code stringlengths 20 4.93k | docstring stringlengths 33 1.27k | source stringclasses 3
values |
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def compute_trigonometric_terms(self, thetas, phis):
if (len(thetas) != len(phis)):
raise ValueError('List of polar and azimuthal angles have to be equal!')
self._pow_sin_t.clear()
self._pow_cos_t.clear()
self._sin_n_p.clear()
self._cos_n_p.clear()
self._pow_sin_t[1] = [sin(float(t)) for... | Computes trigonometric terms that are required to
calculate bond orientational order parameters using
internal variables.
Args:
thetas ([float]): polar angles of all neighbors in radians.
phis ([float]): azimuth angles of all neighbors in radians.
The list of
azimuth angles of all neighbors in radians. The list of
az... | codesearchnet |
def main(args):
args = parse_args(args)
setup_logging(args.loglevel)
_logger.info("Starting GramVaani importer...")
_logger.info("Starting loading GramVaani csv...")
csv = GramVaaniCSV(args.csv_filename)
_logger.info("Starting downloading GramVaani mp3's...")
downloader = GramVaaniDownl... | Main entry point allowing external calls
Args:
args ([str]): command line parameter list | juraj-google-style |
def update_state(self, y_true, y_pred, sample_weight=None):
return metrics_utils.update_confusion_matrix_variables({self._confusion_matrix_cond: self.accumulator}, y_true, y_pred, thresholds=self.thresholds, thresholds_distributed_evenly=self._thresholds_distributed_evenly, sample_weight=sample_weight) | Accumulates the metric statistics.
Args:
y_true: The ground truth values.
y_pred: The predicted values.
sample_weight: Optional weighting of each example. Defaults to `1`.
Can be a tensor whose rank is either 0, or the same rank as
`y_true`, and must be broadcastable to `y_true`. | github-repos |
async def end_takeout(self, success):
try:
async with _TakeoutClient(True, self, None) as takeout:
takeout.success = success
except ValueError:
return False
return True | Finishes a takeout, with specified result sent back to Telegram.
Returns:
``True`` if the operation was successful, ``False`` otherwise. | codesearchnet |
def overload(fn):
if (not isfunction(fn)):
raise TypeError('paco: fn must be a callable object')
spec = getargspec(fn)
args = spec.args
if ((not spec.varargs) and ((len(args) < 2) or (args[1] != 'iterable'))):
raise ValueError('paco: invalid function signature or arity')
@functools.... | Overload a given callable object to be used with ``|`` operator
overloading.
This is especially used for composing a pipeline of
transformation over a single data set.
Arguments:
fn (function): target function to decorate.
Raises:
TypeError: if function or coroutine function is not provided.
Returns:
function: deco... | codesearchnet |
def url(self, value):
if value == self._defaults['url'] and 'url' in self._values:
del self._values['url']
else:
self._values['url'] = value | The url property.
Args:
value (string). the property value. | juraj-google-style |
def nic_s(msg):
tc = typecode(msg)
if tc != 31:
raise RuntimeError("%s: Not a status operation message, expecting TC = 31" % msg)
msgbin = common.hex2bin(msg)
nic_s = int(msgbin[75])
return nic_s | Obtain NIC supplement bit, TC=31 message
Args:
msg (string): 28 bytes hexadecimal message string
Returns:
int: NICs number (0 or 1) | juraj-google-style |
def _full_axis_reduce_along_select_indices(self, func, axis, index):
old_index = (self.index if axis else self.columns)
numeric_indices = [i for (i, name) in enumerate(old_index) if (name in index)]
result = self.data.apply_func_to_select_indices_along_full_axis(axis, func, numeric_indices)
return resul... | Reduce Manger along select indices using function that needs full axis.
Args:
func: Callable that reduces the dimension of the object and requires full
knowledge of the entire axis.
axis: 0 for columns and 1 for rows. Defaults to 0.
index: Index of the resulting QueryCompiler.
Returns:
A new QueryCompiler object with... | codesearchnet |
def _CheckIsDevice(self, file_entry):
if (definitions.FILE_ENTRY_TYPE_DEVICE not in self._file_entry_types):
return False
return file_entry.IsDevice() | Checks the is_device find specification.
Args:
file_entry (FileEntry): file entry.
Returns:
bool: True if the file entry matches the find specification, False if not. | codesearchnet |
def GetTemplateArgs(clean_lines, linenum):
func_line = linenum
while (func_line > 0):
line = clean_lines.elided[func_line]
if Match('^\\s*$', line):
return set()
if (line.find('(') >= 0):
break
func_line -= 1
if (func_line == 0):
return set()
... | Find list of template arguments associated with this function declaration.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: Line number containing the start of the function declaration,
usually one line after the end of the template-argument-list.
Returns:
Set of type names, or empty set if th... | codesearchnet |
def observe(self, terminal, reward, index=0):
fetches = self.episode_output
feed_dict = self.get_feed_dict(terminal=terminal, reward=reward, index=index)
episode = self.monitored_session.run(fetches=fetches, feed_dict=feed_dict)
return episode | Adds an observation (reward and is-terminal) to the model without updating its trainable variables.
Args:
terminal (List[bool]): List of is-terminal signals.
reward (List[float]): List of reward signals.
index: (int) parallel episode you want to observe
Returns:
The value of the model-internal episode counter. | juraj-google-style |
def is_alias_command(subcommands, args):
if (not args):
return False
for subcommand in subcommands:
if (args[:2] == ['alias', subcommand]):
return True
return False | Check if the user is invoking one of the comments in 'subcommands' in the from az alias .
Args:
subcommands: The list of subcommands to check through.
args: The CLI arguments to process.
