code stringlengths 20 4.93k | docstring stringlengths 33 1.27k | source stringclasses 3
values |
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def add(self, layer):
if hasattr(layer, '_keras_history'):
origin_layer = layer._keras_history[0]
if isinstance(origin_layer, input_layer.InputLayer):
layer = origin_layer
logging.warning('Please add `keras.layers.InputLayer` instead of `keras.Input` to Sequential model. `ker... | Adds a layer instance on top of the layer stack.
Args:
layer: layer instance.
Raises:
TypeError: If `layer` is not a layer instance.
ValueError: In case the `layer` argument does not
know its input shape.
ValueError: In case the `layer` argument has
multiple output tensors, or is already connected
somewhere else (for... | github-repos |
def _InfoBackup(component):
info = {}
info['type_name'] = type(component).__name__
info['string_form'] = str(component)
filename, lineno = GetFileAndLine(component)
info['file'] = filename
info['line'] = lineno
info['docstring'] = inspect.getdoc(component)
try:
info['length'] = s... | Returns a dict with information about the given component.
This function is to be called only in the case that IPython's
oinspect module is not available. The info dict it produces may
contain less information that contained in the info dict produced
by oinspect.
Args:
component: The component to analyze.
Returns:
A ... | github-repos |
def selfSignCert(self, cert, pkey):
cert.set_issuer(cert.get_subject())
cert.sign(pkey, self.signing_digest) | Self-sign a certificate.
Args:
cert (OpenSSL.crypto.X509): The certificate to sign.
pkey (OpenSSL.crypto.PKey): The PKey with which to sign the certificate.
Examples:
Sign a given certificate with a given private key:
cdir.selfSignCert(mycert, myotherprivatekey)
Returns:
None | juraj-google-style |
def data(self, rows=None):
rows = tf.range(self._capacity) if rows is None else rows
assert rows.shape.ndims == 1
episode = tools.nested.map(lambda var: tf.gather(var, rows), self._buffers)
length = tf.gather(self._length, rows)
return episode, length | Access a batch of episodes from the memory.
Padding elements after the length of each episode are unspecified and might
contain old data.
Args:
rows: Episodes to select, defaults to all.
Returns:
Tuple containing a tuple of transition quantities with batch and time
dimensions, and a batch of sequence lengths. | juraj-google-style |
def bind_parameters(self, value_dict):
new_circuit = self.copy()
if (value_dict.keys() > self.parameters):
raise QiskitError('Cannot bind parameters ({}) not present in the circuit.'.format([str(p) for p in (value_dict.keys() - self.parameters)]))
for (parameter, value) in value_dict.items():
... | Assign parameters to values yielding a new circuit.
Args:
value_dict (dict): {parameter: value, ...}
Raises:
QiskitError: If value_dict contains parameters not present in the circuit
Returns:
QuantumCircuit: copy of self with assignment substitution. | codesearchnet |
def acos(cls, x: 'TensorFluent') -> 'TensorFluent':
return cls._unary_op(x, tf.acos, tf.float32) | Returns a TensorFluent for the arccos function.
Args:
x: The input fluent.
Returns:
A TensorFluent wrapping the arccos function. | juraj-google-style |
def get_array_for_fit(observables: dict, track_pt_bin: int, jet_pt_bin: int) -> histogram.Histogram1D:
for (name, observable) in observables.items():
if ((observable.track_pt_bin == track_pt_bin) and (observable.jet_pt_bin == jet_pt_bin)):
return histogram.Histogram1D.from_existing_hist(observab... | Get a Histogram1D associated with the selected jet and track pt bins.
This is often used to retrieve data for fitting.
Args:
observables (dict): The observables from which the hist should be retrieved.
track_pt_bin (int): Track pt bin of the desired hist.
jet_ptbin (int): Jet pt bin of the desired hist.
Returns:
Hist... | codesearchnet |
def GetNumberOfEventSources(self):
number_of_event_sources = self._CountStoredAttributeContainers(self._CONTAINER_TYPE_EVENT_SOURCE)
number_of_event_sources += self._GetNumberOfSerializedAttributeContainers(self._CONTAINER_TYPE_EVENT_SOURCE)
return number_of_event_sources | Retrieves the number event sources.
Returns:
int: number of event sources. | codesearchnet |
def index_buffer(self, buffer, index_element_size=4):
if (not (type(buffer) in [moderngl.Buffer, numpy.ndarray, bytes])):
raise VAOError('buffer parameter must be a moderngl.Buffer, numpy.ndarray or bytes instance')
if isinstance(buffer, numpy.ndarray):
buffer = self.ctx.buffer(buffer.tobytes())... | Set the index buffer for this VAO
Args:
buffer: ``moderngl.Buffer``, ``numpy.array`` or ``bytes``
Keyword Args:
index_element_size (int): Byte size of each element. 1, 2 or 4 | codesearchnet |
def get_image_features(self, pixel_values: torch.FloatTensor, vision_feature_layer: Optional[Union[int, List[int]]]=None, vision_feature_select_strategy: Optional[str]=None, **kwargs):
vision_feature_layer = vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
vision_fe... | Obtains image last hidden states from the vision tower and apply multimodal projection.
Args:
pixel_values (`torch.FloatTensor]` of shape `(batch_size, channels, height, width)`):
The tensors corresponding to the input images.
vision_feature_layer (`Union[int, List[int]]`, *optional*):
The index of the layer to select... | github-repos |
def get_changeset(changeset):
url = 'https:
return ET.fromstring(requests.get(url).content) | Get the changeset using the OSM API and return the content as a XML
ElementTree.
Args:
changeset: the id of the changeset. | codesearchnet |
def stitch_values(values_and_indices_list):
length = 0
for values_and_indices in values_and_indices_list:
length += len(values_and_indices[0])
result = [None] * length
for values_and_indices in values_and_indices_list:
if values_and_indices and values_and_indices[0]:
for v, i... | Stitch values together according to their indices.
Args:
values_and_indices_list: a list of tuples of values and indices indicating
the values and positions in the returned list.
