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def _yield_all_clusters(emr_conn, *args, **kwargs): for resp in _repeat(emr_conn.list_clusters, *args, **kwargs): for cluster in getattr(resp, 'clusters', []): (yield cluster)
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make successive api calls .
train
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def unpack_task_input(task): task_input = task.task_input for key in ['import_from', 'import_from_format', 'image_properties']: if (key not in task_input): msg = (_("Input does not contain '%(key)s' field") % {'key': key}) raise exception.Invalid(msg) return task_input
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verifies and returns valid task input dictionary .
train
false
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def findRelease(epObj): release = None provider = None failed_db_con = db.DBConnection('failed.db') results = failed_db_con.select('SELECT release, provider, date FROM history WHERE showid=? AND season=? AND episode=?', [epObj.show.indexerid, epObj.season, epObj.episode]) for result in results: release = str(result['release']) provider = str(result['provider']) date = result['date'] failed_db_con.action('DELETE FROM history WHERE release=? AND date!=?', [release, date]) logger.log(u'Failed release found for season ({0}): ({1})'.format(epObj.season, result['release']), logger.DEBUG) return (release, provider) logger.log(u'No releases found for season ({0}) of ({1})'.format(epObj.season, epObj.show.indexerid), logger.DEBUG) return (release, provider)
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find releases in history by show id and season .
train
false
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def LoadEdges(filename, targets): file = open('dump.json') edges = json.load(file) file.close() target_edges = {} to_visit = targets[:] while to_visit: src = to_visit.pop() if (src in target_edges): continue target_edges[src] = edges[src] to_visit.extend(edges[src]) return target_edges
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load the edges map from the dump file .
train
false
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def _asset_index(request, course_key): course_module = modulestore().get_course(course_key) return render_to_response('asset_index.html', {'context_course': course_module, 'max_file_size_in_mbs': settings.MAX_ASSET_UPLOAD_FILE_SIZE_IN_MB, 'chunk_size_in_mbs': settings.UPLOAD_CHUNK_SIZE_IN_MB, 'max_file_size_redirect_url': settings.MAX_ASSET_UPLOAD_FILE_SIZE_URL, 'asset_callback_url': reverse_course_url('assets_handler', course_key)})
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display an editable asset library .
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def test_mnist(): skip_if_no_gpu() train = load_train_file(os.path.join(pylearn2.__path__[0], 'scripts/papers/maxout/mnist.yaml')) init_value = control.load_data control.load_data = [False] train.dataset = MNIST(which_set='train', axes=['c', 0, 1, 'b'], start=0, stop=100) train.algorithm._set_monitoring_dataset(train.dataset) control.load_data = init_value train.algorithm.termination_criterion = EpochCounter(max_epochs=1) train.extensions.pop(0) train.save_freq = 0 train.main_loop()
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test the mnist .
train
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def render_pages(tasks, dest, opts, notification=(lambda x, y: x)): (failures, pages) = ([], []) for (num, path) in tasks: try: pages.extend(PageProcessor(path, dest, opts, num)) msg = (_('Rendered %s') % path) except: failures.append(path) msg = (_('Failed %s') % path) if opts.verbose: msg += ('\n' + traceback.format_exc()) prints(msg) notification(0.5, msg) return (pages, failures)
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entry point for the job server .
train
false
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def random_mntd(distmat, n, iters): means = [] indices = arange(distmat.shape[0]) for i in range(iters): shuffle(indices) means.append(mntd(reduce_mtx(distmat, indices[:n]))) return (mean(means), std(means))
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calc mean .
train
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def _transformValue(value, policy, transform_type): t_kwargs = {} if ('Transform' in policy): if (transform_type in policy['Transform']): _policydata = _policy_info() if ((transform_type + 'Args') in policy['Transform']): t_kwargs = policy['Transform'][(transform_type + 'Args')] return getattr(_policydata, policy['Transform'][transform_type])(value, **t_kwargs) else: return value else: if ('Registry' in policy): if (value == '(value not set)'): return 'Not Defined' return value
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helper function to transform the policy value into something that more closely matches how the policy is displayed in the gpedit gui .
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def pytest_collection_modifyitems(session, config, items): new_items = [] last_items = [] for item in items: if hasattr(item.obj, 'first'): new_items.insert(0, item) elif hasattr(item.obj, 'last'): last_items.append(item) else: new_items.append(item) items[:] = (new_items + last_items)
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apply @qtwebengine_* markers; skip unittests with qute_bdd_webengine .
train
false
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def strategy_independent_set(G, colors): remaining_nodes = set(G) while (len(remaining_nodes) > 0): nodes = _maximal_independent_set(G.subgraph(remaining_nodes)) remaining_nodes -= nodes for v in nodes: (yield v)
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uses a greedy independent set removal strategy to determine the colors .
train
false
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def delete_comment(request, comment_id): (cc_comment, context) = _get_comment_and_context(request, comment_id) if can_delete(cc_comment, context): cc_comment.delete() comment_deleted.send(sender=None, user=request.user, post=cc_comment) else: raise PermissionDenied
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deletes comment .
