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Cantera/cantera
|
0119484b261967ccb55a0066c020599cacc312e4
|
interfaces/python_sdist/setup.py
|
python
|
lib_def
|
(sources, cflags, include_dirs, macros)
|
return dict(sources=sources, cflags=cflags, include_dirs=include_dirs,
macros=macros)
|
Convenience factory to create the dictionary for a Setuptools library build.
|
Convenience factory to create the dictionary for a Setuptools library build.
|
[
"Convenience",
"factory",
"to",
"create",
"the",
"dictionary",
"for",
"a",
"Setuptools",
"library",
"build",
"."
] |
def lib_def(sources, cflags, include_dirs, macros):
"""Convenience factory to create the dictionary for a Setuptools library build."""
return dict(sources=sources, cflags=cflags, include_dirs=include_dirs,
macros=macros)
|
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https://github.com/Cantera/cantera/blob/0119484b261967ccb55a0066c020599cacc312e4/interfaces/python_sdist/setup.py#L129-L132
|
|
baidu-research/tensorflow-allreduce
|
66d5b855e90b0949e9fa5cca5599fd729a70e874
|
tensorflow/contrib/learn/python/learn/models.py
|
python
|
logistic_regression_zero_init
|
(x, y)
|
return logistic_regression(x, y, init_mean=0.0, init_stddev=0.0)
|
Logistic regression subgraph with zero-value initial weights and bias.
Args:
x: tensor or placeholder for input features.
y: tensor or placeholder for labels.
Returns:
Predictions and loss tensors.
|
Logistic regression subgraph with zero-value initial weights and bias.
|
[
"Logistic",
"regression",
"subgraph",
"with",
"zero",
"-",
"value",
"initial",
"weights",
"and",
"bias",
"."
] |
def logistic_regression_zero_init(x, y):
"""Logistic regression subgraph with zero-value initial weights and bias.
Args:
x: tensor or placeholder for input features.
y: tensor or placeholder for labels.
Returns:
Predictions and loss tensors.
"""
return logistic_regression(x, y, init_mean=0.0, init_stddev=0.0)
|
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https://github.com/baidu-research/tensorflow-allreduce/blob/66d5b855e90b0949e9fa5cca5599fd729a70e874/tensorflow/contrib/learn/python/learn/models.py#L46-L56
|
|
wxWidgets/wxPython-Classic
|
19571e1ae65f1ac445f5491474121998c97a1bf0
|
src/osx_carbon/_gdi.py
|
python
|
DC.LogicalToDeviceY
|
(*args, **kwargs)
|
return _gdi_.DC_LogicalToDeviceY(*args, **kwargs)
|
LogicalToDeviceY(self, int y) -> int
Converts logical Y coordinate to device coordinate, using the current
mapping mode.
|
LogicalToDeviceY(self, int y) -> int
|
[
"LogicalToDeviceY",
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"-",
">",
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] |
def LogicalToDeviceY(*args, **kwargs):
"""
LogicalToDeviceY(self, int y) -> int
Converts logical Y coordinate to device coordinate, using the current
mapping mode.
"""
return _gdi_.DC_LogicalToDeviceY(*args, **kwargs)
|
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_carbon/_gdi.py#L4258-L4265
|
|
FreeCAD/FreeCAD
|
ba42231b9c6889b89e064d6d563448ed81e376ec
|
src/Mod/Draft/draftguitools/gui_arcs.py
|
python
|
Arc.numericInput
|
(self, numx, numy, numz)
|
Validate the entry fields in the user interface.
This function is called by the toolbar or taskpanel interface
when valid x, y, and z have been entered in the input fields.
|
Validate the entry fields in the user interface.
|
[
"Validate",
"the",
"entry",
"fields",
"in",
"the",
"user",
"interface",
"."
] |
def numericInput(self, numx, numy, numz):
"""Validate the entry fields in the user interface.
This function is called by the toolbar or taskpanel interface
when valid x, y, and z have been entered in the input fields.
"""
self.center = App.Vector(numx, numy, numz)
self.node = [self.center]
self.arctrack.setCenter(self.center)
self.arctrack.on()
self.ui.radiusUi()
self.step = 1
self.ui.setNextFocus()
_msg(translate("draft", "Pick radius"))
|
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https://github.com/FreeCAD/FreeCAD/blob/ba42231b9c6889b89e064d6d563448ed81e376ec/src/Mod/Draft/draftguitools/gui_arcs.py#L397-L410
|
||
lmb-freiburg/flownet2
|
b92e198b56b0e52e1ba0a5a98dc0e39fa5ae70cc
|
python/caffe/pycaffe.py
|
python
|
_Net_blobs
|
(self)
|
return self._blobs_dict
|
An OrderedDict (bottom to top, i.e., input to output) of network
blobs indexed by name
|
An OrderedDict (bottom to top, i.e., input to output) of network
blobs indexed by name
|
[
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"bottom",
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] |
def _Net_blobs(self):
"""
An OrderedDict (bottom to top, i.e., input to output) of network
blobs indexed by name
"""
if not hasattr(self, '_blobs_dict'):
self._blobs_dict = OrderedDict(zip(self._blob_names, self._blobs))
return self._blobs_dict
|
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https://github.com/lmb-freiburg/flownet2/blob/b92e198b56b0e52e1ba0a5a98dc0e39fa5ae70cc/python/caffe/pycaffe.py#L25-L32
|
|
catboost/catboost
|
167f64f237114a4d10b2b4ee42adb4569137debe
|
contrib/python/more-itertools/py2/more_itertools/recipes.py
|
python
|
tabulate
|
(function, start=0)
|
return map(function, count(start))
|
Return an iterator over the results of ``func(start)``,
``func(start + 1)``, ``func(start + 2)``...
*func* should be a function that accepts one integer argument.
If *start* is not specified it defaults to 0. It will be incremented each
time the iterator is advanced.
>>> square = lambda x: x ** 2
>>> iterator = tabulate(square, -3)
>>> take(4, iterator)
[9, 4, 1, 0]
|
Return an iterator over the results of ``func(start)``,
``func(start + 1)``, ``func(start + 2)``...
|
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def tabulate(function, start=0):
"""Return an iterator over the results of ``func(start)``,
``func(start + 1)``, ``func(start + 2)``...
*func* should be a function that accepts one integer argument.
If *start* is not specified it defaults to 0. It will be incremented each
time the iterator is advanced.
>>> square = lambda x: x ** 2
>>> iterator = tabulate(square, -3)
>>> take(4, iterator)
[9, 4, 1, 0]
"""
return map(function, count(start))
|
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/more-itertools/py2/more_itertools/recipes.py#L97-L112
|
|
zju3dv/clean-pvnet
|
5870c509e3cc205e1bb28910a7b1a9a3c8add9a8
|
lib/utils/meshrenderer/pysixd/transform.py
|
python
|
random_quaternion
|
(rand=None)
|
return numpy.array([numpy.cos(t2)*r2, numpy.sin(t1)*r1,
numpy.cos(t1)*r1, numpy.sin(t2)*r2])
|
Return uniform random unit quaternion.
rand: array like or None
Three independent random variables that are uniformly distributed
between 0 and 1.
>>> q = random_quaternion()
>>> numpy.allclose(1, vector_norm(q))
True
>>> q = random_quaternion(numpy.random.random(3))
>>> len(q.shape), q.shape[0]==4
(1, True)
|
Return uniform random unit quaternion.
|
[
"Return",
"uniform",
"random",
"unit",
"quaternion",
"."
] |
def random_quaternion(rand=None):
"""Return uniform random unit quaternion.
rand: array like or None
Three independent random variables that are uniformly distributed
between 0 and 1.
>>> q = random_quaternion()
>>> numpy.allclose(1, vector_norm(q))
True
>>> q = random_quaternion(numpy.random.random(3))
>>> len(q.shape), q.shape[0]==4
(1, True)
"""
if rand is None:
rand = numpy.random.rand(3)
else:
assert len(rand) == 3
r1 = numpy.sqrt(1.0 - rand[0])
r2 = numpy.sqrt(rand[0])
pi2 = math.pi * 2.0
t1 = pi2 * rand[1]
t2 = pi2 * rand[2]
return numpy.array([numpy.cos(t2)*r2, numpy.sin(t1)*r1,
numpy.cos(t1)*r1, numpy.sin(t2)*r2])
|
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https://github.com/zju3dv/clean-pvnet/blob/5870c509e3cc205e1bb28910a7b1a9a3c8add9a8/lib/utils/meshrenderer/pysixd/transform.py#L1463-L1488
|
|
baidu-research/tensorflow-allreduce
|
66d5b855e90b0949e9fa5cca5599fd729a70e874
|
tensorflow/python/debug/wrappers/framework.py
|
python
|
BaseDebugWrapperSession.on_run_end
|
(self, request)
|
Callback invoked on run() calls to the debug-wrapper session.
This is a blocking callback.
The invocation happens right before the wrapper exits its run() call.
Args:
request: (`OnRunEndRequest`) callback request object carrying information
such as the actual action performed by the session wrapper for the
run() call.
Returns:
An instance of `OnRunStartResponse`.
|
Callback invoked on run() calls to the debug-wrapper session.
|
[
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"run",
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"calls",
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"debug",
"-",
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"."
] |
def on_run_end(self, request):
"""Callback invoked on run() calls to the debug-wrapper session.
This is a blocking callback.
The invocation happens right before the wrapper exits its run() call.
Args:
request: (`OnRunEndRequest`) callback request object carrying information
such as the actual action performed by the session wrapper for the
run() call.
Returns:
An instance of `OnRunStartResponse`.
"""
|
[
"def",
"on_run_end",
"(",
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",",
"request",
")",
":"
] |
https://github.com/baidu-research/tensorflow-allreduce/blob/66d5b855e90b0949e9fa5cca5599fd729a70e874/tensorflow/python/debug/wrappers/framework.py#L659-L672
|
||
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/pandas/core/generic.py
|
python
|
NDFrame._repr_latex_
|
(self)
|
Returns a LaTeX representation for a particular object.
Mainly for use with nbconvert (jupyter notebook conversion to pdf).
|
Returns a LaTeX representation for a particular object.
Mainly for use with nbconvert (jupyter notebook conversion to pdf).
|
[
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def _repr_latex_(self):
"""
Returns a LaTeX representation for a particular object.
Mainly for use with nbconvert (jupyter notebook conversion to pdf).
"""
if config.get_option("display.latex.repr"):
return self.to_latex()
else:
return None
|
[
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/pandas/core/generic.py#L1981-L1989
|
||
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/pandas/core/dtypes/cast.py
|
python
|
is_nested_object
|
(obj)
|
return False
|
return a boolean if we have a nested object, e.g. a Series with 1 or
more Series elements
This may not be necessarily be performant.
|
return a boolean if we have a nested object, e.g. a Series with 1 or
more Series elements
|
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] |
def is_nested_object(obj) -> bool:
"""
return a boolean if we have a nested object, e.g. a Series with 1 or
more Series elements
This may not be necessarily be performant.
"""
if isinstance(obj, ABCSeries) and is_object_dtype(obj):
if any(isinstance(v, ABCSeries) for v in obj.values):
return True
return False
|
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/pandas/core/dtypes/cast.py#L83-L97
|
|
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/pkg_resources/__init__.py
|
python
|
ResourceManager.extraction_error
|
(self)
|
Give an error message for problems extracting file(s)
|
Give an error message for problems extracting file(s)
|
[
"Give",
"an",
"error",
"message",
"for",
"problems",
"extracting",
"file",
"(",
"s",
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] |
def extraction_error(self):
"""Give an error message for problems extracting file(s)"""
old_exc = sys.exc_info()[1]
cache_path = self.extraction_path or get_default_cache()
tmpl = textwrap.dedent("""
Can't extract file(s) to egg cache
The following error occurred while trying to extract file(s)
to the Python egg cache:
{old_exc}
The Python egg cache directory is currently set to:
{cache_path}
Perhaps your account does not have write access to this directory?
You can change the cache directory by setting the PYTHON_EGG_CACHE
environment variable to point to an accessible directory.
""").lstrip()
err = ExtractionError(tmpl.format(**locals()))
err.manager = self
err.cache_path = cache_path
err.original_error = old_exc
raise err
|
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/pkg_resources/__init__.py#L1167-L1193
|
||
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Tools/Python/3.7.10/windows/Lib/multiprocessing/managers.py
|
python
|
BaseManager._create
|
(*args, **kwds)
|
return Token(typeid, self._address, id), exposed
|
Create a new shared object; return the token and exposed tuple
|
Create a new shared object; return the token and exposed tuple
|
[
"Create",
"a",
"new",
"shared",
"object",
";",
"return",
"the",
"token",
"and",
"exposed",
"tuple"
] |
def _create(*args, **kwds):
'''
Create a new shared object; return the token and exposed tuple
'''
self, typeid, *args = args
args = tuple(args)
assert self._state.value == State.STARTED, 'server not yet started'
conn = self._Client(self._address, authkey=self._authkey)
try:
id, exposed = dispatch(conn, None, 'create', (typeid,)+args, kwds)
finally:
conn.close()
return Token(typeid, self._address, id), exposed
|
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/windows/Lib/multiprocessing/managers.py#L599-L612
|
|
wxWidgets/wxPython-Classic
|
19571e1ae65f1ac445f5491474121998c97a1bf0
|
src/osx_carbon/_windows.py
|
python
|
HScrolledWindow.EstimateTotalWidth
|
(*args, **kwargs)
|
return _windows_.HScrolledWindow_EstimateTotalWidth(*args, **kwargs)
|
EstimateTotalWidth(self) -> int
|
EstimateTotalWidth(self) -> int
|
[
"EstimateTotalWidth",
"(",
"self",
")",
"-",
">",
"int"
] |
def EstimateTotalWidth(*args, **kwargs):
"""EstimateTotalWidth(self) -> int"""
return _windows_.HScrolledWindow_EstimateTotalWidth(*args, **kwargs)
|
[
"def",
"EstimateTotalWidth",
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"_windows_",
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"HScrolledWindow_EstimateTotalWidth",
"(",
"*",
"args",
",",
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"kwargs",
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_carbon/_windows.py#L2524-L2526
|
|
GJDuck/LowFat
|
ecf6a0f0fa1b73a27a626cf493cc39e477b6faea
|
llvm-4.0.0.src/tools/clang/bindings/python/clang/cindex.py
|
python
|
Cursor.result_type
|
(self)
|
return self._result_type
|
Retrieve the Type of the result for this Cursor.
|
Retrieve the Type of the result for this Cursor.
|
[
"Retrieve",
"the",
"Type",
"of",
"the",
"result",
"for",
"this",
"Cursor",
"."
] |
def result_type(self):
"""Retrieve the Type of the result for this Cursor."""
if not hasattr(self, '_result_type'):
self._result_type = conf.lib.clang_getResultType(self.type)
return self._result_type
|
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"result_type",
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"lib",
".",
"clang_getResultType",
"(",
"self",
".",
"type",
")",
"return",
"self",
".",
"_result_type"
] |
https://github.com/GJDuck/LowFat/blob/ecf6a0f0fa1b73a27a626cf493cc39e477b6faea/llvm-4.0.0.src/tools/clang/bindings/python/clang/cindex.py#L1527-L1532
|
|
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Tools/Python/3.7.10/windows/Lib/site-packages/setuptools/command/easy_install.py
|
python
|
easy_install.expand_dirs
|
(self)
|
Calls `os.path.expanduser` on install dirs.
|
Calls `os.path.expanduser` on install dirs.
|
[
"Calls",
"os",
".",
"path",
".",
"expanduser",
"on",
"install",
"dirs",
"."
] |
def expand_dirs(self):
"""Calls `os.path.expanduser` on install dirs."""
dirs = [
'install_purelib',
'install_platlib',
'install_lib',
'install_headers',
'install_scripts',
'install_data',
]
self._expand_attrs(dirs)
|
[
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"'install_data'",
",",
"]",
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/windows/Lib/site-packages/setuptools/command/easy_install.py#L401-L411
|
||
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/pip/_internal/cache.py
|
python
|
Cache._get_cache_path_parts
|
(self, link)
|
return parts
|
Get parts of part that must be os.path.joined with cache_dir
|
Get parts of part that must be os.path.joined with cache_dir
|
[
"Get",
"parts",
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"part",
"that",
"must",
"be",
"os",
".",
"path",
".",
"joined",
"with",
"cache_dir"
] |
def _get_cache_path_parts(self, link):
# type: (Link) -> List[str]
"""Get parts of part that must be os.path.joined with cache_dir
"""
# We want to generate an url to use as our cache key, we don't want to
# just re-use the URL because it might have other items in the fragment
# and we don't care about those.
key_parts = {"url": link.url_without_fragment}
if link.hash_name is not None and link.hash is not None:
key_parts[link.hash_name] = link.hash
if link.subdirectory_fragment:
key_parts["subdirectory"] = link.subdirectory_fragment
# Include interpreter name, major and minor version in cache key
# to cope with ill-behaved sdists that build a different wheel
# depending on the python version their setup.py is being run on,
# and don't encode the difference in compatibility tags.
# https://github.com/pypa/pip/issues/7296
key_parts["interpreter_name"] = interpreter_name()
key_parts["interpreter_version"] = interpreter_version()
# Encode our key url with sha224, we'll use this because it has similar
# security properties to sha256, but with a shorter total output (and
# thus less secure). However the differences don't make a lot of
# difference for our use case here.
hashed = _hash_dict(key_parts)
# We want to nest the directories some to prevent having a ton of top
# level directories where we might run out of sub directories on some
# FS.
parts = [hashed[:2], hashed[2:4], hashed[4:6], hashed[6:]]
return parts
|
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",",
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/pip/_internal/cache.py#L115-L181
|
|
larroy/clearskies_core
|
3574ddf0edc8555454c7044126e786a6c29444dc
|
tools/gyp/pylib/gyp/msvs_emulation.py
|
python
|
MsvsSettings.GetLdflags
|
(self, config, gyp_to_build_path, expand_special,
manifest_base_name, output_name, is_executable, build_dir)
|
return ldflags, intermediate_manifest, manifest_files
|
Returns the flags that need to be added to link commands, and the
manifest files.
|
Returns the flags that need to be added to link commands, and the
manifest files.
|
[
"Returns",
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"flags",
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"be",
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"commands",
"and",
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"manifest",
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"."
] |
def GetLdflags(self, config, gyp_to_build_path, expand_special,
manifest_base_name, output_name, is_executable, build_dir):
"""Returns the flags that need to be added to link commands, and the
manifest files."""
config = self._TargetConfig(config)
ldflags = []
ld = self._GetWrapper(self, self.msvs_settings[config],
'VCLinkerTool', append=ldflags)
self._GetDefFileAsLdflags(ldflags, gyp_to_build_path)
ld('GenerateDebugInformation', map={'true': '/DEBUG'})
ld('TargetMachine', map={'1': 'X86', '17': 'X64'}, prefix='/MACHINE:')
ldflags.extend(self._GetAdditionalLibraryDirectories(
'VCLinkerTool', config, gyp_to_build_path))
ld('DelayLoadDLLs', prefix='/DELAYLOAD:')
ld('TreatLinkerWarningAsErrors', prefix='/WX',
map={'true': '', 'false': ':NO'})
out = self.GetOutputName(config, expand_special)
if out:
ldflags.append('/OUT:' + out)
pdb = self.GetPDBName(config, expand_special, output_name + '.pdb')
if pdb:
ldflags.append('/PDB:' + pdb)
pgd = self.GetPGDName(config, expand_special)
if pgd:
ldflags.append('/PGD:' + pgd)
map_file = self.GetMapFileName(config, expand_special)
ld('GenerateMapFile', map={'true': '/MAP:' + map_file if map_file
else '/MAP'})
ld('MapExports', map={'true': '/MAPINFO:EXPORTS'})
ld('AdditionalOptions', prefix='')
minimum_required_version = self._Setting(
('VCLinkerTool', 'MinimumRequiredVersion'), config, default='')
if minimum_required_version:
minimum_required_version = ',' + minimum_required_version
ld('SubSystem',
map={'1': 'CONSOLE%s' % minimum_required_version,
'2': 'WINDOWS%s' % minimum_required_version},
prefix='/SUBSYSTEM:')
ld('TerminalServerAware', map={'1': ':NO', '2': ''}, prefix='/TSAWARE')
ld('LinkIncremental', map={'1': ':NO', '2': ''}, prefix='/INCREMENTAL')
ld('BaseAddress', prefix='/BASE:')
ld('FixedBaseAddress', map={'1': ':NO', '2': ''}, prefix='/FIXED')
ld('RandomizedBaseAddress',
map={'1': ':NO', '2': ''}, prefix='/DYNAMICBASE')
ld('DataExecutionPrevention',
map={'1': ':NO', '2': ''}, prefix='/NXCOMPAT')
ld('OptimizeReferences', map={'1': 'NOREF', '2': 'REF'}, prefix='/OPT:')
ld('ForceSymbolReferences', prefix='/INCLUDE:')
ld('EnableCOMDATFolding', map={'1': 'NOICF', '2': 'ICF'}, prefix='/OPT:')
ld('LinkTimeCodeGeneration',
map={'1': '', '2': ':PGINSTRUMENT', '3': ':PGOPTIMIZE',
'4': ':PGUPDATE'},
prefix='/LTCG')
ld('IgnoreDefaultLibraryNames', prefix='/NODEFAULTLIB:')
ld('ResourceOnlyDLL', map={'true': '/NOENTRY'})
ld('EntryPointSymbol', prefix='/ENTRY:')
ld('Profile', map={'true': '/PROFILE'})
ld('LargeAddressAware',
map={'1': ':NO', '2': ''}, prefix='/LARGEADDRESSAWARE')
# TODO(scottmg): This should sort of be somewhere else (not really a flag).
ld('AdditionalDependencies', prefix='')
# If the base address is not specifically controlled, DYNAMICBASE should
# be on by default.
base_flags = filter(lambda x: 'DYNAMICBASE' in x or x == '/FIXED',
ldflags)
if not base_flags:
ldflags.append('/DYNAMICBASE')
# If the NXCOMPAT flag has not been specified, default to on. Despite the
# documentation that says this only defaults to on when the subsystem is
# Vista or greater (which applies to the linker), the IDE defaults it on
# unless it's explicitly off.
if not filter(lambda x: 'NXCOMPAT' in x, ldflags):
ldflags.append('/NXCOMPAT')
have_def_file = filter(lambda x: x.startswith('/DEF:'), ldflags)
manifest_flags, intermediate_manifest, manifest_files = \
self._GetLdManifestFlags(config, manifest_base_name, gyp_to_build_path,
is_executable and not have_def_file, build_dir)
ldflags.extend(manifest_flags)
return ldflags, intermediate_manifest, manifest_files
|
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"}",
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"self",
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"_GetLdManifestFlags",
"(",
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",",
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"gyp_to_build_path",
",",
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"(",
"manifest_flags",
")",
"return",
"ldflags",
",",
"intermediate_manifest",
",",
"manifest_files"
] |
https://github.com/larroy/clearskies_core/blob/3574ddf0edc8555454c7044126e786a6c29444dc/tools/gyp/pylib/gyp/msvs_emulation.py#L474-L557
|
|
ChromiumWebApps/chromium
|
c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7
|
third_party/jinja2/runtime.py
|
python
|
Context.resolve
|
(self, key)
|
return self.environment.undefined(name=key)
|
Looks up a variable like `__getitem__` or `get` but returns an
:class:`Undefined` object with the name of the name looked up.
|
Looks up a variable like `__getitem__` or `get` but returns an
:class:`Undefined` object with the name of the name looked up.
|
[
"Looks",
"up",
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"variable",
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"an",
":",
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":",
"Undefined",
"object",
"with",
"the",
"name",
"of",
"the",
"name",
"looked",
"up",
"."
] |
def resolve(self, key):
"""Looks up a variable like `__getitem__` or `get` but returns an
:class:`Undefined` object with the name of the name looked up.
"""
if key in self.vars:
return self.vars[key]
if key in self.parent:
return self.parent[key]
return self.environment.undefined(name=key)
|
[
"def",
"resolve",
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"self",
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"key",
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"return",
"self",
".",
"environment",
".",
"undefined",
"(",
"name",
"=",
"key",
")"
] |
https://github.com/ChromiumWebApps/chromium/blob/c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7/third_party/jinja2/runtime.py#L146-L154
|
|
apache/arrow
|
af33dd1157eb8d7d9bfac25ebf61445b793b7943
|
cpp/build-support/cpplint.py
|
python
|
NestingState.InClassDeclaration
|
(self)
|
return self.stack and isinstance(self.stack[-1], _ClassInfo)
|
Check if we are currently one level inside a class or struct declaration.
