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VladyslavUsenko/basalt-mirror | 6d55fdab9ff2e6901dbcab634cf5025c58ad7371 | python/basalt/experiments.py | python | Experiment.sequences | (self, filter_regex=None) | return list of sequence names found for this experiment
:param filter_regex: if provided, return only they sequences that match the regex | return list of sequence names found for this experiment
:param filter_regex: if provided, return only they sequences that match the regex | [
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] | def sequences(self, filter_regex=None):
"""return list of sequence names found for this experiment
:param filter_regex: if provided, return only they sequences that match the regex
"""
if filter_regex is None:
return self.runs.keys()
else:
return [k for k in self.runs.keys() if re.search(filter_regex, k)] | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_gdi.py | python | AutoBufferedPaintDC.__init__ | (self, *args, **kwargs) | __init__(self, Window win) -> AutoBufferedPaintDC
If the current platform double buffers by default then this DC is the
same as a plain `wx.PaintDC`, otherwise it is a `wx.BufferedPaintDC`.
:see: `wx.AutoBufferedPaintDCFactory` | __init__(self, Window win) -> AutoBufferedPaintDC | [
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"""
__init__(self, Window win) -> AutoBufferedPaintDC
If the current platform double buffers by default then this DC is the
same as a plain `wx.PaintDC`, otherwise it is a `wx.BufferedPaintDC`.
:see: `wx.AutoBufferedPaintDCFactory`
"""
_gdi_.AutoBufferedPaintDC_swiginit(self,_gdi_.new_AutoBufferedPaintDC(*args, **kwargs)) | [
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leanprover/lean | 72a965986fa5aeae54062e98efb3140b2c4e79fd | src/cmake/Modules/cpplint.py | python | FindNextMatchingAngleBracket | (clean_lines, linenum, init_suffix) | return True | Find the corresponding > to close a template.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: Current line number.
init_suffix: Remainder of the current line after the initial <.
Returns:
True if a matching bracket exists. | Find the corresponding > to close a template. | [
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] | def FindNextMatchingAngleBracket(clean_lines, linenum, init_suffix):
"""Find the corresponding > to close a template.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: Current line number.
init_suffix: Remainder of the current line after the initial <.
Returns:
True if a matching bracket exists.
"""
line = init_suffix
nesting_stack = ['<']
while True:
# Find the next operator that can tell us whether < is used as an
# opening bracket or as a less-than operator. We only want to
# warn on the latter case.
#
# We could also check all other operators and terminate the search
# early, e.g. if we got something like this "a<b+c", the "<" is
# most likely a less-than operator, but then we will get false
# positives for default arguments (e.g. http://go/prccd) and
# other template expressions (e.g. http://go/oxcjq).
match = Search(r'^[^<>(),;\[\]]*([<>(),;\[\]])(.*)$', line)
if match:
# Found an operator, update nesting stack
operator = match.group(1)
line = match.group(2)
if nesting_stack[-1] == '<':
# Expecting closing angle bracket
if operator in ('<', '(', '['):
nesting_stack.append(operator)
elif operator == '>':
nesting_stack.pop()
if not nesting_stack:
# Found matching angle bracket
return True
elif operator == ',':
# Got a comma after a bracket, this is most likely a template
# argument. We have not seen a closing angle bracket yet, but
# it's probably a few lines later if we look for it, so just
# return early here.
return True
else:
# Got some other operator.
return False
else:
# Expecting closing parenthesis or closing bracket
if operator in ('<', '(', '['):
nesting_stack.append(operator)
elif operator in (')', ']'):
# We don't bother checking for matching () or []. If we got
# something like (] or [), it would have been a syntax error.
nesting_stack.pop()
else:
# Scan the next line
linenum += 1
if linenum >= len(clean_lines.elided):
break
line = clean_lines.elided[linenum]
# Exhausted all remaining lines and still no matching angle bracket.
# Most likely the input was incomplete, otherwise we should have
# seen a semicolon and returned early.
return True | [
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shedskin/shedskin | ae88dbca7b1d9671cd8be448cb0b497122758936 | examples/msp_ss.py | python | BootStrapLoader.actionChangeBaudrate | (self, baudrate=9600) | change baudrate. first the command is sent, then the comm
port is reprogrammed. only possible with newer MSP430-BSL versions.
(ROM >=1.6, downloadable >=1.5) | change baudrate. first the command is sent, then the comm
port is reprogrammed. only possible with newer MSP430-BSL versions.
(ROM >=1.6, downloadable >=1.5) | [
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] | def actionChangeBaudrate(self, baudrate=9600):
"""change baudrate. first the command is sent, then the comm
port is reprogrammed. only possible with newer MSP430-BSL versions.
(ROM >=1.6, downloadable >=1.5)"""
try:
baudconfigs = bauratetable[self.cpu]
except KeyError:
raise ValueError("unknown CPU type %s, can't switch baudrate" % self.cpu)
try:
a,l = baudconfigs[baudrate]
except KeyError:
raise ValueError("baudrate not valid. valid values are %r" % baudconfigs.keys())
#a, l = 0x87e0, 0x0002
sys.stderr.write("Changing baudrate to %d ...\n" % baudrate)
sys.stderr.flush()
self.bslTxRx(BSL_CHANGEBAUD, #Command: change baudrate
a, l) #args are coded in adr and len
time.sleep(0.010) #recomended delay
self.serialport.setBaudrate(baudrate) | [
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mongodb/mongo | d8ff665343ad29cf286ee2cf4a1960d29371937b | src/third_party/scons-3.1.2/scons-local-3.1.2/SCons/Node/__init__.py | python | Node.build | (self, **kw) | Actually build the node.
This is called by the Taskmaster after it's decided that the
Node is out-of-date and must be rebuilt, and after the prepare()
method has gotten everything, uh, prepared.
This method is called from multiple threads in a parallel build,
so only do thread safe stuff here. Do thread unsafe stuff
in built(). | Actually build the node. | [
"Actually",
"build",
"the",
"node",
"."
] | def build(self, **kw):
"""Actually build the node.
This is called by the Taskmaster after it's decided that the
Node is out-of-date and must be rebuilt, and after the prepare()
method has gotten everything, uh, prepared.
This method is called from multiple threads in a parallel build,
so only do thread safe stuff here. Do thread unsafe stuff
in built().
"""
try:
self.get_executor()(self, **kw)
except SCons.Errors.BuildError as e:
e.node = self
raise | [
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indutny/candor | 48e7260618f5091c80a3416828e2808cad3ea22e | tools/gyp/pylib/gyp/xcode_emulation.py | python | XcodeSettings.GetCflagsObjCC | (self, configname) | return cflags_objcc | Returns flags that need to be added to .mm compilations. | Returns flags that need to be added to .mm compilations. | [
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] | def GetCflagsObjCC(self, configname):
"""Returns flags that need to be added to .mm compilations."""
self.configname = configname
cflags_objcc = []
self._AddObjectiveCGarbageCollectionFlags(cflags_objcc)
if self._Test('GCC_OBJC_CALL_CXX_CDTORS', 'YES', default='NO'):
cflags_objcc.append('-fobjc-call-cxx-cdtors')
self.configname = None
return cflags_objcc | [
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apple/swift-clang | d7403439fc6641751840b723e7165fb02f52db95 | bindings/python/clang/cindex.py | python | TranslationUnit.save | (self, filename) | Saves the TranslationUnit to a file.
This is equivalent to passing -emit-ast to the clang frontend. The
saved file can be loaded back into a TranslationUnit. Or, if it
corresponds to a header, it can be used as a pre-compiled header file.
If an error occurs while saving, a TranslationUnitSaveError is raised.
If the error was TranslationUnitSaveError.ERROR_INVALID_TU, this means
the constructed TranslationUnit was not valid at time of save. In this
case, the reason(s) why should be available via
TranslationUnit.diagnostics().
filename -- The path to save the translation unit to (str or PathLike). | Saves the TranslationUnit to a file. | [
"Saves",
"the",
"TranslationUnit",
"to",
"a",
"file",
"."
] | def save(self, filename):
"""Saves the TranslationUnit to a file.
This is equivalent to passing -emit-ast to the clang frontend. The
saved file can be loaded back into a TranslationUnit. Or, if it
corresponds to a header, it can be used as a pre-compiled header file.
If an error occurs while saving, a TranslationUnitSaveError is raised.
If the error was TranslationUnitSaveError.ERROR_INVALID_TU, this means
the constructed TranslationUnit was not valid at time of save. In this
case, the reason(s) why should be available via
TranslationUnit.diagnostics().
filename -- The path to save the translation unit to (str or PathLike).
"""
options = conf.lib.clang_defaultSaveOptions(self)
result = int(conf.lib.clang_saveTranslationUnit(self, fspath(filename),
options))
if result != 0:
raise TranslationUnitSaveError(result,
'Error saving TranslationUnit.') | [
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hfinkel/llvm-project-cxxjit | 91084ef018240bbb8e24235ff5cd8c355a9c1a1e | llvm/examples/Kaleidoscope/MCJIT/lazy/genk-timing.py | python | generateKScript | (filename, numFuncs, elementsPerFunc, funcsBetweenExec, callWeighting, timingScript) | Generate a random Kaleidoscope script based on the given parameters | Generate a random Kaleidoscope script based on the given parameters | [
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] | def generateKScript(filename, numFuncs, elementsPerFunc, funcsBetweenExec, callWeighting, timingScript):
""" Generate a random Kaleidoscope script based on the given parameters """
print("Generating " + filename)
print(" %d functions, %d elements per function, %d functions between execution" %
(numFuncs, elementsPerFunc, funcsBetweenExec))
print(" Call weighting = %f" % callWeighting)
script = KScriptGenerator(filename)
script.setCallWeighting(callWeighting)
script.writeComment("===========================================================================")
script.writeComment("Auto-generated script")
script.writeComment(" %d functions, %d elements per function, %d functions between execution"
% (numFuncs, elementsPerFunc, funcsBetweenExec))
script.writeComment(" call weighting = %f" % callWeighting)
script.writeComment("===========================================================================")
script.writeEmptyLine()
script.writePredefinedFunctions()
funcsSinceLastExec = 0
for i in range(numFuncs):
script.writeFunction(elementsPerFunc)
funcsSinceLastExec += 1
if funcsSinceLastExec == funcsBetweenExec:
script.writeFunctionCall()
funcsSinceLastExec = 0
# Always end with a function call
if funcsSinceLastExec > 0:
script.writeFunctionCall()
script.writeEmptyLine()
script.writeFinalFunctionCounts()
funcsCalled = len(script.calledFunctions)
print(" Called %d of %d functions, %d total" % (funcsCalled, numFuncs, script.totalCallsExecuted))
timingScript.writeTimingCall(filename, numFuncs, funcsCalled, script.totalCallsExecuted) | [
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microsoft/onnxruntime | f92e47e95b13a240e37caf7b36577983544f98fc | onnxruntime/python/tools/transformers/gpt2_beamsearch_helper.py | python | Gpt2BeamSearchHelper.prepare_io_binding | (ort_session,
input_ids,
position_ids,
attention_mask,
past,
output_buffers,
output_shapes,
beam_select_idx=None,
input_log_probs=None,
input_unfinished_sents=None,
prev_step_results=None,
prev_step_scores=None) | return io_binding | Returnas IO binding object for a session. | Returnas IO binding object for a session. | [
"Returnas",
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"session",
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] | def prepare_io_binding(ort_session,
input_ids,
position_ids,
attention_mask,
past,
output_buffers,
output_shapes,
beam_select_idx=None,
input_log_probs=None,
input_unfinished_sents=None,
prev_step_results=None,
prev_step_scores=None):
"""Returnas IO binding object for a session."""
