code stringlengths 20 4.93k | docstring stringlengths 33 1.27k | source stringclasses 3
values |
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def RunPlugins(
cls, artifacts_registry, file_system, mount_point, knowledge_base):
searcher = file_system_searcher.FileSystemSearcher(file_system, mount_point)
cls.CollectFromFileSystem(
artifacts_registry, knowledge_base, searcher, file_system)
environment_variables = None
... | Runs the preprocessing plugins.
Args:
artifacts_registry (artifacts.ArtifactDefinitionsRegistry): artifacts
definitions registry.
file_system (dfvfs.FileSystem): file system to be preprocessed.
mount_point (dfvfs.PathSpec): mount point path specification that refers
to the base location of the file system.
knowledge_b... | juraj-google-style |
def get_channel_id(turn_context: TurnContext) -> str:
if (turn_context.activity.channel_id is None):
return ''
else:
return turn_context.activity.channel_id | Get the Channel Id from the current Activity on the Turn Context.
Args:
turn_context (TurnContext): The Turn Context to retrieve the Activity's Channel Id from.
Returns:
str: The Channel Id from the Turn Context's Activity. | codesearchnet |
def convert_snapshot(self, shift, instruction):
command_dict = {
'name': 'snapshot',
't0': shift+instruction.start_time,
'label': instruction.name,
'type': instruction.type
}
return self._qobj_model(**command_dict) | Return converted `Snapshot`.
Args:
shift(int): Offset time.
instruction (Snapshot): snapshot instruction.
Returns:
dict: Dictionary of required parameters. | juraj-google-style |
def __format_error(self, error_list_tag):
error = {'domain': self.domain(),
'reason': self.reason(),
'message': self.message()}
error.update(self.extra_fields() or {})
return {'error': {error_list_tag: [error],
'code': self.status_code(),
... | Format this error into a JSON response.
Args:
error_list_tag: A string specifying the name of the tag to use for the
error list.
Returns:
A dict containing the reformatted JSON error response. | juraj-google-style |
def _ParseCmdItem(self, cmd_input, template_file=None):
fsm = textfsm.TextFSM(template_file)
if (not self._keys):
self._keys = set(fsm.GetValuesByAttrib('Key'))
table = texttable.TextTable()
table.header = fsm.header
for record in fsm.ParseText(cmd_input):
table.Append(record)
re... | Creates Texttable with output of command.
Args:
cmd_input: String, Device response.
template_file: File object, template to parse with.
Returns:
TextTable containing command output.
Raises:
CliTableError: A template was not found for the given command. | codesearchnet |
def _ParseShellItemPathSegment(self, shell_item):
path_segment = None
if isinstance(shell_item, pyfwsi.root_folder):
description = shell_folder_ids.DESCRIPTIONS.get(shell_item.shell_folder_identifier, None)
if description:
path_segment = description
else:
path_seg... | Parses a shell item path segment.
Args:
shell_item (pyfwsi.item): shell item.
Returns:
str: shell item path segment. | codesearchnet |
def GetRequestXML(self, method, *args):
packed_args = self._PackArguments(method, args, set_type_attrs=True)
headers = self._GetZeepFormattedSOAPHeaders()
return self.zeep_client.create_message(
self.zeep_client.service, method, *packed_args, _soapheaders=headers) | Get the raw SOAP XML for a request.
Args:
method: The method name.
*args: A list of arguments to be passed to the method.
Returns:
An element containing the raw XML that would be sent as the request. | juraj-google-style |
def __init__(self, parent_xid, relationship):
self.xid = str(uuid.uuid4())
self._action_data = {
'indicatorXid': self.xid,
'relationship': relationship,
'parentIndicatorXid': parent_xid,
}
self._children = [] | Initialize Class Properties.
.. warning:: This code is not complete and may require some update to the API.
Args:
parent_xid (str): The external id of the parent Indicator.
relationship: ??? | juraj-google-style |
def start_at(self, document_fields):
query = query_mod.Query(self)
return query.start_at(document_fields) | Start query at a cursor with this collection as parent.
See
:meth:`~.firestore_v1beta1.query.Query.start_at` for
more information on this method.
Args:
document_fields (Union[~.firestore_v1beta1.\
document.DocumentSnapshot, dict, list, tuple]): a document
snapshot or a dictionary/list/tuple of fields representing a
q... | codesearchnet |
def _AddCampaignsToGroup(client, campaign_group_id, campaign_ids):
campaign_service = client.GetService('CampaignService', version='v201809')
operations = [{'operator': 'SET', 'operand': {'id': campaign_id, 'campaignGroupId': campaign_group_id}} for campaign_id in campaign_ids]
campaign_service.mutate(opera... | Adds multiple campaigns to a campaign group.
Args:
client: an AdWordsClient instance.
campaign_group_id: an integer ID for the campaign group.
campaign_ids: a list of integer IDs for campaigns. | codesearchnet |
def check(self, digest):
path = self.get_file_path(digest)
if (self._calc_digest(path) != digest):
self.logger.warning("found corrupted file: '{0}'".format(path))
return False
return True | Check the integrity of the file with the given digest
Args:
digest -- digest of the file to check
Returns:
True if the file is not corrupted | codesearchnet |
def children(self, sourcepath, recursive=True):
return self._get_recursive_dependancies(self._CHILDREN_MAP, sourcepath, recursive=True) | Recursively find all children that are imported from the given source
path.
Args:
sourcepath (str): Source file path to search for.
Keyword Arguments:
recursive (bool): Switch to enabled recursive finding (if True).
Default to True.
Returns:
set: List of finded parents path. | codesearchnet |
def create_constructor_args(cls, proto_list: List[fra.ForwardRateAgreement], config: ForwardRateAgreementConfig=None) -> Dict[str, Any]:
fra_data = proto_utils.from_protos_v2(proto_list, config)
res = {}
for key in fra_data:
tensor_repr = proto_utils.tensor_repr(fra_data[key])
res[key] = ten... | Creates a dictionary to initialize ForwardRateAgreement.
