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PyMesh/PyMesh | 384ba882b7558ba6e8653ed263c419226c22bddf | python/pymesh/wires/WireNetwork.py | python | WireNetwork.load_from_file | (self, wire_file) | Load vertices and edges from a file.
Args:
wire_file (:py:class:`str`): Input wire file name.
The file should have the following format::
# This is a comment
v x y z
v x y z
...
l i j # where i and j are vertex indices (starting from 1)
l i j
... | Load vertices and edges from a file. | [
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] | def load_from_file(self, wire_file):
""" Load vertices and edges from a file.
Args:
wire_file (:py:class:`str`): Input wire file name.
The file should have the following format::
# This is a comment
v x y z
v x y z
...
l i j # where i and j are vertex indices (starting from 1)
l i j
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"""
self.raw_wires = PyMesh.WireNetwork.create(wire_file)
self.__initialize_wires() | [
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||
hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/protobuf/python/google/protobuf/message.py | python | Message.SerializeToString | (self) | Serializes the protocol message to a binary string.
Returns:
A binary string representation of the message if all of the required
fields in the message are set (i.e. the message is initialized).
Raises:
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Returns:
A binary string representation of the message if all of the required
fields in the message are set (i.e. the message is initialized).
Raises:
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"""
raise NotImplementedError | [
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||
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/setuptools/py2/setuptools/msvc.py | python | EnvironmentInfo.UCRTIncludes | (self) | return [join(include, '%sucrt' % self._ucrt_subdir)] | Microsoft Universal C Runtime SDK Include.
Return
------
list of str
paths | Microsoft Universal C Runtime SDK Include. | [
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] | def UCRTIncludes(self):
"""
Microsoft Universal C Runtime SDK Include.
Return
------
list of str
paths
"""
if self.vs_ver < 14.0:
return []
include = join(self.si.UniversalCRTSdkDir, 'include')
return [join(include, '%sucrt' % self._ucrt_subdir)] | [
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|
mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/nn/metrics/perplexity.py | python | Perplexity.update | (self, *inputs) | Updates the internal evaluation result: math:preds and :math:labels.
Args:
inputs: Input `preds` and `labels`. `preds` and `labels` are a `Tensor`, list or numpy.ndarray.
`preds` is the predicted values, `labels` is the labels of the data.
The shape of `preds` and `labels` are both :math:`(N, C)`.
Raises:
ValueError: If the number of the inputs is not 2.
RuntimeError: If preds and labels have different lengths.
RuntimeError: If label shape is not equal to pred shape. | Updates the internal evaluation result: math:preds and :math:labels. | [
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"""
Updates the internal evaluation result: math:preds and :math:labels.
Args:
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`preds` is the predicted values, `labels` is the labels of the data.
The shape of `preds` and `labels` are both :math:`(N, C)`.
Raises:
ValueError: If the number of the inputs is not 2.
RuntimeError: If preds and labels have different lengths.
RuntimeError: If label shape is not equal to pred shape.
"""
if len(inputs) != 2:
raise ValueError("For 'Perplexity.update', it needs 2 inputs (predicted value, label), but got {}."
.format(len(inputs)))
preds = [self._convert_data(inputs[0])]
labels = [self._convert_data(inputs[1])]
if len(preds) != len(labels):
raise RuntimeError("For 'Perplexity.update', predicted value (input[0]) and label (input[1]) should have "
"the same length, but got predicted value length {}, label length {}."
.format(len(preds), len(labels)))
loss = 0.
num = 0
for label, pred in zip(labels, preds):
if label.size != pred.size / pred.shape[-1]:
raise RuntimeError("For 'Perplexity.update', predicted value (input[0]) and label (input[1]) should "
"have the same shape, but got predicted value shape {}, label shape {}."
.format(pred.shape, label.shape))
label = label.reshape((label.size,))
label_expand = label.astype(int)
label_expand = np.expand_dims(label_expand, axis=1)
first_indices = np.arange(label_expand.shape[0])[:, None]
pred = np.squeeze(pred[first_indices, label_expand])
if self.ignore_label is not None:
ignore = (label == self.ignore_label).astype(pred.dtype)
num -= np.sum(ignore)
pred = pred * (1 - ignore) + ignore
loss -= np.sum(np.log(np.maximum(1e-10, pred)))
num += pred.size
self._sum_metric += loss
self._num_inst += num | [
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||
SoarGroup/Soar | a1c5e249499137a27da60533c72969eef3b8ab6b | scons/scons-local-4.1.0/SCons/Environment.py | python | Base.PrependUnique | (self, delete_existing=0, **kw) | Prepend values to existing construction variables
in an Environment, if they're not already there.
If delete_existing is 1, removes existing values first, so
values move to front. | Prepend values to existing construction variables
in an Environment, if they're not already there.
If delete_existing is 1, removes existing values first, so
values move to front. | [
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"""Prepend values to existing construction variables
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If delete_existing is 1, removes existing values first, so
values move to front.
"""
kw = copy_non_reserved_keywords(kw)
for key, val in kw.items():
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val = _delete_duplicates(val, not delete_existing)
if key not in self._dict or self._dict[key] in ('', None):
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||
mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/numpy/array_ops.py | python | apply_along_axis | (func1d, axis, arr, *args, **kwargs) | return res | Applies a function to 1-D slices along the given axis.
Executes ``func1d(a, *args, **kwargs)`` where `func1d` operates on 1-D arrays and `a` is a
1-D slice of arr along axis.
Args:
func1d (function): Maps `(M,) -> (Nj…)`. This function should accept 1-D arrays. It is
applied to 1-D slices of arr along the specified axis.
axis (int): Axis along which arr is sliced.
arr (Tensor): Input array with shape `(Ni…, M, Nk…)`.
args (any): Additional arguments to `func1d`.
kwargs (any): Additional named arguments to `func1d`.
Returns:
Tensor with shape `(Ni…, Nj…, Nk…)`, the output array. Its shape is identical to the
shape of `arr`, except along the `axis` dimension. This axis is removed, and replaced
with new dimensions equal to the shape of the return value of `func1d`. So if `func1d`
returns a scalar, the output will have one fewer dimensions than `arr`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Raises:
ValueError: If axis is out of the range.
Examples:
>>> import mindspore.numpy as np
>>> b = np.array([[1,2,3], [4,5,6], [7,8,9]])
>>> print(np.apply_along_axis(np.diag, -1, b))
[[[1 0 0]
[0 2 0]
[0 0 3]]
[[4 0 0]
[0 5 0]
[0 0 6]]
[[7 0 0]
[0 8 0]
[0 0 9]]] | Applies a function to 1-D slices along the given axis.
Executes ``func1d(a, *args, **kwargs)`` where `func1d` operates on 1-D arrays and `a` is a
1-D slice of arr along axis. | [
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"""
Applies a function to 1-D slices along the given axis.
Executes ``func1d(a, *args, **kwargs)`` where `func1d` operates on 1-D arrays and `a` is a
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Args:
func1d (function): Maps `(M,) -> (Nj…)`. This function should accept 1-D arrays. It is
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axis (int): Axis along which arr is sliced.
arr (Tensor): Input array with shape `(Ni…, M, Nk…)`.
args (any): Additional arguments to `func1d`.
kwargs (any): Additional named arguments to `func1d`.
Returns:
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Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Raises:
ValueError: If axis is out of the range.
Examples:
>>> import mindspore.numpy as np
>>> b = np.array([[1,2,3], [4,5,6], [7,8,9]])
>>> print(np.apply_along_axis(np.diag, -1, b))
[[[1 0 0]
[0 2 0]
[0 0 3]]
[[4 0 0]
[0 5 0]
[0 0 6]]
[[7 0 0]
[0 8 0]
[0 0 9]]]
"""
ndim = F.rank(arr)
shape = F.shape(arr)
axis = _check_axis_in_range(axis, ndim)
arr = moveaxis(arr, axis, -1)
arr = F.reshape(arr, (-1, F.shape(arr)[-1]))
slices = []
for i in range(F.shape(arr)[0]):
slices.append(func1d(arr[i], *args, **kwargs))
stacked_slices = stack(slices)
shape_stacked = (_tuple_slice(shape, None, axis) + _tuple_slice(shape, axis + 1, None) +
_tuple_slice(F.shape(stacked_slices), 1, None))
res = F.reshape(stacked_slices, shape_stacked)
# moves the dimensions returned by `func1d` back to `axis`
ndim_func = F.rank(res) - ndim + 1
if ndim_func >= 1:
res = moveaxis(res, F.make_range(ndim - 1, F.rank(res)),
F.make_range(axis, axis + ndim_func))
return res | [
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|
google/llvm-propeller | 45c226984fe8377ebfb2ad7713c680d652ba678d | clang/bindings/python/clang/cindex.py | python | Cursor.underlying_typedef_type | (self) | return self._underlying_type | Return the underlying type of a typedef declaration.
Returns a Type for the typedef this cursor is a declaration for. If
the current cursor is not a typedef, this raises. | Return the underlying type of a typedef declaration. | [
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] | def underlying_typedef_type(self):
"""Return the underlying type of a typedef declaration.
Returns a Type for the typedef this cursor is a declaration for. If
the current cursor is not a typedef, this raises.
"""
if not hasattr(self, '_underlying_type'):
assert self.kind.is_declaration()
self._underlying_type = \
conf.lib.clang_getTypedefDeclUnderlyingType(self)
return self._underlying_type | [
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|
JumpingYang001/webrtc | c03d6e965e1f54aeadd670e491eabe5fdb8db968 | tools_webrtc/presubmit_checks_lib/check_package_boundaries.py | python | _CheckBuildFile | (build_file_path, packages) | Iterates over all the targets of the given BUILD.gn file, and verifies that
the source files referenced by it don't belong to any of it's subpackages.
Returns an iterator over PackageBoundaryViolations for this package. | Iterates over all the targets of the given BUILD.gn file, and verifies that
the source files referenced by it don't belong to any of it's subpackages.
Returns an iterator over PackageBoundaryViolations for this package. | [
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"""Iterates over all the targets of the given BUILD.gn file, and verifies that
the source files referenced by it don't belong to any of it's subpackages.
Returns an iterator over PackageBoundaryViolations for this package.
"""
package = os.path.dirname(build_file_path)
subpackages_re = _BuildSubpackagesPattern(packages, package)
build_file_contents = _ReadFileAndPrependLines(build_file_path)
for target_match in TARGET_RE.finditer(build_file_contents):
target_name = target_match.group('target_name')
target_contents = target_match.group('target_contents')
for sources_match in SOURCES_RE.finditer(target_contents):
sources = sources_match.group('sources')
for subpackages_match in subpackages_re.finditer(sources):
subpackage = subpackages_match.group('subpackage')
source_file = subpackages_match.group('source_file')
if subpackage:
yield PackageBoundaryViolation(build_file_path,
target_name, source_file,
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||
bingwin/MicroChat | 81d9a71a212c1cbca5bba497ec42659a7d25dccf | mars/lint/cpplint.py | python | CheckForCopyright | (filename, lines, error) | Logs an error if no Copyright message appears at the top of the file. | Logs an error if no Copyright message appears at the top of the file. | [
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"""Logs an error if no Copyright message appears at the top of the file."""
# We'll say it should occur by line 10. Don't forget there's a
# dummy line at the front.
for line in xrange(1, min(len(lines), 11)):
if re.search(r'Copyright', lines[line], re.I): break
else: # means no copyright line was found
error(filename, 0, 'legal/copyright', 5,
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||
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/grid.py | python | Grid.CanDragColMove | (*args, **kwargs) | return _grid.Grid_CanDragColMove(*args, **kwargs) | CanDragColMove(self) -> bool | CanDragColMove(self) -> bool | [
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"""CanDragColMove(self) -> bool"""
return _grid.Grid_CanDragColMove(*args, **kwargs) | [
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|
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/AWSPythonSDK/1.5.8/docutils/writers/latex2e/__init__.py | python | LaTeXTranslator.__init__ | (self, document, babel_class=Babel) | Heterogeneous stack.
