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def make_dependence_cmp(): depends = make_depends() def dependence(a, b): '\n A cmp function for nodes in a graph - does a depend on b?\n\n Returns\n -------\n int\n Positive number if a depends on b, negative number\n if b depends on a, 0 otherwise.\n\n ' if depends((a, b)): return 1 if depends((b, a)): return (-1) return 0 return dependence
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create a comparator to represent the dependence of nodes in a graph .
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def render_record(response, record, file_url=None, extra_data=None): data = {} if (extra_data is not None): data.update(extra_data) if (record is None): if (extra_data is None): response.set_status(404) response.out.write(render('details.html', data)) return data.update(get_details_data(record, file_url)) response.out.write(render('details.html', data))
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render an appstats record in detail .
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def test_raw_unicode_escape_dashes(): ok = True try: unicode('hey', 'raw_unicode-escape') except LookupError: ok = False Assert(ok, 'dashes and underscores should be interchangable')
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make sure that either dashes or underscores work in raw encoding name .
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def test_missing_data_pandas(): X = np.random.random((10, 5)) X[(1, 2)] = np.nan df = pandas.DataFrame(X) (vals, cnames, rnames) = data.interpret_data(df) np.testing.assert_equal(rnames.tolist(), [0, 2, 3, 4, 5, 6, 7, 8, 9])
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fixes gh: #144 .
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def residue_reduce_to_basic(H, DE, z): i = Dummy('i') s = list(zip(reversed(DE.T), reversed([f(DE.x) for f in DE.Tfuncs]))) return sum((RootSum(a[0].as_poly(z), Lambda(i, (i * log(a[1].as_expr()).subs({z: i}).subs(s)))) for a in H))
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converts the tuple returned by residue_reduce() into a basic expression .
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def get_bs_object_for_view(request, view, user, object=None): with override_provides('service_provider_admin_form', ['shuup.testing.service_forms.PseudoPaymentProcessorForm', 'shuup.admin.modules.service_providers.forms:CustomCarrierForm', 'shuup.admin.modules.service_providers.forms:CustomPaymentProcessorForm']): request = apply_all_middleware(request, user=user) response = view(request, pk=(object.pk if object else None)) if hasattr(response, 'render'): response.render() assert (response.status_code in [200, 302]) return BeautifulSoup(response.content)
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help function to get beautifulsoup object from the html rendered by the edit view .
train
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@register.inclusion_tag('inclusion.html') def inclusion_two_params(one, two): return {'result': ('inclusion_two_params - Expected result: %s, %s' % (one, two))}
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expected inclusion_two_params __doc__ .
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def call_command(name, *args, **options): try: app_name = get_commands()[name] if isinstance(app_name, BaseCommand): klass = app_name else: klass = load_command_class(app_name, name) except KeyError: raise CommandError(('Unknown command: %r' % name)) defaults = dict([(o.dest, o.default) for o in klass.option_list if (o.default is not NO_DEFAULT)]) defaults.update(options) return klass.execute(*args, **defaults)
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calls the given command .
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def HostAPI(*args, **kwargs): compute_api_class_name = _get_compute_api_class_name() compute_api_class = importutils.import_class(compute_api_class_name) class_name = (compute_api_class.__module__ + '.HostAPI') return importutils.import_object(class_name, *args, **kwargs)
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returns the hostapi class from the same module as the configured compute api .
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def run_script_with_context(script_path, cwd, context): (_, extension) = os.path.splitext(script_path) contents = io.open(script_path, 'r', encoding='utf-8').read() with tempfile.NamedTemporaryFile(delete=False, mode='wb', suffix=extension) as temp: env = StrictEnvironment(context=context, keep_trailing_newline=True) template = env.from_string(contents) output = template.render(**context) temp.write(output.encode('utf-8')) run_script(temp.name, cwd)
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execute a script after rendering it with jinja .
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def _filter_ipv4(addresses): return [address for address in addresses if is_valid_ip_address(address=address, family=socket.AF_INET)]
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select ipv4 addresses from the list of ip addresses .
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def bokeh_tree(name, rawtext, text, lineno, inliner, options=None, content=None): app = inliner.document.settings.env.app tag = app.env.config['version'] if ('-' in tag): tag = 'master' url = ('%s/tree/%s/%s' % (BOKEH_GH, tag, text)) _try_url(app, url, 'tree') options = (options or {}) set_classes(options) node = nodes.reference(rawtext, text, refuri=url, **options) return ([node], [])
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link to a url in the bokeh github tree .
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def toptexts(query, texts, index, n=10): sims = index[query] sims = sorted(enumerate(sims), key=(lambda item: (- item[1]))) result = [] for (topid, topcosine) in sims[:n]: result.append((topid, topcosine, texts[topid])) return result
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debug fnc to help inspect the top n most similar documents .