# Bind inputs and outputs to onnxruntime session
io_binding = Gpt2Helper.prepare_io_binding(ort_session,
input_ids,
position_ids,
attention_mask,
past=past,
output_buffers=output_buffers,
output_shapes=output_shapes)
# Bind inputs
data_type = output_buffers[ort_session.get_outputs()[1].name].dtype
float_type = numpy.float16 if data_type == torch.float16 else numpy.float32
if past is not None:
for i, past_i in enumerate(past):
assert past_i.is_contiguous()
data_ptr = past_i.data_ptr()
if data_ptr == 0:
# When past_sequence_length is 0, its data_ptr will be zero. IO Binding asserts that data_ptr shall not be zero.
# Here we workaround and pass data pointer of input_ids. Actual data is not used for past so it does not matter.
data_ptr = input_ids.data_ptr()
io_binding.bind_input(f'past_{i}', past_i.device.type, 0, float_type, list(past_i.size()), data_ptr)
if attention_mask is not None:
assert attention_mask.is_contiguous()
io_binding.bind_input('attention_mask', attention_mask.device.type, 0, float_type,
list(attention_mask.size()), attention_mask.data_ptr())
if beam_select_idx is not None:
assert beam_select_idx.is_contiguous()
io_binding.bind_input(
"beam_select_idx",
beam_select_idx.device.type,
0,
numpy.longlong,
list(beam_select_idx.size()),
beam_select_idx.data_ptr(),
)
if input_log_probs is not None:
assert input_log_probs.is_contiguous()
io_binding.bind_input(
"input_log_probs",
input_log_probs.device.type,
0,
float_type,
list(input_log_probs.size()),
input_log_probs.data_ptr(),
)
if input_unfinished_sents is not None:
assert input_unfinished_sents.is_contiguous()
io_binding.bind_input(
"input_unfinished_sents",
input_unfinished_sents.device.type,
0,
numpy.bool,
list(input_unfinished_sents.size()),
input_unfinished_sents.data_ptr(),
)
if prev_step_results is not None:
assert prev_step_results.is_contiguous()
io_binding.bind_input(
"prev_step_results",
prev_step_results.device.type,
0,
numpy.longlong,
list(prev_step_results.size()),
prev_step_results.data_ptr(),
)
if prev_step_scores is not None:
assert prev_step_scores.is_contiguous()
io_binding.bind_input(
"prev_step_scores",
prev_step_scores.device.type,
0,
float_type,
list(prev_step_scores.size()),
prev_step_scores.data_ptr(),
)
# Bind outputs
for output in ort_session.get_outputs():
output_name = output.name
output_buffer = output_buffers[output_name]
logger.debug(f"{output_name} device type={output_buffer.device.type} shape={list(output_buffer.size())}")
if (output_name == "output_selected_indices" or output_name == "last_state"
or output_name == "current_step_results"):
io_binding.bind_output(
output_name,
output_buffer.device.type,
0,
numpy.longlong,
output_shapes[output_name],
output_buffer.data_ptr(),
)
elif output_name == "output_unfinished_sents":
io_binding.bind_output(
output_name,
output_buffer.device.type,
0,
numpy.bool,
output_shapes[output_name],
output_buffer.data_ptr(),
)
else:
io_binding.bind_output(
output_name,
output_buffer.device.type,
0,
float_type,
output_shapes[output_name],
output_buffer.data_ptr(),
)
return io_binding | [
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] | https://github.com/microsoft/onnxruntime/blob/f92e47e95b13a240e37caf7b36577983544f98fc/onnxruntime/python/tools/transformers/gpt2_beamsearch_helper.py#L686-L819 | |
xenia-project/xenia | 9b1fdac98665ac091b9660a5d0fbb259ed79e578 | third_party/google-styleguide/cpplint/cpplint.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.
TODO(unknown): cpplint spends a fair bit of time matching parentheses.
Ideally we would want to index all opening and closing parentheses once
and have CloseExpression be just a simple lookup, but due to preprocessor
tricks, this is not so easy.
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. | [
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] | 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.
TODO(unknown): cpplint spends a fair bit of time matching parentheses.
Ideally we would want to index all opening and closing parentheses once
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tricks, this is not so easy.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
pos: A position on the line.
Returns:
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"""
line = clean_lines.elided[linenum]
if (line[pos] not in '({[<') or Match(r'<[<=]', line[pos:]):
return (line, clean_lines.NumLines(), -1)
# Check first line
(end_pos, stack) = FindEndOfExpressionInLine(line, pos, [])
if end_pos > -1:
return (line, linenum, end_pos)
# Continue scanning forward
while stack and linenum < clean_lines.NumLines() - 1:
linenum += 1
line = clean_lines.elided[linenum]
(end_pos, stack) = FindEndOfExpressionInLine(line, 0, stack)
if end_pos > -1:
return (line, linenum, end_pos)
# Did not find end of expression before end of file, give up
return (line, clean_lines.NumLines(), -1) | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/distutils/util.py | python | execute | (func, args, msg=None, verbose=0, dry_run=0) | Perform some action that affects the outside world (eg. by
writing to the filesystem). Such actions are special because they
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that bureaucracy for you; all you have to do is supply the
function to call and an argument tuple for it (to embody the
"external action" being performed), and an optional message to
print. | Perform some action that affects the outside world (eg. by
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are disabled by the 'dry_run' flag. This method takes care of all
that bureaucracy for you; all you have to do is supply the
function to call and an argument tuple for it (to embody the
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if msg is None:
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if msg[-2:] == ',)': # correct for singleton tuple
msg = msg[0:-2] + ')'
log.info(msg)
if not dry_run:
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eclipse/sumo | 7132a9b8b6eea734bdec38479026b4d8c4336d03 | tools/contributed/sumopy/agilepy/lib_wx/objpanel.py | python | DirpathWidgetContainer.define_widgetset | (self) | return [('name', self.create_namewidget(), wx.ALIGN_RIGHT | wx.ALIGN_CENTER_VERTICAL),
('value', self.create_valuewidget(), wx.EXPAND),
('unit', self.create_unitwidget(), wx.ALIGN_LEFT | wx.ALIGN_CENTER_VERTICAL), ] | Generates the widgets representing this attribute. | Generates the widgets representing this attribute. | [
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] | def define_widgetset(self):
"""
Generates the widgets representing this attribute.
"""
return [('name', self.create_namewidget(), wx.ALIGN_RIGHT | wx.ALIGN_CENTER_VERTICAL),
('value', self.create_valuewidget(), wx.EXPAND),
('unit', self.create_unitwidget(), wx.ALIGN_LEFT | wx.ALIGN_CENTER_VERTICAL), ] | [
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traveller59/spconv | 647927ce6b64dc51fbec4eb50c7194f8ca5007e5 | spconv/pytorch/core.py | python | SparseConvTensor.__init__ | (self,
features: torch.Tensor,
indices: torch.Tensor,
spatial_shape: Union[List[int], np.ndarray],
batch_size: int,
grid: Optional[torch.Tensor] = None,
voxel_num: Optional[torch.Tensor] = None,
indice_dict: Optional[dict] = None,
benchmark: bool = False,
permanent_thrust_allocator: bool = False,
enable_timer: bool = False) | Args:
features: [num_points, num_features] feature tensor
indices: [num_points, ndim + 1] indice tensor. batch index saved in indices[:, 0]
spatial_shape: spatial shape of your sparse data
batch_size: batch size of your sparse data
grid: pre-allocated grid tensor. should be used when the volume of spatial shape
is very large.
benchmark: whether to enable benchmark. if enabled, all sparse operators will be record to
SparseConvTensor. | Args:
features: [num_points, num_features] feature tensor
indices: [num_points, ndim + 1] indice tensor. batch index saved in indices[:, 0]
spatial_shape: spatial shape of your sparse data
batch_size: batch size of your sparse data
grid: pre-allocated grid tensor. should be used when the volume of spatial shape
is very large.
benchmark: whether to enable benchmark. if enabled, all sparse operators will be record to
SparseConvTensor. | [
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features: torch.Tensor,
indices: torch.Tensor,
spatial_shape: Union[List[int], np.ndarray],
batch_size: int,
grid: Optional[torch.Tensor] = None,
voxel_num: Optional[torch.Tensor] = None,
indice_dict: Optional[dict] = None,
benchmark: bool = False,
permanent_thrust_allocator: bool = False,
enable_timer: bool = False):
"""
Args:
features: [num_points, num_features] feature tensor
indices: [num_points, ndim + 1] indice tensor. batch index saved in indices[:, 0]
spatial_shape: spatial shape of your sparse data
batch_size: batch size of your sparse data
grid: pre-allocated grid tensor. should be used when the volume of spatial shape
is very large.
benchmark: whether to enable benchmark. if enabled, all sparse operators will be record to
SparseConvTensor.
"""
ndim = indices.shape[1] - 1
assert features.ndim == 2
assert indices.ndim == 2
assert len(spatial_shape) == ndim, "spatial shape must equal to ndim"
assert indices.dtype == torch.int32, "only support int32"
assert batch_size > 0
self._features = features
self.indices = indices
self.spatial_shape = [int(v) for v in spatial_shape]
self.batch_size = batch_size
if indice_dict is None:
indice_dict = {}
self.indice_dict = indice_dict
if grid is None:
grid = torch.Tensor() # empty tensor
self.grid = grid
self.voxel_num = voxel_num # for tensorrt
self.benchmark = benchmark
self.benchmark_record = {}
self.thrust_allocator: Optional[ThrustSortAllocator] = None
if permanent_thrust_allocator:
self.thrust_allocator = ThrustSortAllocator(features.device)
self._timer = CUDAKernelTimer(enable_timer) | [
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nasa/astrobee | 9241e67e6692810d6e275abb3165b6d02f4ca5ef | scripts/git/cpplint.py | python | FileInfo.Extension | (self) | return self.Split()[2] | File extension - text following the final period. | File extension - text following the final period. | [
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"""File extension - text following the final period."""
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/lib-tk/ttk.py | python | _format_elemcreate | (etype, script=False, *args, **kw) | return spec, opts | Formats args and kw according to the given element factory etype. | Formats args and kw according to the given element factory etype. | [
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] | def _format_elemcreate(etype, script=False, *args, **kw):
"""Formats args and kw according to the given element factory etype."""
spec = None
opts = ()
if etype in ("image", "vsapi"):
if etype == "image": # define an element based on an image
# first arg should be the default image name
iname = args[0]
# next args, if any, are statespec/value pairs which is almost
# a mapdict, but we just need the value
imagespec = _join(_mapdict_values(args[1:]))
spec = "%s %s" % (iname, imagespec)
else:
# define an element whose visual appearance is drawn using the
# Microsoft Visual Styles API which is responsible for the
# themed styles on Windows XP and Vista.
# Availability: Tk 8.6, Windows XP and Vista.
class_name, part_id = args[:2]
statemap = _join(_mapdict_values(args[2:]))
spec = "%s %s %s" % (class_name, part_id, statemap)
opts = _format_optdict(kw, script)
elif etype == "from": # clone an element
# it expects a themename and optionally an element to clone from,
# otherwise it will clone {} (empty element)
spec = args[0] # theme name
if len(args) > 1: # elementfrom specified
opts = (_format_optvalue(args[1], script),)
if script:
spec = '{%s}' % spec
opts = ' '.join(opts)
return spec, opts | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/setuptools/py3/setuptools/_vendor/pyparsing.py | python | ParseResults.dump | (self, indent='', depth=0, full=True) | return "".join(out) | Diagnostic method for listing out the contents of a C{ParseResults}.
Accepts an optional C{indent} argument so that this string can be embedded
in a nested display of other data.
Example::
integer = Word(nums)
date_str = integer("year") + '/' + integer("month") + '/' + integer("day")
result = date_str.parseString('12/31/1999')
print(result.dump())
prints::
['12', '/', '31', '/', '1999']
- day: 1999
- month: 31
- year: 12 | Diagnostic method for listing out the contents of a C{ParseResults}.
Accepts an optional C{indent} argument so that this string can be embedded
in a nested display of other data. | [
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] | def dump(self, indent='', depth=0, full=True):
"""
Diagnostic method for listing out the contents of a C{ParseResults}.
Accepts an optional C{indent} argument so that this string can be embedded
in a nested display of other data.
Example::
integer = Word(nums)
date_str = integer("year") + '/' + integer("month") + '/' + integer("day")
result = date_str.parseString('12/31/1999')
print(result.dump())
prints::
['12', '/', '31', '/', '1999']
- day: 1999
- month: 31
- year: 12
"""
out = []
NL = '\n'
out.append( indent+_ustr(self.asList()) )
if full:
if self.haskeys():
items = sorted((str(k), v) for k,v in self.items())
for k,v in items:
if out:
out.append(NL)
out.append( "%s%s- %s: " % (indent,(' '*depth), k) )
if isinstance(v,ParseResults):
if v:
out.append( v.dump(indent,depth+1) )
else:
out.append(_ustr(v))
else:
out.append(repr(v))
elif any(isinstance(vv,ParseResults) for vv in self):
v = self
for i,vv in enumerate(v):
if isinstance(vv,ParseResults):
out.append("\n%s%s[%d]:\n%s%s%s" % (indent,(' '*(depth)),i,indent,(' '*(depth+1)),vv.dump(indent,depth+1) ))
else:
out.append("\n%s%s[%d]:\n%s%s%s" % (indent,(' '*(depth)),i,indent,(' '*(depth+1)),_ustr(vv)))
return "".join(out) | [
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turi-code/SFrame | 796b9bdfb2fa1b881d82080754643c7e68629cd2 | oss_src/unity/python/sframe/meta/bytecodetools/pyc_file.py | python | extract | (binary) | return modtime, code | Extract a code object from a binary pyc file.
:param binary: a sequence of bytes from a pyc file. | Extract a code object from a binary pyc file.
:param binary: a sequence of bytes from a pyc file. | [
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'''
Extract a code object from a binary pyc file.
:param binary: a sequence of bytes from a pyc file.
'''
if len(binary) <= 8:
raise Exception("Binary pyc must be greater than 8 bytes (got %i)" % len(binary))
magic = binary[:4]
MAGIC = get_magic()
if magic != MAGIC:
raise Exception("Python version mismatch (%r != %r) Is this a pyc file?" % (magic, MAGIC))
modtime = time.asctime(time.localtime(struct.unpack('i', binary[4:8])[0]))
code = marshal.loads(binary[8:])
return modtime, code | [
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dscharrer/innoextract | 5519d364cc8898f906f6285d81a87ab8c5469cde | cmake/cpplint.py | python | CleansedLines._CollapseStrings | (elided) | return elided | Collapses strings and chars on a line to simple "" or '' blocks.
