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aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/traceback.py
python
TracebackException._load_lines
(self)
Private API. force all lines in the stack to be loaded.
Private API. force all lines in the stack to be loaded.
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def _load_lines(self): """Private API. force all lines in the stack to be loaded.""" for frame in self.stack: frame.line if self.__context__: self.__context__._load_lines() if self.__cause__: self.__cause__._load_lines()
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/traceback.py#L528-L535
adobe/chromium
cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7
third_party/closure_linter/closure_linter/checkerbase.py
python
LintRulesBase.Initialize
(self, checker, limited_doc_checks, is_html)
Initializes to prepare to check a file. Args: checker: Class to report errors to. limited_doc_checks: Whether doc checking is relaxed for this file. is_html: Whether the file is an HTML file with extracted contents.
Initializes to prepare to check a file.
[ "Initializes", "to", "prepare", "to", "check", "a", "file", "." ]
def Initialize(self, checker, limited_doc_checks, is_html): """Initializes to prepare to check a file. Args: checker: Class to report errors to. limited_doc_checks: Whether doc checking is relaxed for this file. is_html: Whether the file is an HTML file with extracted contents. """ self.__checker = checker self._limited_doc_checks = limited_doc_checks self._is_html = is_html
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https://github.com/adobe/chromium/blob/cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7/third_party/closure_linter/closure_linter/checkerbase.py#L48-L58
hfinkel/llvm-project-cxxjit
91084ef018240bbb8e24235ff5cd8c355a9c1a1e
lldb/utils/vim-lldb/python-vim-lldb/vim_panes.py
python
bufwinnr
(name)
return int(vim.eval("bufwinnr('%s')" % name))
Returns window number corresponding with buffer name
Returns window number corresponding with buffer name
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def bufwinnr(name): """ Returns window number corresponding with buffer name """ return int(vim.eval("bufwinnr('%s')" % name))
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https://github.com/hfinkel/llvm-project-cxxjit/blob/91084ef018240bbb8e24235ff5cd8c355a9c1a1e/lldb/utils/vim-lldb/python-vim-lldb/vim_panes.py#L118-L120
cms-sw/cmssw
fd9de012d503d3405420bcbeec0ec879baa57cf2
DQM/Integration/python/config/FrontierCondition_GT_autoExpress_cfi.py
python
Tier0Handler._queryTier0DataSvc
( self, url )
return json.loads( ''.join(stdoutdata).replace( "'", '"').replace(' None', ' "None"') )
Queries Tier0DataSvc. url: Tier0DataSvc URL. @returns: dictionary, from whence the required information must be retrieved according to the API call. Raises if connection error, bad response, or timeout after retries occur.
Queries Tier0DataSvc. url: Tier0DataSvc URL.
[ "Queries", "Tier0DataSvc", ".", "url", ":", "Tier0DataSvc", "URL", "." ]
def _queryTier0DataSvc( self, url ): """ Queries Tier0DataSvc. url: Tier0DataSvc URL. @returns: dictionary, from whence the required information must be retrieved according to the API call. Raises if connection error, bad response, or timeout after retries occur. """ userAgent = "User-Agent: DQMIntegration/2.0 python/%d.%d.%d PycURL/%s" % ( sys.version_info[ :3 ] + ( pycurl.version_info()[ 1 ], ) ) proxy = "" if self._proxy: proxy = ' --proxy %s ' % self._proxy debug = " -s -S " if self._debug: debug = " -v " cmd = '/usr/bin/curl -k -L --user-agent "%s" %s --connect-timeout %i --retry %i %s %s ' % (userAgent, proxy, self._timeOut, self._retries, debug, url) process = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) (stdoutdata, stderrdata) = process.communicate() retcode = process.returncode if retcode != 0 or stderrdata: msg = "looks like curl returned an error: retcode=%s" % (retcode,) msg += ' msg = "'+str(stderrdata)+'"' raise Tier0Error(msg) return json.loads( ''.join(stdoutdata).replace( "'", '"').replace(' None', ' "None"') )
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https://github.com/cms-sw/cmssw/blob/fd9de012d503d3405420bcbeec0ec879baa57cf2/DQM/Integration/python/config/FrontierCondition_GT_autoExpress_cfi.py#L81-L108
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Tools/AWSPythonSDK/1.5.8/docutils/statemachine.py
python
State.add_initial_transitions
(self)
Make and add transitions listed in `self.initial_transitions`.
Make and add transitions listed in `self.initial_transitions`.
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def add_initial_transitions(self): """Make and add transitions listed in `self.initial_transitions`.""" if self.initial_transitions: names, transitions = self.make_transitions( self.initial_transitions) self.add_transitions(names, transitions)
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/AWSPythonSDK/1.5.8/docutils/statemachine.py#L645-L650
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numpy/ma/core.py
python
masked_inside
(x, v1, v2, copy=True)
return masked_where(condition, x, copy=copy)
Mask an array inside a given interval. Shortcut to ``masked_where``, where `condition` is True for `x` inside the interval [v1,v2] (v1 <= x <= v2). The boundaries `v1` and `v2` can be given in either order. See Also -------- masked_where : Mask where a condition is met. Notes ----- The array `x` is prefilled with its filling value. Examples -------- >>> import numpy.ma as ma >>> x = [0.31, 1.2, 0.01, 0.2, -0.4, -1.1] >>> ma.masked_inside(x, -0.3, 0.3) masked_array(data=[0.31, 1.2, --, --, -0.4, -1.1], mask=[False, False, True, True, False, False], fill_value=1e+20) The order of `v1` and `v2` doesn't matter. >>> ma.masked_inside(x, 0.3, -0.3) masked_array(data=[0.31, 1.2, --, --, -0.4, -1.1], mask=[False, False, True, True, False, False], fill_value=1e+20)
Mask an array inside a given interval.
[ "Mask", "an", "array", "inside", "a", "given", "interval", "." ]
def masked_inside(x, v1, v2, copy=True): """ Mask an array inside a given interval. Shortcut to ``masked_where``, where `condition` is True for `x` inside the interval [v1,v2] (v1 <= x <= v2). The boundaries `v1` and `v2` can be given in either order. See Also -------- masked_where : Mask where a condition is met. Notes ----- The array `x` is prefilled with its filling value. Examples -------- >>> import numpy.ma as ma >>> x = [0.31, 1.2, 0.01, 0.2, -0.4, -1.1] >>> ma.masked_inside(x, -0.3, 0.3) masked_array(data=[0.31, 1.2, --, --, -0.4, -1.1], mask=[False, False, True, True, False, False], fill_value=1e+20) The order of `v1` and `v2` doesn't matter. >>> ma.masked_inside(x, 0.3, -0.3) masked_array(data=[0.31, 1.2, --, --, -0.4, -1.1], mask=[False, False, True, True, False, False], fill_value=1e+20) """ if v2 < v1: (v1, v2) = (v2, v1) xf = filled(x) condition = (xf >= v1) & (xf <= v2) return masked_where(condition, x, copy=copy)
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numpy/ma/core.py#L2114-L2151
hughperkins/tf-coriander
970d3df6c11400ad68405f22b0c42a52374e94ca
tensorflow/python/ops/nn_grad.py
python
_BiasAddGradGrad
(op, received_grad)
return array_ops.tile(expanded_grad, tile_mults)
Gradient for the BiasAddGrad op. Args: op: BiasAddGrad op for which we are calculating gradients. received_grad: The gradients passed to the BiasAddGrad op. Returns: A single gradient Tensor for the input to BiasAddGrad (which is the gradient of the bias term in BiasAdd)
Gradient for the BiasAddGrad op.
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def _BiasAddGradGrad(op, received_grad): """Gradient for the BiasAddGrad op. Args: op: BiasAddGrad op for which we are calculating gradients. received_grad: The gradients passed to the BiasAddGrad op. Returns: A single gradient Tensor for the input to BiasAddGrad (which is the gradient of the bias term in BiasAdd) """ try: data_format = op.get_attr("data_format") except ValueError: data_format = None shape = array_ops.shape(op.inputs[0]) rank = array_ops.rank(op.inputs[0]) bias_shape = array_ops.shape(received_grad) if data_format == b"NCHW": expanded_shape = array_ops.concat( 0, [array_ops.ones_like(shape[:-3]), bias_shape, array_ops.ones_like(shape[-2:])] ) tile_mults = array_ops.concat(0, [shape[:-3], [1], shape[-2:]]) else: expanded_shape = array_ops.concat(0, [array_ops.ones_like(shape[:-1]), bias_shape]) tile_mults = array_ops.concat(0, [shape[:-1], [1]]) expanded_grad = array_ops.reshape(received_grad, expanded_shape) return array_ops.tile(expanded_grad, tile_mults)
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https://github.com/hughperkins/tf-coriander/blob/970d3df6c11400ad68405f22b0c42a52374e94ca/tensorflow/python/ops/nn_grad.py#L207-L241
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Tools/Python/3.7.10/windows/Lib/site-packages/pip/_internal/network/session.py
python
looks_like_ci
()
return any(name in os.environ for name in CI_ENVIRONMENT_VARIABLES)
Return whether it looks like pip is running under CI.
Return whether it looks like pip is running under CI.
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def looks_like_ci(): # type: () -> bool """ Return whether it looks like pip is running under CI. """ # We don't use the method of checking for a tty (e.g. using isatty()) # because some CI systems mimic a tty (e.g. Travis CI). Thus that # method doesn't provide definitive information in either direction. return any(name in os.environ for name in CI_ENVIRONMENT_VARIABLES)
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/windows/Lib/site-packages/pip/_internal/network/session.py#L88-L96
catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/python/tornado/tornado-6/tornado/tcpserver.py
python
TCPServer.stop
(self)
Stops listening for new connections. Requests currently in progress may still continue after the server is stopped.
Stops listening for new connections.
[ "Stops", "listening", "for", "new", "connections", "." ]
def stop(self) -> None: """Stops listening for new connections. Requests currently in progress may still continue after the server is stopped. """ if self._stopped: return self._stopped = True for fd, sock in self._sockets.items(): assert sock.fileno() == fd # Unregister socket from IOLoop self._handlers.pop(fd)() sock.close()
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/tornado/tornado-6/tornado/tcpserver.py#L250-L263
albertz/openlierox
d316c14a8eb57848ef56e9bfa7b23a56f694a51b
tools/DedicatedServerVideo/gdata/Crypto/PublicKey/pubkey.py
python
pubkey.publickey
(self)
return self
publickey(): object Return a new key object containing only the public information.
publickey(): object Return a new key object containing only the public information.
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def publickey (self): """publickey(): object Return a new key object containing only the public information. """ return self
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https://github.com/albertz/openlierox/blob/d316c14a8eb57848ef56e9bfa7b23a56f694a51b/tools/DedicatedServerVideo/gdata/Crypto/PublicKey/pubkey.py#L162-L166
catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/python/ipython/py2/IPython/core/interactiveshell.py
python
InteractiveShell.restore_sys_module_state
(self)
Restore the state of the sys module.
Restore the state of the sys module.
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def restore_sys_module_state(self): """Restore the state of the sys module.""" try: for k, v in iteritems(self._orig_sys_module_state): setattr(sys, k, v) except AttributeError: pass # Reset what what done in self.init_sys_modules if self._orig_sys_modules_main_mod is not None: sys.modules[self._orig_sys_modules_main_name] = self._orig_sys_modules_main_mod
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/ipython/py2/IPython/core/interactiveshell.py#L754-L763
ThePhD/sol2
50b62c9346750b7c2c406c9e4c546f96b0bf021d
documentation/source/conf.py
python
generate_doxygen_xml
(app)
Run the doxygen make commands if we're on the ReadTheDocs server
Run the doxygen make commands if we're on the ReadTheDocs server
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def generate_doxygen_xml(app): """Run the doxygen make commands if we're on the ReadTheDocs server""" read_the_docs_build = os.environ.get('READTHEDOCS', None) == 'True' if read_the_docs_build: run_cmake_doxygen()
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https://github.com/ThePhD/sol2/blob/50b62c9346750b7c2c406c9e4c546f96b0bf021d/documentation/source/conf.py#L340-L346
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/osx_cocoa/_gdi.py
python
DC.GetResolution
(*args, **kwargs)
return _gdi_.DC_GetResolution(*args, **kwargs)
GetResolution(self) -> int
GetResolution(self) -> int
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def GetResolution(*args, **kwargs): """GetResolution(self) -> int""" return _gdi_.DC_GetResolution(*args, **kwargs)
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_cocoa/_gdi.py#L4207-L4209
eventql/eventql
7ca0dbb2e683b525620ea30dc40540a22d5eb227
deps/3rdparty/spidermonkey/mozjs/media/webrtc/trunk/tools/gyp/pylib/gyp/generator/msvs.py
python
_AddConfigurationToMSVSProject
(p, spec, config_type, config_name, config)
Adds a configuration to the MSVS project. Many settings in a vcproj file are specific to a configuration. This function the main part of the vcproj file that's configuration specific. Arguments: p: The target project being generated. spec: The target dictionary containing the properties of the target. config_type: The configuration type, a number as defined by Microsoft. config_name: The name of the configuration. config: The dictionnary that defines the special processing to be done for this configuration.
Adds a configuration to the MSVS project.
[ "Adds", "a", "configuration", "to", "the", "MSVS", "project", "." ]
def _AddConfigurationToMSVSProject(p, spec, config_type, config_name, config): """Adds a configuration to the MSVS project. Many settings in a vcproj file are specific to a configuration. This function the main part of the vcproj file that's configuration specific. Arguments: p: The target project being generated. spec: The target dictionary containing the properties of the target. config_type: The configuration type, a number as defined by Microsoft. config_name: The name of the configuration. config: The dictionnary that defines the special processing to be done for this configuration. """ # Get the information for this configuration include_dirs, resource_include_dirs = _GetIncludeDirs(config) libraries = _GetLibraries(spec) out_file, vc_tool, _ = _GetOutputFilePathAndTool(spec, msbuild=False) defines = _GetDefines(config) defines = [_EscapeCppDefineForMSVS(d) for d in defines] disabled_warnings = _GetDisabledWarnings(config) prebuild = config.get('msvs_prebuild') postbuild = config.get('msvs_postbuild') def_file = _GetModuleDefinition(spec) precompiled_header = config.get('msvs_precompiled_header') # Prepare the list of tools as a dictionary. tools = dict() # Add in user specified msvs_settings. msvs_settings = config.get('msvs_settings', {}) MSVSSettings.ValidateMSVSSettings(msvs_settings) for tool in msvs_settings: settings = config['msvs_settings'][tool] for setting in settings: _ToolAppend(tools, tool, setting, settings[setting]) # Add the information to the appropriate tool _ToolAppend(tools, 'VCCLCompilerTool', 'AdditionalIncludeDirectories', include_dirs) _ToolAppend(tools, 'VCResourceCompilerTool', 'AdditionalIncludeDirectories', resource_include_dirs) # Add in libraries. _ToolAppend(tools, 'VCLinkerTool', 'AdditionalDependencies', libraries) if out_file: _ToolAppend(tools, vc_tool, 'OutputFile', out_file, only_if_unset=True) # Add defines. _ToolAppend(tools, 'VCCLCompilerTool', 'PreprocessorDefinitions', defines) _ToolAppend(tools, 'VCResourceCompilerTool', 'PreprocessorDefinitions', defines) # Change program database directory to prevent collisions. _ToolAppend(tools, 'VCCLCompilerTool', 'ProgramDataBaseFileName', '$(IntDir)$(ProjectName)\\vc80.pdb', only_if_unset=True) # Add disabled warnings. _ToolAppend(tools, 'VCCLCompilerTool', 'DisableSpecificWarnings', disabled_warnings) # Add Pre-build. _ToolAppend(tools, 'VCPreBuildEventTool', 'CommandLine', prebuild) # Add Post-build. _ToolAppend(tools, 'VCPostBuildEventTool', 'CommandLine', postbuild) # Turn on precompiled headers if appropriate. if precompiled_header: precompiled_header = os.path.split(precompiled_header)[1] _ToolAppend(tools, 'VCCLCompilerTool', 'UsePrecompiledHeader', '2') _ToolAppend(tools, 'VCCLCompilerTool', 'PrecompiledHeaderThrough', precompiled_header) _ToolAppend(tools, 'VCCLCompilerTool', 'ForcedIncludeFiles', precompiled_header) # Loadable modules don't generate import libraries; # tell dependent projects to not expect one. if spec['type'] == 'loadable_module': _ToolAppend(tools, 'VCLinkerTool', 'IgnoreImportLibrary', 'true') # Set the module definition file if any. if def_file: _ToolAppend(tools, 'VCLinkerTool', 'ModuleDefinitionFile', def_file) _AddConfigurationToMSVS(p, spec, tools, config, config_type, config_name)
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https://github.com/eventql/eventql/blob/7ca0dbb2e683b525620ea30dc40540a22d5eb227/deps/3rdparty/spidermonkey/mozjs/media/webrtc/trunk/tools/gyp/pylib/gyp/generator/msvs.py#L1013-L1087
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/tkinter/__init__.py
python
Misc.winfo_fpixels
(self, number)
return self.tk.getdouble(self.tk.call( 'winfo', 'fpixels', self._w, number))
Return the number of pixels for the given distance NUMBER (e.g. "3c") as float.
Return the number of pixels for the given distance NUMBER (e.g. "3c") as float.
