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def update_emoji(): global RE_EMOJI global URL_EMOJI emoji_list = get_github_emoji() emoji_map = {} if (emoji_list is not None): for emoji in emoji_list: url = emoji_list[emoji] m = RE_ASSET.match(url) if m: emoji_map[emoji] = m.group(u'image') if emoji_map: RE_EMOJI = (u':(%s):' % u'|'.join([re.escape(key) for key in sorted(emoji_map.keys())])) URL_EMOJI = copy.copy(emoji_map)
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update the emoji pattern in memory .
train
false
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def _format_float(value): value_str = '{:.16G}'.format(value) if (('.' not in value_str) and ('E' not in value_str)): value_str += '.0' elif ('E' in value_str): (significand, exponent) = value_str.split('E') if (exponent[0] in ('+', '-')): sign = exponent[0] exponent = exponent[1:] else: sign = '' value_str = '{}E{}{:02d}'.format(significand, sign, int(exponent)) str_len = len(value_str) if (str_len > 20): idx = value_str.find('E') if (idx < 0): value_str = value_str[:20] else: value_str = (value_str[:(20 - (str_len - idx))] + value_str[idx:]) return value_str
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format a floating number to make sure it gets the decimal point .
train
false
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def get_app_dir(app_name, roaming=True, force_posix=False): if WIN: key = ((roaming and 'APPDATA') or 'LOCALAPPDATA') folder = os.environ.get(key) if (folder is None): folder = os.path.expanduser('~') return os.path.join(folder, app_name) if force_posix: return os.path.join(os.path.expanduser(('~/.' + _posixify(app_name)))) if (sys.platform == 'darwin'): return os.path.join(os.path.expanduser('~/Library/Application Support'), app_name) return os.path.join(os.environ.get('XDG_CONFIG_HOME', os.path.expanduser('~/.config')), _posixify(app_name))
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returns the config folder for the application .
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true
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def _import_module_functions(module_path): functions_dict = {} module = __import__(module_path) for part in module_path.split('.')[1:]: module = getattr(module, part) for (k, v) in module.__dict__.items(): try: if (v.__module__ != module_path): continue functions_dict[k] = v except AttributeError: pass return functions_dict
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import a module and get the functions and return them in a dict .
train
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@addon_view @non_atomic_requests def contributions_series(request, addon, group, start, end, format): date_range = check_series_params_or_404(group, start, end, format) check_stats_permission(request, addon, for_contributions=True) qs = Contribution.objects.extra(select={'date_created': 'date(created)'}).filter(addon=addon, amount__gt=0, transaction_id__isnull=False, created__range=date_range).values('date_created').annotate(count=Count('amount'), average=Avg('amount'), total=Sum('amount')) series = sorted(qs, key=(lambda x: x['date_created']), reverse=True) for row in series: row['end'] = row['date'] = row.pop('date_created') if (format == 'csv'): return render_csv(request, addon, series, ['date', 'count', 'total', 'average']) elif (format == 'json'): return render_json(request, addon, series)
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generate summarized contributions grouped by group in format .
train
false
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def arg_to_array(func): def fn(self, arg, *args, **kwargs): 'Function\n\n Parameters\n ----------\n arg : array-like\n Argument to convert.\n *args : tuple\n Arguments.\n **kwargs : dict\n Keyword arguments.\n\n Returns\n -------\n value : object\n The return value of the function.\n ' return func(self, np.array(arg), *args, **kwargs) return fn
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decorator to convert argument to array .
train
true
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def get_user_config_path(): return os.path.join(xdg.get_config_dir(), 'config.py')
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get the path to the user config file .
train
false
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def IsTypePackable(field_type): return (field_type not in NON_PACKABLE_TYPES)
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return true iff packable = true is valid for fields of this type .
train
false
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def sort_collator(): global _sort_collator if (_sort_collator is None): _sort_collator = collator().clone() _sort_collator.strength = _icu.UCOL_SECONDARY _sort_collator.numeric = tweaks[u'numeric_collation'] return _sort_collator
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ignores case differences and recognizes numbers in strings .
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def remove_known_host(reactor, hostname): return run(reactor, ['ssh-keygen', '-R', hostname])
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remove all keys belonging to hostname from a known_hosts file .
train
false
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def Async(proxy): key = id(proxy) if (key in _async_proxy_cache): return _async_proxy_cache[key] else: new_proxy = AsyncNetProxy(proxy) _async_proxy_cache[key] = new_proxy return new_proxy
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a factory for creating asynchronous proxies for existing synchronous ones .
train
false
16,855
def lowess(endog, exog, frac=(2.0 / 3), it=3): x = exog if (exog.ndim != 1): raise ValueError('exog must be a vector') if (endog.ndim != 1): raise ValueError('endog must be a vector') if (endog.shape[0] != x.shape[0]): raise ValueError('exog and endog must have same length') n = exog.shape[0] fitted = np.zeros(n) k = int((frac * n)) index_array = np.argsort(exog) x_copy = np.array(exog[index_array]) y_copy = endog[index_array] (fitted, weights) = _lowess_initial_fit(x_copy, y_copy, k, n) for i in range(it): _lowess_robustify_fit(x_copy, y_copy, fitted, weights, k, n) out = np.array([x_copy, fitted]).T out.shape = (n, 2) return out
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returns y-values estimated using the lowess function in statsmodels .
