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def _temp_proj(ref_2, ref_1, raw_data, n_proj=6): """Remove common signal subspace of ref_2 and ref_1 from raw_data. Parameters ---------- ref_2 : np.ndarray of float, shape (n_sensors_2, n_times) The magnetometer data for CSS. Can use either all magnetometer data or a few selected sens...
def _temp_proj(ref_2, ref_1, raw_data, n_proj=6): """Remove common signal subspace of ref_2 and ref_1 from raw_data. Parameters ---------- ref_2 : np.ndarray of float, shape (n_sensors_2, n_times) The magnetometer data for CSS. Can use either all magnetometer data or a few selected sens...
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def get_pkg_data_path(*path, package=None): """Make path from source-included data directories. Parameters ---------- *path : str Name/location of the desired data file/directory. May be a tuple of strings -- for ``os.path`` intelligent path joining. package : str, optional, keywor...
def get_pkg_data_path(*path, package=None): """Get path from source-included data directories. Parameters ---------- *path : str Name/location of the desired data file/directory. May be a tuple of strings -- for ``os.path`` intelligent path joining. package : str, optional, keyword...
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def _compute_library_size( data: Union[sp_sparse.spmatrix, np.ndarray] ) -> Tuple[np.ndarray, np.ndarray]: sum_counts = data.sum(axis=1) masked_log_sum = np.ma.log(sum_counts) if np.ma.is_masked(masked_log_sum): logger.warning( "This dataset has some empty cells, this might fail scVI...
def _compute_library_size( data: Union[sp_sparse.spmatrix, np.ndarray] ) -> Tuple[np.ndarray, np.ndarray]: sum_counts = data.sum(axis=1) masked_log_sum = np.ma.log(sum_counts) if np.ma.is_masked(masked_log_sum): logger.warning( "This dataset has some empty cells, this might fail infe...
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def qemu_check_kvm_support() -> bool: kvm = Path("/dev/kvm") if not kvm.exists(): return False # some CI runners may present a non-working KVM device try: with kvm.open(): return True except OSError: return False
def qemu_check_kvm_support() -> bool: kvm = Path("/dev/kvm") if not kvm.is_char_device(): return False # some CI runners may present a non-working KVM device try: with kvm.open(): return True except OSError: return False
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def validate_and_parse_detection_start_end_time(args: Dict[str, Any]) -> Tuple[Optional[datetime], Optional[datetime]]: """ Validate and return detection_start_time and detection_end_time as per Chronicle Backstory or \ raise a ValueError if the given inputs are invalid. :type args: dict :param arg...
def validate_and_parse_detection_start_end_time(args: Dict[str, Any]) -> Tuple[Optional[datetime], Optional[datetime]]: """ Validate and return detection_start_time and detection_end_time as per Chronicle Backstory or \ raise a ValueError if the given inputs are invalid. :type args: dict :param arg...
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def _find_excluded_ranges( lines: List[Tuple[int, str]], *, warnings: _ExclusionRangeWarnings, exclude_lines_by_pattern: Optional[str] = None, exclude_branches_by_pattern: Optional[str] = None, exclude_pattern_prefix: str, ) -> Callable[[int], bool]: """ Scan through all lines to find li...
def _find_excluded_ranges( lines: List[Tuple[int, str]], *, warnings: _ExclusionRangeWarnings, exclude_lines_by_pattern: Optional[str] = None, exclude_branches_by_pattern: Optional[str] = None, exclude_pattern_prefix: str, ) -> Callable[[int], bool]: """ Scan through all lines to find li...
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def get_entity_extractors(interpreter: Interpreter) -> Set[Text]: """Finds the names of entity extractors used by the interpreter. Processors are removed since they do not detect the boundaries themselves. """ from rasa.nlu.extractors.extractor import EntityExtractor extractors = set() for c i...
def get_entity_extractors(interpreter: Interpreter) -> Set[Text]: """Finds the names of entity extractors used by the interpreter. Processors are removed since they do not detect the boundaries themselves. """ from rasa.nlu.extractors.extractor import EntityExtractor extractors = set() for c i...
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def test_project_generate(): """ Run project by passing in a pandas.DataFrame as input. """ output = project(center=[0, -1], endpoint=[0, 1], flat_earth=True, generate=0.5) assert isinstance(output, pd.DataFrame) assert output.shape == (5, 3) npt.assert_allclose(output.iloc[1], [3.061617e-17...
def test_project_generate(): """ Run project by passing in center and endpoint as input. """ output = project(center=[0, -1], endpoint=[0, 1], flat_earth=True, generate=0.5) assert isinstance(output, pd.DataFrame) assert output.shape == (5, 3) npt.assert_allclose(output.iloc[1], [3.061617e-1...
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def generate_ast(triple_arch, action, source, config, env): """ Generates ASTs for the current compilation command. """ ast_joined_path = os.path.join(config.ctu_dir, triple_arch, 'ast', os.path.realpath(source)[1:] + '.ast') ast_path = os.path.abspath(ast_joined_path) ...
def generate_ast(triple_arch, action, source, config, env): """ Generates ASTs for the current compilation command. """ ast_joined_path = os.path.join(config.ctu_dir, triple_arch, 'ast', os.path.realpath(source)[1:] + '.ast') ast_path = os.path.abspath(ast_joined_path) ...
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def test_vmc_functions(): ha, sx, ma, sampler, driver = _setup_vmc() driver.advance(500) assert driver.energy.mean == approx(ma.expect(ha).mean, abs=1e-5) state = ma.to_array() n_samples = 16000 ma.n_samples = n_samples ma.n_discard_per_chain = 100 # Check zero gradieent _, grad...
def test_vmc_functions(): ha, sx, ma, sampler, driver = _setup_vmc() driver.advance(500) assert driver.energy.mean == approx(ma.expect(ha).mean, abs=1e-5) state = ma.to_array() n_samples = 16000 ma.n_samples = n_samples ma.n_discard_per_chain = 100 # Check zero gradient _, grads...
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def test_redact(): redacted = wandb_settings._redact_dict({"this": 2, "that": 9, "api_key": "secret"}) assert redacted == {"this": 2, "that": 9, "api_key": "***REDACTED***"} redacted = wandb_settings._redact_dict( {"ok": "keep", "unsafe": 9, "bad": "secret"}, unsafe_keys={"unsafe", "bad"}, ...
def test_redact(): redacted = wandb_settings._redact_dict({"this": 2, "that": 9, "api_key": "secret"}) assert redacted == {"this": 2, "that": 9, "api_key": "***REDACTED***"} redacted = wandb_settings._redact_dict({"this": 2, "that": 9}) assert redacted == {"this": 2, "that": 9} redacted = wandb_sett...
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def extract_trigger_kv(trigger_events: list): """ The main information about the alert is more convenient to have as key/value pairs instead of using field weights as it makes writing mapping for individual values easier. """ trigger_event = None for event in trigger_events: if event.get...
def extract_trigger_kv(trigger_events: list): """ The main information about the alert is more convenient to have as key/value pairs instead of using field weights as it makes writing mapping for individual values easier. """ trigger_event = None for event in trigger_events: if event.get...
