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intel/caffe | 3f494b442ee3f9d17a07b09ecbd5fa2bbda00836 | scripts/cpp_lint.py | python | CheckAltTokens | (filename, clean_lines, linenum, error) | Check alternative keywords being used in boolean expressions.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found. | Check alternative keywords being used in boolean expressions. | [
"Check",
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] | def CheckAltTokens(filename, clean_lines, linenum, error):
"""Check alternative keywords being used in boolean expressions.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
# Avoid preprocessor lines
if Match(r'^\s*#', line):
return
# Last ditch effort to avoid multi-line comments. This will not help
# if the comment started before the current line or ended after the
# current line, but it catches most of the false positives. At least,
# it provides a way to workaround this warning for people who use
# multi-line comments in preprocessor macros.
#
# TODO(unknown): remove this once cpplint has better support for
# multi-line comments.
if line.find('/*') >= 0 or line.find('*/') >= 0:
return
for match in _ALT_TOKEN_REPLACEMENT_PATTERN.finditer(line):
error(filename, linenum, 'readability/alt_tokens', 2,
'Use operator %s instead of %s' % (
_ALT_TOKEN_REPLACEMENT[match.group(1)], match.group(1))) | [
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VelsonWang/HmiFuncDesigner | 439265da17bd3424e678932cbfbc0237b52630f3 | HmiFuncDesigner/libs/qscintilla/Python/configure.py | python | quote | (path) | return path | Return a path with quotes added if it contains spaces. path is the
path. | Return a path with quotes added if it contains spaces. path is the
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""" Return a path with quotes added if it contains spaces. path is the
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"""
if ' ' in path:
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ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/ros/rosunit/src/rosunit/xmlrunner.py | python | _TestInfo.create_error | (test, time, error) | return info | Create a _TestInfo instance for an erroneous test. | Create a _TestInfo instance for an erroneous test. | [
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info = _TestInfo(test, time)
info._error = error
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tensorflow/ngraph-bridge | ea6422491ec75504e78a63db029e7f74ec3479a5 | examples/retrain_ngraph.py | python | save_graph_to_file | (graph_file_name, module_spec, class_count) | Saves an graph to file, creating a valid quantized one if necessary. | Saves an graph to file, creating a valid quantized one if necessary. | [
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] | def save_graph_to_file(graph_file_name, module_spec, class_count):
"""Saves an graph to file, creating a valid quantized one if necessary."""
sess, _, _, _, _, _ = build_eval_session(module_spec, class_count)
graph = sess.graph
output_graph_def = tf.graph_util.convert_variables_to_constants(
sess, graph.as_graph_def(), [FLAGS.final_tensor_name])
with tf.gfile.GFile(graph_file_name, 'wb') as f:
f.write(output_graph_def.SerializeToString()) | [
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rapidsai/cudf | d5b2448fc69f17509304d594f029d0df56984962 | ci/checks/gitutils.py | python | listFilesToCheck | (filesDirs, filter=None) | return allFiles | Utility function to filter the input list of files/dirs based on the input
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"""
allFiles = []
for f in filesDirs:
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allFiles.append(f)
elif os.path.isdir(f):
files = listAllFilesInDir(f)
for f_ in files:
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return allFiles | [
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openthread/openthread | 9fcdbed9c526c70f1556d1ed84099c1535c7cd32 | tools/otci/otci/otci.py | python | OTCI.get_backbone_router_jitter | (self) | return self.__parse_int(self.execute_command('bbr jitter')) | Get jitter (in seconds) for Backbone Router registration for Thread 1.2 FTD. | Get jitter (in seconds) for Backbone Router registration for Thread 1.2 FTD. | [
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"""Get jitter (in seconds) for Backbone Router registration for Thread 1.2 FTD."""
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bigartm/bigartm | 47e37f982de87aa67bfd475ff1f39da696b181b3 | 3rdparty/protobuf-3.0.0/python/google/protobuf/internal/encoder.py | python | _TagSize | (field_number) | return _VarintSize(wire_format.PackTag(field_number, 0)) | Returns the number of bytes required to serialize a tag with this field
number. | Returns the number of bytes required to serialize a tag with this field
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"""Returns the number of bytes required to serialize a tag with this field
number."""
# Just pass in type 0, since the type won't affect the tag+type size.
return _VarintSize(wire_format.PackTag(field_number, 0)) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_controls.py | python | PreToolBar | (*args, **kwargs) | return val | PreToolBar() -> ToolBar | PreToolBar() -> ToolBar | [
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val = _controls_.new_PreToolBar(*args, **kwargs)
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py3/pandas/core/accessor.py | python | PandasDelegate._add_delegate_accessors | (
cls, delegate, accessors, typ: str, overwrite: bool = False
) | Add accessors to cls from the delegate class.
Parameters
----------
cls
Class to add the methods/properties to.
delegate
Class to get methods/properties and doc-strings.
accessors : list of str
List of accessors to add.
typ : {'property', 'method'}
overwrite : bool, default False
Overwrite the method/property in the target class if it exists. | Add accessors to cls from the delegate class. | [
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] | def _add_delegate_accessors(
cls, delegate, accessors, typ: str, overwrite: bool = False
):
"""
Add accessors to cls from the delegate class.
Parameters
----------
cls
Class to add the methods/properties to.
delegate
Class to get methods/properties and doc-strings.
accessors : list of str
List of accessors to add.
typ : {'property', 'method'}
overwrite : bool, default False
Overwrite the method/property in the target class if it exists.
"""
def _create_delegator_property(name):
def _getter(self):
return self._delegate_property_get(name)
def _setter(self, new_values):
return self._delegate_property_set(name, new_values)
_getter.__name__ = name
_setter.__name__ = name
return property(
fget=_getter, fset=_setter, doc=getattr(delegate, name).__doc__
)
def _create_delegator_method(name):
def f(self, *args, **kwargs):
return self._delegate_method(name, *args, **kwargs)
f.__name__ = name
f.__doc__ = getattr(delegate, name).__doc__
return f
for name in accessors:
if typ == "property":
f = _create_delegator_property(name)
else:
f = _create_delegator_method(name)
# don't overwrite existing methods/properties
if overwrite or not hasattr(cls, name):
setattr(cls, name, f) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/core/computation/scope.py | python | Scope.full_scope | (self) | return DeepChainMap(*maps) | Return the full scope for use with passing to engines transparently
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Returns
-------
vars : DeepChainMap
All variables in this scope. | Return the full scope for use with passing to engines transparently
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"""
Return the full scope for use with passing to engines transparently
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Returns
-------
vars : DeepChainMap
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"""
maps = [self.temps] + self.resolvers.maps + self.scope.maps
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/distutils/sysconfig.py | python | customize_compiler | (compiler) | Do any platform-specific customization of a CCompiler instance.
Mainly needed on Unix, so we can plug in the information that
varies across Unices and is stored in Python's Makefile. | Do any platform-specific customization of a CCompiler instance. | [
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] | def customize_compiler(compiler):
"""Do any platform-specific customization of a CCompiler instance.
Mainly needed on Unix, so we can plug in the information that
varies across Unices and is stored in Python's Makefile.
"""
if compiler.compiler_type == "unix":
if sys.platform == "darwin":
# Perform first-time customization of compiler-related
# config vars on OS X now that we know we need a compiler.
# This is primarily to support Pythons from binary
# installers. The kind and paths to build tools on
# the user system may vary significantly from the system
# that Python itself was built on. Also the user OS
# version and build tools may not support the same set
# of CPU architectures for universal builds.
global _config_vars
# Use get_config_var() to ensure _config_vars is initialized.
if not get_config_var('CUSTOMIZED_OSX_COMPILER'):
import _osx_support
_osx_support.customize_compiler(_config_vars)
_config_vars['CUSTOMIZED_OSX_COMPILER'] = 'True'
(cc, cxx, cflags, ccshared, ldshared, shlib_suffix, ar, ar_flags) = \
get_config_vars('CC', 'CXX', 'CFLAGS',
'CCSHARED', 'LDSHARED', 'SHLIB_SUFFIX', 'AR', 'ARFLAGS')
if 'CC' in os.environ:
newcc = os.environ['CC']
if (sys.platform == 'darwin'
and 'LDSHARED' not in os.environ
and ldshared.startswith(cc)):
# On OS X, if CC is overridden, use that as the default
# command for LDSHARED as well
ldshared = newcc + ldshared[len(cc):]
cc = newcc
if 'CXX' in os.environ:
cxx = os.environ['CXX']
if 'LDSHARED' in os.environ:
ldshared = os.environ['LDSHARED']
if 'CPP' in os.environ:
cpp = os.environ['CPP']
else:
cpp = cc + " -E" # not always
if 'LDFLAGS' in os.environ:
ldshared = ldshared + ' ' + os.environ['LDFLAGS']
if 'CFLAGS' in os.environ:
cflags = cflags + ' ' + os.environ['CFLAGS']
ldshared = ldshared + ' ' + os.environ['CFLAGS']
if 'CPPFLAGS' in os.environ:
cpp = cpp + ' ' + os.environ['CPPFLAGS']
cflags = cflags + ' ' + os.environ['CPPFLAGS']
ldshared = ldshared + ' ' + os.environ['CPPFLAGS']
if 'AR' in os.environ:
ar = os.environ['AR']
if 'ARFLAGS' in os.environ:
archiver = ar + ' ' + os.environ['ARFLAGS']
else:
archiver = ar + ' ' + ar_flags
cc_cmd = cc + ' ' + cflags
compiler.set_executables(
preprocessor=cpp,
compiler=cc_cmd,
compiler_so=cc_cmd + ' ' + ccshared,
compiler_cxx=cxx,
linker_so=ldshared,
linker_exe=cc,
archiver=archiver)
compiler.shared_lib_extension = shlib_suffix | [
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miyosuda/TensorFlowAndroidMNIST | 7b5a4603d2780a8a2834575706e9001977524007 | jni-build/jni/include/external/bazel_tools/third_party/py/gflags/__init__.py | python | FlagValues._RegisterFlagByModuleId | (self, module_id, flag) | Records the module that defines a specific flag.
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module_id: An int, the ID of the Python module.
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Args:
module_id: An int, the ID of the Python module.
flag: A Flag object, a flag that is key to the module.
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flags_by_module_id = self.FlagsByModuleIdDict()
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/cgi.py | python | FieldStorage.read_lines_to_eof | (self) | Internal: read lines until EOF. | Internal: read lines until EOF. | [
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self.done = -1
break
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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/msvs_emulation.py | python | _AddPrefix | (element, prefix) | Add |prefix| to |element| or each subelement if element is iterable. | Add |prefix| to |element| or each subelement if element is iterable. | [
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"""Add |prefix| to |element| or each subelement if element is iterable."""
if element is None:
return element
# Note, not Iterable because we don't want to handle strings like that.
if isinstance(element, list) or isinstance(element, tuple):
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chromiumembedded/cef | 80caf947f3fe2210e5344713c5281d8af9bdc295 | tools/cef_parser.py | python | obj_class.is_library_side | (self) | return self.attribs['source'] == 'library' | Returns true if the class is implemented by the library. | Returns true if the class is implemented by the library. | [
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pmq20/node-packer | 12c46c6e44fbc14d9ee645ebd17d5296b324f7e0 | lts/deps/npm/node_modules/node-gyp/gyp/pylib/gyp/generator/eclipse.py | python | GetAllIncludeDirectories | (target_list, target_dicts,
shared_intermediate_dirs, config_name, params,
compiler_path) | return all_includes_list | Calculate the set of include directories to be used.
Returns:
A list including all the include_dir's specified for every target followed
by any include directories that were added as cflag compiler options. | Calculate the set of include directories to be used. | [
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shared_intermediate_dirs, config_name, params,
compiler_path):
"""Calculate the set of include directories to be used.
Returns:
A list including all the include_dir's specified for every target followed
by any include directories that were added as cflag compiler options.
