def save(self, savename, savedir = 'saved_interrogations', **kwargs): """ Save an interrogation as pickle to ``savedir``. >>> o = corpus.interrogate('w', 'any') >>> o.save('savename') will create ``saved_interrogations/savename.p`` :param savename: A name for the saved file :type savename: str :param savedir: Relative path to directory in which to save file :type savedir: str :param print_info: Show/hide stdout :type print_info: bool :returns: None """ from other import save save(self, savename, **kwargs)
def save(self, savename, savedir='saved_interrogations', **kwargs): """ Save an interrogation as pickle to `savedir`. :param savename: A name for the saved file :type savename: str :param savedir: Relative path to directory in which to save file :type savedir: str :param print_info: Show/hide stdout :type print_info: bool :Example: >>> o = corpus.interrogate('w', 'any') ### create ``saved_interrogations/savename.p`` >>> o.save('savename') :returns: None """ from other import save save(self, savename, savedir=savedir, **kwargs)
def _save(self, savename, **kwargs): from corpkit import save save(self, savename, **kwargs)
def pmultiquery(corpus, search, show = 'words', query = 'any', sort_by = 'total', quicksave = False, multiprocess = 'default', function_filter = False, just_speakers = False, root = False, note = False, print_info = True, **kwargs): """Parallel process multiple queries or corpora. This function is used by interrogator() if: a) path is a list of paths b) query is a dict of named queries c) just speakers == 'each', or a list of speakers with len(list) > 1 This function needs joblib 0.8.4 or above in order to run properly. There's no reason to call it yourself.""" import collections import os import pandas as pd import collections from collections import namedtuple from time import strftime, localtime import corpkit from interrogator import interrogator from editor import editor from other import save from interrogation import Interrogation try: from joblib import Parallel, delayed except: pass #raise ValueError('joblib, the module used for multiprocessing, cannot be found. ' \ # 'Install with:\n\n pip install joblib') import multiprocessing def best_num_parallel(num_cores, num_queries): import corpkit """decide how many parallel processes to run the idea, more or less, is to balance the load when possible""" if num_queries <= num_cores: return num_queries if num_queries > num_cores: if (num_queries / num_cores) == num_cores: return int(num_cores) if num_queries % num_cores == 0: try: return max([int(num_queries / n) for n in range(2, num_cores) if int(num_queries / n) <= num_cores]) except ValueError: return num_cores else: import math if (float(math.sqrt(num_queries))).is_integer(): square_root = math.sqrt(num_queries) if square_root <= num_queries / num_cores: return int(square_root) return num_cores num_cores = multiprocessing.cpu_count() # what is our iterable? ... multiple_option = False multiple_queries = False multiple_speakers = False multiple_corpora = False multiple_search = False mult_corp_are_subs = False denom = 1 if hasattr(corpus, '__iter__'): multiple_corpora = True num_cores = best_num_parallel(num_cores, len(corpus)) denom = len(corpus) if all(c.__class__ == corpkit.corpus.Subcorpus for c in corpus): mult_corp_are_subs = True elif (type(query) == list or type(query) == dict) and not hasattr(search, '__iter__'): multiple_queries = True num_cores = best_num_parallel(num_cores, len(query)) denom = len(query) elif hasattr(search, '__iter__') and type(search) != dict: multiple_search = True num_cores = best_num_parallel(num_cores, len(list(search.keys()))) denom = len(list(search.keys())) elif hasattr(function_filter, '__iter__'): multiple_option = True num_cores = best_num_parallel(num_cores, len(list(function_filter.keys()))) denom = len(list(function_filter.keys())) elif just_speakers: from build import get_speaker_names_from_xml_corpus multiple_speakers = True if just_speakers == 'each' or just_speakers == ['each']: just_speakers = get_speaker_names_from_xml_corpus(corpus.path) if len(just_speakers) == 0: print('No speaker name data found.') return num_cores = best_num_parallel(num_cores, len(just_speakers)) denom = len(just_speakers) if