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yongzhuo/Keras-TextClassification | 640e3f44f90d9d8046546f7e1a93a29ebe5c8d30 | keras_textclassification/m03_CharCNN/graph_yoon_kim.py | python | highway_keras | (x) | return high_way | [] | def highway_keras(x):
# writter by my own
# paper； Highway Network(http://arxiv.org/abs/1505.00387).
# 公式
# 1. s = sigmoid(Wx + b)
# 2. z = s * relu(Wx + b) + (1 - s) * x
# x shape : [N * time_depth, sum(filters)]
# Table 1. CIFAR-10 test set accuracy of convolutional highway networks with
# rectified linear activation and sigmoid gates.
# For comparison, results reported by Romero et al. (2014)
# using maxout networks are also shown.
# Fitnets were trained using a two step training procedure using soft targets from the trained Teacher network,
# which was trained using backpropagation. We trained all highway networks directly using backpropagation.
# * indicates networks which were trained only on a set of 40K out of 50K examples in the training set.
# Figure 2. Visualization of certain internals of the blocks in the best 50 hidden layer highway networks trained on MNIST
# (top row) and CIFAR-100 (bottom row). The first hidden layer is a plain layer which changes the dimensionality of the representation to 50. Each of
# the 49 highway layers (y-axis) consists of 50 blocks (x-axis).
# The first column shows the transform gate biases, which were initialized to -2 and -4 respectively.
# In the second column the mean output of the transform gate over 10,000 training examples is depicted.
# The third and forth columns show the output of the transform gates and
# the block outputs for a single random training sample.
gate_transform = Dense(units=K.int_shape(x)[1],
activation='sigmoid',
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer=keras.initializers.Constant(value=-2))(x)
gate_cross = 1 - gate_transform
block_state = Dense(units=K.int_shape(x)[1],
activation='relu',
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zero')(x)
high_way = gate_transform * block_state + gate_cross * x
return high_way | [
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tensorflow/models | 6b8bb0cbeb3e10415c7a87448f08adc3c484c1d3 | research/seq_flow_lite/models/sgnn/sgnn.py | python | sgnn | (texts, hash_seed, ngram_size) | return tf.cast(tf.concat(projection_layer, -1), tf.float32) | Projects the string text to float features.
It first generasts N ngrams of the tokens from given text,
then projects each ngram tensor with a partion of the seeds.
Args:
texts: a string tensor, in shape of [batch_size].
hash_seed: a list of integers, in shape of [projection_size].
ngram_size: max size of ngram to generate features.
Returns:
A float tensor that projects ngrams to the space represented by hash_seed,
in shape of [batch_size, projection_size]. | Projects the string text to float features. | [
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] | def sgnn(texts, hash_seed, ngram_size):
"""Projects the string text to float features.
It first generasts N ngrams of the tokens from given text,
then projects each ngram tensor with a partion of the seeds.
Args:
texts: a string tensor, in shape of [batch_size].
hash_seed: a list of integers, in shape of [projection_size].
ngram_size: max size of ngram to generate features.
Returns:
A float tensor that projects ngrams to the space represented by hash_seed,
in shape of [batch_size, projection_size].
"""
projection_size = len(hash_seed)
partition_size = int(projection_size / ((ngram_size + 1) * ngram_size / 2))
if partition_size == 0:
raise ValueError(
'projection size %d is not enough for %d ngram partitions' %
(projection_size, ngram_size))
indices = [int(i * (i + 1) / 2) * partition_size for i in range(ngram_size)]
indices.append(projection_size)
projection_layer = []
tokens = preprocess(texts)
for i in range(ngram_size):
ngram = get_ngrams(tokens, i + 1)
projection = fused_project(ngram, hash_seed[indices[i]:indices[i + 1]],
0x7FFFFFFF)
projection_layer.append(projection)
return tf.cast(tf.concat(projection_layer, -1), tf.float32) | [
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PaddlePaddle/PaddleX | 2bab73f81ab54e328204e7871e6ae4a82e719f5d | static/paddlex/cv/transforms/seg_transforms.py | python | ResizeByShort.__init__ | (self, short_size=800, max_size=1333) | [] | def __init__(self, short_size=800, max_size=1333):
self.max_size = int(max_size)
if not isinstance(short_size, int):
raise TypeError(
"Type of short_size is invalid. Must be Integer, now is {}".
format(type(short_size)))
self.short_size = short_size
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F8LEFT/DecLLVM | d38e45e3d0dd35634adae1d0cf7f96f3bd96e74c | python/idaapi.py | python | udt_member_t.set_baseclass | (self, *args) | return _idaapi.udt_member_t_set_baseclass(self, *args) | set_baseclass(self) | set_baseclass(self) | [
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set_baseclass(self)
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return _idaapi.udt_member_t_set_baseclass(self, *args) | [
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Hrabal/TemPy | 03b9f22239118f322ba66084b15090133405ae9c | tempy/widgets/tempytable.py | python | TempyTable.pop_row | (self, idr=None, tags=False) | return row if tags else [cell.childs[0] for cell in row] | Pops a row, default the last | Pops a row, default the last | [
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"""Pops a row, default the last"""
idr = idr if idr is not None else len(self.body) - 1
row = self.body.pop(idr)
return row if tags else [cell.childs[0] for cell in row] | [
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IronLanguages/ironpython3 | 7a7bb2a872eeab0d1009fc8a6e24dca43f65b693 | Src/StdLib/Lib/encodings/cp865.py | python | IncrementalDecoder.decode | (self, input, final=False) | return codecs.charmap_decode(input,self.errors,decoding_table)[0] | [] | def decode(self, input, final=False):
return codecs.charmap_decode(input,self.errors,decoding_table)[0] | [
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cantools/cantools | 8d86d61bc010f328cf414150331fecfd4b6f4dc3 | cantools/database/can/formats/arxml.py | python | SystemLoader._get_arxml_children | (self, base_elems, children_location) | return base_elems | Locate a set of ElementTree child nodes at a given location.
This is a method that retrieves a list of ElementTree nodes
that match a given ARXML location. An ARXML location is a list
of strings that specify the nesting order of the XML tag
names; potential references for entries are preceeded by an
'&': If a sub-element exhibits the specified name, it is used
directly and if there is a sub-node called
'{child_tag_name}-REF', it is assumed to contain an ARXML
reference. This reference is then resolved and the remaining
location specification is relative to the result of that
resolution. If a location atom is preceeded by '*', then
multiple sub-elements are possible. The '&' and '*' qualifiers
may be combined.
Example:
.. code:: text
# Return all frame triggerings in any physical channel of a
# CAN cluster, where each conditional, each the physical
# channel and its individual frame triggerings can be
# references
loader._get_arxml_children(can_cluster,
[
'CAN-CLUSTER-VARIANTS',
'*&CAN-CLUSTER-CONDITIONAL',
'PHYSICAL-CHANNELS',
'*&CAN-PHYSICAL-CHANNEL',
'FRAME-TRIGGERINGS',
'*&CAN-FRAME-TRIGGERING'
]) | Locate a set of ElementTree child nodes at a given location. | [
"Locate",
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"set",
"of",
"ElementTree",
"child",
"nodes",
"at",
"a",
"given",
"location",
"."
] | def _get_arxml_children(self, base_elems, children_location):
"""Locate a set of ElementTree child nodes at a given location.
This is a method that retrieves a list of ElementTree nodes
that match a given ARXML location. An ARXML location is a list
of strings that specify the nesting order of the XML tag
names; potential references for entries are preceeded by an
'&': If a sub-element exhibits the specified name, it is used
directly and if there is a sub-node called
'{child_tag_name}-REF', it is assumed to contain an ARXML
reference. This reference is then resolved and the remaining
location specification is relative to the result of that
resolution. If a location atom is preceeded by '*', then
multiple sub-elements are possible. The '&' and '*' qualifiers
may be combined.
Example:
.. code:: text
# Return all frame triggerings in any physical channel of a
# CAN cluster, where each conditional, each the physical
# channel and its individual frame triggerings can be
# references
loader._get_arxml_children(can_cluster,
[
'CAN-CLUSTER-VARIANTS',
'*&CAN-CLUSTER-CONDITIONAL',
'PHYSICAL-CHANNELS',
'*&CAN-PHYSICAL-CHANNEL',
'FRAME-TRIGGERINGS',
'*&CAN-FRAME-TRIGGERING'
])
"""
if base_elems is None:
raise ValueError(
'Cannot retrieve a child element of a non-existing node!')
# make sure that the children_location is a list. for convenience we
# also allow it to be a string. In this case we take it that a
# direct child node needs to be found.
if isinstance(children_location, str):
children_location = [ children_location ]
# make sure that the base elements are iterable. for
# convenience we also allow it to be an individiual node.
if type(base_elems).__name__ == 'Element':
base_elems = [base_elems]
for child_tag_name in children_location:
if len(base_elems) == 0:
return [] # the base elements left are the empty set...
# handle the set and reference specifiers of the current
# sub-location
allow_references = '&' in child_tag_name[:2]
is_nodeset = '*' in child_tag_name[:2]
if allow_references:
child_tag_name = child_tag_name[1:]
if is_nodeset:
child_tag_name = child_tag_name[1:]
# traverse the specified path one level deeper
result = []
for base_elem in base_elems:
local_result = []
for child_elem in base_elem:
ctt = f'{{{self.xml_namespace}}}{child_tag_name}'
cttr = f'{{{self.xml_namespace}}}{child_tag_name}-REF'
if child_elem.tag == ctt:
local_result.append(child_elem)
elif child_elem.tag == cttr:
tmp = self._follow_arxml_reference(
base_elem=base_elem,
arxml_path=child_elem.text,
dest_tag_name=child_elem.attrib.get('DEST'),
refbase_name=child_elem.attrib.get('BASE'))
if tmp is None:
raise ValueError(f'Encountered dangling reference '
f'{child_tag_name}-REF: '
f'{child_elem.text}')
local_result.append(tmp)
if not is_nodeset and len(local_result) > 1:
raise ValueError(f'Encountered a a non-unique child node '
f'of type {child_tag_name} which ought to '
f'be unique')
result.extend(local_result)
base_elems = result
return base_elems | [
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datafolklabs/cement | 2d44d2c1821bda6bdfcfe605d244dc2dfb0b19a6 | cement/cli/contrib/jinja2/debug.py | python | make_traceback | (exc_info, source_hint=None) | return translate_exception(exc_info, initial_skip) | Creates a processed traceback object from the exc_info. | Creates a processed traceback object from the exc_info. | [
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] | def make_traceback(exc_info, source_hint=None):
"""Creates a processed traceback object from the exc_info."""
exc_type, exc_value, tb = exc_info
if isinstance(exc_value, TemplateSyntaxError):
exc_info = translate_syntax_error(exc_value, source_hint)
initial_skip = 0
else:
initial_skip = 1
return translate_exception(exc_info, initial_skip) | [
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Matheus-Garbelini/sweyntooth_bluetooth_low_energy_attacks | 40c985b9a9ff1189ddf278462440b120cf96b196 | libs/scapy/contrib/isotp.py | python | ISOTPSocketImplementation.check_recv | (self) | return not self.rx_queue.empty() | Implementation for SelectableObject | Implementation for SelectableObject | [
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] | def check_recv(self):
"""Implementation for SelectableObject"""
return not self.rx_queue.empty() | [
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implus/PytorchInsight | 2864528f8b83f52c3df76f7c3804aa468b91e5cf | classification/models/imagenet/resnet_se.py | python | Bottleneck.__init__ | (self, inplanes, planes, stride=1, downsample=None) | [] | def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = conv1x1(inplanes, planes)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = conv3x3(planes, planes, stride)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = conv1x1(planes, planes * self.expansion)
self.bn3 = nn.BatchNorm2d(planes * self.expansion)
self.se = SELayer(planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride | [
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wwqgtxx/wwqLyParse | 33136508e52821babd9294fdecffbdf02d73a6fc | wwqLyParse/lib/flask_lib/werkzeug/wsgi.py | python | get_current_url | (environ, root_only=False, strip_querystring=False,
host_only=False, trusted_hosts=None) | return uri_to_iri(''.join(tmp)) | A handy helper function that recreates the full URL as IRI for the
current request or parts of it. Here's an example:
>>> from werkzeug.test import create_environ
>>> env = create_environ("/?param=foo", "http://localhost/script")
>>> get_current_url(env)
'http://localhost/script/?param=foo'
>>> get_current_url(env, root_only=True)
'http://localhost/script/'
>>> get_current_url(env, host_only=True)
'http://localhost/'
>>> get_current_url(env, strip_querystring=True)
'http://localhost/script/'
This optionally it verifies that the host is in a list of trusted hosts.
If the host is not in there it will raise a
:exc:`~werkzeug.exceptions.SecurityError`.
Note that the string returned might contain unicode characters as the
representation is an IRI not an URI. If you need an ASCII only
representation you can use the :func:`~werkzeug.urls.iri_to_uri`
function:
>>> from werkzeug.urls import iri_to_uri
>>> iri_to_uri(get_current_url(env))
'http://localhost/script/?param=foo'
:param environ: the WSGI environment to get the current URL from.
:param root_only: set `True` if you only want the root URL.
:param strip_querystring: set to `True` if you don't want the querystring.
:param host_only: set to `True` if the host URL should be returned.
