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def test_min_samples_split(): X = np.asfortranarray(iris.data.astype(tree._tree.DTYPE)) y = iris.target for (max_leaf_nodes, name) in product((None, 1000), ALL_TREES.keys()): TreeEstimator = ALL_TREES[name] est = TreeEstimator(min_samples_split=10, max_leaf_nodes=max_leaf_nodes, random_state=0) est.fit(X, y) node_samples = est.tree_.n_node_samples[(est.tree_.children_left != (-1))] assert_greater(np.min(node_samples), 9, 'Failed with {0}'.format(name)) est = TreeEstimator(min_samples_split=0.2, max_leaf_nodes=max_leaf_nodes, random_state=0) est.fit(X, y) node_samples = est.tree_.n_node_samples[(est.tree_.children_left != (-1))] assert_greater(np.min(node_samples), 9, 'Failed with {0}'.format(name))
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test min_samples_split parameter .
train
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def validate_bug_tracker_base_hosting_url(input_url): try: (input_url % ()) except TypeError: raise ValidationError([(_(u"The URL '%s' is not valid because it contains a format character. For bug trackers other than 'Custom Bug Tracker', use the base URL of the server. If you need a '%%' character, prepend it with an additional '%%'.") % input_url)])
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check that hosting service bug urls dont contain %s .
train
false
37,035
def is_coverage_running(): if ('coverage' not in sys.modules): return False tracer = sys.gettrace() if (tracer is None): return False try: mod = tracer.__module__ except AttributeError: try: mod = tracer.__class__.__module__ except AttributeError: return False return mod.startswith('coverage')
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return whether coverage is currently running .
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false
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def select_direct_adjacent(cache, left, right): right = (always_in if (right is None) else frozenset(right)) for parent in left: for sibling in cache.itersiblings(parent): if (sibling in right): (yield sibling) break
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right is a sibling immediately after left .
train
false
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def add_visual_box_select(plot): source = ColumnDataSource(data=dict(x=[], y=[], width=[], height=[])) rect = Rect(x='x', y='y', width='width', height='height', fill_alpha=0.3, fill_color='#009933') callback = CustomJS(args=dict(source=source), code="\n // get data source from Callback args\n var data = source.data;\n\n /// get BoxSelectTool dimensions from cb_data parameter of Callback\n var geometry = cb_data['geometry'];\n\n /// calculate Rect attributes\n var width = geometry['x1'] - geometry['x0'];\n var height = geometry['y1'] - geometry['y0'];\n var x = geometry['x0'] + width/2;\n var y = geometry['y0'] + height/2;\n\n /// update data source with new Rect attributes\n data['x'].push(x);\n data['y'].push(y);\n data['width'].push(width);\n data['height'].push(height);\n\n // trigger update of data source\n source.trigger('change');\n ") box_select = BoxSelectTool(callback=callback) plot.add_glyph(source, rect, selection_glyph=rect, nonselection_glyph=rect) plot.add_tools(box_select) return plot
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add a box select tool to your plot which draws a rect on box select .
train
false
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def clean_orphaned_vdis(xenapi, vdi_uuids): for vdi_uuid in vdi_uuids: if CONF.verbose: print ('CLEANING VDI (%s)' % vdi_uuid) vdi_ref = call_xenapi(xenapi, 'VDI.get_by_uuid', vdi_uuid) try: call_xenapi(xenapi, 'VDI.destroy', vdi_ref) except XenAPI.Failure as exc: sys.stderr.write(('Skipping %s: %s' % (vdi_uuid, exc)))
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clean orphaned vdis .
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false
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def yield_source_csv_messages(cls, foreign_cls, csvreader, force_column=None): columns = list(cls.__table__.c) column_names = next(csvreader) assert ([cls.__table__.c[name] for name in column_names] == columns), ','.join((c.name for c in columns)) pk = columns[:len(cls.__table__.primary_key.columns)] first_string_index = len(pk) return _yield_csv_messages(foreign_cls, columns, first_string_index, csvreader, force_column=force_column)
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yield all messages from one source csv file .
train
false
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def vvd(val, valok, dval, func, test, status): assert quantity_allclose(val, (valok * val.unit), atol=(dval * val.unit))
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mimic routine of erfa/src/t_erfa_c .
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false
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def get_async(keys, **kwargs): (keys, multiple) = datastore.NormalizeAndTypeCheckKeys(keys) def extra_hook(entities): if ((not multiple) and (not entities)): return None models = [] for entity in entities: if (entity is None): model = None else: cls1 = class_for_kind(entity.kind()) model = cls1.from_entity(entity) models.append(model) if multiple: return models assert (len(models) == 1) return models[0] return datastore.GetAsync(keys, extra_hook=extra_hook, **kwargs)
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asynchronously fetch the specified model instance(s) from the datastore .
train
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def with_tmpdir(f): @wraps(f) def wrapper(self, *args, **kwargs): tmp = tempfile.mkdtemp(prefix='f2b-temp') try: return f(self, tmp, *args, **kwargs) finally: shutil.rmtree(tmp) return wrapper
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helper decorator to create a temporary directory directory gets removed after function returns .
train
false
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def save_resized_image(image_file, width_, height_, basewidth, aspect, height_size, upload_path, ext='jpg', remove_after_upload=False): filename = '{filename}.{ext}'.format(filename=time.time(), ext=ext) img = Image.open(image_file) if (aspect == 'on'): width_percent = (basewidth / float(img.size[0])) height_size = int((float(img.size[1]) * float(width_percent))) img = img.resize((basewidth, height_size), PIL.Image.ANTIALIAS) img.save(image_file) file = UploadedFile(file_path=image_file, filename=filename) if remove_after_upload: os.remove(image_file) return upload(file, upload_path)
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save the resized version of the background image .