train
false
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def create_groups(): data_dir = settings.DATA_DIR print ('data_dir = %s' % data_dir) for course_dir in os.listdir(data_dir): if course_dir.startswith('.'): continue if (not os.path.isdir((path(data_dir) / course_dir))): continue cxfn = ((path(data_dir) / course_dir) / 'course.xml') try: coursexml = etree.parse(cxfn) except Exception: print ('Oops, cannot read %s, skipping' % cxfn) continue cxmlroot = coursexml.getroot() course = cxmlroot.get('course') if (course is None): print ("oops, can't get course id for %s" % course_dir) continue print ('course=%s for course_dir=%s' % (course, course_dir)) create_group(('staff_%s' % course)) create_group(('instructor_%s' % course))
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create staff and instructor groups for all classes in the data_dir .
train
false
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def password_reset_done(request, response_format='html'): return render_to_response('core/password_reset_done', context_instance=RequestContext(request), response_format=response_format)
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shows success message .
train
false
30,449
def list_windowsfeatures(): choc_path = _find_chocolatey(__context__, __salt__) cmd = [choc_path, 'list', '--source', 'windowsfeatures'] result = __salt__['cmd.run_all'](cmd, python_shell=False) if (result['retcode'] != 0): err = 'Running chocolatey failed: {0}'.format(result['stdout']) raise CommandExecutionError(err) return result['stdout']
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instructs chocolatey to pull a full package list from the windows features list .
train
true
30,450
def _read_pfm(handle): alphabet = dna counts = {} letters = 'ACGT' for (letter, line) in zip(letters, handle): words = line.split() if (words[0] == letter): words = words[1:] counts[letter] = [float(x) for x in words] motif = Motif(matrix_id=None, name=None, alphabet=alphabet, counts=counts) motif.mask = ('*' * motif.length) record = Record() record.append(motif) return record
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read the motif from a jaspar .
train
false
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def GetDefaultStyleForDir(dirname): dirname = os.path.abspath(dirname) while True: style_file = os.path.join(dirname, style.LOCAL_STYLE) if os.path.exists(style_file): return style_file config_file = os.path.join(dirname, style.SETUP_CONFIG) if os.path.exists(config_file): with open(config_file) as fd: config = py3compat.ConfigParser() config.read_file(fd) if config.has_section('yapf'): return config_file dirname = os.path.dirname(dirname) if ((not dirname) or (not os.path.basename(dirname)) or (dirname == os.path.abspath(os.path.sep))): break global_file = os.path.expanduser(style.GLOBAL_STYLE) if os.path.exists(global_file): return global_file return style.DEFAULT_STYLE
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return default style name for a given directory .
train
false
30,452
def mailchimp_subscribe(email, mc_url): r = requests.post(mc_url, data={'EMAIL': email}) return r.text
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do the post request to mailchimp .
train
false
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def tempest_modules(): return {'compute': compute, 'identity.v2': identity.v2, 'identity.v3': identity.v3, 'image.v1': image.v1, 'image.v2': image.v2, 'network': network, 'volume.v1': volume.v1, 'volume.v2': volume.v2, 'volume.v3': volume.v3}
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dict of service client modules available in tempest .
train
false
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def do_get_language_info(parser, token): args = token.contents.split() if ((len(args) != 5) or (args[1] != 'for') or (args[3] != 'as')): raise TemplateSyntaxError(("'%s' requires 'for string as variable' (got %r)" % (args[0], args[1:]))) return GetLanguageInfoNode(args[2], args[4])
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this will store the language information dictionary for the given language code in a context variable .
train
false
30,455
def cse_separate(r, e): d = sift(e, (lambda w: (w.is_Equality and w.lhs.is_Symbol))) r = (r + [w.args for w in d[True]]) e = d[False] return [reps_toposort(r), e]
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move expressions that are in the form out of the expressions and sort them into the replacements using the reps_toposort .
train
false
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def str2bool(value): return (value.lower() in (u'yes', u'1', u'true', u't', u'y'))
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returns a boolean reflecting a human-entered string .
train
false
30,457
def human_file_size(size): if hasattr(size, u'unit'): from .. import units as u size = size.to(u.byte).value suffixes = u' kMGTPEZY' if (size == 0): num_scale = 0 else: num_scale = int(math.floor((math.log(size) / math.log(1000)))) if (num_scale > 7): suffix = u'?' else: suffix = suffixes[num_scale] num_scale = int(math.pow(1000, num_scale)) value = (size / num_scale) str_value = str(value) if (suffix == u' '): str_value = str_value[:str_value.index(u'.')] elif (str_value[2] == u'.'): str_value = str_value[:2] else: str_value = str_value[:3] return u'{0:>3s}{1}'.format(str_value, suffix)
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returns a human-friendly string representing a file size that is 2-4 characters long .
train
false
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def getNewRepository(): return ExportRepository()
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get new repository .
train
false
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def EncodeVarLengthNumber(value): byte_str = '' while (value >= 128): byte_str += struct.pack('>B', ((value & 127) | 128)) value >>= 7 byte_str += struct.pack('>B', (value & 127)) return byte_str
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uses a variable length encoding scheme .
train
false
30,461
def StandardNormalCdf(x): return ((math.erf((x / ROOT2)) + 1) / 2)
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evaluates the cdf of the standard normal distribution .
