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gramps-project/gramps | 04d4651a43eb210192f40a9f8c2bad8ee8fa3753 | gramps/gen/user.py | python | UserBase.warn | (self, title, warning="") | Warn the user.
:param title: the title of the warning
:type title: str
:param warning: the warning
:type warning: str
:returns: none | Warn the user. | [
"Warn",
"the",
"user",
"."
] | def warn(self, title, warning=""):
"""
Warn the user.
:param title: the title of the warning
:type title: str
:param warning: the warning
:type warning: str
:returns: none
""" | [
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||
holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/sympy/calculus/singularities.py | python | singularities | (expression, symbol) | Find singularities of a given function.
Parameters
==========
expression : Expr
The target function in which singularities need to be found.
symbol : Symbol
The symbol over the values of which the singularity in
expression in being searched for.
Returns
=======
Set
A set of values for ``symbol`` for which ``expression`` has a
singularity. An ``EmptySet`` is returned if ``expression`` has no
singularities for any given value of ``Symbol``.
Raises
======
NotImplementedError
The algorithm to find singularities for irrational functions
has not been implemented yet.
Notes
=====
This function does not find non-isolated singularities
nor does it find branch points of the expression.
Currently supported functions are:
- univariate rational (real or complex) functions
References
==========
.. [1] https://en.wikipedia.org/wiki/Mathematical_singularity
Examples
========
>>> from sympy.calculus.singularities import singularities
>>> from sympy import Symbol
>>> x = Symbol('x', real=True)
>>> y = Symbol('y', real=False)
>>> singularities(x**2 + x + 1, x)
EmptySet
>>> singularities(1/(x + 1), x)
FiniteSet(-1)
>>> singularities(1/(y**2 + 1), y)
FiniteSet(I, -I)
>>> singularities(1/(y**3 + 1), y)
FiniteSet(-1, 1/2 - sqrt(3)*I/2, 1/2 + sqrt(3)*I/2) | Find singularities of a given function. | [
"Find",
"singularities",
"of",
"a",
"given",
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] | def singularities(expression, symbol):
"""
Find singularities of a given function.
Parameters
==========
expression : Expr
The target function in which singularities need to be found.
symbol : Symbol
The symbol over the values of which the singularity in
expression in being searched for.
Returns
=======
Set
A set of values for ``symbol`` for which ``expression`` has a
singularity. An ``EmptySet`` is returned if ``expression`` has no
singularities for any given value of ``Symbol``.
Raises
======
NotImplementedError
The algorithm to find singularities for irrational functions
has not been implemented yet.
Notes
=====
This function does not find non-isolated singularities
nor does it find branch points of the expression.
Currently supported functions are:
- univariate rational (real or complex) functions
References
==========
.. [1] https://en.wikipedia.org/wiki/Mathematical_singularity
Examples
========
>>> from sympy.calculus.singularities import singularities
>>> from sympy import Symbol
>>> x = Symbol('x', real=True)
>>> y = Symbol('y', real=False)
>>> singularities(x**2 + x + 1, x)
EmptySet
>>> singularities(1/(x + 1), x)
FiniteSet(-1)
>>> singularities(1/(y**2 + 1), y)
FiniteSet(I, -I)
>>> singularities(1/(y**3 + 1), y)
FiniteSet(-1, 1/2 - sqrt(3)*I/2, 1/2 + sqrt(3)*I/2)
"""
if not expression.is_rational_function(symbol):
raise NotImplementedError(
"Algorithms finding singularities for non-rational"
" functions are not yet implemented."
)
else:
domain = S.Reals if symbol.is_real else S.Complexes
return solveset(simplify(1 / expression), symbol, domain) | [
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||
jython/frozen-mirror | b8d7aa4cee50c0c0fe2f4b235dd62922dd0f3f99 | lib-python/2.7/xml/dom/pulldom.py | python | PullDOM.clear | (self) | clear(): Explicitly release parsing structures | clear(): Explicitly release parsing structures | [
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||
kensho-technologies/graphql-compiler | 4318443b7b2512a059f3616112bfc40bbf8eec06 | graphql_compiler/compiler/emit_sql.py | python | CompilationState.start_global_operations | (self) | Execute a GlobalOperationsStart block. | Execute a GlobalOperationsStart block. | [
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] | def start_global_operations(self) -> None:
"""Execute a GlobalOperationsStart block."""
if self._current_location is None:
raise AssertionError("CompilationState is already in global scope.")
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||
espnet/espnet | ea411f3f627b8f101c211e107d0ff7053344ac80 | espnet/lm/lm_utils.py | python | count_tokens | (data, unk_id=None) | return n_tokens, n_oovs | Count tokens and oovs in token ID sequences.
Args:
data (list[np.ndarray]): list of token ID sequences
unk_id (int): ID of unknown token
Returns:
tuple: tuple of number of token occurrences and number of oov tokens | Count tokens and oovs in token ID sequences. | [
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Args:
data (list[np.ndarray]): list of token ID sequences
unk_id (int): ID of unknown token
Returns:
tuple: tuple of number of token occurrences and number of oov tokens
"""
n_tokens = 0
n_oovs = 0
for sentence in data:
n_tokens += len(sentence)
if unk_id is not None:
n_oovs += np.count_nonzero(sentence == unk_id)
return n_tokens, n_oovs | [
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CLUEbenchmark/CLUEPretrainedModels | b384fd41665a8261f9c689c940cf750b3bc21fce | baselines/models/bert/run_classifier.py | python | ColaProcessor.get_test_examples | (self, data_dir) | return self._create_examples(
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|
cloudera/hue | 23f02102d4547c17c32bd5ea0eb24e9eadd657a4 | desktop/core/ext-py/Django-1.11.29/django/db/utils.py | python | ConnectionHandler.ensure_defaults | (self, alias) | Puts the defaults into the settings dictionary for a given connection
where no settings is provided. | Puts the defaults into the settings dictionary for a given connection
where no settings is provided. | [
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] | def ensure_defaults(self, alias):
"""
Puts the defaults into the settings dictionary for a given connection
where no settings is provided.
"""
try:
conn = self.databases[alias]
except KeyError:
raise ConnectionDoesNotExist("The connection %s doesn't exist" % alias)
conn.setdefault('ATOMIC_REQUESTS', False)
conn.setdefault('AUTOCOMMIT', True)
conn.setdefault('ENGINE', 'django.db.backends.dummy')
if conn['ENGINE'] == 'django.db.backends.' or not conn['ENGINE']:
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conn.setdefault('CONN_MAX_AGE', 0)
conn.setdefault('OPTIONS', {})
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||
dimagi/commcare-hq | d67ff1d3b4c51fa050c19e60c3253a79d3452a39 | corehq/apps/hqwebapp/widgets.py | python | DateRangePickerWidget.render | (self, name, value, attrs=None, renderer=None) | return format_html(
'<div class="input-group hqwebapp-datespan">'
' <span class="input-group-addon"><i class="fa fa-calendar"></i></span>'
' {}'
'</div>',
output
) | [] | def render(self, name, value, attrs=None, renderer=None):
startdate = ''
enddate = ''
if isinstance(self.default_datespan, DateSpan):
if self.default_datespan.startdate is not None:
startdate = self.default_datespan.startdate.strftime('%m/%d/%Y')
if self.default_datespan.enddate is not None:
enddate = self.default_datespan.enddate.strftime('%m/%d/%Y')
attrs.update({
'data-separator': self.separator,
'data-labels': json.dumps(self.range_labels),
'data-start-date': startdate,
'data-end-date': enddate,
})
output = super(DateRangePickerWidget, self).render(name, value, attrs, renderer)
return format_html(
'<div class="input-group hqwebapp-datespan">'
' <span class="input-group-addon"><i class="fa fa-calendar"></i></span>'
' {}'
'</div>',
output
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tgalal/yowsup | dd47d57a950964bab0c4715e9d56fd8450bc94e2 | yowsup/layers/protocol_profiles/protocolentities/iq_statuses_get.py | python | GetStatusesIqProtocolEntity.__init__ | (self, jids, _id = None) | Request the statuses of users. Should be sent once after login.
Args:
- jids: A list of jids representing the users whose statuses you are
trying to get. | Request the statuses of users. Should be sent once after login. | [
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"""
Request the statuses of users. Should be sent once after login.
Args:
- jids: A list of jids representing the users whose statuses you are
trying to get.
"""
super(GetStatusesIqProtocolEntity, self).__init__(self.__class__.XMLNS, _id, _type = "get", to = YowConstants.WHATSAPP_SERVER)
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chainer/chainer | e9da1423255c58c37be9733f51b158aa9b39dc93 | chainer/training/extensions/evaluator.py | python | Evaluator.get_target | (self, name) | return self._targets[name] | Returns the target link of the given name. | Returns the target link of the given name. | [
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|
emmetio/livestyle-sublime-old | c42833c046e9b2f53ebce3df3aa926528f5a33b5 | tornado/web.py | python | Application.reverse_url | (self, name, *args) | Returns a URL path for handler named ``name``
The handler must be added to the application as a named `URLSpec`.
Args will be substituted for capturing groups in the `URLSpec` regex.
They will be converted to strings if necessary, encoded as utf8,
and url-escaped. | Returns a URL path for handler named ``name`` | [
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The handler must be added to the application as a named `URLSpec`.
Args will be substituted for capturing groups in the `URLSpec` regex.
They will be converted to strings if necessary, encoded as utf8,
and url-escaped.
"""
if name in self.named_handlers:
return self.named_handlers[name].reverse(*args)
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larryhastings/gilectomy | 4315ec3f1d6d4f813cc82ce27a24e7f784dbfc1a | Lib/_strptime.py | python | TimeRE.compile | (self, format) | return re_compile(self.pattern(format), IGNORECASE) | Return a compiled re object for the format string. | Return a compiled re object for the format string. | [
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return re_compile(self.pattern(format), IGNORECASE) | [
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|
robotlearn/pyrobolearn | 9cd7c060723fda7d2779fa255ac998c2c82b8436 | pyrobolearn/worlds/world.py | python | World.get_body_color | (self, body_id) | return self.sim.get_visual_shape_data(body_id)[-1] | Return the RGBA color of the given body.
Args:
body_id (int): body id
Returns:
float[4]: RGBA color | Return the RGBA color of the given body. | [
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] | def get_body_color(self, body_id):
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Return the RGBA color of the given body.
Args:
body_id (int): body id
Returns:
float[4]: RGBA color
"""
return self.sim.get_visual_shape_data(body_id)[-1] | [
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|
qibinlou/SinaWeibo-Emotion-Classification | f336fc104abd68b0ec4180fe2ed80fafe49cb790 | nltk/tree.py | python | ImmutableTree._get_node | (self) | return self._node | Get the node value | Get the node value | [
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|
sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/geometry/polyhedral_complex.py | python | PolyhedralComplex._an_element_ | (self) | Return a (maximal) cell of this complex.