Returns:
True if top of the stack is a class/struct, False otherwise.
|
Check if we are currently one level inside a class or struct declaration.
|
[
"Check",
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"we",
"are",
"currently",
"one",
"level",
"inside",
"a",
"class",
"or",
"struct",
"declaration",
"."
] |
def InClassDeclaration(self):
"""Check if we are currently one level inside a class or struct declaration.
Returns:
True if top of the stack is a class/struct, False otherwise.
"""
return self.stack and isinstance(self.stack[-1], _ClassInfo)
|
[
"def",
"InClassDeclaration",
"(",
"self",
")",
":",
"return",
"self",
".",
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"and",
"isinstance",
"(",
"self",
".",
"stack",
"[",
"-",
"1",
"]",
",",
"_ClassInfo",
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] |
https://github.com/apache/arrow/blob/af33dd1157eb8d7d9bfac25ebf61445b793b7943/cpp/build-support/cpplint.py#L2565-L2571
|
|
rdiankov/openrave
|
d1a23023fd4b58f077d2ca949ceaf1b91f3f13d7
|
python/examples/qtexampleselector.py
|
python
|
OpenRaveServer.__init__
|
(self,pipe)
|
Setup the shared memory data structure model and initialize the control parts.
|
Setup the shared memory data structure model and initialize the control parts.
|
[
"Setup",
"the",
"shared",
"memory",
"data",
"structure",
"model",
"and",
"initialize",
"the",
"control",
"parts",
"."
] |
def __init__(self,pipe):
'''
Setup the shared memory data structure model and initialize the control parts.
'''
self.pipe = pipe
self.running = True
self._run()
|
[
"def",
"__init__",
"(",
"self",
",",
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":",
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"pipe",
"=",
"pipe",
"self",
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"running",
"=",
"True",
"self",
".",
"_run",
"(",
")"
] |
https://github.com/rdiankov/openrave/blob/d1a23023fd4b58f077d2ca949ceaf1b91f3f13d7/python/examples/qtexampleselector.py#L82-L88
|
||
stellar-deprecated/stellard
|
67eabb2217bdfa9a6ea317f62338fb6bca458c90
|
src/protobuf/python/mox.py
|
python
|
IsA.equals
|
(self, rhs)
|
Check to see if the RHS is an instance of class_name.
Args:
# rhs: the right hand side of the test
rhs: object
Returns:
bool
|
Check to see if the RHS is an instance of class_name.
|
[
"Check",
"to",
"see",
"if",
"the",
"RHS",
"is",
"an",
"instance",
"of",
"class_name",
"."
] |
def equals(self, rhs):
"""Check to see if the RHS is an instance of class_name.
Args:
# rhs: the right hand side of the test
rhs: object
Returns:
bool
"""
try:
return isinstance(rhs, self._class_name)
except TypeError:
# Check raw types if there was a type error. This is helpful for
# things like cStringIO.StringIO.
return type(rhs) == type(self._class_name)
|
[
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"rhs",
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"==",
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https://github.com/stellar-deprecated/stellard/blob/67eabb2217bdfa9a6ea317f62338fb6bca458c90/src/protobuf/python/mox.py#L807-L823
|
||
snap-stanford/snap-python
|
d53c51b0a26aa7e3e7400b014cdf728948fde80a
|
snapx/snapx/convert.py
|
python
|
to_snapx_graph
|
(data, create_using=None, multigraph_input=False)
|
PORTED FROM NETWORKX
Make a SnapX graph from a known data structure.
The preferred way to call this is automatically
from the class constructor
>>> d = {0: {1: {'weight':1}}} # dict-of-dicts single edge (0,1)
>>> G = nx.Graph(d)
instead of the equivalent
>>> G = nx.from_dict_of_dicts(d)
Parameters
----------
data : object to be converted
Current known types are:
any NetworkX graph
dict-of-dicts
dict-of-lists
container (ie set, list, tuple, iterator) of edges
Pandas DataFrame (row per edge)
numpy matrix
numpy ndarray
scipy sparse matrix
pygraphviz agraph
create_using : NetworkX graph constructor, optional (default=nx.Graph)
Graph type to create. If graph instance, then cleared before populated.
multigraph_input : bool (default False)
If True and data is a dict_of_dicts,
try to create a multigraph assuming dict_of_dict_of_lists.
If data and create_using are both multigraphs then create
a multigraph from a multigraph.
|
PORTED FROM NETWORKX
Make a SnapX graph from a known data structure.
The preferred way to call this is automatically
from the class constructor
>>> d = {0: {1: {'weight':1}}} # dict-of-dicts single edge (0,1)
>>> G = nx.Graph(d)
instead of the equivalent
>>> G = nx.from_dict_of_dicts(d)
Parameters
----------
data : object to be converted
Current known types are:
any NetworkX graph
dict-of-dicts
dict-of-lists
container (ie set, list, tuple, iterator) of edges
Pandas DataFrame (row per edge)
numpy matrix
numpy ndarray
scipy sparse matrix
pygraphviz agraph
create_using : NetworkX graph constructor, optional (default=nx.Graph)
Graph type to create. If graph instance, then cleared before populated.
multigraph_input : bool (default False)
If True and data is a dict_of_dicts,
try to create a multigraph assuming dict_of_dict_of_lists.
If data and create_using are both multigraphs then create
a multigraph from a multigraph.
|
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] |
def to_snapx_graph(data, create_using=None, multigraph_input=False):
"""PORTED FROM NETWORKX
Make a SnapX graph from a known data structure.
The preferred way to call this is automatically
from the class constructor
>>> d = {0: {1: {'weight':1}}} # dict-of-dicts single edge (0,1)
>>> G = nx.Graph(d)
instead of the equivalent
>>> G = nx.from_dict_of_dicts(d)
Parameters
----------
data : object to be converted
Current known types are:
any NetworkX graph
dict-of-dicts
dict-of-lists
container (ie set, list, tuple, iterator) of edges
Pandas DataFrame (row per edge)
numpy matrix
numpy ndarray
scipy sparse matrix
pygraphviz agraph
create_using : NetworkX graph constructor, optional (default=nx.Graph)
Graph type to create. If graph instance, then cleared before populated.
multigraph_input : bool (default False)
If True and data is a dict_of_dicts,
try to create a multigraph assuming dict_of_dict_of_lists.
If data and create_using are both multigraphs then create
a multigraph from a multigraph.
"""
# SX graph
if hasattr(data, "adj"):
try:
result = from_dict_of_dicts(
data.adj,
create_using=create_using,
multigraph_input=data.is_multigraph(),
)
if hasattr(data, "graph"): # data.graph should be dict-like
result.graph.update(data.graph)
if hasattr(data, "nodes"): # data.nodes should be dict-like
# result.add_node_from(data.nodes.items()) possible but
# for custom node_attr_dict_factory which may be hashable
# will be unexpected behavior
for n, dd in data.nodes.items():
result._node[n].update(dd)
return result
except:
raise sx.SnapXError("Input is not a correct SnapX graph.")
# pygraphviz agraph
if hasattr(data, "is_strict"):
raise NotImplementedError("TODO")
#try:
# return nx.nx_agraph.from_agraph(data, create_using=create_using)
#except:
# raise nx.NetworkXError("Input is not a correct pygraphviz graph.")
# dict of dicts/lists
if isinstance(data, dict):
raise NotImplementedError("TODO")
#try:
# return from_dict_of_dicts(
# data, create_using=create_using, multigraph_input=multigraph_input
# )
#except:
# try:
# return from_dict_of_lists(data, create_using=create_using)
# except:
# raise TypeError("Input is not known type.")
# list or generator of edges
if isinstance(data, (list, tuple, set)) or any(
hasattr(data, attr) for attr in ["_adjdict", "next", "__next__"]
):
raise NotImplementedError("TODO")
#try:
# return from_edgelist(data, create_using=create_using)
#except:
# raise nx.NetworkXError("Input is not a valid edge list")
# Pandas DataFrame
try:
import pandas as pd
if isinstance(data, pd.DataFrame):
raise NotImplementedError("TODO")
#if data.shape[0] == data.shape[1]:
# try:
# return nx.from_pandas_adjacency(data, create_using=create_using)
# except:
# msg = "Input is not a correct Pandas DataFrame adjacency matrix."
# raise nx.NetworkXError(msg)
#else:
# try:
# return nx.from_pandas_edgelist(
# data, edge_attr=True, create_using=create_using
# )
# except:
# msg = "Input is not a correct Pandas DataFrame edge-list."
# raise nx.NetworkXError(msg)
except ImportError:
msg = "pandas not found, skipping conversion test."
warnings.warn(msg, ImportWarning)
# numpy matrix or ndarray
try:
import numpy
if isinstance(data, (numpy.matrix, numpy.ndarray)):
raise NotImplementedError("TODO")
#try:
# return nx.from_numpy_matrix(data, create_using=create_using)
#except:
# raise nx.NetworkXError("Input is not a correct numpy matrix or array.")
except ImportError:
warnings.warn("numpy not found, skipping conversion test.", ImportWarning)
# scipy sparse matrix - any format
try:
import scipy
if hasattr(data, "format"):
raise NotImplementedError("TODO")
#try:
# return nx.from_scipy_sparse_matrix(data, create_using=create_using)
#except:
# raise nx.NetworkXError(
# "Input is not a correct scipy sparse matrix type."
# )
except ImportError:
warnings.warn("scipy not found, skipping conversion test.", ImportWarning)
raise sx.SnapXError("Input is not a known data type for conversion.")
|
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"NotImplementedError",
"(",
"\"TODO\"",
")",
"#try:",
"# return nx.from_scipy_sparse_matrix(data, create_using=create_using)",
"#except:",
"# raise nx.NetworkXError(",
"# \"Input is not a correct scipy sparse matrix type.\"",
"# )",
"except",
"ImportError",
":",
"warnings",
".",
"warn",
"(",
"\"scipy not found, skipping conversion test.\"",
",",
"ImportWarning",
")",
"raise",
"sx",
".",
"SnapXError",
"(",
"\"Input is not a known data type for conversion.\"",
")"
] |
https://github.com/snap-stanford/snap-python/blob/d53c51b0a26aa7e3e7400b014cdf728948fde80a/snapx/snapx/convert.py#L4-L138
|
||
cmu-sei/pharos
|
af54b6ada58d50c046fa899452addce80e9ce8da
|
tools/ooanalyzer/ida/OOAnalyzer.py
|
python
|
PyOOAnalyzerExpForm.__load_from_json_file
|
(self, json_file)
|
return res_str, num_classes, num_vcalls, num_usages
|
Parse the JSON file
|
Parse the JSON file
|
[
"Parse",
"the",
"JSON",
"file"
] |
def __load_from_json_file(self, json_file):
'''
Parse the JSON file
'''
if json_file != None:
if self.__ooanalyzer.set_json_file(json_file):
print("Opened JSON file %s" % json_file)
else:
ida_kernwin.warning("Could not open %s" % json_file)
return None, 0, 0, 0
if self.__ooanalyzer.is_parsed() == False: # not parsed yet
result, msg = self.__ooanalyzer.parse()
print("Parsed %s %s" % (result, msg))
if result == False:
ida_kernwin.warning("Could not parse JSON: %s" % msg)
return None, 0, 0, 0
parse_results = self.__ooanalyzer.get_parse_results()
if parse_results == None:
return None, 0, 0, 0
num_classes = 0
num_vcalls = 0
num_usages = 0
if "NumClasses" in parse_results:
num_classes = parse_results["NumClasses"]
if "NumVcalls" in parse_results:
num_vcalls = parse_results["NumVcalls"]
if "NumUsages" in parse_results:
num_usages = parse_results["NumUsages"]
res_str = """
Successfully parsed JSON file: "%s".
The following C++ constructs are ready to apply:
* %s class structures
* %s object usages
* %s virtual function calls
Press \"Yes\" to apply these items to the IDB. Press no to apply them manually (Note that virtual function calls will be resolved automatically)
""" % (self.__ooanalyzer.get_json_filename(), num_classes, num_usages, num_vcalls)
return res_str, num_classes, num_vcalls, num_usages
|
[
"def",
"__load_from_json_file",
"(",
"self",
",",
"json_file",
")",
":",
"if",
"json_file",
"!=",
"None",
":",
"if",
"self",
".",
"__ooanalyzer",
".",
"set_json_file",
"(",
"json_file",
")",
":",
"print",
"(",
"\"Opened JSON file %s\"",
"%",
"json_file",
")",
"else",
":",
"ida_kernwin",
".",
"warning",
"(",
"\"Could not open %s\"",
"%",
"json_file",
")",
"return",
"None",
",",
"0",
",",
"0",
",",
"0",
"if",
"self",
".",
"__ooanalyzer",
".",
"is_parsed",
"(",
")",
"==",
"False",
":",
"# not parsed yet",
"result",
",",
"msg",
"=",
"self",
".",
"__ooanalyzer",
".",
"parse",
"(",
")",
"print",
"(",
"\"Parsed %s %s\"",
"%",
"(",
"result",
",",
"msg",
")",
")",
"if",
"result",
"==",
"False",
":",
"ida_kernwin",
".",
"warning",
"(",
"\"Could not parse JSON: %s\"",
"%",
"msg",
")",
"return",
"None",
",",
"0",
",",
"0",
",",
"0",
"parse_results",
"=",
"self",
".",
"__ooanalyzer",
".",
"get_parse_results",
"(",
")",
"if",
"parse_results",
"==",
"None",
":",
"return",
"None",
",",
"0",
",",
"0",
",",
"0",
"num_classes",
"=",
"0",
"num_vcalls",
"=",
"0",
"num_usages",
"=",
"0",
"if",
"\"NumClasses\"",
"in",
"parse_results",
":",
"num_classes",
"=",
"parse_results",
"[",
"\"NumClasses\"",
"]",
"if",
"\"NumVcalls\"",
"in",
"parse_results",
":",
"num_vcalls",
"=",
"parse_results",
"[",
"\"NumVcalls\"",
"]",
"if",
"\"NumUsages\"",
"in",
"parse_results",
":",
"num_usages",
"=",
"parse_results",
"[",
"\"NumUsages\"",
"]",
"res_str",
"=",
"\"\"\"\nSuccessfully parsed JSON file: \"%s\".\n\nThe following C++ constructs are ready to apply:\n\n * %s class structures\n * %s object usages\n * %s virtual function calls\n\nPress \\\"Yes\\\" to apply these items to the IDB. Press no to apply them manually (Note that virtual function calls will be resolved automatically)\n\"\"\"",
"%",
"(",
"self",
".",
"__ooanalyzer",
".",
"get_json_filename",
"(",
")",
",",
"num_classes",
",",
"num_usages",
",",
"num_vcalls",
")",
"return",
"res_str",
",",
"num_classes",
",",
"num_vcalls",
",",
"num_usages"
] |
https://github.com/cmu-sei/pharos/blob/af54b6ada58d50c046fa899452addce80e9ce8da/tools/ooanalyzer/ida/OOAnalyzer.py#L2175-L2221
|
|
catboost/catboost
|
167f64f237114a4d10b2b4ee42adb4569137debe
|
contrib/python/pandas/py2/pandas/core/resample.py
|
python
|
Resampler.nearest
|
(self, limit=None)
|
return self._upsample('nearest', limit=limit)
|
Resample by using the nearest value.
When resampling data, missing values may appear (e.g., when the
resampling frequency is higher than the original frequency).
The `nearest` method will replace ``NaN`` values that appeared in
the resampled data with the value from the nearest member of the
sequence, based on the index value.
Missing values that existed in the original data will not be modified.
If `limit` is given, fill only this many values in each direction for
each of the original values.
Parameters
----------
limit : int, optional
Limit of how many values to fill.
.. versionadded:: 0.21.0
Returns
-------
Series or DataFrame
An upsampled Series or DataFrame with ``NaN`` values filled with
their nearest value.
See Also
--------
backfill : Backward fill the new missing values in the resampled data.
pad : Forward fill ``NaN`` values.
Examples
--------
>>> s = pd.Series([1, 2],
... index=pd.date_range('20180101',
... periods=2,
... freq='1h'))
>>> s
2018-01-01 00:00:00 1
2018-01-01 01:00:00 2
Freq: H, dtype: int64
>>> s.resample('15min').nearest()
2018-01-01 00:00:00 1
2018-01-01 00:15:00 1
2018-01-01 00:30:00 2
2018-01-01 00:45:00 2
2018-01-01 01:00:00 2
Freq: 15T, dtype: int64
Limit the number of upsampled values imputed by the nearest:
>>> s.resample('15min').nearest(limit=1)
2018-01-01 00:00:00 1.0
2018-01-01 00:15:00 1.0
2018-01-01 00:30:00 NaN
2018-01-01 00:45:00 2.0
2018-01-01 01:00:00 2.0
Freq: 15T, dtype: float64
|
Resample by using the nearest value.
|
[
"Resample",
"by",
"using",
"the",
"nearest",
"value",
"."
] |
def nearest(self, limit=None):
"""
Resample by using the nearest value.
When resampling data, missing values may appear (e.g., when the
resampling frequency is higher than the original frequency).
The `nearest` method will replace ``NaN`` values that appeared in
the resampled data with the value from the nearest member of the
sequence, based on the index value.
Missing values that existed in the original data will not be modified.
If `limit` is given, fill only this many values in each direction for
each of the original values.
Parameters
----------
limit : int, optional
Limit of how many values to fill.
.. versionadded:: 0.21.0
Returns
-------
Series or DataFrame
An upsampled Series or DataFrame with ``NaN`` values filled with
their nearest value.
See Also
--------
backfill : Backward fill the new missing values in the resampled data.
pad : Forward fill ``NaN`` values.
Examples
--------
>>> s = pd.Series([1, 2],
... index=pd.date_range('20180101',
... periods=2,
... freq='1h'))
>>> s
2018-01-01 00:00:00 1
2018-01-01 01:00:00 2
Freq: H, dtype: int64
>>> s.resample('15min').nearest()
2018-01-01 00:00:00 1
2018-01-01 00:15:00 1
2018-01-01 00:30:00 2
2018-01-01 00:45:00 2
2018-01-01 01:00:00 2
Freq: 15T, dtype: int64
Limit the number of upsampled values imputed by the nearest:
>>> s.resample('15min').nearest(limit=1)
2018-01-01 00:00:00 1.0
2018-01-01 00:15:00 1.0
2018-01-01 00:30:00 NaN
2018-01-01 00:45:00 2.0
2018-01-01 01:00:00 2.0
Freq: 15T, dtype: float64
"""
return self._upsample('nearest', limit=limit)
|
[
"def",
"nearest",
"(",
"self",
",",
"limit",
"=",
"None",
")",
":",
"return",
"self",
".",
"_upsample",
"(",
"'nearest'",
",",
"limit",
"=",
"limit",
")"
] |
https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/pandas/py2/pandas/core/resample.py#L436-L496
|
|
borglab/gtsam
|
a5bee157efce6a0563704bce6a5d188c29817f39
|
wrap/gtwrap/matlab_wrapper/mixins.py
|
python
|
FormatMixin._format_global_function
|
(self,
function: Union[parser.GlobalFunction, Any],
separator: str = '')
|
return method[2 * len(separator):]
|
Example:
gtsamPoint3.staticFunction
|
Example:
|
[
"Example",
":"
] |
def _format_global_function(self,
function: Union[parser.GlobalFunction, Any],
separator: str = ''):
"""Example:
gtsamPoint3.staticFunction
"""
method = ''
if isinstance(function, parser.GlobalFunction):
method += "".join([separator + x for x in function.parent.full_namespaces()]) + \
separator
return method[2 * len(separator):]
|
[
"def",
"_format_global_function",
"(",
"self",
",",
"function",
":",
"Union",
"[",
"parser",
".",
"GlobalFunction",
",",
"Any",
"]",
",",
"separator",
":",
"str",
"=",
"''",
")",
":",
"method",
"=",
"''",
"if",
"isinstance",
"(",
"function",
",",
"parser",
".",
"GlobalFunction",
")",
":",
"method",
"+=",
"\"\"",
".",
"join",
"(",
"[",
"separator",
"+",
"x",
"for",
"x",
"in",
"function",
".",
"parent",
".",
"full_namespaces",
"(",
")",
"]",
")",
"+",
"separator",
"return",
"method",
"[",
"2",
"*",
"len",
"(",
"separator",
")",
":",
"]"
] |
https://github.com/borglab/gtsam/blob/a5bee157efce6a0563704bce6a5d188c29817f39/wrap/gtwrap/matlab_wrapper/mixins.py#L204-L217
|
|
domino-team/openwrt-cc
|
8b181297c34d14d3ca521cc9f31430d561dbc688
|
package/gli-pub/openwrt-node-packages-master/node/node-v6.9.1/deps/v8/tools/grokdump.py
|
python
|
InspectionShell.do_u
|
(self, args)
|
Unassemble memory in the region [address, address + size). If the
size is not specified, a default value of 32 bytes is used.
Synopsis: u 0x<address> 0x<size>
|
Unassemble memory in the region [address, address + size). If the
size is not specified, a default value of 32 bytes is used.
Synopsis: u 0x<address> 0x<size>
|
[
"Unassemble",
"memory",
"in",
"the",
"region",
"[",
"address",
"address",
"+",
"size",
")",
".",
"If",
"the",
"size",
"is",
"not",
"specified",
"a",
"default",
"value",
"of",
"32",
"bytes",
"is",
"used",
".",
"Synopsis",
":",
"u",
"0x<address",
">",
"0x<size",
">"
] |
def do_u(self, args):
"""
Unassemble memory in the region [address, address + size). If the
size is not specified, a default value of 32 bytes is used.
Synopsis: u 0x<address> 0x<size>
"""
args = args.split(' ')
start = int(args[0], 16)
size = int(args[1], 16) if len(args) > 1 else 0x20
if not self.reader.IsValidAddress(start):
print "Address is not contained within the minidump!"
return
lines = self.reader.GetDisasmLines(start, size)
for line in lines:
print FormatDisasmLine(start, self.heap, line)
print
|
[
"def",
"do_u",
"(",
"self",
",",
"args",
")",
":",
"args",
"=",
"args",
".",
"split",
"(",
"' '",
")",
"start",
"=",
"int",
"(",
"args",
"[",
"0",
"]",
",",
"16",
")",
"size",
"=",
"int",
"(",
"args",
"[",
"1",
"]",
",",
"16",
")",
"if",
"len",
"(",
"args",
")",
">",
"1",
"else",
"0x20",
"if",
"not",
"self",
".",
"reader",
".",
"IsValidAddress",
"(",
"start",
")",
":",
"print",
"\"Address is not contained within the minidump!\"",
"return",
"lines",
"=",
"self",
".",
"reader",
".",
"GetDisasmLines",
"(",
"start",
",",
"size",
")",
"for",
"line",
"in",
"lines",
":",
"print",
"FormatDisasmLine",
"(",
"start",
",",
"self",
".",
"heap",
",",
"line",
")",
"print"
] |
https://github.com/domino-team/openwrt-cc/blob/8b181297c34d14d3ca521cc9f31430d561dbc688/package/gli-pub/openwrt-node-packages-master/node/node-v6.9.1/deps/v8/tools/grokdump.py#L3068-L3083
|
||
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/pandas/core/indexes/interval.py
|
python
|
interval_range
|
(
start=None, end=None, periods=None, freq=None, name=None, closed="right"
)
|
return IntervalIndex.from_breaks(breaks, name=name, closed=closed)
|
Return a fixed frequency IntervalIndex.
Parameters
----------
start : numeric or datetime-like, default None
Left bound for generating intervals.
end : numeric or datetime-like, default None
Right bound for generating intervals.
periods : int, default None
Number of periods to generate.
freq : numeric, str, or DateOffset, default None
The length of each interval. Must be consistent with the type of start
and end, e.g. 2 for numeric, or '5H' for datetime-like. Default is 1
for numeric and 'D' for datetime-like.
name : str, default None
Name of the resulting IntervalIndex.
closed : {'left', 'right', 'both', 'neither'}, default 'right'
Whether the intervals are closed on the left-side, right-side, both
or neither.