Returns:
True if the user is invoking 'az alias {command}'. | codesearchnet |
def percent_of(percent, whole):
percent = float(percent)
whole = float(whole)
return (percent * whole) / 100 | Calculates the value of a percent of a number
ie: 5% of 20 is what --> 1
Args:
percent (float): The percent of a number
whole (float): The whole of the number
Returns:
float: The value of a percent
Example:
>>> percent_of(25, 100)
25.0
>>> percent_of(5, 20)
1.0 | juraj-google-style |
def _debug(message, color=None, attrs=None):
if attrs is None:
attrs = []
if color is not None:
print colored(message, color, attrs=attrs)
else:
if len(attrs) > 0:
print colored(message, "white", attrs=attrs)
else:
print message | Print a message if the class attribute 'verbose' is enabled
Args:
message (str): Message to print | juraj-google-style |
def remove_profile(self, profile=None):
with self.db:
return self.db.remove((self.query.profile == profile)) | Remove profile from credentials file.
Args:
profile (str): Credentials profile to remove.
Returns:
list: List of affected document IDs. | codesearchnet |
def get_block_size(self, token, resolution=None):
cdims = self.get_metadata(token)['dataset']['cube_dimension']
if (resolution is None):
resolution = min(cdims.keys())
return cdims[str(resolution)] | Gets the block-size for a given token at a given resolution.
Arguments:
token (str): The token to inspect
resolution (int : None): The resolution at which to inspect data.
If none is specified, uses the minimum available.
Returns:
int[3]: The xyz blocksize. | codesearchnet |
def _state_to_task(cls, tstate, shard_state, eta=None, countdown=None):
base_path = tstate.base_path
task_name = MapperWorkerCallbackHandler.get_task_name(tstate.shard_id, tstate.slice_id, tstate.retries)
headers = util._get_task_headers(tstate.mapreduce_spec.mapreduce_id)
headers[util._MR_SHARD_ID_TASK... | Generate task for slice according to current states.
Args:
tstate: An instance of TransientShardState.
shard_state: An instance of ShardState.
eta: Absolute time when the MR should execute. May not be specified
if 'countdown' is also supplied. This may be timezone-aware or
timezone-naive.
countdown: Time in seconds in... | codesearchnet |
def send_update(url_id, dataset):
data = _convert_to_seeder_format(dataset)
if not data:
return
try:
_send_request(url_id, json=data, req_type=requests.patch)
except Exception as e:
sys.stderr.write("Seeder PATCH error: ")
sys.stderr.write(str(e.message))
... | Send request to Seeder's API with data changed by user.
Args:
url_id (str): ID used as identification in Seeder.
dataset (dict): WA-KAT dataset sent from frontend. | juraj-google-style |
def _get_what_to_read_next(fp, previously_read_position, chunk_size):
seek_position = max((previously_read_position - chunk_size), 0)
read_size = chunk_size
while (seek_position > 0):
fp.seek(seek_position)
if _is_partially_read_new_line(fp.read(1)):
seek_position -= 1
... | Return information on which file pointer position to read from and how many bytes.
Args:
fp
past_read_positon (int): The file pointer position that has been read previously
chunk_size(int): ideal io chunk_size
Returns:
(int, int): The next seek position, how many bytes to read next | codesearchnet |
def parse_multiple_json(json_file, offset=None):
json_info_list = []
if (not os.path.exists(json_file)):
return json_info_list
try:
with open(json_file, 'r') as f:
if offset:
f.seek(offset)
for line in f:
if (line[(- 1)] != '\n'):
... | Parse multiple json records from the given file.
Seek to the offset as the start point before parsing
if offset set. return empty list if the json file does
not exists or exception occurs.
Args:
json_file (str): File path to be parsed.
offset (int): Initial seek position of the file.
Returns:
A dict of json info.
Ne... | codesearchnet |
def encode(g, top=None, cls=PENMANCodec, **kwargs):
codec = cls(**kwargs)
return codec.encode(g, top=top) | Serialize the graph *g* from *top* to PENMAN notation.
Args:
g: the Graph object
top: the node identifier for the top of the serialized graph; if
unset, the original top of *g* is used
cls: serialization codec class
kwargs: keyword arguments passed to the constructor of *cls*
Returns:
the PENMAN-serialized string of t... | juraj-google-style |
def __init__(self, concentration, validate_args=False, allow_nan_stats=True, name='Dirichlet'):
parameters = dict(locals())
with ops.name_scope(name, values=[concentration]) as name:
self._concentration = self._maybe_assert_valid_concentration(ops.convert_to_tensor(concentration, name='concentration'), ... | Initialize a batch of Dirichlet distributions.
Args:
concentration: Positive floating-point `Tensor` indicating mean number
of class occurrences; aka "alpha". Implies `self.dtype`, and
`self.batch_shape`, `self.event_shape`, i.e., if
`concentration.shape = [N1, N2, ..., Nm, k]` then
`batch_shape = [N1, N2, ..., Nm]` a... | github-repos |
def furnish(app: web.Application):
app_name = app['config']['name']
prefix = '/' + app_name.lstrip('/')
app.router.add_routes(routes)
cors_middleware.enable_cors(app)
known_resources = set()
for route in list(app.router.routes()):
if route.resource in known_resources:
... | Configures Application routes, readying it for running.
This function modifies routes and resources that were added by calling code,
and must be called immediately prior to `run(app)`.
Args:
app (web.Application):
The Aiohttp Application as created by `create_app()` | juraj-google-style |
def get_ui(ui_type, on_ui_exit=None, available_ui_types=None, config=None):
if available_ui_types is None:
available_ui_types = copy.deepcopy(SUPPORTED_UI_TYPES)
if ui_type and ui_type not in available_ui_types:
raise ValueError("Invalid ui_type: '%s'" % ui_type)
try:
if ui_type == '... | Create a `base_ui.BaseUI` subtype.
This factory method attempts to fallback to other available ui_types on
ImportError.
Args:
ui_type: (`str`) requested UI type. Currently supported:
( readline)
on_ui_exit: (`Callable`) the callback to be called when the UI exits.
available_ui_types: (`None` or `list` of `str`) Manua... | github-repos |
def trace_export(name, step=None, profiler_outdir=None):
global _current_trace_context
if ops.inside_function():
logging.warn('Cannot export trace inside a tf.function.')
return
if not context.executing_eagerly():
logging.warn('Can only export trace while executing eagerly.')
... | Stops and exports the active trace as a Summary and/or profile file.
Stops the trace and exports all metadata collected during the trace to the
default SummaryWriter, if one has been set.