EXAMPLES::
sage: PolyhedralComplex()._an_element_()
Traceback (most recent call last):
...
EmptySetError: the complex is empty
sage: pc = PolyhedralComplex([
....: Polyhedron(vertices=[(1/3, 1/3), (0, 0), (1, 2)]),
....: Polyhedron(vertices=[(1, 2), (0, 0), (0, 1/2)])])
sage: pc._an_element_().vertices_list()
[[0, 0], [0, 1/2], [1, 2]] | Return a (maximal) cell of this complex. | [
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] | def _an_element_(self):
"""
Return a (maximal) cell of this complex.
EXAMPLES::
sage: PolyhedralComplex()._an_element_()
Traceback (most recent call last):
...
EmptySetError: the complex is empty
sage: pc = PolyhedralComplex([
....: Polyhedron(vertices=[(1/3, 1/3), (0, 0), (1, 2)]),
....: Polyhedron(vertices=[(1, 2), (0, 0), (0, 1/2)])])
sage: pc._an_element_().vertices_list()
[[0, 0], [0, 1/2], [1, 2]]
"""
try:
return next(self.maximal_cell_iterator(increasing=False))
except StopIteration:
from sage.categories.sets_cat import EmptySetError
raise EmptySetError("the complex is empty") | [
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||
fabioz/PyDev.Debugger | 0f8c02a010fe5690405da1dd30ed72326191ce63 | pydevd_attach_to_process/winappdbg/breakpoint.py | python | Breakpoint.run_action | (self, event) | return True | Executes the breakpoint action callback, if any was set.
@type event: L{Event}
@param event: Debug event triggered by the breakpoint. | Executes the breakpoint action callback, if any was set. | [
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"""
Executes the breakpoint action callback, if any was set.
@type event: L{Event}
@param event: Debug event triggered by the breakpoint.
"""
action = self.get_action()
if action is not None:
try:
return bool( action(event) )
except Exception:
e = sys.exc_info()[1]
msg = ("Breakpoint action callback %r"
" raised an exception: %s")
msg = msg % (action, traceback.format_exc(e))
warnings.warn(msg, BreakpointCallbackWarning)
return False
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|
InvestmentSystems/static-frame | 0b19d6969bf6c17fb0599871aca79eb3b52cf2ed | static_frame/core/index_level.py | python | IndexLevel.leaf_loc_to_iloc | (self,
key: tp.Union[tp.Iterable[tp.Hashable], ILoc, HLoc]
) | Given an iterable of single-element level keys (a leaf loc), return the iloc value.
Note that key components (level selectors) cannot be slices, lists, or np.ndarray. | Given an iterable of single-element level keys (a leaf loc), return the iloc value. | [
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] | def leaf_loc_to_iloc(self,
key: tp.Union[tp.Iterable[tp.Hashable], ILoc, HLoc]
) -> int:
'''Given an iterable of single-element level keys (a leaf loc), return the iloc value.
Note that key components (level selectors) cannot be slices, lists, or np.ndarray.
'''
if isinstance(key, ILoc):
return key.key #type: ignore [return-value]
node = self
pos = 0
key_depth_max = len(key) - 1 #type: ignore
# NOTE: rather than a for/enumerate, this could use a while loop on an iter() and explicitly look at next() results to determine if the key matches
for key_depth, k in enumerate(key):
if isinstance(k, KEY_MULTIPLE_TYPES):
raise RuntimeError(f'slices cannot be used in a leaf selection into an IndexHierarchy; try HLoc[{key}].')
if node.targets is not None:
node = node.targets[node.index._loc_to_iloc(k)]
pos += node.offset
else: # targets is None, meaning we are at max depth
# k returns an integer
offset = node.index._loc_to_iloc(k)
assert isinstance(offset, INT_TYPES) # enforces leaf loc
if key_depth == key_depth_max:
return pos + offset
break # return exception below if key_depth not max depth
raise KeyError(f'Invalid key length {key_depth_max + 1}; must be length {self.depth}.') | [
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||
pypa/pip | 7f8a6844037fb7255cfd0d34ff8e8cf44f2598d4 | src/pip/_vendor/pep517/dirtools.py | python | tempdir | () | Create a temporary directory in a context manager. | Create a temporary directory in a context manager. | [
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] | def tempdir():
"""Create a temporary directory in a context manager."""
td = tempfile.mkdtemp()
try:
yield td
finally:
shutil.rmtree(td) | [
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||
IronLanguages/main | a949455434b1fda8c783289e897e78a9a0caabb5 | External.LCA_RESTRICTED/Languages/CPython/27/Lib/ast.py | python | dump | (node, annotate_fields=True, include_attributes=False) | return _format(node) | Return a formatted dump of the tree in *node*. This is mainly useful for
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wanted *annotate_fields* must be set to False. Attributes such as line
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*include_attributes* can be set to True.
"""
def _format(node):
if isinstance(node, AST):
fields = [(a, _format(b)) for a, b in iter_fields(node)]
rv = '%s(%s' % (node.__class__.__name__, ', '.join(
('%s=%s' % field for field in fields)
if annotate_fields else
(b for a, b in fields)
))
if include_attributes and node._attributes:
rv += fields and ', ' or ' '
rv += ', '.join('%s=%s' % (a, _format(getattr(node, a)))
for a in node._attributes)
return rv + ')'
elif isinstance(node, list):
return '[%s]' % ', '.join(_format(x) for x in node)
return repr(node)
if not isinstance(node, AST):
raise TypeError('expected AST, got %r' % node.__class__.__name__)
return _format(node) | [
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|
pycrypto/pycrypto | 7acba5f3a6ff10f1424c309d0d34d2b713233019 | lib/Crypto/PublicKey/RSA.py | python | _RSAobj.verify | (self, M, signature) | return pubkey.pubkey.verify(self, M, signature) | Verify the validity of an RSA signature.
:attention: this function performs the plain, primitive RSA encryption
(*textbook*). In real applications, you always need to use proper
cryptographic padding, and you should not directly verify data with
this method. Failure to do so may lead to security vulnerabilities.
It is recommended to use modules
`Crypto.Signature.PKCS1_PSS` or `Crypto.Signature.PKCS1_v1_5` instead.
:Parameter M: The expected message.
:Type M: byte string or long
:Parameter signature: The RSA signature to verify. The first item of
the tuple is the actual signature (a long not larger than the modulus
**n**), whereas the second item is always ignored.
:Type signature: A 2-item tuple as return by `sign`
:Return: True if the signature is correct, False otherwise. | Verify the validity of an RSA signature. | [
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] | def verify(self, M, signature):
"""Verify the validity of an RSA signature.
:attention: this function performs the plain, primitive RSA encryption
(*textbook*). In real applications, you always need to use proper
cryptographic padding, and you should not directly verify data with
this method. Failure to do so may lead to security vulnerabilities.
It is recommended to use modules
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:Parameter M: The expected message.
:Type M: byte string or long
:Parameter signature: The RSA signature to verify. The first item of
the tuple is the actual signature (a long not larger than the modulus
**n**), whereas the second item is always ignored.
:Type signature: A 2-item tuple as return by `sign`
:Return: True if the signature is correct, False otherwise.
"""
return pubkey.pubkey.verify(self, M, signature) | [
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|
eirannejad/pyRevit | 49c0b7eb54eb343458ce1365425e6552d0c47d44 | site-packages/sqlalchemy/engine/default.py | python | DefaultExecutionContext.get_lastrowid | (self) | return self.cursor.lastrowid | return self.cursor.lastrowid, or equivalent, after an INSERT.
This may involve calling special cursor functions,
issuing a new SELECT on the cursor (or a new one),
or returning a stored value that was
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value is used in place of calling get_lastrowid().
Note that this method is *not* equivalent to the
``lastrowid`` method on ``ResultProxy``, which is a
direct proxy to the DBAPI ``lastrowid`` accessor
in all cases. | return self.cursor.lastrowid, or equivalent, after an INSERT. | [
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] | def get_lastrowid(self):
"""return self.cursor.lastrowid, or equivalent, after an INSERT.
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issuing a new SELECT on the cursor (or a new one),
or returning a stored value that was
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Note that this method is *not* equivalent to the
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"""
return self.cursor.lastrowid | [
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|
arizvisa/ida-minsc | 8627a60f047b5e55d3efeecde332039cd1a16eea | base/structure.py | python | structure_t.__contains__ | (self, member) | return member in self.members | Return whether the specified `member` is contained by this structure. | Return whether the specified `member` is contained by this structure. | [
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'''Return whether the specified `member` is contained by this structure.'''
if not isinstance(member, member_t):
raise TypeError(member)
return member in self.members | [
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|
google/timesketch | 1ce6b60e125d104e6644947c6f1dbe1b82ac76b6 | api_client/python/timesketch_api_client/story.py | python | ViewBlock.__init__ | (self, story, index) | [] | def __init__(self, story, index):
super().__init__(story, index)
self._view_id = 0
self._view_name = '' | [
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||||
tensorflow/benchmarks | 16af178ad312e8c1213efb27a5f227044228bfdf | scripts/tf_cnn_benchmarks/models/tf1_only/nasnet_model.py | python | _build_nasnet_base | (images,
normal_cell,
reduction_cell,
num_classes,
hparams,
is_training,
stem_type,
final_endpoint=None) | return logits, end_points | Constructs a NASNet image model. | Constructs a NASNet image model. | [
"Constructs",
"a",
"NASNet",
"image",
"model",
"."
