\twhile bG.has_edge(node, alter): # exclude double edges\n\t\t\t\t\talter = randint(0, m)\n\t\t\t\t#\n\t\t\t\tbG.add_edge(node, alter)\n\t\t\t#\n\t\t\tfor k in range(out_d_migration):\n\t\t\t\talter = node\n\t\t\t\twhile alter==node:\n\t\t\t\t\talter = randint(0, m)\n\t\t\t\t#\n\t\t\t\tmG.add_edge(node, alter)\n\t\t\t#\n\t\t#\n\t\t# test for connectedness\n\t\tuG = networkx.DiGraph() #networkx.union(mG, bG)\n\t\tuG.add_edges_from(bG.edges())\n\t\tuG.add_edges_from(mG.edges())\n\t\tif networkx.is_strongly_connected(uG):\n\t\t\tnotdone = False\n\t\t#\n\t#\n\treturn bG, mG","title":""},{"docid":"d3d97c6cebdc2388fd5b0f0876f942d7","score":"0.59185874","text":"def random_select_revealed_node(self, alpha, index1, index2):\n same_nodes = self.nodes[index1] & self.nodes[index2]\n s = int(alpha * len(same_nodes))\n logging.info(\"graph {}-{} random revealed nodes {}/{}.\".format\n (index1, index2, s, len(same_nodes)))\n revealed_nodes = random.sample(same_nodes, s)\n self.revealed_nodes[(index1, index2)] = set(revealed_nodes)\n self.same_nodes[(index1, index2)] = same_nodes\n return revealed_nodes","title":""},{"docid":"0a2e06e406978ad953657c9859ba4337","score":"0.5917218","text":"def randomSelection(self):\n indA = self.matingPool[random.randint(0, self.popSize - 1)]\n indB = self.matingPool[random.randint(0, self.popSize - 1)]\n return [indA, indB]","title":""},{"docid":"065d79f1085390367e0308ce5e4e831f","score":"0.5916045","text":"def edges():\n return [ e \n for e \n in g().edges \n if e is not None \n and hasattr(e,'source')\n and hasattr(e,'target')\n ]","title":""},{"docid":"98c8f568a5f99f9e1f3160949cf8d9f2","score":"0.59129196","text":"def get_random_neighbour_arbitrary(schedule):\n neighbours = []\n\n while not neighbours:\n neighbours = SchedulingTask.get_neighbours_local_arbitrary(schedule)\n i, j = random.choice(neighbours)\n new_schedule = schedule.copy()\n new_schedule[i], new_schedule[j] = new_schedule[j], new_schedule[i]\n return new_schedule","title":""},{"docid":"c8be0a991b35dc485df1bbc2988f5f33","score":"0.5910603","text":"def _generate_edge(self):\n edges = []\n for node in self._graph_dict:\n for adjacent_node in self._graph_dict[node]:\n if {adjacent_node, node} not in edges:\n edges.append({str(node), str(adjacent_node)})\n return edges","title":""},{"docid":"bf6d5716f736619b0c62bd1bd8ba3e01","score":"0.5904561","text":"def random_painting(self) -> int:\n return random.choice(self.museum_graph_list())","title":""},{"docid":"130159cd7d27f1e575d6c9abdd38927c","score":"0.5904064","text":"def create_random_graph(number_of_vertices, number_of_edges, radius, thickness):\n\n global screen\n\n dist_apart = radius * 3\n\n for i in range(0, number_of_vertices):\n vtx_x, vtx_y = generate_valid_coordinates(radius, dist_apart)\n\n vtx = {\"ID\": i,\n \"x\": vtx_x,\n \"y\": vtx_y,\n \"color\": \"WHITE\",\n \"adjacent\": [],\n }\n\n VERTICES.append(vtx)\n\n # Assign adjacency\n for i in range(0, number_of_edges):\n vtx_one = None\n vtx_two = None\n\n # Do not assign the adjacency of a vertex to itself\n while vtx_one==vtx_two:\n vtx_one = random.randint(0, number_of_vertices - 1)\n vtx_two = random.randint(0, number_of_vertices - 1)\n\n VERTICES[vtx_one][\"adjacent\"].append(VERTICES[vtx_two][\"ID\"])\n VERTICES[vtx_two][\"adjacent\"].append(VERTICES[vtx_one][\"ID\"])","title":""},{"docid":"ad103fbe0abe05331b742c7db6903285","score":"0.59001255","text":"def randomGraph(v,e, graphID = None, connected = False):\r\n graph = Graph(graphID)\r\n graph.connected = connected\r\n \r\n # check edges does not exceed max number possible\r\n if e > v*(v-1)/2:\r\n raise RuntimeError(\"e: number of edges has to be <= v*(v-1)/2 \")\r\n \r\n # check for connected graph have the min number of edges\r\n if connected and e < v-1:\r\n \r\n raise RuntimeError(\"e: number of edges has to be at least >= v-1\")\r\n \r\n # create v vertices with id = i\r\n for vertexID in range(v):\r\n \r\n # generate random location\r\n x = random.random()\r\n y = random.random()\r\n \r\n # create instance and set location\r\n V = Vertex(vertexID)\r\n V.setLocation(x,y)\r\n \r\n # store object key = id, value = vertex object\r\n graph.vertexList[vertexID]=V\r\n \r\n \r\n allVertices = set(range(v))\r\n assignedVertices = set()\r\n remainingVertices = v\r\n \r\n # pick random start vertex\r\n assignedVertices.add(random.choice(list(allVertices))) \r\n \r\n # iterate until create e edges:\r\n edgeID = 0\r\n while (edgeID<e):\r\n \r\n # random pair of vertices (order in pair of vertices doesn't matter)\r\n # (v1,v2) is the same as (v2,v1), but need to store as one\r\n # uniqe key for dictionary, so get (smallest, largest) to be \r\n # consistent\r\n \r\n if (remainingVertices == 0 or not connected ):\r\n pair = randomPair(allVertices, order=False)\r\n v1,v2 = pair\r\n \r\n else:\r\n v1 = random.choice(list(assignedVertices))\r\n v2 = random.choice(list(allVertices.difference(assignedVertices)))\r\n \r\n pair = tuple(sorted((v1,v2)))\r\n \r\n # generate a random pair of vertices until pair does not \r\n # exist in the adjancecyList (as a key in the dictionary)\r\n if (pair not in graph.adjacencyList):\r\n \r\n # get vertex objects and compute weight\r\n \r\n V1 = graph.vertexList[v1]\r\n V2 = graph.vertexList[v2]\r\n weight = findDistance(V1.location, V2.location)\r\n \r\n # create edge and add to edge list\r\n edge = Edge(edgeID, V1,V2, weight) \r\n graph.edgeList[edgeID] = edge\r\n \r\n # add pair as key and value = euclidean distance\r\n graph.adjacencyList[pair]= edge.weight\r\n \r\n # remove v1 and v2 from set unassignedVertices if present.\r\n # no error if not present (might be the case that the pair\r\n # does not exist, but one of the vertices might have already \r\n # been removed because was used in a different edge)\r\n \r\n # only do this for connected graph\r\n if (connected):\r\n assignedVertices.add(v2)\r\n \r\n remainingVertices = len(allVertices.difference(assignedVertices))\r\n \r\n edgeID+= 1 # go to next edge\r\n \r\n return graph","title":""},{"docid":"c73eb76e9d8ee6e060b1899996cf1617","score":"0.5899185","text":"def snowballsampling(g, seed, maxsize=50):\n if g.number_of_nodes() < maxsize:\n return set()\n q = que.Queue()\n q.put(seed)\n subgraph = set(seed)\n while not q.empty():\n for node in g.neighbors(q.get()):\n if len(subgraph) < maxsize:\n q.put(node)\n subgraph.add(node)\n else :\n return subgraph\n return subgraph","title":""},{"docid":"16873bd2a6ef04ad8fa4e4aba2956634","score":"0.58973855","text":"def edges_selected(threshold, edges, weights):\n edges_new = []\n weights_new = []\n for index in range(len(weights)):\n if weights[index] >= threshold:\n edges_new.append(edges[index])\n weights_new.append(weights[index])\n unique_v = set(sum(edges_new, ()))\n return edges_new, weights_new, list(unique_v)","title":""},{"docid":"2166b5c0e254cca56166ce92412c25ee","score":"0.58885175","text":"def _generate_edges(self):\n edges = []\n for vertex in self._graph_dict:\n for (neighbour, label) in self._graph_dict[vertex]:\n if [neighbour, vertex] not in edges:\n edges.append([vertex, (neighbour, label)])\n return edges","title":""},{"docid":"2b7f8ec3d64450336bab7f59bb7ec9be","score":"0.5880869","text":"def generate_random_graph(n, max_iter=10):\n g = get_simple_ordered_tree(n)\n for it in range(max_iter):\n i, j = get_random_node_pair(n)\n if g.has_edge(i, j) is True:\n del_edge(i, j, g)\n else:\n add_edge(i, j, g)\n return g","title":""},{"docid":"c065d4d361e794d29ea50fa6581d76d7","score":"0.5875883","text":"def CreateRandomHyperGraph(num_nodes, num_edges, probability):\n assert probability <= 1\n assert probability >= 0\n assert num_edges >= 0\n assert num_nodes >= 0\n result = Hypergraph()\n for i in range(num_nodes):\n for j in range(num_edges):\n if random() < probability:\n AddNodeToEdge(result, i, j)\n return result","title":""},{"docid":"3e869a922f2254033ee67bb18fbe4197","score":"0.5869381","text":"def test_return_neighbors_at_edge(self):\n exp = [\n (3, 3),\n (3, 0),\n (3, 1),\n (0, 3),\n (0, 1),\n (1, 3),\n (1, 0),\n (1, 1),\n ]\n g = grid.Grid(4, 4)\n act = g.neighbors(0, 0)\n self.assertEqual(exp, act)","title":""},{"docid":"8f3c0e88845bd83011b738d40fe4e4ec","score":"0.58688164","text":"def fractal_model(generation,m,x,e):\n\tG=Graph()\n\tG.add_edge(0,1) #This is the seed for the network (generation 0)\n\tnode_index = 2\n\tfor n in range(1,generation+1):\n\t\tall_links = G.edges()\n\t\twhile all_links:\n\t\t\tlink = all_links.pop()\n\t\t\tnew_nodes_a = range(node_index,node_index + m)\n\t\t\t#random.shuffle(new_nodes_a)\n\t\t\tnode_index += m\n\t\t\tnew_nodes_b = range(node_index,node_index + m)\n\t\t\t#random.shuffle(new_nodes_b)\n\t\t\tnode_index += m\n\t\t\tG.add_edges_from([(link[0],node) for node in new_nodes_a])\n\t\t\tG.add_edges_from([(link[1],node) for node in new_nodes_b])\n\t\t\trepulsive_links = zip(new_nodes_a,new_nodes_b)\n\t\t\tG.add_edges_from([repulsive_links.pop() for i in range(x-1)])\n\t\t\tif random.random() > e:\n\t\t\t\tG.remove_edge(*(link))\n\t\t\t\tG.add_edge(*(repulsive_links.pop()))\n\treturn G","title":""}],"string":"[\n {\n \"docid\": \"2c464bb991f2dfc77bb0900780b7b411\",\n \"score\": \"0.78476745\",\n \"text\": \"def pick_random_edge(self):\\r\\n return random.choice(self.get_edges())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"372f819474a5905adfa5a9d50320c43c\",\n \"score\": \"0.7839612\",\n \"text\": \"def random_edge(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bcbd09927b415081e25c3eebecf32a55\",\n \"score\": \"0.7696494\",\n \"text\": \"def make_random_edge(self):\\n random_edge = tuple(random.sample(self.nodes, 2))\\n return random_edge\",\n \"title\": \"\"\n },\n {\n \"docid\": \"894bde9e3167f2ea8520d27fa7abd0ce\",\n \"score\": \"0.753902\",\n \"text\": \"def re(self):\\n return self.G.edge(*nth(self.G.iter_edges(), randint(self.G.num_edges())))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"464726094dfe7aa04692b769def0c215\",\n \"score\": \"0.7304091\",\n \"text\": \"def generate_edges(self):\\r\\n random.seed(10)\\r\\n for i in range(self.size):\\r\\n for j in range(i + 1, self.size):\\r\\n if random.randrange(0, 100) <= self.connectedness * 100:\\r\\n yield [i, j]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0cdb8725f343005d9cbfddffeca19bb\",\n \"score\": \"0.6980972\",\n \"text\": \"def random_edges(num_edges):\\n if num_edges > 0:\\n e = []\\n # Calculates the required number of vertices.\\n num_vertices = calc_min_number_of_vertices(num_edges)\\n\\n # Creates a list with vertices.\\n # Each vertex is as many times member of the list as edges with that vertex are possible.\\n vertices = []\\n for i in range(0, num_vertices):\\n for j in range(0, num_vertices - 1):\\n vertices.append(i+1)\\n\\n # Creates as many edges as required.\\n for i in range(0, num_edges):\\n\\n # Get a random vertex.\\n a = random_el(vertices)\\n\\n # Get the index of another random vertex.\\n # Loops are not allowed, vertex b must be different to vertex a.\\n b_i = random_ind(vertices, a)\\n\\n # Add the edge (a, b) to the list\\n # if the edge is not already member of the list.\\n duplicate = True\\n while duplicate:\\n duplicate = False\\n for edge in e:\\n if a in edge and vertices[b_i] in edge:\\n duplicate = True\\n b_i = random_ind(vertices, a)\\n\\n b = vertices.pop(b_i)\\n e.append((a,b))\\n\\n return e\\n else:\\n return []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4726b0e130b06cd203fd17c83eed350\",\n \"score\": \"0.6948543\",\n \"text\": \"def random_edge(self):\\n sample = random.sample(self.edges, 1)\\n\\n return sample[0] if len(sample) != 0 else None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78d5dadee3e537b9a0c1a0f00536f6c7\",\n \"score\": \"0.6904613\",\n \"text\": \"def generate_edges(size, connectedness):\\n assert connectedness <= 1\\n random.seed(10)\\n for i in range(size):\\n for j in range(i + 1, size):\\n if random.randrange(0, 100) <= connectedness * 100:\\n w = random.randint(1, 20)\\n yield [i, j, w]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1521c0aeb104be54fad81b9ce5042b19\",\n \"score\": \"0.6851834\",\n \"text\": \"def get_random_edge(G):\\n \\n # List of edges and usage statistics \\n edges = [(u, v) for u, v in G.edges()]\\n usage_stat = [G[u][v]['usage_stat'] for u, v in G.edges()]\\n \\n # Select random edge based on statistics\\n return random.choices(edges, weights=usage_stat)[0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"27aaa0341d4a663e66f9a4d7815ee11e\",\n \"score\": \"0.6818581\",\n \"text\": \"def rnd_edge_filtration(self):\\n G = self.graph\\n nodes = list(G.nodes)\\n edges = list(G.edges)\\n np.random.shuffle(edges)\\n A = np.full((len(nodes), len(nodes)), np.inf)\\n rel_timeline = {}\\n count = 1\\n for edge in edges:\\n i, j = nodes.index(edge[0]), nodes.index(edge[1])\\n # make sure corresponding nodes are introduced prior to introducing edge\\n for ind in [i, j]:\\n if A[ind, ind] == np.inf:\\n A[ind, ind] = count\\n count += 1\\n A[i, j] = count\\n A[j, i] = count\\n rel_timeline[count] = edge\\n count += 1\\n return A, count, rel_timeline\",\n \"title\": \"\"\n },\n {\n \"docid\": \"44d3660612c080e2caaead60f32f910e\",\n \"score\": \"0.6795535\",\n \"text\": \"def random_graph(cls, size):\\n print(cls, size)\\n graph = [cls() for index in range(size)]\\n for node in graph:\\n num_edges = random.randrange(size//2)\\n for neighbor in random.sample(graph, num_edges):\\n if neighbor != node:\\n node.connect(neighbor)\\n return graph, lambda v1, v2: v1.weight(v2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7b682642358dff4fdd55fabc801244d\",\n \"score\": \"0.6720819\",\n \"text\": \"def rand_tree_edges(size):\\n ret = []\\n for u in range(1, size):\\n v = randint(0, u-1)\\n ret.append((u, v))\\n return ret\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f229dcd0d1a7a2a6ea10b0492bdcb1f7\",\n \"score\": \"0.6704576\",\n \"text\": \"def edge_sample(self, size=1):\\n return sample(list(self.edges()), size)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fc0381fc4bb48476697937a42204bc4\",\n \"score\": \"0.67016083\",\n \"text\": \"def _get_graph_with_random_edges(graph, number_seed_edges, random_state):\\n log.debug('making new graph with %d random edges', number_seed_edges)\\n\\n result = BELGraph()\\n\\n for u, v, key in _get_random_u_v_k_triples(graph, number_seed_edges, random_state):\\n result.add_edge(u, v, key=key, attr_dict=graph[u][v][key])\\n\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ea62dbe608bfc408942ac92cf073b29a\",\n \"score\": \"0.6671015\",\n \"text\": \"def AddRandomEdges(graph, num_edges_tried):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"097ea130ed2cb795a9df44302a2d0eea\",\n \"score\": \"0.6632942\",\n \"text\": \"def getRandEdge(adjGrid, dim):\\n loc = getRandLoc(dim)\\n # we need a location that has an edge from it\\n while len(adjGrid[loc]) is 0:\\n loc = genRandLoc(dim)\\n loc2 = adjGrid[loc][np.random.randint(0,len(adjGrid[loc]))]\\n return [loc, loc2]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6b7cb1a1cfd66ff2488ccaa8cb097079\",\n \"score\": \"0.6625906\",\n \"text\": \"def adj():\\n return [random.randrange(3), random.randrange(6), random.randrange(2)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"745dd5f0b35e894a0cb2646e4b5dbd9f\",\n \"score\": \"0.65565914\",\n \"text\": \"def SampleNegativeEdges(graph, num_edges):\\n random_negatives = set()\\n nodes = list(graph.nodes())\\n while len(random_negatives) < num_edges:\\n i1 = random.randint(0, len(nodes) - 1)\\n i2 = random.randint(0, len(nodes) - 1)\\n if i1 == i2:\\n continue\\n if i1 > i2:\\n i1, i2 = i2, i1\\n n1 = nodes[i1]\\n n2 = nodes[i2]\\n if graph.has_edge(n1, n2):\\n continue\\n random_negatives.add((n1, n2))\\n\\n return random_negatives\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9b0065eebe641dc474dabbc9c517807\",\n \"score\": \"0.65495986\",\n \"text\": \"def create_edges(vertices):\\n\\n colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k', 'lightgray', 'rosybrown', 'orange', 'lightgreen', 'lightblue', 'lightpink']\\n cindex = 0\\n for vertex in vertices:\\n\\n \\\"\\\"\\\"\\n Building vertices connected to a given vertex:\\n - select a random number of possible vertices from the whole set\\n - from this selection, select only vertices closer to it than distance_max\\n \\\"\\\"\\\"\\n # vertex.out_vertices = random.sample(vertices, np.random.randint(0, len(vertices)))\\n\\n vertex.out_vertices = [v for v in random.sample(vertices, np.random.randint(0, np.sqrt(len(vertices)))) if vertex.dist(v) < distance_max]\\n\\n out = \\\",\\\".join([\\\"{}\\\".format(v.id) for v in vertex.out_vertices])\\n\\n # print(\\\"create_edges> id={} out={}\\\".format(vertex.id, out))\\n\\n plt.scatter(vertex.x, vertex.y, c=colors[cindex], s=1)\\n\\n for v in vertex.out_vertices:\\n plt.plot([vertex.x, v.x], [vertex.y, v.y], colors[cindex])\\n\\n cindex += 1\\n if cindex >= len(colors):\\n cindex = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"108fbfd1fc5a361e641c9152352476c8\",\n \"score\": \"0.6512314\",\n \"text\": \"def createRandomGraph():\\n g = {}\\n n = random.sample([0,1,2,3,4,5,6,7,8,9], 7)\\n for i in n:\\n g[i] =[]\\n edges = random.randint(10,20)\\n count = 0\\n while count < edges:\\n a = random.choice(n)\\n b = random.choice(n)\\n if b not in g[a] and a != b:\\n g[a].append(b)\\n count +=1\\n return g\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b3518a77571dcb37e8daa6b1919ec7df\",\n \"score\": \"0.6482092\",\n \"text\": \"def add_random_edge(self, total_edges):\\n while len(self.edges) < total_edges:\\n self.add_edge(self.make_random_edge())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4db108591e60765028f47f65d28ca405\",\n \"score\": \"0.63894844\",\n \"text\": \"def adj33():\\n return [random.randrange(3), random.randrange(6), random.randrange(2)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd507cd32b5fb1b2f6e1dd3440cbc7fd\",\n \"score\": \"0.63875794\",\n \"text\": \"def random_graph_descriptors():\\n graph = nx.dense_gnm_random_graph(11, 50, seed=1337)\\n line_graph = nx.line_graph(graph)\\n\\n node_laplacian = np.array(nx.normalized_laplacian_matrix(graph).todense())\\n edge_laplacian = np.array(nx.normalized_laplacian_matrix(line_graph).todense())\\n incidence = np.array(nx.incidence_matrix(graph).todense())\\n\\n return node_laplacian, edge_laplacian, incidence\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d5ab18866bbbd9503986b059338ff52\",\n \"score\": \"0.63832897\",\n \"text\": \"def random_reverse_reachable_set(graph):\\n n = len(graph[1]) - 1\\n start = random.choice(range(n))\\n stack = [start]\\n visited = set()\\n while stack:\\n curr = stack.pop()\\n if curr not in visited:\\n visited.add(curr)\\n\\n # We are getting the value offsets from the second array here\\n value_start = graph[1][curr]\\n value_end = graph[1][curr + 1]\\n\\n # Using the offsets we just extracted, get head of outgoing edges\\n edges = graph[2][value_start:value_end]\\n\\n # Do the same with the values of the edges\\n probabilities = graph[0][value_start:value_end]\\n\\n for i in range(len(edges)):\\n if random.random() < probabilities[i]:\\n stack.append(edges[i])\\n return visited\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d24fffac7b12c4004f6039df72f2076\",\n \"score\": \"0.6377901\",\n \"text\": \"def edges(self) -> typing.List:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0688b644e03cb287e9cb85752d85180a\",\n \"score\": \"0.6369845\",\n \"text\": \"def select_edges(self):\\n guids = compas_rhino.select_lines()\\n edges = [self.artist.guid_edge[guid] for guid in guids if guid in self.artist.guid_edge]\\n return edges\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1261bc937fa4a77f7cf2b83fe2800b40\",\n \"score\": \"0.63674444\",\n \"text\": \"def edges(self):\\n return self.__generate_edges()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1261bc937fa4a77f7cf2b83fe2800b40\",\n \"score\": \"0.63674444\",\n \"text\": \"def edges(self):\\n return self.__generate_edges()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1261bc937fa4a77f7cf2b83fe2800b40\",\n \"score\": \"0.63674444\",\n \"text\": \"def edges(self):\\n return self.__generate_edges()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1261bc937fa4a77f7cf2b83fe2800b40\",\n \"score\": \"0.63674444\",\n \"text\": \"def edges(self):\\n return self.__generate_edges()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"340e8c6f32b95d33543704d2c8bf336f\",\n \"score\": \"0.635858\",\n \"text\": \"def alive_edges():\\n return [ e for e in edges() if get_weight(e) > 0.0 ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fb0a011b19a3c2a70facc8722890180\",\n \"score\": \"0.63278514\",\n \"text\": \"def directedEdges(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82723a6c4ef2a35a854f092d65015b95\",\n \"score\": \"0.6298398\",\n \"text\": \"def remove_random_edges(G, target_user, portion=10):\\n neighbors = set(G.neighbors(target_user))\\n removed_neighbors = sample(neighbors, k=portion)\\n remaining_neighbors = neighbors.difference(set(removed_neighbors))\\n remaining_neighbors = list(remaining_neighbors)\\n\\n for neighbor in removed_neighbors:\\n G.remove_edge(target_user, neighbor)\\n\\n return removed_neighbors, remaining_neighbors\",\n \"title\": \"\"\n },\n {\n \"docid\": \"280c75b4731e6d76a450088b422857a6\",\n \"score\": \"0.62839603\",\n \"text\": \"def edges(self):\\n return self._generate_edges()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"280c75b4731e6d76a450088b422857a6\",\n \"score\": \"0.62839603\",\n \"text\": \"def edges(self):\\n return self._generate_edges()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"089db9c2cac3e498b3d43d8e3b1652d4\",\n \"score\": \"0.6280948\",\n \"text\": \"def initRandomGraph(ctor, n, m):\\n g = ctor(n)\\n addedEdges = 0\\n while addedEdges < m:\\n x = random.randrange(0, n)\\n y = random.randrange(0, n)\\n if not g.isEdge(x, y):\\n g.addEdge(x, y)\\n addedEdges += 1\\n return g\",\n \"title\": \"\"\n },\n {\n \"docid\": \"604fa7f42080e6a32eab942b62bf4f03\",\n \"score\": \"0.6275545\",\n \"text\": \"def genRandomGraph(self,n,p):\\n graph={}\\n for i in range(n):\\n graph[i] = []\\n for i in range(n):\\n for j in range(i+1,n):\\n #edge i-j\\n r = random.random();\\n if r <= p:\\n graph[i].append(j)\\n #edge j-i\\n r = random.random();\\n if r <= p:\\n graph[j].append(i)\\n return graph\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4ed14dc5792c071eb7e72a6c2b27cbe\",\n \"score\": \"0.6262201\",\n \"text\": \"def randomGraph(size, connectedness):\\n graph = [Node() for _ in range(size)]\\n for node in graph:\\n max_edges = size * connectedness\\n if max_edges > 0:\\n num_edges = random.randrange(int(size * connectedness))\\n else:\\n num_edges = 0\\n node.adj.update(random.sample(graph, num_edges))\\n random.shuffle(graph)\\n return graph\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b069e140ea60e0cd667f5fa84175ebcd\",\n \"score\": \"0.6261221\",\n \"text\": \"def list_edges(self):\\n return list(self._generate_edge())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcbc2c6731ea0b89a5af381be660b43b\",\n \"score\": \"0.62581587\",\n \"text\": \"def rand_graph(n, m):\\n g = {v: [] for v in range(n)}\\n i = 0\\n while i < m:\\n x = randint(0, n)\\n y = randint(0, n)\\n if x > y:\\n x, y = y, x\\n if x != y and y not in g[x]:\\n g[x].append(y)\\n i += 1\\n r_graph = ExtendedGraph(FUN.edge_list_from_dict(g))\\n while r_graph.order() < n:\\n r_graph.add_vertex()\\n return r_graph\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2794e6346c5eff1c29eb4da4852fafe3\",\n \"score\": \"0.6249172\",\n \"text\": \"def undirectedEdges(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0495c7ca07d161e1af5705825d669b9c\",\n \"score\": \"0.6248403\",\n \"text\": \"def random_select(self, neighbours):\\n\\n current_node = random.choice(neighbours).node\\n current_node.select_random_attributes()\\n return current_node\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f2fde3789f5019a104aef57616df804f\",\n \"score\": \"0.6242146\",\n \"text\": \"def _make_erdos(n, e):\\r\\n edges = [(i,j) for j in range(n) for i in range(n) if i != j]\\r\\n edges = [edges[i] for i in np.random.choice(len(edges), e, replace=False)]\\r\\n g = {i:list(set([j for k,j in edges if k==i])) for i in range(n)}\\r\\n return g\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8414c3ad2b68634a675f2a4a08754f0\",\n \"score\": \"0.6241641\",\n \"text\": \"def test_edges(self):\\n edges_tuple = (0, 3), (1, 2), (2, 3), (2, 4)\\n stuff_to_find = set(edges_tuple)\\n stuff_calculated = g_01.edges()\\n self.assertSetEqual(stuff_to_find, stuff_calculated,\\n f\\\"error: edges(), {stuff_to_find} is not expected\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6da3d02be3396e7f2c2ec1f19e175597\",\n \"score\": \"0.6231202\",\n \"text\": \"def random_graph(n, m):\\n G = Digraph()\\n for v in range(n):\\n G.add_vertex(v)\\n\\n max_num_edges = n * (n - 1)\\n if m > max_num_edges:\\n raise ValueError(\\\"For {} vertices, you wanted {} edges, but can only have a maximum of {}\\\".format(n, m, max_num_edges))\\n\\n while G.num_edges() < m:\\n G.add_edge(random.sample(range(n), 2))\\n\\n return G\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ed7f0241725645b1537385c902d799f\",\n \"score\": \"0.61847633\",\n \"text\": \"def test_edges(self):\\n edge_list = self.g.edges()\\n self.assertEqual(42, len(edge_list))\\n # p1 p3 and p3 p1 are valid edges\\n t1 = ('p1', 'p3')\\n self.assertTrue(t1 in edge_list)\\n t2 = ('p3', 'p1')\\n self.assertTrue(t2 in edge_list)\\n made_up = ('z1', 'q123')\\n self.assertFalse(made_up in edge_list)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5d3735886a47a68cac4553dcd3eec3c3\",\n \"score\": \"0.6178505\",\n \"text\": \"def choose_edge(size, degrees):\\n prob = map(lambda x: x / float(sum(degrees)), degrees)\\n return np.random.choice(len(degrees), size=size, p=prob)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4bd24ee0afec3141887b0bf2a8821523\",\n \"score\": \"0.6172141\",\n \"text\": \"def findEdge(self, intree, outtree):\\n e1 = random.choice(list(intree))\\n e2 = self.searchOnGraph(outtree, random.choice(list(intree)), 1)[0]\\n return e1, e2\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e7e8f2ba41af81f8ca31ef97a9583500\",\n \"score\": \"0.6136359\",\n \"text\": \"def select_nodes(network, max_neighbors):\\n degree_to_nodes = {}\\n for node, degree in network.degree():\\n if degree in degree_to_nodes:\\n degree_to_nodes[degree].append(node)\\n else:\\n degree_to_nodes[degree] = [node]\\n\\n selected_nodes = []\\n for degree, nodes in sorted(degree_to_nodes.items(), key=lambda x: x[0]):\\n if degree > max_neighbors:\\n break\\n\\n selected_nodes.append(np.random.choice(nodes))\\n\\n return selected_nodes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"444b58e7de5995a8be5ba97f3c57c8d4\",\n \"score\": \"0.6117529\",\n \"text\": \"def randomDAG(nodes, white_edges=(), black_edges=(), prob=.5):\\n \\n #shuffle the nodes\\n N.random.shuffle(nodes)\\n \\n #add the white edges\\n net = Network(nodes, white_edges)\\n \\n for e in itertools.combinations(nodes, 2):\\n if N.random.randn() < prob:\\n net.add_edge(*e)\\n \\n return net\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f76eb340b47cb7f773a2208ccaabea7\",\n \"score\": \"0.6113278\",\n \"text\": \"def bidirectedEdges(self):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c3b986fed0306cba065899a72ed192d5\",\n \"score\": \"0.61092746\",\n \"text\": \"def __generate_edges(self):\\n edges = []\\n for vertex in self.__graph_dict:\\n for neighbour in self.__graph_dict[vertex]:\\n if {neighbour, vertex} not in edges:\\n edges.append({vertex, neighbour}) #povezuje dva cvora (cvor i link)\\n return edges\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bba920d7c0f547bc59e86b3076b1c0e7\",\n \"score\": \"0.61051416\",\n \"text\": \"def getRandomNeighbour(site, exceptSite, graph):\\n\\n # Convert sites to a tuples (tuples can be used as hash keys but not arrays)\\n site = tuple(site)\\n\\n # Get all the neighbours to site\\n # neighbours is as [(i, j+1), ...]\\n neighbours = list(graph[site].keys())\\n\\n if exceptSite is not None:\\n neighbours.remove(tuple(exceptSite))\\n\\n neighbour = random.choice(neighbours)\\n\\n # Since neighbour will be changed a lot, use a list\\n return list(neighbour)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cd7d47352f5f43106db35a5749500ca\",\n \"score\": \"0.61008394\",\n \"text\": \"def generate_graph(n):\\n \\n #graph is represented by an adjacency list\\n graph = {} #dictionnary = {vertex i : [list of neighbours]}\\n \\n for i in range(n):\\n # Each vertex\\n # has more than one neighbour and less than n-1 neighbour\\n nb_neighbours = rd.randint(1,n-1) \\n \\n # we forbid a vertex beeing its own neighbour\\n possible_neighbours = [j for j in range(n) if j!=i]\\n \\n # list of neighbours\\n # defined by taking nb_neighbours out of the whole set of neighbours \\n neighbours = rd.sample(possible_neighbours, nb_neighbours)\\n graph[i] = neighbours\\n \\n return graph\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4bae7e965f961198aeda1d77aae2a19\",\n \"score\": \"0.6099499\",\n \"text\": \"def edges(self):\\n return self._mst\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab3bd80821b407f7ad8b6090cb57ee41\",\n \"score\": \"0.60875195\",\n \"text\": \"def random_subgraph(graph, number_of_nodes):\\n all_nodes = set(graph.nodes())\\n subgraph_nodes = random.sample(all_nodes, number_of_nodes)\\n\\n return graph.subgraph(subgraph_nodes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"678454c9298de035ff737197789ba5b7\",\n \"score\": \"0.6082616\",\n \"text\": \"def random_dag(nodes, edges):\\n G = nx.DiGraph()\\n for i in range(nodes):\\n G.add_node(i)\\n while edges > 0:\\n a = randint(0, nodes-1)\\n b = a\\n while b == a:\\n b = randint(0, nodes-1)\\n G.add_edge(a, b)\\n if nx.is_directed_acyclic_graph(G):\\n edges -= 1\\n else:\\n # we closed a loop!\\n G.remove_edge(a, b)\\n\\n first_node = nodes\\n last_node = nodes + 1\\n G.add_node(first_node)\\n G.add_node(last_node)\\n\\n for node, in_edge in dict(G.in_degree()).items():\\n if in_edge == 0:\\n if node == first_node or node == last_node:\\n continue\\n G.add_edge(first_node, node)\\n\\n for node, out_edge in dict(G.out_degree()).items():\\n if out_edge == 0:\\n if node == first_node or node == last_node:\\n continue\\n G.add_edge(node, last_node)\\n\\n return G\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b3c2bbcfb8c66bd901a9bf376e79b0e\",\n \"score\": \"0.6056807\",\n \"text\": \"def select_elites():\\n elites = []\\n while len(elites) < E: # Keep choosing elites until there are E of them\\n new_elites = popD[max(popD)] # These are the binary lists with the best fitness\\n # If adding all the elites with this fitness would be too many, then discard the surplus at random\\n while len(new_elites) > (E - len(elites)):\\n new_elites.remove(random.sample(new_elites, 1)[0])\\n elites.extend(new_elites)\\n popD.pop(max(popD), None) # Remove the key with the value just added from popD{}\\n return elites\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cd1a917d916e11b0da39d213952a153\",\n \"score\": \"0.60468584\",\n \"text\": \"def random_graph(num_edges):\\n if num_edges <= 0:\\n raise ValueError(\\\"The argument must be a positive integer.\\\")\\n e = random_edges(num_edges)\\n return Graph(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c930a982dc9652dd8b74c44792ce0afd\",\n \"score\": \"0.60441923\",\n \"text\": \"def _get_random_u_v_k_triples(graph, number_seed_edges, random_state):\\n universe_edges = [\\n (u, v)\\n for u in graph\\n for v in graph.edge[u]\\n ]\\n random_state.shuffle(universe_edges)\\n\\n for u, v in universe_edges[:number_seed_edges]:\\n keys = list(graph[u][v])\\n key = random_state.choice(keys)\\n yield u, v, key\",\n \"title\": \"\"\n },\n {\n \"docid\": \"81a275e0b255ea63e6a24039cdd04e2a\",\n \"score\": \"0.6038839\",\n \"text\": \"def get_random_neighbors(self):\\n x = randint(0, 8)\\n y1 = randint(0, 8)\\n y2 = randint(0, 8)\\n while y1 == y2 or (x, y1) in self.read_only_tiles or (x, y2) in self.read_only_tiles:\\n x = randint(0, 8)\\n y1 = randint(0, 8)\\n y2 = randint(0, 8)\\n\\n return (x, y1), (x, y2)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"171125957197731e58cc48698ac4b28d\",\n \"score\": \"0.6024078\",\n \"text\": \"def edges(graph):\\n return gr.edges(graph)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6780daecfb21e00f4f8b82aa3ca439b4\",\n \"score\": \"0.6018661\",\n \"text\": \"def add_random_edges(win_graph: nx.DiGraph, candidates: set) -> nx.DiGraph:\\n to_add = candidates.difference(set(win_graph.nodes))\\n win_graph = win_graph.copy()\\n candidates = set(win_graph.nodes).union(to_add)\\n edge_list = []\\n for u in candidates:\\n for v in candidates:\\n if u != v:\\n edge_list.append((u, v))\\n\\n random.shuffle(edge_list)\\n\\n # Existing edges don't have to be checked\\n for edge in win_graph.edges:\\n edge_list.remove(edge)\\n\\n for c1, c2 in edge_list:\\n try:\\n win_graph.add_edge(c1, c2)\\n nx.find_cycle(win_graph)\\n win_graph.remove_edge(c1, c2)\\n except nx.NetworkXNoCycle:\\n pass\\n\\n return win_graph\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d090ddd13d2316696b3c0bba4b296aa2\",\n \"score\": \"0.6015056\",\n \"text\": \"def random_neighbor(G, node):\\n neighbors = list(G.neighbors(node))\\n randomly_selected_neighbor = choice(neighbors)\\n return randomly_selected_neighbor\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ce8e838dbcb9d4e9523ac2105ff7d8f3\",\n \"score\": \"0.6010255\",\n \"text\": \"def add_edges(graph, n_max_connections = 5) :\\r\\n for i in range(1, graph.number_of_nodes()):\\r\\n for n in range(1, n_max_connections):\\r\\n ni = np.random.randint(1, graph.number_of_nodes())\\r\\n if i != ni : cluster_graph.add_edge(i,ni)\\r\\n return graph\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1a42fbb0a60dd6a1ce3f8fbd553a8f29\",\n \"score\": \"0.600649\",\n \"text\": \"def geo_graph(m,n,d):\\n # Begin with a grid:\\n res_graph = nx.grid_2d_graph(m,n,True)\\n\\n # Next connect each node to random nodes:\\n for x in res_graph.nodes_iter():\\n neis = random.sample(res_graph.nodes(),d)\\n # We dont want to connect to ourselves:\\n try:\\n neis.remove(x)\\n except ValueError:\\n pass\\n\\n # Add the additional edges:\\n for nei in neis:\\n if not res_graph.has_edge(x,nei):\\n res_graph.add_edge(x,nei)\\n\\n return res_graph\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98794bbc9674cb345fe605faacae86e1\",\n \"score\": \"0.59923995\",\n \"text\": \"def get_edges(self):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8641406bc41e89cd21d52d5e35d24524\",\n \"score\": \"0.599198\",\n \"text\": \"def random_graph(V=80, E=50, connected=False):\\n graph = Graph(V)\\n for _ in range(E):\\n graph.add_edge(randrange(0, V), randrange(0, V))\\n if not connected:\\n return graph\\n\\n component = max([graph.connected_component(x) for x in range(len(graph.vertices))])\\n while len(component) < len(graph.vertices):\\n for x in range(len(graph.vertices)):\\n if x not in component:\\n graph.add_edge(x, choice(list(component)))\\n break\\n\\n component = graph.connected_component(component.pop())\\n\\n return graph\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c94bf4f3386041446bbf7c4bdf875bd5\",\n \"score\": \"0.59897614\",\n \"text\": \"def generateRandomSelection(self): \\n rand1 = r.random()\\n rand2 = r.random() \\n \\n if rand2 < rand1: #if the nr of rand2 is bigger swap them\\n temp = rand2\\n rand2 = rand1\\n rand1 = temp\\n #selectionPoints = self.storePointSelection(rand1, rand2) // CHECK IF I NEED TO DELETE THAT\\n selectionPoints = u.getPointSelection (rand1, rand2, self.usablePointSet)\\n return selectionPoints\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50e629025d059d2c8cc5306774a91a3e\",\n \"score\": \"0.5976913\",\n \"text\": \"def __generate_edges(self):\\n edges = []\\n for v in self.__graph:\\n for vi in self.__graph[v]:\\n if v < vi:\\n edges.append((v, vi))\\n return edges\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09da137052d1f627751cfb1e44a63128\",\n \"score\": \"0.59740263\",\n \"text\": \"def getEdges(self, filter=None) -> typing.List:\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a40115d9f0b21f71c6fcdfac0ae39ee5\",\n \"score\": \"0.5968571\",\n \"text\": \"def sample_negative_pairs(graph, size):\\n log(f\\\"Sampling {size} negative pairs\\\")\\n negative_edges = []\\n nodes = list(graph.nodes())\\n while len(negative_edges) < size:\\n u = np.random.randint(low=0, high=len(nodes), size=1)[0]\\n v = np.random.randint(low=0, high=len(nodes), size=1)[0]\\n if u != v and not graph.has_edge(u, v):\\n negative_edges.append((u, v))\\n log(f\\\"{len(negative_edges)}/{size}\\\", cr=True)\\n log()\\n return torch.tensor(negative_edges, dtype=torch.long)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d97b03dc3e837ba2cfc3bdb94f2708af\",\n \"score\": \"0.5967824\",\n \"text\": \"def randomNode(self):\\r\\n availableNodes = []\\r\\n for xNode in self.nodeList:\\r\\n for node in xNode:\\r\\n if node.wall == False:\\r\\n availableNodes.append(node)\\r\\n\\r\\n return random.choice(availableNodes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e55848b13a615a619a0a12716021fb3d\",\n \"score\": \"0.5940899\",\n \"text\": \"def sample_negative_nodes(graph, dist, source, target, size=1):\\n negatives = []\\n while len(negatives) < size:\\n rnd_idx = np.random.randint(0, len(dist), size)[0]\\n rnd_node = dist[rnd_idx]\\n if source != rnd_node != target and not graph.has_edge(source, rnd_node) and \\\\\\n not graph.has_edge(target, rnd_node):\\n negatives.append(rnd_node)\\n return torch.tensor(negatives, dtype=torch.long)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"230209c5387f9db718f1994651e8a091\",\n \"score\": \"0.59373766\",\n \"text\": \"def randomSelection(self):\\r\\n indA = self.matingPool[random.randint(0, self.popSize - 1)]\\r\\n indB = self.matingPool[random.randint(0, self.popSize - 1)]\\r\\n return [indA, indB]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d831db1dfc1a32f0050dadef6ec94ba1\",\n \"score\": \"0.59341425\",\n \"text\": \"def randomize(self):\\n\\n # shortcuts for quick references\\n l = self.data.keys() # make a list of the vertices\\n\\n f = random.choice(l) # pick a random first place\\n visited = list() # initialize an empty visited list\\n visited.append(f) # add the first city to the list\\n\\n # while the number of visited nodes is less\\n # than the number of entries in the list\\n while len(visited) < len(l):\\n n = random.choice(l) # select a random choice\\n\\n # If the node hasn't been visited\\n if n not in visited:\\n visited.append(n) # Append the visited list\\n\\n # Add the final connection between the\\n # last node visited and the first node\\n visited.append(f)\\n self.graph.add_path(visited)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e372bbaf11c6dbec4ba931455483f1a\",\n \"score\": \"0.59324497\",\n \"text\": \"def adj31():\\n return [random.randrange(2), random.randrange(6), random.randrange(2)]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e61456c03e4e49928d70d76ac3406f32\",\n \"score\": \"0.5930999\",\n \"text\": \"def get_edges(self):\\r\\n return self.edges\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8a0aed6adad808ea66d37484d781b05c\",\n \"score\": \"0.5926841\",\n \"text\": \"def randomGraph(node_count, color_count, neighbor_count):\\n graph = Graph()\\n\\n # randomly make the nodes and color them.\\n for i in range(0, node_count - 1):\\n graph.add_node(Node(randint(0, color_count - 1), graph))\\n\\n # randomly make neighbors.\\n for node in graph.nodes():\\n # get all nodes of graph and store it in an array\\n other_nodes = graph.nodes()\\n # remove current node from that array\\n other_nodes.remove(node)\\n # randomly choose some nodes from that array\\n neighbors = random.sample(other_nodes, randint(0, neighbor_count - 1))\\n # loop on the second array and make them neighbors\\n graph.make_neighbor(node, neighbors)\\n\\n return graph\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b9f4289628af0d97eda279ed6a5797a\",\n \"score\": \"0.592557\",\n \"text\": \"def random_dag(self):\\n # add nodes, labeled 0...nodes:\\n \\n for i in range(self.n_nodes):\\n self.randDAG.add_node(self.nodes[i])\\n\\n child_parent = {}\\n shuffled_nodes = self.nodes[:]\\n random.shuffle(shuffled_nodes)\\n \\n # to avoid infinit loop, need to have better solution\\n round = 1000\\n while self.n_edges > 0 and round > 0:\\n round -= 1\\n\\n a = random.choice(self.nodes) \\n b = random.choice(self.nodes)\\n while a == b or self.randDAG.has_edge(a, b):\\n b = random.choice(self.nodes)\\n \\n self.randDAG.add_edge(a, b)\\n if nx.is_directed_acyclic_graph(self.randDAG):\\n self.n_edges -= 1\\n parent = child_parent.get(b)\\n if parent is None:\\n parent = [a]\\n else:\\n parent.append(a)\\n child_parent[b] = parent\\n # print(a,\\\"-> \\\", b)\\n else:\\n # we closed a loop!\\n self.randDAG.remove_edge(a, b)\\n \\n return self.randDAG, child_parent\",\n \"title\": \"\"\n },\n {\n \"docid\": \"60cac2fd2a901f4eed428b25f1a64432\",\n \"score\": \"0.592433\",\n \"text\": \"def make_random_graph(num_nodes, prob):\\n # initialize empty graph\\n random_graph = {}\\n for vertex in range(num_nodes): random_graph[vertex] = []\\n # consider each vertex\\n for vertex in range(num_nodes):\\n # consider each neighbour with greater value\\n for neighbour in range(vertex + 1, num_nodes):\\n random_number = random.random()\\n # add edge from vertex to neighbour with probability prob\\n if random_number < prob:\\n # maybe this method for set union is deprecated in python 3\\n random_graph[vertex] = set(random_graph[vertex]) | set([neighbour])\\n random_graph[neighbour] = set(random_graph[neighbour]) | set([vertex])\\n return random_graph\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee13a7adf5fbd10f193d72fef6a5bfe3\",\n \"score\": \"0.5921974\",\n \"text\": \"def make_random_graph(num_nodes, prob):\\n #initialize empty graph\\n random_graph = {}\\n #consider each vertex\\n for i in range(num_nodes):\\n random_graph[i] = []\\n\\n for vertex in range(num_nodes):\\n for neighbour in range(vertex+1, num_nodes):\\n random_number = random.random()\\n if random_number < prob:\\n random_graph[vertex] += [neighbour]\\n random_graph[neighbour] += [vertex] \\n #add vertex with list of out_ neighbours\\n\\n return random_graph\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8df0bbf613be9c439d2bfaf5e9ae4b2e\",\n \"score\": \"0.5920063\",\n \"text\": \"def generate_edges(self):\\n e = []\\n for v in self._graph_dict:\\n for neigh in self._graph_dict[v]:\\n if {neigh,v} not in e:\\n e.append({v,neigh})\\n return e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e3b421642ebdc12755eb262a5df8f2c7\",\n \"score\": \"0.5919125\",\n \"text\": \"def init(graph):\\n #sol = TP1.approx_steiner(graph, terms)\\n return [round(rd.random()) == 1 for _ in range(len(graph.edges))]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87fce0055a69ec379af8cf8b9ef7ffa3\",\n \"score\": \"0.59188426\",\n \"text\": \"def generate_random_model__regular_digraph(m, out_d_birth, out_d_migration):\\n\\t\\n\\tnotdone = True\\n\\twhile notdone:\\n\\t\\tbG = networkx.DiGraph()\\n\\t\\tmG = networkx.DiGraph()\\n\\t\\tfor node in range(m):\\n\\t\\t\\tfor k in range(out_d_birth):\\n\\t\\t\\t\\talter = randint(0, m)\\n\\t\\t\\t\\twhile bG.has_edge(node, alter): # exclude double edges\\n\\t\\t\\t\\t\\talter = randint(0, m)\\n\\t\\t\\t\\t#\\n\\t\\t\\t\\tbG.add_edge(node, alter)\\n\\t\\t\\t#\\n\\t\\t\\tfor k in range(out_d_migration):\\n\\t\\t\\t\\talter = node\\n\\t\\t\\t\\twhile alter==node:\\n\\t\\t\\t\\t\\talter = randint(0, m)\\n\\t\\t\\t\\t#\\n\\t\\t\\t\\tmG.add_edge(node, alter)\\n\\t\\t\\t#\\n\\t\\t#\\n\\t\\t# test for connectedness\\n\\t\\tuG = networkx.DiGraph() #networkx.union(mG, bG)\\n\\t\\tuG.add_edges_from(bG.edges())\\n\\t\\tuG.add_edges_from(mG.edges())\\n\\t\\tif networkx.is_strongly_connected(uG):\\n\\t\\t\\tnotdone = False\\n\\t\\t#\\n\\t#\\n\\treturn bG, mG\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d3d97c6cebdc2388fd5b0f0876f942d7\",\n \"score\": \"0.59185874\",\n \"text\": \"def random_select_revealed_node(self, alpha, index1, index2):\\n same_nodes = self.nodes[index1] & self.nodes[index2]\\n s = int(alpha * len(same_nodes))\\n logging.info(\\\"graph {}-{} random revealed nodes {}/{}.\\\".format\\n (index1, index2, s, len(same_nodes)))\\n revealed_nodes = random.sample(same_nodes, s)\\n self.revealed_nodes[(index1, index2)] = set(revealed_nodes)\\n self.same_nodes[(index1, index2)] = same_nodes\\n return revealed_nodes\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a2e06e406978ad953657c9859ba4337\",\n \"score\": \"0.5917218\",\n \"text\": \"def randomSelection(self):\\n indA = self.matingPool[random.randint(0, self.popSize - 1)]\\n indB = self.matingPool[random.randint(0, self.popSize - 1)]\\n return [indA, indB]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"065d79f1085390367e0308ce5e4e831f\",\n \"score\": \"0.5916045\",\n \"text\": \"def edges():\\n return [ e \\n for e \\n in g().edges \\n if e is not None \\n and hasattr(e,'source')\\n and hasattr(e,'target')\\n ]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98c8f568a5f99f9e1f3160949cf8d9f2\",\n \"score\": \"0.59129196\",\n \"text\": \"def get_random_neighbour_arbitrary(schedule):\\n neighbours = []\\n\\n while not neighbours:\\n neighbours = SchedulingTask.get_neighbours_local_arbitrary(schedule)\\n i, j = random.choice(neighbours)\\n new_schedule = schedule.copy()\\n new_schedule[i], new_schedule[j] = new_schedule[j], new_schedule[i]\\n return new_schedule\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c8be0a991b35dc485df1bbc2988f5f33\",\n \"score\": \"0.5910603\",\n \"text\": \"def _generate_edge(self):\\n edges = []\\n for node in self._graph_dict:\\n for adjacent_node in self._graph_dict[node]:\\n if {adjacent_node, node} not in edges:\\n edges.append({str(node), str(adjacent_node)})\\n return edges\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bf6d5716f736619b0c62bd1bd8ba3e01\",\n \"score\": \"0.5904561\",\n \"text\": \"def random_painting(self) -> int:\\n return random.choice(self.museum_graph_list())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"130159cd7d27f1e575d6c9abdd38927c\",\n \"score\": \"0.5904064\",\n \"text\": \"def create_random_graph(number_of_vertices, number_of_edges, radius, thickness):\\n\\n global screen\\n\\n dist_apart = radius * 3\\n\\n for i in range(0, number_of_vertices):\\n vtx_x, vtx_y = generate_valid_coordinates(radius, dist_apart)\\n\\n vtx = {\\\"ID\\\": i,\\n \\\"x\\\": vtx_x,\\n \\\"y\\\": vtx_y,\\n \\\"color\\\": \\\"WHITE\\\",\\n \\\"adjacent\\\": [],\\n }\\n\\n VERTICES.append(vtx)\\n\\n # Assign adjacency\\n for i in range(0, number_of_edges):\\n vtx_one = None\\n vtx_two = None\\n\\n # Do not assign the adjacency of a vertex to itself\\n while vtx_one==vtx_two:\\n vtx_one = random.randint(0, number_of_vertices - 1)\\n vtx_two = random.randint(0, number_of_vertices - 1)\\n\\n VERTICES[vtx_one][\\\"adjacent\\\"].append(VERTICES[vtx_two][\\\"ID\\\"])\\n VERTICES[vtx_two][\\\"adjacent\\\"].append(VERTICES[vtx_one][\\\"ID\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ad103fbe0abe05331b742c7db6903285\",\n \"score\": \"0.59001255\",\n \"text\": \"def randomGraph(v,e, graphID = None, connected = False):\\r\\n graph = Graph(graphID)\\r\\n graph.connected = connected\\r\\n \\r\\n # check edges does not exceed max number possible\\r\\n if e > v*(v-1)/2:\\r\\n raise RuntimeError(\\\"e: number of edges has to be <= v*(v-1)/2 \\\")\\r\\n \\r\\n # check for connected graph have the min number of edges\\r\\n if connected and e < v-1:\\r\\n \\r\\n raise RuntimeError(\\\"e: number of edges has to be at least >= v-1\\\")\\r\\n \\r\\n # create v vertices with id = i\\r\\n for vertexID in range(v):\\r\\n \\r\\n # generate random location\\r\\n x = random.random()\\r\\n y = random.random()\\r\\n \\r\\n # create instance and set location\\r\\n V = Vertex(vertexID)\\r\\n V.setLocation(x,y)\\r\\n \\r\\n # store object key = id, value = vertex object\\r\\n graph.vertexList[vertexID]=V\\r\\n \\r\\n \\r\\n allVertices = set(range(v))\\r\\n assignedVertices = set()\\r\\n remainingVertices = v\\r\\n \\r\\n # pick random start vertex\\r\\n assignedVertices.add(random.choice(list(allVertices))) \\r\\n \\r\\n # iterate until create e edges:\\r\\n edgeID = 0\\r\\n while (edgeID<e):\\r\\n \\r\\n # random pair of vertices (order in pair of vertices doesn't matter)\\r\\n # (v1,v2) is the same as (v2,v1), but need to store as one\\r\\n # uniqe key for dictionary, so get (smallest, largest) to be \\r\\n # consistent\\r\\n \\r\\n if (remainingVertices == 0 or not connected ):\\r\\n pair = randomPair(allVertices, order=False)\\r\\n v1,v2 = pair\\r\\n \\r\\n else:\\r\\n v1 = random.choice(list(assignedVertices))\\r\\n v2 = random.choice(list(allVertices.difference(assignedVertices)))\\r\\n \\r\\n pair = tuple(sorted((v1,v2)))\\r\\n \\r\\n # generate a random pair of vertices until pair does not \\r\\n # exist in the adjancecyList (as a key in the dictionary)\\r\\n if (pair not in graph.adjacencyList):\\r\\n \\r\\n # get vertex objects and compute weight\\r\\n \\r\\n V1 = graph.vertexList[v1]\\r\\n V2 = graph.vertexList[v2]\\r\\n weight = findDistance(V1.location, V2.location)\\r\\n \\r\\n # create edge and add to edge list\\r\\n edge = Edge(edgeID, V1,V2, weight) \\r\\n graph.edgeList[edgeID] = edge\\r\\n \\r\\n # add pair as key and value = euclidean distance\\r\\n graph.adjacencyList[pair]= edge.weight\\r\\n \\r\\n # remove v1 and v2 from set unassignedVertices if present.\\r\\n # no error if not present (might be the case that the pair\\r\\n # does not exist, but one of the vertices might have already \\r\\n # been removed because was used in a different edge)\\r\\n \\r\\n # only do this for connected graph\\r\\n if (connected):\\r\\n assignedVertices.add(v2)\\r\\n \\r\\n remainingVertices = len(allVertices.difference(assignedVertices))\\r\\n \\r\\n edgeID+= 1 # go to next edge\\r\\n \\r\\n return graph\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c73eb76e9d8ee6e060b1899996cf1617\",\n \"score\": \"0.5899185\",\n \"text\": \"def snowballsampling(g, seed, maxsize=50):\\n if g.number_of_nodes() < maxsize:\\n return set()\\n q = que.Queue()\\n q.put(seed)\\n subgraph = set(seed)\\n while not q.empty():\\n for node in g.neighbors(q.get()):\\n if len(subgraph) < maxsize:\\n q.put(node)\\n subgraph.add(node)\\n else :\\n return subgraph\\n return subgraph\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16873bd2a6ef04ad8fa4e4aba2956634\",\n \"score\": \"0.58973855\",\n \"text\": \"def edges_selected(threshold, edges, weights):\\n edges_new = []\\n weights_new = []\\n for index in range(len(weights)):\\n if weights[index] >= threshold:\\n edges_new.append(edges[index])\\n weights_new.append(weights[index])\\n unique_v = set(sum(edges_new, ()))\\n return edges_new, weights_new, list(unique_v)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2166b5c0e254cca56166ce92412c25ee\",\n \"score\": \"0.58885175\",\n \"text\": \"def _generate_edges(self):\\n edges = []\\n for vertex in self._graph_dict:\\n for (neighbour, label) in self._graph_dict[vertex]:\\n if [neighbour, vertex] not in edges:\\n edges.append([vertex, (neighbour, label)])\\n return edges\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b7f8ec3d64450336bab7f59bb7ec9be\",\n \"score\": \"0.5880869\",\n \"text\": \"def generate_random_graph(n, max_iter=10):\\n g = get_simple_ordered_tree(n)\\n for it in range(max_iter):\\n i, j = get_random_node_pair(n)\\n if g.has_edge(i, j) is True:\\n del_edge(i, j, g)\\n else:\\n add_edge(i, j, g)\\n return g\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c065d4d361e794d29ea50fa6581d76d7\",\n \"score\": \"0.5875883\",\n \"text\": \"def CreateRandomHyperGraph(num_nodes, num_edges, probability):\\n assert probability <= 1\\n assert probability >= 0\\n assert num_edges >= 0\\n assert num_nodes >= 0\\n result = Hypergraph()\\n for i in range(num_nodes):\\n for j in range(num_edges):\\n if random() < probability:\\n AddNodeToEdge(result, i, j)\\n return result\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e869a922f2254033ee67bb18fbe4197\",\n \"score\": \"0.5869381\",\n \"text\": \"def test_return_neighbors_at_edge(self):\\n exp = [\\n (3, 3),\\n (3, 0),\\n (3, 1),\\n (0, 3),\\n (0, 1),\\n (1, 3),\\n (1, 0),\\n (1, 1),\\n ]\\n g = grid.Grid(4, 4)\\n act = g.neighbors(0, 0)\\n self.assertEqual(exp, act)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f3c0e88845bd83011b738d40fe4e4ec\",\n \"score\": \"0.58688164\",\n \"text\": \"def fractal_model(generation,m,x,e):\\n\\tG=Graph()\\n\\tG.add_edge(0,1) #This is the seed for the network (generation 0)\\n\\tnode_index = 2\\n\\tfor n in range(1,generation+1):\\n\\t\\tall_links = G.edges()\\n\\t\\twhile all_links:\\n\\t\\t\\tlink = all_links.pop()\\n\\t\\t\\tnew_nodes_a = range(node_index,node_index + m)\\n\\t\\t\\t#random.shuffle(new_nodes_a)\\n\\t\\t\\tnode_index += m\\n\\t\\t\\tnew_nodes_b = range(node_index,node_index + m)\\n\\t\\t\\t#random.shuffle(new_nodes_b)\\n\\t\\t\\tnode_index += m\\n\\t\\t\\tG.add_edges_from([(link[0],node) for node in new_nodes_a])\\n\\t\\t\\tG.add_edges_from([(link[1],node) for node in new_nodes_b])\\n\\t\\t\\trepulsive_links = zip(new_nodes_a,new_nodes_b)\\n\\t\\t\\tG.add_edges_from([repulsive_links.pop() for i in range(x-1)])\\n\\t\\t\\tif random.random() > e:\\n\\t\\t\\t\\tG.remove_edge(*(link))\\n\\t\\t\\t\\tG.add_edge(*(repulsive_links.pop()))\\n\\treturn G\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993297,"cells":{"query_id":{"kind":"string","value":"87488fa0f3f8495b51a11ee8ae28a308"},"query":{"kind":"string","value":"This event is triggered when any FlatNotebook page is deleted. When a FlatNotebook page is deleted we remove the associated page object (a TextCtrl) from the pages list. At the same time we give the operator a chance to save modifications."},"positive_passages":{"kind":"list like","value":[{"docid":"8a6345ace9f008266cd1e82075967ffa","score":"0.0","text":"def closing_file_event(self, event):\n\n page = self.notebook.GetCurrentPage()\n filename = page.filename\n\n self.show_notebook_if_not_shown()\n\n if page.GetModify() == True:\n message = 'Discard unsaved modifications?'\n caption = 'Discard?'\n discard_page_ok = self.ask_yes_no(self, message, caption)\n else:\n discard_page_ok = True\n\n if discard_page_ok == True:\n self.pages.pop(self.pages.index(page))\n self.statusbar.SetStatusText('{0} was closed.'.format(filename), 0)\n self.statusbar.SetStatusText('', 1)\n else:\n self.save_file_as()\n # After attempting to save we check for unsaved modifications again.\n # If they exist, it indicates that the save failed. Veto the event.\n if page.GetModify() == True:\n event.Veto()\n self.statusbar.SetStatusText('{0} was not closed.'.format(filename), 0)\n else:\n self.pages.pop(self.pages.index(page))\n self.statusbar.SetStatusText('{0} was closed.'.format(filename), 0)\n self.statusbar.SetStatusText('', 1)","title":""}],"string":"[\n {\n \"docid\": \"8a6345ace9f008266cd1e82075967ffa\",\n \"score\": \"0.0\",\n \"text\": \"def closing_file_event(self, event):\\n\\n page = self.notebook.GetCurrentPage()\\n filename = page.filename\\n\\n self.show_notebook_if_not_shown()\\n\\n if page.GetModify() == True:\\n message = 'Discard unsaved modifications?'\\n caption = 'Discard?'\\n discard_page_ok = self.ask_yes_no(self, message, caption)\\n else:\\n discard_page_ok = True\\n\\n if discard_page_ok == True:\\n self.pages.pop(self.pages.index(page))\\n self.statusbar.SetStatusText('{0} was closed.'.format(filename), 0)\\n self.statusbar.SetStatusText('', 1)\\n else:\\n self.save_file_as()\\n # After attempting to save we check for unsaved modifications again.\\n # If they exist, it indicates that the save failed. Veto the event.\\n if page.GetModify() == True:\\n event.Veto()\\n self.statusbar.SetStatusText('{0} was not closed.'.format(filename), 0)\\n else:\\n self.pages.pop(self.pages.index(page))\\n self.statusbar.SetStatusText('{0} was closed.'.format(filename), 0)\\n self.statusbar.SetStatusText('', 1)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"84272713bfa0b104465c28ac77305ef6","score":"0.67369455","text":"def on_close_page(self, event):\r\n page = event.EventObject\r\n page.Show(False)\r\n # Remove page from MainWindow data structures\r\n if isinstance(page, DatabasePage):\r\n del self.db_pages[page]\r\n page_dbs = [page.db]\r\n main.log(\"Closed database tab for %s.\" % page.db)\r\n else:\r\n del self.merger_pages[page]\r\n page_dbs = [page.db1, page.db2]\r\n main.log(\"Closed comparison tab for %s and %s.\" % (\r\n page.db1, page.db2\r\n ))\r\n # Close databases, if not used in any other page\r\n for db in page_dbs:\r\n if not db.has_consumers():\r\n del self.dbs[db.filename]\r\n db.close()\r\n main.log(\"Closed database %s.\" % db)\r\n # Remove any dangling references\r\n if self.page_merge_latest == page:\r\n self.page_merge_latest = None\r\n if self.page_db_latest == page:\r\n self.page_db_latest = None\r\n self.pages_visited = filter(lambda x: x != page, self.pages_visited)\r\n index_new = 0\r\n if self.pages_visited:\r\n for i in range(self.notebook.GetPageCount()):\r\n if self.notebook.GetPage(i) == self.pages_visited[-1]:\r\n index_new = i\r\n break\r\n self.notebook.SetSelection(index_new)\r\n # Remove page from among notebook pages\r\n for i in range(self.notebook.PageCount):\r\n if self.notebook.GetPage(i) == page:\r\n # Using DeletePage freezes app for some reason\r\n self.notebook.RemovePage(i)\r\n break\r\n self.SendSizeEvent() # Multiline wx.Notebooks need redrawing\r\n self.notebook.RemoveChild(page)\r\n if isinstance(page, DatabasePage):\r\n self.update_database_list(page.db.filename)\r\n else:\r\n self.update_database_list(page.db1.filename)\r\n self.update_database_list(page.db2.filename)\r\n page.Destroy()\r\n self.UpdateAccelerators() # Remove page accelerators\r","title":""},{"docid":"366edf55ce95b000bf92b904eb0d8ec1","score":"0.64634645","text":"def remove(self):\n index = self.notebook.page_num(self)\n self.notebook.remove_page(index)","title":""},{"docid":"ca2d14beb2cd7efcdc82cd8c59c06e92","score":"0.620988","text":"def close(self, event):\n\n # Try deleting the currently selected notebook page.\n # This will trigger the EVT_FLATBOOK_PAGE_CLOSING event.\n # That event is bound to self.closing_file_event.\n self.DeletePage(self.GetSelection())","title":""},{"docid":"07f98f0731021775966ba47f5cb685e6","score":"0.58915406","text":"def onTextDeleted(self, event):\n\n # Ignore changes on the status bar. We handle them in onTextInserted.\n #\n if event.source.getRole() != pyatspi.ROLE_STATUS_BAR:\n default.Script.onTextDeleted(self, event)","title":""},{"docid":"2cd51a0a6a1bf2e8b916eab1ed95560e","score":"0.5830332","text":"def TreeItemDelete(self, event):\n # Find what type of node it is (word or tree root)\n node_type = cf.checkNode(self.current_node)\n # Delete the word or tree\n if node_type == 'word':\n cf.deleteWord(self.current_node)\n elif node_type == 'tree':\n cf.deleteTree(self.current_node)\n self.search_root = self.search_words = None\n # Refresh the tree\n self.DisplayTree(self.search_root, self.search_words)","title":""},{"docid":"961f7058782da73d071c56f325537e8e","score":"0.5803831","text":"def deleteReceived(event):","title":""},{"docid":"790aaf569a6d09ef8e5d4e57ba883722","score":"0.574993","text":"def after_delete(self):\n pass","title":""},{"docid":"e8cf4cdc1ecac8773ca410376d5bfa76","score":"0.57103044","text":"def dispatch(self, *args, **kwargs):\n return super(PageDelete, self).dispatch(*args, **kwargs)","title":""},{"docid":"d8bf104a4ce2ac93178f1c85a219165b","score":"0.5698488","text":"def delete_clicked(self, obj):\n store, the_iter = self.namemodel.get_selected()\n if not the_iter:\n return\n row = self.namemodel.get_selected_row()\n self.bookmarks.pop(row)\n self.namemodel.remove(the_iter)\n self.modified = True\n if row > 0:\n row -= 1\n self.namemodel.select_row(row)","title":""},{"docid":"f4d8a0a99052e1b173b0f890541aa7d8","score":"0.56756574","text":"def delete(self, event):\n\t\tif self.inLastLine():\n\t\t\tif self.output.selectedText:\n\t\t\t\tself.doc.remove(self.output.selectionStart, self.output.selectionEnd - self.output.selectionStart)\n\t\t\telif self.output.caretPosition < self.doc.length:\n\t\t\t\tself.doc.remove(self.output.caretPosition, 1)","title":""},{"docid":"239eb36d55ef79b30c2fd9592444fb9d","score":"0.5665459","text":"def send_page_deletion(deleted_page_id):\n pusher_event = 'page-delete'\n dct = {'id': deleted_page_id}\n pusher_helpers.try_pusher_page_send(deleted_page_id, pusher_event, dct)","title":""},{"docid":"ee5edd3abad86baf1419f9e24f184077","score":"0.5647726","text":"def destroy(self):\r\n logger.info(\"Destroying page: %s\" % self)\r\n if self.doc.nb_pages <= 1:\r\n self.doc.destroy()\r\n return\r\n doc_pages = self.doc.pages[:]\r\n current_doc_nb_pages = self.doc.nb_pages\r\n paths = [\r\n self.__get_box_path(),\r\n self.__get_img_path(),\r\n self._get_thumb_path(),\r\n ]\r\n for path in paths:\r\n if os.access(path, os.F_OK):\r\n os.unlink(path)\r\n for page_nb in range(self.page_nb + 1, current_doc_nb_pages):\r\n page = doc_pages[page_nb]\r\n page.__ch_number(offset=-1)\r\n self.drop_cache()\r\n self.doc.drop_cache()","title":""},{"docid":"8e812b7a9a9431b4595a7a1813599a09","score":"0.558448","text":"def handle_delete(self):\n self.__call_callback('remove')","title":""},{"docid":"f95de9a92330b573e164f8a1bbc48175","score":"0.55704135","text":"def remove_page(self, page):\n self._remove_page(page)","title":""},{"docid":"c506b4de40a64b8140a305c5ea353b51","score":"0.5550883","text":"def post_delete(self):\n pass","title":""},{"docid":"8ef740ecf2d0202f9df948789568ae17","score":"0.5536703","text":"def on_delete(self):\n NavigationView.on_delete(self)\n self._config.save()","title":""},{"docid":"649a97af8b2c6582ef55c5b5c8b89fdf","score":"0.5534997","text":"def on_deleted(self, event):\n try:\n self.prl_indexer.remove_record(event.src_path)\n except Exception as e:\n self.exceptions_queue.put(e)","title":""},{"docid":"69ff643b4782b061741ec8832eb7f578","score":"0.5512228","text":"def remove(self):\n index = self.notebook.page_num(self)\n if self.immortal:\n return False\n else:\n self.notebook.remove_page(index)\n return True","title":""},{"docid":"0e5bb14a7e8a71b0519ea25e4ff83fc3","score":"0.5506258","text":"def delete_page_view(request, pid):\n websites = domain.websites_domain\n page = websites.find_page(pid)\n if not page:\n raise http.Http404()\n if request.method == 'POST':\n websites.remove_page(page)\n return shortcuts.redirect(shortcuts.resolve_url('index'))\n else:\n return shortcuts.render(request, 'websites/delete_page.html', {'page': page})","title":""},{"docid":"35a0d1955bd38a352c4b0a3d0ccad4da","score":"0.54954714","text":"def on_delLt(self):\n selItems = self.twLtTree.selectedItems()\n if selItems:\n self.delConf = pQt.ConfirmDialog(\"Delete ?\", ['Delete'], [self.delLt])\n self.delConf.exec_()","title":""},{"docid":"bd6e5f21cf4d57afd9c543cf00929444","score":"0.54784316","text":"def auto_pre_delete_document_on_delete(sender, instance, **kwargs):\n if instance.document:\n if os.path.isfile(instance.document.path):\n os.remove(instance.document.path)","title":""},{"docid":"6c4c0015ee17534dab24a48e96d79114","score":"0.5448795","text":"def remove_temp_page(self, word, last_page):\n to_be_removed_fn = self.base_filename+\"_{}_{}.html\".format(word, last_page+1)\n to_be_removed_fp = os.path.join(self.data_dir, to_be_removed_fn)\n try:\n os.remove(to_be_removed_fp)\n except:\n## print(\"file path not found:\", to_be_removed_fp)\n pass","title":""},{"docid":"50642478ec50bff299dfb6fb3e95d1b0","score":"0.54445523","text":"def on_remove(self):\r\n pass","title":""},{"docid":"49469658534d078d255052329ab4879b","score":"0.5427577","text":"def delete_page(*args):\n\tprint \"xmlrpc method: delete_page\"\n\treturn True","title":""},{"docid":"7329a2d66b283eb0681a4a53d9b8f6c8","score":"0.5414291","text":"def _on_deleted(self, event):\n\n path = event.src_path\n dbx_path = self.to_dbx_path(path)\n local_rev = self.get_local_rev(dbx_path)\n local_rev = None if local_rev == \"folder\" else local_rev\n\n try:\n self.client.remove(dbx_path, parent_rev=local_rev)\n except (NotFoundError, PathError):\n logger.debug(\"Could not delete '%s': the item does not exist on Dropbox.