Used by visit_* and depart_* functions in conjunction with the tree
traversal. Make sure that the pops correspond to the pushes. | Heterogeneous stack. | [
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] | def __init__(self, document, babel_class=Babel):
nodes.NodeVisitor.__init__(self, document)
# Reporter
# ~~~~~~~~
self.warn = self.document.reporter.warning
self.error = self.document.reporter.error
# Settings
# ~~~~~~~~
self.settings = settings = document.settings
self.latex_encoding = self.to_latex_encoding(settings.output_encoding)
self.use_latex_toc = settings.use_latex_toc
self.use_latex_docinfo = settings.use_latex_docinfo
self._use_latex_citations = settings.use_latex_citations
self._reference_label = settings.reference_label
self.hyperlink_color = settings.hyperlink_color
self.compound_enumerators = settings.compound_enumerators
self.font_encoding = getattr(settings, 'font_encoding', '')
self.section_prefix_for_enumerators = (
settings.section_prefix_for_enumerators)
self.section_enumerator_separator = (
settings.section_enumerator_separator.replace('_', r'\_'))
# literal blocks:
self.literal_block_env = 'alltt'
self.literal_block_options = ''
if settings.literal_block_env != '':
(none,
self.literal_block_env,
self.literal_block_options,
none ) = re.split(r'(\w+)(.*)', settings.literal_block_env)
elif settings.use_verbatim_when_possible:
self.literal_block_env = 'verbatim'
#
if self.settings.use_bibtex:
self.bibtex = self.settings.use_bibtex.split(',',1)
# TODO avoid errors on not declared citations.
else:
self.bibtex = None
# language module for Docutils-generated text
# (labels, bibliographic_fields, and author_separators)
self.language_module = languages.get_language(settings.language_code,
document.reporter)
self.babel = babel_class(settings.language_code, document.reporter)
self.author_separator = self.language_module.author_separators[0]
d_options = [self.settings.documentoptions]
if self.babel.language not in ('english', ''):
d_options.append(self.babel.language)
self.documentoptions = ','.join(filter(None, d_options))
self.d_class = DocumentClass(settings.documentclass,
settings.use_part_section)
# graphic package options:
if self.settings.graphicx_option == '':
self.graphicx_package = r'\usepackage{graphicx}'
elif self.settings.graphicx_option.lower() == 'auto':
self.graphicx_package = PreambleCmds.graphicx_auto
else:
self.graphicx_package = (r'\usepackage[%s]{graphicx}' %
self.settings.graphicx_option)
# footnotes:
self.docutils_footnotes = settings.docutils_footnotes
# @@ table_style: list of values from fixed set: warn?
# for s in self.settings.table_style:
# if s not in Writer.table_style_values:
# self.warn('Ignoring value "%s" in "table-style" setting.' %s)
# Output collection stacks
# ~~~~~~~~~~~~~~~~~~~~~~~~
# Document parts
self.head_prefix = [r'\documentclass[%s]{%s}' %
(self.documentoptions, self.settings.documentclass)]
self.requirements = SortableDict() # made a list in depart_document()
self.requirements['__static'] = r'\usepackage{ifthen}'
self.latex_preamble = [settings.latex_preamble]
self.fallbacks = SortableDict() # made a list in depart_document()
self.pdfsetup = [] # PDF properties (hyperref package)
self.title = []
self.subtitle = []
self.titledata = [] # \title, \author, \date
## self.body_prefix = ['\\begin{document}\n']
self.body_pre_docinfo = [] # \maketitle
self.docinfo = []
self.dedication = []
self.abstract = []
self.body = []
## self.body_suffix = ['\\end{document}\n']
self.context = []
"""Heterogeneous stack.
Used by visit_* and depart_* functions in conjunction with the tree
traversal. Make sure that the pops correspond to the pushes."""
# Title metadata:
self.title_labels = []
self.subtitle_labels = []
# (if use_latex_docinfo: collects lists of
# author/organization/contact/address lines)
self.author_stack = []
self.date = []
# PDF properties: pdftitle, pdfauthor
# TODO?: pdfcreator, pdfproducer, pdfsubject, pdfkeywords
self.pdfinfo = []
self.pdfauthor = []
# Stack of section counters so that we don't have to use_latex_toc.
# This will grow and shrink as processing occurs.
# Initialized for potential first-level sections.
self._section_number = [0]
# The current stack of enumerations so that we can expand
# them into a compound enumeration.
self._enumeration_counters = []
# The maximum number of enumeration counters we've used.
# If we go beyond this number, we need to create a new
# counter; otherwise, just reuse an old one.
self._max_enumeration_counters = 0
self._bibitems = []
# object for a table while proccessing.
self.table_stack = []
self.active_table = Table(self, 'longtable')
# Where to collect the output of visitor methods (default: body)
self.out = self.body
self.out_stack = [] # stack of output collectors
# Process settings
# ~~~~~~~~~~~~~~~~
# Encodings:
# Docutils' output-encoding => TeX input encoding
if self.latex_encoding != 'ascii':
self.requirements['_inputenc'] = (r'\usepackage[%s]{inputenc}'
% self.latex_encoding)
# TeX font encoding
if not self.is_xetex:
if self.font_encoding:
self.requirements['_fontenc'] = (r'\usepackage[%s]{fontenc}' %
self.font_encoding)
# ensure \textquotedbl is defined:
for enc in self.font_encoding.split(','):
enc = enc.strip()
if enc == 'OT1':
self.requirements['_textquotedblOT1'] = (
r'\DeclareTextSymbol{\textquotedbl}{OT1}{`\"}')
elif enc not in ('T1', 'T2A', 'T2B', 'T2C', 'T4', 'T5'):
self.requirements['_textquotedbl'] = (
r'\DeclareTextSymbolDefault{\textquotedbl}{T1}')
# page layout with typearea (if there are relevant document options)
if (settings.documentclass.find('scr') == -1 and
(self.documentoptions.find('DIV') != -1 or
self.documentoptions.find('BCOR') != -1)):
self.requirements['typearea'] = r'\usepackage{typearea}'
# Stylesheets
# (the name `self.stylesheet` is singular because only one
# stylesheet was supported before Docutils 0.6).
self.stylesheet = [self.stylesheet_call(path)
for path in utils.get_stylesheet_list(settings)]
# PDF setup
if self.hyperlink_color in ('0', 'false', 'False', ''):
self.hyperref_options = ''
else:
self.hyperref_options = 'colorlinks=true,linkcolor=%s,urlcolor=%s' % (
self.hyperlink_color, self.hyperlink_color)
if settings.hyperref_options:
self.hyperref_options += ',' + settings.hyperref_options
# LaTeX Toc
# include all supported sections in toc and PDF bookmarks
# (or use documentclass-default (as currently))?
## if self.use_latex_toc:
## self.requirements['tocdepth'] = (r'\setcounter{tocdepth}{%d}' %
## len(self.d_class.sections))
# Section numbering
if settings.sectnum_xform: # section numbering by Docutils
PreambleCmds.secnumdepth = r'\setcounter{secnumdepth}{0}'
else: # section numbering by LaTeX:
secnumdepth = settings.sectnum_depth
# Possible values of settings.sectnum_depth:
# None "sectnum" directive without depth arg -> LaTeX default
# 0 no "sectnum" directive -> no section numbers
# >0 value of "depth" argument -> translate to LaTeX levels:
# -1 part (0 with "article" document class)
# 0 chapter (missing in "article" document class)
# 1 section
# 2 subsection
# 3 subsubsection
# 4 paragraph
# 5 subparagraph
if secnumdepth is not None:
# limit to supported levels
secnumdepth = min(secnumdepth, len(self.d_class.sections))
# adjust to document class and use_part_section settings
if 'chapter' in self.d_class.sections:
secnumdepth -= 1
if self.d_class.sections[0] == 'part':
secnumdepth -= 1
PreambleCmds.secnumdepth = \
r'\setcounter{secnumdepth}{%d}' % secnumdepth
# start with specified number:
if (hasattr(settings, 'sectnum_start') and
settings.sectnum_start != 1):
self.requirements['sectnum_start'] = (
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google/or-tools | 2cb85b4eead4c38e1c54b48044f92087cf165bce | ortools/sat/python/cp_model.py | python | Constraint.OnlyEnforceIf | (self, boolvar) | return self | Adds an enforcement literal to the constraint.
This method adds one or more literals (that is, a boolean variable or its
negation) as enforcement literals. The conjunction of all these literals
determines whether the constraint is active or not. It acts as an
implication, so if the conjunction is true, it implies that the constraint
must be enforced. If it is false, then the constraint is ignored.
BoolOr, BoolAnd, and linear constraints all support enforcement literals.
Args:
boolvar: A boolean literal or a list of boolean literals.
Returns:
self. | Adds an enforcement literal to the constraint. | [
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"""Adds an enforcement literal to the constraint.
This method adds one or more literals (that is, a boolean variable or its
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determines whether the constraint is active or not. It acts as an
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ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/third_party/lib_x86_64/python2.7/dist-packages/yaml/__init__.py | python | add_path_resolver | (tag, path, kind=None, Loader=Loader, Dumper=Dumper) | Add a path based resolver for the given tag.
A path is a list of keys that forms a path
to a node in the representation tree.
Keys can be string values, integers, or None. | Add a path based resolver for the given tag.
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"""
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||
panda3d/panda3d | 833ad89ebad58395d0af0b7ec08538e5e4308265 | direct/src/showbase/Messenger.py | python | Messenger.getAllAccepting | (self, object) | Returns the list of all events accepted by the indicated object. | Returns the list of all events accepted by the indicated object. | [
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"""
Returns the list of all events accepted by the indicated object.
"""
self.lock.acquire()
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id = self._getMessengerId(object)
# Get the list of events this object is listening to
eventDict = self.__objectEvents.get(id)
if eventDict:
return list(eventDict.keys())
return []
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/keras/saving/saving_utils.py | python | _deserialize_nested_config | (deserialize_fn, config) | Deserializes arbitrary Keras `config` using `deserialize_fn`. | Deserializes arbitrary Keras `config` using `deserialize_fn`. | [
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"""Deserializes arbitrary Keras `config` using `deserialize_fn`."""
def _is_single_object(obj):
if isinstance(obj, dict) and 'class_name' in obj:
return True # Serialized Keras object.
if isinstance(obj, str):
return True # Serialized function or string.
return False
if config is None:
return None
if _is_single_object(config):
return deserialize_fn(config)
elif isinstance(config, dict):
return {
k: _deserialize_nested_config(deserialize_fn, v)
for k, v in config.items()
}
elif isinstance(config, (tuple, list)):
return [_deserialize_nested_config(deserialize_fn, obj) for obj in config]
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_misc.py | python | Display.IsOk | (*args, **kwargs) | return _misc_.Display_IsOk(*args, **kwargs) | IsOk(self) -> bool
Return true if the object was initialized successfully | IsOk(self) -> bool | [
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"""
IsOk(self) -> bool
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|
BlzFans/wke | b0fa21158312e40c5fbd84682d643022b6c34a93 | cygwin/lib/python2.6/distutils/ccompiler.py | python | CCompiler._compile | (self, obj, src, ext, cc_args, extra_postargs, pp_opts) | Compile 'src' to product 'obj'. | Compile 'src' to product 'obj'. | [
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FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Part/BOPTools/JoinFeatures.py | python | makeCutout | (name) | return obj | makeCutout(name): makes an Cutout object. | makeCutout(name): makes an Cutout object. | [
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turi-code/SFrame | 796b9bdfb2fa1b881d82080754643c7e68629cd2 | oss_src/unity/python/sframe/connect/main.py | python | is_connected | () | Returns true if connected to the server. | Returns true if connected to the server. | [
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bareos/bareos | 56a10bb368b0a81e977bb51304033fe49d59efb0 | core/src/plugins/filed/python/vmware/BareosFdPluginVMware.py | python | BareosFdPluginVMware.parse_plugin_definition | (self, plugindef) | return bareosfd.bRC_OK | Parses the plugin arguments | Parses the plugin arguments | [
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bareosfd.DebugMessage(
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|
espressomd/espresso | 7e29f9052e710fe1ebf0f5d2a8076b32921fbc6a | src/python/espressomd/visualization_opengl.py | python | Shape.draw | (self) | Draw shape via rasterization. Used as a default draw method.
Can and should be overwritten in child classes to implement a better
draw method. | Draw shape via rasterization. Used as a default draw method.
