code stringlengths 20 4.93k | docstring stringlengths 33 1.27k | source stringclasses 3
values |
|---|---|---|
def get(self, personId):
check_type(personId, basestring, may_be_none=False)
json_data = self._session.get(((API_ENDPOINT + '/') + personId))
return self._object_factory(OBJECT_TYPE, json_data) | Get a person's details, by ID.
Args:
personId(basestring): The ID of the person to be retrieved.
Returns:
Person: A Person object with the details of the requested person.
Raises:
TypeError: If the parameter types are incorrect.
ApiError: If the Webex Teams cloud returns an error. | codesearchnet |
def reformat_python_docstrings(top_dirs: List[str], correct_copyright_lines: List[str], show_only: bool=True, rewrite: bool=False, process_only_filenum: int=None) -> None:
filenum = 0
for top_dir in top_dirs:
for (dirpath, dirnames, filenames) in walk(top_dir):
for filename in filenames:
... | Walk a directory, finding Python files and rewriting them.
Args:
top_dirs: list of directories to descend into
correct_copyright_lines:
list of lines (without newlines) representing the copyright
docstring block, including the transition lines of equals
symbols
show_only: show results (to stdout) only; don't rewrite
r... | codesearchnet |
def chained(self, text=None, fore=None, back=None, style=None):
self.data = ''.join((
self.data,
self.color(text=text, fore=fore, back=back, style=style),
))
return self | Called by the various 'color' methods to colorize a single string.
The RESET_ALL code is appended to the string unless text is empty.
Raises ValueError on invalid color names.
Arguments:
text : String to colorize, or None for BG/Style change.
fore : Name of fore color to use.
back : Name of back color to use.
styl... | juraj-google-style |
def get_nltk_builder(languages):
all_stemmers = []
all_stopwords_filters = []
all_word_characters = set()
for language in languages:
if language == "en":
all_stemmers.append(lunr.stemmer.stemmer)
all_stopwords_filters.append(stop_word_filter)
... | Returns a builder with stemmers for all languages added to it.
Args:
languages (list): A list of supported languages. | juraj-google-style |
def getModPath(self, *paths):
dirn = self.getModDir()
return s_common.genpath(dirn, *paths) | Construct a path relative to this module's working directory.
Args:
*paths: A list of path strings
Notes:
This creates the module specific directory if it does not exist.
Returns:
(str): The full path (or None if no cortex dir is configured). | juraj-google-style |
def _randomize_direction(base_heading, sigma) -> int:
val = MissionWeather._gauss(base_heading, sigma)
val = MissionWeather._normalize_direction(val)
return val | Creates a variation in direction
Args:
base_heading: base direction
sigma: sigma value for gaussian variation
Returns: random direction | codesearchnet |
def get_slot(self, *args, **kwargs):
return self._opt.get_slot(*args, **kwargs) | Return a slot named "name" created for "var" by the Optimizer.
This simply wraps the get_slot() from the actual optimizer.
Args:
*args: Arguments for get_slot().
**kwargs: Keyword arguments for get_slot().
Returns:
The `Variable` for the slot if it was created, `None` otherwise. | github-repos |
def verify(self, obj):
if obj != self._literal:
raise ValidationError("Object is not equal to literal",
reason='%s is not equal to %s' % (str(obj), str(self._literal)), object=obj)
return obj | Verify that the object conforms to this verifier's schema
Args:
obj (object): A python object to verify
Raises:
ValidationError: If there is a problem verifying the dictionary, a
ValidationError is thrown with at least the reason key set indicating
the reason for the lack of validation. | juraj-google-style |
def model_from_json(json_string, custom_objects=None):
from keras.src.saving import serialization_lib
model_config = json.loads(json_string)
return serialization_lib.deserialize_keras_object(model_config, custom_objects=custom_objects) | Parses a JSON model configuration string and returns a model instance.
Example:
>>> model = keras.Sequential([
... keras.layers.Dense(5, input_shape=(3,)),
... keras.layers.Softmax()])
>>> config = model.to_json()
>>> loaded_model = keras.models.model_from_json(config)
Args:
json_string: JSON string encoding... | github-repos |
def dump_orm_object_as_insert_sql(engine: Engine, obj: object, fileobj: TextIO) -> None:
insp = inspect(obj)
meta = MetaData(bind=engine)
table_name = insp.mapper.mapped_table.name
table = Table(table_name, meta, autoload=True)
query = select(table.columns)
for orm_pkcol in insp.mapper.primary_k... | Takes a SQLAlchemy ORM object, and writes ``INSERT`` SQL to replicate it
to the output file-like object.
Args:
engine: SQLAlchemy :class:`Engine`
obj: SQLAlchemy ORM object to write
fileobj: file-like object to write to | codesearchnet |
def from_voigt(cls, voigt_input):
voigt_input = np.array(voigt_input)
rank = (sum(voigt_input.shape)
t = cls(np.zeros(([3] * rank)))
if (voigt_input.shape != t._vscale.shape):
raise ValueError('Invalid shape for voigt matrix')
voigt_input = (voigt_input / t._vscale)
this_voigt_map = t.g... | Constructor based on the voigt notation vector or matrix.
Args:
voigt_input (array-like): voigt input for a given tensor | codesearchnet |
def _client_send(self, msg):
try:
self._client.write(msg.encode("utf8") + b'\n')
self._client.flush()
self.log.debug('Snippet sent %s.', msg)
except socket.error as e:
raise Error(
self._ad,
'Encountered socket erro... | Sends an Rpc message through the connection.
Args:
msg: string, the message to send.
Raises:
Error: a socket error occurred during the send. | juraj-google-style |
def FindCoinsByVins(self, vins):
ret = []
for coin in self.GetCoins():
coinref = coin.Reference
for vin in vins:
if ((coinref.PrevIndex == vin.PrevIndex) and (coinref.PrevHash == vin.PrevHash)):
ret.append(coin)
return ret | Looks through the current collection of coins in a wallet
and chooses coins that match the specified CoinReference objects.
Args:
vins: A list of ``neo.Core.CoinReference`` objects.
