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apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | deps/src/libxml2-2.9.1/python/libxml2.py | python | catalog.add | (self, type, orig, replace) | return ret | Add an entry in the catalog, it may overwrite existing but
different entries. | Add an entry in the catalog, it may overwrite existing but
different entries. | [
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] | def add(self, type, orig, replace):
"""Add an entry in the catalog, it may overwrite existing but
different entries. """
ret = libxml2mod.xmlACatalogAdd(self._o, type, orig, replace)
return ret | [
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|
NREL/EnergyPlus | fadc5973b85c70e8cc923efb69c144e808a26078 | src/EnergyPlus/api/datatransfer.py | python | DataExchange.set_global_value | (self, state: c_void_p, handle: int, value: float) | Set the current value of a plugin global variable in a running simulation. This is only used for Python Plugin
applications!
Global variables are used as a way to share data between running Python Plugins. First a global variable must
be declared in the input file using the PythonPlugin:GlobalVariables object. Once a name has been declared, it
can be accessed in the Plugin by getting a handle to the variable using the get_global_handle function, then
using the get_global_value and this set_global_value functions as needed. Note all global variables are
floating point values.
:param state: An active EnergyPlus "state" that is returned from a call to `api.state_manager.new_state()`.
:param handle: An integer returned from the `get_global_handle` function.
:param value: Floating point value to assign to the global variable | Set the current value of a plugin global variable in a running simulation. This is only used for Python Plugin
applications! | [
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"""
Set the current value of a plugin global variable in a running simulation. This is only used for Python Plugin
applications!
Global variables are used as a way to share data between running Python Plugins. First a global variable must
be declared in the input file using the PythonPlugin:GlobalVariables object. Once a name has been declared, it
can be accessed in the Plugin by getting a handle to the variable using the get_global_handle function, then
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:param state: An active EnergyPlus "state" that is returned from a call to `api.state_manager.new_state()`.
:param handle: An integer returned from the `get_global_handle` function.
:param value: Floating point value to assign to the global variable
"""
if not self.running_as_python_plugin:
raise EnergyPlusException("set_global_handle is only available as part of a Python Plugin workflow")
if not is_number(handle):
raise EnergyPlusException(
"`set_global_value` expects `variable_handle` as an `int`, not "
"'{}'".format(handle))
if not is_number(value):
raise EnergyPlusException(
"`get_global_value` expects `value` as a `float`, not "
"'{}'".format(value))
self.api.setPluginGlobalVariableValue(state, handle, value) | [
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||
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_windows.py | python | StatusBar.SetStatusStyles | (*args, **kwargs) | return _windows_.StatusBar_SetStatusStyles(*args, **kwargs) | SetStatusStyles(self, int styles) | SetStatusStyles(self, int styles) | [
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"""SetStatusStyles(self, int styles)"""
return _windows_.StatusBar_SetStatusStyles(*args, **kwargs) | [
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|
facebook/wangle | 2e7e3fbb3a15c4986d6fe0e36c31daeeba614ce3 | build/fbcode_builder/getdeps/cargo.py | python | CargoBuilder._resolve_config | (self) | return "\n".join(config) | Returns a configuration to be put inside root Cargo.toml file which
patches the dependencies git code with local getdeps versions.
See https://doc.rust-lang.org/cargo/reference/manifest.html#the-patch-section | Returns a configuration to be put inside root Cargo.toml file which
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"""
Returns a configuration to be put inside root Cargo.toml file which
patches the dependencies git code with local getdeps versions.
See https://doc.rust-lang.org/cargo/reference/manifest.html#the-patch-section
"""
dep_to_git = self._resolve_dep_to_git()
dep_to_crates = CargoBuilder._resolve_dep_to_crates(
self.build_source_dir(), dep_to_git
)
config = []
for name in sorted(dep_to_git.keys()):
git_conf = dep_to_git[name]
crates = sorted(dep_to_crates.get(name, []))
if not crates:
continue # nothing to patch, move along
crates_patches = [
'{} = {{ path = "{}" }}'.format(
crate,
CargoBuilder._resolve_crate_to_path(crate, git_conf).replace(
"\\", "\\\\"
),
)
for crate in crates
]
config.append(
'[patch."{0}"]\n'.format(git_conf["repo_url"])
+ "\n".join(crates_patches)
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|
oracle/graaljs | 36a56e8e993d45fc40939a3a4d9c0c24990720f1 | graal-nodejs/tools/configure.d/nodedownload.py | python | unpack | (packedfile, parent_path) | Unpacks packedfile into parent_path. Assumes .zip. Returns parent_path | Unpacks packedfile into parent_path. Assumes .zip. Returns parent_path | [
"Unpacks",
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"into",
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".",
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".",
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"Returns",
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] | def unpack(packedfile, parent_path):
"""Unpacks packedfile into parent_path. Assumes .zip. Returns parent_path"""
if zipfile.is_zipfile(packedfile):
with contextlib.closing(zipfile.ZipFile(packedfile, 'r')) as icuzip:
print(' Extracting zipfile: %s' % packedfile)
icuzip.extractall(parent_path)
return parent_path
elif tarfile.is_tarfile(packedfile):
with contextlib.closing(tarfile.TarFile.open(packedfile, 'r')) as icuzip:
print(' Extracting tarfile: %s' % packedfile)
icuzip.extractall(parent_path)
return parent_path
else:
packedsuffix = packedfile.lower().split('.')[-1] # .zip, .tgz etc
raise Exception('Error: Don\'t know how to unpack %s with extension %s' % (packedfile, packedsuffix)) | [
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||
thalium/icebox | 99d147d5b9269222225443ce171b4fd46d8985d4 | third_party/retdec-3.2/scripts/type_extractor/type_extractor/json_types.py | python | get_array_dimensions | (dimensions) | return [int(d) if d.isdigit() else d for d in dimensions] | Returns list of all dimensions. | Returns list of all dimensions. | [
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"dimensions",
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] | def get_array_dimensions(dimensions):
"""Returns list of all dimensions."""
dimensions = re.sub(r'^\[|\]$', '', dimensions).split('][')
return [int(d) if d.isdigit() else d for d in dimensions] | [
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|
wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/sets.py | python | BaseSet.__or__ | (self, other) | return self.union(other) | Return the union of two sets as a new set.
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"""Return the union of two sets as a new set.
(I.e. all elements that are in either set.)
"""
if not isinstance(other, BaseSet):
return NotImplemented
return self.union(other) | [
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|
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/aui.py | python | AuiPaneInfo.Floatable | (*args, **kwargs) | return _aui.AuiPaneInfo_Floatable(*args, **kwargs) | Floatable(self, bool b=True) -> AuiPaneInfo | Floatable(self, bool b=True) -> AuiPaneInfo | [
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"""Floatable(self, bool b=True) -> AuiPaneInfo"""
return _aui.AuiPaneInfo_Floatable(*args, **kwargs) | [
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|
coinapi/coinapi-sdk | 854f21e7f69ea8599ae35c5403565cf299d8b795 | oeml-sdk/python/openapi_client/model/order_execution_reports.py | python | OrderExecutionReports.__init__ | (self, *args, **kwargs) | OrderExecutionReports - a model defined in OpenAPI
Note that value can be passed either in args or in kwargs, but not in both.
Args:
args[0] ([OrderExecutionReport]): Collection of order execution reports for currently opened orders.. # noqa: E501
Keyword Args:
value ([OrderExecutionReport]): Collection of order execution reports for currently opened orders.. # noqa: E501
_check_type (bool): if True, values for parameters in openapi_types
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raised if the wrong type is input.
Defaults to True
_path_to_item (tuple/list): This is a list of keys or values to
drill down to the model in received_data
when deserializing a response
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_configuration (Configuration): the instance to use when
deserializing a file_type parameter.
If passed, type conversion is attempted
If omitted no type conversion is done.
_visited_composed_classes (tuple): This stores a tuple of
classes that we have traveled through so that
if we see that class again we will not use its
discriminator again.
When traveling through a discriminator, the
composed schema that is
is traveled through is added to this set.
For example if Animal has a discriminator
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once using the discriminator, and pick Dog.
Then in Dog, we will make an instance of the
Animal class but this time we won't travel
through its discriminator because we passed in
_visited_composed_classes = (Animal,) | OrderExecutionReports - a model defined in OpenAPI | [
"OrderExecutionReports",
"-",
"a",
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"OpenAPI"
] | def __init__(self, *args, **kwargs):
"""OrderExecutionReports - a model defined in OpenAPI
Note that value can be passed either in args or in kwargs, but not in both.
Args:
args[0] ([OrderExecutionReport]): Collection of order execution reports for currently opened orders.. # noqa: E501
Keyword Args:
value ([OrderExecutionReport]): Collection of order execution reports for currently opened orders.. # noqa: E501
_check_type (bool): if True, values for parameters in openapi_types
will be type checked and a TypeError will be
raised if the wrong type is input.
Defaults to True
_path_to_item (tuple/list): This is a list of keys or values to
drill down to the model in received_data
when deserializing a response
_spec_property_naming (bool): True if the variable names in the input data
are serialized names, as specified in the OpenAPI document.
False if the variable names in the input data
are pythonic names, e.g. snake case (default)
_configuration (Configuration): the instance to use when
deserializing a file_type parameter.
If passed, type conversion is attempted
If omitted no type conversion is done.
_visited_composed_classes (tuple): This stores a tuple of
classes that we have traveled through so that
if we see that class again we will not use its
discriminator again.
When traveling through a discriminator, the
composed schema that is
is traveled through is added to this set.
For example if Animal has a discriminator
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through its discriminator because we passed in
_visited_composed_classes = (Animal,)
"""
# required up here when default value is not given
_path_to_item = kwargs.pop('_path_to_item', ())
if 'value' in kwargs:
value = kwargs.pop('value')
elif args:
args = list(args)
value = args.pop(0)
else:
raise ApiTypeError(
"value is required, but not passed in args or kwargs and doesn't have default",
path_to_item=_path_to_item,
valid_classes=(self.__class__,),
)
_check_type = kwargs.pop('_check_type', True)
_spec_property_naming = kwargs.pop('_spec_property_naming', False)
_configuration = kwargs.pop('_configuration', None)
_visited_composed_classes = kwargs.pop('_visited_composed_classes', ())
if args:
raise ApiTypeError(
"Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % (
args,
self.__class__.__name__,
),
path_to_item=_path_to_item,
valid_classes=(self.__class__,),
)
self._data_store = {}
self._check_type = _check_type
self._spec_property_naming = _spec_property_naming
self._path_to_item = _path_to_item
self._configuration = _configuration
self._visited_composed_classes = _visited_composed_classes + (self.__class__,)
self.value = value
if kwargs:
raise ApiTypeError(
"Invalid named arguments=%s passed to %s. Remove those invalid named arguments." % (
kwargs,
self.__class__.__name__,
),
path_to_item=_path_to_item,
valid_classes=(self.__class__,),
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||
microsoft/checkedc-clang | a173fefde5d7877b7750e7ce96dd08cf18baebf2 | compiler-rt/lib/sanitizer_common/scripts/cpplint.py | python | CheckSectionSpacing | (filename, clean_lines, class_info, linenum, error) | Checks for additional blank line issues related to sections.
Currently the only thing checked here is blank line before protected/private.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
class_info: A _ClassInfo objects.
linenum: The number of the line to check.
error: The function to call with any errors found. | Checks for additional blank line issues related to sections. | [
"Checks",
"for",
"additional",
"blank",
"line",
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"related",
"to",
"sections",
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] | def CheckSectionSpacing(filename, clean_lines, class_info, linenum, error):
"""Checks for additional blank line issues related to sections.
Currently the only thing checked here is blank line before protected/private.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
class_info: A _ClassInfo objects.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
# Skip checks if the class is small, where small means 25 lines or less.
# 25 lines seems like a good cutoff since that's the usual height of
# terminals, and any class that can't fit in one screen can't really
# be considered "small".
#
# Also skip checks if we are on the first line. This accounts for
# classes that look like
# class Foo { public: ... };
#
# If we didn't find the end of the class, last_line would be zero,
# and the check will be skipped by the first condition.
if (class_info.last_line - class_info.starting_linenum <= 24 or
linenum <= class_info.starting_linenum):
return
matched = Match(r'\s*(public|protected|private):', clean_lines.lines[linenum])
if matched:
# Issue warning if the line before public/protected/private was
# not a blank line, but don't do this if the previous line contains
# "class" or "struct". This can happen two ways:
# - We are at the beginning of the class.
# - We are forward-declaring an inner class that is semantically
# private, but needed to be public for implementation reasons.
# Also ignores cases where the previous line ends with a backslash as can be
# common when defining classes in C macros.
prev_line = clean_lines.lines[linenum - 1]
if (not IsBlankLine(prev_line) and
not Search(r'\b(class|struct)\b', prev_line) and
not Search(r'\\$', prev_line)):
# Try a bit harder to find the beginning of the class. This is to
# account for multi-line base-specifier lists, e.g.:
# class Derived
# : public Base {
end_class_head = class_info.starting_linenum
for i in range(class_info.starting_linenum, linenum):
if Search(r'\{\s*$', clean_lines.lines[i]):
end_class_head = i
break
if end_class_head < linenum - 1:
error(filename, linenum, 'whitespace/blank_line', 3,
'"%s:" should be preceded by a blank line' % matched.group(1)) | [
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||
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_misc.py | python | MimeTypesManager.EnumAllFileTypes | (*args, **kwargs) | return _misc_.MimeTypesManager_EnumAllFileTypes(*args, **kwargs) | EnumAllFileTypes(self) -> PyObject | EnumAllFileTypes(self) -> PyObject | [
"EnumAllFileTypes",
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] | def EnumAllFileTypes(*args, **kwargs):
"""EnumAllFileTypes(self) -> PyObject"""
return _misc_.MimeTypesManager_EnumAllFileTypes(*args, **kwargs) | [
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|
anestisb/oatdump_plus | ba858c1596598f0d9ae79c14d08c708cecc50af3 | tools/common/common.py | python | GetJackClassPath | () | return libdir + '/core-libart-hostdex_intermediates/classes.jack:' \
+ libdir + '/core-oj-hostdex_intermediates/classes.jack' | Returns Jack's classpath. | Returns Jack's classpath. | [
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] | def GetJackClassPath():
"""Returns Jack's classpath."""
top = GetEnvVariableOrError('ANDROID_BUILD_TOP')
libdir = top + '/out/host/common/obj/JAVA_LIBRARIES'
return libdir + '/core-libart-hostdex_intermediates/classes.jack:' \
+ libdir + '/core-oj-hostdex_intermediates/classes.jack' | [
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|
krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/python2_version/klampt/model/trajectory.py | python | HermiteTrajectory.stackDofs | (self,trajs,strict=True) | Stacks the degrees of freedom of multiple trajectories together.
