code stringlengths 20 4.93k | docstring stringlengths 33 1.27k | source stringclasses 3
values |
|---|---|---|
def send(query,
address=DEFAULT_ADDRESS,
port=DEFAULT_PORT,
ttl=DEFAULT_TTL,
local_only=False,
timeout_s=2):
sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
sock.setsockopt(socket.IPPROTO_IP, socket.IP_MULTICAST_TTL, ttl)
if local_only:
sock.sets... | Sends a query to the given multicast socket and returns responses.
Args:
query: The string query to send.
address: Multicast IP address component of the socket to send to.
port: Multicast UDP port component of the socket to send to.
ttl: TTL for multicast messages. 1 to keep traffic in-network.
timeout_s: Seconds to w... | juraj-google-style |
def get_system_time():
now = win32api.GetLocalTime()
meridian = 'AM'
hours = int(now[4])
if (hours == 12):
meridian = 'PM'
elif (hours == 0):
hours = 12
elif (hours > 12):
hours = (hours - 12)
meridian = 'PM'
return '{0:02d}:{1:02d}:{2:02d} {3}'.format(hours, ... | Get the system time.
Returns:
str: Returns the system time in HH:MM:SS AM/PM format.
CLI Example:
.. code-block:: bash
salt 'minion-id' system.get_system_time | codesearchnet |
def base256_encode(n, minwidth=0):
if (n > 0):
arr = []
while n:
(n, rem) = divmod(n, 256)
arr.append(rem)
b = bytearray(reversed(arr))
elif (n == 0):
b = bytearray(b'\x00')
else:
raise ValueError('Negative numbers not supported')
if ((minw... | Encode the input with base256.
Args:
n (int): input value.
minwidth: minimum return value length.
Raises:
ValueError: if a negative number is provided.
Returns:
bytearray: | codesearchnet |
async def verify_chain_of_trust(chain):
log_path = os.path.join(chain.context.config['task_log_dir'], 'chain_of_trust.log')
scriptworker_log = logging.getLogger('scriptworker')
with contextual_log_handler(chain.context, path=log_path, log_obj=scriptworker_log, formatter=AuditLogFormatter(fmt=chain.context.c... | Build and verify the chain of trust.
Args:
chain (ChainOfTrust): the chain we're operating on
Raises:
CoTError: on failure | codesearchnet |
def call(self, inputs):
del inputs
latent_code = ed.MultivariateNormalDiag(loc=tf.zeros(self.latent_size), sample_shape=1, name='latent_code')
state = self.lstm.zero_state(1, dtype=tf.float32)
t = 0
productions = []
stack = [self.grammar.start_symbol]
while stack:
symbol = stack.pop(... | Runs the model forward to generate a sequence of productions.
Args:
inputs: Unused.
Returns:
productions: Tensor of shape [1, num_productions, num_production_rules].
Slices along the `num_productions` dimension represent one-hot vectors. | codesearchnet |
def _Open(self, path_spec=None, mode='rb'):
if ((not self._file_object_set_in_init) and (not path_spec)):
raise ValueError('Missing path specification.')
if (not self._file_object_set_in_init):
if (not path_spec.HasParent()):
raise errors.PathSpecError('Unsupported path specification... | Opens the file-like object.
Args:
path_spec (Optional[PathSpec]): path specification.
mode (Optional[str]): file access mode.
Raises:
AccessError: if the access to open the file was denied.
IOError: if the file-like object could not be opened.
OSError: if the file-like object could not be opened.
PathSpecError: if th... | codesearchnet |
def RestrictFeedItemToGeoTarget(client, feed_item, location_id):
feed_item_target_service = client.GetService(
'FeedItemTargetService', version='v201809')
criterion_target = {
'xsi_type': 'FeedItemCriterionTarget',
'feedId': feed_item['feedId'],
'feedItemId': feed_item['feedItem... | Restrict a feed item to a geo target location.
Args:
client: An AdWordsClient instance.
feed_item: A FeedItem.
location_id: The Id of the location to restrict to. | juraj-google-style |
def SetHeaders(self, soap_headers, http_headers):
self.suds_client.set_options(soapheaders=soap_headers, headers=http_headers) | Set the headers for the underlying client.
Args:
soap_headers: A SOAP element for the SOAP headers.
http_headers: A dictionary for the http headers. | codesearchnet |
def get_or_generate_vocabulary(data_dir,
tmp_dir,
data_prefix,
max_page_size_exp,
approx_vocab_size=32768,
strip=True):
num_pages_for_vocab_generation = approx_... | Get or generate the vocabulary.
Args:
data_dir: a string
tmp_dir: a string
data_prefix: a string
max_page_size_exp: an integer
approx_vocab_size: an integer
strip: a boolean
Returns:
a TextEncoder | juraj-google-style |
def _PrintProcessingTime(self, processing_status):
if (not processing_status):
processing_time = '00:00:00'
else:
processing_time = (time.time() - processing_status.start_time)
time_struct = time.gmtime(processing_time)
processing_time = time.strftime('%H:%M:%S', time_struct)
... | Prints the processing time.
Args:
processing_status (ProcessingStatus): processing status. | codesearchnet |
def get_config(self):
data = self.data
if type(self.data).__module__ == np.__name__:
data = self.data.tolist()
try:
json_data = json.dumps(data)
except TypeError as e:
raise TypeError(f'Data not JSON Serializable: {data}') from e
targets = self.targets
if type(self.target... | Returns the TimeseriesGenerator configuration as Python dictionary.
Returns:
A Python dictionary with the TimeseriesGenerator configuration. | github-repos |
def sort_imports(file: str, check_only: bool=True):
with open(file, encoding='utf-8') as f:
code = f.read()
if '_import_structure' not in code or 'define_import_structure' in code:
return
main_blocks = split_code_in_indented_blocks(code, start_prompt='_import_structure = {', end_prompt='if T... | Sort the imports defined in the `_import_structure` of a given init.