train
false
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def getGearProfileCylinder(teeth, toothProfile): gearProfile = [] toothAngleRadian = ((2.0 * math.pi) / float(teeth)) totalToothAngle = 0.0 for toothIndex in xrange(abs(teeth)): for toothPoint in toothProfile: gearProfile.append((toothPoint * euclidean.getWiddershinsUnitPolar(totalToothAngle))) totalToothAngle += toothAngleRadian return gearProfile
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get gear profile for a cylinder gear .
train
false
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def find_app(app, symbol_by_name=symbol_by_name, imp=import_from_cwd): from .base import Celery try: sym = symbol_by_name(app, imp=imp) except AttributeError: sym = imp(app) if (isinstance(sym, ModuleType) and (u':' not in app)): try: found = sym.app if isinstance(found, ModuleType): raise AttributeError() except AttributeError: try: found = sym.celery if isinstance(found, ModuleType): raise AttributeError() except AttributeError: if getattr(sym, u'__path__', None): try: return find_app(u'{0}.celery'.format(app), symbol_by_name=symbol_by_name, imp=imp) except ImportError: pass for suspect in values(vars(sym)): if isinstance(suspect, Celery): return suspect raise else: return found else: return found return sym
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find app by name .
train
false
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def get_rare_data(otu_table, seqs_per_sample, include_small_samples=False, subsample_f=subsample): with errstate(empty='raise'): if (not include_small_samples): otu_table = filter_samples_from_otu_table(otu_table, otu_table.ids(), seqs_per_sample, inf) def func(x, s_id, s_md): if (x.sum() < seqs_per_sample): return x else: return subsample_f(x.astype(int), seqs_per_sample) subsampled_otu_table = otu_table.transform(func, axis='sample') return subsampled_otu_table
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filter otu table to keep only desired sample sizes .
train
false
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def get_texts_box(texts, fs): max_len = max(map(len, texts)) return (fs, text_len(max_len, fs))
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approximation of multiple texts bounds .
train
true
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def is_admin_context(context): if (not context): LOG.warning(_LW('Use of empty request context is deprecated'), DeprecationWarning) raise Exception('die') return context.is_admin
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indicates if the request context is an administrator .
train
false
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def get_computer_desc(): desc = None hostname_cmd = salt.utils.which('hostnamectl') if hostname_cmd: desc = __salt__['cmd.run']('{0} status --pretty'.format(hostname_cmd)) else: pattern = re.compile('^\\s*PRETTY_HOSTNAME=(.*)$') try: with salt.utils.fopen('/etc/machine-info', 'r') as mach_info: for line in mach_info.readlines(): match = pattern.match(line) if match: desc = _strip_quotes(match.group(1).strip()).replace('\\"', '"') except IOError: return False return desc
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get the windows computer description :return: returns the computer description if found .
train
false
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@Throttle(MIN_TIME_BETWEEN_UPDATES) def send_data(name, msg): import dweepy try: dweepy.dweet_for(name, msg) except dweepy.DweepyError: _LOGGER.error("Error saving data '%s' to Dweet.io", msg)
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send the collected data to dweet .
train
false
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def resize_thumbnails(pelican): global enabled if (not enabled): return in_path = _image_path(pelican) sizes = pelican.settings.get('THUMBNAIL_SIZES', DEFAULT_THUMBNAIL_SIZES) resizers = dict(((k, _resizer(k, v, in_path)) for (k, v) in sizes.items())) logger.debug('Thumbnailer Started') for (dirpath, _, filenames) in os.walk(in_path): for filename in filenames: if (not filename.startswith('.')): for (name, resizer) in resizers.items(): in_filename = path.join(dirpath, filename) out_path = get_out_path(pelican, in_path, in_filename, name) resizer.resize_file_to(in_filename, out_path, pelican.settings.get('THUMBNAIL_KEEP_NAME'))
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resize a directory tree full of images into thumbnails .
train
true
37,058
def ReplaceChunks(chunks): chunks_by_file = _SortChunksByFile(chunks) sorted_file_list = sorted(iterkeys(chunks_by_file)) num_files_to_open = _GetNumNonVisibleFiles(sorted_file_list) if (num_files_to_open > 0): if (not Confirm(FIXIT_OPENING_BUFFERS_MESSAGE_FORMAT.format(num_files_to_open))): return locations = [] for filepath in sorted_file_list: (buffer_num, close_window) = _OpenFileInSplitIfNeeded(filepath) ReplaceChunksInBuffer(chunks_by_file[filepath], vim.buffers[buffer_num], locations) if close_window: vim.command(u'lclose') vim.command(u'hide') if locations: SetQuickFixList(locations) PostVimMessage(u'Applied {0} changes'.format(len(chunks)), warning=False)
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apply the source file deltas supplied in |chunks| to arbitrary files .
train
false
37,059
def subscribe_user_to_notifications(node, user): NotificationSubscription = apps.get_model('osf.NotificationSubscription') if node.is_collection: raise InvalidSubscriptionError('Collections are invalid targets for subscriptions') if node.is_deleted: raise InvalidSubscriptionError('Deleted Nodes are invalid targets for subscriptions') events = constants.NODE_SUBSCRIPTIONS_AVAILABLE notification_type = 'email_transactional' target_id = node._id if user.is_registered: for event in events: event_id = to_subscription_key(target_id, event) global_event_id = to_subscription_key(user._id, ('global_' + event)) global_subscription = NotificationSubscription.load(global_event_id) subscription = NotificationSubscription.load(event_id) if (not (node and node.parent_node and (not subscription) and (node.creator == user))): if (not subscription): subscription = NotificationSubscription(_id=event_id, owner=node, event_name=event) subscription.save() if global_subscription: global_notification_type = get_global_notification_type(global_subscription, user) subscription.add_user_to_subscription(user, global_notification_type) else: subscription.add_user_to_subscription(user, notification_type) subscription.save()
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update the notification settings for the creator or contributors .
