nwo
stringlengths 5
86
| sha
stringlengths 40
40
| path
stringlengths 4
189
| language
stringclasses 1
value | identifier
stringlengths 1
94
| parameters
stringlengths 2
4.03k
| argument_list
stringclasses 1
value | return_statement
stringlengths 0
11.5k
| docstring
stringlengths 1
33.2k
| docstring_summary
stringlengths 0
5.15k
| docstring_tokens
list | function
stringlengths 34
151k
| function_tokens
list | url
stringlengths 90
278
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Tools/AWSPythonSDK/1.5.8/docutils/utils/math/math2html.py
|
python
|
FilePosition.__init__
|
(self, filename)
|
Create the position from a file.
|
Create the position from a file.
|
[
"Create",
"the",
"position",
"from",
"a",
"file",
"."
] |
def __init__(self, filename):
"Create the position from a file."
Position.__init__(self)
self.reader = LineReader(filename)
self.pos = 0
self.checkbytemark()
|
[
"def",
"__init__",
"(",
"self",
",",
"filename",
")",
":",
"Position",
".",
"__init__",
"(",
"self",
")",
"self",
".",
"reader",
"=",
"LineReader",
"(",
"filename",
")",
"self",
".",
"pos",
"=",
"0",
"self",
".",
"checkbytemark",
"(",
")"
] |
https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/AWSPythonSDK/1.5.8/docutils/utils/math/math2html.py#L2114-L2119
|
||
ricardoquesada/Spidermonkey
|
4a75ea2543408bd1b2c515aa95901523eeef7858
|
toolkit/crashreporter/tools/symbolstore.py
|
python
|
Dumper_Mac.ProcessFilesWorkMac
|
(self, file)
|
return result
|
dump_syms on Mac needs to be run on a dSYM bundle produced
by dsymutil(1), so run dsymutil here and pass the bundle name
down to the superclass method instead.
|
dump_syms on Mac needs to be run on a dSYM bundle produced
by dsymutil(1), so run dsymutil here and pass the bundle name
down to the superclass method instead.
|
[
"dump_syms",
"on",
"Mac",
"needs",
"to",
"be",
"run",
"on",
"a",
"dSYM",
"bundle",
"produced",
"by",
"dsymutil",
"(",
"1",
")",
"so",
"run",
"dsymutil",
"here",
"and",
"pass",
"the",
"bundle",
"name",
"down",
"to",
"the",
"superclass",
"method",
"instead",
"."
] |
def ProcessFilesWorkMac(self, file):
"""dump_syms on Mac needs to be run on a dSYM bundle produced
by dsymutil(1), so run dsymutil here and pass the bundle name
down to the superclass method instead."""
self.output_pid(sys.stderr, "Worker running Mac pre-processing on file: %s" % (file,))
# our return is a status and a tuple of files to dump symbols for
# the extra files are fallbacks; as soon as one is dumped successfully, we stop
result = { 'status' : False, 'files' : None, 'file_key' : file }
dsymbundle = file + ".dSYM"
if os.path.exists(dsymbundle):
shutil.rmtree(dsymbundle)
# dsymutil takes --arch=foo instead of -a foo like everything else
subprocess.call(["dsymutil"] + [a.replace('-a ', '--arch=') for a in self.archs if a]
+ [file],
stdout=open("/dev/null","w"))
if not os.path.exists(dsymbundle):
# dsymutil won't produce a .dSYM for files without symbols
self.output_pid(sys.stderr, "No symbols found in file: %s" % (file,))
result['status'] = False
result['files'] = (file, )
return result
result['status'] = True
result['files'] = (dsymbundle, file)
return result
|
[
"def",
"ProcessFilesWorkMac",
"(",
"self",
",",
"file",
")",
":",
"self",
".",
"output_pid",
"(",
"sys",
".",
"stderr",
",",
"\"Worker running Mac pre-processing on file: %s\"",
"%",
"(",
"file",
",",
")",
")",
"# our return is a status and a tuple of files to dump symbols for",
"# the extra files are fallbacks; as soon as one is dumped successfully, we stop",
"result",
"=",
"{",
"'status'",
":",
"False",
",",
"'files'",
":",
"None",
",",
"'file_key'",
":",
"file",
"}",
"dsymbundle",
"=",
"file",
"+",
"\".dSYM\"",
"if",
"os",
".",
"path",
".",
"exists",
"(",
"dsymbundle",
")",
":",
"shutil",
".",
"rmtree",
"(",
"dsymbundle",
")",
"# dsymutil takes --arch=foo instead of -a foo like everything else",
"subprocess",
".",
"call",
"(",
"[",
"\"dsymutil\"",
"]",
"+",
"[",
"a",
".",
"replace",
"(",
"'-a '",
",",
"'--arch='",
")",
"for",
"a",
"in",
"self",
".",
"archs",
"if",
"a",
"]",
"+",
"[",
"file",
"]",
",",
"stdout",
"=",
"open",
"(",
"\"/dev/null\"",
",",
"\"w\"",
")",
")",
"if",
"not",
"os",
".",
"path",
".",
"exists",
"(",
"dsymbundle",
")",
":",
"# dsymutil won't produce a .dSYM for files without symbols",
"self",
".",
"output_pid",
"(",
"sys",
".",
"stderr",
",",
"\"No symbols found in file: %s\"",
"%",
"(",
"file",
",",
")",
")",
"result",
"[",
"'status'",
"]",
"=",
"False",
"result",
"[",
"'files'",
"]",
"=",
"(",
"file",
",",
")",
"return",
"result",
"result",
"[",
"'status'",
"]",
"=",
"True",
"result",
"[",
"'files'",
"]",
"=",
"(",
"dsymbundle",
",",
"file",
")",
"return",
"result"
] |
https://github.com/ricardoquesada/Spidermonkey/blob/4a75ea2543408bd1b2c515aa95901523eeef7858/toolkit/crashreporter/tools/symbolstore.py#L854-L879
|
|
apache/singa
|
93fd9da72694e68bfe3fb29d0183a65263d238a1
|
python/singa/utils.py
|
python
|
get_output_shape
|
(auto_pad, input_spatial_shape, kernel_spatial_shape,
strides_spatial)
|
return out_shape
|
return output shape of conv2d or pooling,
! borrow from onnx
Args:
auto_pad: string
input_spatial_shape: list[int]
kernel_spatial_shape: list[int]
strides_spatial: list[int]
output_spatial_shape: list[int]
Returns:
list[int
|
return output shape of conv2d or pooling,
! borrow from onnx
Args:
auto_pad: string
input_spatial_shape: list[int]
kernel_spatial_shape: list[int]
strides_spatial: list[int]
output_spatial_shape: list[int]
Returns:
list[int
|
[
"return",
"output",
"shape",
"of",
"conv2d",
"or",
"pooling",
"!",
"borrow",
"from",
"onnx",
"Args",
":",
"auto_pad",
":",
"string",
"input_spatial_shape",
":",
"list",
"[",
"int",
"]",
"kernel_spatial_shape",
":",
"list",
"[",
"int",
"]",
"strides_spatial",
":",
"list",
"[",
"int",
"]",
"output_spatial_shape",
":",
"list",
"[",
"int",
"]",
"Returns",
":",
"list",
"[",
"int"
] |
def get_output_shape(auto_pad, input_spatial_shape, kernel_spatial_shape,
strides_spatial):
"""
return output shape of conv2d or pooling,
! borrow from onnx
Args:
auto_pad: string
input_spatial_shape: list[int]
kernel_spatial_shape: list[int]
strides_spatial: list[int]
output_spatial_shape: list[int]
Returns:
list[int
"""
out_shape = [0] * len(input_spatial_shape)
if auto_pad in ('SAME_UPPER', 'SAME_LOWER'):
for i in range(len(input_spatial_shape)):
out_shape[i] = int(
np.ceil(
float(input_spatial_shape[i]) / float(strides_spatial[i])))
elif auto_pad == 'VALID':
for i in range(len(input_spatial_shape)):
out_shape[i] = int(
np.ceil(
float(input_spatial_shape[i] -
(kernel_spatial_shape[i] - 1)) /
float(strides_spatial[i])))
return out_shape
|
[
"def",
"get_output_shape",
"(",
"auto_pad",
",",
"input_spatial_shape",
",",
"kernel_spatial_shape",
",",
"strides_spatial",
")",
":",
"out_shape",
"=",
"[",
"0",
"]",
"*",
"len",
"(",
"input_spatial_shape",
")",
"if",
"auto_pad",
"in",
"(",
"'SAME_UPPER'",
",",
"'SAME_LOWER'",
")",
":",
"for",
"i",
"in",
"range",
"(",
"len",
"(",
"input_spatial_shape",
")",
")",
":",
"out_shape",
"[",
"i",
"]",
"=",
"int",
"(",
"np",
".",
"ceil",
"(",
"float",
"(",
"input_spatial_shape",
"[",
"i",
"]",
")",
"/",
"float",
"(",
"strides_spatial",
"[",
"i",
"]",
")",
")",
")",
"elif",
"auto_pad",
"==",
"'VALID'",
":",
"for",
"i",
"in",
"range",
"(",
"len",
"(",
"input_spatial_shape",
")",
")",
":",
"out_shape",
"[",
"i",
"]",
"=",
"int",
"(",
"np",
".",
"ceil",
"(",
"float",
"(",
"input_spatial_shape",
"[",
"i",
"]",
"-",
"(",
"kernel_spatial_shape",
"[",
"i",
"]",
"-",
"1",
")",
")",
"/",
"float",
"(",
"strides_spatial",
"[",
"i",
"]",
")",
")",
")",
"return",
"out_shape"
] |
https://github.com/apache/singa/blob/93fd9da72694e68bfe3fb29d0183a65263d238a1/python/singa/utils.py#L189-L216
|
|
mingchen/protobuf-ios
|
0958df34558cd54cb7b6e6ca5c8855bf3d475046
|
compiler/python/google/protobuf/reflection.py
|
python
|
GeneratedProtocolMessageType.__new__
|
(cls, name, bases, dictionary)
|
return superclass.__new__(cls, name, bases, dictionary)
|
Custom allocation for runtime-generated class types.
We override __new__ because this is apparently the only place
where we can meaningfully set __slots__ on the class we're creating(?).
(The interplay between metaclasses and slots is not very well-documented).
Args:
name: Name of the class (ignored, but required by the
metaclass protocol).
bases: Base classes of the class we're constructing.
(Should be message.Message). We ignore this field, but
it's required by the metaclass protocol
dictionary: The class dictionary of the class we're
constructing. dictionary[_DESCRIPTOR_KEY] must contain
a Descriptor object describing this protocol message
type.
Returns:
Newly-allocated class.
|
Custom allocation for runtime-generated class types.
|
[
"Custom",
"allocation",
"for",
"runtime",
"-",
"generated",
"class",
"types",
"."
] |
def __new__(cls, name, bases, dictionary):
"""Custom allocation for runtime-generated class types.
We override __new__ because this is apparently the only place
where we can meaningfully set __slots__ on the class we're creating(?).
(The interplay between metaclasses and slots is not very well-documented).
Args:
name: Name of the class (ignored, but required by the
metaclass protocol).
bases: Base classes of the class we're constructing.
(Should be message.Message). We ignore this field, but
it's required by the metaclass protocol
dictionary: The class dictionary of the class we're
constructing. dictionary[_DESCRIPTOR_KEY] must contain
a Descriptor object describing this protocol message
type.
Returns:
Newly-allocated class.
"""
descriptor = dictionary[GeneratedProtocolMessageType._DESCRIPTOR_KEY]
_AddSlots(descriptor, dictionary)
_AddClassAttributesForNestedExtensions(descriptor, dictionary)
superclass = super(GeneratedProtocolMessageType, cls)
return superclass.__new__(cls, name, bases, dictionary)
|
[
"def",
"__new__",
"(",
"cls",
",",
"name",
",",
"bases",
",",
"dictionary",
")",
":",
"descriptor",
"=",
"dictionary",
"[",
"GeneratedProtocolMessageType",
".",
"_DESCRIPTOR_KEY",
"]",
"_AddSlots",
"(",
"descriptor",
",",
"dictionary",
")",
"_AddClassAttributesForNestedExtensions",
"(",
"descriptor",
",",
"dictionary",
")",
"superclass",
"=",
"super",
"(",
"GeneratedProtocolMessageType",
",",
"cls",
")",
"return",
"superclass",
".",
"__new__",
"(",
"cls",
",",
"name",
",",
"bases",
",",
"dictionary",
")"
] |
https://github.com/mingchen/protobuf-ios/blob/0958df34558cd54cb7b6e6ca5c8855bf3d475046/compiler/python/google/protobuf/reflection.py#L97-L122
|
|
wxWidgets/wxPython-Classic
|
19571e1ae65f1ac445f5491474121998c97a1bf0
|
src/osx_cocoa/combo.py
|
python
|
ComboCtrl.GetTextRect
|
(*args, **kwargs)
|
return _combo.ComboCtrl_GetTextRect(*args, **kwargs)
|
GetTextRect(self) -> Rect
Returns area covered by the text field (includes everything except
borders and the dropdown button).
|
GetTextRect(self) -> Rect
|
[
"GetTextRect",
"(",
"self",
")",
"-",
">",
"Rect"
] |
def GetTextRect(*args, **kwargs):
"""
GetTextRect(self) -> Rect
Returns area covered by the text field (includes everything except
borders and the dropdown button).
"""
return _combo.ComboCtrl_GetTextRect(*args, **kwargs)
|
[
"def",
"GetTextRect",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"return",
"_combo",
".",
"ComboCtrl_GetTextRect",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")"
] |
https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_cocoa/combo.py#L377-L384
|
|
apache/incubator-mxnet
|
f03fb23f1d103fec9541b5ae59ee06b1734a51d9
|
python/mxnet/onnx/mx2onnx/_op_translations/_op_translations_opset13.py
|
python
|
convert_max
|
(node, **kwargs)
|
Map MXNet's max operator attributes to onnx's ReduceMax operator
and return the created node.
|
Map MXNet's max operator attributes to onnx's ReduceMax operator
and return the created node.
|
[
"Map",
"MXNet",
"s",
"max",
"operator",
"attributes",
"to",
"onnx",
"s",
"ReduceMax",
"operator",
"and",
"return",
"the",
"created",
"node",
"."
] |
def convert_max(node, **kwargs):
"""Map MXNet's max operator attributes to onnx's ReduceMax operator
and return the created node.
"""
from onnx.helper import make_node
name, input_nodes, attrs = get_inputs(node, kwargs)
mx_axis = str(attrs.get("axis", 'None'))
axes = convert_string_to_list(mx_axis) if mx_axis != 'None' else None
keepdims = get_boolean_attribute_value(attrs, "keepdims")
if axes is not None:
if keepdims:
node = make_node('ReduceMax', input_nodes, [name], axes=axes, keepdims=keepdims)
return [node]
else:
create_tensor([1], name+'_1', kwargs['initializer'])
create_tensor([0], name+'_0', kwargs['initializer'])
create_tensor([len(axes)], name+'_axes_dim', kwargs['initializer'])
nodes = [
make_node('ReduceMax', input_nodes, [name+'_rmax'], axes=axes, keepdims=keepdims),
make_node('Shape', [name+'_rmax'], [name+'_rmax_shape']),
make_node('Shape', [name+'_rmax_shape'], [name+'_rmax_dim']),
make_node('Shape', [input_nodes[0]], [name+'_in_shape']),
make_node('Shape', [name+'_in_shape'], [name+'_in_dim']),
make_node('Equal', [name+'_axes_dim', name+'_in_dim'], [name+'_equal']),
make_node('Where', [name+'_equal', name+'_1', name+'_rmax_dim'], [name+'_where0']),
make_node('Tile', [name+'_0', name+'_where0'], [name+'_tile']),
make_node('Unsqueeze', [name+'_0', name+'_0'], [name+'_unsqueeze']),
make_node('Where', [name+'_equal', name+'_1', name+'_0'], [name+'_where1']),
make_node('ScatterND', [name+'_tile', name+'_unsqueeze', name+'_where1'], [name+'_SND']),
make_node('Reshape', [name+'_rmax', name+'_SND'], [name]),
]
return nodes
else:
if keepdims:
node = make_node('ReduceMax', input_nodes, [name], keepdims=keepdims)
return [node]
else:
create_tensor([1], name+'_1', kwargs['initializer'])
nodes = [
make_node('ReduceMax', input_nodes, [name+'_rmax'], keepdims=keepdims),
make_node('Reshape', [name+'_rmax', name+'_1'], [name])
]
return nodes
|
[
"def",
"convert_max",
"(",
"node",
",",
"*",
"*",
"kwargs",
")",
":",
"from",
"onnx",
".",
"helper",
"import",
"make_node",
"name",
",",
"input_nodes",
",",
"attrs",
"=",
"get_inputs",
"(",
"node",
",",
"kwargs",
")",
"mx_axis",
"=",
"str",
"(",
"attrs",
".",
"get",
"(",
"\"axis\"",
",",
"'None'",
")",
")",
"axes",
"=",
"convert_string_to_list",
"(",
"mx_axis",
")",
"if",
"mx_axis",
"!=",
"'None'",
"else",
"None",
"keepdims",
"=",
"get_boolean_attribute_value",
"(",
"attrs",
",",
"\"keepdims\"",
")",
"if",
"axes",
"is",
"not",
"None",
":",
"if",
"keepdims",
":",
"node",
"=",
"make_node",
"(",
"'ReduceMax'",
",",
"input_nodes",
",",
"[",
"name",
"]",
",",
"axes",
"=",
"axes",
",",
"keepdims",
"=",
"keepdims",
")",
"return",
"[",
"node",
"]",
"else",
":",
"create_tensor",
"(",
"[",
"1",
"]",
",",
"name",
"+",
"'_1'",
",",
"kwargs",
"[",
"'initializer'",
"]",
")",
"create_tensor",
"(",
"[",
"0",
"]",
",",
"name",
"+",
"'_0'",
",",
"kwargs",
"[",
"'initializer'",
"]",
")",
"create_tensor",
"(",
"[",
"len",
"(",
"axes",
")",
"]",
",",
"name",
"+",
"'_axes_dim'",
",",
"kwargs",
"[",
"'initializer'",
"]",
")",
"nodes",
"=",
"[",
"make_node",
"(",
"'ReduceMax'",
",",
"input_nodes",
",",
"[",
"name",
"+",
"'_rmax'",
"]",
",",
"axes",
"=",
"axes",
",",
"keepdims",
"=",
"keepdims",
")",
",",
"make_node",
"(",
"'Shape'",
",",
"[",
"name",
"+",
"'_rmax'",
"]",
",",
"[",
"name",
"+",
"'_rmax_shape'",
"]",
")",
",",
"make_node",
"(",
"'Shape'",
",",
"[",
"name",
"+",
"'_rmax_shape'",
"]",
",",
"[",
"name",
"+",
"'_rmax_dim'",
"]",
")",
",",
"make_node",
"(",
"'Shape'",
",",
"[",
"input_nodes",
"[",
"0",
"]",
"]",
",",
"[",
"name",
"+",
"'_in_shape'",
"]",
")",
",",
"make_node",
"(",
"'Shape'",
",",
"[",
"name",
"+",
"'_in_shape'",
"]",
",",
"[",
"name",
"+",
"'_in_dim'",
"]",
")",
",",
"make_node",
"(",
"'Equal'",
",",
"[",
"name",
"+",
"'_axes_dim'",
",",
"name",
"+",
"'_in_dim'",
"]",
",",
"[",
"name",
"+",
"'_equal'",
"]",
")",
",",
"make_node",
"(",
"'Where'",
",",
"[",
"name",
"+",
"'_equal'",
",",
"name",
"+",
"'_1'",
",",
"name",
"+",
"'_rmax_dim'",
"]",
",",
"[",
"name",
"+",
"'_where0'",
"]",
")",
",",
"make_node",
"(",
"'Tile'",
",",
"[",
"name",
"+",
"'_0'",
",",
"name",
"+",
"'_where0'",
"]",
",",
"[",
"name",
"+",
"'_tile'",
"]",
")",
",",
"make_node",
"(",
"'Unsqueeze'",
",",
"[",
"name",
"+",
"'_0'",
",",
"name",
"+",
"'_0'",
"]",
",",
"[",
"name",
"+",
"'_unsqueeze'",
"]",
")",
",",
"make_node",
"(",
"'Where'",
",",
"[",
"name",
"+",
"'_equal'",
",",
"name",
"+",
"'_1'",
",",
"name",
"+",
"'_0'",
"]",
",",
"[",
"name",
"+",
"'_where1'",
"]",
")",
",",
"make_node",
"(",
"'ScatterND'",
",",
"[",
"name",
"+",
"'_tile'",
",",
"name",
"+",
"'_unsqueeze'",
",",
"name",
"+",
"'_where1'",
"]",
",",
"[",
"name",
"+",
"'_SND'",
"]",
")",
",",
"make_node",
"(",
"'Reshape'",
",",
"[",
"name",
"+",
"'_rmax'",
",",
"name",
"+",
"'_SND'",
"]",
",",
"[",
"name",
"]",
")",
",",
"]",
"return",
"nodes",
"else",
":",
"if",
"keepdims",
":",
"node",
"=",
"make_node",
"(",
"'ReduceMax'",
",",
"input_nodes",
",",
"[",
"name",
"]",
",",
"keepdims",
"=",
"keepdims",
")",
"return",
"[",
"node",
"]",
"else",
":",
"create_tensor",
"(",
"[",
"1",
"]",
",",
"name",
"+",
"'_1'",
",",
"kwargs",
"[",
"'initializer'",
"]",
")",
"nodes",
"=",
"[",
"make_node",
"(",
"'ReduceMax'",
",",
"input_nodes",
",",
"[",
"name",
"+",
"'_rmax'",
"]",
",",
"keepdims",
"=",
"keepdims",
")",
",",
"make_node",
"(",
"'Reshape'",
",",
"[",
"name",
"+",
"'_rmax'",
",",
"name",
"+",
"'_1'",
"]",
",",
"[",
"name",
"]",
")",
"]",
"return",
"nodes"
] |
https://github.com/apache/incubator-mxnet/blob/f03fb23f1d103fec9541b5ae59ee06b1734a51d9/python/mxnet/onnx/mx2onnx/_op_translations/_op_translations_opset13.py#L1470-L1515
|
||
microsoft/TSS.MSR
|
0f2516fca2cd9929c31d5450e39301c9bde43688
|
TSS.Py/src/TpmTypes.py
|
python
|
TPM_HANDLE.fromBytes
|
(buffer)
|
return TpmBuffer(buffer).createObj(TPM_HANDLE)
|
Returns new TPM_HANDLE object constructed from its marshaled
representation in the given byte buffer
|
Returns new TPM_HANDLE object constructed from its marshaled
representation in the given byte buffer
|
[
"Returns",
"new",
"TPM_HANDLE",
"object",
"constructed",
"from",
"its",
"marshaled",
"representation",
"in",
"the",
"given",
"byte",
"buffer"
] |
def fromBytes(buffer):
""" Returns new TPM_HANDLE object constructed from its marshaled
representation in the given byte buffer
"""
return TpmBuffer(buffer).createObj(TPM_HANDLE)
|
[
"def",
"fromBytes",
"(",
"buffer",
")",
":",
"return",
"TpmBuffer",
"(",
"buffer",
")",
".",
"createObj",
"(",
"TPM_HANDLE",
")"
] |
https://github.com/microsoft/TSS.MSR/blob/0f2516fca2cd9929c31d5450e39301c9bde43688/TSS.Py/src/TpmTypes.py#L3455-L3459
|
|
microsoft/ivy
|
9f3c7ecc0b2383129fdd0953e10890d98d09a82d
|
ivy/ivy_parser.py
|
python
|
p_typesymbol_this
|
(p)
|
typesymbol : THIS
|
typesymbol : THIS
|
[
"typesymbol",
":",
"THIS"
] |
def p_typesymbol_this(p):
'typesymbol : THIS'
p[0] = This()
p[0].lineno = get_lineno(p,1)
|
[
"def",
"p_typesymbol_this",
"(",
"p",
")",
":",
"p",
"[",
"0",
"]",
"=",
"This",
"(",
")",
"p",
"[",
"0",
"]",
".",
"lineno",
"=",
"get_lineno",
"(",
"p",
",",
"1",
")"
] |
https://github.com/microsoft/ivy/blob/9f3c7ecc0b2383129fdd0953e10890d98d09a82d/ivy/ivy_parser.py#L1313-L1316
|
||
catboost/catboost
|
167f64f237114a4d10b2b4ee42adb4569137debe
|
contrib/python/pandas/py2/pandas/core/base.py
|
python
|
StringMixin.__bytes__
|
(self)
|
return self.__unicode__().encode(encoding, 'replace')
|
Return a string representation for a particular object.
Invoked by bytes(obj) in py3 only.
Yields a bytestring in both py2/py3.
|
Return a string representation for a particular object.
|
[
"Return",
"a",
"string",
"representation",
"for",
"a",
"particular",
"object",
"."
] |
def __bytes__(self):
"""
Return a string representation for a particular object.
Invoked by bytes(obj) in py3 only.
Yields a bytestring in both py2/py3.
"""
from pandas.core.config import get_option
encoding = get_option("display.encoding")
return self.__unicode__().encode(encoding, 'replace')
|
[
"def",
"__bytes__",
"(",
"self",
")",
":",
"from",
"pandas",
".",
"core",
".",
"config",
"import",
"get_option",
"encoding",
"=",
"get_option",
"(",
"\"display.encoding\"",
")",
"return",
"self",
".",
"__unicode__",
"(",
")",
".",
"encode",
"(",
"encoding",
",",
"'replace'",
")"
] |
https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/pandas/py2/pandas/core/base.py#L60-L70
|
|
blackberry/Boost
|
fc90c3fde129c62565c023f091eddc4a7ed9902b
|
tools/build/v2/util/set.py
|
python
|
equal
|
(a, b)
|
return contains (a, b) and contains (b, a)
|
Returns True iff 'a' contains the same elements as 'b', irrespective of their order.
# TODO: Python 2.4 has a proper set class.
|
Returns True iff 'a' contains the same elements as 'b', irrespective of their order.
# TODO: Python 2.4 has a proper set class.
|
[
"Returns",
"True",
"iff",
"a",
"contains",
"the",
"same",
"elements",
"as",
"b",
"irrespective",
"of",
"their",
"order",
".",
"#",
"TODO",
":",
"Python",
"2",
".",
"4",
"has",
"a",
"proper",
"set",
"class",
"."
] |
def equal (a, b):
""" Returns True iff 'a' contains the same elements as 'b', irrespective of their order.
# TODO: Python 2.4 has a proper set class.
"""
return contains (a, b) and contains (b, a)
|
[
"def",
"equal",
"(",
"a",
",",
"b",
")",
":",
"return",
"contains",
"(",
"a",
",",
"b",
")",
"and",
"contains",
"(",
"b",
",",
"a",
")"
] |
https://github.com/blackberry/Boost/blob/fc90c3fde129c62565c023f091eddc4a7ed9902b/tools/build/v2/util/set.py#L38-L42
|
|
priyankchheda/algorithms
|
c361aa9071573fa9966d5b02d05e524815abcf2b
|
array/min_max_arr.py
|
python
|
getminmax_linear_search
|
(arr)
|
return min_num, max_num
|
Linear method
Initialize values of min and max as minimum and maximum of the first two elements
respectively. Starting from 3rd, compare each element with max and min, and
change max and min accordingly
|
Linear method
Initialize values of min and max as minimum and maximum of the first two elements
respectively. Starting from 3rd, compare each element with max and min, and
change max and min accordingly
|
[
"Linear",
"method",
"Initialize",
"values",
"of",
"min",
"and",
"max",
"as",
"minimum",
"and",
"maximum",
"of",
"the",
"first",
"two",
"elements",
"respectively",
".",
"Starting",
"from",
"3rd",
"compare",
"each",
"element",
"with",
"max",
"and",
"min",
"and",
"change",
"max",
"and",
"min",
"accordingly"
] |
def getminmax_linear_search(arr):
""" Linear method
Initialize values of min and max as minimum and maximum of the first two elements
respectively. Starting from 3rd, compare each element with max and min, and
change max and min accordingly
"""
if len(arr) == 0:
return None, None
if len(arr) == 1:
return arr[0], arr[0]
min_num = None
max_num = None
if arr[0] > arr[1]:
max_num = arr[0]
min_num = arr[1]
else:
max_num = arr[1]
min_num = arr[0]
for idx in range(2, len(arr)):
if min_num > arr[idx]:
min_num = arr[idx]
if max_num < arr[idx]:
max_num = arr[idx]
return min_num, max_num
|
[
"def",
"getminmax_linear_search",
"(",
"arr",
")",
":",
"if",
"len",
"(",
"arr",
")",
"==",
"0",
":",
"return",
"None",
",",
"None",
"if",
"len",
"(",
"arr",
")",
"==",
"1",
":",
"return",
"arr",
"[",
"0",
"]",
",",
"arr",
"[",
"0",
"]",
"min_num",
"=",
"None",
"max_num",
"=",
"None",
"if",
"arr",
"[",
"0",
"]",
">",
"arr",
"[",
"1",
"]",
":",
"max_num",
"=",
"arr",
"[",
"0",
"]",
"min_num",
"=",
"arr",
"[",
"1",
"]",
"else",
":",
"max_num",
"=",
"arr",
"[",
"1",
"]",
"min_num",
"=",
"arr",
"[",
"0",
"]",
"for",
"idx",
"in",
"range",
"(",
"2",
",",
"len",
"(",
"arr",
")",
")",
":",
"if",
"min_num",
">",
"arr",
"[",
"idx",
"]",
":",
"min_num",
"=",
"arr",
"[",
"idx",
"]",
"if",
"max_num",
"<",
"arr",
"[",
"idx",
"]",
":",
"max_num",
"=",
"arr",
"[",
"idx",
"]",
"return",
"min_num",
",",
"max_num"
] |
https://github.com/priyankchheda/algorithms/blob/c361aa9071573fa9966d5b02d05e524815abcf2b/array/min_max_arr.py#L8-L35
|
|
mantidproject/mantid
|
03deeb89254ec4289edb8771e0188c2090a02f32
|
qt/python/mantidqtinterfaces/mantidqtinterfaces/Muon/GUI/Common/fitting_widgets/general_fitting/general_fitting_options_view.py
|
python
|
GeneralFittingOptionsView.simultaneous_fit_by_specifier
|
(self)
|
return self.simul_fit_by_specifier.currentText()
|
Returns the run, group or pair name.
|
Returns the run, group or pair name.
|
[
"Returns",
"the",
"run",
"group",
"or",
"pair",
"name",
"."