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def ini_check (): """set environmental variable and change directory""" # Note: # hal_gremlin gets INI file from os.environ (only) # hal_gremlin expects cwd to be same as INI file ini_filename = get_linuxcnc_ini_file() if ini_filename is not None: os.putenv('INI_FILE_NAME',ini_filena...
def ini_check (): """set environmental variable and change directory""" # Note: # hal_gremlin gets INI file from os.environ (only) # hal_gremlin expects cwd to be same as INI file ini_filename = get_linuxcnc_ini_file() if ini_filename is not None: os.putenv('INI_FILE_NAME',ini_filena...
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def compute_authenticator(raw_payload: Union[bytes, bytearray], dbmsg: Message, authenticator_fn: Callable[[Message, bytearray, int], ...
def compute_authenticator(raw_payload: Union[bytes, bytearray], dbmsg: Message, authenticator_fn: Callable[[Message, bytearray, int], ...
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def fake_random_ds( ndims, peak_value=1.0, fields=None, units=None, particle_fields=None, particle_field_units=None, negative=False, nprocs=1, particles=0, length_unit=1.0, unit_system="cgs", bbox=None, ): from yt.loaders import load_uniform_grid if fields is not...
def fake_random_ds( ndims, peak_value=1.0, fields=None, units=None, particle_fields=None, particle_field_units=None, negative=False, nprocs=1, particles=0, length_unit=1.0, unit_system="cgs", bbox=None, ): from yt.loaders import load_uniform_grid if (fields, unit...
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def merge_grids_of_equal_dim(gb): """Merges all grids that have the same dimension in the GridBucket. Thus the returned GridBucket only has one grid per dimension. See also pp.utils.grid_utils.merge_grids(grids). Parameters: gb (pp.GridBucket): A Grid bucket with possible many grids per dimension ...
def merge_grids_of_equal_dim(gb: pp.GridBucket) -> pp.GridBucket: """Merges all grids that have the same dimension in the GridBucket. Thus the returned GridBucket only has one grid per dimension. See also pp.utils.grid_utils.merge_grids(grids). Parameters: gb (pp.GridBucket): A Grid bucket with pos...
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def distance_to_identity(dist, d_low=None, d_high=None): """ ANI = 1-distance """ if not 0 <= dist <= 1: raise ValueError(f"Error: distance value {dist} is not between 0 and 1!") ident = 1 - dist result = ident id_low, id_high = None, None if any([d_low is not None, d_high is not...
def distance_to_identity(dist, *, d_low=None, d_high=None): """ ANI = 1-distance """ if not 0 <= dist <= 1: raise ValueError(f"Error: distance value {dist} is not between 0 and 1!") ident = 1 - dist result = ident id_low, id_high = None, None if any([d_low is not None, d_high is ...
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def as_chunks(max_size: int, iterator: _Iter[T]) -> _Iter[List[T]]: """A helper function that collects an iterator into chunks of a given size. .. versionadded:: 2.0 Parameters ---------- max_size: :class:`int` The maximum chunk size. iterator: Union[:class:`Iterator`, :class:`...
def as_chunks(max_size: int, iterator: _Iter[T]) -> _Iter[List[T]]: """A helper function that collects an iterator into chunks of a given size. .. versionadded:: 2.0 Parameters ---------- max_size: :class:`int` The maximum chunk size. iterator: Union[:class:`collections.abc.Ite...
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def parse_incident_from_item(item): """ Parses an incident from an item :param item: item to parse :return: Parsed item """ incident = {} labels = [] try: incident["details"] = item.text_body or item.body except AttributeError: incident["details"] = item.body inc...
def parse_incident_from_item(item): """ Parses an incident from an item :param item: item to parse :return: Parsed item """ incident = {} labels = [] try: incident["details"] = item.text_body or item.body except AttributeError: incident["details"] = item.body inc...
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def _flag_missing_timestamps( df: pd.DataFrame, frequency: str, column_name: str, first_time_stamp: pd.Timestamp, last_time_stamp: pd.Timestamp, ) -> namedtuple: """ Utility function to test if input data frame is missing any timestamps relative to expected timestamps generated based on ...
def _flag_missing_timestamps( df: pd.DataFrame, frequency: str, column_name: str, first_time_stamp: pd.Timestamp, last_time_stamp: pd.Timestamp, ) -> namedtuple: """ Utility function to test if input data frame is missing any timestamps relative to expected timestamps generated based on ...
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def map_scim(clientData): try: clientData = json.loads(clientData) except Exception: pass if type(clientData) != dict: raise Exception('Provided client data is not JSON compatible') scim_extension = INPUT_SCIM_EXTENSION_KEY.replace('.', '\.') mapping = { "active": "a...
def map_scim(client_data): try: client_data = json.loads(client_data) except Exception: pass if type(client_data) != dict: raise Exception('Provided client data is not JSON compatible') scim_extension = INPUT_SCIM_EXTENSION_KEY.replace('.', '\.') mapping = { "active"...
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def rk4(f, x, t, dt, stages=4, s=0): """Runge-Kutta (explicit, non-adaptive) numerical (S)ODE solvers. The rule has strong / weak convergence order 1.0 for generic SDEs and order 4.0 convergence for ODEs when stages=4. For stages=1, this becomes the Euler-Maruyama schemefor SDEs (s > 0.0) with strong ...
def rk4(f, x, t, dt, stages=4, s=0): """Runge-Kutta (explicit, non-adaptive) numerical (S)ODE solvers. The rule has strong / weak convergence order 1.0 for generic SDEs and order 4.0 convergence for ODEs when stages=4. For stages=1, this becomes the Euler-Maruyama schemefor SDEs (s > 0.0) with strong ...
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def gather_new_integration(determine_auth: bool) -> Info: """Gather info about new integration from user.""" fields = { "name": { "prompt": "What is the name of your integration?", "validators": [CHECK_EMPTY], }, "codeowner": { "prompt": "What is your ...
def gather_new_integration(determine_auth: bool) -> Info: """Gather info about new integration from user.""" fields = { "name": { "prompt": "What is the name of your integration?", "validators": [CHECK_EMPTY], }, "codeowner": { "prompt": "What is your ...
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def create_user_iam(default_base_dn, default_page_size, args): assert conn is not None user_profile = args.get("user-profile") user_profile_delta = args.get('user-profile-delta') iam_user_profile = IAMUserProfile(user_profile=user_profile, user_profile_delta=user_profile_delta) ad_user = iam_user_...
def create_user_iam(default_base_dn, default_page_size, args): assert conn is not None user_profile = args.get("user-profile") user_profile_delta = args.get('user-profile-delta') iam_user_profile = IAMUserProfile(user_profile=user_profile, user_profile_delta=user_profile_delta) ad_user = iam_user_...
