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scrapinghub/splash | 802d8391984bae049ef95a3fe1a74feaee95a233 | splash/network_manager.py | python | ProxiedQNetworkAccessManager._get_har | (self, request=None) | return self._get_webpage_attribute(request, "har") | Return HarBuilder instance.
:rtype: splash.har_builder.HarBuilder | None | Return HarBuilder instance.
:rtype: splash.har_builder.HarBuilder | None | [
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] | def _get_har(self, request=None):
"""
Return HarBuilder instance.
:rtype: splash.har_builder.HarBuilder | None
"""
if request is None:
request = self.sender().request()
return self._get_webpage_attribute(request, "har") | [
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Source-Python-Dev-Team/Source.Python | d0ffd8ccbd1e9923c9bc44936f20613c1c76b7fb | addons/source-python/packages/site-packages/sqlalchemy/engine/result.py | python | ResultProxy.close | (self) | Close this ResultProxy.
This closes out the underlying DBAPI cursor corresonding
to the statement execution, if one is still present. Note that the
DBAPI cursor is automatically released when the :class:`.ResultProxy`
exhausts all available rows. :meth:`.ResultProxy.close` is generally
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.. versionchanged:: 1.0.0 - the :meth:`.ResultProxy.close` method
has been separated out from the process that releases the underlying
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:class:`.ResultProxy` to still behave as an open-but-exhausted
result set; the actual :meth:`.ResultProxy.close` method is never
called. It is still safe to discard a :class:`.ResultProxy`
that has been fully exhausted without calling this method.
.. seealso::
:ref:`connections_toplevel`
:meth:`.ResultProxy._soft_close` | Close this ResultProxy. | [
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] | def close(self):
"""Close this ResultProxy.
This closes out the underlying DBAPI cursor corresonding
to the statement execution, if one is still present. Note that the
DBAPI cursor is automatically released when the :class:`.ResultProxy`
exhausts all available rows. :meth:`.ResultProxy.close` is generally
an optional method except in the case when discarding a
:class:`.ResultProxy` that still has additional rows pending for fetch.
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:ref:`connectionless execution <dbengine_implicit>`,
the underyling :class:`.Connection` object is also closed, which
:term:`releases` DBAPI connection resources.
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the fetch methods, which will raise a :class:`.ResourceClosedError`
on subsequent use.
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has been separated out from the process that releases the underlying
DBAPI cursor resource. The "auto close" feature of the
:class:`.Connection` now performs a so-called "soft close", which
releases the underlying DBAPI cursor, but allows the
:class:`.ResultProxy` to still behave as an open-but-exhausted
result set; the actual :meth:`.ResultProxy.close` method is never
called. It is still safe to discard a :class:`.ResultProxy`
that has been fully exhausted without calling this method.
.. seealso::
:ref:`connections_toplevel`
:meth:`.ResultProxy._soft_close`
"""
if not self.closed:
self._soft_close()
self.closed = True | [
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frostming/legit | 5119c0a81cb0e87974255dd29463a60b22916baa | legit/utils.py | python | git_version | () | return g.version_info | Return the git version tuple (major, minor, patch) | Return the git version tuple (major, minor, patch) | [
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"""Return the git version tuple (major, minor, patch)"""
from git import Git
g = Git()
return g.version_info | [
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SteveDoyle2/pyNastran | eda651ac2d4883d95a34951f8a002ff94f642a1a | pyNastran/converters/nastran/gui/nastran_io.py | python | NastranIO_._fill_bar_yz | (self, unused_dim_max, model, icase, cases, form, debug=False) | return icase | plots the y, z vectors for CBAR & CBEAM elements | plots the y, z vectors for CBAR & CBEAM elements | [
"plots",
"the",
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] | def _fill_bar_yz(self, unused_dim_max, model, icase, cases, form, debug=False):
"""
plots the y, z vectors for CBAR & CBEAM elements
"""
card_types = ['CBAR', 'CBEAM']
out = model.get_card_ids_by_card_types(card_types=card_types)
bar_beam_eids = out['CBAR'] + out['CBEAM']
bar_pid_to_eids = get_beam_sections_map(model, bar_beam_eids)
bar_nids = get_bar_nids(model, bar_beam_eids)
#ugrid_temp = create_3d_beams(model, bar_pid_to_eids)
self.bar_eids = {}
self.bar_lines = {}
if len(bar_beam_eids) == 0:
return icase
scale = 0.15
# TODO: this should be reworked
bar_nids, bar_types, nid_release_map = self._get_bar_yz_arrays(
model, bar_beam_eids, bar_pid_to_eids,
scale, debug)
self.nid_release_map = nid_release_map
bar_nids = list(bar_nids)
self.gui.create_alternate_vtk_grid(
'Bar Nodes', color=RED_FLOAT, line_width=1, opacity=1.,
point_size=5, representation='point', bar_scale=0., is_visible=False)
msg = ", which is required by 'Bar Nodes'"
self._add_nastran_nodes_to_grid('Bar Nodes', bar_nids, model, msg)
geo_form = form[2]
bar_form = ('CBAR / CBEAM', None, [])
#print('geo_form =', geo_form)
#bar_types2 = {}
bar_eids = []
for bar_type, data in sorted(bar_types.items()):
eids, lines_bar_y, lines_bar_z = data
if len(eids):
bar_eids.append(eids)
ibars = 0
if bar_eids:
bar_eids = np.hstack(bar_eids)
ibars = np.searchsorted(self.element_ids, bar_eids)
for bar_type, data in sorted(bar_types.items()):
eids, lines_bar_y, lines_bar_z = data
if len(eids):
if debug: # pragma: no cover
print('bar_type = %r' % bar_type)
print('eids = %r' % eids)
print('all_eids = %r' % self.element_ids.tolist())
# if bar_type not in ['ROD', 'TUBE']:
bar_y = bar_type + '_y'
bar_z = bar_type + '_z'
self.gui.create_alternate_vtk_grid(
bar_y, color=GREEN_FLOAT, line_width=5, opacity=1.,
point_size=5, representation='bar', bar_scale=scale, is_visible=False)
self.gui.create_alternate_vtk_grid(
bar_z, color=BLUE_FLOAT, line_width=5, opacity=1.,
point_size=5, representation='bar', bar_scale=scale, is_visible=False)
self._add_nastran_lines_xyz_to_grid(bar_y, lines_bar_y, eids)
self._add_nastran_lines_xyz_to_grid(bar_z, lines_bar_z, eids)
# form = ['Geometry', None, []]
i = np.searchsorted(self.element_ids, eids)
is_type = np.full(self.element_ids.shape, -1, dtype='int32')
is_type[ibars] = 0
try:
is_type[i] = 1
except Exception:
#print('self.element_ids =', self.element_ids)
#print('eids =', eids)
ii = np.where(i == len(self.element_ids))[0]
print('ii = %s' % ii)
print('failed eids =', eids[ii])
#assert self.element_ids[i] == eids
raise
bar_form[2].append(['is_%s' % bar_type, icase, []])
msg = 'is_%s' % bar_type
type_res = GuiResult(0, header=msg, title=msg,
location='centroid', scalar=is_type, mask_value=-1)
cases[icase] = (type_res, (0, msg))
icase += 1
# print(geo_form)
if len(bar_form[2]):
geo_form.append(bar_form)
return icase | [
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statsmodels/statsmodels | debbe7ea6ba28fe5bdb78f09f8cac694bef98722 | statsmodels/sandbox/panel/sandwich_covariance_generic.py | python | S_all_hac | (x, d, nlags=1) | return aggregate_cov(x, d, r=r, weights=weights) | HAC independent of categorical group membership | HAC independent of categorical group membership | [
"HAC",
"independent",
"of",
"categorical",
"group",
"membership"
] | def S_all_hac(x, d, nlags=1):
'''HAC independent of categorical group membership
'''
r = np.zeros(d.shape[1])
r[0] = 1
weights = weights_bartlett(nlags)
return aggregate_cov(x, d, r=r, weights=weights) | [
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krintoxi/NoobSec-Toolkit | 38738541cbc03cedb9a3b3ed13b629f781ad64f6 | NoobSecToolkit - MAC OSX/scripts/sshbackdoors/rpyc/core/stream.py | python | SocketStream.read | (self, count) | return BYTES_LITERAL("").join(data) | [] | def read(self, count):
data = []
while count > 0:
try:
buf = self.sock.recv(min(self.MAX_IO_CHUNK, count))
except socket.timeout:
continue
except socket.error:
ex = sys.exc_info()[1]
if get_exc_errno(ex) in retry_errnos:
# windows just has to be a bitch
continue
self.close()
raise EOFError(ex)
if not buf:
self.close()
raise EOFError("connection closed by peer")
data.append(buf)
count -= len(buf)
return BYTES_LITERAL("").join(data) | [
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linxid/Machine_Learning_Study_Path | 558e82d13237114bbb8152483977806fc0c222af | Machine Learning In Action/Chapter8-Regression/venv/Lib/site-packages/pip-9.0.1-py3.6.egg/pip/_vendor/ipaddress.py | python | get_mixed_type_key | (obj) | return NotImplemented | Return a key suitable for sorting between networks and addresses.
Address and Network objects are not sortable by default; they're
fundamentally different so the expression
IPv4Address('192.0.2.0') <= IPv4Network('192.0.2.0/24')
doesn't make any sense. There are some times however, where you may wish
to have ipaddress sort these for you anyway. If you need to do this, you
can use this function as the key= argument to sorted().
Args:
obj: either a Network or Address object.
Returns:
appropriate key. | Return a key suitable for sorting between networks and addresses. | [
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] | def get_mixed_type_key(obj):
"""Return a key suitable for sorting between networks and addresses.
Address and Network objects are not sortable by default; they're
fundamentally different so the expression
IPv4Address('192.0.2.0') <= IPv4Network('192.0.2.0/24')
doesn't make any sense. There are some times however, where you may wish
to have ipaddress sort these for you anyway. If you need to do this, you
can use this function as the key= argument to sorted().
Args:
obj: either a Network or Address object.
Returns:
appropriate key.
"""
if isinstance(obj, _BaseNetwork):
return obj._get_networks_key()
elif isinstance(obj, _BaseAddress):
return obj._get_address_key()
return NotImplemented | [
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cisco/mindmeld | 809c36112e9ea8019fe29d54d136ca14eb4fd8db | mindmeld/augmentation.py | python | MultiLingualParaphraser._prepare_inputs | (self, processed_queries) | return unannotated_queries | Removes any markdown formatting in the query
Args:
queries (list(str)): List of queries to be paraphrased
Returns:
unannotated queries (list(str)) | Removes any markdown formatting in the query | [
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] | def _prepare_inputs(self, processed_queries):
"""Removes any markdown formatting in the query
Args:
queries (list(str)): List of queries to be paraphrased
Returns:
unannotated queries (list(str))
"""
unannotated_queries = [processed_query.query.text.strip()
for processed_query in processed_queries]
return unannotated_queries | [
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pyparallel/pyparallel | 11e8c6072d48c8f13641925d17b147bf36ee0ba3 | Tools/msi/msilib.py | python | Directory.start_component | (self, component = None, feature = None, flags = None, keyfile = None, uuid=None) | Add an entry to the Component table, and make this component the current for this
directory. If no component name is given, the directory name is used. If no feature
is given, the current feature is used. If no flags are given, the directory's default
flags are used. If no keyfile is given, the KeyPath is left null in the Component
table. | Add an entry to the Component table, and make this component the current for this
directory. If no component name is given, the directory name is used. If no feature
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flags are used. If no keyfile is given, the KeyPath is left null in the Component
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"""Add an entry to the Component table, and make this component the current for this
directory. If no component name is given, the directory name is used. If no feature
is given, the current feature is used. If no flags are given, the directory's default
flags are used. If no keyfile is given, the KeyPath is left null in the Component
table."""
if flags is None:
flags = self.componentflags
if uuid is None:
uuid = gen_uuid()
else:
uuid = uuid.upper()
if component is None:
component = self.logical
self.component = component
if Win64:
flags |= 256
if keyfile:
keyid = self.cab.gen_id(self.absolute, keyfile)
self.keyfiles[keyfile] = keyid
else:
keyid = None
add_data(self.db, "Component",
[(component, uuid, self.logical, flags, None, keyid)])
if feature is None:
feature = current_feature
add_data(self.db, "FeatureComponents",
[(feature.id, component)]) | [
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googleads/google-ads-python | 2a1d6062221f6aad1992a6bcca0e7e4a93d2db86 | google/ads/googleads/v7/services/services/group_placement_view_service/client.py | python | GroupPlacementViewServiceClient.parse_common_project_path | (path: str) | return m.groupdict() if m else {} | Parse a project path into its component segments. | Parse a project path into its component segments. | [
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"""Parse a project path into its component segments."""
m = re.match(r"^projects/(?P<project>.+?)$", path)
return m.groupdict() if m else {} | [
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vlomme/Multi-Tacotron-Voice-Cloning | 62598a52260146a3da51b9044881e54a99525e53 | synthesizer/models/custom_decoder.py | python | CustomDecoder.__init__ | (self, cell, helper, initial_state, output_layer=None) | Initialize CustomDecoder.