The output dictionary is such that the instruments can be initialized
as follows:
```
initializer = create_constructor_args(proto_list, config)
fras = [ForwardRateAgreement(**data) for data in initializer.values()]
```
The keys of the output dictionary are uniq... | github-repos |
def pull_release(self, name, version, destfolder='.', force=False):
unique_id = name.replace('/', '_')
depdict = {'name': name, 'unique_id': unique_id, 'required_version': version, 'required_version_string': str(version)}
destdir = os.path.join(destfolder, unique_id)
if os.path.exists(destdir):
... | Download and unpack a released iotile component by name and version range
If the folder that would be created already exists, this command fails unless
you pass force=True
Args:
name (string): The name of the component to download
version (SemanticVersionRange): The valid versions of the component to fetch
destfolder... | codesearchnet |
def run(self, args):
jlink = self.create_jlink(args)
erased = jlink.erase()
print(('Bytes Erased: %d' % erased)) | Erases the device connected to the J-Link.
Args:
self (EraseCommand): the ``EraseCommand`` instance
args (Namespace): the arguments passed on the command-line
Returns:
``None`` | codesearchnet |
def write_float(self, registeraddress, value, numberOfRegisters=2):
_checkNumerical(value, description='input value')
_checkInt(numberOfRegisters, minvalue=2, maxvalue=4, description='number of registers')
self._genericCommand(16, registeraddress, value, numberOfRegisters=numberOfRegisters, payloadformat='f... | Write a floating point number to the slave.
Floats are stored in two or more consecutive 16-bit registers in the slave.
Uses Modbus function code 16.
For discussion on precision, number of registers and on byte order, see :meth:`.read_float`.
Args:
* registeraddress (int): The slave register start address (use deci... | codesearchnet |
def get_membership(self, uuid=None):
group_id = self.get_group_id(uuid=uuid)
uri = 'group/{group_id}/member'
mbr_data = self.get(uri.format(group_id=group_id), params=None)
return mbr_data | Get membership data based on uuid.
Args:
uuid (str): optional uuid. defaults to self.cuuid
Raises:
PyLmodUnexpectedData: No data was returned.
requests.RequestException: Exception connection error
Returns:
dict: membership json | juraj-google-style |
def _execute_and_process_stdout(self, args, shell, handler) -> bytes:
proc = subprocess.Popen(args, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=shell, bufsize=1)
out = '[elided, processed via handler]'
try:
while True:
line = proc.stdout.readline()
if line:
... | Executes adb commands and processes the stdout with a handler.
Args:
args: string or list of strings, program arguments.
See subprocess.Popen() documentation.
shell: bool, True to run this command through the system shell,
False to invoke it directly. See subprocess.Popen() docs.
handler: func, a function to handle ad... | github-repos |
def StaticAdd(cls, queue_urn, rdf_value, mutation_pool=None):
if not isinstance(rdf_value, cls.rdf_type):
raise ValueError("This collection only accepts values of type %s." %
cls.rdf_type.__name__)
if mutation_pool is None:
raise ValueError("Mutation pool can't be none.")... | Adds an rdf value the queue.
Adds an rdf value to a queue. Does not require that the queue be locked, or
even open. NOTE: The caller is responsible for ensuring that the queue
exists and is of the correct type.
Args:
queue_urn: The urn of the queue to add to.
rdf_value: The rdf value to add to the queue.
mutation_p... | juraj-google-style |
def __init__(self, hash_queue, hash_analysis_queue, **kwargs):
super(NsrlsvrAnalyzer, self).__init__(
hash_queue, hash_analysis_queue, **kwargs)
self._host = None
self._port = None
self.hashes_per_batch = 100 | Initializes an nsrlsvr analyzer thread.
Args:
hash_queue (Queue.queue): contains hashes to be analyzed.
hash_analysis_queue (Queue.queue): that the analyzer will append
HashAnalysis objects this queue. | juraj-google-style |
def search(self, search_phrase, limit=None):
query, query_params = self._make_query_from_terms(search_phrase, limit=limit)
self._parsed_query = (str(query), query_params)
assert isinstance(query, TextClause)
datasets = {}
def make_result(vid=None, b_score=0, p_score... | Finds datasets by search phrase.
Args:
search_phrase (str or unicode):
limit (int, optional): how many results to return. None means without limit.
Returns:
list of DatasetSearchResult instances. | juraj-google-style |
def set(self, time):
self._time = time
self._pb.sec = int(self._time)
self._pb.nsec = int((self._time - self._pb.sec) * 10 ** 9) | Sets time in seconds since Epoch
Args:
time (:obj:`float`): time in seconds since Epoch (see time.time())
Returns:
None | juraj-google-style |
def IsLinearOutputModule(cls, name):
name = name.lower()
output_class = cls._output_classes.get(name, None)
if (not output_class):
output_class = cls._disabled_output_classes.get(name, None)
if output_class:
return issubclass(output_class, interface.LinearOutputModule)
return False | Determines if a specific output class is a linear output module.
Args:
name (str): name of the output module.
Returns:
True: if the output module is linear. | codesearchnet |
def parse_func_attrs(attributes, allowlist=None):
if not allowlist:
allowlist = MONOMORPHIC_FUNCTION_ALLOWLIST
attrs = {}
for key, value in attributes.items():
if key not in allowlist:
raise ValueError(f'Allowlist does not support `{key}` as an attribute.')
attrs[key] = _... | Convert the keyword arguments into function_def attributes.
Currently only support primitive types: bool, int, float and string.
Args:
attributes: the dictionary of attributes.
allowlist: set of attribute names allowed.
Returns:
A dict of attributes where the key is the name of attribute and the value
is the AttrValu... | github-repos |
def remove(package_name):
if package_name not in packages:
raise HolodeckException("Unknown package name " + package_name)
for config, path in _iter_packages():
if config["name"] == package_name:
shutil.rmtree(path) | Removes a holodeck package.
Args:
package_name (str): the name of the package to remove | juraj-google-style |
def ParseMessageRow(self, parser_mediator, query, row, **unused_kwargs):
query_hash = hash(query)
event_data = TangoAndroidMessageEventData()
event_data.message_identifier = self._GetRowValue(
query_hash, row, 'msg_id')
event_data.direction = self._GetRowValue(query_hash, ... | Parses a message row from the database.
