code stringlengths 20 4.93k | docstring stringlengths 33 1.27k | source stringclasses 3
values |
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def read_excitation_energies(self):
transitions = list()
with zopen(self.filename, 'r') as f:
line = f.readline()
td = False
while (line != ''):
if re.search('^\\sExcitation energies and oscillator strengths:', line):
td = True
if td:
... | Read a excitation energies after a TD-DFT calculation.
Returns:
A list: A list of tuple for each transition such as
[(energie (eV), lambda (nm), oscillatory strength), ... ] | codesearchnet |
def __init__(self, callback):
super(ThreadedXMLRPCServer, self).__init__(callback)
self._rpc_thread = None
self._xmlrpc_server = None | Initialize a threaded RPC server.
Args:
callback (function): callback function to invoke on get status RPC
request. | juraj-google-style |
def load_filename(self, filename, index=None):
filename = str(filename)
if index is None:
index = self._get_tab_index()
page = self.pages[index]
self.load_dir, _ = os.path.split(filename)
clss = page.clss_load
if len(clss) == 1:
... | Loads file given filename
Args:
filename:
index: tab index to load file into. If not passed, loads into current tab | juraj-google-style |
def orient_averaged_adaptive(tm):
S = np.zeros((2,2), dtype=complex)
Z = np.zeros((4,4))
def Sfunc(beta, alpha, i, j, real):
(S_ang, Z_ang) = tm.get_SZ_single(alpha=alpha, beta=beta)
s = S_ang[i,j].real if real else S_ang[i,j].imag
return s * tm.or_pdf(beta)
in... | Compute the T-matrix using variable orientation scatterers.
This method uses a very slow adaptive routine and should mainly be used
for reference purposes. Uses the set particle orientation PDF, ignoring
the alpha and beta attributes.
Args:
tm: TMatrix (or descendant) instance
Returns:
The amplitude (S) and phase (Z... | juraj-google-style |
class GroundingDinoImageLoss(ImageLoss):
def __init__(self, matcher, focal_alpha, losses):
nn.Module.__init__(self)
self.matcher = matcher
self.focal_alpha = focal_alpha
self.losses = losses
def _get_target_classes_one_hot(self, outputs, targets, indices):
logi... | This class computes the losses for `GroundingDinoForObjectDetection`. The process happens in two steps: 1) we
compute hungarian assignment between ground truth boxes and the outputs of the model 2) we supervise each pair of
matched ground-truth / prediction (supervise class and box).
Args:
matcher (`GroundingDinoHunga... | github-repos |
def __init__(self, minimum=None, maximum=None):
super(IntegerTypeChecker, self).__init__(base_type=int)
self.minimum = minimum
self.maximum = maximum | Initialization method.
Args:
minimum (int): a minimum value (included).
maximum (int): a maximum value (included). | juraj-google-style |
def fill_datetime(self):
if (not self.filled):
raise SlotNotFilledError(('Slot with name "%s", key "%s" not yet filled.' % (self.name, self.key)))
return self._fill_datetime | Returns when the slot was filled.
Returns:
A datetime.datetime.
Raises:
SlotNotFilledError if the value hasn't been filled yet. | codesearchnet |
def shannon_entropy(time_series):
if (not isinstance(time_series, str)):
time_series = list(time_series)
data_set = list(set(time_series))
freq_list = []
for entry in data_set:
counter = 0.0
for i in time_series:
if (i == entry):
counter += 1
f... | Return the Shannon Entropy of the sample data.
Args:
time_series: Vector or string of the sample data
Returns:
The Shannon Entropy as float value | codesearchnet |
def rank_internal(input, name=None, optimize=True):
with ops.name_scope(name, 'Rank', [input]) as name:
if isinstance(input, (sparse_tensor.SparseTensor, sparse_tensor.SparseTensorValue)):
return gen_array_ops.size(input.dense_shape, name=name)
else:
input = ops.convert_to_te... | Returns the rank of a tensor.
Args:
input: A `Tensor` or `SparseTensor`.
name: A name for the operation (optional).
optimize: if true, encode the rank as a constant when possible.
Returns:
A `Tensor` of type `int32`. | github-repos |
def encode(self, input_ids: jnp.ndarray, attention_mask: Optional[jnp.ndarray]=None, position_ids: Optional[jnp.ndarray]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None, train: bool=False, params: Optional[dict]=None, dropout_rng: PRNGKey=None):
... | Returns:
Example:
```python
>>> from transformers import AutoTokenizer, FlaxBlenderbotForConditionalGeneration
>>> model = FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-400M-distill")
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
>>> text = "My frien... | github-repos |
def create_bagit_stream(dir_name, payload_info_list):
zip_file = zipstream.ZipFile(mode='w', compression=zipstream.ZIP_DEFLATED)
_add_path(dir_name, payload_info_list)
payload_byte_count, payload_file_count = _add_payload_files(
zip_file, payload_info_list
)
tag_info_list = _add_tag_fil... | Create a stream containing a BagIt zip archive.
Args:
dir_name : str
The name of the root directory in the zip file, under which all the files
are placed (avoids "zip bombs").
payload_info_list: list
List of payload_info_dict, each dict describing a file.
- keys: pid, filename, iter, checksum, checksum_algorithm
- I... | juraj-google-style |
def process_document_events(events, use_buffers=True):
json_events = []
references = set()
buffers = [] if use_buffers else None
for event in events:
json_events.append(event.generate(references, buffers))
json = {
'events' : json_events,
'references' : reference... | Create a JSON string describing a patch to be applied as well as
any optional buffers.