Args:
cell: An `RNNCell` instance.
helper: A `Helper` instance.
initial_state: A (possibly nested tuple of...) tensors and TensorArrays.
The initial state of the RNNCell.
output_layer: (Optional) An instance of `tf.layers.Layer`, i.e.,
`tf.layers.Dense`. Optional layer to apply to the RNN output prior
to storing the result or sampling.
Raises:
TypeError: if `cell`, `helper` or `output_layer` have an incorrect type. | Initialize CustomDecoder.
Args:
cell: An `RNNCell` instance.
helper: A `Helper` instance.
initial_state: A (possibly nested tuple of...) tensors and TensorArrays.
The initial state of the RNNCell.
output_layer: (Optional) An instance of `tf.layers.Layer`, i.e.,
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Raises:
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helper: A `Helper` instance.
initial_state: A (possibly nested tuple of...) tensors and TensorArrays.
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output_layer: (Optional) An instance of `tf.layers.Layer`, i.e.,
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rnn_cell_impl.assert_like_rnncell(type(cell), cell)
if not isinstance(helper, helper_py.Helper):
raise TypeError("helper must be a Helper, received: %s" % type(helper))
if (output_layer is not None
and not isinstance(output_layer, layers_base.Layer)):
raise TypeError(
"output_layer must be a Layer, received: %s" % type(output_layer))
self._cell = cell
self._helper = helper
self._initial_state = initial_state
self._output_layer = output_layer | [
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linxid/Machine_Learning_Study_Path | 558e82d13237114bbb8152483977806fc0c222af | Machine Learning In Action/Chapter8-Regression/venv/Lib/site-packages/pip-9.0.1-py3.6.egg/pip/_vendor/pyparsing.py | python | And.__init__ | ( self, exprs, savelist = True ) | [] | def __init__( self, exprs, savelist = True ):
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Skycrab/weixin-knife | 0cea6f645d1a94e45f394c32364dfb545f32b6b6 | weixin/lib.py | python | CurlClient.postXmlSSL | (self, xml, url, second=30, cert=True, post=True) | return buff.getvalue() | 使用证书 | 使用证书 | [
"使用证书"
] | def postXmlSSL(self, xml, url, second=30, cert=True, post=True):
"""使用证书"""
self.curl.setopt(pycurl.URL, url)
self.curl.setopt(pycurl.TIMEOUT, second)
#设置证书
#使用证书：cert 与 key 分别属于两个.pem文件
#默认格式为PEM，可以注释
if cert:
self.curl.setopt(pycurl.SSLKEYTYPE, "PEM")
self.curl.setopt(pycurl.SSLKEY, WxPayConf_pub.SSLKEY_PATH)
self.curl.setopt(pycurl.SSLCERTTYPE, "PEM")
self.curl.setopt(pycurl.SSLCERT, WxPayConf_pub.SSLCERT_PATH)
#post提交方式
if post:
self.curl.setopt(pycurl.POST, True)
self.curl.setopt(pycurl.POSTFIELDS, xml)
buff = StringIO()
self.curl.setopt(pycurl.WRITEFUNCTION, buff.write)
self.curl.perform()
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tensorflow/lingvo | ce10019243d954c3c3ebe739f7589b5eebfdf907 | lingvo/tasks/car/point_detector.py | python | PointDetectorBase._Placeholders | (self) | return result | Return a NestedMap of placeholders to fill in for inference.
Runs the configured input pipeline to generate the expected shapes and types
of the inputs.
Returns:
A NestedMap of placeholders matching the input structure of
the inference model. | Return a NestedMap of placeholders to fill in for inference. | [
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"""Return a NestedMap of placeholders to fill in for inference.
Runs the configured input pipeline to generate the expected shapes and types
of the inputs.
Returns:
A NestedMap of placeholders matching the input structure of
the inference model.
"""
p = self.params
with tf.Graph().as_default():
inputs = self.params.input.Instantiate()
# Turn those inputs into placeholders.
placeholders = []
for input_shape, dtype in zip(inputs.Shape().Flatten(),
inputs.DType().Flatten()):
batched_input_shape = [p.inference_batch_size] + input_shape.as_list()
placeholders.append(tf.placeholder(dtype, batched_input_shape))
result = inputs.DType().Pack(placeholders)
return result | [
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securesystemslab/zippy | ff0e84ac99442c2c55fe1d285332cfd4e185e089 | zippy/benchmarks/src/benchmarks/sympy/sympy/mpmath/ctx_mp_python.py | python | PythonMPContext.make_mpc | (ctx, v) | return a | [] | def make_mpc(ctx, v):
a = new(ctx.mpc)
a._mpc_ = v
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open-covid-19/data | d7a495f340ec1f82511e6de949a8d799d0a8568c | src/lib/io.py | python | pbar | (*args, **kwargs) | return tqdm(*args, **{**kwargs, **{"disable": os.getenv(GLOBAL_DISABLE_PROGRESS)}}) | Helper function used to display a tqdm progress bar respecting global settings for whether all
progress bars should be disabled. All arguments are passed through to tqdm but the "disable"
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thunguyenphuoc/RenderNet | f7e2ec12524820907f4adeae1c645d45f32a76e7 | tools/binvox_rw.py | python | dense_to_sparse | (voxel_data, dtype=np.int) | return np.asarray(np.nonzero(voxel_data), dtype) | From dense representation to sparse (coordinate) representation.
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"""
From dense representation to sparse (coordinate) representation.
No coordinate reordering.
"""
if voxel_data.ndim!=3:
raise ValueError('voxel_data is wrong shape; should be 3D array.')
return np.asarray(np.nonzero(voxel_data), dtype) | [
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glue-viz/glue | 840b4c1364b0fa63bf67c914540c93dd71df41e1 | glue/core/visual.py | python | VisualAttributes.color | (self) | return self._color | Color specified using Matplotlib notation
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* A string with a common color (e.g. 'black', 'red', 'orange')
* A string containing a float in the rng [0:1] for a shade of
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* An HTML hexadecimal string (e.g. '#eeefff') | Color specified using Matplotlib notation | [
"Color",
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] | def color(self):
"""
Color specified using Matplotlib notation
Specifically, it can be:
* A string with a common color (e.g. 'black', 'red', 'orange')
* A string containing a float in the rng [0:1] for a shade of
gray ('0.0' = black,'1.0' = white)
* A tuple of three floats in the rng [0:1] for (R, G, B)
* An HTML hexadecimal string (e.g. '#eeefff')
"""
return self._color | [
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Delta-ML/delta | 31dfebc8f20b7cb282b62f291ff25a87e403cc86 | egs/conll2003/pretrain/v1/local/modeling.py | python | embedding_lookup | (input_ids,
vocab_size,
embedding_size=128,
initializer_range=0.02,
word_embedding_name="word_embeddings",
use_one_hot_embeddings=False) | return (output, embedding_table) | Looks up words embeddings for id tensor.
Args:
input_ids: int32 Tensor of shape [batch_size, seq_length] containing word
ids.
vocab_size: int. Size of the embedding vocabulary.
embedding_size: int. Width of the word embeddings.
initializer_range: float. Embedding initialization range.
word_embedding_name: string. Name of the embedding table.
use_one_hot_embeddings: bool. If True, use one-hot method for word
embeddings. If False, use `tf.gather()`.
Returns:
float Tensor of shape [batch_size, seq_length, embedding_size]. | Looks up words embeddings for id tensor. | [
"Looks",
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"id",
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"."
] | def embedding_lookup(input_ids,
vocab_size,
embedding_size=128,
initializer_range=0.02,
word_embedding_name="word_embeddings",
use_one_hot_embeddings=False):
"""Looks up words embeddings for id tensor.
Args:
input_ids: int32 Tensor of shape [batch_size, seq_length] containing word
ids.
vocab_size: int. Size of the embedding vocabulary.
embedding_size: int. Width of the word embeddings.
initializer_range: float. Embedding initialization range.
word_embedding_name: string. Name of the embedding table.
use_one_hot_embeddings: bool. If True, use one-hot method for word
embeddings. If False, use `tf.gather()`.
Returns:
float Tensor of shape [batch_size, seq_length, embedding_size].
"""
# This function assumes that the input is of shape [batch_size, seq_length,
# num_inputs].
#
# If the input is a 2D tensor of shape [batch_size, seq_length], we
# reshape to [batch_size, seq_length, 1].
if input_ids.shape.ndims == 2:
input_ids = tf.expand_dims(input_ids, axis=[-1])
embedding_table = tf.compat.v1.get_variable(
name=word_embedding_name,
shape=[vocab_size, embedding_size],
initializer=create_initializer(initializer_range))
flat_input_ids = tf.reshape(input_ids, [-1])
if use_one_hot_embeddings:
one_hot_input_ids = tf.one_hot(flat_input_ids, depth=vocab_size)
output = tf.matmul(one_hot_input_ids, embedding_table)
else:
output = tf.gather(embedding_table, flat_input_ids)
input_shape = get_shape_list(input_ids)
output = tf.reshape(output,
input_shape[0:-1] + [input_shape[-1] * embedding_size])
return (output, embedding_table) | [
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JiYou/openstack | 8607dd488bde0905044b303eb6e52bdea6806923 | packages/source/nova/nova/service.py | python | Launcher.launch_server | (self, server) | Load and start the given server.
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if self.backdoor_port is not None:
server.backdoor_port = self.backdoor_port
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holoviz/holoviews | cc6b27f01710402fdfee2aeef1507425ca78c91f | holoviews/core/data/spatialpandas.py | python | from_shapely | (data) | return data | Converts shapely based data formats to spatialpandas.GeoDataFrame.
Args:
data: A list of shapely objects or dictionaries containing
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"""Converts shapely based data formats to spatialpandas.GeoDataFrame.
Args:
data: A list of shapely objects or dictionaries containing
shapely objects
Returns:
A GeoDataFrame containing the shapely geometry data.
"""
from spatialpandas import GeoDataFrame, GeoSeries
from shapely.geometry.base import BaseGeometry
if not data:
pass
elif all(isinstance(d, BaseGeometry) for d in data):
data = GeoSeries(data).to_frame()
elif all(isinstance(d, dict) and 'geometry' in d and isinstance(d['geometry'], BaseGeometry)
for d in data):
new_data = {col: [] for col in data[0]}
for d in data:
for col, val in d.items():
new_data[col].append(val if isscalar(val) or isinstance(val, BaseGeometry) else np.asarray(val))
new_data['geometry'] = GeoSeries(new_data['geometry'])
data = GeoDataFrame(new_data)
return data | [
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ckan/ckan | b3b01218ad88ed3fb914b51018abe8b07b07bff3 | ckan/plugins/interfaces.py | python | IDatasetForm.new_template | (self, package_type) | u'''Return the path to the template for the new dataset page.
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daveleroy/sublime_debugger | 3e4d2cf6519ec846cf655b30d8da4732807de54e | modules/libs/pywinpty/st3_windows_x32/winpty/ptyprocess.py | python | PtyProcess.spawn | (cls, argv, cwd=None, env=None, dimensions=(24, 80)) | return inst | Start the given command in a child process in a pseudo terminal.