train
false
37,061
def function_dump(filename, inputs, outputs=None, mode=None, updates=None, givens=None, no_default_updates=False, accept_inplace=False, name=None, rebuild_strict=True, allow_input_downcast=None, profile=None, on_unused_input=None, extra_tag_to_remove=None): assert isinstance(filename, string_types) d = dict(inputs=inputs, outputs=outputs, mode=mode, updates=updates, givens=givens, no_default_updates=no_default_updates, accept_inplace=accept_inplace, name=name, rebuild_strict=rebuild_strict, allow_input_downcast=allow_input_downcast, profile=profile, on_unused_input=on_unused_input) with open(filename, 'wb') as f: import theano.misc.pkl_utils pickler = theano.misc.pkl_utils.StripPickler(f, protocol=(-1), extra_tag_to_remove=extra_tag_to_remove) pickler.dump(d)
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this is helpful to make a reproducible case for problems during theano compilation .
train
false
37,065
def _gp_int(tok): try: return int(tok) except ValueError: return str(tok)
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gets a int from a token .
train
false
37,068
def read_requirements(path, strict_bounds, conda_format=False, filter_names=None): real_path = join(dirname(abspath(__file__)), path) with open(real_path) as f: reqs = _filter_requirements(f.readlines(), filter_names=filter_names, filter_sys_version=(not conda_format)) if (not strict_bounds): reqs = map(_with_bounds, reqs) if conda_format: reqs = map(_conda_format, reqs) return list(reqs)
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read a requirements .
train
true
37,071
def lines(path, comments=None): stream = open(path, 'U') result = stream_lines(stream, comments) stream.close() return result
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return a list of non empty lines in the file located at path .
train
false
37,072
def encode_header_param(param_text): if (not param_text): return '' param_text_utf8 = ustr(param_text).encode('utf-8') param_text_ascii = try_coerce_ascii(param_text_utf8) return (param_text_ascii or Charset('utf8').header_encode(param_text_utf8))
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returns an appropriate rfc2047 encoded representation of the given header parameter value .
train
false
37,074
def hash_args(*args, **kwargs): arg_string = '_'.join([str(arg) for arg in args]) kwarg_string = '_'.join([((str(key) + '=') + str(value)) for (key, value) in iteritems(kwargs)]) combined = ':'.join([arg_string, kwarg_string]) hasher = md5() hasher.update(b(combined)) return hasher.hexdigest()
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define a unique string for any set of representable args .
train
true
37,076
def test_hdf5_topo_view(): skip_if_no_h5py() import h5py (handle, filename) = tempfile.mkstemp() dataset = random_one_hot_topological_dense_design_matrix(np.random.RandomState(1), num_examples=10, shape=(2, 2), channels=3, axes=('b', 0, 1, 'c'), num_classes=3) with h5py.File(filename, 'w') as f: f.create_dataset('topo_view', data=dataset.get_topological_view()) f.create_dataset('y', data=dataset.get_targets()) trainer = yaml_parse.load((topo_view_yaml % {'filename': filename})) trainer.main_loop() os.remove(filename)
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train using an hdf5 dataset with topo_view instead of x .
train
false
37,077
def CreateStyleFromConfig(style_config): def GlobalStyles(): for (style, _) in _DEFAULT_STYLE_TO_FACTORY: (yield style) def_style = False if (style_config is None): for style in GlobalStyles(): if (_style == style): def_style = True break if (not def_style): return _style return _GLOBAL_STYLE_FACTORY() style_factory = _STYLE_NAME_TO_FACTORY.get(style_config.lower()) if (style_factory is not None): return style_factory() if style_config.startswith('{'): config = _CreateConfigParserFromConfigString(style_config) else: config = _CreateConfigParserFromConfigFile(style_config) return _CreateStyleFromConfigParser(config)
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create a style dict from the given config .
train
false
37,078
def scalar_potential_difference(field, frame, point1, point2, origin): _check_frame(frame) if isinstance(field, Vector): scalar_fn = scalar_potential(field, frame) else: scalar_fn = field position1 = express(point1.pos_from(origin), frame, variables=True) position2 = express(point2.pos_from(origin), frame, variables=True) subs_dict1 = {} subs_dict2 = {} for (i, x) in enumerate(frame): subs_dict1[frame[i]] = x.dot(position1) subs_dict2[frame[i]] = x.dot(position2) return (scalar_fn.subs(subs_dict2) - scalar_fn.subs(subs_dict1))
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returns the scalar potential difference between two points in a certain coordinate system .
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def deprecation_warning(message=None): sys.stderr.write(u'WARNING: This function is deprecated.') if message: sys.stderr.write((u' ' + message.strip())) sys.stderr.write(u'\n')
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print a deprecation warning to stderr .
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def shape2geometry(shape, projection, clip): if clip: try: shape = shape.intersection(clip) except TopologicalError: raise _InvisibleBike('Clipping shape resulted in a topological error') if shape.is_empty: raise _InvisibleBike('Clipping shape resulted in a null geometry') geom = shape.__geo_interface__ if (geom['type'] == 'Point'): geom['coordinates'] = _p2p(geom['coordinates'], projection) elif (geom['type'] in ('MultiPoint', 'LineString')): geom['coordinates'] = [_p2p(c, projection) for c in geom['coordinates']] elif (geom['type'] in ('MultiLineString', 'Polygon')): geom['coordinates'] = [[_p2p(c, projection) for c in cs] for cs in geom['coordinates']] elif (geom['type'] == 'MultiPolygon'): geom['coordinates'] = [[[_p2p(c, projection) for c in cs] for cs in ccs] for ccs in geom['coordinates']] return geom
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convert a shapely geometry object to a geojson-suitable geometry dict .
