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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/lib-tk/turtle.py | python | RawTurtle.reset | (self) | Delete the turtle's drawings and restore its default values.
No argument.
,
Delete the turtle's drawings from the screen, re-center the turtle
and set variables to the default values.
Example (for a Turtle instance named turtle):
>>> turtle.position()
(0.00,-22.00)
>>> turtle.heading()
100.0
>>> turtle.reset()
>>> turtle.position()
(0.00,0.00)
>>> turtle.heading()
0.0 | Delete the turtle's drawings and restore its default values. | [
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] | def reset(self):
"""Delete the turtle's drawings and restore its default values.
No argument.
,
Delete the turtle's drawings from the screen, re-center the turtle
and set variables to the default values.
Example (for a Turtle instance named turtle):
>>> turtle.position()
(0.00,-22.00)
>>> turtle.heading()
100.0
>>> turtle.reset()
>>> turtle.position()
(0.00,0.00)
>>> turtle.heading()
0.0
"""
TNavigator.reset(self)
TPen._reset(self)
self._clear()
self._drawturtle()
self._update() | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/site-packages/s3transfer/futures.py | python | ExecutorFuture.__init__ | (self, future) | A future returned from the executor
Currently, it is just a wrapper around a concurrent.futures.Future.
However, this can eventually grow to implement the needed functionality
of concurrent.futures.Future if we move off of the library and not
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"""A future returned from the executor
Currently, it is just a wrapper around a concurrent.futures.Future.
However, this can eventually grow to implement the needed functionality
of concurrent.futures.Future if we move off of the library and not
affect the rest of the codebase.
:type future: concurrent.futures.Future
:param future: The underlying future
"""
self._future = future | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/pip/_internal/models/target_python.py | python | TargetPython.format_given | (self) | return ' '.join(
f'{key}={value!r}' for key, value in key_values
if value is not None
) | Format the given, non-None attributes for display. | [] | def format_given(self):
# type: () -> str
"""
Format the given, non-None attributes for display.
"""
display_version = None
if self._given_py_version_info is not None:
display_version = '.'.join(
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return ' '.join(
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apple/swift-lldb | d74be846ef3e62de946df343e8c234bde93a8912 | examples/python/file_extract.py | python | FileExtract.get_uint16 | (self, fail_value=0) | Extract a single uint16_t from the binary file at the current file position, returns a single integer | Extract a single uint16_t from the binary file at the current file position, returns a single integer | [
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'''Extract a single uint16_t from the binary file at the current file position, returns a single integer'''
s = self.read_size(2)
if s:
v, = struct.unpack(self.byte_order + 'H', s)
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/ipaddress.py | python | IPv6Address.is_unspecified | (self) | return self._ip == 0 | Test if the address is unspecified.
Returns:
A boolean, True if this is the unspecified address as defined in
RFC 2373 2.5.2. | Test if the address is unspecified. | [
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"""Test if the address is unspecified.
Returns:
A boolean, True if this is the unspecified address as defined in
RFC 2373 2.5.2.
"""
return self._ip == 0 | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/ops/partitioned_variables.py | python | create_partitioned_variables | (
shape, slicing, initializer, dtype=dtypes.float32,
trainable=True, collections=None, name=None, reuse=None) | Create a list of partitioned variables according to the given `slicing`.
Currently only one dimension of the full variable can be sliced, and the
full variable can be reconstructed by the concatenation of the returned
list along that dimension.
Args:
shape: List of integers. The shape of the full variable.
slicing: List of integers. How to partition the variable.
Must be of the same length as `shape`. Each value
indicate how many slices to create in the corresponding
dimension. Presently only one of the values can be more than 1;
that is, the variable can only be sliced along one dimension.
For convenience, The requested number of partitions does not have to
divide the corresponding dimension evenly. If it does not, the
shapes of the partitions are incremented by 1 starting from partition
0 until all slack is absorbed. The adjustment rules may change in the
future, but as you can save/restore these variables with different
slicing specifications this should not be a problem.
initializer: A `Tensor` of shape `shape` or a variable initializer
function. If a function, it will be called once for each slice,
passing the shape and data type of the slice as parameters. The
function must return a tensor with the same shape as the slice.
dtype: Type of the variables. Ignored if `initializer` is a `Tensor`.
trainable: If True also add all the variables to the graph collection
`GraphKeys.TRAINABLE_VARIABLES`.
collections: List of graph collections keys to add the variables to.
Defaults to `[GraphKeys.GLOBAL_VARIABLES]`.
name: Optional name for the full variable. Defaults to
`"PartitionedVariable"` and gets uniquified automatically.
reuse: Boolean or `None`; if `True` and name is set, it would reuse
previously created variables. if `False` it will create new variables.
if `None`, it would inherit the parent scope reuse.
Returns:
A list of Variables corresponding to the slicing.
Raises:
ValueError: If any of the arguments is malformed. | Create a list of partitioned variables according to the given `slicing`. | [
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shape, slicing, initializer, dtype=dtypes.float32,
trainable=True, collections=None, name=None, reuse=None):
"""Create a list of partitioned variables according to the given `slicing`.
Currently only one dimension of the full variable can be sliced, and the
full variable can be reconstructed by the concatenation of the returned
list along that dimension.
Args:
shape: List of integers. The shape of the full variable.
slicing: List of integers. How to partition the variable.
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indicate how many slices to create in the corresponding
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that is, the variable can only be sliced along one dimension.
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initializer: A `Tensor` of shape `shape` or a variable initializer
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passing the shape and data type of the slice as parameters. The
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dtype: Type of the variables. Ignored if `initializer` is a `Tensor`.
trainable: If True also add all the variables to the graph collection
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collections: List of graph collections keys to add the variables to.
Defaults to `[GraphKeys.GLOBAL_VARIABLES]`.
name: Optional name for the full variable. Defaults to
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reuse: Boolean or `None`; if `True` and name is set, it would reuse
previously created variables. if `False` it will create new variables.
if `None`, it would inherit the parent scope reuse.
Returns:
A list of Variables corresponding to the slicing.
Raises:
ValueError: If any of the arguments is malformed.
"""
logging.warn(
"create_partitioned_variables is deprecated. Use "
"tf.get_variable with a partitioner set, or "
"tf.get_partitioned_variable_list, instead.")
if len(shape) != len(slicing):
raise ValueError("The 'shape' and 'slicing' of a partitioned Variable "
"must have the length: shape: %s, slicing: %s" %
(shape, slicing))
if len(shape) < 1:
raise ValueError("A partitioned Variable must have rank at least 1: "
"shape: %s" % shape)
# Legacy: we are provided the slicing directly, so just pass it to
# the partitioner.
partitioner = lambda **unused_kwargs: slicing
with variable_scope.variable_scope(
name, "PartitionedVariable", reuse=reuse):
# pylint: disable=protected-access
partitioned_var = variable_scope._get_partitioned_variable(
name=None,
shape=shape,
dtype=dtype,
initializer=initializer,
trainable=trainable,
partitioner=partitioner,
collections=collections)
return list(partitioned_var) | [
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intel/caffe | 3f494b442ee3f9d17a07b09ecbd5fa2bbda00836 | examples/faster-rcnn/lib/roi_data_layer/minibatch.py | python | _project_im_rois | (im_rois, im_scale_factor) | return rois | Project image RoIs into the rescaled training image. | Project image RoIs into the rescaled training image. | [
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] | def _project_im_rois(im_rois, im_scale_factor):
"""Project image RoIs into the rescaled training image."""
rois = im_rois * im_scale_factor
return rois | [
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llvm/llvm-project | ffa6262cb4e2a335d26416fad39a581b4f98c5f4 | llvm/utils/lit/lit/util.py | python | to_unicode | (s) | return s | Return the parameter as type which supports unicode, possibly decoding
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"""Return the parameter as type which supports unicode, possibly decoding
it.
In Python2, this is the unicode type. In Python3 it's the str type.
"""
if isinstance(s, bytes):
# In Python2, this branch is taken for both 'str' and 'bytes'.
# In Python3, this branch is taken only for 'bytes'.
return s.decode('utf-8')
return s | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/mindrecord/tools/tfrecord_to_mr.py | python | TFRecordToMR.transform | (self) | return t.res | Encapsulate the run function to exit normally | Encapsulate the run function to exit normally | [
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t = ExceptionThread(target=self.run)
t.daemon = True
t.start()
t.join()
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mhammond/pywin32 | 44afd86ba8485194df93234639243252deeb40d5 | com/win32com/makegw/makegwparse.py | python | ArgFormatter.GetBuildForInterfacePostCode | (self) | return "" | Get a string of C++ code to be executed after (ie, to finalise) the Py_BuildValue conversion for Interfaces | Get a string of C++ code to be executed after (ie, to finalise) the Py_BuildValue conversion for Interfaces | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/idlelib/macosx.py | python | setupApp | (root, flist) | Perform initial OS X customizations if needed.
Called from pyshell.main() after initial calls to Tk()
There are currently three major versions of Tk in use on OS X:
1. Aqua Cocoa Tk (native default since OS X 10.6)
2. Aqua Carbon Tk (original native, 32-bit only, deprecated)
3. X11 (supported by some third-party distributors, deprecated)
There are various differences among the three that affect IDLE
behavior, primarily with menus, mouse key events, and accelerators.
Some one-time customizations are performed here.
Others are dynamically tested throughout idlelib by calls to the
isAquaTk(), isCarbonTk(), isCocoaTk(), isXQuartz() functions which
are initialized here as well. | Perform initial OS X customizations if needed.
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"""
Perform initial OS X customizations if needed.
Called from pyshell.main() after initial calls to Tk()
There are currently three major versions of Tk in use on OS X:
1. Aqua Cocoa Tk (native default since OS X 10.6)
2. Aqua Carbon Tk (original native, 32-bit only, deprecated)
3. X11 (supported by some third-party distributors, deprecated)
There are various differences among the three that affect IDLE
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if isAquaTk():
hideTkConsole(root)
overrideRootMenu(root, flist)
addOpenEventSupport(root, flist)
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microsoft/ELL | a1d6bacc37a14879cc025d9be2ba40b1a0632315 | tools/utilities/pythonlibs/audio/training/dataset.py | python | Dataset.to_categorical | (self, labels, num_categories) | return result | Convert the labels to vector format useful for training, for example the label 3 out of 10
possible categories would become this vector: "0 0 0 1 0 0 0 0 0 0 0". | Convert the labels to vector format useful for training, for example the label 3 out of 10
possible categories would become this vector: "0 0 0 1 0 0 0 0 0 0 0". | [
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"""
Convert the labels to vector format useful for training, for example the label 3 out of 10
possible categories would become this vector: "0 0 0 1 0 0 0 0 0 0 0".