The result is contained in self.
All evaluations are assumed to take place with the 'halt' endBehavior.
Args:
trajs (list or tuple of HermiteTrajectory): the trajectories to
stack
strict (bool, optional): ignored. Will always warn for invalid
classes.
Returns:
None | Stacks the degrees of freedom of multiple trajectories together.
The result is contained in self. | [
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] | def stackDofs(self,trajs,strict=True):
"""Stacks the degrees of freedom of multiple trajectories together.
The result is contained in self.
All evaluations are assumed to take place with the 'halt' endBehavior.
Args:
trajs (list or tuple of HermiteTrajectory): the trajectories to
stack
strict (bool, optional): ignored. Will always warn for invalid
classes.
Returns:
None
"""
if not isinstance(trajs,(list,tuple)):
raise ValueError("HermiteTrajectory.stackDofs takes in a list of trajectories as input")
for traj in trajs:
if not isinstance(traj,HermiteTrajectory):
raise ValueError("Can't stack non-HermiteTrajectory objects into a HermiteTrajectory")
alltimes = set()
for traj in trajs:
for t in traj.times:
alltimes.add(t)
self.times = sorted(alltimes)
stacktrajs = [traj.remesh(self.times) for traj in trajs]
for traj in stacktrajs:
assert len(traj.milestones) == len(self.times)
self.milestones = []
for i,t in enumerate(self.times):
q = []
v = []
for traj in stacktrajs:
n = len(traj.milestones[i])//2
q += list(traj.milestones[i][:n])
v += list(traj.milestones[i][n:])
self.milestones.append(q + v) | [
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||
smilehao/xlua-framework | a03801538be2b0e92d39332d445b22caca1ef61f | ConfigData/trunk/tools/protobuf-2.5.0/protobuf-2.5.0/python/google/protobuf/text_format.py | python | PrintFieldValue | (field, value, out, indent=0,
as_utf8=False, as_one_line=False) | Print a single field value (not including name). For repeated fields,
the value should be a single element. | Print a single field value (not including name). For repeated fields,
the value should be a single element. | [
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] | def PrintFieldValue(field, value, out, indent=0,
as_utf8=False, as_one_line=False):
"""Print a single field value (not including name). For repeated fields,
the value should be a single element."""
if field.cpp_type == descriptor.FieldDescriptor.CPPTYPE_MESSAGE:
if as_one_line:
out.write(' { ')
PrintMessage(value, out, indent, as_utf8, as_one_line)
out.write('}')
else:
out.write(' {\n')
PrintMessage(value, out, indent + 2, as_utf8, as_one_line)
out.write(' ' * indent + '}')
elif field.cpp_type == descriptor.FieldDescriptor.CPPTYPE_ENUM:
enum_value = field.enum_type.values_by_number.get(value, None)
if enum_value is not None:
out.write(enum_value.name)
else:
out.write(str(value))
elif field.cpp_type == descriptor.FieldDescriptor.CPPTYPE_STRING:
out.write('\"')
if type(value) is unicode:
out.write(_CEscape(value.encode('utf-8'), as_utf8))
else:
out.write(_CEscape(value, as_utf8))
out.write('\"')
elif field.cpp_type == descriptor.FieldDescriptor.CPPTYPE_BOOL:
if value:
out.write("true")
else:
out.write("false")
else:
out.write(str(value)) | [
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||
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py2/sklearn/pipeline.py | python | Pipeline.predict | (self, X) | return self.steps[-1][-1].predict(Xt) | Apply transforms to the data, and predict with the final estimator
Parameters
----------
X : iterable
Data to predict on. Must fulfill input requirements of first step
of the pipeline.
Returns
-------
y_pred : array-like | Apply transforms to the data, and predict with the final estimator | [
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"""Apply transforms to the data, and predict with the final estimator
Parameters
----------
X : iterable
Data to predict on. Must fulfill input requirements of first step
of the pipeline.
Returns
-------
y_pred : array-like
"""
Xt = X
for name, transform in self.steps[:-1]:
if transform is not None:
Xt = transform.transform(Xt)
return self.steps[-1][-1].predict(Xt) | [
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|
Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow2.x/tensorflow_model_optimization/python/core/quantization/keras/vitis/common/vitis_quantize_registry.py | python | VitisQuantizeRegistry.get_input_quantize_config | (self) | return config | Get input quantize config. | Get input quantize config. | [
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"quantize",
"config",
"."
] | def get_input_quantize_config(self):
"""Get input quantize config."""
config = self._configs['input_quantize_config']
input_bit = self.get_input_bit()
if input_bit:
config['input_quantizer']['quantizer_params']['bit_width'] = input_bit
logger.debug('Override default bit_width: input -> {}'.format(input_bit))
symmetry = self.get_symmetry()
if symmetry is not None:
config['input_quantizer']['quantizer_params']['symmetry'] = symmetry
logger.debug('Override default symmetry: input -> {}'.format(symmetry))
return config | [
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|
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/ultimatelistctrl.py | python | UltimateListHeaderData.GetText | (self) | return self._text | Returns the header/footer item text. | Returns the header/footer item text. | [
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] | def GetText(self):
""" Returns the header/footer item text. """
return self._text | [
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|
ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | build/linux/rewrite_dirs.py | python | RewritePath | (path, opts) | Rewrites a path by stripping the prefix and prepending the sysroot. | Rewrites a path by stripping the prefix and prepending the sysroot. | [
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"""Rewrites a path by stripping the prefix and prepending the sysroot."""
sysroot = opts.sysroot
prefix = opts.strip_prefix
if os.path.isabs(path) and not path.startswith(sysroot):
if path.startswith(prefix):
path = path[len(prefix):]
path = path.lstrip('/')
return os.path.join(sysroot, path)
else:
return path | [
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||
potassco/clingo | e0c91d8f95cc28de1c480a871f9c97c30de83d40 | libpyclingo/clingo/ast.py | python | Pool | (location: Location, arguments: Sequence[AST]) | return AST(p_ast[0]) | Construct an AST node of type `ASTType.Pool`. | Construct an AST node of type `ASTType.Pool`. | [
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] | def Pool(location: Location, arguments: Sequence[AST]) -> AST:
'''
Construct an AST node of type `ASTType.Pool`.
'''
p_ast = _ffi.new('clingo_ast_t**')
c_location = _c_location(location)
_handle_error(_lib.clingo_ast_build(
_lib.clingo_ast_type_pool, p_ast,
c_location[0],
_ffi.new('clingo_ast_t*[]', [ x._rep for x in arguments ]),
_ffi.cast('size_t', len(arguments))))
return AST(p_ast[0]) | [
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|
openvinotoolkit/openvino | dedcbeafa8b84cccdc55ca64b8da516682b381c7 | .ci/openvino-onnx/watchdog/src/git_wrapper.py | python | GitWrapper.get_git_time | (self) | return datetime_object | Retrieve time from GitHub.
Used to reliably determine time during Watchdog run.
:return: Datetime object describing current time
:rtype: datetime | Retrieve time from GitHub. | [
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"time",
"from",
"GitHub",
"."
] | def get_git_time(self):
"""Retrieve time from GitHub.
Used to reliably determine time during Watchdog run.
:return: Datetime object describing current time
:rtype: datetime
"""
try:
datetime_object = self._get_git_time()
except ValueError as e:
raise GitWrapperError(str(e))
except GithubException as e:
message = 'GitHub Exception during API status retrieval. Exception: {}'.format(str(e))
raise GitWrapperError(message)
except timeout_decorator.TimeoutError:
message = 'GitHub Exception during API status retrieval. Timeout during API request.'
raise GitWrapperError(message)
return datetime_object | [
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|
wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/fileinput.py | python | close | () | Close the sequence. | Close the sequence. | [
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"."
] | def close():
"""Close the sequence."""
global _state
state = _state
_state = None
if state:
state.close() | [
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||
eventql/eventql | 7ca0dbb2e683b525620ea30dc40540a22d5eb227 | deps/3rdparty/spidermonkey/mozjs/python/requests/requests/sessions.py | python | Session.get | (self, url, **kwargs) | return self.request('GET', url, **kwargs) | Sends a GET request. Returns :class:`Response` object.
:param url: URL for the new :class:`Request` object.
:param \*\*kwargs: Optional arguments that ``request`` takes. | Sends a GET request. Returns :class:`Response` object. | [
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] | def get(self, url, **kwargs):
"""Sends a GET request. Returns :class:`Response` object.
:param url: URL for the new :class:`Request` object.
:param \*\*kwargs: Optional arguments that ``request`` takes.
"""
kwargs.setdefault('allow_redirects', True)
return self.request('GET', url, **kwargs) | [
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|
ivansafrin/Polycode | 37a40fefe194ec7f6e9d1257f3bb3517b0a168bc | Bindings/Scripts/create_lua_library/zipfile.py | python | ZipFile.write | (self, filename, arcname=None, compress_type=None) | Put the bytes from filename into the archive under the name
arcname. | Put the bytes from filename into the archive under the name
arcname. | [
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] | def write(self, filename, arcname=None, compress_type=None):
"""Put the bytes from filename into the archive under the name
arcname."""
if not self.fp:
raise RuntimeError(
"Attempt to write to ZIP archive that was already closed")
st = os.stat(filename)
isdir = stat.S_ISDIR(st.st_mode)
mtime = time.localtime(st.st_mtime)
date_time = mtime[0:6]
# Create ZipInfo instance to store file information
if arcname is None:
arcname = filename
arcname = os.path.normpath(os.path.splitdrive(arcname)[1])
while arcname[0] in (os.sep, os.altsep):
arcname = arcname[1:]
if isdir:
arcname += '/'
zinfo = ZipInfo(arcname, date_time)
zinfo.external_attr = (st[0] & 0xFFFF) << 16L # Unix attributes
if compress_type is None:
zinfo.compress_type = self.compression
else:
zinfo.compress_type = compress_type
zinfo.file_size = st.st_size
zinfo.flag_bits = 0x00
zinfo.header_offset = self.fp.tell() # Start of header bytes
self._writecheck(zinfo)
self._didModify = True
if isdir:
zinfo.file_size = 0
zinfo.compress_size = 0
zinfo.CRC = 0
self.filelist.append(zinfo)
self.NameToInfo[zinfo.filename] = zinfo
self.fp.write(zinfo.FileHeader())
return
with open(filename, "rb") as fp:
# Must overwrite CRC and sizes with correct data later
zinfo.CRC = CRC = 0
zinfo.compress_size = compress_size = 0
zinfo.file_size = file_size = 0
self.fp.write(zinfo.FileHeader())
if zinfo.compress_type == ZIP_DEFLATED:
cmpr = zlib.compressobj(zlib.Z_DEFAULT_COMPRESSION,
zlib.DEFLATED, -15)
else:
cmpr = None
while 1:
buf = fp.read(1024 * 8)
if not buf:
break
file_size = file_size + len(buf)
CRC = crc32(buf, CRC) & 0xffffffff
if cmpr:
buf = cmpr.compress(buf)
compress_size = compress_size + len(buf)
self.fp.write(buf)
if cmpr:
buf = cmpr.flush()
compress_size = compress_size + len(buf)
self.fp.write(buf)
zinfo.compress_size = compress_size
else:
zinfo.compress_size = file_size
zinfo.CRC = CRC
zinfo.file_size = file_size
# Seek backwards and write CRC and file sizes
position = self.fp.tell() # Preserve current position in file
self.fp.seek(zinfo.header_offset + 14, 0)
self.fp.write(struct.pack("<LLL", zinfo.CRC, zinfo.compress_size,
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self.fp.seek(position, 0)
self.filelist.append(zinfo)
self.NameToInfo[zinfo.filename] = zinfo | [
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||
apple/swift-lldb | d74be846ef3e62de946df343e8c234bde93a8912 | examples/python/globals.py | python | globals | (command_args) | Extract all globals from any arguments which must be paths to object files. | Extract all globals from any arguments which must be paths to object files. | [
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] | def globals(command_args):
'''Extract all globals from any arguments which must be paths to object files.'''
usage = "usage: %prog [options] <PATH> [PATH ...]"
description = '''This command will find all globals in the specified object file and return an list() of lldb.SBValue objects (which might be empty).'''
parser = optparse.OptionParser(
description=description,
prog='globals',
usage=usage)
parser.add_option(
'-v',
'--verbose',
action='store_true',
dest='verbose',
help='display verbose debug info',
default=False)
parser.add_option(
'-a',
'--arch',
type='string',
metavar='arch',
dest='arch',
help='Specify an architecture (or triple) to use when extracting from a file.')
parser.add_option(
'-p',
'--platform',
type='string',
metavar='platform',
dest='platform',
help='Specify the platform to use when creating the debug target. Valid values include "localhost", "darwin-kernel", "ios-simulator", "remote-freebsd", "remote-macosx", "remote-ios", "remote-linux".')
try:
(options, args) = parser.parse_args(command_args)
except:
return
for path in args:
get_globals(path, options) | [
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||
Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/ops/nn_impl.py | python | l2_normalize_v2 | (x, axis=None, epsilon=1e-12, name=None) | Normalizes along dimension `axis` using an L2 norm.
For a 1-D tensor with `axis = 0`, computes
output = x / sqrt(max(sum(x**2), epsilon))
For `x` with more dimensions, independently normalizes each 1-D slice along
dimension `axis`.
Args:
x: A `Tensor`.
axis: Dimension along which to normalize. A scalar or a vector of
integers.
epsilon: A lower bound value for the norm. Will use `sqrt(epsilon)` as the
divisor if `norm < sqrt(epsilon)`.
name: A name for this operation (optional).
Returns:
A `Tensor` with the same shape as `x`. | Normalizes along dimension `axis` using an L2 norm. | [
"Normalizes",
"along",
"dimension",
"axis",
"using",
"an",
"L2",
"norm",
"."
] | def l2_normalize_v2(x, axis=None, epsilon=1e-12, name=None):
"""Normalizes along dimension `axis` using an L2 norm.
For a 1-D tensor with `axis = 0`, computes
output = x / sqrt(max(sum(x**2), epsilon))
For `x` with more dimensions, independently normalizes each 1-D slice along
dimension `axis`.
Args:
x: A `Tensor`.
axis: Dimension along which to normalize. A scalar or a vector of
integers.
epsilon: A lower bound value for the norm. Will use `sqrt(epsilon)` as the
divisor if `norm < sqrt(epsilon)`.
name: A name for this operation (optional).
Returns:
A `Tensor` with the same shape as `x`.