[ "Return", "the", "number", "of", "pixels", "for", "the", "given", "distance", "NUMBER", "(", "e", ".", "g", ".", "3c", ")", "as", "float", "." ]
def winfo_fpixels(self, number): """Return the number of pixels for the given distance NUMBER (e.g. "3c") as float.""" return self.tk.getdouble(self.tk.call( 'winfo', 'fpixels', self._w, number))
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/tkinter/__init__.py#L989-L993
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/msw/dataview.py
python
DataViewRenderer.GetVariantType
(*args, **kwargs)
return _dataview.DataViewRenderer_GetVariantType(*args, **kwargs)
GetVariantType(self) -> String
GetVariantType(self) -> String
[ "GetVariantType", "(", "self", ")", "-", ">", "String" ]
def GetVariantType(*args, **kwargs): """GetVariantType(self) -> String""" return _dataview.DataViewRenderer_GetVariantType(*args, **kwargs)
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/msw/dataview.py#L1160-L1162
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/osx_carbon/richtext.py
python
RichTextBuffer.BeginFontSize
(*args, **kwargs)
return _richtext.RichTextBuffer_BeginFontSize(*args, **kwargs)
BeginFontSize(self, int pointSize) -> bool
BeginFontSize(self, int pointSize) -> bool
[ "BeginFontSize", "(", "self", "int", "pointSize", ")", "-", ">", "bool" ]
def BeginFontSize(*args, **kwargs): """BeginFontSize(self, int pointSize) -> bool""" return _richtext.RichTextBuffer_BeginFontSize(*args, **kwargs)
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_carbon/richtext.py#L2357-L2359
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/osx_cocoa/grid.py
python
GridEditorCreatedEvent.SetControl
(*args, **kwargs)
return _grid.GridEditorCreatedEvent_SetControl(*args, **kwargs)
SetControl(self, Control ctrl)
SetControl(self, Control ctrl)
[ "SetControl", "(", "self", "Control", "ctrl", ")" ]
def SetControl(*args, **kwargs): """SetControl(self, Control ctrl)""" return _grid.GridEditorCreatedEvent_SetControl(*args, **kwargs)
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_cocoa/grid.py#L2487-L2489
SmileiPIC/Smilei
07dcb51200029e10f626e1546558c1ae7599c8b1
validation/plot_logs.py
python
switchPlots.on_pick
(self,event)
This method is used to make markers pickable.
This method is used to make markers pickable.
[ "This", "method", "is", "used", "to", "make", "markers", "pickable", "." ]
def on_pick(self,event): """ This method is used to make markers pickable. """ if self.plottype == "summary": pass elif self.plottype == "benchmark": marker_indexes = event.ind print("\n Selected points: {}".format(marker_indexes)) D = self.data[self.ind] for k in marker_indexes: print(" > Commit: {}".format(D["commits"][k])) print(" Branch: {}".format(D["branches"][k])) print(" Date: {}".format(D["date"][k])) print(" Link: https://llrgit.in2p3.fr/smilei/smilei/-/commit/{}".format(D["commit_ids"][k])) print(" ----------------------------------------------------------------------") print(" Timers | Times (s) | Min (s) | Mean (s) | Max (s) |") print(" ----------------------------------------------------------------------") for label, d, min, mean, max in zip( D["labels"]+["Total"], D["processed_data"]+[D["time_in_timeloop"]], D["min_times"], D["mean_times"], D["max_times"] ): print(" {0:15} | {1:.4e} | {2:.4e} | {3:.4e} | {4:.4e} |".format(label, d[k], min, mean, max)) else: raise Exception("Impossible")
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https://github.com/SmileiPIC/Smilei/blob/07dcb51200029e10f626e1546558c1ae7599c8b1/validation/plot_logs.py#L197-L226
catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/tools/python/src/Lib/plat-mac/pimp.py
python
PimpPackage_binary.unpackPackageOnly
(self, output=None)
We don't unpack binary packages until installing
We don't unpack binary packages until installing
[ "We", "don", "t", "unpack", "binary", "packages", "until", "installing" ]
def unpackPackageOnly(self, output=None): """We don't unpack binary packages until installing""" pass
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/tools/python/src/Lib/plat-mac/pimp.py#L793-L795
adobe/chromium
cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7
tools/site_compare/commands/compare2.py
python
ExecuteCompare2
(command)
Executes the Compare2 command.
Executes the Compare2 command.
[ "Executes", "the", "Compare2", "command", "." ]
def ExecuteCompare2(command): """Executes the Compare2 command.""" if command["--url"]: url_list = [command["--url"]] else: startline = command["--startline"] if command["--count"]: endline = startline+command["--count"] else: endline = command["--endline"] url_list = [url.strip() for url in open(command["--list"], "r").readlines()[startline:endline]] log_file = open(command["--logfile"], "w") outdir = command["--outdir"] if not outdir: outdir = tempfile.gettempdir() scrape_info_list = [] class ScrapeInfo(object): """Helper class to hold information about a scrape.""" __slots__ = ["browser_path", "scraper", "outdir", "result"] for index in xrange(1, 3): scrape_info = ScrapeInfo() scrape_info.browser_path = command["--browser%d" % index] scrape_info.scraper = scrapers.GetScraper( (command["--browser"], command["--browser%dver" % index])) if command["--browser%dname" % index]: scrape_info.outdir = os.path.join(outdir, command["--browser%dname" % index]) else: scrape_info.outdir = os.path.join(outdir, str(index)) drivers.windowing.PreparePath(scrape_info.outdir) scrape_info_list.append(scrape_info) compare = operators.GetOperator("equals_with_mask") for url in url_list: success = True for scrape_info in scrape_info_list: scrape_info.result = scrape_info.scraper.Scrape( [url], scrape_info.outdir, command["--size"], (0, 0), command["--timeout"], path=scrape_info.browser_path) if not scrape_info.result: scrape_info.result = "success" else: success = False result = "unknown" if success: result = "equal" file1 = drivers.windowing.URLtoFilename( url, scrape_info_list[0].outdir, ".bmp") file2 = drivers.windowing.URLtoFilename( url, scrape_info_list[1].outdir, ".bmp") comparison_result = compare.Compare(file1, file2, maskdir=command["--maskdir"]) if comparison_result is not None: result = "not-equal" if command["--diffdir"]: comparison_result[1].save( drivers.windowing.URLtoFilename(url, command["--diffdir"], ".bmp")) # TODO(jhaas): maybe use the logging module rather than raw file writes log_file.write("%s %s %s %s\n" % (url, scrape_info_list[0].result, scrape_info_list[1].result, result))
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https://github.com/adobe/chromium/blob/cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7/tools/site_compare/commands/compare2.py#L92-L170
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/gtk/_core.py
python
HeaderColumn.GetTitle
(*args, **kwargs)
return _core_.HeaderColumn_GetTitle(*args, **kwargs)
GetTitle(self) -> String
GetTitle(self) -> String
[ "GetTitle", "(", "self", ")", "-", ">", "String" ]
def GetTitle(*args, **kwargs): """GetTitle(self) -> String""" return _core_.HeaderColumn_GetTitle(*args, **kwargs)
[ "def", "GetTitle", "(", "*", "args", ",", "*", "*", "kwargs", ")", ":", "return", "_core_", ".", "HeaderColumn_GetTitle", "(", "*", "args", ",", "*", "*", "kwargs", ")" ]
https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/gtk/_core.py#L16380-L16382
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
wx/lib/agw/aui/auibar.py
python
AuiToolBar.GetOverflowState
(self)
return self._overflow_state
Returns the state of the overflow button.
Returns the state of the overflow button.
[ "Returns", "the", "state", "of", "the", "overflow", "button", "." ]
def GetOverflowState(self): """ Returns the state of the overflow button. """ return self._overflow_state
[ "def", "GetOverflowState", "(", "self", ")", ":", "return", "self", ".", "_overflow_state" ]
https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/wx/lib/agw/aui/auibar.py#L3181-L3184
martinmoene/variant-lite
f1af3518e4c28f12b09839b9d2ee37984cbf137a
script/upload-conan.py
python
createConanPackage
( args )
Create conan package and upload it.
Create conan package and upload it.
[ "Create", "conan", "package", "and", "upload", "it", "." ]
def createConanPackage( args ): """Create conan package and upload it.""" cmd = tpl_conan_create.format(usr=args.user, chn=args.channel) if args.verbose: print( "> {}".format(cmd) ) if not args.dry_run: subprocess.call( cmd, shell=False )
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https://github.com/martinmoene/variant-lite/blob/f1af3518e4c28f12b09839b9d2ee37984cbf137a/script/upload-conan.py#L38-L44
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Tools/Python/3.7.10/windows/Lib/site-packages/pip/_vendor/distlib/_backport/sysconfig.py
python
get_config_h_filename
()
return os.path.join(inc_dir, 'pyconfig.h')
Return the path of pyconfig.h.
Return the path of pyconfig.h.
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def get_config_h_filename(): """Return the path of pyconfig.h.""" if _PYTHON_BUILD: if os.name == "nt": inc_dir = os.path.join(_PROJECT_BASE, "PC") else: inc_dir = _PROJECT_BASE else: inc_dir = get_path('platinclude') return os.path.join(inc_dir, 'pyconfig.h')
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/windows/Lib/site-packages/pip/_vendor/distlib/_backport/sysconfig.py#L417-L426
Xilinx/Vitis-AI
fc74d404563d9951b57245443c73bef389f3657f
tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/feature_column/feature_column_v2.py
python
IdentityCategoricalColumn._get_config
(self)
return dict(zip(self._fields, self))
See 'FeatureColumn` base class.
See 'FeatureColumn` base class.
[ "See", "FeatureColumn", "base", "class", "." ]
def _get_config(self): """See 'FeatureColumn` base class.""" return dict(zip(self._fields, self))
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https://github.com/Xilinx/Vitis-AI/blob/fc74d404563d9951b57245443c73bef389f3657f/tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/feature_column/feature_column_v2.py#L3904-L3906
ceph/ceph
959663007321a369c83218414a29bd9dbc8bda3a
src/pybind/mgr/rook/rook_cluster.py
python
RookCluster.describe_pods
(self, service_type: Optional[str], service_id: Optional[str], nodename: Optional[str])
return pods_summary
Go query the k8s API about deployment, containers related to this filesystem Example Rook Pod labels for a mgr daemon: Labels: app=rook-ceph-mgr pod-template-hash=2171958073 rook_cluster=rook And MDS containers additionally have `rook_filesystem` label Label filter is rook_cluster=<cluster namespace> rook_file_system=<self.fs_name>
Go query the k8s API about deployment, containers related to this filesystem
[ "Go", "query", "the", "k8s", "API", "about", "deployment", "containers", "related", "to", "this", "filesystem" ]
def describe_pods(self, service_type: Optional[str], service_id: Optional[str], nodename: Optional[str]) -> List[Dict[str, Any]]: """ Go query the k8s API about deployment, containers related to this filesystem Example Rook Pod labels for a mgr daemon: Labels: app=rook-ceph-mgr pod-template-hash=2171958073 rook_cluster=rook And MDS containers additionally have `rook_filesystem` label Label filter is rook_cluster=<cluster namespace> rook_file_system=<self.fs_name> """ def predicate(item): # type: (client.V1Pod) -> bool metadata = item.metadata if service_type is not None: if metadata.labels['app'] != "rook-ceph-{0}".format(service_type): return False if service_id is not None: try: k, v = { "mds": ("rook_file_system", service_id), "osd": ("ceph-osd-id", service_id), "mon": ("mon", service_id), "mgr": ("mgr", service_id), "nfs": ("nfs", service_id), "rgw": ("ceph_rgw", service_id), }[service_type] except KeyError: raise orchestrator.OrchestratorValidationError( '{} not supported'.format(service_type)) if metadata.labels[k] != v: return False if nodename is not None: if item.spec.node_name != nodename: return False return True refreshed = datetime_now() pods = [i for i in self.rook_pods.items if predicate(i)] pods_summary = [] prefix = 'sha256:' for p in pods: d = p.to_dict() image_name = None for c in d['spec']['containers']: # look at the first listed container in the pod... image_name = c['image'] break ls = d['status'].get('container_statuses') if not ls: # ignore pods with no containers continue image_id = ls[0]['image_id'] image_id = image_id.split(prefix)[1] if prefix in image_id else image_id s = { "name": d['metadata']['name'], "hostname": d['spec']['node_name'], "labels": d['metadata']['labels'], 'phase': d['status']['phase'], 'container_image_name': image_name, 'container_image_id': image_id, 'refreshed': refreshed, # these may get set below... 'started': None, 'created': None, } # note: we want UTC if d['metadata'].get('creation_timestamp', None): s['created'] = d['metadata']['creation_timestamp'].astimezone( tz=datetime.timezone.utc) if d['status'].get('start_time', None): s['started'] = d['status']['start_time'].astimezone( tz=datetime.timezone.utc) pods_summary.append(s) return pods_summary
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https://github.com/ceph/ceph/blob/959663007321a369c83218414a29bd9dbc8bda3a/src/pybind/mgr/rook/rook_cluster.py#L747-L837
Xilinx/Vitis-AI
fc74d404563d9951b57245443c73bef389f3657f
tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/learn/python/learn/estimators/estimator.py
python
BaseEstimator.evaluate
(self, x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None, checkpoint_path=None, hooks=None, log_progress=True)
return eval_results
See `Evaluable`. Raises: ValueError: If at least one of `x` or `y` is provided, and at least one of `input_fn` or `feed_fn` is provided. Or if `metrics` is not `None` or `dict`.
See `Evaluable`.
[ "See", "Evaluable", "." ]
def evaluate(self, x=None, y=None, input_fn=None, feed_fn=None, batch_size=None, steps=None, metrics=None, name=None, checkpoint_path=None, hooks=None, log_progress=True): # pylint: disable=g-doc-args,g-doc-return-or-yield """See `Evaluable`. Raises: ValueError: If at least one of `x` or `y` is provided, and at least one of `input_fn` or `feed_fn` is provided. Or if `metrics` is not `None` or `dict`. """ _verify_input_args(x, y, input_fn, feed_fn, batch_size) if x is not None: return SKCompat(self).score(x, y, batch_size, steps, metrics, name) if metrics is not None and not isinstance(metrics, dict): raise ValueError('Metrics argument should be None or dict. ' 'Got %s.' % metrics) eval_results, global_step = self._evaluate_model( input_fn=input_fn, feed_fn=feed_fn, steps=steps, metrics=metrics, name=name, checkpoint_path=checkpoint_path, hooks=hooks, log_progress=log_progress) if eval_results is not None: eval_results.update({'global_step': global_step}) return eval_results
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https://github.com/Xilinx/Vitis-AI/blob/fc74d404563d9951b57245443c73bef389f3657f/tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/learn/python/learn/estimators/estimator.py#L582-L621
FreeCAD/FreeCAD
ba42231b9c6889b89e064d6d563448ed81e376ec
src/Mod/Path/PathScripts/PathThreadMilling.py
python
internalThreadCommands
(loc, cmd, zStart, zFinal, pitch, radius, leadInOut)
return path
internalThreadCommands(loc, cmd, zStart, zFinal, pitch, radius) ... returns the g-code to mill the given internal thread
internalThreadCommands(loc, cmd, zStart, zFinal, pitch, radius) ... returns the g-code to mill the given internal thread
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def internalThreadCommands(loc, cmd, zStart, zFinal, pitch, radius, leadInOut): """internalThreadCommands(loc, cmd, zStart, zFinal, pitch, radius) ... returns the g-code to mill the given internal thread""" thread = _InternalThread(cmd, zStart, zFinal, pitch) yMin = loc.y - radius yMax = loc.y + radius path = [] # at this point the tool is at a safe height (depending on the previous thread), so we can move # into position first, and then drop to the start height. If there is any material in the way this # op hasn't been setup properly. path.append(Path.Command("G0", {"X": loc.x, "Y": loc.y})) path.append(Path.Command("G0", {"Z": thread.zStart})) if leadInOut: path.append(Path.Command(thread.cmd, {"Y": yMax, "J": (yMax - loc.y) / 2})) else: path.append(Path.Command("G1", {"Y": yMax})) z = thread.zStart r = -radius i = 0 while True: z = thread.zStart + i * thread.hPitch if thread.overshoots(z): break if 0 == (i & 0x01): y = yMin else: y = yMax path.append(Path.Command(thread.cmd, {"Y": y, "Z": z + thread.hPitch, "J": r})) r = -r i = i + 1 z = thread.zStart + i * thread.hPitch if PathGeom.isRoughly(z, thread.zFinal): x = loc.x else: n = math.fabs(thread.zFinal - thread.zStart) / thread.hPitch k = n - int(n) dy = math.cos(k * math.pi) dx = math.sin(k * math.pi) y = thread.adjustY(loc.y, r * dy) x = thread.adjustX(loc.x, r * dx) path.append( Path.Command(thread.cmd, {"X": x, "Y": y, "Z": thread.zFinal, "J": r}) ) if leadInOut: path.append( Path.Command( thread.cmd, {"X": loc.x, "Y": loc.y, "I": (loc.x - x) / 2, "J": (loc.y - y) / 2}, ) ) else: path.append(Path.Command("G1", {"X": loc.x, "Y": loc.y})) return path
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https://github.com/FreeCAD/FreeCAD/blob/ba42231b9c6889b89e064d6d563448ed81e376ec/src/Mod/Path/PathScripts/PathThreadMilling.py#L115-L171
apache/arrow
af33dd1157eb8d7d9bfac25ebf61445b793b7943
python/pyarrow/orc.py
python
ORCFile.writer
(self)
return self.reader.writer()
Name of the writer that wrote this file. If the writer is unknown then its Writer ID (a number) is returned
Name of the writer that wrote this file. If the writer is unknown then its Writer ID (a number) is returned
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def writer(self): """Name of the writer that wrote this file. If the writer is unknown then its Writer ID (a number) is returned""" return self.reader.writer()
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https://github.com/apache/arrow/blob/af33dd1157eb8d7d9bfac25ebf61445b793b7943/python/pyarrow/orc.py#L83-L87
catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/python/numpy/py3/numpy/ma/core.py
python
reshape
(a, new_shape, order='C')
Returns an array containing the same data with a new shape. Refer to `MaskedArray.reshape` for full documentation. See Also -------- MaskedArray.reshape : equivalent function
Returns an array containing the same data with a new shape.