Returns:
list: A list of ``neo.Wallet.Coin`` objects. | codesearchnet |
def is_match(self, subject: Union[(Expression, FlatTerm)]) -> bool:
try:
next(self.match(subject))
except StopIteration:
return False
return True | Check if the given subject matches any pattern in the net.
Args:
subject:
The subject that is matched. Must be constant.
Returns:
True, if any pattern matches the subject. | codesearchnet |
def get_cosine_schedule_with_warmup(optimizer: Optimizer, num_warmup_steps: int, num_training_steps: int, num_cycles: float=0.5, last_epoch: int=-1):
lr_lambda = partial(_get_cosine_schedule_with_warmup_lr_lambda, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps, num_cycles=num_cycles)
r... | Create a schedule with a learning rate that decreases following the values of the cosine function between the
initial lr set in the optimizer to 0, after a warmup period during which it increases linearly between 0 and the
initial lr set in the optimizer.
Args:
optimizer ([`~torch.optim.Optimizer`]):
The optimizer for... | github-repos |
def json_compat_obj_encode(data_type, obj, caller_permissions=None, alias_validators=None,
old_style=False, for_msgpack=False, should_redact=False):
serializer = StoneToPythonPrimitiveSerializer(
caller_permissions, alias_validators, for_msgpack, old_style, should_redact)
... | Encodes an object into a JSON-compatible dict based on its type.
Args:
data_type (Validator): Validator for obj.
obj (object): Object to be serialized.
caller_permissions (list): The list of raw-string caller permissions
with which to serialize.
Returns:
An object that when passed to json.dumps() will produce a strin... | juraj-google-style |
def Run(self, request, global_params=None):
config = self.GetMethodConfig('Run')
return self._RunMethod(config, request, global_params=global_params) | Runs a `BuildTrigger` at a particular source revision.
Args:
request: (CloudbuildProjectsLocationsTriggersRunRequest) input message
global_params: (StandardQueryParameters, default: None) global arguments
Returns:
(Operation) The response message. | github-repos |
def call(self, batch_size: Optional[int], input_points: Optional[Tuple[tf.Tensor, tf.Tensor]], input_labels: tf.Tensor | None, input_boxes: tf.Tensor | None, input_masks: tf.Tensor | None) -> Tuple[tf.Tensor, tf.Tensor]:
sparse_embeddings = None
if input_points is not None:
batch_size, point_batch_size ... | Embeds different types of prompts, returning both sparse and dense embeddings.
Args:
points (`tf.Tensor`, *optional*):
point coordinates and labels to embed.
boxes (`tf.Tensor`, *optional*):
boxes to embed
masks (`tf.Tensor`, *optional*):
masks to embed | github-repos |
def get(self, container_id):
resp = self.client.api.inspect_container(container_id)
return self.prepare_model(resp) | Get a container by name or ID.
Args:
container_id (str): Container name or ID.
Returns:
A :py:class:`Container` object.
Raises:
:py:class:`docker.errors.NotFound`
If the container does not exist.
:py:class:`docker.errors.APIError`
If the server returns an error. | codesearchnet |
def padFrameRange(frange, zfill):
def _do_pad(match):
result = list(match.groups())
result[1] = pad(result[1], zfill)
if result[4]:
result[4] = pad(result[4], zfill)
return ''.join((i for i in result if i))
return PAD_... | Return the zero-padded version of the frame range string.
Args:
frange (str): a frame range to test
zfill (int):
Returns:
str: | juraj-google-style |
def _FormatForCommand(token):
if not isinstance(token, str):
token = str(token)
if token.startswith('_'):
return token
return token.replace('_', '-') | Replaces underscores with hyphens, unless the token starts with a token.
This is because we typically prefer hyphens to underscores at the command
line, but we reserve hyphens at the start of a token for flags. This becomes
relevant when --verbose is activated, so that things like __str__ don't get
transformed into --... | github-repos |
def request(self, send_terminator = False):
try:
retA = self.requestA()
retB = self.requestB()
if retA and retB:
self.makeAB()
self.calculateFields()
self.updateObservers()
return True
except:
... | Combined A and B read for V4 meter.
Args:
send_terminator (bool): Send termination string at end of read.
Returns:
bool: True on completion. | juraj-google-style |
def file_modify(filename, settings):
for (k, v) in settings.items():
if (k == 'mode'):
os.chmod(filename, v)
if (k == 'owners'):
os.chown(filename, v) | Modifies file access
Args:
filename (str): Filename.
settings (dict): Can be "mode" or "owners" | codesearchnet |
def Mint(self, wallet, mint_to_addr, attachment_args, invoke_attrs=None):
invoke_args = [self.ScriptHash.ToString(), 'mintTokens', []]
invoke_args = (invoke_args + attachment_args)
(tx, fee, results, num_ops, engine_success) = TestInvokeContract(wallet, invoke_args, None, True, from_addr=mint_to_addr, invok... | Call the "mintTokens" function of the smart contract.
Args:
wallet (neo.Wallets.Wallet): a wallet instance.
mint_to_addr (str): public address of the account to mint the tokens to.
attachment_args: (list): a list of arguments used to attach neo and/or gas to an invoke, eg ['--attach-gas=10.0','--attach-neo=3']
invoke_... | codesearchnet |
def raster_dilation(rasterfile):
if is_string(rasterfile):
origin_raster = RasterUtilClass.read_raster(str(rasterfile))
elif isinstance(rasterfile, Raster):
origin_raster = rasterfile.data
elif isinstance(rasterfile, numpy.ndarray):
origin_raster = rasterfile
else:
return... | Dilate the raster image.
Find the max pixel's value in 8-neighborhood. Then change the compute
pixel's value into the max pixel's value.
Args:
rasterfile: input original raster image, type can be filename(string,
like "test1.tif"), rasterfile(class Raster) or numpy.ndarray.
Returns:
dilation_raster: raster image aft... | codesearchnet |
def dom_processing(self, value):
if value == self._defaults['domProcessing'] and 'domProcessing' in self._values:
del self._values['domProcessing']
else:
self._values['domProcessing'] = value | The dom_processing property.