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def _linda_forecast( precip, precip_lagr_diff, timesteps, fct_gen, precip_pert_gen, vel_pert_gen, n_ensemble_members, seed, measure_time, print_info, return_output, callback, ): """Compute LINDA nowcast.""" # compute convolved difference fields precip_lagr_dif...
def _linda_forecast( precip, precip_lagr_diff, timesteps, forecast_gen, precip_pert_gen, vel_pert_gen, n_ensemble_members, seed, measure_time, print_info, return_output, callback, ): """Compute LINDA nowcast.""" # compute convolved difference fields precip_lag...
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def group_dataset_datavalues( dataset, datavalues_list ) -> list: """ This function evaluates a few cases regarding the 'period' and 'orgUnit': Considerations: The 'period' and 'orgUnit' must be on specified on the same level as the 'completeDate', thus ...
def group_dataset_datavalues( dataset, datavalues_list ) -> list: """ This function evaluates a few cases regarding the 'period' and 'orgUnit': Considerations: The 'period' and 'orgUnit' must be on specified on the same level as the 'completeDate', thus ...
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def combine_evoked(all_evoked, weights): """Merge evoked data by weighted addition or subtraction. Data should have the same channels and the same time instants. Subtraction can be performed by calling ``combine_evoked([evoked1, -evoked2], 'equal')`` .. warning:: If you pass negative weights, it ...
def combine_evoked(all_evoked, weights): """Merge evoked data by weighted addition or subtraction. Data should have the same channels and the same time instants. Subtraction can be performed by calling ``combine_evoked([evoked1, -evoked2], 'equal')`` .. warning:: If you pass negative weights, it ...
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def list_accounts(client, args): title = f'{INTEGRATION_NAME} - List of the Accounts' raws = [] cyberark_ec = [] raw_response = client.get_accounts(offset=args['offset'], limit=args['limit'])['value'] if raw_response: for item in raw_response: raws.append(item) cyber...
def list_accounts(client, args): title = f'{INTEGRATION_NAME} - List of the Accounts' raws = [] cyberark_ec = [] raw_response = client.get_accounts(offset=args['offset'], limit=args['limit'])['value'] if raw_response: for item in raw_response: raws.append(item) cyber...
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def rand_QB(A, target_rank=None, distribution='normal', oversampling=0, powerIterations=0): """ randomisierte QB-Zerlegung See Algorithm 3.1 in [EMKB19]_. Parameters ---------- A : The |VectorArray| for which the randomized QB Decomposition is to be computed. target_rank : int ...
def rand_QB(A, target_rank=None, distribution='normal', oversampling=0, powerIterations=0): """ randomisierte QB-Zerlegung See Algorithm 3.1 in [EMKB19]_. Parameters ---------- A : The |VectorArray| for which the randomized QB Decomposition is to be computed. target_rank : int ...
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def create_data_graph_nodes_and_groups(data, parent_uid, root_uid, whitelist): data_graph = { 'nodes': [], 'edges': [], 'groups': [] } groups = [] for file in data: mime = file['processed_analysis']['file_type']['mime'] if mime not in whitelist or root_uid not i...
def create_data_graph_nodes_and_groups(data, parent_uid, root_uid, whitelist): data_graph = { 'nodes': [], 'edges': [], 'groups': [] } groups = [] for file in data: mime = file['processed_analysis']['file_type']['mime'] if mime not in whitelist or root_uid not i...
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def format_sort(sort_str: str) -> list: """ Format a sort string from "field1:asc,field2:desc" to a list accepted by pymongo.sort() "field1:asc,field2:desc" => [("field1",1),("field2",-1)] Args: sort_str: a sort detailed as a string Returns: list accepted by pymongo.sort() """ ...
def format_sort(sort_str: str) -> list: """ Format a sort string from "field1:asc,field2:desc" to a list accepted by pymongo.sort() "field1:asc,field2:desc" => [("field1",1),("field2",-1)] Args: sort_str: a sort detailed as a string Returns: list accepted by pymongo.sort() """ ...
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def convert_reports(reports: Report, repo_dirs: List[str]) -> Dict: """Convert the given reports to codeclimate format. This function will convert the given report to Code Climate format. reports - list of reports type Report repo_dir - Root directory of the sources, i.e. the directory where the ...
def convert_reports(reports: List[Report], repo_dirs: List[str]) -> Dict: """Convert the given reports to codeclimate format. This function will convert the given report to Code Climate format. reports - list of reports type Report repo_dir - Root directory of the sources, i.e. the directory where the ...
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def fetch_incidents(client: Client) -> list: query_params = {} incidents = [] last_run = demisto.getLastRun() date_format = '%Y-%m-%d %H:%M:%S' start_snow_time, _ = get_fetch_run_time_range( last_run=last_run, first_fetch=client.fetch_time, look_back=client.look_back, date_format=date_form...
def fetch_incidents(client: Client) -> list: query_params = {} incidents = [] last_run = demisto.getLastRun() date_format = '%Y-%m-%d %H:%M:%S' start_snow_time, _ = get_fetch_run_time_range( last_run=last_run, first_fetch=client.fetch_time, look_back=client.look_back, date_format=date_form...
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def _get_secret_key(response): # type: (PipelineResponse) -> str # expecting header containing path to secret key file header = response.http_response.headers.get("Www-Authenticate") if header is None: raise CredentialUnavailableError(message="Did not receive a value from Www-Authenticate header...
def _get_secret_key(response): # type: (PipelineResponse) -> str # expecting header containing path to secret key file header = response.http_response.headers.get("Www-Authenticate") if header is None: raise CredentialUnavailableError(message="Did not receive a value from Www-Authenticate header...
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def plot_dendrogram(model, **kwargs): # Create linkage matrix and then plot the dendrogram # create the counts of samples in each node counts = np.zeros(model.children_.shape[0]) n_samples = len(model.labels_) for i, merge in enumerate(model.children_): current_count = 0 for j in [0...
def plot_dendrogram(model, **kwargs): # Create linkage matrix and then plot the dendrogram # create the counts of samples in each node counts = np.zeros(model.children_.shape[0]) n_samples = len(model.labels_) for i, merge in enumerate(model.children_): current_count = 0 for j in [0...
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def _is_resolved(what): return (what is None or isinstance(what, (dict, list)))
def _is_resolved(what): return isinstance(what, (dict, list, NoneType))
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def set_total_prices(apps, schema_editor): OrderLine = apps.get_model("order", "OrderLine") lines = [] for line in OrderLine.objects.filter(total_price_gross_amount__isnull=True): line.total_price_gross_amount = line.unit_price_gross_amount * line.quantity line.total_price_net_amount = line....
def set_total_prices(apps, schema_editor): OrderLine = apps.get_model("order", "OrderLine") lines = [] for line in OrderLine.objects.filter(total_price_gross_amount__isnull=True).iterator(): line.total_price_gross_amount = line.unit_price_gross_amount * line.quantity line.total_price_net_amo...
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def check_layout_layers(layout, layers): """ Check the layer widget order matches the layers order in the layout Parameters ---------- layout : QLayout Layout to test layers : napar.components.LayersList LayersList to compare to Returns ---------- match : bool ...
def check_layout_layers(layout, layers): """ Check the layer widget order matches the layers order in the layout Parameters ---------- layout : QLayout Layout to test layers : napari.components.LayersList LayersList to compare to Returns ---------- match : bool ...