Args:
name: A name for the summary to be written.
step: Explicit `int64`-castable monotonic step value for this summary. If
omitte... | github-repos |
def delete(self, branch, commit_message, **kwargs):
file_path = self.get_id().replace('/', '%2F')
self.manager.delete(file_path, branch, commit_message, **kwargs) | Delete the file from the server.
Args:
branch (str): Branch from which the file will be removed
commit_message (str): Commit message for the deletion
**kwargs: Extra options to send to the server (e.g. sudo)
Raises:
GitlabAuthenticationError: If authentication is not correct
GitlabDeleteError: If the server cannot pe... | codesearchnet |
def analyze_structures(self, structures, step_freq=10, most_frequent_polyhedra=15):
voro_dict = {}
step = 0
for structure in structures:
step += 1
if ((step % step_freq) != 0):
continue
v = []
for n in range(len(structure)):
v.append(str(self.analyze(s... | Perform Voronoi analysis on a list of Structures.
Note that this might take a significant amount of time depending on the
size and number of structures.
Args:
structures (list): list of Structures
cutoff (float: cutoff distance around an atom to search for
neighbors
step_freq (int): perform analysis every step_freq st... | codesearchnet |
def chop(array, epsilon=1e-10):
ret = np.array(array)
if np.isrealobj(ret):
ret[abs(ret) < epsilon] = 0.0
else:
ret.real[abs(ret.real) < epsilon] = 0.0
ret.imag[abs(ret.imag) < epsilon] = 0.0
return ret | Truncate small values of a complex array.
Args:
array (array_like): array to truncte small values.
epsilon (float): threshold.
Returns:
np.array: A new operator with small values set to zero. | juraj-google-style |
def _CreateLineStringForShape(self, parent, shape):
coordinate_list = [(longitude, latitude) for (latitude, longitude, distance) in shape.points]
return self._CreateLineString(parent, coordinate_list) | Create a KML LineString using coordinates from a shape.
Args:
parent: The parent ElementTree.Element instance.
shape: The transitfeed.Shape instance.
Returns:
The LineString ElementTree.Element instance or None if coordinate_list is
empty. | codesearchnet |
def _save_cached_when_graph_building(self, file_prefix, object_graph_tensor, options, update_ckpt_state=False):
named_saveable_objects, graph_proto, feed_additions, unused_registered_savers = self._gather_saveables(object_graph_tensor=object_graph_tensor)
if self._last_save_object_graph != graph_proto or contex... | Create or retrieve save ops, overrides parents's private method.
Args:
file_prefix: The prefix for saved checkpoint files.
object_graph_tensor: A `Tensor` to which the current object graph will be
fed.
options: `CheckpointOptions` object.
update_ckpt_state: Optional bool flag. Indiciate whether the internal
checkpoint... | github-repos |
def _batch_examples(dataset, batch_size, max_length):
(buckets_min, buckets_max) = _create_min_max_boundaries(max_length)
bucket_batch_sizes = [(batch_size
bucket_batch_sizes = tf.constant(bucket_batch_sizes, dtype=tf.int64)
def example_to_bucket_id(example_input, example_target):
'Return int6... | Group examples by similar lengths, and return batched dataset.
Each batch of similar-length examples are padded to the same length, and may
have different number of elements in each batch, such that:
group_batch_size * padded_length <= batch_size.
This decreases the number of padding tokens per batch, which improves ... | codesearchnet |
def _get_dir_size(self, path: str='.'):
total = 0
for root, _, files in os.walk(path):
for filename in files:
total += os.path.getsize(os.path.join(root, filename))
return total | Get the total size of files and sub-directories under the path.
Args:
path: Path of a directory or a file to calculate the total size.
Returns:
Total size of the directory or a file. | github-repos |
def add_to_buffer(self, content, read_position):
self.read_position = read_position
if (self.read_buffer is None):
self.read_buffer = content
else:
self.read_buffer = (content + self.read_buffer) | Add additional bytes content as read from the read_position.
Args:
content (bytes): data to be added to buffer working BufferWorkSpac.
read_position (int): where in the file pointer the data was read from. | 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['feedItemId'], 'criterion': {'xsi_type': 'Lo... | 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. | codesearchnet |
def create_selected_summaries_dict(summaries_list):
headers_summary = cellpy.parameters.internal_settings.get_headers_summary()
selected_summaries = dict()
for h in summaries_list:
selected_summaries[h] = headers_summary[h]
return selected_summaries | Creates a dictionary with summary column headers.
Examples:
>>> summaries_to_output = ["discharge_capacity", "charge_capacity"]
>>> summaries_to_output_dict = create_selected_summaries_dict(
>>> summaries_to_output
>>> )
>>> print(summaries_to_output_dict)
{'discharge_capacity': "Discharge_Capacity(mAh/g)",
'charge... | codesearchnet |
def do_hook_actions(self, actions, hook_type):
logger.log_debug("call {} hook actions.".format(hook_type))
for action in actions:
if isinstance(action, dict) and len(action) == 1:
var_name, hook_content = list(action.items())[0]
... | call hook actions.
Args:
actions (list): each action in actions list maybe in two format.
format1 (dict): assignment, the value returned by hook function will be assigned to variable.
{"var": "${func()}"}
format2 (str): only call hook functions.
${func()}
hook_type (enum): setup/teardown | juraj-google-style |
def forward(self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, causal_attention_mask: torch.Tensor, output_attentions: Optional[bool]=False) -> Tuple[torch.FloatTensor]:
residual = hidden_states
hidden_states, attn_weights = self.self_attn(hidden_states=hidden_states, attention_mask=attention_mask... | Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
`(config.encoder_attention_heads,)`.
output_attentions (`bo... | github-repos |
def meta_features_path(self, path):
return os.path.join(
path,
app.config['XCESSIV_META_FEATURES_FOLDER'],
str(self.id)
) + '.npy' | Returns path for meta-features
Args:
path (str): Absolute/local path of xcessiv folder | juraj-google-style |
def close_children_tasks(self, parent_task_name):
if (parent_task_name not in self.tasks):
return
while self.tasks:
next_task = reversed(self.tasks.keys()).next()
if (next_task == parent_task_name):
break
del self.tasks[next_task] | Closes all the children tasks that were open
Args:
parent_task_name (str): Name of the parent task
Returns:
None | codesearchnet |
def do_command_line(infile: typing.IO[str]) -> int:
lines = infile.readlines()
tree = ast.parse(''.join(lines))
checker = Checker(tree, lines, infile.name)
checker.load()
errors = []
for func in checker.all_funcs(skip_noqa=True):
try:
errors = list(func.check_all())
e... | Currently a small stub to create an instance of Checker for the passed
``infile`` and run its test functions through linting.
