code stringlengths 20 4.93k | docstring stringlengths 33 1.27k | source stringclasses 3
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def perspective(img, startpoints, endpoints, interpolation=Image.BICUBIC):
if (not _is_pil_image(img)):
raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
coeffs = _get_perspective_coeffs(startpoints, endpoints)
return img.transform(img.size, Image.PERSPECTIVE, coeffs, interpolatio... | Perform perspective transform of the given PIL Image.
Args:
img (PIL Image): Image to be transformed.
coeffs (tuple) : 8-tuple (a, b, c, d, e, f, g, h) which contains the coefficients.
for a perspective transform.
interpolation: Default- Image.BICUBIC
Returns:
PIL Image: Perspectively transformed Image. | codesearchnet |
def query_band(self, value):
self._query_band = value
if (value is None):
try:
del self._connectionXML.attrib['query-band-spec']
except KeyError:
pass
else:
self._connectionXML.set('query-band-spec', value) | Set the connection's query_band property.
Args:
value: New query_band value. String.
Returns:
Nothing. | codesearchnet |
def get_connection_string(params, hide_password=True):
connection_string = params['driver'] + ':
user = params.get('user', None)
password = params.get('password', None)
host = params.get('host', None)
port = params.get('port', None)
database = params.get('databa... | Get a database connection string
Args:
params (dict): database configuration, as defined in :mod:`ozelot.config`
hide_password (bool): if True, the password is hidden in the returned string
(use this for logging purposes).
Returns:
str: connection string | juraj-google-style |
def add_tags(self):
session = boto3.session.Session(profile_name=self.env, region_name=self.region)
resource = session.resource('ec2')
group_id = get_security_group_id(self.app_name, self.env, self.region)
security_group = resource.SecurityGroup(group_id)
try:
tag = security_group.create_tag... | Add tags to security group.
Returns:
True: Upon successful completion. | codesearchnet |
def insert(self, iterable, index=0, data=None, weight=1.0):
if (index == len(iterable)):
self.is_terminal = True
self.key = iterable
self.weight = weight
if data:
self.data.add(data)
else:
if (iterable[index] not in self.children):
self.children[it... | Insert new node into tree
Args:
iterable(hashable): key used to find in the future.
data(object): data associated with the key
index(int): an index used for insertion.
weight(float): the wait given for the item added. | codesearchnet |
def int(name, default=None, allow_none=False, fallback=None):
value = read(name, default, allow_none, fallback=fallback)
if isinstance(value, builtins.str):
value = value.strip()
if value is None and allow_none:
return None
else:
return builtins.int(value) | Get a string environment value or the default.
Args:
name: The environment variable name
default: The default value to use if no environment variable is found
allow_none: If the return value can be `None` (i.e. optional) | juraj-google-style |
def plot_state_hinton(rho, title='', figsize=None):
if not HAS_MATPLOTLIB:
raise ImportError('Must have Matplotlib installed.')
rho = _validate_input_state(rho)
if figsize is None:
figsize = (8, 5)
num = int(np.log2(len(rho)))
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=figsize... | Plot a hinton diagram for the quanum state.
Args:
rho (ndarray): Numpy array for state vector or density matrix.
title (str): a string that represents the plot title
figsize (tuple): Figure size in inches.
Returns:
matplotlib.Figure: The matplotlib.Figure of the visualization
Raises:
ImportError: Requires matplotlib. | juraj-google-style |
def save_image(tensor, filename, nrow=8, padding=2,
normalize=False, range=None, scale_each=False, pad_value=0):
from PIL import Image
grid = make_grid(tensor, nrow=nrow, padding=padding, pad_value=pad_value,
normalize=normalize, range=range, scale_each=scale_each)
... | Save a given Tensor into an image file.
Args:
tensor (Tensor or list): Image to be saved. If given a mini-batch tensor,
saves the tensor as a grid of images by calling ``make_grid``.
**kwargs: Other arguments are documented in ``make_grid``. | juraj-google-style |
def get_token(self):
if (self.token == None):
self.token = self.authenticate(self.username, self.password)
try:
return str(self.token, 'utf-8')
except TypeError:
return self.token | Method to retrieve an auth token.
The cached global token is looked up and returned if it exists. If it
is `None` a new one is requested and returned.
Returns:
Simplenote API token as string | codesearchnet |
def is_diagonal_scale(scale):
if not isinstance(scale, tf.linalg.LinearOperator):
raise TypeError("Expected argument 'scale' to be instance of LinearOperator"
". Found: %s" % scale)
return (isinstance(scale, tf.linalg.LinearOperatorIdentity) or
isinstance(scale, tf.linalg.Linear... | Returns `True` if `scale` is a `LinearOperator` that is known to be diag.
Args:
scale: `LinearOperator` instance.
Returns:
Python `bool`.
Raises:
TypeError: If `scale` is not a `LinearOperator`. | juraj-google-style |
def __init__(self, receive_port):
super(InteractiveDebuggerDataServer, self).__init__(
receive_port, InteractiveDebuggerDataStreamHandler)
self._incoming_channel = queue.Queue()
self._outgoing_channel = comm_channel_lib.CommChannel()
self._run_states = RunStates(breakpoints_func=lambda: se... | Receives health pills from a debugger and writes them to disk.
Args:
receive_port: The port at which to receive health pills from the
TensorFlow debugger.
always_flush: A boolean indicating whether the EventsWriter will be
flushed after every write. Can be used for testing. | juraj-google-style |
def add_streamer(self, binary_descriptor):
streamer = streamer_descriptor.parse_binary_descriptor(binary_descriptor)
try:
self.graph.add_streamer(streamer)
self.streamer_status[(len(self.graph.streamers) - 1)] = StreamerStatus()
return Error.NO_ERROR
except ResourceUsageError:
... | Add a streamer to the sensor_graph using a binary streamer descriptor.
Args:
binary_descriptor (bytes): An encoded binary streamer descriptor.