Args:
events : list of events to be translated into patches
Returns:
str, list :
JSON string which can be applied to make the given updates to obj
as well as any optional buffers | juraj-google-style |
def get_distance_and_image(self, frac_coords1: Vector3Like, frac_coords2: Vector3Like, jimage: Optional[Union[(List[int], np.ndarray)]]=None) -> Tuple[(float, np.ndarray)]:
if (jimage is None):
(v, d2) = pbc_shortest_vectors(self, frac_coords1, frac_coords2, return_d2=True)
fc = ((self.get_fractiona... | Gets distance between two frac_coords assuming periodic boundary
conditions. If the index jimage is not specified it selects the j
image nearest to the i atom and returns the distance and jimage
indices in terms of lattice vector translations. If the index jimage
is specified it returns the distance between the frac_co... | codesearchnet |
def get_default_backend_config(appdirs):
return {
'store': 'sqlalchemy',
'day_start': datetime.time(5, 30, 0),
'fact_min_delta': 1,
'tmpfile_path': os.path.join(appdirs.user_data_dir, '{}.tmp'.format(appdirs.appname)),
'db_engine': 'sqlite',
'db_path': os.path.jo... | Return a default config dictionary.
Args:
appdirs (HamsterAppDirs): ``HamsterAppDirs`` instance encapsulating the apps details.
Returns:
dict: Dictionary with a default configuration.
Note:
Those defaults are independent of the particular config-store. | juraj-google-style |
def get(self, context_id, address_list):
if (context_id not in self._contexts):
return []
for add in address_list:
if (not self.address_is_valid(address=add)):
raise AuthorizationException(address=add)
context = self._contexts[context_id]
addresses_in_ctx = [add for add in ad... | Get the values associated with list of addresses, for a specific
context referenced by context_id.
Args:
context_id (str): the return value of create_context, referencing
a particular context.
address_list (list): a list of address strs
Returns:
values_list (list): a list of (address, value) tuples
Raises:
Authoriza... | codesearchnet |
def ResolveFlats(dem, in_place=False):
if (type(dem) is not rdarray):
raise Exception('A richdem.rdarray or numpy.ndarray is required!')
if (not in_place):
dem = dem.copy()
_AddAnalysis(dem, 'ResolveFlats(dem, in_place={in_place})'.format(in_place=in_place))
demw = dem.wrap()
_richde... | Attempts to resolve flats by imposing a local gradient
Args:
dem (rdarray): An elevation model
in_place (bool): If True, the DEM is modified in place and there is
no return; otherwise, a new, altered DEM is returned.
Returns:
DEM modified such that all flats drain. | codesearchnet |
def __init__(self, var_config, scope_config):
self._substs = {}
self._var_config = var_config
self._scope_config = scope_config
for var_id, var_value in iteritems(var_config):
key = "%%{var}%%".format(var=var_id)
self._substs[key] = str(var_value)
for scope_id, var_config in iteri... | Initializes the substitution environment.
Args:
var_config: A configuration (concrete values) of pattern variables.
scope_config: A configuration (concrete values) of pattern scopes. | juraj-google-style |
def get_actions(self, parent_environ=None):
interp = Python(target_environ={}, passive=True)
executor = self._create_executor(interp, parent_environ)
self._execute(executor)
return executor.actions | Get the list of rex.Action objects resulting from interpreting this
context. This is provided mainly for testing purposes.
Args:
parent_environ Environment to interpret the context within,
defaults to os.environ if None.
Returns:
A list of rex.Action subclass instances. | juraj-google-style |
def get_heading_encoding(response):
encoding = wpull.protocol.http.util.parse_charset(
response.fields.get('content-type', ''))
if encoding:
return wpull.string.normalize_codec_name(encoding)
else:
return None | Return the document encoding from a HTTP header.
Args:
response (Response): An instance of :class:`.http.Response`.
Returns:
``str``, ``None``: The codec name. | juraj-google-style |
def request(self, session=None):
try:
from .tcex_request import TcExRequest
r = TcExRequest(self, session)
if ((session is None) and self.default_args.tc_proxy_external):
self.log.info('Using proxy server for external request {}:{}.'.format(self.default_args.tc_proxy_host, self.d... | Return an instance of the Request Class.
A wrapper on the Python Requests module that provides a different interface for creating
requests. The session property of this instance has built-in logging, session level
retries, and preconfigured proxy configuration.
Returns:
(object): An instance of Request Class | codesearchnet |
def build_variant_query(self, query=None, category='snv', variant_type=['clinical']):
query = (query or {})
mongo_variant_query = {}
LOG.debug(('Building a mongo query for %s' % query))
if query.get('hgnc_symbols'):
mongo_variant_query['hgnc_symbols'] = {'$in': query['hgnc_symbols']}
mongo_v... | Build a mongo query across multiple cases.
Translate query options from a form into a complete mongo query dictionary.
Beware that unindexed queries against a large variant collection will
be extremely slow.
Currently indexed query options:
hgnc_symbols
rank_score
variant_type
category
Args:
query(dict): A query dic... | codesearchnet |
def period_start_day(self, value=None):
if (value is not None):
try:
value = str(value)
except ValueError:
raise ValueError('value {} need to be of type str for field `period_start_day`'.format(value))
if (',' in value):
raise ValueError('value should not ... | Corresponds to IDD Field `period_start_day`
Args:
value (str): value for IDD Field `period_start_day`
if `value` is None it will not be checked against the
specification and is assumed to be a missing value
Raises:
ValueError: if `value` is not a valid value | codesearchnet |
def loss_labels(self, class_queries_logits: Tensor, class_labels: List[Tensor], indices: Tuple[np.array]) -> Dict[str, Tensor]:
pred_logits = class_queries_logits
batch_size, num_queries, _ = pred_logits.shape
criterion = nn.CrossEntropyLoss(weight=self.empty_weight)
idx = self._get_predictions_permutat... | Compute the losses related to the labels using cross entropy.
Args:
class_queries_logits (`torch.Tensor`):
A tensor of shape `batch_size, num_queries, num_labels`
class_labels (`List[torch.Tensor]`):
List of class labels of shape `(labels)`.
indices (`Tuple[np.array])`:
The indices computed by the Hungarian matcher.
... | github-repos |
def ensure_dir(path):
dirpath = os.path.dirname(path)
if dirpath and not os.path.exists(dirpath):
os.makedirs(dirpath) | Ensure directory exists.
Args:
path(str): dir path | juraj-google-style |
def normalize_whitespace(text):
return re.sub('\\s+', ' ', text, flags=re.UNICODE).strip() | Returns the given text with outer whitespace removed and inner whitespace collapsed.