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def create_local_xmlrpc_uri(port): return ('http://%s:%s/' % (get_host_name(), port))
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determine the xmlrpc uri for local servers .
train
false
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def ListComp(xp, fp, it, test=None): xp.set_prefix('') fp.set_prefix(' ') it.set_prefix(' ') for_leaf = Leaf(token.NAME, 'for') for_leaf.set_prefix(' ') in_leaf = Leaf(token.NAME, 'in') in_leaf.set_prefix(' ') inner_args = [for_leaf, fp, in_leaf, it] if test: test.set_prefix(' ') if_leaf = Leaf(token.NAME, 'if') if_leaf.set_prefix(' ') inner_args.append(Node(syms.comp_if, [if_leaf, test])) inner = Node(syms.listmaker, [xp, Node(syms.comp_for, inner_args)]) return Node(syms.atom, [Leaf(token.LBRACE, '['), inner, Leaf(token.RBRACE, ']')])
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a list comprehension of the form [xp for fp in it if test] .
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def use_obj_for_literal_in_memo(expr, obj, lit, memo): for node in pyll.dfs(expr): try: if (node.obj == lit): memo[node] = obj except AttributeError: pass return memo
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set memo[node] = obj for all nodes in expr such that node .
train
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def set_up_gae_environment(sdk_path): if ('google' in sys.modules): reload_module(sys.modules['google']) sys.path.insert(0, sdk_path) import dev_appserver dev_appserver.fix_sys_path() import google.appengine.tools.os_compat
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set up appengine sdk third-party imports .
train
false
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@pytest.mark.network def test_command_line_appends_correctly(script, data): script.environ['PIP_FIND_LINKS'] = ('http://pypi.pinaxproject.com %s' % data.find_links) result = script.pip('install', '-vvv', 'INITools', '--trusted-host', 'pypi.pinaxproject.com', expect_error=True) assert ('Analyzing links from page http://pypi.pinaxproject.com' in result.stdout), result.stdout assert (('Skipping link %s' % data.find_links) in result.stdout)
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test multiple appending options set by environmental variables .
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def decode_result(found): return {True: 'Countermodel found', False: 'No countermodel found', None: 'None'}[found]
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decode the result of model_found() .
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def reset_devices(): temper_devices = get_temper_devices() for (sensor, device) in zip(TEMPER_SENSORS, temper_devices): sensor.set_temper_device(device)
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re-scan for underlying temper sensors and assign them to our devices .
train
false
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def get_volume_labels_from_aseg(mgz_fname, return_colors=False): import nibabel as nib mgz_data = nib.load(mgz_fname).get_data() lut = _get_lut() label_names = [lut[(lut['id'] == ii)]['name'][0].decode('utf-8') for ii in np.unique(mgz_data)] label_colors = [[lut[(lut['id'] == ii)]['R'][0], lut[(lut['id'] == ii)]['G'][0], lut[(lut['id'] == ii)]['B'][0], lut[(lut['id'] == ii)]['A'][0]] for ii in np.unique(mgz_data)] order = np.argsort(label_names) label_names = [label_names[k] for k in order] label_colors = [label_colors[k] for k in order] if return_colors: return (label_names, label_colors) else: return label_names
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return a list of names and colors of segmented volumes .
train
false
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def standardize_patterns(column_names, patterns): try: patterns = dict(((k, pattern_as_function(v)) for (k, v) in patterns.items() if v)) if (not column_names): return patterns p2 = {} for k in patterns: if (k in column_names): idx = column_names.index(k) if (idx in patterns): raise ColumnIdentifierError(('Column %s has index %i which already has a pattern.' % (k, idx))) p2[idx] = patterns[k] else: p2[k] = patterns[k] return p2 except AttributeError: return dict(((i, pattern_as_function(x)) for (i, x) in enumerate(patterns)))
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given patterns in any of the permitted input forms .
train
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def ValidateString(value, name='unused', exception=datastore_errors.BadValueError, max_len=_MAX_STRING_LENGTH, empty_ok=False): if ((value is None) and empty_ok): return if ((not isinstance(value, basestring)) or isinstance(value, Blob)): raise exception(('%s should be a string; received %s (a %s):' % (name, value, typename(value)))) if ((not value) and (not empty_ok)): raise exception(('%s must not be empty.' % name)) if (len(value.encode('utf-8')) > max_len): raise exception(('%s must be under %d bytes.' % (name, max_len)))
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raises an exception if value is not a valid string or a subclass thereof .
train
false
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def libvlc_get_changeset(): f = (_Cfunctions.get('libvlc_get_changeset', None) or _Cfunction('libvlc_get_changeset', (), None, ctypes.c_char_p)) return f()
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retrieve libvlc changeset .
train
false
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def response_truncated(f): @functools.wraps(f) def wrapper(self, *args, **kwargs): if (kwargs.get('hints') is None): return f(self, *args, **kwargs) list_limit = self.driver._get_list_limit() if list_limit: kwargs['hints'].set_limit(list_limit) return f(self, *args, **kwargs) return wrapper
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truncate the list returned by the wrapped function .
train
false
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def validip(ip, defaultaddr='0.0.0.0', defaultport=8080): addr = defaultaddr port = defaultport ip = ip.split(':', 1) if (len(ip) == 1): if (not ip[0]): pass elif validipaddr(ip[0]): addr = ip[0] elif validipport(ip[0]): port = int(ip[0]) else: raise ValueError, (':'.join(ip) + ' is not a valid IP address/port') elif (len(ip) == 2): (addr, port) = ip if ((not validipaddr(addr)) and validipport(port)): raise ValueError, (':'.join(ip) + ' is not a valid IP address/port') port = int(port) else: raise ValueError, (':'.join(ip) + ' is not a valid IP address/port') return (addr, port)
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returns from string ip_addr_port .
