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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/propgrid.py | python | PGProperty.SetAttribute | (*args, **kwargs) | return _propgrid.PGProperty_SetAttribute(*args, **kwargs) | SetAttribute(self, String name, wxVariant value) | SetAttribute(self, String name, wxVariant value) | [
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/python2_version/klampt/src/robotsim.py | python | IKSolver.solve | (self, *args) | return _robotsim.IKSolver_solve(self, *args) | solve(IKSolver self) -> bool
solve(IKSolver self, int iters, double tol) -> PyObject *
Old-style: will be deprecated. Specify # of iterations and tolerance. Tries to
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/devil/devil/utils/find_usb_devices.py | python | USBNode.AllNodes | (self) | Generator that yields this node and all of its descendants.
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"""Generator that yields this node and all of its descendants.
Yields:
[USBNode] First this node, then each of its descendants (recursively)
"""
yield self
for child_node in self._port_to_node.values():
for descendant_node in child_node.AllNodes():
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/site-packages/pip/_vendor/resolvelib/structs.py | python | DirectedGraph.connect | (self, f, t) | Connect two existing vertices.
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"""Connect two existing vertices.
Nothing happens if the vertices are already connected.
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if t not in self._vertices:
raise KeyError(t)
self._forwards[f].add(t)
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/extern/aui/auibar.py | python | AuiToolBar.GetToolEnabled | (self, tool_id) | return False | Returns whether the tool identified by `tool_id` is enabled or not.
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Returns whether the tool identified by `tool_id` is enabled or not.
:param integer `tool_id`: the tool identifier.
"""
tool = self.FindTool(tool_id)
if tool:
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/klampt/src/doxy2swig.py | python | Doxy2SWIG.make_constructor_list | (self, constructor_nodes, classname) | Produces the "Constructors" section and the constructor signatures
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if constructor_nodes == []:
return
self.add_text(['\n', 'Constructors',
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self.add_line_with_subsequent_indent('* ' + self.get_function_signature(n))
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chromiumembedded/cef | 80caf947f3fe2210e5344713c5281d8af9bdc295 | tools/automate/automate-git.py | python | apply_deps_patch | () | Patch the Chromium DEPS file before `gclient sync` if necessary. | Patch the Chromium DEPS file before `gclient sync` if necessary. | [
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""" Patch the Chromium DEPS file before `gclient sync` if necessary. """
deps_path = os.path.join(chromium_src_dir, deps_file)
if os.path.isfile(deps_path):
msg("Chromium DEPS file: %s" % (deps_path))
apply_patch(deps_file)
else:
raise Exception("Path does not exist: %s" % (deps_path)) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | tools/copyright_scanner/copyright_scanner.py | python | _GetDeletedContents | (affected_file) | return deleted_lines | Returns a list of all deleted lines.
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pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | torch/fx/experimental/graph_gradual_typechecker.py | python | Refine.symbolic_relations | (self) | return True | Infers algebraic relations | Infers algebraic relations | [
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"""
Infers algebraic relations
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graph = self.traced.graph
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Slicer/Slicer | ba9fadf332cb0303515b68d8d06a344c82e3e3e5 | Modules/Scripted/DICOMLib/DICOMUtils.py | python | getDatabasePatientUIDByPatientID | (patientID) | return None | Get database patient UID by DICOM patient ID for easy loading of a patient | Get database patient UID by DICOM patient ID for easy loading of a patient | [
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] | def getDatabasePatientUIDByPatientID(patientID):
""" Get database patient UID by DICOM patient ID for easy loading of a patient
"""
if not slicer.dicomDatabase.isOpen:
raise OSError('DICOM module or database cannot be accessed')
patients = slicer.dicomDatabase.patients()
for patientUID in patients:
# Get first file of first series
studies = slicer.dicomDatabase.studiesForPatient(patientUID)
series = [slicer.dicomDatabase.seriesForStudy(study) for study in studies]
seriesUIDs = [uid for uidList in series for uid in uidList]
if len(seriesUIDs) == 0:
continue
filePaths = slicer.dicomDatabase.filesForSeries(seriesUIDs[0], 1)
if len(filePaths) == 0:
continue
firstFile = filePaths[0]
# Get PatientID from first file
currentPatientID = slicer.dicomDatabase.fileValue(slicer.util.longPath(firstFile), "0010,0020")
if currentPatientID == patientID:
return patientUID
return None | [
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libornovax/master_thesis_code | 6eca474ed3cae673afde010caef338cf7349f839 | caffe/python/caffe/net_spec.py | python | param_name_dict | () | return dict(zip(param_type_names, param_names)) | Find out the correspondence between layer names and parameter names. | Find out the correspondence between layer names and parameter names. | [
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"""Find out the correspondence between layer names and parameter names."""
layer = caffe_pb2.LayerParameter()
# get all parameter names (typically underscore case) and corresponding
# type names (typically camel case), which contain the layer names
# (note that not all parameters correspond to layers, but we'll ignore that)
param_names = [f.name for f in layer.DESCRIPTOR.fields if f.name.endswith('_param')]
param_type_names = [type(getattr(layer, s)).__name__ for s in param_names]
# strip the final '_param' or 'Parameter'
param_names = [s[:-len('_param')] for s in param_names]
param_type_names = [s[:-len('Parameter')] for s in param_type_names]
return dict(zip(param_type_names, param_names)) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/ultimatelistctrl.py | python | UltimateListHeaderWindow.SetCustomRenderer | (self, renderer=None) | Associate a custom renderer with the header - all columns will use it
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"""
Associate a custom renderer with the header - all columns will use it
:param `renderer`: a class able to correctly render header buttons
:note: the renderer class **must** implement the methods `DrawHeaderButton`
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"""
if not self._owner.HasAGWFlag(ULC_REPORT):
raise Exception("Custom renderers can be used on with style = ULC_REPORT")
self._headerCustomRenderer = renderer | [
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uber/neuropod | de304c40ec0634a868d7ef41ba7bf89ebc364f10 | source/python/neuropod/backends/python/packager.py | python | create_python_neuropod | (
neuropod_path,
data_paths,
code_path_spec,
entrypoint_package,
entrypoint,
requirements=None,
**kwargs
) | Packages arbitrary python code as a neuropod package.
{common_doc_pre}
:param data_paths: A list of dicts containing the paths to any data files that needs to be packaged.
!!! note ""
***Example***:
```
[{
path: "/path/to/myfile.txt",
packaged_name: "newfilename.txt"
}]
```
:param code_path_spec: The folder paths of all the code that will be packaged. Note that
*.pyc files are ignored.
!!! note ""
This is specified as follows:
```
[{
"python_root": "/some/path/to/a/python/root",
"dirs_to_package": ["relative/path/to/package"]
}, ...]
```
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!!! note ""
For example,
a function like:
```
def neuropod_init(data_path):
def addition_model(x, y):
return {
"output": x + y
}
return addition_model
```
contained in the package 'my.awesome.addition_model' would have
`entrypoint_package='my.awesome.addition_model'` and
`entrypoint='neuropod_init'`
:param requirements: An optional string containing the runtime requirements of this model
(specified in a format that pip understands)
!!! note ""
***Example***:
```
tensorflow=1.15.0
numpy=1.8
```
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] | def create_python_neuropod(
neuropod_path,
data_paths,
code_path_spec,
entrypoint_package,
entrypoint,
requirements=None,
**kwargs
):
"""
Packages arbitrary python code as a neuropod package.
{common_doc_pre}
:param data_paths: A list of dicts containing the paths to any data files that needs to be packaged.
!!! note ""
***Example***:
```
[{
path: "/path/to/myfile.txt",
packaged_name: "newfilename.txt"
}]
```
:param code_path_spec: The folder paths of all the code that will be packaged. Note that
*.pyc files are ignored.
!!! note ""
This is specified as follows:
```
[{
"python_root": "/some/path/to/a/python/root",
"dirs_to_package": ["relative/path/to/package"]
}, ...]
```
:param entrypoint_package: The python package containing the entrypoint (e.g. some.package.something).
This must contain the entrypoint function specified below.
:param entrypoint: The name of a function contained in the `entrypoint_package`. This
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`input_spec` and returns a dict containing the outputs specified in
`output_spec`. The `entrypoint` function will be provided the path to
a directory containing the packaged data as its first parameter.
!!! note ""
For example,
a function like:
```
def neuropod_init(data_path):
def addition_model(x, y):
return {
"output": x + y
}
return addition_model
```
contained in the package 'my.awesome.addition_model' would have
`entrypoint_package='my.awesome.addition_model'` and
`entrypoint='neuropod_init'`
:param requirements: An optional string containing the runtime requirements of this model
(specified in a format that pip understands)
!!! note ""
***Example***:
```
tensorflow=1.15.0
numpy=1.8
```
{common_doc_post}
"""
neuropod_data_path = os.path.join(neuropod_path, "0", "data")
neuropod_code_path = os.path.join(neuropod_path, "0", "code")
# Create a folder to store the packaged data
os.makedirs(neuropod_data_path)
# Copy the data to be packaged
for data_path_spec in data_paths:
shutil.copyfile(
data_path_spec["path"],
os.path.join(neuropod_data_path, data_path_spec["packaged_name"]),
)
# Copy the specified source code while preserving package paths
for copy_spec in code_path_spec:
python_root = copy_spec["python_root"]
if os.path.realpath(neuropod_path).startswith(
os.path.realpath(python_root) + os.sep
):
raise ValueError(
"`neuropod_path` cannot be a subdirectory of `python_root`"
)
for dir_to_package in copy_spec["dirs_to_package"]:
shutil.copytree(
os.path.join(python_root, dir_to_package),
os.path.join(neuropod_code_path, dir_to_package),
ignore=shutil.ignore_patterns("*.pyc"),
)
# Add __init__.py files as needed
for root, subdirs, files in os.walk(neuropod_code_path):
if "__init__.py" not in files:
with open(os.path.join(root, "__init__.py"), "w"):
# We just need to create the file
pass
# Save requirements if specified
if requirements is not None:
# Write requirements to a temp file
with tempfile.NamedTemporaryFile() as requirements_txt:
requirements_txt.write(requirements.encode("utf-8"))
requirements_txt.flush()
# Write the lockfile
lock_path = os.path.join(neuropod_path, "0", "requirements.lock")
compile_requirements(requirements_txt.name, lock_path)
# We also need to save the entrypoint package name so we know what to load at runtime
# This is python specific config so it's not saved in the overall neuropod config
with open(os.path.join(neuropod_path, "0", "config.json"), "w") as config_file:
json.dump(
{"entrypoint_package": entrypoint_package, "entrypoint": entrypoint},
config_file,
) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/pandas/compat/__init__.py | python | set_function_name | (f, name, cls) | return f | Bind the name/qualname attributes of the function. | Bind the name/qualname attributes of the function. | [
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"""
Bind the name/qualname attributes of the function.
"""
f.__name__ = name
f.__qualname__ = f"{cls.__name__}.{name}"
f.__module__ = cls.__module__
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/pip/_vendor/distro.py | python | LinuxDistribution.os_release_info | (self) | return self._os_release_info | Return a dictionary containing key-value pairs for the information
items from the os-release file data source of the OS distribution.
For details, see :func:`distro.os_release_info`. | [] | def os_release_info(self):
"""
Return a dictionary containing key-value pairs for the information
items from the os-release file data source of the OS distribution.
For details, see :func:`distro.os_release_info`.
"""
return self._os_release_info | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/Muon/GUI/ElementalAnalysis/LoadWidget/load_utils.py | python | LModel._load | (self, inputs) | inputs is a dict mapping filepaths to output names | inputs is a dict mapping filepaths to output names | [
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""" inputs is a dict mapping filepaths to output names """
for path, output in inputs.items():
workspace = mantid.LoadAscii(path, OutputWorkspace=output)
workspace.getAxis(0).setUnit("Label").setLabel("Energy", "keV") | [
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apache/impala | 8ddac48f3428c86f2cbd037ced89cfb903298b12 | shell/impala_shell.py | python | ImpalaShell.do_rerun | (self, args) | return self.onecmd(cmd.rstrip(";")) | Rerun a command with an command index in history
Example: @1; | Rerun a command with an command index in history
Example: | [
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] | def do_rerun(self, args):
"""Rerun a command with an command index in history
Example: @1;
"""
history_len = self.readline.get_current_history_length()
# Rerun command shouldn't appear in history
self.readline.remove_history_item(history_len - 1)
history_len -= 1
if not self.readline:
print(READLINE_UNAVAILABLE_ERROR, file=sys.stderr)
return CmdStatus.ERROR
try:
cmd_idx = int(args)
except ValueError:
print("Command index to be rerun must be an integer.", file=sys.stderr)
return CmdStatus.ERROR
if not (0 < cmd_idx <= history_len or -history_len <= cmd_idx < 0):
print("Command index out of range. Valid range: [1, {0}] and [-{0}, -1]"
.format(history_len), file=sys.stderr)
return CmdStatus.ERROR
if cmd_idx < 0:
cmd_idx += history_len + 1
cmd = self.readline.get_history_item(cmd_idx)
print("Rerunning " + cmd, file=sys.stderr)
return self.onecmd(cmd.rstrip(";")) | [
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miyosuda/TensorFlowAndroidMNIST | 7b5a4603d2780a8a2834575706e9001977524007 | jni-build/jni/include/tensorflow/contrib/learn/python/learn/evaluable.py | python | Evaluable.evaluate | (
self, x=None, y=None, input_fn=None, feed_fn=None, batch_size=None,
steps=None, metrics=None, name=None) | Evaluates given model with provided evaluation data.
Evaluates on the given input data. If `input_fn` is provided, that
input function should raise an end-of-input exception (`OutOfRangeError` or
`StopIteration`) after one epoch of the training data has been provided.