"""
gyp_includes_set = set()
compiler_includes_list = []
# Find compiler's default include dirs.
if compiler_path:
command = shlex.split(compiler_path)
command.extend(['-E', '-xc++', '-v', '-'])
proc = subprocess.Popen(args=command, stdin=subprocess.PIPE,
stdout=subprocess.PIPE, stderr=subprocess.PIPE)
output = proc.communicate()[1]
if PY3:
output = output.decode('utf-8')
# Extract the list of include dirs from the output, which has this format:
# ...
# #include "..." search starts here:
# #include <...> search starts here:
# /usr/include/c++/4.6
# /usr/local/include
# End of search list.
# ...
in_include_list = False
for line in output.splitlines():
if line.startswith('#include'):
in_include_list = True
continue
if line.startswith('End of search list.'):
break
if in_include_list:
include_dir = line.strip()
if include_dir not in compiler_includes_list:
compiler_includes_list.append(include_dir)
flavor = gyp.common.GetFlavor(params)
if flavor == 'win':
generator_flags = params.get('generator_flags', {})
for target_name in target_list:
target = target_dicts[target_name]
if config_name in target['configurations']:
config = target['configurations'][config_name]
# Look for any include dirs that were explicitly added via cflags. This
# may be done in gyp files to force certain includes to come at the end.
# TODO(jgreenwald): Change the gyp files to not abuse cflags for this, and
# remove this.
if flavor == 'win':
msvs_settings = gyp.msvs_emulation.MsvsSettings(target, generator_flags)
cflags = msvs_settings.GetCflags(config_name)
else:
cflags = config['cflags']
for cflag in cflags:
if cflag.startswith('-I'):
include_dir = cflag[2:]
if include_dir not in compiler_includes_list:
compiler_includes_list.append(include_dir)
# Find standard gyp include dirs.
if 'include_dirs' in config:
include_dirs = config['include_dirs']
for shared_intermediate_dir in shared_intermediate_dirs:
for include_dir in include_dirs:
include_dir = include_dir.replace('$SHARED_INTERMEDIATE_DIR',
shared_intermediate_dir)
if not os.path.isabs(include_dir):
base_dir = os.path.dirname(target_name)
include_dir = base_dir + '/' + include_dir
include_dir = os.path.abspath(include_dir)
gyp_includes_set.add(include_dir)
# Generate a list that has all the include dirs.
all_includes_list = list(gyp_includes_set)
all_includes_list.sort()
for compiler_include in compiler_includes_list:
if not compiler_include in gyp_includes_set:
all_includes_list.append(compiler_include)
# All done.
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/feature_column/feature_column_v2.py | python | BucketizedColumn.num_buckets | (self) | return (len(self.boundaries) + 1) * self.source_column.shape[0] | See `CategoricalColumn` base class. | See `CategoricalColumn` base class. | [
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"""See `CategoricalColumn` base class."""
# By construction, source_column is always one-dimensional.
return (len(self.boundaries) + 1) * self.source_column.shape[0] | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/eager/context.py | python | Context.get_optimizer_experimental_options | (self) | return options | Get experimental options for the optimizer.
Returns:
Dictionary of current option values | Get experimental options for the optimizer. | [
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] | def get_optimizer_experimental_options(self):
"""Get experimental options for the optimizer.
Returns:
Dictionary of current option values
"""
rewrite_options = self.config.graph_options.rewrite_options
options = {}
def rewriter_toggle(option):
attr = getattr(rewrite_options, option)
if attr != 0:
options[option] = (attr == rewriter_config_pb2.RewriterConfig.ON)
def rewriter_bool(option):
options[option] = getattr(rewrite_options, option)
rewriter_toggle("layout_optimizer")
rewriter_toggle("constant_folding")
rewriter_toggle("shape_optimization")
rewriter_toggle("remapping")
rewriter_toggle("arithmetic_optimization")
rewriter_toggle("dependency_optimization")
rewriter_toggle("loop_optimization")
rewriter_toggle("function_optimization")
rewriter_toggle("debug_stripper")
rewriter_bool("disable_model_pruning")
rewriter_toggle("scoped_allocator_optimization")
rewriter_toggle("pin_to_host_optimization")
rewriter_toggle("implementation_selector")
rewriter_toggle("auto_mixed_precision")
rewriter_toggle("use_plugin_optimizers")
rewriter_bool("disable_meta_optimizer")
if rewrite_options.min_graph_nodes != 0:
options["min_graph_nodes"] = rewrite_options.min_graph_nodes
return options | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | tools/json_schema_compiler/h_generator.py | python | _Generator._GenerateFields | (self, props) | return c | Generates the field declarations when declaring a type. | Generates the field declarations when declaring a type. | [
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"""Generates the field declarations when declaring a type.
"""
c = Code()
needs_blank_line = False
for prop in props:
if needs_blank_line:
c.Append()
needs_blank_line = True
if prop.description:
c.Comment(prop.description)
# ANY is a base::Value which is abstract and cannot be a direct member, so
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is_ptr = prop.optional or prop.type_.property_type == PropertyType.ANY
(c.Append('%s %s;' % (
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google/mysql-protobuf | 467cda676afaa49e762c5c9164a43f6ad31a1fbf | protobuf/python/google/protobuf/internal/decoder.py | python | MapDecoder | (field_descriptor, new_default, is_message_map) | return DecodeMap | Returns a decoder for a map field. | Returns a decoder for a map field. | [
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"""Returns a decoder for a map field."""
key = field_descriptor
tag_bytes = encoder.TagBytes(field_descriptor.number,
wire_format.WIRETYPE_LENGTH_DELIMITED)
tag_len = len(tag_bytes)
local_DecodeVarint = _DecodeVarint
# Can't read _concrete_class yet; might not be initialized.
message_type = field_descriptor.message_type
def DecodeMap(buffer, pos, end, message, field_dict):
submsg = message_type._concrete_class()
value = field_dict.get(key)
if value is None:
value = field_dict.setdefault(key, new_default(message))
while 1:
# Read length.
(size, pos) = local_DecodeVarint(buffer, pos)
new_pos = pos + size
if new_pos > end:
raise _DecodeError('Truncated message.')
# Read sub-message.
submsg.Clear()
if submsg._InternalParse(buffer, pos, new_pos) != new_pos:
# The only reason _InternalParse would return early is if it
# encountered an end-group tag.
raise _DecodeError('Unexpected end-group tag.')
if is_message_map:
value[submsg.key].MergeFrom(submsg.value)
else:
value[submsg.key] = submsg.value
# Predict that the next tag is another copy of the same repeated field.
pos = new_pos + tag_len
if buffer[new_pos:pos] != tag_bytes or new_pos == end:
# Prediction failed. Return.
return new_pos
return DecodeMap | [
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carla-simulator/carla | 8854804f4d7748e14d937ec763a2912823a7e5f5 | Co-Simulation/PTV-Vissim/vissim_integration/carla_simulation.py | python | CarlaSimulation.tick | (self) | Tick to carla simulation. | Tick to carla simulation. | [
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"""
Tick to carla simulation.
"""
self.world.tick()
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self.spawned_actors = current_actors.difference(self._active_actors)
self.destroyed_actors = self._active_actors.difference(current_actors)
self._active_actors = current_actors | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/setuptools/py2/setuptools/command/egg_info.py | python | FileList._remove_files | (self, predicate) | return found | Remove all files from the file list that match the predicate.
Return True if any matching files were removed | Remove all files from the file list that match the predicate.
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"""
found = False
for i in range(len(self.files) - 1, -1, -1):
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found = True
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moderngl/moderngl | 32fe79927e02b0fa893b3603d677bdae39771e14 | moderngl/mock.py | python | Implementation.create_context | (self, *args) | return (None, 0) | create_context | create_context | [
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'''
create_context
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/numbers.py | python | Complex.__pos__ | (self) | +self | +self | [
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/klampt/io/povray.py | python | render_to_animation | (properties,folder) | This will setup your properties dict to not call povray, but rather just
generate a render script. | This will setup your properties dict to not call povray, but rather just
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"""This will setup your properties dict to not call povray, but rather just
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"""
if not os.path.exists(folder):
os.mkdir(folder)
import re
maxId=-1
for f in os.listdir(folder):
if re.match('[0-9]+.pov',f):
maxId=max(maxId,int(f[:len(f)-4]))
maxId+=1
properties['tempfile']=folder+"/"+str(maxId)+".pov"
properties['remove_temp']=False
properties['outfile']=None | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/idlelib/macosxSupport.py | python | setupApp | (root, flist) | Perform setup for the OSX application bundle. | Perform setup for the OSX application bundle. | [
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"""
Perform setup for the OSX application bundle.
"""
if not runningAsOSXApp(): return
hideTkConsole(root)
overrideRootMenu(root, flist)
addOpenEventSupport(root, flist) | [
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hpi-xnor/BMXNet-v2 | af2b1859eafc5c721b1397cef02f946aaf2ce20d | tools/coreml/converter/_layers.py | python | convert_reshape | (net, node, module, builder) | Converts a reshape layer from mxnet to coreml.
This doesn't currently handle the deprecated parameters for the reshape layer.
Parameters
----------
network: net
An mxnet network object.
layer: node
Node to convert.
module: module
A module for MXNet
builder: NeuralNetworkBuilder
A neural network builder object. | Converts a reshape layer from mxnet to coreml. | [
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] | def convert_reshape(net, node, module, builder):
"""Converts a reshape layer from mxnet to coreml.
This doesn't currently handle the deprecated parameters for the reshape layer.
Parameters
----------
network: net
An mxnet network object.
layer: node
Node to convert.
module: module
A module for MXNet
builder: NeuralNetworkBuilder
A neural network builder object.
"""
input_name, output_name = _get_input_output_name(net, node)
name = node['name']
target_shape = node['shape']
if any(item <= 0 for item in target_shape):
raise NotImplementedError('Special dimensional values less than or equal to 0 are not supported yet.'
'Feel free to file an issue here: https://github.com/dmlc/mxnet/issues.')
if 'reverse' in node and node['reverse'] == 'True':
raise NotImplementedError('"reverse" parameter is not supported by yet.'
'Feel free to file an issue here: https://github.com/dmlc/mxnet/issues.')
mode = 0 # CHANNEL_FIRST
builder.add_reshape(name, input_name, output_name, target_shape, mode) | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/share/gdb/python/gdb/command/frame_filters.py | python | _complete_frame_filter_name | (word, printer_dict) | return flist | Worker for frame filter name completion.
Arguments:
word: The most recent word of the command line.
printer_dict: The frame filter dictionary to search for frame
filter name completions.
Returns: A list of suggested frame filter name completions
from word analysis of the frame filter dictionary. This list
can be empty when there are no suggestions for completion. | Worker for frame filter name completion. | [
"Worker",
"for",
"frame",
"filter",
"name",
"completion",
"."
] | def _complete_frame_filter_name(word, printer_dict):
"""Worker for frame filter name completion.
Arguments:
word: The most recent word of the command line.
printer_dict: The frame filter dictionary to search for frame
filter name completions.
Returns: A list of suggested frame filter name completions
from word analysis of the frame filter dictionary. This list
can be empty when there are no suggestions for completion.
"""
printer_keys = printer_dict.keys()
if (word == ""):
return printer_keys
flist = filter(lambda x,y=word:x.startswith(y), printer_keys)
return flist | [
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eclipse/sumo | 7132a9b8b6eea734bdec38479026b4d8c4336d03 | tools/traci/_vehicle.py | python | VehicleDomain.getLeftFollowers | (self, vehID, blockingOnly=False) | return self.getNeighbors(vehID, mode) | bool -> list(pair(string, double))
Convenience method, see getNeighbors() | bool -> list(pair(string, double))
Convenience method, see getNeighbors() | [
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] | def getLeftFollowers(self, vehID, blockingOnly=False):
""" bool -> list(pair(string, double))
Convenience method, see getNeighbors()
"""
if blockingOnly:
mode = 4
else:
mode = 0
return self.getNeighbors(vehID, mode) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/site-packages/pkg_resources/__init__.py | python | find_eggs_in_zip | (importer, path_item, only=False) | Find eggs in zip files; possibly multiple nested eggs. | Find eggs in zip files; possibly multiple nested eggs. | [
"Find",
"eggs",
"in",
"zip",
"files",
";",
"possibly",
"multiple",
"nested",
"eggs",
"."
] | def find_eggs_in_zip(importer, path_item, only=False):
"""
Find eggs in zip files; possibly multiple nested eggs.