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def _get_packages(): exclude = [ "build*", "test*", "torchaudio.csrc*", "third_party*", "build_tools*", ] exclude_prototype = False branch_name = _run_cmd(['git', 'rev-parse', '--abbrev-ref', 'HEAD']) is_on_tag = _run_cmd(['git', 'describe', '--tags', '--exact...
def _get_packages(): exclude = [ "build*", "test*", "torchaudio.csrc*", "third_party*", "tools*", ] exclude_prototype = False branch_name = _run_cmd(['git', 'rev-parse', '--abbrev-ref', 'HEAD']) is_on_tag = _run_cmd(['git', 'describe', '--tags', '--exact-match...
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def log_umount_blockers(mount: Path) -> None: blockers: List[Tuple[str, Path]] = [] for d in Path("/proc").iterdir(): if not d.is_dir(): continue try: int(d.name) except ValueError: continue if not d.joinpath("fd").exists(): cont...
def log_umount_blockers(mount: Path) -> None: blockers: List[Tuple[str, Path]] = [] for d in Path("/proc").iterdir(): if not d.is_dir(): continue if not d.name.isdigit(): continue if not d.joinpath("fd").exists(): continue comm = d.joinpath...
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def _get_doc_content(project, version, doc): storage_path = project.get_storage_path( 'json', version_slug=version.slug, include_file=False, version_type=version.type, ) file_path = build_media_storage.join(storage_path, f'{doc}.fjson') try: with build_media_stora...
def _get_doc_content(project, version, doc): storage_path = project.get_storage_path( 'json', version_slug=version.slug, include_file=False, version_type=version.type, ) file_path = build_media_storage.join(storage_path, f'{doc}.fjson') try: with build_media_stora...
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def main(): """ PARSE AND VALIDATE INTEGRATION PARAMS """ params = demisto.params() username = params.get('credentials').get('identifier') password = params.get('credentials').get('password') # get the service API url base_url = params.get('server_url') verify_certificate = not...
def main(): """ PARSE AND VALIDATE INTEGRATION PARAMS """ params = demisto.params() username = params.get('credentials').get('identifier') password = params.get('credentials').get('password') # get the service API url base_url = params.get('server_url') verify_certificate = not...
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def checkpoint_delete_objects_batch_command(client: Client, object_type: str, name): printable_result = {} readable_output = '' name = argToList(name) del_list = [] for n in name: tmp_dict = {'name': n} del_list.append(tmp_dict) result = current_result = client.delete_objects_b...
def checkpoint_delete_objects_batch_command(client: Client, object_type: str, name): printable_result = {} readable_output = '' name = argToList(name) objects_to_delete = [{'name':object_name} for object_name in object_names] result = current_result = client.delete_objects_batch(object_type, del_l...
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def handle_exception(ex): # pylint: disable=too-many-return-statements # For error code, follow guidelines at https://docs.python.org/2/library/sys.html#sys.exit, from jmespath.exceptions import JMESPathTypeError from msrestazure.azure_exceptions import CloudError from msrest.exceptions import HttpOper...
def handle_exception(ex): # pylint: disable=too-many-return-statements # For error code, follow guidelines at https://docs.python.org/2/library/sys.html#sys.exit, from jmespath.exceptions import JMESPathTypeError from msrestazure.azure_exceptions import CloudError from msrest.exceptions import HttpOper...
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def parse_psm3(fbuf, map_variables): """ Parse an NSRDB PSM3 weather file (formatted as SAM CSV). The NSRDB is described in [1]_ and the SAM CSV format is described in [2]_. .. versionchanged:: 0.9.0 The function now returns a tuple where the first element is a dataframe and the second el...
def parse_psm3(fbuf, map_variables=None): """ Parse an NSRDB PSM3 weather file (formatted as SAM CSV). The NSRDB is described in [1]_ and the SAM CSV format is described in [2]_. .. versionchanged:: 0.9.0 The function now returns a tuple where the first element is a dataframe and the seco...
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def power_transform(X, method="yeo-johnson", *, standardize=True, copy=True): """ Family of parametric, monotonic transformations to make data more Gaussian-like. Power transforms are a family of parametric, monotonic transformations that are applied to make data more Gaussian-like. This is useful ...
def power_transform(X, method="yeo-johnson", *, standardize=True, copy=True): """Parametric, monotonic transformation to make data more Gaussian-like. Power transforms are a family of parametric, monotonic transformations that are applied to make data more Gaussian-like. This is useful for modeling...
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def _get_handler(settings): """ Return a log handler object according to settings """ filename = settings.get('LOG_FILE') if filename: encoding = settings.get('LOG_ENCODING') if settings.get("LOG_ROTATING") is True: max_bytes = settings.get('LOG_MAX_BYTES', 0) log_bac...
def _get_handler(settings): """ Return a log handler object according to settings """ filename = settings.get('LOG_FILE') if filename: encoding = settings.get('LOG_ENCODING') if settings.getbool("LOG_ROTATING") is True: max_bytes = settings.get('LOG_MAX_BYTES', 0) log...
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def _prepare_environ(workspace): new_environ = os.environ.copy() new_environ['TMPDIR'] = workspace # So, pytest is nice, and a little to for our usage. # In order to help user to call seamlessly any piece of python code without requiring to # install it as a full-fledged setuptools distribution for...
def _prepare_environ(workspace): new_environ = os.environ.copy() new_environ['TMPDIR'] = workspace # So, pytest is nice, and a little too nice for our usage. # In order to help user to call seamlessly any piece of python code without requiring to # install it as a full-fledged setuptools distributi...
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def get_user_id(form_id): key = f'xform-{form_id}-user_id' user_id = cache.get(key) if not user_id: try: user_id = FormAccessorSQL.get_form(form_id).metadata.userID except XFormNotFound: return None cache.set(key, user_id, 12 * 60 * 60) return user_id
def get_user_id(form_id): key = f'xform-{form_id}-user_id' user_id = cache.get(key) if not user_id: try: user_id = FormAccessorSQL.get_form(form_id).user_id except XFormNotFound: return None cache.set(key, user_id, 12 * 60 * 60) return user_id
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def main(args): """ function for the initial repositories fetch and manual repositories updates """ if not args: _show_usage() sys.exit(0) logdir = os.path.dirname(CONFIG["path.log.fetch"]) if not os.path.exists(logdir): os.makedirs() logging.basicConfig( f...
def main(args): """ function for the initial repositories fetch and manual repositories updates """ if not args: _show_usage() sys.exit(0) logdir = os.path.dirname(CONFIG["path.log.fetch"]) if not os.path.exists(logdir): os.makedirs(logdir) logging.basicConfig( ...