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 resulting from query. | juraj-google-style |
def write_edges(edges: Mapping[(str, Any)], filename: str, jsonlines: bool=False, gzipflag: bool=False, yaml: bool=False):
pass | Write edges to file
Args:
edges (Mapping[str, Any]): in edges JSON Schema format
filename (str): filename to write
jsonlines (bool): output in JSONLines format?
gzipflag (bool): create gzipped file?
yaml (bool): create yaml file? | codesearchnet |
def isdir(self, path, follow_symlinks=True):
return self._is_of_type(path, S_IFDIR, follow_symlinks) | Determine if path identifies a directory.
Args:
path: Path to filesystem object.
Returns:
`True` if path points to a directory (following symlinks).
Raises:
TypeError: if path is None. | codesearchnet |
def patch_fromText(self, textline):
if (type(textline) == unicode):
textline = textline.encode('ascii')
patches = []
if (not textline):
return patches
text = textline.split('\n')
while (len(text) != 0):
m = re.match('^@@ -(\\d+),?(\\d*) \\+(\\d+),?(\\d*) @@$', text[0])
... | Parse a textual representation of patches and return a list of patch
objects.
Args:
textline: Text representation of patches.
Returns:
Array of Patch objects.
Raises:
ValueError: If invalid input. | codesearchnet |
def _build(self):
if ('w' not in self._initializers):
stddev = (1 / math.sqrt(np.prod(self._shape)))
self._initializers['w'] = tf.truncated_normal_initializer(stddev=stddev)
self._w = tf.get_variable('w', shape=self._shape, dtype=self._dtype, initializer=self._initializers['w'], partitioner=self... | Connects the TrainableTensor module into the graph.
Returns:
A Tensor of shape as determined in the constructor. | codesearchnet |
def _update_token(self, request):
self._source_credentials.refresh(request)
body = {
"delegates": self._delegates,
"scope": self._target_scopes,
"lifetime": str(self._lifetime) + "s"
}
headers = {
'Content-Type': 'appli... | Updates credentials with a new access_token representing
the impersonated account.
Args:
request (google.auth.transport.requests.Request): Request object
to use for refreshing credentials. | juraj-google-style |
def __init__(self, full_shape, var_offset):
if not isinstance(full_shape, (list, tuple)):
raise TypeError('`full_shape` must be a sequence (like tuple or list) instead of ' + type(full_shape).__name__)
if not isinstance(var_offset, (list, tuple)):
raise TypeError('`var_offset` must be a sequence... | Constructor.
Args:
full_shape: Tuple or list of `int` indicating the full combined shape of
the partitioned variables.
var_offset: Tuple or list of `int` specifying offset of this partition
with respect to the full variable for each dimension.
Raises:
TypeError: If `full_shape` or `var_offset` is not a sequence.
Valu... | github-repos |
def label(self):
with self.selenium.context(self.selenium.CONTEXT_CHROME):
return self.root.get_attribute('label') | Provide access to the notification label.
Returns:
str: The notification label | codesearchnet |
def get_imagery(cls, lat, lon, date=None, dim=None, cloud_score=False):
instance = cls('planetary/earth/imagery')
filters = {
'lat': lat,
'lon': lon,
'date': date,
'dim': dim,
'cloud_score': cloud_score
}
return insta... | Returns satellite image
Args:
lat: latitude float
lon: longitude float
date: date instance of available date from `get_assets`
dim: width and height of image in degrees as float
cloud_score: boolean to calculate the percentage of the image covered by clouds
Returns:
json | juraj-google-style |
def get_extra_managed_storage_volume_paths(self, start=0, count=(- 1), filter='', sort=''):
uri = (self.URI + '/repair?alertFixType=ExtraManagedStorageVolumePaths')
return self._client.get_all(start, count, filter=filter, sort=sort, uri=uri) | Gets the list of extra managed storage volume paths.
Args:
start:
The first item to return, using 0-based indexing.
If not specified, the default is 0 - start with the first available item.
count:
The number of resources to return. A count of -1 requests all items.
The actual number of items in the response might diff... | codesearchnet |
def build(self, var_list):
if self.built:
return
if var_list:
dtype = var_list[0].dtype
else:
dtype = backend.floatx()
super().build(var_list)
self._momentums, self._velocities = self.add_optimizer_variables(var_list, ['momentum', 'velocity'])
self._u_product = backend.Va... | Initialize optimizer variables.
Nadam optimizer has 2 types of variables: momentums and velocities.
Args:
var_list: list of model variables to build Nadam variables on. | github-repos |
def make_basket_put_payoff(strikes: types.RealTensor, dtype: tf.DType=None, name: str=None) -> Callable[[types.RealTensor], types.RealTensor]:
name = name or 'put_valuer'
with tf.name_scope(name):
strikes = tf.convert_to_tensor(strikes, dtype=dtype, name='strikes')
dtype = dtype or strikes.dtype... | Produces a callable from samples to payoff of a simple basket put option.
Args:
strikes: A `Tensor` of `dtype` consistent with `samples` and shape
`[num_samples, batch_size]`.
dtype: Optional `dtype`. Either `tf.float32` or `tf.float64`. If supplied,
represents the `dtype` for the 'strikes' as well as for the input
ar... | github-repos |
def wait_for_import(self, connection_id, wait_interval):
self.stdout.write(self.style.NOTICE('Waiting for import'), ending='')
state = utils.ConnectionStates.IMPORT_CONFIGURATION
while (state == utils.ConnectionStates.IMPORT_CONFIGURATION):
self.stdout.write(self.style.NOTICE('.'), ending='')
... | Wait until connection state is no longer ``IMPORT_CONFIGURATION``.
Args:
connection_id (str): Heroku Connect connection to monitor.
wait_interval (int): How frequently to poll in seconds.
Raises:
CommandError: If fetch connection information fails. | codesearchnet |
def can_process_matrix(entry, matrix_tags):
if (len(matrix_tags) == 0):
return True
count = 0
if ('tags' in entry):
for tag in matrix_tags:
if (tag in entry['tags']):
count += 1
return (count > 0) | Check given matrix tags to be in the given list of matric tags.
Args:
entry (dict): matrix item (in yaml).
matrix_tags (list): represents --matrix-tags defined by user in command line.