\", event.src_path)\n\n # remove revision metadata\n self.set_local_rev(dbx_path, None)","title":""},{"docid":"5e138ff15b803bf7e1966d75a62e7faa","score":"0.54128844","text":"def _on_delete(self, *args):\n self.hide()\n return True","title":""},{"docid":"70d5029f9929e02d776c040f886a237d","score":"0.5409877","text":"def tabClose(self, widget, page_widget):\n\t\t# Get the page number\n\t\tpage_number = self.floater.main_window_nb.page_num(page_widget)\n\n\t\t# Invoked when Yes is pressed\n\t\tdef close_tab():\n\t\t\t# Delete the page\n\t\t\tself.floater.main_window_nb.remove_page(page_number)\n\t\t\t# Remove the page entry from pages\n\t\t\tself.floater.pages.pop(page_number)\n\t\t\t# Save the new order to file\n\t\t\tself.floater.file_saver.saveSymbols()\n\n\t\tdialog = YesNoDialog(parent=self.floater.main_window, dialog_title=\"Delete?\",\n\t\t\t\tdialog_text=\"Are you sure you want to delete {0}?\".format(self.floater.pages[page_number]['page_name']),\n\t\t\t\t\t\t\tyes_func=close_tab\n\t\t\t\t\t\t\t)","title":""},{"docid":"6086d7de93c210df935715bf299fed37","score":"0.5402625","text":"def object_deleted(self, wref):\n pass","title":""},{"docid":"d58d6612f715d87e786cfd4ee75d1777","score":"0.5398824","text":"def onDelete(win, event):\n window.hide()\n modules.postQuitMsg()\n return True","title":""},{"docid":"5c0c4e5f8bfd1cff9897c9ace5d8593c","score":"0.53885245","text":"def removeBookmark(self):\n\t\tself.book_marked_page = None","title":""},{"docid":"611be62b1c296cea1ae00947abc3a3af","score":"0.53862375","text":"def on_delete(self):\r\n cmd = undo.removeRowCmd(self, self.imageList[self.imageShowing])\r\n self.undoStack.push(cmd)","title":""},{"docid":"2f882b1a9aecee36da66f43f521122c2","score":"0.5382081","text":"def on_booklist_delete_clicked(self, obj):\n store, the_iter = self.blist.get_selected()\n if not the_iter:\n return\n data = self.blist.get_data(the_iter, [0])\n self.booklist.delete_book(cuni(data[0]))\n self.blist.remove(the_iter)\n self.unsaved_changes = True\n self.top.run()","title":""},{"docid":"5ec377824e3ead71a1bbc9c84ae14c30","score":"0.5379131","text":"def on_change_page(self, event):\r\n p = self.notebook.GetPage(self.notebook.Selection)\r\n if not self.pages_visited or self.pages_visited[-1] != p:\r\n self.pages_visited.append(p)\r\n event.Skip() # Pass event along to next handler\r","title":""},{"docid":"1086be4cfbdf823196bb514976d5b96b","score":"0.53741676","text":"def remove_copies(sender, instance, **kwargs):\n items = PageInheritanceItem.objects.filter(page=instance)\n if not items.exists():\n return\n\n with transaction.atomic():\n for item in items:\n if not item.modified:\n item.inherited_page.delete()\n\n item.delete()","title":""},{"docid":"8f7f20e3ec52398473b7535256b891ac","score":"0.5365019","text":"def on_remove(self):\n pass","title":""},{"docid":"1aaf64ab68c5aac7848672dc46540391","score":"0.534527","text":"def bind_delete_save(self):\n if self.tail_txt[self.selection_index] == \"FILLED\":\n self.tail_txt[self.selection_index] = \"EMPTY\"\n self.save_handler.delete_save(self.selection_index)","title":""},{"docid":"00de6a4fb178b724943641fcdd1ccf96","score":"0.53380704","text":"def pre_delete(self):\n pass","title":""},{"docid":"4dfa12bbe130cc4b8773321241162942","score":"0.53339136","text":"def on_delete_text(self, start, end):","title":""},{"docid":"262d8ab117f8ed64e10e47326cae29bd","score":"0.5332218","text":"def _on_switch_page(self, __notebook, __page, page_num):\r\n\r\n # Remove the existing List Book before adding the new one.\r\n if self.listview.get_child() is not None:\r\n self.listview.remove(self.listview.get_child())\r\n\r\n try:\r\n _listbook = Configuration.RTK_MODULES[page_num].listbook\r\n except IndexError:\r\n _listbook = None\r\n\r\n if _listbook is not None:\r\n self.listview.add(_listbook)\r\n\r\n # Remove the existing Work Book before adding the new one.\r\n if self.workview.get_child() is not None:\r\n self.workview.remove(self.workview.get_child())\r\n\r\n try:\r\n _workbook = Configuration.RTK_MODULES[page_num].workbook\r\n except IndexError:\r\n _workbook = None\r\n\r\n if _workbook is not None:\r\n self.workview.add(_workbook)\r\n\r\n return False","title":""},{"docid":"c26e8c44877eea9a42d25e90b7917927","score":"0.5285082","text":"def del_div(self):\n self.emit(SIGNAL(\"del_div\"))","title":""},{"docid":"e22d68e26c942a3be68db1b439d5f973","score":"0.52761865","text":"def on_workspace_post_delete(sender, instance, **kwargs):\n matter = instance\n matter.actions.deleted_matter(lawyer=matter.lawyer)","title":""},{"docid":"e739c5649f2b2f7891712a8b582b0449","score":"0.5248411","text":"def delete(path, del_page):\n # create a pdf object using PdfReader that could be read\n pdf_obj = PdfReader(path)\n # pdf_obj.pages attribute gives the length of the pages in pdf\n total_pages = len(pdf_obj.pages)\n print(\"Total Pages in PDF are:\", total_pages)\n # Initialising the writer object using the PdfWriter class\n writer = PdfWriter()\n\n # Adding only those pages that we need to this list excluding del_page\n page_list = [\n page for page in range(1, total_pages + 1) if page not in del_page\n ]\n\n # Index of pdf_obj.pages starts from 0.\n for page in page_list:\n writer.addpage(pdf_obj.pages[page - 1])\n\n # removing the original pdf\n os.remove(path)\n # writing the modified file to the memory\n writer.write(path)","title":""},{"docid":"cf276233a92729725cbeb653363eb622","score":"0.5221678","text":"def do_deactivate(self):\n Gtk.Container.remove(self.window.get_statusbar(), self._label)\n if self._doc_changed_id:\n for signal_id in self._doc_changed_id[1]:\n self._doc_changed_id[0].disconnect(signal_id)\n del self._label","title":""},{"docid":"fa859d69ed959e4ce2f6b3f90fc1a916","score":"0.52037174","text":"def OnPageChanged(self, page, trigger=None):\n # When parent changes\n # This is a necessary function for PyCorrFit.\n # This is stuff that should be done when the active page\n # of the notebook changes.\n if trigger in [\"parm_batch\", \"fit_batch\", \"page_add_batch\",\n \"parm_finalize\", \"fit_finalize\"]:\n return\n if self.parent.notebook.GetPageCount() == 0:\n self.panel.Disable()\n return\n \n #idsel = self.WXDropSelMod.GetSelection()\n self.SetValues()\n # Set back user selection:\n #self.WXDropSelMod.SetSelection(idsel)\n\n self.panel.Enable()\n self.Page = page","title":""},{"docid":"2b298a10c0e8a38f796825f8465a209f","score":"0.5203252","text":"def onDataDeleted(self, data):\n if not data or not self.isVisible():\n return\n if data.id == self.data_id:\n self.setWindowTitle(self.window_name)\n self.clearAnyData()\n self.close()","title":""},{"docid":"642f2913e2a7a8c5f4f82d4196347f48","score":"0.52004904","text":"def remove(self): \n # It checks page _id exist and that\n # you have permission to remove that page\n if g.my['rank'] < 15:\n model.pages.remove(page_id=self.page[\"_id\"])\n return 'nada'","title":""},{"docid":"9314e1c5c247fb506127ee2b946c21cc","score":"0.5199811","text":"def on_delete(self, sender, arg=None):\n self.modify_selection(lambda *a: '')","title":""},{"docid":"bd8873c5a229ac326111c8f1bc3dafbf","score":"0.5197803","text":"def removeDeleteCallback(self, *args) -> \"void\":\n return _coin.ScXMLStateMachine_removeDeleteCallback(self, *args)","title":""},{"docid":"b254a005287e838ad95de64b7eda1a43","score":"0.51848257","text":"def __before_deleted(self):\n self.move_to(None)\n this = self.ref()\n for o in list(self.contents):\n if o.location == this:\n o.move_to(None)","title":""},{"docid":"aa1804da0fccdc3c049fcb446c8cb271","score":"0.5181659","text":"def onDelToolClicked(self, event):\n log_func.debug(u'Delete item')\n self.entry_check = ENTRY_DEL_CMD\n self.GetParent().EndModal(wx.ID_OK)\n event.Skip()","title":""},{"docid":"cb649ca1ba0c40c924a643c5fd935b14","score":"0.51697","text":"def handle_delete_visual_keypress(self):\n return self.event_dispatcher.event_visual(\n self.session_handler,\n 'delete'\n )","title":""},{"docid":"412c2bd000b67a65f1dc0cd4268fa05a","score":"0.51627064","text":"def clear(self) -> None:\n for widget in self.viewPort.winfo_children():\n widget.destroy()\n self.pages.clear()","title":""},{"docid":"097cccbffa604051e24049c25232fccc","score":"0.5156696","text":"def remove_hpo(e):\r\n if hpo_selected.curselection():\r\n deletion = hpo_selected.curselection()\r\n hpo_selected.delete(deletion)\r\n else:\r\n pass","title":""},{"docid":"9bff7401d2e0b4ee15f47162a75fd22c","score":"0.51550794","text":"def delete_notebook(self, notebook_id):\n n = Notebook.objects.get(id=notebook_id)\n n.deleted = True\n n.save()","title":""},{"docid":"846ca1065276a0d81b66c183993ead8f","score":"0.51452094","text":"def remove(self):\n print(\"Remove tab\")","title":""},{"docid":"da4d35cd8904ebf2ecbd21d562e65971","score":"0.5145195","text":"def _delete_button_clicked(self):\n current_widget = self._get_selected_widget()\n current_widget.delete_object()","title":""},{"docid":"035a65cfa4ddfea52a509ba667c70e4a","score":"0.51327085","text":"def PalaceObject_post_delete_handler(sender, **kwargs):\n PalaceObject = kwargs['instance']\n storage, path = PalaceObject.objectImage.storage, PalaceObject.objectImage.path\n storage.delete(path)","title":""},{"docid":"52c478eb910a2a43964881a3629db3fa","score":"0.51289463","text":"def __del__(self):\n del(self.editor)\n print \"Document %s has been destroyed!\"","title":""},{"docid":"61f3144412124baf27c9cf2b2b7610a9","score":"0.51113105","text":"def page_changed_event(self, event):\n\n page = self.notebook.GetCurrentPage()\n\n self.statusbar.SetStatusText('You switched to {0}'.format(page.filename), 0)\n self.statusbar.SetStatusText(page.filename, 1)\n\n self.viewmenu.Check(401, page.word_wrap)\n self.viewmenu.Check(403, page.line_numbers)\n self.viewmenu.Check(404, page.folding_symbols)\n self.viewmenu.Check(407, page.python_syntax)","title":""},{"docid":"5ef84591403b3cf83573ba53e11ff902","score":"0.5108674","text":"def on_deleted(self, event):\n super(BackupBayFSEventHandler, self).on_deleted(event)\n what = 'directory' if event.is_directory else 'file'\n self.app.logger.info(\"Deleted %s: %s\", what, event.src_path)\n self.__handle_deleted_event(event.src_path)","title":""},{"docid":"6c8fd2a2e76593d7840d24d989136709","score":"0.50924975","text":"def command_button_del(self):\n\t\t\n\t\tself.bill_to_del = self.bill_management.get_bill_id(self.select_item_tree(self.treeview_main))\n\t\tself.bill_management.delete_bill(self.bill_to_del)\n\t\tself.update_bills()","title":""},{"docid":"a8bb69bf487485162d550eadbf828eee","score":"0.5085551","text":"def log_deletion(self, request, object, object_repr):\n super(ChunkAdmin, self).log_deletion(request, object, object_repr)\n super(ChunkAdmin, self).log_deletion(request, object.content_object,\n object_repr)","title":""},{"docid":"110e21858531db60c7b6fbc4911220b1","score":"0.5078875","text":"def __remove (self, event):\n wx.PostEvent (self.GetEventHandler ( ), custom_events.RemoveEvent (self.GetId ( )))","title":""},{"docid":"1862fb18f1efbd369dd6371ee3e3746e","score":"0.506303","text":"def delete_event(self, widget, event, data=None):\n self.quit_program()","title":""},{"docid":"d64994df1dc6dfbf1a62e3bbc9ef519b","score":"0.5058047","text":"def aboutToDelete(self):\n \n pass","title":""},{"docid":"fcb7b931e2a7530eb767ca41eccc69c9","score":"0.50521165","text":"def addDeleteCallback(self, *args) -> \"void\":\n return _coin.ScXMLStateMachine_addDeleteCallback(self, *args)","title":""},{"docid":"9295c502e5918093a8e13e47a39a384c","score":"0.5051373","text":"def delete(self):\n self.l_info('delete','Oh God I\\'m being deleted. Nooooooooooooooooooooooooooooooooooooooooo.')","title":""},{"docid":"d380cd1fecf2d979fcbddd6eefcfec0e","score":"0.50449795","text":"def handlerTextLineNumbersDelete(self,event):\n \n def tempDef(event):\n \n global widthLineNumbers\n \n print '\\n** In handlerTextLineNumbersDelete'\n# print ' raw index =',eval(self.textMyCode.index(END))\n \n self.textLineNumbers['state']='normal'\n \n# first check is there is a selection-delete\n try:\n print 'Checking for \"Selection Delete\"...'\n indexFirstLine, indexFirstChar = self.textMyCode.index(SEL_FIRST).split('.')\n indexLastLine, indexLastChar = self.textMyCode.index(SEL_LAST).split('.')\n print '\\n SELECTION DELETE found:'\n print ' indexFirstLine, indexFirstChar =',indexFirstLine, indexFirstChar\n print ' indexLastLine, indexLastChar =', indexLastLine, indexLastChar\n# print ' WARNING: no selection-delete has been implemented yet.'\n# print ' delete operation halted.'\n \n diffLines = eval(indexLastLine) - eval(indexFirstLine)\n print ' Number of line numbers to selectively deleted =',diffLines\n \n for line in range(diffLines):\n indexBeginDelete = str(eval(self.textLineNumbers.index(END))-1)\n indexEndDelete = self.textLineNumbers.index(END)\n self.textLineNumbers.delete(indexBeginDelete,indexEndDelete)\n print ' .... deleted line',indexBeginDelete\n \n self.textLineNumbers['state']='disabled'\n \n return\n \n# no selection, so come here for single character delete \n except TclError: \n# these values are strings\n indexCurrentLine, indexCurrentChar = self.textMyCode.index(INSERT).split('.')\n print 'Current line number, char = ',indexCurrentLine, indexCurrentChar\n indexEnd = self.textMyCode.index(END)\n \n# these values are ints \n indexCurrentCharInt = int(eval(indexCurrentChar))\n if indexCurrentCharInt > 0:\n print ' Cursor is not at beginning of line.'\n print ' No line deletion yet, just character deletion.'\n return\n \n indexCurrentLineInt = int(eval(indexCurrentLine))\n if indexCurrentLineInt == 1 and indexCurrentCharInt == 0:\n print ' Cursor is at the beginning of line 1.'\n print ' No deletions allowed at this cursor location.'\n print\n self.textLineNumbers['state']='disabled'\n return\n \n indexBeginDelete = str(eval(self.textLineNumbers.index(END))-1)\n indexEndDelete= self.textLineNumbers.index(END)\n self.textLineNumbers.delete(indexBeginDelete,indexEndDelete)\n \n print ' Deleting from %s to %s in textLineNumbers' % (indexBeginDelete, indexEndDelete)\n \n# line numbers take up 'widthLineNumbers' spaces\n# End=str(eval(indexEnd))\n# self.textLineNumbers.delete(End) \n# newEnd = self.textMyCode.index(END)\n# self.textLineNumbers.delete(str(float(eval(newEnd))),str(float(eval(newEnd)+0.5)))\n \n self.textLineNumbers.delete(indexBeginDelete,indexEndDelete)\n \n \n# plus values scroll line numbers up; neg values scroll down \n# scrollLines = -5\n# self.textLineNumbers.yview_scroll(scrollLines,UNITS)\n# print ' Scroll line numbers in y direction:',scrollLines\n \n# make sure the current line we just moved to is visible\n var=self.textMyCode.index(INSERT)\n self.textLineNumbers.see(var)\n# self.textLineNumbers.see(var)\n# self.textMyCode.see(var)\n print ' \"SEE\" line number index:',str(var)\n print\n \n self.textLineNumbers['state']='disabled'\n \n return tempDef","title":""},{"docid":"874e63e0f0623b01b37415498b6b73f1","score":"0.5044052","text":"def delete(self):\n if self.back is not None:\n self.destroyControls()","title":""},{"docid":"8368c12e41febedef15c788db3b26d7e","score":"0.50384784","text":"def page_delete_cell(file_name, page_num, cell_indx):\n file_bytes = load_file(file_name)\n page = load_page(file_bytes, page_num)\n page = bytearray(page)\n num_cells = struct.unpack(endian + \"h\", page[2:4])[0]\n assert cell_indx <= num_cells - 1 # index starts at 0\n assert num_cells >= 1 # delete CAN empty a page\n assert cell_indx >= 0\n\n cell_content_area_start = struct.unpack(endian + \"h\", page[4:6])[0]\n end_of_array = 16 + 2 * num_cells\n array_idx_top = 16 + 2 * cell_indx\n array_idx_bot = 16 + 2 * (cell_indx + 1)\n\n # if cell is the last cell (but not if theres only one cell left)\n if (cell_indx == num_cells - 1) & (cell_indx != 0):\n cell_top_loc, cell_bot_loc = get_cell_indices(page, cell_indx)\n cell_2_delete = page[cell_top_loc:cell_bot_loc]\n dis2replace = len(cell_2_delete) # overwrite the cell2delete\n page[cell_top_loc:cell_bot_loc] = (\n b\"\\x00\" * dis2replace\n ) # change the cell_start area in header\n page[4:6] = struct.pack(endian + \"h\", cell_content_area_start + dis2replace)\n # delete last entri in cell array\n page[16 + 2 * (num_cells - 1) : 16 + 2 * (num_cells)] = b\"\\x00\" * 2\n # update the number of cells\n page[2:4] = struct.pack(endian + \"h\", num_cells - 1)\n\n else:\n cell_top_loc, cell_bot_loc = get_cell_indices(page, cell_indx)\n cell_2_delete = page[cell_top_loc:cell_bot_loc]\n dis2replace = len(cell_2_delete)\n # shift cell content down\n page = shift_page_content(\n page, cell_content_area_start, cell_top_loc, dis2replace, up=False\n )\n # since we just shifted every cell, every value in cell_array is off\n page = update_array_values(page, cell_indx, num_cells, dis2replace, up=False)\n # change the cell_start area\n page[4:6] = struct.pack(endian + \"h\", cell_content_area_start + dis2replace)\n # shift cell array up (deletes entry for deleted cell)\n page = shift_page_content(page, array_idx_bot, end_of_array, 2, up=True)\n # update num of cells\n page[2:4] = struct.pack(endian + \"h\", num_cells - 1)\n save_page(file_name, page_num, page)\n assert len(page) == PAGE_SIZE # ensure page is same size\n return (num_cells - 1) == 0","title":""},{"docid":"e04dc7e3a7a8f5ffe73e9a5f20819388","score":"0.502725","text":"def onNotebookChanged(self, event): # wxGlade: DataSetPanel.<event_handler>\n self.refreshSelectedPage()\n return","title":""},{"docid":"fb24259c7c6ee0f716d68a2156fe02d8","score":"0.5011361","text":"def delete_by_type(page_type):\n return WebPage.delete_by_type(page_type)","title":""},{"docid":"50626e6757c256479f603817fd92ff81","score":"0.5005604","text":"def model_deleted(sender, instance, *args, **kwargs):\n get_hook_model().handle_model_event(instance, 'deleted')","title":""},{"docid":"74e17dc57f29dbc1123dfdc1b3721608","score":"0.5002597","text":"def on_delete_window(self, *args):\n Gtk.main_quit(*args)","title":""},{"docid":"ecb153a3fd0244a2515abe977f14ad09","score":"0.49905857","text":"def delete(self):\n mysql.deleteProdukt(self.id)\n type(self).__counter -= 1 # Verringere Anzahl der Instanzen\n for widget in [self.name_label, self.expdate_label, self.options_button, self.delete_button]:\n widget.destroy()","title":""},{"docid":"4435fdc9d4efd43b78fa8c53872dbd49","score":"0.4988058","text":"def RemovePage(*args, **kwargs):\n return _aui.AuiTabContainer_RemovePage(*args, **kwargs)","title":""},{"docid":"4ec53403d5e0b84c62f9f71cf2ee9b54","score":"0.49859768","text":"def __on_del_click(self):\n index = self.__tbl.currentIndex()\n row = index.row()\n if row < 0:\n return None\n\n book_id = int(index.sibling(row, 0).data()) - 1\n self.__del_btn.setText(\"Sterge\")\n self.__srv.remove_book(book_id)\n self.__set_table(self.__srv.get_all_srv())","title":""},{"docid":"7b253d7291d6c77938d963fcf2f77e78","score":"0.49819225","text":"def _on_removal(self, item):\n pass","title":""},{"docid":"d393142461e69d378613297d322d94c2","score":"0.4974042","text":"def _on_object_removed(self, event):\n\n self.fire_nodes_removed([event.old_binding])\n\n return","title":""},{"docid":"98fec78a601167355717e50ab70a0627","score":"0.49733984","text":"def handleDelete(self, widget, event):\n widget.hide()\n return True","title":""},{"docid":"815ebb2e092775cebe95c9c055caa56c","score":"0.4973303","text":"def __handle_deleted_event(self, folder):\n username, isAnonymous = self.__handle_event(folder)\n with self.app.app_context():\n user = User.get(username)\n user_service = UserDataService(user)\n user_service.destroy_index()\n del User.users[username]","title":""},{"docid":"464a010664606b1dd6373e7b9e1dd51b","score":"0.49710786","text":"def print_request_auto_delete_file_on_delete(sender, instance, **kwargs):\n # pylint: disable=unused-argument\n if instance.document:\n if os.path.isfile(instance.document.path):\n os.remove(instance.document.path)","title":""},{"docid":"608488b96e4eda7157731de5cf2d16ec","score":"0.49695885","text":"def unload(self):\n for action in self.actions:\n self.iface.removePluginMenu(\n self.tr(u'&GhiniTreePositioner'),\n action)\n self.iface.removeToolBarIcon(action)\n # remove the toolbar\n del self.toolbar","title":""},{"docid":"cd7b22a0908863737d566dc9a6c643a0","score":"0.49687088","text":"def page_delete_cells_on_and_after(file_name, page_num, cell_indx):\n file_bytes = load_file(file_name)\n page = load_page(file_bytes, page_num)\n page = bytearray(page)\n num_cells = struct.unpack(endian + \"h\", page[2:4])[0]\n assert cell_indx <= num_cells - 1 # index starts at 0\n assert num_cells >= 1 # delete CAN empty a page\n assert cell_indx >= 0\n cell_content_area_start = struct.unpack(endian + \"h\", page[4:6])[0]\n cell_top_loc, cell_bot_loc = get_cell_indices(page, cell_indx)\n dis = cell_bot_loc - cell_content_area_start\n page[cell_content_area_start:cell_bot_loc] = b\"\\x00\" * dis\n page[16 + 2 * cell_indx : 16 + 2 * num_cells] = (\n b\"\\x00\" * 2 * (num_cells - cell_indx)\n )\n # update num of cells\n page[2:4] = struct.pack(endian + \"h\", cell_indx)\n # change cell start area\n page[4:6] = struct.pack(endian + \"h\", cell_content_area_start + dis)\n save_page(file_name, page_num, page)\n assert len(page) == PAGE_SIZE # ensure page is same size\n return (num_cells - 1) == 0","title":""},{"docid":"29c94f191590ed4914705d99ccc53ba7","score":"0.49610904","text":"def on_delete_interpretation_button(self,event):\n\n #if self.s in self.redo_specimens.keys():\n # del self.redo_specimens[self.s]\n del self.Data[self.s]['pars']\n self.Data[self.s]['pars']={}\n self.Data[self.s]['pars']['lab_dc_field']=self.Data[self.s]['lab_dc_field']\n self.Data[self.s]['pars']['er_specimen_name']=self.Data[self.s]['er_specimen_name'] \n self.Data[self.s]['pars']['er_sample_name']=self.Data[self.s]['er_sample_name'] \n self.Data[self.s]['pars']['er_sample_name']=self.Data[self.s]['er_sample_name'] \n sample=self.Data_hierarchy['specimens'][self.s]\n if sample in self.Data_samples.keys():\n if self.s in self.Data_samples[sample].keys():\n del self.Data_samples[sample][self.s]\n \n site=self.get_site_from_hierarchy(sample) \n if site in self.Data_sites.keys():\n if self.s in self.Data_sites[site].keys():\n del self.Data_sites[site][self.s]\n\n self.tmin_box.SetValue(\"\")\n self.tmax_box.SetValue(\"\")\n self.clear_boxes()\n self.draw_figure(self.s)\n self.draw_sample_mean()\n self.write_sample_box()","title":""},{"docid":"3dd7a51a16b3a567ffa363f7dfe5b54e","score":"0.4952583","text":"def delete_selected_tree(self, modeladmin, request, queryset):\n # If the user has not yet confirmed the deletion, call the regular delete\n # action that will present a confirmation page\n if not request.POST.get('post'):\n return actions.delete_selected(modeladmin, request, queryset)\n # Otherwise, delete objects one by one\n n = 0\n for obj in queryset:\n obj.delete()\n n += 1\n self.message_user(request, _(\"Successfully deleted %s items.\" % n))","title":""},{"docid":"69129fb88e955e819fb4c229566040ab","score":"0.49481672","text":"def remove_action_observer(observer):","title":""},{"docid":"5aa22785098f1ab15a804f8b5f65d505","score":"0.49462962","text":"def delete(event, context):\n return","title":""},{"docid":"c029cba2b090ffa0cee0b4c9f418a1c8","score":"0.49461254","text":"def delete_event(self, widget, event, data=None):\n\t\treturn False","title":""},{"docid":"9fd1fbd717efcb2967014e702e5d3d00","score":"0.4941474","text":"def delete_dictionary_window():","title":""},{"docid":"6a0ad1109c063c22ccb01888c5d0cacf","score":"0.49408126","text":"def handle_delete(self, sender, instance, *args, **kwargs):\n if is_algolia_managed(instance):\n self.indexer.delete(instance)","title":""},{"docid":"7d129c63dcf36d512ac49476a6a458cf","score":"0.49403518","text":"def at_object_delete(self):\r\n return True","title":""},{"docid":"6feb6aba4530a273b38a170c19e928e7","score":"0.4937442","text":"def deleting_func(self, *args):\n return _ida_idp.IDB_Hooks_deleting_func(self, *args)","title":""},{"docid":"362ba3ca8c527d5fa2ac61634a88f597","score":"0.4927455","text":"def code_resource_revision_post_delete(instance, **kwargs):\n if instance.content_file:\n instance.content_file.delete(save=False)","title":""},{"docid":"f3c333e8f204bdad6001920c65c3a71a","score":"0.49269268","text":"def button_delete_click(self, **event_args):\n pass","title":""},{"docid":"26d6aec36e1c0b87919594adf2806436","score":"0.49222717","text":"def _remove_notebooks(self):\r\n if self._exists:\r\n q = (~models.Notebook.id.in_(self._exists)\r\n & (models.Notebook.action != const.ACTION_CREATE)\r\n & (models.Notebook.action != const.ACTION_CHANGE))\r\n else:\r\n q = ((models.Notebook.action != const.ACTION_CREATE)\r\n & (models.Notebook.action != const.ACTION_CHANGE))\r\n\r\n self.session.query(models.Notebook).filter(\r\n q).delete(synchronize_session='fetch')","title":""},{"docid":"564c177abf920984804fd0e94944b609","score":"0.49158022","text":"def delete_notebook_id(self, notebook_id):\n name = self.mapping[notebook_id]\n super(MongoNotebookManager, self).delete_notebook_id(notebook_id)\n del self.rev_mapping[name]","title":""},{"docid":"a3a9bc5a79f2c431ad2e49f3541ac7c3","score":"0.4915359","text":"def on_remove_clicked(self, obj):\n store, the_iter = self.book_model.get_selected()\n if not the_iter:\n return\n row = self.book_model.get_selected_row()\n self.book.pop_item(row)\n self.book_model.remove(the_iter)","title":""},{"docid":"87d96ad95a9aa0b8e7b1a172a9b8128a","score":"0.49097547","text":"def remove_input_file(self):\n # loop over selected items in the tree\n for idx in self.view.selectedIndexes():\n # get the selected item from the index\n selected_item = idx.model().itemFromIndex(idx)\n # get the parent of the selected item\n parent = selected_item.parent()\n # remove the row\n parent.removeRow(selected_item.row())\n self.renumber_subsection_keys(parent)\n self.update_tab_text()","title":""},{"docid":"d8f62227754f7e2df51f860c73f568d8","score":"0.49026677","text":"def delete_selection():\n configs = self.dirs.settings.last_ard.configs\n # We will visually remove an item from the GUI, ONLY IF we can remove its associated data first\n for item in self.scene.selectedItems():\n if item.data in configs:\n configs.remove(item.data) # Removes backend data associated with GUI object\n # We then use the updated configs to completely reset the progbar and its elements\n # This way, we ensure that what we show on the GUI is 100% representative of backend configurations\n self.set_progbar_scene()","title":""},{"docid":"c03e8e97215a5022c87cd7300c1adda6","score":"0.49010605","text":"def deleting_func_tail(self, *args):\n return _ida_idp.IDB_Hooks_deleting_func_tail(self, *args)","title":""}],"string":"[\n {\n \"docid\": \"84272713bfa0b104465c28ac77305ef6\",\n \"score\": \"0.67369455\",\n \"text\": \"def on_close_page(self, event):\\r\\n page = event.EventObject\\r\\n page.Show(False)\\r\\n # Remove page from MainWindow data structures\\r\\n if isinstance(page, DatabasePage):\\r\\n del self.db_pages[page]\\r\\n page_dbs = [page.db]\\r\\n main.log(\\\"Closed database tab for %s.\\\" % page.db)\\r\\n else:\\r\\n del self.merger_pages[page]\\r\\n page_dbs = [page.db1, page.db2]\\r\\n main.log(\\\"Closed comparison tab for %s and %s.\\\" % (\\r\\n page.db1, page.db2\\r\\n ))\\r\\n # Close databases, if not used in any other page\\r\\n for db in page_dbs:\\r\\n if not db.has_consumers():\\r\\n del self.dbs[db.filename]\\r\\n db.close()\\r\\n main.log(\\\"Closed database %s.\\\" % db)\\r\\n # Remove any dangling references\\r\\n if self.page_merge_latest == page:\\r\\n self.page_merge_latest = None\\r\\n if self.page_db_latest == page:\\r\\n self.page_db_latest = None\\r\\n self.pages_visited = filter(lambda x: x != page, self.pages_visited)\\r\\n index_new = 0\\r\\n if self.pages_visited:\\r\\n for i in range(self.notebook.GetPageCount()):\\r\\n if self.notebook.GetPage(i) == self.pages_visited[-1]:\\r\\n index_new = i\\r\\n break\\r\\n self.notebook.SetSelection(index_new)\\r\\n # Remove page from among notebook pages\\r\\n for i in range(self.notebook.PageCount):\\r\\n if self.notebook.GetPage(i) == page:\\r\\n # Using DeletePage freezes app for some reason\\r\\n self.notebook.RemovePage(i)\\r\\n break\\r\\n self.SendSizeEvent() # Multiline wx.Notebooks need redrawing\\r\\n self.notebook.RemoveChild(page)\\r\\n if isinstance(page, DatabasePage):\\r\\n self.update_database_list(page.db.filename)\\r\\n else:\\r\\n self.update_database_list(page.db1.filename)\\r\\n self.update_database_list(page.db2.filename)\\r\\n page.Destroy()\\r\\n self.UpdateAccelerators() # Remove page accelerators\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"366edf55ce95b000bf92b904eb0d8ec1\",\n \"score\": \"0.64634645\",\n \"text\": \"def remove(self):\\n index = self.notebook.page_num(self)\\n self.notebook.remove_page(index)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca2d14beb2cd7efcdc82cd8c59c06e92\",\n \"score\": \"0.620988\",\n \"text\": \"def close(self, event):\\n\\n # Try deleting the currently selected notebook page.\\n # This will trigger the EVT_FLATBOOK_PAGE_CLOSING event.\\n # That event is bound to self.closing_file_event.\\n self.DeletePage(self.GetSelection())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"07f98f0731021775966ba47f5cb685e6\",\n \"score\": \"0.58915406\",\n \"text\": \"def onTextDeleted(self, event):\\n\\n # Ignore changes on the status bar. We handle them in onTextInserted.\\n #\\n if event.source.getRole() != pyatspi.ROLE_STATUS_BAR:\\n default.Script.onTextDeleted(self, event)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2cd51a0a6a1bf2e8b916eab1ed95560e\",\n \"score\": \"0.5830332\",\n \"text\": \"def TreeItemDelete(self, event):\\n # Find what type of node it is (word or tree root)\\n node_type = cf.checkNode(self.current_node)\\n # Delete the word or tree\\n if node_type == 'word':\\n cf.deleteWord(self.current_node)\\n elif node_type == 'tree':\\n cf.deleteTree(self.current_node)\\n self.search_root = self.search_words = None\\n # Refresh the tree\\n self.DisplayTree(self.search_root, self.search_words)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"961f7058782da73d071c56f325537e8e\",\n \"score\": \"0.5803831\",\n \"text\": \"def deleteReceived(event):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"790aaf569a6d09ef8e5d4e57ba883722\",\n \"score\": \"0.574993\",\n \"text\": \"def after_delete(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e8cf4cdc1ecac8773ca410376d5bfa76\",\n \"score\": \"0.57103044\",\n \"text\": \"def dispatch(self, *args, **kwargs):\\n return super(PageDelete, self).dispatch(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8bf104a4ce2ac93178f1c85a219165b\",\n \"score\": \"0.5698488\",\n \"text\": \"def delete_clicked(self, obj):\\n store, the_iter = self.namemodel.get_selected()\\n if not the_iter:\\n return\\n row = self.namemodel.get_selected_row()\\n self.bookmarks.pop(row)\\n self.namemodel.remove(the_iter)\\n self.modified = True\\n if row > 0:\\n row -= 1\\n self.namemodel.select_row(row)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4d8a0a99052e1b173b0f890541aa7d8\",\n \"score\": \"0.56756574\",\n \"text\": \"def delete(self, event):\\n\\t\\tif self.inLastLine():\\n\\t\\t\\tif self.output.selectedText:\\n\\t\\t\\t\\tself.doc.remove(self.output.selectionStart, self.output.selectionEnd - self.output.selectionStart)\\n\\t\\t\\telif self.output.caretPosition < self.doc.length:\\n\\t\\t\\t\\tself.doc.remove(self.output.caretPosition, 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"239eb36d55ef79b30c2fd9592444fb9d\",\n \"score\": \"0.5665459\",\n \"text\": \"def send_page_deletion(deleted_page_id):\\n pusher_event = 'page-delete'\\n dct = {'id': deleted_page_id}\\n pusher_helpers.try_pusher_page_send(deleted_page_id, pusher_event, dct)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ee5edd3abad86baf1419f9e24f184077\",\n \"score\": \"0.5647726\",\n \"text\": \"def destroy(self):\\r\\n logger.info(\\\"Destroying page: %s\\\" % self)\\r\\n if self.doc.nb_pages <= 1:\\r\\n self.doc.destroy()\\r\\n return\\r\\n doc_pages = self.doc.pages[:]\\r\\n current_doc_nb_pages = self.doc.nb_pages\\r\\n paths = [\\r\\n self.__get_box_path(),\\r\\n self.__get_img_path(),\\r\\n self._get_thumb_path(),\\r\\n ]\\r\\n for path in paths:\\r\\n if os.access(path, os.F_OK):\\r\\n os.unlink(path)\\r\\n for page_nb in range(self.page_nb + 1, current_doc_nb_pages):\\r\\n page = doc_pages[page_nb]\\r\\n page.__ch_number(offset=-1)\\r\\n self.drop_cache()\\r\\n self.doc.drop_cache()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e812b7a9a9431b4595a7a1813599a09\",\n \"score\": \"0.558448\",\n \"text\": \"def handle_delete(self):\\n self.__call_callback('remove')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f95de9a92330b573e164f8a1bbc48175\",\n \"score\": \"0.55704135\",\n \"text\": \"def remove_page(self, page):\\n self._remove_page(page)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c506b4de40a64b8140a305c5ea353b51\",\n \"score\": \"0.5550883\",\n \"text\": \"def post_delete(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8ef740ecf2d0202f9df948789568ae17\",\n \"score\": \"0.5536703\",\n \"text\": \"def on_delete(self):\\n NavigationView.on_delete(self)\\n self._config.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"649a97af8b2c6582ef55c5b5c8b89fdf\",\n \"score\": \"0.5534997\",\n \"text\": \"def on_deleted(self, event):\\n try:\\n self.prl_indexer.remove_record(event.src_path)\\n except Exception as e:\\n self.exceptions_queue.put(e)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69ff643b4782b061741ec8832eb7f578\",\n \"score\": \"0.5512228\",\n \"text\": \"def remove(self):\\n index = self.notebook.page_num(self)\\n if self.immortal:\\n return False\\n else:\\n self.notebook.remove_page(index)\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0e5bb14a7e8a71b0519ea25e4ff83fc3\",\n \"score\": \"0.5506258\",\n \"text\": \"def delete_page_view(request, pid):\\n websites = domain.websites_domain\\n page = websites.find_page(pid)\\n if not page:\\n raise http.Http404()\\n if request.method == 'POST':\\n websites.remove_page(page)\\n return shortcuts.redirect(shortcuts.resolve_url('index'))\\n else:\\n return shortcuts.render(request, 'websites/delete_page.html', {'page': page})\",\n \"title\": \"\"\n },\n {\n \"docid\": \"35a0d1955bd38a352c4b0a3d0ccad4da\",\n \"score\": \"0.54954714\",\n \"text\": \"def on_delLt(self):\\n selItems = self.twLtTree.selectedItems()\\n if selItems:\\n self.delConf = pQt.ConfirmDialog(\\\"Delete ?\\\", ['Delete'], [self.delLt])\\n self.delConf.exec_()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd6e5f21cf4d57afd9c543cf00929444\",\n \"score\": \"0.54784316\",\n \"text\": \"def auto_pre_delete_document_on_delete(sender, instance, **kwargs):\\n if instance.document:\\n if os.path.isfile(instance.document.path):\\n os.remove(instance.document.path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c4c0015ee17534dab24a48e96d79114\",\n \"score\": \"0.5448795\",\n \"text\": \"def remove_temp_page(self, word, last_page):\\n to_be_removed_fn = self.base_filename+\\\"_{}_{}.html\\\".format(word, last_page+1)\\n to_be_removed_fp = os.path.join(self.data_dir, to_be_removed_fn)\\n try:\\n os.remove(to_be_removed_fp)\\n except:\\n## print(\\\"file path not found:\\\", to_be_removed_fp)\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50642478ec50bff299dfb6fb3e95d1b0\",\n \"score\": \"0.54445523\",\n \"text\": \"def on_remove(self):\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"49469658534d078d255052329ab4879b\",\n \"score\": \"0.5427577\",\n \"text\": \"def delete_page(*args):\\n\\tprint \\\"xmlrpc method: delete_page\\\"\\n\\treturn True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7329a2d66b283eb0681a4a53d9b8f6c8\",\n \"score\": \"0.5414291\",\n \"text\": \"def _on_deleted(self, event):\\n\\n path = event.src_path\\n dbx_path = self.to_dbx_path(path)\\n local_rev = self.get_local_rev(dbx_path)\\n local_rev = None if local_rev == \\\"folder\\\" else local_rev\\n\\n try:\\n self.client.remove(dbx_path, parent_rev=local_rev)\\n except (NotFoundError, PathError):\\n logger.debug(\\\"Could not delete '%s': the item does not exist on Dropbox.\\\", event.src_path)\\n\\n # remove revision metadata\\n self.set_local_rev(dbx_path, None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5e138ff15b803bf7e1966d75a62e7faa\",\n \"score\": \"0.54128844\",\n \"text\": \"def _on_delete(self, *args):\\n self.hide()\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"70d5029f9929e02d776c040f886a237d\",\n \"score\": \"0.5409877\",\n \"text\": \"def tabClose(self, widget, page_widget):\\n\\t\\t# Get the page number\\n\\t\\tpage_number = self.floater.main_window_nb.page_num(page_widget)\\n\\n\\t\\t# Invoked when Yes is pressed\\n\\t\\tdef close_tab():\\n\\t\\t\\t# Delete the page\\n\\t\\t\\tself.floater.main_window_nb.remove_page(page_number)\\n\\t\\t\\t# Remove the page entry from pages\\n\\t\\t\\tself.floater.pages.pop(page_number)\\n\\t\\t\\t# Save the new order to file\\n\\t\\t\\tself.floater.file_saver.saveSymbols()\\n\\n\\t\\tdialog = YesNoDialog(parent=self.floater.main_window, dialog_title=\\\"Delete?\\\",\\n\\t\\t\\t\\tdialog_text=\\\"Are you sure you want to delete {0}?\\\".format(self.floater.pages[page_number]['page_name']),\\n\\t\\t\\t\\t\\t\\t\\tyes_func=close_tab\\n\\t\\t\\t\\t\\t\\t\\t)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6086d7de93c210df935715bf299fed37\",\n \"score\": \"0.5402625\",\n \"text\": \"def object_deleted(self, wref):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d58d6612f715d87e786cfd4ee75d1777\",\n \"score\": \"0.5398824\",\n \"text\": \"def onDelete(win, event):\\n window.hide()\\n modules.postQuitMsg()\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5c0c4e5f8bfd1cff9897c9ace5d8593c\",\n \"score\": \"0.53885245\",\n \"text\": \"def removeBookmark(self):\\n\\t\\tself.book_marked_page = None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"611be62b1c296cea1ae00947abc3a3af\",\n \"score\": \"0.53862375\",\n \"text\": \"def on_delete(self):\\r\\n cmd = undo.removeRowCmd(self, self.imageList[self.imageShowing])\\r\\n self.undoStack.push(cmd)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2f882b1a9aecee36da66f43f521122c2\",\n \"score\": \"0.5382081\",\n \"text\": \"def on_booklist_delete_clicked(self, obj):\\n store, the_iter = self.blist.get_selected()\\n if not the_iter:\\n return\\n data = self.blist.get_data(the_iter, [0])\\n self.booklist.delete_book(cuni(data[0]))\\n self.blist.remove(the_iter)\\n self.unsaved_changes = True\\n self.top.run()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ec377824e3ead71a1bbc9c84ae14c30\",\n \"score\": \"0.5379131\",\n \"text\": \"def on_change_page(self, event):\\r\\n p = self.notebook.GetPage(self.notebook.Selection)\\r\\n if not self.pages_visited or self.pages_visited[-1] != p:\\r\\n self.pages_visited.append(p)\\r\\n event.Skip() # Pass event along to next handler\\r\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1086be4cfbdf823196bb514976d5b96b\",\n \"score\": \"0.53741676\",\n \"text\": \"def remove_copies(sender, instance, **kwargs):\\n items = PageInheritanceItem.objects.filter(page=instance)\\n if not items.exists():\\n return\\n\\n with transaction.atomic():\\n for item in items:\\n if not item.modified:\\n item.inherited_page.delete()\\n\\n item.delete()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8f7f20e3ec52398473b7535256b891ac\",\n \"score\": \"0.5365019\",\n \"text\": \"def on_remove(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1aaf64ab68c5aac7848672dc46540391\",\n \"score\": \"0.534527\",\n \"text\": \"def bind_delete_save(self):\\n if self.tail_txt[self.selection_index] == \\\"FILLED\\\":\\n self.tail_txt[self.selection_index] = \\\"EMPTY\\\"\\n self.save_handler.delete_save(self.selection_index)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00de6a4fb178b724943641fcdd1ccf96\",\n \"score\": \"0.53380704\",\n \"text\": \"def pre_delete(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4dfa12bbe130cc4b8773321241162942\",\n \"score\": \"0.53339136\",\n \"text\": \"def on_delete_text(self, start, end):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"262d8ab117f8ed64e10e47326cae29bd\",\n \"score\": \"0.5332218\",\n \"text\": \"def _on_switch_page(self, __notebook, __page, page_num):\\r\\n\\r\\n # Remove the existing List Book before adding the new one.\\r\\n if self.listview.get_child() is not None:\\r\\n self.listview.remove(self.listview.get_child())\\r\\n\\r\\n try:\\r\\n _listbook = Configuration.RTK_MODULES[page_num].listbook\\r\\n except IndexError:\\r\\n _listbook = None\\r\\n\\r\\n if _listbook is not None:\\r\\n self.listview.add(_listbook)\\r\\n\\r\\n # Remove the existing Work Book before adding the new one.\\r\\n if self.workview.get_child() is not None:\\r\\n self.workview.remove(self.workview.get_child())\\r\\n\\r\\n try:\\r\\n _workbook = Configuration.RTK_MODULES[page_num].workbook\\r\\n except IndexError:\\r\\n _workbook = None\\r\\n\\r\\n if _workbook is not None:\\r\\n self.workview.add(_workbook)\\r\\n\\r\\n return False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c26e8c44877eea9a42d25e90b7917927\",\n \"score\": \"0.5285082\",\n \"text\": \"def del_div(self):\\n self.emit(SIGNAL(\\\"del_div\\\"))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e22d68e26c942a3be68db1b439d5f973\",\n \"score\": \"0.52761865\",\n \"text\": \"def on_workspace_post_delete(sender, instance, **kwargs):\\n matter = instance\\n matter.actions.deleted_matter(lawyer=matter.lawyer)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e739c5649f2b2f7891712a8b582b0449\",\n \"score\": \"0.5248411\",\n \"text\": \"def delete(path, del_page):\\n # create a pdf object using PdfReader that could be read\\n pdf_obj = PdfReader(path)\\n # pdf_obj.pages attribute gives the length of the pages in pdf\\n total_pages = len(pdf_obj.pages)\\n print(\\\"Total Pages in PDF are:\\\", total_pages)\\n # Initialising the writer object using the PdfWriter class\\n writer = PdfWriter()\\n\\n # Adding only those pages that we need to this list excluding del_page\\n page_list = [\\n page for page in range(1, total_pages + 1) if page not in del_page\\n ]\\n\\n # Index of pdf_obj.pages starts from 0.\\n for page in page_list:\\n writer.addpage(pdf_obj.pages[page - 1])\\n\\n # removing the original pdf\\n os.remove(path)\\n # writing the modified file to the memory\\n writer.write(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cf276233a92729725cbeb653363eb622\",\n \"score\": \"0.5221678\",\n \"text\": \"def do_deactivate(self):\\n Gtk.Container.remove(self.window.get_statusbar(), self._label)\\n if self._doc_changed_id:\\n for signal_id in self._doc_changed_id[1]:\\n self._doc_changed_id[0].disconnect(signal_id)\\n del self._label\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fa859d69ed959e4ce2f6b3f90fc1a916\",\n \"score\": \"0.52037174\",\n \"text\": \"def OnPageChanged(self, page, trigger=None):\\n # When parent changes\\n # This is a necessary function for PyCorrFit.\\n # This is stuff that should be done when the active page\\n # of the notebook changes.\\n if trigger in [\\\"parm_batch\\\", \\\"fit_batch\\\", \\\"page_add_batch\\\",\\n \\\"parm_finalize\\\", \\\"fit_finalize\\\"]:\\n return\\n if self.parent.notebook.GetPageCount() == 0:\\n self.panel.Disable()\\n return\\n \\n #idsel = self.WXDropSelMod.GetSelection()\\n self.SetValues()\\n # Set back user selection:\\n #self.WXDropSelMod.SetSelection(idsel)\\n\\n self.panel.Enable()\\n self.Page = page\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2b298a10c0e8a38f796825f8465a209f\",\n \"score\": \"0.5203252\",\n \"text\": \"def onDataDeleted(self, data):\\n if not data or not self.isVisible():\\n return\\n if data.id == self.data_id:\\n self.setWindowTitle(self.window_name)\\n self.clearAnyData()\\n self.close()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"642f2913e2a7a8c5f4f82d4196347f48\",\n \"score\": \"0.52004904\",\n \"text\": \"def remove(self): \\n # It checks page _id exist and that\\n # you have permission to remove that page\\n if g.my['rank'] < 15:\\n model.pages.remove(page_id=self.page[\\\"_id\\\"])\\n return 'nada'\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9314e1c5c247fb506127ee2b946c21cc\",\n \"score\": \"0.5199811\",\n \"text\": \"def on_delete(self, sender, arg=None):\\n self.modify_selection(lambda *a: '')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bd8873c5a229ac326111c8f1bc3dafbf\",\n \"score\": \"0.5197803\",\n \"text\": \"def removeDeleteCallback(self, *args) -> \\\"void\\\":\\n return _coin.ScXMLStateMachine_removeDeleteCallback(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b254a005287e838ad95de64b7eda1a43\",\n \"score\": \"0.51848257\",\n \"text\": \"def __before_deleted(self):\\n self.move_to(None)\\n this = self.ref()\\n for o in list(self.contents):\\n if o.location == this:\\n o.move_to(None)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa1804da0fccdc3c049fcb446c8cb271\",\n \"score\": \"0.5181659\",\n \"text\": \"def onDelToolClicked(self, event):\\n log_func.debug(u'Delete item')\\n self.entry_check = ENTRY_DEL_CMD\\n self.GetParent().EndModal(wx.ID_OK)\\n event.Skip()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cb649ca1ba0c40c924a643c5fd935b14\",\n \"score\": \"0.51697\",\n \"text\": \"def handle_delete_visual_keypress(self):\\n return self.event_dispatcher.event_visual(\\n self.session_handler,\\n 'delete'\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"412c2bd000b67a65f1dc0cd4268fa05a\",\n \"score\": \"0.51627064\",\n \"text\": \"def clear(self) -> None:\\n for widget in self.viewPort.winfo_children():\\n widget.destroy()\\n self.pages.clear()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"097cccbffa604051e24049c25232fccc\",\n \"score\": \"0.5156696\",\n \"text\": \"def remove_hpo(e):\\r\\n if hpo_selected.curselection():\\r\\n deletion = hpo_selected.curselection()\\r\\n hpo_selected.delete(deletion)\\r\\n else:\\r\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9bff7401d2e0b4ee15f47162a75fd22c\",\n \"score\": \"0.51550794\",\n \"text\": \"def delete_notebook(self, notebook_id):\\n n = Notebook.objects.get(id=notebook_id)\\n n.deleted = True\\n n.save()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"846ca1065276a0d81b66c183993ead8f\",\n \"score\": \"0.51452094\",\n \"text\": \"def remove(self):\\n print(\\\"Remove tab\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"da4d35cd8904ebf2ecbd21d562e65971\",\n \"score\": \"0.5145195\",\n \"text\": \"def _delete_button_clicked(self):\\n current_widget = self._get_selected_widget()\\n current_widget.delete_object()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"035a65cfa4ddfea52a509ba667c70e4a\",\n \"score\": \"0.51327085\",\n \"text\": \"def PalaceObject_post_delete_handler(sender, **kwargs):\\n PalaceObject = kwargs['instance']\\n storage, path = PalaceObject.objectImage.storage, PalaceObject.objectImage.path\\n storage.delete(path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52c478eb910a2a43964881a3629db3fa\",\n \"score\": \"0.51289463\",\n \"text\": \"def __del__(self):\\n del(self.editor)\\n print \\\"Document %s has been destroyed!\\\"\",\n \"title\": \"\"\n },\n {\n \"docid\": \"61f3144412124baf27c9cf2b2b7610a9\",\n \"score\": \"0.51113105\",\n \"text\": \"def page_changed_event(self, event):\\n\\n page = self.notebook.GetCurrentPage()\\n\\n self.statusbar.SetStatusText('You switched to {0}'.format(page.filename), 0)\\n self.statusbar.SetStatusText(page.filename, 1)\\n\\n self.viewmenu.Check(401, page.word_wrap)\\n self.viewmenu.Check(403, page.line_numbers)\\n self.viewmenu.Check(404, page.folding_symbols)\\n self.viewmenu.Check(407, page.python_syntax)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ef84591403b3cf83573ba53e11ff902\",\n \"score\": \"0.5108674\",\n \"text\": \"def on_deleted(self, event):\\n super(BackupBayFSEventHandler, self).on_deleted(event)\\n what = 'directory' if event.is_directory else 'file'\\n self.app.logger.info(\\\"Deleted %s: %s\\\", what, event.src_path)\\n self.__handle_deleted_event(event.src_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c8fd2a2e76593d7840d24d989136709\",\n \"score\": \"0.50924975\",\n \"text\": \"def command_button_del(self):\\n\\t\\t\\n\\t\\tself.bill_to_del = self.bill_management.get_bill_id(self.select_item_tree(self.treeview_main))\\n\\t\\tself.bill_management.delete_bill(self.bill_to_del)\\n\\t\\tself.update_bills()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a8bb69bf487485162d550eadbf828eee\",\n \"score\": \"0.5085551\",\n \"text\": \"def log_deletion(self, request, object, object_repr):\\n super(ChunkAdmin, self).log_deletion(request, object, object_repr)\\n super(ChunkAdmin, self).log_deletion(request, object.content_object,\\n object_repr)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"110e21858531db60c7b6fbc4911220b1\",\n \"score\": \"0.5078875\",\n \"text\": \"def __remove (self, event):\\n wx.PostEvent (self.GetEventHandler ( ), custom_events.RemoveEvent (self.GetId ( )))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1862fb18f1efbd369dd6371ee3e3746e\",\n \"score\": \"0.506303\",\n \"text\": \"def delete_event(self, widget, event, data=None):\\n self.quit_program()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d64994df1dc6dfbf1a62e3bbc9ef519b\",\n \"score\": \"0.5058047\",\n \"text\": \"def aboutToDelete(self):\\n \\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fcb7b931e2a7530eb767ca41eccc69c9\",\n \"score\": \"0.50521165\",\n \"text\": \"def addDeleteCallback(self, *args) -> \\\"void\\\":\\n return _coin.ScXMLStateMachine_addDeleteCallback(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9295c502e5918093a8e13e47a39a384c\",\n \"score\": \"0.5051373\",\n \"text\": \"def delete(self):\\n self.l_info('delete','Oh God I\\\\'m being deleted. Nooooooooooooooooooooooooooooooooooooooooo.')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d380cd1fecf2d979fcbddd6eefcfec0e\",\n \"score\": \"0.50449795\",\n \"text\": \"def handlerTextLineNumbersDelete(self,event):\\n \\n def tempDef(event):\\n \\n global widthLineNumbers\\n \\n print '\\\\n** In handlerTextLineNumbersDelete'\\n# print ' raw index =',eval(self.textMyCode.index(END))\\n \\n self.textLineNumbers['state']='normal'\\n \\n# first check is there is a selection-delete\\n try:\\n print 'Checking for \\\"Selection Delete\\\"...'\\n indexFirstLine, indexFirstChar = self.textMyCode.index(SEL_FIRST).split('.')\\n indexLastLine, indexLastChar = self.textMyCode.index(SEL_LAST).split('.')\\n print '\\\\n SELECTION DELETE found:'\\n print ' indexFirstLine, indexFirstChar =',indexFirstLine, indexFirstChar\\n print ' indexLastLine, indexLastChar =', indexLastLine, indexLastChar\\n# print ' WARNING: no selection-delete has been implemented yet.'\\n# print ' delete operation halted.'\\n \\n diffLines = eval(indexLastLine) - eval(indexFirstLine)\\n print ' Number of line numbers to selectively deleted =',diffLines\\n \\n for line in range(diffLines):\\n indexBeginDelete = str(eval(self.textLineNumbers.index(END))-1)\\n indexEndDelete = self.textLineNumbers.index(END)\\n self.textLineNumbers.delete(indexBeginDelete,indexEndDelete)\\n print ' .... deleted line',indexBeginDelete\\n \\n self.textLineNumbers['state']='disabled'\\n \\n return\\n \\n# no selection, so come here for single character delete \\n except TclError: \\n# these values are strings\\n indexCurrentLine, indexCurrentChar = self.textMyCode.index(INSERT).split('.')\\n print 'Current line number, char = ',indexCurrentLine, indexCurrentChar\\n indexEnd = self.textMyCode.index(END)\\n \\n# these values are ints \\n indexCurrentCharInt = int(eval(indexCurrentChar))\\n if indexCurrentCharInt > 0:\\n print ' Cursor is not at beginning of line.'\\n print ' No line deletion yet, just character deletion.'\\n return\\n \\n indexCurrentLineInt = int(eval(indexCurrentLine))\\n if indexCurrentLineInt == 1 and indexCurrentCharInt == 0:\\n print ' Cursor is at the beginning of line 1.'\\n print ' No deletions allowed at this cursor location.'\\n print\\n self.textLineNumbers['state']='disabled'\\n return\\n \\n indexBeginDelete = str(eval(self.textLineNumbers.index(END))-1)\\n indexEndDelete= self.textLineNumbers.index(END)\\n self.textLineNumbers.delete(indexBeginDelete,indexEndDelete)\\n \\n print ' Deleting from %s to %s in textLineNumbers' % (indexBeginDelete, indexEndDelete)\\n \\n# line numbers take up 'widthLineNumbers' spaces\\n# End=str(eval(indexEnd))\\n# self.textLineNumbers.delete(End) \\n# newEnd = self.textMyCode.index(END)\\n# self.textLineNumbers.delete(str(float(eval(newEnd))),str(float(eval(newEnd)+0.5)))\\n \\n self.textLineNumbers.delete(indexBeginDelete,indexEndDelete)\\n \\n \\n# plus values scroll line numbers up; neg values scroll down \\n# scrollLines = -5\\n# self.textLineNumbers.yview_scroll(scrollLines,UNITS)\\n# print ' Scroll line numbers in y direction:',scrollLines\\n \\n# make sure the current line we just moved to is visible\\n var=self.textMyCode.index(INSERT)\\n self.textLineNumbers.see(var)\\n# self.textLineNumbers.see(var)\\n# self.textMyCode.see(var)\\n print ' \\\"SEE\\\" line number index:',str(var)\\n print\\n \\n self.textLineNumbers['state']='disabled'\\n \\n return tempDef\",\n \"title\": \"\"\n },\n {\n \"docid\": \"874e63e0f0623b01b37415498b6b73f1\",\n \"score\": \"0.5044052\",\n \"text\": \"def delete(self):\\n if self.back is not None:\\n self.destroyControls()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8368c12e41febedef15c788db3b26d7e\",\n \"score\": \"0.50384784\",\n \"text\": \"def page_delete_cell(file_name, page_num, cell_indx):\\n file_bytes = load_file(file_name)\\n page = load_page(file_bytes, page_num)\\n page = bytearray(page)\\n num_cells = struct.unpack(endian + \\\"h\\\", page[2:4])[0]\\n assert cell_indx <= num_cells - 1 # index starts at 0\\n assert num_cells >= 1 # delete CAN empty a page\\n assert cell_indx >= 0\\n\\n cell_content_area_start = struct.unpack(endian + \\\"h\\\", page[4:6])[0]\\n end_of_array = 16 + 2 * num_cells\\n array_idx_top = 16 + 2 * cell_indx\\n array_idx_bot = 16 + 2 * (cell_indx + 1)\\n\\n # if cell is the last cell (but not if theres only one cell left)\\n if (cell_indx == num_cells - 1) & (cell_indx != 0):\\n cell_top_loc, cell_bot_loc = get_cell_indices(page, cell_indx)\\n cell_2_delete = page[cell_top_loc:cell_bot_loc]\\n dis2replace = len(cell_2_delete) # overwrite the cell2delete\\n page[cell_top_loc:cell_bot_loc] = (\\n b\\\"\\\\x00\\\" * dis2replace\\n ) # change the cell_start area in header\\n page[4:6] = struct.pack(endian + \\\"h\\\", cell_content_area_start + dis2replace)\\n # delete last entri in cell array\\n page[16 + 2 * (num_cells - 1) : 16 + 2 * (num_cells)] = b\\\"\\\\x00\\\" * 2\\n # update the number of cells\\n page[2:4] = struct.pack(endian + \\\"h\\\", num_cells - 1)\\n\\n else:\\n cell_top_loc, cell_bot_loc = get_cell_indices(page, cell_indx)\\n cell_2_delete = page[cell_top_loc:cell_bot_loc]\\n dis2replace = len(cell_2_delete)\\n # shift cell content down\\n page = shift_page_content(\\n page, cell_content_area_start, cell_top_loc, dis2replace, up=False\\n )\\n # since we just shifted every cell, every value in cell_array is off\\n page = update_array_values(page, cell_indx, num_cells, dis2replace, up=False)\\n # change the cell_start area\\n page[4:6] = struct.pack(endian + \\\"h\\\", cell_content_area_start + dis2replace)\\n # shift cell array up (deletes entry for deleted cell)\\n page = shift_page_content(page, array_idx_bot, end_of_array, 2, up=True)\\n # update num of cells\\n page[2:4] = struct.pack(endian + \\\"h\\\", num_cells - 1)\\n save_page(file_name, page_num, page)\\n assert len(page) == PAGE_SIZE # ensure page is same size\\n return (num_cells - 1) == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e04dc7e3a7a8f5ffe73e9a5f20819388\",\n \"score\": \"0.502725\",\n \"text\": \"def onNotebookChanged(self, event): # wxGlade: DataSetPanel.<event_handler>\\n self.refreshSelectedPage()\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fb24259c7c6ee0f716d68a2156fe02d8\",\n \"score\": \"0.5011361\",\n \"text\": \"def delete_by_type(page_type):\\n return WebPage.delete_by_type(page_type)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"50626e6757c256479f603817fd92ff81\",\n \"score\": \"0.5005604\",\n \"text\": \"def model_deleted(sender, instance, *args, **kwargs):\\n get_hook_model().handle_model_event(instance, 'deleted')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"74e17dc57f29dbc1123dfdc1b3721608\",\n \"score\": \"0.5002597\",\n \"text\": \"def on_delete_window(self, *args):\\n Gtk.main_quit(*args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecb153a3fd0244a2515abe977f14ad09\",\n \"score\": \"0.49905857\",\n \"text\": \"def delete(self):\\n mysql.deleteProdukt(self.id)\\n type(self).__counter -= 1 # Verringere Anzahl der Instanzen\\n for widget in [self.name_label, self.expdate_label, self.options_button, self.delete_button]:\\n widget.destroy()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4435fdc9d4efd43b78fa8c53872dbd49\",\n \"score\": \"0.4988058\",\n \"text\": \"def RemovePage(*args, **kwargs):\\n return _aui.AuiTabContainer_RemovePage(*args, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ec53403d5e0b84c62f9f71cf2ee9b54\",\n \"score\": \"0.49859768\",\n \"text\": \"def __on_del_click(self):\\n index = self.__tbl.currentIndex()\\n row = index.row()\\n if row < 0:\\n return None\\n\\n book_id = int(index.sibling(row, 0).data()) - 1\\n self.__del_btn.setText(\\\"Sterge\\\")\\n self.__srv.remove_book(book_id)\\n self.__set_table(self.__srv.get_all_srv())\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7b253d7291d6c77938d963fcf2f77e78\",\n \"score\": \"0.49819225\",\n \"text\": \"def _on_removal(self, item):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d393142461e69d378613297d322d94c2\",\n \"score\": \"0.4974042\",\n \"text\": \"def _on_object_removed(self, event):\\n\\n self.fire_nodes_removed([event.old_binding])\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"98fec78a601167355717e50ab70a0627\",\n \"score\": \"0.49733984\",\n \"text\": \"def handleDelete(self, widget, event):\\n widget.hide()\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"815ebb2e092775cebe95c9c055caa56c\",\n \"score\": \"0.4973303\",\n \"text\": \"def __handle_deleted_event(self, folder):\\n username, isAnonymous = self.__handle_event(folder)\\n with self.app.app_context():\\n user = User.get(username)\\n user_service = UserDataService(user)\\n user_service.destroy_index()\\n del User.users[username]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"464a010664606b1dd6373e7b9e1dd51b\",\n \"score\": \"0.49710786\",\n \"text\": \"def print_request_auto_delete_file_on_delete(sender, instance, **kwargs):\\n # pylint: disable=unused-argument\\n if instance.document:\\n if os.path.isfile(instance.document.path):\\n os.remove(instance.document.path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"608488b96e4eda7157731de5cf2d16ec\",\n \"score\": \"0.49695885\",\n \"text\": \"def unload(self):\\n for action in self.actions:\\n self.iface.removePluginMenu(\\n self.tr(u'&GhiniTreePositioner'),\\n action)\\n self.iface.removeToolBarIcon(action)\\n # remove the toolbar\\n del self.toolbar\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cd7b22a0908863737d566dc9a6c643a0\",\n \"score\": \"0.49687088\",\n \"text\": \"def page_delete_cells_on_and_after(file_name, page_num, cell_indx):\\n file_bytes = load_file(file_name)\\n page = load_page(file_bytes, page_num)\\n page = bytearray(page)\\n num_cells = struct.unpack(endian + \\\"h\\\", page[2:4])[0]\\n assert cell_indx <= num_cells - 1 # index starts at 0\\n assert num_cells >= 1 # delete CAN empty a page\\n assert cell_indx >= 0\\n cell_content_area_start = struct.unpack(endian + \\\"h\\\", page[4:6])[0]\\n cell_top_loc, cell_bot_loc = get_cell_indices(page, cell_indx)\\n dis = cell_bot_loc - cell_content_area_start\\n page[cell_content_area_start:cell_bot_loc] = b\\\"\\\\x00\\\" * dis\\n page[16 + 2 * cell_indx : 16 + 2 * num_cells] = (\\n b\\\"\\\\x00\\\" * 2 * (num_cells - cell_indx)\\n )\\n # update num of cells\\n page[2:4] = struct.pack(endian + \\\"h\\\", cell_indx)\\n # change cell start area\\n page[4:6] = struct.pack(endian + \\\"h\\\", cell_content_area_start + dis)\\n save_page(file_name, page_num, page)\\n assert len(page) == PAGE_SIZE # ensure page is same size\\n return (num_cells - 1) == 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29c94f191590ed4914705d99ccc53ba7\",\n \"score\": \"0.49610904\",\n \"text\": \"def on_delete_interpretation_button(self,event):\\n\\n #if self.s in self.redo_specimens.keys():\\n # del self.redo_specimens[self.s]\\n del self.Data[self.s]['pars']\\n self.Data[self.s]['pars']={}\\n self.Data[self.s]['pars']['lab_dc_field']=self.Data[self.s]['lab_dc_field']\\n self.Data[self.s]['pars']['er_specimen_name']=self.Data[self.s]['er_specimen_name'] \\n self.Data[self.s]['pars']['er_sample_name']=self.Data[self.s]['er_sample_name'] \\n self.Data[self.s]['pars']['er_sample_name']=self.Data[self.s]['er_sample_name'] \\n sample=self.Data_hierarchy['specimens'][self.s]\\n if sample in self.Data_samples.keys():\\n if self.s in self.Data_samples[sample].keys():\\n del self.Data_samples[sample][self.s]\\n \\n site=self.get_site_from_hierarchy(sample) \\n if site in self.Data_sites.keys():\\n if self.s in self.Data_sites[site].keys():\\n del self.Data_sites[site][self.s]\\n\\n self.tmin_box.SetValue(\\\"\\\")\\n self.tmax_box.SetValue(\\\"\\\")\\n self.clear_boxes()\\n self.draw_figure(self.s)\\n self.draw_sample_mean()\\n self.write_sample_box()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3dd7a51a16b3a567ffa363f7dfe5b54e\",\n \"score\": \"0.4952583\",\n \"text\": \"def delete_selected_tree(self, modeladmin, request, queryset):\\n # If the user has not yet confirmed the deletion, call the regular delete\\n # action that will present a confirmation page\\n if not request.POST.get('post'):\\n return actions.delete_selected(modeladmin, request, queryset)\\n # Otherwise, delete objects one by one\\n n = 0\\n for obj in queryset:\\n obj.delete()\\n n += 1\\n self.message_user(request, _(\\\"Successfully deleted %s items.\\\" % n))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69129fb88e955e819fb4c229566040ab\",\n \"score\": \"0.49481672\",\n \"text\": \"def remove_action_observer(observer):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5aa22785098f1ab15a804f8b5f65d505\",\n \"score\": \"0.49462962\",\n \"text\": \"def delete(event, context):\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c029cba2b090ffa0cee0b4c9f418a1c8\",\n \"score\": \"0.49461254\",\n \"text\": \"def delete_event(self, widget, event, data=None):\\n\\t\\treturn False\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9fd1fbd717efcb2967014e702e5d3d00\",\n \"score\": \"0.4941474\",\n \"text\": \"def delete_dictionary_window():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6a0ad1109c063c22ccb01888c5d0cacf\",\n \"score\": \"0.49408126\",\n \"text\": \"def handle_delete(self, sender, instance, *args, **kwargs):\\n if is_algolia_managed(instance):\\n self.indexer.delete(instance)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7d129c63dcf36d512ac49476a6a458cf\",\n \"score\": \"0.49403518\",\n \"text\": \"def at_object_delete(self):\\r\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6feb6aba4530a273b38a170c19e928e7\",\n \"score\": \"0.4937442\",\n \"text\": \"def deleting_func(self, *args):\\n return _ida_idp.IDB_Hooks_deleting_func(self, *args)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"362ba3ca8c527d5fa2ac61634a88f597\",\n \"score\": \"0.4927455\",\n \"text\": \"def code_resource_revision_post_delete(instance, **kwargs):\\n if instance.content_file:\\n instance.content_file.delete(save=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f3c333e8f204bdad6001920c65c3a71a\",\n \"score\": \"0.49269268\",\n \"text\": \"def button_delete_click(self, **event_args):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"26d6aec36e1c0b87919594adf2806436\",\n \"score\": \"0.49222717\",\n \"text\": \"def _remove_notebooks(self):\\r\\n if self._exists:\\r\\n q = (~models.Notebook.id.in_(self._exists)\\r\\n & (models.Notebook.action != const.ACTION_CREATE)\\r\\n & (models.Notebook.action != const.ACTION_CHANGE))\\r\\n else:\\r\\n q = ((models.Notebook.action != const.ACTION_CREATE)\\r\\n & (models.Notebook.action != const.ACTION_CHANGE))\\r\\n\\r\\n self.session.query(models.Notebook).filter(\\r\\n q).delete(synchronize_session='fetch')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"564c177abf920984804fd0e94944b609\",\n \"score\": \"0.49158022\",\n \"text\": \"def delete_notebook_id(self, notebook_id):\\n name = self.mapping[notebook_id]\\n super(MongoNotebookManager, self).delete_notebook_id(notebook_id)\\n del self.rev_mapping[name]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3a9bc5a79f2c431ad2e49f3541ac7c3\",\n \"score\": \"0.4915359\",\n \"text\": \"def on_remove_clicked(self, obj):\\n store, the_iter = self.book_model.get_selected()\\n if not the_iter:\\n return\\n row = self.book_model.get_selected_row()\\n self.book.pop_item(row)\\n self.book_model.remove(the_iter)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"87d96ad95a9aa0b8e7b1a172a9b8128a\",\n \"score\": \"0.49097547\",\n \"text\": \"def remove_input_file(self):\\n # loop over selected items in the tree\\n for idx in self.view.selectedIndexes():\\n # get the selected item from the index\\n selected_item = idx.model().itemFromIndex(idx)\\n # get the parent of the selected item\\n parent = selected_item.parent()\\n # remove the row\\n parent.removeRow(selected_item.row())\\n self.renumber_subsection_keys(parent)\\n self.update_tab_text()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8f62227754f7e2df51f860c73f568d8\",\n \"score\": \"0.49026677\",\n \"text\": \"def delete_selection():\\n configs = self.dirs.settings.last_ard.configs\\n # We will visually remove an item from the GUI, ONLY IF we can remove its associated data first\\n for item in self.scene.selectedItems():\\n if item.data in configs:\\n configs.remove(item.data) # Removes backend data associated with GUI object\\n # We then use the updated configs to completely reset the progbar and its elements\\n # This way, we ensure that what we show on the GUI is 100% representative of backend configurations\\n self.set_progbar_scene()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c03e8e97215a5022c87cd7300c1adda6\",\n \"score\": \"0.49010605\",\n \"text\": \"def deleting_func_tail(self, *args):\\n return _ida_idp.IDB_Hooks_deleting_func_tail(self, *args)\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993298,"cells":{"query_id":{"kind":"string","value":"55f1402bb4366390c6c9b5f6ce663a83"},"query":{"kind":"string","value":"Restart the services in the self.restart_map{} attribute if any of the files identified by the keys changes for the wrapped call."