train
true
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def corefile(process): if context.noptrace: log.warn_once('Skipping corefile since context.noptrace==True') return temp = tempfile.NamedTemporaryFile(prefix='pwn-corefile-') if (version() < (7, 11)): log.warn_once(('The installed GDB (%s) does not emit core-dumps which contain all of the data in the process.\nUpgrade to GDB >= 7.11 for better core-dumps.' % binary())) gdb_args = ['-batch', '-q', '--nx', '-ex', '"set pagination off"', '-ex', '"set height 0"', '-ex', '"set width 0"', '-ex', '"set use-coredump-filter on"', '-ex', ('"generate-core-file %s"' % temp.name), '-ex', 'detach'] with context.local(terminal=['sh', '-c']): with context.quiet: pid = attach(process, gdb_args=gdb_args) os.waitpid(pid, 0) return elf.corefile.Core(temp.name)
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drops a core file for the process .
train
false
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def image_resize_image_big(base64_source, size=(1024, 1024), encoding='base64', filetype=None, avoid_if_small=True): return image_resize_image(base64_source, size, encoding, filetype, avoid_if_small)
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wrapper on image_resize_image .
train
false
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def _dump_function(func): func_info = (func.func_name, func.func_defaults, func.func_closure) code_info = (func.func_code.co_argcount, func.func_code.co_nlocals, func.func_code.co_stacksize, func.func_code.co_flags, func.func_code.co_code, func.func_code.co_consts, func.func_code.co_names, func.func_code.co_varnames, func.func_code.co_filename, func.func_code.co_name, func.func_code.co_firstlineno, func.func_code.co_lnotab, func.func_code.co_freevars, func.func_code.co_cellvars) return pickle.dumps((code_info, func_info, func.func_doc), pickle.HIGHEST_PROTOCOL)
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serializes a function .
train
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def is_empty(filename): try: return (os.stat(filename).st_size == 0) except OSError: return False
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is a file empty? .
train
false
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def get_nginx_port(appid): filename = (('/etc/appscale/port-' + appid) + '.txt') file_handle = open(filename) port = file_handle.read() file_handle.close() return port
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an appscale-specific method that callers can use to find out what port task queue api calls should be routed through .
train
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def application(): settings.hrm.staff_experience = True settings.hrm.use_skills = True settings.search.filter_manager = True def prep(r): method = r.method if ((not method) and (r.representation != 's3json')): r.method = method = 'select' if (method == 'select'): r.custom_action = s3db.deploy_apply return True s3.prep = prep if (('delete' in request.args) or ((request.env.request_method == 'POST') and (auth.permission.format == 's3json'))): return s3_rest_controller() else: return s3_rest_controller('hrm', 'human_resource')
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the main wsgi application .
train
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def wininst_name(pyver): ext = '.exe' return ('scipy-%s.win32-py%s%s' % (FULLVERSION, pyver, ext))
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return the name of the installer built by wininst command .
train
false
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def open_with_editor(filepath): editor_cmd = editor().split() try: subprocess.call((editor_cmd + [filepath])) except OSError: die(('Could not launch ' + editor()))
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open filepath using the editor specified by the environment variables .
train
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def _seed(a=None, max_bytes=8): if (a is None): a = _bigint_from_bytes(os.urandom(max_bytes)) elif isinstance(a, str): a = a.encode('utf8') a += hashlib.sha512(a).digest() a = _bigint_from_bytes(a[:max_bytes]) elif isinstance(a, integer_types): a = (a % (2 ** (8 * max_bytes))) else: raise error.Error('Invalid type for seed: {} ({})'.format(type(a), a)) return a
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create a strong random seed .
train
false
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@utils.arg('server', metavar='<server>', help=_('Name or ID of server.')) @utils.arg('--password', metavar='<password>', dest='password', help=_('The admin password to be set in the rescue environment.')) @utils.arg('--image', metavar='<image>', dest='image', help=_('The image to rescue with.')) def do_rescue(cs, args): kwargs = {} if args.image: kwargs['image'] = _find_image(cs, args.image) if args.password: kwargs['password'] = args.password utils.print_dict(_find_server(cs, args.server).rescue(**kwargs)[1])
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reboots a server into rescue mode .
train
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def send_notify_osd(title, message): global _NTFOSD if (not _HAVE_NTFOSD): return T('Not available') error = 'NotifyOSD not working' icon = os.path.join(sabnzbd.DIR_PROG, 'sabnzbd.ico') _NTFOSD = (_NTFOSD or pynotify.init('icon-summary-body')) if _NTFOSD: logging.info('Send to NotifyOSD: %s / %s', title, message) try: note = pynotify.Notification(title, message, icon) note.show() except: logging.info(error) return error return None else: return error
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send a message to notifyosd .
train
false
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@no_debug_mode def test_train_example(): assert (config.mode != 'DEBUG_MODE') path = pylearn2.__path__[0] train_example_path = os.path.join(path, 'scripts', 'tutorials', 'grbm_smd') if (not os.path.isfile(os.path.join(train_example_path, 'cifar10_preprocessed_train.pkl'))): raise SkipTest cwd = os.getcwd() try: os.chdir(train_example_path) train_yaml_path = os.path.join(train_example_path, 'cifar_grbm_smd.yaml') train_object = load_train_file(train_yaml_path) train_object.algorithm.termination_criterion.prop_decrease = 0.5 train_object.algorithm.termination_criterion.N = 1 train_object.main_loop() finally: os.chdir(cwd)
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tests that the grbm_smd example script runs correctly .
