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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/richtext.py | python | RichTextBuffer.EndRightIndent | (*args, **kwargs) | return _richtext.RichTextBuffer_EndRightIndent(*args, **kwargs) | EndRightIndent(self) -> bool | EndRightIndent(self) -> bool | [
"EndRightIndent",
"(",
"self",
")",
"-",
">",
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] | def EndRightIndent(*args, **kwargs):
"""EndRightIndent(self) -> bool"""
return _richtext.RichTextBuffer_EndRightIndent(*args, **kwargs) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_controls.py | python | ListCtrl.DeleteAllItems | (*args, **kwargs) | return _controls_.ListCtrl_DeleteAllItems(*args, **kwargs) | DeleteAllItems(self) -> bool | DeleteAllItems(self) -> bool | [
"DeleteAllItems",
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"""DeleteAllItems(self) -> bool"""
return _controls_.ListCtrl_DeleteAllItems(*args, **kwargs) | [
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hughperkins/tf-coriander | 970d3df6c11400ad68405f22b0c42a52374e94ca | tensorflow/contrib/layers/python/layers/feature_column_ops.py | python | sequence_input_from_feature_columns | (columns_to_tensors,
feature_columns,
weight_collections=None,
trainable=True,
scope=None) | return _input_from_feature_columns(
columns_to_tensors,
feature_columns,
weight_collections,
trainable,
scope,
output_rank=3,
default_name='sequence_input_from_feature_columns') | Builds inputs for sequence models from `FeatureColumn`s.
See documentation for `input_from_feature_columns`. The following types of
`FeatureColumn` are permitted in `feature_columns`: `_OneHotColumn`,
`_EmbeddingColumn`, `_HashedEmbeddingColumn`, `_RealValuedColumn`,
`_DataFrameColumn`. In addition, columns in `feature_columns` may not be
constructed using any of the following: `HashedEmbeddingColumn`,
`BucketizedColumn`, `CrossedColumn`.
Args:
columns_to_tensors: A mapping from feature column to tensors. 'string' key
means a base feature (not-transformed). It can have FeatureColumn as a
key too. That means that FeatureColumn is already transformed by input
pipeline. For example, `inflow` may have handled transformations.
feature_columns: A set containing all the feature columns. All items in the
set should be instances of classes derived by FeatureColumn.
weight_collections: List of graph collections to which weights are added.
trainable: If `True` also add variables to the graph collection
`GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable).
scope: Optional scope for variable_scope.
Returns:
A Tensor which can be consumed by hidden layers in the neural network.
Raises:
ValueError: if FeatureColumn cannot be consumed by a neural network. | Builds inputs for sequence models from `FeatureColumn`s. | [
"Builds",
"inputs",
"for",
"sequence",
"models",
"from",
"FeatureColumn",
"s",
"."
] | def sequence_input_from_feature_columns(columns_to_tensors,
feature_columns,
weight_collections=None,
trainable=True,
scope=None):
"""Builds inputs for sequence models from `FeatureColumn`s.
See documentation for `input_from_feature_columns`. The following types of
`FeatureColumn` are permitted in `feature_columns`: `_OneHotColumn`,
`_EmbeddingColumn`, `_HashedEmbeddingColumn`, `_RealValuedColumn`,
`_DataFrameColumn`. In addition, columns in `feature_columns` may not be
constructed using any of the following: `HashedEmbeddingColumn`,
`BucketizedColumn`, `CrossedColumn`.
Args:
columns_to_tensors: A mapping from feature column to tensors. 'string' key
means a base feature (not-transformed). It can have FeatureColumn as a
key too. That means that FeatureColumn is already transformed by input
pipeline. For example, `inflow` may have handled transformations.
feature_columns: A set containing all the feature columns. All items in the
set should be instances of classes derived by FeatureColumn.
weight_collections: List of graph collections to which weights are added.
trainable: If `True` also add variables to the graph collection
`GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable).
scope: Optional scope for variable_scope.
Returns:
A Tensor which can be consumed by hidden layers in the neural network.
Raises:
ValueError: if FeatureColumn cannot be consumed by a neural network.
"""
_check_supported_sequence_columns(feature_columns)
_check_forbidden_sequence_columns(feature_columns)
return _input_from_feature_columns(
columns_to_tensors,
feature_columns,
weight_collections,
trainable,
scope,
output_rank=3,
default_name='sequence_input_from_feature_columns') | [
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google/mozc | 7329757e1ad30e327c1ae823a8302c79482d6b9c | src/win32/installer/postbuilds_win.py | python | RunOrDie | (argv) | Run the command, or die if it failed. | Run the command, or die if it failed. | [
"Run",
"the",
"command",
"or",
"die",
"if",
"it",
"failed",
"."
] | def RunOrDie(argv):
"""Run the command, or die if it failed."""
# Rest are the target program name and the parameters, but we special
# case if the target program name ends with '.py'
if argv[0].endswith('.py'):
argv.insert(0, sys.executable) # Inject the python interpreter path.
# We don't capture stdout and stderr from Popen. The output will just
# be emitted to a terminal or console.
process = subprocess.Popen(argv, stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
out, err = process.communicate()
if process.wait() != 0:
raise RunOrDieError('\n'.join(['',
'==========',
' ERROR: %s' % ' '.join(argv),
' Stdout', out.decode('utf-8'),
' Stderr', err.decode('utf-8'),
'=========='])) | [
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klzgrad/naiveproxy | ed2c513637c77b18721fe428d7ed395b4d284c83 | src/build/landmines.py | python | clobber_if_necessary | (new_landmines, src_dir, landmines_path) | Does the work of setting, planting, and triggering landmines. | Does the work of setting, planting, and triggering landmines. | [
"Does",
"the",
"work",
"of",
"setting",
"planting",
"and",
"triggering",
"landmines",
"."
] | def clobber_if_necessary(new_landmines, src_dir, landmines_path):
"""Does the work of setting, planting, and triggering landmines."""
out_dir = get_build_dir(src_dir)
try:
os.makedirs(out_dir)
except OSError as e:
if e.errno == errno.EEXIST:
pass
if os.path.exists(landmines_path):
with open(landmines_path, 'r') as f:
old_landmines = f.readlines()
if old_landmines != new_landmines:
old_date = time.ctime(os.stat(landmines_path).st_ctime)
diff = difflib.unified_diff(old_landmines, new_landmines,
fromfile='old_landmines', tofile='new_landmines',
fromfiledate=old_date, tofiledate=time.ctime(), n=0)
sys.stdout.write('Clobbering due to:\n')
sys.stdout.writelines(diff)
sys.stdout.flush()
clobber.clobber(out_dir)
# Save current set of landmines for next time.
with open(landmines_path, 'w') as f:
f.writelines(new_landmines) | [
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apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | src/external/boost/boost_1_68_0/tools/build/src/build/generators.py | python | __construct_really | (project, name, target_type, prop_set, sources) | return result | Attempts to construct target by finding viable generators, running them
and selecting the dependency graph. | Attempts to construct target by finding viable generators, running them
and selecting the dependency graph. | [
"Attempts",
"to",
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"target",
"by",
"finding",
"viable",
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"and",
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"dependency",
"graph",
"."
] | def __construct_really (project, name, target_type, prop_set, sources):
""" Attempts to construct target by finding viable generators, running them
and selecting the dependency graph.
"""
if __debug__:
from .targets import ProjectTarget
assert isinstance(project, ProjectTarget)
assert isinstance(name, basestring) or name is None
assert isinstance(target_type, basestring)
assert isinstance(prop_set, property_set.PropertySet)
assert is_iterable_typed(sources, virtual_target.VirtualTarget)
viable_generators = find_viable_generators (target_type, prop_set)
result = []
dout(" *** %d viable generators" % len (viable_generators))
generators_that_succeeded = []
for g in viable_generators:
__active_generators.append(g)
r = try_one_generator (project, name, g, target_type, prop_set, sources)
del __active_generators[-1]
if r:
generators_that_succeeded.append(g)
if result:
output = cStringIO.StringIO()
print >>output, "ambiguity found when searching for best transformation"
print >>output, "Trying to produce type '%s' from: " % (target_type)
for s in sources:
print >>output, " - " + s.str()
print >>output, "Generators that succeeded:"
for g in generators_that_succeeded:
print >>output, " - " + g.id()
print >>output, "First generator produced: "
for t in result[1:]:
print >>output, " - " + str(t)
print >>output, "Second generator produced:"
for t in r[1:]:
print >>output, " - " + str(t)
get_manager().errors()(output.getvalue())
else:
result = r;
return result; | [
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dmlc/nnvm | dab5ce8ab6adbf4edd8bd2fa89f1a99f343b6e38 | python/nnvm/top/tensor.py | python | _compute_binary_scalar | (f) | return _compute | auxiliary function | auxiliary function | [
"auxiliary",
"function"
] | def _compute_binary_scalar(f):
"""auxiliary function"""
@tvm.tag_scope(topi.tag.ELEMWISE)
def _compute(attrs, x, _):
x = x[0]
scalar = attrs.get_float("scalar")
scalar = tvm.const(scalar, x.dtype)
return tvm.compute(x.shape, lambda *i: f(x(*i), scalar))
return _compute | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/python/client/timeline.py | python | _ChromeTraceFormatter._create_event | (self, ph, category, name, pid, tid, timestamp) | return event | Creates a new Chrome Trace event.
For details of the file format, see:
https://github.com/catapult-project/catapult/blob/master/tracing/README.md
Args:
ph: The type of event - usually a single character.
category: The event category as a string.
name: The event name as a string.
pid: Identifier of the process generating this event as an integer.
tid: Identifier of the thread generating this event as an integer.
timestamp: The timestamp of this event as a long integer.
Returns:
A JSON compatible event object. | Creates a new Chrome Trace event. | [
"Creates",
"a",
"new",
"Chrome",
"Trace",
"event",
"."
] | def _create_event(self, ph, category, name, pid, tid, timestamp):
"""Creates a new Chrome Trace event.
For details of the file format, see:
https://github.com/catapult-project/catapult/blob/master/tracing/README.md
Args:
ph: The type of event - usually a single character.
category: The event category as a string.
name: The event name as a string.
pid: Identifier of the process generating this event as an integer.
tid: Identifier of the thread generating this event as an integer.
timestamp: The timestamp of this event as a long integer.
Returns:
A JSON compatible event object.
"""
event = {}
event['ph'] = ph
event['cat'] = category
event['name'] = name
event['pid'] = pid
event['tid'] = tid
event['ts'] = timestamp
return event | [
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albertz/openlierox | d316c14a8eb57848ef56e9bfa7b23a56f694a51b | tools/DedicatedServerVideo/gdata/tlslite/utils/keyfactory.py | python | generateRSAKey | (bits, implementations=["openssl", "python"]) | Generate an RSA key with the specified bit length.
@type bits: int
@param bits: Desired bit length of the new key's modulus.
@rtype: L{tlslite.utils.RSAKey.RSAKey}
@return: A new RSA private key. | Generate an RSA key with the specified bit length. | [
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"""Generate an RSA key with the specified bit length.
@type bits: int
@param bits: Desired bit length of the new key's modulus.
@rtype: L{tlslite.utils.RSAKey.RSAKey}
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"""
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twhui/LiteFlowNet | 00925aebf2db9ac50f4b1666f718688b10dd10d1 | python/caffe/detector.py | python | Detector.configure_crop | (self, context_pad) | Configure crop dimensions and amount of context for cropping.
If context is included, make the special input mean for context padding.
Parameters
----------
context_pad : amount of context for cropping. | Configure crop dimensions and amount of context for cropping.
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context_pad : amount of context for cropping.
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mean = self.transformer.mean.get(in_)
if mean is not None:
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crop_mean = crop_mean[:, :, channel_order_inverse]
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Harick1/caffe-yolo | eea92bf3ddfe4d0ff6b0b3ba9b15c029a83ed9a3 | python/caffe/io.py | python | Transformer.set_transpose | (self, in_, order) | Set the input channel order for e.g. RGB to BGR conversion
as needed for the reference ImageNet model.