Returns
-------
IntervalIndex
See Also
--------
IntervalIndex : An Index of intervals that are all closed on the same side.
Notes
-----
Of the four parameters ``start``, ``end``, ``periods``, and ``freq``,
exactly three must be specified. If ``freq`` is omitted, the resulting
``IntervalIndex`` will have ``periods`` linearly spaced elements between
``start`` and ``end``, inclusively.
To learn more about datetime-like frequency strings, please see `this link
<https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__.
Examples
--------
Numeric ``start`` and ``end`` is supported.
>>> pd.interval_range(start=0, end=5)
IntervalIndex([(0, 1], (1, 2], (2, 3], (3, 4], (4, 5]],
closed='right', dtype='interval[int64]')
Additionally, datetime-like input is also supported.
>>> pd.interval_range(start=pd.Timestamp('2017-01-01'),
... end=pd.Timestamp('2017-01-04'))
IntervalIndex([(2017-01-01, 2017-01-02], (2017-01-02, 2017-01-03],
(2017-01-03, 2017-01-04]],
closed='right', dtype='interval[datetime64[ns]]')
The ``freq`` parameter specifies the frequency between the left and right.
endpoints of the individual intervals within the ``IntervalIndex``. For
numeric ``start`` and ``end``, the frequency must also be numeric.
>>> pd.interval_range(start=0, periods=4, freq=1.5)
IntervalIndex([(0.0, 1.5], (1.5, 3.0], (3.0, 4.5], (4.5, 6.0]],
closed='right', dtype='interval[float64]')
Similarly, for datetime-like ``start`` and ``end``, the frequency must be
convertible to a DateOffset.
>>> pd.interval_range(start=pd.Timestamp('2017-01-01'),
... periods=3, freq='MS')
IntervalIndex([(2017-01-01, 2017-02-01], (2017-02-01, 2017-03-01],
(2017-03-01, 2017-04-01]],
closed='right', dtype='interval[datetime64[ns]]')
Specify ``start``, ``end``, and ``periods``; the frequency is generated
automatically (linearly spaced).
>>> pd.interval_range(start=0, end=6, periods=4)
IntervalIndex([(0.0, 1.5], (1.5, 3.0], (3.0, 4.5], (4.5, 6.0]],
closed='right',
dtype='interval[float64]')
The ``closed`` parameter specifies which endpoints of the individual
intervals within the ``IntervalIndex`` are closed.
>>> pd.interval_range(end=5, periods=4, closed='both')
IntervalIndex([[1, 2], [2, 3], [3, 4], [4, 5]],
closed='both', dtype='interval[int64]')
|
Return a fixed frequency IntervalIndex.
|
[
"Return",
"a",
"fixed",
"frequency",
"IntervalIndex",
"."
] |
def interval_range(
start=None, end=None, periods=None, freq=None, name=None, closed="right"
):
"""
Return a fixed frequency IntervalIndex.
Parameters
----------
start : numeric or datetime-like, default None
Left bound for generating intervals.
end : numeric or datetime-like, default None
Right bound for generating intervals.
periods : int, default None
Number of periods to generate.
freq : numeric, str, or DateOffset, default None
The length of each interval. Must be consistent with the type of start
and end, e.g. 2 for numeric, or '5H' for datetime-like. Default is 1
for numeric and 'D' for datetime-like.
name : str, default None
Name of the resulting IntervalIndex.
closed : {'left', 'right', 'both', 'neither'}, default 'right'
Whether the intervals are closed on the left-side, right-side, both
or neither.
Returns
-------
IntervalIndex
See Also
--------
IntervalIndex : An Index of intervals that are all closed on the same side.
Notes
-----
Of the four parameters ``start``, ``end``, ``periods``, and ``freq``,
exactly three must be specified. If ``freq`` is omitted, the resulting
``IntervalIndex`` will have ``periods`` linearly spaced elements between
``start`` and ``end``, inclusively.
To learn more about datetime-like frequency strings, please see `this link
<https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__.
Examples
--------
Numeric ``start`` and ``end`` is supported.
>>> pd.interval_range(start=0, end=5)
IntervalIndex([(0, 1], (1, 2], (2, 3], (3, 4], (4, 5]],
closed='right', dtype='interval[int64]')
Additionally, datetime-like input is also supported.
>>> pd.interval_range(start=pd.Timestamp('2017-01-01'),
... end=pd.Timestamp('2017-01-04'))
IntervalIndex([(2017-01-01, 2017-01-02], (2017-01-02, 2017-01-03],
(2017-01-03, 2017-01-04]],
closed='right', dtype='interval[datetime64[ns]]')
The ``freq`` parameter specifies the frequency between the left and right.
endpoints of the individual intervals within the ``IntervalIndex``. For
numeric ``start`` and ``end``, the frequency must also be numeric.
>>> pd.interval_range(start=0, periods=4, freq=1.5)
IntervalIndex([(0.0, 1.5], (1.5, 3.0], (3.0, 4.5], (4.5, 6.0]],
closed='right', dtype='interval[float64]')
Similarly, for datetime-like ``start`` and ``end``, the frequency must be
convertible to a DateOffset.
>>> pd.interval_range(start=pd.Timestamp('2017-01-01'),
... periods=3, freq='MS')
IntervalIndex([(2017-01-01, 2017-02-01], (2017-02-01, 2017-03-01],
(2017-03-01, 2017-04-01]],
closed='right', dtype='interval[datetime64[ns]]')
Specify ``start``, ``end``, and ``periods``; the frequency is generated
automatically (linearly spaced).
>>> pd.interval_range(start=0, end=6, periods=4)
IntervalIndex([(0.0, 1.5], (1.5, 3.0], (3.0, 4.5], (4.5, 6.0]],
closed='right',
dtype='interval[float64]')
The ``closed`` parameter specifies which endpoints of the individual
intervals within the ``IntervalIndex`` are closed.
>>> pd.interval_range(end=5, periods=4, closed='both')
IntervalIndex([[1, 2], [2, 3], [3, 4], [4, 5]],
closed='both', dtype='interval[int64]')
"""
start = com.maybe_box_datetimelike(start)
end = com.maybe_box_datetimelike(end)
endpoint = start if start is not None else end
if freq is None and com.any_none(periods, start, end):
freq = 1 if is_number(endpoint) else "D"
if com.count_not_none(start, end, periods, freq) != 3:
raise ValueError(
"Of the four parameters: start, end, periods, and "
"freq, exactly three must be specified"
)
if not _is_valid_endpoint(start):
raise ValueError(f"start must be numeric or datetime-like, got {start}")
elif not _is_valid_endpoint(end):
raise ValueError(f"end must be numeric or datetime-like, got {end}")
if is_float(periods):
periods = int(periods)
elif not is_integer(periods) and periods is not None:
raise TypeError(f"periods must be a number, got {periods}")
if freq is not None and not is_number(freq):
try:
freq = to_offset(freq)
except ValueError:
raise ValueError(
f"freq must be numeric or convertible to DateOffset, got {freq}"
)
# verify type compatibility
if not all(
[
_is_type_compatible(start, end),
_is_type_compatible(start, freq),
_is_type_compatible(end, freq),
]
):
raise TypeError("start, end, freq need to be type compatible")
# +1 to convert interval count to breaks count (n breaks = n-1 intervals)
if periods is not None:
periods += 1
if is_number(endpoint):
# force consistency between start/end/freq (lower end if freq skips it)
if com.all_not_none(start, end, freq):
end -= (end - start) % freq
# compute the period/start/end if unspecified (at most one)
if periods is None:
periods = int((end - start) // freq) + 1
elif start is None:
start = end - (periods - 1) * freq
elif end is None:
end = start + (periods - 1) * freq
breaks = np.linspace(start, end, periods)
if all(is_integer(x) for x in com.not_none(start, end, freq)):
# np.linspace always produces float output
breaks = maybe_downcast_to_dtype(breaks, "int64")
else:
# delegate to the appropriate range function
if isinstance(endpoint, Timestamp):
range_func = date_range
else:
range_func = timedelta_range
breaks = range_func(start=start, end=end, periods=periods, freq=freq)
return IntervalIndex.from_breaks(breaks, name=name, closed=closed)
|
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",",
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",",
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")"
] |
https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/pandas/core/indexes/interval.py#L1222-L1383
|
|
hanpfei/chromium-net
|
392cc1fa3a8f92f42e4071ab6e674d8e0482f83f
|
third_party/protobuf/python/google/protobuf/internal/well_known_types.py
|
python
|
Timestamp.FromMilliseconds
|
(self, millis)
|
Converts milliseconds since epoch to Timestamp.
|
Converts milliseconds since epoch to Timestamp.
|
[
"Converts",
"milliseconds",
"since",
"epoch",
"to",
"Timestamp",
"."
] |
def FromMilliseconds(self, millis):
"""Converts milliseconds since epoch to Timestamp."""
self.seconds = millis // _MILLIS_PER_SECOND
self.nanos = (millis % _MILLIS_PER_SECOND) * _NANOS_PER_MILLISECOND
|
[
"def",
"FromMilliseconds",
"(",
"self",
",",
"millis",
")",
":",
"self",
".",
"seconds",
"=",
"millis",
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"self",
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"nanos",
"=",
"(",
"millis",
"%",
"_MILLIS_PER_SECOND",
")",
"*",
"_NANOS_PER_MILLISECOND"
] |
https://github.com/hanpfei/chromium-net/blob/392cc1fa3a8f92f42e4071ab6e674d8e0482f83f/third_party/protobuf/python/google/protobuf/internal/well_known_types.py#L216-L219
|
||
CaoWGG/TensorRT-YOLOv4
|
4d7c2edce99e8794a4cb4ea3540d51ce91158a36
|
tools/yolo_to_onnx.py
|
python
|
WeightLoader._load_one_param_type
|
(self, conv_params, param_category, suffix)
|
return param_name, param_data, param_shape
|
Deserializes the weights from a file stream in the DarkNet order.
Keyword arguments:
conv_params -- a ConvParams object
param_category -- the category of parameters to be created ('bn' or 'conv')
suffix -- a string determining the sub-type of above param_category (e.g.,
'weights' or 'bias')
|
Deserializes the weights from a file stream in the DarkNet order.
|
[
"Deserializes",
"the",
"weights",
"from",
"a",
"file",
"stream",
"in",
"the",
"DarkNet",
"order",
"."
] |
def _load_one_param_type(self, conv_params, param_category, suffix):
"""Deserializes the weights from a file stream in the DarkNet order.
Keyword arguments:
conv_params -- a ConvParams object
param_category -- the category of parameters to be created ('bn' or 'conv')
suffix -- a string determining the sub-type of above param_category (e.g.,
'weights' or 'bias')
"""
param_name = conv_params.generate_param_name(param_category, suffix)
channels_out, channels_in, filter_h, filter_w = conv_params.conv_weight_dims
if param_category == 'bn':
param_shape = [channels_out]
elif param_category == 'conv':
if suffix == 'weights':
param_shape = [channels_out, channels_in, filter_h, filter_w]
elif suffix == 'bias':
param_shape = [channels_out]
param_size = np.product(np.array(param_shape))
param_data = np.ndarray(
shape=param_shape,
dtype='float32',
buffer=self.weights_file.read(param_size * 4))
param_data = param_data.flatten().astype(float)
return param_name, param_data, param_shape
|
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",",
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"(",
"float",
")",
"return",
"param_name",
",",
"param_data",
",",
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] |
https://github.com/CaoWGG/TensorRT-YOLOv4/blob/4d7c2edce99e8794a4cb4ea3540d51ce91158a36/tools/yolo_to_onnx.py#L256-L280
|
|
wlanjie/AndroidFFmpeg
|
7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf
|
tools/fdk-aac-build/x86/toolchain/lib/python2.7/threading.py
|
python
|
Thread.start
|
(self)
|
Start the thread's activity.
It must be called at most once per thread object. It arranges for the
object's run() method to be invoked in a separate thread of control.
This method will raise a RuntimeError if called more than once on the
same thread object.
|
Start the thread's activity.
|
[
"Start",
"the",
"thread",
"s",
"activity",
"."
] |
def start(self):
"""Start the thread's activity.
It must be called at most once per thread object. It arranges for the
object's run() method to be invoked in a separate thread of control.
This method will raise a RuntimeError if called more than once on the
same thread object.
"""
if not self.__initialized:
raise RuntimeError("thread.__init__() not called")
if self.__started.is_set():
raise RuntimeError("threads can only be started once")
if __debug__:
self._note("%s.start(): starting thread", self)
with _active_limbo_lock:
_limbo[self] = self
try:
_start_new_thread(self.__bootstrap, ())
except Exception:
with _active_limbo_lock:
del _limbo[self]
raise
self.__started.wait()
|
[
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"wait",
"(",
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] |
https://github.com/wlanjie/AndroidFFmpeg/blob/7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf/tools/fdk-aac-build/x86/toolchain/lib/python2.7/threading.py#L724-L748
|
||
NVIDIA/DALI
|
bf16cc86ba8f091b145f91962f21fe1b6aff243d
|
dali/python/nvidia/dali/math.py
|
python
|
sin
|
(input)
|
return _arithm_op("sin", input)
|
Computes sine of values in ``input``.
:rtype: TensorList of sin(input). If input is an integer, the result will be float,
otherwise the type is preserved.
|
Computes sine of values in ``input``.
|
[
"Computes",
"sine",
"of",
"values",
"in",
"input",
"."
] |
def sin(input):
"""Computes sine of values in ``input``.
:rtype: TensorList of sin(input). If input is an integer, the result will be float,
otherwise the type is preserved.
"""
return _arithm_op("sin", input)
|
[
"def",
"sin",
"(",
"input",
")",
":",
"return",
"_arithm_op",
"(",
"\"sin\"",
",",
"input",
")"
] |
https://github.com/NVIDIA/DALI/blob/bf16cc86ba8f091b145f91962f21fe1b6aff243d/dali/python/nvidia/dali/math.py#L112-L118
|
|
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/_pydecimal.py
|
python
|
Decimal.to_eng_string
|
(self, context=None)
|
return self.__str__(eng=True, context=context)
|
Convert to a string, using engineering notation if an exponent is needed.
Engineering notation has an exponent which is a multiple of 3. This
can leave up to 3 digits to the left of the decimal place and may
require the addition of either one or two trailing zeros.
|
Convert to a string, using engineering notation if an exponent is needed.
|
[
"Convert",
"to",
"a",
"string",
"using",
"engineering",
"notation",
"if",
"an",
"exponent",
"is",
"needed",
"."
] |
def to_eng_string(self, context=None):
"""Convert to a string, using engineering notation if an exponent is needed.
Engineering notation has an exponent which is a multiple of 3. This
can leave up to 3 digits to the left of the decimal place and may
require the addition of either one or two trailing zeros.
"""
return self.__str__(eng=True, context=context)
|
[
"def",
"to_eng_string",
"(",
"self",
",",
"context",
"=",
"None",
")",
":",
"return",
"self",
".",
"__str__",
"(",
"eng",
"=",
"True",
",",
"context",
"=",
"context",
")"
] |
https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/_pydecimal.py#L1083-L1090
|
|
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/idlelib/calltip_w.py
|
python
|
CalltipWindow.__init__
|
(self, text_widget)
|
Create a call-tip; shown by showtip().
text_widget: a Text widget with code for which call-tips are desired
|
Create a call-tip; shown by showtip().
|
[
"Create",
"a",
"call",
"-",
"tip",
";",
"shown",
"by",
"showtip",
"()",
"."
] |
def __init__(self, text_widget):
"""Create a call-tip; shown by showtip().
text_widget: a Text widget with code for which call-tips are desired
"""
# Note: The Text widget will be accessible as self.anchor_widget
super(CalltipWindow, self).__init__(text_widget)
self.label = self.text = None
self.parenline = self.parencol = self.lastline = None
self.hideid = self.checkhideid = None
self.checkhide_after_id = None
|
[
"def",
"__init__",
"(",
"self",
",",
"text_widget",
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":",
"# Note: The Text widget will be accessible as self.anchor_widget",
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"self",
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"=",
"self",
".",
"checkhideid",
"=",
"None",
"self",
".",
"checkhide_after_id",
"=",
"None"
] |
https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/idlelib/calltip_w.py#L22-L33
|
||
wxWidgets/wxPython-Classic
|
19571e1ae65f1ac445f5491474121998c97a1bf0
|
src/osx_carbon/calendar.py
|
python
|
GenericCalendarCtrl.Create
|
(*args, **kwargs)
|
return _calendar.GenericCalendarCtrl_Create(*args, **kwargs)
|
Create(self, Window parent, int id, DateTime date=DefaultDateTime,
Point pos=DefaultPosition, Size size=DefaultSize,
long style=wxCAL_SHOW_HOLIDAYS|wxWANTS_CHARS,
String name=CalendarNameStr) -> bool
Acutally create the GUI portion of the CalendarCtrl for 2-phase
creation.
|
Create(self, Window parent, int id, DateTime date=DefaultDateTime,
Point pos=DefaultPosition, Size size=DefaultSize,
long style=wxCAL_SHOW_HOLIDAYS|wxWANTS_CHARS,
String name=CalendarNameStr) -> bool
|
[
"Create",
"(",
"self",
"Window",
"parent",
"int",
"id",
"DateTime",
"date",
"=",
"DefaultDateTime",
"Point",
"pos",
"=",
"DefaultPosition",
"Size",
"size",
"=",
"DefaultSize",
"long",
"style",
"=",
"wxCAL_SHOW_HOLIDAYS|wxWANTS_CHARS",
"String",
"name",
"=",
"CalendarNameStr",
")",
"-",
">",
"bool"
] |
def Create(*args, **kwargs):
"""
Create(self, Window parent, int id, DateTime date=DefaultDateTime,
Point pos=DefaultPosition, Size size=DefaultSize,
long style=wxCAL_SHOW_HOLIDAYS|wxWANTS_CHARS,
String name=CalendarNameStr) -> bool
Acutally create the GUI portion of the CalendarCtrl for 2-phase
creation.
"""
return _calendar.GenericCalendarCtrl_Create(*args, **kwargs)
|
[
"def",
"Create",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"return",
"_calendar",
".",
"GenericCalendarCtrl_Create",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")"
] |
https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_carbon/calendar.py#L503-L513
|
|
wxWidgets/wxPython-Classic
|
19571e1ae65f1ac445f5491474121998c97a1bf0
|
src/osx_carbon/_windows.py
|
python
|
Printout.HasPage
|
(*args, **kwargs)
|
return _windows_.Printout_HasPage(*args, **kwargs)
|
HasPage(self, int page) -> bool
|
HasPage(self, int page) -> bool
|
[
"HasPage",
"(",
"self",
"int",
"page",
")",
"-",
">",
"bool"
] |
def HasPage(*args, **kwargs):
"""HasPage(self, int page) -> bool"""
return _windows_.Printout_HasPage(*args, **kwargs)
|
[
"def",
"HasPage",
"(",
"*",
"args",
",",
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".",
"Printout_HasPage",
"(",
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",",
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")"
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_carbon/_windows.py#L5403-L5405
|
|
tensorflow/tensorflow
|
419e3a6b650ea4bd1b0cba23c4348f8a69f3272e
|
tensorflow/python/ops/bincount_ops.py
|
python
|
validate_dense_weights
|
(values, weights, dtype=None)
|
return weights
|
Validates the passed weight tensor or creates an empty one.
|
Validates the passed weight tensor or creates an empty one.
|
[
"Validates",
"the",
"passed",
"weight",
"tensor",
"or",
"creates",
"an",
"empty",
"one",
"."
] |
def validate_dense_weights(values, weights, dtype=None):
"""Validates the passed weight tensor or creates an empty one."""
if weights is None:
if dtype:
return array_ops.constant([], dtype=dtype)
return array_ops.constant([], dtype=values.dtype)
if not isinstance(weights, ops.Tensor):
raise ValueError(
"Argument `weights` must be a tf.Tensor if `values` is a tf.Tensor. "
f"Received weights={weights} of type: {type(weights).__name__}")
return weights
|
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"f\"Received weights={weights} of type: {type(weights).__name__}\"",
")",
"return",
"weights"
] |
https://github.com/tensorflow/tensorflow/blob/419e3a6b650ea4bd1b0cba23c4348f8a69f3272e/tensorflow/python/ops/bincount_ops.py#L452-L464
|
|
catboost/catboost
|
167f64f237114a4d10b2b4ee42adb4569137debe
|
contrib/python/scipy/scipy/stats/_multivariate.py
|
python
|
_pinv_1d
|
(v, eps=1e-5)
|
return np.array([0 if abs(x) <= eps else 1/x for x in v], dtype=float)
|
A helper function for computing the pseudoinverse.
Parameters
----------
v : iterable of numbers
This may be thought of as a vector of eigenvalues or singular values.
eps : float
Values with magnitude no greater than eps are considered negligible.
Returns
-------
v_pinv : 1d float ndarray
A vector of pseudo-inverted numbers.
|
A helper function for computing the pseudoinverse.
|
[
"A",
"helper",
"function",
"for",
"computing",
"the",
"pseudoinverse",
"."
] |
def _pinv_1d(v, eps=1e-5):
"""
A helper function for computing the pseudoinverse.
Parameters
----------
v : iterable of numbers
This may be thought of as a vector of eigenvalues or singular values.
eps : float
Values with magnitude no greater than eps are considered negligible.
Returns
-------
v_pinv : 1d float ndarray
A vector of pseudo-inverted numbers.
"""
return np.array([0 if abs(x) <= eps else 1/x for x in v], dtype=float)
|
[
"def",
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")"
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/scipy/scipy/stats/_multivariate.py#L83-L100
|
|
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/core/generic.py
|
python
|
NDFrame._get_cacher
|
(self)
|
return cacher
|
return my cacher or None
|
return my cacher or None
|
[
"return",
"my",
"cacher",
"or",
"None"
] |
def _get_cacher(self):
"""return my cacher or None"""
cacher = getattr(self, "_cacher", None)
if cacher is not None:
cacher = cacher[1]()
return cacher
|
[
"def",
"_get_cacher",
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"=",
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",",
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"None",
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"cacher",
"=",
"cacher",
"[",
"1",
"]",
"(",
")",
"return",
"cacher"
] |
https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/core/generic.py#L3248-L3253
|
|
cvxpy/cvxpy
|
5165b4fb750dfd237de8659383ef24b4b2e33aaf
|
cvxpy/reductions/solvers/conic_solvers/scs_conif.py
|
python
|
SCS.name
|
(self)
|
return s.SCS
|
The name of the solver.
|
The name of the solver.
|
[
"The",
"name",
"of",
"the",
"solver",
"."
] |
def name(self):
"""The name of the solver.
"""
return s.SCS
|
[
"def",
"name",
"(",
"self",
")",
":",
"return",
"s",
".",
"SCS"
] |
https://github.com/cvxpy/cvxpy/blob/5165b4fb750dfd237de8659383ef24b4b2e33aaf/cvxpy/reductions/solvers/conic_solvers/scs_conif.py#L136-L139
|
|
NREL/EnergyPlus
|
fadc5973b85c70e8cc923efb69c144e808a26078
|
src/EnergyPlus/api/datatransfer.py
|
python
|
DataExchange.today_weather_outdoor_barometric_pressure_at_time
|
(self, state: c_void_p, hour: int,
time_step_number: int)
|
return self.api.todayWeatherOutBarometricPressureAtTime(state, hour, time_step_number)
|
Gets the specified weather data at the specified hour and time step index within that hour
:param state: An active EnergyPlus "state" that is returned from a call to `api.state_manager.new_state()`.
:param hour: Integer hour of day (0 to 23)
:param time_step_number: Time step index in hour, from 1 to the number of zone time steps per hour
:return: Value of the weather condition at the specified time
|
Gets the specified weather data at the specified hour and time step index within that hour
|
[
"Gets",
"the",
"specified",
"weather",
"data",
"at",
"the",
"specified",
"hour",
"and",
"time",
"step",
"index",
"within",
"that",
"hour"
] |
def today_weather_outdoor_barometric_pressure_at_time(self, state: c_void_p, hour: int,
time_step_number: int) -> float:
"""
Gets the specified weather data at the specified hour and time step index within that hour
:param state: An active EnergyPlus "state" that is returned from a call to `api.state_manager.new_state()`.