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"""Start the given command in a child process in a pseudo terminal.
This does all the setting up the pty, and returns an instance of
PtyProcess.
Dimensions of the psuedoterminal used for the subprocess can be
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"""
if isinstance(argv, str):
argv = shlex.split(argv, posix=False)
if not isinstance(argv, (list, tuple)):
raise TypeError("Expected a list or tuple for argv, got %r" % argv)
# Shallow copy of argv so we can modify it
argv = argv[:]
command = argv[0]
env = env or os.environ
path = env.get('PATH', os.defpath)
command_with_path = which(command, path=path)
if command_with_path is None:
raise FileNotFoundError(
'The command was not found or was not ' +
'executable: %s.' % command
)
command = command_with_path
argv[0] = command
cmdline = ' ' + subprocess.list2cmdline(argv[1:])
cwd = cwd or os.getcwd()
proc = PTY(dimensions[1], dimensions[0])
# Create the environemnt string.
envStrs = []
for (key, value) in env.items():
envStrs.append('%s=%s' % (key, value))
env = '\0'.join(envStrs) + '\0'
if PY2:
command = _unicode(command)
cwd = _unicode(cwd)
cmdline = _unicode(cmdline)
env = _unicode(env)
if len(argv) == 1:
proc.spawn(command, cwd=cwd, env=env)
else:
proc.spawn(command, cwd=cwd, env=env, cmdline=cmdline)
inst = cls(proc)
inst._winsize = dimensions
# Set some informational attributes
inst.argv = argv
if env is not None:
inst.env = env
if cwd is not None:
inst.launch_dir = cwd
return inst | [
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Samsung/cotopaxi | d19178b1235017257fec20d0a41edc918de55574 | cotopaxi/http_utils.py | python | HTTPTester.response_parser | () | return HTTPResponse | Provide Scapy class implementing parsing of protocol responses. | Provide Scapy class implementing parsing of protocol responses. | [
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] | https://github.com/Samsung/cotopaxi/blob/d19178b1235017257fec20d0a41edc918de55574/cotopaxi/http_utils.py#L72-L74 | |
IBM/pytorchpipe | 9cb17271666061cb19fe24197ecd5e4c8d32c5da | ptp/components/losses/loss.py | python | Loss.collect_statistics | (self, stat_col, data_streams) | Collects statistics (loss) for given episode.
:param stat_col: ``StatisticsCollector``. | Collects statistics (loss) for given episode. | [
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"""
Collects statistics (loss) for given episode.
:param stat_col: ``StatisticsCollector``.
"""
stat_col[self.key_loss] = data_streams[self.key_loss].item() | [
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rcorcs/NatI | fdf014f4292afdc95250add7b6658468043228e1 | en/parser/nltk_lite/contrib/toolbox/lexicon.py | python | Lexicon.get_entries | (self) | This method obtains all of the entries found in a
parsed Shoebox lexicon.
@return: all of the entries in the Lexicon
@rtype: list of Entry objects | This method obtains all of the entries found in a
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"""
This method obtains all of the entries found in a
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@return: all of the entries in the Lexicon
@rtype: list of Entry objects
"""
keys = self._entries.keys()
keys.sort()
for k in keys :
v = self._entries[k]
for e in v :
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nipreps/mriqc | ad8813035136acc1a80c96ac1fbbdde1d82b14ee | mriqc/reports/group.py | python | _format_labels | (row, id_labels) | return "_".join(crow) | format participant labels | format participant labels | [
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"""format participant labels"""
crow = []
for col_id, prefix in list(BIDS_COMP.items()):
if col_id in id_labels:
crow.append("%s-%s" % (prefix, row[[col_id]].values[0]))
return "_".join(crow) | [
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google-research/sound-separation | 0b23ae22123b041b9538295f32a92151cb77bff9 | models/train/signal_util.py | python | static_or_dynamic_dim_size | (tensor, i) | return (static_shape[i].value if hasattr(static_shape[i], 'value')
else static_shape[i]) or dyn_shape[i] | Static size for dimension `i` if available, otherwise dynamic size. | Static size for dimension `i` if available, otherwise dynamic size. | [
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] | def static_or_dynamic_dim_size(tensor, i):
"""Static size for dimension `i` if available, otherwise dynamic size."""
static_shape = tensor.shape
dyn_shape = tf.shape(tensor)
return (static_shape[i].value if hasattr(static_shape[i], 'value')
else static_shape[i]) or dyn_shape[i] | [
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NoGameNoLife00/mybolg | afe17ea5bfe405e33766e5682c43a4262232ee12 | libs/jinja2/environment.py | python | Environment._generate | (self, source, name, filename, defer_init=False) | return generate(source, self, name, filename, defer_init=defer_init) | Internal hook that can be overridden to hook a different generate
method in.
.. versionadded:: 2.5 | Internal hook that can be overridden to hook a different generate
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"""Internal hook that can be overridden to hook a different generate
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return generate(source, self, name, filename, defer_init=defer_init) | [
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jtriley/StarCluster | bc7c950e73f193eac9aab986b6764939cfdad978 | starcluster/cluster.py | python | ClusterValidator.validate_zone | (self) | return True | Validates that the cluster's availability zone exists and is available.
The 'zone' property additionally checks that all EBS volumes are in the
same zone and that the cluster's availability zone setting, if
specified, matches the EBS volume(s) zone. | Validates that the cluster's availability zone exists and is available.
The 'zone' property additionally checks that all EBS volumes are in the
same zone and that the cluster's availability zone setting, if
specified, matches the EBS volume(s) zone. | [
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"""
Validates that the cluster's availability zone exists and is available.
The 'zone' property additionally checks that all EBS volumes are in the
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"""
zone = self.cluster.zone
if zone and zone.state != 'available':
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PokemonGoF/PokemonGo-Bot-Desktop | 4bfa94f0183406c6a86f93645eff7abd3ad4ced8 | build/pywin/Lib/posixpath.py | python | realpath | (filename) | return abspath(path) | Return the canonical path of the specified filename, eliminating any
symbolic links encountered in the path. | Return the canonical path of the specified filename, eliminating any
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] | def realpath(filename):
"""Return the canonical path of the specified filename, eliminating any
symbolic links encountered in the path."""
path, ok = _joinrealpath('', filename, {})
return abspath(path) | [
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llSourcell/AI_Artist | 3038c06c2e389b9c919c881c9a169efe2fd7810e | lib/python2.7/site-packages/pip/req/req_install.py | python | InstallRequirement.remove_temporary_source | (self) | Remove the source files from this requirement, if they are marked
for deletion | Remove the source files from this requirement, if they are marked
for deletion | [
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] | def remove_temporary_source(self):
"""Remove the source files from this requirement, if they are marked
for deletion"""
if self.source_dir and os.path.exists(
os.path.join(self.source_dir, PIP_DELETE_MARKER_FILENAME)):
logger.debug('Removing source in %s', self.source_dir)
rmtree(self.source_dir)
self.source_dir = None
if self._temp_build_dir and os.path.exists(self._temp_build_dir):
rmtree(self._temp_build_dir)
self._temp_build_dir = None | [
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Blizzard/heroprotocol | 3d36eaf44fc4c8ff3331c2ae2f1dc08a94535f1c | heroprotocol/versions/protocol80293.py | python | decode_replay_header | (contents) | return decoder.instance(replay_header_typeid) | Decodes and return the replay header from the contents byte string. | Decodes and return the replay header from the contents byte string. | [
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] | def decode_replay_header(contents):
"""Decodes and return the replay header from the contents byte string."""
decoder = VersionedDecoder(contents, typeinfos)
return decoder.instance(replay_header_typeid) | [
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akanazawa/hmr | f149abeb0a7e2a3412eb68274a94a9232f7cb667 | src/tf_smpl/batch_lbs.py | python | batch_global_rigid_transformation | (Rs, Js, parent, rotate_base=False) | Computes absolute joint locations given pose.
rotate_base: if True, rotates the global rotation by 90 deg in x axis.
if False, this is the original SMPL coordinate.
Args:
Rs: N x 24 x 3 x 3 rotation vector of K joints
Js: N x 24 x 3, joint locations before posing
parent: 24 holding the parent id for each index
Returns
new_J : `Tensor`: N x 24 x 3 location of absolute joints
A : `Tensor`: N x 24 4 x 4 relative joint transformations for LBS. | Computes absolute joint locations given pose. | [
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"."
] | def batch_global_rigid_transformation(Rs, Js, parent, rotate_base=False):
"""
Computes absolute joint locations given pose.
rotate_base: if True, rotates the global rotation by 90 deg in x axis.
if False, this is the original SMPL coordinate.
Args:
Rs: N x 24 x 3 x 3 rotation vector of K joints
Js: N x 24 x 3, joint locations before posing
parent: 24 holding the parent id for each index
Returns
new_J : `Tensor`: N x 24 x 3 location of absolute joints
A : `Tensor`: N x 24 4 x 4 relative joint transformations for LBS.
"""
with tf.name_scope("batch_forward_kinematics", [Rs, Js]):
N = Rs.shape[0].value
if rotate_base:
print('Flipping the SMPL coordinate frame!!!!')
rot_x = tf.constant(
[[1, 0, 0], [0, -1, 0], [0, 0, -1]], dtype=Rs.dtype)
rot_x = tf.reshape(tf.tile(rot_x, [N, 1]), [N, 3, 3])
root_rotation = tf.matmul(Rs[:, 0, :, :], rot_x)
else:
root_rotation = Rs[:, 0, :, :]
# Now Js is N x 24 x 3 x 1
Js = tf.expand_dims(Js, -1)
def make_A(R, t, name=None):
# Rs is N x 3 x 3, ts is N x 3 x 1
with tf.name_scope(name, "Make_A", [R, t]):
R_homo = tf.pad(R, [[0, 0], [0, 1], [0, 0]])
t_homo = tf.concat([t, tf.ones([N, 1, 1])], 1)
return tf.concat([R_homo, t_homo], 2)
A0 = make_A(root_rotation, Js[:, 0])
results = [A0]
for i in range(1, parent.shape[0]):
j_here = Js[:, i] - Js[:, parent[i]]
A_here = make_A(Rs[:, i], j_here)
res_here = tf.matmul(
results[parent[i]], A_here, name="propA%d" % i)
results.append(res_here)
# 10 x 24 x 4 x 4
results = tf.stack(results, axis=1)
new_J = results[:, :, :3, 3]
# --- Compute relative A: Skinning is based on
# how much the bone moved (not the final location of the bone)
# but (final_bone - init_bone)
# ---
Js_w0 = tf.concat([Js, tf.zeros([N, 24, 1, 1])], 2)
init_bone = tf.matmul(results, Js_w0)
# Append empty 4 x 3:
init_bone = tf.pad(init_bone, [[0, 0], [0, 0], [0, 0], [3, 0]])
A = results - init_bone
return new_J, A | [
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jimmysong/programmingbitcoin | 3fba6b992ece443e4256df057595cfbe91edda75 | code-ch11/tx.py | python | TxIn.script_pubkey | (self, testnet=False) | return tx.tx_outs[self.prev_index].script_pubkey | Get the ScriptPubKey by looking up the tx hash
Returns a Script object | Get the ScriptPubKey by looking up the tx hash
Returns a Script object | [
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] | def script_pubkey(self, testnet=False):
'''Get the ScriptPubKey by looking up the tx hash
Returns a Script object
'''
# use self.fetch_tx to get the transaction
tx = self.fetch_tx(testnet=testnet)
# get the output at self.prev_index
# return the script_pubkey property
return tx.tx_outs[self.prev_index].script_pubkey | [
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osmr/imgclsmob | f2993d3ce73a2f7ddba05da3891defb08547d504 | tensorflow_/tensorflowcv/models/mobilenetv2.py | python | linear_bottleneck | (x,
in_channels,
out_channels,
strides,
expansion,
training,
data_format,
name="linear_bottleneck") | return x | So-called 'Linear Bottleneck' layer. It is used as a MobileNetV2 unit.