Parameters
----------
in_ : which input to assign this channel order
order : the order to transpose the dimensions | Set the input channel order for e.g. RGB to BGR conversion
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Set the input channel order for e.g. RGB to BGR conversion
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Parameters
----------
in_ : which input to assign this channel order
order : the order to transpose the dimensions
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self.__check_input(in_)
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natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/contrib/learn/python/learn/estimators/base.py | python | TensorFlowEstimator.predict_proba | (self, x, batch_size=None) | return self._predict(x, batch_size=batch_size) | Predict class probability of the input samples `x`.
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x: array-like matrix, [n_samples, n_features...] or iterator.
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batch_size: If test set is too big, use batch size to split
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"""
return self._predict(x, batch_size=batch_size) | [
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natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/python/ops/variables.py | python | Variable.initial_value | (self) | return self._initial_value | Returns the Tensor used as the initial value for the variable.
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Note that this is different from `initialized_value()` which runs
the op that initializes the variable before returning its value.
This method returns the tensor that is used by the op that initializes
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return self._initial_value | [
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lmb-freiburg/ogn | 974f72ef4bf840d6f6693d22d1843a79223e77ce | examples/pycaffe/tools.py | python | CaffeSolver.add_from_file | (self, filepath) | Reads a caffe solver prototxt file and updates the Caffesolver
instance parameters. | Reads a caffe solver prototxt file and updates the Caffesolver
instance parameters. | [
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Reads a caffe solver prototxt file and updates the Caffesolver
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_core.py | python | Point2D.__isub__ | (*args, **kwargs) | return _core_.Point2D___isub__(*args, **kwargs) | __isub__(self, Point2D pt) -> Point2D | __isub__(self, Point2D pt) -> Point2D | [
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MTG/gaia | 0f7214dbdec6f9b651ca34211824841ffba0bc77 | src/doc/doxy2swig.py | python | Doxy2SWIG.extract_text | (self, node) | return ret | Return the string representation of the node or list of nodes by parsing the
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nasa/fprime | 595cf3682d8365943d86c1a6fe7c78f0a116acf0 | Autocoders/Python/src/fprime_ac/generators/InstanceDictHeader.py | python | InstanceDictHeader.addVisitor | (self, visitor) | Add a visitor to the list of visitors.
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"""
if issubclass(visitor.__class__, AbstractVisitor.AbstractVisitor):
self.__visitor_list.append(visitor)
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raise Exception(
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PrincetonUniversity/athena-public-version | 9c266692b9423743d8e23509b3ab266a232a92d2 | vis/python/athena_read.py | python | error_dat | (filename, **kwargs) | return data | Wrapper to np.loadtxt() for applying optional checks used in regression tests | Wrapper to np.loadtxt() for applying optional checks used in regression tests | [
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facebookincubator/mvfst | 034a40c797485113d00127852d4df3c5bb44b3ed | build/fbcode_builder/fbcode_builder.py | python | FBCodeBuilder.diagnostics | (self) | return self.step(
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] | def diagnostics(self):
"Log some system diagnostics before/after setup for ease of debugging"
# The builder's repr is not used in a command to avoid pointlessly
# invalidating Docker's build cache.
return self.step(
"Diagnostics",
[
self.comment("Builder {0}".format(repr(self))),
self.run(ShellQuoted("hostname")),
self.run(ShellQuoted("cat /etc/issue || echo no /etc/issue")),
self.run(ShellQuoted("g++ --version || echo g++ not installed")),
self.run(ShellQuoted("cmake --version || echo cmake not installed")),
],
) | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/training/input.py | python | string_input_producer | (string_tensor,
num_epochs=None,
shuffle=True,
seed=None,
capacity=32,
shared_name=None,
name=None,
cancel_op=None) | Output strings (e.g. filenames) to a queue for an input pipeline.
Note: if `num_epochs` is not `None`, this function creates local counter
`epochs`. Use `local_variables_initializer()` to initialize local variables.
Args:
string_tensor: A 1-D string tensor with the strings to produce.
num_epochs: An integer (optional). If specified, `string_input_producer`
produces each string from `string_tensor` `num_epochs` times before
generating an `OutOfRange` error. If not specified,
`string_input_producer` can cycle through the strings in `string_tensor`
an unlimited number of times.
shuffle: Boolean. If true, the strings are randomly shuffled within each
epoch.
seed: An integer (optional). Seed used if shuffle == True.
capacity: An integer. Sets the queue capacity.
shared_name: (optional). If set, this queue will be shared under the given
name across multiple sessions. All sessions open to the device which has
this queue will be able to access it via the shared_name. Using this in
a distributed setting means each name will only be seen by one of the
sessions which has access to this operation.
name: A name for the operations (optional).
cancel_op: Cancel op for the queue (optional).
Returns:
A queue with the output strings. A `QueueRunner` for the Queue
is added to the current `Graph`'s `QUEUE_RUNNER` collection.
Raises:
ValueError: If the string_tensor is a null Python list. At runtime,
will fail with an assertion if string_tensor becomes a null tensor. | Output strings (e.g. filenames) to a queue for an input pipeline. | [
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] | def string_input_producer(string_tensor,
num_epochs=None,
shuffle=True,
seed=None,
capacity=32,
shared_name=None,
name=None,
cancel_op=None):
"""Output strings (e.g. filenames) to a queue for an input pipeline.
Note: if `num_epochs` is not `None`, this function creates local counter
`epochs`. Use `local_variables_initializer()` to initialize local variables.
Args:
string_tensor: A 1-D string tensor with the strings to produce.
num_epochs: An integer (optional). If specified, `string_input_producer`
produces each string from `string_tensor` `num_epochs` times before
generating an `OutOfRange` error. If not specified,
`string_input_producer` can cycle through the strings in `string_tensor`
an unlimited number of times.
shuffle: Boolean. If true, the strings are randomly shuffled within each
epoch.
seed: An integer (optional). Seed used if shuffle == True.
capacity: An integer. Sets the queue capacity.
shared_name: (optional). If set, this queue will be shared under the given
name across multiple sessions. All sessions open to the device which has
this queue will be able to access it via the shared_name. Using this in
a distributed setting means each name will only be seen by one of the
sessions which has access to this operation.
name: A name for the operations (optional).
cancel_op: Cancel op for the queue (optional).
Returns:
A queue with the output strings. A `QueueRunner` for the Queue
is added to the current `Graph`'s `QUEUE_RUNNER` collection.
Raises:
ValueError: If the string_tensor is a null Python list. At runtime,
will fail with an assertion if string_tensor becomes a null tensor.
"""
not_null_err = "string_input_producer requires a non-null input tensor"
if not isinstance(string_tensor, ops.Tensor) and not string_tensor:
raise ValueError(not_null_err)
with ops.name_scope(name, "input_producer", [string_tensor]) as name:
string_tensor = ops.convert_to_tensor(string_tensor, dtype=dtypes.string)
with ops.control_dependencies([
control_flow_ops.Assert(
math_ops.greater(array_ops.size(string_tensor), 0),
[not_null_err])]):
string_tensor = array_ops.identity(string_tensor)
return input_producer(
input_tensor=string_tensor,
element_shape=[],
num_epochs=num_epochs,
shuffle=shuffle,
seed=seed,
capacity=capacity,
shared_name=shared_name,
name=name,
summary_name="fraction_of_%d_full" % capacity,
cancel_op=cancel_op) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py3/pandas/plotting/_misc.py | python | deregister | () | Remove pandas formatters and converters.
Removes the custom converters added by :func:`register`. This
attempts to set the state of the registry back to the state before
pandas registered its own units. Converters for pandas' own types like
Timestamp and Period are removed completely. Converters for types
pandas overwrites, like ``datetime.datetime``, are restored to their
original value.
See Also
--------
register_matplotlib_converters : Register pandas formatters and converters
with matplotlib. | Remove pandas formatters and converters. | [
"Remove",
"pandas",
"formatters",
"and",
"converters",
"."
] | def deregister():
"""
Remove pandas formatters and converters.
Removes the custom converters added by :func:`register`. This
attempts to set the state of the registry back to the state before
pandas registered its own units. Converters for pandas' own types like
Timestamp and Period are removed completely. Converters for types
pandas overwrites, like ``datetime.datetime``, are restored to their
original value.
See Also
--------
register_matplotlib_converters : Register pandas formatters and converters
with matplotlib.
"""
plot_backend = _get_plot_backend("matplotlib")
plot_backend.deregister() | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/protobuf/python/google/protobuf/internal/enum_type_wrapper.py | python | EnumTypeWrapper.Value | (self, name) | Returns the value coresponding to the given enum name. | Returns the value coresponding to the given enum name. | [
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return self._enum_type.values_by_name[name].number
raise ValueError('Enum %s has no value defined for name %s' % (
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/SimpleXMLRPCServer.py | python | SimpleXMLRPCDispatcher.system_listMethods | (self) | return methods | system.listMethods() => ['add', 'subtract', 'multiple']
Returns a list of the methods supported by the server. | system.listMethods() => ['add', 'subtract', 'multiple'] | [
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] | def system_listMethods(self):
"""system.listMethods() => ['add', 'subtract', 'multiple']
Returns a list of the methods supported by the server."""
methods = self.funcs.keys()
if self.instance is not None:
# Instance can implement _listMethod to return a list of
# methods
if hasattr(self.instance, '_listMethods'):
methods = remove_duplicates(
methods + self.instance._listMethods()
)
# if the instance has a _dispatch method then we
# don't have enough information to provide a list
# of methods
elif not hasattr(self.instance, '_dispatch'):
methods = remove_duplicates(
methods + list_public_methods(self.instance)
)
methods.sort()
return methods | [
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pmq20/node-packer | 12c46c6e44fbc14d9ee645ebd17d5296b324f7e0 | lts/tools/gyp/pylib/gyp/MSVSVersion.py | python | VisualStudioVersion.ProjectVersion | (self) | return self.project_version | Get the version number of the vcproj or vcxproj files. | Get the version number of the vcproj or vcxproj files. | [
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] | def ProjectVersion(self):
"""Get the version number of the vcproj or vcxproj files."""