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def _inner_out_flow_constraint_hamiltonian( graph: nx.DiGraph, node: int ) -> Tuple[List[float], List[qml.operation.Observable]]: r"""Calculates the expanded inner portion of the Hamiltonian in :func:`out_flow_constraint`. For a given :math:`i`, this function returns: .. math:: d_{i}^{out}(d_...
def _inner_out_flow_constraint_hamiltonian( graph: nx.DiGraph, node: int ) -> Tuple[List[float], List[qml.operation.Observable]]: r"""Calculates the inner portion of the Hamiltonian in :func:`out_flow_constraint`. For a given :math:`i`, this function returns: .. math:: d_{i}^{out}(d_{i}^{out}...
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def _receive_cap_nak(bot: SopelWrapper, trigger: Trigger) -> None: was_completed = bot.cap_requests.is_complete cap_ack = bot.capabilities.handle_nak(bot, trigger) try: result: Optional[ List[Tuple[bool, Optional[plugin.CapabilityNegotiation]]] ] = bot.cap_requests.deny(bot, cap...
def _receive_cap_nak(bot: SopelWrapper, trigger: Trigger) -> None: was_completed = bot.cap_requests.is_complete cap_ack = bot.capabilities.handle_nak(bot, trigger) try: result: Optional[ List[Tuple[bool, Optional[plugin.CapabilityNegotiation]]] ] = bot.cap_requests.deny(bot, cap...
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def _api_template_string(wrapped, instance, args, kwargs): tracer = _get_tracer(instance) with tracer.trace("responder.render_template"): return wrapped(*args, **kwargs)
def _api_template_string(wrapped, instance, args, kwargs): tracer = _get_tracer(instance) with tracer.trace("responder.render_template", span_type=SpanTypes.TEMPLATE): return wrapped(*args, **kwargs)
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def test_type_error_complex_index_non_scalar_return(): # GH 31605 def fct(group): return group[1].values.flatten() df = pd.DataFrame([("A", 1), ("A", 2), ("A", 3), ("B", 4), ("B", 5), ("C", np.nan)]) result = df.groupby(0).apply(fct) expected = pd.Series( [[1.0, 2.0, 3.0], [4.0, 5.0...
def test_apply_function_returns_numpy_array(): # GH 31605 def fct(group): return group[1].values.flatten() df = pd.DataFrame([("A", 1), ("A", 2), ("A", 3), ("B", 4), ("B", 5), ("C", np.nan)]) result = df.groupby(0).apply(fct) expected = pd.Series( [[1.0, 2.0, 3.0], [4.0, 5.0], [np.n...
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def fetch_incidents(service): last_run = demisto.getLastRun() and demisto.getLastRun()['time'] search_offset = demisto.getLastRun().get('offset', 0) incidents = [] current_time_for_fetch = datetime.utcnow() if demisto.get(demisto.params(), 'timezone'): timezone = demisto.params()['timezone'...
def fetch_incidents(service): last_run = demisto.getLastRun() and demisto.getLastRun()['time'] search_offset = demisto.getLastRun().get('offset', 0) incidents = [] current_time_for_fetch = datetime.utcnow() if demisto.get(demisto.params(), 'timezone'): timezone = demisto.params()['timezone'...
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def convert_to_unicode(s, is_msg_header=True): global ENCODINGS_TYPES try: res = '' # utf encoded result if is_msg_header: # Mime encoded words used on message headers only try: word_mime_encoded = s and MIME_ENCODED_WORD.search(s) if word_mime_encod...
def convert_to_unicode(s, is_msg_header=True): global ENCODINGS_TYPES try: res = '' # utf encoded result if is_msg_header: # Mime encoded words used on message headers only try: word_mime_encoded = s and MIME_ENCODED_WORD.search(s) if word_mime_encod...
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def main(argv=None): parser = get_parser() arguments = parser.parse_args(argv) verbose = arguments.v set_loglevel(verbose=verbose) # See if there's a configuration file and import those options if arguments.config is not None: print('configuring') with open(arguments.config, 'r'...
def main(argv=None): parser = get_parser() arguments = parser.parse_args(argv) verbose = arguments.v set_loglevel(verbose=verbose) # See if there's a configuration file and import those options if arguments.config is not None: print('configuring') with open(arguments.config, 'r'...
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def run( uri, entry_point="main", version=None, parameters=None, docker_args=None, experiment_name=None, experiment_id=None, backend="local", backend_config=None, use_conda=None, storage_dir=None, synchronous=True, run_id=None, run_name=None, env_manager=None,...
def run( uri, entry_point="main", version=None, parameters=None, docker_args=None, experiment_name=None, experiment_id=None, backend="local", backend_config=None, use_conda=None, storage_dir=None, synchronous=True, run_id=None, run_name=None, env_manager=None,...
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def test_batch(): assert list(Batch()) == [] assert Batch().is_empty() assert not Batch(b={'c': {}}).is_empty() assert Batch(b={'c': {}}).is_empty(recurse=True) assert not Batch(a=Batch(), b=Batch(c=Batch())).is_empty() assert Batch(a=Batch(), b=Batch(c=Batch())).is_empty(recurse=True) asser...
def test_batch(): assert list(Batch()) == [] assert Batch().is_empty() assert not Batch(b={'c': {}}).is_empty() assert Batch(b={'c': {}}).is_empty(recurse=True) assert not Batch(a=Batch(), b=Batch(c=Batch())).is_empty() assert Batch(a=Batch(), b=Batch(c=Batch())).is_empty(recurse=True) asser...
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def create_fake_bids_dataset(base_dir='', n_sub=10, n_ses=2, tasks=['localizer', 'main'], n_runs=[1, 3], with_derivatives=True, with_confounds=True, confounds_tag="desc-confounds_timeseries", ...
def create_fake_bids_dataset(base_dir='', n_sub=10, n_ses=2, tasks=['localizer', 'main'], n_runs=[1, 3], with_derivatives=True, with_confounds=True, confounds_tag="desc-confounds_timeseries", ...
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def file_open(file_path, mode="rb", kwargs=None): if isinstance(file_path, str): match = S3_ADDRESS_REGEX.search(file_path) if match: import s3fs as S3FS from botocore.exceptions import NoCredentialsError s3fs = S3FS.S3FileSystem(anon=False) try: ...
def file_open(file_path, mode="rb", kwargs=None): if isinstance(file_path, str): match = S3_ADDRESS_REGEX.search(file_path) if match: import s3fs as S3FS from botocore.exceptions import NoCredentialsError s3fs = S3FS.S3FileSystem(anon=False) try: ...
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def get_other_source_component_file_query(session): """ Get filter query for the auto-generated Others component. If there are no user defined source components in the database this function will return with None. The returned query will look like this: (Files NOT IN Component_1) AND (Files NOT...
def get_other_source_component_file_query(session): """ Get filter query for the auto-generated Others component. If there are no user defined source components in the database this function will return with None. The returned query will look like this: (Files NOT LIKE Component_1) AND (Files N...