},"positive_passages":{"kind":"list like","value":[{"docid":"b2e1cce208f93ac248afb91bf797ba85","score":"0.59367985","text":"def restart_on_change(self):\n return ch_core.host.restart_on_change(\n self.full_restart_map,\n stopstart=True,\n restart_functions=getattr(self, 'restart_functions', None),\n can_restart_now_f=deferred_events.check_and_record_restart_request,\n post_svc_restart_f=deferred_events.process_svc_restart)","title":""}],"string":"[\n {\n \"docid\": \"b2e1cce208f93ac248afb91bf797ba85\",\n \"score\": \"0.59367985\",\n \"text\": \"def restart_on_change(self):\\n return ch_core.host.restart_on_change(\\n self.full_restart_map,\\n stopstart=True,\\n restart_functions=getattr(self, 'restart_functions', None),\\n can_restart_now_f=deferred_events.check_and_record_restart_request,\\n post_svc_restart_f=deferred_events.process_svc_restart)\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"6f1df8eff29503c62e0f02bf5cc83958","score":"0.74130356","text":"def restart_on_change(self):\n checksums = {path: ch_host.path_hash(path)\n for path in self.full_restart_map.keys()}\n yield\n restarts = []\n for path in self.full_restart_map:\n if ch_host.path_hash(path) != checksums[path]:\n restarts += self.full_restart_map[path]\n services_list = list(collections.OrderedDict.fromkeys(restarts).keys())\n for service_name in services_list:\n ch_host.service_stop(service_name)\n for service_name in services_list:\n ch_host.service_start(service_name)","title":""},{"docid":"bfbdcdf0ae81a19a1a285bbf99ddb89c","score":"0.6244993","text":"def restart_map(self):\n _restart_map = {\n self.workloadmgr_conf: self.services,\n self.api_paste_ini: [\"wlm-api\"],\n self.alembic_ini: [],\n }\n if self.backup_target_type == 's3':\n _restart_map[self.object_store_conf] = ['tvault-object-store']\n _restart_map[\n charms_openstack.plugins.trilio.S3_SSL_CERT_FILE] = [\n 'tvault-object-store']\n return _restart_map","title":""},{"docid":"0a386a07b8d259ba209cd76f57bfe662","score":"0.6071259","text":"def restart_all(self):\n for svc in self.services:\n ch_host.service_restart(svc)","title":""},{"docid":"d9b6c8d59687268068eb18b38d4ce23d","score":"0.6045352","text":"def restart_map(self):\n # Note that we use the standard config render features of\n # charms.openstack to just copy this file in place hence no\n # service attached.\n # The charm will configure the system-id at runtime in the\n # ``configure_ovs`` method. The openvswitch-switch init script will\n # use the on-disk file on service restart.\n _restart_map = {\n '/etc/openvswitch/system-id.conf': [],\n '/etc/default/openvswitch-switch': [],\n # Netplan config snippets will be rendered when SR-IOV or Hardware\n # Offload is enabled. However we always want to render the file\n # to ensure configuration is removed when any of those features\n # are disabled.\n '/etc/netplan/150-charm-ovn.yaml': [],\n }\n # The ``dpdk`` system init script takes care of binding devices\n # to the driver specified in configuration at run- and boot- time.\n #\n # NOTE: we must take care to perform device lookup and store\n # mapping information in the system before binding the interfaces\n # as important information such as hardware ethernet address (MAC)\n # will be harder to get at once bound. (The device disappears from\n # sysfs)\n if self.options.enable_dpdk:\n _restart_map.update({\n '/etc/dpdk/interfaces': ['dpdk']})\n if self.enable_openstack:\n _restart_map.update(self.openstack_restart_map)\n if self.options.enable_sriov:\n _restart_map.update({\n '/etc/neutron/neutron.conf': ['neutron-sriov-agent'],\n '/etc/neutron/plugins/ml2/sriov_agent.ini': [\n 'neutron-sriov-agent'],\n })\n elif self.options.enable_dpdk:\n # Note that we use the standard config render features of\n # charms.openstack to just copy this file in place hence no\n # service attached. systemd-tmpfiles-setup will take care of\n # it at boot and we will do a first-time initialization in the\n # ``install`` method.\n _restart_map.update({self.nova_vhost_user_file: []})\n return _restart_map","title":""},{"docid":"a3b46b8993821e3781b8f59d8f6dce7f","score":"0.59419286","text":"def services_restart():\n _require_target()\n _supervisor_command('stop all')\n\n _supervisor_command('update')\n _supervisor_command('reload')\n time.sleep(1)\n _supervisor_command('start all')","title":""},{"docid":"20621966f50f5c19ef81e9062d15c631","score":"0.5937132","text":"def full_restart_map(self):\n _restart_map = super(OpenStackAPICharm, self).full_restart_map.copy()\n if self.enable_memcache():\n _restart_map[self.MEMCACHE_CONF] = ['memcached']\n return _restart_map","title":""},{"docid":"8d403c3d97b03cf82088ef6f4b499ec5","score":"0.5895145","text":"def restart(self):\r\n cfg.CONF.reload_config_files()\r\n self.services.restart()","title":""},{"docid":"580294a27494f2f4d51aaa8b971394ef","score":"0.57792073","text":"def restart(self, timeStamp):\n print('example.restart() called')\n\n # services = self.services\n # workdir = services.get_working_dir()\n\n # Get restart files listed in config file.\n # Get global configuration parameters and set internal data\n\n return 0","title":""},{"docid":"3bf6850dbaf13d9b215a71adbac0b7ae","score":"0.57774436","text":"def full_restart_map(self):\n _restart_map = super(HAOpenStackCharm, self).full_restart_map\n if self.haproxy_enabled():\n _restart_map[self.HAPROXY_CONF] = ['haproxy']\n if self.apache_enabled():\n _restart_map[self.apache_vhost_file] = ['apache2']\n return _restart_map","title":""},{"docid":"7ef9a5814c3c92f1f49b340c4abd11d0","score":"0.57418406","text":"def check_and_record_restart_request(service, changed_files):\n changed_files = sorted(list(set(changed_files)))\n permitted = is_restart_permitted()\n if not permitted:\n save_event(ServiceEvent(\n timestamp=round(time.time()),\n service=service,\n reason='File(s) changed: {}'.format(\n ', '.join(changed_files)),\n action='restart'))\n return permitted","title":""},{"docid":"1561cb6218817bd64eb2e2d6480f407b","score":"0.5705848","text":"def process_svc_restart(service):\n clear_deferred_restarts([service])","title":""},{"docid":"088208df5444067c2d0e9017a4e63b1e","score":"0.55748874","text":"def extract_restart(self): # pragma: no cover\n try:\n self._extract_update()\n\n if jms_utils.system.get_system() == 'win':\n self._win_overwrite_app_restart()\n else:\n self._overwrite_app()\n self._restart()\n except ClientError as err:\n log.error(str(err))\n log.debug(str(err), exc_info=True)","title":""},{"docid":"0b673b4d05d1f4c7b0bf67fdae5a8927","score":"0.55056995","text":"def restart(self):\n return self.service.post(self.base+'restart/')","title":""},{"docid":"90cab6103b742292c1e56a123e2feb48","score":"0.5463502","text":"def restart_service(service_paths=list()):\n for service_path in service_paths:\n sudo('%s restart' % service_path)","title":""},{"docid":"9740c44e7238fcccdc4a16e8ddcb5464","score":"0.5409788","text":"def test_service_restart():\n mock = MagicMock(return_value=True)\n with patch.dict(slackware_service.__salt__, {\"cmd.retcode\": mock}):\n assert not slackware_service.restart(\"name\")","title":""},{"docid":"ae4ea0c5319d797a9be3b2c733150b54","score":"0.53806067","text":"def process_restart_icloud3_service_request(call):\n\n Gb.restart_icloud3_request_flag = True","title":""},{"docid":"c5f41add5a047490812735f613dcfb99","score":"0.53654873","text":"def mtce_restart():\n restart_service(\"mtcClient\")\n restart_service(\"hbsClient\")\n restart_service(\"pmon\")","title":""},{"docid":"65ab3b8c9de02230421a69c4a4cc1198","score":"0.5345265","text":"def mark_for_restart(self):\n self._should_restart = True","title":""},{"docid":"85ff9f1035940404f2257d811a00ec20","score":"0.5328938","text":"def test_workers_need_restart(self):\n params = WorkerPoolParams()\n\n with self.subTest('not initialized'):\n for func, worker_init, worker_exit, worker_lifespan, enable_insights in product(\n [self._f1, self._f2],\n [None, self._init1, self._init2],\n [None, self._exit1, self._exit2],\n [None, 42, 1337],\n [False, True]\n ):\n self.assertTrue(params.workers_need_restart(func, worker_init, worker_exit, worker_lifespan,\n enable_insights))\n\n params.set_map_params(self._f1, self._init1, self._exit1, 42, True)\n\n with self.subTest('initialized and nothing changed'):\n self.assertFalse(params.workers_need_restart(self._f1, self._init1, self._exit1, 42, True))\n\n with self.subTest('initialized and a parameter changed'):\n self.assertTrue(params.workers_need_restart(self._f2, self._init1, self._exit1, 42, True))\n self.assertTrue(params.workers_need_restart(self._f1, self._init2, self._exit1, 42, True))\n self.assertTrue(params.workers_need_restart(self._f1, self._init1, self._exit2, 42, True))\n self.assertTrue(params.workers_need_restart(self._f1, self._init1, self._exit1, 1337, True))\n self.assertTrue(params.workers_need_restart(self._f1, self._init1, self._exit1, 42, False))","title":""},{"docid":"c99dc397d9f760e7b269feb211b07f7d","score":"0.5314654","text":"async def on_restart(self) -> None:\n ...","title":""},{"docid":"f22f9a992d362888b313c6c99b04a871","score":"0.52900183","text":"def restartAll(self):\n for name in self.processes.keys():\n self.stopProcess(name)","title":""},{"docid":"393e155c14b562f0a19f5e12f5bd2269","score":"0.5287844","text":"def restartable_services(self):\n # Due to Bug #1861321 ceilometer-collector does not reliably\n # restart.\n _services = copy.deepcopy(self.services)\n if self.current_release <= CeilometerTest.TRUSTY_MITAKA:\n try:\n _services.remove('ceilometer-collector')\n except ValueError:\n pass\n return _services","title":""},{"docid":"acfcc2b0dda52308258ca894aed217a2","score":"0.527303","text":"def refresh(self, key=None):\n keys = [key] if key else self._conf.keys()\n for k in keys:\n self.add_key(k, watch=False)","title":""},{"docid":"88021130ca325654bf92ccf4387e06ef","score":"0.5259236","text":"def restart(self):\n return self.default_restart(self.__apache_restarter)","title":""},{"docid":"90ac8aadee08d06b16f1aaec3830efb8","score":"0.5248133","text":"def test_service_reload_():\n mock = MagicMock(return_value=True)\n with patch.dict(slackware_service.__salt__, {\"cmd.retcode\": mock}):\n assert not slackware_service.reload_(\"name\")","title":""},{"docid":"32a9eafd6c149da732705b3ef098992a","score":"0.52437323","text":"def run(self):\r\n for filename in self.sysfiles() | self.files:\r\n if filename:\r\n if filename.endswith('.pyc'):\r\n filename = filename[:-1]\r\n\r\n oldtime = self.mtimes.get(filename, 0)\r\n if oldtime is None:\r\n # Module with no .py file. Skip it.\r\n continue\r\n\r\n try:\r\n mtime = os.stat(filename).st_mtime\r\n except OSError:\r\n # Either a module with no .py file, or it's been deleted.\r\n mtime = None\r\n\r\n if filename not in self.mtimes:\r\n # If a module has no .py file, this will be None.\r\n self.mtimes[filename] = mtime\r\n else:\r\n if mtime is None or mtime > oldtime:\r\n # The file has been deleted or modified.\r\n self.bus.log(\"Restarting because %s changed.\" % filename)\r\n self.thread.cancel()\r\n self.bus.log(\"Stopped thread %r.\" % self.thread.getName())\r\n self.bus.restart()\r\n return","title":""},{"docid":"f1adcdd0dbe804a5808b908013a19f9a","score":"0.5216333","text":"def trigger_remote_restart(self, restart_key=None):\n if not restart_key:\n restart_key = uuid.uuid1()\n for relation in self.relations:\n relation.to_publish_raw['restart_trigger'] = restart_key","title":""},{"docid":"93015f7062d6364325819ef5dd9ac2dc","score":"0.52040523","text":"def restart():\n set_user(superuser=True)\n with settings(hide('warnings'), warn_only=True, ):\n execute(Server.restart_services, hosts=env.hosts)","title":""},{"docid":"de53fd336ee00a3a3c050ed7d44eb3c0","score":"0.5203045","text":"def restart(self, instances):\n return self._request(instances, self._do_restart_instance)","title":""},{"docid":"3e22fab56f6bf04cad1af7a5b2de83e8","score":"0.5160064","text":"def restart_service(services, host=None):\n if isinstance(services, str):\n run('service %s restart' % services, host=host)\n elif isinstance(services, list):\n for service in services:\n run('service %s restart' % service, host=host)\n else:\n LOGGER.error(\"%s should be a str or list\", services)","title":""},{"docid":"03cabc95d7a476905e57c9f198939f83","score":"0.5158204","text":"def check_file_change(self):\n for f, mtime in watched_files_mtimes:\n if getmtime(f) > mtime:\n self.restart()","title":""},{"docid":"7325ffd7da97f624c8a5118ab1e9328b","score":"0.5144581","text":"def _update_docker_services(self):\n # Read services from all docker-compose files.\n docker_services = {}\n for compose in self.docker_composes:\n services = yield hm_utils.parse_docker_state(\n compose, docker_compose_bin=self.docker_compose_bin)\n docker_services.update(services)\n\n # Mark containers that have disappeared.\n dead = {}\n for k in self.docker_services:\n if k not in docker_services:\n dead[k] = self.docker_services.pop(k)\n\n # Everything else is good.\n self.docker_services.update(docker_services)\n returnValue(dead)","title":""},{"docid":"8d76504efa977911d00fd0a29f74c72b","score":"0.5140465","text":"def change_key(self, key):\n\n services = {}\n for service_name in self.list_services():\n services[service_name] = self.get_service(service_name)\n\n orgs = {}\n for org_name in self.list_orgs():\n orgs[org_name] = self.get_org(org_name)\n\n self.key = key\n\n if orgs:\n for org_name, org_config in list(orgs.items()):\n self.set_org(org_config)\n\n if services:\n for service_name, service_config in list(services.items()):\n self.set_service(service_name, service_config)\n\n self._convert_connected_app()","title":""},{"docid":"7654783b20527097e757d6c5f1a7eba0","score":"0.5075957","text":"def _synchronize(self):\n cached_files = glob.glob(os.path.join(self.tm_env.cache_dir, '*'))\n running_links = glob.glob(os.path.join(self.tm_env.running_dir, '*'))\n\n # Calculate the instance names from every event file\n cached_instances = {\n os.path.basename(filename)\n for filename in cached_files\n }\n # Calculate the container names from every event file\n cached_containers = {\n appcfg.eventfile_unique_name(filename)\n for filename in cached_files\n }\n # Calculate the instance names from every event running link\n running_instances = {\n os.path.basename(linkname)\n for linkname in running_links\n if os.path.islink(linkname)\n }\n removed_instances = set()\n added_instances = set()\n\n _LOGGER.info('running %r', running_instances)\n # If instance is extra, remove the symlink and force svscan to\n # re-evaluate\n for instance_link in running_links:\n # Skip any files and directories (created by svscan). The footprint\n # that defines the app is the symlink:\n # - running/<instance_name> -> apps/<container_name>\n #\n # Iterate over all symlinks, skipping other files and directories.\n if not os.path.islink(instance_link):\n continue\n\n instance_name = os.path.basename(instance_link)\n _LOGGER.info('checking %r', instance_link)\n if not os.path.exists(instance_link):\n # Broken symlink, something went wrong.\n _LOGGER.warning('broken link %r', instance_link)\n os.unlink(instance_link)\n removed_instances.add(instance_name)\n continue\n\n elif instance_name not in cached_instances:\n self._terminate(instance_name)\n removed_instances.add(instance_name)\n\n else:\n # Now check that the link match the intended container\n container_dir = self._resolve_running_link(instance_link)\n container_name = os.path.basename(container_dir)\n if container_name not in cached_containers:\n self._terminate(instance_name)\n removed_instances.add(instance_name)\n\n # For all new apps, read the manifest and configure the app. When all\n # apps are configured, force rescan again.\n for instance_name in cached_instances:\n instance_link = os.path.join(\n self.tm_env.running_dir,\n instance_name\n )\n if self._configure(instance_name):\n added_instances.add(instance_name)\n\n _LOGGER.debug('End resuld: %r / %r - %r + %r',\n cached_containers,\n running_instances,\n removed_instances,\n added_instances)\n\n running_instances -= removed_instances\n running_instances |= added_instances\n _LOGGER.info('running post cleanup: %r', running_instances)\n self._refresh_supervisor(instance_names=running_instances)","title":""},{"docid":"43f29f222398a41a3f5fac000a698882","score":"0.506318","text":"def restart(file):\n if is_win32:\n print \"The 'restart' command is not supported on Windows.\"\n sys.exit(1)\n \n try:\n config = setup_environment(file)\n \n stop(file)\n start(file)\n except KeyboardInterrupt:\n pass","title":""},{"docid":"78b4c7f5a8aec7ed1de9b3954a13a1b6","score":"0.5044088","text":"def test_service_force_reload():\n mock = MagicMock(return_value=True)\n with patch.dict(slackware_service.__salt__, {\"cmd.retcode\": mock}):\n assert not slackware_service.force_reload(\"name\")","title":""},{"docid":"9b1b8ab71cc222e957772d72049e6f13","score":"0.5038991","text":"def __restart__():\n\n CalmTask.Exec.ssh(name=\"Task1\", script=\"echo 'Service create in ENV=@@{ENV}@@'\")","title":""},{"docid":"7e1d027f5e9b38757f31709a06d6ec1e","score":"0.5020628","text":"def restart_services(flags):\n global wan_interface, wan_method\n global bool_lan_dhcp_on, bool_lan_dhcp_was_on\n\n tasks = set()\n\n if HOSTNAME_CHANGED in flags: tasks.add('hostname')\n if DNS_CHANGED in flags: tasks.add('dns')\n if WAN_INTERFACE_CHANGED in flags: tasks.add('networking')\n if WAN_METHOD_CHANGED in flags: tasks.add('networking')\n if (wan_method == 'static') and (WAN_STATIC_CHANGED in flags):\n tasks.add('networking')\n if (LAN_ADDRESS_CHANGED in flags) or (LAN_NETMASK_CHANGED in flags):\n tasks.add('networking')\n tasks.add('dhcp_restart')\n if (LAN_ADDRESS_CHANGED in flags):\n tasks.add('squid')\n if (wan_interface == 'eth1') and (LAN_GATEWAY_CHANGED in flags):\n tasks.add('networking')\n if bool_lan_dhcp_on and \\\n ((LAN_ADDRESS_CHANGED in flags) or \\\n (LAN_NETMASK_CHANGED in flags) or \\\n (DHCPD_PARAM_CHANGED in flags)):\n if bool_lan_dhcp_was_on:\n tasks.add('dhcp_restart')\n else:\n tasks.add('dhcp_start')\n elif bool_lan_dhcp_was_on:\n tasks.add('dhcp_stop')\n\n # configure DHCP to start at boot or not\n call([CHKCONFIG, 'dhcp3-server', ['off', 'on'][bool_lan_dhcp_on]])\n\n # run the restarter as NOHUP so it will run to completion\n path = abspath(dirname(sys.argv[0]))\n return Popen(ENV + ['nohup', join(path, RESTARTER)] + list(tasks) + ['&']).pid","title":""},{"docid":"a4f91e2ec5ed2e8c77cb9143e4dbbf37","score":"0.5005713","text":"def expose_transients_regex(self, regexes, *class_build_args, **class_build_kwargs):\n # Important : no effect if names is empty list. only return empty diff. functional style.\n\n # look through the new service names received by reconfigure, and add\n # those services which are not in the existing service args\n for tst_regex in [r for r in regexes if not r in self.transients_args]:\n self.transients_args.add(tst_regex)\n logging.info('[{name}] Exposing {desc} regex : {regex}'.format(name=__name__, desc=self.transients_desc, regex=tst_regex))\n # TODO : check here for bugs & add test : what if we add multiple regexes ? wont we miss some add_names ?\n\n # look through the current service args and delete those values which\n # will not be valid when the args are replaced with the new ones. run on\n # a copy so that we will remove from the original without crashing\n for tst_regex in [r for r in self.transients_args if not r in regexes]:\n logging.info('[{name}] Withholding {desc} regex : {regex}'.format(name=__name__, desc=self.transients_desc, regex=tst_regex))\n self.transients_args.remove(tst_regex)\n\n return self.transient_change_detect(\n *class_build_args,\n **class_build_kwargs\n )","title":""},{"docid":"2e4202e76d895ea372d38338e6ee655a","score":"0.50007385","text":"def full_restart_map(self):\n return self.restart_map","title":""},{"docid":"00a6b91cd72c3aaa704adb48185b5848","score":"0.49869463","text":"def _actual_restarts(self, service_name, restart_data):\n actual_restarts = 0\n\n finished = os.path.join(self.services_dir, service_name, 'finished')\n\n restart_rate_exceeded = False\n try:\n with open(finished) as f:\n limit, interval = (\n restart_data['limit'],\n restart_data['interval']\n )\n lines = f.readlines()\n actual_restarts = len(lines)\n if limit == 0:\n # Do not allow any restart\n restart_rate_exceeded = True\n\n elif len(lines) >= limit:\n # See if we went over the limit\n timestamp, _rc, _sig = lines[-limit].split()\n if int(timestamp) + interval > time.time():\n restart_rate_exceeded = True\n\n except IOError as err:\n if err.errno != errno.ENOENT:\n raise\n\n return restart_rate_exceeded, actual_restarts","title":""},{"docid":"dc2ae57ee8aa5ebe41451206f1043296","score":"0.4977543","text":"def restart():\n reload_nginx()\n restart_gunicorn()","title":""},{"docid":"4ce8453e535846ae72ddc1a6a5a07835","score":"0.4970081","text":"def __call__(self, renewed=None):\n if renewed is None:\n if 'RENEWED_DOMAINS' not in os.environ:\n raise ValueError('No domains to renew and no RENEWED_DOMAIN environment variable')\n renewed = os.environ['RENEWED_DOMAINS'].split()\n\n for domain in renewed:\n if domain in self.domains:\n print('Hook {} matches {}'.format(\n self.name, domain)\n )\n for service in self.services:\n # Allow handler to be either a module with\n # a 'handler' function or just a function\n if hasattr(self.handler, 'handler'):\n self.handler.handler(service, domain)\n else:\n self.handler(service, domain)\n if self.once:\n break","title":""},{"docid":"2760d7760fb646f9fd52dc33599f42b7","score":"0.49542162","text":"def restart(self): # pragma: no cover\n # On windows we write a batch file to move the update\n # binary to the correct location and restart app.\n if jms_utils.system.get_system() == 'win':\n log.warning('Only supported on Unix like systems')\n return\n try:\n self._overwrite_app()\n self._restart()\n except ClientError as err:\n log.error(str(err))\n log.debug(str(err), exc_info=True)","title":""},{"docid":"3431895ac75e2054fe631e895bfea539","score":"0.49429658","text":"def processRetries(self, files):\n if len(files) < 1:\n # We got no files?\n return\n\n propList = []\n fileList = self.loadFilesFromList(recList = files)\n logging.debug(\"Files in cooloff %s\" % fileList)\n # Now we should have the files\n propList = self.selectFilesToRetry(fileList)\n logging.debug(\"Files to retry %s\" % propList)\n now = str(datetime.datetime.now())\n for file in propList:\n # update couch\n self.logger.debug(\"Trying to resubmit %s\" % file['id'])\n try:\n document = self.db.document(file['id'])\n except Exception, ex:\n msg = \"Error loading document from couch\"\n msg += str(ex)\n msg += str(traceback.format_exc())\n self.logger.error(msg)\n continue\n if document['state'] != 'killed':\n data = {}\n data['state'] = 'new'\n data['last_update'] = time.time()\n data['retry'] = now\n updateUri = \"/\" + self.db.name + \"/_design/AsyncTransfer/_update/updateJobs/\" + file['id']\n updateUri += \"?\" + urllib.urlencode(data)\n try:\n self.db.makeRequest(uri = updateUri, type = \"PUT\", decode = False)\n except Exception, ex:\n msg = \"Error updating document in couch\"\n msg += str(ex)\n msg += str(traceback.format_exc())\n self.logger.error(msg)\n continue\n self.logger.debug(\"%s resubmitted\" % file['id'])\n else:\n continue\n return","title":""},{"docid":"78187a61e560567cc505f6af92e08564","score":"0.49322882","text":"def check_for_reboot(self, any_dict):\n\n for key, val in any_dict.iteritems():\n if len(val) == 2:\n if val[0] > val[1]:\n self.device_potential_reboot_counter += 1\n self.reboot_flag = True\n del val[0]\n if self.device_potential_reboot_counter is 16:\n if not MAINTAIN_STATE.file_created:\n MAINTAIN_STATE.set_reboot_data_filename(generate_timestamped_txt('reboot_data'))\n MAINTAIN_STATE.set_file_created(True)\n reboot_info = 'Reboot at %f for %s' % (self.timestamp,\n self.filename)\n logging.debug(reboot_info)\n write_reboot_data_to_txt(reboot_info, MAINTAIN_STATE.reboot_data_filename)\n self.device_potential_reboot_counter = 0","title":""},{"docid":"93ee4e77c45151a652fbb1c65f28ae4d","score":"0.4923302","text":"def deferrable_svc_restart(service, reason=None):\n if is_restart_permitted():\n host.service_restart(service)\n else:\n save_event(ServiceEvent(\n timestamp=round(time.time()),\n service=service,\n reason=reason,\n action='restart'))","title":""},{"docid":"f28351318c0ba9d2abaeac1fbe11503f","score":"0.49088147","text":"def restart():\n pid = os.getpid()\n print(INFO + \"%s : Restarting the service (%i)...\" % (current_time(), pid))\n os.execl(\"./setup/restart.sh\", \"./setup/restart.sh\")","title":""},{"docid":"370090f28f4d45440a5a5bf1d5bc0299","score":"0.49067613","text":"def configure_application_restart(\n self, cmd_callback: 'Callable[[str], List[str]]') -> None:\n UsageExpectation.of(self).enforce()\n if self._restart_strategy is None:\n self._restart_strategy = detect_restart_strategy()\n self._restart_cmd_callback = cmd_callback\n # Prevent second call to this method and to the\n # use_alternate_restart_strategy() method.\n allowed_calls = UsageExpectation.of(self).allowed_calls\n del allowed_calls[self.configure_application_restart]\n if self.use_alternate_restart_strategy in allowed_calls:\n del allowed_calls[self.use_alternate_restart_strategy]","title":""},{"docid":"e0a59c6433f7817a47d80e148dc59ccf","score":"0.49067572","text":"def restart(name, sig=None):\n\n proxy_fn = \"ssh_sample.service_restart\"\n return __proxy__[proxy_fn](name)","title":""},{"docid":"28047d7a43cdb085088362a2673a5f3b","score":"0.48873818","text":"def clear_deferred_restarts(services):\n clear_deferred_events(services, 'restart')","title":""},{"docid":"d71a7e787c7db3afd16cb84212b44cae","score":"0.48666334","text":"def restart(ctx):\n run(\n \"docker compose restart {}\".format(\" \".join(_get_cluster_services())),\n shell=True,\n check=True,\n cwd=PROJ_ROOT,\n )","title":""},{"docid":"3d75f3bd0c1105a64fd899a795e108ed","score":"0.48420784","text":"def restart(): # type: ignore","title":""},{"docid":"cbce4571b04777c49043d33b7b326ca1","score":"0.483355","text":"def restart(self):\n\t\tself.stop_server()\n\t\tself.start_server()","title":""},{"docid":"8af9de5369b65d557f25e04ce61b934f","score":"0.482892","text":"def service_restart(self, *a):\n try:\n retcode = call('shorewall restart', shell=True)\n if retcode > 0:\n l = open('/usr/local/tcs/tums/shorewallBroken', 'wt')\n l.write(str(retcode))\n print \"Firewall failed to start\"\n else: \n retcode = call('rm /usr/local/tcs/tums/shorewallBroken > /dev/null 2>&1', shell=True)\n print \"Firewall Restarted\"\n except OSError, e:\n print \"Execution failed:\", e","title":""},{"docid":"700703fefa20adada554cc4629f8dabd","score":"0.48268744","text":"def restart_func(self):\r\n self.restart_game()","title":""},{"docid":"e1e9010362fb168e1f8ca7764c0bbb1c","score":"0.4823742","text":"async def reload_scripts_handler(call):\n _LOGGER.debug(\"reload: yaml, reloading scripts, and restarting\")\n\n await update_yaml_config(hass, config_entry)\n State.set_pyscript_config(config_entry.data)\n\n await State.get_service_params()\n\n global_ctx_only = call.data.get(\"global_ctx\", None)\n\n if global_ctx_only is not None and not GlobalContextMgr.get(global_ctx_only):\n _LOGGER.error(\"pyscript.reload: no global context '%s' to reload\", global_ctx_only)\n return\n\n await unload_scripts(global_ctx_only=global_ctx_only)\n\n await install_requirements(hass, config_entry, pyscript_folder)\n await load_scripts(hass, config_entry.data, global_ctx_only=global_ctx_only)\n\n start_global_contexts(global_ctx_only=global_ctx_only)","title":""},{"docid":"e4ae5d7073da629dc86d2eeac8f5f5fb","score":"0.48101065","text":"def restart(self, **kwargs):\n del kwargs\n self.debug_log(\"Restarting (resetting then enabling)\")\n self.reset()\n self.enable()","title":""},{"docid":"9260cb6df3cd50f01bd48a3a36c12784","score":"0.48025313","text":"def pool_restart(self, modules=None, reload=False, reloader=None,\n destination=None, **kwargs):\n return self.broadcast(\n 'pool_restart',\n arguments={\n 'modules': modules,\n 'reload': reload,\n 'reloader': reloader,\n },\n destination=destination, **kwargs)","title":""},{"docid":"67570405ca3b291078e41a0bd40983d0","score":"0.47988692","text":"def restart(self, wfcfile, dft_energies):\n print(\"=================== Restarting run =======================\")\n try:\n self.dft_driver.restart_wfc(wfcfile, dft_energies)\n \n except:\n print(\" Restart failed! \")\n e = sys.exc_info()\n print(e)\n print(\"Quitting DFT driver\")\n self.dft_driver.exit()","title":""},{"docid":"875f28dfe4bb5e2e8fca24fa4c5e299f","score":"0.47932616","text":"def restart(self):\n return sync(self.loop, self._restart)","title":""},{"docid":"fe7e3e54122acceb7779f5d36516d2ca","score":"0.47914538","text":"def start_service(self, service_name):\n # TODO(boysson): remove this. There is no reason why a manifest should\n # lack restart_data.\n try:\n restart_data = self.services[service_name]['restart']\n except TypeError:\n _LOGGER.error('Incorrect settings for restart')\n restart_data = {\n 'limit': _MAX_RESTART_RATE,\n 'interval': _RESTART_RATE_INTERVAL,\n }\n\n svc_dir = os.path.join(self.services_dir, service_name)\n\n restart_rate_exceeded, actual_restarts = (\n self._actual_restarts(service_name, restart_data))\n _LOGGER.info('starting %s, retries %s/%s in %s',\n service_name, actual_restarts,\n restart_data['limit'], restart_data['interval'])\n\n # If for whatever reason presence exited before reporting last exit\n # status, do it now. The method is no-op if last status was reported\n # successfully.\n self.update_exit_status(service_name)\n\n if restart_rate_exceeded:\n _LOGGER.info('Exceeded number of restarts per interval')\n return False\n\n if is_down(svc_dir):\n if os.path.exists(os.path.join(svc_dir, 'down')):\n subproc.check_call(['s6_svc', '-o', svc_dir])\n self.report_running(service_name)\n else:\n _LOGGER.info('Service %s already running', service_name)\n\n subproc.check_call(['s6_svwait', '-u', svc_dir])\n return True","title":""},{"docid":"8e5b488ab80fc290746a2254a7ef9214","score":"0.47743413","text":"def restart_salt():\n stop_salt()\n start_salt()","title":""},{"docid":"8d3edf85753726e3a5616d72800955a5","score":"0.4770703","text":"def do_step(self):\n for update in self.updates:\n self.fs_pairs, cnf = update(self.fs_pairs, self.lookup_cache)\n\n key = pre_hash(self.fs_pairs)\n if cnf and key not in self.lookup_cache:\n # first time here, cache it\n self.lookup_cache[key] = cnf[\"FSfq\"]\n self.rcache[key] = dict( (k, cnf[k]) \n for k in [\"FSfq\", \"P\", \"roots\", \"c\"] )","title":""},{"docid":"f6012769d945d93803f927cf226603b8","score":"0.47538352","text":"def _restart_components(self, env, result):\n result.log(\"Restarting converter app.\")\n env.converter.restart()\n result.log(\n \"App is now listening on {}:{}\".format(\n env.converter.host, env.converter.port\n )\n )\n env.server.accept_connection()\n env.client.restart()","title":""},{"docid":"1adb6045cfe15d1327a816f3e846b387","score":"0.4745147","text":"def restart():\n stop()\n start()","title":""},{"docid":"1adb6045cfe15d1327a816f3e846b387","score":"0.4745147","text":"def restart():\n stop()\n start()","title":""},{"docid":"32f6dac0d3647532b3994f1765eb2ad9","score":"0.47360694","text":"def reboot(self, restart_services=True):\n if self.isBootloaderMode:\n self.fastboot.reboot()\n return\n\n if self.isTcpFastbootdMode:\n self.fastboot.reboot()\n return\n\n if restart_services:\n has_adb_log = self.isAdbLogcatOn\n has_vts_agent = True if self.vts_agent_process else False\n if has_adb_log:\n self.stopAdbLogcat()\n if has_vts_agent:\n self.stopVtsAgent()\n\n self.adb.reboot()\n self.waitForBootCompletion()\n self.rootAdb()\n\n if restart_services:\n if has_adb_log:\n self.startAdbLogcat()\n if has_vts_agent:\n self.startVtsAgent()","title":""},{"docid":"1e2f212a03b663e5261a974b01ce4a77","score":"0.47277263","text":"def do_restart(self, host_id):\n r = requests.put(self.api_url + \"hosts/\" + self.real_id(host_id) + \"/restart\")\n self.print_result(r)","title":""},{"docid":"319e6fd92316a6e76ace23697386cb7a","score":"0.47240332","text":"def force_restart(self) -> pulumi.Output[bool]:\n return pulumi.get(self, \"force_restart\")","title":""},{"docid":"8289c15e00640a47b23462dc1be8a450","score":"0.4718915","text":"def restart():\n shutdown()\n startup()\n return","title":""},{"docid":"a4ff15c0a6b944f64c21397b3794ad70","score":"0.47108525","text":"def restart(c, containers):\n c.run(f'{DOCKER_COMPOSE_COMMAND} restart {containers}', pty=True)\n\n # When restarting a container that nginx is connected to, we need to restart\n # nginx as well because it has the IP cached\n need_nginx_restart = [\n 'web',\n 'proxito',\n 'storage',\n ]\n for extra in need_nginx_restart:\n if extra in containers:\n c.run(f'{DOCKER_COMPOSE_COMMAND} restart nginx', pty=True)\n break","title":""},{"docid":"115a8adc8bc02d73d63750a376b16b1c","score":"0.47093368","text":"def force_restart():\n kill()\n start()","title":""},{"docid":"5a3cfbf495ff8d5812a86599c2dd679b","score":"0.47088304","text":"def restart(self):\n pass","title":""},{"docid":"2df18fb446317df7b23dd33eae204919","score":"0.4704457","text":"def ReloadMaps():\n importlib.reload(map_data)\n importlib.reload(map_data2)\n REGIONS[1] = map_data.map_data\n REGIONS[2] = map_data2.map_data","title":""},{"docid":"559b72d0c3bdf2807b153e28bc16cb23","score":"0.47043926","text":"def __call__(self, editFiles):\n editFiles = self.config.get('KEYVAL_Files', editFiles)\n reg=r'[\\s\\t]*=[\\s\\t]*'\n methodDict = self.methodDictBuild(files=editFiles)\n self.logger.debug('dict_of_functions_and_text=%s' % self.functions_and_text)\n self.logger.info('methodDict = %s'%methodDict)\n self.logger.info('functions_and_text = %s'%self.functions_and_text)\n\n for file in editFiles:\n self.string_on_file[file] = self.BuildDict(startwith=os.path.basename(file), \n hash=self.config, hashFiles1={})\n tmp = []\n for k,v in self.string_on_file[file].iteritems():\n tmp.append({ re.split(reg, v, 1)[0] : re.split(reg, v, 1)[1] })\n self.string_on_file[file] = tmp\n self.logger.debug('dict_of_string_on_file=%s' % self.string_on_file)\n super(conf_app, self).__call__(editFiles=editFiles, methodDict=methodDict)","title":""},{"docid":"8105208a52574c2033944896d694171e","score":"0.46994665","text":"def on_swift_restart(self, q, roothashes):\n for c in self._communities.itervalues():\n if isinstance(c, MyCommunity):\n c.swift_community.put_endpoint_calls(q, roothashes)","title":""},{"docid":"75faf2f6667ab9cd57b72792bb691742","score":"0.46910605","text":"def restart(self):\n self.stop()\n self.start()\n return","title":""},{"docid":"244d1eb8deae5a5d395ac95ad92b0cbd","score":"0.46907112","text":"def __restartManifest(self, service, manifest):\r\n self.__LOG.info(\"restart active Manifest %s\" % (service))\r\n self._execPackages('restart', service, manifest, 10, 90, activateFlow = False)","title":""},{"docid":"ceb95f8271a62c5d4369874b99a5ff2d","score":"0.46765342","text":"def perform_local_restart(self) -> None:\n if self.local_restart:\n if self.local_restart == \"IPOP\" and self.mu > 512: \n self.mu *= self.ipop_factor\n self.lambda_ *= self.ipop_factor\n\n elif self.local_restart == \"BIPOP\":\n self.bipop_parameters.adapt(self.used_budget)\n self.sigma = self.bipop_parameters.sigma\n self.lambda_ = self.bipop_parameters.lambda_\n self.mu = self.bipop_parameters.mu\n\n self.init_selection_parameters()\n self.init_adaptation_parameters()\n self.init_dynamic_parameters()\n self.init_local_restart_parameters()\n self.restarts.append(self.t)\n else:\n warnings.warn(\n \"Termination criteria met: {}\".format(\n \", \".join(\n name\n for name, value in self.termination_criteria.items()\n if value\n )\n ),\n RuntimeWarning,\n )","title":""},{"docid":"b7a462ff722c03874d486a7c957b5411","score":"0.46743345","text":"def reload(self):\n raise NotImplementedError('\"reload\" method not available for '\n 'service %s' % self.name)","title":""},{"docid":"4ddfc32e90450eab7e70176e65f7099d","score":"0.46735638","text":"def check_restart(self):\n return path.exists(config.GRAB_DAEMON_RESTART_FLAG_PATH)","title":""},{"docid":"0c0b53ec2330a776869fd6007e8b3bff","score":"0.46724764","text":"def run_mapping_flags(self, flags, keys):\n self.set_flags_false()\n self.set_flags_loop(flags, keys)","title":""},{"docid":"0bc07a56fa0081a67dca8ee7faa53761","score":"0.46703753","text":"def restartFinished(self) -> None:\n\t\t# Look for the shutdown script(s) and run them. \n\t\tself.runEventScripts(_metaOnRestart)","title":""},{"docid":"fd38c20c5385f642316c519a781d5e0b","score":"0.46699303","text":"def proper_handling_of_errors_in_restart():","title":""},{"docid":"d9f49b6e2a9e6fb59becacd55b7c5c6a","score":"0.46645284","text":"def configure_deferred_restarts(self):\n if 'enable-auto-restarts' in ch_core.hookenv.config().keys():\n deferred_events.configure_deferred_restarts(\n self.deferable_services)\n # Reactive charms execute perm missing.\n os.chmod(\n '/var/lib/charm/{}/policy-rc.d'.format(\n ch_core.hookenv.service_name()),\n 0o755)","title":""},{"docid":"153dc5f59f51122e2fce0f77bf9bc552","score":"0.46611124","text":"def test_900_restart_on_config_change(self):\n config_name = 'debug'\n\n if self.application_name == 'ceilometer-agent':\n config_name = 'use-internal-endpoints'\n\n # Expected default and alternate values\n current_value = openstack_utils.get_application_config_option(\n self.application_name, config_name\n )\n assert type(current_value) is bool\n new_value = not current_value\n\n # Convert bool to str\n current_value = str(current_value)\n new_value = str(new_value)\n\n set_default = {config_name: current_value}\n set_alternate = {config_name: new_value}\n\n default_entry = {'DEFAULT': {'debug': [current_value]}}\n alternate_entry = {'DEFAULT': {'debug': [new_value]}}\n\n if self.application_name == 'ceilometer-agent':\n default_entry = None\n alternate_entry = {\n 'service_credentials': {'interface': ['internalURL']}\n }\n\n logging.info('changing config: {}'.format(set_alternate))\n self.restart_on_changed(\n CeilometerTest.CONF_FILE,\n set_default,\n set_alternate,\n default_entry,\n alternate_entry,\n self.restartable_services)","title":""},{"docid":"dd805dcf79cb0f0da10745f8d1d50e45","score":"0.46483374","text":"def restart_ike_daemon(self):\n # Remove the stale information from the NSS database\n self._nss_clear_database()\n\n f = open(self.IPSEC_CONF, \"w\")\n f.write(self.CONF_HEADER)\n f.close()\n\n f = open(self.IPSEC_SECRETS, \"w\")\n f.write(FILE_HEADER)\n f.close()\n\n vlog.info(\"Restarting LibreSwan\")\n subprocess.call([self.IPSEC, \"restart\"])","title":""},{"docid":"9aee510142150149330e7c9fb36e745b","score":"0.46279284","text":"async def reload_service_handler(service_call: ServiceCall) -> None:\n conf = await component.async_prepare_reload(skip_reset=True)\n if conf is None:\n return\n await yaml_collection.async_load(\n [\n {CONF_ID: id_, **(conf or {})}\n for id_, conf in conf.get(DOMAIN, {}).items()\n ]\n )","title":""},{"docid":"734bfdbe589537f3c9cc4101fb4792d7","score":"0.46163067","text":"def restart (self):\n\n for pid in self._workers:\n # we have HUP and TERM registered because HUP is not legal on Windows\n # both signals result in the same action\n os.kill(pid, signal.SIGTERM)","title":""},{"docid":"73187aa713d2326aaacb8a9167a1ac6b","score":"0.4616095","text":"def run(self):\n # Start idle\n self._is_active = False\n\n # Setup the watchdog\n watchdog_lease = self.tm_env.watchdogs.create(\n name='svc-{svc_name}'.format(svc_name=self.name),\n timeout='{hb:d}s'.format(hb=_WATCHDOG_TIMEOUT_SEC),\n content='Service %r failed' % self.name\n )\n\n watch = dirwatch.DirWatcher(self.tm_env.cache_dir)\n watch.on_created = self._on_created\n watch.on_modified = self._on_modified\n watch.on_deleted = self._on_deleted\n\n # Start the timer\n watchdog_lease.heartbeat()\n\n while True:\n if watch.wait_for_events(timeout=_HEARTBEAT_SEC):\n watch.process_events(max_events=5)\n else:\n if self._is_active is True:\n cached_files = glob.glob(\n os.path.join(self.tm_env.cache_dir, '*')\n )\n running_links = glob.glob(\n os.path.join(self.tm_env.running_dir, '*')\n )\n # Calculate the container names from every event file\n cached_containers = {\n appcfg.eventfile_unique_name(filename)\n for filename in cached_files\n }\n # Calculate the instance names from every event running\n # link\n running_instances = {\n os.path.basename(linkname)\n for linkname in running_links\n }\n _LOGGER.debug('content of %r and %r: %r <-> %r',\n self.tm_env.cache_dir,\n self.tm_env.running_dir,\n cached_containers,\n running_instances)\n\n else:\n _LOGGER.info('Still inactive during heartbeat event.')\n\n watchdog_lease.heartbeat()\n\n # Graceful shutdown.\n _LOGGER.info('service shutdown.')\n watchdog_lease.remove()","title":""},{"docid":"65acb2451affd34ecbc6643590571acb","score":"0.46148628","text":"def restart(self):\n raise NotImplementedError()","title":""},{"docid":"eb8459c975ac92a6083c1a8f779080aa","score":"0.46090245","text":"def run_mapping(self, keys):\n for ((tosca_path, flags), map_sol6) in keys.mapping:\n self.run_mapping_flags(flags, keys)\n self.run_mapping_map_needed(tosca_path, map_sol6)","title":""},{"docid":"d1123adbce4da2d76a51e518573d041f","score":"0.45906487","text":"def __restart(self):\n\t\tself.__restart_counter += 1\n\t\t\n\t\tif self.__debug:\n\t\t\tstring1 = '\\nRestarting...'\n\t\t\tstring2 = '\\n\\n\\t\\t======== STOCK SIMULATION #{} ========\\n'.format(self.__restart_counter)\n\t\t\t#print(string1)\n\t\t\t#print(string2)\n\t\t\tself.__debug_str += string1\n\t\t\tself.__debug_str += string2\n\t\t\n\t\t# Reset some lists\n\t\tself.__current_stocks = []\n\t\tself.__daily_stocks.clear()\n\t\t\n\t\tadded_items = self.__added_items.copy()\n\t\tself.__added_items.clear()\n\t\t\n\t\tdemand = self.__demand.copy()\n\t\tself.__demand.clear()\n\t\t\n\t\tself.__temp_orders = self.__orders.copy()\n\t\t\n\t\t# Add initial items\n\t\tfor date in added_items:\n\t\t\titems = added_items[date]\n\t\t\tfor name in items:\n\t\t\t\tamount = items[name]\n\t\t\t\tself.add(name, amount, date)\n\t\t\n\t\t# Try buy stuff\n\t\tfor date in demand:\n\t\t\titems = demand[date]\n\t\t\tfor name in items:\n\t\t\t\tamount = items[name]\n\t\t\t\tself.sell(name, amount, date)","title":""},{"docid":"650b55fd3801517676eac75eccb1ad17","score":"0.45902014","text":"def watch_files(self, files):","title":""},{"docid":"b05214d0b102cbc8816490f4454a94f0","score":"0.45782933","text":"def restart_ike_daemon(self):\n f = open(self.CHARON_CONF, \"w\")\n f.write(self.STRONGSWAN_CONF)\n f.close()\n\n f = open(self.IPSEC_CONF, \"w\")\n f.write(self.CONF_HEADER)\n f.close()\n\n f = open(self.IPSEC_SECRETS, \"w\")\n f.write(FILE_HEADER)\n f.close()\n\n vlog.info(\"Restarting StrongSwan\")\n subprocess.call([self.IPSEC, \"restart\"])","title":""},{"docid":"fd33a5cab88b9c495f0ea12bc5fa1dda","score":"0.45664304","text":"def periodicUpdate(self):\n self.state['CurrentProcess'] = self.executionMgr.currentTask.process\n try:\n self.state.updateState()\n except Exception, ex:\n if self.ignoreErrors:\n msg = \"WARNING: Error ignored in Monitoring System:\\n\"\n msg += \"Caught Updating MonitorState:\\n\"\n msg += str(ex)\n print msg\n else:\n raise ex\n \n for key in self.keys():\n try:\n self[key].periodicUpdate(self.state)\n except Exception, ex:\n if self.ignoreErrors:\n msg = \"WARNING: Error ignored in Monitoring System:\\n\"\n msg += \"Caught on PeriodicUpdate from %s\\n\" % key\n msg += str(ex)\n print msg\n continue\n else:\n raise ex\n return","title":""},{"docid":"1b996d3abb59a85696d60e0a3498792a","score":"0.4561073","text":"def restart_build_jobs():\n for job_name in job_name_list:\n start_build_job(job_name)\n return True","title":""},{"docid":"69a76be61d7a6f11676c33be83479d08","score":"0.4558351","text":"def restart_flask():\n sudo('service supervisor restart')\n sudo('supervisorctl reload')\n sudo('supervisorctl restart ertc')","title":""},{"docid":"0857d0e56a3ec2600b1446550ad6740e","score":"0.4549719","text":"def restart(self):\n self.stop()\n self.start()","title":""}],"string":"[\n {\n \"docid\": \"6f1df8eff29503c62e0f02bf5cc83958\",\n \"score\": \"0.74130356\",\n \"text\": \"def restart_on_change(self):\\n checksums = {path: ch_host.path_hash(path)\\n for path in self.full_restart_map.keys()}\\n yield\\n restarts = []\\n for path in self.full_restart_map:\\n if ch_host.path_hash(path) != checksums[path]:\\n restarts += self.full_restart_map[path]\\n services_list = list(collections.OrderedDict.fromkeys(restarts).keys())\\n for service_name in services_list:\\n ch_host.service_stop(service_name)\\n for service_name in services_list:\\n ch_host.service_start(service_name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bfbdcdf0ae81a19a1a285bbf99ddb89c\",\n \"score\": \"0.6244993\",\n \"text\": \"def restart_map(self):\\n _restart_map = {\\n self.workloadmgr_conf: self.services,\\n self.api_paste_ini: [\\\"wlm-api\\\"],\\n self.alembic_ini: [],\\n }\\n if self.backup_target_type == 's3':\\n _restart_map[self.object_store_conf] = ['tvault-object-store']\\n _restart_map[\\n charms_openstack.plugins.trilio.S3_SSL_CERT_FILE] = [\\n 'tvault-object-store']\\n return _restart_map\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a386a07b8d259ba209cd76f57bfe662\",\n \"score\": \"0.6071259\",\n \"text\": \"def restart_all(self):\\n for svc in self.services:\\n ch_host.service_restart(svc)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9b6c8d59687268068eb18b38d4ce23d\",\n \"score\": \"0.6045352\",\n \"text\": \"def restart_map(self):\\n # Note that we use the standard config render features of\\n # charms.openstack to just copy this file in place hence no\\n # service attached.\\n # The charm will configure the system-id at runtime in the\\n # ``configure_ovs`` method. The openvswitch-switch init script will\\n # use the on-disk file on service restart.\\n _restart_map = {\\n '/etc/openvswitch/system-id.conf': [],\\n '/etc/default/openvswitch-switch': [],\\n # Netplan config snippets will be rendered when SR-IOV or Hardware\\n # Offload is enabled. However we always want to render the file\\n # to ensure configuration is removed when any of those features\\n # are disabled.\\n '/etc/netplan/150-charm-ovn.yaml': [],\\n }\\n # The ``dpdk`` system init script takes care of binding devices\\n # to the driver specified in configuration at run- and boot- time.\\n #\\n # NOTE: we must take care to perform device lookup and store\\n # mapping information in the system before binding the interfaces\\n # as important information such as hardware ethernet address (MAC)\\n # will be harder to get at once bound. (The device disappears from\\n # sysfs)\\n if self.options.enable_dpdk:\\n _restart_map.update({\\n '/etc/dpdk/interfaces': ['dpdk']})\\n if self.enable_openstack:\\n _restart_map.update(self.openstack_restart_map)\\n if self.options.enable_sriov:\\n _restart_map.update({\\n '/etc/neutron/neutron.conf': ['neutron-sriov-agent'],\\n '/etc/neutron/plugins/ml2/sriov_agent.ini': [\\n 'neutron-sriov-agent'],\\n })\\n elif self.options.enable_dpdk:\\n # Note that we use the standard config render features of\\n # charms.openstack to just copy this file in place hence no\\n # service attached. systemd-tmpfiles-setup will take care of\\n # it at boot and we will do a first-time initialization in the\\n # ``install`` method.\\n _restart_map.update({self.nova_vhost_user_file: []})\\n return _restart_map\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a3b46b8993821e3781b8f59d8f6dce7f\",\n \"score\": \"0.59419286\",\n \"text\": \"def services_restart():\\n _require_target()\\n _supervisor_command('stop all')\\n\\n _supervisor_command('update')\\n _supervisor_command('reload')\\n time.sleep(1)\\n _supervisor_command('start all')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"20621966f50f5c19ef81e9062d15c631\",\n \"score\": \"0.5937132\",\n \"text\": \"def full_restart_map(self):\\n _restart_map = super(OpenStackAPICharm, self).full_restart_map.copy()\\n if self.enable_memcache():\\n _restart_map[self.MEMCACHE_CONF] = ['memcached']\\n return _restart_map\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d403c3d97b03cf82088ef6f4b499ec5\",\n \"score\": \"0.5895145\",\n \"text\": \"def restart(self):\\r\\n cfg.CONF.reload_config_files()\\r\\n self.services.restart()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"580294a27494f2f4d51aaa8b971394ef\",\n \"score\": \"0.57792073\",\n \"text\": \"def restart(self, timeStamp):\\n print('example.restart() called')\\n\\n # services = self.services\\n # workdir = services.get_working_dir()\\n\\n # Get restart files listed in config file.\\n # Get global configuration parameters and set internal data\\n\\n return 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3bf6850dbaf13d9b215a71adbac0b7ae\",\n \"score\": \"0.57774436\",\n \"text\": \"def full_restart_map(self):\\n _restart_map = super(HAOpenStackCharm, self).full_restart_map\\n if self.haproxy_enabled():\\n _restart_map[self.HAPROXY_CONF] = ['haproxy']\\n if self.apache_enabled():\\n _restart_map[self.apache_vhost_file] = ['apache2']\\n return _restart_map\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7ef9a5814c3c92f1f49b340c4abd11d0\",\n \"score\": \"0.57418406\",\n \"text\": \"def check_and_record_restart_request(service, changed_files):\\n changed_files = sorted(list(set(changed_files)))\\n permitted = is_restart_permitted()\\n if not permitted:\\n save_event(ServiceEvent(\\n timestamp=round(time.time()),\\n service=service,\\n reason='File(s) changed: {}'.format(\\n ', '.join(changed_files)),\\n action='restart'))\\n return permitted\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1561cb6218817bd64eb2e2d6480f407b\",\n \"score\": \"0.5705848\",\n \"text\": \"def process_svc_restart(service):\\n clear_deferred_restarts([service])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"088208df5444067c2d0e9017a4e63b1e\",\n \"score\": \"0.55748874\",\n \"text\": \"def extract_restart(self): # pragma: no cover\\n try:\\n self._extract_update()\\n\\n if jms_utils.system.get_system() == 'win':\\n self._win_overwrite_app_restart()\\n else:\\n self._overwrite_app()\\n self._restart()\\n except ClientError as err:\\n log.error(str(err))\\n log.debug(str(err), exc_info=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0b673b4d05d1f4c7b0bf67fdae5a8927\",\n \"score\": \"0.55056995\",\n \"text\": \"def restart(self):\\n return self.service.post(self.base+'restart/')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90cab6103b742292c1e56a123e2feb48\",\n \"score\": \"0.5463502\",\n \"text\": \"def restart_service(service_paths=list()):\\n for service_path in service_paths:\\n sudo('%s restart' % service_path)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9740c44e7238fcccdc4a16e8ddcb5464\",\n \"score\": \"0.5409788\",\n \"text\": \"def test_service_restart():\\n mock = MagicMock(return_value=True)\\n with patch.dict(slackware_service.__salt__, {\\\"cmd.retcode\\\": mock}):\\n assert not slackware_service.restart(\\\"name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae4ea0c5319d797a9be3b2c733150b54\",\n \"score\": \"0.53806067\",\n \"text\": \"def process_restart_icloud3_service_request(call):\\n\\n Gb.restart_icloud3_request_flag = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c5f41add5a047490812735f613dcfb99\",\n \"score\": \"0.53654873\",\n \"text\": \"def mtce_restart():\\n restart_service(\\\"mtcClient\\\")\\n restart_service(\\\"hbsClient\\\")\\n restart_service(\\\"pmon\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65ab3b8c9de02230421a69c4a4cc1198\",\n \"score\": \"0.5345265\",\n \"text\": \"def mark_for_restart(self):\\n self._should_restart = True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85ff9f1035940404f2257d811a00ec20\",\n \"score\": \"0.5328938\",\n \"text\": \"def test_workers_need_restart(self):\\n params = WorkerPoolParams()\\n\\n with self.subTest('not initialized'):\\n for func, worker_init, worker_exit, worker_lifespan, enable_insights in product(\\n [self._f1, self._f2],\\n [None, self._init1, self._init2],\\n [None, self._exit1, self._exit2],\\n [None, 42, 1337],\\n [False, True]\\n ):\\n self.assertTrue(params.workers_need_restart(func, worker_init, worker_exit, worker_lifespan,\\n enable_insights))\\n\\n params.set_map_params(self._f1, self._init1, self._exit1, 42, True)\\n\\n with self.subTest('initialized and nothing changed'):\\n self.assertFalse(params.workers_need_restart(self._f1, self._init1, self._exit1, 42, True))\\n\\n with self.subTest('initialized and a parameter changed'):\\n self.assertTrue(params.workers_need_restart(self._f2, self._init1, self._exit1, 42, True))\\n self.assertTrue(params.workers_need_restart(self._f1, self._init2, self._exit1, 42, True))\\n self.assertTrue(params.workers_need_restart(self._f1, self._init1, self._exit2, 42, True))\\n self.assertTrue(params.workers_need_restart(self._f1, self._init1, self._exit1, 1337, True))\\n self.assertTrue(params.workers_need_restart(self._f1, self._init1, self._exit1, 42, False))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c99dc397d9f760e7b269feb211b07f7d\",\n \"score\": \"0.5314654\",\n \"text\": \"async def on_restart(self) -> None:\\n ...\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f22f9a992d362888b313c6c99b04a871\",\n \"score\": \"0.52900183\",\n \"text\": \"def restartAll(self):\\n for name in self.processes.keys():\\n self.stopProcess(name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"393e155c14b562f0a19f5e12f5bd2269\",\n \"score\": \"0.5287844\",\n \"text\": \"def restartable_services(self):\\n # Due to Bug #1861321 ceilometer-collector does not reliably\\n # restart.\\n _services = copy.deepcopy(self.services)\\n if self.current_release <= CeilometerTest.TRUSTY_MITAKA:\\n try:\\n _services.remove('ceilometer-collector')\\n except ValueError:\\n pass\\n return _services\",\n \"title\": \"\"\n },\n {\n \"docid\": \"acfcc2b0dda52308258ca894aed217a2\",\n \"score\": \"0.527303\",\n \"text\": \"def refresh(self, key=None):\\n keys = [key] if key else self._conf.keys()\\n for k in keys:\\n self.add_key(k, watch=False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"88021130ca325654bf92ccf4387e06ef\",\n \"score\": \"0.5259236\",\n \"text\": \"def restart(self):\\n return self.default_restart(self.__apache_restarter)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"90ac8aadee08d06b16f1aaec3830efb8\",\n \"score\": \"0.5248133\",\n \"text\": \"def test_service_reload_():\\n mock = MagicMock(return_value=True)\\n with patch.dict(slackware_service.__salt__, {\\\"cmd.retcode\\\": mock}):\\n assert not slackware_service.reload_(\\\"name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32a9eafd6c149da732705b3ef098992a\",\n \"score\": \"0.52437323\",\n \"text\": \"def run(self):\\r\\n for filename in self.sysfiles() | self.files:\\r\\n if filename:\\r\\n if filename.endswith('.pyc'):\\r\\n filename = filename[:-1]\\r\\n\\r\\n oldtime = self.mtimes.get(filename, 0)\\r\\n if oldtime is None:\\r\\n # Module with no .py file. Skip it.\\r\\n continue\\r\\n\\r\\n try:\\r\\n mtime = os.stat(filename).st_mtime\\r\\n except OSError:\\r\\n # Either a module with no .py file, or it's been deleted.\\r\\n mtime = None\\r\\n\\r\\n if filename not in self.mtimes:\\r\\n # If a module has no .py file, this will be None.\\r\\n self.mtimes[filename] = mtime\\r\\n else:\\r\\n if mtime is None or mtime > oldtime:\\r\\n # The file has been deleted or modified.\\r\\n self.bus.log(\\\"Restarting because %s changed.\\\" % filename)\\r\\n self.thread.cancel()\\r\\n self.bus.log(\\\"Stopped thread %r.\\\" % self.thread.getName())\\r\\n self.bus.restart()\\r\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f1adcdd0dbe804a5808b908013a19f9a\",\n \"score\": \"0.5216333\",\n \"text\": \"def trigger_remote_restart(self, restart_key=None):\\n if not restart_key:\\n restart_key = uuid.uuid1()\\n for relation in self.relations:\\n relation.to_publish_raw['restart_trigger'] = restart_key\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93015f7062d6364325819ef5dd9ac2dc\",\n \"score\": \"0.52040523\",\n \"text\": \"def restart():\\n set_user(superuser=True)\\n with settings(hide('warnings'), warn_only=True, ):\\n execute(Server.restart_services, hosts=env.hosts)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"de53fd336ee00a3a3c050ed7d44eb3c0\",\n \"score\": \"0.5203045\",\n \"text\": \"def restart(self, instances):\\n return self._request(instances, self._do_restart_instance)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3e22fab56f6bf04cad1af7a5b2de83e8\",\n \"score\": \"0.5160064\",\n \"text\": \"def restart_service(services, host=None):\\n if isinstance(services, str):\\n run('service %s restart' % services, host=host)\\n elif isinstance(services, list):\\n for service in services:\\n run('service %s restart' % service, host=host)\\n else:\\n LOGGER.error(\\\"%s should be a str or list\\\", services)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"03cabc95d7a476905e57c9f198939f83\",\n \"score\": \"0.5158204\",\n \"text\": \"def check_file_change(self):\\n for f, mtime in watched_files_mtimes:\\n if getmtime(f) > mtime:\\n self.restart()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7325ffd7da97f624c8a5118ab1e9328b\",\n \"score\": \"0.5144581\",\n \"text\": \"def _update_docker_services(self):\\n # Read services from all docker-compose files.\\n docker_services = {}\\n for compose in self.docker_composes:\\n services = yield hm_utils.parse_docker_state(\\n compose, docker_compose_bin=self.docker_compose_bin)\\n docker_services.update(services)\\n\\n # Mark containers that have disappeared.\\n dead = {}\\n for k in self.docker_services:\\n if k not in docker_services:\\n dead[k] = self.docker_services.pop(k)\\n\\n # Everything else is good.\\n self.docker_services.update(docker_services)\\n returnValue(dead)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d76504efa977911d00fd0a29f74c72b\",\n \"score\": \"0.5140465\",\n \"text\": \"def change_key(self, key):\\n\\n services = {}\\n for service_name in self.list_services():\\n services[service_name] = self.get_service(service_name)\\n\\n orgs = {}\\n for org_name in self.list_orgs():\\n orgs[org_name] = self.get_org(org_name)\\n\\n self.key = key\\n\\n if orgs:\\n for org_name, org_config in list(orgs.items()):\\n self.set_org(org_config)\\n\\n if services:\\n for service_name, service_config in list(services.items()):\\n self.set_service(service_name, service_config)\\n\\n self._convert_connected_app()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7654783b20527097e757d6c5f1a7eba0\",\n \"score\": \"0.5075957\",\n \"text\": \"def _synchronize(self):\\n cached_files = glob.glob(os.path.join(self.tm_env.cache_dir, '*'))\\n running_links = glob.glob(os.path.join(self.tm_env.running_dir, '*'))\\n\\n # Calculate the instance names from every event file\\n cached_instances = {\\n os.path.basename(filename)\\n for filename in cached_files\\n }\\n # Calculate the container names from every event file\\n cached_containers = {\\n appcfg.eventfile_unique_name(filename)\\n for filename in cached_files\\n }\\n # Calculate the instance names from every event running link\\n running_instances = {\\n os.path.basename(linkname)\\n for linkname in running_links\\n if os.path.islink(linkname)\\n }\\n removed_instances = set()\\n added_instances = set()\\n\\n _LOGGER.info('running %r', running_instances)\\n # If instance is extra, remove the symlink and force svscan to\\n # re-evaluate\\n for instance_link in running_links:\\n # Skip any files and directories (created by svscan). The footprint\\n # that defines the app is the symlink:\\n # - running/<instance_name> -> apps/<container_name>\\n #\\n # Iterate over all symlinks, skipping other files and directories.\\n if not os.path.islink(instance_link):\\n continue\\n\\n instance_name = os.path.basename(instance_link)\\n _LOGGER.info('checking %r', instance_link)\\n if not os.path.exists(instance_link):\\n # Broken symlink, something went wrong.\\n _LOGGER.warning('broken link %r', instance_link)\\n os.unlink(instance_link)\\n removed_instances.add(instance_name)\\n continue\\n\\n elif instance_name not in cached_instances:\\n self._terminate(instance_name)\\n removed_instances.add(instance_name)\\n\\n else:\\n # Now check that the link match the intended container\\n container_dir = self._resolve_running_link(instance_link)\\n container_name = os.path.basename(container_dir)\\n if container_name not in cached_containers:\\n self._terminate(instance_name)\\n removed_instances.add(instance_name)\\n\\n # For all new apps, read the manifest and configure the app. When all\\n # apps are configured, force rescan again.\\n for instance_name in cached_instances:\\n instance_link = os.path.join(\\n self.tm_env.running_dir,\\n instance_name\\n )\\n if self._configure(instance_name):\\n added_instances.add(instance_name)\\n\\n _LOGGER.debug('End resuld: %r / %r - %r + %r',\\n cached_containers,\\n running_instances,\\n removed_instances,\\n added_instances)\\n\\n running_instances -= removed_instances\\n running_instances |= added_instances\\n _LOGGER.info('running post cleanup: %r', running_instances)\\n self._refresh_supervisor(instance_names=running_instances)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43f29f222398a41a3f5fac000a698882\",\n \"score\": \"0.506318\",\n \"text\": \"def restart(file):\\n if is_win32:\\n print \\\"The 'restart' command is not supported on Windows.\\\"\\n sys.exit(1)\\n \\n try:\\n config = setup_environment(file)\\n \\n stop(file)\\n start(file)\\n except KeyboardInterrupt:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78b4c7f5a8aec7ed1de9b3954a13a1b6\",\n \"score\": \"0.5044088\",\n \"text\": \"def test_service_force_reload():\\n mock = MagicMock(return_value=True)\\n with patch.dict(slackware_service.