Can and should be overwritten in child classes to implement a better
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fifengine/fifengine | 4b62c42e85bec19893cef8e63e6855927cff2c47 | engine/python/fife/extensions/pychan/widgets/widget.py | python | Widget.requestModalMouseInputFocus | (self) | Requests modal mouse input focus.
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receiving input even if the input occurs outside of the widget and
no matter what the input is.
The widget must be modal mouse input focusable in order for this to work.
Therefore, no other widget should has modal input focus. | Requests modal mouse input focus.
When a widget has modal input focus that widget will be the only widget
receiving input even if the input occurs outside of the widget and
no matter what the input is.
The widget must be modal mouse input focusable in order for this to work.
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daijifeng001/caffe-rfcn | 543f8f6a4b7c88256ea1445ae951a12d1ad9cffd | scripts/cpp_lint.py | python | IsCppString | (line) | return ((line.count('"') - line.count(r'\"') - line.count("'\"'")) & 1) == 1 | Does line terminate so, that the next symbol is in string constant.
This function does not consider single-line nor multi-line comments.
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Returns:
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cathywu/Sentiment-Analysis | eb501fd1375c0c3f3ab430f963255f1bb858e659 | PyML-0.7.9/PyML/classifiers/svm.py | python | SVM.__init__ | (self, arg = None, **args) | :Parameters:
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'gist' - use a gist-like optimizer.
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- `C` - the svm C parameter
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- `cacheSize` - size of the kernel cache (in MB).
"""
Classifier.__init__(self, arg, **args)
self.kernel = None
if arg.__class__ == self.__class__ :
if arg.kernel is not None :
self.kernel = arg.kernel.__class__(arg.kernel)
elif hasattr(arg, 'type') and arg.type == 'kernel' :
self.kernel = arg.__class__(arg)
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | tools/grit/grit/scons.py | python | _SetDependencies | (env, base_dir, res_file, rc_alls, translated_files,
static_files) | Sets dependencies in the environment.
Args:
env: The SCons environment.
base_dir: The base directory for filenames.
res_file: The res_file specified in the RC flags.
rc_alls: All non-rc_header outputs.
translated_files: Files that are structures or skeletons, and get
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base_dir: The base directory for filenames.
res_file: The res_file specified in the RC flags.
rc_alls: All non-rc_header outputs.
translated_files: Files that are structures or skeletons, and get
translated by GRIT.
static_files: Files that are includes, and are used directly by res files.
"""
if res_file:
env.Depends(os.path.join(base_dir, res_file), static_files)
else:
# Make a best effort dependency setup when no res file is specified.
translated_files.extend(static_files)
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/xrc.py | python | XmlResource.LoadObject | (*args, **kwargs) | return _xrc.XmlResource_LoadObject(*args, **kwargs) | LoadObject(self, Window parent, String name, String classname) -> Object | LoadObject(self, Window parent, String name, String classname) -> Object | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/core/algorithms.py | python | _get_hashtable_algo | (values) | return htable, values | Parameters
----------
values : arraylike
Returns
-------
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values : ndarray | Parameters
----------
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"""
Parameters
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values : arraylike
Returns
-------
htable : HashTable subclass
values : ndarray
"""
values, _ = _ensure_data(values)
ndtype = _check_object_for_strings(values)
htable = _hashtables[ndtype]
return htable, values | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/mindrecord/shardwriter.py | python | ShardWriter._merge_blob | (self, blob_data) | return merged | Merge multiple blob data whose type is bytes or ndarray
Args:
blob_data (dict): Dict of blob data
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Merge multiple blob data whose type is bytes or ndarray
Args:
blob_data (dict): Dict of blob data
Returns:
bytes, merged blob data
"""
if len(blob_data) == 1:
values = [v for v in blob_data.values()]
return bytes(values[0])
# convert int to bytes
def int_to_bytes(x: int) -> bytes:
return x.to_bytes(8, 'big')
merged = bytes()
for field, v in blob_data.items():
# convert ndarray to bytes
if isinstance(v, np.ndarray):
v = v.astype(self._header.schema[field]["type"]).tobytes()
merged += int_to_bytes(len(v))
merged += v
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eclipse/sumo | 7132a9b8b6eea734bdec38479026b4d8c4336d03 | tools/contributed/sumopy/agilepy/lib_wx/objpanel.py | python | BooleanWidgetContainer.get_valuewidget_write | (self) | return widget | Return widget to edit numeric value of attribute
This is effectively the parametrisation of the masked.NumCtrl widget. | Return widget to edit numeric value of attribute
This is effectively the parametrisation of the masked.NumCtrl widget. | [
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"""
Return widget to edit numeric value of attribute
This is effectively the parametrisation of the masked.NumCtrl widget.
"""
# print 'get_numeditwidget',value
widget = wx.CheckBox(self.parent, -1)
if self.immediate_apply:
self.parent.Bind(wx.EVT_CHECKBOX, self.on_apply_immediate, widget)
value = self.get_value_obj()
widget.SetValue(value)
return widget | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/requests/api.py | python | head | (url, **kwargs) | return request('head', url, **kwargs) | r"""Sends a HEAD request.
:param url: URL for the new :class:`Request` object.
:param \*\*kwargs: Optional arguments that ``request`` takes. If
`allow_redirects` is not provided, it will be set to `False` (as
opposed to the default :meth:`request` behavior).
:return: :class:`Response <Response>` object
:rtype: requests.Response | r"""Sends a HEAD request. | [
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r"""Sends a HEAD request.
:param url: URL for the new :class:`Request` object.
:param \*\*kwargs: Optional arguments that ``request`` takes. If
`allow_redirects` is not provided, it will be set to `False` (as
opposed to the default :meth:`request` behavior).
:return: :class:`Response <Response>` object
:rtype: requests.Response
"""
kwargs.setdefault('allow_redirects', False)
return request('head', url, **kwargs) | [
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|
benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/contrib/timeseries/python/timeseries/head.py | python | _TimeSeriesRegressionHead._train_ops | (self, features) | return estimator_lib.EstimatorSpec(
loss=model_outputs.loss,
mode=estimator_lib.ModeKeys.TRAIN,
train_op=train_op) | Add training ops to the graph. | Add training ops to the graph. | [
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] | def _train_ops(self, features):
"""Add training ops to the graph."""
with variable_scope.variable_scope("model"):
model_outputs = self.state_manager.define_loss(
self.model, features, estimator_lib.ModeKeys.TRAIN)
train_op = optimizers.optimize_loss(
model_outputs.loss,
global_step=variables.get_global_step(),
optimizer=self.optimizer,
# Learning rate is set in the Optimizer object
learning_rate=None)
return estimator_lib.EstimatorSpec(
loss=model_outputs.loss,
mode=estimator_lib.ModeKeys.TRAIN,
train_op=train_op) | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/contrib/distributions/python/ops/vector_exponential_linear_operator.py | python | VectorExponentialLinearOperator._mode_mean_shape | (self) | return shape | Shape for the mode/mean Tensors. | Shape for the mode/mean Tensors. | [
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"""Shape for the mode/mean Tensors."""
shape = self.batch_shape.concatenate(self.event_shape)
has_static_shape = shape.is_fully_defined()
if not has_static_shape:
shape = array_ops.concat([
self.batch_shape_tensor(),
self.event_shape_tensor(),
], 0)
return shape | [
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|
sdhash/sdhash | b9eff63e4e5867e910f41fd69032bbb1c94a2a5e | sdhash-server/gen-py/sdhashsrv/sdhashsrv.py | python | Iface.saveSet | (self, num1, filename) | Parameters:
- num1
- filename | Parameters:
- num1
- filename | [
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- filename
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||
bumptop/BumpTop | 466d23597a07ae738f4265262fa01087fc6e257c | trunk/win/Source/bin/jinja2/meta.py | python | TrackingCodeGenerator.write | (self, x) | Don't write. | Don't write. | [
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||
sdhash/sdhash | b9eff63e4e5867e910f41fd69032bbb1c94a2a5e | sdhash-ui/jinja2/environment.py | python | Environment.compile | (self, source, name=None, filename=None, raw=False,
defer_init=False) | Compile a node or template source code. The `name` parameter is
the load name of the template after it was joined using
:meth:`join_path` if necessary, not the filename on the file system.
the `filename` parameter is the estimated filename of the template on
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parameter is `True` the return value will be a string with python
code equivalent to the bytecode returned otherwise. This method is
mainly used internally.
`defer_init` is use internally to aid the module code generator. This
causes the generated code to be able to import without the global
environment variable to be set.
.. versionadded:: 2.4
`defer_init` parameter added. | Compile a node or template source code. The `name` parameter is
the load name of the template after it was joined using
:meth:`join_path` if necessary, not the filename on the file system.
the `filename` parameter is the estimated filename of the template on
the file system. If the template came from a database or memory this
can be omitted. | [
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the load name of the template after it was joined using
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the `filename` parameter is the estimated filename of the template on
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source = self._parse(source, name, filename)
if self.optimized:
source = optimize(source, self)
source = self._generate(source, name, filename,
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if raw:
return source
if filename is None:
filename = '<template>'
else:
filename = _encode_filename(filename)
return self._compile(source, filename)
except TemplateSyntaxError:
exc_info = sys.exc_info()
self.handle_exception(exc_info, source_hint=source) | [
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||
windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/rexec.py | python | RExec.s_reload | (self, *args) | return self.s_apply(self.r_reload, args) | Reload the module object, re-parsing and re-initializing it.
This method is implicitly called by code executing in the
restricted environment. Overriding this method in a subclass is
used to change the policies enforced by a restricted environment.
Similar to the r_reload() method, but has access to restricted
versions of the standard I/O streams sys.stdin, sys.stderr, and
sys.stdout. | Reload the module object, re-parsing and re-initializing it. | [
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"""Reload the module object, re-parsing and re-initializing it.
This method is implicitly called by code executing in the
restricted environment. Overriding this method in a subclass is
used to change the policies enforced by a restricted environment.
Similar to the r_reload() method, but has access to restricted
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"""
return self.s_apply(self.r_reload, args) | [
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|
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/distutils/command/check.py | python | check.check_metadata | (self) | Ensures that all required elements of meta-data are supplied.
name, version, URL, (author and author_email) or
(maintainer and maintainer_email)).
Warns if any are missing. | Ensures that all required elements of meta-data are supplied. | [
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"""Ensures that all required elements of meta-data are supplied.
name, version, URL, (author and author_email) or
(maintainer and maintainer_email)).
Warns if any are missing.
"""
metadata = self.distribution.metadata
missing = []
for attr in ('name', 'version', 'url'):
if not (hasattr(metadata, attr) and getattr(metadata, attr)):
missing.append(attr)
if missing:
self.warn("missing required meta-data: %s" % ', '.join(missing))
if metadata.author:
if not metadata.author_email:
self.warn("missing meta-data: if 'author' supplied, " +
"'author_email' must be supplied too")
elif metadata.maintainer:
if not metadata.maintainer_email:
self.warn("missing meta-data: if 'maintainer' supplied, " +
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else:
self.warn("missing meta-data: either (author and author_email) " +
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||
wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/shelve.py | python | open | (filename, flag='c', protocol=None, writeback=False) | return DbfilenameShelf(filename, flag, protocol, writeback) | Open a persistent dictionary for reading and writing.
The filename parameter is the base filename for the underlying
database. As a side-effect, an extension may be added to the
filename and more than one file may be created. The optional flag
parameter has the same interpretation as the flag parameter of
anydbm.open(). The optional protocol parameter specifies the
version of the pickle protocol (0, 1, or 2).
See the module's __doc__ string for an overview of the interface. | Open a persistent dictionary for reading and writing. | [
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] | def open(filename, flag='c', protocol=None, writeback=False):
"""Open a persistent dictionary for reading and writing.
The filename parameter is the base filename for the underlying
database. As a side-effect, an extension may be added to the
filename and more than one file may be created. The optional flag
parameter has the same interpretation as the flag parameter of
anydbm.open(). The optional protocol parameter specifies the
version of the pickle protocol (0, 1, or 2).
See the module's __doc__ string for an overview of the interface.