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def _methodFunction(classObject, methodName): methodObject = getattr(classObject, methodName) if _PY3: return methodObject return methodObject.im_func
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retrieve the function object implementing a method name given the class its on and a method name .
train
false
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def simple_preprocess(doc, deacc=False, min_len=2, max_len=15): tokens = [token for token in tokenize(doc, lower=True, deacc=deacc, errors='ignore') if ((min_len <= len(token) <= max_len) and (not token.startswith('_')))] return tokens
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convert a document into a list of tokens .
train
true
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def fix_missing_locations(node): def _fix(node, lineno, col_offset): if ('lineno' in node._attributes): if (not hasattr(node, 'lineno')): node.lineno = lineno else: lineno = node.lineno if ('col_offset' in node._attributes): if (not hasattr(node, 'col_offset')): node.col_offset = col_offset else: col_offset = node.col_offset for child in iter_child_nodes(node): _fix(child, lineno, col_offset) _fix(node, 1, 0) return node
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some nodes require a line number and the column offset .
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true
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def mygettext(string): return _(string)
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used instead of _ when creating translationproxies .
train
false
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def _set_powercfg_value(scheme, sub_group, setting_guid, power, value): if (scheme is None): scheme = _get_current_scheme() cmd = 'powercfg /set{0}valueindex {1} {2} {3} {4}'.format(power, scheme, sub_group, setting_guid, value) return __salt__['cmd.run'](cmd, python_shell=False)
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sets the value of a setting with a given power to the given scheme .
train
true
16,864
def get_locale_from_accept_header(request): header = request.headers.get('Accept-Language', '') parsed = parse_accept_language_header(header) if (parsed is None): return None pairs_sorted_by_q = sorted(parsed.items(), key=(lambda (lang, q): q), reverse=True) locale = Locale.negotiate([lang for (lang, q) in pairs_sorted_by_q], request.app.config.get('locales'), sep='_') return str(locale)
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detect locale from request .
train
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def is_pep8_available(): try: import pep8 if (not hasattr(pep8, 'BaseReport')): raise ImportError() return True except ImportError: return False
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checks if pep8 is availalbe .
train
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def ascontiguousarray(a, dtype=None): return core.ascontiguousarray(a, dtype)
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returns a c-contiguous array .
train
false
16,868
def cbs_download(url, output_dir='.', merge=True, info_only=False, **kwargs): html = get_content(url) pid = match1(html, "video\\.settings\\.pid\\s*=\\s*\\'([^\\']+)\\'") title = match1(html, 'video\\.settings\\.title\\s*=\\s*\\"([^\\"]+)\\"') theplatform_download_by_pid(pid, title, output_dir=output_dir, merge=merge, info_only=info_only)
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downloads cbs videos by url .
train
true
16,870
def expect_json(view_function): @wraps(view_function) def parse_json_into_request(request, *args, **kwargs): if (('application/json' in request.META.get('CONTENT_TYPE', '')) and request.body): try: request.json = json.loads(request.body) except ValueError: return JsonResponseBadRequest({'error': 'Invalid JSON'}) else: request.json = {} return view_function(request, *args, **kwargs) return parse_json_into_request
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view decorator for simplifying handing of requests that expect json .
train
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16,871
def is_matching_event(expected_event, actual_event, tolerate=None): return (len(get_event_differences(expected_event, actual_event, tolerate=tolerate)) == 0)
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return true iff the actual_event matches the expected_event given the tolerances .
train
false
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def commit_on_success_unless_managed(using=None, savepoint=False): connection = get_connection(using) if (connection.get_autocommit() or connection.in_atomic_block): return atomic(using, savepoint) else: def entering(using): pass def exiting(exc_type, using): set_dirty(using=using) return _transaction_func(entering, exiting, using)
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transitory api to preserve backwards-compatibility while refactoring .
train
false
16,874
def role_add(user, role): ret = {} roles = role.split(',') known_roles = role_list().keys() valid_roles = [r for r in roles if (r in known_roles)] log.debug('rbac.role_add - roles={0}, known_roles={1}, valid_roles={2}'.format(roles, known_roles, valid_roles)) if (len(valid_roles) > 0): res = __salt__['cmd.run_all']('usermod -R "{roles}" {login}'.format(login=user, roles=','.join(set((role_get(user) + valid_roles))))) if (res['retcode'] > 0): ret['Error'] = {'retcode': res['retcode'], 'message': (res['stderr'] if ('stderr' in res) else res['stdout'])} return ret active_roles = role_get(user) for r in roles: if (r not in valid_roles): ret[r] = 'Unknown' elif (r in active_roles): ret[r] = 'Added' else: ret[r] = 'Failed' return ret
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add role to user user : string username role : string role name cli example: .