Args:
text (str): The text to normalize.
Returns:
str: The normalized text. | codesearchnet |
def group_device_names(devices, group_size):
num_devices = len(devices)
if group_size > num_devices:
raise ValueError(
"only %d devices, but group_size=%d" % (num_devices, group_size))
num_groups = (
num_devices
(num_devices % group_size... | Group device names into groups of group_size.
Args:
devices: list of strings naming devices.
group_size: int >= 1
Returns:
list of lists of devices, where each inner list is group_size long,
and each device appears at least once in an inner list. If
len(devices) % group_size = 0 then each device will appear
exactly ... | juraj-google-style |
def _random_stateless_uniform(shape: types.IntTensor, num_digits: types.IntTensor, seed: int, validate_args: bool=False, dtype: tf.DType=None, name: str=None) -> types.IntTensor:
with tf.name_scope(name or 'random_stateless_uniform'):
dtype = dtype or tf.int32
shape = tf.convert_to_tensor(shape, dty... | Returns a `Tensor` drawn from a uniform distribution with a given `shape`.
Args:
shape: Positive scalar `Tensor` of integers with rank 1. The shape of the
returned `Tensor`.
num_digits: Positive scalar `Tensor` of integers with rank 0. the base-2
precision of the points which can be sampled from `generating_matrices`.... | github-repos |
def post_process_depth_estimation(self, outputs: 'DepthProDepthEstimatorOutput', target_sizes: Optional[Union[TensorType, List[Tuple[int, int]], None]]=None) -> Dict[str, List[TensorType]]:
requires_backends(self, 'torch')
predicted_depth = outputs.predicted_depth
fov = outputs.field_of_view
batch_size ... | Post-processes the raw depth predictions from the model to generate
final depth predictions which is caliberated using the field of view if provided
and resized to specified target sizes if provided.
Args:
outputs ([`DepthProDepthEstimatorOutput`]):
Raw outputs of the model.
target_sizes (`Optional[Union[TensorType, L... | github-repos |
def _handle_stop_workflow(self, request):
self._stop_workflow = True
for (name, dag) in self._dags_running.items():
if (name not in self._stop_dags):
self._stop_dags.append(name)
return Response(success=True, uid=request.uid) | The handler for the stop_workflow request.
The stop_workflow request adds all running dags to the list of dags
that should be stopped and prevents new dags from being started. The dags will
then stop queueing new tasks, which will terminate the dags and in turn the
workflow.
Args:
request (Request): Reference to a re... | codesearchnet |
def initialize(self, table):
check_table_dtypes(table, self.key_dtype, self.value_dtype)
with ops.name_scope(self._name, 'text_file_init', (table.resource_handle,)):
filename = ops.convert_to_tensor(self._filename, dtypes.string, name='asset_filepath')
init_op = gen_lookup_ops.initialize_table_f... | Initializes the table from a text file.
Args:
table: The table to be initialized.
Returns:
The operation that initializes the table.
Raises:
TypeError: when the keys and values data types do not match the table
key and value data types. | github-repos |
def potential_jumps( self ):
jumps = []
if self.number_of_occupied_sites <= self.number_of_sites / 2:
for occupied_site in self.occupied_sites():
unoccupied_neighbours = [ site for site in [ self.site_with_id( n ) for n in occupied_site.neighbours ] if not site.is_oc... | All nearest-neighbour jumps not blocked by volume exclusion
(i.e. from occupied to neighbouring unoccupied sites).
Args:
None
Returns:
(List(Jump)): List of possible jumps. | juraj-google-style |
def from_event(cls, ion_event):
if (ion_event.value is not None):
(args, kwargs) = cls._to_constructor_args(ion_event.value)
else:
(args, kwargs) = ((), {})
value = cls(*args, **kwargs)
value.ion_event = ion_event
value.ion_type = ion_event.ion_type
value.ion_annotations = ion_ev... | Constructs the given native extension from the properties of an event.
Args:
ion_event (IonEvent): The event to construct the native value from. | codesearchnet |
def HandleExceptionsAndRebuildHttpConnections(retry_args):
retry_after = None
if isinstance(retry_args.exc, (http_client.BadStatusLine,
http_client.IncompleteRead,
http_client.ResponseNotReady)):
logging.debug('Ca... | Exception handler for http failures.
This catches known failures and rebuilds the underlying HTTP connections.
Args:
retry_args: An ExceptionRetryArgs tuple. | juraj-google-style |
def getObjective(self, name):
return lock_and_call(
lambda: Objective(self._impl.getObjective(name)),
self._lock
) | Get the objective with the corresponding name.
Args:
name: Name of the objective to be found.
Raises:
TypeError: if the specified objective does not exist. | juraj-google-style |
def _convert_to_dict(data):
if isinstance(data, dict):
return data
if (isinstance(data, list) or isinstance(data, tuple)):
if _all_correct_list(data):
return dict(data)
else:
data = zip(data[::2], data[1::2])
return dict(data)
else:
raise M... | Convert `data` to dictionary.
Tries to get sense in multidimensional arrays.
Args:
data: List/dict/tuple of variable dimension.
Returns:
dict: If the data can be converted to dictionary.
Raises:
MetaParsingException: When the data are unconvertible to dict. | codesearchnet |
def file_md5(filename):
with zopen(filename, 'r') as f:
file_string = f.read()
try:
file_string = file_string.decode()
except AttributeError:
pass
return md5sum(file_string) | Generate the md5 checksum for a file
Args:
filename (Str): The file to be checksummed.
Returns:
(Str): The hex checksum
Notes:
If the file is gzipped, the md5 checksum returned is
for the uncompressed ASCII file. | codesearchnet |
def load_architecture(self, name, arch_info, disassembler, translator):
self.name = name
self.arch_info = arch_info
self.disassembler = disassembler
self.ir_translator = translator
self._setup_analysis_modules() | Translate to REIL instructions.