"""
return DbfilenameShelf(filename, flag, protocol, writeback) | [
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|
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/generator.py | python | Html.TransformText | (self, text) | return text | Does character substitution on a string and returns
the html equivalent of the given string.
@param text: text to transform
@return: text with all special characters transformed | Does character substitution on a string and returns
the html equivalent of the given string.
@param text: text to transform
@return: text with all special characters transformed | [
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"""Does character substitution on a string and returns
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@param text: text to transform
@return: text with all special characters transformed
"""
text = text.replace('&', "&") # Ampersands
text = text.replace('<', "<") # Less Than Symbols
text = text.replace('>', ">") # Greater Than Symbols
text = text.replace("\"", """)
return text | [
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|
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_core.py | python | ImageHandler.GetExtension | (*args, **kwargs) | return _core_.ImageHandler_GetExtension(*args, **kwargs) | GetExtension(self) -> String | GetExtension(self) -> String | [
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|
tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/lite/python/tflite_convert.py | python | _get_tflite_converter | (flags) | return converter_fn(**converter_kwargs) | Makes a TFLiteConverter object based on the flags provided.
Args:
flags: argparse.Namespace object containing TFLite flags.
Returns:
TFLiteConverter object.
Raises:
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"""Makes a TFLiteConverter object based on the flags provided.
Args:
flags: argparse.Namespace object containing TFLite flags.
Returns:
TFLiteConverter object.
Raises:
ValueError: Invalid flags.
"""
# Parse input and output arrays.
input_arrays = _parse_array(flags.input_arrays)
input_shapes = None
if flags.input_shapes:
input_shapes_list = [
_parse_array(shape, type_fn=int)
for shape in six.ensure_str(flags.input_shapes).split(":")
]
input_shapes = dict(list(zip(input_arrays, input_shapes_list)))
output_arrays = _parse_array(flags.output_arrays)
converter_kwargs = {
"input_arrays": input_arrays,
"input_shapes": input_shapes,
"output_arrays": output_arrays
}
# Create TFLiteConverter.
if flags.graph_def_file:
converter_fn = lite.TFLiteConverter.from_frozen_graph
converter_kwargs["graph_def_file"] = flags.graph_def_file
elif flags.saved_model_dir:
converter_fn = lite.TFLiteConverter.from_saved_model
converter_kwargs["saved_model_dir"] = flags.saved_model_dir
converter_kwargs["tag_set"] = _parse_set(flags.saved_model_tag_set)
converter_kwargs["signature_key"] = flags.saved_model_signature_key
elif flags.keras_model_file:
converter_fn = lite.TFLiteConverter.from_keras_model_file
converter_kwargs["model_file"] = flags.keras_model_file
else:
raise ValueError("--graph_def_file, --saved_model_dir, or "
"--keras_model_file must be specified.")
return converter_fn(**converter_kwargs) | [
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|
ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | third_party/tlslite/tlslite/X509.py | python | X509.getCommonName | (self) | Get the Subject's Common Name from the certificate.
The cryptlib_py module must be installed in order to use this
function.
@rtype: str or None
@return: The CN component of the certificate's subject DN, if
present. | Get the Subject's Common Name from the certificate. | [
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] | def getCommonName(self):
"""Get the Subject's Common Name from the certificate.
The cryptlib_py module must be installed in order to use this
function.
@rtype: str or None
@return: The CN component of the certificate's subject DN, if
present.
"""
import cryptlib_py
import array
c = cryptlib_py.cryptImportCert(self.bytes, cryptlib_py.CRYPT_UNUSED)
name = cryptlib_py.CRYPT_CERTINFO_COMMONNAME
try:
try:
length = cryptlib_py.cryptGetAttributeString(c, name, None)
returnVal = array.array('B', [0] * length)
cryptlib_py.cryptGetAttributeString(c, name, returnVal)
returnVal = returnVal.tostring()
except cryptlib_py.CryptException, e:
if e[0] == cryptlib_py.CRYPT_ERROR_NOTFOUND:
returnVal = None
return returnVal
finally:
cryptlib_py.cryptDestroyCert(c) | [
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||
assimp/assimp | 97c7e084c2f7f8c9355ea42f73605890481bddc5 | port/PyAssimp/scripts/transformations.py | python | Arcball.drag | (self, point) | Update current cursor window coordinates. | Update current cursor window coordinates. | [
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] | def drag(self, point):
"""Update current cursor window coordinates."""
vnow = arcball_map_to_sphere(point, self._center, self._radius)
if self._axis is not None:
vnow = arcball_constrain_to_axis(vnow, self._axis)
self._qpre = self._qnow
t = numpy.cross(self._vdown, vnow)
if numpy.dot(t, t) < _EPS:
self._qnow = self._qdown
else:
q = [t[0], t[1], t[2], numpy.dot(self._vdown, vnow)]
self._qnow = quaternion_multiply(q, self._qdown) | [
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LiquidPlayer/LiquidCore | 9405979363f2353ac9a71ad8ab59685dd7f919c9 | deps/node-10.15.3/deps/v8/third_party/jinja2/bccache.py | python | BytecodeCache.dump_bytecode | (self, bucket) | Subclasses have to override this method to write the bytecode
from a bucket back to the cache. If it unable to do so it must not
fail silently but raise an exception. | Subclasses have to override this method to write the bytecode
from a bucket back to the cache. If it unable to do so it must not
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||
ValveSoftware/source-sdk-2013 | 0d8dceea4310fde5706b3ce1c70609d72a38efdf | sp/src/thirdparty/protobuf-2.3.0/python/google/protobuf/service_reflection.py | python | _ServiceStubBuilder.BuildServiceStub | (self, cls) | Constructs the stub class.
Args:
cls: The class that will be constructed. | Constructs the stub class. | [
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cls: The class that will be constructed.
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def _ServiceStubInit(stub, rpc_channel):
stub.rpc_channel = rpc_channel
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] | https://github.com/ValveSoftware/source-sdk-2013/blob/0d8dceea4310fde5706b3ce1c70609d72a38efdf/sp/src/thirdparty/protobuf-2.3.0/python/google/protobuf/service_reflection.py#L251-L263 |
||
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/turtle.py | python | read_docstrings | (lang) | Read in docstrings from lang-specific docstring dictionary.
Transfer docstrings, translated to lang, from a dictionary-file
to the methods of classes Screen and Turtle and - in revised form -
to the corresponding functions. | Read in docstrings from lang-specific docstring dictionary. | [
"Read",
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"docstring",
"dictionary",
"."
] | def read_docstrings(lang):
"""Read in docstrings from lang-specific docstring dictionary.
Transfer docstrings, translated to lang, from a dictionary-file
to the methods of classes Screen and Turtle and - in revised form -
to the corresponding functions.
"""
modname = "turtle_docstringdict_%(language)s" % {'language':lang.lower()}
module = __import__(modname)
docsdict = module.docsdict
for key in docsdict:
try:
# eval(key).im_func.__doc__ = docsdict[key]
eval(key).__doc__ = docsdict[key]
except Exception:
print("Bad docstring-entry: %s" % key) | [
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||
macchina-io/macchina.io | ef24ba0e18379c3dd48fb84e6dbf991101cb8db0 | platform/JS/V8/v8/tools/stats-viewer.py | python | Main | (data_file, name_filter) | Run the stats counter.
Args:
data_file: The counters file to monitor.
name_filter: The regexp filter to apply to counter names. | Run the stats counter. | [
"Run",
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"stats",
"counter",
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] | def Main(data_file, name_filter):
"""Run the stats counter.
Args:
data_file: The counters file to monitor.
name_filter: The regexp filter to apply to counter names.
"""
StatsViewer(data_file, name_filter).Run() | [
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||
miyosuda/TensorFlowAndroidMNIST | 7b5a4603d2780a8a2834575706e9001977524007 | jni-build/jni/include/tensorflow/python/training/learning_rate_decay.py | python | natural_exp_decay | (learning_rate, global_step, decay_steps, decay_rate,
staircase=False, name=None) | Applies natural exponential decay to the initial learning rate.
When training a model, it is often recommended to lower the learning rate as
the training progresses. This function applies an exponential decay function
to a provided initial learning rate. It requires an `global_step` value to
compute the decayed learning rate. You can just pass a TensorFlow variable
that you increment at each training step.
The function returns the decayed learning rate. It is computed as:
```python
decayed_learning_rate = learning_rate * exp(-decay_rate * global_step)
```
Example: decay exponetially with a base of 0.96:
```python
...
global_step = tf.Variable(0, trainable=False)
learning_rate = 0.1
k = 0.5
learning_rate = tf.train.exponential_time_decay(learning_rate, global_step, k)
# Passing global_step to minimize() will increment it at each step.
learning_step = (
tf.train.GradientDescentOptimizer(learning_rate)
.minimize(...my loss..., global_step=global_step)
)
```
Args:
learning_rate: A scalar `float32` or `float64` `Tensor` or a
Python number. The initial learning rate.
global_step: A Python number.
Global step to use for the decay computation. Must not be negative.
decay_rate: A Python number. The decay rate.
name: String. Optional name of the operation. Defaults to
'ExponentialTimeDecay'
Returns:
A scalar `Tensor` of the same type as `learning_rate`. The decayed
learning rate. | Applies natural exponential decay to the initial learning rate. | [
"Applies",
"natural",
"exponential",
"decay",
"to",
"the",
"initial",
"learning",
"rate",
"."
] | def natural_exp_decay(learning_rate, global_step, decay_steps, decay_rate,
staircase=False, name=None):
"""Applies natural exponential decay to the initial learning rate.
When training a model, it is often recommended to lower the learning rate as
the training progresses. This function applies an exponential decay function
to a provided initial learning rate. It requires an `global_step` value to
compute the decayed learning rate. You can just pass a TensorFlow variable
that you increment at each training step.
The function returns the decayed learning rate. It is computed as:
```python
decayed_learning_rate = learning_rate * exp(-decay_rate * global_step)
```
Example: decay exponetially with a base of 0.96:
```python
...
global_step = tf.Variable(0, trainable=False)
learning_rate = 0.1
k = 0.5
learning_rate = tf.train.exponential_time_decay(learning_rate, global_step, k)
# Passing global_step to minimize() will increment it at each step.
learning_step = (
tf.train.GradientDescentOptimizer(learning_rate)
.minimize(...my loss..., global_step=global_step)
)
```
Args:
learning_rate: A scalar `float32` or `float64` `Tensor` or a
Python number. The initial learning rate.
global_step: A Python number.
Global step to use for the decay computation. Must not be negative.
decay_rate: A Python number. The decay rate.
name: String. Optional name of the operation. Defaults to
'ExponentialTimeDecay'
Returns:
A scalar `Tensor` of the same type as `learning_rate`. The decayed
learning rate.