Args:
value (string). the property value. | juraj-google-style |
def __init__(self, app):
self.app = app
self.user_manager = app.user_manager
self.password_crypt_context = CryptContext(
schemes=self.user_manager.USER_PASSLIB_CRYPTCONTEXT_SCHEMES,
**self.user_manager.USER_PASSLIB_CRYPTCONTEXT_KEYWORDS) | Create a passlib CryptContext.
Args:
password_hash(str): The name of a valid passlib password hash.
Examples: ``'bcrypt', 'pbkdf2_sha512', 'sha512_crypt' or 'argon2'``.
Example:
``password_manager = PasswordManager('bcrypt')`` | juraj-google-style |
def picture_view(request, user_id, year=None):
try:
user = User.objects.get(id=user_id)
except User.DoesNotExist:
raise Http404
default_image_path = os.path.join(settings.PROJECT_ROOT, 'static/img/default_profile_pic.png')
if (user is None):
raise Http404
else:
if (ye... | Displays a view of a user's picture.
Args:
user_id
The ID of the user whose picture is being fetched.
year
The user's picture from this year is fetched. If not
specified, use the preferred picture. | codesearchnet |
def call_later(self, delay, callback):
if hasattr(self._connection.ioloop, "call_later"):
self._connection.ioloop.call_later(delay, callback)
else:
self._connection.ioloop.add_timeout(delay, callback) | Schedule a one-shot timeout given delay seconds.
This method is only useful for compatibility with older versions of pika.
Args:
delay (float): Non-negative number of seconds from now until
expiration
callback (method): The callback method, having the signature
`callback()` | juraj-google-style |
def collect(val, collections, default_collections):
if collections is None:
collections = default_collections
for key in collections:
ops.add_to_collection(key, val) | Adds keys to a collection.
Args:
val: The value to add per each key.
collections: A collection of keys to add.
default_collections: Used if collections is None. | github-repos |
def __init__(self, user_assist_guid):
key_path = self._KEY_PATH_FORMAT.format(user_assist_guid)
super(UserAssistWindowsRegistryKeyPathFilter, self).__init__(key_path) | Initializes Windows Registry key filter.
Args:
user_assist_guid (str): UserAssist GUID. | juraj-google-style |
def _GetTableNames(self, database):
table_names = []
for esedb_table in database.tables:
table_names.append(esedb_table.name)
return table_names | Retrieves the table names in a database.
Args:
database (pyesedb.file): ESE database.
Returns:
list[str]: table names. | codesearchnet |
def filter_lines(lines, filter_regex, groups=None):
pattern = re.compile(filter_regex)
for line in lines:
match = pattern.search(line)
if match:
if groups is None:
yield line
elif len(groups) == 1:
yield match.group(groups[0])
... | Filters out the lines not matching the pattern.
Args:
lines: list[string]: lines to filter.
pattern: string: regular expression to filter out lines.
Returns: list[string]: the list of filtered lines. | juraj-google-style |
def main(params=None):
parser = getParser()
if params != None:
args = parser.parse_args(params)
else:
args = parser.parse_args()
print(general.title(banner.text))
sayingHello = + general.LICENSE_URL + "\n"
print(general.info(sayingHello))
urlDict = {}
... | Main loop for the enumeration
Args:
-----
params: A list with the parameters as grabbed by the terminal. It is
None when this is called by an entry_point. | juraj-google-style |
def _generate_key_map(entity_list, key, entity_class):
key_map = {}
for obj in entity_list:
key_map[obj[key]] = entity_class(**obj)
return key_map | Helper method to generate map from key to entity object for given list of dicts.
Args:
entity_list: List consisting of dict.
key: Key in each dict which will be key in the map.
entity_class: Class representing the entity.
Returns:
Map mapping key to entity object. | juraj-google-style |
def type(self, value):
if value == self._defaults['type'] and 'type' in self._values:
del self._values['type']
else:
self._values['type'] = value | The type property.
Args:
value (string). the property value. | juraj-google-style |
def register(self, name):
def decorator(func):
'Inner decorator, not used directly.\n\n Args:\n func: obj. Parameterless function to register.\n\n Returns:\n func: decorated function.\n '
self.logic[name] = func
@wraps(func)
... | Decorator for registering a named function in the sesion logic.
Args:
name: str. Function name.
func: obj. Parameterless function to register.
The following named functions must be registered:
'LaunchRequest' - logic for launch request.
'SessionEndedRequest': logic for session ended request.
In addition, all intents... | codesearchnet |
def stop_standing_subprocess(proc):
import psutil
pid = proc.pid
logging.debug('Stopping standing subprocess %d', pid)
process = psutil.Process(pid)
failed = []
try:
children = process.children(recursive=True)
except AttributeError:
children = process.get_children(recursive=T... | Stops a subprocess started by start_standing_subprocess.
Before killing the process, we check if the process is running, if it has
terminated, Error is raised.
Catches and ignores the PermissionError which only happens on Macs.
Args:
proc: Subprocess to terminate.
Raises:
Error: if the subprocess could not be stopp... | codesearchnet |
def order_verification(self, institute, case, user, link, variant):
LOG.info("Creating event for ordering validation for variant" \
" {0}".format(variant['display_name']))
updated_variant = self.variant_collection.find_one_and_update(
{'_id': variant['_id']},
... | Create an event for a variant verification for a variant
and an event for a variant verification for a case
Arguments:
institute (dict): A Institute object
case (dict): Case object
user (dict): A User object
link (str): The url to be used in the event
variant (dict): A variant object
Returns:
updated_variant(dict) | juraj-google-style |
def scalar_spec(value_spec: pg.typing.ValueSpec) -> pg.typing.ValueSpec:
return pg.typing.Union([value_spec, pg.typing.Callable([pg.typing.Int()], returns=value_spec)]) | Returns the value spec for a schedule scalar.
Args:
value_spec: a value spec for the schedule-based scalar type.