return self.project_version | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/pydoc.py | python | Doc.getdocloc | (self, object) | return docloc | Return the location of module docs or None | Return the location of module docs or None | [
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"""Return the location of module docs or None"""
try:
file = inspect.getabsfile(object)
except TypeError:
file = '(built-in)'
docloc = os.environ.get("PYTHONDOCS",
"http://docs.python.org/library")
basedir = os.path.join(sys.exec_prefix, "lib",
"python"+sys.version[0:3])
if (isinstance(object, type(os)) and
(object.__name__ in ('errno', 'exceptions', 'gc', 'imp',
'marshal', 'posix', 'signal', 'sys',
'thread', 'zipimport') or
(file.startswith(basedir) and
not file.startswith(os.path.join(basedir, 'site-packages')))) and
object.__name__ not in ('xml.etree', 'test.pydoc_mod')):
if docloc.startswith("http://"):
docloc = "%s/%s" % (docloc.rstrip("/"), object.__name__)
else:
docloc = os.path.join(docloc, object.__name__ + ".html")
else:
docloc = None
return docloc | [
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ceph/ceph | 959663007321a369c83218414a29bd9dbc8bda3a | qa/tasks/rgw.py | python | task | (ctx, config) | For example, to run rgw on all clients::
tasks:
- ceph:
- rgw:
To only run on certain clients::
tasks:
- ceph:
- rgw: [client.0, client.3]
or
tasks:
- ceph:
- rgw:
client.0:
client.3:
To run radosgw through valgrind:
tasks:
- ceph:
- rgw:
client.0:
valgrind: [--tool=memcheck]
client.3:
valgrind: [--tool=memcheck]
To configure data or index pool pg_size:
overrides:
rgw:
data_pool_pg_size: 256
index_pool_pg_size: 128 | For example, to run rgw on all clients:: | [
"For",
"example",
"to",
"run",
"rgw",
"on",
"all",
"clients",
"::"
] | def task(ctx, config):
"""
For example, to run rgw on all clients::
tasks:
- ceph:
- rgw:
To only run on certain clients::
tasks:
- ceph:
- rgw: [client.0, client.3]
or
tasks:
- ceph:
- rgw:
client.0:
client.3:
To run radosgw through valgrind:
tasks:
- ceph:
- rgw:
client.0:
valgrind: [--tool=memcheck]
client.3:
valgrind: [--tool=memcheck]
To configure data or index pool pg_size:
overrides:
rgw:
data_pool_pg_size: 256
index_pool_pg_size: 128
"""
if config is None:
config = dict(('client.{id}'.format(id=id_), None)
for id_ in teuthology.all_roles_of_type(
ctx.cluster, 'client'))
elif isinstance(config, list):
config = dict((name, None) for name in config)
clients = config.keys() # http://tracker.ceph.com/issues/20417
overrides = ctx.config.get('overrides', {})
teuthology.deep_merge(config, overrides.get('rgw', {}))
ctx.rgw = argparse.Namespace()
ctx.rgw.ec_data_pool = bool(config.pop('ec-data-pool', False))
ctx.rgw.erasure_code_profile = config.pop('erasure_code_profile', {})
ctx.rgw.cache_pools = bool(config.pop('cache-pools', False))
ctx.rgw.frontend = config.pop('frontend', 'beast')
ctx.rgw.compression_type = config.pop('compression type', None)
ctx.rgw.storage_classes = config.pop('storage classes', None)
default_cert = config.pop('ssl certificate', None)
ctx.rgw.data_pool_pg_size = config.pop('data_pool_pg_size', 64)
ctx.rgw.index_pool_pg_size = config.pop('index_pool_pg_size', 64)
ctx.rgw.datacache = bool(config.pop('datacache', False))
ctx.rgw.datacache_path = config.pop('datacache_path', None)
ctx.rgw.config = config
log.debug("config is {}".format(config))
log.debug("client list is {}".format(clients))
ctx.rgw.role_endpoints = assign_endpoints(ctx, config, default_cert)
subtasks = [
lambda: create_pools(ctx=ctx, clients=clients),
]
if ctx.rgw.compression_type:
subtasks.extend([
lambda: configure_compression(ctx=ctx, clients=clients,
compression=ctx.rgw.compression_type),
])
if ctx.rgw.datacache:
subtasks.extend([
lambda: configure_datacache(ctx=ctx, clients=clients,
datacache_path=ctx.rgw.datacache_path),
])
if ctx.rgw.storage_classes:
subtasks.extend([
lambda: configure_storage_classes(ctx=ctx, clients=clients,
storage_classes=ctx.rgw.storage_classes),
])
subtasks.extend([
lambda: start_rgw(ctx=ctx, config=config, clients=clients),
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with contextutil.nested(*subtasks):
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/AWS/resource-manager-code/lib/setuptools/msvc.py | python | EnvironmentInfo.FxTools | (self) | return tools | Microsoft .NET Framework Tools.
Return
------
list of str
paths | Microsoft .NET Framework Tools. | [
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] | def FxTools(self):
"""
Microsoft .NET Framework Tools.
Return
------
list of str
paths
"""
pi = self.pi
si = self.si
if self.vs_ver <= 10.0:
include32 = True
include64 = not pi.target_is_x86() and not pi.current_is_x86()
else:
include32 = pi.target_is_x86() or pi.current_is_x86()
include64 = pi.current_cpu == 'amd64' or pi.target_cpu == 'amd64'
tools = []
if include32:
tools += [join(si.FrameworkDir32, ver)
for ver in si.FrameworkVersion32]
if include64:
tools += [join(si.FrameworkDir64, ver)
for ver in si.FrameworkVersion64]
return tools | [
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GoSSIP-SJTU/TripleDoggy | 03648d6b19c812504b14e8b98c8c7b3f443f4e54 | bindings/python/llvm/object.py | python | Relocation.cache | (self) | Cache all cacheable properties on this instance. | Cache all cacheable properties on this instance. | [
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"""Cache all cacheable properties on this instance."""
getattr(self, 'address')
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getattr(self, 'symbol')
getattr(self, 'type')
getattr(self, 'type_name')
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_misc.py | python | MimeTypesManager_IsOfType | (*args, **kwargs) | return _misc_.MimeTypesManager_IsOfType(*args, **kwargs) | MimeTypesManager_IsOfType(String mimeType, String wildcard) -> bool | MimeTypesManager_IsOfType(String mimeType, String wildcard) -> bool | [
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"""MimeTypesManager_IsOfType(String mimeType, String wildcard) -> bool"""
return _misc_.MimeTypesManager_IsOfType(*args, **kwargs) | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/graph_editor/util.py | python | flatten_tree | (tree, leaves=None) | return leaves | Flatten a tree into a list.
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tree: iterable or not. If iterable, its elements (child) can also be
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leaves: list to which the tree leaves are appended (None by default).
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tree: iterable or not. If iterable, its elements (child) can also be
iterable or not.
leaves: list to which the tree leaves are appended (None by default).
Returns:
A list of all the leaves in the tree.
"""
if leaves is None:
leaves = []
if isinstance(tree, dict):
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flatten_tree(child, leaves)
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for child in tree:
flatten_tree(child, leaves)
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leaves.append(tree)
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arangodb/arangodb | 0d658689c7d1b721b314fa3ca27d38303e1570c8 | 3rdParty/V8/v7.9.317/third_party/jinja2/environment.py | python | Environment._parse | (self, source, name, filename) | return Parser(self, source, name, encode_filename(filename)).parse() | Internal parsing function used by `parse` and `compile`. | Internal parsing function used by `parse` and `compile`. | [
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BlzFans/wke | b0fa21158312e40c5fbd84682d643022b6c34a93 | cygwin/lib/python2.6/json/decoder.py | python | JSONDecoder.__init__ | (self, encoding=None, object_hook=None, parse_float=None,
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objects decoded by this instance (utf-8 by default). It has no
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``parse_constant``, if specified, will be called with one of the
following strings: -Infinity, Infinity, NaN, null, true, false.
This can be used to raise an exception if invalid JSON numbers
are encountered. | ``encoding`` determines the encoding used to interpret any ``str``
objects decoded by this instance (utf-8 by default). It has no
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"""``encoding`` determines the encoding used to interpret any ``str``
objects decoded by this instance (utf-8 by default). It has no
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/enum.py | python | EnumMeta._create_ | (cls, class_name, names, *, module=None, qualname=None, type=None, start=1) | return enum_class | Convenience method to create a new Enum class.
`names` can be:
* A string containing member names, separated either with spaces or
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* An iterable of member names. Values are incremented by 1 from `start`.
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"""
Convenience method to create a new Enum class.
`names` can be:
* A string containing member names, separated either with spaces or
commas. Values are incremented by 1 from `start`.
* An iterable of member names. Values are incremented by 1 from `start`.
* An iterable of (member name, value) pairs.
* A mapping of member name -> value pairs.
"""
metacls = cls.__class__
bases = (cls, ) if type is None else (type, cls)
_, first_enum = cls._get_mixins_(cls, bases)
classdict = metacls.__prepare__(class_name, bases)
# special processing needed for names?
if isinstance(names, str):
names = names.replace(',', ' ').split()
if isinstance(names, (tuple, list)) and names and isinstance(names[0], str):
original_names, names = names, []
last_values = []
for count, name in enumerate(original_names):
value = first_enum._generate_next_value_(name, start, count, last_values[:])
last_values.append(value)
names.append((name, value))
# Here, names is either an iterable of (name, value) or a mapping.
for item in names:
if isinstance(item, str):
member_name, member_value = item, names[item]
else:
member_name, member_value = item
classdict[member_name] = member_value
enum_class = metacls.__new__(metacls, class_name, bases, classdict)
# TODO: replace the frame hack if a blessed way to know the calling
# module is ever developed
if module is None:
try:
module = sys._getframe(2).f_globals['__name__']
except (AttributeError, ValueError, KeyError):
pass
if module is None:
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else:
enum_class.__module__ = module
if qualname is not None:
enum_class.__qualname__ = qualname
return enum_class | [
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nRF24/RF24 | f9e507544686af23bcfe9578a1558bbb08d382c9 | examples_linux/acknowledgement_payloads.py | python | master | () | Transmits a message and an incrementing integer every second. | Transmits a message and an incrementing integer every second. | [
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] | def master():
"""Transmits a message and an incrementing integer every second."""
radio.stopListening() # put radio in TX mode
failures = 0
while failures < 6:
# construct a payload to send
buffer = b"Hello \x00" + bytes(counter)
# send the payload and prompt
start_timer = time.monotonic_ns() # start timer
result = radio.write(buffer) # save the report
end_timer = time.monotonic_ns() # stop timer
if result:
# print timer results upon transmission success
print(
"Transmission successful! Time to transmit: "
"{} us. Sent: {}{}".format(
int((end_timer - start_timer) / 1000),
buffer[:6].decode("utf-8"),
counter[0]
),
end=" "
)
has_payload, pipe_number = radio.available_pipe()
if has_payload:
# print the received ACK that was automatically sent
length = radio.getDynamicPayloadSize()
response = radio.read(length)
print(
"Received {} on pipe {}: {}{}".format(
length,
pipe_number,
bytes(response[:6]).decode("utf-8"),
response[7:8][0]
)
)
# increment counter from received payload
if response[7:8][0] < 255:
counter[0] = response[7:8][0] + 1
else:
counter[0] = 0
else:
print("Received an empty ACK packet")
else:
failures += 1
print("Transmission failed or timed out")
time.sleep(1) # let the RX node prepare a new ACK payload
print(failures, "failures detected. Leaving TX role.") | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py2/pandas/core/sparse/scipy_sparse.py | python | _coo_to_sparse_series | (A, dense_index=False) | return s | Convert a scipy.sparse.coo_matrix to a SparseSeries.
Use the defaults given in the SparseSeries constructor. | Convert a scipy.sparse.coo_matrix to a SparseSeries.
Use the defaults given in the SparseSeries constructor. | [
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] | def _coo_to_sparse_series(A, dense_index=False):
""" Convert a scipy.sparse.coo_matrix to a SparseSeries.
Use the defaults given in the SparseSeries constructor.
"""
s = Series(A.data, MultiIndex.from_arrays((A.row, A.col)))
s = s.sort_index()
s = s.to_sparse() # TODO: specify kind?
if dense_index:
# is there a better constructor method to use here?
i = range(A.shape[0])
j = range(A.shape[1])
ind = MultiIndex.from_product([i, j])
s = s.reindex(ind)
return s | [
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moflow/moflow | 2dfb27c799c90c6caf1477508eca3eec616ef7d2 | bap/libtracewrap/libtrace/protobuf/python/mox.py | python | UnorderedGroup.IsSatisfied | (self) | return len(self._methods) == 0 | Return True if there are not any methods in this group. | Return True if there are not any methods in this group. | [
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"not",
"any",
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] | def IsSatisfied(self):
"""Return True if there are not any methods in this group."""
return len(self._methods) == 0 | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/keras/distribute/distributed_training_utils_v1.py | python | unwrap_outputs | (distribution_strategy, grouped_outputs,
with_loss_tensor=False) | return [loss] + all_outputs | Unwrap the list of outputs contained in the PerReplica parameters.
This function calls `flatten_per_replica_values` to parse each of the input
parameters into a list of outputs on the different devices. If we set
`with_loss_tensor` to be True, we also call `reduce` on the list of losses on
the different devices to give us one loss tensor.
Args:
distribution_strategy: DistributionStrategy used to distribute training and
validation.
grouped_outputs: PerReplica outputs returned from the train or test function
that we ran on each device.
with_loss_tensor: Boolean that indicates if we need to add the reduced loss
tensor as one of the outputs.
