code stringlengths 20 4.93k | docstring stringlengths 33 1.27k | source stringclasses 3
values |
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def from_row_limits(cls, row_limits, validate=True, dtype=None, dtype_hint=None):
if not isinstance(validate, bool):
raise TypeError('validate must have type bool')
with ops.name_scope(None, 'RowPartitionFromRowLimits', [row_limits]):
row_limits = cls._convert_row_partition(row_limits, 'row_limi... | Creates a `RowPartition` with rows partitioned by `row_limits`.
Equivalent to: `from_row_splits(values, concat([0, row_limits], axis=0))`.
Args:
row_limits: A 1-D integer tensor with shape `[nrows]`. Must be sorted in
ascending order.
validate: If true, then use assertions to check that the arguments form a
valid `R... | github-repos |
def attribute(*args, **kw):
return operator(kind=Operator.Type.ATTRIBUTE, *args, **kw) | Registers a new attribute only operator function in the test engine.
Arguments:
*args: variadic arguments.
**kw: variadic keyword arguments.
Returns:
function | juraj-google-style |
def with_min_occurrence(self, min_occurrence):
self._options['min_occurrence'] = min_occurrence
return self | Only show profiler nodes including no less than 'min_occurrence' graph nodes.
A "node" means a profiler output node, which can be a python line
(code view), an operation type (op view), or a graph node
(graph/scope view). A python line includes all graph nodes created by that
line, while an operation type includes all... | github-repos |
def _CreateFolder(self, parent, name, visible=True, description=None):
folder = ET.SubElement(parent, 'Folder')
name_tag = ET.SubElement(folder, 'name')
name_tag.text = name
if (description is not None):
desc_tag = ET.SubElement(folder, 'description')
desc_tag.text = description
if (... | Create a KML Folder element.
Args:
parent: The parent ElementTree.Element instance.
name: The folder name as a string.
visible: Whether the folder is initially visible or not.
description: A description string or None.
Returns:
The folder ElementTree.Element instance. | codesearchnet |
def _ReadTableHeader(self, file_object, table_header_offset):
data_type_map = self._GetDataTypeMap('keychain_table_header')
(table_header, _) = self._ReadStructureFromFileObject(file_object, table_header_offset, data_type_map)
return table_header | Reads the table header.
Args:
file_object (file): file-like object.
table_header_offset (int): offset of the tables header relative to
the start of the file.
Returns:
keychain_table_header: table header.
Raises:
ParseError: if the table header cannot be read. | codesearchnet |
def __init__(self, datafile, logger, error_handler):
config = json.loads(datafile)
self.logger = logger
self.error_handler = error_handler
self.version = config.get('version')
if self.version not in SUPPORTED_VERSIONS:
raise exceptions.UnsupportedDatafileVersionException(
enums.E... | ProjectConfig init method to load and set project config data.
Args:
datafile: JSON string representing the project.
logger: Provides a log message to send log messages to.
error_handler: Provides a handle_error method to handle exceptions. | juraj-google-style |
def call(self, sequence_output, cell_index, cell_mask, allow_empty_column_selection) -> tf.Tensor:
token_logits = tf.einsum('bsj,j->bs', sequence_output, self.column_output_weights) + self.column_output_bias
cell_logits, cell_logits_index = reduce_mean(token_logits, cell_index)
column_index = cell_index.pro... | Computes the column logits.
Args:
sequence_output (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
Also known as last_hidden_state. Sequence of hidden-states at the output of the last layer of the
model.
cell_index (`ProductIndexMap`):
Index that groups tokens into cells.
cell_mask (`tf.Tensor` of ... | github-repos |
def generate_password(length=32):
return ''.join(random.SystemRandom().choice(string.ascii_letters + '!@ | Generate a cryptographically secure random string to use for passwords
Args:
length (int): Length of password, defaults to 32 characters
Returns:
Randomly generated string | juraj-google-style |
def save_counter(self):
return self.checkpointer().save_counter | An integer variable numbering the checkpoint events.
This is maintained by the underlying tf.train.Checkpoint object employed by
AsyncCheckpoint class. The number starts at 0 and gets incremented for each
checkpoint event.
Returns:
The save counter variable. | github-repos |
def generate_hashfile(directory, blacklist=_BLACKLIST):
checksums = generate_checksums(directory, blacklist)
out = ""
for fn, checksum in sorted(checksums.items()):
out += "%s %s\n" % (checksum, fn)
return out | Compute checksum for each file in `directory`, with exception of files
specified in `blacklist`.
Args:
directory (str): Absolute or relative path to the directory.
blacklist (list/set/tuple): List of blacklisted filenames. Only
filenames are checked, not paths!
Returns:
str: Content of hashfile as it is specified in ... | juraj-google-style |
def handle_worker_messages(self, timeout):
msgs = self.messaging_backend.popn(self.incoming_mailbox, n=20)
for msg in msgs:
self.handle_single_message(msg) | Read messages that are placed in self.incoming_mailbox,
and then update the job states corresponding to each message.
Args:
timeout: How long to wait for an incoming message, if the mailbox is empty right now.
Returns: None | codesearchnet |
def load_flag_values(self, flags=None):
if flags is None:
flags = self._flags
for keyval in flags.config_value:
k, v = keyval.split('=', 1)
v = self._modules['yaml'].load(v) if isinstance(v, str) else v
k = k.decode() if isinstance(k, bytes) else k
v = v.decode() if is... | Load flag values given from command line flags.
Args:
flags: An argparse Namespace containing the command line flags. | juraj-google-style |
def get_template_edit_url(self, template_id):
request = self._get_request()
return request.get(self.EMBEDDED_TEMPLATE_EDIT_URL + template_id) | Retrieves a embedded template for editing
Retrieves an embedded object containing a template url that can be opened in an iFrame.
Args:
template_id (str): The id of the template to get a signature url for
Returns:
An Embedded object | juraj-google-style |
def get_substring_idxs(substr, string):
return [match.start() for match in re.finditer(substr, string)] | Return a list of indexes of substr. If substr not found, list is
empty.
Arguments:
substr (str): Substring to match.
string (str): String to match in.