train
false
30,462
def apply_sorting(records, sorting): result = list(records) if (not result): return result first_record = result[0] def column(first, record, name): empty = first.get(name, float('inf')) subfields = name.split('.') value = record for subfield in subfields: value = value.get(subfield, empty) if (not isinstance(value, dict)): break return value for sort in reversed(sorting): result = sorted(result, key=(lambda r: column(first_record, r, sort.field)), reverse=(sort.direction < 0)) return result
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sort the specified records .
train
false
30,464
def Mandatory(cls, **_kwargs): kwargs = dict(min_occurs=1, nillable=False) if (cls.get_type_name() is not cls.Empty): kwargs['type_name'] = ('%s%s%s' % (const.MANDATORY_PREFIX, cls.get_type_name(), const.MANDATORY_SUFFIX)) kwargs.update(_kwargs) if issubclass(cls, Unicode): kwargs.update(dict(min_len=1)) elif issubclass(cls, Array): ((k, v),) = cls._type_info.items() if (v.Attributes.min_occurs == 0): cls._type_info[k] = Mandatory(v) return cls.customize(**kwargs)
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customizes the given type to be a mandatory one .
train
false
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def offset_bounds_from_strides(context, builder, arrty, arr, shapes, strides): itemsize = arr.itemsize zero = itemsize.type(0) one = zero.type(1) if (arrty.layout in 'CF'): lower = zero upper = builder.mul(itemsize, arr.nitems) else: lower = zero upper = zero for i in range(arrty.ndim): max_axis_offset = builder.mul(strides[i], builder.sub(shapes[i], one)) is_upwards = builder.icmp_signed('>=', max_axis_offset, zero) upper = builder.select(is_upwards, builder.add(upper, max_axis_offset), upper) lower = builder.select(is_upwards, lower, builder.add(lower, max_axis_offset)) upper = builder.add(upper, itemsize) is_empty = builder.icmp_signed('==', arr.nitems, zero) upper = builder.select(is_empty, zero, upper) lower = builder.select(is_empty, zero, lower) return (lower, upper)
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compute a half-open range [lower .
train
false
30,466
def parse_lt_objects(lt_objects): text_content = [] for lt_object in lt_objects: if (isinstance(lt_object, LTTextBox) or isinstance(lt_object, LTTextLine)): text_content.append(lt_object.get_text().encode('utf-8')) elif isinstance(lt_object, LTFigure): text_content.append(parse_lt_objects(lt_object._objs)) return '\n'.join(text_content)
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iterate through the list of lt* objects and capture the text data contained in each object .
train
false
30,468
def buildTopo(topos, topoStr): (topo, args, kwargs) = splitArgs(topoStr) if (topo not in topos): raise Exception(('Invalid topo name %s' % topo)) return topos[topo](*args, **kwargs)
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create topology from string with format .
train
false
30,469
def set_target(alias, target): if (alias == ''): raise SaltInvocationError('alias can not be an empty string') if (target == ''): raise SaltInvocationError('target can not be an empty string') if (get_target(alias) == target): return True lines = __parse_aliases() out = [] ovr = False for (line_alias, line_target, line_comment) in lines: if (line_alias == alias): if (not ovr): out.append((alias, target, line_comment)) ovr = True else: out.append((line_alias, line_target, line_comment)) if (not ovr): out.append((alias, target, '')) __write_aliases_file(out) return True
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set the entry in the aliases file for the given alias .
train
true
30,470
def bw_usage_get(context, uuid, start_period, mac): return IMPL.bw_usage_get(context, uuid, start_period, mac)
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return bw usage for instance and mac in a given audit period .
train
false
30,472
def defdict_to_dict(defdict, constructor=dict): if isinstance(defdict, dict): new = constructor() for (key, value) in defdict.items(): new[key] = defdict_to_dict(value, constructor) return new else: return defdict
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recursively convert default dicts to regular dicts .
train
false
30,473
def get_versions_list(): return (([('Weblate', '', GIT_VERSION)] + get_versions()) + get_optional_versions())
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returns list with version information summary .
train
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def find_css_with_wait(context, id_str, **kwargs): return _find_elem_with_wait(context, (By.CSS_SELECTOR, id_str), **kwargs)
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tries to find an element with given css selector with an explicit timeout .
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def _GetDriver(driver_name=None): if (not driver_name): base_pkg_path = 'google.storage.speckle.python.api.' if apiproxy_stub_map.apiproxy.GetStub('rdbms'): driver_name = (base_pkg_path + 'rdbms_apiproxy') else: driver_name = (base_pkg_path + 'rdbms_googleapi') __import__(driver_name) return sys.modules[driver_name]
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imports the driver module specified by the given module name .
train
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def insert_into_pipeline(pipeline, transform, before=None, after=None): assert (before or after) cls = (before or after) for (i, t) in enumerate(pipeline): if isinstance(t, cls): break if after: i += 1 return ((pipeline[:i] + [transform]) + pipeline[i:])
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insert a new transform into the pipeline after or before an instance of the given class .