"""
if importer.archive.endswith('.whl'):
# wheels are not supported with this finder
# they don't have PKG-INFO metadata, and won't ever contain eggs
return
metadata = EggMetadata(importer)
if metadata.has_metadata('PKG-INFO'):
yield Distribution.from_filename(path_item, metadata=metadata)
if only:
# don't yield nested distros
return
for subitem in metadata.resource_listdir(''):
if _is_egg_path(subitem):
subpath = os.path.join(path_item, subitem)
dists = find_eggs_in_zip(zipimport.zipimporter(subpath), subpath)
for dist in dists:
yield dist
elif subitem.lower().endswith('.dist-info'):
subpath = os.path.join(path_item, subitem)
submeta = EggMetadata(zipimport.zipimporter(subpath))
submeta.egg_info = subpath
yield Distribution.from_location(path_item, subitem, submeta) | [
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TimoSaemann/caffe-segnet-cudnn5 | abcf30dca449245e101bf4ced519f716177f0885 | scripts/cpp_lint.py | python | CheckStyle | (filename, clean_lines, linenum, file_extension, nesting_state,
error) | Checks rules from the 'C++ style rules' section of cppguide.html.
Most of these rules are hard to test (naming, comment style), but we
do what we can. In particular we check for 2-space indents, line lengths,
tab usage, spaces inside code, etc.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
file_extension: The extension (without the dot) of the filename.
nesting_state: A _NestingState instance which maintains information about
the current stack of nested blocks being parsed.
error: The function to call with any errors found. | Checks rules from the 'C++ style rules' section of cppguide.html. | [
"Checks",
"rules",
"from",
"the",
"C",
"++",
"style",
"rules",
"section",
"of",
"cppguide",
".",
"html",
"."
] | def CheckStyle(filename, clean_lines, linenum, file_extension, nesting_state,
error):
"""Checks rules from the 'C++ style rules' section of cppguide.html.
Most of these rules are hard to test (naming, comment style), but we
do what we can. In particular we check for 2-space indents, line lengths,
tab usage, spaces inside code, etc.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
file_extension: The extension (without the dot) of the filename.
nesting_state: A _NestingState instance which maintains information about
the current stack of nested blocks being parsed.
error: The function to call with any errors found.
"""
# Don't use "elided" lines here, otherwise we can't check commented lines.
# Don't want to use "raw" either, because we don't want to check inside C++11
# raw strings,
raw_lines = clean_lines.lines_without_raw_strings
line = raw_lines[linenum]
if line.find('\t') != -1:
error(filename, linenum, 'whitespace/tab', 1,
'Tab found; better to use spaces')
# One or three blank spaces at the beginning of the line is weird; it's
# hard to reconcile that with 2-space indents.
# NOTE: here are the conditions rob pike used for his tests. Mine aren't
# as sophisticated, but it may be worth becoming so: RLENGTH==initial_spaces
# if(RLENGTH > 20) complain = 0;
# if(match($0, " +(error|private|public|protected):")) complain = 0;
# if(match(prev, "&& *$")) complain = 0;
# if(match(prev, "\\|\\| *$")) complain = 0;
# if(match(prev, "[\",=><] *$")) complain = 0;
# if(match($0, " <<")) complain = 0;
# if(match(prev, " +for \\(")) complain = 0;
# if(prevodd && match(prevprev, " +for \\(")) complain = 0;
initial_spaces = 0
cleansed_line = clean_lines.elided[linenum]
while initial_spaces < len(line) and line[initial_spaces] == ' ':
initial_spaces += 1
if line and line[-1].isspace():
error(filename, linenum, 'whitespace/end_of_line', 4,
'Line ends in whitespace. Consider deleting these extra spaces.')
# There are certain situations we allow one space, notably for section labels
elif ((initial_spaces == 1 or initial_spaces == 3) and
not Match(r'\s*\w+\s*:\s*$', cleansed_line)):
error(filename, linenum, 'whitespace/indent', 3,
'Weird number of spaces at line-start. '
'Are you using a 2-space indent?')
# Check if the line is a header guard.
is_header_guard = False
if file_extension == 'h':
cppvar = GetHeaderGuardCPPVariable(filename)
if (line.startswith('#ifndef %s' % cppvar) or
line.startswith('#define %s' % cppvar) or
line.startswith('#endif // %s' % cppvar)):
is_header_guard = True
# #include lines and header guards can be long, since there's no clean way to
# split them.
#
# URLs can be long too. It's possible to split these, but it makes them
# harder to cut&paste.
#
# The "$Id:...$" comment may also get very long without it being the
# developers fault.
if (not line.startswith('#include') and not is_header_guard and
not Match(r'^\s*//.*http(s?)://\S*$', line) and
not Match(r'^// \$Id:.*#[0-9]+ \$$', line)):
line_width = GetLineWidth(line)
extended_length = int((_line_length * 1.25))
if line_width > extended_length:
error(filename, linenum, 'whitespace/line_length', 4,
'Lines should very rarely be longer than %i characters' %
extended_length)
elif line_width > _line_length:
error(filename, linenum, 'whitespace/line_length', 2,
'Lines should be <= %i characters long' % _line_length)
if (cleansed_line.count(';') > 1 and
# for loops are allowed two ;'s (and may run over two lines).
cleansed_line.find('for') == -1 and
(GetPreviousNonBlankLine(clean_lines, linenum)[0].find('for') == -1 or
GetPreviousNonBlankLine(clean_lines, linenum)[0].find(';') != -1) and
# It's ok to have many commands in a switch case that fits in 1 line
not ((cleansed_line.find('case ') != -1 or
cleansed_line.find('default:') != -1) and
cleansed_line.find('break;') != -1)):
error(filename, linenum, 'whitespace/newline', 0,
'More than one command on the same line')
# Some more style checks
CheckBraces(filename, clean_lines, linenum, error)
CheckEmptyBlockBody(filename, clean_lines, linenum, error)
CheckAccess(filename, clean_lines, linenum, nesting_state, error)
CheckSpacing(filename, clean_lines, linenum, nesting_state, error)
CheckCheck(filename, clean_lines, linenum, error)
CheckAltTokens(filename, clean_lines, linenum, error)
classinfo = nesting_state.InnermostClass()
if classinfo:
CheckSectionSpacing(filename, clean_lines, classinfo, linenum, error) | [
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pmq20/node-packer | 12c46c6e44fbc14d9ee645ebd17d5296b324f7e0 | current/tools/inspector_protocol/jinja2/optimizer.py | python | optimize | (node, environment) | return optimizer.visit(node) | The context hint can be used to perform an static optimization
based on the context given. | The context hint can be used to perform an static optimization
based on the context given. | [
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"context",
"given",
"."
] | def optimize(node, environment):
"""The context hint can be used to perform an static optimization
based on the context given."""
optimizer = Optimizer(environment)
return optimizer.visit(node) | [
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] | https://github.com/pmq20/node-packer/blob/12c46c6e44fbc14d9ee645ebd17d5296b324f7e0/current/tools/inspector_protocol/jinja2/optimizer.py#L23-L27 | |
hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/pyasn1/pyasn1/type/base.py | python | Asn1ItemBase.isSuperTypeOf | (self, other) | return self._tagSet.isSuperTagSetOf(other.getTagSet()) and \
self._subtypeSpec.isSuperTypeOf(other.getSubtypeSpec()) | Returns true if argument is a ASN1 subtype of ourselves | Returns true if argument is a ASN1 subtype of ourselves | [
"Returns",
"true",
"if",
"argument",
"is",
"a",
"ASN1",
"subtype",
"of",
"ourselves"
] | def isSuperTypeOf(self, other):
"""Returns true if argument is a ASN1 subtype of ourselves"""
return self._tagSet.isSuperTagSetOf(other.getTagSet()) and \
self._subtypeSpec.isSuperTypeOf(other.getSubtypeSpec()) | [
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kiwix/kiwix-xulrunner | 38f4a10ae4b1585c16cb11730bb0dcc4924ae19f | android/update-play-store.py | python | download_remote_file | (url, path) | download url to path | download url to path | [
"download",
"url",
"to",
"path"
] | def download_remote_file(url, path):
''' download url to path '''
syscall('wget -c -O {path} {url}'.format(path=path, url=url)) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/difflib.py | python | HtmlDiff.__init__ | (self,tabsize=8,wrapcolumn=None,linejunk=None,
charjunk=IS_CHARACTER_JUNK) | HtmlDiff instance initializer
Arguments:
tabsize -- tab stop spacing, defaults to 8.
wrapcolumn -- column number where lines are broken and wrapped,
defaults to None where lines are not wrapped.
linejunk,charjunk -- keyword arguments passed into ndiff() (used by
HtmlDiff() to generate the side by side HTML differences). See
ndiff() documentation for argument default values and descriptions. | HtmlDiff instance initializer | [
"HtmlDiff",
"instance",
"initializer"
] | def __init__(self,tabsize=8,wrapcolumn=None,linejunk=None,
charjunk=IS_CHARACTER_JUNK):
"""HtmlDiff instance initializer
Arguments:
tabsize -- tab stop spacing, defaults to 8.
wrapcolumn -- column number where lines are broken and wrapped,
defaults to None where lines are not wrapped.
linejunk,charjunk -- keyword arguments passed into ndiff() (used by
HtmlDiff() to generate the side by side HTML differences). See
ndiff() documentation for argument default values and descriptions.
"""
self._tabsize = tabsize
self._wrapcolumn = wrapcolumn
self._linejunk = linejunk
self._charjunk = charjunk | [
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google/clif | cab24d6a105609a65c95a36a1712ae3c20c7b5df | clif/python/pyext.py | python | Module.WrapEnum | (self, e, unused_ln, cpp_namespace, unused_class_ns='') | Process AST.EnumDecl e. | Process AST.EnumDecl e. | [
"Process",
"AST",
".",
"EnumDecl",
"e",
"."
] | def WrapEnum(self, e, unused_ln, cpp_namespace, unused_class_ns=''):
"""Process AST.EnumDecl e."""
# Enum(pyname, ((name, value),...))
pytype = 'Enum' if e.enum_class else 'IntEnum'
items = []
for m in e.members:
if ':' in m.cpp_name:
name = m.cpp_name # FQN populated by matcher.
else:
name = e.name.cpp_name + '::' + m.cpp_name
items.append((
'PyUnicode_FromString("%s")' % (m.native or Ident(m.cpp_name)),
'PyLong_FromLong(\n%s%s)' % (
4*I,
types.AsType(types.EnumIntType(e.name.cpp_name), name))))
assert items, 'matcher should populate enum members'
wclass = '_'+e.name.native
genw = 'wrap'+Ident(e.name.cpp_name)
pyname = '.'.join([f.pyname for f in self.nested] + [e.name.native])
t = types.EnumType(e.name.cpp_name, pyname, pytype, self.CppName(wclass),
cpp_namespace)
self.types.append(t)
self.dict.append((e.name.native,
'(%s=%s())' % (self.CppName(wclass), self.CppName(genw))))
if not self.enums:
self.enums = True
self.init.extend([
'{PyObject* em = PyImport_ImportModule("enum");',
' if (em == nullptr) goto err;',
' _Enum = PyObject_GetAttrString(em, "Enum");',
' _IntEnum = PyObject_GetAttrString(em, "IntEnum");',
' Py_DECREF(em);}',
'if (!_Enum || !_IntEnum) {',
I+'Py_XDECREF(_Enum);',
I+'Py_XDECREF(_IntEnum);',
I+'goto err;',
'}'])
yield ''
for s in t.CreateEnum(genw, wclass, items):
yield s | [
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sailing-pmls/pmls-caffe | 49e98bced9c6d5af7cd701d18ab235b5fd0e4b3a | scripts/cpp_lint.py | python | CheckForNonStandardConstructs | (filename, clean_lines, linenum,
nesting_state, error) | r"""Logs an error if we see certain non-ANSI constructs ignored by gcc-2.
Complain about several constructs which gcc-2 accepts, but which are
not standard C++. Warning about these in lint is one way to ease the
transition to new compilers.
- put storage class first (e.g. "static const" instead of "const static").
- "%lld" instead of %qd" in printf-type functions.
- "%1$d" is non-standard in printf-type functions.
- "\%" is an undefined character escape sequence.
- text after #endif is not allowed.
- invalid inner-style forward declaration.
- >? and <? operators, and their >?= and <?= cousins.
Additionally, check for constructor/destructor style violations and reference
members, as it is very convenient to do so while checking for
gcc-2 compliance.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
nesting_state: A _NestingState instance which maintains information about
the current stack of nested blocks being parsed.
error: A callable to which errors are reported, which takes 4 arguments:
filename, line number, error level, and message | r"""Logs an error if we see certain non-ANSI constructs ignored by gcc-2. | [
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"error",
"if",
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"see",
"certain",
"non",
"-",
"ANSI",
"constructs",
"ignored",
"by",
"gcc",
"-",
"2",
"."