Returns:
list of int: Start indices of substr. | juraj-google-style |
def run(self):
for aws_region in AWS_REGIONS:
self.log.debug('Checking trails for {}/{}'.format(self.account.account_name, aws_region))
ct = self.session.client('cloudtrail', region_name=aws_region)
trails = ct.describe_trails()
if (len(trails['trailList']) == 0):
if (aws... | Configures and enables a CloudTrail trail and logging on a single AWS Account.
Has the capability to create both single region and multi-region trails.
Will automatically create SNS topics, subscribe to SQS queues and turn on logging for the account in question,
as well as reverting any manual changes to the trails i... | codesearchnet |
def get_collection(self, lang=None, task=None):
if lang:
id = '{}{}'.format(Downloader.LANG_PREFIX, lang)
elif task:
id = '{}{}'.format(Downloader.TASK_PREFIX, task)
else:
raise ValueError('You should pass either the task or the lang')
try:
return self.info(id)
except... | Return the collection that represents a specific language or task.
Args:
lang (string): Language code.
task (string): Task name. | codesearchnet |
def remove_duplicate_sg(security_groups):
for (each_sg, duplicate_sg_name) in SECURITYGROUP_REPLACEMENTS.items():
if ((each_sg in security_groups) and (duplicate_sg_name in security_groups)):
LOG.info('Duplicate SG found. Removing %s in favor of %s.', duplicate_sg_name, each_sg)
secu... | Removes duplicate Security Groups that share a same name alias
Args:
security_groups (list): A list of security group id to compare against SECURITYGROUP_REPLACEMENTS
Returns:
security_groups (list): A list of security groups with duplicate aliases removed | codesearchnet |
def numeric_columns(self, include_bool=True):
columns = []
for (col, dtype) in zip(self.columns, self.dtypes):
if (is_numeric_dtype(dtype) and (include_bool or ((not include_bool) and (dtype != np.bool_)))):
columns.append(col)
return columns | Returns the numeric columns of the Manager.
Returns:
List of index names. | codesearchnet |
def rematch_entry(envkernel, gamma = 0.1, threshold = 1e-6):
n, m = envkernel.shape
K = np.exp(-(1 - envkernel) / gamma)
u = np.ones((n,)) / n
v = np.ones((m,)) / m
en = np.ones((n,)) / float(n)
em = np.ones((m,)) / float(m)
Kp = (1 / en).reshape(-1, 1) * K
itercount =... | Compute the global similarity between two structures A and B.
It uses the Sinkhorn algorithm as reported in:
Phys. Chem. Chem. Phys., 2016, 18, p. 13768
Args:
envkernel: NxM matrix of structure A with
N and structure B with M atoms
gamma: parameter to control between best match gamma = 0
and average kernel gamma = inf. | juraj-google-style |
def build(cls, seqs: Iterable[int], uid: bool=False) -> 'SequenceSet':
seqs_list = sorted(set(seqs))
groups: List[Union[(int, Tuple[(int, int)])]] = []
group: Union[(int, Tuple[(int, int)])] = seqs_list[0]
for i in range(1, len(seqs_list)):
group_i = seqs_list[i]
if isinstance(group, int... | Build a new sequence set that contains the given values using as
few groups as possible.
Args:
seqs: The sequence values to build.
uid: True if the sequences refer to message UIDs. | codesearchnet |
def match_global_phase(a: np.ndarray,
b: np.ndarray
) -> Tuple[np.ndarray, np.ndarray]:
if a.shape != b.shape:
return a, b
k = max(np.ndindex(*a.shape), key=lambda t: abs(b[t]))
def dephase(v):
r = np.real(v)
i = np.imag... | Phases the given matrices so that they agree on the phase of one entry.
To maximize precision, the position with the largest entry from one of the
matrices is used when attempting to compute the phase difference between
the two matrices.
Args:
a: A numpy array.
b: Another numpy array.
Returns:
A tuple (a', b') where... | juraj-google-style |
def _find_index_of_defining_frame(tb):
size = len(tb)
filenames = [frame.filename for frame in tb]
for idx, filename in enumerate(reversed(filenames)):
is_framework = _is_framework_filename(filename)
if not is_framework:
return size - idx - 1
return 0 | Return index in op.traceback with first 'useful' frame.
This method reads through the stack stored in op.traceback looking for the
innermost frame which (hopefully) belongs to the caller. It accomplishes this
by rejecting frames deemed to be part of the TensorFlow framework (by
pattern matching the filename).
Args:
... | github-repos |
def authentication(self, username, password):
_auth_text = '{}:{}'.format(username, password)
if int(sys.version[0]) > 2:
_auth_bin = base64.encodebytes(_auth_text.encode())
_auth = _auth_bin.decode()
_auth = _auth.replace('\n', '')
... | Configures the user authentication for eAPI
This method configures the username and password combination to use
for authenticating to eAPI.
Args:
username (str): The username to use to authenticate the eAPI
connection with
password (str): The password in clear text to use to authenticate
the eAPI connection with | juraj-google-style |
def list_vnets(access_token, subscription_id):
endpoint = ''.join([get_rm_endpoint(), '/subscriptions/', subscription_id, '/providers/Microsoft.Network/', '/virtualNetworks?api-version=', NETWORK_API])
return do_get(endpoint, access_token) | List the VNETs in a subscription .
Args:
access_token (str): A valid Azure authentication token.
subscription_id (str): Azure subscription id.
Returns:
HTTP response. JSON body of VNets list with properties. | codesearchnet |
def reserve(self, *args, **kwargs):
data = self.get_data('floating_ips/', type=POST, params={'region': self.region_slug})
if data:
self.ip = data['floating_ip']['ip']
self.region = data['floating_ip']['region']
return self | Creates a FloatingIP in a region without assigning
it to a specific Droplet.
Note: Every argument and parameter given to this method will be
assigned to the object.
Args:
region_slug: str - region's slug (e.g. 'nyc3') | codesearchnet |
def convolution_kernel(self, name='convolution_kernel'):
with self._name_scope(name):
h = self._ifft(_to_complex(self.spectrum))
return math_ops.cast(h, self.dtype) | Convolution kernel corresponding to `self.spectrum`.