Args:
infile
Returns:
int: Number of flake8 errors raised. | codesearchnet |
def do_get_next(endpoint, access_token):
headers = {"Authorization": 'Bearer ' + access_token}
headers['User-Agent'] = get_user_agent()
looping = True
value_list = []
vm_dict = {}
while looping:
get_return = requests.get(endpoint, headers=headers).json()
if not 'value' in ge... | Do an HTTP GET request, follow the nextLink chain and return JSON.
Args:
endpoint (str): Azure Resource Manager management endpoint.
access_token (str): A valid Azure authentication token.
Returns:
HTTP response. JSON body. | juraj-google-style |
def _getScalesDiag(self,termx=0):
assert self.P>1, 'CVarianceDecomposition:: diagonal init_method allowed only for multi trait models'
assert self.noisPos!=None, 'CVarianceDecomposition:: noise term has to be set'
assert termx<self.n_terms-1, 'CVarianceDecomposition:: termx>=n_terms-1'... | Uses 2 term single trait model to get covar params for initialization
Args:
termx: non-noise term terms that is used for initialization | juraj-google-style |
def waitOnUpdate(self, timeout: float=0) -> bool:
if timeout:
with suppress(asyncio.TimeoutError):
util.run(asyncio.wait_for(self.updateEvent, timeout))
else:
util.run(self.updateEvent)
return True | Wait on any new update to arrive from the network.
Args:
timeout: Maximum time in seconds to wait.
If 0 then no timeout is used.
.. note::
A loop with ``waitOnUpdate`` should not be used to harvest
tick data from tickers, since some ticks can go missing.
This happens when multiple updates occur almost simultaneously;... | codesearchnet |
def GetFeedItems(client, feed):
feed_item_service = client.GetService('FeedItemService', 'v201809')
feed_items = []
more_pages = True
selector = {'fields': ['FeedItemId', 'AttributeValues'], 'predicates': [{'field': 'Status', 'operator': 'EQUALS', 'values': ['ENABLED']}, {'field': 'FeedId', 'operator': ... | Returns the Feed Items for a given Feed.
Args:
client: an AdWordsClient instance.
feed: the Feed we are retrieving Feed Items from.
Returns:
The Feed Items associated with the given Feed. | codesearchnet |
def flux_up(self, fluxUpBottom, emission=None):
if (emission is None):
emission = np.zeros_like(self.absorptivity)
E = np.concatenate((emission, np.atleast_1d(fluxUpBottom)), axis=(- 1))
return np.squeeze(matrix_multiply(self.Tup, E[(..., np.newaxis)])) | Compute downwelling radiative flux at interfaces between layers.
Inputs:
* fluxDownTop: flux down at top
* emission: emission from atmospheric levels (N)
defaults to zero if not given
Returns:
* vector of downwelling radiative flux between levels (N+1)
element 0 is the flux down to the surface. | codesearchnet |
def sg_queue_context(sess=None):
sess = (tf.get_default_session() if (sess is None) else sess)
coord = tf.train.Coordinator()
try:
threads = tf.train.start_queue_runners(sess, coord)
(yield)
finally:
coord.request_stop()
coord.join(threads) | r"""Context helper for queue routines.
Args:
sess: A session to open queues. If not specified, a new session is created.
Returns:
None | codesearchnet |
def run_attack_work(self, work_id):
adv_batch_id = self.attack_work.work[work_id]['output_adversarial_batch_id']
adv_batch = self.adv_batches[adv_batch_id]
dataset_batch_id = adv_batch['dataset_batch_id']
submission_id = adv_batch['submission_id']
epsilon = self.dataset_batches[dataset_batch_id]['ep... | Runs one attack work.
Args:
work_id: ID of the piece of work to run
Returns:
elapsed_time_sec, submission_id - elapsed time and id of the submission
Raises:
WorkerError: if error occurred during execution. | codesearchnet |
def norm(self, valu):
func = self._type_norms.get(type(valu))
if func is None:
raise s_exc.NoSuchFunc(name=self.name, mesg='no norm for type: %r' % (type(valu),))
return func(valu) | Normalize the value for a given type.
Args:
valu (obj): The value to normalize.
Returns:
((obj,dict)): The normalized valu, info tuple.
Notes:
The info dictionary uses the following key conventions:
subs (dict): The normalized sub-fields as name: valu entries. | juraj-google-style |
def __init__(self, package, device):
self.package = package
self.log = device.log
self.verbose_logging = True
self._device = device
self._counter = None
self._lock = threading.Lock()
self._event_client = None | Initializes the instance of ClientBase.
Args:
package: str, the user-visible name of the snippet library being
communicated with.
device: DeviceController, the device object associated with a client. | github-repos |
def pop_all(self, event_name):
if (not self.started):
raise IllegalStateError('Dispatcher needs to be started before popping.')
results = []
try:
self.lock.acquire()
while True:
e = self.event_dict[event_name].get(block=False)
results.append(e)
except (que... | Return and remove all stored events of a specified name.
Pops all events from their queue. May miss the latest ones.
If no event is available, return immediately.
Args:
event_name: Name of the events to be popped.
Returns:
List of the desired events.
Raises:
IllegalStateError: Raised if pop is called before the dis... | codesearchnet |
def _enroll_users(cls, request, enterprise_customer, emails, mode, course_id=None, program_details=None, notify=True):
pending_messages = []
if course_id:
(succeeded, pending, failed) = cls.enroll_users_in_course(enterprise_customer=enterprise_customer, course_id=course_id, course_mode=mode, emails=emai... | Enroll the users with the given email addresses to the courses specified, either specifically or by program.