__salt__, {\\\"cmd.retcode\\\": mock}):\\n assert not slackware_service.force_reload(\\\"name\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9b1b8ab71cc222e957772d72049e6f13\",\n \"score\": \"0.5038991\",\n \"text\": \"def __restart__():\\n\\n CalmTask.Exec.ssh(name=\\\"Task1\\\", script=\\\"echo 'Service create in ENV=@@{ENV}@@'\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7e1d027f5e9b38757f31709a06d6ec1e\",\n \"score\": \"0.5020628\",\n \"text\": \"def restart_services(flags):\\n global wan_interface, wan_method\\n global bool_lan_dhcp_on, bool_lan_dhcp_was_on\\n\\n tasks = set()\\n\\n if HOSTNAME_CHANGED in flags: tasks.add('hostname')\\n if DNS_CHANGED in flags: tasks.add('dns')\\n if WAN_INTERFACE_CHANGED in flags: tasks.add('networking')\\n if WAN_METHOD_CHANGED in flags: tasks.add('networking')\\n if (wan_method == 'static') and (WAN_STATIC_CHANGED in flags):\\n tasks.add('networking')\\n if (LAN_ADDRESS_CHANGED in flags) or (LAN_NETMASK_CHANGED in flags):\\n tasks.add('networking')\\n tasks.add('dhcp_restart')\\n if (LAN_ADDRESS_CHANGED in flags):\\n tasks.add('squid')\\n if (wan_interface == 'eth1') and (LAN_GATEWAY_CHANGED in flags):\\n tasks.add('networking')\\n if bool_lan_dhcp_on and \\\\\\n ((LAN_ADDRESS_CHANGED in flags) or \\\\\\n (LAN_NETMASK_CHANGED in flags) or \\\\\\n (DHCPD_PARAM_CHANGED in flags)):\\n if bool_lan_dhcp_was_on:\\n tasks.add('dhcp_restart')\\n else:\\n tasks.add('dhcp_start')\\n elif bool_lan_dhcp_was_on:\\n tasks.add('dhcp_stop')\\n\\n # configure DHCP to start at boot or not\\n call([CHKCONFIG, 'dhcp3-server', ['off', 'on'][bool_lan_dhcp_on]])\\n\\n # run the restarter as NOHUP so it will run to completion\\n path = abspath(dirname(sys.argv[0]))\\n return Popen(ENV + ['nohup', join(path, RESTARTER)] + list(tasks) + ['&']).pid\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4f91e2ec5ed2e8c77cb9143e4dbbf37\",\n \"score\": \"0.5005713\",\n \"text\": \"def expose_transients_regex(self, regexes, *class_build_args, **class_build_kwargs):\\n # Important : no effect if names is empty list. only return empty diff. functional style.\\n\\n # look through the new service names received by reconfigure, and add\\n # those services which are not in the existing service args\\n for tst_regex in [r for r in regexes if not r in self.transients_args]:\\n self.transients_args.add(tst_regex)\\n logging.info('[{name}] Exposing {desc} regex : {regex}'.format(name=__name__, desc=self.transients_desc, regex=tst_regex))\\n # TODO : check here for bugs & add test : what if we add multiple regexes ? wont we miss some add_names ?\\n\\n # look through the current service args and delete those values which\\n # will not be valid when the args are replaced with the new ones. run on\\n # a copy so that we will remove from the original without crashing\\n for tst_regex in [r for r in self.transients_args if not r in regexes]:\\n logging.info('[{name}] Withholding {desc} regex : {regex}'.format(name=__name__, desc=self.transients_desc, regex=tst_regex))\\n self.transients_args.remove(tst_regex)\\n\\n return self.transient_change_detect(\\n *class_build_args,\\n **class_build_kwargs\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2e4202e76d895ea372d38338e6ee655a\",\n \"score\": \"0.50007385\",\n \"text\": \"def full_restart_map(self):\\n return self.restart_map\",\n \"title\": \"\"\n },\n {\n \"docid\": \"00a6b91cd72c3aaa704adb48185b5848\",\n \"score\": \"0.49869463\",\n \"text\": \"def _actual_restarts(self, service_name, restart_data):\\n actual_restarts = 0\\n\\n finished = os.path.join(self.services_dir, service_name, 'finished')\\n\\n restart_rate_exceeded = False\\n try:\\n with open(finished) as f:\\n limit, interval = (\\n restart_data['limit'],\\n restart_data['interval']\\n )\\n lines = f.readlines()\\n actual_restarts = len(lines)\\n if limit == 0:\\n # Do not allow any restart\\n restart_rate_exceeded = True\\n\\n elif len(lines) >= limit:\\n # See if we went over the limit\\n timestamp, _rc, _sig = lines[-limit].split()\\n if int(timestamp) + interval > time.time():\\n restart_rate_exceeded = True\\n\\n except IOError as err:\\n if err.errno != errno.ENOENT:\\n raise\\n\\n return restart_rate_exceeded, actual_restarts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dc2ae57ee8aa5ebe41451206f1043296\",\n \"score\": \"0.4977543\",\n \"text\": \"def restart():\\n reload_nginx()\\n restart_gunicorn()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ce8453e535846ae72ddc1a6a5a07835\",\n \"score\": \"0.4970081\",\n \"text\": \"def __call__(self, renewed=None):\\n if renewed is None:\\n if 'RENEWED_DOMAINS' not in os.environ:\\n raise ValueError('No domains to renew and no RENEWED_DOMAIN environment variable')\\n renewed = os.environ['RENEWED_DOMAINS'].split()\\n\\n for domain in renewed:\\n if domain in self.domains:\\n print('Hook {} matches {}'.format(\\n self.name, domain)\\n )\\n for service in self.services:\\n # Allow handler to be either a module with\\n # a 'handler' function or just a function\\n if hasattr(self.handler, 'handler'):\\n self.handler.handler(service, domain)\\n else:\\n self.handler(service, domain)\\n if self.once:\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2760d7760fb646f9fd52dc33599f42b7\",\n \"score\": \"0.49542162\",\n \"text\": \"def restart(self): # pragma: no cover\\n # On windows we write a batch file to move the update\\n # binary to the correct location and restart app.\\n if jms_utils.system.get_system() == 'win':\\n log.warning('Only supported on Unix like systems')\\n return\\n try:\\n self._overwrite_app()\\n self._restart()\\n except ClientError as err:\\n log.error(str(err))\\n log.debug(str(err), exc_info=True)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3431895ac75e2054fe631e895bfea539\",\n \"score\": \"0.49429658\",\n \"text\": \"def processRetries(self, files):\\n if len(files) < 1:\\n # We got no files?\\n return\\n\\n propList = []\\n fileList = self.loadFilesFromList(recList = files)\\n logging.debug(\\\"Files in cooloff %s\\\" % fileList)\\n # Now we should have the files\\n propList = self.selectFilesToRetry(fileList)\\n logging.debug(\\\"Files to retry %s\\\" % propList)\\n now = str(datetime.datetime.now())\\n for file in propList:\\n # update couch\\n self.logger.debug(\\\"Trying to resubmit %s\\\" % file['id'])\\n try:\\n document = self.db.document(file['id'])\\n except Exception, ex:\\n msg = \\\"Error loading document from couch\\\"\\n msg += str(ex)\\n msg += str(traceback.format_exc())\\n self.logger.error(msg)\\n continue\\n if document['state'] != 'killed':\\n data = {}\\n data['state'] = 'new'\\n data['last_update'] = time.time()\\n data['retry'] = now\\n updateUri = \\\"/\\\" + self.db.name + \\\"/_design/AsyncTransfer/_update/updateJobs/\\\" + file['id']\\n updateUri += \\\"?\\\" + urllib.urlencode(data)\\n try:\\n self.db.makeRequest(uri = updateUri, type = \\\"PUT\\\", decode = False)\\n except Exception, ex:\\n msg = \\\"Error updating document in couch\\\"\\n msg += str(ex)\\n msg += str(traceback.format_exc())\\n self.logger.error(msg)\\n continue\\n self.logger.debug(\\\"%s resubmitted\\\" % file['id'])\\n else:\\n continue\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78187a61e560567cc505f6af92e08564\",\n \"score\": \"0.49322882\",\n \"text\": \"def check_for_reboot(self, any_dict):\\n\\n for key, val in any_dict.iteritems():\\n if len(val) == 2:\\n if val[0] > val[1]:\\n self.device_potential_reboot_counter += 1\\n self.reboot_flag = True\\n del val[0]\\n if self.device_potential_reboot_counter is 16:\\n if not MAINTAIN_STATE.file_created:\\n MAINTAIN_STATE.set_reboot_data_filename(generate_timestamped_txt('reboot_data'))\\n MAINTAIN_STATE.set_file_created(True)\\n reboot_info = 'Reboot at %f for %s' % (self.timestamp,\\n self.filename)\\n logging.debug(reboot_info)\\n write_reboot_data_to_txt(reboot_info, MAINTAIN_STATE.reboot_data_filename)\\n self.device_potential_reboot_counter = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"93ee4e77c45151a652fbb1c65f28ae4d\",\n \"score\": \"0.4923302\",\n \"text\": \"def deferrable_svc_restart(service, reason=None):\\n if is_restart_permitted():\\n host.service_restart(service)\\n else:\\n save_event(ServiceEvent(\\n timestamp=round(time.time()),\\n service=service,\\n reason=reason,\\n action='restart'))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f28351318c0ba9d2abaeac1fbe11503f\",\n \"score\": \"0.49088147\",\n \"text\": \"def restart():\\n pid = os.getpid()\\n print(INFO + \\\"%s : Restarting the service (%i)...\\\" % (current_time(), pid))\\n os.execl(\\\"./setup/restart.sh\\\", \\\"./setup/restart.sh\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"370090f28f4d45440a5a5bf1d5bc0299\",\n \"score\": \"0.49067613\",\n \"text\": \"def configure_application_restart(\\n self, cmd_callback: 'Callable[[str], List[str]]') -> None:\\n UsageExpectation.of(self).enforce()\\n if self._restart_strategy is None:\\n self._restart_strategy = detect_restart_strategy()\\n self._restart_cmd_callback = cmd_callback\\n # Prevent second call to this method and to the\\n # use_alternate_restart_strategy() method.\\n allowed_calls = UsageExpectation.of(self).allowed_calls\\n del allowed_calls[self.configure_application_restart]\\n if self.use_alternate_restart_strategy in allowed_calls:\\n del allowed_calls[self.use_alternate_restart_strategy]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e0a59c6433f7817a47d80e148dc59ccf\",\n \"score\": \"0.49067572\",\n \"text\": \"def restart(name, sig=None):\\n\\n proxy_fn = \\\"ssh_sample.service_restart\\\"\\n return __proxy__[proxy_fn](name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"28047d7a43cdb085088362a2673a5f3b\",\n \"score\": \"0.48873818\",\n \"text\": \"def clear_deferred_restarts(services):\\n clear_deferred_events(services, 'restart')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d71a7e787c7db3afd16cb84212b44cae\",\n \"score\": \"0.48666334\",\n \"text\": \"def restart(ctx):\\n run(\\n \\\"docker compose restart {}\\\".format(\\\" \\\".join(_get_cluster_services())),\\n shell=True,\\n check=True,\\n cwd=PROJ_ROOT,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3d75f3bd0c1105a64fd899a795e108ed\",\n \"score\": \"0.48420784\",\n \"text\": \"def restart(): # type: ignore\",\n \"title\": \"\"\n },\n {\n \"docid\": \"cbce4571b04777c49043d33b7b326ca1\",\n \"score\": \"0.483355\",\n \"text\": \"def restart(self):\\n\\t\\tself.stop_server()\\n\\t\\tself.start_server()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8af9de5369b65d557f25e04ce61b934f\",\n \"score\": \"0.482892\",\n \"text\": \"def service_restart(self, *a):\\n try:\\n retcode = call('shorewall restart', shell=True)\\n if retcode > 0:\\n l = open('/usr/local/tcs/tums/shorewallBroken', 'wt')\\n l.write(str(retcode))\\n print \\\"Firewall failed to start\\\"\\n else: \\n retcode = call('rm /usr/local/tcs/tums/shorewallBroken > /dev/null 2>&1', shell=True)\\n print \\\"Firewall Restarted\\\"\\n except OSError, e:\\n print \\\"Execution failed:\\\", e\",\n \"title\": \"\"\n },\n {\n \"docid\": \"700703fefa20adada554cc4629f8dabd\",\n \"score\": \"0.48268744\",\n \"text\": \"def restart_func(self):\\r\\n self.restart_game()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e1e9010362fb168e1f8ca7764c0bbb1c\",\n \"score\": \"0.4823742\",\n \"text\": \"async def reload_scripts_handler(call):\\n _LOGGER.debug(\\\"reload: yaml, reloading scripts, and restarting\\\")\\n\\n await update_yaml_config(hass, config_entry)\\n State.set_pyscript_config(config_entry.data)\\n\\n await State.get_service_params()\\n\\n global_ctx_only = call.data.get(\\\"global_ctx\\\", None)\\n\\n if global_ctx_only is not None and not GlobalContextMgr.get(global_ctx_only):\\n _LOGGER.error(\\\"pyscript.reload: no global context '%s' to reload\\\", global_ctx_only)\\n return\\n\\n await unload_scripts(global_ctx_only=global_ctx_only)\\n\\n await install_requirements(hass, config_entry, pyscript_folder)\\n await load_scripts(hass, config_entry.data, global_ctx_only=global_ctx_only)\\n\\n start_global_contexts(global_ctx_only=global_ctx_only)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4ae5d7073da629dc86d2eeac8f5f5fb\",\n \"score\": \"0.48101065\",\n \"text\": \"def restart(self, **kwargs):\\n del kwargs\\n self.debug_log(\\\"Restarting (resetting then enabling)\\\")\\n self.reset()\\n self.enable()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9260cb6df3cd50f01bd48a3a36c12784\",\n \"score\": \"0.48025313\",\n \"text\": \"def pool_restart(self, modules=None, reload=False, reloader=None,\\n destination=None, **kwargs):\\n return self.broadcast(\\n 'pool_restart',\\n arguments={\\n 'modules': modules,\\n 'reload': reload,\\n 'reloader': reloader,\\n },\\n destination=destination, **kwargs)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"67570405ca3b291078e41a0bd40983d0\",\n \"score\": \"0.47988692\",\n \"text\": \"def restart(self, wfcfile, dft_energies):\\n print(\\\"=================== Restarting run =======================\\\")\\n try:\\n self.dft_driver.restart_wfc(wfcfile, dft_energies)\\n \\n except:\\n print(\\\" Restart failed! \\\")\\n e = sys.exc_info()\\n print(e)\\n print(\\\"Quitting DFT driver\\\")\\n self.dft_driver.exit()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"875f28dfe4bb5e2e8fca24fa4c5e299f\",\n \"score\": \"0.47932616\",\n \"text\": \"def restart(self):\\n return sync(self.loop, self._restart)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fe7e3e54122acceb7779f5d36516d2ca\",\n \"score\": \"0.47914538\",\n \"text\": \"def start_service(self, service_name):\\n # TODO(boysson): remove this. There is no reason why a manifest should\\n # lack restart_data.\\n try:\\n restart_data = self.services[service_name]['restart']\\n except TypeError:\\n _LOGGER.error('Incorrect settings for restart')\\n restart_data = {\\n 'limit': _MAX_RESTART_RATE,\\n 'interval': _RESTART_RATE_INTERVAL,\\n }\\n\\n svc_dir = os.path.join(self.services_dir, service_name)\\n\\n restart_rate_exceeded, actual_restarts = (\\n self._actual_restarts(service_name, restart_data))\\n _LOGGER.info('starting %s, retries %s/%s in %s',\\n service_name, actual_restarts,\\n restart_data['limit'], restart_data['interval'])\\n\\n # If for whatever reason presence exited before reporting last exit\\n # status, do it now. The method is no-op if last status was reported\\n # successfully.\\n self.update_exit_status(service_name)\\n\\n if restart_rate_exceeded:\\n _LOGGER.info('Exceeded number of restarts per interval')\\n return False\\n\\n if is_down(svc_dir):\\n if os.path.exists(os.path.join(svc_dir, 'down')):\\n subproc.check_call(['s6_svc', '-o', svc_dir])\\n self.report_running(service_name)\\n else:\\n _LOGGER.info('Service %s already running', service_name)\\n\\n subproc.check_call(['s6_svwait', '-u', svc_dir])\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e5b488ab80fc290746a2254a7ef9214\",\n \"score\": \"0.47743413\",\n \"text\": \"def restart_salt():\\n stop_salt()\\n start_salt()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8d3edf85753726e3a5616d72800955a5\",\n \"score\": \"0.4770703\",\n \"text\": \"def do_step(self):\\n for update in self.updates:\\n self.fs_pairs, cnf = update(self.fs_pairs, self.lookup_cache)\\n\\n key = pre_hash(self.fs_pairs)\\n if cnf and key not in self.lookup_cache:\\n # first time here, cache it\\n self.lookup_cache[key] = cnf[\\\"FSfq\\\"]\\n self.rcache[key] = dict( (k, cnf[k]) \\n for k in [\\\"FSfq\\\", \\\"P\\\", \\\"roots\\\", \\\"c\\\"] )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f6012769d945d93803f927cf226603b8\",\n \"score\": \"0.47538352\",\n \"text\": \"def _restart_components(self, env, result):\\n result.log(\\\"Restarting converter app.\\\")\\n env.converter.restart()\\n result.log(\\n \\\"App is now listening on {}:{}\\\".format(\\n env.converter.host, env.converter.port\\n )\\n )\\n env.server.accept_connection()\\n env.client.restart()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1adb6045cfe15d1327a816f3e846b387\",\n \"score\": \"0.4745147\",\n \"text\": \"def restart():\\n stop()\\n start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1adb6045cfe15d1327a816f3e846b387\",\n \"score\": \"0.4745147\",\n \"text\": \"def restart():\\n stop()\\n start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"32f6dac0d3647532b3994f1765eb2ad9\",\n \"score\": \"0.47360694\",\n \"text\": \"def reboot(self, restart_services=True):\\n if self.isBootloaderMode:\\n self.fastboot.reboot()\\n return\\n\\n if self.isTcpFastbootdMode:\\n self.fastboot.reboot()\\n return\\n\\n if restart_services:\\n has_adb_log = self.isAdbLogcatOn\\n has_vts_agent = True if self.vts_agent_process else False\\n if has_adb_log:\\n self.stopAdbLogcat()\\n if has_vts_agent:\\n self.stopVtsAgent()\\n\\n self.adb.reboot()\\n self.waitForBootCompletion()\\n self.rootAdb()\\n\\n if restart_services:\\n if has_adb_log:\\n self.startAdbLogcat()\\n if has_vts_agent:\\n self.startVtsAgent()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1e2f212a03b663e5261a974b01ce4a77\",\n \"score\": \"0.47277263\",\n \"text\": \"def do_restart(self, host_id):\\n r = requests.put(self.api_url + \\\"hosts/\\\" + self.real_id(host_id) + \\\"/restart\\\")\\n self.print_result(r)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"319e6fd92316a6e76ace23697386cb7a\",\n \"score\": \"0.47240332\",\n \"text\": \"def force_restart(self) -> pulumi.Output[bool]:\\n return pulumi.get(self, \\\"force_restart\\\")\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8289c15e00640a47b23462dc1be8a450\",\n \"score\": \"0.4718915\",\n \"text\": \"def restart():\\n shutdown()\\n startup()\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a4ff15c0a6b944f64c21397b3794ad70\",\n \"score\": \"0.47108525\",\n \"text\": \"def restart(c, containers):\\n c.run(f'{DOCKER_COMPOSE_COMMAND} restart {containers}', pty=True)\\n\\n # When restarting a container that nginx is connected to, we need to restart\\n # nginx as well because it has the IP cached\\n need_nginx_restart = [\\n 'web',\\n 'proxito',\\n 'storage',\\n ]\\n for extra in need_nginx_restart:\\n if extra in containers:\\n c.run(f'{DOCKER_COMPOSE_COMMAND} restart nginx', pty=True)\\n break\",\n \"title\": \"\"\n },\n {\n \"docid\": \"115a8adc8bc02d73d63750a376b16b1c\",\n \"score\": \"0.47093368\",\n \"text\": \"def force_restart():\\n kill()\\n start()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5a3cfbf495ff8d5812a86599c2dd679b\",\n \"score\": \"0.47088304\",\n \"text\": \"def restart(self):\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2df18fb446317df7b23dd33eae204919\",\n \"score\": \"0.4704457\",\n \"text\": \"def ReloadMaps():\\n importlib.reload(map_data)\\n importlib.reload(map_data2)\\n REGIONS[1] = map_data.map_data\\n REGIONS[2] = map_data2.map_data\",\n \"title\": \"\"\n },\n {\n \"docid\": \"559b72d0c3bdf2807b153e28bc16cb23\",\n \"score\": \"0.47043926\",\n \"text\": \"def __call__(self, editFiles):\\n editFiles = self.config.get('KEYVAL_Files', editFiles)\\n reg=r'[\\\\s\\\\t]*=[\\\\s\\\\t]*'\\n methodDict = self.methodDictBuild(files=editFiles)\\n self.logger.debug('dict_of_functions_and_text=%s' % self.functions_and_text)\\n self.logger.info('methodDict = %s'%methodDict)\\n self.logger.info('functions_and_text = %s'%self.functions_and_text)\\n\\n for file in editFiles:\\n self.string_on_file[file] = self.BuildDict(startwith=os.path.basename(file), \\n hash=self.config, hashFiles1={})\\n tmp = []\\n for k,v in self.string_on_file[file].iteritems():\\n tmp.append({ re.split(reg, v, 1)[0] : re.split(reg, v, 1)[1] })\\n self.string_on_file[file] = tmp\\n self.logger.debug('dict_of_string_on_file=%s' % self.string_on_file)\\n super(conf_app, self).__call__(editFiles=editFiles, methodDict=methodDict)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8105208a52574c2033944896d694171e\",\n \"score\": \"0.46994665\",\n \"text\": \"def on_swift_restart(self, q, roothashes):\\n for c in self._communities.itervalues():\\n if isinstance(c, MyCommunity):\\n c.swift_community.put_endpoint_calls(q, roothashes)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"75faf2f6667ab9cd57b72792bb691742\",\n \"score\": \"0.46910605\",\n \"text\": \"def restart(self):\\n self.stop()\\n self.start()\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"244d1eb8deae5a5d395ac95ad92b0cbd\",\n \"score\": \"0.46907112\",\n \"text\": \"def __restartManifest(self, service, manifest):\\r\\n self.__LOG.info(\\\"restart active Manifest %s\\\" % (service))\\r\\n self._execPackages('restart', service, manifest, 10, 90, activateFlow = False)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ceb95f8271a62c5d4369874b99a5ff2d\",\n \"score\": \"0.46765342\",\n \"text\": \"def perform_local_restart(self) -> None:\\n if self.local_restart:\\n if self.local_restart == \\\"IPOP\\\" and self.mu > 512: \\n self.mu *= self.ipop_factor\\n self.lambda_ *= self.ipop_factor\\n\\n elif self.local_restart == \\\"BIPOP\\\":\\n self.bipop_parameters.adapt(self.used_budget)\\n self.sigma = self.bipop_parameters.sigma\\n self.lambda_ = self.bipop_parameters.lambda_\\n self.mu = self.bipop_parameters.mu\\n\\n self.init_selection_parameters()\\n self.init_adaptation_parameters()\\n self.init_dynamic_parameters()\\n self.init_local_restart_parameters()\\n self.restarts.append(self.t)\\n else:\\n warnings.warn(\\n \\\"Termination criteria met: {}\\\".format(\\n \\\", \\\".join(\\n name\\n for name, value in self.termination_criteria.items()\\n if value\\n )\\n ),\\n RuntimeWarning,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b7a462ff722c03874d486a7c957b5411\",\n \"score\": \"0.46743345\",\n \"text\": \"def reload(self):\\n raise NotImplementedError('\\\"reload\\\" method not available for '\\n 'service %s' % self.name)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4ddfc32e90450eab7e70176e65f7099d\",\n \"score\": \"0.46735638\",\n \"text\": \"def check_restart(self):\\n return path.exists(config.GRAB_DAEMON_RESTART_FLAG_PATH)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0c0b53ec2330a776869fd6007e8b3bff\",\n \"score\": \"0.46724764\",\n \"text\": \"def run_mapping_flags(self, flags, keys):\\n self.set_flags_false()\\n self.set_flags_loop(flags, keys)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0bc07a56fa0081a67dca8ee7faa53761\",\n \"score\": \"0.46703753\",\n \"text\": \"def restartFinished(self) -> None:\\n\\t\\t# Look for the shutdown script(s) and run them. \\n\\t\\tself.runEventScripts(_metaOnRestart)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd38c20c5385f642316c519a781d5e0b\",\n \"score\": \"0.46699303\",\n \"text\": \"def proper_handling_of_errors_in_restart():\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d9f49b6e2a9e6fb59becacd55b7c5c6a\",\n \"score\": \"0.46645284\",\n \"text\": \"def configure_deferred_restarts(self):\\n if 'enable-auto-restarts' in ch_core.hookenv.config().keys():\\n deferred_events.configure_deferred_restarts(\\n self.deferable_services)\\n # Reactive charms execute perm missing.\\n os.chmod(\\n '/var/lib/charm/{}/policy-rc.d'.format(\\n ch_core.hookenv.service_name()),\\n 0o755)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"153dc5f59f51122e2fce0f77bf9bc552\",\n \"score\": \"0.46611124\",\n \"text\": \"def test_900_restart_on_config_change(self):\\n config_name = 'debug'\\n\\n if self.application_name == 'ceilometer-agent':\\n config_name = 'use-internal-endpoints'\\n\\n # Expected default and alternate values\\n current_value = openstack_utils.get_application_config_option(\\n self.application_name, config_name\\n )\\n assert type(current_value) is bool\\n new_value = not current_value\\n\\n # Convert bool to str\\n current_value = str(current_value)\\n new_value = str(new_value)\\n\\n set_default = {config_name: current_value}\\n set_alternate = {config_name: new_value}\\n\\n default_entry = {'DEFAULT': {'debug': [current_value]}}\\n alternate_entry = {'DEFAULT': {'debug': [new_value]}}\\n\\n if self.application_name == 'ceilometer-agent':\\n default_entry = None\\n alternate_entry = {\\n 'service_credentials': {'interface': ['internalURL']}\\n }\\n\\n logging.info('changing config: {}'.format(set_alternate))\\n self.restart_on_changed(\\n CeilometerTest.CONF_FILE,\\n set_default,\\n set_alternate,\\n default_entry,\\n alternate_entry,\\n self.restartable_services)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"dd805dcf79cb0f0da10745f8d1d50e45\",\n \"score\": \"0.46483374\",\n \"text\": \"def restart_ike_daemon(self):\\n # Remove the stale information from the NSS database\\n self._nss_clear_database()\\n\\n f = open(self.IPSEC_CONF, \\\"w\\\")\\n f.write(self.CONF_HEADER)\\n f.close()\\n\\n f = open(self.IPSEC_SECRETS, \\\"w\\\")\\n f.write(FILE_HEADER)\\n f.close()\\n\\n vlog.info(\\\"Restarting LibreSwan\\\")\\n subprocess.call([self.IPSEC, \\\"restart\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9aee510142150149330e7c9fb36e745b\",\n \"score\": \"0.46279284\",\n \"text\": \"async def reload_service_handler(service_call: ServiceCall) -> None:\\n conf = await component.async_prepare_reload(skip_reset=True)\\n if conf is None:\\n return\\n await yaml_collection.async_load(\\n [\\n {CONF_ID: id_, **(conf or {})}\\n for id_, conf in conf.get(DOMAIN, {}).items()\\n ]\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"734bfdbe589537f3c9cc4101fb4792d7\",\n \"score\": \"0.46163067\",\n \"text\": \"def restart (self):\\n\\n for pid in self._workers:\\n # we have HUP and TERM registered because HUP is not legal on Windows\\n # both signals result in the same action\\n os.kill(pid, signal.SIGTERM)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"73187aa713d2326aaacb8a9167a1ac6b\",\n \"score\": \"0.4616095\",\n \"text\": \"def run(self):\\n # Start idle\\n self._is_active = False\\n\\n # Setup the watchdog\\n watchdog_lease = self.tm_env.watchdogs.create(\\n name='svc-{svc_name}'.format(svc_name=self.name),\\n timeout='{hb:d}s'.format(hb=_WATCHDOG_TIMEOUT_SEC),\\n content='Service %r failed' % self.name\\n )\\n\\n watch = dirwatch.DirWatcher(self.tm_env.cache_dir)\\n watch.on_created = self._on_created\\n watch.on_modified = self._on_modified\\n watch.on_deleted = self._on_deleted\\n\\n # Start the timer\\n watchdog_lease.heartbeat()\\n\\n while True:\\n if watch.wait_for_events(timeout=_HEARTBEAT_SEC):\\n watch.process_events(max_events=5)\\n else:\\n if self._is_active is True:\\n cached_files = glob.glob(\\n os.path.join(self.tm_env.cache_dir, '*')\\n )\\n running_links = glob.glob(\\n os.path.join(self.tm_env.running_dir, '*')\\n )\\n # Calculate the container names from every event file\\n cached_containers = {\\n appcfg.eventfile_unique_name(filename)\\n for filename in cached_files\\n }\\n # Calculate the instance names from every event running\\n # link\\n running_instances = {\\n os.path.basename(linkname)\\n for linkname in running_links\\n }\\n _LOGGER.debug('content of %r and %r: %r <-> %r',\\n self.tm_env.cache_dir,\\n self.tm_env.running_dir,\\n cached_containers,\\n running_instances)\\n\\n else:\\n _LOGGER.info('Still inactive during heartbeat event.')\\n\\n watchdog_lease.heartbeat()\\n\\n # Graceful shutdown.\\n _LOGGER.info('service shutdown.')\\n watchdog_lease.remove()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"65acb2451affd34ecbc6643590571acb\",\n \"score\": \"0.46148628\",\n \"text\": \"def restart(self):\\n raise NotImplementedError()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"eb8459c975ac92a6083c1a8f779080aa\",\n \"score\": \"0.46090245\",\n \"text\": \"def run_mapping(self, keys):\\n for ((tosca_path, flags), map_sol6) in keys.mapping:\\n self.run_mapping_flags(flags, keys)\\n self.run_mapping_map_needed(tosca_path, map_sol6)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d1123adbce4da2d76a51e518573d041f\",\n \"score\": \"0.45906487\",\n \"text\": \"def __restart(self):\\n\\t\\tself.__restart_counter += 1\\n\\t\\t\\n\\t\\tif self.__debug:\\n\\t\\t\\tstring1 = '\\\\nRestarting...'\\n\\t\\t\\tstring2 = '\\\\n\\\\n\\\\t\\\\t======== STOCK SIMULATION #{} ========\\\\n'.format(self.__restart_counter)\\n\\t\\t\\t#print(string1)\\n\\t\\t\\t#print(string2)\\n\\t\\t\\tself.__debug_str += string1\\n\\t\\t\\tself.__debug_str += string2\\n\\t\\t\\n\\t\\t# Reset some lists\\n\\t\\tself.__current_stocks = []\\n\\t\\tself.__daily_stocks.clear()\\n\\t\\t\\n\\t\\tadded_items = self.__added_items.copy()\\n\\t\\tself.__added_items.clear()\\n\\t\\t\\n\\t\\tdemand = self.__demand.copy()\\n\\t\\tself.__demand.clear()\\n\\t\\t\\n\\t\\tself.__temp_orders = self.__orders.copy()\\n\\t\\t\\n\\t\\t# Add initial items\\n\\t\\tfor date in added_items:\\n\\t\\t\\titems = added_items[date]\\n\\t\\t\\tfor name in items:\\n\\t\\t\\t\\tamount = items[name]\\n\\t\\t\\t\\tself.add(name, amount, date)\\n\\t\\t\\n\\t\\t# Try buy stuff\\n\\t\\tfor date in demand:\\n\\t\\t\\titems = demand[date]\\n\\t\\t\\tfor name in items:\\n\\t\\t\\t\\tamount = items[name]\\n\\t\\t\\t\\tself.sell(name, amount, date)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"650b55fd3801517676eac75eccb1ad17\",\n \"score\": \"0.45902014\",\n \"text\": \"def watch_files(self, files):\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b05214d0b102cbc8816490f4454a94f0\",\n \"score\": \"0.45782933\",\n \"text\": \"def restart_ike_daemon(self):\\n f = open(self.CHARON_CONF, \\\"w\\\")\\n f.write(self.STRONGSWAN_CONF)\\n f.close()\\n\\n f = open(self.IPSEC_CONF, \\\"w\\\")\\n f.write(self.CONF_HEADER)\\n f.close()\\n\\n f = open(self.IPSEC_SECRETS, \\\"w\\\")\\n f.write(FILE_HEADER)\\n f.close()\\n\\n vlog.info(\\\"Restarting StrongSwan\\\")\\n subprocess.call([self.IPSEC, \\\"restart\\\"])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fd33a5cab88b9c495f0ea12bc5fa1dda\",\n \"score\": \"0.45664304\",\n \"text\": \"def periodicUpdate(self):\\n self.state['CurrentProcess'] = self.executionMgr.currentTask.process\\n try:\\n self.state.updateState()\\n except Exception, ex:\\n if self.ignoreErrors:\\n msg = \\\"WARNING: Error ignored in Monitoring System:\\\\n\\\"\\n msg += \\\"Caught Updating MonitorState:\\\\n\\\"\\n msg += str(ex)\\n print msg\\n else:\\n raise ex\\n \\n for key in self.keys():\\n try:\\n self[key].periodicUpdate(self.state)\\n except Exception, ex:\\n if self.ignoreErrors:\\n msg = \\\"WARNING: Error ignored in Monitoring System:\\\\n\\\"\\n msg += \\\"Caught on PeriodicUpdate from %s\\\\n\\\" % key\\n msg += str(ex)\\n print msg\\n continue\\n else:\\n raise ex\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1b996d3abb59a85696d60e0a3498792a\",\n \"score\": \"0.4561073\",\n \"text\": \"def restart_build_jobs():\\n for job_name in job_name_list:\\n start_build_job(job_name)\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69a76be61d7a6f11676c33be83479d08\",\n \"score\": \"0.4558351\",\n \"text\": \"def restart_flask():\\n sudo('service supervisor restart')\\n sudo('supervisorctl reload')\\n sudo('supervisorctl restart ertc')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0857d0e56a3ec2600b1446550ad6740e\",\n \"score\": \"0.4549719\",\n \"text\": \"def restart(self):\\n self.stop()\\n self.start()\",\n \"title\": \"\"\n }\n]","isConversational":false}}},{"rowIdx":993299,"cells":{"query_id":{"kind":"string","value":"e5c0ce3678f425329a83576e15dd2772"},"query":{"kind":"string","value":"This method finds the the forces and moments calculated from the near field."},"positive_passages":{"kind":"list like","value":[{"docid":"05ede3869caeb37b83413ef8d2cc596c","score":"0.66304636","text":"def calculate_near_field_forces_and_moments(self):\n\n # Initialize a variable to hold the global panel position as the panel's are\n # iterate through.\n global_panel_position = 0\n\n # Initialize three lists of variables, which will hold the effective strength\n # of the line vortices comprising\n # each panel's ring vortex.\n effective_right_vortex_line_strengths = np.zeros(self.airplane.num_panels)\n effective_front_vortex_line_strengths = np.zeros(self.airplane.num_panels)\n effective_left_vortex_line_strengths = np.zeros(self.airplane.num_panels)\n\n # Iterate through the current_airplane's wings.\n for wing in self.airplane.wings:\n\n # Convert this wing's 2D array of panels into a 1D array.\n panels = np.ravel(wing.panels)\n\n # Iterate through this wing's 1D array panels.\n for panel in panels:\n\n # Check if this panel is on its wing's right edge.\n if panel.is_right_edge:\n\n # Change the effective right vortex line strength from zero to\n # this panel's ring vortex's strength.\n effective_right_vortex_line_strengths[\n global_panel_position\n ] = self.vortex_strengths[global_panel_position]\n\n else:\n\n # Get the panel directly to the right of this panel.\n panel_to_right = wing.panels[\n panel.local_chordwise_position,\n panel.local_spanwise_position + 1,\n ]\n\n # Change the effective right vortex line strength from zero to\n # the difference between this panel's\n # ring vortex's strength, and the ring vortex strength of the\n # panel to the right of it.\n effective_right_vortex_line_strengths[global_panel_position] = (\n self.vortex_strengths[global_panel_position]\n - panel_to_right.ring_vortex.strength\n )\n\n # Check if this panel is on its wing's leading edge.\n if panel.is_leading_edge:\n\n # Change the effective front vortex line strength from zero to\n # this panel's ring vortex's strength.\n effective_front_vortex_line_strengths[\n global_panel_position\n ] = self.vortex_strengths[global_panel_position]\n else:\n\n # Get the panel directly in front of this panel.\n panel_to_front = wing.panels[\n panel.local_chordwise_position - 1,\n panel.local_spanwise_position,\n ]\n\n # Change the effective front vortex line strength from zero to\n # the difference between this panel's\n # ring vortex's strength, and the ring vortex strength of the\n # panel in front of it.\n effective_front_vortex_line_strengths[global_panel_position] = (\n self.vortex_strengths[global_panel_position]\n - panel_to_front.ring_vortex.strength\n )\n\n # Check if this panel is on its wing's left edge.\n if panel.is_left_edge:\n\n # Change the effective left vortex line strength from zero to\n # this panel's ring vortex's strength.\n effective_left_vortex_line_strengths[\n global_panel_position\n ] = self.vortex_strengths[global_panel_position]\n else:\n\n # Get the panel directly to the left of this panel.\n panel_to_left = wing.panels[\n panel.local_chordwise_position,\n panel.local_spanwise_position - 1,\n ]\n\n # Change the effective left vortex line strength from zero to the\n # difference between this panel's\n # ring vortex's strength, and the ring vortex strength of the\n # panel to the left of it.\n effective_left_vortex_line_strengths[global_panel_position] = (\n self.vortex_strengths[global_panel_position]\n - panel_to_left.ring_vortex.strength\n )\n\n # Increment the global panel position.\n global_panel_position += 1\n\n # Calculate the solution velocities at the centers of the panel's front leg,\n # left leg, and right leg.\n velocities_at_ring_vortex_front_leg_centers = self.calculate_solution_velocity(\n points=self.panel_front_vortex_centers\n )\n velocities_at_ring_vortex_left_leg_centers = self.calculate_solution_velocity(\n points=self.panel_left_vortex_centers\n )\n velocities_at_ring_vortex_right_leg_centers = self.calculate_solution_velocity(\n points=self.panel_right_vortex_centers\n )\n\n # Using the effective line vortex strengths, and the Kutta-Joukowski theorem\n # to find the near field force in\n # geometry axes on the front leg, left leg, and right leg.\n near_field_forces_on_ring_vortex_right_legs_geometry_axes = (\n self.operating_point.density\n * np.expand_dims(effective_right_vortex_line_strengths, axis=1)\n * np.cross(\n velocities_at_ring_vortex_right_leg_centers,\n self.panel_right_vortex_vectors,\n axis=-1,\n )\n )\n near_field_forces_on_ring_vortex_front_legs_geometry_axes = (\n self.operating_point.density\n * np.expand_dims(effective_front_vortex_line_strengths, axis=1)\n * np.cross(\n velocities_at_ring_vortex_front_leg_centers,\n self.panel_front_vortex_vectors,\n axis=-1,\n )\n )\n near_field_forces_on_ring_vortex_left_legs_geometry_axes = (\n self.operating_point.density\n * np.expand_dims(effective_left_vortex_line_strengths, axis=1)\n * np.cross(\n velocities_at_ring_vortex_left_leg_centers,\n self.panel_left_vortex_vectors,\n axis=-1,\n )\n )\n\n # Sum the forces on the legs to calculate the total near field force,\n # in geometry axes, on each panel.\n near_field_forces_geometry_axes = (\n near_field_forces_on_ring_vortex_front_legs_geometry_axes\n + near_field_forces_on_ring_vortex_left_legs_geometry_axes\n + near_field_forces_on_ring_vortex_right_legs_geometry_axes\n )\n\n # Find the near field moment in geometry axes on the front leg, left leg,\n # and right leg.\n near_field_moments_on_ring_vortex_front_legs_geometry_axes = np.cross(\n self.panel_front_vortex_centers - self.airplane.xyz_ref,\n near_field_forces_on_ring_vortex_front_legs_geometry_axes,\n axis=-1,\n )\n near_field_moments_on_ring_vortex_left_legs_geometry_axes = np.cross(\n self.panel_left_vortex_centers - self.airplane.xyz_ref,\n near_field_forces_on_ring_vortex_left_legs_geometry_axes,\n axis=-1,\n )\n near_field_moments_on_ring_vortex_right_legs_geometry_axes = np.cross(\n self.panel_right_vortex_centers - self.airplane.xyz_ref,\n near_field_forces_on_ring_vortex_right_legs_geometry_axes,\n axis=-1,\n )\n\n # Sum the moments on the legs to calculate the total near field moment,\n # in geometry axes, on each panel.\n near_field_moments_geometry_axes = (\n near_field_moments_on_ring_vortex_front_legs_geometry_axes\n + near_field_moments_on_ring_vortex_left_legs_geometry_axes\n + near_field_moments_on_ring_vortex_right_legs_geometry_axes\n )\n\n # Initialize a variable to hold the global panel position.\n global_panel_position = 0\n\n # Iterate through this solver's panels.\n for panel in self.panels:\n # Update the force and moment on this panel.\n panel.near_field_force_geometry_axes = near_field_forces_geometry_axes[\n global_panel_position, :\n ]\n panel.near_field_moment_geometry_axes = near_field_moments_geometry_axes[\n global_panel_position, :\n ]\n\n # Update the pressure on this panel.\n panel.update_pressure()\n\n # Increment the global panel position.\n global_panel_position += 1\n\n # Sum up the near field forces and moments on every panel to find the total\n # force and moment on the geometry.\n total_near_field_force_geometry_axes = np.sum(\n near_field_forces_geometry_axes, axis=0\n )\n total_near_field_moment_geometry_axes = np.sum(\n near_field_moments_geometry_axes, axis=0\n )\n\n # Find the total near field force in wind axes from the rotation matrix and\n # the total near field force in\n # geometry axes.\n self.airplane.total_near_field_force_wind_axes = (\n np.transpose(\n self.operating_point.calculate_rotation_matrix_wind_axes_to_geometry_axes()\n )\n @ total_near_field_force_geometry_axes\n )\n\n # Find the total near field moment in wind axes from the rotation matrix and\n # the total near field moment in\n # geometry axes.\n self.airplane.total_near_field_moment_wind_axes = (\n np.transpose(\n self.operating_point.calculate_rotation_matrix_wind_axes_to_geometry_axes()\n )\n @ total_near_field_moment_geometry_axes\n )\n\n # Calculate the current_airplane's induced drag coefficient\n induced_drag_coefficient = (\n -self.airplane.total_near_field_force_wind_axes[0]\n / self.operating_point.calculate_dynamic_pressure()\n / self.airplane.s_ref\n )\n\n # Calculate the current_airplane's side force coefficient.\n side_force_coefficient = (\n self.airplane.total_near_field_force_wind_axes[1]\n / self.operating_point.calculate_dynamic_pressure()\n / self.airplane.s_ref\n )\n\n # Calculate the current_airplane's lift coefficient.\n lift_coefficient = (\n -self.airplane.total_near_field_force_wind_axes[2]\n / self.operating_point.calculate_dynamic_pressure()\n / self.airplane.s_ref\n )\n\n # Calculate the current_airplane's rolling moment coefficient.\n rolling_moment_coefficient = (\n self.airplane.total_near_field_moment_wind_axes[0]\n / self.operating_point.calculate_dynamic_pressure()\n / self.airplane.s_ref\n / self.airplane.b_ref\n )\n\n # Calculate the current_airplane's pitching moment coefficient.\n pitching_moment_coefficient = (\n self.airplane.total_near_field_moment_wind_axes[1]\n / self.operating_point.calculate_dynamic_pressure()\n / self.airplane.s_ref\n / self.airplane.c_ref\n )\n\n # Calculate the current_airplane's yawing moment coefficient.\n yawing_moment_coefficient = (\n self.airplane.total_near_field_moment_wind_axes[2]\n / self.operating_point.calculate_dynamic_pressure()\n / self.airplane.s_ref\n / self.airplane.b_ref\n )\n\n self.airplane.total_near_field_force_coefficients_wind_axes = np.array(\n [induced_drag_coefficient, side_force_coefficient, lift_coefficient]\n )\n self.airplane.total_near_field_moment_coefficients_wind_axes = np.array(\n [\n rolling_moment_coefficient,\n pitching_moment_coefficient,\n yawing_moment_coefficient,\n ]\n )","title":""}],"string":"[\n {\n \"docid\": \"05ede3869caeb37b83413ef8d2cc596c\",\n \"score\": \"0.66304636\",\n \"text\": \"def calculate_near_field_forces_and_moments(self):\\n\\n # Initialize a variable to hold the global panel position as the panel's are\\n # iterate through.\\n global_panel_position = 0\\n\\n # Initialize three lists of variables, which will hold the effective strength\\n # of the line vortices comprising\\n # each panel's ring vortex.\\n effective_right_vortex_line_strengths = np.zeros(self.airplane.num_panels)\\n effective_front_vortex_line_strengths = np.zeros(self.airplane.num_panels)\\n effective_left_vortex_line_strengths = np.zeros(self.airplane.num_panels)\\n\\n # Iterate through the current_airplane's wings.\\n for wing in self.airplane.wings:\\n\\n # Convert this wing's 2D array of panels into a 1D array.\\n panels = np.ravel(wing.panels)\\n\\n # Iterate through this wing's 1D array panels.\\n for panel in panels:\\n\\n # Check if this panel is on its wing's right edge.\\n if panel.is_right_edge:\\n\\n # Change the effective right vortex line strength from zero to\\n # this panel's ring vortex's strength.\\n effective_right_vortex_line_strengths[\\n global_panel_position\\n ] = self.vortex_strengths[global_panel_position]\\n\\n else:\\n\\n # Get the panel directly to the right of this panel.\\n panel_to_right = wing.panels[\\n panel.local_chordwise_position,\\n panel.local_spanwise_position + 1,\\n ]\\n\\n # Change the effective right vortex line strength from zero to\\n # the difference between this panel's\\n # ring vortex's strength, and the ring vortex strength of the\\n # panel to the right of it.\\n effective_right_vortex_line_strengths[global_panel_position] = (\\n self.vortex_strengths[global_panel_position]\\n - panel_to_right.ring_vortex.strength\\n )\\n\\n # Check if this panel is on its wing's leading edge.\\n if panel.is_leading_edge:\\n\\n # Change the effective front vortex line strength from zero to\\n # this panel's ring vortex's strength.\\n effective_front_vortex_line_strengths[\\n global_panel_position\\n ] = self.vortex_strengths[global_panel_position]\\n else:\\n\\n # Get the panel directly in front of this panel.\\n panel_to_front = wing.panels[\\n panel.local_chordwise_position - 1,\\n panel.local_spanwise_position,\\n ]\\n\\n # Change the effective front vortex line strength from zero to\\n # the difference between this panel's\\n # ring vortex's strength, and the ring vortex strength of the\\n # panel in front of it.\\n effective_front_vortex_line_strengths[global_panel_position] = (\\n self.vortex_strengths[global_panel_position]\\n - panel_to_front.ring_vortex.strength\\n )\\n\\n # Check if this panel is on its wing's left edge.\\n if panel.is_left_edge:\\n\\n # Change the effective left vortex line strength from zero to\\n # this panel's ring vortex's strength.\\n effective_left_vortex_line_strengths[\\n global_panel_position\\n ] = self.vortex_strengths[global_panel_position]\\n else:\\n\\n # Get the panel directly to the left of this panel.\\n panel_to_left = wing.panels[\\n panel.local_chordwise_position,\\n panel.local_spanwise_position - 1,\\n ]\\n\\n # Change the effective left vortex line strength from zero to the\\n # difference between this panel's\\n # ring vortex's strength, and the ring vortex strength of the\\n # panel to the left of it.\\n effective_left_vortex_line_strengths[global_panel_position] = (\\n self.vortex_strengths[global_panel_position]\\n - panel_to_left.ring_vortex.strength\\n )\\n\\n # Increment the global panel position.\\n global_panel_position += 1\\n\\n # Calculate the solution velocities at the centers of the panel's front leg,\\n # left leg, and right leg.\\n velocities_at_ring_vortex_front_leg_centers = self.calculate_solution_velocity(\\n points=self.panel_front_vortex_centers\\n )\\n velocities_at_ring_vortex_left_leg_centers = self.calculate_solution_velocity(\\n points=self.panel_left_vortex_centers\\n )\\n velocities_at_ring_vortex_right_leg_centers = self.calculate_solution_velocity(\\n points=self.panel_right_vortex_centers\\n )\\n\\n # Using the effective line vortex strengths, and the Kutta-Joukowski theorem\\n # to find the near field force in\\n # geometry axes on the front leg, left leg, and right leg.\\n near_field_forces_on_ring_vortex_right_legs_geometry_axes = (\\n self.operating_point.density\\n * np.expand_dims(effective_right_vortex_line_strengths, axis=1)\\n * np.cross(\\n velocities_at_ring_vortex_right_leg_centers,\\n self.panel_right_vortex_vectors,\\n axis=-1,\\n )\\n )\\n near_field_forces_on_ring_vortex_front_legs_geometry_axes = (\\n self.operating_point.density\\n * np.expand_dims(effective_front_vortex_line_strengths, axis=1)\\n * np.cross(\\n velocities_at_ring_vortex_front_leg_centers,\\n self.panel_front_vortex_vectors,\\n axis=-1,\\n )\\n )\\n near_field_forces_on_ring_vortex_left_legs_geometry_axes = (\\n self.operating_point.density\\n * np.expand_dims(effective_left_vortex_line_strengths, axis=1)\\n * np.cross(\\n velocities_at_ring_vortex_left_leg_centers,\\n self.panel_left_vortex_vectors,\\n axis=-1,\\n )\\n )\\n\\n # Sum the forces on the legs to calculate the total near field force,\\n # in geometry axes, on each panel.\\n near_field_forces_geometry_axes = (\\n near_field_forces_on_ring_vortex_front_legs_geometry_axes\\n + near_field_forces_on_ring_vortex_left_legs_geometry_axes\\n + near_field_forces_on_ring_vortex_right_legs_geometry_axes\\n )\\n\\n # Find the near field moment in geometry axes on the front leg, left leg,\\n # and right leg.\\n near_field_moments_on_ring_vortex_front_legs_geometry_axes = np.cross(\\n self.panel_front_vortex_centers - self.airplane.xyz_ref,\\n near_field_forces_on_ring_vortex_front_legs_geometry_axes,\\n axis=-1,\\n )\\n near_field_moments_on_ring_vortex_left_legs_geometry_axes = np.cross(\\n self.panel_left_vortex_centers - self.airplane.xyz_ref,\\n near_field_forces_on_ring_vortex_left_legs_geometry_axes,\\n axis=-1,\\n )\\n near_field_moments_on_ring_vortex_right_legs_geometry_axes = np.cross(\\n self.panel_right_vortex_centers - self.airplane.xyz_ref,\\n near_field_forces_on_ring_vortex_right_legs_geometry_axes,\\n axis=-1,\\n )\\n\\n # Sum the moments on the legs to calculate the total near field moment,\\n # in geometry axes, on each panel.\\n near_field_moments_geometry_axes = (\\n near_field_moments_on_ring_vortex_front_legs_geometry_axes\\n + near_field_moments_on_ring_vortex_left_legs_geometry_axes\\n + near_field_moments_on_ring_vortex_right_legs_geometry_axes\\n )\\n\\n # Initialize a variable to hold the global panel position.\\n global_panel_position = 0\\n\\n # Iterate through this solver's panels.\\n for panel in self.panels:\\n # Update the force and moment on this panel.\\n panel.near_field_force_geometry_axes = near_field_forces_geometry_axes[\\n global_panel_position, :\\n ]\\n panel.near_field_moment_geometry_axes = near_field_moments_geometry_axes[\\n global_panel_position, :\\n ]\\n\\n # Update the pressure on this panel.\\n panel.update_pressure()\\n\\n # Increment the global panel position.\\n global_panel_position += 1\\n\\n # Sum up the near field forces and moments on every panel to find the total\\n # force and moment on the geometry.\\n total_near_field_force_geometry_axes = np.sum(\\n near_field_forces_geometry_axes, axis=0\\n )\\n total_near_field_moment_geometry_axes = np.sum(\\n near_field_moments_geometry_axes, axis=0\\n )\\n\\n # Find the total near field force in wind axes from the rotation matrix and\\n # the total near field force in\\n # geometry axes.\\n self.airplane.total_near_field_force_wind_axes = (\\n np.transpose(\\n self.operating_point.calculate_rotation_matrix_wind_axes_to_geometry_axes()\\n )\\n @ total_near_field_force_geometry_axes\\n )\\n\\n # Find the total near field moment in wind axes from the rotation matrix and\\n # the total near field moment in\\n # geometry axes.\\n self.airplane.total_near_field_moment_wind_axes = (\\n np.transpose(\\n self.operating_point.calculate_rotation_matrix_wind_axes_to_geometry_axes()\\n )\\n @ total_near_field_moment_geometry_axes\\n )\\n\\n # Calculate the current_airplane's induced drag coefficient\\n induced_drag_coefficient = (\\n -self.airplane.total_near_field_force_wind_axes[0]\\n / self.operating_point.calculate_dynamic_pressure()\\n / self.airplane.s_ref\\n )\\n\\n # Calculate the current_airplane's side force coefficient.\\n side_force_coefficient = (\\n self.airplane.total_near_field_force_wind_axes[1]\\n / self.operating_point.calculate_dynamic_pressure()\\n / self.airplane.s_ref\\n )\\n\\n # Calculate the current_airplane's lift coefficient.\\n lift_coefficient = (\\n -self.airplane.total_near_field_force_wind_axes[2]\\n / self.operating_point.calculate_dynamic_pressure()\\n / self.airplane.s_ref\\n )\\n\\n # Calculate the current_airplane's rolling moment coefficient.\\n rolling_moment_coefficient = (\\n self.airplane.total_near_field_moment_wind_axes[0]\\n / self.operating_point.calculate_dynamic_pressure()\\n / self.airplane.s_ref\\n / self.airplane.b_ref\\n )\\n\\n # Calculate the current_airplane's pitching moment coefficient.\\n pitching_moment_coefficient = (\\n self.airplane.total_near_field_moment_wind_axes[1]\\n / self.operating_point.calculate_dynamic_pressure()\\n / self.airplane.s_ref\\n / self.airplane.c_ref\\n )\\n\\n # Calculate the current_airplane's yawing moment coefficient.\\n yawing_moment_coefficient = (\\n self.airplane.total_near_field_moment_wind_axes[2]\\n / self.operating_point.calculate_dynamic_pressure()\\n / self.airplane.s_ref\\n / self.airplane.b_ref\\n )\\n\\n self.airplane.total_near_field_force_coefficients_wind_axes = np.array(\\n [induced_drag_coefficient, side_force_coefficient, lift_coefficient]\\n )\\n self.airplane.total_near_field_moment_coefficients_wind_axes = np.array(\\n [\\n rolling_moment_coefficient,\\n pitching_moment_coefficient,\\n yawing_moment_coefficient,\\n ]\\n )\",\n \"title\": \"\"\n }\n]","isConversational":false},"negative_passages":{"kind":"list like","value":[{"docid":"e462cf8d09116c9d6ae444287f7f8205","score":"0.7421039","text":"def calculate_near_field_forces_and_moments(self):\n # Calculate the velocities induced at every panel's bound vortex center.\n induced_velocities = aerodynamics.collapsed_velocities_from_horseshoe_vortices(\n points=self.panel_bound_vortex_centers,\n back_right_vortex_vertices=self.panel_back_right_vortex_vertices,\n front_right_vortex_vertices=self.panel_front_right_vortex_vertices,\n front_left_vortex_vertices=self.panel_front_left_vortex_vertices,\n back_left_vortex_vertices=self.panel_back_left_vortex_vertices,\n strengths=self.vortex_strengths,\n )\n\n # Add the freestream velocity to the induced velocities to calculate the\n # total velocity at every panel's bound vortex center.\n total_velocities = induced_velocities + self.freestream_velocity\n\n # Calculate the near field force, in geometry axes, on each panel's bound\n # vortex.\n near_field_forces_geometry_axes = (\n self.operating_point.density\n * np.expand_dims(self.vortex_strengths, axis=1)\n * np.cross(total_velocities, self.panel_bound_vortex_vectors, axis=-1)\n )\n\n # Calculate the near field moments, in geometry axes, on each panel's bound\n # vortex.\n near_field_moments_geometry_axes = np.cross(\n self.panel_bound_vortex_centers - self.airplane.xyz_ref,\n near_field_forces_geometry_axes,\n axis=-1,\n )\n\n functions.process_steady_solver_forces(\n steady_solver=self,\n near_field_forces_geometry_axes=near_field_forces_geometry_axes,\n near_field_moments_geometry_axes=near_field_moments_geometry_axes,\n )","title":""},{"docid":"026f950d8d6b924e3aad4dfe849d37b5","score":"0.59816235","text":"def get_forces(self):\n return self.forces","title":""},{"docid":"ca24d27f1fbfb3bd7d06067dc607f37b","score":"0.59468377","text":"def __apply_forces(self):\n to_remove = []\n\n for [force_vector, location, end_time] in self.forces:\n self.entity_addresses['rhonrad'].apply_force_at_local_point(\n force_vector, location)\n if end_time != -1 and end_time >= self.current_time:\n to_remove = [force_vector, location, end_time]\n\n for force in to_remove:\n self.remove_force_on_wheel(force)","title":""},{"docid":"18ddf28a336c8a6ca51ab6a156ce6c43","score":"0.58091074","text":"def calculate_forces(box, coords, elems, bonds, exclude, ff):\n # initialize forces\n force = [[0.0, 0.0, 0.0] for i in range(len(coords))]\n\n start_time_bonded = time.time()\n bonded_energy, force = calculate_bonded_forces(box, coords, elems, bonds, force, ff[\"bond_length\"], ff[\"bond_force_const\"])\n print('time for bonded force: ', time.time() - start_time_bonded)\n\n start_time_nonbonded = time.time()\n nonbonded_energy, force = calculate_nonbonded_forces(box, coords, elems, exclude, force, ff[\"sigma\"], ff[\"epsilon\"], ff[\"charge\"])\n print('time for nonbonded force: ', time.time() - start_time_nonbonded)\n\n return bonded_energy + nonbonded_energy, force","title":""},{"docid":"64c1dc7f9531aec76e98b01db4dcca63","score":"0.57691175","text":"def _get_forces(charges):\r\n\r\n all_charges = np.concatenate((charges, -charges))\r\n all_charges = all_charges[:, None]\r\n r = charges - all_charges\r\n r_mag = np.sqrt((r*r).sum(-1))[:, :, None]\r\n with warnings.catch_warnings():\r\n warnings.simplefilter(\"ignore\")\r\n force = r / r_mag**3\r\n potential = 1. / r_mag\r\n\r\n d = np.arange(len(charges))\r\n force[d,d] = 0\r\n force = force.sum(0)\r\n force_r_comp = (charges*force).sum(-1)[:, None]\r\n f_theta = force - force_r_comp*charges\r\n potential[d,d] = 0\r\n potential = 2*potential.sum()\r\n return f_theta, potential","title":""},{"docid":"69baf116f14f94ae22789d9ae8875c1a","score":"0.5766241","text":"def get_positions_and_forces(self, filename=\"OUTCAR\", lines=None, n_atoms=None):\n if n_atoms is None:\n n_atoms = self.get_number_of_atoms(filename=filename, lines=lines)\n trigger_indices, lines = _get_trigger(\n lines=lines, filename=filename, trigger=\"TOTAL-FORCE (eV/Angst)\"\n )\n return self._get_positions_and_forces_parser(\n lines=lines,\n trigger_indices=trigger_indices,\n n_atoms=n_atoms,\n pos_flag=True,\n force_flag=True,\n )","title":""},{"docid":"ae313c98182a3b6a24243b9bef4082b6","score":"0.57572204","text":"def get_forces(self, filename=\"OUTCAR\", lines=None, n_atoms=None):\n if n_atoms is None:\n n_atoms = self.get_number_of_atoms(filename=filename, lines=lines)\n trigger_indices, lines = _get_trigger(\n lines=lines, filename=filename, trigger=\"TOTAL-FORCE (eV/Angst)\"\n )\n return self._get_positions_and_forces_parser(\n lines=lines,\n trigger_indices=trigger_indices,\n n_atoms=n_atoms,\n pos_flag=False,\n force_flag=True,\n )","title":""},{"docid":"ecc866726d0346e93a418d0c937e9a47","score":"0.56925327","text":"def calculate_force(self, pos):\n force = np.array((0., 0.))\n r = self.par_dic[\"resolution\"]\n for name, (objects, calculator) in self.calculator_dic.iteritems():\n if name == \"source\": # exclude the force from other source\n continue\n for obj_par in objects:\n force += calculator.get_force(np.array(obj_par)/r, np.array(pos + [self.par_dic[\"ball_size\"]])/r)\n return force","title":""},{"docid":"b4ba8b1acdbef9ea2183e164eff2bd13","score":"0.56105226","text":"def calc_force(self):\n\n total_force = 0.\n\n # Loop through elements on the squirmer surface and compute the\n # hydrodynamic stresses on each one\n for elem_S in self.mesh.elems_in_region(\"sphere\"):\n # get the \"bulk\" element adjacent to the surface element.\n _S, elem_V = elem_S.adj_map['*']\n # get the element mapping\n x_cyl = elem_V.get_mapping()\n jac = x_cyl.jacobian()\n detJ, invJ = det_inv_2x2(jac)\n\n # coordinates in cylindrical and polar form\n x_cyl_S = elem_S.get_mapping()\n # let *_S denote quantities defined at the element surface only\n # theta = np.arctan2(x_cyl_S[0], x_cyl_S[1]) # polar angle\n sin_th = x_cyl_S[0] # here, r = 1\n sin2_th = sin_th**2\n cos_th = x_cyl_S[1]\n\n # surface slip velocity\n slip_profile = self.phys_params[\"slip_profile\"]\n vslip = slip_profile(sin_th, cos_th)\n\n # solution for vorticity field\n vort_gl = self.soln_vec[1::2]\n vort = elem_V.get_coeffs(vort_gl)\n\n invJ_S = invJ.get_boundary(_S)\n # compute d{vorticity}/d(xi, eta, ...)\n dw_du_S = vort.jacobian().get_boundary(_S)\n # d(rho, z)/d(xi, eta, ...)\n drhoz_dr_S = x_cyl.get_boundary(_S)\n # d{vorticity}/dr at squirmer surface\n dw_dr_S = np.einsum('im,ijm,jm->m',\n dw_du_S, invJ_S, drhoz_dr_S)\n\n # compute stresses\n vort_S = vort.get_boundary(_S)\n n_rey = self.phys_params[\"N_Re\"]\n bernouli_stress = np.pi * n_rey * vslip**2 * sin_th * cos_th\n w_asym_stress = np.pi * (dw_dr_S + vort_S) * sin2_th\n pressure_stress = bernouli_stress + w_asym_stress\n viscous_stress = -2*np.pi * vort_S * sin2_th\n total_stress = pressure_stress + viscous_stress\n\n # differential arc length\n t_vec = x_cyl_S.jacobian() # tangent vector\n d_arc = np.sqrt(t_vec[0]**2 + t_vec[1]**2)\n # compute integrands\n total_force += bs.CoeffArray.integrate(total_stress * d_arc)\n\n return total_force","title":""},{"docid":"ac5eeadadc70c23a9ea810375e1ab968","score":"0.55544335","text":"def _forces_moments(self, delta):\n\n # extract the states\n pn = self._state.item(0)\n pe = self._state.item(1)\n pd = self._state.item(2)\n u = self._state.item(3)\n v = self._state.item(4)\n w = self._state.item(5)\n e0 = self._state.item(6)\n e1 = self._state.item(7)\n e2 = self._state.item(8)\n e3 = self._state.item(9)\n p = self._state.item(10)\n q = self._state.item(11)\n r = self._state.item(12)\n\n Va = self._Va\n # if Va == 0:\n # Va = 0.000001\n alpha = self._alpha\n beta = self._beta\n\n delta_a = delta.item(0)\n delta_e = delta.item(1)\n delta_t = delta.item(2)\n delta_r = delta.item(3)\n\n phi, theta, psi = Quaternion2Euler(e0, e1, e2, e3)\n\n # gravity\n f_g = MAV.mass * MAV.gravity * np.array(\n [[-np.sin(theta)], [np.cos(theta) * np.sin(phi)], [np.cos(theta) * np.cos(phi)]])\n self.f_g = f_g\n\n # Motor Forces\n V_in = MAV.V_max * delta_t\n # Quadratic formula to solve for motor speed\n a = MAV.rho * np.power(MAV.D_prop, 5) * MAV.C_Q0 / ((2. * np.pi) ** 2)\n b = (MAV.rho * np.power(MAV.D_prop, 4) / (2. * np.pi)) ** 2 * MAV.C_Q1 * Va + MAV.KQ * MAV.KQ / MAV.R_motor\n c = MAV.rho * np.power(MAV.D_prop, 3) * MAV.C_Q2 * Va ** 2 - (MAV.KQ / MAV.R_motor) * V_in + MAV.KQ * MAV.i0\n # Consider only positive root\n Omega_op = (-b + np.sqrt(b ** 2 - 4 * a * c)) / (2. * a)\n # add thrust and torque due to propeller\n T_p = (MAV.rho * np.power(MAV.D_prop, 4) * MAV.C_T0 / (4 * np.pi ** 2)) * Omega_op ** 2 + (\n MAV.rho * np.power(MAV.D_prop, 3) * MAV.C_T1 * Va / (2 * np.pi)) * Omega_op + (\n MAV.rho * MAV.D_prop ** 2 * MAV.C_T2 * Va ** 2)\n Q_p = (MAV.rho * MAV.D_prop ** 5 * MAV.C_Q0 / (4 * np.pi ** 2)) * Omega_op ** 2 + (\n MAV.rho * MAV.D_prop ** 4 * MAV.C_Q1 * Va / (2 * np.pi)) + (\n MAV.rho * MAV.D_prop ** 3 * MAV.C_Q2 * Va ** 2)\n\n self.thrust = T_p\n\n # Use the non-linear system to compute lift and drag\n sigma = (1 + np.exp(-MAV.M * (alpha - MAV.alpha0)) + np.exp(MAV.M * (alpha + MAV.alpha0))) / (\n (1 + np.exp(-MAV.M * (alpha - MAV.alpha0))) * (1 + np.exp(MAV.M * (alpha + MAV.alpha0))))\n\n C_L = (1 - sigma) * (MAV.C_L_0 + MAV.C_L_alpha * alpha) + sigma * (\n 2 * np.sign(alpha) * np.sin(alpha) ** 2 * np.cos(alpha))\n C_D = MAV.C_D_p + (MAV.C_L_0 + MAV.C_L_alpha * alpha) ** 2 / (np.pi * MAV.e * MAV.AR)\n\n # 4.25\n C_chi = -C_D * np.cos(alpha) + C_L * np.sin(alpha)\n C_chi_q = -MAV.C_D_q * np.cos(alpha) + MAV.C_L_q * np.sin(alpha)\n C_chi_delta_e = -MAV.C_D_delta_e * np.cos(alpha) + MAV.C_L_delta_e * np.sin(alpha)\n C_Z = -C_D * np.sin(alpha) - C_L * np.cos(alpha)\n C_Z_q = -MAV.C_D_q * np.sin(alpha) - MAV.C_L_q * np.cos(alpha)\n C_Z_delta_e = -MAV.C_D_delta_e * np.sin(alpha) - MAV.C_L_delta_e * np.cos(alpha)\n\n forces = f_g + np.array([[T_p], [0], [0]]) + 0.5 * MAV.rho * Va ** 2 * MAV.S_wing * np.array(\n [[C_chi + C_chi_q * MAV.c / (2 * Va) * q],\n [MAV.C_Y_0 + MAV.C_Y_beta * beta + MAV.C_Y_p * b / (2 * Va) * p + MAV.C_Y_r * b / (2 * Va) * r],\n [C_Z + C_Z_q * MAV.c / (2 * Va) * q]]) + 0.5 * MAV.rho * Va ** 2 * MAV.S_wing * np.array(\n [[C_chi_delta_e * delta_e], [MAV.C_Y_delta_a * delta_a + MAV.C_Y_delta_r * delta_r],\n [C_Z_delta_e * delta_e]])\n\n fx = forces.item(0)\n fy = forces.item(1)\n fz = forces.item(2)\n\n moments = 0.5 * MAV.rho * Va ** 2 * MAV.S_wing * np.array([[MAV.b * (\n MAV.C_ell_0 + MAV.C_ell_beta * beta + MAV.C_ell_p * b / (2 * Va) * p + MAV.C_ell_r * b / (\n 2 * Va) * r)], [MAV.c * (MAV.C_m_0 + MAV.C_m_alpha * alpha + MAV.C_m_q * MAV.c / (2 * Va) * q)],\n [MAV.b * (\n MAV.C_n_0 + MAV.C_n_beta * beta + MAV.C_n_p * b / (\n 2 * Va) * p + MAV.C_n_r * b / (\n 2 * Va) * r)]]) + 0.5 * MAV.rho * Va ** 2 * MAV.S_wing * np.array(\n [[MAV.b * (MAV.C_ell_delta_a * delta_a + MAV.C_ell_delta_r * delta_r)],\n [MAV.c * (MAV.C_m_delta_e * delta_e)],\n [MAV.b * (MAV.C_n_delta_a * delta_a + MAV.C_n_delta_r * delta_r)]]) + np.array([[Q_p], [0], [0]])\n\n l = moments.item(0)\n m = moments.item(1)\n n = moments.item(2)\n\n Mx = l\n My = m\n Mz = n\n\n forcesandmoments = np.array([[fx, fy, fz, Mx, My, Mz]])\n return forcesandmoments","title":""},{"docid":"aa81ae68f967e6120eab9e0fd6a8a836","score":"0.55542964","text":"def _get_energies_forces(self, simulator):\n energies = (\n simulator.system.properties[self.energy_handle].float()\n * self.energy_conversion\n )\n forces = simulator.system.forces\n return energies, forces","title":""},{"docid":"ab68246bfcb4081433714fa12180f2cc","score":"0.5477652","text":"def getForce(obj1, obj2, eps=1e-12):\n\n # Calculate the distance between two positions (\"eps\" saves the day when 2 objects are on the exactly same \n # spot)\n dist = np.linalg.norm(obj1.pos - obj2.pos) + eps\n\n # Calculate the force\n force_vect = -GRAV_CONSTANT*obj1.m*obj2.m * (obj1.pos - obj2.pos)/dist**3\n\n return force_vect","title":""},{"docid":"147f343d5f9d786708249be3471b6193","score":"0.54687405","text":"def analyze(self, event):\n \n triggerObjects = Collection(event, \"TrigObj\")\n\n tightMuons = self.tightMuonsCollection(event)\n tightElectrons = self.tightElectronsCollection(event)\n\n looseMuons = self.looseMuonCollection(event)\n looseElectrons = self.looseElectronCollection(event)\n \n cpfCandidates = Collection(event,\"cpf\")\n\n\n for lepton in tightMuons+looseMuons:\n lepton.isMuon = 1\n lepton.isElectron = 0\n lepton.relIso = lepton.pfRelIso04_all\n lepton.cpf_match = None\n lepton.cpf_match_dR = 100.\n for cpfCandidate in cpfCandidates:\n if cpfCandidate.matchedMuon>0.5:\n pt = math.sqrt(cpfCandidate.px**2+cpfCandidate.py**2)\n p = math.sqrt(cpfCandidate.px**2+cpfCandidate.py**2+cpfCandidate.pz**2)\n eta = math.atanh(cpfCandidate.pz/p)\n phi = math.atan2(cpfCandidate.py,cpfCandidate.px)\n dR = math.sqrt((eta-lepton.eta)**2+deltaPhi(phi,lepton.phi)**2)\n if math.fabs(pt/lepton.pt-1)<0.1 and dR<0.02 and dR<lepton.cpf_match_dR:\n lepton.cpf_match = cpfCandidate\n lepton.cpf_match_dR = dR\n\n for lepton in tightElectrons+looseElectrons:\n lepton.isMuon = 0\n lepton.isElectron = 1\n lepton.relIso = lepton.pfRelIso03_all\n lepton.cpf_match = None\n lepton.cpf_match_dR = 100.\n \n for cpfCandidate in cpfCandidates:\n if cpfCandidate.matchedElectron>0.5:\n pt = math.sqrt(cpfCandidate.px**2+cpfCandidate.py**2)\n p = math.sqrt(cpfCandidate.px**2+cpfCandidate.py**2+cpfCandidate.pz**2)\n eta = math.atanh(cpfCandidate.pz/p)\n phi = math.atan2(cpfCandidate.py,cpfCandidate.px)\n dR = math.sqrt((eta-lepton.eta)**2+deltaPhi(phi,lepton.phi)**2)\n if math.fabs(pt/lepton.pt-1)<0.1 and dR<0.02 and dR<lepton.cpf_match_dR:\n lepton.cpf_match = cpfCandidate\n lepton.cpf_match_dR = dR\n\n tightLeptons = []\n looseLeptons = []\n\n tightLeptons = tightMuons+tightElectrons\n looseLeptons = looseMuons+looseElectrons\n\n tightLeptons = sorted(tightLeptons, key=lambda x: x.pt, reverse=True)\n \n # select leading only, move subleading to \"loose\"\n looseLeptons.extend(tightLeptons[1:])\n tightLeptons = tightLeptons[:1]\n looseLeptons = sorted(looseLeptons, key=lambda x: x.pt, reverse=True)\n\n muonmuon = 0\n electronelectron = 0\n muonelectron = 0\n electronmuon = 0\n muonjets = 0\n electronjets = 0\n\n ## flavour categorisation :\n if len(tightLeptons) > 0 and len(looseLeptons) > 0:\n # Ensure pt(l1) > pt(l2)\n if tightLeptons[0].pt < looseLeptons[0].pt:\n return False\n \n if tightLeptons[0].isMuon and looseLeptons[0].isMuon:\n muonmuon = 1\n\n elif tightLeptons[0].isElectron and looseLeptons[0].isElectron:\n electronelectron= 1\n\n elif tightLeptons[0].isMuon and looseLeptons[0].isElectron:\n muonelectron = 1\n \n elif tightLeptons[0].isElectron and looseLeptons[0].isMuon:\n electronmuon = 1\n\n elif len(tightLeptons) > 0:\n if tightLeptons[0].isMuon:\n muonjets = 1\n elif tightLeptons[0].isElectron:\n electronjets = 1\n\n for lepton in tightLeptons+looseLeptons:\n if self.triggerMatched(lepton, triggerObjects):\n setattr(lepton,\"isTriggerMatched\",1)\n else:\n setattr(lepton,\"isTriggerMatched\",0)\n\n for lepton in tightLeptons+looseLeptons:\n if lepton.dxyErr < 1e-6:\n setattr(lepton, \"dxysig\", -1.)