Args:
file (`str`): The path to the init to check/fix.
check_only (`bool`, *optional*, defaults to `True`): Whether or not to just check (and not auto-fix) the init. | github-repos |
def cast_to_seq(obj, alphabet=IUPAC.extended_protein):
if isinstance(obj, Seq):
return obj
if isinstance(obj, SeqRecord):
return obj.seq
if isinstance(obj, str):
obj = obj.upper()
return Seq(obj, alphabet)
else:
raise ValueError('Must provide a string, Seq, or Seq... | Return a Seq representation of a string or SeqRecord object.
Args:
obj (str, Seq, SeqRecord): Sequence string or Biopython SeqRecord object
alphabet: See Biopython SeqRecord docs
Returns:
Seq: Seq representation of the sequence | codesearchnet |
def get_files(self, retrieve=False):
if self.exists and hasattr(self.rdf.triples, 'pcdm') and hasattr(self.rdf.triples.pcdm, 'hasFile'):
files = [ self.repo.parse_uri(uri) for uri in self.rdf.triples.pcdm.hasFile ]
return files
else:
return [] | get pcdm:hasFile for this resource
Args:
retrieve (bool): if True, issue .refresh() on resource thereby confirming existence and retrieving payload | juraj-google-style |
def add(self, email):
if (email not in self._collaborators):
self._collaborators[email] = ShareRequestValue.Add
self._dirty = True | Add a collaborator.
Args:
str : Collaborator email address. | codesearchnet |
def get_cases(variant_source, case_lines=None, case_type='ped',
variant_type='snv', variant_mode='vcf'):
individuals = get_individuals(
variant_source=variant_source,
case_lines=case_lines,
case_type=case_type,
variant_mode=variant_mode
... | Create a cases and populate it with individuals
Args:
variant_source (str): Path to vcf files
case_lines (Iterable): Ped like lines
case_type (str): Format of case lines
Returns:
case_objs (list(puzzle.models.Case)) | juraj-google-style |
def slot(self):
if (self.type == EventType.TOUCH_FRAME):
raise AttributeError(_wrong_prop.format(self.type))
return self._libinput.libinput_event_touch_get_slot(self._handle) | The slot of this touch event.
See the kernel's multitouch protocol B documentation for more
information.
If the touch event has no assigned slot, for example if it is from
a single touch device, this property returns -1.
For events not of type :attr:`~libinput.constant.EventType.TOUCH_DOWN`,
:attr:`~libinput.constan... | codesearchnet |
def check_imports(filename: Union[str, os.PathLike]) -> list[str]:
imports = get_imports(filename)
missing_packages = []
for imp in imports:
try:
importlib.import_module(imp)
except ImportError as exception:
logger.warning(f'Encountered exception while importing {imp}... | Check if the current Python environment contains all the libraries that are imported in a file. Will raise if a
library is missing.
Args:
filename (`str` or `os.PathLike`): The module file to check.
Returns:
`list[str]`: The list of relative imports in the file. | github-repos |
def persist_project(project):
from benchbuild.utils.schema import Project, Session
session = Session()
projects = session.query(Project) \
.filter(Project.name == project.name) \
.filter(Project.group_name == project.group)
name = project.name
desc = project.__doc__
domain ... | Persist this project in the benchbuild database.
Args:
project: The project we want to persist. | juraj-google-style |
def cancel(self, identifier: typing.Any, exc_type: typing.Optional[type]=None) -> bool:
raise NotImplementedError() | Cancel an active coroutine and remove it from the schedule.
Args:
identifier (typing.Any): The identifier returned from add.
exc_type (typing.Optional[type]): The exception type to throw into
the coroutine on cancel. No exception is thrown if nothing is
given. Instead the coroutine is no longer processed.
Returns:
bo... | codesearchnet |
def _get_recursive_dependancies(self, dependencies_map, sourcepath, recursive=True):
collected = set([])
collected.update(dependencies_map.get(sourcepath, []))
sequence = collected.copy()
walkthrough = []
if recursive:
while True:
if (not sequence):
break
... | Return all dependencies of a source, recursively searching through its
dependencies.
This is a common method used by ``children`` and ``parents`` methods.
Args:
dependencies_map (dict): Internal buffer (internal buffers
``_CHILDREN_MAP`` or ``_PARENTS_MAP``) to use for searching.
sourcepath (str): Source file path to... | codesearchnet |
def _find_relation(self, span_doc: doc, r: List) -> Dict:
rule = r[1][0]
span_pivot = 0
relation = {}
for e_id, element in enumerate(rule):
if not span_doc[span_pivot:]:
for extra_id, _, in enumerate(rule[e_id:]):
relation[e_id+ex... | Get the relations between the each pattern in the spacy rule and the matches
Args:
span_doc: doc
r: List
Returns: Dict | juraj-google-style |
def eig(tensor, name=None):
if tensor.dtype == dtypes.float32 or tensor.dtype == dtypes.complex64:
out_dtype = dtypes.complex64
elif tensor.dtype == dtypes.float64 or tensor.dtype == dtypes.complex128:
out_dtype = dtypes.complex128
e, v = gen_linalg_ops.eig(tensor, Tout=out_dtype, compute_v=... | Computes the eigen decomposition of a batch of matrices.
The eigenvalues
and eigenvectors for a non-Hermitian matrix in general are complex. The
eigenvectors are not guaranteed to be linearly independent.
Computes the eigenvalues and right eigenvectors of the innermost
N-by-N matrices in `tensor` such that
`tensor[..... | github-repos |
def has_datastore(self):
(success, result) = self._read_from_hdx('datastore', self.data['id'], 'resource_id', self.actions()['datastore_search'])
if (not success):
logger.debug(result)
elif result:
return True
return False | Check if the resource has a datastore.
Returns:
bool: Whether the resource has a datastore or not | codesearchnet |
def _find_uninitialized(self):
return set((name for (name, prop) in self._properties.iteritems() if (not prop._is_initialized(self)))) | Internal helper to find uninitialized properties.