We nix strings first so we're not fooled by text like '"http://"'
Args:
elided: The line being processed.
Returns:
The line with collapsed strings. | Collapses strings and chars on a line to simple "" or '' blocks. | [
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"and",
"chars",
"on",
"a",
"line",
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"simple",
"or",
"blocks",
"."
] | def _CollapseStrings(elided):
"""Collapses strings and chars on a line to simple "" or '' blocks.
We nix strings first so we're not fooled by text like '"http://"'
Args:
elided: The line being processed.
Returns:
The line with collapsed strings.
"""
if not _RE_PATTERN_INCLUDE.match(elided):
# Remove escaped characters first to make quote/single quote collapsing
# basic. Things that look like escaped characters shouldn't occur
# outside of strings and chars.
elided = _RE_PATTERN_CLEANSE_LINE_ESCAPES.sub('', elided)
elided = _RE_PATTERN_CLEANSE_LINE_SINGLE_QUOTES.sub("''", elided)
elided = _RE_PATTERN_CLEANSE_LINE_DOUBLE_QUOTES.sub('""', elided)
return elided | [
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zerollzeng/tiny-tensorrt | e7bdb8f82934342a0f22ce68dfefdb8e15eb72b2 | third_party/pybind11/tools/clang/cindex.py | python | Config.set_compatibility_check | (check_status) | Perform compatibility check when loading libclang
The python bindings are only tested and evaluated with the version of
libclang they are provided with. To ensure correct behavior a (limited)
compatibility check is performed when loading the bindings. This check
will throw an exception, as soon as it fails.
In case these bindings are used with an older version of libclang, parts
that have been stable between releases may still work. Users of the
python bindings can disable the compatibility check. This will cause
the python bindings to load, even though they are written for a newer
version of libclang. Failures now arise if unsupported or incompatible
features are accessed. The user is required to test themselves if the
features they are using are available and compatible between different
libclang versions. | Perform compatibility check when loading libclang | [
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] | def set_compatibility_check(check_status):
""" Perform compatibility check when loading libclang
The python bindings are only tested and evaluated with the version of
libclang they are provided with. To ensure correct behavior a (limited)
compatibility check is performed when loading the bindings. This check
will throw an exception, as soon as it fails.
In case these bindings are used with an older version of libclang, parts
that have been stable between releases may still work. Users of the
python bindings can disable the compatibility check. This will cause
the python bindings to load, even though they are written for a newer
version of libclang. Failures now arise if unsupported or incompatible
features are accessed. The user is required to test themselves if the
features they are using are available and compatible between different
libclang versions.
"""
if Config.loaded:
raise Exception("compatibility_check must be set before before " \
"using any other functionalities in libclang.")
Config.compatibility_check = check_status | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/nntplib.py | python | NNTP.getline | (self) | return line | Internal: return one line from the server, stripping CRLF.
Raise EOFError if the connection is closed. | Internal: return one line from the server, stripping CRLF.
Raise EOFError if the connection is closed. | [
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] | def getline(self):
"""Internal: return one line from the server, stripping CRLF.
Raise EOFError if the connection is closed."""
line = self.file.readline(_MAXLINE + 1)
if len(line) > _MAXLINE:
raise NNTPDataError('line too long')
if self.debugging > 1:
print '*get*', repr(line)
if not line: raise EOFError
if line[-2:] == CRLF: line = line[:-2]
elif line[-1:] in CRLF: line = line[:-1]
return line | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/util/nest.py | python | flatten | (structure, expand_composites=False) | return _pywrap_tensorflow.Flatten(structure, expand_composites) | Returns a flat list from a given nested structure.
If nest is not a sequence, tuple, or dict, then returns a single-element list:
[nest].
In the case of dict instances, the sequence consists of the values, sorted by
key to ensure deterministic behavior. This is true also for OrderedDict
instances: their sequence order is ignored, the sorting order of keys is used
instead. The same convention is followed in pack_sequence_as. This correctly
repacks dicts and OrderedDicts after they have been flattened, and also allows
flattening an OrderedDict and then repacking it back using a corresponding
plain dict, or vice-versa. Dictionaries with non-sortable keys cannot be
flattened.
Users must not modify any collections used in nest while this function is
running.
Args:
structure: an arbitrarily nested structure or a scalar object. Note, numpy
arrays are considered scalars.
expand_composites: If true, then composite tensors such as tf.SparseTensor
and tf.RaggedTensor are expanded into their component tensors.
Returns:
A Python list, the flattened version of the input.
Raises:
TypeError: The nest is or contains a dict with non-sortable keys. | Returns a flat list from a given nested structure. | [
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"flat",
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"from",
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] | def flatten(structure, expand_composites=False):
"""Returns a flat list from a given nested structure.
If nest is not a sequence, tuple, or dict, then returns a single-element list:
[nest].
In the case of dict instances, the sequence consists of the values, sorted by
key to ensure deterministic behavior. This is true also for OrderedDict
instances: their sequence order is ignored, the sorting order of keys is used
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structure: an arbitrarily nested structure or a scalar object. Note, numpy
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expand_composites: If true, then composite tensors such as tf.SparseTensor
and tf.RaggedTensor are expanded into their component tensors.
Returns:
A Python list, the flattened version of the input.
Raises:
TypeError: The nest is or contains a dict with non-sortable keys.
"""
return _pywrap_tensorflow.Flatten(structure, expand_composites) | [
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gnuradio/gnuradio | 09c3c4fa4bfb1a02caac74cb5334dfe065391e3b | gr-utils/modtool/core/rm.py | python | ModToolRemove._run_subdir | (self, path, globs, makefile_vars, cmakeedit_func=None) | return files_deleted | Delete all files that match a certain pattern in path.
path - The directory in which this will take place
globs - A tuple of standard UNIX globs of files to delete (e.g. *.yml)
makefile_vars - A tuple with a list of CMakeLists.txt-variables which
may contain references to the globbed files
cmakeedit_func - If the CMakeLists.txt needs special editing, use this | Delete all files that match a certain pattern in path.
path - The directory in which this will take place
globs - A tuple of standard UNIX globs of files to delete (e.g. *.yml)
makefile_vars - A tuple with a list of CMakeLists.txt-variables which
may contain references to the globbed files
cmakeedit_func - If the CMakeLists.txt needs special editing, use this | [
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""" Delete all files that match a certain pattern in path.
path - The directory in which this will take place
globs - A tuple of standard UNIX globs of files to delete (e.g. *.yml)
makefile_vars - A tuple with a list of CMakeLists.txt-variables which
may contain references to the globbed files
cmakeedit_func - If the CMakeLists.txt needs special editing, use this
"""
if self.cli:
from ..cli import cli_input
# 1. Create a filtered list
files = []
for g in globs:
files = files + sorted(glob.glob(f"{path}/{g}"))
files_filt = []
logger.info(f"Searching for matching files in {path}/:")
if self.info['blockname']:
# Ensure the blockname given is not confused with similarly named blocks
blockname_pattern = ''
if path == self.info['pydir']:
blockname_pattern = f"^(qa_)?{self.info['blockname']}.py$"
elif path == os.path.join(self.info['pydir'], 'bindings'):
blockname_pattern = f"^{self.info['blockname']}_python.cc$"
elif path == os.path.join(self.info['pydir'], 'bindings', 'docstrings'):
blockname_pattern = f"^{self.info['blockname']}_pydoc_template.h$"
elif path == 'lib':
blockname_pattern = f"^{self.info['blockname']}_impl(\\.h|\\.cc)$"
elif path == self.info['includedir']:
blockname_pattern = f"^{self.info['blockname']}.h$"
elif path == 'grc':
blockname_pattern = f"^{self.info['modname']}_{self.info['blockname']}.block.yml$"
for f in files:
if re.search(blockname_pattern, os.path.basename(f)) is not None:
files_filt.append(f)
elif self.info['pattern']:
# A regex resembling one or several blocks were given as stdin
for f in files:
if re.search(self.info['pattern'], os.path.basename(f)) is not None:
files_filt.append(f)
if len(files_filt) == 0:
logger.info("None found.")
return []
# 2. Delete files, Makefile entries and other occurrences
files_deleted = []
yes = self.info['yes']
for f in files_filt:
b = os.path.basename(f)
if not yes and self.cli:
ans = cli_input(
f"Really delete {f}? [Y/n/a/q]: ").lower().strip()
if ans == 'a':
yes = True
if ans == 'q':
sys.exit(0)
if ans == 'n':
continue
files_deleted.append(b)
logger.info(f"Deleting {f}.")
self.scm.remove_file(f)
os.unlink(f)
if (os.path.exists(f'{path}/CMakeLists.txt')):
ed = CMakeFileEditor(f'{path}/CMakeLists.txt')
logger.info(
f"Deleting occurrences of {b} from {path}/CMakeLists.txt...")
for var in makefile_vars:
ed.remove_value(var, b)
if cmakeedit_func is not None:
cmakeedit_func(b, ed)
ed.write()
self.scm.mark_files_updated((f'{path}/CMakeLists.txt'))
return files_deleted | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/ipython/py3/IPython/core/ultratb.py | python | VerboseTB.format_records | (self, records, last_unique, recursion_repeat) | return frames | Format the stack frames of the traceback | Format the stack frames of the traceback | [
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"""Format the stack frames of the traceback"""
frames = []
skipped = 0
lastrecord = len(records) - 1
for i, r in enumerate(records[: last_unique + recursion_repeat + 1]):
if self.skip_hidden:
if r[0].f_locals.get("__tracebackhide__", 0) and i != lastrecord:
skipped += 1
continue
if skipped:
Colors = self.Colors # just a shorthand + quicker name lookup
ColorsNormal = Colors.Normal # used a lot
frames.append(
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% (Colors.excName, skipped, ColorsNormal)
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skipped = 0
frames.append(self.format_record(*r))
if skipped:
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ColorsNormal = Colors.Normal # used a lot
frames.append(
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if recursion_repeat:
frames.append('... last %d frames repeated, from the frame below ...\n' % recursion_repeat)
frames.append(self.format_record(*records[last_unique+recursion_repeat+1]))
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Slicer/SlicerGitSVNArchive | 65e92bb16c2b32ea47a1a66bee71f238891ee1ca | Base/Python/slicer/util.py | python | arrayFromMarkupsControlPoints | (markupsNode, world = False) | return narray | Return control point positions of a markups node as rows in a numpy array (of size Nx3).
:param world: if set to True then the control points coordinates are returned in world coordinate system
(effect of parent transform to the node is applied).
The returned array is just a copy and so any modification in the array will not affect the markup node.
To modify markup control points based on a numpy array, use :py:meth:`updateMarkupsControlPointsFromArray`. | Return control point positions of a markups node as rows in a numpy array (of size Nx3).
:param world: if set to True then the control points coordinates are returned in world coordinate system
(effect of parent transform to the node is applied).
The returned array is just a copy and so any modification in the array will not affect the markup node.
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numberOfControlPoints = markupsNode.GetNumberOfControlPoints()
import numpy as np
narray = np.zeros([numberOfControlPoints, 3])
for controlPointIndex in range(numberOfControlPoints):
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markupsNode.GetNthControlPointPositionWorld(controlPointIndex, narray[controlPointIndex,:])
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markupsNode.GetNthControlPointPosition(controlPointIndex, narray[controlPointIndex,:])
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smilehao/xlua-framework | a03801538be2b0e92d39332d445b22caca1ef61f | ConfigData/trunk/tools/protobuf-2.5.0/protobuf-2.5.0/python/google/protobuf/service_reflection.py | python | _ServiceStubBuilder.__init__ | (self, service_descriptor) | Initializes an instance of the service stub class builder.
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service_descriptor: ServiceDescriptor to use when constructing the
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Args:
service_descriptor: ServiceDescriptor to use when constructing the
stub class.
"""
self.descriptor = service_descriptor | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | Framework/PythonInterface/plugins/algorithms/LoadEmptyVesuvio.py | python | LoadEmptyVesuvio._load_ip_file | (self, workspace, ip_file) | return update_inst.getProperty("Workspace").value | If provided, load the instrument parameter file into the result workspace.
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"""
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@param ip_file A string containing the full path to an IP file
@return Updated workspace
"""
ip_header = self._get_header_format(ip_file)
# More verbose until the child algorithm stuff is sorted
update_inst = self.createChildAlgorithm("UpdateInstrumentFromFile")
update_inst.setLogging(_LOGGING_)
update_inst.setProperty("Workspace", workspace)
update_inst.setProperty("Filename", ip_file)
update_inst.setProperty("MoveMonitors", False)
update_inst.setProperty("IgnorePhi", True)
update_inst.setProperty("AsciiHeader", ip_header)
update_inst.execute()
return update_inst.getProperty("Workspace").value | [
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numworks/epsilon | 8952d2f8b1de1c3f064eec8ffcea804c5594ba4c | build/device/usb/_objfinalizer.py | python | _AutoFinalizedObjectBase._finalize_object | (self) | Actually finalizes the object (frees allocated resources etc.).