:param trusted_hosts: a list of trusted hosts, see :func:`host_is_trusted`
for more information. | A handy helper function that recreates the full URL as IRI for the
current request or parts of it. Here's an example: | [
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host_only=False, trusted_hosts=None):
"""A handy helper function that recreates the full URL as IRI for the
current request or parts of it. Here's an example:
>>> from werkzeug.test import create_environ
>>> env = create_environ("/?param=foo", "http://localhost/script")
>>> get_current_url(env)
'http://localhost/script/?param=foo'
>>> get_current_url(env, root_only=True)
'http://localhost/script/'
>>> get_current_url(env, host_only=True)
'http://localhost/'
>>> get_current_url(env, strip_querystring=True)
'http://localhost/script/'
This optionally it verifies that the host is in a list of trusted hosts.
If the host is not in there it will raise a
:exc:`~werkzeug.exceptions.SecurityError`.
Note that the string returned might contain unicode characters as the
representation is an IRI not an URI. If you need an ASCII only
representation you can use the :func:`~werkzeug.urls.iri_to_uri`
function:
>>> from werkzeug.urls import iri_to_uri
>>> iri_to_uri(get_current_url(env))
'http://localhost/script/?param=foo'
:param environ: the WSGI environment to get the current URL from.
:param root_only: set `True` if you only want the root URL.
:param strip_querystring: set to `True` if you don't want the querystring.
:param host_only: set to `True` if the host URL should be returned.
:param trusted_hosts: a list of trusted hosts, see :func:`host_is_trusted`
for more information.
"""
tmp = [environ['wsgi.url_scheme'], '://', get_host(environ, trusted_hosts)]
cat = tmp.append
if host_only:
return uri_to_iri(''.join(tmp) + '/')
cat(url_quote(wsgi_get_bytes(environ.get('SCRIPT_NAME', ''))).rstrip('/'))
cat('/')
if not root_only:
cat(url_quote(wsgi_get_bytes(environ.get('PATH_INFO', '')).lstrip(b'/')))
if not strip_querystring:
qs = get_query_string(environ)
if qs:
cat('?' + qs)
return uri_to_iri(''.join(tmp)) | [
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"... | https://github.com/wwqgtxx/wwqLyParse/blob/33136508e52821babd9294fdecffbdf02d73a6fc/wwqLyParse/lib/flask_lib/werkzeug/wsgi.py#L51-L99 | |
awslabs/gluon-ts | 066ec3b7f47aa4ee4c061a28f35db7edbad05a98 | src/gluonts/nursery/sagemaker_sdk/model.py | python | GluonTSPredictor.__init__ | (
self, endpoint_name: str, sagemaker_session: session.Session = None
) | Initialize an ``GluonTSPredictor``.
Parameters
----------
endpoint_name:
The name of the endpoint to perform inference on.
sagemaker_session :
Session object which manages interactions with Amazon SageMaker APIs and any other
AWS services needed. If not specified, the estimator creates one
using the default AWS configuration chain. | Initialize an ``GluonTSPredictor``. | [
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"GluonTSPredictor",
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] | def __init__(
self, endpoint_name: str, sagemaker_session: session.Session = None
):
"""Initialize an ``GluonTSPredictor``.
Parameters
----------
endpoint_name:
The name of the endpoint to perform inference on.
sagemaker_session :
Session object which manages interactions with Amazon SageMaker APIs and any other
AWS services needed. If not specified, the estimator creates one
using the default AWS configuration chain.
"""
# TODO: implement custom data serializer and deserializer: convert between gluonts dataset and bytes
# Use the default functions from MXNet (they handle more than we need
# (e.g: np.ndarrays), but that should be fine)
super(GluonTSPredictor, self).__init__(
endpoint_name,
sagemaker_session,
json_serializer, # change this
json_deserializer, # change this
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mdiazcl/fuzzbunch-debian | 2b76c2249ade83a389ae3badb12a1bd09901fd2c | windows/Resources/Python/Core/Lib/lib-tk/ttk.py | python | Treeview.selection | (self, selop=None, items=None) | return self.tk.call(self._w, 'selection', selop, items) | If selop is not specified, returns selected items. | If selop is not specified, returns selected items. | [
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"""If selop is not specified, returns selected items."""
return self.tk.call(self._w, 'selection', selop, items) | [
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ifwe/digsby | f5fe00244744aa131e07f09348d10563f3d8fa99 | digsby/src/gui/infobox/emailpanels.py | python | EmailList.OnMouseOut | (self, event) | Unselects the email and hide links on mouse out of list. | Unselects the email and hide links on mouse out of list. | [
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] | def OnMouseOut(self, event):
'Unselects the email and hide links on mouse out of list.'
if not self.linkage.Rect.Contains(event.Position) and self.Selection != -1:
self.RefreshLine(self.Selection)
self.Selection = -1
self.linkage.Show(False) | [
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inkandswitch/livebook | 93c8d467734787366ad084fc3566bf5cbe249c51 | public/pypyjs/modules/logging/__init__.py | python | Handler.createLock | (self) | Acquire a thread lock for serializing access to the underlying I/O. | Acquire a thread lock for serializing access to the underlying I/O. | [
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"""
Acquire a thread lock for serializing access to the underlying I/O.
"""
if thread:
self.lock = threading.RLock()
else:
self.lock = None | [
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bbc/brave | 88d4454412ee5acfa5ecf2ac5bc8cf75766c7be5 | brave/outputs/file.py | python | FileOutput.set_pipeline_state | (self, new_state) | return super().set_pipeline_state(new_state) | [] | def set_pipeline_state(self, new_state):
sent_eos = False
# If this is ending the file creation (identified by moving to READY or NULL)
# we must send an EOS so that the file is completed correctly.
if (new_state == Gst.State.READY or new_state == Gst.State.NULL):
for encoder_name in ['video_encoder', 'audio_encoder']:
if hasattr(self, encoder_name):
encoder = getattr(self, encoder_name)
encoder_state = encoder.get_state(0).state
if encoder_state in [Gst.State.PAUSED, Gst.State.PLAYING]:
if encoder.send_event(Gst.Event.new_eos()):
self.logger.debug('Successfully send EOS event to the ' + encoder_name)
sent_eos = True
else:
self.logger.warning('Failed to send EOS event to the %s' % encoder_name)
# If we've sent an EOS, allow that to propogate the pipeline.
# (Separate code will then catch the EOS successful message and cause a state change.)
# Otherwise, lets go ahead and set the state of the pipeline.
if sent_eos:
return
return super().set_pipeline_state(new_state) | [
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artefactual/archivematica | 4f4605453d5a8796f6a739fa9664921bdb3418f2 | src/dashboard/src/fpr/utils.py | python | determine_what_replaces_model_instance | (model, instance) | return replaces | Determine what object, if any, will be replaced by creating a new
revision. | Determine what object, if any, will be replaced by creating a new
revision. | [
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] | def determine_what_replaces_model_instance(model, instance):
"""Determine what object, if any, will be replaced by creating a new
revision."""
if instance:
# if replacing the latest version or base on old version
if instance.enabled:
replaces = model.objects.get(pk=instance.pk)
else:
replaces = get_current_revision_using_ancestor(model, instance.uuid)
else:
replaces = None
return replaces | [
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proycon/pynlpl | 7707f69a91caaa6cde037f0d0379f1d42500a68b | pynlpl/formats/folia.py | python | AbstractElement.setparents | (self) | Correct all parent relations for elements within the scop. There is sually no need to call this directly, invoked implicitly by :meth:`copy` | Correct all parent relations for elements within the scop. There is sually no need to call this directly, invoked implicitly by :meth:`copy` | [
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"""Correct all parent relations for elements within the scop. There is sually no need to call this directly, invoked implicitly by :meth:`copy`"""
for c in self:
if isinstance(c, AbstractElement):
c.parent = self
c.setparents() | [
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materialsproject/pymatgen | 8128f3062a334a2edd240e4062b5b9bdd1ae6f58 | pymatgen/io/vasp/outputs.py | python | VolumetricData.write_file | (self, file_name, vasp4_compatible=False) | Write the VolumetricData object to a vasp compatible file.
Args:
file_name (str): Path to a file
vasp4_compatible (bool): True if the format is vasp4 compatible | Write the VolumetricData object to a vasp compatible file. | [
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] | def write_file(self, file_name, vasp4_compatible=False):
"""
Write the VolumetricData object to a vasp compatible file.
Args:
file_name (str): Path to a file
vasp4_compatible (bool): True if the format is vasp4 compatible
"""
def _print_fortran_float(f):
"""
Fortran codes print floats with a leading zero in scientific
notation. When writing CHGCAR files, we adopt this convention
to ensure written CHGCAR files are byte-to-byte identical to
their input files as far as possible.
:param f: float
:return: str
"""
s = f"{f:.10E}"
if f >= 0:
return "0." + s[0] + s[2:12] + "E" + f"{int(s[13:]) + 1:+03}"
return "-." + s[1] + s[3:13] + "E" + f"{int(s[14:]) + 1:+03}"
with zopen(file_name, "wt") as f:
p = Poscar(self.structure)
# use original name if it's been set (e.g. from Chgcar)
comment = getattr(self, "name", p.comment)
lines = comment + "\n"
lines += " 1.00000000000000\n"
latt = self.structure.lattice.matrix
lines += " %12.6f%12.6f%12.6f\n" % tuple(latt[0, :])
lines += " %12.6f%12.6f%12.6f\n" % tuple(latt[1, :])
lines += " %12.6f%12.6f%12.6f\n" % tuple(latt[2, :])
if not vasp4_compatible:
lines += "".join(["%5s" % s for s in p.site_symbols]) + "\n"
lines += "".join(["%6d" % x for x in p.natoms]) + "\n"
lines += "Direct\n"
for site in self.structure:
lines += "%10.6f%10.6f%10.6f\n" % tuple(site.frac_coords)
lines += " \n"
f.write(lines)
a = self.dim
def write_spin(data_type):
lines = []
count = 0
f.write(f" {a[0]} {a[1]} {a[2]}\n")
for (k, j, i) in itertools.product(list(range(a[2])), list(range(a[1])), list(range(a[0]))):
lines.append(_print_fortran_float(self.data[data_type][i, j, k]))
count += 1
if count % 5 == 0:
f.write(" " + "".join(lines) + "\n")
lines = []
else:
lines.append(" ")
if count % 5 != 0:
f.write(" " + "".join(lines) + " \n")
f.write("".join(self.data_aug.get(data_type, [])))
write_spin("total")
if self.is_spin_polarized and self.is_soc:
write_spin("diff_x")
write_spin("diff_y")
write_spin("diff_z")
elif self.is_spin_polarized:
write_spin("diff") | [
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sametmax/Django--an-app-at-a-time | 99eddf12ead76e6dfbeb09ce0bae61e282e22f8a | ignore_this_directory/django/views/generic/list.py | python | MultipleObjectMixin.get_context_data | (self, *, object_list=None, **kwargs) | return super().get_context_data(**context) | Get the context for this view. | Get the context for this view. | [
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"."
] | def get_context_data(self, *, object_list=None, **kwargs):
"""Get the context for this view."""
queryset = object_list if object_list is not None else self.object_list
page_size = self.get_paginate_by(queryset)
context_object_name = self.get_context_object_name(queryset)
if page_size:
paginator, page, queryset, is_paginated = self.paginate_queryset(queryset, page_size)
context = {
'paginator': paginator,
'page_obj': page,
'is_paginated': is_paginated,
'object_list': queryset
}
else:
context = {
'paginator': None,
'page_obj': None,
'is_paginated': False,
'object_list': queryset
}
if context_object_name is not None:
context[context_object_name] = queryset
context.update(kwargs)
return super().get_context_data(**context) | [
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openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | openshift/installer/vendored/openshift-ansible-3.11.28-1/roles/lib_vendored_deps/library/oc_service.py | python | OCService.update | (self) | return self._replace_content(self.kind, self.config.name, self.user_svc.yaml_dict) | create a service | create a service | [
"create",
"a",
"service"
] | def update(self):
'''create a service '''
# Need to copy over the portalIP and the serviceIP settings
self.user_svc.add_cluster_ip(self.service.get('spec.clusterIP'))
self.user_svc.add_portal_ip(self.service.get('spec.portalIP'))
return self._replace_content(self.kind, self.config.name, self.user_svc.yaml_dict) | [
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Sarcasm/compdb | 62b4c0f6daa0654256a0ae9036d59cd5026f280f | compdb/backend/json.py | python | JSONCompilationDatabase.probe_directory | (cls, directory) | return super(JSONCompilationDatabase, cls).probe_directory(directory) | Automatically create a CompilationDatabase from build directory. | Automatically create a CompilationDatabase from build directory. | [
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] | def probe_directory(cls, directory):
"""Automatically create a CompilationDatabase from build directory."""
db_path = os.path.join(directory, 'compile_commands.json')
if os.path.exists(db_path):
return cls(db_path)
return super(JSONCompilationDatabase, cls).probe_directory(directory) | [
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bendmorris/static-python | 2e0f8c4d7ed5b359dc7d8a75b6fb37e6b6c5c473 | Lib/difflib.py | python | unified_diff | (a, b, fromfile='', tofile='', fromfiledate='',
tofiledate='', n=3, lineterm='\n') | r"""
Compare two sequences of lines; generate the delta as a unified diff.