"""
labels_arr = np.array(labels, dtype="int").ravel()
num_rows = len(labels_arr)
result = np.zeros((num_rows, num_categories))
result[np.arange(num_rows), labels_arr] = 1
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openvinotoolkit/openvino | dedcbeafa8b84cccdc55ca64b8da516682b381c7 | tools/pot/tools/frame_extractor/extractor.py | python | extract_frames_and_make_dataset | (
video_file, output_dir, dataset_size, frame_step, ext='png') | Extracts frames with the highest motion value and creates dataset in specified directory
:param video_file: path to video file
:param output_dir: directory to save extracted images as dataset
:param dataset_size: number of images to extract
:param frame_step: step to drop frames from video and exclude launching of algorithm for them
:param ext: extension of images in dataset | Extracts frames with the highest motion value and creates dataset in specified directory
:param video_file: path to video file
:param output_dir: directory to save extracted images as dataset
:param dataset_size: number of images to extract
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video_file, output_dir, dataset_size, frame_step, ext='png'):
"""
Extracts frames with the highest motion value and creates dataset in specified directory
:param video_file: path to video file
:param output_dir: directory to save extracted images as dataset
:param dataset_size: number of images to extract
:param frame_step: step to drop frames from video and exclude launching of algorithm for them
:param ext: extension of images in dataset
"""
frames_by_motion = sort_frame_indices_by_motion(video_file, frame_step)
loader = video_loader.VideoLoader(video_file)
if dataset_size is None:
dataset_size = len(loader)
if dataset_size > len(loader):
raise RuntimeError('Number of images in output dataset should'
' not be bigger than number of images in video')
dataset_indices = sorted(frames_by_motion[:dataset_size])
output_dir = Path(output_dir)
for idx, dataset_idx in enumerate(dataset_indices):
frame = loader[dataset_idx]
cv.imwrite(str(output_dir / '{}.{}'.format(idx, ext)), frame) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/logging/handlers.py | python | NTEventLogHandler.close | (self) | Clean up this handler.
You can remove the application name from the registry as a
source of event log entries. However, if you do this, you will
not be able to see the events as you intended in the Event Log
Viewer - it needs to be able to access the registry to get the
DLL name. | Clean up this handler. | [
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] | def close(self):
"""
Clean up this handler.
You can remove the application name from the registry as a
source of event log entries. However, if you do this, you will
not be able to see the events as you intended in the Event Log
Viewer - it needs to be able to access the registry to get the
DLL name.
"""
#self._welu.RemoveSourceFromRegistry(self.appname, self.logtype)
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apache/incubator-mxnet | f03fb23f1d103fec9541b5ae59ee06b1734a51d9 | python/mxnet/base.py | python | _valid_error_name | (name) | return all(x.isalnum() or x in "_." for x in name) | Check whether name is a valid error name. | Check whether name is a valid error name. | [
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return all(x.isalnum() or x in "_." for x in name) | [
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hpi-xnor/BMXNet | ed0b201da6667887222b8e4b5f997c4f6b61943d | python/mxnet/symbol/symbol.py | python | Symbol.debug_str | (self) | return py_str(debug_str.value) | Gets a debug string of symbol.
It contains Symbol output, variables and operators in the computation graph
with their inputs, variables and attributes.
Returns
-------
string
Debug string of the symbol.
Examples
--------
>>> a = mx.sym.Variable('a')
>>> b = mx.sym.sin(a)
>>> c = 2 * a + b
>>> d = mx.sym.FullyConnected(data=c, num_hidden=10)
>>> d.debug_str()
>>> print d.debug_str()
Symbol Outputs:
output[0]=fullyconnected0(0)
Variable:a
--------------------
Op:_mul_scalar, Name=_mulscalar0
Inputs:
arg[0]=a(0) version=0
Attrs:
scalar=2
--------------------
Op:sin, Name=sin0
Inputs:
arg[0]=a(0) version=0
--------------------
Op:elemwise_add, Name=_plus0
Inputs:
arg[0]=_mulscalar0(0)
arg[1]=sin0(0)
Variable:fullyconnected0_weight
Variable:fullyconnected0_bias
--------------------
Op:FullyConnected, Name=fullyconnected0
Inputs:
arg[0]=_plus0(0)
arg[1]=fullyconnected0_weight(0) version=0
arg[2]=fullyconnected0_bias(0) version=0
Attrs:
num_hidden=10 | Gets a debug string of symbol. | [
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] | def debug_str(self):
"""Gets a debug string of symbol.
It contains Symbol output, variables and operators in the computation graph
with their inputs, variables and attributes.
Returns
-------
string
Debug string of the symbol.
Examples
--------
>>> a = mx.sym.Variable('a')
>>> b = mx.sym.sin(a)
>>> c = 2 * a + b
>>> d = mx.sym.FullyConnected(data=c, num_hidden=10)
>>> d.debug_str()
>>> print d.debug_str()
Symbol Outputs:
output[0]=fullyconnected0(0)
Variable:a
--------------------
Op:_mul_scalar, Name=_mulscalar0
Inputs:
arg[0]=a(0) version=0
Attrs:
scalar=2
--------------------
Op:sin, Name=sin0
Inputs:
arg[0]=a(0) version=0
--------------------
Op:elemwise_add, Name=_plus0
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arg[1]=sin0(0)
Variable:fullyconnected0_weight
Variable:fullyconnected0_bias
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Inputs:
arg[0]=_plus0(0)
arg[1]=fullyconnected0_weight(0) version=0
arg[2]=fullyconnected0_bias(0) version=0
Attrs:
num_hidden=10
"""
debug_str = ctypes.c_char_p()
check_call(_LIB.MXSymbolPrint(
self.handle, ctypes.byref(debug_str)))
return py_str(debug_str.value) | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/contrib/learn/python/learn/models.py | python | linear_regression | (x, y, init_mean=None, init_stddev=1.0) | Creates linear regression TensorFlow subgraph.
Args:
x: tensor or placeholder for input features.
y: tensor or placeholder for labels.
init_mean: the mean value to use for initialization.
init_stddev: the standard deviation to use for initialization.
Returns:
Predictions and loss tensors.
Side effects:
The variables linear_regression.weights and linear_regression.bias are
initialized as follows. If init_mean is not None, then initialization
will be done using a random normal initializer with the given init_mean
and init_stddv. (These may be set to 0.0 each if a zero initialization
is desirable for convex use cases.) If init_mean is None, then the
uniform_unit_scaling_initialzer will be used. | Creates linear regression TensorFlow subgraph. | [
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] | def linear_regression(x, y, init_mean=None, init_stddev=1.0):
"""Creates linear regression TensorFlow subgraph.
Args:
x: tensor or placeholder for input features.
y: tensor or placeholder for labels.
init_mean: the mean value to use for initialization.
init_stddev: the standard deviation to use for initialization.
Returns:
Predictions and loss tensors.
Side effects:
The variables linear_regression.weights and linear_regression.bias are
initialized as follows. If init_mean is not None, then initialization
will be done using a random normal initializer with the given init_mean
and init_stddv. (These may be set to 0.0 each if a zero initialization
is desirable for convex use cases.) If init_mean is None, then the
uniform_unit_scaling_initialzer will be used.
"""
with vs.variable_scope('linear_regression'):
scope_name = vs.get_variable_scope().name
summary.histogram('%s.x' % scope_name, x)
summary.histogram('%s.y' % scope_name, y)
dtype = x.dtype.base_dtype
y_shape = y.get_shape()
if len(y_shape) == 1:
output_shape = 1
else:
output_shape = y_shape[1]
# Set up the requested initialization.
if init_mean is None:
weights = vs.get_variable(
'weights', [x.get_shape()[1], output_shape], dtype=dtype)
bias = vs.get_variable('bias', [output_shape], dtype=dtype)
else:
weights = vs.get_variable(
'weights', [x.get_shape()[1], output_shape],
initializer=init_ops.random_normal_initializer(
init_mean, init_stddev, dtype=dtype),
dtype=dtype)
bias = vs.get_variable(
'bias', [output_shape],
initializer=init_ops.random_normal_initializer(
init_mean, init_stddev, dtype=dtype),
dtype=dtype)
summary.histogram('%s.weights' % scope_name, weights)
summary.histogram('%s.bias' % scope_name, bias)
return losses_ops.mean_squared_error_regressor(x, y, weights, bias) | [
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ROCmSoftwarePlatform/hipCaffe | 4ec5d482515cce532348553b6db6d00d015675d5 | scripts/cpp_lint.py | python | CleanseComments | (line) | return _RE_PATTERN_CLEANSE_LINE_C_COMMENTS.sub('', line) | Removes //-comments and single-line C-style /* */ comments.
Args:
line: A line of C++ source.
Returns:
The line with single-line comments removed. | Removes //-comments and single-line C-style /* */ comments. | [
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"""Removes //-comments and single-line C-style /* */ comments.
Args:
line: A line of C++ source.
Returns:
The line with single-line comments removed.
"""
commentpos = line.find('//')
if commentpos != -1 and not IsCppString(line[:commentpos]):
line = line[:commentpos].rstrip()
# get rid of /* ... */
return _RE_PATTERN_CLEANSE_LINE_C_COMMENTS.sub('', line) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/prompt-toolkit/py2/prompt_toolkit/key_binding/bindings/named_commands.py | python | beginning_of_line | (event) | Move to the start of the current line. | Move to the start of the current line. | [
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] | def beginning_of_line(event):
" Move to the start of the current line. "
buff = event.current_buffer
buff.cursor_position += buff.document.get_start_of_line_position(after_whitespace=False) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py3/scipy/signal/wavelets.py | python | ricker | (points, a) | return total | Return a Ricker wavelet, also known as the "Mexican hat wavelet".
It models the function:
``A (1 - x^2/a^2) exp(-x^2/2 a^2)``,
where ``A = 2/sqrt(3a)pi^1/4``.
Parameters
----------
points : int
Number of points in `vector`.
Will be centered around 0.
a : scalar
Width parameter of the wavelet.
Returns
-------
vector : (N,) ndarray
Array of length `points` in shape of ricker curve.
Examples
--------
>>> from scipy import signal
>>> import matplotlib.pyplot as plt
>>> points = 100
>>> a = 4.0
>>> vec2 = signal.ricker(points, a)
>>> print(len(vec2))
100
>>> plt.plot(vec2)
>>> plt.show() | Return a Ricker wavelet, also known as the "Mexican hat wavelet". | [
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"Ricker",
"wavelet",
"also",
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"the",
"Mexican",
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] | def ricker(points, a):
"""
Return a Ricker wavelet, also known as the "Mexican hat wavelet".
It models the function:
``A (1 - x^2/a^2) exp(-x^2/2 a^2)``,
where ``A = 2/sqrt(3a)pi^1/4``.
Parameters
----------
points : int
Number of points in `vector`.
Will be centered around 0.
a : scalar
Width parameter of the wavelet.
Returns
-------
vector : (N,) ndarray
Array of length `points` in shape of ricker curve.
Examples
--------
>>> from scipy import signal
>>> import matplotlib.pyplot as plt
>>> points = 100
>>> a = 4.0
>>> vec2 = signal.ricker(points, a)
>>> print(len(vec2))
100
>>> plt.plot(vec2)
>>> plt.show()
"""
A = 2 / (np.sqrt(3 * a) * (np.pi**0.25))
wsq = a**2
vec = np.arange(0, points) - (points - 1.0) / 2
xsq = vec**2
mod = (1 - xsq / wsq)
gauss = np.exp(-xsq / (2 * wsq))
total = A * mod * gauss
return total | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/threading.py | python | _Event.set | (self) | Set the internal flag to true.
All threads waiting for the flag to become true are awakened. Threads
that call wait() once the flag is true will not block at all. | Set the internal flag to true. | [
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] | def set(self):
"""Set the internal flag to true.
All threads waiting for the flag to become true are awakened. Threads
that call wait() once the flag is true will not block at all.
"""
self.__cond.acquire()
try:
self.__flag = True
self.__cond.notify_all()
finally:
self.__cond.release() | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/cgi.py | python | parse_header | (line) | return key, pdict | Parse a Content-type like header.