Returns:
a stitched list of values. | github-repos |
def _ref(self):
return self._variable | Returns a reference to this variable.
You usually do not need to call this method as all ops that need a reference
to the variable call it automatically.
Returns is a `Tensor` which holds a reference to the variable. You can
assign a new value to the variable by passing the tensor to an assign op.
See `tf.Variable.v... | github-repos |
def peek(self) -> str:
try:
return self.input[self.offset]
except IndexError:
raise EndOfInput(self) | Return the next character without advancing offset.
Raises:
EndOfInput: If past the end of `self.input`. | codesearchnet |
def device_id_to_slug(did):
try:
device_slug = IOTileDeviceSlug(did, allow_64bits=False)
except ValueError:
raise ArgumentError('Unable to recognize {} as a device id'.format(did))
return str(device_slug) | Converts a device id into a correct device slug.
Args:
did (long) : A device id
did (string) : A device slug in the form of XXXX, XXXX-XXXX-XXXX, d--XXXX, d--XXXX-XXXX-XXXX-XXXX
Returns:
str: The device slug in the d--XXXX-XXXX-XXXX-XXXX format
Raises:
ArgumentError: if the ID is not in the [1, 16**12] range, or if no... | codesearchnet |
def _detect(self):
results = []
for c in self.contracts:
for f in c.functions:
if (f.contract != c):
continue
if (f.view or f.pure):
if f.contains_assembly:
attr = ('view' if f.view else 'pure')
info = '{}.{}... | Detect the constant function changing the state
Recursively visit the calls
Returns:
list: {'vuln', 'filename,'contract','func','#varsWritten'} | codesearchnet |
def extract_cookies(self, response, request, referrer_host=None):
new_response = HTTPResponseInfoWrapper(response)
new_request = convert_http_request(request, referrer_host)
self._cookie_jar.extract_cookies(new_response, new_request) | Wrapped ``extract_cookies``.
Args:
response: An instance of :class:`.http.request.Response`.
request: An instance of :class:`.http.request.Request`.
referrer_host (str): An hostname or IP address of the referrer
URL. | codesearchnet |
def Serialize(self, writer):
writer.WriteHashes(self.HashStart)
if self.HashStop is not None:
writer.WriteUInt256(self.HashStop) | Serialize object.
Args:
writer (neo.IO.BinaryWriter): | juraj-google-style |
def save_checkpoint(model, filename, optimizer=None, meta=None):
if meta is None:
meta = {}
elif not isinstance(meta, dict):
raise TypeError('meta must be a dict or None, but got {}'.format(
type(meta)))
meta.update(mmcv_version=mmcv.__version__, time=time.asctime())
mm... | Save checkpoint to file.
The checkpoint will have 3 fields: ``meta``, ``state_dict`` and
``optimizer``. By default ``meta`` will contain version and time info.
Args:
model (Module): Module whose params are to be saved.
filename (str): Checkpoint filename.
optimizer (:obj:`Optimizer`, optional): Optimizer to be saved.... | juraj-google-style |
def create_alias(alias_name, alias_command):
(alias_name, alias_command) = (alias_name.strip(), alias_command.strip())
alias_table = get_alias_table()
if (alias_name not in alias_table.sections()):
alias_table.add_section(alias_name)
alias_table.set(alias_name, 'command', alias_command)
_com... | Create an alias.
Args:
alias_name: The name of the alias.
alias_command: The command that the alias points to. | codesearchnet |
def sg_transpose(tensor, opt):
assert (opt.perm is not None), 'perm is mandatory'
return tf.transpose(tensor, opt.perm, name=opt.name) | r"""Permutes the dimensions according to `opt.perm`.
See `tf.transpose()` in tensorflow.
Args:
tensor: A `Tensor` (automatically given by chain).
opt:
perm: A permutation of the dimensions of `tensor`. The target shape.
name: If provided, replace current tensor's name.
Returns:
A `Tensor`. | codesearchnet |
def _get_format(format, fname, inp=None):
fmt = None
err = True
if (format is not None):
if (format in fmt_to_exts):
fmt = format
err = False
elif fname:
file_ext = os.path.splitext(fname)[1][len(os.path.extsep):]
for (fmt_name, exts) in fmt_to_exts.items(... | Try to guess markup format of given input.
Args:
format: explicit format override to use
fname: name of file, if a file was used to read `inp`
inp: optional bytestring to guess format of (can be None, if markup
format is to be guessed only from `format` and `fname`)
Returns:
guessed format (a key of fmt_to_exts dict)
... | codesearchnet |
def accuracy(y_true: [list, np.ndarray], y_predicted: [list, np.ndarray]) -> float:
examples_len = len(y_true)
correct = sum([(y1 == y2) for (y1, y2) in zip(y_true, y_predicted)])
return ((correct / examples_len) if examples_len else 0) | Calculate accuracy in terms of absolute coincidence
Args:
y_true: array of true values
y_predicted: array of predicted values
Returns:
portion of absolutely coincidental samples | codesearchnet |
def AddForwardedIp(self, address, interface):
address = address if IP_ALIAS_REGEX.match(address) else '%s/32' % address
args = ['add', 'to', 'local', address]
options = self._CreateRouteOptions(dev=interface)
self._RunIpRoute(args=args, options=options) | Configure a new IP address on the network interface.
Args:
address: string, the IP address to configure.
interface: string, the output device to use. | juraj-google-style |
def convert_drive(self, shift, instruction):
command_dict = {'name': instruction.command.name, 't0': (shift + instruction.start_time), 'ch': instruction.channels[0].name}
return self._qobj_model(**command_dict) | Return converted `PulseInstruction`.
Args:
shift(int): Offset time.
instruction (PulseInstruction): drive instruction.
Returns:
dict: Dictionary of required parameters. | codesearchnet |
def __directory_list_descriptor(self, configs):
descriptor = {'kind': 'discovery
items = []
for config in configs:
item_descriptor = self.__item_descriptor(config)
if item_descriptor:
items.append(item_descriptor)
if items:
descriptor['items'] = items
return descr... | Builds a directory list for an API.