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def KernelVersion(): rtl_osversioninfoexw = RtlOSVersionInfoExw() try: RtlGetVersion(rtl_osversioninfoexw) except exceptions.WindowsError: return 'unknown' return ('%d.%d.%d' % (rtl_osversioninfoexw.dwMajorVersion, rtl_osversioninfoexw.dwMinorVersion, rtl_osversioninfoexw.dwBuildNumber))
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gets the kernel version as string .
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@tasklets.tasklet def fetch_data_async(blob, start_index, end_index, **options): if isinstance(blob, BlobInfo): blob = blob.key() rpc = blobstore.create_rpc(**options) rpc = blobstore.fetch_data_async(blob, start_index, end_index, rpc=rpc) result = (yield rpc) raise tasklets.Return(result)
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async version of fetch_data() .
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def csd(x, y, fs=1.0, window='hann', nperseg=None, noverlap=None, nfft=None, detrend='constant', return_onesided=True, scaling='density', axis=(-1)): (freqs, _, Pxy) = _spectral_helper(x, y, fs, window, nperseg, noverlap, nfft, detrend, return_onesided, scaling, axis, mode='psd') if ((len(Pxy.shape) >= 2) and (Pxy.size > 0)): if (Pxy.shape[(-1)] > 1): Pxy = Pxy.mean(axis=(-1)) else: Pxy = np.reshape(Pxy, Pxy.shape[:(-1)]) return (freqs, Pxy)
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estimate the cross power spectral density .
train
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def run_pyreverse(): from pylint.pyreverse.main import Run Run(sys.argv[1:])
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run pyreverse .
train
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def getIndexedLoopFromIndexedGrid(indexedGrid): indexedLoop = indexedGrid[0][:] for row in indexedGrid[1:(-1)]: indexedLoop.append(row[(-1)]) indexedLoop += indexedGrid[(-1)][::(-1)] for row in indexedGrid[(len(indexedGrid) - 2):0:(-1)]: indexedLoop.append(row[0]) return indexedLoop
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get indexed loop from around the indexed grid .
train
false
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@_api_version(1.21) @_client_version('1.5.0') def connect_container_to_network(container, network_id): response = _client_wrapper('connect_container_to_network', container, network_id) _clear_context() return response
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connect container to network .
train
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def addListToListTable(elementList, key, listTable): if (key in listTable): listTable[key] += elementList else: listTable[key] = elementList
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add a list to the list table .
train
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def test_isotonic_regression_ties_secondary_(): x = [8, 8, 8, 10, 10, 10, 12, 12, 12, 14, 14] y = [21, 23.5, 23, 24, 21, 25, 21.5, 22, 19, 23.5, 25] y_true = [22.22222, 22.22222, 22.22222, 22.22222, 22.22222, 22.22222, 22.22222, 22.22222, 22.22222, 24.25, 24.25] ir = IsotonicRegression() ir.fit(x, y) assert_array_almost_equal(ir.transform(x), y_true, 4) assert_array_almost_equal(ir.fit_transform(x, y), y_true, 4)
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test isotonic regression fit .
train
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def publish_badges(request): return render(request, 'ecosystem/publish_badges.html', {'page': 'badges', 'category': 'publish'})
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publish - marketplace badges .
train
false
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def native(s): if (not isinstance(s, (binary_type, text_type))): raise TypeError(('%r is neither bytes nor unicode' % s)) if PY3: if isinstance(s, binary_type): return s.decode('utf-8') elif isinstance(s, text_type): return s.encode('utf-8') return s
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convert :py:class:bytes or :py:class:unicode to the native :py:class:str type .
train
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def test_init(): ratio = 1.0 cc = ClusterCentroids(ratio=ratio, random_state=RND_SEED) assert_equal(cc.ratio, ratio)
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test the initialisation of the object .
train
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def test_ncr_fit_sample(): ncr = NeighbourhoodCleaningRule(random_state=RND_SEED) (X_resampled, y_resampled) = ncr.fit_sample(X, Y) X_gt = np.array([[(-1.20809175), (-1.49917302)], [(-0.60497017), (-0.66630228)], [(-0.91735824), 0.93110278], [(-0.20413357), 0.64628718], [0.35967591, 2.61186964], [(-1.55581933), 1.09609604], [1.55157493, (-1.6981518)]]) y_gt = np.array([0, 0, 1, 1, 2, 1, 2]) assert_array_equal(X_resampled, X_gt) assert_array_equal(y_resampled, y_gt)
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test the fit sample routine .
train
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def _get_or_default(mylist, i, default=None): if (i >= len(mylist)): return default else: return mylist[i]
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return list item number .
train
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def pseudo_quoteattr(value): return ('"%s"' % value)
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quote attributes for pseudo-xml .
train
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def getFillOfSurroundings(surroundingLoops): fillOfSurroundings = [] for surroundingLoop in surroundingLoops: fillOfSurroundings += surroundingLoop.getFillLoops() return fillOfSurroundings
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get extra fill loops of surrounding loops .
