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oracle/graaljs
36a56e8e993d45fc40939a3a4d9c0c24990720f1
graal-nodejs/deps/v8/third_party/jinja2/filters.py
python
do_join
(eval_ctx, value, d=u'', attribute=None)
return soft_unicode(d).join(imap(soft_unicode, value))
Return a string which is the concatenation of the strings in the sequence. The separator between elements is an empty string per default, you can define it with the optional parameter: .. sourcecode:: jinja {{ [1, 2, 3]|join('|') }} -> 1|2|3 {{ [1, 2, 3]|join }} -> 123 It is also possible to join certain attributes of an object: .. sourcecode:: jinja {{ users|join(', ', attribute='username') }} .. versionadded:: 2.6 The `attribute` parameter was added.
Return a string which is the concatenation of the strings in the sequence. The separator between elements is an empty string per default, you can define it with the optional parameter:
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def do_join(eval_ctx, value, d=u'', attribute=None): """Return a string which is the concatenation of the strings in the sequence. The separator between elements is an empty string per default, you can define it with the optional parameter: .. sourcecode:: jinja {{ [1, 2, 3]|join('|') }} -> 1|2|3 {{ [1, 2, 3]|join }} -> 123 It is also possible to join certain attributes of an object: .. sourcecode:: jinja {{ users|join(', ', attribute='username') }} .. versionadded:: 2.6 The `attribute` parameter was added. """ if attribute is not None: value = imap(make_attrgetter(eval_ctx.environment, attribute), value) # no automatic escaping? joining is a lot eaiser then if not eval_ctx.autoescape: return text_type(d).join(imap(text_type, value)) # if the delimiter doesn't have an html representation we check # if any of the items has. If yes we do a coercion to Markup if not hasattr(d, '__html__'): value = list(value) do_escape = False for idx, item in enumerate(value): if hasattr(item, '__html__'): do_escape = True else: value[idx] = text_type(item) if do_escape: d = escape(d) else: d = text_type(d) return d.join(value) # no html involved, to normal joining return soft_unicode(d).join(imap(soft_unicode, value))
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https://github.com/oracle/graaljs/blob/36a56e8e993d45fc40939a3a4d9c0c24990720f1/graal-nodejs/deps/v8/third_party/jinja2/filters.py#L378-L424
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Tools/Python/3.7.10/windows/Lib/lib2to3/pytree.py
python
Node.prefix
(self)
return self.children[0].prefix
The whitespace and comments preceding this node in the input.
The whitespace and comments preceding this node in the input.
[ "The", "whitespace", "and", "comments", "preceding", "this", "node", "in", "the", "input", "." ]
def prefix(self): """ The whitespace and comments preceding this node in the input. """ if not self.children: return "" return self.children[0].prefix
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/windows/Lib/lib2to3/pytree.py#L275-L281
miyosuda/TensorFlowAndroidDemo
35903e0221aa5f109ea2dbef27f20b52e317f42d
jni-build/jni/include/tensorflow/contrib/learn/python/learn/estimators/dnn.py
python
DNNClassifier._get_train_ops
(self, features, targets)
return super(DNNClassifier, self)._get_train_ops(features, targets)
See base class.
See base class.
[ "See", "base", "class", "." ]
def _get_train_ops(self, features, targets): """See base class.""" self._validate_dnn_feature_columns(features) return super(DNNClassifier, self)._get_train_ops(features, targets)
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https://github.com/miyosuda/TensorFlowAndroidDemo/blob/35903e0221aa5f109ea2dbef27f20b52e317f42d/jni-build/jni/include/tensorflow/contrib/learn/python/learn/estimators/dnn.py#L181-L184
raspberrypi/tools
13474ee775d0c5ec8a7da4fb0a9fa84187abfc87
arm-bcm2708/gcc-linaro-arm-linux-gnueabihf-raspbian/share/gdb/python/gdb/types.py
python
apply_type_recognizers
(recognizers, type_obj)
return None
Apply the given list of type recognizers to the type TYPE_OBJ. If any recognizer in the list recognizes TYPE_OBJ, returns the name given by the recognizer. Otherwise, this returns None.
Apply the given list of type recognizers to the type TYPE_OBJ. If any recognizer in the list recognizes TYPE_OBJ, returns the name given by the recognizer. Otherwise, this returns None.
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def apply_type_recognizers(recognizers, type_obj): """Apply the given list of type recognizers to the type TYPE_OBJ. If any recognizer in the list recognizes TYPE_OBJ, returns the name given by the recognizer. Otherwise, this returns None.""" for r in recognizers: result = r.recognize(type_obj) if result is not None: return result return None
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https://github.com/raspberrypi/tools/blob/13474ee775d0c5ec8a7da4fb0a9fa84187abfc87/arm-bcm2708/gcc-linaro-arm-linux-gnueabihf-raspbian/share/gdb/python/gdb/types.py#L158-L166
catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/python/scipy/py2/scipy/spatial/kdtree.py
python
Rectangle.max_distance_point
(self, x, p=2.)
return minkowski_distance(0, np.maximum(self.maxes-x,x-self.mins),p)
Return the maximum distance between input and points in the hyperrectangle. Parameters ---------- x : array_like Input array. p : float, optional Input.
Return the maximum distance between input and points in the hyperrectangle.
[ "Return", "the", "maximum", "distance", "between", "input", "and", "points", "in", "the", "hyperrectangle", "." ]
def max_distance_point(self, x, p=2.): """ Return the maximum distance between input and points in the hyperrectangle. Parameters ---------- x : array_like Input array. p : float, optional Input. """ return minkowski_distance(0, np.maximum(self.maxes-x,x-self.mins),p)
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/scipy/py2/scipy/spatial/kdtree.py#L133-L145
windystrife/UnrealEngine_NVIDIAGameWorks
b50e6338a7c5b26374d66306ebc7807541ff815e
Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/site-packages/winpython/utils.py
python
get_python_long_version
(path)
return ver
Return long version (X.Y.Z) for the Python distribution located in *path*
Return long version (X.Y.Z) for the Python distribution located in *path*
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def get_python_long_version(path): """Return long version (X.Y.Z) for the Python distribution located in *path*""" ver = python_query("import sys; print('%d.%d.%d' % "\ "(sys.version_info.major, sys.version_info.minor,"\ "sys.version_info.micro))", path) if re.match(r'([0-9]*)\.([0-9]*)\.([0-9]*)', ver) is None: ver = None return ver
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https://github.com/windystrife/UnrealEngine_NVIDIAGameWorks/blob/b50e6338a7c5b26374d66306ebc7807541ff815e/Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/site-packages/winpython/utils.py#L235-L243
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Tools/Python/3.7.10/windows/Lib/idlelib/parenmatch.py
python
ParenMatch.set_timeout_last
(self)
The last highlight created will be removed after FLASH_DELAY millisecs
The last highlight created will be removed after FLASH_DELAY millisecs
[ "The", "last", "highlight", "created", "will", "be", "removed", "after", "FLASH_DELAY", "millisecs" ]
def set_timeout_last(self): """The last highlight created will be removed after FLASH_DELAY millisecs""" # associate a counter with an event; only disable the "paren" # tag if the event is for the most recent timer. self.counter += 1 self.editwin.text_frame.after( self.FLASH_DELAY, lambda self=self, c=self.counter: self.handle_restore_timer(c))
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/windows/Lib/idlelib/parenmatch.py#L168-L175
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/site-packages/pip/_vendor/pyparsing.py
python
Regex.sub
(self, repl)
return self.addParseAction(pa)
r""" Return Regex with an attached parse action to transform the parsed result as if called using `re.sub(expr, repl, string) <https://docs.python.org/3/library/re.html#re.sub>`_. Example:: make_html = Regex(r"(\w+):(.*?):").sub(r"<\1>\2</\1>") print(make_html.transformString("h1:main title:")) # prints "<h1>main title</h1>"
r""" Return Regex with an attached parse action to transform the parsed result as if called using `re.sub(expr, repl, string) <https://docs.python.org/3/library/re.html#re.sub>`_.
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def sub(self, repl): r""" Return Regex with an attached parse action to transform the parsed result as if called using `re.sub(expr, repl, string) <https://docs.python.org/3/library/re.html#re.sub>`_. Example:: make_html = Regex(r"(\w+):(.*?):").sub(r"<\1>\2</\1>") print(make_html.transformString("h1:main title:")) # prints "<h1>main title</h1>" """ if self.asGroupList: warnings.warn("cannot use sub() with Regex(asGroupList=True)", SyntaxWarning, stacklevel=2) raise SyntaxError() if self.asMatch and callable(repl): warnings.warn("cannot use sub() with a callable with Regex(asMatch=True)", SyntaxWarning, stacklevel=2) raise SyntaxError() if self.asMatch: def pa(tokens): return tokens[0].expand(repl) else: def pa(tokens): return self.re.sub(repl, tokens[0]) return self.addParseAction(pa)
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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/site-packages/pip/_vendor/pyparsing.py#L3381-L3408
wlanjie/AndroidFFmpeg
7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf
tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/lib-tk/Tix.py
python
CheckList.setstatus
(self, entrypath, mode='on')
Sets the status of entryPath to be status. A bitmap will be displayed next to the entry its status is on, off or default.
Sets the status of entryPath to be status. A bitmap will be displayed next to the entry its status is on, off or default.
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def setstatus(self, entrypath, mode='on'): '''Sets the status of entryPath to be status. A bitmap will be displayed next to the entry its status is on, off or default.''' self.tk.call(self._w, 'setstatus', entrypath, mode)
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https://github.com/wlanjie/AndroidFFmpeg/blob/7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf/tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/lib-tk/Tix.py#L1612-L1615
catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/python/Jinja2/py3/jinja2/filters.py
python
do_reverse
(value: t.Union[str, t.Iterable[V]])
Reverse the object or return an iterator that iterates over it the other way round.
Reverse the object or return an iterator that iterates over it the other way round.
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def do_reverse(value: t.Union[str, t.Iterable[V]]) -> t.Union[str, t.Iterable[V]]: """Reverse the object or return an iterator that iterates over it the other way round. """ if isinstance(value, str): return value[::-1] try: return reversed(value) # type: ignore except TypeError: try: rv = list(value) rv.reverse() return rv except TypeError as e: raise FilterArgumentError("argument must be iterable") from e
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/Jinja2/py3/jinja2/filters.py#L1339-L1354
catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/tools/python3/src/Lib/concurrent/futures/_base.py
python
Future.set_result
(self, result)
Sets the return value of work associated with the future. Should only be used by Executor implementations and unit tests.
Sets the return value of work associated with the future.
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def set_result(self, result): """Sets the return value of work associated with the future. Should only be used by Executor implementations and unit tests. """ with self._condition: if self._state in {CANCELLED, CANCELLED_AND_NOTIFIED, FINISHED}: raise InvalidStateError('{}: {!r}'.format(self._state, self)) self._result = result self._state = FINISHED for waiter in self._waiters: waiter.add_result(self) self._condition.notify_all() self._invoke_callbacks()
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/tools/python3/src/Lib/concurrent/futures/_base.py#L527-L540
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/idlelib/calltip.py
python
Calltip.force_open_calltip_event
(self, event)
return "break"
The user selected the menu entry or hotkey, open the tip.
The user selected the menu entry or hotkey, open the tip.
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def force_open_calltip_event(self, event): "The user selected the menu entry or hotkey, open the tip." self.open_calltip(True) return "break"
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/idlelib/calltip.py#L41-L44
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/idlelib/grep.py
python
GrepDialog.default_command
(self, event=None)
Grep for search pattern in file path. The default command is bound to <Return>. If entry values are populated, set OutputWindow as stdout and perform search. The search dialog is closed automatically when the search begins.
Grep for search pattern in file path. The default command is bound to <Return>.
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def default_command(self, event=None): """Grep for search pattern in file path. The default command is bound to <Return>. If entry values are populated, set OutputWindow as stdout and perform search. The search dialog is closed automatically when the search begins. """ prog = self.engine.getprog() if not prog: return path = self.globvar.get() if not path: self.top.bell() return from idlelib.outwin import OutputWindow # leave here! save = sys.stdout try: sys.stdout = OutputWindow(self.flist) self.grep_it(prog, path) finally: sys.stdout = save
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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/idlelib/grep.py#L129-L150
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/osx_cocoa/_misc.py
python
Clipboard.Clear
(*args, **kwargs)
return _misc_.Clipboard_Clear(*args, **kwargs)
Clear(self) Clears data from the clipboard object and also the system's clipboard if possible.
Clear(self)
[ "Clear", "(", "self", ")" ]
def Clear(*args, **kwargs): """ Clear(self) Clears data from the clipboard object and also the system's clipboard if possible. """ return _misc_.Clipboard_Clear(*args, **kwargs)
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_cocoa/_misc.py#L5865-L5872
Illumina/manta
75b5c38d4fcd2f6961197b28a41eb61856f2d976
src/python/lib/mantaWorkflow.py
python
runLocusGraph
(self,taskPrefix="",dependencies=None)
return nextStepWait
Create the full SV locus graph
Create the full SV locus graph
[ "Create", "the", "full", "SV", "locus", "graph" ]
def runLocusGraph(self,taskPrefix="",dependencies=None): """ Create the full SV locus graph """ statsPath=self.paths.getStatsPath() graphPath=self.paths.getGraphPath() graphStatsPath=self.paths.getGraphStatsPath() tmpGraphDir=self.paths.getTmpGraphDir() makeTmpGraphDirCmd = getMkdirCmd() + [tmpGraphDir] dirTask = self.addTask(preJoin(taskPrefix,"makeGraphTmpDir"), makeTmpGraphDirCmd, dependencies=dependencies, isForceLocal=True) tmpGraphFiles = [] graphTasks = set() for gsegGroup in getGenomeSegmentGroups(getNextGenomeSegment(self.params)) : assert(len(gsegGroup) != 0) gid=gsegGroup[0].id if len(gsegGroup) > 1 : gid += "_to_"+gsegGroup[-1].id tmpGraphFiles.append(self.paths.getTmpGraphFile(gid)) graphCmd = [ self.params.mantaGraphBin ] graphCmd.extend(["--output-file", tmpGraphFiles[-1]]) graphCmd.extend(["--align-stats",statsPath]) for gseg in gsegGroup : graphCmd.extend(["--region",gseg.bamRegion]) graphCmd.extend(["--min-candidate-sv-size", self.params.minCandidateVariantSize]) graphCmd.extend(["--min-edge-observations", self.params.minEdgeObservations]) graphCmd.extend(["--ref",self.params.referenceFasta]) for bamPath in self.params.normalBamList : graphCmd.extend(["--align-file",bamPath]) for bamPath in self.params.tumorBamList : graphCmd.extend(["--tumor-align-file",bamPath]) if self.params.isHighDepthFilter : graphCmd.extend(["--chrom-depth", self.paths.getChromDepth()]) if self.params.isIgnoreAnomProperPair : graphCmd.append("--ignore-anom-proper-pair") if self.params.isRNA : graphCmd.append("--rna") graphTask=preJoin(taskPrefix,"makeLocusGraph_"+gid) graphTasks.add(self.addTask(graphTask,graphCmd,dependencies=dirTask,memMb=self.params.estimateMemMb)) if len(tmpGraphFiles) == 0 : raise Exception("No SV Locus graphs to create. Possible target region parse error.") tmpGraphFileList = self.paths.getTmpGraphFileListPath() tmpGraphFileListTask = preJoin(taskPrefix,"mergeLocusGraphInputList") self.addWorkflowTask(tmpGraphFileListTask,listFileWorkflow(tmpGraphFileList,tmpGraphFiles),dependencies=graphTasks) mergeCmd = [ self.params.mantaGraphMergeBin ] mergeCmd.extend(["--output-file", graphPath]) mergeCmd.extend(["--graph-file-list",tmpGraphFileList]) mergeTask = self.addTask(preJoin(taskPrefix,"mergeLocusGraph"),mergeCmd,dependencies=tmpGraphFileListTask,memMb=self.params.mergeMemMb) # Run a separate process to rigorously check that the final graph is valid, the sv candidate generators will check as well, but # this makes the check much more clear: checkCmd = [ self.params.mantaGraphCheckBin ] checkCmd.extend(["--graph-file", graphPath]) checkTask = self.addTask(preJoin(taskPrefix,"checkLocusGraph"),checkCmd,dependencies=mergeTask,memMb=self.params.mergeMemMb) if not self.params.isRetainTempFiles : rmGraphTmpCmd = getRmdirCmd() + [tmpGraphDir] rmTask=self.addTask(preJoin(taskPrefix,"removeTmpDir"),rmGraphTmpCmd,dependencies=mergeTask) graphStatsCmd = [self.params.mantaGraphStatsBin,"--global"] graphStatsCmd.extend(["--graph-file",graphPath]) graphStatsCmd.extend(["--output-file",graphStatsPath]) graphStatsTask = self.addTask(preJoin(taskPrefix,"locusGraphStats"),graphStatsCmd,dependencies=mergeTask,memMb=self.params.mergeMemMb) nextStepWait = set() nextStepWait.add(checkTask) return nextStepWait
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Possible target region parse error.\"", ")", "tmpGraphFileList", "=", "self", ".", "paths", ".", "getTmpGraphFileListPath", "(", ")", "tmpGraphFileListTask", "=", "preJoin", "(", "taskPrefix", ",", "\"mergeLocusGraphInputList\"", ")", "self", ".", "addWorkflowTask", "(", "tmpGraphFileListTask", ",", "listFileWorkflow", "(", "tmpGraphFileList", ",", "tmpGraphFiles", ")", ",", "dependencies", "=", "graphTasks", ")", "mergeCmd", "=", "[", "self", ".", "params", ".", "mantaGraphMergeBin", "]", "mergeCmd", ".", "extend", "(", "[", "\"--output-file\"", ",", "graphPath", "]", ")", "mergeCmd", ".", "extend", "(", "[", "\"--graph-file-list\"", ",", "tmpGraphFileList", "]", ")", "mergeTask", "=", "self", ".", "addTask", "(", "preJoin", "(", "taskPrefix", ",", "\"mergeLocusGraph\"", ")", ",", "mergeCmd", ",", "dependencies", "=", "tmpGraphFileListTask", ",", "memMb", "=", "self", ".", "params", ".", "mergeMemMb", ")", "# Run a separate process to rigorously check that the final graph is valid, the sv candidate generators will check as well, but", "# this makes the check much more clear:", "checkCmd", "=", "[", "self", ".", "params", ".", "mantaGraphCheckBin", "]", "checkCmd", ".", "extend", "(", "[", "\"--graph-file\"", ",", "graphPath", "]", ")", "checkTask", "=", "self", ".", "addTask", "(", "preJoin", "(", "taskPrefix", ",", "\"checkLocusGraph\"", ")", ",", "checkCmd", ",", "dependencies", "=", "mergeTask", ",", "memMb", "=", "self", ".", "params", ".", "mergeMemMb", ")", "if", "not", "self", ".", "params", ".", "isRetainTempFiles", ":", "rmGraphTmpCmd", "=", "getRmdirCmd", "(", ")", "+", "[", "tmpGraphDir", "]", "rmTask", "=", "self", ".", "addTask", "(", "preJoin", "(", "taskPrefix", ",", "\"removeTmpDir\"", ")", ",", "rmGraphTmpCmd", ",", "dependencies", "=", "mergeTask", ")", "graphStatsCmd", "=", "[", "self", ".", "params", ".", "mantaGraphStatsBin", ",", "\"--global\"", "]", "graphStatsCmd", ".", "extend", "(", "[", "\"--graph-file\"", ",", "graphPath", "]", ")", "graphStatsCmd", ".", "extend", "(", "[", "\"--output-file\"", ",", "graphStatsPath", "]", ")", "graphStatsTask", "=", "self", ".", "addTask", "(", "preJoin", "(", "taskPrefix", ",", "\"locusGraphStats\"", ")", ",", "graphStatsCmd", ",", "dependencies", "=", "mergeTask", ",", "memMb", "=", "self", ".", "params", ".", "mergeMemMb", ")", "nextStepWait", "=", "set", "(", ")", "nextStepWait", ".", "add", "(", "checkTask", ")", "return", "nextStepWait" ]
https://github.com/Illumina/manta/blob/75b5c38d4fcd2f6961197b28a41eb61856f2d976/src/python/lib/mantaWorkflow.py#L235-L313
wlanjie/AndroidFFmpeg
7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf
tools/fdk-aac-build/x86/toolchain/lib/python2.7/distutils/command/bdist_rpm.py
python
bdist_rpm._format_changelog
(self, changelog)
return new_changelog
Format the changelog correctly and convert it to a list of strings
Format the changelog correctly and convert it to a list of strings
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def _format_changelog(self, changelog): """Format the changelog correctly and convert it to a list of strings """ if not changelog: return changelog new_changelog = [] for line in string.split(string.strip(changelog), '\n'): line = string.strip(line) if line[0] == '*': new_changelog.extend(['', line]) elif line[0] == '-': new_changelog.append(line) else: new_changelog.append(' ' + line) # strip trailing newline inserted by first changelog entry if not new_changelog[0]: del new_changelog[0] return new_changelog
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https://github.com/wlanjie/AndroidFFmpeg/blob/7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf/tools/fdk-aac-build/x86/toolchain/lib/python2.7/distutils/command/bdist_rpm.py#L564-L583
facebook/fboss
60063db1df37c2ec0e7dcd0955c54885ea9bf7f0
build/fbcode_builder/fbcode_builder.py
python
FBCodeBuilder.render
(self, steps)
return res
Converts nested actions to your builder's expected output format. Typically takes the output of build().