Args:
name (str): Architecture's name.
arch_info (ArchitectureInformation): Architecture information object.
disassembler (Disassembler): Disassembler for the architecture.
translator (Translator): Translator for the architecture. | juraj-google-style |
def get_pattern_actual_step(self, patternnumber):
_checkPatternNumber(patternnumber)
address = _calculateRegisterAddress('actualstep', patternnumber)
return self.read_register(address, 0) | Get the 'actual step' parameter for a given pattern.
Args:
patternnumber (integer): 0-7
Returns:
The 'actual step' parameter (int). | juraj-google-style |
def validate(self, corpus):
overflow_segments = {}
for utterance in corpus.utterances.values():
utt_segments = self.validate_utterance(utterance)
if len(utt_segments) > 0:
overflow_segments[utterance.idx] = utt_segments
passed = len(overflow_s... | Perform the validation on the given corpus.
Args:
corpus (Corpus): The corpus to test/validate.
Returns:
InvalidUtterancesResult: Validation result. | juraj-google-style |
def add(self, layer, rebuild=True):
if not self._layers:
if getattr(layer, '_input_shape_arg', None) is not None:
self.add(InputLayer(shape=layer._input_shape_arg))
if hasattr(layer, '_keras_history'):
origin_layer = layer._keras_history[0]
if isinstance(origin_layer, InputLa... | Adds a layer instance on top of the layer stack.
Args:
layer: layer instance. | github-repos |
def _test_streaming(self, with_attributes):
state_verifier = PipelineStateMatcher(PipelineState.RUNNING)
expected_messages = self.EXPECTED_OUTPUT_MESSAGES[self.runner_name]
if not with_attributes:
expected_messages = [pubsub_msg.data for pubsub_msg in expected_messages]
if self.runner_name == 'T... | Runs IT pipeline with message verifier.
Args:
with_attributes: False - Reads and writes message data only.
True - Reads and writes message data and attributes. Also verifies
id_label and timestamp_attribute features. | github-repos |
def process_tokens(self, tokens):
for (tok_type, token, (start_row, start_col), _, _) in tokens:
if (tok_type == tokenize.STRING):
self._process_string_token(token, start_row, start_col) | Process the token stream.
This is required to override the parent class' implementation.
Args:
tokens: the tokens from the token stream to process. | codesearchnet |
def to_numbers(self, flatten: bool=True) -> Union[List[Union[int, float, str]], utils.Nestable[Union[int, float, str]]]:
if flatten:
decisions = [self.value] if self.value is not None else []
for c in self.children:
decisions.extend(c.to_numbers(flatten))
return decisions
eli... | Returns a (maybe) nested structure of numbers as decisions.
Args:
flatten: If True, the hierarchy of the numbers will not be preserved.
Decisions will be returned as a flat list in DFS order. Otherwise, a
nestable structure of numbers will be returned.
Returns:
A flat list or a hierarchical structure of numbers as th... | github-repos |
def parse_done(self, buf: memoryview) -> Tuple[bool, memoryview]:
match = self._pattern.match(buf)
if not match:
raise NotParseable(buf)
done = match.group(1).upper() == self.continuation
buf = buf[match.end(0):]
return done, buf | Parse the continuation line sent by the client to end the ``IDLE``
command.
Args:
buf: The continuation line to parse. | juraj-google-style |
def export_model(model, model_type, export_dir, model_column_fn):
wide_columns, deep_columns = model_column_fn()
if model_type == 'wide':
columns = wide_columns
elif model_type == 'deep':
columns = deep_columns
else:
columns = wide_columns + deep_columns
feature_spec = tf.feature_column.make_pa... | Export to SavedModel format.
Args:
model: Estimator object
model_type: string indicating model type. "wide", "deep" or "wide_deep"
export_dir: directory to export the model.
model_column_fn: Function to generate model feature columns. | juraj-google-style |
def comments(self, case_id=None, variant_id=None, username=None):
logger.debug("Looking for comments")
comment_objs = self.query(Comment)
if case_id:
comment_objs = comment_objs.filter_by(case_id=case_id)
if variant_id:
comment_objs = comment_objs.filte... | Return comments for a case or variant.
Args:
case_id (str): id for a related case
variant_id (Optional[str]): id for a related variant | juraj-google-style |
def put(self, url, params=None, data=None, files=None, **kwargs):
return self.call_api(
"PUT",
url,
params=params,
data=data,
files=files,
**kwargs
) | Call the API with a PUT request.
Args:
url (str): Resource location relative to the base URL.
params (dict or None): Query-string parameters.
data (dict or None): Request body contents.
files (dict or None: Files to be passed to the request.
Returns:
An instance of ResultParser or ErrorParser. | juraj-google-style |
def _load_config_file(path):
with io.open(utils.abs_path(path), 'r', encoding='utf-8') as f:
conf = yaml.safe_load(f)
return conf | Loads a test config file.
The test config file has to be in YAML format.
Args:
path: A string that is the full path to the config file, including the
file name.
Returns:
A dict that represents info in the config file. | github-repos |
def read_local_files(*file_paths: str) -> str:
def _read_single_file(file_path):
with open(file_path) as f:
filename = os.path.splitext(file_path)[0]
title = f'{filename}\n{"=" * len(filename)}'
return '\n\n'.join((title, f.read()))
return '\n' + '\n\n'.join(ma... | Reads one or more text files and returns them joined together.
A title is automatically created based on the file name.
Args:
*file_paths: list of files to aggregate
Returns: content of files | juraj-google-style |
def distance_to_line(a, b, p):
return distance(closest_point(a, b, p), p) | Closest distance between a line segment and a point
Args:
a ([float, float]): x and y coordinates. Line start
b ([float, float]): x and y coordinates. Line end
p ([float, float]): x and y coordinates. Point to compute the distance
Returns:
float | juraj-google-style |
def apply_inverse(self, y, in_place=False):
return cho_solve(self._factor, y, overwrite_b=in_place) | r"""
Apply the inverse of the covariance matrix to the input by solving
.. math::
K\,x = y
Args:
y (ndarray[nsamples] or ndadrray[nsamples, nrhs]): The vector or
matrix :math:`y`.
in_place (Optional[bool]): Should the data in ``y`` be overwritten
with the result :math:`x`? (default: ``False``) | codesearchnet |
def ParseLastVisitedRow(self, parser_mediator, query, row, cache=None, database=None, **unused_kwargs):
query_hash = hash(query)
hidden = self._GetRowValue(query_hash, row, 'hidden')
transition = self._GetRowValue(query_hash, row, 'transition')
visit_identifier = self._GetRowValue(query_hash, row, 'visi... | Parses a last visited row.