:param hour: Integer hour of day (0 to 23)
:param time_step_number: Time step index in hour, from 1 to the number of zone time steps per hour
:return: Value of the weather condition at the specified time
"""
return self.api.todayWeatherOutBarometricPressureAtTime(state, hour, time_step_number)
|
[
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"todayWeatherOutBarometricPressureAtTime",
"(",
"state",
",",
"hour",
",",
"time_step_number",
")"
] |
https://github.com/NREL/EnergyPlus/blob/fadc5973b85c70e8cc923efb69c144e808a26078/src/EnergyPlus/api/datatransfer.py#L1163-L1173
|
|
miyosuda/TensorFlowAndroidMNIST
|
7b5a4603d2780a8a2834575706e9001977524007
|
jni-build/jni/include/tensorflow/contrib/learn/python/learn/utils/checkpoints.py
|
python
|
load_variable
|
(checkpoint_dir, name)
|
return checkpoint_utils.load_variable(checkpoint_dir, name)
|
See `tf.contrib.framework.load_variable`.
|
See `tf.contrib.framework.load_variable`.
|
[
"See",
"tf",
".",
"contrib",
".",
"framework",
".",
"load_variable",
"."
] |
def load_variable(checkpoint_dir, name):
"""See `tf.contrib.framework.load_variable`."""
return checkpoint_utils.load_variable(checkpoint_dir, name)
|
[
"def",
"load_variable",
"(",
"checkpoint_dir",
",",
"name",
")",
":",
"return",
"checkpoint_utils",
".",
"load_variable",
"(",
"checkpoint_dir",
",",
"name",
")"
] |
https://github.com/miyosuda/TensorFlowAndroidMNIST/blob/7b5a4603d2780a8a2834575706e9001977524007/jni-build/jni/include/tensorflow/contrib/learn/python/learn/utils/checkpoints.py#L35-L37
|
|
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Gems/CloudGemFramework/v1/AWS/common-code/lib/requests/utils.py
|
python
|
prepend_scheme_if_needed
|
(url, new_scheme)
|
return urlunparse((scheme, netloc, path, params, query, fragment))
|
Given a URL that may or may not have a scheme, prepend the given scheme.
Does not replace a present scheme with the one provided as an argument.
:rtype: str
|
Given a URL that may or may not have a scheme, prepend the given scheme.
Does not replace a present scheme with the one provided as an argument.
|
[
"Given",
"a",
"URL",
"that",
"may",
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"not",
"have",
"a",
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"replace",
"a",
"present",
"scheme",
"with",
"the",
"one",
"provided",
"as",
"an",
"argument",
"."
] |
def prepend_scheme_if_needed(url, new_scheme):
"""Given a URL that may or may not have a scheme, prepend the given scheme.
Does not replace a present scheme with the one provided as an argument.
:rtype: str
"""
scheme, netloc, path, params, query, fragment = urlparse(url, new_scheme)
# urlparse is a finicky beast, and sometimes decides that there isn't a
# netloc present. Assume that it's being over-cautious, and switch netloc
# and path if urlparse decided there was no netloc.
if not netloc:
netloc, path = path, netloc
return urlunparse((scheme, netloc, path, params, query, fragment))
|
[
"def",
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",",
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",",
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemFramework/v1/AWS/common-code/lib/requests/utils.py#L894-L908
|
|
MTG/gaia
|
0f7214dbdec6f9b651ca34211824841ffba0bc77
|
src/doc/doxy2swig.py
|
python
|
Doxy2SWIG.parse
|
(self, node)
|
Parse a given node. This function in turn calls the
`parse_<nodeType>` functions which handle the respective
nodes.
|
Parse a given node. This function in turn calls the
`parse_<nodeType>` functions which handle the respective
nodes.
|
[
"Parse",
"a",
"given",
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"parse_<nodeType",
">",
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"."
] |
def parse(self, node):
"""Parse a given node. This function in turn calls the
`parse_<nodeType>` functions which handle the respective
nodes.
"""
pm = getattr(self, "parse_%s" % node.__class__.__name__)
pm(node)
|
[
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] |
https://github.com/MTG/gaia/blob/0f7214dbdec6f9b651ca34211824841ffba0bc77/src/doc/doxy2swig.py#L171-L178
|
||
MirrorYuChen/ncnn_example
|
a42608e6e0e51ed68d3bd8ada853595980935220
|
ncnn-20210525-full-source/python/ncnn/model_zoo/mobilenetv3ssdlite.py
|
python
|
MobileNetV3_SSDLite.__call__
|
(self, img)
|
return objects
|
#method 2, use ncnn.Mat->numpy.array to get the result, no memory copy too
out = np.array(mat_out)
for i in range(len(out)):
values = out[i]
obj = Detect_Object()
obj.label = values[0]
obj.prob = values[1]
x1 = clamp(values[2] * self.img_width, 0.0, float(self.img_width - 1)) / self.img_width * img_w
y1 = clamp(values[3] * self.img_height, 0.0, float(self.img_height - 1)) / self.img_height * img_h
x2 = clamp(values[4] * self.img_width, 0.0, float(self.img_width - 1)) / self.img_width * img_w
y2 = clamp(values[5] * self.img_height, 0.0, float(self.img_height - 1)) / self.img_height * img_h
obj.rect.x = x1
obj.rect.y = y1
obj.rect.w = x2 - x1
obj.rect.h = y2 - y1
objects.append(obj)
|
#method 2, use ncnn.Mat->numpy.array to get the result, no memory copy too
out = np.array(mat_out)
for i in range(len(out)):
values = out[i]
obj = Detect_Object()
obj.label = values[0]
obj.prob = values[1]
|
[
"#method",
"2",
"use",
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"Mat",
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"array",
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"0",
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"[",
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"]"
] |
def __call__(self, img):
img_h = img.shape[0]
img_w = img.shape[1]
mat_in = ncnn.Mat.from_pixels_resize(
img,
ncnn.Mat.PixelType.PIXEL_BGR2RGB,
img.shape[1],
img.shape[0],
self.target_size,
self.target_size,
)
mat_in.substract_mean_normalize([], self.norm_vals)
mat_in.substract_mean_normalize(self.mean_vals, [])
ex = self.net.create_extractor()
ex.set_light_mode(True)
ex.set_num_threads(self.num_threads)
ex.input("input", mat_in)
ret, mat_out = ex.extract("detection_out")
objects = []
# printf("%d %d %d\n", mat_out.w, mat_out.h, mat_out.c)
# method 1, use ncnn.Mat.row to get the result, no memory copy
for i in range(mat_out.h):
values = mat_out.row(i)
obj = Detect_Object()
obj.label = values[0]
obj.prob = values[1]
x1 = (
clamp(values[2] * self.target_size, 0.0, float(self.target_size - 1))
/ self.target_size
* img_w
)
y1 = (
clamp(values[3] * self.target_size, 0.0, float(self.target_size - 1))
/ self.target_size
* img_h
)
x2 = (
clamp(values[4] * self.target_size, 0.0, float(self.target_size - 1))
/ self.target_size
* img_w
)
y2 = (
clamp(values[5] * self.target_size, 0.0, float(self.target_size - 1))
/ self.target_size
* img_h
)
if np.isnan(x1) or np.isnan(y1) or np.isnan(x2) or np.isnan(y2):
continue
obj.rect.x = x1
obj.rect.y = y1
obj.rect.w = x2 - x1
obj.rect.h = y2 - y1
objects.append(obj)
"""
#method 2, use ncnn.Mat->numpy.array to get the result, no memory copy too
out = np.array(mat_out)
for i in range(len(out)):
values = out[i]
obj = Detect_Object()
obj.label = values[0]
obj.prob = values[1]
x1 = clamp(values[2] * self.img_width, 0.0, float(self.img_width - 1)) / self.img_width * img_w
y1 = clamp(values[3] * self.img_height, 0.0, float(self.img_height - 1)) / self.img_height * img_h
x2 = clamp(values[4] * self.img_width, 0.0, float(self.img_width - 1)) / self.img_width * img_w
y2 = clamp(values[5] * self.img_height, 0.0, float(self.img_height - 1)) / self.img_height * img_h
obj.rect.x = x1
obj.rect.y = y1
obj.rect.w = x2 - x1
obj.rect.h = y2 - y1
objects.append(obj)
"""
return objects
|
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"append",
"(",
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")",
"return",
"objects"
] |
https://github.com/MirrorYuChen/ncnn_example/blob/a42608e6e0e51ed68d3bd8ada853595980935220/ncnn-20210525-full-source/python/ncnn/model_zoo/mobilenetv3ssdlite.py#L74-L162
|
|
priyankchheda/algorithms
|
c361aa9071573fa9966d5b02d05e524815abcf2b
|
red_black_tree/red_black_tree.py
|
python
|
RedBlackTree.max
|
(self)
|
return current.data
|
returns right-most item present in red black tree which is also
the maximum element in rb-tree
|
returns right-most item present in red black tree which is also
the maximum element in rb-tree
|
[
"returns",
"right",
"-",
"most",
"item",
"present",
"in",
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"black",
"tree",
"which",
"is",
"also",
"the",
"maximum",
"element",
"in",
"rb",
"-",
"tree"
] |
def max(self):
""" returns right-most item present in red black tree which is also
the maximum element in rb-tree
"""
if self.root is None:
raise Exception("tree is empty")
current = self.root
while current.right is not None:
current = current.right
return current.data
|
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https://github.com/priyankchheda/algorithms/blob/c361aa9071573fa9966d5b02d05e524815abcf2b/red_black_tree/red_black_tree.py#L168-L178
|
|
BitMEX/api-connectors
|
37a3a5b806ad5d0e0fc975ab86d9ed43c3bcd812
|
auto-generated/python/swagger_client/models/position.py
|
python
|
Position.opening_cost
|
(self, opening_cost)
|
Sets the opening_cost of this Position.
:param opening_cost: The opening_cost of this Position. # noqa: E501
:type: float
|
Sets the opening_cost of this Position.
|
[
"Sets",
"the",
"opening_cost",
"of",
"this",
"Position",
"."
] |
def opening_cost(self, opening_cost):
"""Sets the opening_cost of this Position.
:param opening_cost: The opening_cost of this Position. # noqa: E501
:type: float
"""
self._opening_cost = opening_cost
|
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"def",
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"(",
"self",
",",
"opening_cost",
")",
":",
"self",
".",
"_opening_cost",
"=",
"opening_cost"
] |
https://github.com/BitMEX/api-connectors/blob/37a3a5b806ad5d0e0fc975ab86d9ed43c3bcd812/auto-generated/python/swagger_client/models/position.py#L892-L900
|
||
catboost/catboost
|
167f64f237114a4d10b2b4ee42adb4569137debe
|
contrib/tools/python3/src/Lib/ipaddress.py
|
python
|
_BaseV6._explode_shorthand_ip_string
|
(self)
|
return ':'.join(parts)
|
Expand a shortened IPv6 address.
Args:
ip_str: A string, the IPv6 address.
Returns:
A string, the expanded IPv6 address.
|
Expand a shortened IPv6 address.
|
[
"Expand",
"a",
"shortened",
"IPv6",
"address",
"."
] |
def _explode_shorthand_ip_string(self):
"""Expand a shortened IPv6 address.
Args:
ip_str: A string, the IPv6 address.
Returns:
A string, the expanded IPv6 address.
"""
if isinstance(self, IPv6Network):
ip_str = str(self.network_address)
elif isinstance(self, IPv6Interface):
ip_str = str(self.ip)
else:
ip_str = str(self)
ip_int = self._ip_int_from_string(ip_str)
hex_str = '%032x' % ip_int
parts = [hex_str[x:x+4] for x in range(0, 32, 4)]
if isinstance(self, (_BaseNetwork, IPv6Interface)):
return '%s/%d' % (':'.join(parts), self._prefixlen)
return ':'.join(parts)
|
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/tools/python3/src/Lib/ipaddress.py#L1812-L1834
|
|
wxWidgets/wxPython-Classic
|
19571e1ae65f1ac445f5491474121998c97a1bf0
|
wx/lib/buttons.py
|
python
|
GenButton._GetLabelSize
|
(self)
|
return w, h, True
|
used internally
|
used internally
|
[
"used",
"internally"
] |
def _GetLabelSize(self):
""" used internally """
w, h = self.GetTextExtent(self.GetLabel())
return w, h, True
|
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"def",
"_GetLabelSize",
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"GetLabel",
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")",
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"w",
",",
"h",
",",
"True"
] |
https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/wx/lib/buttons.py#L212-L215
|
|
bulletphysics/bullet3
|
f0f2a952e146f016096db6f85cf0c44ed75b0b9a
|
examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur.py
|
python
|
Minitaur._RecordInertiaInfoFromURDF
|
(self)
|
Record the inertia of each body from URDF file.
|
Record the inertia of each body from URDF file.
|
[
"Record",
"the",
"inertia",
"of",
"each",
"body",
"from",
"URDF",
"file",
"."
] |
def _RecordInertiaInfoFromURDF(self):
"""Record the inertia of each body from URDF file."""
self._link_urdf = []
num_bodies = self._pybullet_client.getNumJoints(self.quadruped)
for body_id in range(-1, num_bodies): # -1 is for the base link.
inertia = self._pybullet_client.getDynamicsInfo(self.quadruped, body_id)[2]
self._link_urdf.append(inertia)
# We need to use id+1 to index self._link_urdf because it has the base
# (index = -1) at the first element.
self._base_inertia_urdf = [
self._link_urdf[chassis_id + 1] for chassis_id in self._chassis_link_ids
]
self._leg_inertia_urdf = [self._link_urdf[leg_id + 1] for leg_id in self._leg_link_ids]
self._leg_inertia_urdf.extend(
[self._link_urdf[motor_id + 1] for motor_id in self._motor_link_ids])
|
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https://github.com/bulletphysics/bullet3/blob/f0f2a952e146f016096db6f85cf0c44ed75b0b9a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur.py#L184-L198
|
||
hpi-xnor/BMXNet-v2
|
af2b1859eafc5c721b1397cef02f946aaf2ce20d
|
example/image-classification/common/fit.py
|
python
|
add_fit_args
|
(parser)
|
return train
|
parser : argparse.ArgumentParser
return a parser added with args required by fit
|
parser : argparse.ArgumentParser
return a parser added with args required by fit
|
[
"parser",
":",
"argparse",
".",
"ArgumentParser",
"return",
"a",
"parser",
"added",
"with",
"args",
"required",
"by",
"fit"
] |
def add_fit_args(parser):
"""
parser : argparse.ArgumentParser
return a parser added with args required by fit
"""
train = parser.add_argument_group('Training', 'model training')
train.add_argument('--network', type=str,
help='the neural network to use')
train.add_argument('--num-layers', type=int,
help='number of layers in the neural network, \
required by some networks such as resnet')
train.add_argument('--gpus', type=str,
help='list of gpus to run, e.g. 0 or 0,2,5. empty means using cpu')
train.add_argument('--kv-store', type=str, default='device',
help='key-value store type')
train.add_argument('--num-epochs', type=int, default=100,
help='max num of epochs')
train.add_argument('--lr', type=float, default=0.1,
help='initial learning rate')
train.add_argument('--lr-factor', type=float, default=0.1,
help='the ratio to reduce lr on each step')
train.add_argument('--lr-step-epochs', type=str,
help='the epochs to reduce the lr, e.g. 30,60')
train.add_argument('--initializer', type=str, default='default',
help='the initializer type')
train.add_argument('--optimizer', type=str, default='sgd',
help='the optimizer type')
train.add_argument('--mom', type=float, default=0.9,
help='momentum for sgd')
train.add_argument('--wd', type=float, default=0.0001,
help='weight decay for sgd')
train.add_argument('--batch-size', type=int, default=128,
help='the batch size')
train.add_argument('--disp-batches', type=int, default=20,
help='show progress for every n batches')
train.add_argument('--model-prefix', type=str,
help='model prefix')
train.add_argument('--save-period', type=int, default=1, help='params saving period')
parser.add_argument('--monitor', dest='monitor', type=int, default=0,
help='log network parameters every N iters if larger than 0')
train.add_argument('--load-epoch', type=int,
help='load the model on an epoch using the model-load-prefix')
train.add_argument('--top-k', type=int, default=0,
help='report the top-k accuracy. 0 means no report.')
train.add_argument('--loss', type=str, default='',
help='show the cross-entropy or nll loss. ce strands for cross-entropy, nll-loss stands for likelihood loss')
train.add_argument('--test-io', type=int, default=0,
help='1 means test reading speed without training')
train.add_argument('--dtype', type=str, default='float32',
help='precision: float32 or float16')
train.add_argument('--gc-type', type=str, default='none',
help='type of gradient compression to use, \
takes `2bit` or `none` for now')
train.add_argument('--gc-threshold', type=float, default=0.5,
help='threshold for 2bit gradient compression')
# additional parameters for large batch sgd
train.add_argument('--macrobatch-size', type=int, default=0,
help='distributed effective batch size')
train.add_argument('--warmup-epochs', type=int, default=5,
help='the epochs to ramp-up lr to scaled large-batch value')
train.add_argument('--warmup-strategy', type=str, default='linear',
help='the ramping-up strategy for large batch sgd')
train.add_argument('--profile-worker-suffix', type=str, default='',
help='profile workers actions into this file. During distributed training\
filename saved will be rank1_ followed by this suffix')
train.add_argument('--profile-server-suffix', type=str, default='',
help='profile server actions into a file with name like rank1_ followed by this suffix \
during distributed training')
return train
|
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] |
https://github.com/hpi-xnor/BMXNet-v2/blob/af2b1859eafc5c721b1397cef02f946aaf2ce20d/example/image-classification/common/fit.py#L77-L145
|
|
baidu-research/tensorflow-allreduce
|
66d5b855e90b0949e9fa5cca5599fd729a70e874
|
tensorflow/contrib/learn/python/learn/estimators/dnn.py
|
python
|
DNNRegressor.__init__
|
(self,
hidden_units,
feature_columns,
model_dir=None,
weight_column_name=None,
optimizer=None,
activation_fn=nn.relu,
dropout=None,
gradient_clip_norm=None,
enable_centered_bias=False,
config=None,
feature_engineering_fn=None,
label_dimension=1,
embedding_lr_multipliers=None,
input_layer_min_slice_size=None)
|
Initializes a `DNNRegressor` instance.
Args:
hidden_units: List of hidden units per layer. All layers are fully
connected. Ex. `[64, 32]` means first layer has 64 nodes and second one
has 32.
feature_columns: An iterable containing all the feature columns used by
the model. All items in the set should be instances of classes derived
from `FeatureColumn`.
model_dir: Directory to save model parameters, graph and etc. This can
also be used to load checkpoints from the directory into a estimator to
continue training a previously saved model.
weight_column_name: A string defining feature column name representing
weights. It is used to down weight or boost examples during training. It
will be multiplied by the loss of the example.
optimizer: An instance of `tf.Optimizer` used to train the model. If
`None`, will use an Adagrad optimizer.
activation_fn: Activation function applied to each layer. If `None`, will
use `tf.nn.relu`. Note that a string containing the unqualified name of
the op may also be provided, e.g., "relu", "tanh", or "sigmoid".
dropout: When not `None`, the probability we will drop out a given
coordinate.
gradient_clip_norm: A `float` > 0. If provided, gradients are clipped
to their global norm with this clipping ratio. See
`tf.clip_by_global_norm` for more details.
enable_centered_bias: A bool. If True, estimator will learn a centered
bias variable for each class. Rest of the model structure learns the
residual after centered bias.
config: `RunConfig` object to configure the runtime settings.
feature_engineering_fn: Feature engineering function. Takes features and
labels which are the output of `input_fn` and
returns features and labels which will be fed
into the model.
label_dimension: Number of regression targets per example. This is the
size of the last dimension of the labels and logits `Tensor` objects
(typically, these have shape `[batch_size, label_dimension]`).
embedding_lr_multipliers: Optional. A dictionary from `EbeddingColumn` to
a `float` multiplier. Multiplier will be used to multiply with
learning rate for the embedding variables.
input_layer_min_slice_size: Optional. The min slice size of input layer
partitions. If not provided, will use the default of 64M.
Returns:
A `DNNRegressor` estimator.
|
Initializes a `DNNRegressor` instance.
|
[
"Initializes",
"a",
"DNNRegressor",
"instance",
"."
] |
def __init__(self,
hidden_units,
feature_columns,
model_dir=None,
weight_column_name=None,
optimizer=None,
activation_fn=nn.relu,
dropout=None,
gradient_clip_norm=None,
enable_centered_bias=False,
config=None,
feature_engineering_fn=None,
label_dimension=1,
embedding_lr_multipliers=None,
input_layer_min_slice_size=None):
"""Initializes a `DNNRegressor` instance.
Args:
hidden_units: List of hidden units per layer. All layers are fully
connected. Ex. `[64, 32]` means first layer has 64 nodes and second one
has 32.
feature_columns: An iterable containing all the feature columns used by
the model. All items in the set should be instances of classes derived
from `FeatureColumn`.
model_dir: Directory to save model parameters, graph and etc. This can
also be used to load checkpoints from the directory into a estimator to
continue training a previously saved model.
weight_column_name: A string defining feature column name representing
weights. It is used to down weight or boost examples during training. It
will be multiplied by the loss of the example.
optimizer: An instance of `tf.Optimizer` used to train the model. If
`None`, will use an Adagrad optimizer.
activation_fn: Activation function applied to each layer. If `None`, will
use `tf.nn.relu`. Note that a string containing the unqualified name of
the op may also be provided, e.g., "relu", "tanh", or "sigmoid".
dropout: When not `None`, the probability we will drop out a given
coordinate.
gradient_clip_norm: A `float` > 0. If provided, gradients are clipped
to their global norm with this clipping ratio. See
`tf.clip_by_global_norm` for more details.
enable_centered_bias: A bool. If True, estimator will learn a centered
bias variable for each class. Rest of the model structure learns the
residual after centered bias.
config: `RunConfig` object to configure the runtime settings.
feature_engineering_fn: Feature engineering function. Takes features and
labels which are the output of `input_fn` and
returns features and labels which will be fed
into the model.
label_dimension: Number of regression targets per example. This is the
size of the last dimension of the labels and logits `Tensor` objects
(typically, these have shape `[batch_size, label_dimension]`).
embedding_lr_multipliers: Optional. A dictionary from `EbeddingColumn` to
a `float` multiplier. Multiplier will be used to multiply with
learning rate for the embedding variables.
input_layer_min_slice_size: Optional. The min slice size of input layer
partitions. If not provided, will use the default of 64M.
Returns:
A `DNNRegressor` estimator.
"""
self._feature_columns = tuple(feature_columns or [])
super(DNNRegressor, self).__init__(
model_fn=_dnn_model_fn,
model_dir=model_dir,
config=config,
params={
"head":
head_lib.regression_head(
label_dimension=label_dimension,
weight_column_name=weight_column_name,
enable_centered_bias=enable_centered_bias),
"hidden_units": hidden_units,
"feature_columns": self._feature_columns,
"optimizer": optimizer,
"activation_fn": activation_fn,
"dropout": dropout,
"gradient_clip_norm": gradient_clip_norm,
"embedding_lr_multipliers": embedding_lr_multipliers,
"input_layer_min_slice_size": input_layer_min_slice_size,
},
feature_engineering_fn=feature_engineering_fn)
|
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https://github.com/baidu-research/tensorflow-allreduce/blob/66d5b855e90b0949e9fa5cca5599fd729a70e874/tensorflow/contrib/learn/python/learn/estimators/dnn.py#L575-L655
|
||
netket/netket
|
0d534e54ecbf25b677ea72af6b85947979420652
|
netket/optimizer/qgt/qgt_jacobian_pytree_logic.py
|
python
|
jacobian_cplx
|
(
forward_fn: Callable,
params: PyTree,
samples: Array,
chunk_size: int = None,
_build_fn: Callable = partial(jax.tree_multimap, jax.lax.complex),
)
|
return vmap_chunked(
_jacobian_cplx, in_axes=(None, None, 0, None), chunk_size=chunk_size
)(forward_fn, params, samples, _build_fn)
|
Calculates Jacobian entries by vmapping grad.