] | def CheckForNonStandardConstructs(filename, clean_lines, linenum,
nesting_state, error):
r"""Logs an error if we see certain non-ANSI constructs ignored by gcc-2.
Complain about several constructs which gcc-2 accepts, but which are
not standard C++. Warning about these in lint is one way to ease the
transition to new compilers.
- put storage class first (e.g. "static const" instead of "const static").
- "%lld" instead of %qd" in printf-type functions.
- "%1$d" is non-standard in printf-type functions.
- "\%" is an undefined character escape sequence.
- text after #endif is not allowed.
- invalid inner-style forward declaration.
- >? and <? operators, and their >?= and <?= cousins.
Additionally, check for constructor/destructor style violations and reference
members, as it is very convenient to do so while checking for
gcc-2 compliance.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
nesting_state: A _NestingState instance which maintains information about
the current stack of nested blocks being parsed.
error: A callable to which errors are reported, which takes 4 arguments:
filename, line number, error level, and message
"""
# Remove comments from the line, but leave in strings for now.
line = clean_lines.lines[linenum]
if Search(r'printf\s*\(.*".*%[-+ ]?\d*q', line):
error(filename, linenum, 'runtime/printf_format', 3,
'%q in format strings is deprecated. Use %ll instead.')
if Search(r'printf\s*\(.*".*%\d+\$', line):
error(filename, linenum, 'runtime/printf_format', 2,
'%N$ formats are unconventional. Try rewriting to avoid them.')
# Remove escaped backslashes before looking for undefined escapes.
line = line.replace('\\\\', '')
if Search(r'("|\').*\\(%|\[|\(|{)', line):
error(filename, linenum, 'build/printf_format', 3,
'%, [, (, and { are undefined character escapes. Unescape them.')
# For the rest, work with both comments and strings removed.
line = clean_lines.elided[linenum]
if Search(r'\b(const|volatile|void|char|short|int|long'
r'|float|double|signed|unsigned'
r'|schar|u?int8|u?int16|u?int32|u?int64)'
r'\s+(register|static|extern|typedef)\b',
line):
error(filename, linenum, 'build/storage_class', 5,
'Storage class (static, extern, typedef, etc) should be first.')
if Match(r'\s*#\s*endif\s*[^/\s]+', line):
error(filename, linenum, 'build/endif_comment', 5,
'Uncommented text after #endif is non-standard. Use a comment.')
if Match(r'\s*class\s+(\w+\s*::\s*)+\w+\s*;', line):
error(filename, linenum, 'build/forward_decl', 5,
'Inner-style forward declarations are invalid. Remove this line.')
if Search(r'(\w+|[+-]?\d+(\.\d*)?)\s*(<|>)\?=?\s*(\w+|[+-]?\d+)(\.\d*)?',
line):
error(filename, linenum, 'build/deprecated', 3,
'>? and <? (max and min) operators are non-standard and deprecated.')
if Search(r'^\s*const\s*string\s*&\s*\w+\s*;', line):
# TODO(unknown): Could it be expanded safely to arbitrary references,
# without triggering too many false positives? The first
# attempt triggered 5 warnings for mostly benign code in the regtest, hence
# the restriction.
# Here's the original regexp, for the reference:
# type_name = r'\w+((\s*::\s*\w+)|(\s*<\s*\w+?\s*>))?'
# r'\s*const\s*' + type_name + '\s*&\s*\w+\s*;'
error(filename, linenum, 'runtime/member_string_references', 2,
'const string& members are dangerous. It is much better to use '
'alternatives, such as pointers or simple constants.')
# Everything else in this function operates on class declarations.
# Return early if the top of the nesting stack is not a class, or if
# the class head is not completed yet.
classinfo = nesting_state.InnermostClass()
if not classinfo or not classinfo.seen_open_brace:
return
# The class may have been declared with namespace or classname qualifiers.
# The constructor and destructor will not have those qualifiers.
base_classname = classinfo.name.split('::')[-1]
# Look for single-argument constructors that aren't marked explicit.
# Technically a valid construct, but against style.
args = Match(r'\s+(?:inline\s+)?%s\s*\(([^,()]+)\)'
% re.escape(base_classname),
line)
if (args and
args.group(1) != 'void' and
not Match(r'(const\s+)?%s(\s+const)?\s*(?:<\w+>\s*)?&'
% re.escape(base_classname), args.group(1).strip())):
error(filename, linenum, 'runtime/explicit', 5,
'Single-argument constructors should be marked explicit.') | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/os.py | python | removedirs | (name) | removedirs(path)
Super-rmdir; remove a leaf directory and all empty intermediate
ones. Works like rmdir except that, if the leaf directory is
successfully removed, directories corresponding to rightmost path
segments will be pruned away until either the whole path is
consumed or an error occurs. Errors during this latter phase are
ignored -- they generally mean that a directory was not empty. | removedirs(path) | [
"removedirs",
"(",
"path",
")"
] | def removedirs(name):
"""removedirs(path)
Super-rmdir; remove a leaf directory and all empty intermediate
ones. Works like rmdir except that, if the leaf directory is
successfully removed, directories corresponding to rightmost path
segments will be pruned away until either the whole path is
consumed or an error occurs. Errors during this latter phase are
ignored -- they generally mean that a directory was not empty.
"""
rmdir(name)
head, tail = path.split(name)
if not tail:
head, tail = path.split(head)
while head and tail:
try:
rmdir(head)
except error:
break
head, tail = path.split(head) | [
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/klampt/vis/ipython/widgets.py | python | KlamptWidget.addTriangle | (self,name="Tri1",a=(0,0,0),b=(1,0,0),c=(0,1,0)) | Adds a new triangle with vertices a,b,c. a,b, and c are 3-lists or 3-tuples. | Adds a new triangle with vertices a,b,c. a,b, and c are 3-lists or 3-tuples. | [
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] | def addTriangle(self,name="Tri1",a=(0,0,0),b=(1,0,0),c=(0,1,0)):
"""Adds a new triangle with vertices a,b,c. a,b, and c are 3-lists or 3-tuples."""
verts = a+b+c
self._extras[name] = ('Trilist',verts)
self._do_rpc({'type':'add_trilist','name':name,'verts':verts}) | [
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mitsuba-renderer/mitsuba2 | 4e7628c6eed365904ca2ba536b795d1b03410344 | docs/docs_api/conf.py | python | generate_list_api_callback | (app) | Generate a RST file listing all the python members (classes or functions)
to be parsed and extracted. This function will recursively explore submodules
and packages. | Generate a RST file listing all the python members (classes or functions)
to be parsed and extracted. This function will recursively explore submodules
and packages. | [
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] | def generate_list_api_callback(app):
"""Generate a RST file listing all the python members (classes or functions)
to be parsed and extracted. This function will recursively explore submodules
and packages."""
import importlib
from inspect import isclass, isfunction, ismodule, ismethod
def process(f, obj, lib, name):
if re.match(r'__[a-zA-Z\_0-9]+__', name):
return
if ismodule(obj):
# 'python' is a package, so it needs to be treated differently
if name == 'python':
# Iterate recursively on all the python files
for root, dirs, files in os.walk(obj.__path__[0]):
root = root.replace(obj.__path__[0], '').replace('/', '.')
if root.endswith('__pycache__'):
continue
for f_name in files:
if not f_name == '__init__.py' and f_name.endswith('.py'):
module = importlib.import_module(
'mitsuba.python%s.%s' % (root, f_name[:-3]))
for x in dir(module):
obj2 = getattr(module, x)
# Skip the imported modules (e.g. enoki)
if not ismodule(obj2):
process(f, obj2, '.python%s.%s' %
(root, f_name[:-3]), x)
else:
for x in dir(obj):
process(f, getattr(obj, x), '%s.%s' % (lib, name), x)
else:
full_name = 'mitsuba%s.%s' % (lib, name)
# Check if this block should be excluded from the API documentation
for pattern in excluded_api:
if re.fullmatch(pattern, full_name):
return
f.write('.. auto%s:: %s\n\n' %
('class' if isclass(obj) else 'function', full_name))
with open(list_api_filename, 'w') as f:
print('Generate API list file: %s' % list_api_filename)
for lib in ['core', 'render', 'python']:
module = importlib.import_module('mitsuba.%s' % lib)
process(f, module, '', lib) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/mailbox.py | python | Babyl._pre_mailbox_hook | (self, f) | Called before writing the mailbox to file f. | Called before writing the mailbox to file f. | [
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] | def _pre_mailbox_hook(self, f):
"""Called before writing the mailbox to file f."""
babyl = b'BABYL OPTIONS:' + linesep
babyl += b'Version: 5' + linesep
labels = self.get_labels()
labels = (label.encode() for label in labels)
babyl += b'Labels:' + b','.join(labels) + linesep
babyl += b'\037'
f.write(babyl) | [
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ideawu/ssdb | f229ba277c7f7d0ca5a441c0c6fb3d1209af68e4 | deps/cpy/antlr3/recognizers.py | python | TokenSource.next | (self) | return token | Return next token or raise StopIteration.
Note that this will raise StopIteration when hitting the EOF token,
so EOF will not be part of the iteration. | Return next token or raise StopIteration. | [
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"raise",
"StopIteration",
"."
] | def next(self):
"""Return next token or raise StopIteration.
Note that this will raise StopIteration when hitting the EOF token,
so EOF will not be part of the iteration.
"""
token = self.nextToken()
if token is None or token.type == EOF:
raise StopIteration
return token | [
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hpi-xnor/BMXNet-v2 | af2b1859eafc5c721b1397cef02f946aaf2ce20d | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | python | get_inputs | (node, kwargs) | return name, input_nodes, attrs | Helper function to get inputs | Helper function to get inputs | [
"Helper",
"function",
"to",
"get",
"inputs"
] | def get_inputs(node, kwargs):
"""Helper function to get inputs"""
name = node["name"]
proc_nodes = kwargs["proc_nodes"]
index_lookup = kwargs["index_lookup"]
inputs = node["inputs"]
attrs = node.get("attrs", {})
input_nodes = []
for ip in inputs:
input_node_id = index_lookup[ip[0]]
input_nodes.append(proc_nodes[input_node_id].name)
return name, input_nodes, attrs | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/io/json/_json.py | python | FrameParser._process_converter | (self, f, filt=None) | Take a conversion function and possibly recreate the frame. | Take a conversion function and possibly recreate the frame. | [
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"."
] | def _process_converter(self, f, filt=None):
"""
Take a conversion function and possibly recreate the frame.
"""
if filt is None:
filt = lambda col, c: True
needs_new_obj = False
new_obj = dict()
for i, (col, c) in enumerate(self.obj.items()):
if filt(col, c):
new_data, result = f(col, c)
if result:
c = new_data
needs_new_obj = True
new_obj[i] = c
if needs_new_obj:
# possibly handle dup columns
new_obj = DataFrame(new_obj, index=self.obj.index)
new_obj.columns = self.obj.columns
self.obj = new_obj | [
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Z3Prover/z3 | d745d03afdfdf638d66093e2bfbacaf87187f35b | src/api/python/z3/z3.py | python | is_distinct | (a) | return is_app_of(a, Z3_OP_DISTINCT) | Return `True` if `a` is a Z3 distinct expression.
>>> x, y, z = Ints('x y z')
>>> is_distinct(x == y)
False
>>> is_distinct(Distinct(x, y, z))
True | Return `True` if `a` is a Z3 distinct expression. | [
"Return",
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"is",
"a",
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] | def is_distinct(a):
"""Return `True` if `a` is a Z3 distinct expression.
>>> x, y, z = Ints('x y z')
>>> is_distinct(x == y)
False
>>> is_distinct(Distinct(x, y, z))
True
"""
return is_app_of(a, Z3_OP_DISTINCT) | [
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arangodb/arangodb | 0d658689c7d1b721b314fa3ca27d38303e1570c8 | 3rdParty/V8/gyp/generator/ninja.py | python | ComputeOutputDir | (params) | return os.path.normpath(os.path.join(generator_dir, output_dir)) | Returns the path from the toplevel_dir to the build output directory. | Returns the path from the toplevel_dir to the build output directory. | [
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] | def ComputeOutputDir(params):
"""Returns the path from the toplevel_dir to the build output directory."""