train
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def update_exploration_summary(exploration_id, contributor_id_to_add): exploration = get_exploration_by_id(exploration_id) exp_summary = compute_summary_of_exploration(exploration, contributor_id_to_add) save_exploration_summary(exp_summary)
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update the summary of an exploration .
train
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def clean_fuzzable_request(fuzzable_request, dc_handler=clean_data_container): res = ('(%s)-' % fuzzable_request.get_method().upper()) res += clean_url(fuzzable_request.get_uri(), dc_handler=dc_handler) raw_data = fuzzable_request.get_raw_data() if raw_data: res += ('!' + dc_handler(raw_data)) return res
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we receive a fuzzable request and output includes the http method and any parameters which might be sent over http post-data in the request are appended to the result as query string params .
train
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@register.simple_tag def absolute_asset_url(module, path): return absolute_uri(get_asset_url(module, path))
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returns a versioned absolute asset url .
train
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@pytest.fixture def keyhint(qtbot, config_stub, key_config_stub): config_stub.data = {'colors': {'keyhint.fg': 'white', 'keyhint.fg.suffix': 'yellow', 'keyhint.bg': 'black'}, 'fonts': {'keyhint': 'Comic Sans'}, 'ui': {'keyhint-blacklist': '', 'status-position': 'bottom'}} keyhint = KeyHintView(0, None) qtbot.add_widget(keyhint) assert (keyhint.text() == '') return keyhint
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fixture to initialize a keyhintview .
train
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def aggregate_metadata_add(context, aggregate_id, metadata, set_delete=False): IMPL.aggregate_metadata_add(context, aggregate_id, metadata, set_delete)
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add/update metadata .
train
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def ensure_distance(dist): try: km = dist.km except AttributeError: raise SpatialError((u"'%s' does not appear to be a 'Distance' object." % dist)) return dist
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makes sure the parameter passed in is a distance object .
train
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def iter_style_files(style_dir): for path in os.listdir(style_dir): filename = os.path.basename(path) if is_style_file(filename): match = STYLE_FILE_PATTERN.match(filename) path = os.path.abspath(os.path.join(style_dir, path)) (yield (path, match.groups()[0]))
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yield file path and name of styles in the given directory .
train
false
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def is_trunk_service_port(port_name): return (is_trunk_bridge(port_name) or (port_name[:2] in (tman.TrunkParentPort.DEV_PREFIX, tman.SubPort.DEV_PREFIX)))
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true if the port is any of the ports used to realize a trunk .
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def spatial_dist_connectivity(src, dist, verbose=None): return spatio_temporal_dist_connectivity(src, 1, dist)
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compute connectivity from distances in a source space .
train
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def get_can_enable_dns(): if has_module(u'DNS'): return (True, None) else: return (False, _(u'PyDNS, which is required to find the domain controller, is not installed.'))
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check whether we can query dns to find the domain controller to use .
train
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def add_check(module, check): if ((not check.name) and (not service_id)): module.fail_json(msg='a check name is required for a node level check, one not attached to a service') consul_api = get_consul_api(module) check.register(consul_api) module.exit_json(changed=True, check_id=check.check_id, check_name=check.name, script=check.script, interval=check.interval, ttl=check.ttl, http=check.http, timeout=check.timeout, service_id=check.service_id)
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registers a check with the given agent .
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false
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def make_scorer(score_func, greater_is_better=True, needs_proba=False, needs_threshold=False, **kwargs): sign = (1 if greater_is_better else (-1)) if (needs_proba and needs_threshold): raise ValueError('Set either needs_proba or needs_threshold to True, but not both.') if needs_proba: cls = _ProbaScorer elif needs_threshold: cls = _ThresholdScorer else: cls = _PredictScorer return cls(score_func, sign, kwargs)
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make a scorer from a performance metric or loss function .
train
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def splitByCommas(str): out = [] if (not str): return out current = '' openQuotes = '"\'<({' closeQuotes = '"\'>)}' matchingQuote = '' insideQuotes = False nextIsLiteral = False for char in str: if nextIsLiteral: current += char nextIsLiteral = False elif insideQuotes: if (char == '\\'): nextIsLiteral = True else: current += char if (char == matchingQuote): insideQuotes = False elif (char == ','): out.append(current.strip()) current = '' else: current += char quoteIndex = openQuotes.find(char) if (quoteIndex > (-1)): matchingQuote = closeQuotes[quoteIndex] insideQuotes = True out.append(current.strip()) return out
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split a string by unenclosed commas: that is .
train
false
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def read_style_directory(style_dir): styles = dict() for (path, name) in iter_style_files(style_dir): with warnings.catch_warnings(record=True) as warns: styles[name] = rc_params_from_file(path, use_default_template=False) for w in warns: message = (u'In %s: %s' % (path, w.message)) warnings.warn(message) return styles
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return dictionary of styles defined in style_dir .
train
false
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def render_home(framework_name): template = env.get_template('index.html') return template.render(providers=ASSEMBLED_CONFIG, oauth2_providers=OAUTH2_PROVIDERS, oauth1_providers=OAUTH1_PROVIDERS, openid_providers=OPENID_PROVIDERS, framework_name=framework_name)
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renders the homepage .
train
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def educateDashesOldSchoolInverted(str): str = re.sub('---', '&#8211;', str) str = re.sub('--', '&#8212;', str) return str
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parameter: string .