By default, the whole evaluation dataset is used. If `steps` is provided,
only `steps` batches of size `batch_size` are processed.
The return value is a dict containing the metrics specified in `metrics`, as
well as an entry `global_step` which contains the value of the global step
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Args:
x: Matrix of shape [n_samples, n_features...]. Can be iterator that
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model. If set, `input_fn` must be `None`.
y: Vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
iterator that returns array of targets. The training target values
(class labels in classification, real numbers in regression). If set,
`input_fn` must be `None`.
input_fn: Input function. If set, `x`, `y`, and `batch_size` must be
`None`.
feed_fn: Function creating a feed dict every time it is called. Called
once per iteration. Must be `None` if `input_fn` is provided.
batch_size: minibatch size to use on the input, defaults to first
dimension of `x`, if specified. Must be `None` if `input_fn` is
provided.
steps: Number of steps for which to evaluate model. If `None`, evaluate
until running tensors generated by `metrics` raises an exception.
metrics: Dict of metric ops to run. If `None`, the default metric
functions are used; if `{}`, no metrics are used. If model has one
output (i.e., returning single predction), keys are `str`, e.g.
`'accuracy'` - just a name of the metric that will show up in
the logs / summaries. Otherwise, keys are tuple of two `str`, e.g.
`('accuracy', 'classes')`- name of the metric and name of `Tensor` in
the predictions to run this metric on.
Metric ops should support streaming, e.g., returning
update_op and value tensors. See more details in
../../../metrics/python/metrics/ops/streaming_metrics.py.
name: Name of the evaluation if user needs to run multiple evaluations on
different data sets, such as on training data vs test data.
Returns:
Returns `dict` with evaluation results. | Evaluates given model with provided evaluation data. | [
"Evaluates",
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"."
] | def evaluate(
self, x=None, y=None, input_fn=None, feed_fn=None, batch_size=None,
steps=None, metrics=None, name=None):
"""Evaluates given model with provided evaluation data.
Evaluates on the given input data. If `input_fn` is provided, that
input function should raise an end-of-input exception (`OutOfRangeError` or
`StopIteration`) after one epoch of the training data has been provided.
By default, the whole evaluation dataset is used. If `steps` is provided,
only `steps` batches of size `batch_size` are processed.
The return value is a dict containing the metrics specified in `metrics`, as
well as an entry `global_step` which contains the value of the global step
for which this evaluation was performed.
Args:
x: Matrix of shape [n_samples, n_features...]. Can be iterator that
returns arrays of features. The training input samples for fitting the
model. If set, `input_fn` must be `None`.
y: Vector or matrix [n_samples] or [n_samples, n_outputs]. Can be
iterator that returns array of targets. The training target values
(class labels in classification, real numbers in regression). If set,
`input_fn` must be `None`.
input_fn: Input function. If set, `x`, `y`, and `batch_size` must be
`None`.
feed_fn: Function creating a feed dict every time it is called. Called
once per iteration. Must be `None` if `input_fn` is provided.
batch_size: minibatch size to use on the input, defaults to first
dimension of `x`, if specified. Must be `None` if `input_fn` is
provided.
steps: Number of steps for which to evaluate model. If `None`, evaluate
until running tensors generated by `metrics` raises an exception.
metrics: Dict of metric ops to run. If `None`, the default metric
functions are used; if `{}`, no metrics are used. If model has one
output (i.e., returning single predction), keys are `str`, e.g.
`'accuracy'` - just a name of the metric that will show up in
the logs / summaries. Otherwise, keys are tuple of two `str`, e.g.
`('accuracy', 'classes')`- name of the metric and name of `Tensor` in
the predictions to run this metric on.
Metric ops should support streaming, e.g., returning
update_op and value tensors. See more details in
../../../metrics/python/metrics/ops/streaming_metrics.py.
name: Name of the evaluation if user needs to run multiple evaluations on
different data sets, such as on training data vs test data.
Returns:
Returns `dict` with evaluation results.
"""
raise NotImplementedError | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/smtplib.py | python | SMTP.help | (self, args='') | return self.getreply()[1] | SMTP 'help' command.
Returns help text from server. | SMTP 'help' command.
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"""SMTP 'help' command.
Returns help text from server."""
self.putcmd("help", args)
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Z3Prover/z3 | d745d03afdfdf638d66093e2bfbacaf87187f35b | src/api/python/z3/z3num.py | python | Numeral.__ne__ | (self, other) | return Z3_algebraic_neq(self.ctx_ref(), self.ast, _to_numeral(other, self.ctx).ast) | Return True if `self != other`.
>>> Numeral(Sqrt(2)) != 2
True
>>> Numeral(Sqrt(3)) != Numeral(Sqrt(2))
True
>>> Numeral(Sqrt(2)) != Numeral(Sqrt(2))
False | Return True if `self != other`. | [
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] | def __ne__(self, other):
""" Return True if `self != other`.
>>> Numeral(Sqrt(2)) != 2
True
>>> Numeral(Sqrt(3)) != Numeral(Sqrt(2))
True
>>> Numeral(Sqrt(2)) != Numeral(Sqrt(2))
False
"""
return Z3_algebraic_neq(self.ctx_ref(), self.ast, _to_numeral(other, self.ctx).ast) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | demo/Grid_MegaExample.py | python | MegaGrid.rowPopup | (self, row, evt) | return | (row, evt) -> display a popup menu when a row label is right clicked | (row, evt) -> display a popup menu when a row label is right clicked | [
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"""(row, evt) -> display a popup menu when a row label is right clicked"""
appendID = wx.NewId()
deleteID = wx.NewId()
x = self.GetRowSize(row)/2
if not self.GetSelectedRows():
self.SelectRow(row)
menu = wx.Menu()
xo, yo = evt.GetPosition()
menu.Append(appendID, "Append Row")
menu.Append(deleteID, "Delete Row(s)")
def append(event, self=self, row=row):
self._table.AppendRow(row)
self.Reset()
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self.PopupMenu(menu)
menu.Destroy()
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miyosuda/TensorFlowAndroidMNIST | 7b5a4603d2780a8a2834575706e9001977524007 | jni-build/jni/include/tensorflow/contrib/distributions/python/ops/mvn.py | python | MultivariateNormalOperatorPD.validate_args | (self) | return self._validate_args | `Boolean` describing behavior on invalid input. | `Boolean` describing behavior on invalid input. | [
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"""`Boolean` describing behavior on invalid input."""
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/s3fs/core.py | python | S3FileSystem.checksum | (self, path, refresh=False) | Unique value for current version of file
If the checksum is the same from one moment to another, the contents
are guaranteed to be the same. If the checksum changes, the contents
*might* have changed.
Parameters
----------
path : string/bytes
path of file to get checksum for
refresh : bool (=False)
if False, look in local cache for file details first | Unique value for current version of file | [
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"for",
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"version",
"of",
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] | def checksum(self, path, refresh=False):
"""
Unique value for current version of file
If the checksum is the same from one moment to another, the contents
are guaranteed to be the same. If the checksum changes, the contents
*might* have changed.
Parameters
----------
path : string/bytes
path of file to get checksum for
refresh : bool (=False)
if False, look in local cache for file details first
"""
info = self.info(path, refresh=refresh)
if info["type"] != 'directory':
return int(info["ETag"].strip('"'), 16)
else:
return int(tokenize(info), 16) | [
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ceph/ceph | 959663007321a369c83218414a29bd9dbc8bda3a | src/pybind/mgr/dashboard/services/access_control.py | python | ac_user_delete_cmd | (_, username: str) | Delete user | Delete user | [
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] | def ac_user_delete_cmd(_, username: str):
'''
Delete user
'''
try:
mgr.ACCESS_CTRL_DB.delete_user(username)
mgr.ACCESS_CTRL_DB.save()
return 0, "User '{}' deleted".format(username), ""
except UserDoesNotExist as ex:
return -errno.ENOENT, '', str(ex) | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/keras/engine/training_v1.py | python | Model.get_weights | (self) | return base_layer.Layer.get_weights(self) | Retrieves the weights of the model.
Returns:
A flat list of Numpy arrays. | Retrieves the weights of the model. | [
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] | def get_weights(self):
"""Retrieves the weights of the model.
Returns:
A flat list of Numpy arrays.
"""
strategy = (self._distribution_strategy or
self._compile_time_distribution_strategy)
if strategy:
with strategy.scope():
return base_layer.Layer.get_weights(self)
return base_layer.Layer.get_weights(self) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/combo.py | python | ComboPopup.GetAdjustedSize | (*args, **kwargs) | return _combo.ComboPopup_GetAdjustedSize(*args, **kwargs) | GetAdjustedSize(self, int minWidth, int prefHeight, int maxHeight) -> Size
The derived class may implement this method to return adjusted size
for the popup control, according to the variables given. It is called
on every popup, just prior to `OnPopup`.
:param minWidth: Preferred minimum width.
:param prefHeight: Preferred height. May be -1 to indicate no preference.
:maxWidth: Max height for window, as limited by screen size, and
should only be rounded down, if necessary. | GetAdjustedSize(self, int minWidth, int prefHeight, int maxHeight) -> Size | [
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] | def GetAdjustedSize(*args, **kwargs):
"""
GetAdjustedSize(self, int minWidth, int prefHeight, int maxHeight) -> Size
The derived class may implement this method to return adjusted size
for the popup control, according to the variables given. It is called
on every popup, just prior to `OnPopup`.
:param minWidth: Preferred minimum width.
:param prefHeight: Preferred height. May be -1 to indicate no preference.
:maxWidth: Max height for window, as limited by screen size, and
should only be rounded down, if necessary.
"""
return _combo.ComboPopup_GetAdjustedSize(*args, **kwargs) | [
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] | https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_carbon/combo.py#L721-L735 | |
bulletphysics/bullet3 | f0f2a952e146f016096db6f85cf0c44ed75b0b9a | examples/pybullet/gym/pybullet_envs/agents/tools/in_graph_batch_env.py | python | InGraphBatchEnv.reset | (self, indices=None) | Reset the batch of environments.
Args:
indices: The batch indices of the environments to reset; defaults to all.
Returns:
Batch tensor of the new observations. | Reset the batch of environments. | [
"Reset",
"the",
"batch",
"of",
"environments",
"."
] | def reset(self, indices=None):
"""Reset the batch of environments.
Args:
indices: The batch indices of the environments to reset; defaults to all.
Returns:
Batch tensor of the new observations.
"""
if indices is None:
indices = tf.range(len(self._batch_env))
observ_dtype = self._parse_dtype(self._batch_env.observation_space)
observ = tf.py_func(self._batch_env.reset, [indices], observ_dtype, name='reset')
observ = tf.check_numerics(observ, 'observ')
reward = tf.zeros_like(indices, tf.float32)
done = tf.zeros_like(indices, tf.bool)
with tf.control_dependencies([
tf.scatter_update(self._observ, indices, observ),
tf.scatter_update(self._reward, indices, reward),
tf.scatter_update(self._done, indices, done)
]):
return tf.identity(observ) | [
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... | https://github.com/bulletphysics/bullet3/blob/f0f2a952e146f016096db6f85cf0c44ed75b0b9a/examples/pybullet/gym/pybullet_envs/agents/tools/in_graph_batch_env.py#L102-L123 | ||
miyosuda/TensorFlowAndroidMNIST | 7b5a4603d2780a8a2834575706e9001977524007 | jni-build/jni/include/tensorflow/contrib/learn/python/learn/graph_actions.py | python | get_summary_writer | (logdir) | return summary_writer_cache.SummaryWriterCache.get(logdir) | Returns single SummaryWriter per logdir in current run.
Args:
logdir: str, folder to write summaries.
Returns:
Existing `SummaryWriter` object or new one if never wrote to given
directory. | Returns single SummaryWriter per logdir in current run. | [
"Returns",
"single",
"SummaryWriter",
"per",
"logdir",
"in",
"current",
"run",
"."
] | def get_summary_writer(logdir):
"""Returns single SummaryWriter per logdir in current run.
Args:
logdir: str, folder to write summaries.
Returns:
Existing `SummaryWriter` object or new one if never wrote to given
directory.
"""
return summary_writer_cache.SummaryWriterCache.get(logdir) | [
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")"
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/AWS/resource-manager-code/lib/pkg_resources/_vendor/pyparsing.py | python | tokenMap | (func, *args) | return pa | Helper to define a parse action by mapping a function to all elements of a ParseResults list.If any additional
args are passed, they are forwarded to the given function as additional arguments after
the token, as in C{hex_integer = Word(hexnums).setParseAction(tokenMap(int, 16))}, which will convert the
parsed data to an integer using base 16.
Example (compare the last to example in L{ParserElement.transformString}::
hex_ints = OneOrMore(Word(hexnums)).setParseAction(tokenMap(int, 16))
hex_ints.runTests('''
00 11 22 aa FF 0a 0d 1a
''')
upperword = Word(alphas).setParseAction(tokenMap(str.upper))
OneOrMore(upperword).runTests('''
my kingdom for a horse
''')
wd = Word(alphas).setParseAction(tokenMap(str.title))
OneOrMore(wd).setParseAction(' '.join).runTests('''
now is the winter of our discontent made glorious summer by this sun of york
''')
prints::
00 11 22 aa FF 0a 0d 1a
[0, 17, 34, 170, 255, 10, 13, 26]
my kingdom for a horse
['MY', 'KINGDOM', 'FOR', 'A', 'HORSE']
now is the winter of our discontent made glorious summer by this sun of york
['Now Is The Winter Of Our Discontent Made Glorious Summer By This Sun Of York'] | Helper to define a parse action by mapping a function to all elements of a ParseResults list.If any additional
args are passed, they are forwarded to the given function as additional arguments after
the token, as in C{hex_integer = Word(hexnums).setParseAction(tokenMap(int, 16))}, which will convert the
parsed data to an integer using base 16. | [
"Helper",
"to",
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"function",
"to",
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"are",
"passed",
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"the",
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"func... | def tokenMap(func, *args):
"""
Helper to define a parse action by mapping a function to all elements of a ParseResults list.If any additional
args are passed, they are forwarded to the given function as additional arguments after
the token, as in C{hex_integer = Word(hexnums).setParseAction(tokenMap(int, 16))}, which will convert the
parsed data to an integer using base 16.