train
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def list_vns(call=None): if (call == 'action'): raise SaltCloudSystemExit('The list_vns function must be called with -f or --function.') (server, user, password) = _get_xml_rpc() auth = ':'.join([user, password]) vn_pool = server.one.vnpool.info(auth, (-2), (-1), (-1))[1] vns = {} for v_network in _get_xml(vn_pool): vns[v_network.find('NAME').text] = _xml_to_dict(v_network) return vns
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lists all virtual networks available to the user and the users groups .
train
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def render_and_create_dir(dirname, context, output_dir, environment, overwrite_if_exists=False): name_tmpl = environment.from_string(dirname) rendered_dirname = name_tmpl.render(**context) dir_to_create = os.path.normpath(os.path.join(output_dir, rendered_dirname)) logger.debug(u'Rendered dir {} must exist in output_dir {}'.format(dir_to_create, output_dir)) output_dir_exists = os.path.exists(dir_to_create) if overwrite_if_exists: if output_dir_exists: logger.debug(u'Output directory {} already exists,overwriting it'.format(dir_to_create)) elif output_dir_exists: msg = u'Error: "{}" directory already exists'.format(dir_to_create) raise OutputDirExistsException(msg) make_sure_path_exists(dir_to_create) return dir_to_create
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render name of a directory .
train
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def typecast_string(s): if ((not s) and (not isinstance(s, str))): return s return smart_unicode(s)
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cast all returned strings to unicode strings .
train
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def get_all_cluster_mors(session): try: results = session._call_method(vim_util, 'get_objects', 'ClusterComputeResource', ['name']) session._call_method(vutil, 'cancel_retrieval', results) if (results.objects is None): return [] else: return results.objects except Exception as excep: LOG.warning(_LW('Failed to get cluster references %s'), excep)
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get all the clusters in the vcenter .
train
false
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def test_pickle_indexed_table(protocol): t = simple_table() t.add_index('a') t.add_index(['a', 'b']) ts = pickle.dumps(t) tp = pickle.loads(ts) assert (len(t.indices) == len(tp.indices)) for (index, indexp) in zip(t.indices, tp.indices): assert np.all((index.data.data == indexp.data.data)) assert (index.data.data.colnames == indexp.data.data.colnames)
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ensure that any indices that have been added will survive pickling .
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def make_caller_id(name): return make_global_ns(ns_join(get_ros_namespace(), name))
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resolve a local name to the caller id based on ros environment settings .
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def _check_for_default_values(fname, arg_val_dict, compat_args): for key in arg_val_dict: try: v1 = arg_val_dict[key] v2 = compat_args[key] if (((v1 is not None) and (v2 is None)) or ((v1 is None) and (v2 is not None))): match = False else: match = (v1 == v2) if (not is_bool(match)): raise ValueError("'match' is not a boolean") except: match = (arg_val_dict[key] is compat_args[key]) if (not match): raise ValueError("the '{arg}' parameter is not supported in the pandas implementation of {fname}()".format(fname=fname, arg=key))
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check that the keys in arg_val_dict are mapped to their default values as specified in compat_args .
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def GetAllFieldInDocument(document, field_name): fields = [] for f in document.field_list(): if (f.name() == field_name): fields.append(f) return fields
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find and return all fields with the provided name in the document .
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@pytest.mark.skipif('True', reason='Refactor a few parser things first.') def test_basic_parsing(): prs = ParserWithRecovery(load_grammar(), code_basic_features) diff_code_assert(code_basic_features, prs.module.get_code())
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validate the parsing features .
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def _validate_timestamp_fields(query, field_name, operator_list, allow_timestamps): for item in query: if (item.field == field_name): if (not allow_timestamps): raise wsme.exc.UnknownArgument(field_name, ('not valid for ' + 'this resource')) if (item.op not in operator_list): raise wsme.exc.InvalidInput('op', item.op, ('unimplemented operator for %s' % item.field)) return True return False
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validates the timestamp related constraints in a query if there are any .
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def _swap_curly(string): return string.replace('{{ ', '{{').replace('{{', '\x00').replace('{', '{{').replace('\x00', '{').replace(' }}', '}}').replace('}}', '\x00').replace('}', '}}').replace('\x00', '}')
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swap single and double curly brackets .
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def read_crypto_key(key_path, key_type=None): from keyczar.keys import AesKey key_type = (key_type or AesKey) with open(key_path) as key_file: key = key_type.Read(key_file.read()) return key
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return the crypto key given a path to key file and the key type .
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@with_device @no_emulator def unlock_bootloader(): AdbClient().reboot_bootloader() fastboot(['oem', 'unlock']) fastboot(['continue'])
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unlocks the bootloader of the device .
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def collect_dependencies(attributes, all_attributes): dependencies = {attr.ref: attr.dependencies for attr in all_attributes} depsorted = depsort_attributes([attr.ref for attr in attributes], dependencies) return depsorted
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collect all original and dependant cube attributes for attributes .
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@verbose def ica_find_ecg_events(raw, ecg_source, event_id=999, tstart=0.0, l_freq=5, h_freq=35, qrs_threshold='auto', verbose=None): logger.info('Using ICA source to identify heart beats') ecg_events = qrs_detector(raw.info['sfreq'], ecg_source.ravel(), tstart=tstart, thresh_value=qrs_threshold, l_freq=l_freq, h_freq=h_freq) n_events = len(ecg_events) ecg_events = np.c_[((ecg_events + raw.first_samp), np.zeros(n_events), (event_id * np.ones(n_events)))] return ecg_events
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find ecg peaks from one selected ica source .
train
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def getRadiusArealizedBasedOnAreaRadius(elementNode, radius, sides): if elementNode.getCascadeBoolean(False, 'radiusAreal'): return radius return (radius * euclidean.getRadiusArealizedMultiplier(sides))
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get the areal radius from the radius .