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def get_optional_parameters(dictionary): return {key: dictionary[key] for key in OPTIONAL_PARAMETERS if (key in dictionary)}
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extract all optional lti parameters from a dictionary .
train
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def retention_action(client, stream_name, retention_period=24, action='increase', check_mode=False): success = False err_msg = '' params = {'StreamName': stream_name} try: if (not check_mode): if (action == 'increase'): params['RetentionPeriodHours'] = retention_period client.increase_stream_retention_period(**params) success = True err_msg = 'Retention Period increased successfully to {0}'.format(retention_period) elif (action == 'decrease'): params['RetentionPeriodHours'] = retention_period client.decrease_stream_retention_period(**params) success = True err_msg = 'Retention Period decreased successfully to {0}'.format(retention_period) else: err_msg = 'Invalid action {0}'.format(action) elif (action == 'increase'): success = True elif (action == 'decrease'): success = True else: err_msg = 'Invalid action {0}'.format(action) except botocore.exceptions.ClientError as e: err_msg = str(e) return (success, err_msg)
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increase or decreaste the retention of messages in the kinesis stream .
train
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def test_javascript_url(): s = 'javascript:document.vulnerable' eq_(s, linkify(s))
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javascript: urls should never be linkified .
train
false
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def getSquareLoopWiddershins(beginComplex, endComplex): loop = [beginComplex, complex(endComplex.real, beginComplex.imag), endComplex] loop.append(complex(beginComplex.real, endComplex.imag)) return loop
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get a square loop from the beginning to the end and back .
train
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def ipython_display(clip, filetype=None, maxduration=60, t=None, fps=None, rd_kwargs=None, center=True, **html_kwargs): if (not ipython_available): raise ImportError('Only works inside an IPython Notebook') if (rd_kwargs is None): rd_kwargs = {} if (fps is not None): rd_kwargs['fps'] = fps if (t is not None): clip = clip.to_ImageClip(t) return HTML2(html_embed(clip, filetype=filetype, maxduration=maxduration, center=center, rd_kwargs=rd_kwargs, **html_kwargs))
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clip either the name of a file .
train
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def load_csr(*names): loader = _guess_loader(names[(-1)], OpenSSL.crypto.FILETYPE_PEM, OpenSSL.crypto.FILETYPE_ASN1) return OpenSSL.crypto.load_certificate_request(loader, load_vector(*names))
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load certificate request .
train
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def auto_openstack_logging(interface, original): return interface_decorator('auto_openstack_logging', interface, _openstack_logged_method, original)
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create a class decorator which will add openstack-specific exception logging versions versions of all of the methods on interface .
train
false
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def join_options(options): rv = [] any_prefix_is_slash = False for opt in options: prefix = split_opt(opt)[0] if (prefix == '/'): any_prefix_is_slash = True rv.append((len(prefix), opt)) rv.sort(key=(lambda x: x[0])) rv = ', '.join((x[1] for x in rv)) return (rv, any_prefix_is_slash)
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given a list of option strings this joins them in the most appropriate way and returns them in the form where the second item in the tuple is a flag that indicates if any of the option prefixes was a slash .
train
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def fake_view(request): return HttpResponse(json.dumps(request.META))
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fake view that returns the request meta as a json-encoded string .
train
false
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def user_field(user, field, *args): if (field and hasattr(user, field)): if args: v = args[0] if v: User = get_user_model() v = v[0:User._meta.get_field(field).max_length] setattr(user, field, v) else: return getattr(user, field)
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gets or sets user model fields .
train
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def set_marker(folder): name = cfg.marker_file() if name: path = os.path.join(folder, name) logging.debug('Create marker file %s', path) try: fp = open(path, 'w') fp.close() except: logging.info('Cannot create marker file %s', path) logging.info('Traceback: ', exc_info=True) name = None return name
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set marker file and return name .
train
false
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def commit_transaction(hostname, username, password, label): bigip_session = _build_session(username, password) trans_id = __salt__['grains.get']('bigip_f5_trans:{label}'.format(label=label)) if trans_id: payload = {} payload['state'] = 'VALIDATING' try: response = bigip_session.patch((BIG_IP_URL_BASE.format(host=hostname) + '/transaction/{trans_id}'.format(trans_id=trans_id)), data=json.dumps(payload)) return _load_response(response) except requests.exceptions.ConnectionError as e: return _load_connection_error(hostname, e) else: return 'Error: the label for this transaction was not defined as a grain. Begin a new transaction using the bigip.start_transaction function'
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a function to connect to a bigip device and commit an existing transaction .