The `D` dimensional DFT of this kernel is the frequency domain spectrum of
this operator.
Args:
name: A name to give this `Op`.
Returns:
`Tensor` with `dtype` `self.dtype`. | github-repos |
def to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False, utc=None, box=True, format=None, exact=True, unit=None, infer_datetime_format=False, origin='unix', cache=False):
if (not isinstance(arg, DataFrame)):
return pandas.to_datetime(arg, errors=errors, dayfirst=dayfirst, yearfirst=yearfirst, u... | Convert the arg to datetime format. If not Ray DataFrame, this falls
back on pandas.
Args:
errors ('raise' or 'ignore'): If 'ignore', errors are silenced.
Pandas blatantly ignores this argument so we will too.
dayfirst (bool): Date format is passed in as day first.
yearfirst (bool): Date format is passed in as year fi... | codesearchnet |
def _UpdateEtag(self, response):
etag = response.headers.get('etag', self.etag)
etag_updated = self.etag != etag
self.etag = etag
return etag_updated | Update the etag from an API response.
Args:
response: HTTP response with a header field.
Returns:
bool, True if the etag in the response header updated. | juraj-google-style |
def List(self, request, global_params=None):
config = self.GetMethodConfig('List')
return self._RunMethod(config, request, global_params=global_params) | List all `BitbucketServerConfigs` for a given project. This API is experimental.
Args:
request: (CloudbuildProjectsLocationsBitbucketServerConfigsListRequest) input message
global_params: (StandardQueryParameters, default: None) global arguments
Returns:
(ListBitbucketServerConfigsResponse) The response message. | github-repos |
def _parse_line(cls, line):
try:
pkg, rest = line.split(None, 1)
except ValueError:
rpm = cls._parse_package(line.strip())
return rpm
rpm = cls._parse_package(pkg)
rest = rest.split('\t')
for i, value in enumerate(rest):
rp... | Helper method for parsing package line with or without SOS report information.
Args:
line (str): package line with or without SOS report information
Returns:
dict: dictionary containing 'name', 'version', 'release' and 'arch' keys plus
additionally 'installtime', 'buildtime', 'vendor', 'buildserver', 'pgpsig',
'pgpsi... | juraj-google-style |
def GetPointWithDistanceTraveled(self, shape_dist_traveled):
if not self.distance:
return None
if shape_dist_traveled <= self.distance[0]:
return self.points[0]
if shape_dist_traveled >= self.distance[-1]:
return self.points[-1]
index = bisect.bisect(self.distance, shape_dist_tra... | Returns a point on the shape polyline with the input shape_dist_traveled.
Args:
shape_dist_traveled: The input shape_dist_traveled.
Returns:
The shape point as a tuple (lat, lng, shape_dist_traveled), where lat and
lng is the location of the shape point, and shape_dist_traveled is an
increasing metric representing th... | juraj-google-style |
def SetStorageProfiler(self, storage_profiler):
self._storage_profiler = storage_profiler
if self._storage_file:
self._storage_file.SetStorageProfiler(storage_profiler) | Sets the storage profiler.
Args:
storage_profiler (StorageProfiler): storage profiler. | juraj-google-style |
def refresh(self, id_or_uri, timeout=(- 1)):
uri = (self._client.build_uri(id_or_uri) + '/refresh')
return self._client.update_with_zero_body(uri, timeout=timeout) | The Refresh action reclaims the top-of-rack switches in a logical switch.
Args:
id_or_uri:
Can be either the Logical Switch ID or URI
timeout:
Timeout in seconds. Wait for task completion by default. The timeout does not abort the operation
in OneView, just stop waiting for its completion.
Returns:
dict: The Logical ... | codesearchnet |
def generate_flat_data(self):
all_statements = []
all_targets = []
self.sequence_sizes_in = []
self.sequence_sizes_out = []
for _ in six.moves.range(self._batch_size):
(length, nest) = self.curriculum_obj.fetch()
seq_size_in = self._max_seq_length
is_valid_sample = False
... | Generates batched data in flat numpy arrays.
Raises:
ValueError: When too many generate calls are required. | codesearchnet |
def request_status(r, detailed=False):
base_string = "HTTP {r.request.method} {r.request.url}: {r.status_code}"
if r.status_code in range(200,99):
string = base_string
if detailed is True:
string += " - {r.json()}"
else:
string += " - 👍"
return string.format(r=r)
else:
string = base_string
r... | Returns a formatted string about the status, useful for logging.
args:
r - takes requests.models.Response | juraj-google-style |
def add_observer(self, observer, identify_observed=False):
if hasattr(observer, '__self__'):
result = self._add_bound_method(observer, identify_observed)
else:
result = self._add_function(observer, identify_observed)
return result | Register an observer to observe me.
Args:
observer: The callable to register as an observer.
identify_observed: If True, then the observer will get myself
passed as an additional first argument whenever it is invoked.
See ObserverFunction and ObserverBoundMethod to see how this
works.
Returns:
True if the observer wa... | codesearchnet |
def retrieve_clang_version(clang_executable):
stderr = open(os.devnull, 'wb')
curr_version = run_shell([clang_executable, '--version'], allow_non_zero=True, stderr=stderr)
curr_version_split = curr_version.lower().split('clang version ')
if len(curr_version_split) > 1:
curr_version = curr_versio... | Retrieve installed clang version.
Args:
clang_executable: (String) path to clang executable
Returns:
The clang version detected. | github-repos |
def name(self):
return ctypes.cast(self.sName, ctypes.c_char_p).value.decode() | Returns the name of the device.
Args:
self (JLinkDeviceInfo): the ``JLinkDeviceInfo`` instance
Returns:
Device name. | codesearchnet |
def _GetPathSegmentIndexForValueWeights(self, value_weights):
largest_weight = value_weights.GetLargestWeight()
if (largest_weight > 0):
value_weight_indexes = value_weights.GetIndexesForWeight(largest_weight)
else:
value_weight_indexes = []
if value_weight_indexes:
path_segment_... | Retrieves the index of the path segment based on value weights.