Returns:
bool: True when matrix entry can be processed. | codesearchnet |
def _ParseHeader(self, parser_mediator, structure):
_, month, day, hours, minutes, seconds, year = structure.date_time
month = timelib.MONTH_DICT.get(month.lower(), 0)
time_elements_tuple = (year, month, day, hours, minutes, seconds)
try:
date_time = dfdatetime_time_elements.TimeElements(
... | Parses a log header.
Args:
parser_mediator (ParserMediator): mediates interactions between parsers
and other components, such as storage and dfvfs.
structure (pyparsing.ParseResults): structure of tokens derived from
a line of a text file. | juraj-google-style |
def softplus(x):
if any_symbolic_tensors((x,)):
return Softplus().symbolic_call(x)
return backend.nn.softplus(x) | Softplus activation function.
It is defined as `f(x) = log(exp(x) + 1)`, where `log` is the natural
logarithm and `exp` is the exponential function.
Args:
x: Input tensor.
Returns:
A tensor with the same shape as `x`.
Example:
>>> x = keras.ops.convert_to_tensor([-0.555, 0.0, 0.555])
>>> keras.ops.softplus(x)
arra... | github-repos |
def get_data_dirs(__pkg: str) -> List[str]:
dirs = [user_data(__pkg), ]
dirs.extend(path.expanduser(path.sep.join([d, __pkg]))
for d in getenv('XDG_DATA_DIRS',
'/usr/local/share/:/usr/share/').split(':'))
return [d for d in dirs if path.isdir(d)] | Return all data directories for given package.
Args:
__pkg: Package name | juraj-google-style |
def get_object(self, dn, filter, attributes, _connection=None):
connection = _connection
if (not connection):
connection = self._make_connection(bind_user=self.config.get('LDAP_BIND_USER_DN'), bind_password=self.config.get('LDAP_BIND_USER_PASSWORD'))
connection.bind()
connection.search(searc... | Gets an object at the specified dn and returns it.
Args:
dn (str): The dn of the object to find.
filter (str): The LDAP syntax search filter.
attributes (list): A list of LDAP attributes to get when searching.
_connection (ldap3.Connection): A connection object to use when
searching. If not given, a temporary connecti... | codesearchnet |
def output_csv(filehandle: TextIO, values: Iterable[str]) -> None:
line = ','.join(values)
filehandle.write((line + '\n')) | Write a line of CSV. POOR; does not escape things properly. DEPRECATED.
Args:
filehandle: file to write to
values: values | codesearchnet |
def squeeze(input: ragged_tensor.Ragged, axis=None, name=None):
with ops.name_scope(name, 'RaggedSqueeze', [input]):
input = ragged_tensor.convert_to_tensor_or_ragged_tensor(input)
if isinstance(input, tensor.Tensor):
return array_ops.squeeze(input, axis, name)
if axis is None:
... | Ragged compatible squeeze.
If `input` is a `tf.Tensor`, then this calls `tf.squeeze`.
If `input` is a `tf.RaggedTensor`, then this operation takes `O(N)` time,
where `N` is the number of elements in the squeezed dimensions.
Args:
input: A potentially ragged tensor. The input to squeeze.
axis: An optional list of int... | github-repos |
def sample(self, qubits: List[ops.Qid], repetitions: int=1):
return self._stepper.sample_measurements(indices=[self.qubit_map[q] for q in qubits], repetitions=repetitions) | Samples from the wave function at this point in the computation.
Note that this does not collapse the wave function.
Returns:
Measurement results with True corresponding to the `|1>` state.
The outer list is for repetitions, and the inner corresponds to
measurements ordered by the supplied qubits. | codesearchnet |
def fill(self, background_shape, img):
background_shape = tuple(background_shape)
return self._fill(background_shape, img) | Return a proper background image of background_shape, given img.
Args:
background_shape (tuple): a shape (h, w)
img: an image
Returns:
a background image | juraj-google-style |
def _get_lp_matrix(spin_states, nodes, edges, offset_weight, gap_weight):
if len(spin_states) == 0:
return None
n_states = len(spin_states)
m_linear = len(nodes)
m_quadratic = len(edges)
matrix = np.empty((n_states, m_linear + m_quadratic + 2))
if spin_states.ndim == ... | Creates an linear programming matrix based on the spin states, graph, and scalars provided.
LP matrix:
[spin_states, corresponding states of edges, offset_weight, gap_weight]
Args:
spin_states: Numpy array of spin states
nodes: Iterable
edges: Iterable of tuples
offset_weight: Numpy 1-D array or number
gap_weight: Num... | juraj-google-style |
def __init__(self, parameters, confirms=True):
self.confirms = confirms
self.protocol = FedoraMessagingProtocolV2
self._parameters = parameters
self._client_deferred = defer.Deferred()
self._client = None
self._consumers = {} | Create a new factory for protocol objects.
Any exchanges, queues, or bindings provided here will be declared and
set up each time a new protocol instance is created. In other words,
each time a new connection is set up to the broker, it will start with
the declaration of these objects.
Args:
parameters (pika.Connecti... | juraj-google-style |
def LSTMCell(weights, m_prev, c_prev, x, pad):
xm = array_ops.concat([x, m_prev], 1)
xmw = math_ops.matmul(xm, weights)
in_value, in_gate, forget_gate, out_gate = array_ops.split(value=xmw, num_or_size_splits=4, axis=1)
in_value = math_ops.tanh(in_value)
in_gate = math_ops.sigmoid(in_gate)
forge... | Unrolls a single LSTM cell with clipped activations forward by one step.
Args:
weights: Weight matrix with shape LSTMCellWeightsShape.
m_prev: Previous m states with shape [batch_size, num_nodes].
c_prev: Previous c states with shape [batch_size, num_nodes].
x: Input with shape [batch_size, num_inputs].
pad: Padding w... | github-repos |
def mark_as_done(self, **kwargs):
path = ('%s/%s/mark_as_done' % (self.manager.path, self.id))
server_data = self.manager.gitlab.http_post(path, **kwargs)
self._update_attrs(server_data) | Mark the todo as done.