Parameters:
----------
x : Tensor
Input tensor.
in_channels : int
Number of input channels.
out_channels : int
Number of output channels.
strides : int or tuple/list of 2 int
Strides of the convolution.
expansion : bool
Whether do expansion of channels.
training : bool, or a TensorFlow boolean scalar tensor
Whether to return the output in training mode or in inference mode.
data_format : str
The ordering of the dimensions in tensors.
name : str, default 'linear_bottleneck'
Unit name.
Returns:
-------
Tensor
Resulted tensor. | So-called 'Linear Bottleneck' layer. It is used as a MobileNetV2 unit. | [
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] | def linear_bottleneck(x,
in_channels,
out_channels,
strides,
expansion,
training,
data_format,
name="linear_bottleneck"):
"""
So-called 'Linear Bottleneck' layer. It is used as a MobileNetV2 unit.
Parameters:
----------
x : Tensor
Input tensor.
in_channels : int
Number of input channels.
out_channels : int
Number of output channels.
strides : int or tuple/list of 2 int
Strides of the convolution.
expansion : bool
Whether do expansion of channels.
training : bool, or a TensorFlow boolean scalar tensor
Whether to return the output in training mode or in inference mode.
data_format : str
The ordering of the dimensions in tensors.
name : str, default 'linear_bottleneck'
Unit name.
Returns:
-------
Tensor
Resulted tensor.
"""
residual = (in_channels == out_channels) and (strides == 1)
mid_channels = in_channels * 6 if expansion else in_channels
if residual:
identity = x
x = conv1x1_block(
x=x,
in_channels=in_channels,
out_channels=mid_channels,
activation="relu6",
training=training,
data_format=data_format,
name=name + "/conv1")
x = dwconv3x3_block(
x=x,
in_channels=mid_channels,
out_channels=mid_channels,
strides=strides,
activation="relu6",
training=training,
data_format=data_format,
name=name + "/conv2")
x = conv1x1_block(
x=x,
in_channels=mid_channels,
out_channels=out_channels,
activation=None,
training=training,
data_format=data_format,
name=name + "/conv3")
if residual:
x = x + identity
return x | [
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edfungus/Crouton | ada98b3930192938a48909072b45cb84b945f875 | clients/esp8266_clients/venv/lib/python2.7/site-packages/pkg_resources/__init__.py | python | get_provider | (moduleOrReq) | return _find_adapter(_provider_factories, loader)(module) | Return an IResourceProvider for the named module or requirement | Return an IResourceProvider for the named module or requirement | [
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"""Return an IResourceProvider for the named module or requirement"""
if isinstance(moduleOrReq, Requirement):
return working_set.find(moduleOrReq) or require(str(moduleOrReq))[0]
try:
module = sys.modules[moduleOrReq]
except KeyError:
__import__(moduleOrReq)
module = sys.modules[moduleOrReq]
loader = getattr(module, '__loader__', None)
return _find_adapter(_provider_factories, loader)(module) | [
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graalvm/mx | 29c0debab406352df3af246be2f8973be5db69ae | mx_compat.py | python | MxCompatibility500.__str__ | (self) | return str("MxCompatibility({})".format(self.version())) | [] | def __str__(self):
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] | https://github.com/graalvm/mx/blob/29c0debab406352df3af246be2f8973be5db69ae/mx_compat.py#L157-L158 | |||
nopernik/mpDNS | b17dc39e7068406df82cb3431b3042e74e520cf9 | circuits/web/parsers/multipart.py | python | MultiDict.__contains__ | (self, key) | return key in self.dict | [] | def __contains__(self, key):
return key in self.dict | [
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plotdevice/plotdevice | 598f66a19cd58b8cfea8295024998b322ed66adf | plotdevice/lib/foundry.py | python | parse_display_name | (dname) | return weight, wgt_val, width, wid_val, variant | Try to extract style attributes from the font's display name | Try to extract style attributes from the font's display name | [
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] | def parse_display_name(dname):
"""Try to extract style attributes from the font's display name"""
# break the string on spaces and on lc/uc transitions
elts = filter(None, re.sub(r'(?<=[^ ])([A-Z][a-z]+)',r' \1',dname).split(' '))
# disregard the first italic-y word in the name (if any)
for i in xrange(len(elts)-1,-1,-1):
# look for full italic/oblique/slanted spellings first
if italicky(elts[i], strict=True):
elts.pop(i)
break
else:
# if one wasn't found, settle for an it/a/l/ic/s fragment
for i in xrange(len(elts)-1,-1,-1):
if italicky(elts[i], strict=False):
elts.pop(i)
break
# next search for width-y words
width = None
wid_val = 0
for i in xrange(len(elts)-2,-1,-1):
# first look for modifier+width combinations
prefix, suffix = elts[i:i+2]
if widthy(prefix+suffix) and sanitized(prefix) in wid_mods:
if sanitized(prefix)=='semi':
width = prefix + suffix.lower()
else:
width = " ".join([prefix,suffix])
_, wid_val = standardized('width', width)
del elts[i:i+2]
break
else:
# otherwise just look for a single-word width (leave width==None if no match)
for i in xrange(len(elts)-1,-1,-1):
if widthy(elts[i]):
width = elts[i]
_, wid_val = standardized('width', width)
elts.pop(i)
break
else:
wid_val = 0
# search for weighty words in what's left
weight = None
wgt_val = 5
for i in xrange(len(elts)-2,-1,-1):
# first look for modifier+weight combinations
prefix, suffix = elts[i:i+2]
if weighty(prefix+suffix) and sanitized(prefix) in wgt_mods:
spacer = '' if prefix.lower() in ('semi','demi') else ' '
weight = spacer.join([prefix,suffix]).title()
_, wgt_val = standardized('weight', weight)
del elts[i:i+2]
break
else:
# otherwise just look for a single-word weight (leave weight==None if no match)
for i in xrange(len(elts)-1,-1,-1):
if weighty(elts[i]):
weight = elts[i]
_, wgt_val = standardized('weight', weight)
# the standard weights allow leaving out the `bold' and just using
# a modifier name. fill that back in...
if sanitized(weight) in wgt_mods:
suffix = 'bold' if sanitized(weight) in ('semi','demi') else ' Bold'
weight += suffix
elts.pop(i)
break
# concat all the non-filtered text as the `variant`
variant = None
if elts:
variant = "".join(elts)
return weight, wgt_val, width, wid_val, variant | [
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lambdaji/tf_repos | b531ff12cdab65acc9551025f73fade2b6f425a7 | DeepMTL/Model_pipeline/DeepCvrMTL.py | python | input_fn | (filenames, batch_size=32, num_epochs=1, perform_shuffle=False) | return batch_features, batch_labels | [] | def input_fn(filenames, batch_size=32, num_epochs=1, perform_shuffle=False):
print('Parsing', filenames)
def _parse_fn(record):
features = {
"y": tf.FixedLenFeature([], tf.float32),
"z": tf.FixedLenFeature([], tf.float32),
"feat_ids": tf.FixedLenFeature([FLAGS.field_size], tf.int64),
#"feat_vals": tf.FixedLenFeature([None], tf.float32),
"u_catids": tf.VarLenFeature(tf.int64),
"u_catvals": tf.VarLenFeature(tf.float32),
"u_shopids": tf.VarLenFeature(tf.int64),
"u_shopvals": tf.VarLenFeature(tf.float32),
"u_intids": tf.VarLenFeature(tf.int64),
"u_intvals": tf.VarLenFeature(tf.float32),
"u_brandids": tf.VarLenFeature(tf.int64),
"u_brandvals": tf.VarLenFeature(tf.float32),
"a_catids": tf.FixedLenFeature([], tf.int64),
"a_shopids": tf.FixedLenFeature([], tf.int64),
"a_brandids": tf.FixedLenFeature([], tf.int64),
"a_intids": tf.VarLenFeature(tf.int64)
}
parsed = tf.parse_single_example(record, features)
y = parsed.pop('y')
z = parsed.pop('z')
return parsed, {"y": y, "z": z}
# Extract lines from input files using the Dataset API, can pass one filename or filename list
dataset = tf.data.TFRecordDataset(filenames).map(_parse_fn, num_parallel_calls=10).prefetch(500000) # multi-thread pre-process then prefetch
# Randomizes input using a window of 256 elements (read into memory)
if perform_shuffle:
dataset = dataset.shuffle(buffer_size=256)
# epochs from blending together.
dataset = dataset.repeat(num_epochs)
dataset = dataset.batch(batch_size) # Batch size to use
#dataset = dataset.padded_batch(batch_size, padded_shapes=({"feeds_ids": [None], "feeds_vals": [None], "title_ids": [None]}, [None])) #不定长补齐
#return dataset.make_one_shot_iterator()
iterator = dataset.make_one_shot_iterator()
batch_features, batch_labels = iterator.get_next()
#return tf.reshape(batch_ids,shape=[-1,field_size]), tf.reshape(batch_vals,shape=[-1,field_size]), batch_labels
return batch_features, batch_labels | [
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tensorflow/models | 6b8bb0cbeb3e10415c7a87448f08adc3c484c1d3 | official/legacy/detection/utils/box_utils.py | python | yxyx_to_xywh | (boxes) | return new_boxes | Converts boxes from ymin, xmin, ymax, xmax to xmin, ymin, width, height.
Args:
boxes: a numpy array whose last dimension is 4 representing the coordinates
of boxes in ymin, xmin, ymax, xmax order.
Returns:
boxes: a numpy array whose shape is the same as `boxes` in new format.
Raises:
ValueError: If the last dimension of boxes is not 4. | Converts boxes from ymin, xmin, ymax, xmax to xmin, ymin, width, height. | [
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] | def yxyx_to_xywh(boxes):
"""Converts boxes from ymin, xmin, ymax, xmax to xmin, ymin, width, height.
Args:
boxes: a numpy array whose last dimension is 4 representing the coordinates
of boxes in ymin, xmin, ymax, xmax order.
Returns:
boxes: a numpy array whose shape is the same as `boxes` in new format.
Raises:
ValueError: If the last dimension of boxes is not 4.
"""
if boxes.shape[-1] != 4:
raise ValueError('boxes.shape[-1] is {:d}, but must be 4.'.format(
boxes.shape[-1]))
boxes_ymin = boxes[..., 0]
boxes_xmin = boxes[..., 1]
boxes_width = boxes[..., 3] - boxes[..., 1]
boxes_height = boxes[..., 2] - boxes[..., 0]
new_boxes = np.stack([boxes_xmin, boxes_ymin, boxes_width, boxes_height],
axis=-1)
return new_boxes | [
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nature-of-code/noc-examples-python | a6d823b6998304daa972a21affbf3e13444fe093 | chp04_systems/NOC_4_06_ParticleSystemForces/particle_system.py | python | ParticleSystem.apply_force | (self, f) | A function to apply force to all particles | A function to apply force to all particles | [
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"to",
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] | def apply_force(self, f):
"""A function to apply force to all particles"""
for p in self.particles:
p.apply_force(f) | [
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buke/GreenOdoo | 3d8c55d426fb41fdb3f2f5a1533cfe05983ba1df | runtime/python/lib/python2.7/site-packages/gdata/tlslite/utils/cipherfactory.py | python | createTripleDES | (key, IV, implList=None) | Create a new 3DES object.
@type key: str
@param key: A 24 byte string.
@type IV: str
@param IV: An 8 byte string
@rtype: L{tlslite.utils.TripleDES}
@return: A 3DES object. | Create a new 3DES object. | [
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"."
] | def createTripleDES(key, IV, implList=None):
"""Create a new 3DES object.
@type key: str
@param key: A 24 byte string.
@type IV: str
@param IV: An 8 byte string
@rtype: L{tlslite.utils.TripleDES}
@return: A 3DES object.