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def main(): params = demisto.params() args = demisto.args() url = params.get('url') verify_certificate = not params.get('insecure', False) proxy = params.get('proxy', False) headers = {} mock_data = str(args.get('mock-data', '')) if mock_data.lower() == "true": headers['Mock-Data...
def main(): params = demisto.params() args = demisto.args() url = params.get('url') verify_certificate = not params.get('insecure', False) proxy = params.get('proxy', False) headers = {} mock_data = str(args.get('mock-data', '')) if mock_data.lower() == "true": headers['Mock-Data...
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def test_prex_builder_script_from_pex_path(tmpdir): # type: (Any) -> None pex_with_script = os.path.join(str(tmpdir), "script.pex") with built_wheel( name="my_project", entry_points={"console_scripts": ["my_app = my_project.my_module:do_something"]}, ) as my_whl: pb = PEXBuilder...
def test_pex_builder_script_from_pex_path(tmpdir): # type: (Any) -> None pex_with_script = os.path.join(str(tmpdir), "script.pex") with built_wheel( name="my_project", entry_points={"console_scripts": ["my_app = my_project.my_module:do_something"]}, ) as my_whl: pb = PEXBuilder(...
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def s3_server(xprocess): """ Mock a local S3 server using `minio` This requires: - pytest-xprocess: runs the background process - minio: the executable must be in PATH Note it will be given EMPTY! The test function needs to populate it. You can use `conda.testing.helpers.populate_s3_se...
def s3_server(xprocess): """ Mock a local S3 server using `minio` This requires: - pytest-xprocess: runs the background process - minio: the executable must be in PATH Note it will be given EMPTY! The test function needs to populate it. You can use `conda.testing.helpers.populate_s3_se...
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def clone(estimator, safe=True): """Constructs a new estimator with the same parameters. Clone does a deep copy of the model in an estimator without actually copying attached data. It yields a new estimator with the same parameters that has not been fit on any data. Parameters ---------- e...
def clone(estimator, safe=True): """Constructs a new estimator with the same parameters. Clone does a deep copy of the model in an estimator without actually copying attached data. It yields a new estimator with the same parameters that has not been fit on any data. Parameters ---------- e...
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def get_used_dockers_images() -> CommandResults: md = None active_docker_list_integration = {} active_docker_list_automation = {} result_dict: Dict[str, List[str]] = {} active_integration_instances = demisto.internalHttpRequest(POST_COMMAND, "%s" % SETTING_INTEGRATION_SEARCH, ...
def get_used_dockers_images() -> CommandResults: md = None active_docker_list_integration = {} active_docker_list_automation = {} result_dict: Dict[str, List[str]] = {} active_integration_instances = demisto.internalHttpRequest(POST_COMMAND, "%s" % SETTING_INTEGRATION_SEARCH, ...
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def main(argv=None): try: # Step One: Parse The Command Line parser = build_parser() opts = parser.parse_args(argv or None) # Step Two: "Do not run as root" checks try: check_not_root() except RuntimeError as err: stderr('%s' % err) ...
def main(argv=None): try: # Step One: Parse The Command Line parser = build_parser() opts = parser.parse_args(argv or None) # Step Two: "Do not run as root" checks try: check_not_root() except RuntimeError as err: stderr('%s' % err) ...
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def test_create_with_spectral_coord(): spectral_coord = SpectralCoord(np.arange(5100, 5150)*u.AA, radial_velocity = u.Quantity(1000.0, "km/s")) flux = np.random.randn(50)*u.Jy spec = Spectrum1D(spectral_axis = spectral_coord, flux = flux) assert spec.radial_velocity == u.Quantity(1000.0, "km/s") a...
def test_create_with_spectral_coord(): spectral_coord = SpectralCoord(np.arange(5100, 5150)*u.AA, radial_velocity = u.Quantity(1000.0, "km/s")) flux = np.random.randn(50)*u.Jy spec = Spectrum1D(spectral_axis = spectral_coord, flux=flux) assert spec.radial_velocity == u.Quantity(1000.0, "km/s") ass...
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def cve_to_context(cve) -> Dict[str, str]: """Returning a cve structure with the following fields: * ID: The cve ID. * CVSS: The cve score scale/ * Published: The date the cve was published. * Modified: The date the cve was modified. * Description: the cve's description Args: ...
def cve_to_context(cve) -> Dict[str, str]: """Returning a cve structure with the following fields: * ID: The cve ID. * CVSS: The cve score scale/ * Published: The date the cve was published. * Modified: The date the cve was modified. * Description: the cve's description Args: ...
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def item_purchase_command(client: Client, args: Dict[str, Any]) -> Union[CommandResults, None]: try: item_id = str(args.get('item_id')) bot_id = '' room_id = '' # Get mentions list: mentions_list_res = client.get_mention_list() if isinstance(mentions_list_res, dict) ...
def item_purchase_command(client: Client, args: Dict[str, Any]) -> Union[CommandResults, None]: try: item_id = str(args.get('item_id')) bot_id = '' room_id = '' # Get mentions list: mentions_list_res = client.get_mention_list() if isinstance(mentions_list_res, dict) ...
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def compare_measurements(meas1, meas2): """ Helper function to compare measurements """ assert meas1.return_type.name == meas2.return_type.name obs1 = meas1.obs obs2 = meas2.obs assert np.array(obs1.name == obs2.name).all() assert set(obs1.wires.tolist()) == set(obs2.wires.tolist())
def compare_measurements(meas1, meas2): """ Helper function to compare measurements """ assert meas1.return_type.name == meas2.return_type.name obs1 = meas1.obs obs2 = meas2.obs assert np.array(obs1.name == obs2.name).all() assert set(obs1.wires) == set(obs2.wires)
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def cluster_pool_from_config( app_config: config.RawConfig, prefix: str = "rediscluster.", **kwargs: Any ) -> rediscluster.ClusterConnectionPool: """Make a ClusterConnectionPool from a configuration dictionary. The keys useful to :py:func:`clusterpool_from_config` should be prefixed, e.g. ``rediscluste...
def cluster_pool_from_config( app_config: config.RawConfig, prefix: str = "rediscluster.", **kwargs: Any ) -> rediscluster.ClusterConnectionPool: """Make a ClusterConnectionPool from a configuration dictionary. The keys useful to :py:func:`clusterpool_from_config` should be prefixed, e.g. ``rediscluste...
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def remove_unnecessary_nrt_usage(function, context, fndesc): """ Remove unnecessary NRT incref/decref in the given LLVM function. It uses highlevel type info to determine if the function does not need NRT. Such a function does not: - return array object; - take arguments that need refcount exce...
def remove_unnecessary_nrt_usage(function, context, fndesc): """ Remove unnecessary NRT incref/decref in the given LLVM function. It uses highlevel type info to determine if the function does not need NRT. Such a function does not: - return array object; - take arguments that need refcount exce...
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def traverse_dir( directory, topdown=True, ignore=None, only=None, recursive=True, include_subdir=True, ): """ Recursively traverse all files and sub directories in a directory and get a list of relative paths. :param directory: Path to a directory that will be traversed. :t...
def traverse_dir( directory, topdown=True, ignore=None, only=None, recursive=True, include_subdir=True, ): """ Recursively traverse all files and sub directories in a directory and get a list of relative paths. :param directory: Path to a directory that will be traversed. :t...