Returns:
A value spec for either the value itself or a callable that produces such
value based on a step (integer). | github-repos |
def fermi_fourier_trans_inverse_4(qubits):
(yield (fswap(qubits[1], qubits[2]),))
(yield fermi_fourier_trans_2(qubits[0], qubits[1]))
(yield fermi_fourier_trans_2(qubits[2], qubits[3]))
(yield fswap(qubits[1], qubits[2]))
(yield fermi_fourier_trans_2(qubits[0], qubits[1]))
(yield cirq.S(qubits[2... | The reverse fermionic Fourier transformation implemented on 4 qubits
on a line, which maps the momentum picture to the position picture.
Using the fast Fourier transformation algorithm, the circuit can be
decomposed into 2-mode fermionic Fourier transformation, the fermionic
SWAP gates, and single-qubit rotations.
Arg... | codesearchnet |
def load_tiff_multipage(tiff_filename, dtype='float32'):
if not os.path.isfile(tiff_filename):
raise RuntimeError('could not find file "%s"' % tiff_filename)
data = tiff.imread(tiff_filename)
im = []
while True:
Xi = numpy.array(data, dtype=dtype)
if Xi.ndim == 2:
... | Load a multipage tiff into a single variable in x,y,z format.
Arguments:
tiff_filename: Filename of source data
dtype: data type to use for the returned tensor
Returns:
Array containing contents from input tiff file in xyz order | juraj-google-style |
def read(self, queue, name=None):
if isinstance(queue, tensor_lib.Tensor):
queue_ref = queue
else:
queue_ref = queue.queue_ref
if self._reader_ref.dtype == dtypes.resource:
return gen_io_ops.reader_read_v2(self._reader_ref, queue_ref, name=name)
else:
old_queue_op = gen_d... | Returns the next record (key, value) pair produced by a reader.
Will dequeue a work unit from queue if necessary (e.g. when the
Reader needs to start reading from a new file since it has
finished with the previous file).
Args:
queue: A Queue or a mutable string Tensor representing a handle
to a Queue, with string wor... | github-repos |
def upload_file(filename: str, config: Config, full_table_id: str, action: Action=Action.APPEND, service_account_email: Optional[str]=None) -> None:
if service_account_email:
auth_config = AuthConfig(service_account_email=service_account_email)
else:
auth_config = None
table_metadata = build... | Upload a data file conforming to the given config to BigQuery.
Args:
* filename: Local path to csv file to be uploaded
* config: Config key from configs.CONFIGS dictionary
* full_table_id: BigQuery table id
* action: APPEND to table or REPLACE table
* service_account_email: Email address of service account | github-repos |
def register_tensor_conversion_function_internal(base_type, conversion_func, priority=100):
base_types = base_type if isinstance(base_type, tuple) else (base_type,)
if any((not isinstance(x, type) for x in base_types)):
raise TypeError(f'Argument `base_type` must be a type or a tuple of types. Obtained:... | Internal version of register_tensor_conversion_function.
See docstring of `register_tensor_conversion_function` for details.
The internal version of the function allows registering conversions
for types in the _UNCONVERTIBLE_TYPES tuple.
Args:
base_type: The base type or tuple of base types for all objects that
`con... | github-repos |
def LockRetryWrapper(self,
subject,
retrywrap_timeout=1,
retrywrap_max_timeout=10,
blocking=True,
lease_time=None):
timeout = 0
while timeout < retrywrap_max_timeout:
try:
return... | Retry a DBSubjectLock until it succeeds.
Args:
subject: The subject which the lock applies to.
retrywrap_timeout: How long to wait before retrying the lock.
retrywrap_max_timeout: The maximum time to wait for a retry until we
raise.
blocking: If False, raise on first lock failure.
lease_time: lock lease time in second... | juraj-google-style |
def run_user_main(wrapped_test_module):
tree = ast.parse(tf_inspect.getsource(wrapped_test_module))
target = ast.dump(ast.parse('if __name__ == "__main__": pass').body[0].test)
for expr in reversed(tree.body):
if isinstance(expr, ast.If) and ast.dump(expr.test) == target:
break
else:... | Runs the "if __name__ == '__main__'" at the bottom of a module.
TensorFlow practice is to have a main if at the bottom of the module which
might call an API compat function before calling test.main().
Since this is a statement, not a function, we can't cleanly reference it, but
we can inspect it from the user module ... | github-repos |
def get_reference_points(spatial_shapes, valid_ratios, device):
reference_points_list = []
for level, (height, width) in enumerate(spatial_shapes):
ref_y, ref_x = meshgrid(torch.linspace(0.5, height - 0.5, height, dtype=torch.float32, device=device), torch.linspace(0.5, width - 0.5, width, dtype=torch.f... | Get reference points for each feature map. Used in decoder.
Args:
spatial_shapes (`torch.LongTensor` of shape `(num_feature_levels, 2)`):
Spatial shapes of each feature map.
valid_ratios (`torch.FloatTensor` of shape `(batch_size, num_feature_levels, 2)`):
Valid ratios of each feature map.
device (`torch.device`):
Dev... | github-repos |
def get(self):
if self.call_queue:
return self.apply((lambda df: df)).data
else:
return self.data.copy() | Flushes the call_queue and returns the data.
Note: Since this object is a simple wrapper, just return the data.
Returns:
The object that was `put`. | codesearchnet |
def wulff_from_chempot(self, delu_dict=None, delu_default=0, symprec=1e-05, no_clean=False, no_doped=False):
latt = SpacegroupAnalyzer(self.ucell_entry.structure).get_conventional_standard_structure().lattice
miller_list = self.all_slab_entries.keys()
e_surf_list = []
for hkl in miller_list:
gam... | Method to get the Wulff shape at a specific chemical potential.
Args:
delu_dict (Dict): Dictionary of the chemical potentials to be set as
constant. Note the key should be a sympy Symbol object of the
format: Symbol("delu_el") where el is the name of the element.
delu_default (float): Default value for all unset chemi... | codesearchnet |
def destroy_walker(self, walker):
if walker.buffered:
self._queue_walkers.remove(walker)
else:
self._virtual_walkers.remove(walker) | Destroy a previously created stream walker.