train
true
16,877
def matches(what, glob_patterns): return any((fnmatch.fnmatch(what, glob_pattern) for glob_pattern in glob_patterns))
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check for matches in both cdao and obo namespaces .
train
false
16,878
def _status_csf(): cmd = 'test -e /etc/csf/csf.disable' out = __salt__['cmd.run_all'](cmd) return bool(out['retcode'])
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return true if csf is running otherwise return false .
train
false
16,879
def getJoinedPath(path, subName=''): if (subName == ''): return path return os.path.join(path, subName)
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get the joined file path .
train
false
16,883
def get_standard_freq(freq): msg = 'get_standard_freq is deprecated. Use to_offset(freq).rule_code instead.' warnings.warn(msg, FutureWarning, stacklevel=2) return to_offset(freq).rule_code
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return the standardized frequency string .
train
false
16,884
def test_newlines(): superConsole.SendKeys('outputRedirectStart{(}{)}{ENTER}') superConsole.SendKeys('{ENTER}') superConsole.SendKeys('None{ENTER}') superConsole.SendKeys('{ENTER}{ENTER}{ENTER}') superConsole.SendKeys('outputRedirectStop{(}{)}{ENTER}') for lines in getTestOutput(): AreEqual(removePrompts(lines), [])
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ensure empty lines do not break the console .
train
false
16,885
def skills_filter(req_id): rstable = s3db.req_req_skill rows = db((rstable.req_id == req_id)).select(rstable.skill_id) filter_opts = [] for r in rows: multi_skill_id = r.skill_id for skill_id in multi_skill_id: filter_opts.append(skill_id) if (len(filter_opts) == 1): field = s3db.req_commit_skill.skill_id field.default = skill_id field.writable = False return s3db.req_commit_skill.skill_id.requires = IS_ONE_OF(db, 'hrm_skill.id', s3db.hrm_multi_skill_represent, filterby='id', filter_opts=filter_opts, sort=True, multiple=True)
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limit commit skills to skills from the request - dry helper function .
train
false
16,887
def load_yaml(filename): try: with open(filename, 'r') as f: return yaml.load(f, Loader=Loader) except (IOError, yaml.error.YAMLError) as exc: raise ConfigReadError(filename, exc)
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read a yaml document from a file .
train
true
16,888
def get_tau_sd(tau=None, sd=None): if (tau is None): if (sd is None): sd = 1.0 tau = 1.0 else: tau = (sd ** (-2.0)) elif (sd is not None): raise ValueError("Can't pass both tau and sd") else: sd = (tau ** (-0.5)) tau = (1.0 * tau) sd = (1.0 * sd) return (tau, sd)
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find precision and standard deviation .
train
false
16,889
@register(None) def _check_generic(brain, match_kind, match, target_dict, cred_dict): match = (match % target_dict) if (match_kind in cred_dict): return (match == unicode(cred_dict[match_kind])) return False
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check an individual match .
train
false
16,891
def deploy_script(scriptpath, *args): put(local_path=scriptpath, remote_path='', mirror_local_mode=True) scriptfile = os.path.split(scriptpath)[1] args_str = ' '.join(args) run(((('./' + scriptfile) + ' ') + args_str))
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copies to remote and executes local script .
train
false
16,893
@requires_application() def test_functionality_proxy(): _test_basics('gl2 debug')
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test gl proxy class for basic functionality .
train
false
16,894
def is_valid_ethernet_tag_id(etag_id): return (isinstance(etag_id, numbers.Integral) and (0 <= etag_id <= 4294967295))
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returns true if the given evpn ethernet tag id is valid .
train
false
16,895
def get_lus_version(): fsvers = '/proc/fs/lustre/version' logging.debug((' opening file ' + fsvers)) try: fobj = open(fsvers) except IOError: logging.debug(('ERROR opening file ' + fsvers)) exit() vals = [] for line in fobj: item = re.split('\\s+', line.rstrip()) vals.append(item) for n in range(len(vals)): if ('lustre:' in vals[n]): vers_string = vals[n][1] fir = vers_string.find('.') sec = vers_string.find('.', (fir + 1)) lus_version = ((vers_string[0:fir] + '.') + vers_string[(fir + 1):sec]) logging.debug((' lus_version: ' + str(lus_version))) return float(lus_version)
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return lustre version number .
train
false
16,896
def getNewRepository(): return ExportRepository()
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get new repository .