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def comb(N, k, exact=False, repetition=False): if repetition: return comb(((N + k) - 1), k, exact) if exact: return _comb_int(N, k) else: (k, N) = (asarray(k), asarray(N)) cond = (((k <= N) & (N >= 0)) & (k >= 0)) vals = binom(N, k) if isinstance(vals, np.ndarray): vals[(~ cond)] = 0 elif (not cond): vals = np.float64(0) return vals
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the number of combinations of n things taken k at a time .
train
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def str_rsplit(arr, pat=None, n=None): if ((n is None) or (n == 0)): n = (-1) f = (lambda x: x.rsplit(pat, n)) res = _na_map(f, arr) return res
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split each string in the series/index by the given delimiter string .
train
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def _find_all_vdis_and_system_vdis(xenapi, all_vdi_uuids, connected_vdi_uuids): def _system_owned(vdi_rec): vdi_name = vdi_rec['name_label'] return (vdi_name.startswith('USB') or vdi_name.endswith('.iso') or (vdi_rec['type'] == 'system')) for vdi_ref in call_xenapi(xenapi, 'VDI.get_all'): try: vdi_rec = call_xenapi(xenapi, 'VDI.get_record', vdi_ref) except XenAPI.Failure as e: if (e.details[0] != 'HANDLE_INVALID'): raise continue vdi_uuid = vdi_rec['uuid'] all_vdi_uuids.add(vdi_uuid) if _system_owned(vdi_rec): print_xen_object('SYSTEM VDI', vdi_rec, indent_level=0) connected_vdi_uuids.add(vdi_uuid) elif (not vdi_rec['managed']): print_xen_object('UNMANAGED VDI', vdi_rec, indent_level=0) connected_vdi_uuids.add(vdi_uuid)
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collects all vdis and adds system vdis to the connected set .
train
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def get_system_runners_base_path(): return cfg.CONF.content.system_runners_base_path
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return a path to the directory where system runners are stored .
train
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def build_markdown_header(title, date, author, categories, tags, slug, status=None, attachments=None): header = (u'Title: %s\n' % title) if date: header += (u'Date: %s\n' % date) if author: header += (u'Author: %s\n' % author) if categories: header += (u'Category: %s\n' % u', '.join(categories)) if tags: header += (u'Tags: %s\n' % u', '.join(tags)) if slug: header += (u'Slug: %s\n' % slug) if status: header += (u'Status: %s\n' % status) if attachments: header += (u'Attachments: %s\n' % u', '.join(attachments)) header += u'\n' return header
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build a header from a list of fields .
train
false
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def GetFieldInDocument(document, field_name, return_type=None): if (return_type is not None): field_list = [f for f in document.field_list() if (f.name() == field_name)] field_types_dict = {} for f in field_list: field_types_dict.setdefault(f.value().type(), f) if (return_type == EXPRESSION_RETURN_TYPE_TEXT): if (document_pb.FieldValue.HTML in field_types_dict): return field_types_dict[document_pb.FieldValue.HTML] if (document_pb.FieldValue.ATOM in field_types_dict): return field_types_dict[document_pb.FieldValue.ATOM] return field_types_dict.get(document_pb.FieldValue.TEXT) elif (return_type == EXPRESSION_RETURN_TYPE_NUMERIC): if (document_pb.FieldValue.NUMBER in field_types_dict): return field_types_dict[document_pb.FieldValue.NUMBER] return field_types_dict.get(document_pb.FieldValue.DATE) else: return field_types_dict.get(return_type) else: for f in document.field_list(): if (f.name() == field_name): return f return None
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find and return the field with the provided name and type .
train
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def _poll_for_status(poll_fn, obj_id, action, final_ok_states, poll_period=5, show_progress=True, status_field='status', silent=False): def print_progress(progress): if show_progress: msg = (_('\rServer %(action)s... %(progress)s%% complete') % dict(action=action, progress=progress)) else: msg = (_('\rServer %(action)s...') % dict(action=action)) sys.stdout.write(msg) sys.stdout.flush() if (not silent): print() while True: obj = poll_fn(obj_id) status = getattr(obj, status_field) if status: status = status.lower() progress = (getattr(obj, 'progress', None) or 0) if (status in final_ok_states): if (not silent): print_progress(100) print(_('\nFinished')) break elif (status == 'error'): if (not silent): print((_('\nError %s server') % action)) raise exceptions.ResourceInErrorState(obj) elif (status == 'deleted'): if (not silent): print((_('\nDeleted %s server') % action)) raise exceptions.InstanceInDeletedState(obj.fault['message']) if (not silent): print_progress(progress) time.sleep(poll_period)
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block while an action is being performed .
train
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def _group_name_from_id(project, group_id): return 'projects/{project}/groups/{group_id}'.format(project=project, group_id=group_id)
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build the group name given the project and group id .
train
false
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def parse_afm(fh): _sanity_check(fh) dhead = _parse_header(fh) (dcmetrics_ascii, dcmetrics_name) = _parse_char_metrics(fh) doptional = _parse_optional(fh) return (dhead, dcmetrics_ascii, dcmetrics_name, doptional[0], doptional[1])
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parse the adobe font metics file in file handle *fh* .
train
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def _unserialize_packer_dict(serialized_packer_dict): packer_dict = {} for item in serialized_packer_dict.split('%!(PACKER_COMMA)'): (key, value) = item.split(':') packer_dict[key] = value return freeze(packer_dict)
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parse a packer serialized dictionary .
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def parse_lang(rule): parser = argparse.ArgumentParser() rules = shlex.split(rule) rules.pop(0) parser.add_argument('lang') args = clean_args(vars(parser.parse_args(rules))) parser = None return args
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parse the lang line .