Args:
walker (StreamWalker): The walker to remove from internal updating
lists. | juraj-google-style |
def get_service_credentials(pipeline_options):
return _Credentials.get_service_credentials(pipeline_options) | For internal use only; no backwards-compatibility guarantees.
Get credentials to access Google services.
Args:
pipeline_options: Pipeline options, used in creating credentials
like impersonated credentials.
Returns:
A ``_ApitoolsCredentialsAdapter`` object or None if credentials
not found. Returned object is thread-s... | github-repos |
def __init__(self, devpath):
self._fd = None
self._devpath = None
self._open(devpath) | Instantiate an I2C object and open the i2c-dev device at the
specified path.
Args:
devpath (str): i2c-dev device path.
Returns:
I2C: I2C object.
Raises:
I2CError: if an I/O or OS error occurs. | juraj-google-style |
def is_valid_package_name(name, raise_error=False):
is_valid = PACKAGE_NAME_REGEX.match(name)
if (raise_error and (not is_valid)):
raise PackageRequestError(('Not a valid package name: %r' % name))
return is_valid | Test the validity of a package name string.
Args:
name (str): Name to test.
raise_error (bool): If True, raise an exception on failure
Returns:
bool. | codesearchnet |
def process_event(event):
if event.type == EventType.ON_CONVERSATION_TURN_STARTED:
print()
print(event)
if (event.type == EventType.ON_CONVERSATION_TURN_FINISHED and
event.args and not event.args['with_follow_on_turn']):
print()
if event.type == EventType.ON_DEVICE_ACT... | Pretty prints events.
Prints all events that occur with two spaces between each new
conversation and a single space between turns of a conversation.
Args:
event(event.Event): The current event to process. | juraj-google-style |
def retrieve_template(self):
links = self.retrieve_instance_links()
self.log.debug('Links is \n%s', pformat(links))
self.pipeline_config['instance_links'].update(links)
jsondata = get_template(template_file='infrastructure/app_data.json.j2', appinfo=self.appinfo, pipeline_config=self.pipeline_config, fo... | Sets the instance links with pipeline_configs and then renders template files
Returns:
jsondata: A json objects containing templates | codesearchnet |
def value_report(self, address, zipcode, report_type="full", format_type="json"):
query_params = {
"report_type": report_type,
"format": format_type,
"address": address,
"zipcode": zipcode
}
return self._api_client.fetch_synchronous("prop... | Call the value_report component
Value Report only supports a single address.
Args:
- address
- zipcode
Kwargs:
- report_type - "full" or "summary". Default is "full".
- format_type - "json", "pdf", "xlsx" or "all". Default is "json". | juraj-google-style |
def get_enterprise_customer_or_404(enterprise_uuid):
EnterpriseCustomer = apps.get_model('enterprise', 'EnterpriseCustomer')
try:
enterprise_uuid = UUID(enterprise_uuid)
return EnterpriseCustomer.objects.get(uuid=enterprise_uuid)
except (TypeError, ValueError, EnterpriseCustomer.DoesNotExist... | Given an EnterpriseCustomer UUID, return the corresponding EnterpriseCustomer or raise a 404.
Arguments:
enterprise_uuid (str): The UUID (in string form) of the EnterpriseCustomer to fetch.
Returns:
(EnterpriseCustomer): The EnterpriseCustomer given the UUID. | codesearchnet |
def create(cls, application_namespace, application_data):
namespace = ApplicationNamespace(application_namespace)
data = ApplicationData(application_data)
return ApplicationSpecificInformation(application_namespace=namespace, application_data=data) | Construct an ApplicationSpecificInformation object from provided data
and namespace values.
Args:
application_namespace (str): The name of the application namespace.
application_data (str): Application data related to the namespace.
Returns:
ApplicationSpecificInformation: The newly created set of
application informa... | codesearchnet |
def set_quickchart_resource(self, resource):
if (isinstance(resource, int) and (not isinstance(resource, bool))):
resource = self.get_resources()[resource]
if (isinstance(resource, hdx.data.resource.Resource) or isinstance(resource, dict)):
res = resource.get('id')
if (res is None):
... | Set the resource that will be used for displaying QuickCharts in dataset preview
Args:
resource (Union[hdx.data.resource.Resource,Dict,str,int]): Either resource id or name, resource metadata from a Resource object or a dictionary or position
Returns:
bool: Returns True if resource for QuickCharts in dataset preview ... | codesearchnet |
def post_process_object_detection(self, outputs, threshold: float=0.5, target_sizes: Union[TensorType, List[Tuple]]=None):
out_logits, out_bbox = (outputs.logits, outputs.pred_boxes)
if target_sizes is not None:
if len(out_logits) != len(target_sizes):
raise ValueError('Make sure that you pa... | Converts the raw output of [`DetrForObjectDetection`] into final bounding boxes in (top_left_x, top_left_y,
bottom_right_x, bottom_right_y) format. Only supports PyTorch.
Args:
outputs ([`DetrObjectDetectionOutput`]):
Raw outputs of the model.
threshold (`float`, *optional*):
Score threshold to keep object detection p... | github-repos |
def setup(self, socket_type, complete_or_error_queue):
try:
if self._secured:
if self._server_public_key is None or \
self._server_private_key is None:
raise LocalConfigurationError(
"Attempting to start soc... | Setup the asyncio event loop.
Args:
socket_type (int from zmq.*): One of zmq.DEALER or zmq.ROUTER
complete_or_error_queue (queue.Queue): A way to propagate errors
back to the calling thread. Needed since this function is
directly used in Thread.
Returns:
None | juraj-google-style |
def enc(self, byts, asscd=None):
iv = os.urandom(16)
encryptor = AESGCM(self.ekey)
byts = encryptor.encrypt(iv, byts, asscd)
envl = {'iv': iv, 'data': byts, 'asscd': asscd}
return s_msgpack.en(envl) | Encrypt the given bytes and return an envelope dict in msgpack form.
Args:
byts (bytes): The message to be encrypted.
asscd (bytes): Extra data that needs to be authenticated (but not encrypted).