"""
if implList == None:
implList = ["cryptlib", "openssl", "pycrypto"]
for impl in implList:
if impl == "cryptlib" and cryptomath.cryptlibpyLoaded:
return Cryptlib_TripleDES.new(key, 2, IV)
elif impl == "openssl" and cryptomath.m2cryptoLoaded:
return OpenSSL_TripleDES.new(key, 2, IV)
elif impl == "pycrypto" and cryptomath.pycryptoLoaded:
return PyCrypto_TripleDES.new(key, 2, IV)
raise NotImplementedError() | [
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openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | openshift/installer/vendored/openshift-ansible-3.9.14-1/roles/lib_vendored_deps/library/oc_adm_policy_group.py | python | OpenShiftCLI.__init__ | (self,
namespace,
kubeconfig='/etc/origin/master/admin.kubeconfig',
verbose=False,
all_namespaces=False) | Constructor for OpenshiftCLI | Constructor for OpenshiftCLI | [
"Constructor",
"for",
"OpenshiftCLI"
] | def __init__(self,
namespace,
kubeconfig='/etc/origin/master/admin.kubeconfig',
verbose=False,
all_namespaces=False):
''' Constructor for OpenshiftCLI '''
self.namespace = namespace
self.verbose = verbose
self.kubeconfig = Utils.create_tmpfile_copy(kubeconfig)
self.all_namespaces = all_namespaces
self.oc_binary = locate_oc_binary() | [
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hclhkbu/dlbench | 978b034e9c34e6aaa38782bb1e4a2cea0c01d0f9 | tools/tensorflow/cnn/alexnet/cifar10_eval.py | python | evaluate | () | Eval CIFAR-10 for a number of steps. | Eval CIFAR-10 for a number of steps. | [
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"-",
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"for",
"a",
"number",
"of",
"steps",
"."
] | def evaluate():
"""Eval CIFAR-10 for a number of steps."""
with tf.Graph().as_default() as g:
# Get images and labels for CIFAR-10.
eval_data = FLAGS.eval_data == 'test'
images, labels = cifar10_input.inputs(eval_data, FLAGS.data_dir, FLAGS.batch_size)
# Build a Graph that computes the logits predictions from the
# inference model.
logits = inference(images)
# Calculate predictions.
top_k_op = tf.nn.in_top_k(logits, labels, 1)
# Restore the moving average version of the learned variables for eval.
#variable_averages = tf.train.ExponentialMovingAverage(
# cifar10.MOVING_AVERAGE_DECAY)
#variables_to_restore = variable_averages.variables_to_restore()
#saver = tf.train.Saver(variables_to_restore)
saver = tf.train.Saver()
# Build the summary operation based on the TF collection of Summaries.
summary_op = tf.merge_all_summaries()
summary_writer = tf.train.SummaryWriter(FLAGS.eval_dir, g)
while True:
eval_once(saver, summary_writer, top_k_op, summary_op)
if FLAGS.run_once:
break
time.sleep(FLAGS.eval_interval_secs) | [
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joeferraro/MavensMate-SublimeText | af36de7ffaa9b0541f446145736a48b1c66ac0cf | mavensmate.py | python | GetOrgWideTestCoverageCommand.run | (self) | [] | def run(self):
mm.call('get-coverage', True, context=self, body={'global': True}, message="Retrieving org-wide test coverage...") | [
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graalvm/mx | 29c0debab406352df3af246be2f8973be5db69ae | mx_benchmark.py | python | BenchmarkSuite.workingDirectory | (self, benchmarks, bmSuiteArgs) | return None | Returns the desired working directory for running the benchmark.
By default it returns `None`, meaning that the working directory is not be
changed. It is meant to be overridden in subclasses when necessary. | Returns the desired working directory for running the benchmark. | [
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] | def workingDirectory(self, benchmarks, bmSuiteArgs):
"""Returns the desired working directory for running the benchmark.
By default it returns `None`, meaning that the working directory is not be
changed. It is meant to be overridden in subclasses when necessary.
"""
return None | [
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pymedusa/Medusa | 1405fbb6eb8ef4d20fcca24c32ddca52b11f0f38 | ext/mako/runtime.py | python | supports_caller | (func) | return wrap_stackframe | Apply a caller_stack compatibility decorator to a plain
Python function.
See the example in :ref:`namespaces_python_modules`. | Apply a caller_stack compatibility decorator to a plain
Python function. | [
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"."
] | def supports_caller(func):
"""Apply a caller_stack compatibility decorator to a plain
Python function.
See the example in :ref:`namespaces_python_modules`.
"""
def wrap_stackframe(context, *args, **kwargs):
context.caller_stack._push_frame()
try:
return func(context, *args, **kwargs)
finally:
context.caller_stack._pop_frame()
return wrap_stackframe | [
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voicy-ai/DialogStateTracking | a102672be468a5246c8c1183f15f55fa320f33c8 | src/memn2n/main.py | python | prepare_data | (args, task_id) | [] | def prepare_data(args, task_id):
# get candidates (restaurants)
candidates, candid2idx, idx2candid = data_utils.load_candidates(task_id=task_id,
candidates_f= DATA_DIR + 'dialog-babi-candidates.txt')
# get data
train, test, val = data_utils.load_dialog_task(
data_dir= DATA_DIR,
task_id= task_id,
candid_dic= candid2idx,
isOOV= False)
##
# get metadata
metadata = data_utils.build_vocab(train + test + val, candidates)
###
# write data to file
data_ = {
'candidates' : candidates,
'train' : train,
'test' : test,
'val' : val
}
with open(P_DATA_DIR + str(task_id) + '.data.pkl', 'wb') as f:
pkl.dump(data_, f)
###
# save metadata to disk
metadata['candid2idx'] = candid2idx
metadata['idx2candid'] = idx2candid
with open(P_DATA_DIR + str(task_id) + '.metadata.pkl', 'wb') as f:
pkl.dump(metadata, f) | [
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renatoviolin/Question-Answering-Albert-Electra | 8ca885c27c89af16bb2484ea0e6aeb960801259a | electra/model/modeling.py | python | embedding_lookup | (input_ids,
vocab_size,
embedding_size=128,
initializer_range=0.02,
word_embedding_name="word_embeddings",
use_one_hot_embeddings=False) | return output, embedding_table | Looks up words embeddings for id tensor.
Args:
input_ids: int32 Tensor of shape [batch_size, seq_length] containing word
ids.
vocab_size: int. Size of the embedding vocabulary.
embedding_size: int. Width of the word embeddings.
initializer_range: float. Embedding initialization range.
word_embedding_name: string. Name of the embedding table.
use_one_hot_embeddings: bool. If True, use one-hot method for word
embeddings. If False, use `tf.nn.embedding_lookup()`. One hot is better
for TPUs.
Returns:
float Tensor of shape [batch_size, seq_length, embedding_size]. | Looks up words embeddings for id tensor. | [
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] | def embedding_lookup(input_ids,
vocab_size,
embedding_size=128,
initializer_range=0.02,
word_embedding_name="word_embeddings",
use_one_hot_embeddings=False):
"""Looks up words embeddings for id tensor.
Args:
input_ids: int32 Tensor of shape [batch_size, seq_length] containing word
ids.
vocab_size: int. Size of the embedding vocabulary.
embedding_size: int. Width of the word embeddings.
initializer_range: float. Embedding initialization range.
word_embedding_name: string. Name of the embedding table.
use_one_hot_embeddings: bool. If True, use one-hot method for word
embeddings. If False, use `tf.nn.embedding_lookup()`. One hot is better
for TPUs.
Returns:
float Tensor of shape [batch_size, seq_length, embedding_size].
"""
# This function assumes that the input is of shape [batch_size, seq_length,
# num_inputs].
#
# If the input is a 2D tensor of shape [batch_size, seq_length], we
# reshape to [batch_size, seq_length, 1].
original_dims = input_ids.shape.ndims
if original_dims == 2:
input_ids = tf.expand_dims(input_ids, axis=[-1])
embedding_table = tf.get_variable(
name=word_embedding_name,
shape=[vocab_size, embedding_size],
initializer=create_initializer(initializer_range))
if original_dims == 3:
input_shape = get_shape_list(input_ids)
tf.reshape(input_ids, [-1, input_shape[-1]])
output = tf.matmul(input_ids, embedding_table)
output = tf.reshape(output,
[input_shape[0], input_shape[1], embedding_size])
else:
if use_one_hot_embeddings:
flat_input_ids = tf.reshape(input_ids, [-1])
one_hot_input_ids = tf.one_hot(flat_input_ids, depth=vocab_size)
output = tf.matmul(one_hot_input_ids, embedding_table)
else:
output = tf.nn.embedding_lookup(embedding_table, input_ids)
input_shape = get_shape_list(input_ids)
output = tf.reshape(output,
input_shape[0:-1] + [input_shape[-1] * embedding_size])
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mrDoctorWho/vk4xmpp | e8f25a16832adb6b93fe8b50afdc9547e429389b | extensions/.example.py | python | evt07_handler | (user) | Linear handler
Called when user is connecting (if authenticated)
Parameters:
user: User class object | Linear handler
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Parameters:
user: User class object | [
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"""
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TencentCloud/tencentcloud-sdk-python | 3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2 | tencentcloud/mps/v20190612/models.py | python | ProhibitedConfigureInfo.__init__ | (self) | r"""
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google/clusterfuzz | f358af24f414daa17a3649b143e71ea71871ef59 | src/clusterfuzz/_internal/bot/fuzzers/libFuzzer/stats.py | python | calculate_log_lines | (log_lines) | return other_lines_count, libfuzzer_lines_count, ignored_lines_count | Calculate number of logs lines of different kind in the given log. | Calculate number of logs lines of different kind in the given log. | [
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# Counters to be returned.
libfuzzer_lines_count = 0
other_lines_count = 0
ignored_lines_count = 0
lines_after_last_libfuzzer_line_count = 0
libfuzzer_inited = False
found_libfuzzer_crash = False
for line in log_lines:
if not libfuzzer_inited:
# Skip to start of libFuzzer log output.
if LIBFUZZER_LOG_START_INITED_REGEX.match(line):
libfuzzer_inited = True
else:
ignored_lines_count += 1
continue
if LIBFUZZER_LOG_IGNORE_REGEX.match(line):
# We should ignore lines like sanitizer warnings, etc.
ignored_lines_count += 1
continue
if LIBFUZZER_ANY_CRASH_TYPE_REGEX.match(line):
# We should ignore whole block if a libfuzzer crash is found.
# E.g. slow units.
found_libfuzzer_crash = True
elif LIBFUZZER_LOG_LINE_REGEX.match(line):
if found_libfuzzer_crash:
# Ignore previous block.
other_lines_count -= lines_after_last_libfuzzer_line_count
ignored_lines_count += lines_after_last_libfuzzer_line_count
libfuzzer_lines_count += 1
lines_after_last_libfuzzer_line_count = 0
found_libfuzzer_crash = False
elif LIBFUZZER_LOG_END_REGEX.match(line):
libfuzzer_lines_count += 1
break
else:
other_lines_count += 1
lines_after_last_libfuzzer_line_count += 1
# Ignore the lines after the last libfuzzer line.
other_lines_count -= lines_after_last_libfuzzer_line_count
ignored_lines_count += lines_after_last_libfuzzer_line_count
return other_lines_count, libfuzzer_lines_count, ignored_lines_count | [
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openembedded/openembedded-core | 9154f71c7267e9731156c1dfd57397103e9e6a2b | scripts/lib/wic/filemap.py | python | FilemapSeek.get_mapped_ranges | (self, start, count) | return self._get_ranges(start, count, _SEEK_DATA, _SEEK_HOLE) | Refer the '_FilemapBase' class for the documentation. | Refer the '_FilemapBase' class for the documentation. | [
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self._log.debug("FilemapSeek: get_mapped_ranges(%d, %d(%d))"
% (start, count, start + count - 1))
return self._get_ranges(start, count, _SEEK_DATA, _SEEK_HOLE) | [
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facebookresearch/CrypTen | 90bf38b4f80726c808f322efb0ce430dcdf5e5ec | crypten/nn/onnx_converter.py | python | _export_pytorch_model | (f, pytorch_model, dummy_input) | return f | Returns a binary I/O stream containing ONNX-exported pytorch_model that was
traced with input `dummy_input`. | Returns a binary I/O stream containing ONNX-exported pytorch_model that was
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"""
Returns a binary I/O stream containing ONNX-exported pytorch_model that was
traced with input `dummy_input`.