[]
def render(self, steps): """ Converts nested actions to your builder's expected output format. Typically takes the output of build(). """ res = self._render_impl(steps) # Implementation-dependent # Now that the output is rendered, we expect all options to have # been used. unused_options = set(self._options_do_not_access) unused_options -= self.options_used if unused_options: raise RuntimeError( "Unused options: {0} -- please check if you made a typo " "in any of them. Those that are truly not useful should " "be not be set so that this typo detection can be useful.".format( unused_options ) ) return res
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https://github.com/facebook/fboss/blob/60063db1df37c2ec0e7dcd0955c54885ea9bf7f0/build/fbcode_builder/fbcode_builder.py#L120-L140
pmq20/node-packer
12c46c6e44fbc14d9ee645ebd17d5296b324f7e0
lts/deps/npm/node_modules/node-gyp/gyp/pylib/gyp/generator/xcode.py
python
EscapeXcodeDefine
(s)
return re.sub(_xcode_define_re, r'\\\1', s)
We must escape the defines that we give to XCode so that it knows not to split on spaces and to respect backslash and quote literals. However, we must not quote the define, or Xcode will incorrectly intepret variables especially $(inherited).
We must escape the defines that we give to XCode so that it knows not to split on spaces and to respect backslash and quote literals. However, we must not quote the define, or Xcode will incorrectly intepret variables especially $(inherited).
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def EscapeXcodeDefine(s): """We must escape the defines that we give to XCode so that it knows not to split on spaces and to respect backslash and quote literals. However, we must not quote the define, or Xcode will incorrectly intepret variables especially $(inherited).""" return re.sub(_xcode_define_re, r'\\\1', s)
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https://github.com/pmq20/node-packer/blob/12c46c6e44fbc14d9ee645ebd17d5296b324f7e0/lts/deps/npm/node_modules/node-gyp/gyp/pylib/gyp/generator/xcode.py#L558-L563
yrnkrn/zapcc
c6a8aa30006d997eff0d60fd37b0e62b8aa0ea50
tools/clang/tools/scan-build-py/libscanbuild/report.py
python
chop
(prefix, filename)
return filename if not len(prefix) else os.path.relpath(filename, prefix)
Create 'filename' from '/prefix/filename'
Create 'filename' from '/prefix/filename'
[ "Create", "filename", "from", "/", "prefix", "/", "filename" ]
def chop(prefix, filename): """ Create 'filename' from '/prefix/filename' """ return filename if not len(prefix) else os.path.relpath(filename, prefix)
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https://github.com/yrnkrn/zapcc/blob/c6a8aa30006d997eff0d60fd37b0e62b8aa0ea50/tools/clang/tools/scan-build-py/libscanbuild/report.py#L439-L442
catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/tools/python3/src/Lib/ftplib.py
python
FTP.sendeprt
(self, host, port)
return self.voidcmd(cmd)
Send an EPRT command with the current host and the given port number.
Send an EPRT command with the current host and the given port number.
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def sendeprt(self, host, port): '''Send an EPRT command with the current host and the given port number.''' af = 0 if self.af == socket.AF_INET: af = 1 if self.af == socket.AF_INET6: af = 2 if af == 0: raise error_proto('unsupported address family') fields = ['', repr(af), host, repr(port), ''] cmd = 'EPRT ' + '|'.join(fields) return self.voidcmd(cmd)
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/tools/python3/src/Lib/ftplib.py#L298-L309
tiny-dnn/tiny-dnn
c0f576f5cb7b35893f62127cb7aec18f77a3bcc5
third_party/cpplint.py
python
RemoveMultiLineCommentsFromRange
(lines, begin, end)
Clears a range of lines for multi-line comments.
Clears a range of lines for multi-line comments.
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def RemoveMultiLineCommentsFromRange(lines, begin, end): """Clears a range of lines for multi-line comments.""" # Having // dummy comments makes the lines non-empty, so we will not get # unnecessary blank line warnings later in the code. for i in range(begin, end): lines[i] = '/**/'
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https://github.com/tiny-dnn/tiny-dnn/blob/c0f576f5cb7b35893f62127cb7aec18f77a3bcc5/third_party/cpplint.py#L1554-L1559
eventql/eventql
7ca0dbb2e683b525620ea30dc40540a22d5eb227
deps/3rdparty/spidermonkey/mozjs/python/psutil/psutil/_pswindows.py
python
per_cpu_times
()
return ret
Return system per-CPU times as a list of named tuples.
Return system per-CPU times as a list of named tuples.
[ "Return", "system", "per", "-", "CPU", "times", "as", "a", "list", "of", "named", "tuples", "." ]
def per_cpu_times(): """Return system per-CPU times as a list of named tuples.""" ret = [] for cpu_t in cext.per_cpu_times(): user, system, idle = cpu_t item = scputimes(user, system, idle) ret.append(item) return ret
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https://github.com/eventql/eventql/blob/7ca0dbb2e683b525620ea30dc40540a22d5eb227/deps/3rdparty/spidermonkey/mozjs/python/psutil/psutil/_pswindows.py#L135-L142
apache/incubator-mxnet
f03fb23f1d103fec9541b5ae59ee06b1734a51d9
python/mxnet/numpy/linalg.py
python
lstsq
(a, b, rcond='warn')
return _mx_nd_np.linalg.lstsq(a, b, rcond)
r""" Return the least-squares solution to a linear matrix equation. Solves the equation :math:`a x = b` by computing a vector `x` that minimizes the squared Euclidean 2-norm :math:`\| b - a x \|^2_2`. The equation may be under-, well-, or over-determined (i.e., the number of linearly independent rows of `a` can be less than, equal to, or greater than its number of linearly independent columns). If `a` is square and of full rank, then `x` (but for round-off error) is the "exact" solution of the equation. Parameters ---------- a : (M, N) ndarray "Coefficient" matrix. b : {(M,), (M, K)} ndarray Ordinate or "dependent variable" values. If `b` is two-dimensional, the least-squares solution is calculated for each of the `K` columns of `b`. rcond : float, optional Cut-off ratio for small singular values of `a`. For the purposes of rank determination, singular values are treated as zero if they are smaller than `rcond` times the largest singular value of `a` The default of ``warn`` or ``-1`` will use the machine precision as `rcond` parameter. The default of ``None`` will use the machine precision times `max(M, N)`. Returns ------- x : {(N,), (N, K)} ndarray Least-squares solution. If `b` is two-dimensional, the solutions are in the `K` columns of `x`. residuals : {(1,), (K,), (0,)} ndarray Sums of residuals. Squared Euclidean 2-norm for each column in ``b - a*x``. If the rank of `a` is < N or M <= N, this is an empty array. If `b` is 1-dimensional, this is a (1,) shape array. Otherwise the shape is (K,). rank : int Rank of matrix `a`. s : (min(M, N),) ndarray Singular values of `a`. Raises ------ MXNetError If computation does not converge. Notes ----- If `b` is a matrix, then all array results are returned as matrices. Examples -------- >>> x = np.array([0, 1, 2, 3]) >>> y = np.array([-1, 0.2, 0.9, 2.1]) >>> A = np.vstack([x, np.ones(len(x))]).T >>> A array([[ 0., 1.], [ 1., 1.], [ 2., 1.], [ 3., 1.]]) >>> m, c = np.linalg.lstsq(A, y, rcond=None)[0] >>> m, c (1.0 -0.95) # may vary
r""" Return the least-squares solution to a linear matrix equation.
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def lstsq(a, b, rcond='warn'): r""" Return the least-squares solution to a linear matrix equation. Solves the equation :math:`a x = b` by computing a vector `x` that minimizes the squared Euclidean 2-norm :math:`\| b - a x \|^2_2`. The equation may be under-, well-, or over-determined (i.e., the number of linearly independent rows of `a` can be less than, equal to, or greater than its number of linearly independent columns). If `a` is square and of full rank, then `x` (but for round-off error) is the "exact" solution of the equation. Parameters ---------- a : (M, N) ndarray "Coefficient" matrix. b : {(M,), (M, K)} ndarray Ordinate or "dependent variable" values. If `b` is two-dimensional, the least-squares solution is calculated for each of the `K` columns of `b`. rcond : float, optional Cut-off ratio for small singular values of `a`. For the purposes of rank determination, singular values are treated as zero if they are smaller than `rcond` times the largest singular value of `a` The default of ``warn`` or ``-1`` will use the machine precision as `rcond` parameter. The default of ``None`` will use the machine precision times `max(M, N)`. Returns ------- x : {(N,), (N, K)} ndarray Least-squares solution. If `b` is two-dimensional, the solutions are in the `K` columns of `x`. residuals : {(1,), (K,), (0,)} ndarray Sums of residuals. Squared Euclidean 2-norm for each column in ``b - a*x``. If the rank of `a` is < N or M <= N, this is an empty array. If `b` is 1-dimensional, this is a (1,) shape array. Otherwise the shape is (K,). rank : int Rank of matrix `a`. s : (min(M, N),) ndarray Singular values of `a`. Raises ------ MXNetError If computation does not converge. Notes ----- If `b` is a matrix, then all array results are returned as matrices. Examples -------- >>> x = np.array([0, 1, 2, 3]) >>> y = np.array([-1, 0.2, 0.9, 2.1]) >>> A = np.vstack([x, np.ones(len(x))]).T >>> A array([[ 0., 1.], [ 1., 1.], [ 2., 1.], [ 3., 1.]]) >>> m, c = np.linalg.lstsq(A, y, rcond=None)[0] >>> m, c (1.0 -0.95) # may vary """ return _mx_nd_np.linalg.lstsq(a, b, rcond)
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https://github.com/apache/incubator-mxnet/blob/f03fb23f1d103fec9541b5ae59ee06b1734a51d9/python/mxnet/numpy/linalg.py#L438-L506
apache/incubator-mxnet
f03fb23f1d103fec9541b5ae59ee06b1734a51d9
python/mxnet/image/image.py
python
CastAug.__call__
(self, src)
return src
Augmenter body
Augmenter body
[ "Augmenter", "body" ]
def __call__(self, src): """Augmenter body""" src = src.astype(self.typ) return src
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https://github.com/apache/incubator-mxnet/blob/f03fb23f1d103fec9541b5ae59ee06b1734a51d9/python/mxnet/image/image.py#L1165-L1168
catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/python/attrs/attr/_make.py
python
_CountingAttr.default
(self, meth)
return meth
Decorator that allows to set the default for an attribute. Returns *meth* unchanged. :raises DefaultAlreadySetError: If default has been set before. .. versionadded:: 17.1.0
Decorator that allows to set the default for an attribute.
[ "Decorator", "that", "allows", "to", "set", "the", "default", "for", "an", "attribute", "." ]
def default(self, meth): """ Decorator that allows to set the default for an attribute. Returns *meth* unchanged. :raises DefaultAlreadySetError: If default has been set before. .. versionadded:: 17.1.0 """ if self._default is not NOTHING: raise DefaultAlreadySetError() self._default = Factory(meth, takes_self=True) return meth
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/attrs/attr/_make.py#L2810-L2825
catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/tools/python/src/Lib/gzip.py
python
GzipFile.fileno
(self)
return self.fileobj.fileno()
Invoke the underlying file object's fileno() method. This will raise AttributeError if the underlying file object doesn't support fileno().
Invoke the underlying file object's fileno() method.
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def fileno(self): """Invoke the underlying file object's fileno() method. This will raise AttributeError if the underlying file object doesn't support fileno(). """ return self.fileobj.fileno()
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/tools/python/src/Lib/gzip.py#L394-L400
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/gtk/xrc.py
python
XmlNodeEasy
(*args, **kwargs)
return val
XmlNodeEasy(int type, String name, String content=EmptyString) -> XmlNode
XmlNodeEasy(int type, String name, String content=EmptyString) -> XmlNode
[ "XmlNodeEasy", "(", "int", "type", "String", "name", "String", "content", "=", "EmptyString", ")", "-", ">", "XmlNode" ]
def XmlNodeEasy(*args, **kwargs): """XmlNodeEasy(int type, String name, String content=EmptyString) -> XmlNode""" val = _xrc.new_XmlNodeEasy(*args, **kwargs) return val
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/gtk/xrc.py#L498-L501
lightvector/KataGo
20d34784703c5b4000643d3ccc43bb37d418f3b5
python/sgfmill/sgf.py
python
Node.get_setup_stones
(self)
return bp, wp, ep
Retrieve Add Black / Add White / Add Empty properties from a node. Returns a tuple (black_points, white_points, empty_points) Each value is a set of pairs (row, col).
Retrieve Add Black / Add White / Add Empty properties from a node.
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def get_setup_stones(self): """Retrieve Add Black / Add White / Add Empty properties from a node. Returns a tuple (black_points, white_points, empty_points) Each value is a set of pairs (row, col). """ try: bp = self.get("AB") except KeyError: bp = set() try: wp = self.get("AW") except KeyError: wp = set() try: ep = self.get("AE") except KeyError: ep = set() return bp, wp, ep
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https://github.com/lightvector/KataGo/blob/20d34784703c5b4000643d3ccc43bb37d418f3b5/python/sgfmill/sgf.py#L236-L256
snap-stanford/snap-python
d53c51b0a26aa7e3e7400b014cdf728948fde80a
setup/snap.py
python
TNEANet.IntAttrValueEI
(self, *args)
return _snap.TNEANet_IntAttrValueEI(self, *args)
IntAttrValueEI(TNEANet self, TInt EId, TIntV Values) Parameters: EId: TInt const & Values: TIntV & IntAttrValueEI(TNEANet self, TInt EId, TStrIntPrH::TIter EdgeHI, TIntV Values) Parameters: EId: TInt const & EdgeHI: TStrIntPrH::TIter Values: TIntV &
IntAttrValueEI(TNEANet self, TInt EId, TIntV Values)
[ "IntAttrValueEI", "(", "TNEANet", "self", "TInt", "EId", "TIntV", "Values", ")" ]
def IntAttrValueEI(self, *args): """ IntAttrValueEI(TNEANet self, TInt EId, TIntV Values) Parameters: EId: TInt const & Values: TIntV & IntAttrValueEI(TNEANet self, TInt EId, TStrIntPrH::TIter EdgeHI, TIntV Values) Parameters: EId: TInt const & EdgeHI: TStrIntPrH::TIter Values: TIntV & """ return _snap.TNEANet_IntAttrValueEI(self, *args)
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https://github.com/snap-stanford/snap-python/blob/d53c51b0a26aa7e3e7400b014cdf728948fde80a/setup/snap.py#L21635-L21651
RGF-team/rgf
272afb85b4c91571f576e5fc83ecfacce3672eb4
python-package/rgf/utils.py
python
RGFClassifierMixin.predict
(self, X)
return np.asarray(list(self._classes_map.values()))[np.searchsorted(list(self._classes_map.keys()), y)]
Predict class for X. The predicted class of an input sample is computed. Parameters ---------- X : array-like or sparse matrix of shape = [n_samples, n_features] The input samples. Returns ------- y : array of shape = [n_samples] The predicted classes.