Returns:
int: A packed error code | codesearchnet |
def get_config_parameter(config: ConfigParser,
section: str,
param: str,
fn: Callable[[Any], Any],
default: Any) -> Any:
try:
value = fn(config.get(section, param))
except (TypeError, ValueError, NoO... | Fetch parameter from ``configparser`` ``.INI`` file.
Args:
config: :class:`ConfigParser` object
section: section name within config file
param: name of parameter within section
fn: function to apply to string parameter (e.g. ``int``)
default: default value
Returns:
parameter value, or ``None`` if ``default is None``,... | juraj-google-style |
def log_error(cls, msg):
cls.error_logger.error(msg)
cls.debug_logger.debug(msg) | Logs the provided error message to both the error logger and the debug logger logging
instances.
Args:
msg: `str`. The error message to log. | codesearchnet |
def element(self, using, value):
return self._execute(Command.FIND_CHILD_ELEMENT, {'using': using, 'value': value}) | find an element in the current element.
Support:
Android iOS Web(WebView)
Args:
using(str): The element location strategy.
value(str): The value of the location strategy.
Returns:
WebElement Object.
Raises:
WebDriverException. | codesearchnet |
def _get_dense_tensor(self, inputs, weight_collections=None, trainable=None):
del weight_collections
del trainable
return inputs.get(self) | Returns dense `Tensor` representing numeric feature.
Args:
inputs: A `_LazyBuilder` object to access inputs.
weight_collections: Unused `weight_collections` since no variables are
created in this function.
trainable: Unused `trainable` bool since no variables are created in this
function.
Returns:
Dense `Tensor` crea... | github-repos |
def stringize(self, rnf_profile):
coor_width = max(rnf_profile.coor_width, len(str(self.left)), len(str(self.right)))
return '({},{},{},{},{})'.format(str(self.genome_id).zfill(rnf_profile.genome_id_width), str(self.chr_id).zfill(rnf_profile.chr_id_width), self.direction, str(self.left).zfill(coor_width), str(s... | Create RNF representation of this segment.
Args:
rnf_profile (rnftools.rnfformat.RnfProfile): RNF profile (with widths). | codesearchnet |
def get_kpoint_weights(self, kpoints, atol=1e-05):
kpts = np.array(kpoints)
shift = []
mesh = []
for i in range(3):
nonzero = [i for i in kpts[(:, i)] if (abs(i) > 1e-05)]
if (len(nonzero) != len(kpts)):
if (not nonzero):
mesh.append(1)
else:
... | Calculate the weights for a list of kpoints.
Args:
kpoints (Sequence): Sequence of kpoints. np.arrays is fine. Note
that the code does not check that the list of kpoints
provided does not contain duplicates.
atol (float): Tolerance for fractional coordinates comparisons.
Returns:
List of weights, in the SAME order as... | codesearchnet |
def additive_coupling(name, x, mid_channels=512, reverse=False,
activation="relu", dropout=0.0):
with tf.variable_scope(name, reuse=tf.AUTO_REUSE):
output_channels = common_layers.shape_list(x)[-1]
x1, x2 = tf.split(x, num_or_size_splits=2, axis=-1)
z1 = x1
shift = conv_stac... | Reversible additive coupling layer.
Args:
name: variable scope.
x: 4-D Tensor, shape=(NHWC).
mid_channels: number of channels in the coupling layer.
reverse: Forward or reverse operation.
activation: "relu" or "gatu"
dropout: default, 0.0
Returns:
output: 4-D Tensor, shape=(NHWC)
objective: 0.0 | juraj-google-style |
def TrimVariableTable(self, new_size):
def ProcessBufferFull(variables):
for variable in variables:
var_index = variable.get('varTableIndex')
if var_index is not None and (var_index >= new_size):
variable['varTableIndex'] = 0
members = variable.get('members')
... | Trims the variable table in the formatted breakpoint message.
Removes trailing entries in variables table. Then scans the entire
breakpoint message and replaces references to the trimmed variables to
point to var_index of 0 ("buffer full").
Args:
new_size: desired size of variables table. | juraj-google-style |
def get_course_final_price(self, mode, currency='$', enterprise_catalog_uuid=None):
try:
price_details = self.client.baskets.calculate.get(sku=[mode['sku']], username=self.user.username, catalog=enterprise_catalog_uuid)
except (SlumberBaseException, ConnectionError, Timeout) as exc:
LOGGER.excep... | Get course mode's SKU discounted price after applying any entitlement available for this user.
Returns:
str: Discounted price of the course mode. | codesearchnet |
def ExpandWindowsEnvironmentVariables(data_string, knowledge_base):
r
win_environ_regex = re.compile(r"%([^%]+?)%")
components = []
offset = 0
for match in win_environ_regex.finditer(data_string):
components.append(data_string[offset:match.start()])
kb_value = getattr(knowledge_base, "environ_%s... | r"""Take a string and expand any windows environment variables.
Args:
data_string: A string, e.g. "%SystemRoot%\\LogFiles"
knowledge_base: A knowledgebase object.
Returns:
A string with available environment variables expanded. If we can't expand
we just return the string with the original variables. | juraj-google-style |
def HasOutputClass(cls, name):
if not isinstance(name, py2to3.STRING_TYPES):
return False
return name.lower() in cls._output_classes | Determines if a specific output class is registered with the manager.
Args:
name (str): name of the output module.
Returns:
bool: True if the output class is registered. | juraj-google-style |
def get_country_info_from_m49(cls, m49, use_live=True, exception=None):
iso3 = cls.get_iso3_from_m49(m49, use_live=use_live, exception=exception)
if iso3 is not None:
return cls.get_country_info_from_iso3(iso3, exception=exception)
return None | Get country name from M49 code
Args:
m49 (int): M49 numeric code for which to get country information
use_live (bool): Try to get use latest data from web rather than file in package. Defaults to True.
exception (Optional[ExceptionUpperBound]): An exception to raise if country not found. Defaults to None.