train
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@register_stabilize @register_specialize @register_canonicalize @gof.local_optimizer([T.Elemwise]) def local_expm1(node): if (isinstance(node.op, T.Elemwise) and isinstance(node.op.scalar_op, theano.scalar.basic.Sub)): (in1, in2) = node.inputs out = node.outputs[0] if (in1.owner and isinstance(in1.owner.op, T.Elemwise) and isinstance(in1.owner.op.scalar_op, theano.scalar.basic.Exp) and (T.extract_constant(in2, only_process_constants=False) == 1)): in11 = in1.owner.inputs[0] new_out = T.expm1(in11) if (new_out.dtype != out.dtype): new_out = T.cast(new_out, dtype=out.dtype) if (new_out.type != out.type): return return [new_out]
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this optimization detects exp(a)-1 and converts this to expm1(a) .
train
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def firstof(parser, token): bits = token.split_contents()[1:] if (len(bits) < 1): raise TemplateSyntaxError("'firstof' statement requires at least one argument") return FirstOfNode([parser.compile_filter(bit) for bit in bits])
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this is the future version of firstof with auto-escaping .
train
false
54,364
def _list_interface_private_addrs(eni_desc): primary = eni_desc.get('privateIpAddress') if (not primary): return None addresses = [primary] lst = eni_desc.get('privateIpAddressesSet', {}).get('item', []) if (not isinstance(lst, list)): return addresses for entry in lst: if (entry.get('primary') == 'true'): continue if entry.get('privateIpAddress'): addresses.append(entry.get('privateIpAddress')) return addresses
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returns a list of all of the private ip addresses attached to a network interface .
train
true
54,365
def _detect_gce_environment(): http = transport.get_http_object(timeout=GCE_METADATA_TIMEOUT) try: (response, _) = transport.request(http, _GCE_METADATA_URI, headers=_GCE_HEADERS) return ((response.status == http_client.OK) and (response.get(_METADATA_FLAVOR_HEADER) == _DESIRED_METADATA_FLAVOR)) except socket.error: logger.info('Timeout attempting to reach GCE metadata service.') return False
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determine if the current environment is compute engine .
train
true
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def test_cache_remove_data(config_stub, tmpdir): config_stub.data = {'storage': {'cache-size': 1024}, 'general': {'private-browsing': False}} url = 'http://qutebrowser.org' disk_cache = cache.DiskCache(str(tmpdir)) preload_cache(disk_cache, url) assert (disk_cache.cacheSize() > 0) assert disk_cache.remove(QUrl(url)) assert (disk_cache.cacheSize() == 0)
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test if a previously inserted entry can be removed from the cache .
train
false
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def _executeExternalCmdAndReapStdout(args): _debugOut(('_executeExternalCmdAndReapStdout: Starting...\n<%s>' % (args,))) p = subprocess.Popen(args, env=os.environ, stdout=subprocess.PIPE, stderr=subprocess.PIPE) _debugOut(('Process started for <%s>' % (args,))) (stdoutData, stderrData) = p.communicate() _debugOut((('Process completed for <%s>: exit status=%s, ' + 'stdoutDataType=%s, stdoutData=<%s>, stderrData=<%s>') % (args, p.returncode, type(stdoutData), stdoutData, stderrData))) result = dict(exitStatus=p.returncode, stdoutData=stdoutData, stderrData=stderrData) _debugOut(('_executeExternalCmdAndReapStdout for <%s>: result=\n%s' % (args, pprint.pformat(result, indent=4)))) return result
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args: args list as defined for the args parameter in subprocess .
train
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def sfv_check(sfv_path): failed = [] try: fp = open(sfv_path, 'r') except: logging.info('Cannot open SFV file %s', sfv_path) failed.append(unicoder(sfv_path)) return failed root = os.path.split(sfv_path)[0] for line in fp: line = line.strip('\n\r ') if (line and (line[0] != ';')): x = line.rfind(' ') if (x > 0): filename = platform_encode(line[:x].strip()) checksum = line[x:].strip() path = os.path.join(root, filename) if os.path.exists(path): if crc_check(path, checksum): logging.debug('File %s passed SFV check', path) else: logging.info('File %s did not pass SFV check', path) failed.append(unicoder(filename)) else: logging.info('File %s missing in SFV check', path) failed.append(unicoder(filename)) fp.close() return failed
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verify files using sfv file .
train
false
54,369
def home_link(): return {'title': 'home', 'href': '/'}
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returns a link to the api entry point/home page .