] |
def simultaneous_fit_by_specifier(self) -> str:
"""Returns the run, group or pair name."""
return self.simul_fit_by_specifier.currentText()
|
[
"def",
"simultaneous_fit_by_specifier",
"(",
"self",
")",
"->",
"str",
":",
"return",
"self",
".",
"simul_fit_by_specifier",
".",
"currentText",
"(",
")"
] |
https://github.com/mantidproject/mantid/blob/03deeb89254ec4289edb8771e0188c2090a02f32/qt/python/mantidqtinterfaces/mantidqtinterfaces/Muon/GUI/Common/fitting_widgets/general_fitting/general_fitting_options_view.py#L62-L64
|
|
MythTV/mythtv
|
d282a209cb8be85d036f85a62a8ec971b67d45f4
|
mythtv/programs/scripts/internetcontent/nv_python_libs/thewb/thewb_api.py
|
python
|
Videos.getSeasonEpisode
|
(self, title)
|
return s_e
|
Check is there is any season or episode number information in an item's title
return array of season and/or episode numbers plus any duration in minutes and seconds
return array with None values
|
Check is there is any season or episode number information in an item's title
return array of season and/or episode numbers plus any duration in minutes and seconds
return array with None values
|
[
"Check",
"is",
"there",
"is",
"any",
"season",
"or",
"episode",
"number",
"information",
"in",
"an",
"item",
"s",
"title",
"return",
"array",
"of",
"season",
"and",
"/",
"or",
"episode",
"numbers",
"plus",
"any",
"duration",
"in",
"minutes",
"and",
"seconds",
"return",
"array",
"with",
"None",
"values"
] |
def getSeasonEpisode(self, title):
''' Check is there is any season or episode number information in an item's title
return array of season and/or episode numbers plus any duration in minutes and seconds
return array with None values
'''
s_e = []
for index in range(len(self.s_e_Patterns)):
match = self.s_e_Patterns[index].match(title)
if not match:
continue
return match.groups()
return s_e
|
[
"def",
"getSeasonEpisode",
"(",
"self",
",",
"title",
")",
":",
"s_e",
"=",
"[",
"]",
"for",
"index",
"in",
"range",
"(",
"len",
"(",
"self",
".",
"s_e_Patterns",
")",
")",
":",
"match",
"=",
"self",
".",
"s_e_Patterns",
"[",
"index",
"]",
".",
"match",
"(",
"title",
")",
"if",
"not",
"match",
":",
"continue",
"return",
"match",
".",
"groups",
"(",
")",
"return",
"s_e"
] |
https://github.com/MythTV/mythtv/blob/d282a209cb8be85d036f85a62a8ec971b67d45f4/mythtv/programs/scripts/internetcontent/nv_python_libs/thewb/thewb_api.py#L205-L216
|
|
eventql/eventql
|
7ca0dbb2e683b525620ea30dc40540a22d5eb227
|
deps/3rdparty/spidermonkey/mozjs/build/pymake/pymake/parserdata.py
|
python
|
Location.offset
|
(self, s, start, end)
|
return Location(self.path, line, column)
|
Returns a new location offset by
the specified string.
|
Returns a new location offset by
the specified string.
|
[
"Returns",
"a",
"new",
"location",
"offset",
"by",
"the",
"specified",
"string",
"."
] |
def offset(self, s, start, end):
"""
Returns a new location offset by
the specified string.
"""
if start == end:
return self
skiplines = s.count('\n', start, end)
line = self.line + skiplines
if skiplines:
lastnl = s.rfind('\n', start, end)
assert lastnl != -1
start = lastnl + 1
column = 0
else:
column = self.column
while True:
j = s.find('\t', start, end)
if j == -1:
column += end - start
break
column += j - start
column += _tabwidth
column -= column % _tabwidth
start = j + 1
return Location(self.path, line, column)
|
[
"def",
"offset",
"(",
"self",
",",
"s",
",",
"start",
",",
"end",
")",
":",
"if",
"start",
"==",
"end",
":",
"return",
"self",
"skiplines",
"=",
"s",
".",
"count",
"(",
"'\\n'",
",",
"start",
",",
"end",
")",
"line",
"=",
"self",
".",
"line",
"+",
"skiplines",
"if",
"skiplines",
":",
"lastnl",
"=",
"s",
".",
"rfind",
"(",
"'\\n'",
",",
"start",
",",
"end",
")",
"assert",
"lastnl",
"!=",
"-",
"1",
"start",
"=",
"lastnl",
"+",
"1",
"column",
"=",
"0",
"else",
":",
"column",
"=",
"self",
".",
"column",
"while",
"True",
":",
"j",
"=",
"s",
".",
"find",
"(",
"'\\t'",
",",
"start",
",",
"end",
")",
"if",
"j",
"==",
"-",
"1",
":",
"column",
"+=",
"end",
"-",
"start",
"break",
"column",
"+=",
"j",
"-",
"start",
"column",
"+=",
"_tabwidth",
"column",
"-=",
"column",
"%",
"_tabwidth",
"start",
"=",
"j",
"+",
"1",
"return",
"Location",
"(",
"self",
".",
"path",
",",
"line",
",",
"column",
")"
] |
https://github.com/eventql/eventql/blob/7ca0dbb2e683b525620ea30dc40540a22d5eb227/deps/3rdparty/spidermonkey/mozjs/build/pymake/pymake/parserdata.py#L24-L54
|
|
p4lang/PI
|
38d87e81253feff9fff0660d662c885be78fb719
|
tools/cpplint.py
|
python
|
CheckForNonStandardConstructs
|
(filename, clean_lines, linenum,
nesting_state, error)
|
r"""Logs an error if we see certain non-ANSI constructs ignored by gcc-2.
Complain about several constructs which gcc-2 accepts, but which are
not standard C++. Warning about these in lint is one way to ease the
transition to new compilers.
- put storage class first (e.g. "static const" instead of "const static").
- "%lld" instead of %qd" in printf-type functions.
- "%1$d" is non-standard in printf-type functions.
- "\%" is an undefined character escape sequence.
- text after #endif is not allowed.
- invalid inner-style forward declaration.
- >? and <? operators, and their >?= and <?= cousins.
Additionally, check for constructor/destructor style violations and reference
members, as it is very convenient to do so while checking for
gcc-2 compliance.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
nesting_state: A NestingState instance which maintains information about
the current stack of nested blocks being parsed.
error: A callable to which errors are reported, which takes 4 arguments:
filename, line number, error level, and message
|
r"""Logs an error if we see certain non-ANSI constructs ignored by gcc-2.
|
[
"r",
"Logs",
"an",
"error",
"if",
"we",
"see",
"certain",
"non",
"-",
"ANSI",
"constructs",
"ignored",
"by",
"gcc",
"-",
"2",
"."
] |
def CheckForNonStandardConstructs(filename, clean_lines, linenum,
nesting_state, error):
r"""Logs an error if we see certain non-ANSI constructs ignored by gcc-2.
Complain about several constructs which gcc-2 accepts, but which are
not standard C++. Warning about these in lint is one way to ease the
transition to new compilers.
- put storage class first (e.g. "static const" instead of "const static").
- "%lld" instead of %qd" in printf-type functions.
- "%1$d" is non-standard in printf-type functions.
- "\%" is an undefined character escape sequence.
- text after #endif is not allowed.
- invalid inner-style forward declaration.
- >? and <? operators, and their >?= and <?= cousins.
Additionally, check for constructor/destructor style violations and reference
members, as it is very convenient to do so while checking for
gcc-2 compliance.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
nesting_state: A NestingState instance which maintains information about
the current stack of nested blocks being parsed.
error: A callable to which errors are reported, which takes 4 arguments:
filename, line number, error level, and message
"""
# Remove comments from the line, but leave in strings for now.
line = clean_lines.lines[linenum]
if Search(r'printf\s*\(.*".*%[-+ ]?\d*q', line):
error(filename, linenum, 'runtime/printf_format', 3,
'%q in format strings is deprecated. Use %ll instead.')
if Search(r'printf\s*\(.*".*%\d+\$', line):
error(filename, linenum, 'runtime/printf_format', 2,
'%N$ formats are unconventional. Try rewriting to avoid them.')
# Remove escaped backslashes before looking for undefined escapes.
line = line.replace('\\\\', '')
if Search(r'("|\').*\\(%|\[|\(|{)', line):
error(filename, linenum, 'build/printf_format', 3,
'%, [, (, and { are undefined character escapes. Unescape them.')
# For the rest, work with both comments and strings removed.
line = clean_lines.elided[linenum]
if Search(r'\b(const|volatile|void|char|short|int|long'
r'|float|double|signed|unsigned'
r'|schar|u?int8|u?int16|u?int32|u?int64)'
r'\s+(register|static|extern|typedef)\b',
line):
error(filename, linenum, 'build/storage_class', 5,
'Storage-class specifier (static, extern, typedef, etc) should be '
'at the beginning of the declaration.')
if Match(r'\s*#\s*endif\s*[^/\s]+', line):
error(filename, linenum, 'build/endif_comment', 5,
'Uncommented text after #endif is non-standard. Use a comment.')
if Match(r'\s*class\s+(\w+\s*::\s*)+\w+\s*;', line):
error(filename, linenum, 'build/forward_decl', 5,
'Inner-style forward declarations are invalid. Remove this line.')
if Search(r'(\w+|[+-]?\d+(\.\d*)?)\s*(<|>)\?=?\s*(\w+|[+-]?\d+)(\.\d*)?',
line):
error(filename, linenum, 'build/deprecated', 3,
'>? and <? (max and min) operators are non-standard and deprecated.')
if Search(r'^\s*const\s*string\s*&\s*\w+\s*;', line):
# TODO(unknown): Could it be expanded safely to arbitrary references,
# without triggering too many false positives? The first
# attempt triggered 5 warnings for mostly benign code in the regtest, hence
# the restriction.
# Here's the original regexp, for the reference:
# type_name = r'\w+((\s*::\s*\w+)|(\s*<\s*\w+?\s*>))?'
# r'\s*const\s*' + type_name + '\s*&\s*\w+\s*;'
error(filename, linenum, 'runtime/member_string_references', 2,
'const string& members are dangerous. It is much better to use '
'alternatives, such as pointers or simple constants.')
# Everything else in this function operates on class declarations.
# Return early if the top of the nesting stack is not a class, or if
# the class head is not completed yet.
classinfo = nesting_state.InnermostClass()
if not classinfo or not classinfo.seen_open_brace:
return
# The class may have been declared with namespace or classname qualifiers.
# The constructor and destructor will not have those qualifiers.
base_classname = classinfo.name.split('::')[-1]
# Look for single-argument constructors that aren't marked explicit.
# Technically a valid construct, but against style.
explicit_constructor_match = Match(
r'\s+(?:(?:inline|constexpr)\s+)*(explicit\s+)?'
r'(?:(?:inline|constexpr)\s+)*%s\s*'
r'\(((?:[^()]|\([^()]*\))*)\)'
% re.escape(base_classname),
line)
if explicit_constructor_match:
is_marked_explicit = explicit_constructor_match.group(1)
if not explicit_constructor_match.group(2):
constructor_args = []
else:
constructor_args = explicit_constructor_match.group(2).split(',')
# collapse arguments so that commas in template parameter lists and function
# argument parameter lists don't split arguments in two
i = 0
while i < len(constructor_args):
constructor_arg = constructor_args[i]
while (constructor_arg.count('<') > constructor_arg.count('>') or
constructor_arg.count('(') > constructor_arg.count(')')):
constructor_arg += ',' + constructor_args[i + 1]
del constructor_args[i + 1]
constructor_args[i] = constructor_arg
i += 1
variadic_args = [arg for arg in constructor_args if '&&...' in arg]
defaulted_args = [arg for arg in constructor_args if '=' in arg]
noarg_constructor = (not constructor_args or # empty arg list
# 'void' arg specifier
(len(constructor_args) == 1 and
constructor_args[0].strip() == 'void'))
onearg_constructor = ((len(constructor_args) == 1 and # exactly one arg
not noarg_constructor) or
# all but at most one arg defaulted
(len(constructor_args) >= 1 and
not noarg_constructor and
len(defaulted_args) >= len(constructor_args) - 1) or
# variadic arguments with zero or one argument
(len(constructor_args) <= 2 and
len(variadic_args) >= 1))
initializer_list_constructor = bool(
onearg_constructor and
Search(r'\bstd\s*::\s*initializer_list\b', constructor_args[0]))
copy_constructor = bool(
onearg_constructor and
Match(r'((const\s+(volatile\s+)?)?|(volatile\s+(const\s+)?))?'
r'%s(\s*<[^>]*>)?(\s+const)?\s*(?:<\w+>\s*)?&'
% re.escape(base_classname), constructor_args[0].strip()))
if (not is_marked_explicit and
onearg_constructor and
not initializer_list_constructor and
not copy_constructor):
if defaulted_args or variadic_args:
error(filename, linenum, 'runtime/explicit', 5,
'Constructors callable with one argument '
'should be marked explicit.')
else:
error(filename, linenum, 'runtime/explicit', 5,
'Single-parameter constructors should be marked explicit.')
elif is_marked_explicit and not onearg_constructor:
if noarg_constructor:
error(filename, linenum, 'runtime/explicit', 5,
'Zero-parameter constructors should not be marked explicit.')
|
[
"def",
"CheckForNonStandardConstructs",
"(",
"filename",
",",
"clean_lines",
",",
"linenum",
",",
"nesting_state",
",",
"error",
")",
":",
"# Remove comments from the line, but leave in strings for now.",
"line",
"=",
"clean_lines",
".",
"lines",
"[",
"linenum",
"]",
"if",
"Search",
"(",
"r'printf\\s*\\(.*\".*%[-+ ]?\\d*q'",
",",
"line",
")",
":",
"error",
"(",
"filename",
",",
"linenum",
",",
"'runtime/printf_format'",
",",
"3",
",",
"'%q in format strings is deprecated. Use %ll instead.'",
")",
"if",
"Search",
"(",
"r'printf\\s*\\(.*\".*%\\d+\\$'",
",",
"line",
")",
":",
"error",
"(",
"filename",
",",
"linenum",
",",
"'runtime/printf_format'",
",",
"2",
",",
"'%N$ formats are unconventional. Try rewriting to avoid them.'",
")",
"# Remove escaped backslashes before looking for undefined escapes.",
"line",
"=",
"line",
".",
"replace",
"(",
"'\\\\\\\\'",
",",
"''",
")",
"if",
"Search",
"(",
"r'(\"|\\').*\\\\(%|\\[|\\(|{)'",
",",
"line",
")",
":",
"error",
"(",
"filename",
",",
"linenum",
",",
"'build/printf_format'",
",",
"3",
",",
"'%, [, (, and { are undefined character escapes. Unescape them.'",
")",
"# For the rest, work with both comments and strings removed.",
"line",
"=",
"clean_lines",
".",
"elided",
"[",
"linenum",
"]",
"if",
"Search",
"(",
"r'\\b(const|volatile|void|char|short|int|long'",
"r'|float|double|signed|unsigned'",
"r'|schar|u?int8|u?int16|u?int32|u?int64)'",
"r'\\s+(register|static|extern|typedef)\\b'",
",",
"line",
")",
":",
"error",
"(",
"filename",
",",
"linenum",
",",
"'build/storage_class'",
",",
"5",
",",
"'Storage-class specifier (static, extern, typedef, etc) should be '",
"'at the beginning of the declaration.'",
")",
"if",
"Match",
"(",
"r'\\s*#\\s*endif\\s*[^/\\s]+'",
",",
"line",
")",
":",
"error",
"(",
"filename",
",",
"linenum",
",",
"'build/endif_comment'",
",",
"5",
",",
"'Uncommented text after #endif is non-standard. Use a comment.'",
")",
"if",
"Match",
"(",
"r'\\s*class\\s+(\\w+\\s*::\\s*)+\\w+\\s*;'",
",",
"line",
")",
":",
"error",
"(",
"filename",
",",
"linenum",
",",
"'build/forward_decl'",
",",
"5",
",",
"'Inner-style forward declarations are invalid. Remove this line.'",
")",
"if",
"Search",
"(",
"r'(\\w+|[+-]?\\d+(\\.\\d*)?)\\s*(<|>)\\?=?\\s*(\\w+|[+-]?\\d+)(\\.\\d*)?'",
",",
"line",
")",
":",
"error",
"(",
"filename",
",",
"linenum",
",",
"'build/deprecated'",
",",
"3",
",",
"'>? and <? (max and min) operators are non-standard and deprecated.'",
")",
"if",
"Search",
"(",
"r'^\\s*const\\s*string\\s*&\\s*\\w+\\s*;'",
",",
"line",
")",
":",
"# TODO(unknown): Could it be expanded safely to arbitrary references,",
"# without triggering too many false positives? The first",
"# attempt triggered 5 warnings for mostly benign code in the regtest, hence",
"# the restriction.",
"# Here's the original regexp, for the reference:",
"# type_name = r'\\w+((\\s*::\\s*\\w+)|(\\s*<\\s*\\w+?\\s*>))?'",
"# r'\\s*const\\s*' + type_name + '\\s*&\\s*\\w+\\s*;'",
"error",
"(",
"filename",
",",
"linenum",
",",
"'runtime/member_string_references'",
",",
"2",
",",
"'const string& members are dangerous. It is much better to use '",
"'alternatives, such as pointers or simple constants.'",
")",
"# Everything else in this function operates on class declarations.",
"# Return early if the top of the nesting stack is not a class, or if",
"# the class head is not completed yet.",
"classinfo",
"=",
"nesting_state",
".",
"InnermostClass",
"(",
")",
"if",
"not",
"classinfo",
"or",
"not",
"classinfo",
".",
"seen_open_brace",
":",
"return",
"# The class may have been declared with namespace or classname qualifiers.",
"# The constructor and destructor will not have those qualifiers.",
"base_classname",
"=",
"classinfo",
".",
"name",
".",
"split",
"(",
"'::'",
")",
"[",
"-",
"1",
"]",
"# Look for single-argument constructors that aren't marked explicit.",
"# Technically a valid construct, but against style.",
"explicit_constructor_match",
"=",
"Match",
"(",
"r'\\s+(?:(?:inline|constexpr)\\s+)*(explicit\\s+)?'",
"r'(?:(?:inline|constexpr)\\s+)*%s\\s*'",
"r'\\(((?:[^()]|\\([^()]*\\))*)\\)'",
"%",
"re",
".",
"escape",
"(",
"base_classname",
")",
",",
"line",
")",
"if",
"explicit_constructor_match",
":",
"is_marked_explicit",
"=",
"explicit_constructor_match",
".",
"group",
"(",
"1",
")",
"if",
"not",
"explicit_constructor_match",
".",
"group",
"(",
"2",
")",
":",
"constructor_args",
"=",
"[",
"]",
"else",
":",
"constructor_args",
"=",
"explicit_constructor_match",
".",
"group",
"(",
"2",
")",
".",
"split",
"(",
"','",
")",
"# collapse arguments so that commas in template parameter lists and function",
"# argument parameter lists don't split arguments in two",
"i",
"=",
"0",
"while",
"i",
"<",
"len",
"(",
"constructor_args",
")",
":",
"constructor_arg",
"=",
"constructor_args",
"[",
"i",
"]",
"while",
"(",
"constructor_arg",
".",
"count",
"(",
"'<'",
")",
">",
"constructor_arg",
".",
"count",
"(",
"'>'",
")",
"or",
"constructor_arg",
".",
"count",
"(",
"'('",
")",
">",
"constructor_arg",
".",
"count",
"(",
"')'",
")",
")",
":",
"constructor_arg",
"+=",
"','",
"+",
"constructor_args",
"[",
"i",
"+",
"1",
"]",
"del",
"constructor_args",
"[",
"i",
"+",
"1",
"]",
"constructor_args",
"[",
"i",
"]",
"=",
"constructor_arg",
"i",
"+=",
"1",
"variadic_args",
"=",
"[",
"arg",
"for",
"arg",
"in",
"constructor_args",
"if",
"'&&...'",
"in",
"arg",
"]",
"defaulted_args",
"=",
"[",
"arg",
"for",
"arg",
"in",
"constructor_args",
"if",
"'='",
"in",
"arg",
"]",
"noarg_constructor",
"=",
"(",
"not",
"constructor_args",
"or",
"# empty arg list",
"# 'void' arg specifier",
"(",
"len",
"(",
"constructor_args",
")",
"==",
"1",
"and",
"constructor_args",
"[",
"0",
"]",
".",
"strip",
"(",
")",
"==",
"'void'",
")",
")",
"onearg_constructor",
"=",
"(",
"(",
"len",
"(",
"constructor_args",
")",
"==",
"1",
"and",
"# exactly one arg",
"not",
"noarg_constructor",
")",
"or",
"# all but at most one arg defaulted",
"(",
"len",
"(",
"constructor_args",
")",
">=",
"1",
"and",
"not",
"noarg_constructor",
"and",
"len",
"(",
"defaulted_args",
")",
">=",
"len",
"(",
"constructor_args",
")",
"-",
"1",
")",
"or",
"# variadic arguments with zero or one argument",
"(",
"len",
"(",
"constructor_args",
")",
"<=",
"2",
"and",
"len",
"(",
"variadic_args",
")",
">=",
"1",
")",
")",
"initializer_list_constructor",
"=",
"bool",
"(",
"onearg_constructor",
"and",
"Search",
"(",
"r'\\bstd\\s*::\\s*initializer_list\\b'",
",",
"constructor_args",
"[",
"0",
"]",
")",
")",
"copy_constructor",
"=",
"bool",
"(",
"onearg_constructor",
"and",
"Match",
"(",
"r'((const\\s+(volatile\\s+)?)?|(volatile\\s+(const\\s+)?))?'",
"r'%s(\\s*<[^>]*>)?(\\s+const)?\\s*(?:<\\w+>\\s*)?&'",
"%",
"re",
".",
"escape",
"(",
"base_classname",
")",
",",
"constructor_args",
"[",
"0",
"]",
".",
"strip",
"(",
")",
")",
")",
"if",
"(",
"not",
"is_marked_explicit",
"and",
"onearg_constructor",
"and",
"not",
"initializer_list_constructor",
"and",
"not",
"copy_constructor",
")",
":",
"if",
"defaulted_args",
"or",
"variadic_args",
":",
"error",
"(",
"filename",
",",
"linenum",
",",
"'runtime/explicit'",
",",
"5",
",",
"'Constructors callable with one argument '",
"'should be marked explicit.'",
")",
"else",
":",
"error",
"(",
"filename",
",",
"linenum",
",",
"'runtime/explicit'",
",",
"5",
",",
"'Single-parameter constructors should be marked explicit.'",
")",
"elif",
"is_marked_explicit",
"and",
"not",
"onearg_constructor",
":",
"if",
"noarg_constructor",
":",
"error",
"(",
"filename",
",",
"linenum",
",",
"'runtime/explicit'",
",",
"5",
",",
"'Zero-parameter constructors should not be marked explicit.'",
")"
] |
https://github.com/p4lang/PI/blob/38d87e81253feff9fff0660d662c885be78fb719/tools/cpplint.py#L3271-L3433
|
||
dartsim/dart
|
495c82120c836005f2d136d4a50c8cc997fb879b
|
tools/cpplint.py
|
python
|
GetHeaderGuardCPPVariable
|
(filename)
|
return re.sub(r'[-./\s]', '_', file_path_from_root).upper() + '_'
|
Returns the CPP variable that should be used as a header guard.
Args:
filename: The name of a C++ header file.
Returns:
The CPP variable that should be used as a header guard in the
named file.
|
Returns the CPP variable that should be used as a header guard.
|
[
"Returns",
"the",
"CPP",
"variable",
"that",
"should",
"be",
"used",
"as",
"a",
"header",
"guard",
"."
] |
def GetHeaderGuardCPPVariable(filename):
"""Returns the CPP variable that should be used as a header guard.
Args:
filename: The name of a C++ header file.
Returns:
The CPP variable that should be used as a header guard in the
named file.
"""
# Restores original filename in case that cpplint is invoked from Emacs's
# flymake.
filename = re.sub(r'_flymake\.h$', '.h', filename)
filename = re.sub(r'/\.flymake/([^/]*)$', r'/\1', filename)
fileinfo = FileInfo(filename)
file_path_from_root = fileinfo.RepositoryName()
if _root:
file_path_from_root = re.sub('^' + _root + os.sep, '', file_path_from_root)
return re.sub(r'[-./\s]', '_', file_path_from_root).upper() + '_'
|
[
"def",
"GetHeaderGuardCPPVariable",
"(",
"filename",
")",
":",
"# Restores original filename in case that cpplint is invoked from Emacs's",
"# flymake.",
"filename",
"=",
"re",
".",
"sub",
"(",
"r'_flymake\\.h$'",
",",
"'.h'",
",",
"filename",
")",
"filename",
"=",
"re",
".",
"sub",
"(",
"r'/\\.flymake/([^/]*)$'",
",",
"r'/\\1'",
",",
"filename",
")",
"fileinfo",
"=",
"FileInfo",
"(",
"filename",
")",
"file_path_from_root",
"=",
"fileinfo",
".",
"RepositoryName",
"(",
")",
"if",
"_root",
":",
"file_path_from_root",
"=",
"re",
".",
"sub",
"(",
"'^'",
"+",
"_root",
"+",
"os",
".",
"sep",
",",
"''",
",",
"file_path_from_root",
")",
"return",
"re",
".",
"sub",
"(",
"r'[-./\\s]'",
",",
"'_'",
",",
"file_path_from_root",
")",
".",
"upper",
"(",
")",
"+",
"'_'"
] |
https://github.com/dartsim/dart/blob/495c82120c836005f2d136d4a50c8cc997fb879b/tools/cpplint.py#L1362-L1383
|
|
eventql/eventql
|
7ca0dbb2e683b525620ea30dc40540a22d5eb227
|
deps/3rdparty/spidermonkey/mozjs/python/mock-1.0.0/mock.py
|
python
|
NonCallableMagicMock.mock_add_spec
|
(self, spec, spec_set=False)
|
Add a spec to a mock. `spec` can either be an object or a
list of strings. Only attributes on the `spec` can be fetched as
attributes from the mock.
If `spec_set` is True then only attributes on the spec can be set.
|
Add a spec to a mock. `spec` can either be an object or a
list of strings. Only attributes on the `spec` can be fetched as
attributes from the mock.
|
[
"Add",
"a",
"spec",
"to",
"a",
"mock",
".",
"spec",
"can",
"either",
"be",
"an",
"object",
"or",
"a",
"list",
"of",
"strings",
".",
"Only",
"attributes",
"on",
"the",
"spec",
"can",
"be",
"fetched",
"as",
"attributes",
"from",
"the",
"mock",
"."
] |
def mock_add_spec(self, spec, spec_set=False):
"""Add a spec to a mock. `spec` can either be an object or a
list of strings. Only attributes on the `spec` can be fetched as
attributes from the mock.
If `spec_set` is True then only attributes on the spec can be set."""
self._mock_add_spec(spec, spec_set)
self._mock_set_magics()
|
[
"def",
"mock_add_spec",
"(",
"self",
",",
"spec",
",",
"spec_set",
"=",
"False",
")",
":",
"self",
".",
"_mock_add_spec",
"(",
"spec",
",",
"spec_set",
")",
"self",
".",
"_mock_set_magics",
"(",
")"
] |
https://github.com/eventql/eventql/blob/7ca0dbb2e683b525620ea30dc40540a22d5eb227/deps/3rdparty/spidermonkey/mozjs/python/mock-1.0.0/mock.py#L1868-L1875
|
||
moderngl/moderngl
|
32fe79927e02b0fa893b3603d677bdae39771e14
|
moderngl/framebuffer.py
|
python
|
Framebuffer.size
|
(self)
|
return self._size
|
tuple: The size of the framebuffer.
Framebuffers created by a window will only report its initial size.
It's better get size information from the window itself.
|
tuple: The size of the framebuffer.
|
[
"tuple",
":",
"The",
"size",
"of",
"the",
"framebuffer",
"."
] |
def size(self) -> tuple:
'''
tuple: The size of the framebuffer.
Framebuffers created by a window will only report its initial size.
It's better get size information from the window itself.
'''
return self._size
|
[
"def",
"size",
"(",
"self",
")",
"->",
"tuple",
":",
"return",
"self",
".",
"_size"
] |
https://github.com/moderngl/moderngl/blob/32fe79927e02b0fa893b3603d677bdae39771e14/moderngl/framebuffer.py#L177-L185
|
|
macchina-io/macchina.io
|
ef24ba0e18379c3dd48fb84e6dbf991101cb8db0
|
platform/JS/V8/tools/gyp/pylib/gyp/generator/cmake.py
|
python
|
NormjoinPathForceCMakeSource
|
(base_path, rel_path)
|
return os.path.join('${CMAKE_CURRENT_LIST_DIR}',
os.path.normpath(os.path.join(base_path, rel_path)))
|
Resolves rel_path against base_path and returns the result.
If rel_path is an absolute path it is returned unchanged.
Otherwise it is resolved against base_path and normalized.
If the result is a relative path, it is forced to be relative to the
CMakeLists.txt.
|
Resolves rel_path against base_path and returns the result.
|
[
"Resolves",
"rel_path",
"against",
"base_path",
"and",
"returns",
"the",
"result",
"."
] |
def NormjoinPathForceCMakeSource(base_path, rel_path):
"""Resolves rel_path against base_path and returns the result.
If rel_path is an absolute path it is returned unchanged.
Otherwise it is resolved against base_path and normalized.
If the result is a relative path, it is forced to be relative to the
CMakeLists.txt.
"""
if os.path.isabs(rel_path):
return rel_path
if any([rel_path.startswith(var) for var in FULL_PATH_VARS]):
return rel_path
# TODO: do we need to check base_path for absolute variables as well?
return os.path.join('${CMAKE_CURRENT_LIST_DIR}',
os.path.normpath(os.path.join(base_path, rel_path)))
|
[
"def",
"NormjoinPathForceCMakeSource",
"(",
"base_path",
",",
"rel_path",
")",
":",
"if",
"os",
".",
"path",
".",
"isabs",
"(",
"rel_path",
")",
":",
"return",
"rel_path",
"if",
"any",
"(",
"[",
"rel_path",
".",
"startswith",
"(",
"var",
")",
"for",
"var",
"in",
"FULL_PATH_VARS",
"]",
")",
":",
"return",
"rel_path",
"# TODO: do we need to check base_path for absolute variables as well?",
"return",
"os",
".",
"path",
".",
"join",
"(",
"'${CMAKE_CURRENT_LIST_DIR}'",
",",
"os",
".",
"path",
".",
"normpath",
"(",
"os",
".",
"path",
".",
"join",
"(",
"base_path",
",",
"rel_path",
")",
")",
")"
] |
https://github.com/macchina-io/macchina.io/blob/ef24ba0e18379c3dd48fb84e6dbf991101cb8db0/platform/JS/V8/tools/gyp/pylib/gyp/generator/cmake.py#L94-L108
|
|
windystrife/UnrealEngine_NVIDIAGameWorks
|
b50e6338a7c5b26374d66306ebc7807541ff815e
|
Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/logging/__init__.py
|
python
|
error
|
(msg, *args, **kwargs)
|
Log a message with severity 'ERROR' on the root logger.
|
Log a message with severity 'ERROR' on the root logger.
|
[
"Log",
"a",
"message",
"with",
"severity",
"ERROR",
"on",
"the",
"root",
"logger",
"."
] |
def error(msg, *args, **kwargs):
"""
Log a message with severity 'ERROR' on the root logger.