Args:
value_weights: the value weights object (instance of _PathSegmentWeights).
Returns:
An integer containing the path segment index.
Raises:
RuntimeError: is no path segment index can be found. | codesearchnet |
def matches_all(expected):
def _matches(actual):
from hamcrest.core import assert_that as hamcrest_assert
from hamcrest.library.collection import contains_inanyorder
expected_list = list(expected)
hamcrest_assert(actual, contains_inanyorder(*expected_list))
return _matches | Matcher used by assert_that to check a set of matchers.
Args:
expected: A list of elements or hamcrest matchers to be used to match
the elements of a single PCollection. | github-repos |
def transform(self, data, data_type='S3Prefix', content_type=None, compression_type=None, split_type=None, job_name=None):
local_mode = self.sagemaker_session.local_mode
if ((not local_mode) and (not data.startswith('s3:
raise ValueError('Invalid S3 URI: {}'.format(data))
if (job_name is not None):
... | Start a new transform job.
Args:
data (str): Input data location in S3.
data_type (str): What the S3 location defines (default: 'S3Prefix'). Valid values:
* 'S3Prefix' - the S3 URI defines a key name prefix. All objects with this prefix will be used as
inputs for the transform job.
* 'ManifestFile' - the S3 URI point... | codesearchnet |
def make_processor(self, name, mappings, processor_type, **kwargs):
from .processor import Processor
if self.processors.get(name):
raise LookupError("processor has already been created")
if isinstance(mappings, list):
mappings = [self.get_rml(item) for item in ma... | Instantiates a RmlProcessor and registers it in the manager
Args:
-----
name: the name to register the processor
mappings: the list RML mapping definitions to use
processor_type: the name of the RML processor to use | juraj-google-style |
def _compute_elemwise_op_output_shape(self, shape1, shape2):
if None in [shape1, shape2]:
return None
elif len(shape1) < len(shape2):
return self._compute_elemwise_op_output_shape(shape2, shape1)
elif not shape2:
return shape1
output_shape = list(shape1[:-len(shape2)])
for i,... | Computes the shape of the resultant of an elementwise operation.
Args:
shape1: tuple or None. Shape of the first tensor
shape2: tuple or None. Shape of the second tensor
Returns:
expected output shape when an element-wise operation is
carried out on 2 tensors with shapes shape1 and shape2.
tuple or None.
Raises:
Val... | github-repos |
def export(self, path, session):
if (self._graph is not tf_v1.get_default_graph()):
raise RuntimeError('default graph differs from the graph where the module was instantiated.')
if (self._graph is not session.graph):
raise RuntimeError('session graph differs from the graph where the module was i... | Exports the module with the variables from the session in `path`.
Note that it is the module definition in the ModuleSpec used to create this
module that gets exported. The session is only used to provide the value
of variables.
Args:
path: path where to export the module to.
session: session where to export the vari... | codesearchnet |
def reduce_max(x,
disable_positional_args=None,
output_shape=None,
reduced_dim=None,
name=None):
output_shape = convert_to_shape(output_shape)
reduced_dim = convert_to_dimension(reduced_dim)
assert disable_positional_args is None
output_shape = _red... | Reduction on 1 or more axes.
Args:
x: a Tensor
disable_positional_args: None
output_shape: an optional Shape. Must be a subsequence of x.shape.
reduced_dim: an optional Dimension
name: an optional string
Returns:
a Tensor | juraj-google-style |
def __init__(self, reader, genTexts=False):
if genTexts is not None:
self.genTexts = genTexts
self._reader = reader | Creates an instance of *Borrower* class.
Args:
reader: a *reader* object
Keyword Args:
genText: indicates whether this borrower should be looking
for transformed MIBs that include human-oriented texts | juraj-google-style |
def Match(self, encoded):
logging.log(1, 'Decoding %s: %s', self.name, encoded)
decoded = self.msg.encoding.ParseFromString(encoded, self.msg)
logging.info('Matching message value:\nExpected: %s\nActual: %s\n', self.value_dict_or_array, decoded)
return MessageValue._MatchValue(self.value_dict_or_array, ... | Whether or not |encoded| is compatible with this message instance.
If |encoded| has all required fields, and values of all fields are same to
those of this message instance, it is compatible. Otherwise, i.e
1) it doesn't have some required fields
2) it has some values of fields different from specified in |value_dict|... | github-repos |
def force_in_A_to_force_in_B(force_A, torque_A, pose_A_in_B):
pos_A_in_B = pose_A_in_B[(:3, 3)]
rot_A_in_B = pose_A_in_B[(:3, :3)]
skew_symm = _skew_symmetric_translation(pos_A_in_B)
force_B = rot_A_in_B.T.dot(force_A)
torque_B = ((- rot_A_in_B.T.dot(skew_symm.dot(force_A))) + rot_A_in_B.T.dot(torqu... | Converts linear and rotational force at a point in frame A to the equivalent in frame B.
Args:
force_A: 3-dim iterable for linear force in A
torque_A: 3-dim iterable for rotational force (moment) in A
pose_A_in_B: numpy array of shape (4,4) corresponding to the pose of A in frame B
Returns:
force_B, torque_B: two num... | codesearchnet |
def remove_item(self, item):
for (idx, _item) in enumerate(self.items):
if (item == _item):
del self.items[idx]
return True
return False | Remove the specified item from the menu.
Args:
item (MenuItem): the item to be removed.
Returns:
bool: True if the item was removed; False otherwise. | codesearchnet |
def box_area(boxes):
boxes = _upcast(boxes)
return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]) | Computes the area of a set of bounding boxes, which are specified by its (x1, y1, x2, y2) coordinates.
Args:
boxes (`torch.FloatTensor` of shape `(number_of_boxes, 4)`):
Boxes for which the area will be computed. They are expected to be in (x1, y1, x2, y2) format with `0 <= x1
< x2` and `0 <= y1 < y2`.