] | def _build_nasnet_base(images,
normal_cell,
reduction_cell,
num_classes,
hparams,
is_training,
stem_type,
final_endpoint=None):
"""Constructs a NASNet image model."""
end_points = {}
def add_and_check_endpoint(endpoint_name, net):
end_points[endpoint_name] = net
return final_endpoint and (endpoint_name == final_endpoint)
# Find where to place the reduction cells or stride normal cells
reduction_indices = nasnet_utils.calc_reduction_layers(
hparams.num_cells, hparams.num_reduction_layers)
stem_cell = reduction_cell
if stem_type == 'imagenet':
stem = lambda: _imagenet_stem(images, hparams, stem_cell)
elif stem_type == 'cifar':
stem = lambda: _cifar_stem(images, hparams)
else:
raise ValueError('Unknown stem_type: ', stem_type)
net, cell_outputs = stem()
if add_and_check_endpoint('Stem', net):
return net, end_points
# Setup for building in the auxiliary head.
aux_head_cell_idxes = []
if len(reduction_indices) >= 2:
aux_head_cell_idxes.append(reduction_indices[1] - 1)
# Run the cells
filter_scaling = 1.0
# true_cell_num accounts for the stem cells
true_cell_num = 2 if stem_type == 'imagenet' else 0
for cell_num in range(hparams.num_cells):
stride = 1
if hparams.skip_reduction_layer_input:
prev_layer = cell_outputs[-2]
if cell_num in reduction_indices:
filter_scaling *= hparams.filter_scaling_rate
net = reduction_cell(
net,
scope='reduction_cell_{}'.format(reduction_indices.index(cell_num)),
filter_scaling=filter_scaling,
stride=2,
prev_layer=cell_outputs[-2],
cell_num=true_cell_num)
if add_and_check_endpoint(
'Reduction_Cell_{}'.format(reduction_indices.index(cell_num)), net):
return net, end_points
true_cell_num += 1
cell_outputs.append(net)
if not hparams.skip_reduction_layer_input:
prev_layer = cell_outputs[-2]
net = normal_cell(
net,
scope='cell_{}'.format(cell_num),
filter_scaling=filter_scaling,
stride=stride,
prev_layer=prev_layer,
cell_num=true_cell_num)
if add_and_check_endpoint('Cell_{}'.format(cell_num), net):
return net, end_points
true_cell_num += 1
if (hparams.use_aux_head and cell_num in aux_head_cell_idxes and
num_classes and is_training):
aux_net = tf.nn.relu(net)
_build_aux_head(
aux_net,
end_points,
num_classes,
hparams,
scope='aux_{}'.format(cell_num))
cell_outputs.append(net)
# Final softmax layer
with tf.variable_scope('final_layer'):
net = tf.nn.relu(net)
net = nasnet_utils.global_avg_pool(net)
if add_and_check_endpoint('global_pool', net) or num_classes is None:
return net, end_points
net = slim.dropout(net, hparams.dense_dropout_keep_prob, scope='dropout')
logits = slim.fully_connected(net, num_classes)
if add_and_check_endpoint('Logits', logits):
return net, end_points
predictions = tf.nn.softmax(logits, name='predictions')
if add_and_check_endpoint('Predictions', predictions):
return net, end_points
return logits, end_points | [
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|
aws-quickstart/quickstart-redhat-openshift | 2b87dd38b72e7e4c439a606c5a9ea458d72da612 | functions/source/DeleteBucketContents/requests/api.py | python | delete | (url, **kwargs) | return request('delete', url, **kwargs) | r"""Sends a DELETE request.
:param url: URL for the new :class:`Request` object.
:param \*\*kwargs: Optional arguments that ``request`` takes.
:return: :class:`Response <Response>` object
:rtype: requests.Response | r"""Sends a DELETE request. | [
"r",
"Sends",
"a",
"DELETE",
"request",
"."
] | def delete(url, **kwargs):
r"""Sends a DELETE request.
:param url: URL for the new :class:`Request` object.
:param \*\*kwargs: Optional arguments that ``request`` takes.
:return: :class:`Response <Response>` object
:rtype: requests.Response
"""
return request('delete', url, **kwargs) | [
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|
AppScale/gts | 46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9 | AppServer/google/appengine/api/datastore_types.py | python | PackInteger | (name, value, pbvalue) | Packs an integer property into a entity_pb.PropertyValue.
Args:
name: The name of the property as a string.
value: An int or long instance.
pbvalue: The entity_pb.PropertyValue to pack this value into. | Packs an integer property into a entity_pb.PropertyValue. | [
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"entity_pb",
".",
"PropertyValue",
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] | def PackInteger(name, value, pbvalue):
"""Packs an integer property into a entity_pb.PropertyValue.
Args:
name: The name of the property as a string.
value: An int or long instance.
pbvalue: The entity_pb.PropertyValue to pack this value into.
"""
pbvalue.set_int64value(value) | [
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||
jgagneastro/coffeegrindsize | 22661ebd21831dba4cf32bfc6ba59fe3d49f879c | App/venv/lib/python3.7/site-packages/pip/_vendor/lockfile/pidlockfile.py | python | PIDLockFile.acquire | (self, timeout=None) | Acquire the lock.
Creates the PID file for this lock, or raises an error if
the lock could not be acquired. | Acquire the lock. | [
"Acquire",
"the",
"lock",
"."
] | def acquire(self, timeout=None):
""" Acquire the lock.
Creates the PID file for this lock, or raises an error if
the lock could not be acquired.
"""
timeout = timeout if timeout is not None else self.timeout
end_time = time.time()
if timeout is not None and timeout > 0:
end_time += timeout
while True:
try:
write_pid_to_pidfile(self.path)
except OSError as exc:
if exc.errno == errno.EEXIST:
# The lock creation failed. Maybe sleep a bit.
if time.time() > end_time:
if timeout is not None and timeout > 0:
raise LockTimeout("Timeout waiting to acquire"
" lock for %s" %
self.path)
else:
raise AlreadyLocked("%s is already locked" %
self.path)
time.sleep(timeout is not None and timeout / 10 or 0.1)
else:
raise LockFailed("failed to create %s" % self.path)
else:
return | [
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||
usnistgov/fipy | 6809b180b41a11de988a48655575df7e142c93b9 | fipy/tools/dimensions/physicalField.py | python | PhysicalField.arcsin | (self) | return PhysicalField(value = umath.arcsin(self.inDimensionless()), unit = "rad") | Return the inverse sine of the `PhysicalField` in radians
>>> print(PhysicalField(1).arcsin().allclose("1.57079632679 rad"))
1
The input `PhysicalField` must be dimensionless
>>> print(numerix.round_(PhysicalField("1 m").arcsin(), 6))
Traceback (most recent call last):
...
TypeError: Incompatible units | Return the inverse sine of the `PhysicalField` in radians | [
"Return",
"the",
"inverse",
"sine",
"of",
"the",
"PhysicalField",
"in",
"radians"
] | def arcsin(self):
"""
Return the inverse sine of the `PhysicalField` in radians
>>> print(PhysicalField(1).arcsin().allclose("1.57079632679 rad"))
1
The input `PhysicalField` must be dimensionless
>>> print(numerix.round_(PhysicalField("1 m").arcsin(), 6))
Traceback (most recent call last):
...
TypeError: Incompatible units
"""
return PhysicalField(value = umath.arcsin(self.inDimensionless()), unit = "rad") | [
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|
sqall01/alertR | e1d1a83e54f876cc4cd7bd87387e05cb75d4dc13 | alertClientRaspberryPi/lib/client/util.py | python | MsgBuilder.build_ping_msg | () | return json.dumps(message) | Internal function that builds the ping message.
:return: | Internal function that builds the ping message. | [
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] | def build_ping_msg() -> str:
"""
Internal function that builds the ping message.
:return:
"""
payload = {"type": "request"}
utc_timestamp = int(time.time())
message = {"msgTime": utc_timestamp,
"message": "ping",
"payload": payload}
return json.dumps(message) | [
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|
Rapptz/RoboDanny | 1fb95d76d1b7685e2e2ff950e11cddfc96efbfec | cogs/utils/db.py | python | Table.migrate | (cls, *, directory='migrations', index=-1, downgrade=False, verbose=False, connection=None) | Actually run the latest migration pointed by the data file.
Parameters
-----------
directory: str
The directory of where the migration data file resides.
index: int
The index of the migration array to use.
downgrade: bool
Whether to run an upgrade or a downgrade.
verbose: bool
Whether to output some information to stdout.
connection: Optional[asyncpg.Connection]
The connection to use, if not provided will acquire one from
the internal pool. | Actually run the latest migration pointed by the data file. | [
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] | async def migrate(cls, *, directory='migrations', index=-1, downgrade=False, verbose=False, connection=None):
"""Actually run the latest migration pointed by the data file.
Parameters
-----------
directory: str
The directory of where the migration data file resides.
index: int
The index of the migration array to use.
downgrade: bool
Whether to run an upgrade or a downgrade.
verbose: bool
Whether to output some information to stdout.
connection: Optional[asyncpg.Connection]
The connection to use, if not provided will acquire one from
the internal pool.
"""
directory = Path(directory) / cls.__tablename__
p = directory.with_suffix('.json')
if not p.exists():
raise RuntimeError('Could not find migration file.')
with p.open('r', encoding='utf-8') as fp:
data = json.load(fp)
migrations = data['migrations']
try:
migration = migrations[index]
except IndexError:
return False
diff = SchemaDiff(cls, migration['upgrade'], migration['downgrade'])
if diff.is_empty():
return False
async with MaybeAcquire(connection, pool=cls._pool) as con:
sql = diff.to_sql(downgrade=downgrade)
if verbose:
print(sql)
await con.execute(sql)
current = directory.with_name('current-' + p.name)
with current.open('w', encoding='utf-8') as fp:
json.dump(cls.to_dict(), fp, indent=4, ensure_ascii=True) | [
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||
oilshell/oil | 94388e7d44a9ad879b12615f6203b38596b5a2d3 | Python-2.7.13/Lib/idlelib/configDialog.py | python | ConfigDialog.KeyBindingSelected | (self, event) | [] | def KeyBindingSelected(self, event):
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||||
cloudera/hue | 23f02102d4547c17c32bd5ea0eb24e9eadd657a4 | desktop/core/ext-py/boto-2.46.1/boto/swf/layer2.py | python | ActivityWorker.poll | (self, **kwargs) | return task | PollForActivityTask. | PollForActivityTask. | [
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"."
] | def poll(self, **kwargs):
"""PollForActivityTask."""
task_list = self.task_list
if 'task_list' in kwargs:
task_list = kwargs.get('task_list')
del kwargs['task_list']
task = self._swf.poll_for_activity_task(self.domain, task_list,
**kwargs)
self.last_tasktoken = task.get('taskToken')
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|
fabioz/PyDev.Debugger | 0f8c02a010fe5690405da1dd30ed72326191ce63 | third_party/pep8/pycodestyle.py | python | Checker.init_checker_state | (self, name, argument_names) | Prepare custom state for the specific checker plugin. | Prepare custom state for the specific checker plugin. | [
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if 'checker_state' in argument_names:
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||
leo-editor/leo-editor | 383d6776d135ef17d73d935a2f0ecb3ac0e99494 | leo/core/leoAst.py | python | TokenOrderGenerator.do_arg | (self, node) | This is one argument of a list of ast.Function or ast.Lambda arguments. | This is one argument of a list of ast.Function or ast.Lambda arguments. | [
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lohriialo/photoshop-scripting-python | 6b97da967a5d0a45e54f7c99631b29773b923f09 | api_reference/photoshop_CC_2018.py | python | ArtLayers.__iter__ | (self) | return win32com.client.util.Iterator(ob, '{16BE80A3-57B1-4871-83AC-7F844EEEB1CA}') | Return a Python iterator for this object | Return a Python iterator for this object | [
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except pythoncom.error:
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|
andresriancho/w3af | cd22e5252243a87aaa6d0ddea47cf58dacfe00a9 | w3af/plugins/grep/clamav.py | python | clamav._is_properly_configured | (self) | :return: True if the plugin can connect to the ClamAV daemon. | :return: True if the plugin can connect to the ClamAV daemon. | [
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] | def _is_properly_configured(self):
"""
:return: True if the plugin can connect to the ClamAV daemon.