Unified diffs are a compact way of showing line changes and a few
lines of context. The number of context lines is set by 'n' which
defaults to three.
By default, the diff control lines (those with ---, +++, or @@) are
created with a trailing newline. This is helpful so that inputs
created from file.readlines() result in diffs that are suitable for
file.writelines() since both the inputs and outputs have trailing
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For inputs that do not have trailing newlines, set the lineterm
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'fromfile', 'tofile', 'fromfiledate', and 'tofiledate'.
The modification times are normally expressed in the ISO 8601 format.
Example:
>>> for line in unified_diff('one two three four'.split(),
... 'zero one tree four'.split(), 'Original', 'Current',
... '2005-01-26 23:30:50', '2010-04-02 10:20:52',
... lineterm=''):
... print(line) # doctest: +NORMALIZE_WHITESPACE
--- Original 2005-01-26 23:30:50
+++ Current 2010-04-02 10:20:52
@@ -1,4 +1,4 @@
+zero
one
-two
-three
+tree
four | r"""
Compare two sequences of lines; generate the delta as a unified diff. | [
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tofiledate='', n=3, lineterm='\n'):
r"""
Compare two sequences of lines; generate the delta as a unified diff.
Unified diffs are a compact way of showing line changes and a few
lines of context. The number of context lines is set by 'n' which
defaults to three.
By default, the diff control lines (those with ---, +++, or @@) are
created with a trailing newline. This is helpful so that inputs
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For inputs that do not have trailing newlines, set the lineterm
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'fromfile', 'tofile', 'fromfiledate', and 'tofiledate'.
The modification times are normally expressed in the ISO 8601 format.
Example:
>>> for line in unified_diff('one two three four'.split(),
... 'zero one tree four'.split(), 'Original', 'Current',
... '2005-01-26 23:30:50', '2010-04-02 10:20:52',
... lineterm=''):
... print(line) # doctest: +NORMALIZE_WHITESPACE
--- Original 2005-01-26 23:30:50
+++ Current 2010-04-02 10:20:52
@@ -1,4 +1,4 @@
+zero
one
-two
-three
+tree
four
"""
started = False
for group in SequenceMatcher(None,a,b).get_grouped_opcodes(n):
if not started:
started = True
fromdate = '\t{}'.format(fromfiledate) if fromfiledate else ''
todate = '\t{}'.format(tofiledate) if tofiledate else ''
yield '--- {}{}{}'.format(fromfile, fromdate, lineterm)
yield '+++ {}{}{}'.format(tofile, todate, lineterm)
first, last = group[0], group[-1]
file1_range = _format_range_unified(first[1], last[2])
file2_range = _format_range_unified(first[3], last[4])
yield '@@ -{} +{} @@{}'.format(file1_range, file2_range, lineterm)
for tag, i1, i2, j1, j2 in group:
if tag == 'equal':
for line in a[i1:i2]:
yield ' ' + line
continue
if tag in {'replace', 'delete'}:
for line in a[i1:i2]:
yield '-' + line
if tag in {'replace', 'insert'}:
for line in b[j1:j2]:
yield '+' + line | [
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BillBillBillBill/Tickeys-linux | 2df31b8665004c58a5d4ab05277f245267d96364 | tickeys/kivy/core/image/__init__.py | python | ImageData.iterate_mipmaps | (self) | Iterate over all mipmap images available.
.. versionadded:: 1.0.7 | Iterate over all mipmap images available. | [
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'''Iterate over all mipmap images available.
.. versionadded:: 1.0.7
'''
mm = self.mipmaps
for x in range(len(mm)):
item = mm.get(x, None)
if item is None:
raise Exception('Invalid mipmap level, found empty one')
yield x, item[0], item[1], item[2], item[3] | [
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linxid/Machine_Learning_Study_Path | 558e82d13237114bbb8152483977806fc0c222af | Machine Learning In Action/Chapter5-LogisticRegression/venv/Lib/site-packages/setuptools/command/install.py | python | install._called_from_setup | (run_frame) | return (
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SFDO-Tooling/CumulusCI | 825ae1f122b25dc41761c52a4ddfa1938d2a4b6e | cumulusci/core/config/project_config.py | python | BaseProjectConfig.construct_subproject_config | (self, **kwargs) | return self.__class__(
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ask/carrot | 5889a25cd2e274642071c9bba39772f4b3e3d9da | carrot/backends/base.py | python | BaseBackend.consume | (self, *args, **kwargs) | Iterate over the declared consumers. | Iterate over the declared consumers. | [
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krintoxi/NoobSec-Toolkit | 38738541cbc03cedb9a3b3ed13b629f781ad64f6 | NoobSecToolkit /scripts/sshbackdoors/backdoors/shell/pupy/pupy/packages/windows/x86/psutil/_pslinux.py | python | Process.open_files | (self) | return retlist | [] | def open_files(self):
retlist = []
files = os.listdir("/proc/%s/fd" % self.pid)
hit_enoent = False
for fd in files:
file = "/proc/%s/fd/%s" % (self.pid, fd)
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# ENOENT == file which is gone in the meantime
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hit_enoent = True
continue
elif err.errno == errno.EINVAL:
# not a link
continue
else:
raise
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# to tell whether it's a regular file or not,
# so we skip it. A regular file is always supposed
# to be absolutized though.
if file.startswith('/') and isfile_strict(file):
ntuple = _common.popenfile(file, int(fd))
retlist.append(ntuple)
if hit_enoent:
# raise NSP if the process disappeared on us
os.stat('/proc/%s' % self.pid)
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demisto/content | 5c664a65b992ac8ca90ac3f11b1b2cdf11ee9b07 | Packs/CentrifyVault/Integrations/CentrifyVault/CentrifyVault.py | python | Client.get_token_request | (self) | return token_response | Sends token request
:rtype ``str``
:return: bearer token | Sends token request | [
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"""
Sends token request
:rtype ``str``
:return: bearer token
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if not token_response:
err_msg = 'Authorization Error: User has no authorization to create a token.' \
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raise Exception(err_msg)
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securesystemslab/zippy | ff0e84ac99442c2c55fe1d285332cfd4e185e089 | zippy/benchmarks/src/benchmarks/sympy/sympy/physics/quantum/matrixutils.py | python | _scipy_sparse_tensor_product | (*product) | return sparse.csr_matrix(answer) | scipy.sparse version of tensor product of multiple arguments. | scipy.sparse version of tensor product of multiple arguments. | [
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"""scipy.sparse version of tensor product of multiple arguments."""
if not sparse:
raise ImportError
answer = product[0]
for item in product[1:]:
answer = sparse.kron(answer, item)
# The final matrices will just be multiplied, so csr is a good final
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gitpython-developers/GitPython | fac603789d66c0fd7c26e75debb41b06136c5026 | git/config.py | python | GitConfigParser.items_all | (self, section_name: str) | return rv.items_all() | :return: list((option, [values...]), ...) pairs of all items in the given section | :return: list((option, [values...]), ...) pairs of all items in the given section | [
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fooying/3102 | 0faee38c30b2e24154f41e68457cfd8f7a61c040 | thirdparty/dns/resolver.py | python | Cache._maybe_clean | (self) | Clean the cache if it's time to do so. | Clean the cache if it's time to do so. | [
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lxtGH/OctaveConv_pytorch | 079f7da29d55c2eeed8985d33f0b2f765d7a469e | libs/nn/resnet_sk.py | python | conv1x1 | (in_planes, out_planes, stride=1) | return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) | 1x1 convolution | 1x1 convolution | [
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PyLops/pylops | 33eb807c6f429dd2efe697627c0d3955328af81f | pylops/waveeqprocessing/wavedecomposition.py | python | UpDownComposition3D | (
nt,
nr,
dt,
dr,
rho,
vel,
nffts=(None, None, None),
critical=100.0,
ntaper=10,
scaling=1.0,
backend="numpy",
dtype="complex128",
) | return UDop | r"""3D Up-down wavefield composition.
Apply multi-component seismic wavefield composition from its
up- and down-going constituents. The input model required by the operator
should be created by flattening the separated wavefields of
size :math:`\lbrack n_{r_y} \times n_{r_x} \times n_t \rbrack`
concatenated along the first spatial axis.
Similarly, the data is also a flattened concatenation of pressure and
vertical particle velocity wavefields.
Parameters
----------
nt : :obj:`int`
Number of samples along the time axis
nr : :obj:`tuple`
Number of samples along the receiver axes
dt : :obj:`float`
Sampling along the time axis
dr : :obj:`tuple`
Samplings along the receiver array
rho : :obj:`float`
Density along the receiver array (must be constant)
vel : :obj:`float`
Velocity along the receiver array (must be constant)
nffts : :obj:`tuple`, optional
Number of samples along the wavenumbers and frequency axes (for the
wavenumbers axes the same order as ``nr`` and ``dr`` must be followed)
critical : :obj:`float`, optional
Percentage of angles to retain in obliquity factor. For example, if
``critical=100`` only angles below the critical angle
:math:`\sqrt{k_y^2 + k_x^2} < \frac{\omega}{vel}` will be retained
ntaper : :obj:`float`, optional
Number of samples of taper applied to obliquity factor around critical
angle
scaling : :obj:`float`, optional
Scaling to apply to the operator (see Notes for more details)
backend : :obj:`str`, optional
Backend used for creation of obliquity factor operator
(``numpy`` or ``cupy``)
dtype : :obj:`str`, optional
Type of elements in input array.
Returns
-------
UDop : :obj:`pylops.LinearOperator`
Up-down wavefield composition operator
See Also
--------
UpDownComposition2D: 2D Wavefield composition
WavefieldDecomposition: Wavefield decomposition
Notes
-----
Multi-component seismic data (:math:`p(y, x, t)` and :math:`v_z(y, x, t)`)
can be synthesized in the frequency-wavenumber domain
as the superposition of the up- and downgoing constituents of
the pressure wavefield (:math:`p^-(y, x, t)` and :math:`p^+(y, x, t)`)
as described :class:`pylops.waveeqprocessing.UpDownComposition2D`.
Here the vertical wavenumber :math:`k_z` is defined as
:math:`k_z=\sqrt{\omega^2/c^2 - k_y^2 - k_x^2}`. | r"""3D Up-down wavefield composition. | [
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] | def UpDownComposition3D(
nt,
nr,
dt,
dr,
rho,
vel,
nffts=(None, None, None),
critical=100.0,
ntaper=10,
scaling=1.0,
backend="numpy",
dtype="complex128",
):
r"""3D Up-down wavefield composition.
Apply multi-component seismic wavefield composition from its
up- and down-going constituents. The input model required by the operator
should be created by flattening the separated wavefields of
size :math:`\lbrack n_{r_y} \times n_{r_x} \times n_t \rbrack`
concatenated along the first spatial axis.
Similarly, the data is also a flattened concatenation of pressure and
vertical particle velocity wavefields.
Parameters
----------
nt : :obj:`int`
Number of samples along the time axis
nr : :obj:`tuple`
Number of samples along the receiver axes
dt : :obj:`float`
Sampling along the time axis
dr : :obj:`tuple`
Samplings along the receiver array
rho : :obj:`float`
Density along the receiver array (must be constant)
vel : :obj:`float`
Velocity along the receiver array (must be constant)
nffts : :obj:`tuple`, optional
Number of samples along the wavenumbers and frequency axes (for the
wavenumbers axes the same order as ``nr`` and ``dr`` must be followed)
critical : :obj:`float`, optional
Percentage of angles to retain in obliquity factor. For example, if
``critical=100`` only angles below the critical angle
:math:`\sqrt{k_y^2 + k_x^2} < \frac{\omega}{vel}` will be retained
ntaper : :obj:`float`, optional
Number of samples of taper applied to obliquity factor around critical
angle
scaling : :obj:`float`, optional
Scaling to apply to the operator (see Notes for more details)
backend : :obj:`str`, optional
Backend used for creation of obliquity factor operator
(``numpy`` or ``cupy``)
dtype : :obj:`str`, optional
Type of elements in input array.
Returns
-------
UDop : :obj:`pylops.LinearOperator`
Up-down wavefield composition operator
See Also
--------
UpDownComposition2D: 2D Wavefield composition
WavefieldDecomposition: Wavefield decomposition
Notes
-----
Multi-component seismic data (:math:`p(y, x, t)` and :math:`v_z(y, x, t)`)
can be synthesized in the frequency-wavenumber domain
as the superposition of the up- and downgoing constituents of
the pressure wavefield (:math:`p^-(y, x, t)` and :math:`p^+(y, x, t)`)
as described :class:`pylops.waveeqprocessing.UpDownComposition2D`.
Here the vertical wavenumber :math:`k_z` is defined as
:math:`k_z=\sqrt{\omega^2/c^2 - k_y^2 - k_x^2}`.