Returns:
`torch... | github-repos |
def parse_cartouche_text(lines):
indent_lines = unindent(lines)
indent_lines = pad_blank_lines(indent_lines)
indent_lines = first_paragraph_indent(indent_lines)
indent_paragraphs = gather_lines(indent_lines)
parse_tree = group_paragraphs(indent_paragraphs)
syntax_tree = extract_structure(pa... | Parse text in cartouche format and return a reStructuredText equivalent
Args:
lines: A sequence of strings representing the lines of a single
docstring as read from the source by Sphinx. This string should be
in a format that can be parsed by cartouche.
Returns:
A list of lines containing the transformed docstring as... | juraj-google-style |
def query_parameters(param_list, defaults=None):
script_params = collections.OrderedDict(([k, []] for k in param_list))
for (param, default) in zip(list(script_params.keys()), defaults):
user_input = click.prompt(('%s' % param), default=default)
script_params[param] = ast.literal_eval(user_input... | Asks the user for parameters. If available, proposes some defaults.
Args:
param_list (list): List of parameters to ask the user for values.
defaults (list): A list of proposed defaults. It must be a list of the
same length as param_list. A value of None in one element of the
list means that no default will be proposed... | codesearchnet |
def __init__(self, text: str, sctx: SchemaContext):
super().__init__(text)
self.sctx = sctx | Initialize the parser instance.
Args:
sctx: Schema context for XPath expression parsing. | juraj-google-style |
def ExportClientsByKeywords(keywords, filename, token=None):
index = client_index.CreateClientIndex(token=token)
client_list = index.LookupClients(keywords)
logging.info('found %d clients', len(client_list))
if (not client_list):
return
writer = csv.DictWriter([u'client_id', u'hostname', u'l... | r"""A script to export clients summaries selected by a keyword search.
This script does a client search for machines matching all of keywords and
writes a .csv summary of the results to filename. Multi-value fields are '\n'
separated.
Args:
keywords: a list of keywords to search for
filename: the name of the file to ... | codesearchnet |
def construct(name, exec_, terminal=False, additional_opts={}):
desktop_file = '[Desktop Entry]\n'
desktop_file_dict = {
'Name': name,
'Exec': exec_,
'Terminal': 'true' if terminal else 'false',
'Comment': additional_opts.get('Comment', name)
}
desktop_file = ('[Desktop Entry]\nName={name}\nExec={exec_... | Construct a .desktop file and return it as a string.
Create a standards-compliant .desktop file, returning it as a string.
Args:
name (str) : The program's name.
exec\_ (str) : The command.
terminal (bool): Determine if program should be run in a terminal emulator or not. Defaults to ``False``.
additional_opts (d... | juraj-google-style |
def get_services_health(self) -> dict:
services_health = {}
services_ids = self._get_services()
for service_id in services_ids:
service_name = DC.get_service_name(service_id)
if (DC.get_replicas(service_id) != DC.get_actual_replica(service_id)):
services_health[service_name] = 'U... | Get the health of all services.
Returns:
dict, services id and health status | codesearchnet |
def Delete(self, request, global_params=None):
config = self.GetMethodConfig('Delete')
return self._RunMethod(config, request, global_params=global_params) | Deletes the dataset specified by the datasetId value. Before you can delete a dataset, you must delete all its tables, either manually or by specifying deleteContents. Immediately after deletion, you can create another dataset with the same name.
Args:
request: (BigqueryDatasetsDeleteRequest) input message
global_para... | github-repos |
class Kosmos2VisionEncoder(nn.Module):
def __init__(self, config: Kosmos2VisionConfig):
super().__init__()
self.config = config
self.layers = nn.ModuleList([Kosmos2VisionEncoderLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forw... | Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
[`Kosmos2VisionEncoderLayer`].
Args:
config: Kosmos2VisionConfig | github-repos |
def depth_november_average_ground_temperature(self, value=None):
if (value is not None):
try:
value = float(value)
except ValueError:
raise ValueError('value {} need to be of type float for field `depth_november_average_ground_temperature`'.format(value))
self._depth_nove... | Corresponds to IDD Field `depth_november_average_ground_temperature`
Args:
value (float): value for IDD Field `depth_november_average_ground_temperature`
Unit: C
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 consume_json(request):
client = OEmbedConsumer()
urls = request.GET.getlist('urls')
width = request.GET.get('width')
height = request.GET.get('height')
template_dir = request.GET.get('template_dir')
output = {}
ctx = RequestContext(request)
for url in urls:
try:
p... | Extract and return oembed content for given urls.
Required GET params:
urls - list of urls to consume
Optional GET params:
width - maxwidth attribute for oembed content
height - maxheight attribute for oembed content
template_dir - template_dir to use when rendering oembed
Returns:
list of dictionaries with oembed m... | codesearchnet |
def __init__(self, graph_view):
self._graph_view = graph_view
if context.executing_eagerly():
self._cache = None
self._saveables_cache = None
else:
self._cache = object_identity.ObjectIdentityWeakKeyDictionary()
self._saveables_cache = object_identity.ObjectIdentityWeakKeyDic... | Configure saving.
Args:
graph_view: An `ObjectGraphView` object containing a description of the
object graph to save. | github-repos |
def split_data(*inputs, splits=[0.5, 0.5], shuffle=True, stratify_by=None, index_only=False, seed=None):
def fractions_to_counts(fracs, n):
'Converts a list of fractions to a list of counts that sum to n'
counts = [int(np.round((n * frac))) for frac in fracs]
counts[(- 1)] = (n - sum(counts... | Splits inputs into multiple splits of defined sizes
Args:
inputs: correlated tuples/lists/arrays/matrices/tensors to split
splits: list containing split sizes (fractions or counts);
shuffle: if True, shuffle the data before splitting
stratify_by: (None or an input) if not None, use these labels to
stratify the splits ... | codesearchnet |
def parse_mapping(mapping_file: Optional[str]) -> configparser.ConfigParser:
LOGGER.debug('Parsing mapping file. Command line: %s', mapping_file)
def parse(mapping_file):
config = configparser.ConfigParser()
config.read_file(mapping_file)
return config
if mapping_file is ... | Parse the file containing the mappings from hosts to pass entries.