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def reshape(a, new_shape, order='C'): """ Returns an array containing the same data with a new shape. Refer to `MaskedArray.reshape` for full documentation. See Also -------- MaskedArray.reshape : equivalent function """ # We can't use 'frommethod', it whine about some parameters. Dmmit. try: return a.reshape(new_shape, order=order) except AttributeError: _tmp = narray(a, copy=False).reshape(new_shape, order=order) return _tmp.view(MaskedArray)
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/numpy/py3/numpy/ma/core.py#L7142-L7158
ros-planning/moveit
ee48dc5cedc981d0869352aa3db0b41469c2735c
moveit_commander/src/moveit_commander/move_group.py
python
MoveGroupCommander.clear_trajectory_constraints
(self)
Specify that no trajectory constraints are to be used during motion planning
Specify that no trajectory constraints are to be used during motion planning
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def clear_trajectory_constraints(self): """ Specify that no trajectory constraints are to be used during motion planning """ self._g.clear_trajectory_constraints()
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https://github.com/ros-planning/moveit/blob/ee48dc5cedc981d0869352aa3db0b41469c2735c/moveit_commander/src/moveit_commander/move_group.py#L520-L522
natanielruiz/android-yolo
1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f
jni-build/jni/include/tensorflow/contrib/slim/python/slim/nets/alexnet.py
python
alexnet_v2
(inputs, num_classes=1000, dropout_keep_prob=0.5, is_training=True, spatial_squeeze=True, scope='alexnet_v2')
AlexNet version 2. Described in: http://arxiv.org/pdf/1404.5997v2.pdf Parameters from: github.com/akrizhevsky/cuda-convnet2/blob/master/layers/ layers-imagenet-1gpu.cfg Note: All the fully_connected layers have been transformed to conv2d layers. To use in classification mode, resize input to 224x224. To use in fully convolutional mode, set spatial_squeeze to false. The LRN layers have been removed and change the initializers from random_normal_initializer to xavier_initializer. Args: inputs: a tensor of size [batch_size, height, width, channels]. num_classes: number of predicted classes. dropout_keep_prob: the probability that activations are kept in the dropout layers during training. is_training: whether or not the model is being trained. spatial_squeeze: whether or not should squeeze the spatial dimensions of the outputs. Useful to remove unnecessary dimensions for classification. scope: Optional scope for the variables. Returns: the last op containing the log predictions and end_points dict.
AlexNet version 2.
[ "AlexNet", "version", "2", "." ]
def alexnet_v2(inputs, num_classes=1000, dropout_keep_prob=0.5, is_training=True, spatial_squeeze=True, scope='alexnet_v2'): """AlexNet version 2. Described in: http://arxiv.org/pdf/1404.5997v2.pdf Parameters from: github.com/akrizhevsky/cuda-convnet2/blob/master/layers/ layers-imagenet-1gpu.cfg Note: All the fully_connected layers have been transformed to conv2d layers. To use in classification mode, resize input to 224x224. To use in fully convolutional mode, set spatial_squeeze to false. The LRN layers have been removed and change the initializers from random_normal_initializer to xavier_initializer. Args: inputs: a tensor of size [batch_size, height, width, channels]. num_classes: number of predicted classes. dropout_keep_prob: the probability that activations are kept in the dropout layers during training. is_training: whether or not the model is being trained. spatial_squeeze: whether or not should squeeze the spatial dimensions of the outputs. Useful to remove unnecessary dimensions for classification. scope: Optional scope for the variables. Returns: the last op containing the log predictions and end_points dict. """ with tf.variable_op_scope([inputs], scope, 'alexnet_v2') as sc: end_points_collection = sc.name + '_end_points' # Collect outputs for conv2d, fully_connected and max_pool2d. with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d], outputs_collections=[end_points_collection]): net = slim.conv2d(inputs, 64, [11, 11], 4, padding='VALID', scope='conv1') net = slim.max_pool2d(net, [3, 3], 2, scope='pool1') net = slim.conv2d(net, 192, [5, 5], scope='conv2') net = slim.max_pool2d(net, [3, 3], 2, scope='pool2') net = slim.conv2d(net, 384, [3, 3], scope='conv3') net = slim.conv2d(net, 384, [3, 3], scope='conv4') net = slim.conv2d(net, 256, [3, 3], scope='conv5') net = slim.max_pool2d(net, [3, 3], 2, scope='pool5') # Use conv2d instead of fully_connected layers. with slim.arg_scope([slim.conv2d], weights_initializer=trunc_normal(0.005), biases_initializer=tf.constant_initializer(0.1)): net = slim.conv2d(net, 4096, [5, 5], padding='VALID', scope='fc6') net = slim.dropout(net, dropout_keep_prob, is_training=is_training, scope='dropout6') net = slim.conv2d(net, 4096, [1, 1], scope='fc7') net = slim.dropout(net, dropout_keep_prob, is_training=is_training, scope='dropout7') net = slim.conv2d(net, num_classes, [1, 1], activation_fn=None, normalizer_fn=None, biases_initializer=tf.zeros_initializer, scope='fc8') # Convert end_points_collection into a end_point dict. end_points = dict(tf.get_collection(end_points_collection)) if spatial_squeeze: net = tf.squeeze(net, [1, 2], name='fc8/squeezed') end_points[sc.name + '/fc8'] = net return net, end_points
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https://github.com/natanielruiz/android-yolo/blob/1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f/jni-build/jni/include/tensorflow/contrib/slim/python/slim/nets/alexnet.py#L51-L120
baidu-research/tensorflow-allreduce
66d5b855e90b0949e9fa5cca5599fd729a70e874
tensorflow/python/estimator/canned/head.py
python
_multi_class_head_with_softmax_cross_entropy_loss
(n_classes, weight_column=None, label_vocabulary=None)
return _MultiClassHeadWithSoftmaxCrossEntropyLoss(n_classes, weight_column, label_vocabulary)
Creates a '_Head' for multi class classification. This head expects to be fed integer labels specifying the class index. Args: n_classes: Number of classes, must be greater than 2 (for 2 classes, use `_BinaryLogisticHeadWithSigmoidCrossEntropyLoss`). weight_column: A string or a `_NumericColumn` created by `tf.feature_column.numeric_column` defining feature column representing weights. It is used to down weight or boost examples during training. It will be multiplied by the loss of the example. label_vocabulary: A list of strings represents possible label values. If it is not given, that means labels are already encoded as integer within [0, n_classes). If given, labels must be string type and have any value in `label_vocabulary`. Also there will be errors if vocabulary is not provided and labels are string. Returns: An instance of `_Head` for multi class classification. Raises: ValueError: if `n_classes`, `metric_class_ids` or `label_keys` is invalid.
Creates a '_Head' for multi class classification.
[ "Creates", "a", "_Head", "for", "multi", "class", "classification", "." ]
def _multi_class_head_with_softmax_cross_entropy_loss(n_classes, weight_column=None, label_vocabulary=None): """Creates a '_Head' for multi class classification. This head expects to be fed integer labels specifying the class index. Args: n_classes: Number of classes, must be greater than 2 (for 2 classes, use `_BinaryLogisticHeadWithSigmoidCrossEntropyLoss`). weight_column: A string or a `_NumericColumn` created by `tf.feature_column.numeric_column` defining feature column representing weights. It is used to down weight or boost examples during training. It will be multiplied by the loss of the example. label_vocabulary: A list of strings represents possible label values. If it is not given, that means labels are already encoded as integer within [0, n_classes). If given, labels must be string type and have any value in `label_vocabulary`. Also there will be errors if vocabulary is not provided and labels are string. Returns: An instance of `_Head` for multi class classification. Raises: ValueError: if `n_classes`, `metric_class_ids` or `label_keys` is invalid. """ if label_vocabulary is not None and not isinstance(label_vocabulary, (list, tuple)): raise ValueError('label_vocabulary should be a list. Given type: {}'.format( type(label_vocabulary))) return _MultiClassHeadWithSoftmaxCrossEntropyLoss(n_classes, weight_column, label_vocabulary)
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https://github.com/baidu-research/tensorflow-allreduce/blob/66d5b855e90b0949e9fa5cca5599fd729a70e874/tensorflow/python/estimator/canned/head.py#L283-L315
apple/swift-lldb
d74be846ef3e62de946df343e8c234bde93a8912
scripts/Python/static-binding/lldb.py
python
SBCommunication.AdoptFileDesriptor
(self, fd, owns_fd)
return _lldb.SBCommunication_AdoptFileDesriptor(self, fd, owns_fd)
AdoptFileDesriptor(SBCommunication self, int fd, bool owns_fd) -> lldb::ConnectionStatus
AdoptFileDesriptor(SBCommunication self, int fd, bool owns_fd) -> lldb::ConnectionStatus
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def AdoptFileDesriptor(self, fd, owns_fd): """AdoptFileDesriptor(SBCommunication self, int fd, bool owns_fd) -> lldb::ConnectionStatus""" return _lldb.SBCommunication_AdoptFileDesriptor(self, fd, owns_fd)
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https://github.com/apple/swift-lldb/blob/d74be846ef3e62de946df343e8c234bde93a8912/scripts/Python/static-binding/lldb.py#L3032-L3034
francinexue/xuefu
b6ff79747a42e020588c0c0a921048e08fe4680c
api/ctpx/ctptd.py
python
CtpTd.onRspFromFutureToBankByFuture
(self, ReqTransferField, RspInfoField, requestId, final)
期货发起期货资金转银行应答
期货发起期货资金转银行应答
[ "期货发起期货资金转银行应答" ]
def onRspFromFutureToBankByFuture(self, ReqTransferField, RspInfoField, requestId, final): """期货发起期货资金转银行应答""" pass
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https://github.com/francinexue/xuefu/blob/b6ff79747a42e020588c0c0a921048e08fe4680c/api/ctpx/ctptd.py#L527-L529
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/osx_cocoa/_misc.py
python
GetLocalTime
(*args)
return _misc_.GetLocalTime(*args)
GetLocalTime() -> long
GetLocalTime() -> long
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def GetLocalTime(*args): """GetLocalTime() -> long""" return _misc_.GetLocalTime(*args)
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_cocoa/_misc.py#L4785-L4787
Slicer/Slicer
ba9fadf332cb0303515b68d8d06a344c82e3e3e5
Modules/Scripted/DICOMLib/DICOMUtils.py
python
refreshDICOMWidget
()
return True
Refresh DICOM browser from database. It is useful when the database is changed via a database object that is different from the one stored in the DICOM browser. There may be multiple database connection (through different database objects) in the same process.
Refresh DICOM browser from database. It is useful when the database is changed via a database object that is different from the one stored in the DICOM browser. There may be multiple database connection (through different database objects) in the same process.
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def refreshDICOMWidget(): """ Refresh DICOM browser from database. It is useful when the database is changed via a database object that is different from the one stored in the DICOM browser. There may be multiple database connection (through different database objects) in the same process. """ try: slicer.modules.DICOMInstance.browserWidget.dicomBrowser.dicomTableManager().updateTableViews() except AttributeError: logging.error('DICOM module or browser cannot be accessed') return False return True
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https://github.com/Slicer/Slicer/blob/ba9fadf332cb0303515b68d8d06a344c82e3e3e5/Modules/Scripted/DICOMLib/DICOMUtils.py#L672-L683
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/gtk/grid.py
python
GridCellEditor.IsAcceptedKey
(*args, **kwargs)
return _grid.GridCellEditor_IsAcceptedKey(*args, **kwargs)
IsAcceptedKey(self, KeyEvent event) -> bool
IsAcceptedKey(self, KeyEvent event) -> bool
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def IsAcceptedKey(*args, **kwargs): """IsAcceptedKey(self, KeyEvent event) -> bool""" return _grid.GridCellEditor_IsAcceptedKey(*args, **kwargs)
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/gtk/grid.py#L320-L322
catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/tools/python3/src/Lib/ftplib.py
python
FTP.rename
(self, fromname, toname)
return self.voidcmd('RNTO ' + toname)
Rename a file.
Rename a file.
[ "Rename", "a", "file", "." ]
def rename(self, fromname, toname): '''Rename a file.''' resp = self.sendcmd('RNFR ' + fromname) if resp[0] != '3': raise error_reply(resp) return self.voidcmd('RNTO ' + toname)
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/tools/python3/src/Lib/ftplib.py#L599-L604
Xilinx/Vitis-AI
fc74d404563d9951b57245443c73bef389f3657f
tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/keras/backend_config.py
python
floatx
()
return _FLOATX
Returns the default float type, as a string. E.g. 'float16', 'float32', 'float64'. Returns: String, the current default float type. Example: ```python keras.backend.floatx() >>> 'float32' ```
Returns the default float type, as a string.
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def floatx(): """Returns the default float type, as a string. E.g. 'float16', 'float32', 'float64'. Returns: String, the current default float type. Example: ```python keras.backend.floatx() >>> 'float32' ``` """ return _FLOATX
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https://github.com/Xilinx/Vitis-AI/blob/fc74d404563d9951b57245443c73bef389f3657f/tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/keras/backend_config.py#L61-L74
kamyu104/LeetCode-Solutions
77605708a927ea3b85aee5a479db733938c7c211
Python/matrix-diagonal-sum.py
python
Solution.diagonalSum
(self, mat)
return sum(mat[i][i]+mat[~i][i] for i in xrange(len(mat))) - (mat[len(mat)//2][len(mat)//2] if len(mat)%2 == 1 else 0)
:type mat: List[List[int]] :rtype: int
:type mat: List[List[int]] :rtype: int
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def diagonalSum(self, mat): """ :type mat: List[List[int]] :rtype: int """ return sum(mat[i][i]+mat[~i][i] for i in xrange(len(mat))) - (mat[len(mat)//2][len(mat)//2] if len(mat)%2 == 1 else 0)
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https://github.com/kamyu104/LeetCode-Solutions/blob/77605708a927ea3b85aee5a479db733938c7c211/Python/matrix-diagonal-sum.py#L5-L10
libornovax/master_thesis_code
6eca474ed3cae673afde010caef338cf7349f839
scripts/compute_pr_curve.py
python
PRPlotter._add_curve
(self, tps, fps, fns, fnsr, fpsd, category)
Puts a new PR curve into the plot. Input: tps: np.array of true positives' counts (length N) fps: np.array of false positives' counts (length N) fns: np.array of false negatives' counts (length N) fnsr: np.array of false negatives' counts on required gt (length N) fpsd: np.array of false negatives' counts outside of don't care regions (length N) category: Object category (label), which the curve corresponds to
Puts a new PR curve into the plot.