Args:
configs: List of dicts containing the service configurations to list.
Returns:
A dictionary that can be deserialized into JSON in discovery list format.
Raises:
ApiConfigurationError: If there's something wrong with the API
configuration, such as a multiclass API decorated w... | codesearchnet |
def _LastEntryTimestamp(dct, upper_bound_timestamp):
if upper_bound_timestamp is None:
upper_bound = lambda _: True
else:
upper_bound = lambda key: key <= upper_bound_timestamp
try:
return max(filter(upper_bound, iterkeys(dct)))
except ValueError:
return None | Searches for greatest timestamp lower than the specified one.
Args:
dct: A dictionary from timestamps to some items.
upper_bound_timestamp: An upper bound for timestamp to be returned.
Returns:
Greatest timestamp that is lower than the specified one. If no such value
exists, `None` is returned. | juraj-google-style |
def AddSerializedFile(self, serialized_file_desc_proto):
from google.protobuf import descriptor_pb2
file_desc_proto = descriptor_pb2.FileDescriptorProto.FromString(serialized_file_desc_proto)
self.Add(file_desc_proto) | Adds the FileDescriptorProto and its types to this pool.
Args:
serialized_file_desc_proto: A bytes string, serialization of the
FileDescriptorProto to add. | codesearchnet |
def _parse_description(details):
description = details.find("div", {"class": "detailPopis"})
if not description:
return None
ekniha = description[0].find("div", {"class": "ekniha"})
if ekniha:
ekniha[0].replaceWith(dhtmlparser.HTMLElement(""))
detail = descript... | Parse description of the book.
Args:
details (obj): HTMLElement containing slice of the page with details.
Returns:
str/None: Details as string with currency or None if not found. | juraj-google-style |
def encode(self):
blob = bytearray()
for record in self.records:
blob += record.encode()
header = struct.pack('<LL', self.SCRIPT_MAGIC, (len(blob) + self.SCRIPT_HEADER_LENGTH))
blob = (header + blob)
sha = hashlib.sha256()
sha.update(blob)
hash_value = sha.digest()[:16]
return (b... | Encode this record into a binary blob.
This binary blob could be parsed via a call to FromBinary().
Returns:
bytearray: The binary encoded script. | codesearchnet |
def EncodeMessages(self, message_list, result, destination=None, timestamp=None, api_version=3):
if (api_version not in [3]):
raise RuntimeError(('Unsupported api version: %s, expected 3.' % api_version))
if (destination is None):
destination = self.server_name
cipher = self._GetServerCi... | Accepts a list of messages and encodes for transmission.
This function signs and then encrypts the payload.
Args:
message_list: A MessageList rdfvalue containing a list of GrrMessages.
result: A ClientCommunication rdfvalue which will be filled in.
destination: The CN of the remote system this should go to.
timestamp... | codesearchnet |
def get_poi_types(self, **kwargs):
params = {
'cultureInfo': util.language_code(kwargs.get('lang'))
}
result = self.make_request('geo', 'get_poi_types', **params)
values = result.get('types', [])
return True, [emtype.PoiType(**a) ... | Obtain POI types.
Args:
lang (str): Language code (*es* or *en*).
Returns:
Status boolean and parsed response (list[PoiType]), or message string
in case of error. | juraj-google-style |
def lookup_symbol(self, name, namespace_stack):
symbol = Symbol(name, name.split('::'), namespace_stack)
assert symbol.parts
if (symbol.parts[0] == ''):
symbol.parts = symbol.parts[1:]
elif (namespace_stack is not None):
result = self._lookup_in_all_namespaces(symbol)
if result:
... | Returns AST node and module for symbol if found.
Args:
name: 'name of the symbol to lookup'
namespace_stack: None or ['namespaces', 'in', 'current', 'scope']
Returns:
(ast.Node, module (ie, any object stored with symbol)) if found
Raises:
Error if the symbol cannot be found. | codesearchnet |
def get_summary(self):
func_summaries = [f.get_summary() for f in self.functions]
modif_summaries = [f.get_summary() for f in self.modifiers]
return (self.name, [str(x) for x in self.inheritance], [str(x) for x in self.variables], func_summaries, modif_summaries) | Return the function summary
Returns:
(str, list, list, list, list): (name, inheritance, variables, fuction summaries, modifier summaries) | codesearchnet |
def join_dags(self, names=None):
return self._client.send(Request(action='join_dags', payload={'names': names})).success | Wait for the specified dags to terminate.
This function blocks until the specified dags terminate. If no dags are specified
wait for all dags of the workflow, except the dag of the task calling this signal,
to terminate.
Args:
names (list): The names of the dags that have to terminate.
Returns:
bool: True if all the... | codesearchnet |
def ParseLines(lines, message, allow_unknown_extension=False,
allow_field_number=False):
parser = _Parser(allow_unknown_extension, allow_field_number)
return parser.ParseLines(lines, message) | Parses an text representation of a protocol message into a message.
Args:
lines: An iterable of lines of a message's text representation.
message: A protocol buffer message to merge into.
allow_unknown_extension: if True, skip over missing extensions and keep
parsing
allow_field_number: if True, both field number and ... | juraj-google-style |
def getUserForHost(self, user, host):
for name in iterFqdnUp(host):
usercert = '%s@%s' % (user, name)
if self.isUserCert(usercert):
return usercert | Gets the name of the first existing user cert for a given user and host.
Args:
user (str): The name of the user.
host (str): The name of the host.
Examples:
Get the name for the "myuser" user cert at "cool.vertex.link":
usercertname = cdir.getUserForHost('myuser', 'cool.vertex.link')
Returns:
str: The cert name, if... | juraj-google-style |
def postprocess(self, args: argparse.Namespace):
names = {k for k in self.pytype_single_args if hasattr(args, k)}
opt_map = {k: self._pytype_arg_map[k].long_opt for k in names}
pytype_config.Postprocessor(names, opt_map, args).process() | Postprocesses the subset of pytype_single_args that appear in args.