Returns:
Values of each of the PerReplica outputs. | Unwrap the list of outputs contained in the PerReplica parameters. | [
"Unwrap",
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] | def unwrap_outputs(distribution_strategy, grouped_outputs,
with_loss_tensor=False):
"""Unwrap the list of outputs contained in the PerReplica parameters.
This function calls `flatten_per_replica_values` to parse each of the input
parameters into a list of outputs on the different devices. If we set
`with_loss_tensor` to be True, we also call `reduce` on the list of losses on
the different devices to give us one loss tensor.
Args:
distribution_strategy: DistributionStrategy used to distribute training and
validation.
grouped_outputs: PerReplica outputs returned from the train or test function
that we ran on each device.
with_loss_tensor: Boolean that indicates if we need to add the reduced loss
tensor as one of the outputs.
Returns:
Values of each of the PerReplica outputs.
"""
if not with_loss_tensor:
return flatten_per_replica_values(distribution_strategy,
grouped_outputs)
if not isinstance(grouped_outputs, list):
grouped_outputs = [grouped_outputs]
# reduce loss tensor before adding it to the list of fetches
loss = distribution_strategy.reduce(reduce_util.ReduceOp.SUM,
grouped_outputs[0], axis=None)
all_outputs = flatten_per_replica_values(distribution_strategy,
grouped_outputs[1:])
if (backend.is_tpu_strategy(distribution_strategy) and
ops.executing_eagerly_outside_functions()):
# Choose 1 value per replica in the TPU case since all replicas produce the
# same output.
# We only do this in eager mode for now since this function is used in
# both graph and eager mode and in the graph case we currently don't use
# experimental_run so would need to be removed when we converge the graph
# code path as well.
all_outputs = all_outputs[::distribution_strategy.num_replicas_in_sync]
return [loss] + all_outputs | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/scipy/stats/stats.py | python | sem | (a, axis=0, ddof=1, nan_policy='propagate') | return s | Calculates the standard error of the mean (or standard error of
measurement) of the values in the input array.
Parameters
----------
a : array_like
An array containing the values for which the standard error is
returned.
axis : int or None, optional
Axis along which to operate. Default is 0. If None, compute over
the whole array `a`.
ddof : int, optional
Delta degrees-of-freedom. How many degrees of freedom to adjust
for bias in limited samples relative to the population estimate
of variance. Defaults to 1.
nan_policy : {'propagate', 'raise', 'omit'}, optional
Defines how to handle when input contains nan. 'propagate' returns nan,
'raise' throws an error, 'omit' performs the calculations ignoring nan
values. Default is 'propagate'.
Returns
-------
s : ndarray or float
The standard error of the mean in the sample(s), along the input axis.
Notes
-----
The default value for `ddof` is different to the default (0) used by other
ddof containing routines, such as np.std and np.nanstd.
Examples
--------
Find standard error along the first axis:
>>> from scipy import stats
>>> a = np.arange(20).reshape(5,4)
>>> stats.sem(a)
array([ 2.8284, 2.8284, 2.8284, 2.8284])
Find standard error across the whole array, using n degrees of freedom:
>>> stats.sem(a, axis=None, ddof=0)
1.2893796958227628 | Calculates the standard error of the mean (or standard error of
measurement) of the values in the input array. | [
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"""
Calculates the standard error of the mean (or standard error of
measurement) of the values in the input array.
Parameters
----------
a : array_like
An array containing the values for which the standard error is
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axis : int or None, optional
Axis along which to operate. Default is 0. If None, compute over
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ddof : int, optional
Delta degrees-of-freedom. How many degrees of freedom to adjust
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The default value for `ddof` is different to the default (0) used by other
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Examples
--------
Find standard error along the first axis:
>>> from scipy import stats
>>> a = np.arange(20).reshape(5,4)
>>> stats.sem(a)
array([ 2.8284, 2.8284, 2.8284, 2.8284])
Find standard error across the whole array, using n degrees of freedom:
>>> stats.sem(a, axis=None, ddof=0)
1.2893796958227628
"""
a, axis = _chk_asarray(a, axis)
contains_nan, nan_policy = _contains_nan(a, nan_policy)
if contains_nan and nan_policy == 'omit':
a = ma.masked_invalid(a)
return mstats_basic.sem(a, axis, ddof)
n = a.shape[axis]
s = np.std(a, axis=axis, ddof=ddof) / np.sqrt(n)
return s | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/timeseries/python/timeseries/model.py | python | TimeSeriesModel.initialize_graph | (self, input_statistics=None) | Define ops for the model, not depending on any previously defined ops.
Args:
input_statistics: A math_utils.InputStatistics object containing input
statistics. If None, data-independent defaults are used, which may
result in longer or unstable training. | Define ops for the model, not depending on any previously defined ops. | [
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"""Define ops for the model, not depending on any previously defined ops.
Args:
input_statistics: A math_utils.InputStatistics object containing input
statistics. If None, data-independent defaults are used, which may
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"""
self._graph_initialized = True
self._input_statistics = input_statistics | [
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PaddlePaddle/PaddleOCR | b756bf5f8c90142e0d89d3db0163965c686b6ffe | ppocr/losses/det_basic_loss.py | python | BalanceLoss.forward | (self, pred, gt, mask=None) | return balance_loss | The BalanceLoss for Differentiable Binarization text detection
args:
pred (variable): predicted feature maps.
gt (variable): ground truth feature maps.
mask (variable): masked maps.
return: (variable) balanced loss | The BalanceLoss for Differentiable Binarization text detection
args:
pred (variable): predicted feature maps.
gt (variable): ground truth feature maps.
mask (variable): masked maps.
return: (variable) balanced loss | [
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"""
The BalanceLoss for Differentiable Binarization text detection
args:
pred (variable): predicted feature maps.
gt (variable): ground truth feature maps.
mask (variable): masked maps.
return: (variable) balanced loss
"""
positive = gt * mask
negative = (1 - gt) * mask
positive_count = int(positive.sum())
negative_count = int(
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loss = self.loss(pred, gt, mask=mask)
if not self.balance_loss:
return loss
positive_loss = positive * loss
negative_loss = negative * loss
negative_loss = paddle.reshape(negative_loss, shape=[-1])
if negative_count > 0:
sort_loss = negative_loss.sort(descending=True)
negative_loss = sort_loss[:negative_count]
# negative_loss, _ = paddle.topk(negative_loss, k=negative_count_int)
balance_loss = (positive_loss.sum() + negative_loss.sum()) / (
positive_count + negative_count + self.eps)
else:
balance_loss = positive_loss.sum() / (positive_count + self.eps)
if self.return_origin:
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return balance_loss | [
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klzgrad/naiveproxy | ed2c513637c77b18721fe428d7ed395b4d284c83 | src/build/android/gyp/dex.py | python | _IntermediateDexFilePathsFromInputJars | (class_inputs, incremental_dir) | return dex_files | Returns a list of all intermediate dex file paths. | Returns a list of all intermediate dex file paths. | [
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"""Returns a list of all intermediate dex file paths."""
dex_files = []
for jar in class_inputs:
with zipfile.ZipFile(jar, 'r') as z:
for subpath in z.namelist():
if _IsClassFile(subpath):
subpath = subpath[:-5] + 'dex'
dex_files.append(os.path.join(incremental_dir, subpath))
return dex_files | [
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tpfister/caffe-heatmap | 4db69ef53e6b8a0b3b4ebb29328b0ab3dbf67c4e | scripts/cpp_lint.py | python | _SetVerboseLevel | (level) | return _cpplint_state.SetVerboseLevel(level) | Sets the module's verbosity, and returns the previous setting. | Sets the module's verbosity, and returns the previous setting. | [
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"""Sets the module's verbosity, and returns the previous setting."""
return _cpplint_state.SetVerboseLevel(level) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/protorpc/protorpc/remote.py | python | _ServiceClass.__init__ | (cls, name, bases, dct) | Create uninitialized state on new class. | Create uninitialized state on new class. | [
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"""Create uninitialized state on new class."""
type.__init__(cls, name, bases, dct)
# Only service implementation classes should have remote methods and stub
# sub classes created. Stub implementations have their own methods passed
# in to the type constructor.
if StubBase not in bases:
# Create list of remote methods.
cls.__remote_methods = dict(cls.__base_methods)
for attribute, value in dct.items():
value = getattr(cls, attribute)
remote_method_info = get_remote_method_info(value)
if remote_method_info:
cls.__remote_methods[attribute] = value
# Build asynchronous stub class.
stub_attributes = {'Service': cls}
async_methods = cls.__create_async_methods(cls.__remote_methods)
stub_attributes.update(async_methods)
async_class = type('AsyncStub', (StubBase, cls), stub_attributes)
cls.AsyncStub = async_class
# Constructor for synchronous stub class.
def __init__(self, transport):
"""Constructor.
Args:
transport: Underlying transport to communicate with remote service.
"""
super(cls.Stub, self).__init__(transport)
self.async = cls.AsyncStub(transport)
# Build synchronous stub class.
stub_attributes = {'Service': cls,
'__init__': __init__}
stub_attributes.update(cls.__create_sync_methods(async_methods))
cls.Stub = type('Stub', (StubBase, cls), stub_attributes) | [
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nyuwireless-unipd/ns3-mmwave | 4ff9e87e8079764e04cbeccd8e85bff15ae16fb3 | utils/grid.py | python | ScaleRenderer.get_position | (self, x) | return real_x | ! Get Position
@param self this object
@param x x
@return real x | ! Get Position | [
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] | def get_position(self, x):
"""! Get Position
@param self this object
@param x x
@return real x
"""
real_x = (x - self.__lo ) * self.__width / (self.__hi - self.__lo)
return real_x | [
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google/iree | 1224bbdbe65b0d1fdf40e7324f60f68beeaf7c76 | integrations/tensorflow/iree-dialects/python/iree/compiler/dialects/iree_pydm/importer/util.py | python | ImportContext.abort | (self, message: str) | Emits an error diagnostic and raises an exception to abort. | Emits an error diagnostic and raises an exception to abort. | [
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loc = self.loc
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/AWSPythonSDK/1.5.8/boto3/resources/model.py | python | DefinitionWithParams.params | (self) | return params | Get a list of auto-filled parameters for this request.
:type: list(:py:class:`Parameter`) | Get a list of auto-filled parameters for this request. | [
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"""
Get a list of auto-filled parameters for this request.
:type: list(:py:class:`Parameter`)
"""
params = []
for item in self._definition.get('params', []):
params.append(Parameter(**item))
return params | [
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OPAE/opae-sdk | 221124343c8275243a249eb72d69e0ea2d568d1b | binaries/utilities/vc_image_convert/merge_device_table.py | python | main | (input_file, dtb_file) | function reads and checks max10_device_table.bin to ensure
the file is under the alloted size
the section is written into the table section of the input file | function reads and checks max10_device_table.bin to ensure
the file is under the alloted size
the section is written into the table section of the input file | [
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""" function reads and checks max10_device_table.bin to ensure
the file is under the alloted size
the section is written into the table section of the input file """
LOGGER.info("Reading: %s" % dtb_file)
with open(dtb_file, "rb") as max_table_file:
LOGGER.info("max10_device_table.bin size: %x" %
os.path.getsize(max_table_file.name))
LOGGER.info("Max max10 table size: %x" % MAX10_TABLE_SIZE)
if (os.path.getsize(max_table_file.name) > MAX10_TABLE_SIZE):
raise Exception(LOGGER.error("max10_device_table.bin is too big"))
max10_table = max_table_file.read()
LOGGER.info("Writing file: %s" % input_file)
with open(input_file, "rb+") as rpd_file:
rpd_file.seek(MAX10_TABLE_START)
rpd_file.write(bytearray(max10_table))
LOGGER.info("Done merging Max10 device table") | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/encodings/base64_codec.py | python | base64_encode | (input,errors='strict') | return (output, len(input)) | Encodes the object input and returns a tuple (output
object, length consumed).
errors defines the error handling to apply. It defaults to
'strict' handling which is the only currently supported
error handling for this codec. | Encodes the object input and returns a tuple (output
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""" Encodes the object input and returns a tuple (output
object, length consumed).
errors defines the error handling to apply. It defaults to
'strict' handling which is the only currently supported
error handling for this codec.