Args:
parser_mediator (ParserMediator): mediates interactions between parsers
and other components, such as storage and dfvfs.
query (str): query that created the row.
row (sqlite3.Row): row.
cache (SQLiteCache): cache which contains cached results from querying
the visits and urls tables.
d... | codesearchnet |
def button_state(self):
if (self.type != EventType.POINTER_BUTTON):
raise AttributeError(_wrong_prop.format(self.type))
return self._libinput.libinput_event_pointer_get_button_state(self._handle) | The button state that triggered this event.
For pointer events that are not of type
:attr:`~libinput.constant.EventType.POINTER_BUTTON`, this property
raises :exc:`AttributeError`.
Returns:
~libinput.constant.ButtonState: The button state triggering this
event.
Raises:
AttributeError | codesearchnet |
def __register_services(api_name_version_map, api_config_registry):
generator = api_config.ApiConfigGenerator()
protorpc_services = []
for service_factories in api_name_version_map.itervalues():
service_classes = [service_factory.service_class for service_factory in service_factories]
config... | Register & return a list of each URL and class that handles that URL.
This finds every service class in api_name_version_map, registers it with
the given ApiConfigRegistry, builds the URL for that class, and adds
the URL and its factory to a list that's returned.
Args:
api_name_version_map: A mapping from (api name, ... | codesearchnet |
def halo_exchange(x, blocks_dim, block_size_dim, halo_size, wrap=False):
if halo_size == 0:
return x
block_size = block_size_dim.size
partial_size = halo_size % block_size
num_complete_blocks = halo_size
parts = [x]
for i in xrange(1, num_complete_blocks + 1):
parts = ([shift(x, i, blocks_dim,... | Concat each block with the margins of adjacent blocks.
Get left and right blocks_dim and concatenate along block_size_dim.
Args:
x: a Tensor.
blocks_dim: a Dimension in x.shape
block_size_dim: a Dimension in x.shape
halo_size: an integer
wrap: a boolean
Returns:
a Tensor with the same shape as x, other than in block... | juraj-google-style |
def plot_correlation(self, freq=None, title=None, figsize=(12, 6), **kwargs):
if (title is None):
title = self._get_default_plot_title(freq, 'Return Correlation Matrix')
rets = self._get_series(freq).to_returns().dropna()
return rets.plot_corr_heatmap(title=title, figsize=figsize, **kwargs) | Utility function to plot correlations.
Args:
* freq (str): Pandas data frequency alias string
* title (str): Plot title
* figsize (tuple (x,y)): figure size
* kwargs: passed to Pandas' plot_corr_heatmap function | codesearchnet |
def tag(self, name, formatter=None):
tag = Tag(name, formatter)
for tag_data in self._tags:
if (tag_data.name == name):
tag = tag_data
break
else:
self._tags.append(tag)
return tag | Return instance of Tag.
Args:
name (str): The value for this tag.
formatter (method, optional): A method that take a tag value and returns a
formatted tag.
Returns:
obj: An instance of Tag. | codesearchnet |
def colored(cls, color, message):
return ((getattr(cls, color.upper()) + message) + cls.DEFAULT) | Small function to wrap a string around a color
Args:
color (str): name of the color to wrap the string with, must be one
of the class properties
message (str): String to wrap with the color
Returns:
str: the colored string | codesearchnet |
def get_vmss(access_token, subscription_id, resource_group, vmss_name):
endpoint = ''.join([get_rm_endpoint(),
'/subscriptions/', subscription_id,
'/resourceGroups/', resource_group,
'/providers/Microsoft.Compute/virtualMachineScaleSets/',... | Get virtual machine scale set details.
Args:
access_token (str): A valid Azure authentication token.
subscription_id (str): Azure subscription id.
resource_group (str): Azure resource group name.
vmss_name (str): Name of the virtual machine scale set.
Returns:
HTTP response. JSON body of scale set properties. | juraj-google-style |
def _send_notification(self, handle, payload):
self.bable.notify(
connection_handle=self._connection_handle,
attribute_handle=handle,
value=payload
) | Send a notification over BLE
It is executed in the baBLE working thread: should not be blocking.
Args:
handle (int): The handle to notify on
payload (bytearray): The value to notify | juraj-google-style |
def get_timestamped_export_dir(export_dir_base):
attempts = 0
while attempts < MAX_DIRECTORY_CREATION_ATTEMPTS:
timestamp = int(time.time())
result_dir = os.path.join(compat.as_bytes(export_dir_base), compat.as_bytes(str(timestamp)))
if not gfile.Exists(result_dir):
return re... | Builds a path to a new subdirectory within the base directory.
Each export is written into a new subdirectory named using the
current time. This guarantees monotonically increasing version
numbers even across multiple runs of the pipeline.
The timestamp used is the number of seconds since epoch UTC.
Args:
export_dir... | github-repos |
def _unpack(formatstring, packed):
_checkString(formatstring, description='formatstring', minlength=1)
_checkString(packed, description='packed string', minlength=1)
if (sys.version_info[0] > 2):
packed = bytes(packed, encoding='latin1')
try:
value = struct.unpack(formatstring, packed)[0... | Unpack a bytestring into a value.
Uses the built-in :mod:`struct` Python module.
Args:
* formatstring (str): String for the packing. See the :mod:`struct` module for details.
* packed (str): The bytestring to be unpacked.
Returns:
A value. The type depends on the formatstring.
Raises:
ValueError
Note that the :mod... | codesearchnet |
def resolve_object_property(obj, path: str):
value = obj
for path_part in path.split('.'):
value = getattr(value, path_part)
return value | Resolves the value of a property on an object.