Returns:
Op... | juraj-google-style |
async def remove_participant(self, p: Participant):
(await self.connection('DELETE', 'tournaments/{}/participants/{}'.format(self._id, p._id)))
if (p in self.participants):
self.participants.remove(p) | remove a participant from the tournament
|methcoro|
Args:
p: the participant to remove
Raises:
APIException | codesearchnet |
def from_raw(self, raw: RawScalar) -> Optional[ScalarValue]:
if isinstance(raw, str):
return raw | Return a cooked value of the receiver type.
Args:
raw: Raw value obtained from JSON parser. | codesearchnet |
def userinfo(self, access_token):
return self.get(url='https: | Returns the user information based on the Auth0 access token.
This endpoint will work only if openid was granted as a scope for the access_token.
Args:
access_token (str): Auth0 access token (obtained during login).
Returns:
The user profile. | codesearchnet |
def missing_values(self, flatten: bool=True) -> Dict[Union[str, int], Any]: | Returns missing values from this object.
Args:
flatten: If True, convert nested structures into a flattened dict using
key path (delimited by '.' and '[]') as key.
Returns:
A dict of key to MISSING_VALUE. | github-repos |
def _MaybeCaptured(t):
if not isinstance(t, ops.EagerTensor) and _IsFunction(t.op.graph) and (t.op.type == 'Placeholder'):
for input_t, placeholder_t in _Captures(t.op.graph):
if t is placeholder_t:
return _MaybeCaptured(input_t)
return t | If t is a captured value placeholder, returns the original captured value.
Args:
t: Tensor
Returns:
A tensor, potentially from a different Graph/FuncGraph. | github-repos |
def sanity_check_type(self, other):
if type(self) is not type(other):
raise ValueError('No TypeSpec is compatible with both %s and %s' % (self, other))
if self._input_workers.serialize() != other._input_workers.serialize():
raise ValueError('_input_workers is not compatible with both %s and %s' ... | Returns the most specific TypeSpec compatible with `self` and `other`.
Args:
other: A `TypeSpec`.
Raises:
ValueError: If there is no TypeSpec that is compatible with both `self`
and `other`. | github-repos |
def _DownloadScript(self, url, dest_dir):
if url.startswith(r'gs:
url = re.sub('^gs:
return self._DownloadAuthUrl(url, dest_dir)
header = r'http[s]?:
domain = r'storage\.googleapis\.com'
bucket = r'(?P<bucket>[a-z0-9][-_.a-z0-9]*[a-z0-9])'
obj... | Download the contents of the URL to the destination.
Args:
url: string, the URL to download.
dest_dir: string, the path to a directory for storing metadata scripts.
Returns:
string, the path to the file storing the metadata script. | juraj-google-style |
def update_branch(profile, name, sha):
ref = ('heads/' + name)
data = refs.update_ref(profile, ref, sha)
return data | Move a branch's HEAD to a new SHA.
Args:
profile
A profile generated from ``simplygithub.authentication.profile``.
Such profiles tell this module (i) the ``repo`` to connect to,
and (ii) the ``token`` to connect with.
name
The name of the branch to update.
sha
The commit SHA to point the branch's HEAD to.
Returns:... | codesearchnet |
def check_email_exists_by_subject(self, subject, match_recipient=None):
self._mail.select("inbox")
try:
matches = self.__search_email_by_subject(subject, match_recipient)
if len(matches) <= 0:
return False
else:
retur... | Searches for Email by Subject. Returns True or False.
Args:
subject (str): Subject to search for.
Kwargs:
match_recipient (str) : Recipient to match exactly. (don't care if not specified)
Returns:
True - email found, False - email not found | juraj-google-style |
def ConfigsToTest():
def Config(input_size, filter_size, out_size, stride=1, padding='SAME', dilations=None):
return (input_size, filter_size, out_size, stride, padding, dilations)
return [Config([4, 5, 5, 48], [1, 1, 48, 2], [4, 5, 5, 96]), Config([4, 8, 8, 84], [1, 3, 84, 1], [4, 8, 8, 84]), Config([... | Iterator for different convolution shapes, strides and paddings.
Returns:
List of tuples (input_size, filter_size, out_size, stride, padding,
dilations), the depthwise convolution parameters. | github-repos |
def notify_by(self, invoice_id, medium, **kwargs):
url = "{}/{}/notify_by/{}".format(self.base_url, invoice_id, medium)
return self.post_url(url, {}, **kwargs) | Send/Resend notifications to customer via email/sms
Args:
invoice_id : Id for trigger notify
medium : Medium for triggering notification via email or sms
Returns:
{"success": true} | juraj-google-style |
def __init__(self, kind=None, project=None, namespace=None, ancestor=None, filters=(), projection=(), order=(), distinct_on=(), limit=None):
self.kind = kind
self.project = project
self.namespace = namespace
self.ancestor = ancestor
self.filters = filters or ()
self.projection = projection
s... | Represents a Datastore query.
Args:
kind: (str) The kind to query.
project: (str) Required. Project associated with query.
namespace: (str, ValueProvider(str)) (Optional) Namespace to restrict
results to.
ancestor: (:class:`~apache_beam.io.gcp.datastore.v1new.types.Key`)
(Optional) key of the ancestor to which this qu... | github-repos |
def _post_process(self, feed_item, item):
campaign = self._campaign_dao.get(feed_item, required=True)
feed_item[FieldMap.CAMPAIGN_ID] = campaign['id']
feed_item[FieldMap.CAMPAIGN_NAME] = campaign['name']
landing_page = self._landing_page_dao.get(feed_item, required=True)
if landing_page:
fee... | Maps ids and names of related entities so they can be updated in the Bulkdozer feed.
When Bulkdozer is done processing an item, it writes back the updated names
and ids of related objects, this method makes sure those are updated in the
ad feed.
Args:
feed_item: Feed item representing the ad from the Bulkdozer feed.
... | github-repos |
def read_at(self, d, index=False):
for i, iv in enumerate(self):
if iv.spans(d):
return i if index else iv
return None | Get the index of the interval at a particular 'depth' (though this
might be an elevation or age or anything).
Args:
d (Number): The 'depth' to query.
index (bool): Whether to return the index instead of the interval.