Return the main content-type and a dictionary of options. | Parse a Content-type like header. | [
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] | def parse_header(line):
"""Parse a Content-type like header.
Return the main content-type and a dictionary of options.
"""
parts = _parseparam(';' + line)
key = parts.next()
pdict = {}
for p in parts:
i = p.find('=')
if i >= 0:
name = p[:i].strip().lower()
value = p[i+1:].strip()
if len(value) >= 2 and value[0] == value[-1] == '"':
value = value[1:-1]
value = value.replace('\\\\', '\\').replace('\\"', '"')
pdict[name] = value
return key, pdict | [
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BlzFans/wke | b0fa21158312e40c5fbd84682d643022b6c34a93 | cygwin/lib/python2.6/email/_parseaddr.py | python | AddrlistClass.getaddrspec | (self) | return EMPTYSTRING.join(aslist) + self.getdomain() | Parse an RFC 2822 addr-spec. | Parse an RFC 2822 addr-spec. | [
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] | def getaddrspec(self):
"""Parse an RFC 2822 addr-spec."""
aslist = []
self.gotonext()
while self.pos < len(self.field):
if self.field[self.pos] == '.':
aslist.append('.')
self.pos += 1
elif self.field[self.pos] == '"':
aslist.append('"%s"' % self.getquote())
elif self.field[self.pos] in self.atomends:
break
else:
aslist.append(self.getatom())
self.gotonext()
if self.pos >= len(self.field) or self.field[self.pos] != '@':
return EMPTYSTRING.join(aslist)
aslist.append('@')
self.pos += 1
self.gotonext()
return EMPTYSTRING.join(aslist) + self.getdomain() | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/python/ops/variable_scope.py | python | variable_op_scope | (values,
name_or_scope,
default_name=None,
initializer=None,
regularizer=None,
caching_device=None,
partitioner=None,
custom_getter=None,
reuse=None,
dtype=None,
use_resource=None) | Deprecated: context manager for defining an op that creates variables. | Deprecated: context manager for defining an op that creates variables. | [
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] | def variable_op_scope(values,
name_or_scope,
default_name=None,
initializer=None,
regularizer=None,
caching_device=None,
partitioner=None,
custom_getter=None,
reuse=None,
dtype=None,
use_resource=None):
"""Deprecated: context manager for defining an op that creates variables."""
logging.warn("tf.variable_op_scope(values, name, default_name) is deprecated,"
" use tf.variable_scope(name, default_name, values)")
with variable_scope(name_or_scope,
default_name=default_name,
values=values,
initializer=initializer,
regularizer=regularizer,
caching_device=caching_device,
partitioner=partitioner,
custom_getter=custom_getter,
reuse=reuse,
dtype=dtype,
use_resource=use_resource) as scope:
yield scope | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/richtext.py | python | RichTextBuffer.BeginTextColour | (*args, **kwargs) | return _richtext.RichTextBuffer_BeginTextColour(*args, **kwargs) | BeginTextColour(self, Colour colour) -> bool | BeginTextColour(self, Colour colour) -> bool | [
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] | def BeginTextColour(*args, **kwargs):
"""BeginTextColour(self, Colour colour) -> bool"""
return _richtext.RichTextBuffer_BeginTextColour(*args, **kwargs) | [
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microsoft/onnxruntime | f92e47e95b13a240e37caf7b36577983544f98fc | orttraining/orttraining/python/training/ortmodule/_graph_execution_manager.py | python | GraphExecutionManager._get_session_config | (self) | return session_options, providers, provider_options | Creates and returns the session configuration to be used for the ExecutionAgent | Creates and returns the session configuration to be used for the ExecutionAgent | [
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] | def _get_session_config(self):
"""Creates and returns the session configuration to be used for the ExecutionAgent"""
if _are_deterministic_algorithms_enabled():
if self._debug_options.logging.log_level <= _logger.LogLevel.INFO:
warnings.warn("ORTModule's determinism will be enabled because PyTorch's determinism is enabled.",
UserWarning)
providers = None
provider_options = None
if self._device.type == 'cuda':
# Configure the InferenceSessions to use the specific GPU on which the model is placed.
providers = (["ROCMExecutionProvider"] if self.is_rocm_pytorch else [
"CUDAExecutionProvider"])
providers.append("CPUExecutionProvider")
provider_option_map = {"device_id": str(self._device.index)}
if not self.is_rocm_pytorch:
# Set Conv algo search mode to HEURISTIC, which is same as PyTorch's default setting.
provider_option_map["cudnn_conv_algo_search"] = "HEURISTIC"
provider_option_map["cudnn_conv_use_max_workspace"] = "1"
if self._use_external_gpu_allocator:
provider_option_map["gpu_external_alloc"] = str(self._torch_alloc)
provider_option_map["gpu_external_free"] = str(self._torch_free)
provider_option_map["gpu_external_empty_cache"] = str(self._torch_empty_cache)
provider_options = [provider_option_map, {}]
elif self._device.type == 'cpu':
providers = ["CPUExecutionProvider"]
provider_options = [{}]
elif self._device.type == 'ort':
provider_info = C.get_ort_device_provider_info(self._device.index)
assert len(provider_info.keys()) == 1
providers = list(provider_info.keys())
provider_options = [provider_info[providers[0]]]
session_options = onnxruntime.SessionOptions()
session_options.enable_mem_pattern = False
session_options.enable_mem_reuse = False
session_options.use_deterministic_compute = _are_deterministic_algorithms_enabled()
# default to PRIORITY_BASED execution order
session_options.execution_order = onnxruntime.ExecutionOrder.PRIORITY_BASED
# 0:Verbose, 1:Info, 2:Warning. 3:Error, 4:Fatal. Default is 2.
session_options.log_severity_level = int(
self._debug_options.logging.log_level)
if self._debug_options.save_onnx_models.save:
session_options.optimized_model_filepath = \
os.path.join(self._debug_options.save_onnx_models.path,
_onnx_models._get_onnx_file_name(
self._debug_options.save_onnx_models.name_prefix,
'execution_model', self._export_mode))
return session_options, providers, provider_options | [
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llvm/llvm-project | ffa6262cb4e2a335d26416fad39a581b4f98c5f4 | mlir/python/mlir/dialects/linalg/opdsl/lang/affine.py | python | AffineBinaryExprDef.visit_affine_exprs | (self, callback) | Visits all AffineExprDefs including self. | Visits all AffineExprDefs including self. | [
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] | def visit_affine_exprs(self, callback):
"""Visits all AffineExprDefs including self."""
super().visit_affine_exprs(callback)
self.lhs.visit_affine_exprs(callback)
self.rhs.visit_affine_exprs(callback) | [
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apache/incubator-mxnet | f03fb23f1d103fec9541b5ae59ee06b1734a51d9 | python/mxnet/gluon/probability/distributions/binomial.py | python | Binomial.prob | (self) | return logit2prob(self.logit, True) | Get the probability of sampling `1`.
Returns
-------
Tensor
Parameter tensor. | Get the probability of sampling `1`. | [
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] | def prob(self):
"""Get the probability of sampling `1`.
Returns
-------
Tensor
Parameter tensor.
"""
# pylint: disable=method-hidden
return logit2prob(self.logit, True) | [
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apache/incubator-mxnet | f03fb23f1d103fec9541b5ae59ee06b1734a51d9 | python/mxnet/symbol/symbol.py | python | split_v2 | (ary, indices_or_sections, axis=0, squeeze_axis=False) | return _internal._split_v2(ary, indices, axis, squeeze_axis, sections) | Split an array into multiple sub-arrays.
Parameters
----------
ary : NDArray
Array to be divided into sub-arrays.
indices_or_sections : int or tuple of ints
If `indices_or_sections` is an integer, N, the array will be divided
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- ary[:2]
- ary[2:3]
- ary[3:]
If an index exceeds the dimension of the array along `axis`,
an empty sub-array is returned correspondingly.
axis : int, optional
The axis along which to split, default is 0.
squeeze_axis: boolean, optional
Whether to squeeze the axis of sub-arrays or not, only useful when size
of the sub-arrays are 1 on the `axis`. Default is False.
Returns
-------
out : Symbol
The created Symbol | Split an array into multiple sub-arrays. | [
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] | def split_v2(ary, indices_or_sections, axis=0, squeeze_axis=False):
"""Split an array into multiple sub-arrays.
Parameters
----------
ary : NDArray
Array to be divided into sub-arrays.
indices_or_sections : int or tuple of ints
If `indices_or_sections` is an integer, N, the array will be divided
into N equal arrays along `axis`. If such a split is not possible,
an error is raised.
If `indices_or_sections` is a 1-D array of sorted integers, the entries
indicate where along `axis` the array is split. For example,
``[2, 3]`` would, for ``axis=0``, result in
- ary[:2]
- ary[2:3]
- ary[3:]
If an index exceeds the dimension of the array along `axis`,
an empty sub-array is returned correspondingly.
axis : int, optional
The axis along which to split, default is 0.
squeeze_axis: boolean, optional
Whether to squeeze the axis of sub-arrays or not, only useful when size
of the sub-arrays are 1 on the `axis`. Default is False.
Returns
-------
out : Symbol
The created Symbol
"""
indices = []
sections = 0
if isinstance(indices_or_sections, int):
sections = indices_or_sections
elif isinstance(indices_or_sections, tuple):
indices = [0] + list(indices_or_sections)
else:
raise ValueError('indices_or_sections must either int or tuple of ints')
return _internal._split_v2(ary, indices, axis, squeeze_axis, sections) | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/stringold.py | python | lower | (s) | return s.lower() | lower(s) -> string
Return a copy of the string s converted to lowercase. | lower(s) -> string | [
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"""lower(s) -> string
Return a copy of the string s converted to lowercase.
"""
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/klampt/robotsim.py | python | Geometry3D.getGeometricPrimitive | (self) | return _robotsim.Geometry3D_getGeometricPrimitive(self) | r"""
Returns a GeometricPrimitive if this geometry is of type GeometricPrimitive. | r"""
Returns a GeometricPrimitive if this geometry is of type GeometricPrimitive. | [
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r"""
Returns a GeometricPrimitive if this geometry is of type GeometricPrimitive.