"""
nffts = (
int(nffts[0]) if nffts[0] is not None else nr[0],
int(nffts[1]) if nffts[1] is not None else nr[1],
int(nffts[2]) if nffts[2] is not None else nt,
)
# create obliquity operator
FFTop, OBLop = _obliquity3D(
nt,
nr,
dt,
dr,
rho,
vel,
nffts=nffts,
critical=critical,
ntaper=ntaper,
composition=True,
backend=backend,
dtype=dtype,
)
# create up-down modelling operator
UDop = (
BlockDiag([FFTop.H, scaling * FFTop.H])
* Block(
[
[
Identity(nffts[0] * nffts[1] * nffts[2], dtype=dtype),
Identity(nffts[0] * nffts[1] * nffts[2], dtype=dtype),
],
[OBLop, -OBLop],
]
)
* BlockDiag([FFTop, FFTop])
)
return UDop | [
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maurosoria/dirsearch | b83e68c8fdf360ab06be670d7b92b263262ee5b1 | thirdparty/jinja2/compiler.py | python | UndeclaredNameVisitor.visit_Block | (self, node: nodes.Block) | Stop visiting a blocks. | Stop visiting a blocks. | [
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] | https://github.com/maurosoria/dirsearch/blob/b83e68c8fdf360ab06be670d7b92b263262ee5b1/thirdparty/jinja2/compiler.py#L289-L290 | ||
Qirky/Troop | 529c5eb14e456f683e6d23fd4adcddc8446aa115 | src/OSC3.py | python | OSCStreamingServer.start | (self) | Start the server thread. | Start the server thread. | [
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] | def start(self):
""" Start the server thread. """
self._server_thread = threading.Thread(target=self.serve_forever)
self._server_thread.setDaemon(True)
self._server_thread.start() | [
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twilio/twilio-python | 6e1e811ea57a1edfadd5161ace87397c563f6915 | twilio/rest/api/v2010/account/incoming_phone_number/mobile.py | python | MobileInstance.status_callback_method | (self) | return self._properties['status_callback_method'] | :returns: The HTTP method we use to call status_callback
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"""
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:rtype: unicode
"""
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jaychsu/algorithm | 87dac5456b74a515dd97507ac68e9b8588066a04 | leetcode/388_longest_absolute_file_path.py | python | Solution.lengthLongestPath | (self, path) | return ans | :type path: str
:rtype: int | :type path: str
:rtype: int | [
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"""
:type path: str
:rtype: int
"""
ans = 0
if not path:
return ans
dep2size = {0: 0}
for line in path.split('\n'):
name = line.lstrip('\t')
size = len(name)
depth = len(line) - len(name)
if '.' in name:
ans = max(ans, dep2size[depth] + size)
else:
dep2size[depth + 1] = dep2size[depth] + size + 1
return ans | [
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rll/rllab | ba78e4c16dc492982e648f117875b22af3965579 | rllab/spaces/base.py | python | Space.flat_dim | (self) | The dimension of the flattened vector of the tensor representation | The dimension of the flattened vector of the tensor representation | [
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buke/GreenOdoo | 3d8c55d426fb41fdb3f2f5a1533cfe05983ba1df | runtime/python/lib/python2.7/locale.py | python | setlocale | (category, locale=None) | return _setlocale(category, locale) | Set the locale for the given category. The locale can be
a string, an iterable of two strings (language code and encoding),
or None.
Iterables are converted to strings using the locale aliasing
engine. Locale strings are passed directly to the C lib.
category may be given as one of the LC_* values. | Set the locale for the given category. The locale can be
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""" Set the locale for the given category. The locale can be
a string, an iterable of two strings (language code and encoding),
or None.
Iterables are converted to strings using the locale aliasing
engine. Locale strings are passed directly to the C lib.
category may be given as one of the LC_* values.
"""
if locale and type(locale) is not type(""):
# convert to string
locale = normalize(_build_localename(locale))
return _setlocale(category, locale) | [
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NoGameNoLife00/mybolg | afe17ea5bfe405e33766e5682c43a4262232ee12 | libs/wtforms/form.py | python | FormMeta.__call__ | (cls, *args, **kwargs) | return type.__call__(cls, *args, **kwargs) | Construct a new `Form` instance.
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"""
Construct a new `Form` instance.
Creates the `_unbound_fields` list and the internal `_wtforms_meta`
subclass of the class Meta in order to allow a proper inheritance
hierarchy.
"""
if cls._unbound_fields is None:
fields = []
for name in dir(cls):
if not name.startswith('_'):
unbound_field = getattr(cls, name)
if hasattr(unbound_field, '_formfield'):
fields.append((name, unbound_field))
# We keep the name as the second element of the sort
# to ensure a stable sort.
fields.sort(key=lambda x: (x[1].creation_counter, x[0]))
cls._unbound_fields = fields
# Create a subclass of the 'class Meta' using all the ancestors.
if cls._wtforms_meta is None:
bases = []
for mro_class in cls.__mro__:
if 'Meta' in mro_class.__dict__:
bases.append(mro_class.Meta)
cls._wtforms_meta = type('Meta', tuple(bases), {})
return type.__call__(cls, *args, **kwargs) | [
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smart-mobile-software/gitstack | d9fee8f414f202143eb6e620529e8e5539a2af56 | python/Lib/email/iterators.py | python | body_line_iterator | (msg, decode=False) | Iterate over the parts, returning string payloads line-by-line.
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"""
for subpart in msg.walk():
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jaraco/inflect | 98e19e3eb3ad28f9fa882baaad01674c44b59952 | inflect.py | python | joinstem | (cutpoint: Optional[int] = 0, words: Optional[Iterable[str]] = None) | return enclose("|".join(w[:cutpoint] for w in words)) | join stem of each word in words into a string for regex
each word is truncated at cutpoint
cutpoint is usually negative indicating the number of letters to remove
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e.g.
joinstem(-2, ["ephemeris", "iris", ".*itis"]) returns
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each word is truncated at cutpoint
cutpoint is usually negative indicating the number of letters to remove
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"""
join stem of each word in words into a string for regex
each word is truncated at cutpoint
cutpoint is usually negative indicating the number of letters to remove
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joinstem(-2, ["ephemeris", "iris", ".*itis"]) returns
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coursera/dataduct | 83aea17c1b1abd376270bc8fd4a180ce09181cc5 | dataduct/s3/s3_path.py | python | S3Path.append | (self, new_key, is_directory=False) | Appends new key to the current key
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new_key (str): Key for the S3 path
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Args:
new_key (str): Key for the S3 path
is_directory (bool): Is the specified S3 path a directory
"""
assert self.is_directory or self.key is None, \
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# If new key is list we want to flatten it out
if isinstance(new_key, list):
new_key = join(*new_key)
# Remove duplicate, leading, and trailing '/'
new_key = [a for a in new_key.split("/") if a != '']
# AWS prevents us from using periods in paths
# Substitute them with '_'
if is_directory:
directory_path = new_key
file_name = ''
else:
directory_path = new_key[:-1]
file_name = new_key[-1]
# Remove periods
new_key = [sub(r'\.', '_', a) for a in directory_path]
new_key.append(file_name)
new_key = join(*new_key)
if self.key:
self.key = join(self.key, new_key)
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self.key = new_key
self.is_directory = is_directory | [
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joelgrus/data-science-from-scratch | d5d0f117f41b3ccab3b07f1ee1fa21cfcf69afa1 | first-edition/code-python3/databases.py | python | Table.limit | (self, num_rows=None) | return limit_table | return only the first num_rows rows | return only the first num_rows rows | [
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"""return only the first num_rows rows"""
limit_table = Table(self.columns)
limit_table.rows = (self.rows[:num_rows]
if num_rows is not None
else self.rows)
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scipy/scipy | e0a749f01e79046642ccfdc419edbf9e7ca141ad | scipy/integrate/_quadrature.py | python | _cached_roots_legendre | (n) | return _cached_roots_legendre.cache[n] | Cache roots_legendre results to speed up calls of the fixed_quad
function. | Cache roots_legendre results to speed up calls of the fixed_quad
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"""
Cache roots_legendre results to speed up calls of the fixed_quad
function.
"""
if n in _cached_roots_legendre.cache:
return _cached_roots_legendre.cache[n]
_cached_roots_legendre.cache[n] = roots_legendre(n)
return _cached_roots_legendre.cache[n] | [
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pinterest/mysql_utils | 7ab237699b85de8b503b09f36e0309ac807689fe | mysql_backup_csv.py | python | mysql_backup_csv.mysql_backup_one_partition | (self, table_tuple, tmp_dir_db, conn) | Back up a single partition of a single table
Args:
table_tuple - the table_tuple (db, partition name, partition number)
to be backed up
tmp_dir_db - temporary storage used for all tables in the db
conn - a connection the the mysql instance | Back up a single partition of a single table | [
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""" Back up a single partition of a single table
Args:
table_tuple - the table_tuple (db, partition name, partition number)
to be backed up
tmp_dir_db - temporary storage used for all tables in the db
conn - a connection the the mysql instance
"""
proc_id = multiprocessing.current_process().name
(_, data_path, _) = backup.get_csv_backup_paths(self.instance,
*table_tuple[0].split('.'),
date=self.datestamp,
partition_number=table_tuple[2])
log.debug('{proc_id}: {tbl} partition {p} dump to {path} started'
''.format(proc_id=proc_id,
tbl=table_tuple[0],
p=table_tuple[2],
path=data_path))
self.upload_schema(*table_tuple[0].split('.'), tmp_dir_db=tmp_dir_db)
fifo = os.path.join(tmp_dir_db,
'{tbl}{part}'.format(tbl=table_tuple[0].split('.')[1],
part=table_tuple[2]))
procs = dict()
try:
# giant try so we can try to clean things up in case of errors
self.create_fifo(fifo)
# Start creating processes
procs['cat'] = subprocess.Popen(['cat', fifo],
stdout=subprocess.PIPE)
procs['nullescape'] = subprocess.Popen(['nullescape'],
stdin=procs['cat'].stdout,
stdout=subprocess.PIPE)
procs['lzop'] = subprocess.Popen(['lzop'],
stdin=procs['nullescape'].stdout,
stdout=subprocess.PIPE)
# Start dump query
return_value = set()
query_thread = threading.Thread(target=self.run_dump_query,
args=(table_tuple, fifo, conn,
procs['cat'], return_value))
query_thread.daemon = True
query_thread.start()
# And run the upload
safe_uploader.safe_upload(precursor_procs=procs,
stdin=procs['lzop'].stdout,
bucket=self.upload_bucket,
key=data_path,
check_func=self.check_dump_success,
check_arg=return_value)
os.remove(fifo)
log.debug('{proc_id}: {tbl} partition {p} clean up complete'
''.format(proc_id=proc_id,
tbl=table_tuple[0],
p=table_tuple[2]))
except:
log.debug('{}: in exception handling for failed table '
'upload'.format(proc_id))
if os.path.exists(fifo):
self.cleanup_fifo(fifo)
raise | [
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triaquae/triaquae | bbabf736b3ba56a0c6498e7f04e16c13b8b8f2b9 | TriAquae/models/django/views/generic/dates.py | python | MonthMixin._get_current_month | (self, date) | return date.replace(day=1) | Return the start date of the previous interval. | Return the start date of the previous interval. | [
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"""
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msracver/Deep-Exemplar-based-Colorization | 964026106bb51171a3c58be58d5c117e2f62bb4c | colorization_subnet/lib/functional.py | python | crop | (img, i, j, h, w) | return img.crop((j, i, j + w, i + h)) | Crop the given PIL Image.
Args:
img (PIL Image): Image to be cropped.
i: Upper pixel coordinate.
j: Left pixel coordinate.
h: Height of the cropped image.
w: Width of the cropped image.
Returns:
PIL Image: Cropped image. | Crop the given PIL Image. | [
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] | def crop(img, i, j, h, w):
"""Crop the given PIL Image.
Args:
img (PIL Image): Image to be cropped.
i: Upper pixel coordinate.
j: Left pixel coordinate.
h: Height of the cropped image.
w: Width of the cropped image.
Returns:
PIL Image: Cropped image.
"""
if not _is_pil_image(img):
raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
return img.crop((j, i, j + w, i + h)) | [
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djblets/djblets | 0496e1ec49e43d43d776768c9fc5b6f8af56ec2c | djblets/registries/registry.py | python | Registry.populated | (self) | return self._populated | Whether or not the registry is populated.
Returns:
bool: Whether or not the registry is populated. | Whether or not the registry is populated. | [
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"""Whether or not the registry is populated.
Returns:
bool: Whether or not the registry is populated.
"""
return self._populated | [
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ChineseGLUE/ChineseGLUE | 1591b85cf5427c2ff60f718d359ecb71d2b44879 | baselines/models/xlnet/classifier_utils.py | python | convert_single_example_for_inews | (ex_index, tokens_a, tokens_b, label_map, max_seq_length,
tokenizer, example) | return feature | [] | def convert_single_example_for_inews(ex_index, tokens_a, tokens_b, label_map, max_seq_length,
tokenizer, example):
if tokens_b:
# Modifies `tokens_a` and `tokens_b` in place so that the total
# length is less than the specified length.
# Account for two [SEP] & one [CLS] with "- 3"
_truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
else:
# Account for one [SEP] & one [CLS] with "- 2"
if len(tokens_a) > max_seq_length - 2:
tokens_a = tokens_a[:max_seq_length - 2]
tokens = []
segment_ids = []
for token in tokens_a:
tokens.append(token)
segment_ids.append(SEG_ID_A)
tokens.append(SEP_ID)
segment_ids.append(SEG_ID_A)
if tokens_b:
for token in tokens_b:
tokens.append(token)
segment_ids.append(SEG_ID_B)
tokens.append(SEP_ID)
segment_ids.append(SEG_ID_B)
tokens.append(CLS_ID)
segment_ids.append(SEG_ID_CLS)
input_ids = tokens
# The mask has 0 for real tokens and 1 for padding tokens. Only real
# tokens are attended to.
input_mask = [0] * len(input_ids)
# Zero-pad up to the sequence length.
if len(input_ids) < max_seq_length:
delta_len = max_seq_length - len(input_ids)
input_ids = [0] * delta_len + input_ids
input_mask = [1] * delta_len + input_mask
segment_ids = [SEG_ID_PAD] * delta_len + segment_ids
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
if label_map is not None:
label_id = label_map[example.label]
else:
label_id = example.label
if ex_index < 5:
tf.logging.info("*** Example ***")
tf.logging.info("guid: %s" % (example.guid))
tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
tf.logging.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
tf.logging.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
tf.logging.info("label: {} (id = {})".format(example.label, label_id))
feature = InputFeatures(
input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids,
label_id=label_id)
return feature | [
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onnx/onnx-tensorflow | 6194294c9f2f1c9270a614f6ae5078f2095587b7 | onnx_tf/backend.py | python | TensorflowBackend.prepare | (cls,
model,
device='CPU',
strict=True,
logging_level='INFO',
auto_cast=False,
**kwargs) | return cls.onnx_model_to_tensorflow_rep(model, strict, **kwargs) | Prepare an ONNX model for Tensorflow Backend.