train
false
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def assert_array_less(x, y, err_msg='', verbose=True): numpy.testing.assert_array_less(cupy.asnumpy(x), cupy.asnumpy(y), err_msg=err_msg, verbose=verbose)
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raises an assertionerror if array_like objects are not ordered by less than .
train
false
54,371
def load_token(session_file): if (not os.path.exists(session_file)): return with open(session_file) as handle: data = json.load(handle) for (mx_id, token) in data.items(): AUTH_TOKENS[mx_id] = token
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load authentication tokens from persistent storage .
train
false
54,372
@testing.requires_testing_data def test_make_field_map_eeg(): evoked = read_evokeds(evoked_fname, condition='Left Auditory') evoked.info['bads'] = ['MEG 2443', 'EEG 053'] surf = get_head_surf('sample', subjects_dir=subjects_dir) assert_raises(ValueError, _make_surface_mapping, evoked.info, surf, 'eeg') evoked.pick_types(meg=False, eeg=True) fmd = make_field_map(evoked, trans_fname, subject='sample', subjects_dir=subjects_dir) assert_raises(RuntimeError, make_field_map, evoked, None, subject='sample', subjects_dir=subjects_dir) fmd = make_field_map(evoked, trans_fname, subject='sample', subjects_dir=subjects_dir) assert_true((len(fmd) == 1)) assert_array_equal(fmd[0]['data'].shape, (642, 59)) assert_true(len(fmd[0]['ch_names']), 59)
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test interpolation of eeg field onto head .
train
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def get_pending_file_rename(): vnames = ('PendingFileRenameOperations', 'PendingFileRenameOperations2') key = 'SYSTEM\\CurrentControlSet\\Control\\Session Manager' for vname in vnames: reg_ret = __salt__['reg.read_value']('HKLM', key, vname) if reg_ret['success']: log.debug('Found key: %s', key) if (reg_ret['vdata'] and (reg_ret['vdata'] != '(value not set)')): return True else: log.debug('Unable to access key: %s', key) return False
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determine whether there are pending file rename operations that require a reboot .
train
false
54,376
def fix_html(container, raw): root = container.parse_xhtml(raw) return serialize(root, u'text/html')
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fix any parsing errors in the html represented as a string in raw .
train
false
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def bivariate_normal(X, Y, sigmax=1.0, sigmay=1.0, mux=0.0, muy=0.0, sigmaxy=0.0): Xmu = (X - mux) Ymu = (Y - muy) rho = (sigmaxy / (sigmax * sigmay)) z = ((((Xmu ** 2) / (sigmax ** 2)) + ((Ymu ** 2) / (sigmay ** 2))) - ((((2 * rho) * Xmu) * Ymu) / (sigmax * sigmay))) denom = ((((2 * np.pi) * sigmax) * sigmay) * np.sqrt((1 - (rho ** 2)))) return (np.exp(((- z) / (2 * (1 - (rho ** 2))))) / denom)
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bivariate gaussian distribution for equal shape *x* .
train
false
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def sample_ids_from_metadata_description(mapping_f, valid_states_str): (map_data, map_header, map_comments) = parse_mapping_file(mapping_f) valid_states = parse_metadata_state_descriptions(valid_states_str) sample_ids = get_sample_ids(map_data, map_header, valid_states) if (len(sample_ids) < 1): raise ValueError('All samples have been filtered out for the criteria described in the valid states') return sample_ids
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given a description of metadata .
train
false
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def graph_process(mg, lenmavlist): mg.show(lenmavlist)
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process for a graph .
train
false
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def get_profile_image_names(username): name = _make_profile_image_name(username) return {size: _get_profile_image_filename(name, size) for size in _PROFILE_IMAGE_SIZES}
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returns a dict containing the filenames for a complete set of profile images .
train
false
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def randomRange(start=0, stop=1000, seed=None): if (seed is not None): _ = getCurrentThreadData().random _.seed(seed) randint = _.randint else: randint = random.randint return int(randint(start, stop))
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returns random integer value in given range .
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def correlate_output_formatter(bt, test_stats, pvals, fdr_pvals, bon_pvals, md_key): header = ['Feature ID', 'Test stat.', 'pval', 'pval_fdr', 'pval_bon', md_key] num_lines = len(test_stats) lines = [' DCTB '.join(header)] for i in range(num_lines): tmp = [bt.ids(axis='observation')[i], test_stats[i], pvals[i], fdr_pvals[i], bon_pvals[i]] lines.append(' DCTB '.join(map(str, tmp))) nls = _add_metadata(bt, md_key, lines) return nls
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produce lines for a tab delimited text file for correlations .
train
false
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def format_unixtime_ms(unixtime): if unixtime: return str(datetime.datetime.fromtimestamp((unixtime / 1000)).strftime('%x %X %Z')) else: return ''
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format a unix timestamp in ms to a human readable string .