"""
assert errors == 'strict'
output = base64.encodestring(input)
return (output, len(input)) | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/labeled_tensor/python/ops/_typecheck.py | python | accepts | (*types) | return check_accepts | A decorator which checks the input types of a function.
Based on:
http://stackoverflow.com/questions/15299878/how-to-use-python-decorators-to-check-function-arguments
The above draws from:
https://www.python.org/dev/peps/pep-0318/
Args:
*types: A list of Python types.
Returns:
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"""A decorator which checks the input types of a function.
Based on:
http://stackoverflow.com/questions/15299878/how-to-use-python-decorators-to-check-function-arguments
The above draws from:
https://www.python.org/dev/peps/pep-0318/
Args:
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Returns:
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"""
def check_accepts(f):
"""Check the types."""
spec = tf_inspect.getargspec(f)
num_function_arguments = len(spec.args)
if len(types) != num_function_arguments:
raise Error(
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if spec.defaults:
num_defaults = len(spec.defaults)
for (name, a, t) in zip(spec.args[-num_defaults:],
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types[-num_defaults:]):
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if not isinstance(a, allowed_type):
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% (a, type(a), _type_repr(allowed_type), name, f))
@functools.wraps(f)
def new_f(*args, **kwds):
"""A helper function."""
for (a, t) in zip(args, types):
allowed_type = _replace_forward_references(t, f.__globals__)
if not isinstance(a, allowed_type):
raise Error("%r of type %r is not an instance of the allowed type %s "
"for %r" % (a, type(a), _type_repr(allowed_type), f))
return f(*args, **kwds)
return new_f
return check_accepts | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/richtext.py | python | RichTextBuffer.SetFontTable | (*args, **kwargs) | return _richtext.RichTextBuffer_SetFontTable(*args, **kwargs) | SetFontTable(self, RichTextFontTable table) | SetFontTable(self, RichTextFontTable table) | [
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"""SetFontTable(self, RichTextFontTable table)"""
return _richtext.RichTextBuffer_SetFontTable(*args, **kwargs) | [
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netket/netket | 0d534e54ecbf25b677ea72af6b85947979420652 | netket/utils/mpi/primitives.py | python | mpi_max | (x, *, comm=MPI_py_comm) | return ar | Computes the elementwise logical OR of an array or a scalar across all MPI
processes, effectively equivalent to an elementwise any
Args:
a: The input array, which will usually be overwritten in place.
Returns:
out: The reduced array. | Computes the elementwise logical OR of an array or a scalar across all MPI
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"""
Computes the elementwise logical OR of an array or a scalar across all MPI
processes, effectively equivalent to an elementwise any
Args:
a: The input array, which will usually be overwritten in place.
Returns:
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ar = np.asarray(x)
if n_nodes > 1:
comm.Allreduce(MPI.IN_PLACE, ar.reshape(-1), op=MPI.MAX)
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemDefectReporter/v1/AWS/common-code/Lib/setuptools/msvc.py | python | EnvironmentInfo.HTMLHelpWorkshop | (self) | return [os.path.join(self.si.ProgramFilesx86, 'HTML Help Workshop')] | Microsoft HTML Help Workshop | Microsoft HTML Help Workshop | [
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] | def HTMLHelpWorkshop(self):
"""
Microsoft HTML Help Workshop
"""
if self.vc_ver < 11.0:
return []
return [os.path.join(self.si.ProgramFilesx86, 'HTML Help Workshop')] | [
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redpony/cdec | f7c4899b174d86bc70b40b1cae68dcad364615cb | realtime/rt/rt.py | python | RealtimeTranslator.learn | (self, source, target, ctx_name=None) | Learn from training instance (inc extracting grammar if needed)
Threadsafe, FIFO | Learn from training instance (inc extracting grammar if needed)
Threadsafe, FIFO | [
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'''Learn from training instance (inc extracting grammar if needed)
Threadsafe, FIFO'''
lock = self.ctx_locks[ctx_name]
lock.acquire()
self.lazy_ctx(ctx_name)
if '' in (source.strip(), target.strip()):
logger.info('({}) ERROR: empty source or target: {} ||| {}'.format(ctx_name, source, target))
lock.release()
return
if self.norm:
source = self.tokenize(source)
target = self.tokenize(target)
# Align instance
alignment = self.aligner.align(source, target)
grammar_file = self.grammar(source, ctx_name)
# MIRA update before adding data to grammar extractor
decoder = self.decoders[ctx_name]
mira_log = decoder.decoder.update(source, grammar_file, target)
logger.info('({}) MIRA HBF: {}'.format(ctx_name, mira_log))
# Add to HPYPLM by writing to fifo (read on next translation)
if self.hpyplm:
logger.info('({}) Adding to HPYPLM: {}'.format(ctx_name, target))
decoder.ref_fifo.write('{}\n'.format(target))
decoder.ref_fifo.flush()
# Store incremental data for save/load
self.ctx_data[ctx_name].append((source, target, alignment))
# Add aligned sentence pair to grammar extractor
logger.info('({}) Adding to bitext: {} ||| {} ||| {}'.format(ctx_name, source, target, alignment))
self.extractor.add_instance(source, target, alignment, ctx_name)
# Clear (old) cached grammar
rm_grammar = self.grammar_dict[ctx_name].pop(source)
os.remove(rm_grammar)
lock.release() | [
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pmq20/node-packer | 12c46c6e44fbc14d9ee645ebd17d5296b324f7e0 | current/tools/inspector_protocol/jinja2/sandbox.py | python | SandboxedEnvironment.is_safe_callable | (self, obj) | return not (getattr(obj, 'unsafe_callable', False) or
getattr(obj, 'alters_data', False)) | Check if an object is safely callable. Per default a function is
considered safe unless the `unsafe_callable` attribute exists and is
True. Override this method to alter the behavior, but this won't
affect the `unsafe` decorator from this module. | Check if an object is safely callable. Per default a function is
considered safe unless the `unsafe_callable` attribute exists and is
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RegrowthStudios/SoACode-Public | c3ddd69355b534d5e70e2e6d0c489b4e93ab1ffe | utils/git-hooks/pep8.py | python | BaseReport.increment_logical_line | (self) | Signal a new logical line. | Signal a new logical line. | [
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"""Signal a new logical line."""
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pmq20/node-packer | 12c46c6e44fbc14d9ee645ebd17d5296b324f7e0 | lts/deps/npm/node_modules/node-gyp/gyp/pylib/gyp/MSVSSettings.py | python | _MSVSOnly | (tool, name, setting_type) | Defines a setting that is only found in MSVS.
Args:
tool: a dictionary that gives the names of the tool for MSVS and MSBuild.
name: the name of the setting.
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"""Defines a setting that is only found in MSVS.
Args:
tool: a dictionary that gives the names of the tool for MSVS and MSBuild.
name: the name of the setting.
setting_type: the type of this setting.
"""
def _Translate(unused_value, unused_msbuild_settings):
# Since this is for MSVS only settings, no translation will happen.
pass
_msvs_validators[tool.msvs_name][name] = setting_type.ValidateMSVS
_msvs_to_msbuild_converters[tool.msvs_name][name] = _Translate | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py2/pandas/core/arrays/period.py | python | PeriodArray._add_timedeltalike_scalar | (self, other) | return ordinals | Parameters
----------
other : timedelta, Tick, np.timedelta64
Returns
-------
result : ndarray[int64] | Parameters
----------
other : timedelta, Tick, np.timedelta64 | [
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"""
Parameters
----------
other : timedelta, Tick, np.timedelta64
Returns
-------
result : ndarray[int64]
"""
assert isinstance(self.freq, Tick) # checked by calling function
assert isinstance(other, (timedelta, np.timedelta64, Tick))
if notna(other):
# special handling for np.timedelta64("NaT"), avoid calling
# _check_timedeltalike_freq_compat as that would raise TypeError
other = self._check_timedeltalike_freq_compat(other)
# Note: when calling parent class's _add_timedeltalike_scalar,
# it will call delta_to_nanoseconds(delta). Because delta here
# is an integer, delta_to_nanoseconds will return it unchanged.
ordinals = super(PeriodArray, self)._add_timedeltalike_scalar(other)
return ordinals | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/ops/_op_impl/tbe/squeeze.py | python | _squeeze_tbe | () | return | Squeeze TBE register | Squeeze TBE register | [
"Squeeze",
"TBE",
"register"
] | def _squeeze_tbe():
"""Squeeze TBE register"""
return | [
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Polidea/SiriusObfuscator | b0e590d8130e97856afe578869b83a209e2b19be | SymbolExtractorAndRenamer/lldb/scripts/Python/static-binding/lldb.py | python | SBSection.GetTargetByteSize | (self) | return _lldb.SBSection_GetTargetByteSize(self) | GetTargetByteSize(self) -> uint32_t
Return the size of a target's byte represented by this section
in numbers of host bytes. Note that certain architectures have
varying minimum addressable unit (i.e. byte) size for their
CODE or DATA buses.
@return
The number of host (8-bit) bytes needed to hold a target byte | GetTargetByteSize(self) -> uint32_t | [
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"""
GetTargetByteSize(self) -> uint32_t
Return the size of a target's byte represented by this section
in numbers of host bytes. Note that certain architectures have
varying minimum addressable unit (i.e. byte) size for their
CODE or DATA buses.
@return
The number of host (8-bit) bytes needed to hold a target byte
"""
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microsoft/checkedc-clang | a173fefde5d7877b7750e7ce96dd08cf18baebf2 | compiler-rt/lib/sanitizer_common/scripts/cpplint.py | python | CheckComment | (line, filename, linenum, next_line_start, error) | Checks for common mistakes in comments.
Args:
line: The line in question.
filename: The name of the current file.
linenum: The number of the line to check.
next_line_start: The first non-whitespace column of the next line.
error: The function to call with any errors found. | Checks for common mistakes in comments. | [
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"in",
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"."
] | def CheckComment(line, filename, linenum, next_line_start, error):
"""Checks for common mistakes in comments.
Args:
line: The line in question.
filename: The name of the current file.
linenum: The number of the line to check.
next_line_start: The first non-whitespace column of the next line.
error: The function to call with any errors found.