"""
if len(root.handlers) == 0:
basicConfig()
root.error(msg, *args, **kwargs)
|
[
"def",
"error",
"(",
"msg",
",",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"if",
"len",
"(",
"root",
".",
"handlers",
")",
"==",
"0",
":",
"basicConfig",
"(",
")",
"root",
".",
"error",
"(",
"msg",
",",
"*",
"args",
",",
"*",
"*",
"kwargs",
")"
] |
https://github.com/windystrife/UnrealEngine_NVIDIAGameWorks/blob/b50e6338a7c5b26374d66306ebc7807541ff815e/Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/logging/__init__.py#L1579-L1585
|
||
catboost/catboost
|
167f64f237114a4d10b2b4ee42adb4569137debe
|
contrib/python/tornado/tornado-6/tornado/tcpserver.py
|
python
|
TCPServer.add_sockets
|
(self, sockets: Iterable[socket.socket])
|
Makes this server start accepting connections on the given sockets.
The ``sockets`` parameter is a list of socket objects such as
those returned by `~tornado.netutil.bind_sockets`.
`add_sockets` is typically used in combination with that
method and `tornado.process.fork_processes` to provide greater
control over the initialization of a multi-process server.
|
Makes this server start accepting connections on the given sockets.
|
[
"Makes",
"this",
"server",
"start",
"accepting",
"connections",
"on",
"the",
"given",
"sockets",
"."
] |
def add_sockets(self, sockets: Iterable[socket.socket]) -> None:
"""Makes this server start accepting connections on the given sockets.
The ``sockets`` parameter is a list of socket objects such as
those returned by `~tornado.netutil.bind_sockets`.
`add_sockets` is typically used in combination with that
method and `tornado.process.fork_processes` to provide greater
control over the initialization of a multi-process server.
"""
for sock in sockets:
self._sockets[sock.fileno()] = sock
self._handlers[sock.fileno()] = add_accept_handler(
sock, self._handle_connection
)
|
[
"def",
"add_sockets",
"(",
"self",
",",
"sockets",
":",
"Iterable",
"[",
"socket",
".",
"socket",
"]",
")",
"->",
"None",
":",
"for",
"sock",
"in",
"sockets",
":",
"self",
".",
"_sockets",
"[",
"sock",
".",
"fileno",
"(",
")",
"]",
"=",
"sock",
"self",
".",
"_handlers",
"[",
"sock",
".",
"fileno",
"(",
")",
"]",
"=",
"add_accept_handler",
"(",
"sock",
",",
"self",
".",
"_handle_connection",
")"
] |
https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/tornado/tornado-6/tornado/tcpserver.py#L154-L167
|
||
9miao/CrossApp
|
1f5375e061bf69841eb19728598f5ae3f508d620
|
tools/bindings-generator/backup/clang-llvm-3.3-pybinding/cindex.py
|
python
|
SourceLocation.offset
|
(self)
|
return self._get_instantiation()[3]
|
Get the file offset represented by this source location.
|
Get the file offset represented by this source location.
|
[
"Get",
"the",
"file",
"offset",
"represented",
"by",
"this",
"source",
"location",
"."
] |
def offset(self):
"""Get the file offset represented by this source location."""
return self._get_instantiation()[3]
|
[
"def",
"offset",
"(",
"self",
")",
":",
"return",
"self",
".",
"_get_instantiation",
"(",
")",
"[",
"3",
"]"
] |
https://github.com/9miao/CrossApp/blob/1f5375e061bf69841eb19728598f5ae3f508d620/tools/bindings-generator/backup/clang-llvm-3.3-pybinding/cindex.py#L213-L215
|
|
LiquidPlayer/LiquidCore
|
9405979363f2353ac9a71ad8ab59685dd7f919c9
|
deps/node-10.15.3/tools/gyp/pylib/gyp/generator/ninja.py
|
python
|
NinjaWriter.WriteMacXCassets
|
(self, xcassets, bundle_depends)
|
return partial_info_plist
|
Writes ninja edges for 'mac_bundle_resources' .xcassets files.
This add an invocation of 'actool' via the 'mac_tool.py' helper script.
It assumes that the assets catalogs define at least one imageset and
thus an Assets.car file will be generated in the application resources
directory. If this is not the case, then the build will probably be done
at each invocation of ninja.
|
Writes ninja edges for 'mac_bundle_resources' .xcassets files.
|
[
"Writes",
"ninja",
"edges",
"for",
"mac_bundle_resources",
".",
"xcassets",
"files",
"."
] |
def WriteMacXCassets(self, xcassets, bundle_depends):
"""Writes ninja edges for 'mac_bundle_resources' .xcassets files.
This add an invocation of 'actool' via the 'mac_tool.py' helper script.
It assumes that the assets catalogs define at least one imageset and
thus an Assets.car file will be generated in the application resources
directory. If this is not the case, then the build will probably be done
at each invocation of ninja."""
if not xcassets:
return
extra_arguments = {}
settings_to_arg = {
'XCASSETS_APP_ICON': 'app-icon',
'XCASSETS_LAUNCH_IMAGE': 'launch-image',
}
settings = self.xcode_settings.xcode_settings[self.config_name]
for settings_key, arg_name in settings_to_arg.iteritems():
value = settings.get(settings_key)
if value:
extra_arguments[arg_name] = value
partial_info_plist = None
if extra_arguments:
partial_info_plist = self.GypPathToUniqueOutput(
'assetcatalog_generated_info.plist')
extra_arguments['output-partial-info-plist'] = partial_info_plist
outputs = []
outputs.append(
os.path.join(
self.xcode_settings.GetBundleResourceFolder(),
'Assets.car'))
if partial_info_plist:
outputs.append(partial_info_plist)
keys = QuoteShellArgument(json.dumps(extra_arguments), self.flavor)
extra_env = self.xcode_settings.GetPerTargetSettings()
env = self.GetSortedXcodeEnv(additional_settings=extra_env)
env = self.ComputeExportEnvString(env)
bundle_depends.extend(self.ninja.build(
outputs, 'compile_xcassets', xcassets,
variables=[('env', env), ('keys', keys)]))
return partial_info_plist
|
[
"def",
"WriteMacXCassets",
"(",
"self",
",",
"xcassets",
",",
"bundle_depends",
")",
":",
"if",
"not",
"xcassets",
":",
"return",
"extra_arguments",
"=",
"{",
"}",
"settings_to_arg",
"=",
"{",
"'XCASSETS_APP_ICON'",
":",
"'app-icon'",
",",
"'XCASSETS_LAUNCH_IMAGE'",
":",
"'launch-image'",
",",
"}",
"settings",
"=",
"self",
".",
"xcode_settings",
".",
"xcode_settings",
"[",
"self",
".",
"config_name",
"]",
"for",
"settings_key",
",",
"arg_name",
"in",
"settings_to_arg",
".",
"iteritems",
"(",
")",
":",
"value",
"=",
"settings",
".",
"get",
"(",
"settings_key",
")",
"if",
"value",
":",
"extra_arguments",
"[",
"arg_name",
"]",
"=",
"value",
"partial_info_plist",
"=",
"None",
"if",
"extra_arguments",
":",
"partial_info_plist",
"=",
"self",
".",
"GypPathToUniqueOutput",
"(",
"'assetcatalog_generated_info.plist'",
")",
"extra_arguments",
"[",
"'output-partial-info-plist'",
"]",
"=",
"partial_info_plist",
"outputs",
"=",
"[",
"]",
"outputs",
".",
"append",
"(",
"os",
".",
"path",
".",
"join",
"(",
"self",
".",
"xcode_settings",
".",
"GetBundleResourceFolder",
"(",
")",
",",
"'Assets.car'",
")",
")",
"if",
"partial_info_plist",
":",
"outputs",
".",
"append",
"(",
"partial_info_plist",
")",
"keys",
"=",
"QuoteShellArgument",
"(",
"json",
".",
"dumps",
"(",
"extra_arguments",
")",
",",
"self",
".",
"flavor",
")",
"extra_env",
"=",
"self",
".",
"xcode_settings",
".",
"GetPerTargetSettings",
"(",
")",
"env",
"=",
"self",
".",
"GetSortedXcodeEnv",
"(",
"additional_settings",
"=",
"extra_env",
")",
"env",
"=",
"self",
".",
"ComputeExportEnvString",
"(",
"env",
")",
"bundle_depends",
".",
"extend",
"(",
"self",
".",
"ninja",
".",
"build",
"(",
"outputs",
",",
"'compile_xcassets'",
",",
"xcassets",
",",
"variables",
"=",
"[",
"(",
"'env'",
",",
"env",
")",
",",
"(",
"'keys'",
",",
"keys",
")",
"]",
")",
")",
"return",
"partial_info_plist"
] |
https://github.com/LiquidPlayer/LiquidCore/blob/9405979363f2353ac9a71ad8ab59685dd7f919c9/deps/node-10.15.3/tools/gyp/pylib/gyp/generator/ninja.py#L824-L868
|
|
apache/incubator-mxnet
|
f03fb23f1d103fec9541b5ae59ee06b1734a51d9
|
python/mxnet/gluon/parameter.py
|
python
|
Parameter.initialize
|
(self, init=None, device=None, default_init=initializer.Uniform(),
force_reinit=False)
|
Initializes parameter and gradient arrays. Only used for :py:class:`NDArray` API.
Parameters
----------
init : Initializer
The initializer to use. Overrides :py:meth:`Parameter.init` and default_init.
device : Device or list of Device, default :py:meth:`device.current_device()`.
Assign Parameter to given device. If device is a list of Device, a
copy will be made for each device.
.. note::
Copies are independent arrays. User is responsible for keeping
their values consistent when updating.
Normally :py:class:`gluon.Trainer` does this for you.
default_init : Initializer
Default initializer is used when both :py:func:`init`
and :py:meth:`Parameter.init` are ``None``.
force_reinit : bool, default False
Whether to force re-initialization if parameter is already initialized.
Examples
--------
>>> weight = mx.gluon.Parameter('weight', shape=(2, 2))
>>> weight.initialize(device=mx.cpu(0))
>>> weight.data()
[[-0.01068833 0.01729892]
[ 0.02042518 -0.01618656]]
<NDArray 2x2 @cpu(0)>
>>> weight.grad()
[[ 0. 0.]
[ 0. 0.]]
<NDArray 2x2 @cpu(0)>
>>> weight.initialize(device=[mx.gpu(0), mx.gpu(1)])
>>> weight.data(mx.gpu(0))
[[-0.00873779 -0.02834515]
[ 0.05484822 -0.06206018]]
<NDArray 2x2 @gpu(0)>
>>> weight.data(mx.gpu(1))
[[-0.00873779 -0.02834515]
[ 0.05484822 -0.06206018]]
<NDArray 2x2 @gpu(1)>
|
Initializes parameter and gradient arrays. Only used for :py:class:`NDArray` API.
|
[
"Initializes",
"parameter",
"and",
"gradient",
"arrays",
".",
"Only",
"used",
"for",
":",
"py",
":",
"class",
":",
"NDArray",
"API",
"."
] |
def initialize(self, init=None, device=None, default_init=initializer.Uniform(),
force_reinit=False):
"""Initializes parameter and gradient arrays. Only used for :py:class:`NDArray` API.
Parameters
----------
init : Initializer
The initializer to use. Overrides :py:meth:`Parameter.init` and default_init.
device : Device or list of Device, default :py:meth:`device.current_device()`.
Assign Parameter to given device. If device is a list of Device, a
copy will be made for each device.
.. note::
Copies are independent arrays. User is responsible for keeping
their values consistent when updating.
Normally :py:class:`gluon.Trainer` does this for you.
default_init : Initializer
Default initializer is used when both :py:func:`init`
and :py:meth:`Parameter.init` are ``None``.
force_reinit : bool, default False
Whether to force re-initialization if parameter is already initialized.
Examples
--------
>>> weight = mx.gluon.Parameter('weight', shape=(2, 2))
>>> weight.initialize(device=mx.cpu(0))
>>> weight.data()
[[-0.01068833 0.01729892]
[ 0.02042518 -0.01618656]]
<NDArray 2x2 @cpu(0)>
>>> weight.grad()
[[ 0. 0.]
[ 0. 0.]]
<NDArray 2x2 @cpu(0)>
>>> weight.initialize(device=[mx.gpu(0), mx.gpu(1)])
>>> weight.data(mx.gpu(0))
[[-0.00873779 -0.02834515]
[ 0.05484822 -0.06206018]]
<NDArray 2x2 @gpu(0)>
>>> weight.data(mx.gpu(1))
[[-0.00873779 -0.02834515]
[ 0.05484822 -0.06206018]]
<NDArray 2x2 @gpu(1)>
"""
if self._data is not None and not force_reinit:
warnings.warn("Parameter '%s' is already initialized, ignoring. " \
"Set force_reinit=True to re-initialize."%self.name,
stacklevel=2)
return
self._data = self._grad = None
if device is None:
device = [_device.current_device()]
if isinstance(device, Device):
device = [device]
if isinstance(self.init, initializer.RNNFused):
self.init.set_initializer(init if init else default_init)
init = default_init = self.init
if init is None:
init = default_init if self.init is None else self.init
if not shape_is_known(self.shape):
if self._allow_deferred_init:
self._deferred_init = (init, device, default_init, None)
return
raise ValueError("Cannot initialize Parameter '%s' because it has " \
"invalid shape: %s."%(self.name, str(self.shape)))
self._deferred_init = (init, device, default_init, None)
self._finish_deferred_init()
|
[
"def",
"initialize",
"(",
"self",
",",
"init",
"=",
"None",
",",
"device",
"=",
"None",
",",
"default_init",
"=",
"initializer",
".",
"Uniform",
"(",
")",
",",
"force_reinit",
"=",
"False",
")",
":",
"if",
"self",
".",
"_data",
"is",
"not",
"None",
"and",
"not",
"force_reinit",
":",
"warnings",
".",
"warn",
"(",
"\"Parameter '%s' is already initialized, ignoring. \"",
"\"Set force_reinit=True to re-initialize.\"",
"%",
"self",
".",
"name",
",",
"stacklevel",
"=",
"2",
")",
"return",
"self",
".",
"_data",
"=",
"self",
".",
"_grad",
"=",
"None",
"if",
"device",
"is",
"None",
":",
"device",
"=",
"[",
"_device",
".",
"current_device",
"(",
")",
"]",
"if",
"isinstance",
"(",
"device",
",",
"Device",
")",
":",
"device",
"=",
"[",
"device",
"]",
"if",
"isinstance",
"(",
"self",
".",
"init",
",",
"initializer",
".",
"RNNFused",
")",
":",
"self",
".",
"init",
".",
"set_initializer",
"(",
"init",
"if",
"init",
"else",
"default_init",
")",
"init",
"=",
"default_init",
"=",
"self",
".",
"init",
"if",
"init",
"is",
"None",
":",
"init",
"=",
"default_init",
"if",
"self",
".",
"init",
"is",
"None",
"else",
"self",
".",
"init",
"if",
"not",
"shape_is_known",
"(",
"self",
".",
"shape",
")",
":",
"if",
"self",
".",
"_allow_deferred_init",
":",
"self",
".",
"_deferred_init",
"=",
"(",
"init",
",",
"device",
",",
"default_init",
",",
"None",
")",
"return",
"raise",
"ValueError",
"(",
"\"Cannot initialize Parameter '%s' because it has \"",
"\"invalid shape: %s.\"",
"%",
"(",
"self",
".",
"name",
",",
"str",
"(",
"self",
".",
"shape",
")",
")",
")",
"self",
".",
"_deferred_init",
"=",
"(",
"init",
",",
"device",
",",
"default_init",
",",
"None",
")",
"self",
".",
"_finish_deferred_init",
"(",
")"
] |
https://github.com/apache/incubator-mxnet/blob/f03fb23f1d103fec9541b5ae59ee06b1734a51d9/python/mxnet/gluon/parameter.py#L425-L492
|
||
microsoft/checkedc-clang
|
a173fefde5d7877b7750e7ce96dd08cf18baebf2
|
compiler-rt/lib/sanitizer_common/scripts/cpplint.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.
Args:
line: is a partial line of code starting from the 0..n.
Returns:
True, if next character appended to 'line' is inside a
string constant.
|
Does line terminate so, that the next symbol is in string constant.
|
[
"Does",
"line",
"terminate",
"so",
"that",
"the",
"next",
"symbol",
"is",
"in",
"string",
"constant",
"."
] |
def IsCppString(line):
"""Does line terminate so, that the next symbol is in string constant.
This function does not consider single-line nor multi-line comments.
Args:
line: is a partial line of code starting from the 0..n.
Returns:
True, if next character appended to 'line' is inside a
string constant.
"""
line = line.replace(r'\\', 'XX') # after this, \\" does not match to \"
return ((line.count('"') - line.count(r'\"') - line.count("'\"'")) & 1) == 1
|
[
"def",
"IsCppString",
"(",
"line",
")",
":",
"line",
"=",
"line",
".",
"replace",
"(",
"r'\\\\'",
",",
"'XX'",
")",
"# after this, \\\\\" does not match to \\\"",
"return",
"(",
"(",
"line",
".",
"count",
"(",
"'\"'",
")",
"-",
"line",
".",
"count",
"(",
"r'\\\"'",
")",
"-",
"line",
".",
"count",
"(",
"\"'\\\"'\"",
")",
")",
"&",
"1",
")",
"==",
"1"
] |
https://github.com/microsoft/checkedc-clang/blob/a173fefde5d7877b7750e7ce96dd08cf18baebf2/compiler-rt/lib/sanitizer_common/scripts/cpplint.py#L1271-L1285
|
|
catboost/catboost
|
167f64f237114a4d10b2b4ee42adb4569137debe
|
contrib/python/numpy/py3/numpy/core/_string_helpers.py
|
python
|
english_lower
|
(s)
|
return lowered
|
Apply English case rules to convert ASCII strings to all lower case.
This is an internal utility function to replace calls to str.lower() such
that we can avoid changing behavior with changing locales. In particular,
Turkish has distinct dotted and dotless variants of the Latin letter "I" in
both lowercase and uppercase. Thus, "I".lower() != "i" in a "tr" locale.
Parameters
----------
s : str
Returns
-------
lowered : str
Examples
--------
>>> from numpy.core.numerictypes import english_lower
>>> english_lower('ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789_')
'abcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyz0123456789_'
>>> english_lower('')
''
|
Apply English case rules to convert ASCII strings to all lower case.
|
[
"Apply",
"English",
"case",
"rules",
"to",
"convert",
"ASCII",
"strings",
"to",
"all",
"lower",
"case",
"."
] |
def english_lower(s):
""" Apply English case rules to convert ASCII strings to all lower case.
This is an internal utility function to replace calls to str.lower() such
that we can avoid changing behavior with changing locales. In particular,
Turkish has distinct dotted and dotless variants of the Latin letter "I" in
both lowercase and uppercase. Thus, "I".lower() != "i" in a "tr" locale.
Parameters
----------
s : str
Returns
-------
lowered : str
Examples
--------
>>> from numpy.core.numerictypes import english_lower
>>> english_lower('ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789_')
'abcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyz0123456789_'
>>> english_lower('')
''
"""
lowered = s.translate(LOWER_TABLE)
return lowered
|
[
"def",
"english_lower",
"(",
"s",
")",
":",
"lowered",
"=",
"s",
".",
"translate",
"(",
"LOWER_TABLE",
")",
"return",
"lowered"
] |
https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/numpy/py3/numpy/core/_string_helpers.py#L16-L41
|
|
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numpy/lib/twodim_base.py
|
python
|
tril
|
(m, k=0)
|
return where(mask, m, zeros(1, m.dtype))
|
Lower triangle of an array.
Return a copy of an array with elements above the `k`-th diagonal zeroed.
Parameters
----------
m : array_like, shape (M, N)
Input array.
k : int, optional
Diagonal above which to zero elements. `k = 0` (the default) is the
main diagonal, `k < 0` is below it and `k > 0` is above.
Returns
-------
tril : ndarray, shape (M, N)
Lower triangle of `m`, of same shape and data-type as `m`.
See Also
--------
triu : same thing, only for the upper triangle
Examples
--------
>>> np.tril([[1,2,3],[4,5,6],[7,8,9],[10,11,12]], -1)
array([[ 0, 0, 0],
[ 4, 0, 0],
[ 7, 8, 0],
[10, 11, 12]])
|
Lower triangle of an array.
|
[
"Lower",
"triangle",
"of",
"an",
"array",
"."
] |
def tril(m, k=0):
"""
Lower triangle of an array.
Return a copy of an array with elements above the `k`-th diagonal zeroed.
Parameters
----------
m : array_like, shape (M, N)
Input array.
k : int, optional
Diagonal above which to zero elements. `k = 0` (the default) is the
main diagonal, `k < 0` is below it and `k > 0` is above.
Returns
-------
tril : ndarray, shape (M, N)
Lower triangle of `m`, of same shape and data-type as `m`.
See Also
--------
triu : same thing, only for the upper triangle
Examples
--------
>>> np.tril([[1,2,3],[4,5,6],[7,8,9],[10,11,12]], -1)
array([[ 0, 0, 0],
[ 4, 0, 0],
[ 7, 8, 0],
[10, 11, 12]])
"""
m = asanyarray(m)
mask = tri(*m.shape[-2:], k=k, dtype=bool)
return where(mask, m, zeros(1, m.dtype))
|
[
"def",
"tril",
"(",
"m",
",",
"k",
"=",
"0",
")",
":",
"m",
"=",
"asanyarray",
"(",
"m",
")",
"mask",
"=",
"tri",
"(",
"*",
"m",
".",
"shape",
"[",
"-",
"2",
":",
"]",
",",
"k",
"=",
"k",
",",
"dtype",
"=",
"bool",
")",
"return",
"where",
"(",
"mask",
",",
"m",
",",
"zeros",
"(",
"1",
",",
"m",
".",
"dtype",
")",
")"
] |
https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numpy/lib/twodim_base.py#L403-L438
|
|
wxWidgets/wxPython-Classic
|
19571e1ae65f1ac445f5491474121998c97a1bf0
|
src/gtk/_controls.py
|
python
|
StaticBox.Create
|
(*args, **kwargs)
|
return _controls_.StaticBox_Create(*args, **kwargs)
|
Create(self, Window parent, int id=-1, String label=EmptyString,
Point pos=DefaultPosition, Size size=DefaultSize,
long style=0, String name=StaticBoxNameStr) -> bool
|
Create(self, Window parent, int id=-1, String label=EmptyString,
Point pos=DefaultPosition, Size size=DefaultSize,
long style=0, String name=StaticBoxNameStr) -> bool
|
[
"Create",
"(",
"self",
"Window",
"parent",
"int",
"id",
"=",
"-",
"1",
"String",
"label",
"=",
"EmptyString",
"Point",
"pos",
"=",
"DefaultPosition",
"Size",
"size",
"=",
"DefaultSize",
"long",
"style",
"=",
"0",
"String",
"name",
"=",
"StaticBoxNameStr",
")",
"-",
">",
"bool"
] |
def Create(*args, **kwargs):
"""
Create(self, Window parent, int id=-1, String label=EmptyString,
Point pos=DefaultPosition, Size size=DefaultSize,
long style=0, String name=StaticBoxNameStr) -> bool
"""
return _controls_.StaticBox_Create(*args, **kwargs)
|
[
"def",
"Create",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"return",
"_controls_",
".",
"StaticBox_Create",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")"
] |
https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/gtk/_controls.py#L849-L855
|
|
weolar/miniblink49
|
1c4678db0594a4abde23d3ebbcc7cd13c3170777
|
third_party/WebKit/Source/bindings/scripts/v8_interface.py
|
python
|
resolution_tests_methods
|
(effective_overloads)
|
Yields resolution test and associated method, in resolution order, for effective overloads of a given length.
This is the heart of the resolution algorithm.
http://heycam.github.io/webidl/#dfn-overload-resolution-algorithm
Note that a given method can be listed multiple times, with different tests!
This is to handle implicit type conversion.
Returns:
[(test, method)]
|
Yields resolution test and associated method, in resolution order, for effective overloads of a given length.
|
[
"Yields",
"resolution",
"test",
"and",
"associated",
"method",
"in",
"resolution",
"order",
"for",
"effective",
"overloads",
"of",
"a",
"given",
"length",
"."
] |
def resolution_tests_methods(effective_overloads):
"""Yields resolution test and associated method, in resolution order, for effective overloads of a given length.
This is the heart of the resolution algorithm.
http://heycam.github.io/webidl/#dfn-overload-resolution-algorithm
Note that a given method can be listed multiple times, with different tests!
This is to handle implicit type conversion.
Returns:
[(test, method)]
"""
methods = [effective_overload[0]
for effective_overload in effective_overloads]
if len(methods) == 1:
# If only one method with a given length, no test needed
yield 'true', methods[0]
return
# 6. If there is more than one entry in S, then set d to be the
# distinguishing argument index for the entries of S.
index = distinguishing_argument_index(effective_overloads)
# (7-9 are for handling |undefined| values for optional arguments before
# the distinguishing argument (as “missing”), so you can specify only some
# optional arguments. We don’t support this, so we skip these steps.)
# 10. If i = d, then:
# (d is the distinguishing argument index)
# 1. Let V be argi.
# Note: This is the argument that will be used to resolve which
# overload is selected.
cpp_value = 'info[%s]' % index
# Extract argument and IDL type to simplify accessing these in each loop.
arguments = [method['arguments'][index] for method in methods]
arguments_methods = zip(arguments, methods)
idl_types = [argument['idl_type_object'] for argument in arguments]
idl_types_methods = zip(idl_types, methods)
# We can’t do a single loop through all methods or simply sort them, because
# a method may be listed in multiple steps of the resolution algorithm, and
# which test to apply differs depending on the step.
#
# Instead, we need to go through all methods at each step, either finding
# first match (if only one test is allowed) or filtering to matches (if
# multiple tests are allowed), and generating an appropriate tests.
# 2. If V is undefined, and there is an entry in S whose list of
# optionality values has “optional” at index i, then remove from S all
# other entries.
try:
method = next(method for argument, method in arguments_methods
if argument['is_optional'])
test = '%s->IsUndefined()' % cpp_value
yield test, method
except StopIteration:
pass
# 3. Otherwise: if V is null or undefined, and there is an entry in S that
# has one of the following types at position i of its type list,
# • a nullable type
try:
method = next(method for idl_type, method in idl_types_methods
if idl_type.is_nullable)
test = 'isUndefinedOrNull(%s)' % cpp_value
yield test, method
except StopIteration:
pass
# 4. Otherwise: if V is a platform object – but not a platform array
# object – and there is an entry in S that has one of the following
# types at position i of its type list,
# • an interface type that V implements
# (Unlike most of these tests, this can return multiple methods, since we
# test if it implements an interface. Thus we need a for loop, not a next.)
# (We distinguish wrapper types from built-in interface types.)
for idl_type, method in ((idl_type, method)
for idl_type, method in idl_types_methods
if idl_type.is_wrapper_type):
test = 'V8{idl_type}::hasInstance({cpp_value}, info.GetIsolate())'.format(idl_type=idl_type.base_type, cpp_value=cpp_value)
yield test, method
# 13. Otherwise: if IsCallable(V) is true, and there is an entry in S that
# has one of the following types at position i of its type list,
# • a callback function type
# ...
#
# FIXME:
# We test for functions rather than callability, which isn't strictly the
# same thing.
try:
method = next(method for idl_type, method in idl_types_methods
if idl_type.is_callback_function)
test = '%s->IsFunction()' % cpp_value
yield test, method
except StopIteration:
pass
# 14. Otherwise: if V is any kind of object except for a native Date object,
# a native RegExp object, and there is an entry in S that has one of the
# following types at position i of its type list,
# • a sequence type
# ...
#
# 15. Otherwise: if V is any kind of object except for a native Date object,
# a native RegExp object, and there is an entry in S that has one of the
# following types at position i of its type list,
# • an array type
# ...
# • a dictionary
#
# FIXME:
# We don't strictly follow the algorithm here. The algorithm says "remove
# all other entries" if there is "one entry" matching, but we yield all
# entries to support following constructors:
# [constructor(sequence<DOMString> arg), constructor(Dictionary arg)]
# interface I { ... }
# (Need to check array types before objects because an array is an object)
for idl_type, method in idl_types_methods:
if idl_type.native_array_element_type:
# (We test for Array instead of generic Object to type-check.)
# FIXME: test for Object during resolution, then have type check for
# Array in overloaded method: http://crbug.com/262383
yield '%s->IsArray()' % cpp_value, method
for idl_type, method in idl_types_methods:
if idl_type.is_dictionary or idl_type.name == 'Dictionary':
# FIXME: should be '{1}->IsObject() && !{1}->IsDate() && !{1}->IsRegExp()'.format(cpp_value)
# FIXME: the IsDate and IsRegExp checks can be skipped if we've
# already generated tests for them.
yield '%s->IsObject()' % cpp_value, method
# (Check for exact type matches before performing automatic type conversion;
# only needed if distinguishing between primitive types.)
if len([idl_type.is_primitive_type for idl_type in idl_types]) > 1:
# (Only needed if match in step 11, otherwise redundant.)
if any(idl_type.is_string_type or idl_type.is_enum
for idl_type in idl_types):
# 10. Otherwise: if V is a Number value, and there is an entry in S
# that has one of the following types at position i of its type
# list,
# • a numeric type
try:
method = next(method for idl_type, method in idl_types_methods
if idl_type.is_numeric_type)
test = '%s->IsNumber()' % cpp_value
yield test, method
except StopIteration:
pass
# (Perform automatic type conversion, in order. If any of these match,
# that’s the end, and no other tests are needed.) To keep this code simple,
# we rely on the C++ compiler's dead code elimination to deal with the
# redundancy if both cases below trigger.
# 11. Otherwise: if there is an entry in S that has one of the following
# types at position i of its type list,
# • DOMString
# • ByteString
# • USVString
# • an enumeration type
try:
method = next(method for idl_type, method in idl_types_methods
if idl_type.is_string_type or idl_type.is_enum)
yield 'true', method
except StopIteration:
pass
# 12. Otherwise: if there is an entry in S that has one of the following
# types at position i of its type list,
# • a numeric type
try:
method = next(method for idl_type, method in idl_types_methods
if idl_type.is_numeric_type)
yield 'true', method
except StopIteration:
pass
|
[
"def",
"resolution_tests_methods",
"(",
"effective_overloads",
")",
":",
"methods",
"=",
"[",
"effective_overload",
"[",
"0",
"]",
"for",
"effective_overload",
"in",
"effective_overloads",
"]",
"if",
"len",
"(",
"methods",
")",
"==",
"1",
":",
"# If only one method with a given length, no test needed",
"yield",
"'true'",
",",
"methods",
"[",
"0",
"]",
"return",
"# 6. If there is more than one entry in S, then set d to be the",
"# distinguishing argument index for the entries of S.",
"index",
"=",
"distinguishing_argument_index",
"(",
"effective_overloads",
")",
"# (7-9 are for handling |undefined| values for optional arguments before",
"# the distinguishing argument (as “missing”), so you can specify only some",
"# optional arguments. We don’t support this, so we skip these steps.)",
"# 10. If i = d, then:",
"# (d is the distinguishing argument index)",
"# 1. Let V be argi.",
"# Note: This is the argument that will be used to resolve which",
"# overload is selected.",
"cpp_value",
"=",
"'info[%s]'",
"%",
"index",
"# Extract argument and IDL type to simplify accessing these in each loop.",
"arguments",
"=",
"[",
"method",
"[",
"'arguments'",
"]",
"[",
"index",
"]",
"for",
"method",
"in",
"methods",
"]",
"arguments_methods",
"=",
"zip",
"(",
"arguments",
",",
"methods",
")",
"idl_types",
"=",
"[",
"argument",
"[",
"'idl_type_object'",
"]",
"for",
"argument",
"in",
"arguments",
"]",
"idl_types_methods",
"=",
"zip",
"(",
"idl_types",
",",
"methods",
")",
"# We can’t do a single loop through all methods or simply sort them, because",
"# a method may be listed in multiple steps of the resolution algorithm, and",
"# which test to apply differs depending on the step.",
"#",
"# Instead, we need to go through all methods at each step, either finding",
"# first match (if only one test is allowed) or filtering to matches (if",
"# multiple tests are allowed), and generating an appropriate tests.",
"# 2. If V is undefined, and there is an entry in S whose list of",
"# optionality values has “optional” at index i, then remove from S all",
"# other entries.",
"try",
":",
"method",
"=",
"next",
"(",
"method",
"for",
"argument",
",",
"method",
"in",
"arguments_methods",
"if",
"argument",
"[",
"'is_optional'",
"]",
")",
"test",
"=",
"'%s->IsUndefined()'",
"%",
"cpp_value",
"yield",
"test",
",",
"method",
"except",
"StopIteration",
":",
"pass",
"# 3. Otherwise: if V is null or undefined, and there is an entry in S that",
"# has one of the following types at position i of its type list,",
"# • a nullable type",
"try",
":",
"method",
"=",
"next",
"(",
"method",
"for",
"idl_type",
",",
"method",
"in",
"idl_types_methods",
"if",
"idl_type",
".",
"is_nullable",
")",
"test",
"=",
"'isUndefinedOrNull(%s)'",
"%",
"cpp_value",
"yield",
"test",
",",
"method",
"except",
"StopIteration",
":",
"pass",
"# 4. Otherwise: if V is a platform object – but not a platform array",
"# object – and there is an entry in S that has one of the following",
"# types at position i of its type list,",
"# • an interface type that V implements",
"# (Unlike most of these tests, this can return multiple methods, since we",
"# test if it implements an interface. Thus we need a for loop, not a next.)",
"# (We distinguish wrapper types from built-in interface types.)",
"for",
"idl_type",
",",
"method",
"in",
"(",
"(",
"idl_type",
",",
"method",
")",
"for",
"idl_type",
",",
"method",
"in",
"idl_types_methods",
"if",
"idl_type",
".",
"is_wrapper_type",
")",
":",
"test",
"=",
"'V8{idl_type}::hasInstance({cpp_value}, info.GetIsolate())'",
".",
"format",
"(",
"idl_type",
"=",
"idl_type",
".",
"base_type",
",",
"cpp_value",
"=",
"cpp_value",
")",
"yield",
"test",
",",
"method",
"# 13. Otherwise: if IsCallable(V) is true, and there is an entry in S that",
"# has one of the following types at position i of its type list,",
"# • a callback function type",
"# ...",
"#",
"# FIXME:",
"# We test for functions rather than callability, which isn't strictly the",
"# same thing.",
"try",
":",
"method",
"=",
"next",
"(",
"method",
"for",
"idl_type",
",",
"method",
"in",
"idl_types_methods",
"if",
"idl_type",
".",
"is_callback_function",
")",
"test",
"=",
"'%s->IsFunction()'",
"%",
"cpp_value",
"yield",
"test",
",",
"method",
"except",
"StopIteration",
":",
"pass",
"# 14. Otherwise: if V is any kind of object except for a native Date object,",
"# a native RegExp object, and there is an entry in S that has one of the",
"# following types at position i of its type list,",
"# • a sequence type",
"# ...",
"#",
"# 15. Otherwise: if V is any kind of object except for a native Date object,",
"# a native RegExp object, and there is an entry in S that has one of the",
"# following types at position i of its type list,",
"# • an array type",
"# ...",
"# • a dictionary",
"#",
"# FIXME:",
"# We don't strictly follow the algorithm here. The algorithm says \"remove",
"# all other entries\" if there is \"one entry\" matching, but we yield all",
"# entries to support following constructors:",
"# [constructor(sequence<DOMString> arg), constructor(Dictionary arg)]",
"# interface I { ... }",
"# (Need to check array types before objects because an array is an object)",
"for",
"idl_type",
",",
"method",
"in",
"idl_types_methods",
":",
"if",
"idl_type",
".",
"native_array_element_type",
":",
"# (We test for Array instead of generic Object to type-check.)",
"# FIXME: test for Object during resolution, then have type check for",
"# Array in overloaded method: http://crbug.com/262383",
"yield",
"'%s->IsArray()'",
"%",
"cpp_value",
",",
"method",
"for",
"idl_type",
",",
"method",
"in",
"idl_types_methods",
":",
"if",
"idl_type",
".",
"is_dictionary",
"or",
"idl_type",
".",
"name",
"==",
"'Dictionary'",
":",
"# FIXME: should be '{1}->IsObject() && !{1}->IsDate() && !{1}->IsRegExp()'.format(cpp_value)",
"# FIXME: the IsDate and IsRegExp checks can be skipped if we've",
"# already generated tests for them.",
"yield",
"'%s->IsObject()'",
"%",
"cpp_value",
",",
"method",
"# (Check for exact type matches before performing automatic type conversion;",
"# only needed if distinguishing between primitive types.)",
"if",
"len",
"(",
"[",
"idl_type",
".",
"is_primitive_type",
"for",
"idl_type",
"in",
"idl_types",
"]",
")",
">",
"1",
":",
"# (Only needed if match in step 11, otherwise redundant.)",
"if",
"any",
"(",
"idl_type",
".",
"is_string_type",
"or",
"idl_type",
".",
"is_enum",
"for",
"idl_type",
"in",
"idl_types",
")",
":",
"# 10. Otherwise: if V is a Number value, and there is an entry in S",
"# that has one of the following types at position i of its type",
"# list,",
"# • a numeric type",
"try",
":",
"method",
"=",
"next",
"(",
"method",
"for",
"idl_type",
",",
"method",
"in",
"idl_types_methods",
"if",
"idl_type",
".",
"is_numeric_type",
")",
"test",
"=",
"'%s->IsNumber()'",
"%",
"cpp_value",
"yield",
"test",
",",
"method",
"except",
"StopIteration",
":",
"pass",
"# (Perform automatic type conversion, in order. If any of these match,",
"# that’s the end, and no other tests are needed.) To keep this code simple,",
"# we rely on the C++ compiler's dead code elimination to deal with the",
"# redundancy if both cases below trigger.",
"# 11. Otherwise: if there is an entry in S that has one of the following",
"# types at position i of its type list,",
"# • DOMString",
"# • ByteString",
"# • USVString",
"# • an enumeration type",
"try",
":",
"method",
"=",
"next",
"(",
"method",
"for",
"idl_type",
",",
"method",
"in",
"idl_types_methods",
"if",
"idl_type",
".",
"is_string_type",
"or",
"idl_type",
".",
"is_enum",
")",
"yield",
"'true'",
",",
"method",
"except",
"StopIteration",
":",
"pass",
"# 12. Otherwise: if there is an entry in S that has one of the following",
"# types at position i of its type list,",
"# • a numeric type",
"try",
":",
"method",
"=",
"next",
"(",
"method",
"for",
"idl_type",
",",
"method",
"in",
"idl_types_methods",
"if",
"idl_type",
".",
"is_numeric_type",
")",
"yield",
"'true'",
",",
"method",
"except",
"StopIteration",
":",
"pass"
] |
https://github.com/weolar/miniblink49/blob/1c4678db0594a4abde23d3ebbcc7cd13c3170777/third_party/WebKit/Source/bindings/scripts/v8_interface.py#L963-L1137
|
||
trilinos/Trilinos
|
6168be6dd51e35e1cd681e9c4b24433e709df140
|
packages/seacas/scripts/exomerge3.py
|
python
|
ExodusModel._get_coordinates_at_time
|
(self, timestep)
|
return [(x + dx, y + dy, z + dz)
for (x, y,
z), dx, dy, dz in zip(self.nodes, *displacement_values)]
|
Return the node coordinates list at the given timestep.
This includes the displacement if it exists.
|
Return the node coordinates list at the given timestep.
|
[
"Return",
"the",
"node",
"coordinates",
"list",
"at",
"the",
"given",
"timestep",
"."
] |
def _get_coordinates_at_time(self, timestep):
"""
Return the node coordinates list at the given timestep.
This includes the displacement if it exists.
"""
timestep = self._format_id_list(timestep, self.get_timesteps(),
'timestep')
if len(timestep) > 1:
self._error(
'Ambiguous timestep.',
'More than one timestep was specified. We expected '
'one or zero timesteps.')
if not timestep:
return [tuple(x) for x in self.nodes]
timestep_index = self.timesteps.index(timestep[0])
displacement_values = [
x[timestep_index] for x in self._get_displacement_field_values()
]
return [(x + dx, y + dy, z + dz)
for (x, y,
z), dx, dy, dz in zip(self.nodes, *displacement_values)]
|
[
"def",
"_get_coordinates_at_time",
"(",
"self",
",",
"timestep",
")",
":",
"timestep",
"=",
"self",
".",
"_format_id_list",
"(",
"timestep",
",",
"self",
".",
"get_timesteps",
"(",
")",
",",
"'timestep'",
")",
"if",
"len",
"(",
"timestep",
")",
">",
"1",
":",
"self",
".",
"_error",
"(",
"'Ambiguous timestep.'",
",",
"'More than one timestep was specified. We expected '",
"'one or zero timesteps.'",
")",
"if",
"not",
"timestep",
":",
"return",
"[",
"tuple",
"(",
"x",
")",
"for",
"x",
"in",
"self",
".",
"nodes",
"]",
"timestep_index",
"=",
"self",
".",
"timesteps",
".",
"index",
"(",
"timestep",
"[",
"0",
"]",
")",
"displacement_values",
"=",
"[",
"x",
"[",
"timestep_index",
"]",
"for",
"x",
"in",
"self",
".",
"_get_displacement_field_values",
"(",
")",
"]",
"return",
"[",
"(",
"x",
"+",
"dx",
",",
"y",
"+",
"dy",
",",
"z",
"+",
"dz",
")",
"for",
"(",
"x",
",",
"y",
",",
"z",
")",
",",
"dx",
",",
"dy",
",",
"dz",
"in",
"zip",
"(",
"self",
".",
"nodes",
",",
"*",
"displacement_values",
")",
"]"
] |
https://github.com/trilinos/Trilinos/blob/6168be6dd51e35e1cd681e9c4b24433e709df140/packages/seacas/scripts/exomerge3.py#L1611-L1633
|
|
baidu-research/tensorflow-allreduce
|
66d5b855e90b0949e9fa5cca5599fd729a70e874
|
tensorflow/contrib/data/python/ops/dataset_ops.py
|
python
|
TensorDataset.__init__
|
(self, tensors)
|
See `Dataset.from_tensors()` for details.
|
See `Dataset.from_tensors()` for details.
|
[
"See",
"Dataset",
".",
"from_tensors",
"()",
"for",
"details",
"."
] |
def __init__(self, tensors):
"""See `Dataset.from_tensors()` for details."""
super(TensorDataset, self).__init__()
with ops.name_scope("tensors"):
self._tensors = nest.pack_sequence_as(tensors, [
ops.convert_to_tensor(t, name="component_%d" % i)
for i, t in enumerate(nest.flatten(tensors))
])
|
[
"def",
"__init__",
"(",
"self",
",",
"tensors",
")",
":",
"super",
"(",
"TensorDataset",
",",
"self",
")",
".",
"__init__",
"(",
")",
"with",
"ops",
".",
"name_scope",
"(",
"\"tensors\"",
")",
":",
"self",
".",
"_tensors",
"=",
"nest",
".",
"pack_sequence_as",
"(",
"tensors",
",",
"[",
"ops",
".",
"convert_to_tensor",
"(",
"t",
",",
"name",
"=",
"\"component_%d\"",
"%",
"i",
")",
"for",
"i",
",",
"t",
"in",
"enumerate",
"(",
"nest",
".",
"flatten",
"(",
"tensors",
")",
")",
"]",
")"
] |
https://github.com/baidu-research/tensorflow-allreduce/blob/66d5b855e90b0949e9fa5cca5599fd729a70e874/tensorflow/contrib/data/python/ops/dataset_ops.py#L1078-L1085
|
||
benoitsteiner/tensorflow-opencl
|
cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5
|
tensorflow/python/ops/metrics_impl.py
|
python
|
specificity_at_sensitivity
|
(
labels, predictions, sensitivity, weights=None, num_thresholds=200,
metrics_collections=None, updates_collections=None, name=None)
|
Computes the specificity at a given sensitivity.
The `specificity_at_sensitivity` function creates four local
variables, `true_positives`, `true_negatives`, `false_positives` and
`false_negatives` that are used to compute the specificity at the given
sensitivity value. The threshold for the given sensitivity value is computed
and used to evaluate the corresponding specificity.
For estimation of the metric over a stream of data, the function creates an
`update_op` operation that updates these variables and returns the
`specificity`. `update_op` increments the `true_positives`, `true_negatives`,
`false_positives` and `false_negatives` counts with the weight of each case
found in the `predictions` and `labels`.
If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
For additional information about specificity and sensitivity, see the
following: https://en.wikipedia.org/wiki/Sensitivity_and_specificity
Args:
labels: The ground truth values, a `Tensor` whose dimensions must match
`predictions`. Will be cast to `bool`.
predictions: A floating point `Tensor` of arbitrary shape and whose values
are in the range `[0, 1]`.
sensitivity: A scalar value in range `[0, 1]`.
weights: Optional `Tensor` whose rank is either 0, or the same rank as
`labels`, and must be broadcastable to `labels` (i.e., all dimensions must
be either `1`, or the same as the corresponding `labels` dimension).
num_thresholds: The number of thresholds to use for matching the given
sensitivity.
metrics_collections: An optional list of collections that `specificity`
should be added to.
updates_collections: An optional list of collections that `update_op` should
be added to.
name: An optional variable_scope name.
Returns:
specificity: A scalar `Tensor` representing the specificity at the given
`specificity` value.
update_op: An operation that increments the `true_positives`,
`true_negatives`, `false_positives` and `false_negatives` variables
appropriately and whose value matches `specificity`.
Raises:
ValueError: If `predictions` and `labels` have mismatched shapes, if
`weights` is not `None` and its shape doesn't match `predictions`, or if
`sensitivity` is not between 0 and 1, or if either `metrics_collections`
or `updates_collections` are not a list or tuple.
|
Computes the specificity at a given sensitivity.
|
[
"Computes",
"the",
"specificity",
"at",
"a",
"given",
"sensitivity",
"."
] |
def specificity_at_sensitivity(
labels, predictions, sensitivity, weights=None, num_thresholds=200,
metrics_collections=None, updates_collections=None, name=None):
"""Computes the specificity at a given sensitivity.
The `specificity_at_sensitivity` function creates four local
variables, `true_positives`, `true_negatives`, `false_positives` and
`false_negatives` that are used to compute the specificity at the given
sensitivity value. The threshold for the given sensitivity value is computed
and used to evaluate the corresponding specificity.
For estimation of the metric over a stream of data, the function creates an
`update_op` operation that updates these variables and returns the
`specificity`. `update_op` increments the `true_positives`, `true_negatives`,
`false_positives` and `false_negatives` counts with the weight of each case
found in the `predictions` and `labels`.
If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
For additional information about specificity and sensitivity, see the
following: https://en.wikipedia.org/wiki/Sensitivity_and_specificity
Args:
labels: The ground truth values, a `Tensor` whose dimensions must match
`predictions`. Will be cast to `bool`.
predictions: A floating point `Tensor` of arbitrary shape and whose values
are in the range `[0, 1]`.
sensitivity: A scalar value in range `[0, 1]`.
weights: Optional `Tensor` whose rank is either 0, or the same rank as
`labels`, and must be broadcastable to `labels` (i.e., all dimensions must
be either `1`, or the same as the corresponding `labels` dimension).
num_thresholds: The number of thresholds to use for matching the given
sensitivity.
metrics_collections: An optional list of collections that `specificity`
should be added to.
updates_collections: An optional list of collections that `update_op` should
be added to.
name: An optional variable_scope name.
Returns:
specificity: A scalar `Tensor` representing the specificity at the given
`specificity` value.
update_op: An operation that increments the `true_positives`,
`true_negatives`, `false_positives` and `false_negatives` variables
appropriately and whose value matches `specificity`.
Raises:
ValueError: If `predictions` and `labels` have mismatched shapes, if
`weights` is not `None` and its shape doesn't match `predictions`, or if
`sensitivity` is not between 0 and 1, or if either `metrics_collections`
or `updates_collections` are not a list or tuple.
"""
if sensitivity < 0 or sensitivity > 1:
raise ValueError('`sensitivity` must be in the range [0, 1].')
with variable_scope.variable_scope(name, 'specificity_at_sensitivity',
(predictions, labels, weights)):
kepsilon = 1e-7 # to account for floating point imprecisions
thresholds = [(i + 1) * 1.0 / (num_thresholds - 1)
for i in range(num_thresholds-2)]
thresholds = [0.0 - kepsilon] + thresholds + [1.0 - kepsilon]
values, update_ops = _confusion_matrix_at_thresholds(
labels, predictions, thresholds, weights)
def compute_specificity_at_sensitivity(tp, tn, fp, fn, name):
"""Computes the specificity at the given sensitivity.
Args:
tp: True positives.
tn: True negatives.
fp: False positives.
fn: False negatives.
name: The name of the operation.
Returns:
The specificity using the aggregated values.
"""
sensitivities = math_ops.div(tp, tp + fn + kepsilon)
# We'll need to use this trick until tf.argmax allows us to specify
# whether we should use the first or last index in case of ties.
min_val = math_ops.reduce_min(math_ops.abs(sensitivities - sensitivity))
indices_at_minval = math_ops.equal(
math_ops.abs(sensitivities - sensitivity), min_val)
indices_at_minval = math_ops.to_int64(indices_at_minval)
indices_at_minval = math_ops.cumsum(indices_at_minval)
tf_index = math_ops.argmax(indices_at_minval, 0)
tf_index = math_ops.cast(tf_index, dtypes.int32)
# Now, we have the implicit threshold, so compute the specificity:
return math_ops.div(tn[tf_index],
tn[tf_index] + fp[tf_index] + kepsilon,
name)
specificity = compute_specificity_at_sensitivity(
values['tp'], values['tn'], values['fp'], values['fn'], 'value')
update_op = compute_specificity_at_sensitivity(
update_ops['tp'], update_ops['tn'], update_ops['fp'], update_ops['fn'],
'update_op')
if metrics_collections:
ops.add_to_collections(metrics_collections, specificity)
if updates_collections:
ops.add_to_collections(updates_collections, update_op)
return specificity, update_op
|
[
"def",
"specificity_at_sensitivity",
"(",
"labels",
",",
"predictions",
",",
"sensitivity",
",",
"weights",
"=",
"None",
",",
"num_thresholds",
"=",
"200",
",",
"metrics_collections",
"=",
"None",
",",
"updates_collections",
"=",
"None",
",",
"name",
"=",
"None",
")",
":",
"if",
"sensitivity",
"<",
"0",
"or",
"sensitivity",
">",
"1",
":",
"raise",
"ValueError",
"(",
"'`sensitivity` must be in the range [0, 1].'",
")",
"with",
"variable_scope",
".",
"variable_scope",
"(",
"name",
",",
"'specificity_at_sensitivity'",
",",
"(",
"predictions",
",",
"labels",
",",
"weights",
")",
")",
":",
"kepsilon",
"=",
"1e-7",
"# to account for floating point imprecisions",
"thresholds",
"=",
"[",
"(",
"i",
"+",
"1",
")",
"*",
"1.0",
"/",
"(",
"num_thresholds",
"-",
"1",
")",
"for",
"i",
"in",
"range",
"(",
"num_thresholds",
"-",
"2",
")",
"]",
"thresholds",
"=",
"[",
"0.0",
"-",
"kepsilon",
"]",
"+",
"thresholds",
"+",
"[",
"1.0",
"-",
"kepsilon",
"]",
"values",
",",
"update_ops",
"=",
"_confusion_matrix_at_thresholds",
"(",
"labels",
",",
"predictions",
",",
"thresholds",
",",
"weights",
")",
"def",
"compute_specificity_at_sensitivity",
"(",
"tp",
",",
"tn",
",",
"fp",
",",
"fn",
",",
"name",
")",
":",
"\"\"\"Computes the specificity at the given sensitivity.\n\n Args:\n tp: True positives.\n tn: True negatives.\n fp: False positives.\n fn: False negatives.\n name: The name of the operation.\n\n Returns:\n The specificity using the aggregated values.\n \"\"\"",
"sensitivities",
"=",
"math_ops",
".",
"div",
"(",
"tp",
",",
"tp",
"+",
"fn",
"+",
"kepsilon",
")",
"# We'll need to use this trick until tf.argmax allows us to specify",
"# whether we should use the first or last index in case of ties.",
"min_val",
"=",
"math_ops",
".",
"reduce_min",
"(",
"math_ops",
".",
"abs",
"(",
"sensitivities",
"-",
"sensitivity",
")",
")",
"indices_at_minval",
"=",
"math_ops",
".",
"equal",
"(",
"math_ops",
".",
"abs",
"(",
"sensitivities",
"-",
"sensitivity",
")",
",",
"min_val",
")",
"indices_at_minval",
"=",
"math_ops",
".",
"to_int64",
"(",
"indices_at_minval",
")",
"indices_at_minval",
"=",
"math_ops",
".",
"cumsum",
"(",
"indices_at_minval",
")",
"tf_index",
"=",
"math_ops",
".",
"argmax",
"(",
"indices_at_minval",
",",
"0",
")",
"tf_index",
"=",
"math_ops",
".",
"cast",
"(",
"tf_index",
",",
"dtypes",
".",
"int32",
")",
"# Now, we have the implicit threshold, so compute the specificity:",
"return",
"math_ops",
".",
"div",
"(",
"tn",
"[",
"tf_index",
"]",
",",
"tn",
"[",
"tf_index",
"]",
"+",
"fp",
"[",
"tf_index",
"]",
"+",
"kepsilon",
",",
"name",
")",
"specificity",
"=",
"compute_specificity_at_sensitivity",
"(",
"values",
"[",
"'tp'",
"]",
",",
"values",
"[",
"'tn'",
"]",
",",
"values",
"[",
"'fp'",
"]",
",",
"values",
"[",
"'fn'",
"]",
",",
"'value'",
")",
"update_op",
"=",
"compute_specificity_at_sensitivity",
"(",
"update_ops",
"[",
"'tp'",
"]",
",",
"update_ops",
"[",
"'tn'",
"]",
",",
"update_ops",
"[",
"'fp'",
"]",
",",
"update_ops",
"[",
"'fn'",
"]",
",",
"'update_op'",
")",
"if",
"metrics_collections",
":",
"ops",
".",
"add_to_collections",
"(",
"metrics_collections",
",",
"specificity",
")",
"if",
"updates_collections",
":",
"ops",
".",
"add_to_collections",
"(",
"updates_collections",
",",
"update_op",
")",
"return",
"specificity",
",",
"update_op"
] |
https://github.com/benoitsteiner/tensorflow-opencl/blob/cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5/tensorflow/python/ops/metrics_impl.py#L3066-L3173
|
||
klzgrad/naiveproxy
|
ed2c513637c77b18721fe428d7ed395b4d284c83
|
src/third_party/abseil-cpp/absl/abseil.podspec.gen.py
|
python
|
generate
|
(args)
|
Generates a podspec file from all BUILD files under absl directory.
|
Generates a podspec file from all BUILD files under absl directory.
|
[
"Generates",
"a",
"podspec",
"file",
"from",
"all",
"BUILD",
"files",
"under",
"absl",
"directory",
"."
] |
def generate(args):
"""Generates a podspec file from all BUILD files under absl directory."""
rules = filter(relevant_rule, collect_rules("absl"))
with open(args.output, "wt") as f:
write_podspec(f, rules, vars(args))
|
[
"def",
"generate",
"(",
"args",
")",
":",
"rules",
"=",
"filter",
"(",
"relevant_rule",
",",
"collect_rules",
"(",
"\"absl\"",
")",
")",
"with",
"open",
"(",
"args",
".",
"output",
",",
"\"wt\"",
")",
"as",
"f",
":",
"write_podspec",
"(",
"f",
",",
"rules",
",",
"vars",
"(",
"args",
")",
")"
] |
https://github.com/klzgrad/naiveproxy/blob/ed2c513637c77b18721fe428d7ed395b4d284c83/src/third_party/abseil-cpp/absl/abseil.podspec.gen.py#L200-L204
|
||
NVIDIA/DALI
|
bf16cc86ba8f091b145f91962f21fe1b6aff243d
|
docs/examples/use_cases/pytorch/resnet50/main.py
|
python
|
adjust_learning_rate
|
(optimizer, epoch, step, len_epoch)
|
LR schedule that should yield 76% converged accuracy with batch size 256
|
LR schedule that should yield 76% converged accuracy with batch size 256
|
[
"LR",
"schedule",
"that",
"should",
"yield",
"76%",
"converged",
"accuracy",
"with",
"batch",
"size",
"256"
] |
def adjust_learning_rate(optimizer, epoch, step, len_epoch):
"""LR schedule that should yield 76% converged accuracy with batch size 256"""
factor = epoch // 30
if epoch >= 80:
factor = factor + 1
lr = args.lr*(0.1**factor)
"""Warmup"""
if epoch < 5:
lr = lr*float(1 + step + epoch*len_epoch)/(5.*len_epoch)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
|
[
"def",
"adjust_learning_rate",
"(",
"optimizer",
",",
"epoch",
",",
"step",
",",
"len_epoch",
")",
":",
"factor",
"=",
"epoch",
"//",
"30",
"if",
"epoch",
">=",
"80",
":",
"factor",
"=",
"factor",
"+",
"1",
"lr",
"=",
"args",
".",
"lr",
"*",
"(",
"0.1",
"**",
"factor",
")",
"\"\"\"Warmup\"\"\"",
"if",
"epoch",
"<",
"5",
":",
"lr",
"=",
"lr",
"*",
"float",
"(",
"1",
"+",
"step",
"+",
"epoch",
"*",
"len_epoch",
")",
"/",
"(",
"5.",
"*",
"len_epoch",
")",
"for",
"param_group",
"in",
"optimizer",
".",
"param_groups",
":",
"param_group",
"[",
"'lr'",
"]",
"=",
"lr"
] |
https://github.com/NVIDIA/DALI/blob/bf16cc86ba8f091b145f91962f21fe1b6aff243d/docs/examples/use_cases/pytorch/resnet50/main.py#L536-L550
|
||
apple/swift-lldb
|
d74be846ef3e62de946df343e8c234bde93a8912
|
scripts/Python/static-binding/lldb.py
|
python
|
SBWatchpoint.GetWatchpointEventTypeFromEvent
|
(event)
|
return _lldb.SBWatchpoint_GetWatchpointEventTypeFromEvent(event)
|
GetWatchpointEventTypeFromEvent(SBEvent event) -> lldb::WatchpointEventType
|
GetWatchpointEventTypeFromEvent(SBEvent event) -> lldb::WatchpointEventType
|
[
"GetWatchpointEventTypeFromEvent",
"(",
"SBEvent",
"event",
")",
"-",
">",
"lldb",
"::",
"WatchpointEventType"
] |
def GetWatchpointEventTypeFromEvent(event):
"""GetWatchpointEventTypeFromEvent(SBEvent event) -> lldb::WatchpointEventType"""
return _lldb.SBWatchpoint_GetWatchpointEventTypeFromEvent(event)
|
[
"def",
"GetWatchpointEventTypeFromEvent",
"(",
"event",
")",
":",
"return",
"_lldb",
".",
"SBWatchpoint_GetWatchpointEventTypeFromEvent",
"(",
"event",
")"
] |
https://github.com/apple/swift-lldb/blob/d74be846ef3e62de946df343e8c234bde93a8912/scripts/Python/static-binding/lldb.py#L15257-L15259
|
|
mapnik/mapnik
|
f3da900c355e1d15059c4a91b00203dcc9d9f0ef
|
scons/scons-local-4.1.0/SCons/Node/FS.py
|
python
|
EntryProxy.__get_windows_path
|
(self)
|
r"""Return the path with \ as the path separator,
regardless of platform.
|
r"""Return the path with \ as the path separator,
regardless of platform.
|
[
"r",
"Return",
"the",
"path",
"with",
"\\",
"as",
"the",
"path",
"separator",
"regardless",
"of",
"platform",
"."
] |
def __get_windows_path(self):
r"""Return the path with \ as the path separator,
regardless of platform."""
if OS_SEP == '\\':
return self
else:
entry = self.get()
r = entry.get_path().replace(OS_SEP, '\\')
return SCons.Subst.SpecialAttrWrapper(r, entry.name + "_windows")
|
[
"def",
"__get_windows_path",
"(",
"self",
")",
":",
"if",
"OS_SEP",
"==",
"'\\\\'",
":",
"return",
"self",
"else",
":",
"entry",
"=",
"self",
".",
"get",
"(",
")",
"r",
"=",
"entry",
".",
"get_path",
"(",
")",
".",
"replace",
"(",
"OS_SEP",
",",
"'\\\\'",
")",
"return",
"SCons",
".",
"Subst",
".",
"SpecialAttrWrapper",
"(",
"r",
",",
"entry",
".",
"name",
"+",
"\"_windows\"",
")"
] |
https://github.com/mapnik/mapnik/blob/f3da900c355e1d15059c4a91b00203dcc9d9f0ef/scons/scons-local-4.1.0/SCons/Node/FS.py#L476-L484
|
||
francinexue/xuefu
|
b6ff79747a42e020588c0c0a921048e08fe4680c
|
ctpx/ctp2/ctptd.py
|
python
|
CtpTd.onRspQryTransferBank
|
(self, TransferBankField, RspInfoField, requestId, final)
|
请求查询转帐银行响应
|
请求查询转帐银行响应
|
[
"请求查询转帐银行响应"
] |
def onRspQryTransferBank(self, TransferBankField, RspInfoField, requestId, final):
"""请求查询转帐银行响应"""
pass
|
[
"def",
"onRspQryTransferBank",
"(",
"self",
",",
"TransferBankField",
",",
"RspInfoField",
",",
"requestId",
",",
"final",
")",
":",
"pass"
] |
https://github.com/francinexue/xuefu/blob/b6ff79747a42e020588c0c0a921048e08fe4680c/ctpx/ctp2/ctptd.py#L245-L247
|
||
wxWidgets/wxPython-Classic
|
19571e1ae65f1ac445f5491474121998c97a1bf0
|
wx/lib/agw/flatmenu.py
|
python
|
FlatMenu.GetLabel
|
(self, itemId)
|
return item.GetText()
|
Returns the label of a :class:`FlatMenuItem`.