Args:
args: an argparse.Namespace. | github-repos |
def get_controller(self, path):
path_info = path.lstrip('/').split('/', 2)
try:
return self._routes.get(((path_info[0] + '/') + path_info[1]))
except (IndexError, KeyError):
return self._routes.get((path_info[0] or 'index')) | Return controller that handle given path.
Args:
- path: requested path, like: /blog/post_view/15 | codesearchnet |
def plot_title(ax, pretitle='', title='Figure', posttitle='', title_fontsize=14, title_arg=None):
current_title = ax.get_title()
if (not current_title):
current_title = ((pretitle + title) + posttitle)
title_arg = dict_if_none(title_arg)
ax.set_title(current_title, fontsize=title_fontsize, **tit... | Set title options of a matplotlib plot
Args:
ax: matplotlib axes
pretitle(str): String to include before the general title of the figure
posttitle (str): String to include after the general title of the figure
title (str): Set the title for the figure
title_fontsize (int): Defines the size of the title's font
title_ar... | codesearchnet |
def get_available_palettes(chosen_palette):
result = None
try:
result = ALL_PALETTES[:(ALL_PALETTES.index(chosen_palette) + 1)]
except ValueError:
pass
return result | Given a chosen palette, returns tuple of those available,
or None when not found.
Because palette support of a particular level is almost always a
superset of lower levels, this should return all available palettes.
Returns:
Boolean, None: is tty or None if not found. | codesearchnet |
def load(cls, fh):
dat = fh.read()
try:
ret = cls.from_json(dat)
except:
ret = cls.from_yaml(dat)
return ret | Load json or yaml data from file handle.
Args:
fh (file): File handle to load from.
Examlple:
>>> with open('data.json', 'r') as json:
>>> jsdata = composite.load(json)
>>>
>>> with open('data.yml', 'r') as yml:
>>> ymldata = composite.load(yml) | juraj-google-style |
def sample(self, num_rows=1):
self.check_fit()
res = {}
means = np.zeros(self.covariance.shape[0])
size = (num_rows,)
clean_cov = np.nan_to_num(self.covariance)
samples = np.random.multivariate_normal(means, clean_cov, size=size)
for i, (label, distrib... | Creates sintentic values stadistically similar to the original dataset.
Args:
num_rows: `int` amount of samples to generate.
Returns:
np.ndarray: Sampled data. | juraj-google-style |
def image_summary(predictions, targets, hparams):
del hparams
results = tf.cast(tf.argmax(predictions, axis=-1), tf.uint8)
gold = tf.cast(targets, tf.uint8)
summary1 = tf.summary.image("prediction", results, max_outputs=2)
summary2 = tf.summary.image("data", gold, max_outputs=2)
summary = tf.summary.merg... | Reshapes predictions and passes it to tensorboard.
Args:
predictions : The predicted image (logits).
targets : The ground truth.
hparams: model hparams.
Returns:
summary_proto: containing the summary images.
weights: A Tensor of zeros of the same shape as predictions. | juraj-google-style |
def get_status(self, batch_id):
with self._lock:
if self._batch_committed(batch_id):
return ClientBatchStatus.COMMITTED
if (batch_id in self._invalid):
return ClientBatchStatus.INVALID
if (batch_id in self._pending):
return ClientBatchStatus.PENDING
... | Returns the status enum for a batch.
Args:
batch_id (str): The id of the batch to get the status for
Returns:
int: The status enum | codesearchnet |
class IntSoftmax(nn.Module):
def __init__(self, output_bit, quant_mode=False, force_dequant='none'):
super().__init__()
self.output_bit = output_bit
self.max_bit = 32
self.quant_mode = quant_mode
if force_dequant in ['nonlinear', 'softmax']:
logger.info('Force de... | Quantized version of `torch.nn.Softmax`. Adds quantization-specific arguments on top of `torch.nn.Softmax`.
Args:
output_bit (`int`):
Bitwidth for the layer output activation.
quant_mode (`bool`, *optional*, defaults to `False`):
Whether or not the layer is quantized.
force_dequant (`str`, *optional*, defaults to `"no... | github-repos |
def _merge_doc(original, to_merge):
if not original:
return to_merge or ''
if not to_merge:
return original or ''
sections = []
for name in ('usage', 'arguments', 'options'):
sections.append(_merge_section(
_get_section(name, original),
_get_sect... | Merge two usage strings together.
Args:
original: The source of headers and initial section lines.
to_merge: The source for the additional section lines to append.
Returns:
A new usage string that contains information from both usage strings. | juraj-google-style |
def recipe_dynamic_costs(config, dcm_account, auth_read, configuration_sheet_url, auth_write, bigquery_dataset):
dynamic_costs(config, {'auth': auth_read, 'account': dcm_account, 'sheet': {'template': {'url': 'https: | Calculate DV360 cost at the dynamic creative combination level.
Args:
dcm_account (string) - NA
auth_read (authentication) - Credentials used for reading data.
configuration_sheet_url (string) - NA
auth_write (authentication) - Credentials used for writing data.
bigquery_dataset (string) - NA | github-repos |
def sample_with_temperature(logits, temperature, sampling_keep_top_k=-1):
if temperature == 0.0:
logits_shape = shape_list(logits)
argmax = tf.argmax(tf.reshape(logits, [-1, logits_shape[-1]]), axis=1)
return tf.reshape(argmax, logits_shape[:-1])
else:
assert temperature > 0.0
if sampli... | Either argmax or random sampling.
Args:
logits: a Tensor.
temperature: a float 0.0=argmax 1.0=random
sampling_keep_top_k: If not -1, only sample from the top k logits.
Returns:
a Tensor with one fewer dimension than logits. | juraj-google-style |
def unzip_file(source_file, dest_dir=None, mkdir=False):
if dest_dir is None:
dest_dir, fname = os.path.split(source_file)
elif not os.path.isdir(dest_dir):
if mkdir:
preparedir(dest_dir)
else:
created = preparedir(dest_dir, False)
if not cre... | Unzip a compressed file.