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return _robotsim.Geometry3D_getGeometricPrimitive(self) | [
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jackaudio/jack2 | 21b293dbc37d42446141a08922cdec0d2550c6a0 | waflib/Tools/c_aliases.py | python | get_extensions | (lst) | return ret | Returns the file extensions for the list of files given as input
:param lst: files to process
:list lst: list of string or :py:class:`waflib.Node.Node`
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"""
Returns the file extensions for the list of files given as input
:param lst: files to process
:list lst: list of string or :py:class:`waflib.Node.Node`
:return: list of file extensions
:rtype: list of string
"""
ret = []
for x in Utils.to_list(lst):
if not isinstance(x, str):
x = x.name
ret.append(x[x.rfind('.') + 1:])
return ret | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | tools/telemetry/third_party/png/png.py | python | Writer.write | (self, outfile, rows) | Write a PNG image to the output file. `rows` should be
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The rows should be the rows of the original image, so there
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a = array(fmt, itertools.chain(*rows))
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else:
nrows = self.write_passes(outfile, rows)
if nrows != self.height:
raise ValueError(
"rows supplied (%d) does not match height (%d)" %
(nrows, self.height)) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/build/waf-1.7.13/waflib/Tools/errcheck.py | python | enhance_lib | () | modify existing classes and methods | modify existing classes and methods | [
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] | def enhance_lib():
"""
modify existing classes and methods
"""
for m in meths_typos:
replace(m)
# catch '..' in ant_glob patterns
def ant_glob(self, *k, **kw):
if k:
lst=Utils.to_list(k[0])
for pat in lst:
if '..' in pat.split('/'):
Logs.error("In ant_glob pattern %r: '..' means 'two dots', not 'parent directory'" % k[0])
if kw.get('remove', True):
try:
if self.is_child_of(self.ctx.bldnode) and not kw.get('quiet', False):
Logs.error('Using ant_glob on the build folder (%r) is dangerous (quiet=True to disable this warning)' % self)
except AttributeError:
pass
return self.old_ant_glob(*k, **kw)
Node.Node.old_ant_glob = Node.Node.ant_glob
Node.Node.ant_glob = ant_glob
# catch conflicting ext_in/ext_out/before/after declarations
old = Task.is_before
def is_before(t1, t2):
ret = old(t1, t2)
if ret and old(t2, t1):
Logs.error('Contradictory order constraints in classes %r %r' % (t1, t2))
return ret
Task.is_before = is_before
# check for bld(feature='cshlib') where no 'c' is given - this can be either a mistake or on purpose
# so we only issue a warning
def check_err_features(self):
lst = self.to_list(self.features)
if 'shlib' in lst:
Logs.error('feature shlib -> cshlib, dshlib or cxxshlib')
for x in ('c', 'cxx', 'd', 'fc'):
if not x in lst and lst and lst[0] in [x+y for y in ('program', 'shlib', 'stlib')]:
Logs.error('%r features is probably missing %r' % (self, x))
TaskGen.feature('*')(check_err_features)
# check for erroneous order constraints
def check_err_order(self):
if not hasattr(self, 'rule') and not 'subst' in Utils.to_list(self.features):
for x in ('before', 'after', 'ext_in', 'ext_out'):
if hasattr(self, x):
Logs.warn('Erroneous order constraint %r on non-rule based task generator %r' % (x, self))
else:
for x in ('before', 'after'):
for y in self.to_list(getattr(self, x, [])):
if not Task.classes.get(y, None):
Logs.error('Erroneous order constraint %s=%r on %r (no such class)' % (x, y, self))
TaskGen.feature('*')(check_err_order)
# check for @extension used with @feature/@before_method/@after_method
def check_compile(self):
check_invalid_constraints(self)
try:
ret = self.orig_compile()
finally:
check_same_targets(self)
return ret
Build.BuildContext.orig_compile = Build.BuildContext.compile
Build.BuildContext.compile = check_compile
# check for invalid build groups #914
def use_rec(self, name, **kw):
try:
y = self.bld.get_tgen_by_name(name)
except Errors.WafError:
pass
else:
idx = self.bld.get_group_idx(self)
odx = self.bld.get_group_idx(y)
if odx > idx:
msg = "Invalid 'use' across build groups:"
if Logs.verbose > 1:
msg += '\n target %r\n uses:\n %r' % (self, y)
else:
msg += " %r uses %r (try 'waf -v -v' for the full error)" % (self.name, name)
raise Errors.WafError(msg)
self.orig_use_rec(name, **kw)
TaskGen.task_gen.orig_use_rec = TaskGen.task_gen.use_rec
TaskGen.task_gen.use_rec = use_rec
# check for env.append
def getattri(self, name, default=None):
if name == 'append' or name == 'add':
raise Errors.WafError('env.append and env.add do not exist: use env.append_value/env.append_unique')
elif name == 'prepend':
raise Errors.WafError('env.prepend does not exist: use env.prepend_value')
if name in self.__slots__:
return object.__getattr__(self, name, default)
else:
return self[name]
ConfigSet.ConfigSet.__getattr__ = getattri | [
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daijifeng001/caffe-rfcn | 543f8f6a4b7c88256ea1445ae951a12d1ad9cffd | scripts/cpp_lint.py | python | _CppLintState.PrintErrorCounts | (self) | Print a summary of errors by category, and the total. | Print a summary of errors by category, and the total. | [
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"""Print a summary of errors by category, and the total."""
for category, count in self.errors_by_category.iteritems():
sys.stderr.write('Category \'%s\' errors found: %d\n' %
(category, count))
sys.stderr.write('Total errors found: %d\n' % self.error_count) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/scipy/optimize/optimize.py | python | _minimize_scalar_brent | (func, brack=None, args=(),
xtol=1.48e-8, maxiter=500,
**unknown_options) | return OptimizeResult(fun=fval, x=x, nit=nit, nfev=nfev,
success=nit < maxiter) | Options
-------
maxiter : int
Maximum number of iterations to perform.
xtol : float
Relative error in solution `xopt` acceptable for convergence.
Notes
-----
Uses inverse parabolic interpolation when possible to speed up
convergence of golden section method. | Options
-------
maxiter : int
Maximum number of iterations to perform.
xtol : float
Relative error in solution `xopt` acceptable for convergence. | [
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**unknown_options):
"""
Options
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maxiter : int
Maximum number of iterations to perform.
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Relative error in solution `xopt` acceptable for convergence.
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Uses inverse parabolic interpolation when possible to speed up
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"""
_check_unknown_options(unknown_options)
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brent.set_bracket(brack)
brent.optimize()
x, fval, nit, nfev = brent.get_result(full_output=True)
return OptimizeResult(fun=fval, x=x, nit=nit, nfev=nfev,
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/telemetry/third_party/web-page-replay/third_party/dns/resolver.py | python | zone_for_name | (name, rdclass=dns.rdataclass.IN, tcp=False, resolver=None) | Find the name of the zone which contains the specified name.
@param name: the query name
@type name: absolute dns.name.Name object or string
@param rdclass: The query class
@type rdclass: int
@param tcp: use TCP to make the query (default is False).
@type tcp: bool
@param resolver: the resolver to use
@type resolver: dns.resolver.Resolver object or None
@rtype: dns.name.Name | Find the name of the zone which contains the specified name. | [
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"""Find the name of the zone which contains the specified name.
@param name: the query name
@type name: absolute dns.name.Name object or string
@param rdclass: The query class
@type rdclass: int
@param tcp: use TCP to make the query (default is False).
@type tcp: bool
@param resolver: the resolver to use
@type resolver: dns.resolver.Resolver object or None
@rtype: dns.name.Name"""
if isinstance(name, (str, unicode)):
name = dns.name.from_text(name, dns.name.root)
if resolver is None:
resolver = get_default_resolver()
if not name.is_absolute():
raise NotAbsolute(name)
while 1:
try:
answer = resolver.query(name, dns.rdatatype.SOA, rdclass, tcp)
return name
except (dns.resolver.NXDOMAIN, dns.resolver.NoAnswer):
try:
name = name.parent()
except dns.name.NoParent:
raise NoRootSOA | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py2/scipy/sparse/linalg/_expm_multiply.py | python | LazyOperatorNormInfo.alpha | (self, p) | return max(self.d(p), self.d(p+1)) | Lazily compute max(d(p), d(p+1)). | Lazily compute max(d(p), d(p+1)). | [
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"""
Lazily compute max(d(p), d(p+1)).
"""
return max(self.d(p), self.d(p+1)) | [
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MythTV/mythtv | d282a209cb8be85d036f85a62a8ec971b67d45f4 | mythtv/bindings/python/MythTV/methodheap.py | python | MythDB.searchRecord | (self, init=False, key=None, value=None) | return None | obj.searchRecord(**kwargs) -> list of Record objects
Supports the following keywords:
type, chanid, starttime, startdate, endtime
enddate, title, subtitle, category, profile
recgroup, station, seriesid, programid, playgroup,
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"""
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Supports the following keywords:
type, chanid, starttime, startdate, endtime
enddate, title, subtitle, category, profile
recgroup, station, seriesid, programid, playgroup,
inetref
"""
if init:
init.table = 'record'
init.handler = Record
return None
if key in ('type','chanid','starttime','startdate','endtime','enddate',
'title','subtitle','category','profile','recgroup',
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return ('%s=?' % key, value, 0)
return None | [
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include-what-you-use/include-what-you-use | 208fbfffa5d69364b9f78e427caa443441279283 | fix_includes.py | python | _LinesAreAllBlank | (file_lines, start_line, end_line) | return True | Returns true iff all lines in [start_line, end_line) are blank/deleted. | Returns true iff all lines in [start_line, end_line) are blank/deleted. | [
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facebookincubator/BOLT | 88c70afe9d388ad430cc150cc158641701397f70 | libcxx/utils/libcxx/sym_check/extract.py | python | NMExtractor.__init__ | (self, static_lib) | Initialize the nm executable and flags that will be used to extract
symbols from shared libraries. | Initialize the nm executable and flags that will be used to extract
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"""
Initialize the nm executable and flags that will be used to extract
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"""
self.nm_exe = self.find_tool()
if self.nm_exe is None:
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sys.exit(1)
self.static_lib = static_lib
self.flags = ['-P', '-g'] | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/httplib.py | python | HTTP.getreply | (self, buffering=False) | return response.status, response.reason, response.msg | Compat definition since superclass does not define it.
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- server status code (e.g. '200' if all goes well)
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"""Compat definition since superclass does not define it.
Returns a tuple consisting of:
- server status code (e.g. '200' if all goes well)
- server "reason" corresponding to status code
- any RFC822 headers in the response from the server
"""
try:
if not buffering:
response = self._conn.getresponse()
else:
#only add this keyword if non-default for compatibility
#with other connection classes
response = self._conn.getresponse(buffering)
except BadStatusLine, e:
### hmm. if getresponse() ever closes the socket on a bad request,
### then we are going to have problems with self.sock
### should we keep this behavior? do people use it?
# keep the socket open (as a file), and return it
self.file = self._conn.sock.makefile('rb', 0)
# close our socket -- we want to restart after any protocol error
self.close()
self.headers = None
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/lib2to3/fixer_util.py | python | touch_import | (package, name, node) | Works like `does_tree_import` but adds an import statement
if it was not imported. | Works like `does_tree_import` but adds an import statement
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def is_import_stmt(node):
return (node.type == syms.simple_stmt and node.children and
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root = find_root(node)
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insert_pos = offset = 0
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thalium/icebox | 99d147d5b9269222225443ce171b4fd46d8985d4 | third_party/virtualbox/src/VBox/Devices/EFI/Firmware/AppPkg/Applications/Python/PyMod-2.7.2/Lib/pydoc.py | python | writedocs | (dir, pkgpath='', done=None) | return | Write out HTML documentation for all modules in a directory tree. | Write out HTML documentation for all modules in a directory tree. | [
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"directory",
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] | def writedocs(dir, pkgpath='', done=None):
"""Write out HTML documentation for all modules in a directory tree."""
if done is None: done = {}
for importer, modname, ispkg in pkgutil.walk_packages([dir], pkgpath):
writedoc(modname)
return | [
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larroy/clearskies_core | 3574ddf0edc8555454c7044126e786a6c29444dc | tools/gyp/pylib/gyp/generator/ninja.py | python | CalculateGeneratorInputInfo | (params) | Called by __init__ to initialize generator values based on params. | Called by __init__ to initialize generator values based on params. | [
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"""Called by __init__ to initialize generator values based on params."""