Returns: None
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Returns: None
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"""
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eclipse/sumo | 7132a9b8b6eea734bdec38479026b4d8c4336d03 | tools/traci/_parkingarea.py | python | ParkingAreaDomain.getVehicleCount | (self, stopID) | return self._getUniversal(tc.VAR_STOP_STARTING_VEHICLES_NUMBER, stopID) | getParkingAreaWaiting() -> integer
Get the total number of vehicles stopped at the named parking area. | getParkingAreaWaiting() -> integer
Get the total number of vehicles stopped at the named parking area. | [
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potassco/clingo | e0c91d8f95cc28de1c480a871f9c97c30de83d40 | libpyclingo/clingo/symbolic_atoms.py | python | SymbolicAtoms.signatures | (self) | return [ (_to_str(_lib.clingo_signature_name(c_sig)),
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'''
The list of predicate signatures occurring in the program.
The Boolean indicates the sign of the signature.
'''
size = _c_call('size_t', _lib.clingo_symbolic_atoms_signatures_size, self._rep)
p_sigs = _ffi.new('clingo_signature_t[]', size)
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/imaplib.py | python | ParseFlags | (resp) | return tuple(mo.group('flags').split()) | Convert IMAP4 flags response to python tuple. | Convert IMAP4 flags response to python tuple. | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/html.py | python | HtmlWordCell.SetPreviousWord | (*args, **kwargs) | return _html.HtmlWordCell_SetPreviousWord(*args, **kwargs) | SetPreviousWord(self, HtmlWordCell cell) | SetPreviousWord(self, HtmlWordCell cell) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_core.py | python | MouseState.SetRightDown | (*args, **kwargs) | return _core_.MouseState_SetRightDown(*args, **kwargs) | SetRightDown(self, bool down) | SetRightDown(self, bool down) | [
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natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/python/ops/logging_ops.py | python | Print | (input_, data, message=None, first_n=None, summarize=None,
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input_: A tensor passed through this op.
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zeakey/DeepSkeleton | dc70170f8fd2ec8ca1157484ce66129981104486 | python/caffe/io.py | python | Transformer.set_raw_scale | (self, in_, scale) | Set the scale of raw features s.t. the input blob = input * scale.
While Python represents images in [0, 1], certain Caffe models
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of these models must be 255.
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in_ : which input to assign this scale factor
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_windows.py | python | PyScrolledWindow.DoGetVirtualSize | (*args, **kwargs) | return _windows_.PyScrolledWindow_DoGetVirtualSize(*args, **kwargs) | DoGetVirtualSize(self) -> Size | DoGetVirtualSize(self) -> Size | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_gdi.py | python | DC.MinY | (*args, **kwargs) | return _gdi_.DC_MinY(*args, **kwargs) | MinY(self) -> int
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apple/swift-lldb | d74be846ef3e62de946df343e8c234bde93a8912 | scripts/Python/static-binding/lldb.py | python | SBCommandReturnObject.GetStatus | (self) | return _lldb.SBCommandReturnObject_GetStatus(self) | GetStatus(SBCommandReturnObject self) -> lldb::ReturnStatus | GetStatus(SBCommandReturnObject self) -> lldb::ReturnStatus | [
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kamyu104/LeetCode-Solutions | 77605708a927ea3b85aee5a479db733938c7c211 | Python/count-unhappy-friends.py | python | Solution.unhappyFriends | (self, n, preferences, pairs) | return sum(any(friends[i][j] < friends[i][pairing[i]] and friends[j][i] < friends[j][pairing[j]]
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/io/json/_json.py | python | JSONTableWriter.__init__ | (
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ensure_ascii: bool,
date_unit: str,
index: bool,
default_handler: Optional[Callable[[Any], JSONSerializable]] = None,
indent: int = 0,
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the index (can't do in caller, because the schema inference needs
to know what the index is, forces orient to records, and forces
date_format to 'iso'. | Adds a `schema` attribute with the Table Schema, resets
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if obj.ndim == 2 and isinstance(obj.columns, MultiIndex):
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if (
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if len(timedeltas):
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# Convert PeriodIndex to datetimes before serializing
if is_period_dtype(obj.index):
obj.index = obj.index.to_timestamp()
# exclude index from obj if index=False
if not self.index:
self.obj = obj.reset_index(drop=True)
else:
self.obj = obj.reset_index(drop=False)
self.date_format = "iso"
self.orient = "records"
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sigmaai/self-driving-golf-cart | 8d891600af3d851add27a10ae45cf3c2108bb87c | ros/src/ros_carla_bridge/carla_ros_bridge/src/carla_ros_bridge/debug_helper.py | python | DebugHelper.draw_arrow | (self, marker, lifetime, color) | draw arrow from ros marker | draw arrow from ros marker | [
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"""
draw arrow from ros marker
"""
if marker.points:
if not len(marker.points) == 2:
rospy.logwarn(
"Drawing arrow from points requires two points. Received {}".format(
len(marker.points)))
return
thickness = marker.scale.x
arrow_size = marker.scale.y
start = carla.Location(
x=marker.points[0].x, y=-marker.points[0].y, z=marker.points[0].z)
end = carla.Location(
x=marker.points[1].x, y=-marker.points[1].y, z=marker.points[1].z)
rospy.loginfo("Draw Arrow from {} to {} (color: {}, lifetime: {}, "
"thickness: {}, arrow_size: {})".format(
start, end, color, lifetime, thickness, arrow_size))
self.debug.draw_arrow(
start,
end,
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rospy.logwarn(
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cinder/Cinder | e83f5bb9c01a63eec20168d02953a0879e5100f7 | docs/libs/bs4/dammit.py | python | EntitySubstitution.substitute_xml | (cls, value, make_quoted_attribute=False) | return value | Substitute XML entities for special XML characters.
:param value: A string to be substituted. The less-than sign
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that appear to be part of an entity definition to be left
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# Escape angle brackets and ampersands.
value = cls.AMPERSAND_OR_BRACKET.sub(
cls._substitute_xml_entity, value)
if make_quoted_attribute:
value = cls.quoted_attribute_value(value)
return value | [
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Tencent/CMONGO | c40380caa14e05509f46993aa8b8da966b09b0b5 | src/third_party/scons-2.5.0/scons-local-2.5.0/SCons/Tool/aixlink.py | python | generate | (env) | Add Builders and construction variables for Visual Age linker to
an Environment. | Add Builders and construction variables for Visual Age linker to
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"""
Add Builders and construction variables for Visual Age linker to
an Environment.
"""
link.generate(env)
env['SMARTLINKFLAGS'] = smart_linkflags
env['LINKFLAGS'] = SCons.Util.CLVar('$SMARTLINKFLAGS')
env['SHLINKFLAGS'] = SCons.Util.CLVar('$LINKFLAGS -qmkshrobj -qsuppress=1501-218')
env['SHLIBSUFFIX'] = '.a' | [
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chromiumembedded/cef | 80caf947f3fe2210e5344713c5281d8af9bdc295 | tools/yapf/yapf/yapflib/split_penalty.py | python | _SetStronglyConnected | (*nodes) | Set a STRONGLY_CONNECTED penalty annotation for the given nodes. | Set a STRONGLY_CONNECTED penalty annotation for the given nodes. | [
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] | def _SetStronglyConnected(*nodes):
"""Set a STRONGLY_CONNECTED penalty annotation for the given nodes."""
for node in nodes:
_RecAnnotate(node, pytree_utils.Annotation.SPLIT_PENALTY,
STRONGLY_CONNECTED) | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/layers/python/layers/layers.py | python | dropout | (inputs,
keep_prob=0.5,
noise_shape=None,
is_training=True,
outputs_collections=None,
scope=None) | Returns a dropout op applied to the input.
With probability `keep_prob`, outputs the input element scaled up by
`1 / keep_prob`, otherwise outputs `0`. The scaling is so that the expected
sum is unchanged.
Args:
inputs: The tensor to pass to the nn.dropout op.
keep_prob: A scalar `Tensor` with the same type as x. The probability
that each element is kept.
noise_shape: A 1-D `Tensor` of type `int32`, representing the
shape for randomly generated keep/drop flags.
is_training: A bool `Tensor` indicating whether or not the model
is in training mode. If so, dropout is applied and values scaled.
Otherwise, inputs is returned.
outputs_collections: Collection to add the outputs.
scope: Optional scope for name_scope.
Returns:
A tensor representing the output of the operation. | Returns a dropout op applied to the input. | [
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] | def dropout(inputs,
keep_prob=0.5,
noise_shape=None,
is_training=True,
outputs_collections=None,
scope=None):
"""Returns a dropout op applied to the input.
With probability `keep_prob`, outputs the input element scaled up by
`1 / keep_prob`, otherwise outputs `0`. The scaling is so that the expected
sum is unchanged.
Args:
inputs: The tensor to pass to the nn.dropout op.
keep_prob: A scalar `Tensor` with the same type as x. The probability
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noise_shape: A 1-D `Tensor` of type `int32`, representing the
shape for randomly generated keep/drop flags.
is_training: A bool `Tensor` indicating whether or not the model
is in training mode. If so, dropout is applied and values scaled.
Otherwise, inputs is returned.
outputs_collections: Collection to add the outputs.
scope: Optional scope for name_scope.
Returns:
A tensor representing the output of the operation.
"""
with variable_scope.variable_scope(
scope, 'Dropout', [inputs], custom_getter=_model_variable_getter) as sc:
inputs = ops.convert_to_tensor(inputs)
layer = core_layers.Dropout(rate=1 - keep_prob,
noise_shape=noise_shape,
name=sc.name,
_scope=sc)
outputs = layer.apply(inputs, training=is_training)
return utils.collect_named_outputs(
outputs_collections, sc.original_name_scope, outputs) | [
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openvinotoolkit/openvino | dedcbeafa8b84cccdc55ca64b8da516682b381c7 | tools/mo/openvino/tools/mo/front/caffe/extractors/native_caffe.py | python | blob_name | (i) | Implements legacy schema for blobs naming:
0-th blob is called 'weights'
1-th blob is called 'biases'
then, next blobs are called according to the new default schema
with 'custom_' prefix: custom_2, custom_3 and so on. | Implements legacy schema for blobs naming:
0-th blob is called 'weights'
1-th blob is called 'biases'
then, next blobs are called according to the new default schema
with 'custom_' prefix: custom_2, custom_3 and so on. | [
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"""
Implements legacy schema for blobs naming:
0-th blob is called 'weights'
1-th blob is called 'biases'
then, next blobs are called according to the new default schema
with 'custom_' prefix: custom_2, custom_3 and so on.
"""
predefined_names = ['weights', 'biases']
if i < len(predefined_names):
return predefined_names[i]
else:
return 'custom_{}'.format(i) | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | tools/perf/metrics/io.py | python | IOMetric.AddSummaryResults | (self, browser, results) | Add summary results to the results object. | Add summary results to the results object. | [
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"to",
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"results",
"object",
"."
] | def AddSummaryResults(self, browser, results):
"""Add summary results to the results object."""
io_stats = browser.io_stats
if not io_stats['Browser']:
return
def AddSummariesForProcessType(process_type_io, process_type_trace):
"""For a given process type, add all relevant summary results.
Args:
process_type_io: Type of process (eg Browser or Renderer).
process_type_trace: String to be added to the trace name in the results.
"""
if 'ReadOperationCount' in io_stats[process_type_io]:
results.AddSummary('read_operations_' + process_type_trace, 'count',
io_stats[process_type_io]
['ReadOperationCount'],
data_type='unimportant')
if 'WriteOperationCount' in io_stats[process_type_io]:
results.AddSummary('write_operations_' + process_type_trace, 'count',
io_stats[process_type_io]
['WriteOperationCount'],
data_type='unimportant')
if 'ReadTransferCount' in io_stats[process_type_io]:
results.AddSummary('read_bytes_' + process_type_trace, 'kb',
io_stats[process_type_io]
['ReadTransferCount'] / 1024,
data_type='unimportant')
if 'WriteTransferCount' in io_stats[process_type_io]:
results.AddSummary('write_bytes_' + process_type_trace, 'kb',
io_stats[process_type_io]
['WriteTransferCount'] / 1024,
data_type='unimportant')
AddSummariesForProcessType('Browser', 'browser')
AddSummariesForProcessType('Renderer', 'renderer')
AddSummariesForProcessType('Gpu', 'gpu') | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_core.py | python | KeyboardState.SetMetaDown | (*args, **kwargs) | return _core_.KeyboardState_SetMetaDown(*args, **kwargs) | SetMetaDown(self, bool down) | SetMetaDown(self, bool down) | [
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"""SetMetaDown(self, bool down)"""
return _core_.KeyboardState_SetMetaDown(*args, **kwargs) | [
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bulletphysics/bullet3 | f0f2a952e146f016096db6f85cf0c44ed75b0b9a | examples/pybullet/gym/pybullet_envs/agents/tools/wrappers.py | python | ExternalProcess._receive | (self) | Wait for a message from the worker process and return its payload.
Raises:
Exception: An exception was raised inside the worker process.
KeyError: The reveived message is of an unknown type.
Returns:
Payload object of the message. | Wait for a message from the worker process and return its payload. | [
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"""Wait for a message from the worker process and return its payload.
Raises:
Exception: An exception was raised inside the worker process.