Args:
mapping_file:
Name of the file to parse. If ``None``, the default file from the
XDG location is used. | juraj-google-style |
def share(self, group_id, group_access, expires_at=None, **kwargs):
path = ('/projects/%s/share' % self.get_id())
data = {'group_id': group_id, 'group_access': group_access, 'expires_at': expires_at}
self.manager.gitlab.http_post(path, post_data=data, **kwargs) | Share the project with a group.
Args:
group_id (int): ID of the group.
group_access (int): Access level for the group.
**kwargs: Extra options to send to the server (e.g. sudo)
Raises:
GitlabAuthenticationError: If authentication is not correct
GitlabCreateError: If the server failed to perform the request | codesearchnet |
def collapse_address_list(addresses):
i = 0
addrs = []
ips = []
nets = []
for ip in addresses:
if isinstance(ip, _BaseIP):
if ips and ips[-1]._version != ip._version:
raise TypeError("%s and %s are not of the same version" % (
st... | Collapse a list of IP objects.
Example:
collapse_address_list([IPv4('1.1.0.0/24'), IPv4('1.1.1.0/24')]) ->
[IPv4('1.1.0.0/23')]
Args:
addresses: A list of IPv4Network or IPv6Network objects.
Returns:
A list of IPv4Network or IPv6Network objects depending on what we
were passed.
Raises:
TypeError: If passed a list o... | juraj-google-style |
def _get_input_to_checker_function(self, flag_values):
return dict([key, flag_values[key].value] for key in self.flag_names) | Given flag values, returns the input to be given to checker.
Args:
flag_values: flags.FlagValues, the FlagValues instance to get flags from.
Returns:
dict, with keys() being self.lag_names, and value for each key
being the value of the corresponding flag (string, boolean, etc). | juraj-google-style |
def read(self, vals):
i = 0
if len(vals[i]) == 0:
self.holiday_name = None
else:
self.holiday_name = vals[i]
i += 1
if len(vals[i]) == 0:
self.holiday_day = None
else:
self.holiday_day = vals[i]
i += 1 | Read values.
Args:
vals (list): list of strings representing values | juraj-google-style |
def _ragged_tensor_apply_loss(loss_fn, y_true, y_pred, y_pred_extra_dim=False):
def rt_is_equiv_dense(rt):
return math_ops.reduce_all([math_ops.equal(math_ops.reduce_variance(math_ops.cast(row_lens, backend.floatx())), constant_op.constant([0.0])) for row_lens in rt.nested_row_lengths()])
def... | Apply a loss function on a per batch basis.
Args:
loss_fn: The loss function
y_true: truth values (RaggedTensor)
y_pred: predicted values (RaggedTensor)
y_pred_extra_dim: whether y_pred has an additional dimension compared to
y_true
Returns:
Loss-function result. A dense tensor if the output has a single dimension
(p... | github-repos |
def dump(self, include_address=True, include_id=True) -> str:
d = {'crypto': self.keystore['crypto'], 'version': self.keystore['version']}
if (include_address and (self.address is not None)):
d['address'] = remove_0x_prefix(encode_hex(self.address))
if (include_id and (self.uuid is not None)):
... | Dump the keystore for later disk storage.
The result inherits the entries `'crypto'` and `'version`' from `account.keystore`, and
adds `'address'` and `'id'` in accordance with the parameters `'include_address'` and
`'include_id`'.
If address or id are not known, they are not added, even if requested.
Args:
include_... | codesearchnet |
def step_preprocess(x, step, hparams):
original_channel_size = common_layers.shape_list(x)[(- 1)]
if hparams.add_position_timing_signal:
x = add_position_timing_signal(x, step, hparams)
if hparams.add_step_timing_signal:
x = add_step_timing_signal(x, step, hparams)
if ((hparams.add_posit... | Preprocess the input at the beginning of each step.
Args:
x: input tensor
step: step
hparams: model hyper-parameters
Returns:
preprocessed input. | codesearchnet |
def verify_firebase_token(id_token, request, audience=None):
return verify_token(id_token, request, audience=audience, certs_url=_GOOGLE_APIS_CERTS_URL) | Verifies an ID Token issued by Firebase Authentication.
Args:
id_token (Union[str, bytes]): The encoded token.
request (google.auth.transport.Request): The object used to make
HTTP requests.
audience (str): The audience that this token is intended for. This is
typically your Firebase application ID. If None then the a... | codesearchnet |
def _CreateTaskStorageWriter(self, path, task):
return SQLiteStorageFileWriter(
self._session, path,
storage_type=definitions.STORAGE_TYPE_TASK, task=task) | Creates a task storage writer.
Args:
path (str): path to the storage file.
task (Task): task.
Returns:
SQLiteStorageFileWriter: storage writer. | juraj-google-style |
def CreateExtensionSetting(client, feed_items, campaign_feed, feed_item_ids, platform_restrictions=None):
campaign_extension_setting_service = client.GetService('CampaignExtensionSettingService', 'v201809')
extension_feed_items = [{CreateSitelinkFeedItem(feed_items, feed_item_id)} for feed_item_id in feed_item_... | Creates the extension setting for a list of Feed Items.
Args:
client: an AdWordsClient instance.
feed_items: the list of all Feed Items.
campaign_feed: the original Campaign Feed.
feed_item_ids: the Ids of the feed items for which extension settings should
be created.
platform_restrictions: an optional Platform Restri... | codesearchnet |
def load(cls, path, reader=None):
if reader is None:
from . import io
reader = io.DefaultReader()
elif type(reader) == str:
from . import io
reader = io.create_reader_of_type(reader)
return reader.load(path) | Loads the corpus from the given path, using the given reader. If no reader is given the
:py:class:`audiomate.corpus.io.DefaultReader` is used.
Args:
path (str): Path to load the corpus from.
reader (str, CorpusReader): The reader or the name of the reader to use.
Returns:
Corpus: The loaded corpus. | juraj-google-style |
def _read_data_handler(length, whence, ctx, skip=False, stream_event=ION_STREAM_INCOMPLETE_EVENT):
trans = None
queue = ctx.queue
if length > ctx.remaining:
raise IonException('Length overrun: %d bytes, %d remaining' % (length, ctx.remaining))
queue_len = len(queue)
if queue_len ... | Creates a co-routine for retrieving data up to a requested size.