train
false
16,898
def http_error(httpexception, errno=None, code=None, error=None, message=None, info=None, details=None): errno = (errno or ERRORS.UNDEFINED) if isinstance(errno, Enum): errno = errno.value logger.bind(errno=errno) body = {'code': (code or httpexception.code), 'errno': errno, 'error': (error or httpexception.title)} if (message is not None): body['message'] = message if (info is not None): body['info'] = info if (details is not None): body['details'] = details response = httpexception response.body = json.dumps(body).encode('utf-8') response.content_type = 'application/json' return response
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return a json formated response matching the error http api .
train
false
16,900
def working_path(filename): return os.path.join(WORKING_DIR, filename)
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return an absolute path for .
train
false
16,901
def publish_doctree(source, source_path=None, source_class=io.StringInput, reader=None, reader_name='standalone', parser=None, parser_name='restructuredtext', settings=None, settings_spec=None, settings_overrides=None, config_section=None, enable_exit_status=False): pub = Publisher(reader=reader, parser=parser, writer=None, settings=settings, source_class=source_class, destination_class=io.NullOutput) pub.set_components(reader_name, parser_name, 'null') pub.process_programmatic_settings(settings_spec, settings_overrides, config_section) pub.set_source(source, source_path) pub.set_destination(None, None) output = pub.publish(enable_exit_status=enable_exit_status) return pub.document
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set up & run a publisher for programmatic use with string i/o .
train
false
16,902
def _get_sentences_with_word_count(sentences, word_count): length = 0 selected_sentences = [] for sentence in sentences: words_in_sentence = len(sentence.text.split()) if (abs(((word_count - length) - words_in_sentence)) > abs((word_count - length))): return selected_sentences selected_sentences.append(sentence) length += words_in_sentence return selected_sentences
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given a list of sentences .
train
true
16,903
def _reverse(viewname, args=None, kwargs=None, request=None, format=None, **extra): if (format is not None): kwargs = (kwargs or {}) kwargs['format'] = format return reverse(viewname, args=args, kwargs=kwargs, **extra)
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same as django .
train
false
16,904
@lru_cache(maxsize=None) def cache_installed(): has_key = bool(getattr(settings, u'NEVERCACHE_KEY', u'')) def flatten(seqs): return (item for seq in seqs for item in seq) middleware_classes = map(import_string, get_middleware_setting()) middleware_ancestors = set(flatten(map(getmro, middleware_classes))) mezzanine_cache_middleware_classes = {import_string(u'mezzanine.core.middleware.UpdateCacheMiddleware'), import_string(u'mezzanine.core.middleware.FetchFromCacheMiddleware')} return (has_key and settings.CACHES and (not settings.TESTING) and mezzanine_cache_middleware_classes.issubset(middleware_ancestors))
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returns true if a cache backend is configured .
train
false
16,905
def create_multiple_choice_problem(problem_name): xml_data = create_multiple_choice_xml() return XBlockFixtureDesc('problem', problem_name, data=xml_data, metadata={'rerandomize': 'always'})
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return the multiple choice problem descriptor .
train
false
16,907
def sh_string(s): if ('\x00' in s): log.error('sh_string(): Cannot create a null-byte') chars = set(s) very_good = set(((string.ascii_letters + string.digits) + '_+.,/')) if (chars <= very_good): return s if (not (chars & set(ESCAPED))): return ("'%s'" % s) quoted_string = '' quoted = False for char in s: if (char not in ESCAPED): if (not quoted): quoted_string += SINGLE_QUOTE quoted = True quoted_string += char else: if quoted: quoted = False quoted_string += SINGLE_QUOTE quoted_string += ESCAPED[char] if quoted: quoted_string += SINGLE_QUOTE return quoted_string
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outputs a string in a format that will be understood by /bin/sh .
train
false
16,908
def test_calibration_nan_imputer(): (X, y) = make_classification(n_samples=10, n_features=2, n_informative=2, n_redundant=0, random_state=42) X[(0, 0)] = np.nan clf = Pipeline([('imputer', Imputer()), ('rf', RandomForestClassifier(n_estimators=1))]) clf_c = CalibratedClassifierCV(clf, cv=2, method='isotonic') clf_c.fit(X, y) clf_c.predict(X)
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test that calibration can accept nan .
train
false
16,909
def get_Http(): global Http return Http
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return current transport class .
train
false
16,911
def indefinite_article(word): return 'a'
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returns the indefinite article for a given word .
train
false
16,912
def _check_for_misordered_fields(entries, expected): actual = [field for field in entries.keys() if (field in expected)] expected = [field for field in expected if (field in actual)] if (actual != expected): actual_label = ', '.join(actual) expected_label = ', '.join(expected) raise ValueError(("The fields in a section of the document are misordered. It should be '%s' but was '%s'" % (actual_label, expected_label)))
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to be valid a network status documents fiends need to appear in a specific order .
train
false
16,913
@utils.arg('server', metavar='<server>', help=_('Name or ID of server.')) def do_volume_attachments(cs, args): volumes = cs.volumes.get_server_volumes(_find_server(cs, args.server).id) _translate_volume_attachments_keys(volumes) utils.print_list(volumes, ['ID', 'DEVICE', 'SERVER ID', 'VOLUME ID'])
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list all the volumes attached to a server .