train
false
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def _AddSetListenerMethod(cls): def SetListener(self, listener): if (listener is None): self._listener = message_listener_mod.NullMessageListener() else: self._listener = listener cls._SetListener = SetListener
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helper for _addmessagemethods() .
train
false
54,388
@register_opt() @local_optimizer([SparseBlockOuter, GpuFromHost]) def gpu_sparse_block_outer_opt(node): if (isinstance(node.op, SparseBlockOuter) and any((_owner_isinstance(inp, HostFromGpu) for inp in node.inputs))): inputs = _clear_host_from_gpu(node.inputs) return [host_from_gpu(GpuSparseBlockOuter()(*inputs))] elif (isinstance(node.op, GpuFromHost) and _owner_isinstance(node.inputs[0], SparseBlockOuter)): meta_node = node.inputs[0].owner inputs = _clear_host_from_gpu(meta_node.inputs) return [GpuSparseBlockOuter()(*inputs)]
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sparseblockouter(hostfromgpu) -> hostfromgpu(gpusparseblockouter) or gpufromhost -> gpusparseblockouter .
train
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def lerp(a, b, t): if (t < 0.0): return a if (t > 1.0): return b return (a + ((b - a) * t))
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returns the linear interpolation between a and b at time t between 0 .
train
true
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def create_relationship(model, instance, relation): result = {} pk_value = primary_key_value(instance) self_link = url_for(model, pk_value, relation, relationship=True) related_link = url_for(model, pk_value, relation) result['links'] = {'self': self_link} try: related_model = get_related_model(model, relation) url_for(related_model) except ValueError: pass else: result['links']['related'] = related_link related_value = getattr(instance, relation) if is_like_list(instance, relation): result['data'] = list(map(simple_relationship_dump, related_value)) elif (related_value is not None): result['data'] = simple_relationship_dump(related_value) else: result['data'] = None return result
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creates a relationship from the given relation name .
train
false
54,392
def _validate_post_params(params): try: username = params.get('username') user = User.objects.get(username=username) except User.DoesNotExist: msg = _('User {username} does not exist').format(username=username) return (None, HttpResponseBadRequest(msg)) try: course_key = CourseKey.from_string(params.get('course_key')) except InvalidKeyError: msg = _('{course_key} is not a valid course key').format(course_key=params.get('course_key')) return (None, HttpResponseBadRequest(msg)) return ({'user': user, 'course_key': course_key}, None)
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validate request post parameters to the generate and regenerate certificates end-point .
train
false
54,396
def testIBP(): from pybrain.tools.plotting.colormaps import ColorMap import pylab n = 50 ps = [(10, 0.1), (10,), (50,), (50, 0.5)] ms = [] for p in ps: if (len(p) > 1): m = generateIBP(n, p[0], p[1]) else: m = generateIBP(n, p[0]) ms.append(leftordered(m)) for m in ms: ColorMap(m, pixelspervalue=3) pylab.show()
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plot matrices generated by an ibp .
train
false
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def p_command_let(p): p[0] = ('LET', p[2], p[4])
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command : let variable equals expr .
train
false
54,399
def queryset_iterator(queryset, chunksize=1000): if queryset.exists(): primary_key = 0 last_pk = queryset.order_by('-pk')[0].pk queryset = queryset.order_by('pk') while (primary_key < last_pk): for row in queryset.filter(pk__gt=primary_key)[:chunksize]: primary_key = row.pk (yield row) gc.collect()
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the queryset iterator helps to keep the memory consumption down .
train
true
54,400
def _remove_intercept_patsy(terms): from patsy.desc import INTERCEPT if (INTERCEPT in terms): terms.remove(INTERCEPT) return terms
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remove intercept from patsy terms .
train
false
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def compat_tee(iterable): def gen(next, data={}, cnt=[0]): dpop = data.pop for i in itertools.count(): if (i == cnt[0]): item = data[i] = next() cnt[0] += 1 else: item = dpop(i) (yield item) next = iter(iterable).next return (gen(next), gen(next))
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return two independent iterators from a single iterable .
train
false
54,402
def get_protocol_from_tool_shed_url(tool_shed_url): try: if (tool_shed_url.find('://') > 0): return tool_shed_url.split('://')[0].lower() except Exception as e: if (tool_shed_url is not None): log.exception('Handled exception getting the protocol from Tool Shed URL %s:\n%s', str(tool_shed_url), e) return 'http'
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return the protocol from the received tool_shed_url if it exists .
train
false
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def format_acl_v2(acl_dict): return json.dumps(acl_dict, ensure_ascii=True, separators=(',', ':'), sort_keys=True)
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returns a version-2 swift acl json string .
train
false
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def test_version(): assert virtualenv.virtualenv_version, 'Should have version'
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should have a version string .