Args:
cls (type): The EnterpriseCustomerManageLearnersView class itself
request: The HTTP request the enrollment is being created by
enterprise_customer: The instance of EnterpriseCustomer whose attached users ... | codesearchnet |
def variable(self, var_name, shape, init, dt=tf.float32, train=None):
dt = tf.as_dtype(dt).base_dtype
if (var_name in self.vars):
v = self.vars[var_name]
if (v.get_shape() != shape):
raise ValueError(('Shape mismatch: %s vs %s. Perhaps a UnboundVariable had incompatible values within... | Adds a named variable to this bookkeeper or returns an existing one.
Variables marked train are returned by the training_variables method. If
the requested name already exists and it is compatible (same shape, dt and
train) then it is returned. In case of an incompatible type, an exception is
thrown.
Args:
var_name: ... | codesearchnet |
def parse_GSM(filepath, entry_name=None):
if isinstance(filepath, str):
with utils.smart_open(filepath) as f:
soft = []
has_table = False
for line in f:
if "_table_begin" in line or (not line.startswith(("^", "!", "
has_table = Tru... | Parse GSM entry from SOFT file.
Args:
filepath (:obj:`str` or :obj:`Iterable`): Path to file with 1 GSM entry
or list of lines representing GSM from GSE file.
entry_name (:obj:`str`, optional): Name of the entry. By default it is
inferred from the data.
Returns:
:obj:`GEOparse.GSM`: A GSM object. | juraj-google-style |
def _setup_transitions(tdef, states, prev=()):
trs = list(prev)
for transition in tdef:
if len(transition) == 3:
(name, source, target) = transition
if is_string(source) or isinstance(source, State):
source = [source]
source = [states[src] for src... | Create a TransitionList object from a 'transitions' Workflow attribute.
Args:
tdef: list of transition definitions
states (StateList): already parsed state definitions.
prev (TransitionList): transition definitions from a parent.
Returns:
TransitionList: the list of transitions defined in the 'tdef' argument. | juraj-google-style |
def sg_summary_activation(tensor, prefix=None, name=None):
prefix = ('' if (prefix is None) else (prefix + '/'))
name = ((prefix + _pretty_name(tensor)) if (name is None) else (prefix + name))
_scalar((name + '/ratio'), tf.reduce_mean(tf.cast(tf.greater(tensor, 0), tf.sg_floatx)))
_histogram((name + '/r... | r"""Register `tensor` to summary report as `activation`
Args:
tensor: A `Tensor` to log as activation
prefix: A `string`. A prefix to display in the tensor board web UI.
name: A `string`. A name to display in the tensor board web UI.
Returns:
None | codesearchnet |
def __init__(self, img, gaussian_kernel_1d=None, size=None):
self.img = img if not isinstance(img, compat.basestring) \
else compat.Image.open(img)
if size and size != self.img.size:
self.img = self.img.resize(size, Image.ANTIALIAS)
... | Create an SSIMImage.
Args:
img (str or PIL.Image): PIL Image object or file name.
gaussian_kernel_1d (np.ndarray, optional): Gaussian kernel
that was generated with utils.get_gaussian_kernel is used
to precompute common objects for SSIM computation
size (tuple, optional): New image size to resize image to. | juraj-google-style |
def IsDataVisible(self, path):
if path is None:
return (False, RESPONSES['UNKNOWN_TYPE'])
if _Matches(path, self.blacklist_patterns):
return (False, RESPONSES['BLACKLISTED'])
if not _Matches(path, self.whitelist_patterns):
return (False, RESPONSES['NOT_WHITELISTED'])
return (Tr... | Returns a tuple (visible, reason) stating if the data should be visible.
Args:
path: A dot separated path that represents a package, class, method or
variable. The format is identical to pythons "import" statement.
Returns:
(visible, reason) where visible is a boolean that is True if the data
should be visible. Rea... | juraj-google-style |
def split_range(self):
if self.is_single_namespace:
return [self]
mid_point = ((_namespace_to_ord(self.namespace_start) + _namespace_to_ord(self.namespace_end))
return [NamespaceRange(self.namespace_start, _ord_to_namespace(mid_point), _app=self.app), NamespaceRange(_ord_to_namespace((mid_point + 1... | Splits the NamespaceRange into two nearly equal-sized ranges.
Returns:
If this NamespaceRange contains a single namespace then a list containing
this NamespaceRange is returned. Otherwise a two-element list containing
two NamespaceRanges whose total range is identical to this
NamespaceRange's is returned. | codesearchnet |
def ParseFromUnicode(self, value):
precondition.AssertType(value, Text)
value = value.strip()
super(ClientURN, self).ParseFromUnicode(value)
match = self.CLIENT_ID_RE.match(self._string_urn)
if (not match):
raise type_info.TypeValueError(('Client urn malformed: %s' % value))
clientid = m... | Parse a string into a client URN.
Convert case so that all URNs are of the form C.[0-9a-f].
Args:
value: string value to parse | codesearchnet |
def strip_quotes(self, content):
error_msg = 'Following rule is badly quoted: {}'
if ((content.startswith('"') and content.endswith('"')) or (content.startswith("'") and content.endswith("'"))):
return content[1:(- 1)]
elif ((content.startswith('"') and (not content.endswith('"'))) or (content.start... | Unquote given rule.
Args:
content (str): An import rule.
Raises:
InvalidImportRule: Raise exception if the rule is badly quoted
(not started or not ended quotes).
Returns:
string: The given rule unquoted. | codesearchnet |
def find(self, name):
collectors = self.get_collectors()
for collector in collectors:
if name.lower() == collector['name'].lower():
self.collector_id = collector['id']
return collector
return {'status': 'No results found.'} | Returns a dict of collector's details if found.
Args:
name (str): name of collector searching for | juraj-google-style |
def verify_edge_segments(edge_infos):
if (edge_infos is None):
return
for edge_info in edge_infos:
num_segments = len(edge_info)
for index in six.moves.xrange((- 1), (num_segments - 1)):
(index1, start1, end1) = edge_info[index]
if (not (0.0 <= start1 < end1 <= 1.... | Verify that the edge segments in an intersection are valid.