# generator_dir: relative path from pwd to where make puts build files.
# Makes migrating from make to ninja easier, ninja doesn't put anything here.
generator_dir = os.path.relpath(params['options'].generator_output or '.')
# output_dir: relative path from generator_dir to the build directory.
output_dir = params.get('generator_flags', {}).get('output_dir', 'out')
# Relative path from source root to our output files. e.g. "out"
return os.path.normpath(os.path.join(generator_dir, output_dir)) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/numpy/py2/numpy/ma/extras.py | python | intersect1d | (ar1, ar2, assume_unique=False) | return aux[:-1][aux[1:] == aux[:-1]] | Returns the unique elements common to both arrays.
Masked values are considered equal one to the other.
The output is always a masked array.
See `numpy.intersect1d` for more details.
See Also
--------
numpy.intersect1d : Equivalent function for ndarrays.
Examples
--------
>>> x = array([1, 3, 3, 3], mask=[0, 0, 0, 1])
>>> y = array([3, 1, 1, 1], mask=[0, 0, 0, 1])
>>> intersect1d(x, y)
masked_array(data = [1 3 --],
mask = [False False True],
fill_value = 999999) | Returns the unique elements common to both arrays. | [
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"."
] | def intersect1d(ar1, ar2, assume_unique=False):
"""
Returns the unique elements common to both arrays.
Masked values are considered equal one to the other.
The output is always a masked array.
See `numpy.intersect1d` for more details.
See Also
--------
numpy.intersect1d : Equivalent function for ndarrays.
Examples
--------
>>> x = array([1, 3, 3, 3], mask=[0, 0, 0, 1])
>>> y = array([3, 1, 1, 1], mask=[0, 0, 0, 1])
>>> intersect1d(x, y)
masked_array(data = [1 3 --],
mask = [False False True],
fill_value = 999999)
"""
if assume_unique:
aux = ma.concatenate((ar1, ar2))
else:
# Might be faster than unique( intersect1d( ar1, ar2 ) )?
aux = ma.concatenate((unique(ar1), unique(ar2)))
aux.sort()
return aux[:-1][aux[1:] == aux[:-1]] | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | gpu/command_buffer/build_gles2_cmd_buffer.py | python | Argument.GetValidClientSideArg | (self, func, offset, index) | return str(offset + 1) | Gets a valid value for this argument. | Gets a valid value for this argument. | [
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] | def GetValidClientSideArg(self, func, offset, index):
"""Gets a valid value for this argument."""
return str(offset + 1) | [
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hpi-xnor/BMXNet | ed0b201da6667887222b8e4b5f997c4f6b61943d | example/rcnn/rcnn/pycocotools/cocoeval.py | python | COCOeval.evaluate | (self) | Run per image evaluation on given images and store results (a list of dict) in self.evalImgs
:return: None | Run per image evaluation on given images and store results (a list of dict) in self.evalImgs
:return: None | [
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] | def evaluate(self):
'''
Run per image evaluation on given images and store results (a list of dict) in self.evalImgs
:return: None
'''
tic = time.time()
print('Running per image evaluation...')
p = self.params
# add backward compatibility if useSegm is specified in params
if not p.useSegm is None:
p.iouType = 'segm' if p.useSegm == 1 else 'bbox'
print('useSegm (deprecated) is not None. Running {} evaluation'.format(p.iouType))
print('Evaluate annotation type *{}*'.format(p.iouType))
p.imgIds = list(np.unique(p.imgIds))
if p.useCats:
p.catIds = list(np.unique(p.catIds))
p.maxDets = sorted(p.maxDets)
self.params=p
self._prepare()
# loop through images, area range, max detection number
catIds = p.catIds if p.useCats else [-1]
if p.iouType == 'segm' or p.iouType == 'bbox':
computeIoU = self.computeIoU
elif p.iouType == 'keypoints':
computeIoU = self.computeOks
self.ious = {(imgId, catId): computeIoU(imgId, catId) \
for imgId in p.imgIds
for catId in catIds}
evaluateImg = self.evaluateImg
maxDet = p.maxDets[-1]
self.evalImgs = [evaluateImg(imgId, catId, areaRng, maxDet)
for catId in catIds
for areaRng in p.areaRng
for imgId in p.imgIds
]
self._paramsEval = copy.deepcopy(self.params)
toc = time.time()
print('DONE (t={:0.2f}s).'.format(toc-tic)) | [
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pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | torch/ao/quantization/fx/_convert_do_not_use.py | python | _convert_do_not_use | (
model: GraphModule, is_reference: bool = False,
convert_custom_config_dict: Dict[str, Any] = None,
is_standalone_module: bool = False,
_remove_qconfig_flag: bool = True,
backend_config_dict: Optional[Dict[str, Any]] = None) | return model | We will convert an observed model (a module with observer calls) to a reference
quantized model, the rule is simple:
1. for each observer module call in the graph, we'll convert it to calls to
quantize and dequantize functions based on the observer instance
2. for weighted operations like linear/conv, we need to convert them to reference
quantized module, this requires us to know whether the dtype configured for the
weight is supported in the backend, this is done in prepare step and the result
is stored in observed_node_names, we can decide whether we need to swap the
module based on this set
standalone_module means it a submodule that is not inlined in
parent module, and will be quantized separately as one unit.
Returns a quantized standalone module, whether input/output is quantized is
specified by prepare_custom_config_dict, with
input_quantized_idxs, output_quantized_idxs, please
see docs for prepare_fx for details | We will convert an observed model (a module with observer calls) to a reference
quantized model, the rule is simple:
1. for each observer module call in the graph, we'll convert it to calls to
quantize and dequantize functions based on the observer instance
2. for weighted operations like linear/conv, we need to convert them to reference
quantized module, this requires us to know whether the dtype configured for the
weight is supported in the backend, this is done in prepare step and the result
is stored in observed_node_names, we can decide whether we need to swap the
module based on this set | [
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model: GraphModule, is_reference: bool = False,
convert_custom_config_dict: Dict[str, Any] = None,
is_standalone_module: bool = False,
_remove_qconfig_flag: bool = True,
backend_config_dict: Optional[Dict[str, Any]] = None) -> torch.nn.Module:
"""
We will convert an observed model (a module with observer calls) to a reference
quantized model, the rule is simple:
1. for each observer module call in the graph, we'll convert it to calls to
quantize and dequantize functions based on the observer instance
2. for weighted operations like linear/conv, we need to convert them to reference
quantized module, this requires us to know whether the dtype configured for the
weight is supported in the backend, this is done in prepare step and the result
is stored in observed_node_names, we can decide whether we need to swap the
module based on this set
standalone_module means it a submodule that is not inlined in
parent module, and will be quantized separately as one unit.
Returns a quantized standalone module, whether input/output is quantized is
specified by prepare_custom_config_dict, with
input_quantized_idxs, output_quantized_idxs, please
see docs for prepare_fx for details
"""
if convert_custom_config_dict is None:
convert_custom_config_dict = {}
patterns, node_name_to_scope, prepare_custom_config_dict, observed_node_names = restore_state(model)
qconfig_map: Dict[str, QConfigAny] = model._qconfig_map # type: ignore[assignment]
assert is_reference, "_convert_do_not_use only supports reference option"
# mapping from fully qualified module name to module instance
# for example,
# {
# '': Model(...),
# 'linear': Linear(...),
# 'linear.weight_fake_quant': PerChannelMinMaxObserver(...),
# }
# We use remove_duplicate=False here because torch.cat uses
# the same activation_post_process module instance but different names
modules = dict(model.named_modules(remove_duplicate=False))
custom_module_classes = get_custom_module_class_keys(
convert_custom_config_dict,
"observed_to_quantized_custom_module_class")
if model._equalization_qconfig_map is not None:
# If we want to do equalization then do the following:
# Calculate the equalization scale, update the observers with the scaled
# inputs, and scale the weight
weight_eq_obs_dict = update_obs_for_equalization(model, modules)
convert_eq_obs(model, modules, weight_eq_obs_dict)
graph_inputs: List[str] = []
for node in model.graph.nodes:
if node.op == 'placeholder':
graph_inputs.append(node.name)
def replace_observer_with_quantize_dequantize_node(graph: Graph, node: Node, modules: Dict[str, torch.nn.Module]) -> None:
""" Replace activation_post_process module call node with quantize and
dequantize node
Before:
... -> observer_0(x) -> ...
After:
... -> torch.quantize_per_tensor(x, ...) -> x.dequantize() -> ...
"""
assert modules is not None
assert isinstance(node.target, str)
observer_module = modules[node.target]
root_module = modules[""]
if observer_module.dtype == torch.float32:
# remove the node for now
# TODO: support dynamic quant
with graph.inserting_before(node):
node.replace_all_uses_with(node.args[0])
graph.erase_node(node)
elif observer_module.dtype in [torch.quint8, torch.qint8, torch.float16]:
node_type, quantize_op, qparams = get_quantize_node_info(observer_module)
# replace observer node with quant - dequant node
with graph.inserting_before(node):
input_node = node.args[0]
inputs = [input_node]
for key, value in qparams.items():
if key in ['_scale_', '_zero_point_']:
# For scale and zero_point values we register them as buffers in the root module.
# TODO: maybe need more complex attr name here
qparam_node = create_getattr_from_value(root_module, graph, key, value)
inputs.append(qparam_node)
else:
# for qparams that are not scale/zero_point (like axis, dtype) we store them as literals in the graph.
inputs.append(value)
quantized_node = graph.create_node(node_type, quantize_op, tuple(inputs), {})
dequantized_node = graph.call_method("dequantize", args=(quantized_node,))
node.replace_all_uses_with(dequantized_node)
graph.erase_node(node)
# additional state to override inputs to be quantized, if specified
# by the user
placeholder_node_seen_cnt = 0
output_node_seen_cnt = 0
input_quantized_idxs: List[int] = prepare_custom_config_dict.get(
"input_quantized_idxs", [])
output_quantized_idxs: List[int] = prepare_custom_config_dict.get(
"output_quantized_idxs", [])
if backend_config_dict is None:
backend_config_dict = {}
quantized_reference_module_mapping = get_quantized_reference_module_mapping(backend_config_dict)
# convert tuples so that it can work with isinstance(module, tuple_of_classes)
weighted_module_classes = tuple(quantized_reference_module_mapping.keys())
for node in list(model.graph.nodes):
if node.op == 'placeholder':
cur_placeholder_node_idx = placeholder_node_seen_cnt
placeholder_node_seen_cnt += 1
if cur_placeholder_node_idx in input_quantized_idxs:
# Inputs are assumed to be quantized if the user specifid the
# input_quantized_idxs override.
# we need to dequantize the inputs since all operators took
# floating point inputs in reference quantized models
insert_dequantize_node(node, model.graph)
elif node.op == "output":
cur_output_node_idx = output_node_seen_cnt
output_node_seen_cnt += 1
if cur_output_node_idx in output_quantized_idxs:
# Result are kept quantized if the user specified the
# output_quantized_idxs override.
# Remove the dequantize operator in the end
maybe_dequantize_node = node.args[0]
if isinstance(maybe_dequantize_node, Node) and \
maybe_dequantize_node.op == "call_method" and \
maybe_dequantize_node.target == "dequantize":
quantize_node = maybe_dequantize_node.args[0]
maybe_dequantize_node.replace_all_uses_with(quantize_node)
model.graph.erase_node(maybe_dequantize_node)
elif node.op == "call_module":
if is_activation_post_process(modules[node.target]):
replace_observer_with_quantize_dequantize_node(model.graph, node, modules)
elif is_observed_standalone_module(modules[node.target]):
# TODO: move this to a separate function
convert = torch.ao.quantization._quantize_fx_do_not_use._convert_do_not_use # type: ignore[attr-defined]
# We know that observed standalone module is a GraphModule since
# it's produced by us
observed_standalone_module : GraphModule = modules[str(node.target)] # type: ignore[assignment]
sm_input_quantized_idxs = \
observed_standalone_module \
._standalone_module_input_quantized_idxs\
.tolist() # type: ignore[operator]
# remove the dequantize nodes for inputs
args = list(node.args)
for idx in range(len(args)):
if idx in sm_input_quantized_idxs:
arg = args[idx]
if arg.op == "call_method" and arg.target == "dequantize":
quantize_node = arg.args[0]
node.replace_input_with(arg, quantize_node)
if len(arg.users) == 0:
model.graph.erase_node(arg)
# add dequantize node for output
sm_output_quantized_idxs = \
observed_standalone_module \
._standalone_module_output_quantized_idxs \
.tolist() # type: ignore[operator]
if len(sm_output_quantized_idxs) > 0:
assert sm_output_quantized_idxs[0] == 0, "Currently only quantized"
"output idxs = [0] is supported"
# if it's non-empty, then it means the output is kept in quantized form
# we'll just add a dequantize node after this node
insert_dequantize_node(node, model.graph)
# TODO: allow convert_custom_config_dict to override backend_config_dict
# for standalone module
quantized_standalone_module = convert(
observed_standalone_module,
is_reference=True,
backend_config_dict=backend_config_dict)
parent_name, name = _parent_name(node.target)
# update the modules dict
setattr(modules[parent_name], name, quantized_standalone_module)
modules[str(node.target)] = quantized_standalone_module
elif type(modules[node.target]) in set(
weighted_module_classes).union(QAT_MODULE_CLASSES).union(FUSED_MODULE_CLASSES):
# TODO: refactor this part to a function
original_module = modules[node.target]
qconfig = original_module.qconfig
is_observed = node.name in observed_node_names
is_activation_quantized = activation_is_int8_quantized(qconfig)
is_weight_quantized = weight_is_statically_quantized(qconfig)
# TODO: rename weight_is_statically_quantized to weight_is_int8_quantized
if qconfig is None or \
not is_observed or \
not is_weight_quantized or \
not is_activation_quantized:
continue
float_module = original_module
fused_module = None
if isinstance(
original_module,
QAT_MODULE_CLASSES):
# case 1. converting qat module to
# a float module, we need to attch
# weight fake_quant to the module,
# weight fake_quant is assumed to be run during
# QAT so we don't need to run it again here
float_module = original_module.to_float() # type: ignore[operator]
# change qat conv to conv
parent_name, name = _parent_name(node.target)
setattr(modules[parent_name], name, float_module)
if isinstance(float_module, torch.nn.intrinsic._FusedModule):
fused_module = float_module
float_module = fused_module[0]
weight_post_process = original_module.weight_fake_quant
else:
# case 2. converting a float module/fused float module
# to float module, we need to attach
# weight observer to the conv module and run it
# with conv weight
if isinstance(original_module, torch.nn.intrinsic._FusedModule):
fused_module = original_module
float_module = fused_module[0] # type: ignore[index]
assert qconfig is not None
weight_post_process = qconfig.weight()
# run weight observer
weight_post_process(float_module.weight) # type: ignore[operator]
weight_qparams = get_qparam_dict(weight_post_process)
# TODO: may need to change the mapping when we support dynamic quantization
ref_qmodule_cls = quantized_reference_module_mapping.get(type(float_module), None)
assert ref_qmodule_cls is not None, f"No reference quantized module class configured for {type(float_module)}"
ref_qmodule = ref_qmodule_cls.from_float(float_module, weight_qparams) # type: ignore[attr-defined]
if fused_module is not None:
fused_module[0] = ref_qmodule
else:
parent_name, name = _parent_name(node.target)
setattr(modules[parent_name], name, ref_qmodule)
# removes qconfig and activation_post_process modules
if _remove_qconfig_flag:
_remove_qconfig(model)
preserved_attributes = set(convert_custom_config_dict.get("preserved_attributes", []))
model = QuantizedGraphModule(model, model.graph, preserved_attributes)
return model | [
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intel/llvm | e6d0547e9d99b5a56430c4749f6c7e328bf221ab | libcxx/utils/libcxx/sym_check/extract.py | python | extract_symbols | (lib_file, static_lib=None) | return extractor.extract(lib_file) | Extract and return a list of symbols extracted from a static or dynamic
library. The symbols are extracted using NM or readelf. They are then
filtered and formated. Finally they symbols are made unique. | Extract and return a list of symbols extracted from a static or dynamic
library. The symbols are extracted using NM or readelf. They are then
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"""
Extract and return a list of symbols extracted from a static or dynamic
library. The symbols are extracted using NM or readelf. They are then
filtered and formated. Finally they symbols are made unique.
"""
if static_lib is None:
_, ext = os.path.splitext(lib_file)
static_lib = True if ext in ['.a'] else False
if ReadElfExtractor.find_tool() and not static_lib:
extractor = ReadElfExtractor(static_lib=static_lib)
else:
extractor = NMExtractor(static_lib=static_lib)
return extractor.extract(lib_file) | [
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gabyx/ApproxMVBB | 838f3ff7690a938f1e4199a5f41b6feefc32a603 | example/kdTreeFiltering/python/Tools/Transformations/Transformations.py | python | concatenate_matrices | (*matrices) | return M | Return concatenation of series of transformation matrices.
>>> M = numpy.random.rand(16).reshape((4, 4)) - 0.5
>>> numpy.allclose(M, concatenate_matrices(M))
True
>>> numpy.allclose(numpy.dot(M, M.T), concatenate_matrices(M, M.T))
True | Return concatenation of series of transformation matrices. | [
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"""Return concatenation of series of transformation matrices.
>>> M = numpy.random.rand(16).reshape((4, 4)) - 0.5
>>> numpy.allclose(M, concatenate_matrices(M))
True
>>> numpy.allclose(numpy.dot(M, M.T), concatenate_matrices(M, M.T))
True
"""
M = numpy.identity(4)
for i in matrices:
M = numpy.dot(M, i)
return M | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numba/targets/cpu.py | python | remove_refct_calls | (func) | Remove redundant incref/decref within on a per block basis | Remove redundant incref/decref within on a per block basis | [
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"""
Remove redundant incref/decref within on a per block basis
"""
for bb in func.basic_blocks:
remove_null_refct_call(bb)
remove_refct_pairs(bb) | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/pdb.py | python | Pdb.displayhook | (self, obj) | Custom displayhook for the exec in default(), which prevents
assignment of the _ variable in the builtins. | Custom displayhook for the exec in default(), which prevents
assignment of the _ variable in the builtins. | [
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"""Custom displayhook for the exec in default(), which prevents
assignment of the _ variable in the builtins.
"""
# reproduce the behavior of the standard displayhook, not printing None
if obj is not None:
print repr(obj) | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/multiprocessing/sharedctypes.py | python | RawValue | (typecode_or_type, *args) | return obj | Returns a ctypes object allocated from shared memory | Returns a ctypes object allocated from shared memory | [
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'''
type_ = typecode_to_type.get(typecode_or_type, typecode_or_type)
obj = _new_value(type_)
ctypes.memset(ctypes.addressof(obj), 0, ctypes.sizeof(obj))
obj.__init__(*args)
return obj | [
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pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | torch/nn/quantized/_reference/modules/linear.py | python | Linear.forward | (self, x: torch.Tensor) | return result | we have:
w(float) -- quant - dequant \
x(float) ------------- F.linear ---
In the full model, we will see
w(float) -- quant - *dequant \
x -- quant --- *dequant -- *F.linear --- *quant - dequant
and the backend should be able to fuse the ops with `*` into a quantized linear | we have:
w(float) -- quant - dequant \
x(float) ------------- F.linear --- | [
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weight_dequant = self.get_weight()
result = F.linear(x, weight_dequant, self.bias)
return result | [
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/klampt/robotsim.py | python | Mass.__init__ | (self) | r""" | r""" | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/multiprocessing/process.py | python | Process.authkey | (self, authkey) | Set authorization key of process | Set authorization key of process | [
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'''
Set authorization key of process
'''
self._authkey = AuthenticationString(authkey) | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/ops/operations/_inner_ops.py | python | MatrixDiag.__init__ | (self) | Initialize MatrixDiag | Initialize MatrixDiag | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/sets.py | python | Set.remove | (self, element) | Remove an element from a set; it must be a member.
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"""Remove an element from a set; it must be a member.
If the element is not a member, raise a KeyError.
"""
try:
del self._data[element]
except TypeError:
transform = getattr(element, "__as_temporarily_immutable__", None)
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/ops/math_grad.py | python | _PolygammaGrad | (op, grad) | Returns gradient of psi(n, x) with respect to n and x. | Returns gradient of psi(n, x) with respect to n and x. | [
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"""Returns gradient of psi(n, x) with respect to n and x."""
# TODO(tillahoffmann): Add derivative with respect to n
n = op.inputs[0]
x = op.inputs[1]
# Broadcast gradients
sn = array_ops.shape(n)
sx = array_ops.shape(x)
unused_rn, rx = gen_array_ops.broadcast_gradient_args(sn, sx)
# Evaluate gradient
with ops.control_dependencies([grad]):
n = math_ops.conj(n)
x = math_ops.conj(x)
partial_x = math_ops.polygamma(n + 1, x)
return (None,
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/training/saver.py | python | update_checkpoint_state | (save_dir,
model_checkpoint_path,
all_model_checkpoint_paths=None,
latest_filename=None) | Updates the content of the 'checkpoint' file.
This updates the checkpoint file containing a CheckpointState
proto.
Args:
save_dir: Directory where the model was saved.
model_checkpoint_path: The checkpoint file.
all_model_checkpoint_paths: List of strings. Paths to all not-yet-deleted
checkpoints, sorted from oldest to newest. If this is a non-empty list,
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are also saved in the CheckpointState proto.
latest_filename: Optional name of the checkpoint file. Default to
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Raises:
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] | def update_checkpoint_state(save_dir,
model_checkpoint_path,
all_model_checkpoint_paths=None,
latest_filename=None):
"""Updates the content of the 'checkpoint' file.
This updates the checkpoint file containing a CheckpointState
proto.
Args:
save_dir: Directory where the model was saved.
model_checkpoint_path: The checkpoint file.
all_model_checkpoint_paths: List of strings. Paths to all not-yet-deleted
checkpoints, sorted from oldest to newest. If this is a non-empty list,
the last element must be equal to model_checkpoint_path. These paths
are also saved in the CheckpointState proto.
latest_filename: Optional name of the checkpoint file. Default to
'checkpoint'.
Raises:
RuntimeError: If any of the model checkpoint paths conflict with the file
containing CheckpointSate.
"""
_update_checkpoint_state(
save_dir=save_dir,
model_checkpoint_path=model_checkpoint_path,
all_model_checkpoint_paths=all_model_checkpoint_paths,
latest_filename=latest_filename,
save_relative_paths=False) | [
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BitMEX/api-connectors | 37a3a5b806ad5d0e0fc975ab86d9ed43c3bcd812 | auto-generated/python/swagger_client/models/announcement.py | python | Announcement.to_str | (self) | return pprint.pformat(self.to_dict()) | Returns the string representation of the model | Returns the string representation of the model | [
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"""Returns the string representation of the model"""
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indutny/candor | 48e7260618f5091c80a3416828e2808cad3ea22e | tools/gyp/pylib/gyp/MSVSProject.py | python | Writer._GetSpecForConfiguration | (self, config_type, config_name, attrs, tools) | return specification | Returns the specification for a configuration.
Args:
config_type: Type of configuration node.
config_name: Configuration name.
attrs: Dict of configuration attributes; may be None.
tools: List of tools (strings or Tool objects); may be None.
Returns: | Returns the specification for a configuration. | [
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] | def _GetSpecForConfiguration(self, config_type, config_name, attrs, tools):
"""Returns the specification for a configuration.
Args:
config_type: Type of configuration node.
config_name: Configuration name.
attrs: Dict of configuration attributes; may be None.
tools: List of tools (strings or Tool objects); may be None.
Returns:
"""
# Handle defaults
if not attrs:
attrs = {}
if not tools:
tools = []
# Add configuration node and its attributes
node_attrs = attrs.copy()
node_attrs['Name'] = config_name
specification = [config_type, node_attrs]
# Add tool nodes and their attributes
if tools:
for t in tools:
if isinstance(t, Tool):
specification.append(t._GetSpecification())
else:
specification.append(Tool(t)._GetSpecification())
return specification | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/slim/python/slim/nets/resnet_utils.py | python | resnet_arg_scope | (is_training=True,
weight_decay=0.0001,
batch_norm_decay=0.997,
batch_norm_epsilon=1e-5,
batch_norm_scale=True) | Defines the default ResNet arg scope.
TODO(gpapan): The batch-normalization related default values above are
appropriate for use in conjunction with the reference ResNet models
released at https://github.com/KaimingHe/deep-residual-networks. When
training ResNets from scratch, they might need to be tuned.