train
false
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def package_relationship_update(context, data_dict): model = context['model'] schema = (context.get('schema') or schema_.default_update_relationship_schema()) (id, id2, rel) = _get_or_bust(data_dict, ['subject', 'object', 'type']) pkg1 = model.Package.get(id) pkg2 = model.Package.get(id2) if (not pkg1): raise NotFound(('Subject package %r was not found.' % id)) if (not pkg2): return NotFound(('Object package %r was not found.' % id2)) (data, errors) = _validate(data_dict, schema, context) if errors: model.Session.rollback() raise ValidationError(errors) _check_access('package_relationship_update', context, data_dict) existing_rels = pkg1.get_relationships_with(pkg2, rel) if (not existing_rels): raise NotFound('This relationship between the packages was not found.') entity = existing_rels[0] comment = data_dict.get('comment', u'') context['relationship'] = entity return _update_package_relationship(entity, comment, context)
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update a relationship between two datasets .
train
false
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def add_required_label_tag(original_function): def required_label_tag(self, contents=None, attrs=None): 'Required label tag' contents = (contents or escape(self.label)) if self.field.required: if (not self.label.endswith(' *')): self.label += ' *' contents += ' *' attrs = {'class': 'required'} return original_function(self, contents, attrs) return required_label_tag
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adds the required css class and an asterisks to required field labels .
train
false
54,408
def make_script_tests(executable): class ScriptTests(TestCase, ): @skipUnless(which(executable), (executable + ' not installed')) def test_version(self): '\n The script is a command available on the system path.\n ' result = run_process(([executable] + ['--version'])) self.assertEqual(result.output, ('%s\n' % (__version__,))) @skipUnless(which(executable), (executable + ' not installed')) def test_identification(self): '\n The script identifies itself as what it is.\n ' result = run_process(([executable] + ['--help'])) self.assertIn(executable, result.output) return ScriptTests
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generate a test suite which applies to any flocker-installed node script .
train
false
54,409
def _find_clickable_elem_with_wait(context, by, wait_time=MAX_WAIT_TIME): return WebDriverWait(context.browser, wait_time).until(EC.element_to_be_clickable(by))
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tries to find an enabled element with an explicit timeout .
train
false
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def get_issues_from_bulk(bulk_data, **additional_fields): return [models.Issue(subject=line, **additional_fields) for line in text.split_in_lines(bulk_data)]
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convert bulk_data into a list of issues .
train
false
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def question_metadata(): output = s3_rest_controller() return output
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restful crud controller .
train
false
54,412
def _find_es_dict_by_key(lookup_dict, term): if (term in lookup_dict): return (lookup_dict, term) dict_cursor = lookup_dict subkeys = term.split('.') subkey = '' while (len(subkeys) > 0): subkey += subkeys.pop(0) if (subkey in dict_cursor): if (len(subkeys) == 0): break dict_cursor = dict_cursor[subkey] subkey = '' elif (len(subkeys) == 0): dict_cursor = None subkey = None else: subkey += '.' return (dict_cursor, subkey)
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performs iterative dictionary search based upon the following conditions: 1 .
train
false
54,413
def set_ip_nonlocal_bind_for_namespace(namespace): failed = set_ip_nonlocal_bind(value=0, namespace=namespace, log_fail_as_error=False) if failed: LOG.warning(_LW('%s will not be set to 0 in the root namespace in order to not break DVR, which requires this value be set to 1. This may introduce a race between moving a floating IP to a different network node, and the peer side getting a populated ARP cache for a given floating IP address.'), IP_NONLOCAL_BIND)
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set ip_nonlocal_bind but dont raise exception on failure .
train
false
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def get_today_all(): ct._write_head() df = _parsing_dayprice_json(1) if (df is not None): for i in range(2, ct.PAGE_NUM[0]): newdf = _parsing_dayprice_json(i) df = df.append(newdf, ignore_index=True) return df
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return dataframe .
train
false
54,415
def sort_otu_table(otu_table, sorted_sample_ids): sorted_sample_ids_set = set(sorted_sample_ids) if (set(otu_table.ids()) - sorted_sample_ids_set): raise KeyError(('Sample IDs present in OTU table but not sorted sample id list: ' + ' '.join(list((set(otu_table.ids()) - set(sorted_sample_ids)))))) if (len(sorted_sample_ids_set) != len(sorted_sample_ids)): raise ValueError('Duplicate sample IDs are present in sorted sample id list.') safe_sorted_sample_ids = [] for k in sorted_sample_ids: if otu_table.exists(k): safe_sorted_sample_ids.append(k) sorted_table = otu_table.sort_order(safe_sorted_sample_ids) return sorted_table
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sort an otu table by sorted sample ids .
train
false
54,418
def prepro(I): I = I[35:195] I = I[::2, ::2, 0] I[(I == 144)] = 0 I[(I == 109)] = 0 I[(I != 0)] = 1 return I.astype(np.float).ravel()
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prepro 210x160x3 uint8 frame into 6400 1d float vector .
train
true