train
false
16,914
def _stc_src_sel(src, stc): if isinstance(stc, VolSourceEstimate): vertices = [stc.vertices] else: vertices = stc.vertices if (not (len(src) == len(vertices))): raise RuntimeError(('Mismatch between number of source spaces (%s) and STC vertices (%s)' % (len(src), len(vertices)))) src_sels = [] offset = 0 for (s, v) in zip(src, vertices): src_sel = np.intersect1d(s['vertno'], v) src_sel = np.searchsorted(s['vertno'], src_sel) src_sels.append((src_sel + offset)) offset += len(s['vertno']) src_sel = np.concatenate(src_sels) return src_sel
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select the vertex indices of a source space using a source estimate .
train
false
16,915
def in_exception_handler(): return (sys.exc_info() != (None, None, None))
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train
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16,916
def fake_get_platform(): if (sys.platform == 'darwin'): return 'macosx-' else: return 'linux-'
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fake distutils .
train
false
16,917
@mock_streams('stdout') def test_puts_with_prefix(): s = 'my output' h = 'localhost' with settings(host_string=h): puts(s) eq_(sys.stdout.getvalue(), ('[%s] %s' % (h, (s + '\n'))))
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puts() should prefix output with env .
train
false
16,918
@receiver(post_delete, sender=Project) @receiver(post_delete, sender=SubProject) def delete_object_dir(sender, instance, **kwargs): if (hasattr(instance, 'is_repo_link') and instance.is_repo_link): return project_path = instance.get_path() if os.path.exists(project_path): shutil.rmtree(project_path)
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handler to delete project directory on project deletion .
train
false
16,919
def complete_signin(request, redirect_field_name=REDIRECT_FIELD_NAME, openid_form=OpenidSigninForm, auth_form=AuthenticationForm, on_success=signin_success, on_failure=signin_failure, extra_context=None): return complete(request, on_success, on_failure, (get_url_host(request) + reverse('user_complete_signin')), redirect_field_name=redirect_field_name, openid_form=openid_form, auth_form=auth_form, extra_context=extra_context)
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in case of complete signin with openid :attr request: request object :attr openid_form: form use for openid signin .
train
false
16,921
def set_other_config_pci(session, vm_ref, params): other_config = session.call_xenapi('VM.get_other_config', vm_ref) other_config['pci'] = params session.call_xenapi('VM.set_other_config', vm_ref, other_config)
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set the pci key of other-config parameter to params .
train
false
16,922
def _prefix_only_url_replace_regex(pattern): return re.compile(u'\n (?x) # flags=re.VERBOSE\n (?P<quote>\\\\?[\'"]) # the opening quotes\n {}\n (?P=quote) # the first matching closing quote\n '.format(pattern))
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match urls in quotes pulling out the fields from pattern .
train
false
16,923
def is_inverse_pair(node_op, prev_op, inv_pair): node_is_op0 = isinstance(node_op, inv_pair[0]) node_is_op1 = isinstance(node_op, inv_pair[1]) prev_is_op0 = isinstance(prev_op, inv_pair[0]) prev_is_op1 = isinstance(prev_op, inv_pair[1]) return ((node_is_op0 and prev_is_op1) or (node_is_op1 and prev_is_op0))
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given two consecutive operations .
train
false
16,924
def requires_network(test): def _is_unreachable_err(err): return (getattr(err, 'errno', None) in (errno.ENETUNREACH, errno.EHOSTUNREACH)) def _has_route(): try: sock = socket.create_connection((TARPIT_HOST, 80), 0.0001) sock.close() return True except socket.timeout: return True except socket.error as e: if _is_unreachable_err(e): return False else: raise @functools.wraps(test) def wrapper(*args, **kwargs): global _requires_network_has_route if (_requires_network_has_route is None): _requires_network_has_route = _has_route() if _requires_network_has_route: return test(*args, **kwargs) else: msg = "Can't run {name} because the network is unreachable".format(name=test.__name__) raise SkipTest(msg) return wrapper
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helps you skip tests that require the network .