:param integer `id`: the menu item identifier;
:see: :meth:`~FlatMenu.SetLabel`.
|
Returns the label of a :class:`FlatMenuItem`.
|
[
"Returns",
"the",
"label",
"of",
"a",
":",
"class",
":",
"FlatMenuItem",
"."
] |
def GetLabel(self, itemId):
"""
Returns the label of a :class:`FlatMenuItem`.
:param integer `id`: the menu item identifier;
:see: :meth:`~FlatMenu.SetLabel`.
"""
item = self.FindItem(itemId)
return item.GetText()
|
[
"def",
"GetLabel",
"(",
"self",
",",
"itemId",
")",
":",
"item",
"=",
"self",
".",
"FindItem",
"(",
"itemId",
")",
"return",
"item",
".",
"GetText",
"(",
")"
] |
https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/wx/lib/agw/flatmenu.py#L6881-L6891
|
|
catboost/catboost
|
167f64f237114a4d10b2b4ee42adb4569137debe
|
contrib/python/path.py/path.py
|
python
|
Path.write_bytes
|
(self, bytes, append=False)
|
Open this file and write the given bytes to it.
Default behavior is to overwrite any existing file.
Call ``p.write_bytes(bytes, append=True)`` to append instead.
|
Open this file and write the given bytes to it.
|
[
"Open",
"this",
"file",
"and",
"write",
"the",
"given",
"bytes",
"to",
"it",
"."
] |
def write_bytes(self, bytes, append=False):
""" Open this file and write the given bytes to it.
Default behavior is to overwrite any existing file.
Call ``p.write_bytes(bytes, append=True)`` to append instead.
"""
if append:
mode = 'ab'
else:
mode = 'wb'
with self.open(mode) as f:
f.write(bytes)
|
[
"def",
"write_bytes",
"(",
"self",
",",
"bytes",
",",
"append",
"=",
"False",
")",
":",
"if",
"append",
":",
"mode",
"=",
"'ab'",
"else",
":",
"mode",
"=",
"'wb'",
"with",
"self",
".",
"open",
"(",
"mode",
")",
"as",
"f",
":",
"f",
".",
"write",
"(",
"bytes",
")"
] |
https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/path.py/path.py#L767-L778
|
||
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/pip/_vendor/distlib/database.py
|
python
|
InstalledDistribution.read_exports
|
(self)
|
return result
|
Read exports data from a file in .ini format.
:return: A dictionary of exports, mapping an export category to a list
of :class:`ExportEntry` instances describing the individual
export entries.
|
[] |
def read_exports(self):
"""
Read exports data from a file in .ini format.
:return: A dictionary of exports, mapping an export category to a list
of :class:`ExportEntry` instances describing the individual
export entries.
"""
result = {}
r = self.get_distinfo_resource(EXPORTS_FILENAME)
if r:
with contextlib.closing(r.as_stream()) as stream:
result = read_exports(stream)
return result
|
[
"def",
"read_exports",
"(",
"self",
")",
":",
"result",
"=",
"{",
"}",
"r",
"=",
"self",
".",
"get_distinfo_resource",
"(",
"EXPORTS_FILENAME",
")",
"if",
"r",
":",
"with",
"contextlib",
".",
"closing",
"(",
"r",
".",
"as_stream",
"(",
")",
")",
"as",
"stream",
":",
"result",
"=",
"read_exports",
"(",
"stream",
")",
"return",
"result"
] |
https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/pip/_vendor/distlib/database.py#L1233-L1259
|
||
albertz/openlierox
|
d316c14a8eb57848ef56e9bfa7b23a56f694a51b
|
tools/DedicatedServerVideo/gdata/gauth.py
|
python
|
AuthSubToken.from_url
|
(str_or_uri)
|
return AuthSubToken(token_and_scopes[0], token_and_scopes[1])
|
Creates a new AuthSubToken using information in the URL.
Uses auth_sub_string_from_url.
Args:
str_or_uri: The current page's URL (as a str or atom.http_core.Uri)
which should contain a token query parameter since the
Google auth server redirected the user's browser to this
URL.
|
Creates a new AuthSubToken using information in the URL.
|
[
"Creates",
"a",
"new",
"AuthSubToken",
"using",
"information",
"in",
"the",
"URL",
"."
] |
def from_url(str_or_uri):
"""Creates a new AuthSubToken using information in the URL.
Uses auth_sub_string_from_url.
Args:
str_or_uri: The current page's URL (as a str or atom.http_core.Uri)
which should contain a token query parameter since the
Google auth server redirected the user's browser to this
URL.
"""
token_and_scopes = auth_sub_string_from_url(str_or_uri)
return AuthSubToken(token_and_scopes[0], token_and_scopes[1])
|
[
"def",
"from_url",
"(",
"str_or_uri",
")",
":",
"token_and_scopes",
"=",
"auth_sub_string_from_url",
"(",
"str_or_uri",
")",
"return",
"AuthSubToken",
"(",
"token_and_scopes",
"[",
"0",
"]",
",",
"token_and_scopes",
"[",
"1",
"]",
")"
] |
https://github.com/albertz/openlierox/blob/d316c14a8eb57848ef56e9bfa7b23a56f694a51b/tools/DedicatedServerVideo/gdata/gauth.py#L335-L347
|
|
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/_config/config.py
|
python
|
register_option
|
(key: str, defval: object, doc="", validator=None, cb=None)
|
Register an option in the package-wide pandas config object
Parameters
----------
key - a fully-qualified key, e.g. "x.y.option - z".
defval - the default value of the option
doc - a string description of the option
validator - a function of a single argument, should raise `ValueError` if
called with a value which is not a legal value for the option.
cb - a function of a single argument "key", which is called
immediately after an option value is set/reset. key is
the full name of the option.
Returns
-------
Nothing.
Raises
------
ValueError if `validator` is specified and `defval` is not a valid value.
|
Register an option in the package-wide pandas config object
|
[
"Register",
"an",
"option",
"in",
"the",
"package",
"-",
"wide",
"pandas",
"config",
"object"
] |
def register_option(key: str, defval: object, doc="", validator=None, cb=None):
"""Register an option in the package-wide pandas config object
Parameters
----------
key - a fully-qualified key, e.g. "x.y.option - z".
defval - the default value of the option
doc - a string description of the option
validator - a function of a single argument, should raise `ValueError` if
called with a value which is not a legal value for the option.
cb - a function of a single argument "key", which is called
immediately after an option value is set/reset. key is
the full name of the option.
Returns
-------
Nothing.
Raises
------
ValueError if `validator` is specified and `defval` is not a valid value.
"""
import tokenize
import keyword
key = key.lower()
if key in _registered_options:
raise OptionError(f"Option '{key}' has already been registered")
if key in _reserved_keys:
raise OptionError(f"Option '{key}' is a reserved key")
# the default value should be legal
if validator:
validator(defval)
# walk the nested dict, creating dicts as needed along the path
path = key.split(".")
for k in path:
# NOTE: tokenize.Name is not a public constant
# error: Module has no attribute "Name" [attr-defined]
if not re.match("^" + tokenize.Name + "$", k): # type: ignore
raise ValueError(f"{k} is not a valid identifier")
if keyword.iskeyword(k):
raise ValueError(f"{k} is a python keyword")
cursor = _global_config
msg = "Path prefix to option '{option}' is already an option"
for i, p in enumerate(path[:-1]):
if not isinstance(cursor, dict):
raise OptionError(msg.format(option=".".join(path[:i])))
if p not in cursor:
cursor[p] = {}
cursor = cursor[p]
if not isinstance(cursor, dict):
raise OptionError(msg.format(option=".".join(path[:-1])))
cursor[path[-1]] = defval # initialize
# save the option metadata
_registered_options[key] = RegisteredOption(
key=key, defval=defval, doc=doc, validator=validator, cb=cb
)
|
[
"def",
"register_option",
"(",
"key",
":",
"str",
",",
"defval",
":",
"object",
",",
"doc",
"=",
"\"\"",
",",
"validator",
"=",
"None",
",",
"cb",
"=",
"None",
")",
":",
"import",
"tokenize",
"import",
"keyword",
"key",
"=",
"key",
".",
"lower",
"(",
")",
"if",
"key",
"in",
"_registered_options",
":",
"raise",
"OptionError",
"(",
"f\"Option '{key}' has already been registered\"",
")",
"if",
"key",
"in",
"_reserved_keys",
":",
"raise",
"OptionError",
"(",
"f\"Option '{key}' is a reserved key\"",
")",
"# the default value should be legal",
"if",
"validator",
":",
"validator",
"(",
"defval",
")",
"# walk the nested dict, creating dicts as needed along the path",
"path",
"=",
"key",
".",
"split",
"(",
"\".\"",
")",
"for",
"k",
"in",
"path",
":",
"# NOTE: tokenize.Name is not a public constant",
"# error: Module has no attribute \"Name\" [attr-defined]",
"if",
"not",
"re",
".",
"match",
"(",
"\"^\"",
"+",
"tokenize",
".",
"Name",
"+",
"\"$\"",
",",
"k",
")",
":",
"# type: ignore",
"raise",
"ValueError",
"(",
"f\"{k} is not a valid identifier\"",
")",
"if",
"keyword",
".",
"iskeyword",
"(",
"k",
")",
":",
"raise",
"ValueError",
"(",
"f\"{k} is a python keyword\"",
")",
"cursor",
"=",
"_global_config",
"msg",
"=",
"\"Path prefix to option '{option}' is already an option\"",
"for",
"i",
",",
"p",
"in",
"enumerate",
"(",
"path",
"[",
":",
"-",
"1",
"]",
")",
":",
"if",
"not",
"isinstance",
"(",
"cursor",
",",
"dict",
")",
":",
"raise",
"OptionError",
"(",
"msg",
".",
"format",
"(",
"option",
"=",
"\".\"",
".",
"join",
"(",
"path",
"[",
":",
"i",
"]",
")",
")",
")",
"if",
"p",
"not",
"in",
"cursor",
":",
"cursor",
"[",
"p",
"]",
"=",
"{",
"}",
"cursor",
"=",
"cursor",
"[",
"p",
"]",
"if",
"not",
"isinstance",
"(",
"cursor",
",",
"dict",
")",
":",
"raise",
"OptionError",
"(",
"msg",
".",
"format",
"(",
"option",
"=",
"\".\"",
".",
"join",
"(",
"path",
"[",
":",
"-",
"1",
"]",
")",
")",
")",
"cursor",
"[",
"path",
"[",
"-",
"1",
"]",
"]",
"=",
"defval",
"# initialize",
"# save the option metadata",
"_registered_options",
"[",
"key",
"]",
"=",
"RegisteredOption",
"(",
"key",
"=",
"key",
",",
"defval",
"=",
"defval",
",",
"doc",
"=",
"doc",
",",
"validator",
"=",
"validator",
",",
"cb",
"=",
"cb",
")"
] |
https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/_config/config.py#L413-L479
|
||
OkCupid/okws
|
1c337392c676ccb4e9a4c92d11d5d2fada6427d2
|
contrib/pub2-upgrade.py
|
python
|
PubParser.p_pub_block
|
(self, p)
|
pub_block : LBP block_inner RBP
|
pub_block : LBP block_inner RBP
|
[
"pub_block",
":",
"LBP",
"block_inner",
"RBP"
] |
def p_pub_block (self, p):
'''pub_block : LBP block_inner RBP'''
p[0] = PubBlock (p[2], p[1], p[3])
|
[
"def",
"p_pub_block",
"(",
"self",
",",
"p",
")",
":",
"p",
"[",
"0",
"]",
"=",
"PubBlock",
"(",
"p",
"[",
"2",
"]",
",",
"p",
"[",
"1",
"]",
",",
"p",
"[",
"3",
"]",
")"
] |
https://github.com/OkCupid/okws/blob/1c337392c676ccb4e9a4c92d11d5d2fada6427d2/contrib/pub2-upgrade.py#L274-L276
|
||
pytorch/pytorch
|
7176c92687d3cc847cc046bf002269c6949a21c2
|
torch/optim/_multi_tensor/_functional.py
|
python
|
adamax
|
(params: List[Tensor],
grads: List[Tensor],
exp_avgs: List[Tensor],
exp_infs: List[Tensor],
state_steps: List[Tensor],
*,
beta1: float,
beta2: float,
lr: float,
weight_decay: float,
eps: float)
|
r"""Functional API that performs Adamax algorithm computation.
See :class:`~torch.optim.Adamax` for details.
|
r"""Functional API that performs Adamax algorithm computation.
|
[
"r",
"Functional",
"API",
"that",
"performs",
"Adamax",
"algorithm",
"computation",
"."
] |
def adamax(params: List[Tensor],
grads: List[Tensor],
exp_avgs: List[Tensor],
exp_infs: List[Tensor],
state_steps: List[Tensor],
*,
beta1: float,
beta2: float,
lr: float,
weight_decay: float,
eps: float):
r"""Functional API that performs Adamax algorithm computation.
See :class:`~torch.optim.Adamax` for details.
"""
if not all([isinstance(t, torch.Tensor) for t in state_steps]):
raise RuntimeError("API has changed, `state_steps` argument must contain a list of singleton tensors")
# Update steps
torch._foreach_add_(state_steps, 1)
if weight_decay != 0:
torch._foreach_add_(grads, params, alpha=weight_decay)
# Update biased first moment estimate.
torch._foreach_mul_(exp_avgs, beta1)
torch._foreach_add_(exp_avgs, grads, alpha=1 - beta1)
# Update the exponentially weighted infinity norm.
torch._foreach_mul_(exp_infs, beta2)
for exp_inf, grad in zip(exp_infs, grads):
norm_buf = torch.cat([
exp_inf.unsqueeze(0),
grad.abs().add_(eps).unsqueeze_(0)
], 0)
torch.max(norm_buf, 0, keepdim=False, out=(exp_inf, exp_inf.new().long()))
bias_corrections = [1 - beta1 ** step.item() for step in state_steps]
clr = [-1 * (lr / bias_correction) for bias_correction in bias_corrections]
torch._foreach_addcdiv_(params, exp_avgs, exp_infs, clr)
|
[
"def",
"adamax",
"(",
"params",
":",
"List",
"[",
"Tensor",
"]",
",",
"grads",
":",
"List",
"[",
"Tensor",
"]",
",",
"exp_avgs",
":",
"List",
"[",
"Tensor",
"]",
",",
"exp_infs",
":",
"List",
"[",
"Tensor",
"]",
",",
"state_steps",
":",
"List",
"[",
"Tensor",
"]",
",",
"*",
",",
"beta1",
":",
"float",
",",
"beta2",
":",
"float",
",",
"lr",
":",
"float",
",",
"weight_decay",
":",
"float",
",",
"eps",
":",
"float",
")",
":",
"if",
"not",
"all",
"(",
"[",
"isinstance",
"(",
"t",
",",
"torch",
".",
"Tensor",
")",
"for",
"t",
"in",
"state_steps",
"]",
")",
":",
"raise",
"RuntimeError",
"(",
"\"API has changed, `state_steps` argument must contain a list of singleton tensors\"",
")",
"# Update steps",
"torch",
".",
"_foreach_add_",
"(",
"state_steps",
",",
"1",
")",
"if",
"weight_decay",
"!=",
"0",
":",
"torch",
".",
"_foreach_add_",
"(",
"grads",
",",
"params",
",",
"alpha",
"=",
"weight_decay",
")",
"# Update biased first moment estimate.",
"torch",
".",
"_foreach_mul_",
"(",
"exp_avgs",
",",
"beta1",
")",
"torch",
".",
"_foreach_add_",
"(",
"exp_avgs",
",",
"grads",
",",
"alpha",
"=",
"1",
"-",
"beta1",
")",
"# Update the exponentially weighted infinity norm.",
"torch",
".",
"_foreach_mul_",
"(",
"exp_infs",
",",
"beta2",
")",
"for",
"exp_inf",
",",
"grad",
"in",
"zip",
"(",
"exp_infs",
",",
"grads",
")",
":",
"norm_buf",
"=",
"torch",
".",
"cat",
"(",
"[",
"exp_inf",
".",
"unsqueeze",
"(",
"0",
")",
",",
"grad",
".",
"abs",
"(",
")",
".",
"add_",
"(",
"eps",
")",
".",
"unsqueeze_",
"(",
"0",
")",
"]",
",",
"0",
")",
"torch",
".",
"max",
"(",
"norm_buf",
",",
"0",
",",
"keepdim",
"=",
"False",
",",
"out",
"=",
"(",
"exp_inf",
",",
"exp_inf",
".",
"new",
"(",
")",
".",
"long",
"(",
")",
")",
")",
"bias_corrections",
"=",
"[",
"1",
"-",
"beta1",
"**",
"step",
".",
"item",
"(",
")",
"for",
"step",
"in",
"state_steps",
"]",
"clr",
"=",
"[",
"-",
"1",
"*",
"(",
"lr",
"/",
"bias_correction",
")",
"for",
"bias_correction",
"in",
"bias_corrections",
"]",
"torch",
".",
"_foreach_addcdiv_",
"(",
"params",
",",
"exp_avgs",
",",
"exp_infs",
",",
"clr",
")"
] |
https://github.com/pytorch/pytorch/blob/7176c92687d3cc847cc046bf002269c6949a21c2/torch/optim/_multi_tensor/_functional.py#L75-L116
|
||
NVIDIAGameWorks/kaolin
|
e5148d05e9c1e2ce92a07881ce3593b1c5c3f166
|
kaolin/ops/spc/spc.py
|
python
|
unbatched_query
|
(octree, exsum, query_points, level)
|
return _C.ops.spc.query_cuda(octree.contiguous(), exsum.contiguous(),
query_points.contiguous(), level).long()
|
r"""Query point indices from the octree.
Given a point hierarchy, this function will efficiently find the corresponding indices of the
points in the points tensor. For each input in query_points, returns a index to the points tensor.
Returns -1 if the point does not exist.
Args:
octree (torch.ByteTensor): The octree, of shape :math:`(\text{num_bytes})`.
exsum (torch.IntTensor): The exclusive sum of the octree bytes,
of shape :math:`(\text{num_bytes} + 1)`.
See :ref:`spc_pyramids` for more details.
query_points (torch.ShortTensor): A collection of query indices,
of shape :math:`(\text{num_query}, 3)`.
level (int): The level of the octree to query from.
|
r"""Query point indices from the octree.
|
[
"r",
"Query",
"point",
"indices",
"from",
"the",
"octree",
"."
] |
def unbatched_query(octree, exsum, query_points, level):
r"""Query point indices from the octree.
Given a point hierarchy, this function will efficiently find the corresponding indices of the
points in the points tensor. For each input in query_points, returns a index to the points tensor.
Returns -1 if the point does not exist.
Args:
octree (torch.ByteTensor): The octree, of shape :math:`(\text{num_bytes})`.
exsum (torch.IntTensor): The exclusive sum of the octree bytes,
of shape :math:`(\text{num_bytes} + 1)`.
See :ref:`spc_pyramids` for more details.
query_points (torch.ShortTensor): A collection of query indices,
of shape :math:`(\text{num_query}, 3)`.
level (int): The level of the octree to query from.
"""
return _C.ops.spc.query_cuda(octree.contiguous(), exsum.contiguous(),
query_points.contiguous(), level).long()
|
[
"def",
"unbatched_query",
"(",
"octree",
",",
"exsum",
",",
"query_points",
",",
"level",
")",
":",
"return",
"_C",
".",
"ops",
".",
"spc",
".",
"query_cuda",
"(",
"octree",
".",
"contiguous",
"(",
")",
",",
"exsum",
".",
"contiguous",
"(",
")",
",",
"query_points",
".",
"contiguous",
"(",
")",
",",
"level",
")",
".",
"long",
"(",
")"
] |
https://github.com/NVIDIAGameWorks/kaolin/blob/e5148d05e9c1e2ce92a07881ce3593b1c5c3f166/kaolin/ops/spc/spc.py#L242-L259
|
|
Xilinx/Vitis-AI
|
fc74d404563d9951b57245443c73bef389f3657f
|
tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/feature_column/feature_column_v2.py
|
python
|
VocabularyFileCategoricalColumn.transform_feature
|
(self, transformation_cache, state_manager)
|
return self._transform_input_tensor(input_tensor, state_manager)
|
Creates a lookup table for the vocabulary.
|
Creates a lookup table for the vocabulary.
|
[
"Creates",
"a",
"lookup",
"table",
"for",
"the",
"vocabulary",
"."
] |
def transform_feature(self, transformation_cache, state_manager):
"""Creates a lookup table for the vocabulary."""
input_tensor = _to_sparse_input_and_drop_ignore_values(
transformation_cache.get(self.key, state_manager))
return self._transform_input_tensor(input_tensor, state_manager)
|
[
"def",
"transform_feature",
"(",
"self",
",",
"transformation_cache",
",",
"state_manager",
")",
":",
"input_tensor",
"=",
"_to_sparse_input_and_drop_ignore_values",
"(",
"transformation_cache",
".",
"get",
"(",
"self",
".",
"key",
",",
"state_manager",
")",
")",
"return",
"self",
".",
"_transform_input_tensor",
"(",
"input_tensor",
",",
"state_manager",
")"
] |
https://github.com/Xilinx/Vitis-AI/blob/fc74d404563d9951b57245443c73bef389f3657f/tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/feature_column/feature_column_v2.py#L3621-L3625
|
|
cvxpy/cvxpy
|
5165b4fb750dfd237de8659383ef24b4b2e33aaf
|
cvxpy/problems/problem.py
|
python
|
Problem._sort_candidate_solvers
|
(solvers)
|
Sorts candidate solvers lists according to slv_def.CONIC_SOLVERS/QP_SOLVERS
Arguments
---------
candidates : dict
Dictionary of candidate solvers divided in qp_solvers
and conic_solvers
Returns
-------
None
|
Sorts candidate solvers lists according to slv_def.CONIC_SOLVERS/QP_SOLVERS
|
[
"Sorts",
"candidate",
"solvers",
"lists",
"according",
"to",
"slv_def",
".",
"CONIC_SOLVERS",
"/",
"QP_SOLVERS"
] |
def _sort_candidate_solvers(solvers) -> None:
"""Sorts candidate solvers lists according to slv_def.CONIC_SOLVERS/QP_SOLVERS
Arguments
---------
candidates : dict
Dictionary of candidate solvers divided in qp_solvers
and conic_solvers
Returns
-------
None
"""
if len(solvers['conic_solvers']) > 1:
solvers['conic_solvers'] = sorted(
solvers['conic_solvers'], key=lambda s: slv_def.CONIC_SOLVERS.index(s)
)
if len(solvers['qp_solvers']) > 1:
solvers['qp_solvers'] = sorted(
solvers['qp_solvers'], key=lambda s: slv_def.QP_SOLVERS.index(s)
)
|
[
"def",
"_sort_candidate_solvers",
"(",
"solvers",
")",
"->",
"None",
":",
"if",
"len",
"(",
"solvers",
"[",
"'conic_solvers'",
"]",
")",
">",
"1",
":",
"solvers",
"[",
"'conic_solvers'",
"]",
"=",
"sorted",
"(",
"solvers",
"[",
"'conic_solvers'",
"]",
",",
"key",
"=",
"lambda",
"s",
":",
"slv_def",
".",
"CONIC_SOLVERS",
".",
"index",
"(",
"s",
")",
")",
"if",
"len",
"(",
"solvers",
"[",
"'qp_solvers'",
"]",
")",
">",
"1",
":",
"solvers",
"[",
"'qp_solvers'",
"]",
"=",
"sorted",
"(",
"solvers",
"[",
"'qp_solvers'",
"]",
",",
"key",
"=",
"lambda",
"s",
":",
"slv_def",
".",
"QP_SOLVERS",
".",
"index",
"(",
"s",
")",
")"
] |
https://github.com/cvxpy/cvxpy/blob/5165b4fb750dfd237de8659383ef24b4b2e33aaf/cvxpy/problems/problem.py#L815-L834
|
||
wxWidgets/wxPython-Classic
|
19571e1ae65f1ac445f5491474121998c97a1bf0
|
wx/tools/Editra/src/eclib/finddlg.py
|
python
|
FindBox.__init__
|
(self, parent, fdata, id=wx.ID_ANY, pos=wx.DefaultPosition,
size=wx.DefaultSize, style=AFR_STYLE_FINDDIALOG,
name=FindBoxName)
|
Create the container box
@param fdata: wx.FindReplaceData
|
Create the container box
@param fdata: wx.FindReplaceData
|
[
"Create",
"the",
"container",
"box",
"@param",
"fdata",
":",
"wx",
".",
"FindReplaceData"
] |
def __init__(self, parent, fdata, id=wx.ID_ANY, pos=wx.DefaultPosition,
size=wx.DefaultSize, style=AFR_STYLE_FINDDIALOG,
name=FindBoxName):
"""Create the container box
@param fdata: wx.FindReplaceData
"""
super(FindBox, self).__init__(parent, id, pos, size,
wx.TAB_TRAVERSAL|wx.NO_BORDER, name)
# Attributes
self._fpanel = FindPanel(self, fdata, style=style)
ctrlbar = ctrlbox.ControlBar(self, style=ctrlbox.CTRLBAR_STYLE_GRADIENT)
bmp = wx.ArtProvider.GetBitmap(wx.ART_FIND, wx.ART_MENU)
self.find = platebtn.PlateButton(ctrlbar, label=_("Find"), bmp=bmp,
style=platebtn.PB_STYLE_NOBG)
bmp = wx.ArtProvider.GetBitmap(wx.ART_FIND_AND_REPLACE, wx.ART_MENU)
self.replace = platebtn.PlateButton(ctrlbar, label=_("Replace"),
bmp=bmp,
style=platebtn.PB_STYLE_NOBG)
# Setup
if wx.Platform == '__WXGTK__':
ctrlbar.SetWindowStyle(ctrlbox.CTRLBAR_STYLE_BORDER_BOTTOM)
ctrlbar.SetVMargin(2, 2)
ctrlbar.AddControl(self.find, wx.ALIGN_LEFT)
ctrlbar.AddControl(self.replace, wx.ALIGN_LEFT)
self.SetControlBar(ctrlbar)
self.SetWindow(self._fpanel)
if style & AFR_STYLE_NO_MODE_SELECT:
self.GetControlBar().Hide()
# Event Handlers
self.Bind(wx.EVT_BUTTON, self.OnButton)
|
[
"def",
"__init__",
"(",
"self",
",",
"parent",
",",
"fdata",
",",
"id",
"=",
"wx",
".",
"ID_ANY",
",",
"pos",
"=",
"wx",
".",
"DefaultPosition",
",",
"size",
"=",
"wx",
".",
"DefaultSize",
",",
"style",
"=",
"AFR_STYLE_FINDDIALOG",
",",
"name",
"=",
"FindBoxName",
")",
":",
"super",
"(",
"FindBox",
",",
"self",
")",
".",
"__init__",
"(",
"parent",
",",
"id",
",",
"pos",
",",
"size",
",",
"wx",
".",
"TAB_TRAVERSAL",
"|",
"wx",
".",
"NO_BORDER",
",",
"name",
")",
"# Attributes",
"self",
".",
"_fpanel",
"=",
"FindPanel",
"(",
"self",
",",
"fdata",
",",
"style",
"=",
"style",
")",
"ctrlbar",
"=",
"ctrlbox",
".",
"ControlBar",
"(",
"self",
",",
"style",
"=",
"ctrlbox",
".",
"CTRLBAR_STYLE_GRADIENT",
")",
"bmp",
"=",
"wx",
".",
"ArtProvider",
".",
"GetBitmap",
"(",
"wx",
".",
"ART_FIND",
",",
"wx",
".",
"ART_MENU",
")",
"self",
".",
"find",
"=",
"platebtn",
".",
"PlateButton",
"(",
"ctrlbar",
",",
"label",
"=",
"_",
"(",
"\"Find\"",
")",
",",
"bmp",
"=",
"bmp",
",",
"style",
"=",
"platebtn",
".",
"PB_STYLE_NOBG",
")",
"bmp",
"=",
"wx",
".",
"ArtProvider",
".",
"GetBitmap",
"(",
"wx",
".",
"ART_FIND_AND_REPLACE",
",",
"wx",
".",
"ART_MENU",
")",
"self",
".",
"replace",
"=",
"platebtn",
".",
"PlateButton",
"(",
"ctrlbar",
",",
"label",
"=",
"_",
"(",
"\"Replace\"",
")",
",",
"bmp",
"=",
"bmp",
",",
"style",
"=",
"platebtn",
".",
"PB_STYLE_NOBG",
")",
"# Setup",
"if",
"wx",
".",
"Platform",
"==",
"'__WXGTK__'",
":",
"ctrlbar",
".",
"SetWindowStyle",
"(",
"ctrlbox",
".",
"CTRLBAR_STYLE_BORDER_BOTTOM",
")",
"ctrlbar",
".",
"SetVMargin",
"(",
"2",
",",
"2",
")",
"ctrlbar",
".",
"AddControl",
"(",
"self",
".",
"find",
",",
"wx",
".",
"ALIGN_LEFT",
")",
"ctrlbar",
".",
"AddControl",
"(",
"self",
".",
"replace",
",",
"wx",
".",
"ALIGN_LEFT",
")",
"self",
".",
"SetControlBar",
"(",
"ctrlbar",
")",
"self",
".",
"SetWindow",
"(",
"self",
".",
"_fpanel",
")",
"if",
"style",
"&",
"AFR_STYLE_NO_MODE_SELECT",
":",
"self",
".",
"GetControlBar",
"(",
")",
".",
"Hide",
"(",
")",
"# Event Handlers",
"self",
".",
"Bind",
"(",
"wx",
".",
"EVT_BUTTON",
",",
"self",
".",
"OnButton",
")"
] |
https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/wx/tools/Editra/src/eclib/finddlg.py#L637-L672
|
||
cornell-zhang/heterocl
|
6d9e4b4acc2ee2707b2d25b27298c0335bccedfd
|
python/heterocl/devices.py
|
python
|
device_to_str
|
(dtype)
|
Convert a device type to string format.
Parameters
----------
dtype : Device or str
The device type to be converted
Returns
-------
str
The converted device type in string format.
|
Convert a device type to string format.
|
[
"Convert",
"a",
"device",
"type",
"to",
"string",
"format",
"."
] |
def device_to_str(dtype):
"""Convert a device type to string format.
Parameters
----------
dtype : Device or str
The device type to be converted
Returns
-------
str
The converted device type in string format.