Args:
**kwargs: Extra options to send to the server (e.g. sudo)
Raises:
GitlabAuthenticationError: If authentication is not correct
GitlabTodoError: If the server failed to perform the request | codesearchnet |
def get_all_configs():
all_functions = collections.OrderedDict([('Platform', get_platform()), ('CPU', get_cpu_type()), ('CPU arch', get_cpu_arch()), ('Distribution', get_distrib()), ('Distribution version', get_distrib_version()), ('GPU', get_gpu_type()[1]), ('GPU count', get_gpu_count()), ('CUDA version (default)'... | Runs all functions for detecting user machine configurations.
Returns:
Tuple
(List of all configurations found,
List of all missing configurations,
List of all configurations found with warnings,
Dict of all configurations) | github-repos |
def read_message(self, timeout):
with self._reader_lock:
raw_header = self._transport.read(struct.calcsize(AdbMessage.HEADER_STRUCT_FORMAT), timeout.remaining_ms)
if (not raw_header):
raise usb_exceptions.AdbProtocolError('Adb connection lost')
try:
raw_message = RawA... | Read an AdbMessage from this transport.
Args:
timeout: Timeout for the entire read operation, in the form of a
timeouts.PolledTimeout instance. Note that for packets with a data
payload, two USB reads are performed.
Returns:
The ADB message read from the device.
Raises:
UsbReadFailedError: There's an error during r... | codesearchnet |
def _to_tensor(x, dtype):
return tensor_conversion.convert_to_tensor_v2_with_dispatch(x, dtype=dtype) | Convert the input `x` to a tensor of type `dtype`.
Args:
x: An object to be converted (numpy array, list, tensors).
dtype: The destination type.
Returns:
A tensor. | github-repos |
def format_error_message(exception_message, task_exception=False):
lines = exception_message.split("\n")
if task_exception:
lines = lines[0:1] + lines[3:]
pass
return "\n".join(lines) | Improve the formatting of an exception thrown by a remote function.
This method takes a traceback from an exception and makes it nicer by
removing a few uninformative lines and adding some space to indent the
remaining lines nicely.
Args:
exception_message (str): A message generated by traceback.format_exc().
Return... | juraj-google-style |
def robust_zscore(mat, ctrl_mat=None, min_mad=0.1):
if ctrl_mat is not None:
medians = ctrl_mat.median(axis=1)
median_devs = abs(ctrl_mat.subtract(medians, axis=0))
else:
medians = mat.median(axis=1)
median_devs = abs(mat.subtract(medians, axis=0))
sub = mat... | Robustly z-score a pandas df along the rows.
Args:
mat (pandas df): Matrix of data that z-scoring will be applied to
ctrl_mat (pandas df): Optional matrix from which to compute medians and MADs
(e.g. vehicle control)
min_mad (float): Minimum MAD to threshold to; tiny MAD values will cause
z-scores to blow up
Returns:... | juraj-google-style |
def Equals(self, other):
if (other is None):
return False
if ((other.PrevHash.ToBytes() == self.PrevHash.ToBytes()) and (other.PrevIndex == self.PrevIndex)):
return True
return False | Test for equality.
Args:
other (obj):
Returns:
bool: True `other` equals self. | codesearchnet |
def decode(self, ids):
(_, tmp_file_path) = tempfile.mkstemp()
wavfile.write(tmp_file_path, self._sample_rate, np.asarray(ids))
return tmp_file_path | Transform a sequence of float32 into a waveform.
Args:
ids: list of integers to be converted.
Returns:
Path to the temporary file where the waveform was saved.
Raises:
ValueError: if the ids are not of the appropriate size. | codesearchnet |
def grid_destroy_from_ids(oargrid_jobids):
jobs = grid_reload_from_ids(oargrid_jobids)
for job in jobs:
job.delete()
logger.info(('Killing the jobs %s' % oargrid_jobids)) | Destroy all the jobs with corresponding ids
Args:
oargrid_jobids (list): the ``(site, oar_job_id)`` list of tuple
identifying the jobs for each site. | codesearchnet |
def _parallel_part_functions(fns: Sequence[PartWithMatchFn], part: _T, with_default_output: bool=False, with_always_output: bool=False) -> AsyncIterable[_T]:
c_iters = [_eager_run_fn(fn, part) for fn, match_fn in fns if match_fn(part)]
async def result_iter():
has_output = False
for c_iter in c... | Executes each part function in a sequence of part functions concurrently.
This method is similar to `_chain_part_functions` except that all of the
PartFns are exectued on exactly `part` instead of being chained together.
The resulting AsyncIterables returned by call each fn are concatenated
together in the provided fn... | github-repos |
def init(images, num_channels, dim='2d', stride=2, kernel_size=7, maxpool=True, training=True, scope='init'):
conv = CONFIG[dim]['conv']
pool = CONFIG[dim]['max_pool']
with tf.variable_scope(scope):
net = conv(images, num_channels, kernel_size, strides=stride, padding='SAME', activation=None)
... | Standard ResNet initial block used as first RevNet block.
Args:
images: [N, H, W, 3] tensor of input images to the model.
num_channels: Output depth of convolutional layer in initial block.
dim: '2d' if 2-dimensional, '3d' if 3-dimensional.
stride: stride for the convolution and pool layer.
kernel_size: Size of the in... | codesearchnet |
def physical_name(self):
pchar = self._libinput.libinput_seat_get_physical_name(self._handle)
return string_at(pchar).decode() | The physical name of the seat.
For libinput contexts created from udev, this is always the same value
as passed into :meth:`~libinput.LibInputUdev.assign_seat` and all
seats from that context will have the same physical name.
The physical name of the seat is one that is usually set by the system
or lower levels of th... | codesearchnet |
def load_many(self, fobjs=None):
if fobjs is not None:
if not hasattr(fobjs, "__iter__"):
fobjs = [fobjs]
for index, (fobj, page) in enumerate(zip(fobjs, self.pages)):
if fobj is None:
continue
eli... | Loads as many files as the number of pages
Args:
fobjs: [filename or DataFile obj, ...] | juraj-google-style |
def extract_attribute_array(self, data_array, var_name):
if (var_name not in self.attributes.keys()):
self.attributes[var_name] = []
for t in range(self.times.size):
self.attributes[var_name].append(data_array[(self.i[t], self.j[t])]) | Extracts data from a 2D array that has the same dimensions as the grid used to identify the object.