Args:
is_training: Whether or not we are training the parameters in the batch
normalization layers of the model. (deprecated)
weight_decay: The weight decay to use for regularizing the model.
batch_norm_decay: The moving average decay when estimating layer activation
statistics in batch normalization.
batch_norm_epsilon: Small constant to prevent division by zero when
normalizing activations by their variance in batch normalization.
batch_norm_scale: If True, uses an explicit `gamma` multiplier to scale the
activations in the batch normalization layer.
Returns:
An `arg_scope` to use for the resnet models. | Defines the default ResNet arg scope. | [
"Defines",
"the",
"default",
"ResNet",
"arg",
"scope",
"."
] | def resnet_arg_scope(is_training=True,
weight_decay=0.0001,
batch_norm_decay=0.997,
batch_norm_epsilon=1e-5,
batch_norm_scale=True):
"""Defines the default ResNet arg scope.
TODO(gpapan): The batch-normalization related default values above are
appropriate for use in conjunction with the reference ResNet models
released at https://github.com/KaimingHe/deep-residual-networks. When
training ResNets from scratch, they might need to be tuned.
Args:
is_training: Whether or not we are training the parameters in the batch
normalization layers of the model. (deprecated)
weight_decay: The weight decay to use for regularizing the model.
batch_norm_decay: The moving average decay when estimating layer activation
statistics in batch normalization.
batch_norm_epsilon: Small constant to prevent division by zero when
normalizing activations by their variance in batch normalization.
batch_norm_scale: If True, uses an explicit `gamma` multiplier to scale the
activations in the batch normalization layer.
Returns:
An `arg_scope` to use for the resnet models.
"""
batch_norm_params = {
'is_training': is_training,
'decay': batch_norm_decay,
'epsilon': batch_norm_epsilon,
'scale': batch_norm_scale,
'updates_collections': ops.GraphKeys.UPDATE_OPS,
}
with arg_scope(
[layers_lib.conv2d],
weights_regularizer=regularizers.l2_regularizer(weight_decay),
weights_initializer=initializers.variance_scaling_initializer(),
activation_fn=nn_ops.relu,
normalizer_fn=layers.batch_norm):
with arg_scope([layers.batch_norm], **batch_norm_params):
# The following implies padding='SAME' for pool1, which makes feature
# alignment easier for dense prediction tasks. This is also used in
# https://github.com/facebook/fb.resnet.torch. However the accompanying
# code of 'Deep Residual Learning for Image Recognition' uses
# padding='VALID' for pool1. You can switch to that choice by setting
# tf.contrib.framework.arg_scope([tf.contrib.layers.max_pool2d], padding='VALID').
with arg_scope([layers.max_pool2d], padding='SAME') as arg_sc:
return arg_sc | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/tkinter/__init__.py | python | Entry.delete | (self, first, last=None) | Delete text from FIRST to LAST (not included). | Delete text from FIRST to LAST (not included). | [
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"""Delete text from FIRST to LAST (not included)."""
self.tk.call(self._w, 'delete', first, last) | [
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BlzFans/wke | b0fa21158312e40c5fbd84682d643022b6c34a93 | cygwin/lib/python2.6/random.py | python | SystemRandom._stub | (self, *args, **kwds) | return None | Stub method. Not used for a system random number generator. | Stub method. Not used for a system random number generator. | [
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"Stub method. Not used for a system random number generator."
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pirobot/rbx2 | 2a6544799fcf062e7b6bd5cf2981b2a84c0c7d2a | rbx2_msgs/src/rbx2_msgs/srv/_SetBatteryLevel.py | python | SetBatteryLevelResponse._get_types | (self) | return self._slot_types | internal API method | internal API method | [
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"""
internal API method
"""
return self._slot_types | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/threading.py | python | BoundedSemaphore | (*args, **kwargs) | return _BoundedSemaphore(*args, **kwargs) | A factory function that returns a new bounded semaphore.
A bounded semaphore checks to make sure its current value doesn't exceed its
initial value. If it does, ValueError is raised. In most situations
semaphores are used to guard resources with limited capacity.
If the semaphore is released too many times it's a sign of a bug. If not
given, value defaults to 1.
Like regular semaphores, bounded semaphores manage a counter representing
the number of release() calls minus the number of acquire() calls, plus an
initial value. The acquire() method blocks if necessary until it can return
without making the counter negative. If not given, value defaults to 1. | A factory function that returns a new bounded semaphore. | [
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] | def BoundedSemaphore(*args, **kwargs):
"""A factory function that returns a new bounded semaphore.
A bounded semaphore checks to make sure its current value doesn't exceed its
initial value. If it does, ValueError is raised. In most situations
semaphores are used to guard resources with limited capacity.
If the semaphore is released too many times it's a sign of a bug. If not
given, value defaults to 1.
Like regular semaphores, bounded semaphores manage a counter representing
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return _BoundedSemaphore(*args, **kwargs) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_misc.py | python | SystemSettings.GetScreenType | (*args, **kwargs) | return _misc_.SystemSettings_GetScreenType(*args, **kwargs) | GetScreenType() -> int | GetScreenType() -> int | [
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"""GetScreenType() -> int"""
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/codecs.py | python | IncrementalDecoder.setstate | (self, state) | Set the current state of the decoder.
state must have been returned by getstate(). The effect of
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Set the current state of the decoder.
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LiquidPlayer/LiquidCore | 9405979363f2353ac9a71ad8ab59685dd7f919c9 | deps/node-10.15.3/deps/v8/third_party/jinja2/compiler.py | python | CodeGenerator.push_assign_tracking | (self) | Pushes a new layer for assignment tracking. | Pushes a new layer for assignment tracking. | [
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"""Pushes a new layer for assignment tracking."""
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FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Draft/draftguitools/gui_edit.py | python | Edit.getObjsFromSelection | (self) | return self.edited_objects | Evaluate selection and return a valid object to edit.
#to be used for app link support
for selobj in Gui.Selection.getSelectionEx('', 0):
for sub in selobj.SubElementNames:
obj = selobj.Object
obj_matrix = selobj.Object.getSubObject(sub, retType=4) | Evaluate selection and return a valid object to edit. | [
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] | def getObjsFromSelection(self):
"""Evaluate selection and return a valid object to edit.
#to be used for app link support
for selobj in Gui.Selection.getSelectionEx('', 0):
for sub in selobj.SubElementNames:
obj = selobj.Object
obj_matrix = selobj.Object.getSubObject(sub, retType=4)
"""
selection = Gui.Selection.getSelection()
self.edited_objects = []
if len(selection) > self.maxObjects:
_err = translate("draft", "Too many objects selected, max number set to:")
App.Console.PrintMessage(_err + " " + str(self.maxObjects) + "\n")
return None
for obj in selection:
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/httplib.py | python | HTTPMessage.addheader | (self, key, value) | Add header for field key handling repeats. | Add header for field key handling repeats. | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_core.py | python | GBSpan.SetRowspan | (*args, **kwargs) | return _core_.GBSpan_SetRowspan(*args, **kwargs) | SetRowspan(self, int rowspan) | SetRowspan(self, int rowspan) | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/ops/_op_impl/tbe/extract_volume_patches.py | python | _extract_volume_patches_tbe | () | return | ExtractVolumePatches TBE register | ExtractVolumePatches TBE register | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/numpy/py2/numpy/polynomial/legendre.py | python | legadd | (c1, c2) | return pu.trimseq(ret) | Add one Legendre series to another.
Returns the sum of two Legendre series `c1` + `c2`. The arguments
are sequences of coefficients ordered from lowest order term to
highest, i.e., [1,2,3] represents the series ``P_0 + 2*P_1 + 3*P_2``.
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----------
c1, c2 : array_like
1-D arrays of Legendre series coefficients ordered from low to
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Returns
-------
out : ndarray
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See Also
--------
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Notes
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Unlike multiplication, division, etc., the sum of two Legendre series
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the basis set) so addition, just like that of "standard" polynomials,
is simply "component-wise."
Examples
--------
>>> from numpy.polynomial import legendre as L
>>> c1 = (1,2,3)
>>> c2 = (3,2,1)
>>> L.legadd(c1,c2)
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Add one Legendre series to another.
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>>> c2 = (3,2,1)
>>> L.legadd(c1,c2)
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"""
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[c1, c2] = pu.as_series([c1, c2])
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/AWS/resource-manager-code/lib/swagger_spec_validator/ref_validators.py | python | in_scope | (resolver, ref_dict) | Context manager to assume the given scope for the passed in resolver.
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MtnViewJohn/context-free | 757d7bde9742f201cec61bd195dda98093edd1e8 | src-scintilla/scripts/FileGenerator.py | python | UpdateLineInPlistFile | (path, key, value) | Replace a single string value preceded by 'key' in an XML plist file. | Replace a single string value preceded by 'key' in an XML plist file. | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/coverage/coverage/plugin.py | python | FileReporter.translate_arcs | (self, arcs) | return arcs | Translate recorded arcs into reported arcs.
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/klampt/math/symbolic.py | python | Function.setArgType | (self,arg,type) | Sets an argument type specifier.
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/decimal.py | python | Decimal._round_ceiling | (self, prec) | Rounds up (not away from 0 if negative.) | Rounds up (not away from 0 if negative.) | [
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google/shaka-player-embedded | dabbeb5b47cc257b37b9a254661546352aaf0afe | shaka/tools/webidl/webidl/parser.py | python | IdlParser.p_DictionaryMember | (self, p) | return p[2]._replace(doc=p[1], docDebug=docDebug) | r"""DictionaryMember : MaybeDoc DictionaryMemberRest | r"""DictionaryMember : MaybeDoc DictionaryMemberRest | [
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zlgopen/awtk | 2c49e854a78749d9092907c027a7fba9062be549 | 3rd/mbedtls/scripts/config.py | python | ConfigFile._format_template | (self, name, indent, middle) | return ''.join([indent,
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perilouswithadollarsign/cstrike15_src | f82112a2388b841d72cb62ca48ab1846dfcc11c8 | thirdparty/protobuf-2.5.0/python/google/protobuf/internal/python_message.py | python | _ExtensionDict._FindExtensionByName | (self, name) | return self._extended_message._extensions_by_name.get(name, None) | Tries to find a known extension with the specified name.
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smilehao/xlua-framework | a03801538be2b0e92d39332d445b22caca1ef61f | ConfigData/trunk/tools/protobuf-2.5.0/protobuf-2.5.0/python/google/protobuf/service.py | python | RpcController.NotifyOnCancel | (self, callback) | Sets a callback to invoke on cancel.
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sailing-pmls/pmls-caffe | 49e98bced9c6d5af7cd701d18ab235b5fd0e4b3a | 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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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/mailbox.py | python | BabylMessage.set_visible | (self, visible) | Set the Message representation of visible headers. | Set the Message representation of visible headers. | [
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synfig/synfig | a5ec91db5b751dc12e4400ccfb5c063fd6d2d928 | synfig-studio/plugins/lottie-exporter/common/Bline.py | python | Bline.get | (self) | return self.bline | Returns the original param | Returns the original param | [
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] | https://github.com/synfig/synfig/blob/a5ec91db5b751dc12e4400ccfb5c063fd6d2d928/synfig-studio/plugins/lottie-exporter/common/Bline.py#L34-L38 | |
stan-dev/math | 5fd79f89933269a4ca4d8dd1fde2a36d53d4768c | lib/boost_1.75.0/libs/metaparse/tools/benchmark/benchmark.py | python | main | () | The main function of the script | The main function of the script | [
"The",
"main",
"function",
"of",
"the",
"script"
] | def main():
"""The main function of the script"""
desc = 'Benchmark the files generated by generate.py'
parser = argparse.ArgumentParser(description=desc)
parser.add_argument(
'--src',
dest='src_dir',
default='generated',
help='The directory containing the sources to benchmark'
)
parser.add_argument(
'--out',
dest='out_dir',
default='../../doc',
help='The output directory'
)
parser.add_argument(
'--include',
dest='include',
default='include',
help='The directory containing the headeres for the benchmark'
)
parser.add_argument(
'--boost_headers',
dest='boost_headers',
default='../../../..',
help='The directory containing the Boost headers (the boost directory)'
)
parser.add_argument(
'--compiler',
dest='compiler',
default='g++',
help='The compiler to do the benchmark with'
)
parser.add_argument(
'--repeat_count',
dest='repeat_count',
type=int,
default=5,
help='How many times a measurement should be repeated.'