"""
with ops.name_scope(name, "l2_normalize", [x]) as name:
x = ops.convert_to_tensor(x, name="x")
square_sum = math_ops.reduce_sum(math_ops.square(x), axis, keepdims=True)
x_inv_norm = math_ops.rsqrt(math_ops.maximum(square_sum, epsilon))
return math_ops.multiply(x, x_inv_norm, name=name) | [
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||
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/setuptools/py3/setuptools/_vendor/more_itertools/more.py | python | split_when | (iterable, pred, maxsplit=-1) | Split *iterable* into pieces based on the output of *pred*.
*pred* should be a function that takes successive pairs of items and
returns ``True`` if the iterable should be split in between them.
For example, to find runs of increasing numbers, split the iterable when
element ``i`` is larger than element ``i + 1``:
>>> list(split_when([1, 2, 3, 3, 2, 5, 2, 4, 2], lambda x, y: x > y))
[[1, 2, 3, 3], [2, 5], [2, 4], [2]]
At most *maxsplit* splits are done. If *maxsplit* is not specified or -1,
then there is no limit on the number of splits:
>>> list(split_when([1, 2, 3, 3, 2, 5, 2, 4, 2],
... lambda x, y: x > y, maxsplit=2))
[[1, 2, 3, 3], [2, 5], [2, 4, 2]] | Split *iterable* into pieces based on the output of *pred*.
*pred* should be a function that takes successive pairs of items and
returns ``True`` if the iterable should be split in between them. | [
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] | def split_when(iterable, pred, maxsplit=-1):
"""Split *iterable* into pieces based on the output of *pred*.
*pred* should be a function that takes successive pairs of items and
returns ``True`` if the iterable should be split in between them.
For example, to find runs of increasing numbers, split the iterable when
element ``i`` is larger than element ``i + 1``:
>>> list(split_when([1, 2, 3, 3, 2, 5, 2, 4, 2], lambda x, y: x > y))
[[1, 2, 3, 3], [2, 5], [2, 4], [2]]
At most *maxsplit* splits are done. If *maxsplit* is not specified or -1,
then there is no limit on the number of splits:
>>> list(split_when([1, 2, 3, 3, 2, 5, 2, 4, 2],
... lambda x, y: x > y, maxsplit=2))
[[1, 2, 3, 3], [2, 5], [2, 4, 2]]
"""
if maxsplit == 0:
yield list(iterable)
return
it = iter(iterable)
try:
cur_item = next(it)
except StopIteration:
return
buf = [cur_item]
for next_item in it:
if pred(cur_item, next_item):
yield buf
if maxsplit == 1:
yield [next_item] + list(it)
return
buf = []
maxsplit -= 1
buf.append(next_item)
cur_item = next_item
yield buf | [
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||
tiann/android-native-debug | 198903ed9346dc4a74327a63cb98d449b97d8047 | app/source/art/tools/cpplint.py | python | _CppLintState.PrintErrorCounts | (self) | Print a summary of errors by category, and the total. | Print a summary of errors by category, and the total. | [
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] | def PrintErrorCounts(self):
"""Print a summary of errors by category, and the total."""
for category, count in self.errors_by_category.iteritems():
sys.stderr.write('Category \'%s\' errors found: %d\n' %
(category, count))
sys.stderr.write('Total errors found: %d\n' % self.error_count) | [
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||
bitsai/book-exercises | 3a52330c6066090662bc2123d03aac270ef3ed10 | Learn Python the Hard Way/ex25.py | python | sort_words | (words) | return sorted(words) | Sorts the words. | Sorts the words. | [
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] | def sort_words(words):
"""Sorts the words."""
return sorted(words) | [
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|
echronos/echronos | c996f1d2c8af6c6536205eb319c1bf1d4d84569c | external_tools/ply_info/example/BASIC/basparse.py | python | p_command_for | (p) | command : FOR ID EQUALS expr TO expr optstep | command : FOR ID EQUALS expr TO expr optstep | [
"command",
":",
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"EQUALS",
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] | def p_command_for(p):
'''command : FOR ID EQUALS expr TO expr optstep'''
p[0] = ('FOR',p[2],p[4],p[6],p[7]) | [
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||
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py2/scipy/signal/ltisys.py | python | impulse2 | (system, X0=None, T=None, N=None, **kwargs) | return Tr, Yr | Impulse response of a single-input, continuous-time linear system.
Parameters
----------
system : an instance of the LTI class or a tuple of array_like
describing the system.
The following gives the number of elements in the tuple and
the interpretation:
* 1 (instance of `lti`)
* 2 (num, den)
* 3 (zeros, poles, gain)
* 4 (A, B, C, D)
X0 : 1-D array_like, optional
The initial condition of the state vector. Default: 0 (the
zero vector).
T : 1-D array_like, optional
The time steps at which the input is defined and at which the
output is desired. If `T` is not given, the function will
generate a set of time samples automatically.
N : int, optional
Number of time points to compute. Default: 100.
kwargs : various types
Additional keyword arguments are passed on to the function
`scipy.signal.lsim2`, which in turn passes them on to
`scipy.integrate.odeint`; see the latter's documentation for
information about these arguments.
Returns
-------
T : ndarray
The time values for the output.
yout : ndarray
The output response of the system.
See Also
--------
impulse, lsim2, integrate.odeint
Notes
-----
The solution is generated by calling `scipy.signal.lsim2`, which uses
the differential equation solver `scipy.integrate.odeint`.
If (num, den) is passed in for ``system``, coefficients for both the
numerator and denominator should be specified in descending exponent
order (e.g. ``s^2 + 3s + 5`` would be represented as ``[1, 3, 5]``).
.. versionadded:: 0.8.0
Examples
--------
Second order system with a repeated root: x''(t) + 2*x(t) + x(t) = u(t)
>>> from scipy import signal
>>> system = ([1.0], [1.0, 2.0, 1.0])
>>> t, y = signal.impulse2(system)
>>> import matplotlib.pyplot as plt
>>> plt.plot(t, y) | Impulse response of a single-input, continuous-time linear system. | [
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"""
Impulse response of a single-input, continuous-time linear system.
Parameters
----------
system : an instance of the LTI class or a tuple of array_like
describing the system.
The following gives the number of elements in the tuple and
the interpretation:
* 1 (instance of `lti`)
* 2 (num, den)
* 3 (zeros, poles, gain)
* 4 (A, B, C, D)
X0 : 1-D array_like, optional
The initial condition of the state vector. Default: 0 (the
zero vector).
T : 1-D array_like, optional
The time steps at which the input is defined and at which the
output is desired. If `T` is not given, the function will
generate a set of time samples automatically.
N : int, optional
Number of time points to compute. Default: 100.
kwargs : various types
Additional keyword arguments are passed on to the function
`scipy.signal.lsim2`, which in turn passes them on to
`scipy.integrate.odeint`; see the latter's documentation for
information about these arguments.
Returns
-------
T : ndarray
The time values for the output.
yout : ndarray
The output response of the system.
See Also
--------
impulse, lsim2, integrate.odeint
Notes
-----
The solution is generated by calling `scipy.signal.lsim2`, which uses
the differential equation solver `scipy.integrate.odeint`.
If (num, den) is passed in for ``system``, coefficients for both the
numerator and denominator should be specified in descending exponent
order (e.g. ``s^2 + 3s + 5`` would be represented as ``[1, 3, 5]``).
.. versionadded:: 0.8.0
Examples
--------
Second order system with a repeated root: x''(t) + 2*x(t) + x(t) = u(t)
>>> from scipy import signal
>>> system = ([1.0], [1.0, 2.0, 1.0])
>>> t, y = signal.impulse2(system)
>>> import matplotlib.pyplot as plt
>>> plt.plot(t, y)
"""
if isinstance(system, lti):
sys = system._as_ss()
elif isinstance(system, dlti):
raise AttributeError('impulse2 can only be used with continuous-time '
'systems.')
else:
sys = lti(*system)._as_ss()
B = sys.B
if B.shape[-1] != 1:
raise ValueError("impulse2() requires a single-input system.")
B = B.squeeze()
if X0 is None:
X0 = zeros_like(B)
if N is None:
N = 100
if T is None:
T = _default_response_times(sys.A, N)
# Move the impulse in the input to the initial conditions, and then
# solve using lsim2().
ic = B + X0
Tr, Yr, Xr = lsim2(sys, T=T, X0=ic, **kwargs)
return Tr, Yr | [
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|
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_controls.py | python | Slider.GetThumbLength | (*args, **kwargs) | return _controls_.Slider_GetThumbLength(*args, **kwargs) | GetThumbLength(self) -> int | GetThumbLength(self) -> int | [
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")",
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"""GetThumbLength(self) -> int"""
return _controls_.Slider_GetThumbLength(*args, **kwargs) | [
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|
adobe/chromium | cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7 | tools/valgrind/gdb_helper.py | python | AddressTable.Add | (self, binary, address) | Register a lookup request. | Register a lookup request. | [
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] | def Add(self, binary, address):
''' Register a lookup request. '''
if binary == '':
logging.warn('adding address %s in empty binary?' % address)
if binary in self._binaries:
self._binaries[binary].append(address)
else:
self._binaries[binary] = [address]
self._all_resolved = False | [
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||
SmileiPIC/Smilei | 07dcb51200029e10f626e1546558c1ae7599c8b1 | happi/_SmileiSimulation.py | python | SmileiSimulation.fieldInfo | (self, diag) | return dict( diagNumber=diagNumber, diagName=diagName, fields=fields ) | Information on a specific Field diagnostic
Parameters:
-----------
diag: the number or name of a Field diagnostic
Returns:
--------
A dictionnary containing:
* "diagNumber": the diagnostic number
* "diagName": the diagnostic name
* "fields": list of the available fields in this diagnostic. In the case of
`AMcylindrical` geometry, this is a dictionnary with a list of modes for each field. | Information on a specific Field diagnostic
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-----------
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--------
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* "diagNumber": the diagnostic number
* "diagName": the diagnostic name
* "fields": list of the available fields in this diagnostic. In the case of
`AMcylindrical` geometry, this is a dictionnary with a list of modes for each field. | [
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""" Information on a specific Field diagnostic
Parameters:
-----------
diag: the number or name of a Field diagnostic
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--------
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* "diagNumber": the diagnostic number
* "diagName": the diagnostic name
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"""
diag_numbers, diag_names = self.getDiags("Fields")
if type(diag) is str:
if diag not in diag_names:
raise Exception("No Field diagnostic `"+diag+"` found")
i = diag_names.index( diag )
else:
if diag not in diag_numbers:
raise Exception("No Field diagnostic #"+str(diag)+" found")
i = diag_numbers.index( diag )
diagNumber = diag_numbers[i]
diagName = diag_names[i]
raw_fields = set()
for path in self._results_path:
file = path+self._os.sep+'Fields'+str(diagNumber)+'.h5'
try:
f = self._h5py.File(file, 'r')
except Exception as e:
continue
values = f["data"].values()
if len(values)==0:
continue
these_fields = set(next(iter(values)).keys())
raw_fields = (raw_fields & these_fields) or these_fields
f.close()
# Case of a cylindrical geometry
if self.cylindrical:
from ._Diagnostics.Field import Field
fields = {}
for f in raw_fields:
fname, imode, envelope = Field._cylindricalMode(f)
if fname not in fields:
fields[fname] = []
fields[fname] += [int(imode)]
else:
fields = sorted(list(raw_fields))
return dict( diagNumber=diagNumber, diagName=diagName, fields=fields ) | [
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|
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemDefectReporter/v1/AWS/common-code/Lib/oauthlib/oauth2/rfc6749/request_validator.py | python | RequestValidator.get_default_redirect_uri | (self, client_id, request, *args, **kwargs) | Get the default redirect URI for the client.
:param client_id: Unicode client identifier
:param request: The HTTP Request (oauthlib.common.Request)
:rtype: The default redirect URI for the client
Method is used by:
- Authorization Code Grant
- Implicit Grant | Get the default redirect URI for the client. | [
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] | def get_default_redirect_uri(self, client_id, request, *args, **kwargs):
"""Get the default redirect URI for the client.
:param client_id: Unicode client identifier
:param request: The HTTP Request (oauthlib.common.Request)
:rtype: The default redirect URI for the client
Method is used by:
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raise NotImplementedError('Subclasses must implement this method.') | [
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||
microsoft/DirectXShaderCompiler | 8348ff8d9e0287610ba05d3a828e10af981a1c05 | utils/llvm-build/llvmbuild/main.py | python | add_magic_target_components | (parser, project, opts) | add_magic_target_components(project, opts) -> None
Add the "magic" target based components to the project, which can only be
determined based on the target configuration options.
This currently is responsible for populating the required_libraries list of
the "all-targets", "Native", "NativeCodeGen", and "Engine" components. | add_magic_target_components(project, opts) -> None | [
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"""add_magic_target_components(project, opts) -> None
Add the "magic" target based components to the project, which can only be
determined based on the target configuration options.
This currently is responsible for populating the required_libraries list of
the "all-targets", "Native", "NativeCodeGen", and "Engine" components.