.. note::
This is a helper used only by :func:`generic_intersect`.
Args:
edge_infos (Optional[list]): List of "edge info" lists. Each list
represents a curved polygon and contains 3-tuples of edge index,
start and end (see the output of :func:`ends_to_curve... | codesearchnet |
def put_image(self, name, val):
assert isinstance(val, np.ndarray)
arr = image_to_nhwc(val)
self._dispatch(lambda m: m.process_image(name, arr))
s = create_image_summary(name, arr)
self._dispatch(lambda m: m.process_summary(s)) | Put an image.
Args:
name (str):
val (np.ndarray): 2D, 3D (HWC) or 4D (NHWC) numpy array of images
in range [0,255]. If channel is 3, assumed to be RGB. | juraj-google-style |
def convert_sum(params, w_name, scope_name, inputs, layers, weights, names):
print('Converting Sum ...')
def target_layer(x):
import keras.backend as K
return K.sum(x)
lambda_layer = keras.layers.Lambda(target_layer)
layers[scope_name] = lambda_layer(layers[inputs[0]]) | Convert sum.
Args:
params: dictionary with layer parameters
w_name: name prefix in state_dict
scope_name: pytorch scope name
inputs: pytorch node inputs
layers: dictionary with keras tensors
weights: pytorch state_dict
names: use short names for keras layers | codesearchnet |
def not_storable(_type):
return Storable(_type, handlers=StorableHandler(poke=fake_poke, peek=fail_peek(_type))) | Helper for tagging unserializable types.
Arguments:
_type (type): type to be ignored.
Returns:
Storable: storable instance that does not poke. | codesearchnet |
def ed25519_public_key_from_string(string):
try:
return Ed25519PublicKey.from_public_bytes(base64.b64decode(string))
except (UnsupportedAlgorithm, Base64Error) as exc:
raise ScriptWorkerEd25519Error("Can't create Ed25519PublicKey: {}!".format(str(exc))) | Create an ed25519 public key from ``string``, which is a seed.
Args:
string (str): the string to use as a seed.
Returns:
Ed25519PublicKey: the public key | codesearchnet |
def QA_fetch_get_sh_margin(date):
if (date in trade_date_sse):
data = pd.read_excel(_sh_url.format(QA_util_date_str2int(date)), 1).assign(date=date).assign(sse='sh')
data.columns = ['code', 'name', 'leveraged_balance', 'leveraged_buyout', 'leveraged_payoff', 'margin_left', 'margin_sell', 'margin_rep... | return shanghai margin data
Arguments:
date {str YYYY-MM-DD} -- date format
Returns:
pandas.DataFrame -- res for margin data | codesearchnet |
def _Build(self, storage_file):
self._index = {}
for event_tag in storage_file.GetEventTags():
self.SetEventTag(event_tag) | Builds the event tag index.
Args:
storage_file (BaseStorageFile): storage file. | codesearchnet |
def included(self, start, stop):
for event in self:
if (start <= event.begin <= stop
and start <= event.end <= stop):
yield event | Iterates (in chronological order) over every event that is included
in the timespan between `start` and `stop`
Args:
start : (Arrow object)
stop : (Arrow object) | juraj-google-style |
def add_keywords_from_dict(self, keyword_dict):
for (clean_name, keywords) in keyword_dict.items():
if (not isinstance(keywords, list)):
raise AttributeError('Value of key {} should be a list'.format(clean_name))
for keyword in keywords:
self.add_keyword(keyword, clean_name) | To add keywords from a dictionary
Args:
keyword_dict (dict): A dictionary with `str` key and (list `str`) as value
Examples:
>>> keyword_dict = {
"java": ["java_2e", "java programing"],
"product management": ["PM", "product manager"]
}
>>> keyword_processor.add_keywords_from_dict(keyword_dict)
Raises:
AttributeError... | codesearchnet |
def encode_all_features(dataset, vocabulary):
def my_fn(features):
ret = {}
for (k, v) in features.items():
v = vocabulary.encode_tf(v)
v = tf.concat([tf.to_int64(v), [1]], 0)
ret[k] = v
return ret
return dataset.map(my_fn, num_parallel_calls=tf.data.... | Encode all features.
Args:
dataset: a tf.data.Dataset
vocabulary: a vocabulary.Vocabulary
Returns:
a tf.data.Dataset | codesearchnet |
def word_ids(self, batch_index: int=0) -> List[Optional[int]]:
if not self._encodings:
raise ValueError('word_ids() is not available when using non-fast tokenizers (e.g. instance of a `XxxTokenizerFast` class).')
return self._encodings[batch_index].word_ids | Return a list mapping the tokens to their actual word in the initial sentence for a fast tokenizer.
Args:
batch_index (`int`, *optional*, defaults to 0): The index to access in the batch.
Returns:
`List[Optional[int]]`: A list indicating the word corresponding to each token. Special tokens added by the
tokenizer are ... | github-repos |
def should_execute_serially(self, applied_ptransform):
if isinstance(applied_ptransform.transform, (_GroupByKeyOnly, _StreamingGroupByKeyOnly, _StreamingGroupAlsoByWindow)):
return True
elif isinstance(applied_ptransform.transform, core.ParDo) and is_stateful_dofn(applied_ptransform.transform.dofn):
... | Returns True if this applied_ptransform should run one bundle at a time.
Some TransformEvaluators use a global state object to keep track of their
global execution state. For example evaluator for _GroupByKeyOnly uses this
state as an in memory dictionary to buffer keys.
Serially executed evaluators will act as synci... | github-repos |
def get_cards(self, **query_params):
cards = self.get_cards_json(self.base_uri, query_params=query_params)
cards_list = []
for card_json in cards:
cards_list.append(self.create_card(card_json))
return cards_list | Get all cards this member is attached to. Return a list of Card
objects.