Predict class for X.
[ "Predict", "class", "for", "X", "." ]
def predict(self, X): """ Predict class for X. The predicted class of an input sample is computed. Parameters ---------- X : array-like or sparse matrix of shape = [n_samples, n_features] The input samples. Returns ------- y : array of shape = [n_samples] The predicted classes. """ y = self.predict_proba(X) y = np.argmax(y, axis=1) return np.asarray(list(self._classes_map.values()))[np.searchsorted(list(self._classes_map.keys()), y)]
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https://github.com/RGF-team/rgf/blob/272afb85b4c91571f576e5fc83ecfacce3672eb4/python-package/rgf/utils.py#L569-L587
microsoft/TSS.MSR
0f2516fca2cd9929c31d5450e39301c9bde43688
TSS.Py/src/TpmTypes.py
python
TPM2B_DIGEST_SYMCIPHER.fromTpm
(buf)
return buf.createObj(TPM2B_DIGEST_SYMCIPHER)
Returns new TPM2B_DIGEST_SYMCIPHER object constructed from its marshaled representation in the given TpmBuffer buffer
Returns new TPM2B_DIGEST_SYMCIPHER object constructed from its marshaled representation in the given TpmBuffer buffer
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def fromTpm(buf): """ Returns new TPM2B_DIGEST_SYMCIPHER object constructed from its marshaled representation in the given TpmBuffer buffer """ return buf.createObj(TPM2B_DIGEST_SYMCIPHER)
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https://github.com/microsoft/TSS.MSR/blob/0f2516fca2cd9929c31d5450e39301c9bde43688/TSS.Py/src/TpmTypes.py#L18248-L18252
devosoft/avida
c6179ffc617fdbc962b5a9c4de3e889e0ef2d94c
avida-core/support/utils/AvidaUtils/BoostPythonTool.py
python
generate
(env)
Adds builders and construction variables for Boost.Python to an Environment.
Adds builders and construction variables for Boost.Python to an Environment.
[ "Adds", "builders", "and", "construction", "variables", "for", "Boost", ".", "Python", "to", "an", "Environment", "." ]
def generate(env): """ Adds builders and construction variables for Boost.Python to an Environment. """ env.SetDefault( BOOST_PYTHON_TOOL_ERR = '', BOOST_PYTHON_CPPFLAGS = ['$PYTHON_BASECFLAGS'], BOOST_PYTHON_CPPDEFINES = [], BOOST_PYTHON_CPPPATH = ['$boostIncludeDir', '$PYTHON_INCLUDEPY'], BOOST_PYTHON_CXX_SUFFIX = '.boost.python.cpp', BOOST_PYTHON_LIBPATH = ['$boostPythonLibDir'], BOOST_PYTHON_LIBS = ['$boostPythonLib'], BOOST_PYTHON_SHLINK = '$PYTHON_LDSHARED', BOOST_PYTHON_LDMODULE = '$BOOST_PYTHON_SHLINK', BOOST_PYTHON_LDMODULEFLAGS = '', _BOOST_PYTHON_CPPINCFLAGS = '$( ${_concat(INCPREFIX, BOOST_PYTHON_CPPPATH, INCSUFFIX, __env__, RDirs, TARGET)} $)', _BOOST_PYTHON_CPPDEFFLAGS = '${_defines(CPPDEFPREFIX, BOOST_PYTHON_CPPDEFINES, CPPDEFSUFFIX, __env__)}', _BOOST_PYTHON_LIBFLAGS = '${_stripixes(LIBLINKPREFIX, BOOST_PYTHON_LIBS, LIBLINKSUFFIX, LIBPREFIX, LIBSUFFIX, __env__)}', _BOOST_PYTHON_LIBDIRFLAGS = '$( ${_concat(LIBDIRPREFIX, BOOST_PYTHON_LIBPATH, LIBDIRSUFFIX, __env__, RDirs, TARGET)} $)', BOOST_PYTHON_SHCXXCOM = '$SHCXX $SHCXXFLAGS $CPPFLAGS $BOOST_PYTHON_CPPFLAGS $_CPPDEFFLAGS $_BOOST_PYTHON_CPPDEFFLAGS $_CPPINCFLAGS $_BOOST_PYTHON_CPPINCFLAGS -c -o $TARGET $SOURCES', BOOST_PYTHON_LDMODULECOM = '$BOOST_PYTHON_LDMODULE $BOOST_PYTHON_LDMODULEFLAGS -o ${TARGET} $SOURCES $_LIBDIRFLAGS $_BOOST_PYTHON_LIBDIRFLAGS $_LIBFLAGS $_BOOST_PYTHON_LIBFLAGS', ) boost_python_ld_module_link_action = SCons.Action.Action("$BOOST_PYTHON_LDMODULECOM", "$BOOST_PYTHON_LDMODULECOMSTR") boost_python_shared_object_builder = SCons.Builder.Builder( action = [ ShCXXAction ], prefix = '$SHOBJPREFIX', suffix = '$SHOBJSUFFIX', src_suffix = '$BOOST_PYTHON_CXX_SUFFIX', source_scanner = SCons.Tool.SourceFileScanner, single_source = True ) boost_python_module_builder = SCons.Builder.Builder( action = [ LdModuleLinkAction ], prefix = '', suffix = '$PYTHON_SO', target_scanner = SCons.Tool.ProgramScanner, src_suffix = '$SHOBJSUFFIX', src_builder = 'BoostPythonSharedObject', single_source = True ) env.AppendUnique(BUILDERS = {'BoostPythonSharedObject' : boost_python_shared_object_builder}) env.AppendUnique(BUILDERS = {'BoostPythonModule' : boost_python_module_builder}) if env.subst('$runConfTests') in ['yes', '1']: find(env)
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https://github.com/devosoft/avida/blob/c6179ffc617fdbc962b5a9c4de3e889e0ef2d94c/avida-core/support/utils/AvidaUtils/BoostPythonTool.py#L83-L129
catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/python/scipy/py3/scipy/cluster/vq.py
python
_missing_warn
()
Print a warning when called.
Print a warning when called.
[ "Print", "a", "warning", "when", "called", "." ]
def _missing_warn(): """Print a warning when called.""" warnings.warn("One of the clusters is empty. " "Re-run kmeans with a different initialization.")
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/scipy/py3/scipy/cluster/vq.py#L578-L581
catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/tools/python3/src/Lib/runpy.py
python
_run_code
(code, run_globals, init_globals=None, mod_name=None, mod_spec=None, pkg_name=None, script_name=None)
return run_globals
Helper to run code in nominated namespace
Helper to run code in nominated namespace
[ "Helper", "to", "run", "code", "in", "nominated", "namespace" ]
def _run_code(code, run_globals, init_globals=None, mod_name=None, mod_spec=None, pkg_name=None, script_name=None): """Helper to run code in nominated namespace""" if init_globals is not None: run_globals.update(init_globals) if mod_spec is None: loader = None fname = script_name cached = None else: loader = mod_spec.loader fname = mod_spec.origin cached = mod_spec.cached if pkg_name is None: pkg_name = mod_spec.parent run_globals.update(__name__ = mod_name, __file__ = fname, __cached__ = cached, __doc__ = None, __loader__ = loader, __package__ = pkg_name, __spec__ = mod_spec) exec(code, run_globals) return run_globals
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/tools/python3/src/Lib/runpy.py#L64-L88
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/osx_carbon/combo.py
python
ComboPopup._setCallbackInfo
(*args, **kwargs)
return _combo.ComboPopup__setCallbackInfo(*args, **kwargs)
_setCallbackInfo(self, PyObject self, PyObject _class)
_setCallbackInfo(self, PyObject self, PyObject _class)
[ "_setCallbackInfo", "(", "self", "PyObject", "self", "PyObject", "_class", ")" ]
def _setCallbackInfo(*args, **kwargs): """_setCallbackInfo(self, PyObject self, PyObject _class)""" return _combo.ComboPopup__setCallbackInfo(*args, **kwargs)
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_carbon/combo.py#L607-L609
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/pandas/core/series.py
python
Series.sort_values
( self, axis=0, ascending=True, inplace=False, kind="quicksort", na_position="last", ignore_index=False, )
Sort by the values. Sort a Series in ascending or descending order by some criterion. Parameters ---------- axis : {0 or 'index'}, default 0 Axis to direct sorting. The value 'index' is accepted for compatibility with DataFrame.sort_values. ascending : bool, default True If True, sort values in ascending order, otherwise descending. inplace : bool, default False If True, perform operation in-place. kind : {'quicksort', 'mergesort' or 'heapsort'}, default 'quicksort' Choice of sorting algorithm. See also :func:`numpy.sort` for more information. 'mergesort' is the only stable algorithm. na_position : {'first' or 'last'}, default 'last' Argument 'first' puts NaNs at the beginning, 'last' puts NaNs at the end. ignore_index : bool, default False If True, the resulting axis will be labeled 0, 1, …, n - 1. .. versionadded:: 1.0.0 Returns ------- Series Series ordered by values. See Also -------- Series.sort_index : Sort by the Series indices. DataFrame.sort_values : Sort DataFrame by the values along either axis. DataFrame.sort_index : Sort DataFrame by indices. Examples -------- >>> s = pd.Series([np.nan, 1, 3, 10, 5]) >>> s 0 NaN 1 1.0 2 3.0 3 10.0 4 5.0 dtype: float64 Sort values ascending order (default behaviour) >>> s.sort_values(ascending=True) 1 1.0 2 3.0 4 5.0 3 10.0 0 NaN dtype: float64 Sort values descending order >>> s.sort_values(ascending=False) 3 10.0 4 5.0 2 3.0 1 1.0 0 NaN dtype: float64 Sort values inplace >>> s.sort_values(ascending=False, inplace=True) >>> s 3 10.0 4 5.0 2 3.0 1 1.0 0 NaN dtype: float64 Sort values putting NAs first >>> s.sort_values(na_position='first') 0 NaN 1 1.0 2 3.0 4 5.0 3 10.0 dtype: float64 Sort a series of strings >>> s = pd.Series(['z', 'b', 'd', 'a', 'c']) >>> s 0 z 1 b 2 d 3 a 4 c dtype: object >>> s.sort_values() 3 a 1 b 4 c 2 d 0 z dtype: object
Sort by the values.
[ "Sort", "by", "the", "values", "." ]
def sort_values( self, axis=0, ascending=True, inplace=False, kind="quicksort", na_position="last", ignore_index=False, ): """ Sort by the values. Sort a Series in ascending or descending order by some criterion. Parameters ---------- axis : {0 or 'index'}, default 0 Axis to direct sorting. The value 'index' is accepted for compatibility with DataFrame.sort_values. ascending : bool, default True If True, sort values in ascending order, otherwise descending. inplace : bool, default False If True, perform operation in-place. kind : {'quicksort', 'mergesort' or 'heapsort'}, default 'quicksort' Choice of sorting algorithm. See also :func:`numpy.sort` for more information. 'mergesort' is the only stable algorithm. na_position : {'first' or 'last'}, default 'last' Argument 'first' puts NaNs at the beginning, 'last' puts NaNs at the end. ignore_index : bool, default False If True, the resulting axis will be labeled 0, 1, …, n - 1. .. versionadded:: 1.0.0 Returns ------- Series Series ordered by values. See Also -------- Series.sort_index : Sort by the Series indices. DataFrame.sort_values : Sort DataFrame by the values along either axis. DataFrame.sort_index : Sort DataFrame by indices. Examples -------- >>> s = pd.Series([np.nan, 1, 3, 10, 5]) >>> s 0 NaN 1 1.0 2 3.0 3 10.0 4 5.0 dtype: float64 Sort values ascending order (default behaviour) >>> s.sort_values(ascending=True) 1 1.0 2 3.0 4 5.0 3 10.0 0 NaN dtype: float64 Sort values descending order >>> s.sort_values(ascending=False) 3 10.0 4 5.0 2 3.0 1 1.0 0 NaN dtype: float64 Sort values inplace >>> s.sort_values(ascending=False, inplace=True) >>> s 3 10.0 4 5.0 2 3.0 1 1.0 0 NaN dtype: float64 Sort values putting NAs first >>> s.sort_values(na_position='first') 0 NaN 1 1.0 2 3.0 4 5.0 3 10.0 dtype: float64 Sort a series of strings >>> s = pd.Series(['z', 'b', 'd', 'a', 'c']) >>> s 0 z 1 b 2 d 3 a 4 c dtype: object >>> s.sort_values() 3 a 1 b 4 c 2 d 0 z dtype: object """ inplace = validate_bool_kwarg(inplace, "inplace") # Validate the axis parameter self._get_axis_number(axis) # GH 5856/5853 if inplace and self._is_cached: raise ValueError( "This Series is a view of some other array, to " "sort in-place you must create a copy" ) def _try_kind_sort(arr): # easier to ask forgiveness than permission try: # if kind==mergesort, it can fail for object dtype return arr.argsort(kind=kind) except TypeError: # stable sort not available for object dtype # uses the argsort default quicksort return arr.argsort(kind="quicksort") arr = self._values sorted_index = np.empty(len(self), dtype=np.int32) bad = isna(arr) good = ~bad idx = ibase.default_index(len(self)) argsorted = _try_kind_sort(arr[good]) if is_list_like(ascending): if len(ascending) != 1: raise ValueError( f"Length of ascending ({len(ascending)}) must be 1 for Series" ) ascending = ascending[0] if not is_bool(ascending): raise ValueError("ascending must be boolean") if not ascending: argsorted = argsorted[::-1] if na_position == "last": n = good.sum() sorted_index[:n] = idx[good][argsorted] sorted_index[n:] = idx[bad] elif na_position == "first": n = bad.sum() sorted_index[n:] = idx[good][argsorted] sorted_index[:n] = idx[bad] else: raise ValueError(f"invalid na_position: {na_position}") result = self._constructor(arr[sorted_index], index=self.index[sorted_index]) if ignore_index: result.index = ibase.default_index(len(sorted_index)) if inplace: self._update_inplace(result) else: return result.__finalize__(self)
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/pandas/core/series.py#L2816-L2996
facebook/wangle
2e7e3fbb3a15c4986d6fe0e36c31daeeba614ce3
build/fbcode_builder/fbcode_builder.py
python
FBCodeBuilder.fb_github_project_workdir
(self, project_and_path, github_org="facebook")
return self.github_project_workdir(github_org + "/" + project, path)
This helper lets Facebook-internal CI special-cases FB projects
This helper lets Facebook-internal CI special-cases FB projects
[ "This", "helper", "lets", "Facebook", "-", "internal", "CI", "special", "-", "cases", "FB", "projects" ]
def fb_github_project_workdir(self, project_and_path, github_org="facebook"): "This helper lets Facebook-internal CI special-cases FB projects" project, path = project_and_path.split("/", 1) return self.github_project_workdir(github_org + "/" + project, path)
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https://github.com/facebook/wangle/blob/2e7e3fbb3a15c4986d6fe0e36c31daeeba614ce3/build/fbcode_builder/fbcode_builder.py#L393-L396
tensorflow/tensorflow
419e3a6b650ea4bd1b0cba23c4348f8a69f3272e
tensorflow/python/ops/tensor_array_ops.py
python
_GraphTensorArrayV2._check_element_shape
(self, shape)
Changes the element shape of the array given a shape to merge with. Args: shape: A `TensorShape` object to merge with. Raises: ValueError: if the provided shape is incompatible with the current element shape of the `TensorArray`.
Changes the element shape of the array given a shape to merge with.
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def _check_element_shape(self, shape): """Changes the element shape of the array given a shape to merge with. Args: shape: A `TensorShape` object to merge with. Raises: ValueError: if the provided shape is incompatible with the current element shape of the `TensorArray`. """ if not shape.is_compatible_with(self.element_shape): raise ValueError("Inconsistent shapes: saw %s but expected %s " % (shape, self.element_shape)) if self._infer_shape: self._element_shape[0] = self.element_shape.merge_with(shape)
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https://github.com/tensorflow/tensorflow/blob/419e3a6b650ea4bd1b0cba23c4348f8a69f3272e/tensorflow/python/ops/tensor_array_ops.py#L501-L515
Xilinx/Vitis-AI
fc74d404563d9951b57245443c73bef389f3657f
tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/losses/python/metric_learning/metric_loss_ops.py
python
npairs_loss_multilabel
(sparse_labels, embeddings_anchor, embeddings_positive, reg_lambda=0.002, print_losses=False)
r"""Computes the npairs loss with multilabel data. Npairs loss expects paired data where a pair is composed of samples from the same labels and each pairs in the minibatch have different labels. The loss has two components. The first component is the L2 regularizer on the embedding vectors. The second component is the sum of cross entropy loss which takes each row of the pair-wise similarity matrix as logits and the remapped one-hot labels as labels. Here, the similarity is defined by the dot product between two embedding vectors. S_{i,j} = f(x_i)^T f(x_j) To deal with multilabel inputs, we use the count of label intersection i.e. L_{i,j} = | set_of_labels_for(i) \cap set_of_labels_for(j) | Then we normalize each rows of the count based label matrix so that each row sums to one. Args: sparse_labels: List of 1-D Boolean `SparseTensor` of dense_shape [batch_size/2, num_classes] labels for the anchor-pos pairs. embeddings_anchor: 2-D `Tensor` of shape [batch_size/2, embedding_dim] for the embedding vectors for the anchor images. Embeddings should not be l2 normalized. embeddings_positive: 2-D `Tensor` of shape [batch_size/2, embedding_dim] for the embedding vectors for the positive images. Embeddings should not be l2 normalized. reg_lambda: Float. L2 regularization term on the embedding vectors. print_losses: Boolean. Option to print the xent and l2loss. Returns: npairs_loss: tf.float32 scalar. Raises: TypeError: When the specified sparse_labels is not a `SparseTensor`.
r"""Computes the npairs loss with multilabel data.