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def _two_element_tuple(int_or_tuple): if isinstance(int_or_tuple, (list, tuple)): if (len(int_or_tuple) != 2): raise ValueError(('Must be a list with 2 elements: %s' % int_or_tuple)) return (int(int_or_tuple[0]), int(int_or_tuple[1])) if isinstance(int_or_tuple, int): return (int(int_or_tuple), int(int_or_tuple)) if isinstance(int_or_tuple, tf.TensorShape): if (len(int_or_tuple) == 2): return (int_or_tuple[0], int_or_tuple[1]) raise ValueError('Must be an int, a list with 2 elements or a TensorShape of length 2')
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converts int_or_tuple to height .
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def elem_style(style_rules, cls, inherited_style): classes = cls.split() style = inherited_style.copy() for cls in classes: style.update(style_rules.get(cls, {})) wt = style.get(u'font-weight', None) pwt = inherited_style.get(u'font-weight', u'400') if (wt == u'bolder'): style[u'font-weight'] = {u'100': u'400', u'200': u'400', u'300': u'400', u'400': u'700', u'500': u'700'}.get(pwt, u'900') elif (wt == u'lighter'): style[u'font-weight'] = {u'600': u'400', u'700': u'400', u'800': u'700', u'900': u'700'}.get(pwt, u'100') return style
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find the effective style for the given element .
train
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def apply_dropout(computation_graph, variables, drop_prob, rng=None, seed=None, custom_divisor=None): if ((not rng) and (not seed)): seed = config.default_seed if (not rng): rng = MRG_RandomStreams(seed) if (custom_divisor is None): divisor = (1 - drop_prob) else: divisor = custom_divisor replacements = [(var, ((var * rng.binomial(var.shape, p=(1 - drop_prob), dtype=theano.config.floatX)) / divisor)) for var in variables] for (variable, replacement) in replacements: add_role(replacement, DROPOUT) replacement.tag.replacement_of = variable return computation_graph.replace(replacements)
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apply dropout to specified variables in a graph .
train
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def grid_graph(dim, periodic=False): dlabel = ('%s' % str(dim)) if (not dim): G = empty_graph(0) G.name = ('grid_graph(%s)' % dlabel) return G if periodic: func = cycle_graph else: func = path_graph G = func(dim[0]) for current_dim in dim[1:]: Gold = G.copy() Gnew = func(current_dim) G = nx.cartesian_product(Gnew, Gold) H = nx.relabel_nodes(G, flatten) H.name = ('grid_graph(%s)' % dlabel) return H
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return the n-dimensional grid graph .
train
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@sync_performer @do def perform_download_s3_key_recursively(dispatcher, intent): keys = (yield Effect(ListS3Keys(prefix=(intent.source_prefix + '/'), bucket=intent.source_bucket))) for key in keys: if (not key.endswith(intent.filter_extensions)): continue path = intent.target_path.preauthChild(key) if (not path.parent().exists()): path.parent().makedirs() source_key = os.path.join(intent.source_prefix, key) (yield Effect(DownloadS3Key(source_bucket=intent.source_bucket, source_key=source_key, target_path=path)))
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see :class:downloads3keyrecursively .
train
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def kegg_conv(target_db, source_db, option=None): if (option and (option not in ['turtle', 'n-triple'])): raise Exception('Invalid option arg for kegg conv request.') if isinstance(source_db, list): source_db = '+'.join(source_db) if ((target_db in ['ncbi-gi', 'ncbi-geneid', 'uniprot']) or (source_db in ['ncbi-gi', 'ncbi-geneid', 'uniprot']) or ((target_db in ['drug', 'compound', 'glycan']) and (source_db in ['pubchem', 'glycan'])) or ((target_db in ['pubchem', 'glycan']) and (source_db in ['drug', 'compound', 'glycan']))): if option: resp = _q('conv', target_db, source_db, option) else: resp = _q('conv', target_db, source_db) return resp else: raise Exception('Bad argument target_db or source_db for kegg conv request.')
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kegg conv - convert kegg identifiers to/from outside identifiers target_db - target database source_db_or_dbentries - source database or database entries option - can be "turtle" or "n-triple" .
train
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@register.inclusion_tag('zinnia/tags/dummy.html') def get_archives_entries(template='zinnia/tags/entries_archives.html'): return {'template': template, 'archives': Entry.published.datetimes('publication_date', 'month', order='DESC')}
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return archives entries .
train
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def upload_training_video(videos, api_key=None, env_id=None): with tempfile.TemporaryFile() as archive_file: write_archive(videos, archive_file, env_id=env_id) archive_file.seek(0) logger.info('[%s] Uploading videos of %d training episodes (%d bytes)', env_id, len(videos), util.file_size(archive_file)) file_upload = resource.FileUpload.create(purpose='video', content_type='application/vnd.openai.video+x-compressed', api_key=api_key) file_upload.put(archive_file, encode=None) return file_upload
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videos: should be list of tuples .
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def external_download(song, filename, url): cmd = config.DOWNLOAD_COMMAND.get (ddir, basename) = (config.DDIR.get, os.path.basename(filename)) cmd_list = shlex.split(cmd) def list_string_sub(orig, repl, lst): ' Replace substrings for items in a list. ' return [(x if (orig not in x) else x.replace(orig, repl)) for x in lst] cmd_list = list_string_sub('%F', filename, cmd_list) cmd_list = list_string_sub('%d', ddir, cmd_list) cmd_list = list_string_sub('%f', basename, cmd_list) cmd_list = list_string_sub('%u', url, cmd_list) cmd_list = list_string_sub('%i', song.ytid, cmd_list) util.dbg('Downloading using: %s', ' '.join(cmd_list)) subprocess.call(cmd_list)
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perform download using external application .