"""
with self._config_check_lock:
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# Return the cached response
return self._properly_configured
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msg = 'Using %s for scanning HTTP response bodies.'
om.out.information(msg % self._get_clamd_version())
self._properly_configured = True
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om.out.error(msg % self._clamd_socket)
self._properly_configured = False
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||
python/cpython | e13cdca0f5224ec4e23bdd04bb3120506964bc8b | Lib/enum.py | python | Enum.name | (self) | return self._name_ | The name of the Enum member. | The name of the Enum member. | [
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"""The name of the Enum member."""
return self._name_ | [
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|
CvvT/dumpDex | 92ab3b7e996194a06bf1dd5538a4954e8a5ee9c1 | python/idautils.py | python | GetRegisterList | () | return idaapi.ph_get_regnames() | Returns the register list | Returns the register list | [
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] | def GetRegisterList():
"""Returns the register list"""
return idaapi.ph_get_regnames() | [
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|
rucio/rucio | 6d0d358e04f5431f0b9a98ae40f31af0ddff4833 | lib/rucio/daemons/conveyor/preparer.py | python | stop | () | Graceful exit. | Graceful exit. | [
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] | def stop():
"""
Graceful exit.
"""
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||
asyml/texar | a23f021dae289a3d768dc099b220952111da04fd | texar/tf/modules/qnets/qnets.py | python | CategoricalQNet._build | (self, inputs, mode=None) | return outputs | Takes in states and outputs Q values.
Args:
inputs: Inputs to the Q net with the first dimension
the batch dimension.
mode (optional): A tensor taking value in
:tf_main:`tf.estimator.ModeKeys <estimator/ModeKeys>`, including
`TRAIN`, `EVAL`, and `PREDICT`. If `None`,
:func:`texar.tf.global_mode` is used.
Returns
A `dict` including fields `"qvalues"`.
where
- **"qvalues"**: A Tensor of shape \
`[batch_size] + action_space size` containing Q values of all\
possible actions. | Takes in states and outputs Q values. | [
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] | def _build(self, inputs, mode=None):
"""Takes in states and outputs Q values.
Args:
inputs: Inputs to the Q net with the first dimension
the batch dimension.
mode (optional): A tensor taking value in
:tf_main:`tf.estimator.ModeKeys <estimator/ModeKeys>`, including
`TRAIN`, `EVAL`, and `PREDICT`. If `None`,
:func:`texar.tf.global_mode` is used.
Returns
A `dict` including fields `"qvalues"`.
where
- **"qvalues"**: A Tensor of shape \
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"""
outputs = {
"qvalues": self._network(inputs, mode=mode)
}
if not self._built:
self._add_internal_trainable_variables()
self._add_trainable_variable(self._network.trainable_variables)
self._built = True
return outputs | [
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|
securesystemslab/zippy | ff0e84ac99442c2c55fe1d285332cfd4e185e089 | zippy/lib-python/3/plat-sunos5/IN.py | python | NSEC_TO_TICK_ROUNDUP | (nsec) | return | [] | def NSEC_TO_TICK_ROUNDUP(nsec): return | [
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|||
ladybug-tools/butterfly | c8fc0bbe317bb41bfe5f28305782a82347b8c776 | butterfly/sampleDict.py | python | SampleDict.points | (self) | return self._pts | Get and set probe locations from list of tuples. | Get and set probe locations from list of tuples. | [
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] | def points(self):
"""Get and set probe locations from list of tuples."""
return self._pts | [
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|
lambdaji/tf_repos | b531ff12cdab65acc9551025f73fade2b6f425a7 | DeepMTL/Feature_pipeline/get_remap_mapper.py | python | load_fcnts | (if_str) | return feat_cnts_dict | [] | def load_fcnts(if_str):
feat_cnts_dict = defaultdict(lambda: 0)
new_id = 20
with open(if_str) as f:
for line in f:
fid, cnts = line.strip().split('\t')
if feat_cnts_dict.get(fid):
continue
if int(cnts) >= 20: #cutoff=20
feat_cnts_dict[fid] = new_id
new_id = new_id + 1
return feat_cnts_dict | [
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|||
securesystemslab/zippy | ff0e84ac99442c2c55fe1d285332cfd4e185e089 | zippy/benchmarks/src/benchmarks/sympy/sympy/combinatorics/perm_groups.py | python | PermutationGroup.derived_series | (self) | return res | r"""Return the derived series for the group.
The derived series for a group ``G`` is defined as
``G = G_0 > G_1 > G_2 > \ldots`` where ``G_i = [G_{i-1}, G_{i-1}]``,
i.e. ``G_i`` is the derived subgroup of ``G_{i-1}``, for
``i\in\mathbb{N}``. When we have ``G_k = G_{k-1}`` for some
``k\in\mathbb{N}``, the series terminates.
Returns
=======
A list of permutation groups containing the members of the derived
series in the order ``G = G_0, G_1, G_2, \ldots``.
Examples
========
>>> from sympy.combinatorics.named_groups import (SymmetricGroup,
... AlternatingGroup, DihedralGroup)
>>> A = AlternatingGroup(5)
>>> len(A.derived_series())
1
>>> S = SymmetricGroup(4)
>>> len(S.derived_series())
4
>>> S.derived_series()[1].is_subgroup(AlternatingGroup(4))
True
>>> S.derived_series()[2].is_subgroup(DihedralGroup(2))
True
See Also
========
derived_subgroup | r"""Return the derived series for the group. | [
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"derived",
"series",
"for",
"the",
"group",
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] | def derived_series(self):
r"""Return the derived series for the group.
The derived series for a group ``G`` is defined as
``G = G_0 > G_1 > G_2 > \ldots`` where ``G_i = [G_{i-1}, G_{i-1}]``,
i.e. ``G_i`` is the derived subgroup of ``G_{i-1}``, for
``i\in\mathbb{N}``. When we have ``G_k = G_{k-1}`` for some
``k\in\mathbb{N}``, the series terminates.
Returns
=======
A list of permutation groups containing the members of the derived
series in the order ``G = G_0, G_1, G_2, \ldots``.
Examples
========
>>> from sympy.combinatorics.named_groups import (SymmetricGroup,
... AlternatingGroup, DihedralGroup)
>>> A = AlternatingGroup(5)
>>> len(A.derived_series())
1
>>> S = SymmetricGroup(4)
>>> len(S.derived_series())
4
>>> S.derived_series()[1].is_subgroup(AlternatingGroup(4))
True
>>> S.derived_series()[2].is_subgroup(DihedralGroup(2))
True
See Also
========
derived_subgroup
"""
res = [self]
current = self
next = self.derived_subgroup()
while not current.is_subgroup(next):
res.append(next)
current = next
next = next.derived_subgroup()
return res | [
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|
weiaicunzai/pytorch-cifar100 | 2149cb57f517c6e5fa7262f958652227225d125b | models/stochasticdepth.py | python | stochastic_depth_resnet34 | () | return StochasticDepthResNet(StochasticDepthBasicBlock, [3, 4, 6, 3]) | return a ResNet 34 object | return a ResNet 34 object | [
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""" return a ResNet 34 object
"""
return StochasticDepthResNet(StochasticDepthBasicBlock, [3, 4, 6, 3]) | [
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|
dpgaspar/Flask-AppBuilder | 557249f33b66d02a48c1322ef21324b815abe18e | flask_appbuilder/models/base.py | python | BaseInterface.get_values_json | (self, lst, list_columns) | return result | Converts list of objects from query to JSON | Converts list of objects from query to JSON | [
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for item in self.get_values(lst, list_columns):
for key, value in list(item.items()):
if isinstance(value, datetime.datetime) or isinstance(
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value = value.isoformat()
item[key] = value
if isinstance(value, list):
item[key] = [str(v) for v in value]
result.append(item)
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|
Lausannen/NAS-FCOS | d586b28c0fae72a9f30119797732e614a2517de5 | maskrcnn_benchmark/utils/comm.py | python | decode | (encoded_data) | return pickle.loads(bytearray(encoded_tensor.tolist())) | Decode data ,usually used in process or gpu which receive data coming from
main process
Arguments:
encoded_data (list): encoded data coming from main process
Returns:
data (tensor): decoded data | Decode data ,usually used in process or gpu which receive data coming from
main process
Arguments:
encoded_data (list): encoded data coming from main process
Returns:
data (tensor): decoded data | [
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"""Decode data ,usually used in process or gpu which receive data coming from
main process
Arguments:
encoded_data (list): encoded data coming from main process
Returns:
data (tensor): decoded data
"""
size = encoded_data[0]
encoded_tensor = encoded_data[1 : (size + 1)].to("cpu")
return pickle.loads(bytearray(encoded_tensor.tolist())) | [
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|
OneDrive/onedrive-sdk-python | e5642f8cad8eea37a4f653c1a23dfcfc06c37110 | src/onedrivesdk/model/thumbnail_set.py | python | ThumbnailSet.source | (self) | return None | Gets and sets the source
Returns:
:class:`Thumbnail<onedrivesdk.model.thumbnail.Thumbnail>`:
The source | Gets and sets the source
Returns:
:class:`Thumbnail<onedrivesdk.model.thumbnail.Thumbnail>`:
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"""
Gets and sets the source
Returns:
:class:`Thumbnail<onedrivesdk.model.thumbnail.Thumbnail>`:
The source
"""
if "source" in self._prop_dict:
if isinstance(self._prop_dict["source"], OneDriveObjectBase):
return self._prop_dict["source"]
else :
self._prop_dict["source"] = Thumbnail(self._prop_dict["source"])
return self._prop_dict["source"]
return None | [
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|
golismero/golismero | 7d605b937e241f51c1ca4f47b20f755eeefb9d76 | thirdparty_libs/nltk/collocations.py | python | AbstractCollocationFinder.apply_word_filter | (self, fn) | Removes candidate ngrams (w1, w2, ...) where any of (fn(w1), fn(w2),
...) evaluates to True. | Removes candidate ngrams (w1, w2, ...) where any of (fn(w1), fn(w2),
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"""Removes candidate ngrams (w1, w2, ...) where any of (fn(w1), fn(w2),
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self._apply_filter(lambda ng, f: any(fn(w) for w in ng)) | [
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||
IronLanguages/ironpython3 | 7a7bb2a872eeab0d1009fc8a6e24dca43f65b693 | Src/StdLib/Lib/pprint.py | python | isreadable | (object) | return _safe_repr(object, {}, None, 0)[1] | Determine if saferepr(object) is readable by eval(). | Determine if saferepr(object) is readable by eval(). | [
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] | def isreadable(object):
"""Determine if saferepr(object) is readable by eval()."""
return _safe_repr(object, {}, None, 0)[1] | [
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|
hatRiot/zarp | 2e772350a01c2aeed3f4da9685cd0cc5d6b3ecad | src/lib/scapy/utils6.py | python | in6_getnsma | (a) | return r | Return link-local solicited-node multicast address for given
address. Passed address must be provided in network format.