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def npairs_loss_multilabel(sparse_labels, embeddings_anchor, embeddings_positive, reg_lambda=0.002, print_losses=False): r"""Computes the npairs loss with multilabel data. Npairs loss expects paired data where a pair is composed of samples from the same labels and each pairs in the minibatch have different labels. The loss has two components. The first component is the L2 regularizer on the embedding vectors. The second component is the sum of cross entropy loss which takes each row of the pair-wise similarity matrix as logits and the remapped one-hot labels as labels. Here, the similarity is defined by the dot product between two embedding vectors. S_{i,j} = f(x_i)^T f(x_j) To deal with multilabel inputs, we use the count of label intersection i.e. L_{i,j} = | set_of_labels_for(i) \cap set_of_labels_for(j) | Then we normalize each rows of the count based label matrix so that each row sums to one. Args: sparse_labels: List of 1-D Boolean `SparseTensor` of dense_shape [batch_size/2, num_classes] labels for the anchor-pos pairs. embeddings_anchor: 2-D `Tensor` of shape [batch_size/2, embedding_dim] for the embedding vectors for the anchor images. Embeddings should not be l2 normalized. embeddings_positive: 2-D `Tensor` of shape [batch_size/2, embedding_dim] for the embedding vectors for the positive images. Embeddings should not be l2 normalized. reg_lambda: Float. L2 regularization term on the embedding vectors. print_losses: Boolean. Option to print the xent and l2loss. Returns: npairs_loss: tf.float32 scalar. Raises: TypeError: When the specified sparse_labels is not a `SparseTensor`. """ if False in [isinstance( l, sparse_tensor.SparseTensor) for l in sparse_labels]: raise TypeError( 'sparse_labels must be a list of SparseTensors, but got %s' % str( sparse_labels)) with ops.name_scope('NpairsLossMultiLabel'): # Add the regularizer on the embedding. reg_anchor = math_ops.reduce_mean( math_ops.reduce_sum(math_ops.square(embeddings_anchor), 1)) reg_positive = math_ops.reduce_mean( math_ops.reduce_sum(math_ops.square(embeddings_positive), 1)) l2loss = math_ops.multiply(0.25 * reg_lambda, reg_anchor + reg_positive, name='l2loss') # Get per pair similarities. similarity_matrix = math_ops.matmul( embeddings_anchor, embeddings_positive, transpose_a=False, transpose_b=True) # TODO(coreylynch): need to check the sparse values # TODO(coreylynch): are composed only of 0's and 1's. multilabel_adjacency_matrix = _build_multilabel_adjacency(sparse_labels) labels_remapped = math_ops.cast(multilabel_adjacency_matrix, dtypes.float32) labels_remapped /= math_ops.reduce_sum(labels_remapped, 1, keepdims=True) # Add the softmax loss. xent_loss = nn.softmax_cross_entropy_with_logits( logits=similarity_matrix, labels=labels_remapped) xent_loss = math_ops.reduce_mean(xent_loss, name='xentropy') if print_losses: xent_loss = logging_ops.Print( xent_loss, ['cross entropy:', xent_loss, 'l2loss:', l2loss]) return l2loss + xent_loss
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https://github.com/Xilinx/Vitis-AI/blob/fc74d404563d9951b57245443c73bef389f3657f/tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/losses/python/metric_learning/metric_loss_ops.py#L337-L408
Slicer/Slicer
ba9fadf332cb0303515b68d8d06a344c82e3e3e5
Modules/Scripted/DICOMLib/DICOMUtils.py
python
getDatabasePatientUIDByPatientName
(name)
return None
Get patient UID by patient name for easy loading of a patient
Get patient UID by patient name for easy loading of a patient
[ "Get", "patient", "UID", "by", "patient", "name", "for", "easy", "loading", "of", "a", "patient" ]
def getDatabasePatientUIDByPatientName(name): """ Get patient UID by patient name for easy loading of a patient """ if not slicer.dicomDatabase.isOpen: raise OSError('DICOM module or database cannot be accessed') patients = slicer.dicomDatabase.patients() for patientUID in patients: currentName = slicer.dicomDatabase.nameForPatient(patientUID) if currentName == name: return patientUID return None
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https://github.com/Slicer/Slicer/blob/ba9fadf332cb0303515b68d8d06a344c82e3e3e5/Modules/Scripted/DICOMLib/DICOMUtils.py#L76-L87
baidu-research/tensorflow-allreduce
66d5b855e90b0949e9fa5cca5599fd729a70e874
tensorflow/python/framework/sparse_tensor.py
python
SparseTensor.indices
(self)
return self._indices
The indices of non-zero values in the represented dense tensor. Returns: A 2-D Tensor of int64 with dense_shape `[N, ndims]`, where `N` is the number of non-zero values in the tensor, and `ndims` is the rank.
The indices of non-zero values in the represented dense tensor.
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def indices(self): """The indices of non-zero values in the represented dense tensor. Returns: A 2-D Tensor of int64 with dense_shape `[N, ndims]`, where `N` is the number of non-zero values in the tensor, and `ndims` is the rank. """ return self._indices
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https://github.com/baidu-research/tensorflow-allreduce/blob/66d5b855e90b0949e9fa5cca5599fd729a70e874/tensorflow/python/framework/sparse_tensor.py#L151-L158
Xilinx/Vitis-AI
fc74d404563d9951b57245443c73bef389f3657f
tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/ops/signal/util_ops.py
python
gcd
(a, b, name=None)
Returns the greatest common divisor via Euclid's algorithm. Args: a: The dividend. A scalar integer `Tensor`. b: The divisor. A scalar integer `Tensor`. name: An optional name for the operation. Returns: A scalar `Tensor` representing the greatest common divisor between `a` and `b`. Raises: ValueError: If `a` or `b` are not scalar integers.
Returns the greatest common divisor via Euclid's algorithm.
[ "Returns", "the", "greatest", "common", "divisor", "via", "Euclid", "s", "algorithm", "." ]
def gcd(a, b, name=None): """Returns the greatest common divisor via Euclid's algorithm. Args: a: The dividend. A scalar integer `Tensor`. b: The divisor. A scalar integer `Tensor`. name: An optional name for the operation. Returns: A scalar `Tensor` representing the greatest common divisor between `a` and `b`. Raises: ValueError: If `a` or `b` are not scalar integers. """ with ops.name_scope(name, 'gcd', [a, b]): a = ops.convert_to_tensor(a) b = ops.convert_to_tensor(b) a.shape.assert_has_rank(0) b.shape.assert_has_rank(0) if not a.dtype.is_integer: raise ValueError('a must be an integer type. Got: %s' % a.dtype) if not b.dtype.is_integer: raise ValueError('b must be an integer type. Got: %s' % b.dtype) # TPU requires static shape inference. GCD is used for subframe size # computation, so we should prefer static computation where possible. const_a = tensor_util.constant_value(a) const_b = tensor_util.constant_value(b) if const_a is not None and const_b is not None: return ops.convert_to_tensor(fractions.gcd(const_a, const_b)) cond = lambda _, b: math_ops.greater(b, array_ops.zeros_like(b)) body = lambda a, b: [b, math_ops.mod(a, b)] a, b = control_flow_ops.while_loop(cond, body, [a, b], back_prop=False) return a
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https://github.com/Xilinx/Vitis-AI/blob/fc74d404563d9951b57245443c73bef389f3657f/tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/ops/signal/util_ops.py#L30-L67
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/osx_cocoa/stc.py
python
StyledTextCtrl.SetEmptySelection
(*args, **kwargs)
return _stc.StyledTextCtrl_SetEmptySelection(*args, **kwargs)
SetEmptySelection(self, int pos)
SetEmptySelection(self, int pos)
[ "SetEmptySelection", "(", "self", "int", "pos", ")" ]
def SetEmptySelection(*args, **kwargs): """SetEmptySelection(self, int pos)""" return _stc.StyledTextCtrl_SetEmptySelection(*args, **kwargs)
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_cocoa/stc.py#L3460-L3462
ricardoquesada/Spidermonkey
4a75ea2543408bd1b2c515aa95901523eeef7858
python/configobj/configobj.py
python
Section.as_bool
(self, key)
Accepts a key as input. The corresponding value must be a string or the objects (``True`` or 1) or (``False`` or 0). We allow 0 and 1 to retain compatibility with Python 2.2. If the string is one of ``True``, ``On``, ``Yes``, or ``1`` it returns ``True``. If the string is one of ``False``, ``Off``, ``No``, or ``0`` it returns ``False``. ``as_bool`` is not case sensitive. Any other input will raise a ``ValueError``. >>> a = ConfigObj() >>> a['a'] = 'fish' >>> a.as_bool('a') Traceback (most recent call last): ValueError: Value "fish" is neither True nor False >>> a['b'] = 'True' >>> a.as_bool('b') 1 >>> a['b'] = 'off' >>> a.as_bool('b') 0
Accepts a key as input. The corresponding value must be a string or the objects (``True`` or 1) or (``False`` or 0). We allow 0 and 1 to retain compatibility with Python 2.2. If the string is one of ``True``, ``On``, ``Yes``, or ``1`` it returns ``True``. If the string is one of ``False``, ``Off``, ``No``, or ``0`` it returns ``False``. ``as_bool`` is not case sensitive. Any other input will raise a ``ValueError``. >>> a = ConfigObj() >>> a['a'] = 'fish' >>> a.as_bool('a') Traceback (most recent call last): ValueError: Value "fish" is neither True nor False >>> a['b'] = 'True' >>> a.as_bool('b') 1 >>> a['b'] = 'off' >>> a.as_bool('b') 0
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def as_bool(self, key): """ Accepts a key as input. The corresponding value must be a string or the objects (``True`` or 1) or (``False`` or 0). We allow 0 and 1 to retain compatibility with Python 2.2. If the string is one of ``True``, ``On``, ``Yes``, or ``1`` it returns ``True``. If the string is one of ``False``, ``Off``, ``No``, or ``0`` it returns ``False``. ``as_bool`` is not case sensitive. Any other input will raise a ``ValueError``. >>> a = ConfigObj() >>> a['a'] = 'fish' >>> a.as_bool('a') Traceback (most recent call last): ValueError: Value "fish" is neither True nor False >>> a['b'] = 'True' >>> a.as_bool('b') 1 >>> a['b'] = 'off' >>> a.as_bool('b') 0 """ val = self[key] if val == True: return True elif val == False: return False else: try: if not isinstance(val, basestring): # TODO: Why do we raise a KeyError here? raise KeyError() else: return self.main._bools[val.lower()] except KeyError: raise ValueError('Value "%s" is neither True nor False' % val)
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https://github.com/ricardoquesada/Spidermonkey/blob/4a75ea2543408bd1b2c515aa95901523eeef7858/python/configobj/configobj.py#L940-L981
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/pkg_resources/__init__.py
python
Environment.can_add
(self, dist)
return py_compat and compatible_platforms(dist.platform, self.platform)
Is distribution `dist` acceptable for this environment? The distribution must match the platform and python version requirements specified when this environment was created, or False is returned.
Is distribution `dist` acceptable for this environment?
[ "Is", "distribution", "dist", "acceptable", "for", "this", "environment?" ]
def can_add(self, dist): """Is distribution `dist` acceptable for this environment? The distribution must match the platform and python version requirements specified when this environment was created, or False is returned. """ py_compat = ( self.python is None or dist.py_version is None or dist.py_version == self.python ) return py_compat and compatible_platforms(dist.platform, self.platform)
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/pkg_resources/__init__.py#L987-L999
FreeCAD/FreeCAD
ba42231b9c6889b89e064d6d563448ed81e376ec
src/Mod/Import/App/ap203_configuration_controlled_3d_design_of_mechanical_parts_and_assemblies_mim_lf.py
python
gbsf_check_point
(pnt,)
return FALSE
:param pnt :type pnt:point
:param pnt :type pnt:point
[ ":", "param", "pnt", ":", "type", "pnt", ":", "point" ]
def gbsf_check_point(pnt,): ''' :param pnt :type pnt:point ''' if ('AP203_CONFIGURATION_CONTROLLED_3D_DESIGN_OF_MECHANICAL_PARTS_AND_ASSEMBLIES_MIM_LF.CARTESIAN_POINT' == TYPEOF(pnt)): return TRUE else: if ('AP203_CONFIGURATION_CONTROLLED_3D_DESIGN_OF_MECHANICAL_PARTS_AND_ASSEMBLIES_MIM_LF.POINT_ON_CURVE' == TYPEOF(pnt)): return gbsf_check_curve(pnt.point_on_curve.basis_curve) else: if ('AP203_CONFIGURATION_CONTROLLED_3D_DESIGN_OF_MECHANICAL_PARTS_AND_ASSEMBLIES_MIM_LF.POINT_ON_SURFACE' == TYPEOF(pnt)): return gbsf_check_surface(pnt.point_on_surface.basis_surface) else: if ('AP203_CONFIGURATION_CONTROLLED_3D_DESIGN_OF_MECHANICAL_PARTS_AND_ASSEMBLIES_MIM_LF.DEGENERATE_PCURVE' == TYPEOF(pnt)): return gbsf_check_curve(pnt.degenerate_pcurve.reference_to_curve.representation.items[1]) and gbsf_check_surface(pnt.degenerate_pcurve.basis_surface) return FALSE
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https://github.com/FreeCAD/FreeCAD/blob/ba42231b9c6889b89e064d6d563448ed81e376ec/src/Mod/Import/App/ap203_configuration_controlled_3d_design_of_mechanical_parts_and_assemblies_mim_lf.py#L39463-L39479
jackaudio/jack2
21b293dbc37d42446141a08922cdec0d2550c6a0
waflib/Tools/ar.py
python
configure
(conf)
Finds the ar program and sets the default flags in ``conf.env.ARFLAGS``
Finds the ar program and sets the default flags in ``conf.env.ARFLAGS``
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def configure(conf): """Finds the ar program and sets the default flags in ``conf.env.ARFLAGS``""" conf.find_program('ar', var='AR') conf.add_os_flags('ARFLAGS') if not conf.env.ARFLAGS: conf.env.ARFLAGS = ['rcs']
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https://github.com/jackaudio/jack2/blob/21b293dbc37d42446141a08922cdec0d2550c6a0/waflib/Tools/ar.py#L18-L23
albertz/openlierox
d316c14a8eb57848ef56e9bfa7b23a56f694a51b
tools/DedicatedServerVideo/gdata/sites/client.py
python
SitesClient.download_attachment
(self, uri_or_entry, file_path)
Downloads an attachment file to disk. Args: uri_or_entry: string The full URL to download the file from. file_path: string The full path to save the file to. Raises: gdata.client.RequestError: on error response from server.
Downloads an attachment file to disk.
[ "Downloads", "an", "attachment", "file", "to", "disk", "." ]
def download_attachment(self, uri_or_entry, file_path): """Downloads an attachment file to disk. Args: uri_or_entry: string The full URL to download the file from. file_path: string The full path to save the file to. Raises: gdata.client.RequestError: on error response from server. """ uri = uri_or_entry if isinstance(uri_or_entry, gdata.sites.data.ContentEntry): uri = uri_or_entry.content.src f = open(file_path, 'wb') try: f.write(self._get_file_content(uri)) except gdata.client.RequestError, e: f.close() raise e f.flush() f.close()
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https://github.com/albertz/openlierox/blob/d316c14a8eb57848ef56e9bfa7b23a56f694a51b/tools/DedicatedServerVideo/gdata/sites/client.py#L339-L360
baidu/AnyQ
d94d450d2aaa5f7ed73424b10aa4539835b97527
tools/simnet/train/paddle/nets/gru.py
python
GRU.predict
(self, left, right)
Forward network
Forward network
[ "Forward", "network" ]
def predict(self, left, right): """ Forward network """ # embedding layer emb_layer = layers.EmbeddingLayer(self.dict_size, self.emb_dim, "emb") left_emb = emb_layer.ops(left) right_emb = emb_layer.ops(right) # Presentation context gru_layer = layers.DynamicGRULayer(self.gru_dim, "gru") left_gru = gru_layer.ops(left_emb) right_gru = gru_layer.ops(right_emb) last_layer = layers.SequenceLastStepLayer() left_last = last_layer.ops(left_gru) right_last = last_layer.ops(right_gru) # matching layer if self.task_mode == "pairwise": relu_layer = layers.FCLayer(self.hidden_dim, "relu", "relu") left_relu = relu_layer.ops(left_last) right_relu = relu_layer.ops(right_last) cos_sim_layer = layers.CosSimLayer() pred = cos_sim_layer.ops(left_relu, right_relu) return left_relu, pred else: concat_layer = layers.ConcatLayer(1) concat = concat_layer.ops([left_last, right_last]) relu_layer = layers.FCLayer(self.hidden_dim, "relu", "relu") concat_fc = relu_layer.ops(concat) softmax_layer = layers.FCLayer(2, "softmax", "cos_sim") pred = softmax_layer.ops(concat_fc) return left_last, pred
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https://github.com/baidu/AnyQ/blob/d94d450d2aaa5f7ed73424b10aa4539835b97527/tools/simnet/train/paddle/nets/gru.py#L34-L64
microsoft/checkedc-clang
a173fefde5d7877b7750e7ce96dd08cf18baebf2
lldb/third_party/Python/module/pexpect-4.6/pexpect/screen.py
python
screen.put
(self, ch)
This puts a characters at the current cursor position.