type(multiprocess) == int: num_cores = multiprocess if multiprocess is False: num_cores = 1 # make sure quicksaves are right type if quicksave is True: raise ValueError('quicksave must be string when using pmultiquery.') # the options that don't change d = { #'paralleling': True, 'function': 'interrogator', 'root': root, 'note': note, 'denominator': denom} # add kwargs to query for k, v in list(kwargs.items()): d[k] = v # make a list of dicts to pass to interrogator, # with the iterable unique in every one ds = [] if multiple_corpora: for index, p in enumerate(corpus): name = p.name a_dict = dict(d) a_dict['corpus'] = p a_dict['search'] = search a_dict['query'] = query a_dict['show'] = show a_dict['outname'] = name.replace('-parsed', '') a_dict['just_speakers'] = just_speakers a_dict['paralleling'] = index a_dict['printstatus'] = False ds.append(a_dict) elif multiple_queries: for index, (name, q) in enumerate(query.items()): a_dict = dict(d) a_dict['corpus'] = corpus a_dict['search'] = search a_dict['query'] = q a_dict['show'] = show a_dict['outname'] = name a_dict['just_speakers'] = just_speakers a_dict['paralleling'] = index a_dict['printstatus'] = False ds.append(a_dict) elif multiple_option: for index, (name, q) in enumerate(function_filter.items()): a_dict = dict(d) a_dict['corpus'] = corpus a_dict['search'] = search a_dict['query'] = query a_dict['show'] = show a_dict['outname'] = name a_dict['just_speakers'] = just_speakers a_dict['paralleling'] = index a_dict['function_filter'] = q a_dict['printstatus'] = False ds.append(a_dict) elif multiple_speakers: for index, name in enumerate(just_speakers): a_dict = dict(d) a_dict['corpus'] = corpus a_dict['search'] = search a_dict['query'] = query a_dict['show'] = show a_dict['outname'] = name a_dict['just_speakers'] = [name] a_dict['function_filter'] = function_filter a_dict['paralleling'] = index a_dict['printstatus'] = False ds.append(a_dict) elif multiple_search: for index, val in enumerate(search): a_dict = dict(d) a_dict['corpus'] = corpus a_dict['search'] = val a_dict['query'] = query a_dict['show'] = show a_dict['outname'] = name a_dict['just_speakers'] = just_speakers a_dict['function_filter'] = function_filter a_dict['paralleling'] = index a_dict['printstatus'] = False ds.append(a_dict) if kwargs.get('do_concordancing') is False: message = 'Interrogating' elif kwargs.get('do_concordancing') is True: message = 'Interrogating and concordancing' elif kwargs.get('do_concordancing').lower() == 'only': message = 'Concordancing' time = strftime("%H:%M:%S", localtime()) sformat = '' for i, (k, v) in enumerate(list(search.items())): if type(v) == list: vformat = ', '.join(v[:5]) if len(v) > 5: vformat += ' ...' else: vformat = v sformat += '%s: %s' %(k, vformat) if i < len(search.keys()) - 1: sformat += '\n ' if multiple_corpora and not multiple_option: corplist = "\n ".join([i.name for i in corpus[:20]]) if len(corpus) > 20: corplist += '\n ... and %d more ...\n' % (len(corpus) - 20) print(("\n%s: Beginning %d corpus interrogations (in %d parallel processes):\n %s" \ "\n Query: '%s'\n %s corpus ... \n" % (time, len(corpus), num_cores, corplist, sformat, message))) elif multiple_queries: print(("\n%s: Beginning %d corpus interrogations (in %d parallel processes): %s" \ "\n Queries: '%s'\n %s corpus ... \n" % (time, len(search), num_cores, corpus.name, "', '".join(list(search.values())), message) )) elif multiple_search: print(("\n%s: Beginning %d corpus interrogations (in %d parallel processes): %s" \ "\n Queries: '%s'\n %s corpus ... \n" % (time, len(list(search.keys())), num_cores, corpus.name, str(list(search.values())), message))) elif multiple_option: print(("\n%s: Beginning %d parallel corpus interrogations (multiple options): %s" \ "\n Query: '%s'\n %s corpus ... \n" % (time, num_cores, corpus.name, sformat, message) )) elif multiple_speakers: print(("\n%s: Beginning %d parallel corpus interrogations: %s" \ "\n Query: '%s'\n %s corpus ... \n" % (time, num_cores, corpus.name, sformat, message) )) # run in parallel, get either a list of tuples (non-c option) # or a dataframe (c option) #import sys #reload(sys) #stdout=sys.stdout failed = False terminal = False used_joblib = False #ds = ds[::-1] if not root: from blessings import Terminal