# E.g. "out/gypfiles"
toplevel = params['options'].toplevel_dir
qualified_out_dir = os.path.normpath(os.path.join(
toplevel, ComputeOutputDir(params), 'gypfiles'))
global generator_filelist_paths
generator_filelist_paths = {
'toplevel': toplevel,
'qualified_out_dir': qualified_out_dir,
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/closure_compiler/compile2.py | python | Checker._create_temp_file | (self, contents) | return tmp_file.name | Creates an owned temporary file with |contents|.
Args:
content: A string of the file contens to write to a temporary file.
Return:
The filepath of the newly created, written, and closed temporary file. | Creates an owned temporary file with |contents|. | [
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"""Creates an owned temporary file with |contents|.
Args:
content: A string of the file contens to write to a temporary file.
Return:
The filepath of the newly created, written, and closed temporary file.
"""
with tempfile.NamedTemporaryFile(mode="wt", delete=False) as tmp_file:
self._temp_files.append(tmp_file.name)
tmp_file.write(contents)
return tmp_file.name | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/Muon/GUI/ElementalAnalysis/PeriodicTable/periodic_table.py | python | PeriodicTable.setSelection | (self, symbols) | Set selected elements.
This causes the sigSelectionChanged signal
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"""Set selected elements.
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:param List[str] symbols: List of symbols of elements to be selected
(e.g. *["Fe", "Hg", "Li"]*)
"""
# accept list of PeriodicTableItems as input, because getSelection
# returns these objects and it makes sense to have getter and setter
# use same type of data
if isinstance(symbols[0], PeriodicTableItem):
symbols = [elmt.symbol for elmt in symbols]
for (e, b) in self._eltButtons.items():
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self.sigSelectionChanged.emit(self.getSelection()) | [
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freesurfer/freesurfer | 6dbe527d43ffa611acb2cd112e9469f9bfec8e36 | python/freesurfer/subfields/hippocampus.py | python | HippoAmygdalaSubfields.get_label_groups | (self) | return labelGroups | Return a group (list of lists) of label names that determine the class reductions for
the primary image-fitting stage. | Return a group (list of lists) of label names that determine the class reductions for
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"""
Return a group (list of lists) of label names that determine the class reductions for
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if not self.highResImage:
labelGroups = [
['Left-Cerebral-Cortex', 'Left-Hippocampus', 'Left-Amygdala', 'subiculum-head', 'subiculum-body',
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else:
labelGroups = [
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labelGroups.append(['Left-Cerebral-White-Matter', 'fimbria'])
labelGroups.append(['alveus'])
labelGroups.append(['Left-Lateral-Ventricle', 'Background-CSF', 'SUSPICIOUS', 'Left-hippocampus-intensity-abnormality'])
labelGroups.append(['hippocampal-fissure'])
labelGroups.append(['Left-Pallidum'])
labelGroups.append(['Left-Putamen'])
labelGroups.append(['Left-Caudate'])
labelGroups.append(['Left-Thalamus-Proper'])
labelGroups.append(['Left-choroid-plexus'])
labelGroups.append(['Left-VentralDC'])
labelGroups.append(['Left-Accumbens-area'])
labelGroups.append(['Unknown', 'Background-tissue'])
return labelGroups | [
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giuspen/cherrytree | 84712f206478fcf9acf30174009ad28c648c6344 | pygtk2/modules/core.py | python | CherryTree.find_in_selected_node | (self, *args) | Search for a pattern in the selected Node | Search for a pattern in the selected Node | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/ops/ragged/ragged_map_ops.py | python | _convert_declared_ragged | (current, declared) | Converts an output with RaggedTensorType into a _RaggedTensorComponents. | Converts an output with RaggedTensorType into a _RaggedTensorComponents. | [
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# Check that dtypes match up.
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raise ValueError(
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if (isinstance(current, ragged_tensor.RaggedTensor) and
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current = current.with_row_splits_dtype(declared.row_splits_dtype)
if isinstance(current, ragged_tensor.RaggedTensor):
return current
else:
nrows = array_ops.shape(current, out_type=declared.row_splits_dtype)[0]
row_length = array_ops.expand_dims(nrows, axis=0)
return _RaggedTensorComponents(
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CRYTEK/CRYENGINE | 232227c59a220cbbd311576f0fbeba7bb53b2a8c | Code/Tools/waf-1.7.13/waflib/Node.py | python | Node.find_resource | (self, lst) | return node | Try to find a declared build node or a source file
:param lst: path
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Try to find a declared build node or a source file
:param lst: path
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"""
if isinstance(lst, str):
lst = [x for x in split_path(lst) if x and x != '.']
node = self.get_bld().search_node(lst)
if not node:
self = self.get_src()
node = self.find_node(lst)
if node:
if os.path.isdir(node.abspath()):
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return node | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/ed_stc.py | python | EditraStc.SetCurrentCol | (self, column) | Set the current column position on the currently line
extending the selection.
@param column: Column to move to | Set the current column position on the currently line
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lstart = self.PositionFromLine(cline)
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/xrc.py | python | XmlResource.LoadOnDialog | (*args, **kwargs) | return _xrc.XmlResource_LoadOnDialog(*args, **kwargs) | LoadOnDialog(self, wxDialog dlg, Window parent, String name) -> bool | LoadOnDialog(self, wxDialog dlg, Window parent, String name) -> bool | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/site-packages/setuptools/command/easy_install.py | python | WindowsScriptWriter._get_script_args | (cls, type_, name, header, script_text) | For Windows, add a .py extension | For Windows, add a .py extension | [
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ext = dict(console='.pya', gui='.pyw')[type_]
if ext not in os.environ['PATHEXT'].lower().split(';'):
msg = (
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warnings.warn(msg, UserWarning)
old = ['.pya', '.py', '-script.py', '.pyc', '.pyo', '.pyw', '.exe']
old.remove(ext)
header = cls._adjust_header(type_, header)
blockers = [name + x for x in old]
yield name + ext, header + script_text, 't', blockers | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py2/sklearn/metrics/scorer.py | python | _passthrough_scorer | (estimator, *args, **kwargs) | return estimator.score(*args, **kwargs) | Function that wraps estimator.score | Function that wraps estimator.score | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numpy/ma/core.py | python | _convert2ma.getdoc | (self) | return doc | Return the doc of the function (from the doc of the method). | Return the doc of the function (from the doc of the method). | [
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doc = getattr(self._func, '__doc__', None)
sig = get_object_signature(self._func)
if doc:
# Add the signature of the function at the beginning of the doc
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sig = "%s%s\n" % (self._func.__name__, sig)
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numba/unicode.py | python | unicode_isidentifier | (data) | return impl | Implements UnicodeType.isidentifier() | Implements UnicodeType.isidentifier() | [
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return True
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openvinotoolkit/openvino | dedcbeafa8b84cccdc55ca64b8da516682b381c7 | tools/mo/openvino/tools/mo/utils/cli_parser.py | python | readable_dirs_or_empty | (paths: str) | return paths | Checks that comma separated list of paths are readable directories of if it is empty.
:param paths: comma separated list of paths.
:return: comma separated list of paths. | Checks that comma separated list of paths are readable directories of if it is empty.
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/linalg/python/ops/linear_operator.py | python | LinearOperator.to_dense | (self, name="to_dense") | Return a dense (batch) matrix representing this operator. | Return a dense (batch) matrix representing this operator. | [
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] | https://github.com/baidu-research/tensorflow-allreduce/blob/66d5b855e90b0949e9fa5cca5599fd729a70e874/tensorflow/contrib/linalg/python/ops/linear_operator.py#L860-L863 | ||
Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/lib/io/file_io.py | python | read_file_to_string | (filename, binary_mode=False) | return f.read() | Reads the entire contents of a file to a string.
Args:
filename: string, path to a file
binary_mode: whether to open the file in binary mode or not. This changes
the type of the object returned.
Returns:
contents of the file as a string or bytes.
Raises:
errors.OpError: Raises variety of errors that are subtypes e.g.
NotFoundError etc. | Reads the entire contents of a file to a string. | [
"Reads",
"the",
"entire",
"contents",
"of",
"a",
"file",
"to",
"a",
"string",
"."
] | def read_file_to_string(filename, binary_mode=False):
"""Reads the entire contents of a file to a string.
Args:
filename: string, path to a file
binary_mode: whether to open the file in binary mode or not. This changes
the type of the object returned.
Returns:
contents of the file as a string or bytes.
Raises:
errors.OpError: Raises variety of errors that are subtypes e.g.
NotFoundError etc.
"""
if binary_mode:
f = FileIO(filename, mode="rb")
else:
f = FileIO(filename, mode="r")
return f.read() | [
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microsoft/TSS.MSR | 0f2516fca2cd9929c31d5450e39301c9bde43688 | TSS.Py/src/TpmTypes.py | python | RSA_DecryptResponse.fromTpm | (buf) | return buf.createObj(RSA_DecryptResponse) | Returns new RSA_DecryptResponse object constructed from its
marshaled representation in the given TpmBuffer buffer | Returns new RSA_DecryptResponse object constructed from its
marshaled representation in the given TpmBuffer buffer | [
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] | def fromTpm(buf):
""" Returns new RSA_DecryptResponse object constructed from its
marshaled representation in the given TpmBuffer buffer
"""
return buf.createObj(RSA_DecryptResponse) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/aui.py | python | AuiMDIParentFrame.SetArtProvider | (*args, **kwargs) | return _aui.AuiMDIParentFrame_SetArtProvider(*args, **kwargs) | SetArtProvider(self, AuiTabArt provider) | SetArtProvider(self, AuiTabArt provider) | [
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"""SetArtProvider(self, AuiTabArt provider)"""
return _aui.AuiMDIParentFrame_SetArtProvider(*args, **kwargs) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_core.py | python | Window.Navigate | (*args, **kwargs) | return _core_.Window_Navigate(*args, **kwargs) | Navigate(self, int flags=NavigationKeyEvent.IsForward) -> bool
Does keyboard navigation starting from this window to another. This is
equivalient to self.GetParent().NavigateIn(). | Navigate(self, int flags=NavigationKeyEvent.IsForward) -> bool | [
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"""
Navigate(self, int flags=NavigationKeyEvent.IsForward) -> bool
Does keyboard navigation starting from this window to another. This is
equivalient to self.GetParent().NavigateIn().
"""
return _core_.Window_Navigate(*args, **kwargs) | [
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Jittor/jittor | e9aca0444c2bdc8e2389d99122954cd0903eec46 | python/jittor/init.py | python | zero_ | (var) | return var.assign(zero(var.shape, var.dtype)) | Inplace initialize variable with zero.
Args:
var (Jittor Var):
Var to initialize with zero.
Return:
var itself.
Example::
from jittor import init
from jittor import nn
linear = nn.Linear(2,2)
init.zero_(linear.weight)
print(linear.weight)
# output: [[0.,0.],[0.,0.]]
linear.weight.zero_() # This is ok too | Inplace initialize variable with zero. | [
"Inplace",
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"with",
"zero",
"."
] | def zero_(var):
''' Inplace initialize variable with zero.
Args:
var (Jittor Var):
Var to initialize with zero.
Return:
var itself.