This puts a characters at the current cursor position.
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def put (self, ch): '''This puts a characters at the current cursor position. ''' if isinstance(ch, bytes): ch = self._decode(ch) self.put_abs (self.cur_r, self.cur_c, ch)
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https://github.com/microsoft/checkedc-clang/blob/a173fefde5d7877b7750e7ce96dd08cf18baebf2/lldb/third_party/Python/module/pexpect-4.6/pexpect/screen.py#L211-L218
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numba/targets/listobj.py
python
_ListPayloadMixin.clamp_index
(self, idx)
return builder.load(idxptr)
Clamp the index in [0, size].
Clamp the index in [0, size].
[ "Clamp", "the", "index", "in", "[", "0", "size", "]", "." ]
def clamp_index(self, idx): """ Clamp the index in [0, size]. """ builder = self._builder idxptr = cgutils.alloca_once_value(builder, idx) zero = ir.Constant(idx.type, 0) size = self.size underflow = self._builder.icmp_signed('<', idx, zero) with builder.if_then(underflow, likely=False): builder.store(zero, idxptr) overflow = self._builder.icmp_signed('>=', idx, size) with builder.if_then(overflow, likely=False): builder.store(size, idxptr) return builder.load(idxptr)
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numba/targets/listobj.py#L86-L103
pytorch/pytorch
7176c92687d3cc847cc046bf002269c6949a21c2
torch/optim/optimizer.py
python
Optimizer.zero_grad
(self, set_to_none: bool = False)
r"""Sets the gradients of all optimized :class:`torch.Tensor` s to zero. Args: set_to_none (bool): instead of setting to zero, set the grads to None. This will in general have lower memory footprint, and can modestly improve performance. However, it changes certain behaviors. For example: 1. When the user tries to access a gradient and perform manual ops on it, a None attribute or a Tensor full of 0s will behave differently. 2. If the user requests ``zero_grad(set_to_none=True)`` followed by a backward pass, ``.grad``\ s are guaranteed to be None for params that did not receive a gradient. 3. ``torch.optim`` optimizers have a different behavior if the gradient is 0 or None (in one case it does the step with a gradient of 0 and in the other it skips the step altogether).
r"""Sets the gradients of all optimized :class:`torch.Tensor` s to zero.
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def zero_grad(self, set_to_none: bool = False): r"""Sets the gradients of all optimized :class:`torch.Tensor` s to zero. Args: set_to_none (bool): instead of setting to zero, set the grads to None. This will in general have lower memory footprint, and can modestly improve performance. However, it changes certain behaviors. For example: 1. When the user tries to access a gradient and perform manual ops on it, a None attribute or a Tensor full of 0s will behave differently. 2. If the user requests ``zero_grad(set_to_none=True)`` followed by a backward pass, ``.grad``\ s are guaranteed to be None for params that did not receive a gradient. 3. ``torch.optim`` optimizers have a different behavior if the gradient is 0 or None (in one case it does the step with a gradient of 0 and in the other it skips the step altogether). """ foreach = self.defaults.get('foreach', False) if not hasattr(self, "_zero_grad_profile_name"): self._hook_for_profile() if foreach: per_device_and_dtype_grads = defaultdict(lambda: defaultdict(list)) with torch.autograd.profiler.record_function(self._zero_grad_profile_name): for group in self.param_groups: for p in group['params']: if p.grad is not None: if set_to_none: p.grad = None else: if p.grad.grad_fn is not None: p.grad.detach_() else: p.grad.requires_grad_(False) if (not foreach or p.grad.is_sparse): p.grad.zero_() else: per_device_and_dtype_grads[p.grad.device][p.grad.dtype].append(p.grad) if foreach: for _, per_dtype_grads in per_device_and_dtype_grads.items(): for grads in per_dtype_grads.values(): torch._foreach_zero_(grads)
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https://github.com/pytorch/pytorch/blob/7176c92687d3cc847cc046bf002269c6949a21c2/torch/optim/optimizer.py#L189-L228
hanpfei/chromium-net
392cc1fa3a8f92f42e4071ab6e674d8e0482f83f
third_party/catapult/third_party/Paste/paste/exceptions/errormiddleware.py
python
ErrorMiddleware.__call__
(self, environ, start_response)
The WSGI application interface.
The WSGI application interface.
[ "The", "WSGI", "application", "interface", "." ]
def __call__(self, environ, start_response): """ The WSGI application interface. """ # We want to be careful about not sending headers twice, # and the content type that the app has committed to (if there # is an exception in the iterator body of the response) if environ.get('paste.throw_errors'): return self.application(environ, start_response) environ['paste.throw_errors'] = True try: __traceback_supplement__ = Supplement, self, environ sr_checker = ResponseStartChecker(start_response) app_iter = self.application(environ, sr_checker) return self.make_catching_iter(app_iter, environ, sr_checker) except: exc_info = sys.exc_info() try: for expect in environ.get('paste.expected_exceptions', []): if isinstance(exc_info[1], expect): raise start_response('500 Internal Server Error', [('content-type', 'text/html')], exc_info) # @@: it would be nice to deal with bad content types here response = self.exception_handler(exc_info, environ) if six.PY3: response = response.encode('utf8') return [response] finally: # clean up locals... exc_info = None
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https://github.com/hanpfei/chromium-net/blob/392cc1fa3a8f92f42e4071ab6e674d8e0482f83f/third_party/catapult/third_party/Paste/paste/exceptions/errormiddleware.py#L128-L160
ucbrise/confluo
578883a4f7fbbb4aea78c342d366f5122ef598f7
pyclient/confluo/rpc/client.py
python
RpcClient.archive
(self, offset=-1)
Archives the atomic multilog until provided offset. Args: offset: Offset until which multilog should be archived (-1 for full archival). Raises: ValueError.
Archives the atomic multilog until provided offset.
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def archive(self, offset=-1): """ Archives the atomic multilog until provided offset. Args: offset: Offset until which multilog should be archived (-1 for full archival). Raises: ValueError. """ if self.cur_m_id_ == -1: raise ValueError("Must set atomic multilog first.") self.client_.archive(self.cur_m_id_, offset)
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https://github.com/ucbrise/confluo/blob/578883a4f7fbbb4aea78c342d366f5122ef598f7/pyclient/confluo/rpc/client.py#L206-L216
catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/tools/python/src/Lib/lib-tk/Tkinter.py
python
Canvas.addtag_below
(self, newtag, tagOrId)
Add tag NEWTAG to all items below TAGORID.
Add tag NEWTAG to all items below TAGORID.
[ "Add", "tag", "NEWTAG", "to", "all", "items", "below", "TAGORID", "." ]
def addtag_below(self, newtag, tagOrId): """Add tag NEWTAG to all items below TAGORID.""" self.addtag(newtag, 'below', tagOrId)
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/tools/python/src/Lib/lib-tk/Tkinter.py#L2251-L2253
wlanjie/AndroidFFmpeg
7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf
tools/fdk-aac-build/x86/toolchain/lib/python2.7/_abcoll.py
python
Mapping.get
(self, key, default=None)
D.get(k[,d]) -> D[k] if k in D, else d. d defaults to None.
D.get(k[,d]) -> D[k] if k in D, else d. d defaults to None.
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def get(self, key, default=None): 'D.get(k[,d]) -> D[k] if k in D, else d. d defaults to None.' try: return self[key] except KeyError: return default
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https://github.com/wlanjie/AndroidFFmpeg/blob/7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf/tools/fdk-aac-build/x86/toolchain/lib/python2.7/_abcoll.py#L360-L365
Yijunmaverick/GenerativeFaceCompletion
f72dea0fa27c779fef7b65d2f01e82bcc23a0eb2
scripts/cpp_lint.py
python
_CppLintState.SetCountingStyle
(self, counting_style)
Sets the module's counting options.
Sets the module's counting options.
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def SetCountingStyle(self, counting_style): """Sets the module's counting options.""" self.counting = counting_style
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https://github.com/Yijunmaverick/GenerativeFaceCompletion/blob/f72dea0fa27c779fef7b65d2f01e82bcc23a0eb2/scripts/cpp_lint.py#L713-L715
PaddlePaddle/Paddle
1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c
python/paddle/fluid/nets.py
python
simple_img_conv_pool
(input, num_filters, filter_size, pool_size, pool_stride, pool_padding=0, pool_type='max', global_pooling=False, conv_stride=1, conv_padding=0, conv_dilation=1, conv_groups=1, param_attr=None, bias_attr=None, act=None, use_cudnn=True)
return pool_out
r""" :api_attr: Static Graph The simple_img_conv_pool api is composed of :ref:`api_fluid_layers_conv2d` and :ref:`api_fluid_layers_pool2d` . Args: input (Variable): 4-D Tensor, shape is [N, C, H, W], data type can be float32 or float64. num_filters(int): The number of filters. It is the same as the output channels. filter_size (int|list|tuple): The filter size. If filter_size is a list or tuple, it must contain two integers, (filter_size_H, filter_size_W). Otherwise, the filter_size_H = filter_size_W = filter_size. pool_size (int|list|tuple): The pooling size of pool2d layer. If pool_size is a list or tuple, it must contain two integers, (pool_size_H, pool_size_W). Otherwise, the pool_size_H = pool_size_W = pool_size. pool_stride (int|list|tuple): The pooling stride of pool2d layer. If pool_stride is a list or tuple, it must contain two integers, (pooling_stride_H, pooling_stride_W). Otherwise, the pooling_stride_H = pooling_stride_W = pool_stride. pool_padding (int|list|tuple): The padding of pool2d layer. If pool_padding is a list or tuple, it must contain two integers, (pool_padding_H, pool_padding_W). Otherwise, the pool_padding_H = pool_padding_W = pool_padding. Default 0. pool_type (str): Pooling type can be :math:`max` for max-pooling or :math:`avg` for average-pooling. Default :math:`max`. global_pooling (bool): Whether to use the global pooling. If global_pooling = true, pool_size and pool_padding while be ignored. Default False conv_stride (int|list|tuple): The stride size of the conv2d Layer. If stride is a list or tuple, it must contain two integers, (conv_stride_H, conv_stride_W). Otherwise, the conv_stride_H = conv_stride_W = conv_stride. Default: conv_stride = 1. conv_padding (int|list|tuple): The padding size of the conv2d Layer. If padding is a list or tuple, it must contain two integers, (conv_padding_H, conv_padding_W). Otherwise, the conv_padding_H = conv_padding_W = conv_padding. Default: conv_padding = 0. conv_dilation (int|list|tuple): The dilation size of the conv2d Layer. If dilation is a list or tuple, it must contain two integers, (conv_dilation_H, conv_dilation_W). Otherwise, the conv_dilation_H = conv_dilation_W = conv_dilation. Default: conv_dilation = 1. conv_groups (int): The groups number of the conv2d Layer. According to grouped convolution in Alex Krizhevsky's Deep CNN paper: when group=2, the first half of the filters is only connected to the first half of the input channels, while the second half of the filters is only connected to the second half of the input channels. Default: groups=1. param_attr (ParamAttr|None): The parameter attribute for learnable parameters/weights of conv2d. If it is set to None or one attribute of ParamAttr, conv2d will create ParamAttr as param_attr. If the Initializer of the param_attr is not set, the parameter is initialized with :math:`Normal(0.0, std)`, and the :math:`std` is :math:`(\\frac{2.0 }{filter\_elem\_num})^{0.5}`. Default: None. bias_attr (ParamAttr|bool|None): The parameter attribute for the bias of conv2d. If it is set to False, no bias will be added to the output units. If it is set to None or one attribute of ParamAttr, conv2d will create ParamAttr as bias_attr. If the Initializer of the bias_attr is not set, the bias is initialized zero. Default: None. act (str): Activation type for conv2d, if it is set to None, activation is not appended. Default: None. use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn library is installed. Default: True Return: 4-D Tensor, the result of input after conv2d and pool2d, with the same data type as :attr:`input` Return Type: Variable Examples: .. code-block:: python import paddle.fluid as fluid import paddle paddle.enable_static() img = fluid.data(name='img', shape=[100, 1, 28, 28], dtype='float32') conv_pool = fluid.nets.simple_img_conv_pool(input=img, filter_size=5, num_filters=20, pool_size=2, pool_stride=2, act="relu")
r""" :api_attr: Static Graph
[ "r", ":", "api_attr", ":", "Static", "Graph" ]
def simple_img_conv_pool(input, num_filters, filter_size, pool_size, pool_stride, pool_padding=0, pool_type='max', global_pooling=False, conv_stride=1, conv_padding=0, conv_dilation=1, conv_groups=1, param_attr=None, bias_attr=None, act=None, use_cudnn=True): r""" :api_attr: Static Graph The simple_img_conv_pool api is composed of :ref:`api_fluid_layers_conv2d` and :ref:`api_fluid_layers_pool2d` . Args: input (Variable): 4-D Tensor, shape is [N, C, H, W], data type can be float32 or float64. num_filters(int): The number of filters. It is the same as the output channels. filter_size (int|list|tuple): The filter size. If filter_size is a list or tuple, it must contain two integers, (filter_size_H, filter_size_W). Otherwise, the filter_size_H = filter_size_W = filter_size. pool_size (int|list|tuple): The pooling size of pool2d layer. If pool_size is a list or tuple, it must contain two integers, (pool_size_H, pool_size_W). Otherwise, the pool_size_H = pool_size_W = pool_size. pool_stride (int|list|tuple): The pooling stride of pool2d layer. If pool_stride is a list or tuple, it must contain two integers, (pooling_stride_H, pooling_stride_W). Otherwise, the pooling_stride_H = pooling_stride_W = pool_stride. pool_padding (int|list|tuple): The padding of pool2d layer. If pool_padding is a list or tuple, it must contain two integers, (pool_padding_H, pool_padding_W). Otherwise, the pool_padding_H = pool_padding_W = pool_padding. Default 0. pool_type (str): Pooling type can be :math:`max` for max-pooling or :math:`avg` for average-pooling. Default :math:`max`. global_pooling (bool): Whether to use the global pooling. If global_pooling = true, pool_size and pool_padding while be ignored. Default False conv_stride (int|list|tuple): The stride size of the conv2d Layer. If stride is a list or tuple, it must contain two integers, (conv_stride_H, conv_stride_W). Otherwise, the conv_stride_H = conv_stride_W = conv_stride. Default: conv_stride = 1. conv_padding (int|list|tuple): The padding size of the conv2d Layer. If padding is a list or tuple, it must contain two integers, (conv_padding_H, conv_padding_W). Otherwise, the conv_padding_H = conv_padding_W = conv_padding. Default: conv_padding = 0. conv_dilation (int|list|tuple): The dilation size of the conv2d Layer. If dilation is a list or tuple, it must contain two integers, (conv_dilation_H, conv_dilation_W). Otherwise, the conv_dilation_H = conv_dilation_W = conv_dilation. Default: conv_dilation = 1. conv_groups (int): The groups number of the conv2d Layer. According to grouped convolution in Alex Krizhevsky's Deep CNN paper: when group=2, the first half of the filters is only connected to the first half of the input channels, while the second half of the filters is only connected to the second half of the input channels. Default: groups=1. param_attr (ParamAttr|None): The parameter attribute for learnable parameters/weights of conv2d. If it is set to None or one attribute of ParamAttr, conv2d will create ParamAttr as param_attr. If the Initializer of the param_attr is not set, the parameter is initialized with :math:`Normal(0.0, std)`, and the :math:`std` is :math:`(\\frac{2.0 }{filter\_elem\_num})^{0.5}`. Default: None. bias_attr (ParamAttr|bool|None): The parameter attribute for the bias of conv2d. If it is set to False, no bias will be added to the output units. If it is set to None or one attribute of ParamAttr, conv2d will create ParamAttr as bias_attr. If the Initializer of the bias_attr is not set, the bias is initialized zero. Default: None. act (str): Activation type for conv2d, if it is set to None, activation is not appended. Default: None. use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn library is installed. Default: True Return: 4-D Tensor, the result of input after conv2d and pool2d, with the same data type as :attr:`input` Return Type: Variable Examples: .. code-block:: python import paddle.fluid as fluid import paddle paddle.enable_static() img = fluid.data(name='img', shape=[100, 1, 28, 28], dtype='float32') conv_pool = fluid.nets.simple_img_conv_pool(input=img, filter_size=5, num_filters=20, pool_size=2, pool_stride=2, act="relu") """ conv_out = layers.conv2d( input=input, num_filters=num_filters, filter_size=filter_size, stride=conv_stride, padding=conv_padding, dilation=conv_dilation, groups=conv_groups, param_attr=param_attr, bias_attr=bias_attr, act=act, use_cudnn=use_cudnn) pool_out = layers.pool2d( input=conv_out, pool_size=pool_size, pool_type=pool_type, pool_stride=pool_stride, pool_padding=pool_padding, global_pooling=global_pooling, use_cudnn=use_cudnn) return pool_out
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https://github.com/PaddlePaddle/Paddle/blob/1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c/python/paddle/fluid/nets.py#L30-L141
google-ar/WebARonTango
e86965d2cbc652156b480e0fcf77c716745578cd
chromium/src/gpu/command_buffer/build_gles2_cmd_buffer.py
python
ImmediatePointerArgument.GetLogArg
(self)
return "static_cast<const void*>(%s)" % self.name
Overridden from Argument.
Overridden from Argument.