Returns:
A set of property names. | codesearchnet |
def _genBgTerm_fromXX(self, vTot, vCommon, XX, a=None, c=None):
vSpecific = (vTot - vCommon)
SP.random.seed(0)
if (c == None):
c = SP.randn(self.P)
XX += (0.001 * SP.eye(XX.shape[0]))
L = LA.cholesky(XX, lower=True)
R = self.genWeights(self.N, self.P)
A = self.genTraitEffect()
if... | generate background term from SNPs
Args:
vTot: variance of Yc+Yi
vCommon: variance of Yc
XX: kinship matrix
a: common scales, it can be set for debugging purposes
c: indipendent scales, it can be set for debugging purposes | codesearchnet |
def get_data_xlsx(file_name, file_contents=None, on_demand=False):
return get_data_xls(file_name, file_contents=file_contents, on_demand=on_demand) | Loads the new excel format files. Old format files will automatically get loaded as well.
Args:
file_name: The name of the local file, or the holder for the
extension type when the file_contents are supplied.
file_contents: The file-like object holding contents of file_name.
If left as None, then file_name is directly... | codesearchnet |
def age(self):
date = datetime.today().date()
b = self.birthday
if b:
return int(((date - b).days / 365))
return None | Returns a user's age, based on their birthday.
Returns:
integer | codesearchnet |
def send_location(self, room_id, geo_uri, name, thumb_url=None, thumb_info=None, timestamp=None):
content_pack = {'geo_uri': geo_uri, 'msgtype': 'm.location', 'body': name}
if thumb_url:
content_pack['thumbnail_url'] = thumb_url
if thumb_info:
content_pack['thumbnail_info'] = thumb_info
... | Send m.location message event
Args:
room_id (str): The room ID to send the event in.
geo_uri (str): The geo uri representing the location.
name (str): Description for the location.
thumb_url (str): URL to the thumbnail of the location.
thumb_info (dict): Metadata about the thumbnail, type ImageInfo.
timestamp (int): S... | codesearchnet |
def find_and_replace_channel_refs(self, text):
match = True
pattern = re.compile('<
while match:
match = pattern.search(text)
if match:
text = text.replace(match.group(0), ('
return text | Find occurrences of Slack channel referenfces and attempts to
replace them with just channel names.
Args:
text (string): The message text
Returns:
string: The message text with channel references replaced. | codesearchnet |
def read(self, size=None):
data = self.rfile.read(size)
self.bytes_read += len(data)
self._check_length()
return data | Read a chunk from rfile buffer and return it.
Args:
size (int): amount of data to read
Returns:
bytes: Chunk from rfile, limited by size if specified. | juraj-google-style |
def _load_schema(file_path, name=None):
if name is None:
name = os.path.splitext(os.path.basename(file_path))[0]
if name not in _SCHEMAS:
with open(file_path, 'r') as schema_file:
_SCHEMAS[name] = json.load(schema_file)
return _SCHEMAS[name] | Loads the QObj schema for use in future validations.
Caches schema in _SCHEMAS module attribute.
Args:
file_path(str): Path to schema.
name(str): Given name for schema. Defaults to file_path filename
without schema.
Return:
schema(dict): Loaded schema. | juraj-google-style |
def __call__(self, utterances_batch: List[str], history_batch: List[List[str]],
states_batch: Optional[list] = None) -> Tuple[List[str], List[float]]:
responses, confidences = self.model(utterances_batch)
if isinstance(confidences[0], list):
confidences = ... | It returns the skill inference result.
Output is batches of the skill inference results and estimated confidences.
Args:
utterances_batch: A batch of utterances.
history_batch: A batch of list typed histories for each utterance.
states_batch: Optional. A batch of arbitrary typed states for
each utterance.
Returns:
B... | juraj-google-style |
def write(self, data):
ctx = context.get()
if len(data) != 2:
logging.error("Got bad tuple of length %d (2-tuple expected): %s",
len(data), data)
try:
key = str(data[0])
value = str(data[1])
except TypeError:
logging.error("Expecting a tuple, but got %s:... | Write data.
Args:
data: actual data yielded from handler. Type is writer-specific. | juraj-google-style |
def Send(self, message):
if not isinstance(message, common_pb2.Message):
raise ValueError("Send requires a fleetspeak.Message")
if message.destination.service_name == "system":
raise ValueError(
"Only predefined messages can have destination.service_name == \"system\"")
return s... | Send a message through Fleetspeak.
Args:
message: A message protocol buffer.
Returns:
Size of the message in bytes.
Raises:
ValueError: If message is not a common_pb2.Message. | juraj-google-style |
def _safe_setattr(obj, name, value):
okey = id(obj)
if okey in _set_failures or okey in _final_objs:
return False
import inspect
try:
if inspect.ismethod(obj):
setattr(obj.__func__, name, value)
return True
else:
if isinstance(obj, di... | Safely sets the attribute of the specified object. This includes not
setting attributes for final objects and setting __func__ for instancemethod
typed objects.
Args:
obj: object to set an attribute for.
name (str): new attribute name.
value: new attribute value.
Returns:
bool: True if the set attribute was successfu... | juraj-google-style |
def retrieve_token(self, token):
headers = self.client._get_private_headers()
endpoint = '/tokens/{}'.format(token)
return self.client._get((self.client.URL_BASE + endpoint), headers=headers) | Retrieve Token details for a specific Token.
Args:
token: The identifier of the token.
Returns: | codesearchnet |
def iter_packages(self, name, range_=None, paths=None):
for package in iter_packages(name, range_, paths):
if (not self.excludes(package)):
(yield package) | Same as iter_packages in packages.py, but also applies this filter.
Args:
name (str): Name of the package, eg 'maya'.
range_ (VersionRange or str): If provided, limits the versions returned
to those in `range_`.
paths (list of str, optional): paths to search for packages, defaults
to `config.packages_path`.