[ "Puts", "a", "new", "PR", "curve", "into", "the", "plot", "." ]
def _add_curve(self, tps, fps, fns, fnsr, fpsd, category): """ Puts a new PR curve into the plot. Input: tps: np.array of true positives' counts (length N) fps: np.array of false positives' counts (length N) fns: np.array of false negatives' counts (length N) fnsr: np.array of false negatives' counts on required gt (length N) fpsd: np.array of false negatives' counts outside of don't care regions (length N) category: Object category (label), which the curve corresponds to """ # Compute the precision and recall for the PR curve precisions, recalls = pr_curve_points(tps, fps, fns) precisionsr, recallsr = pr_curve_points(tps, fps, fnsr) precisionsd, recallsd = pr_curve_points(tps, fpsd, fns) precisionsrd, recallsrd = pr_curve_points(tps, fpsd, fnsr) plt.plot(precisions, recalls, label=category, color=COLORS[category], linewidth=2) plt.plot(precisionsd, recallsd, label=category+' - don\'t care', color=COLORS[category]) plt.plot(precisionsr, recallsr, label=category+' - required', color=COLORS[category], linestyle='--') plt.plot(precisionsrd, recallsrd, label=category+' - required, don\'t care', color=COLORS[category], linestyle=':') self.categories.append(category) self.precisions.append(precisions) self.recalls.append(recalls) self.precisionsr.append(precisionsr) self.recallsr.append(recallsr) self.precisionsd.append(precisionsd) self.recallsd.append(recallsd) self.precisionsrd.append(precisionsrd) self.recallsrd.append(recallsrd) self.tps.append(tps) self.fps.append(fps) self.fns.append(fns) self.fnsr.append(fnsr) self.fpsd.append(fpsd)
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https://github.com/libornovax/master_thesis_code/blob/6eca474ed3cae673afde010caef338cf7349f839/scripts/compute_pr_curve.py#L238-L276
catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/python/pandas/py2/pandas/core/frame.py
python
DataFrame.combine
(self, other, func, fill_value=None, overwrite=True)
return self._constructor(result, index=new_index, columns=new_columns)
Perform column-wise combine with another DataFrame based on a passed function. Combines a DataFrame with `other` DataFrame using `func` to element-wise combine columns. The row and column indexes of the resulting DataFrame will be the union of the two. Parameters ---------- other : DataFrame The DataFrame to merge column-wise. func : function Function that takes two series as inputs and return a Series or a scalar. Used to merge the two dataframes column by columns. fill_value : scalar value, default None The value to fill NaNs with prior to passing any column to the merge func. overwrite : boolean, default True If True, columns in `self` that do not exist in `other` will be overwritten with NaNs. Returns ------- result : DataFrame See Also -------- DataFrame.combine_first : Combine two DataFrame objects and default to non-null values in frame calling the method. Examples -------- Combine using a simple function that chooses the smaller column. >>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]}) >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]}) >>> take_smaller = lambda s1, s2: s1 if s1.sum() < s2.sum() else s2 >>> df1.combine(df2, take_smaller) A B 0 0 3 1 0 3 Example using a true element-wise combine function. >>> df1 = pd.DataFrame({'A': [5, 0], 'B': [2, 4]}) >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]}) >>> df1.combine(df2, np.minimum) A B 0 1 2 1 0 3 Using `fill_value` fills Nones prior to passing the column to the merge function. >>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]}) >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]}) >>> df1.combine(df2, take_smaller, fill_value=-5) A B 0 0 -5.0 1 0 4.0 However, if the same element in both dataframes is None, that None is preserved >>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]}) >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [None, 3]}) >>> df1.combine(df2, take_smaller, fill_value=-5) A B 0 0 NaN 1 0 3.0 Example that demonstrates the use of `overwrite` and behavior when the axis differ between the dataframes. >>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]}) >>> df2 = pd.DataFrame({'B': [3, 3], 'C': [-10, 1],}, index=[1, 2]) >>> df1.combine(df2, take_smaller) A B C 0 NaN NaN NaN 1 NaN 3.0 -10.0 2 NaN 3.0 1.0 >>> df1.combine(df2, take_smaller, overwrite=False) A B C 0 0.0 NaN NaN 1 0.0 3.0 -10.0 2 NaN 3.0 1.0 Demonstrating the preference of the passed in dataframe. >>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1],}, index=[1, 2]) >>> df2.combine(df1, take_smaller) A B C 0 0.0 NaN NaN 1 0.0 3.0 NaN 2 NaN 3.0 NaN >>> df2.combine(df1, take_smaller, overwrite=False) A B C 0 0.0 NaN NaN 1 0.0 3.0 1.0 2 NaN 3.0 1.0
Perform column-wise combine with another DataFrame based on a passed function.
[ "Perform", "column", "-", "wise", "combine", "with", "another", "DataFrame", "based", "on", "a", "passed", "function", "." ]
def combine(self, other, func, fill_value=None, overwrite=True): """ Perform column-wise combine with another DataFrame based on a passed function. Combines a DataFrame with `other` DataFrame using `func` to element-wise combine columns. The row and column indexes of the resulting DataFrame will be the union of the two. Parameters ---------- other : DataFrame The DataFrame to merge column-wise. func : function Function that takes two series as inputs and return a Series or a scalar. Used to merge the two dataframes column by columns. fill_value : scalar value, default None The value to fill NaNs with prior to passing any column to the merge func. overwrite : boolean, default True If True, columns in `self` that do not exist in `other` will be overwritten with NaNs. Returns ------- result : DataFrame See Also -------- DataFrame.combine_first : Combine two DataFrame objects and default to non-null values in frame calling the method. Examples -------- Combine using a simple function that chooses the smaller column. >>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]}) >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]}) >>> take_smaller = lambda s1, s2: s1 if s1.sum() < s2.sum() else s2 >>> df1.combine(df2, take_smaller) A B 0 0 3 1 0 3 Example using a true element-wise combine function. >>> df1 = pd.DataFrame({'A': [5, 0], 'B': [2, 4]}) >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]}) >>> df1.combine(df2, np.minimum) A B 0 1 2 1 0 3 Using `fill_value` fills Nones prior to passing the column to the merge function. >>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]}) >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]}) >>> df1.combine(df2, take_smaller, fill_value=-5) A B 0 0 -5.0 1 0 4.0 However, if the same element in both dataframes is None, that None is preserved >>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]}) >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [None, 3]}) >>> df1.combine(df2, take_smaller, fill_value=-5) A B 0 0 NaN 1 0 3.0 Example that demonstrates the use of `overwrite` and behavior when the axis differ between the dataframes. >>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]}) >>> df2 = pd.DataFrame({'B': [3, 3], 'C': [-10, 1],}, index=[1, 2]) >>> df1.combine(df2, take_smaller) A B C 0 NaN NaN NaN 1 NaN 3.0 -10.0 2 NaN 3.0 1.0 >>> df1.combine(df2, take_smaller, overwrite=False) A B C 0 0.0 NaN NaN 1 0.0 3.0 -10.0 2 NaN 3.0 1.0 Demonstrating the preference of the passed in dataframe. >>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1],}, index=[1, 2]) >>> df2.combine(df1, take_smaller) A B C 0 0.0 NaN NaN 1 0.0 3.0 NaN 2 NaN 3.0 NaN >>> df2.combine(df1, take_smaller, overwrite=False) A B C 0 0.0 NaN NaN 1 0.0 3.0 1.0 2 NaN 3.0 1.0 """ other_idxlen = len(other.index) # save for compare this, other = self.align(other, copy=False) new_index = this.index if other.empty and len(new_index) == len(self.index): return self.copy() if self.empty and len(other) == other_idxlen: return other.copy() # sorts if possible new_columns = this.columns.union(other.columns) do_fill = fill_value is not None result = {} for col in new_columns: series = this[col] otherSeries = other[col] this_dtype = series.dtype other_dtype = otherSeries.dtype this_mask = isna(series) other_mask = isna(otherSeries) # don't overwrite columns unecessarily # DO propagate if this column is not in the intersection if not overwrite and other_mask.all(): result[col] = this[col].copy() continue if do_fill: series = series.copy() otherSeries = otherSeries.copy() series[this_mask] = fill_value otherSeries[other_mask] = fill_value if col not in self.columns: # If self DataFrame does not have col in other DataFrame, # try to promote series, which is all NaN, as other_dtype. new_dtype = other_dtype try: series = series.astype(new_dtype, copy=False) except ValueError: # e.g. new_dtype is integer types pass else: # if we have different dtypes, possibly promote new_dtype = find_common_type([this_dtype, other_dtype]) if not is_dtype_equal(this_dtype, new_dtype): series = series.astype(new_dtype) if not is_dtype_equal(other_dtype, new_dtype): otherSeries = otherSeries.astype(new_dtype) arr = func(series, otherSeries) arr = maybe_downcast_to_dtype(arr, this_dtype) result[col] = arr # convert_objects just in case return self._constructor(result, index=new_index, columns=new_columns)
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/pandas/py2/pandas/core/frame.py#L5122-L5288
catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/python/numpy/py2/numpy/lib/nanfunctions.py
python
nanmean
(a, axis=None, dtype=None, out=None, keepdims=np._NoValue)
return avg
Compute the arithmetic mean along the specified axis, ignoring NaNs. Returns the average of the array elements. The average is taken over the flattened array by default, otherwise over the specified axis. `float64` intermediate and return values are used for integer inputs. For all-NaN slices, NaN is returned and a `RuntimeWarning` is raised. .. versionadded:: 1.8.0 Parameters ---------- a : array_like Array containing numbers whose mean is desired. If `a` is not an array, a conversion is attempted. axis : {int, tuple of int, None}, optional Axis or axes along which the means are computed. The default is to compute the mean of the flattened array. dtype : data-type, optional Type to use in computing the mean. For integer inputs, the default is `float64`; for inexact inputs, it is the same as the input dtype. out : ndarray, optional Alternate output array in which to place the result. The default is ``None``; if provided, it must have the same shape as the expected output, but the type will be cast if necessary. See `doc.ufuncs` for details. keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original `a`. If the value is anything but the default, then `keepdims` will be passed through to the `mean` or `sum` methods of sub-classes of `ndarray`. If the sub-classes methods does not implement `keepdims` any exceptions will be raised. Returns ------- m : ndarray, see dtype parameter above If `out=None`, returns a new array containing the mean values, otherwise a reference to the output array is returned. Nan is returned for slices that contain only NaNs. See Also -------- average : Weighted average mean : Arithmetic mean taken while not ignoring NaNs var, nanvar Notes ----- The arithmetic mean is the sum of the non-NaN elements along the axis divided by the number of non-NaN elements. Note that for floating-point input, the mean is computed using the same precision the input has. Depending on the input data, this can cause the results to be inaccurate, especially for `float32`. Specifying a higher-precision accumulator using the `dtype` keyword can alleviate this issue. Examples -------- >>> a = np.array([[1, np.nan], [3, 4]]) >>> np.nanmean(a) 2.6666666666666665 >>> np.nanmean(a, axis=0) array([ 2., 4.]) >>> np.nanmean(a, axis=1) array([ 1., 3.5])
Compute the arithmetic mean along the specified axis, ignoring NaNs.
[ "Compute", "the", "arithmetic", "mean", "along", "the", "specified", "axis", "ignoring", "NaNs", "." ]
def nanmean(a, axis=None, dtype=None, out=None, keepdims=np._NoValue): """ Compute the arithmetic mean along the specified axis, ignoring NaNs. Returns the average of the array elements. The average is taken over the flattened array by default, otherwise over the specified axis. `float64` intermediate and return values are used for integer inputs. For all-NaN slices, NaN is returned and a `RuntimeWarning` is raised. .. versionadded:: 1.8.0 Parameters ---------- a : array_like Array containing numbers whose mean is desired. If `a` is not an array, a conversion is attempted. axis : {int, tuple of int, None}, optional Axis or axes along which the means are computed. The default is to compute the mean of the flattened array. dtype : data-type, optional Type to use in computing the mean. For integer inputs, the default is `float64`; for inexact inputs, it is the same as the input dtype. out : ndarray, optional Alternate output array in which to place the result. The default is ``None``; if provided, it must have the same shape as the expected output, but the type will be cast if necessary. See `doc.ufuncs` for details. keepdims : bool, optional If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the original `a`. If the value is anything but the default, then `keepdims` will be passed through to the `mean` or `sum` methods of sub-classes of `ndarray`. If the sub-classes methods does not implement `keepdims` any exceptions will be raised. Returns ------- m : ndarray, see dtype parameter above If `out=None`, returns a new array containing the mean values, otherwise a reference to the output array is returned. Nan is returned for slices that contain only NaNs. See Also -------- average : Weighted average mean : Arithmetic mean taken while not ignoring NaNs var, nanvar Notes ----- The arithmetic mean is the sum of the non-NaN elements along the axis divided by the number of non-NaN elements. Note that for floating-point input, the mean is computed using the same precision the input has. Depending on the input data, this can cause the results to be inaccurate, especially for `float32`. Specifying a higher-precision accumulator using the `dtype` keyword can alleviate this issue. Examples -------- >>> a = np.array([[1, np.nan], [3, 4]]) >>> np.nanmean(a) 2.6666666666666665 >>> np.nanmean(a, axis=0) array([ 2., 4.]) >>> np.nanmean(a, axis=1) array([ 1., 3.5]) """ arr, mask = _replace_nan(a, 0) if mask is None: return np.mean(arr, axis=axis, dtype=dtype, out=out, keepdims=keepdims) if dtype is not None: dtype = np.dtype(dtype) if dtype is not None and not issubclass(dtype.type, np.inexact): raise TypeError("If a is inexact, then dtype must be inexact") if out is not None and not issubclass(out.dtype.type, np.inexact): raise TypeError("If a is inexact, then out must be inexact") cnt = np.sum(~mask, axis=axis, dtype=np.intp, keepdims=keepdims) tot = np.sum(arr, axis=axis, dtype=dtype, out=out, keepdims=keepdims) avg = _divide_by_count(tot, cnt, out=out) isbad = (cnt == 0) if isbad.any(): warnings.warn("Mean of empty slice", RuntimeWarning, stacklevel=2) # NaN is the only possible bad value, so no further # action is needed to handle bad results. return avg
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/numpy/py2/numpy/lib/nanfunctions.py#L829-L923
hanpfei/chromium-net
392cc1fa3a8f92f42e4071ab6e674d8e0482f83f
third_party/catapult/third_party/gsutil/third_party/apitools/apitools/gen/gen_client_lib.py
python
DescriptorGenerator.WriteIntermediateInit
(self, out)
Write a simple __init__.py for an intermediate directory.
Write a simple __init__.py for an intermediate directory.
[ "Write", "a", "simple", "__init__", ".", "py", "for", "an", "intermediate", "directory", "." ]
def WriteIntermediateInit(self, out): """Write a simple __init__.py for an intermediate directory.""" printer = self._GetPrinter(out) printer('#!/usr/bin/env python') printer('"""Shared __init__.py for apitools."""') printer() printer('from pkgutil import extend_path') printer('__path__ = extend_path(__path__, __name__)')
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https://github.com/hanpfei/chromium-net/blob/392cc1fa3a8f92f42e4071ab6e674d8e0482f83f/third_party/catapult/third_party/gsutil/third_party/apitools/apitools/gen/gen_client_lib.py#L174-L181
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
wx/lib/agw/ultimatelistctrl.py
python
UltimateListCtrl.GetFirstGradientColour
(self)
return self._mainWin.GetFirstGradientColour()
Returns the first gradient colour for gradient-style selections.
Returns the first gradient colour for gradient-style selections.
[ "Returns", "the", "first", "gradient", "colour", "for", "gradient", "-", "style", "selections", "." ]
def GetFirstGradientColour(self): """ Returns the first gradient colour for gradient-style selections. """ return self._mainWin.GetFirstGradientColour()
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/wx/lib/agw/ultimatelistctrl.py#L13181-L13184
ApolloAuto/apollo-platform
86d9dc6743b496ead18d597748ebabd34a513289
ros/ros_comm/rosbag/src/rosbag/bag.py
python
Bag.write
(self, topic, msg, t=None, raw=False)
Write a message to the bag. @param topic: name of topic @type topic: str @param msg: message to add to bag, or tuple (if raw) @type msg: Message or tuple of raw message data @param t: ROS time of message publication, if None specifed, use current time [optional] @type t: U{genpy.Time} @param raw: if True, msg is in raw format, i.e. (msg_type, serialized_bytes, md5sum, pytype) @type raw: bool @raise ValueError: if arguments are invalid or bag is closed
Write a message to the bag.
[ "Write", "a", "message", "to", "the", "bag", "." ]
def write(self, topic, msg, t=None, raw=False): """ Write a message to the bag. @param topic: name of topic @type topic: str @param msg: message to add to bag, or tuple (if raw) @type msg: Message or tuple of raw message data @param t: ROS time of message publication, if None specifed, use current time [optional] @type t: U{genpy.Time} @param raw: if True, msg is in raw format, i.e. (msg_type, serialized_bytes, md5sum, pytype) @type raw: bool @raise ValueError: if arguments are invalid or bag is closed """ if not self._file: raise ValueError('I/O operation on closed bag') if not topic: raise ValueError('topic is invalid') if not msg: raise ValueError('msg is invalid') if t is None: t = genpy.Time.from_sec(time.time()) # Seek to end (in case previous operation was a read) self._file.seek(0, os.SEEK_END) # Open a chunk, if needed if not self._chunk_open: self._start_writing_chunk(t) # Unpack raw if raw: if len(msg) == 5: msg_type, serialized_bytes, md5sum, pos, pytype = msg elif len(msg) == 4: msg_type, serialized_bytes, md5sum, pytype = msg else: raise ValueError('msg must be of length 4 or 5') # Write connection record, if necessary (currently using a connection per topic; ignoring message connection header) if topic in self._topic_connections: connection_info = self._topic_connections[topic] conn_id = connection_info.id else: conn_id = len(self._connections) if raw: if pytype is None: try: pytype = genpy.message.get_message_class(msg_type) except Exception: pytype = None if pytype is None: raise ROSBagException('cannot locate message class and no message class provided for [%s]' % msg_type) if pytype._md5sum != md5sum: print('WARNING: md5sum of loaded type [%s] does not match that specified' % msg_type, file=sys.stderr) #raise ROSBagException('md5sum of loaded type does not match that of data being recorded') header = { 'topic' : topic, 'type' : msg_type, 'md5sum' : md5sum, 'message_definition' : pytype._full_text } else: if issubclass(msg.__class__, google.protobuf.message.Message): roslib.message.add_rosmsg_interface_for_protobuf(msg.__class__) header = { 'topic' : topic, 'type' : msg.__class__._type, 'md5sum' : msg.__class__._md5sum, 'message_definition' : 'protobuf' } else: header = { 'topic' : topic, 'type' : msg.__class__._type, 'md5sum' : msg.__class__._md5sum, 'message_definition' : msg._full_text } connection_info = _ConnectionInfo(conn_id, topic, header) self._write_connection_record(connection_info) self._connections[conn_id] = connection_info self._topic_connections[topic] = connection_info # Create an index entry index_entry = _IndexEntry200(t, self._curr_chunk_info.pos, self._get_chunk_offset()) # Update the indexes and current chunk info if conn_id not in self._curr_chunk_connection_indexes: # This is the first message on this connection in the chunk self._curr_chunk_connection_indexes[conn_id] = [index_entry] self._curr_chunk_info.connection_counts[conn_id] = 1 else: curr_chunk_connection_index = self._curr_chunk_connection_indexes[conn_id] if index_entry >= curr_chunk_connection_index[-1]: # Test if we're writing chronologically. Can skip binary search if so. curr_chunk_connection_index.append(index_entry) else: bisect.insort_right(curr_chunk_connection_index, index_entry) self._curr_chunk_info.connection_counts[conn_id] += 1 if conn_id not in self._connection_indexes: self._connection_indexes[conn_id] = [index_entry] else: bisect.insort_right(self._connection_indexes[conn_id], index_entry) # Update the chunk start/end times if t > self._curr_chunk_info.end_time: self._curr_chunk_info.end_time = t elif t < self._curr_chunk_info.start_time: self._curr_chunk_info.start_time = t if not raw: # Serialize the message to the buffer self._buffer.seek(0) self._buffer.truncate(0) msg.serialize(self._buffer) serialized_bytes = self._buffer.getvalue() # Write message data record self._write_message_data_record(conn_id, t, serialized_bytes) # Check if we want to stop this chunk chunk_size = self._get_chunk_offset() if chunk_size > self._chunk_threshold: self._stop_writing_chunk()
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https://github.com/ApolloAuto/apollo-platform/blob/86d9dc6743b496ead18d597748ebabd34a513289/ros/ros_comm/rosbag/src/rosbag/bag.py#L289-L406
google/clif
cab24d6a105609a65c95a36a1712ae3c20c7b5df
clif/python/pytd2proto.py
python
_TypeEntry.set_nested_type
(self, pyname, cpp_name)
Set the C++ type for a Python type, checking for conflicts. This is usually used to add a specific mapping for which there is only one C++ type, such as a class definition in a clif file -- in such cases, there should only be a single C++ type. Args: pyname: str, the Python name for the type. No dots allowed. If the name already exists, then `cpp_name` must match the pre-existing entry. cpp_name: str, the C++ type name.