"""
if isinstance(dtype, Device):
if isinstance(dtype, CPU):
return "cpu_" + str(dtype.model)
elif isinstance(dtype, FPGA):
return "fpga_" + str(dtype.model)
else:
if not isinstance(dtype, str):
raise DeviceError("Unsupported device type format")
return dtype
|
[
"def",
"device_to_str",
"(",
"dtype",
")",
":",
"if",
"isinstance",
"(",
"dtype",
",",
"Device",
")",
":",
"if",
"isinstance",
"(",
"dtype",
",",
"CPU",
")",
":",
"return",
"\"cpu_\"",
"+",
"str",
"(",
"dtype",
".",
"model",
")",
"elif",
"isinstance",
"(",
"dtype",
",",
"FPGA",
")",
":",
"return",
"\"fpga_\"",
"+",
"str",
"(",
"dtype",
".",
"model",
")",
"else",
":",
"if",
"not",
"isinstance",
"(",
"dtype",
",",
"str",
")",
":",
"raise",
"DeviceError",
"(",
"\"Unsupported device type format\"",
")",
"return",
"dtype"
] |
https://github.com/cornell-zhang/heterocl/blob/6d9e4b4acc2ee2707b2d25b27298c0335bccedfd/python/heterocl/devices.py#L328-L349
|
||
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numpy/lib/histograms.py
|
python
|
_hist_bin_scott
|
(x, range)
|
return (24.0 * np.pi**0.5 / x.size)**(1.0 / 3.0) * np.std(x)
|
Scott histogram bin estimator.
The binwidth is proportional to the standard deviation of the data
and inversely proportional to the cube root of data size
(asymptotically optimal).
Parameters
----------
x : array_like
Input data that is to be histogrammed, trimmed to range. May not
be empty.
Returns
-------
h : An estimate of the optimal bin width for the given data.
|
Scott histogram bin estimator.
|
[
"Scott",
"histogram",
"bin",
"estimator",
"."
] |
def _hist_bin_scott(x, range):
"""
Scott histogram bin estimator.
The binwidth is proportional to the standard deviation of the data
and inversely proportional to the cube root of data size
(asymptotically optimal).
Parameters
----------
x : array_like
Input data that is to be histogrammed, trimmed to range. May not
be empty.
Returns
-------
h : An estimate of the optimal bin width for the given data.
"""
del range # unused
return (24.0 * np.pi**0.5 / x.size)**(1.0 / 3.0) * np.std(x)
|
[
"def",
"_hist_bin_scott",
"(",
"x",
",",
"range",
")",
":",
"del",
"range",
"# unused",
"return",
"(",
"24.0",
"*",
"np",
".",
"pi",
"**",
"0.5",
"/",
"x",
".",
"size",
")",
"**",
"(",
"1.0",
"/",
"3.0",
")",
"*",
"np",
".",
"std",
"(",
"x",
")"
] |
https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numpy/lib/histograms.py#L103-L122
|
|
adobe/chromium
|
cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7
|
third_party/closure_linter/closure_linter/closurizednamespacesinfo.py
|
python
|
ClosurizedNamespacesInfo.IsFirstProvide
|
(self, token)
|
return self._provide_tokens and token == self._provide_tokens[0]
|
Returns whether token is the first provide token.
|
Returns whether token is the first provide token.
|
[
"Returns",
"whether",
"token",
"is",
"the",
"first",
"provide",
"token",
"."
] |
def IsFirstProvide(self, token):
"""Returns whether token is the first provide token."""
return self._provide_tokens and token == self._provide_tokens[0]
|
[
"def",
"IsFirstProvide",
"(",
"self",
",",
"token",
")",
":",
"return",
"self",
".",
"_provide_tokens",
"and",
"token",
"==",
"self",
".",
"_provide_tokens",
"[",
"0",
"]"
] |
https://github.com/adobe/chromium/blob/cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7/third_party/closure_linter/closure_linter/closurizednamespacesinfo.py#L264-L266
|
|
rdkit/rdkit
|
ede860ae316d12d8568daf5ee800921c3389c84e
|
rdkit/ML/Descriptors/Descriptors.py
|
python
|
DescriptorCalculator.ShowDescriptors
|
(self)
|
prints out a list of the descriptors
|
prints out a list of the descriptors
|
[
"prints",
"out",
"a",
"list",
"of",
"the",
"descriptors"
] |
def ShowDescriptors(self):
""" prints out a list of the descriptors
"""
if self.simpleList is None:
raise NotImplementedError('Need to have a simpleList defined')
print('#---------')
print('Simple:')
for desc in self.simpleList:
print(desc)
if self.compoundList:
print('#---------')
print('Compound:')
for desc in self.compoundList:
print(desc)
|
[
"def",
"ShowDescriptors",
"(",
"self",
")",
":",
"if",
"self",
".",
"simpleList",
"is",
"None",
":",
"raise",
"NotImplementedError",
"(",
"'Need to have a simpleList defined'",
")",
"print",
"(",
"'#---------'",
")",
"print",
"(",
"'Simple:'",
")",
"for",
"desc",
"in",
"self",
".",
"simpleList",
":",
"print",
"(",
"desc",
")",
"if",
"self",
".",
"compoundList",
":",
"print",
"(",
"'#---------'",
")",
"print",
"(",
"'Compound:'",
")",
"for",
"desc",
"in",
"self",
".",
"compoundList",
":",
"print",
"(",
"desc",
")"
] |
https://github.com/rdkit/rdkit/blob/ede860ae316d12d8568daf5ee800921c3389c84e/rdkit/ML/Descriptors/Descriptors.py#L28-L42
|
||
ChromiumWebApps/chromium
|
c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7
|
ppapi/generators/idl_parser.py
|
python
|
IDLParser.p_label_cont
|
(self, p)
|
label_cont : ',' label_list
|
|
label_cont : ',' label_list
|
|
[
"label_cont",
":",
"label_list",
"|"
] |
def p_label_cont(self, p):
"""label_cont : ',' label_list
|"""
if len(p) > 1: p[0] = p[2]
if self.parse_debug: DumpReduction('label_cont', p)
|
[
"def",
"p_label_cont",
"(",
"self",
",",
"p",
")",
":",
"if",
"len",
"(",
"p",
")",
">",
"1",
":",
"p",
"[",
"0",
"]",
"=",
"p",
"[",
"2",
"]",
"if",
"self",
".",
"parse_debug",
":",
"DumpReduction",
"(",
"'label_cont'",
",",
"p",
")"
] |
https://github.com/ChromiumWebApps/chromium/blob/c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7/ppapi/generators/idl_parser.py#L717-L721
|
||
catboost/catboost
|
167f64f237114a4d10b2b4ee42adb4569137debe
|
contrib/python/pandas/py3/pandas/io/formats/info.py
|
python
|
TableBuilderAbstract.add_dtypes_line
|
(self)
|
Add summary line with dtypes present in dataframe.
|
Add summary line with dtypes present in dataframe.
|
[
"Add",
"summary",
"line",
"with",
"dtypes",
"present",
"in",
"dataframe",
"."
] |
def add_dtypes_line(self) -> None:
"""Add summary line with dtypes present in dataframe."""
collected_dtypes = [
f"{key}({val:d})" for key, val in sorted(self.dtype_counts.items())
]
self._lines.append(f"dtypes: {', '.join(collected_dtypes)}")
|
[
"def",
"add_dtypes_line",
"(",
"self",
")",
"->",
"None",
":",
"collected_dtypes",
"=",
"[",
"f\"{key}({val:d})\"",
"for",
"key",
",",
"val",
"in",
"sorted",
"(",
"self",
".",
"dtype_counts",
".",
"items",
"(",
")",
")",
"]",
"self",
".",
"_lines",
".",
"append",
"(",
"f\"dtypes: {', '.join(collected_dtypes)}\"",
")"
] |
https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/pandas/py3/pandas/io/formats/info.py#L451-L456
|
||
wlanjie/AndroidFFmpeg
|
7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf
|
tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/fractions.py
|
python
|
gcd
|
(a, b)
|
return a
|
Calculate the Greatest Common Divisor of a and b.
Unless b==0, the result will have the same sign as b (so that when
b is divided by it, the result comes out positive).
|
Calculate the Greatest Common Divisor of a and b.
|
[
"Calculate",
"the",
"Greatest",
"Common",
"Divisor",
"of",
"a",
"and",
"b",
"."
] |
def gcd(a, b):
"""Calculate the Greatest Common Divisor of a and b.
Unless b==0, the result will have the same sign as b (so that when
b is divided by it, the result comes out positive).
"""
while b:
a, b = b, a%b
return a
|
[
"def",
"gcd",
"(",
"a",
",",
"b",
")",
":",
"while",
"b",
":",
"a",
",",
"b",
"=",
"b",
",",
"a",
"%",
"b",
"return",
"a"
] |
https://github.com/wlanjie/AndroidFFmpeg/blob/7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf/tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/fractions.py#L18-L26
|
|
catboost/catboost
|
167f64f237114a4d10b2b4ee42adb4569137debe
|
contrib/python/scipy/scipy/signal/filter_design.py
|
python
|
cheby2
|
(N, rs, Wn, btype='low', analog=False, output='ba')
|
return iirfilter(N, Wn, rs=rs, btype=btype, analog=analog,
output=output, ftype='cheby2')
|
Chebyshev type II digital and analog filter design.
Design an Nth-order digital or analog Chebyshev type II filter and
return the filter coefficients.
Parameters
----------
N : int
The order of the filter.
rs : float
The minimum attenuation required in the stop band.
Specified in decibels, as a positive number.
Wn : array_like
A scalar or length-2 sequence giving the critical frequencies.
For Type II filters, this is the point in the transition band at which
the gain first reaches -`rs`.
For digital filters, `Wn` is normalized from 0 to 1, where 1 is the
Nyquist frequency, pi radians/sample. (`Wn` is thus in
half-cycles / sample.)
For analog filters, `Wn` is an angular frequency (e.g. rad/s).
btype : {'lowpass', 'highpass', 'bandpass', 'bandstop'}, optional
The type of filter. Default is 'lowpass'.
analog : bool, optional
When True, return an analog filter, otherwise a digital filter is
returned.
output : {'ba', 'zpk', 'sos'}, optional
Type of output: numerator/denominator ('ba'), pole-zero ('zpk'), or
second-order sections ('sos'). Default is 'ba'.
Returns
-------
b, a : ndarray, ndarray
Numerator (`b`) and denominator (`a`) polynomials of the IIR filter.
Only returned if ``output='ba'``.
z, p, k : ndarray, ndarray, float
Zeros, poles, and system gain of the IIR filter transfer
function. Only returned if ``output='zpk'``.
sos : ndarray
Second-order sections representation of the IIR filter.
Only returned if ``output=='sos'``.
See Also
--------
cheb2ord, cheb2ap
Notes
-----
The Chebyshev type II filter maximizes the rate of cutoff between the
frequency response's passband and stopband, at the expense of ripple in
the stopband and increased ringing in the step response.
Type II filters do not roll off as fast as Type I (`cheby1`).
The ``'sos'`` output parameter was added in 0.16.0.
Examples
--------
Plot the filter's frequency response, showing the critical points:
>>> from scipy import signal
>>> import matplotlib.pyplot as plt
>>> b, a = signal.cheby2(4, 40, 100, 'low', analog=True)
>>> w, h = signal.freqs(b, a)
>>> plt.semilogx(w, 20 * np.log10(abs(h)))
>>> plt.title('Chebyshev Type II frequency response (rs=40)')
>>> plt.xlabel('Frequency [radians / second]')
>>> plt.ylabel('Amplitude [dB]')
>>> plt.margins(0, 0.1)
>>> plt.grid(which='both', axis='both')
>>> plt.axvline(100, color='green') # cutoff frequency
>>> plt.axhline(-40, color='green') # rs
>>> plt.show()
|
Chebyshev type II digital and analog filter design.
|
[
"Chebyshev",
"type",
"II",
"digital",
"and",
"analog",
"filter",
"design",
"."
] |
def cheby2(N, rs, Wn, btype='low', analog=False, output='ba'):
"""
Chebyshev type II digital and analog filter design.
Design an Nth-order digital or analog Chebyshev type II filter and
return the filter coefficients.
Parameters
----------
N : int
The order of the filter.
rs : float
The minimum attenuation required in the stop band.
Specified in decibels, as a positive number.
Wn : array_like
A scalar or length-2 sequence giving the critical frequencies.
For Type II filters, this is the point in the transition band at which
the gain first reaches -`rs`.
For digital filters, `Wn` is normalized from 0 to 1, where 1 is the
Nyquist frequency, pi radians/sample. (`Wn` is thus in
half-cycles / sample.)
For analog filters, `Wn` is an angular frequency (e.g. rad/s).
btype : {'lowpass', 'highpass', 'bandpass', 'bandstop'}, optional
The type of filter. Default is 'lowpass'.
analog : bool, optional
When True, return an analog filter, otherwise a digital filter is
returned.
output : {'ba', 'zpk', 'sos'}, optional
Type of output: numerator/denominator ('ba'), pole-zero ('zpk'), or
second-order sections ('sos'). Default is 'ba'.
Returns
-------
b, a : ndarray, ndarray
Numerator (`b`) and denominator (`a`) polynomials of the IIR filter.
Only returned if ``output='ba'``.
z, p, k : ndarray, ndarray, float
Zeros, poles, and system gain of the IIR filter transfer
function. Only returned if ``output='zpk'``.
sos : ndarray
Second-order sections representation of the IIR filter.
Only returned if ``output=='sos'``.
See Also
--------
cheb2ord, cheb2ap
Notes
-----
The Chebyshev type II filter maximizes the rate of cutoff between the
frequency response's passband and stopband, at the expense of ripple in
the stopband and increased ringing in the step response.
Type II filters do not roll off as fast as Type I (`cheby1`).
The ``'sos'`` output parameter was added in 0.16.0.
Examples
--------
Plot the filter's frequency response, showing the critical points:
>>> from scipy import signal
>>> import matplotlib.pyplot as plt
>>> b, a = signal.cheby2(4, 40, 100, 'low', analog=True)
>>> w, h = signal.freqs(b, a)
>>> plt.semilogx(w, 20 * np.log10(abs(h)))
>>> plt.title('Chebyshev Type II frequency response (rs=40)')
>>> plt.xlabel('Frequency [radians / second]')
>>> plt.ylabel('Amplitude [dB]')
>>> plt.margins(0, 0.1)
>>> plt.grid(which='both', axis='both')
>>> plt.axvline(100, color='green') # cutoff frequency
>>> plt.axhline(-40, color='green') # rs
>>> plt.show()
"""
return iirfilter(N, Wn, rs=rs, btype=btype, analog=analog,
output=output, ftype='cheby2')
|
[
"def",
"cheby2",
"(",
"N",
",",
"rs",
",",
"Wn",
",",
"btype",
"=",
"'low'",
",",
"analog",
"=",
"False",
",",
"output",
"=",
"'ba'",
")",
":",
"return",
"iirfilter",
"(",
"N",
",",
"Wn",
",",
"rs",
"=",
"rs",
",",
"btype",
"=",
"btype",
",",
"analog",
"=",
"analog",
",",
"output",
"=",
"output",
",",
"ftype",
"=",
"'cheby2'",
")"
] |
https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/scipy/scipy/signal/filter_design.py#L2203-L2281
|
|
NREL/EnergyPlus
|
fadc5973b85c70e8cc923efb69c144e808a26078
|
src/EnergyPlus/api/datatransfer.py
|
python
|
DataExchange.today_weather_outdoor_relative_humidity_at_time
|
(self, state: c_void_p, hour: int,
time_step_number: int)
|
return self.api.todayWeatherOutRelativeHumidityAtTime(state, hour, time_step_number)
|
Gets the specified weather data at the specified hour and time step index within that hour
:param state: An active EnergyPlus "state" that is returned from a call to `api.state_manager.new_state()`.
:param hour: Integer hour of day (0 to 23)
:param time_step_number: Time step index in hour, from 1 to the number of zone time steps per hour
:return: Value of the weather condition at the specified time
|
Gets the specified weather data at the specified hour and time step index within that hour
|
[
"Gets",
"the",
"specified",
"weather",
"data",
"at",
"the",
"specified",
"hour",
"and",
"time",
"step",
"index",
"within",
"that",
"hour"
] |
def today_weather_outdoor_relative_humidity_at_time(self, state: c_void_p, hour: int,
time_step_number: int) -> float:
"""
Gets the specified weather data at the specified hour and time step index within that hour
:param state: An active EnergyPlus "state" that is returned from a call to `api.state_manager.new_state()`.
:param hour: Integer hour of day (0 to 23)
:param time_step_number: Time step index in hour, from 1 to the number of zone time steps per hour
:return: Value of the weather condition at the specified time
"""
return self.api.todayWeatherOutRelativeHumidityAtTime(state, hour, time_step_number)
|
[
"def",
"today_weather_outdoor_relative_humidity_at_time",
"(",
"self",
",",
"state",
":",
"c_void_p",
",",
"hour",
":",
"int",
",",
"time_step_number",
":",
"int",
")",
"->",
"float",
":",
"return",
"self",
".",
"api",
".",
"todayWeatherOutRelativeHumidityAtTime",
"(",
"state",
",",
"hour",
",",
"time_step_number",
")"
] |
https://github.com/NREL/EnergyPlus/blob/fadc5973b85c70e8cc923efb69c144e808a26078/src/EnergyPlus/api/datatransfer.py#L1175-L1185
|
|
Xilinx/Vitis-AI
|
fc74d404563d9951b57245443c73bef389f3657f
|
tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/keras/saving/saved_model/save.py
|
python
|
_reset_layer_losses
|
(parent_layer)
|
return losses_dict
|
Resets losses of layer and its sublayers, and returns original losses.
|
Resets losses of layer and its sublayers, and returns original losses.
|
[
"Resets",
"losses",
"of",
"layer",
"and",
"its",
"sublayers",
"and",
"returns",
"original",
"losses",
"."
] |
def _reset_layer_losses(parent_layer):
"""Resets losses of layer and its sublayers, and returns original losses."""
losses_dict = {}
for layer in _list_all_layers(parent_layer) + [parent_layer]:
losses_dict[layer] = {'losses': layer._losses[:],
'eager_losses': layer._eager_losses[:]}
with trackable.no_automatic_dependency_tracking_scope(layer):
layer._losses = []
layer._eager_losses = []
return losses_dict
|
[
"def",
"_reset_layer_losses",
"(",
"parent_layer",
")",
":",
"losses_dict",
"=",
"{",
"}",
"for",
"layer",
"in",
"_list_all_layers",
"(",
"parent_layer",
")",
"+",
"[",
"parent_layer",
"]",
":",
"losses_dict",
"[",
"layer",
"]",
"=",
"{",
"'losses'",
":",
"layer",
".",
"_losses",
"[",
":",
"]",
",",
"'eager_losses'",
":",
"layer",
".",
"_eager_losses",
"[",
":",
"]",
"}",
"with",
"trackable",
".",
"no_automatic_dependency_tracking_scope",
"(",
"layer",
")",
":",
"layer",
".",
"_losses",
"=",
"[",
"]",
"layer",
".",
"_eager_losses",
"=",
"[",
"]",
"return",
"losses_dict"
] |
https://github.com/Xilinx/Vitis-AI/blob/fc74d404563d9951b57245443c73bef389f3657f/tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/keras/saving/saved_model/save.py#L372-L381
|
|
NERSC/timemory
|
431912b360ff50d1a160d7826e2eea04fbd1037f
|
scripts/gprof2dot.py
|
python
|
Profile._tarjan
|
(self, function, order, stack, data)
|
return order
|
Tarjan's strongly connected components algorithm.
See also:
- http://en.wikipedia.org/wiki/Tarjan's_strongly_connected_components_algorithm
|
Tarjan's strongly connected components algorithm.
|
[
"Tarjan",
"s",
"strongly",
"connected",
"components",
"algorithm",
"."
] |
def _tarjan(self, function, order, stack, data):
"""Tarjan's strongly connected components algorithm.
See also:
- http://en.wikipedia.org/wiki/Tarjan's_strongly_connected_components_algorithm
"""
try:
func_data = data[function.id]
return order
except KeyError:
func_data = self._TarjanData(order)
data[function.id] = func_data
order += 1
pos = len(stack)
stack.append(function)
func_data.onstack = True
for call in compat_itervalues(function.calls):
try:
callee_data = data[call.callee_id]
if callee_data.onstack:
func_data.lowlink = min(func_data.lowlink, callee_data.order)
except KeyError:
callee = self.functions[call.callee_id]
order = self._tarjan(callee, order, stack, data)
callee_data = data[call.callee_id]
func_data.lowlink = min(func_data.lowlink, callee_data.lowlink)
if func_data.lowlink == func_data.order:
# Strongly connected component found
members = stack[pos:]
del stack[pos:]
if len(members) > 1:
cycle = Cycle()
for member in members:
cycle.add_function(member)
data[member.id].onstack = False
else:
for member in members:
data[member.id].onstack = False
return order
|
[
"def",
"_tarjan",
"(",
"self",
",",
"function",
",",
"order",
",",
"stack",
",",
"data",
")",
":",
"try",
":",
"func_data",
"=",
"data",
"[",
"function",
".",
"id",
"]",
"return",
"order",
"except",
"KeyError",
":",
"func_data",
"=",
"self",
".",
"_TarjanData",
"(",
"order",
")",
"data",
"[",
"function",
".",
"id",
"]",
"=",
"func_data",
"order",
"+=",
"1",
"pos",
"=",
"len",
"(",
"stack",
")",
"stack",
".",
"append",
"(",
"function",
")",
"func_data",
".",
"onstack",
"=",
"True",
"for",
"call",
"in",
"compat_itervalues",
"(",
"function",
".",
"calls",
")",
":",
"try",
":",
"callee_data",
"=",
"data",
"[",
"call",
".",
"callee_id",
"]",
"if",
"callee_data",
".",
"onstack",
":",
"func_data",
".",
"lowlink",
"=",
"min",
"(",
"func_data",
".",
"lowlink",
",",
"callee_data",
".",
"order",
")",
"except",
"KeyError",
":",
"callee",
"=",
"self",
".",
"functions",
"[",
"call",
".",
"callee_id",
"]",
"order",
"=",
"self",
".",
"_tarjan",
"(",
"callee",
",",
"order",
",",
"stack",
",",
"data",
")",
"callee_data",
"=",
"data",
"[",
"call",
".",
"callee_id",
"]",
"func_data",
".",
"lowlink",
"=",
"min",
"(",
"func_data",
".",
"lowlink",
",",
"callee_data",
".",
"lowlink",
")",
"if",
"func_data",
".",
"lowlink",
"==",
"func_data",
".",
"order",
":",
"# Strongly connected component found",
"members",
"=",
"stack",
"[",
"pos",
":",
"]",
"del",
"stack",
"[",
"pos",
":",
"]",
"if",
"len",
"(",
"members",
")",
">",
"1",
":",
"cycle",
"=",
"Cycle",
"(",
")",
"for",
"member",
"in",
"members",
":",
"cycle",
".",
"add_function",
"(",
"member",
")",
"data",
"[",
"member",
".",
"id",
"]",
".",
"onstack",
"=",
"False",
"else",
":",
"for",
"member",
"in",
"members",
":",
"data",
"[",
"member",
".",
"id",
"]",
".",
"onstack",
"=",
"False",
"return",
"order"
] |
https://github.com/NERSC/timemory/blob/431912b360ff50d1a160d7826e2eea04fbd1037f/scripts/gprof2dot.py#L402-L441
|
|
miyosuda/TensorFlowAndroidMNIST
|
7b5a4603d2780a8a2834575706e9001977524007
|
jni-build/jni/include/tensorflow/contrib/quantization/tools/quantize_graph.py
|
python
|
GraphRewriter.remove_redundant_quantization
|
(self, old_graph)
|
return self.output_graph
|
Removes unneeded pairs of quantize/dequantize ops from the graph.
This is a bit of a tricky function, because it's attempting to spot the
pattern of dequantizing from eight-bit up to float, and then immediately
quantizing back down to eight bits again, that's introduced by previous
passes that do 'key-hole' conversions of individual nodes but have to
convert back to float to match the previous output interface, since they
don't know that the next op can handle quantized tensors.
It works by:
- Looking for Quantize nodes.
- Checking to see if their first input is a Dequantize node.
- Seeing if their min/max inputs come from Min/Max nodes.
- Making sure those Min/Max nodes are being fed from the same Dequantize.
- Or that the Min is indirectly being fed from the same Dequantize as Max.
- Making sure the Dequantize is going through a Reshape (which we add
during the previous pass when we create the quantize sub-graph).
- Looking for the dims Const op for the Min/Max dims.
If all of these conditions are met, then it's a sub-graph pattern that
we know how to optimize out (and is likely the common one we've introduced).
We then rewire the graph to skip it entirely, and then rely on the dead node
removal pass to get rid of any nodes that are no longer needed.
Args:
old_graph: The model we'll be stripping redundant nodes from.
Returns:
A graph with the unnecessary nodes removed.
Raises:
ValueError: Two nodes with the same name were found in the graph.
|
Removes unneeded pairs of quantize/dequantize ops from the graph.
|
[
"Removes",
"unneeded",
"pairs",
"of",
"quantize",
"/",
"dequantize",
"ops",
"from",
"the",
"graph",
"."
] |
def remove_redundant_quantization(self, old_graph):
"""Removes unneeded pairs of quantize/dequantize ops from the graph.
This is a bit of a tricky function, because it's attempting to spot the
pattern of dequantizing from eight-bit up to float, and then immediately
quantizing back down to eight bits again, that's introduced by previous
passes that do 'key-hole' conversions of individual nodes but have to
convert back to float to match the previous output interface, since they
don't know that the next op can handle quantized tensors.
It works by:
- Looking for Quantize nodes.
- Checking to see if their first input is a Dequantize node.
- Seeing if their min/max inputs come from Min/Max nodes.
- Making sure those Min/Max nodes are being fed from the same Dequantize.
- Or that the Min is indirectly being fed from the same Dequantize as Max.
- Making sure the Dequantize is going through a Reshape (which we add
during the previous pass when we create the quantize sub-graph).
- Looking for the dims Const op for the Min/Max dims.
If all of these conditions are met, then it's a sub-graph pattern that
we know how to optimize out (and is likely the common one we've introduced).
We then rewire the graph to skip it entirely, and then rely on the dead node
removal pass to get rid of any nodes that are no longer needed.
Args:
old_graph: The model we'll be stripping redundant nodes from.
Returns:
A graph with the unnecessary nodes removed.
Raises:
ValueError: Two nodes with the same name were found in the graph.
"""
old_nodes_map = self.create_nodes_map(old_graph)
self.output_graph = tf.GraphDef()
inputs_to_rename = {}
# We go through all the nodes, looking for any that match the patterns we
# know how to optimize away.
for node in old_graph.node:
# We always start with a Quantize node, and examine its inputs to see if
# they are in a form that can be removed.
if node.op not in ["Quantize", "QuantizeV2"]:
continue
dequantize_node_name = node_name_from_input(node.input[0])
if dequantize_node_name not in old_nodes_map:
raise ValueError("Input node name '" + dequantize_node_name +
"' not found in node '" + node.name + "'")
dequantize_node = old_nodes_map[dequantize_node_name]
# Do we have a Dequantize feeding in, with the same type as the Quantize?
if dequantize_node.op != "Dequantize":
continue
if node.attr["T"] != dequantize_node.attr["T"]:
continue
# Now look at the other inputs, and ensure they're Min/Max nodes.
min_node_name = node_name_from_input(node.input[1])
max_node_name = node_name_from_input(node.input[2])
min_node = old_nodes_map[min_node_name]
max_node = old_nodes_map[max_node_name]
is_min_right_type = (min_node.op in ["Min", "Dequantize"])
is_max_right_type = (max_node.op in ["Max", "Dequantize"])
if not is_min_right_type or not is_max_right_type:
print("Didn't find expected types on inputs : %s, %s." % (
min_node.op, max_node.op))
continue
min_node_input_name = node_name_from_input(min_node.input[0])
max_node_input_name = node_name_from_input(max_node.input[0])
# There are two different patterns for Min nodes we can recognize, one
# where the input comes directly from the same one as the Max, and
# another where we run it through another Min first, so check for both.
is_same_input = False
if min_node_input_name == max_node_input_name:
is_same_input = True
else:
first_min_node_input = old_nodes_map[min_node_input_name]
if first_min_node_input.op == "Concat":
second_min_node_name = node_name_from_input(
first_min_node_input.input[1])
second_min_node = old_nodes_map[second_min_node_name]
if second_min_node.op == "Min":
second_min_node_input_name = node_name_from_input(
second_min_node.input[0])
is_same_input = (second_min_node_input_name == max_node_input_name)
if not is_same_input:
print("Different min/max inputs: " + min_node_input_name)
continue
# We recognize this pattern, so mark the graph edges to be rewired to
# route around it entirely, since we know it's a no-op.
dequantize_source_name = node_name_from_input(dequantize_node.input[0])
node_tensor_name = ensure_tensor_name_has_port(node.name)
min_tensor_name = node.name + ":1"
max_tensor_name = node.name + ":2"
inputs_to_rename[node_tensor_name] = dequantize_source_name
inputs_to_rename[min_tensor_name] = dequantize_node.input[1]
inputs_to_rename[max_tensor_name] = dequantize_node.input[2]