Returns:
Interval: The interval, or if ``index==True`` the index of the
interval, at the specified 'd... | juraj-google-style |
def from_keras_log(csv_path, output_dir_path, **kwargs):
data = pd.read_csv(csv_path, sep=None, engine='python')
_from_keras_log_format(data, output_dir_path=output_dir_path, **kwargs) | Plot accuracy and loss from a Keras CSV log.
Args:
csv_path: The path to the CSV log with the actual data.
output_dir_path: The path to the directory where the resultings plots
should end up. | codesearchnet |
def transform_and_overwrite_file(self, file_path: str, transformation: Optional[Callable[[Iterator[str]], Iterator[str]]]=None) -> None:
if transformation is None:
transformation = self.annotate_test_file
with open(file_path, mode='r') as original_file, tempfile.NamedTemporaryFile(mode='w', delete=False... | Transforms the contents of `file_path`, overwriting the file.
Args:
file_path: The path to the file whose contents are to be transformed.
transformation: A function that takes an iterator over the lines of an HLO
file and returns an iterator over the lines of the transformed file. If
this is left as `None`, `self.anno... | github-repos |
def restore(cdiff, a):
left = a.splitlines(1) if isinstance(a, string_types) else a
lrest = []
iline = 0
for i, line in enumerate(left):
if iline not in cdiff:
lrest.append(" " + line)
iline += 1
else:
cs = [l[0] for l in cdiff[iline]]
... | Restores the full text of either the edited text using the
compressed diff.
Args:
cdiff (dict): compressed diff returned by
:func:`~acorn.logging.diff.compress`.
a (str or list): *original* string or list of strings to use as a
reference to restore the edited version. | juraj-google-style |
def get_table_schema(schema):
if schema is None:
return schema
elif isinstance(schema, str):
return bigquery_tools.parse_table_schema_from_json(schema)
elif isinstance(schema, dict):
return bigquery_tools.parse_table_schema_from_json(json.dumps(schema))
else:
raise TypeEr... | Transform the table schema into a bigquery.TableSchema instance.
Args:
schema: The schema to be used if the BigQuery table to write has to be
created. This is a dictionary object created in the WriteToBigQuery
transform.
Returns:
table_schema: The schema to be used if the BigQuery table to write has
to be created but ... | github-repos |
def replace_code(code: str, replace_pattern: str) -> str:
if len(replace_pattern) > 0:
patterns = replace_pattern.replace('with', '').split(',')
patterns = [_re_replace_pattern.search(p) for p in patterns]
for pattern in patterns:
if pattern is None:
continue
... | Replace `code` by a pattern of the form `with X1->X2,Y1->Y2,Z1->Z2`.
Args:
code (`str`): The code to be modified.
replace_pattern (`str`): The pattern used to modify `code`.
Returns:
`str`: The modified code. | github-repos |
def _assert_sparse_compatible(sparse_tensors):
checks = []
first = sparse_tensors[0]
for t in sparse_tensors[1:]:
checks.append(check_ops.assert_equal(first.dense_shape, t.dense_shape, message='Mismatched shapes!'))
checks.append(check_ops.assert_equal(first.indices, t.indices, message='Mism... | Check that all of `sparse_tensors` have same `indices` and `dense_shape`.
Args:
sparse_tensors: A list of sparse tensors.
Returns:
An op to be used as a control dependency. | github-repos |
def print_summary(model, line_length=None, positions=None, print_fn=None):
if print_fn is None:
print_fn = print
if model.__class__.__name__ == 'Sequential':
sequential_like = True
elif not model._is_graph_network:
sequential_like = True
else:
sequential_like = True
... | Prints a summary of a model.
Args:
model: Keras model instance.
line_length: Total length of printed lines
(e.g. set this to adapt the display to different
terminal window sizes).
positions: Relative or absolute positions of log elements in each line.
If not provided, defaults to `[.33, .55, .67, 1.]`.
print_fn: Print... | github-repos |
def __init__(self, graph, fetches, feeds, feed_handles=None):
with graph.as_default():
self._fetch_mapper = _FetchMapper.for_fetch(fetches)
self._fetches = []
self._targets = []
self._feeds = feeds
self._feed_handles = feed_handles or {}
self._ops = []
self._fetch_handles = {}
fo... | Creates a fetch handler.
Args:
graph: Graph of the fetches. Used to check for fetchability and to
convert all fetches to tensors or ops as needed.
fetches: An arbitrary fetch structure: singleton, list, tuple, namedtuple,
or dict.
feeds: A feed dict where keys are Tensors.
feed_handles: A dict from feed Tensors to T... | github-repos |
def group(self, group_type, name, **kwargs):
group_obj = Group(group_type, name, **kwargs)
return self._group(group_obj) | Add Group data to Batch object.
Args:
group_type (str): The ThreatConnect define Group type.
name (str): The name for this Group.
date_added (str, kwargs): The date timestamp the Indicator was created.
xid (str, kwargs): The external id for this Group.
Returns:
obj: An instance of Group. | juraj-google-style |
def _get_elmt_amt_in_rxt(self, rxt):
return sum([rxt.get_el_amount(e) for e in self.pd.elements]) | Computes total number of atoms in a reaction formula for elements
not in external reservoir. This method is used in the calculation
of reaction energy per mol of reaction formula.
Args:
rxt (Reaction): a reaction.
Returns:
Total number of atoms for non_reservoir elements. | juraj-google-style |
def query(self, connection, query, fetch=True):
self.install_module(connection)
statements = sqlparse.parse(sqlparse.format(query, strip_comments=True))
logger.debug('Finding and installing all partitions from query. \n query: {}'.format(query))
new_query... | Creates virtual tables for all partitions found in the query and executes query.
Args:
query (str): sql query
fetch (bool): fetch result from database if True, do not fetch overwise. | juraj-google-style |
def process_result_value(self, value, dialect):
masks = list()
if value:
for e in enums.CryptographicUsageMask:
if e.value & value:
masks.append(e)
return masks | Returns a new list of enums.CryptographicUsageMask Enums. This converts
the integer value into the list of enums.