"""
commentpos = line.find('//')
if commentpos != -1:
# Check if the // may be in quotes. If so, ignore it
if re.sub(r'\\.', '', line[0:commentpos]).count('"') % 2 == 0:
# Allow one space for new scopes, two spaces otherwise:
if (not (Match(r'^.*{ *//', line) and next_line_start == commentpos) and
((commentpos >= 1 and
line[commentpos-1] not in string.whitespace) or
(commentpos >= 2 and
line[commentpos-2] not in string.whitespace))):
error(filename, linenum, 'whitespace/comments', 2,
'At least two spaces is best between code and comments')
# Checks for common mistakes in TODO comments.
comment = line[commentpos:]
match = _RE_PATTERN_TODO.match(comment)
if match:
# One whitespace is correct; zero whitespace is handled elsewhere.
leading_whitespace = match.group(1)
if len(leading_whitespace) > 1:
error(filename, linenum, 'whitespace/todo', 2,
'Too many spaces before TODO')
username = match.group(2)
if not username:
error(filename, linenum, 'readability/todo', 2,
'Missing username in TODO; it should look like '
'"// TODO(my_username): Stuff."')
middle_whitespace = match.group(3)
# Comparisons made explicit for correctness -- pylint: disable=g-explicit-bool-comparison
if middle_whitespace != ' ' and middle_whitespace != '':
error(filename, linenum, 'whitespace/todo', 2,
'TODO(my_username) should be followed by a space')
# If the comment contains an alphanumeric character, there
# should be a space somewhere between it and the // unless
# it's a /// or //! Doxygen comment.
if (Match(r'//[^ ]*\w', comment) and
not Match(r'(///|//\!)(\s+|$)', comment)):
error(filename, linenum, 'whitespace/comments', 4,
'Should have a space between // and comment') | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | scripts/SANS/ISISCommandInterface.py | python | LOQ | (idf_path='LOQ_Definition_20020226-.xml') | return True | Initialises the instrument settings for LOQ
@return True on success | Initialises the instrument settings for LOQ | [
"Initialises",
"the",
"instrument",
"settings",
"for",
"LOQ"
] | def LOQ(idf_path='LOQ_Definition_20020226-.xml'):
"""
Initialises the instrument settings for LOQ
@return True on success
"""
_printMessage('LOQ()')
try:
instrument = isis_instrument.LOQ(idf_path)
if instrument is None:
raise RuntimeError("The provided idf path seems to have been incorrect")
ReductionSingleton().set_instrument(instrument)
config['default.instrument'] = 'LOQ'
except(Exception, Warning):
return False
return True | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/prompt-toolkit/py3/prompt_toolkit/layout/scrollable_pane.py | python | ScrollablePane._copy_over_write_positions | (
self, screen: Screen, temp_screen: Screen, write_position: WritePosition
) | Copy over window write positions. | Copy over window write positions. | [
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"window",
"write",
"positions",
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] | def _copy_over_write_positions(
self, screen: Screen, temp_screen: Screen, write_position: WritePosition
) -> None:
"""
Copy over window write positions.
"""
ypos = write_position.ypos
xpos = write_position.xpos
for win, write_pos in temp_screen.visible_windows_to_write_positions.items():
screen.visible_windows_to_write_positions[win] = WritePosition(
xpos=write_pos.xpos + xpos,
ypos=write_pos.ypos + ypos - self.vertical_scroll,
# TODO: if the window is only partly visible, then truncate width/height.
# This could be important if we have nested ScrollablePanes.
height=write_pos.height,
width=write_pos.width,
) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/asyncio/queues.py | python | Queue.put_nowait | (self, item) | Put an item into the queue without blocking.
If no free slot is immediately available, raise QueueFull. | Put an item into the queue without blocking. | [
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] | def put_nowait(self, item):
"""Put an item into the queue without blocking.
If no free slot is immediately available, raise QueueFull.
"""
if self.full():
raise QueueFull
self._put(item)
self._unfinished_tasks += 1
self._finished.clear()
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/eager/graph_callable.py | python | _VariableCapturingScope.initializing_scope | (self) | Context manager to capture variable creations.
Forcibly initializes all created variables.
Yields:
nothing | Context manager to capture variable creations. | [
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] | def initializing_scope(self):
"""Context manager to capture variable creations.
Forcibly initializes all created variables.
Yields:
nothing
"""
# TODO(apassos) ignoring the regularizer and partitioner here; figure out
# how to deal with these.
def _custom_getter(getter=None, name=None, shape=None, dtype=dtypes.float32, # pylint: disable=missing-docstring
initializer=None, regularizer=None, reuse=None,
trainable=True, collections=None, caching_device=None, # pylint: disable=redefined-outer-name
partitioner=None, validate_shape=True,
use_resource=None):
del getter, regularizer, collections, caching_device, partitioner
del use_resource, validate_shape
if name in self.tf_variables:
if reuse:
return self.tf_variables[name].initialized_value()
else:
raise ValueError("Specified reuse=%s but tried to reuse variables."
% reuse)
# TODO(apassos): ensure this is on the same device as above
v = _CapturedVariable(name, initializer, shape, dtype, trainable)
self.variables[name] = v
graph_mode_resource = v.variable.handle
if initializer is None:
initializer = _default_initializer(name, shape, dtype)
resource_variable_ops.assign_variable_op(
graph_mode_resource, initializer(shape, dtype))
return v.variable
scope = variable_scope.get_variable_scope()
with variable_scope.variable_scope(scope, custom_getter=_custom_getter):
yield | [
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BitMEX/api-connectors | 37a3a5b806ad5d0e0fc975ab86d9ed43c3bcd812 | auto-generated/python/swagger_client/models/wallet.py | python | Wallet.to_dict | (self) | return result | Returns the model properties as a dict | Returns the model properties as a dict | [
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"model",
"properties",
"as",
"a",
"dict"
] | def to_dict(self):
"""Returns the model properties as a dict"""
result = {}
for attr, _ in six.iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
if issubclass(Wallet, dict):
for key, value in self.items():
result[key] = value
return result | [
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pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py | python | C10dRendezvousBackend.get_state | (self) | return self._decode_state(base64_state) | See base class. | See base class. | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_controls.py | python | ListCtrl.GetItemCount | (*args, **kwargs) | return _controls_.ListCtrl_GetItemCount(*args, **kwargs) | GetItemCount(self) -> int | GetItemCount(self) -> int | [
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SpenceKonde/megaTinyCore | 1c4a70b18a149fe6bcb551dfa6db11ca50b8997b | megaavr/tools/libs/pyedbglib/protocols/jtagice3protocol.py | python | Jtagice3Protocol.set_le16 | (self, context, offset, value) | Sets a little-endian 16-bit parameter
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/pyprogress.py | python | PyProgress.GetFirstGradientColour | (self) | return self._gauge.GetFirstGradientColour() | Returns the gauge first gradient colour. | Returns the gauge first gradient colour. | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/distutils/dist.py | python | Distribution.parse_command_line | (self) | return True | Parse the setup script's command line, taken from the
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NVIDIA/TensorRT | 42805f078052daad1a98bc5965974fcffaad0960 | tools/pytorch-quantization/pytorch_quantization/nn/modules/_utils.py | python | pop_quant_desc_in_kwargs | (quant_cls, input_only=False, **kwargs) | return quant_desc_input, quant_desc_weight | Pop quant descriptors in kwargs
If there is no descriptor in kwargs, the default one in quant_cls will be used
Arguments:
quant_cls: A class that has default quantization descriptors
input_only: A boolean. If True, pop quant_desc_input only, not quant_desc_weight. Default false.
Keyword Arguments:
quant_desc_input: An instance of :class:`QuantDescriptor <pytorch_quantization.tensor_quant.QuantDescriptor>`.
Quantization descriptor of input.
quant_desc_weight: An instance of :class:`QuantDescriptor <pytorch_quantization.tensor_quant.QuantDescriptor>`.
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"""Pop quant descriptors in kwargs
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Arguments:
quant_cls: A class that has default quantization descriptors
input_only: A boolean. If True, pop quant_desc_input only, not quant_desc_weight. Default false.
Keyword Arguments:
quant_desc_input: An instance of :class:`QuantDescriptor <pytorch_quantization.tensor_quant.QuantDescriptor>`.
Quantization descriptor of input.
quant_desc_weight: An instance of :class:`QuantDescriptor <pytorch_quantization.tensor_quant.QuantDescriptor>`.
Quantization descriptor of weight.
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quant_desc_input = kwargs.pop('quant_desc_input', quant_cls.default_quant_desc_input)
if not input_only:
quant_desc_weight = kwargs.pop('quant_desc_weight', quant_cls.default_quant_desc_weight)
# Check if anything is left in **kwargs
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echronos/echronos | c996f1d2c8af6c6536205eb319c1bf1d4d84569c | external_tools/ply_info/example/classcalc/calc.py | python | Calc.t_NUMBER | (self, t) | return t | r'\d+ | r'\d+ | [
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r'\d+'
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t.value = int(t.value)
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PlatformLab/RAMCloud | b1866af19124325a6dfd8cbc267e2e3ef1f965d1 | cpplint.py | python | ProcessFile | (filename, vlevel) | Does google-lint on a single file.
Args:
filename: The name of the file to parse.
vlevel: The level of errors to report. Every error of confidence
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filename: The name of the file to parse.
vlevel: The level of errors to report. Every error of confidence
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"""
_SetVerboseLevel(vlevel)
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lines[linenum] = lines[linenum].rstrip('\r')
carriage_return_found = True
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return
# Note, if no dot is found, this will give the entire filename as the ext.
file_extension = filename[filename.rfind('.') + 1:]
# When reading from stdin, the extension is unknown, so no cpplint tests
# should rely on the extension.
if (filename != '-' and file_extension != 'cc' and file_extension != 'h'
and file_extension != 'cpp' and file_extension != 'c'):
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Error(filename, 0, 'whitespace/newline', 1,
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/boto3/resources/factory.py | python | ResourceFactory._create_identifier | (factory_self, identifier, resource_name) | return property(get_identifier) | Creates a read-only property for identifier attributes. | Creates a read-only property for identifier attributes. | [
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"""
Creates a read-only property for identifier attributes.
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def get_identifier(self):
# The default value is set to ``None`` instead of
# raising an AttributeError because when resources are
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return getattr(self, '_' + identifier.name, None)
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get_identifier.__doc__ = docstring.IdentifierDocstring(
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_controls.py | python | PyControl.DoSetClientSize | (*args, **kwargs) | return _controls_.PyControl_DoSetClientSize(*args, **kwargs) | DoSetClientSize(self, int width, int height) | DoSetClientSize(self, int width, int height) | [
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mongodb/mongo | d8ff665343ad29cf286ee2cf4a1960d29371937b | buildscripts/idl/idl/syntax.py | python | SymbolTable.get_generic_reply_field_list | (self, name) | return None | Get a generic reply field list from the SymbolTable based on the list name. | Get a generic reply field list from the SymbolTable based on the list name. | [
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Netflix/NfWebCrypto | 499faf4eb9f9ccf0b21dc728e974970f54bd6c52 | plugin/ppapi/ppapi/generators/idl_parser.py | python | IDLParser.p_value | (self, p) | value : FLOAT
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pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | torch/distributions/transformed_distribution.py | python | TransformedDistribution.rsample | (self, sample_shape=torch.Size()) | return x | Generates a sample_shape shaped reparameterized sample or sample_shape
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trilinos/Trilinos | 6168be6dd51e35e1cd681e9c4b24433e709df140 | packages/seacas/libraries/ioss/src/visualization/catalyst/phactori/phactori.py | python | PhactoriImagesetBlock.ClearPvViewAndPvRepAfterWriteImage | (self) | Since it turns out to be much more memory efficient to reuse a single
render view rather than having one per image, this routine is called
immediately after WriteImage in order to make stuff invisible again
before the next item gets a chance to do WriteImage | Since it turns out to be much more memory efficient to reuse a single
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#if PhactoriDbg(150):
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#cube axes invisible
ShowCubeAxesXX(self.mSharedPvRenderView2, 'off')
#3d/pointset dataset invisible (plot or 3d viewing)
self.mPvDataRepresentation2.Visibility = 0
#color legend invisible, if 3d viewing
for oneColorLegendRepRef in self.mColorLegendRepRefs:
if oneColorLegendRepRef != None:
if PhactoriDbg(100):
myDebugPrint3("C inColorLegendRepRef was " + \
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oneColorLegendRepRef.Visibility = 0
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timeAnnStngs = self.mRepresentation.mTimeAnnotationSettings
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gPipeAndViewsState.mTimeAnnotationPv.\
mParaViewRepresentation.Visibility = 0
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for oneMarker in self.mVisibleMarkers:
oneMarker.MakeInvisible()
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oneTextAnnotation.MakeInvisible()
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ii = 1
while ii < len(self.mVisibleReps):
oneVisPvDataRep = self.mVisiblePvDataReps[ii]
if(oneVisPvDataRep != None):
oneVisPvDataRep.Visibility = 0
#this is already done above
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/AWS/common-code/lib/OpenSSL/SSL.py | python | Context.get_verify_mode | (self) | return _lib.SSL_CTX_get_verify_mode(self._context) | Retrieve the Context object's verify mode, as set by
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leela-zero/leela-zero | e3ed6310d33d75078ba74c3adf887d18439fc2e3 | scripts/cpplint.py | python | CheckForFunctionLengths | (filename, clean_lines, linenum,
function_state, error) | Reports for long function bodies.