Returns:
bytes: The encrypted message. This is a msgpacked dictionary
containing the IV, ciphertext, and associated data. | codesearchnet |
def __init__(self, name: str, snap_type: str):
self._type = snap_type
self._channel = SnapshotChannel()
Command.__init__(self, duration=0, name=name)
Instruction.__init__(self, self, self._channel, name=name) | Create new snapshot command.
Args:
name (str): Snapshot name which is used to identify the snapshot in the output.
snap_type (str): Type of snapshot, e.g., “state” (take a snapshot of the quantum state).
The types of snapshots offered are defined in a separate specification
document for simulators. | juraj-google-style |
def step(self, actions):
if self._store_rollouts and \
self._rollouts_by_epoch_and_split[self.current_epoch]:
raise ValueError(
"Data for current epoch has already been loaded from disk."
)
(obs, unclipped_rewards, dones) = self._step(actions)
obs = self._preprocess_observ... | Makes a step in all environments.
Does any preprocessing and records frames.
Args:
actions: Batch of actions.
Returns:
(obs, rewards, dones) - batches of observations, rewards and done flags
respectively.
Raises:
ValueError: when the data for current epoch has already been loaded. | juraj-google-style |
def forward(self, image_embeddings: torch.Tensor, image_positional_embeddings: torch.Tensor, sparse_prompt_embeddings: torch.Tensor, dense_prompt_embeddings: torch.Tensor, multimask_output: bool, output_attentions: Optional[bool]=None, attention_similarity: Optional[torch.Tensor]=None, target_embedding: Optional[torch.... | Predict masks given image and prompt embeddings.
Args:
image_embeddings (`torch.Tensor`):
the embeddings from the image encoder
image_positional_embedding (`torch.Tensor`):
positional encoding with the shape of image_embeddings
sparse_prompt_embeddings (`torch.Tensor`):
The embeddings of the points and boxes
dense_pro... | github-repos |
def _add_remove_team_member(self, url, email_address=None, account_id=None):
if not email_address and not account_id:
raise HSException("No email address or account_id specified")
data = {}
if account_id is not None:
data = {
"account_id": accou... | Add or Remove a team member
We use this function for two different tasks because they have the same
API call
Args:
email_address (str): Email address of the Account to add/remove
account_id (str): ID of the Account to add/remove
Returns:
A Team object | juraj-google-style |
def _scope_vals(self, vals):
if isinstance(vals, (list, tuple)):
return vals
elif isinstance(vals, dict):
return vals.values()
else:
return [vals] | Return a list of values to pass to `name_scope()`.
Args:
vals: A tensor, a list or tuple of tensors, or a dictionary.
Returns:
The values in vals as a list. | github-repos |
def _profile_table(self, batch_id):
message = self._execute_command(batch_id, 'RAY.TABLE_LOOKUP', ray.gcs_utils.TablePrefix.PROFILE, '', batch_id.binary())
if (message is None):
return []
gcs_entries = ray.gcs_utils.GcsTableEntry.GetRootAsGcsTableEntry(message, 0)
profile_events = []
for i i... | Get the profile events for a given batch of profile events.
Args:
batch_id: An identifier for a batch of profile events.
Returns:
A list of the profile events for the specified batch. | 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 specificatio... | 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... | juraj-google-style |
def search(self, search_space, valid_data, init_args=[], train_args=[], init_kwargs={}, train_kwargs={}, module_args={}, module_kwargs={}, max_search=None, shuffle=True, verbose=True, seed=None, **score_kwargs):
self._clear_state(seed)
self.search_space = search_space
n_models_scored = 0
for (bracket_in... | Performs hyperband search according to the generated schedule.
At the beginning of each bracket, we generate a
list of random configurations and perform
successive halving on it; we repeat this process
for the number of brackets in the schedule.
Args:
init_args: (list) positional args for initializing the model
train... | codesearchnet |
def get_serialization_context(self, driver_id):
with self.lock:
if driver_id not in self.serialization_context_map:
_initialize_serialization(driver_id)
return self.serialization_context_map[driver_id] | Get the SerializationContext of the driver that this worker is processing.
Args:
driver_id: The ID of the driver that indicates which driver to get
the serialization context for.
Returns:
The serialization context of the given driver. | juraj-google-style |
def _VerifyExplicitPaddings(self, tensor_in_sizes, filter_in_sizes, strides, padding, dilations=(1, 1), test_grappler_layout_optimizer=False, tol=1e-05, fp16_tol=0.001):
input_tensor = self._CreateNumpyTensor(tensor_in_sizes)
filter_tensor = self._CreateNumpyTensor(filter_in_sizes)
input_tensor = array_ops.... | Verifies Conv2D with explicit padding generates correct values.
It does this by comparing with Conv2D without explicit padding. This
function assumes Conv2D without explicit padding works correctly.
Args:
tensor_in_sizes: Input tensor dimensions in [batch, input_rows,
input_cols, input_depth].
filter_in_sizes: Filter... | github-repos |
def heightmap_normalize(hm: np.ndarray, mi: float=0.0, ma: float=1.0) -> None:
lib.TCOD_heightmap_normalize(_heightmap_cdata(hm), mi, ma) | Normalize heightmap values between ``mi`` and ``ma``.
Args:
mi (float): The lowest value after normalization.
ma (float): The highest value after normalization. | codesearchnet |
def get_app_names(self):
app_names = set()
for name in self.apps:
app_names.add(name)
return app_names | Return application names.
Return the list of application names that are available in the
database.
Returns:
set of str. | codesearchnet |
def doit(self, classes=None, recursive=True, indices=None, max_terms=None, **kwargs):
return super().doit(classes, recursive, indices=indices, max_terms=max_terms, **kwargs) | Write out the indexed sum explicitly
If `classes` is None or :class:`IndexedSum` is in `classes`,
(partially) write out the indexed sum in to an explicit sum of terms.
If `recursive` is True, write out each of the new sum's summands by
calling its :meth:`doit` method.