Returns:
... | codesearchnet |
def __init__(self, iterable=None, modify_time=None, update_time=None):
if self.__class__ is Map:
raise TypeError('Map is an abstract class.')
self._data = {}
self._index = []
self._last_modification_timestamp = modify_time
self._last_update_timestamp = update_time
self.log = logging.getL... | Construct a Map object.
Args:
iterable: A tuple or list that can be iterated over and added to the Map,
defaults to None.
modify_time: An optional modify time for this Map, defaults to None.
defaults to None.
update_time: An optional update time for this Map, defaults to None.
defaults to None.
Raises:
TypeError: If ... | github-repos |
def _process_datum(self, data, input_reader, ctx, transient_shard_state):
if (data is not input_readers.ALLOW_CHECKPOINT):
self.slice_context.incr(context.COUNTER_MAPPER_CALLS)
handler = transient_shard_state.handler
if isinstance(handler, map_job.Mapper):
handler(self.slice_cont... | Process a single data piece.
Call mapper handler on the data.
Args:
data: a datum to process.
input_reader: input reader.
ctx: mapreduce context
transient_shard_state: transient shard state.
Returns:
True if scan should be continued, False if scan should be stopped. | codesearchnet |
def check(self, solution):
return self.func(*(solution[v] for v in self.variables)) | Check that a solution satisfies the constraint.
Args:
solution (container):
An assignment for the variables in the constraint.
Returns:
bool: True if the solution satisfies the constraint; otherwise False.
Examples:
This example creates a constraint that :math:`a \\ne b` on binary variables
and tests it for two cand... | codesearchnet |
def _examples_from_path_handler(self, request):
examples_count = int(request.args.get('max_examples'))
examples_path = request.args.get('examples_path')
sampling_odds = float(request.args.get('sampling_odds'))
self.example_class = (tf.train.SequenceExample
if request.args.get('sequence_exam... | Returns JSON of the specified examples.
Args:
request: A request that should contain 'examples_path' and 'max_examples'.
Returns:
JSON of up to max_examlpes of the examples in the path. | juraj-google-style |
def __resource_descriptor(self, resource_path, methods):
descriptor = {}
method_map = {}
sub_resource_index = collections.defaultdict(list)
sub_resource_map = {}
resource_path_tokens = resource_path.split('.')
for (service, protorpc_meth_info) in methods:
method_info = getattr(protorpc_m... | Describes a resource.
Args:
resource_path: string, the path of the resource (e.g., 'entries.items')
methods: list of tuples of type
(endpoints.Service, protorpc.remote._RemoteMethodInfo), the methods
that serve this resource.
Returns:
Dictionary describing the resource. | codesearchnet |
def _prepare_4d_causal_attention_mask_with_cache_position(attention_mask: torch.Tensor, sequence_length: int, target_length: int, dtype: torch.dtype, cache_position: torch.Tensor, batch_size: int, **kwargs):
if attention_mask is not None and attention_mask.dim() == 4:
causal_mask = attention_mask
else:
... | Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
`(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
Args:
attention_mask (`torch.Tensor`):
A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attentio... | github-repos |
def load_validator(schema_path, schema):
if (os.name == 'nt'):
file_prefix = 'file:
else:
file_prefix = 'file:'
resolver = RefResolver((file_prefix + schema_path.replace('\\', '/')), schema)
validator = Draft4Validator(schema, resolver=resolver)
return validator | Create a JSON schema validator for the given schema.
Args:
schema_path: The filename of the JSON schema.
schema: A Python object representation of the same schema.
Returns:
An instance of Draft4Validator. | codesearchnet |
def unset(entity, *types):
if not types:
types = (TypedField,)
fields = list(entity._fields.keys())
remove = (x for x in fields if isinstance(x, types))
for field in remove:
del entity._fields[field] | Unset the TypedFields on the input `entity`.
Args:
entity: A mixbox.Entity object.
*types: A variable-length list of TypedField subclasses. If not
provided, defaults to TypedField. | juraj-google-style |
def _os_release_info(self):
if os.path.isfile(self.os_release_file):
with open(self.os_release_file) as release_file:
return self._parse_os_release_content(release_file)
return {} | Get the information items from the specified os-release file.
Returns:
A dictionary containing all information items. | codesearchnet |
def to_dict(self):
return {'name': self.name, 'id': self.id, 'type': self.type, 'workflow_id': self.workflow_id, 'queue': self.queue, 'start_time': self.start_time, 'arguments': self.arguments, 'acknowledged': self.acknowledged, 'func_name': self.func_name, 'hostname': self.hostname, 'worker_name': self.worker_name... | Return a dictionary of the job stats.
Returns:
dict: Dictionary of the stats. | codesearchnet |
def op_list_to_dict(op_list, convert_variable_to_tensor=True):
if not isinstance(op_list, (list, tuple, set)):
raise TypeError(f'Variables to save should be passed in a dict or a list. Got {op_list}')
op_list = nest.flatten(list(op_list))
op_list = sorted(op_list, key=lambda x: x.name)
names_to_... | Create a dictionary of names to operation lists.
This method is only used when the variable name matters (e.g. when saving
or restoring from a TF1 name-based checkpoint). In TF2, this can be called
from `tf.train.Checkpoint.restore` when loading from a name-based checkpoint.
Args:
op_list: A (nested) list, tuple, or ... | github-repos |
def filter_embeddings(embeddings, vocab, dim):
if not isinstance(embeddings, dict):
return
_embeddings = np.zeros([len(vocab), dim])
for word in vocab:
if word in embeddings:
word_idx = vocab[word]
_embeddings[word_idx] = embeddings[word]
return _embeddings | Loads word vectors in numpy array.
Args:
embeddings (dict): a dictionary of numpy array.
vocab (dict): word_index lookup table.