Example (compare the last to example in L{ParserElement.transformString}::
hex_ints = OneOrMore(Word(hexnums)).setParseAction(tokenMap(int, 16))
hex_ints.runTests('''
00 11 22 aa FF 0a 0d 1a
''')
upperword = Word(alphas).setParseAction(tokenMap(str.upper))
OneOrMore(upperword).runTests('''
my kingdom for a horse
''')
wd = Word(alphas).setParseAction(tokenMap(str.title))
OneOrMore(wd).setParseAction(' '.join).runTests('''
now is the winter of our discontent made glorious summer by this sun of york
''')
prints::
00 11 22 aa FF 0a 0d 1a
[0, 17, 34, 170, 255, 10, 13, 26]
my kingdom for a horse
['MY', 'KINGDOM', 'FOR', 'A', 'HORSE']
now is the winter of our discontent made glorious summer by this sun of york
['Now Is The Winter Of Our Discontent Made Glorious Summer By This Sun Of York']
"""
def pa(s,l,t):
return [func(tokn, *args) for tokn in t]
try:
func_name = getattr(func, '__name__',
getattr(func, '__class__').__name__)
except Exception:
func_name = str(func)
pa.__name__ = func_name
return pa | [
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sdhash/sdhash | b9eff63e4e5867e910f41fd69032bbb1c94a2a5e | sdhash-ui/cherrypy/lib/jsontools.py | python | json_in | (content_type=[ntou('application/json'), ntou('text/javascript')],
force=True, debug=False, processor = json_processor) | Add a processor to parse JSON request entities:
The default processor places the parsed data into request.json.
Incoming request entities which match the given content_type(s) will
be deserialized from JSON to the Python equivalent, and the result
stored at cherrypy.request.json. The 'content_type' argument may
be a Content-Type string or a list of allowable Content-Type strings.
If the 'force' argument is True (the default), then entities of other
content types will not be allowed; "415 Unsupported Media Type" is
raised instead.
Supply your own processor to use a custom decoder, or to handle the parsed
data differently. The processor can be configured via
tools.json_in.processor or via the decorator method.
Note that the deserializer requires the client send a Content-Length
request header, or it will raise "411 Length Required". If for any
other reason the request entity cannot be deserialized from JSON,
it will raise "400 Bad Request: Invalid JSON document".
You must be using Python 2.6 or greater, or have the 'simplejson'
package importable; otherwise, ValueError is raised during processing. | Add a processor to parse JSON request entities:
The default processor places the parsed data into request.json. | [
"Add",
"a",
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":",
"The",
"default",
"processor",
"places",
"the",
"parsed",
"data",
"into",
"request",
".",
"json",
"."
] | def json_in(content_type=[ntou('application/json'), ntou('text/javascript')],
force=True, debug=False, processor = json_processor):
"""Add a processor to parse JSON request entities:
The default processor places the parsed data into request.json.
Incoming request entities which match the given content_type(s) will
be deserialized from JSON to the Python equivalent, and the result
stored at cherrypy.request.json. The 'content_type' argument may
be a Content-Type string or a list of allowable Content-Type strings.
If the 'force' argument is True (the default), then entities of other
content types will not be allowed; "415 Unsupported Media Type" is
raised instead.
Supply your own processor to use a custom decoder, or to handle the parsed
data differently. The processor can be configured via
tools.json_in.processor or via the decorator method.
Note that the deserializer requires the client send a Content-Length
request header, or it will raise "411 Length Required". If for any
other reason the request entity cannot be deserialized from JSON,
it will raise "400 Bad Request: Invalid JSON document".
You must be using Python 2.6 or greater, or have the 'simplejson'
package importable; otherwise, ValueError is raised during processing.
"""
request = cherrypy.serving.request
if isinstance(content_type, basestring):
content_type = [content_type]
if force:
if debug:
cherrypy.log('Removing body processors %s' %
repr(request.body.processors.keys()), 'TOOLS.JSON_IN')
request.body.processors.clear()
request.body.default_proc = cherrypy.HTTPError(
415, 'Expected an entity of content type %s' %
', '.join(content_type))
for ct in content_type:
if debug:
cherrypy.log('Adding body processor for %s' % ct, 'TOOLS.JSON_IN')
request.body.processors[ct] = processor | [
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... | https://github.com/sdhash/sdhash/blob/b9eff63e4e5867e910f41fd69032bbb1c94a2a5e/sdhash-ui/cherrypy/lib/jsontools.py#L16-L58 | ||
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numba/targets/arrayobj.py | python | _array_copy | (context, builder, sig, args) | return impl_ret_new_ref(context, builder, sig.return_type, ret._getvalue()) | Array copy. | Array copy. | [
"Array",
"copy",
"."
] | def _array_copy(context, builder, sig, args):
"""
Array copy.
"""
arytype = sig.args[0]
ary = make_array(arytype)(context, builder, value=args[0])
shapes = cgutils.unpack_tuple(builder, ary.shape)
rettype = sig.return_type
ret = _empty_nd_impl(context, builder, rettype, shapes)
src_data = ary.data
dest_data = ret.data
assert rettype.layout in "CF"
if arytype.layout == rettype.layout:
# Fast path: memcpy
cgutils.raw_memcpy(builder, dest_data, src_data, ary.nitems,
ary.itemsize, align=1)
else:
src_strides = cgutils.unpack_tuple(builder, ary.strides)
dest_strides = cgutils.unpack_tuple(builder, ret.strides)
intp_t = context.get_value_type(types.intp)
with cgutils.loop_nest(builder, shapes, intp_t) as indices:
src_ptr = cgutils.get_item_pointer2(context, builder, src_data,
shapes, src_strides,
arytype.layout, indices)
dest_ptr = cgutils.get_item_pointer2(context, builder, dest_data,
shapes, dest_strides,
rettype.layout, indices)
builder.store(builder.load(src_ptr), dest_ptr)
return impl_ret_new_ref(context, builder, sig.return_type, ret._getvalue()) | [
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"[... | https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numba/targets/arrayobj.py#L3934-L3968 | |
NervanaSystems/ngraph | f677a119765ca30636cf407009dabd118664951f | python/src/ngraph/ops.py | python | selu | (
data: NodeInput, alpha: NodeInput, lambda_value: NodeInput, name: Optional[str] = None
) | return _get_node_factory().create("Selu", as_nodes(data, alpha, lambda_value)) | Perform a Scaled Exponential Linear Unit (SELU) operation on input node element-wise.
:param data: input node, array or scalar.
:param alpha: Alpha coefficient of SELU operation
:param lambda_value: Lambda coefficient of SELU operation
:param name: The optional output node name.
:return: The new node performing relu operation on its input element-wise. | Perform a Scaled Exponential Linear Unit (SELU) operation on input node element-wise. | [
"Perform",
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"Linear",
"Unit",
"(",
"SELU",
")",
"operation",
"on",
"input",
"node",
"element",
"-",
"wise",
"."
] | def selu(
data: NodeInput, alpha: NodeInput, lambda_value: NodeInput, name: Optional[str] = None
) -> Node:
"""Perform a Scaled Exponential Linear Unit (SELU) operation on input node element-wise.
:param data: input node, array or scalar.
:param alpha: Alpha coefficient of SELU operation
:param lambda_value: Lambda coefficient of SELU operation
:param name: The optional output node name.
:return: The new node performing relu operation on its input element-wise.
"""
return _get_node_factory().create("Selu", as_nodes(data, alpha, lambda_value)) | [
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"crea... | https://github.com/NervanaSystems/ngraph/blob/f677a119765ca30636cf407009dabd118664951f/python/src/ngraph/ops.py#L932-L943 | |
chipsalliance/verible | aa14e0074ff89945bf65eecfb9ef78684d996058 | third_party/py/dataclasses/dataclasses/__init__.py | python | is_dataclass | (obj) | return hasattr(obj, _FIELDS) | Returns True if obj is a dataclass or an instance of a
dataclass. | Returns True if obj is a dataclass or an instance of a
dataclass. | [
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"is",
"a",
"dataclass",
"or",
"an",
"instance",
"of",
"a",
"dataclass",
"."
] | def is_dataclass(obj):
"""Returns True if obj is a dataclass or an instance of a
dataclass."""
return hasattr(obj, _FIELDS) | [
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] | https://github.com/chipsalliance/verible/blob/aa14e0074ff89945bf65eecfb9ef78684d996058/third_party/py/dataclasses/dataclasses/__init__.py#L879-L882 | |
INK-USC/USC-DS-RelationExtraction | eebcfa7fd2eda5bba92f3ef8158797cdf91e6981 | code/Classifier/DataIO.py | python | save_from_list | (filename, indexes, data) | Save data(a list of list) to a file.
:param filename:
:param data:
:return: | Save data(a list of list) to a file.
:param filename:
:param data:
:return: | [
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"filename",
":",
":",
"param",
"data",
":",
":",
"return",
":"
] | def save_from_list(filename, indexes, data):
"""
Save data(a list of list) to a file.
:param filename:
:param data:
:return:
"""
with open(filename, 'w') as f:
for i in xrange(len(indexes)):
index = indexes[i]
labels = data[i]
if len(labels) > 0: ### only detected RMs are written
for l in labels:
f.write(str(index) + '\t' +str(l) + '\t1\n') | [
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... | https://github.com/INK-USC/USC-DS-RelationExtraction/blob/eebcfa7fd2eda5bba92f3ef8158797cdf91e6981/code/Classifier/DataIO.py#L47-L60 | ||
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/core/dtypes/cast.py | python | astype_nansafe | (arr, dtype, copy: bool = True, skipna: bool = False) | return arr.view(dtype) | Cast the elements of an array to a given dtype a nan-safe manner.
Parameters
----------
arr : ndarray
dtype : np.dtype
copy : bool, default True
If False, a view will be attempted but may fail, if
e.g. the item sizes don't align.
skipna: bool, default False
Whether or not we should skip NaN when casting as a string-type.
Raises
------
ValueError
The dtype was a datetime64/timedelta64 dtype, but it had no unit. | Cast the elements of an array to a given dtype a nan-safe manner. | [
"Cast",
"the",
"elements",
"of",
"an",
"array",
"to",
"a",
"given",
"dtype",
"a",
"nan",
"-",
"safe",
"manner",
"."
] | def astype_nansafe(arr, dtype, copy: bool = True, skipna: bool = False):
"""
Cast the elements of an array to a given dtype a nan-safe manner.
Parameters
----------
arr : ndarray
dtype : np.dtype
copy : bool, default True
If False, a view will be attempted but may fail, if
e.g. the item sizes don't align.
skipna: bool, default False
Whether or not we should skip NaN when casting as a string-type.
Raises
------
ValueError
The dtype was a datetime64/timedelta64 dtype, but it had no unit.
"""
# dispatch on extension dtype if needed
if is_extension_array_dtype(dtype):
return dtype.construct_array_type()._from_sequence(arr, dtype=dtype, copy=copy)
if not isinstance(dtype, np.dtype):
dtype = pandas_dtype(dtype)
if issubclass(dtype.type, str):
return lib.astype_str(arr.ravel(), skipna=skipna).reshape(arr.shape)
elif is_datetime64_dtype(arr):
if is_object_dtype(dtype):
return tslib.ints_to_pydatetime(arr.view(np.int64))
elif dtype == np.int64:
if isna(arr).any():
raise ValueError("Cannot convert NaT values to integer")
return arr.view(dtype)
# allow frequency conversions
if dtype.kind == "M":
return arr.astype(dtype)
raise TypeError(f"cannot astype a datetimelike from [{arr.dtype}] to [{dtype}]")
elif is_timedelta64_dtype(arr):
if is_object_dtype(dtype):
return tslibs.ints_to_pytimedelta(arr.view(np.int64))
elif dtype == np.int64:
if isna(arr).any():
raise ValueError("Cannot convert NaT values to integer")
return arr.view(dtype)
if dtype not in [_INT64_DTYPE, _TD_DTYPE]:
# allow frequency conversions
# we return a float here!
if dtype.kind == "m":
mask = isna(arr)
result = arr.astype(dtype).astype(np.float64)
result[mask] = np.nan
return result
elif dtype == _TD_DTYPE:
return arr.astype(_TD_DTYPE, copy=copy)
raise TypeError(f"cannot astype a timedelta from [{arr.dtype}] to [{dtype}]")
elif np.issubdtype(arr.dtype, np.floating) and np.issubdtype(dtype, np.integer):
if not np.isfinite(arr).all():
raise ValueError("Cannot convert non-finite values (NA or inf) to integer")
elif is_object_dtype(arr):
# work around NumPy brokenness, #1987
if np.issubdtype(dtype.type, np.integer):
return lib.astype_intsafe(arr.ravel(), dtype).reshape(arr.shape)
# if we have a datetime/timedelta array of objects
# then coerce to a proper dtype and recall astype_nansafe
elif is_datetime64_dtype(dtype):
from pandas import to_datetime
return astype_nansafe(to_datetime(arr).values, dtype, copy=copy)
elif is_timedelta64_dtype(dtype):
from pandas import to_timedelta
return astype_nansafe(to_timedelta(arr).values, dtype, copy=copy)
if dtype.name in ("datetime64", "timedelta64"):
msg = (
f"The '{dtype.name}' dtype has no unit. Please pass in "
f"'{dtype.name}[ns]' instead."