\n else:\n setattr(lepton, \"dxysig\", math.fabs(lepton.dxy)/math.fabs(lepton.dxyErr))\n\n if lepton.dzErr < 1e-6:\n setattr(lepton, \"dzsig\", -1.)\n else:\n setattr(lepton, \"dzsig\", math.fabs(lepton.dz)/math.fabs(lepton.dzErr))\n\n\n lepton_id = -1\n lepton_ptErr = -100.\n if len(looseLeptons) > 0:\n if looseLeptons[0].isMuon: \n\t\tlepton_id = looseLeptons[0].tightId\n\t\tlepton_ptErr = looseLeptons[0].ptErr\n\t\n if looseLeptons[0].isElectron: \n lepton_id = looseLeptons[0].mvaFall17V1Iso_WP90\n\t\tlepton_ptErr = looseLeptons[0].energyErr\n\n self.out.fillBranch(\"nleading\"+self.outputName, len(tightLeptons))\n self.out.fillBranch(\"nsubleading\"+self.outputName, len(looseLeptons))\n\n for variable in self.storeLeadingKinematics:\n self.out.fillBranch(\"leading\"+self.outputName+\"_\"+variable,map(lambda lepton: getattr(lepton,variable),tightLeptons))\n for variable in self.storeSubleadingKinematics:\n self.out.fillBranch(\"subleading\"+self.outputName+\"_\"+variable,map(lambda lepton: getattr(lepton,variable),looseLeptons))\n\n '''\n self.out.fillBranch(\"subleading\"+self.outputName+\"_cpfMatch\", [1 if lepton.cpf_match!=None else 0 for lepton in looseLeptons])\n for cpfFeature in cpfFeatures:\n arr = [getattr(lepton.cpf_match,cpfFeature) if lepton.cpf_match!=None else 0 for lepton in looseLeptons]\n self.out.fillBranch(\"subleading\"+self.outputName+\"_\"+cpfFeature,arr)\n '''\n self.out.fillBranch(self.outputName+\"_muonmuon\", muonmuon)\n self.out.fillBranch(self.outputName+\"_electronelectron\", electronelectron)\n self.out.fillBranch(self.outputName+\"_muonelectron\", muonelectron)\n self.out.fillBranch(self.outputName+\"_electronmuon\", electronmuon)\n self.out.fillBranch(self.outputName+\"_muonjets\", muonjets)\n self.out.fillBranch(self.outputName+\"_electronjets\", electronjets)\n self.out.fillBranch(self.outputName+\"_subLeptonTightId\", lepton_id)\n self.out.fillBranch(self.outputName+\"_subLeptonPtErr\", lepton_ptErr) \n setattr(event, \"leading\"+self.outputName, tightLeptons)\n setattr(event, \"subleading\"+self.outputName, looseLeptons)\n\n return True","title":""},{"docid":"1bf7b221bedf2d46b9dac9ba2e05a233","score":"0.54687405","text":"def analyze(self, event):\n \n triggerObjects = Collection(event, \"TrigObj\")\n\n tightMuons = self.tightMuonsCollection(event)\n tightElectrons = self.tightElectronsCollection(event)\n\n looseMuons = self.looseMuonCollection(event)\n looseElectrons = self.looseElectronCollection(event)\n \n cpfCandidates = Collection(event,\"cpf\")\n\n\n for lepton in tightMuons+looseMuons:\n lepton.isMuon = 1\n lepton.isElectron = 0\n lepton.relIso = lepton.pfRelIso04_all\n lepton.cpf_match = None\n lepton.cpf_match_dR = 100.\n for cpfCandidate in cpfCandidates:\n if cpfCandidate.matchedMuon>0.5:\n pt = math.sqrt(cpfCandidate.px**2+cpfCandidate.py**2)\n p = math.sqrt(cpfCandidate.px**2+cpfCandidate.py**2+cpfCandidate.pz**2)\n eta = math.atanh(cpfCandidate.pz/p)\n phi = math.atan2(cpfCandidate.py,cpfCandidate.px)\n dR = math.sqrt((eta-lepton.eta)**2+deltaPhi(phi,lepton.phi)**2)\n if math.fabs(pt/lepton.pt-1)<0.1 and dR<0.02 and dR<lepton.cpf_match_dR:\n lepton.cpf_match = cpfCandidate\n lepton.cpf_match_dR = dR\n\n for lepton in tightElectrons+looseElectrons:\n lepton.isMuon = 0\n lepton.isElectron = 1\n lepton.relIso = lepton.pfRelIso03_all\n lepton.cpf_match = None\n lepton.cpf_match_dR = 100.\n \n for cpfCandidate in cpfCandidates:\n if cpfCandidate.matchedElectron>0.5:\n pt = math.sqrt(cpfCandidate.px**2+cpfCandidate.py**2)\n p = math.sqrt(cpfCandidate.px**2+cpfCandidate.py**2+cpfCandidate.pz**2)\n eta = math.atanh(cpfCandidate.pz/p)\n phi = math.atan2(cpfCandidate.py,cpfCandidate.px)\n dR = math.sqrt((eta-lepton.eta)**2+deltaPhi(phi,lepton.phi)**2)\n if math.fabs(pt/lepton.pt-1)<0.1 and dR<0.02 and dR<lepton.cpf_match_dR:\n lepton.cpf_match = cpfCandidate\n lepton.cpf_match_dR = dR\n\n tightLeptons = []\n looseLeptons = []\n\n tightLeptons = tightMuons+tightElectrons\n looseLeptons = looseMuons+looseElectrons\n\n tightLeptons = sorted(tightLeptons, key=lambda x: x.pt, reverse=True)\n\n if len(tightLeptons)<2:\n print(\"Need at least two tight leptons\")\n return False\n\n # if len(tightLeptons+looseLeptons)<3:\n # print(\"Need at least three leptons\")\n # return False\n\n # select leading only, move subleading to \"loose\"\n looseLeptons.extend(tightLeptons[2:])\n tightLeptons = tightLeptons[:2]\n if len(looseLeptons)>0:\n looseLeptons = sorted(looseLeptons, key=lambda x: x.pt, reverse=True)\n\n muonmuonelectron = 0\n electronelectronelectron = 0\n\n ## flavour categorisation :\n if len(tightLeptons) > 0 and len(looseLeptons) > 0:\n # Ensure pt(l1) > pt(l3) and pt(l2) > pt(l3)\n if (tightLeptons[0].pt < looseLeptons[0].pt) or (tightLeptons[1].pt < looseLeptons[0].pt):\n return False\n \n if tightLeptons[0].isMuon and tightLeptons[1].isMuon and looseLeptons[0].isElectron:\n muonmuonelectron = 1\n\n elif tightLeptons[0].isElectron and tightLeptons[1].isElectron and looseLeptons[0].isElectron:\n electronelectronelectron = 1\n\n for lepton in tightLeptons+looseLeptons:\n if self.triggerMatched(lepton, triggerObjects):\n setattr(lepton,\"isTriggerMatched\",1)\n else:\n setattr(lepton,\"isTriggerMatched\",0)\n\n for lepton in tightLeptons+looseLeptons:\n if lepton.dxyErr < 1e-6:\n setattr(lepton, \"dxysig\", -1.)\n else:\n setattr(lepton, \"dxysig\", math.fabs(lepton.dxy)/math.fabs(lepton.dxyErr))\n\n if lepton.dzErr < 1e-6:\n setattr(lepton, \"dzsig\", -1.)\n else:\n setattr(lepton, \"dzsig\", math.fabs(lepton.dz)/math.fabs(lepton.dzErr))\n\n # self.out.fillBranch(\"nleading\"+self.outputName, int(float(len(tightLeptons))/2.))\n # self.out.fillBranch(\"nsubleading\"+self.outputName, int(float(len(tightLeptons))/2.))\n self.out.fillBranch(\"nleading\"+self.outputName, 1)\n self.out.fillBranch(\"nsubleading\"+self.outputName, 1)\n self.out.fillBranch(\"ntrailing\"+self.outputName, len(looseLeptons))\n\n for variable in self.storeLeadingKinematics:\n #self.out.fillBranch(\"leading\"+self.outputName+\"_\"+variable,map(lambda lepton: getattr(lepton,variable),tightLeptons))\n self.out.fillBranch(\"leading\"+self.outputName+\"_\"+variable,getattr(tightLeptons[0], variable))\n for variable in self.storeSubleadingKinematics:\n #self.out.fillBranch(\"subleading\"+self.outputName+\"_\"+variable,map(lambda lepton: getattr(lepton,variable),tightLeptons))\n self.out.fillBranch(\"subleading\"+self.outputName+\"_\"+variable,getattr(tightLeptons[1], variable))\n if len(looseLeptons)>0:\n for variable in self.storeTrailingKinematics:\n #self.out.fillBranch(\"trailing\"+self.outputName+\"_\"+variable,map(lambda lepton: getattr(lepton,variable),looseLeptons))\n self.out.fillBranch(\"trailing\"+self.outputName+\"_\"+variable,getattr(looseLeptons[0], variable))\n else:\n for variable in self.storeTrailingKinematics:\n self.out.fillBranch(\"trailing\"+self.outputName+\"_\"+variable, -99)\n\n\n self.out.fillBranch(self.outputName+\"_muonmuonelectron\", muonmuonelectron)\n self.out.fillBranch(self.outputName+\"_electronelectronelectron\", electronelectronelectron)\n\n if len(looseLeptons)>0:\n trilepton = ROOT.TLorentzVector()\n trilepton = tightLeptons[0].p4()+tightLeptons[1].p4()+looseLeptons[0].p4()\n trilepton_charge = tightLeptons[0].charge*tightLeptons[1].charge*looseLeptons[0].charge\n\n self.out.fillBranch(\"trilepton_mass\", trilepton.M())\n self.out.fillBranch(\"trilepton_pt\", trilepton.Pt())\n self.out.fillBranch(\"trilepton_eta\", trilepton.Eta())\n self.out.fillBranch(\"trilepton_charge\", trilepton_charge)\n self.out.fillBranch(self.outputName+\"_dPhi_l1l3\", math.fabs(deltaPhi(tightLeptons[0],looseLeptons[0])))\n self.out.fillBranch(self.outputName+\"_dEta_l1l3\", math.fabs(tightLeptons[0].eta-looseLeptons[0].eta))\n self.out.fillBranch(self.outputName+\"_dR_l1l3\", deltaR(tightLeptons[0],looseLeptons[0]))\n self.out.fillBranch(self.outputName+\"_dPhi_l2l3\", math.fabs(deltaPhi(tightLeptons[1],looseLeptons[0])))\n self.out.fillBranch(self.outputName+\"_dEta_l2l3\", math.fabs(tightLeptons[1].eta-looseLeptons[0].eta))\n self.out.fillBranch(self.outputName+\"_dR_l2l3\", deltaR(tightLeptons[1],looseLeptons[0]))\n else:\n self.out.fillBranch(\"trilepton_mass\", -99)\n self.out.fillBranch(\"trilepton_pt\", -99)\n self.out.fillBranch(\"trilepton_eta\", -99)\n self.out.fillBranch(\"trilepton_charge\", -99)\n self.out.fillBranch(self.outputName+\"_dPhi_l1l3\", -99)\n self.out.fillBranch(self.outputName+\"_dEta_l1l3\", -99)\n self.out.fillBranch(self.outputName+\"_dR_l1l3\", -99)\n self.out.fillBranch(self.outputName+\"_dPhi_l2l3\", -99)\n self.out.fillBranch(self.outputName+\"_dEta_l2l3\", -99)\n self.out.fillBranch(self.outputName+\"_dR_l2l3\", -99)\n\n setattr(event, \"leading\"+self.outputName, tightLeptons[0])\n setattr(event, \"subleading\"+self.outputName, tightLeptons[1])\n if len(looseLeptons)>0:\n setattr(event, \"trailing\"+self.outputName, looseLeptons[0])\n else:\n setattr(event, \"trailing\"+self.outputName, None)\n\n return True","title":""},{"docid":"7a6672f99c4c403fd07c8a1b71772a39","score":"0.5468047","text":"def read_forces(self, atoms):\n forces = self.read_output(self.log, 'forces')\n return forces","title":""},{"docid":"63ac0aa927c1df4fd13b305519600b76","score":"0.5459774","text":"def do_near(self, data):\n near_me.main(data)","title":""},{"docid":"fdb43b064434bdf6cf68d48f2638552b","score":"0.54474926","text":"def get_forces(self, robot_cell, goal_point, robot_map):\n forces = {'gen_force': None, 'attr_force': None, 'rep_force': None}\n forces['attr_force'] = self.__get_attractive_force(robot_cell, goal_point)\n forces['rep_force'] = self.__get_repulsive_force(robot_cell, robot_map)\n forces['gen_force'] = {'x': forces['attr_force']['x'] + forces['rep_force']['x'], \n 'y': forces['attr_force']['y'] + forces['rep_force']['y']}\n return forces","title":""},{"docid":"c38d92c6157c04d949c44a5a46c5c9ab","score":"0.5446808","text":"def get_forces():\n forcefile = open('cfd/postProcessing/forceCoeffs1/0/forceCoeffs.dat')\n for line in forcefile:\n if not line.startswith('#'):\n line = line.strip()\n info = line.split('\\t')\n info = [float(strval) for strval in info]\n forces = {'time':info[0], \n 'pitchmoment':info[1],\n 'drag':info[2],\n 'lift':info[3], \n 'frontlift':info[4], \n 'rearlift':info[5]}\n return forces","title":""},{"docid":"5cdca46a23b0c9cb80dbabb26ef1265c","score":"0.5446389","text":"def _calc_forces(self):\n df, learn, sampl_row, model_mean, fields, max_row_used = self.df, self.learn, self.sampl_row, self.model_mean, self.fields, self.max_row_used\n\n forces = OrderedDict()\n # build big table with all the variants to check all the data in one run\n huge_df = pd.DataFrame()\n cur_dfs = []\n for field in fields:\n cur_df = self._shuffle_cols(sampl_col=field)\n cur_df['group'] = self._list_to_key(field)\n cur_dfs.append(cur_df)\n\n huge_df = pd.concat(cur_dfs)\n del cur_dfs\n\n # predict on it\n huge_df['preds'] = self._predict_df(df=huge_df)\n # predict actual data (just predict sampl_row)\n actual = self._predict_row(row=sampl_row)\n\n # divide back by fields\n for field in fields:\n cur_df = huge_df.query(f\"group == '{self._list_to_key(field)}'\")\n # calculate force\n force = float(actual - cur_df['preds'].mean())\n key = f\"{field} ({sampl_row[field]})\" if isinstance(field, str) else ', '.join(\n f\"{e} ({sampl_row[e]})\" for e in field)\n forces[key] = force\n\n self.batch_forces = OrderedDict(sorted(forces.items(), key=lambda kv: abs(kv[1]), reverse=True))","title":""},{"docid":"deb95ae6d18f4b784660f430ce3de5dc","score":"0.5437343","text":"def dynamics(x0, v0, dt, tmax=10):\n \n N = len(x0) #number of objects\n nsteps = int(tmax/dt)\n x = np.zeros((nsteps/10,N,3))\n dx = np.copy(x0)\n v = np.copy(v0)\n vhalf = np.zeros((N,3))\n Ut = np.zeros(nsteps)\n kinetic = np.zeros(nsteps)\n totalE = np.zeros(nsteps)\n \n Ft, Ut[0] = gravity(dx)\n \n for i in range(nsteps):\n for j in range(N):\n vhalf[j] = v[j] + 0.5 * dt * Ft[j] / mass[j]\n dx[j] += dt * vhalf[j]\n Ft, Ut[i]= gravity(dx)\n for j in range(N):\n v[j] = vhalf[j] + 0.5 * dt * Ft[j] / mass[j]\n kinetic[i] += 0.5 * mass[j] * np.sum(v[j]**2) \n if i%10 == 0:\n x[int(i/10)] = dx\n#<<<<<<< HEAD\n \n #get position of earth and rama and determine distance between the two.\n #----------------------------------------------------------------------\n earth_pos = np.zeros(len(x[:]))\n rama_pos = np.zeros_like(earth_pos)\n dist = np.zeros_like(earth_pos) \n dist = np.abs(earth_pos - rama_pos)\n\n earth_pos = x[:,3]\n rama_pos = x[:,9]\n #distance between the two\n dist = np.abs(earth_pos - rama_pos)\n #array to store the closer values\n close = np.zeros((nsteps,), dtype=np.float64)\n dist_mag = np.zeros((len(earth_pos)), dtype=np.float64)\n for i in range(len(earth_pos)):\n dist_mag[i] = np.linalg.norm(dist[i])\n if dist_mag[i] < 0.2:\n print(\"Iteration:\",i,\",\",\n \"Rama distance from Earth (au):\", dist_mag[i])\n\n \n \n#=======\n \n#>>>>>>> 1f69f9476821ec1398d4c3b4e304f2e9563594e2\n return x, v, kinetic, Ut, totalE","title":""},{"docid":"8e583545061e23ae2481cca942044382","score":"0.5342062","text":"def find_folowing_numerical(self, delta_t, c_arr_d, c_arr_g, theta_direction):\n\t\tt = time.time()\n\t\terreur = \"ABS(1000*(%s-delta_t)) + ABS(57*(%s-theta_direction)) + ABS(1000*(%s-c_arr_d)) + ABS(1000*(%s-c_arr_g)) + ABS(100*(%s-vx_in)) + ABS(100*(%s-vy_in)) + ABS(573*(%s-w_in))\" % (\n\t\t\tdelta_t, theta_direction, c_arr_d, c_arr_g, self.vitesses_translation[-1][\"x\"], self.vitesses_translation[-1][\"y\"], self.vitesses_angulaire[-1])\n\t\tself.cursor.execute(\"\"\"\n\t\t\t\t\t\tSELECT vx_out, vy_out, w_out, x_out, y_out, angle, %s\n\t\t\t\t\t\tFROM simulation\n\t\t\t\t\t\tORDER BY (%s)\n\t\t\t\t\t\tLIMIT 1\"\"\" % (erreur, erreur))\n\t\tresultat = list(self.cursor)\n\t\tif len(resultat):\n\t\t\tr = resultat[0]\n\t\t\tif resultat[0][6] < 0.2:\n\t\t\t\tresultat_num = {\"vx\":r[0], \"vy\":r[1], \"omega_z\":r[2],\n\t\t\t\t\t\"x\":r[3]+self.positions[-1][\"x\"], \"y\":r[4]+self.positions[-1][\"y\"], \"angle\":r[5]+self.angles[-1]}\n\t\t\t\treturn resultat_num\n\n\t\tdt = min(2e-3, delta_t) # on ne fait pas des pas de plus de 2 ms\n\t\tdt = delta_t/float(int(delta_t/dt))\n\t\tresultat_num = {\"vx\":self.vitesses_translation[-1][\"x\"], \"vy\":self.vitesses_translation[-1][\"y\"], \"omega_z\":self.vitesses_angulaire[-1],\n\t\t\t\t\t\t\"x\":self.positions[-1][\"x\"], \"y\":self.positions[-1][\"y\"], \"angle\":self.angles[-1]}\n\t\tangle_relatif = 0.0\n\t\tx_relatif = 0.0\n\t\ty_relatif = 0.0\n\t\tresultat_form = {}\n\t\tresultat_form[\"vx\"] = self.U_vx.subs({\"dt\":dt, \"c_arr_d\":c_arr_d, \"c_arr_g\":c_arr_g, \"theta_direction\":theta_direction})\n\t\tresultat_form[\"vy\"] = self.U_vy.subs({\"dt\":dt, \"c_arr_d\":c_arr_d, \"c_arr_g\":c_arr_g, \"theta_direction\":theta_direction})\n\t\tresultat_form[\"omega_z\"] = self.U_omega_z.subs({\"dt\":dt, \"c_arr_d\":c_arr_d, \"c_arr_g\":c_arr_g, \"theta_direction\":theta_direction})\n\t\tfor i in range(int(delta_t/dt)):\n\t\t\tresultat_num = {\n\t\t\t\t\"vx\":resultat_form[\"vx\"].subs({\"vx\":resultat_num[\"vx\"], \"vy\":resultat_num[\"vy\"], \"omega_z\":resultat_num[\"omega_z\"]}),\n\t\t\t\t\"vy\":resultat_form[\"vy\"].subs({\"vx\":resultat_num[\"vx\"], \"vy\":resultat_num[\"vy\"], \"omega_z\":resultat_num[\"omega_z\"]}),\n\t\t\t\t\"omega_z\":resultat_form[\"omega_z\"].subs({\"vx\":resultat_num[\"vx\"], \"vy\":resultat_num[\"vy\"], \"omega_z\":resultat_num[\"omega_z\"]}),\n\t\t\t}\n\t\t\tangle_relatif += dt*resultat_num[\"omega_z\"]\n\t\t\tx_relatif += dt*(resultat_num[\"vx\"]*math.cos(self.angles[-1]+angle_relatif) - resultat_num[\"vy\"]*math.sin(self.angles[-1]+angle_relatif))\n\t\t\ty_relatif += dt*(resultat_num[\"vx\"]*math.sin(self.angles[-1]+angle_relatif) + resultat_num[\"vy\"]*math.cos(self.angles[-1]+angle_relatif))\n\n\n\t\tself.cursor.execute(\"\"\"INSERT INTO simulation\n\t\t\t(delta_t, theta_direction, c_arr_d, c_arr_g, vx_in, vy_in, w_in, vx_out, vy_out, w_out, x_out, y_out, angle)\n\t\t\tVALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)\"\"\" %\n\t\t\t(delta_t, theta_direction, c_arr_d, c_arr_g, self.vitesses_translation[-1][\"x\"], self.vitesses_translation[-1][\"y\"], self.vitesses_angulaire[-1],\n\t\t\t\tresultat_num[\"vx\"], resultat_num[\"vy\"], resultat_num[\"omega_z\"], x_relatif, y_relatif, angle_relatif))\n\t\tself.bdd.commit()\n\n\t\tresultat_num[\"x\"] = self.positions[-1][\"x\"] + x_relatif\n\t\tresultat_num[\"y\"] = self.positions[-1][\"y\"] + y_relatif\n\t\tresultat_num[\"angle\"] = self.angles[-1] + angle_relatif\n\t\treturn resultat_num","title":""},{"docid":"05570e041bde0f8e3a19201b6214d347","score":"0.5341981","text":"def getNearbyPlaces(self):\n\t\tself.nearby = self.gmaps.places_radar(\n\t\t\t(self.centre['lat'], self.centre['lng']),\n\t\t\tradius=self.radius*self.radius_factor,\n\t\t\tkeyword=self.keywords\n\t\t\t)['results']\n\n\t\t##Lets create a master dict in the object which has all the place info\n\t\t##we can then pull the day out after the fact instead of constantly adding terms\n\t\tfor i in xrange(len(self.nearby)):\n\t\t\tself.nearby[i]['place_info'] = self.gmaps.place(self.nearby[i]['place_id'])\n\n\t\t##some ratings are missing - lets add them in as zero\n\t\tfor i in xrange(len(self.nearby)):\n\t\t\tif 'rating' not in self.nearby[i]['place_info']['result'].keys():\n\t\t\t\tself.nearby[i]['place_info']['result']['rating'] = 0","title":""},{"docid":"788e211a4ed13f8761f19abd9d93bcbc","score":"0.53289604","text":"def forces_values(self):\n\n forces = self.forces\n if forces is None:\n return None\n else:\n return self.forces.value_in_unit(self.forces_unit)","title":""},{"docid":"1ab262544a0b24cd837028738f45a16b","score":"0.5313329","text":"def calculate_non_bonded_force(self):\n\n if self.qm['charges_engine'] == 'chargemol':\n self.extract_params_chargemol()\n\n elif self.qm['charges_engine'] == 'onetep':\n self.extract_params_onetep()\n\n else:\n raise KeyError('Invalid charges engine provided, cannot extract charges.')\n\n # Calculate initial a_is and b_is\n self.append_ais_bis()\n\n # Use the a_is and b_is to calculate the non_bonded_force dict\n self.calculate_sig_eps()\n\n # Tweak for polar Hydrogens\n self.correct_polar_hydrogens()\n\n # Tweak the charge, sigma and epsilon for symmetry\n self.apply_symmetrisation()\n\n # Find extra site positions in local coords if present and tweak the charges of the parent\n if self.qm['charges_engine'] == 'onetep':\n self.extract_extra_sites()\n\n return self.non_bonded_force","title":""},{"docid":"9c0288e35b894d25b3d5ca97c3b0a0c8","score":"0.5312327","text":"def calc_end_forces(self, result, nodal_displ):\n elements = self._model.elements\n # Initialize dictionary with results of the end forces\n end_forces = dict()\n # Get the node freedom map table\n nfmt = self.nfmt\n # Get the node freedom allocation of every node\n nfat = self.nfat\n # Calculate end forces for each element\n for num, element in elements.items():\n # Get the transformation matrix for the element\n Te = element.transformation_matrix\n # Get the stiffness matrix of the element in global coordinates\n # FIXME!\n Ke = element._Ke\n # Get the displacements of the corresponding DOFs in global coordinates\n # - Initialize matrix\n v_i = np.zeros(element._k_size, dtype=np.float64)\n P_e_i = np.zeros(element._k_size, dtype=np.float64)\n # Get the node freedom allocation map table of the current element\n enfmt = element.enfmt\n # Initiate element load vector\n # Assemble matrix with element nodal displacements of the current\n # element\n for n_node_e, node in element._nodal_connectivity.items():\n # Indices for the array v_i\n index_base = element.get_element_active_dof(n_node_e)\n i_index = index_base + enfmt[n_node_e]\n # Indices corresponding to the position of the DOF of\n # the current node analyzed (global system)\n index_base_n = element.get_node_active_dof(n_node_e)\n j_index = index_base_n + nfmt[node.number]\n # Add the results for these DOFs to the v_i array\n v_i[i_index] = nodal_displ[j_index] # DOF of node selected\n # Account for element loads\n if len(element._loads) > 0:\n # Same indices are used here\n for load in element._loads:\n P_e_i[i_index] += load._load_vector_global[i_index]\n\n # Get the End Forces of the element in global coordinates\n P_i_global = Ke @ v_i - P_e_i\n # Transform End Forces to local coordinates\n P_i_local = Te @ P_i_global\n # Add End Forces to the dictionary of End Forces of the result object\n # for the current element\n end_forces[element.number] = P_i_local\n # Add the corresponding nodal forces to the matrix to\n # calculate the sectional forces\n # - Axial force\n element._poly_sec_force[0, 0] += -P_i_local[0]\n # - Shear force\n element._poly_sec_force[0, 1] += P_i_local[1]\n # - Moment\n element._poly_sec_force[0, 2] += -P_i_local[2]\n element._poly_sec_force[1, 2] += P_i_local[1]\n\n # Add results to the result object\n result.add_result('end forces', end_forces)\n\n return end_forces","title":""},{"docid":"2fb7167b6f15816ae27d9bbe47f2898c","score":"0.5259555","text":"def _calc_forces_repeats(self):\n num_tests = self.num_tests\n forces = pd.DataFrame()\n for tests in progress_bar(range(num_tests)):\n self._calc_forces()\n forces = forces.append(self.batch_forces, ignore_index=True)\n clear_output()\n\n forces = forces.mean()\n fc_od = OrderedDict()\n for k, v in forces.iteritems():\n fc_od[k] = v\n\n self.forces = OrderedDict(sorted(fc_od.items(), key=lambda kv: abs(kv[1]), reverse=True))","title":""},{"docid":"78f1a1bb18a59340370e50fa7bcd6023","score":"0.52554226","text":"def mmff94_potential(mol):\n mp = AllChem.MMFFGetMoleculeProperties(mol, mmffVariant=\"MMFF94\")\n ff = AllChem.MMFFGetMoleculeForceField(mol, mp)\n opt_fail = ff.Minimize(maxIts=1000,forceTol=0.0001,energyTol=1e-06)\n energy = ff.CalcEnergy()\n return energy","title":""},{"docid":"0a8226aa8c01cbdb2122170a1670c849","score":"0.52412826","text":"def force ( box, r, d ):\n\n import numpy as np\n \n # It is assumed that positions are in units where box = 1\n # Forces are calculated in units where sigma = 1 and epsilon = 1\n # Note that this is the force-shifted LJ potential with a linear smoothing term\n # S Mossa, E La Nave, HE Stanley, C Donati, F Sciortino, P Tartaglia, Phys Rev E, 65, 041205 (2002)\n\n n, ndim = r.shape\n assert ndim==3, 'Dimension error for r'\n nn, nna, ndim = d.shape\n assert nna==na and ndim==3, 'Dimension error for d'\n assert n==nn, 'Dimension mismatch for r and d'\n \n sr2_ovr = 1.77 # Overlap threshold (pot > 100)\n rm_cut_box = ( r_cut + diameter ) / box # Molecular cutoff in box=1 units\n rm_cut_box_sq = rm_cut_box**2 # squared\n assert rm_cut_box<0.5, 'rm_cut/box too large'\n r_cut_sq = r_cut ** 2\n\n # Initialize\n f = np.zeros_like(r)\n tau = np.zeros_like(r)\n total = PotentialType ( pot=0.0, vir=0.0, ovr=False )\n\n if fast:\n for i in range(n-1):\n rij = r[i,:]-r[i+1:,:] # Separation vectors for j>i\n rij = rij - np.rint(rij) # Periodic boundary conditions in box=1 units\n rij = rij * box # Now in sigma=1 units\n for a in range(na):\n for b in range(na):\n rab = rij + d[i,a,:] - d[i+1:,b,:] # All atom-atom vectors for given a and b\n rab_sq = np.sum(rab**2,axis=1) # Squared separations\n in_range = rab_sq < r_cut_sq # Set flags for within cutoff\n sr2 = 1.0 / rab_sq # (sigma/rab)**2\n ovr = sr2 > sr2_ovr # Set flags for any overlaps\n rmag = np.sqrt(rab_sq)\n sr6 = sr2 ** 3\n sr12 = sr6 ** 2\n pot = np.where ( in_range,\n 4.0*(sr12-sr6) + lambda1 + lambda2*rmag, 0.0 ) # force-shifted pair potentials\n virab = np.where ( in_range,\n 24.0*(2.0*sr12-sr6) - lambda2*rmag, 0.0 ) # pair virials\n fab = virab * sr2\n fab = rab * fab[:,np.newaxis] # atom-atom pair forces\n\n total = total + PotentialType ( pot=np.sum(pot), vir=np.sum(rij*fab), ovr=np.any(ovr) )\n fia = np.sum(fab,axis=0)\n f[i,:] = f[i,:] + fia\n f[i+1:,:] = f[i+1:,:] - fab\n tau[i,:] = tau[i,:] + np.cross ( d[i,a,:], fia )\n tau[i+1:,:] = tau[i+1:,:] - np.cross ( d[i+1:,b,:], fab )\n\n else:\n for i in range(n-1): # Outer loop\n for j in range(i+1,n): # Inner loop\n rij = r[i,:]-r[j,:] # Separation vector\n rij = rij - np.rint(rij) # Periodic boundary conditions in box=1 units\n rij_sq = np.sum(rij**2) # Squared separation\n\n if rij_sq < rm_cut_box_sq: # Check within cutoff\n rij = rij * box # Now in sigma=1 units\n for a in range(na):\n for b in range(na):\n rab = rij + d[i,a,:] - d[j,b,:] # Atom-atom vector for given a and b\n rab_sq = np.sum(rab**2) # Squared separation\n if rab_sq < r_cut_sq: # Test within potential cutoff\n sr2 = 1.0 / rab_sq # (sigma/rab)**2\n ovr = sr2 > sr2_ovr # Set flag for overlap\n rmag = np.sqrt(rab_sq)\n sr6 = sr2 ** 3\n sr12 = sr6 ** 2\n pot = 4.0*(sr12-sr6) + lambda1 + lambda2*rmag # force-shifted pair potential\n virab = 24.0*(2.0*sr12-sr6) - lambda2*rmag # pair virial\n fab = virab * sr2\n fab = rab * fab # atom-atom pair force\n\n total = total + PotentialType ( pot=pot, vir=np.sum(rij*fab), ovr=ovr )\n f[i,:] = f[i,:] + fab\n f[j,:] = f[j,:] - fab\n tau[i,:] = tau[i,:] + np.cross ( d[i,a,:], fab )\n tau[j,:] = tau[j,:] - np.cross ( d[j,b,:], fab )\n\n # Multiply results by numerical factors\n total.vir = total.vir / 3.0 # Divide virial by 3\n \n return total, f, tau","title":""},{"docid":"3c2b4c357b84232d662ada8d55b6a125","score":"0.5226326","text":"def compute_gravity_resistance(contacts, normals, mu, gamma, object_mass):\n # YOUR CODE HERE (contact forces exist may be useful here)\n gravity_wrench = np.array([0, 0, -9.81 * object_mass, 0, 0, 0])\n return contact_forces_exist(contacts, normals, mu, gamma, gravity_wrench)","title":""},{"docid":"328a4824c1f451de2257f5b68173378d","score":"0.5166418","text":"def _update_forces_thermostat(self, kinetic_energy):\n # Compute forces on thermostat (R x M)\n self.t_forces[..., 0] = (\n kinetic_energy - self.degrees_of_freedom_particles * self.kb_temperature\n ) / self.t_masses[..., 0]\n\n # Get kinetic energy of barostat (R x M)\n kinetic_energy_cell = self._compute_kinetic_energy_cell()\n # Compute forces on cell thermostat\n self.t_forces_cell[..., 0] = (\n kinetic_energy_cell - self.kb_temperature\n ) / self.t_masses_cell[..., 0]","title":""},{"docid":"3506709c3945f06751269a0747a8a713","score":"0.516037","text":"def calculate(self, atoms):\n boundary = ''\n for i in range(3):\n if atoms.pbc[i]:\n boundary += 'p ' \n else:\n boundary += 'f '\n if boundary in ['f f p ', 'p p f ']: #needs some work later\n boundary = 'p p p '\n self.boundary = boundary\n if self.molecule:\n self.write_lammps_data_water(atoms)\n else:\n self.write_lammps_data(atoms)\n self.write_lammps_in()\n self.lmp.file(self.lammps_in)\n # Extract the forces and energy\n self.lmp.command('variable pxx equal pxx')\n self.lmp.command('variable pyy equal pyy')\n self.lmp.command('variable pzz equal pzz')\n self.lmp.command('variable pxy equal pxy')\n self.lmp.command('variable pxz equal pxz')\n self.lmp.command('variable pyz equal pyz')\n self.lmp.command('variable fx atom fx')\n self.lmp.command('variable fy atom fy')\n self.lmp.command('variable fz atom fz')\n self.lmp.command('variable pe equal pe')\n if self.calc_type.find('GB') >= 0:\n self.lmp.command('variable Etot equal c_eatoms')\n self.gb_energy = self.lmp.extract_variable(\"Etot\", None, 0)\n #print('gb_energy from lammps: ', self.gb_energy)\n #print('update lammps GB energy')\n\n pos = np.array(\n [x for x in self.lmp.gather_atoms(\"x\", 1, 3)]).reshape(-1, 3)\n \n self.energy = self.lmp.extract_variable('pe', None, 0) \n #print('update lammps energy')\n\n xlo = self.lmp.extract_global(\"boxxlo\", 1)\n xhi = self.lmp.extract_global(\"boxxhi\", 1)\n ylo = self.lmp.extract_global(\"boxylo\", 1)\n yhi = self.lmp.extract_global(\"boxyhi\", 1)\n zlo = self.lmp.extract_global(\"boxzlo\", 1)\n zhi = self.lmp.extract_global(\"boxzhi\", 1)\n xy = self.lmp.extract_global(\"xy\", 1)\n yz = self.lmp.extract_global(\"yz\", 1)\n xz = self.lmp.extract_global(\"xz\", 1)\n unitcell = np.array([[xhi-xlo, xy, xz],\n [0, yhi-ylo, yz],\n [0, 0, zhi-zlo]]).T\n\n stress = np.empty(6)\n stress_vars = ['pxx', 'pyy', 'pzz', 'pyz', 'pxz', 'pxy']\n\n for i, var in enumerate(stress_vars):\n stress[i] = self.lmp.extract_variable(var, None, 0)\n #print('update lammps stress')\n\n stress_mat = np.zeros((3, 3))\n stress_mat[0, 0] = stress[0]\n stress_mat[1, 1] = stress[1]\n stress_mat[2, 2] = stress[2]\n stress_mat[1, 2] = stress[3]\n stress_mat[2, 1] = stress[3]\n stress_mat[0, 2] = stress[4]\n stress_mat[2, 0] = stress[4]\n stress_mat[0, 1] = stress[5]\n stress_mat[1, 0] = stress[5]\n stress[0] = stress_mat[0, 0]\n stress[1] = stress_mat[1, 1]\n stress[2] = stress_mat[2, 2]\n stress[3] = stress_mat[1, 2]\n stress[4] = stress_mat[0, 2]\n stress[5] = stress_mat[0, 1]\n\n self.stress = -stress * 1e5 * ase.units.Pascal\n f = (np.array(self.lmp.gather_atoms(\"f\", 1, 3)).reshape(-1,3) *\n (ase.units.eV/ase.units.Angstrom))\n #print('update lammps force')\n self.forces = f.copy()\n atoms.positions = pos.copy()\n atoms.cell = unitcell.copy()\n if self.molecule:\n atoms.positions *= 0.529\n atoms.cell *= 0.529\n self.atoms = atoms.copy()\n #self.atoms.info['GB_energy'] = self.gb_energy\n #print('update lammps all')","title":""},{"docid":"ffb43eb1901c341a9a63209c9f56bd98","score":"0.5123564","text":"def _get_potential():\n agent_pos = env.agent_pos\n goal_pos = (16,16)\n mannh_dist = goal_pos[0] - agent_pos[0] + goal_pos[1] - agent_pos[1]\n return (-1)*mannh_dist","title":""},{"docid":"d2aedad6da3938d9533fb0ce1f9a46a8","score":"0.5109667","text":"def update_dynamics(self):\n # mass matrix\n fd = rbd.ForwardDynamics(self.kine_dyn.mb)\n fd.computeH(self.kine_dyn.mb, self.kine_dyn.mbc)\n self.M = fd.H()\n self.Minv = self.M.inverse()\n # nonlinear effects vector\n fd.computeC(self.kine_dyn.mb, self.kine_dyn.mbc)\n self.h = fd.C()\n\n return M, Minv, h","title":""},{"docid":"64b8abcce7f4895b3262362293d4bc72","score":"0.51049143","text":"def energy_forces(self):\n ene, frc = energy_forces(as_numpy=True)\n return ene, frc.reshape((self.natom, 3))","title":""},{"docid":"a80b375c041b7c5e4982a8ed1a731a8b","score":"0.50943166","text":"def energy_forces(as_numpy=False):\n global APPLY_UNITS\n e, f = _pys.energy_forces()\n if as_numpy:\n f = _np.asarray(f)\n if APPLY_UNITS:\n return (_apply_units_to_struct(e, u.kilocalories_per_mole),\n u.Quantity(f, u.kilocalories_per_mole/u.angstroms))\n return e, f","title":""},{"docid":"09ac0a328ca063de02e150599b26f039","score":"0.5093232","text":"def _get_positions_and_forces_parser(\n lines, trigger_indices, n_atoms, pos_flag=True, force_flag=True\n ):\n positions = []\n forces = []\n for j in trigger_indices:\n pos = []\n force = []\n for line in lines[j + 2 : j + n_atoms + 2]:\n line = line.strip()\n line = _clean_line(line)\n if pos_flag:\n pos.append([float(l) for l in line.split()[0:3]])\n if force_flag:\n force.append([float(l) for l in line.split()[3:]])\n forces.append(force)\n positions.append(pos)\n if pos_flag and force_flag:\n return np.array(positions), np.array(forces)\n elif pos_flag:\n return np.array(positions)\n elif force_flag:\n return np.array(forces)","title":""},{"docid":"85da39827b781985f7ed2329edc15ca5","score":"0.5090978","text":"def potential_field(self, obstacle, curr_pos): \n epsilon = self.params[\"force_max\"]\n\n force = np.empty((3,))\n\n dist = obstacle-curr_pos\n dist_norm = np.linalg.norm(dist)\n direction = dist/dist_norm\n if dist_norm < epsilon:\n dist_norm = epsilon\n \n return direction/dist_norm","title":""},{"docid":"2778ae395f578ca52adb0e39d0d58410","score":"0.50805146","text":"def _update_forces_thermostat(self, kinetic_energy):\n # Compute Ekin from tensor\n # kinetic_energy = torch.einsum(\"abii->ab\", kinetic_energy)\n # Compute forces on thermostat (R x M)\n self.t_forces[..., 0] = (\n kinetic_energy - self.degrees_of_freedom_particles * self.kb_temperature\n ) / self.t_masses[..., 0]\n\n # Get kinetic energy of barostat (R x M)\n kinetic_energy_cell = self._compute_kinetic_energy_cell()\n # Compute forces on cell thermostat\n self.t_forces_cell[..., 0] = (\n kinetic_energy_cell - 9.0 * self.kb_temperature\n ) / self.t_masses_cell[..., 0]","title":""},{"docid":"090f8af58bf28f07ebc4921c67483c0a","score":"0.50768507","text":"def find_force(cell_1: Cell, cell_2: Cell) -> Vector:\n # vector from cell_1 to cell_2\n vr = cell_1.coordinates - cell_2.coordinates\n r = abs(vr) # absolute distance between cells\n abs_force = lj_force(r, Cell.epsilon, Cell.sigma)\n return Vector(\n projection(abs_force, vr.x, vr),\n projection(abs_force, vr.y, vr),\n projection(abs_force, vr.z, vr)\n )","title":""},{"docid":"ae25604d0c4278104bbc859c0dfd78e4","score":"0.5076336","text":"def find_nearby_atoms(\n i_seq,\n xray_structure,\n pdb_atoms,\n asu_mappings,\n asu_table,\n connectivity,\n far_distance_cutoff=3.0,\n near_distance_cutoff=1.5,\n filter_by_bonding=True):\n contacts = []\n unit_cell = xray_structure.unit_cell()\n sites_frac = xray_structure.sites_frac()\n site_frac = sites_frac[i_seq]\n asu_dict = asu_table[i_seq]\n site_i = sites_frac[i_seq]\n rt_mx_i_inv = asu_mappings.get_rt_mx(i_seq, 0).inverse()\n atom_i = pdb_atoms[i_seq]\n # Create the primary list of contacts\n for j_seq, j_sym_groups in asu_dict.items():\n site_j = sites_frac[j_seq]\n atom_j = pdb_atoms[j_seq]\n # Filter out hydrogens\n if atom_j.element.upper().strip() in [\"H\", \"D\"]:\n continue\n # Filter out alternate conformations of this atom\n if _same_atom_different_altloc(atom_i, atom_j):\n continue\n # Gather up contacts with all symmetric copies\n for j_sym_group in j_sym_groups:\n for j_sym_id in j_sym_group :\n rt_mx = rt_mx_i_inv.multiply(asu_mappings.get_rt_mx(j_seq, j_sym_id))\n site_ji = rt_mx * site_j\n site_ji_cart = unit_cell.orthogonalize(site_ji)\n vec_i = col(unit_cell.orthogonalize(site_frac=site_frac))\n vec_ji = col(site_ji_cart)\n assert abs(vec_i - vec_ji) < far_distance_cutoff + 0.5\n contact = atom_contact(\n atom = atom_j,\n vector = vec_i - vec_ji,\n site_cart = site_ji_cart,\n rt_mx = rt_mx)\n # XXX I have no idea why the built-in handling of special positions\n # doesn't catch this for us\n if (j_seq == i_seq) and (not rt_mx.is_unit_mx()):\n continue\n if contact.distance() < near_distance_cutoff:\n continue\n contacts.append(contact)\n # Filter out carbons that are judged to be \"contacts\", but are actually\n # just bonded to genuine coordinating atoms. This is basically just a\n # way to handle sidechains such as His, Asp/Glu, or Cys where the carbons\n # may be relatively close to the metal site.\n if filter_by_bonding and (connectivity is not None):\n filtered = []\n all_i_seqs = [contact.atom_i_seq() for contact in contacts]\n for contact in contacts:\n # oxygen is always allowed, other elements may not be\n if contact.element not in [\"C\", \"P\", \"S\", \"N\"]:\n filtered.append(contact)\n continue\n # Remove atoms within 1.9 A contact distance\n if any(other_contact != contact and\n contact.distance_from(other_contact) < 1.9 and\n contact.distance() > other_contact.distance()\n for other_contact in contacts):\n continue\n # Examine the mode connectivity to catch more closely bonded atoms\n bonded_j_seqs = []\n for j_seq in connectivity[contact.atom_i_seq()]:\n if (j_seq in all_i_seqs):\n bonded_j_seqs.append(j_seq)\n for j_seq in bonded_j_seqs:\n other_contact = contacts[all_i_seqs.index(j_seq)]\n if other_contact.element in [\"N\", \"O\", \"S\"] and \\\n abs(other_contact) < abs(contact):\n break\n else:\n filtered.append(contact)\n contacts = filtered\n return contacts","title":""},{"docid":"1844e1f9bd9a09301057d4e912a77182","score":"0.5059125","text":"def run_forward_solves(self, params):\n\n print('Running forward solves')\n self.make_forward_sim(params)\n iter = self.optimizer.iteration if self.store_all_simulations else 0\n self.sim.run(name = 'forward', iter = iter)\n \n get_eps = True\n get_D = not self.use_deps\n nointerpolation = not self.geometry.use_interpolation()\n \n self.forward_fields = get_fields(self.sim.fdtd,\n monitor_name = 'opt_fields',\n field_result_name = 'forward_fields',\n get_eps = get_eps,\n get_D = get_D,\n get_H = False,\n nointerpolation = nointerpolation,\n unfold_symmetry = self.unfold_symmetry)\n fom = self.fom.get_fom(self.sim)\n\n if self.store_all_simulations:\n self.sim.remove_data_and_save() #< Remove the data from the file to save disk space. TODO: Make optional?\n\n self.fomHist.append(fom)\n print('FOM = {}'.format(fom))\n return fom","title":""},{"docid":"d0e236d123d49239ffb679f5a2c48890","score":"0.50420284","text":"def get_potential_field(self):\n ticks = np.linspace(0, 700, 700/self.par_dic[\"resolution\"])\n num_ticks = len(ticks)\n potential = np.zeros((num_ticks, num_ticks), dtype=np.float64)\n for i in range(num_ticks):\n for j in range(num_ticks):\n potential[i][j] = self.calculate_potential([ticks[i], ticks[j]])\n return potential","title":""},{"docid":"ccf296f8a9211e03244c987b53a9ff8b","score":"0.5021543","text":"def total_forces(self, zeta_pole=np.zeros((3,))):\n\n if not hasattr(self, 'Gamma') or not hasattr(self, 'Fqs'):\n self.reshape()\n\n self.Ftot = np.zeros((3,))\n self.Mtot = np.zeros((3,))\n\n for ss in range(self.MS.n_surf):\n M, N = self.MS.Surfs[ss].maps.M, self.MS.Surfs[ss].maps.N\n for nn in range(N + 1):\n for mm in range(M + 1):\n zeta_node = self.MS.Surfs[ss].zeta[:, mm, nn]\n fnode = self.Fqs[ss][:, mm, nn]\n self.Ftot += fnode\n self.Mtot += np.cross(zeta_node - zeta_pole, fnode)\n # for cc in range(3):\n # \tself.Ftot[cc]+=np.sum(self.Fqs[ss][cc,:,:])","title":""},{"docid":"51f0b7f32e01212e43ded36ca5d47118","score":"0.5016431","text":"def get_force(self):\n # @todo: make this work. Probably need to go through self.sim to get pod velocity, etc. \n\n \"\"\"\n # Numerical simulation run at 6 different velocities -- see Keith's graph \n # A34 data -- drag is for both brakes, lift is for one brake. Force_y has to do with the difference in force due to magnetic interactions and can be disregarded\n v = self.sim.pod.velocity\n air_gap = .024 # Should be self.sim.pod.brakes.gap, or brakes[i].gap if we're using an array of brakes, which we probably will\n \n # Fdrag(v) = gap_coefficient * (-e^(-.3*v)+1)*(1.5*e^(-.02*v)+1)\n # gap_coefficient = 5632e^-202gap\n \n # @todo: Either the drag force or the lift force is for a single brake, the other is for both. Which is which? \n gap_coefficient = 5632 * np.exp(-202 * air_gap)\n f_drag = gap_coefficient * (-np.exp(-.3*v) + 1) * (1.5 * np.exp(-.02*v)+1)\n #print \"Brake drag at air gap {}: {}\".format(air_gap, -f_drag)\n \"\"\"\n\n #f_drag = self.sim.brake_1.drag_force * 2 # *2 for both brakes. Just testing right now\n \n f_drag = self.sim.pod.brakes.get_drag() \n \n return self.data(f_drag, 0, 0)","title":""},{"docid":"d8fc2caff7036f9e1b274c855eb7b5c3","score":"0.49988088","text":"def computeForce(s): \n\n fv = vector(0.0, 0.0, 0.0) # Overall force vector\n \n for f in s.PForces:\n # Determine if the force is a vector or a function,\n # and act appropriately:\n if hasattr(f, '__call__') == True:\n fv = fv + f(s)\n else:\n fv = fv + f\n\n # Forces on ALL parts of the object contribute to overall force vector\n for f in s.lForces:\n if hasattr(f, '__call__') == True:\n fv = fv + f(s)\n else:\n fv = fv + f \n\n for f in s.rForces:\n if hasattr(f, '__call__') == True:\n fv = fv + f(s)\n else:\n fv = fv + f\n\n for f in s.nForces:\n if hasattr(f, '__call__') == True:\n fv = fv + f(s)\n else:\n fv = fv + f\n\n for f in s.tForces:\n if hasattr(f, '__call__') == True:\n fv = fv + f(s)\n else:\n fv = fv + f\n\n \n return fv.scale(TIMESTEP)","title":""},{"docid":"0defcfe72b6852ece75ffde2ff4e6aac","score":"0.49946612","text":"async def get_near(self, request: Request) -> OkResult:\n pass","title":""},{"docid":"2de315fffcf7abcc2fcfb597eaecfae2","score":"0.49818745","text":"def get_force(self):\n return self.rob.get_force(wait=True) - self.offset_force","title":""},{"docid":"e70999e96b23d059034e8d0ec431b373","score":"0.49759325","text":"def get_dynamics(self, speed, copter_status):\n velocity = copter_status.velocity\n # convert axis from copter to world frame\n ang_vel = copter_status.to_world_direction(copter_status.angular_velocity)\n axis = copter_status.to_world_direction(self._axis)\n pos = copter_status.to_world_direction(self._position)\n\n F = force(self, speed, axis, velocity, ang_vel)\n M = moment(self, speed, axis, velocity, ang_vel)\n # remove the part that accelerates the propeller\n ma = np.dot(M, axis)\n M -= ma * axis\n # and add what the force does through the lever.\n M += np.cross(pos, F)\n return F, M, ma","title":""},{"docid":"591a6b675b65e7a7b286ff71a3c3c061","score":"0.49697343","text":"def calculate_hemispheres(self):\n\n # Figure out hemisphere of each line\n self.fiber_hemisphere = numpy.zeros(self.number_of_fibers)\n # percentage in left hemisphere\n test = sum(self.fiber_array_r.T < 0) / float(self.points_per_fiber)\n self.fiber_hemisphere[numpy.nonzero(test > 0.95)] = -1\n self.fiber_hemisphere[numpy.nonzero(test < 0.05)] = 1\n # otherwise hem stays 0 for commissural\n\n # output boolean arrays for each hemisphere and callosal fibers\n self.is_left_hem = (self.fiber_hemisphere == -1)\n self.is_right_hem = (self.fiber_hemisphere == 1)\n self.is_commissure = (self.fiber_hemisphere == 0)\n\n # output indices of each type above\n self.index_left_hem = numpy.nonzero(self.is_left_hem)[0]\n self.index_right_hem = numpy.nonzero(self.is_right_hem)[0]\n self.index_commissure = numpy.nonzero(self.is_commissure)[0]\n self.index_hem = \\\n numpy.nonzero(self.is_left_hem | self.is_right_hem)[0]\n\n # output totals of each type also\n self.number_left_hem = len(self.index_left_hem)\n self.number_right_hem = len(self.index_right_hem)\n self.number_commissure = len(self.index_commissure)\n\n # test\n if __debug__:\n test = self.number_of_fibers == \\\n (self.number_left_hem + self.number_right_hem \\\n + self.number_commissure)\n if not test:\n print \"<fibers.py> ERROR: fiber numbers don't add up.\"\n raise AssertionError","title":""},{"docid":"af0441346a35ed99651c2abcdb4c0370","score":"0.49606064","text":"def get_bader_coulomb_energy(self, structure):\n energy = 0\n force = np.zeros((len(structure), 3))\n a = -1\n b = -1\n for i, sitei in enumerate(structure.as_dict()['sites']):\n for j, sitej in enumerate(structure.as_dict()['sites']):\n if i != j:\n dij = structure.get_distance(i, j)\n d_vec = structure.frac_coords[i] - structure.frac_coords[j]\n Zi = sitei['species'][0]['oxidation_state']\n Zj = sitej['species'][0]['oxidation_state']\n energy += 0.5 * Zi * Zj / dij\n force[i][0] += Zi * Zj / (dij ** 2) * (d_vec[0] / dij)\n force[i][1] += Zi * Zj / (dij ** 2) * (d_vec[1] / dij)\n force[i][2] += Zi * Zj / (dij ** 2) * (d_vec[2] / dij)\n print force[i]\n # to work on definition of forces\n print np.sum(force[:, 0]), np.sum(force[:, 1]), np.sum(\n force[:, 2]) # total force on cell in x, y, z ?\n return energy","title":""},{"docid":"e4db5019ce493a2b46fee42fa1a6c245","score":"0.49512225","text":"def getForce(self):\n force = 0\n # A number to multiply the weight by to make the teeter totter just right.\n forceScalar = 0.1\n for distance, obj in self.Objects:\n leverage = distance / (self.width/2)\n force += leverage * self.WEIGHT * forceScalar\n return force","title":""},{"docid":"85cbd7935cd8338eca8c0a790297a7c5","score":"0.49396554","text":"def compute_distance_field(self, entity_type):\n \n # initialize the distance field and visited array \n visited = []\n distance_field= []\n for _ in range(self._grid_height):\n dist_inner=[]\n visited_inner = []\n for _ in range(self._grid_width):\n dist_inner.append( self._grid_width * self._grid_height )\n visited_inner.append(False)\n\n distance_field.append(dist_inner)\n visited.append(visited_inner)\n\n \n # which entity\n if entity_type == HUMAN:\n entity = self._human_list \n #neb_func = self.eight_neighbors\n elif entity_type == ZOMBIE:\n entity= self._zombie_list \n #neb_func = self.four_neighbors\n \n # initialize boundary, vistied and distance_field \n boundary = poc_queue.Queue()\n for ele in entity:\n boundary.enqueue(ele)\n visited[ele[0]][ele[1]] = True\n distance_field[ele[0]][ele[1]] = 0\n \n # BFS search\n while [ ele for ele in boundary] != []:\n cell = boundary.dequeue()\n neighbors = self.four_neighbors(cell[0],cell[1])\n for neighbor in neighbors:\n if visited[neighbor[0]][neighbor[1]] == False:\n if self.is_empty(neighbor[0],neighbor[1]):\n visited[neighbor[0]][neighbor[1]] = True\n boundary.enqueue(neighbor)\n #if self.is_empty(neighbor[0],neighbor[1]):\n distance_field[neighbor[0]][neighbor[1]] = distance_field[cell[0]][cell[1]] + 1 \n \n return distance_field","title":""},{"docid":"ae3efba09fa292e22afb64e136b30621","score":"0.49363986","text":"def forces_moments(self, forces_moments):\n\n self._forces_moments = forces_moments","title":""},{"docid":"a749f40fccffa0c7ebea1f5f25d1f26c","score":"0.4922669","text":"def extract_energy_and_forces(atom_frames,calc=None,atomic=True):\n energies_dft, forces_dft = [], {}\n energies_gap, forces_gap = [], {}\n\n for atoms in atom_frames: # free energy per atom\n # basic info\n symbols = atoms.get_chemical_symbols()\n if atomic:\n natoms = len(atoms)\n else:\n natoms = 1\n # energy\n free_energy = atoms.get_potential_energy(force_consistent=True) # electronic free energy\n energies_dft.append(free_energy/natoms)\n # force\n forces = atoms.get_forces()\n for sym, force in zip(symbols,forces):\n if sym in forces_dft.keys():\n forces_dft[sym].extend(force)\n else:\n forces_dft[sym] = list(force)\n if calc:\n # use quip to calculate gap predicted energy\n atoms.set_calculator(calc)\n free_energy = atoms.get_potential_energy(force_consistent=True) # electronic free energy\n energies_gap.append(free_energy/natoms)\n # force\n forces = atoms.get_forces()\n for sym, force in zip(symbols,forces):\n if sym in forces_gap.keys():\n forces_gap[sym].extend(force)\n else:\n forces_gap[sym] = list(force)\n\n return forces_dft, forces_gap, energies_dft, energies_gap","title":""},{"docid":"9a074b8ff1ea7758f57bb822f3e4805c","score":"0.49200338","text":"def calculate_potential(self, pos):\n potential = 0.\n r = self.par_dic[\"resolution\"]\n for objects, calculator in self.calculator_dic.values():\n for obj_par in objects:\n potential += calculator.get_potential(np.array(obj_par)/r, np.array(pos + [self.par_dic[\"ball_size\"]])/r)\n return potential","title":""},{"docid":"e15e44ccf9753527d28e41077a145e76","score":"0.49186394","text":"def forces(self):\n try:\n return self.sim_state.getForces(asNumpy=True)\n except:\n warn(\"Unknown exception handled from `self.sim_state.getForces()`, \"\n \"this is probably because this attribute is not in the State.\")\n return None","title":""},{"docid":"f4d8ec1c6c0a28dd2b1ab398d2a841ec","score":"0.4916737","text":"def find_hp_force(hp_centre_x, hp_centre_y, hp_kappa_x, hp_kappa_y, X , Y, min_grid, max_grid, grid_space, periodic):\n\t#Calculate x-force\n\tF_harmonic_x = hp_kappa_x * (X - hp_centre_x)\n\tif periodic == 1:\n\t\tgrid_length = max_grid[0] - min_grid[0]\n\t\tgrid_centre = min_grid[0] + grid_length/2\n\t\tif hp_centre_x < grid_centre:\n\t\t\tindex_period = index(hp_centre_x + grid_length/2, min_grid[0], grid_space[0])\n\t\t\tF_harmonic_x[:, index_period:] = hp_kappa_x * (X[:, index_period:] - hp_centre_x - grid_length)\n\t\telif hp_centre_x > grid_centre:\n\t\t\tindex_period = index(hp_centre_x - grid_length/2, min_grid[0], grid_space[0])\n\t\t\tF_harmonic_x[:, :index_period] = hp_kappa_x * (X[:, :index_period] - hp_centre_x + grid_length)\n\t#Calculate y-force\n\tF_harmonic_y = hp_kappa_y * (Y - hp_centre_y)\n\tif periodic == 1:\n\t\tgrid_length = max_grid[0] - min_grid[0]\n\t\tgrid_centre = min_grid[0] + grid_length / 2\n\t\tif hp_centre_y < grid_centre:\n\t\t\tindex_period = index(hp_centre_y + grid_length/2, min_grid[1], grid_space[1])\n\t\t\tF_harmonic_y[index_period:, :] = hp_kappa_y * (Y[index_period:, :] - hp_centre_y - grid_length)\n\t\telif hp_centre_y > grid_centre:\n\t\t\tindex_period = index(hp_centre_y - grid_length/2, min_grid[1], grid_space[1])\n\t\t\tF_harmonic_y[:index_period, :] = hp_kappa_y * (Y[:index_period, :] - hp_centre_y + grid_length)\n\n\treturn [F_harmonic_x, F_harmonic_y]","title":""},{"docid":"bad4c4414505b48f3669330cf958ce8e","score":"0.4893941","text":"def gravity(coordinates):\n \n N = len(coordinates) #number of objects\n r = np.zeros((N, 3)) #distance between objects\n force = np.zeros((N, 3)) #force between objects\n U = 0 #potential energy\n \n for i in range(0, N - 1):\n for j in range(i + 1, N):\n #calculates distance vector between each object\n r[i] = coordinates[j] - coordinates[i]\n \n #force calculation\n rmag = np.sqrt(r[i, 0]**2 + r[i, 1]**2 + r[i, 2]**2)\n rhat = r[i] / rmag\n dF = rhat * G_gravity * mass[i] * mass[j] / (rmag**2)\n force[j] += -dF\n force[i] += dF\n U += -G_gravity * mass[i] * mass[j] / rmag \n \n return force, U","title":""},{"docid":"d5215042c5e14f44508c68cdab40a093","score":"0.4882773","text":"def get_force(self):\n x = -self.air_resistance_k * self.sim.pod.velocity ** 2\n y = 0 # No y force. y force isn't used in the simulator right now\n z = 0 # No z force for aero\n return self.data(x, y, z)","title":""},{"docid":"1fbf792dbf52e48a1f180c831c8e7bf0","score":"0.48756886","text":"def force_hertzian(self):\n N = self.N\n pos, rad = self.pos, self.rad\n \n f = np.zeros_like(pos)\n for i in range(N):\n rij = pos[i] - pos\n dij = rad[i] + rad\n dist = np.sqrt((rij**2).sum(axis=-1))\n \n mask = (dist > 0)&(dist < dij)\n rij = rij[mask]\n dij = dij[mask][:,None]\n dist = dist[mask][:,None]\n \n if len(rij) > 0:\n forces = self.epsilon*(1-dist/dij)**2 * rij/dist\n f[i] += forces.sum(axis=0)\n \n return f","title":""},{"docid":"d941dde0602a6b78d6b5cd35e116e151","score":"0.48747146","text":"def _force(self, x: float, y: float, z: float) -> TupleOf3Floats:\n if not self.is_Coord_Inside_Vacuum(x, y, z):\n return np.nan, np.nan, np.nan\n FySymmetryFact = 1.0 if y >= 0.0 else -1.0 # take advantage of symmetry\n FzSymmetryFact = 1.0 if z >= 0.0 else -1.0\n y = abs(y) # confine to upper right quadrant\n z = abs(z)\n if -self.extra_field_length <= x <= self.L_cap: # at beginning of lens\n Fx, Fy, Fz = self._force_Func_Outer(x, y, z)\n elif self.L_cap < x <= self.L - self.L_cap: # if long enough, model interior as uniform in x\n Fx, Fy, Fz = self._force_Func_Inner(y, z)\n elif self.L - self.L_cap <= x <= self.L + self.extra_field_length: # at end of lens\n x = self.L - x\n Fx, Fy, Fz = self._force_Func_Outer(x, y, z)\n Fx = -Fx\n else:\n raise Exception(\"Particle outside field region\") # this may be triggered when itentionally misligned\n Fx *= self.field_fact\n Fy *= FySymmetryFact * self.field_fact\n Fz *= FzSymmetryFact * self.field_fact\n # Fx, Fy, Fz = self.baseClass.rotate_Force_For_Misalignment(Fx, Fy, Fz)\n return Fx, Fy, Fz","title":""},{"docid":"961f2c0fe80433b42457a604bfec2ad6","score":"0.48666638","text":"def test_forces_result(parse_result):\n from aiida.orm import DataFactory\n _, nodes = parse_result()\n nodes = dict(nodes)\n assert isinstance(nodes['forces'], DataFactory('array'))\n assert numpy.all(nodes['forces'].get_array('forces')[0] == numpy.array(\n [-0.23272115, -0.01115905, 0.03449686]))\n assert numpy.all(nodes['forces'].get_array('forces')[-1] == numpy.array(\n [-0.00300438, 0.00453998, 0.00066599]))","title":""},{"docid":"a6d795221978880ad87839c99fd89e99","score":"0.4861695","text":"def apply_forces(\n self, system_one: SystemType, index_one, system_two: SystemType, index_two\n ):\n end_distance_vector = (\n system_two.position_collection[..., index_two]\n - system_one.position_collection[..., index_one]\n )\n elastic_force = self.k * end_distance_vector\n\n relative_velocity = (\n system_two.velocity_collection[..., index_two]\n - system_one.velocity_collection[..., index_one]\n )\n damping_force = self.nu * relative_velocity\n\n contact_force = elastic_force + damping_force\n system_one.external_forces[..., index_one] += contact_force\n system_two.external_forces[..., index_two] -= contact_force\n\n return","title":""},{"docid":"2025ec37fdbfe53980550f2adbac2d5e","score":"0.48610002","text":"def compute_hydration_energies(molecules, parameters):\n\n energies = dict() # energies[index] is the computed solvation energy of molecules[index]\n\n platform = openmm.Platform.getPlatformByName(\"Reference\")\n\n moleculeIndex = -1\n for molecule in molecules:\n moleculeIndex += 1\n # Create OpenMM System.\n system = openmm.System()\n for atom in molecule.GetAtoms():\n mass = OEGetDefaultMass(atom.GetAtomicNum())\n system.addParticle(mass * units.amu)\n\n # Add nonbonded term.\n # nonbonded_force = openmm.NonbondedSoftcoreForce()\n # nonbonded_force.setNonbondedMethod(openmm.NonbondedForce.NoCutoff)\n # for atom in molecule.GetAtoms():\n # charge = 0.0 * units.elementary_charge\n # sigma = 1.0 * units.angstrom\n # epsilon = 0.0 * units.kilocalories_per_mole\n # nonbonded_force.addParticle(charge, sigma, epsilon)\n # system.addForce(nonbonded_force)\n\n # Add GBVI term\n # gbvi_force = openmm.GBVISoftcoreForce()\n gbvi_force = openmm.GBVIForce() \n gbvi_force.setNonbondedMethod(openmm.GBVIForce.NoCutoff) # set no cutoff\n gbvi_force.setSoluteDielectric(1)\n gbvi_force.setSolventDielectric(78.3)\n\n # Use scaling method.\n # gbvi_force.setBornRadiusScalingMethod(openmm.GBVISoftcoreForce.QuinticSpline)\n # gbvi_force.setQuinticLowerLimitFactor(0.75)\n # gbvi_force.setQuinticUpperBornRadiusLimit(50.0*units.nanometers)\n\n # Build indexable list of atoms.\n atoms = [atom for atom in molecule.GetAtoms()] \n \n # Assign GB/VI parameters.\n for atom in molecule.GetAtoms(): \n atomtype = atom.GetStringData(\"gbvi_type\") # GBVI atomtype\n charge = atom.GetPartialCharge() * units.elementary_charge\n radius = parameters['%s_%s' % (atomtype, 'radius')] * units.angstroms\n gamma = parameters['%s_%s' % (atomtype, 'gamma')] * units.kilocalories_per_mole \n # gamma *= -1.0 # DEBUG\n lambda_ = 1.0 # fully interacting\n # gbvi_force.addParticle(charge, radius, gamma, lambda_) # for GBVISoftcoreForce\n gbvi_force.addParticle(charge, radius, gamma) # for GBVIForce\n\n # Add bonds.\n for bond in molecule.GetBonds():\n # Get atom indices.\n iatom = bond.GetBgnIdx()\n jatom = bond.GetEndIdx()\n # Get bond length.\n (xi, yi, zi) = molecule.GetCoords(atoms[iatom])\n (xj, yj, zj) = molecule.GetCoords(atoms[jatom])\n distance = math.sqrt((xi-xj)**2 + (yi-yj)**2 + (zi-zj)**2) * units.angstroms\n # Identify bonded atoms to GBVI.\n gbvi_force.addBond(iatom, jatom, distance)\n\n # Add the force to the system.\n system.addForce(gbvi_force)\n \n # Build coordinate array.\n natoms = len(atoms)\n coordinates = units.Quantity(numpy.zeros([natoms, 3]), units.angstroms)\n positions = []\n for (index,atom) in enumerate(atoms):\n (x,y,z) = molecule.GetCoords(atom)\n coordinates[index,:] = units.Quantity(numpy.array([x,y,z]),units.angstroms) \n positions.append( [x*0.1,y*0.1,z*0.1] )\n #positions.append( [x,y,z] )\n \n # Create OpenMM Context.\n timestep = 1.0 * units.femtosecond # arbitrary\n integrator = openmm.VerletIntegrator(timestep)\n context = openmm.Context(system, integrator, platform)\n\n # Set the coordinates.\n context.setPositions(coordinates)\n serializeDirectory = '/home/friedrim/source/gbff/examples/gbvi/serialization'\n serializeFileName = 'mol_' + (\"%d\" % (moleculeIndex))\n serialize( system, positions, serializeDirectory, serializeFileName )\n \n # Get the energy\n state = context.getState(getEnergy=True)\n energies[molecule] = state.getPotentialEnergy()\n\n return energies","title":""},{"docid":"5ad94c111d7e684999e2e838c9ddf638","score":"0.48538497","text":"def generalized_forces(self, q , dq , ddq , t = 0 ): \n \n H = self.H( q )\n C = self.C( q , dq )\n g = self.g( q )\n d = self.d( q , dq )\n \n # Generalized forces\n forces = np.dot( H , ddq ) + np.dot( C , dq ) + g + d\n \n return forces","title":""},{"docid":"e5e4b02ed346ad5a64351d42f051a224","score":"0.48517668","text":"def calc_mom_transport(self):\n y = self.y_R*R\n z = self.z_H*H\n self.ddy_upvp = np.zeros(self.grdims)\n self.ddz_upwp = np.zeros(self.grdims)\n self.d2Udy2 = np.zeros(self.grdims)\n self.d2Udz2 = np.zeros(self.grdims)\n self.dUdy = np.zeros(self.grdims)\n self.dUdz = np.zeros(self.grdims)\n for n in range(len(z)):\n self.ddy_upvp[n, :] = \\\n fdiff.second_order_diff(self.df.mean_upvp.iloc[n, :], y)\n self.dUdy[n, :] = \\\n fdiff.second_order_diff(self.df.mean_u.iloc[n, :], y)\n self.d2Udy2[n, :] = fdiff.second_order_diff(self.dUdy[n, :], y)\n for n in range(len(y)):\n self.ddz_upwp[:, n] = \\\n fdiff.second_order_diff(self.df.mean_upwp.iloc[:, n], z)\n self.dUdz[:, n] = \\\n fdiff.second_order_diff(self.df.mean_u.iloc[:, n], z)\n self.d2Udz2[:, n] = fdiff.second_order_diff(self.dUdz[:, n], z)","title":""},{"docid":"d41bcf9a7f67579fd34239da8fd3c0c2","score":"0.484313","text":"def meteorite_fell_near(location: str):\n output = []\n given_longitude = commonLocations.find({\"name\": location})[0]['long']\n given_latitude = commonLocations.find({\"name\": location})[0]['lat']\n for entry in meteoriteLandings.find({}):\n #pprint(entry)\n entry_longitude, entry_latitude = entry[\"geolocation\"][\"coordinates\"]\n entry_latitude = float(entry_latitude)\n entry_longitude = float(entry_longitude)\n if ((entry_longitude - given_longitude) ** 2 + (entry_latitude - given_latitude) ** 2) ** .5 < 25:\n output.append(entry)\n\n pprint(\"DONE\")\n return output","title":""},{"docid":"6d3c4b425c2adf894bfe090378206722","score":"0.48427597","text":"def compute_distance_field(self, entity_type):\n # store the height and width of the grid\n height = poc_grid.Grid.get_grid_height(self)\n width = poc_grid.Grid.get_grid_width(self)\n # create a grid that tracks the visited cells of the grid\n # and intialize all locations to be EMPTY i.e. not visited\n visited = [[EMPTY for dummy_col in range(width)] \n for dummy_row in range(height)]\n \n # create a distance field to keep track of the shortest\n # distance from a entity type and initialize it as height*width\n # since the distance larger than any possible distance\n distance_field = [[height * width for dummy_col in range(width)] \n for dummy_row in range(height)]\n \n # create a queue for breath first search\n boundary = poc_queue.Queue()\n # map the respective entity type to its generator function\n map_entity_type = {HUMAN: self.humans, ZOMBIE: self.zombies}\n # add all human or zombie locations to the queue \n # and mark those locations as visited and the\n # distance at that location as zero\n for row, col in map_entity_type[entity_type]():\n boundary.enqueue((row, col))\n visited[row][col] = FULL\n distance_field[row][col] = 0\n # begin the breath first search\n while(len(boundary) > 0 ):\n # get the current cell i.e the grid location\n # of the zombie/human\n current_cell = boundary.dequeue()\n # get all of the current cells four neighbours and iterate\n # over them\n for neighbor_cell in poc_grid.Grid.four_neighbors(self, \n current_cell[0], \n current_cell[1]):\n # if neigboring cell is passable and has not yet been visited\n # add it to the queue for BFS, mark it as visited and \n # update the distance. \n if (poc_grid.Grid.is_empty(self, neighbor_cell[0], neighbor_cell[1]) \n and visited[neighbor_cell[0]][neighbor_cell[1]] == EMPTY):\n boundary.enqueue(neighbor_cell)\n visited[neighbor_cell[0]][neighbor_cell[1]] = FULL\n distance_field[neighbor_cell[0]][neighbor_cell[1]] = (\n distance_field[current_cell[0]][current_cell[1]] + 1)\n \n return distance_field","title":""},{"docid":"82ed37aee75d00dea5092afa8ae04352","score":"0.4840184","text":"def updatePos(self):\n posspheres = self._spherepos()\n p = _np.exp(\n _ne.evaluate('sum(-pos**rho*s, axis=1)', local_dict={'s': _np.log(2) * (2 / self.fwhm) ** self.rho, 'pos': posspheres, 'rho': self.rho})\n )\n n = self.rng.poisson(p * (self._N / _np.sum(p)))\n missing = self._N - _np.sum(n)\n while missing:\n ids = self.rng.choice(len(n), int(abs(missing)), replace=True, p=p / (_np.sum(p)))\n _np.add.at(n, ids, _np.sign(missing))\n n[n < 0] = 0\n missing = self._N - _np.sum(n)\n nc = _np.cumsum(n)\n if self.rsphere > 0:\n posatoms = _rndSphere(self.rsphere, int(self._N))\n else:\n posatoms = _np.zeros((self._N, 3))\n multisphere._staggeredadd(posspheres, posatoms, nc)\n self._debug = (len(posspheres), _np.min(n), _np.max(n), _np.mean(n))\n self._pos = posatoms","title":""},{"docid":"52d9a95819ef0d7fa86b3cca8ca2496b","score":"0.48382258","text":"def remove_all_forces(self):\n self.forces = []","title":""},{"docid":"14fd50105b4aa4359ae49db62a245c38","score":"0.4827966","text":"def find_near_assets(self, asset_id: str, data: dict) -> dict:\n\n query = {'center': {'$near': {'$geometry': data['center'], '$maxDistance': 500}}}\n qs = self.collection.find(query, {'asset_id': 1, 'year': 1, 'iiif_identifier': 1})\n\n # Merge with similar and set distance as 0\n similar = data.get('similar', [])\n similar = similar + [\n py_.pick(i, 'asset_id', 'year', 'iiif_identifier') for i in qs if i.get('year', None) is not None\n ]\n out = {'similar': similar}\n\n return out","title":""},{"docid":"d62ca60fc0cab06461d22cb340d12add","score":"0.48248664","text":"def calculate_bonded_forces(box, coords, elems, bonds, force, bond_length, force_constant):\n energy = 0\n for bond in bonds:\n dx = distance_pbc(box, coords[bond[0]], coords[bond[1]])\n dx2 = np.dot(dx, dx)\n dx1 = math.sqrt(dx2)\n bond_name = elems[bond[0]] + '-' + elems[bond[1]]\n if(not bond_name in bond_length or not bond_name in force_constant):\n sys.exit(\"quitting program because of unknown %s bond\"%bond_name)\n ddx = dx1 - bond_length[bond_name]\n energy += 0.5 * force_constant[bond_name]*ddx**2\n # force on one vector\n force_a = -force_constant[bond_name] * ddx / dx1\n # on xyz vectors\n for m in range(3):\n force[bond[0]][m] += force_a * dx[m]\n force[bond[1]][m] -= force_a * dx[m]\n\n return energy, force","title":""},{"docid":"d616cb450c3a3390c5892225009ab1e4","score":"0.481471","text":"def calculateFitness(self, fitness_forces, modules):\n fitness = 0\n for item in fitness_forces:\n response = self.calculateUnitFitness(item, modules)\n if response is None:\n return None\n else:\n fitness += response\n return fitness","title":""},{"docid":"43a7167aa6439a2a418a6fa18d12fdf5","score":"0.48118192","text":"def getKinectData(self):\n #Figuring out the corner points on the outside of the sphere\n (junk, leftPoint,rightPoint,lowest,highest)=self.getFieldOfView()\n #Create the rays that go from the center and hit each of the outside points, treat as 2D, represented as length and slope \n angle=SimSensors.angle((leftPoint[1],leftPoint[0]),(rightPoint[1],rightPoint[0]))-math.pi/2\n rays=[]\n i=0\n prev=(-1,-1)\n while True:\n i+=1\n (x,y)=(int(leftPoint[0]+math.cos(angle)*i),int(leftPoint[1]-math.sin(angle)*i))\n #print (x,y)\n #import time\n #time.sleep(1)\n if (rightPoint[0]-leftPoint[0])*(x-rightPoint[0])>=0 and (rightPoint[1]-leftPoint[1])*(y-rightPoint[1])>=0:\n break\n if prev[0]!=x or prev[1]!=y:#Checking for duplicates\n rays.append((SimSensors.angle(self.pos,(x,y)),SimSensors.euclid(self.pos,(x,y))))\n prev=[x,y]\n rays.append((SimSensors.angle(self.pos,rightPoint),SimSensors.euclid(self.pos,rightPoint)))\n points=[]\n #Tracing each ray from the beginning...