Set the C++ type for a Python type, checking for conflicts.
[ "Set", "the", "C", "++", "type", "for", "a", "Python", "type", "checking", "for", "conflicts", "." ]
def set_nested_type(self, pyname, cpp_name): """Set the C++ type for a Python type, checking for conflicts. This is usually used to add a specific mapping for which there is only one C++ type, such as a class definition in a clif file -- in such cases, there should only be a single C++ type. Args: pyname: str, the Python name for the type. No dots allowed. If the name already exists, then `cpp_name` must match the pre-existing entry. cpp_name: str, the C++ type name. """ assert '.' not in pyname, ('pyname {!r} cannot contain dots: you probably ' 'meant to use _TypeTable').format(pyname) try: existing = self._types[pyname] except KeyError: entry = self._create_child_entry(pyname, cpp_name) self._types[pyname] = entry else: desired_cpp_names = [cpp_name] existing_cpp_names = existing._cpp_names # pylint: disable=protected-access if existing_cpp_names != desired_cpp_names: raise ValueError( 'Python type {!r}: existing C++ types {!r} don\'t match desired ' 'types {!r}'.format(pyname, existing_cpp_names, desired_cpp_names))
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https://github.com/google/clif/blob/cab24d6a105609a65c95a36a1712ae3c20c7b5df/clif/python/pytd2proto.py#L1229-L1255
bastibl/gr-ieee802-15-4
1a2999ce2778df279870f028a4ce15d94e60fbd9
docs/doxygen/update_pydoc.py
python
utoascii
(text)
return str(out)
Convert unicode text into ascii and escape quotes and backslashes.
Convert unicode text into ascii and escape quotes and backslashes.
[ "Convert", "unicode", "text", "into", "ascii", "and", "escape", "quotes", "and", "backslashes", "." ]
def utoascii(text): """ Convert unicode text into ascii and escape quotes and backslashes. """ if text is None: return '' out = text.encode('ascii', 'replace') # swig will require us to replace blackslash with 4 backslashes # TODO: evaluate what this should be for pybind11 out = out.replace(b'\\', b'\\\\\\\\') out = out.replace(b'"', b'\\"').decode('ascii') return str(out)
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https://github.com/bastibl/gr-ieee802-15-4/blob/1a2999ce2778df279870f028a4ce15d94e60fbd9/docs/doxygen/update_pydoc.py#L70-L81
windystrife/UnrealEngine_NVIDIAGameWorks
b50e6338a7c5b26374d66306ebc7807541ff815e
Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/idlelib/SearchEngine.py
python
SearchEngine.search_text
(self, text, prog=None, ok=0)
return res
Search a text widget for the pattern. If prog is given, it should be the precompiled pattern. Return a tuple (lineno, matchobj); None if not found. This obeys the wrap and direction (back) settings. The search starts at the selection (if there is one) or at the insert mark (otherwise). If the search is forward, it starts at the right of the selection; for a backward search, it starts at the left end. An empty match exactly at either end of the selection (or at the insert mark if there is no selection) is ignored unless the ok flag is true -- this is done to guarantee progress. If the search is allowed to wrap around, it will return the original selection if (and only if) it is the only match.
Search a text widget for the pattern.
[ "Search", "a", "text", "widget", "for", "the", "pattern", "." ]
def search_text(self, text, prog=None, ok=0): """Search a text widget for the pattern. If prog is given, it should be the precompiled pattern. Return a tuple (lineno, matchobj); None if not found. This obeys the wrap and direction (back) settings. The search starts at the selection (if there is one) or at the insert mark (otherwise). If the search is forward, it starts at the right of the selection; for a backward search, it starts at the left end. An empty match exactly at either end of the selection (or at the insert mark if there is no selection) is ignored unless the ok flag is true -- this is done to guarantee progress. If the search is allowed to wrap around, it will return the original selection if (and only if) it is the only match. """ if not prog: prog = self.getprog() if not prog: return None # Compilation failed -- stop wrap = self.wrapvar.get() first, last = get_selection(text) if self.isback(): if ok: start = last else: start = first line, col = get_line_col(start) res = self.search_backward(text, prog, line, col, wrap, ok) else: if ok: start = first else: start = last line, col = get_line_col(start) res = self.search_forward(text, prog, line, col, wrap, ok) return res
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https://github.com/windystrife/UnrealEngine_NVIDIAGameWorks/blob/b50e6338a7c5b26374d66306ebc7807541ff815e/Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/idlelib/SearchEngine.py#L94-L134
ApolloAuto/apollo-platform
86d9dc6743b496ead18d597748ebabd34a513289
ros/ros_comm/roslaunch/src/roslaunch/pmon.py
python
ProcessMonitor.do_main_thread_jobs
(self)
Execute tasks that need to be run in the main thread. Must be called from main thread.
Execute tasks that need to be run in the main thread. Must be called from main thread.
[ "Execute", "tasks", "that", "need", "to", "be", "run", "in", "the", "main", "thread", ".", "Must", "be", "called", "from", "main", "thread", "." ]
def do_main_thread_jobs(self): """ Execute tasks that need to be run in the main thread. Must be called from main thread. """ #not entirely threadsafe sigs = [s for s in self.reacquire_signals] for s in sigs: _signal_chain[s] = signal.signal(s, rl_signal) self.reacquire_signals.remove(s)
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https://github.com/ApolloAuto/apollo-platform/blob/86d9dc6743b496ead18d597748ebabd34a513289/ros/ros_comm/roslaunch/src/roslaunch/pmon.py#L410-L419
facebook/folly
744a0a698074d1b013813065fe60f545aa2c9b94
build/fbcode_builder/getdeps/buildopts.py
python
BuildOptions.get_context_generator
(self, host_tuple=None)
return ContextGenerator( { "os": host_type.ostype, "distro": host_type.distro, "distro_vers": host_type.distrovers, "fb": "on" if self.facebook_internal else "off", "fbsource": "on" if self.fbsource_dir else "off", "test": "off", "shared_libs": "on" if self.shared_libs else "off", } )
Create a manifest ContextGenerator for the specified target platform.
Create a manifest ContextGenerator for the specified target platform.
[ "Create", "a", "manifest", "ContextGenerator", "for", "the", "specified", "target", "platform", "." ]
def get_context_generator(self, host_tuple=None): """Create a manifest ContextGenerator for the specified target platform.""" if host_tuple is None: host_type = self.host_type elif isinstance(host_tuple, HostType): host_type = host_tuple else: host_type = HostType.from_tuple_string(host_tuple) return ContextGenerator( { "os": host_type.ostype, "distro": host_type.distro, "distro_vers": host_type.distrovers, "fb": "on" if self.facebook_internal else "off", "fbsource": "on" if self.fbsource_dir else "off", "test": "off", "shared_libs": "on" if self.shared_libs else "off", } )
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https://github.com/facebook/folly/blob/744a0a698074d1b013813065fe60f545aa2c9b94/build/fbcode_builder/getdeps/buildopts.py#L178-L197
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/osx_cocoa/propgrid.py
python
PGProperty.GetIndexInParent
(*args, **kwargs)
return _propgrid.PGProperty_GetIndexInParent(*args, **kwargs)
GetIndexInParent(self) -> int
GetIndexInParent(self) -> int
[ "GetIndexInParent", "(", "self", ")", "-", ">", "int" ]
def GetIndexInParent(*args, **kwargs): """GetIndexInParent(self) -> int""" return _propgrid.PGProperty_GetIndexInParent(*args, **kwargs)
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_cocoa/propgrid.py#L647-L649
seqan/seqan
f5f658343c366c9c3d44ba358ffc9317e78a09ed
apps/ngs_roi/tool_shed/roi_details.py
python
DetailedRoiGenerator.run
(self)
return 0
Run report generation, return status code. :return: integer with the result.
Run report generation, return status code.
[ "Run", "report", "generation", "return", "status", "code", "." ]
def run(self): """Run report generation, return status code. :return: integer with the result. """ print >>sys.stderr, 'Loading ROI' records = ngs_roi.io.load(self.args.in_file, self.args.max_rois) keys = records[0].data_keys self.writeHtml(keys, records) self.writePlots(records) return 0
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https://github.com/seqan/seqan/blob/f5f658343c366c9c3d44ba358ffc9317e78a09ed/apps/ngs_roi/tool_shed/roi_details.py#L71-L82
tensorflow/tensorflow
419e3a6b650ea4bd1b0cba23c4348f8a69f3272e
tensorflow/python/ops/summary_ops_v2.py
python
graph_v1
(param, step=None, name=None)
Writes a TensorFlow graph to the summary interface. The graph summary is, strictly speaking, not a summary. Conditions like `tf.summary.should_record_summaries` do not apply. Only a single graph can be associated with a particular run. If multiple graphs are written, then only the last one will be considered by TensorBoard. When not using eager execution mode, the user should consider passing the `graph` parameter to `tf.compat.v1.summary.initialize` instead of calling this function. Otherwise special care needs to be taken when using the graph to record the graph. Args: param: A `tf.Tensor` containing a serialized graph proto. When eager execution is enabled, this function will automatically coerce `tf.Graph`, `tf.compat.v1.GraphDef`, and string types. step: The global step variable. This doesn't have useful semantics for graph summaries, but is used anyway, due to the structure of event log files. This defaults to the global step. name: A name for the operation (optional). Returns: The created `tf.Operation` or a `tf.no_op` if summary writing has not been enabled for this context. Raises: TypeError: If `param` isn't already a `tf.Tensor` in graph mode.
Writes a TensorFlow graph to the summary interface.
[ "Writes", "a", "TensorFlow", "graph", "to", "the", "summary", "interface", "." ]
def graph_v1(param, step=None, name=None): """Writes a TensorFlow graph to the summary interface. The graph summary is, strictly speaking, not a summary. Conditions like `tf.summary.should_record_summaries` do not apply. Only a single graph can be associated with a particular run. If multiple graphs are written, then only the last one will be considered by TensorBoard. When not using eager execution mode, the user should consider passing the `graph` parameter to `tf.compat.v1.summary.initialize` instead of calling this function. Otherwise special care needs to be taken when using the graph to record the graph. Args: param: A `tf.Tensor` containing a serialized graph proto. When eager execution is enabled, this function will automatically coerce `tf.Graph`, `tf.compat.v1.GraphDef`, and string types. step: The global step variable. This doesn't have useful semantics for graph summaries, but is used anyway, due to the structure of event log files. This defaults to the global step. name: A name for the operation (optional). Returns: The created `tf.Operation` or a `tf.no_op` if summary writing has not been enabled for this context. Raises: TypeError: If `param` isn't already a `tf.Tensor` in graph mode. """ if not context.executing_eagerly() and not isinstance(param, ops.Tensor): raise TypeError("graph() needs a argument `param` to be tf.Tensor " "(e.g. tf.placeholder) in graph mode, but received " f"param={param} of type {type(param).__name__}.") writer = _summary_state.writer if writer is None: return control_flow_ops.no_op() with ops.device("cpu:0"): if isinstance(param, (ops.Graph, graph_pb2.GraphDef)): tensor = ops.convert_to_tensor(_serialize_graph(param), dtypes.string) else: tensor = array_ops.identity(param) return gen_summary_ops.write_graph_summary( writer._resource, _choose_step(step), tensor, name=name)
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https://github.com/tensorflow/tensorflow/blob/419e3a6b650ea4bd1b0cba23c4348f8a69f3272e/tensorflow/python/ops/summary_ops_v2.py#L965-L1008
MegEngine/MegEngine
ce9ad07a27ec909fb8db4dd67943d24ba98fb93a
imperative/python/megengine/traced_module/traced_module.py
python
InternalGraph.top_graph
(self)
return None
r"""Get the parent graph of this graph. Returns: An ``InternalGraph``.
r"""Get the parent graph of this graph.
[ "r", "Get", "the", "parent", "graph", "of", "this", "graph", "." ]
def top_graph(self): r"""Get the parent graph of this graph. Returns: An ``InternalGraph``. """ if self._top_graph: return self._top_graph() return None
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https://github.com/MegEngine/MegEngine/blob/ce9ad07a27ec909fb8db4dd67943d24ba98fb93a/imperative/python/megengine/traced_module/traced_module.py#L635-L643
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/s3transfer/tasks.py
python
SubmissionTask._submit
(self, transfer_future, **kwargs)
The submition method to be implemented :type transfer_future: s3transfer.futures.TransferFuture :param transfer_future: The transfer future associated with the transfer request that tasks are being submitted for :param kwargs: Any additional keyword arguments you want to be passed in
The submition method to be implemented
[ "The", "submition", "method", "to", "be", "implemented" ]
def _submit(self, transfer_future, **kwargs): """The submition method to be implemented :type transfer_future: s3transfer.futures.TransferFuture :param transfer_future: The transfer future associated with the transfer request that tasks are being submitted for :param kwargs: Any additional keyword arguments you want to be passed in """ raise NotImplementedError('_submit() must be implemented')
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/s3transfer/tasks.py#L280-L290
klzgrad/naiveproxy
ed2c513637c77b18721fe428d7ed395b4d284c83
src/build/android/gyp/util/jar_info_utils.py
python
ReadAarSourceInfo
(info_path)
Returns the source= path from an .aar's source.info file.
Returns the source= path from an .aar's source.info file.
[ "Returns", "the", "source", "=", "path", "from", "an", ".", "aar", "s", "source", ".", "info", "file", "." ]
def ReadAarSourceInfo(info_path): """Returns the source= path from an .aar's source.info file.""" # The .info looks like: "source=path/to/.aar\n". with open(info_path) as f: return f.read().rstrip().split('=', 1)[1]
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https://github.com/klzgrad/naiveproxy/blob/ed2c513637c77b18721fe428d7ed395b4d284c83/src/build/android/gyp/util/jar_info_utils.py#L16-L20
catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/tools/python3/src/Lib/asyncio/selector_events.py
python
BaseSelectorEventLoop._remove_writer
(self, fd)
Remove a writer callback.
Remove a writer callback.
[ "Remove", "a", "writer", "callback", "." ]
def _remove_writer(self, fd): """Remove a writer callback.""" if self.is_closed(): return False try: key = self._selector.get_key(fd) except KeyError: return False else: mask, (reader, writer) = key.events, key.data # Remove both writer and connector. mask &= ~selectors.EVENT_WRITE if not mask: self._selector.unregister(fd) else: self._selector.modify(fd, mask, (reader, None)) if writer is not None: writer.cancel() return True else: return False
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/tools/python3/src/Lib/asyncio/selector_events.py#L310-L331
tensorflow/tensorflow
419e3a6b650ea4bd1b0cba23c4348f8a69f3272e
tensorflow/python/keras/mixed_precision/autocast_variable.py
python
AutoCastVariable.gather_nd
(self, indices, name=None)
return math_ops.cast(val, self._cast_dtype)
Gather slices of the variable into a Tensor.
Gather slices of the variable into a Tensor.
[ "Gather", "slices", "of", "the", "variable", "into", "a", "Tensor", "." ]
def gather_nd(self, indices, name=None): """Gather slices of the variable into a Tensor.""" val = self._variable.gather_nd(indices, name=name) return math_ops.cast(val, self._cast_dtype)
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https://github.com/tensorflow/tensorflow/blob/419e3a6b650ea4bd1b0cba23c4348f8a69f3272e/tensorflow/python/keras/mixed_precision/autocast_variable.py#L125-L128
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/shutil.py
python
_get_gid
(name)
return None
Returns a gid, given a group name.
Returns a gid, given a group name.
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def _get_gid(name): """Returns a gid, given a group name.""" if getgrnam is None or name is None: return None try: result = getgrnam(name) except KeyError: result = None if result is not None: return result[2] return None
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/shutil.py#L593-L603
ApolloAuto/apollo-platform
86d9dc6743b496ead18d597748ebabd34a513289
ros/third_party/lib_x86_64/python2.7/dist-packages/rospkg/manifest.py
python
_check_rosdeps
(n, filename)
Validator for stack rosdeps. :raises: :exc:`InvalidManifest` If validation fails
Validator for stack rosdeps.