Assumes the function is R→C, backpropagates 1 and -1j
Args:
forward_fn: the log wavefunction ln Ψ
params : a pytree of parameters p
samples : an array of n samples σ
Returns:
The Jacobian matrix ∂/∂pₖ ln Ψ(σⱼ) as a PyTree
|
Calculates Jacobian entries by vmapping grad.
Assumes the function is R→C, backpropagates 1 and -1j
|
[
"Calculates",
"Jacobian",
"entries",
"by",
"vmapping",
"grad",
".",
"Assumes",
"the",
"function",
"is",
"R→C",
"backpropagates",
"1",
"and",
"-",
"1j"
] |
def jacobian_cplx(
forward_fn: Callable,
params: PyTree,
samples: Array,
chunk_size: int = None,
_build_fn: Callable = partial(jax.tree_multimap, jax.lax.complex),
) -> PyTree:
"""Calculates Jacobian entries by vmapping grad.
Assumes the function is R→C, backpropagates 1 and -1j
Args:
forward_fn: the log wavefunction ln Ψ
params : a pytree of parameters p
samples : an array of n samples σ
Returns:
The Jacobian matrix ∂/∂pₖ ln Ψ(σⱼ) as a PyTree
"""
def _jacobian_cplx(forward_fn, params, samples, _build_fn):
y, vjp_fun = jax.vjp(single_sample(forward_fn), params, samples)
gr, _ = vjp_fun(np.array(1.0, dtype=jnp.result_type(y)))
gi, _ = vjp_fun(np.array(-1.0j, dtype=jnp.result_type(y)))
return _build_fn(gr, gi)
return vmap_chunked(
_jacobian_cplx, in_axes=(None, None, 0, None), chunk_size=chunk_size
)(forward_fn, params, samples, _build_fn)
|
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https://github.com/netket/netket/blob/0d534e54ecbf25b677ea72af6b85947979420652/netket/optimizer/qgt/qgt_jacobian_pytree_logic.py#L78-L105
|
|
Xilinx/Vitis-AI
|
fc74d404563d9951b57245443c73bef389f3657f
|
tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/lite/python/convert_saved_model.py
|
python
|
freeze_saved_model
|
(saved_model_dir, input_arrays, input_shapes,
output_arrays, tag_set, signature_key)
|
Converts a SavedModel to a frozen graph.
Args:
saved_model_dir: SavedModel directory to convert.
input_arrays: List of input tensors to freeze graph with. Uses input arrays
from SignatureDef when none are provided.
input_shapes: Dict of strings representing input tensor names to list of
integers representing input shapes (e.g., {"foo": : [1, 16, 16, 3]}).
Automatically determined when input shapes is None (e.g., {"foo" : None}).
output_arrays: List of output tensors to freeze graph with. Uses output
arrays from SignatureDef when none are provided.
tag_set: Set of tags identifying the MetaGraphDef within the SavedModel to
analyze. All tags in the tag set must be present.
signature_key: Key identifying SignatureDef containing inputs and outputs.
Returns:
frozen_graph_def: Frozen GraphDef.
in_tensors: List of input tensors for the graph.
out_tensors: List of output tensors for the graph.
graph: `Graph` object.
Raises:
ValueError:
SavedModel doesn't contain a MetaGraphDef identified by tag_set.
signature_key is not in the MetaGraphDef.
assets/ directory is in the MetaGraphDef.
input_shapes does not match the length of input_arrays.
input_arrays or output_arrays are not valid.
|
Converts a SavedModel to a frozen graph.
|
[
"Converts",
"a",
"SavedModel",
"to",
"a",
"frozen",
"graph",
"."
] |
def freeze_saved_model(saved_model_dir, input_arrays, input_shapes,
output_arrays, tag_set, signature_key):
"""Converts a SavedModel to a frozen graph.
Args:
saved_model_dir: SavedModel directory to convert.
input_arrays: List of input tensors to freeze graph with. Uses input arrays
from SignatureDef when none are provided.
input_shapes: Dict of strings representing input tensor names to list of
integers representing input shapes (e.g., {"foo": : [1, 16, 16, 3]}).
Automatically determined when input shapes is None (e.g., {"foo" : None}).
output_arrays: List of output tensors to freeze graph with. Uses output
arrays from SignatureDef when none are provided.
tag_set: Set of tags identifying the MetaGraphDef within the SavedModel to
analyze. All tags in the tag set must be present.
signature_key: Key identifying SignatureDef containing inputs and outputs.
Returns:
frozen_graph_def: Frozen GraphDef.
in_tensors: List of input tensors for the graph.
out_tensors: List of output tensors for the graph.
graph: `Graph` object.
Raises:
ValueError:
SavedModel doesn't contain a MetaGraphDef identified by tag_set.
signature_key is not in the MetaGraphDef.
assets/ directory is in the MetaGraphDef.
input_shapes does not match the length of input_arrays.
input_arrays or output_arrays are not valid.
"""
# Read SignatureDef.
meta_graph = get_meta_graph_def(saved_model_dir, tag_set)
signature_def = get_signature_def(meta_graph, signature_key)
inputs, outputs = get_inputs_outputs(signature_def)
# Check SavedModel for assets directory.
collection_def = meta_graph.collection_def
if constants.ASSETS_KEY in collection_def:
raise ValueError("SavedModels with assets/ directory are not supported.")
graph = ops.Graph()
with session.Session(graph=graph) as sess:
loader.load(sess, meta_graph.meta_info_def.tags, saved_model_dir)
# Gets input and output tensors.
# TODO(zhixianyan): Use TFLite supported Op list to filter outputs.
in_tensors = _get_tensors(graph, inputs, input_arrays)
out_tensors = _get_tensors(graph, outputs, output_arrays)
util.set_tensor_shapes(in_tensors, input_shapes)
frozen_graph_def = util.freeze_graph(sess, in_tensors, out_tensors)
return frozen_graph_def, in_tensors, out_tensors, sess.graph
|
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https://github.com/Xilinx/Vitis-AI/blob/fc74d404563d9951b57245443c73bef389f3657f/tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/lite/python/convert_saved_model.py#L155-L207
|
||
eventql/eventql
|
7ca0dbb2e683b525620ea30dc40540a22d5eb227
|
deps/3rdparty/spidermonkey/mozjs/python/virtualenv/virtualenv.py
|
python
|
make_relative_path
|
(source, dest, dest_is_directory=True)
|
return os.path.sep.join(full_parts)
|
Make a filename relative, where the filename is dest, and it is
being referred to from the filename source.
>>> make_relative_path('/usr/share/something/a-file.pth',
... '/usr/share/another-place/src/Directory')
'../another-place/src/Directory'
>>> make_relative_path('/usr/share/something/a-file.pth',
... '/home/user/src/Directory')
'../../../home/user/src/Directory'
>>> make_relative_path('/usr/share/a-file.pth', '/usr/share/')
'./'
|
Make a filename relative, where the filename is dest, and it is
being referred to from the filename source.
|
[
"Make",
"a",
"filename",
"relative",
"where",
"the",
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"dest",
"and",
"it",
"is",
"being",
"referred",
"to",
"from",
"the",
"filename",
"source",
"."
] |
def make_relative_path(source, dest, dest_is_directory=True):
"""
Make a filename relative, where the filename is dest, and it is
being referred to from the filename source.
>>> make_relative_path('/usr/share/something/a-file.pth',
... '/usr/share/another-place/src/Directory')
'../another-place/src/Directory'
>>> make_relative_path('/usr/share/something/a-file.pth',
... '/home/user/src/Directory')
'../../../home/user/src/Directory'
>>> make_relative_path('/usr/share/a-file.pth', '/usr/share/')
'./'
"""
source = os.path.dirname(source)
if not dest_is_directory:
dest_filename = os.path.basename(dest)
dest = os.path.dirname(dest)
dest = os.path.normpath(os.path.abspath(dest))
source = os.path.normpath(os.path.abspath(source))
dest_parts = dest.strip(os.path.sep).split(os.path.sep)
source_parts = source.strip(os.path.sep).split(os.path.sep)
while dest_parts and source_parts and dest_parts[0] == source_parts[0]:
dest_parts.pop(0)
source_parts.pop(0)
full_parts = ['..']*len(source_parts) + dest_parts
if not dest_is_directory:
full_parts.append(dest_filename)
if not full_parts:
# Special case for the current directory (otherwise it'd be '')
return './'
return os.path.sep.join(full_parts)
|
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".",
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".",
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".",
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"(",
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] |
https://github.com/eventql/eventql/blob/7ca0dbb2e683b525620ea30dc40540a22d5eb227/deps/3rdparty/spidermonkey/mozjs/python/virtualenv/virtualenv.py#L1763-L1794
|
|
ChromiumWebApps/chromium
|
c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7
|
gpu/command_buffer/build_gles2_cmd_buffer.py
|
python
|
CreateHandler.WriteHandlerImplementation
|
(self, func, file)
|
Overrriden from TypeHandler.
|
Overrriden from TypeHandler.
|
[
"Overrriden",
"from",
"TypeHandler",
"."
] |
def WriteHandlerImplementation (self, func, file):
"""Overrriden from TypeHandler."""
file.Write(" uint32 client_id = c.client_id;\n")
file.Write(" if (!%sHelper(%s)) {\n" %
(func.name, func.MakeCmdArgString("")))
file.Write(" return error::kInvalidArguments;\n")
file.Write(" }\n")
|
[
"def",
"WriteHandlerImplementation",
"(",
"self",
",",
"func",
",",
"file",
")",
":",
"file",
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"Write",
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"\" if (!%sHelper(%s)) {\\n\"",
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"Write",
"(",
"\" return error::kInvalidArguments;\\n\"",
")",
"file",
".",
"Write",
"(",
"\" }\\n\"",
")"
] |
https://github.com/ChromiumWebApps/chromium/blob/c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7/gpu/command_buffer/build_gles2_cmd_buffer.py#L4190-L4196
|
||
krishauser/Klampt
|
972cc83ea5befac3f653c1ba20f80155768ad519
|
Python/klampt/model/geometry.py
|
python
|
fit_plane
|
(points : Sequence[Vector3])
|
return normal[0],normal[1],normal[2],-vectorops.dot(centroid,normal)
|
Returns a 3D plane equation that is a least squares fit
through the points (len(points) >= 3).
|
Returns a 3D plane equation that is a least squares fit
through the points (len(points) >= 3).
|
[
"Returns",
"a",
"3D",
"plane",
"equation",
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"a",
"least",
"squares",
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"through",
"the",
"points",
"(",
"len",
"(",
"points",
")",
">",
"=",
"3",
")",
"."
] |
def fit_plane(points : Sequence[Vector3]) -> Tuple[float,float,float,float]:
"""Returns a 3D plane equation that is a least squares fit
through the points (len(points) >= 3)."""
centroid,normal = fit_plane_centroid(points)
return normal[0],normal[1],normal[2],-vectorops.dot(centroid,normal)
|
[
"def",
"fit_plane",
"(",
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":",
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"Vector3",
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] |
https://github.com/krishauser/Klampt/blob/972cc83ea5befac3f653c1ba20f80155768ad519/Python/klampt/model/geometry.py#L320-L324
|
|
hanpfei/chromium-net
|
392cc1fa3a8f92f42e4071ab6e674d8e0482f83f
|
third_party/catapult/third_party/WebOb/webob/request.py
|
python
|
BaseRequest.as_bytes
|
(self, skip_body=False)
|
return b'\r\n'.join(parts)
|
Return HTTP bytes representing this request.
If skip_body is True, exclude the body.
If skip_body is an integer larger than one, skip body
only if its length is bigger than that number.
|
Return HTTP bytes representing this request.
If skip_body is True, exclude the body.
If skip_body is an integer larger than one, skip body
only if its length is bigger than that number.
|
[
"Return",
"HTTP",
"bytes",
"representing",
"this",
"request",
".",
"If",
"skip_body",
"is",
"True",
"exclude",
"the",
"body",
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"skip_body",
"is",
"an",
"integer",
"larger",
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"one",
"skip",
"body",
"only",
"if",
"its",
"length",
"is",
"bigger",
"than",
"that",
"number",
"."
] |
def as_bytes(self, skip_body=False):
"""
Return HTTP bytes representing this request.
If skip_body is True, exclude the body.
If skip_body is an integer larger than one, skip body
only if its length is bigger than that number.
"""
url = self.url
host = self.host_url
assert url.startswith(host)
url = url[len(host):]
parts = [bytes_('%s %s %s' % (self.method, url, self.http_version))]
#self.headers.setdefault('Host', self.host)
# acquire body before we handle headers so that
# content-length will be set
body = None
if http_method_probably_has_body.get(self.method):
if skip_body > 1:
if len(self.body) > skip_body:
body = bytes_('<body skipped (len=%s)>' % len(self.body))
else:
skip_body = False
if not skip_body:
body = self.body
for k, v in sorted(self.headers.items()):
header = bytes_('%s: %s' % (k, v))
parts.append(header)
if body:
parts.extend([b'', body])
# HTTP clearly specifies CRLF
return b'\r\n'.join(parts)
|
[
"def",
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":",
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"# HTTP clearly specifies CRLF",
"return",
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"join",
"(",
"parts",
")"
] |
https://github.com/hanpfei/chromium-net/blob/392cc1fa3a8f92f42e4071ab6e674d8e0482f83f/third_party/catapult/third_party/WebOb/webob/request.py#L1129-L1162
|
|
apache/incubator-mxnet
|
f03fb23f1d103fec9541b5ae59ee06b1734a51d9
|
python/mxnet/image/image.py
|
python
|
ImageIter.augmentation_transform
|
(self, data)
|
return data
|
Transforms input data with specified augmentation.
|
Transforms input data with specified augmentation.
|
[
"Transforms",
"input",
"data",
"with",
"specified",
"augmentation",
"."
] |
def augmentation_transform(self, data):
"""Transforms input data with specified augmentation."""
for aug in self.auglist:
data = aug(data)
return data
|
[
"def",
"augmentation_transform",
"(",
"self",
",",
"data",
")",
":",
"for",
"aug",
"in",
"self",
".",
"auglist",
":",
"data",
"=",
"aug",
"(",
"data",
")",
"return",
"data"
] |
https://github.com/apache/incubator-mxnet/blob/f03fb23f1d103fec9541b5ae59ee06b1734a51d9/python/mxnet/image/image.py#L1603-L1607
|
|
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/idlelib/tree.py
|
python
|
wheel_event
|
(event, widget=None)
|
return 'break'
|
Handle scrollwheel event.
For wheel up, event.delta = 120*n on Windows, -1*n on darwin,
where n can be > 1 if one scrolls fast. Flicking the wheel
generates up to maybe 20 events with n up to 10 or more 1.
Macs use wheel down (delta = 1*n) to scroll up, so positive
delta means to scroll up on both systems.
X-11 sends Control-Button-4,5 events instead.
The widget parameter is needed so browser label bindings can pass
the underlying canvas.
This function depends on widget.yview to not be overridden by
a subclass.
|
Handle scrollwheel event.
|
[
"Handle",
"scrollwheel",
"event",
"."
] |
def wheel_event(event, widget=None):
"""Handle scrollwheel event.
For wheel up, event.delta = 120*n on Windows, -1*n on darwin,
where n can be > 1 if one scrolls fast. Flicking the wheel
generates up to maybe 20 events with n up to 10 or more 1.
Macs use wheel down (delta = 1*n) to scroll up, so positive
delta means to scroll up on both systems.
X-11 sends Control-Button-4,5 events instead.
The widget parameter is needed so browser label bindings can pass
the underlying canvas.
This function depends on widget.yview to not be overridden by
a subclass.
"""
up = {EventType.MouseWheel: event.delta > 0,
EventType.ButtonPress: event.num == 4}
lines = -5 if up[event.type] else 5
widget = event.widget if widget is None else widget
widget.yview(SCROLL, lines, 'units')
return 'break'
|
[
"def",
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"event",
",",
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"None",
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":",
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"yview",
"(",
"SCROLL",
",",
"lines",
",",
"'units'",
")",
"return",
"'break'"
] |
https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/idlelib/tree.py#L59-L81
|
|
HackWebRTC/webrtc
|
7abfc990c00ab35090fff285fcf635d1d7892433
|
PRESUBMIT.py
|
python
|
_CalculateAddedDeps
|
(os_path, old_contents, new_contents)
|
return results
|
Helper method for _CheckAddedDepsHaveTargetApprovals. Returns
a set of DEPS entries that we should look up.
For a directory (rather than a specific filename) we fake a path to
a specific filename by adding /DEPS. This is chosen as a file that
will seldom or never be subject to per-file include_rules.
|
Helper method for _CheckAddedDepsHaveTargetApprovals. Returns
a set of DEPS entries that we should look up.
|
[
"Helper",
"method",
"for",
"_CheckAddedDepsHaveTargetApprovals",
".",
"Returns",
"a",
"set",
"of",
"DEPS",
"entries",
"that",
"we",
"should",
"look",
"up",
"."
] |
def _CalculateAddedDeps(os_path, old_contents, new_contents):
"""Helper method for _CheckAddedDepsHaveTargetApprovals. Returns
a set of DEPS entries that we should look up.
For a directory (rather than a specific filename) we fake a path to
a specific filename by adding /DEPS. This is chosen as a file that
will seldom or never be subject to per-file include_rules.
"""
# We ignore deps entries on auto-generated directories.
auto_generated_dirs = ['grit', 'jni']
old_deps = _ExtractAddRulesFromParsedDeps(_ParseDeps(old_contents))
new_deps = _ExtractAddRulesFromParsedDeps(_ParseDeps(new_contents))
added_deps = new_deps.difference(old_deps)
results = set()
for added_dep in added_deps:
if added_dep.split('/')[0] in auto_generated_dirs:
continue
# Assume that a rule that ends in .h is a rule for a specific file.
if added_dep.endswith('.h'):
results.add(added_dep)
else:
results.add(os_path.join(added_dep, 'DEPS'))
return results
|
[
"def",
"_CalculateAddedDeps",
"(",
"os_path",
",",
"old_contents",
",",
"new_contents",
")",
":",
"# We ignore deps entries on auto-generated directories.",
"auto_generated_dirs",
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"]",
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"if",
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"'.h'",
")",
":",
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"add",
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"(",
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".",
"join",
"(",
"added_dep",
",",
"'DEPS'",
")",
")",
"return",
"results"
] |
https://github.com/HackWebRTC/webrtc/blob/7abfc990c00ab35090fff285fcf635d1d7892433/PRESUBMIT.py#L1116-L1141
|
|
llvm/llvm-project
|
ffa6262cb4e2a335d26416fad39a581b4f98c5f4
|
lldb/third_party/Python/module/six/six.py
|
python
|
_SixMetaPathImporter.is_package
|
(self, fullname)
|
return hasattr(self.__get_module(fullname), "__path__")
|
Return true, if the named module is a package.
We need this method to get correct spec objects with
Python 3.4 (see PEP451)
|
Return true, if the named module is a package.
|
[
"Return",
"true",
"if",
"the",
"named",
"module",
"is",
"a",
"package",
"."
] |
def is_package(self, fullname):
"""
Return true, if the named module is a package.
We need this method to get correct spec objects with
Python 3.4 (see PEP451)
"""
return hasattr(self.__get_module(fullname), "__path__")
|
[
"def",
"is_package",
"(",
"self",
",",
"fullname",
")",
":",
"return",
"hasattr",
"(",
"self",
".",
"__get_module",
"(",
"fullname",
")",
",",
"\"__path__\"",
")"
] |
https://github.com/llvm/llvm-project/blob/ffa6262cb4e2a335d26416fad39a581b4f98c5f4/lldb/third_party/Python/module/six/six.py#L209-L216
|
|
Polidea/SiriusObfuscator
|
b0e590d8130e97856afe578869b83a209e2b19be
|
SymbolExtractorAndRenamer/clang/tools/scan-build-py/libscanbuild/shell.py
|
python
|
encode
|
(command)
|
return " ".join([escape(arg) for arg in command])
|
Takes a command as list and returns a string.
|
Takes a command as list and returns a string.
|
[
"Takes",
"a",
"command",
"as",
"list",
"and",
"returns",
"a",
"string",
"."
] |
def encode(command):
""" Takes a command as list and returns a string. """
def needs_quote(word):
""" Returns true if arguments needs to be protected by quotes.
Previous implementation was shlex.split method, but that's not good
for this job. Currently is running through the string with a basic
state checking. """
reserved = {' ', '$', '%', '&', '(', ')', '[', ']', '{', '}', '*', '|',
'<', '>', '@', '?', '!'}
state = 0
for current in word:
if state == 0 and current in reserved:
return True
elif state == 0 and current == '\\':
state = 1
elif state == 1 and current in reserved | {'\\'}:
state = 0
elif state == 0 and current == '"':
state = 2
elif state == 2 and current == '"':
state = 0
elif state == 0 and current == "'":
state = 3
elif state == 3 and current == "'":
state = 0
return state != 0
def escape(word):
""" Do protect argument if that's needed. """
table = {'\\': '\\\\', '"': '\\"'}
escaped = ''.join([table.get(c, c) for c in word])
return '"' + escaped + '"' if needs_quote(word) else escaped
return " ".join([escape(arg) for arg in command])
|
[
"def",
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")",
":",
"def",
"needs_quote",
"(",
"word",
")",
":",
"\"\"\" Returns true if arguments needs to be protected by quotes.\n\n Previous implementation was shlex.split method, but that's not good\n for this job. Currently is running through the string with a basic\n state checking. \"\"\"",
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"current",
"in",
"word",
":",
"if",
"state",
"==",
"0",
"and",
"current",
"in",
"reserved",
":",
"return",
"True",
"elif",
"state",
"==",
"0",
"and",
"current",
"==",
"'\\\\'",
":",
"state",
"=",
"1",
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"==",
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"current",
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"|",
"{",
"'\\\\'",
"}",
":",
"state",
"=",
"0",
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"state",
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":",
"state",
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":",
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"3",
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"==",
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"and",
"current",
"==",
"\"'\"",
":",
"state",
"=",
"0",
"return",
"state",
"!=",
"0",
"def",
"escape",
"(",
"word",
")",
":",
"\"\"\" Do protect argument if that's needed. \"\"\"",
"table",
"=",
"{",
"'\\\\'",
":",
"'\\\\\\\\'",
",",
"'\"'",
":",
"'\\\\\"'",
"}",
"escaped",
"=",
"''",
".",
"join",
"(",
"[",
"table",
".",
"get",
"(",
"c",
",",
"c",
")",
"for",
"c",
"in",
"word",
"]",
")",
"return",
"'\"'",
"+",
"escaped",
"+",
"'\"'",
"if",
"needs_quote",
"(",
"word",
")",
"else",
"escaped",
"return",
"\" \"",
".",
"join",
"(",
"[",
"escape",
"(",
"arg",
")",
"for",
"arg",
"in",
"command",
"]",
")"
] |
https://github.com/Polidea/SiriusObfuscator/blob/b0e590d8130e97856afe578869b83a209e2b19be/SymbolExtractorAndRenamer/clang/tools/scan-build-py/libscanbuild/shell.py#L14-L52
|
|
zhuli19901106/leetcode-zhuli
|
0f8fc29ccb8c33ea91149ecb2d4e961024c11db7
|
explore/queue-stack/1337_design-circular-queue_1_AC.py
|
python
|
MyCircularQueue.Rear
|
(self)
|
return self.arr[(self.head + self.cap) % (self.cap + 1)]
|
Get the last item from the queue.
|
Get the last item from the queue.
|
[
"Get",
"the",
"last",
"item",
"from",
"the",
"queue",
"."
] |
def Rear(self) -> int:
"""
Get the last item from the queue.
"""
if self.isEmpty():
return MyCircularQueue.NULL_VAL
return self.arr[(self.head + self.cap) % (self.cap + 1)]
|
[
"def",
"Rear",
"(",
"self",
")",
"->",
"int",
":",
"if",
"self",
".",
"isEmpty",
"(",
")",
":",
"return",
"MyCircularQueue",
".",
"NULL_VAL",
"return",
"self",
".",
"arr",
"[",
"(",
"self",
".",
"head",
"+",
"self",
".",
"cap",
")",
"%",
"(",
"self",
".",
"cap",
"+",
"1",
")",
"]"
] |
https://github.com/zhuli19901106/leetcode-zhuli/blob/0f8fc29ccb8c33ea91149ecb2d4e961024c11db7/explore/queue-stack/1337_design-circular-queue_1_AC.py#L46-L52
|
|
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numba/targets/codegen.py
|
python
|
JITCodeLibrary.get_pointer_to_function
|
(self, name)
|
Generate native code for function named *name* and return a pointer
to the start of the function (as an integer).