Args:
classes (None or list): see :meth:`.Express... | codesearchnet |
def delete_issue(self, issue_id, params=None):
return self._delete((self.API_URL + 'issue/{}'.format(issue_id)), params=params) | Deletes an individual issue.
If the issue has sub-tasks you must set the deleteSubtasks=true parameter to delete the issue. You cannot delete
an issue without deleting its sub-tasks.
Args:
issue_id:
params:
Returns: | codesearchnet |
def get_details(app='groupproject', env='dev', region='us-east-1'):
url = '{host}/applications/{app}'.format(host=API_URL, app=app)
request = requests.get(url, verify=GATE_CA_BUNDLE, cert=GATE_CLIENT_CERT)
if not request.ok:
raise SpinnakerAppNotFound('"{0}" not found.'.format(app))
app_... | Extract details for Application.
Args:
app (str): Application Name
env (str): Environment/account to get details from
Returns:
collections.namedtuple with _group_, _policy_, _profile_, _role_,
_user_. | juraj-google-style |
def _GetComparable(self, sub_comparable_string=''):
string_parts = []
string_parts.append(getattr(self.parent, 'comparable', ''))
string_parts.append('type: {0:s}'.format(self.type_indicator))
if sub_comparable_string:
string_parts.append(', {0:s}'.format(sub_comparable_string))
string_... | Retrieves the comparable representation.
This is a convenience function for constructing comparables.
Args:
sub_comparable_string (str): sub comparable string.
Returns:
str: comparable representation of the path specification. | juraj-google-style |
def evaluate_cut(uncut_subsystem, cut, unpartitioned_ces):
log.debug('Evaluating %s...', cut)
cut_subsystem = uncut_subsystem.apply_cut(cut)
if config.ASSUME_CUTS_CANNOT_CREATE_NEW_CONCEPTS:
mechanisms = unpartitioned_ces.mechanisms
else:
mechanisms = set(
... | Compute the system irreducibility for a given cut.
Args:
uncut_subsystem (Subsystem): The subsystem without the cut applied.
cut (Cut): The cut to evaluate.
unpartitioned_ces (CauseEffectStructure): The cause-effect structure of
the uncut subsystem.
Returns:
SystemIrreducibilityAnalysis: The |SystemIrreducibilityAnal... | juraj-google-style |
def CreateFeedItemAddOperation(name, price, date, ad_customizer_feed):
feed_item = {'feedId': ad_customizer_feed['feedId'], 'attributeValues': [{'feedAttributeId': ad_customizer_feed['feedAttributes'][0]['id'], 'stringValue': name}, {'feedAttributeId': ad_customizer_feed['feedAttributes'][1]['id'], 'stringValue': p... | Creates a FeedItemOperation.
The generated FeedItemOperation will create a FeedItem with the specified
values when sent to FeedItemService.mutate.
Args:
name: the value for the name attribute of the FeedItem.
price: the value for the price attribute of the FeedItem.
date: the value for the date attribute of the FeedI... | codesearchnet |
def get_site_t2g_eg_resolved_dos(self, site):
t2g_dos = []
eg_dos = []
for s, atom_dos in self.pdos.items():
if s == site:
for orb, pdos in atom_dos.items():
if orb in (Orbital.dxy, Orbital.dxz, Orbital.dyz):
t2g_do... | Get the t2g, eg projected DOS for a particular site.
Args:
site: Site in Structure associated with CompleteDos.
Returns:
A dict {"e_g": Dos, "t2g": Dos} containing summed e_g and t2g DOS
for the site. | juraj-google-style |
def sequence_like(instance, args):
if _is_mutable_mapping(instance):
result = dict(zip(_tf_core_sorted(instance), args))
instance_type = type(instance)
if instance_type == _collections.defaultdict:
d = _collections.defaultdict(instance.default_factory)
else:
d... | Converts the sequence `args` to the same type as `instance`.
Args:
instance: an instance of `tuple`, `list`, `namedtuple`, `dict`,
`collections.OrderedDict`, or `composite_tensor.Composite_Tensor` or
`type_spec.TypeSpec`.
args: items to be converted to the `instance` type.
Returns:
`args` with the type of `instance`. | github-repos |
def request_stop(self):
raise StopIteration('step_fn has requested the iterations to stop.') | Exit the training loop by causing `should_stop()` to return `True`.
Causes `step_fn` to exit by raising an exception.
Raises:
StopIteration | github-repos |
def _preprocess_resize_output_shape(image, output_shape):
output_shape = tuple(output_shape)
output_ndim = len(output_shape)
input_shape = image.shape
if output_ndim > image.ndim:
input_shape += (1,) * (output_ndim - image.ndim)
image = np.reshape(image, input_shape)
elif output_ndim... | Validate resize output shape according to input image.
Args:
image (`np.ndarray`):
Image to be resized.
output_shape (`iterable`):
Size of the generated output image `(rows, cols[, ...][, dim])`. If `dim` is not provided, the number of
channels is preserved.
Returns
image (`np.ndarray`):
The input image, but with add... | github-repos |
def keypoint_flip(bbox, d, rows, cols):
if (d == 0):
bbox = keypoint_vflip(bbox, rows, cols)
elif (d == 1):
bbox = keypoint_hflip(bbox, rows, cols)
elif (d == (- 1)):
bbox = keypoint_hflip(bbox, rows, cols)
bbox = keypoint_vflip(bbox, rows, cols)
else:
raise Value... | Flip a keypoint either vertically, horizontally or both depending on the value of `d`.
Raises:
ValueError: if value of `d` is not -1, 0 or 1. | codesearchnet |
def argv(cls, name, short_name=None, type=None, help=None):
cls.__hierarchy.append(argv.Argv(name, short_name, type, help)) | Set command line arguments as a source
Parses the command line arguments described by the parameters.
Args:
name: the long name of the argument (foo)
short_name: the optional short name of the argument (f)
type: the optional type of the argument, defaults to bool
help: the optional help text for the argument | codesearchnet |
def path_to_string(path):
if isinstance(path, os.PathLike):
return os.fspath(path)
return path | Convert `PathLike` objects to their string representation.
If given a non-string typed path object, converts it to its string
representation.