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def _wait_until_complete(operation, max_attempts=5): def _operation_complete(result): return result retry = RetryResult(_operation_complete, max_tries=max_attempts) return retry(operation.poll)()
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wait until an operation has completed .
train
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def _getattr(obj, name, default): value = getattr(obj, name, default) if value: return value else: return default
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like getattr but return default if none or false .
train
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def _parse_nested_sequence(context, typ): if isinstance(typ, (types.Buffer,)): raise TypingError('%r not allowed in a homogenous sequence') elif isinstance(typ, (types.Sequence,)): (n, dtype) = _parse_nested_sequence(context, typ.dtype) return ((n + 1), dtype) elif isinstance(typ, (types.BaseTuple,)): if (typ.count == 0): return (1, types.float64) (n, dtype) = _parse_nested_sequence(context, typ[0]) dtypes = [dtype] for i in range(1, typ.count): (_n, dtype) = _parse_nested_sequence(context, typ[i]) if (_n != n): raise TypingError(('type %r does not have a regular shape' % (typ,))) dtypes.append(dtype) dtype = context.unify_types(*dtypes) if (dtype is None): raise TypingError('cannot convert %r to a homogenous type') return ((n + 1), dtype) else: as_dtype(typ) return (0, typ)
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parse a nested sequence type .
train
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def GetScriptModuleName(handler_path): if handler_path.startswith((PYTHON_LIB_VAR + '/')): handler_path = handler_path[len(PYTHON_LIB_VAR):] handler_path = os.path.normpath(handler_path) extension_index = handler_path.rfind('.py') if (extension_index != (-1)): handler_path = handler_path[:extension_index] module_fullname = handler_path.replace(os.sep, '.') module_fullname = module_fullname.strip('.') module_fullname = re.sub('\\.+', '.', module_fullname) if module_fullname.endswith('.__init__'): module_fullname = module_fullname[:(- len('.__init__'))] return module_fullname
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determines the fully-qualified python module name of a script on disk .
train
false
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def windows_memory_usage(): from ctypes import windll, Structure, c_uint64, sizeof, byref from ctypes.wintypes import DWORD class MemoryStatus(Structure, ): _fields_ = [('dwLength', DWORD), ('dwMemoryLoad', DWORD), ('ullTotalPhys', c_uint64), ('ullAvailPhys', c_uint64), ('ullTotalPageFile', c_uint64), ('ullAvailPageFile', c_uint64), ('ullTotalVirtual', c_uint64), ('ullAvailVirtual', c_uint64), ('ullAvailExtendedVirtual', c_uint64)] memorystatus = MemoryStatus() memorystatus.dwLength = sizeof(memorystatus) windll.kernel32.GlobalMemoryStatusEx(byref(memorystatus)) return float(memorystatus.dwMemoryLoad)
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return physical memory usage works on windows platforms only .
train
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def parse_string(text): if ((len(text) >= 2) and (text[0] in ('"', "'")) and (text[(-1)] in ('"', "'"))): text = text[1:(-1)] return text.strip()
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parse a string to a string .
train
false
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def save_all_figures(image_path): figure_list = [] (image_dir, image_fmt_str) = image_path.psplit() fig_mngr = matplotlib._pylab_helpers.Gcf.get_all_fig_managers() for fig_num in (m.num for m in fig_mngr): plt.figure(fig_num) plt.savefig(image_path.format(fig_num)) figure_list.append(image_fmt_str.format(fig_num)) return figure_list
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save all matplotlib figures .
train
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def test_config_noastropy_fallback(monkeypatch): from ...tests.helper import pytest monkeypatch.setenv(str(u'XDG_CONFIG_HOME'), u'foo') monkeypatch.delenv(str(u'XDG_CONFIG_HOME')) monkeypatch.setattr(paths.set_temp_config, u'_temp_path', None) def osraiser(dirnm, linkto): raise OSError monkeypatch.setattr(paths, u'_find_or_create_astropy_dir', osraiser) monkeypatch.setattr(configuration, u'_cfgobjs', {}) with pytest.raises(OSError): paths.get_config_dir() with catch_warnings(configuration.ConfigurationMissingWarning) as w: test_configitem() assert (len(w) == 1) w = w[0] assert (u'Configuration defaults will be used' in str(w.message))
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tests to make sure configuration items fall back to their defaults when theres a problem accessing the astropy directory .
train
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def on_color(clip, size=None, color=(0, 0, 0), pos=None, col_opacity=None): if (size is None): size = clip.size if (pos is None): pos = 'center' colorclip = ColorClip(size, color) if col_opacity: colorclip = colorclip.with_mask().set_opacity(col_opacity) return CompositeVideoClip([colorclip, clip.set_pos(pos)], transparent=(col_opacity is not None))
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returns a clip made of the current clip overlaid on a color clip of a possibly bigger size .
train
false
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def all_pairs(singles): pairs = [] singles = list(singles) while singles: suitor = singles.pop(0) pairs.extend(((suitor, single) for single in singles)) return pairs
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generate pairs list for all-against-all alignments .
train
false
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def set_name_reuse(enable=True): set_keep['name_reuse'] = enable
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enable or disable reuse layer name .