This function implicitly calls .finalize().
Returns
-------
pointer : int
- zero (null) if no symbol of *name* is defined by this code
library.
- non-zero if the symbol is defined.
|
Generate native code for function named *name* and return a pointer
to the start of the function (as an integer).
|
[
"Generate",
"native",
"code",
"for",
"function",
"named",
"*",
"name",
"*",
"and",
"return",
"a",
"pointer",
"to",
"the",
"start",
"of",
"the",
"function",
"(",
"as",
"an",
"integer",
")",
"."
] |
def get_pointer_to_function(self, name):
"""
Generate native code for function named *name* and return a pointer
to the start of the function (as an integer).
This function implicitly calls .finalize().
Returns
-------
pointer : int
- zero (null) if no symbol of *name* is defined by this code
library.
- non-zero if the symbol is defined.
"""
self._ensure_finalized()
ee = self._codegen._engine
if not ee.is_symbol_defined(name):
return 0
else:
return self._codegen._engine.get_function_address(name)
|
[
"def",
"get_pointer_to_function",
"(",
"self",
",",
"name",
")",
":",
"self",
".",
"_ensure_finalized",
"(",
")",
"ee",
"=",
"self",
".",
"_codegen",
".",
"_engine",
"if",
"not",
"ee",
".",
"is_symbol_defined",
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"name",
")",
":",
"return",
"0",
"else",
":",
"return",
"self",
".",
"_codegen",
".",
"_engine",
".",
"get_function_address",
"(",
"name",
")"
] |
https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numba/targets/codegen.py#L475-L494
|
||
wxWidgets/wxPython-Classic
|
19571e1ae65f1ac445f5491474121998c97a1bf0
|
src/osx_cocoa/_controls.py
|
python
|
TreeCtrl.SetFocusedItem
|
(*args, **kwargs)
|
return _controls_.TreeCtrl_SetFocusedItem(*args, **kwargs)
|
SetFocusedItem(self, TreeItemId item)
|
SetFocusedItem(self, TreeItemId item)
|
[
"SetFocusedItem",
"(",
"self",
"TreeItemId",
"item",
")"
] |
def SetFocusedItem(*args, **kwargs):
"""SetFocusedItem(self, TreeItemId item)"""
return _controls_.TreeCtrl_SetFocusedItem(*args, **kwargs)
|
[
"def",
"SetFocusedItem",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"return",
"_controls_",
".",
"TreeCtrl_SetFocusedItem",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")"
] |
https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_cocoa/_controls.py#L5379-L5381
|
|
catboost/catboost
|
167f64f237114a4d10b2b4ee42adb4569137debe
|
contrib/python/pandas/py3/pandas/core/dtypes/concat.py
|
python
|
cast_to_common_type
|
(arr: ArrayLike, dtype: DtypeObj)
|
return arr.astype(dtype, copy=False)
|
Helper function for `arr.astype(common_dtype)` but handling all special
cases.
|
Helper function for `arr.astype(common_dtype)` but handling all special
cases.
|
[
"Helper",
"function",
"for",
"arr",
".",
"astype",
"(",
"common_dtype",
")",
"but",
"handling",
"all",
"special",
"cases",
"."
] |
def cast_to_common_type(arr: ArrayLike, dtype: DtypeObj) -> ArrayLike:
"""
Helper function for `arr.astype(common_dtype)` but handling all special
cases.
"""
if is_dtype_equal(arr.dtype, dtype):
return arr
if (
is_categorical_dtype(arr.dtype)
and isinstance(dtype, np.dtype)
and np.issubdtype(dtype, np.integer)
):
# problem case: categorical of int -> gives int as result dtype,
# but categorical can contain NAs -> fall back to object dtype
try:
return arr.astype(dtype, copy=False)
except ValueError:
return arr.astype(object, copy=False)
if is_sparse(arr) and not is_sparse(dtype):
# problem case: SparseArray.astype(dtype) doesn't follow the specified
# dtype exactly, but converts this to Sparse[dtype] -> first manually
# convert to dense array
arr = cast(SparseArray, arr)
return arr.to_dense().astype(dtype, copy=False)
if (
isinstance(arr, np.ndarray)
and arr.dtype.kind in ["m", "M"]
and dtype is np.dtype("object")
):
# wrap datetime-likes in EA to ensure astype(object) gives Timestamp/Timedelta
# this can happen when concat_compat is called directly on arrays (when arrays
# are not coming from Index/Series._values), eg in BlockManager.quantile
arr = ensure_wrapped_if_datetimelike(arr)
if isinstance(dtype, ExtensionDtype):
if isinstance(arr, np.ndarray):
# numpy's astype cannot handle ExtensionDtypes
return pd_array(arr, dtype=dtype, copy=False)
return arr.astype(dtype, copy=False)
return arr.astype(dtype, copy=False)
|
[
"def",
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":",
"ArrayLike",
",",
"dtype",
":",
"DtypeObj",
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",",
"dtype",
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":",
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",",
"np",
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",",
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"integer",
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":",
"# problem case: categorical of int -> gives int as result dtype,",
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",",
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",",
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"dtype",
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":",
"# problem case: SparseArray.astype(dtype) doesn't follow the specified",
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",",
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"\"M\"",
"]",
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")",
")",
":",
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"# this can happen when concat_compat is called directly on arrays (when arrays",
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"arr",
"=",
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"(",
"arr",
")",
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"(",
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",",
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":",
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",",
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"ndarray",
")",
":",
"# numpy's astype cannot handle ExtensionDtypes",
"return",
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"(",
"arr",
",",
"dtype",
"=",
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",",
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")",
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"astype",
"(",
"dtype",
",",
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"=",
"False",
")",
"return",
"arr",
".",
"astype",
"(",
"dtype",
",",
"copy",
"=",
"False",
")"
] |
https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/pandas/py3/pandas/core/dtypes/concat.py#L33-L75
|
|
eventql/eventql
|
7ca0dbb2e683b525620ea30dc40540a22d5eb227
|
deps/3rdparty/spidermonkey/mozjs/python/requests/requests/sessions.py
|
python
|
Session.merge_environment_settings
|
(self, url, proxies, stream, verify, cert)
|
return {'verify': verify, 'proxies': proxies, 'stream': stream,
'cert': cert}
|
Check the environment and merge it with some settings.
|
Check the environment and merge it with some settings.
|
[
"Check",
"the",
"environment",
"and",
"merge",
"it",
"with",
"some",
"settings",
"."
] |
def merge_environment_settings(self, url, proxies, stream, verify, cert):
"""Check the environment and merge it with some settings."""
# Gather clues from the surrounding environment.
if self.trust_env:
# Set environment's proxies.
env_proxies = get_environ_proxies(url) or {}
for (k, v) in env_proxies.items():
proxies.setdefault(k, v)
# Look for requests environment configuration and be compatible
# with cURL.
if verify is True or verify is None:
verify = (os.environ.get('REQUESTS_CA_BUNDLE') or
os.environ.get('CURL_CA_BUNDLE'))
# Merge all the kwargs.
proxies = merge_setting(proxies, self.proxies)
stream = merge_setting(stream, self.stream)
verify = merge_setting(verify, self.verify)
cert = merge_setting(cert, self.cert)
return {'verify': verify, 'proxies': proxies, 'stream': stream,
'cert': cert}
|
[
"def",
"merge_environment_settings",
"(",
"self",
",",
"url",
",",
"proxies",
",",
"stream",
",",
"verify",
",",
"cert",
")",
":",
"# Gather clues from the surrounding environment.",
"if",
"self",
".",
"trust_env",
":",
"# Set environment's proxies.",
"env_proxies",
"=",
"get_environ_proxies",
"(",
"url",
")",
"or",
"{",
"}",
"for",
"(",
"k",
",",
"v",
")",
"in",
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"(",
")",
":",
"proxies",
".",
"setdefault",
"(",
"k",
",",
"v",
")",
"# Look for requests environment configuration and be compatible",
"# with cURL.",
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"verify",
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"True",
"or",
"verify",
"is",
"None",
":",
"verify",
"=",
"(",
"os",
".",
"environ",
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"get",
"(",
"'REQUESTS_CA_BUNDLE'",
")",
"or",
"os",
".",
"environ",
".",
"get",
"(",
"'CURL_CA_BUNDLE'",
")",
")",
"# Merge all the kwargs.",
"proxies",
"=",
"merge_setting",
"(",
"proxies",
",",
"self",
".",
"proxies",
")",
"stream",
"=",
"merge_setting",
"(",
"stream",
",",
"self",
".",
"stream",
")",
"verify",
"=",
"merge_setting",
"(",
"verify",
",",
"self",
".",
"verify",
")",
"cert",
"=",
"merge_setting",
"(",
"cert",
",",
"self",
".",
"cert",
")",
"return",
"{",
"'verify'",
":",
"verify",
",",
"'proxies'",
":",
"proxies",
",",
"'stream'",
":",
"stream",
",",
"'cert'",
":",
"cert",
"}"
] |
https://github.com/eventql/eventql/blob/7ca0dbb2e683b525620ea30dc40540a22d5eb227/deps/3rdparty/spidermonkey/mozjs/python/requests/requests/sessions.py#L614-L636
|
|
catboost/catboost
|
167f64f237114a4d10b2b4ee42adb4569137debe
|
contrib/python/traitlets/py2/traitlets/config/configurable.py
|
python
|
Configurable.section_names
|
(cls)
|
return [c.__name__ for c in reversed(cls.__mro__) if
issubclass(c, Configurable) and issubclass(cls, c)
]
|
return section names as a list
|
return section names as a list
|
[
"return",
"section",
"names",
"as",
"a",
"list"
] |
def section_names(cls):
"""return section names as a list"""
return [c.__name__ for c in reversed(cls.__mro__) if
issubclass(c, Configurable) and issubclass(cls, c)
]
|
[
"def",
"section_names",
"(",
"cls",
")",
":",
"return",
"[",
"c",
".",
"__name__",
"for",
"c",
"in",
"reversed",
"(",
"cls",
".",
"__mro__",
")",
"if",
"issubclass",
"(",
"c",
",",
"Configurable",
")",
"and",
"issubclass",
"(",
"cls",
",",
"c",
")",
"]"
] |
https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/traitlets/py2/traitlets/config/configurable.py#L100-L104
|
|
bkaradzic/bgfx
|
e42fe374c33138a84d5e959566be4a77546310f6
|
3rdparty/glslang/build_info.py
|
python
|
deduce_software_version
|
(directory)
|
Returns a software version number parsed from the CHANGES.md file
in the given directory.
The CHANGES.md file describes most recent versions first.
|
Returns a software version number parsed from the CHANGES.md file
in the given directory.
|
[
"Returns",
"a",
"software",
"version",
"number",
"parsed",
"from",
"the",
"CHANGES",
".",
"md",
"file",
"in",
"the",
"given",
"directory",
"."
] |
def deduce_software_version(directory):
"""Returns a software version number parsed from the CHANGES.md file
in the given directory.
The CHANGES.md file describes most recent versions first.
"""
# Match the first well-formed version-and-date line.
# Allow trailing whitespace in the checked-out source code has
# unexpected carriage returns on a linefeed-only system such as
# Linux.
pattern = re.compile(r'^#* +(\d+)\.(\d+)\.(\d+)(-\w+)? (\d\d\d\d-\d\d-\d\d)? *$')
changes_file = os.path.join(directory, 'CHANGES.md')
with open(changes_file, mode='r') as f:
for line in f.readlines():
match = pattern.match(line)
if match:
flavor = match.group(4)
if flavor == None:
flavor = ""
return {
"major": match.group(1),
"minor": match.group(2),
"patch": match.group(3),
"flavor": flavor.lstrip("-"),
"-flavor": flavor,
"date": match.group(5),
}
raise Exception('No version number found in {}'.format(changes_file))
|
[
"def",
"deduce_software_version",
"(",
"directory",
")",
":",
"# Match the first well-formed version-and-date line.",
"# Allow trailing whitespace in the checked-out source code has",
"# unexpected carriage returns on a linefeed-only system such as",
"# Linux.",
"pattern",
"=",
"re",
".",
"compile",
"(",
"r'^#* +(\\d+)\\.(\\d+)\\.(\\d+)(-\\w+)? (\\d\\d\\d\\d-\\d\\d-\\d\\d)? *$'",
")",
"changes_file",
"=",
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",",
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",",
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",",
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",",
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":",
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":",
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",",
"\"-flavor\"",
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"flavor",
",",
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":",
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",",
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"(",
"'No version number found in {}'",
".",
"format",
"(",
"changes_file",
")",
")"
] |
https://github.com/bkaradzic/bgfx/blob/e42fe374c33138a84d5e959566be4a77546310f6/3rdparty/glslang/build_info.py#L86-L114
|
||
google/earthenterprise
|
0fe84e29be470cd857e3a0e52e5d0afd5bb8cee9
|
earth_enterprise/src/scons/khEnvironment.py
|
python
|
EmitBuildDateFunc
|
(target, build_date)
|
Emits build date information to target file.
|
Emits build date information to target file.
|
[
"Emits",
"build",
"date",
"information",
"to",
"target",
"file",
"."
] |
def EmitBuildDateFunc(target, build_date):
"""Emits build date information to target file."""
fp = open(target, 'w')
fp.writelines(['// DO NOT MODIFY - auto-generated file\n',
'extern const char *const BUILD_DATE = "' +
time.strftime('%Y-%m-%d', build_date) + '";\n',
'extern const char *const BUILD_YEAR = "' +
time.strftime('%Y', build_date) + '";\n',
'extern const char *const BUILD_MONTH = "' +
time.strftime('%m', build_date) + '";\n',
'extern const char *const BUILD_DAY = "' +
time.strftime('%d', build_date) + '";\n',
])
fp.close()
|
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https://github.com/google/earthenterprise/blob/0fe84e29be470cd857e3a0e52e5d0afd5bb8cee9/earth_enterprise/src/scons/khEnvironment.py#L170-L183
|
||
LiquidPlayer/LiquidCore
|
9405979363f2353ac9a71ad8ab59685dd7f919c9
|
deps/boost_1_66_0/tools/build/src/build/scanner.py
|
python
|
get
|
(scanner_class, properties)
|
return __scanner_cache[scanner_id]
|
Returns an instance of previously registered scanner
with the specified properties.
|
Returns an instance of previously registered scanner
with the specified properties.
|
[
"Returns",
"an",
"instance",
"of",
"previously",
"registered",
"scanner",
"with",
"the",
"specified",
"properties",
"."
] |
def get(scanner_class, properties):
""" Returns an instance of previously registered scanner
with the specified properties.
"""
assert issubclass(scanner_class, Scanner)
assert is_iterable_typed(properties, basestring)
scanner_name = str(scanner_class)
if not registered(scanner_name):
raise BaseException ("attempt to get unregisted scanner: %s" % scanner_name)
relevant_properties = __scanners[scanner_name]
r = property.select(relevant_properties, properties)
scanner_id = scanner_name + '.' + '-'.join(r)
if scanner_id not in __scanner_cache:
__scanner_cache[scanner_id] = scanner_class(r)
return __scanner_cache[scanner_id]
|
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"__scanner_cache",
"[",
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] |
https://github.com/LiquidPlayer/LiquidCore/blob/9405979363f2353ac9a71ad8ab59685dd7f919c9/deps/boost_1_66_0/tools/build/src/build/scanner.py#L68-L87
|
|
quantOS-org/DataCore
|
e2ef9bd2c22ee9e2845675b6435a14fa607f3551
|
mdlink/deps/windows/protobuf-2.5.0/python/google/protobuf/descriptor.py
|
python
|
FieldDescriptor.__init__
|
(self, name, full_name, index, number, type, cpp_type, label,
default_value, message_type, enum_type, containing_type,
is_extension, extension_scope, options=None,
has_default_value=True)
|
The arguments are as described in the description of FieldDescriptor
attributes above.
Note that containing_type may be None, and may be set later if necessary
(to deal with circular references between message types, for example).
Likewise for extension_scope.
|
The arguments are as described in the description of FieldDescriptor
attributes above.
|
[
"The",
"arguments",
"are",
"as",
"described",
"in",
"the",
"description",
"of",
"FieldDescriptor",
"attributes",
"above",
"."
] |
def __init__(self, name, full_name, index, number, type, cpp_type, label,
default_value, message_type, enum_type, containing_type,
is_extension, extension_scope, options=None,
has_default_value=True):
"""The arguments are as described in the description of FieldDescriptor
attributes above.
Note that containing_type may be None, and may be set later if necessary
(to deal with circular references between message types, for example).
Likewise for extension_scope.
"""
super(FieldDescriptor, self).__init__(options, 'FieldOptions')
self.name = name
self.full_name = full_name
self.index = index
self.number = number
self.type = type
self.cpp_type = cpp_type
self.label = label
self.has_default_value = has_default_value
self.default_value = default_value
self.containing_type = containing_type
self.message_type = message_type
self.enum_type = enum_type
self.is_extension = is_extension
self.extension_scope = extension_scope
if api_implementation.Type() == 'cpp':
if is_extension:
if api_implementation.Version() == 2:
self._cdescriptor = _message.GetExtensionDescriptor(full_name)
else:
self._cdescriptor = cpp_message.GetExtensionDescriptor(full_name)
else:
if api_implementation.Version() == 2:
self._cdescriptor = _message.GetFieldDescriptor(full_name)
else:
self._cdescriptor = cpp_message.GetFieldDescriptor(full_name)
else:
self._cdescriptor = None
|
[
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"(",
"full_name",
")",
"else",
":",
"self",
".",
"_cdescriptor",
"=",
"None"
] |
https://github.com/quantOS-org/DataCore/blob/e2ef9bd2c22ee9e2845675b6435a14fa607f3551/mdlink/deps/windows/protobuf-2.5.0/python/google/protobuf/descriptor.py#L428-L466
|
||
openvinotoolkit/openvino
|
dedcbeafa8b84cccdc55ca64b8da516682b381c7
|
src/bindings/python/src/compatibility/ngraph/opset1/ops.py
|
python
|
result
|
(data: NodeInput, name: Optional[str] = None)
|
return _get_node_factory_opset1().create("Result", [data])
|
Return a node which represents an output of a graph (Function).
:param data: The tensor containing the input data
:return: Result node
|
Return a node which represents an output of a graph (Function).
|
[
"Return",
"a",
"node",
"which",
"represents",
"an",
"output",
"of",
"a",
"graph",
"(",
"Function",
")",
"."
] |
def result(data: NodeInput, name: Optional[str] = None) -> Node:
"""Return a node which represents an output of a graph (Function).
:param data: The tensor containing the input data
:return: Result node
"""
return _get_node_factory_opset1().create("Result", [data])
|
[
"def",
"result",
"(",
"data",
":",
"NodeInput",
",",
"name",
":",
"Optional",
"[",
"str",
"]",
"=",
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"->",
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"return",
"_get_node_factory_opset1",
"(",
")",
".",
"create",
"(",
"\"Result\"",
",",
"[",
"data",
"]",
")"
] |
https://github.com/openvinotoolkit/openvino/blob/dedcbeafa8b84cccdc55ca64b8da516682b381c7/src/bindings/python/src/compatibility/ngraph/opset1/ops.py#L2442-L2448
|
|
leggedrobotics/free_gait
|
93e6c2f385fe9ac7107153965e14f6b7a1e0d702
|
free_gait_python/src/free_gait/free_gait.py
|
python
|
LocalTransformListener.unregister
|
(self)
|
Unregisters all tf subscribers.
|
Unregisters all tf subscribers.
|
[
"Unregisters",
"all",
"tf",
"subscribers",
"."
] |
def unregister(self):
"""
Unregisters all tf subscribers.
"""
self.tf_sub.unregister()
self.tf_static_sub.unregister()
|
[
"def",
"unregister",
"(",
"self",
")",
":",
"self",
".",
"tf_sub",
".",
"unregister",
"(",
")",
"self",
".",
"tf_static_sub",
".",
"unregister",
"(",
")"
] |
https://github.com/leggedrobotics/free_gait/blob/93e6c2f385fe9ac7107153965e14f6b7a1e0d702/free_gait_python/src/free_gait/free_gait.py#L583-L588
|
||
pytorch/pytorch
|
7176c92687d3cc847cc046bf002269c6949a21c2
|
torch/cuda/amp/grad_scaler.py
|
python
|
GradScaler.unscale_
|
(self, optimizer)
|
Divides ("unscales") the optimizer's gradient tensors by the scale factor.
:meth:`unscale_` is optional, serving cases where you need to
:ref:`modify or inspect gradients<working-with-unscaled-gradients>`
between the backward pass(es) and :meth:`step`.
If :meth:`unscale_` is not called explicitly, gradients will be unscaled automatically during :meth:`step`.
Simple example, using :meth:`unscale_` to enable clipping of unscaled gradients::
...
scaler.scale(loss).backward()
scaler.unscale_(optimizer)
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm)
scaler.step(optimizer)
scaler.update()
Args:
optimizer (torch.optim.Optimizer): Optimizer that owns the gradients to be unscaled.
.. note::
:meth:`unscale_` does not incur a CPU-GPU sync.
.. warning::
:meth:`unscale_` should only be called once per optimizer per :meth:`step` call,
and only after all gradients for that optimizer's assigned parameters have been accumulated.
Calling :meth:`unscale_` twice for a given optimizer between each :meth:`step` triggers a RuntimeError.
.. warning::
:meth:`unscale_` may unscale sparse gradients out of place, replacing the ``.grad`` attribute.
|
Divides ("unscales") the optimizer's gradient tensors by the scale factor.
|
[
"Divides",
"(",
"unscales",
")",
"the",
"optimizer",
"s",
"gradient",
"tensors",
"by",
"the",
"scale",
"factor",
"."
] |
def unscale_(self, optimizer):
"""
Divides ("unscales") the optimizer's gradient tensors by the scale factor.
:meth:`unscale_` is optional, serving cases where you need to
:ref:`modify or inspect gradients<working-with-unscaled-gradients>`
between the backward pass(es) and :meth:`step`.
If :meth:`unscale_` is not called explicitly, gradients will be unscaled automatically during :meth:`step`.
Simple example, using :meth:`unscale_` to enable clipping of unscaled gradients::
...
scaler.scale(loss).backward()
scaler.unscale_(optimizer)
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm)
scaler.step(optimizer)
scaler.update()
Args:
optimizer (torch.optim.Optimizer): Optimizer that owns the gradients to be unscaled.
.. note::
:meth:`unscale_` does not incur a CPU-GPU sync.
.. warning::
:meth:`unscale_` should only be called once per optimizer per :meth:`step` call,
and only after all gradients for that optimizer's assigned parameters have been accumulated.
Calling :meth:`unscale_` twice for a given optimizer between each :meth:`step` triggers a RuntimeError.
.. warning::
:meth:`unscale_` may unscale sparse gradients out of place, replacing the ``.grad`` attribute.
"""
if not self._enabled:
return
self._check_scale_growth_tracker("unscale_")
optimizer_state = self._per_optimizer_states[id(optimizer)]
if optimizer_state["stage"] is OptState.UNSCALED:
raise RuntimeError("unscale_() has already been called on this optimizer since the last update().")
elif optimizer_state["stage"] is OptState.STEPPED:
raise RuntimeError("unscale_() is being called after step().")
# FP32 division can be imprecise for certain compile options, so we carry out the reciprocal in FP64.
assert self._scale is not None
inv_scale = self._scale.double().reciprocal().float()
found_inf = torch.full((1,), 0.0, dtype=torch.float32, device=self._scale.device)
optimizer_state["found_inf_per_device"] = self._unscale_grads_(optimizer, inv_scale, found_inf, False)
optimizer_state["stage"] = OptState.UNSCALED
|
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] |
https://github.com/pytorch/pytorch/blob/7176c92687d3cc847cc046bf002269c6949a21c2/torch/cuda/amp/grad_scaler.py#L230-L280
|
||
hanpfei/chromium-net
|
392cc1fa3a8f92f42e4071ab6e674d8e0482f83f
|
third_party/catapult/third_party/gsutil/third_party/protorpc/demos/tunes_db/server/model.py
|
python
|
Info.search
|
(cls, name_prefix=None)
|
return query
|
Create search query based on info record name prefix.