Returns:
numpy array: an array of word embeddings. | juraj-google-style |
def astype(array, y):
if isinstance(y, autograd.core.Node):
return array.astype(numpy.array(y.value).dtype)
return array.astype(numpy.array(y).dtype) | A functional form of the `astype` method.
Args:
array: The array or number to cast.
y: An array or number, as the input, whose type should be that of array.
Returns:
An array or number with the same dtype as `y`. | juraj-google-style |
def create_token_type_ids_from_sequences(self, token_ids_0: List[int], token_ids_1: Optional[List[int]]=None) -> List[int]:
sep = [self.sep_token_id]
cls = [self.cls_token_id]
if token_ids_1 is None:
return len(cls + token_ids_0 + sep) * [0]
return len(cls + token_ids_0 + sep + sep + token_ids_1... | Create a mask from the two sequences passed to be used in a sequence-pair classification task. XLM-RoBERTa does
not make use of token type ids, therefore a list of zeros is returned.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optional*):
Optional second list of IDs for sequence pairs.
Re... | github-repos |
def log_flush_for_interval(self, log_type, interval):
if (not log_type):
log_type = 'policies'
interval = interval.replace(' ', '+')
flush_url = '{}/{}/interval/{}'.format(self.url, log_type, interval)
self.jss.delete(flush_url) | Flush logs for an interval of time.
Args:
log_type (str): Only documented type is "policies". This
will be applied by default if nothing is passed.
interval (str): Combination of "Zero", "One", "Two",
"Three", "Six", and "Day", "Week", "Month", "Year". e.g.
("Three+Months") Please note: The documentation for this
spec... | codesearchnet |
def set_config(self, key, value):
keyname = ('config:' + key)
self.kvstore.set(keyname, value) | Set a persistent config key to a value, stored in the registry
Args:
key (string): The key name
value (string): The key value | codesearchnet |
def __init__(self, ascii_codepage='cp1252', registry_file_reader=None):
super(WinRegistry, self).__init__()
self._ascii_codepage = ascii_codepage
self._registry_file_reader = registry_file_reader
self._registry_files = {}
self._user_registry_files = {} | Initializes the Windows Registry.
Args:
ascii_codepage (Optional[str]): ASCII string codepage.
registry_file_reader (Optional[WinRegistryFileReader]): Windows Registry
file reader. | juraj-google-style |
def get_padding_value(padding=None, kernel_size=7, stride=1, dilation=1) -> Tuple[Tuple, bool]:
dynamic = False
if padding is None:
padding = (stride - 1 + dilation * (kernel_size - 1))
return (padding, dynamic)
if isinstance(padding, str):
padding = padding.lower()
if paddi... | Utility function to get the tuple padding value given the kernel_size and padding.
Args:
padding (Union[`str`, `int`], *optional*):
Padding value, can be either `"same"`, `"valid"`. If a different value is provided the default padding from
PyTorch is used.
kernel_size (`int`, *optional*, defaults to 7):
Kernel size of... | github-repos |
def imshow_bboxes(img, bboxes, colors='green', top_k=(- 1), thickness=1, show=True, win_name='', wait_time=0, out_file=None):
img = imread(img)
if isinstance(bboxes, np.ndarray):
bboxes = [bboxes]
if (not isinstance(colors, list)):
colors = [colors for _ in range(len(bboxes))]
colors = [... | Draw bboxes on an image.
Args:
img (str or ndarray): The image to be displayed.
bboxes (list or ndarray): A list of ndarray of shape (k, 4).
colors (list[str or tuple or Color]): A list of colors.
top_k (int): Plot the first k bboxes only if set positive.
thickness (int): Thickness of lines.
show (bool): Whether to sh... | codesearchnet |
def _copy(src, dst, src_is_storage, dst_is_storage):
if (src_is_storage and dst_is_storage):
system_src = get_instance(src)
system_dst = get_instance(dst)
if (system_src is system_dst):
if (system_src.relpath(src) == system_dst.relpath(dst)):
raise same_file_error... | Copies file from source to destination
Args:
src (str or file-like object): Source file.
dst (str or file-like object): Destination file.
src_is_storage (bool): Source is storage.
dst_is_storage (bool): Destination is storage. | codesearchnet |
def _probe_services(self, handle):
code = 10240
def event_filter_func(event):
if ((event.command_class == 4) and (event.command == 2)):
(event_handle,) = unpack('B', event.payload[0:1])
return (event_handle == handle)
return False
def end_filter_func(event):
... | Probe for all primary services and characteristics in those services
Args:
handle (int): the connection handle to probe | codesearchnet |
def is_valid_callsign(self, callsign, timestamp=timestamp_now):
try:
if self.get_all(callsign, timestamp):
return True
except KeyError:
return False | Checks if a callsign is valid
Args:
callsign (str): Amateur Radio callsign
timestamp (datetime, optional): datetime in UTC (tzinfo=pytz.UTC)
Returns:
bool: True / False
Example:
The following checks if "DH1TW" is a valid callsign
>>> from pyhamtools import LookupLib, Callinfo
>>> my_lookuplib = LookupLib(lookuptype... | codesearchnet |
def __init__(self, port=None,
max_length=ControllerMaxLen.OFPCML_NO_BUFFER):
super().__init__(action_type=ActionType.OFPAT_OUTPUT, length=16)
self.port = port
self.max_length = max_length | Create a ActionOutput with the optional parameters below.
Args:
port (:class:`Port` or :class:`int`): Output port.
max_length (int): Max length to send to controller. | juraj-google-style |
def stack(self, trees: Iterable[Tree[Array['*s']]]) -> Tree[Array['n_trees *s']]:
return self.backend.map(_stack, *trees) | Stack a tree of `Iterable[Array]`.
Supports `jax`, `tf`, `np`.
Example:
```python
etree.stack([
{'a': np.array([1])},
{'a': np.array([2])},
{'a': np.array([3])},
]) == {
'a': np.array([[1], [2], [3]])
}
```
Args:
trees: The list of tree to stack
Returns:
Tree of arrays. | github-repos |
def insert_top(self, node):
if (not isinstance(node, grammar.STATEMENTS)):
raise ValueError
self.to_insert_top.append(node) | Insert statements at the top of the function body.