# Finally we apply all the rewiring we've marked to the graph.
for node in old_graph.node:
for index, input_full_name in enumerate(node.input):
input_name = ensure_tensor_name_has_port(input_full_name)
if input_name in inputs_to_rename:
node.input[index] = inputs_to_rename[input_name]
self.add_output_graph_node(node)
return self.output_graph
|
[
"def",
"remove_redundant_quantization",
"(",
"self",
",",
"old_graph",
")",
":",
"old_nodes_map",
"=",
"self",
".",
"create_nodes_map",
"(",
"old_graph",
")",
"self",
".",
"output_graph",
"=",
"tf",
".",
"GraphDef",
"(",
")",
"inputs_to_rename",
"=",
"{",
"}",
"# We go through all the nodes, looking for any that match the patterns we",
"# know how to optimize away.",
"for",
"node",
"in",
"old_graph",
".",
"node",
":",
"# We always start with a Quantize node, and examine its inputs to see if",
"# they are in a form that can be removed.",
"if",
"node",
".",
"op",
"not",
"in",
"[",
"\"Quantize\"",
",",
"\"QuantizeV2\"",
"]",
":",
"continue",
"dequantize_node_name",
"=",
"node_name_from_input",
"(",
"node",
".",
"input",
"[",
"0",
"]",
")",
"if",
"dequantize_node_name",
"not",
"in",
"old_nodes_map",
":",
"raise",
"ValueError",
"(",
"\"Input node name '\"",
"+",
"dequantize_node_name",
"+",
"\"' not found in node '\"",
"+",
"node",
".",
"name",
"+",
"\"'\"",
")",
"dequantize_node",
"=",
"old_nodes_map",
"[",
"dequantize_node_name",
"]",
"# Do we have a Dequantize feeding in, with the same type as the Quantize?",
"if",
"dequantize_node",
".",
"op",
"!=",
"\"Dequantize\"",
":",
"continue",
"if",
"node",
".",
"attr",
"[",
"\"T\"",
"]",
"!=",
"dequantize_node",
".",
"attr",
"[",
"\"T\"",
"]",
":",
"continue",
"# Now look at the other inputs, and ensure they're Min/Max nodes.",
"min_node_name",
"=",
"node_name_from_input",
"(",
"node",
".",
"input",
"[",
"1",
"]",
")",
"max_node_name",
"=",
"node_name_from_input",
"(",
"node",
".",
"input",
"[",
"2",
"]",
")",
"min_node",
"=",
"old_nodes_map",
"[",
"min_node_name",
"]",
"max_node",
"=",
"old_nodes_map",
"[",
"max_node_name",
"]",
"is_min_right_type",
"=",
"(",
"min_node",
".",
"op",
"in",
"[",
"\"Min\"",
",",
"\"Dequantize\"",
"]",
")",
"is_max_right_type",
"=",
"(",
"max_node",
".",
"op",
"in",
"[",
"\"Max\"",
",",
"\"Dequantize\"",
"]",
")",
"if",
"not",
"is_min_right_type",
"or",
"not",
"is_max_right_type",
":",
"print",
"(",
"\"Didn't find expected types on inputs : %s, %s.\"",
"%",
"(",
"min_node",
".",
"op",
",",
"max_node",
".",
"op",
")",
")",
"continue",
"min_node_input_name",
"=",
"node_name_from_input",
"(",
"min_node",
".",
"input",
"[",
"0",
"]",
")",
"max_node_input_name",
"=",
"node_name_from_input",
"(",
"max_node",
".",
"input",
"[",
"0",
"]",
")",
"# There are two different patterns for Min nodes we can recognize, one",
"# where the input comes directly from the same one as the Max, and",
"# another where we run it through another Min first, so check for both.",
"is_same_input",
"=",
"False",
"if",
"min_node_input_name",
"==",
"max_node_input_name",
":",
"is_same_input",
"=",
"True",
"else",
":",
"first_min_node_input",
"=",
"old_nodes_map",
"[",
"min_node_input_name",
"]",
"if",
"first_min_node_input",
".",
"op",
"==",
"\"Concat\"",
":",
"second_min_node_name",
"=",
"node_name_from_input",
"(",
"first_min_node_input",
".",
"input",
"[",
"1",
"]",
")",
"second_min_node",
"=",
"old_nodes_map",
"[",
"second_min_node_name",
"]",
"if",
"second_min_node",
".",
"op",
"==",
"\"Min\"",
":",
"second_min_node_input_name",
"=",
"node_name_from_input",
"(",
"second_min_node",
".",
"input",
"[",
"0",
"]",
")",
"is_same_input",
"=",
"(",
"second_min_node_input_name",
"==",
"max_node_input_name",
")",
"if",
"not",
"is_same_input",
":",
"print",
"(",
"\"Different min/max inputs: \"",
"+",
"min_node_input_name",
")",
"continue",
"# We recognize this pattern, so mark the graph edges to be rewired to",
"# route around it entirely, since we know it's a no-op.",
"dequantize_source_name",
"=",
"node_name_from_input",
"(",
"dequantize_node",
".",
"input",
"[",
"0",
"]",
")",
"node_tensor_name",
"=",
"ensure_tensor_name_has_port",
"(",
"node",
".",
"name",
")",
"min_tensor_name",
"=",
"node",
".",
"name",
"+",
"\":1\"",
"max_tensor_name",
"=",
"node",
".",
"name",
"+",
"\":2\"",
"inputs_to_rename",
"[",
"node_tensor_name",
"]",
"=",
"dequantize_source_name",
"inputs_to_rename",
"[",
"min_tensor_name",
"]",
"=",
"dequantize_node",
".",
"input",
"[",
"1",
"]",
"inputs_to_rename",
"[",
"max_tensor_name",
"]",
"=",
"dequantize_node",
".",
"input",
"[",
"2",
"]",
"# Finally we apply all the rewiring we've marked to the graph.",
"for",
"node",
"in",
"old_graph",
".",
"node",
":",
"for",
"index",
",",
"input_full_name",
"in",
"enumerate",
"(",
"node",
".",
"input",
")",
":",
"input_name",
"=",
"ensure_tensor_name_has_port",
"(",
"input_full_name",
")",
"if",
"input_name",
"in",
"inputs_to_rename",
":",
"node",
".",
"input",
"[",
"index",
"]",
"=",
"inputs_to_rename",
"[",
"input_name",
"]",
"self",
".",
"add_output_graph_node",
"(",
"node",
")",
"return",
"self",
".",
"output_graph"
] |
https://github.com/miyosuda/TensorFlowAndroidMNIST/blob/7b5a4603d2780a8a2834575706e9001977524007/jni-build/jni/include/tensorflow/contrib/quantization/tools/quantize_graph.py#L804-L904
|
|
PaddlePaddle/PaddleOCR
|
b756bf5f8c90142e0d89d3db0163965c686b6ffe
|
PPOCRLabel/PPOCRLabel.py
|
python
|
MainWindow.newShape
|
(self, value=True)
|
Pop-up and give focus to the label editor.
position MUST be in global coordinates.
|
Pop-up and give focus to the label editor.
|
[
"Pop",
"-",
"up",
"and",
"give",
"focus",
"to",
"the",
"label",
"editor",
"."
] |
def newShape(self, value=True):
"""Pop-up and give focus to the label editor.
position MUST be in global coordinates.
"""
if len(self.labelHist) > 0:
self.labelDialog = LabelDialog(
parent=self, listItem=self.labelHist)
if value:
text = self.labelDialog.popUp(text=self.prevLabelText)
self.lastLabel = text
else:
text = self.prevLabelText
if text is not None:
self.prevLabelText = self.stringBundle.getString('tempLabel')
# generate_color = generateColorByText(text)
shape = self.canvas.setLastLabel(text, None, None)#generate_color, generate_color
self.addLabel(shape)
if self.beginner(): # Switch to edit mode.
self.canvas.setEditing(True)
self.actions.create.setEnabled(True)
self.actions.undoLastPoint.setEnabled(False)
self.actions.undo.setEnabled(True)
else:
self.actions.editMode.setEnabled(True)
self.setDirty()
else:
# self.canvas.undoLastLine()
self.canvas.resetAllLines()
|
[
"def",
"newShape",
"(",
"self",
",",
"value",
"=",
"True",
")",
":",
"if",
"len",
"(",
"self",
".",
"labelHist",
")",
">",
"0",
":",
"self",
".",
"labelDialog",
"=",
"LabelDialog",
"(",
"parent",
"=",
"self",
",",
"listItem",
"=",
"self",
".",
"labelHist",
")",
"if",
"value",
":",
"text",
"=",
"self",
".",
"labelDialog",
".",
"popUp",
"(",
"text",
"=",
"self",
".",
"prevLabelText",
")",
"self",
".",
"lastLabel",
"=",
"text",
"else",
":",
"text",
"=",
"self",
".",
"prevLabelText",
"if",
"text",
"is",
"not",
"None",
":",
"self",
".",
"prevLabelText",
"=",
"self",
".",
"stringBundle",
".",
"getString",
"(",
"'tempLabel'",
")",
"# generate_color = generateColorByText(text)",
"shape",
"=",
"self",
".",
"canvas",
".",
"setLastLabel",
"(",
"text",
",",
"None",
",",
"None",
")",
"#generate_color, generate_color",
"self",
".",
"addLabel",
"(",
"shape",
")",
"if",
"self",
".",
"beginner",
"(",
")",
":",
"# Switch to edit mode.",
"self",
".",
"canvas",
".",
"setEditing",
"(",
"True",
")",
"self",
".",
"actions",
".",
"create",
".",
"setEnabled",
"(",
"True",
")",
"self",
".",
"actions",
".",
"undoLastPoint",
".",
"setEnabled",
"(",
"False",
")",
"self",
".",
"actions",
".",
"undo",
".",
"setEnabled",
"(",
"True",
")",
"else",
":",
"self",
".",
"actions",
".",
"editMode",
".",
"setEnabled",
"(",
"True",
")",
"self",
".",
"setDirty",
"(",
")",
"else",
":",
"# self.canvas.undoLastLine()",
"self",
".",
"canvas",
".",
"resetAllLines",
"(",
")"
] |
https://github.com/PaddlePaddle/PaddleOCR/blob/b756bf5f8c90142e0d89d3db0163965c686b6ffe/PPOCRLabel/PPOCRLabel.py#L1185-L1216
|
||
wxWidgets/wxPython-Classic
|
19571e1ae65f1ac445f5491474121998c97a1bf0
|
src/msw/_core.py
|
python
|
PyApp.WakeUpIdle
|
(*args, **kwargs)
|
return _core_.PyApp_WakeUpIdle(*args, **kwargs)
|
WakeUpIdle(self)
Make sure that idle events are sent again.
:see: `wx.WakeUpIdle`
|
WakeUpIdle(self)
|
[
"WakeUpIdle",
"(",
"self",
")"
] |
def WakeUpIdle(*args, **kwargs):
"""
WakeUpIdle(self)
Make sure that idle events are sent again.
:see: `wx.WakeUpIdle`
"""
return _core_.PyApp_WakeUpIdle(*args, **kwargs)
|
[
"def",
"WakeUpIdle",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"return",
"_core_",
".",
"PyApp_WakeUpIdle",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")"
] |
https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/msw/_core.py#L7926-L7933
|
|
krishauser/Klampt
|
972cc83ea5befac3f653c1ba20f80155768ad519
|
Python/klampt/robotsim.py
|
python
|
Simulator.getTime
|
(self)
|
return _robotsim.Simulator_getTime(self)
|
r"""
Returns the simulation time.
|
r"""
Returns the simulation time.
|
[
"r",
"Returns",
"the",
"simulation",
"time",
"."
] |
def getTime(self) ->float:
r"""
Returns the simulation time.
"""
return _robotsim.Simulator_getTime(self)
|
[
"def",
"getTime",
"(",
"self",
")",
"->",
"float",
":",
"return",
"_robotsim",
".",
"Simulator_getTime",
"(",
"self",
")"
] |
https://github.com/krishauser/Klampt/blob/972cc83ea5befac3f653c1ba20f80155768ad519/Python/klampt/robotsim.py#L7864-L7869
|
|
catboost/catboost
|
167f64f237114a4d10b2b4ee42adb4569137debe
|
contrib/tools/python3/src/Lib/http/cookiejar.py
|
python
|
CookiePolicy.set_ok
|
(self, cookie, request)
|
Return true if (and only if) cookie should be accepted from server.
Currently, pre-expired cookies never get this far -- the CookieJar
class deletes such cookies itself.
|
Return true if (and only if) cookie should be accepted from server.
|
[
"Return",
"true",
"if",
"(",
"and",
"only",
"if",
")",
"cookie",
"should",
"be",
"accepted",
"from",
"server",
"."
] |
def set_ok(self, cookie, request):
"""Return true if (and only if) cookie should be accepted from server.
Currently, pre-expired cookies never get this far -- the CookieJar
class deletes such cookies itself.
"""
raise NotImplementedError()
|
[
"def",
"set_ok",
"(",
"self",
",",
"cookie",
",",
"request",
")",
":",
"raise",
"NotImplementedError",
"(",
")"
] |
https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/tools/python3/src/Lib/http/cookiejar.py#L843-L850
|
||
fasiondog/hikyuu
|
842751aa25283f9fdafc6f560ea262f79e67a307
|
hikyuu/draw/drawplot/bokeh_draw.py
|
python
|
mkplot
|
(kdata, new=True, axes=None, colorup='r', colordown='g', ticksize=3)
|
return None
|
绘制美式K线图
:param KData kdata: K线数据
:param bool new: 是否在新窗口中显示，只在没有指定axes时生效
:param axes: 指定的坐标轴
:param colorup: the color of the lines where close >= open
:param colordown: the color of the lines where close < open
:param ticksize: open/close tick marker in points
|
绘制美式K线图
:param KData kdata: K线数据
:param bool new: 是否在新窗口中显示，只在没有指定axes时生效
:param axes: 指定的坐标轴
:param colorup: the color of the lines where close >= open
:param colordown: the color of the lines where close < open
:param ticksize: open/close tick marker in points
|
[
"绘制美式K线图",
":",
"param",
"KData",
"kdata",
":",
"K线数据",
":",
"param",
"bool",
"new",
":",
"是否在新窗口中显示，只在没有指定axes时生效",
":",
"param",
"axes",
":",
"指定的坐标轴",
":",
"param",
"colorup",
":",
"the",
"color",
"of",
"the",
"lines",
"where",
"close",
">",
"=",
"open",
":",
"param",
"colordown",
":",
"the",
"color",
"of",
"the",
"lines",
"where",
"close",
"<",
"open",
":",
"param",
"ticksize",
":",
"open",
"/",
"close",
"tick",
"marker",
"in",
"points"
] |
def mkplot(kdata, new=True, axes=None, colorup='r', colordown='g', ticksize=3):
"""绘制美式K线图
:param KData kdata: K线数据
:param bool new: 是否在新窗口中显示，只在没有指定axes时生效
:param axes: 指定的坐标轴
:param colorup: the color of the lines where close >= open
:param colordown: the color of the lines where close < open
:param ticksize: open/close tick marker in points
"""
print("Bokeh 暂不支持绘制美式K线图, 请使用 matplotlib")
return None
|
[
"def",
"mkplot",
"(",
"kdata",
",",
"new",
"=",
"True",
",",
"axes",
"=",
"None",
",",
"colorup",
"=",
"'r'",
",",
"colordown",
"=",
"'g'",
",",
"ticksize",
"=",
"3",
")",
":",
"print",
"(",
"\"Bokeh 暂不支持绘制美式K线图, 请使用 matplotlib\")",
"",
"return",
"None"
] |
https://github.com/fasiondog/hikyuu/blob/842751aa25283f9fdafc6f560ea262f79e67a307/hikyuu/draw/drawplot/bokeh_draw.py#L212-L223
|
|
pytorch/pytorch
|
7176c92687d3cc847cc046bf002269c6949a21c2
|
torch/distributed/distributed_c10d.py
|
python
|
all_reduce
|
(tensor, op=ReduceOp.SUM, group=None, async_op=False)
|
Reduces the tensor data across all machines in such a way that all get
the final result.
After the call ``tensor`` is going to be bitwise identical in all processes.
Complex tensors are supported.
Args:
tensor (Tensor): Input and output of the collective. The function
operates in-place.
op (optional): One of the values from
``torch.distributed.ReduceOp``
enum. Specifies an operation used for element-wise reductions.
group (ProcessGroup, optional): The process group to work on. If None,
the default process group will be used.
async_op (bool, optional): Whether this op should be an async op
Returns:
Async work handle, if async_op is set to True.
None, if not async_op or if not part of the group
Examples:
>>> # All tensors below are of torch.int64 type.
>>> # We have 2 process groups, 2 ranks.
>>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank
>>> tensor
tensor([1, 2]) # Rank 0
tensor([3, 4]) # Rank 1
>>> dist.all_reduce(tensor, op=ReduceOp.SUM)
>>> tensor
tensor([4, 6]) # Rank 0
tensor([4, 6]) # Rank 1
>>> # All tensors below are of torch.cfloat type.
>>> # We have 2 process groups, 2 ranks.
>>> tensor = torch.tensor([1+1j, 2+2j], dtype=torch.cfloat) + 2 * rank * (1+1j)
>>> tensor
tensor([1.+1.j, 2.+2.j]) # Rank 0
tensor([3.+3.j, 4.+4.j]) # Rank 1
>>> dist.all_reduce(tensor, op=ReduceOp.SUM)
>>> tensor
tensor([4.+4.j, 6.+6.j]) # Rank 0
tensor([4.+4.j, 6.+6.j]) # Rank 1
|
Reduces the tensor data across all machines in such a way that all get
the final result.
|
[
"Reduces",
"the",
"tensor",
"data",
"across",
"all",
"machines",
"in",
"such",
"a",
"way",
"that",
"all",
"get",
"the",
"final",
"result",
"."
] |
def all_reduce(tensor, op=ReduceOp.SUM, group=None, async_op=False):
"""
Reduces the tensor data across all machines in such a way that all get
the final result.
After the call ``tensor`` is going to be bitwise identical in all processes.
Complex tensors are supported.
Args:
tensor (Tensor): Input and output of the collective. The function
operates in-place.
op (optional): One of the values from
``torch.distributed.ReduceOp``
enum. Specifies an operation used for element-wise reductions.
group (ProcessGroup, optional): The process group to work on. If None,
the default process group will be used.
async_op (bool, optional): Whether this op should be an async op
Returns:
Async work handle, if async_op is set to True.
None, if not async_op or if not part of the group
Examples:
>>> # All tensors below are of torch.int64 type.
>>> # We have 2 process groups, 2 ranks.
>>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank
>>> tensor
tensor([1, 2]) # Rank 0
tensor([3, 4]) # Rank 1
>>> dist.all_reduce(tensor, op=ReduceOp.SUM)
>>> tensor
tensor([4, 6]) # Rank 0
tensor([4, 6]) # Rank 1
>>> # All tensors below are of torch.cfloat type.
>>> # We have 2 process groups, 2 ranks.
>>> tensor = torch.tensor([1+1j, 2+2j], dtype=torch.cfloat) + 2 * rank * (1+1j)
>>> tensor
tensor([1.+1.j, 2.+2.j]) # Rank 0
tensor([3.+3.j, 4.+4.j]) # Rank 1
>>> dist.all_reduce(tensor, op=ReduceOp.SUM)
>>> tensor
tensor([4.+4.j, 6.+6.j]) # Rank 0
tensor([4.+4.j, 6.+6.j]) # Rank 1
"""
_check_single_tensor(tensor, "tensor")
if _rank_not_in_group(group):
_warn_not_in_group("all_reduce")
return
if tensor.is_complex():
if not supports_complex(op):
raise RuntimeError(f"all_reduce does not support {op} on complex tensors")
tensor = torch.view_as_real(tensor)
opts = AllreduceOptions()
opts.reduceOp = op
if group is None:
default_pg = _get_default_group()
work = default_pg.allreduce([tensor], opts)
else:
work = group.allreduce([tensor], opts)
if async_op:
return work
else:
work.wait()
|
[
"def",
"all_reduce",
"(",
"tensor",
",",
"op",
"=",
"ReduceOp",
".",
"SUM",
",",
"group",
"=",
"None",
",",
"async_op",
"=",
"False",
")",
":",
"_check_single_tensor",
"(",
"tensor",
",",
"\"tensor\"",
")",
"if",
"_rank_not_in_group",
"(",
"group",
")",
":",
"_warn_not_in_group",
"(",
"\"all_reduce\"",
")",
"return",
"if",
"tensor",
".",
"is_complex",
"(",
")",
":",
"if",
"not",
"supports_complex",
"(",
"op",
")",
":",
"raise",
"RuntimeError",
"(",
"f\"all_reduce does not support {op} on complex tensors\"",
")",
"tensor",
"=",
"torch",
".",
"view_as_real",
"(",
"tensor",
")",
"opts",
"=",
"AllreduceOptions",
"(",
")",
"opts",
".",
"reduceOp",
"=",
"op",
"if",
"group",
"is",
"None",
":",
"default_pg",
"=",
"_get_default_group",
"(",
")",
"work",
"=",
"default_pg",
".",
"allreduce",
"(",
"[",
"tensor",
"]",
",",
"opts",
")",
"else",
":",
"work",
"=",
"group",
".",
"allreduce",
"(",
"[",
"tensor",
"]",
",",
"opts",
")",
"if",
"async_op",
":",
"return",
"work",
"else",
":",
"work",
".",
"wait",
"(",
")"
] |
https://github.com/pytorch/pytorch/blob/7176c92687d3cc847cc046bf002269c6949a21c2/torch/distributed/distributed_c10d.py#L1253-L1321
|
||
pmq20/node-packer
|
12c46c6e44fbc14d9ee645ebd17d5296b324f7e0
|
lts/deps/v8/tools/grokdump.py
|
python
|
InspectionShell.do_disassemble
|
(self, args)
|
Unassemble memory in the region [address, address + size).
If the size is not specified, a default value of 32 bytes is used.
Synopsis: u 0x<address> 0x<size>
|
Unassemble memory in the region [address, address + size).
|
[
"Unassemble",
"memory",
"in",
"the",
"region",
"[",
"address",
"address",
"+",
"size",
")",
"."
] |
def do_disassemble(self, args):
"""
Unassemble memory in the region [address, address + size).
If the size is not specified, a default value of 32 bytes is used.
Synopsis: u 0x<address> 0x<size>
"""
if len(args) != 0:
args = args.split(' ')
self.u_start = self.ParseAddressExpr(args[0])
self.u_size = self.ParseAddressExpr(args[1]) if len(args) > 1 else 0x20
skip = False
else:
# Skip the first instruction if we reuse the last address.
skip = True
if not self.reader.IsValidAddress(self.u_start):
print("Address %s is not contained within the minidump!" % (
self.reader.FormatIntPtr(self.u_start)))
return
lines = self.reader.GetDisasmLines(self.u_start, self.u_size)
if len(lines) == 0:
print("Address %s could not be disassembled!" % (
self.reader.FormatIntPtr(self.u_start)))
print(" Could not disassemble using %s." % OBJDUMP_BIN)
print(" Pass path to architecture specific objdump via --objdump?")
return
for line in lines:
if skip:
skip = False
continue
print(FormatDisasmLine(self.u_start, self.heap, line))
# Set the next start address = last line
self.u_start += lines[-1][0]
print()
|
[
"def",
"do_disassemble",
"(",
"self",
",",
"args",
")",
":",
"if",
"len",
"(",
"args",
")",
"!=",
"0",
":",
"args",
"=",
"args",
".",
"split",
"(",
"' '",
")",
"self",
".",
"u_start",
"=",
"self",
".",
"ParseAddressExpr",
"(",
"args",
"[",
"0",
"]",
")",
"self",
".",
"u_size",
"=",
"self",
".",
"ParseAddressExpr",
"(",
"args",
"[",
"1",
"]",
")",
"if",
"len",
"(",
"args",
")",
">",
"1",
"else",
"0x20",
"skip",
"=",
"False",
"else",
":",
"# Skip the first instruction if we reuse the last address.",
"skip",
"=",
"True",
"if",
"not",
"self",
".",
"reader",
".",
"IsValidAddress",
"(",
"self",
".",
"u_start",
")",
":",
"print",
"(",
"\"Address %s is not contained within the minidump!\"",
"%",
"(",
"self",
".",
"reader",
".",
"FormatIntPtr",
"(",
"self",
".",
"u_start",
")",
")",
")",
"return",
"lines",
"=",
"self",
".",
"reader",
".",
"GetDisasmLines",
"(",
"self",
".",
"u_start",
",",
"self",
".",
"u_size",
")",
"if",
"len",
"(",
"lines",
")",
"==",
"0",
":",
"print",
"(",
"\"Address %s could not be disassembled!\"",
"%",
"(",
"self",
".",
"reader",
".",
"FormatIntPtr",
"(",
"self",
".",
"u_start",
")",
")",
")",
"print",
"(",
"\" Could not disassemble using %s.\"",
"%",
"OBJDUMP_BIN",
")",
"print",
"(",
"\" Pass path to architecture specific objdump via --objdump?\"",
")",
"return",
"for",
"line",
"in",
"lines",
":",
"if",
"skip",
":",
"skip",
"=",
"False",
"continue",
"print",
"(",
"FormatDisasmLine",
"(",
"self",
".",
"u_start",
",",
"self",
".",
"heap",
",",
"line",
")",
")",
"# Set the next start address = last line",
"self",
".",
"u_start",
"+=",
"lines",
"[",
"-",
"1",
"]",
"[",
"0",
"]",
"print",
"(",
")"
] |
https://github.com/pmq20/node-packer/blob/12c46c6e44fbc14d9ee645ebd17d5296b324f7e0/lts/deps/v8/tools/grokdump.py#L3740-L3774
|
||
catboost/catboost
|
167f64f237114a4d10b2b4ee42adb4569137debe
|
contrib/python/scipy/py2/scipy/integrate/_ivp/base.py
|
python
|
check_arguments
|
(fun, y0, support_complex)
|
return fun_wrapped, y0
|
Helper function for checking arguments common to all solvers.
|
Helper function for checking arguments common to all solvers.
|
[
"Helper",
"function",
"for",
"checking",
"arguments",
"common",
"to",
"all",
"solvers",
"."
] |
def check_arguments(fun, y0, support_complex):
"""Helper function for checking arguments common to all solvers."""
y0 = np.asarray(y0)
if np.issubdtype(y0.dtype, np.complexfloating):
if not support_complex:
raise ValueError("`y0` is complex, but the chosen solver does "
"not support integration in a complex domain.")
dtype = complex
else:
dtype = float
y0 = y0.astype(dtype, copy=False)
if y0.ndim != 1:
raise ValueError("`y0` must be 1-dimensional.")
def fun_wrapped(t, y):
return np.asarray(fun(t, y), dtype=dtype)
return fun_wrapped, y0
|
[
"def",
"check_arguments",
"(",
"fun",
",",
"y0",
",",
"support_complex",
")",
":",
"y0",
"=",
"np",
".",
"asarray",
"(",
"y0",
")",
"if",
"np",
".",
"issubdtype",
"(",
"y0",
".",
"dtype",
",",
"np",
".",
"complexfloating",
")",
":",
"if",
"not",
"support_complex",
":",
"raise",
"ValueError",
"(",
"\"`y0` is complex, but the chosen solver does \"",
"\"not support integration in a complex domain.\"",
")",
"dtype",
"=",
"complex",
"else",
":",
"dtype",
"=",
"float",
"y0",
"=",
"y0",
".",
"astype",
"(",
"dtype",
",",
"copy",
"=",
"False",
")",
"if",
"y0",
".",
"ndim",
"!=",
"1",
":",
"raise",
"ValueError",
"(",
"\"`y0` must be 1-dimensional.\"",
")",
"def",
"fun_wrapped",
"(",
"t",
",",
"y",
")",
":",
"return",
"np",
".",
"asarray",
"(",
"fun",
"(",
"t",
",",
"y",
")",
",",
"dtype",
"=",
"dtype",
")",
"return",
"fun_wrapped",
",",
"y0"
] |
https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/scipy/py2/scipy/integrate/_ivp/base.py#L5-L23
|
|
arangodb/arangodb
|
0d658689c7d1b721b314fa3ca27d38303e1570c8
|
3rdParty/V8/v7.9.317/third_party/jinja2/compiler.py
|
python
|
CodeGenerator.pop_parameter_definitions
|
(self)
|
Pops the current parameter definitions set.
|
Pops the current parameter definitions set.
|
[
"Pops",
"the",
"current",
"parameter",
"definitions",
"set",
"."
] |
def pop_parameter_definitions(self):
"""Pops the current parameter definitions set."""
self._param_def_block.pop()
|
[
"def",
"pop_parameter_definitions",
"(",
"self",
")",
":",
"self",
".",
"_param_def_block",
".",
"pop",
"(",
")"
] |
https://github.com/arangodb/arangodb/blob/0d658689c7d1b721b314fa3ca27d38303e1570c8/3rdParty/V8/v7.9.317/third_party/jinja2/compiler.py#L623-L625
|
||
SequoiaDB/SequoiaDB
|
2894ed7e5bd6fe57330afc900cf76d0ff0df9f64
|
tools/server/php_linux/libxml2/lib/python2.4/site-packages/libxml2.py
|
python
|
xpathParserContext.xpathRoundFunction
|
(self, nargs)
|
Implement the round() XPath function number round(number)
The round function returns the number that is closest to
the argument and that is an integer. If there are two such
numbers, then the one that is even is returned.
|
Implement the round() XPath function number round(number)
The round function returns the number that is closest to
the argument and that is an integer. If there are two such
numbers, then the one that is even is returned.
|
[
"Implement",
"the",
"round",
"()",
"XPath",
"function",
"number",
"round",
"(",
"number",
")",
"The",
"round",
"function",
"returns",
"the",
"number",
"that",
"is",
"closest",
"to",
"the",
"argument",
"and",
"that",
"is",
"an",
"integer",
".",
"If",
"there",
"are",
"two",
"such",
"numbers",
"then",
"the",
"one",
"that",
"is",
"even",
"is",
"returned",
"."