Is able to resolve nested properties. For example,
a path can be specified:
'other.beer.name'
Raises:
AttributeError:
In case the property could not be resolved.
Returns:
The value of the specified property. | codesearchnet |
def _WriteAttributeContainer(self, attribute_container):
if attribute_container.CONTAINER_TYPE == self._CONTAINER_TYPE_EVENT:
timestamp, serialized_data = self._serialized_event_heap.PopEvent()
else:
serialized_data = self._SerializeAttributeContainer(attribute_container)
if self.compressi... | Writes an attribute container.
The table for the container type must exist.
Args:
attribute_container (AttributeContainer): attribute container. | juraj-google-style |
def load(f, _dict=dict, decoder=None):
if _ispath(f):
with io.open(_getpath(f), encoding='utf-8') as ffile:
return loads(ffile.read(), _dict, decoder)
elif isinstance(f, list):
from os import path as op
from warnings import warn
if (not [path for path in f if op.exist... | Parses named file or files as toml and returns a dictionary
Args:
f: Path to the file to open, array of files to read into single dict
or a file descriptor
_dict: (optional) Specifies the class of the returned toml dictionary
Returns:
Parsed toml file represented as a dictionary
Raises:
TypeError -- When f is invali... | codesearchnet |
def _read_output(self, stream, callback, output_file):
if (callback is None and output_file is None) or stream.closed:
return False
line = stream.readline()
if line:
if callback is not None:
callback(line.decode(),
self._... | Read the output of the process, executed the callback and save the output.
Args:
stream: A file object pointing to the output stream that should be read.
callback(callable, None): A callback function that is called for each new
line of output.
output_file: A file object to which the full output is written.
Returns:
b... | juraj-google-style |
def hurst_compare_nvals(data, nvals=None):
import matplotlib.pyplot as plt
data = np.asarray(data)
n_all = np.arange(2,len(data)+1)
dd_all = nolds.hurst_rs(data, nvals=n_all, debug_data=True, fit="poly")
dd_def = nolds.hurst_rs(data, debug_data=True, fit="poly")
n_def = np.round(np.exp(dd_def[1][0])).ast... | Creates a plot that compares the results of different choices for nvals
for the function hurst_rs.
Args:
data (array-like of float):
the input data from which the hurst exponent should be estimated
Kwargs:
nvals (array of int):
a manually selected value for the nvals parameter that should be plotted
in comparison to ... | juraj-google-style |
def valUserCert(self, byts, cacerts=None):
cert = crypto.load_certificate(crypto.FILETYPE_PEM, byts)
if (cacerts is None):
cacerts = self.getCaCerts()
store = crypto.X509Store()
[store.add_cert(cacert) for cacert in cacerts]
ctx = crypto.X509StoreContext(store, cert)
ctx.verify_certifica... | Validate the PEM encoded x509 user certificate bytes and return it.
Args:
byts (bytes): The bytes for the User Certificate.
cacerts (tuple): A tuple of OpenSSL.crypto.X509 CA Certificates.
Raises:
OpenSSL.crypto.X509StoreContextError: If the certificate is not valid.
Returns:
OpenSSL.crypto.X509: The certificate, if... | codesearchnet |
def add_arguments(self, parser):
group = parser.add_mutually_exclusive_group(required=True)
group.add_argument('-l', '--list', nargs='?',
type=str.lower, default='_',
choices=['usb', 'ip'],
help='list all the conne... | Adds the arguments for the emulator command.
Args:
self (EmulatorCommand): the ``EmulatorCommand`` instance
parser (argparse.ArgumentParser): parser to add the commands to
Returns:
``None`` | juraj-google-style |
def download_apcor(self, uri):
local_file = os.path.basename(uri)
if os.access(local_file, os.F_OK):
fobj = open(local_file)
else:
fobj = storage.vofile(uri, view='data')
fobj.seek(0)
str = fobj.read()
fobj.close()
apcor_str = str
return ApcorData.from_string(apcor_st... | Downloads apcor data.
Args:
uri: The URI of the apcor data file.
Returns:
apcor: ossos.downloads.core.ApcorData | codesearchnet |
def read_local_files(*file_paths: str) -> str:
def _read_single_file(file_path):
with open(file_path) as f:
filename = os.path.splitext(file_path)[0]
title = f
return '\n\n'.join((title, f.read()))
return ('\n' + '\n\n'.join(map(_read_single_file, file_paths))) | Reads one or more text files and returns them joined together.
A title is automatically created based on the file name.
Args:
*file_paths: list of files to aggregate
Returns: content of files | codesearchnet |
def _zip_request_params(self, urls, query_params, data):
if (not isinstance(urls, list)):
urls = [urls]
if (not isinstance(query_params, list)):
query_params = [query_params]
if (not isinstance(data, list)):
data = [data]
url_count = len(urls)
query_param_count = len(query_pa... | Massages inputs and returns a list of 3-tuples zipping them up.
This is all the smarts behind deciding how many requests to issue.
It's fine for an input to have 0, 1, or a list of values.
If there are two inputs each with a list of values, the cardinality of those lists much match.
Args:
urls - 1 string URL or a lis... | codesearchnet |
def register(self, user_dict):
endpoint = os.path.join(self._config.get('napps', 'api'), 'users', '')
res = self.make_request(endpoint, method='POST', json=user_dict)
return res.content.decode('utf-8') | Send an user_dict to NApps server using POST request.
Args:
user_dict(dict): Dictionary with user attributes.
Returns:
result(string): Return the response of Napps server. | codesearchnet |
def _set_details(self, content):
try:
self.details = str(content)
except UnicodeEncodeError:
if (sys.version_info < (3, 0)):
self.details = unicode(content)
else:
logging.error('Unable to decode "%s" in Py3, encoding in utf-8.', content)
self.details =... | Sets the `details` field.