train
false
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def HandleRequest(unused_environ, handler_name, unused_url, post_data, unused_error, application_root, python_lib, import_hook=None): body = cStringIO.StringIO() module_name = _FileToModuleName(handler_name) (parent_module, _, submodule_name) = module_name.rpartition('.') parent_module = _GetModuleOrNone(parent_module) main = None if (module_name in sys.modules): module = sys.modules[module_name] main = _GetValidMain(module) if (not main): module = imp.new_module('__main__') if (import_hook is not None): module.__loader__ = import_hook saved_streams = (sys.stdin, sys.stdout) try: sys.modules['__main__'] = module module.__dict__['__name__'] = '__main__' sys.stdin = post_data sys.stdout = body if main: os.environ['PATH_TRANSLATED'] = module.__file__ main() else: filename = _AbsolutePath(handler_name, application_root, python_lib) if filename.endswith((os.sep + '__init__.py')): module.__path__ = [os.path.dirname(filename)] if (import_hook is None): (code, filename) = _LoadModuleCode(filename) else: code = import_hook.get_code(module_name) if (not code): return {'error': 2} os.environ['PATH_TRANSLATED'] = filename module.__file__ = filename try: sys.modules[module_name] = module eval(code, module.__dict__) except: del sys.modules[module_name] if (parent_module and (submodule_name in parent_module.__dict__)): del parent_module.__dict__[submodule_name] raise else: if parent_module: parent_module.__dict__[submodule_name] = module return _ParseResponse(body) except: exception = sys.exc_info() message = ''.join(traceback.format_exception(exception[0], exception[1], exception[2].tb_next)) logging.error(message) return {'error': 1} finally: (sys.stdin, sys.stdout) = saved_streams module.__name__ = module_name if ('__main__' in sys.modules): del sys.modules['__main__']
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handle a single wsgi request .
train
false
16,926
def nsmallest(arr, n, keep='first'): if (keep == 'last'): arr = arr[::(-1)] narr = len(arr) n = min(n, narr) sdtype = str(arr.dtype) arr = arr.view(_dtype_map.get(sdtype, sdtype)) kth_val = algos.kth_smallest(arr.copy(), (n - 1)) return _finalize_nsmallest(arr, kth_val, n, keep, narr)
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find the n smallest elements in a dataset .
train
false
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def instance_group_create(context, values, policies=None, members=None): return IMPL.instance_group_create(context, values, policies, members)
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create a new group .
train
false
16,928
def _onPygletMousePress(x, y, button, modifiers, emulated=False): global mouseButtons, mouseClick, mouseTimes now = psychopy.clock.getTime() if emulated: label = 'Emulated' else: label = '' if (button & LEFT): mouseButtons[0] = 1 mouseTimes[0] = (now - mouseClick[0].getLastResetTime()) label += ' Left' if (button & MIDDLE): mouseButtons[1] = 1 mouseTimes[1] = (now - mouseClick[1].getLastResetTime()) label += ' Middle' if (button & RIGHT): mouseButtons[2] = 1 mouseTimes[2] = (now - mouseClick[2].getLastResetTime()) label += ' Right' logging.data(('Mouse: %s button down, pos=(%i,%i)' % (label.strip(), x, y)))
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button left=1 .
train
false
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def Comma(): return Leaf(token.COMMA, u',')
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a comma leaf .
train
false
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@requires_segment_info def tab_modified_indicator(pl, segment_info, text=u'+'): for buf_segment_info in list_tabpage_buffers_segment_info(segment_info): if int(vim_getbufoption(buf_segment_info, u'modified')): return [{u'contents': text, u'highlight_groups': [u'tab_modified_indicator', u'modified_indicator']}] return None
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return a file modified indicator for tabpages .
train
false
16,933
def CDLHIGHWAVE(barDs, count): return call_talib_with_ohlc(barDs, count, talib.CDLHIGHWAVE)
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high-wave candle .
train
false
16,936
def test_shared_cudandarray(): a = cuda.shared_constructor(cuda.CudaNdarray.zeros((2, 3))) assert isinstance(a.type, tcn.CudaNdarrayType)
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test that we can create a cudandarraysharedvariable from a cudandarray .
train
false
16,937
def lock_held(): return False
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return false since threading is not supported .
train
false
16,939
def OutDedent(format='', *args): if format: VaOutput(format, args) DedentLevel()
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combine output() followed by dedentlevel() .
train
false
16,941
def parse_multipart_headers(iterable): result = [] for line in iterable: line = to_native(line) (line, line_terminated) = _line_parse(line) if (not line_terminated): raise ValueError('unexpected end of line in multipart header') if (not line): break elif ((line[0] in ' DCTB ') and result): (key, value) = result[(-1)] result[(-1)] = (key, ((value + '\n ') + line[1:])) else: parts = line.split(':', 1) if (len(parts) == 2): result.append((parts[0].strip(), parts[1].strip())) return Headers(result)
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parses multipart headers from an iterable that yields lines .
train
true
16,942
def mkFilter(parts): if (parts is None): parts = [BasicServiceEndpoint] try: parts = list(parts) except TypeError: return mkCompoundFilter([parts]) else: return mkCompoundFilter(parts)
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convert a filter-convertable thing into a filter .
train
true
16,943
def _compute_n_patches(i_h, i_w, p_h, p_w, max_patches=None): n_h = ((i_h - p_h) + 1) n_w = ((i_w - p_w) + 1) all_patches = (n_h * n_w) if max_patches: if (isinstance(max_patches, numbers.Integral) and (max_patches < all_patches)): return max_patches elif (isinstance(max_patches, numbers.Real) and (0 < max_patches < 1)): return int((max_patches * all_patches)) else: raise ValueError(('Invalid value for max_patches: %r' % max_patches)) else: return all_patches
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compute the number of patches that will be extracted in an image .