"""
kwargs = {
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"export_params": True,
"enable_onnx_checker": True,
"input_names": ["input"],
"operator_export_type": OperatorExportTypes.ONNX,
"output_names": ["output"],
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torch.onnx.export(pytorch_model, dummy_input, f, **kwargs)
return f | [
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postgres/pgadmin4 | 374c5e952fa594d749fadf1f88076c1cba8c5f64 | web/pgadmin/browser/server_groups/servers/databases/schemas/tables/constraints/foreign_key/utils.py | python | _get_sql_for_delete_fk_constraint | (data, constraint, sql, template_path,
conn) | Get sql for delete foreign key constraints.
:param data:
:param constraint:
:param sql: sql for append
:param template_path: template path for sql.
:param conn:
:return: | Get sql for delete foreign key constraints.
:param data:
:param constraint:
:param sql: sql for append
:param template_path: template path for sql.
:param conn:
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:param data:
:param constraint:
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"""
if 'deleted' in constraint:
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c['schema'] = data['schema']
c['table'] = data['name']
# Sql for drop
sql.append(
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wistbean/fxxkpython | 88e16d79d8dd37236ba6ecd0d0ff11d63143968c | vip/qyxuan/projects/Snake/venv/lib/python3.6/site-packages/pip-19.0.3-py3.6.egg/pip/_vendor/distlib/_backport/tarfile.py | python | _FileInFile.tell | (self) | return self.position | Return the current file position. | Return the current file position. | [
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"""Return the current file position.
"""
return self.position | [
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nortikin/sverchok | 7b460f01317c15f2681bfa3e337c5e7346f3711b | nodes/analyzer/deformation.py | python | SvDeformationNode.ready | (self) | return ready | check if there are the needed links | check if there are the needed links | [
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] | def ready(self):
'''check if there are the needed links'''
si = self.inputs
so = self.outputs
ready = any(s.is_linked for s in so)
ready = ready and si[0].is_linked and si[1].is_linked
ready = ready and (si[2].is_linked or si[3].is_linked)
return ready | [
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krintoxi/NoobSec-Toolkit | 38738541cbc03cedb9a3b3ed13b629f781ad64f6 | NoobSecToolkit - MAC OSX/scripts/nextpenbox.py | python | xsstracer | () | [] | def xsstracer():
clearScr()
print("XSSTracer is a small python script that checks remote web servers for Clickjacking, Cross-Frame Scripting, Cross-Site Tracing and Host Header Injection.")
os.system("git clone https://github.com/1N3/XSSTracer.git")
clearScr ()
xsstracerchoice = raw_input("Select a Target: ")
os.system("cd XSSTracer && chmod 777 xsstracer.py && python xsstracer.py %s 80"%xsstracerchoice) | [
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andyzsf/TuShare | 92787ad0cd492614bdb6389b71a19c80d1c8c9ae | tushare/datayes/macro.py | python | Macro.KoreaDataGDP | (self, indicID='', indicName='', beginDate='', endDate='', field='') | return _ret_data(code, result) | 包含韩国GDP数据，具体指标可参见API文档；历史数据从1970年开始，按季更新。 | 包含韩国GDP数据，具体指标可参见API文档；历史数据从1970年开始，按季更新。 | [
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] | def KoreaDataGDP(self, indicID='', indicName='', beginDate='', endDate='', field=''):
"""
包含韩国GDP数据，具体指标可参见API文档；历史数据从1970年开始，按季更新。
"""
code, result = self.client.getData(vs.KOREADATAGDP%(indicID, indicName, beginDate, endDate, field))
return _ret_data(code, result) | [
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nfstream/nfstream | aa14fd1606ce4757f8b1589faeb032a0f5a93c5e | nfstream/system.py | python | ConnCache.scan | (self, current_time) | Scan and delete LRU entries based on a defined timeout | Scan and delete LRU entries based on a defined timeout | [
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if (current_time - self.last_scan_time) > 10:
remaining = True # We suppose that there is something to expire
scanned = 0
while remaining and scanned <= 1000: # Each 10 ms we scan with 1000 entries budget
try:
lru_key = self.get_lru_key() # will return the LRU conn key.
lru_last_update_time = self[lru_key]
if current_time - lru_last_update_time >= self.timeout:
del self[lru_key]
self.channel.put(NFSocket(NFEvent.SOCKET_REMOVE, lru_key, None, None)) # Send to streamer
scanned += 1
else:
remaining = False # LRU flow is not yet idle.
except StopIteration: # Empty cache
remaining = False
self.last_scan_time = current_time | [
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pycontribs/pyrax | a0c022981f76a4cba96a22ecc19bb52843ac4fbe | pyrax/image.py | python | ImageManager.create | (self, name, img_format=None, img_container_format=None,
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be supplied directly in the 'data' parameter, or it can be an image
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if img_format is None:
img_format = "vhd"
if img_container_format is None:
img_container_format = "bare"
headers = {
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if data:
img_data = data
else:
ident = self.api.identity
region = self.api.region_name
clt = ident.get_client("object_store", region)
if not isinstance(obj, StorageObject):
obj = clt.get_object(container, obj)
img_data = obj.fetch()
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StepNeverStop/RLs | 25cc97c96cbb19fe859c9387b7547cbada2c89f2 | rls/utils/torch_utils.py | python | squash_action | (pi, log_pi, *, is_independent=True) | return pi, log_pi | Enforcing action bounds.
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pi: sample of gaussian distribution
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jliljebl/flowblade | 995313a509b80e99eb1ad550d945bdda5995093b | flowblade-trunk/Flowblade/extraeditors.py | python | ThreeBandColorBox._press_event | (self, event) | [] | def _press_event(self, event):
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quodlibet/quodlibet | e3099c89f7aa6524380795d325cc14630031886c | quodlibet/plugins/playlist.py | python | PlaylistPluginHandler.handle | (self, plugin_id, library, browser, playlists) | Start a plugin directly without a menu | Start a plugin directly without a menu | [
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marinho/geraldo | 868ebdce67176d9b6205cddc92476f642c783fff | site/newsite/django_1_0/django/dispatch/dispatcher.py | python | _removeSender | (senderkey) | Remove senderkey from connections. | Remove senderkey from connections. | [
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pantsbuild/pex | 473c6ac732ed4bc338b4b20a9ec930d1d722c9b4 | pex/vendor/_vendored/pip/pip/_vendor/html5lib/_trie/py.py | python | Trie.__init__ | (self, data) | [] | def __init__(self, data):
if not all(isinstance(x, text_type) for x in data.keys()):
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biolab/orange2 | db40a9449cb45b507d63dcd5739b223f9cffb8e6 | Orange/classification/bayes.py | python | NaiveClassifier.p | (self, class_, instance) | return self.native_bayes_classifier.p(class_, instance) | Return probability of a single class.
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:type class_: :class:`~Orange.data.Value`
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AcidWeb/CurseBreaker | 1a8cb60f4db0cc8b7e0702441e1adc0f1829003e | CurseBreaker.py | python | TUI.print_log | (self) | [] | def print_log(self):
if self.headless:
html = self.console.export_html(inline_styles=True, theme=HEADLESS_TERMINAL_THEME)
with open('CurseBreaker.html', 'a+', encoding='utf-8') as log:
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pyocd/pyOCD | 7d164d99e816c4ef6c99f257014543188a0ca5a6 | pyocd/coresight/cortex_m_core_registers.py | python | index_for_reg | (name) | return CortexMCoreRegisterInfo.get(name).index | ! @brief Utility to easily convert register name to index. | ! | [
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"""! @brief Utility to easily convert register name to index."""
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django/djangosnippets.org | 0b273ce13135e157267009631d460835387d9975 | ratings/utils.py | python | query_as_sql | (query) | return query.get_compiler(connection=connection).as_sql() | [] | def query_as_sql(query):
return query.get_compiler(connection=connection).as_sql() | [
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digidotcom/xbee-python | 0757f4be0017530c205175fbee8f9f61be9614d1 | digi/xbee/devices.py | python | Connection.scan_counter_b2a | (self, new_scan_counter_b2a) | Configures the scan counter for this connection, discovered by its B node.
Args:
new_scan_counter_b2a (Integer): The scan counter for this
connection, discovered by its B node. | Configures the scan counter for this connection, discovered by its B node. | [
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"""
Configures the scan counter for this connection, discovered by its B node.
Args:
new_scan_counter_b2a (Integer): The scan counter for this
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self.__scan_counter_b2a = new_scan_counter_b2a | [
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bokeh/bokeh | a00e59da76beb7b9f83613533cfd3aced1df5f06 | bokeh/core/property/descriptors.py | python | DataSpecPropertyDescriptor.set_from_json | (self, obj, json, *, models=None, setter=None) | Sets the value of this property from a JSON value.
This method first
Args:
obj (HasProps) :
json (JSON-dict) :
models(seq[Model], optional) :
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""" Sets the value of this property from a JSON value.
This method first
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obj (HasProps) :
json (JSON-dict) :
models(seq[Model], optional) :
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old = getattr(obj, self.name)
if old is not None:
try:
self.property._type.validate(old, False)
if 'value' in json:
json = json['value']
except ValueError:
if isinstance(old, str) and 'field' in json:
json = json['field']
# leave it as a dict if 'old' was a dict
super().set_from_json(obj, json, models=models, setter=setter) | [
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explosion/srsly | 8617ecc099d1f34a60117b5287bef5424ea2c837 | srsly/ruamel_yaml/comments.py | python | CommentedMap.__delitem__ | (self, key) | [] | def __delitem__(self, key):
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mila-iqia/myia | 56774a39579b4ec4123f44843ad4ca688acc859b | myia_backend_relay/myia_backend_relay/relay.py | python | CompileGraph.on_constant | (self, node) | return self.make_const(node.value, node.abstract) | Convert a constant node. | Convert a constant node. | [
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cloudera/hue | 23f02102d4547c17c32bd5ea0eb24e9eadd657a4 | desktop/core/ext-py/celery-4.2.1/celery/platforms.py | python | Signals.ignore | (self, *names) | Ignore signal using :const:`SIG_IGN`.
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buke/GreenOdoo | 3d8c55d426fb41fdb3f2f5a1533cfe05983ba1df | runtime/python/lib/python2.7/site-packages/docutils/utils/math/math2html.py | python | VerticalSpace.process | (self) | Set the correct tag | Set the correct tag | [
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noahgolmant/pytorch-hessian-eigenthings | dce2e54a19963b0dfa41b93f531fb7742d46ea04 | hessian_eigenthings/power_iter.py | python | deflated_power_iteration | (
operator: Operator,
num_eigenthings: int = 10,
power_iter_steps: int = 20,
power_iter_err_threshold: float = 1e-4,
momentum: float = 0.0,
use_gpu: bool = True,
fp16: bool = False,
to_numpy: bool = True,
) | return eigenvals, eigenvecs | Compute top k eigenvalues by repeatedly subtracting out dyads
operator: linear operator that gives us access to matrix vector product
num_eigenvals number of eigenvalues to compute
power_iter_steps: number of steps per run of power iteration
power_iter_err_threshold: early stopping threshold for power iteration
returns: np.ndarray of top eigenvalues, np.ndarray of top eigenvectors | Compute top k eigenvalues by repeatedly subtracting out dyads
operator: linear operator that gives us access to matrix vector product
num_eigenvals number of eigenvalues to compute
power_iter_steps: number of steps per run of power iteration
power_iter_err_threshold: early stopping threshold for power iteration
returns: np.ndarray of top eigenvalues, np.ndarray of top eigenvectors | [
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power_iter_err_threshold: float = 1e-4,
momentum: float = 0.0,
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fp16: bool = False,
to_numpy: bool = True,
) -> Tuple[np.ndarray, np.ndarray]:
"""
Compute top k eigenvalues by repeatedly subtracting out dyads
operator: linear operator that gives us access to matrix vector product
num_eigenvals number of eigenvalues to compute
power_iter_steps: number of steps per run of power iteration
power_iter_err_threshold: early stopping threshold for power iteration
returns: np.ndarray of top eigenvalues, np.ndarray of top eigenvectors
"""
eigenvals = []
eigenvecs = []
current_op = operator
prev_vec = None
def _deflate(x, val, vec):
return val * vec.dot(x) * vec
utils.log("beginning deflated power iteration")
for i in range(num_eigenthings):
utils.log("computing eigenvalue/vector %d of %d" % (i + 1, num_eigenthings))
eigenval, eigenvec = power_iteration(
current_op,
power_iter_steps,
power_iter_err_threshold,
momentum=momentum,
use_gpu=use_gpu,
fp16=fp16,
init_vec=prev_vec,
)
utils.log("eigenvalue %d: %.4f" % (i + 1, eigenval))
def _new_op_fn(x, op=current_op, val=eigenval, vec=eigenvec):
return utils.maybe_fp16(op.apply(x), fp16) - _deflate(x, val, vec)
current_op = LambdaOperator(_new_op_fn, operator.size)
prev_vec = eigenvec
eigenvals.append(eigenval)
eigenvec = eigenvec.cpu()
if to_numpy:
# Clone so that power_iteration can continue to use torch.
numpy_eigenvec = eigenvec.detach().clone().numpy()
eigenvecs.append(numpy_eigenvec)
else:
eigenvecs.append(eigenvec)
eigenvals = np.array(eigenvals)
eigenvecs = np.array(eigenvecs)
# sort them in descending order
sorted_inds = np.argsort(eigenvals)
eigenvals = eigenvals[sorted_inds][::-1]
eigenvecs = eigenvecs[sorted_inds][::-1]
return eigenvals, eigenvecs | [
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anonymous47823493/EagleEye | ba312d99587e4c1b9ffeb4f56ab81d909f2021a0 | distiller/utils.py | python | denormalize_module_name | (parallel_model, normalized_name) | Convert back from the normalized form of the layer name, to PyTorch's name
which contains "artifacts" if DataParallel is used. | Convert back from the normalized form of the layer name, to PyTorch's name
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] | def denormalize_module_name(parallel_model, normalized_name):
"""Convert back from the normalized form of the layer name, to PyTorch's name
which contains "artifacts" if DataParallel is used.
"""
fully_qualified_name = [
mod_name
for mod_name, _ in parallel_model.named_modules()
if normalize_module_name(mod_name) == normalized_name
]
if len(fully_qualified_name) > 0:
return fully_qualified_name[-1]
else:
return normalized_name | [
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sympy/sympy | d822fcba181155b85ff2b29fe525adbafb22b448 | sympy/plotting/plot.py | python | MatplotlibBackend.get_segments | (x, y, z=None) | return np.ma.concatenate([points[:-1], points[1:]], axis=1) | Convert two list of coordinates to a list of segments to be used
with Matplotlib's LineCollection.
Parameters
==========
x: list
List of x-coordinates
y: list
List of y-coordinates
z: list
List of z-coordinates for a 3D line. | Convert two list of coordinates to a list of segments to be used
with Matplotlib's LineCollection. | [
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] | def get_segments(x, y, z=None):
""" Convert two list of coordinates to a list of segments to be used
with Matplotlib's LineCollection.
Parameters
==========
x: list
List of x-coordinates
y: list
List of y-coordinates
z: list
List of z-coordinates for a 3D line.
"""
np = import_module('numpy')
if z is not None:
dim = 3
points = (x, y, z)
else:
dim = 2
points = (x, y)
points = np.ma.array(points).T.reshape(-1, 1, dim)
return np.ma.concatenate([points[:-1], points[1:]], axis=1) | [
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auDeep/auDeep | 07df37b4fde5b10cd96a0c94d8804a1612c10d6f | audeep/backend/parsers/meta.py | python | MetaParser.__init__ | (self,
basedir: Path) | Creates and initializes a new MetaParser for the specified data set base directory.
Parameters
----------
basedir: pathlib.Path
The data set base directory | Creates and initializes a new MetaParser for the specified data set base directory.
Parameters
----------
basedir: pathlib.Path
The data set base directory | [
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basedir: Path):
"""
Creates and initializes a new MetaParser for the specified data set base directory.
Parameters
----------
basedir: pathlib.Path
The data set base directory
"""
super().__init__(basedir)
self._parser = None
self._can_parse = None
self._parsers = [
DCASEParser(basedir),
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leo-editor/leo-editor | 383d6776d135ef17d73d935a2f0ecb3ac0e99494 | leo/core/leoCommands.py | python | Commands.diff_file | (self, fn, rev1='HEAD', rev2='') | Create an outline describing the git diffs for all files changed
between rev1 and rev2. | Create an outline describing the git diffs for all files changed
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] | def diff_file(self, fn, rev1='HEAD', rev2=''):
"""
Create an outline describing the git diffs for all files changed
between rev1 and rev2.
"""
from leo.commands import editFileCommands as efc
x = efc.GitDiffController(c=self)
x.diff_file(fn=fn, rev1=rev1, rev2=rev2) | [
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IntelLabs/nlp-architect | 60afd0dd1bfd74f01b4ac8f613cb484777b80284 | nlp_architect/models/temporal_convolutional_network.py | python | WeightNorm.build | (self, input_shape) | Build `Layer` | Build `Layer` | [
"Build",
"Layer"
] | def build(self, input_shape):
"""Build `Layer`"""
input_shape = tensor_shape.TensorShape(input_shape).as_list()
self.input_spec = InputSpec(shape=input_shape)
if not self.layer.built:
self.layer.build(input_shape)
self.layer.built = False
if not hasattr(self.layer, "kernel"):
raise ValueError(
"`WeightNorm` must wrap a layer that" " contains a `kernel` for weights"
)
# The kernel's filter or unit dimension is -1
self.layer_depth = int(self.layer.kernel.shape[-1])
self.norm_axes = list(range(self.layer.kernel.shape.ndims - 1))
self.layer.v = self.layer.kernel
self.layer.g = self.layer.add_variable(
name="g",
shape=(self.layer_depth,),
initializer=initializers.get("ones"),
dtype=self.layer.kernel.dtype,
trainable=True,
)
with ops.control_dependencies([self.layer.g.assign(self._init_norm(self.layer.v))]):
self._compute_weights()
self.layer.built = True
super(WeightNorm, self).build()
self.built = True | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/Python-2.7.9/Lib/filecmp.py | python | dircmp.__getattr__ | (self, attr) | return getattr(self, attr) | [] | def __getattr__(self, attr):
if attr not in self.methodmap:
raise AttributeError, attr
self.methodmap[attr](self)
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open-cogsci/OpenSesame | c4a3641b097a80a76937edbd8c365f036bcc9705 | libqtopensesame/extensions/_base_extension.py | python | base_extension.create_action | (self) | desc:
Creates a QAction for the extension, and adds it to the menubar
and/ or the toolbar. | desc:
Creates a QAction for the extension, and adds it to the menubar
and/ or the toolbar. | [
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] | def create_action(self):
"""
desc:
Creates a QAction for the extension, and adds it to the menubar
and/ or the toolbar.
"""
if self.label() is not None:
self.action = self.qaction(self.icon(), self.label(),
self._activate, checkable=self.checkable(),
tooltip=self.tooltip(), shortcut=self.shortcut())
# Insert the action into the menu
if self.menu_pos() is not None:
submenu_text, index, separator_before, separator_after = \
self.menu_pos()
submenu = self.get_submenu(submenu_text)
self.add_action(submenu, self.action, index, separator_before,
separator_after)
# Insert the action into the toolbar
if self.toolbar_pos() is not None:
index, separator_before, separator_after = self.toolbar_pos()
self.add_action(self.toolbar, self.action, index,
separator_before, separator_after)
else:
self.action = None | [
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JaniceWuo/MovieRecommend | 4c86db64ca45598917d304f535413df3bc9fea65 | movierecommend/venv1/Lib/site-packages/pip-9.0.1-py3.6.egg/pip/_vendor/pyparsing.py | python | CharsNotIn.__init__ | ( self, notChars, min=1, max=0, exact=0 ) | [] | def __init__( self, notChars, min=1, max=0, exact=0 ):
super(CharsNotIn,self).__init__()
self.skipWhitespace = False
self.notChars = notChars
if min < 1:
raise ValueError("cannot specify a minimum length < 1; use Optional(CharsNotIn()) if zero-length char group is permitted")
self.minLen = min
if max > 0:
self.maxLen = max
else:
self.maxLen = _MAX_INT
if exact > 0:
self.maxLen = exact
self.minLen = exact
self.name = _ustr(self)
self.errmsg = "Expected " + self.name
self.mayReturnEmpty = ( self.minLen == 0 )
self.mayIndexError = False | [
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flow-project/flow | a511c41c48e6b928bb2060de8ad1ef3c3e3d9554 | flow/networks/ring.py | python | RingNetwork.__init__ | (self,
name,
vehicles,
net_params,
initial_config=InitialConfig(),
traffic_lights=TrafficLightParams()) | Initialize a ring scenario. | Initialize a ring scenario. | [
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name,
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net_params,
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"""Initialize a ring scenario."""
for p in ADDITIONAL_NET_PARAMS.keys():
if p not in net_params.additional_params:
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super().__init__(name, vehicles, net_params, initial_config,
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AppScale/gts | 46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9 | AppServer/lib/django-1.3/django/contrib/auth/views.py | python | password_reset_confirm | (request, uidb36=None, token=None,
template_name='registration/password_reset_confirm.html',
token_generator=default_token_generator,
set_password_form=SetPasswordForm,
post_reset_redirect=None,
current_app=None, extra_context=None) | return render_to_response(template_name, context,
context_instance=RequestContext(request, current_app=current_app)) | View that checks the hash in a password reset link and presents a
form for entering a new password. | View that checks the hash in a password reset link and presents a
form for entering a new password. | [
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] | def password_reset_confirm(request, uidb36=None, token=None,
template_name='registration/password_reset_confirm.html',
token_generator=default_token_generator,
set_password_form=SetPasswordForm,
post_reset_redirect=None,
current_app=None, extra_context=None):
"""
View that checks the hash in a password reset link and presents a
form for entering a new password.
"""
assert uidb36 is not None and token is not None # checked by URLconf
if post_reset_redirect is None:
post_reset_redirect = reverse('django.contrib.auth.views.password_reset_complete')
try:
uid_int = base36_to_int(uidb36)
user = User.objects.get(id=uid_int)
except (ValueError, User.DoesNotExist):
user = None
if user is not None and token_generator.check_token(user, token):
validlink = True
if request.method == 'POST':
form = set_password_form(user, request.POST)
if form.is_valid():
form.save()
return HttpResponseRedirect(post_reset_redirect)
else:
form = set_password_form(None)
else:
validlink = False
form = None
context = {
'form': form,
'validlink': validlink,
}
context.update(extra_context or {})
return render_to_response(template_name, context,
context_instance=RequestContext(request, current_app=current_app)) | [
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nose-devs/nose2 | e0dc345da06995fdf00abeb3ed4ae65050d552bd | nose2/util.py | python | safe_decode | (string) | Safely decode a byte string into unicode | Safely decode a byte string into unicode | [
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try:
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openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | openshift/installer/vendored/openshift-ansible-3.10.0-0.29.0/roles/lib_openshift/library/oc_serviceaccount.py | python | Yedit.write | (self) | return (True, self.yaml_dict) | write to file | write to file | [
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] | def write(self):
''' write to file '''
if not self.filename:
raise YeditException('Please specify a filename.')
if self.backup and self.file_exists():
shutil.copy(self.filename, '{}{}'.format(self.filename, self.backup_ext))
# Try to set format attributes if supported
try:
self.yaml_dict.fa.set_block_style()
except AttributeError:
pass
# Try to use RoundTripDumper if supported.
if self.content_type == 'yaml':
try:
Yedit._write(self.filename, yaml.dump(self.yaml_dict, Dumper=yaml.RoundTripDumper))
except AttributeError:
Yedit._write(self.filename, yaml.safe_dump(self.yaml_dict, default_flow_style=False))
elif self.content_type == 'json':
Yedit._write(self.filename, json.dumps(self.yaml_dict, indent=4, sort_keys=True))
else:
raise YeditException('Unsupported content_type: {}.'.format(self.content_type) +
'Please specify a content_type of yaml or json.')