)
raise ValueError(msg)
if copy or is_object_dtype(arr) or is_object_dtype(dtype):
# Explicit copy, or required since NumPy can't view from / to object.
return arr.astype(dtype, copy=True)
return arr.view(dtype) | [
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apache/incubator-mxnet | f03fb23f1d103fec9541b5ae59ee06b1734a51d9 | python/mxnet/ndarray/numpy/_op.py | python | rad2deg | (x, out=None, **kwargs) | return _pure_unary_func_helper(x, _api_internal.rad2deg, _np.rad2deg, out=out) | r"""
Convert angles from radians to degrees.
Parameters
----------
x : ndarray or scalar
Angles in degrees.
out : ndarray or None, optional
A location into which the result is stored. If not provided or `None`,
a freshly-allocated array is returned.
Returns
-------
y : ndarray or scalar
The corresponding angle in radians.
This is a scalar if `x` is a scalar.
Notes
-----
"rad2deg(x)" is "x *180 / pi".
This function differs from the original numpy.arange in the following aspects:
- Only support float32 and float64.
- `out` must be in the same size of input.
Examples
--------
>>> np.rad2deg(np.pi/2)
90.0 | r"""
Convert angles from radians to degrees. | [
"r",
"Convert",
"angles",
"from",
"radians",
"to",
"degrees",
"."
] | def rad2deg(x, out=None, **kwargs):
r"""
Convert angles from radians to degrees.
Parameters
----------
x : ndarray or scalar
Angles in degrees.
out : ndarray or None, optional
A location into which the result is stored. If not provided or `None`,
a freshly-allocated array is returned.
Returns
-------
y : ndarray or scalar
The corresponding angle in radians.
This is a scalar if `x` is a scalar.
Notes
-----
"rad2deg(x)" is "x *180 / pi".
This function differs from the original numpy.arange in the following aspects:
- Only support float32 and float64.
- `out` must be in the same size of input.
Examples
--------
>>> np.rad2deg(np.pi/2)
90.0
"""
return _pure_unary_func_helper(x, _api_internal.rad2deg, _np.rad2deg, out=out) | [
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apache/incubator-mxnet | f03fb23f1d103fec9541b5ae59ee06b1734a51d9 | python/mxnet/ndarray/sparse.py | python | _check_shape | (s1, s2) | check s1 == s2 if both are not None | check s1 == s2 if both are not None | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/protobuf/py3/google/protobuf/internal/encoder.py | python | GroupEncoder | (field_number, is_repeated, is_packed) | Returns an encoder for a group field. | Returns an encoder for a group field. | [
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"""Returns an encoder for a group field."""
start_tag = TagBytes(field_number, wire_format.WIRETYPE_START_GROUP)
end_tag = TagBytes(field_number, wire_format.WIRETYPE_END_GROUP)
assert not is_packed
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def EncodeRepeatedField(write, value, deterministic):
for element in value:
write(start_tag)
element._InternalSerialize(write, deterministic)
write(end_tag)
return EncodeRepeatedField
else:
def EncodeField(write, value, deterministic):
write(start_tag)
value._InternalSerialize(write, deterministic)
return write(end_tag)
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HyeonwooNoh/caffe | d9e8494a2832d67b25dee37194c7bcb9d52d0e42 | scripts/cpp_lint.py | python | FileInfo.IsSource | (self) | return self.Extension()[1:] in ('c', 'cc', 'cpp', 'cxx') | File has a source file extension. | File has a source file extension. | [
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quantOS-org/DataCore | e2ef9bd2c22ee9e2845675b6435a14fa607f3551 | mdlink/deps/windows/protobuf-2.5.0/python/google/protobuf/service_reflection.py | python | _ServiceBuilder._GetRequestClass | (self, method_descriptor) | return method_descriptor.input_type._concrete_class | Returns the class of the request protocol message.
Args:
method_descriptor: Descriptor of the method for which to return the
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Returns:
A class that represents the input protocol message of the specified
method. | Returns the class of the request protocol message. | [
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] | def _GetRequestClass(self, method_descriptor):
"""Returns the class of the request protocol message.
Args:
method_descriptor: Descriptor of the method for which to return the
request protocol message class.
Returns:
A class that represents the input protocol message of the specified
method.
"""
if method_descriptor.containing_service != self.descriptor:
raise RuntimeError(
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return method_descriptor.input_type._concrete_class | [
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kismetwireless/kismet | a7c0dc270c960fb1f58bd9cec4601c201885fd4e | capture_sdr_rtladsb/KismetCaptureRtladsb/kismetexternal/__init__.py | python | Datasource.send_datasource_configure_report | (self, seqno, success=False, channel=None, hop_rate=None,
hop_channels=None, spectrum=None, message=None,
full_hopping=None, warning=None, **kwargs) | When acting as a Kismet datasource, send a response to a configuration request. This
is called with the response to the open datasource command.
:param seqno: Sequence number of open source command
:param success: Source configuration success
:param channel: Optional source single-channel configuration
:param hop_rate: Optional source hop speed, if hopping
:param hop_channels: Optional vector of string channels, if hopping
:param message: Optional message
:param full_hopping: Optional full datasource_pb2.SubChanset
:param warning: Optional warning text to be set in datasource detailed info
:param spectrum: Optional spectral data
:param kwargs: Unused additional arguments
:return: None | When acting as a Kismet datasource, send a response to a configuration request. This
is called with the response to the open datasource command. | [
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hop_channels=None, spectrum=None, message=None,
full_hopping=None, warning=None, **kwargs):
"""
When acting as a Kismet datasource, send a response to a configuration request. This
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"""
report = datasource_pb2.ConfigureReport()
report.success.success = success
report.success.seqno = seqno
if message:
report.message.msgtext = message
if success:
report.message.msgtype = self.MSG_INFO
else:
report.message.msgtype = self.MSG_ERROR
if hop_channels:
report.hopping.channels.extend(hop_channels)
if hop_rate:
report.hopping.hop_rate = hop_rate
if channel:
report.channel.channel = channel
if full_hopping:
report.hopping.CopyFrom(full_hopping)
if spectrum:
report.spectrum.CopyFrom(spectrum)
if warning:
report.warning = warning
self.write_ext_packet("KDSCONFIGUREREPORT", report) | [
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acado/acado | b4e28f3131f79cadfd1a001e9fff061f361d3a0f | misc/cpplint.py | python | CheckVlogArguments | (filename, clean_lines, linenum, error) | Checks that VLOG() is only used for defining a logging level.
For example, VLOG(2) is correct. VLOG(INFO), VLOG(WARNING), VLOG(ERROR), and
VLOG(FATAL) are not.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found. | Checks that VLOG() is only used for defining a logging level. | [
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"""Checks that VLOG() is only used for defining a logging level.
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Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
if Search(r'\bVLOG\((INFO|ERROR|WARNING|DFATAL|FATAL)\)', line):
error(filename, linenum, 'runtime/vlog', 5,
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apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | src/python/turicreate/toolkits/object_detector/_tf_model_architecture.py | python | ODTensorFlowModel.conv_layer | (self, inputs, shape, name, batch_name, init_weights, batch_norm=True) | return conv | Defines conv layer, batch norm and leaky ReLU
Parameters
----------
inputs: TensorFlow Tensor
4d tensor of NHWC format
shape: list
Shape of the conv layer
batch_norm: Bool
(True or False) to add batch norm layer. This is used to add batch norm to all conv layers but the last.
name: string
Name for the conv layer
init_weights: Dict of numpy arrays
A mapping of layer names to init weights
batch_name: string
Name for the batch norm layer
Returns
-------
conv: TensorFlow Tensor
Return result from combining conv, batch norm and leaky ReLU or conv and bias as needed | Defines conv layer, batch norm and leaky ReLU | [
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"batch",
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] | def conv_layer(self, inputs, shape, name, batch_name, init_weights, batch_norm=True):
"""
Defines conv layer, batch norm and leaky ReLU
Parameters
----------
inputs: TensorFlow Tensor
4d tensor of NHWC format
shape: list
Shape of the conv layer
batch_norm: Bool
(True or False) to add batch norm layer. This is used to add batch norm to all conv layers but the last.
name: string
Name for the conv layer
init_weights: Dict of numpy arrays
A mapping of layer names to init weights
batch_name: string
Name for the batch norm layer
Returns
-------
conv: TensorFlow Tensor
Return result from combining conv, batch norm and leaky ReLU or conv and bias as needed
"""
_tf = _lazy_import_tensorflow()
weight = _tf.Variable(
init_weights[name + "weight"].transpose(2, 3, 1, 0),
trainable=True,
name=name + "weight",
)
conv = _tf.nn.conv2d(
inputs, weight, strides=[1, 1, 1, 1], padding="SAME", name=name
)
if batch_norm:
conv = self.batch_norm_wrapper(conv, batch_name, init_weights, is_training=self.is_train)
alpha = 0.1
conv = _tf.maximum(alpha * conv, conv)
else:
bias = _tf.Variable(_tf.constant(0.1, shape=[shape[3]]), name=name + "bias")
conv = _tf.add(conv, bias)
return conv | [
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apache/incubator-mxnet | f03fb23f1d103fec9541b5ae59ee06b1734a51d9 | python/mxnet/ndarray/ndarray.py | python | logical_xor | (lhs, rhs) | return _ufunc_helper(
lhs,
rhs,
op.broadcast_logical_xor,
lambda x, y: 1 if bool(x) ^ bool(y) else 0,
_internal._logical_xor_scalar,
None) | Returns the result of element-wise **logical xor** comparison
operation with broadcasting.
For each element in input arrays, return 1(true) if lhs elements or rhs elements
are true, otherwise return 0(false).
Equivalent to ``bool(lhs) ^ bool(rhs)`` and ``mx.nd.broadcast_logical_xor(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First input of the function.
rhs : scalar or mxnet.ndarray.array
Second input of the function. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> mx.nd.logical_xor(x, y).asnumpy()
array([[ 1., 1., 1.],
[ 0., 0., 0.]], dtype=float32) | Returns the result of element-wise **logical xor** comparison
operation with broadcasting. | [
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"""Returns the result of element-wise **logical xor** comparison
operation with broadcasting.
For each element in input arrays, return 1(true) if lhs elements or rhs elements
are true, otherwise return 0(false).
Equivalent to ``bool(lhs) ^ bool(rhs)`` and ``mx.nd.broadcast_logical_xor(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First input of the function.
rhs : scalar or mxnet.ndarray.array
Second input of the function. If ``lhs.shape != rhs.shape``, they must be
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Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> mx.nd.logical_xor(x, y).asnumpy()
array([[ 1., 1., 1.],
[ 0., 0., 0.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_logical_xor,
lambda x, y: 1 if bool(x) ^ bool(y) else 0,
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/tkinter/__init__.py | python | Scrollbar.fraction | (self, x, y) | return self.tk.getdouble(self.tk.call(self._w, 'fraction', x, y)) | Return the fractional value which corresponds to a slider
position of X,Y. | Return the fractional value which corresponds to a slider
position of X,Y. | [
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] | def fraction(self, x, y):
"""Return the fractional value which corresponds to a slider
position of X,Y."""
return self.tk.getdouble(self.tk.call(self._w, 'fraction', x, y)) | [
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microsoft/clang | 86d4513d3e0daa4d5a29b0b1de7c854ca15f9fe5 | bindings/python/clang/cindex.py | python | Cursor.is_default_constructor | (self) | return conf.lib.clang_CXXConstructor_isDefaultConstructor(self) | Returns True if the cursor refers to a C++ default constructor. | Returns True if the cursor refers to a C++ default constructor. | [
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] | def is_default_constructor(self):
"""Returns True if the cursor refers to a C++ default constructor.
"""
return conf.lib.clang_CXXConstructor_isDefaultConstructor(self) | [
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] | https://github.com/microsoft/clang/blob/86d4513d3e0daa4d5a29b0b1de7c854ca15f9fe5/bindings/python/clang/cindex.py#L1442-L1445 | |
facebookresearch/minirts | 859e747a5e2fab2355bea083daffa6a36820a7f2 | scripts/behavior_clone/rnn_coach.py | python | ConvRnnCoach.rl_forward | (self, batch) | return output | forward function use by RL | forward function use by RL | [
"forward",
"function",
"use",
"by",
"RL"
] | def rl_forward(self, batch):
"""forward function use by RL
"""
batch = self._format_rl_language_input(batch)
glob_feat = self._forward(batch)
v = self.value(glob_feat).squeeze()
cont_prob = self.cont_cls.compute_prob(glob_feat)
inst_prob = self.inst_selector.compute_prob(
batch['cand_inst'], batch['cand_inst_len'], glob_feat)
output = {
'cont_pi': cont_prob,
'inst_pi': inst_prob,
'v': v
}
return output | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/telemetry/telemetry/benchmark.py | python | Benchmark.Run | (self, finder_options) | return story_runner.RunBenchmark(self, finder_options) | Do not override this method. | Do not override this method. | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py2/sklearn/mixture/base.py | python | BaseMixture.sample | (self, n_samples=1) | return (X, y) | Generate random samples from the fitted Gaussian distribution.
Parameters
----------
n_samples : int, optional
Number of samples to generate. Defaults to 1.
Returns
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X : array, shape (n_samples, n_features)
Randomly generated sample
y : array, shape (nsamples,)
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] | def sample(self, n_samples=1):
"""Generate random samples from the fitted Gaussian distribution.
Parameters
----------
n_samples : int, optional
Number of samples to generate. Defaults to 1.