Args:
value(int): The integer value stored in the database that is used
to create the list of enums.CryptographicUsageMask Enums.
dialect(string): SQL dialect | juraj-google-style |
def get_metrics(self, name=None):
return self._get_elements(self.metrics, 'metrics', Metric, name=name) | Get metrics for this operator.
Args:
name(str, optional): Only return metrics matching `name`, where `name` can be a regular expression. If
`name` is not supplied, then all metrics for this operator are returned.
Returns:
list(Metric): List of matching metrics.
Retrieving a list of metrics whose name contains the s... | codesearchnet |
def get_available_transcript_languages(video_id):
available_languages = VideoTranscript.objects.filter(
video__edx_video_id=video_id
).values_list(
'language_code', flat=True
)
return list(available_languages) | Get available transcript languages
Arguments:
video_id(unicode): An id identifying the Video.
Returns:
A list containing transcript language codes for the Video. | juraj-google-style |
def iter(self):
page = 1
fetch_all = True
url = '{}/{}'.format(__endpoint__, self.type.RESOURCE)
if ('page' in self.params):
page = self.params['page']
fetch_all = False
response = RestClient.get(url, self.params)[self.type.RESOURCE]
while len(response):
for item in respo... | Gets all resources, automating paging through data
Returns:
iterable of object: Iterable of resource objects | codesearchnet |
def _ProcessAMCacheFileKey(self, am_entry, parser_mediator):
amcache_datetime = am_entry.get_value_by_name(
self._AMCACHE_DATETIME).get_data_as_integer()
event_data = AmcacheEventData()
event_data.full_path = am_entry.get_value_by_name(
self._AMCACHE_FULL_PATH).get_data_as_string()
... | Parses an Amcache Root/File key for events.
Args:
am_entry (pyregf.key): amcache File key.
parser_mediator (ParserMediator): mediates interactions between parsers
and other components, such as storage and dfvfs. | juraj-google-style |
def ask_stories(self, raw=False, limit=None):
ask_stories = self._get_stories('askstories', limit)
if raw:
ask_stories = [story.raw for story in ask_stories]
return ask_stories | Returns list of item ids of latest Ask HN stories
Args:
limit (int): specifies the number of stories to be returned.
raw (bool): Flag to indicate whether to transform all
objects into raw json.
Returns:
`list` object containing ids of Ask HN stories. | codesearchnet |
def ListPlugins(logdir):
plugins_dir = os.path.join(logdir, _PLUGINS_DIR)
try:
entries = tf.io.gfile.listdir(plugins_dir)
except tf.errors.NotFoundError:
return []
return [x.rstrip('/') for x in entries
if x.endswith('/') or _IsDirectory(plugins_dir, x)] | List all the plugins that have registered assets in logdir.
If the plugins_dir does not exist, it returns an empty list. This maintains
compatibility with old directories that have no plugins written.
Args:
logdir: A directory that was created by a TensorFlow events writer.
Returns:
a list of plugin names, as string... | juraj-google-style |
def get_descriptor_defaults(self, api_info, hostname=None):
if self.__request:
hostname = self.__request.reconstruct_hostname()
protocol = self.__request.url_scheme
else:
hostname = (hostname or util.get_app_hostname() or
api_info.hostname)
protocol = 'http' if ((h... | Gets a default configuration for a service.
Args:
api_info: _ApiInfo object for this service.
hostname: string, Hostname of the API, to override the value set on the
current service. Defaults to None.
Returns:
A dictionary with the default configuration. | juraj-google-style |
def synthesize(self, duration):
sr = self.samplerate.samples_per_second
seconds = (duration / Seconds(1))
samples = np.random.uniform(low=(- 1.0), high=1.0, size=int((sr * seconds)))
return AudioSamples(samples, self.samplerate) | Synthesize white noise
Args:
duration (numpy.timedelta64): The duration of the synthesized sound | codesearchnet |
def populations():
city_pops = {}
fname = pkg_resources.resource_filename(__name__, 'resources/CityPops.csv')
with open(fname, 'rU') as csvfile:
reader = csv.reader(csvfile, delimiter=',')
for row in reader:
city_pops[row[0]] = int(row[1])
return city_pops | Get a dictionary of Backpage city names mapped to their citizen populations.
Returns:
dictionary of Backpage city names mapped to their populations (integers) | codesearchnet |
def unique_name(self, name, mark_as_used=True):
scope_name = tf.get_variable_scope().name
if scope_name:
name = ((scope_name + '/') + name)
name_key = name.lower()
i = self._names_in_use.get(name_key, 0)
if mark_as_used:
self._names_in_use[name_key] = (i + 1)
if (i > 0):
... | Like tf.Graph.unique_name, returns a unique operation name for `name`.
Args:
name: The name for an operation.
mark_as_used: whether to mark this name as being used.
Returns:
A string to use as the name for the operation. | codesearchnet |
def get_events(self, event_title, regex=False):
regex_val = 0
if regex:
regex_val = 1
r = requests.get('{0}/events/?api_key={1}&username={2}&c-title={3}®ex={4}'.format(self.url, self.api_key, self.username, event_title, regex_val), verify=self.verify)
if (r.status_code == 200):
json_o... | Search for events with the provided title
Args:
event_title: The title of the event
Returns:
An event JSON object returned from the server with the following:
{
"meta":{
"limit": 20, "next": null, "offset": 0,
"previous": null, "total_count": 3
},
"objects": [{}, {}, etc]
}
or None if an error occurred. | codesearchnet |
def load_requires_from_file(filepath):
with open(filepath) as fp:
return [pkg_name.strip() for pkg_name in fp.readlines()] | Read a package list from a given file path.
Args:
filepath: file path of the package list.
Returns:
a list of package names. | codesearchnet |
def __init__(self, _channel, loop=None, executor=None, standalone_pool_for_streaming=False):
self._channel = _channel
if loop is None:
loop = _asyncio.get_event_loop()
self._loop = loop
self._executor = executor
self._standalone_pool = standalone_pool_for_str... | Constructor.