\n for ray in rays:\n lowestAngle=-self.scanAngleV#Keeps track of the angle that it can see that is not blocked by another object\n for i in range(int(ray[1])+1):\n x=int(self.getFrontPos()[0]+i*math.cos(ray[0]))\n y=int(self.getFrontPos()[1]+i*math.sin(ray[0]))\n if min(x,y)<0 or max(x-len(self.heights),y-len(self.heights[0]))>0:\n continue\n z=self.kinectHeight+i*math.sin(lowestAngle)#Minimum height that can be seen at this point\n if x<0 or x>=len(self.heights) or y<0 or y>=len(self.heights[0]):\n continue\n if self.heights[x][y]>z:\n points.append((x,y,self.heights[x][y]))\n #setting lowestAngle to angle from kinect to top of object\n if self.kinectHeight>self.heights[x][y]:\n if i==0:\n if self.kinectHeight-self.heights[x][y]>0:\n lowestAngle=math.pi/2\n else:\n lowestAngle=math.pi/-2\n else:\n lowestAngle=-math.atan(float(self.kinectHeight-self.heights[x][y])/float(i))\n elif self.kinectHeight<self.heights[x][y]:\n if i==0:\n if self.heights[x][y]-self.kinectHeight>0:\n lowestAngle=math.pi/2\n else:\n lowestAngle=math.pi/-2\n else:\n lowestAngle=math.atan(float(self.heights[x][y]-self.kinectHeight)/float(i))\n else:\n lowestAngle=0.0 \n return points","title":""},{"docid":"4560ff102bb2419d6fa81854f6cb1c65","score":"0.4809997","text":"def _contact_geometry_GCS(self, q):\n # create distance object\n # print \"q =\", q\n # print \"i =\", self.body_id_i\n # print \"j =\", self.body_id_j\n # print \"i =\", q2R_i(q, self.body_id_i)\n # print \"j =\", q2R_i(q, self.body_id_j)\n self._distance_obj = Distance(q2R_i(q, self.body_id_i), q2R_i(q, self.body_id_j))\n\n # distance\n _d = self._distance_obj._distance\n\n # delta\n _delta = self._distance_obj._distance - self._distance0\n\n # normal in GCS\n _n_GCS = -self._distance_obj._distance_vector / self._distance_obj._distance\n\n # tangent in GCS\n _t_GCS = n2t(_n_GCS)\n\n return _d, _delta, _n_GCS, _t_GCS","title":""},{"docid":"f4e1c2587bcc083ef6b5b92845e14201","score":"0.4807392","text":"def find_valid(self, obj1coor, obj2coor, obs, lims=[[0,2*np.pi],[-np.pi/2,np.pi/2]]):\n \n #print obs*np.pi/180\n \n \n n=self.comp_power\n \n obsth = obs[0]\n obsph = obs[1]\n \n # Initalize variable space\n A = np.linspace(lims[0][0],lims[0][1],n)\n B = np.linspace(lims[1][0],lims[1][1],n)\n grid = np.meshgrid(A,B)\n \n flat = np.array([grid[0].flatten(),grid[1].flatten()]).T\n \n # calculate observation vectors\n obj1_xyz = self.xyz(obj1coor[0],obj1coor[1]) # xyz(obj1[0],obj1[1])\n obj2_xyz = self.xyz(obj2coor[0],obj2coor[1]) # (obj2[0],obj2[1])\n\n\n # For each potential A and B vector, calculate the theoretical change in theta and phi \n thp = []\n php = []\n for x in flat:\n # Calculate the vectors in a frame [alpha, beta] on the surface of the earth\n [thp1,php1] = self.vec_prime(x[0],x[1],obj1_xyz,form='th-ph')\n [thp2,php2] = self.vec_prime(x[0],x[1],obj2_xyz,form='th-ph')\n\n # Calculate theoretical difference between the two angles\n thp += [thp2-thp1]\n php += [php2-php1]\n\n #back from column to 2d grid\n thp= np.array(thp).reshape(n,n)\n php= np.array(php).reshape(n,n)\n \n #print thp\n \n # Create surface that represents the True change in alt-az coords (as observed)\n obs12_th = np.ones((n,n))*obsth\n obs12_ph = np.ones((n,n))*obsph\n\n # Take the observed delta-theta and delta-phi and compare it to our theoretical ones to figure out\n # which values of A and B would allow for the observed changes.\n \n # Set up empty array for output valeues\n sel_fin = np.array([])\n \n # Start real small with the binsize, extremely restrictive\n stdth = np.std(thp.flatten())\n stdph = np.std(php.flatten())*2\n \n #width = (max(thp.flatten())-min(thp.flatten()))/2\n \n mod=0.01\n dist = np.ones((n,n))*10**-12\n \n \n while mod*stdth < 3*self.obserrs[0] and mod*stdph < 3*self.obserrs[1]:\n \n mod*=1.01\n \n thdist = np.exp(- ((thp-obs12_th)/(mod*stdth))**2 )\n phdist = np.exp(- ((php-obs12_ph)/(mod*stdph))**2 )\n \n #thdist = np.exp(- ((thp-obs12_th)/(self.obserrs[0]))**2 )\n #phdist = np.exp(- ((php-obs12_ph)/(self.obserrs[1]))**2 ) \n \n dist = (thdist/np.sum(thdist))*(phdist/np.sum(phdist))\n dist_norm = dist/np.sum(dist)\n \n self.chain+=[dist_norm]\n \n return grid,dist_norm","title":""},{"docid":"828e97789ab10fc3087f66480d1411df","score":"0.48013788","text":"def calc_force(a, b, dt):\n\n r = ((b['x'] - a['x']) ** 2 + (b['y'] - a['y']) ** 2 + (b['z']\n - a['z']) ** 2) ** 0.5\n a['vx'] += G * a['m'] * b['m'] / r ** 2 * ((b['x'] - a['x']) / r) \\\n / a['m'] * dt\n a['vy'] += G * a['m'] * b['m'] / r ** 2 * ((b['y'] - a['y']) / r) \\\n / a['m'] * dt\n a['vz'] += G * a['m'] * b['m'] / r ** 2 * ((b['z'] - a['z']) / r) \\\n / a['m'] * dt","title":""},{"docid":"5ad5d9f47c24125cd478eb46cdcd465f","score":"0.47910753","text":"def distance_displacement(self):\n AtomIterator = np.ascontiguousarray(np.vstack((np.fromiter(itertools.chain(*[[i]*(self.na-i-1) for i in range(self.na)]),dtype=np.int32),\n np.fromiter(itertools.chain(*[range(i+1,self.na) for i in range(self.na)]),dtype=np.int32))).T)\n if hasattr(self, 'boxes') and pbc:\n boxes = np.array([[self.boxes[i].a, self.boxes[i].b, self.boxes[i].c] for i in range(len(self))])\n drij, dxij = AtomContact(np.array(self.xyzs), AtomIterator, box=boxes, displace=True)\n else:\n drij, dxij = AtomContact(np.array(self.xyzs), AtomIterator, box=None, displace=True)\n return AtomIterator, list(drij), list(dxij)","title":""},{"docid":"9ddac155d6a9476e6a8f8372b841d185","score":"0.479047","text":"def calculate_plastic_force(\n self, u: np.ndarray, p: np.ndarray\n ) -> Tuple[float, float]:\n # initialise variables\n (f_top, f_bot) = (0, 0)\n (ea_top, ea_bot) = (0, 0)\n (qx_top, qx_bot) = (0, 0)\n (qy_top, qy_bot) = (0, 0)\n\n top_geoms, bot_geoms = self.geometry.split_section(point_i=p, vector=u)\n\n if top_geoms:\n for top_geom in top_geoms:\n e = top_geom.material.elastic_modulus\n f_y = top_geom.material.yield_strength\n area_top = top_geom.calculate_area()\n ea_top += e * area_top\n cx, cy = top_geom.calculate_centroid()\n qx_top += cy * area_top\n qy_top += cx * area_top\n f_top += f_y * area_top\n\n if bot_geoms:\n for bot_geom in bot_geoms:\n e = bot_geom.material.elastic_modulus\n f_y = bot_geom.material.yield_strength\n area_bot = bot_geom.calculate_area()\n ea_bot += e * area_bot\n cx, cy = bot_geom.calculate_centroid()\n qx_bot += cy * area_bot\n qy_bot += cx * area_bot\n f_bot += f_y * area_bot\n\n try:\n self.c_top = [qy_top / ea_top, qx_top / ea_top]\n self.f_top = f_top\n except ZeroDivisionError:\n self.c_top = [0, 0]\n self.f_top = 0\n\n try:\n self.c_bot = [qy_bot / ea_bot, qx_bot / ea_bot]\n except ZeroDivisionError:\n self.c_bot = [0, 0]\n\n return (f_top, f_bot)","title":""},{"docid":"46870c467aa0d0cf76849ffc08fa8f4f","score":"0.47887427","text":"def get_positions(self, filename=\"OUTCAR\", lines=None, n_atoms=None):\n if n_atoms is None:\n n_atoms = self.get_number_of_atoms(filename=filename, lines=lines)\n trigger_indices, lines = _get_trigger(\n lines=lines, filename=filename, trigger=\"TOTAL-FORCE (eV/Angst)\"\n )\n return self._get_positions_and_forces_parser(\n lines=lines,\n trigger_indices=trigger_indices,\n n_atoms=n_atoms,\n pos_flag=True,\n force_flag=False,\n )","title":""},{"docid":"46d42bade72f2c3c7e41b34460dc18c7","score":"0.47878733","text":"def filter_npc_vehicles(self):\n near_npc_vehicles = [] # carla.Vehicles\n\n for index, veh in enumerate(self.npc_vehicles):\n veh_info = self.get_veh_info(veh) # return a info dict\n # add distance into veh_info\n is_near = self.check_is_near(veh_info)\n\n if is_near:\n near_npc_vehicles.append(veh_info)\n\n # select vehicles in front of ego vehicle\n candidate_npc_vehicles = []\n for veh_info in near_npc_vehicles:\n is_front = self.check_is_front(veh_info)\n if is_front:\n candidate_npc_vehicles.append(veh_info)\n\n # select npc vehicles for state representation\n selected_npc_vehicles = heapq.nsmallest(self.state_npc_number, # int, number of npc vehicles\n candidate_npc_vehicles, # list in which stores the dict\n key=lambda s: s['distance'])\n\n return selected_npc_vehicles","title":""},{"docid":"5de1d988ec92138a8524792c137f9038","score":"0.47865242","text":"def generalized_forces(self, q , dq , ddq , t = 0 ): \n \n H = self.H( q )\n C = self.C( q , dq )\n g = self.g( q )\n \n u = self.ubar\n d = self.d( q , dq , u )\n \n # Generalized forces\n forces = np.dot( H , ddq ) + np.dot( C , dq ) + g + d\n \n return forces","title":""},{"docid":"38682cceb9b9e7403eb5bb0681e0cfd3","score":"0.47845912","text":"def analyze(self):\n self.calculate_FOM()\n self.generate_FOM_image()\n self.generate_plot()\n ##self.generate_spot_mask()","title":""},{"docid":"028a429f7bbec0b6730d5ae12ea3feef","score":"0.47789276","text":"def get_force(self):\n # @todo: decide whether or not we want to have gimbaling provide x force and lift for 4 of the engines, or to do x force (drag) for all engines in force_hover_engines.py\n return self.data(0.0, 0.0, 0.0)","title":""},{"docid":"57886eb1b9ee1654916afa094afbe5e7","score":"0.47713766","text":"def for_md_calculate_force(simul_box, ion_dict):\n with tf.name_scope(\"for_md_calculate_force\"):\n pef = _particle_electrostatic_force(simul_box, ion_dict)\n erw = _electrostatic_right_wall_force(simul_box, ion_dict)\n elw = _electrostatic_left_wall_force(simul_box, ion_dict)\n plj = _particle_lj_force(simul_box, ion_dict)\n lw_lj = _left_wall_lj_force(simul_box, ion_dict)\n rw_lj = _right_wall_lj_force(simul_box, ion_dict)\n ion_dict[interface.ion_for_str] = plj + lw_lj + rw_lj + erw + elw + pef\n return ion_dict","title":""},{"docid":"55e74fa68123c3f03a7a693ceb8e3559","score":"0.47644803","text":"def get_constant(self):\n\t\tforce_essieu_avant = MASSE*G_TERRE*L_AR/(L_AR + L_AV) # regle des moments\n\t\tforce_essieu_arriere = MASSE*G_TERRE*L_AV/(L_AR + L_AV)# en faisant l'hypotese que la voiture est tres deformable\n\t\tself.force_arriere_droit[\"z\"] = force_essieu_arriere/2.0\n\t\tself.force_arriere_gauche[\"z\"] = force_essieu_arriere/2.0\n\t\tself.force_avant_droit[\"z\"] = force_essieu_avant/2.0\n\t\tself.force_avant_gauche[\"z\"] = force_essieu_avant/2.0","title":""},{"docid":"a0ad752d34a7a8aeb51059ff688df385","score":"0.47494587","text":"def direct_solve(self):\n # initialize global stiffness, force, displacement\n glob_stiff = np.zeros((self.num_node, self.num_node))\n glob_force = np.zeros(self.num_node)\n disp = np.zeros(self.num_node)\n\n # Assemble the global stiffness and force\n for elem in self.elements:\n\n # element nodes, coordinates, and displacement\n elem_nodes = self.conn[elem.num][:elem.num_points]\n elem_coords = self.coords[elem_nodes]\n elem_disp = disp[elem_nodes]\n\n # element stiffness and force\n elem_stiff = elem.stiffness(elem_coords, elem_disp)\n elem_force = elem.force(self.dist_load, elem_coords)\n\n # Add element contribution to global force and stiffness\n for a in range(elem.num_points):\n I = elem_nodes[a]\n glob_force[I] += elem_force[a]\n for b in range(elem.num_points):\n J = elem_nodes[b]\n glob_stiff[I, J] += elem_stiff[a, b]\n\n # Concentrated forces\n for (node, mag) in self.conc_forces:\n glob_force[node] += mag\n\n # Apply boundary conditions\n for (n, alpha, beta, gamma) in self.boundary_conditions:\n glob_stiff[n, n] += alpha / beta\n glob_force[n] += gamma / beta\n\n # Now solve\n disp[:] = np.linalg.solve(glob_stiff, glob_force)\n\n self._disp[:] = disp\n\n # post process\n stress = np.empty(self.num_elem)\n stran = np.empty(self.num_elem)\n for elem in self.elements:\n # element nodes, coordinates, and displacement\n elem_nodes = self.conn[elem.num][:elem.num_points]\n elem_coords = self.coords[elem_nodes]\n elem_disp = disp[elem_nodes]\n elem_stran = []\n elem_stress = []\n\n for (n, xi) in enumerate(elem.gauss_points):\n B = elem.b_matrix(xi, elem_coords)\n e = np.dot(B, elem_disp)\n s = elem.mat.get_stress(e)\n elem_stran.append(e)\n elem_stress.append(s)\n stress[elem.num] = np.sum(elem_stress) / len(elem_stress)\n stran[elem.num] = np.sum(elem_stran) / len(elem_stran)\n\n self.snapshot(stress, stran, self.disp)\n\n return","title":""},{"docid":"29d840d3494bc40f6ac54f92380f6e8a","score":"0.47363195","text":"def best_nearby(self, delta, point, prevbest, nvars, f, c_funevals):\n\n z = [0]*self.VARS\n\n minf = prevbest\n\n i = 0\n\n while (i < nvars):\n z[i] = point[i]\n\n i += 1\n\n i = 0\n\n while (i < nvars):\n z[i] = point[i] + delta[i]\n\n ftmp = f(z, nvars, c_funevals)\n\n if (ftmp < minf):\n minf = ftmp\n else:\n delta[i] = 0.0 - delta[i]\n z[i] = point[i] + delta[i]\n\n ftmp = f(z, nvars, c_funevals)\n\n if (ftmp < minf):\n minf = ftmp\n else:\n z[i] = point[i]\n\n i += 1\n\n i = 0\n\n while (i < nvars):\n point[i] = z[i]\n\n i += 1\n\n return minf","title":""},{"docid":"31ad1003dbd8efa76df4df0addb3fd88","score":"0.4734121","text":"def forcesIntoMOIBasis(atoms, index):\r\n \r\n forces = atoms.get_forces()\r\n positions = atoms.get_positions()\r\n\r\n # find MOIs\r\n pvalues, pdirs = get_moments_of_inertia(positions=positions,\r\n masses=atoms.get_masses(),\r\n center=positions[index],\r\n vectors=True)\r\n\r\n # Normalizing the vectors\r\n pdirs[0] /= np.linalg.norm(pdirs[0])\r\n pdirs[1] /= np.linalg.norm(pdirs[1])\r\n pdirs[2] /= np.linalg.norm(pdirs[2])\r\n \r\n # Calculating the center of mass in the pdirs coordinate basis.\r\n xtilde, ytilde, ztilde = calculate_image_center(atoms=atoms,\r\n watchindex=index,\r\n pdirs=pdirs)\r\n\r\n # If the coordinates of the center of mass are negative, \r\n # then the principle direction is flipped.\r\n # We want the first moment to be positive in all three directions.\r\n if xtilde < 0.:\r\n pdirs[0] *= -1.\r\n if ytilde < 0.:\r\n pdirs[1] *= -1.\r\n if ztilde < 0.:\r\n pdirs[2] *= -1.\r\n\r\n # Forces on atom index 'index' are transformed into the principle directions.\r\n transformed_forces = transform_forces(pdirections=pdirs,\r\n forces=forces[index])\r\n \r\n return transformed_forces, pdirs","title":""},{"docid":"16d1262cc9f04e36f30647521e834eab","score":"0.4730221","text":"def force(potential):\n forcex = np.zeros_like(potential)\n forcey = np.zeros_like(potential)\n\n lengthx, lengthy = potential.shape\n\n for i in range(lengthx):\n for j in range(1, lengthy - 1):\n forcex[i, j] = -0.5 * (potential[(i + 1) % lengthx, j] - potential[i - 1, j])\n forcey[i, j] = -0.5 * (potential[i, j + 1] - potential[i, j - 1])\n\n return forcex, forcey","title":""},{"docid":"ab157bc5804ef801b876efe19de8710c","score":"0.47300896","text":"def _magnetic_potential(self, x: float, y: float, z: float) -> float:\n if not self.is_Coord_Inside_Vacuum(x, y, z):\n return np.nan\n y = abs(y)\n z = abs(z)\n if -self.extra_field_length <= x <= self.L_cap:\n V0 = self._magnetic_potential_Func_Fringe(x, y, z)\n elif self.L_cap < x <= self.L - self.L_cap:\n V0 = self._magnetic_potential_Func_Inner(x, y, z)\n elif 0 <= x <= self.L + self.extra_field_length:\n x = self.L - x\n V0 = self._magnetic_potential_Func_Fringe(x, y, z)\n else:\n raise Exception(\"Particle outside field region\")\n V0 *= self.field_fact\n return V0","title":""},{"docid":"a27c8ebb23476444b90789163792efb2","score":"0.4729782","text":"def get_thermodynamic(self):\n res = {}\n res = Montecarlo.get_thermodynamic(self)\n at_count = self.count_atoms()\n\n # Inser the insertion enegies\n for move in self.insertion_moves:\n key = self.get_key(move[0], move[1])\n name = \"insert_energy_{}\".format(key)\n N = self.averager_track[key].num_samples\n inf_temp = float(at_count[move[0]])/(at_count[move[1]]+1)\n E0 = self.averager_track[key].ref_value\n\n res[name] = E0 - kB * self.T * \\\n np.log(self.averager_track[key].average * inf_temp)\n\n name = \"raw_insert_energy_{}\".format(key)\n res[name] = self.raw_insertion_energy[key] / N\n\n name = \"boltzmann_avg_insert_energy_{}\".format(key)\n res[name] = self.boltzmann_weight_ins_energy[key].average / \\\n self.averager_track[key].average\n\n name = \"boltzmann_avg_insert_energy_sq_{}\".format(key)\n res[name] = self.boltzmann_weight_ins_eng_sq[key].average / \\\n self.averager_track[key].average\n return res","title":""},{"docid":"ffc6f50b8b046ed36e8e736939a17dc2","score":"0.47294286","text":"def get_potential():\n # print GetRegister('Atom')\n # type(GetRegister('Atom'))\n\tres = []\n\tfor k1, atom1 in GetRegister('Atom'):\n\t\tv = 0\n\t\tfor k2, atom2 in GetRegister('Atom'):\n\t\t\tif atom2._parent != atom1._parent:\n\t\t\t\tv += atom2.potential_at(atom1._pos) # add the potential at atom2 from atom1\n\t\tres.append(v) \n\treturn np.matrix(res) # return the result as a numpy matrix object","title":""},{"docid":"2574178e0a532a0a22db308b853f4637","score":"0.47279984","text":"def calculate_onobject_gaze(self):\r\n onint_gaze, onboring_gaze, onfam_gaze = 0,0,0\r\n for k, v in self._dict.items():\r\n if v[0] == \"INT\":\r\n onint_gaze += v[2]\r\n elif v[0] == \"BOR\":\r\n onboring_gaze += v[2]\r\n elif v[0] == \"FAM\":\r\n onfam_gaze += v[2]\r\n\r\n all_gaze = onint_gaze + onboring_gaze + onfam_gaze\r\n onint = onint_gaze / all_gaze if all_gaze != 0 else 0\r\n onboring = onboring_gaze / all_gaze if all_gaze != 0 else 0\r\n onfam = onfam_gaze / all_gaze if all_gaze != 0 else 0\r\n\r\n return onint, onboring, onfam","title":""},{"docid":"f85c78cc3096da00b58f9cd350a20991","score":"0.47214985","text":"def post_calc(self):\r\n\r\n\t\tself.mfp = fcl.mean_free_path(self.atmosphere.T, self.atmosphere.p,\r\n\t\t\tself.atmosphere.d)\r\n\t\tself.Kn = self.mfp / self.spacecraft.L\r\n#\t\tself.Re = fcl.KnReMa(self.atmosphere.k, Kn=self.Kn,\r\n#\t\t\tMa=self.Ma)\r\n\t\tself.Re = fcl.Reynolds(self.atmosphere.rho, self.V, self.spacecraft.L,\r\n\t\t\tself.atmosphere.mu)\r\n\r\n\t\t# Continuum: 0 < Kn < 0.1\r\n\t\t# Slip: 0.1 <= Kn < 1.0\r\n\t\t# Transition: 1.0 <= Kn < 10\r\n\t\t# Free molecular: 10 < Kn\r\n\r\n\t\tself.regimes = placeholder()\r\n\r\n\t\tif len(np.argwhere(self.Kn > 10)) != 0:\r\n\t\t\tself.index_tran_freemol = np.argwhere(self.Kn > 10)[-1]\r\n\t\t\tself.regimes.free_molecular = np.argwhere(self.Kn >= 10)\r\n\t\telse:\r\n\t\t\tself.index_tran_freemol = None\r\n\r\n\t\tif len(np.argwhere(self.Kn > 1.0)) != 0:\r\n\t\t\tself.index_slip_tran = np.argwhere(self.Kn > 1.0)[-1]\r\n\t\t\tself.regimes.transition = np.argwhere((self.Kn < 10) & (self.Kn >= 1.0))\r\n\t\telse:\r\n\t\t\tself.index_slip_tran = None\r\n\r\n\t\tif len(np.argwhere(self.Kn > 0.1)) != 0:\r\n\t\t\tself.index_cont_slip = np.argwhere(self.Kn > 0.1)[-1]\r\n\t\t\tself.regimes.slip = np.argwhere((self.Kn < 1.0) & (self.Kn >= 0.1))\r\n\t\telse:\r\n\t\t\tself.index_cont_slip = None\r\n\r\n\t\tif len(np.argwhere((self.Kn > 0) & (self.Kn <= 0.1))) != 0:\r\n\t\t\tself.regimes.continuum = np.argwhere((self.Kn < 0.1) & (self.Kn >= 0))\r\n\t\telse:\r\n\t\t\tself.index_cont_slip = None\r\n\r\n\t\treturn [self.mfp, self.Kn, self.Re]","title":""},{"docid":"b55deed435212910627555fa2a0df0d2","score":"0.47197974","text":"def _get_contact_geometry_data(self, q):\n # normal in GCS of body i, j\n self._n_GCS_list = [+self._n_GCS, -self._n_GCS]\n\n # tangent in GCS of body i, j\n self._t_GCS_list = [+self._t_GCS, -self._t_GCS]","title":""},{"docid":"aa0f8bab954b8c31073b63e8dc484ef2","score":"0.47193244","text":"def get_sect_forces_gain(self):\n\n Ntot = 0\n for ss in range(self.MS.n_surf):\n Ntot += self.MS.NN[ss] + 1\n self.Kfsec = np.zeros((3 * Ntot, 3 * self.Kzeta))\n self.Kmsec = np.zeros((3 * Ntot, 3 * self.Kzeta))\n\n Kzeta_start = 0\n II_start = 0\n for ss in range(self.MS.n_surf):\n M, N = self.MS.MM[ss], self.MS.NN[ss]\n\n for nn in range(N + 1):\n zeta_sec = self.MS.Surfs[ss].zeta[:, :, nn]\n\n # section indices\n iivec = [II_start + np.ravel_multi_index((cc, nn),\n (3, N + 1)) for cc in range(3)]\n # iivec = [II_start + cc+6*nn for cc in range(6)]\n # iivec = [II_start + cc*(N+1) + nn for cc in range(6)]\n\n for mm in range(M + 1):\n # vertex indices\n jjvec = [Kzeta_start + np.ravel_multi_index((cc, mm, nn),\n (3, M + 1, N + 1)) for cc in range(3)]\n\n # sectional force\n self.Kfsec[iivec, jjvec] = 1.0\n\n # sectional moment\n dx, dy, dz = zeta_sec[:, mm] - zeta_sec[:, M // 2]\n self.Kmsec[np.ix_(iivec, jjvec)] = np.array([[0, -dz, dy],\n [dz, 0, -dx],\n [-dy, dx, 0]])\n Kzeta_start += 3 * self.MS.KKzeta[ss]\n II_start += 3 * (N + 1)","title":""},{"docid":"2d2aede5659b6da08818b51715196b8c","score":"0.4713425","text":"def get_potential(self,atom,grid):\n position=self.PI.GetPosition(atom)\n return self.get_potential_atpos(position,grid)","title":""},{"docid":"6c3f6dcd7df9985de286e813d4edbbd7","score":"0.47111088","text":"def force(p1,p2):\n return (-(4.982338253E-10)*p1.mass*p2.mass/(np.linalg.norm(Particle.Vect_Sep(p1,p2)))**3)*Particle.Vect_Sep(p1,p2) # convert unit of G to km^3 kg^(-1) day^(-2) to match with the input units from file_handle","title":""},{"docid":"d6de14740395850d689970b1378f8022","score":"0.47041842","text":"def q_show_forces(self, forces):\n\n c = [intco.f_show_factor for f in self.all_fragments for intco in f.intcos]\n c = np.asarray(c)\n qaJ = c * forces\n return qaJ","title":""}],"string":"[\n {\n \"docid\": \"e462cf8d09116c9d6ae444287f7f8205\",\n \"score\": \"0.7421039\",\n \"text\": \"def calculate_near_field_forces_and_moments(self):\\n # Calculate the velocities induced at every panel's bound vortex center.\\n induced_velocities = aerodynamics.collapsed_velocities_from_horseshoe_vortices(\\n points=self.panel_bound_vortex_centers,\\n back_right_vortex_vertices=self.panel_back_right_vortex_vertices,\\n front_right_vortex_vertices=self.panel_front_right_vortex_vertices,\\n front_left_vortex_vertices=self.panel_front_left_vortex_vertices,\\n back_left_vortex_vertices=self.panel_back_left_vortex_vertices,\\n strengths=self.vortex_strengths,\\n )\\n\\n # Add the freestream velocity to the induced velocities to calculate the\\n # total velocity at every panel's bound vortex center.\\n total_velocities = induced_velocities + self.freestream_velocity\\n\\n # Calculate the near field force, in geometry axes, on each panel's bound\\n # vortex.\\n near_field_forces_geometry_axes = (\\n self.operating_point.density\\n * np.expand_dims(self.vortex_strengths, axis=1)\\n * np.cross(total_velocities, self.panel_bound_vortex_vectors, axis=-1)\\n )\\n\\n # Calculate the near field moments, in geometry axes, on each panel's bound\\n # vortex.\\n near_field_moments_geometry_axes = np.cross(\\n self.panel_bound_vortex_centers - self.airplane.xyz_ref,\\n near_field_forces_geometry_axes,\\n axis=-1,\\n )\\n\\n functions.process_steady_solver_forces(\\n steady_solver=self,\\n near_field_forces_geometry_axes=near_field_forces_geometry_axes,\\n near_field_moments_geometry_axes=near_field_moments_geometry_axes,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"026f950d8d6b924e3aad4dfe849d37b5\",\n \"score\": \"0.59816235\",\n \"text\": \"def get_forces(self):\\n return self.forces\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ca24d27f1fbfb3bd7d06067dc607f37b\",\n \"score\": \"0.59468377\",\n \"text\": \"def __apply_forces(self):\\n to_remove = []\\n\\n for [force_vector, location, end_time] in self.forces:\\n self.entity_addresses['rhonrad'].apply_force_at_local_point(\\n force_vector, location)\\n if end_time != -1 and end_time >= self.current_time:\\n to_remove = [force_vector, location, end_time]\\n\\n for force in to_remove:\\n self.remove_force_on_wheel(force)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"18ddf28a336c8a6ca51ab6a156ce6c43\",\n \"score\": \"0.58091074\",\n \"text\": \"def calculate_forces(box, coords, elems, bonds, exclude, ff):\\n # initialize forces\\n force = [[0.0, 0.0, 0.0] for i in range(len(coords))]\\n\\n start_time_bonded = time.time()\\n bonded_energy, force = calculate_bonded_forces(box, coords, elems, bonds, force, ff[\\\"bond_length\\\"], ff[\\\"bond_force_const\\\"])\\n print('time for bonded force: ', time.time() - start_time_bonded)\\n\\n start_time_nonbonded = time.time()\\n nonbonded_energy, force = calculate_nonbonded_forces(box, coords, elems, exclude, force, ff[\\\"sigma\\\"], ff[\\\"epsilon\\\"], ff[\\\"charge\\\"])\\n print('time for nonbonded force: ', time.time() - start_time_nonbonded)\\n\\n return bonded_energy + nonbonded_energy, force\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64c1dc7f9531aec76e98b01db4dcca63\",\n \"score\": \"0.57691175\",\n \"text\": \"def _get_forces(charges):\\r\\n\\r\\n all_charges = np.concatenate((charges, -charges))\\r\\n all_charges = all_charges[:, None]\\r\\n r = charges - all_charges\\r\\n r_mag = np.sqrt((r*r).sum(-1))[:, :, None]\\r\\n with warnings.catch_warnings():\\r\\n warnings.simplefilter(\\\"ignore\\\")\\r\\n force = r / r_mag**3\\r\\n potential = 1. / r_mag\\r\\n\\r\\n d = np.arange(len(charges))\\r\\n force[d,d] = 0\\r\\n force = force.sum(0)\\r\\n force_r_comp = (charges*force).sum(-1)[:, None]\\r\\n f_theta = force - force_r_comp*charges\\r\\n potential[d,d] = 0\\r\\n potential = 2*potential.sum()\\r\\n return f_theta, potential\",\n \"title\": \"\"\n },\n {\n \"docid\": \"69baf116f14f94ae22789d9ae8875c1a\",\n \"score\": \"0.5766241\",\n \"text\": \"def get_positions_and_forces(self, filename=\\\"OUTCAR\\\", lines=None, n_atoms=None):\\n if n_atoms is None:\\n n_atoms = self.get_number_of_atoms(filename=filename, lines=lines)\\n trigger_indices, lines = _get_trigger(\\n lines=lines, filename=filename, trigger=\\\"TOTAL-FORCE (eV/Angst)\\\"\\n )\\n return self._get_positions_and_forces_parser(\\n lines=lines,\\n trigger_indices=trigger_indices,\\n n_atoms=n_atoms,\\n pos_flag=True,\\n force_flag=True,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae313c98182a3b6a24243b9bef4082b6\",\n \"score\": \"0.57572204\",\n \"text\": \"def get_forces(self, filename=\\\"OUTCAR\\\", lines=None, n_atoms=None):\\n if n_atoms is None:\\n n_atoms = self.get_number_of_atoms(filename=filename, lines=lines)\\n trigger_indices, lines = _get_trigger(\\n lines=lines, filename=filename, trigger=\\\"TOTAL-FORCE (eV/Angst)\\\"\\n )\\n return self._get_positions_and_forces_parser(\\n lines=lines,\\n trigger_indices=trigger_indices,\\n n_atoms=n_atoms,\\n pos_flag=False,\\n force_flag=True,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ecc866726d0346e93a418d0c937e9a47\",\n \"score\": \"0.56925327\",\n \"text\": \"def calculate_force(self, pos):\\n force = np.array((0., 0.))\\n r = self.par_dic[\\\"resolution\\\"]\\n for name, (objects, calculator) in self.calculator_dic.iteritems():\\n if name == \\\"source\\\": # exclude the force from other source\\n continue\\n for obj_par in objects:\\n force += calculator.get_force(np.array(obj_par)/r, np.array(pos + [self.par_dic[\\\"ball_size\\\"]])/r)\\n return force\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b4ba8b1acdbef9ea2183e164eff2bd13\",\n \"score\": \"0.56105226\",\n \"text\": \"def calc_force(self):\\n\\n total_force = 0.\\n\\n # Loop through elements on the squirmer surface and compute the\\n # hydrodynamic stresses on each one\\n for elem_S in self.mesh.elems_in_region(\\\"sphere\\\"):\\n # get the \\\"bulk\\\" element adjacent to the surface element.\\n _S, elem_V = elem_S.adj_map['*']\\n # get the element mapping\\n x_cyl = elem_V.get_mapping()\\n jac = x_cyl.jacobian()\\n detJ, invJ = det_inv_2x2(jac)\\n\\n # coordinates in cylindrical and polar form\\n x_cyl_S = elem_S.get_mapping()\\n # let *_S denote quantities defined at the element surface only\\n # theta = np.arctan2(x_cyl_S[0], x_cyl_S[1]) # polar angle\\n sin_th = x_cyl_S[0] # here, r = 1\\n sin2_th = sin_th**2\\n cos_th = x_cyl_S[1]\\n\\n # surface slip velocity\\n slip_profile = self.phys_params[\\\"slip_profile\\\"]\\n vslip = slip_profile(sin_th, cos_th)\\n\\n # solution for vorticity field\\n vort_gl = self.soln_vec[1::2]\\n vort = elem_V.get_coeffs(vort_gl)\\n\\n invJ_S = invJ.get_boundary(_S)\\n # compute d{vorticity}/d(xi, eta, ...)\\n dw_du_S = vort.jacobian().get_boundary(_S)\\n # d(rho, z)/d(xi, eta, ...)\\n drhoz_dr_S = x_cyl.get_boundary(_S)\\n # d{vorticity}/dr at squirmer surface\\n dw_dr_S = np.einsum('im,ijm,jm->m',\\n dw_du_S, invJ_S, drhoz_dr_S)\\n\\n # compute stresses\\n vort_S = vort.get_boundary(_S)\\n n_rey = self.phys_params[\\\"N_Re\\\"]\\n bernouli_stress = np.pi * n_rey * vslip**2 * sin_th * cos_th\\n w_asym_stress = np.pi * (dw_dr_S + vort_S) * sin2_th\\n pressure_stress = bernouli_stress + w_asym_stress\\n viscous_stress = -2*np.pi * vort_S * sin2_th\\n total_stress = pressure_stress + viscous_stress\\n\\n # differential arc length\\n t_vec = x_cyl_S.jacobian() # tangent vector\\n d_arc = np.sqrt(t_vec[0]**2 + t_vec[1]**2)\\n # compute integrands\\n total_force += bs.CoeffArray.integrate(total_stress * d_arc)\\n\\n return total_force\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ac5eeadadc70c23a9ea810375e1ab968\",\n \"score\": \"0.55544335\",\n \"text\": \"def _forces_moments(self, delta):\\n\\n # extract the states\\n pn = self._state.item(0)\\n pe = self._state.item(1)\\n pd = self._state.item(2)\\n u = self._state.item(3)\\n v = self._state.item(4)\\n w = self._state.item(5)\\n e0 = self._state.item(6)\\n e1 = self._state.item(7)\\n e2 = self._state.item(8)\\n e3 = self._state.item(9)\\n p = self._state.item(10)\\n q = self._state.item(11)\\n r = self._state.item(12)\\n\\n Va = self._Va\\n # if Va == 0:\\n # Va = 0.000001\\n alpha = self._alpha\\n beta = self._beta\\n\\n delta_a = delta.item(0)\\n delta_e = delta.item(1)\\n delta_t = delta.item(2)\\n delta_r = delta.item(3)\\n\\n phi, theta, psi = Quaternion2Euler(e0, e1, e2, e3)\\n\\n # gravity\\n f_g = MAV.mass * MAV.gravity * np.array(\\n [[-np.sin(theta)], [np.cos(theta) * np.sin(phi)], [np.cos(theta) * np.cos(phi)]])\\n self.f_g = f_g\\n\\n # Motor Forces\\n V_in = MAV.V_max * delta_t\\n # Quadratic formula to solve for motor speed\\n a = MAV.rho * np.power(MAV.D_prop, 5) * MAV.C_Q0 / ((2. * np.pi) ** 2)\\n b = (MAV.rho * np.power(MAV.D_prop, 4) / (2. * np.pi)) ** 2 * MAV.C_Q1 * Va + MAV.KQ * MAV.KQ / MAV.R_motor\\n c = MAV.rho * np.power(MAV.D_prop, 3) * MAV.C_Q2 * Va ** 2 - (MAV.KQ / MAV.R_motor) * V_in + MAV.KQ * MAV.i0\\n # Consider only positive root\\n Omega_op = (-b + np.sqrt(b ** 2 - 4 * a * c)) / (2. * a)\\n # add thrust and torque due to propeller\\n T_p = (MAV.rho * np.power(MAV.D_prop, 4) * MAV.C_T0 / (4 * np.pi ** 2)) * Omega_op ** 2 + (\\n MAV.rho * np.power(MAV.D_prop, 3) * MAV.C_T1 * Va / (2 * np.pi)) * Omega_op + (\\n MAV.rho * MAV.D_prop ** 2 * MAV.C_T2 * Va ** 2)\\n Q_p = (MAV.rho * MAV.D_prop ** 5 * MAV.C_Q0 / (4 * np.pi ** 2)) * Omega_op ** 2 + (\\n MAV.rho * MAV.D_prop ** 4 * MAV.C_Q1 * Va / (2 * np.pi)) + (\\n MAV.rho * MAV.D_prop ** 3 * MAV.C_Q2 * Va ** 2)\\n\\n self.thrust = T_p\\n\\n # Use the non-linear system to compute lift and drag\\n sigma = (1 + np.exp(-MAV.M * (alpha - MAV.alpha0)) + np.exp(MAV.M * (alpha + MAV.alpha0))) / (\\n (1 + np.exp(-MAV.M * (alpha - MAV.alpha0))) * (1 + np.exp(MAV.M * (alpha + MAV.alpha0))))\\n\\n C_L = (1 - sigma) * (MAV.C_L_0 + MAV.C_L_alpha * alpha) + sigma * (\\n 2 * np.sign(alpha) * np.sin(alpha) ** 2 * np.cos(alpha))\\n C_D = MAV.C_D_p + (MAV.C_L_0 + MAV.C_L_alpha * alpha) ** 2 / (np.pi * MAV.e * MAV.AR)\\n\\n # 4.25\\n C_chi = -C_D * np.cos(alpha) + C_L * np.sin(alpha)\\n C_chi_q = -MAV.C_D_q * np.cos(alpha) + MAV.C_L_q * np.sin(alpha)\\n C_chi_delta_e = -MAV.C_D_delta_e * np.cos(alpha) + MAV.C_L_delta_e * np.sin(alpha)\\n C_Z = -C_D * np.sin(alpha) - C_L * np.cos(alpha)\\n C_Z_q = -MAV.C_D_q * np.sin(alpha) - MAV.C_L_q * np.cos(alpha)\\n C_Z_delta_e = -MAV.C_D_delta_e * np.sin(alpha) - MAV.C_L_delta_e * np.cos(alpha)\\n\\n forces = f_g + np.array([[T_p], [0], [0]]) + 0.5 * MAV.rho * Va ** 2 * MAV.S_wing * np.array(\\n [[C_chi + C_chi_q * MAV.c / (2 * Va) * q],\\n [MAV.C_Y_0 + MAV.C_Y_beta * beta + MAV.C_Y_p * b / (2 * Va) * p + MAV.C_Y_r * b / (2 * Va) * r],\\n [C_Z + C_Z_q * MAV.c / (2 * Va) * q]]) + 0.5 * MAV.rho * Va ** 2 * MAV.S_wing * np.array(\\n [[C_chi_delta_e * delta_e], [MAV.C_Y_delta_a * delta_a + MAV.C_Y_delta_r * delta_r],\\n [C_Z_delta_e * delta_e]])\\n\\n fx = forces.item(0)\\n fy = forces.item(1)\\n fz = forces.item(2)\\n\\n moments = 0.5 * MAV.rho * Va ** 2 * MAV.S_wing * np.array([[MAV.b * (\\n MAV.C_ell_0 + MAV.C_ell_beta * beta + MAV.C_ell_p * b / (2 * Va) * p + MAV.C_ell_r * b / (\\n 2 * Va) * r)], [MAV.c * (MAV.C_m_0 + MAV.C_m_alpha * alpha + MAV.C_m_q * MAV.c / (2 * Va) * q)],\\n [MAV.b * (\\n MAV.C_n_0 + MAV.C_n_beta * beta + MAV.C_n_p * b / (\\n 2 * Va) * p + MAV.C_n_r * b / (\\n 2 * Va) * r)]]) + 0.5 * MAV.rho * Va ** 2 * MAV.S_wing * np.array(\\n [[MAV.b * (MAV.C_ell_delta_a * delta_a + MAV.C_ell_delta_r * delta_r)],\\n [MAV.c * (MAV.C_m_delta_e * delta_e)],\\n [MAV.b * (MAV.C_n_delta_a * delta_a + MAV.C_n_delta_r * delta_r)]]) + np.array([[Q_p], [0], [0]])\\n\\n l = moments.item(0)\\n m = moments.item(1)\\n n = moments.item(2)\\n\\n Mx = l\\n My = m\\n Mz = n\\n\\n forcesandmoments = np.array([[fx, fy, fz, Mx, My, Mz]])\\n return forcesandmoments\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa81ae68f967e6120eab9e0fd6a8a836\",\n \"score\": \"0.55542964\",\n \"text\": \"def _get_energies_forces(self, simulator):\\n energies = (\\n simulator.system.properties[self.energy_handle].float()\\n * self.energy_conversion\\n )\\n forces = simulator.system.forces\\n return energies, forces\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab68246bfcb4081433714fa12180f2cc\",\n \"score\": \"0.5477652\",\n \"text\": \"def getForce(obj1, obj2, eps=1e-12):\\n\\n # Calculate the distance between two positions (\\\"eps\\\" saves the day when 2 objects are on the exactly same \\n # spot)\\n dist = np.linalg.norm(obj1.pos - obj2.pos) + eps\\n\\n # Calculate the force\\n force_vect = -GRAV_CONSTANT*obj1.m*obj2.m * (obj1.pos - obj2.pos)/dist**3\\n\\n return force_vect\",\n \"title\": \"\"\n },\n {\n \"docid\": \"147f343d5f9d786708249be3471b6193\",\n \"score\": \"0.54687405\",\n \"text\": \"def analyze(self, event):\\n \\n triggerObjects = Collection(event, \\\"TrigObj\\\")\\n\\n tightMuons = self.tightMuonsCollection(event)\\n tightElectrons = self.tightElectronsCollection(event)\\n\\n looseMuons = self.looseMuonCollection(event)\\n looseElectrons = self.looseElectronCollection(event)\\n \\n cpfCandidates = Collection(event,\\\"cpf\\\")\\n\\n\\n for lepton in tightMuons+looseMuons:\\n lepton.isMuon = 1\\n lepton.isElectron = 0\\n lepton.relIso = lepton.pfRelIso04_all\\n lepton.cpf_match = None\\n lepton.cpf_match_dR = 100.\\n for cpfCandidate in cpfCandidates:\\n if cpfCandidate.matchedMuon>0.5:\\n pt = math.sqrt(cpfCandidate.px**2+cpfCandidate.py**2)\\n p = math.sqrt(cpfCandidate.px**2+cpfCandidate.py**2+cpfCandidate.pz**2)\\n eta = math.atanh(cpfCandidate.pz/p)\\n phi = math.atan2(cpfCandidate.py,cpfCandidate.px)\\n dR = math.sqrt((eta-lepton.eta)**2+deltaPhi(phi,lepton.phi)**2)\\n if math.fabs(pt/lepton.pt-1)<0.1 and dR<0.02 and dR<lepton.cpf_match_dR:\\n lepton.cpf_match = cpfCandidate\\n lepton.cpf_match_dR = dR\\n\\n for lepton in tightElectrons+looseElectrons:\\n lepton.isMuon = 0\\n lepton.isElectron = 1\\n lepton.relIso = lepton.pfRelIso03_all\\n lepton.cpf_match = None\\n lepton.cpf_match_dR = 100.\\n \\n for cpfCandidate in cpfCandidates:\\n if cpfCandidate.matchedElectron>0.5:\\n pt = math.sqrt(cpfCandidate.px**2+cpfCandidate.py**2)\\n p = math.sqrt(cpfCandidate.px**2+cpfCandidate.py**2+cpfCandidate.pz**2)\\n eta = math.atanh(cpfCandidate.pz/p)\\n phi = math.atan2(cpfCandidate.py,cpfCandidate.px)\\n dR = math.sqrt((eta-lepton.eta)**2+deltaPhi(phi,lepton.phi)**2)\\n if math.fabs(pt/lepton.pt-1)<0.1 and dR<0.02 and dR<lepton.cpf_match_dR:\\n lepton.cpf_match = cpfCandidate\\n lepton.cpf_match_dR = dR\\n\\n tightLeptons = []\\n looseLeptons = []\\n\\n tightLeptons = tightMuons+tightElectrons\\n looseLeptons = looseMuons+looseElectrons\\n\\n tightLeptons = sorted(tightLeptons, key=lambda x: x.pt, reverse=True)\\n \\n # select leading only, move subleading to \\\"loose\\\"\\n looseLeptons.extend(tightLeptons[1:])\\n tightLeptons = tightLeptons[:1]\\n looseLeptons = sorted(looseLeptons, key=lambda x: x.pt, reverse=True)\\n\\n muonmuon = 0\\n electronelectron = 0\\n muonelectron = 0\\n electronmuon = 0\\n muonjets = 0\\n electronjets = 0\\n\\n ## flavour categorisation :\\n if len(tightLeptons) > 0 and len(looseLeptons) > 0:\\n # Ensure pt(l1) > pt(l2)\\n if tightLeptons[0].pt < looseLeptons[0].pt:\\n return False\\n \\n if tightLeptons[0].isMuon and looseLeptons[0].isMuon:\\n muonmuon = 1\\n\\n elif tightLeptons[0].isElectron and looseLeptons[0].isElectron:\\n electronelectron= 1\\n\\n elif tightLeptons[0].isMuon and looseLeptons[0].isElectron:\\n muonelectron = 1\\n \\n elif tightLeptons[0].isElectron and looseLeptons[0].isMuon:\\n electronmuon = 1\\n\\n elif len(tightLeptons) > 0:\\n if tightLeptons[0].isMuon:\\n muonjets = 1\\n elif tightLeptons[0].isElectron:\\n electronjets = 1\\n\\n for lepton in tightLeptons+looseLeptons:\\n if self.triggerMatched(lepton, triggerObjects):\\n setattr(lepton,\\\"isTriggerMatched\\\",1)\\n else:\\n setattr(lepton,\\\"isTriggerMatched\\\",0)\\n\\n for lepton in tightLeptons+looseLeptons:\\n if lepton.dxyErr < 1e-6:\\n setattr(lepton, \\\"dxysig\\\", -1.)\\n else:\\n setattr(lepton, \\\"dxysig\\\", math.fabs(lepton.dxy)/math.fabs(lepton.dxyErr))\\n\\n if lepton.dzErr < 1e-6:\\n setattr(lepton, \\\"dzsig\\\", -1.)\\n else:\\n setattr(lepton, \\\"dzsig\\\", math.fabs(lepton.dz)/math.fabs(lepton.dzErr))\\n\\n\\n lepton_id = -1\\n lepton_ptErr = -100.\\n if len(looseLeptons) > 0:\\n if looseLeptons[0].isMuon: \\n\\t\\tlepton_id = looseLeptons[0].tightId\\n\\t\\tlepton_ptErr = looseLeptons[0].ptErr\\n\\t\\n if looseLeptons[0].isElectron: \\n lepton_id = looseLeptons[0].mvaFall17V1Iso_WP90\\n\\t\\tlepton_ptErr = looseLeptons[0].energyErr\\n\\n self.out.fillBranch(\\\"nleading\\\"+self.outputName, len(tightLeptons))\\n self.out.fillBranch(\\\"nsubleading\\\"+self.outputName, len(looseLeptons))\\n\\n for variable in self.storeLeadingKinematics:\\n self.out.fillBranch(\\\"leading\\\"+self.outputName+\\\"_\\\"+variable,map(lambda lepton: getattr(lepton,variable),tightLeptons))\\n for variable in self.storeSubleadingKinematics:\\n self.out.fillBranch(\\\"subleading\\\"+self.outputName+\\\"_\\\"+variable,map(lambda lepton: getattr(lepton,variable),looseLeptons))\\n\\n '''\\n self.out.fillBranch(\\\"subleading\\\"+self.outputName+\\\"_cpfMatch\\\", [1 if lepton.cpf_match!=None else 0 for lepton in looseLeptons])\\n for cpfFeature in cpfFeatures:\\n arr = [getattr(lepton.cpf_match,cpfFeature) if lepton.cpf_match!=None else 0 for lepton in looseLeptons]\\n self.out.fillBranch(\\\"subleading\\\"+self.outputName+\\\"_\\\"+cpfFeature,arr)\\n '''\\n self.out.fillBranch(self.outputName+\\\"_muonmuon\\\", muonmuon)\\n self.out.fillBranch(self.outputName+\\\"_electronelectron\\\", electronelectron)\\n self.out.fillBranch(self.outputName+\\\"_muonelectron\\\", muonelectron)\\n self.out.fillBranch(self.outputName+\\\"_electronmuon\\\", electronmuon)\\n self.out.fillBranch(self.outputName+\\\"_muonjets\\\", muonjets)\\n self.out.fillBranch(self.outputName+\\\"_electronjets\\\", electronjets)\\n self.out.fillBranch(self.outputName+\\\"_subLeptonTightId\\\", lepton_id)\\n self.out.fillBranch(self.outputName+\\\"_subLeptonPtErr\\\", lepton_ptErr) \\n setattr(event, \\\"leading\\\"+self.outputName, tightLeptons)\\n setattr(event, \\\"subleading\\\"+self.outputName, looseLeptons)\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1bf7b221bedf2d46b9dac9ba2e05a233\",\n \"score\": \"0.54687405\",\n \"text\": \"def analyze(self, event):\\n \\n triggerObjects = Collection(event, \\\"TrigObj\\\")\\n\\n tightMuons = self.tightMuonsCollection(event)\\n tightElectrons = self.tightElectronsCollection(event)\\n\\n looseMuons = self.looseMuonCollection(event)\\n looseElectrons = self.looseElectronCollection(event)\\n \\n cpfCandidates = Collection(event,\\\"cpf\\\")\\n\\n\\n for lepton in tightMuons+looseMuons:\\n lepton.isMuon = 1\\n lepton.isElectron = 0\\n lepton.relIso = lepton.pfRelIso04_all\\n lepton.cpf_match = None\\n lepton.cpf_match_dR = 100.\\n for cpfCandidate in cpfCandidates:\\n if cpfCandidate.matchedMuon>0.5:\\n pt = math.sqrt(cpfCandidate.px**2+cpfCandidate.py**2)\\n p = math.sqrt(cpfCandidate.px**2+cpfCandidate.py**2+cpfCandidate.pz**2)\\n eta = math.atanh(cpfCandidate.pz/p)\\n phi = math.atan2(cpfCandidate.py,cpfCandidate.px)\\n dR = math.sqrt((eta-lepton.eta)**2+deltaPhi(phi,lepton.phi)**2)\\n if math.fabs(pt/lepton.pt-1)<0.1 and dR<0.02 and dR<lepton.cpf_match_dR:\\n lepton.cpf_match = cpfCandidate\\n lepton.cpf_match_dR = dR\\n\\n for lepton in tightElectrons+looseElectrons:\\n lepton.isMuon = 0\\n lepton.isElectron = 1\\n lepton.relIso = lepton.pfRelIso03_all\\n lepton.cpf_match = None\\n lepton.cpf_match_dR = 100.\\n \\n for cpfCandidate in cpfCandidates:\\n if cpfCandidate.matchedElectron>0.5:\\n pt = math.sqrt(cpfCandidate.px**2+cpfCandidate.py**2)\\n p = math.sqrt(cpfCandidate.px**2+cpfCandidate.py**2+cpfCandidate.pz**2)\\n eta = math.atanh(cpfCandidate.pz/p)\\n phi = math.atan2(cpfCandidate.py,cpfCandidate.px)\\n dR = math.sqrt((eta-lepton.eta)**2+deltaPhi(phi,lepton.phi)**2)\\n if math.fabs(pt/lepton.pt-1)<0.1 and dR<0.02 and dR<lepton.cpf_match_dR:\\n lepton.cpf_match = cpfCandidate\\n lepton.cpf_match_dR = dR\\n\\n tightLeptons = []\\n looseLeptons = []\\n\\n tightLeptons = tightMuons+tightElectrons\\n looseLeptons = looseMuons+looseElectrons\\n\\n tightLeptons = sorted(tightLeptons, key=lambda x: x.pt, reverse=True)\\n\\n if len(tightLeptons)<2:\\n print(\\\"Need at least two tight leptons\\\")\\n return False\\n\\n # if len(tightLeptons+looseLeptons)<3:\\n # print(\\\"Need at least three leptons\\\")\\n # return False\\n\\n # select leading only, move subleading to \\\"loose\\\"\\n looseLeptons.extend(tightLeptons[2:])\\n tightLeptons = tightLeptons[:2]\\n if len(looseLeptons)>0:\\n looseLeptons = sorted(looseLeptons, key=lambda x: x.pt, reverse=True)\\n\\n muonmuonelectron = 0\\n electronelectronelectron = 0\\n\\n ## flavour categorisation :\\n if len(tightLeptons) > 0 and len(looseLeptons) > 0:\\n # Ensure pt(l1) > pt(l3) and pt(l2) > pt(l3)\\n if (tightLeptons[0].pt < looseLeptons[0].pt) or (tightLeptons[1].pt < looseLeptons[0].pt):\\n return False\\n \\n if tightLeptons[0].isMuon and tightLeptons[1].isMuon and looseLeptons[0].isElectron:\\n muonmuonelectron = 1\\n\\n elif tightLeptons[0].isElectron and tightLeptons[1].isElectron and looseLeptons[0].isElectron:\\n electronelectronelectron = 1\\n\\n for lepton in tightLeptons+looseLeptons:\\n if self.triggerMatched(lepton, triggerObjects):\\n setattr(lepton,\\\"isTriggerMatched\\\",1)\\n else:\\n setattr(lepton,\\\"isTriggerMatched\\\",0)\\n\\n for lepton in tightLeptons+looseLeptons:\\n if lepton.dxyErr < 1e-6:\\n setattr(lepton, \\\"dxysig\\\", -1.)\\n else:\\n setattr(lepton, \\\"dxysig\\\", math.fabs(lepton.dxy)/math.fabs(lepton.dxyErr))\\n\\n if lepton.dzErr < 1e-6:\\n setattr(lepton, \\\"dzsig\\\", -1.)\\n else:\\n setattr(lepton, \\\"dzsig\\\", math.fabs(lepton.dz)/math.fabs(lepton.dzErr))\\n\\n # self.out.fillBranch(\\\"nleading\\\"+self.outputName, int(float(len(tightLeptons))/2.))\\n # self.out.fillBranch(\\\"nsubleading\\\"+self.outputName, int(float(len(tightLeptons))/2.))\\n self.out.fillBranch(\\\"nleading\\\"+self.outputName, 1)\\n self.out.fillBranch(\\\"nsubleading\\\"+self.outputName, 1)\\n self.out.fillBranch(\\\"ntrailing\\\"+self.outputName, len(looseLeptons))\\n\\n for variable in self.storeLeadingKinematics:\\n #self.out.fillBranch(\\\"leading\\\"+self.outputName+\\\"_\\\"+variable,map(lambda lepton: getattr(lepton,variable),tightLeptons))\\n self.out.fillBranch(\\\"leading\\\"+self.outputName+\\\"_\\\"+variable,getattr(tightLeptons[0], variable))\\n for variable in self.storeSubleadingKinematics:\\n #self.out.fillBranch(\\\"subleading\\\"+self.outputName+\\\"_\\\"+variable,map(lambda lepton: getattr(lepton,variable),tightLeptons))\\n self.out.fillBranch(\\\"subleading\\\"+self.outputName+\\\"_\\\"+variable,getattr(tightLeptons[1], variable))\\n if len(looseLeptons)>0:\\n for variable in self.storeTrailingKinematics:\\n #self.out.fillBranch(\\\"trailing\\\"+self.outputName+\\\"_\\\"+variable,map(lambda lepton: getattr(lepton,variable),looseLeptons))\\n self.out.fillBranch(\\\"trailing\\\"+self.outputName+\\\"_\\\"+variable,getattr(looseLeptons[0], variable))\\n else:\\n for variable in self.storeTrailingKinematics:\\n self.out.fillBranch(\\\"trailing\\\"+self.outputName+\\\"_\\\"+variable, -99)\\n\\n\\n self.out.fillBranch(self.outputName+\\\"_muonmuonelectron\\\", muonmuonelectron)\\n self.out.fillBranch(self.outputName+\\\"_electronelectronelectron\\\", electronelectronelectron)\\n\\n if len(looseLeptons)>0:\\n trilepton = ROOT.TLorentzVector()\\n trilepton = tightLeptons[0].p4()+tightLeptons[1].p4()+looseLeptons[0].p4()\\n trilepton_charge = tightLeptons[0].charge*tightLeptons[1].charge*looseLeptons[0].charge\\n\\n self.out.fillBranch(\\\"trilepton_mass\\\", trilepton.M())\\n self.out.fillBranch(\\\"trilepton_pt\\\", trilepton.Pt())\\n self.out.fillBranch(\\\"trilepton_eta\\\", trilepton.Eta())\\n self.out.fillBranch(\\\"trilepton_charge\\\", trilepton_charge)\\n self.out.fillBranch(self.outputName+\\\"_dPhi_l1l3\\\", math.fabs(deltaPhi(tightLeptons[0],looseLeptons[0])))\\n self.out.fillBranch(self.outputName+\\\"_dEta_l1l3\\\", math.fabs(tightLeptons[0].eta-looseLeptons[0].eta))\\n self.out.fillBranch(self.outputName+\\\"_dR_l1l3\\\", deltaR(tightLeptons[0],looseLeptons[0]))\\n self.out.fillBranch(self.outputName+\\\"_dPhi_l2l3\\\", math.fabs(deltaPhi(tightLeptons[1],looseLeptons[0])))\\n self.out.fillBranch(self.outputName+\\\"_dEta_l2l3\\\", math.fabs(tightLeptons[1].eta-looseLeptons[0].eta))\\n self.out.fillBranch(self.outputName+\\\"_dR_l2l3\\\", deltaR(tightLeptons[1],looseLeptons[0]))\\n else:\\n self.out.fillBranch(\\\"trilepton_mass\\\", -99)\\n self.out.fillBranch(\\\"trilepton_pt\\\", -99)\\n self.out.fillBranch(\\\"trilepton_eta\\\", -99)\\n self.out.fillBranch(\\\"trilepton_charge\\\", -99)\\n self.out.fillBranch(self.outputName+\\\"_dPhi_l1l3\\\", -99)\\n self.out.fillBranch(self.outputName+\\\"_dEta_l1l3\\\", -99)\\n self.out.fillBranch(self.outputName+\\\"_dR_l1l3\\\", -99)\\n self.out.fillBranch(self.outputName+\\\"_dPhi_l2l3\\\", -99)\\n self.out.fillBranch(self.outputName+\\\"_dEta_l2l3\\\", -99)\\n self.out.fillBranch(self.outputName+\\\"_dR_l2l3\\\", -99)\\n\\n setattr(event, \\\"leading\\\"+self.outputName, tightLeptons[0])\\n setattr(event, \\\"subleading\\\"+self.outputName, tightLeptons[1])\\n if len(looseLeptons)>0:\\n setattr(event, \\\"trailing\\\"+self.outputName, looseLeptons[0])\\n else:\\n setattr(event, \\\"trailing\\\"+self.outputName, None)\\n\\n return True\",\n \"title\": \"\"\n },\n {\n \"docid\": \"7a6672f99c4c403fd07c8a1b71772a39\",\n \"score\": \"0.5468047\",\n \"text\": \"def read_forces(self, atoms):\\n forces = self.read_output(self.log, 'forces')\\n return forces\",\n \"title\": \"\"\n },\n {\n \"docid\": \"63ac0aa927c1df4fd13b305519600b76\",\n \"score\": \"0.5459774\",\n \"text\": \"def do_near(self, data):\\n near_me.main(data)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"fdb43b064434bdf6cf68d48f2638552b\",\n \"score\": \"0.54474926\",\n \"text\": \"def get_forces(self, robot_cell, goal_point, robot_map):\\n forces = {'gen_force': None, 'attr_force': None, 'rep_force': None}\\n forces['attr_force'] = self.__get_attractive_force(robot_cell, goal_point)\\n forces['rep_force'] = self.__get_repulsive_force(robot_cell, robot_map)\\n forces['gen_force'] = {'x': forces['attr_force']['x'] + forces['rep_force']['x'], \\n 'y': forces['attr_force']['y'] + forces['rep_force']['y']}\\n return forces\",\n \"title\": \"\"\n },\n {\n \"docid\": \"c38d92c6157c04d949c44a5a46c5c9ab\",\n \"score\": \"0.5446808\",\n \"text\": \"def get_forces():\\n forcefile = open('cfd/postProcessing/forceCoeffs1/0/forceCoeffs.dat')\\n for line in forcefile:\\n if not line.startswith('#'):\\n line = line.strip()\\n info = line.split('\\\\t')\\n info = [float(strval) for strval in info]\\n forces = {'time':info[0], \\n 'pitchmoment':info[1],\\n 'drag':info[2],\\n 'lift':info[3], \\n 'frontlift':info[4], \\n 'rearlift':info[5]}\\n return forces\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5cdca46a23b0c9cb80dbabb26ef1265c\",\n \"score\": \"0.5446389\",\n \"text\": \"def _calc_forces(self):\\n df, learn, sampl_row, model_mean, fields, max_row_used = self.df, self.learn, self.sampl_row, self.model_mean, self.fields, self.max_row_used\\n\\n forces = OrderedDict()\\n # build big table with all the variants to check all the data in one run\\n huge_df = pd.DataFrame()\\n cur_dfs = []\\n for field in fields:\\n cur_df = self._shuffle_cols(sampl_col=field)\\n cur_df['group'] = self._list_to_key(field)\\n cur_dfs.append(cur_df)\\n\\n huge_df = pd.concat(cur_dfs)\\n del cur_dfs\\n\\n # predict on it\\n huge_df['preds'] = self._predict_df(df=huge_df)\\n # predict actual data (just predict sampl_row)\\n actual = self._predict_row(row=sampl_row)\\n\\n # divide back by fields\\n for field in fields:\\n cur_df = huge_df.query(f\\\"group == '{self._list_to_key(field)}'\\\")\\n # calculate force\\n force = float(actual - cur_df['preds'].mean())\\n key = f\\\"{field} ({sampl_row[field]})\\\" if isinstance(field, str) else ', '.join(\\n f\\\"{e} ({sampl_row[e]})\\\" for e in field)\\n forces[key] = force\\n\\n self.batch_forces = OrderedDict(sorted(forces.items(), key=lambda kv: abs(kv[1]), reverse=True))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"deb95ae6d18f4b784660f430ce3de5dc\",\n \"score\": \"0.5437343\",\n \"text\": \"def dynamics(x0, v0, dt, tmax=10):\\n \\n N = len(x0) #number of objects\\n nsteps = int(tmax/dt)\\n x = np.zeros((nsteps/10,N,3))\\n dx = np.copy(x0)\\n v = np.copy(v0)\\n vhalf = np.zeros((N,3))\\n Ut = np.zeros(nsteps)\\n kinetic = np.zeros(nsteps)\\n totalE = np.zeros(nsteps)\\n \\n Ft, Ut[0] = gravity(dx)\\n \\n for i in range(nsteps):\\n for j in range(N):\\n vhalf[j] = v[j] + 0.5 * dt * Ft[j] / mass[j]\\n dx[j] += dt * vhalf[j]\\n Ft, Ut[i]= gravity(dx)\\n for j in range(N):\\n v[j] = vhalf[j] + 0.5 * dt * Ft[j] / mass[j]\\n kinetic[i] += 0.5 * mass[j] * np.sum(v[j]**2) \\n if i%10 == 0:\\n x[int(i/10)] = dx\\n#<<<<<<< HEAD\\n \\n #get position of earth and rama and determine distance between the two.\\n #----------------------------------------------------------------------\\n earth_pos = np.zeros(len(x[:]))\\n rama_pos = np.zeros_like(earth_pos)\\n dist = np.zeros_like(earth_pos) \\n dist = np.abs(earth_pos - rama_pos)\\n\\n earth_pos = x[:,3]\\n rama_pos = x[:,9]\\n #distance between the two\\n dist = np.abs(earth_pos - rama_pos)\\n #array to store the closer values\\n close = np.zeros((nsteps,), dtype=np.float64)\\n dist_mag = np.zeros((len(earth_pos)), dtype=np.float64)\\n for i in range(len(earth_pos)):\\n dist_mag[i] = np.linalg.norm(dist[i])\\n if dist_mag[i] < 0.2:\\n print(\\\"Iteration:\\\",i,\\\",\\\",\\n \\\"Rama distance from Earth (au):\\\", dist_mag[i])\\n\\n \\n \\n#=======\\n \\n#>>>>>>> 1f69f9476821ec1398d4c3b4e304f2e9563594e2\\n return x, v, kinetic, Ut, totalE\",\n \"title\": \"\"\n },\n {\n \"docid\": \"8e583545061e23ae2481cca942044382\",\n \"score\": \"0.5342062\",\n \"text\": \"def find_folowing_numerical(self, delta_t, c_arr_d, c_arr_g, theta_direction):\\n\\t\\tt = time.time()\\n\\t\\terreur = \\\"ABS(1000*(%s-delta_t)) + ABS(57*(%s-theta_direction)) + ABS(1000*(%s-c_arr_d)) + ABS(1000*(%s-c_arr_g)) + ABS(100*(%s-vx_in)) + ABS(100*(%s-vy_in)) + ABS(573*(%s-w_in))\\\" % (\\n\\t\\t\\tdelta_t, theta_direction, c_arr_d, c_arr_g, self.vitesses_translation[-1][\\\"x\\\"], self.vitesses_translation[-1][\\\"y\\\"], self.vitesses_angulaire[-1])\\n\\t\\tself.cursor.execute(\\\"\\\"\\\"\\n\\t\\t\\t\\t\\t\\tSELECT vx_out, vy_out, w_out, x_out, y_out, angle, %s\\n\\t\\t\\t\\t\\t\\tFROM simulation\\n\\t\\t\\t\\t\\t\\tORDER BY (%s)\\n\\t\\t\\t\\t\\t\\tLIMIT 1\\\"\\\"\\\" % (erreur, erreur))\\n\\t\\tresultat = list(self.cursor)\\n\\t\\tif len(resultat):\\n\\t\\t\\tr = resultat[0]\\n\\t\\t\\tif resultat[0][6] < 0.2:\\n\\t\\t\\t\\tresultat_num = {\\\"vx\\\":r[0], \\\"vy\\\":r[1], \\\"omega_z\\\":r[2],\\n\\t\\t\\t\\t\\t\\\"x\\\":r[3]+self.positions[-1][\\\"x\\\"], \\\"y\\\":r[4]+self.positions[-1][\\\"y\\\"], \\\"angle\\\":r[5]+self.angles[-1]}\\n\\t\\t\\t\\treturn resultat_num\\n\\n\\t\\tdt = min(2e-3, delta_t) # on ne fait pas des pas de plus de 2 ms\\n\\t\\tdt = delta_t/float(int(delta_t/dt))\\n\\t\\tresultat_num = {\\\"vx\\\":self.vitesses_translation[-1][\\\"x\\\"], \\\"vy\\\":self.vitesses_translation[-1][\\\"y\\\"], \\\"omega_z\\\":self.vitesses_angulaire[-1],\\n\\t\\t\\t\\t\\t\\t\\\"x\\\":self.positions[-1][\\\"x\\\"], \\\"y\\\":self.positions[-1][\\\"y\\\"], \\\"angle\\\":self.angles[-1]}\\n\\t\\tangle_relatif = 0.0\\n\\t\\tx_relatif = 0.0\\n\\t\\ty_relatif = 0.0\\n\\t\\tresultat_form = {}\\n\\t\\tresultat_form[\\\"vx\\\"] = self.U_vx.subs({\\\"dt\\\":dt, \\\"c_arr_d\\\":c_arr_d, \\\"c_arr_g\\\":c_arr_g, \\\"theta_direction\\\":theta_direction})\\n\\t\\tresultat_form[\\\"vy\\\"] = self.U_vy.subs({\\\"dt\\\":dt, \\\"c_arr_d\\\":c_arr_d, \\\"c_arr_g\\\":c_arr_g, \\\"theta_direction\\\":theta_direction})\\n\\t\\tresultat_form[\\\"omega_z\\\"] = self.U_omega_z.subs({\\\"dt\\\":dt, \\\"c_arr_d\\\":c_arr_d, \\\"c_arr_g\\\":c_arr_g, \\\"theta_direction\\\":theta_direction})\\n\\t\\tfor i in range(int(delta_t/dt)):\\n\\t\\t\\tresultat_num = {\\n\\t\\t\\t\\t\\\"vx\\\":resultat_form[\\\"vx\\\"].subs({\\\"vx\\\":resultat_num[\\\"vx\\\"], \\\"vy\\\":resultat_num[\\\"vy\\\"], \\\"omega_z\\\":resultat_num[\\\"omega_z\\\"]}),\\n\\t\\t\\t\\t\\\"vy\\\":resultat_form[\\\"vy\\\"].subs({\\\"vx\\\":resultat_num[\\\"vx\\\"], \\\"vy\\\":resultat_num[\\\"vy\\\"], \\\"omega_z\\\":resultat_num[\\\"omega_z\\\"]}),\\n\\t\\t\\t\\t\\\"omega_z\\\":resultat_form[\\\"omega_z\\\"].subs({\\\"vx\\\":resultat_num[\\\"vx\\\"], \\\"vy\\\":resultat_num[\\\"vy\\\"], \\\"omega_z\\\":resultat_num[\\\"omega_z\\\"]}),\\n\\t\\t\\t}\\n\\t\\t\\tangle_relatif += dt*resultat_num[\\\"omega_z\\\"]\\n\\t\\t\\tx_relatif += dt*(resultat_num[\\\"vx\\\"]*math.cos(self.angles[-1]+angle_relatif) - resultat_num[\\\"vy\\\"]*math.sin(self.angles[-1]+angle_relatif))\\n\\t\\t\\ty_relatif += dt*(resultat_num[\\\"vx\\\"]*math.sin(self.angles[-1]+angle_relatif) + resultat_num[\\\"vy\\\"]*math.cos(self.angles[-1]+angle_relatif))\\n\\n\\n\\t\\tself.cursor.execute(\\\"\\\"\\\"INSERT INTO simulation\\n\\t\\t\\t(delta_t, theta_direction, c_arr_d, c_arr_g, vx_in, vy_in, w_in, vx_out, vy_out, w_out, x_out, y_out, angle)\\n\\t\\t\\tVALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)\\\"\\\"\\\" %\\n\\t\\t\\t(delta_t, theta_direction, c_arr_d, c_arr_g, self.vitesses_translation[-1][\\\"x\\\"], self.vitesses_translation[-1][\\\"y\\\"], self.vitesses_angulaire[-1],\\n\\t\\t\\t\\tresultat_num[\\\"vx\\\"], resultat_num[\\\"vy\\\"], resultat_num[\\\"omega_z\\\"], x_relatif, y_relatif, angle_relatif))\\n\\t\\tself.bdd.commit()\\n\\n\\t\\tresultat_num[\\\"x\\\"] = self.positions[-1][\\\"x\\\"] + x_relatif\\n\\t\\tresultat_num[\\\"y\\\"] = self.positions[-1][\\\"y\\\"] + y_relatif\\n\\t\\tresultat_num[\\\"angle\\\"] = self.angles[-1] + angle_relatif\\n\\t\\treturn resultat_num\",\n \"title\": \"\"\n },\n {\n \"docid\": \"05570e041bde0f8e3a19201b6214d347\",\n \"score\": \"0.5341981\",\n \"text\": \"def getNearbyPlaces(self):\\n\\t\\tself.nearby = self.gmaps.places_radar(\\n\\t\\t\\t(self.centre['lat'], self.centre['lng']),\\n\\t\\t\\tradius=self.radius*self.radius_factor,\\n\\t\\t\\tkeyword=self.keywords\\n\\t\\t\\t)['results']\\n\\n\\t\\t##Lets create a master dict in the object which has all the place info\\n\\t\\t##we can then pull the day out after the fact instead of constantly adding terms\\n\\t\\tfor i in xrange(len(self.nearby)):\\n\\t\\t\\tself.nearby[i]['place_info'] = self.gmaps.place(self.nearby[i]['place_id'])\\n\\n\\t\\t##some ratings are missing - lets add them in as zero\\n\\t\\tfor i in xrange(len(self.nearby)):\\n\\t\\t\\tif 'rating' not in self.nearby[i]['place_info']['result'].keys():\\n\\t\\t\\t\\tself.nearby[i]['place_info']['result']['rating'] = 0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"788e211a4ed13f8761f19abd9d93bcbc\",\n \"score\": \"0.53289604\",\n \"text\": \"def forces_values(self):\\n\\n forces = self.forces\\n if forces is None:\\n return None\\n else:\\n return self.forces.value_in_unit(self.forces_unit)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1ab262544a0b24cd837028738f45a16b\",\n \"score\": \"0.5313329\",\n \"text\": \"def calculate_non_bonded_force(self):\\n\\n if self.qm['charges_engine'] == 'chargemol':\\n self.extract_params_chargemol()\\n\\n elif self.qm['charges_engine'] == 'onetep':\\n self.extract_params_onetep()\\n\\n else:\\n raise KeyError('Invalid charges engine provided, cannot extract charges.')\\n\\n # Calculate initial a_is and b_is\\n self.append_ais_bis()\\n\\n # Use the a_is and b_is to calculate the non_bonded_force dict\\n self.calculate_sig_eps()\\n\\n # Tweak for polar Hydrogens\\n self.correct_polar_hydrogens()\\n\\n # Tweak the charge, sigma and epsilon for symmetry\\n self.apply_symmetrisation()\\n\\n # Find extra site positions in local coords if present and tweak the charges of the parent\\n if self.qm['charges_engine'] == 'onetep':\\n self.extract_extra_sites()\\n\\n return self.non_bonded_force\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9c0288e35b894d25b3d5ca97c3b0a0c8\",\n \"score\": \"0.5312327\",\n \"text\": \"def calc_end_forces(self, result, nodal_displ):\\n elements = self._model.elements\\n # Initialize dictionary with results of the end forces\\n end_forces = dict()\\n # Get the node freedom map table\\n nfmt = self.nfmt\\n # Get the node freedom allocation of every node\\n nfat = self.nfat\\n # Calculate end forces for each element\\n for num, element in elements.items():\\n # Get the transformation matrix for the element\\n Te = element.transformation_matrix\\n # Get the stiffness matrix of the element in global coordinates\\n # FIXME!