Returned value is also in network format. | Return link-local solicited-node multicast address for given
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Returned value is also in network format. | [
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] | def in6_getnsma(a):
"""
Return link-local solicited-node multicast address for given
address. Passed address must be provided in network format.
Returned value is also in network format.
"""
r = in6_and(a, inet_pton(socket.AF_INET6, '::ff:ffff'))
r = in6_or(inet_pton(socket.AF_INET6, 'ff02::1:ff00:0'), r)
return r | [
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|
Zehaos/MobileNet | bb02b10fbd211d717f7a207245feac229f6bb23e | tools/quantize_graph.py | python | GraphRewriter.create_nodes_map | (self, graph) | return nodes_map | Builds a mapping of node names to their defs from the graph. | Builds a mapping of node names to their defs from the graph. | [
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"""Builds a mapping of node names to their defs from the graph."""
nodes_map = {}
for node in graph.node:
if node.name not in nodes_map.keys():
nodes_map[node.name] = node
else:
raise ValueError("Duplicate node names detected.")
return nodes_map | [
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|
sahana/eden | 1696fa50e90ce967df69f66b571af45356cc18da | modules/templates/IFRC/survey.py | python | survey_answerlist_dataTable_pre | () | The answer list has been removed for the moment. Currently it
displays all answers for a summary it would be better to
be able to display just a few select answers | The answer list has been removed for the moment. Currently it
displays all answers for a summary it would be better to
be able to display just a few select answers | [
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"""
The answer list has been removed for the moment. Currently it
displays all answers for a summary it would be better to
be able to display just a few select answers
"""
list_fields = ["created_on", "series_id", "location", "modified_by"]
current.s3db.configure("survey_complete", list_fields=list_fields) | [
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||
openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | openshift/installer/vendored/openshift-ansible-3.10.0-0.29.0/roles/lib_utils/filter_plugins/oo_filters.py | python | lib_utils_oo_l_of_d_to_csv | (input_list) | return ','.join(json.dumps(x) for x in input_list) | Map a list of dictionaries, input_list, into a csv string
of json values.
Example input:
[{'var1': 'val1', 'var2': 'val2'}, {'var1': 'val3', 'var2': 'val4'}]
Example output:
u'{"var1": "val1", "var2": "val2"},{"var1": "val3", "var2": "val4"}' | Map a list of dictionaries, input_list, into a csv string
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"""Map a list of dictionaries, input_list, into a csv string
of json values.
Example input:
[{'var1': 'val1', 'var2': 'val2'}, {'var1': 'val3', 'var2': 'val4'}]
Example output:
u'{"var1": "val1", "var2": "val2"},{"var1": "val3", "var2": "val4"}'
"""
return ','.join(json.dumps(x) for x in input_list) | [
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|
uqfoundation/mystic | 154e6302d1f2f94e8f13e88ecc5f24241cc28ac7 | models/circle.py | python | gendata | (coeffs,npts=20) | return xy | Generate a 2D dataset of npts enclosed in circle of given coeffs,
where coeffs = (x,y,r).
NOTE: if npts == None, constrain all points to circle of given radius | Generate a 2D dataset of npts enclosed in circle of given coeffs,
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"""Generate a 2D dataset of npts enclosed in circle of given coeffs,
where coeffs = (x,y,r).
NOTE: if npts == None, constrain all points to circle of given radius"""
if not npts:
return gencircle(coeffs)
def points_circle(N):
# generate N random points in a unit circle
n = 0
while n < N:
x = random.random()*2.-1.
y = random.random()*2.-1.
if x*x + y*y <= 1:
n = n+1
yield [x,y]
x0,y0,R0 = coeffs
xy = array(list(points_circle(npts)))*R0
xy[:,0] += x0
xy[:,1] += y0
return xy | [
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|
autorope/donkeycar | a3df79a3573127dff31fc9b2953588056875b214 | donkeycar/templates/cv_control.py | python | LineFollower.debug_display | (self, cam_img, mask, max_yellow, confidense) | composite mask on top the original image.
show some values we are using for control | composite mask on top the original image.
show some values we are using for control | [
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] | def debug_display(self, cam_img, mask, max_yellow, confidense):
'''
composite mask on top the original image.
show some values we are using for control
'''
mask_exp = np.stack((mask,)*3, axis=-1)
iSlice = self.scan_y
img = np.copy(cam_img)
img[iSlice : iSlice + self.scan_height, :, :] = mask_exp
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
display_str = []
display_str.append("STEERING:{:.1f}".format(self.steering))
display_str.append("THROTTLE:{:.2f}".format(self.throttle))
display_str.append("I YELLOW:{:d}".format(max_yellow))
display_str.append("CONF:{:.2f}".format(confidense))
y = 10
x = 10
for s in display_str:
cv2.putText(img, s, color=(0,255,255), org=(x,y), fontFace=cv2.FONT_HERSHEY_SIMPLEX, fontScale=0.4)
y += 10
cv2.namedWindow('image', cv2.WINDOW_NORMAL)
cv2.imshow("image", img)
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facebookresearch/FixRes | c9be6acc7a6b32f896e62c28a97c20c2348327d3 | imnet_finetune/Res.py | python | resnet101 | (pretrained=False, progress=True, **kwargs) | return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress,
**kwargs) | Constructs a ResNet-101 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
progress (bool): If True, displays a progress bar of the download to stderr | Constructs a ResNet-101 model.
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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qilingframework/qiling | 32cc674f2f6fa4b4c9d64a35a1a57853fe1e4142 | qiling/os/memory.py | python | del_mapinfo | (self, mem_s: int, mem_e: int) | Subtract a memory range from map.
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mem_s: memory range start
mem_e: memory range end
"""
tmp_map_info: MutableSequence[MapInfoEntry] = []
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if s < mem_s:
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if e > mem_e:
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self.map_info = tmp_map_info | [
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n1nj4sec/pupy | a5d766ea81fdfe3bc2c38c9bdaf10e9b75af3b39 | pupy/network/lib/streams/PupySocketStream.py | python | PupyUDPSocketStream.close | (self) | [] | def close(self):
self._send_packet(self.END)
if self.close_callback:
self.close_callback('{}:{}'.format(
self.dst_addr[0], self.dst_addr[1]))
self.closed = True
self.kcp = None
if self.client_side:
self.sock.close() | [
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triaquae/triaquae | bbabf736b3ba56a0c6498e7f04e16c13b8b8f2b9 | TriAquae/models/Centos_6.4/pyasn1/codec/ber/decoder.py | python | BitStringDecoder.indefLenValueDecoder | (self, fullSubstrate, substrate, asn1Spec, tagSet,
length, state, decodeFun, substrateFun) | return r, substrate | [] | def indefLenValueDecoder(self, fullSubstrate, substrate, asn1Spec, tagSet,
length, state, decodeFun, substrateFun):
r = self._createComponent(asn1Spec, tagSet, '')
if substrateFun:
return substrateFun(r, substrate, length)
while substrate:
component, substrate = decodeFun(substrate)
if eoo.endOfOctets.isSameTypeWith(component) and \
component == eoo.endOfOctets:
break
r = r + component
else:
raise error.SubstrateUnderrunError(
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return r, substrate | [
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IronLanguages/main | a949455434b1fda8c783289e897e78a9a0caabb5 | External.LCA_RESTRICTED/Languages/CPython/27/Lib/traceback.py | python | print_stack | (f=None, limit=None, file=None) | Print a stack trace from its invocation point.
The optional 'f' argument can be used to specify an alternate
stack frame at which to start. The optional 'limit' and 'file'
arguments have the same meaning as for print_exception(). | Print a stack trace from its invocation point. | [
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] | def print_stack(f=None, limit=None, file=None):
"""Print a stack trace from its invocation point.
The optional 'f' argument can be used to specify an alternate
stack frame at which to start. The optional 'limit' and 'file'
arguments have the same meaning as for print_exception().
"""
if f is None:
try:
raise ZeroDivisionError
except ZeroDivisionError:
f = sys.exc_info()[2].tb_frame.f_back
print_list(extract_stack(f, limit), file) | [
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leo-editor/leo-editor | 383d6776d135ef17d73d935a2f0ecb3ac0e99494 | leo/commands/commanderFileCommands.py | python | editRecentFiles | (self, event=None) | Opens recent files list in a new node for editing. | Opens recent files list in a new node for editing. | [
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kylebebak/Requester | 4a9f9f051fa5fc951a8f7ad098a328261ca2db97 | deps/oauthlib/oauth1/rfc5849/request_validator.py | python | RequestValidator.get_realms | (self, token, request) | Get realms associated with a request token.
:param token: The request token string.
:param request: An oauthlib.common.Request object.
:returns: The list of realms associated with the request token.
This method is used by
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* AccessTokenEndpoint | Get realms associated with a request token. | [
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"""Get realms associated with a request token.
:param token: The request token string.
:param request: An oauthlib.common.Request object.
:returns: The list of realms associated with the request token.
This method is used by
* AuthorizationEndpoint
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raise self._subclass_must_implement("get_realms") | [
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sosreport/sos | 900e8bea7f3cd36c1dd48f3cbb351ab92f766654 | sos/report/plugins/__init__.py | python | Plugin.is_installed | (self, package_name) | return self.policy.pkg_by_name(package_name) is not None | Is the package $package_name installed?
:param package_name: The name of the package to check
:type package_name: ``str``
:returns: ``True`` id the package is installed, else ``False``
:rtype: ``bool`` | Is the package $package_name installed? | [
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"""Is the package $package_name installed?
:param package_name: The name of the package to check
:type package_name: ``str``
:returns: ``True`` id the package is installed, else ``False``
:rtype: ``bool``
"""
return self.policy.pkg_by_name(package_name) is not None | [
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douban/pymesos | 047c7bac8ca98772f63192aed063148fdf399b55 | pymesos/interface.py | python | OperatorMasterDriver.getMaintenanceSchedule | (self) | This call retrieves the cluster's maintenance status. | This call retrieves the cluster's maintenance status. | [
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||
bloomberg/phabricator-tools | 09bd1587fe8945d93a891162fd4c89640c6fada7 | py/phl/phlurl_watcher.py | python | Watcher.load | (self, f) | Load data from the supplied file pointer, overwriting existing data.