"""
# Determine the available targets.
available_targets = dict((ci.name,ci)
for ci in project.component_infos
if ci.type_name == 'TargetGroup')
# Find the configured native target.
# We handle a few special cases of target names here for historical
# reasons, as these are the names configure currently comes up with.
native_target_name = { 'x86' : 'X86',
'x86_64' : 'X86',
'Unknown' : None }.get(opts.native_target,
opts.native_target)
if native_target_name is None:
native_target = None
else:
native_target = available_targets.get(native_target_name)
if native_target is None:
parser.error("invalid native target: %r (not in project)" % (
opts.native_target,))
if native_target.type_name != 'TargetGroup':
parser.error("invalid native target: %r (not a target)" % (
opts.native_target,))
# Find the list of targets to enable.
if opts.enable_targets is None:
enable_targets = available_targets.values()
else:
# We support both space separated and semi-colon separated lists.
if opts.enable_targets == '':
enable_target_names = []
elif ' ' in opts.enable_targets:
enable_target_names = opts.enable_targets.split()
else:
enable_target_names = opts.enable_targets.split(';')
enable_targets = []
for name in enable_target_names:
if name == "None": continue # HLSL Change
target = available_targets.get(name)
if target is None:
parser.error("invalid target to enable: %r (not in project)" % (
name,))
if target.type_name != 'TargetGroup':
parser.error("invalid target to enable: %r (not a target)" % (
name,))
enable_targets.append(target)
# Find the special library groups we are going to populate. We enforce that
# these appear in the project (instead of just adding them) so that they at
# least have an explicit representation in the project LLVMBuild files (and
# comments explaining how they are populated).
def find_special_group(name):
info = info_map.get(name)
if info is None:
fatal("expected project to contain special %r component" % (
name,))
if info.type_name != 'LibraryGroup':
fatal("special component %r should be a LibraryGroup" % (
name,))
if info.required_libraries:
fatal("special component %r must have empty %r list" % (
name, 'required_libraries'))
if info.add_to_library_groups:
fatal("special component %r must have empty %r list" % (
name, 'add_to_library_groups'))
info._is_special_group = True
return info
info_map = dict((ci.name, ci) for ci in project.component_infos)
all_targets = find_special_group('all-targets')
native_group = find_special_group('Native')
native_codegen_group = find_special_group('NativeCodeGen')
engine_group = find_special_group('Engine')
# Set the enabled bit in all the target groups, and append to the
# all-targets list.
for ci in enable_targets:
all_targets.required_libraries.append(ci.name)
ci.enabled = True
# If we have a native target, then that defines the native and
# native_codegen libraries.
if native_target and native_target.enabled:
native_group.required_libraries.append(native_target.name)
native_codegen_group.required_libraries.append(
'%sCodeGen' % native_target.name)
# If we have a native target with a JIT, use that for the engine. Otherwise,
# use the interpreter.
if native_target and native_target.enabled and native_target.has_jit:
engine_group.required_libraries.append('MCJIT')
engine_group.required_libraries.append(native_group.name)
else:
engine_group.required_libraries.append('Interpreter') | [
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] | https://github.com/microsoft/DirectXShaderCompiler/blob/8348ff8d9e0287610ba05d3a828e10af981a1c05/utils/llvm-build/llvmbuild/main.py#L708-L815 |
||
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/plugins/codebrowser/codebrowser/tagload.py | python | _TagLoader.GetGenerator | (self, lang_id) | return None | Get the tag generator method for the given language id
@param lang_id: Editra language identifier id
@return: Generator Method or None | Get the tag generator method for the given language id
@param lang_id: Editra language identifier id
@return: Generator Method or None | [
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":",
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] | def GetGenerator(self, lang_id):
"""Get the tag generator method for the given language id
@param lang_id: Editra language identifier id
@return: Generator Method or None
"""
if lang_id in LOAD_MAP:
modname = LOAD_MAP[lang_id]
self.LoadModule(modname)
if modname in _TagLoader._loaded:
return TagLoader._loaded[modname].GenerateTags
return None | [
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ceph/ceph | 959663007321a369c83218414a29bd9dbc8bda3a | src/pybind/mgr/balancer/module.py | python | Module.plan_rm | (self, plan: str) | return (0, '', '') | Discard an optimization plan | Discard an optimization plan | [
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] | def plan_rm(self, plan: str) -> Tuple[int, str, str]:
"""
Discard an optimization plan
"""
if plan in self.plans:
del self.plans[plan]
return (0, '', '') | [
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|
pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | torch/nn/utils/spectral_norm.py | python | remove_spectral_norm | (module: T_module, name: str = 'weight') | return module | r"""Removes the spectral normalization reparameterization from a module.
Args:
module (Module): containing module
name (str, optional): name of weight parameter
Example:
>>> m = spectral_norm(nn.Linear(40, 10))
>>> remove_spectral_norm(m) | r"""Removes the spectral normalization reparameterization from a module. | [
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"reparameterization",
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] | def remove_spectral_norm(module: T_module, name: str = 'weight') -> T_module:
r"""Removes the spectral normalization reparameterization from a module.
Args:
module (Module): containing module
name (str, optional): name of weight parameter
Example:
>>> m = spectral_norm(nn.Linear(40, 10))
>>> remove_spectral_norm(m)
"""
for k, hook in module._forward_pre_hooks.items():
if isinstance(hook, SpectralNorm) and hook.name == name:
hook.remove(module)
del module._forward_pre_hooks[k]
break
else:
raise ValueError("spectral_norm of '{}' not found in {}".format(
name, module))
for k, hook in module._state_dict_hooks.items():
if isinstance(hook, SpectralNormStateDictHook) and hook.fn.name == name:
del module._state_dict_hooks[k]
break
for k, hook in module._load_state_dict_pre_hooks.items():
if isinstance(hook, SpectralNormLoadStateDictPreHook) and hook.fn.name == name:
del module._load_state_dict_pre_hooks[k]
break
return module | [
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|
root-project/root | fcd3583bb14852bf2e8cd2415717cbaac0e75896 | build/unix/makepchinput.py | python | getParams | () | return rootSrcDir, modules, legacyPyROOT == 'ON', clingetpchList | Extract parameters from the commandline, which looks like
makePCHInput.py WWW XXX YYY ZZZ -- CXXFLAGS | Extract parameters from the commandline, which looks like
makePCHInput.py WWW XXX YYY ZZZ -- CXXFLAGS | [
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"""
Extract parameters from the commandline, which looks like
makePCHInput.py WWW XXX YYY ZZZ -- CXXFLAGS
"""
argv = sys.argv
rootSrcDir, modules, legacyPyROOT = argv[1:4]
clingetpchList = argv[4:]
return rootSrcDir, modules, legacyPyROOT == 'ON', clingetpchList | [
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|
hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/apitools/apitools/gen/extended_descriptor.py | python | _ProtoRpcPrinter.__PrintAdditionalImports | (self, imports) | Print additional imports needed for protorpc. | Print additional imports needed for protorpc. | [
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] | def __PrintAdditionalImports(self, imports):
"""Print additional imports needed for protorpc."""
google_imports = [x for x in imports if 'google' in x]
other_imports = [x for x in imports if 'google' not in x]
if other_imports:
for import_ in sorted(other_imports):
self.__printer(import_)
self.__printer()
# Note: If we ever were going to add imports from this package, we'd
# need to sort those out and put them at the end.
if google_imports:
for import_ in sorted(google_imports):
self.__printer(import_)
self.__printer() | [
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||
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/mailcap.py | python | getcaps | () | return caps | Return a dictionary containing the mailcap database.
The dictionary maps a MIME type (in all lowercase, e.g. 'text/plain')
to a list of dictionaries corresponding to mailcap entries. The list
collects all the entries for that MIME type from all available mailcap
files. Each dictionary contains key-value pairs for that MIME type,
where the viewing command is stored with the key "view". | Return a dictionary containing the mailcap database. | [
"Return",
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"dictionary",
"containing",
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"mailcap",
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] | def getcaps():
"""Return a dictionary containing the mailcap database.
The dictionary maps a MIME type (in all lowercase, e.g. 'text/plain')
to a list of dictionaries corresponding to mailcap entries. The list
collects all the entries for that MIME type from all available mailcap
files. Each dictionary contains key-value pairs for that MIME type,
where the viewing command is stored with the key "view".
"""
caps = {}
for mailcap in listmailcapfiles():
try:
fp = open(mailcap, 'r')
except IOError:
continue
with fp:
morecaps = readmailcapfile(fp)
for key, value in morecaps.iteritems():
if not key in caps:
caps[key] = value
else:
caps[key] = caps[key] + value
return caps | [
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|
lighttransport/nanort | 74063967336311f54ede5dffdfa242123825033b | deps/cpplint.py | python | IsErrorSuppressedByNolint | (category, linenum) | return (linenum in _error_suppressions.get(category, set()) or
linenum in _error_suppressions.get(None, set())) | Returns true if the specified error category is suppressed on this line.
Consults the global error_suppressions map populated by
ParseNolintSuppressions/ResetNolintSuppressions.
Args:
category: str, the category of the error.
linenum: int, the current line number.
Returns:
bool, True iff the error should be suppressed due to a NOLINT comment. | Returns true if the specified error category is suppressed on this line. | [
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] | def IsErrorSuppressedByNolint(category, linenum):
"""Returns true if the specified error category is suppressed on this line.
Consults the global error_suppressions map populated by
ParseNolintSuppressions/ResetNolintSuppressions.
Args:
category: str, the category of the error.
linenum: int, the current line number.
Returns:
bool, True iff the error should be suppressed due to a NOLINT comment.
"""
return (linenum in _error_suppressions.get(category, set()) or
linenum in _error_suppressions.get(None, set())) | [
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|
hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/closure_linter/closure_linter/ecmalintrules.py | python | EcmaScriptLintRules._HandleStartBracket | (self, token, last_non_space_token) | Handles a token that is an open bracket.
Args:
token: The token to handle.
last_non_space_token: The last token that was not a space. | Handles a token that is an open bracket. | [
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] | def _HandleStartBracket(self, token, last_non_space_token):
"""Handles a token that is an open bracket.
Args:
token: The token to handle.
last_non_space_token: The last token that was not a space.
"""
if (not token.IsFirstInLine() and token.previous.type == Type.WHITESPACE and
last_non_space_token and
last_non_space_token.type in Type.EXPRESSION_ENDER_TYPES):
self._HandleError(
errors.EXTRA_SPACE, 'Extra space before "["',
token.previous, position=Position.All(token.previous.string))
# If the [ token is the first token in a line we shouldn't complain
# about a missing space before [. This is because some Ecma script
# languages allow syntax like:
# [Annotation]
# class MyClass {...}
# So we don't want to blindly warn about missing spaces before [.
# In the the future, when rules for computing exactly how many spaces
# lines should be indented are added, then we can return errors for
# [ tokens that are improperly indented.
# For example:
# var someVeryVeryVeryVeryVeryVeryVeryVeryVeryVeryVeryLongVariableName =
# [a,b,c];
# should trigger a proper indentation warning message as [ is not indented
# by four spaces.
elif (not token.IsFirstInLine() and token.previous and
token.previous.type not in (
[Type.WHITESPACE, Type.START_PAREN, Type.START_BRACKET] +
Type.EXPRESSION_ENDER_TYPES)):
self._HandleError(errors.MISSING_SPACE, 'Missing space before "["',
token, position=Position.AtBeginning()) | [
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||
hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/pyfakefs/pyfakefs/fake_filesystem_shutil.py | python | FakeShutilModule.rmtree | (self, path, ignore_errors=False, onerror=None) | Remove a directory and all its contents.
Args:
path: (str) Directory tree to remove.
ignore_errors: (bool) unimplemented
onerror: (func) unimplemented | Remove a directory and all its contents. | [
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"a",
"directory",
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"its",
"contents",
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] | def rmtree(self, path, ignore_errors=False, onerror=None):
"""Remove a directory and all its contents.
Args:
path: (str) Directory tree to remove.
ignore_errors: (bool) unimplemented
onerror: (func) unimplemented
"""
self.filesystem.RemoveObject(path) | [
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||
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numba/targets/npdatetime.py | python | is_not_nat | (builder, val) | return builder.icmp(lc.ICMP_NE, val, NAT) | Return a predicate which is true if *val* is not NaT. | Return a predicate which is true if *val* is not NaT. | [
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"""
Return a predicate which is true if *val* is not NaT.
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return builder.icmp(lc.ICMP_NE, val, NAT) | [
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|
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/graphviz/py2/graphviz/files.py | python | File._view | (self, filepath, format, quiet) | Start the right viewer based on file format and platform. | Start the right viewer based on file format and platform. | [
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] | def _view(self, filepath, format, quiet):
"""Start the right viewer based on file format and platform."""
methodnames = [
'_view_%s_%s' % (format, backend.PLATFORM),
'_view_%s' % backend.PLATFORM,
]
for name in methodnames:
view_method = getattr(self, name, None)
if view_method is not None:
break
else:
raise RuntimeError('%r has no built-in viewer support for %r'
' on %r platform' % (self.__class__, format,
backend.PLATFORM))
view_method(filepath, quiet) | [
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weolar/miniblink49 | 1c4678db0594a4abde23d3ebbcc7cd13c3170777 | v8_5_1/tools/stats-viewer.py | python | ChromeCounter.__init__ | (self, data, name_offset, value_offset) | Create a new instance.
Args:
data: the shared data access object containing the counter
name_offset: the byte offset of the start of this counter's name
value_offset: the byte offset of the start of this counter's value | Create a new instance. | [
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"""Create a new instance.
Args:
data: the shared data access object containing the counter
name_offset: the byte offset of the start of this counter's name
value_offset: the byte offset of the start of this counter's value
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self.data = data
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Komnomnomnom/swigibpy | cfd307fdbfaffabc69a2dc037538d7e34a8b8daf | swigibpy.py | python | ContractDetails.__init__ | (self) | __init__(ContractDetails self) -> ContractDetails | __init__(ContractDetails self) -> ContractDetails | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/site-packages/pip/_vendor/distlib/_backport/tarfile.py | python | _Stream.seek | (self, pos=0) | return self.pos | Set the stream's file pointer to pos. Negative seeking
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/share/doc/python3.7/examples/Tools/scripts/patchcheck.py | python | n_files_str | (count) | return "{} file{}".format(count, "s" if count != 1 else "") | Return 'N file(s)' with the proper plurality on 'file'. | Return 'N file(s)' with the proper plurality on 'file'. | [
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"""Return 'N file(s)' with the proper plurality on 'file'."""
return "{} file{}".format(count, "s" if count != 1 else "") | [
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microsoft/checkedc-clang | a173fefde5d7877b7750e7ce96dd08cf18baebf2 | llvm/examples/Kaleidoscope/MCJIT/cached/genk-timing.py | python | KScriptGenerator.updateCalledFunctionList | (self, callee) | Maintains a list of functions that will actually be called | Maintains a list of functions that will actually be called | [
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# Update the total call count
self.updateTotalCallCount(callee)
# If this function is already in the list, don't do anything else
if callee in self.calledFunctions:
return
# Add this function to the list of those that will be called.
self.calledFunctions.append(callee)
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if callee in self.calledFunctionTable:
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/dataview.py | python | PyDataViewModel._setCallbackInfo | (*args, **kwargs) | return _dataview.PyDataViewModel__setCallbackInfo(*args, **kwargs) | _setCallbackInfo(self, PyObject self, PyObject _class) | _setCallbackInfo(self, PyObject self, PyObject _class) | [
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return _dataview.PyDataViewModel__setCallbackInfo(*args, **kwargs) | [
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htcondor/htcondor | 4829724575176d1d6c936e4693dfd78a728569b0 | src/condor_contrib/condor_pigeon/src/condor_pigeon_client/skype_linux_tools/Skype4Py/utils.py | python | chop | (s, n=1, d=None) | return spl | Chops initial words from a string and returns a list of them and the rest of the string.
@param s: String to chop from.
@type s: str or unicode
@param n: Number of words to chop.
@type n: int
@param d: Optional delimeter. Any white-char by default.
@type d: str or unicode
@return: A list of n first words from the string followed by the rest of the string
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@rtype: list of str or unicode | Chops initial words from a string and returns a list of them and the rest of the string. | [
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@param s: String to chop from.
@type s: str or unicode
@param n: Number of words to chop.