This function converts an ONNX model to an internel representation
of the computational graph called TensorflowRep and returns
the converted representation.
:param model: The ONNX model to be converted.
:param device: The device to execute this model on. It can be either CPU (default) or CUDA.
:param strict: Whether to enforce semantic equivalence between the original model
and the converted tensorflow model, defaults to True (yes, enforce semantic equivalence).
Changing to False is strongly discouraged.
Currently, the strict flag only affects the behavior of MaxPool and AveragePool ops.
:param logging_level: The logging level, default is INFO. Change it to DEBUG
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:param auto_cast: Whether to auto cast data types that might lose precision for the tensors
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:returns: A TensorflowRep class object representing the ONNX model | Prepare an ONNX model for Tensorflow Backend. | [
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model,
device='CPU',
strict=True,
logging_level='INFO',
auto_cast=False,
**kwargs):
"""Prepare an ONNX model for Tensorflow Backend.
This function converts an ONNX model to an internel representation
of the computational graph called TensorflowRep and returns
the converted representation.
:param model: The ONNX model to be converted.
:param device: The device to execute this model on. It can be either CPU (default) or CUDA.
:param strict: Whether to enforce semantic equivalence between the original model
and the converted tensorflow model, defaults to True (yes, enforce semantic equivalence).
Changing to False is strongly discouraged.
Currently, the strict flag only affects the behavior of MaxPool and AveragePool ops.
:param logging_level: The logging level, default is INFO. Change it to DEBUG
to see more conversion details or to WARNING to see less
:param auto_cast: Whether to auto cast data types that might lose precision for the tensors
with types not natively supported by Tensorflow, default is False
:returns: A TensorflowRep class object representing the ONNX model
"""
super(TensorflowBackend, cls).prepare(model, device, **kwargs)
common.logger.setLevel(logging_level)
common.logger.handlers[0].setLevel(logging_level)
common.sys_config.auto_cast = auto_cast
common.sys_config.device = device
return cls.onnx_model_to_tensorflow_rep(model, strict, **kwargs) | [
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kumar-shridhar/PyTorch-BayesianCNN | d93bad543c3226cd0fe05c0cb0ba033c41b3caa6 | Mixtures/gmm.py | python | GaussianMixture.__p_k | (self, x, mu, var) | return prefactor * exponent | Returns a tensor with dimensions (n, k, 1) indicating the likelihood of data belonging to the k-th Gaussian.
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x: torch.Tensor (n, k, d)
mu: torch.Tensor (1, k, d)
var: torch.Tensor (1, k, d)
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var = var.expand(x.size(0), self.n_components, self.n_features)
# (n, k, d) --> (n, k, 1)
exponent = torch.exp(-.5 * torch.sum((x - mu) * (x - mu) / var, 2, keepdim=True))
# (n, k, d) --> (n, k, 1)
prefactor = torch.rsqrt(((2. * pi) ** self.n_features) * torch.prod(var, dim=2, keepdim=True) + self.eps)
return prefactor * exponent | [
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google-research/language | 61fa7260ac7d690d11ef72ca863e45a37c0bdc80 | language/canine/bert_optimization.py | python | AdamWeightDecayOptimizer.__init__ | (self,
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weight_decay_rate=0.0,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-6,
exclude_from_weight_decay=None,
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learning_rate,
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beta_1=0.9,
beta_2=0.999,
epsilon=1e-6,
exclude_from_weight_decay=None,
name="AdamWeightDecayOptimizer"):
"""Constructs a AdamWeightDecayOptimizer."""
super(AdamWeightDecayOptimizer, self).__init__(False, name)
self.learning_rate = learning_rate
self.weight_decay_rate = weight_decay_rate
self.beta_1 = beta_1
self.beta_2 = beta_2
self.epsilon = epsilon
self.exclude_from_weight_decay = exclude_from_weight_decay | [
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bruderstein/PythonScript | df9f7071ddf3a079e3a301b9b53a6dc78cf1208f | PythonLib/full/asyncio/windows_events.py | python | _BaseWaitHandleFuture.set_exception | (self, exception) | [] | def set_exception(self, exception):
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super().set_exception(exception) | [
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/plot/plot3d/tachyon.py | python | FCylinder.__init__ | (self, base, apex, radius, texture) | r"""
Create a finite cylinder object.
EXAMPLES::
sage: from sage.plot.plot3d.tachyon import FCylinder
sage: fc = FCylinder((0,0,0),(1,1,1),.1,'s')
sage: fc.str()
'\n fcylinder base 0.0 0.0 0.0 apex 1.0 1.0 1.0 rad 0.1 s\n ' | r"""
Create a finite cylinder object. | [
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] | def __init__(self, base, apex, radius, texture):
r"""
Create a finite cylinder object.
EXAMPLES::
sage: from sage.plot.plot3d.tachyon import FCylinder
sage: fc = FCylinder((0,0,0),(1,1,1),.1,'s')
sage: fc.str()
'\n fcylinder base 0.0 0.0 0.0 apex 1.0 1.0 1.0 rad 0.1 s\n '
"""
x, y, z = base
self._center = (float(x), float(y), float(z))
x, y, z = apex
self._axis = (float(x), float(y), float(z))
self._radius = float(radius)
self._texture = texture | [
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zachwill/flask-engine | 7c8ad4bfe36382a8c9286d873ec7b785715832a4 | libs/werkzeug/routing.py | python | RuleFactory.get_rules | (self, map) | Subclasses of `RuleFactory` have to override this method and return
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fonttools/fonttools | 892322aaff6a89bea5927379ec06bc0da3dfb7df | Lib/fontTools/ttLib/ttFont.py | python | _TTGlyphSet.__init__ | (self, ttFont, glyphs, glyphType) | Construct a new glyphset.
Args:
font (TTFont): The font object (used to get metrics).
glyphs (dict): A dictionary mapping glyph names to ``_TTGlyph`` objects.
glyphType (class): Either ``_TTGlyphCFF`` or ``_TTGlyphGlyf``. | Construct a new glyphset. | [
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Args:
font (TTFont): The font object (used to get metrics).
glyphs (dict): A dictionary mapping glyph names to ``_TTGlyph`` objects.
glyphType (class): Either ``_TTGlyphCFF`` or ``_TTGlyphGlyf``.
"""
self._glyphs = glyphs
self._hmtx = ttFont['hmtx']
self._vmtx = ttFont['vmtx'] if 'vmtx' in ttFont else None
self._glyphType = glyphType | [
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pyparallel/pyparallel | 11e8c6072d48c8f13641925d17b147bf36ee0ba3 | Lib/site-packages/pip-7.1.2-py3.3.egg/pip/_vendor/lockfile/pidlockfile.py | python | PIDLockFile.is_locked | (self) | return os.path.exists(self.path) | Test if the lock is currently held.
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PythonJS/PythonJS | 591a80afd8233fb715493591db2b68f1748558d9 | pythonjs/lib/python2.7/StringIO.py | python | StringIO.seek | (self, pos, mode = 0) | Set the file's current position.
The mode argument is optional and defaults to 0 (absolute file
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sebastien/cuisine | f6f70268ef1361db66815383017f7c8969002154 | src/cuisine.py | python | stringify | ( value ) | Turns the given value in a user-friendly string that can be displayed | Turns the given value in a user-friendly string that can be displayed | [
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freedombox/FreedomBox | 335a7f92cc08f27981f838a7cddfc67740598e54 | plinth/glib.py | python | _run | () | Connect to D-Bus and run main loop. | Connect to D-Bus and run main loop. | [
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dbus.init()
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omz/PythonistaAppTemplate | f560f93f8876d82a21d108977f90583df08d55af | PythonistaAppTemplate/PythonistaKit.framework/pylib_ext/matplotlib/colors.py | python | LinearSegmentedColormap.from_list | (name, colors, N=256, gamma=1.0) | return LinearSegmentedColormap(name, cdict, N, gamma) | Make a linear segmented colormap with *name* from a sequence
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"]",... | def from_list(name, colors, N=256, gamma=1.0):
"""
Make a linear segmented colormap with *name* from a sequence
of *colors* which evenly transitions from colors[0] at val=0
to colors[-1] at val=1. *N* is the number of rgb quantization
levels.
Alternatively, a list of (value, color) tuples can be given
to divide the range unevenly.
"""
if not cbook.iterable(colors):
raise ValueError('colors must be iterable')
if cbook.iterable(colors[0]) and len(colors[0]) == 2 and \
not cbook.is_string_like(colors[0]):
# List of value, color pairs
vals, colors = zip(*colors)
else:
vals = np.linspace(0., 1., len(colors))
cdict = dict(red=[], green=[], blue=[], alpha=[])
for val, color in zip(vals, colors):
r, g, b, a = colorConverter.to_rgba(color)
cdict['red'].append((val, r, r))
cdict['green'].append((val, g, g))
cdict['blue'].append((val, b, b))
cdict['alpha'].append((val, a, a))
return LinearSegmentedColormap(name, cdict, N, gamma) | [
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ipython/ipython | c0abea7a6dfe52c1f74c9d0387d4accadba7cc14 | IPython/core/history.py | python | HistoryAccessor.init_db | (self) | Connect to the database, and create tables if necessary. | Connect to the database, and create tables if necessary. | [
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"""Connect to the database, and create tables if necessary."""
if not self.enabled:
self.db = DummyDB()
return
# use detect_types so that timestamps return datetime objects
kwargs = dict(detect_types=sqlite3.PARSE_DECLTYPES|sqlite3.PARSE_COLNAMES)
kwargs.update(self.connection_options)
self.db = sqlite3.connect(str(self.hist_file), **kwargs)
with self.db:
self.db.execute(
"""CREATE TABLE IF NOT EXISTS sessions (session integer
primary key autoincrement, start timestamp,
end timestamp, num_cmds integer, remark text)"""
)
self.db.execute(
"""CREATE TABLE IF NOT EXISTS history
(session integer, line integer, source text, source_raw text,
PRIMARY KEY (session, line))"""
)
# Output history is optional, but ensure the table's there so it can be
# enabled later.
self.db.execute(
"""CREATE TABLE IF NOT EXISTS output_history
(session integer, line integer, output text,
PRIMARY KEY (session, line))"""
)
# success! reset corrupt db count
self._corrupt_db_counter = 0 | [
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AwesomeTTS/awesometts-anki-addon | c7c2c94479b610b9767ec44cdbb825002bc0c2b7 | awesometts/gui/common.py | python | key_event_combo | (event) | return key + sum(flag
for flag in key_event_combo.MOD_FLAGS
if modifiers & flag) | Given a key event, returns an integer representing the combination
of keys that was pressed or released.
Certain keys are blacklisted (see BLACKLIST) and key_event_combo()
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key codes for Alt and Ctrl are different from the modifier flag
codes for Alt and Ctrl), and clashes with input navigation. | Given a key event, returns an integer representing the combination
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] | def key_event_combo(event):
"""
Given a key event, returns an integer representing the combination
of keys that was pressed or released.
Certain keys are blacklisted (see BLACKLIST) and key_event_combo()
will return None if it sees these keys in the primary key() slot for
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"""
key = event.key()
if key < 32 or key in key_event_combo.BLACKLIST:
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modifiers = event.modifiers()
return key + sum(flag
for flag in key_event_combo.MOD_FLAGS
if modifiers & flag) | [
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edfungus/Crouton | ada98b3930192938a48909072b45cb84b945f875 | clients/python_clients/cf_demo_client/cf_env/lib/python2.7/site-packages/pip/_vendor/distlib/locators.py | python | DependencyFinder.__init__ | (self, locator=None) | Initialise an instance, using the specified locator
to locate distributions. | Initialise an instance, using the specified locator
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"""
Initialise an instance, using the specified locator
to locate distributions.