Args:
length (int): The minimum length requested.
whence (Coroutine): The co-routine to return to after the data is satisfied.
ctx (_HandlerContext): The context for the read.
skip (Optional[bool]): Whether the requested number of bytes should be skipped... | juraj-google-style |
def start(self) -> None:
self._server.start() | Starts this server.
Raises:
tf.errors.OpError: Or one of its subclasses if an error occurs while
starting the server. | github-repos |
def _get_type(points, soma_class):
assert soma_class in (SOMA_CONTOUR, SOMA_CYLINDER)
npoints = len(points)
if soma_class == SOMA_CONTOUR:
return {0: None,
1: SomaSinglePoint,
2: None}.get(npoints, SomaSimpleContour)
if(npoints == 3 and
points[0][COL... | get the type of the soma
Args:
points: Soma points
soma_class(str): one of 'contour' or 'cylinder' to specify the type | juraj-google-style |
def find_stacks(node, strict=False):
fso = FindStackOps()
fso.visit(node)
AnnotateStacks(fso.push_pop_pairs, strict).visit(node)
return node | Find pushes and pops to the stack and annotate them as such.
Args:
node: An AST node that might contain stack pushes and pops.
strict: A boolean indicating whether to stringently test whether each
push and pop are matched. This is not always possible when taking
higher-order derivatives of code generated in split-moti... | juraj-google-style |
def Append(self, value=None, **kwarg):
if (self.rdf_type is not None):
if (isinstance(value, rdfvalue.RDFValue) and (value.__class__ != self.rdf_type)):
raise ValueError(('Can only accept %s' % self.rdf_type))
try:
value = self.rdf_type(value, **kwarg)
except (TypeErr... | Add another member to the array.
Args:
value: The new data to append to the array.
**kwarg: Create a new element from these keywords.
Returns:
The value which was added. This can be modified further by the caller and
changes will be propagated here.
Raises:
ValueError: If the value to add is not allowed. | codesearchnet |
def run_from_ufos(self, ufos, output=(), **kwargs):
if set(output) == {"ufo"}:
return
ufo_paths = []
if isinstance(ufos, basestring):
ufo_paths = glob.glob(ufos)
ufos = [Font(x) for x in ufo_paths]
elif isinstance(ufos, lis... | Run toolchain from UFO sources.
Args:
ufos: List of UFO sources, as either paths or opened objects.
output: List of output formats to generate.
kwargs: Arguments passed along to save_otfs. | juraj-google-style |
def summarize(values, epsilon):
values = np.reshape(values, [-1])
values = np.sort(values)
elements = np.size(values)
num_buckets = 1.0 / epsilon
increment = elements / num_buckets
start = increment
step = max(increment, 1)
boundaries = values[int(start)::int(step)]
weights = np.ones... | Reduce a 1D sequence of values to a summary.
This algorithm is based on numpy.quantiles but modified to allow for
intermediate steps between multiple data sets. It first finds the target
number of bins as the reciprocal of epsilon and then takes the individual
values spaced at appropriate intervals to arrive at that t... | github-repos |
def pnum_to_group(mesh_shape, group_dims, pnum):
coord = pnum_to_processor_coordinates(mesh_shape, pnum)
remaining_shape = Shape(
[d for i, d in enumerate(mesh_shape) if i not in group_dims])
remaining_coord = [d for i, d in enumerate(coord) if i not in group_dims]
return processor_coordinates_to_pnum(... | Group number for grouped allreduce.
Args:
mesh_shape: a Shape
group_dims: a list of integers (the dimensions reduced over)
pnum: an integer
Returns:
an integer | juraj-google-style |
def closest(self, coords=[], **kwargs):
if self.ndims > 1:
raise NotImplementedError("Closest method currently only "
"implemented for 1D Elements")
if kwargs:
if len(kwargs) > 1:
raise NotImplementedError("Closest m... | Snaps coordinate(s) to closest coordinate in Dataset
Args:
coords: List of coordinates expressed as tuples
**kwargs: Coordinates defined as keyword pairs
Returns:
List of tuples of the snapped coordinates
Raises:
NotImplementedError: Raised if snapping is not supported | juraj-google-style |
def binary_cross_entropy_with_logits(input_, target, name=PROVIDED, loss_weight=None, per_example_weights=None, per_output_weights=None):
if (target is None):
raise ValueError('target must be set')
target = _convert_and_assert_tensors_compatible(input_, target)
with tf.name_scope('stats'):
(... | Calculates the binary cross entropy of the input_ vs inputs.
Expects unscaled logits. Do not pass in results of sigmoid operation.
Args:
input_: A rank 2 Tensor or a Pretty Tensor holding the logits.
target: A rank 2 tf.float32 or tf.float64 tensor containing class label
probabilities. Note that binary cross entropy ... | codesearchnet |
def __init__(self, xid=None, multipart_type=None, flags=0, body=b''):
super().__init__(xid)
self.multipart_type = multipart_type
self.flags = flags
self.body = body | Create a MultipartRequest with the optional parameters below.
Args:
xid (int): xid to the header.
multipart_type (int): One of the OFPMP_* constants.
flags (int): OFPMPF_REQ_* flags.
body (bytes): Body of the request. | juraj-google-style |
def get_port(self, id_or_uri, port_id_or_uri):
uri = self._client.build_subresource_uri(id_or_uri, port_id_or_uri, "ports")
return self._client.get(uri) | Gets an interconnect port.
Args:
id_or_uri: Can be either the interconnect id or uri.
port_id_or_uri: The interconnect port id or uri.
Returns:
dict: The interconnect port. | juraj-google-style |
def parse_timers(self):
filenames = list(filter(os.path.exists, [task.output_file.path for task in self]))
parser = AbinitTimerParser()
parser.parse(filenames)
return parser | Parse the TIMER section reported in the ABINIT output files.
Returns:
:class:`AbinitTimerParser` object | codesearchnet |
def authenticate(self, username, password, attribute=None, base_dn=None, search_filter=None, search_scope=SUBTREE):
valid_dn = False
try:
parse_dn(username)
valid_dn = True
except LDAPInvalidDnError:
pass
if (valid_dn is False):
user_filter = '({0}={1})'.format(attribute,... | Attempts to bind a user to the LDAP server.