Note that multiple calls to `insert_top` will result in the statements
being prepended in that order; this is different behavior from `prepend`.
Args:
node: The statement to prepend.
Raises:
ValueError: If the given node is not a statement. | codesearchnet |
def get_dirty_items(item_list, flag_list):
assert (len(item_list) == len(flag_list))
dirty_items = [item for (item, flag) in zip(item_list, flag_list) if (not flag)]
return dirty_items | Returns each item in item_list where not flag in flag_list
Args:
item_list (list):
flag_list (list):
Returns:
dirty_items | codesearchnet |
def complain(distribution_name):
try:
pkg_resources.get_distribution(distribution_name)
warnings.warn('The {pkg} distribution is now obsolete. Please `pip uninstall {pkg}`. In the future, this warning will become an ImportError.'.format(pkg=distribution_name), DeprecationWarning)
except pkg_reso... | Issue a warning if `distribution_name` is installed.
In a future release, this method will be updated to raise ImportError
rather than just send a warning.
Args:
distribution_name (str): The name of the obsolete distribution. | codesearchnet |
def load_panel_app(adapter, panel_id=None, institute='cust000'):
base_url = 'https:
hgnc_map = adapter.genes_by_alias()
if panel_id:
panel_ids = [panel_id]
if not panel_id:
LOG.info("Fetching all panel app panels")
data = get_request(base_url.format('list... | Load PanelApp panels into scout database
If no panel_id load all PanelApp panels
Args:
adapter(scout.adapter.MongoAdapter)
panel_id(str): The panel app panel id | juraj-google-style |
def create_redis_client(redis_address, password=None):
redis_ip_address, redis_port = redis_address.split(":")
return redis.StrictRedis(
host=redis_ip_address, port=int(redis_port), password=password) | Create a Redis client.
Args:
The IP address, port, and password of the Redis server.
Returns:
A Redis client. | juraj-google-style |
def gaussian_pdf(std=10.0, mean=0.0):
norm_const = 1.0
def pdf(x):
return norm_const*np.exp(-0.5 * ((x-mean)/std)**2) * \
np.sin(np.pi/180.0 * x)
norm_dev = quad(pdf, 0.0, 180.0)[0]
norm_const /= norm_dev
return pdf | Gaussian PDF for orientation averaging.
Args:
std: The standard deviation in degrees of the Gaussian PDF
mean: The mean in degrees of the Gaussian PDF. This should be a number
in the interval [0, 180)
Returns:
pdf(x), a function that returns the value of the spherical Jacobian-
normalized Gaussian PDF with the given... | juraj-google-style |
def setup_service(api_name, api_version, credentials=None):
if (not credentials):
credentials = oauth2client.client.GoogleCredentials.get_application_default()
return apiclient.discovery.build(api_name, api_version, credentials=credentials) | Configures genomics API client.
Args:
api_name: Name of the Google API (for example: "genomics")
api_version: Version of the API (for example: "v2alpha1")
credentials: Credentials to be used for the gcloud API calls.
Returns:
A configured Google Genomics API client with appropriate credentials. | codesearchnet |
def add_institute(self, institute_obj):
internal_id = institute_obj['internal_id']
display_name = institute_obj['internal_id']
if self.institute(institute_id=internal_id):
raise IntegrityError('Institute {0} already exists in database'.format(display_name))
LOG.info('Adding institute with intern... | Add a institute to the database
Args:
institute_obj(Institute) | codesearchnet |
def reset(self, *args):
self.resource = self.resource.reset(list(args))
return self | Resets any of the tokens for this Application.
Note that you may have to reauthenticate afterwards.
Usage:
application.reset('api_token')
application.reset('api_token', 'totp_secret')
Args:
*args (list of str): one or more of
['api_token', 'subscription_token', 'totp_secret']
Returns:
The Application. | codesearchnet |
def sg_log(tensor, opt):
r
return tf.log(tensor + tf.sg_eps, name=opt.name) | r"""Log transform a dense tensor
See `tf.log()` in tensorflow.
Args:
tensor: A `Tensor` ( automatically given by chain )
opt:
name: If provided, replace current tensor's name.
Returns:
A `Tensor`. | juraj-google-style |
def convert_to_experiment_list(experiments):
exp_list = experiments
if (experiments is None):
exp_list = []
elif isinstance(experiments, Experiment):
exp_list = [experiments]
elif (type(experiments) is dict):
exp_list = [Experiment.from_json(name, spec) for (name, spec) in experi... | Produces a list of Experiment objects.
Converts input from dict, single experiment, or list of
experiments to list of experiments. If input is None,
will return an empty list.
Arguments:
experiments (Experiment | list | dict): Experiments to run.
Returns:
List of experiments. | codesearchnet |
def translate_file(estimator, subtokenizer, input_file, output_file=None, print_all_translations=True):
batch_size = _DECODE_BATCH_SIZE
(sorted_inputs, sorted_keys) = _get_sorted_inputs(input_file)
num_decode_batches = (((len(sorted_inputs) - 1)
def input_generator():
'Yield encoded strings fr... | Translate lines in file, and save to output file if specified.
Args:
estimator: tf.Estimator used to generate the translations.
subtokenizer: Subtokenizer object for encoding and decoding source and
translated lines.
input_file: file containing lines to translate
output_file: file that stores the generated translation... | codesearchnet |
def AddNEP5Token(self, token):
if (token.ScriptHash.ToBytes() in self._tokens.keys()):
logger.error('Token already in wallet')
return
self._tokens[token.ScriptHash.ToBytes()] = token | Add a NEP-5 compliant token to the wallet.
Args:
token (NEP5Token): an instance of type neo.Wallets.NEP5Token.