"""
self.locator = locator or default_locator
self.scheme = get_scheme(self.locator.scheme) | [
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/tkinter/__init__.py | python | Text.insert | (self, index, chars, *args) | Insert CHARS before the characters at INDEX. An additional
tag can be given in ARGS. Additional CHARS and tags can follow in ARGS. | Insert CHARS before the characters at INDEX. An additional
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"""Insert CHARS before the characters at INDEX. An additional
tag can be given in ARGS. Additional CHARS and tags can follow in ARGS."""
self.tk.call((self._w, 'insert', index, chars) + args) | [
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securesystemslab/zippy | ff0e84ac99442c2c55fe1d285332cfd4e185e089 | zippy/lib-python/3/binhex.py | python | _Hqxdecoderengine.close | (self) | [] | def close(self):
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ganeti/ganeti | d340a9ddd12f501bef57da421b5f9b969a4ba905 | lib/utils/text.py | python | BuildShellCmd | (template, *args) | return template % args | Build a safe shell command line from the given arguments.
This function will check all arguments in the args list so that they
are valid shell parameters (i.e. they don't contain shell
metacharacters). If everything is ok, it will return the result of
template % args.
@type template: str
@param template: the string holding the template for the
string formatting
@rtype: str
@return: the expanded command line | Build a safe shell command line from the given arguments. | [
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] | def BuildShellCmd(template, *args):
"""Build a safe shell command line from the given arguments.
This function will check all arguments in the args list so that they
are valid shell parameters (i.e. they don't contain shell
metacharacters). If everything is ok, it will return the result of
template % args.
@type template: str
@param template: the string holding the template for the
string formatting
@rtype: str
@return: the expanded command line
"""
for word in args:
if not IsValidShellParam(word):
raise errors.ProgrammerError("Shell argument '%s' contains"
" invalid characters" % word)
return template % args | [
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googleapis/python-dialogflow | e48ea001b7c8a4a5c1fe4b162bad49ea397458e9 | google/cloud/dialogflow_v2beta1/services/documents/client.py | python | DocumentsClient.list_documents | (
self,
request: Union[document.ListDocumentsRequest, dict] = None,
*,
parent: str = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: float = None,
metadata: Sequence[Tuple[str, str]] = (),
) | return response | r"""Returns the list of all documents of the knowledge base.
Note: The ``projects.agent.knowledgeBases.documents`` resource
is deprecated; only use ``projects.knowledgeBases.documents``.
Args:
request (Union[google.cloud.dialogflow_v2beta1.types.ListDocumentsRequest, dict]):
The request object. Request message for
[Documents.ListDocuments][google.cloud.dialogflow.v2beta1.Documents.ListDocuments].
parent (str):
Required. The knowledge base to list all documents for.
Format:
``projects/<Project ID>/locations/<Location ID>/knowledgeBases/<Knowledge Base ID>``.
This corresponds to the ``parent`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
retry (google.api_core.retry.Retry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.cloud.dialogflow_v2beta1.services.documents.pagers.ListDocumentsPager:
Response message for
[Documents.ListDocuments][google.cloud.dialogflow.v2beta1.Documents.ListDocuments].
Iterating over this object will yield results and
resolve additional pages automatically. | r"""Returns the list of all documents of the knowledge base. | [
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self,
request: Union[document.ListDocumentsRequest, dict] = None,
*,
parent: str = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: float = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> pagers.ListDocumentsPager:
r"""Returns the list of all documents of the knowledge base.
Note: The ``projects.agent.knowledgeBases.documents`` resource
is deprecated; only use ``projects.knowledgeBases.documents``.
Args:
request (Union[google.cloud.dialogflow_v2beta1.types.ListDocumentsRequest, dict]):
The request object. Request message for
[Documents.ListDocuments][google.cloud.dialogflow.v2beta1.Documents.ListDocuments].
parent (str):
Required. The knowledge base to list all documents for.
Format:
``projects/<Project ID>/locations/<Location ID>/knowledgeBases/<Knowledge Base ID>``.
This corresponds to the ``parent`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
retry (google.api_core.retry.Retry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.cloud.dialogflow_v2beta1.services.documents.pagers.ListDocumentsPager:
Response message for
[Documents.ListDocuments][google.cloud.dialogflow.v2beta1.Documents.ListDocuments].
Iterating over this object will yield results and
resolve additional pages automatically.
"""
# Create or coerce a protobuf request object.
# Sanity check: If we got a request object, we should *not* have
# gotten any keyword arguments that map to the request.
has_flattened_params = any([parent])
if request is not None and has_flattened_params:
raise ValueError(
"If the `request` argument is set, then none of "
"the individual field arguments should be set."
)
# Minor optimization to avoid making a copy if the user passes
# in a document.ListDocumentsRequest.
# There's no risk of modifying the input as we've already verified
# there are no flattened fields.
if not isinstance(request, document.ListDocumentsRequest):
request = document.ListDocumentsRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if parent is not None:
request.parent = parent
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[self._transport.list_documents]
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (
gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)),
)
# Send the request.
response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,)
# This method is paged; wrap the response in a pager, which provides
# an `__iter__` convenience method.
response = pagers.ListDocumentsPager(
method=rpc, request=request, response=response, metadata=metadata,
)
# Done; return the response.
return response | [
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eirannejad/pyRevit | 49c0b7eb54eb343458ce1365425e6552d0c47d44 | site-packages/sqlalchemy/engine/base.py | python | Connection.execution_options | (self, **opt) | return c | r""" Set non-SQL options for the connection which take effect
during execution.
The method returns a copy of this :class:`.Connection` which references
the same underlying DBAPI connection, but also defines the given
execution options which will take effect for a call to
:meth:`execute`. As the new :class:`.Connection` references the same
underlying resource, it's usually a good idea to ensure that the copies
will be discarded immediately, which is implicit if used as in::
result = connection.execution_options(stream_results=True).\
execute(stmt)
Note that any key/value can be passed to
:meth:`.Connection.execution_options`, and it will be stored in the
``_execution_options`` dictionary of the :class:`.Connection`. It
is suitable for usage by end-user schemes to communicate with
event listeners, for example.
The keywords that are currently recognized by SQLAlchemy itself
include all those listed under :meth:`.Executable.execution_options`,
as well as others that are specific to :class:`.Connection`.
:param autocommit: Available on: Connection, statement.
When True, a COMMIT will be invoked after execution
when executed in 'autocommit' mode, i.e. when an explicit
transaction is not begun on the connection. Note that DBAPI
connections by default are always in a transaction - SQLAlchemy uses
rules applied to different kinds of statements to determine if
COMMIT will be invoked in order to provide its "autocommit" feature.
Typically, all INSERT/UPDATE/DELETE statements as well as
CREATE/DROP statements have autocommit behavior enabled; SELECT
constructs do not. Use this option when invoking a SELECT or other
specific SQL construct where COMMIT is desired (typically when
calling stored procedures and such), and an explicit
transaction is not in progress.
:param compiled_cache: Available on: Connection.
A dictionary where :class:`.Compiled` objects
will be cached when the :class:`.Connection` compiles a clause
expression into a :class:`.Compiled` object.
It is the user's responsibility to
manage the size of this dictionary, which will have keys
corresponding to the dialect, clause element, the column
names within the VALUES or SET clause of an INSERT or UPDATE,
as well as the "batch" mode for an INSERT or UPDATE statement.
The format of this dictionary is not guaranteed to stay the
same in future releases.
Note that the ORM makes use of its own "compiled" caches for
some operations, including flush operations. The caching
used by the ORM internally supersedes a cache dictionary
specified here.
:param isolation_level: Available on: :class:`.Connection`.
Set the transaction isolation level for
the lifespan of this :class:`.Connection` object (*not* the
underlying DBAPI connection, for which the level is reset
to its original setting upon termination of this
:class:`.Connection` object).
Valid values include
those string values accepted by the
:paramref:`.create_engine.isolation_level`
parameter passed to :func:`.create_engine`. These levels are
semi-database specific; see individual dialect documentation for
valid levels.
Note that this option necessarily affects the underlying
DBAPI connection for the lifespan of the originating
:class:`.Connection`, and is not per-execution. This
setting is not removed until the underlying DBAPI connection
is returned to the connection pool, i.e.
the :meth:`.Connection.close` method is called.
.. warning:: The ``isolation_level`` execution option should
**not** be used when a transaction is already established, that
is, the :meth:`.Connection.begin` method or similar has been
called. A database cannot change the isolation level on a
transaction in progress, and different DBAPIs and/or
SQLAlchemy dialects may implicitly roll back or commit
the transaction, or not affect the connection at all.
.. versionchanged:: 0.9.9 A warning is emitted when the
``isolation_level`` execution option is used after a
transaction has been started with :meth:`.Connection.begin`
or similar.
.. note:: The ``isolation_level`` execution option is implicitly
reset if the :class:`.Connection` is invalidated, e.g. via
the :meth:`.Connection.invalidate` method, or if a
disconnection error occurs. The new connection produced after
the invalidation will not have the isolation level re-applied
to it automatically.
.. seealso::
:paramref:`.create_engine.isolation_level`
- set per :class:`.Engine` isolation level
:meth:`.Connection.get_isolation_level` - view current level
:ref:`SQLite Transaction Isolation <sqlite_isolation_level>`
:ref:`PostgreSQL Transaction Isolation <postgresql_isolation_level>`
:ref:`MySQL Transaction Isolation <mysql_isolation_level>`
:ref:`SQL Server Transaction Isolation <mssql_isolation_level>`
:ref:`session_transaction_isolation` - for the ORM
:param no_parameters: When ``True``, if the final parameter
list or dictionary is totally empty, will invoke the
statement on the cursor as ``cursor.execute(statement)``,
not passing the parameter collection at all.
Some DBAPIs such as psycopg2 and mysql-python consider
percent signs as significant only when parameters are
present; this option allows code to generate SQL
containing percent signs (and possibly other characters)
that is neutral regarding whether it's executed by the DBAPI
or piped into a script that's later invoked by
command line tools.
.. versionadded:: 0.7.6
:param stream_results: Available on: Connection, statement.
Indicate to the dialect that results should be
"streamed" and not pre-buffered, if possible. This is a limitation
of many DBAPIs. The flag is currently understood only by the
psycopg2, mysqldb and pymysql dialects.
:param schema_translate_map: Available on: Connection, Engine.
A dictionary mapping schema names to schema names, that will be
applied to the :paramref:`.Table.schema` element of each
:class:`.Table` encountered when SQL or DDL expression elements
are compiled into strings; the resulting schema name will be
converted based on presence in the map of the original name.
.. versionadded:: 1.1
.. seealso::
:ref:`schema_translating` | r""" Set non-SQL options for the connection which take effect
during execution. | [
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r""" Set non-SQL options for the connection which take effect
during execution.
The method returns a copy of this :class:`.Connection` which references
the same underlying DBAPI connection, but also defines the given
execution options which will take effect for a call to
:meth:`execute`. As the new :class:`.Connection` references the same
underlying resource, it's usually a good idea to ensure that the copies
will be discarded immediately, which is implicit if used as in::
result = connection.execution_options(stream_results=True).\
execute(stmt)
Note that any key/value can be passed to
:meth:`.Connection.execution_options`, and it will be stored in the
``_execution_options`` dictionary of the :class:`.Connection`. It
is suitable for usage by end-user schemes to communicate with
event listeners, for example.
The keywords that are currently recognized by SQLAlchemy itself
include all those listed under :meth:`.Executable.execution_options`,
as well as others that are specific to :class:`.Connection`.
:param autocommit: Available on: Connection, statement.
When True, a COMMIT will be invoked after execution
when executed in 'autocommit' mode, i.e. when an explicit
transaction is not begun on the connection. Note that DBAPI
connections by default are always in a transaction - SQLAlchemy uses
rules applied to different kinds of statements to determine if
COMMIT will be invoked in order to provide its "autocommit" feature.
Typically, all INSERT/UPDATE/DELETE statements as well as
CREATE/DROP statements have autocommit behavior enabled; SELECT
constructs do not. Use this option when invoking a SELECT or other
specific SQL construct where COMMIT is desired (typically when
calling stored procedures and such), and an explicit
transaction is not in progress.
:param compiled_cache: Available on: Connection.
A dictionary where :class:`.Compiled` objects
will be cached when the :class:`.Connection` compiles a clause
expression into a :class:`.Compiled` object.
It is the user's responsibility to
manage the size of this dictionary, which will have keys
corresponding to the dialect, clause element, the column
names within the VALUES or SET clause of an INSERT or UPDATE,
as well as the "batch" mode for an INSERT or UPDATE statement.
The format of this dictionary is not guaranteed to stay the
same in future releases.
Note that the ORM makes use of its own "compiled" caches for
some operations, including flush operations. The caching
used by the ORM internally supersedes a cache dictionary
specified here.
:param isolation_level: Available on: :class:`.Connection`.
Set the transaction isolation level for
the lifespan of this :class:`.Connection` object (*not* the
underlying DBAPI connection, for which the level is reset
to its original setting upon termination of this
:class:`.Connection` object).
Valid values include
those string values accepted by the
:paramref:`.create_engine.isolation_level`
parameter passed to :func:`.create_engine`. These levels are
semi-database specific; see individual dialect documentation for
valid levels.
Note that this option necessarily affects the underlying
DBAPI connection for the lifespan of the originating
:class:`.Connection`, and is not per-execution. This
setting is not removed until the underlying DBAPI connection
is returned to the connection pool, i.e.
the :meth:`.Connection.close` method is called.