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def test_raid(verbosity): """checks all MD arrays on local machine, returns status code""" raid_devices = find_arrays(verbosity) status = OK message = "" arrays_not_ok = 0 number_arrays = len(raid_devices) for array in raid_devices: if verbosity >= 2: print('Now testing...
def test_raid(verbosity): """checks all MD arrays on local machine, returns status code""" raid_devices = find_arrays(verbosity) status = OK message = "" arrays_not_ok = 0 number_arrays = len(raid_devices) for array in raid_devices: if verbosity >= 2: print('Now testing...
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def test_no_eager_gc_in_equal_splitting_lazy_dataset(ray_start_regular_shared): ds = ( ray.data.range(100, parallelism=10).map_batches(lambda x: x).experimental_lazy() ) for batch in ds.iter_batches(): pass assert ds._lazy assert not ds._used_from_dataset_pipeline # Splitting 10 ...
def test_no_eager_gc_in_equal_splitting_lazy_dataset(ray_start_regular_shared): ds = ( ray.data.range(100, parallelism=10).map_batches(lambda x: x).experimental_lazy() ) for batch in ds.iter_batches(): pass assert ds._lazy assert not ds._used_from_dataset_pipeline # Splitting 10 ...
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def _get_parser(): """ Parses command line inputs for tedana Returns ------- parser.parse_args() : argparse dict """ parser = argparse.ArgumentParser() # Argument parser follow templtate provided by RalphyZ # https://stackoverflow.com/a/43456577 optional = parser._action_groups....
def _get_parser(): """ Parses command line inputs for tedana Returns ------- parser.parse_args() : argparse dict """ parser = argparse.ArgumentParser() # Argument parser follow templtate provided by RalphyZ # https://stackoverflow.com/a/43456577 optional = parser._action_groups....
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def lintify(meta, recipe_dir=None, conda_forge=False): lints = [] hints = [] major_sections = list(meta.keys()) # If the recipe_dir exists (no guarantee within this function) , we can # find the meta.yaml within it. meta_fname = os.path.join(recipe_dir or "", "meta.yaml") sources_section =...
def lintify(meta, recipe_dir=None, conda_forge=False): lints = [] hints = [] major_sections = list(meta.keys()) # If the recipe_dir exists (no guarantee within this function) , we can # find the meta.yaml within it. meta_fname = os.path.join(recipe_dir or "", "meta.yaml") sources_section =...
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def test_module(): """ Performs basic get request to get item samples """ request_api_token() r = requests.get(url=ARC_URL + '/watchlists', headers=CLIENT_HEADERS, verify=VERIFY_CERT) try: _ = r.json() if r.text else {} if not r.ok: demisto.results('Cannot connect to ...
def test_module(): """ Performs basic get request to get item samples """ request_api_token() r = requests.get(url=ARC_URL + '/watchlists', headers=CLIENT_HEADERS, verify=VERIFY_CERT) try: _ = r.json() if r.text else {} if not r.ok: demisto.results('Cannot connect to ...
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def test_all_attribute(): """Verify all trait types are added to to `traitlets.__all__`""" names = dir(traitlets) for name in names: value = getattr(traitlets, name) if not name.startswith("_") and isinstance(value, type) and issubclass(value, TraitType): if name not in traitlets...
def test_all_attribute(): """Verify all trait types are added to `traitlets.__all__`""" names = dir(traitlets) for name in names: value = getattr(traitlets, name) if not name.startswith("_") and isinstance(value, type) and issubclass(value, TraitType): if name not in traitlets.__...
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def validate_component_keys(pipeline: List["Component"]) -> None: """Validates that all keys for a component are valid. Raises: InvalidConfigError: If any component has a key specified that is not used by the component class, it is likely a mistake in the pipeline Args: pipelin...
def validate_component_keys(pipeline: List["Component"]) -> None: """Validates that all keys for a component are valid. Raises: InvalidConfigError: If any component has a key specified that is not used by the component class, it is likely a mistake in the pipeline Args: pipelin...
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def __reload_config(args): """ Sends the CodeChecker server process a SIGHUP signal, causing it to reread it's configuration files. """ for i in instance_manager.get_instances(): if i['hostname'] != socket.gethostname(): continue # A RELOAD only reloads the server associ...
def __reload_config(args): """ Sends the CodeChecker server process a SIGHUP signal, causing it to reread it's configuration files. """ for i in instance_manager.get_instances(): if i['hostname'] != socket.gethostname(): continue # A RELOAD only reloads the server associ...
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def get_app(git_url, branch=None, bench_path='.', skip_assets=False, verbose=False, postprocess=True, overwrite=False): if not os.path.exists(git_url): if git_url.startswith('git@'): pass elif not check_url(git_url, raise_err=False): orgs = ['frappe', 'erpnext'] for org in orgs: url = 'https://api.git...
def get_app(git_url, branch=None, bench_path='.', skip_assets=False, verbose=False, postprocess=True, overwrite=False): if not os.path.exists(git_url): if not git_url.startswith('git@') and not check_url(git_url, raise_err=False): orgs = ['frappe', 'erpnext'] for org in orgs: url = 'https://api.github.com/...
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def test_default_properties_assignment(): """Test that the default properties value can be assigned to properties see https://github.com/napari/napari/issues/2477 """ np.random.seed(0) data = np.random.randint(20, size=(10, 15)) layer = Labels(data) layer.properties = layer.properties
def test_default_properties_assignment(): """Test that the default properties value can be assigned to properties see https://github.com/napari/napari/issues/2477 """ np.random.seed(0) data = np.random.randint(20, size=(10, 15)) layer = Labels(data) layer.properties = {} assert layer.pr...
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def main(): parser = argparse.ArgumentParser( description='CLI tool to start a local instance of Pebble or Boulder CA server.') parser.add_argument('--server-type', '-s', choices=['pebble', 'boulder-v1', 'boulder-v2'], default='pebble', help='type of CA se...
def main(): parser = argparse.ArgumentParser( description='CLI tool to start a local instance of Pebble or Boulder CA server.') parser.add_argument('--server-type', '-s', choices=['pebble', 'boulder-v1', 'boulder-v2'], default='pebble', help='type of CA se...
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def format_array(arr): """ Returns the given array as a string, using the python builtin str function on a piecewise basis. Useful for xml representation of arrays. For customisations, use the :mod:`numpy.core.arrayprint` directly. """ summary_threshold = 85 summary_insert = "..." if...
def format_array(arr): """ Returns the given array as a string, using the python builtin str function on a piecewise basis. Useful for xml representation of arrays. For customisations, use the :mod:`numpy.core.arrayprint` directly. """ summary_threshold = 85 summary_insert = "..." if...
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def load_module(path): """Creates model from configuration file. Args: path (string): Path to the configuration file relative to pymor_source. Returns: model: model as loaded from the file. """ with open(path, "r") as stream: try: load_dict = yaml.safe_load(stre...
def load_module(path): """Creates model from configuration file. Args: path (string): Path to the configuration file relative to pymor_source. Returns: model: |Model| as loaded from the file. """ with open(path, "r") as stream: try: load_dict = yaml.safe_load(st...