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def GetLogArg(self): """Overridden from Argument.""" return "static_cast<const void*>(%s)" % self.name
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https://github.com/google-ar/WebARonTango/blob/e86965d2cbc652156b480e0fcf77c716745578cd/chromium/src/gpu/command_buffer/build_gles2_cmd_buffer.py#L8914-L8916
lammps/lammps
b75c3065430a75b1b5543a10e10f46d9b4c91913
tools/i-pi/ipi/engine/barostats.py
python
BaroMHT.get_ebaro
(self)
return self.thermostat.ethermo + self.kin + self.pot
Calculates the barostat conserved quantity.
Calculates the barostat conserved quantity.
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def get_ebaro(self): """Calculates the barostat conserved quantity.""" return self.thermostat.ethermo + self.kin + self.pot
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https://github.com/lammps/lammps/blob/b75c3065430a75b1b5543a10e10f46d9b4c91913/tools/i-pi/ipi/engine/barostats.py#L415-L418
vgteam/vg
cf4d516a5e9ee5163c783e4437ddf16b18a4b561
vgci/mine-logs.py
python
load_mapeval_runtimes
(map_times_path)
return None
read the runtimes out of map_times.txt, assuming first line is a header
read the runtimes out of map_times.txt, assuming first line is a header
[ "read", "the", "runtimes", "out", "of", "map_times", ".", "txt", "assuming", "first", "line", "is", "a", "header" ]
def load_mapeval_runtimes(map_times_path): """ read the runtimes out of map_times.txt, assuming first line is a header """ try: map_times_dict = {} with open(map_times_path) as map_times_file: lines = [line for line in map_times_file] for line in lines[1:]: toks = line.split('\t') map_times_dict[toks[0]] = toks[1] return map_times_dict except: pass return None
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https://github.com/vgteam/vg/blob/cf4d516a5e9ee5163c783e4437ddf16b18a4b561/vgci/mine-logs.py#L55-L69
rampageX/firmware-mod-kit
c94cd6aeee50d92ec5280a6dba6d74828fd3606b
src/binwalk-2.1.1/src/binwalk/modules/hashmatch.py
python
HashMatch._compare_files
(self, file1, file2)
return None
Fuzzy diff two files. @file1 - The first file to diff. @file2 - The second file to diff. Returns the match percentage. Returns None on error.
Fuzzy diff two files.
[ "Fuzzy", "diff", "two", "files", "." ]
def _compare_files(self, file1, file2): ''' Fuzzy diff two files. @file1 - The first file to diff. @file2 - The second file to diff. Returns the match percentage. Returns None on error. ''' status = 0 file1_dup = False file2_dup = False if not self.filter_by_name or os.path.basename(file1) == os.path.basename(file2): if os.path.exists(file1) and os.path.exists(file2): hash1 = ctypes.create_string_buffer(self.FUZZY_MAX_RESULT) hash2 = ctypes.create_string_buffer(self.FUZZY_MAX_RESULT) # Check if the last file1 or file2 matches this file1 or file2; no need to re-hash if they match. if file1 == self.last_file1.name and self.last_file1.hash: file1_dup = True else: self.last_file1.name = file1 if file2 == self.last_file2.name and self.last_file2.hash: file2_dup = True else: self.last_file2.name = file2 try: if self.strings: if file1_dup: file1_strings = self.last_file1.strings else: self.last_file1.strings = file1_strings = self._get_strings(file1) if file2_dup: file2_strings = self.last_file2.strings else: self.last_file2.strings = file2_strings = self._get_strings(file2) if file1_strings == file2_strings: return 100 else: if file1_dup: hash1 = self.last_file1.hash else: status |= self.lib.fuzzy_hash_buf(file1_strings, len(file1_strings), hash1) if file2_dup: hash2 = self.last_file2.hash else: status |= self.lib.fuzzy_hash_buf(file2_strings, len(file2_strings), hash2) else: if file1_dup: hash1 = self.last_file1.hash else: status |= self.lib.fuzzy_hash_filename(file1, hash1) if file2_dup: hash2 = self.last_file2.hash else: status |= self.lib.fuzzy_hash_filename(file2, hash2) if status == 0: if not file1_dup: self.last_file1.hash = hash1 if not file2_dup: self.last_file2.hash = hash2 if hash1.raw == hash2.raw: return 100 else: return self.lib.fuzzy_compare(hash1, hash2) except Exception as e: binwalk.core.common.warning("Exception while doing fuzzy hash: %s" % str(e)) return None
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https://github.com/rampageX/firmware-mod-kit/blob/c94cd6aeee50d92ec5280a6dba6d74828fd3606b/src/binwalk-2.1.1/src/binwalk/modules/hashmatch.py#L120-L200
microsoft/ELL
a1d6bacc37a14879cc025d9be2ba40b1a0632315
tools/importers/common/importer.py
python
ImporterEngine.get_nodes_in_import_order
(self, nodes: typing.Mapping[str, typing.Any])
return result
Returns the nodes in an order that is suitable to import. That means each node is guaranteed to appear after the nodes it relies on.
Returns the nodes in an order that is suitable to import. That means each node is guaranteed to appear after the nodes it relies on.
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def get_nodes_in_import_order(self, nodes: typing.Mapping[str, typing.Any]): """ Returns the nodes in an order that is suitable to import. That means each node is guaranteed to appear after the nodes it relies on. """ pending_nodes = list(nodes.values()) ordered_nodes = [] node_processed = len(pending_nodes) > 0 outputs_available = {} while node_processed: node_processed = False for current_node in pending_nodes: # Find a node which already has all of its input nodes in the ordered list. if current_node.operation_type != "Skip": if all((input_id in outputs_available) for input_id in current_node.inputs if input_id): pending_nodes.remove(current_node) ordered_nodes.append(current_node) node_processed = True for o in current_node.outputs: outputs_available[o] = True pending_nodes = [n for n in pending_nodes if n.operation_type != "Skip"] if len(pending_nodes) > 0: _logger.info("### ignoring the following nodes because their inputs are not satisfiable:") for node in pending_nodes: _logger.info(" {}({})".format(node.operation_type, node.id)) result = [] for current_node in ordered_nodes: if current_node.operation_type != "Input" or any(current_node.outputs[0] in node.inputs for node in ordered_nodes): result.append(current_node) return result
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https://github.com/microsoft/ELL/blob/a1d6bacc37a14879cc025d9be2ba40b1a0632315/tools/importers/common/importer.py#L341-L374
snap-stanford/snap-python
d53c51b0a26aa7e3e7400b014cdf728948fde80a
setup/snap.py
python
TIntHI.GetDat
(self, *args)
return _snap.TIntHI_GetDat(self, *args)
GetDat(TIntHI self) -> TInt GetDat(TIntHI self) -> TInt Parameters: self: THashKeyDatI< TInt,TInt > *
GetDat(TIntHI self) -> TInt GetDat(TIntHI self) -> TInt
[ "GetDat", "(", "TIntHI", "self", ")", "-", ">", "TInt", "GetDat", "(", "TIntHI", "self", ")", "-", ">", "TInt" ]
def GetDat(self, *args): """ GetDat(TIntHI self) -> TInt GetDat(TIntHI self) -> TInt Parameters: self: THashKeyDatI< TInt,TInt > * """ return _snap.TIntHI_GetDat(self, *args)
[ "def", "GetDat", "(", "self", ",", "*", "args", ")", ":", "return", "_snap", ".", "TIntHI_GetDat", "(", "self", ",", "*", "args", ")" ]
https://github.com/snap-stanford/snap-python/blob/d53c51b0a26aa7e3e7400b014cdf728948fde80a/setup/snap.py#L19067-L19076
glotzerlab/hoomd-blue
f7f97abfa3fcc2522fa8d458d65d0aeca7ba781a
hoomd/md/compute.py
python
ThermodynamicQuantities.pressure_tensor
(self)
return self._cpp_obj.pressure_tensor
Instantaneous pressure tensor of the group \ :math:`[\\mathrm{pressure}]`. (:math:`P_{xx}`, :math:`P_{xy}`, :math:`P_{xz}`, :math:`P_{yy}`, :math:`P_{yz}`, :math:`P_{zz}`). calculated as: .. math:: P_{ij} = \\left[\\sum_{k \\in \\mathrm{filter}} m_k \\vec{v}_{k,i} \\cdot \\vec{v}_{k,j} + \\sum_{k \\in \\mathrm{filter}} \\sum_{l} \\frac{1}{2} \\left(\\vec{r}_{kl,i} \\cdot \\vec{F}_{kl,j} + \\vec{r}_{kl,j} \\cdot \\vec{F}_{kl,i} \\right) \\right]/V where :math:`V` is the total simulation box volume (or area in 2D).
Instantaneous pressure tensor of the group \ :math:`[\\mathrm{pressure}]`.
[ "Instantaneous", "pressure", "tensor", "of", "the", "group", "\\", ":", "math", ":", "[", "\\\\", "mathrm", "{", "pressure", "}", "]", "." ]
def pressure_tensor(self): """Instantaneous pressure tensor of the group \ :math:`[\\mathrm{pressure}]`. (:math:`P_{xx}`, :math:`P_{xy}`, :math:`P_{xz}`, :math:`P_{yy}`, :math:`P_{yz}`, :math:`P_{zz}`). calculated as: .. math:: P_{ij} = \\left[\\sum_{k \\in \\mathrm{filter}} m_k \\vec{v}_{k,i} \\cdot \\vec{v}_{k,j} + \\sum_{k \\in \\mathrm{filter}} \\sum_{l} \\frac{1}{2} \\left(\\vec{r}_{kl,i} \\cdot \\vec{F}_{kl,j} + \\vec{r}_{kl,j} \\cdot \\vec{F}_{kl,i} \\right) \\right]/V where :math:`V` is the total simulation box volume (or area in 2D). """ self._cpp_obj.compute(self._simulation.timestep) return self._cpp_obj.pressure_tensor
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https://github.com/glotzerlab/hoomd-blue/blob/f7f97abfa3fcc2522fa8d458d65d0aeca7ba781a/hoomd/md/compute.py#L96-L114
perilouswithadollarsign/cstrike15_src
f82112a2388b841d72cb62ca48ab1846dfcc11c8
thirdparty/protobuf-2.5.0/python/google/protobuf/reflection.py
python
ParseMessage
(descriptor, byte_str)
return new_msg
Generate a new Message instance from this Descriptor and a byte string. Args: descriptor: Protobuf Descriptor object byte_str: Serialized protocol buffer byte string Returns: Newly created protobuf Message object.
Generate a new Message instance from this Descriptor and a byte string.
[ "Generate", "a", "new", "Message", "instance", "from", "this", "Descriptor", "and", "a", "byte", "string", "." ]
def ParseMessage(descriptor, byte_str): """Generate a new Message instance from this Descriptor and a byte string. Args: descriptor: Protobuf Descriptor object byte_str: Serialized protocol buffer byte string Returns: Newly created protobuf Message object. """ class _ResultClass(message.Message): __metaclass__ = GeneratedProtocolMessageType DESCRIPTOR = descriptor new_msg = _ResultClass() new_msg.ParseFromString(byte_str) return new_msg
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https://github.com/perilouswithadollarsign/cstrike15_src/blob/f82112a2388b841d72cb62ca48ab1846dfcc11c8/thirdparty/protobuf-2.5.0/python/google/protobuf/reflection.py#L152-L169
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/typing.py
python
get_type_hints
(obj, globalns=None, localns=None)
return hints
Return type hints for an object. This is often the same as obj.__annotations__, but it handles forward references encoded as string literals, and if necessary adds Optional[t] if a default value equal to None is set. The argument may be a module, class, method, or function. The annotations are returned as a dictionary. For classes, annotations include also inherited members. TypeError is raised if the argument is not of a type that can contain annotations, and an empty dictionary is returned if no annotations are present. BEWARE -- the behavior of globalns and localns is counterintuitive (unless you are familiar with how eval() and exec() work). The search order is locals first, then globals. - If no dict arguments are passed, an attempt is made to use the globals from obj (or the respective module's globals for classes), and these are also used as the locals. If the object does not appear to have globals, an empty dictionary is used. - If one dict argument is passed, it is used for both globals and locals. - If two dict arguments are passed, they specify globals and locals, respectively.
Return type hints for an object.
[ "Return", "type", "hints", "for", "an", "object", "." ]
def get_type_hints(obj, globalns=None, localns=None): """Return type hints for an object. This is often the same as obj.__annotations__, but it handles forward references encoded as string literals, and if necessary adds Optional[t] if a default value equal to None is set. The argument may be a module, class, method, or function. The annotations are returned as a dictionary. For classes, annotations include also inherited members. TypeError is raised if the argument is not of a type that can contain annotations, and an empty dictionary is returned if no annotations are present. BEWARE -- the behavior of globalns and localns is counterintuitive (unless you are familiar with how eval() and exec() work). The search order is locals first, then globals. - If no dict arguments are passed, an attempt is made to use the globals from obj (or the respective module's globals for classes), and these are also used as the locals. If the object does not appear to have globals, an empty dictionary is used. - If one dict argument is passed, it is used for both globals and locals. - If two dict arguments are passed, they specify globals and locals, respectively. """ if getattr(obj, '__no_type_check__', None): return {} # Classes require a special treatment. if isinstance(obj, type): hints = {} for base in reversed(obj.__mro__): if globalns is None: base_globals = sys.modules[base.__module__].__dict__ else: base_globals = globalns ann = base.__dict__.get('__annotations__', {}) for name, value in ann.items(): if value is None: value = type(None) if isinstance(value, str): value = ForwardRef(value, is_argument=False) value = _eval_type(value, base_globals, localns) hints[name] = value return hints if globalns is None: if isinstance(obj, types.ModuleType): globalns = obj.__dict__ else: nsobj = obj # Find globalns for the unwrapped object. while hasattr(nsobj, '__wrapped__'): nsobj = nsobj.__wrapped__ globalns = getattr(nsobj, '__globals__', {}) if localns is None: localns = globalns elif localns is None: localns = globalns hints = getattr(obj, '__annotations__', None) if hints is None: # Return empty annotations for something that _could_ have them. if isinstance(obj, _allowed_types): return {} else: raise TypeError('{!r} is not a module, class, method, ' 'or function.'.format(obj)) defaults = _get_defaults(obj) hints = dict(hints) for name, value in hints.items(): if value is None: value = type(None) if isinstance(value, str): value = ForwardRef(value) value = _eval_type(value, globalns, localns) if name in defaults and defaults[name] is None: value = Optional[value] hints[name] = value return hints
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/typing.py#L934-L1017
LiquidPlayer/LiquidCore
9405979363f2353ac9a71ad8ab59685dd7f919c9
deps/node-10.15.3/deps/npm/node_modules/node-gyp/gyp/pylib/gyp/MSVSNew.py
python
MSVSProject.__init__
(self, path, name = None, dependencies = None, guid = None, spec = None, build_file = None, config_platform_overrides = None, fixpath_prefix = None)
Initializes the project. Args: path: Absolute path to the project file. name: Name of project. If None, the name will be the same as the base name of the project file. dependencies: List of other Project objects this project is dependent upon, if not None. guid: GUID to use for project, if not None. spec: Dictionary specifying how to build this project. build_file: Filename of the .gyp file that the vcproj file comes from. config_platform_overrides: optional dict of configuration platforms to used in place of the default for this target. fixpath_prefix: the path used to adjust the behavior of _fixpath
Initializes the project.
[ "Initializes", "the", "project", "." ]
def __init__(self, path, name = None, dependencies = None, guid = None, spec = None, build_file = None, config_platform_overrides = None, fixpath_prefix = None): """Initializes the project. Args: path: Absolute path to the project file. name: Name of project. If None, the name will be the same as the base name of the project file. dependencies: List of other Project objects this project is dependent upon, if not None. guid: GUID to use for project, if not None. spec: Dictionary specifying how to build this project. build_file: Filename of the .gyp file that the vcproj file comes from. config_platform_overrides: optional dict of configuration platforms to used in place of the default for this target. fixpath_prefix: the path used to adjust the behavior of _fixpath """ self.path = path self.guid = guid self.spec = spec self.build_file = build_file # Use project filename if name not specified self.name = name or os.path.splitext(os.path.basename(path))[0] # Copy passed lists (or set to empty lists) self.dependencies = list(dependencies or []) self.entry_type_guid = ENTRY_TYPE_GUIDS['project'] if config_platform_overrides: self.config_platform_overrides = config_platform_overrides else: self.config_platform_overrides = {} self.fixpath_prefix = fixpath_prefix self.msbuild_toolset = None
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https://github.com/LiquidPlayer/LiquidCore/blob/9405979363f2353ac9a71ad8ab59685dd7f919c9/deps/node-10.15.3/deps/npm/node_modules/node-gyp/gyp/pylib/gyp/MSVSNew.py#L112-L147
catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/python/pandas/py3/pandas/core/internals/array_manager.py
python
SingleArrayManager.idelete
(self, indexer)
return self
Delete selected locations in-place (new array, same ArrayManager)
Delete selected locations in-place (new array, same ArrayManager)
[ "Delete", "selected", "locations", "in", "-", "place", "(", "new", "array", "same", "ArrayManager", ")" ]
def idelete(self, indexer) -> SingleArrayManager: """ Delete selected locations in-place (new array, same ArrayManager) """ to_keep = np.ones(self.shape[0], dtype=np.bool_) to_keep[indexer] = False self.arrays = [self.arrays[0][to_keep]] self._axes = [self._axes[0][to_keep]] return self
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/pandas/py3/pandas/core/internals/array_manager.py#L1292-L1301
benoitsteiner/tensorflow-opencl
cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5
tensorflow/python/ops/data_flow_ops.py
python
ConditionalAccumulator.take_grad
(self, num_required, name=None)
return out
Attempts to extract the average gradient from the accumulator. The operation blocks until sufficient number of gradients have been successfully applied to the accumulator. Once successful, the following actions are also triggered: - Counter of accumulated gradients is reset to 0. - Aggregated gradient is reset to 0 tensor. - Accumulator's internal time step is incremented by 1. Args: num_required: Number of gradients that needs to have been aggregated name: Optional name for the operation Returns: A tensor holding the value of the average gradient. Raises: InvalidArgumentError: If num_required < 1
Attempts to extract the average gradient from the accumulator.