KeyError: The reveived message is of an unknown type.
Returns:
Payload object of the message.
"""
message, payload = self._conn.recv()
# Re-raise exceptions in the main process.
if message == self._EXCEPTION:
stacktrace = payload
raise Exception(stacktrace)
if message == self._RESULT:
return payload
raise KeyError('Received message of unexpected type {}'.format(message)) | [
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PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | python/paddle/distributed/fleet/dataset/dataset.py | python | FileInstantDataset.init | (self, **kwargs) | should be called only once in user's python scripts to initialize setings of dataset instance | should be called only once in user's python scripts to initialize setings of dataset instance | [
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"""
should be called only once in user's python scripts to initialize setings of dataset instance
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mhammond/pywin32 | 44afd86ba8485194df93234639243252deeb40d5 | adodbapi/adodbapi.py | python | Connection._rollback | (self) | In case a database does provide transactions this method causes the the database to roll back to
the start of any pending transaction. Closing a connection without committing the changes first will
cause an implicit rollback to be performed.
If the database does not support the functionality required by the method, the interface should
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the method available. These interfaces should then raise a NotSupportedError to indicate the
non-ability to perform the roll back when the method is invoked. | In case a database does provide transactions this method causes the the database to roll back to
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"""In case a database does provide transactions this method causes the the database to roll back to
the start of any pending transaction. Closing a connection without committing the changes first will
cause an implicit rollback to be performed.
If the database does not support the functionality required by the method, the interface should
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"""
self.messages = []
if (
self.transaction_level
): # trying to roll back with no open transaction causes an error
try:
self.transaction_level = self.connector.RollbackTrans()
if verbose > 1:
print("rollback done on connection at %X" % id(self))
if not self._autocommit and not (
self.connector.Attributes & adc.adXactAbortRetaining
):
# If attributes has adXactAbortRetaining it performs retaining aborts that is,
# calling RollbackTrans automatically starts a new transaction. Not all providers support this.
# If not, we will have to start a new transaction by this command:
if (
not self.transaction_level
): # if self.transaction_level == 0 or self.transaction_level is None:
self.transaction_level = self.connector.BeginTrans()
except Exception as e:
self._raiseConnectionError(api.ProgrammingError, e) | [
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BVLC/caffe | 9b891540183ddc834a02b2bd81b31afae71b2153 | scripts/cpp_lint.py | python | IsBlankLine | (line) | return not line or line.isspace() | Returns true if the given line is blank.
We consider a line to be blank if the line is empty or consists of
only white spaces.
Args:
line: A line of a string.
Returns:
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"""Returns true if the given line is blank.
We consider a line to be blank if the line is empty or consists of
only white spaces.
Args:
line: A line of a string.
Returns:
True, if the given line is blank.
"""
return not line or line.isspace() | [
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Kitware/kwiver | 7ed70308905698b6e88d27ae3dc028c9b016ca0a | python/kwiver/vital/modules/loaders.py | python | ModuleLoader._findPluginFilePaths | (self, namespace) | Searches for modules in `namespace` that are reachable from the paths
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Args:
namespace (str): the importable name of a python module or package
Yields:
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Args:
namespace (str): the importable name of a python module or package
Yields:
str: mod_rel_path - the paths (relative to PYTHONPATH) of
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"""
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for ext in py_exts:
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logger.warn(('do not specify .py extension for the {} '
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for possible in os.listdir(namespace_path):
poss_path = os.path.join(namespace_path, possible)
if os.path.isdir(poss_path):
if not self._isPackage(poss_path):
continue
base = possible
else:
base, ext = os.path.splitext(possible)
if base == '__init__' or ext != '.py':
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mod_rel_path = os.path.join(namespace_rel_path,
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yield mod_rel_path
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# namespace was not a package, check if it was a pyfile
base = namespace_path
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if os.path.isfile(mod_fpath):
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mod_rel_path = namespace_rel_path + ext
yield mod_rel_path
# Dont test remaining pyc / pyo extensions.
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domino-team/openwrt-cc | 8b181297c34d14d3ca521cc9f31430d561dbc688 | package/gli-pub/openwrt-node-packages-master/node/node-v6.9.1/deps/v8/build/vs_toolchain.py | python | _CopyRuntime2015 | (target_dir, source_dir, dll_pattern) | Copy both the msvcp and vccorlib runtime DLLs, only if the target doesn't
exist, but the target directory does exist. | Copy both the msvcp and vccorlib runtime DLLs, only if the target doesn't
exist, but the target directory does exist. | [
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for file_part in ('msvcp', 'vccorlib'):
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target = os.path.join(target_dir, dll)
source = os.path.join(source_dir, dll)
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opengauss-mirror/openGauss-server | e383f1b77720a00ddbe4c0655bc85914d9b02a2b | src/gausskernel/dbmind/tools/anomaly_detection/detector/tools/slow_sql/diagnosing.py | python | diagnose_user | (dbagent, query, start_time) | return zip_rca | Get RCA of system resource.
:param dbagent: obj, interface for sqlite3.
:param query: str, query.
:param start_time: int, start_time.
:return | Get RCA of system resource.
:param dbagent: obj, interface for sqlite3.
:param query: str, query.
:param start_time: int, start_time.
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"""
Get RCA of system resource.
:param dbagent: obj, interface for sqlite3.
:param query: str, query.
:param start_time: int, start_time.
:return
"""
rca = []
suggestion = []
plan = Plan()
timestamp, start_time, finish_time, explain = dbagent.fetch_all_result('select timestamp, start_time, finish_time, '
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.format(start_time=start_time))[0]
plan.parse(explain)
operators = plan.sorted_operators
if not len(operators):
return []
heaviest_opt = operators[0]
for operator in operators:
if str.startswith(operator.name, 'Vector Streaming(type: REDISTRIBUTE)'):
rca.append(PLAN_CAUSE['Redistribute']['DataSkew'])
suggestion.append(PLAN_SUGGESTION['Redistribute']['DataSkew'])
zip_rca = [[x_rca, x_sug] for x_rca, x_sug in zip(rca, suggestion)]
zip_rca.insert(0, finish_time)
zip_rca.insert(0, start_time)
return zip_rca
if heaviest_opt.type == 'Scan':
analyze_scan(dbagent, query, timestamp, rca, suggestion)
elif heaviest_opt.type == 'Sort':
analyze_sort(dbagent, start_time, finish_time, heaviest_opt, rca, suggestion)
elif heaviest_opt.type == 'Other':
if str.startswith(heaviest_opt.name, 'Update'):
analyze_scan(dbagent, query, timestamp, rca, suggestion)
elif str.startswith(heaviest_opt.name, 'Insert'):
rca.append(PLAN_CAUSE['Insert']['DataRes'])
suggestion.append(PLAN_SUGGESTION['Insert']['DataRes'])
elif str.startswith(heaviest_opt.name, 'Delete'):
rca.append(PLAN_CAUSE['Delete']['DataRes'])
suggestion.append(PLAN_SUGGESTION['Delete']['DataRes'])
analyze_join(heaviest_opt, rca, suggestion)
elif heaviest_opt.type == 'Aggregate':
analyze_agg(heaviest_opt, rca, suggestion)
do_resource_check(dbagent, query, timestamp, rca, suggestion)
zip_rca = [[x_rca, x_sug] for x_rca, x_sug in zip(rca, suggestion)]
zip_rca.insert(0, finish_time)
zip_rca.insert(0, start_time)
return zip_rca | [
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lballabio/quantlib-old | 136336947ed4fea9ecc1da6edad188700e821739 | gensrc/gensrc/addins/excel.py | python | ExcelAddin.generateRegisterFunctions | (self, cat) | Generate the code for registering addin functions with Excel. | Generate the code for registering addin functions with Excel. | [
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] | def generateRegisterFunctions(self, cat):
"""Generate the code for registering addin functions with Excel."""
registerCode = ''
unregisterCode = ''
for func in cat.functions(self.name_, supportedplatform.MANUAL):
self.functionCount_ += 1
registerCode += self.generateRegisterFunction(func,
cat.xlFunctionWizardCategory())
unregisterCode += self.generateRegisterFunction(func,
cat.xlFunctionWizardCategory(), False)
self.outputRegisterFile(registerCode, unregisterCode, cat.name()) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py2/scipy/interpolate/fitpack2.py | python | UnivariateSpline._from_tck | (cls, tck, ext=0) | return self | Construct a spline object from given tck | Construct a spline object from given tck | [
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"""Construct a spline object from given tck"""
self = cls.__new__(cls)
t, c, k = tck
self._eval_args = tck
# _data == x,y,w,xb,xe,k,s,n,t,c,fp,fpint,nrdata,ier
self._data = (None, None, None, None, None, k, None, len(t), t,
c, None, None, None, None)
self.ext = ext
return self | [
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ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/third_party/lib_x86_64/python2.7/dist-packages/numpy/ma/extras.py | python | issequence | (seq) | return False | Is seq a sequence (ndarray, list or tuple)? | Is seq a sequence (ndarray, list or tuple)? | [
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CRYTEK/CRYENGINE | 232227c59a220cbbd311576f0fbeba7bb53b2a8c | Editor/Python/windows/Lib/site-packages/pip/_vendor/requests/api.py | python | get | (url, params=None, **kwargs) | return request('get', url, params=params, **kwargs) | Sends a GET request.
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_controls.py | python | RadioBox.FindString | (*args, **kwargs) | return _controls_.RadioBox_FindString(*args, **kwargs) | FindString(self, String s) -> int | FindString(self, String s) -> int | [
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gromacs/gromacs | 7dec3a3f99993cf5687a122de3e12de31c21c399 | docs/doxygen/reporter.py | python | Reporter._write | (self, message) | Implement actual message writing. | Implement actual message writing. | [
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SequoiaDB/SequoiaDB | 2894ed7e5bd6fe57330afc900cf76d0ff0df9f64 | tools/server/php_linux/libxml2/lib/python2.4/site-packages/libxml2.py | python | uCSIsCJKCompatibility | (code) | return ret | Check whether the character is part of CJKCompatibility UCS
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"""Check whether the character is part of CJKCompatibility UCS
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ret = libxml2mod.xmlUCSIsCJKCompatibility(code)
return ret | [
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miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/tensorflow/python/framework/ops.py | python | get_to_proto_function | (collection_name) | Returns the to_proto function for collection_name. | Returns the to_proto function for collection_name. | [
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"""Returns the to_proto function for collection_name."""
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except LookupError:
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Z3Prover/z3 | d745d03afdfdf638d66093e2bfbacaf87187f35b | src/api/python/z3/z3.py | python | is_array | (a) | return isinstance(a, ArrayRef) | Return `True` if `a` is a Z3 array expression.
>>> a = Array('a', IntSort(), IntSort())
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/site-packages/pip/_internal/network/download.py | python | parse_content_disposition | (content_disposition, default_filename) | return filename or default_filename | Parse the "filename" value from a Content-Disposition header, and
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"""
_type, params = cgi.parse_header(content_disposition)
filename = params.get('filename')
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forkineye/ESPixelStick | 22926f1c0d1131f1369fc7cad405689a095ae3cb | dist/bin/pyserial/serial/serialutil.py | python | SerialBase.inter_byte_timeout | (self, ic_timeout) | Change inter-byte timeout setting. | Change inter-byte timeout setting. | [
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raise ValueError("Not a valid timeout: {!r}".format(ic_timeout))
self._inter_byte_timeout = ic_timeout
if self.is_open:
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/ed_basestc.py | python | EditraBaseStc.SetBlockCaret | (self) | Change caret style to block | Change caret style to block | [
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InsightSoftwareConsortium/ITK | 87acfce9a93d928311c38bc371b666b515b9f19d | Modules/ThirdParty/pygccxml/src/pygccxml/parser/source_reader.py | python | source_reader_t.create_xml_file | (self, source_file, destination=None) | return xml_file | This method will generate a xml file using an external tool.
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"""
This method will generate a xml file using an external tool.
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:param source_file: path to the source file that should be parsed.
:type source_file: str
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:type destination: str
:rtype: path to xml file.
"""
xml_file = destination
# If file specified, remove it to start else create new file name
if xml_file:
utils.remove_file_no_raise(xml_file, self.__config)
else:
xml_file = utils.create_temp_file_name(suffix='.xml')
ffname = source_file
if not os.path.isabs(ffname):
ffname = self.__file_full_name(source_file)
command_line = self.__create_command_line(ffname, xml_file)
process = subprocess.Popen(
args=command_line,
shell=True,
stdout=subprocess.PIPE)
try:
results = []
while process.poll() is None:
line = process.stdout.readline()
if line.strip():
results.append(line.rstrip())
for line in process.stdout.readlines():
if line.strip():
results.append(line.rstrip())
exit_status = process.returncode
msg = os.linesep.join([str(s) for s in results])
if self.__config.ignore_gccxml_output:
if not os.path.isfile(xml_file):
raise RuntimeError(
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self.__config.xml_generator.upper() +
": %s status:%s" %
(msg, exit_status))
else:
if msg or exit_status or not \
os.path.isfile(xml_file):
if not os.path.isfile(xml_file):
raise RuntimeError(
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self.__config.xml_generator.upper() +
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else:
raise RuntimeError(
"Error occurred while running " +
self.__config.xml_generator.upper() +
": %s status:%s" % (msg, exit_status))
except Exception:
utils.remove_file_no_raise(xml_file, self.__config)
raise
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process.stdout.close()
return xml_file | [
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bigartm/bigartm | 47e37f982de87aa67bfd475ff1f39da696b181b3 | 3rdparty/protobuf-3.0.0/python/google/protobuf/internal/python_message.py | python | _AddPropertiesForRepeatedField | (field, cls) | Adds a public property for a "repeated" protocol message field. Clients
can use this property to get the value of the field, which will be either a
_RepeatedScalarFieldContainer or _RepeatedCompositeFieldContainer (see
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Note that when clients add values to these containers, we perform
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/cubecolourdialog.py | python | rad2deg | (x) | return 180.0*x/pi | Transforms radians into degrees.