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if Match(r'TEST', function): # Handle TEST... macros
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function += parameter_regexp.group(1)
else:
function += '()'
function_state.Begin(function)
break
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error(filename, linenum, 'readability/fn_size', 5,
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/idlelib/tooltip.py | python | OnHoverTooltipBase.hidetip | (self) | hide the tooltip | hide the tooltip | [
"hide",
"the",
"tooltip"
] | def hidetip(self):
"""hide the tooltip"""
try:
self.unschedule()
except TclError: # pragma: no cover
pass
super(OnHoverTooltipBase, self).hidetip() | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/numpy/py3/numpy/matlib.py | python | identity | (n,dtype=None) | return b | Returns the square identity matrix of given size.
Parameters
----------
n : int
Size of the returned identity matrix.
dtype : data-type, optional
Data-type of the output. Defaults to ``float``.
Returns
-------
out : matrix
`n` x `n` matrix with its main diagonal set to one,
and all other elements zero.
See Also
--------
numpy.identity : Equivalent array function.
matlib.eye : More general matrix identity function.
Examples
--------
>>> import numpy.matlib
>>> np.matlib.identity(3, dtype=int)
matrix([[1, 0, 0],
[0, 1, 0],
[0, 0, 1]]) | Returns the square identity matrix of given size. | [
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"""
Returns the square identity matrix of given size.
Parameters
----------
n : int
Size of the returned identity matrix.
dtype : data-type, optional
Data-type of the output. Defaults to ``float``.
Returns
-------
out : matrix
`n` x `n` matrix with its main diagonal set to one,
and all other elements zero.
See Also
--------
numpy.identity : Equivalent array function.
matlib.eye : More general matrix identity function.
Examples
--------
>>> import numpy.matlib
>>> np.matlib.identity(3, dtype=int)
matrix([[1, 0, 0],
[0, 1, 0],
[0, 0, 1]])
"""
a = array([1]+n*[0], dtype=dtype)
b = empty((n, n), dtype=dtype)
b.flat = a
return b | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_core.py | python | HeaderColumn.GetBitmap | (*args, **kwargs) | return _core_.HeaderColumn_GetBitmap(*args, **kwargs) | GetBitmap(self) -> Bitmap | GetBitmap(self) -> Bitmap | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py3/scipy/spatial/transform/rotation.py | python | Rotation.as_quat | (self) | Represent as quaternions.
Rotations in 3 dimensions can be represented using unit norm
quaternions [1]_. The mapping from quaternions to rotations is
two-to-one, i.e. quaternions `q` and `-q`, where `-q` simply reverses
the sign of each component, represent the same spatial rotation.
Returns
-------
quat : `numpy.ndarray`, shape (4,) or (N, 4)
Shape depends on shape of inputs used for initialization.
References
----------
.. [1] `Quaternions and Spatial Rotation
<https://en.wikipedia.org/wiki/Quaternions_and_spatial_rotation>`_
Examples
--------
>>> from scipy.spatial.transform import Rotation as R
Represent a single rotation:
>>> r = R.from_dcm([
... [0, -1, 0],
... [1, 0, 0],
... [0, 0, 1]])
>>> r.as_quat()
array([0. , 0. , 0.70710678, 0.70710678])
>>> r.as_quat().shape
(4,)
Represent a stack with a single rotation:
>>> r = R.from_quat([[0, 0, 0, 1]])
>>> r.as_quat().shape
(1, 4)
Represent multiple rotaions in a single object:
>>> r = R.from_rotvec([[np.pi, 0, 0], [0, 0, np.pi/2]])
>>> r.as_quat().shape
(2, 4) | Represent as quaternions. | [
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"""Represent as quaternions.
Rotations in 3 dimensions can be represented using unit norm
quaternions [1]_. The mapping from quaternions to rotations is
two-to-one, i.e. quaternions `q` and `-q`, where `-q` simply reverses
the sign of each component, represent the same spatial rotation.
Returns
-------
quat : `numpy.ndarray`, shape (4,) or (N, 4)
Shape depends on shape of inputs used for initialization.
References
----------
.. [1] `Quaternions and Spatial Rotation
<https://en.wikipedia.org/wiki/Quaternions_and_spatial_rotation>`_
Examples
--------
>>> from scipy.spatial.transform import Rotation as R
Represent a single rotation:
>>> r = R.from_dcm([
... [0, -1, 0],
... [1, 0, 0],
... [0, 0, 1]])
>>> r.as_quat()
array([0. , 0. , 0.70710678, 0.70710678])
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>>> r = R.from_quat([[0, 0, 0, 1]])
>>> r.as_quat().shape
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>>> r = R.from_rotvec([[np.pi, 0, 0], [0, 0, np.pi/2]])
>>> r.as_quat().shape
(2, 4)
"""
if self._single:
return self._quat[0].copy()
else:
return self._quat.copy() | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/Muon/GUI/Common/plot_widget/plotting_canvas/plotting_canvas_view.py | python | PlottingCanvasView.clear_all_workspaces_from_plot | (self) | Clears all workspaces from the plot | Clears all workspaces from the plot | [
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"""Clears all workspaces from the plot"""
for ax in self.fig.axes:
ax.cla()
ax.tracked_workspaces.clear()
ax.set_prop_cycle(None)
for color_queue in self._color_queue:
color_queue.reset()
for shaded_region in self._shaded_regions:
shaded_region.remove()
self._shaded_regions={}
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqt/mantidqt/widgets/embedded_find_replace_dialog/presenter.py | python | EmbeddedFindReplaceDialog.strings_different | (self, string1, string2, case_sensitive) | return (not case_sensitive and string1.lower() != string2.lower()) or (case_sensitive and string1 != string2) | :param case_sensitive: If case_sensitive is NOT selected, then both strings will be made lowercase
Else the strings will be compared as they are without changes | [] | def strings_different(self, string1, string2, case_sensitive):
"""
:param case_sensitive: If case_sensitive is NOT selected, then both strings will be made lowercase
Else the strings will be compared as they are without changes
"""
return (not case_sensitive and string1.lower() != string2.lower()) or (case_sensitive and string1 != string2) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/setuptools/msvc.py | python | msvc14_get_vc_env | (plat_spec) | Patched "distutils._msvccompiler._get_vc_env" for support extra
Microsoft Visual C++ 14.X compilers.
Set environment without use of "vcvarsall.bat".
Parameters
----------
plat_spec: str
Target architecture.
Return
------
dict
environment | Patched "distutils._msvccompiler._get_vc_env" for support extra
Microsoft Visual C++ 14.X compilers. | [
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"""
Patched "distutils._msvccompiler._get_vc_env" for support extra
Microsoft Visual C++ 14.X compilers.
Set environment without use of "vcvarsall.bat".
Parameters
----------
plat_spec: str
Target architecture.
Return
------
dict
environment
"""
# Always use backport from CPython 3.8
try:
return _msvc14_get_vc_env(plat_spec)
except distutils.errors.DistutilsPlatformError as exc:
_augment_exception(exc, 14.0)
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apple/foundationdb | f7118ad406f44ab7a33970fc8370647ed0085e18 | layers/taskbucket/__init__.py | python | TaskBucket.get_one | (self, tr) | return taskDict | Gets a single task from the bucket, locks it so that only the
caller will work on it for a while, and returns its taskDict.
If there are no tasks in the bucket, returns None. | Gets a single task from the bucket, locks it so that only the
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"""Gets a single task from the bucket, locks it so that only the
caller will work on it for a while, and returns its taskDict.
If there are no tasks in the bucket, returns None."""
if self.system_access:
tr.options.set_access_system_keys()
k = tr.snapshot.get_key(fdb.KeySelector.last_less_or_equal(self.available.pack((random_key(),))))
if not k or k < self.available.pack(("",)):
k = tr.snapshot.get_key(fdb.KeySelector.last_less_or_equal(self.available.pack((chr(255) * 16,))))
if not k or k < self.available.pack(("",)):
if self.check_timeouts(tr):
return self.get_one(tr)
return None
key = self.available.unpack(k)[0]
avail = self.available[key]
timeout = tr.get_read_version().wait() + long(self.timeout * (0.9 + 0.2 * random.random()))
taskDict = {}
for k, v in tr[avail.range(())]:
tk, = avail.unpack(k)
taskDict[tk] = v
if tk != "type" or v != "":
tr[self.timeouts.pack((timeout, key, tk))] = v
del tr[avail.range(())]
tr[self.active.key()] = random_key()
taskDict["__task_key"] = key
taskDict["__task_timeout"] = timeout
return taskDict | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/telemetry/telemetry/web_perf/metrics/webrtc_rendering_stats.py | python | WebMediaPlayerMsRenderingStats._GetSmoothnessStats | (self, norm_drift_time) | return (percent_badly_oos, percent_out_of_sync, smoothness_score) | Get the smoothness stats from the normalized drift time.
This method will calculate the smoothness score, along with the percentage
of frames badly out of sync and the percentage of frames out of sync. To be
considered badly out of sync, a frame has to have missed rendering by at
least 2*VSYNC_DURATION. To be considered out of sync, a frame has to have
missed rendering by at least one VSYNC_DURATION.
The smoothness score is a measure of how out of sync the frames are.
Args:
norm_drift_time: normalized drift time.
Returns:
a tuple of (percent_badly_oos, percent_out_of_sync, smoothness_score) | Get the smoothness stats from the normalized drift time. | [
"Get",
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"."
] | def _GetSmoothnessStats(self, norm_drift_time):
"""Get the smoothness stats from the normalized drift time.
This method will calculate the smoothness score, along with the percentage
of frames badly out of sync and the percentage of frames out of sync. To be
considered badly out of sync, a frame has to have missed rendering by at
least 2*VSYNC_DURATION. To be considered out of sync, a frame has to have
missed rendering by at least one VSYNC_DURATION.
The smoothness score is a measure of how out of sync the frames are.
Args:
norm_drift_time: normalized drift time.
Returns:
a tuple of (percent_badly_oos, percent_out_of_sync, smoothness_score)
"""
# How many times is a frame later/earlier than T=2*VSYNC_DURATION. Time is
# in microseconds.
frames_severely_out_of_sync = len(
[x for x in norm_drift_time if abs(x) > 2 * VSYNC_DURATION])
percent_badly_oos = (
100.0 * frames_severely_out_of_sync / len(norm_drift_time))
# How many times is a frame later/earlier than VSYNC_DURATION.
frames_out_of_sync = len(
[x for x in norm_drift_time if abs(x) > VSYNC_DURATION])
percent_out_of_sync = (
100.0 * frames_out_of_sync / len(norm_drift_time))
frames_oos_only_once = frames_out_of_sync - frames_severely_out_of_sync
# Calculate smoothness metric. From the formula, we can see that smoothness
# score can be negative.
smoothness_score = 100.0 - 100.0 * (frames_oos_only_once +
SEVERITY * frames_severely_out_of_sync) / len(norm_drift_time)
# Minimum smoothness_score value allowed is zero.
if smoothness_score < 0:
smoothness_score = 0
return (percent_badly_oos, percent_out_of_sync, smoothness_score) | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/bdb.py | python | Bdb.user_call | (self, frame, argument_list) | This method is called when there is the remote possibility
that we ever need to stop in this function. | This method is called when there is the remote possibility
that we ever need to stop in this function. | [
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] | def user_call(self, frame, argument_list):
"""This method is called when there is the remote possibility
that we ever need to stop in this function."""
pass | [
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PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | python/paddle/nn/utils/spectral_norm_hook.py | python | spectral_norm | (layer,
name='weight',
n_power_iterations=1,
eps=1e-12,
dim=None) | return layer | r"""
This spectral_norm layer applies spectral normalization to a parameter according to the
following Calculation:
Step 1:
Generate vector U in shape of [H], and V in shape of [W].