\\n Ke = element._Ke\\n # Get the displacements of the corresponding DOFs in global coordinates\\n # - Initialize matrix\\n v_i = np.zeros(element._k_size, dtype=np.float64)\\n P_e_i = np.zeros(element._k_size, dtype=np.float64)\\n # Get the node freedom allocation map table of the current element\\n enfmt = element.enfmt\\n # Initiate element load vector\\n # Assemble matrix with element nodal displacements of the current\\n # element\\n for n_node_e, node in element._nodal_connectivity.items():\\n # Indices for the array v_i\\n index_base = element.get_element_active_dof(n_node_e)\\n i_index = index_base + enfmt[n_node_e]\\n # Indices corresponding to the position of the DOF of\\n # the current node analyzed (global system)\\n index_base_n = element.get_node_active_dof(n_node_e)\\n j_index = index_base_n + nfmt[node.number]\\n # Add the results for these DOFs to the v_i array\\n v_i[i_index] = nodal_displ[j_index] # DOF of node selected\\n # Account for element loads\\n if len(element._loads) > 0:\\n # Same indices are used here\\n for load in element._loads:\\n P_e_i[i_index] += load._load_vector_global[i_index]\\n\\n # Get the End Forces of the element in global coordinates\\n P_i_global = Ke @ v_i - P_e_i\\n # Transform End Forces to local coordinates\\n P_i_local = Te @ P_i_global\\n # Add End Forces to the dictionary of End Forces of the result object\\n # for the current element\\n end_forces[element.number] = P_i_local\\n # Add the corresponding nodal forces to the matrix to\\n # calculate the sectional forces\\n # - Axial force\\n element._poly_sec_force[0, 0] += -P_i_local[0]\\n # - Shear force\\n element._poly_sec_force[0, 1] += P_i_local[1]\\n # - Moment\\n element._poly_sec_force[0, 2] += -P_i_local[2]\\n element._poly_sec_force[1, 2] += P_i_local[1]\\n\\n # Add results to the result object\\n result.add_result('end forces', end_forces)\\n\\n return end_forces\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2fb7167b6f15816ae27d9bbe47f2898c\",\n \"score\": \"0.5259555\",\n \"text\": \"def _calc_forces_repeats(self):\\n num_tests = self.num_tests\\n forces = pd.DataFrame()\\n for tests in progress_bar(range(num_tests)):\\n self._calc_forces()\\n forces = forces.append(self.batch_forces, ignore_index=True)\\n clear_output()\\n\\n forces = forces.mean()\\n fc_od = OrderedDict()\\n for k, v in forces.iteritems():\\n fc_od[k] = v\\n\\n self.forces = OrderedDict(sorted(fc_od.items(), key=lambda kv: abs(kv[1]), reverse=True))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"78f1a1bb18a59340370e50fa7bcd6023\",\n \"score\": \"0.52554226\",\n \"text\": \"def mmff94_potential(mol):\\n mp = AllChem.MMFFGetMoleculeProperties(mol, mmffVariant=\\\"MMFF94\\\")\\n ff = AllChem.MMFFGetMoleculeForceField(mol, mp)\\n opt_fail = ff.Minimize(maxIts=1000,forceTol=0.0001,energyTol=1e-06)\\n energy = ff.CalcEnergy()\\n return energy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0a8226aa8c01cbdb2122170a1670c849\",\n \"score\": \"0.52412826\",\n \"text\": \"def force ( box, r, d ):\\n\\n import numpy as np\\n \\n # It is assumed that positions are in units where box = 1\\n # Forces are calculated in units where sigma = 1 and epsilon = 1\\n # Note that this is the force-shifted LJ potential with a linear smoothing term\\n # S Mossa, E La Nave, HE Stanley, C Donati, F Sciortino, P Tartaglia, Phys Rev E, 65, 041205 (2002)\\n\\n n, ndim = r.shape\\n assert ndim==3, 'Dimension error for r'\\n nn, nna, ndim = d.shape\\n assert nna==na and ndim==3, 'Dimension error for d'\\n assert n==nn, 'Dimension mismatch for r and d'\\n \\n sr2_ovr = 1.77 # Overlap threshold (pot > 100)\\n rm_cut_box = ( r_cut + diameter ) / box # Molecular cutoff in box=1 units\\n rm_cut_box_sq = rm_cut_box**2 # squared\\n assert rm_cut_box<0.5, 'rm_cut/box too large'\\n r_cut_sq = r_cut ** 2\\n\\n # Initialize\\n f = np.zeros_like(r)\\n tau = np.zeros_like(r)\\n total = PotentialType ( pot=0.0, vir=0.0, ovr=False )\\n\\n if fast:\\n for i in range(n-1):\\n rij = r[i,:]-r[i+1:,:] # Separation vectors for j>i\\n rij = rij - np.rint(rij) # Periodic boundary conditions in box=1 units\\n rij = rij * box # Now in sigma=1 units\\n for a in range(na):\\n for b in range(na):\\n rab = rij + d[i,a,:] - d[i+1:,b,:] # All atom-atom vectors for given a and b\\n rab_sq = np.sum(rab**2,axis=1) # Squared separations\\n in_range = rab_sq < r_cut_sq # Set flags for within cutoff\\n sr2 = 1.0 / rab_sq # (sigma/rab)**2\\n ovr = sr2 > sr2_ovr # Set flags for any overlaps\\n rmag = np.sqrt(rab_sq)\\n sr6 = sr2 ** 3\\n sr12 = sr6 ** 2\\n pot = np.where ( in_range,\\n 4.0*(sr12-sr6) + lambda1 + lambda2*rmag, 0.0 ) # force-shifted pair potentials\\n virab = np.where ( in_range,\\n 24.0*(2.0*sr12-sr6) - lambda2*rmag, 0.0 ) # pair virials\\n fab = virab * sr2\\n fab = rab * fab[:,np.newaxis] # atom-atom pair forces\\n\\n total = total + PotentialType ( pot=np.sum(pot), vir=np.sum(rij*fab), ovr=np.any(ovr) )\\n fia = np.sum(fab,axis=0)\\n f[i,:] = f[i,:] + fia\\n f[i+1:,:] = f[i+1:,:] - fab\\n tau[i,:] = tau[i,:] + np.cross ( d[i,a,:], fia )\\n tau[i+1:,:] = tau[i+1:,:] - np.cross ( d[i+1:,b,:], fab )\\n\\n else:\\n for i in range(n-1): # Outer loop\\n for j in range(i+1,n): # Inner loop\\n rij = r[i,:]-r[j,:] # Separation vector\\n rij = rij - np.rint(rij) # Periodic boundary conditions in box=1 units\\n rij_sq = np.sum(rij**2) # Squared separation\\n\\n if rij_sq < rm_cut_box_sq: # Check within cutoff\\n rij = rij * box # Now in sigma=1 units\\n for a in range(na):\\n for b in range(na):\\n rab = rij + d[i,a,:] - d[j,b,:] # Atom-atom vector for given a and b\\n rab_sq = np.sum(rab**2) # Squared separation\\n if rab_sq < r_cut_sq: # Test within potential cutoff\\n sr2 = 1.0 / rab_sq # (sigma/rab)**2\\n ovr = sr2 > sr2_ovr # Set flag for overlap\\n rmag = np.sqrt(rab_sq)\\n sr6 = sr2 ** 3\\n sr12 = sr6 ** 2\\n pot = 4.0*(sr12-sr6) + lambda1 + lambda2*rmag # force-shifted pair potential\\n virab = 24.0*(2.0*sr12-sr6) - lambda2*rmag # pair virial\\n fab = virab * sr2\\n fab = rab * fab # atom-atom pair force\\n\\n total = total + PotentialType ( pot=pot, vir=np.sum(rij*fab), ovr=ovr )\\n f[i,:] = f[i,:] + fab\\n f[j,:] = f[j,:] - fab\\n tau[i,:] = tau[i,:] + np.cross ( d[i,a,:], fab )\\n tau[j,:] = tau[j,:] - np.cross ( d[j,b,:], fab )\\n\\n # Multiply results by numerical factors\\n total.vir = total.vir / 3.0 # Divide virial by 3\\n \\n return total, f, tau\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3c2b4c357b84232d662ada8d55b6a125\",\n \"score\": \"0.5226326\",\n \"text\": \"def compute_gravity_resistance(contacts, normals, mu, gamma, object_mass):\\n # YOUR CODE HERE (contact forces exist may be useful here)\\n gravity_wrench = np.array([0, 0, -9.81 * object_mass, 0, 0, 0])\\n return contact_forces_exist(contacts, normals, mu, gamma, gravity_wrench)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"328a4824c1f451de2257f5b68173378d\",\n \"score\": \"0.5166418\",\n \"text\": \"def _update_forces_thermostat(self, kinetic_energy):\\n # Compute forces on thermostat (R x M)\\n self.t_forces[..., 0] = (\\n kinetic_energy - self.degrees_of_freedom_particles * self.kb_temperature\\n ) / self.t_masses[..., 0]\\n\\n # Get kinetic energy of barostat (R x M)\\n kinetic_energy_cell = self._compute_kinetic_energy_cell()\\n # Compute forces on cell thermostat\\n self.t_forces_cell[..., 0] = (\\n kinetic_energy_cell - self.kb_temperature\\n ) / self.t_masses_cell[..., 0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"3506709c3945f06751269a0747a8a713\",\n \"score\": \"0.516037\",\n \"text\": \"def calculate(self, atoms):\\n boundary = ''\\n for i in range(3):\\n if atoms.pbc[i]:\\n boundary += 'p ' \\n else:\\n boundary += 'f '\\n if boundary in ['f f p ', 'p p f ']: #needs some work later\\n boundary = 'p p p '\\n self.boundary = boundary\\n if self.molecule:\\n self.write_lammps_data_water(atoms)\\n else:\\n self.write_lammps_data(atoms)\\n self.write_lammps_in()\\n self.lmp.file(self.lammps_in)\\n # Extract the forces and energy\\n self.lmp.command('variable pxx equal pxx')\\n self.lmp.command('variable pyy equal pyy')\\n self.lmp.command('variable pzz equal pzz')\\n self.lmp.command('variable pxy equal pxy')\\n self.lmp.command('variable pxz equal pxz')\\n self.lmp.command('variable pyz equal pyz')\\n self.lmp.command('variable fx atom fx')\\n self.lmp.command('variable fy atom fy')\\n self.lmp.command('variable fz atom fz')\\n self.lmp.command('variable pe equal pe')\\n if self.calc_type.find('GB') >= 0:\\n self.lmp.command('variable Etot equal c_eatoms')\\n self.gb_energy = self.lmp.extract_variable(\\\"Etot\\\", None, 0)\\n #print('gb_energy from lammps: ', self.gb_energy)\\n #print('update lammps GB energy')\\n\\n pos = np.array(\\n [x for x in self.lmp.gather_atoms(\\\"x\\\", 1, 3)]).reshape(-1, 3)\\n \\n self.energy = self.lmp.extract_variable('pe', None, 0) \\n #print('update lammps energy')\\n\\n xlo = self.lmp.extract_global(\\\"boxxlo\\\", 1)\\n xhi = self.lmp.extract_global(\\\"boxxhi\\\", 1)\\n ylo = self.lmp.extract_global(\\\"boxylo\\\", 1)\\n yhi = self.lmp.extract_global(\\\"boxyhi\\\", 1)\\n zlo = self.lmp.extract_global(\\\"boxzlo\\\", 1)\\n zhi = self.lmp.extract_global(\\\"boxzhi\\\", 1)\\n xy = self.lmp.extract_global(\\\"xy\\\", 1)\\n yz = self.lmp.extract_global(\\\"yz\\\", 1)\\n xz = self.lmp.extract_global(\\\"xz\\\", 1)\\n unitcell = np.array([[xhi-xlo, xy, xz],\\n [0, yhi-ylo, yz],\\n [0, 0, zhi-zlo]]).T\\n\\n stress = np.empty(6)\\n stress_vars = ['pxx', 'pyy', 'pzz', 'pyz', 'pxz', 'pxy']\\n\\n for i, var in enumerate(stress_vars):\\n stress[i] = self.lmp.extract_variable(var, None, 0)\\n #print('update lammps stress')\\n\\n stress_mat = np.zeros((3, 3))\\n stress_mat[0, 0] = stress[0]\\n stress_mat[1, 1] = stress[1]\\n stress_mat[2, 2] = stress[2]\\n stress_mat[1, 2] = stress[3]\\n stress_mat[2, 1] = stress[3]\\n stress_mat[0, 2] = stress[4]\\n stress_mat[2, 0] = stress[4]\\n stress_mat[0, 1] = stress[5]\\n stress_mat[1, 0] = stress[5]\\n stress[0] = stress_mat[0, 0]\\n stress[1] = stress_mat[1, 1]\\n stress[2] = stress_mat[2, 2]\\n stress[3] = stress_mat[1, 2]\\n stress[4] = stress_mat[0, 2]\\n stress[5] = stress_mat[0, 1]\\n\\n self.stress = -stress * 1e5 * ase.units.Pascal\\n f = (np.array(self.lmp.gather_atoms(\\\"f\\\", 1, 3)).reshape(-1,3) *\\n (ase.units.eV/ase.units.Angstrom))\\n #print('update lammps force')\\n self.forces = f.copy()\\n atoms.positions = pos.copy()\\n atoms.cell = unitcell.copy()\\n if self.molecule:\\n atoms.positions *= 0.529\\n atoms.cell *= 0.529\\n self.atoms = atoms.copy()\\n #self.atoms.info['GB_energy'] = self.gb_energy\\n #print('update lammps all')\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffb43eb1901c341a9a63209c9f56bd98\",\n \"score\": \"0.5123564\",\n \"text\": \"def _get_potential():\\n agent_pos = env.agent_pos\\n goal_pos = (16,16)\\n mannh_dist = goal_pos[0] - agent_pos[0] + goal_pos[1] - agent_pos[1]\\n return (-1)*mannh_dist\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d2aedad6da3938d9533fb0ce1f9a46a8\",\n \"score\": \"0.5109667\",\n \"text\": \"def update_dynamics(self):\\n # mass matrix\\n fd = rbd.ForwardDynamics(self.kine_dyn.mb)\\n fd.computeH(self.kine_dyn.mb, self.kine_dyn.mbc)\\n self.M = fd.H()\\n self.Minv = self.M.inverse()\\n # nonlinear effects vector\\n fd.computeC(self.kine_dyn.mb, self.kine_dyn.mbc)\\n self.h = fd.C()\\n\\n return M, Minv, h\",\n \"title\": \"\"\n },\n {\n \"docid\": \"64b8abcce7f4895b3262362293d4bc72\",\n \"score\": \"0.51049143\",\n \"text\": \"def energy_forces(self):\\n ene, frc = energy_forces(as_numpy=True)\\n return ene, frc.reshape((self.natom, 3))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a80b375c041b7c5e4982a8ed1a731a8b\",\n \"score\": \"0.50943166\",\n \"text\": \"def energy_forces(as_numpy=False):\\n global APPLY_UNITS\\n e, f = _pys.energy_forces()\\n if as_numpy:\\n f = _np.asarray(f)\\n if APPLY_UNITS:\\n return (_apply_units_to_struct(e, u.kilocalories_per_mole),\\n u.Quantity(f, u.kilocalories_per_mole/u.angstroms))\\n return e, f\",\n \"title\": \"\"\n },\n {\n \"docid\": \"09ac0a328ca063de02e150599b26f039\",\n \"score\": \"0.5093232\",\n \"text\": \"def _get_positions_and_forces_parser(\\n lines, trigger_indices, n_atoms, pos_flag=True, force_flag=True\\n ):\\n positions = []\\n forces = []\\n for j in trigger_indices:\\n pos = []\\n force = []\\n for line in lines[j + 2 : j + n_atoms + 2]:\\n line = line.strip()\\n line = _clean_line(line)\\n if pos_flag:\\n pos.append([float(l) for l in line.split()[0:3]])\\n if force_flag:\\n force.append([float(l) for l in line.split()[3:]])\\n forces.append(force)\\n positions.append(pos)\\n if pos_flag and force_flag:\\n return np.array(positions), np.array(forces)\\n elif pos_flag:\\n return np.array(positions)\\n elif force_flag:\\n return np.array(forces)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85da39827b781985f7ed2329edc15ca5\",\n \"score\": \"0.5090978\",\n \"text\": \"def potential_field(self, obstacle, curr_pos): \\n epsilon = self.params[\\\"force_max\\\"]\\n\\n force = np.empty((3,))\\n\\n dist = obstacle-curr_pos\\n dist_norm = np.linalg.norm(dist)\\n direction = dist/dist_norm\\n if dist_norm < epsilon:\\n dist_norm = epsilon\\n \\n return direction/dist_norm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2778ae395f578ca52adb0e39d0d58410\",\n \"score\": \"0.50805146\",\n \"text\": \"def _update_forces_thermostat(self, kinetic_energy):\\n # Compute Ekin from tensor\\n # kinetic_energy = torch.einsum(\\\"abii->ab\\\", kinetic_energy)\\n # Compute forces on thermostat (R x M)\\n self.t_forces[..., 0] = (\\n kinetic_energy - self.degrees_of_freedom_particles * self.kb_temperature\\n ) / self.t_masses[..., 0]\\n\\n # Get kinetic energy of barostat (R x M)\\n kinetic_energy_cell = self._compute_kinetic_energy_cell()\\n # Compute forces on cell thermostat\\n self.t_forces_cell[..., 0] = (\\n kinetic_energy_cell - 9.0 * self.kb_temperature\\n ) / self.t_masses_cell[..., 0]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"090f8af58bf28f07ebc4921c67483c0a\",\n \"score\": \"0.50768507\",\n \"text\": \"def find_force(cell_1: Cell, cell_2: Cell) -> Vector:\\n # vector from cell_1 to cell_2\\n vr = cell_1.coordinates - cell_2.coordinates\\n r = abs(vr) # absolute distance between cells\\n abs_force = lj_force(r, Cell.epsilon, Cell.sigma)\\n return Vector(\\n projection(abs_force, vr.x, vr),\\n projection(abs_force, vr.y, vr),\\n projection(abs_force, vr.z, vr)\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae25604d0c4278104bbc859c0dfd78e4\",\n \"score\": \"0.5076336\",\n \"text\": \"def find_nearby_atoms(\\n i_seq,\\n xray_structure,\\n pdb_atoms,\\n asu_mappings,\\n asu_table,\\n connectivity,\\n far_distance_cutoff=3.0,\\n near_distance_cutoff=1.5,\\n filter_by_bonding=True):\\n contacts = []\\n unit_cell = xray_structure.unit_cell()\\n sites_frac = xray_structure.sites_frac()\\n site_frac = sites_frac[i_seq]\\n asu_dict = asu_table[i_seq]\\n site_i = sites_frac[i_seq]\\n rt_mx_i_inv = asu_mappings.get_rt_mx(i_seq, 0).inverse()\\n atom_i = pdb_atoms[i_seq]\\n # Create the primary list of contacts\\n for j_seq, j_sym_groups in asu_dict.items():\\n site_j = sites_frac[j_seq]\\n atom_j = pdb_atoms[j_seq]\\n # Filter out hydrogens\\n if atom_j.element.upper().strip() in [\\\"H\\\", \\\"D\\\"]:\\n continue\\n # Filter out alternate conformations of this atom\\n if _same_atom_different_altloc(atom_i, atom_j):\\n continue\\n # Gather up contacts with all symmetric copies\\n for j_sym_group in j_sym_groups:\\n for j_sym_id in j_sym_group :\\n rt_mx = rt_mx_i_inv.multiply(asu_mappings.get_rt_mx(j_seq, j_sym_id))\\n site_ji = rt_mx * site_j\\n site_ji_cart = unit_cell.orthogonalize(site_ji)\\n vec_i = col(unit_cell.orthogonalize(site_frac=site_frac))\\n vec_ji = col(site_ji_cart)\\n assert abs(vec_i - vec_ji) < far_distance_cutoff + 0.5\\n contact = atom_contact(\\n atom = atom_j,\\n vector = vec_i - vec_ji,\\n site_cart = site_ji_cart,\\n rt_mx = rt_mx)\\n # XXX I have no idea why the built-in handling of special positions\\n # doesn't catch this for us\\n if (j_seq == i_seq) and (not rt_mx.is_unit_mx()):\\n continue\\n if contact.distance() < near_distance_cutoff:\\n continue\\n contacts.append(contact)\\n # Filter out carbons that are judged to be \\\"contacts\\\", but are actually\\n # just bonded to genuine coordinating atoms. This is basically just a\\n # way to handle sidechains such as His, Asp/Glu, or Cys where the carbons\\n # may be relatively close to the metal site.\\n if filter_by_bonding and (connectivity is not None):\\n filtered = []\\n all_i_seqs = [contact.atom_i_seq() for contact in contacts]\\n for contact in contacts:\\n # oxygen is always allowed, other elements may not be\\n if contact.element not in [\\\"C\\\", \\\"P\\\", \\\"S\\\", \\\"N\\\"]:\\n filtered.append(contact)\\n continue\\n # Remove atoms within 1.9 A contact distance\\n if any(other_contact != contact and\\n contact.distance_from(other_contact) < 1.9 and\\n contact.distance() > other_contact.distance()\\n for other_contact in contacts):\\n continue\\n # Examine the mode connectivity to catch more closely bonded atoms\\n bonded_j_seqs = []\\n for j_seq in connectivity[contact.atom_i_seq()]:\\n if (j_seq in all_i_seqs):\\n bonded_j_seqs.append(j_seq)\\n for j_seq in bonded_j_seqs:\\n other_contact = contacts[all_i_seqs.index(j_seq)]\\n if other_contact.element in [\\\"N\\\", \\\"O\\\", \\\"S\\\"] and \\\\\\n abs(other_contact) < abs(contact):\\n break\\n else:\\n filtered.append(contact)\\n contacts = filtered\\n return contacts\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1844e1f9bd9a09301057d4e912a77182\",\n \"score\": \"0.5059125\",\n \"text\": \"def run_forward_solves(self, params):\\n\\n print('Running forward solves')\\n self.make_forward_sim(params)\\n iter = self.optimizer.iteration if self.store_all_simulations else 0\\n self.sim.run(name = 'forward', iter = iter)\\n \\n get_eps = True\\n get_D = not self.use_deps\\n nointerpolation = not self.geometry.use_interpolation()\\n \\n self.forward_fields = get_fields(self.sim.fdtd,\\n monitor_name = 'opt_fields',\\n field_result_name = 'forward_fields',\\n get_eps = get_eps,\\n get_D = get_D,\\n get_H = False,\\n nointerpolation = nointerpolation,\\n unfold_symmetry = self.unfold_symmetry)\\n fom = self.fom.get_fom(self.sim)\\n\\n if self.store_all_simulations:\\n self.sim.remove_data_and_save() #< Remove the data from the file to save disk space. TODO: Make optional?\\n\\n self.fomHist.append(fom)\\n print('FOM = {}'.format(fom))\\n return fom\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d0e236d123d49239ffb679f5a2c48890\",\n \"score\": \"0.50420284\",\n \"text\": \"def get_potential_field(self):\\n ticks = np.linspace(0, 700, 700/self.par_dic[\\\"resolution\\\"])\\n num_ticks = len(ticks)\\n potential = np.zeros((num_ticks, num_ticks), dtype=np.float64)\\n for i in range(num_ticks):\\n for j in range(num_ticks):\\n potential[i][j] = self.calculate_potential([ticks[i], ticks[j]])\\n return potential\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ccf296f8a9211e03244c987b53a9ff8b\",\n \"score\": \"0.5021543\",\n \"text\": \"def total_forces(self, zeta_pole=np.zeros((3,))):\\n\\n if not hasattr(self, 'Gamma') or not hasattr(self, 'Fqs'):\\n self.reshape()\\n\\n self.Ftot = np.zeros((3,))\\n self.Mtot = np.zeros((3,))\\n\\n for ss in range(self.MS.n_surf):\\n M, N = self.MS.Surfs[ss].maps.M, self.MS.Surfs[ss].maps.N\\n for nn in range(N + 1):\\n for mm in range(M + 1):\\n zeta_node = self.MS.Surfs[ss].zeta[:, mm, nn]\\n fnode = self.Fqs[ss][:, mm, nn]\\n self.Ftot += fnode\\n self.Mtot += np.cross(zeta_node - zeta_pole, fnode)\\n # for cc in range(3):\\n # \\tself.Ftot[cc]+=np.sum(self.Fqs[ss][cc,:,:])\",\n \"title\": \"\"\n },\n {\n \"docid\": \"51f0b7f32e01212e43ded36ca5d47118\",\n \"score\": \"0.5016431\",\n \"text\": \"def get_force(self):\\n # @todo: make this work. Probably need to go through self.sim to get pod velocity, etc. \\n\\n \\\"\\\"\\\"\\n # Numerical simulation run at 6 different velocities -- see Keith's graph \\n # A34 data -- drag is for both brakes, lift is for one brake. Force_y has to do with the difference in force due to magnetic interactions and can be disregarded\\n v = self.sim.pod.velocity\\n air_gap = .024 # Should be self.sim.pod.brakes.gap, or brakes[i].gap if we're using an array of brakes, which we probably will\\n \\n # Fdrag(v) = gap_coefficient * (-e^(-.3*v)+1)*(1.5*e^(-.02*v)+1)\\n # gap_coefficient = 5632e^-202gap\\n \\n # @todo: Either the drag force or the lift force is for a single brake, the other is for both. Which is which? \\n gap_coefficient = 5632 * np.exp(-202 * air_gap)\\n f_drag = gap_coefficient * (-np.exp(-.3*v) + 1) * (1.5 * np.exp(-.02*v)+1)\\n #print \\\"Brake drag at air gap {}: {}\\\".format(air_gap, -f_drag)\\n \\\"\\\"\\\"\\n\\n #f_drag = self.sim.brake_1.drag_force * 2 # *2 for both brakes. Just testing right now\\n \\n f_drag = self.sim.pod.brakes.get_drag() \\n \\n return self.data(f_drag, 0, 0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d8fc2caff7036f9e1b274c855eb7b5c3\",\n \"score\": \"0.49988088\",\n \"text\": \"def computeForce(s): \\n\\n fv = vector(0.0, 0.0, 0.0) # Overall force vector\\n \\n for f in s.PForces:\\n # Determine if the force is a vector or a function,\\n # and act appropriately:\\n if hasattr(f, '__call__') == True:\\n fv = fv + f(s)\\n else:\\n fv = fv + f\\n\\n # Forces on ALL parts of the object contribute to overall force vector\\n for f in s.lForces:\\n if hasattr(f, '__call__') == True:\\n fv = fv + f(s)\\n else:\\n fv = fv + f \\n\\n for f in s.rForces:\\n if hasattr(f, '__call__') == True:\\n fv = fv + f(s)\\n else:\\n fv = fv + f\\n\\n for f in s.nForces:\\n if hasattr(f, '__call__') == True:\\n fv = fv + f(s)\\n else:\\n fv = fv + f\\n\\n for f in s.tForces:\\n if hasattr(f, '__call__') == True:\\n fv = fv + f(s)\\n else:\\n fv = fv + f\\n\\n \\n return fv.scale(TIMESTEP)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"0defcfe72b6852ece75ffde2ff4e6aac\",\n \"score\": \"0.49946612\",\n \"text\": \"async def get_near(self, request: Request) -> OkResult:\\n pass\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2de315fffcf7abcc2fcfb597eaecfae2\",\n \"score\": \"0.49818745\",\n \"text\": \"def get_force(self):\\n return self.rob.get_force(wait=True) - self.offset_force\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e70999e96b23d059034e8d0ec431b373\",\n \"score\": \"0.49759325\",\n \"text\": \"def get_dynamics(self, speed, copter_status):\\n velocity = copter_status.velocity\\n # convert axis from copter to world frame\\n ang_vel = copter_status.to_world_direction(copter_status.angular_velocity)\\n axis = copter_status.to_world_direction(self._axis)\\n pos = copter_status.to_world_direction(self._position)\\n\\n F = force(self, speed, axis, velocity, ang_vel)\\n M = moment(self, speed, axis, velocity, ang_vel)\\n # remove the part that accelerates the propeller\\n ma = np.dot(M, axis)\\n M -= ma * axis\\n # and add what the force does through the lever.\\n M += np.cross(pos, F)\\n return F, M, ma\",\n \"title\": \"\"\n },\n {\n \"docid\": \"591a6b675b65e7a7b286ff71a3c3c061\",\n \"score\": \"0.49697343\",\n \"text\": \"def calculate_hemispheres(self):\\n\\n # Figure out hemisphere of each line\\n self.fiber_hemisphere = numpy.zeros(self.number_of_fibers)\\n # percentage in left hemisphere\\n test = sum(self.fiber_array_r.T < 0) / float(self.points_per_fiber)\\n self.fiber_hemisphere[numpy.nonzero(test > 0.95)] = -1\\n self.fiber_hemisphere[numpy.nonzero(test < 0.05)] = 1\\n # otherwise hem stays 0 for commissural\\n\\n # output boolean arrays for each hemisphere and callosal fibers\\n self.is_left_hem = (self.fiber_hemisphere == -1)\\n self.is_right_hem = (self.fiber_hemisphere == 1)\\n self.is_commissure = (self.fiber_hemisphere == 0)\\n\\n # output indices of each type above\\n self.index_left_hem = numpy.nonzero(self.is_left_hem)[0]\\n self.index_right_hem = numpy.nonzero(self.is_right_hem)[0]\\n self.index_commissure = numpy.nonzero(self.is_commissure)[0]\\n self.index_hem = \\\\\\n numpy.nonzero(self.is_left_hem | self.is_right_hem)[0]\\n\\n # output totals of each type also\\n self.number_left_hem = len(self.index_left_hem)\\n self.number_right_hem = len(self.index_right_hem)\\n self.number_commissure = len(self.index_commissure)\\n\\n # test\\n if __debug__:\\n test = self.number_of_fibers == \\\\\\n (self.number_left_hem + self.number_right_hem \\\\\\n + self.number_commissure)\\n if not test:\\n print \\\"<fibers.py> ERROR: fiber numbers don't add up.\\\"\\n raise AssertionError\",\n \"title\": \"\"\n },\n {\n \"docid\": \"af0441346a35ed99651c2abcdb4c0370\",\n \"score\": \"0.49606064\",\n \"text\": \"def get_bader_coulomb_energy(self, structure):\\n energy = 0\\n force = np.zeros((len(structure), 3))\\n a = -1\\n b = -1\\n for i, sitei in enumerate(structure.as_dict()['sites']):\\n for j, sitej in enumerate(structure.as_dict()['sites']):\\n if i != j:\\n dij = structure.get_distance(i, j)\\n d_vec = structure.frac_coords[i] - structure.frac_coords[j]\\n Zi = sitei['species'][0]['oxidation_state']\\n Zj = sitej['species'][0]['oxidation_state']\\n energy += 0.5 * Zi * Zj / dij\\n force[i][0] += Zi * Zj / (dij ** 2) * (d_vec[0] / dij)\\n force[i][1] += Zi * Zj / (dij ** 2) * (d_vec[1] / dij)\\n force[i][2] += Zi * Zj / (dij ** 2) * (d_vec[2] / dij)\\n print force[i]\\n # to work on definition of forces\\n print np.sum(force[:, 0]), np.sum(force[:, 1]), np.sum(\\n force[:, 2]) # total force on cell in x, y, z ?\\n return energy\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e4db5019ce493a2b46fee42fa1a6c245\",\n \"score\": \"0.49512225\",\n \"text\": \"def getForce(self):\\n force = 0\\n # A number to multiply the weight by to make the teeter totter just right.\\n forceScalar = 0.1\\n for distance, obj in self.Objects:\\n leverage = distance / (self.width/2)\\n force += leverage * self.WEIGHT * forceScalar\\n return force\",\n \"title\": \"\"\n },\n {\n \"docid\": \"85cbd7935cd8338eca8c0a790297a7c5\",\n \"score\": \"0.49396554\",\n \"text\": \"def compute_distance_field(self, entity_type):\\n \\n # initialize the distance field and visited array \\n visited = []\\n distance_field= []\\n for _ in range(self._grid_height):\\n dist_inner=[]\\n visited_inner = []\\n for _ in range(self._grid_width):\\n dist_inner.append( self._grid_width * self._grid_height )\\n visited_inner.append(False)\\n\\n distance_field.append(dist_inner)\\n visited.append(visited_inner)\\n\\n \\n # which entity\\n if entity_type == HUMAN:\\n entity = self._human_list \\n #neb_func = self.eight_neighbors\\n elif entity_type == ZOMBIE:\\n entity= self._zombie_list \\n #neb_func = self.four_neighbors\\n \\n # initialize boundary, vistied and distance_field \\n boundary = poc_queue.Queue()\\n for ele in entity:\\n boundary.enqueue(ele)\\n visited[ele[0]][ele[1]] = True\\n distance_field[ele[0]][ele[1]] = 0\\n \\n # BFS search\\n while [ ele for ele in boundary] != []:\\n cell = boundary.dequeue()\\n neighbors = self.four_neighbors(cell[0],cell[1])\\n for neighbor in neighbors:\\n if visited[neighbor[0]][neighbor[1]] == False:\\n if self.is_empty(neighbor[0],neighbor[1]):\\n visited[neighbor[0]][neighbor[1]] = True\\n boundary.enqueue(neighbor)\\n #if self.is_empty(neighbor[0],neighbor[1]):\\n distance_field[neighbor[0]][neighbor[1]] = distance_field[cell[0]][cell[1]] + 1 \\n \\n return distance_field\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ae3efba09fa292e22afb64e136b30621\",\n \"score\": \"0.49363986\",\n \"text\": \"def forces_moments(self, forces_moments):\\n\\n self._forces_moments = forces_moments\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a749f40fccffa0c7ebea1f5f25d1f26c\",\n \"score\": \"0.4922669\",\n \"text\": \"def extract_energy_and_forces(atom_frames,calc=None,atomic=True):\\n energies_dft, forces_dft = [], {}\\n energies_gap, forces_gap = [], {}\\n\\n for atoms in atom_frames: # free energy per atom\\n # basic info\\n symbols = atoms.get_chemical_symbols()\\n if atomic:\\n natoms = len(atoms)\\n else:\\n natoms = 1\\n # energy\\n free_energy = atoms.get_potential_energy(force_consistent=True) # electronic free energy\\n energies_dft.append(free_energy/natoms)\\n # force\\n forces = atoms.get_forces()\\n for sym, force in zip(symbols,forces):\\n if sym in forces_dft.keys():\\n forces_dft[sym].extend(force)\\n else:\\n forces_dft[sym] = list(force)\\n if calc:\\n # use quip to calculate gap predicted energy\\n atoms.set_calculator(calc)\\n free_energy = atoms.get_potential_energy(force_consistent=True) # electronic free energy\\n energies_gap.append(free_energy/natoms)\\n # force\\n forces = atoms.get_forces()\\n for sym, force in zip(symbols,forces):\\n if sym in forces_gap.keys():\\n forces_gap[sym].extend(force)\\n else:\\n forces_gap[sym] = list(force)\\n\\n return forces_dft, forces_gap, energies_dft, energies_gap\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9a074b8ff1ea7758f57bb822f3e4805c\",\n \"score\": \"0.49200338\",\n \"text\": \"def calculate_potential(self, pos):\\n potential = 0.\\n r = self.par_dic[\\\"resolution\\\"]\\n for objects, calculator in self.calculator_dic.values():\\n for obj_par in objects:\\n potential += calculator.get_potential(np.array(obj_par)/r, np.array(pos + [self.par_dic[\\\"ball_size\\\"]])/r)\\n return potential\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e15e44ccf9753527d28e41077a145e76\",\n \"score\": \"0.49186394\",\n \"text\": \"def forces(self):\\n try:\\n return self.sim_state.getForces(asNumpy=True)\\n except:\\n warn(\\\"Unknown exception handled from `self.sim_state.getForces()`, \\\"\\n \\\"this is probably because this attribute is not in the State.\\\")\\n return None\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4d8ec1c6c0a28dd2b1ab398d2a841ec\",\n \"score\": \"0.4916737\",\n \"text\": \"def find_hp_force(hp_centre_x, hp_centre_y, hp_kappa_x, hp_kappa_y, X , Y, min_grid, max_grid, grid_space, periodic):\\n\\t#Calculate x-force\\n\\tF_harmonic_x = hp_kappa_x * (X - hp_centre_x)\\n\\tif periodic == 1:\\n\\t\\tgrid_length = max_grid[0] - min_grid[0]\\n\\t\\tgrid_centre = min_grid[0] + grid_length/2\\n\\t\\tif hp_centre_x < grid_centre:\\n\\t\\t\\tindex_period = index(hp_centre_x + grid_length/2, min_grid[0], grid_space[0])\\n\\t\\t\\tF_harmonic_x[:, index_period:] = hp_kappa_x * (X[:, index_period:] - hp_centre_x - grid_length)\\n\\t\\telif hp_centre_x > grid_centre:\\n\\t\\t\\tindex_period = index(hp_centre_x - grid_length/2, min_grid[0], grid_space[0])\\n\\t\\t\\tF_harmonic_x[:, :index_period] = hp_kappa_x * (X[:, :index_period] - hp_centre_x + grid_length)\\n\\t#Calculate y-force\\n\\tF_harmonic_y = hp_kappa_y * (Y - hp_centre_y)\\n\\tif periodic == 1:\\n\\t\\tgrid_length = max_grid[0] - min_grid[0]\\n\\t\\tgrid_centre = min_grid[0] + grid_length / 2\\n\\t\\tif hp_centre_y < grid_centre:\\n\\t\\t\\tindex_period = index(hp_centre_y + grid_length/2, min_grid[1], grid_space[1])\\n\\t\\t\\tF_harmonic_y[index_period:, :] = hp_kappa_y * (Y[index_period:, :] - hp_centre_y - grid_length)\\n\\t\\telif hp_centre_y > grid_centre:\\n\\t\\t\\tindex_period = index(hp_centre_y - grid_length/2, min_grid[1], grid_space[1])\\n\\t\\t\\tF_harmonic_y[:index_period, :] = hp_kappa_y * (Y[:index_period, :] - hp_centre_y + grid_length)\\n\\n\\treturn [F_harmonic_x, F_harmonic_y]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"bad4c4414505b48f3669330cf958ce8e\",\n \"score\": \"0.4893941\",\n \"text\": \"def gravity(coordinates):\\n \\n N = len(coordinates) #number of objects\\n r = np.zeros((N, 3)) #distance between objects\\n force = np.zeros((N, 3)) #force between objects\\n U = 0 #potential energy\\n \\n for i in range(0, N - 1):\\n for j in range(i + 1, N):\\n #calculates distance vector between each object\\n r[i] = coordinates[j] - coordinates[i]\\n \\n #force calculation\\n rmag = np.sqrt(r[i, 0]**2 + r[i, 1]**2 + r[i, 2]**2)\\n rhat = r[i] / rmag\\n dF = rhat * G_gravity * mass[i] * mass[j] / (rmag**2)\\n force[j] += -dF\\n force[i] += dF\\n U += -G_gravity * mass[i] * mass[j] / rmag \\n \\n return force, U\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d5215042c5e14f44508c68cdab40a093\",\n \"score\": \"0.4882773\",\n \"text\": \"def get_force(self):\\n x = -self.air_resistance_k * self.sim.pod.velocity ** 2\\n y = 0 # No y force. y force isn't used in the simulator right now\\n z = 0 # No z force for aero\\n return self.data(x, y, z)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"1fbf792dbf52e48a1f180c831c8e7bf0\",\n \"score\": \"0.48756886\",\n \"text\": \"def force_hertzian(self):\\n N = self.N\\n pos, rad = self.pos, self.rad\\n \\n f = np.zeros_like(pos)\\n for i in range(N):\\n rij = pos[i] - pos\\n dij = rad[i] + rad\\n dist = np.sqrt((rij**2).sum(axis=-1))\\n \\n mask = (dist > 0)&(dist < dij)\\n rij = rij[mask]\\n dij = dij[mask][:,None]\\n dist = dist[mask][:,None]\\n \\n if len(rij) > 0:\\n forces = self.epsilon*(1-dist/dij)**2 * rij/dist\\n f[i] += forces.sum(axis=0)\\n \\n return f\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d941dde0602a6b78d6b5cd35e116e151\",\n \"score\": \"0.48747146\",\n \"text\": \"def _force(self, x: float, y: float, z: float) -> TupleOf3Floats:\\n if not self.is_Coord_Inside_Vacuum(x, y, z):\\n return np.nan, np.nan, np.nan\\n FySymmetryFact = 1.0 if y >= 0.0 else -1.0 # take advantage of symmetry\\n FzSymmetryFact = 1.0 if z >= 0.0 else -1.0\\n y = abs(y) # confine to upper right quadrant\\n z = abs(z)\\n if -self.extra_field_length <= x <= self.L_cap: # at beginning of lens\\n Fx, Fy, Fz = self._force_Func_Outer(x, y, z)\\n elif self.L_cap < x <= self.L - self.L_cap: # if long enough, model interior as uniform in x\\n Fx, Fy, Fz = self._force_Func_Inner(y, z)\\n elif self.L - self.L_cap <= x <= self.L + self.extra_field_length: # at end of lens\\n x = self.L - x\\n Fx, Fy, Fz = self._force_Func_Outer(x, y, z)\\n Fx = -Fx\\n else:\\n raise Exception(\\\"Particle outside field region\\\") # this may be triggered when itentionally misligned\\n Fx *= self.field_fact\\n Fy *= FySymmetryFact * self.field_fact\\n Fz *= FzSymmetryFact * self.field_fact\\n # Fx, Fy, Fz = self.baseClass.rotate_Force_For_Misalignment(Fx, Fy, Fz)\\n return Fx, Fy, Fz\",\n \"title\": \"\"\n },\n {\n \"docid\": \"961f2c0fe80433b42457a604bfec2ad6\",\n \"score\": \"0.48666638\",\n \"text\": \"def test_forces_result(parse_result):\\n from aiida.orm import DataFactory\\n _, nodes = parse_result()\\n nodes = dict(nodes)\\n assert isinstance(nodes['forces'], DataFactory('array'))\\n assert numpy.all(nodes['forces'].get_array('forces')[0] == numpy.array(\\n [-0.23272115, -0.01115905, 0.03449686]))\\n assert numpy.all(nodes['forces'].get_array('forces')[-1] == numpy.array(\\n [-0.00300438, 0.00453998, 0.00066599]))\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a6d795221978880ad87839c99fd89e99\",\n \"score\": \"0.4861695\",\n \"text\": \"def apply_forces(\\n self, system_one: SystemType, index_one, system_two: SystemType, index_two\\n ):\\n end_distance_vector = (\\n system_two.position_collection[..., index_two]\\n - system_one.position_collection[..., index_one]\\n )\\n elastic_force = self.k * end_distance_vector\\n\\n relative_velocity = (\\n system_two.velocity_collection[..., index_two]\\n - system_one.velocity_collection[..., index_one]\\n )\\n damping_force = self.nu * relative_velocity\\n\\n contact_force = elastic_force + damping_force\\n system_one.external_forces[..., index_one] += contact_force\\n system_two.external_forces[..., index_two] -= contact_force\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2025ec37fdbfe53980550f2adbac2d5e\",\n \"score\": \"0.48610002\",\n \"text\": \"def compute_hydration_energies(molecules, parameters):\\n\\n energies = dict() # energies[index] is the computed solvation energy of molecules[index]\\n\\n platform = openmm.Platform.getPlatformByName(\\\"Reference\\\")\\n\\n moleculeIndex = -1\\n for molecule in molecules:\\n moleculeIndex += 1\\n # Create OpenMM System.\\n system = openmm.System()\\n for atom in molecule.GetAtoms():\\n mass = OEGetDefaultMass(atom.GetAtomicNum())\\n system.addParticle(mass * units.amu)\\n\\n # Add nonbonded term.\\n # nonbonded_force = openmm.NonbondedSoftcoreForce()\\n # nonbonded_force.setNonbondedMethod(openmm.NonbondedForce.NoCutoff)\\n # for atom in molecule.GetAtoms():\\n # charge = 0.0 * units.elementary_charge\\n # sigma = 1.0 * units.angstrom\\n # epsilon = 0.0 * units.kilocalories_per_mole\\n # nonbonded_force.addParticle(charge, sigma, epsilon)\\n # system.addForce(nonbonded_force)\\n\\n # Add GBVI term\\n # gbvi_force = openmm.GBVISoftcoreForce()\\n gbvi_force = openmm.GBVIForce() \\n gbvi_force.setNonbondedMethod(openmm.GBVIForce.NoCutoff) # set no cutoff\\n gbvi_force.setSoluteDielectric(1)\\n gbvi_force.setSolventDielectric(78.3)\\n\\n # Use scaling method.\\n # gbvi_force.setBornRadiusScalingMethod(openmm.GBVISoftcoreForce.QuinticSpline)\\n # gbvi_force.setQuinticLowerLimitFactor(0.75)\\n # gbvi_force.setQuinticUpperBornRadiusLimit(50.0*units.nanometers)\\n\\n # Build indexable list of atoms.\\n atoms = [atom for atom in molecule.GetAtoms()] \\n \\n # Assign GB/VI parameters.\\n for atom in molecule.GetAtoms(): \\n atomtype = atom.GetStringData(\\\"gbvi_type\\\") # GBVI atomtype\\n charge = atom.GetPartialCharge() * units.elementary_charge\\n radius = parameters['%s_%s' % (atomtype, 'radius')] * units.angstroms\\n gamma = parameters['%s_%s' % (atomtype, 'gamma')] * units.kilocalories_per_mole \\n # gamma *= -1.0 # DEBUG\\n lambda_ = 1.0 # fully interacting\\n # gbvi_force.addParticle(charge, radius, gamma, lambda_) # for GBVISoftcoreForce\\n gbvi_force.addParticle(charge, radius, gamma) # for GBVIForce\\n\\n # Add bonds.\\n for bond in molecule.GetBonds():\\n # Get atom indices.\\n iatom = bond.GetBgnIdx()\\n jatom = bond.GetEndIdx()\\n # Get bond length.\\n (xi, yi, zi) = molecule.GetCoords(atoms[iatom])\\n (xj, yj, zj) = molecule.GetCoords(atoms[jatom])\\n distance = math.sqrt((xi-xj)**2 + (yi-yj)**2 + (zi-zj)**2) * units.angstroms\\n # Identify bonded atoms to GBVI.\\n gbvi_force.addBond(iatom, jatom, distance)\\n\\n # Add the force to the system.\\n system.addForce(gbvi_force)\\n \\n # Build coordinate array.\\n natoms = len(atoms)\\n coordinates = units.Quantity(numpy.zeros([natoms, 3]), units.angstroms)\\n positions = []\\n for (index,atom) in enumerate(atoms):\\n (x,y,z) = molecule.GetCoords(atom)\\n coordinates[index,:] = units.Quantity(numpy.array([x,y,z]),units.angstroms) \\n positions.append( [x*0.1,y*0.1,z*0.1] )\\n #positions.append( [x,y,z] )\\n \\n # Create OpenMM Context.\\n timestep = 1.0 * units.femtosecond # arbitrary\\n integrator = openmm.VerletIntegrator(timestep)\\n context = openmm.Context(system, integrator, platform)\\n\\n # Set the coordinates.\\n context.setPositions(coordinates)\\n serializeDirectory = '/home/friedrim/source/gbff/examples/gbvi/serialization'\\n serializeFileName = 'mol_' + (\\\"%d\\\" % (moleculeIndex))\\n serialize( system, positions, serializeDirectory, serializeFileName )\\n \\n # Get the energy\\n state = context.getState(getEnergy=True)\\n energies[molecule] = state.getPotentialEnergy()\\n\\n return energies\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ad94c111d7e684999e2e838c9ddf638\",\n \"score\": \"0.48538497\",\n \"text\": \"def generalized_forces(self, q , dq , ddq , t = 0 ): \\n \\n H = self.H( q )\\n C = self.C( q , dq )\\n g = self.g( q )\\n d = self.d( q , dq )\\n \\n # Generalized forces\\n forces = np.dot( H , ddq ) + np.dot( C , dq ) + g + d\\n \\n return forces\",\n \"title\": \"\"\n },\n {\n \"docid\": \"e5e4b02ed346ad5a64351d42f051a224\",\n \"score\": \"0.48517668\",\n \"text\": \"def calc_mom_transport(self):\\n y = self.y_R*R\\n z = self.z_H*H\\n self.ddy_upvp = np.zeros(self.grdims)\\n self.ddz_upwp = np.zeros(self.grdims)\\n self.d2Udy2 = np.zeros(self.grdims)\\n self.d2Udz2 = np.zeros(self.grdims)\\n self.dUdy = np.zeros(self.grdims)\\n self.dUdz = np.zeros(self.grdims)\\n for n in range(len(z)):\\n self.ddy_upvp[n, :] = \\\\\\n fdiff.second_order_diff(self.df.mean_upvp.iloc[n, :], y)\\n self.dUdy[n, :] = \\\\\\n fdiff.second_order_diff(self.df.mean_u.iloc[n, :], y)\\n self.d2Udy2[n, :] = fdiff.second_order_diff(self.dUdy[n, :], y)\\n for n in range(len(y)):\\n self.ddz_upwp[:, n] = \\\\\\n fdiff.second_order_diff(self.df.mean_upwp.iloc[:, n], z)\\n self.dUdz[:, n] = \\\\\\n fdiff.second_order_diff(self.df.mean_u.iloc[:, n], z)\\n self.d2Udz2[:, n] = fdiff.second_order_diff(self.dUdz[:, n], z)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d41bcf9a7f67579fd34239da8fd3c0c2\",\n \"score\": \"0.484313\",\n \"text\": \"def meteorite_fell_near(location: str):\\n output = []\\n given_longitude = commonLocations.find({\\\"name\\\": location})[0]['long']\\n given_latitude = commonLocations.find({\\\"name\\\": location})[0]['lat']\\n for entry in meteoriteLandings.find({}):\\n #pprint(entry)\\n entry_longitude, entry_latitude = entry[\\\"geolocation\\\"][\\\"coordinates\\\"]\\n entry_latitude = float(entry_latitude)\\n entry_longitude = float(entry_longitude)\\n if ((entry_longitude - given_longitude) ** 2 + (entry_latitude - given_latitude) ** 2) ** .5 < 25:\\n output.append(entry)\\n\\n pprint(\\\"DONE\\\")\\n return output\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6d3c4b425c2adf894bfe090378206722\",\n \"score\": \"0.48427597\",\n \"text\": \"def compute_distance_field(self, entity_type):\\n # store the height and width of the grid\\n height = poc_grid.Grid.get_grid_height(self)\\n width = poc_grid.Grid.get_grid_width(self)\\n # create a grid that tracks the visited cells of the grid\\n # and intialize all locations to be EMPTY i.e. not visited\\n visited = [[EMPTY for dummy_col in range(width)] \\n for dummy_row in range(height)]\\n \\n # create a distance field to keep track of the shortest\\n # distance from a entity type and initialize it as height*width\\n # since the distance larger than any possible distance\\n distance_field = [[height * width for dummy_col in range(width)] \\n for dummy_row in range(height)]\\n \\n # create a queue for breath first search\\n boundary = poc_queue.Queue()\\n # map the respective entity type to its generator function\\n map_entity_type = {HUMAN: self.humans, ZOMBIE: self.zombies}\\n # add all human or zombie locations to the queue \\n # and mark those locations as visited and the\\n # distance at that location as zero\\n for row, col in map_entity_type[entity_type]():\\n boundary.enqueue((row, col))\\n visited[row][col] = FULL\\n distance_field[row][col] = 0\\n # begin the breath first search\\n while(len(boundary) > 0 ):\\n # get the current cell i.e the grid location\\n # of the zombie/human\\n current_cell = boundary.dequeue()\\n # get all of the current cells four neighbours and iterate\\n # over them\\n for neighbor_cell in poc_grid.Grid.four_neighbors(self, \\n current_cell[0], \\n current_cell[1]):\\n # if neigboring cell is passable and has not yet been visited\\n # add it to the queue for BFS, mark it as visited and \\n # update the distance. \\n if (poc_grid.Grid.is_empty(self, neighbor_cell[0], neighbor_cell[1]) \\n and visited[neighbor_cell[0]][neighbor_cell[1]] == EMPTY):\\n boundary.enqueue(neighbor_cell)\\n visited[neighbor_cell[0]][neighbor_cell[1]] = FULL\\n distance_field[neighbor_cell[0]][neighbor_cell[1]] = (\\n distance_field[current_cell[0]][current_cell[1]] + 1)\\n \\n return distance_field\",\n \"title\": \"\"\n },\n {\n \"docid\": \"82ed37aee75d00dea5092afa8ae04352\",\n \"score\": \"0.4840184\",\n \"text\": \"def updatePos(self):\\n posspheres = self._spherepos()\\n p = _np.exp(\\n _ne.evaluate('sum(-pos**rho*s, axis=1)', local_dict={'s': _np.log(2) * (2 / self.fwhm) ** self.rho, 'pos': posspheres, 'rho': self.rho})\\n )\\n n = self.rng.poisson(p * (self._N / _np.sum(p)))\\n missing = self._N - _np.sum(n)\\n while missing:\\n ids = self.rng.choice(len(n), int(abs(missing)), replace=True, p=p / (_np.sum(p)))\\n _np.add.at(n, ids, _np.sign(missing))\\n n[n < 0] = 0\\n missing = self._N - _np.sum(n)\\n nc = _np.cumsum(n)\\n if self.rsphere > 0:\\n posatoms = _rndSphere(self.rsphere, int(self._N))\\n else:\\n posatoms = _np.zeros((self._N, 3))\\n multisphere._staggeredadd(posspheres, posatoms, nc)\\n self._debug = (len(posspheres), _np.min(n), _np.max(n), _np.mean(n))\\n self._pos = posatoms\",\n \"title\": \"\"\n },\n {\n \"docid\": \"52d9a95819ef0d7fa86b3cca8ca2496b\",\n \"score\": \"0.48382258\",\n \"text\": \"def remove_all_forces(self):\\n self.forces = []\",\n \"title\": \"\"\n },\n {\n \"docid\": \"14fd50105b4aa4359ae49db62a245c38\",\n \"score\": \"0.4827966\",\n \"text\": \"def find_near_assets(self, asset_id: str, data: dict) -> dict:\\n\\n query = {'center': {'$near': {'$geometry': data['center'], '$maxDistance': 500}}}\\n qs = self.collection.find(query, {'asset_id': 1, 'year': 1, 'iiif_identifier': 1})\\n\\n # Merge with similar and set distance as 0\\n similar = data.get('similar', [])\\n similar = similar + [\\n py_.pick(i, 'asset_id', 'year', 'iiif_identifier') for i in qs if i.get('year', None) is not None\\n ]\\n out = {'similar': similar}\\n\\n return out\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d62ca60fc0cab06461d22cb340d12add\",\n \"score\": \"0.48248664\",\n \"text\": \"def calculate_bonded_forces(box, coords, elems, bonds, force, bond_length, force_constant):\\n energy = 0\\n for bond in bonds:\\n dx = distance_pbc(box, coords[bond[0]], coords[bond[1]])\\n dx2 = np.dot(dx, dx)\\n dx1 = math.sqrt(dx2)\\n bond_name = elems[bond[0]] + '-' + elems[bond[1]]\\n if(not bond_name in bond_length or not bond_name in force_constant):\\n sys.exit(\\\"quitting program because of unknown %s bond\\\"%bond_name)\\n ddx = dx1 - bond_length[bond_name]\\n energy += 0.5 * force_constant[bond_name]*ddx**2\\n # force on one vector\\n force_a = -force_constant[bond_name] * ddx / dx1\\n # on xyz vectors\\n for m in range(3):\\n force[bond[0]][m] += force_a * dx[m]\\n force[bond[1]][m] -= force_a * dx[m]\\n\\n return energy, force\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d616cb450c3a3390c5892225009ab1e4\",\n \"score\": \"0.481471\",\n \"text\": \"def calculateFitness(self, fitness_forces, modules):\\n fitness = 0\\n for item in fitness_forces:\\n response = self.calculateUnitFitness(item, modules)\\n if response is None:\\n return None\\n else:\\n fitness += response\\n return fitness\",\n \"title\": \"\"\n },\n {\n \"docid\": \"43a7167aa6439a2a418a6fa18d12fdf5\",\n \"score\": \"0.48118192\",\n \"text\": \"def getKinectData(self):\\n #Figuring out the corner points on the outside of the sphere\\n (junk, leftPoint,rightPoint,lowest,highest)=self.getFieldOfView()\\n #Create the rays that go from the center and hit each of the outside points, treat as 2D, represented as length and slope \\n angle=SimSensors.angle((leftPoint[1],leftPoint[0]),(rightPoint[1],rightPoint[0]))-math.pi/2\\n rays=[]\\n i=0\\n prev=(-1,-1)\\n while True:\\n i+=1\\n (x,y)=(int(leftPoint[0]+math.cos(angle)*i),int(leftPoint[1]-math.sin(angle)*i))\\n #print (x,y)\\n #import time\\n #time.sleep(1)\\n if (rightPoint[0]-leftPoint[0])*(x-rightPoint[0])>=0 and (rightPoint[1]-leftPoint[1])*(y-rightPoint[1])>=0:\\n break\\n if prev[0]!=x or prev[1]!=y:#Checking for duplicates\\n rays.append((SimSensors.angle(self.pos,(x,y)),SimSensors.euclid(self.pos,(x,y))))\\n prev=[x,y]\\n rays.append((SimSensors.angle(self.pos,rightPoint),SimSensors.euclid(self.pos,rightPoint)))\\n points=[]\\n #Tracing each ray from the beginning...\\n for ray in rays:\\n lowestAngle=-self.scanAngleV#Keeps track of the angle that it can see that is not blocked by another object\\n for i in range(int(ray[1])+1):\\n x=int(self.getFrontPos()[0]+i*math.cos(ray[0]))\\n y=int(self.getFrontPos()[1]+i*math.sin(ray[0]))\\n if min(x,y)<0 or max(x-len(self.heights),y-len(self.heights[0]))>0:\\n continue\\n z=self.kinectHeight+i*math.sin(lowestAngle)#Minimum height that can be seen at this point\\n if x<0 or x>=len(self.heights) or y<0 or y>=len(self.heights[0]):\\n continue\\n if self.heights[x][y]>z:\\n points.append((x,y,self.heights[x][y]))\\n #setting lowestAngle to angle from kinect to top of object\\n if self.kinectHeight>self.heights[x][y]:\\n if i==0:\\n if self.kinectHeight-self.heights[x][y]>0:\\n lowestAngle=math.pi/2\\n else:\\n lowestAngle=math.pi/-2\\n else:\\n lowestAngle=-math.atan(float(self.kinectHeight-self.heights[x][y])/float(i))\\n elif self.kinectHeight<self.heights[x][y]:\\n if i==0:\\n if self.heights[x][y]-self.kinectHeight>0:\\n lowestAngle=math.pi/2\\n else:\\n lowestAngle=math.pi/-2\\n else:\\n lowestAngle=math.atan(float(self.heights[x][y]-self.kinectHeight)/float(i))\\n else:\\n lowestAngle=0.0 \\n return points\",\n \"title\": \"\"\n },\n {\n \"docid\": \"4560ff102bb2419d6fa81854f6cb1c65\",\n \"score\": \"0.4809997\",\n \"text\": \"def _contact_geometry_GCS(self, q):\\n # create distance object\\n # print \\\"q =\\\", q\\n # print \\\"i =\\\", self.body_id_i\\n # print \\\"j =\\\", self.body_id_j\\n # print \\\"i =\\\", q2R_i(q, self.body_id_i)\\n # print \\\"j =\\\", q2R_i(q, self.body_id_j)\\n self._distance_obj = Distance(q2R_i(q, self.body_id_i), q2R_i(q, self.body_id_j))\\n\\n # distance\\n _d = self._distance_obj._distance\\n\\n # delta\\n _delta = self._distance_obj._distance - self._distance0\\n\\n # normal in GCS\\n _n_GCS = -self._distance_obj._distance_vector / self._distance_obj._distance\\n\\n # tangent in GCS\\n _t_GCS = n2t(_n_GCS)\\n\\n return _d, _delta, _n_GCS, _t_GCS\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f4e1c2587bcc083ef6b5b92845e14201\",\n \"score\": \"0.4807392\",\n \"text\": \"def find_valid(self, obj1coor, obj2coor, obs, lims=[[0,2*np.pi],[-np.pi/2,np.pi/2]]):\\n \\n #print obs*np.pi/180\\n \\n \\n n=self.comp_power\\n \\n obsth = obs[0]\\n obsph = obs[1]\\n \\n # Initalize variable space\\n A = np.linspace(lims[0][0],lims[0][1],n)\\n B = np.linspace(lims[1][0],lims[1][1],n)\\n grid = np.meshgrid(A,B)\\n \\n flat = np.array([grid[0].flatten(),grid[1].flatten()]).T\\n \\n # calculate observation vectors\\n obj1_xyz = self.xyz(obj1coor[0],obj1coor[1]) # xyz(obj1[0],obj1[1])\\n obj2_xyz = self.xyz(obj2coor[0],obj2coor[1]) # (obj2[0],obj2[1])\\n\\n\\n # For each potential A and B vector, calculate the theoretical change in theta and phi \\n thp = []\\n php = []\\n for x in flat:\\n # Calculate the vectors in a frame [alpha, beta] on the surface of the earth\\n [thp1,php1] = self.vec_prime(x[0],x[1],obj1_xyz,form='th-ph')\\n [thp2,php2] = self.vec_prime(x[0],x[1],obj2_xyz,form='th-ph')\\n\\n # Calculate theoretical difference between the two angles\\n thp += [thp2-thp1]\\n php += [php2-php1]\\n\\n #back from column to 2d grid\\n thp= np.array(thp).reshape(n,n)\\n php= np.array(php).reshape(n,n)\\n \\n #print thp\\n \\n # Create surface that represents the True change in alt-az coords (as observed)\\n obs12_th = np.ones((n,n))*obsth\\n obs12_ph = np.ones((n,n))*obsph\\n\\n # Take the observed delta-theta and delta-phi and compare it to our theoretical ones to figure out\\n # which values of A and B would allow for the observed changes.\\n \\n # Set up empty array for output valeues\\n sel_fin = np.array([])\\n \\n # Start real small with the binsize, extremely restrictive\\n stdth = np.std(thp.flatten())\\n stdph = np.std(php.flatten())*2\\n \\n #width = (max(thp.flatten())-min(thp.flatten()))/2\\n \\n mod=0.01\\n dist = np.ones((n,n))*10**-12\\n \\n \\n while mod*stdth < 3*self.obserrs[0] and mod*stdph < 3*self.obserrs[1]:\\n \\n mod*=1.01\\n \\n thdist = np.exp(- ((thp-obs12_th)/(mod*stdth))**2 )\\n phdist = np.exp(- ((php-obs12_ph)/(mod*stdph))**2 )\\n \\n #thdist = np.exp(- ((thp-obs12_th)/(self.obserrs[0]))**2 )\\n #phdist = np.exp(- ((php-obs12_ph)/(self.obserrs[1]))**2 ) \\n \\n dist = (thdist/np.sum(thdist))*(phdist/np.sum(phdist))\\n dist_norm = dist/np.sum(dist)\\n \\n self.chain+=[dist_norm]\\n \\n return grid,dist_norm\",\n \"title\": \"\"\n },\n {\n \"docid\": \"828e97789ab10fc3087f66480d1411df\",\n \"score\": \"0.48013788\",\n \"text\": \"def calc_force(a, b, dt):\\n\\n r = ((b['x'] - a['x']) ** 2 + (b['y'] - a['y']) ** 2 + (b['z']\\n - a['z']) ** 2) ** 0.5\\n a['vx'] += G * a['m'] * b['m'] / r ** 2 * ((b['x'] - a['x']) / r) \\\\\\n / a['m'] * dt\\n a['vy'] += G * a['m'] * b['m'] / r ** 2 * ((b['y'] - a['y']) / r) \\\\\\n / a['m'] * dt\\n a['vz'] += G * a['m'] * b['m'] / r ** 2 * ((b['z'] - a['z']) / r) \\\\\\n / a['m'] * dt\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5ad5d9f47c24125cd478eb46cdcd465f\",\n \"score\": \"0.47910753\",\n \"text\": \"def distance_displacement(self):\\n AtomIterator = np.ascontiguousarray(np.vstack((np.fromiter(itertools.chain(*[[i]*(self.na-i-1) for i in range(self.na)]),dtype=np.int32),\\n np.fromiter(itertools.chain(*[range(i+1,self.na) for i in range(self.na)]),dtype=np.int32))).T)\\n if hasattr(self, 'boxes') and pbc:\\n boxes = np.array([[self.boxes[i].a, self.boxes[i].b, self.boxes[i].c] for i in range(len(self))])\\n drij, dxij = AtomContact(np.array(self.xyzs), AtomIterator, box=boxes, displace=True)\\n else:\\n drij, dxij = AtomContact(np.array(self.xyzs), AtomIterator, box=None, displace=True)\\n return AtomIterator, list(drij), list(dxij)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"9ddac155d6a9476e6a8f8372b841d185\",\n \"score\": \"0.479047\",\n \"text\": \"def calculate_plastic_force(\\n self, u: np.ndarray, p: np.ndarray\\n ) -> Tuple[float, float]:\\n # initialise variables\\n (f_top, f_bot) = (0, 0)\\n (ea_top, ea_bot) = (0, 0)\\n (qx_top, qx_bot) = (0, 0)\\n (qy_top, qy_bot) = (0, 0)\\n\\n top_geoms, bot_geoms = self.geometry.split_section(point_i=p, vector=u)\\n\\n if top_geoms:\\n for top_geom in top_geoms:\\n e = top_geom.material.elastic_modulus\\n f_y = top_geom.material.yield_strength\\n area_top = top_geom.calculate_area()\\n ea_top += e * area_top\\n cx, cy = top_geom.calculate_centroid()\\n qx_top += cy * area_top\\n qy_top += cx * area_top\\n f_top += f_y * area_top\\n\\n if bot_geoms:\\n for bot_geom in bot_geoms:\\n e = bot_geom.material.elastic_modulus\\n f_y = bot_geom.material.yield_strength\\n area_bot = bot_geom.calculate_area()\\n ea_bot += e * area_bot\\n cx, cy = bot_geom.calculate_centroid()\\n qx_bot += cy * area_bot\\n qy_bot += cx * area_bot\\n f_bot += f_y * area_bot\\n\\n try:\\n self.c_top = [qy_top / ea_top, qx_top / ea_top]\\n self.f_top = f_top\\n except ZeroDivisionError:\\n self.c_top = [0, 0]\\n self.f_top = 0\\n\\n try:\\n self.c_bot = [qy_bot / ea_bot, qx_bot / ea_bot]\\n except ZeroDivisionError:\\n self.c_bot = [0, 0]\\n\\n return (f_top, f_bot)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46870c467aa0d0cf76849ffc08fa8f4f\",\n \"score\": \"0.47887427\",\n \"text\": \"def get_positions(self, filename=\\\"OUTCAR\\\", lines=None, n_atoms=None):\\n if n_atoms is None:\\n n_atoms = self.get_number_of_atoms(filename=filename, lines=lines)\\n trigger_indices, lines = _get_trigger(\\n lines=lines, filename=filename, trigger=\\\"TOTAL-FORCE (eV/Angst)\\\"\\n )\\n return self._get_positions_and_forces_parser(\\n lines=lines,\\n trigger_indices=trigger_indices,\\n n_atoms=n_atoms,\\n pos_flag=True,\\n force_flag=False,\\n )\",\n \"title\": \"\"\n },\n {\n \"docid\": \"46d42bade72f2c3c7e41b34460dc18c7\",\n \"score\": \"0.47878733\",\n \"text\": \"def filter_npc_vehicles(self):\\n near_npc_vehicles = [] # carla.Vehicles\\n\\n for index, veh in enumerate(self.npc_vehicles):\\n veh_info = self.get_veh_info(veh) # return a info dict\\n # add distance into veh_info\\n is_near = self.check_is_near(veh_info)\\n\\n if is_near:\\n near_npc_vehicles.append(veh_info)\\n\\n # select vehicles in front of ego vehicle\\n candidate_npc_vehicles = []\\n for veh_info in near_npc_vehicles:\\n is_front = self.check_is_front(veh_info)\\n if is_front:\\n candidate_npc_vehicles.append(veh_info)\\n\\n # select npc vehicles for state representation\\n selected_npc_vehicles = heapq.nsmallest(self.state_npc_number, # int, number of npc vehicles\\n candidate_npc_vehicles, # list in which stores the dict\\n key=lambda s: s['distance'])\\n\\n return selected_npc_vehicles\",\n \"title\": \"\"\n },\n {\n \"docid\": \"5de1d988ec92138a8524792c137f9038\",\n \"score\": \"0.47865242\",\n \"text\": \"def generalized_forces(self, q , dq , ddq , t = 0 ): \\n \\n H = self.H( q )\\n C = self.C( q , dq )\\n g = self.g( q )\\n \\n u = self.ubar\\n d = self.d( q , dq , u )\\n \\n # Generalized forces\\n forces = np.dot( H , ddq ) + np.dot( C , dq ) + g + d\\n \\n return forces\",\n \"title\": \"\"\n },\n {\n \"docid\": \"38682cceb9b9e7403eb5bb0681e0cfd3\",\n \"score\": \"0.47845912\",\n \"text\": \"def analyze(self):\\n self.calculate_FOM()\\n self.generate_FOM_image()\\n self.generate_plot()\\n ##self.generate_spot_mask()\",\n \"title\": \"\"\n },\n {\n \"docid\": \"028a429f7bbec0b6730d5ae12ea3feef\",\n \"score\": \"0.47789276\",\n \"text\": \"def get_force(self):\\n # @todo: decide whether or not we want to have gimbaling provide x force and lift for 4 of the engines, or to do x force (drag) for all engines in force_hover_engines.py\\n return self.data(0.0, 0.0, 0.0)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"57886eb1b9ee1654916afa094afbe5e7\",\n \"score\": \"0.47713766\",\n \"text\": \"def for_md_calculate_force(simul_box, ion_dict):\\n with tf.name_scope(\\\"for_md_calculate_force\\\"):\\n pef = _particle_electrostatic_force(simul_box, ion_dict)\\n erw = _electrostatic_right_wall_force(simul_box, ion_dict)\\n elw = _electrostatic_left_wall_force(simul_box, ion_dict)\\n plj = _particle_lj_force(simul_box, ion_dict)\\n lw_lj = _left_wall_lj_force(simul_box, ion_dict)\\n rw_lj = _right_wall_lj_force(simul_box, ion_dict)\\n ion_dict[interface.ion_for_str] = plj + lw_lj + rw_lj + erw + elw + pef\\n return ion_dict\",\n \"title\": \"\"\n },\n {\n \"docid\": \"55e74fa68123c3f03a7a693ceb8e3559\",\n \"score\": \"0.47644803\",\n \"text\": \"def get_constant(self):\\n\\t\\tforce_essieu_avant = MASSE*G_TERRE*L_AR/(L_AR + L_AV) # regle des moments\\n\\t\\tforce_essieu_arriere = MASSE*G_TERRE*L_AV/(L_AR + L_AV)# en faisant l'hypotese que la voiture est tres deformable\\n\\t\\tself.force_arriere_droit[\\\"z\\\"] = force_essieu_arriere/2.0\\n\\t\\tself.force_arriere_gauche[\\\"z\\\"] = force_essieu_arriere/2.0\\n\\t\\tself.force_avant_droit[\\\"z\\\"] = force_essieu_avant/2.0\\n\\t\\tself.force_avant_gauche[\\\"z\\\"] = force_essieu_avant/2.0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a0ad752d34a7a8aeb51059ff688df385\",\n \"score\": \"0.47494587\",\n \"text\": \"def direct_solve(self):\\n # initialize global stiffness, force, displacement\\n glob_stiff = np.zeros((self.num_node, self.num_node))\\n glob_force = np.zeros(self.num_node)\\n disp = np.zeros(self.num_node)\\n\\n # Assemble the global stiffness and force\\n for elem in self.elements:\\n\\n # element nodes, coordinates, and displacement\\n elem_nodes = self.conn[elem.num][:elem.num_points]\\n elem_coords = self.coords[elem_nodes]\\n elem_disp = disp[elem_nodes]\\n\\n # element stiffness and force\\n elem_stiff = elem.stiffness(elem_coords, elem_disp)\\n elem_force = elem.force(self.dist_load, elem_coords)\\n\\n # Add element contribution to global force and stiffness\\n for a in range(elem.num_points):\\n I = elem_nodes[a]\\n glob_force[I] += elem_force[a]\\n for b in range(elem.num_points):\\n J = elem_nodes[b]\\n glob_stiff[I, J] += elem_stiff[a, b]\\n\\n # Concentrated forces\\n for (node, mag) in self.conc_forces:\\n glob_force[node] += mag\\n\\n # Apply boundary conditions\\n for (n, alpha, beta, gamma) in self.boundary_conditions:\\n glob_stiff[n, n] += alpha / beta\\n glob_force[n] += gamma / beta\\n\\n # Now solve\\n disp[:] = np.linalg.solve(glob_stiff, glob_force)\\n\\n self._disp[:] = disp\\n\\n # post process\\n stress = np.empty(self.num_elem)\\n stran = np.empty(self.num_elem)\\n for elem in self.elements:\\n # element nodes, coordinates, and displacement\\n elem_nodes = self.conn[elem.num][:elem.num_points]\\n elem_coords = self.coords[elem_nodes]\\n elem_disp = disp[elem_nodes]\\n elem_stran = []\\n elem_stress = []\\n\\n for (n, xi) in enumerate(elem.gauss_points):\\n B = elem.b_matrix(xi, elem_coords)\\n e = np.dot(B, elem_disp)\\n s = elem.mat.get_stress(e)\\n elem_stran.append(e)\\n elem_stress.append(s)\\n stress[elem.num] = np.sum(elem_stress) / len(elem_stress)\\n stran[elem.num] = np.sum(elem_stran) / len(elem_stran)\\n\\n self.snapshot(stress, stran, self.disp)\\n\\n return\",\n \"title\": \"\"\n },\n {\n \"docid\": \"29d840d3494bc40f6ac54f92380f6e8a\",\n \"score\": \"0.47363195\",\n \"text\": \"def best_nearby(self, delta, point, prevbest, nvars, f, c_funevals):\\n\\n z = [0]*self.VARS\\n\\n minf = prevbest\\n\\n i = 0\\n\\n while (i < nvars):\\n z[i] = point[i]\\n\\n i += 1\\n\\n i = 0\\n\\n while (i < nvars):\\n z[i] = point[i] + delta[i]\\n\\n ftmp = f(z, nvars, c_funevals)\\n\\n if (ftmp < minf):\\n minf = ftmp\\n else:\\n delta[i] = 0.0 - delta[i]\\n z[i] = point[i] + delta[i]\\n\\n ftmp = f(z, nvars, c_funevals)\\n\\n if (ftmp < minf):\\n minf = ftmp\\n else:\\n z[i] = point[i]\\n\\n i += 1\\n\\n i = 0\\n\\n while (i < nvars):\\n point[i] = z[i]\\n\\n i += 1\\n\\n return minf\",\n \"title\": \"\"\n },\n {\n \"docid\": \"31ad1003dbd8efa76df4df0addb3fd88\",\n \"score\": \"0.4734121\",\n \"text\": \"def forcesIntoMOIBasis(atoms, index):\\r\\n \\r\\n forces = atoms.get_forces()\\r\\n positions = atoms.get_positions()\\r\\n\\r\\n # find MOIs\\r\\n pvalues, pdirs = get_moments_of_inertia(positions=positions,\\r\\n masses=atoms.get_masses(),\\r\\n center=positions[index],\\r\\n vectors=True)\\r\\n\\r\\n # Normalizing the vectors\\r\\n pdirs[0] /= np.linalg.norm(pdirs[0])\\r\\n pdirs[1] /= np.linalg.norm(pdirs[1])\\r\\n pdirs[2] /= np.linalg.norm(pdirs[2])\\r\\n \\r\\n # Calculating the center of mass in the pdirs coordinate basis.\\r\\n xtilde, ytilde, ztilde = calculate_image_center(atoms=atoms,\\r\\n watchindex=index,\\r\\n pdirs=pdirs)\\r\\n\\r\\n # If the coordinates of the center of mass are negative, \\r\\n # then the principle direction is flipped.\\r\\n # We want the first moment to be positive in all three directions.\\r\\n if xtilde < 0.:\\r\\n pdirs[0] *= -1.\\r\\n if ytilde < 0.:\\r\\n pdirs[1] *= -1.\\r\\n if ztilde < 0.:\\r\\n pdirs[2] *= -1.\\r\\n\\r\\n # Forces on atom index 'index' are transformed into the principle directions.\\r\\n transformed_forces = transform_forces(pdirections=pdirs,\\r\\n forces=forces[index])\\r\\n \\r\\n return transformed_forces, pdirs\",\n \"title\": \"\"\n },\n {\n \"docid\": \"16d1262cc9f04e36f30647521e834eab\",\n \"score\": \"0.4730221\",\n \"text\": \"def force(potential):\\n forcex = np.zeros_like(potential)\\n forcey = np.zeros_like(potential)\\n\\n lengthx, lengthy = potential.shape\\n\\n for i in range(lengthx):\\n for j in range(1, lengthy - 1):\\n forcex[i, j] = -0.5 * (potential[(i + 1) % lengthx, j] - potential[i - 1, j])\\n forcey[i, j] = -0.5 * (potential[i, j + 1] - potential[i, j - 1])\\n\\n return forcex, forcey\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ab157bc5804ef801b876efe19de8710c\",\n \"score\": \"0.47300896\",\n \"text\": \"def _magnetic_potential(self, x: float, y: float, z: float) -> float:\\n if not self.is_Coord_Inside_Vacuum(x, y, z):\\n return np.nan\\n y = abs(y)\\n z = abs(z)\\n if -self.extra_field_length <= x <= self.L_cap:\\n V0 = self._magnetic_potential_Func_Fringe(x, y, z)\\n elif self.L_cap < x <= self.L - self.L_cap:\\n V0 = self._magnetic_potential_Func_Inner(x, y, z)\\n elif 0 <= x <= self.L + self.extra_field_length:\\n x = self.L - x\\n V0 = self._magnetic_potential_Func_Fringe(x, y, z)\\n else:\\n raise Exception(\\\"Particle outside field region\\\")\\n V0 *= self.field_fact\\n return V0\",\n \"title\": \"\"\n },\n {\n \"docid\": \"a27c8ebb23476444b90789163792efb2\",\n \"score\": \"0.4729782\",\n \"text\": \"def get_thermodynamic(self):\\n res = {}\\n res = Montecarlo.get_thermodynamic(self)\\n at_count = self.count_atoms()\\n\\n # Inser the insertion enegies\\n for move in self.insertion_moves:\\n key = self.get_key(move[0], move[1])\\n name = \\\"insert_energy_{}\\\".format(key)\\n N = self.averager_track[key].num_samples\\n inf_temp = float(at_count[move[0]])/(at_count[move[1]]+1)\\n E0 = self.averager_track[key].ref_value\\n\\n res[name] = E0 - kB * self.T * \\\\\\n np.log(self.averager_track[key].average * inf_temp)\\n\\n name = \\\"raw_insert_energy_{}\\\".format(key)\\n res[name] = self.raw_insertion_energy[key] / N\\n\\n name = \\\"boltzmann_avg_insert_energy_{}\\\".format(key)\\n res[name] = self.boltzmann_weight_ins_energy[key].average / \\\\\\n self.averager_track[key].average\\n\\n name = \\\"boltzmann_avg_insert_energy_sq_{}\\\".format(key)\\n res[name] = self.boltzmann_weight_ins_eng_sq[key].average / \\\\\\n self.averager_track[key].average\\n return res\",\n \"title\": \"\"\n },\n {\n \"docid\": \"ffc6f50b8b046ed36e8e736939a17dc2\",\n \"score\": \"0.47294286\",\n \"text\": \"def get_potential():\\n # print GetRegister('Atom')\\n # type(GetRegister('Atom'))\\n\\tres = []\\n\\tfor k1, atom1 in GetRegister('Atom'):\\n\\t\\tv = 0\\n\\t\\tfor k2, atom2 in GetRegister('Atom'):\\n\\t\\t\\tif atom2._parent != atom1._parent:\\n\\t\\t\\t\\tv += atom2.potential_at(atom1._pos) # add the potential at atom2 from atom1\\n\\t\\tres.append(v) \\n\\treturn np.matrix(res) # return the result as a numpy matrix object\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2574178e0a532a0a22db308b853f4637\",\n \"score\": \"0.47279984\",\n \"text\": \"def calculate_onobject_gaze(self):\\r\\n onint_gaze, onboring_gaze, onfam_gaze = 0,0,0\\r\\n for k, v in self._dict.items():\\r\\n if v[0] == \\\"INT\\\":\\r\\n onint_gaze += v[2]\\r\\n elif v[0] == \\\"BOR\\\":\\r\\n onboring_gaze += v[2]\\r\\n elif v[0] == \\\"FAM\\\":\\r\\n onfam_gaze += v[2]\\r\\n\\r\\n all_gaze = onint_gaze + onboring_gaze + onfam_gaze\\r\\n onint = onint_gaze / all_gaze if all_gaze != 0 else 0\\r\\n onboring = onboring_gaze / all_gaze if all_gaze != 0 else 0\\r\\n onfam = onfam_gaze / all_gaze if all_gaze != 0 else 0\\r\\n\\r\\n return onint, onboring, onfam\",\n \"title\": \"\"\n },\n {\n \"docid\": \"f85c78cc3096da00b58f9cd350a20991\",\n \"score\": \"0.47214985\",\n \"text\": \"def post_calc(self):\\r\\n\\r\\n\\t\\tself.mfp = fcl.mean_free_path(self.atmosphere.T, self.atmosphere.p,\\r\\n\\t\\t\\tself.atmosphere.d)\\r\\n\\t\\tself.Kn = self.mfp / self.spacecraft.L\\r\\n#\\t\\tself.Re = fcl.KnReMa(self.atmosphere.k, Kn=self.Kn,\\r\\n#\\t\\t\\tMa=self.Ma)\\r\\n\\t\\tself.Re = fcl.Reynolds(self.atmosphere.rho, self.V, self.spacecraft.L,\\r\\n\\t\\t\\tself.atmosphere.mu)\\r\\n\\r\\n\\t\\t# Continuum: 0 < Kn < 0.1\\r\\n\\t\\t# Slip: 0.1 <= Kn < 1.0\\r\\n\\t\\t# Transition: 1.0 <= Kn < 10\\r\\n\\t\\t# Free molecular: 10 < Kn\\r\\n\\r\\n\\t\\tself.regimes = placeholder()\\r\\n\\r\\n\\t\\tif len(np.argwhere(self.Kn > 10)) != 0:\\r\\n\\t\\t\\tself.index_tran_freemol = np.argwhere(self.Kn > 10)[-1]\\r\\n\\t\\t\\tself.regimes.free_molecular = np.argwhere(self.Kn >= 10)\\r\\n\\t\\telse:\\r\\n\\t\\t\\tself.index_tran_freemol = None\\r\\n\\r\\n\\t\\tif len(np.argwhere(self.Kn > 1.0)) != 0:\\r\\n\\t\\t\\tself.index_slip_tran = np.argwhere(self.Kn > 1.0)[-1]\\r\\n\\t\\t\\tself.regimes.transition = np.argwhere((self.Kn < 10) & (self.Kn >= 1.0))\\r\\n\\t\\telse:\\r\\n\\t\\t\\tself.index_slip_tran = None\\r\\n\\r\\n\\t\\tif len(np.argwhere(self.Kn > 0.1)) != 0:\\r\\n\\t\\t\\tself.index_cont_slip = np.argwhere(self.Kn > 0.1)[-1]\\r\\n\\t\\t\\tself.regimes.slip = np.argwhere((self.Kn < 1.0) & (self.Kn >= 0.1))\\r\\n\\t\\telse:\\r\\n\\t\\t\\tself.index_cont_slip = None\\r\\n\\r\\n\\t\\tif len(np.argwhere((self.Kn > 0) & (self.Kn <= 0.1))) != 0:\\r\\n\\t\\t\\tself.regimes.continuum = np.argwhere((self.Kn < 0.1) & (self.Kn >= 0))\\r\\n\\t\\telse:\\r\\n\\t\\t\\tself.index_cont_slip = None\\r\\n\\r\\n\\t\\treturn [self.mfp, self.Kn, self.Re]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"b55deed435212910627555fa2a0df0d2\",\n \"score\": \"0.47197974\",\n \"text\": \"def _get_contact_geometry_data(self, q):\\n # normal in GCS of body i, j\\n self._n_GCS_list = [+self._n_GCS, -self._n_GCS]\\n\\n # tangent in GCS of body i, j\\n self._t_GCS_list = [+self._t_GCS, -self._t_GCS]\",\n \"title\": \"\"\n },\n {\n \"docid\": \"aa0f8bab954b8c31073b63e8dc484ef2\",\n \"score\": \"0.47193244\",\n \"text\": \"def get_sect_forces_gain(self):\\n\\n Ntot = 0\\n for ss in range(self.MS.n_surf):\\n Ntot += self.MS.NN[ss] + 1\\n self.Kfsec = np.zeros((3 * Ntot, 3 * self.Kzeta))\\n self.Kmsec = np.zeros((3 * Ntot, 3 * self.Kzeta))\\n\\n Kzeta_start = 0\\n II_start = 0\\n for ss in range(self.MS.n_surf):\\n M, N = self.MS.MM[ss], self.MS.NN[ss]\\n\\n for nn in range(N + 1):\\n zeta_sec = self.MS.Surfs[ss].zeta[:, :, nn]\\n\\n # section indices\\n iivec = [II_start + np.ravel_multi_index((cc, nn),\\n (3, N + 1)) for cc in range(3)]\\n # iivec = [II_start + cc+6*nn for cc in range(6)]\\n # iivec = [II_start + cc*(N+1) + nn for cc in range(6)]\\n\\n for mm in range(M + 1):\\n # vertex indices\\n jjvec = [Kzeta_start + np.ravel_multi_index((cc, mm, nn),\\n (3, M + 1, N + 1)) for cc in range(3)]\\n\\n # sectional force\\n self.Kfsec[iivec, jjvec] = 1.0\\n\\n # sectional moment\\n dx, dy, dz = zeta_sec[:, mm] - zeta_sec[:, M // 2]\\n self.Kmsec[np.ix_(iivec, jjvec)] = np.array([[0, -dz, dy],\\n [dz, 0, -dx],\\n [-dy, dx, 0]])\\n Kzeta_start += 3 * self.MS.KKzeta[ss]\\n II_start += 3 * (N + 1)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"2d2aede5659b6da08818b51715196b8c\",\n \"score\": \"0.4713425\",\n \"text\": \"def get_potential(self,atom,grid):\\n position=self.PI.GetPosition(atom)\\n return self.get_potential_atpos(position,grid)\",\n \"title\": \"\"\n },\n {\n \"docid\": \"6c3f6dcd7df9985de286e813d4edbbd7\",\n \"score\": \"0.47111088\",\n \"text\": \"def force(p1,p2):\\n return (-(4.982338253E-10)*p1.mass*p2.mass/(np.linalg.norm(Particle.Vect_Sep(p1,p2)))**3)*Particle.Vect_Sep(p1,p2) # convert unit of G to km^3 kg^(-1) day^(-2) to match with the input units from file_handle\",\n \"title\": \"\"\n },\n {\n \"docid\": \"d6de14740395850d689970b1378f8022\",\n \"score\": \"0.47041842\",\n \"text\": \"def q_show_forces(self, forces):\\n\\n c = [intco.f_show_factor for f in self.all_fragments for intco in f.intcos]\\n c = np.asarray(c)\\n qaJ = c * forces\\n return qaJ\",\n \"title\": \"\"\n }\n]","isConversational":false}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":9932,"numItemsPerPage":100,"numTotalItems":994482,"offset":993200,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NDkwNzI4NSwianRpIjoiMWYwYTQ5ZmYtMmYzMi00NDRhLWI0OWItNWQ5MjcxMjM5ZDJkIiwic3ViIjoiL2RhdGFzZXRzL250aGFrdXIvY29ybnN0YWNrLXB5dGhvbi12MS10ZXZhdHJvbi0xTSIsImV4cCI6MTc4NDkxMDg4NSwiaXNzIjoiaHR0cHM6Ly9odWdnaW5nZmFjZS5jbyJ9.tIPyn5Gqr48fEOTwJvKB0B2UVy8FWFuQu0fTgZ976uCcpFkoie1G68ZIZPoi7bNU35T0bjziuXe-tf-c80h9Cw","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":994482,"numBytesParquet":23972089656}]},"discussionsStats":{"closed":0,"open":1,"total":1},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query_id stringlengths 32 32 | query stringlengths 9 4.01k | positive_passages listlengths 1 1 | negative_passages listlengths 88 101 |
|---|---|---|---|
34c8e706e99e08935d6f7c512a7f359d | Queries all rankings for all golf tournaments. | [
{
"docid": "54e180015bba8793ef146b17c5e7362c",
"score": "0.538462",
"text": "def golf():\n # http://stackoverflow.com/questions/5334531/python-documentation-standard-for-docstring#5339352\n out = rankings_helper(\n url=RANKINGS_URL + \"golf/final.asp\",\n parser_func=parse_golf_soup\... | [
{
"docid": "4aac33a95c1acdbc792b4b0d0321c420",
"score": "0.6398167",
"text": "def rankings(self):\n return self.open_dota.get_ranking(self.steam_id)",
"title": ""
},
{
"docid": "988f313be5519410eea044d004294ba2",
"score": "0.6312738",
"text": "def get_user_rankings(self, reque... |
ac6a21266fb96ac5ad47efee748fa5e5 | NullableClass a model defined in OpenAPI | [
{
"docid": "f00ff4e2b66758efffaa904e06fd6438",
"score": "0.53666735",