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/distutils/text_file.py | python | TextFile.open | (self, filename) | Open a new file named 'filename'. This overrides both the
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/warnings.py | python | formatwarning | (message, category, filename, lineno, line=None) | return s | Function to format a warning the standard way. | Function to format a warning the standard way. | [
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unicodetype = ()
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message = str(message)
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s += " %s\n" % line
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/scipy/spatial/kdtree.py | python | minkowski_distance | (x, y, p=2) | Compute the L**p distance between two arrays.
Parameters
----------
x : (M, K) array_like
Input array.
y : (N, K) array_like
Input array.
p : float, 1 <= p <= infinity
Which Minkowski p-norm to use.
Examples
--------
>>> from scipy.spatial import minkowski_distance
>>> minkowski_distance([[0,0],[0,0]], [[1,1],[0,1]])
array([ 1.41421356, 1. ]) | Compute the L**p distance between two arrays. | [
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"""
Compute the L**p distance between two arrays.
Parameters
----------
x : (M, K) array_like
Input array.
y : (N, K) array_like
Input array.
p : float, 1 <= p <= infinity
Which Minkowski p-norm to use.
Examples
--------
>>> from scipy.spatial import minkowski_distance
>>> minkowski_distance([[0,0],[0,0]], [[1,1],[0,1]])
array([ 1.41421356, 1. ])
"""
x = np.asarray(x)
y = np.asarray(y)
if p == np.inf or p == 1:
return minkowski_distance_p(x, y, p)
else:
return minkowski_distance_p(x, y, p)**(1./p) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/protorpc/protorpc/util.py | python | TimeZoneOffset.utcoffset | (self, dt) | return datetime.timedelta(minutes=self.__offset) | Get the a timedelta with the time zone's offset from UTC.
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_controls.py | python | PyPickerBase.UpdateTextCtrlFromPicker | (*args, **kwargs) | return _controls_.PyPickerBase_UpdateTextCtrlFromPicker(*args, **kwargs) | UpdateTextCtrlFromPicker(self) | UpdateTextCtrlFromPicker(self) | [
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miyosuda/TensorFlowAndroidMNIST | 7b5a4603d2780a8a2834575706e9001977524007 | jni-build/jni/include/tensorflow/contrib/graph_editor/match.py | python | OpMatcher.input_ops | (self, *args) | return self | Add input matches. | Add input matches. | [
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YosysHQ/nextpnr | 74c99f9195eeb47d106ca74b7abb894cfd47cc03 | 3rdparty/pybind11/pybind11/setup_helpers.py | python | ParallelCompile.function | (self) | return compile_function | Builds a function object usable as distutils.ccompiler.CCompiler.compile. | Builds a function object usable as distutils.ccompiler.CCompiler.compile. | [
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threads = 1
if threads == 0:
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threads = self.max if self.max and self.max < threads else threads
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qgis/QGIS | 15a77662d4bb712184f6aa60d0bd663010a76a75 | python/pyplugin_installer/installer_data.py | python | Repositories.all | (self) | return self.mRepositories | return dict of all repositories | return dict of all repositories | [
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] | def all(self) -> Dict[str, Dict[str, Any]]:
""" return dict of all repositories """
return self.mRepositories | [
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goldeneye-source/ges-code | 2630cd8ef3d015af53c72ec2e19fc1f7e7fe8d9d | thirdparty/protobuf-2.3.0/python/google/protobuf/descriptor.py | python | EnumValueDescriptor.__init__ | (self, name, index, number, type=None, options=None) | Arguments are as described in the attribute description above. | Arguments are as described in the attribute description above. | [
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self.index = index
self.number = number
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eclipse/sumo | 7132a9b8b6eea734bdec38479026b4d8c4336d03 | tools/contributed/sumopy/agilepy/lib_wx/objpanel.py | python | ScalarPanel._prepend_name | (self, names, name) | Predends name if not in list | Predends name if not in list | [
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cms-sw/cmssw | fd9de012d503d3405420bcbeec0ec879baa57cf2 | HLTrigger/Configuration/python/customizeHLTforPatatrack.py | python | customiseEcalLocalReconstruction | (process) | return process | from RecoLocalCalo.EcalRecProducers.ecalRecHitGPU_cfi import ecalRecHitGPU as _ecalRecHitGPU
process.hltEcalRecHitGPU = _ecalRecHitGPU.clone(
uncalibrecHitsInLabelEB = ("hltEcalUncalibRecHitGPU","EcalUncalibRecHitsEB"),
uncalibrecHitsInLabelEE = ("hltEcalUncalibRecHitGPU","EcalUncalibRecHitsEE"),
)
from RecoLocalCalo.EcalRecProducers.ecalCPURecHitProducer_cfi import ecalCPURecHitProducer as _ecalCPURecHitProducer
process.hltEcalRecHitSoA = _ecalCPURecHitProducer.clone(
recHitsInLabelEB = ("hltEcalRecHitGPU", "EcalRecHitsEB"),
recHitsInLabelEE = ("hltEcalRecHitGPU", "EcalRecHitsEE"),
)
# SwitchProducer wrapping the legacy ECAL calibrated rechits producer or a converter from SoA to legacy format
from RecoLocalCalo.EcalRecProducers.ecalRecHitConvertGPU2CPUFormat_cfi import ecalRecHitConvertGPU2CPUFormat as _ecalRecHitConvertGPU2CPUFormat
process.hltEcalRecHit = SwitchProducerCUDA(
# legacy producer
cpu = process.hltEcalRecHit,
# convert the ECAL calibrated rechits from SoA to legacy format
cuda = _ecalRecHitConvertGPU2CPUFormat.clone(
recHitsLabelGPUEB = ("hltEcalRecHitSoA", "EcalRecHitsEB"),
recHitsLabelGPUEE = ("hltEcalRecHitSoA", "EcalRecHitsEE"),
)
) | from RecoLocalCalo.EcalRecProducers.ecalRecHitGPU_cfi import ecalRecHitGPU as _ecalRecHitGPU
process.hltEcalRecHitGPU = _ecalRecHitGPU.clone(
uncalibrecHitsInLabelEB = ("hltEcalUncalibRecHitGPU","EcalUncalibRecHitsEB"),
uncalibrecHitsInLabelEE = ("hltEcalUncalibRecHitGPU","EcalUncalibRecHitsEE"),
) | [
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hasHLTEcalPreshowerSeq = any(seq in process.__dict__ for seq in ['HLTDoFullUnpackingEgammaEcalMFSequence', 'HLTDoFullUnpackingEgammaEcalSequence'])
if not (hasHLTEcalPreshowerSeq or 'HLTDoFullUnpackingEgammaEcalWithoutPreshowerSequence' in process.__dict__):
return process
# FIXME replace the Sequences with empty ones to avoid expanding them during the (re)definition of Modules and EDAliases
process.HLTDoFullUnpackingEgammaEcalWithoutPreshowerSequence = cms.Sequence()
if hasHLTEcalPreshowerSeq:
process.HLTDoFullUnpackingEgammaEcalMFSequence = cms.Sequence()
process.HLTDoFullUnpackingEgammaEcalSequence = cms.Sequence()
# Event Setup
process.load("EventFilter.EcalRawToDigi.ecalElectronicsMappingGPUESProducer_cfi")
process.load("RecoLocalCalo.EcalRecProducers.ecalGainRatiosGPUESProducer_cfi")
process.load("RecoLocalCalo.EcalRecProducers.ecalPedestalsGPUESProducer_cfi")
process.load("RecoLocalCalo.EcalRecProducers.ecalPulseCovariancesGPUESProducer_cfi")
process.load("RecoLocalCalo.EcalRecProducers.ecalPulseShapesGPUESProducer_cfi")
process.load("RecoLocalCalo.EcalRecProducers.ecalSamplesCorrelationGPUESProducer_cfi")
process.load("RecoLocalCalo.EcalRecProducers.ecalTimeBiasCorrectionsGPUESProducer_cfi")
process.load("RecoLocalCalo.EcalRecProducers.ecalTimeCalibConstantsGPUESProducer_cfi")
process.load("RecoLocalCalo.EcalRecProducers.ecalMultifitParametersGPUESProducer_cfi")
process.load("RecoLocalCalo.EcalRecProducers.ecalRechitADCToGeVConstantGPUESProducer_cfi")
process.load("RecoLocalCalo.EcalRecProducers.ecalRechitChannelStatusGPUESProducer_cfi")
process.load("RecoLocalCalo.EcalRecProducers.ecalIntercalibConstantsGPUESProducer_cfi")
process.load("RecoLocalCalo.EcalRecProducers.ecalLaserAPDPNRatiosGPUESProducer_cfi")
process.load("RecoLocalCalo.EcalRecProducers.ecalLaserAPDPNRatiosRefGPUESProducer_cfi")
process.load("RecoLocalCalo.EcalRecProducers.ecalLaserAlphasGPUESProducer_cfi")
process.load("RecoLocalCalo.EcalRecProducers.ecalLinearCorrectionsGPUESProducer_cfi")
process.load("RecoLocalCalo.EcalRecProducers.ecalRecHitParametersGPUESProducer_cfi")
# Modules and EDAliases
# ECAL unpacker running on gpu
from EventFilter.EcalRawToDigi.ecalRawToDigiGPU_cfi import ecalRawToDigiGPU as _ecalRawToDigiGPU
process.hltEcalDigisGPU = _ecalRawToDigiGPU.clone()
# SwitchProducer wrapping the legacy ECAL unpacker or the ECAL digi converter from SoA format on gpu to legacy format on cpu
if not isinstance(process.hltEcalDigis, SwitchProducerCUDA):
if 'hltEcalDigisLegacy' in process.__dict__:
raise Exception('unsupported configuration: "process.hltEcalDigis" is not SwitchProducerCUDA, but "process.hltEcalDigisLegacy" already exists')
process.hltEcalDigisLegacy = process.hltEcalDigis.clone()
process.hltEcalDigis = SwitchProducerCUDA(
# legacy producer
cpu = cms.EDAlias(
hltEcalDigisLegacy = cms.VPSet(
cms.PSet(type = cms.string("EBDigiCollection")),
cms.PSet(type = cms.string("EEDigiCollection")),
cms.PSet(type = cms.string("EBDetIdedmEDCollection")),
cms.PSet(type = cms.string("EEDetIdedmEDCollection")),
cms.PSet(type = cms.string("EBSrFlagsSorted")),
cms.PSet(type = cms.string("EESrFlagsSorted")),
cms.PSet(type = cms.string("EcalElectronicsIdedmEDCollection"), fromProductInstance = cms.string("EcalIntegrityBlockSizeErrors")),
cms.PSet(type = cms.string("EcalElectronicsIdedmEDCollection"), fromProductInstance = cms.string("EcalIntegrityTTIdErrors")),
cms.PSet(type = cms.string("EcalElectronicsIdedmEDCollection"), fromProductInstance = cms.string("EcalIntegrityZSXtalIdErrors")),
cms.PSet(type = cms.string("EcalPnDiodeDigisSorted")),
cms.PSet(type = cms.string("EcalPseudoStripInputDigisSorted"), fromProductInstance = cms.string("EcalPseudoStripInputs")),
cms.PSet(type = cms.string("EcalTriggerPrimitiveDigisSorted"), fromProductInstance = cms.string("EcalTriggerPrimitives")),
)
)
)
# convert ECAL digis from SoA format on gpu to legacy format on cpu
from EventFilter.EcalRawToDigi.ecalCPUDigisProducer_cfi import ecalCPUDigisProducer as _ecalCPUDigisProducer
process.hltEcalDigis.cuda = _ecalCPUDigisProducer.clone(
digisInLabelEB = ("hltEcalDigisGPU", "ebDigis"),
digisInLabelEE = ("hltEcalDigisGPU", "eeDigis"),
produceDummyIntegrityCollections = True
)
# ECAL multifit running on gpu
from RecoLocalCalo.EcalRecProducers.ecalUncalibRecHitProducerGPU_cfi import ecalUncalibRecHitProducerGPU as _ecalUncalibRecHitProducerGPU
process.hltEcalUncalibRecHitGPU = _ecalUncalibRecHitProducerGPU.clone(
digisLabelEB = ("hltEcalDigisGPU", "ebDigis"),
digisLabelEE = ("hltEcalDigisGPU", "eeDigis"),
shouldRunTimingComputation = False
)
# copy the ECAL uncalibrated rechits from gpu to cpu in SoA format
from RecoLocalCalo.EcalRecProducers.ecalCPUUncalibRecHitProducer_cfi import ecalCPUUncalibRecHitProducer as _ecalCPUUncalibRecHitProducer
process.hltEcalUncalibRecHitSoA = _ecalCPUUncalibRecHitProducer.clone(
recHitsInLabelEB = ("hltEcalUncalibRecHitGPU", "EcalUncalibRecHitsEB"),
recHitsInLabelEE = ("hltEcalUncalibRecHitGPU", "EcalUncalibRecHitsEE"),
)
# SwitchProducer wrapping the legacy ECAL uncalibrated rechits producer or a converter from SoA to legacy format
if not isinstance(process.hltEcalUncalibRecHit, SwitchProducerCUDA):
process.hltEcalUncalibRecHit = SwitchProducerCUDA(
# legacy producer
cpu = process.hltEcalUncalibRecHit
)
# convert the ECAL uncalibrated rechits from SoA to legacy format
from RecoLocalCalo.EcalRecProducers.ecalUncalibRecHitConvertGPU2CPUFormat_cfi import ecalUncalibRecHitConvertGPU2CPUFormat as _ecalUncalibRecHitConvertGPU2CPUFormat
process.hltEcalUncalibRecHit.cuda = _ecalUncalibRecHitConvertGPU2CPUFormat.clone(
recHitsLabelGPUEB = ("hltEcalUncalibRecHitSoA", "EcalUncalibRecHitsEB"),
recHitsLabelGPUEE = ("hltEcalUncalibRecHitSoA", "EcalUncalibRecHitsEE"),
)