"""
with ops.op_scope([learning_rate, global_step, decay_rate],
name, "NaturalExpDecay") as name:
learning_rate = ops.convert_to_tensor(learning_rate, name="learning_rate")
dtype = learning_rate.dtype
global_step = math_ops.cast(global_step, dtype)
decay_steps = math_ops.cast(decay_steps, dtype)
decay_rate = math_ops.cast(decay_rate, dtype)
p = global_step / decay_steps
if staircase:
p = math_ops.floor(p)
exponent = math_ops.exp(math_ops.mul(math_ops.neg(decay_rate), p))
return math_ops.mul(learning_rate, exponent, name=name) | [
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||
Polidea/SiriusObfuscator | b0e590d8130e97856afe578869b83a209e2b19be | SymbolExtractorAndRenamer/lldb/scripts/Python/static-binding/lldb.py | python | SBExpressionOptions.GetPlaygroundTransformEnabled | (self) | return _lldb.SBExpressionOptions_GetPlaygroundTransformEnabled(self) | GetPlaygroundTransformEnabled(self) -> bool | GetPlaygroundTransformEnabled(self) -> bool | [
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")",
"-",
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] | def GetPlaygroundTransformEnabled(self):
"""GetPlaygroundTransformEnabled(self) -> bool"""
return _lldb.SBExpressionOptions_GetPlaygroundTransformEnabled(self) | [
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|
hyperledger-archives/iroha | ed579f85126d0e86532a1f4f1f6ce5681bbcd3a9 | example/python/irohalib.py | python | IrohaGrpc.tx_status | (self, transaction) | return status_name, status_code, error_message | Request a status of a transaction
:param transaction: the transaction, which status is about to be known
:return: a tuple with the symbolic status description,
integral status code, and error message string (will be empty if no error occurred) | Request a status of a transaction
:param transaction: the transaction, which status is about to be known
:return: a tuple with the symbolic status description,
integral status code, and error message string (will be empty if no error occurred) | [
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"""
Request a status of a transaction
:param transaction: the transaction, which status is about to be known
:return: a tuple with the symbolic status description,
integral status code, and error message string (will be empty if no error occurred)
"""
request = endpoint_pb2.TxStatusRequest()
request.tx_hash = binascii.hexlify(IrohaCrypto.hash(transaction))
response = self._command_service_stub.Status(request)
status_code = response.tx_status
status_name = endpoint_pb2.TxStatus.Name(response.tx_status)
error_message = response.error_message
return status_name, status_code, error_message | [
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|
hfinkel/llvm-project-cxxjit | 91084ef018240bbb8e24235ff5cd8c355a9c1a1e | libcxx/utils/libcxx/util.py | python | capture | (args, env=None) | return out | capture(command) - Run the given command (or argv list) in a shell and
return the standard output. Raises a CalledProcessError if the command
exits with a non-zero status. | capture(command) - Run the given command (or argv list) in a shell and
return the standard output. Raises a CalledProcessError if the command
exits with a non-zero status. | [
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] | def capture(args, env=None):
"""capture(command) - Run the given command (or argv list) in a shell and
return the standard output. Raises a CalledProcessError if the command
exits with a non-zero status."""
p = subprocess.Popen(args, stdout=subprocess.PIPE, stderr=subprocess.PIPE,
env=env)
out, err = p.communicate()
out = convert_string(out)
err = convert_string(err)
if p.returncode != 0:
raise subprocess.CalledProcessError(cmd=args,
returncode=p.returncode,
output="{}\n{}".format(out, err))
return out | [
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|
wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/distutils/command/check.py | python | check.warn | (self, msg) | return Command.warn(self, msg) | Counts the number of warnings that occurs. | Counts the number of warnings that occurs. | [
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"warnings",
"that",
"occurs",
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] | def warn(self, msg):
"""Counts the number of warnings that occurs."""
self._warnings += 1
return Command.warn(self, msg) | [
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|
Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/tpu/feature_column.py | python | _record_variable_scope_and_name | (embedding_var_name,
embedding_var_name_in_fc,
is_shared_embedding=False) | Add embedding variable name and scope to collection. | Add embedding variable name and scope to collection. | [
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"collection",
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] | def _record_variable_scope_and_name(embedding_var_name,
embedding_var_name_in_fc,
is_shared_embedding=False):
"""Add embedding variable name and scope to collection."""
g = ops.get_default_graph()
collection = g.get_collection_ref(_TPU_FC_TO_SCOPE)
if not collection:
collection.append({})
var_def_dict = collection[0]
captured_scope = variable_scope.get_variable_scope()
captured_scope_name = captured_scope.name
if embedding_var_name in var_def_dict:
if (var_def_dict[embedding_var_name][0] != captured_scope_name
and not is_shared_embedding):
raise ValueError(
'For embedding var name {}, the variable scope name is different, '
'got {}; expected {}'.format(embedding_var_name,
captured_scope_name,
var_def_dict[embedding_var_name][0]))
if var_def_dict[embedding_var_name][1] != embedding_var_name_in_fc:
raise ValueError(
'For embedding var name {}, the embedding name is different, '
'got {}; expected {}'.format(embedding_var_name,
embedding_var_name_in_fc,
var_def_dict[embedding_var_name][1]))
else:
var_def_dict[embedding_var_name] = (captured_scope_name,
embedding_var_name_in_fc) | [
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||
ablab/quast | 5f6709528129a6ad266a6b24ef3f40b88f0fe04b | quast_libs/busco/GeneSetAnalysis.py | python | GeneSetAnalysis.run_analysis | (self) | This function calls all needed steps for running the analysis. | This function calls all needed steps for running the analysis. | [
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"needed",
"steps",
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"running",
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"analysis",
"."
] | def run_analysis(self):
"""
This function calls all needed steps for running the analysis.
"""
super(GeneSetAnalysis, self).run_analysis()
# validate sequence file
if super(GeneSetAnalysis, self).check_protein_file() is False:
GeneSetAnalysis._logger.error('Please provide a protein file as input')
raise SystemExit
self._load_score()
self._load_length()
self._run_hmmer()
self._produce_short_summary()
self.cleanup()
if self._tarzip:
self._run_tarzip_hmmer_output() | [
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||
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/AWS/resource-manager-code/lib/setuptools/command/egg_info.py | python | FileList.prune | (self, dir) | return self._remove_files(match.match) | Filter out files from 'dir/'. | Filter out files from 'dir/'. | [
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] | def prune(self, dir):
"""Filter out files from 'dir/'."""
match = translate_pattern(os.path.join(dir, '**'))
return self._remove_files(match.match) | [
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|
microsoft/CNTK | e9396480025b9ca457d26b6f33dd07c474c6aa04 | bindings/python/cntk/debugging/__init__.py | python | dump_signature | (root, tag=None) | Debug helper that prints the signature of a Function. | Debug helper that prints the signature of a Function. | [
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] | def dump_signature(root, tag=None):
'''
Debug helper that prints the signature of a Function.
'''
print(str(root)) | [
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||
kushview/Element | 1cc16380caa2ab79461246ba758b9de1f46db2a5 | waflib/Tools/c_config.py | python | cxx_add_flags | (conf) | Adds CXXFLAGS / CPPFLAGS from os.environ to conf.env | Adds CXXFLAGS / CPPFLAGS from os.environ to conf.env | [
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"""
Adds CXXFLAGS / CPPFLAGS from os.environ to conf.env
"""
conf.add_os_flags('CPPFLAGS', dup=False)
conf.add_os_flags('CXXFLAGS', dup=False) | [
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||
GeometryCollective/boundary-first-flattening | 8250e5a0e85980ec50b5e8aa8f49dd6519f915cd | deps/nanogui/ext/pybind11/tools/clang/cindex.py | python | CompileCommand.directory | (self) | return conf.lib.clang_CompileCommand_getDirectory(self.cmd) | Get the working directory for this CompileCommand | Get the working directory for this CompileCommand | [
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] | def directory(self):
"""Get the working directory for this CompileCommand"""
return conf.lib.clang_CompileCommand_getDirectory(self.cmd) | [
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|
SFTtech/openage | d6a08c53c48dc1e157807471df92197f6ca9e04d | openage/util/profiler.py | python | Tracemalloc.report | (self, sortby='lineno', cumulative=True, limit=100) | Return the snapshot statistics to the console. | Return the snapshot statistics to the console. | [
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] | def report(self, sortby='lineno', cumulative=True, limit=100):
"""
Return the snapshot statistics to the console.
"""
for stat in self.snapshot.statistics(sortby, cumulative)[:limit]:
print(stat) | [
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||
miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/tensorflow/contrib/slim/python/slim/model_analyzer.py | python | analyze_ops | (graph, print_info=False) | return total_size | Compute the estimated size of the ops.outputs in the graph.
Args:
graph: the graph containing the operations.
print_info: Optional, if true print ops and their outputs.
Returns:
total size of the ops.outputs | Compute the estimated size of the ops.outputs in the graph. | [
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] | def analyze_ops(graph, print_info=False):
"""Compute the estimated size of the ops.outputs in the graph.
Args:
graph: the graph containing the operations.
print_info: Optional, if true print ops and their outputs.
Returns:
total size of the ops.outputs
"""
if print_info:
print('---------')
print('Operations: name -> (type shapes) [size]')
print('---------')
total_size = 0
for op in graph.get_operations():
op_size = 0
shapes = []
for output in op.outputs:
# if output.num_elements() is None or [] assume size 0.
output_size = output.get_shape().num_elements() or 0
if output.get_shape():
shapes.append(tensor_description(output))
op_size += output_size
if print_info:
print(op.name, '\t->', ', '.join(shapes), '[' + str(op_size) + ']')
total_size += op_size
return total_size | [
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|
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | samples/ide/activegrid/tool/AbstractEditor.py | python | EditorCanvasShapeEvtHandler.OnMovePre | (self, dc, x, y, oldX, oldY, display) | return ogl.ShapeEvtHandler.OnMovePre(self, dc, x, y, oldX, oldY, display) | Prevent objects from being dragged outside of viewable area | Prevent objects from being dragged outside of viewable area | [
"Prevent",
"objects",
"from",
"being",
"dragged",
"outside",
"of",
"viewable",
"area"
] | def OnMovePre(self, dc, x, y, oldX, oldY, display):
""" Prevent objects from being dragged outside of viewable area """
if (x < 0) or (y < 0) or (x > self._view._maxWidth) or (y > self._view._maxHeight):
return False
return ogl.ShapeEvtHandler.OnMovePre(self, dc, x, y, oldX, oldY, display) | [
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|
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numba/array_analysis.py | python | EquivSet.insert_equiv | (self, *objs) | return self._insert(objs) | Insert a set of equivalent objects by modifying self. This
method can be overloaded to transform object type before insertion. | Insert a set of equivalent objects by modifying self. This
method can be overloaded to transform object type before insertion. | [
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] | def insert_equiv(self, *objs):
"""Insert a set of equivalent objects by modifying self. This
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"""
return self._insert(objs) | [
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|
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numpy/fft/_pocketfft.py | python | irfft2 | (a, s=None, axes=(-2, -1), norm=None) | return irfftn(a, s, axes, norm) | Compute the 2-dimensional inverse FFT of a real array.
Parameters
----------
a : array_like
The input array
s : sequence of ints, optional
Shape of the real output to the inverse FFT.
axes : sequence of ints, optional
The axes over which to compute the inverse fft.
Default is the last two axes.
norm : {None, "ortho"}, optional
.. versionadded:: 1.10.0
Normalization mode (see `numpy.fft`). Default is None.
Returns
-------
out : ndarray
The result of the inverse real 2-D FFT.
See Also
--------
irfftn : Compute the inverse of the N-dimensional FFT of real input.
Notes
-----
This is really `irfftn` with different defaults.
For more details see `irfftn`. | Compute the 2-dimensional inverse FFT of a real array. | [
"Compute",
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] | def irfft2(a, s=None, axes=(-2, -1), norm=None):
"""
Compute the 2-dimensional inverse FFT of a real array.
Parameters
----------
a : array_like
The input array
s : sequence of ints, optional
Shape of the real output to the inverse FFT.
axes : sequence of ints, optional
The axes over which to compute the inverse fft.
Default is the last two axes.
norm : {None, "ortho"}, optional
.. versionadded:: 1.10.0
Normalization mode (see `numpy.fft`). Default is None.
Returns
-------
out : ndarray
The result of the inverse real 2-D FFT.
See Also
--------
irfftn : Compute the inverse of the N-dimensional FFT of real input.
Notes
-----
This is really `irfftn` with different defaults.
For more details see `irfftn`.
"""
return irfftn(a, s, axes, norm) | [
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SFTtech/openage | d6a08c53c48dc1e157807471df92197f6ca9e04d | openage/convert/service/read/string_resource.py | python | read_age2_hd_3x_stringresources | (stringres, srcdir) | return count | HD Edition 3.x and below store language .txt files
in the Bin/ folder.
Specific language strings are in Bin/$LANG/*.txt.
The data is stored in the `stringres` storage. | HD Edition 3.x and below store language .txt files
in the Bin/ folder.
Specific language strings are in Bin/$LANG/*.txt. | [
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"""
HD Edition 3.x and below store language .txt files
in the Bin/ folder.
Specific language strings are in Bin/$LANG/*.txt.
The data is stored in the `stringres` storage.