Example::
from jittor import init
from jittor import nn
linear = nn.Linear(2,2)
init.zero_(linear.weight)
print(linear.weight)
# output: [[0.,0.],[0.,0.]]
linear.weight.zero_() # This is ok too
'''
return var.assign(zero(var.shape, var.dtype)) | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/lib-tk/Tkinter.py | python | Spinbox.insert | (self, index, s) | return self.tk.call(self._w, 'insert', index, s) | Insert string s at index
Returns an empty string. | Insert string s at index | [
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] | def insert(self, index, s):
"""Insert string s at index
Returns an empty string.
"""
return self.tk.call(self._w, 'insert', index, s) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/logging/handlers.py | python | SysLogHandler.emit | (self, record) | Emit a record.
The record is formatted, and then sent to the syslog server. If
exception information is present, it is NOT sent to the server. | Emit a record. | [
"Emit",
"a",
"record",
"."
] | def emit(self, record):
"""
Emit a record.
The record is formatted, and then sent to the syslog server. If
exception information is present, it is NOT sent to the server.
"""
try:
msg = self.format(record)
if self.ident:
msg = self.ident + msg
if self.append_nul:
msg += '\000'
# We need to convert record level to lowercase, maybe this will
# change in the future.
prio = '<%d>' % self.encodePriority(self.facility,
self.mapPriority(record.levelname))
prio = prio.encode('utf-8')
# Message is a string. Convert to bytes as required by RFC 5424
msg = msg.encode('utf-8')
msg = prio + msg
if self.unixsocket:
try:
self.socket.send(msg)
except OSError:
self.socket.close()
self._connect_unixsocket(self.address)
self.socket.send(msg)
elif self.socktype == socket.SOCK_DGRAM:
self.socket.sendto(msg, self.address)
else:
self.socket.sendall(msg)
except Exception:
self.handleError(record) | [
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google/syzygy | 8164b24ebde9c5649c9a09e88a7fc0b0fcbd1bc5 | third_party/numpy/files/numpy/oldnumeric/ma.py | python | masked_binary_operation.__call__ | (self, a, b, *args, **kwargs) | return masked_array(result, m) | Execute the call behavior. | Execute the call behavior. | [
"Execute",
"the",
"call",
"behavior",
"."
] | def __call__ (self, a, b, *args, **kwargs):
"Execute the call behavior."
m = mask_or(getmask(a), getmask(b))
d1 = filled(a, self.fillx)
d2 = filled(b, self.filly)
result = self.f(d1, d2, *args, **kwargs)
if isinstance(result, ndarray) \
and m.ndim != 0 \
and m.shape != result.shape:
m = mask_or(getmaskarray(a), getmaskarray(b))
return masked_array(result, m) | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/Muon/GUI/Common/corrections_tab_widget/background_corrections_model.py | python | BackgroundCorrectionsModel.set_background_correction_mode | (self, mode: str) | Sets the current background correction mode in the context. | Sets the current background correction mode in the context. | [
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] | def set_background_correction_mode(self, mode: str) -> None:
"""Sets the current background correction mode in the context."""
self._corrections_context.background_corrections_mode = mode | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/ops/_op_impl/cpu/arg_max_with_value.py | python | _arg_max_with_value_cpu | () | return | ArgMaxWithValue cpu register | ArgMaxWithValue cpu register | [
"ArgMaxWithValue",
"cpu",
"register"
] | def _arg_max_with_value_cpu():
"""ArgMaxWithValue cpu register"""
return | [
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] | https://github.com/mindspore-ai/mindspore/blob/fb8fd3338605bb34fa5cea054e535a8b1d753fab/mindspore/python/mindspore/ops/_op_impl/cpu/arg_max_with_value.py#L29-L31 | |
apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | src/external/coremltools_wrap/coremltools/coremltools/converters/keras/_layers2.py | python | convert_bidirectional | (
builder, layer, input_names, output_names, keras_layer, respect_train
) | Convert a bidirectional layer from keras to coreml.
Currently assumes the units are LSTMs.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
respect_train: boolean
Whether to honor Keras' "trainable" flag (unsupported). | Convert a bidirectional layer from keras to coreml.
Currently assumes the units are LSTMs. | [
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"are",
"LSTMs",
"."
] | def convert_bidirectional(
builder, layer, input_names, output_names, keras_layer, respect_train
):
"""
Convert a bidirectional layer from keras to coreml.
Currently assumes the units are LSTMs.
Parameters
----------
keras_layer: layer
A keras layer object.
builder: NeuralNetworkBuilder
A neural network builder object.
respect_train: boolean
Whether to honor Keras' "trainable" flag (unsupported).
"""
input_size = keras_layer.input_shape[-1]
lstm_layer = keras_layer.forward_layer
if type(lstm_layer) != _keras.layers.recurrent.LSTM:
raise TypeError("Bidirectional layers only supported with LSTM")
if lstm_layer.go_backwards:
raise TypeError(" 'go_backwards' mode not supported with Bidirectional layers")
output_all = keras_layer.return_sequences
hidden_size = lstm_layer.units
# Keras: I C F O; W_x, W_h, b
# CoreML: I F O G; W_h and W_x are separated
# Keras has all forward weights, followed by backward in the same order
W_h, W_x, b = ([], [], [])
keras_W_h = keras_layer.forward_layer.get_weights()[1].T
W_h.append(keras_W_h[0 * hidden_size :][:hidden_size])
W_h.append(keras_W_h[1 * hidden_size :][:hidden_size])
W_h.append(keras_W_h[3 * hidden_size :][:hidden_size])
W_h.append(keras_W_h[2 * hidden_size :][:hidden_size])
keras_W_x = keras_layer.forward_layer.get_weights()[0].T
W_x.append(keras_W_x[0 * hidden_size :][:hidden_size])
W_x.append(keras_W_x[1 * hidden_size :][:hidden_size])
W_x.append(keras_W_x[3 * hidden_size :][:hidden_size])
W_x.append(keras_W_x[2 * hidden_size :][:hidden_size])
if keras_layer.forward_layer.use_bias:
keras_b = keras_layer.forward_layer.get_weights()[2]
b.append(keras_b[0 * hidden_size :][:hidden_size])
b.append(keras_b[1 * hidden_size :][:hidden_size])
b.append(keras_b[3 * hidden_size :][:hidden_size])
b.append(keras_b[2 * hidden_size :][:hidden_size])
if len(b) == 0:
b = None
W_h_back, W_x_back, b_back = ([], [], [])
keras_W_h = keras_layer.backward_layer.get_weights()[1].T
W_h_back.append(keras_W_h[0 * hidden_size :][:hidden_size])
W_h_back.append(keras_W_h[1 * hidden_size :][:hidden_size])
W_h_back.append(keras_W_h[3 * hidden_size :][:hidden_size])
W_h_back.append(keras_W_h[2 * hidden_size :][:hidden_size])
keras_W_x = keras_layer.backward_layer.get_weights()[0].T
W_x_back.append(keras_W_x[0 * hidden_size :][:hidden_size])
W_x_back.append(keras_W_x[1 * hidden_size :][:hidden_size])
W_x_back.append(keras_W_x[3 * hidden_size :][:hidden_size])
W_x_back.append(keras_W_x[2 * hidden_size :][:hidden_size])
if keras_layer.backward_layer.use_bias:
keras_b = keras_layer.backward_layer.get_weights()[2]
b_back.append(keras_b[0 * hidden_size :][:hidden_size])
b_back.append(keras_b[1 * hidden_size :][:hidden_size])
b_back.append(keras_b[3 * hidden_size :][:hidden_size])
b_back.append(keras_b[2 * hidden_size :][:hidden_size])
if len(b_back) == 0:
b_back = None
if (b == None and b_back != None) or (b != None and b_back == None):
raise ValueError(
"Unsupported Bi-directional LSTM configuration. Bias "
"must be enabled/disabled for both directions."
)
# Set activation type
inner_activation_str = _get_recurrent_activation_name_from_keras(
lstm_layer.recurrent_activation
)
activation_str = _get_recurrent_activation_name_from_keras(lstm_layer.activation)
output_name_1 = output_names[0]
if hasattr(keras_layer, "merge_mode"):
merge_mode = keras_layer.merge_mode
if merge_mode not in ["concat", "sum", "mul", "ave"]:
raise NotImplementedError(
"merge_mode '%s' in Bidirectional LSTM "
"not supported currently" % merge_mode
)
if merge_mode != "concat":
output_name_1 += "_concatenated_bilstm_output"
# Add to the network
builder.add_bidirlstm(
name=layer,
W_h=W_h,
W_x=W_x,
b=b,
W_h_back=W_h_back,
W_x_back=W_x_back,
b_back=b_back,
hidden_size=hidden_size,
input_size=input_size,
input_names=input_names,
output_names=[output_name_1] + output_names[1:],
inner_activation=inner_activation_str,
cell_state_update_activation=activation_str,
output_activation=activation_str,
forget_bias=lstm_layer.unit_forget_bias,
output_all=output_all,
)
if output_name_1 != output_names[0]:
mode = "CONCAT"
if merge_mode == "sum":
mode = "ADD"
elif merge_mode == "ave":
mode = "AVE"
elif merge_mode == "mul":
mode = "MULTIPLY"
builder.add_split(
name=layer + "_split",
input_name=output_name_1,
output_names=[output_names[0] + "_forward", output_names[0] + "_backward"],
)
builder.add_elementwise(
name=layer + "_elementwise",
input_names=[output_names[0] + "_forward", output_names[0] + "_backward"],
output_name=output_names[0],
mode=mode,
)
if respect_train and keras_layer.trainable:
logging.warning(
"Bidirectional layer '%s' is marked updatable, but "
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/boto/boto/route53/status.py | python | Status.update | (self) | return status | Update the status of this request. | Update the status of this request. | [
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""" Update the status of this request."""
status = self.route53connection.get_change(self.id)['GetChangeResponse']['ChangeInfo']['Status']
self.status = status
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_gdi.py | python | DC.DrawRectangleRect | (*args, **kwargs) | return _gdi_.DC_DrawRectangleRect(*args, **kwargs) | DrawRectangleRect(self, Rect rect)
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DrawRectangleRect(self, Rect rect)
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return _gdi_.DC_DrawRectangleRect(*args, **kwargs) | [
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hpi-xnor/BMXNet | ed0b201da6667887222b8e4b5f997c4f6b61943d | python/mxnet/symbol/symbol.py | python | Symbol.split | (self, *args, **kwargs) | return op.split(self, *args, **kwargs) | Convenience fluent method for :py:func:`split`.