Returns:
list(Card): Return all cards this member is attached to | codesearchnet |
def _partitions_list(N):
if N < (_NUM_PRECOMPUTED_PARTITION_LISTS):
return list(_partition_lists[N])
else:
raise ValueError(
'Partition lists not yet available for system with {} '
'nodes or more'.format(_NUM_PRECOMPUTED_PARTITION_LISTS)) | Return a list of partitions of the |N| binary nodes.
Args:
N (int): The number of nodes under consideration.
Returns:
list[list]: A list of lists, where each inner list is the set of
micro-elements corresponding to a macro-element.
Example:
>>> _partitions_list(3)
[[[0, 1], [2]], [[0, 2], [1]], [[0], [1, 2]], [[0], ... | juraj-google-style |
def calculate_embedding(self, batch_image_bytes):
return self.tf_session.run(self.embedding, feed_dict={self.input_jpeg: batch_image_bytes}) | Get the embeddings for a given JPEG image.
Args:
batch_image_bytes: As if returned from [ff.read() for ff in file_list].
Returns:
The Inception embeddings (bottleneck layer output) | codesearchnet |
def respond(self, prompt_id, response):
_LOG.debug('Responding to prompt (%s): "%s"', prompt_id, response)
with self._cond:
if not (self._prompt and self._prompt.id == prompt_id):
return False
self._response = response
self.last_response = (prompt_id, response)
self.remove_p... | Respond to the prompt with the given ID.
If there is no active prompt or the given ID doesn't match the active
prompt, do nothing.
Args:
prompt_id: A string uniquely identifying the prompt.
response: A string response to the given prompt.
Returns:
True if the prompt with the given ID was active, otherwise False. | juraj-google-style |
def _FlushExportBuffer(self, output_module, deduplicate_events=True):
last_macb_group_identifier = None
last_content_identifier = None
macb_group = []
generator = self._export_event_heap.PopEvents()
for (macb_group_identifier, content_identifier, event) in generator:
if (deduplicate_events a... | Flushes buffered events and writes them to the output module.
Args:
output_module (OutputModule): output module.
deduplicate_events (Optional[bool]): True if events should be
deduplicated. | codesearchnet |
def __init__(self, latitude, longitude, name, units='km'):
super(NumberedPoint, self).__init__(latitude, longitude, units)
self.name = name | Initialise a new ``NumberedPoint`` object.
Args:
latitude (float): Location's latitude
longitude (float): Location's longitude
name (str): Location's name or command line position
units (str): Unit type to be used for distances | juraj-google-style |
def fail_request(self, orig_request, message, start_response):
cors_handler = self._create_cors_handler(orig_request)
return util.send_wsgi_error_response(message, start_response, cors_handler=cors_handler) | Write an immediate failure response to outfile, no redirect.
This calls start_response and returns the error body.
Args:
orig_request: An ApiRequest, the original request from the user.
message: A string containing the error message to be displayed to user.
start_response: A function with semantics defined in PEP-333... | codesearchnet |
def passthrough_context_definition(context_params):
check.inst_param(context_params, 'context', ExecutionContext)
context_definition = PipelineContextDefinition(context_fn=(lambda *_args: context_params))
return {DEFAULT_CONTEXT_NAME: context_definition} | Create a context definition from a pre-existing context. This can be useful
in testing contexts where you may want to create a context manually and then
pass it into a one-off PipelineDefinition
Args:
context (ExecutionContext): The context that will provided to the pipeline.
Returns:
PipelineContextDefinition: The pa... | codesearchnet |
def MakeStatResponse(self, tsk_file, tsk_attribute=None, append_name=None):
precondition.AssertOptionalType(append_name, Text)
info = tsk_file.info
response = rdf_client_fs.StatEntry()
meta = info.meta
if meta:
response.st_ino = meta.addr
for attribute in ['mode', 'nlink', 'uid', 'gi... | Given a TSK info object make a StatEntry.
Note that tsk uses two things to uniquely identify a data stream - the inode
object given in tsk_file and the attribute object which may correspond to an
ADS of this file for filesystems which support ADS. We store both of these
in the stat response.
Args:
tsk_file: A TSK Fil... | codesearchnet |
def parse_ids(chrom, pos, ref, alt, case_id, variant_type):
ids = {}
pos = str(pos)
ids['simple_id'] = parse_simple_id(chrom, pos, ref, alt)
ids['variant_id'] = parse_variant_id(chrom, pos, ref, alt, variant_type)
ids['display_name'] = parse_display_name(chrom, pos, ref, alt, variant_type)
ids['... | Construct the necessary ids for a variant
Args:
chrom(str): Variant chromosome
pos(int): Variant position
ref(str): Variant reference
alt(str): Variant alternative
case_id(str): Unique case id
variant_type(str): 'clinical' or 'research'
Returns:
ids(dict): Dictionary with the relevant ids | codesearchnet |
def __init__(self, num_points):
self.num_points = num_points
self.column_names = []
self.name_to_values = {} | Constructs a metadata for an embedding of the specified size.
Args:
num_points: Number of points in the embedding. | juraj-google-style |
def _prep_binary_content(self):
if not self.data and not self.location and 'Content-Location' not in self.resource.headers.keys():
raise Exception('creating/updating NonRDFSource requires content from self.binary.data, self.binary.location, or the Content-Location header')
elif 'Content-Location' in sel... | Sets delivery method of either payload or header
Favors Content-Location header if set
Args:
None
Returns:
None: sets attributes in self.binary and headers | juraj-google-style |
def individuals(self, ind_ids=None):
if ind_ids:
for ind_id in ind_ids:
for ind in self.individual_objs:
if ind.ind_id == ind_id:
yield ind
else:
for ind in self.individual_objs:
yield ind | Return information about individuals
Args:
ind_ids (list(str)): List of individual ids
Returns:
individuals (Iterable): Iterable with Individuals | juraj-google-style |
def is_array(self, data_type):
data_type = data_type.split('[')[0].strip()
return data_type.lower() in self.array_types | Check if a type is a known array type
Args:
data_type (str): Name of type to check
Returns:
True if ``data_type`` is a known array type. | juraj-google-style |
def ParseDownloadsRow(
self, parser_mediator, query, row, **unused_kwargs):
query_hash = hash(query)
event_data = FirefoxDownloadEventData()
event_data.full_path = self._GetRowValue(query_hash, row, 'target')
event_data.mime_type = self._GetRowValue(query_hash, row, 'mimeType')
event_dat... | Parses a downloads row.