If the object passed to `path` is not among the above, then it is
returned unchanged. This allows e.g. passthrough of file objects
through this function.
Args:
path: `PathLike... | github-repos |
def quarter_ellipsis_functions(xx, yy):
npxx = np.array(xx)
npyy = np.array(yy)
if np.any((npxx == npyy)):
raise RuntimeError('Invalid points for quarter_ellipsis_functions')
if (np.all((npxx < npyy)) or np.all((npxx > npyy))):
if (npxx[0] < npyy[0]):
p1 = npxx
p2... | Method that creates two quarter-ellipse functions based on points xx and yy. The ellipsis is supposed to
be aligned with the axes. The two ellipsis pass through the two points xx and yy.
Args:
xx:
First point
yy:
Second point
Returns:
A dictionary with the lower and upper quarter ellipsis functions. | codesearchnet |
def _replace_oov(original_vocab, line):
return u' '.join([(word if (word in original_vocab) else u'UNK') for word in line.split()]) | Replace out-of-vocab words with "UNK".
This maintains compatibility with published results.
Args:
original_vocab: a set of strings (The standard vocabulary for the dataset)
line: a unicode string - a space-delimited sequence of words.
Returns:
a unicode string - a space-delimited sequence of words. | codesearchnet |
def GetRealPath(filename):
if os.path.isabs(filename):
return filename
if filename.startswith('./') or filename.startswith('../'):
return os.path.abspath(filename)
path = os.getenv('PATH', '')
for directory in path.split(':'):
tryname = os.path.join(directory, filename)
if... | Given an executable filename, find in the PATH or find absolute path.
Args:
filename An executable filename (string)
Returns:
Absolute version of filename.
None if filename could not be found locally, absolutely, or in PATH | juraj-google-style |
def _fetch_events_files_on_disk(self):
all_files = tf.io.gfile.listdir(self._events_directory)
relevant_files = [file_name for file_name in all_files if _DEBUGGER_EVENTS_FILE_NAME_REGEX.match(file_name)]
return sorted(relevant_files, key=self._obtain_file_index) | Obtains the names of debugger-related events files within the directory.
Returns:
The names of the debugger-related events files written to disk. The names
are sorted in increasing events file index. | codesearchnet |
def __init__(self, device, configs=None):
self._device = device
self._configs = configs | Constructor of the class.
The constructor is the only place to pass in a config. If you need to
change the config later, you should unregister the service instance
from `ServiceManager` and register again with the new config.
Args:
device: the device object this service is associated with.
config: optional configurat... | juraj-google-style |
def process_tag(self, tag_proc_name, tag):
tag_processor = self.tag_procs[tag_proc_name]
db_entry = (tag_processor.get_name(tag), tag_processor.get_entry_type(tag), tag_processor.get_filename(tag))
self.zeal_db.insert(*db_entry)
self.entry_count += 1 | Process a tag with a tag processor and insert a DB entry.
Args:
tag_proc_name: A string key that maps to the TagProcessor to use.
tag: A BeautifulSoup Tag to process. | codesearchnet |
def protocol_version_to_kmip_version(value):
if (not isinstance(value, ProtocolVersion)):
return None
if (value.major == 1):
if (value.minor == 0):
return enums.KMIPVersion.KMIP_1_0
elif (value.minor == 1):
return enums.KMIPVersion.KMIP_1_1
elif (value.min... | Convert a ProtocolVersion struct to its KMIPVersion enumeration equivalent.
Args:
value (ProtocolVersion): A ProtocolVersion struct to be converted into
a KMIPVersion enumeration.
Returns:
KMIPVersion: The enumeration equivalent of the struct. If the struct
cannot be converted to a valid enumeration, None is returned... | codesearchnet |
def append_paulis(self, paulis=None, pauli_labels=None):
return self.insert_paulis(None, paulis=paulis, pauli_labels=pauli_labels) | Append pauli at the end.
Args:
paulis (Pauli): the to-be-inserted or appended pauli
pauli_labels (list[str]): the to-be-inserted or appended pauli label
Returns:
Pauli: self | juraj-google-style |
def get_account_info(self):
request = self._get_request()
response = request.get(self.ACCOUNT_INFO_URL)
self.account.json_data = response['account']
return self.account | Get current account information
The information then will be saved in `self.account` so that you can
access the information like this:
>>> hsclient = HSClient()
>>> acct = hsclient.get_account_info()
>>> print acct.email_address
Returns:
An Account object | codesearchnet |
def set_mode(self, name, value=None, default=False, disable=False):
string = 'switchport mode'
command = self.command_builder(string, value=value, default=default, disable=disable)
return self.configure_interface(name, command) | Configures the switchport mode
Args:
name (string): The interface identifier to create the logical
layer 2 switchport for. The name must be the full interface
name and not an abbreviated interface name (eg Ethernet1, not
Et1)
value (string): The value to set the mode to. Accepted values
for this argument are access... | codesearchnet |
def Run(self, conf, args):
try:
options, args = self.parser.parse_args(args)
except SystemExit as e:
return e.code
if options.maps:
self.log.info('Setting configured maps to %s', options.maps)
conf.maps = options.maps
if not options.incremental:
self.log.debug('pe... | Run the Update command.
See Command.Run() for full documentation on the Run() method.
Args:
conf: a nss_cache.config.Config object
args: a list of arguments to be parsed by this command
Returns:
0 on success, nonzero on error | github-repos |
def _tag_sharding_attribute_for_dequeued_tensor(tensor, dims):
if dims is None:
return xla_sharding.replicate(tensor, assign_tuple_sharding=True)
elif np.prod(dims) == 1:
return xla_sharding.assign_device(tensor, 0, assign_tuple_sharding=True)
else:
tile_assignment = np.arange(np.pro... | Tags appropriate XLA sharding attribute to the dequeued tensor.
The sharding attribute of the dequeued tensor will be a tuple.
Args:
tensor: The dequeued tensor on TPU.
dims: A list of integer describes how the tensor is partitioned.
Returns:
The same tensor with the xla_sharding attribute. | github-repos |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.