Args:
source_file: Full path to a valid compressed file (e.g. c:/ladybug/testPts.zip)
dest_dir: Target folder to extract to (e.g. c:/ladybug).
Default is set to the same directory as the source file.
mkdir: Set to True to create the directory if doesn't exist (Default: False) | juraj-google-style |
def best_case(self, matrix, m_list, indices_left):
m_indices = []
fraction_list = []
for m in m_list:
m_indices.extend(m[2])
fraction_list.extend([m[0]] * m[1])
indices = list(indices_left.intersection(m_indices))
interaction_matrix = matrix[ind... | Computes a best case given a matrix and manipulation list.
Args:
matrix: the current matrix (with some permutations already
performed)
m_list: [(multiplication fraction, number_of_indices, indices,
species)] describing the manipulation
indices: Set of indices which haven't had a permutation
performed on them. | juraj-google-style |
def get_tag(self, main_type, sub_type, unique_id, tag, owner=None, params=None):
params = params or {}
return self.tag(main_type, sub_type, unique_id, tag, owner=owner, params=params) | Args:
owner:
main_type:
sub_type:
unique_id:
tag:
params:
Return: | juraj-google-style |
def get_component(self, colour, tolerance=0, default=None):
if not (0 <= tolerance <= np.sqrt(195075)):
raise LegendError('Tolerance must be between 0 and 441.67')
for decor in self.__list:
if colour.lower() == decor.colour:
return decor.component
... | Get the component corresponding to a display colour. This is for
generating a Striplog object from a colour image of a striplog.
Args:
colour (str): The hex colour string to look up.
tolerance (float): The colourspace distance within which to match.
default (component or None): The component to return in the event
of ... | juraj-google-style |
def load(hdf5_filename):
hdf5_filename = os.path.expanduser(hdf5_filename)
try:
f = h5py.File(hdf5_filename, "r")
data_layers = f.get('image').get('CUTOUT')
except Exception as e:
raise ValueError("Could not load file {0} for conversion. {}".format(
... | Import a HDF5 file into a numpy array.
Arguments:
hdf5_filename: A string filename of a HDF5 datafile
Returns:
A numpy array with data from the HDF5 file | juraj-google-style |
def search(self, resources_request=None):
name_pattern, version_range = self._parse_request(resources_request)
family_names = set(
x.name for x in iter_package_families(paths=self.package_paths)
if fnmatch.fnmatch(x.name, name_pattern)
)
famil... | Search for resources.
Args:
resources_request (str): Resource to search, glob-style patterns
are supported. If None, returns all matching resource types.
Returns:
2-tuple:
- str: resource type (family, package, variant);
- List of `ResourceSearchResult`: Matching resources. Will be in
alphabetical order if families, ... | juraj-google-style |
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. | juraj-google-style |
def read_from_file(self, filename, negative_occupancies='warn'):
valid_negative_occupancies = ['warn', 'raise', 'ignore', 'zero']
if (negative_occupancies not in valid_negative_occupancies):
raise ValueError('"{}" is not a valid value for the keyword `negative_occupancies`.'.format(negative_occupancies)... | Reads the projected wavefunction character of each band from a VASP PROCAR file.
Args:
filename (str): Filename of the PROCAR file.
negative_occupancies (:obj:Str, optional): Sets the behaviour for handling
negative occupancies. Default is `warn`.
Returns:
None
Note:
Valid options for `negative_occupancies` are:
`wa... | codesearchnet |
def regroup_if_changed(group, op_list, name=None):
has_deltas = isinstance(op_list, sequence_with_deltas.SequenceWithDeltas)
if (group is None or len(group.control_inputs) != len(op_list) or
(has_deltas and op_list.has_changed())):
if has_deltas:
op_list.mark()
if op_list:
return tf.gro... | Creates a new group for op_list if it has changed.
Args:
group: The current group. It is returned if op_list is unchanged.
op_list: The list of operations to check.
name: The name to use if a new group is created.
Returns:
Either group or a new group (or if op_list is empty then no_op). | juraj-google-style |
def Scalars(self, run, tag):
accumulator = self.GetAccumulator(run)
return accumulator.Scalars(tag) | Retrieve the scalar events associated with a run and tag.
Args:
run: A string name of the run for which values are retrieved.
tag: A string name of the tag for which values are retrieved.
Raises:
KeyError: If the run is not found, or the tag is not available for
the given run.
Returns:
An array of `event_accumulator... | juraj-google-style |
def period_neighborhood_probability(self, radius, smoothing, threshold, stride,start_time,end_time):
neighbor_x = self.x[::stride, ::stride]
neighbor_y = self.y[::stride, ::stride]
neighbor_kd_tree = cKDTree(np.vstack((neighbor_x.ravel(), neighbor_y.ravel())).T)
neighbor_prob = ... | Calculate the neighborhood probability over the full period of the forecast
Args:
radius: circular radius from each point in km
smoothing: width of Gaussian smoother in km
threshold: intensity of exceedance
stride: number of grid points to skip for reduced neighborhood grid
Returns:
(neighborhood probabilities) | juraj-google-style |
def _convert_to_sparse_tensors(sp_inputs):
if isinstance(sp_inputs, list):
return [_convert_to_sparse_tensor(sp_input) for sp_input in sp_inputs]
if isinstance(sp_inputs, tuple):
return (_convert_to_sparse_tensor(sp_input) for sp_input in sp_inputs)
raise TypeError('Inputs must be a list or ... | Convert `sp_inputs` to `SparseTensor` objects and return them.
Args:
sp_inputs: `list` or `tuple` of `SparseTensor` or `SparseTensorValue`
objects.
Returns:
`sp_inputs` converted to `SparseTensor` objects.