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def _get_engine_from_magic_number(filename_or_obj): # check byte header to determine file type if isinstance(filename_or_obj, bytes): magic_number = filename_or_obj[:8] else: if filename_or_obj.tell() != 0: raise ValueError( "file-like object read/write pointer no...
def _get_engine_from_magic_number(filename_or_obj): # check byte header to determine file type if isinstance(filename_or_obj, bytes): magic_number = filename_or_obj[:8] else: if filename_or_obj.tell() != 0: raise ValueError( "file-like object read/write pointer no...
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def common_verify(client, expected_keys): for user, filename, keys in expected_keys: # Ensure key is in the key file contents = client.read_from_file(filename) if user in ['ubuntu', 'root']: # Our personal public key gets added by pycloudlib lines = contents.split('\n...
def common_verify(client, expected_keys): for user, filename, keys in expected_keys: # Ensure key is in the key file contents = client.read_from_file(filename) if user in ['ubuntu', 'root']: # Our personal public key gets added by pycloudlib lines = contents.split('\n...
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def slack_send(): """ Sends a message to slack """ args = demisto.args() message = args.get('message', '') to = args.get('to') original_channel = args.get('channel') channel_id = demisto.args().get('channel_id', '') group = args.get('group') message_type = args.get('messageType'...
def slack_send(): """ Sends a message to slack """ args = demisto.args() message = args.get('message', '') to = args.get('to') original_channel = args.get('channel') channel_id = demisto.args().get('channel_id', '') group = args.get('group') message_type = args.get('messageType'...
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def update_link_metadata(checks, core_workflow=True): root = get_root() ensure_dir_exists(path_join(root, LINK_DIR)) # Sign only what affects each wheel products = [] for check in checks: products.append(path_join(check, 'datadog_checks')) products.append(path_join(check, 'setup.py'...
def update_link_metadata(checks, core_workflow=True): root = get_root() ensure_dir_exists(path_join(root, LINK_DIR)) # Sign only what affects each wheel products = [] for check in checks: products.append(path_join(check, 'datadog_checks')) products.append(path_join(check, 'setup.py'...
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def test_get_channels_data_repsonse_structure(): # Given api_token = "test_token" response_data = { "ok": True, "channels": [ { "id": "id1", "name": "channel1", "is_channel": True, "num_members": 3, }, ...
def test_get_channels_data_response_structure(): # Given api_token = "test_token" response_data = { "ok": True, "channels": [ { "id": "id1", "name": "channel1", "is_channel": True, "num_members": 3, }, ...
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def CRoty(theta): r"""Two-qubit controlled rotation about the y axis. Args: theta (float): rotation angle Returns: array: unitary 4x4 rotation matrix ` """ return np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, np.cos(theta/2), -1*np.sin(theta/2)], [0, 0, np.sin(theta/2), np.cos(theta/...
def CRoty(theta): r"""Two-qubit controlled rotation about the y axis. Args: theta (float): rotation angle Returns: array: unitary 4x4 rotation matrix """ return np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, np.cos(theta/2), -1*np.sin(theta/2)], [0, 0, np.sin(theta/2), np.cos(theta/2)...
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def wildfire_get_verdict_command(): file_hashes = hash_args_handler(demisto.args().get('hash', '')) urls = argToList(demisto.args().get('url', '')) if not urls and not file_hashes: raise Exception('Specify exactly 1 of the following arguments: url, hash.') if file_hashes: for file_hash i...
def wildfire_get_verdict_command(): file_hashes = hash_args_handler(demisto.args().get('hash', '')) urls = argToList(demisto.args().get('url', '')) if not urls and not file_hashes: raise Exception('Either hash or url must be provided.') if file_hashes: for file_hash in file_hashes: ...
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def add(name, url, scope, args={}): """Add a named mirror in the given scope""" mirrors = spack.config.get('mirrors', scope=scope) if not mirrors: mirrors = syaml_dict() if name in mirrors: tty.die("Mirror with name %s already exists." % name) items = [(n, u) for n, u in mirrors.it...
def add(name, url, scope, args={}): """Add a named mirror in the given scope""" mirrors = spack.config.get('mirrors', scope=scope) if not mirrors: mirrors = syaml_dict() if name in mirrors: tty.die("Mirror with name %s already exists." % name) items = [(n, u) for n, u in mirrors.it...
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def _mapping_repr(mapping, title, summarizer, col_width=None, max_rows=None): if col_width is None: col_width = _calculate_col_width(mapping) if max_rows is None: max_rows = OPTIONS["display_max_rows"] summary = [f"{title}:"] if mapping: if len(mapping) > max_rows: fi...
def _mapping_repr(mapping, title, summarizer, col_width=None, max_rows=None): if col_width is None: col_width = _calculate_col_width(mapping) if max_rows is None: max_rows = OPTIONS["display_max_rows"] summary = [f"{title}:"] if mapping: if len(mapping) > max_rows: fi...
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def test_should_hide_feature_notifs_for_non_pro_with_groups(): with case_sharing_groups_patch(['agroupid']), active_service_type_patch("not_IMPLEMENTATION_or_SANDBOX"): hide = NotificationsServiceRMIView._should_hide_feature_notifs("test", None) assert not hide, "notifications should not be hidden f...
def test_should_hide_feature_notifs_for_non_pro_with_groups(): with case_sharing_groups_patch([]), active_service_type_patch("not_IMPLEMENTATION_or_SANDBOX"): hide = NotificationsServiceRMIView._should_hide_feature_notifs("test", None) assert not hide, "notifications should not be hidden for non pro...
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def finalize_on_12(spec, state, epoch, sufficient_support, messed_up_target): assert epoch > 2 state.slot = (spec.SLOTS_PER_EPOCH * epoch) - 1 # skip ahead to just before epoch # 43210 -- epochs ago # 210xx -- justification bitfield indices (pre shift) # 3210x -- justification bitfield indices (p...
def finalize_on_12(spec, state, epoch, sufficient_support, messed_up_target): assert epoch > 2 state.slot = (spec.SLOTS_PER_EPOCH * epoch) - 1 # skip ahead to just before epoch # 43210 -- epochs ago # 210xx -- justification bitfield indices (pre shift) # 3210x -- justification bitfield indices (p...
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def _mix_latent_gp(W, g_mu, g_var, full_cov, full_output_cov): r""" Takes the mean and variance of a uncorrelated L-dimensional latent GP and returns the mean and the variance of the mixed GP, `f = W \times g`, where both f and g are GPs. :param W: [P, L] :param g_mu: [..., N, L] :param g_v...
def _mix_latent_gp(W, g_mu, g_var, full_cov, full_output_cov): r""" Takes the mean and variance of an uncorrelated L-dimensional latent GP and returns the mean and the variance of the mixed GP, `f = W \times g`, where both f and g are GPs. :param W: [P, L] :param g_mu: [..., N, L] :param g_...