Note:
Prints a warning to the console if the token already exists in the wallet. | codesearchnet |
def list_pop(list_, i, opts):
assert isinstance(opts, ListPopOpts)
if isinstance(list_, tensor_array_ops.TensorArray):
raise ValueError('TensorArray does not support item removal')
elif tensor_util.is_tf_type(list_):
if list_.dtype == dtypes.variant:
return _tf_tensor_list_pop(li... | The list pop function.
Note: it is unspecified where list_ will be mutated or not. If list_ is
a TensorFlow entity, it will not be typically mutated. If list_ is a plain
list, it will be. In general, if the list is mutated then the return value
should point to the original entity.
Args:
list_: An entity that supports... | github-repos |
def device_type_from_string(cl_device_type_str):
cl_device_type_str = cl_device_type_str.upper()
if hasattr(cl.device_type, cl_device_type_str):
return getattr(cl.device_type, cl_device_type_str)
return None | Converts values like ``gpu`` to a pyopencl device type string.
Supported values are: ``accelerator``, ``cpu``, ``custom``, ``gpu``. If ``all`` is given, None is returned.
Args:
cl_device_type_str (str): The string we want to convert to a device type.
Returns:
cl.device_type: the pyopencl device type. | juraj-google-style |
def list_experiments(self, collection_name):
exp = ExperimentResource(name='', collection_name=collection_name, coord_frame='foo')
return self._list_resource(exp) | List all experiments that belong to a collection.
Args:
collection_name (string): Name of the parent collection.
Returns:
(list)
Raises:
requests.HTTPError on failure. | codesearchnet |
def bofh_excuse(how_many=1):
excuse_path = os.path.join(os.path.dirname(__file__), 'bofh_excuses.json')
with open(excuse_path, 'r') as _f:
excuse_dict = json.load(_f)
return [generate_random_string(excuse_dict) for _ in range(int(how_many))] | Generate random BOFH themed technical excuses!
Args:
how_many: Number of excuses to generate. (Default: 1)
Returns:
A list of BOFH excuses. | codesearchnet |
def get_rng(obj=None):
seed = (((id(obj) + os.getpid()) + int(datetime.now().strftime('%Y%m%d%H%M%S%f'))) % 4294967295)
if (_RNG_SEED is not None):
seed = _RNG_SEED
return np.random.RandomState(seed) | Get a good RNG seeded with time, pid and the object.
Args:
obj: some object to use to generate random seed.
Returns:
np.random.RandomState: the RNG. | codesearchnet |
def Create(self, request, global_params=None):
config = self.GetMethodConfig('Create')
return self._RunMethod(config, request, global_params=global_params) | Create an association between a GCP project and a GitHub Enterprise server.
Args:
request: (CloudbuildProjectsGithubEnterpriseConfigsCreateRequest) input message
global_params: (StandardQueryParameters, default: None) global arguments
Returns:
(Operation) The response message. | github-repos |
def FindClonedClients(token=None):
index = client_index.CreateClientIndex(token=token)
clients = index.LookupClients(['.'])
hw_infos = _GetHWInfos(clients, token=token)
clients_with_multiple_serials = [client_id for (client_id, serials) in iteritems(hw_infos) if (len(serials) > 1)]
client_list = aff... | A script to find multiple machines reporting the same client_id.
This script looks at the hardware serial numbers that a client reported in
over time (they get collected with each regular interrogate). We have seen
that sometimes those serial numbers change - for example when a disk is put
in a new machine - so report... | codesearchnet |
def build_from_token_counts(self, token_counts, min_count, num_iterations=4, reserved_tokens=None, max_subtoken_length=None):
if (reserved_tokens is None):
reserved_tokens = RESERVED_TOKENS
else:
for (default, proposed) in zip(RESERVED_TOKENS, reserved_tokens):
if (default != propose... | Train a SubwordTextEncoder based on a dictionary of word counts.
Args:
token_counts: a dictionary of Unicode strings to int.
min_count: an integer - discard subtokens with lower counts.
num_iterations: an integer. how many iterations of refinement.
reserved_tokens: List of reserved tokens. The global variable
`RESERV... | codesearchnet |
def read_from_file(path, file_type='text', exception=ScriptWorkerException):
FILE_TYPE_MAP = {'text': 'r', 'binary': 'rb'}
if (file_type not in FILE_TYPE_MAP):
raise exception('Unknown file_type {} not in {}!'.format(file_type, FILE_TYPE_MAP))
try:
with open(path, FILE_TYPE_MAP[file_type]) a... | Read from ``path``.
Small helper function to read from ``file``.
Args:
path (str): the path to read from.
file_type (str, optional): the type of file. Currently accepts
``text`` or ``binary``. Defaults to ``text``.
exception (Exception, optional): the exception to raise
if unable to read from the file. Defaults to `... | codesearchnet |
def write_compacted(g):
d_nodes = {}
d_edges = {}
def conv(value):
if isinstance(value, basestring):
return value.strip('"')
else:
return value
for node in g.nodes():
label = None
attrs = []
for (k, v) in sorted(g.node_attributes(node)):
... | Write a graph in our own compacted format.
Returns:
str. | codesearchnet |
def validate_with_tags(self, tags, confidence):
result = {'intent_type': self.name}
intent_confidence = 0.0
local_tags = tags[:]
used_tags = []
for (require_type, attribute_name) in self.requires:
(required_tag, canonical_form, confidence) = find_first_tag(local_tags, require_type)
i... | Validate weather tags has required entites for this intent to fire
Args:
tags(list): Tags and Entities used for validation
confidence(float): ?
Returns:
intent, tags: Returns intent and tags used by the intent on
falure to meat required entities then returns intent with confidence
of 0.0 and an empty list for tags. | codesearchnet |
def _ParseFileData(self, knowledge_base, file_object):
line_reader = line_reader_file.BinaryLineReader(file_object)
try:
reader = line_reader_file.BinaryDSVReader(line_reader, b':')
except csv.Error as exception:
raise errors.PreProcessFail(
'Unable to read: {0:s} with error: {1!... | Parses file content (data) for user account preprocessing attributes.