Args:
parser_mediator (ParserMediator): mediates interactions between parsers
and other components, such as storage and dfvfs.
query (str): query that created the row.
row (sqlite3.Row): row. | juraj-google-style |
def _parse_username(self, config):
(username, priv, role, nopass, fmt, secret, sshkey) = config
resource = dict()
resource['privilege'] = priv
resource['role'] = role
resource['nopassword'] = (nopass == 'nopassword')
resource['format'] = fmt
resource['secret'] = secret
resource['sshkey']... | Scans the config block and returns the username as a dict
Args:
config (str): The config block to parse
Returns:
dict: A resource dict that is intended to be merged into the
user resource | codesearchnet |
def block(self, **kwargs):
path = '/users/%s/block' % self.id
server_data = self.manager.gitlab.http_post(path, **kwargs)
if server_data is True:
self._attrs['state'] = 'blocked'
return server_data | Block the user.
Args:
**kwargs: Extra options to send to the server (e.g. sudo)
Raises:
GitlabAuthenticationError: If authentication is not correct
GitlabBlockError: If the user could not be blocked
Returns:
bool: Whether the user status has been changed | juraj-google-style |
def _set_spawn_exe_path():
if sys.argv[0].endswith('.py'):
def guess_path(package_root):
if 'bazel-out' in sys.argv[0] and package_root in sys.argv[0]:
package_root_base = sys.argv[0][:sys.argv[0].rfind(package_root)]
binary = os.environ['TEST_TARGET'][2:].replac... | Set the path to the executable for spawned processes.
This utility searches for the binary the parent process is using, and sets
the executable of multiprocessing's context accordingly.
Raises:
RuntimeError: If the binary path cannot be determined. | github-repos |
def gumbel_sample(shape):
uniform_samples = tf.random_uniform(shape, minval=0.00001, maxval=0.99998)
return -tf.log(-tf.log(uniform_samples)) | Sample from the Gumbel distribution, protect from overflows.
Args:
shape: Shape of Gumbel samples.
Returns:
Noise drawn from Gumbel distribution. | juraj-google-style |
def __init__(self, channel):
self.Invoke = channel.unary_unary(
'/pulumirpc.ResourceMonitor/Invoke',
request_serializer=provider__pb2.InvokeRequest.SerializeToString,
response_deserializer=provider__pb2.InvokeResponse.FromString,
)
self.ReadResource = channel.unary_unary(
... | Constructor.
Args:
channel: A grpc.Channel. | juraj-google-style |
def _add_sv_coordinates(self, variant):
variant.stop_chrom = variant.CHROM
variant.start = int(variant.POS)
if ':' in variant.ALT:
other_coordinates = variant.ALT.strip('ACGTN[]').split(':')
variant.stop_chrom = other_coordinates[0].lstrip('chrC... | Add the neccesary sv coordinates for a variant
Args:
variant (puzzle.models.variant) | juraj-google-style |
def __call__(self, fn):
def fail(app, *args, **kwargs):
data = fn(app, *args, **kwargs)
if isinstance(self.enable, bool):
enabled = self.enable
app.tcex.log.debug('Fail on output is ({}).'.format(self.enable))
... | Implement __call__ function for decorator.
Args:
fn (function): The decorated function.
Returns:
function: The custom decorator function. | juraj-google-style |
def get_data_csv(file_name, encoding='utf-8', file_contents=None, on_demand=False):
def yield_csv(csv_contents, csv_file):
try:
for line in csv_contents:
yield line
finally:
try:
csv_file.close()
except:
pass
... | Gets good old csv data from a file.
Args:
file_name: The name of the local file, or the holder for the
extension type when the file_contents are supplied.
encoding: Loads the file with the specified cell encoding.
file_contents: The file-like object holding contents of file_name.
If left as None, then file_name is dir... | juraj-google-style |
def __init__(self, direct_subclasses=None, any_also_is_bottom=True):
self.direct_subclasses = direct_subclasses or {}
self.any_also_is_bottom = any_also_is_bottom
self.solver = booleq.Solver()
self._implications = {} | Construct.
Args:
direct_subclasses: A dictionary, mapping pytd.Type to lists of pytd.Type.
any_also_is_bottom: Whether we should, (if True) consider
pytd.AnythingType() to also be at the bottom of the type hierarchy, thus
making it a subclass of everything, or (if False) to be only at the top. | github-repos |
def set_scheduler(self, host, username='root', password=None, private_key=None, private_key_pass=None):
self._remote = RemoteClient(host, username, password, private_key, private_key_pass)
self._remote_id = uuid.uuid4().hex | Defines the remote scheduler
Args:
host (str): the hostname or ip address of the remote scheduler
username (str, optional): the username used to connect to the remote scheduler. Default is 'root'
password (str, optional): the password for username on the remote scheduler. Either the password or the private_key must be... | codesearchnet |
def stage_tc_associations(self, entity1, entity2):
entity1 = self.tcex.playbook.read(entity1)
entity1_id = entity1.get('id')
entity1_owner = entity1.get('ownerName')
entity1_type = entity1.get('type')
if entity1.get('type') in self.tcex.indicator_types:
... | Add an attribute to a resource.
Args:
entity1 (str): A Redis variable containing a TCEntity.
entity2 (str): A Redis variable containing a TCEntity. | juraj-google-style |
def RegisterDefinition(self, data_type_definition):
name_lower = data_type_definition.name.lower()
if name_lower in self._definitions:
raise KeyError('Definition already set for name: {0:s}.'.format(
data_type_definition.name))
if data_type_definition.name in self._aliases:
raise... | Registers a data type definition.
The data type definitions are identified based on their lower case name.
Args:
data_type_definition (DataTypeDefinition): data type definitions.
Raises:
KeyError: if data type definition is already set for the corresponding
name. | juraj-google-style |
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