terminal = Terminal() print('\n' * (len(ds) - 2)) for dobj in ds: linenum = dobj['paralleling'] # this try handles nosetest problems in sublime text try: with terminal.location(0, terminal.height - (linenum + 1)): # this is a really bad idea. thetime = strftime("%H:%M:%S", localtime()) num_spaces = 26 - len(dobj['outname']) print('%s: QUEUED: %s' % (thetime, dobj['outname'])) except: pass if not root and multiprocess: #res = Parallel(n_jobs=num_cores)(delayed(interrogator)(**x) for x in ds) try: #ds = sorted(ds, key=lambda k: k['paralleling'], reverse = True) res = Parallel(n_jobs=num_cores)(delayed(interrogator)(**x) for x in ds) used_joblib = True except: failed = True print('Multiprocessing failed.') raise if not res: failed = True else: res = [] for index, d in enumerate(ds): d['startnum'] = (100 / denom) * index res.append(interrogator(**d)) try: res = sorted(res) except: pass # multiprocessing way #from multiprocessing import Process #from interrogator import interrogator #jobs = [] ##for d in ds: ## p = multiprocessing.Process(target=interrogator, kwargs=(**d,)) ## jobs.append(p) ## p.start() ## while p.is_alive(): ## import time ## time.sleep(2) ## if root: ## root.update() #result_queue = multiprocessing.Queue() # #for d in ds: #funs = [interrogator(result_queue, **kwargs) for kwargs in ds] #jobs = [multiprocessing.Process(mc) for mc in funs] #for job in jobs: job.start() #for job in jobs: job.join() #results = [result_queue.get() for mc in funs] import corpkit from interrogation import Concordance if kwargs.get('do_concordancing') == 'only': concs = pd.concat([x for x in res]) thetime = strftime("%H:%M:%S", localtime()) print('\n\n%s: Finished! %d results.\n\n' % (thetime, len(concs.index))) return Concordance(concs) from collections import OrderedDict if not all(type(i.results) == pd.core.series.Series for i in res): out = OrderedDict() for interrog, d in zip(res, ds): for unpicklable in ['note', 'root']: interrog.query.pop(unpicklable, None) out[interrog.query['outname']] = interrog if quicksave: fullpath = os.path.join('saved_interrogations', quicksave) while os.path.isdir(fullpath): selection = input("\nSave error: %s already exists in %s.\n\nType 'o' to overwrite, or enter a new name: " % (quicksave, 'saved_interrogations')) if selection == 'o' or selection == 'O': import shutil shutil.rmtree(fullpath) else: import os fullpath = os.path.join('saved_interrogations', selection) for k, v in list(out.items()): save(v, k, savedir = fullpath, print_info = False) time = strftime("%H:%M:%S", localtime()) print("\n%s: %d files saved to %s" % ( time, len(list(out.keys())), fullpath)) time = strftime("%H:%M:%S", localtime()) print("\n\n%s: Finished! Output is a dictionary with keys:\n\n '%s'\n" % (time, "'\n '".join(sorted(out.keys())))) from interrogation import Interrodict return Interrodict(out) # make query and total branch, save, return else: #print sers #print ds if multiple_corpora and not mult_corp_are_subs: sers = [i.results for i in res] out = pd.DataFrame(sers, index = [i.query['outname'] for i in res]) out = out.reindex_axis(sorted(out.columns), axis=1) # sort cols out = out.fillna(0) # nan to zero out = out.astype(int) # float to int out = out.T else: out = pd.concat([r.results for r in res], axis = 1) # format like normal out = out[sorted(list(out.columns))] out = out.T out = out.fillna(0) # nan to zero out = out.astype(int) if 'c' in show and mult_corp_are_subs: out = out.sum() out.index = sorted(list(out.index)) # sort by total if type(out) == pd.core.frame.DataFrame: out.ix['Total-tmp'] = out.sum() tot = out.ix['Total-tmp'] out = out[tot.argsort()[::-1]] out = out.drop('Total-tmp', axis = 0) out = out.edit(sort_by = sort_by, print_info = False, keep_stats = False, \ df1_always_df = kwargs.get('df1_always_df')) if len(out.results.columns) == 1: out.results = out.results.sort_index() if kwargs.get('do_concordancing') is True: concs = pd.concat([x.concordance for x in res], ignore_index = True) concs = concs.sort_values(by='c') concs = concs.reset_index(drop=True) out.concordance = Concordance(concs) thetime = strftime("%H:%M:%S", localtime()) if terminal: with terminal.location(0, terminal.height): print('\n\n%s: Finished! %d unique results, %d total.%s' % (thetime, len(out.results.columns), out.totals.sum(), '\n')) else: print('\n\n%s: Finished! %d unique results, %d total.%s' % (thetime, len(out.results.columns), out.totals.sum(), '\n')) #if used_joblib: if quicksave: from other import save save(out, quicksave) print('\n') return out