Args:
data_array: 2D numpy array | codesearchnet |
def make_iaf_stack(total_event_size, num_hidden_layers=2, seed=None, dtype=tf.float32):
seed = tfd.SeedStream(seed, 'make_iaf_stack')
def make_iaf():
'Create an IAF.'
initializer = tf.compat.v2.keras.initializers.VarianceScaling((2 * 0.01), seed=(seed() % ((2 ** 31) - 1)))
made = tfb.Au... | Creates an stacked IAF bijector.
This bijector operates on vector-valued events.
Args:
total_event_size: Number of dimensions to operate over.
num_hidden_layers: How many hidden layers to use in each IAF.
seed: Random seed for the initializers.
dtype: DType for the variables.
Returns:
bijector: The created bijector. | codesearchnet |
def context_managers(self, kwargs):
del kwargs
return [] | Return context managers for running the test combination.
The test combination will run under all context managers that all
`TestCombination` instances return.
Args:
kwargs: Arguments and their values that are passed to the test
combination.
Returns:
A list of instantiated context managers. | github-repos |
def synthetic_source(self, value):
if value == self._defaults['ai.operation.syntheticSource'] and 'ai.operation.syntheticSource' in self._values:
del self._values['ai.operation.syntheticSource']
else:
self._values['ai.operation.syntheticSource'] = value | The synthetic_source property.
Args:
value (string). the property value. | juraj-google-style |
def Dump(self, output):
data = {'current_content_length': self._current_content_length, 'is_last': self._is_last, 'server': self._request_builder.GetServer(), 'upload_url': self._upload_url, 'version': self._request_builder.GetVersion()}
try:
yaml.dump(data, output)
except yaml.YAMLError as e:
... | Serialize the IncrementalUploadHelper and store in file-like object.
Args:
output: a file-like object where the status of the IncrementalUploadHelper
will be written.
Raises:
GoogleAdsError: If a YAMLError occurs while writing to the file. | codesearchnet |
def _DeserializeAttributeContainer(self, container_type, serialized_data):
if (not serialized_data):
return None
if self._serializers_profiler:
self._serializers_profiler.StartTiming(container_type)
try:
serialized_string = serialized_data.decode('utf-8')
except UnicodeDecodeErro... | Deserializes an attribute container.
Args:
container_type (str): attribute container type.
serialized_data (bytes): serialized attribute container data.
Returns:
AttributeContainer: attribute container or None.
Raises:
IOError: if the serialized data cannot be decoded.
OSError: if the serialized data cannot be decod... | codesearchnet |
def is_github_repo_owner_the_official_one(context, repo_owner):
official_repo_owner = context.config['official_github_repos_owner']
if not official_repo_owner:
raise ConfigError(
'This worker does not have a defined owner for official GitHub repositories. '
'Given "official_... | Given a repo_owner, check if it matches the one configured to be the official one.
Args:
context (scriptworker.context.Context): the scriptworker context.
repo_owner (str): the repo_owner to verify
Raises:
scriptworker.exceptions.ConfigError: when no official owner was defined
Returns:
bool: True when ``repo_owner``... | juraj-google-style |
def _ExpectedKeysForEntry(self, entry):
return [entry.name] | Generate a list of expected cache keys for this type of map.
Args:
entry: A SshkeyMapEntry
Returns:
A list of strings | github-repos |
def FindHeader(self, header):
for section_list in self.include_list:
for f in section_list:
if f[0] == header:
return f[1]
return -1 | Check if a header has already been included.
Args:
header: header to check.
Returns:
Line number of previous occurrence, or -1 if the header has not
been seen before. | juraj-google-style |
def power_spectral_density(x, time_step, freq_range = None):
N = len(x)
P = 2 * np.abs(np.fft.rfft(x))**2 / N * time_step
F = np.fft.rfftfreq(len(x), time_step)
if freq_range is not None:
brange = np.all([F >= freq_range[0], F <= freq_range[1]], axis=0)
P = P[brange]
F = F[... | returns the *single sided* power spectral density of the time trace x which is sampled at intervals time_step
Args:
x (array): timetrace
time_step (float): sampling interval of x
freq_range (array or tuple): frequency range in the form [f_min, f_max] to return only the spectrum within this range
Returns: | juraj-google-style |
def set_flat(self, new_weights):
self._check_sess()
shapes = [v.get_shape().as_list() for v in self.variables.values()]
arrays = unflatten(new_weights, shapes)
placeholders = [self.placeholders[k] for (k, v) in self.variables.items()]
self.sess.run(list(self.assignment_nodes.values()), feed_dict=dic... | Sets the weights to new_weights, converting from a flat array.
Note:
You can only set all weights in the network using this function,
i.e., the length of the array must match get_flat_size.
Args:
new_weights (np.ndarray): Flat array containing weights. | codesearchnet |
def getStreamNetworkAsWkt(self, session, withNodes=True):
wkt_list = []
for link in self.streamLinks:
wkt_link = link.getAsWkt(session)
if wkt_link:
wkt_list.append(wkt_link)
if withNodes:
for node in link.nodes:
... | Retrieve the stream network geometry in Well Known Text format.
Args:
session (:mod:`sqlalchemy.orm.session.Session`): SQLAlchemy session object bound to PostGIS enabled database
withNodes (bool, optional): Include nodes. Defaults to False.
Returns:
str: Well Known Text string. | juraj-google-style |
def refresh(self, refresh_binary=True):
updated_self = self.repo.get_resource(self.uri)
if (not isinstance(self, type(updated_self))):
raise Exception(('Instantiated %s, but repository reports this resource is %s' % (type(updated_self), type(self))))
if updated_self:
self.status_code = updat... | Performs GET request and refreshes RDF information for resource.
Args:
None
Returns:
None | codesearchnet |
def object_hook(obj):
try:
if '__type' in obj:
obj_type = obj['__type']
cls = getattr(cloud_inquisitor.schema, obj_type)
if hasattr(cls, 'from_json'):
return cls.from_json(obj)
key, value = next(iter(obj.items(... | Checks to see if the `__type`-hinting field is available in the object being de-serialized. If present, and
the class referenced has a `from_json` function it will return the generated object, else a standard dic
will be returned
Args:
obj: Object to be deserialized
Returns:
Deserialized object or regular python obje... | juraj-google-style |
def read(self, size=-1):
if not self._readable:
raise UnsupportedOperation('read')
if self._seek == self._size:
return b''
if size == self._buffer_size:
queue_index = self._seek
if queue_index == 0:
... | Read and return up to size bytes,
with at most one call to the underlying raw stream’s.