"""
count = 0
for lang in srcdir["bin"].list():
lang_path = srcdir["bin", lang.decode()]
# There are some .txt files immediately in bin/, but they don't
# seem to contain anything useful. (Everything is overridden by
# files in Bin/$LANG/.)
if not lang_path.is_dir():
continue
# Sometimes we can have language DLLs in Bin/$LANG/
# e.g. HD Edition 2.0
# We do _not_ want to treat these as text files
# so first check explicitly
if lang_path["language.dll"].is_file():
for name in ["language.dll",
"language_x1.dll",
"language_x1_p1.dll"]:
pefile = PEFile(lang_path[name].open('rb'))
stringres.fill_from(pefile.resources().strings)
count += 1
else:
for basename in lang_path.list():
with lang_path[basename].open('rb') as langfile:
# No utf-8 :(
stringres.fill_from(
read_hd_language_file_old(
langfile, lang, enc='iso-8859-1'))
count += 1
return count | [
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|
PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | python/paddle/fluid/contrib/decoder/beam_search_decoder.py | python | BeamSearchDecoder.read_array | (self, init, is_ids=False, is_scores=False) | return read_value | Read an array to get the decoded ids and scores generated by previous
RNN step. At the first step of RNN, the init variable mut be used to
initialize the array.
Args:
init (Variable): The initial variable for first step usage. init
must be provided.
is_ids (bool): Specify whether the variable is an id.
is_scores (bool): Specify whether the variable is a score.
Returns:
The associated variable generated during previous RNN steps.
Examples:
.. code-block:: python
prev_ids = decoder.read_array(init=init_ids, is_ids=True)
prev_scores = decoder.read_array(init=init_scores, is_scores=True) | Read an array to get the decoded ids and scores generated by previous
RNN step. At the first step of RNN, the init variable mut be used to
initialize the array. | [
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] | def read_array(self, init, is_ids=False, is_scores=False):
"""
Read an array to get the decoded ids and scores generated by previous
RNN step. At the first step of RNN, the init variable mut be used to
initialize the array.
Args:
init (Variable): The initial variable for first step usage. init
must be provided.
is_ids (bool): Specify whether the variable is an id.
is_scores (bool): Specify whether the variable is a score.
Returns:
The associated variable generated during previous RNN steps.
Examples:
.. code-block:: python
prev_ids = decoder.read_array(init=init_ids, is_ids=True)
prev_scores = decoder.read_array(init=init_scores, is_scores=True)
"""
self._assert_in_decoder_block('read_array')
if is_ids and is_scores:
raise ValueError('Shouldn\'t mark current array be ids array and'
'scores array at the same time.')
if not isinstance(init, Variable):
raise TypeError('The input argument `init` must be a Variable.')
parent_block = self._parent_block()
array = parent_block.create_var(
name=unique_name.generate('beam_search_decoder_array'),
type=core.VarDesc.VarType.LOD_TENSOR_ARRAY,
dtype=init.dtype)
parent_block.append_op(
type='write_to_array',
inputs={'X': init,
'I': self._zero_idx},
outputs={'Out': array})
if is_ids:
self._ids_array = array
elif is_scores:
self._scores_array = array
read_value = layers.array_read(array=array, i=self._counter)
self._array_dict[read_value.name] = array
return read_value | [
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|
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_misc.py | python | DateTime.Set | (*args, **kwargs) | return _misc_.DateTime_Set(*args, **kwargs) | Set(self, int day, int month=Inv_Month, int year=Inv_Year, int hour=0,
int minute=0, int second=0, int millisec=0) -> DateTime | Set(self, int day, int month=Inv_Month, int year=Inv_Year, int hour=0,
int minute=0, int second=0, int millisec=0) -> DateTime | [
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"""
Set(self, int day, int month=Inv_Month, int year=Inv_Year, int hour=0,
int minute=0, int second=0, int millisec=0) -> DateTime
"""
return _misc_.DateTime_Set(*args, **kwargs) | [
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|
apache/singa | 93fd9da72694e68bfe3fb29d0183a65263d238a1 | python/singa/image_tool.py | python | ImageTool.color_cast | (self, offset=20, inplace=True) | Add a random value from [-offset, offset] to each channel
Args:
offset: cast offset, >0 and <255
inplace: inplace imgs or not ( return new_imgs) | Add a random value from [-offset, offset] to each channel | [
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Args:
offset: cast offset, >0 and <255
inplace: inplace imgs or not ( return new_imgs)
'''
new_imgs = []
if offset < 0 or offset > 255:
raise Exception('offset must be >0 and <255')
for img in self.imgs:
new_img = color_cast(img, offset)
new_imgs.append(new_img)
if inplace:
self.imgs = new_imgs
return self
else:
return new_imgs | [
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||
kamyu104/LeetCode-Solutions | 77605708a927ea3b85aee5a479db733938c7c211 | Python/compare-version-numbers.py | python | Solution2.compareVersion2 | (self, version1, version2) | return cmp(v1, v2) | :type version1: str
:type version2: str
:rtype: int | :type version1: str
:type version2: str
:rtype: int | [
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] | def compareVersion2(self, version1, version2):
"""
:type version1: str
:type version2: str
:rtype: int
"""
v1 = [int(x) for x in version1.split('.')]
v2 = [int(x) for x in version2.split('.')]
while len(v1) != len(v2):
if len(v1) > len(v2):
v2.append(0)
else:
v1.append(0)
return cmp(v1, v2) | [
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|
livecode/livecode | 4606a10ea10b16d5071d0f9f263ccdd7ede8b31d | gyp/pylib/gyp/MSVSSettings.py | python | ConvertVCMacrosToMSBuild | (s) | return s | Convert the the MSVS macros found in the string to the MSBuild equivalent.
This list is probably not exhaustive. Add as needed. | Convert the the MSVS macros found in the string to the MSBuild equivalent. | [
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] | def ConvertVCMacrosToMSBuild(s):
"""Convert the the MSVS macros found in the string to the MSBuild equivalent.
This list is probably not exhaustive. Add as needed.
"""
if '$' in s:
replace_map = {
'$(ConfigurationName)': '$(Configuration)',
'$(InputDir)': '%(RelativeDir)',
'$(InputExt)': '%(Extension)',
'$(InputFileName)': '%(Filename)%(Extension)',
'$(InputName)': '%(Filename)',
'$(InputPath)': '%(Identity)',
'$(ParentName)': '$(ProjectFileName)',
'$(PlatformName)': '$(Platform)',
'$(SafeInputName)': '%(Filename)',
}
for old, new in replace_map.iteritems():
s = s.replace(old, new)
s = FixVCMacroSlashes(s)
return s | [
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|
ideawu/ssdb | f229ba277c7f7d0ca5a441c0c6fb3d1209af68e4 | deps/cpy/antlr3/tree.py | python | CommonTreeNodeStream.push | (self, index) | Make stream jump to a new location, saving old location.
Switch back with pop(). | Make stream jump to a new location, saving old location.
Switch back with pop(). | [
"Make",
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] | def push(self, index):
"""
Make stream jump to a new location, saving old location.
Switch back with pop().
"""
self.calls.append(self.p) # save current index
self.seek(index) | [
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||
Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/profiler/internal/flops_registry.py | python | _l2_loss_flops | (graph, node) | return ops.OpStats("flops", in_shape.num_elements() * 3 - 1) | Compute flops for L2Loss operation. | Compute flops for L2Loss operation. | [
"Compute",
"flops",
"for",
"L2Loss",
"operation",
"."
] | def _l2_loss_flops(graph, node):
"""Compute flops for L2Loss operation."""
in_shape = graph_util.tensor_shape_from_node_def_name(graph, node.input[0])
in_shape.assert_is_fully_defined()
# Tensorflow uses inefficient implementation, with (3*N-1) flops:
# Optimal implementation is 2*N flops
return ops.OpStats("flops", in_shape.num_elements() * 3 - 1) | [
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|
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/propgrid.py | python | FileProperty_GetClassValidator | (*args) | return _propgrid.FileProperty_GetClassValidator(*args) | FileProperty_GetClassValidator() -> Validator | FileProperty_GetClassValidator() -> Validator | [
"FileProperty_GetClassValidator",
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"-",
">",
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] | def FileProperty_GetClassValidator(*args):
"""FileProperty_GetClassValidator() -> Validator"""
return _propgrid.FileProperty_GetClassValidator(*args) | [
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|
ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | gpu/command_buffer/build_gles2_cmd_buffer.py | python | StateSetNamedParameter.WriteHandlerImplementation | (self, func, file) | Overridden from TypeHandler. | Overridden from TypeHandler. | [
"Overridden",
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"TypeHandler",
"."
] | def WriteHandlerImplementation(self, func, file):
"""Overridden from TypeHandler."""
state_name = func.GetInfo('state')
state = _STATES[state_name]
states = state['states']
args = func.GetOriginalArgs()
num_args = len(args)
assert num_args == 2
file.Write(" switch (%s) {\n" % args[0].name)
for state in states:
file.Write(" case %s:\n" % state['enum'])
file.Write(" if (state_.%s != %s) {\n" %
(state['name'], args[1].name))
file.Write(" state_.%s = %s;\n" % (state['name'], args[1].name))
if not func.GetInfo("no_gl"):
file.Write(" %s(%s);\n" %
(func.GetGLFunctionName(), func.MakeOriginalArgString("")))
file.Write(" }\n")
file.Write(" break;\n")
file.Write(" default:\n")
file.Write(" NOTREACHED();\n")
file.Write(" }\n") | [
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] | https://github.com/ChromiumWebApps/chromium/blob/c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7/gpu/command_buffer/build_gles2_cmd_buffer.py#L3400-L3421 |
||
y123456yz/reading-and-annotate-mongodb-3.6 | 93280293672ca7586dc24af18132aa61e4ed7fcf | mongo/src/third_party/scons-2.5.0/scons-local-2.5.0/SCons/SConsign.py | python | Base.get_entry | (self, filename) | return self.entries[filename] | Fetch the specified entry attribute. | Fetch the specified entry attribute. | [
"Fetch",
"the",
"specified",
"entry",
"attribute",
"."
] | def get_entry(self, filename):
"""
Fetch the specified entry attribute.
"""
return self.entries[filename] | [
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] | https://github.com/y123456yz/reading-and-annotate-mongodb-3.6/blob/93280293672ca7586dc24af18132aa61e4ed7fcf/mongo/src/third_party/scons-2.5.0/scons-local-2.5.0/SCons/SConsign.py#L173-L177 |
|
hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/boto/boto/mws/connection.py | python | MWSConnection.list_carts | (self, request, response, **kw) | return self._post_request(request, kw, response) | Returns a list of shopping carts in your Webstore that were last
updated during the time range that you specify. | Returns a list of shopping carts in your Webstore that were last
updated during the time range that you specify. | [
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"that",
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"specify",
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] | def list_carts(self, request, response, **kw):
"""Returns a list of shopping carts in your Webstore that were last
updated during the time range that you specify.
"""
return self._post_request(request, kw, response) | [
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|
raymondlu/super-animation-samples | 04234269112ff0dc32447f27a761dbbb00b8ba17 | samples/cocos2d-x-3.1/CocosLuaGame2/frameworks/cocos2d-x/tools/bindings-generator/clang/cindex.py | python | CursorKind.is_preprocessing | (self) | return conf.lib.clang_isPreprocessing(self) | Test if this is a preprocessing kind. | Test if this is a preprocessing kind. | [
"Test",
"if",
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"preprocessing",
"kind",
"."
] | def is_preprocessing(self):
"""Test if this is a preprocessing kind."""
return conf.lib.clang_isPreprocessing(self) | [
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] | https://github.com/raymondlu/super-animation-samples/blob/04234269112ff0dc32447f27a761dbbb00b8ba17/samples/cocos2d-x-3.1/CocosLuaGame2/frameworks/cocos2d-x/tools/bindings-generator/clang/cindex.py#L660-L662 |
|
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/stc.py | python | StyledTextCtrl.SetHScrollBar | (*args, **kwargs) | return _stc.StyledTextCtrl_SetHScrollBar(*args, **kwargs) | SetHScrollBar(self, ScrollBar bar)
Set the horizontal scrollbar to use instead of the ont that's built-in. | SetHScrollBar(self, ScrollBar bar) | [
"SetHScrollBar",
"(",
"self",
"ScrollBar",
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")"
] | def SetHScrollBar(*args, **kwargs):
"""
SetHScrollBar(self, ScrollBar bar)
Set the horizontal scrollbar to use instead of the ont that's built-in.