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|
deepmind/open_spiel | 4ca53bea32bb2875c7385d215424048ae92f78c8 | open_spiel/python/algorithms/best_response.py | python | CPPBestResponsePolicy.decision_nodes | (self, parent_state) | Yields a (state, cf_prob) pair for each descendant decision node. | Yields a (state, cf_prob) pair for each descendant decision node. | [
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"""Yields a (state, cf_prob) pair for each descendant decision node."""
if not parent_state.is_terminal():
if parent_state.current_player() == self.best_responder_id:
yield (parent_state, 1.0)
for action, p_action in self.transitions(parent_state):
for state, p_state in self.decision_nodes(parent_state.child(action)):
yield (state, p_state * p_action) | [
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acbull/Unbiased_LambdaMart | 7c39abe5caa18ca07df2d23c2db392916d92956c | evaluation/scripts/click_models.py | python | ClickModel.setClickProb | (self, neg_click_prob, pos_click_prob, relevance_grading_num) | Generate noisy click probability based on the relevance grading
number.
Inspired by ERR.
Parameters
----------
neg_click_prob : [type]
[description]
pos_click_prob : [type]
[description]
relevance_grading_num : [type]
[description] | Generate noisy click probability based on the relevance grading
number.
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"""Generate noisy click probability based on the relevance grading
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Inspired by ERR.
Parameters
----------
neg_click_prob : [type]
[description]
pos_click_prob : [type]
[description]
relevance_grading_num : [type]
[description]
"""
b = (pos_click_prob - neg_click_prob) / \
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a = neg_click_prob - b
self.click_prob = [
a + pow(2, i)*b for i in range(relevance_grading_num+1)] | [
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cocos-creator/engine-native | 984c4c9f5838253313b44ccd429bd8fac4ec8a6a | tools/bindings-generator/generator.py | python | Generator.sorted_classes | (self) | return no_dupes | sorted classes in order of inheritance | sorted classes in order of inheritance | [
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'''
sorted classes in order of inheritance
'''
sorted_list = []
for class_name in iter(self.generated_classes.keys()):
nclass = self.generated_classes[class_name]
sorted_list += self._sorted_parents(nclass)
# remove dupes from the list
no_dupes = []
[no_dupes.append(i) for i in sorted_list if not no_dupes.count(i)]
return no_dupes | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/nn/probability/distribution/_utils/utils.py | python | cast_type_for_device | (dtype) | return dtype | use the alternative dtype supported by the device.
Args:
dtype (mindspore.dtype): input dtype.
Returns:
mindspore.dtype. | use the alternative dtype supported by the device.
Args:
dtype (mindspore.dtype): input dtype.
Returns:
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Returns:
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"""
if context.get_context("device_target") == "GPU":
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OSGeo/gdal | 3748fc4ba4fba727492774b2b908a2130c864a83 | swig/python/osgeo/ogr.py | python | FieldDefn.SetDomainName | (self, *args) | return _ogr.FieldDefn_SetDomainName(self, *args) | r"""
SetDomainName(FieldDefn self, char const * name)
void
OGR_Fld_SetDomainName(OGRFieldDefnH hDefn, const char *pszFieldName)
Set the name of the field domain for this field.
Field domains ( OGRFieldDomain) are attached at the GDALDataset level.
This method is the same as the C++ method
OGRFieldDefn::SetDomainName().
Parameters:
-----------
hDefn: handle to the field definition
pszFieldName: Field domain name.
GDAL 3.3 | r"""
SetDomainName(FieldDefn self, char const * name)
void
OGR_Fld_SetDomainName(OGRFieldDefnH hDefn, const char *pszFieldName) | [
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Set the name of the field domain for this field.
Field domains ( OGRFieldDomain) are attached at the GDALDataset level.
This method is the same as the C++ method
OGRFieldDefn::SetDomainName().
Parameters:
-----------
hDefn: handle to the field definition
pszFieldName: Field domain name.
GDAL 3.3
"""
return _ogr.FieldDefn_SetDomainName(self, *args) | [
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|
domino-team/openwrt-cc | 8b181297c34d14d3ca521cc9f31430d561dbc688 | package/gli-pub/openwrt-node-packages-master/node/node-v6.9.1/tools/gyp/pylib/gyp/xcode_emulation.py | python | XcodeSettings.GetCflagsObjC | (self, configname) | return cflags_objc | Returns flags that need to be added to .m compilations. | Returns flags that need to be added to .m compilations. | [
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"""Returns flags that need to be added to .m compilations."""
self.configname = configname
cflags_objc = []
self._AddObjectiveCGarbageCollectionFlags(cflags_objc)
self._AddObjectiveCARCFlags(cflags_objc)
self._AddObjectiveCMissingPropertySynthesisFlags(cflags_objc)
self.configname = None
return cflags_objc | [
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Polidea/SiriusObfuscator | b0e590d8130e97856afe578869b83a209e2b19be | SymbolExtractorAndRenamer/lldb/scripts/Python/static-binding/lldb.py | python | SBUnixSignals.IsValid | (self) | return _lldb.SBUnixSignals_IsValid(self) | IsValid(self) -> bool | IsValid(self) -> bool | [
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"""IsValid(self) -> bool"""
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|
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/prompt-toolkit/py2/prompt_toolkit/contrib/telnet/protocol.py | python | TelnetProtocolParser.wont_received | (self, data) | Received telnet WONT command. | Received telnet WONT command. | [
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""" Received telnet WONT command. """
logger.info('WONT %r', data) | [
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||
zju3dv/clean-pvnet | 5870c509e3cc205e1bb28910a7b1a9a3c8add9a8 | lib/csrc/ransac_voting/ransac_voting_gpu.py | python | ransac_voting_layer_v3 | (mask, vertex, round_hyp_num, inlier_thresh=0.999, confidence=0.99, max_iter=20,
min_num=5, max_num=30000) | return batch_win_pts | :param mask: [b,h,w]
:param vertex: [b,h,w,vn,2]
:param round_hyp_num:
:param inlier_thresh:
:return: [b,vn,2] | :param mask: [b,h,w]
:param vertex: [b,h,w,vn,2]
:param round_hyp_num:
:param inlier_thresh:
:return: [b,vn,2] | [
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min_num=5, max_num=30000):
'''
:param mask: [b,h,w]
:param vertex: [b,h,w,vn,2]
:param round_hyp_num:
:param inlier_thresh:
:return: [b,vn,2]
'''
b, h, w, vn, _ = vertex.shape
batch_win_pts = []
for bi in range(b):
hyp_num = 0
cur_mask = (mask[bi]).byte()
foreground_num = torch.sum(cur_mask)
# if too few points, just skip it
if foreground_num < min_num:
win_pts = torch.zeros([1, vn, 2], dtype=torch.float32, device=mask.device)
batch_win_pts.append(win_pts) # [1,vn,2]
continue
# if too many inliers, we randomly down sample it
if foreground_num > max_num:
selection = torch.zeros(cur_mask.shape, dtype=torch.float32, device=mask.device).uniform_(0, 1)
selected_mask = (selection < (max_num / foreground_num.float())).byte()
cur_mask *= selected_mask
coords = torch.nonzero(cur_mask).float() # [tn,2]
coords = coords[:, [1, 0]]
direct = vertex[bi].masked_select(torch.unsqueeze(torch.unsqueeze(cur_mask, 2), 3)) # [tn,vn,2]
direct = direct.view([coords.shape[0], vn, 2])
tn = coords.shape[0]
idxs = torch.zeros([round_hyp_num, vn, 2], dtype=torch.int32, device=mask.device).random_(0, direct.shape[0])
all_win_ratio = torch.zeros([vn], dtype=torch.float32, device=mask.device)
all_win_pts = torch.zeros([vn, 2], dtype=torch.float32, device=mask.device)
cur_iter = 0
while True:
# generate hypothesis
cur_hyp_pts = ransac_voting.generate_hypothesis(direct, coords, idxs) # [hn,vn,2]
# voting for hypothesis
cur_inlier = torch.zeros([round_hyp_num, vn, tn], dtype=torch.uint8, device=mask.device)
ransac_voting.voting_for_hypothesis(direct, coords, cur_hyp_pts, cur_inlier, inlier_thresh) # [hn,vn,tn]
# find max
cur_inlier_counts = torch.sum(cur_inlier, 2) # [hn,vn]
cur_win_counts, cur_win_idx = torch.max(cur_inlier_counts, 0) # [vn]
cur_win_pts = cur_hyp_pts[cur_win_idx, torch.arange(vn)]
cur_win_ratio = cur_win_counts.float() / tn
# update best point
larger_mask = all_win_ratio < cur_win_ratio
all_win_pts[larger_mask, :] = cur_win_pts[larger_mask, :]
all_win_ratio[larger_mask] = cur_win_ratio[larger_mask]
# check confidence
hyp_num += round_hyp_num
cur_iter += 1
cur_min_ratio = torch.min(all_win_ratio)
if (1 - (1 - cur_min_ratio ** 2) ** hyp_num) > confidence or cur_iter > max_iter:
break
# compute mean intersection again
normal = torch.zeros_like(direct) # [tn,vn,2]
normal[:, :, 0] = direct[:, :, 1]
normal[:, :, 1] = -direct[:, :, 0]
all_inlier = torch.zeros([1, vn, tn], dtype=torch.uint8, device=mask.device)
all_win_pts = torch.unsqueeze(all_win_pts, 0) # [1,vn,2]
ransac_voting.voting_for_hypothesis(direct, coords, all_win_pts, all_inlier, inlier_thresh) # [1,vn,tn]
# coords [tn,2] normal [vn,tn,2]
all_inlier = torch.squeeze(all_inlier.float(), 0) # [vn,tn]
normal = normal.permute(1, 0, 2) # [vn,tn,2]
normal = normal*torch.unsqueeze(all_inlier, 2) # [vn,tn,2] outlier is all zero
b = torch.sum(normal*torch.unsqueeze(coords, 0), 2) # [vn,tn]
ATA = torch.matmul(normal.permute(0, 2, 1), normal) # [vn,2,2]
ATb = torch.sum(normal*torch.unsqueeze(b, 2), 1) # [vn,2]
# try:
all_win_pts = torch.matmul(b_inv(ATA), torch.unsqueeze(ATb, 2)) # [vn,2,1]
# except:
# __import__('ipdb').set_trace()
batch_win_pts.append(all_win_pts[None,:,:, 0])
batch_win_pts = torch.cat(batch_win_pts)
return batch_win_pts | [
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|
yuxng/PoseCNN | 9f3dd7b7bce21dcafc05e8f18ccc90da3caabd04 | lib/datasets/ycb_single.py | python | ycb_single.image_path_at | (self, i) | return self.image_path_from_index(self.image_index[i]) | Return the absolute path to image i in the image sequence. | Return the absolute path to image i in the image sequence. | [
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"""
Return the absolute path to image i in the image sequence.
"""
return self.image_path_from_index(self.image_index[i]) | [
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|
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py2/pandas/core/sparse/frame.py | python | SparseDataFrame.applymap | (self, func) | return self.apply(lambda x: lmap(func, x)) | Apply a function to a DataFrame that is intended to operate
elementwise, i.e. like doing map(func, series) for each series in the
DataFrame
Parameters
----------
func : function
Python function, returns a single value from a single value
Returns
-------
applied : DataFrame | Apply a function to a DataFrame that is intended to operate
elementwise, i.e. like doing map(func, series) for each series in the
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"""
Apply a function to a DataFrame that is intended to operate
elementwise, i.e. like doing map(func, series) for each series in the
DataFrame
Parameters
----------
func : function
Python function, returns a single value from a single value
Returns
-------
applied : DataFrame
"""
return self.apply(lambda x: lmap(func, x)) | [
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|
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/scripts/doxypy.py | python | Doxypy.parseFile | (self, filename) | Parses a python file given as input string and returns the doxygen-
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@param input the python code to parse
@returns the modified python code | Parses a python file given as input string and returns the doxygen-
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"""Parses a python file given as input string and returns the doxygen-
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@param input the python code to parse
@returns the modified python code
"""
f = open(filename, 'r')
for line in f:
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if self.fsm.current_state == "DEFCLASS":
self.__closeComment()
self.__flushBuffer()
f.close() | [
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||
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/mailcap.py | python | findmatch | (caps, MIMEtype, key='view', filename="/dev/null", plist=[]) | return None, None | Find a match for a mailcap entry.
Return a tuple containing the command line, and the mailcap entry
used; (None, None) if no match is found. This may invoke the
'test' command of several matching entries before deciding which
entry to use. | Find a match for a mailcap entry. | [
"Find",
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] | def findmatch(caps, MIMEtype, key='view', filename="/dev/null", plist=[]):
"""Find a match for a mailcap entry.
Return a tuple containing the command line, and the mailcap entry
used; (None, None) if no match is found. This may invoke the
'test' command of several matching entries before deciding which
entry to use.
"""
entries = lookup(caps, MIMEtype, key)
# XXX This code should somehow check for the needsterminal flag.
for e in entries:
if 'test' in e:
test = subst(e['test'], filename, plist)
if test and os.system(test) != 0:
continue
command = subst(e[key], MIMEtype, filename, plist)
return command, e
return None, None | [
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|
cyberbotics/webots | af7fa7d68dcf7b4550f1f2e132092b41e83698fc | resources/osm_importer/webots_objects/road.py | python | Road.add_to_list | (osmid, tags, refs) | Add a new road to the list of roads. | Add a new road to the list of roads. | [
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] | def add_to_list(osmid, tags, refs):
"""Add a new road to the list of roads."""
settingsSection = Settings.get_section('road', tags['highway'])
if settingsSection is None:
return
road = Road()
road.osmid = osmid
road.id = str(osmid)
road.refs = refs
road.tags = tags
if road.parse_tags():
# Only add the road if valid.
Road.roads.append(road)
for ref in road.refs:
node = OSMNode.nodeDictionnary[ref]
if 'highway' in node.tags and node.tags['highway'] == 'crossing':
road.crossings.append(node) | [
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||
wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/cmd.py | python | Cmd.parseline | (self, line) | return cmd, arg, line | Parse the line into a command name and a string containing
the arguments. Returns a tuple containing (command, args, line).
'command' and 'args' may be None if the line couldn't be parsed. | Parse the line into a command name and a string containing
the arguments. Returns a tuple containing (command, args, line).
'command' and 'args' may be None if the line couldn't be parsed. | [
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] | def parseline(self, line):
"""Parse the line into a command name and a string containing
the arguments. Returns a tuple containing (command, args, line).
'command' and 'args' may be None if the line couldn't be parsed.
"""
line = line.strip()
if not line:
return None, None, line
elif line[0] == '?':
line = 'help ' + line[1:]
elif line[0] == '!':
if hasattr(self, 'do_shell'):
line = 'shell ' + line[1:]
else:
return None, None, line
i, n = 0, len(line)
while i < n and line[i] in self.identchars: i = i+1
cmd, arg = line[:i], line[i:].strip()
return cmd, arg, line | [
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|
tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/keras/utils/metrics_utils.py | python | ragged_assert_compatible_and_get_flat_values | (values, mask=None) | return values, mask | If ragged, it checks the compatibility and then returns the flat_values.