"text": "def __init__(self, integer_prop=None, number_prop=None, boolean_prop=None, string_prop=None, date_prop=None, datetime_prop=None, array_nullable_prop=None, array_and_items_nullable_prop=None, array_items_nullable=None, object_... | [
{
"docid": "c27eaa285624d6ab8e5767d4f5c3c135",
"score": "0.583286",
"text": "def __init__(self): # noqa: E501\n self.openapi_types = {\n }\n\n self.attribute_map = {\n }",
"title": ""
},
{
"docid": "31278bb08ce1cbc89e9a444d3a7c6917",
"score": "0.5685518",
... |
a2fc8bb2a5c3260073f5a26b999f3b9f | monsave A command that will save an entry to the Mongo database. | [
{
"docid": "af5ecabe2c9fd3c02de25af0a5cb7cb0",
"score": "0.6818327",
"text": "async def monsave(self, ctx, *, entry):\r\n\r\n self.connect()\r\n discord_id = str(ctx.message.author.id)\r\n\r\n self.database.entries.insert_one({\r\n \"discord_id\": discord_id,\r\n ... | [
{
"docid": "a49ddbc0248dc4afd13e1a51f9c01563",
"score": "0.6202329",
"text": "def MongoSave(message):\n client = pymongo.MongoClient(\"localhost\",27017)\n db = client.PortfolioTracker\n db.AllPortfolios.save(message)#this must be a dictionary for proper insertion http://docs.python.org/2/tutor... |
54155040dec3cd5fe89af8c936b46bdd | Make seperate lists of conditions and conditional effects that will actually be encoded. (Problem) > None | [
{
"docid": "c80aee50170f2ae480783f949c58e2a5",
"score": "0.74035025",
"text": "def make_cond_and_cond_eff_lists(self):\n for cond in list(self.code_conds.values()):\n if cond in self.encode_conds:\n if isinstance(cond, ConditionalEffect):\n self.condit... | [
{
"docid": "3370b023fee9fb74b904e8c60608dd8f",
"score": "0.64567184",
"text": "def assign_cond_codes(self):\n cond_index = [0]\n self.goal.assign_cond_code(cond_index, self.code_conds)\n for action in list(self.actions.values()):\n if action.precondition:\n ... |
eaab0e91847e2a5c0cf6b28cf5470489 | Parse a single path segment. If the segment is an identifier or wildcard, it's constraint is also derived. | [
{
"docid": "f6897198e51df02893b19104052aa780",
"score": "0.7384426",
"text": "def _parse_path_segment(acc, segment):\n match = _token_re.match(segment)\n if match is not None:\n # XXX: Is it a problem that we're using strings for parameters and\n # strings for literal parts?\n ... | [
{
"docid": "ecfb2be83212ae44fb20a2e0a31bb3c2",
"score": "0.58996576",
"text": "def segment_path(self, segment_path):\n # type: (string_types) -> None\n\n if segment_path is not None:\n if not isinstance(segment_path, string_types):\n raise TypeError(\"Invalid type... |
aa6430fa7dd7fb2fdc1c4f5d95ee8312 | Given a color, estimate if it's better to show block or white text on top of it. | [
{
"docid": "bc8d5f0f579b04c9e661417afb9da33e",
"score": "0.5743493",
"text": "def text_color(color):\n\n r = int(color[0:2], base=16)\n g = int(color[2:4], base=16)\n b = int(color[4:6], base=16)\n yiq = 0.299 * r + 0.587 * g + 0.114 * b\n\n return \"000000\" if yiq > 128 else \"FFFFFF\""... | [
{
"docid": "d80767e8caf5a51d397c8f43aec7cc63",
"score": "0.6462235",
"text": "def is_colorized(self):",
"title": ""
},
{
"docid": "62050a6599742bd17e5b293dc01bc4c1",
"score": "0.6453103",
"text": "def detects_color(self, color):\n diff_c, diff_r, diff_g, diff_b = self.get_perc... |
b0c37801e07c8ddfbb03159eae153208 | Returns an int64_list from a bool / enum / int / uint. | [
{
"docid": "59ed2bfd6211f1674fb31803eedb4032",
"score": "0.5494831",
"text": "def _int64_feature(value):\n return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))",
"title": ""
}
] | [
{
"docid": "e2f053683e59ade4f7ff10d232477d03",
"score": "0.6754303",
"text": "def _int64_feature_list(values):\n return tf.train.FeatureList(feature=[wrap_int64(v) for v in values])",
"title": ""
},
{
"docid": "67a83f99ad929027279d8d5a45495c01",
"score": "0.6549031",
"text": "def ... |
15fcdd1674220f6c1a7549f66fa99fa5 | DW doesn't understand async functions well enough to make it a runtime test, but it was causing a compiletime failure at one point | [
{
"docid": "3816fc3f02b6a44307fff4ec73656d71",
"score": "0.5690496",
"text": "async def async_func():\n if variable := 1:\n pass",
"title": ""
}
] | [
{
"docid": "58029603e6c527c84c592ec631d8eed4",
"score": "0.6563841",
"text": "async def test_sync_in_async():\n test_sync()",
"title": ""
},
{
"docid": "712e5f910546efee674330bf9b5c1859",
"score": "0.65307885",
"text": "def test_call_async(rroot_async, loop):\n async def test()... |
f6dc744b3fcabf54d68abc32dc3f5c8c | An optional description of this resource. Provide this property when you create the resource. | [
{
"docid": "a8491c0b84893def7353ab2232d185e5",
"score": "0.0",
"text": "def description(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"description\")",
"title": ""
}
] | [
{
"docid": "32e07fb05930562371948b451032b2e5",
"score": "0.7329538",
"text": "def description(self):\n if self._description is None:\n self.__init()\n return self._description",
"title": ""
},
{
"docid": "32e07fb05930562371948b451032b2e5",
"score": "0.7329538",
... |
2e2dfcb37d6f2ad121fabf1ae5071834 | given a sklearn confusion matrix (cm), make a nice plot Arguments | [
{
"docid": "bcf8df7f3366eec53b591dd621ace859",
"score": "0.0",
"text": "def plot_confusion_matrix(cm,\n target_names,\n title='Confusion matrix',\n cmap=None,\n normalize=True,saveFileName='foo'):\n im... | [
{
"docid": "bdbf58d6563642a82a4f39f44f2446d3",
"score": "0.8023947",
"text": "def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Purples):\n if normalize:\n cm = cm.astyp... |
84dcd4dfcc9f039666e733a8f8a8ef68 | Getter on the catalogue business Parameter associated to self | [
{
"docid": "44a479c6c1bb9d1dd776fdeff10a0abc",
"score": "0.6820848",
"text": "def get_parameter(self):\n return self._parameter",
"title": ""
}
] | [
{
"docid": "77f619a468509205824a233ffa1649aa",
"score": "0.71139556",
"text": "def __get_parameter(self):\n return self.__parameter",
"title": ""
},
{
"docid": "840ab197a58dd11c295570b21fd03a6d",
"score": "0.7003249",
"text": "def get_parameter(self):\n return self._my_... |
8069f575c1b1e5fa08d0b225ce32d317 | this function generates training_set, also for testing | [
{
"docid": "a5cb49956ffef56ac1b73786fcc5df00",
"score": "0.0",
"text": "def sample_generater(descriptors):\n response = np.arange(MODEL_NUM)\n response = np.tile(response, TRAINING_SIZE / MODEL_NUM)\n \n training_set = np.empty(\n [TRAINING_SIZE, MIN_DESCRIPTOR], dtype=np.float32)\n ... | [
{
"docid": "e72bc7fbcfdcd80b61c7ce9328ba8157",
"score": "0.7856855",
"text": "def train_set(self):",
"title": ""
},
{
"docid": "61f1aa1ee27ee49679f3287c44430de8",
"score": "0.7494893",
"text": "def create_training_test_sets(self):\n # load input data\n input_data = np.a... |
5fda6dbd99524ecdfb1b915809eb59b0 | Convert Peptide sequences to unified format. This conversion simplifies the alphabet of the amino acids, removes special characters, replaces digits to written numbers, removes nterminal modifications, rewrites Xmod modifications to modX format and splits the amino acid string into a list. | [
{
"docid": "11caafd96e2f50778648de05dfbac5c9",
"score": "0.4715476",
"text": "def prepare_seqs(psms_df, seq_cols=[\"Peptide1\", \"Peptide2\"]):\n # for all sequence columns in the dataframe perform the processing\n # if linear only a single column, if cross-linked two columns are processed\n fo... | [
{
"docid": "4a73a617e9e89e678a0f5abc48d44b79",
"score": "0.6161142",
"text": "def generate_sequences(long_peptide,aa_type=\"natural\"):\n \n # List of the amino acids that can be included\n natural=[\"A\",\"C\",\"D\",\"E\",\"F\",\"G\",\"H\",\"I\",\"K\",\"L\",\"M\",\"N\",\"P\",\"Q\",\"R\",\"S\",... |
46be3fe4bfe7e117f4651abbdd084af3 | An iterative matcher tries to match different patterns that appear in the filename. The 'filetype' argument indicates which type of file you want to match. If it is 'autodetect', the matcher will try to see whether it can guess that the file corresponds to an episode, or otherwise will assume it is a movie. | [
{
"docid": "27c7d0b44a115b1318ab90815e059075",
"score": "0.70966053",
"text": "def __init__(self, filename, filetype='autodetect', opts=None):\n\n valid_filetypes = ('autodetect', 'subtitle', 'video',\n 'movie', 'moviesubtitle',\n 'episode', 'ep... | [
{
"docid": "14820715cc730d0ccbb4099b1fd063c7",
"score": "0.57484794",
"text": "def examine_file (file):\n fileType = \"\"\n\n for ext in fileExtensions.keys ():\n\tif get_ext_re (ext).search (file):\n\t fileType = ext\n\t break\n else:\n\treturn UNKNOWN\n\n return fileType",
"title... |
3035ce5453e942559fa2b977fedc4834 | Integrates the density over the area of this cell. Let (width,height) be the shape of the density array. The bounding box of the voronoi cells is assumed to be [0,width]x[0,height]. | [
{
"docid": "713118aa93c7d81544c4680c26a17bec",
"score": "0.65445966",
"text": "def integrate(self, density):\n\n # Note: the vertices in the cell are ordered clockwise\n\n width, height = density.shape\n # compute bounds of the cell\n min_x = width-1\n min_y = height-1... | [
{
"docid": "177abe6c70cc77093307027bd04c32bc",
"score": "0.57263225",
"text": "def section_integral(v, xmin, xmax):\n minind, maxind = utils.get_indrange(v.x, xmin, xmax)\n if v.surface_field:\n dx = v.cell_widths_as_data[:,minind:maxind]\n return np.sum(v.data[:,minind:maxind] * dx,... |
b63692932d6fab64feff4df5e1a783b8 | Add command 'fends' arguments to parser. | [
{
"docid": "c1e46a029dc10961db7725ed5971e0fe",
"score": "0.7020121",
"text": "def add_fends_subparser(subparsers):\n parser = subparsers.add_parser(\"fends\", help=\"HiFive Fend Creation Function: Read RE fragment data from either a BED file or a HiCPipe-style tabular fend file and create a HiFive 'F... | [
{
"docid": "00db2eb8473f3a94138906a5d8a50a58",
"score": "0.56442916",
"text": "def add_args(self, parser):",
"title": ""
},
{
"docid": "79bacc3f2766a5e4be3aeb5abc86601a",
"score": "0.56194735",
"text": "def add_arguments(self, parser):",
"title": ""
},
{
"docid": "4f29259... |
fdf6f694cb1e3838bfa48135ff0402ad | Train the network for one epoch | [
{
"docid": "3e94aa52367775bbf34fd544089347d2",
"score": "0.0",
"text": "def train(self):\n self.module.resetDerivatives()\n errors = 0\n ponderation = 0\n for seq in self.ds._provideSequences():\n e, p = self._calcDerivs(seq)\n errors += e\n p... | [
{
"docid": "ddf480db659d1c346cc742bf9a9fae48",
"score": "0.83493596",
"text": "def train(self):\n self.learner.fit_one_cycle(self.config.EPOCHS)",
"title": ""
},
{
"docid": "71e6ccaf184a0e11175e8c89f61e3700",
"score": "0.8324999",
"text": "def train_one_epoch(self):\n r... |
b6c07ecfe56842e17f2af75a281e3e9e | Replace all occurencens of 'old' with 'new' starting at the right hand side of the string. If occurences is specified, the number of replacements is limited to occurences. | [
{
"docid": "28acc5f001264aefd78d9c3a856a6ec1",
"score": "0.7681911",
"text": "def rreplace(s, old, new, occurences=-1):\n li = s.rsplit(old, occurences)\n return new.join(li)",
"title": ""
}
] | [
{
"docid": "1cdce6d8002ba96e113559604122a8ad",
"score": "0.71731454",
"text": "def rreplace(s, old, new, occurrence):\n li = s.rsplit(old, occurrence)\n return new.join(li)",
"title": ""
},
{
"docid": "1cdce6d8002ba96e113559604122a8ad",
"score": "0.71731454",
"text": "def rrepl... |
509ce9754367019a20dfcbf8f803723c | Publish payment response to async nats. | [
{
"docid": "e670274aab61864312b95abba7d841a9",
"score": "0.68084496",
"text": "def publish_response(payload):\n asyncio.run(publish(payload=payload))",
"title": ""
}
] | [
{
"docid": "eccc54ce5aa83f776edf36cb0d731c93",
"score": "0.62804866",
"text": "def on_post(self, req, res):\n account_addr = str(req.body['account_addr']).lower()\n session_id = str(req.body['session_id'])\n token = str(req.body['token'])\n signature = {\n 'hash': ... |
b5f907abb27800eaf3cd53c43f25771f | Take in a hex string and convert it to a list of bytes. (This should effectively "undo" the question 3.) | [
{
"docid": "311f04eab1a5b9a6b7848352c5e32e4b",
"score": "0.83116025",
"text": "def hex_string_to_bytes(the_input):\n # for fun:\n # list(map(lambda i, j: int(i+j, 16), the_input[::2], the_input[1::2]))\n return [int(str(the_input[i:i+2]), 16) for i in range(0, len(the_input), 2)]",
"title":... | [
{
"docid": "1374219d9d3d8d9e3c5c85e01f39a532",
"score": "0.7718656",
"text": "def hexify(hexStr):\n bytes = []\n hexStr = ''.join( hexStr.split() )\n for i in range(0, len(hexStr), 2):\n bytes.append( signed1( int (hexStr[i:i+2], 16 ) ) )\n return bytes",
"title": ""
},
{
... |
eb9332be39b92465995c1480da3d3495 | SM electrolyte with LFP homog, sep + cathode, significant elyte polarization | [
{
"docid": "b242848edc3e7f0aa794661c72e99956",
"score": "0.0",
"text": "def test019(testDir, dirDict, pflag):\n shutil.copy(osp.join(dirDict[\"baseConfig\"], \"params_system.cfg\"), testDir)\n shutil.copy(osp.join(dirDict[\"baseConfig\"], \"params_c.cfg\"), testDir)\n psys = osp.join(testDir, \... | [
{
"docid": "1fc9acad187035ee7206d7755133a958",
"score": "0.6152157",
"text": "def test_el_below_lcl():\n p = [902.1554, 897.9034, 893.6506, 889.4047, 883.063, 874.6284, 866.2387, 857.887,\n 849.5506, 841.2686, 833.0042, 824.7891, 812.5049, 796.2104, 776.0027, 751.9025,\n 727.9612, 704... |
a9ebaff0390576dcd5be733f835b19d2 | Create a ``treq``alike which can dispatch to a local issuer. | [
{
"docid": "4f3b3bcb1187d356bbb30b82a85bbcd1",
"score": "0.6673055",
"text": "def treq_for_loopback_ristretto(local_issuer: IResource) -> StubTreq:\n v1 = Resource()\n v1.putChild(b\"redeem\", local_issuer)\n root = Resource()\n root.putChild(b\"v1\", v1)\n return StubTreq(root)",
"ti... | [
{
"docid": "42ffe4f487c54996648cbf0aaf99c69c",
"score": "0.55943376",
"text": "def req_handler(args):\n key = _get_key(args)\n subject = get_subject_arguments()\n req = create_certificate_request(key, subject=subject, file_name=args.req_out)\n if not args.req_out:\n print(print_certif... |
9c4a351aac4a8bfa8404f4f18c3c2921 | Migrate an old config entry. | [
{
"docid": "4b2a0d08ad7d29ca59dfd48cfd0a492b",
"score": "0.68754864",
"text": "async def async_migrate_entry(hass: HomeAssistant, config_entry: ConfigEntry) -> bool:\n version = config_entry.version\n\n # 1-> 2->3: Migration format\n # Add CONF_TIMEOUT, CONF_LEAGUE_ID, CONF_SPORT_PATH, and CONF... | [
{
"docid": "d1f2111e9a6b30aa09557d5b50434252",
"score": "0.63393617",
"text": "async def async_migrate_entry(hass: HomeAssistant, entry: ConfigEntry) -> bool:\n version = entry.version\n data = {**entry.data}\n\n LOGGER.debug(\"Migrating from version %s\", version)\n\n # 1 -> 2: Remove unuse... |
818278552afa8400e375ae6427463bdd | Is the size correct? | [
{
"docid": "f603ef4dcb43e25fae1d811c14bf6e2d",
"score": "0.0",
"text": "def test_bg_size(self):\n\n bg = driver.find_element_by_class_name(\"container\")\n size = bg.size\n print('\\n') # adds line break\n print \"size of the background is:\"\n print (size)\n\n ... | [
{
"docid": "a4d15465befda3068197d7c325a8ca0e",
"score": "0.82047963",
"text": "def size(self):",
"title": ""
},
{
"docid": "422172fa4cbc7b0f9a71e3309d0a0772",
"score": "0.80646",
"text": "def size(self) -> int:",
"title": ""
},
{
"docid": "3768dfa33ac8c793669943652ba74f50... |
d5276c7c98001128c4f6b4241c6e74fa | Runs policy using a single state as input z estimate of the hidden parameter, used only if self.z_input = True eta scalar representing uncertainty in estimated z, used only if self.eta_input==True | [
{
"docid": "2e412d908d33c47a5962c0e86c2a232a",
"score": "0.6730271",
"text": "def run_actor(self, state, z, sess, epsilon=0, eta=None):\n if np.random.rand(1) < epsilon:\n action = np.random.randint(0,self.l_action)\n else:\n feed = {self.state : np.array([state])}\n ... | [
{
"docid": "78f8fa2fbca74918f47d0736b568fa9f",
"score": "0.6601634",
"text": "def policy(self, state):\n if np.random.rand() > self.epsilon:\n action = np.argmax(self.function_approximator(state))\n else:\n action = np.random.choice([0,1], p=[0.5, 0.5])\n self.epsilon *= 0.99\n ret... |
2a59a9d84493973ad6577758a14d0e21 | Checks for nodes who are built with Plantower boards and are sending data, but are missing Plantower measurements. | [
{
"docid": "5a3f63269c583f87fb928d58c046e46c",
"score": "0.73408353",
"text": "def check_plantower(node_info: Dict[str, dict], node_status: Dict[str, dict], measurements: List[dict]) -> Tuple[Iterable[str], str]:\n logging.info('running check_plantower')\n\n plan_regex = re.compile('.*\\[.*P.*\\].... | [
{
"docid": "5776637f267f5b72594d3a684eed5450",
"score": "0.62628645",
"text": "def check_nodes(self):\n with self.rlock:\n try:\n self.logger.info('Checking if new nodes are needed')\n api = self.driver_class(self.args.api_server, self.logger)\n ... |
9934a9be178a36d0f216f5ef6d3dc133 | Updates local database with its remote counterpart. Use it with only one server. | [
{
"docid": "a75cbee02b0a3c86574fb0ec95a3fa82",
"score": "0.5777243",
"text": "def pull_down_remote_db():\n with cd('{tmp_dir}'):\n run('pg_dump -U {pg_user} {db_name} > {db_name}.sql')\n print(\"Dumping database on remote...\")\n get('{db_name}.sql', '{tmp_dir}')\n print(\... | [
{
"docid": "19bf0c3ec0588a84e1722966ddbaa744",
"score": "0.65133363",
"text": "def update_db():\n fetchHosts(NEWHOSTS_FILES)",
"title": ""
},
{
"docid": "3a59f3d4f14084f9ccc8e936e5e1d7f6",
"score": "0.6444025",
"text": "def sync(self):\n self.database = self.read()",
"t... |
397365f0b60d43389fc0a3083940d283 | Returns orientation list given layup string following convention. Separate angles with /. Use '_n' to repeat an angle n times. prefix angle with 'p' or 'm' for plusmins or minusplus respectively. Append with s (no extra /) for symmetric | [
{
"docid": "aeb7a35a9c2432ceaefc0d3ab4c20640",
"score": "0.7460502",
"text": "def parse_layup(layup_input):\n\n\tlayup = layup_input\n\torientation=[]\n\tsymmetric = False\n\tif layup.endswith('s'):\n\t\tlayup=layup[:-1]\n\t\tif 's' in layup:\n\t\t\tprint \"s must be added at the very end. You wrote : %... | [
{
"docid": "1011fabee140dd00a76db1a74ff26ca0",
"score": "0.5986028",
"text": "def orientation_from_string(string_ornt):\r\n orientation_dict = dict(r=(0,1), l=(0,-1), a=(1,1), \r\n p=(1,-1), s=(2,1), i=(2,-1))\r\n ornt = tuple(orientation_dict[ii] for ii in string_ornt.l... |
a350e691f2f969f4861b5090778a1997 | Handle an event from the SSH interface. If a zone level was changed either by external means such as a Pico remote or by a command sent from us, the new level will appear on the SSH shell and the response is handled by this function. | [
{
"docid": "41c092491dde9b5da9b03970916dacf8",
"score": "0.55854744",
"text": "def _handle_response(self, resp_json):\n comm_type = resp_json['CommuniqueType']\n if comm_type == 'ReadResponse':\n body = resp_json['Body']\n zone = body['ZoneStatus']['Zone']['href']\n ... | [
{
"docid": "61b1eb16840ef04d0784d29758dbe4c1",
"score": "0.52538294",
"text": "def zones_updated_callback(data):\n _LOGGER.info(\"Envisalink sent a zone update event. Updating zones...\")\n dispatcher.send(signal=SIGNAL_ZONE_UPDATE,\n sender=None,\n ... |
bcc59fb2fa950aa77a2770f8384c186f | returns the marginal distribution y given an x using the parameters of the model | [
{
"docid": "f93c5e59196b3ddda4c2cbf81a2334a8",
"score": "0.0",
"text": "def marg(self, x, e, c):\n tmp = exp(dot(x, self.W) + dot(e, self.E) + dot(c, self.S) + self.b)\n marg = tmp / (1 + tmp)\n return marg",
"title": ""
}
] | [
{
"docid": "b6d7b9ec7bdbdc43490eac454453d849",
"score": "0.6826737",
"text": "def marginal(self):\n\t\tif self.is_fitted:\n\t\t\thalfn = self.n / 2\n\t\t\tprec = 1. / self.sigma_\n\t\t\t# calculate error function on posterior mean\n\t\t\tE_mn = (prec / 2.) * self._sse + (self.alpha_ / 2.) * np.dot(self.... |
60d6b87fe250c98400da8be052fbac0f | Returns true if this is an AFNI path. | [
{
"docid": "108a02aee98f71fa2cf3f2bb2d15eb3c",
"score": "0.69678634",
"text": "def is_afni(self) -> bool:\n is_afni = False\n\n stem = self.path.stem\n if re.search(pattern=r\"(\\+tlrc|\\+orig)$\", string=stem):\n is_afni = True\n\n return is_afni",
"title": ""... | [
{
"docid": "98f112d383097e0e6d966f14f749df3e",
"score": "0.71940494",
"text": "def is_ancillary_path(self, path: str) -> Tuple[str, bool]:",
"title": ""
},
{
"docid": "ab67b9d514a1c0ed396adb9a3f7a04b5",
"score": "0.6779986",
"text": "def is_ancillary_path(self, path: str) -> Tuple[st... |
9db5e56fe5d27d593ca82ede04b49076 | Add an item to the queue. | [
{
"docid": "15b679b40caf0fd17afa3055453c4f17",
"score": "0.75617635",
"text": "def enqueue(self, item):\n self.body += [item]",
"title": ""
}
] | [
{
"docid": "50f0ecee0b858f34de792c0e50772c79",
"score": "0.9292446",
"text": "def add(self, item):\n self._queue.append(item)",
"title": ""
},
{
"docid": "1a359aa66aabc2b86fae23a7005dd7d7",
"score": "0.8446879",
"text": "def enqueue(self, item):\n self._queue.insert(0, ... |
36ceea0c3184531e646edfbbb1aabc7e | Define a function to convert xtime character array to numeric time values using local arithmetic | [
{
"docid": "7440ae67beaffbd28ee6a21a967a6962",
"score": "0.7542322",
"text": "def xtime2numtimeMy(xtime):\n # First parse the xtime character array into a string\n xtimestr = netCDF4.chartostring(xtime) # convert from the character array to an array of strings using the netCDF4 module's function\n\n ... | [
{
"docid": "efc25bdf6ce8c6198c6f257baabf7d61",
"score": "0.7195972",
"text": "def xtime2numtime(xtime):\n # First parse the xtime character array into a string\n xtimestr = netCDF4.chartostring(xtime) # convert from the character array to an array of strings using the netCDF4 module's function\n\n dt... |
f63ce0fd6ed9fd8505329ba6eb1f4e48 | GetV(TFlt Val1) > TFltV | [
{
"docid": "0db23296c3b9b0ad1d77b442aa1ee576",
"score": "0.6113632",
"text": "def TFltV_GetV(*args):\n return _SnapTime.TFltV_GetV(*args)",
"title": ""
}
] | [
{
"docid": "b16df4f230f18dbf6d3d81c2de0bc905",
"score": "0.64146775",
"text": "def GetVal(self, *args):\n return _snap.TFltV_GetVal(self, *args)",
"title": ""
},
{
"docid": "bee369ed864dee0b28dc5d58b1ada427",
"score": "0.63197136",
"text": "def __gt__(self, *args):\n re... |
d79de72826c32a9f0086cfd0daf776a1 | Submit ensemble NAMD equilibrationsimulation jobs to the SuperMUC or similar machines. The job results will be stored with a name pattern as defined in the environment, e.g. cylinderabcd1234legion256 | [
{
"docid": "2239c90fbf8d86b8ed518fe6b326fba6",
"score": "0.61851496",
"text": "def bac_namd_supermuclike(config,**args):\n if not args.get('cores'):\n args[\"cores\"] = 7000\n\n update_environment(args)\n if not env.get('replicas'):\n env.replicas=25\n calc_nodes()\n env.nodes_new = \"%s\" % ... | [
{
"docid": "ef33669e73836de24bb3fe9b5665ac62",
"score": "0.656956",
"text": "def bac_nm_remote_archerlike(**args):\n if not args.get('cores'):\n args[\"cores\"] = 1200\n\n update_environment(args)\n with_config('')\n #execute(put_configs,config)\n #print \"$results_path\"\n job(dict(config='',s... |
39b1a8b6359cec35cfdd12e8572f759f | Sets the synapse config bits and the bias currents | [
{
"docid": "83cc760821ed4ea0d544b79863ba2c63",
"score": "0.5745245",
"text": "def set_analog():\n HAL.driver.SetDACCount(CORE, bd.bdpars.BDHornEP.DAC_SYN_PD, SYN_PD_PU)\n HAL.driver.SetDACCount(CORE, bd.bdpars.BDHornEP.DAC_SYN_PU, SYN_PD_PU)\n for n_idx in range(NRN_N):\n HAL.driver.SetS... | [
{
"docid": "fec66140165a77668a9fb4dcf03a43c1",
"score": "0.66797674",
"text": "def set_bias(self, bias_obj):\n configs_list = self.setup_configs_list(\n self.ext_input_has_generator,\n use_bias_simple=\"bias_simple\" in bias_obj,\n bias_simple_key=bias_obj[\"bias_... |
808793aeb9e4f96068c1e83f973ecad6 | Test case for get | [
{
"docid": "890ec65f81a1817d580ad777a43a9f28",
"score": "0.0",
"text": "def test_get404(self):\n with self.assertRaises(Exception) as context:\n self.api.get(\"tmpl_fake\")\n self.assertTrue(\"template not found\" in context.exception.__str__())",
"title": ""
}
] | [
{
"docid": "0447207dd4d65759fd7650c47ffbff4c",
"score": "0.847384",
"text": "def test_get(self):\n pass",
"title": ""
},
{
"docid": "8b1ef8345fdadfe8b8084c72c011341f",
"score": "0.7856544",
"text": "def test_get2(self):\n pass",
"title": ""
},
{
"docid": "f9... |
f53646582a2086f5ed8346845c83a272 | redirecting nonauthorized users to their respective pages | [
{
"docid": "859dbcfe0790fcfefbb504b0fa3227a1",
"score": "0.0",
"text": "def index(request):\n try:\n user_obj = get_object_or_404(UserProfile, pk = request.user.id, user=request.user)\n if user_obj.isMech:\n return redirect('/mechanic')\n except:\n return redirect('... | [
{
"docid": "a286dd94c20c4f864d26a9a9d45019ba",
"score": "0.7639997",
"text": "def unauthorized():\n return redirect(url_for('home', err=\"login_required\"))",
"title": ""
},
{
"docid": "9b6798a1b2e77df9443685205d091bc1",
"score": "0.75805795",
"text": "def unauthorized():\n fla... |
9d4cb1534d08f86e356143a51004df1c | Writes |data| to a file. | [
{
"docid": "a9acd8b2935095b1c6c94b4cdb1a72fc",
"score": "0.65376437",
"text": "def write_file(path, data):\n assert isinstance(path, (list, tuple))\n with open(os.path.join(*path), 'w') as f:\n f.write(data)",
"title": ""
}
] | [
{
"docid": "c410593128a2c54d41702c4ed0ce71ec",
"score": "0.8281608",
"text": "def write_data(self, data, data_file):\n utils.save(data, data_file)",
"title": ""
},
{
"docid": "339e035583a92a5816d850372d6d6e6c",
"score": "0.81348616",
"text": "def write_data_file(data_file):\r\... |
0978e03778f7d773885f4468cc045d83 | (str) > Dictionary Return Dictionary created of the same json file | [
{
"docid": "647e1cdffd867aa0f89b46077ae1708c",
"score": "0.6001661",
"text": "def json_to_dict(path):\n with open(path, 'r', encoding = 'utf-8') as f:\n result = json.load(f)\n return into_atd(result)",
"title": ""
}
] | [
{
"docid": "78045d247a465f96af76a92c5cae741f",
"score": "0.69984156",
"text": "def __load_json(cls, file_name):\n\n with open(file_name, 'r') as f:\n data = json.load(f)\n dic = json.loads(data)\n k = dic.keys()\n v = dic.values()\n k1 = [eva... |
e700043439a253b7a119b58ca110299d | Return a json dictionary representing this model. | [
{
"docid": "772c4ddfaf7a2dec87230514f27a693a",
"score": "0.0",
"text": "def _to_dict(self):\n return self.to_dict()",
"title": ""
}
] | [
{
"docid": "be154a4c630562a4de9ae9a448baad12",
"score": "0.84946454",
"text": "def to_json(self):\r\n return self.__dict__",
"title": ""
},
{
"docid": "8792761ecb900f1b0615ea64606e576d",
"score": "0.8432328",
"text": "def to_json(self):\n return self.__dict__",
"tit... |
1238ee88955f2c73d239159725b1cc1a | Returns whether the given port is connected. | [
{
"docid": "2f3ab98e0b22a25f077f7fa4b5782da1",
"score": "0.8949384",
"text": "def is_connected(self, port: str) -> bool:\n return self._communicator.get_port(port).is_connected()",
"title": ""
}
] | [
{
"docid": "70ed07ea59a14f0b69c58b15a181124b",
"score": "0.8899737",
"text": "def isPortConnected(port):\n ports = port.getConnectedPorts()\n if ports:\n return True\n else:\n return False",
"title": ""
},
{
"docid": "d580bd622ded52669633599cdd400e8e",
"score": "0.... |
067093619e96fe096e7999cda74e8c81 | This function calculates the the leaveoneoutCrossValidation | [
{
"docid": "151900294cff39a180031f32c6316c14",
"score": "0.0",
"text": "def looCV(clim, predic, fnc):\n\n err = 0\n for lo_index in range(len(clim)):\n\n clim_mask = np.ma.array(clim, mask=False)\n clim_mask.mask[lo_index] = True\n clim_lo = clim_mask.compressed()\n\n p... | [
{
"docid": "5d77341b4346ef17ec87fd8df452c313",
"score": "0.6955173",
"text": "def cross_validate(self):\n pass",
"title": ""
},
{
"docid": "a64b7ce38a2ca42f0d31564275b6edfb",
"score": "0.6743403",
"text": "def cross_validation():",
"title": ""
},
{
"docid": "0894c7... |
031d153fe45bee57412bf040f537c673 | Load config file at either `dotenv_path`, env var `PF_CONFIG_DOTENV_PATH`. | [
{
"docid": "ab88c124f7879ba066e35c1e4ee84e26",
"score": "0.76902",
"text": "def load_dotenv_config(dotenv_path=None, verbose=False, **kwargs):\n return dotenv_values(dotenv_path=dotenv_path, verbose=verbose, **kwargs)",
"title": ""
}
] | [
{
"docid": "445f334816909b633d2274341346842c",
"score": "0.74384344",
"text": "def load():\r\n\r\n BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\r\n\r\n stage_config = {\r\n \"local\": \".env\",\r\n \"dev\": \"configmap-dev.env\"\r\n }\r\n\r\n dotenv_co... |
9bdae45b4ce85ff01a0648a83a7b723f | Find the account for a user or all users in a channel. | [
{
"docid": "87b7d043566bbbe86a8495aa2be33552",
"score": "0.0",
"text": "def identify(self, target):\n tag, query = self._WHO_IDENTIFY\n self.sendLine('WHO {} {}t,{}'.format(target, query, tag))\n pass",
"title": ""
}
] | [
{
"docid": "a7d48507829e3b9f081f36dad934ab1b",
"score": "0.65918094",
"text": "def find_by_account_name(cls,account_name):\n\n for user in cls.user_list:\n if user.account_name == account_name:\n return user",
"title": ""
},
{
"docid": "928e682a79a84fc16830d8... |
73f7f94b1edbd641ccb38cd2ac670864 | Use a device configuration and custom SSL auth for simplicity | [
{
"docid": "b7ce69e66456374ac172f683c45be484",
"score": "0.5207255",
"text": "async def session(custom_ssl_context):\n async with IoTHubSession(\n hostname=FAKE_HOSTNAME, device_id=FAKE_DEVICE_ID, ssl_context=custom_ssl_context\n ) as session:\n yield session",
"title": ""
}
] | [
{
"docid": "a6b2bc6d7d2b664bb1ec12ca0917094a",
"score": "0.59794635",
"text": "def __init__(self, uri, user, password, ssl_verify=False):\n self.query_url = uri + '/api/device/'\n self.uri = uri\n self.user = user\n self.upass = password\n self.verify = ssl_verify\n ... |
e41de01a9df8ef78235eb79adeeb467b | r"""Normalizes the whitespace in a string; \s+ becomes one space. | [
{
"docid": "82ff702a3a430d6a341ff352427a9993",
"score": "0.7673004",
"text": "def _normalizeWhitespace(s, removeNewline=True, newLineChar=\" \"):\n if not s:\n return str(s) # not the same reference\n s = html.unescape(s)\n try:\n s = _strip_tags(s)\n except:\n pass\n ... | [
{
"docid": "fea5ffb7e8cc661fdf41853043a9bf6a",
"score": "0.8499005",
"text": "def normalize_spaces(s):\n if not s:\n return ''\n return ' '.join(s.split())",
"title": ""
},
{
"docid": "62a1653f48059089205ff46608160063",
"score": "0.81921566",
"text": "def normalize_white... |
4fb7b84c2981a5eefa2b109a23681879 | the effective end date of the task. for a non repeating task, this is the end date of the task. for a repeating task, this is calculated as t.end_date t.start_date + t.repeat_end | [
{
"docid": "05c3d6c25c8c302cd4b0e75658cd9945",
"score": "0.8365589",
"text": "def effective_end_date(self):\n if self.repeat_state > 0:\n return self.end_date - self.start_date + self.repeat_end\n else:\n return self.end_date",
"title": ""
}
] | [
{
"docid": "b9f7083b9184346d7c75c91d484a0854",
"score": "0.63661",
"text": "def end_date(self): \n return self._end_date",
"title": ""
},
{
"docid": "cb3392aa308b792764cd2fd8673a329c",
"score": "0.63541704",
"text": "def end_date(self) -> date:\n return self._end_date"... |
9f0604fef134c76cf13f57e22f40be5f | Loads data from the data repo to database. | [
{
"docid": "306119030f495c007a37c9bf10055f4a",
"score": "0.6502804",
"text": "def load_data(no_pull=False):\n from data_loader.arangoload import run\n\n run(no_pull=no_pull)",
"title": ""
}
] | [
{
"docid": "a1baffcf78d5245b01860ad904ee45f5",
"score": "0.6960813",
"text": "def load_data(self):\n pass",
"title": ""
},
{
"docid": "a1baffcf78d5245b01860ad904ee45f5",
"score": "0.6960813",
"text": "def load_data(self):\n pass",
"title": ""
},
{
"docid": "... |
f26f0d694f4becc7de930f28494a4e72 | Return the mirroring mode this ROM uses. | [
{
"docid": "e45cf69ff1cda9bf339e7d60dd898f6d",
"score": "0.0",
"text": "def is_vertical_mirroring(self):\n return bool(int(self.flags_6[7]))",
"title": ""
}
] | [
{
"docid": "86508fbb39de895baf91c71f28171df0",
"score": "0.8158034",
"text": "def getMirrorMode(node):\n return mirrorData.getMirrorData(node, 'mirrorMode')",
"title": ""
},
{
"docid": "bdcb99b616a0745ab70115904696d696",
"score": "0.7072534",
"text": "def get_mode(self):\n ... |
720f2c6f6fcd6a7c2b60b4b69d9c1812 | Download necessary information from Kegg Database for parsing. | [
{
"docid": "4ec508dd8350908757f415e10ef746e3",
"score": "0.55843097",
"text": "def downloadKegg(args):\n\n urllib.urlretrieve(\"http://rest.kegg.jp/list/\" + args.specie, args.gen2keggs)\n urllib.urlretrieve(\"http://rest.kegg.jp/link/pathway/\" + args.specie, args.kgen2pathways)\n urllib.urlre... | [
{
"docid": "f0ab19c936c4d5adaee06c4eb0393119",
"score": "0.6366967",
"text": "def _download_data(self) -> None:\n # Download info\n data_dir = f\"{self._data_dir}GENE_INFO/Mammalia/\"\n fn = f\"ncbi_info_{self._date_today}.tsv\"\n data_fn = \"Homo_sapiens.gene_info.gz\"\n ... |
86391a6f19ae2fe7ebbeed2c35e898e8 | This function parses a given code and return the root node. | [
{
"docid": "f040cb4341fa90482fc771c575958fe1",
"score": "0.7506941",
"text": "def parse_code(\n self,\n code: Union[str, bytes]\n ) -> tree_sitter.Node:\n if isinstance(code, bytes):\n tree = self.parser.parse(code)\n elif isinstance(code, str):\n ... | [
{
"docid": "6bbc418bb14f0854bffe5cc29f594cfb",
"score": "0.81535655",
"text": "def parse(code):\n\n code = canonicalize_code(code)\n py_ast = ast.parse(code)\n\n tree = python_ast_to_parse_tree(py_ast.body[0])\n\n tree = add_root(tree)\n\n return tree",
"title": ""
},
{
"docid... |
44b3d2e5bb7c4ede38b9b07e60548c49 | Checks the word can be reduced. | [
{
"docid": "15c1269d4739156bf6a91df47c035723",
"score": "0.7008527",
"text": "def is_reducible(word, words) -> bool:\n\n if word == 'a' or word == 'i' or word == '':\n return True\n\n if word not in words:\n return False\n\n for i in range(len(word)):\n if is_reducible(word... | [
{
"docid": "478549a57316c9f060c35860f6d34cbc",
"score": "0.71950686",
"text": "def is_reducible(word, word_dict):\r\n # if the word has already been checked return whether it reduces\r\n if word_dict[word][0] is not None:\r\n return word_dict[word][0]\r\n # otherwise check all possib... |
ad5643f3cf577e09e1f9b7d73d2d64de | Fetch data (messages + members) from one guild | [
{
"docid": "a28e1862062da57b0b27630a41a38110",
"score": "0.5994161",
"text": "async def save_guild_data(self, guild: discord.Guild) -> None:\n\n logger.info('Fetching members, message and activities from channels of guild %s',\n guild.name)\n\n await self.bot.db.save_gui... | [
{
"docid": "3ce218971aaa4b71925095f00b42261e",
"score": "0.7467778",
"text": "async def guild_members(self, ctx, *, guild: str):\n user = ctx.message.author\n color = self.getColor(user)\n guild = guild.replace(' ', '%20')\n scopes = [\"guilds\"]\n endpoint_id = \"guil... |
95d7665b460f01efb39084d1470c0625 | Get coordinates of grid nodes in the x direction. | [
{
"docid": "8be1729b7a7b85e60ed55480c85c37ab",
"score": "0.78737",
"text": "def get_grid_x(self, grid: int, x: np.ndarray) -> np.ndarray:\n ...",
"title": ""
}
] | [
{
"docid": "cd12c890313162a7fb71ddbd7c1ae534",
"score": "0.764228",
"text": "def grid_xdimension(self):\n return ((self.number_of_node_columns - 1) * self._dx)",
"title": ""
},
{
"docid": "cce369820dc04d9da19dfdb84ed7d51b",
"score": "0.71676946",
"text": "def _get_coordinates(... |
5b47f601cf476ab3802221037dcf0a8d | Builds an expense using input from the command line | [
{
"docid": "004b742f4285f8f335900daca8364cc6",
"score": "0.6994802",
"text": "def build_expense():\n name = input(\"What is this expense called?\\n> \")\n \n amnt = float(input(\"How much is the expense?\\n> $\"))\n \n while True:\n raw_freq = input(msgs.freq)\n\n if raw_fre... | [
{
"docid": "6bb81049769d7b6ecf7000ab1d8dcd0e",
"score": "0.6160386",
"text": "def addExpenseCommand(expensesList, params, undoList):\n if len(params) != 2:\n print(\"Invalid parameters list\")\n else:\n if not readInt(params[0]):\n print(\"Please input positive integer val... |
ab857517844a01614aa76a5dafe39c8e | a generator over documents tree | [
{
"docid": "a46c7fb576199651ac45aea666b94d05",
"score": "0.64883244",
"text": "def __parse_docs(self, tree):\n\n # iter over original-translation pairs\n for para in tree:\n cells = para.getchildren()\n\n _text = str()\n _transl = str()\n _idx = ... | [
{
"docid": "b718d683bed68545db5457b73578c87d",
"score": "0.7002977",
"text": "def generate_doc(self, recursive=False):",
"title": ""
},
{
"docid": "3a324803edac78751a7c0a4501a910eb",
"score": "0.6489271",
"text": "def getTreeDoc(self):\r\n raise NotImplementedError",
"titl... |
9e42fa5e303b22d8ed9f731c217c2f56 | Try to get a page by key, returns the data or False for a miss | [
{
"docid": "443690144e72d61a8689769f29a8a1be",
"score": "0.74597764",
"text": "def get(self, key: str):\n if key in self.pages:\n self.buffer_operation(OP_UPDATE, key)\n return self.pages[key]\n return False",
"title": ""
}
] | [
{
"docid": "1c1ad86a9c8aff263c1133a9669d363b",
"score": "0.6815208",
"text": "def get(self, key: str):\n page = self.database.get(key)\n\n if page is None:\n self.dbg(\"datastore_event\", \"Page for {} was empty\", [key])\n return None\n return page.data",
... |
479d58be037a028ec322fe9821da342c | Initialize a new Game object. | [
{
"docid": "5aa7f184e2fabcb8b811afb12eccee01",
"score": "0.0",
"text": "def __init__(self, board_size, ships):\r\n self.__board_size = board_size\r\n self.__ships = ships\r\n self.bombs = {}\r\n self.__game_status = self.GAME_STATUS_ONGOING\r\n self.intact_cells = []\r... | [
{
"docid": "adca8e88971d5eb9a515579168d68516",
"score": "0.8526202",
"text": "def newGame(self):\n self.__init__()",
"title": ""
},
{
"docid": "8219e98b176a823ccca676e644e5cee1",
"score": "0.79667807",
"text": "def initGame(self):\n\n # Populate everything\n self... |
e09d19f2f450b884fbe9ebd108492b27 | Check that necessary tools, gcloud, gsutil, and kubectl are available if necessary. If execution of one of these tools is unsuccessful it will throw UserReportError exception. | [
{
"docid": "ff85da58e2983378dbabddb6fb56fcbc",
"score": "0.7900791",
"text": "def check_prerequisites() -> None:\n try:\n p = safe_exec('gcloud --version')\n except SafeExecError as e:\n message = f\"Required pre-requisite 'gcloud' doesn't work, check installation of GCP SDK.\\nDetai... | [
{
"docid": "2849f7b05fd7fadae20b904004e7c9d7",
"score": "0.6719025",
"text": "def check_prerequisites():\n # Check 1: Is there a usercert ?\n for key_name in (\"USERCERT\", \"USERKEY\"):\n key_path = PARAMETERS[key_name]\n if not os.access(key_path, os.R_OK):\n print(\"ERROR: Can't access t... |
d2b82ad838c3f125dd615ac7d143f25d | Ensures that given function is a generator function, i.e. a function that returns a generator. This way all properties can be checked in the same manner, regardless of whether they use `yield` internally or not. | [
{
"docid": "109a19b07570110808eefd63110f528e",
"score": "0.70348686",
"text": "def __coerce_to_generator_func(self, func):\r\n if inspect.isgeneratorfunction(func):\r\n return func\r\n\r\n @functools.wraps(func)\r\n def generator_func(*args, **kwargs):\r\n func... | [
{
"docid": "aed8ff0f80b8a06145dbb3bdc0bbf85e",
"score": "0.7716181",
"text": "def check_not_generatorfunction(func):\n if inspect.isgeneratorfunction(func):\n raise RuntimeError(\"{}: must not be decorated, is a generator test\"\n .format(func.__name__))",
"title"... |
ddfdc4641f0ebd7551e79f22d8d89603 | Return boolean representing if given candidate is a valid biological sex. | [
{
"docid": "78ce336c7923fb2a22c4f12843f89f07",
"score": "0.5236249",
"text": "def validate(self, candidate, **kwargs) -> bool:\n return super().validate(candidate, **kwargs) and self._predictor.validate(candidate)",
"title": ""
}
] | [
{
"docid": "af7154605773eac2768871b8d44de057",
"score": "0.5943835",
"text": "def is_valid_candidate_single(self, candidate):\n # idx_array = np.meshgrid(*[list(range(0, n)) for n in candidates.shape[:-2]], indexing='ij')\n # idx_array = np.column_stack([idx.reshape(-1, 1) for idx in idx_a... |
1b3efb1048e2d750918594a39aabf842 | fig, ax = marginal_scalar_pdf(Est, ind) plots the marginal scalar pdf | [
{
"docid": "422ef32a41eebff39a968dac03e4e673",
"score": "0.8348277",
"text": "def plot_marginal_scalar_pdf(Est, ind,\n addto=None, \n color='blue',\n linestyle='-',\n labelprefix='',\n ... | [
{
"docid": "94239d1b19943ff5247ffeec4f4c9350",
"score": "0.8333177",
"text": "def marginal_scalar_pdf(Est, ind, lims=None):\n \n # extract the needed parameters from the estimation result dictionary\n # extract the needed parameters from the estimation result dictionary\n if 'x_est' in Est:\... |
6993effc9868ea8809c4197f0e6dc138 | Create instance of Custom field type | [
{
"docid": "c0cb51e2f1ad9dd0e229fd0825c02630",
"score": "0.0",
"text": "def __init__(self, base, func):\n\n if not hasattr(func, '__call__'):\n raise TypeError('`func` must be a function')\n \n self.base = base\n self.func = func",
"title": ""
}
] | [
{
"docid": "a2cff9f42106817703bba40c006e3f46",
"score": "0.7500207",
"text": "def _instanciate(self, field_data):\n attrs = self._instanciate_attrs(field_data)\n if attrs:\n return fields.Field.by_type[field_data['ttype']](**attrs)",
"title": ""
},
{
"docid": "e8463d... |
ee0bf73415e63b462d088fbb19440dc0 | Numpy implementation of the Frechet Distance. The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1) and X_2 ~ N(mu_2, C_2) is d^2 = ||mu_1 mu_2||^2 + Tr(C_1 + C_2 2sqrt(C_1C_2)). Stable version by Dougal J. Sutherland. | [
{
"docid": "90aaf7f26878246676a06c122c5aa5ad",
"score": "0.765344",
"text": "def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):\n\n mu1 = np.atleast_1d(mu1)\n mu2 = np.atleast_1d(mu2)\n\n sigma1 = np.atleast_2d(sigma1)\n sigma2 = np.atleast_2d(sigma2)\n\n assert mu1.shape... | [
{
"docid": "d22e400f08af653bc3ad334e28fee177",
"score": "0.76049113",
"text": "def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):\r\n\r\n mu1 = np.atleast_1d(mu1)\r\n mu2 = np.atleast_1d(mu2)\r\n\r\n sigma1 = np.atleast_2d(sigma1)\r\n sigma2 = np.atleast_2d(sigma2)\r\n\r\n ... |
407f4af27bf17bc2e6944d8f49e5e536 | Returns some post data to create an article against this view | [
{
"docid": "62fab48fa8b454dc7f8810bfb7da2116",
"score": "0.61425453",
"text": "def _art_data(self):\n return {'title': 'Test article', 'contents': 'Test content'}",
"title": ""
}
] | [
{
"docid": "f8a87bd3b74b8d1c8c488e9ab7d65941",
"score": "0.7187008",
"text": "def create_article(self):\n token = self.user2.token()\n self.client.credentials(\n HTTP_AUTHORIZATION='Bearer ' + token)\n return self.client.post(self.article_url,\n ... |
2b54202fc563b893b8ab5c691c731dfa | Return only the children that represent tags (i.e. exclude any DataParsers) | [
{
"docid": "43b7ca08b47f2144e4f77d6071d9a1a5",
"score": "0.7936607",
"text": "def tag_children(self):\n return [t for t in self.children if t.tag is not None]",
"title": ""
}
] | [
{
"docid": "f852add60a2c6e09ac8c7504d3fc1c47",
"score": "0.72802025",
"text": "def children(self):\n\n return (obj for obj in self._content if isinstance(obj, Tag))",
"title": ""
},
{
"docid": "0b1e84c3599f7e293b159cd549106813",
"score": "0.7091907",
"text": "def get_child_tag... |
3c7f78b13116465cd7ea04b23b33136d | Takes all of the .ta1 files and "shelves" them into tiny directories, the timespan of which is regulated by the user | [
{
"docid": "769f0b823045916036640d6d11d17cbf",
"score": "0.6171974",
"text": "def shelf(location, **ts):\n\tlocation += '/' # needs to be marked as a directory\n\n\n\thours = ts.pop(\"hours\",0)\n\tminutes = ts.pop(\"minutes\",0)\n\tseconds = ts.pop(\"seconds\",0)\n\tif hours==minutes==seconds==0:\n\t\t... | [
{
"docid": "0c394496a1614d300291a321b56ab237",
"score": "0.5822624",
"text": "def makeFinalFiles():\n from ROOT import TChain, TFile, TTree, TNamed, MGTWaveform\n\n # here is where you might put some last minute cuts\n tCut = \"tOffset < 4000\"\n\n cutType = \"frb%d\" % pctTot\n # outType... |
676fa315d8a1f7d5fa3ef30c0c723d9c | pulls soup list from front part of mangapanda url and selector | [
{
"docid": "84b78c04950d8c926831dec8d0a56e06",
"score": "0.79778224",
"text": "def pullSoup(front_url, selector):\n\t#add base url to front url to get complete url for page to be opened\n\tfull_url = \"http://www.mangapanda.com\" + front_url\n\t#open up page\n\tresPage = requests.get(full_url)\n\t#check... | [
{
"docid": "dc6c271d50837bf478ff6000fb86f793",
"score": "0.6672189",
"text": "def getChapters(manga_url):\n\t#gets the list of soup elements from url (first input) using the selector (second input)\n\tsoupList = pullSoup(manga_url,'#listing a')\n\t#reshape so that it is a list of front link+title elemen... |
81fcd009c74629703007d9aca88f642a | This method returns the function's output when the class is called. | [
{
"docid": "2644af6d3a882a635aa0db6234a6a4bf",
"score": "0.0",
"text": "def __call__(self, x):\n\n # Calculating the Ridge's function\n f = x[1:] ** 2\n\n return x[0] + np.sum(f) ** 0.5",
"title": ""
}
] | [
{
"docid": "0a91a476fa8777f5c58ab3058d9b7111",
"score": "0.7639285",
"text": "def getOutput(self):\n pass",
"title": ""
},
{
"docid": "befa74be37040eadded7efa0be3a8454",
"score": "0.75097024",
"text": "def __call__(self):\n return self.out",
"title": ""
},
{
... |
6df2705c11d30eb8209ecb0748477d75 | Function that returns the view page for a blog. | [
{
"docid": "2367d1a9b749a62cc21e21d01adfa256",
"score": "0.7342799",
"text": "def view_blog(blog_id):\n\n blog = Blog.query.filter_by(id=blog_id).first()\n comments = Comment.get_comments(blog_id)\n \n title = \"View Blog\"\n \n return render_template('view.html', title=title, blog=blo... | [
{
"docid": "4d9aca7b2e1de6d48570604a588a6461",
"score": "0.7157125",
"text": "def blog_post(request, url):\n\n blog = get_object_or_404(Post, url=url)\n\n context = {\n \"blog\": blog,\n }\n\n return render(request, \"blog/blog_post.html\", context)",
"title": ""
},
{
"doc... |
be3b167740ad91196d1c07de6ce0dc5d | Raw Github should be up to download stuff from it. | [
{
"docid": "05695d218b9fc640233c8404cbd1b1c2",
"score": "0.0",
"text": "def test_is_raw_gh_online(self):\n is_online = ONLINE_CHECKER.is_gh_raw_online()\n\n self.assertTrue(is_online)",
"title": ""
}
] | [
{
"docid": "edab69dbea84f039aa1f2d2090f0d221",
"score": "0.67514336",
"text": "def test_repo_get_raw_file(self):\n pass",
"title": ""
},
{
"docid": "6bf6846ba20a0a34fbadfa4a08855a71",
"score": "0.6747231",
"text": "def test_raw(self):\n\t\thtml = http.page_text(\"https://raw.g... |
b20c42a9fbbc7c21fa416b7ee9fb2ad1 | List viewable contentish and folderish subobjects. | [
{
"docid": "0eba71dcef16c58921abf110ab3d2a89",
"score": "0.56333256",
"text": "def listFolderContents(self, contentFilter=None):\n fc_list = []\n for id, obj in self.contentItems(contentFilter):\n # validate() can either raise Unauthorized or return 0 to\n # mean unau... | [
{
"docid": "e8c114717b6787b3dbe00695cf0581b5",
"score": "0.6454088",
"text": "def children(self, obj):\n return obj.subfolders()",
"title": ""
},
{
"docid": "04aee46d33afd7c0f8c16422f831928d",
"score": "0.6329941",
"text": "def get_objs(request, my_item_container, allow_copy):... |
381cb68d20b0a9c53d9d956a7e5dc3be | Tests that updates are atomic; when part of an update is not allowed, the entire update does not happen. Try to make the bikeshed twice as large and change its color. Both will fail, since the useful operation blocks the entire change. | [
{
"docid": "11861073c42597eec5cec495d4aa107e",
"score": "0.0",
"text": "def test_partiallyUpdatableAttributes(self):\n self.headers.setRawHeaders(\"Content-Type\", [\"application/json\"])\n self.body = dict(disallowedUpdateState, **allowedUpdateState)\n return self._testUpdate(http.... | [
{
"docid": "ba96f00006c63359a37bb8c6b94ea478",
"score": "0.5960921",
"text": "def _test_staledata_error_on_concurrent_object_update(self, model, dbid):\n lock = functools.partial(lockutils.lock, uuidutils.generate_uuid())\n self._blocked_on_lock = False\n\n def _lock_blocked_name_up... |
3a9a13fe2dfdd04567227bf7a5c59ed4 | Sets the launch_date_utc of this Roadster. | [
{
"docid": "fc4e976a06f01a0fa403acf4f1bc5702",
"score": "0.8859175",
"text": "def launch_date_utc(self, launch_date_utc):\n\n\n self._launch_date_utc = launch_date_utc",
"title": ""
}
] | [
{
"docid": "4d38cc69dc6b63af9664399878087caa",
"score": "0.68396974",
"text": "def set_utc_date(self, date_utc):\n self.date_utc = utils.to_datetime(date_utc)\n self.date_localized = utils.to_datetime(date_utc, self._config['__tzinfo__'])",
"title": ""
},
{
"docid": "6ae675e5d0... |
705c5590fb9aa8ff7593df29d2fe4f51 | Expose containers externally via secured routes | [
{
"docid": "5ce9fcc8aa8ba80fc79d0c172d9fe9c4",
"score": "0.0",
"text": "def create(self, name: str, route_type: \"Types\" = Types.EDGE, **kwargs):\n cmd_args = [f\"--{k}={v}\" for k, v in kwargs.items()]\n self._client.do_action(\"create\", [\"route\", route_type.value, name, cmd_args])",
... | [
{
"docid": "352d9f89089989cc330798ccbba9a78e",
"score": "0.57136905",
"text": "def runpublic():\n app.run(host='0.0.0.0')",
"title": ""
},
{
"docid": "82e3d8128d7d5e40b4e4b8eb9e5c7c9c",
"score": "0.5393183",
"text": "def expose_endpoint(self, exec_endpoint: str, path: Optional[str... |
c7f65ee6fe54c5b3c1cbe41880875758 | Parses the contour file name | [
{
"docid": "326eb45f8cd05476a05715f7cff2d048",
"score": "0.69574046",
"text": "def parse_name(cls, fn_contour: str):\n\n matches = re.match('IM-[\\d]+-([\\d]+)-[a-z]contour-manual.txt', fn_contour)\n if matches is not None:\n slice_num = matches.groups()[0]\n slice_nu... | [
{
"docid": "0cb71a9653db83d5d3b061704811f9a0",
"score": "0.5983463",
"text": "def parse_calib_file_name(fname) :\n fnfields = os.path.splitext(fname) # '123-end.data' -> ('123-end', 'data') \n if len(fnfields) != 2 : return None # check that file name like '123-end.data' has splited exten... |
79690304e21762f3fdc554260faa7dea | Return all terms of a type (``s``, ``p``, or ``o``) in the store. | [
{
"docid": "b7cab220e8d1f97a27d2ca1e4037bc13",
"score": "0.7552455",
"text": "def all_terms(self, term_type):\n if term_type == 's':\n idx_label = 's:po'\n elif term_type == 'p':\n idx_label = 'p:so'\n elif term_type == 'o':\n idx_label = 'o:sp'\n ... | [
{
"docid": "ea3a4c7357c27467aa72934cc79bc3c6",
"score": "0.6069078",
"text": "def terms(self):\n return []",
"title": ""
},
{
"docid": "228c8943f7f273ea3eb8a925309b356d",
"score": "0.6005916",
"text": "def terms(self) -> Set[Term]:",
"title": ""
},
{
"docid": "3bd7... |
797140e4a92b3e65ae68a8b07ea7704a | itkIntensityWindowingImageFilterID3ID3_Superclass_cast(itkLightObject obj) > itkIntensityWindowingImageFilterID3ID3_Superclass | [
{
"docid": "b319a8b048c55a305bd49d2cfc34c65c",
"score": "0.88690996",
"text": "def itkIntensityWindowingImageFilterID3ID3_Superclass_cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterID3ID3_Superclass_cast(*args)",
"title": ""
}
] | [
{
"docid": "f12da4b506e1d9a13378f3eff1c90259",
"score": "0.88566303",
"text": "def itkIntensityWindowingImageFilterIF3ID3_Superclass_cast(*args):\n return _itkIntensityWindowingImageFilterPython.itkIntensityWindowingImageFilterIF3ID3_Superclass_cast(*args)",
"title": ""
},
{
"docid": "02cc0... |
b0685b59a1a8c19bded0b78253b1a4e5 | NTSTATUS NtUnloadDriver( PUNICODE_STRING DriverServiceName ) | [
{
"docid": "d95d989b5badc51e4b44659759f8de6d",
"score": "0.8218995",
"text": "def ntdll_NtUnloadDriver(jitter):\n ret_ad, args = jitter.func_args_stdcall([\"DriverServiceName\"])\n raise RuntimeError('API not implemented')\n jitter.func_ret_stdcall(ret_ad, ret_value)",
"title": ""
}
] | [
{
"docid": "b6361d2362e8373d980c4b2cbddfac6c",
"score": "0.5760336",
"text": "def ntdll_NtLoadDriver(jitter):\n ret_ad, args = jitter.func_args_stdcall([\"DriverServiceName\"])\n raise RuntimeError('API not implemented')\n jitter.func_ret_stdcall(ret_ad, ret_value)",
"title": ""
},
{
... |
3ee24e0c0f2e5a6987f2da134b1c9cd3 | Forward message to a specific node. | [
{
"docid": "fbad94a39b5cad96916cd8b5c3a9d74b",
"score": "0.6388381",
"text": "def forward(message, socket):\n try:\n time.sleep(1)\n dest = message[MSG_FIELD.DESTINATION]\n worker = socket_handler.get(dest)\n if worker:\n content = message[MSG_FIELD.CONTENT]\n ... | [
{
"docid": "6bd3382d4ecd4e07e411a9bc93d58ae9",
"score": "0.67194664",
"text": "def send_message(self, request, node):\n\n request.set(\"src\", self.node)\n node.process_request(request)",
"title": ""
},
{
"docid": "fe13c38927d4d74f9291554d03f3cfb4",
"score": "0.66790444",
... |
8006ac81663c8ab8f5fef2bcd6b06a80 | fitness(target) will return the fitness value of permutation named "target". Higher scores are better and are equal to the total value (Benefit/ profit) of items in the permutation. If total_weight is higher than the capacity, return 0 because the permutation cannot be used. | [
{
"docid": "2e3e16c6025faf95d5530088c9e2daa3",
"score": "0.75124675",
"text": "def fitness(target):\r\n total_value = 0\r\n total_weight = 0\r\n index = 0\r\n for i in target: \r\n if index >= len(ITEMS):\r\n break\r\n if (i == 1):\r\n total_value +... | [
{
"docid": "06ec5d66c638fd314e2e509c2bef6eb2",
"score": "0.64524364",
"text": "def fitness(self, target):\n target = list(target)\n counter = 0\n\n for i in range(len(self.dna)):\n if self.dna[i] == target[i]:\n counter += 1\n self.score = counter / ... |
09357b88590bf7b1fa9eab0cd18851ed | Extracts the name of the month from the given filename/path (assumes /path/to/file/name_yearmonth.txt). | [
{
"docid": "cfee2b92a61ea058ac742355226f2e33",
"score": "0.82106656",
"text": "def getMonthName (filename):\n #\n m = re.search('-([0-9]+).txt', filename)\n if m != None:\n return m.group(1)\n else:\n return \"current\"",
"title": ""
}
] | [
{
"docid": "2893cf73a9bdea5d2db1fb4382594252",
"score": "0.80404526",
"text": "def get_month_info(filename):\n\n pass",
"title": ""
},
{
"docid": "e8e9e2b2121ec1bc1f0e5a3fbb8f6a2c",
"score": "0.6470807",
"text": "def getYear (filename):\n m = re.search('_([0-9]+)-', filename)\n ... |
6f6d9a1e725ce0f4ef8522cd99bf1a2c | State machine to pop data out of the PIFO upon request | [
{
"docid": "5dd07dc1fdf8fabcca914cb97b2bd203",
"score": "0.6355643",
"text": "def pop_sm(self):\n while True:\n # wait to receive a read request\n req = yield self.r_in_pipe.get()\n # model read latency\n yield self.wait_sys_clks(self.read_latency)\n ... | [
{
"docid": "e30a400a9997ad8e4203002e5c1eb6ec",
"score": "0.67517585",
"text": "def PopState(self):",
"title": ""
},
{
"docid": "dc63bf5591eac12daa4bf1eadab9c6df",
"score": "0.6186308",
"text": "def pop_state(self, *_):\n self.next_state = (StateOperations.POP, None)",
"tit... |
b9cf588c58336fdc1b9c6583123cb0f6 | Get the servo's angle | [
{
"docid": "0d1484fbf00fcb6d4ac7f5bebc16412c",
"score": "0.71814936",
"text": "def angle(self):\n return int(self.pos() * 180)",
"title": ""
}
] | [
{
"docid": "c8dd4df30d5c0ddd61b456737131d079",
"score": "0.8747807",
"text": "def getAngle(self):\n a = self.servo.duty()\n a -= 64\n a *= SERVO_ANGLE / (SERVO_MAX - SERVO_MIN)\n a -= (SERVO_ANGLE / 2)\n return a",
"title": ""
},
{
"docid": "8dee146df205737500fe028fb4a8c074",
... |
b4e4a98c0bb277e3fced207af52e5038 | returns the window of AA's around the Cterm of an epitope, given defined upstream and downstream window sizes and a row from a pandas df with ending position and full origin sequence of the epitope. | [
{
"docid": "591ed3df5d9c1747a1df54ec0071ae3b",
"score": "0.62344474",
"text": "def get_peptide_window(sequence, starting_position, ending_position, upstream=6,\n downstream=6, c_terminal=True):\n # set cleavage site index based on function flag\n if c_terminal:\n cleav... | [
{
"docid": "7b3624355498b38ef47c40a368055fa7",
"score": "0.5745897",
"text": "def _get_window(start, end):\n window = 1 - np.r_[hann(4)[:2], np.ones(np.abs(end - start) - 4), hann(4)[-2:]].T\n return window",
"title": ""
},
{
"docid": "3e6e2fb9cb52192f65c76b59f681e9e1",
"score": "0... |
06aff972765844d6171c4bea536cf1d3 | Gets reference row. This is the row that contains the names of the intersecting columns, allowing fetching of cells in this row via passage of a reference string | [
{
"docid": "16be2a035dc6ea6c68d226582881e787",
"score": "0.7425297",
"text": "def reference_row(self) -> 'Row':\n return self.sheet.reference_row",
"title": ""
}
] | [
{
"docid": "17e80d7b4aa4487e8671cc28a483a5f8",
"score": "0.6681498",
"text": "def reference_row_index(self) -> int:\n return self._reference_row_index",
"title": ""
},
{
"docid": "43cff7c6a2da37b232d6ddf915a04635",
"score": "0.60901815",
"text": "def getRow(self, row):\r\n\t\t... |
1df76c97edfb3f851e3fe52c7227b956 | Inline progress bar implementation. | [
{
"docid": "6be4c47277503cd9c3f51d77398d3cd5",
"score": "0.0",
"text": "def progress(count, total, suffix=''):\n\n bar_len = 60\n filled_len = int(round(bar_len * count / float(total)))\n percents = round(100.0 * count / float(total), 1)\n bar = '=' * filled_len + '-' * (bar_len - filled_len... | [
{
"docid": "9933aeb88ac3e6df00a8f820b8f7ead5",
"score": "0.7358753",
"text": "def update_progress(self, progress):\n barLength = 20\n status = \"\"\n if isinstance(progress, int):\n progress = float(progress)\n\n block = int(round(barLength * progress))\n te... |
4710b2e29726e9d10bc8a2453f410619 | Initialize class data from input file. | [
{
"docid": "d48e294bf82fbfb6008b8dd04a824560",
"score": "0.0",
"text": "def init_from_file(filename, verbose=False):\n FuelPrice._data.clear()\n\n if verbose:\n omega_log.logwrite('\\nInitializing database from %s...' % filename)\n\n # don't forget to update the module do... | [
{
"docid": "21043b5751e1a501c0fcddd9d86c1228",
"score": "0.780452",
"text": "def __init__(self, input_file):\n self.data = []\n self.data_file = input_file #open(file_name, 'r')",
"title": ""
},
{
"docid": "6712e933ef0ba97c90073c986a49206d",
"score": "0.7547976",
"text"... |
81be948beb5b7c253fda04cf4fa33cd4 | Set up the whirlwind AP with provided network info. | [
{
"docid": "23d3808e96ea08c1b69423e883cd8f32",
"score": "0.70799476",
"text": "def ap_setup(ap, network, bandwidth=80):\n log = logging.getLogger()\n bss_settings = []\n ssid = network[wutils.WifiEnums.SSID_KEY]\n if \"password\" in network.keys():\n password = network[\"password\"]\n... | [
{
"docid": "ba63a9cd6fd3b985f72e362db7c83127",
"score": "0.63868827",
"text": "def setup_network(cfg):\n display_msg(\"Init Network...\", msg2=\"Looking for WiFI\",\n msg3=cfg.cfg_json['WIFI_SSID'])\n\n # wait 2 seconds to see if the network comes up\n time.sleep(2)\n\n sta_if... |
ed849fdc0a449e95110b4776d88c6cf0 | Strips featured artists from the artist title according to Billboard Hot 100 formatting. | [
{
"docid": "014f37cfdd10332cf25d9d9b2e75247c",
"score": "0.5266041",
"text": "def stripFeat(s):\n if \" Featuring\" in s:\n return s[: s.index(\" Featuring\")]\n elif \" x \" in s:\n return s[: s.index(\" x \")]\n else:\n return s",
"title": ""
}
] | [
{
"docid": "1b1b36d7b110cab664480b03386f60c9",
"score": "0.6408811",
"text": "def ft_in_title(item, drop_feat):\r\n artist = item.artist.strip()\r\n albumartist = item.albumartist.strip()\r\n\r\n # Check whether there is a featured artist on this track and the\r\n # artist field does not exa... |
86d422850502985ed56e7163dde3b78d | Converts Pascal bounding box from (x, y, w, h) into (top, left, bottom, right) format, and switch x/y coordinates to make a converted array indexes consistent with Numpy. | [
{
"docid": "24d3fd752b23b30dc0ec8f6dc6236ef5",
"score": "0.63246226",
"text": "def from_voc(bbox):\n x, y, w, h = bbox\n new_box = [y, x, y + h - 1, x + w - 1]\n return np.array(new_box)",
"title": ""
}
] | [
{
"docid": "e3ca31e55f39475243fc71f65d0b1a93",
"score": "0.65984535",
"text": "def bbox_xywh_to_xyxy(self, boxes):\n w, h = boxes[:, 2], boxes[:, 3]\n boxes[:, 0] -= w / 2.0 # x1 = center_x - width/2\n boxes[:, 1] -= h / 2.0 # y1 = center_y - height/2\n boxes[:, 2] = boxes[... |
8ac1622dac757385eda28bbed5586a8a | Take the required arguments and publish the given command. | [
{
"docid": "5a3433795b72113360cfeb450d179318",
"score": "0.5254576",
"text": "def pub(\n self,\n tgt,\n fun,\n arg=(),\n tgt_type=\"glob\",\n ret=\"\",\n jid=\"\",\n timeout=5,\n listen=False,\n **kwargs\n ):\n # Make sure t... | [
{
"docid": "50a2eb172754fe6beac7e71b4cda7d1c",
"score": "0.688723",
"text": "def _publish_task(publish_args: str = '') -> DoitTask:\n return debug_task([\n Interactive('poetry run nox --session build_dist build_check'),\n f'poetry publish {publish_args}',\n ])",
"title": ""
},
... |
d113d1c69349f622deed5b357a9170df | Checks that the value conforms to the LanguageID | [
{
"docid": "f66b4f0de21fc3bd0a8ee041d82e2728",
"score": "0.6371621",
"text": "def checkLanguageID(lang):\n ret = libxml2mod.xmlCheckLanguageID(lang)\n return ret",
"title": ""
}
] | [
{
"docid": "9589b0955ee39d7c15634647a6924963",
"score": "0.6502951",
"text": "def validate_lang(lang):\n if lang in LANGUAGE_OPTIONS.keys():\n return True",
"title": ""
},
{
"docid": "1a3cba627ecaba3ecbd81c3dc38e4493",
"score": "0.64360905",
"text": "def validate_language(l... |
4e42cacc035c23b0856ffd1be1d2e98c | Upgrades alpha beta values of the parent node. | [
{
"docid": "0a798d6e7655c15903d7fbeb1a1f259e",
"score": "0.7142382",
"text": "def updateAB(self, root, nodeNEW):\n if root.max == 1 and root.beta > nodeNEW.score:\n root.beta = nodeNEW.score\n elif root.max == 0 and root.alpha < nodeNEW.score:\n root.alpha = nodeNEW.s... | [
{
"docid": "22c9877fad8bf4bb48f144db7fb7bb9c",
"score": "0.73812336",
"text": "def update_before(self):\n if self.node_type == MAX_NODE:\n # get the beta from parent\n if self.parent:\n self.beta = self.parent.beta\n else:\n ... |
fb5b30afd0e5c179978936780110729e | Upload muscles and the neurons that connect to them | [
{
"docid": "6f3054becbc9fe63dc97044698ece539",
"score": "0.67196697",
"text": "def upload_muscles():\n try:\n with open(CELL_NAMES_SOURCE) as csvfile:\n csvreader = csv.reader(csvfile)\n\n ev = P.Evidence(key=\"wormbase\", title=\"C. elegans Cell List - WormBase.csv\")\n ... | [
{
"docid": "aad469f7cdda8f21594f47b702bbf18b",
"score": "0.63252246",
"text": "def upload_tracks():\n auto_upload_tracks()",
"title": ""
},
{
"docid": "061181d642add61974fefd7bcd56de38",
"score": "0.6084998",
"text": "def upload(self):",
"title": ""
},
{
"docid": "0611... |
65fbb237ba48a42183e928e84ee4eb93 | returns some edges, randomly selected | [
{
"docid": "0f4b0aa3bdb80a832900079ee0262c18",
"score": "0.7515471",
"text": "def random_edges(how_many,only_alive=True): # FIXME: more elegant than parametrized?? Boh!\n pool = None\n if only_alive:\n pool = alive_edges()\n else:\n pool = edges()\n\n how_many = min(how_many,le... | [
{
"docid": "2c464bb991f2dfc77bb0900780b7b411",
"score": "0.78476745",
"text": "def pick_random_edge(self):\r\n return random.choice(self.get_edges())",
"title": ""
},
{
"docid": "372f819474a5905adfa5a9d50320c43c",
"score": "0.7839612",
"text": "def random_edge(self):\n ... |
87488fa0f3f8495b51a11ee8ae28a308 | This event is triggered when any FlatNotebook page is deleted. When a FlatNotebook page is deleted we remove the associated page object (a TextCtrl) from the pages list. At the same time we give the operator a chance to save modifications. | [
{
"docid": "8a6345ace9f008266cd1e82075967ffa",
"score": "0.0",
"text": "def closing_file_event(self, event):\n\n page = self.notebook.GetCurrentPage()\n filename = page.filename\n\n self.show_notebook_if_not_shown()\n\n if page.GetModify() == True:\n message = 'Dis... | [
{
"docid": "84272713bfa0b104465c28ac77305ef6",
"score": "0.67369455",
"text": "def on_close_page(self, event):\r\n page = event.EventObject\r\n page.Show(False)\r\n # Remove page from MainWindow data structures\r\n if isinstance(page, DatabasePage):\r\n del self.db... |
55f1402bb4366390c6c9b5f6ce663a83 | Restart the services in the self.restart_map{} attribute if any of the files identified by the keys changes for the wrapped call. | [
{
"docid": "b2e1cce208f93ac248afb91bf797ba85",
"score": "0.59367985",
"text": "def restart_on_change(self):\n return ch_core.host.restart_on_change(\n self.full_restart_map,\n stopstart=True,\n restart_functions=getattr(self, 'restart_functions', None),\n ... | [
{
"docid": "6f1df8eff29503c62e0f02bf5cc83958",
"score": "0.74130356",
"text": "def restart_on_change(self):\n checksums = {path: ch_host.path_hash(path)\n for path in self.full_restart_map.keys()}\n yield\n restarts = []\n for path in self.full_restart_map... |
e5c0ce3678f425329a83576e15dd2772 | This method finds the the forces and moments calculated from the near field. | [
{
"docid": "05ede3869caeb37b83413ef8d2cc596c",
"score": "0.66304636",
"text": "def calculate_near_field_forces_and_moments(self):\n\n # Initialize a variable to hold the global panel position as the panel's are\n # iterate through.\n global_panel_position = 0\n\n # Initialize... | [
{
"docid": "e462cf8d09116c9d6ae444287f7f8205",
"score": "0.7421039",
"text": "def calculate_near_field_forces_and_moments(self):\n # Calculate the velocities induced at every panel's bound vortex center.\n induced_velocities = aerodynamics.collapsed_velocities_from_horseshoe_vortices(\n ... |