Args:
_channel: wrapped grpc.Channel
loop: asyncio event loop
executor: a thread pool, or None to use the default pool of the loop
standalone_pool_for_streaming: create a new thread pool (with 1 thread) for each streaming
method | juraj-google-style |
def reward(self, action=None):
reward = 0.
if self._check_success():
reward = 1.0
if self.reward_shaping:
cube_pos = self.sim.data.body_xpos[self.cube_body_id]
gripper_site_pos = self.sim.data.site_xpos[self.eef_site_... | Reward function for the task.
The dense reward has three components.
Reaching: in [0, 1], to encourage the arm to reach the cube
Grasping: in {0, 0.25}, non-zero if arm is grasping the cube
Lifting: in {0, 1}, non-zero if arm has lifted the cube
The sparse reward only consists of the lifting component.
Args:
action... | juraj-google-style |
def exists(self, filename):
if is_package(filename):
filepath = os.path.join(self.connection["mount_point"],
"Packages", filename)
else:
filepath = os.path.join(self.connection["mount_point"],
"Scrip... | Report whether a file exists on the distribution point.
Determines file type by extension.
Args:
filename: Filename you wish to check. (No path! e.g.:
"AdobeFlashPlayer-14.0.0.176.pkg") | juraj-google-style |
def parse(self, arguments):
new_values = self._parse(arguments)
if self.present:
self.value.extend(new_values)
else:
self.value = new_values
self.present += len(new_values) | Parses one or more arguments with the installed parser.
Args:
arguments: a single argument or a list of arguments (typically a
list of default values); a single argument is converted
internally into a list containing one item. | juraj-google-style |
def xxd_output_to_bytes(input_cc_file):
pattern = re.compile('\\W*(0x[0-9a-fA-F,x ]+).*')
model_bytearray = bytearray()
with open(input_cc_file) as file_handle:
for line in file_handle:
values_match = pattern.match(line)
if values_match is None:
continue
... | Converts xxd output C++ source file to bytes (immutable).
Args:
input_cc_file: Full path name to th C++ source file dumped by xxd
Raises:
RuntimeError: If input_cc_file path is invalid.
IOError: If input_cc_file cannot be opened.
Returns:
A bytearray corresponding to the input cc file array. | github-repos |
def __fetch_route53_zone_records(self, zone_id):
route53 = self.session.client('route53')
done = False
nextName = nextType = None
records = {}
try:
while (not done):
if (nextName and nextType):
response = route53.list_resource_record_sets(HostedZoneId=zone_id, Sta... | Return all resource records for a specific Route53 zone
Args:
zone_id (`str`): Name / ID of the hosted zone
Returns:
`dict` | codesearchnet |
def peek(self, iroute: "InstanceRoute") -> Optional[Value]:
val = self.value
sn = self.schema_node
for sel in iroute:
val, sn = sel.peek_step(val, sn)
if val is None:
return None
return val | Return a value within the receiver's subtree.
Args:
iroute: Instance route (relative to the receiver). | juraj-google-style |
def post_process_semantic_segmentation(self, outputs, target_sizes: Optional[List[Tuple]]=None):
logits = outputs.logits
if target_sizes is not None:
if len(logits) != len(target_sizes):
raise ValueError('Make sure that you pass in as many target sizes as the batch dimension of the logits')
... | Converts the output of [`MobileNetV2ForSemanticSegmentation`] into semantic segmentation maps. Only supports PyTorch.
Args:
outputs ([`MobileNetV2ForSemanticSegmentation`]):
Raw outputs of the model.
target_sizes (`List[Tuple]` of length `batch_size`, *optional*):
List of tuples corresponding to the requested final si... | github-repos |
def fixity(self, response_format=None):
if (not response_format):
response_format = self.repo.default_serialization
response = self.repo.api.http_request('GET', ('%s/fcr:fixity' % self.uri))
fixity_graph = self.repo.api.parse_rdf_payload(response.content, response.headers)
for outcome in fixity_... | Issues fixity check, return parsed graph
Args:
None
Returns:
(dict): ('verdict':(bool): verdict of fixity check, 'premis_graph':(rdflib.Graph): parsed PREMIS graph from check) | codesearchnet |
def _add_value(self, field_name: str, value, provenance_path=None) -> bool:
if not isinstance(value, list):
value = [value]
all_valid = True
for x in value:
valid = self._add_single_value(field_name, x, provenance_path=provenance_path)
all_valid = al... | Helper function to add values to a knowledge graph
Args:
field_name: a field in the knowledge graph, assumed correct
value: any Python type
Returns: True if the value is compliant with the field schema, False otherwise | juraj-google-style |
def _execute(self, command, params=None):
if (not params):
params = {}
params['id'] = self._id
return self._parent.execute(command, params) | Executes a command against the underlying HTML element.
Args:
command: The name of the command to _execute as a string.
params: A dictionary of named parameters to send with the command.
Returns:
The command's JSON response loaded into a dictionary object. | codesearchnet |
def read_var_bytes(self, max_size=sys.maxsize) -> bytes:
length = self.read_var_int(max_size)
return self.read_bytes(length) | Read a variable length of bytes from the stream.
Args:
max_size (int): (Optional) maximum number of bytes to read.
Returns:
bytes: | codesearchnet |
def content_metadata_uploads(self, mirror=False):
excludes_str = ''
includes_cmds = []
cmd_base = self._get_upload_cmd(mirror=mirror)
for content in self.s3props.get('content_metadata'):
full_path = os.path.join(self.artifact_path, content['path'])
if no... | Finds all specified encoded directories and uploads in multiple parts,
setting metadata for objects.
Args:
mirror (bool): If true, uses a flat directory structure instead of nesting under a version.
Returns:
bool: True if uploaded | juraj-google-style |
def _create_graph(structure_dict):
graph = pydot.Dot()
for node in structure_dict['nodes']:
graph.add_node(pydot.Node(node))
for (node1, node2) in structure_dict['edges']:
graph.add_edge(pydot.Edge(node1, node2))
return graph | Creates pydot graph from the pipeline structure dict.