.. warning:: The ``isolation_level`` execution option should
**not** be used when a transaction is already established, that
is, the :meth:`.Connection.begin` method or similar has been
called. A database cannot change the isolation level on a
transaction in progress, and different DBAPIs and/or
SQLAlchemy dialects may implicitly roll back or commit
the transaction, or not affect the connection at all.
.. versionchanged:: 0.9.9 A warning is emitted when the
``isolation_level`` execution option is used after a
transaction has been started with :meth:`.Connection.begin`
or similar.
.. note:: The ``isolation_level`` execution option is implicitly
reset if the :class:`.Connection` is invalidated, e.g. via
the :meth:`.Connection.invalidate` method, or if a
disconnection error occurs. The new connection produced after
the invalidation will not have the isolation level re-applied
to it automatically.
.. seealso::
:paramref:`.create_engine.isolation_level`
- set per :class:`.Engine` isolation level
:meth:`.Connection.get_isolation_level` - view current level
:ref:`SQLite Transaction Isolation <sqlite_isolation_level>`
:ref:`PostgreSQL Transaction Isolation <postgresql_isolation_level>`
:ref:`MySQL Transaction Isolation <mysql_isolation_level>`
:ref:`SQL Server Transaction Isolation <mssql_isolation_level>`
:ref:`session_transaction_isolation` - for the ORM
:param no_parameters: When ``True``, if the final parameter
list or dictionary is totally empty, will invoke the
statement on the cursor as ``cursor.execute(statement)``,
not passing the parameter collection at all.
Some DBAPIs such as psycopg2 and mysql-python consider
percent signs as significant only when parameters are
present; this option allows code to generate SQL
containing percent signs (and possibly other characters)
that is neutral regarding whether it's executed by the DBAPI
or piped into a script that's later invoked by
command line tools.
.. versionadded:: 0.7.6
:param stream_results: Available on: Connection, statement.
Indicate to the dialect that results should be
"streamed" and not pre-buffered, if possible. This is a limitation
of many DBAPIs. The flag is currently understood only by the
psycopg2, mysqldb and pymysql dialects.
:param schema_translate_map: Available on: Connection, Engine.
A dictionary mapping schema names to schema names, that will be
applied to the :paramref:`.Table.schema` element of each
:class:`.Table` encountered when SQL or DDL expression elements
are compiled into strings; the resulting schema name will be
converted based on presence in the map of the original name.
.. versionadded:: 1.1
.. seealso::
:ref:`schema_translating`
"""
c = self._clone()
c._execution_options = c._execution_options.union(opt)
if self._has_events or self.engine._has_events:
self.dispatch.set_connection_execution_options(c, opt)
self.dialect.set_connection_execution_options(c, opt)
return c | [
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brendano/tweetmotif | 1b0b1e3a941745cd5a26eba01f554688b7c4b27e | everything_else/djfrontend/django-1.0.2/db/backends/__init__.py | python | BaseDatabaseOperations.lookup_cast | (self, lookup_type) | return "%s" | Returns the string to use in a query when performing lookups
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dropbox/dropbox-sdk-python | 015437429be224732990041164a21a0501235db1 | dropbox/sharing.py | python | ViewerInfoPolicy.is_disabled | (self) | return self._tag == 'disabled' | Check if the union tag is ``disabled``.
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stephenmcd/mezzanine | e38ffc69f732000ce44b7ed5c9d0516d258b8af2 | mezzanine/blog/management/base.py | python | BaseImporterCommand.add_post | (
self,
title=None,
content=None,
old_url=None,
pub_date=None,
tags=None,
categories=None,
comments=None,
) | return self.posts[-1] | Adds a post to the post list for processing.
- ``title`` and ``content`` are strings for the post.
- ``old_url`` is a string that a redirect will be created for.
- ``pub_date`` is assumed to be a ``datetime`` object.
- ``tags`` and ``categories`` are sequences of strings.
- ``comments`` is a sequence of dicts - each dict should be the
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old_url=None,
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tags=None,
categories=None,
comments=None,
):
"""
Adds a post to the post list for processing.
- ``title`` and ``content`` are strings for the post.
- ``old_url`` is a string that a redirect will be created for.
- ``pub_date`` is assumed to be a ``datetime`` object.
- ``tags`` and ``categories`` are sequences of strings.
- ``comments`` is a sequence of dicts - each dict should be the
return value of ``add_comment``.
"""
if not title:
title = strip_tags(content).split(". ")[0]
title = decode_entities(title)
if categories is None:
categories = []
if tags is None:
tags = []
if comments is None:
comments = []
self.posts.append(
{
"title": force_text(title),
"publish_date": pub_date,
"content": force_text(content),
"categories": categories,
"tags": tags,
"comments": comments,
"old_url": old_url,
}
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oilshell/oil | 94388e7d44a9ad879b12615f6203b38596b5a2d3 | Python-2.7.13/Lib/lib-tk/turtle.py | python | TPen.isvisible | (self) | return self._shown | Return True if the Turtle is shown, False if it's hidden.
No argument.
Example (for a Turtle instance named turtle):
>>> turtle.hideturtle()
>>> print turtle.isvisible():
False | Return True if the Turtle is shown, False if it's hidden. | [
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"""Return True if the Turtle is shown, False if it's hidden.
No argument.
Example (for a Turtle instance named turtle):
>>> turtle.hideturtle()
>>> print turtle.isvisible():
False
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DLR-RM/stable-baselines3 | e9a8979022d7005560d43b7a9c1dc1ba85f7989a | stable_baselines3/common/monitor.py | python | ResultsWriter.close | (self) | Close the file handler | Close the file handler | [
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"""
Close the file handler
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self.file_handler.close() | [
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GoogleCloudPlatform/PerfKitBenchmarker | 6e3412d7d5e414b8ca30ed5eaf970cef1d919a67 | perfkitbenchmarker/linux_packages/mxnet.py | python | GetMXNetVersion | (vm) | return stdout.strip() | Returns the version of MXNet installed on the vm.
Args:
vm: the target vm on which to check the MXNet version
Returns:
installed python MXNet version as a string | Returns the version of MXNet installed on the vm. | [
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"""Returns the version of MXNet installed on the vm.
Args:
vm: the target vm on which to check the MXNet version
Returns:
installed python MXNet version as a string
"""
stdout, _ = vm.RemoteCommand(
('echo -e "import mxnet\nprint(mxnet.__version__)" | {0} python'
.format(GetEnvironmentVars(vm)))
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wagtail/wagtail | ba8207a5d82c8a1de8f5f9693a7cd07421762999 | wagtail/admin/ui/tables.py | python | Column.get_cell | (self, instance) | return Column.Cell(self, instance) | Return an object encapsulating this column and an item of data, which we can use for
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eriklindernoren/ML-From-Scratch | a2806c6732eee8d27762edd6d864e0c179d8e9e8 | mlfromscratch/deep_learning/neural_network.py | python | NeuralNetwork.predict | (self, X) | return self._forward_pass(X, training=False) | Use the trained model to predict labels of X | Use the trained model to predict labels of X | [
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emcconville/wand | 03682680c351645f16c3b8ea23bde79fbb270305 | wand/color.py | python | Color.blue_int8 | (self) | return max(0, min(255, int(255.0 * self.blue))) | (:class:`numbers.Integral`) Blue as 8bit integer which is
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aiidateam/aiida-core | c743a335480f8bb3a5e4ebd2463a31f9f3b9f9b2 | aiida/manage/manager.py | python | Manager.get_communicator | (self) | return self._communicator | Return the communicator
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jaungiers/LSTM-Neural-Network-for-Time-Series-Prediction | da44411c91135b64c02eb60da3c7f574c7c4c253 | core/data_processor.py | python | DataLoader._next_window | (self, i, seq_len, normalise) | return x, y | Generates the next data window from the given index location i | Generates the next data window from the given index location i | [
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window = self.data_train[i:i+seq_len]
window = self.normalise_windows(window, single_window=True)[0] if normalise else window
x = window[:-1]
y = window[-1, [0]]
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scikit-learn/scikit-learn | 1d1aadd0711b87d2a11c80aad15df6f8cf156712 | sklearn/gaussian_process/_gpc.py | python | _BinaryGaussianProcessClassifierLaplace.predict | (self, X) | return np.where(f_star > 0, self.classes_[1], self.classes_[0]) | Perform classification on an array of test vectors X.
Parameters
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X : array-like of shape (n_samples, n_features) or list of object
Query points where the GP is evaluated for classification.
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C : ndarray of shape (n_samples,)
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X : array-like of shape (n_samples, n_features) or list of object
Query points where the GP is evaluated for classification.
Returns
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C : ndarray of shape (n_samples,)
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"""
check_is_fitted(self)
# As discussed on Section 3.4.2 of GPML, for making hard binary
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K_star = self.kernel_(self.X_train_, X) # K_star =k(x_star)
f_star = K_star.T.dot(self.y_train_ - self.pi_) # Algorithm 3.2,Line 4
return np.where(f_star > 0, self.classes_[1], self.classes_[0]) | [
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Calysto/calysto_scheme | 15bf81987870bcae1264e5a0a06feb9a8ee12b8b | calysto_scheme/src/Scheme.py | python | string_is__q | (s1, s2) | return s1 == s2 | [] | def string_is__q(s1, s2):
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frescobaldi/frescobaldi | 301cc977fc4ba7caa3df9e4bf905212ad5d06912 | frescobaldi_app/svgview/view.py | python | View.initJavaScript | (self) | return self._initJavaScript | Return a string containing all JavaScript to run in a page. | Return a string containing all JavaScript to run in a page. | [
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js = []
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js.append(getJsScript('pointandclick.js'))
# for now only editable in dev (git) or when the user explicitly allows experimental features
if app.is_git_controlled() or QSettings().value("experimental-features", False, bool):
js.append(getJsScript('editsvg.js'))
self._initJavaScript = '\n'.join(js)
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wxWidgets/Phoenix | b2199e299a6ca6d866aa6f3d0888499136ead9d6 | wx/lib/agw/aui/auibook.py | python | AuiNotebook.GetPageCount | (self) | return self._tabs.GetPageCount() | Returns the number of pages in the notebook. | Returns the number of pages in the notebook. | [
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] | def GetPageCount(self):
""" Returns the number of pages in the notebook. """
return self._tabs.GetPageCount() | [
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inkandswitch/livebook | 93c8d467734787366ad084fc3566bf5cbe249c51 | public/pypyjs/modules/numpy/fft/fftpack.py | python | irfft | (a, n=None, axis=-1, norm=None) | return output * (1 / (sqrt(n) if unitary else n)) | Compute the inverse of the n-point DFT for real input.
This function computes the inverse of the one-dimensional *n*-point
discrete Fourier Transform of real input computed by `rfft`.
In other words, ``irfft(rfft(a), len(a)) == a`` to within numerical
accuracy. (See Notes below for why ``len(a)`` is necessary here.)
The input is expected to be in the form returned by `rfft`, i.e. the
real zero-frequency term followed by the complex positive frequency terms
in order of increasing frequency. Since the discrete Fourier Transform of
real input is Hermitian-symmetric, the negative frequency terms are taken
to be the complex conjugates of the corresponding positive frequency terms.
Parameters
----------
a : array_like
The input array.
n : int, optional
Length of the transformed axis of the output.
For `n` output points, ``n//2+1`` input points are necessary. If the
input is longer than this, it is cropped. If it is shorter than this,
it is padded with zeros. If `n` is not given, it is determined from
the length of the input along the axis specified by `axis`.
axis : int, optional
Axis over which to compute the inverse FFT. If not given, the last
axis is used.
norm : {None, "ortho"}, optional
.. versionadded:: 1.10.0
Normalization mode (see `numpy.fft`). Default is None.
Returns
-------
out : ndarray
The truncated or zero-padded input, transformed along the axis
indicated by `axis`, or the last one if `axis` is not specified.
The length of the transformed axis is `n`, or, if `n` is not given,
``2*(m-1)`` where ``m`` is the length of the transformed axis of the
input. To get an odd number of output points, `n` must be specified.
Raises
------
IndexError
If `axis` is larger than the last axis of `a`.
See Also
--------
numpy.fft : For definition of the DFT and conventions used.
rfft : The one-dimensional FFT of real input, of which `irfft` is inverse.
fft : The one-dimensional FFT.
irfft2 : The inverse of the two-dimensional FFT of real input.
irfftn : The inverse of the *n*-dimensional FFT of real input.
Notes
-----
Returns the real valued `n`-point inverse discrete Fourier transform
of `a`, where `a` contains the non-negative frequency terms of a
Hermitian-symmetric sequence. `n` is the length of the result, not the
input.
If you specify an `n` such that `a` must be zero-padded or truncated, the
extra/removed values will be added/removed at high frequencies. One can
thus resample a series to `m` points via Fourier interpolation by:
``a_resamp = irfft(rfft(a), m)``.
Examples
--------
>>> np.fft.ifft([1, -1j, -1, 1j])
array([ 0.+0.j, 1.+0.j, 0.+0.j, 0.+0.j])
>>> np.fft.irfft([1, -1j, -1])
array([ 0., 1., 0., 0.])
Notice how the last term in the input to the ordinary `ifft` is the
complex conjugate of the second term, and the output has zero imaginary
part everywhere. When calling `irfft`, the negative frequencies are not
specified, and the output array is purely real. | Compute the inverse of the n-point DFT for real input. | [
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"""
Compute the inverse of the n-point DFT for real input.