Returns
-------
X : array, shape (n_samples, n_features)
Randomly generated sample
y : array, shape (nsamples,)
Component labels
"""
self._check_is_fitted()
if n_samples < 1:
raise ValueError(
"Invalid value for 'n_samples': %d . The sampling requires at "
"least one sample." % (self.n_components))
_, n_features = self.means_.shape
rng = check_random_state(self.random_state)
n_samples_comp = rng.multinomial(n_samples, self.weights_)
if self.covariance_type == 'full':
X = np.vstack([
rng.multivariate_normal(mean, covariance, int(sample))
for (mean, covariance, sample) in zip(
self.means_, self.covariances_, n_samples_comp)])
elif self.covariance_type == "tied":
X = np.vstack([
rng.multivariate_normal(mean, self.covariances_, int(sample))
for (mean, sample) in zip(
self.means_, n_samples_comp)])
else:
X = np.vstack([
mean + rng.randn(sample, n_features) * np.sqrt(covariance)
for (mean, covariance, sample) in zip(
self.means_, self.covariances_, n_samples_comp)])
y = np.concatenate([j * np.ones(sample, dtype=int)
for j, sample in enumerate(n_samples_comp)])
return (X, y) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/eclib/finddlg.py | python | FindReplaceDlgBase.SetLookinPath | (self, path) | Set the lookin path, adding it to the collection if it is
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/http/cookiejar.py | python | FileCookieJar.__init__ | (self, filename=None, delayload=False, policy=None) | Cookies are NOT loaded from the named file until either the .load() or
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"""
Cookies are NOT loaded from the named file until either the .load() or
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"""
CookieJar.__init__(self, policy)
if filename is not None:
try:
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except:
raise ValueError("filename must be string-like")
self.filename = filename
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snap-stanford/snap-python | d53c51b0a26aa7e3e7400b014cdf728948fde80a | setup/snap.py | python | TAscFlt.Save | (self, *args) | return _snap.TAscFlt_Save(self, *args) | Save(TAscFlt self, TSOut SOut)
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natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/contrib/slim/python/slim/data/tfexample_decoder.py | python | TFExampleDecoder.list_items | (self) | return self._items_to_handlers.keys() | See base class. | See base class. | [
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adobe/chromium | cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7 | tools/code_coverage/croc.py | python | Coverage.AddFiles | (self, src_dir) | Adds files to coverage information.
LCOV files only contains files which are compiled and instrumented as part
of running coverage. This function finds missing files and adds them.
Args:
src_dir: Directory on disk at which to start search. May be a relative
path on disk starting with '.' or '..', or an absolute path, or a
path relative to an alt_name for one of the roots
(for example, '_/src'). If the alt_name matches more than one root,
all matches will be attempted.
Note that dirs not underneath one of the root dirs and covered by an
inclusion rule will be ignored. | Adds files to coverage information. | [
"Adds",
"files",
"to",
"coverage",
"information",
"."
] | def AddFiles(self, src_dir):
"""Adds files to coverage information.
LCOV files only contains files which are compiled and instrumented as part
of running coverage. This function finds missing files and adds them.
Args:
src_dir: Directory on disk at which to start search. May be a relative
path on disk starting with '.' or '..', or an absolute path, or a
path relative to an alt_name for one of the roots
(for example, '_/src'). If the alt_name matches more than one root,
all matches will be attempted.
Note that dirs not underneath one of the root dirs and covered by an
inclusion rule will be ignored.
"""
# Check for root dir alt_names in the path and replace with the actual
# root dirs, then recurse.
found_root = False
for root, alt_name in self.root_dirs:
replaced_root = re.sub('^' + re.escape(alt_name) + '(?=(/|$))', root,
src_dir)
if replaced_root != src_dir:
found_root = True
self.AddFiles(replaced_root)
if found_root:
return # Replaced an alt_name with a root_dir, so already recursed.
for (dirpath, dirnames, filenames) in self.add_files_walk(src_dir):
# Make a copy of the dirnames list so we can modify the original to
# prune subdirs we don't need to walk.
for d in list(dirnames):
# Add trailing '/' to directory names so dir-based regexps can match
# '/' instead of needing to specify '(/|$)'.
dpath = self.CleanupFilename(dirpath + '/' + d) + '/'
attrs = self.ClassifyFile(dpath)
if not attrs.get('include'):
# Directory has been excluded, so don't traverse it
# TODO: Document the slight weirdness caused by this: If you
# AddFiles('./A'), and the rules include 'A/B/C/D' but not 'A/B',
# then it won't recurse into './A/B' so won't find './A/B/C/D'.
# Workarounds are to AddFiles('./A/B/C/D') or AddFiles('./A/B/C').
# The latter works because it explicitly walks the contents of the
# path passed to AddFiles(), so it finds './A/B/C/D'.
dirnames.remove(d)
for f in filenames:
local_path = dirpath + '/' + f
covf = self.GetCoveredFile(local_path, add=True)
if not covf:
continue
# Save where we found the file, for generating line-by-line HTML output
covf.local_path = local_path
if covf.in_lcov:
# File already instrumented and doesn't need to be scanned
continue
if not covf.attrs.get('add_if_missing', 1):
# Not allowed to add the file
self.RemoveCoveredFile(covf)
continue
# Scan file to find potentially-executable lines
lines = self.scan_file(covf.local_path, covf.attrs.get('language'))
if lines:
for l in lines:
covf.lines[l] = None
covf.UpdateCoverage()
else:
# File has no executable lines, so don't count it
self.RemoveCoveredFile(covf) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/lib2to3/fixer_util.py | python | is_tuple | (node) | return (isinstance(node, Node)
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return (isinstance(node, Node)
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_gdi.py | python | Bitmap.SaveFile | (*args, **kwargs) | return _gdi_.Bitmap_SaveFile(*args, **kwargs) | SaveFile(self, String name, int type, Palette palette=None) -> bool
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hfinkel/llvm-project-cxxjit | 91084ef018240bbb8e24235ff5cd8c355a9c1a1e | clang/docs/tools/dump_ast_matchers.py | python | unify_arguments | (args) | return args | Gets rid of anything the user doesn't care about in the argument list. | Gets rid of anything the user doesn't care about in the argument list. | [
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args = re.sub(r'internal::', r'', args)
args = re.sub(r'extern const\s+(.*)&', r'\1 ', args)
args = re.sub(r'&', r' ', args)
args = re.sub(r'(^|\s)M\d?(\s)', r'\1Matcher<*>\2', args)
return args | [
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htcondor/htcondor | 4829724575176d1d6c936e4693dfd78a728569b0 | src/condor_contrib/campus_factory/python-lib/campus_factory/OfflineAds/OfflineAds.py | python | OfflineAds.GetOfflineAds | (self, site) | return ad_list | Get the full classads of the offline startds
@param site: The site to restrict the offline ads
@return: list of ClassAd objects | Get the full classads of the offline startds | [
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"""
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@param site: The site to restrict the offline ads
@return: list of ClassAd objects
"""
#def OfflineAds(data):
# if data.has_key("Offline"):
# if data["Offline"] == True and data[self.siteunique] == site:
# return True
# return False
#fetched = self.condor_status.fetchStored(OfflineAds)
cmd = "condor_status -l -const '(IsUndefined(Offline) == FALSE) && (Offline == true) && (%(uniquesite)s =?= %(sitename)s)'"
query_opts = {"uniquesite": self.siteunique, "sitename": site}
new_cmd = cmd % query_opts
(stdout, stderr) = RunExternal(new_cmd)
ad_list = []
for str_classad in stdout.split('\n\n'):
if len(str_classad) > 0:
ad_list.append(ClassAd(str_classad))
return ad_list | [
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PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | python/paddle/fluid/dygraph/dygraph_to_static/convert_operators.py | python | convert_while_loop | (cond, body, loop_vars) | return loop_vars | A function representation of a Python ``while`` statement.
Args:
cond(Callable): A callable object that returns a boolean variable to control whether to execute the loop body. It takes ``loop_vars`` as arguments.
body(Callable): A callable object that returns a tuple or list of variables with the same arguments ``loops_vars`` as ``cond`` .
loop_vars(list|tuple): A list or tuple of variables passed to ``cond`` and ``body`` .
Returns:
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"""
A function representation of a Python ``while`` statement.
Args:
cond(Callable): A callable object that returns a boolean variable to control whether to execute the loop body. It takes ``loop_vars`` as arguments.
body(Callable): A callable object that returns a tuple or list of variables with the same arguments ``loops_vars`` as ``cond`` .
loop_vars(list|tuple): A list or tuple of variables passed to ``cond`` and ``body`` .
Returns:
A list or tuple of variables which returned by ``body``.
"""
# NOTE: It may be slower if cond is very expensive, but usually cond is just O(1).
# If loop_vars is changed during cond callable, then it causes bug, but current logical_and/logical_not/... doesn't change the loop_vars.
pred = cond(*loop_vars)
if isinstance(pred, Variable):
loop_vars = _run_paddle_while_loop(cond, body, loop_vars)
else:
loop_vars = _run_py_while(cond, body, loop_vars)
return loop_vars | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/urllib/request.py | python | urlretrieve | (url, filename=None, reporthook=None, data=None) | return result | Retrieve a URL into a temporary location on disk.
Requires a URL argument. If a filename is passed, it is used as
the temporary file location. The reporthook argument should be
a callable that accepts a block number, a read size, and the
total file size of the URL target. The data argument should be
valid URL encoded data.
If a filename is passed and the URL points to a local resource,
the result is a copy from local file to new file.
Returns a tuple containing the path to the newly created
data file as well as the resulting HTTPMessage object. | Retrieve a URL into a temporary location on disk. | [
"Retrieve",
"a",
"URL",
"into",
"a",
"temporary",
"location",
"on",
"disk",
"."
] | def urlretrieve(url, filename=None, reporthook=None, data=None):
"""
Retrieve a URL into a temporary location on disk.
Requires a URL argument. If a filename is passed, it is used as
the temporary file location. The reporthook argument should be
a callable that accepts a block number, a read size, and the
total file size of the URL target. The data argument should be
valid URL encoded data.
If a filename is passed and the URL points to a local resource,
the result is a copy from local file to new file.
Returns a tuple containing the path to the newly created
data file as well as the resulting HTTPMessage object.
"""
url_type, path = _splittype(url)
with contextlib.closing(urlopen(url, data)) as fp:
headers = fp.info()
# Just return the local path and the "headers" for file://
# URLs. No sense in performing a copy unless requested.
if url_type == "file" and not filename:
return os.path.normpath(path), headers
# Handle temporary file setup.
if filename:
tfp = open(filename, 'wb')
else:
tfp = tempfile.NamedTemporaryFile(delete=False)
filename = tfp.name
_url_tempfiles.append(filename)
with tfp:
result = filename, headers
bs = 1024*8
size = -1
read = 0
blocknum = 0
if "content-length" in headers:
size = int(headers["Content-Length"])
if reporthook:
reporthook(blocknum, bs, size)
while True:
block = fp.read(bs)
if not block:
break
read += len(block)
tfp.write(block)
blocknum += 1
if reporthook:
reporthook(blocknum, bs, size)
if size >= 0 and read < size:
raise ContentTooShortError(
"retrieval incomplete: got only %i out of %i bytes"
% (read, size), result)
return result | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | tools/telemetry/third_party/pyserial/serial/serialwin32.py | python | Win32Serial.inWaiting | (self) | return comstat.cbInQue | Return the number of characters currently in the input buffer. | Return the number of characters currently in the input buffer. | [
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] | def inWaiting(self):
"""Return the number of characters currently in the input buffer."""
flags = win32.DWORD()
comstat = win32.COMSTAT()
if not win32.ClearCommError(self.hComPort, ctypes.byref(flags), ctypes.byref(comstat)):
raise SerialException('call to ClearCommError failed')
return comstat.cbInQue | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py2/sklearn/utils/multiclass.py | python | is_multilabel | (y) | Check if ``y`` is in a multilabel format.
Parameters
----------
y : numpy array of shape [n_samples]
Target values.
Returns
-------
out : bool,
Return ``True``, if ``y`` is in a multilabel format, else ```False``.
Examples
--------
>>> import numpy as np
>>> from sklearn.utils.multiclass import is_multilabel
>>> is_multilabel([0, 1, 0, 1])
False
>>> is_multilabel([[1], [0, 2], []])
False
>>> is_multilabel(np.array([[1, 0], [0, 0]]))
True
>>> is_multilabel(np.array([[1], [0], [0]]))
False
>>> is_multilabel(np.array([[1, 0, 0]]))
True | Check if ``y`` is in a multilabel format. | [
"Check",
"if",
"y",
"is",
"in",
"a",
"multilabel",
"format",
"."
] | def is_multilabel(y):
""" Check if ``y`` is in a multilabel format.
Parameters
----------
y : numpy array of shape [n_samples]
Target values.
Returns
-------
out : bool,
Return ``True``, if ``y`` is in a multilabel format, else ```False``.
Examples
--------
>>> import numpy as np
>>> from sklearn.utils.multiclass import is_multilabel
>>> is_multilabel([0, 1, 0, 1])
False
>>> is_multilabel([[1], [0, 2], []])
False
>>> is_multilabel(np.array([[1, 0], [0, 0]]))
True
>>> is_multilabel(np.array([[1], [0], [0]]))
False
>>> is_multilabel(np.array([[1, 0, 0]]))
True
"""
if hasattr(y, '__array__'):
y = np.asarray(y)
if not (hasattr(y, "shape") and y.ndim == 2 and y.shape[1] > 1):
return False
if issparse(y):
if isinstance(y, (dok_matrix, lil_matrix)):
y = y.tocsr()
return (len(y.data) == 0 or np.unique(y.data).size == 1 and
(y.dtype.kind in 'biu' or # bool, int, uint
_is_integral_float(np.unique(y.data))))
else:
labels = np.unique(y)
return len(labels) < 3 and (y.dtype.kind in 'biu' or # bool, int, uint
_is_integral_float(labels)) | [
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nasa/fprime | 595cf3682d8365943d86c1a6fe7c78f0a116acf0 | Autocoders/Python/src/fprime_ac/generators/visitors/CommandVisitor.py | python | CommandVisitor._writeTmpl | (self, c, fp, visit_str) | Wrapper to write tmpl to files desc. | Wrapper to write tmpl to files desc. | [
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"."