# Reconstructing the ECAL calibrated rechits on gpu works, but is extremely slow.
# Disable it for the time being, until the performance has been addressed.
"""
from RecoLocalCalo.EcalRecProducers.ecalRecHitGPU_cfi import ecalRecHitGPU as _ecalRecHitGPU
process.hltEcalRecHitGPU = _ecalRecHitGPU.clone(
uncalibrecHitsInLabelEB = ("hltEcalUncalibRecHitGPU","EcalUncalibRecHitsEB"),
uncalibrecHitsInLabelEE = ("hltEcalUncalibRecHitGPU","EcalUncalibRecHitsEE"),
)
from RecoLocalCalo.EcalRecProducers.ecalCPURecHitProducer_cfi import ecalCPURecHitProducer as _ecalCPURecHitProducer
process.hltEcalRecHitSoA = _ecalCPURecHitProducer.clone(
recHitsInLabelEB = ("hltEcalRecHitGPU", "EcalRecHitsEB"),
recHitsInLabelEE = ("hltEcalRecHitGPU", "EcalRecHitsEE"),
)
# SwitchProducer wrapping the legacy ECAL calibrated rechits producer or a converter from SoA to legacy format
from RecoLocalCalo.EcalRecProducers.ecalRecHitConvertGPU2CPUFormat_cfi import ecalRecHitConvertGPU2CPUFormat as _ecalRecHitConvertGPU2CPUFormat
process.hltEcalRecHit = SwitchProducerCUDA(
# legacy producer
cpu = process.hltEcalRecHit,
# convert the ECAL calibrated rechits from SoA to legacy format
cuda = _ecalRecHitConvertGPU2CPUFormat.clone(
recHitsLabelGPUEB = ("hltEcalRecHitSoA", "EcalRecHitsEB"),
recHitsLabelGPUEE = ("hltEcalRecHitSoA", "EcalRecHitsEE"),
)
)
"""
# SwitchProducer wrapping the legacy ECAL rechits producer
# the gpu unpacker does not produce the TPs used for the recovery, so the SwitchProducer alias does not provide them:
# - the cpu uncalibrated rechit producer may mark them for recovery, read the TPs explicitly from the legacy unpacker
# - the gpu uncalibrated rechit producer does not flag them for recovery, so the TPs are not necessary
if not isinstance(process.hltEcalRecHit, SwitchProducerCUDA):
process.hltEcalRecHit = SwitchProducerCUDA(
cpu = process.hltEcalRecHit.clone(
triggerPrimitiveDigiCollection = ('hltEcalDigisLegacy', 'EcalTriggerPrimitives')
),
cuda = process.hltEcalRecHit.clone(
triggerPrimitiveDigiCollection = 'unused'
)
)
# Tasks and Sequences
process.HLTDoFullUnpackingEgammaEcalWithoutPreshowerTask = cms.Task(
process.hltEcalDigisGPU, # unpack ECAL digis on gpu
process.hltEcalDigisLegacy, # legacy producer, referenced in the SwitchProducer
process.hltEcalDigis, # SwitchProducer
process.hltEcalUncalibRecHitGPU, # run ECAL local reconstruction and multifit on gpu
process.hltEcalUncalibRecHitSoA, # needed by hltEcalPhiSymFilter - copy to host
process.hltEcalUncalibRecHit, # needed by hltEcalPhiSymFilter - convert to legacy format
# process.hltEcalRecHitGPU, # make ECAL calibrated rechits on gpu
# process.hltEcalRecHitSoA, # copy to host
process.hltEcalDetIdToBeRecovered, # legacy producer
process.hltEcalRecHit) # legacy producer
process.HLTDoFullUnpackingEgammaEcalWithoutPreshowerSequence = cms.Sequence(
process.HLTDoFullUnpackingEgammaEcalWithoutPreshowerTask)
if hasHLTEcalPreshowerSeq:
process.HLTPreshowerTask = cms.Task(
process.hltEcalPreshowerDigis, # unpack ECAL preshower digis on the host
process.hltEcalPreshowerRecHit) # build ECAL preshower rechits on the host
process.HLTPreshowerSequence = cms.Sequence(process.HLTPreshowerTask)
process.HLTDoFullUnpackingEgammaEcalTask = cms.Task(
process.HLTDoFullUnpackingEgammaEcalWithoutPreshowerTask,
process.HLTPreshowerTask)
process.HLTDoFullUnpackingEgammaEcalSequence = cms.Sequence(
process.HLTDoFullUnpackingEgammaEcalTask)
process.HLTDoFullUnpackingEgammaEcalMFSequence = cms.Sequence(
process.HLTDoFullUnpackingEgammaEcalTask)
# done
return process | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/ogl/_composit.py | python | DivisionShape.GetLeftSideStyle | (self) | return self._leftSideStyle | Return the style used for the left side of the division. | Return the style used for the left side of the division. | [
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return self._leftSideStyle | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_controls.py | python | AnyButton.SetBitmapCurrent | (*args, **kwargs) | return _controls_.AnyButton_SetBitmapCurrent(*args, **kwargs) | SetBitmapCurrent(self, Bitmap bitmap) | SetBitmapCurrent(self, Bitmap bitmap) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py2/pandas/core/ops.py | python | _arith_method_SERIES | (cls, op, special) | return wrapper | Wrapper function for Series arithmetic operations, to avoid
code duplication. | Wrapper function for Series arithmetic operations, to avoid
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"""
Wrapper function for Series arithmetic operations, to avoid
code duplication.
"""
str_rep = _get_opstr(op, cls)
op_name = _get_op_name(op, special)
eval_kwargs = _gen_eval_kwargs(op_name)
fill_zeros = _gen_fill_zeros(op_name)
construct_result = (_construct_divmod_result
if op in [divmod, rdivmod] else _construct_result)
def na_op(x, y):
import pandas.core.computation.expressions as expressions
try:
result = expressions.evaluate(op, str_rep, x, y, **eval_kwargs)
except TypeError:
result = masked_arith_op(x, y, op)
result = missing.fill_zeros(result, x, y, op_name, fill_zeros)
return result
def safe_na_op(lvalues, rvalues):
"""
return the result of evaluating na_op on the passed in values
try coercion to object type if the native types are not compatible
Parameters
----------
lvalues : array-like
rvalues : array-like
Raises
------
TypeError: invalid operation
"""
try:
with np.errstate(all='ignore'):
return na_op(lvalues, rvalues)
except Exception:
if is_object_dtype(lvalues):
return libalgos.arrmap_object(lvalues,
lambda x: op(x, rvalues))
raise
def wrapper(left, right):
if isinstance(right, ABCDataFrame):
return NotImplemented
left, right = _align_method_SERIES(left, right)
res_name = get_op_result_name(left, right)
right = maybe_upcast_for_op(right)
if is_categorical_dtype(left):
raise TypeError("{typ} cannot perform the operation "
"{op}".format(typ=type(left).__name__, op=str_rep))
elif is_datetime64_dtype(left) or is_datetime64tz_dtype(left):
# Give dispatch_to_index_op a chance for tests like
# test_dt64_series_add_intlike, which the index dispatching handles
# specifically.
result = dispatch_to_index_op(op, left, right, pd.DatetimeIndex)
return construct_result(left, result,
index=left.index, name=res_name,
dtype=result.dtype)
elif (is_extension_array_dtype(left) or
(is_extension_array_dtype(right) and not is_scalar(right))):
# GH#22378 disallow scalar to exclude e.g. "category", "Int64"
return dispatch_to_extension_op(op, left, right)
elif is_timedelta64_dtype(left):
result = dispatch_to_index_op(op, left, right, pd.TimedeltaIndex)
return construct_result(left, result,
index=left.index, name=res_name)
elif is_timedelta64_dtype(right):
# We should only get here with non-scalar or timedelta64('NaT')
# values for right
# Note: we cannot use dispatch_to_index_op because
# that may incorrectly raise TypeError when we
# should get NullFrequencyError
result = op(pd.Index(left), right)
return construct_result(left, result,
index=left.index, name=res_name,
dtype=result.dtype)
lvalues = left.values
rvalues = right
if isinstance(rvalues, ABCSeries):
rvalues = rvalues.values
result = safe_na_op(lvalues, rvalues)
return construct_result(left, result,
index=left.index, name=res_name, dtype=None)
wrapper.__name__ = op_name
return wrapper | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/imaplib.py | python | IMAP4.enable | (self, capability) | return typ, data | Send an RFC5161 enable string to the server.
(typ, [data]) = <intance>.enable(capability) | Send an RFC5161 enable string to the server. | [
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] | def enable(self, capability):
"""Send an RFC5161 enable string to the server.
(typ, [data]) = <intance>.enable(capability)
"""
if 'ENABLE' not in self.capabilities:
raise IMAP4.error("Server does not support ENABLE")
typ, data = self._simple_command('ENABLE', capability)
if typ == 'OK' and 'UTF8=ACCEPT' in capability.upper():
self._mode_utf8()
return typ, data | [
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cms-sw/cmssw | fd9de012d503d3405420bcbeec0ec879baa57cf2 | Validation/RecoTrack/python/plotting/plotting.py | python | PlotGroup.onlyForPileup | (self) | return self._onlyForPileup | Return True if the PlotGroup is intended only for pileup samples | Return True if the PlotGroup is intended only for pileup samples | [
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"""Return True if the PlotGroup is intended only for pileup samples"""
return self._onlyForPileup | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/ops/weights_broadcast_ops.py | python | assert_broadcastable | (weights, values) | Asserts `weights` can be broadcast to `values`.
In `tf.losses` and `tf.metrics`, we support limited weight broadcasting. We
let weights be either scalar, or the same rank as the target values, with each
dimension either 1, or the same as the corresponding values dimension.
Args:
weights: `Tensor` of weights.
values: `Tensor` of values to which weights are applied.
Returns:
`Operation` raising `InvalidArgumentError` if `weights` has incorrect shape.
`no_op` if static checks determine `weights` has correct shape.
Raises:
ValueError: If static checks determine `weights` has incorrect shape. | Asserts `weights` can be broadcast to `values`. | [
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] | def assert_broadcastable(weights, values):
"""Asserts `weights` can be broadcast to `values`.
In `tf.losses` and `tf.metrics`, we support limited weight broadcasting. We
let weights be either scalar, or the same rank as the target values, with each
dimension either 1, or the same as the corresponding values dimension.
Args:
weights: `Tensor` of weights.
values: `Tensor` of values to which weights are applied.
Returns:
`Operation` raising `InvalidArgumentError` if `weights` has incorrect shape.
`no_op` if static checks determine `weights` has correct shape.
Raises:
ValueError: If static checks determine `weights` has incorrect shape.