[ "Attempts", "to", "extract", "the", "average", "gradient", "from", "the", "accumulator", "." ]
def take_grad(self, num_required, name=None): """Attempts to extract the average gradient from the accumulator. The operation blocks until sufficient number of gradients have been successfully applied to the accumulator. Once successful, the following actions are also triggered: - Counter of accumulated gradients is reset to 0. - Aggregated gradient is reset to 0 tensor. - Accumulator's internal time step is incremented by 1. Args: num_required: Number of gradients that needs to have been aggregated name: Optional name for the operation Returns: A tensor holding the value of the average gradient. Raises: InvalidArgumentError: If num_required < 1 """ out = gen_data_flow_ops.accumulator_take_gradient( self._accumulator_ref, num_required, dtype=self._dtype, name=name) out.set_shape(self._shape) return out
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https://github.com/benoitsteiner/tensorflow-opencl/blob/cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5/tensorflow/python/ops/data_flow_ops.py#L1212-L1237
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/msw/_gdi.py
python
Font.GetDefaultEncoding
(*args, **kwargs)
return _gdi_.Font_GetDefaultEncoding(*args, **kwargs)
GetDefaultEncoding() -> int Returns the encoding used for all fonts created with an encoding of ``wx.FONTENCODING_DEFAULT``.
GetDefaultEncoding() -> int
[ "GetDefaultEncoding", "()", "-", ">", "int" ]
def GetDefaultEncoding(*args, **kwargs): """ GetDefaultEncoding() -> int Returns the encoding used for all fonts created with an encoding of ``wx.FONTENCODING_DEFAULT``. """ return _gdi_.Font_GetDefaultEncoding(*args, **kwargs)
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/msw/_gdi.py#L2597-L2604
catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/python/scipy/py3/scipy/sparse/base.py
python
spmatrix.tocsr
(self, copy=False)
return self.tocoo(copy=copy).tocsr(copy=False)
Convert this matrix to Compressed Sparse Row format. With copy=False, the data/indices may be shared between this matrix and the resultant csr_matrix.
Convert this matrix to Compressed Sparse Row format.
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def tocsr(self, copy=False): """Convert this matrix to Compressed Sparse Row format. With copy=False, the data/indices may be shared between this matrix and the resultant csr_matrix. """ return self.tocoo(copy=copy).tocsr(copy=False)
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/scipy/py3/scipy/sparse/base.py#L884-L890
openvinotoolkit/openvino
dedcbeafa8b84cccdc55ca64b8da516682b381c7
cmake/developer_package/cpplint/cpplint.py
python
IsBlockInNameSpace
(nesting_state, is_forward_declaration)
return (len(nesting_state.stack) > 1 and nesting_state.stack[-1].check_namespace_indentation and isinstance(nesting_state.stack[-2], _NamespaceInfo))
Checks that the new block is directly in a namespace. Args: nesting_state: The _NestingState object that contains info about our state. is_forward_declaration: If the class is a forward declared class. Returns: Whether or not the new block is directly in a namespace.
Checks that the new block is directly in a namespace.
[ "Checks", "that", "the", "new", "block", "is", "directly", "in", "a", "namespace", "." ]
def IsBlockInNameSpace(nesting_state, is_forward_declaration): """Checks that the new block is directly in a namespace. Args: nesting_state: The _NestingState object that contains info about our state. is_forward_declaration: If the class is a forward declared class. Returns: Whether or not the new block is directly in a namespace. """ if is_forward_declaration: return len(nesting_state.stack) >= 1 and ( isinstance(nesting_state.stack[-1], _NamespaceInfo)) return (len(nesting_state.stack) > 1 and nesting_state.stack[-1].check_namespace_indentation and isinstance(nesting_state.stack[-2], _NamespaceInfo))
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https://github.com/openvinotoolkit/openvino/blob/dedcbeafa8b84cccdc55ca64b8da516682b381c7/cmake/developer_package/cpplint/cpplint.py#L6005-L6021
klzgrad/naiveproxy
ed2c513637c77b18721fe428d7ed395b4d284c83
src/tools/grit/grit/node/base.py
python
Node.GetNodeById
(self, id)
return None
Returns the node in the subtree parented by this node that has a 'name' attribute matching 'id'. Returns None if no such node is found.
Returns the node in the subtree parented by this node that has a 'name' attribute matching 'id'. Returns None if no such node is found.
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def GetNodeById(self, id): '''Returns the node in the subtree parented by this node that has a 'name' attribute matching 'id'. Returns None if no such node is found. ''' for node in self: if 'name' in node.attrs and node.attrs['name'] == id: return node return None
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https://github.com/klzgrad/naiveproxy/blob/ed2c513637c77b18721fe428d7ed395b4d284c83/src/tools/grit/grit/node/base.py#L444-L451
Polidea/SiriusObfuscator
b0e590d8130e97856afe578869b83a209e2b19be
SymbolExtractorAndRenamer/lldb/scripts/Python/static-binding/lldb.py
python
SBTarget.DisableAllWatchpoints
(self)
return _lldb.SBTarget_DisableAllWatchpoints(self)
DisableAllWatchpoints(self) -> bool
DisableAllWatchpoints(self) -> bool
[ "DisableAllWatchpoints", "(", "self", ")", "-", ">", "bool" ]
def DisableAllWatchpoints(self): """DisableAllWatchpoints(self) -> bool""" return _lldb.SBTarget_DisableAllWatchpoints(self)
[ "def", "DisableAllWatchpoints", "(", "self", ")", ":", "return", "_lldb", ".", "SBTarget_DisableAllWatchpoints", "(", "self", ")" ]
https://github.com/Polidea/SiriusObfuscator/blob/b0e590d8130e97856afe578869b83a209e2b19be/SymbolExtractorAndRenamer/lldb/scripts/Python/static-binding/lldb.py#L9217-L9219
hpi-xnor/BMXNet-v2
af2b1859eafc5c721b1397cef02f946aaf2ce20d
example/image-classification/symbols/resnext.py
python
resnext
(units, num_stages, filter_list, num_classes, num_group, image_shape, bottle_neck=True, bn_mom=0.9, workspace=256, dtype='float32', memonger=False)
return mx.sym.SoftmaxOutput(data=fc1, name='softmax')
Return ResNeXt symbol of Parameters ---------- units : list Number of units in each stage num_stages : int Number of stage filter_list : list Channel size of each stage num_classes : int Ouput size of symbol num_groupes: int Number of conv groups dataset : str Dataset type, only cifar10 and imagenet supports workspace : int Workspace used in convolution operator dtype : str Precision (float32 or float16)
Return ResNeXt symbol of Parameters ---------- units : list Number of units in each stage num_stages : int Number of stage filter_list : list Channel size of each stage num_classes : int Ouput size of symbol num_groupes: int Number of conv groups dataset : str Dataset type, only cifar10 and imagenet supports workspace : int Workspace used in convolution operator dtype : str Precision (float32 or float16)
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def resnext(units, num_stages, filter_list, num_classes, num_group, image_shape, bottle_neck=True, bn_mom=0.9, workspace=256, dtype='float32', memonger=False): """Return ResNeXt symbol of Parameters ---------- units : list Number of units in each stage num_stages : int Number of stage filter_list : list Channel size of each stage num_classes : int Ouput size of symbol num_groupes: int Number of conv groups dataset : str Dataset type, only cifar10 and imagenet supports workspace : int Workspace used in convolution operator dtype : str Precision (float32 or float16) """ num_unit = len(units) assert(num_unit == num_stages) data = mx.sym.Variable(name='data') if dtype == 'float32': data = mx.sym.identity(data=data, name='id') else: if dtype == 'float16': data = mx.sym.Cast(data=data, dtype=np.float16) data = mx.sym.BatchNorm(data=data, fix_gamma=True, eps=2e-5, momentum=bn_mom, name='bn_data') (nchannel, height, width) = image_shape if height <= 32: # such as cifar10 body = mx.sym.Convolution(data=data, num_filter=filter_list[0], kernel=(3, 3), stride=(1,1), pad=(1, 1), no_bias=True, name="conv0", workspace=workspace) else: # often expected to be 224 such as imagenet body = mx.sym.Convolution(data=data, num_filter=filter_list[0], kernel=(7, 7), stride=(2,2), pad=(3, 3), no_bias=True, name="conv0", workspace=workspace) body = mx.sym.BatchNorm(data=body, fix_gamma=False, eps=2e-5, momentum=bn_mom, name='bn0') body = mx.sym.Activation(data=body, act_type='relu', name='relu0') body = mx.sym.Pooling(data=body, kernel=(3, 3), stride=(2,2), pad=(1,1), pool_type='max') for i in range(num_stages): body = residual_unit(body, filter_list[i+1], (1 if i==0 else 2, 1 if i==0 else 2), False, name='stage%d_unit%d' % (i + 1, 1), bottle_neck=bottle_neck, num_group=num_group, bn_mom=bn_mom, workspace=workspace, memonger=memonger) for j in range(units[i]-1): body = residual_unit(body, filter_list[i+1], (1,1), True, name='stage%d_unit%d' % (i + 1, j + 2), bottle_neck=bottle_neck, num_group=num_group, bn_mom=bn_mom, workspace=workspace, memonger=memonger) pool1 = mx.sym.Pooling(data=body, global_pool=True, kernel=(7, 7), pool_type='avg', name='pool1') flat = mx.sym.Flatten(data=pool1) fc1 = mx.sym.FullyConnected(data=flat, num_hidden=num_classes, name='fc1') if dtype == 'float16': fc1 = mx.sym.Cast(data=fc1, dtype=np.float32) return mx.sym.SoftmaxOutput(data=fc1, name='softmax')
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https://github.com/hpi-xnor/BMXNet-v2/blob/af2b1859eafc5c721b1397cef02f946aaf2ce20d/example/image-classification/symbols/resnext.py#L101-L155
apple/swift-lldb
d74be846ef3e62de946df343e8c234bde93a8912
scripts/Python/static-binding/lldb.py
python
SBVariablesOptions.SetIncludeRecognizedArguments
(self, arg2)
return _lldb.SBVariablesOptions_SetIncludeRecognizedArguments(self, arg2)
SetIncludeRecognizedArguments(SBVariablesOptions self, bool arg2)
SetIncludeRecognizedArguments(SBVariablesOptions self, bool arg2)
[ "SetIncludeRecognizedArguments", "(", "SBVariablesOptions", "self", "bool", "arg2", ")" ]
def SetIncludeRecognizedArguments(self, arg2): """SetIncludeRecognizedArguments(SBVariablesOptions self, bool arg2)""" return _lldb.SBVariablesOptions_SetIncludeRecognizedArguments(self, arg2)
[ "def", "SetIncludeRecognizedArguments", "(", "self", ",", "arg2", ")", ":", "return", "_lldb", ".", "SBVariablesOptions_SetIncludeRecognizedArguments", "(", "self", ",", "arg2", ")" ]
https://github.com/apple/swift-lldb/blob/d74be846ef3e62de946df343e8c234bde93a8912/scripts/Python/static-binding/lldb.py#L15063-L15065
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/pip/_internal/network/cache.py
python
suppressed_cache_errors
()
If we can't access the cache then we can just skip caching and process requests as if caching wasn't enabled.
If we can't access the cache then we can just skip caching and process
[ "If", "we", "can", "t", "access", "the", "cache", "then", "we", "can", "just", "skip", "caching", "and", "process" ]
def suppressed_cache_errors(): # type: () -> Iterator[None] """If we can't access the cache then we can just skip caching and process requests as if caching wasn't enabled. """ try: yield except OSError: pass
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/pip/_internal/network/cache.py#L49-L65
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/gtk/_windows.py
python
PageSetupDialogData.GetEnablePrinter
(*args, **kwargs)
return _windows_.PageSetupDialogData_GetEnablePrinter(*args, **kwargs)
GetEnablePrinter(self) -> bool
GetEnablePrinter(self) -> bool
[ "GetEnablePrinter", "(", "self", ")", "-", ">", "bool" ]
def GetEnablePrinter(*args, **kwargs): """GetEnablePrinter(self) -> bool""" return _windows_.PageSetupDialogData_GetEnablePrinter(*args, **kwargs)
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/gtk/_windows.py#L4902-L4904
wlanjie/AndroidFFmpeg
7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf
tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/random.py
python
WichmannHill.getstate
(self)
return self.VERSION, self._seed, self.gauss_next
Return internal state; can be passed to setstate() later.
Return internal state; can be passed to setstate() later.
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def getstate(self): """Return internal state; can be passed to setstate() later.""" return self.VERSION, self._seed, self.gauss_next
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https://github.com/wlanjie/AndroidFFmpeg/blob/7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf/tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/random.py#L715-L717
PaddlePaddle/PaddleOCR
b756bf5f8c90142e0d89d3db0163965c686b6ffe
ppocr/data/imaug/east_process.py
python
EASTProcessTrain.check_and_validate_polys
(self, polys, tags, img_height, img_width)
return np.array(validated_polys), np.array(validated_tags)
check so that the text poly is in the same direction, and also filter some invalid polygons :param polys: :param tags: :return:
check so that the text poly is in the same direction, and also filter some invalid polygons :param polys: :param tags: :return:
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def check_and_validate_polys(self, polys, tags, img_height, img_width): """ check so that the text poly is in the same direction, and also filter some invalid polygons :param polys: :param tags: :return: """ h, w = img_height, img_width if polys.shape[0] == 0: return polys polys[:, :, 0] = np.clip(polys[:, :, 0], 0, w - 1) polys[:, :, 1] = np.clip(polys[:, :, 1], 0, h - 1) validated_polys = [] validated_tags = [] for poly, tag in zip(polys, tags): p_area = self.polygon_area(poly) #invalid poly if abs(p_area) < 1: continue if p_area > 0: #'poly in wrong direction' if not tag: tag = True #reversed cases should be ignore poly = poly[(0, 3, 2, 1), :] validated_polys.append(poly) validated_tags.append(tag) return np.array(validated_polys), np.array(validated_tags)
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https://github.com/PaddlePaddle/PaddleOCR/blob/b756bf5f8c90142e0d89d3db0163965c686b6ffe/ppocr/data/imaug/east_process.py#L107-L135
apache/qpid-proton
6bcdfebb55ea3554bc29b1901422532db331a591
python/proton/_common.py
python
unicode2utf8
(string: Optional[str])
Some Proton APIs expect a null terminated string. Convert python text types to UTF8 to avoid zero bytes introduced by other multi-byte encodings. This method will throw if the string cannot be converted.
Some Proton APIs expect a null terminated string. Convert python text types to UTF8 to avoid zero bytes introduced by other multi-byte encodings. This method will throw if the string cannot be converted.
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def unicode2utf8(string: Optional[str]) -> Optional[str]: """Some Proton APIs expect a null terminated string. Convert python text types to UTF8 to avoid zero bytes introduced by other multi-byte encodings. This method will throw if the string cannot be converted. """ if string is None: return None elif isinstance(string, str): # The swig binding converts py3 str -> utf8 char* and back automatically return string # Anything else illegal - specifically python3 bytes raise TypeError("Unrecognized string type: %r (%s)" % (string, type(string)))
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https://github.com/apache/qpid-proton/blob/6bcdfebb55ea3554bc29b1901422532db331a591/python/proton/_common.py#L48-L59
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Tools/Python/3.7.10/windows/Lib/mailbox.py
python
MaildirMessage.set_flags
(self, flags)
Set the given flags and unset all others.
Set the given flags and unset all others.
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def set_flags(self, flags): """Set the given flags and unset all others.""" self._info = '2,' + ''.join(sorted(flags))
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/windows/Lib/mailbox.py#L1553-L1555
OSGeo/gdal
3748fc4ba4fba727492774b2b908a2130c864a83
swig/python/osgeo/gdal.py
python
IdentifyDriver
(*args)
return _gdal.IdentifyDriver(*args)
r"""IdentifyDriver(char const * utf8_path, char ** papszSiblings=None) -> Driver
r"""IdentifyDriver(char const * utf8_path, char ** papszSiblings=None) -> Driver
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def IdentifyDriver(*args): r"""IdentifyDriver(char const * utf8_path, char ** papszSiblings=None) -> Driver""" return _gdal.IdentifyDriver(*args)
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https://github.com/OSGeo/gdal/blob/3748fc4ba4fba727492774b2b908a2130c864a83/swig/python/osgeo/gdal.py#L4141-L4143
llvm-mirror/lldb
d01083a850f577b85501a0902b52fd0930de72c7
utils/vim-lldb/python-vim-lldb/lldb_controller.py
python
LLDBController.doAttach
(self, process_name)
Handle process attach.
Handle process attach.
[ "Handle", "process", "attach", "." ]
def doAttach(self, process_name): """ Handle process attach. """ error = lldb.SBError() self.processListener = lldb.SBListener("process_event_listener") self.target = self.dbg.CreateTarget('') self.process = self.target.AttachToProcessWithName( self.processListener, process_name, False, error) if not error.Success(): sys.stderr.write("Error during attach: " + str(error)) return self.ui.activate() self.pid = self.process.GetProcessID() print("Attached to %s (pid=%d)" % (process_name, self.pid))
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https://github.com/llvm-mirror/lldb/blob/d01083a850f577b85501a0902b52fd0930de72c7/utils/vim-lldb/python-vim-lldb/lldb_controller.py#L154-L169
wlanjie/AndroidFFmpeg
7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf
tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/xml/dom/expatbuilder.py
python
ExpatBuilder.parseString
(self, string)
return doc
Parse a document from a string, returning the document node.