Args:
content: the content to extract details from. | codesearchnet |
def _get_req_fp(self, op):
if(op):
op = op.lower()
if op == 'get':
return requests.get, None
if op == 'put':
return requests.put, {'Content-Type': 'application/x-www-form-urlencoded'}
if op == 'post':
return requests.post, {'Content-Type': 'application/json'}
if op == 'delete':
retur... | Decisions on what verb to use and content headers happen here
Args:
op a string specifying a http verb | juraj-google-style |
def _handle_message_for_stream(self, stream_transport, message, timeout):
if (message.command not in ('OKAY', 'CLSE', 'WRTE')):
raise usb_exceptions.AdbProtocolError('%s received unexpected message: %s', self, message)
if (message.arg1 == stream_transport.local_id):
if (message.command == 'WRTE'... | Handle an incoming message, check if it's for the given stream.
If the message is not for the stream, then add it to the appropriate
message queue.
Args:
stream_transport: AdbStreamTransport currently waiting on a message.
message: Message to check and handle.
timeout: Timeout to use for the operation, should be an i... | codesearchnet |
def ParseDict(js_dict, message, ignore_unknown_fields=False):
parser = _Parser(ignore_unknown_fields)
parser.ConvertMessage(js_dict, message)
return message | Parses a JSON dictionary representation into a message.
Args:
js_dict: Dict representation of a JSON message.
message: A protocol buffer message to merge into.
ignore_unknown_fields: If True, do not raise errors for unknown fields.
Returns:
The same message passed as argument. | juraj-google-style |
def GetZipInfo(self):
if (not self._zip_info):
location = getattr(self.path_spec, 'location', None)
if (location is None):
raise errors.PathSpecError('Path specification missing location.')
if (not location.startswith(self._file_system.LOCATION_ROOT)):
raise errors.Pa... | Retrieves the ZIP info object.
Returns:
zipfile.ZipInfo: a ZIP info object or None if not available.
Raises:
PathSpecError: if the path specification is incorrect. | codesearchnet |
def stop(self, name: str) -> None:
if (not self._timing):
return
now = get_now_utc_pendulum()
if (not self._stack):
raise AssertionError('MultiTimer.stop() when nothing running')
if (self._stack[(- 1)] != name):
raise AssertionError('MultiTimer.stop({}) when {} is running'.format... | Stop a named timer.
Args:
name: timer to stop | codesearchnet |
def word_list(sowpods=False, start="", end=""):
location = os.path.join(
os.path.dirname(os.path.realpath(__file__)),
"wordlists",
)
if sowpods:
filename = "sowpods.txt"
else:
filename = "twl.txt"
filepath = os.path.join(location, filename)
with open(file... | Opens the word list file.
Args:
sowpods: a boolean to declare using the sowpods list or TWL (default)
start: a string of starting characters to find anagrams based on
end: a string of ending characters to find anagrams based on
Yeilds:
a word at a time out of 178691 words for TWL, 267751 for sowpods. Much
less if eit... | juraj-google-style |
def __init__(self, n, key=None, reverse=False):
self._n = n
self._key = key
self._reverse = reverse | Creates a per-key Top operation.
The arguments 'key' and 'reverse' may be passed as keyword arguments,
and have the same meaning as for Python's sort functions.
Args:
n: number of elements to extract from pcoll.
key: (optional) a mapping of elements to a comparable key, similar to
the key argument of Python's sorting... | github-repos |
def set_data(self, data):
for name in self._fields:
setattr(self, name, data.get(name))
return self | Fills form with data
Args:
data (dict): Data to assign form fields.
Returns:
Self. Form object. | juraj-google-style |
def Compile(self, filter_implementation):
self.attribute = self.swap_source.get(self.attribute, self.attribute)
arguments = [self.attribute]
op_str = self.operator.lower()
operator = filter_implementation.OPS.get(op_str, None)
if (not operator):
raise errors.ParseError('Unknown operator {0:s... | Compiles the filter implementation.
Args:
filter_implementation: a filter object (instance of objectfilter.TODO).
Returns:
A filter operator (instance of TODO).
Raises:
ParserError: if an unknown operator is provided. | codesearchnet |
def _unsorted_segment_N(data, segment_ids, num_segments):
num_segments = ops.convert_to_tensor(num_segments)
segment_ids_shape = array_ops.shape_internal(segment_ids)
ones_tensor = array_ops.ones(segment_ids_shape, dtype=data.dtype)
n = gen_math_ops.unsorted_segment_sum(ones_tensor, segment_ids, num_seg... | Helper function for unsorted_segment_mean/_sqrtN.
Computes the number of segment entries with 0-entries set to 1 to allow
division by N.
Args:
data: A `Tensor` with data that will be assembled in the output.
segment_ids: An integer tensor whose shape is a prefix of `data.shape`. The
values must be in the range `[0, n... | github-repos |
def set_epsilon(value):
global _EPSILON
_EPSILON = value | Set the value of the fuzz factor used in numeric expressions.
Args:
value: float. New value of epsilon.
Examples:
>>> keras.config.epsilon()
1e-07
>>> keras.config.set_epsilon(1e-5)
>>> keras.config.epsilon()
1e-05
>>> # Set it back to the default value.
>>> keras.config.set_epsilon(1e-7) | github-repos |
async def addNode(self, name, valu, props=None):
try:
fnib = self._getNodeFnib(name, valu)
retn = await self._addNodeFnib(fnib, props=props)
return retn
except asyncio.CancelledError:
raise
except Exception:
mesg = f'Error... | Add a node by form name and value with optional props.
Args:
name (str): The form of node to add.
valu (obj): The value for the node.
props (dict): Optional secondary properties for the node. | juraj-google-style |
def are_equal_xml(a_xml, b_xml):
a_dom = xml.dom.minidom.parseString(a_xml)
b_dom = xml.dom.minidom.parseString(b_xml)
return are_equal_elements(a_dom.documentElement, b_dom.documentElement) | Normalize and compare XML documents for equality. The document may or may not be
a DataONE type.
Args:
a_xml: str
b_xml: str
XML documents to compare for equality.