:f: a text file pointer to load from
:returns: None | Load data from the supplied file pointer, overwriting existing data. | [
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"""Load data from the supplied file pointer, overwriting existing data.
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results = json.load(f)
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(k, _HashHexdigestHasChanged(*v)) for k, v in results.iteritems()) | [
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wikimedia/pywikibot | 81a01ffaec7271bf5b4b170f85a80388420a4e78 | pywikibot/site/_datasite.py | python | DataSite.property_namespace | (self) | return self._property_namespace | Return namespace for properties.
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"""
Return namespace for properties.
:return: property namespace
:rtype: Namespace
"""
if self._property_namespace is None:
self._property_namespace = self.get_namespace_for_entity_type(
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mrlesmithjr/Ansible | d44f0dc0d942bdf3bf7334b307e6048f0ee16e36 | roles/ansible-vsphere-management/scripts/pdns/lib/python2.7/site-packages/pip/_vendor/requests/models.py | python | Response.is_permanent_redirect | (self) | return ('location' in self.headers and self.status_code in (codes.moved_permanently, codes.permanent_redirect)) | True if this Response one of the permanent versions of redirect | True if this Response one of the permanent versions of redirect | [
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"""True if this Response one of the permanent versions of redirect"""
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] | https://github.com/mrlesmithjr/Ansible/blob/d44f0dc0d942bdf3bf7334b307e6048f0ee16e36/roles/ansible-vsphere-management/scripts/pdns/lib/python2.7/site-packages/pip/_vendor/requests/models.py#L646-L648 |
|
biolab/orange3 | 41685e1c7b1d1babe680113685a2d44bcc9fec0b | Orange/widgets/utils/textimport.py | python | number_parser | (groupsep, decimalsep) | [] | def number_parser(groupsep, decimalsep):
# type: (str, str) -> Callable[[str], float]
if groupsep == "" and decimalsep == ".":
return float
elif groupsep == "":
return lambda value: float(value.replace(decimalsep, "."))
elif decimalsep != groupsep and decimalsep != "" and groupsep != "":
table = {ord(groupsep): None, ord(decimalsep): ord(".")}
return lambda value: float(value.translate(table))
else:
return float | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/pip/_vendor/pkg_resources/__init__.py | python | __getstate__ | () | return state | [] | def __getstate__():
state = {}
g = globals()
for k, v in _state_vars.items():
state[k] = g['_sget_' + v](g[k])
return state | [
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ValvePython/steam | 7aef9d2df57c2195f35bd85013e1b5ccb04624a5 | steam/client/__init__.py | python | SteamClient.send | (self, message, body_params=None) | Send a message to CM
:param message: a message instance
:type message: :class:`.Msg`, :class:`.MsgProto`
:param body_params: a dict with params to the body (only :class:`.MsgProto`)
:type body_params: dict | Send a message to CM | [
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"""Send a message to CM
:param message: a message instance
:type message: :class:`.Msg`, :class:`.MsgProto`
:param body_params: a dict with params to the body (only :class:`.MsgProto`)
:type body_params: dict
"""
if not self.connected:
self._LOG.debug("Trying to send message when not connected. (discarded)")
else:
if body_params and isinstance(message, MsgProto):
proto_fill_from_dict(message.body, body_params)
CMClient.send(self, message) | [
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||
IntelAI/nauta | bbedb114a755cf1f43b834a58fc15fb6e3a4b291 | applications/cli/scripts/mypy_check.py | python | filter_mypy_results | (mypy_results: List[str]) | return [error for error in mypy_results
if not any(ignored_error in error for ignored_error in IGNORED_ERRORS)] | [] | def filter_mypy_results(mypy_results: List[str]) -> List[str]:
return [error for error in mypy_results
if not any(ignored_error in error for ignored_error in IGNORED_ERRORS)] | [
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guildai/guildai | 1665985a3d4d788efc1a3180ca51cc417f71ca78 | guild/external/setuptools/command/egg_info.py | python | FileList.global_exclude | (self, pattern) | return self._remove_files(match.match) | Exclude all files anywhere that match the pattern. | Exclude all files anywhere that match the pattern. | [
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"""
Exclude all files anywhere that match the pattern.
"""
match = translate_pattern(os.path.join('**', pattern))
return self._remove_files(match.match) | [
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dipu-bd/lightnovel-crawler | eca7a71f217ce7a6b0a54d2e2afb349571871880 | lncrawl/bots/telegram/__init__.py | python | TelegramBot.error_handler | (self, bot, update, error) | Log Errors caused by Updates. | Log Errors caused by Updates. | [
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"""Log Errors caused by Updates."""
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heimlich1024/OD_CopyPasteExternal | 943b993198e16d19f1fb4ba44049e498abf1e993 | C4D/C4D_PasteFromExternal.py | python | vertDataToObj | (outputfile) | [] | def vertDataToObj(outputfile):
output = ""
inputfile = tempfile.gettempdir() + os.sep + "ODVertexData.txt"
file = open(inputfile, "r")
lines = file.readlines()
file.close()
#Parse File to see what Data we have
vertline = []; polyline = []; vtxnormals = []; uvMaps = []; morphMaps = []; weightMaps = []
count = 0
for line in lines:
if line.startswith("VERTICES:"):
vertline.append([int(line.strip().split(":")[1].strip()), count])
if line.startswith("POLYGONS:"):
polyline.append([int(line.strip().split(":")[1].strip()), count])
if line.startswith("VERTEXNORMALS:"):
vtxnormals.append([int(line.strip().split(":")[1].strip()), count])
if line.startswith("UV:"):
if line.strip().split(":")[1:][1] != "0":
uvMaps.append([line.strip().split(":")[1:], count]) # changed this to add the # of uv coordinates into the mix
count += 1
#write header
output += "o ODVertexData.obj\n"
output += "g default\n"
#rewrite verts
for verts in vertline:
for i in xrange(verts[1] + 1, verts[1] + verts[0] + 1):
x = map(float, lines[i].split())
output += "v " + str(x[0]) + " " + str(x[1]) + " " + str(x[2]) + "\n"
uvforobj = []
values = []
assignment = []
for uvMap in uvMaps:
count = 0
for i in range(int(uvMap[0][1])):
split = lines[uvMap[1]+1+count].split(":")
if str(float(split[0].split(" ")[0])) + " " + str(float(split[0].split(" ")[1])) not in values:
values.append(str(float(split[0].split(" ")[0])) + " " + str(float(split[0].split(" ")[1])))
assignment.append(str(float(split[0].split(" ")[0])) + " " + str(float(split[0].split(" ")[1])))
count +=1
values.sort()
for val in values:
output += "vt " + val + "\n"
for norm in vtxnormals:
for i in xrange(norm[1] + 1, norm[1] + norm[0] + 1):
x = map(float, lines[i].split())
output += "vn " + str(x[0]) + " " + str(x[1]) + " " + str(x[2]) + "\n"
#create Polygons
for polygons in polyline:
polys = []
count = 0
ncount = 0
mat = ""
testnorm = []
for i in xrange(polygons[1] + 1, polygons[1] + polygons[0] + 1):
pts = lines[i].split(";;")[0].split(",")
newpts = []
#indices in an obj start at 1, so we gotta add one to each index
testpts = []
testidx = []
for p in range(len(pts)):
if len(uvMaps) < 1:
newpts.append(str(int(pts[p]) + 1))
if len(vtxnormals) > 0:
newpts[-1] = str(newpts[-1]) + "//" + str(count+1)
else:
testpts.append(str(int(pts[p])+1))
testidx.append(str(values.index(assignment[count])+1))
if len(vtxnormals) > 0:
testnorm.append(count)
count += 1
string = ""
for t in range(len(testpts)):
string += " " + testpts[t] + "/" + testidx[len(testpts)-1-t]
if len(testnorm) > 0:
string += "/" + str(testnorm[ncount]+1)
ncount += 1
if lines[i].split(";;")[1].strip() != mat:
output += "g " + lines[i].split(";;")[1].strip() + "\n"
output += "usemtl " + lines[i].split(";;")[1].strip() + "\n"
#output += "s 1\n"
mat = lines[i].split(";;")[1].strip()
if string != "":
output += "f " + string.strip() + "\n"
else:
output += "f " + " ".join(newpts) + "\n"
#writing output file
f = open(outputfile, "w")
f.write(output)
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||||
fooying/3102 | 0faee38c30b2e24154f41e68457cfd8f7a61c040 | thirdparty/dns/name.py | python | from_unicode | (text, origin = root) | return Name(labels) | Convert unicode text into a Name object.
Lables are encoded in IDN ACE form.
@rtype: dns.name.Name object | Convert unicode text into a Name object. | [
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"""Convert unicode text into a Name object.
Lables are encoded in IDN ACE form.
@rtype: dns.name.Name object
"""
if not isinstance(text, unicode):
raise ValueError("input to from_unicode() must be a unicode string")
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raise ValueError("origin must be a Name or None")
labels = []
label = u''
escaping = False
edigits = 0
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if text == u'@':
text = u''
if text:
if text == u'.':
return Name(['']) # no Unicode "u" on this constant!