Note: If two tensors are dense, it does not check their compatibility.
Note: Although two ragged tensors with different ragged ranks could have
identical overall rank and dimension sizes and hence be compatible,
we do not support those cases.
Args:
values: A list of potentially ragged tensor of the same ragged_rank.
mask: A potentially ragged tensor of the same ragged_rank as elements in
Values.
Returns:
A tuple in which the first element is the list of tensors and the second
is the mask tensor. ([Values], mask). Mask and the element in Values
are equal to the flat_values of the input arguments (if they were ragged). | If ragged, it checks the compatibility and then returns the flat_values. | [
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] | def ragged_assert_compatible_and_get_flat_values(values, mask=None):
"""If ragged, it checks the compatibility and then returns the flat_values.
Note: If two tensors are dense, it does not check their compatibility.
Note: Although two ragged tensors with different ragged ranks could have
identical overall rank and dimension sizes and hence be compatible,
we do not support those cases.
Args:
values: A list of potentially ragged tensor of the same ragged_rank.
mask: A potentially ragged tensor of the same ragged_rank as elements in
Values.
Returns:
A tuple in which the first element is the list of tensors and the second
is the mask tensor. ([Values], mask). Mask and the element in Values
are equal to the flat_values of the input arguments (if they were ragged).
"""
if isinstance(values, list):
is_all_ragged = \
all(isinstance(rt, ragged_tensor.RaggedTensor) for rt in values)
is_any_ragged = \
any(isinstance(rt, ragged_tensor.RaggedTensor) for rt in values)
else:
is_all_ragged = isinstance(values, ragged_tensor.RaggedTensor)
is_any_ragged = is_all_ragged
if (is_all_ragged and
((mask is None) or isinstance(mask, ragged_tensor.RaggedTensor))):
to_be_stripped = False
if not isinstance(values, list):
values = [values]
to_be_stripped = True
# NOTE: we leave the flat_values compatibility to
# tf.TensorShape `assert_is_compatible_with`
# check if both dynamic dimensions are equal and then use the flat_values.
nested_row_split_list = [rt.nested_row_splits for rt in values]
assertion_list = _assert_splits_match(nested_row_split_list)
# if both are ragged sample_weights also should be ragged with same dims.
if isinstance(mask, ragged_tensor.RaggedTensor):
assertion_list_for_mask = _assert_splits_match(
[nested_row_split_list[0], mask.nested_row_splits])
with ops.control_dependencies(assertion_list_for_mask):
mask = array_ops.expand_dims(mask.flat_values, -1)
# values has at least 1 element.
flat_values = []
for value in values:
with ops.control_dependencies(assertion_list):
flat_values.append(array_ops.expand_dims(value.flat_values, -1))
values = flat_values[0] if to_be_stripped else flat_values
elif is_any_ragged:
raise TypeError('One of the inputs does not have acceptable types.')
# values are empty or value are not ragged and mask is ragged.
elif isinstance(mask, ragged_tensor.RaggedTensor):
raise TypeError('Ragged mask is not allowed with non-ragged inputs.')
return values, mask | [
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|
Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/data/experimental/ops/resampling.py | python | rejection_resample | (class_func, target_dist, initial_dist=None, seed=None) | return _apply_fn | A transformation that resamples a dataset to achieve a target distribution.
**NOTE** Resampling is performed via rejection sampling; some fraction
of the input values will be dropped.
Args:
class_func: A function mapping an element of the input dataset to a scalar
`tf.int32` tensor. Values should be in `[0, num_classes)`.
target_dist: A floating point type tensor, shaped `[num_classes]`.
initial_dist: (Optional.) A floating point type tensor, shaped
`[num_classes]`. If not provided, the true class distribution is
estimated live in a streaming fashion.
seed: (Optional.) Python integer seed for the resampler.
Returns:
A `Dataset` transformation function, which can be passed to
`tf.data.Dataset.apply`. | A transformation that resamples a dataset to achieve a target distribution. | [
"A",
"transformation",
"that",
"resamples",
"a",
"dataset",
"to",
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"target",
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] | def rejection_resample(class_func, target_dist, initial_dist=None, seed=None):
"""A transformation that resamples a dataset to achieve a target distribution.
**NOTE** Resampling is performed via rejection sampling; some fraction
of the input values will be dropped.
Args:
class_func: A function mapping an element of the input dataset to a scalar
`tf.int32` tensor. Values should be in `[0, num_classes)`.
target_dist: A floating point type tensor, shaped `[num_classes]`.
initial_dist: (Optional.) A floating point type tensor, shaped
`[num_classes]`. If not provided, the true class distribution is
estimated live in a streaming fashion.
seed: (Optional.) Python integer seed for the resampler.
Returns:
A `Dataset` transformation function, which can be passed to
`tf.data.Dataset.apply`.
"""
def _apply_fn(dataset):
"""Function from `Dataset` to `Dataset` that applies the transformation."""
target_dist_t = ops.convert_to_tensor(target_dist, name="target_dist")
class_values_ds = dataset.map(class_func)
# Get initial distribution.
if initial_dist is not None:
initial_dist_t = ops.convert_to_tensor(initial_dist, name="initial_dist")
acceptance_dist, prob_of_original = (
_calculate_acceptance_probs_with_mixing(initial_dist_t,
target_dist_t))
initial_dist_ds = dataset_ops.Dataset.from_tensors(
initial_dist_t).repeat()
acceptance_dist_ds = dataset_ops.Dataset.from_tensors(
acceptance_dist).repeat()
prob_of_original_ds = dataset_ops.Dataset.from_tensors(
prob_of_original).repeat()
else:
initial_dist_ds = _estimate_initial_dist_ds(
target_dist_t, class_values_ds)
acceptance_and_original_prob_ds = initial_dist_ds.map(
lambda initial: _calculate_acceptance_probs_with_mixing( # pylint: disable=g-long-lambda
initial, target_dist_t))
acceptance_dist_ds = acceptance_and_original_prob_ds.map(
lambda accept_prob, _: accept_prob)
prob_of_original_ds = acceptance_and_original_prob_ds.map(
lambda _, prob_original: prob_original)
filtered_ds = _filter_ds(dataset, acceptance_dist_ds, initial_dist_ds,
class_values_ds, seed)
# Prefetch filtered dataset for speed.
filtered_ds = filtered_ds.prefetch(3)
prob_original_static = _get_prob_original_static(
initial_dist_t, target_dist_t) if initial_dist is not None else None
if prob_original_static == 1:
return dataset_ops.Dataset.zip((class_values_ds, dataset))
elif prob_original_static == 0:
return filtered_ds
else:
return interleave_ops.sample_from_datasets(
[dataset_ops.Dataset.zip((class_values_ds, dataset)), filtered_ds],
weights=prob_of_original_ds.map(lambda prob: [(prob, 1.0 - prob)]),
seed=seed)
return _apply_fn | [
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|
snap-stanford/snap-python | d53c51b0a26aa7e3e7400b014cdf728948fde80a | setup/snap.py | python | TStr_IsAbsFPath | (*args) | return _snap.TStr_IsAbsFPath(*args) | TStr_IsAbsFPath(TStr FPath) -> bool
Parameters:
FPath: TStr const & | TStr_IsAbsFPath(TStr FPath) -> bool | [
"TStr_IsAbsFPath",
"(",
"TStr",
"FPath",
")",
"-",
">",
"bool"
] | def TStr_IsAbsFPath(*args):
"""
TStr_IsAbsFPath(TStr FPath) -> bool
Parameters:
FPath: TStr const &
"""
return _snap.TStr_IsAbsFPath(*args) | [
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|
hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/telemetry/telemetry/internal/platform/posix_platform_backend.py | python | PosixPlatformBackend._GetTopOutput | (self, pid, columns) | return self.RunCommand(args).splitlines() | Returns output of the 'top' command as a list of lines.
Args:
pid: pid of process to examine.
columns: A list of require columns, e.g., ['idlew', 'vsize']. | Returns output of the 'top' command as a list of lines. | [
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] | def _GetTopOutput(self, pid, columns):
"""Returns output of the 'top' command as a list of lines.
Args:
pid: pid of process to examine.
columns: A list of require columns, e.g., ['idlew', 'vsize'].
"""
args = ['top']
args.extend(['-pid', str(pid), '-l', '1', '-s', '0', '-stats',
','.join(columns)])
return self.RunCommand(args).splitlines() | [
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|
hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | tools/android/loading/dependency_graph.py | python | RequestDependencyGraph.Cost | (self, from_first_request=True, path_list=None, costs_out=None) | Returns the cost of the graph, that is the costliest path.
Args:
from_first_request: (boolean) If True, only considers paths that originate
from the first request node.
path_list: (list) See graph.Cost().
costs_out: (list) See graph.Cost(). | Returns the cost of the graph, that is the costliest path. | [
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] | def Cost(self, from_first_request=True, path_list=None, costs_out=None):
"""Returns the cost of the graph, that is the costliest path.
Args:
from_first_request: (boolean) If True, only considers paths that originate
from the first request node.
path_list: (list) See graph.Cost().
costs_out: (list) See graph.Cost().
"""
if from_first_request:
return self._deps_graph.Cost(
[self._first_request_node], path_list, costs_out)
else:
return self._deps_graph.Cost(path_list=path_list, costs_out=costs_out) | [
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||
facebookresearch/faiss | eb8781557f556505ca93f6f21fff932e17f0d9e0 | benchs/bench_gpu_1bn.py | python | compute_populated_index | (preproc) | return gpu_index, indexall | Add elements to a sharded index. Return the index and if available
a sharded gpu_index that contains the same data. | Add elements to a sharded index. Return the index and if available
a sharded gpu_index that contains the same data. | [
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"that",
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] | def compute_populated_index(preproc):
"""Add elements to a sharded index. Return the index and if available
a sharded gpu_index that contains the same data. """
indexall = prepare_trained_index(preproc)
co = faiss.GpuMultipleClonerOptions()
co.useFloat16 = use_float16
co.useFloat16CoarseQuantizer = False
co.usePrecomputed = use_precomputed_tables
co.indicesOptions = faiss.INDICES_CPU
co.verbose = True
co.reserveVecs = max_add if max_add > 0 else xb.shape[0]
co.shard = True
assert co.shard_type in (0, 1, 2)
vres, vdev = make_vres_vdev()
gpu_index = faiss.index_cpu_to_gpu_multiple(
vres, vdev, indexall, co)
print("add...")
t0 = time.time()
nb = xb.shape[0]
for i0, xs in dataset_iterator(xb, preproc, add_batch_size):
i1 = i0 + xs.shape[0]
gpu_index.add_with_ids(xs, np.arange(i0, i1))
if max_add > 0 and gpu_index.ntotal > max_add:
print("Flush indexes to CPU")
for i in range(ngpu):
index_src_gpu = faiss.downcast_index(gpu_index.at(i))
index_src = faiss.index_gpu_to_cpu(index_src_gpu)
print(" index %d size %d" % (i, index_src.ntotal))
index_src.copy_subset_to(indexall, 0, 0, nb)
index_src_gpu.reset()
index_src_gpu.reserveMemory(max_add)
gpu_index.sync_with_shard_indexes()
print('\r%d/%d (%.3f s) ' % (
i0, nb, time.time() - t0), end=' ')
sys.stdout.flush()
print("Add time: %.3f s" % (time.time() - t0))
print("Aggregate indexes to CPU")
t0 = time.time()
if hasattr(gpu_index, 'at'):
# it is a sharded index
for i in range(ngpu):
index_src = faiss.index_gpu_to_cpu(gpu_index.at(i))
print(" index %d size %d" % (i, index_src.ntotal))
index_src.copy_subset_to(indexall, 0, 0, nb)
else:
# simple index
index_src = faiss.index_gpu_to_cpu(gpu_index)
index_src.copy_subset_to(indexall, 0, 0, nb)
print(" done in %.3f s" % (time.time() - t0))
if max_add > 0:
# it does not contain all the vectors
gpu_index = None
return gpu_index, indexall | [
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|
Tokutek/mongo | 0653eabe2c5b9d12b4814617cb7fb2d799937a0f | buildscripts/cpplint.py | python | _SetFilters | (filters) | Sets the module's error-message filters.
These filters are applied when deciding whether to emit a given
error message.
Args:
filters: A string of comma-separated filters (eg "whitespace/indent").
Each filter should start with + or -; else we die. | Sets the module's error-message filters. | [
"Sets",
"the",
"module",
"s",
"error",
"-",
"message",
"filters",
"."
] | def _SetFilters(filters):
"""Sets the module's error-message filters.
These filters are applied when deciding whether to emit a given
error message.
Args:
filters: A string of comma-separated filters (eg "whitespace/indent").
Each filter should start with + or -; else we die.
"""
_cpplint_state.SetFilters(filters) | [
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] | https://github.com/Tokutek/mongo/blob/0653eabe2c5b9d12b4814617cb7fb2d799937a0f/buildscripts/cpplint.py#L601-L611 |
||
mapnik/mapnik | f3da900c355e1d15059c4a91b00203dcc9d9f0ef | scons/scons-local-4.1.0/SCons/Variables/__init__.py | python | Variables.Update | (self, env, args=None) | Update an environment with the option variables.
env - the environment to update. | Update an environment with the option variables. | [
"Update",
"an",
"environment",
"with",
"the",
"option",
"variables",
"."
] | def Update(self, env, args=None):
"""
Update an environment with the option variables.
env - the environment to update.