Args:
knowledge_base (KnowledgeBase): to fill with preprocessing information.
file_object (dfvfs.FileIO): file-like object that contains the artifact
value data.
Raises:
errors.PreProcessFail: if the preprocessing fails. | juraj-google-style |
def expandvars(text, environ=None):
if '$' not in text:
return text
i = 0
if environ is None:
environ = os.environ
while True:
m = ENV_VAR_REGEX.search(text, i)
if not m:
break
i, j = m.span(0)
name = m.group(1)
if name.startswit... | Expand shell variables of form $var and ${var}.
Unknown variables are left unchanged.
Args:
text (str): String to expand.
environ (dict): Environ dict to use for expansions, defaults to
os.environ.
Returns:
The expanded string. | juraj-google-style |
def __init__(self, logger, script_type, default_shell=None):
self.logger = logger
self.script_type = script_type
self.default_shell = default_shell or '/bin/bash' | Constructor.
Args:
logger: logger object, used to write to SysLog and serial port.
script_type: string, the type of the script we are running.
default_shell: string, the default shell to execute the script. | juraj-google-style |
def addgroup(name, group):
if six.PY2:
name = _to_unicode(name)
group = _to_unicode(group)
name = _cmd_quote(name)
group = _cmd_quote(group).lstrip('\'').rstrip('\'')
user = info(name)
if not user:
return False
if group in user['groups']:
return True
c... | Add user to a group
Args:
name (str): The user name to add to the group
group (str): The name of the group to which to add the user
Returns:
bool: True if successful, otherwise False
CLI Example:
.. code-block:: bash
salt '*' user.addgroup jsnuffy 'Power Users' | juraj-google-style |
def ListClients(self, request, timeout=None):
return self._RetryLoop((lambda t: self._stub.ListClients(request, timeout=t))) | Provides basic information about Fleetspeak clients.
Args:
request: fleetspeak.admin.ListClientsRequest
timeout: How many seconds to try for.
Returns: fleetspeak.admin.ListClientsResponse | codesearchnet |
def _CalculateHashesFileEntry(
self, file_system, file_entry, parent_full_path, output_writer):
full_path = file_system.JoinPath([parent_full_path, file_entry.name])
for data_stream in file_entry.data_streams:
hash_value = self._CalculateHashDataStream(file_entry, data_stream.nam... | Recursive calculates hashes starting with the file entry.
Args:
file_system (dfvfs.FileSystem): file system.
file_entry (dfvfs.FileEntry): file entry.
parent_full_path (str): full path of the parent file entry.
output_writer (StdoutWriter): output writer. | juraj-google-style |
def return_handler(
self,
call_node,
function_nodes,
saved_function_call_index,
first_node
):
if any(isinstance(node, YieldNode) for node in function_nodes):
rhs_prefix = 'yld_'
elif any(isinstance(node, ConnectToExitNode) for... | Handle the return from a function during a function call.
Args:
call_node(ast.Call) : The node that calls the definition.
function_nodes(list[Node]): List of nodes of the function being called.
saved_function_call_index(int): Unique number for each call.
first_node(EntryOrExitNode or RestoreNode): Used to connect prev... | juraj-google-style |
def ExamineEvent(self, mediator, event):
self._EnsureRequesterStarted()
path_spec = event.pathspec
event_identifiers = self._event_identifiers_by_pathspec[path_spec]
event_identifier = event.GetIdentifier()
event_identifiers.append(event_identifier)
if event.data_type not in self.DATA_TY... | Evaluates whether an event contains the right data for a hash lookup.
Args:
mediator (AnalysisMediator): mediates interactions between
analysis plugins and other components, such as storage and dfvfs.
event (EventObject): event. | juraj-google-style |
def _Fail(self, msg):
raise TruthAssertionError(msg) | Fail unconditionally.
Args:
msg: string to include in the exception.
Raises:
TruthAssertionError: always, by design. | github-repos |
def serialize_to_transport(self, encoding='utf-8', xslt_url=None):
assert (encoding in ('utf-8', 'UTF-8'))
dataone_exception_pyxb = self.get_pyxb()
return d1_common.xml.serialize_for_transport(dataone_exception_pyxb, xslt_url=xslt_url) | Serialize to XML ``bytes`` with prolog.
Args:
encoding: str
Encoding to use for XML doc bytes
xslt_url: str
If specified, add a processing instruction to the XML doc that specifies the
download location for an XSLT stylesheet.
Returns:
bytes: XML holding a DataONEError based type. | codesearchnet |
def close(self):
if self.reuse:
logger.debug("Ipcontroller not shutting down: reuse enabled")
return
if self.mode == "manual":
logger.debug("Ipcontroller not shutting down: Manual mode")
return
try:
pgid = os.getpgid(self.pro... | Terminate the controller process and its child processes.
Args:
- None | juraj-google-style |
def call(self, inputs: List[Any], global_state: pg.geno.AttributeDict, step: int=0) -> List[Any]:
raise NotImplementedError() | Subclasses should override this method.
The `global_state` and `step` are optional for the subclasses' call
signature.
Args:
inputs: A list of values as inputs.
global_state: An `AttributeDict` object as the global state container,
which is readable/writable during the operation.
step: Number of examples historically... | github-repos |
def play_human(env):
try:
play(env, fps=env.metadata['video.frames_per_second'])
except KeyboardInterrupt:
pass
env.close() | Play the environment using keyboard as a human.
Args:
env (gym.Env): the initialized gym environment to play
Returns:
None | juraj-google-style |
def delete(filething):
t = OggSpeex(filething)
filething.fileobj.seek(0)
t.delete(filething) | delete(filething)
Arguments:
filething (filething)
Raises:
mutagen.MutagenError
Remove tags from a file. | juraj-google-style |
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