"""
return _stc.StyledTextCtrl_SetHScrollBar(*args, **kwargs) | [
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|
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/prompt-toolkit/py2/prompt_toolkit/contrib/telnet/server.py | python | TelnetServer._handle_incoming_data | (self, conn) | Handle incoming data on socket. | Handle incoming data on socket. | [
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] | def _handle_incoming_data(self, conn):
"""
Handle incoming data on socket.
"""
connection = [c for c in self.connections if c.conn == conn][0]
data = conn.recv(1024)
if data:
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||
wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/ftplib.py | python | parse229 | (resp, peer) | return host, port | Parse the '229' response for a EPSV request.
Raises error_proto if it does not contain '(|||port|)'
Return ('host.addr.as.numbers', port#) tuple. | Parse the '229' response for a EPSV request.
Raises error_proto if it does not contain '(|||port|)'
Return ('host.addr.as.numbers', port#) tuple. | [
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'''Parse the '229' response for a EPSV request.
Raises error_proto if it does not contain '(|||port|)'
Return ('host.addr.as.numbers', port#) tuple.'''
if resp[:3] != '229':
raise error_reply, resp
left = resp.find('(')
if left < 0: raise error_proto, resp
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if right < 0:
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host = peer[0]
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return host, port | [
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|
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/AWS/common-code/lib/pycparser/c_lexer.py | python | CLexer.t_ppline_NEWLINE | (self, t) | r'\n | r'\n | [
"r",
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] | def t_ppline_NEWLINE(self, t):
r'\n'
if self.pp_line is None:
self._error('line number missing in #line', t)
else:
self.lexer.lineno = int(self.pp_line)
if self.pp_filename is not None:
self.filename = self.pp_filename
t.lexer.begin('INITIAL') | [
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||
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/protobuf/py3/google/protobuf/internal/wire_format.py | python | IsTypePackable | (field_type) | return field_type not in NON_PACKABLE_TYPES | Return true iff packable = true is valid for fields of this type.
Args:
field_type: a FieldDescriptor::Type value.
Returns:
True iff fields of this type are packable. | Return true iff packable = true is valid for fields of this type. | [
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"""Return true iff packable = true is valid for fields of this type.
Args:
field_type: a FieldDescriptor::Type value.
Returns:
True iff fields of this type are packable.
"""
return field_type not in NON_PACKABLE_TYPES | [
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|
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/queue.py | python | Queue.join | (self) | Blocks until all items in the Queue have been gotten and processed.
The count of unfinished tasks goes up whenever an item is added to the
queue. The count goes down whenever a consumer thread calls task_done()
to indicate the item was retrieved and all work on it is complete.
When the count of unfinished tasks drops to zero, join() unblocks. | Blocks until all items in the Queue have been gotten and processed. | [
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'''Blocks until all items in the Queue have been gotten and processed.
The count of unfinished tasks goes up whenever an item is added to the
queue. The count goes down whenever a consumer thread calls task_done()
to indicate the item was retrieved and all work on it is complete.
When the count of unfinished tasks drops to zero, join() unblocks.
'''
with self.all_tasks_done:
while self.unfinished_tasks:
self.all_tasks_done.wait() | [
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||
NVIDIAGameWorks/kaolin | e5148d05e9c1e2ce92a07881ce3593b1c5c3f166 | kaolin/io/usd.py | python | export_meshes | (file_path, scene_paths=None, vertices=None, faces=None,
uvs=None, face_uvs_idx=None, face_normals=None, materials_order=None, materials=None,
up_axis='Y', times=None) | return stage | r"""Export multiple meshes to a new USD stage.
Export multiple meshes defined by lists vertices and faces and save the stage to disk.
Args:
file_path (str): Path to usd file (\*.usd, \*.usda).
scene_paths (list of str, optional): Absolute paths of meshes within the USD file scene. Must have the same
number ofpaths as the number of meshes ``N``. Must be a valid Sdf.Path. If no path is provided, a default
path is used.
vertices (list of torch.FloatTensor, optional): Vertices with shape ``(num_vertices, 3)``.
faces (list of torch.LongTensor, optional): Vertex indices for each face with shape ``(num_faces, face_size)``.
Mesh must be homogenous (consistent number of vertices per face).
uvs (list of torch.FloatTensor, optional): of shape ``(num_uvs, 2)``.
face_uvs_idx (list of torch.LongTensor, optional): of shape ``(num_faces, face_size)``. If provided, `uvs`
must also be specified.
face_normals (list of torch.Tensor, optional): of shape ``(num_vertices, num_faces, 3)``.
materials_order (torch.LongTensor): of shape (N, 2)
showing the order in which materials are used over **face_uvs_idx** and the first indices
in which they start to be used. A material can be used multiple times.
materials (list of Material): a list of materials
up_axis (str, optional): Specifies the scene's up axis. Choose from ``['Y', 'Z']``.
times (list of int, optional): Positive integers defining the time at which the supplied parameters
correspond to.
Returns:
(Usd.Stage)
Example:
>>> vertices_list = [torch.rand(3, 3) for _ in range(3)]
>>> faces_list = [torch.tensor([[0, 1, 2]]) for _ in range(3)]
>>> stage = export_meshes('./new_stage.usd', vertices=vertices_list, faces=faces_list) | r"""Export multiple meshes to a new USD stage. | [
"r",
"Export",
"multiple",
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"to",
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"USD",
"stage",
"."
] | def export_meshes(file_path, scene_paths=None, vertices=None, faces=None,
uvs=None, face_uvs_idx=None, face_normals=None, materials_order=None, materials=None,
up_axis='Y', times=None):
r"""Export multiple meshes to a new USD stage.
Export multiple meshes defined by lists vertices and faces and save the stage to disk.
Args:
file_path (str): Path to usd file (\*.usd, \*.usda).
scene_paths (list of str, optional): Absolute paths of meshes within the USD file scene. Must have the same
number ofpaths as the number of meshes ``N``. Must be a valid Sdf.Path. If no path is provided, a default
path is used.
vertices (list of torch.FloatTensor, optional): Vertices with shape ``(num_vertices, 3)``.
faces (list of torch.LongTensor, optional): Vertex indices for each face with shape ``(num_faces, face_size)``.
Mesh must be homogenous (consistent number of vertices per face).
uvs (list of torch.FloatTensor, optional): of shape ``(num_uvs, 2)``.
face_uvs_idx (list of torch.LongTensor, optional): of shape ``(num_faces, face_size)``. If provided, `uvs`
must also be specified.
face_normals (list of torch.Tensor, optional): of shape ``(num_vertices, num_faces, 3)``.
materials_order (torch.LongTensor): of shape (N, 2)
showing the order in which materials are used over **face_uvs_idx** and the first indices
in which they start to be used. A material can be used multiple times.
materials (list of Material): a list of materials
up_axis (str, optional): Specifies the scene's up axis. Choose from ``['Y', 'Z']``.
times (list of int, optional): Positive integers defining the time at which the supplied parameters
correspond to.
Returns:
(Usd.Stage)
Example:
>>> vertices_list = [torch.rand(3, 3) for _ in range(3)]
>>> faces_list = [torch.tensor([[0, 1, 2]]) for _ in range(3)]
>>> stage = export_meshes('./new_stage.usd', vertices=vertices_list, faces=faces_list)
"""
stage = create_stage(file_path, up_axis)
mesh_parameters = {'vertices': vertices, 'faces': faces, 'uvs': uvs,
'face_uvs_idx': face_uvs_idx, 'face_normals': face_normals,
'materials_order': materials_order, 'materials': materials}
supplied_parameters = {k: p for k, p in mesh_parameters.items() if p is not None}
length = len(list(supplied_parameters.values())[0])
assert all([len(p) == length for p in supplied_parameters.values()])
if scene_paths is None:
if not stage.GetPrimAtPath('/World/Meshes'):
stage.DefinePrim('/World/Meshes', 'Xform')
scene_paths = [f'/World/Meshes/mesh_{i}' for i in range(len(vertices))]
assert len(scene_paths) == length
if times is None:
times = [Usd.TimeCode.Default()] * len(scene_paths)
for i, scene_path in enumerate(scene_paths):
mesh_params = {k: p[i] for k, p in supplied_parameters.items()}
add_mesh(stage, scene_path, **mesh_params)
stage.Save()
return stage | [
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|
nnrg/opennero | 43e12a1bcba6e228639db3886fec1dc47ddc24cb | mods/Maze/environment.py | python | MazeEnvironment.mark_maze | (self, r, c, marker) | mark a maze cell with the specified color | mark a maze cell with the specified color | [
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""" mark a maze cell with the specified color """
# remove the previous object, if necessary
if (r,c) in self.marker_map:
removeObject(self.marker_map[(r,c)])
# remember the ID of the marker
self.marker_map[(r,c)] = addObject(marker, Vector3f( (r+1) * GRID_DX, (c+1) * GRID_DY, -1)) | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | Framework/PythonInterface/mantid/plots/plotfunctions.py | python | _validate_plot_inputs | (workspaces, spectrum_nums, wksp_indices, tiled=False, overplot=False) | Raises a ValueError if any arguments have the incorrect types | Raises a ValueError if any arguments have the incorrect types | [
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"""Raises a ValueError if any arguments have the incorrect types"""
if spectrum_nums is not None and wksp_indices is not None:
raise ValueError("Both spectrum_nums and wksp_indices supplied. "
"Please supply only 1.")
if tiled and overplot:
raise ValueError("Both tiled and overplot flags set to true. "
"Please set only one to true.")
raise_if_not_sequence(workspaces, 'workspaces', MatrixWorkspace)
if spectrum_nums is not None:
raise_if_not_sequence(spectrum_nums, 'spectrum_nums')
if wksp_indices is not None:
raise_if_not_sequence(wksp_indices, 'wksp_indices') | [
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anestisb/oatdump_plus | ba858c1596598f0d9ae79c14d08c708cecc50af3 | tools/bisection_search/bisection_search.py | python | EqualsOutputCheck.__init__ | (self, expected_output) | Constructor.
Args:
expected_output: string, expected output. | Constructor. | [
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expected_output: string, expected output.
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wyrover/book-code | 7f4883d9030d553bc6bcfa3da685e34789839900 | 3rdparty/protobuf/python/google/protobuf/text_format.py | python | Tokenizer.ParseErrorPreviousToken | (self, message) | return ParseError(message, self._previous_line + 1,
self._previous_column + 1) | Creates and *returns* a ParseError for the previously read token.
Args:
message: A message to set for the exception.
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Args:
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Returns:
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return ParseError(message, self._previous_line + 1,
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devsisters/libquic | 8954789a056d8e7d5fcb6452fd1572ca57eb5c4e | src/third_party/protobuf/python/mox.py | python | MockMethod._PopNextMethod | (self) | Pop the next method from our call queue. | Pop the next method from our call queue. | [
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gnina/gnina | b9ae032f52fc7a8153987bde09c0efa3620d8bb6 | caffe/tools/extra/extract_seconds.py | python | get_log_created_year | (input_file) | return log_created_year | Get year from log file system timestamp | Get year from log file system timestamp | [
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] | def get_log_created_year(input_file):
"""Get year from log file system timestamp
"""
log_created_time = os.path.getctime(input_file)
log_created_year = datetime.datetime.fromtimestamp(log_created_time).year
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/site-packages/botocore/docs/method.py | python | document_custom_method | (section, method_name, method) | Documents a non-data driven method
:param section: The section to write the documentation to.
:param method_name: The name of the method
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"""Documents a non-data driven method
:param section: The section to write the documentation to.
:param method_name: The name of the method
:param method: The handle to the method being documented
"""
document_custom_signature(
section, method_name, method)
method_intro_section = section.add_new_section('method-intro')
method_intro_section.writeln('')
doc_string = inspect.getdoc(method)
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||
ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/third_party/lib_x86_64/python2.7/dist-packages/numpy/ma/core.py | python | _DomainTan.__init__ | (self, eps) | domain_tan(eps) = true where abs(cos(x)) < eps) | domain_tan(eps) = true where abs(cos(x)) < eps) | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/python/training/sync_replicas_optimizer.py | python | SyncReplicasOptimizer.get_chief_queue_runner | (self) | return self._chief_queue_runner | Returns the QueueRunner for the chief to execute.
This includes the operations to synchronize replicas: aggregate gradients,
apply to variables, increment global step, insert tokens to token queue.