"""
values = {}
# first set the defaults:
for option in self.options:
if option.default is not None:
values[option.key] = option.default
# next set the value specified in the options file
for filename in self.files:
if os.path.exists(filename):
dir = os.path.split(os.path.abspath(filename))[0]
if dir:
sys.path.insert(0, dir)
try:
values['__name__'] = filename
with open(filename, 'r') as f:
contents = f.read()
exec(contents, {}, values)
finally:
if dir:
del sys.path[0]
del values['__name__']
# set the values specified on the command line
if args is None:
args = self.args
for arg, value in args.items():
added = False
for option in self.options:
if arg in list(option.aliases) + [ option.key ]:
values[option.key] = value
added = True
if not added:
self.unknown[arg] = value
# put the variables in the environment:
# (don't copy over variables that are not declared as options)
for option in self.options:
try:
env[option.key] = values[option.key]
except KeyError:
pass
# Call the convert functions:
for option in self.options:
if option.converter and option.key in values:
value = env.subst('${%s}'%option.key)
try:
try:
env[option.key] = option.converter(value)
except TypeError:
env[option.key] = option.converter(value, env)
except ValueError as x:
raise SCons.Errors.UserError('Error converting option: %s\n%s'%(option.key, x))
# Finally validate the values:
for option in self.options:
if option.validator and option.key in values:
option.validator(option.key, env.subst('${%s}'%option.key), env) | [
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qgis/QGIS | 15a77662d4bb712184f6aa60d0bd663010a76a75 | python/plugins/processing/gui/HistoryDialog.py | python | TreeLogEntryItem.as_qgis_process_command | (self) | return self.entry.entry.get('process_command') | Returns the entry as a qgis_process command, if possible | Returns the entry as a qgis_process command, if possible | [
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"""
Returns the entry as a qgis_process command, if possible
"""
return self.entry.entry.get('process_command') | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/timeseries/python/timeseries/state_management.py | python | ChainingStateManager._update_cached_states | (self, model, features, mode) | return loss_op, end_state, batch_predictions | Read, process, and write chunks to the cache. | Read, process, and write chunks to the cache. | [
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"""Read, process, and write chunks to the cache."""
times = features[feature_keys.TrainEvalFeatures.TIMES]
looked_up_state = self._get_cached_states(times[:, 0])
(model_loss, intermediate_states,
batch_predictions) = model.per_step_batch_loss(
features=features,
mode=mode,
state=looked_up_state)
# We need to at least write to the bucket after the one we read from.
min_chunk_numbers = self._get_chunk_number(times) + 1
# We write to the bucket that would have been read had the window started at
# the next sample (except for the last sample in the window, which gets
# written to the next bucket). This assumes fixed missing times (i.e. if we
# were presented with times [10, 50] we will never see times [30, 50]).
#
# TODO(allenl): Retrieve the highest time less than the current time rather
# than relying on fixed bucketing.
write_chunk_numbers = math_ops.maximum(
self._get_chunk_number(array_ops.concat(
[times[:, 1:], times[:, -1:] + 1], axis=1)),
min_chunk_numbers)
# Write once for every computed state; this may mean that we write multiple
# times to the same cell, but later writes will take precedence.
save_ops = [
self._cached_states.insert(
keys=write_chunk_numbers,
values=intermediate_states)]
end_state = nest.pack_sequence_as(
intermediate_states,
[state_element[:, -1]
for state_element in nest.flatten(intermediate_states)])
with ops.control_dependencies(save_ops):
# Make sure end states get saved at each iteration
loss_op = array_ops.identity(model_loss)
return loss_op, end_state, batch_predictions | [
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|
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_gdi.py | python | IconLocation.GetIndex | (*args, **kwargs) | return _gdi_.IconLocation_GetIndex(*args, **kwargs) | GetIndex(self) -> int | GetIndex(self) -> int | [
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"""GetIndex(self) -> int"""
return _gdi_.IconLocation_GetIndex(*args, **kwargs) | [
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|
okex/V3-Open-API-SDK | c5abb0db7e2287718e0055e17e57672ce0ec7fd9 | okex-python-sdk-api/venv/Lib/site-packages/pip-19.0.3-py3.8.egg/pip/_vendor/pkg_resources/__init__.py | python | non_empty_lines | (path) | Yield non-empty lines from file at path | Yield non-empty lines from file at path | [
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"""
Yield non-empty lines from file at path
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||
apple/swift-lldb | d74be846ef3e62de946df343e8c234bde93a8912 | scripts/Python/static-binding/lldb.py | python | SBTypeFilter.__ne__ | (self, rhs) | return _lldb.SBTypeFilter___ne__(self, rhs) | __ne__(SBTypeFilter self, SBTypeFilter rhs) -> bool | __ne__(SBTypeFilter self, SBTypeFilter rhs) -> bool | [
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|
rdkit/rdkit | ede860ae316d12d8568daf5ee800921c3389c84e | rdkit/sping/WX/pidWxDc.py | python | PiddleWxDc.fontAscent | (self, font=None) | return self.dc.GetCharHeight() - self.fontDescent(font) | Find the ascent (height above base) of the given font. | Find the ascent (height above base) of the given font. | [
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'''Find the ascent (height above base) of the given font.'''
wx_font = self._setWXfont(font)
return self.dc.GetCharHeight() - self.fontDescent(font) | [
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|
SpenceKonde/megaTinyCore | 1c4a70b18a149fe6bcb551dfa6db11ca50b8997b | megaavr/tools/libs/pymcuprog/nvmspi.py | python | NvmAccessProviderCmsisDapSpi.stop | (self) | Stop programming session | Stop programming session | [
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"""
Stop programming session
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||
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py2/sklearn/model_selection/_validation.py | python | permutation_test_score | (estimator, X, y, groups=None, cv=None,
n_permutations=100, n_jobs=1, random_state=0,
verbose=0, scoring=None) | return score, permutation_scores, pvalue | Evaluate the significance of a cross-validated score with permutations
Read more in the :ref:`User Guide <cross_validation>`.
Parameters
----------
estimator : estimator object implementing 'fit'
The object to use to fit the data.
X : array-like of shape at least 2D
The data to fit.
y : array-like
The target variable to try to predict in the case of
supervised learning.
groups : array-like, with shape (n_samples,), optional
Labels to constrain permutation within groups, i.e. ``y`` values
are permuted among samples with the same group identifier.
When not specified, ``y`` values are permuted among all samples.
When a grouped cross-validator is used, the group labels are
also passed on to the ``split`` method of the cross-validator. The
cross-validator uses them for grouping the samples while splitting
the dataset into train/test set.
scoring : string, callable or None, optional, default: None
A string (see model evaluation documentation) or
a scorer callable object / function with signature
``scorer(estimator, X, y)``.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross validation,
- integer, to specify the number of folds in a `(Stratified)KFold`,
- An object to be used as a cross-validation generator.
- An iterable yielding train, test splits.
For integer/None inputs, if the estimator is a classifier and ``y`` is
either binary or multiclass, :class:`StratifiedKFold` is used. In all
other cases, :class:`KFold` is used.
Refer :ref:`User Guide <cross_validation>` for the various
cross-validation strategies that can be used here.
n_permutations : integer, optional
Number of times to permute ``y``.
n_jobs : integer, optional
The number of CPUs to use to do the computation. -1 means
'all CPUs'.
random_state : RandomState or an int seed (0 by default)
A random number generator instance to define the state of the
random permutations generator.
verbose : integer, optional
The verbosity level.
Returns
-------
score : float
The true score without permuting targets.
permutation_scores : array, shape (n_permutations,)
The scores obtained for each permutations.
pvalue : float
The returned value equals p-value if `scoring` returns bigger
numbers for better scores (e.g., accuracy_score). If `scoring` is
rather a loss function (i.e. when lower is better such as with
`mean_squared_error`) then this is actually the complement of the
p-value: 1 - p-value.
Notes
-----
This function implements Test 1 in:
Ojala and Garriga. Permutation Tests for Studying Classifier
Performance. The Journal of Machine Learning Research (2010)
vol. 11 | Evaluate the significance of a cross-validated score with permutations | [
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"with",
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n_permutations=100, n_jobs=1, random_state=0,
verbose=0, scoring=None):
"""Evaluate the significance of a cross-validated score with permutations
Read more in the :ref:`User Guide <cross_validation>`.
Parameters
----------
estimator : estimator object implementing 'fit'
The object to use to fit the data.
X : array-like of shape at least 2D
The data to fit.
y : array-like
The target variable to try to predict in the case of
supervised learning.
groups : array-like, with shape (n_samples,), optional
Labels to constrain permutation within groups, i.e. ``y`` values
are permuted among samples with the same group identifier.
When not specified, ``y`` values are permuted among all samples.
When a grouped cross-validator is used, the group labels are
also passed on to the ``split`` method of the cross-validator. The
cross-validator uses them for grouping the samples while splitting
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scoring : string, callable or None, optional, default: None
A string (see model evaluation documentation) or
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``scorer(estimator, X, y)``.
cv : int, cross-validation generator or an iterable, optional
Determines the cross-validation splitting strategy.
Possible inputs for cv are:
- None, to use the default 3-fold cross validation,
- integer, to specify the number of folds in a `(Stratified)KFold`,
- An object to be used as a cross-validation generator.
- An iterable yielding train, test splits.
For integer/None inputs, if the estimator is a classifier and ``y`` is
either binary or multiclass, :class:`StratifiedKFold` is used. In all
other cases, :class:`KFold` is used.
Refer :ref:`User Guide <cross_validation>` for the various
cross-validation strategies that can be used here.
n_permutations : integer, optional
Number of times to permute ``y``.
n_jobs : integer, optional
The number of CPUs to use to do the computation. -1 means
'all CPUs'.
random_state : RandomState or an int seed (0 by default)
A random number generator instance to define the state of the
random permutations generator.
verbose : integer, optional
The verbosity level.
Returns
-------
score : float
The true score without permuting targets.
permutation_scores : array, shape (n_permutations,)
The scores obtained for each permutations.
pvalue : float
The returned value equals p-value if `scoring` returns bigger
numbers for better scores (e.g., accuracy_score). If `scoring` is
rather a loss function (i.e. when lower is better such as with
`mean_squared_error`) then this is actually the complement of the
p-value: 1 - p-value.
Notes
-----
This function implements Test 1 in:
Ojala and Garriga. Permutation Tests for Studying Classifier
Performance. The Journal of Machine Learning Research (2010)
vol. 11
"""
X, y, groups = indexable(X, y, groups)
cv = check_cv(cv, y, classifier=is_classifier(estimator))
scorer = check_scoring(estimator, scoring=scoring)
random_state = check_random_state(random_state)
# We clone the estimator to make sure that all the folds are
# independent, and that it is pickle-able.
score = _permutation_test_score(clone(estimator), X, y, groups, cv, scorer)
permutation_scores = Parallel(n_jobs=n_jobs, verbose=verbose)(
delayed(_permutation_test_score)(
clone(estimator), X, _shuffle(y, groups, random_state),
groups, cv, scorer)
for _ in range(n_permutations))
permutation_scores = np.array(permutation_scores)
pvalue = (np.sum(permutation_scores >= score) + 1.0) / (n_permutations + 1)
return score, permutation_scores, pvalue | [
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SequoiaDB/SequoiaDB | 2894ed7e5bd6fe57330afc900cf76d0ff0df9f64 | tools/server/php_linux/libxml2/lib/python2.4/site-packages/libxml2.py | python | xmlDoc.xincludeProcessFlags | (self, flags) | return ret | Implement the XInclude substitution on the XML document @doc | Implement the XInclude substitution on the XML document | [
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] | def xincludeProcessFlags(self, flags):
"""Implement the XInclude substitution on the XML document @doc """
ret = libxml2mod.xmlXIncludeProcessFlags(self._o, flags)
return ret | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/eager/python/examples/revnet/main_estimator.py | python | get_input_fn | (config, data_dir, split) | return input_fn | Get the input function that is required by the `tf.estimator` API.
Args:
config: Customized hyperparameters
data_dir: Directory where the data is stored
split: One of `train`, `validation`, `train_all`, and `test`
Returns:
Input function required by the `tf.estimator` API | Get the input function that is required by the `tf.estimator` API. | [
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"""Get the input function that is required by the `tf.estimator` API.
Args:
config: Customized hyperparameters
data_dir: Directory where the data is stored
split: One of `train`, `validation`, `train_all`, and `test`
Returns:
Input function required by the `tf.estimator` API
"""
data_dir = os.path.join(data_dir, config.dataset)
# Fix split-dependent hyperparameters
if split == "train_all" or split == "train":
data_aug = True
batch_size = config.batch_size
epochs = config.epochs
shuffle = True
prefetch = config.batch_size
else:
data_aug = False
batch_size = config.eval_batch_size
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def input_fn():
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split=split,
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prefetch=prefetch,
data_format=config.data_format)
return input_fn | [
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|
bilibili/biliobs | 573613dc3b2b63fe7c1506cc94717609a2c52c0c | third_party/freetype/src/tools/docmaker/content.py | python | ContentProcessor.set_section | ( self, section_name ) | Set current section during parsing. | Set current section during parsing. | [
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"""Set current section during parsing."""
if not section_name in self.sections:
section = DocSection( section_name )
self.sections[section_name] = section
self.section = section
else:
self.section = self.sections[section_name] | [
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apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | deps/src/libxml2-2.9.1/python/libxml2.py | python | xmlDoc.saveFormatFileEnc | (self, filename, encoding, format) | return ret | Dump an XML document to a file or an URL. | Dump an XML document to a file or an URL. | [
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"""Dump an XML document to a file or an URL. """
ret = libxml2mod.xmlSaveFormatFileEnc(filename, self._o, encoding, format)
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|
google/syzygy | 8164b24ebde9c5649c9a09e88a7fc0b0fcbd1bc5 | third_party/numpy/files/numpy/lib/function_base.py | python | gradient | (f, *varargs) | Return the gradient of an N-dimensional array.
The gradient is computed using central differences in the interior
and first differences at the boundaries. The returned gradient hence has
the same shape as the input array.
Parameters
----------
f : array_like
An N-dimensional array containing samples of a scalar function.
`*varargs` : scalars
0, 1, or N scalars specifying the sample distances in each direction,
that is: `dx`, `dy`, `dz`, ... The default distance is 1.
Returns
-------
g : ndarray
N arrays of the same shape as `f` giving the derivative of `f` with
respect to each dimension.
Examples
--------
>>> x = np.array([1, 2, 4, 7, 11, 16], dtype=np.float)
>>> np.gradient(x)
array([ 1. , 1.5, 2.5, 3.5, 4.5, 5. ])
>>> np.gradient(x, 2)
array([ 0.5 , 0.75, 1.25, 1.75, 2.25, 2.5 ])
>>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float))
[array([[ 2., 2., -1.],
[ 2., 2., -1.]]),
array([[ 1. , 2.5, 4. ],
[ 1. , 1. , 1. ]])] | Return the gradient of an N-dimensional array. | [
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"""
Return the gradient of an N-dimensional array.
The gradient is computed using central differences in the interior
and first differences at the boundaries. The returned gradient hence has
the same shape as the input array.
Parameters
----------
f : array_like
An N-dimensional array containing samples of a scalar function.
`*varargs` : scalars
0, 1, or N scalars specifying the sample distances in each direction,
that is: `dx`, `dy`, `dz`, ... The default distance is 1.