Raises:
ValueError: if any item in `sp_inputs` is neither `SparseTensor` nor
`SparseTensorValue`. | github-repos |
def depth_june_average_ground_temperature(self, value=None):
if value is not None:
try:
value = float(value)
except ValueError:
raise ValueError(
'value {} need to be of type float '
'for field `depth_june_a... | Corresponds to IDD Field `depth_june_average_ground_temperature`
Args:
value (float): value for IDD Field `depth_june_average_ground_temperature`
Unit: C
if `value` is None it will not be checked against the
specification and is assumed to be a missing value
Raises:
ValueError: if `value` is not a valid value | juraj-google-style |
def write(self, brightness):
if not isinstance(brightness, (bool, int)):
raise TypeError("Invalid brightness type, should be bool or int.")
if isinstance(brightness, bool):
brightness = self._max_brightness if brightness else 0
else:
if not 0 <= brig... | Set the brightness of the LED to `brightness`.
`brightness` can be a boolean for on/off, or integer value for a
specific brightness.
Args:
brightness (bool, int): Brightness value to set.
Raises:
LEDError: if an I/O or OS error occurs.
TypeError: if `brightness` type is not bool or int. | juraj-google-style |
def __contains__(self, nurest_object):
for obj in self:
if obj.equals(nurest_object):
return True
return False | Verify if the fetcher contains the given NURESTObject
Args:
nurest_object (bambou.NURESTObject): the NURESTObject object to verify
Returns:
Returns True if the object has been found. False otherwise | juraj-google-style |
def fuzzy_match(self, proc):
return any(((proc in row[self.command_name]) for row in self.data)) | Are there any commands that contain the given text?
Returns:
boolean: ``True`` if the word ``proc`` appears in the command column.
.. note::
'proc' can match anywhere in the command path, name or arguments. | codesearchnet |
def _linear(self, inputs):
first_dims = shape_list(inputs)[:-1]
x = tf.reshape(inputs, [-1, self.hidden_size])
logits = tf.matmul(x, self.weight, transpose_b=True)
return tf.reshape(logits, first_dims + [self.vocab_size]) | Computes logits by running inputs through a linear layer.
Args:
inputs: A float32 tensor with shape [..., hidden_size]
Returns:
float32 tensor with shape [..., vocab_size]. | github-repos |
def write_compartments(self, stream, compartments, adjacencies, properties=None):
def convert(entry):
return self.convert_compartment_entry(entry, adjacencies.get(entry.id))
self._write_entries(stream, compartments, convert, properties) | Write iterable of compartments as YAML object to stream.
Args:
stream: File-like object.
compartments: Iterable of compartment entries.
adjacencies: Dictionary mapping IDs to adjacent compartment IDs.
properties: Set of compartment properties to output (or None to
output all). | codesearchnet |
def get():
result = runCommand('facter --json', raise_error_on_fail=True)
json_facts = result[1]
facts = json.loads(json_facts)
return facts | Get local facts about this machine.
Returns:
json-compatible dict with all facts of this host | codesearchnet |
def take_profit_replace(self, accountID, orderID, **kwargs):
return self.replace(
accountID,
orderID,
order=TakeProfitOrderRequest(**kwargs)
) | Shortcut to replace a pending Take Profit Order in an Account
Args:
accountID : The ID of the Account
orderID : The ID of the Take Profit Order to replace
kwargs : The arguments to create a TakeProfitOrderRequest
Returns:
v20.response.Response containing the results from submitting
the request | juraj-google-style |
def read(self, input_stream, kmip_version=enums.KMIPVersion.KMIP_1_0):
super(Credential, self).read(input_stream, kmip_version=kmip_version)
local_stream = BytearrayStream(input_stream.read(self.length))
if self.is_tag_next(enums.Tags.CREDENTIAL_TYPE, local_stream):
self._credential_type = primitive... | Read the data encoding the Credential struct and decode it into its
constituent parts.
Args:
input_stream (stream): A data stream containing encoded object
data, supporting a read method; usually a BytearrayStream
object.
kmip_version (KMIPVersion): An enumeration defining the KMIP
version with which the object will b... | codesearchnet |
def get_graph(self, item_ids, language=None):
def _related(item_ids):
if item_ids is None:
items = Item.objects.filter(active=True).prefetch_related('parents', 'children')
else:
item_ids = [ii for iis in item_ids.values() for ii in iis]
... | Get a subgraph of items reachable from the given set of items through
any relation.
Args:
item_ids (list): items which are taken as roots for the reachability
language (str): if specified, filter out items which are not
available in the given language
Returns:
dict: item id -> list of items (parent items), root items... | juraj-google-style |
def register_mbr_plugin(self, fs_id, plugin):
self.logger.debug('MBR: {}, FS ID: {}'.format(self.__get_plugin_name(plugin), fs_id))
self.__mbr_plugins[fs_id].append(plugin) | Used in plugin's registration routine,
to associate it's detection method with given filesystem id
Args:
fs_id: filesystem id that is read from MBR partition entry
plugin: plugin that supports this filesystem | codesearchnet |
def hwvtep_add_loopback_interface(self, **kwargs):
name = kwargs.pop('name')
id = kwargs.pop('int_id')
ip_args = dict(name=name, loopback_id=id)
method_name = 'overlay_gateway_ip_interface_loopback_loopback_id'
method_class = self._brocade_tunnels
gw_attr = getattr(method_class, method_name)
... | Add loopback interface to the overlay-gateway
Args:
name (str): gateway-name
int_id (int): loopback inteface id
callback (function): A function executed upon completion of the
method.
Returns:
Return value of `callback`.
Raises:
None | codesearchnet |
def audio(self, audio, sample_rate, name=None, subdir=''):
from chainerui.report.audio_report import check_available
if not check_available():
return
from chainerui.report.audio_report import report as _audio
col_name = self.get_col_name(name, 'audio')
out_... | Summary audio to listen on web browser.
Args:
audio (:class:`numpy.ndarray` or :class:`cupy.ndarray` or \
:class:`chainer.Variable`): sampled wave array.
sample_rate (int): sampling rate.
name (str): name of image. set as column name. when not setting,
assigned ``'audio'`` + sequential number.
subdir (str): sub-direct... | juraj-google-style |
def do_post(endpoint, body, access_token):
headers = {'content-type': 'application/json', 'Authorization': ('Bearer ' + access_token)}
headers['User-Agent'] = get_user_agent()
return requests.post(endpoint, data=body, headers=headers) | Do an HTTP POST request and return JSON.