Returns:
bool: ``True`` if the XML documents are semantically equivalent. | juraj-google-style |
def coresight_configure(self, ir_pre=0, dr_pre=0, ir_post=0, dr_post=0, ir_len=0, perform_tif_init=True):
if (self.tif == enums.JLinkInterfaces.SWD):
res = self._dll.JLINKARM_CORESIGHT_Configure('')
if (res < 0):
raise errors.JLinkException(res)
return None
config_string = 'I... | Prepares target and J-Link for CoreSight function usage.
Args:
self (JLink): the ``JLink`` instance
ir_pre (int): sum of instruction register length of all JTAG devices
in the JTAG chain, close to TDO than the actual one, that J-Link
shall communicate with
dr_pre (int): number of JTAG devices in the JTAG chain, closer... | codesearchnet |
def FormatTypeSummaryTable(self, level_name, name_to_problist):
output = []
output.append('<table>')
for classname in sorted(name_to_problist.keys()):
problist = name_to_problist[classname]
human_name = MaybePluralizeWord(problist.count, UnCamelCase(classname))
output.append(('<tr><t... | Return an HTML table listing the number of problems by class name.
Args:
level_name: string such as "Error" or "Warning"
name_to_problist: dict mapping class name to an BoundedProblemList object
Returns:
HTML in a string | codesearchnet |
def __init__(self, channel):
self.Health = channel.unary_unary(
'/health.Health/Health',
request_serializer=google_dot_protobuf_dot_empty__pb2.Empty.SerializeToString,
response_deserializer=google_dot_protobuf_dot_empty__pb2.Empty.FromString,
) | Constructor.
Args:
channel: A grpc.Channel. | juraj-google-style |
def escape(inp, quote='"'):
output = ""
for c in inp:
if c == quote:
output += '\\'
output += c
return output | Escape `quote` in string `inp`.
Example usage::
>>> escape('hello "')
'hello \\"'
>>> escape('hello \\"')
'hello \\\\"'
Args:
inp (str): String in which `quote` will be escaped.
quote (char, default "): Specify which character will be escaped.
Returns:
str: Escaped string. | juraj-google-style |
def _create_controller_info_record(self, controller_module_name):
module = self._controller_modules[controller_module_name]
controller_info = None
try:
controller_info = module.get_info(copy.copy(self._controller_objects[controller_module_name]))
except AttributeError:
logging.warning('N... | Creates controller info record for a particular controller type.
Info is retrieved from all the controller objects spawned from the
specified module, using the controller module's `get_info` function.
Args:
controller_module_name: string, the name of the controller module
to retrieve info from.
Returns:
A records.Co... | github-repos |
def save_b26_file(filename, instruments=None, scripts=None, probes=None, overwrite=False, verbose=False):
if os.path.isfile(filename) and overwrite == False:
data_dict = load_b26_file(filename)
else:
data_dict = {}
if instruments is not None:
if 'instruments' in data_dict... | save instruments, scripts and probes as a json file
Args:
filename:
instruments:
scripts:
probes: dictionary of the form {instrument_name : probe_1_of_intrument, probe_2_of_intrument, ...}
Returns: | juraj-google-style |
def validate(self, corpus):
passed = True
results = {}
for validator in self.validators:
sub_result = validator.validate(corpus)
results[validator.name()] = sub_result
if not sub_result.passed:
passed = False
return Combine... | Perform validation on the given corpus.
Args:
corpus (Corpus): The corpus to test/validate. | juraj-google-style |
def GetSortedEvents(self, time_range=None):
if not self._storage_file:
raise IOError('Unable to read from closed storage writer.')
return self._storage_file.GetSortedEvents(time_range=time_range) | Retrieves the events in increasing chronological order.
This includes all events written to the storage including those pending
being flushed (written) to the storage.
Args:
time_range (Optional[TimeRange]): time range used to filter events
that fall in a specific period.
Returns:
generator(EventObject): event gener... | juraj-google-style |
def validate(request: Union[(Dict, List)], schema: dict) -> Union[(Dict, List)]:
jsonschema_validate(request, schema)
return request | Wraps jsonschema.validate, returning the same object passed in.
Args:
request: The deserialized-from-json request.
schema: The jsonschema schema to validate against.
Raises:
jsonschema.ValidationError | codesearchnet |
def __init__(self, min_obs=10):
self.min_obs = min_obs
self.label_encoder = LabelEncoder(min_obs) | Initialize the OneHotEncoder class object.
Args:
min_obs (int): minimum number of observation to create a dummy variable
label_encoder (LabelEncoder): LabelEncoder that transofrm | juraj-google-style |
def removeRow(self, triggered):
if triggered:
model = self.tableView.model()
selection = self.tableView.selectedIndexes()
rows = [index.row() for index in selection]
model.removeDataFrameRows(set(rows))
self.sender().setChecked(False) | Removes a row to the model.
This method is also a slot.
Args:
triggered (bool): If the corresponding button was
activated, the selected row will be removed
from the model. | juraj-google-style |
def overlay(self, feature, color='Blue', opacity=0.6):
result = self.copy()
if (type(feature) == Table):
if ('feature' in feature):
feature = feature['feature']
else:
feature = Circle.map_table(feature)
if (type(feature) in [list, np.ndarray]):
for f in featur... | Overlays ``feature`` on the map. Returns a new Map.
Args:
``feature``: a ``Table`` of map features, a list of map features,
a Map, a Region, or a circle marker map table. The features will
be overlayed on the Map with specified ``color``.
``color`` (``str``): Color of feature. Defaults to 'Blue'
``opacity`` (``float... | codesearchnet |
def get_vulnerability(source, sink, triggers, lattice, cfg, interactive, blackbox_mapping):
nodes_in_constraint = [secondary for secondary in reversed(source.secondary_nodes) if lattice.in_constraint(secondary, sink.cfg_node)]
nodes_in_constraint.append(source.cfg_node)
if sink.trigger.all_arguments_propaga... | Get vulnerability between source and sink if it exists.
Uses triggers to find sanitisers.
Note: When a secondary node is in_constraint with the sink
but not the source, the secondary is a save_N_LHS
node made in process_function in expr_visitor.
Args:
source(TriggerNode): TriggerNode of the source.
sink(TriggerNode)... | codesearchnet |
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