train
false
16,945
def get_stay_open(): return stay_open
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get stay_open variable .
train
false
16,946
def usage_key_with_run(usage_key_string): usage_key = UsageKey.from_string(usage_key_string) usage_key = usage_key.replace(course_key=modulestore().fill_in_run(usage_key.course_key)) return usage_key
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converts usage_key_string to a usagekey .
train
false
16,947
@builtin(u'Capitalize text', capitalize, apply_func_to_match_groups) def replace_capitalize(match, number, file_name, metadata, dictionaries, data, functions, *args, **kwargs): return apply_func_to_match_groups(match, capitalize)
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capitalize matched text .
train
false
16,948
def unsqueeze(data, axis, oldshape): newshape = list(oldshape) newshape[axis] = 1 return data.reshape(newshape)
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unsqueeze a collapsed array .
train
false
16,949
def extend_environ(**kwargs): env = os.environ.copy() env.update(kwargs) return env
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return a copy of os .
train
false
16,951
def raw_highlight(classified_text): result = [] for (kind, text) in classified_text: result.append(('%15s: %r\n' % ((kind or 'plain'), text))) return ''.join(result)
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straight text display of text classifications .
train
false
16,952
def export_users(path_prefix='/', region=None, key=None, keyid=None, profile=None): conn = _get_conn(region=region, key=key, keyid=keyid, profile=profile) if (not conn): return None results = odict.OrderedDict() users = get_all_users(path_prefix, region, key, keyid, profile) for user in users: name = user.user_name _policies = conn.get_all_user_policies(name, max_items=100) _policies = _policies.list_user_policies_response.list_user_policies_result.policy_names policies = {} for policy_name in _policies: _policy = conn.get_user_policy(name, policy_name) _policy = json.loads(_unquote(_policy.get_user_policy_response.get_user_policy_result.policy_document)) policies[policy_name] = _policy user_sls = [] user_sls.append({'name': name}) user_sls.append({'policies': policies}) user_sls.append({'path': user.path}) results[('manage user ' + name)] = {'boto_iam.user_present': user_sls} return _safe_dump(results)
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get all iam user details .
train
true
16,953
def allFrameObjs(): f = sys._getframe() objs = [] while (f is not None): objs.append(f) objs.append(f.f_code) f = f.f_back return objs
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return list of frame objects in current stack .
train
false
16,954
@testing.requires_testing_data def test_read_labels_from_annot_annot2labels(): label_fnames = glob.glob((label_dir + '/*.label')) label_fnames.sort() labels_mne = [read_label(fname) for fname in label_fnames] labels = read_labels_from_annot('sample', subjects_dir=subjects_dir) _assert_labels_equal(labels, labels_mne, ignore_pos=True)
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test reading labels from parc .
train
false
16,955
def _solve_explike_DE(f, x, DE, g, k): from sympy.solvers import rsolve for t in Add.make_args(DE): (coeff, d) = t.as_independent(g) if coeff.free_symbols: return RE = exp_re(DE, g, k) init = {} for i in range(len(Add.make_args(RE))): if i: f = f.diff(x) init[g(k).subs(k, i)] = f.limit(x, 0) sol = rsolve(RE, g(k), init) if sol: return ((sol / factorial(k)), S.Zero, S.Zero)
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solves de with constant coefficients .
train
false
16,958
def _recursive_compare(v1, v2): if isinstance(v1, list): if (len(v1) != len(v2)): return False v1.sort() v2.sort() for (x, y) in zip(v1, v2): if (not _recursive_compare(x, y)): return False return True elif isinstance(v1, dict): v1 = dict(v1) v2 = dict(v2) if (sorted(v1) != sorted(v2)): return False for k in v1: if (not _recursive_compare(v1[k], v2[k])): return False return True else: return (v1 == v2)
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return v1 == v2 .
train
true
16,959
def _donothing_func(*args, **kwargs): pass
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perform no good and no bad .
train
false
16,960
def test_stochasatic_pool_samples(): batch = 1 channel = 1 pool_shape = (2, 2) pool_stride = (2, 2) image_shape = (2, 2) rng = numpy.random.RandomState(12345) data = rng.uniform(0, 10, size=(batch, channel, image_shape[0], image_shape[1])).astype('float32') x = theano.tensor.tensor4() s_max = stochastic_max_pool_bc01(x, pool_shape, pool_stride, image_shape) f = theano.function([x], s_max) samples = [] for i in xrange(300): samples.append(numpy.asarray(f(data))[(0, 0, 0, 0)]) counts = Counter(samples) data = data.reshape(numpy.prod(image_shape)) data.sort() data = data[::(-1)] for i in range((len(data) - 1)): assert (counts[data[i]] >= counts[data[(i + 1)]])
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check if the order of frequency of samples from stochastic max pool are same as the order of input values .