def pmultiquery(corpus, search, show='words', query='any', sort_by='total', quicksave=False, multiprocess='default', just_speakers=False, root=False, note=False, print_info=True, **kwargs): """Parallel process multiple queries or corpora. This function is used by interrogator() for multiprocessing. There's no reason to call this function yourself.""" import collections import os import pandas as pd import collections from collections import namedtuple from time import strftime, localtime import corpkit from interrogator import interrogator from editor import editor from other import save from interrogation import Interrogation try: from joblib import Parallel, delayed except: pass #raise ValueError('joblib, the module used for multiprocessing, cannot be found. ' \ # 'Install with:\n\n pip install joblib') import multiprocessing locs = locals() for k, v in kwargs.items(): locs[k] = v def best_num_parallel(num_cores, num_queries): import corpkit """decide how many parallel processes to run the idea, more or less, is to balance the load when possible""" if num_queries <= num_cores: return num_queries if num_queries > num_cores: if (num_queries / num_cores) == num_cores: return int(num_cores) if num_queries % num_cores == 0: try: return max([ int(num_queries / n) for n in range(2, num_cores) if int(num_queries / n) <= num_cores ]) except ValueError: return num_cores else: import math if (float(math.sqrt(num_queries))).is_integer(): square_root = math.sqrt(num_queries) if square_root <= num_queries / num_cores: return int(square_root) return num_cores num_cores = multiprocessing.cpu_count() # what is our iterable? ... multiple_option = False multiple_queries = False multiple_speakers = False multiple_corpora = False multiple_search = False mult_corp_are_subs = False denom = 1 if hasattr(corpus, '__iter__'): multiple_corpora = True num_cores = best_num_parallel(num_cores, len(corpus)) denom = len(corpus) if all(c.__class__ == corpkit.corpus.Subcorpus for c in corpus): mult_corp_are_subs = True elif (type(query) == list or type(query) == dict) and not hasattr(search, '__iter__'): multiple_queries = True num_cores = best_num_parallel(num_cores, len(query)) denom = len(query) elif hasattr(search, '__iter__') and all( type(i) == dict for i in list(search.values())): multiple_search = True num_cores = best_num_parallel(num_cores, len(list(search.keys()))) denom = len(list(search.keys())) elif just_speakers: from build import get_speaker_names_from_xml_corpus multiple_speakers = True if just_speakers == 'each' or just_speakers == ['each']: just_speakers = get_speaker_names_from_xml_corpus(corpus.path) if len(just_speakers) == 0: print('No speaker name data found.') return num_cores = best_num_parallel(num_cores, len(just_speakers)) denom = len(just_speakers) # if this thing has already come through multiquery, don't multiprocess this time #if kwargs.get('outname'): # multiprocess = False if multiple_corpora and any(x is True for x in [ multiple_speakers, multiple_queries, multiple_search, multiple_option ]): from corpus import Corpus, Corpora if corpus.__class__ == Corpora: multiprocess = False else: corpus = Corpus(corpus) if type(multiprocess) == int: num_cores = multiprocess if multiprocess is False: num_cores = 1 # make sure quicksaves are right type if quicksave is True: raise ValueError('quicksave must be string when using pmultiquery.') # the options that don't change d = { #'paralleling': True, 'function': 'interrogator', 'root': root, 'note': note, 'denominator': denom } # add kwargs to query for k, v in list(kwargs.items()): d[k] = v # make a list of dicts to pass to interrogator, # with the iterable unique in every one ds = [] if multiple_corpora: for index, p in enumerate(corpus): name = p.name a_dict = dict(d) a_dict['corpus'] = p a_dict['search'] = search a_dict['query'] = query a_dict['show'] = show a_dict['outname'] = name.replace('-parsed', '') a_dict['just_speakers'] = just_speakers