Parse a document from a string, returning the document node.
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def parseString(self, string): """Parse a document from a string, returning the document node.""" parser = self.getParser() try: parser.Parse(string, True) self._setup_subset(string) except ParseEscape: pass doc = self.document self.reset() self._parser = None return doc
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https://github.com/wlanjie/AndroidFFmpeg/blob/7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf/tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/xml/dom/expatbuilder.py#L219-L230
miyosuda/TensorFlowAndroidDemo
35903e0221aa5f109ea2dbef27f20b52e317f42d
jni-build/jni/include/tensorflow/python/ops/random_ops.py
python
parameterized_truncated_normal
(shape, means=0.0, stddevs=1.0, minvals=-2.0, maxvals=2.0, dtype=dtypes.float32, seed=None, name=None)
Outputs random values from a truncated normal distribution. The generated values follow a normal distribution with specified mean and standard deviation, except that values whose magnitude is more than 2 standard deviations from the mean are dropped and re-picked. Args: shape: A 1-D integer Tensor or Python array. The shape of the output tensor. means: A 0-D Tensor or Python value of type `dtype`. The mean of the truncated normal distribution. stddevs: A 0-D Tensor or Python value of type `dtype`. The standard deviation of the truncated normal distribution. minvals: A 0-D Tensor or Python value of type `dtype`. The minimum value of the truncated normal distribution. maxvals: A 0-D Tensor or Python value of type `dtype`. The maximum value of the truncated normal distribution. dtype: The type of the output. seed: A Python integer. Used to create a random seed for the distribution. See [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed) for behavior. name: A name for the operation (optional). Returns: A tensor of the specified shape filled with random truncated normal values.
Outputs random values from a truncated normal distribution.
[ "Outputs", "random", "values", "from", "a", "truncated", "normal", "distribution", "." ]
def parameterized_truncated_normal(shape, means=0.0, stddevs=1.0, minvals=-2.0, maxvals=2.0, dtype=dtypes.float32, seed=None, name=None): """Outputs random values from a truncated normal distribution. The generated values follow a normal distribution with specified mean and standard deviation, except that values whose magnitude is more than 2 standard deviations from the mean are dropped and re-picked. Args: shape: A 1-D integer Tensor or Python array. The shape of the output tensor. means: A 0-D Tensor or Python value of type `dtype`. The mean of the truncated normal distribution. stddevs: A 0-D Tensor or Python value of type `dtype`. The standard deviation of the truncated normal distribution. minvals: A 0-D Tensor or Python value of type `dtype`. The minimum value of the truncated normal distribution. maxvals: A 0-D Tensor or Python value of type `dtype`. The maximum value of the truncated normal distribution. dtype: The type of the output. seed: A Python integer. Used to create a random seed for the distribution. See [`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed) for behavior. name: A name for the operation (optional). Returns: A tensor of the specified shape filled with random truncated normal values. """ with ops.op_scope([shape, means, stddevs, minvals, maxvals], name, "parameterized_truncated_normal") as name: shape_tensor = _ShapeTensor(shape) means_tensor = ops.convert_to_tensor(means, dtype=dtype, name="means") stddevs_tensor = ops.convert_to_tensor(stddevs, dtype=dtype, name="stddevs") minvals_tensor = ops.convert_to_tensor(minvals, dtype=dtype, name="minvals") maxvals_tensor = ops.convert_to_tensor(maxvals, dtype=dtype, name="maxvals") seed1, seed2 = random_seed.get_seed(seed) rnd = gen_random_ops._parameterized_truncated_normal(shape_tensor, means_tensor, stddevs_tensor, minvals_tensor, maxvals_tensor, seed=seed1, seed2=seed2) return rnd
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https://github.com/miyosuda/TensorFlowAndroidDemo/blob/35903e0221aa5f109ea2dbef27f20b52e317f42d/jni-build/jni/include/tensorflow/python/ops/random_ops.py#L90-L139
mantidproject/mantid
03deeb89254ec4289edb8771e0188c2090a02f32
qt/python/mantidqtinterfaces/mantidqtinterfaces/Muon/GUI/Common/corrections_tab_widget/background_corrections_presenter.py
python
BackgroundCorrectionsPresenter._update_start_and_end_x_in_view_and_model
(self, run: str, group: str, start_x: float, end_x: float)
Updates the start and end x in the view and model using the provided values.
Updates the start and end x in the view and model using the provided values.
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def _update_start_and_end_x_in_view_and_model(self, run: str, group: str, start_x: float, end_x: float) -> None: """Updates the start and end x in the view and model using the provided values.""" if self.view.is_run_group_displayed(run, group): self.view.set_start_x(run, group, start_x) self.view.set_end_x(run, group, end_x) self.model.set_start_x(run, group, start_x) self.model.set_end_x(run, group, end_x)
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https://github.com/mantidproject/mantid/blob/03deeb89254ec4289edb8771e0188c2090a02f32/qt/python/mantidqtinterfaces/mantidqtinterfaces/Muon/GUI/Common/corrections_tab_widget/background_corrections_presenter.py#L174-L180
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/_pyio.py
python
BufferedReader.__init__
(self, raw, buffer_size=DEFAULT_BUFFER_SIZE)
Create a new buffered reader using the given readable raw IO object.
Create a new buffered reader using the given readable raw IO object.
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def __init__(self, raw, buffer_size=DEFAULT_BUFFER_SIZE): """Create a new buffered reader using the given readable raw IO object. """ if not raw.readable(): raise OSError('"raw" argument must be readable.') _BufferedIOMixin.__init__(self, raw) if buffer_size <= 0: raise ValueError("invalid buffer size") self.buffer_size = buffer_size self._reset_read_buf() self._read_lock = Lock()
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/_pyio.py#L992-L1003
ApolloAuto/apollo-platform
86d9dc6743b496ead18d597748ebabd34a513289
ros/third_party/lib_x86_64/python2.7/dist-packages/numpy/distutils/misc_util.py
python
gpaths
(paths, local_path='', include_non_existing=True)
return _fix_paths(paths, local_path, include_non_existing)
Apply glob to paths and prepend local_path if needed.
Apply glob to paths and prepend local_path if needed.
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def gpaths(paths, local_path='', include_non_existing=True): """Apply glob to paths and prepend local_path if needed. """ if is_string(paths): paths = (paths,) return _fix_paths(paths, local_path, include_non_existing)
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https://github.com/ApolloAuto/apollo-platform/blob/86d9dc6743b496ead18d597748ebabd34a513289/ros/third_party/lib_x86_64/python2.7/dist-packages/numpy/distutils/misc_util.py#L244-L249
windystrife/UnrealEngine_NVIDIAGameWorks
b50e6338a7c5b26374d66306ebc7807541ff815e
Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/mailbox.py
python
mbox.__init__
(self, path, factory=None, create=True)
Initialize an mbox mailbox.
Initialize an mbox mailbox.
[ "Initialize", "an", "mbox", "mailbox", "." ]
def __init__(self, path, factory=None, create=True): """Initialize an mbox mailbox.""" self._message_factory = mboxMessage _mboxMMDF.__init__(self, path, factory, create)
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https://github.com/windystrife/UnrealEngine_NVIDIAGameWorks/blob/b50e6338a7c5b26374d66306ebc7807541ff815e/Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/mailbox.py#L819-L822
cms-sw/cmssw
fd9de012d503d3405420bcbeec0ec879baa57cf2
PhysicsTools/HeppyCore/python/utils/castorBaseDir.py
python
getUserAndArea
(user)
return user, area
Factor out the magic user hack for use in other classes
Factor out the magic user hack for use in other classes
[ "Factor", "out", "the", "magic", "user", "hack", "for", "use", "in", "other", "classes" ]
def getUserAndArea(user): """Factor out the magic user hack for use in other classes""" area = 'user' tokens = user.split('_') if tokens and len(tokens) > 1: user = tokens[0] area = tokens[1] return user, area
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https://github.com/cms-sw/cmssw/blob/fd9de012d503d3405420bcbeec0ec879baa57cf2/PhysicsTools/HeppyCore/python/utils/castorBaseDir.py#L7-L16
D-X-Y/caffe-faster-rcnn
eb50c97ff48f3df115d0e85fe0a32b0c7e2aa4cb
scripts/cpp_lint.py
python
CloseExpression
(clean_lines, linenum, pos)
return (line, clean_lines.NumLines(), -1)
If input points to ( or { or [ or <, finds the position that closes it. If lines[linenum][pos] points to a '(' or '{' or '[' or '<', finds the linenum/pos that correspond to the closing of the expression. Args: clean_lines: A CleansedLines instance containing the file. linenum: The number of the line to check. pos: A position on the line. Returns: A tuple (line, linenum, pos) pointer *past* the closing brace, or (line, len(lines), -1) if we never find a close. Note we ignore strings and comments when matching; and the line we return is the 'cleansed' line at linenum.
If input points to ( or { or [ or <, finds the position that closes it.
[ "If", "input", "points", "to", "(", "or", "{", "or", "[", "or", "<", "finds", "the", "position", "that", "closes", "it", "." ]
def CloseExpression(clean_lines, linenum, pos): """If input points to ( or { or [ or <, finds the position that closes it. If lines[linenum][pos] points to a '(' or '{' or '[' or '<', finds the linenum/pos that correspond to the closing of the expression. Args: clean_lines: A CleansedLines instance containing the file. linenum: The number of the line to check. pos: A position on the line. Returns: A tuple (line, linenum, pos) pointer *past* the closing brace, or (line, len(lines), -1) if we never find a close. Note we ignore strings and comments when matching; and the line we return is the 'cleansed' line at linenum. """ line = clean_lines.elided[linenum] startchar = line[pos] if startchar not in '({[<': return (line, clean_lines.NumLines(), -1) if startchar == '(': endchar = ')' if startchar == '[': endchar = ']' if startchar == '{': endchar = '}' if startchar == '<': endchar = '>' # Check first line (end_pos, num_open) = FindEndOfExpressionInLine( line, pos, 0, startchar, endchar) if end_pos > -1: return (line, linenum, end_pos) # Continue scanning forward while linenum < clean_lines.NumLines() - 1: linenum += 1 line = clean_lines.elided[linenum] (end_pos, num_open) = FindEndOfExpressionInLine( line, 0, num_open, startchar, endchar) if end_pos > -1: return (line, linenum, end_pos) # Did not find endchar before end of file, give up return (line, clean_lines.NumLines(), -1)
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https://github.com/D-X-Y/caffe-faster-rcnn/blob/eb50c97ff48f3df115d0e85fe0a32b0c7e2aa4cb/scripts/cpp_lint.py#L1258-L1301
OAID/Caffe-HRT
aae71e498ab842c6f92bcc23fc668423615a4d65
scripts/cpp_lint.py
python
Search
(pattern, s)
return _regexp_compile_cache[pattern].search(s)
Searches the string for the pattern, caching the compiled regexp.
Searches the string for the pattern, caching the compiled regexp.
[ "Searches", "the", "string", "for", "the", "pattern", "caching", "the", "compiled", "regexp", "." ]
def Search(pattern, s): """Searches the string for the pattern, caching the compiled regexp.""" if pattern not in _regexp_compile_cache: _regexp_compile_cache[pattern] = sre_compile.compile(pattern) return _regexp_compile_cache[pattern].search(s)
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https://github.com/OAID/Caffe-HRT/blob/aae71e498ab842c6f92bcc23fc668423615a4d65/scripts/cpp_lint.py#L543-L547
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/osx_carbon/_windows.py
python
PreSplitterWindow
(*args, **kwargs)
return val
PreSplitterWindow() -> SplitterWindow Precreate a SplitterWindow for 2-phase creation.
PreSplitterWindow() -> SplitterWindow
[ "PreSplitterWindow", "()", "-", ">", "SplitterWindow" ]
def PreSplitterWindow(*args, **kwargs): """ PreSplitterWindow() -> SplitterWindow Precreate a SplitterWindow for 2-phase creation. """ val = _windows_.new_PreSplitterWindow(*args, **kwargs) return val
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_carbon/_windows.py#L1672-L1679
kushview/Element
1cc16380caa2ab79461246ba758b9de1f46db2a5
libs/lv2/lv2specgen/lv2specgen.py
python
rdfsPropertyInfo
(term, m)
return doc
Generate HTML for properties: Domain, range
Generate HTML for properties: Domain, range
[ "Generate", "HTML", "for", "properties", ":", "Domain", "range" ]
def rdfsPropertyInfo(term, m): """Generate HTML for properties: Domain, range""" global classranges global classdomains doc = "" range = "" domain = "" # Find subPropertyOf information rlist = '' first = True for st in findStatements(m, term, rdfs.subPropertyOf, None): k = getTermLink(getObject(st), term, rdfs.subPropertyOf) rlist += getProperty(k, first) first = False if rlist != '': doc += '<tr><th>Sub-property of</th>' + rlist # Domain stuff domains = findStatements(m, term, rdfs.domain, None) domainsdoc = "" first = True for d in domains: union = findOne(m, getObject(d), owl.unionOf, None) if union: uris = parseCollection(m, getObject(union)) for uri in uris: domainsdoc += getProperty(getTermLink(uri, term, rdfs.domain), first) add(classdomains, uri, term) else: if not isBlank(getObject(d)): domainsdoc += getProperty(getTermLink(getObject(d), term, rdfs.domain), first) first = False if (len(domainsdoc) > 0): doc += "<tr><th>Domain</th>%s" % domainsdoc # Range stuff ranges = findStatements(m, term, rdfs.range, None) rangesdoc = "" first = True for r in ranges: union = findOne(m, getObject(r), owl.unionOf, None) if union: uris = parseCollection(m, getObject(union)) for uri in uris: rangesdoc += getProperty(getTermLink(uri, term, rdfs.range), first) add(classranges, uri, term) first = False else: if not isBlank(getObject(r)): rangesdoc += getProperty(getTermLink(getObject(r), term, rdfs.range), first) first = False if (len(rangesdoc) > 0): doc += "<tr><th>Range</th>%s" % rangesdoc return doc
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https://github.com/kushview/Element/blob/1cc16380caa2ab79461246ba758b9de1f46db2a5/libs/lv2/lv2specgen/lv2specgen.py#L377-L432
apache/arrow
af33dd1157eb8d7d9bfac25ebf61445b793b7943
python/pyarrow/parquet.py
python
ParquetDatasetPiece.open
(self)
return reader
Return instance of ParquetFile.
Return instance of ParquetFile.
[ "Return", "instance", "of", "ParquetFile", "." ]
def open(self): """ Return instance of ParquetFile. """ reader = self.open_file_func(self.path) if not isinstance(reader, ParquetFile): reader = ParquetFile(reader, **self.file_options) return reader
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https://github.com/apache/arrow/blob/af33dd1157eb8d7d9bfac25ebf61445b793b7943/python/pyarrow/parquet.py#L867-L874
kristjankorjus/Replicating-DeepMind
68539394e792b34a4d6b430a2eb73b8b8f91d8db
sandbox/chessboard.py
python
make_chessboard
(n)
return make_chessboard_any_size(84, n)
Create a 84x84 chessboard with small square size n pixels.
Create a 84x84 chessboard with small square size n pixels.
[ "Create", "a", "84x84", "chessboard", "with", "small", "square", "size", "n", "pixels", "." ]
def make_chessboard(n): """ Create a 84x84 chessboard with small square size n pixels. """ return make_chessboard_any_size(84, n)
[ "def", "make_chessboard", "(", "n", ")", ":", "return", "make_chessboard_any_size", "(", "84", ",", "n", ")" ]
https://github.com/kristjankorjus/Replicating-DeepMind/blob/68539394e792b34a4d6b430a2eb73b8b8f91d8db/sandbox/chessboard.py#L31-L34
hanpfei/chromium-net
392cc1fa3a8f92f42e4071ab6e674d8e0482f83f
third_party/catapult/third_party/gsutil/third_party/boto/boto/dynamodb/batch.py
python
Batch.to_dict
(self)
return batch_dict
Convert the Batch object into the format required for Layer1.
Convert the Batch object into the format required for Layer1.
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def to_dict(self): """ Convert the Batch object into the format required for Layer1. """ batch_dict = {} key_list = [] for key in self.keys: if isinstance(key, tuple): hash_key, range_key = key else: hash_key = key range_key = None k = self.table.layer2.build_key_from_values(self.table.schema, hash_key, range_key) key_list.append(k) batch_dict['Keys'] = key_list if self.attributes_to_get: batch_dict['AttributesToGet'] = self.attributes_to_get if self.consistent_read: batch_dict['ConsistentRead'] = True else: batch_dict['ConsistentRead'] = False return batch_dict
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https://github.com/hanpfei/chromium-net/blob/392cc1fa3a8f92f42e4071ab6e674d8e0482f83f/third_party/catapult/third_party/gsutil/third_party/boto/boto/dynamodb/batch.py#L58-L80
qgis/QGIS
15a77662d4bb712184f6aa60d0bd663010a76a75
python/plugins/processing/gui/wrappers.py
python
ExpressionWidgetWrapper.__init__
(self, param, dialog, row=0, col=0, **kwargs)
.. deprecated:: 3.4 Do not use, will be removed in QGIS 4.0
.. deprecated:: 3.4 Do not use, will be removed in QGIS 4.0
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def __init__(self, param, dialog, row=0, col=0, **kwargs): """ .. deprecated:: 3.4 Do not use, will be removed in QGIS 4.0 """ from warnings import warn warn("StringWidgetWrapper is deprecated and will be removed in QGIS 4.0", DeprecationWarning) super().__init__(param, dialog, row, col, **kwargs) self.context = dataobjects.createContext()
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https://github.com/qgis/QGIS/blob/15a77662d4bb712184f6aa60d0bd663010a76a75/python/plugins/processing/gui/wrappers.py#L1391-L1401