] | def _writeTmpl(self, c, fp, visit_str):
"""
Wrapper to write tmpl to files desc.
"""
DEBUG.debug("CommandVisitor:%s" % visit_str)
DEBUG.debug("===================================")
DEBUG.debug(c)
fp.writelines(c.__str__())
DEBUG.debug("===================================") | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/closure_linter/closure_linter/tokenutil.py | python | TokensToString | (token_iterable) | return buf.getvalue() | Convert a number of tokens into a string.
Newlines will be inserted whenever the line_number of two neighboring
strings differ.
Args:
token_iterable: The tokens to turn to a string.
Returns:
A string representation of the given tokens. | Convert a number of tokens into a string. | [
"Convert",
"a",
"number",
"of",
"tokens",
"into",
"a",
"string",
"."
] | def TokensToString(token_iterable):
"""Convert a number of tokens into a string.
Newlines will be inserted whenever the line_number of two neighboring
strings differ.
Args:
token_iterable: The tokens to turn to a string.
Returns:
A string representation of the given tokens.
"""
buf = StringIO.StringIO()
token_list = list(token_iterable)
if not token_list:
return ''
line_number = token_list[0].line_number
for token in token_list:
while line_number < token.line_number:
line_number += 1
buf.write('\n')
if line_number > token.line_number:
line_number = token.line_number
buf.write('\n')
buf.write(token.string)
return buf.getvalue() | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/contrib/factorization/python/ops/factorization_ops.py | python | WALSModel._create_factors | (cls, rows, cols, num_shards, init, name) | return sharded_matrix | Helper function to create row and column factors. | Helper function to create row and column factors. | [
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"row",
"and",
"column",
"factors",
"."
] | def _create_factors(cls, rows, cols, num_shards, init, name):
"""Helper function to create row and column factors."""
if callable(init):
init = init()
if isinstance(init, list):
assert len(init) == num_shards
elif isinstance(init, str) and init == "random":
pass
elif num_shards == 1:
init = [init]
sharded_matrix = []
sizes = cls._shard_sizes(rows, num_shards)
assert len(sizes) == num_shards
def make_initializer(i, size):
def initializer():
if init == "random":
return random_ops.random_normal([size, cols])
else:
return init[i]
return initializer
for i, size in enumerate(sizes):
var_name = "%s_shard_%d" % (name, i)
var_init = make_initializer(i, size)
sharded_matrix.append(
variable_scope.variable(
var_init, dtype=dtypes.float32, name=var_name))
return sharded_matrix | [
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p4lang/p4c | 3272e79369f20813cc1a555a5eb26f44432f84a4 | tools/cpplint.py | python | RemoveMultiLineCommentsFromRange | (lines, begin, end) | Clears a range of lines for multi-line comments. | Clears a range of lines for multi-line comments. | [
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"of",
"lines",
"for",
"multi",
"-",
"line",
"comments",
"."
] | def RemoveMultiLineCommentsFromRange(lines, begin, end):
"""Clears a range of lines for multi-line comments."""
# Having // <empty> comments makes the lines non-empty, so we will not get
# unnecessary blank line warnings later in the code.
for i in range(begin, end):
lines[i] = '/**/' | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py2/pandas/io/stata.py | python | StataWriter._write_expansion_fields | (self) | Write 5 zeros for expansion fields | Write 5 zeros for expansion fields | [
"Write",
"5",
"zeros",
"for",
"expansion",
"fields"
] | def _write_expansion_fields(self):
"""Write 5 zeros for expansion fields"""
self._write(_pad_bytes("", 5)) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py3/sklearn/model_selection/_validation.py | python | validation_curve | (estimator, X, y, param_name, param_range, groups=None,
cv=None, scoring=None, n_jobs=None, pre_dispatch="all",
verbose=0, error_score=np.nan) | return out[0], out[1] | Validation curve.
Determine training and test scores for varying parameter values.
Compute scores for an estimator with different values of a specified
parameter. This is similar to grid search with one parameter. However, this
will also compute training scores and is merely a utility for plotting the
results.
Read more in the :ref:`User Guide <learning_curve>`.
Parameters
----------
estimator : object type that implements the "fit" and "predict" methods
An object of that type which is cloned for each validation.
X : array-like, shape (n_samples, n_features)
Training vector, where n_samples is the number of samples and
n_features is the number of features.
y : array-like, shape (n_samples) or (n_samples, n_features), optional
Target relative to X for classification or regression;
None for unsupervised learning.
param_name : string
Name of the parameter that will be varied.
param_range : array-like, shape (n_values,)
The values of the parameter that will be evaluated.
groups : array-like, with shape (n_samples,), optional
Group labels for the samples used while splitting the dataset into
train/test set. Only used in conjunction with a "Group" :term:`cv`
instance (e.g., :class:`GroupKFold`).
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 5-fold cross validation,
- integer, to specify the number of folds in a `(Stratified)KFold`,
- :term:`CV splitter`,
- An iterable yielding (train, test) splits as arrays of indices.
For integer/None inputs, if the estimator is a classifier and ``y`` is
either binary or multiclass, :class:`StratifiedKFold` is used. In all
other cases, :class:`KFold` is used.
Refer :ref:`User Guide <cross_validation>` for the various
cross-validation strategies that can be used here.
.. versionchanged:: 0.22
``cv`` default value if None changed from 3-fold to 5-fold.
scoring : string, callable or None, optional, default: None
A string (see model evaluation documentation) or
a scorer callable object / function with signature
``scorer(estimator, X, y)``.
n_jobs : int or None, optional (default=None)
Number of jobs to run in parallel.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.
pre_dispatch : integer or string, optional
Number of predispatched jobs for parallel execution (default is
all). The option can reduce the allocated memory. The string can
be an expression like '2*n_jobs'.
verbose : integer, optional
Controls the verbosity: the higher, the more messages.
error_score : 'raise' or numeric
Value to assign to the score if an error occurs in estimator fitting.
If set to 'raise', the error is raised.
If a numeric value is given, FitFailedWarning is raised. This parameter
does not affect the refit step, which will always raise the error.
Returns
-------
train_scores : array, shape (n_ticks, n_cv_folds)
Scores on training sets.
test_scores : array, shape (n_ticks, n_cv_folds)
Scores on test set.
Notes
-----
See :ref:`sphx_glr_auto_examples_model_selection_plot_validation_curve.py` | Validation curve. | [
"Validation",
"curve",
"."
] | def validation_curve(estimator, X, y, param_name, param_range, groups=None,
cv=None, scoring=None, n_jobs=None, pre_dispatch="all",
verbose=0, error_score=np.nan):
"""Validation curve.
Determine training and test scores for varying parameter values.
Compute scores for an estimator with different values of a specified
parameter. This is similar to grid search with one parameter. However, this
will also compute training scores and is merely a utility for plotting the
results.
Read more in the :ref:`User Guide <learning_curve>`.
Parameters
----------
estimator : object type that implements the "fit" and "predict" methods
An object of that type which is cloned for each validation.
X : array-like, shape (n_samples, n_features)
Training vector, where n_samples is the number of samples and
n_features is the number of features.
y : array-like, shape (n_samples) or (n_samples, n_features), optional
Target relative to X for classification or regression;
None for unsupervised learning.
param_name : string
Name of the parameter that will be varied.
param_range : array-like, shape (n_values,)
The values of the parameter that will be evaluated.
groups : array-like, with shape (n_samples,), optional
Group labels for the samples used while splitting the dataset into
train/test set. Only used in conjunction with a "Group" :term:`cv`
instance (e.g., :class:`GroupKFold`).
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 5-fold cross validation,
- integer, to specify the number of folds in a `(Stratified)KFold`,
- :term:`CV splitter`,
- An iterable yielding (train, test) splits as arrays of indices.
For integer/None inputs, if the estimator is a classifier and ``y`` is
either binary or multiclass, :class:`StratifiedKFold` is used. In all
other cases, :class:`KFold` is used.
Refer :ref:`User Guide <cross_validation>` for the various
cross-validation strategies that can be used here.
.. versionchanged:: 0.22
``cv`` default value if None changed from 3-fold to 5-fold.
scoring : string, callable or None, optional, default: None
A string (see model evaluation documentation) or
a scorer callable object / function with signature
``scorer(estimator, X, y)``.
n_jobs : int or None, optional (default=None)
Number of jobs to run in parallel.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.
pre_dispatch : integer or string, optional
Number of predispatched jobs for parallel execution (default is
all). The option can reduce the allocated memory. The string can
be an expression like '2*n_jobs'.
verbose : integer, optional
Controls the verbosity: the higher, the more messages.
error_score : 'raise' or numeric
Value to assign to the score if an error occurs in estimator fitting.
If set to 'raise', the error is raised.
If a numeric value is given, FitFailedWarning is raised. This parameter
does not affect the refit step, which will always raise the error.
Returns
-------
train_scores : array, shape (n_ticks, n_cv_folds)
Scores on training sets.
test_scores : array, shape (n_ticks, n_cv_folds)
Scores on test set.
Notes
-----
See :ref:`sphx_glr_auto_examples_model_selection_plot_validation_curve.py`
"""
X, y, groups = indexable(X, y, groups)
cv = check_cv(cv, y, classifier=is_classifier(estimator))
scorer = check_scoring(estimator, scoring=scoring)
parallel = Parallel(n_jobs=n_jobs, pre_dispatch=pre_dispatch,
verbose=verbose)
out = parallel(delayed(_fit_and_score)(
clone(estimator), X, y, scorer, train, test, verbose,
parameters={param_name: v}, fit_params=None, return_train_score=True,
error_score=error_score)
# NOTE do not change order of iteration to allow one time cv splitters
for train, test in cv.split(X, y, groups) for v in param_range)
out = np.asarray(out)
n_params = len(param_range)
n_cv_folds = out.shape[0] // n_params
out = out.reshape(n_cv_folds, n_params, 2).transpose((2, 1, 0))
return out[0], out[1] | [
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hughperkins/tf-coriander | 970d3df6c11400ad68405f22b0c42a52374e94ca | tensorflow/contrib/graph_editor/transform.py | python | Transformer._transform_t | (self, t) | return t_ | Transform a tf.Tensor.
Args:
t: the tensor to be transformed.
Returns:
The transformed tensor. | Transform a tf.Tensor. | [
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".",
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"."
] | def _transform_t(self, t):
"""Transform a tf.Tensor.
Args:
t: the tensor to be transformed.
Returns:
The transformed tensor.
"""
if t in self._info.transformed_ts:
return self._info.transformed_ts[t]
op, op_index = t.op, t.value_index
# If op is not in the subgraph:
if op not in self._info.ops:
# t_ is an input of the subgraph
if t in self._info.sgv_inputs_set:
t_ = self.transform_external_input_handler(self._info, t)
# t_ is a hidden input of the subgraph
else:
t_ = self.transform_external_hidden_input_handler(self._info, t)
# If op is in the subgraph, just transform it:
else:
op_ = self._transform_op(op)
t_ = op_.outputs[op_index]
# assign to collection
if t is not t_:
self.assign_collections_handler(self._info, t, t_)
self._info.transformed_ts[t] = t_
return t_ | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/propgrid.py | python | PropertyGridInterface.GetFirstChild | (*args, **kwargs) | return _propgrid.PropertyGridInterface_GetFirstChild(*args, **kwargs) | GetFirstChild(self, PGPropArg id) -> PGProperty | GetFirstChild(self, PGPropArg id) -> PGProperty | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/ftplib.py | python | FTP.abort | (self) | return resp | Abort a file transfer. Uses out-of-band data.
This does not follow the procedure from the RFC to send Telnet
IP and Synch; that doesn't seem to work with the servers I've
tried. Instead, just send the ABOR command as OOB data. | Abort a file transfer. Uses out-of-band data.
This does not follow the procedure from the RFC to send Telnet
IP and Synch; that doesn't seem to work with the servers I've
tried. Instead, just send the ABOR command as OOB data. | [
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'''Abort a file transfer. Uses out-of-band data.
This does not follow the procedure from the RFC to send Telnet
IP and Synch; that doesn't seem to work with the servers I've
tried. Instead, just send the ABOR command as OOB data.'''
line = b'ABOR' + B_CRLF
if self.debugging > 1:
print('*put urgent*', self.sanitize(line))
self.sock.sendall(line, MSG_OOB)
resp = self.getmultiline()
if resp[:3] not in {'426', '225', '226'}:
raise error_proto(resp)
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/pandas/io/stata.py | python | StataWriter._prepare_categoricals | (self, data) | return DataFrame.from_dict(dict(data_formatted)) | Check for categorical columns, retain categorical information for
Stata file and convert categorical data to int | Check for categorical columns, retain categorical information for
Stata file and convert categorical data to int | [
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] | def _prepare_categoricals(self, data):
"""Check for categorical columns, retain categorical information for
Stata file and convert categorical data to int"""
is_cat = [is_categorical_dtype(data[col]) for col in data]
self._is_col_cat = is_cat
self._value_labels = []
if not any(is_cat):
return data
get_base_missing_value = StataMissingValue.get_base_missing_value
data_formatted = []
for col, col_is_cat in zip(data, is_cat):
if col_is_cat:
svl = StataValueLabel(data[col], encoding=self._encoding)
self._value_labels.append(svl)
dtype = data[col].cat.codes.dtype
if dtype == np.int64:
raise ValueError(
"It is not possible to export "
"int64-based categorical data to Stata."