Args:
endpoint (str): Azure Resource Manager management endpoint.
body (str): JSON body of information to post.
access_token (str): A valid Azure authentication token.
Returns:
HTTP response. JSON body. | codesearchnet |
def difference(self, *other):
from_frozenset = self.items.difference(*map(set, other))
return self.from_iterable(from_frozenset, sort=True) | Returns a new :class:`FrameSet` with elements in `self` but not in
`other`.
Args:
other (:class:`FrameSet`): or objects that can cast to :class:`FrameSet`
Returns:
:class:`FrameSet`: | juraj-google-style |
def onScreen(x, y=None):
x, y = _unpackXY(x, y)
x = int(x)
y = int(y)
width, height = platformModule._size()
return 0 <= x < width and 0 <= y < height | Returns whether the given xy coordinates are on the screen or not.
Args:
Either the arguments are two separate values, first arg for x and second
for y, or there is a single argument of a sequence with two values, the
first x and the second y.
Example: onScreen(x, y) or onScreen([x, y])
Returns:
bool: True if the xy ... | juraj-google-style |
def merge_variables(variables, **kwargs):
var_dict = OrderedDict()
for v in variables:
if v.name not in var_dict:
var_dict[v.name] = []
var_dict[v.name].append(v)
return [merge_variables(vars_, **kwargs)
for vars_ in list(var_dict.... | Concatenates Variables along row axis.
Args:
variables (list): List of Variables to merge. Variables can have
different names (and all Variables that share a name will be
concatenated together).
Returns:
A list of Variables. | juraj-google-style |
def _longToBytestring(value, signed=False, numberOfRegisters=2):
_checkInt(value, description='inputvalue')
_checkBool(signed, description='signed parameter')
_checkInt(numberOfRegisters, minvalue=2, maxvalue=2, description='number of registers')
formatcode = '>'
if signed:
formatcod... | Convert a long integer to a bytestring.
Long integers (32 bits = 4 bytes) are stored in two consecutive 16-bit registers in the slave.
Args:
* value (int): The numerical value to be converted.
* signed (bool): Whether large positive values should be interpreted as negative values.
* numberOfRegisters (int): Should be... | juraj-google-style |
def __eq__(self, other):
if not isinstance(other, SemanticTime):
return False
return self._SORT_ORDER == other._SORT_ORDER | Determines if the date time values are equal to other.
Args:
other (DateTimeValues): date time values to compare against.
Returns:
bool: True if the date time values are equal to other. | juraj-google-style |
def get_schema_descendant(
self, route: SchemaRoute) -> Optional[SchemaNode]:
node = self
for p in route:
node = node.get_child(*p)
if node is None:
return None
return node | Return descendant schema node or ``None`` if not found.
Args:
route: Schema route to the descendant node
(relative to the receiver). | juraj-google-style |
def eval_rs(gains, losses):
count = (len(gains) + len(losses))
avg_gains = (stats.avg(gains, count=count) if gains else 1)
avg_losses = (stats.avg(losses, count=count) if losses else 1)
if (avg_losses == 0):
return avg_gains
else:
return (avg_gains / avg_losses) | Evaluates the RS variable in RSI algorithm
Args:
gains: List of price gains.
losses: List of prices losses.
Returns:
Float of average gains over average losses. | codesearchnet |
def ValidateSyntax(rdf_artifact):
if (not rdf_artifact.doc):
raise rdf_artifacts.ArtifactSyntaxError(rdf_artifact, 'missing doc')
for supp_os in rdf_artifact.supported_os:
valid_os = rdf_artifact.SUPPORTED_OS_LIST
if (supp_os not in valid_os):
detail = ("invalid `supported_os... | Validates artifact syntax.
This method can be used to validate individual artifacts as they are loaded,
without needing all artifacts to be loaded first, as for Validate().
Args:
rdf_artifact: RDF object artifact.
Raises:
ArtifactSyntaxError: If artifact syntax is invalid. | codesearchnet |
def tuplize(nested):
if isinstance(nested, str):
return nested
try:
return tuple(map(tuplize, nested))
except TypeError:
return nested | Recursively converts iterables into tuples.
Args:
nested: A nested structure of items and iterables.
Returns:
A nested structure of items and tuples. | codesearchnet |
def _get_ground_truth_detections(instances_file, allowlist_file=None, num_images=None):
with open(instances_file, 'r') as annotation_dump:
data_dict = ast.literal_eval(annotation_dump.readline())
image_data = collections.OrderedDict()
if allowlist_file is not None:
with open(allowlist_file, ... | Processes the annotations JSON file and returns ground truth data corresponding to allowlisted image IDs.
Args:
instances_file: COCO instances JSON file, usually named as
instances_val20xx.json.
allowlist_file: File containing COCO minival image IDs to allowlist for
evaluation, one per line.
num_images: Number of allo... | github-repos |
def accept_confirm(self, text=None, wait=None):
with self.driver.accept_modal('confirm', text=text, wait=wait):
(yield) | Execute the wrapped code, accepting a confirm.
Args:
text (str | RegexObject, optional): Text to match against the text in the modal.
wait (int | float, optional): Maximum time to wait for the modal to appear after
executing the wrapped code.
Raises:
ModalNotFound: If a modal dialog hasn't been found. | codesearchnet |
def receive(self):
pickled_request = self._connection.connection.lpop(self._request_key)
return (pickle.loads(pickled_request) if (pickled_request is not None) else None) | Returns a single request.
Takes the first request from the list of requests and returns it. If the list
is empty, None is returned.
Returns:
Response: If a new request is available a Request object is returned,
otherwise None is returned. | codesearchnet |
def on_item_changed(self, item, new_value, row, column):
return (item, new_value, row, column) | Event for the item change.