)
args = parser.parse_args()
compiler = compiler_info(args.compiler)
results = benchmark(
args.src_dir,
args.compiler,
[args.include, args.boost_headers],
args.repeat_count
)
plot_diagrams(results, configs_in(args.src_dir), compiler, args.out_dir) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/inspect.py | python | isgeneratorfunction | (obj) | return _has_code_flag(obj, CO_GENERATOR) | Return true if the object is a user-defined generator function.
Generator function objects provide the same attributes as functions.
See help(isfunction) for a list of attributes. | Return true if the object is a user-defined generator function. | [
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"true",
"if",
"the",
"object",
"is",
"a",
"user",
"-",
"defined",
"generator",
"function",
"."
] | def isgeneratorfunction(obj):
"""Return true if the object is a user-defined generator function.
Generator function objects provide the same attributes as functions.
See help(isfunction) for a list of attributes."""
return _has_code_flag(obj, CO_GENERATOR) | [
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] | https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/tools/python3/src/Lib/inspect.py#L183-L188 | |
miyosuda/TensorFlowAndroidMNIST | 7b5a4603d2780a8a2834575706e9001977524007 | jni-build/jni/include/tensorflow/python/ops/math_ops.py | python | reduce_max | (input_tensor, reduction_indices=None, keep_dims=False,
name=None) | return gen_math_ops._max(input_tensor, _ReductionDims(input_tensor,
reduction_indices),
keep_dims, name=name) | Computes the maximum of elements across dimensions of a tensor.
Reduces `input_tensor` along the dimensions given in `reduction_indices`.
Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each
entry in `reduction_indices`. If `keep_dims` is true, the reduced dimensions
are retained with length 1.
If `reduction_indices` has no entries, all dimensions are reduced, and a
tensor with a single element is returned.
Args:
input_tensor: The tensor to reduce. Should have numeric type.
reduction_indices: The dimensions to reduce. If `None` (the default),
reduces all dimensions.
keep_dims: If true, retains reduced dimensions with length 1.
name: A name for the operation (optional).
Returns:
The reduced tensor. | Computes the maximum of elements across dimensions of a tensor. | [
"Computes",
"the",
"maximum",
"of",
"elements",
"across",
"dimensions",
"of",
"a",
"tensor",
"."
] | def reduce_max(input_tensor, reduction_indices=None, keep_dims=False,
name=None):
"""Computes the maximum of elements across dimensions of a tensor.
Reduces `input_tensor` along the dimensions given in `reduction_indices`.
Unless `keep_dims` is true, the rank of the tensor is reduced by 1 for each
entry in `reduction_indices`. If `keep_dims` is true, the reduced dimensions
are retained with length 1.
If `reduction_indices` has no entries, all dimensions are reduced, and a
tensor with a single element is returned.
Args:
input_tensor: The tensor to reduce. Should have numeric type.
reduction_indices: The dimensions to reduce. If `None` (the default),
reduces all dimensions.
keep_dims: If true, retains reduced dimensions with length 1.
name: A name for the operation (optional).
Returns:
The reduced tensor.
"""
return gen_math_ops._max(input_tensor, _ReductionDims(input_tensor,
reduction_indices),
keep_dims, name=name) | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/ops/operations/_grad_ops.py | python | Conv2DBackpropFilter.__init__ | (self,
out_channel,
kernel_size,
pad_mode="valid",
pad=0,
pad_list=(0, 0, 0, 0),
mode=1,
stride=(1, 1),
dilation=(1, 1, 1, 1),
group=1,
data_format="NCHW") | Initialize Convolution | Initialize Convolution | [
"Initialize",
"Convolution"
] | def __init__(self,
out_channel,
kernel_size,
pad_mode="valid",
pad=0,
pad_list=(0, 0, 0, 0),
mode=1,
stride=(1, 1),
dilation=(1, 1, 1, 1),
group=1,
data_format="NCHW"):
"""Initialize Convolution"""
self.init_prim_io_names(inputs=['out_backprop', 'input', 'filter_sizes'], outputs=['output'])
self.out_channel = out_channel
self.kernel_size = kernel_size
self.mode = mode
pad_mode = pad_mode.upper()
self.add_prim_attr('pad_mode', pad_mode)
if isinstance(pad, int):
pad = (pad,) * 4
else:
validator.check_equal_int(len(pad), 4, 'pad size', self.name)
self.add_prim_attr("pad", pad)
if isinstance(stride, tuple) and len(stride) == 4:
self.stride = (stride[2], stride[3])
self.add_prim_attr('stride', self.stride)
self.dilation = dilation
self.group = group
self.add_prim_attr('groups', group)
self.format = validator.check_string(data_format, ['NCHW', 'NHWC'], 'format', self.name)
if context.get_context("device_target") != "GPU" and self.format == "NHWC":
raise ValueError("NHWC format only support in GPU target.")
self.add_prim_attr('data_format', self.format) | [
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... | https://github.com/mindspore-ai/mindspore/blob/fb8fd3338605bb34fa5cea054e535a8b1d753fab/mindspore/python/mindspore/ops/operations/_grad_ops.py#L410-L442 | ||
LiquidPlayer/LiquidCore | 9405979363f2353ac9a71ad8ab59685dd7f919c9 | deps/node-10.15.3/deps/npm/node_modules/node-gyp/gyp/pylib/gyp/xcode_emulation.py | python | XcodeArchsDefault._VariableMapping | (self, sdkroot) | Returns the dictionary of variable mapping depending on the SDKROOT. | Returns the dictionary of variable mapping depending on the SDKROOT. | [
"Returns",
"the",
"dictionary",
"of",
"variable",
"mapping",
"depending",
"on",
"the",
"SDKROOT",
"."
] | def _VariableMapping(self, sdkroot):
"""Returns the dictionary of variable mapping depending on the SDKROOT."""
sdkroot = sdkroot.lower()
if 'iphoneos' in sdkroot:
return self._archs['ios']
elif 'iphonesimulator' in sdkroot:
return self._archs['iossim']
else:
return self._archs['mac'] | [
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"sdkroot",... | https://github.com/LiquidPlayer/LiquidCore/blob/9405979363f2353ac9a71ad8ab59685dd7f919c9/deps/node-10.15.3/deps/npm/node_modules/node-gyp/gyp/pylib/gyp/xcode_emulation.py#L53-L61 | ||
google/shaka-packager | e1b0c7c45431327fd3ce193514a5407d07b39b22 | packager/third_party/protobuf/third_party/six/six.py | python | _SixMetaPathImporter.is_package | (self, fullname) | return hasattr(self.__get_module(fullname), "__path__") | Return true, if the named module is a package.
We need this method to get correct spec objects with
Python 3.4 (see PEP451) | Return true, if the named module is a package. | [
"Return",
"true",
"if",
"the",
"named",
"module",
"is",
"a",
"package",
"."
] | def is_package(self, fullname):
"""
Return true, if the named module is a package.
We need this method to get correct spec objects with
Python 3.4 (see PEP451)
"""
return hasattr(self.__get_module(fullname), "__path__") | [
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"(",
"fullname",
")",
",",
"\"__path__\"",
")"
] | https://github.com/google/shaka-packager/blob/e1b0c7c45431327fd3ce193514a5407d07b39b22/packager/third_party/protobuf/third_party/six/six.py#L209-L216 | |
google/earthenterprise | 0fe84e29be470cd857e3a0e52e5d0afd5bb8cee9 | earth_enterprise/src/fusion/portableglobe/servers/portable_server.py | python | CompositeQueryHandler.get | (self, layer_id) | Handle GET request for JSON file for plugin. | Handle GET request for JSON file for plugin. | [
"Handle",
"GET",
"request",
"for",
"JSON",
"file",
"for",
"plugin",
"."
] | def get(self, layer_id):
"""Handle GET request for JSON file for plugin."""
if self.request.arguments["request"][0] == "Json":
self.set_header("Content-Type", "text/plain; charset=utf-8")
if ("is2d" in self.request.arguments.keys() and
self.request.arguments["is2d"][0] == "t"):
tornado.web.local_server_.LocalJsonHandler(self, True)
else:
tornado.web.local_server_.LocalJsonHandler(self, False)
elif self.request.arguments["request"][0] == "ImageryMaps":
if tornado.web.globe_.IsMbtiles():
self.set_header("Content-Type", "image/png")
tornado.web.local_server_.LocalMapTileHandler(
self, True, portable_globe.COMPOSITE_BASE_LAYER)
else:
self.set_header("Content-Type", "image/jpeg")
tornado.web.local_server_.LocalMapTileHandler(
self, True, int(layer_id))
elif self.request.arguments["request"][0] == "VectorMapsRaster":
self.set_header("Content-Type", "image/png")
tornado.web.local_server_.LocalMapTileHandler(
self, False, int(layer_id))
elif self.request.arguments["request"][0] == "Icon":
self.set_header("Content-Type", "image/png")
(icon_path, use_layer, use_local) = (
tornado.web.local_server_.ConvertIconPath(
self.request.arguments["icon_path"][0]))
layer_id = int(layer_id)
if not use_layer:
layer_id = portable_globe.NON_COMPOSITE_LAYER
tornado.web.local_server_.LocalIconHandler(
self, icon_path, layer_id, use_local)
else:
self.set_header("Content-Type", "text/plain")
print "Unknown query request: ", self.request.uri
self.finish() | [
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"... | https://github.com/google/earthenterprise/blob/0fe84e29be470cd857e3a0e52e5d0afd5bb8cee9/earth_enterprise/src/fusion/portableglobe/servers/portable_server.py#L247-L287 | ||
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/grid.py | python | GridCellAttrProvider.UpdateAttrCols | (*args, **kwargs) | return _grid.GridCellAttrProvider_UpdateAttrCols(*args, **kwargs) | UpdateAttrCols(self, size_t pos, int numCols) | UpdateAttrCols(self, size_t pos, int numCols) | [
"UpdateAttrCols",
"(",
"self",
"size_t",
"pos",
"int",
"numCols",
")"
] | def UpdateAttrCols(*args, **kwargs):
"""UpdateAttrCols(self, size_t pos, int numCols)"""
return _grid.GridCellAttrProvider_UpdateAttrCols(*args, **kwargs) | [
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] | https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/msw/grid.py#L706-L708 | |
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/propgrid.py | python | PropertyGridPopulator.SetGrid | (*args, **kwargs) | return _propgrid.PropertyGridPopulator_SetGrid(*args, **kwargs) | SetGrid(self, PropertyGrid pg) | SetGrid(self, PropertyGrid pg) | [
"SetGrid",
"(",
"self",
"PropertyGrid",
"pg",
")"
] | def SetGrid(*args, **kwargs):
"""SetGrid(self, PropertyGrid pg)"""
return _propgrid.PropertyGridPopulator_SetGrid(*args, **kwargs) | [
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] | https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/msw/propgrid.py#L2581-L2583 | |
Cisco-Talos/moflow | ed71dfb0540d9e0d7a4c72f0881b58958d573728 | BAP-0.7-moflow/libtracewrap/libtrace/protobuf/python/mox.py | python | SameElementsAs.__init__ | (self, expected_seq) | Initialize.
Args:
expected_seq: a sequence | Initialize. | [
"Initialize",
"."
] | def __init__(self, expected_seq):
"""Initialize.
Args:
expected_seq: a sequence
"""
self._expected_seq = expected_seq | [
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] | https://github.com/Cisco-Talos/moflow/blob/ed71dfb0540d9e0d7a4c72f0881b58958d573728/BAP-0.7-moflow/libtracewrap/libtrace/protobuf/python/mox.py#L1012-L1019 | ||
hfinkel/llvm-project-cxxjit | 91084ef018240bbb8e24235ff5cd8c355a9c1a1e | lldb/utils/vim-lldb/python-vim-lldb/vim_panes.py | python | get_selected_frame | (target) | return (frame, "") | Returns a tuple with (frame, error) where frame == None if error occurs | Returns a tuple with (frame, error) where frame == None if error occurs | [
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"with",
"(",
"frame",
"error",
")",
"where",
"frame",
"==",
"None",
"if",
"error",
"occurs"
] | def get_selected_frame(target):
""" Returns a tuple with (frame, error) where frame == None if error occurs """
(thread, error) = get_selected_thread(target)
if thread is None:
return (None, error)
frame = thread.GetSelectedFrame()
if frame is None or not frame.IsValid():
return (None, VimPane.MSG_NO_FRAME)
return (frame, "") | [
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