[ "Validator", "for", "stack", "rosdeps", "." ]
def _check_rosdeps(n, filename): """ Validator for stack rosdeps. :raises: :exc:`InvalidManifest` If validation fails """ try: nodes = _get_nodes_by_name(n, 'rosdep') rosdeps = [e.attributes for e in nodes] names = [d['name'].value for d in rosdeps] return [RosDep(n) for n in names] except KeyError: raise InvalidManifest("invalid rosdep tag in [%s]"%(filename))
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https://github.com/ApolloAuto/apollo-platform/blob/86d9dc6743b496ead18d597748ebabd34a513289/ros/third_party/lib_x86_64/python2.7/dist-packages/rospkg/manifest.py#L131-L143
Xilinx/Vitis-AI
fc74d404563d9951b57245443c73bef389f3657f
tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/learn/python/learn/utils/input_fn_utils.py
python
build_parsing_serving_input_fn
(feature_spec, default_batch_size=None)
return input_fn
Build an input_fn appropriate for serving, expecting fed tf.Examples. Creates an input_fn that expects a serialized tf.Example fed into a string placeholder. The function parses the tf.Example according to the provided feature_spec, and returns all parsed Tensors as features. This input_fn is for use at serving time, so the labels return value is always None. Args: feature_spec: a dict of string to `VarLenFeature`/`FixedLenFeature`. default_batch_size: the number of query examples expected per batch. Leave unset for variable batch size (recommended). Returns: An input_fn suitable for use in serving.
Build an input_fn appropriate for serving, expecting fed tf.Examples.
[ "Build", "an", "input_fn", "appropriate", "for", "serving", "expecting", "fed", "tf", ".", "Examples", "." ]
def build_parsing_serving_input_fn(feature_spec, default_batch_size=None): """Build an input_fn appropriate for serving, expecting fed tf.Examples. Creates an input_fn that expects a serialized tf.Example fed into a string placeholder. The function parses the tf.Example according to the provided feature_spec, and returns all parsed Tensors as features. This input_fn is for use at serving time, so the labels return value is always None. Args: feature_spec: a dict of string to `VarLenFeature`/`FixedLenFeature`. default_batch_size: the number of query examples expected per batch. Leave unset for variable batch size (recommended). Returns: An input_fn suitable for use in serving. """ def input_fn(): """An input_fn that expects a serialized tf.Example.""" serialized_tf_example = array_ops.placeholder(dtype=dtypes.string, shape=[default_batch_size], name='input_example_tensor') inputs = {'examples': serialized_tf_example} features = parsing_ops.parse_example(serialized_tf_example, feature_spec) labels = None # these are not known in serving! return InputFnOps(features, labels, inputs) return input_fn
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https://github.com/Xilinx/Vitis-AI/blob/fc74d404563d9951b57245443c73bef389f3657f/tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/learn/python/learn/utils/input_fn_utils.py#L69-L94
adobe/chromium
cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7
third_party/mesa/MesaLib/src/mapi/glapi/gen-es/gl_parse_header.py
python
HeaderParser._postprocess_dict
(self, hdict)
return hlist
Post-process a header dict and return an ordered list.
Post-process a header dict and return an ordered list.
[ "Post", "-", "process", "a", "header", "dict", "and", "return", "an", "ordered", "list", "." ]
def _postprocess_dict(self, hdict): """Post-process a header dict and return an ordered list.""" hlist = [] largest = 0 for key, cat in hdict.iteritems(): size = len(cat["enums"]) + len(cat["types"]) + len(cat["functions"]) # ignore empty category if not size: continue cat["enums"].sort(self._cmp_enum) # remove duplicates dup = [] for i in xrange(1, len(cat["enums"])): if cat["enums"][i] == cat["enums"][i - 1]: dup.insert(0, i) for i in dup: e = cat["enums"].pop(i) if self.verbose: print "remove duplicate enum %s" % e[0] cat["types"].sort(self._cmp_type) cat["functions"].sort(self._cmp_function) # largest category comes first if size > largest: hlist.insert(0, (key, cat)) largest = size else: hlist.append((key, cat)) return hlist
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https://github.com/adobe/chromium/blob/cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7/third_party/mesa/MesaLib/src/mapi/glapi/gen-es/gl_parse_header.py#L271-L301
mindspore-ai/mindspore
fb8fd3338605bb34fa5cea054e535a8b1d753fab
mindspore/python/mindspore/numpy/math_ops.py
python
exp2
(x, dtype=None)
return _apply_tensor_op(lambda x: F.tensor_pow(2, x), x, dtype=dtype)
Calculates ``2**p`` for all p in the input array. Note: Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are not supported. On GPU, the supported dtypes are np.float16, and np.float32. Args: x (Tensor): input values. dtype (:class:`mindspore.dtype`, optional): Defaults to :class:`None`. Overrides the dtype of the output Tensor. Returns: Tensor or scalar, element-wise 2 to the power `x`. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> import mindspore.numpy as np >>> x = np.array([2, 3]).astype(np.float32) >>> output = np.exp2(x) >>> print(output) [4. 8.]
Calculates ``2**p`` for all p in the input array.
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def exp2(x, dtype=None): """ Calculates ``2**p`` for all p in the input array. Note: Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are not supported. On GPU, the supported dtypes are np.float16, and np.float32. Args: x (Tensor): input values. dtype (:class:`mindspore.dtype`, optional): Defaults to :class:`None`. Overrides the dtype of the output Tensor. Returns: Tensor or scalar, element-wise 2 to the power `x`. Supported Platforms: ``Ascend`` ``GPU`` ``CPU`` Examples: >>> import mindspore.numpy as np >>> x = np.array([2, 3]).astype(np.float32) >>> output = np.exp2(x) >>> print(output) [4. 8.] """ return _apply_tensor_op(lambda x: F.tensor_pow(2, x), x, dtype=dtype)
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https://github.com/mindspore-ai/mindspore/blob/fb8fd3338605bb34fa5cea054e535a8b1d753fab/mindspore/python/mindspore/numpy/math_ops.py#L2810-L2837
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
wx/lib/agw/flatmenu.py
python
FlatMenuBar.GetRendererManager
(self)
return self._rendererMgr
Returns the :class:`FlatMenuBar` renderer manager.
Returns the :class:`FlatMenuBar` renderer manager.
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def GetRendererManager(self): """ Returns the :class:`FlatMenuBar` renderer manager. """ return self._rendererMgr
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/wx/lib/agw/flatmenu.py#L2584-L2589
wlanjie/AndroidFFmpeg
7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf
tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/lib-tk/ttk.py
python
Scrollbar.__init__
(self, master=None, **kw)
Construct a Ttk Scrollbar with parent master. STANDARD OPTIONS class, cursor, style, takefocus WIDGET-SPECIFIC OPTIONS command, orient
Construct a Ttk Scrollbar with parent master.
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def __init__(self, master=None, **kw): """Construct a Ttk Scrollbar with parent master. STANDARD OPTIONS class, cursor, style, takefocus WIDGET-SPECIFIC OPTIONS command, orient """ Widget.__init__(self, master, "ttk::scrollbar", kw)
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https://github.com/wlanjie/AndroidFFmpeg/blob/7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf/tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/lib-tk/ttk.py#L1101-L1112
xhzdeng/crpn
a5aef0f80dbe486103123f740c634fb01e6cc9a1
caffe-fast-rcnn/python/caffe/coord_map.py
python
compose
(base_map, next_map)
return ax, a1 * a2, a1 * b2 + b1
Compose a base coord map with scale a1, shift b1 with a further coord map with scale a2, shift b2. The scales multiply and the further shift, b2, is scaled by base coord scale a1.
Compose a base coord map with scale a1, shift b1 with a further coord map with scale a2, shift b2. The scales multiply and the further shift, b2, is scaled by base coord scale a1.
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def compose(base_map, next_map): """ Compose a base coord map with scale a1, shift b1 with a further coord map with scale a2, shift b2. The scales multiply and the further shift, b2, is scaled by base coord scale a1. """ ax1, a1, b1 = base_map ax2, a2, b2 = next_map if ax1 is None: ax = ax2 elif ax2 is None or ax1 == ax2: ax = ax1 else: raise AxisMismatchException return ax, a1 * a2, a1 * b2 + b1
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https://github.com/xhzdeng/crpn/blob/a5aef0f80dbe486103123f740c634fb01e6cc9a1/caffe-fast-rcnn/python/caffe/coord_map.py#L89-L103
plumonito/dtslam
5994bb9cf7a11981b830370db206bceb654c085d
3rdparty/opencv-git/3rdparty/jinja2/filters.py
python
do_rejectattr
(*args, **kwargs)
return _select_or_reject(args, kwargs, lambda x: not x, True)
Filters a sequence of objects by appying a test to either the object or the attribute and rejecting the ones with the test succeeding. .. sourcecode:: jinja {{ users|rejectattr("is_active") }} {{ users|rejectattr("email", "none") }} .. versionadded:: 2.7
Filters a sequence of objects by appying a test to either the object or the attribute and rejecting the ones with the test succeeding.
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def do_rejectattr(*args, **kwargs): """Filters a sequence of objects by appying a test to either the object or the attribute and rejecting the ones with the test succeeding. .. sourcecode:: jinja {{ users|rejectattr("is_active") }} {{ users|rejectattr("email", "none") }} .. versionadded:: 2.7 """ return _select_or_reject(args, kwargs, lambda x: not x, True)
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https://github.com/plumonito/dtslam/blob/5994bb9cf7a11981b830370db206bceb654c085d/3rdparty/opencv-git/3rdparty/jinja2/filters.py#L893-L904
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/wsgiref/handlers.py
python
BaseHandler._convert_string_type
(self, value, title)
Convert/check value type.
Convert/check value type.
[ "Convert", "/", "check", "value", "type", "." ]
def _convert_string_type(self, value, title): """Convert/check value type.""" if type(value) is str: return value raise AssertionError( "{0} must be of type str (got {1})".format(title, repr(value)) )
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/wsgiref/handlers.py#L253-L259
baidu-research/tensorflow-allreduce
66d5b855e90b0949e9fa5cca5599fd729a70e874
tensorflow/contrib/keras/python/keras/engine/training.py
python
Model._predict_loop
(self, f, ins, batch_size=32, verbose=0)
return outs
Abstract method to loop over some data in batches. Arguments: f: Keras function returning a list of tensors. ins: list of tensors to be fed to `f`. batch_size: integer batch size. verbose: verbosity mode. Returns: Array of predictions (if the model has a single output) or list of arrays of predictions (if the model has multiple outputs).
Abstract method to loop over some data in batches.
[ "Abstract", "method", "to", "loop", "over", "some", "data", "in", "batches", "." ]
def _predict_loop(self, f, ins, batch_size=32, verbose=0): """Abstract method to loop over some data in batches. Arguments: f: Keras function returning a list of tensors. ins: list of tensors to be fed to `f`. batch_size: integer batch size. verbose: verbosity mode. Returns: Array of predictions (if the model has a single output) or list of arrays of predictions (if the model has multiple outputs). """ if ins and hasattr(ins[0], 'shape'): samples = ins[0].shape[0] else: # May happen if we are running `predict` without Numpy input data, # i.e. if all inputs to the models are data tensors # instead of placeholders. # In that case we will run `predict` over a single batch. samples = batch_size verbose = 2 outs = [] if verbose == 1: progbar = Progbar(target=samples) batches = _make_batches(samples, batch_size) index_array = np.arange(samples) for batch_index, (batch_start, batch_end) in enumerate(batches): batch_ids = index_array[batch_start:batch_end] if ins and isinstance(ins[-1], float): # Do not slice the training phase flag. ins_batch = _slice_arrays(ins[:-1], batch_ids) + [ins[-1]] else: ins_batch = _slice_arrays(ins, batch_ids) batch_outs = f(ins_batch) if not isinstance(batch_outs, list): batch_outs = [batch_outs] if batch_index == 0: for batch_out in batch_outs: shape = (samples,) + batch_out.shape[1:] outs.append(np.zeros(shape, dtype=batch_out.dtype)) for i, batch_out in enumerate(batch_outs): outs[i][batch_start:batch_end] = batch_out if verbose == 1: progbar.update(batch_end) if len(outs) == 1: return outs[0] return outs
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https://github.com/baidu-research/tensorflow-allreduce/blob/66d5b855e90b0949e9fa5cca5599fd729a70e874/tensorflow/contrib/keras/python/keras/engine/training.py#L1104-L1154
openmm/openmm
cb293447c4fc8b03976dfe11399f107bab70f3d9
wrappers/python/openmm/app/internal/charmm/topologyobjects.py
python
_CmapGrid.switch_range
(self)
return newgrid
Returns a grid object whose range is 0 to 360 degrees in both dimensions instead of -180 to 180 degrees (or -180 to 180 degrees if the range is already 0 to 360 degrees)
Returns a grid object whose range is 0 to 360 degrees in both dimensions instead of -180 to 180 degrees (or -180 to 180 degrees if the range is already 0 to 360 degrees)
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def switch_range(self): """ Returns a grid object whose range is 0 to 360 degrees in both dimensions instead of -180 to 180 degrees (or -180 to 180 degrees if the range is already 0 to 360 degrees) """ res = self.resolution mid = res // 2 newgrid = _CmapGrid(res) for i in range(res): for j in range(res): # Start from the middle newgrid[i, j] = self[(i+mid)%res, (j+mid)%res] return newgrid
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https://github.com/openmm/openmm/blob/cb293447c4fc8b03976dfe11399f107bab70f3d9/wrappers/python/openmm/app/internal/charmm/topologyobjects.py#L1240-L1253
plumonito/dtslam
5994bb9cf7a11981b830370db206bceb654c085d
3rdparty/opencv-git/modules/java/generator/gen_java.py
python
JavaWrapperGenerator.isSmartClass
(self, classname)
return classname in self.classes and self.classes[classname].base
Check if class stores Ptr<T>* instead of T* in nativeObj field
Check if class stores Ptr<T>* instead of T* in nativeObj field
[ "Check", "if", "class", "stores", "Ptr<T", ">", "*", "instead", "of", "T", "*", "in", "nativeObj", "field" ]
def isSmartClass(self, classname): ''' Check if class stores Ptr<T>* instead of T* in nativeObj field ''' return classname in self.classes and self.classes[classname].base
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https://github.com/plumonito/dtslam/blob/5994bb9cf7a11981b830370db206bceb654c085d/3rdparty/opencv-git/modules/java/generator/gen_java.py#L1565-L1569
FreeCAD/FreeCAD
ba42231b9c6889b89e064d6d563448ed81e376ec
src/Mod/Draft/draftobjects/pathtwistedarray.py
python
PathTwistedArray.set_properties
(self, obj)
Set properties only if they don't exist.
Set properties only if they don't exist.
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def set_properties(self, obj): """Set properties only if they don't exist.""" if hasattr(obj, "PropertiesList"): properties = obj.PropertiesList else: properties = [] if "Base" not in properties: obj.addProperty("App::PropertyLink", "Base", "Objects", QT_TRANSLATE_NOOP("App::Property","The base object that will be duplicated.")) obj.Base = None if "PathObject" not in properties: obj.addProperty("App::PropertyLink", "PathObject", "Objects", QT_TRANSLATE_NOOP("App::Property","The object along which the copies will be distributed. It must contain 'Edges'.")) obj.PathObject = None if "Count" not in properties: obj.addProperty("App::PropertyInteger", "Count", "Objects", QT_TRANSLATE_NOOP("App::Property","Number of copies to create.")) obj.Count = 15 if "RotationFactor" not in properties: obj.addProperty("App::PropertyFloat", "RotationFactor", "Objects", QT_TRANSLATE_NOOP("App::Property","Rotation factor of the twisted array.")) obj.RotationFactor = 0.25 if self.use_link and "ExpandArray" not in properties: obj.addProperty("App::PropertyBool", "ExpandArray", "Objects", QT_TRANSLATE_NOOP("App::Property","Show the individual array elements (only for Link arrays)")) obj.ExpandArray = False obj.setPropertyStatus('Shape', 'Transient')
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https://github.com/FreeCAD/FreeCAD/blob/ba42231b9c6889b89e064d6d563448ed81e376ec/src/Mod/Draft/draftobjects/pathtwistedarray.py#L75-L116
adobe/chromium
cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7
build/android/valgrind_tools.py
python
MemcheckTool.GetFilesForTool
(self)
return ['tools/valgrind/android/vg-chrome-wrapper.sh', 'tools/valgrind/memcheck/suppressions.txt', 'tools/valgrind/memcheck/suppressions_android.txt']
Returns a list of file names for the tool.
Returns a list of file names for the tool.