Returns
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g : ndarray
N arrays of the same shape as `f` giving the derivative of `f` with
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Examples
--------
>>> x = np.array([1, 2, 4, 7, 11, 16], dtype=np.float)
>>> np.gradient(x)
array([ 1. , 1.5, 2.5, 3.5, 4.5, 5. ])
>>> np.gradient(x, 2)
array([ 0.5 , 0.75, 1.25, 1.75, 2.25, 2.5 ])
>>> np.gradient(np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float))
[array([[ 2., 2., -1.],
[ 2., 2., -1.]]),
array([[ 1. , 2.5, 4. ],
[ 1. , 1. , 1. ]])]
"""
N = len(f.shape) # number of dimensions
n = len(varargs)
if n == 0:
dx = [1.0]*N
elif n == 1:
dx = [varargs[0]]*N
elif n == N:
dx = list(varargs)
else:
raise SyntaxError(
"invalid number of arguments")
# use central differences on interior and first differences on endpoints
outvals = []
# create slice objects --- initially all are [:, :, ..., :]
slice1 = [slice(None)]*N
slice2 = [slice(None)]*N
slice3 = [slice(None)]*N
otype = f.dtype.char
if otype not in ['f', 'd', 'F', 'D']:
otype = 'd'
for axis in range(N):
# select out appropriate parts for this dimension
out = np.zeros_like(f).astype(otype)
slice1[axis] = slice(1, -1)
slice2[axis] = slice(2, None)
slice3[axis] = slice(None, -2)
# 1D equivalent -- out[1:-1] = (f[2:] - f[:-2])/2.0
out[slice1] = (f[slice2] - f[slice3])/2.0
slice1[axis] = 0
slice2[axis] = 1
slice3[axis] = 0
# 1D equivalent -- out[0] = (f[1] - f[0])
out[slice1] = (f[slice2] - f[slice3])
slice1[axis] = -1
slice2[axis] = -1
slice3[axis] = -2
# 1D equivalent -- out[-1] = (f[-1] - f[-2])
out[slice1] = (f[slice2] - f[slice3])
# divide by step size
outvals.append(out / dx[axis])
# reset the slice object in this dimension to ":"
slice1[axis] = slice(None)
slice2[axis] = slice(None)
slice3[axis] = slice(None)
if N == 1:
return outvals[0]
else:
return outvals | [
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||
kamyu104/LeetCode-Solutions | 77605708a927ea3b85aee5a479db733938c7c211 | Python/robot-return-to-origin.py | python | Solution.judgeCircle | (self, moves) | return v == 0 and h == 0 | :type moves: str
:rtype: bool | :type moves: str
:rtype: bool | [
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"""
:type moves: str
:rtype: bool
"""
v, h = 0, 0
for move in moves:
if move == 'U':
v += 1
elif move == 'D':
v -= 1
elif move == 'R':
h += 1
elif move == 'L':
h -= 1
return v == 0 and h == 0 | [
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|
CGRU/cgru | 1881a4128530e3d31ac6c25314c18314fc50c2c7 | plugins/maya/afanasy/__init__.py | python | UI.set_field_value | (self, control, value, *args, **kwargs) | sets the given field value
:param control: the UI control
:param value: the value, can be a callable
:return: | sets the given field value | [
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"""sets the given field value
:param control: the UI control
:param value: the value, can be a callable
:return:
"""
try:
v = value()
except TypeError:
v = value
control.setValue(v) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/telemetry/telemetry/internal/platform/tracing_agent/__init__.py | python | TracingAgent.StartAgentTracing | (self, config, timeout) | Override to add tracing agent's custom logic to start tracing.
Depending on trace_options and category_filter, the tracing agent may choose
to start or not start tracing.
Args:
config: tracing_config instance that contains trace_option and
category_filter
trace_options: an instance of tracing_options.TracingOptions that
control which core tracing systems should be enabled.
category_filter: an instance of
chrome_trace_category_filter.ChromeTraceCategoryFilter
timeout: number of seconds that this tracing agent should try to start
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Returns:
True if tracing agent started successfully. | Override to add tracing agent's custom logic to start tracing. | [
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""" Override to add tracing agent's custom logic to start tracing.
Depending on trace_options and category_filter, the tracing agent may choose
to start or not start tracing.
Args:
config: tracing_config instance that contains trace_option and
category_filter
trace_options: an instance of tracing_options.TracingOptions that
control which core tracing systems should be enabled.
category_filter: an instance of
chrome_trace_category_filter.ChromeTraceCategoryFilter
timeout: number of seconds that this tracing agent should try to start
tracing until time out.
Returns:
True if tracing agent started successfully.
"""
raise NotImplementedError | [
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||
eric612/Caffe-YOLOv3-Windows | 6736ca6e16781789b828cc64218ff77cc3454e5d | scripts/cpp_lint.py | python | _NestingState.SeenOpenBrace | (self) | return (not self.stack) or self.stack[-1].seen_open_brace | Check if we have seen the opening brace for the innermost block.
Returns:
True if we have seen the opening brace, False if the innermost
block is still expecting an opening brace. | Check if we have seen the opening brace for the innermost block. | [
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"""Check if we have seen the opening brace for the innermost block.
Returns:
True if we have seen the opening brace, False if the innermost
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/richtext.py | python | RichTextBuffer.LoadStream | (*args, **kwargs) | return _richtext.RichTextBuffer_LoadStream(*args, **kwargs) | LoadStream(self, InputStream stream, int type=RICHTEXT_TYPE_ANY) -> bool | LoadStream(self, InputStream stream, int type=RICHTEXT_TYPE_ANY) -> bool | [
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intel/caffe | 3f494b442ee3f9d17a07b09ecbd5fa2bbda00836 | examples/rfcn/lib/pycocotools/cocoeval.py | python | COCOeval._prepare | (self) | Prepare ._gts and ._dts for evaluation based on params
:return: None | Prepare ._gts and ._dts for evaluation based on params
:return: None | [
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'''
Prepare ._gts and ._dts for evaluation based on params
:return: None
'''
#
def _toMask(objs, coco):
# modify segmentation by reference
for obj in objs:
t = coco.imgs[obj['image_id']]
if type(obj['segmentation']) == list:
if type(obj['segmentation'][0]) == dict:
print 'debug'
obj['segmentation'] = mask.frPyObjects(obj['segmentation'],t['height'],t['width'])
if len(obj['segmentation']) == 1:
obj['segmentation'] = obj['segmentation'][0]
else:
# an object can have multiple polygon regions
# merge them into one RLE mask
obj['segmentation'] = mask.merge(obj['segmentation'])
elif type(obj['segmentation']) == dict and type(obj['segmentation']['counts']) == list:
obj['segmentation'] = mask.frPyObjects([obj['segmentation']],t['height'],t['width'])[0]
elif type(obj['segmentation']) == dict and \
type(obj['segmentation']['counts'] == unicode or type(obj['segmentation']['counts']) == str):
pass
else:
raise Exception('segmentation format not supported.')
p = self.params
if p.useCats:
gts=self.cocoGt.loadAnns(self.cocoGt.getAnnIds(imgIds=p.imgIds, catIds=p.catIds))
dts=self.cocoDt.loadAnns(self.cocoDt.getAnnIds(imgIds=p.imgIds, catIds=p.catIds))
else:
gts=self.cocoGt.loadAnns(self.cocoGt.getAnnIds(imgIds=p.imgIds))
dts=self.cocoDt.loadAnns(self.cocoDt.getAnnIds(imgIds=p.imgIds))
if p.useSegm:
_toMask(gts, self.cocoGt)
_toMask(dts, self.cocoDt)
self._gts = defaultdict(list) # gt for evaluation
self._dts = defaultdict(list) # dt for evaluation
for gt in gts:
self._gts[gt['image_id'], gt['category_id']].append(gt)
for dt in dts:
self._dts[dt['image_id'], dt['category_id']].append(dt)
self.evalImgs = defaultdict(list) # per-image per-category evaluation results
self.eval = {} | [
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||
arangodb/arangodb | 0d658689c7d1b721b314fa3ca27d38303e1570c8 | 3rdParty/V8/gyp/easy_xml.py | python | XmlToString | (content, encoding='utf-8', pretty=False) | return ''.join(xml_parts) | Writes the XML content to disk, touching the file only if it has changed.
Visual Studio files have a lot of pre-defined structures. This function makes
it easy to represent these structures as Python data structures, instead of
having to create a lot of function calls.
Each XML element of the content is represented as a list composed of:
1. The name of the element, a string,
2. The attributes of the element, a dictionary (optional), and
3+. The content of the element, if any. Strings are simple text nodes and
lists are child elements.
Example 1:
<test/>
becomes
['test']
Example 2:
<myelement a='value1' b='value2'>
<childtype>This is</childtype>
<childtype>it!</childtype>
</myelement>
becomes
['myelement', {'a':'value1', 'b':'value2'},
['childtype', 'This is'],
['childtype', 'it!'],
]
Args:
content: The structured content to be converted.
encoding: The encoding to report on the first XML line.
pretty: True if we want pretty printing with indents and new lines.
Returns:
The XML content as a string. | Writes the XML content to disk, touching the file only if it has changed. | [
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""" Writes the XML content to disk, touching the file only if it has changed.
Visual Studio files have a lot of pre-defined structures. This function makes
it easy to represent these structures as Python data structures, instead of
having to create a lot of function calls.
Each XML element of the content is represented as a list composed of:
1. The name of the element, a string,
2. The attributes of the element, a dictionary (optional), and
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Args:
content: The structured content to be converted.
encoding: The encoding to report on the first XML line.
pretty: True if we want pretty printing with indents and new lines.
Returns:
The XML content as a string.
"""
# We create a huge list of all the elements of the file.
xml_parts = ['<?xml version="1.0" encoding="%s"?>' % encoding]
if pretty:
xml_parts.append('\n')
_ConstructContentList(xml_parts, content, pretty)
# Convert it to a string
return ''.join(xml_parts) | [
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|
HyeonwooNoh/caffe | d9e8494a2832d67b25dee37194c7bcb9d52d0e42 | scripts/cpp_lint.py | python | _CppLintState.PrintErrorCounts | (self) | Print a summary of errors by category, and the total. | Print a summary of errors by category, and the total. | [
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"""Print a summary of errors by category, and the total."""
for category, count in self.errors_by_category.iteritems():
sys.stderr.write('Category \'%s\' errors found: %d\n' %
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sys.stderr.write('Total errors found: %d\n' % self.error_count) | [
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microsoft/ELL | a1d6bacc37a14879cc025d9be2ba40b1a0632315 | tools/importers/common/converters.py | python | ConvertInput.convert_node | (self, conversion_parameters: typing.Mapping[str, typing.Any]) | Derived classes override to return the appropriate ELL node | Derived classes override to return the appropriate ELL node | [
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"""
Derived classes override to return the appropriate ELL node
"""
model = conversion_parameters["model"]
builder = conversion_parameters["builder"]
lookup_table = conversion_parameters["lookup_table"]
step_interval_msec = conversion_parameters["step_interval_msec"]
lag_threshold_msec = conversion_parameters["lag_threshold_msec"]
function_prefix = ""
# Add the InputNode to the model
shape_entry = self.importer_node.output_shapes[0]
ell_shape = self.get_ell_shape(shape_entry[0], shape_entry[1], 0)
original_input_node = None
if step_interval_msec is not None:
# in the steppable case the input is a clock ticks (which is a double)
input_node = builder.AddInputNode(
model, ell.model.PortMemoryLayout([1]), ell.nodes.PortType.real)
if lag_threshold_msec is None:
lag_threshold_msec = 2 * step_interval_msec
clock_node = builder.AddClockNode(
model, ell.nodes.PortElements(input_node.GetOutputPort("output")),
float(step_interval_msec), float(lag_threshold_msec),
"{}LagNotification".format(function_prefix))
source_node = builder.AddSourceNode(
model, ell.nodes.PortElements(clock_node.GetOutputPort("output")),
ell.nodes.PortType.smallReal, ell.model.PortMemoryLayout(ell_shape),
"{}InputCallback".format(function_prefix))
original_input_node = input_node
input_node = source_node
else:
input_node = builder.AddInputNode(
model, ell.model.PortMemoryLayout(ell_shape), ell.nodes.PortType.smallReal)
original_input_node = input_node
# Register the mapping
lookup_table.add_imported_ell_node(self.importer_node, input_node)
if step_interval_msec is not None:
lookup_table.add_imported_ell_node(self.importer_node, clock_node)
lookup_table.add_imported_ell_node(self.importer_node, source_node)
# Special case: If output requires padding e.g. Input is connected to a
# Convolutional node that requires padding, add a ReorderData node to
# ensure proper memory layout. This can be skipped once Input supports
# different memory layouts of the output.
padding = self.importer_node.output_padding["size"]
if padding > 0:
# Create the reorder node
port_elements = lookup_table.get_output_port_elements_for_node(input_node)
input_memory_layout = memory_shapes.get_ell_port_memory_layout(shape_entry[0], shape_entry[1], 0)
output_memory_layout = memory_shapes.get_ell_port_memory_layout(shape_entry[0], shape_entry[1], padding)
reorder_node = builder.AddReorderDataNode(model, port_elements, input_memory_layout, output_memory_layout,
[0, 1, 2])
# Register the mapping
lookup_table.add_imported_ell_node(self.importer_node, reorder_node)
lookup_table.add_ell_input(original_input_node) | [
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||
Atarity/Lightpack | 4dee73a443cba4c4073291febe450e6c1941f3af | Software/apiexamples/liOSC/OSC.py | python | OSCMessage.__delitem__ | (self, i) | Removes the indicated argument (or slice) | Removes the indicated argument (or slice) | [
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SpaceNetChallenge/BuildingDetectors | 3def3c44b5847c744cd2f3356182892d92496579 | qinhaifang/src/caffe-mnc/python/caffe/pycaffe.py | python | _Net_forward_backward_all | (self, blobs=None, diffs=None, **kwargs) | return all_outs, all_diffs | Run net forward + backward in batches.
Parameters
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diffs: list of diffs to extract as in backward()
kwargs: Keys are input (for forward) and output (for backward) blob names
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Prefilled variants are called for lack of input or output blobs.
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Run net forward + backward in batches.
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diffs: list of diffs to extract as in backward()
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all_diffs: {blob name: diff ndarray} dict.
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# Batch blobs and diffs.
all_outs = {out: [] for out in set(self.outputs + (blobs or []))}
all_diffs = {diff: [] for diff in set(self.inputs + (diffs or []))}
forward_batches = self._batch({in_: kwargs[in_]
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all_diffs[diff].extend(out_diffs.copy())
# Package in ndarray.
for out, diff in zip(all_outs, all_diffs):
all_outs[out] = np.asarray(all_outs[out])
all_diffs[diff] = np.asarray(all_diffs[diff])
# Discard padding at the end and package in ndarray.
pad = len(all_outs.itervalues().next()) - len(kwargs.itervalues().next())
if pad:
for out, diff in zip(all_outs, all_diffs):
all_outs[out] = all_outs[out][:-pad]
all_diffs[diff] = all_diffs[diff][:-pad]
return all_outs, all_diffs | [
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