While H is the :attr:`dim` th dimension of the input weights,
and W is the product result of remaining dimensions.
Step 2:
:attr:`n_power_iterations` should be a positive integer, do following
calculations with U and V for :attr:`power_iters` rounds.
.. math::
\mathbf{v} := \frac{\mathbf{W}^{T} \mathbf{u}}{\|\mathbf{W}^{T} \mathbf{u}\|_2}
\mathbf{u} := \frac{\mathbf{W} \mathbf{v}}{\|\mathbf{W} \mathbf{v}\|_2}
Step 3:
Calculate :math:`\sigma(\mathbf{W})` and normalize weight values.
.. math::
\sigma(\mathbf{W}) = \mathbf{u}^{T} \mathbf{W} \mathbf{v}
\mathbf{W} = \frac{\mathbf{W}}{\sigma(\mathbf{W})}
Refer to `Spectral Normalization <https://arxiv.org/abs/1802.05957>`_ .
Parameters:
layer(Layer): Layer of paddle, which has weight.
name(str, optional): Name of the weight parameter. Default: 'weight'.
n_power_iterations(int, optional): The number of power iterations to calculate spectral norm. Default: 1.
eps(float, optional): The epsilon for numerical stability in calculating norms. Default: 1e-12.
dim(int, optional): The index of dimension which should be permuted to the first before reshaping Input(Weight) to matrix, it should be set as 0 if Input(Weight) is the weight of fc layer, and should be set as 1 if Input(Weight) is the weight of conv layer. Default: None.
Returns:
The original layer with the spectral norm hook
Examples:
.. code-block:: python
from paddle.nn import Conv2D
from paddle.nn.utils import Spectralnorm
conv = Conv2D(3, 1, 3)
sn_conv = spectral_norm(conv)
print(sn_conv)
# Conv2D(3, 1, kernel_size=[3, 3], data_format=NCHW)
print(sn_conv.weight)
# Tensor(shape=[1, 3, 3, 3], dtype=float32, place=CUDAPlace(0), stop_gradient=False,
# [[[[-0.21090528, 0.18563725, -0.14127982],
# [-0.02310637, 0.03197737, 0.34353802],
# [-0.17117859, 0.33152047, -0.28408015]],
#
# [[-0.13336606, -0.01862637, 0.06959272],
# [-0.02236020, -0.27091628, -0.24532901],
# [ 0.27254242, 0.15516677, 0.09036587]],
#
# [[ 0.30169338, -0.28146112, -0.11768346],
# [-0.45765871, -0.12504843, -0.17482486],
# [-0.36866254, -0.19969313, 0.08783543]]]]) | r"""
This spectral_norm layer applies spectral normalization to a parameter according to the
following Calculation: | [
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"normalization",
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"Calculation",
":"
] | def spectral_norm(layer,
name='weight',
n_power_iterations=1,
eps=1e-12,
dim=None):
r"""
This spectral_norm layer applies spectral normalization to a parameter according to the
following Calculation:
Step 1:
Generate vector U in shape of [H], and V in shape of [W].
While H is the :attr:`dim` th dimension of the input weights,
and W is the product result of remaining dimensions.
Step 2:
:attr:`n_power_iterations` should be a positive integer, do following
calculations with U and V for :attr:`power_iters` rounds.
.. math::
\mathbf{v} := \frac{\mathbf{W}^{T} \mathbf{u}}{\|\mathbf{W}^{T} \mathbf{u}\|_2}
\mathbf{u} := \frac{\mathbf{W} \mathbf{v}}{\|\mathbf{W} \mathbf{v}\|_2}
Step 3:
Calculate :math:`\sigma(\mathbf{W})` and normalize weight values.
.. math::
\sigma(\mathbf{W}) = \mathbf{u}^{T} \mathbf{W} \mathbf{v}
\mathbf{W} = \frac{\mathbf{W}}{\sigma(\mathbf{W})}
Refer to `Spectral Normalization <https://arxiv.org/abs/1802.05957>`_ .
Parameters:
layer(Layer): Layer of paddle, which has weight.
name(str, optional): Name of the weight parameter. Default: 'weight'.
n_power_iterations(int, optional): The number of power iterations to calculate spectral norm. Default: 1.
eps(float, optional): The epsilon for numerical stability in calculating norms. Default: 1e-12.
dim(int, optional): The index of dimension which should be permuted to the first before reshaping Input(Weight) to matrix, it should be set as 0 if Input(Weight) is the weight of fc layer, and should be set as 1 if Input(Weight) is the weight of conv layer. Default: None.
Returns:
The original layer with the spectral norm hook
Examples:
.. code-block:: python
from paddle.nn import Conv2D
from paddle.nn.utils import Spectralnorm
conv = Conv2D(3, 1, 3)
sn_conv = spectral_norm(conv)
print(sn_conv)
# Conv2D(3, 1, kernel_size=[3, 3], data_format=NCHW)
print(sn_conv.weight)
# Tensor(shape=[1, 3, 3, 3], dtype=float32, place=CUDAPlace(0), stop_gradient=False,
# [[[[-0.21090528, 0.18563725, -0.14127982],
# [-0.02310637, 0.03197737, 0.34353802],
# [-0.17117859, 0.33152047, -0.28408015]],
#
# [[-0.13336606, -0.01862637, 0.06959272],
# [-0.02236020, -0.27091628, -0.24532901],
# [ 0.27254242, 0.15516677, 0.09036587]],
#
# [[ 0.30169338, -0.28146112, -0.11768346],
# [-0.45765871, -0.12504843, -0.17482486],
# [-0.36866254, -0.19969313, 0.08783543]]]])
"""
if dim is None:
if isinstance(layer, (Conv1DTranspose, Conv2DTranspose, Conv3DTranspose,
Linear)):
dim = 1
else:
dim = 0
SpectralNorm.apply(layer, name, n_power_iterations, dim, eps)
return layer | [
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facebookincubator/BOLT | 88c70afe9d388ad430cc150cc158641701397f70 | clang/bindings/python/clang/cindex.py | python | Type.is_function_variadic | (self) | return conf.lib.clang_isFunctionTypeVariadic(self) | Determine whether this function Type is a variadic function type. | Determine whether this function Type is a variadic function type. | [
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] | def is_function_variadic(self):
"""Determine whether this function Type is a variadic function type."""
assert self.kind == TypeKind.FUNCTIONPROTO
return conf.lib.clang_isFunctionTypeVariadic(self) | [
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CRYTEK/CRYENGINE | 232227c59a220cbbd311576f0fbeba7bb53b2a8c | Editor/Python/windows/Lib/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. | [
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] | 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 is not None and timeout or 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 timeout is not None and time.time() > end_time:
if 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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cyberbotics/webots | af7fa7d68dcf7b4550f1f2e132092b41e83698fc | resources/osm_importer/osm_objects.py | python | OSMCoord.center_coordinates | (minlat, minlon, maxlat, maxlon) | return xOffset, yOffset | Center the coordinate around (0,0) and returns the offsets between earth and local world coordinate. | Center the coordinate around (0,0) and returns the offsets between earth and local world coordinate. | [
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] | def center_coordinates(minlat, minlon, maxlat, maxlon):
"""Center the coordinate around (0,0) and returns the offsets between earth and local world coordinate."""
x1, y1 = Projection.project(minlon, minlat)
x2, y2 = Projection.project(maxlon, maxlat)
xOffset = (x1 + x2) / 2
yOffset = (y1 + y2) / 2
for osmid in OSMCoord.coordDictionnary:
# inverse X, because OSM and Webots X are inversed
OSMCoord.coordDictionnary[osmid].x = OSMCoord.coordDictionnary[osmid].x - xOffset
OSMCoord.coordDictionnary[osmid].y = OSMCoord.coordDictionnary[osmid].y - yOffset
return xOffset, yOffset | [
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enjalot/adventures_in_opencl | c222d15c076ee3f5f81b529eb47e87c8d8057096 | python/part2/initialize.py | python | fountain_loopy | (num) | return pos, col, vel | This is a slower way of initializing the points (by 10x for large num)
but more illustrative of whats going on | This is a slower way of initializing the points (by 10x for large num)
but more illustrative of whats going on | [
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"""This is a slower way of initializing the points (by 10x for large num)
but more illustrative of whats going on"""
from math import sqrt, sin, cos
import numpy
pos = numpy.ndarray((num, 4), dtype=numpy.float32)
col = numpy.ndarray((num, 4), dtype=numpy.float32)
vel = numpy.ndarray((num, 4), dtype=numpy.float32)
import random
random.seed()
for i in xrange(0, num):
rad = random.uniform(.2, .5);
x = sin(2*3.14 * i/num)*rad
z = 0.
y = cos(2*3.14 * i/num)*rad
pos[i,0] = x
pos[i,1] = y
pos[i,2] = z
pos[i,3] = 1.
col[i,0] = 0.
col[i,1] = 1.
col[i,2] = 0.
col[i,3] = 1.
life = random.random()
vel[i,0] = x*2.
vel[i,1] = y*2.
vel[i,2] = 3.
vel[i,3] = life
return pos, col, vel | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/platebtn.py | python | PlateButton.GetBitmapDisabled | (self) | return self.BitmapDisabled | Get the bitmap of the disable state
:return: :class:`Bitmap` or None | Get the bitmap of the disable state | [
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"""Get the bitmap of the disable state
:return: :class:`Bitmap` or None
"""
return self.BitmapDisabled | [
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alibaba/MNN | c4d9566171d589c3ded23aa18ffb197016995a12 | pymnn/pip_package/MNN/expr/__init__.py | python | equal | (x, y) | return _F.equal(x, y) | equal(x, y)
Return the ``x == y``, element-wise.
Parameters
----------
x : var_like, input value.
y : var_like, input value.
Returns
-------
z : Var. The ``x == y`` of `x` and `y`, dtype is int32.
Example:
-------
>>> expr.equal([-9., 0.5], [1.2, 0.5])
var([0, 1]) | equal(x, y)
Return the ``x == y``, element-wise. | [
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'''
equal(x, y)
Return the ``x == y``, element-wise.
Parameters
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x : var_like, input value.
y : var_like, input value.
Returns
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z : Var. The ``x == y`` of `x` and `y`, dtype is int32.
Example:
-------
>>> expr.equal([-9., 0.5], [1.2, 0.5])
var([0, 1])
'''
x = _to_var(x)
y = _to_var(y)
x, y = _match_dtype(x, y)
return _F.equal(x, y) | [
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facebookresearch/ELF | 1f790173095cd910976d9f651b80beb872ec5d12 | vendor/pybind11/tools/clang/cindex.py | python | Cursor.is_converting_constructor | (self) | return conf.lib.clang_CXXConstructor_isConvertingConstructor(self) | Returns True if the cursor refers to a C++ converting constructor. | Returns True if the cursor refers to a C++ converting constructor. | [
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] | def is_converting_constructor(self):
"""Returns True if the cursor refers to a C++ converting constructor.
"""
return conf.lib.clang_CXXConstructor_isConvertingConstructor(self) | [
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dmlc/nnvm | dab5ce8ab6adbf4edd8bd2fa89f1a99f343b6e38 | python/nnvm/symbol.py | python | Symbol.get_children | (self) | return ret | Gets a new grouped symbol whose output contains
inputs to output nodes of the original symbol. | Gets a new grouped symbol whose output contains
inputs to output nodes of the original symbol. | [
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"""Gets a new grouped symbol whose output contains
inputs to output nodes of the original symbol."""
handle = _base.SymbolHandle()
_check_call(_LIB.NNSymbolGetChildren(
self.handle, _ctypes.byref(handle)))
ret = Symbol(handle=handle)
if not ret.list_output_names():
return None
return ret | [
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