Note that this can only be called after calling apply_gradients() which
actually generates this queuerunner.
Returns:
A `QueueRunner` for chief to execute.
Raises:
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"""Returns the QueueRunner for the chief to execute.
This includes the operations to synchronize replicas: aggregate gradients,
apply to variables, increment global step, insert tokens to token queue.
Note that this can only be called after calling apply_gradients() which
actually generates this queuerunner.
Returns:
A `QueueRunner` for chief to execute.
Raises:
ValueError: If this is called before apply_gradients().
"""
if self._gradients_applied is False:
raise ValueError("Should be called after apply_gradients().")
return self._chief_queue_runner | [
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|
apache/arrow | af33dd1157eb8d7d9bfac25ebf61445b793b7943 | cpp/build-support/cpplint.py | python | NestingState.SeenOpenBrace | (self) | return (not self.stack) or self.stack[-1].seen_open_brace | Check if we have seen the opening brace for the innermost block.
Returns:
True if we have seen the opening brace, False if the innermost
block is still expecting an opening brace. | Check if we have seen the opening brace for the innermost block. | [
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"""Check if we have seen the opening brace for the innermost block.
Returns:
True if we have seen the opening brace, False if the innermost
block is still expecting an opening brace.
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/tools/compatibility/tf_upgrade_v2.py | python | _add_uniform_scaling_initializer_transformer | (
parent, node, full_name, name, logs) | return node | Updates references to uniform_unit_scaling_initializer.
Transforms:
tf.uniform_unit_scaling_initializer(factor, seed, dtype) to
tf.compat.v1.keras.initializers.VarianceScaling(
scale=factor, distribution="uniform", seed=seed)
Note: to apply this transformation, symbol must be added
to reordered_function_names above. | Updates references to uniform_unit_scaling_initializer. | [
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] | def _add_uniform_scaling_initializer_transformer(
parent, node, full_name, name, logs):
"""Updates references to uniform_unit_scaling_initializer.
Transforms:
tf.uniform_unit_scaling_initializer(factor, seed, dtype) to
tf.compat.v1.keras.initializers.VarianceScaling(
scale=factor, distribution="uniform", seed=seed)
Note: to apply this transformation, symbol must be added
to reordered_function_names above.
"""
for keyword_arg in node.keywords:
if keyword_arg.arg == "factor":
keyword_arg.arg = "scale"
distribution_value = "\"uniform\""
# Parse with pasta instead of ast to avoid emitting a spurious trailing \n.
ast_value = pasta.parse(distribution_value)
node.keywords.append(ast.keyword(arg="distribution", value=ast_value))
lineno = node.func.value.lineno
col_offset = node.func.value.col_offset
node.func.value = ast_edits.full_name_node("tf.compat.v1.keras.initializers")
node.func.value.lineno = lineno
node.func.value.col_offset = col_offset
node.func.attr = "VarianceScaling"
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|
microsoft/TSS.MSR | 0f2516fca2cd9929c31d5450e39301c9bde43688 | TSS.Py/src/TpmTypes.py | python | TPM2_PolicyCounterTimer_REQUEST.fromTpm | (buf) | return buf.createObj(TPM2_PolicyCounterTimer_REQUEST) | Returns new TPM2_PolicyCounterTimer_REQUEST object constructed from
its marshaled representation in the given TpmBuffer buffer | Returns new TPM2_PolicyCounterTimer_REQUEST object constructed from
its marshaled representation in the given TpmBuffer buffer | [
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""" Returns new TPM2_PolicyCounterTimer_REQUEST object constructed from
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return buf.createObj(TPM2_PolicyCounterTimer_REQUEST) | [
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|
WeitaoVan/L-GM-loss | 598582f0631bac876b3eeb8d6c4cd1d780269e03 | scripts/cpp_lint.py | python | RemoveMultiLineCommentsFromRange | (lines, begin, end) | Clears a range of lines for multi-line comments. | Clears a range of lines for multi-line comments. | [
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"""Clears a range of lines for multi-line comments."""
# Having // dummy comments makes the lines non-empty, so we will not get
# unnecessary blank line warnings later in the code.
for i in range(begin, end):
lines[i] = '// dummy' | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/boto3/resources/collection.py | python | ResourceCollection.page_size | (self, count) | return self._clone(page_size=count) | Fetch at most this many resources per service request.
>>> for obj in s3.Bucket('boto3').objects.page_size(100):
... print(obj.key)
:type count: int
:param count: Fetch this many items per request
:rtype: :py:class:`ResourceCollection` | Fetch at most this many resources per service request. | [
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"""
Fetch at most this many resources per service request.
>>> for obj in s3.Bucket('boto3').objects.page_size(100):
... print(obj.key)
:type count: int
:param count: Fetch this many items per request
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return self._clone(page_size=count) | [
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|
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py3/pandas/core/series.py | python | Series.unstack | (self, level=-1, fill_value=None) | return unstack(self, level, fill_value) | Unstack, also known as pivot, Series with MultiIndex to produce DataFrame.
Parameters
----------
level : int, str, or list of these, default last level
Level(s) to unstack, can pass level name.
fill_value : scalar value, default None
Value to use when replacing NaN values.
Returns
-------
DataFrame
Unstacked Series.
Examples
--------
>>> s = pd.Series([1, 2, 3, 4],
... index=pd.MultiIndex.from_product([['one', 'two'],
... ['a', 'b']]))
>>> s
one a 1
b 2
two a 3
b 4
dtype: int64
>>> s.unstack(level=-1)
a b
one 1 2
two 3 4
>>> s.unstack(level=0)
one two
a 1 3
b 2 4 | Unstack, also known as pivot, Series with MultiIndex to produce DataFrame. | [
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"""
Unstack, also known as pivot, Series with MultiIndex to produce DataFrame.
Parameters
----------
level : int, str, or list of these, default last level
Level(s) to unstack, can pass level name.
fill_value : scalar value, default None
Value to use when replacing NaN values.
Returns
-------
DataFrame
Unstacked Series.
Examples
--------
>>> s = pd.Series([1, 2, 3, 4],
... index=pd.MultiIndex.from_product([['one', 'two'],
... ['a', 'b']]))
>>> s
one a 1
b 2
two a 3
b 4
dtype: int64
>>> s.unstack(level=-1)
a b
one 1 2
two 3 4
>>> s.unstack(level=0)
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a 1 3
b 2 4
"""
from pandas.core.reshape.reshape import unstack
return unstack(self, level, fill_value) | [
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google/syzygy | 8164b24ebde9c5649c9a09e88a7fc0b0fcbd1bc5 | third_party/numpy/files/numpy/lib/stride_tricks.py | python | broadcast_arrays | (*args) | return broadcasted | Broadcast any number of arrays against each other.
Parameters
----------
`*args` : array_likes
The arrays to broadcast.
Returns
-------
broadcasted : list of arrays
These arrays are views on the original arrays. They are typically
not contiguous. Furthermore, more than one element of a
broadcasted array may refer to a single memory location. If you
need to write to the arrays, make copies first.
Examples
--------
>>> x = np.array([[1,2,3]])
>>> y = np.array([[1],[2],[3]])
>>> np.broadcast_arrays(x, y)
[array([[1, 2, 3],
[1, 2, 3],
[1, 2, 3]]), array([[1, 1, 1],
[2, 2, 2],
[3, 3, 3]])]
Here is a useful idiom for getting contiguous copies instead of
non-contiguous views.
>>> map(np.array, np.broadcast_arrays(x, y))
[array([[1, 2, 3],
[1, 2, 3],
[1, 2, 3]]), array([[1, 1, 1],
[2, 2, 2],
[3, 3, 3]])] | Broadcast any number of arrays against each other. | [
"Broadcast",
"any",
"number",
"of",
"arrays",
"against",
"each",
"other",
"."
] | def broadcast_arrays(*args):
"""
Broadcast any number of arrays against each other.
Parameters
----------
`*args` : array_likes
The arrays to broadcast.
Returns
-------
broadcasted : list of arrays
These arrays are views on the original arrays. They are typically
not contiguous. Furthermore, more than one element of a
broadcasted array may refer to a single memory location. If you
need to write to the arrays, make copies first.
Examples
--------
>>> x = np.array([[1,2,3]])
>>> y = np.array([[1],[2],[3]])
>>> np.broadcast_arrays(x, y)
[array([[1, 2, 3],
[1, 2, 3],
[1, 2, 3]]), array([[1, 1, 1],
[2, 2, 2],
[3, 3, 3]])]
Here is a useful idiom for getting contiguous copies instead of
non-contiguous views.
>>> map(np.array, np.broadcast_arrays(x, y))
[array([[1, 2, 3],
[1, 2, 3],
[1, 2, 3]]), array([[1, 1, 1],
[2, 2, 2],
[3, 3, 3]])]
"""
args = map(np.asarray, args)
shapes = [x.shape for x in args]
if len(set(shapes)) == 1:
# Common case where nothing needs to be broadcasted.
return args
shapes = [list(s) for s in shapes]
strides = [list(x.strides) for x in args]
nds = [len(s) for s in shapes]
biggest = max(nds)
# Go through each array and prepend dimensions of length 1 to each of the
# shapes in order to make the number of dimensions equal.
for i in range(len(args)):
diff = biggest - nds[i]
if diff > 0:
shapes[i] = [1] * diff + shapes[i]
strides[i] = [0] * diff + strides[i]
# Chech each dimension for compatibility. A dimension length of 1 is
# accepted as compatible with any other length.
common_shape = []
for axis in range(biggest):
lengths = [s[axis] for s in shapes]
unique = set(lengths + [1])
if len(unique) > 2:
# There must be at least two non-1 lengths for this axis.
raise ValueError("shape mismatch: two or more arrays have "
"incompatible dimensions on axis %r." % (axis,))
elif len(unique) == 2:
# There is exactly one non-1 length. The common shape will take this
# value.
unique.remove(1)
new_length = unique.pop()
common_shape.append(new_length)
# For each array, if this axis is being broadcasted from a length of
# 1, then set its stride to 0 so that it repeats its data.
for i in range(len(args)):
if shapes[i][axis] == 1:
shapes[i][axis] = new_length
strides[i][axis] = 0
else:
# Every array has a length of 1 on this axis. Strides can be left
# alone as nothing is broadcasted.
common_shape.append(1)
# Construct the new arrays.
broadcasted = [as_strided(x, shape=sh, strides=st) for (x,sh,st) in
zip(args, shapes, strides)]
return broadcasted | [
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] | https://github.com/google/syzygy/blob/8164b24ebde9c5649c9a09e88a7fc0b0fcbd1bc5/third_party/numpy/files/numpy/lib/stride_tricks.py#L30-L115 |
|
bairdzhang/smallhardface | 76fa1d87a9602d9b13d7a7fe693fc7aec91cab80 | external/marcopede-face-eval-f2870fd85d48/database.py | python | getRecord | (data, total=-1, pos=True, pose=False, facial=False) | return arrPos | return all the gt data in a record | return all the gt data in a record | [
"return",
"all",
"the",
"gt",
"data",
"in",
"a",
"record"
] | def getRecord(data, total=-1, pos=True, pose=False, facial=False):
"""return all the gt data in a record"""
if total == -1:
total = data.getTotal()
else:
total = min(data.getTotal(), total)
arrPos = numpy.zeros(
total, dtype=[("id", numpy.int32), ("name", object), ("bbox", list)])
if facial:
arrPos = numpy.zeros(total, dtype=[
("id", numpy.int32), ("name", object), ("bbox", list), ("facial", object)])
if pose:
arrPos = numpy.zeros(total, dtype=[
("id", numpy.int32), ("name", object), ("bbox", list), ("facial", object), ("pose", object)])
for i in range(total):
arrPos[i]["id"] = i
arrPos[i]["name"] = data.getImageName(i)
arrPos[i]["bbox"] = data.getBBox(i)
if pose:
arrPos[i]["pose"] = data.getPose(i)
if facial:
arrPos[i]["facial"] = data.getFacial(i)
return arrPos | [
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] | https://github.com/bairdzhang/smallhardface/blob/76fa1d87a9602d9b13d7a7fe693fc7aec91cab80/external/marcopede-face-eval-f2870fd85d48/database.py#L121-L143 |
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