Args:
name_prefix: User input name-prefix to search for. If name_prefix
is empty string or None returns all records of Info sub-class. Records
are sorted by their encoded name.
Returns:
Datastore query pointing to search results.
|
Create search query based on info record name prefix.
|
[
"Create",
"search",
"query",
"based",
"on",
"info",
"record",
"name",
"prefix",
"."
] |
def search(cls, name_prefix=None):
"""Create search query based on info record name prefix.
Args:
name_prefix: User input name-prefix to search for. If name_prefix
is empty string or None returns all records of Info sub-class. Records
are sorted by their encoded name.
Returns:
Datastore query pointing to search results.
"""
name_prefix = _normalize_name(name_prefix)
query = cls.all().order('encoded_name')
if name_prefix:
query.filter('encoded_name >=', db.ByteString(name_prefix))
# Do not need to worry about name_prefix + '\xff\xff' because not
# a unicode character.
query.filter('encoded_name <=', db.ByteString(name_prefix + '\xff'))
return query
|
[
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] |
https://github.com/hanpfei/chromium-net/blob/392cc1fa3a8f92f42e4071ab6e674d8e0482f83f/third_party/catapult/third_party/gsutil/third_party/protorpc/demos/tunes_db/server/model.py#L85-L103
|
|
linyouhappy/kongkongxiyou
|
7a69b2913eb29f4be77f9a62fb90cdd72c4160f1
|
cocosjs/frameworks/cocos2d-x/tools/bindings-generator/clang/cindex.py
|
python
|
SourceRange.end
|
(self)
|
return conf.lib.clang_getRangeEnd(self)
|
Return a SourceLocation representing the last character within a
source range.
|
Return a SourceLocation representing the last character within a
source range.
|
[
"Return",
"a",
"SourceLocation",
"representing",
"the",
"last",
"character",
"within",
"a",
"source",
"range",
"."
] |
def end(self):
"""
Return a SourceLocation representing the last character within a
source range.
"""
return conf.lib.clang_getRangeEnd(self)
|
[
"def",
"end",
"(",
"self",
")",
":",
"return",
"conf",
".",
"lib",
".",
"clang_getRangeEnd",
"(",
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")"
] |
https://github.com/linyouhappy/kongkongxiyou/blob/7a69b2913eb29f4be77f9a62fb90cdd72c4160f1/cocosjs/frameworks/cocos2d-x/tools/bindings-generator/clang/cindex.py#L256-L261
|
|
windystrife/UnrealEngine_NVIDIAGameWorks
|
b50e6338a7c5b26374d66306ebc7807541ff815e
|
Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/rexec.py
|
python
|
RExec.s_import
|
(self, *args)
|
return self.s_apply(self.r_import, args)
|
Import a module, raising an ImportError exception if the module
is considered unsafe.
This method is implicitly called by code executing in the
restricted environment. Overriding this method in a subclass is
used to change the policies enforced by a restricted environment.
Similar to the r_import() method, but has access to restricted
versions of the standard I/O streams sys.stdin, sys.stderr, and
sys.stdout.
|
Import a module, raising an ImportError exception if the module
is considered unsafe.
|
[
"Import",
"a",
"module",
"raising",
"an",
"ImportError",
"exception",
"if",
"the",
"module",
"is",
"considered",
"unsafe",
"."
] |
def s_import(self, *args):
"""Import a module, raising an ImportError exception if the module
is considered unsafe.
This method is implicitly called by code executing in the
restricted environment. Overriding this method in a subclass is
used to change the policies enforced by a restricted environment.
Similar to the r_import() method, but has access to restricted
versions of the standard I/O streams sys.stdin, sys.stderr, and
sys.stdout.
"""
return self.s_apply(self.r_import, args)
|
[
"def",
"s_import",
"(",
"self",
",",
"*",
"args",
")",
":",
"return",
"self",
".",
"s_apply",
"(",
"self",
".",
"r_import",
",",
"args",
")"
] |
https://github.com/windystrife/UnrealEngine_NVIDIAGameWorks/blob/b50e6338a7c5b26374d66306ebc7807541ff815e/Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/rexec.py#L462-L475
|
|
wxWidgets/wxPython-Classic
|
19571e1ae65f1ac445f5491474121998c97a1bf0
|
src/osx_cocoa/html.py
|
python
|
HtmlHelpWindow.WriteCustomization
|
(*args, **kwargs)
|
return _html.HtmlHelpWindow_WriteCustomization(*args, **kwargs)
|
WriteCustomization(self, ConfigBase cfg, String path=wxEmptyString)
|
WriteCustomization(self, ConfigBase cfg, String path=wxEmptyString)
|
[
"WriteCustomization",
"(",
"self",
"ConfigBase",
"cfg",
"String",
"path",
"=",
"wxEmptyString",
")"
] |
def WriteCustomization(*args, **kwargs):
"""WriteCustomization(self, ConfigBase cfg, String path=wxEmptyString)"""
return _html.HtmlHelpWindow_WriteCustomization(*args, **kwargs)
|
[
"def",
"WriteCustomization",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"return",
"_html",
".",
"HtmlHelpWindow_WriteCustomization",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")"
] |
https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_cocoa/html.py#L1630-L1632
|
|
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Tools/Python/3.7.10/windows/Lib/site-packages/pip/_vendor/pkg_resources/__init__.py
|
python
|
IResourceProvider.resource_listdir
|
(resource_name)
|
List of resource names in the directory (like ``os.listdir()``)
|
List of resource names in the directory (like ``os.listdir()``)
|
[
"List",
"of",
"resource",
"names",
"in",
"the",
"directory",
"(",
"like",
"os",
".",
"listdir",
"()",
")"
] |
def resource_listdir(resource_name):
"""List of resource names in the directory (like ``os.listdir()``)"""
|
[
"def",
"resource_listdir",
"(",
"resource_name",
")",
":"
] |
https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/windows/Lib/site-packages/pip/_vendor/pkg_resources/__init__.py#L549-L550
|
||
ApolloAuto/apollo-platform
|
86d9dc6743b496ead18d597748ebabd34a513289
|
ros/ros_comm/roslaunch/src/roslaunch/core.py
|
python
|
generate_run_id
|
()
|
return str(uuid.uuid1())
|
Utility routine for generating run IDs (UUIDs)
:returns: guid, ``str``
|
Utility routine for generating run IDs (UUIDs)
:returns: guid, ``str``
|
[
"Utility",
"routine",
"for",
"generating",
"run",
"IDs",
"(",
"UUIDs",
")",
":",
"returns",
":",
"guid",
"str"
] |
def generate_run_id():
"""
Utility routine for generating run IDs (UUIDs)
:returns: guid, ``str``
"""
import uuid
return str(uuid.uuid1())
|
[
"def",
"generate_run_id",
"(",
")",
":",
"import",
"uuid",
"return",
"str",
"(",
"uuid",
".",
"uuid1",
"(",
")",
")"
] |
https://github.com/ApolloAuto/apollo-platform/blob/86d9dc6743b496ead18d597748ebabd34a513289/ros/ros_comm/roslaunch/src/roslaunch/core.py#L668-L674
|
|
tensorflow/tensorflow
|
419e3a6b650ea4bd1b0cba23c4348f8a69f3272e
|
tensorflow/python/ops/ragged/ragged_math_ops.py
|
python
|
tensor_not_equals
|
(self: ragged_tensor.RaggedOrDense,
other: ragged_tensor.RaggedOrDense)
|
Ragged version of the operation invoked by `Tensor.__ne__`.
|
Ragged version of the operation invoked by `Tensor.__ne__`.
|
[
"Ragged",
"version",
"of",
"the",
"operation",
"invoked",
"by",
"Tensor",
".",
"__ne__",
"."
] |
def tensor_not_equals(self: ragged_tensor.RaggedOrDense,
other: ragged_tensor.RaggedOrDense):
"""Ragged version of the operation invoked by `Tensor.__ne__`."""
if other is None:
return False
elif _use_legacy_mode_for_tensor_equality(self):
return self is not other
else:
try:
return math_ops.not_equal(self, other)
except (errors.InvalidArgumentError, ValueError):
return True
|
[
"def",
"tensor_not_equals",
"(",
"self",
":",
"ragged_tensor",
".",
"RaggedOrDense",
",",
"other",
":",
"ragged_tensor",
".",
"RaggedOrDense",
")",
":",
"if",
"other",
"is",
"None",
":",
"return",
"False",
"elif",
"_use_legacy_mode_for_tensor_equality",
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"self",
")",
":",
"return",
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"else",
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"try",
":",
"return",
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"self",
",",
"other",
")",
"except",
"(",
"errors",
".",
"InvalidArgumentError",
",",
"ValueError",
")",
":",
"return",
"True"
] |
https://github.com/tensorflow/tensorflow/blob/419e3a6b650ea4bd1b0cba23c4348f8a69f3272e/tensorflow/python/ops/ragged/ragged_math_ops.py#L1123-L1134
|
||
hanpfei/chromium-net
|
392cc1fa3a8f92f42e4071ab6e674d8e0482f83f
|
third_party/catapult/third_party/pipeline/pipeline/pipeline.py
|
python
|
Pipeline.__init__
|
(self, *args, **kwargs)
|
Initializer.
Args:
*args: The positional arguments for this function-object.
**kwargs: The keyword arguments for this function-object.
|
Initializer.
|
[
"Initializer",
"."
] |
def __init__(self, *args, **kwargs):
"""Initializer.
Args:
*args: The positional arguments for this function-object.
**kwargs: The keyword arguments for this function-object.
"""
self.args = args
self.kwargs = kwargs
self.outputs = None
self.backoff_seconds = _DEFAULT_BACKOFF_SECONDS
self.backoff_factor = _DEFAULT_BACKOFF_FACTOR
self.max_attempts = _DEFAULT_MAX_ATTEMPTS
self.target = None
self.task_retry = False
self._current_attempt = 0
self._root_pipeline_key = None
self._pipeline_key = None
self._context = None
self._result_status = None
self._set_class_path()
# Introspectively set the target so pipelines stick to the version it
# started.
self.target = mr_util._get_task_target()
if _TEST_MODE:
self._context = _PipelineContext('', 'default', '')
self._root_pipeline_key = _TEST_ROOT_PIPELINE_KEY
self._pipeline_key = db.Key.from_path(
_PipelineRecord.kind(), uuid.uuid4().hex)
self.outputs = PipelineFuture(self.output_names)
self._context.evaluate_test(self)
|
[
"def",
"__init__",
"(",
"self",
",",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"self",
".",
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"=",
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"kwargs",
"=",
"kwargs",
"self",
".",
"outputs",
"=",
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"backoff_seconds",
"=",
"_DEFAULT_BACKOFF_SECONDS",
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".",
"backoff_factor",
"=",
"_DEFAULT_BACKOFF_FACTOR",
"self",
".",
"max_attempts",
"=",
"_DEFAULT_MAX_ATTEMPTS",
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".",
"target",
"=",
"None",
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"task_retry",
"=",
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"self",
".",
"_current_attempt",
"=",
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"=",
"None",
"self",
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"_context",
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"_result_status",
"=",
"None",
"self",
".",
"_set_class_path",
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")",
"# Introspectively set the target so pipelines stick to the version it",
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".",
"target",
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"mr_util",
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")",
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"_TEST_MODE",
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"self",
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"_context",
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"_PipelineContext",
"(",
"''",
",",
"'default'",
",",
"''",
")",
"self",
".",
"_root_pipeline_key",
"=",
"_TEST_ROOT_PIPELINE_KEY",
"self",
".",
"_pipeline_key",
"=",
"db",
".",
"Key",
".",
"from_path",
"(",
"_PipelineRecord",
".",
"kind",
"(",
")",
",",
"uuid",
".",
"uuid4",
"(",
")",
".",
"hex",
")",
"self",
".",
"outputs",
"=",
"PipelineFuture",
"(",
"self",
".",
"output_names",
")",
"self",
".",
"_context",
".",
"evaluate_test",
"(",
"self",
")"
] |
https://github.com/hanpfei/chromium-net/blob/392cc1fa3a8f92f42e4071ab6e674d8e0482f83f/third_party/catapult/third_party/pipeline/pipeline/pipeline.py#L448-L479
|
||
ChromiumWebApps/chromium
|
c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7
|
tools/telemetry/third_party/pyserial/serial/serialutil.py
|
python
|
SerialBase.setInterCharTimeout
|
(self, interCharTimeout)
|
Change inter-character timeout setting.
|
Change inter-character timeout setting.
|
[
"Change",
"inter",
"-",
"character",
"timeout",
"setting",
"."
] |
def setInterCharTimeout(self, interCharTimeout):
"""Change inter-character timeout setting."""
if interCharTimeout is not None:
if interCharTimeout < 0: raise ValueError("Not a valid timeout: %r" % interCharTimeout)
try:
interCharTimeout + 1 # test if it's a number, will throw a TypeError if not...
except TypeError:
raise ValueError("Not a valid timeout: %r" % interCharTimeout)
self._interCharTimeout = interCharTimeout
if self._isOpen: self._reconfigurePort()
|
[
"def",
"setInterCharTimeout",
"(",
"self",
",",
"interCharTimeout",
")",
":",
"if",
"interCharTimeout",
"is",
"not",
"None",
":",
"if",
"interCharTimeout",
"<",
"0",
":",
"raise",
"ValueError",
"(",
"\"Not a valid timeout: %r\"",
"%",
"interCharTimeout",
")",
"try",
":",
"interCharTimeout",
"+",
"1",
"# test if it's a number, will throw a TypeError if not...",
"except",
"TypeError",
":",
"raise",
"ValueError",
"(",
"\"Not a valid timeout: %r\"",
"%",
"interCharTimeout",
")",
"self",
".",
"_interCharTimeout",
"=",
"interCharTimeout",
"if",
"self",
".",
"_isOpen",
":",
"self",
".",
"_reconfigurePort",
"(",
")"
] |
https://github.com/ChromiumWebApps/chromium/blob/c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7/tools/telemetry/third_party/pyserial/serial/serialutil.py#L468-L478
|
||
FreeCAD/FreeCAD
|
ba42231b9c6889b89e064d6d563448ed81e376ec
|
src/Mod/Draft/drafttaskpanels/task_circulararray.py
|
python
|
TaskPanelCircularArray.print_link_state
|
(self, use_link)
|
Print the link state translated.
|
Print the link state translated.
|
[
"Print",
"the",
"link",
"state",
"translated",
"."
] |
def print_link_state(self, use_link):
"""Print the link state translated."""
if use_link:
state = self.tr_true
else:
state = self.tr_false
_msg(translate("draft","Create Link array:") + " {}".format(state))
|
[
"def",
"print_link_state",
"(",
"self",
",",
"use_link",
")",
":",
"if",
"use_link",
":",
"state",
"=",
"self",
".",
"tr_true",
"else",
":",
"state",
"=",
"self",
".",
"tr_false",
"_msg",
"(",
"translate",
"(",
"\"draft\"",
",",
"\"Create Link array:\"",
")",
"+",
"\" {}\"",
".",
"format",
"(",
"state",
")",
")"
] |
https://github.com/FreeCAD/FreeCAD/blob/ba42231b9c6889b89e064d6d563448ed81e376ec/src/Mod/Draft/drafttaskpanels/task_circulararray.py#L359-L365
|
||
wxWidgets/wxPython-Classic
|
19571e1ae65f1ac445f5491474121998c97a1bf0
|
wx/tools/Editra/src/ed_vim.py
|
python
|
EditraCommander.WordEndBig
|
(self, repeat=1)
|
Move to end of this Word (words are separated by space)
|
Move to end of this Word (words are separated by space)
|
[
"Move",
"to",
"end",
"of",
"this",
"Word",
"(",
"words",
"are",
"separated",
"by",
"space",
")"
] |
def WordEndBig(self, repeat=1):
"""Move to end of this Word (words are separated by space)"""
# TODO:CJP Test on empty document, possible error condition
for i in range(repeat):
self.stc.WordRightEnd()
while self.GetChar() and not self.GetChar().isspace():
self.stc.WordRightEnd()
|
[
"def",
"WordEndBig",
"(",
"self",
",",
"repeat",
"=",
"1",
")",
":",
"# TODO:CJP Test on empty document, possible error condition",
"for",
"i",
"in",
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"(",
"repeat",
")",
":",
"self",
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"stc",
".",
"WordRightEnd",
"(",
")",
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"self",
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"GetChar",
"(",
")",
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"GetChar",
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")",
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"isspace",
"(",
")",
":",
"self",
".",
"stc",
".",
"WordRightEnd",
"(",
")"
] |
https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/wx/tools/Editra/src/ed_vim.py#L386-L392
|
||
benoitsteiner/tensorflow-opencl
|
cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5
|
tensorflow/python/keras/_impl/keras/preprocessing/sequence.py
|
python
|
skipgrams
|
(sequence,
vocabulary_size,
window_size=4,
negative_samples=1.,
shuffle=True,
categorical=False,
sampling_table=None,
seed=None)
|
return couples, labels
|
Generates skipgram word pairs.
Takes a sequence (list of indexes of words),
returns couples of [word_index, other_word index] and labels (1s or 0s),
where label = 1 if 'other_word' belongs to the context of 'word',
and label=0 if 'other_word' is randomly sampled
Arguments:
sequence: a word sequence (sentence), encoded as a list
of word indices (integers). If using a `sampling_table`,
word indices are expected to match the rank
of the words in a reference dataset (e.g. 10 would encode
the 10-th most frequently occurring token).
Note that index 0 is expected to be a non-word and will be skipped.
vocabulary_size: int. maximum possible word index + 1
window_size: int. actually half-window.
The window of a word wi will be [i-window_size, i+window_size+1]
negative_samples: float >= 0. 0 for no negative (=random) samples.
1 for same number as positive samples. etc.
shuffle: whether to shuffle the word couples before returning them.
categorical: bool. if False, labels will be
integers (eg. [0, 1, 1 .. ]),
if True labels will be categorical eg. [[1,0],[0,1],[0,1] .. ]
sampling_table: 1D array of size `vocabulary_size` where the entry i
encodes the probabibily to sample a word of rank i.
seed: Random seed.
Returns:
couples, labels: where `couples` are int pairs and
`labels` are either 0 or 1.
# Note
By convention, index 0 in the vocabulary is
a non-word and will be skipped.
|
Generates skipgram word pairs.
|
[
"Generates",
"skipgram",
"word",
"pairs",
"."
] |
def skipgrams(sequence,
vocabulary_size,
window_size=4,
negative_samples=1.,
shuffle=True,
categorical=False,
sampling_table=None,
seed=None):
"""Generates skipgram word pairs.
Takes a sequence (list of indexes of words),
returns couples of [word_index, other_word index] and labels (1s or 0s),
where label = 1 if 'other_word' belongs to the context of 'word',
and label=0 if 'other_word' is randomly sampled
Arguments:
sequence: a word sequence (sentence), encoded as a list
of word indices (integers). If using a `sampling_table`,
word indices are expected to match the rank
of the words in a reference dataset (e.g. 10 would encode
the 10-th most frequently occurring token).
Note that index 0 is expected to be a non-word and will be skipped.
vocabulary_size: int. maximum possible word index + 1
window_size: int. actually half-window.
The window of a word wi will be [i-window_size, i+window_size+1]
negative_samples: float >= 0. 0 for no negative (=random) samples.
1 for same number as positive samples. etc.
shuffle: whether to shuffle the word couples before returning them.
categorical: bool. if False, labels will be
integers (eg. [0, 1, 1 .. ]),
if True labels will be categorical eg. [[1,0],[0,1],[0,1] .. ]
sampling_table: 1D array of size `vocabulary_size` where the entry i
encodes the probabibily to sample a word of rank i.
seed: Random seed.
Returns:
couples, labels: where `couples` are int pairs and
`labels` are either 0 or 1.
# Note
By convention, index 0 in the vocabulary is
a non-word and will be skipped.
"""
couples = []
labels = []
for i, wi in enumerate(sequence):
if not wi:
continue
if sampling_table is not None:
if sampling_table[wi] < random.random():
continue
window_start = max(0, i - window_size)
window_end = min(len(sequence), i + window_size + 1)
for j in range(window_start, window_end):
if j != i:
wj = sequence[j]
if not wj:
continue
couples.append([wi, wj])
if categorical:
labels.append([0, 1])
else:
labels.append(1)
if negative_samples > 0:
num_negative_samples = int(len(labels) * negative_samples)
words = [c[0] for c in couples]
random.shuffle(words)
couples += [[words[i % len(words)],
random.randint(1, vocabulary_size - 1)]
for i in range(num_negative_samples)]
if categorical:
labels += [[1, 0]] * num_negative_samples
else:
labels += [0] * num_negative_samples
if shuffle:
if seed is None:
seed = random.randint(0, 10e6)
random.seed(seed)
random.shuffle(couples)
random.seed(seed)
random.shuffle(labels)
return couples, labels
|
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https://github.com/benoitsteiner/tensorflow-opencl/blob/cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5/tensorflow/python/keras/_impl/keras/preprocessing/sequence.py#L140-L226
|
|
NVIDIA/MDL-SDK
|
aa9642b2546ad7b6236b5627385d882c2ed83c5d
|
src/mdl/jit/llvm/dist/examples/Kaleidoscope/MCJIT/cached/genk-timing.py
|
python
|
TimingScriptGenerator.writeTimingCall
|
(self, filename, numFuncs, funcsCalled, totalCalls)
|
Echo some comments and invoke both versions of toy
|
Echo some comments and invoke both versions of toy
|
[
"Echo",
"some",
"comments",
"and",
"invoke",
"both",
"versions",
"of",
"toy"
] |
def writeTimingCall(self, filename, numFuncs, funcsCalled, totalCalls):
"""Echo some comments and invoke both versions of toy"""
rootname = filename
if '.' in filename:
rootname = filename[:filename.rfind('.')]
self.shfile.write("echo \"%s: Calls %d of %d functions, %d total\" >> %s\n" % (filename, funcsCalled, numFuncs, totalCalls, self.timeFile))
self.shfile.write("echo \"\" >> %s\n" % self.timeFile)
self.shfile.write("echo \"With MCJIT\" >> %s\n" % self.timeFile)
self.shfile.write("/usr/bin/time -f \"Command %C\\n\\tuser time: %U s\\n\\tsytem time: %S s\\n\\tmax set: %M kb\"")
self.shfile.write(" -o %s -a " % self.timeFile)
self.shfile.write("./toy-mcjit < %s > %s-mcjit.out 2> %s-mcjit.err\n" % (filename, rootname, rootname))
self.shfile.write("echo \"\" >> %s\n" % self.timeFile)
self.shfile.write("echo \"With JIT\" >> %s\n" % self.timeFile)
self.shfile.write("/usr/bin/time -f \"Command %C\\n\\tuser time: %U s\\n\\tsytem time: %S s\\n\\tmax set: %M kb\"")
self.shfile.write(" -o %s -a " % self.timeFile)
self.shfile.write("./toy-jit < %s > %s-jit.out 2> %s-jit.err\n" % (filename, rootname, rootname))
self.shfile.write("echo \"\" >> %s\n" % self.timeFile)
self.shfile.write("echo \"\" >> %s\n" % self.timeFile)
|
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] |
https://github.com/NVIDIA/MDL-SDK/blob/aa9642b2546ad7b6236b5627385d882c2ed83c5d/src/mdl/jit/llvm/dist/examples/Kaleidoscope/MCJIT/cached/genk-timing.py#L13-L30
|
||
bcrusco/Forward-Plus-Renderer
|
1f130f1ae58882f651d94695823044f9833cfa30
|
Forward-Plus/Forward-Plus/external/assimp-3.1.1/port/PyAssimp/pyassimp/core.py
|
python
|
_get_properties
|
(properties, length)
|
return PropertyGetter(result)
|
Convenience Function to get the material properties as a dict
and values in a python format.
|
Convenience Function to get the material properties as a dict
and values in a python format.
|
[
"Convenience",
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"material",
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"as",
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"dict",
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"python",
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"."
] |
def _get_properties(properties, length):
"""
Convenience Function to get the material properties as a dict
and values in a python format.