for c in text:
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raise EmptyLabel
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label = u''
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else:
label += c
if escaping:
raise BadEscape
if len(label) > 0:
labels.append(encodings.idna.ToASCII(label))
else:
labels.append('')
if (len(labels) == 0 or labels[-1] != '') and not origin is None:
labels.extend(list(origin.labels))
return Name(labels) | [
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|
KalleHallden/AutoTimer | 2d954216700c4930baa154e28dbddc34609af7ce | env/lib/python2.7/site-packages/CoreMedia/_macros.py | python | CMTIME_IS_INDEFINITE | (time) | return CMTIME_IS_VALID(time) and (time.flags & CoreMedia.kCMTimeFlags_Indefinite) != 0 | [] | def CMTIME_IS_INDEFINITE(time):
return CMTIME_IS_VALID(time) and (time.flags & CoreMedia.kCMTimeFlags_Indefinite) != 0 | [
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|||
CaptainEven/Vehicle-Car-detection-and-multilabel-classification | 0b0ab3ad8478c5a0ac29819b4fce3ae110d44d82 | Clipper.py | python | Car_DR.cls_draw_bbox | (self, output, orig_img) | orig_img是通过opencv读取的numpy array格式: 通道顺序BGR
在bbox基础上预测车辆属性
将bbox绘制到原图上 | orig_img是通过opencv读取的numpy array格式: 通道顺序BGR
在bbox基础上预测车辆属性
将bbox绘制到原图上 | [
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"""
orig_img是通过opencv读取的numpy array格式: 通道顺序BGR
在bbox基础上预测车辆属性
将bbox绘制到原图上
"""
labels = []
pt_1s = []
pt_2s = []
# 获取车辆属性labels
for det in output:
# rectangle points
pt_1 = tuple(det[1:3].int()) # the left-up point
pt_2 = tuple(det[3:5].int()) # the right down point
pt_1s.append(pt_1)
pt_2s.append(pt_2)
# 调用分类器预测车辆属性: BGR => RGB
ROI = Image.fromarray(
orig_img[pt_1[1]: pt_2[1],
pt_1[0]: pt_2[0]][:, :, ::-1])
# ROI.show()
car_color, car_direction, car_type = self.manager.predict(ROI)
label = str(car_color + ' ' + car_direction + ' ' + car_type)
labels.append(label)
print('=> predicted label: ', label)
# 将bbox绘制到原图
color = (0, 215, 255)
for i, det in enumerate(output):
pt_1 = pt_1s[i]
pt_2 = pt_2s[i]
# 绘制bounding box
cv2.rectangle(orig_img, pt_1, pt_2, color, thickness=2)
# 获取文本大小
txt_size = cv2.getTextSize(
label, cv2.FONT_HERSHEY_PLAIN, 2, 2)[0] # 文字大小
# pt_2 = pt_1[0] + txt_size[0] + 3, pt_1[1] + txt_size[1] + 5
pt_2 = pt_1[0] + txt_size[0] + 3, pt_1[1] - txt_size[1] - 5
# 绘制文本底色矩形
cv2.rectangle(orig_img, pt_1, pt_2, color, thickness=-1) # text
# 绘制文本
cv2.putText(orig_img, labels[i], (pt_1[0], pt_1[1]), # pt_1[1] + txt_size[1] + 4
cv2.FONT_HERSHEY_PLAIN, 2, [225, 255, 255], 2) | [
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||
carbonblack/cbapi-python | 24d677ffd99aee911c2c76ecb5528e4e9320c7cc | src/cbapi/auth.py | python | FileCredentialStore.get_credentials | (self, profile=None) | return Credentials(retval) | [] | def get_credentials(self, profile=None):
credential_profile = profile or "default"
if credential_profile not in self.get_profiles():
raise CredentialError("Cannot find credential profile '%s' after searching in these files: %s." %
(credential_profile, ", ".join(self.credential_search_path)))
retval = {}
for k, v in six.iteritems(default_profile):
retval[k] = self.credentials.get(credential_profile, k)
if not retval["url"] or not retval["token"]:
raise CredentialError("Token and/or URL not available for profile %s" % credential_profile)
return Credentials(retval) | [
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|||
dansoutner/LSTM | 1817b881d2f03e9d3746f39e734426c00134fda7 | ArpaLM.py | python | ArpaLM.mgrams | (self, m) | Return an iterator over N-Grams of order M+1.
@param m: Length of history (i.e. order-1) of desired N-Grams.
@type m: int
@return: Iterator over N-Grams
@rtype: generator(NGram) | Return an iterator over N-Grams of order M+1. | [
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"""
Return an iterator over N-Grams of order M+1.
@param m: Length of history (i.e. order-1) of desired N-Grams.
@type m: int
@return: Iterator over N-Grams
@rtype: generator(NGram)
"""
for ng, ngid in self.ngmap[m].iteritems():
if isinstance(ng, str):
ng = (ng,)
yield self.NGram(ng, *self.ngrams[m][ngid,:]) | [
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||
git-cola/git-cola | b48b8028e0c3baf47faf7b074b9773737358163d | cola/widgets/dag.py | python | ReaderThread.run | (self) | [] | def run(self):
context = self.context
repo = dag.RepoReader(context, self.params)
repo.reset()
self.begin.emit()
commits = []
for c in repo.get():
self._mutex.lock()
if self._stop:
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self._mutex.unlock()
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iclavera/learning_to_adapt | bd7d99ba402521c96631e7d09714128f549db0f1 | learning_to_adapt/mujoco_py/mjtypes.py | python | MjvCameraWrapper.pose | (self) | return self._wrapped.contents.pose | [] | def pose(self):
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|||
pyqtgraph/pyqtgraph | ac3887abfca4e529aac44f022f8e40556a2587b0 | pyqtgraph/opengl/GLGraphicsItem.py | python | GLGraphicsItem.visible | (self) | return self.__visible | Return True if the item is currently set to be visible.
Note that this does not guarantee that the item actually appears in the
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anitab-org/vms | 0189ce8260b705e0e75e0ab5418593f35066f106 | vms/registration/views.py | python | load_states | (request) | return render(
request,
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{'states': states}
) | Renders the options of states dropdown list
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"""
Renders the options of states dropdown list
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states = Region.objects.filter(country__name=country_name).order_by('name')
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kdexd/virtex | 2baba8a4f3a4d80d617b3bc59e4be25b1052db57 | virtex/optim/lookahead.py | python | Lookahead.zero_grad | (self) | r"""Clear all grad buffers at the start of new forward pass. | r"""Clear all grad buffers at the start of new forward pass. | [
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Pymol-Scripts/Pymol-script-repo | bcd7bb7812dc6db1595953dfa4471fa15fb68c77 | modules/pdb2pqr/contrib/numpy-1.1.0/numpy/distutils/misc_util.py | python | Configuration.__init__ | (self,
package_name=None,
parent_name=None,
top_path=None,
package_path=None,
caller_level=1,
setup_name='setup.py',
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package_name -- name of the package
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parent_name -- name of the parent package
Ex.: 'numpy'
top_path -- directory of the toplevel package
Ex.: the directory where the numpy package source sits
package_path -- directory of package. Will be computed by magic from the
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package_name=None,
parent_name=None,
top_path=None,
package_path=None,
caller_level=1,
setup_name='setup.py',
**attrs):
"""Construct configuration instance of a package.
package_name -- name of the package
Ex.: 'distutils'
parent_name -- name of the parent package
Ex.: 'numpy'
top_path -- directory of the toplevel package
Ex.: the directory where the numpy package source sits
package_path -- directory of package. Will be computed by magic from the
directory of the caller module if not specified
Ex.: the directory where numpy.distutils is
caller_level -- frame level to caller namespace, internal parameter.
"""
self.name = dot_join(parent_name, package_name)
self.version = None
caller_frame = get_frame(caller_level)
self.local_path = get_path_from_frame(caller_frame, top_path)
# local_path -- directory of a file (usually setup.py) that
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# local_path -- directory of a file (usually setup.py) that
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top_path = self.local_path
self.local_path = ''
if package_path is None:
package_path = self.local_path
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self.package_path = package_path
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self.list_keys = self._list_keys[:]
self.dict_keys = self._dict_keys[:]
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setattr(self, n, as_list(v))
for n in self.dict_keys:
v = copy.copy(attrs.get(n, {}))
setattr(self, n, v)
known_keys = self.list_keys + self.dict_keys
self.extra_keys = self._extra_keys[:]
for n in attrs.keys():
if n in known_keys:
continue
a = attrs[n]
setattr(self,n,a)
if isinstance(a, list):
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elif isinstance(a, dict):
self.dict_keys.append(n)
else:
self.extra_keys.append(n)
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self.packages.append(self.name)
self.package_dir[self.name] = package_path
self.options = dict(
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assume_default_configuration = False,
delegate_options_to_subpackages = False,
quiet = False,
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break
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if isinstance(caller_instance, self.__class__):
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edisonlz/fastor | 342078a18363ac41d3c6b1ab29dbdd44fdb0b7b3 | base/site-packages/django/db/backends/__init__.py | python | BaseDatabaseWrapper.rollback | (self) | Rolls back a transaction and resets the dirty flag. | Rolls back a transaction and resets the dirty flag. | [
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] | def rollback(self):
"""
Rolls back a transaction and resets the dirty flag.
"""
self.validate_thread_sharing()
self.validate_no_atomic_block()
self._rollback()
self.set_clean() | [
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materialsproject/pymatgen | 8128f3062a334a2edd240e4062b5b9bdd1ae6f58 | pymatgen/transformations/advanced_transformations.py | python | MultipleSubstitutionTransformation.__init__ | (
self,
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charge_balance_species=None,
order=True,
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Args:
sp_to_replace: species to be replaced
r_fraction: fraction of that specie to replace
substitution_dict: dictionary of the format
{2: ["Mg", "Ti", "V", "As", "Cr", "Ta", "N", "Nb"],
3: ["Ru", "Fe", "Co", "Ce", "As", "Cr", "Ta", "N", "Nb"],
4: ["Ru", "V", "Cr", "Ta", "N", "Nb"],
5: ["Ru", "W", "Mn"]
}
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):
"""
Performs multiple fractional substitutions on a transmuter.
Args:
sp_to_replace: species to be replaced
r_fraction: fraction of that specie to replace
substitution_dict: dictionary of the format
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3: ["Ru", "Fe", "Co", "Ce", "As", "Cr", "Ta", "N", "Nb"],
4: ["Ru", "V", "Cr", "Ta", "N", "Nb"],
5: ["Ru", "W", "Mn"]
}
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(an element can be present in multiple lists)
charge_balance_species: If specified, will balance the charge on
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self.sp_to_replace = sp_to_replace
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self.charge_balance_species = charge_balance_species
self.order = order | [
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omz/PythonistaAppTemplate | f560f93f8876d82a21d108977f90583df08d55af | PythonistaAppTemplate/PythonistaKit.framework/pylib_ext/sympy/physics/quantum/qexpr.py | python | split_qexpr_parts | (e) | return expr_part, qexpr_part | Split an expression into Expr and noncommutative QExpr parts. | Split an expression into Expr and noncommutative QExpr parts. | [
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|
AppScale/gts | 46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9 | AppServer/lib/cherrypy/cherrypy/wsgiserver/wsgiserver3.py | python | ThreadPool.start | (self) | Start the pool of threads. | Start the pool of threads. | [
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"""Start the pool of threads."""
for i in range(self.min):
self._threads.append(WorkerThread(self.server))
for worker in self._threads:
worker.setName("CP Server " + worker.getName())
worker.start()
for worker in self._threads:
while not worker.ready:
time.sleep(.1) | [
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||
zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/sqlalchemy/util/_collections.py | python | OrderedSet.intersection | (self, other) | return self.__class__(a for a in self if a in other) | [] | def intersection(self, other):
other = set(other)
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|||
xonsh/xonsh | b76d6f994f22a4078f602f8b386f4ec280c8461f | xonsh/events.py | python | Event.fire | (self, **kwargs) | return vals | Fires an event, calling registered handlers with the given arguments. A non-unique iterable
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----------
**kwargs
Keyword arguments to pass to each handler
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vals : iterable
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Fires an event, calling registered handlers with the given arguments. A non-unique iterable
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Each handler is called immediately. Exceptions are turned in to warnings.