Args:
emitter (TableWidget): The emitter of the event.
item (TableItem): The TableItem instance.
new_value (str): New text content.
row (int): row index.
column (int): column index. | codesearchnet |
def bsp_new_with_size(x: int, y: int, w: int, h: int) -> tcod.bsp.BSP:
return Bsp(x, y, w, h) | Create a new BSP instance with the given rectangle.
Args:
x (int): Rectangle left coordinate.
y (int): Rectangle top coordinate.
w (int): Rectangle width.
h (int): Rectangle height.
Returns:
BSP: A new BSP instance.
.. deprecated:: 2.0
Call the :any:`BSP` class instead. | juraj-google-style |
async def get_action_context_and_template(chain, parent_link, decision_link):
actions_path = decision_link.get_artifact_full_path('public/actions.json')
all_actions = load_json_or_yaml(actions_path, is_path=True)['actions']
action_name = get_action_callback_name(parent_link.task)
action_defn = _get... | Get the appropriate json-e context and template for an action task.
Args:
chain (ChainOfTrust): the chain of trust.
parent_link (LinkOfTrust): the parent link to test.
decision_link (LinkOfTrust): the parent link's decision task link.
tasks_for (str): the reason the parent link was created (cron,
hg-push, action)
Ret... | juraj-google-style |
def _build_js(inputs, outputs, name, implementation, support_code):
input_fields = json.dumps([f[0] for f in inputs])
output_fields = [{'name': f[0], 'type': f[1]} for f in outputs]
output_fields = json.dumps(output_fields, sort_keys=True)
if support_code is None:
s... | Creates a BigQuery SQL UDF javascript object.
Args:
inputs: a list of (name, type) tuples representing the schema of input.
outputs: a list of (name, type) tuples representing the schema of the output.
name: the name of the function
implementation: a javascript function defining the UDF logic.
support_code: additional... | juraj-google-style |
def is_object_new(self, func):
self.load_lazy_attribute('__new__')
self.load_lazy_attribute('__new__extra_args')
return [func] == self.members['__new__'].data or [func] == self.members['__new__extra_args'].data | Whether the given function is object.__new__.
Args:
func: A function.
Returns:
True if func equals either of the pytd definitions for object.__new__,
False otherwise. | github-repos |
def convert_wav(org_wav_fn: Path, tgt_wav_fn: Path) -> None:
if (not org_wav_fn.exists()):
raise FileNotFoundError
args = [config.FFMPEG_PATH, '-i', str(org_wav_fn), '-ac', '1', '-ar', '16000', str(tgt_wav_fn)]
subprocess.run(args) | Converts the wav into a 16bit mono 16000Hz wav.
Args:
org_wav_fn: A `Path` to the original wave file
tgt_wav_fn: The `Path` to output the processed wave file | codesearchnet |
class CustomObjectScope:
def __init__(self, custom_objects):
self.custom_objects = custom_objects or {}
self.backup = None
def __enter__(self):
self.backup = global_state.get_global_attribute('custom_objects_scope_dict', {}).copy()
global_state.set_global_attribute('custom_obje... | Exposes custom classes/functions to Keras deserialization internals.
Under a scope `with custom_object_scope(objects_dict)`, Keras methods such
as `keras.models.load_model()` or
`keras.models.model_from_config()` will be able to deserialize any
custom object referenced by a saved config (e.g. a custom layer or metric)... | github-repos |
def _worker(self, constructor, conn):
try:
env = constructor()
while True:
try:
if (not conn.poll(0.1)):
continue
(message, payload) = conn.recv()
except (EOFError, KeyboardInterrupt):
break
if (m... | The process waits for actions and sends back environment results.
Args:
constructor: Constructor for the OpenAI Gym environment.
conn: Connection for communication to the main process.
Raises:
KeyError: When receiving a message of unknown type. | codesearchnet |
def add_ref(self, timestamp: int) -> None:
self._ref_times.append(timestamp) | Adds a reference to this tensor with the specified timestamp.
Args:
timestamp: Timestamp of object reference as an integer. | github-repos |
def CheckBracesSpacing(filename, clean_lines, linenum, nesting_state, error):
line = clean_lines.elided[linenum]
match = Match(r'^(.*[^ ({>]){', line)
if match:
... | Checks for horizontal spacing near commas.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
nesting_state: A NestingState instance which maintains information about
the current stack of nested blocks being parsed.
error: ... | juraj-google-style |
def build_graph(device, input_shape, output_sizes, axis):
with ops.device('/%s:0' % device):
inp = array_ops.zeros(input_shape)
outputs = []
for _ in range(100):
outputs.extend(array_ops.split(inp, output_sizes, axis))
return control_flow_ops.group(*outputs) | Build a graph containing a sequence of split operations.
Args:
device: string, the device to run on.
input_shape: shape of the input tensor.
output_sizes: size of each output along axis.
axis: axis to be split along.
Returns:
An array of tensors to run() | github-repos |
def build_inputs_with_special_tokens(self, token_ids_0: List[int], token_ids_1: Optional[List[int]]=None) -> List[int]:
if token_ids_1 is None:
raise ValueError('With TAPAS, you must provide both question IDs and table IDs.')
return [self.cls_token_id] + token_ids_0 + [self.sep_token_id] + token_ids_1 | Build model inputs from a question and flattened table for question answering or sequence classification tasks
by concatenating and adding special tokens.
Args:
token_ids_0 (`List[int]`): The ids of the question.
token_ids_1 (`List[int]`, *optional*): The ids of the flattened table.
Returns:
`List[int]`: The model in... | github-repos |
def shell(cmd, *args, **kwargs):
if (kwargs.get('rel_path') and (not cmd.startswith('/'))):
cmd = os.path.join(kwargs['rel_path'], cmd)
status = 0
try:
output = subprocess.check_output(((cmd,) + args), stderr=kwargs.get('stderr'))
except subprocess.CalledProcessError as e:
if kwa... | Execute shell command and return output
Args:
cmd (str): the command itself, i.e. part until the first space
*args: positional arguments, i.e. other space-separated parts
rel_path (bool): execute relative to the path (default: `False`)
raise_on_status(bool): bool, raise exception if command
exited with non-zero status... | codesearchnet |
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