Args:
structure_dict (dict): dict returned by step.upstream_structure
Returns:
graph (pydot.Dot): object representing upstream pipeline structure (with regard to the current Step). | codesearchnet |
def loop(coord, timer_interval_secs, target, args=None, kwargs=None):
looper = LooperThread(coord, timer_interval_secs, target=target, args=args, kwargs=kwargs)
looper.start()
return looper | Start a LooperThread that calls a function periodically.
If `timer_interval_secs` is None the thread calls `target(args)`
repeatedly. Otherwise `target(args)` is called every `timer_interval_secs`
seconds. The thread terminates when a stop of the coordinator is
requested.
Args:
coord: A Coordinator.
timer_interval_... | github-repos |
def get(self, request, *args, **kwargs):
context = self.get_context_data(**kwargs)
context.update(self.extra_context)
context['crumbs'] = self.get_crumbs()
context['title'] = self.title
context['suit'] = 'suit' in settings.INSTALLED_APPS
if context.get('dashboard... | Django view get function.
Add items of extra_context, crumbs and grid to context.
Args:
request (): Django's request object.
*args (): request args.
**kwargs (): request kwargs.
Returns:
response: render to response with context. | juraj-google-style |
def add_extension_to_message(extension: message.Message, msg: message.Message) -> None:
desc = msg.DESCRIPTOR
fields_by_url = {get_inlined_extension_url(field): field for field in desc.fields if field.name != 'id'}
id_field = desc.fields_by_name.get('id')
if proto_utils.field_is_set(extension, id_field)... | Recursively parses extension and adds to message.
Args:
extension: The FHIR extension to serialize and add.
msg: The message to add the extension onto
Raises:
InvalidFhirError: In the event that a value is set on the extension, but the
corresponding message field to copy it to is repeated (extension values are
singul... | github-repos |
def _as_document(self, dataset):
assert isinstance(dataset, Dataset)
execute = object_session(dataset).connection().execute
query = text()
columns = u('\n').join(
[u(' ').join(list(text_type(e) for e in t)) for t in execute(query, dataset_vid=str(datase... | Converts dataset to document indexed by to FTS index.
Args:
dataset (orm.Dataset): dataset to convert.
Returns:
dict with structure matches to BaseDatasetIndex._schema. | juraj-google-style |
def scan_directory(self, dirname, exclude_exts=(), exclude_fnames=()):
for i, ext in enumerate(exclude_exts):
if not ext.strip().startswith("."):
exclude_exts[i] = "." + ext.strip()
paths = []
for fname in os.listdir(dirname):
root, ext... | Analyze the files contained in directory dirname.
Args:
dirname: directory path
exclude_exts: list of file extensions that should be skipped.
exclude_fnames: list of file names that should be skipped.
Returns:
List of pseudopotential objects. | juraj-google-style |
def as_vartype(vartype):
if isinstance(vartype, Vartype):
return vartype
try:
if isinstance(vartype, str):
vartype = Vartype[vartype]
elif isinstance(vartype, frozenset):
vartype = Vartype(vartype)
else:
vartype = Vartype(frozenset(vartyp... | Cast various inputs to a valid vartype object.
Args:
vartype (:class:`.Vartype`/str/set):
Variable type. Accepted input values:
* :class:`.Vartype.SPIN`, ``'SPIN'``, ``{-1, 1}``
* :class:`.Vartype.BINARY`, ``'BINARY'``, ``{0, 1}``
Returns:
:class:`.Vartype`: Either :class:`.Vartype.SPIN` or
:class:`.Vartype.BINARY`.... | juraj-google-style |
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str]=None) -> Tuple[str]:
if not os.path.isdir(save_directory):
logger.error(f'Vocabulary path ({save_directory}) should be a directory')
return
out_vocab_file = os.path.join(save_directory, (filename_prefix + '-' if filena... | Saves the tokenizer's vocabulary dictionary to the provided save_directory.
Args:
save_directory (`str`):
A path to the directory where to saved. It will be created if it doesn't exist.
filename_prefix (`Optional[str]`, *optional*):
A prefix to add to the names of the files saved by the tokenizer. | github-repos |
def _as_variant_tensor(self):
raise NotImplementedError(f'{type(self)}.as_variant_tensor()') | Creates a scalar `tf.Tensor` of `tf.variant` representing this dataset.
Returns:
A scalar `tf.Tensor` of `tf.variant` type, which represents this dataset. | github-repos |
def prepare_image_question_encoder(image_feat, question, hparams):
encoder_input = tf.concat([image_feat, question], axis=1)
encoder_padding = common_attention.embedding_to_padding(encoder_input)
ignore_padding = common_attention.attention_bias_ignore_padding(
encoder_padding)
encoder_self_attention_b... | Prepare encoder.
Args:
image_feat: a Tensor.
question: a Tensor.
hparams: run hyperparameters
Returns:
encoder_input: a Tensor, bottom of encoder stack
encoder_self_attention_bias: a bias tensor for use in encoder self-attention | juraj-google-style |
def load_region(adapter, case_id, hgnc_id=None, chrom=None, start=None, end=None):
if hgnc_id:
gene_obj = adapter.hgnc_gene(hgnc_id)
if not gene_obj:
ValueError("Gene {} does not exist in database".format(hgnc_id))
chrom = gene_obj['chromosome']
start = gene_obj['sta... | Load all variants in a region defined by a HGNC id
Args:
adapter (MongoAdapter)
case_id (str): Case id
hgnc_id (int): If all variants from a gene should be uploaded
chrom (str): If variants from coordinates should be uploaded
start (int): Start position for region
end (int): Stop position for region | juraj-google-style |
def _StopAnalysisProcesses(self, abort=False):
logger.debug('Stopping analysis processes.')
self._StopMonitoringProcesses()
if abort:
self._AbortTerminate()
if not self._use_zeromq:
logger.debug('Emptying queues.')
for event_queue in self._event_queues.val... | Stops the analysis processes.