)
values = data[col].cat.codes.values.copy()
# Upcast if needed so that correct missing values can be set
if values.max() >= get_base_missing_value(dtype):
if dtype == np.int8:
dtype = np.int16
elif dtype == np.int16:
dtype = np.int32
else:
dtype = np.float64
values = np.array(values, dtype=dtype)
# Replace missing values with Stata missing value for type
values[values == -1] = get_base_missing_value(dtype)
data_formatted.append((col, values))
else:
data_formatted.append((col, data[col]))
return DataFrame.from_dict(dict(data_formatted)) | [
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eventql/eventql | 7ca0dbb2e683b525620ea30dc40540a22d5eb227 | deps/3rdparty/spidermonkey/mozjs/python/requests/requests/packages/urllib3/filepost.py | python | choose_boundary | () | return uuid4().hex | Our embarassingly-simple replacement for mimetools.choose_boundary. | Our embarassingly-simple replacement for mimetools.choose_boundary. | [
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] | def choose_boundary():
"""
Our embarassingly-simple replacement for mimetools.choose_boundary.
"""
return uuid4().hex | [
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MythTV/mythtv | d282a209cb8be85d036f85a62a8ec971b67d45f4 | mythtv/programs/scripts/internetcontent/nv_python_libs/mashups/mashups_api.py | python | OutStreamEncoder.__getattr__ | (self, attr) | return getattr(self.out, attr) | Delegate everything but write to the stream | Delegate everything but write to the stream | [
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] | def __getattr__(self, attr):
"""Delegate everything but write to the stream"""
return getattr(self.out, attr) | [
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moflow/moflow | 2dfb27c799c90c6caf1477508eca3eec616ef7d2 | bap/libtracewrap/libtrace/protobuf/python/google/protobuf/internal/python_message.py | python | _ExtensionDict.__setitem__ | (self, extension_handle, value) | If extension_handle specifies a non-repeated, scalar extension
field, sets the value of that field. | If extension_handle specifies a non-repeated, scalar extension
field, sets the value of that field. | [
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] | def __setitem__(self, extension_handle, value):
"""If extension_handle specifies a non-repeated, scalar extension
field, sets the value of that field.
"""
_VerifyExtensionHandle(self._extended_message, extension_handle)
if (extension_handle.label == _FieldDescriptor.LABEL_REPEATED or
extension_handle.cpp_type == _FieldDescriptor.CPPTYPE_MESSAGE):
raise TypeError(
'Cannot assign to extension "%s" because it is a repeated or '
'composite type.' % extension_handle.full_name)
# It's slightly wasteful to lookup the type checker each time,
# but we expect this to be a vanishingly uncommon case anyway.
type_checker = type_checkers.GetTypeChecker(
extension_handle.cpp_type, extension_handle.type)
type_checker.CheckValue(value)
self._extended_message._fields[extension_handle] = value
self._extended_message._Modified() | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | third_party/closure_linter/closure_linter/common/erroraccumulator.py | python | ErrorAccumulator.HandleError | (self, error) | Append the error to the list.
Args:
error: The error object | Append the error to the list. | [
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"""Append the error to the list.
Args:
error: The error object
"""
self._errors.append(error) | [
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etotheipi/BitcoinArmory | 2a6fc5355bb0c6fe26e387ccba30a5baafe8cd98 | armoryengine/ArmoryUtils.py | python | checkAddrBinValid | (addrBin, validPrefixes=None) | return (checkAddrType(addrBin) in validPrefixes) | Checks whether this address is valid for the given network
(set at the top of pybtcengine.py) | Checks whether this address is valid for the given network
(set at the top of pybtcengine.py) | [
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] | def checkAddrBinValid(addrBin, validPrefixes=None):
"""
Checks whether this address is valid for the given network
(set at the top of pybtcengine.py)
"""
if validPrefixes is None:
validPrefixes = [ADDRBYTE, P2SHBYTE]
if not isinstance(validPrefixes, list):
validPrefixes = [validPrefixes]
return (checkAddrType(addrBin) in validPrefixes) | [
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xhzdeng/crpn | a5aef0f80dbe486103123f740c634fb01e6cc9a1 | lib/setup.py | python | locate_cuda | () | return cudaconfig | Locate the CUDA environment on the system
Returns a dict with keys 'home', 'nvcc', 'include', and 'lib64'
and values giving the absolute path to each directory.
Starts by looking for the CUDAHOME env variable. If not found, everything
is based on finding 'nvcc' in the PATH. | Locate the CUDA environment on the system | [
"Locate",
"the",
"CUDA",
"environment",
"on",
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"system"
] | def locate_cuda():
"""Locate the CUDA environment on the system
Returns a dict with keys 'home', 'nvcc', 'include', and 'lib64'
and values giving the absolute path to each directory.
Starts by looking for the CUDAHOME env variable. If not found, everything
is based on finding 'nvcc' in the PATH.
"""
# first check if the CUDAHOME env variable is in use
if 'CUDAHOME' in os.environ:
home = os.environ['CUDAHOME']
nvcc = pjoin(home, 'bin', 'nvcc')
else:
# otherwise, search the PATH for NVCC
default_path = pjoin(os.sep, 'usr', 'local', 'cuda', 'bin')
nvcc = find_in_path('nvcc', os.environ['PATH'] + os.pathsep + default_path)
if nvcc is None:
raise EnvironmentError('The nvcc binary could not be '
'located in your $PATH. Either add it to your path, or set $CUDAHOME')
home = os.path.dirname(os.path.dirname(nvcc))
cudaconfig = {'home':home, 'nvcc':nvcc,
'include': pjoin(home, 'include'),
'lib64': pjoin(home, 'lib64')}
for k, v in cudaconfig.iteritems():
if not os.path.exists(v):
raise EnvironmentError('The CUDA %s path could not be located in %s' % (k, v))
return cudaconfig | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemDefectReporter/v1/AWS/common-code/Lib/jira/client.py | python | JIRA.resolutions | (self) | return resolutions | Get a list of resolution Resources from the server. | Get a list of resolution Resources from the server. | [
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"""Get a list of resolution Resources from the server."""
r_json = self._get_json('resolution')
resolutions = [Resolution(
self._options, self._session, raw_res_json) for raw_res_json in r_json]
return resolutions | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/telemetry/telemetry/benchmark.py | python | Benchmark.CustomizeBrowserOptions | (self, options) | Add browser options that are required by this benchmark. | Add browser options that are required by this benchmark. | [
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] | def CustomizeBrowserOptions(self, options):
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ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/third_party/lib_x86_64/python2.7/dist-packages/numpy/polynomial/chebyshev.py | python | chebfit | (x, y, deg, rcond=None, full=False, w=None) | Least squares fit of Chebyshev series to data.
Return the coefficients of a Legendre series of degree `deg` that is the
least squares fit to the data values `y` given at points `x`. If `y` is
1-D the returned coefficients will also be 1-D. If `y` is 2-D multiple
fits are done, one for each column of `y`, and the resulting
coefficients are stored in the corresponding columns of a 2-D return.
The fitted polynomial(s) are in the form
.. math:: p(x) = c_0 + c_1 * T_1(x) + ... + c_n * T_n(x),
where `n` is `deg`.
Since numpy version 1.7.0, chebfit also supports NA. If any of the
elements of `x`, `y`, or `w` are NA, then the corresponding rows of the
linear least squares problem (see Notes) are set to 0. If `y` is 2-D,
then an NA in any row of `y` invalidates that whole row.
Parameters
----------
x : array_like, shape (M,)
x-coordinates of the M sample points ``(x[i], y[i])``.
y : array_like, shape (M,) or (M, K)
y-coordinates of the sample points. Several data sets of sample
points sharing the same x-coordinates can be fitted at once by
passing in a 2D-array that contains one dataset per column.
deg : int
Degree of the fitting series
rcond : float, optional
Relative condition number of the fit. Singular values smaller than
this relative to the largest singular value will be ignored. The
default value is len(x)*eps, where eps is the relative precision of
the float type, about 2e-16 in most cases.
full : bool, optional
Switch determining nature of return value. When it is False (the
default) just the coefficients are returned, when True diagnostic
information from the singular value decomposition is also returned.
w : array_like, shape (`M`,), optional
Weights. If not None, the contribution of each point
``(x[i],y[i])`` to the fit is weighted by `w[i]`. Ideally the
weights are chosen so that the errors of the products ``w[i]*y[i]``
all have the same variance. The default value is None.
.. versionadded:: 1.5.0
Returns
-------
coef : ndarray, shape (M,) or (M, K)
Chebyshev coefficients ordered from low to high. If `y` was 2-D,
the coefficients for the data in column k of `y` are in column
`k`.
[residuals, rank, singular_values, rcond] : present when `full` = True
Residuals of the least-squares fit, the effective rank of the
scaled Vandermonde matrix and its singular values, and the
specified value of `rcond`. For more details, see `linalg.lstsq`.
Warns
-----
RankWarning
The rank of the coefficient matrix in the least-squares fit is
deficient. The warning is only raised if `full` = False. The
warnings can be turned off by
>>> import warnings
>>> warnings.simplefilter('ignore', RankWarning)
See Also
--------
polyfit, legfit, lagfit, hermfit, hermefit
chebval : Evaluates a Chebyshev series.
chebvander : Vandermonde matrix of Chebyshev series.
chebweight : Chebyshev weight function.
linalg.lstsq : Computes a least-squares fit from the matrix.
scipy.interpolate.UnivariateSpline : Computes spline fits.
Notes
-----
The solution is the coefficients of the Chebyshev series `p` that
minimizes the sum of the weighted squared errors
.. math:: E = \\sum_j w_j^2 * |y_j - p(x_j)|^2,
where :math:`w_j` are the weights. This problem is solved by setting up
as the (typically) overdetermined matrix equation
.. math:: V(x) * c = w * y,
where `V` is the weighted pseudo Vandermonde matrix of `x`, `c` are the
coefficients to be solved for, `w` are the weights, and `y` are the
observed values. This equation is then solved using the singular value
decomposition of `V`.
If some of the singular values of `V` are so small that they are
neglected, then a `RankWarning` will be issued. This means that the
coefficient values may be poorly determined. Using a lower order fit
will usually get rid of the warning. The `rcond` parameter can also be
set to a value smaller than its default, but the resulting fit may be
spurious and have large contributions from roundoff error.
Fits using Chebyshev series are usually better conditioned than fits
using power series, but much can depend on the distribution of the
sample points and the smoothness of the data. If the quality of the fit
is inadequate splines may be a good alternative.
References
----------
.. [1] Wikipedia, "Curve fitting",
http://en.wikipedia.org/wiki/Curve_fitting
Examples
-------- | Least squares fit of Chebyshev series to data. | [
"Least",
"squares",
"fit",
"of",
"Chebyshev",
"series",
"to",
"data",
"."
] | def chebfit(x, y, deg, rcond=None, full=False, w=None):
"""
Least squares fit of Chebyshev series to data.
Return the coefficients of a Legendre series of degree `deg` that is the
least squares fit to the data values `y` given at points `x`. If `y` is
1-D the returned coefficients will also be 1-D. If `y` is 2-D multiple
fits are done, one for each column of `y`, and the resulting
coefficients are stored in the corresponding columns of a 2-D return.
The fitted polynomial(s) are in the form
.. math:: p(x) = c_0 + c_1 * T_1(x) + ... + c_n * T_n(x),
where `n` is `deg`.
Since numpy version 1.7.0, chebfit also supports NA. If any of the
elements of `x`, `y`, or `w` are NA, then the corresponding rows of the
linear least squares problem (see Notes) are set to 0. If `y` is 2-D,
then an NA in any row of `y` invalidates that whole row.
Parameters
----------
x : array_like, shape (M,)
x-coordinates of the M sample points ``(x[i], y[i])``.
y : array_like, shape (M,) or (M, K)
y-coordinates of the sample points. Several data sets of sample
points sharing the same x-coordinates can be fitted at once by
passing in a 2D-array that contains one dataset per column.
deg : int
Degree of the fitting series
rcond : float, optional
Relative condition number of the fit. Singular values smaller than
this relative to the largest singular value will be ignored. The
default value is len(x)*eps, where eps is the relative precision of
the float type, about 2e-16 in most cases.
full : bool, optional
Switch determining nature of return value. When it is False (the
default) just the coefficients are returned, when True diagnostic
information from the singular value decomposition is also returned.
w : array_like, shape (`M`,), optional
Weights. If not None, the contribution of each point
``(x[i],y[i])`` to the fit is weighted by `w[i]`. Ideally the
weights are chosen so that the errors of the products ``w[i]*y[i]``
all have the same variance. The default value is None.
.. versionadded:: 1.5.0
Returns
-------
coef : ndarray, shape (M,) or (M, K)
Chebyshev coefficients ordered from low to high. If `y` was 2-D,
the coefficients for the data in column k of `y` are in column
`k`.
[residuals, rank, singular_values, rcond] : present when `full` = True
Residuals of the least-squares fit, the effective rank of the
scaled Vandermonde matrix and its singular values, and the
specified value of `rcond`. For more details, see `linalg.lstsq`.
Warns
-----
RankWarning
The rank of the coefficient matrix in the least-squares fit is
deficient. The warning is only raised if `full` = False. The
warnings can be turned off by
>>> import warnings
>>> warnings.simplefilter('ignore', RankWarning)
See Also
--------
polyfit, legfit, lagfit, hermfit, hermefit
chebval : Evaluates a Chebyshev series.
chebvander : Vandermonde matrix of Chebyshev series.
chebweight : Chebyshev weight function.
linalg.lstsq : Computes a least-squares fit from the matrix.
scipy.interpolate.UnivariateSpline : Computes spline fits.