"""
result = {}
#read all properties
for p in [properties[i] for i in range(length)]:
#the name
p = p.contents
key = (str(p.mKey.data.decode("utf-8")).split('.')[1], p.mSemantic)
#the data
from ctypes import POINTER, cast, c_int, c_float, sizeof
if p.mType == 1:
arr = cast(p.mData, POINTER(c_float * int(p.mDataLength/sizeof(c_float)) )).contents
value = [x for x in arr]
elif p.mType == 3: #string can't be an array
value = cast(p.mData, POINTER(structs.MaterialPropertyString)).contents.data.decode("utf-8")
elif p.mType == 4:
arr = cast(p.mData, POINTER(c_int * int(p.mDataLength/sizeof(c_int)) )).contents
value = [x for x in arr]
else:
value = p.mData[:p.mDataLength]
if len(value) == 1:
[value] = value
result[key] = value
return PropertyGetter(result)
|
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https://github.com/bcrusco/Forward-Plus-Renderer/blob/1f130f1ae58882f651d94695823044f9833cfa30/Forward-Plus/Forward-Plus/external/assimp-3.1.1/port/PyAssimp/pyassimp/core.py#L371-L401
|
|
benoitsteiner/tensorflow-opencl
|
cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5
|
tensorflow/tools/api/lib/python_object_to_proto_visitor.py
|
python
|
PythonObjectToProtoVisitor.GetProtos
|
(self)
|
return self._protos
|
Return the list of protos stored.
|
Return the list of protos stored.
|
[
"Return",
"the",
"list",
"of",
"protos",
"stored",
"."
] |
def GetProtos(self):
"""Return the list of protos stored."""
return self._protos
|
[
"def",
"GetProtos",
"(",
"self",
")",
":",
"return",
"self",
".",
"_protos"
] |
https://github.com/benoitsteiner/tensorflow-opencl/blob/cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5/tensorflow/tools/api/lib/python_object_to_proto_visitor.py#L112-L114
|
|
tangzhenyu/Scene-Text-Understanding
|
0f7ffc7aea5971a50cdc03d33d0a41075285948b
|
ctpn_crnn_ocr/CTPN/caffe/tools/extra/parse_log.py
|
python
|
parse_log
|
(path_to_log)
|
return train_dict_list, test_dict_list
|
Parse log file
Returns (train_dict_list, train_dict_names, test_dict_list, test_dict_names)
train_dict_list and test_dict_list are lists of dicts that define the table
rows
train_dict_names and test_dict_names are ordered tuples of the column names
for the two dict_lists
|
Parse log file
Returns (train_dict_list, train_dict_names, test_dict_list, test_dict_names)
|
[
"Parse",
"log",
"file",
"Returns",
"(",
"train_dict_list",
"train_dict_names",
"test_dict_list",
"test_dict_names",
")"
] |
def parse_log(path_to_log):
"""Parse log file
Returns (train_dict_list, train_dict_names, test_dict_list, test_dict_names)
train_dict_list and test_dict_list are lists of dicts that define the table
rows
train_dict_names and test_dict_names are ordered tuples of the column names
for the two dict_lists
"""
regex_iteration = re.compile('Iteration (\d+)')
regex_train_output = re.compile('Train net output #(\d+): (\S+) = ([\.\deE+-]+)')
regex_test_output = re.compile('Test net output #(\d+): (\S+) = ([\.\deE+-]+)')
regex_learning_rate = re.compile('lr = ([-+]?[0-9]*\.?[0-9]+([eE]?[-+]?[0-9]+)?)')
# Pick out lines of interest
iteration = -1
learning_rate = float('NaN')
train_dict_list = []
test_dict_list = []
train_row = None
test_row = None
logfile_year = extract_seconds.get_log_created_year(path_to_log)
with open(path_to_log) as f:
start_time = extract_seconds.get_start_time(f, logfile_year)
for line in f:
iteration_match = regex_iteration.search(line)
if iteration_match:
iteration = float(iteration_match.group(1))
if iteration == -1:
# Only start parsing for other stuff if we've found the first
# iteration
continue
time = extract_seconds.extract_datetime_from_line(line,
logfile_year)
seconds = (time - start_time).total_seconds()
learning_rate_match = regex_learning_rate.search(line)
if learning_rate_match:
learning_rate = float(learning_rate_match.group(1))
train_dict_list, train_row = parse_line_for_net_output(
regex_train_output, train_row, train_dict_list,
line, iteration, seconds, learning_rate
)
test_dict_list, test_row = parse_line_for_net_output(
regex_test_output, test_row, test_dict_list,
line, iteration, seconds, learning_rate
)
fix_initial_nan_learning_rate(train_dict_list)
fix_initial_nan_learning_rate(test_dict_list)
return train_dict_list, test_dict_list
|
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https://github.com/tangzhenyu/Scene-Text-Understanding/blob/0f7ffc7aea5971a50cdc03d33d0a41075285948b/ctpn_crnn_ocr/CTPN/caffe/tools/extra/parse_log.py#L17-L74
|
|
microsoft/checkedc-clang
|
a173fefde5d7877b7750e7ce96dd08cf18baebf2
|
clang/bindings/python/clang/cindex.py
|
python
|
Cursor.location
|
(self)
|
return self._loc
|
Return the source location (the starting character) of the entity
pointed at by the cursor.
|
Return the source location (the starting character) of the entity
pointed at by the cursor.
|
[
"Return",
"the",
"source",
"location",
"(",
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"character",
")",
"of",
"the",
"entity",
"pointed",
"at",
"by",
"the",
"cursor",
"."
] |
def location(self):
"""
Return the source location (the starting character) of the entity
pointed at by the cursor.
"""
if not hasattr(self, '_loc'):
self._loc = conf.lib.clang_getCursorLocation(self)
return self._loc
|
[
"def",
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https://github.com/microsoft/checkedc-clang/blob/a173fefde5d7877b7750e7ce96dd08cf18baebf2/clang/bindings/python/clang/cindex.py#L1574-L1582
|
|
Xilinx/Vitis-AI
|
fc74d404563d9951b57245443c73bef389f3657f
|
tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/data/experimental/ops/stats_ops.py
|
python
|
latency_stats
|
(tag)
|
return _apply_fn
|
Records the latency of producing each element of the input dataset.
To consume the statistics, associate a `StatsAggregator` with the output
dataset.
Args:
tag: String. All statistics recorded by the returned transformation will
be associated with the given `tag`.
Returns:
A `Dataset` transformation function, which can be passed to
`tf.data.Dataset.apply`.
|
Records the latency of producing each element of the input dataset.
|
[
"Records",
"the",
"latency",
"of",
"producing",
"each",
"element",
"of",
"the",
"input",
"dataset",
"."
] |
def latency_stats(tag):
"""Records the latency of producing each element of the input dataset.
To consume the statistics, associate a `StatsAggregator` with the output
dataset.
Args:
tag: String. All statistics recorded by the returned transformation will
be associated with the given `tag`.
Returns:
A `Dataset` transformation function, which can be passed to
`tf.data.Dataset.apply`.
"""
def _apply_fn(dataset):
return _StatsDataset(
dataset, gen_experimental_dataset_ops.latency_stats_dataset, tag)
return _apply_fn
|
[
"def",
"latency_stats",
"(",
"tag",
")",
":",
"def",
"_apply_fn",
"(",
"dataset",
")",
":",
"return",
"_StatsDataset",
"(",
"dataset",
",",
"gen_experimental_dataset_ops",
".",
"latency_stats_dataset",
",",
"tag",
")",
"return",
"_apply_fn"
] |
https://github.com/Xilinx/Vitis-AI/blob/fc74d404563d9951b57245443c73bef389f3657f/tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/data/experimental/ops/stats_ops.py#L75-L94
|
|
perilouswithadollarsign/cstrike15_src
|
f82112a2388b841d72cb62ca48ab1846dfcc11c8
|
thirdparty/protobuf-2.5.0/python/mox.py
|
python
|
UnknownMethodCallError.__init__
|
(self, unknown_method_name)
|
Init exception.
Args:
# unknown_method_name: Method call that is not part of the mocked class's
# public interface.
unknown_method_name: str
|
Init exception.
|
[
"Init",
"exception",
"."
] |
def __init__(self, unknown_method_name):
"""Init exception.
Args:
# unknown_method_name: Method call that is not part of the mocked class's
# public interface.
unknown_method_name: str
"""
Error.__init__(self)
self._unknown_method_name = unknown_method_name
|
[
"def",
"__init__",
"(",
"self",
",",
"unknown_method_name",
")",
":",
"Error",
".",
"__init__",
"(",
"self",
")",
"self",
".",
"_unknown_method_name",
"=",
"unknown_method_name"
] |
https://github.com/perilouswithadollarsign/cstrike15_src/blob/f82112a2388b841d72cb62ca48ab1846dfcc11c8/thirdparty/protobuf-2.5.0/python/mox.py#L133-L143
|
||
benoitsteiner/tensorflow-opencl
|
cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5
|
tensorflow/python/keras/_impl/keras/backend.py
|
python
|
conv1d
|
(x,
kernel,
strides=1,
padding='valid',
data_format=None,
dilation_rate=1)
|
return x
|
1D convolution.
Arguments:
x: Tensor or variable.
kernel: kernel tensor.
strides: stride integer.
padding: string, `"same"`, `"causal"` or `"valid"`.
data_format: string, one of "channels_last", "channels_first".
dilation_rate: integer dilate rate.
Returns:
A tensor, result of 1D convolution.
|
1D convolution.
|
[
"1D",
"convolution",
"."
] |
def conv1d(x,
kernel,
strides=1,
padding='valid',
data_format=None,
dilation_rate=1):
"""1D convolution.
Arguments:
x: Tensor or variable.
kernel: kernel tensor.
strides: stride integer.
padding: string, `"same"`, `"causal"` or `"valid"`.
data_format: string, one of "channels_last", "channels_first".
dilation_rate: integer dilate rate.
Returns:
A tensor, result of 1D convolution.
"""
kernel_shape = kernel.get_shape().as_list()
if padding == 'causal':
# causal (dilated) convolution:
left_pad = dilation_rate * (kernel_shape[0] - 1)
x = temporal_padding(x, (left_pad, 0))
padding = 'valid'
padding = _preprocess_padding(padding)
if data_format == 'channels_last':
tf_data_format = 'NWC'
else:
tf_data_format = 'NCW'
x = nn.convolution(
input=x,
filter=kernel,
dilation_rate=(dilation_rate,),
strides=(strides,),
padding=padding,
data_format=tf_data_format)
return x
|
[
"def",
"conv1d",
"(",
"x",
",",
"kernel",
",",
"strides",
"=",
"1",
",",
"padding",
"=",
"'valid'",
",",
"data_format",
"=",
"None",
",",
"dilation_rate",
"=",
"1",
")",
":",
"kernel_shape",
"=",
"kernel",
".",
"get_shape",
"(",
")",
".",
"as_list",
"(",
")",
"if",
"padding",
"==",
"'causal'",
":",
"# causal (dilated) convolution:",
"left_pad",
"=",
"dilation_rate",
"*",
"(",
"kernel_shape",
"[",
"0",
"]",
"-",
"1",
")",
"x",
"=",
"temporal_padding",
"(",
"x",
",",
"(",
"left_pad",
",",
"0",
")",
")",
"padding",
"=",
"'valid'",
"padding",
"=",
"_preprocess_padding",
"(",
"padding",
")",
"if",
"data_format",
"==",
"'channels_last'",
":",
"tf_data_format",
"=",
"'NWC'",
"else",
":",
"tf_data_format",
"=",
"'NCW'",
"x",
"=",
"nn",
".",
"convolution",
"(",
"input",
"=",
"x",
",",
"filter",
"=",
"kernel",
",",
"dilation_rate",
"=",
"(",
"dilation_rate",
",",
")",
",",
"strides",
"=",
"(",
"strides",
",",
")",
",",
"padding",
"=",
"padding",
",",
"data_format",
"=",
"tf_data_format",
")",
"return",
"x"
] |
https://github.com/benoitsteiner/tensorflow-opencl/blob/cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5/tensorflow/python/keras/_impl/keras/backend.py#L3246-L3283
|
|
JumpingYang001/webrtc
|
c03d6e965e1f54aeadd670e491eabe5fdb8db968
|
tools_webrtc/version_updater/update_version.py
|
python
|
_UploadCL
|
(commit_queue_mode)
|
Upload the committed changes as a changelist to Gerrit.
commit_queue_mode:
- 2: Submit to commit queue.
- 1: Run trybots but do not submit to CQ.
- 0: Skip CQ, upload only.
|
Upload the committed changes as a changelist to Gerrit.
|
[
"Upload",
"the",
"committed",
"changes",
"as",
"a",
"changelist",
"to",
"Gerrit",
"."
] |
def _UploadCL(commit_queue_mode):
"""Upload the committed changes as a changelist to Gerrit.
commit_queue_mode:
- 2: Submit to commit queue.
- 1: Run trybots but do not submit to CQ.
- 0: Skip CQ, upload only.
"""
cmd = ['git', 'cl', 'upload', '--force', '--bypass-hooks',
'--cc=""', '--bypass-watchlist']
if commit_queue_mode >= 2:
logging.info('Sending the CL to the CQ...')
cmd.extend(['--use-commit-queue'])
elif commit_queue_mode >= 1:
logging.info('Starting CQ dry run...')
cmd.extend(['--cq-dry-run'])
subprocess.check_call(cmd)
|
[
"def",
"_UploadCL",
"(",
"commit_queue_mode",
")",
":",
"cmd",
"=",
"[",
"'git'",
",",
"'cl'",
",",
"'upload'",
",",
"'--force'",
",",
"'--bypass-hooks'",
",",
"'--cc=\"\"'",
",",
"'--bypass-watchlist'",
"]",
"if",
"commit_queue_mode",
">=",
"2",
":",
"logging",
".",
"info",
"(",
"'Sending the CL to the CQ...'",
")",
"cmd",
".",
"extend",
"(",
"[",
"'--use-commit-queue'",
"]",
")",
"elif",
"commit_queue_mode",
">=",
"1",
":",
"logging",
".",
"info",
"(",
"'Starting CQ dry run...'",
")",
"cmd",
".",
"extend",
"(",
"[",
"'--cq-dry-run'",
"]",
")",
"subprocess",
".",
"check_call",
"(",
"cmd",
")"
] |
https://github.com/JumpingYang001/webrtc/blob/c03d6e965e1f54aeadd670e491eabe5fdb8db968/tools_webrtc/version_updater/update_version.py#L122-L138
|
||
hanpfei/chromium-net
|
392cc1fa3a8f92f42e4071ab6e674d8e0482f83f
|
third_party/catapult/telemetry/third_party/pyfakefs/pyfakefs/fake_tempfile.py
|
python
|
FakeTempfileModule.mkstemp
|
(self, suffix='', prefix=None, dir=None, text=False)
|
return (fd, filename)
|
Create temp file, returning a 2-tuple: (9999, filename).
Important: Returns 9999 instead of a real file descriptor!
Python 2.4.1 tempfile.mkstemp.__doc__ =
>mkstemp([suffix, [prefix, [dir, [text]]]])
>
>User-callable function to create and return a unique temporary file.
>The return value is a pair (fd, name) where fd is the file descriptor
>returned by os.open, and name is the filename.
>
>...[snip args]...
>
>The file is readable and writable only by the creating user ID.
>If the operating system uses permission bits to indicate whether
>a file is executable, the file is executable by no one. The file
>descriptor is not inherited by children of this process.
>
>Caller is responsible for deleting the file when done with it.
NOTE: if dir is unspecified, this call creates a directory.
Output: self.tempdir is initialized if unset
Args:
suffix: optional string, filename suffix
prefix: optional string, filename prefix
dir: optional string, directory for temp file; must exist before call
text: optional boolean, True = open file in text mode.
default False = open file in binary mode.
Returns:
2-tuple containing
[0] = int, file descriptor number for the file object
[1] = string, absolute pathname of a file
Raises:
OSError: when dir= is specified but does not exist
|
Create temp file, returning a 2-tuple: (9999, filename).
|
[
"Create",
"temp",
"file",
"returning",
"a",
"2",
"-",
"tuple",
":",
"(",
"9999",
"filename",
")",
"."
] |
def mkstemp(self, suffix='', prefix=None, dir=None, text=False):
"""Create temp file, returning a 2-tuple: (9999, filename).
Important: Returns 9999 instead of a real file descriptor!
Python 2.4.1 tempfile.mkstemp.__doc__ =
>mkstemp([suffix, [prefix, [dir, [text]]]])
>
>User-callable function to create and return a unique temporary file.
>The return value is a pair (fd, name) where fd is the file descriptor
>returned by os.open, and name is the filename.
>
>...[snip args]...
>
>The file is readable and writable only by the creating user ID.
>If the operating system uses permission bits to indicate whether
>a file is executable, the file is executable by no one. The file
>descriptor is not inherited by children of this process.
>
>Caller is responsible for deleting the file when done with it.
NOTE: if dir is unspecified, this call creates a directory.
Output: self.tempdir is initialized if unset
Args:
suffix: optional string, filename suffix
prefix: optional string, filename prefix
dir: optional string, directory for temp file; must exist before call
text: optional boolean, True = open file in text mode.
default False = open file in binary mode.
Returns:
2-tuple containing
[0] = int, file descriptor number for the file object
[1] = string, absolute pathname of a file
Raises:
OSError: when dir= is specified but does not exist
"""
# pylint: disable-msg=C6002
# TODO: optional boolean text is unused?
# default dir affected by "global"
filename = self._TempEntryname(suffix, prefix, dir)
fh = self._filesystem.CreateFile(filename, st_mode=stat.S_IFREG|0o600)
fd = self._filesystem.AddOpenFile(fh)
self._mktemp_retvals.append(filename)
return (fd, filename)
|
[
"def",
"mkstemp",
"(",
"self",
",",
"suffix",
"=",
"''",
",",
"prefix",
"=",
"None",
",",
"dir",
"=",
"None",
",",
"text",
"=",
"False",
")",
":",
"# pylint: disable-msg=C6002",
"# TODO: optional boolean text is unused?",
"# default dir affected by \"global\"",
"filename",
"=",
"self",
".",
"_TempEntryname",
"(",
"suffix",
",",
"prefix",
",",
"dir",
")",
"fh",
"=",
"self",
".",
"_filesystem",
".",
"CreateFile",
"(",
"filename",
",",
"st_mode",
"=",
"stat",
".",
"S_IFREG",
"|",
"0o600",
")",
"fd",
"=",
"self",
".",
"_filesystem",
".",
"AddOpenFile",
"(",
"fh",
")",
"self",
".",
"_mktemp_retvals",
".",
"append",
"(",
"filename",
")",
"return",
"(",
"fd",
",",
"filename",
")"
] |
https://github.com/hanpfei/chromium-net/blob/392cc1fa3a8f92f42e4071ab6e674d8e0482f83f/third_party/catapult/telemetry/third_party/pyfakefs/pyfakefs/fake_tempfile.py#L152-L197
|
|
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/asyncio/events.py
|
python
|
AbstractEventLoop.start_tls
|
(self, transport, protocol, sslcontext, *,
server_side=False,
server_hostname=None,
ssl_handshake_timeout=None)
|
Upgrade a transport to TLS.
Return a new transport that *protocol* should start using
immediately.
|
Upgrade a transport to TLS.
|
[
"Upgrade",
"a",
"transport",
"to",
"TLS",
"."
] |
async def start_tls(self, transport, protocol, sslcontext, *,
server_side=False,
server_hostname=None,
ssl_handshake_timeout=None):
"""Upgrade a transport to TLS.
Return a new transport that *protocol* should start using
immediately.
"""
raise NotImplementedError
|
[
"async",
"def",
"start_tls",
"(",
"self",
",",
"transport",
",",
"protocol",
",",
"sslcontext",
",",
"*",
",",
"server_side",
"=",
"False",
",",
"server_hostname",
"=",
"None",
",",
"ssl_handshake_timeout",
"=",
"None",
")",
":",
"raise",
"NotImplementedError"
] |
https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/asyncio/events.py#L372-L381
|
||
psi4/psi4
|
be533f7f426b6ccc263904e55122899b16663395
|
psi4/driver/procrouting/proc.py
|
python
|
select_mp2p5_gradient
|
(name, **kwargs)
|
Function selecting the algorithm for a MP2.5 gradient call
and directing to specified or best-performance default modules.
|
Function selecting the algorithm for a MP2.5 gradient call
and directing to specified or best-performance default modules.
|
[
"Function",
"selecting",
"the",
"algorithm",
"for",
"a",
"MP2",
".",
"5",
"gradient",
"call",
"and",
"directing",
"to",
"specified",
"or",
"best",
"-",
"performance",
"default",
"modules",
"."
] |
def select_mp2p5_gradient(name, **kwargs):
"""Function selecting the algorithm for a MP2.5 gradient call
and directing to specified or best-performance default modules.
"""
reference = core.get_option('SCF', 'REFERENCE')
mtd_type = core.get_global_option('MP_TYPE') if core.has_global_option_changed("MP_TYPE") else "DF"
module = core.get_global_option('QC_MODULE')
all_electron = (core.get_global_option('FREEZE_CORE') == "FALSE")
# Considering only [df]occ
func = None
if reference in ['RHF', 'UHF']:
if mtd_type == 'CONV':
if all_electron:
if module in ['', 'OCC']:
func = run_occ_gradient
elif mtd_type == 'DF':
if module in ['', 'OCC']:
func = run_dfocc_gradient
if func is None:
raise ManagedMethodError(['select_mp2p5_gradient', name, 'MP_TYPE', mtd_type, reference, module, all_electron])
if kwargs.pop('probe', False):
return
else:
return func(name, **kwargs)
|
[
"def",
"select_mp2p5_gradient",
"(",
"name",
",",
"*",
"*",
"kwargs",
")",
":",
"reference",
"=",
"core",
".",
"get_option",
"(",
"'SCF'",
",",
"'REFERENCE'",
")",
"mtd_type",
"=",
"core",
".",
"get_global_option",
"(",
"'MP_TYPE'",
")",
"if",
"core",
".",
"has_global_option_changed",
"(",
"\"MP_TYPE\"",
")",
"else",
"\"DF\"",
"module",
"=",
"core",
".",
"get_global_option",
"(",
"'QC_MODULE'",
")",
"all_electron",
"=",
"(",
"core",
".",
"get_global_option",
"(",
"'FREEZE_CORE'",
")",
"==",
"\"FALSE\"",
")",
"# Considering only [df]occ",
"func",
"=",
"None",
"if",
"reference",
"in",
"[",
"'RHF'",
",",
"'UHF'",
"]",
":",
"if",
"mtd_type",
"==",
"'CONV'",
":",
"if",
"all_electron",
":",
"if",
"module",
"in",
"[",
"''",
",",
"'OCC'",
"]",
":",
"func",
"=",
"run_occ_gradient",
"elif",
"mtd_type",
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":",
"if",
"module",
"in",
"[",
"''",
",",
"'OCC'",
"]",
":",
"func",
"=",
"run_dfocc_gradient",
"if",
"func",
"is",
"None",
":",
"raise",
"ManagedMethodError",
"(",
"[",
"'select_mp2p5_gradient'",
",",
"name",
",",
"'MP_TYPE'",
",",
"mtd_type",
",",
"reference",
",",
"module",
",",
"all_electron",
"]",
")",
"if",
"kwargs",
".",
"pop",
"(",
"'probe'",
",",
"False",
")",
":",
"return",
"else",
":",
"return",
"func",
"(",
"name",
",",
"*",
"*",
"kwargs",
")"
] |
https://github.com/psi4/psi4/blob/be533f7f426b6ccc263904e55122899b16663395/psi4/driver/procrouting/proc.py#L550-L577
|
||
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/llvmlite/binding/ffi.py
|
python
|
ret_string
|
(ptr)
|
To wrap string return-value from C-API.
|
To wrap string return-value from C-API.
|
[
"To",
"wrap",
"string",
"return",
"-",
"value",
"from",
"C",
"-",
"API",
"."
] |
def ret_string(ptr):
"""To wrap string return-value from C-API.
"""
if ptr is not None:
return str(OutputString.from_return(ptr))
|
[
"def",
"ret_string",
"(",
"ptr",
")",
":",
"if",
"ptr",
"is",
"not",
"None",
":",
"return",
"str",
"(",
"OutputString",
".",
"from_return",
"(",
"ptr",
")",
")"
] |
https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/llvmlite/binding/ffi.py#L228-L232
|
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