train
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def make_shell(init_func=None, banner=None, use_ipython=True): if (banner is None): banner = 'Interactive Werkzeug Shell' if (init_func is None): init_func = dict def action(ipython=use_ipython): 'Start a new interactive python session.' namespace = init_func() if ipython: try: try: from IPython.frontend.terminal.embed import InteractiveShellEmbed sh = InteractiveShellEmbed(banner1=banner) except ImportError: from IPython.Shell import IPShellEmbed sh = IPShellEmbed(banner=banner) except ImportError: pass else: sh(global_ns={}, local_ns=namespace) return from code import interact interact(banner, local=namespace) return action
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returns an action callback that spawns a new interactive python shell .
train
true
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def pass1(arg, *args, **kwargs): return arg
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parse the bytecode int a list of easy-usable tokens: .
train
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def remove_notifications(email_notification_ids=None): for email_id in email_notification_ids: NotificationDigest.remove(Q('_id', 'eq', email_id))
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remove sent emails .
train
false
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@requires_application() def test_arrow_transform_draw(): if (os.getenv('APPVEYOR', '').lower() == 'true'): raise SkipTest('AppVeyor has unknown failure') old_numpy = (LooseVersion(np.__version__) < '1.8') if ((os.getenv('TRAVIS', 'false') == 'true') and ((sys.version[:3] == '2.6') or old_numpy)): raise SkipTest('Travis fails due to FB stack problem') with TestingCanvas() as c: for arrow_type in ARROW_TYPES: arrow = visuals.Arrow(pos=vertices, arrow_type=arrow_type, arrows=arrows, arrow_size=10, color='red', connect='segments', parent=c.scene) arrow.transform = transforms.STTransform(scale=(0.5, 0.75), translate=((-20), (-20))) assert_image_approved(c.render(), ('visuals/arrow_transform_type_%s.png' % arrow_type)) arrow.parent = None
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tests the arrowvisual when a transform is applied .
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def is_task_module(a): if (isinstance(a, types.ModuleType) and (a not in _seen)): _seen.add(a) return True
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determine if the provided value is a task module .
train
false
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def Client(version=None, unstable=False, session=None, **kwargs): if (not session): session = client_session.Session._construct(kwargs) d = discover.Discover(session=session, **kwargs) return d.create_client(version=version, unstable=unstable)
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create new http client .
train
false
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def _GetPropertyValue(entity, property): if (property in datastore_types._SPECIAL_PROPERTIES): if (property == datastore_types._UNAPPLIED_LOG_TIMESTAMP_SPECIAL_PROPERTY): raise KeyError(property) assert (property == datastore_types.KEY_SPECIAL_PROPERTY) return entity.key() else: return entity[property]
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returns an entitys value for a given property name .
train
false
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def get_test_cases(test_files, cfg, tracer=None): matcher = compile_matcher(cfg.test_regex) results = [] for file in test_files: module = import_module(file, cfg, tracer=tracer) if (tracer is not None): test_suite = tracer.runfunc(module.test_suite) else: test_suite = module.test_suite() if (test_suite is None): continue if cfg.warn_omitted: all_classes = set(get_all_test_cases(module)) classes_in_suite = get_test_classes_from_testsuite(test_suite) difference = (all_classes - classes_in_suite) for test_class in difference: stderr(('\n%s: WARNING: %s not in test suite\n' % (file, test_class.__name__))) if ((cfg.level is not None) and (getattr(test_suite, 'level', 0) > cfg.level)): continue filtered = filter_testsuite(test_suite, matcher, cfg.level) results.extend(filtered) return results
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returns a list of test cases from a given list of test modules .
train
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def places_nearby(client, location, radius=None, keyword=None, language=None, min_price=None, max_price=None, name=None, open_now=False, rank_by=None, type=None, page_token=None): if (rank_by == 'distance'): if (not (keyword or name or type)): raise ValueError('either a keyword, name, or type arg is required when rank_by is set to distance') elif (radius is not None): raise ValueError('radius cannot be specified when rank_by is set to distance') return _places(client, 'nearby', location=location, radius=radius, keyword=keyword, language=language, min_price=min_price, max_price=max_price, name=name, open_now=open_now, rank_by=rank_by, type=type, page_token=page_token)
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performs nearby search for places .
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def authenticate_with_password(request, page_view_restriction_id, page_id): restriction = get_object_or_404(PageViewRestriction, id=page_view_restriction_id) page = get_object_or_404(Page, id=page_id).specific if (request.method == u'POST'): form = PasswordPageViewRestrictionForm(request.POST, instance=restriction) if form.is_valid(): has_existing_session = (settings.SESSION_COOKIE_NAME in request.COOKIES) passed_restrictions = request.session.setdefault(u'passed_page_view_restrictions', []) if (restriction.id not in passed_restrictions): passed_restrictions.append(restriction.id) request.session[u'passed_page_view_restrictions'] = passed_restrictions if (not has_existing_session): request.session.set_expiry(0) return redirect(form.cleaned_data[u'return_url']) else: form = PasswordPageViewRestrictionForm(instance=restriction) action_url = reverse(u'wagtailcore_authenticate_with_password', args=[restriction.id, page.id]) return page.serve_password_required_response(request, form, action_url)
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handle a submission of passwordpageviewrestrictionform to grant view access over a subtree that is protected by a pageviewrestriction .
train
false
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def get_colormap(name, *args, **kwargs): if isinstance(name, BaseColormap): cmap = name else: if (not isinstance(name, string_types)): raise TypeError('colormap must be a Colormap or string name') if (name not in _colormaps): raise KeyError(('colormap name %s not found' % name)) cmap = _colormaps[name] if inspect.isclass(cmap): cmap = cmap(*args, **kwargs) return cmap
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obtain a colormap some colormaps can have additional configuration parameters .