return (True, self.yaml_dict) | [
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"... | https://github.com/openshift/openshift-tools/blob/1188778e728a6e4781acf728123e5b356380fe6f/openshift/installer/vendored/openshift-ansible-3.10.0-0.29.0/roles/lib_openshift/library/oc_serviceaccount.py#L347-L373 | |
bugcrowd/HUNT | ed3e1adee724bf6c98750f377f6c40cd656c82d3 | Burp/lib/issues.py | python | Issues.create_scanner_issues | (self, view, callbacks, helpers, vuln_parameters, request_response) | [] | def create_scanner_issues(self, view, callbacks, helpers, vuln_parameters, request_response):
issues = self.issues
json = self.json
# Takes into account if there is more than one vulnerable parameter
for vuln_parameter in vuln_parameters:
issue_name = vuln_parameter["vuln_name"]
vuln_param = vuln_parameter["vuln_param"]
param_name = vuln_parameter["param"]
param_value = vuln_parameter["value"]
url = helpers.analyzeRequest(request_response).getUrl()
url = urlparse.urlsplit(str(url))
hostname = url.hostname
path = url.path
url = url.scheme + "://" + url.hostname + url.path
http_service = request_response.getHttpService()
http_messages = [callbacks.applyMarkers(request_response, None, None)]
detail = json["issues"][issue_name]["detail"]
severity = "Medium"
scanner_issue = ScannerIssue(url, issue_name, param_name, vuln_param, param_value, hostname, path, http_service, http_messages, detail, severity, request_response)
is_scanner_issue_dupe = self.check_duplicate_issue(scanner_issue)
if is_scanner_issue_dupe:
continue
else:
self.set_scanner_issues(scanner_issue)
issue_count = self.set_issue_count(issue_name, vuln_param)
total_count = self.total_count[issue_name]
view.set_scanner_count(issue_name, vuln_param, issue_count, total_count)
view.scanner_table_models.set_scanner_table_model(scanner_issue, issue_name, param_name, vuln_param) | [
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triaquae/triaquae | bbabf736b3ba56a0c6498e7f04e16c13b8b8f2b9 | TriAquae/models/django_wsgiserver/wsgiserver/wsgiserver2.py | python | ThreadPool.shrink | (self, amount) | Kill off worker threads (not below self.min). | Kill off worker threads (not below self.min). | [
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] | def shrink(self, amount):
"""Kill off worker threads (not below self.min)."""
# Grow/shrink the pool if necessary.
# Remove any dead threads from our list
for t in self._threads:
if not t.isAlive():
self._threads.remove(t)
amount -= 1
# calculate the number of threads above the minimum
n_extra = max(len(self._threads) - self.min, 0)
# don't remove more than amount
n_to_remove = min(amount, n_extra)
# put shutdown requests on the queue equal to the number of threads
# to remove. As each request is processed by a worker, that worker
# will terminate and be culled from the list.
for n in range(n_to_remove):
self._queue.put(_SHUTDOWNREQUEST) | [
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rembo10/headphones | b3199605be1ebc83a7a8feab6b1e99b64014187c | lib/apscheduler/schedulers/twisted.py | python | TwistedScheduler._configure | (self, config) | [] | def _configure(self, config):
self._reactor = maybe_ref(config.pop('reactor', default_reactor))
super(TwistedScheduler, self)._configure(config) | [
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chribsen/simple-machine-learning-examples | dc94e52a4cebdc8bb959ff88b81ff8cfeca25022 | venv/lib/python2.7/site-packages/scipy/linalg/basic.py | python | pinvh | (a, cond=None, rcond=None, lower=True, return_rank=False,
check_finite=True) | Compute the (Moore-Penrose) pseudo-inverse of a Hermitian matrix.
Calculate a generalized inverse of a Hermitian or real symmetric matrix
using its eigenvalue decomposition and including all eigenvalues with
'large' absolute value.
Parameters
----------
a : (N, N) array_like
Real symmetric or complex hermetian matrix to be pseudo-inverted
cond, rcond : float or None
Cutoff for 'small' eigenvalues.
Singular values smaller than rcond * largest_eigenvalue are considered
zero.
If None or -1, suitable machine precision is used.
lower : bool, optional
Whether the pertinent array data is taken from the lower or upper
triangle of a. (Default: lower)
return_rank : bool, optional
if True, return the effective rank of the matrix
check_finite : bool, optional
Whether to check that the input matrix contains only finite numbers.
Disabling may give a performance gain, but may result in problems
(crashes, non-termination) if the inputs do contain infinities or NaNs.
Returns
-------
B : (N, N) ndarray
The pseudo-inverse of matrix `a`.
rank : int
The effective rank of the matrix. Returned if return_rank == True
Raises
------
LinAlgError
If eigenvalue does not converge
Examples
--------
>>> from scipy.linalg import pinvh
>>> a = np.random.randn(9, 6)
>>> a = np.dot(a, a.T)
>>> B = pinvh(a)
>>> np.allclose(a, np.dot(a, np.dot(B, a)))
True
>>> np.allclose(B, np.dot(B, np.dot(a, B)))
True | Compute the (Moore-Penrose) pseudo-inverse of a Hermitian matrix. | [
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] | def pinvh(a, cond=None, rcond=None, lower=True, return_rank=False,
check_finite=True):
"""
Compute the (Moore-Penrose) pseudo-inverse of a Hermitian matrix.
Calculate a generalized inverse of a Hermitian or real symmetric matrix
using its eigenvalue decomposition and including all eigenvalues with
'large' absolute value.
Parameters
----------
a : (N, N) array_like
Real symmetric or complex hermetian matrix to be pseudo-inverted
cond, rcond : float or None
Cutoff for 'small' eigenvalues.
Singular values smaller than rcond * largest_eigenvalue are considered
zero.
If None or -1, suitable machine precision is used.
lower : bool, optional
Whether the pertinent array data is taken from the lower or upper
triangle of a. (Default: lower)
return_rank : bool, optional
if True, return the effective rank of the matrix
check_finite : bool, optional
Whether to check that the input matrix contains only finite numbers.
Disabling may give a performance gain, but may result in problems
(crashes, non-termination) if the inputs do contain infinities or NaNs.
Returns
-------
B : (N, N) ndarray
The pseudo-inverse of matrix `a`.
rank : int
The effective rank of the matrix. Returned if return_rank == True
Raises
------
LinAlgError
If eigenvalue does not converge
Examples
--------
>>> from scipy.linalg import pinvh
>>> a = np.random.randn(9, 6)
>>> a = np.dot(a, a.T)
>>> B = pinvh(a)
>>> np.allclose(a, np.dot(a, np.dot(B, a)))
True
>>> np.allclose(B, np.dot(B, np.dot(a, B)))
True
"""
a = _asarray_validated(a, check_finite=check_finite)
s, u = decomp.eigh(a, lower=lower, check_finite=False)
if rcond is not None:
cond = rcond
if cond in [None, -1]:
t = u.dtype.char.lower()
factor = {'f': 1E3, 'd': 1E6}
cond = factor[t] * np.finfo(t).eps
# For Hermitian matrices, singular values equal abs(eigenvalues)
above_cutoff = (abs(s) > cond * np.max(abs(s)))
psigma_diag = 1.0 / s[above_cutoff]
u = u[:, above_cutoff]
B = np.dot(u * psigma_diag, np.conjugate(u).T)
if return_rank:
return B, len(psigma_diag)
else:
return B | [
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josiah-wolf-oberholtzer/supriya | 5ca725a6b97edfbe016a75666d420ecfdf49592f | supriya/intervals/Moment.py | python | Moment.start_intervals | (self) | return self._start_intervals | Gets the intervals in this moment which start at this
moment's start offset. | Gets the intervals in this moment which start at this
moment's start offset. | [
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] | def start_intervals(self):
"""
Gets the intervals in this moment which start at this
moment's start offset.
"""
return self._start_intervals | [
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securesystemslab/zippy | ff0e84ac99442c2c55fe1d285332cfd4e185e089 | zippy/lib-python/3/plat-sunos5/IN.py | python | btopr | (x) | return ((((x) + PAGEOFFSET) >> PAGESHIFT)) | [] | def btopr(x): return ((((x) + PAGEOFFSET) >> PAGESHIFT)) | [
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tp4a/teleport | 1fafd34f1f775d2cf80ea4af6e44468d8e0b24ad | server/www/packages/packages-windows/x86/ldap3/core/server.py | python | Server._is_ipv6 | (host) | return True | [] | def _is_ipv6(host):
try:
socket.inet_pton(socket.AF_INET6, host)
except (socket.error, AttributeError, ValueError):
return False
return True | [
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wiseman/mavelous | eef41c096cc282bb3acd33a747146a88d2bd1eee | mavproxy.py | python | beep | () | [] | def beep():
f = open("/dev/tty", mode="w")
f.write(chr(7))
f.close() | [
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python-discord/bot | 26c5587ac13e5414361bb6e7ada42983b81014d2 | bot/utils/messages.py | python | send_attachments | (
message: discord.Message,
destination: Union[discord.TextChannel, discord.Webhook],
link_large: bool = True,
use_cached: bool = False,
**kwargs
) | return urls | Re-upload the message's attachments to the destination and return a list of their new URLs.
Each attachment is sent as a separate message to more easily comply with the request/file size
limit. If link_large is True, attachments which are too large are instead grouped into a single
embed which links to them. Extra kwargs will be passed to send() when sending the attachment. | Re-upload the message's attachments to the destination and return a list of their new URLs. | [
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] | async def send_attachments(
message: discord.Message,
destination: Union[discord.TextChannel, discord.Webhook],
link_large: bool = True,
use_cached: bool = False,
**kwargs
) -> List[str]:
"""
Re-upload the message's attachments to the destination and return a list of their new URLs.
Each attachment is sent as a separate message to more easily comply with the request/file size
limit. If link_large is True, attachments which are too large are instead grouped into a single
embed which links to them. Extra kwargs will be passed to send() when sending the attachment.
"""
webhook_send_kwargs = {
'username': message.author.display_name,
'avatar_url': message.author.display_avatar.url,
}
webhook_send_kwargs.update(kwargs)
webhook_send_kwargs['username'] = sub_clyde(webhook_send_kwargs['username'])
large = []
urls = []
for attachment in message.attachments:
failure_msg = (
f"Failed to re-upload attachment {attachment.filename} from message {message.id}"
)
try:
# Allow 512 bytes of leeway for the rest of the request.
# This should avoid most files that are too large,
# but some may get through hence the try-catch.
if attachment.size <= destination.guild.filesize_limit - 512:
with BytesIO() as file:
await attachment.save(file, use_cached=use_cached)
attachment_file = discord.File(file, filename=attachment.filename)
if isinstance(destination, discord.TextChannel):
msg = await destination.send(file=attachment_file, **kwargs)
urls.append(msg.attachments[0].url)
else:
await destination.send(file=attachment_file, **webhook_send_kwargs)
elif link_large:
large.append(attachment)
else:
log.info(f"{failure_msg} because it's too large.")
except discord.HTTPException as e:
if link_large and e.status == 413:
large.append(attachment)
else:
log.warning(f"{failure_msg} with status {e.status}.", exc_info=e)
if link_large and large:
desc = "\n".join(f"[{attachment.filename}]({attachment.url})" for attachment in large)
embed = discord.Embed(description=desc)
embed.set_footer(text="Attachments exceed upload size limit.")
if isinstance(destination, discord.TextChannel):
await destination.send(embed=embed, **kwargs)
else:
await destination.send(embed=embed, **webhook_send_kwargs)
return urls | [
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"... | https://github.com/python-discord/bot/blob/26c5587ac13e5414361bb6e7ada42983b81014d2/bot/utils/messages.py#L108-L170 |
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