Use at most one call to the underlying raw stream’s read method.
Args:
size (int): Number of bytes to read. -1 to read the
stream until end.
Returns:
bytes: Object content | juraj-google-style |
def local_batch_predict(model_dir, csv_file_pattern, output_dir, output_format, batch_size=100):
file_io.recursive_create_dir(output_dir)
csv_files = file_io.get_matching_files(csv_file_pattern)
if (len(csv_files) == 0):
raise ValueError(('No files found given ' + csv_file_pattern))
with tf.Grap... | Batch Predict with a specified model.
It does batch prediction, saves results to output files and also creates an output
schema file. The output file names are input file names prepended by 'predict_results_'.
Args:
model_dir: The model directory containing a SavedModel (usually saved_model.pb).
csv_file_pattern: a p... | codesearchnet |
def concatenate_context_input(context_input, sequence_input):
seq_rank_check = check_ops.assert_rank(sequence_input, 3, message='sequence_input must have rank 3', data=[array_ops.shape(sequence_input)])
seq_type_check = check_ops.assert_type(sequence_input, dtypes.float32, message='sequence_input must have dtyp... | Replicates `context_input` across all timesteps of `sequence_input`.
Expands dimension 1 of `context_input` then tiles it `sequence_length` times.
This value is appended to `sequence_input` on dimension 2 and the result is
returned.
Args:
context_input: A `Tensor` of dtype `float32` and shape `[batch_size, d1]`.
sequ... | github-repos |
def mean_area_distance(item_a, item_b, max_value):
mean_area_a = np.mean([item_a.size(t) for t in item_a.times])
mean_area_b = np.mean([item_b.size(t) for t in item_b.times])
return (np.abs((mean_area_a - mean_area_b)) / float(max_value)) | Absolute difference in the means of the areas of each track over time.
Args:
item_a: STObject from the first set in TrackMatcher
item_b: STObject from the second set in TrackMatcher
max_value: Maximum distance value used as scaling value and upper constraint.
Returns:
Distance value between 0 and 1. | codesearchnet |
def corrcoef(x):
if any_symbolic_tensors((x,)):
return Corrcoef().symbolic_call(x)
return backend.numpy.corrcoef(x) | Compute the Pearson correlation coefficient matrix.
Args:
x: A 2D tensor of shape `(N, D)`, where N is the number of variables
and D is the number of observations.
Returns:
A tensor of shape `(N, N)` representing the correlation matrix. | github-repos |
def rasterize(layer, rast):
driver = ImageDriver('MEM')
r2 = driver.raster(driver.ShortName, rast.size)
r2.affine = rast.affine
sref = rast.sref
if not sref.srid:
sref = SpatialReference(4326)
r2.sref = sref
ml = MemoryLayer(sref, layer.GetGeomType())
ml.load(layer)
stat... | Returns a Raster from layer features.
Arguments:
layer -- Layer to rasterize
rast -- Raster with target affine, size, and sref | juraj-google-style |
def get_local_filter_directives(ast, current_schema_type, inner_vertex_fields):
result = []
if ast.directives:
for directive_obj in ast.directives:
if (directive_obj.name.value == 'filter'):
filtered_field_name = get_ast_field_name_or_none(ast)
if is_filter_wi... | Get all filter directives that apply to the current field.
This helper abstracts away the fact that some vertex field filtering operators apply on the
inner scope (the scope of the inner vertex field on which they are applied), whereas some apply
on the outer scope (the scope that contains the inner vertex field).
See... | codesearchnet |
def get_cursor(self):
(x, y) = self._cursor
(width, height) = self.parent.get_size()
while (x >= width):
x -= width
y += 1
if ((y >= height) and (self.scrollMode == 'scroll')):
y = (height - 1)
return (x, y) | Return the virtual cursor position.
The cursor can be moved with the :any:`move` method.
Returns:
Tuple[int, int]: The (x, y) coordinate of where :any:`print_str`
will continue from.
.. seealso:: :any:move` | codesearchnet |
def scan_file(path):
path = os.path.abspath(path)
assert os.path.exists(path), ("Unreachable file '%s'." % path)
result = sh.clamscan(path, no_summary=True, infected=True, _ok_code=[0, 1])
return _parse_result(result) | Scan `path` for viruses using ``clamscan`` program.
Args:
path (str): Relative or absolute path of file/directory you need to
scan.
Returns:
dict: ``{filename: ("FOUND", "virus type")}`` or blank dict.
Raises:
AssertionError: When the internal file doesn't exists. | codesearchnet |
def ParseBookmarkAnnotationRow(self, parser_mediator, query, row, **unused_kwargs):
query_hash = hash(query)
event_data = FirefoxPlacesBookmarkAnnotationEventData()
event_data.content = self._GetRowValue(query_hash, row, 'content')
event_data.offset = self._GetRowValue(query_hash, row, 'id')
event_d... | Parses a bookmark annotation 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. | codesearchnet |
def lstat(self, entry_path, dir_fd=None):
entry_path = self._path_with_dir_fd(entry_path, self.lstat, dir_fd)
return self.filesystem.stat(entry_path, follow_symlinks=False) | Return the os.stat-like tuple for entry_path, not following symlinks.
Args:
entry_path: path to filesystem object to retrieve.
dir_fd: If not `None`, the file descriptor of a directory, with
`entry_path` being relative to this directory.
New in Python 3.3.
Returns:
the FakeStatResult object corresponding to `entry_p... | codesearchnet |
def write_config(config, filename=None):
if not filename:
filename = CONFIG_DEFAULT_PATH
with open(filename, 'w') as f:
json.dump(config, f, indent=4) | Write the provided configuration to a specific location.
Args:
config (dict): a dictionary with the configuration to load.
filename (str): the name of the file that will store the new configuration. Defaults to ``None``.