Parameters
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**kwargs
Keyword arguments to pass to each handler
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vals = []
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self._delayed_adds = None
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self._handlers.difference_update(self._delayed_discards)
self._delayed_discards = None
return vals | [
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|
quantumlib/OpenFermion | 6187085f2a7707012b68370b625acaeed547e62b | src/openfermion/circuits/primitives/state_preparation.py | python | _generic_gaussian_circuit | (
qubits: Sequence[cirq.Qid],
quadratic_hamiltonian: 'openfermion.QuadraticHamiltonian',
occupied_orbitals: Optional[Sequence[int]],
initial_state: Union[int, Sequence[int]]) | [] | def _generic_gaussian_circuit(
qubits: Sequence[cirq.Qid],
quadratic_hamiltonian: 'openfermion.QuadraticHamiltonian',
occupied_orbitals: Optional[Sequence[int]],
initial_state: Union[int, Sequence[int]]) -> cirq.OP_TREE:
n_qubits = len(qubits)
circuit_description, start_orbitals = (gaussian_state_preparation_circuit(
quadratic_hamiltonian, occupied_orbitals))
if isinstance(initial_state, int):
initially_occupied_orbitals = _occupied_orbitals(
initial_state, n_qubits)
else:
initially_occupied_orbitals = initial_state # type: ignore
# Flip bits so that the correct starting orbitals are occupied
yield (cirq.X(qubits[j])
for j in range(n_qubits)
if (j in initially_occupied_orbitals) != (j in start_orbitals))
yield _ops_from_givens_rotations_circuit_description(
qubits, circuit_description) | [
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||||
leancloud/satori | 701caccbd4fe45765001ca60435c0cb499477c03 | satori-rules/plugin/libs/pymongo/results.py | python | UpdateResult.__init__ | (self, raw_result, acknowledged) | [] | def __init__(self, raw_result, acknowledged):
self.__raw_result = raw_result
super(UpdateResult, self).__init__(acknowledged) | [
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||||
mjwestcott/Goodrich | dc2516591bd28488516c0337a62e64248debe47c | ch11/binary_search_tree.py | python | TreeMap._subtree_last_position | (self, p) | return walk | Return Position of last item in subtree rooted at p. | Return Position of last item in subtree rooted at p. | [
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] | def _subtree_last_position(self, p):
"""Return Position of last item in subtree rooted at p."""
walk = p
while self.right(walk) is not None: # keep walking right
walk = self.right(walk)
return walk | [
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|
pypa/pip | 7f8a6844037fb7255cfd0d34ff8e8cf44f2598d4 | src/pip/_vendor/distlib/_backport/sysconfig.py | python | _init_non_posix | (vars) | Initialize the module as appropriate for NT | Initialize the module as appropriate for NT | [
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vars['LIBDEST'] = get_path('stdlib')
vars['BINLIBDEST'] = get_path('platstdlib')
vars['INCLUDEPY'] = get_path('include')
vars['SO'] = '.pyd'
vars['EXE'] = '.exe'
vars['VERSION'] = _PY_VERSION_SHORT_NO_DOT
vars['BINDIR'] = os.path.dirname(_safe_realpath(sys.executable)) | [
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||
bruderstein/PythonScript | df9f7071ddf3a079e3a301b9b53a6dc78cf1208f | PythonLib/full/ast.py | python | _pad_whitespace | (source) | return result | r"""Replace all chars except '\f\t' in a line with spaces. | r"""Replace all chars except '\f\t' in a line with spaces. | [
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r"""Replace all chars except '\f\t' in a line with spaces."""
result = ''
for c in source:
if c in '\f\t':
result += c
else:
result += ' '
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|
readbeyond/aeneas | 4d200a050690903b30b3d885b44714fecb23f18a | aeneas/task.py | python | TaskConfiguration.aba_parameters | (self) | return {
"algorithm": (aba_algorithm, ABA_MAP[aba_algorithm]),
"nonspeech": (ns_min, ns_string),
"nozero": nozero
} | Return a dictionary representing the
:class:`~aeneas.adjustboundaryalgorithm.AdjustBoundaryAlgorithm`
parameters stored in this task configuration.
Available keys:
* ``algorithm``, tuple: (string, list)
* ``nonspeech``, tuple: (TimeValue or None, string)
* ``nozero``, bool
:rtype: dict | Return a dictionary representing the
:class:`~aeneas.adjustboundaryalgorithm.AdjustBoundaryAlgorithm`
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"""
Return a dictionary representing the
:class:`~aeneas.adjustboundaryalgorithm.AdjustBoundaryAlgorithm`
parameters stored in this task configuration.
Available keys:
* ``algorithm``, tuple: (string, list)
* ``nonspeech``, tuple: (TimeValue or None, string)
* ``nozero``, bool
:rtype: dict
"""
ABA_MAP = {
AdjustBoundaryAlgorithm.AFTERCURRENT: [self[gc.PPN_TASK_ADJUST_BOUNDARY_AFTERCURRENT_VALUE]],
AdjustBoundaryAlgorithm.AUTO: [],
AdjustBoundaryAlgorithm.BEFORENEXT: [self[gc.PPN_TASK_ADJUST_BOUNDARY_BEFORENEXT_VALUE]],
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AdjustBoundaryAlgorithm.PERCENT: [self[gc.PPN_TASK_ADJUST_BOUNDARY_PERCENT_VALUE]],
AdjustBoundaryAlgorithm.RATE: [self[gc.PPN_TASK_ADJUST_BOUNDARY_RATE_VALUE]],
AdjustBoundaryAlgorithm.RATEAGGRESSIVE: [self[gc.PPN_TASK_ADJUST_BOUNDARY_RATE_VALUE]]
}
aba_algorithm = self[gc.PPN_TASK_ADJUST_BOUNDARY_ALGORITHM] or AdjustBoundaryAlgorithm.AUTO
ns_min = self[gc.PPN_TASK_ADJUST_BOUNDARY_NONSPEECH_MIN]
ns_string = self[gc.PPN_TASK_ADJUST_BOUNDARY_NONSPEECH_STRING]
nozero = self[gc.PPN_TASK_ADJUST_BOUNDARY_NO_ZERO] or False
return {
"algorithm": (aba_algorithm, ABA_MAP[aba_algorithm]),
"nonspeech": (ns_min, ns_string),
"nozero": nozero
} | [
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|
angr/angr | 4b04d56ace135018083d36d9083805be8146688b | angr/analyses/bindiff.py | python | FunctionDiff.probably_identical | (self) | return True | :returns: Whether or not these two functions are identical. | :returns: Whether or not these two functions are identical. | [
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"""
:returns: Whether or not these two functions are identical.
"""
if len(self._unmatched_blocks_from_a | self._unmatched_blocks_from_b) > 0:
return False
for (a, b) in self._block_matches:
if not self.blocks_probably_identical(a, b):
return False
return True | [
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|
huggingface/transformers | 623b4f7c63f60cce917677ee704d6c93ee960b4b | src/transformers/models/rembert/modeling_tf_rembert.py | python | TFRemBertSelfAttention.call | (
self,
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attention_mask: tf.Tensor,
head_mask: tf.Tensor,
encoder_hidden_states: tf.Tensor,
encoder_attention_mask: tf.Tensor,
past_key_value: Tuple[tf.Tensor],
output_attentions: bool,
training: bool = False,
) | return outputs | [] | def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
head_mask: tf.Tensor,
encoder_hidden_states: tf.Tensor,
encoder_attention_mask: tf.Tensor,
past_key_value: Tuple[tf.Tensor],
output_attentions: bool,
training: bool = False,
) -> Tuple[tf.Tensor]:
batch_size = shape_list(hidden_states)[0]
mixed_query_layer = self.query(inputs=hidden_states)
# If this is instantiated as a cross-attention module, the keys
# and values come from an encoder; the attention mask needs to be
# such that the encoder's padding tokens are not attended to.
is_cross_attention = encoder_hidden_states is not None
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_layer = past_key_value[0]
value_layer = past_key_value[1]
attention_mask = encoder_attention_mask
elif is_cross_attention:
key_layer = self.transpose_for_scores(self.key(inputs=encoder_hidden_states), batch_size)
value_layer = self.transpose_for_scores(self.value(inputs=encoder_hidden_states), batch_size)
attention_mask = encoder_attention_mask
elif past_key_value is not None:
key_layer = self.transpose_for_scores(self.key(inputs=hidden_states), batch_size)
value_layer = self.transpose_for_scores(self.value(inputs=hidden_states), batch_size)
key_layer = tf.concatenate([past_key_value[0], key_layer], dim=2)
value_layer = tf.concatenate([past_key_value[1], value_layer], dim=2)
else:
key_layer = self.transpose_for_scores(self.key(inputs=hidden_states), batch_size)
value_layer = self.transpose_for_scores(self.value(inputs=hidden_states), batch_size)
query_layer = self.transpose_for_scores(mixed_query_layer, batch_size)
if self.is_decoder:
# if cross_attention save Tuple(tf.Tensor, tf.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (decoder) save Tuple(tf.Tensor, tf.Tensor) of
# all previous decoder key/value_states. Further calls to uni-directional self-attention
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
# if encoder bi-directional self-attention `past_key_value` is always `None`
past_key_value = (key_layer, value_layer)
# Take the dot product between "query" and "key" to get the raw attention scores.
# (batch size, num_heads, seq_len_q, seq_len_k)
attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype)
attention_scores = tf.divide(attention_scores, dk)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in TFRemBertModel call() function)
attention_scores = tf.add(attention_scores, attention_mask)
# Normalize the attention scores to probabilities.
attention_probs = tf.nn.softmax(logits=attention_scores, axis=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(inputs=attention_probs, training=training)
# Mask heads if we want to
if head_mask is not None:
attention_probs = tf.multiply(attention_probs, head_mask)
attention_output = tf.matmul(attention_probs, value_layer)
attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3])
# (batch_size, seq_len_q, all_head_size)
attention_output = tf.reshape(tensor=attention_output, shape=(batch_size, -1, self.all_head_size))
outputs = (attention_output, attention_probs) if output_attentions else (attention_output,)
if self.is_decoder:
outputs = outputs + (past_key_value,)
return outputs | [
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] | https://github.com/huggingface/transformers/blob/623b4f7c63f60cce917677ee704d6c93ee960b4b/src/transformers/models/rembert/modeling_tf_rembert.py#L184-L263 |