Args:
abort (bool): True to indicated the stop is issued on abort. | juraj-google-style |
def get(self, uri):
uri = self.URI + uri
return self._client.get(uri) | Gets an index resource by URI.
Args:
uri: The resource URI.
Returns:
dict: The index resource. | juraj-google-style |
def __remove_alias(type_):
if isinstance(type_, cpptypes.declarated_t) and \
isinstance(type_.declaration, typedef.typedef_t):
return __remove_alias(type_.declaration.decl_type)
if isinstance(type_, cpptypes.compound_t):
type_.base = __remove_alias(type_.base)
return typ... | Implementation detail.
Args:
type_ (type_t): type
Returns:
type_t: the type associated to the inputted type | juraj-google-style |
def get_fieldset_index(fieldsets, index_or_name):
if isinstance(index_or_name, six.integer_types):
return index_or_name
for key, value in enumerate(fieldsets):
if value[0] == index_or_name:
return key
raise KeyError("Key not found: '{}'.".format(index_or_name)) | Return the index of a fieldset in the ``fieldsets`` list.
Args:
fieldsets (list): The original ``fieldsets`` list.
index_or_name (int or str): The value of the reference element, or directly its numeric index.
Returns:
(int) The index of the fieldset in the ``fieldsets`` list. | juraj-google-style |
def PushEvent(self, event):
event_string = event.GetAttributeValuesString()
heap_values = (event.timestamp, event.timestamp_desc, event_string, event)
heapq.heappush(self._heap, heap_values) | Pushes an event onto the heap.
Args:
event (EventObject): event. | juraj-google-style |
def execute(self, sensor_graph, scope_stack):
parent = scope_stack[-1]
alloc = parent.allocator
output = alloc.allocate_stream(DataStream.UnbufferedType, attach=True)
trigger_stream, trigger_cond = parent.trigger_chain()
streamer_const = alloc.allocate_stream... | Execute this statement on the sensor_graph given the current scope tree.
This adds a single node to the sensor graph with the trigger_streamer function
as is processing function.
Args:
sensor_graph (SensorGraph): The sensor graph that we are building or
modifying
scope_stack (list(Scope)): A stack of nested scopes th... | juraj-google-style |
def params_size(num_components, component_params_size, name=None):
with tf.compat.v1.name_scope(name, 'MixtureSameFamily_params_size', [num_components, component_params_size]):
num_components = tf.convert_to_tensor(value=num_components, name='num_components', dtype_hint=tf.int32)
component_params_si... | Number of `params` needed to create a `MixtureSameFamily` distribution.
Arguments:
num_components: Number of component distributions in the mixture
distribution.
component_params_size: Number of parameters needed to create a single
component distribution.
name: The name to use for the op to compute the number of param... | codesearchnet |
def arctan(self: EventSetOrNode) -> EventSetOrNode:
from temporian.core.operators.unary import arctan
return arctan(self) | Calculates the inverse tangent of an [`EventSet`][temporian.EventSet]'s features.
Can only be used on floating point features.
Example:
```python
>>> a = tp.event_set(
... timestamps=[1, 2, 3, 4],
... features={"M": [0, 1.0, -1.0, 5.0]},
... )
>>> a.arctan()
indexes: ...
timestamps: [1. 2. 3. 4.]
'M': [ 0. ... | github-repos |
def update_email_asset(self, asset_id, name, asset_type):
self.update_asset('EMAIL', asset_id, name, asset_type) | Updates a Email Asset
Args:
name: The name provided to the email asset
asset_type: The type provided to the email asset
asset_id:
Returns: | juraj-google-style |
def add_handler(self, handler):
handler['logger'] = self._get_logger(handler)
handler['reads'] = 0
handler['data_read'] = 0
self.capture_handlers.append(handler) | Add an additional handler
Args:
handler:
A dictionary of handler configuration for the handler
that should be added. See :func:`__init__` for details
on valid parameters. | juraj-google-style |
def _RunIpRoute(self, args=None, options=None):
args = args or []
options = options or {}
command = ['ip', 'route']
command.extend(args)
for item in options.items():
command.extend(item)
try:
process = subprocess.Popen(
command, stdout=subprocess.PIPE, stderr=subproces... | Run a command with ip route and return the response.
Args:
args: list, the string ip route command args to execute.
options: dict, the string parameters to append to the ip route command.
Returns:
string, the standard output from the ip route command execution. | juraj-google-style |
def backward_propagation(parameters, cache, X, Y):
m = X.shape[1]
W1 = parameters["W1"]
W2 = parameters["W2"]
A1 = cache["A1"]
A2 = cache["A2"]
dZ2 = A2 - Y
dW2 = 1.0 / m * np.dot(dZ2, A1.T)
db2 = 1.0 / m * np.sum(dZ2, axis=1, keepdims=True)
dZ1 = W2.T * dZ2 * ... | Implement the backward propagation using the instructions above.
Arguments:
parameters -- python dictionary containing our parameters
cache -- a dictionary containing "Z1", "A1", "Z2" and "A2".
X -- input data of shape (2, number of examples)
Y -- "true" labels vector of shape (1, number of examples)
Returns:
grads -... | juraj-google-style |
def cmd_list(options):
(i_info, param_str) = gather_data(options)
if i_info:
awsc.get_all_aminames(i_info)
param_str = "Instance List - " + param_str + "\n"
list_instances(i_info, param_str)
else:
print("No instances found with parameters: {}".format(param_str)) | Gather data for instances matching args and call display func.
Args:
options (object): contains args and data from parser. | juraj-google-style |
def numbafy(fn, args, compiler='jit', **nbkws):
kwargs = {}
if (not isinstance(args, (tuple, list))):
args = (args,)
if isinstance(compiler, six.string_types):
compiler_ = getattr(nb, compiler, None)
if (compiler is None):
raise AttributeError('No numba function with name... | Compile a string, sympy expression or symengine expression using numba.
Not all functions are supported by Python's numerical package (numpy). For
difficult cases, valid Python code (as string) may be more suitable than
symbolic expressions coming from sympy, symengine, etc. When compiling
vectorized functions, includ... | codesearchnet |
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