"""
with ops.name_scope(None, "assert_broadcastable", (weights, values)) as scope:
with ops.name_scope(None, "weights", (weights,)) as weights_scope:
weights = ops.convert_to_tensor(weights, name=weights_scope)
weights_shape = array_ops.shape(weights, name="shape")
weights_rank = array_ops.rank(weights, name="rank")
weights_rank_static = tensor_util.constant_value(weights_rank)
with ops.name_scope(None, "values", (values,)) as values_scope:
values = ops.convert_to_tensor(values, name=values_scope)
values_shape = array_ops.shape(values, name="shape")
values_rank = array_ops.rank(values, name="rank")
values_rank_static = tensor_util.constant_value(values_rank)
# Try static checks.
if weights_rank_static is not None and values_rank_static is not None:
if weights_rank_static == 0:
return control_flow_ops.no_op(name="static_scalar_check_success")
if weights_rank_static != values_rank_static:
raise ValueError(
f"{_ASSERT_BROADCASTABLE_ERROR_PREFIX} values.rank="
f"{values_rank_static}. weights.rank={weights_rank_static}. "
f"values.shape={values.shape}. weights.shape={weights.shape}. "
f"Received weights={weights}, values={values}")
weights_shape_static = tensor_util.constant_value(weights_shape)
values_shape_static = tensor_util.constant_value(values_shape)
if weights_shape_static is not None and values_shape_static is not None:
# Sanity check, this should always be true since we checked rank above.
ndims = len(values_shape_static)
assert ndims == len(weights_shape_static)
for i in range(ndims):
if weights_shape_static[i] not in (1, values_shape_static[i]):
raise ValueError(
f"{_ASSERT_BROADCASTABLE_ERROR_PREFIX} Mismatch at dim {i}. "
f"values.shape={values_shape_static}, weights.shape="
f"{weights_shape_static}. Received weights={weights}, "
f"values={values}")
return control_flow_ops.no_op(name="static_dims_check_success")
# Dynamic checks.
is_scalar = math_ops.equal(0, weights_rank, name="is_scalar")
data = (
_ASSERT_BROADCASTABLE_ERROR_PREFIX,
"weights.shape=", weights.name, weights_shape,
"values.shape=", values.name, values_shape,
"is_scalar=", is_scalar,
)
is_valid_shape = control_flow_ops.cond(
is_scalar,
lambda: is_scalar,
lambda: _has_valid_nonscalar_shape( # pylint: disable=g-long-lambda
weights_rank, weights_shape, values_rank, values_shape),
name="is_valid_shape")
return control_flow_ops.Assert(is_valid_shape, data, name=scope) | [
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apple/swift-lldb | d74be846ef3e62de946df343e8c234bde93a8912 | scripts/Python/static-binding/lldb.py | python | SBAddress.GetLineEntry | (self) | return _lldb.SBAddress_GetLineEntry(self) | GetLineEntry(SBAddress self) -> SBLineEntry | GetLineEntry(SBAddress self) -> SBLineEntry | [
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] | def GetLineEntry(self):
"""GetLineEntry(SBAddress self) -> SBLineEntry"""
return _lldb.SBAddress_GetLineEntry(self) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/flatmenu.py | python | FlatMenuItem.SetLabel | (self, text) | Sets the label text for this item from the text (excluding the accelerator).
:param string `text`: the new item label (excluding the accelerator). | Sets the label text for this item from the text (excluding the accelerator). | [
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Sets the label text for this item from the text (excluding the accelerator).
:param string `text`: the new item label (excluding the accelerator).
"""
if text:
indx = text.find("\t")
if indx >= 0:
self._accelStr = text[indx+1:]
label = text[0:indx]
else:
self._accelStr = ""
label = text
self._mnemonicIdx, self._label = GetAccelIndex(label)
else:
self._mnemonicIdx = wx.NOT_FOUND
self._label = ""
if self._parentMenu:
self._parentMenu.UpdateItem(self) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_core.py | python | Sizer.FitInside | (*args, **kwargs) | return _core_.Sizer_FitInside(*args, **kwargs) | FitInside(self, Window window)
Tell the sizer to resize the *virtual size* of the *window* to match the
sizer's minimal size. This will not alter the on screen size of the
window, but may cause the addition/removal/alteration of scrollbars
required to view the virtual area in windows which manage it.
:see: `wx.ScrolledWindow.SetScrollbars` | FitInside(self, Window window) | [
"FitInside",
"(",
"self",
"Window",
"window",
")"
] | def FitInside(*args, **kwargs):
"""
FitInside(self, Window window)
Tell the sizer to resize the *virtual size* of the *window* to match the
sizer's minimal size. This will not alter the on screen size of the
window, but may cause the addition/removal/alteration of scrollbars
required to view the virtual area in windows which manage it.
:see: `wx.ScrolledWindow.SetScrollbars`
"""
return _core_.Sizer_FitInside(*args, **kwargs) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/asyncio/events.py | python | set_event_loop | (loop) | Equivalent to calling get_event_loop_policy().set_event_loop(loop). | Equivalent to calling get_event_loop_policy().set_event_loop(loop). | [
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deepmind/open_spiel | 4ca53bea32bb2875c7385d215424048ae92f78c8 | open_spiel/python/algorithms/jpsro.py | python | _rmwcce | (meta_game, per_player_repeats, ignore_repeats=False) | return dist, dict() | Random maximum welfare CCE. | Random maximum welfare CCE. | [
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"."
] | def _rmwcce(meta_game, per_player_repeats, ignore_repeats=False):
"""Random maximum welfare CCE."""
del ignore_repeats
num_players = len(per_player_repeats)
cost = np.ravel(np.sum(meta_game, axis=0))
cost += np.ravel(np.random.normal(size=meta_game.shape[1:])) * 1e-6
a_mat, _ = _cce_constraints(
meta_game, [0.0] * num_players, remove_null=True,
zero_tolerance=1e-8)
e_vec = np.zeros([a_mat.shape[0]])
x, _ = _linear(meta_game, a_mat, e_vec, cost=cost)
dist = np.reshape(x, meta_game.shape[1:])
return dist, dict() | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_misc.py | python | LogTextCtrl.__init__ | (self, *args, **kwargs) | __init__(self, wxTextCtrl pTextCtrl) -> LogTextCtrl | __init__(self, wxTextCtrl pTextCtrl) -> LogTextCtrl | [
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"""__init__(self, wxTextCtrl pTextCtrl) -> LogTextCtrl"""
_misc_.LogTextCtrl_swiginit(self,_misc_.new_LogTextCtrl(*args, **kwargs)) | [
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ricardoquesada/Spidermonkey | 4a75ea2543408bd1b2c515aa95901523eeef7858 | ipc/ipdl/ipdl/parser.py | python | p_ActorType | (p) | ActorType : ID ':' State | ActorType : ID ':' State | [
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loc = locFromTok(p, 1)
p[0] = TypeSpec(loc, QualifiedId(loc, p[1]), state=p[3]) | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/ops/_op_impl/cpu/dropout.py | python | _dropout_cpu | () | return | Dropout cpu register | Dropout cpu register | [
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"register"
] | def _dropout_cpu():
"""Dropout cpu register"""
return | [
"def",
"_dropout_cpu",
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/html/parser.py | python | HTMLParser.__init__ | (self, *, convert_charrefs=True) | Initialize and reset this instance.
If convert_charrefs is True (the default), all character references
are automatically converted to the corresponding Unicode characters. | Initialize and reset this instance. | [
"Initialize",
"and",
"reset",
"this",
"instance",
"."
] | def __init__(self, *, convert_charrefs=True):
"""Initialize and reset this instance.
If convert_charrefs is True (the default), all character references
are automatically converted to the corresponding Unicode characters.
"""
self.convert_charrefs = convert_charrefs
self.reset() | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | scripts/abins/atomsdata.py | python | AtomsData._check_item | (item: Dict[str, Any], n_atoms: Optional[int] = None) | return item | Raise an error if Atoms data item is unsuitable
:param item: element to be added
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"""
Raise an error if Atoms data item is unsuitable
:param item: element to be added
:param n_atoms: Number of atoms in data. If provided, check that "sort" value is not higher than expected.
"""
if not isinstance(item, dict):
raise ValueError("Every element of AtomsData should be a dictionary.")
if not sorted(item.keys()) == sorted(abins.constants.ALL_KEYWORDS_ATOMS_DATA):
raise ValueError("Invalid structure of the dictionary to be added.")
# "symbol"
if not item["symbol"] in abins.constants.ALL_SYMBOLS:
# Check is symbol was loaded as type bytes
utf8_symbol = item["symbol"].decode("utf-8")
if utf8_symbol in abins.constants.ALL_SYMBOLS:
item["symbol"] = utf8_symbol
else:
raise ValueError("Invalid value of symbol.")
# "coord"
coord = item["coord"]
if not isinstance(coord, np.ndarray):
raise ValueError("Coordinates of an atom should have a form of a numpy array.")
if len(coord.shape) != 1:
raise ValueError("Coordinates should have a form of 1D numpy array.")
if coord.shape[0] != 3:
raise ValueError("Coordinates should have a form of numpy array with three elements.")
if coord.dtype.num != abins.constants.FLOAT_ID:
raise ValueError("Coordinates array should have real float dtype.")
# "sort"
sort = item["sort"]
if not isinstance(sort, numbers.Integral):
raise ValueError("Parameter 'sort' should be integer.")
if sort < 0:
raise ValueError("Parameter 'sort' cannot be negative.")
if n_atoms is not None and (sort + 1) > n_atoms:
raise ValueError("Parameter 'sort' should not exceed atom indices")
# "mass"
mass = item["mass"]
if not isinstance(mass, numbers.Real):
raise ValueError("Mass of atom should be a real number.")
if mass < 0:
raise ValueError("Mass of atom cannot be negative.")
return item | [
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gtcasl/gpuocelot | fa63920ee7c5f9a86e264cd8acd4264657cbd190 | ocelot/scripts/build_environment.py | python | getDebianArchitecture | () | return arch[0] | Determines the debian architecture
return {deb_arch} | Determines the debian architecture
return {deb_arch} | [
"Determines",
"the",
"debian",
"architecture",
"return",
"{",
"deb_arch",
"}"
] | def getDebianArchitecture():
"""Determines the debian architecture
return {deb_arch}
"""
# check for supported OS
if os.name != 'posix':
raise ValueError, 'Error: unknown OS. Can only build .deb on linux.'
try:
dpkg_arch_path = which('dpkg-architecture')
except:
raise ValueError, "Failed to find 'dpkg-architecture' needed for .deb" \
". Try installing dpkg-dev"
# setup .deb environment variables
arch = os.popen( \
'dpkg-architecture -c \'echo $DEB_BUILD_ARCH\'').read().split()
if len(arch) == 0:
raise ValueError, 'Failed to get architecture from dpkg-architecture'
return arch[0] | [
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] | https://github.com/gtcasl/gpuocelot/blob/fa63920ee7c5f9a86e264cd8acd4264657cbd190/ocelot/scripts/build_environment.py#L11-L34 | |
ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | native_client_sdk/src/build_tools/update_nacl_manifest.py | python | VersionFinder._GetAvailableArchivesFor | (self, version_string) | return filter(lambda a: a + '.json' in manifests, archives) | Downloads a list of all available archives for a given version.
Args:
version_string: The version to find archives for. (e.g. "18.0.1025.164")
Returns:
A list of strings, each of which is a platform-specific archive URL. (e.g.
"gs://nativeclient_mirror/nacl/nacl_sdk/18.0.1025.164/"
"naclsdk_linux.tar.bz2").
All returned URLs will use the gs:// schema. | Downloads a list of all available archives for a given version. | [
"Downloads",
"a",
"list",
"of",
"all",
"available",
"archives",
"for",
"a",
"given",
"version",
"."
] | def _GetAvailableArchivesFor(self, version_string):
"""Downloads a list of all available archives for a given version.
Args:
version_string: The version to find archives for. (e.g. "18.0.1025.164")
Returns:
A list of strings, each of which is a platform-specific archive URL. (e.g.
"gs://nativeclient_mirror/nacl/nacl_sdk/18.0.1025.164/"
"naclsdk_linux.tar.bz2").
All returned URLs will use the gs:// schema."""
files = self.delegate.GsUtil_ls(GS_BUCKET_PATH + version_string)
assert all(file.startswith('gs://') for file in files)
archives = [f for f in files if not f.endswith('.json')]
manifests = [f for f in files if f.endswith('.json')]
# don't include any archives that don't have an associated manifest.
return filter(lambda a: a + '.json' in manifests, archives) | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/site.py | python | addsitepackages | (known_paths) | return known_paths | Add site-packages (and possibly site-python) to sys.path | Add site-packages (and possibly site-python) to sys.path | [
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"-",
"packages",
"(",
"and",
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"site",
"-",
"python",
")",
"to",
"sys",
".",
"path"
] | def addsitepackages(known_paths):
"""Add site-packages (and possibly site-python) to sys.path"""
for sitedir in getsitepackages():
if os.path.isdir(sitedir):
addsitedir(sitedir, known_paths)
return known_paths | [
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google/usd_from_gltf | 6d288cce8b68744494a226574ae1d7ba6a9c46eb | tools/ufginstall/ufginstall.py | python | DracoDep.install | (self) | Installs Draco dependency. | Installs Draco dependency. | [
"Installs",
"Draco",
"dependency",
"."
] | def install(self):
"""Installs Draco dependency."""
url = 'https://github.com/google/draco/archive/1.3.5.zip'
path = os.path.join(cfg.src_dir, 'draco.zip')
force = self.forced()
dl_dir = download_archive(url, path, force)
with cwd(dl_dir):
extra_args = ['-DCMAKE_POSITION_INDEPENDENT_CODE=1']
# Fix case-sensitivity bug in cmake script.
patches = [
('${draco_build_dir}/DracoConfig.cmake',
'${draco_build_dir}/dracoConfig.cmake')]
patch_file_text('CMakeLists.txt', patches)
run_cmake(force, extra_args) | [
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