train
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def _mul_as_two_parts(f): gs = _mul_args(f) if (len(gs) < 2): return None if (len(gs) == 2): return [tuple(gs)] return [(Mul(*x), Mul(*y)) for (x, y) in multiset_partitions(gs, 2)]
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find all the ways to split f into a product of two terms .
train
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def test_binary_descriptors_unequal_descriptor_sizes_error(): descs1 = np.array([[True, True, False, True], [False, True, False, True]]) descs2 = np.array([[True, False, False, True, False], [False, True, True, True, False]]) assert_raises(ValueError, match_descriptors, descs1, descs2)
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sizes of descriptors of keypoints to be matched should be equal .
train
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def is_quad_residue(a, p): (a, p) = (as_int(a), as_int(p)) if (p < 1): raise ValueError('p must be > 0') if ((a >= p) or (a < 0)): a = (a % p) if ((a < 2) or (p < 3)): return True if (not isprime(p)): if ((p % 2) and (jacobi_symbol(a, p) == (-1))): return False r = sqrt_mod(a, p) if (r is None): return False else: return True return (pow(a, ((p - 1) // 2), p) == 1)
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returns true if a is in the set of squares mod p .
train
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def action_finish(context, values): return IMPL.action_finish(context, values)
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finish an action for an instance .
train
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@register.tag def get_latest_posts(parser, token): try: (tag_name, arg) = token.contents.split(None, 1) except ValueError: raise template.TemplateSyntaxError, ('%s tag requires arguments' % token.contents.split()[0]) m = re.search('(.*?) as (\\w+)', arg) if (not m): raise template.TemplateSyntaxError, ('%s tag had invalid arguments' % tag_name) (format_string, var_name) = m.groups() return LatestPosts(format_string, var_name)
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gets any number of latest posts and stores them in a varable .
train
false
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@register_opt() @local_optimizer([GpuFromHost, tensor.blas.Dot22Scalar]) def local_gpu_dot22scalar(node): if isinstance(node.op, GpuFromHost): host_input = node.inputs[0] if (host_input.owner and isinstance(host_input.owner.op, tensor.blas.Dot22Scalar)): (x, y, scalar) = host_input.owner.inputs return [gpu_dot22scalar(as_cuda_ndarray_variable(x), as_cuda_ndarray_variable(y), tensor.blas._as_scalar(scalar))] if isinstance(node.op, tensor.blas.Dot22Scalar): if any([(i.owner and isinstance(i.owner.op, HostFromGpu)) for i in node.inputs]): (x, y, scalar) = node.inputs return [host_from_gpu(gpu_dot22scalar(as_cuda_ndarray_variable(x), as_cuda_ndarray_variable(y), tensor.blas._as_scalar(scalar)))] return False
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gpu_from_host -> gpudot dot -> host_from_gpu .
train
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def get_time_server(): ret = salt.utils.mac_utils.execute_return_result('systemsetup -getnetworktimeserver') return salt.utils.mac_utils.parse_return(ret)
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display the currently set network time server .
train
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def uses_mandrill(func): @wraps(func) def wrapped_func(*args, **kwargs): kwargs['mail_client'] = get_mandrill_client() return func(*args, **kwargs) return wrapped_func
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this decorator takes a function with keyword argument "mail_client" and fills it in with the mail_client for the mandrill account .
train
false
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def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False): return _apply_scalar_per_pixel(generic_cy._maximum, image, selem, out=out, mask=mask, shift_x=shift_x, shift_y=shift_y)
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element-wise maximum of input variables .
train
false
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def skipped(*a, **kw): item = a[0] if (type(item) != dict): raise errors.AnsibleFilterError('|skipped expects a dictionary') skipped = item.get('skipped', False) return skipped
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test if task result yields skipped .
train
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@pytest.mark.network def test_download_should_download_dependencies(script): result = script.pip('download', 'Paste[openid]==1.7.5.1', '-d', '.', expect_error=True) assert ((Path('scratch') / 'Paste-1.7.5.1.tar.gz') in result.files_created) openid_tarball_prefix = str((Path('scratch') / 'python-openid-')) assert any((path.startswith(openid_tarball_prefix) for path in result.files_created)) assert ((script.site_packages / 'openid') not in result.files_created)
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it should download dependencies .
train
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def _GetConnection(): __InitConnection() return _thread_local.connection_stack[(-1)]
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internal method to retrieve a datastore connection local to the thread .
train
false
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def setup_path(invoke_minversion=None): if (not os.path.isdir(TASKS_VENDOR_DIR)): print(('SKIP: TASKS_VENDOR_DIR=%s is missing' % TASKS_VENDOR_DIR)) return elif (os.path.abspath(TASKS_VENDOR_DIR) in sys.path): pass use_vendor_bundles = os.environ.get('INVOKE_TASKS_USE_VENDOR_BUNDLES', 'no') if need_vendor_bundles(invoke_minversion): use_vendor_bundles = 'yes' if (use_vendor_bundles == 'yes'): syspath_insert(0, os.path.abspath(TASKS_VENDOR_DIR)) if setup_path_for_bundle(INVOKE_BUNDLE, pos=1): import invoke bundle_path = os.path.relpath(INVOKE_BUNDLE, os.getcwd()) print(('USING: %s (version: %s)' % (bundle_path, invoke.__version__))) else: syspath_append(os.path.abspath(TASKS_VENDOR_DIR)) setup_path_for_bundle(INVOKE_BUNDLE, pos=len(sys.path)) if DEBUG_SYSPATH: for (index, p) in enumerate(sys.path): print((' %d. %s' % (index, p)))
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setup python search and add tasks_vendor_dir .