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simsong/bulk_extractor | 738911df22b7066ca9e1662f4131fb44090a4196 | python/identify_filenames.py | python | byterundb.process_fi | (self,fi) | Read an XML file and add each byte run to this database | Read an XML file and add each byte run to this database | [
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for run in fi.byte_runs():
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fname = gval(fi.filename())
md5val = gval(fi.md5())
if not fi.allocated():
fname = b'*' + fname;
if args.mactimes:
fileinfo = (fname, md5val, gval(fi.crtime()), gval(fi.ctime()), gval(fi.mtime()), gval(fi.atime()))
else:
fileinfo = (fname, md5val)
self.add_extent(run.img_offset,run.len,fileinfo)
except TypeError as e:
pass | [
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pristineio/webrtc-mirror | 7a5bcdffaab90a05bc1146b2b1ea71c004e54d71 | webrtc/rtc_tools/py_event_log_analyzer/rtp_analyzer.py | python | RTPStatistics.PlotStatistics | (self) | Plots changes in delay and average bandwidth. | Plots changes in delay and average bandwidth. | [
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] | def PlotStatistics(self):
"""Plots changes in delay and average bandwidth."""
start_ms = self.data_points[0].real_send_time_ms
stop_ms = self.data_points[-1].real_send_time_ms
time_axis = numpy.arange(start_ms / 1000, stop_ms / 1000,
RTPStatistics.PLOT_RESOLUTION_MS / 1000)
delay = CalculateDelay(start_ms, stop_ms,
RTPStatistics.PLOT_RESOLUTION_MS,
self.data_points)
plt.figure(1)
plt.plot(time_axis, delay[:len(time_axis)])
plt.xlabel("Send time [s]")
plt.ylabel("Relative transport delay [ms]")
plt.figure(2)
plt.plot(time_axis[:len(self.smooth_bw_kbps)], self.smooth_bw_kbps)
plt.xlabel("Send time [s]")
plt.ylabel("Bandwidth [kbps]")
plt.show() | [
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intel/caffe | 3f494b442ee3f9d17a07b09ecbd5fa2bbda00836 | examples/rfcn/lib/rpn/proposal_target_layer.py | python | ProposalTargetLayer.reshape | (self, bottom, top) | Reshaping happens during the call to forward. | Reshaping happens during the call to forward. | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/importlib/_bootstrap_external.py | python | ExtensionFileLoader.is_package | (self, fullname) | return any(file_name == '__init__' + suffix
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kushview/Element | 1cc16380caa2ab79461246ba758b9de1f46db2a5 | waflib/extras/msvs.py | python | vsnode_project.get_key | (self, node) | return 'ClInclude' | required for writing the source files | required for writing the source files | [
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perilouswithadollarsign/cstrike15_src | f82112a2388b841d72cb62ca48ab1846dfcc11c8 | thirdparty/protobuf-2.5.0/python/mox.py | python | ExpectedMethodCallsError.__init__ | (self, expected_methods) | Init exception.
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root-project/root | fcd3583bb14852bf2e8cd2415717cbaac0e75896 | bindings/experimental/distrdf/python/DistRDF/ComputationGraphGenerator.py | python | ComputationGraphGenerator.trigger_computation_graph | (self, starting_node, range_id) | return actions | Trigger the computation graph.
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range_id (int): The id of the current range. Needed to assign a
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Trigger the computation graph.
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eventql/eventql | 7ca0dbb2e683b525620ea30dc40540a22d5eb227 | deps/3rdparty/spidermonkey/mozjs/python/mozbuild/mozbuild/util.py | python | FileAvoidWrite.close | (self) | return existed, True | Stop accepting writes, compare file contents, and rewrite if needed.
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underlying file was changed, ``.diff`` will be populated with the diff
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buf = self.getvalue()
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self.diff = 'Binary or non-ascii file changed: %s' % self.name
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/profiler/internal/flops_registry.py | python | _list_product | (lst) | return result | Computes product of element of the list. | Computes product of element of the list. | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/scipy/sparse/sputils.py | python | upcast_char | (*args) | return t | Same as `upcast` but taking dtype.char as input (faster). | Same as `upcast` but taking dtype.char as input (faster). | [
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mapnik/mapnik | f3da900c355e1d15059c4a91b00203dcc9d9f0ef | scons/scons-local-4.1.0/SCons/Builder.py | python | DictCmdGenerator.add_action | (self, suffix, action) | Add a suffix-action pair to the mapping. | Add a suffix-action pair to the mapping. | [
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pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | torch/cuda/_utils.py | python | _get_device_index | (device: Any, optional: bool = False,
allow_cpu: bool = False) | return _torch_get_device_index(device, optional, allow_cpu) | r"""Gets the device index from :attr:`device`, which can be a torch.device
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natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/external/bazel_tools/tools/android/merge_manifests.py | python | MergeManifests._ReplaceArgumentPlaceholders | (self, dom) | Replaces argument placeholders with their values.
Modifies the attribute values of the input node.
Args:
dom: Xml node that should get placeholders replaced. | Replaces argument placeholders with their values. | [
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] | def _ReplaceArgumentPlaceholders(self, dom):
"""Replaces argument placeholders with their values.
Modifies the attribute values of the input node.
Args:
dom: Xml node that should get placeholders replaced.
"""
placeholders = {
'packageName': self._merger_dom.getElementsByTagName(
self._MANIFEST).item(0).getAttribute(self._PACKAGE),
}
for element in dom.getElementsByTagName('*'):
for i in range(element.attributes.length):
attr = element.attributes.item(i)
attr.value = self._ReplaceArgumentHelper(placeholders, attr.value) | [
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apache/arrow | af33dd1157eb8d7d9bfac25ebf61445b793b7943 | python/pyarrow/dataset.py | python | parquet_dataset | (metadata_path, schema=None, filesystem=None, format=None,
partitioning=None, partition_base_dir=None) | return factory.finish(schema) | Create a FileSystemDataset from a `_metadata` file created via
`pyarrrow.parquet.write_metadata`.
Parameters
----------
metadata_path : path,
Path pointing to a single file parquet metadata file
schema : Schema, optional
Optionally provide the Schema for the Dataset, in which case it will
not be inferred from the source.
filesystem : FileSystem or URI string, default None
If a single path is given as source and filesystem is None, then the
filesystem will be inferred from the path.
If an URI string is passed, then a filesystem object is constructed
using the URI's optional path component as a directory prefix. See the
examples below.
Note that the URIs on Windows must follow 'file:///C:...' or
'file:/C:...' patterns.
format : ParquetFileFormat
An instance of a ParquetFileFormat if special options needs to be
passed.
partitioning : Partitioning, PartitioningFactory, str, list of str
The partitioning scheme specified with the ``partitioning()``
function. A flavor string can be used as shortcut, and with a list of
field names a DirectionaryPartitioning will be inferred.
partition_base_dir : str, optional
For the purposes of applying the partitioning, paths will be
stripped of the partition_base_dir. Files not matching the
partition_base_dir prefix will be skipped for partitioning discovery.
The ignored files will still be part of the Dataset, but will not
have partition information.
Returns
-------
FileSystemDataset | Create a FileSystemDataset from a `_metadata` file created via
`pyarrrow.parquet.write_metadata`. | [
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] | def parquet_dataset(metadata_path, schema=None, filesystem=None, format=None,
partitioning=None, partition_base_dir=None):
"""
Create a FileSystemDataset from a `_metadata` file created via
`pyarrrow.parquet.write_metadata`.
Parameters
----------
metadata_path : path,
Path pointing to a single file parquet metadata file
schema : Schema, optional
Optionally provide the Schema for the Dataset, in which case it will
not be inferred from the source.
filesystem : FileSystem or URI string, default None
If a single path is given as source and filesystem is None, then the
filesystem will be inferred from the path.
If an URI string is passed, then a filesystem object is constructed
using the URI's optional path component as a directory prefix. See the
examples below.
Note that the URIs on Windows must follow 'file:///C:...' or
'file:/C:...' patterns.
format : ParquetFileFormat
An instance of a ParquetFileFormat if special options needs to be
passed.
partitioning : Partitioning, PartitioningFactory, str, list of str
The partitioning scheme specified with the ``partitioning()``
function. A flavor string can be used as shortcut, and with a list of
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partition_base_dir : str, optional
For the purposes of applying the partitioning, paths will be
stripped of the partition_base_dir. Files not matching the
partition_base_dir prefix will be skipped for partitioning discovery.
The ignored files will still be part of the Dataset, but will not
have partition information.
Returns
-------
FileSystemDataset
"""
from pyarrow.fs import LocalFileSystem, _ensure_filesystem
if format is None:
format = ParquetFileFormat()
elif not isinstance(format, ParquetFileFormat):
raise ValueError("format argument must be a ParquetFileFormat")
if filesystem is None:
filesystem = LocalFileSystem()
else:
filesystem = _ensure_filesystem(filesystem)
metadata_path = filesystem.normalize_path(_stringify_path(metadata_path))
options = ParquetFactoryOptions(
partition_base_dir=partition_base_dir,
partitioning=_ensure_partitioning(partitioning)
)
factory = ParquetDatasetFactory(
metadata_path, filesystem, format, options=options)
return factory.finish(schema) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/build/waf-1.7.13/waflib/extras/review.py | python | ReviewContext.format_option | (self, name, help, actual, default, term_width) | return out | Return the string representing the option specified. | Return the string representing the option specified. | [
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] | def format_option(self, name, help, actual, default, term_width):
"""
Return the string representing the option specified.
"""
def val_to_str(val):
if val == None or val == '':
return "(void)"
return str(val)
max_name_len = 20
sep_len = 2
w = textwrap.TextWrapper()
w.width = term_width - 1
if w.width < 60: w.width = 60
out = ""
# format the help
out += w.fill(help) + "\n"
# format the name
name_len = len(name)
out += Logs.colors.CYAN + name + Logs.colors.NORMAL
# set the indentation used when the value wraps to the next line
w.subsequent_indent = " ".rjust(max_name_len + sep_len)
w.width -= (max_name_len + sep_len)
# the name string is too long, switch to the next line
if name_len > max_name_len:
out += "\n" + w.subsequent_indent
# fill the remaining of the line with spaces
else:
out += " ".rjust(max_name_len + sep_len - name_len)
# format the actual value, if there is one
if actual != None:
out += Logs.colors.BOLD + w.fill(val_to_str(actual)) + Logs.colors.NORMAL + "\n" + w.subsequent_indent
# format the default value
default_fmt = val_to_str(default)
if actual != None:
default_fmt = "default: " + default_fmt
out += Logs.colors.NORMAL + w.fill(default_fmt) + Logs.colors.NORMAL
return out | [
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simsong/bulk_extractor | 738911df22b7066ca9e1662f4131fb44090a4196 | python/dfxml.py | python | extentdb.run_for_sector | (self,sector_number,count=1) | return byte_run(len=count*self.sectorsize,img_offset=sector_number * self.sectorsize) | Returns the run for a specified sector, and optionally a count of sectors | Returns the run for a specified sector, and optionally a count of sectors | [
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] | def run_for_sector(self,sector_number,count=1):
"""Returns the run for a specified sector, and optionally a count of sectors"""
return byte_run(len=count*self.sectorsize,img_offset=sector_number * self.sectorsize) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/_pydecimal.py | python | Decimal.__add__ | (self, other, context=None) | return ans | Returns self + other.