train
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def env_vars_from_file(filename): if (not os.path.exists(filename)): raise ConfigurationError((u"Couldn't find env file: %s" % filename)) elif (not os.path.isfile(filename)): raise ConfigurationError((u'%s is not a file.' % filename)) env = {} for line in codecs.open(filename, u'r', u'utf-8'): line = line.strip() if (line and (not line.startswith(u'#'))): (k, v) = split_env(line) env[k] = v return env
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read in a line delimited file of environment variables .
train
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def test_word(word): return ((len(word) <= 2) or (word in SAME_BLACKLIST))
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test whether word should be ignored .
train
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def ApprovalFind(object_id, token=None): user = getpass.getuser() object_id = rdfvalue.RDFURN(object_id) try: approved_token = security.Approval.GetApprovalForObject(object_id, token=token, username=user) print ('Found token %s' % str(approved_token)) return approved_token except access_control.UnauthorizedAccess: print ('No token available for access to %s' % object_id)
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find approvals issued for a specific client .
train
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def extract_msg_options(options, keep=MSG_OPTIONS): return dict(((name, options.get(name)) for name in keep))
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extracts known options to basic_publish from a dict .
train
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def cache_node_list(nodes, provider, opts): if (('update_cachedir' not in opts) or (not opts['update_cachedir'])): return base = os.path.join(init_cachedir(), 'active') driver = next(six.iterkeys(opts['providers'][provider])) prov_dir = os.path.join(base, driver, provider) if (not os.path.exists(prov_dir)): os.makedirs(prov_dir) missing_node_cache(prov_dir, nodes, provider, opts) for node in nodes: diff_node_cache(prov_dir, node, nodes[node], opts) path = os.path.join(prov_dir, '{0}.p'.format(node)) with salt.utils.fopen(path, 'w') as fh_: msgpack.dump(nodes[node], fh_)
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if configured to do so .
train
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def CheckSection(sec): try: CFG[sec] return True except: CFG[sec] = {} return False
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check if ini section exists .
train
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def download_template(name=None, url=None): if (url is None): url = ('http://download.openvz.org/template/precreated/%s.tar.gz' % name) with cd('/var/lib/vz/template/cache'): run_as_root(('wget --progress=dot:mega "%s"' % url))
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download an openvz template .
train
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def on_plugin_start(config): pass
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called once after plugin is loaded .
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@not_implemented_for('undirected') def immediate_dominators(G, start): if (start not in G): raise nx.NetworkXError('start is not in G') idom = {start: start} order = list(nx.dfs_postorder_nodes(G, start)) dfn = {u: i for (i, u) in enumerate(order)} order.pop() order.reverse() def intersect(u, v): while (u != v): while (dfn[u] < dfn[v]): u = idom[u] while (dfn[u] > dfn[v]): v = idom[v] return u changed = True while changed: changed = False for u in order: new_idom = reduce(intersect, (v for v in G.pred[u] if (v in idom))) if ((u not in idom) or (idom[u] != new_idom)): idom[u] = new_idom changed = True return idom
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returns the immediate dominators of all nodes of a directed graph .
train
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def clear_index(index_name): index = gae_search.Index(index_name) while True: doc_ids = [document.doc_id for document in index.get_range(ids_only=True)] if (not doc_ids): break index.delete(doc_ids)
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clears an index completely .
train
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def as_list(arg): if _is_list(arg): return arg return [arg]
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coerces arg into a list .
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def cluster(S, k, ndim): if (sum(abs((S - S.T))) > 1e-10): print 'not symmetric' rowsum = sum(abs(S), axis=0) D = diag((1 / sqrt((rowsum + 1e-06)))) L = dot(D, dot(S, D)) (U, sigma, V) = linalg.svd(L, full_matrices=False) features = array(V[:ndim]).T features = whiten(features) (centroids, distortion) = kmeans(features, k) (code, distance) = vq(features, centroids) return (code, V)
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spectral clustering from a similarity matrix .
train
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def _params_default(app=None): p = Storage() p.name = (app or 'BASE') p.default_application = (app or 'init') p.default_controller = 'default' p.default_function = 'index' p.routes_app = [] p.routes_in = [] p.routes_out = [] p.routes_onerror = [] p.routes_apps_raw = [] p.error_handler = None p.error_message = '<html><body><h1>%s</h1></body></html>' p.error_message_ticket = (('<html><body><h1>Internal error</h1>Ticket issued: <a href="/admin/default/ticket/%(ticket)s" target="_blank">%(ticket)s</a></body><!-- this is junk text else IE does not display the page: ' + ('x' * 512)) + ' //--></html>') p.routers = None p.logging = 'off' return p
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returns a new copy of default parameters .
train
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def after_this_request(f): _request_ctx_stack.top._after_request_functions.append(f) return f
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executes a function after this request .
train
false
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def lambda_min(X): X = Expression.cast_to_const(X) return (- lambda_max((- X)))
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miximum eigenvalue; :math:lambda_{min}(a) .
train
false
37,178
def faster_could_be_isomorphic(G1, G2): if (G1.order() != G2.order()): return False d1 = sorted((d for (n, d) in G1.degree())) d2 = sorted((d for (n, d) in G2.degree())) if (d1 != d2): return False return True
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returns false if graphs are definitely not isomorphic .
train
false
37,179
def get_email_from_username(username): user_model = user_models.UserSettingsModel.get_by_normalized_username(UserSettings.normalize_username(username)) if (user_model is None): return None else: return user_model.email
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gets the email for a given username .
train
false
37,180
def get_expirer_container(x_delete_at, expirer_divisor, acc, cont, obj): shard_int = (int(hash_path(acc, cont, obj), 16) % 100) return normalize_delete_at_timestamp((((int(x_delete_at) / expirer_divisor) * expirer_divisor) - shard_int))
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returns a expiring object container name for given x-delete-at and a/c/o .
train
false