train
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def new(rsa_key): return PKCS115_SigScheme(rsa_key)
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return a fresh instance of a shake256 object .
train
false
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def _hmac_etag(key, etag): result = hmac.new(key, etag, digestmod=hashlib.sha256).digest() return base64.b64encode(result).decode()
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compute an hmac-sha256 using given key and etag .
train
false
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def parse_etag_response(value): if (value and (not value.startswith('W/'))): unquote_match = QUOTES_RE.match(value) if (unquote_match is not None): value = unquote_match.group(1) value = value.replace('\\"', '"') return value
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parse a response etag .
train
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def lock(instance_id, profile=None): conn = _auth(profile) return conn.lock(instance_id)
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get lock path the path in zookeeper where the lock is zk_hosts zookeeper connect string identifier name to identify this minion .
train
true
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@sync_performer @do def perform_upload_s3_key_recursively(dispatcher, intent): for child in intent.files: path = intent.source_path.preauthChild(child) if path.isfile(): (yield Effect(UploadToS3(source_path=intent.source_path, target_bucket=intent.target_bucket, target_key=('%s/%s' % (intent.target_key, child)), file=path)))
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see :class:uploadtos3recursively .
train
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@image_comparison(baseline_images=[u'colorbar_extensions_shape_uniform', u'colorbar_extensions_shape_proportional'], extensions=[u'png']) def test_colorbar_extension_shape(): _colorbar_extension_shape(u'uniform') _colorbar_extension_shape(u'proportional')
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test rectangular colorbar extensions .
train
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def RenderValue(value, limit_lists=(-1)): if (value is None): return None renderer = ApiValueRenderer.GetRendererForValueOrClass(value, limit_lists=limit_lists) return renderer.RenderValue(value)
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render given rdfvalue as plain old python objects .
train
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def getComplexByWords(words, wordIndex=0): try: return complex(float(words[wordIndex]), float(words[(wordIndex + 1)])) except: pass return None
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get the complex by the first two words .
train
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def moments_normalized(mu, order=3): if (mu.ndim != 2): raise TypeError('Image moments must be 2-dimension') if ((mu.shape[0] <= order) or (mu.shape[1] <= order)): raise TypeError('Shape of image moments must be >= `order`') return _moments_cy.moments_normalized(mu.astype(np.double), order)
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calculate all normalized central image moments up to a certain order .
train
false
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def to_unicode_from_fs(string): if (not is_string(string)): string = to_text_string(string.toUtf8(), 'utf-8') elif is_binary_string(string): try: unic = string.decode(FS_ENCODING) except (UnicodeError, TypeError): pass else: return unic return string
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return a unicode version of string decoded using the file system encoding .
train
true
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def parse_lookaround(source, info, behind, positive): saved_flags = info.flags try: subpattern = _parse_pattern(source, info) source.expect(')') finally: info.flags = saved_flags source.ignore_space = bool((info.flags & VERBOSE)) return LookAround(behind, positive, subpattern)
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parses a lookaround .
train
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def label_sign_flip(label, src): if (len(src) != 2): raise ValueError('Only source spaces with 2 hemisphers are accepted') lh_vertno = src[0]['vertno'] rh_vertno = src[1]['vertno'] if (label.hemi == 'lh'): vertno_sel = np.intersect1d(lh_vertno, label.vertices) if (len(vertno_sel) == 0): return np.array([], int) ori = src[0]['nn'][vertno_sel] elif (label.hemi == 'rh'): vertno_sel = np.intersect1d(rh_vertno, label.vertices) if (len(vertno_sel) == 0): return np.array([], int) ori = src[1]['nn'][vertno_sel] else: raise Exception('Unknown hemisphere type') (_, _, Vh) = linalg.svd(ori, full_matrices=False) flip = np.sign(np.dot(ori, Vh[0])) return flip
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compute sign for label averaging .
train
false
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def indent_block(text, indention=' '): temp = '' while (text and (text[(-1)] == '\n')): temp += text[(-1)] text = text[:(-1)] lines = text.split('\n') return ('\n'.join(map((lambda s: (indention + s)), lines)) + temp)
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this function indents a text block with a default of four spaces .
train
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def getAroundsFromPathPoints(points, radius, thresholdRatio=0.9): centers = getCentersFromPoints(points, (0.8 * radius)) arounds = [] for center in centers: if euclidean.isWiddershins(center): arounds.append(euclidean.getSimplifiedPath(center, radius)) return arounds
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get the arounds from the path .
train
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def parse_image_id(image_ref): return image_ref.rsplit('/')[(-1)]
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return the image id from a given image ref this function just returns the last word of the given image ref string splitting with / .
train
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def read_feather(path): feather = _try_import() return feather.read_dataframe(path)
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load a feather-format object from the file path .
train
false