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def GetFilesForTool(self): """Returns a list of file names for the tool.""" return ['tools/valgrind/android/vg-chrome-wrapper.sh', 'tools/valgrind/memcheck/suppressions.txt', 'tools/valgrind/memcheck/suppressions_android.txt']
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https://github.com/adobe/chromium/blob/cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7/build/android/valgrind_tools.py#L124-L128
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Tools/AWSPythonSDK/1.5.8/docutils/utils/math/math2html.py
python
LineReader.readline
(self)
Read a line from elyxer.file
Read a line from elyxer.file
[ "Read", "a", "line", "from", "elyxer", ".", "file" ]
def readline(self): "Read a line from elyxer.file" self.current = self.file.readline() if not isinstance(self.file, codecs.StreamReaderWriter): self.current = self.current.decode('utf-8') if len(self.current) == 0: self.depleted = True self.current = self.current.rstrip('\n\r') self.linenumber += 1 self.mustread = False Trace.prefix = 'Line ' + unicode(self.linenumber) + ': ' if self.linenumber % 1000 == 0: Trace.message('Parsing')
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/AWSPythonSDK/1.5.8/docutils/utils/math/math2html.py#L1754-L1766
PrincetonUniversity/athena-public-version
9c266692b9423743d8e23509b3ab266a232a92d2
tst/style/cpplint.py
python
CleansedLines._CollapseStrings
(elided)
return collapsed
Collapses strings and chars on a line to simple "" or '' blocks. We nix strings first so we're not fooled by text like '"http://"' Args: elided: The line being processed. Returns: The line with collapsed strings.
Collapses strings and chars on a line to simple "" or '' blocks.
[ "Collapses", "strings", "and", "chars", "on", "a", "line", "to", "simple", "or", "blocks", "." ]
def _CollapseStrings(elided): """Collapses strings and chars on a line to simple "" or '' blocks. We nix strings first so we're not fooled by text like '"http://"' Args: elided: The line being processed. Returns: The line with collapsed strings. """ if _RE_PATTERN_INCLUDE.match(elided): return elided # Remove escaped characters first to make quote/single quote collapsing # basic. Things that look like escaped characters shouldn't occur # outside of strings and chars. elided = _RE_PATTERN_CLEANSE_LINE_ESCAPES.sub('', elided) # Replace quoted strings and digit separators. Both single quotes # and double quotes are processed in the same loop, otherwise # nested quotes wouldn't work. collapsed = '' while True: # Find the first quote character match = Match(r'^([^\'"]*)([\'"])(.*)$', elided) if not match: collapsed += elided break head, quote, tail = match.groups() if quote == '"': # Collapse double quoted strings second_quote = tail.find('"') if second_quote >= 0: collapsed += head + '""' elided = tail[second_quote + 1:] else: # Unmatched double quote, don't bother processing the rest # of the line since this is probably a multiline string. collapsed += elided break else: # Found single quote, check nearby text to eliminate digit separators. # # There is no special handling for floating point here, because # the integer/fractional/exponent parts would all be parsed # correctly as long as there are digits on both sides of the # separator. So we are fine as long as we don't see something # like "0.'3" (gcc 4.9.0 will not allow this literal). if Search(r'\b(?:0[bBxX]?|[1-9])[0-9a-fA-F]*$', head): match_literal = Match(r'^((?:\'?[0-9a-zA-Z_])*)(.*)$', "'" + tail) collapsed += head + match_literal.group(1).replace("'", '') elided = match_literal.group(2) else: second_quote = tail.find('\'') if second_quote >= 0: collapsed += head + "''" elided = tail[second_quote + 1:] else: # Unmatched single quote collapsed += elided break return collapsed
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https://github.com/PrincetonUniversity/athena-public-version/blob/9c266692b9423743d8e23509b3ab266a232a92d2/tst/style/cpplint.py#L1683-L1747
Kitware/ParaView
f760af9124ff4634b23ebbeab95a4f56e0261955
Wrapping/Python/paraview/simple.py
python
GetCameraTrack
(view=None)
return cue
Returns the camera animation track for the given view. If no view is specified, active view will be used. If no existing camera animation track is found, a new one will be created.
Returns the camera animation track for the given view. If no view is specified, active view will be used. If no existing camera animation track is found, a new one will be created.
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def GetCameraTrack(view=None): """Returns the camera animation track for the given view. If no view is specified, active view will be used. If no existing camera animation track is found, a new one will be created.""" if not view: view = GetActiveView() if not view: raise ValueError ("No view specified") scene = GetAnimationScene() for cue in scene.Cues: if cue.AnimatedProxy == view and\ cue.GetXMLName() == "CameraAnimationCue": return cue # no cue was found, create a new one. cue = CameraAnimationCue() cue.AnimatedProxy = view scene.Cues.append(cue) return cue
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https://github.com/Kitware/ParaView/blob/f760af9124ff4634b23ebbeab95a4f56e0261955/Wrapping/Python/paraview/simple.py#L2177-L2194
albertz/openlierox
d316c14a8eb57848ef56e9bfa7b23a56f694a51b
tools/DedicatedServerVideo/gdata/tlslite/mathtls.py
python
MAC_SSL.update
(self, msg)
Update this hashing object with the string msg.
Update this hashing object with the string msg.
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def update(self, msg): """Update this hashing object with the string msg. """ self.inner.update(msg)
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https://github.com/albertz/openlierox/blob/d316c14a8eb57848ef56e9bfa7b23a56f694a51b/tools/DedicatedServerVideo/gdata/tlslite/mathtls.py#L138-L141
tensorflow/tensorflow
419e3a6b650ea4bd1b0cba23c4348f8a69f3272e
tensorflow/python/ops/linalg/linear_operator_algebra.py
python
adjoint
(lin_op_a, name=None)
Get the adjoint associated to lin_op_a. Args: lin_op_a: The LinearOperator to take the adjoint of. name: Name to use for this operation. Returns: A LinearOperator that represents the adjoint of `lin_op_a`. Raises: NotImplementedError: If no Adjoint method is defined for the LinearOperator type of `lin_op_a`.
Get the adjoint associated to lin_op_a.
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def adjoint(lin_op_a, name=None): """Get the adjoint associated to lin_op_a. Args: lin_op_a: The LinearOperator to take the adjoint of. name: Name to use for this operation. Returns: A LinearOperator that represents the adjoint of `lin_op_a`. Raises: NotImplementedError: If no Adjoint method is defined for the LinearOperator type of `lin_op_a`. """ adjoint_fn = _registered_adjoint(type(lin_op_a)) if adjoint_fn is None: raise ValueError("No adjoint registered for {}".format( type(lin_op_a))) with ops.name_scope(name, "Adjoint"): return adjoint_fn(lin_op_a)
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https://github.com/tensorflow/tensorflow/blob/419e3a6b650ea4bd1b0cba23c4348f8a69f3272e/tensorflow/python/ops/linalg/linear_operator_algebra.py#L72-L92
apache/incubator-mxnet
f03fb23f1d103fec9541b5ae59ee06b1734a51d9
python/mxnet/gluon/nn/basic_layers.py
python
Sequential.add
(self, *blocks)
Adds block on top of the stack.
Adds block on top of the stack.
[ "Adds", "block", "on", "top", "of", "the", "stack", "." ]
def add(self, *blocks): """Adds block on top of the stack.""" for block in blocks: self._layers.append(block) self.register_child(block)
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https://github.com/apache/incubator-mxnet/blob/f03fb23f1d103fec9541b5ae59ee06b1734a51d9/python/mxnet/gluon/nn/basic_layers.py#L49-L53
wlanjie/AndroidFFmpeg
7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf
tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/plat-mac/findertools.py
python
update
(file)
return finder.update(file_alias)
Update the display of the specified object(s) to match their on-disk representation. Specify file by name, fsref or fsspec.
Update the display of the specified object(s) to match their on-disk representation. Specify file by name, fsref or fsspec.
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def update(file): """Update the display of the specified object(s) to match their on-disk representation. Specify file by name, fsref or fsspec.""" finder = _getfinder() fsr = Carbon.File.FSRef(file) file_alias = fsr.FSNewAliasMinimal() return finder.update(file_alias)
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https://github.com/wlanjie/AndroidFFmpeg/blob/7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf/tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/plat-mac/findertools.py#L115-L121
apple/turicreate
cce55aa5311300e3ce6af93cb45ba791fd1bdf49
src/external/boost/boost_1_68_0/libs/mpl/preprocessed/fix_boost_mpl_preprocess.py
python
check_header_comment
(filename)
return True
Checks if the header-comment of the given file needs fixing.
Checks if the header-comment of the given file needs fixing.
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def check_header_comment(filename): """Checks if the header-comment of the given file needs fixing.""" # Check input file. name = os.path.basename( filename ) # Read content of input file. sourcefile = open( filename, "rU" ) content = sourcefile.read() sourcefile.close() # Search content for '$Id$'. match = re.search(r'\$Id\$', content) if match == None: # Make sure that the correct value for '$Id$' was already set. match = re.search(r'\$Id: ' + name + r'\s+[^$]+\$', content) if match != None: # The given file needs no fixing. return False # The given file needs fixing. return True
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https://github.com/apple/turicreate/blob/cce55aa5311300e3ce6af93cb45ba791fd1bdf49/src/external/boost/boost_1_68_0/libs/mpl/preprocessed/fix_boost_mpl_preprocess.py#L19-L36
krishauser/Klampt
972cc83ea5befac3f653c1ba20f80155768ad519
Python/klampt/math/autodiff/ad.py
python
finite_differences_hessian
(f,x,h)
return g/h**2
Performs forward differences to approximate the Hessian of f(x) w.r.t. x.
Performs forward differences to approximate the Hessian of f(x) w.r.t. x.
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def finite_differences_hessian(f,x,h): """Performs forward differences to approximate the Hessian of f(x) w.r.t. x. """ f0 = f(x) g = np.empty((_size(f0),_size(x),_size(x))) if hasattr(x,'__iter__'): xtemp = x.astype(float,copy=True) fx = [] for i in range(len(x)): xtemp[i] += h fx.append(np.copy(f(xtemp))) xtemp[i] = x[i] for i in range(len(x)): xtemp[i] -= h g[:,i,i] = (fx[i] - 2*f0 + f(xtemp)) xtemp[i] = x[i] xtemp[i] += h for j in range(i): xtemp[j] += h g[:,j,i] = g[:,i,j] = (f(xtemp) - fx[i]) - (fx[j] - f0) xtemp[j] = x[j] xtemp[i] = x[i] else: fx = f(x+h) g[:,0,0] = (fx - 2*f0 + f(x-h)) return g/h**2
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https://github.com/krishauser/Klampt/blob/972cc83ea5befac3f653c1ba20f80155768ad519/Python/klampt/math/autodiff/ad.py#L1118-L1144
Cantera/cantera
0119484b261967ccb55a0066c020599cacc312e4
interfaces/cython/cantera/composite.py
python
SolutionArray.append
(self, state=None, normalize=True, **kwargs)
Append an element to the array with the specified state. Elements can only be appended in cases where the array of states is one-dimensional. The state may be specified in one of three ways: - as the array of [temperature, density, mass fractions] which is returned by `Solution.state`:: mystates.append(gas.state) - as a tuple of three elements that corresponds to any of the full-state setters of `Solution`, e.g. `TPY` or `HPX`:: mystates.append(TPX=(300, 101325, 'O2:1.0, N2:3.76')) - as separate keywords for each of the elements corresponding to one of the full-state setters:: mystates.append(T=300, P=101325, X={'O2':1.0, 'N2':3.76}) By default, the mass or mole fractions will be normalized i.e negative values are truncated and the mass or mole fractions sum up to 1.0. If this is not desired, the ``normalize`` argument can be set to ``False``.
Append an element to the array with the specified state. Elements can only be appended in cases where the array of states is one-dimensional.
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def append(self, state=None, normalize=True, **kwargs): """ Append an element to the array with the specified state. Elements can only be appended in cases where the array of states is one-dimensional. The state may be specified in one of three ways: - as the array of [temperature, density, mass fractions] which is returned by `Solution.state`:: mystates.append(gas.state) - as a tuple of three elements that corresponds to any of the full-state setters of `Solution`, e.g. `TPY` or `HPX`:: mystates.append(TPX=(300, 101325, 'O2:1.0, N2:3.76')) - as separate keywords for each of the elements corresponding to one of the full-state setters:: mystates.append(T=300, P=101325, X={'O2':1.0, 'N2':3.76}) By default, the mass or mole fractions will be normalized i.e negative values are truncated and the mass or mole fractions sum up to 1.0. If this is not desired, the ``normalize`` argument can be set to ``False``. """ if len(self._shape) != 1: raise IndexError("Can only append to 1D SolutionArray") # This check must go before we start appending to any arrays so that # array lengths don't get out of sync. missing_extra_kwargs = self._extra.keys() - kwargs.keys() if missing_extra_kwargs: raise TypeError( "Missing keyword arguments for extra values: " "'{}'".format(", ".join(missing_extra_kwargs)) ) # For the checks of the state below, the kwargs dictionary can # only contain keywords that match properties of the state. Here # we pop any kwargs that have to do with the extra items so they # aren't included in that check. They are put into a temporary # storage so that appending can be done at the end of the function # all at once. extra_temp = {} for name in self._extra: extra_temp[name] = kwargs.pop(name) if state is not None: self._phase.state = state elif len(kwargs) == 1: attr, value = kwargs.popitem() if frozenset(attr) not in self._phase._full_states: raise KeyError( "'{}' does not specify a full thermodynamic state".format(attr) ) if normalize or attr.endswith("Q"): setattr(self._phase, attr, value) else: if attr.endswith("X"): self._phase.set_unnormalized_mole_fractions(value[-1]) elif attr.endswith("Y"): self._phase.set_unnormalized_mass_fractions(value[-1]) attr = attr[:-1] value = value[:-1] setattr(self._phase, attr, value) else: try: attr = self._phase._full_states[frozenset(kwargs)] except KeyError: raise KeyError( "{} is not a valid combination of properties for setting " "the thermodynamic state".format(tuple(kwargs)) ) from None if normalize or attr.endswith("Q"): setattr(self._phase, attr, list(kwargs.values())) else: if attr.endswith("X"): self._phase.set_unnormalized_mole_fractions(kwargs.pop("X")) elif attr.endswith("Y"): self._phase.set_unnormalized_mass_fractions(kwargs.pop("Y")) attr = attr[:-1] setattr(self._phase, attr, list(kwargs.values())) for name, value in self._extra.items(): new = extra_temp[name] if len(value): if (value.ndim == 1 and hasattr(new, '__len__') and not isinstance(new, str)): raise ValueError( "Encountered incompatible value '{}' for extra column '{}'." "".format(new, name)) elif value.ndim > 1 and value.shape[1:] != np.array(new).shape: raise ValueError( "Shape of new element does not match existing extra " "column '{}'".format(name)) # Casting to a list before appending is ~5x faster than using # np.append when appending a single item. v = value.tolist() v.append(new) extra_temp[name] = np.array(v) for name, value in extra_temp.items(): self._extra[name] = value self._states.append(self._phase.state) self._indices.append(len(self._indices)) self._shape = (len(self._indices),)
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https://github.com/Cantera/cantera/blob/0119484b261967ccb55a0066c020599cacc312e4/interfaces/cython/cantera/composite.py#L648-L757
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
wx/tools/Editra/src/autocomp/htmlcomp.py
python
Completer.OnCompletionInserted
(self, pos, text)
Handle adjusting caret position after some insertions. @param pos: position caret was at before insertion @param text: text that was inserted
Handle adjusting caret position after some insertions. @param pos: position caret was at before insertion @param text: text that was inserted
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def OnCompletionInserted(self, pos, text): """Handle adjusting caret position after some insertions. @param pos: position caret was at before insertion @param text: text that was inserted """ buff = self.GetBuffer() if text.strip().startswith(u"</"): buff.SetCurrentPos(pos) # move caret back between the tags # HACK: SetCurrentPos causes text to be selected buff.SetSelection(pos, pos)
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/wx/tools/Editra/src/autocomp/htmlcomp.py#L164-L174
hanpfei/chromium-net
392cc1fa3a8f92f42e4071ab6e674d8e0482f83f
tools/auto_bisect/bisect_perf_regression.py
python
BisectPerformanceMetrics._SyncRevision
(self, depot, revision, sync_client)
return source_control.SyncToRevision(revision, sync_client)
Syncs depot to particular revision. Args: depot: The depot that's being used at the moment (src, webkit, etc.) revision: The revision to sync to. sync_client: Program used to sync, e.g. "gclient". Can be None. Returns: True if successful, False otherwise.
Syncs depot to particular revision.
[ "Syncs", "depot", "to", "particular", "revision", "." ]
def _SyncRevision(self, depot, revision, sync_client): """Syncs depot to particular revision. Args: depot: The depot that's being used at the moment (src, webkit, etc.) revision: The revision to sync to. sync_client: Program used to sync, e.g. "gclient". Can be None. Returns: True if successful, False otherwise. """ self.depot_registry.ChangeToDepotDir(depot) if sync_client: self.PerformPreBuildCleanup() # When using gclient to sync, you need to specify the depot you # want so that all the dependencies sync properly as well. # i.e. gclient sync src@<SHA1> if sync_client == 'gclient' and revision: revision = '%s@%s' % (bisect_utils.DEPOT_DEPS_NAME[depot]['src'], revision) if depot == 'chromium' and self.opts.target_platform == 'android-chrome': return self._SyncRevisionsForAndroidChrome(revision) return source_control.SyncToRevision(revision, sync_client)
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https://github.com/hanpfei/chromium-net/blob/392cc1fa3a8f92f42e4071ab6e674d8e0482f83f/tools/auto_bisect/bisect_perf_regression.py#L1543-L1568
mantidproject/mantid
03deeb89254ec4289edb8771e0188c2090a02f32
qt/python/mantidqt/mantidqt/widgets/workspacedisplay/matrix/model.py
python
MatrixWorkspaceDisplayModel.supports
(cls, ws)
Checks that the provided workspace is supported by this display. :param ws: Workspace to be checked for support :raises ValueError: if the workspace is not supported
Checks that the provided workspace is supported by this display. :param ws: Workspace to be checked for support :raises ValueError: if the workspace is not supported
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def supports(cls, ws): """ Checks that the provided workspace is supported by this display. :param ws: Workspace to be checked for support :raises ValueError: if the workspace is not supported """ if not any(isinstance(ws, allowed_type) for allowed_type in cls.ALLOWED_WORKSPACE_TYPES): raise ValueError("The workspace type is not supported: {0}".format(ws))
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https://github.com/mantidproject/mantid/blob/03deeb89254ec4289edb8771e0188c2090a02f32/qt/python/mantidqt/mantidqt/widgets/workspacedisplay/matrix/model.py#L24-L31
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/gtk/_core.py
python
GridSizer.CalcRowsCols
(self)
return (rows, cols)
CalcRowsCols() -> (rows, cols) Calculates how many rows and columns will be in the sizer based on the current number of items and also the rows, cols specified in the constructor.