train
false
16,961
def test_iht_fit_sample_linear_svm(): est = 'linear-svm' iht = InstanceHardnessThreshold(est, random_state=RND_SEED) (X_resampled, y_resampled) = iht.fit_sample(X, Y) X_gt = np.array([[(-0.3879569), 0.6894251], [(-0.09322739), 1.28177189], [(-0.77740357), 0.74097941], [0.91542919, (-0.65453327)], [(-0.03852113), 0.40910479], [(-0.43877303), 1.07366684], [(-0.18430329), 0.52328473], [(-0.65571327), 0.42412021], [(-0.28305528), 0.30284991], [1.06446472, (-1.09279772)], [0.30543283, (-0.02589502)], [(-0.00717161), 0.00318087]]) y_gt = np.array([0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0]) assert_array_equal(X_resampled, X_gt) assert_array_equal(y_resampled, y_gt)
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test the fit sample routine with linear svm .
train
false
16,962
def list_all_sysfonts(): filepath = [] searchpath = list(set((TTFSearchPath + rl_config.TTFSearchPath))) for dirname in searchpath: for filename in glob.glob(os.path.join(os.path.expanduser(dirname), '*.[Tt][Tt][FfCc]')): filepath.append(filename) return filepath
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this function returns list of font directories of system .
train
false
16,963
def group_patch(context, data_dict): _check_access('group_patch', context, data_dict) show_context = {'model': context['model'], 'session': context['session'], 'user': context['user'], 'auth_user_obj': context['auth_user_obj']} group_dict = _get_action('group_show')(show_context, {'id': _get_or_bust(data_dict, 'id')}) patched = dict(group_dict) patched.pop('display_name', None) patched.update(data_dict) return _update.group_update(context, patched)
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patch a group .
train
false
16,964
def numerical_sort(string_int_list): as_int_list = [] as_str_list = [] for vlan in string_int_list: as_int_list.append(int(vlan)) as_int_list.sort() for vlan in as_int_list: as_str_list.append(str(vlan)) return as_str_list
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sorts list of strings/integers that are digits in numerical order .
train
false
16,966
def get_oauth_settings(): out = frappe._dict({u'skip_authorization': frappe.db.get_value(u'OAuth Provider Settings', None, u'skip_authorization')}) return out
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returns oauth settings .
train
false
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def get_support_mail(): from sentry.options import get return (get('system.support-email') or get('system.admin-email') or None)
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returns the most appropriate support email address .
train
false
16,969
def _join_relationships(workflow, parent, child_el): if ('to' not in child_el.attrib): raise RuntimeError((_("Node %s has a link that is missing 'to' attribute.") % parent.name)) to = child_el.attrib['to'] try: child = Node.objects.get(workflow=workflow, name=to) except Node.DoesNotExist as e: raise RuntimeError((_('Node %s has not been defined.') % to)) obj = Link.objects.create(name='to', parent=parent, child=child) obj.save()
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resolves join node links .
train
false
16,970
def split_on_arrow(eq): for arrow in ARROWS: (left, a, right) = eq.partition(arrow) if (a != ''): return (left, a, right) return (eq, '', '')
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split a string on an arrow .
train
false
16,971
def ricker(points, a): A = (2 / (np.sqrt((3 * a)) * (np.pi ** 0.25))) wsq = (a ** 2) vec = (np.arange(0, points) - ((points - 1.0) / 2)) xsq = (vec ** 2) mod = (1 - (xsq / wsq)) gauss = np.exp(((- xsq) / (2 * wsq))) total = ((A * mod) * gauss) return total
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return a ricker wavelet .
train
false
16,972
def SAR(barDs, count, acceleration=(-4e+37), maximum=(-4e+37)): return call_talib_with_hl(barDs, count, talib.SAR, acceleration, maximum)
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parabolic sar .
train
false
16,973
@pytest.fixture(scope='session') def cli_runner(): runner = CliRunner() def cli_main(*cli_args): 'Run cookiecutter cli main with the given args.' return runner.invoke(main, cli_args) return cli_main
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fixture that returns a helper function to run the cookiecutter cli .
train
false
16,974
def xticks(*args, **kwargs): ax = gca() if (len(args) == 0): locs = ax.get_xticks() labels = ax.get_xticklabels() elif (len(args) == 1): locs = ax.set_xticks(args[0]) labels = ax.get_xticklabels() elif (len(args) == 2): locs = ax.set_xticks(args[0]) labels = ax.set_xticklabels(args[1], **kwargs) else: raise TypeError('Illegal number of arguments to xticks') if len(kwargs): for l in labels: l.update(kwargs) draw_if_interactive() return (locs, silent_list('Text xticklabel', labels))
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get or set the *x*-limits of the current tick locations and labels .
train
false