a_dict['paralleling'] = index a_dict['printstatus'] = False ds.append(a_dict) elif multiple_queries: for index, (name, q) in enumerate(query.items()): a_dict = dict(d) a_dict['corpus'] = corpus a_dict['search'] = search a_dict['query'] = q a_dict['show'] = show a_dict['outname'] = name a_dict['just_speakers'] = just_speakers a_dict['paralleling'] = index a_dict['printstatus'] = False ds.append(a_dict) elif multiple_speakers: for index, name in enumerate(just_speakers): a_dict = dict(d) a_dict['corpus'] = corpus a_dict['search'] = search a_dict['query'] = query a_dict['show'] = show a_dict['outname'] = name a_dict['just_speakers'] = [name] a_dict['paralleling'] = index a_dict['printstatus'] = False ds.append(a_dict) elif multiple_search: for index, (name, val) in enumerate(search.items()): a_dict = dict(d) a_dict['corpus'] = corpus a_dict['search'] = val a_dict['query'] = query a_dict['show'] = show a_dict['outname'] = name a_dict['just_speakers'] = just_speakers a_dict['paralleling'] = index a_dict['printstatus'] = False ds.append(a_dict) if kwargs.get('do_concordancing') is False: message = 'Interrogating' elif kwargs.get('do_concordancing') is True: message = 'Interrogating and concordancing' elif kwargs.get('do_concordancing').lower() == 'only': message = 'Concordancing' time = strftime("%H:%M:%S", localtime()) sformat = '' if multiple_queries: to_it_over = query else: to_it_over = search for i, (k, v) in enumerate(list(to_it_over.items())): if type(v) == list: vformat = ', '.join(v[:5]) if len(v) > 5: vformat += ' ...' elif type(v) == dict: vformat = '' for kk, vv in v.items(): if type(vv) == list: vv = ', '.join(vv[:5]) vformat += '\n %s: %s' % (kk, vv) if len(vv) > 5: vformat += ' ...' else: vformat = v sformat += '%s: %s' % (k, vformat) if i < len(to_it_over.keys()) - 1: sformat += '\n ' if print_info: if multiple_corpora and not multiple_option: corplist = "\n ".join([i.name for i in corpus[:20]]) if len(corpus) > 20: corplist += '\n ... and %d more ...\n' % (len(corpus) - 20) print(("\n%s: Beginning %d corpus interrogations (in %d parallel processes):\n %s" \ "\n Query: %s\n %s corpus ... \n" % (time, len(corpus), num_cores, corplist, sformat, message))) elif multiple_queries: print(("\n%s: Beginning %d corpus interrogations (in %d parallel processes): %s" \ "\n Queries: %s\n %s corpus ... \n" % (time, len(query), num_cores, corpus.name, sformat, message) )) elif multiple_search: print(("\n%s: Beginning %d corpus interrogations (in %d parallel processes): %s" \ "\n Queries: %s\n %s corpus ... \n" % (time, len(list(search.keys())), num_cores, corpus.name, sformat, message))) elif multiple_option: print(("\n%s: Beginning %d parallel corpus interrogations (multiple options): %s" \ "\n Query: %s\n %s corpus ... \n" % (time, num_cores, corpus.name, sformat, message) )) elif multiple_speakers: print(("\n%s: Beginning %d parallel corpus interrogations: %s" \ "\n Query: %s\n %s corpus ... \n" % (time, num_cores, corpus.name, sformat, message) )) # run in parallel, get either a list of tuples (non-c option) # or a dataframe (c option) #import sys #reload(sys) #stdout=sys.stdout failed = False terminal = False used_joblib = False #ds = ds[::-1] if not root and print_info: from blessings import Terminal terminal = Terminal() print('\n' * (len(ds) - 2)) for dobj in ds: linenum = dobj['paralleling'] # this try handles nosetest problems in sublime text try: with terminal.location(0, terminal.height - (linenum + 1)): # this is a really bad idea. thetime = strftime("%H:%M:%S", localtime()) num_spaces = 26 - len(dobj['outname']) print('%s: QUEUED: %s' % (thetime, dobj['outname'])) except: pass if not root and multiprocess: #res = Parallel(n_jobs=num_cores)(delayed(interrogator)(**x) for x in ds) try: #ds = sorted(ds, key=lambda k: k['paralleling'], reverse = True) res = Parallel(n_jobs=num_cores)(delayed(interrogator)(**x) for x in ds) used_joblib = True except: failed = True print('Multiprocessing failed.') raise if not res: failed = True else: res = [] for index, d in enumerate(ds): d['startnum'] = (100 / denom) * index res.append(interrogator(**d)) try: res = sorted(res) except: pass # multiprocessing way #from multiprocessing import Process #from interrogator import interrogator #jobs = [] ##for d in ds: ## p = multiprocessing.Process(target=interrogator, kwargs=(**d,)) ## jobs.append(p) ## p.start() ## while p.is_alive(): ## import time ## time.sleep(2) ## if root: ## root.update() #result_queue = multiprocessing.Queue() # #for d in ds: #funs = [interrogator(result_queue, **kwargs) for kwargs in ds] #jobs = [multiprocessing.Process(mc) for mc in funs] #for job in jobs: job.start() #for job in jobs: job.join() #results = [result_queue.get() for mc in funs] import corpkit from interrogation import Concordance if kwargs.get('do_concordancing') == 'only': concs = pd.concat([x for x in res]) thetime = strftime("%H:%M:%S", localtime()) if print_info: print('\n\n%s: Finished! %d results.\n\n' % (thetime, len(concs.index))) return Concordance(concs) from collections import OrderedDict if not all(type(i.results) == pd.core.series.Series for i in res): out = OrderedDict() for interrog, d in zip(res, ds): for unpicklable in ['note', 'root']: interrog.query.pop(unpicklable, None) try: out[interrog.query['outname']] = interrog except KeyError: out[d['outname']] = interrog if quicksave: fullpath = os.path.join('saved_interrogations', quicksave) while os.path.isdir(fullpath): selection = input( "\nSave error: %s already exists in %s.\n\nType 'o' to overwrite, or enter a new name: " % (quicksave, 'saved_interrogations')) if selection == 'o' or selection == 'O': import shutil shutil.rmtree(fullpath) else: import os fullpath = os.path.join('saved_interrogations', selection) for k, v in list(out.items()): save(v, k, savedir=fullpath, print_info=False) time = strftime("%H:%M:%S", localtime()) print("\n%s: %d files saved to %s" % (time, len(list(out.keys())), fullpath)) time = strftime("%H:%M:%S", localtime()) if print_info: print( "\n\n%s: Finished! Output is a dictionary with keys:\n\n '%s'\n" % (time, "'\n '".join(sorted(out.keys())))) from interrogation import Interrodict idict = Interrodict(out) # remove unpicklable bits from query from types import ModuleType, FunctionType, BuiltinMethodType, BuiltinFunctionType locs = {k: v for k, v in locs.items() if not isinstance(v, ModuleType) \ and not isinstance(v, FunctionType) \ and not isinstance(v, BuiltinFunctionType) \ and not isinstance(v, BuiltinMethodType)} idict.query = locs return idict # make query and total branch, save, return else: #print sers #print ds if multiple_corpora and not mult_corp_are_subs: sers = [i.results for i in res] out = pd.DataFrame(sers, index=[i.query['outname'] for i in res]) out = out.reindex_axis(sorted(out.columns), axis=1) # sort cols out = out.fillna(0) # nan to zero out = out.astype(int) # float to int out = out.T else: try: out = pd.concat([r.results for r in res], axis=1) except ValueError: return None # format like normal out = out[sorted(list(out.columns))] out = out.T out = out.fillna(0) # nan to zero out = out.astype(int) if 'c' in show and mult_corp_are_subs: out = out.sum() out.index = sorted(list(out.index)) # sort by total if type(out) == pd.core.frame.DataFrame: out.ix['Total-tmp'] = out.sum() tot = out.ix['Total-tmp'] out = out[tot.argsort()[::-1]] out = out.drop('Total-tmp', axis=0) out = out.edit(sort_by = sort_by, print_info = False, keep_stats = False, \ df1_always_df = kwargs.get('df1_always_df')) if len(out.results.columns) == 1: out.results = out.results.sort_index() if kwargs.get('do_concordancing') is True: concs = pd.concat([x.concordance for x in res], ignore_index=True) concs = concs.sort_values(by='c') concs = concs.reset_index(drop=True) out.concordance = Concordance(concs) thetime = strftime("%H:%M:%S", localtime()) if terminal and print_info: with terminal.location(0, terminal.height): print('\n\n%s: Finished! %d unique results, %d total.%s' % (thetime, len( out.results.columns), out.totals.sum(), '\n')) else: if print_info: print('\n\n%s: Finished! %d unique results, %d total.%s' % (thetime, len( out.results.columns), out.totals.sum(), '\n')) #if used_joblib: if quicksave: from other import save save(out, quicksave) return out
def savefunc(self, savename, *args, **kwargs): from corpkit import save save(self, savename, *args, **kwargs)
def save(self, savename, *args, **kwargs): """Save data to pickle file""" from other import save save(self, savename, *args, **kwargs)