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if other is NotImplemented:
return other
if context is None:
context = getcontext()
if self._is_special or other._is_special:
ans = self._check_nans(other, context)
if ans:
return ans
if self._isinfinity():
# If both INF, same sign => same as both, opposite => error.
if self._sign != other._sign and other._isinfinity():
return context._raise_error(InvalidOperation, '-INF + INF')
return Decimal(self)
if other._isinfinity():
return Decimal(other) # Can't both be infinity here
exp = min(self._exp, other._exp)
negativezero = 0
if context.rounding == ROUND_FLOOR and self._sign != other._sign:
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negativezero = 1
if not self and not other:
sign = min(self._sign, other._sign)
if negativezero:
sign = 1
ans = _dec_from_triple(sign, '0', exp)
ans = ans._fix(context)
return ans
if not self:
exp = max(exp, other._exp - context.prec-1)
ans = other._rescale(exp, context.rounding)
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return ans
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exp = max(exp, self._exp - context.prec-1)
ans = self._rescale(exp, context.rounding)
ans = ans._fix(context)
return ans
op1 = _WorkRep(self)
op2 = _WorkRep(other)
op1, op2 = _normalize(op1, op2, context.prec)
result = _WorkRep()
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if op1.int == op2.int:
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ans = ans._fix(context)
return ans
if op1.int < op2.int:
op1, op2 = op2, op1
# OK, now abs(op1) > abs(op2)
if op1.sign == 1:
result.sign = 1
op1.sign, op2.sign = op2.sign, op1.sign
else:
result.sign = 0
# So we know the sign, and op1 > 0.
elif op1.sign == 1:
result.sign = 1
op1.sign, op2.sign = (0, 0)
else:
result.sign = 0
# Now, op1 > abs(op2) > 0
if op2.sign == 0:
result.int = op1.int + op2.int
else:
result.int = op1.int - op2.int
result.exp = op1.exp
ans = Decimal(result)
ans = ans._fix(context)
return ans | [
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Harick1/caffe-yolo | eea92bf3ddfe4d0ff6b0b3ba9b15c029a83ed9a3 | scripts/cpp_lint.py | python | _ClassifyInclude | (fileinfo, include, is_system) | return _OTHER_HEADER | Figures out what kind of header 'include' is.
Args:
fileinfo: The current file cpplint is running over. A FileInfo instance.
include: The path to a #included file.
is_system: True if the #include used <> rather than "".
Returns:
One of the _XXX_HEADER constants.
For example:
>>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'stdio.h', True)
_C_SYS_HEADER
>>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'string', True)
_CPP_SYS_HEADER
>>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'foo/foo.h', False)
_LIKELY_MY_HEADER
>>> _ClassifyInclude(FileInfo('foo/foo_unknown_extension.cc'),
... 'bar/foo_other_ext.h', False)
_POSSIBLE_MY_HEADER
>>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'foo/bar.h', False)
_OTHER_HEADER | Figures out what kind of header 'include' is. | [
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] | def _ClassifyInclude(fileinfo, include, is_system):
"""Figures out what kind of header 'include' is.
Args:
fileinfo: The current file cpplint is running over. A FileInfo instance.
include: The path to a #included file.
is_system: True if the #include used <> rather than "".
Returns:
One of the _XXX_HEADER constants.
For example:
>>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'stdio.h', True)
_C_SYS_HEADER
>>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'string', True)
_CPP_SYS_HEADER
>>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'foo/foo.h', False)
_LIKELY_MY_HEADER
>>> _ClassifyInclude(FileInfo('foo/foo_unknown_extension.cc'),
... 'bar/foo_other_ext.h', False)
_POSSIBLE_MY_HEADER
>>> _ClassifyInclude(FileInfo('foo/foo.cc'), 'foo/bar.h', False)
_OTHER_HEADER
"""
# This is a list of all standard c++ header files, except
# those already checked for above.
is_cpp_h = include in _CPP_HEADERS
if is_system:
if is_cpp_h:
return _CPP_SYS_HEADER
else:
return _C_SYS_HEADER
# If the target file and the include we're checking share a
# basename when we drop common extensions, and the include
# lives in . , then it's likely to be owned by the target file.
target_dir, target_base = (
os.path.split(_DropCommonSuffixes(fileinfo.RepositoryName())))
include_dir, include_base = os.path.split(_DropCommonSuffixes(include))
if target_base == include_base and (
include_dir == target_dir or
include_dir == os.path.normpath(target_dir + '/../public')):
return _LIKELY_MY_HEADER
# If the target and include share some initial basename
# component, it's possible the target is implementing the
# include, so it's allowed to be first, but we'll never
# complain if it's not there.
target_first_component = _RE_FIRST_COMPONENT.match(target_base)
include_first_component = _RE_FIRST_COMPONENT.match(include_base)
if (target_first_component and include_first_component and
target_first_component.group(0) ==
include_first_component.group(0)):
return _POSSIBLE_MY_HEADER
return _OTHER_HEADER | [
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AngoraFuzzer/Angora | 80e81c8590077bc0ac069dbd367da8ce405ff618 | llvm_mode/dfsan_rt/sanitizer_common/scripts/cpplint.py | python | _OutputFormat | () | return _cpplint_state.output_format | Gets the module's output format. | Gets the module's output format. | [
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] | def _OutputFormat():
"""Gets the module's output format."""
return _cpplint_state.output_format | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/customtreectrl.py | python | CustomTreeCtrl.AssignButtonsImageList | (self, imageList) | Assigns the button image list.
:param `imageList`: an instance of :class:`ImageList`. | Assigns the button image list. | [
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] | def AssignButtonsImageList(self, imageList):
"""
Assigns the button image list.
:param `imageList`: an instance of :class:`ImageList`.
"""
self.SetButtonsImageList(imageList)
self._ownsImageListButtons = True | [
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hpi-xnor/BMXNet | ed0b201da6667887222b8e4b5f997c4f6b61943d | python/mxnet/ndarray/ndarray.py | python | NDArray.log2 | (self, *args, **kwargs) | return op.log2(self, *args, **kwargs) | Convenience fluent method for :py:func:`log2`.
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The arguments are the same as for :py:func:`log2`, with
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"""
return op.log2(self, *args, **kwargs) | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | scripts/CrystalTools/PeakReport.py | python | PeakReport.set_show_background | (self, show_background) | Arguments:
show_background -- True to show background | Arguments:
show_background -- True to show background | [
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Arguments:
show_background -- True to show background
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smartdevicelink/sdl_core | 68f082169e0a40fccd9eb0db3c83911c28870f07 | tools/infrastructure/api_compare.py | python | console_print | (summary_result) | Function which prints summary result to console | Function which prints summary result to console | [
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"""Function which prints summary result to console"""
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print("\n" + "---" * 60)
print(colors.HEADER + rpc_name + colors.ENDC)
for problematic_item in summary_result[rpc_name]:
item = summary_result[rpc_name][problematic_item]
if len(item) > 0:
print(colors.UNDERLINE + problematic_item + colors.ENDC)
if type(item) is not dict:
print("{}{}{}".format(colors.WARN, item, colors.ENDC))
elif type(item) is dict:
for param in item.keys():
item_print = colors.UNDERLINE + param + colors.ENDC
print("{} {}".format("Parameter name: ", item_print))
res_val = item[param]
for key in res_val:
print(key, ":", colors.FAIL, res_val[key], colors.ENDC) | [
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apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | src/external/boost/boost_1_68_0/tools/build/src/build/property.py | python | validate | (properties) | Exit with error if any of the properties is not valid.
properties may be a single property or a sequence of properties. | Exit with error if any of the properties is not valid.
properties may be a single property or a sequence of properties. | [
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""" Exit with error if any of the properties is not valid.
properties may be a single property or a sequence of properties.
"""
if isinstance(properties, Property):
properties = [properties]
assert is_iterable_typed(properties, Property)
for p in properties:
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numba/numpy_support.py | python | ufunc_can_cast | (from_, to, has_mixed_inputs, casting='safe') | return np.can_cast(from_, to, casting) | A variant of np.can_cast() that can allow casting any integer to
any real or complex type, in case the operation has mixed-kind
inputs.
For example we want `np.power(float32, int32)` to be computed using
SP arithmetic and return `float32`.
However, `np.sqrt(int32)` should use DP arithmetic and return `float64`. | A variant of np.can_cast() that can allow casting any integer to
any real or complex type, in case the operation has mixed-kind
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Tools/msi/msilib.py | python | Directory.start_component | (self, component = None, feature = None, flags = None, keyfile = None, uuid=None) | Add an entry to the Component table, and make this component the current for this
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if flags is None:
flags = self.componentflags
if uuid is None:
uuid = gen_uuid()
else:
uuid = uuid.upper()
if component is None:
component = self.logical
self.component = component
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flags |= 256
if keyfile:
keyid = self.cab.gen_id(self.absolute, keyfile)
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add_data(self.db, "FeatureComponents",
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mongodb/mongo | d8ff665343ad29cf286ee2cf4a1960d29371937b | buildscripts/task_generation/suite_split_strategies.py | python | greedy_division | (tests_runtimes: List[TestRuntime], max_time_seconds: float,
max_suites: Optional[int] = None, max_tests_per_suite: Optional[int] = None,
logger: Any = LOGGER) | return [[test.test_name for test in test_list] for test_list in suites] | Divide the given tests into suites.
Each suite should be able to execute in less than the max time specified. If a single
test has a runtime greater than `max_time_seconds`, it will be run in a suite on its own.
If max_suites is reached before assigning all tests to a suite, the remaining tests will be
divided up among the created suites.
Note: If `max_suites` is hit, suites may have more tests than `max_tests_per_suite` and may have
runtimes longer than `max_time_seconds`.
:param tests_runtimes: List of tuples containing test names and test runtimes.
:param max_time_seconds: Maximum runtime to add to a single bucket.
:param max_suites: Maximum number of suites to create.
:param max_tests_per_suite: Maximum number of tests to add to a single suite.
:param logger: Logger to write log output to.
:return: List of Suite objects representing grouping of tests. | Divide the given tests into suites. | [
"Divide",
"the",
"given",
"tests",
"into",
"suites",
"."
] | def greedy_division(tests_runtimes: List[TestRuntime], max_time_seconds: float,
max_suites: Optional[int] = None, max_tests_per_suite: Optional[int] = None,
logger: Any = LOGGER) -> List[List[str]]:
"""
Divide the given tests into suites.
Each suite should be able to execute in less than the max time specified. If a single
test has a runtime greater than `max_time_seconds`, it will be run in a suite on its own.
If max_suites is reached before assigning all tests to a suite, the remaining tests will be
divided up among the created suites.
Note: If `max_suites` is hit, suites may have more tests than `max_tests_per_suite` and may have
runtimes longer than `max_time_seconds`.
:param tests_runtimes: List of tuples containing test names and test runtimes.
:param max_time_seconds: Maximum runtime to add to a single bucket.
:param max_suites: Maximum number of suites to create.
:param max_tests_per_suite: Maximum number of tests to add to a single suite.
:param logger: Logger to write log output to.
:return: List of Suite objects representing grouping of tests.
"""
suites = []
last_test_processed = len(tests_runtimes)
logger.debug("Determines suites for runtime", max_runtime_seconds=max_time_seconds,
max_suites=max_suites, max_tests_per_suite=max_tests_per_suite)
current_test_list = []
for idx, test_instance in enumerate(tests_runtimes):
logger.debug("Adding test", test=test_instance, suite_index=len(suites))
if _new_suite_needed(current_test_list, test_instance.runtime, max_time_seconds,
max_tests_per_suite):
logger.debug("Finished suite", test_runtime=test_instance.runtime,
max_time=max_time_seconds, suite_index=len(suites))
if current_test_list:
suites.append(current_test_list)
current_test_list = []
if max_suites and len(suites) >= max_suites:
last_test_processed = idx
break
current_test_list.append(test_instance)
if current_test_list:
suites.append(current_test_list)
if max_suites and last_test_processed < len(tests_runtimes):
# We must have hit the max suite limit, just randomly add the remaining tests to suites.
divide_remaining_tests_among_suites(tests_runtimes[last_test_processed:], suites)
return [[test.test_name for test in test_list] for test_list in suites] | [
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] | https://github.com/mongodb/mongo/blob/d8ff665343ad29cf286ee2cf4a1960d29371937b/buildscripts/task_generation/suite_split_strategies.py#L54-L103 |
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