Args:
username (str): DN or the username to attempt to bind with.
password (str): The password of the username.
attribute (str): The LDAP attribute for the username.
base_dn (str): The LDAP basedn to search on.
search_filter (str): LDAP searchfilter to attempt the user
searc... | codesearchnet |
def GetFeedItemIdsForCampaign(campaign_feed):
feed_item_ids = set()
try:
lhs_operand = campaign_feed['matchingFunction']['lhsOperand']
except KeyError:
lhs_operand = None
if (lhs_operand and lhs_operand[0]['FunctionArgumentOperand.Type'] ==
'RequestContextOperand'):
request_context_operan... | Gets the Feed Item Ids used by a campaign through a given Campaign Feed.
Args:
campaign_feed: the Campaign Feed we are retrieving Feed Item Ids from.
Returns:
A list of Feed Item IDs. | juraj-google-style |
def _read_single(parser, filepath):
from os import path
global packages
if path.isfile(filepath):
parser.readfp(open(filepath)) | Reads a single config file into the parser, silently failing if the file
does not exist.
Args:
parser (ConfigParser): parser to read the file into.
filepath (str): full path to the config file. | juraj-google-style |
def retrieve_pwd_from_config(msg, cfg):
msg_type = msg.__class__.__name__.lower()
key_fmt = ((msg.profile + '_') + msg_type)
pwd = cfg.pwd[key_fmt].split(' :: ')
if (len(pwd) == 1):
msg.auth = pwd[0]
else:
msg.auth = tuple(pwd) | Retrieve auth from profile configuration and set in msg.auth attr.
Args:
:msg: (Message class) an instance of a message class.
:cfg: (jsonconfig.Config) config instance. | codesearchnet |
def output(self, filename):
info = 'Inheritance\n'
if not self.contracts:
return
info += blue('Child_Contract -> ') + green('Immediate_Base_Contracts')
info += green(' [Not_Immediate_Base_Contracts]')
for child in self.contracts:
info += blue(f'... | Output the inheritance relation
_filename is not used
Args:
_filename(string) | juraj-google-style |
def _get_ami_file(region='us-east-1'):
LOG.info("Getting AMI from Gitlab")
lookup = FileLookup(git_short='devops/ansible')
filename = 'scripts/{0}.json'.format(region)
ami_contents = lookup.remote_file(filename=filename, branch='master')
LOG.debug('AMI file contents in %s: %s', filename, ami_co... | Get file from Gitlab.
Args:
region (str): AWS Region to find AMI ID.
Returns:
str: Contents in json format. | juraj-google-style |
def search(self, query, results=10, suggestion=False):
self._check_query(query, 'Query must be specified')
search_params = {'list': 'search', 'srprop': '', 'srlimit': results, 'srsearch': query}
if suggestion:
search_params['srinfo'] = 'suggestion'
raw_results = self.wiki_request(search_params)
... | Search for similar titles
Args:
query (str): Page title
results (int): Number of pages to return
suggestion (bool): Use suggestion
Returns:
tuple or list: tuple (list results, suggestion) if \
suggestion is **True**; list of results \
otherwise | codesearchnet |
def disconnect(self, container, *args, **kwargs):
if isinstance(container, Container):
container = container.id
return self.client.api.disconnect_container_from_network(container, self.id, *args, **kwargs) | Disconnect a container from this network.
Args:
container (str): Container to disconnect from this network, as
either an ID, name, or
:py:class:`~docker.models.containers.Container` object.
force (bool): Force the container to disconnect from a network.
Default: ``False``
Raises:
:py:class:`docker.errors.APIError`
If... | codesearchnet |
def SetDefault(self, name, value):
fl = self.FlagDict()
if name not in fl:
self._SetUnknownFlag(name, value)
return
if self.IsParsed():
logging.warn(
'FLAGS.SetDefault called on flag "%s" after flag parsing. Call this '
'method at the top level of a module to avoid... | Changes the default value (and current value) of the named flag object.
Call this method at the top level of a module to avoid overwriting the value
passed at the command line.
Args:
name: A string, the name of the flag to modify.
value: The new default value.
Raises:
UnrecognizedFlagError: When there is no register... | juraj-google-style |
def format_formula(formula):
formatted_formula = ""
number_format = ""
for i, s in enumerate(formula):
if s.isdigit():
if not number_format:
number_format = "_{"
number_format += s
if i == len(formula) - 1:
number_format += "}... | Converts str of chemical formula into
latex format for labelling purposes
Args:
formula (str): Chemical formula | juraj-google-style |
def _serialize_normalized_array(array, fmt='png', quality=70):
dtype = array.dtype
assert np.issubdtype(dtype, np.unsignedinteger)
assert np.max(array) <= np.iinfo(dtype).max
assert array.shape[-1] > 1
image = PIL.Image.fromarray(array)
image_bytes = BytesIO()
image.save(image_bytes, fmt, quality=qu... | Given a normalized array, returns byte representation of image encoding.
Args:
array: NumPy array of dtype uint8 and range 0 to 255
fmt: string describing desired file format, defaults to 'png'
quality: specifies compression quality from 0 to 100 for lossy formats
Returns:
image data as BytesIO buffer | juraj-google-style |
def validate(data):
try:
return Schema(Validator.SCHEMA).validate(data)
except SchemaError as exception:
logging.getLogger(__name__).error(exception)
return None | Validate data against the schema.
Args:
data(dict): data structure to validate.
Returns:
dict: data as provided and defaults where defined in schema. | codesearchnet |
def remove_token(self, *, payer_id, credit_card_token_id):
payload = {'language': self.client.language.value, 'command': PaymentCommand.REMOVE_TOKEN.value, 'merchant': {'apiLogin': self.client.api_login, 'apiKey': self.client.api_key}, 'removeCreditCardToken': {'payerId': payer_id, 'creditCardTokenId': credit_card_... | This feature allows you to delete a tokenized credit card register.
Args:
payer_id:
credit_card_token_id:
Returns: | codesearchnet |
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