Notes
-----
The solution is the coefficients of the Chebyshev series `p` that
minimizes the sum of the weighted squared errors
.. math:: E = \\sum_j w_j^2 * |y_j - p(x_j)|^2,
where :math:`w_j` are the weights. This problem is solved by setting up
as the (typically) overdetermined matrix equation
.. math:: V(x) * c = w * y,
where `V` is the weighted pseudo Vandermonde matrix of `x`, `c` are the
coefficients to be solved for, `w` are the weights, and `y` are the
observed values. This equation is then solved using the singular value
decomposition of `V`.
If some of the singular values of `V` are so small that they are
neglected, then a `RankWarning` will be issued. This means that the
coefficient values may be poorly determined. Using a lower order fit
will usually get rid of the warning. The `rcond` parameter can also be
set to a value smaller than its default, but the resulting fit may be
spurious and have large contributions from roundoff error.
Fits using Chebyshev series are usually better conditioned than fits
using power series, but much can depend on the distribution of the
sample points and the smoothness of the data. If the quality of the fit
is inadequate splines may be a good alternative.
References
----------
.. [1] Wikipedia, "Curve fitting",
http://en.wikipedia.org/wiki/Curve_fitting
Examples
--------
"""
order = int(deg) + 1
x = np.asarray(x) + 0.0
y = np.asarray(y) + 0.0
# check arguments.
if deg < 0 :
raise ValueError("expected deg >= 0")
if x.ndim != 1:
raise TypeError("expected 1D vector for x")
if x.size == 0:
raise TypeError("expected non-empty vector for x")
if y.ndim < 1 or y.ndim > 2 :
raise TypeError("expected 1D or 2D array for y")
if len(x) != len(y):
raise TypeError("expected x and y to have same length")
# set up the least squares matrices in transposed form
lhs = chebvander(x, deg).T
rhs = y.T
if w is not None:
w = np.asarray(w) + 0.0
if w.ndim != 1:
raise TypeError("expected 1D vector for w")
if len(x) != len(w):
raise TypeError("expected x and w to have same length")
# apply weights. Don't use inplace operations as they
# can cause problems with NA.
lhs = lhs * w
rhs = rhs * w
# set rcond
if rcond is None :
rcond = len(x)*np.finfo(x.dtype).eps
# Determine the norms of the design matrix columns.
if issubclass(lhs.dtype.type, np.complexfloating):
scl = np.sqrt((np.square(lhs.real) + np.square(lhs.imag)).sum(1))
else:
scl = np.sqrt(np.square(lhs).sum(1))
scl[scl == 0] = 1
# Solve the least squares problem.
c, resids, rank, s = la.lstsq(lhs.T/scl, rhs.T, rcond)
c = (c.T/scl).T
# warn on rank reduction
if rank != order and not full:
msg = "The fit may be poorly conditioned"
warnings.warn(msg, pu.RankWarning)
if full :
return c, [resids, rank, s, rcond]
else :
return c | [
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stellar-deprecated/stellard | 67eabb2217bdfa9a6ea317f62338fb6bca458c90 | src/protobuf/python/google/protobuf/descriptor_pool.py | python | DescriptorPool._ExtractMessages | (self, desc_protos) | Pulls out all the message protos from descriptos.
Args:
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infinit/memo | 3a8394d0f647efe03ccb8bfe885a7279cb8be8a6 | elle/drake/src/drake/__init__.py | python | include | (path, *args, **kwargs) | Include a sub-drakefile.
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args, kwargs -- Arguments for the drakefile's configure.
Load the drakefile found in the specified directory, merge its
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defined globally by the sub-drakefile as attributes.
"""
path = Path(path)
with Drake.current.recurse(path):
drakefile = None
names = ['drakefile', 'drakefile.py']
for name in names:
path = drake.path_source(name)
if path.exists():
drakefile = path
break
if drakefile is None:
raise Exception('cannot find %s or %s in %s' % \
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/mailbox.py | python | _ProxyFile._read | (self, size, read_method) | return result | Read size bytes using read_method. | Read size bytes using read_method. | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | samples/pydocview/FindService.py | python | FindService.GetReplaceString | (self) | return wx.ConfigBase_Get().Read(FIND_MATCHREPLACE, "") | Load the replace pattern from registry | Load the replace pattern from registry | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/numpy/py3/numpy/lib/recfunctions.py | python | _izip_fields | (iterable) | Returns an iterator of concatenated fields from a sequence of arrays. | Returns an iterator of concatenated fields from a sequence of arrays. | [
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"""
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"""
for element in iterable:
if (hasattr(element, '__iter__') and
not isinstance(element, str)):
yield from _izip_fields(element)
elif isinstance(element, np.void) and len(tuple(element)) == 1:
# this statement is the same from the previous expression
yield from _izip_fields(element)
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yield element | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/ops/_grad/grad_array_ops.py | python | get_bprop_flatten | (self) | return bprop | Generate bprop for Flatten | Generate bprop for Flatten | [
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idaholab/moose | 9eeebc65e098b4c30f8205fb41591fd5b61eb6ff | python/moosesqa/check_syntax.py | python | file_is_stub | (filename) | return False | Helper for getting stub status for markdown file | Helper for getting stub status for markdown file | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numpy/linalg/linalg.py | python | _multi_dot_matrix_chain_order | (arrays, return_costs=False) | return (s, m) if return_costs else s | Return a np.array that encodes the optimal order of mutiplications.
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multiplication.
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cost[i, j] = min([
cost[prefix] + cost[suffix] + cost_mult(prefix, suffix)
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/tkinter/__init__.py | python | PanedWindow.sash_place | (self, index, x, y) | return self.sash("place", index, x, y) | Place the sash given by index at the given coordinates | Place the sash given by index at the given coordinates | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/mapreduce/mapreduce/input_readers.py | python | BlobstoreZipLineInputReader.validate | (cls, mapper_spec) | Validates mapper spec and all mapper parameters.
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Args:
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Raises:
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"""
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/graphviz/py2/graphviz/backend.py | python | run | (cmd, input=None, capture_output=False, check=False, encoding=None,
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"'run %r'",... | https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/graphviz/py2/graphviz/backend.py#L150-L186 | |
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/core/series.py | python | Series.to_timestamp | (self, freq=None, how="start", copy=True) | return self._constructor(new_values, index=new_index).__finalize__(self) | Cast to DatetimeIndex of Timestamps, at *beginning* of period.
Parameters
----------
freq : str, default frequency of PeriodIndex
Desired frequency.
how : {'s', 'e', 'start', 'end'}
Convention for converting period to timestamp; start of period
vs. end.
copy : bool, default True
Whether or not to return a copy.
Returns
-------
Series with DatetimeIndex | Cast to DatetimeIndex of Timestamps, at *beginning* of period. | [
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"""
Cast to DatetimeIndex of Timestamps, at *beginning* of period.
Parameters
----------
freq : str, default frequency of PeriodIndex
Desired frequency.
how : {'s', 'e', 'start', 'end'}
Convention for converting period to timestamp; start of period
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copy : bool, default True
Whether or not to return a copy.
Returns
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"""
new_values = self._values
if copy:
new_values = new_values.copy()
new_index = self.index.to_timestamp(freq=freq, how=how)
return self._constructor(new_values, index=new_index).__finalize__(self) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/prompt-toolkit/py2/prompt_toolkit/layout/controls.py | python | TokenListControl.mouse_handler | (self, cli, mouse_event) | return NotImplemented | Handle mouse events.
(When the token list contained mouse handlers and the user clicked on
on any of these, the matching handler is called. This handler can still
return `NotImplemented` in case we want the `Window` to handle this
particular event.) | Handle mouse events. | [
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"""
Handle mouse events.
(When the token list contained mouse handlers and the user clicked on
on any of these, the matching handler is called. This handler can still
return `NotImplemented` in case we want the `Window` to handle this
particular event.)
"""
if self._tokens:
# Read the generator.
tokens_for_line = list(split_lines(self._tokens))
try:
tokens = tokens_for_line[mouse_event.position.y]
except IndexError:
return NotImplemented
else:
# Find position in the token list.
xpos = mouse_event.position.x
# Find mouse handler for this character.
count = 0
for item in tokens:
count += len(item[1])
if count >= xpos:
if len(item) >= 3:
# Handler found. Call it.
# (Handler can return NotImplemented, so return
# that result.)
handler = item[2]
return handler(cli, mouse_event)
else:
break
# Otherwise, don't handle here.
return NotImplemented | [
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linyouhappy/kongkongxiyou | 7a69b2913eb29f4be77f9a62fb90cdd72c4160f1 | cocosjs/frameworks/cocos2d-x/tools/bindings-generator/clang/cindex.py | python | TranslationUnit.save | (self, filename) | Saves the TranslationUnit to a file.
This is equivalent to passing -emit-ast to the clang frontend. The
saved file can be loaded back into a TranslationUnit. Or, if it
corresponds to a header, it can be used as a pre-compiled header file.
If an error occurs while saving, a TranslationUnitSaveError is raised.
If the error was TranslationUnitSaveError.ERROR_INVALID_TU, this means
the constructed TranslationUnit was not valid at time of save. In this
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filename -- The path to save the translation unit to. | Saves the TranslationUnit to a file. | [
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"""Saves the TranslationUnit to a file.
This is equivalent to passing -emit-ast to the clang frontend. The
saved file can be loaded back into a TranslationUnit. Or, if it
corresponds to a header, it can be used as a pre-compiled header file.
If an error occurs while saving, a TranslationUnitSaveError is raised.
If the error was TranslationUnitSaveError.ERROR_INVALID_TU, this means
the constructed TranslationUnit was not valid at time of save. In this
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filename -- The path to save the translation unit to.
"""
options = conf.lib.clang_defaultSaveOptions(self)
result = int(conf.lib.clang_saveTranslationUnit(self, filename,
options))
if result != 0:
raise TranslationUnitSaveError(result,
'Error saving TranslationUnit.') | [
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sonyxperiadev/WebGL | 0299b38196f78c6d5f74bcf6fa312a3daee6de60 | Tools/Scripts/webkitpy/style/checkers/cpp.py | python | check_check | (clean_lines, line_number, error) | Checks the use of CHECK and EXPECT macros.
Args:
clean_lines: A CleansedLines instance containing the file.
line_number: The number of the line to check.
error: The function to call with any errors found. | Checks the use of CHECK and EXPECT macros. | [
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"""Checks the use of CHECK and EXPECT macros.
Args:
clean_lines: A CleansedLines instance containing the file.
line_number: The number of the line to check.
error: The function to call with any errors found.
"""
# Decide the set of replacement macros that should be suggested
raw_lines = clean_lines.raw_lines
current_macro = ''
for macro in _CHECK_MACROS:
if raw_lines[line_number].find(macro) >= 0:
current_macro = macro
break
if not current_macro:
# Don't waste time here if line doesn't contain 'CHECK' or 'EXPECT'
return
line = clean_lines.elided[line_number] # get rid of comments and strings
# Encourage replacing plain CHECKs with CHECK_EQ/CHECK_NE/etc.
for operator in ['==', '!=', '>=', '>', '<=', '<']:
if replaceable_check(operator, current_macro, line):
error(line_number, 'readability/check', 2,
'Consider using %s instead of %s(a %s b)' % (
_CHECK_REPLACEMENT[current_macro][operator],
current_macro, operator))
break | [
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openmm/openmm | cb293447c4fc8b03976dfe11399f107bab70f3d9 | wrappers/python/openmm/app/pdbreporter.py | python | PDBxReporter.report | (self, simulation, state) | Generate a report.
Parameters
----------
simulation : Simulation
The Simulation to generate a report for
state : State
The current state of the simulation | Generate a report. | [
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"""Generate a report.
Parameters
----------
simulation : Simulation
The Simulation to generate a report for
state : State
The current state of the simulation
"""
if self._nextModel == 0:
PDBxFile.writeHeader(simulation.topology, self._out)
self._nextModel += 1
PDBxFile.writeModel(simulation.topology, state.getPositions(), self._out, self._nextModel)
self._nextModel += 1
if hasattr(self._out, 'flush') and callable(self._out.flush):
self._out.flush() | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_windows.py | python | PyWindow.DoSetVirtualSize | (*args, **kwargs) | return _windows_.PyWindow_DoSetVirtualSize(*args, **kwargs) | DoSetVirtualSize(self, int x, int y) | DoSetVirtualSize(self, int x, int y) | [
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return _windows_.PyWindow_DoSetVirtualSize(*args, **kwargs) | [
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SpenceKonde/megaTinyCore | 1c4a70b18a149fe6bcb551dfa6db11ca50b8997b | megaavr/tools/libs/pymcuprog/pymcuprog.py | python | _parse_literal | (literal) | Literals can either be integers or float values. Default is Integer | Literals can either be integers or float values. Default is Integer | [
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"""
Literals can either be integers or float values. Default is Integer
"""
try:
return int(literal, 0)
except ValueError:
return float(literal) | [
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snap-stanford/snap-python | d53c51b0a26aa7e3e7400b014cdf728948fde80a | setup/snap.py | python | TNEANet.AttrValueEI | (self, *args) | return _snap.TNEANet_AttrValueEI(self, *args) | AttrValueEI(TNEANet self, TInt EId, TStrV Values)
Parameters:
EId: TInt const &
Values: TStrV &
AttrValueEI(TNEANet self, TInt EId, TStrIntPrH::TIter EdgeHI, TStrV Values)
Parameters:
EId: TInt const &
EdgeHI: TStrIntPrH::TIter
Values: TStrV & | AttrValueEI(TNEANet self, TInt EId, TStrV Values) | [
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"(",
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"""
AttrValueEI(TNEANet self, TInt EId, TStrV Values)
Parameters:
EId: TInt const &
Values: TStrV &
AttrValueEI(TNEANet self, TInt EId, TStrIntPrH::TIter EdgeHI, TStrV Values)
Parameters:
EId: TInt const &
EdgeHI: TStrIntPrH::TIter
Values: TStrV &
"""
return _snap.TNEANet_AttrValueEI(self, *args) | [
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