CalcRowsCols() -> (rows, cols)
[ "CalcRowsCols", "()", "-", ">", "(", "rows", "cols", ")" ]
def CalcRowsCols(self): """ CalcRowsCols() -> (rows, cols) Calculates how many rows and columns will be in the sizer based on the current number of items and also the rows, cols specified in the constructor. """ nitems = len(self.GetChildren()) rows = self.GetRows() cols = self.GetCols() assert rows != 0 or cols != 0, "Grid sizer must have either rows or columns fixed" if cols != 0: rows = (nitems + cols - 1) / cols elif rows != 0: cols = (nitems + rows - 1) / rows return (rows, cols)
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/gtk/_core.py#L15273-L15289
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/urllib3/contrib/_securetransport/low_level.py
python
_cert_array_from_pem
(pem_bundle)
return cert_array
Given a bundle of certs in PEM format, turns them into a CFArray of certs that can be used to validate a cert chain.
Given a bundle of certs in PEM format, turns them into a CFArray of certs that can be used to validate a cert chain.
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def _cert_array_from_pem(pem_bundle): """ Given a bundle of certs in PEM format, turns them into a CFArray of certs that can be used to validate a cert chain. """ # Normalize the PEM bundle's line endings. pem_bundle = pem_bundle.replace(b"\r\n", b"\n") der_certs = [ base64.b64decode(match.group(1)) for match in _PEM_CERTS_RE.finditer(pem_bundle) ] if not der_certs: raise ssl.SSLError("No root certificates specified") cert_array = CoreFoundation.CFArrayCreateMutable( CoreFoundation.kCFAllocatorDefault, 0, ctypes.byref(CoreFoundation.kCFTypeArrayCallBacks), ) if not cert_array: raise ssl.SSLError("Unable to allocate memory!") try: for der_bytes in der_certs: certdata = _cf_data_from_bytes(der_bytes) if not certdata: raise ssl.SSLError("Unable to allocate memory!") cert = Security.SecCertificateCreateWithData( CoreFoundation.kCFAllocatorDefault, certdata ) CoreFoundation.CFRelease(certdata) if not cert: raise ssl.SSLError("Unable to build cert object!") CoreFoundation.CFArrayAppendValue(cert_array, cert) CoreFoundation.CFRelease(cert) except Exception: # We need to free the array before the exception bubbles further. # We only want to do that if an error occurs: otherwise, the caller # should free. CoreFoundation.CFRelease(cert_array) return cert_array
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/urllib3/contrib/_securetransport/low_level.py#L105-L147
catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/python/traitlets/py2/traitlets/config/configurable.py
python
Configurable.class_get_trait_help
(cls, trait, inst=None)
return '\n'.join(lines)
Get the help string for a single trait. If `inst` is given, it's current trait values will be used in place of the class default.
Get the help string for a single trait.
[ "Get", "the", "help", "string", "for", "a", "single", "trait", "." ]
def class_get_trait_help(cls, trait, inst=None): """Get the help string for a single trait. If `inst` is given, it's current trait values will be used in place of the class default. """ assert inst is None or isinstance(inst, cls) lines = [] header = "--%s.%s=<%s>" % (cls.__name__, trait.name, trait.__class__.__name__) lines.append(header) if inst is not None: lines.append(indent('Current: %r' % getattr(inst, trait.name), 4)) else: try: dvr = trait.default_value_repr() except Exception: dvr = None # ignore defaults we can't construct if dvr is not None: if len(dvr) > 64: dvr = dvr[:61]+'...' lines.append(indent('Default: %s' % dvr, 4)) if 'Enum' in trait.__class__.__name__: # include Enum choices lines.append(indent('Choices: %r' % (trait.values,))) help = trait.help if help != '': help = '\n'.join(wrap_paragraphs(help, 76)) lines.append(indent(help, 4)) return '\n'.join(lines)
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/traitlets/py2/traitlets/config/configurable.py#L221-L250
natanielruiz/android-yolo
1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f
jni-build/jni/include/tensorflow/python/ops/variable_scope.py
python
VariableScope._get_partitioned_variable
(self, var_store, name, shape=None, dtype=None, initializer=None, regularizer=None, trainable=True, collections=None, caching_device=None, partitioner=None, validate_shape=True)
Gets an existing variable with this name or create a new one.
Gets an existing variable with this name or create a new one.
[ "Gets", "an", "existing", "variable", "with", "this", "name", "or", "create", "a", "new", "one", "." ]
def _get_partitioned_variable(self, var_store, name, shape=None, dtype=None, initializer=None, regularizer=None, trainable=True, collections=None, caching_device=None, partitioner=None, validate_shape=True): """Gets an existing variable with this name or create a new one.""" if initializer is None: initializer = self._initializer if regularizer is None: regularizer = self._regularizer if caching_device is None: caching_device = self._caching_device if partitioner is None: partitioner = self._partitioner if dtype is None: dtype = self._dtype if self._custom_getter is not None: raise ValueError( "Private access to _get_partitioned_variable is not allowed when " "a custom getter is set. Current custom getter: %s. " "It is likely that you're using create_partitioned_variables. " "If so, consider instead using get_variable with a non-empty " "partitioner parameter instead." % self._custom_getter) if partitioner is None: raise ValueError("No partitioner was specified") # This allows the variable scope name to be used as the variable name if # this function is invoked with an empty name arg, for backward # compatibility with create_partitioned_variables(). full_name_list = [] if self.name: full_name_list.append(self.name) if name: full_name_list.append(name) full_name = "/".join(full_name_list) # Variable names only depend on variable_scope (full_name here), # not name_scope, so we reset it below for the time of variable creation. with ops.name_scope(None): # pylint: disable=protected-access return var_store._get_partitioned_variable( full_name, shape=shape, dtype=dtype, initializer=initializer, regularizer=regularizer, reuse=self.reuse, trainable=trainable, collections=collections, caching_device=caching_device, partitioner=partitioner, validate_shape=validate_shape)
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https://github.com/natanielruiz/android-yolo/blob/1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f/jni-build/jni/include/tensorflow/python/ops/variable_scope.py#L702-L755
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numba/dataflow.py
python
DataFlowAnalysis.op_SLICE_1
(self, info, inst)
TOS = TOS1[TOS:]
TOS = TOS1[TOS:]
[ "TOS", "=", "TOS1", "[", "TOS", ":", "]" ]
def op_SLICE_1(self, info, inst): """ TOS = TOS1[TOS:] """ tos = info.pop() tos1 = info.pop() res = info.make_temp() slicevar = info.make_temp() indexvar = info.make_temp() nonevar = info.make_temp() info.append(inst, base=tos1, start=tos, res=res, slicevar=slicevar, indexvar=indexvar, nonevar=nonevar) info.push(res)
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numba/dataflow.py#L469-L481
root-project/root
fcd3583bb14852bf2e8cd2415717cbaac0e75896
interpreter/llvm/src/utils/benchmark/mingw.py
python
main
()
Invoked when the script is run directly by the python interpreter
Invoked when the script is run directly by the python interpreter
[ "Invoked", "when", "the", "script", "is", "run", "directly", "by", "the", "python", "interpreter" ]
def main(): ''' Invoked when the script is run directly by the python interpreter ''' parser = argparse.ArgumentParser( description = 'Downloads a specific version of MinGW', formatter_class = argparse.ArgumentDefaultsHelpFormatter ) parser.add_argument('--location', help = 'the location to download the compiler to', default = os.path.join(tempfile.gettempdir(), 'mingw-builds')) parser.add_argument('--arch', required = True, choices = ['i686', 'x86_64'], help = 'the target MinGW architecture string') parser.add_argument('--version', type = str2ver, help = 'the version of GCC to download') parser.add_argument('--threading', choices = ['posix', 'win32'], help = 'the threading type of the compiler') parser.add_argument('--exceptions', choices = ['sjlj', 'seh', 'dwarf'], help = 'the method to throw exceptions') parser.add_argument('--revision', type=int, help = 'the revision of the MinGW release') group = parser.add_mutually_exclusive_group() group.add_argument('-v', '--verbose', action='store_true', help='increase the script output verbosity') group.add_argument('-q', '--quiet', action='store_true', help='only print errors and warning') args = parser.parse_args() # Create the logger logger = logging.getLogger('mingw') handler = logging.StreamHandler() formatter = logging.Formatter('%(message)s') handler.setFormatter(formatter) logger.addHandler(handler) logger.setLevel(logging.INFO) if args.quiet: logger.setLevel(logging.WARN) if args.verbose: logger.setLevel(logging.DEBUG) # Get MinGW root_dir = root(location = args.location, arch = args.arch, version = args.version, threading = args.threading, exceptions = args.exceptions, revision = args.revision, log = logger) sys.stdout.write('%s\n' % os.path.join(root_dir, 'bin'))
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https://github.com/root-project/root/blob/fcd3583bb14852bf2e8cd2415717cbaac0e75896/interpreter/llvm/src/utils/benchmark/mingw.py#L261-L307
tensorflow/tensorflow
419e3a6b650ea4bd1b0cba23c4348f8a69f3272e
tensorflow/python/saved_model/load.py
python
Loader._retrieve_all_filtered_nodes
(self)
return all_filtered_nodes
Traverses through the object graph to get the IDs of all nodes to load. As a side-effect, if node_filters is a dictionary that contains already- created objects, then the children tracked by those objects will be added to node_filters. Returns: List of all nodes to load, or None if all nodes should be loaded.
Traverses through the object graph to get the IDs of all nodes to load.
[ "Traverses", "through", "the", "object", "graph", "to", "get", "the", "IDs", "of", "all", "nodes", "to", "load", "." ]
def _retrieve_all_filtered_nodes(self): """Traverses through the object graph to get the IDs of all nodes to load. As a side-effect, if node_filters is a dictionary that contains already- created objects, then the children tracked by those objects will be added to node_filters. Returns: List of all nodes to load, or None if all nodes should be loaded. """ if self._node_filters is None: return None # All nodes should be loaded. all_filtered_nodes = set() nodes_to_visit = list(self._node_filters) while nodes_to_visit: node_path = nodes_to_visit.pop(0) node_id = self._node_path_to_id[node_path] if node_id in all_filtered_nodes: continue all_filtered_nodes.add(node_id) node, setter = self._loaded_nodes.get(node_id, (None, None)) if node is not None: if not isinstance(node, base.Trackable): raise TypeError( "Error when processing dictionary values passed to nodes_to_load." f"Object at {node_path} is expected to be a checkpointable (i.e. " "'trackable') TensorFlow object (e.g. tf.Variable, tf.Module or " "Keras layer).") node._maybe_initialize_trackable() # pylint: disable=protected-access for reference in self._proto.nodes[node_id].children: child_object, _ = self._loaded_nodes.get( reference.node_id, (None, None)) # See if node already tracks the child reference, in which case add the # child to the loaded_nodes dict. if child_object is None and node is not None: child_object = node._lookup_dependency(reference.local_name) # pylint: disable=protected-access if isinstance(child_object, data_structures.TrackableDataStructure): # Make setattr a noop to avoid overwriting already existing data # structures. setter = lambda *args: None self._loaded_nodes[reference.node_id] = (child_object, setter) child_path = "{}.{}".format(node_path, reference.local_name) self._node_path_to_id[child_path] = reference.node_id nodes_to_visit.append(child_path) if 0 in all_filtered_nodes: return None return all_filtered_nodes
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https://github.com/tensorflow/tensorflow/blob/419e3a6b650ea4bd1b0cba23c4348f8a69f3272e/tensorflow/python/saved_model/load.py#L216-L271
hpi-xnor/BMXNet-v2
af2b1859eafc5c721b1397cef02f946aaf2ce20d
python/mxnet/context.py
python
gpu_memory_info
(device_id=0)
return (free.value, total.value)
Query CUDA for the free and total bytes of GPU global memory. Parameters ---------- device_id : int, optional The device id of the GPU device. Raises ------ Will raise an exception on any CUDA error. Returns ------- (free, total) : (int, int) The number of GPUs.
Query CUDA for the free and total bytes of GPU global memory.
[ "Query", "CUDA", "for", "the", "free", "and", "total", "bytes", "of", "GPU", "global", "memory", "." ]
def gpu_memory_info(device_id=0): """Query CUDA for the free and total bytes of GPU global memory. Parameters ---------- device_id : int, optional The device id of the GPU device. Raises ------ Will raise an exception on any CUDA error. Returns ------- (free, total) : (int, int) The number of GPUs. """ free = ctypes.c_uint64() total = ctypes.c_uint64() dev_id = ctypes.c_int(device_id) check_call(_LIB.MXGetGPUMemoryInformation64(dev_id, ctypes.byref(free), ctypes.byref(total))) return (free.value, total.value)
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https://github.com/hpi-xnor/BMXNet-v2/blob/af2b1859eafc5c721b1397cef02f946aaf2ce20d/python/mxnet/context.py#L279-L301
gemrb/gemrb
730206eed8d1dd358ca5e69a62f9e099aa22ffc6
gemrb/GUIScripts/Actor.py
python
Actor.ClassNames
(self)
return self.__classnames
Returns a list will all the class names.
Returns a list will all the class names.
[ "Returns", "a", "list", "will", "all", "the", "class", "names", "." ]
def ClassNames (self): """Returns a list will all the class names.""" if self.__classnames == None: self.__classnames = GUICommon.GetClassRowName (self.classid, "class").split("_") if self.IsDualSwap(): self.__classnames.reverse() return self.__classnames
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https://github.com/gemrb/gemrb/blob/730206eed8d1dd358ca5e69a62f9e099aa22ffc6/gemrb/GUIScripts/Actor.py#L74-L81
wlanjie/AndroidFFmpeg
7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf
tools/fdk-aac-build/x86/toolchain/lib/python2.7/plat-mac/lib-scriptpackages/Finder/Standard_Suite.py
python
Standard_Suite_Events.quit
(self, _no_object=None, _attributes={}, **_arguments)
quit: Quit the Finder Keyword argument _attributes: AppleEvent attribute dictionary
quit: Quit the Finder Keyword argument _attributes: AppleEvent attribute dictionary
[ "quit", ":", "Quit", "the", "Finder", "Keyword", "argument", "_attributes", ":", "AppleEvent", "attribute", "dictionary" ]
def quit(self, _no_object=None, _attributes={}, **_arguments): """quit: Quit the Finder Keyword argument _attributes: AppleEvent attribute dictionary """ _code = 'aevt' _subcode = 'quit' if _arguments: raise TypeError, 'No optional args expected' if _no_object is not None: raise TypeError, 'No direct arg expected' _reply, _arguments, _attributes = self.send(_code, _subcode, _arguments, _attributes) if _arguments.get('errn', 0): raise aetools.Error, aetools.decodeerror(_arguments) # XXXX Optionally decode result if _arguments.has_key('----'): return _arguments['----']
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https://github.com/wlanjie/AndroidFFmpeg/blob/7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf/tools/fdk-aac-build/x86/toolchain/lib/python2.7/plat-mac/lib-scriptpackages/Finder/Standard_Suite.py#L280-L297
albertz/openlierox
d316c14a8eb57848ef56e9bfa7b23a56f694a51b
tools/DedicatedServerVideo/gdata/spreadsheet/text_db.py
python
Table.SetFields
(self, fields)
Changes the contents of the cells in the first row of this worksheet. Args: fields: list of strings The names in the list comprise the first row of the worksheet. These names are converted into XML tags by the server. To avoid changes during the translation process I recommend using all lowercase alphabetic names. For example ['somelongname', 'theothername']
Changes the contents of the cells in the first row of this worksheet.
[ "Changes", "the", "contents", "of", "the", "cells", "in", "the", "first", "row", "of", "this", "worksheet", "." ]
def SetFields(self, fields): """Changes the contents of the cells in the first row of this worksheet. Args: fields: list of strings The names in the list comprise the first row of the worksheet. These names are converted into XML tags by the server. To avoid changes during the translation process I recommend using all lowercase alphabetic names. For example ['somelongname', 'theothername'] """ # TODO: If the table already had fields, we might want to clear out the, # current column headers. self.fields = fields i = 0 for column_name in fields: i = i + 1 # TODO: speed this up by using a batch request to update cells. self.client._GetSpreadsheetsClient().UpdateCell(1, i, column_name, self.spreadsheet_key, self.worksheet_id)
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https://github.com/albertz/openlierox/blob/d316c14a8eb57848ef56e9bfa7b23a56f694a51b/tools/DedicatedServerVideo/gdata/spreadsheet/text_db.py#L311-L329