If ``None``, the HOME of the current user and the string ``.bigchaindb`` will be used. | juraj-google-style |
def value_text(tensor, is_repr=False) -> AnyStr:
if tensor._prefer_custom_summarizer():
text = tensor._summarize_value()
if is_repr:
text = 'value=' + text
else:
text = numpy_text(tensor, is_repr=is_repr)
if is_repr:
text = 'numpy=' + text
return text | Either the NumPy value or a custom TensorFlow formatting of `tensor`.
Custom formatting is used for custom device tensors, e.g. parallel tensors
with multiple components on different devices.
Args:
tensor: The tensor to format.
is_repr: Controls the style/verbosity of formatting.
Returns:
The formatted tensor. | github-repos |
def set_fore(self, x: int, y: int, r: int, g: int, b: int, char: str) -> None:
i = ((self.width * y) + x)
self.fore_r[i] = r
self.fore_g[i] = g
self.fore_b[i] = b
self.char[i] = ord(char) | Set the character and foreground color of one cell.
Args:
x (int): X position to change.
y (int): Y position to change.
r (int): Red foreground color, from 0 to 255.
g (int): Green foreground color, from 0 to 255.
b (int): Blue foreground color, from 0 to 255.
char (AnyStr): A single character str or bytes object. | codesearchnet |
def lowercase_term_id(term_id: str) -> str:
(ns, val) = term_id.split(":", maxsplit=1)
term_id = f"{ns}:{val.lower()}"
return term_id | Lowercase the term value (not the namespace prefix)
Args:
term_id (str): term identifier with namespace prefix, e.g. MESH:Atherosclerosis
Returns:
str: lowercased, e.g. MESH:atherosclerosis | juraj-google-style |
def then_by(self, key_selector=identity):
if self.closed():
raise ValueError('Attempt to call then_by() on a closed OrderedQueryable.')
if (not is_callable(key_selector)):
raise TypeError('then_by() parameter key_selector={key_selector} is not callable'.format(key_selector=repr(key_selector)))
... | Introduce subsequent ordering to the sequence with an optional key.
The returned sequence will be sorted in ascending order by the
selected key.
Note: This method uses deferred execution.
Args:
key_selector: A unary function the only positional argument to
which is the element value from which the key will be
select... | codesearchnet |
def verify(token, key, algorithms, verify=True):
(header, payload, signing_input, signature) = _load(token)
if verify:
_verify_signature(signing_input, header, signature, key, algorithms)
return payload | Verifies a JWS string's signature.
Args:
token (str): A signed JWS to be verified.
key (str or dict): A key to attempt to verify the payload with. Can be
individual JWK or JWK set.
algorithms (str or list): Valid algorithms that should be used to verify the JWS.
Returns:
str: The str representation of the payload, as... | codesearchnet |
def _prompt_split_image(self, aspect_ratio, num_patches_per_chunk):
img_string = '<|image_start|>'
ratio_h, ratio_w = aspect_ratio
if ratio_h * ratio_w > 1:
for yy in range(ratio_h):
for xx in range(ratio_w):
img_string += '<|patch|>' * num_patches_per_chunk
... | Create a structured string representation of image tokens
Args:
num_patches: Number of patches in the image
Returns:
String with appropriate image tokens | github-repos |
def update_dataset(self, dataset, fields, retry=DEFAULT_RETRY):
partial = dataset._build_resource(fields)
if (dataset.etag is not None):
headers = {'If-Match': dataset.etag}
else:
headers = None
api_response = self._call_api(retry, method='PATCH', path=dataset.path, data=partial, headers... | Change some fields of a dataset.
Use ``fields`` to specify which fields to update. At least one field
must be provided. If a field is listed in ``fields`` and is ``None`` in
``dataset``, it will be deleted.
If ``dataset.etag`` is not ``None``, the update will only
succeed if the dataset on the server has the same ETa... | codesearchnet |
async def _send(self, request_bytes, body_bytes, h11_connection):
await self.sock.send_all(h11_connection.send(request_bytes))
if body_bytes is not None:
await self.sock.send_all(h11_connection.send(body_bytes))
await self.sock.send_all(h11_connection.send(h11.EndOfMessage()... | Takes a package and body, combines then, then shoots 'em off in to
the ether.
Args:
package (list of str): The header package.
body (str): The str representation of the body. | juraj-google-style |
def set_secondary_ips(self, name, vrid, secondary_ips, run=True):
cmds = []
curr_sec_ips = []
vrrps = self.get(name)
if (vrrps and (vrid in vrrps)):
curr_sec_ips = vrrps[vrid]['secondary_ip']
for sec_ip in secondary_ips:
if ((type(sec_ip) is not str) or (not re.match('^\\d+\\.\\d+\\.... | Configure the secondary_ip property of the vrrp
Notes:
set_secondary_ips takes a list of secondary ip addresses
which are to be set on the virtal router. An empty list will
remove any existing secondary ip addresses from the vrrp.
A list containing addresses will configure the virtual router
with only the addresses sp... | codesearchnet |
def _compute_edges(self):
(nodes1, nodes2, nodes3) = _surface_helpers.compute_edge_nodes(self._nodes, self._degree)
edge1 = _curve_mod.Curve(nodes1, self._degree, _copy=False)
edge2 = _curve_mod.Curve(nodes2, self._degree, _copy=False)
edge3 = _curve_mod.Curve(nodes3, self._degree, _copy=False)
retu... | Compute the edges of the current surface.
Returns:
Tuple[~curve.Curve, ~curve.Curve, ~curve.Curve]: The edges of
the surface. | codesearchnet |
def dropout_with_broadcast_dims(x, keep_prob, broadcast_dims=None, **kwargs):
assert ('noise_shape' not in kwargs)
if broadcast_dims:
shape = tf.shape(x)
ndims = len(x.get_shape())
broadcast_dims = [((dim + ndims) if (dim < 0) else dim) for dim in broadcast_dims]
kwargs['noise_sh... | Like tf.nn.dropout but takes broadcast_dims instead of noise_shape.
Instead of specifying noise_shape, this function takes broadcast_dims -
a list of dimension numbers in which noise_shape should be 1. The random
keep/drop tensor has dimensionality 1 along these dimensions.
Args:
x: a floating point tensor.
keep_pro... | codesearchnet |
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