This function computes the inverse of the one-dimensional *n*-point
discrete Fourier Transform of real input computed by `rfft`.
In other words, ``irfft(rfft(a), len(a)) == a`` to within numerical
accuracy. (See Notes below for why ``len(a)`` is necessary here.)
The input is expected to be in the form returned by `rfft`, i.e. the
real zero-frequency term followed by the complex positive frequency terms
in order of increasing frequency. Since the discrete Fourier Transform of
real input is Hermitian-symmetric, the negative frequency terms are taken
to be the complex conjugates of the corresponding positive frequency terms.
Parameters
----------
a : array_like
The input array.
n : int, optional
Length of the transformed axis of the output.
For `n` output points, ``n//2+1`` input points are necessary. If the
input is longer than this, it is cropped. If it is shorter than this,
it is padded with zeros. If `n` is not given, it is determined from
the length of the input along the axis specified by `axis`.
axis : int, optional
Axis over which to compute the inverse FFT. If not given, the last
axis is used.
norm : {None, "ortho"}, optional
.. versionadded:: 1.10.0
Normalization mode (see `numpy.fft`). Default is None.
Returns
-------
out : ndarray
The truncated or zero-padded input, transformed along the axis
indicated by `axis`, or the last one if `axis` is not specified.
The length of the transformed axis is `n`, or, if `n` is not given,
``2*(m-1)`` where ``m`` is the length of the transformed axis of the
input. To get an odd number of output points, `n` must be specified.
Raises
------
IndexError
If `axis` is larger than the last axis of `a`.
See Also
--------
numpy.fft : For definition of the DFT and conventions used.
rfft : The one-dimensional FFT of real input, of which `irfft` is inverse.
fft : The one-dimensional FFT.
irfft2 : The inverse of the two-dimensional FFT of real input.
irfftn : The inverse of the *n*-dimensional FFT of real input.
Notes
-----
Returns the real valued `n`-point inverse discrete Fourier transform
of `a`, where `a` contains the non-negative frequency terms of a
Hermitian-symmetric sequence. `n` is the length of the result, not the
input.
If you specify an `n` such that `a` must be zero-padded or truncated, the
extra/removed values will be added/removed at high frequencies. One can
thus resample a series to `m` points via Fourier interpolation by:
``a_resamp = irfft(rfft(a), m)``.
Examples
--------
>>> np.fft.ifft([1, -1j, -1, 1j])
array([ 0.+0.j, 1.+0.j, 0.+0.j, 0.+0.j])
>>> np.fft.irfft([1, -1j, -1])
array([ 0., 1., 0., 0.])
Notice how the last term in the input to the ordinary `ifft` is the
complex conjugate of the second term, and the output has zero imaginary
part everywhere. When calling `irfft`, the negative frequencies are not
specified, and the output array is purely real.
"""
# The copy may be required for multithreading.
a = array(a, copy=True, dtype=complex)
if n is None:
n = (a.shape[axis] - 1) * 2
unitary = _unitary(norm)
output = _raw_fft(a, n, axis, fftpack.rffti, fftpack.rfftb,
_real_fft_cache)
return output * (1 / (sqrt(n) if unitary else n)) | [
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creatorrr/pyAIML | bda3c8d85e8e0f5fa2d0133fca59a2d4e0baf641 | WordSub.py | python | WordSub.sub | (self, text) | return self._regex.sub(self, text) | Translate text, returns the modified text. | Translate text, returns the modified text. | [
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"""Translate text, returns the modified text."""
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fkie/multimaster_fkie | 3d23df29d25d71a75c66bbd3cc6e9cbb255724d8 | fkie_master_discovery/src/fkie_master_discovery/interface_finder.py | python | get_refresh_service | (masteruri, wait=True, check_host=True) | return _get_service(masteruri, 'refresh', wait, check_host) | Search in services of ROS master for a service with name ending by
`refresh` and returns his name, if it runs on the local host. Returns
empty list if no service was found and `wait` is ``False``.
:param masteruri: the URI of the ROS master
:type masteruri: str
:param wait: check every second for the service
:type wait: boo
:param check_host: check for eqaul hostname of topic provider and master uri.
:type check_host: bool
:return: the list with names of the services ending with `refresh`
:rtype: list of strings | Search in services of ROS master for a service with name ending by
`refresh` and returns his name, if it runs on the local host. Returns
empty list if no service was found and `wait` is ``False``. | [
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'''
Search in services of ROS master for a service with name ending by
`refresh` and returns his name, if it runs on the local host. Returns
empty list if no service was found and `wait` is ``False``.
:param masteruri: the URI of the ROS master
:type masteruri: str
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:type wait: boo
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algorhythms/LeetCode | 3fb14aeea62a960442e47dfde9f964c7ffce32be | 140 Word Break II.py | python | Solution.build_result | (self, dp, cur_index, cur_sentence, result) | dfs recursive
from right to left | dfs recursive | [
"dfs",
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] | def build_result(self, dp, cur_index, cur_sentence, result):
"""
dfs recursive
from right to left
"""
# reached, build the result from cur_sentence
if cur_index == 0:
result.append(" ".join(cur_sentence))
return
# dfs
for prefix in dp[cur_index]:
cur_sentence.appendleft(prefix)
self.build_result(dp, cur_index - len(prefix), cur_sentence, result)
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clinton-hall/nzbToMedia | 27669389216902d1085660167e7bda0bd8527ecf | libs/common/beetsplug/replaygain.py | python | Bs1770gainBackend.parse_tool_output | (self, text, path_list, is_album) | return out | Given the output from bs1770gain, parse the text and
return a list of dictionaries
containing information about each analyzed file. | Given the output from bs1770gain, parse the text and
return a list of dictionaries
containing information about each analyzed file. | [
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"""Given the output from bs1770gain, parse the text and
return a list of dictionaries
containing information about each analyzed file.
"""
per_file_gain = {}
album_gain = {} # mutable variable so it can be set from handlers
parser = xml.parsers.expat.ParserCreate(encoding='utf-8')
state = {'file': None, 'gain': None, 'peak': None}
def start_element_handler(name, attrs):
if name == u'track':
state['file'] = bytestring_path(attrs[u'file'])
if state['file'] in per_file_gain:
raise ReplayGainError(
u'duplicate filename in bs1770gain output')
elif name == u'integrated':
state['gain'] = float(attrs[u'lu'])
elif name == u'sample-peak':
state['peak'] = float(attrs[u'factor'])
def end_element_handler(name):
if name == u'track':
if state['gain'] is None or state['peak'] is None:
raise ReplayGainError(u'could not parse gain or peak from '
'the output of bs1770gain')
per_file_gain[state['file']] = Gain(state['gain'],
state['peak'])
state['gain'] = state['peak'] = None
elif name == u'summary':
if state['gain'] is None or state['peak'] is None:
raise ReplayGainError(u'could not parse gain or peak from '
'the output of bs1770gain')
album_gain["album"] = Gain(state['gain'], state['peak'])
state['gain'] = state['peak'] = None
parser.StartElementHandler = start_element_handler
parser.EndElementHandler = end_element_handler
parser.Parse(text, True)
if len(per_file_gain) != len(path_list):
raise ReplayGainError(
u'the number of results returned by bs1770gain does not match '
'the number of files passed to it')
# bs1770gain does not return the analysis results in the order that
# files are passed on the command line, because it is sorting the files
# internally. We must recover the order from the filenames themselves.
try:
out = [per_file_gain[os.path.basename(p)] for p in path_list]
except KeyError:
raise ReplayGainError(
u'unrecognized filename in bs1770gain output '
'(bs1770gain can only deal with utf-8 file names)')
if is_album:
out.append(album_gain["album"])
return out | [
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gevent/gevent | ae2cb5aeb2aea8987efcb90a4c50ca4e1ee12c31 | src/gevent/_patcher.py | python | _collect_stdlib_gevent_modules | () | return result | Return a map from standard library name to
imported gevent module that provides the same API.
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Return a map from standard library name to
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"""
result = {}
for gevent_name, stdlib_name in iteritems(MAPPING):
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Drakkar-Software/OctoBot | c80ed2270e5d085994213955c0f56b9e3b70b476 | octobot/task_manager.py | python | TaskManager._create_new_asyncio_main_loop | (self) | [] | def _create_new_asyncio_main_loop(self):
self.async_loop = asyncio.new_event_loop()
self.async_loop.set_debug(constants.FORCE_ASYNCIO_DEBUG_OPTION)
self.async_loop.set_exception_handler(self._loop_exception_handler)
asyncio.set_event_loop(self.async_loop)
self.current_loop_thread = threading.Thread(target=self.async_loop.run_forever,
name=f"{self.get_name()} new asyncio main loop")
self.current_loop_thread.start() | [
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DataBiosphere/toil | 2e148eee2114ece8dcc3ec8a83f36333266ece0d | src/toil/common.py | python | ToilMetrics._containerRunning | (containerName) | return result | [] | def _containerRunning(containerName):
try:
result = subprocess.check_output(["docker", "inspect", "-f",
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except subprocess.CalledProcessError:
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mongodb/mongo-python-driver | c760f900f2e4109a247c2ffc8ad3549362007772 | pymongo/message.py | python | _query | (options, collection_name, num_to_skip, num_to_return,
query, field_selector, opts, ctx=None) | return _query_uncompressed(options, collection_name, num_to_skip,
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if ctx:
return _query_compressed(options, collection_name, num_to_skip,
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grnet/synnefo | d06ec8c7871092131cdaabf6b03ed0b504c93e43 | ci/utils.py | python | SynnefoCI.setup_fabric | (self) | Setup fabric environment | Setup fabric environment | [
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] | def setup_fabric(self):
"""Setup fabric environment"""
self.logger.info("Setup fabric parameters..")
fabric.env.user = self.read_temp_config('server_user')
fabric.env.host_string = self.read_temp_config('server_ip')
fabric.env.port = int(self.read_temp_config('server_port'))
fabric.env.password = self.read_temp_config('server_passwd')
fabric.env.connection_attempts = 10
fabric.env.shell = "/bin/bash -c"
fabric.env.disable_known_hosts = True
fabric.env.output_prefix = None
no_agent = SSH_NO_AGENT or self.get_config(
'Global', 'no_agent', False, 'boolean')
fabric.env.no_agent = no_agent
forward_agent = self.get_config(
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fabric.env.forward_agent = forward_agent | [
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ialbert/biostar-central | 2dc7bd30691a50b2da9c2833ba354056bc686afa | biostar/forum/auth.py | python | is_suspended | (user) | return False | [] | def is_suspended(user):
if user.is_authenticated and user.profile.state in (Profile.BANNED, Profile.SUSPENDED, Profile.SPAMMER):
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... | https://github.com/ialbert/biostar-central/blob/2dc7bd30691a50b2da9c2833ba354056bc686afa/biostar/forum/auth.py#L345-L349 | |||
numba/numba | bf480b9e0da858a65508c2b17759a72ee6a44c51 | numba/core/ir_utils.py | python | enforce_no_phis | (func_ir) | Enforce there being no ir.Expr.phi nodes in the IR. | Enforce there being no ir.Expr.phi nodes in the IR. | [
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"there",
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"no",
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"Expr",
".",
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] | def enforce_no_phis(func_ir):
"""
Enforce there being no ir.Expr.phi nodes in the IR.
"""
for blk in func_ir.blocks.values():
phis = [x for x in blk.find_exprs(op='phi')]
if phis:
msg = "Illegal IR, phi found at: %s" % phis[0]
raise CompilerError(msg, loc=phis[0].loc) | [
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":... | https://github.com/numba/numba/blob/bf480b9e0da858a65508c2b17759a72ee6a44c51/numba/core/ir_utils.py#L2201-L2209 | ||
jgagneastro/coffeegrindsize | 22661ebd21831dba4cf32bfc6ba59fe3d49f879c | App/dist/coffeegrindsize.app/Contents/Resources/lib/python3.7/PIL/ContainerIO.py | python | ContainerIO.read | (self, n=0) | return self.fh.read(n) | Read data.
:param n: Number of bytes to read. If omitted or zero,
read until end of region.
:returns: An 8-bit string. | Read data. | [
"Read",
"data",
"."
] | def read(self, n=0):
"""
Read data.
:param n: Number of bytes to read. If omitted or zero,
read until end of region.
:returns: An 8-bit string.
"""
if n:
n = min(n, self.length - self.pos)
else:
n = self.length - self.pos
if not n: # EOF
return ""
self.pos = self.pos + n
return self.fh.read(n) | [
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pymedusa/Medusa | 1405fbb6eb8ef4d20fcca24c32ddca52b11f0f38 | ext/pyparsing.py | python | ParserElement.setDebugActions | (self, startAction, successAction, exceptionAction) | return self | Enable display of debugging messages while doing pattern matching. | Enable display of debugging messages while doing pattern matching. | [
"Enable",
"display",
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] | def setDebugActions(self, startAction, successAction, exceptionAction):
"""
Enable display of debugging messages while doing pattern matching.
"""
self.debugActions = (startAction or _defaultStartDebugAction,
successAction or _defaultSuccessDebugAction,
exceptionAction or _defaultExceptionDebugAction)
self.debug = True
return self | [
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... | https://github.com/pymedusa/Medusa/blob/1405fbb6eb8ef4d20fcca24c32ddca52b11f0f38/ext/pyparsing.py#L2492-L2500 |
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