] |
def xpathRoundFunction(self, nargs):
"""Implement the round() XPath function number round(number)
The round function returns the number that is closest to
the argument and that is an integer. If there are two such
numbers, then the one that is even is returned. """
libxml2mod.xmlXPathRoundFunction(self._o, nargs)
|
[
"def",
"xpathRoundFunction",
"(",
"self",
",",
"nargs",
")",
":",
"libxml2mod",
".",
"xmlXPathRoundFunction",
"(",
"self",
".",
"_o",
",",
"nargs",
")"
] |
https://github.com/SequoiaDB/SequoiaDB/blob/2894ed7e5bd6fe57330afc900cf76d0ff0df9f64/tools/server/php_linux/libxml2/lib/python2.4/site-packages/libxml2.py#L7772-L7777
|
||
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numba/parfor.py
|
python
|
ParforDiagnostics.sort_pf_by_line
|
(self, pf_id, parfors_simple)
|
return line
|
pd_id - the parfors id
parfors_simple - the simple parfors map
|
pd_id - the parfors id
parfors_simple - the simple parfors map
|
[
"pd_id",
"-",
"the",
"parfors",
"id",
"parfors_simple",
"-",
"the",
"simple",
"parfors",
"map"
] |
def sort_pf_by_line(self, pf_id, parfors_simple):
"""
pd_id - the parfors id
parfors_simple - the simple parfors map
"""
# this sorts parfors by source line number
pf = parfors_simple[pf_id][0]
pattern = pf.patterns[0]
line = max(0, pf.loc.line - 1) # why are these out by 1 ?!
filename = self.func_ir.loc.filename
nadj, nroots = self.compute_graph_info(self.nested_fusion_info)
fadj, froots = self.compute_graph_info(self.fusion_info)
graphs = [nadj, fadj]
# If the parfor is internal, like internal prange, then the
# default line number is from its location in the numba source
# To get a more accurate line number, this first checks the
# adjacency graph for fused parfors that might not be internal
# and uses the minimum line number from there. If that fails
# (case where there's just a single internal parfor) the IR
# is walked backwards from the parfor location and the first non
# parfor statement line number is used.
if isinstance(pattern, tuple):
if pattern[1] == 'internal':
reported_loc = pattern[2][1]
if reported_loc.filename == filename:
return max(0, reported_loc.line - 1)
else:
# first recurse and check the adjacency list for
# something that is not an in internal parfor
tmp = []
for adj in graphs:
if adj: # graph may be empty, e.g. no nesting
for k in adj[pf_id]:
tmp.append(self.sort_pf_by_line(k, parfors_simple))
if tmp:
return max(0, min(tmp) - 1)
# second run through the parfor block to see if there's
# and reference to a line number in the user source
for blk in pf.loop_body.values():
for stmt in blk.body:
if stmt.loc.filename == filename:
return max(0, stmt.loc.line - 1)
# finally run through the func_ir and look for the
# first non-parfor statement prior to this one and
# grab the line from that
for blk in self.func_ir.blocks.values():
try:
idx = blk.body.index(pf)
for i in range(idx - 1, 0, -1):
stmt = blk.body[i]
if not isinstance(stmt, Parfor):
line = max(0, stmt.loc.line - 1)
break
except ValueError:
pass
return line
|
[
"def",
"sort_pf_by_line",
"(",
"self",
",",
"pf_id",
",",
"parfors_simple",
")",
":",
"# this sorts parfors by source line number",
"pf",
"=",
"parfors_simple",
"[",
"pf_id",
"]",
"[",
"0",
"]",
"pattern",
"=",
"pf",
".",
"patterns",
"[",
"0",
"]",
"line",
"=",
"max",
"(",
"0",
",",
"pf",
".",
"loc",
".",
"line",
"-",
"1",
")",
"# why are these out by 1 ?!",
"filename",
"=",
"self",
".",
"func_ir",
".",
"loc",
".",
"filename",
"nadj",
",",
"nroots",
"=",
"self",
".",
"compute_graph_info",
"(",
"self",
".",
"nested_fusion_info",
")",
"fadj",
",",
"froots",
"=",
"self",
".",
"compute_graph_info",
"(",
"self",
".",
"fusion_info",
")",
"graphs",
"=",
"[",
"nadj",
",",
"fadj",
"]",
"# If the parfor is internal, like internal prange, then the",
"# default line number is from its location in the numba source",
"# To get a more accurate line number, this first checks the",
"# adjacency graph for fused parfors that might not be internal",
"# and uses the minimum line number from there. If that fails",
"# (case where there's just a single internal parfor) the IR",
"# is walked backwards from the parfor location and the first non",
"# parfor statement line number is used.",
"if",
"isinstance",
"(",
"pattern",
",",
"tuple",
")",
":",
"if",
"pattern",
"[",
"1",
"]",
"==",
"'internal'",
":",
"reported_loc",
"=",
"pattern",
"[",
"2",
"]",
"[",
"1",
"]",
"if",
"reported_loc",
".",
"filename",
"==",
"filename",
":",
"return",
"max",
"(",
"0",
",",
"reported_loc",
".",
"line",
"-",
"1",
")",
"else",
":",
"# first recurse and check the adjacency list for",
"# something that is not an in internal parfor",
"tmp",
"=",
"[",
"]",
"for",
"adj",
"in",
"graphs",
":",
"if",
"adj",
":",
"# graph may be empty, e.g. no nesting",
"for",
"k",
"in",
"adj",
"[",
"pf_id",
"]",
":",
"tmp",
".",
"append",
"(",
"self",
".",
"sort_pf_by_line",
"(",
"k",
",",
"parfors_simple",
")",
")",
"if",
"tmp",
":",
"return",
"max",
"(",
"0",
",",
"min",
"(",
"tmp",
")",
"-",
"1",
")",
"# second run through the parfor block to see if there's",
"# and reference to a line number in the user source",
"for",
"blk",
"in",
"pf",
".",
"loop_body",
".",
"values",
"(",
")",
":",
"for",
"stmt",
"in",
"blk",
".",
"body",
":",
"if",
"stmt",
".",
"loc",
".",
"filename",
"==",
"filename",
":",
"return",
"max",
"(",
"0",
",",
"stmt",
".",
"loc",
".",
"line",
"-",
"1",
")",
"# finally run through the func_ir and look for the",
"# first non-parfor statement prior to this one and",
"# grab the line from that",
"for",
"blk",
"in",
"self",
".",
"func_ir",
".",
"blocks",
".",
"values",
"(",
")",
":",
"try",
":",
"idx",
"=",
"blk",
".",
"body",
".",
"index",
"(",
"pf",
")",
"for",
"i",
"in",
"range",
"(",
"idx",
"-",
"1",
",",
"0",
",",
"-",
"1",
")",
":",
"stmt",
"=",
"blk",
".",
"body",
"[",
"i",
"]",
"if",
"not",
"isinstance",
"(",
"stmt",
",",
"Parfor",
")",
":",
"line",
"=",
"max",
"(",
"0",
",",
"stmt",
".",
"loc",
".",
"line",
"-",
"1",
")",
"break",
"except",
"ValueError",
":",
"pass",
"return",
"line"
] |
https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numba/parfor.py#L776-L832
|
|
hanpfei/chromium-net
|
392cc1fa3a8f92f42e4071ab6e674d8e0482f83f
|
tools/grit/grit/extern/tclib.py
|
python
|
Message.SetIsHidden
|
(self, is_hidden)
|
Sets whether this message should be hidden.
Args:
is_hidden : 0 or 1 - if the message should be hidden, 0 otherwise
|
Sets whether this message should be hidden.
|
[
"Sets",
"whether",
"this",
"message",
"should",
"be",
"hidden",
"."
] |
def SetIsHidden(self, is_hidden):
"""Sets whether this message should be hidden.
Args:
is_hidden : 0 or 1 - if the message should be hidden, 0 otherwise
"""
if is_hidden not in [0, 1]:
raise MessageTranslationError, "is_hidden must be 0 or 1, got %s"
self.__is_hidden = is_hidden
|
[
"def",
"SetIsHidden",
"(",
"self",
",",
"is_hidden",
")",
":",
"if",
"is_hidden",
"not",
"in",
"[",
"0",
",",
"1",
"]",
":",
"raise",
"MessageTranslationError",
",",
"\"is_hidden must be 0 or 1, got %s\"",
"self",
".",
"__is_hidden",
"=",
"is_hidden"
] |
https://github.com/hanpfei/chromium-net/blob/392cc1fa3a8f92f42e4071ab6e674d8e0482f83f/tools/grit/grit/extern/tclib.py#L439-L447
|
||
giuspen/cherrytree
|
84712f206478fcf9acf30174009ad28c648c6344
|
pygtk2/modules/machines.py
|
python
|
get_encoded_buffer_from_pixbuf
|
(pixbuf)
|
return encoded_buffer
|
Pixbuf To Encoded Buffer
|
Pixbuf To Encoded Buffer
|
[
"Pixbuf",
"To",
"Encoded",
"Buffer"
] |
def get_encoded_buffer_from_pixbuf(pixbuf):
"""Pixbuf To Encoded Buffer"""
io = StringIO.StringIO()
pixbuf.save_to_callback(io.write, "png")
encoded_buffer = base64.b64encode(io.getvalue())
return encoded_buffer
|
[
"def",
"get_encoded_buffer_from_pixbuf",
"(",
"pixbuf",
")",
":",
"io",
"=",
"StringIO",
".",
"StringIO",
"(",
")",
"pixbuf",
".",
"save_to_callback",
"(",
"io",
".",
"write",
",",
"\"png\"",
")",
"encoded_buffer",
"=",
"base64",
".",
"b64encode",
"(",
"io",
".",
"getvalue",
"(",
")",
")",
"return",
"encoded_buffer"
] |
https://github.com/giuspen/cherrytree/blob/84712f206478fcf9acf30174009ad28c648c6344/pygtk2/modules/machines.py#L56-L61
|
|
catboost/catboost
|
167f64f237114a4d10b2b4ee42adb4569137debe
|
contrib/python/pandas/py2/pandas/core/arrays/period.py
|
python
|
_period_array_cmp
|
(cls, op)
|
return compat.set_function_name(wrapper, opname, cls)
|
Wrap comparison operations to convert Period-like to PeriodDtype
|
Wrap comparison operations to convert Period-like to PeriodDtype
|
[
"Wrap",
"comparison",
"operations",
"to",
"convert",
"Period",
"-",
"like",
"to",
"PeriodDtype"
] |
def _period_array_cmp(cls, op):
"""
Wrap comparison operations to convert Period-like to PeriodDtype
"""
opname = '__{name}__'.format(name=op.__name__)
nat_result = True if opname == '__ne__' else False
def wrapper(self, other):
op = getattr(self.asi8, opname)
if isinstance(other, (ABCDataFrame, ABCSeries, ABCIndexClass)):
return NotImplemented
if is_list_like(other) and len(other) != len(self):
raise ValueError("Lengths must match")
if isinstance(other, Period):
self._check_compatible_with(other)
result = op(other.ordinal)
elif isinstance(other, cls):
self._check_compatible_with(other)
result = op(other.asi8)
mask = self._isnan | other._isnan
if mask.any():
result[mask] = nat_result
return result
elif other is NaT:
result = np.empty(len(self.asi8), dtype=bool)
result.fill(nat_result)
else:
other = Period(other, freq=self.freq)
result = op(other.ordinal)
if self._hasnans:
result[self._isnan] = nat_result
return result
return compat.set_function_name(wrapper, opname, cls)
|
[
"def",
"_period_array_cmp",
"(",
"cls",
",",
"op",
")",
":",
"opname",
"=",
"'__{name}__'",
".",
"format",
"(",
"name",
"=",
"op",
".",
"__name__",
")",
"nat_result",
"=",
"True",
"if",
"opname",
"==",
"'__ne__'",
"else",
"False",
"def",
"wrapper",
"(",
"self",
",",
"other",
")",
":",
"op",
"=",
"getattr",
"(",
"self",
".",
"asi8",
",",
"opname",
")",
"if",
"isinstance",
"(",
"other",
",",
"(",
"ABCDataFrame",
",",
"ABCSeries",
",",
"ABCIndexClass",
")",
")",
":",
"return",
"NotImplemented",
"if",
"is_list_like",
"(",
"other",
")",
"and",
"len",
"(",
"other",
")",
"!=",
"len",
"(",
"self",
")",
":",
"raise",
"ValueError",
"(",
"\"Lengths must match\"",
")",
"if",
"isinstance",
"(",
"other",
",",
"Period",
")",
":",
"self",
".",
"_check_compatible_with",
"(",
"other",
")",
"result",
"=",
"op",
"(",
"other",
".",
"ordinal",
")",
"elif",
"isinstance",
"(",
"other",
",",
"cls",
")",
":",
"self",
".",
"_check_compatible_with",
"(",
"other",
")",
"result",
"=",
"op",
"(",
"other",
".",
"asi8",
")",
"mask",
"=",
"self",
".",
"_isnan",
"|",
"other",
".",
"_isnan",
"if",
"mask",
".",
"any",
"(",
")",
":",
"result",
"[",
"mask",
"]",
"=",
"nat_result",
"return",
"result",
"elif",
"other",
"is",
"NaT",
":",
"result",
"=",
"np",
".",
"empty",
"(",
"len",
"(",
"self",
".",
"asi8",
")",
",",
"dtype",
"=",
"bool",
")",
"result",
".",
"fill",
"(",
"nat_result",
")",
"else",
":",
"other",
"=",
"Period",
"(",
"other",
",",
"freq",
"=",
"self",
".",
"freq",
")",
"result",
"=",
"op",
"(",
"other",
".",
"ordinal",
")",
"if",
"self",
".",
"_hasnans",
":",
"result",
"[",
"self",
".",
"_isnan",
"]",
"=",
"nat_result",
"return",
"result",
"return",
"compat",
".",
"set_function_name",
"(",
"wrapper",
",",
"opname",
",",
"cls",
")"
] |
https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/pandas/py2/pandas/core/arrays/period.py#L44-L86
|
|
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Tools/Python/3.7.10/windows/Lib/tkinter/__init__.py
|
python
|
Misc.winfo_height
|
(self)
|
return self.tk.getint(
self.tk.call('winfo', 'height', self._w))
|
Return height of this widget.
|
Return height of this widget.
|
[
"Return",
"height",
"of",
"this",
"widget",
"."
] |
def winfo_height(self):
"""Return height of this widget."""
return self.tk.getint(
self.tk.call('winfo', 'height', self._w))
|
[
"def",
"winfo_height",
"(",
"self",
")",
":",
"return",
"self",
".",
"tk",
".",
"getint",
"(",
"self",
".",
"tk",
".",
"call",
"(",
"'winfo'",
",",
"'height'",
",",
"self",
".",
"_w",
")",
")"
] |
https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/windows/Lib/tkinter/__init__.py#L997-L1000
|
|
0ad/0ad
|
f58db82e0e925016d83f4e3fa7ca599e3866e2af
|
source/tools/i18n/updateTemplates.py
|
python
|
warnAboutUntouchedMods
|
()
|
Warn about mods that are not properly configured to get their messages extracted.
|
Warn about mods that are not properly configured to get their messages extracted.
|
[
"Warn",
"about",
"mods",
"that",
"are",
"not",
"properly",
"configured",
"to",
"get",
"their",
"messages",
"extracted",
"."
] |
def warnAboutUntouchedMods():
"""
Warn about mods that are not properly configured to get their messages extracted.
"""
modsRootFolder = os.path.join(projectRootDirectory, "binaries", "data", "mods")
untouchedMods = {}
for modFolder in os.listdir(modsRootFolder):
if modFolder[0] != "_" and modFolder[0] != '.':
if not os.path.exists(os.path.join(modsRootFolder, modFolder, l10nFolderName)):
untouchedMods[modFolder] = "There is no '{folderName}' folder in the root folder of this mod.".format(folderName=l10nFolderName)
elif not os.path.exists(os.path.join(modsRootFolder, modFolder, l10nFolderName, messagesFilename)):
untouchedMods[modFolder] = "There is no '{filename}' file within the '{folderName}' folder in the root folder of this mod.".format(folderName=l10nFolderName, filename=messagesFilename)
if untouchedMods:
print(""
"Warning: No messages were extracted from the following mods:"
"")
for mod in untouchedMods:
print("• {modName}: {warningMessage}".format(modName=mod, warningMessage=untouchedMods[mod]))
print(""
f"For this script to extract messages from a mod folder, this mod folder must contain a '{l10nFolderName}' "
f"folder, and this folder must contain a '{messagesFilename}' file that describes how to extract messages for the "
f"mod. See the folder of the main mod ('public') for an example, and see the documentation for more "
f"information."
)
|
[
"def",
"warnAboutUntouchedMods",
"(",
")",
":",
"modsRootFolder",
"=",
"os",
".",
"path",
".",
"join",
"(",
"projectRootDirectory",
",",
"\"binaries\"",
",",
"\"data\"",
",",
"\"mods\"",
")",
"untouchedMods",
"=",
"{",
"}",
"for",
"modFolder",
"in",
"os",
".",
"listdir",
"(",
"modsRootFolder",
")",
":",
"if",
"modFolder",
"[",
"0",
"]",
"!=",
"\"_\"",
"and",
"modFolder",
"[",
"0",
"]",
"!=",
"'.'",
":",
"if",
"not",
"os",
".",
"path",
".",
"exists",
"(",
"os",
".",
"path",
".",
"join",
"(",
"modsRootFolder",
",",
"modFolder",
",",
"l10nFolderName",
")",
")",
":",
"untouchedMods",
"[",
"modFolder",
"]",
"=",
"\"There is no '{folderName}' folder in the root folder of this mod.\"",
".",
"format",
"(",
"folderName",
"=",
"l10nFolderName",
")",
"elif",
"not",
"os",
".",
"path",
".",
"exists",
"(",
"os",
".",
"path",
".",
"join",
"(",
"modsRootFolder",
",",
"modFolder",
",",
"l10nFolderName",
",",
"messagesFilename",
")",
")",
":",
"untouchedMods",
"[",
"modFolder",
"]",
"=",
"\"There is no '{filename}' file within the '{folderName}' folder in the root folder of this mod.\"",
".",
"format",
"(",
"folderName",
"=",
"l10nFolderName",
",",
"filename",
"=",
"messagesFilename",
")",
"if",
"untouchedMods",
":",
"print",
"(",
"\"\"",
"\"Warning: No messages were extracted from the following mods:\"",
"\"\"",
")",
"for",
"mod",
"in",
"untouchedMods",
":",
"print",
"(",
"\"• {modName}: {warningMessage}\".f",
"o",
"rmat(m",
"o",
"dName=m",
"o",
"d, ",
"w",
"rningMessage=u",
"n",
"touchedMods[m",
"o",
"d])",
")",
"",
"",
"print",
"(",
"\"\"",
"f\"For this script to extract messages from a mod folder, this mod folder must contain a '{l10nFolderName}' \"",
"f\"folder, and this folder must contain a '{messagesFilename}' file that describes how to extract messages for the \"",
"f\"mod. See the folder of the main mod ('public') for an example, and see the documentation for more \"",
"f\"information.\"",
")"
] |
https://github.com/0ad/0ad/blob/f58db82e0e925016d83f4e3fa7ca599e3866e2af/source/tools/i18n/updateTemplates.py#L31-L54
|
||
tensorflow/tensorflow
|
419e3a6b650ea4bd1b0cba23c4348f8a69f3272e
|
tensorflow/python/autograph/operators/control_flow.py
|
python
|
_is_none_or_undef
|
(value)
|
return ((value is None)
or isinstance(value, variables.UndefinedReturnValue)
or isinstance(value, variables.Undefined))
|
Tests whether a value is None or undefined.
AutoGraph represents undefined symbols using special objects of type Undefined
or UndefinedReturnValue.
Args:
value: value to test
Returns:
Boolean
|
Tests whether a value is None or undefined.
|
[
"Tests",
"whether",
"a",
"value",
"is",
"None",
"or",
"undefined",
"."
] |
def _is_none_or_undef(value):
"""Tests whether a value is None or undefined.
AutoGraph represents undefined symbols using special objects of type Undefined
or UndefinedReturnValue.
Args:
value: value to test
Returns:
Boolean
"""
return ((value is None)
or isinstance(value, variables.UndefinedReturnValue)
or isinstance(value, variables.Undefined))
|
[
"def",
"_is_none_or_undef",
"(",
"value",
")",
":",
"return",
"(",
"(",
"value",
"is",
"None",
")",
"or",
"isinstance",
"(",
"value",
",",
"variables",
".",
"UndefinedReturnValue",
")",
"or",
"isinstance",
"(",
"value",
",",
"variables",
".",
"Undefined",
")",
")"
] |
https://github.com/tensorflow/tensorflow/blob/419e3a6b650ea4bd1b0cba23c4348f8a69f3272e/tensorflow/python/autograph/operators/control_flow.py#L100-L114
|
|
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numba/unsafe/numbers.py
|
python
|
leading_zeros
|
(typeingctx, src)
|
return src(src), codegen
|
Counts leading zeros in the binary representation of an integer.
|
Counts leading zeros in the binary representation of an integer.
|
[
"Counts",
"leading",
"zeros",
"in",
"the",
"binary",
"representation",
"of",
"an",
"integer",
"."
] |
def leading_zeros(typeingctx, src):
"""Counts leading zeros in the binary representation of an integer."""
if not isinstance(src, types.Integer):
raise TypeError(
"leading_zeros is only defined for integers, but passed value was "
"'{}'.".format(src)
)
def codegen(context, builder, signature, args):
[src] = args
return builder.ctlz(src, ir.Constant(ir.IntType(1), 0))
return src(src), codegen
|
[
"def",
"leading_zeros",
"(",
"typeingctx",
",",
"src",
")",
":",
"if",
"not",
"isinstance",
"(",
"src",
",",
"types",
".",
"Integer",
")",
":",
"raise",
"TypeError",
"(",
"\"leading_zeros is only defined for integers, but passed value was \"",
"\"'{}'.\"",
".",
"format",
"(",
"src",
")",
")",
"def",
"codegen",
"(",
"context",
",",
"builder",
",",
"signature",
",",
"args",
")",
":",
"[",
"src",
"]",
"=",
"args",
"return",
"builder",
".",
"ctlz",
"(",
"src",
",",
"ir",
".",
"Constant",
"(",
"ir",
".",
"IntType",
"(",
"1",
")",
",",
"0",
")",
")",
"return",
"src",
"(",
"src",
")",
",",
"codegen"
] |
https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numba/unsafe/numbers.py#L44-L55
|
|
deepmind/open_spiel
|
4ca53bea32bb2875c7385d215424048ae92f78c8
|
open_spiel/python/algorithms/adidas_utils/solvers/nonsymmetric/ate_anneal.py
|
python
|
Solver.init_vars
|
(self, num_strats, num_players)
|
return (init_dist, init_y, init_anneal_steps)
|
Initialize solver parameters.
|
Initialize solver parameters.
|
[
"Initialize",
"solver",
"parameters",
"."
] |
def init_vars(self, num_strats, num_players):
"""Initialize solver parameters."""
self.num_players = num_players
if len(num_strats) != num_players:
raise ValueError('Must specify num strategies for each player')
init_dist = []
for num_strats_i in num_strats:
if self.rnd_init:
init_dist_i = self.random.rand(num_strats_i)
else:
init_dist_i = np.ones(num_strats_i)
init_dist_i /= init_dist_i.sum()
init_dist.append(init_dist_i)
init_y = [np.zeros_like(dist_i) for dist_i in init_dist]
init_anneal_steps = 0
return (init_dist, init_y, init_anneal_steps)
|
[
"def",
"init_vars",
"(",
"self",
",",
"num_strats",
",",
"num_players",
")",
":",
"self",
".",
"num_players",
"=",
"num_players",
"if",
"len",
"(",
"num_strats",
")",
"!=",
"num_players",
":",
"raise",
"ValueError",
"(",
"'Must specify num strategies for each player'",
")",
"init_dist",
"=",
"[",
"]",
"for",
"num_strats_i",
"in",
"num_strats",
":",
"if",
"self",
".",
"rnd_init",
":",
"init_dist_i",
"=",
"self",
".",
"random",
".",
"rand",
"(",
"num_strats_i",
")",
"else",
":",
"init_dist_i",
"=",
"np",
".",
"ones",
"(",
"num_strats_i",
")",
"init_dist_i",
"/=",
"init_dist_i",
".",
"sum",
"(",
")",
"init_dist",
".",
"append",
"(",
"init_dist_i",
")",
"init_y",
"=",
"[",
"np",
".",
"zeros_like",
"(",
"dist_i",
")",
"for",
"dist_i",
"in",
"init_dist",
"]",
"init_anneal_steps",
"=",
"0",
"return",
"(",
"init_dist",
",",
"init_y",
",",
"init_anneal_steps",
")"
] |
https://github.com/deepmind/open_spiel/blob/4ca53bea32bb2875c7385d215424048ae92f78c8/open_spiel/python/algorithms/adidas_utils/solvers/nonsymmetric/ate_anneal.py#L58-L73
|
|
catboost/catboost
|
167f64f237114a4d10b2b4ee42adb4569137debe
|
contrib/python/subprocess32/subprocess32.py
|
python
|
Popen.communicate
|
(self, input=None, timeout=None)
|
return (stdout, stderr)
|
Interact with process: Send data to stdin. Read data from
stdout and stderr, until end-of-file is reached. Wait for
process to terminate. The optional input argument should be a
string to be sent to the child process, or None, if no data
should be sent to the child.
communicate() returns a tuple (stdout, stderr).
|
Interact with process: Send data to stdin. Read data from
stdout and stderr, until end-of-file is reached. Wait for
process to terminate. The optional input argument should be a
string to be sent to the child process, or None, if no data
should be sent to the child.
|
[
"Interact",
"with",
"process",
":",
"Send",
"data",
"to",
"stdin",
".",
"Read",
"data",
"from",
"stdout",
"and",
"stderr",
"until",
"end",
"-",
"of",
"-",
"file",
"is",
"reached",
".",
"Wait",
"for",
"process",
"to",
"terminate",
".",
"The",
"optional",
"input",
"argument",
"should",
"be",
"a",
"string",
"to",
"be",
"sent",
"to",
"the",
"child",
"process",
"or",
"None",
"if",
"no",
"data",
"should",
"be",
"sent",
"to",
"the",
"child",
"."
] |
def communicate(self, input=None, timeout=None):
"""Interact with process: Send data to stdin. Read data from
stdout and stderr, until end-of-file is reached. Wait for
process to terminate. The optional input argument should be a
string to be sent to the child process, or None, if no data
should be sent to the child.
communicate() returns a tuple (stdout, stderr)."""
if self._communication_started and input:
raise ValueError("Cannot send input after starting communication")
if timeout is not None:
endtime = time.time() + timeout
else:
endtime = None
# Optimization: If we are not worried about timeouts, we haven't
# started communicating, and we have one or zero pipes, using select()
# or threads is unnecessary.
if (endtime is None and not self._communication_started and
[self.stdin, self.stdout, self.stderr].count(None) >= 2):
stdout = None
stderr = None
if self.stdin:
self._stdin_write(input)
elif self.stdout:
stdout = _eintr_retry_call(self.stdout.read)
self.stdout.close()
elif self.stderr:
stderr = _eintr_retry_call(self.stderr.read)
self.stderr.close()
self.wait()
return (stdout, stderr)
try:
stdout, stderr = self._communicate(input, endtime, timeout)
finally:
self._communication_started = True
sts = self.wait(timeout=self._remaining_time(endtime))
return (stdout, stderr)
|
[
"def",
"communicate",
"(",
"self",
",",
"input",
"=",
"None",
",",
"timeout",
"=",
"None",
")",
":",
"if",
"self",
".",
"_communication_started",
"and",
"input",
":",
"raise",
"ValueError",
"(",
"\"Cannot send input after starting communication\"",
")",
"if",
"timeout",
"is",
"not",
"None",
":",
"endtime",
"=",
"time",
".",
"time",
"(",
")",
"+",
"timeout",
"else",
":",
"endtime",
"=",
"None",
"# Optimization: If we are not worried about timeouts, we haven't",
"# started communicating, and we have one or zero pipes, using select()",
"# or threads is unnecessary.",
"if",
"(",
"endtime",
"is",
"None",
"and",
"not",
"self",
".",
"_communication_started",
"and",
"[",
"self",
".",
"stdin",
",",
"self",
".",
"stdout",
",",
"self",
".",
"stderr",
"]",
".",
"count",
"(",
"None",
")",
">=",
"2",
")",
":",
"stdout",
"=",
"None",
"stderr",
"=",
"None",
"if",
"self",
".",
"stdin",
":",
"self",
".",
"_stdin_write",
"(",
"input",
")",
"elif",
"self",
".",
"stdout",
":",
"stdout",
"=",
"_eintr_retry_call",
"(",
"self",
".",
"stdout",
".",
"read",
")",
"self",
".",
"stdout",
".",
"close",
"(",
")",
"elif",
"self",
".",
"stderr",
":",
"stderr",
"=",
"_eintr_retry_call",
"(",
"self",
".",
"stderr",
".",
"read",
")",
"self",
".",
"stderr",
".",
"close",
"(",
")",
"self",
".",
"wait",
"(",
")",
"return",
"(",
"stdout",
",",
"stderr",
")",
"try",
":",
"stdout",
",",
"stderr",
"=",
"self",
".",
"_communicate",
"(",
"input",
",",
"endtime",
",",
"timeout",
")",
"finally",
":",
"self",
".",
"_communication_started",
"=",
"True",
"sts",
"=",
"self",
".",
"wait",
"(",
"timeout",
"=",
"self",
".",
"_remaining_time",
"(",
"endtime",
")",
")",
"return",
"(",
"stdout",
",",
"stderr",
")"
] |
https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/subprocess32/subprocess32.py#L712-L754
|
|
GrammaTech/gtirb
|
415dd72e1e3c475004d013723c16cdcb29c0826e
|
python/gtirb/symbol.py
|
python
|
Symbol.__init__
|
(
self,
name: str,
uuid: typing.Optional[UUID] = None,
payload: typing.Optional[Payload] = None,
at_end: bool = False,
module: typing.Optional["Module"] = None,
)
|
:param name: The name of this symbol.
:param uuid: The UUID of this ``Symbol``,
or None if a new UUID needs generated via :func:`uuid.uuid4`.
Defaults to None.
:param payload: The value this symbol points to.
May be an address, a Node, or None.
:param at_end: True if this symbol is at the end of its referent,
rather than at the beginning.
:param module: The :class:`Module` this symbol belongs to.
|
:param name: The name of this symbol.
:param uuid: The UUID of this ``Symbol``,
or None if a new UUID needs generated via :func:`uuid.uuid4`.
Defaults to None.
:param payload: The value this symbol points to.
May be an address, a Node, or None.
:param at_end: True if this symbol is at the end of its referent,
rather than at the beginning.
:param module: The :class:`Module` this symbol belongs to.
|
[
":",
"param",
"name",
":",
"The",
"name",
"of",
"this",
"symbol",
".",
":",
"param",
"uuid",
":",
"The",
"UUID",
"of",
"this",
"Symbol",
"or",
"None",
"if",
"a",
"new",
"UUID",
"needs",
"generated",
"via",
":",
"func",
":",
"uuid",
".",
"uuid4",
".",
"Defaults",
"to",
"None",
".",
":",
"param",
"payload",
":",
"The",
"value",
"this",
"symbol",
"points",
"to",
".",
"May",
"be",
"an",
"address",
"a",
"Node",
"or",
"None",
".",
":",
"param",
"at_end",
":",
"True",
"if",
"this",
"symbol",
"is",
"at",
"the",
"end",
"of",
"its",
"referent",
"rather",
"than",
"at",
"the",
"beginning",
".",
":",
"param",
"module",
":",
"The",
":",
"class",
":",
"Module",
"this",
"symbol",
"belongs",
"to",
"."
] |
def __init__(
self,
name: str,
uuid: typing.Optional[UUID] = None,
payload: typing.Optional[Payload] = None,
at_end: bool = False,
module: typing.Optional["Module"] = None,
):
"""
:param name: The name of this symbol.
:param uuid: The UUID of this ``Symbol``,
or None if a new UUID needs generated via :func:`uuid.uuid4`.
Defaults to None.
:param payload: The value this symbol points to.
May be an address, a Node, or None.
:param at_end: True if this symbol is at the end of its referent,
rather than at the beginning.
:param module: The :class:`Module` this symbol belongs to.
"""
super().__init__(uuid)
self._module: typing.Optional["Module"] = None
self.name = name
self.at_end = at_end
self._payload = payload
# Use the property setter to ensure correct invariants.
self.module = module
|
[
"def",
"__init__",
"(",
"self",
",",
"name",
":",
"str",
",",
"uuid",
":",
"typing",
".",
"Optional",
"[",
"UUID",
"]",
"=",
"None",
",",
"payload",
":",
"typing",
".",
"Optional",
"[",
"Payload",
"]",
"=",
"None",
",",
"at_end",
":",
"bool",
"=",
"False",
",",
"module",
":",
"typing",
".",
"Optional",
"[",
"\"Module\"",
"]",
"=",
"None",
",",
")",
":",
"super",
"(",
")",
".",
"__init__",
"(",
"uuid",
")",
"self",
".",
"_module",
":",
"typing",
".",
"Optional",
"[",
"\"Module\"",
"]",
"=",
"None",
"self",
".",
"name",
"=",
"name",
"self",
".",
"at_end",
"=",
"at_end",
"self",
".",
"_payload",
"=",
"payload",
"# Use the property setter to ensure correct invariants.",
"self",
".",
"module",
"=",
"module"
] |
https://github.com/GrammaTech/gtirb/blob/415dd72e1e3c475004d013723c16cdcb29c0826e/python/gtirb/symbol.py#L29-L55
|
||
catboost/catboost
|
167f64f237114a4d10b2b4ee42adb4569137debe
|
contrib/python/scipy/py3/scipy/special/basic.py
|
python
|
assoc_laguerre
|
(x, n, k=0.0)
|
return orthogonal.eval_genlaguerre(n, k, x)
|
Compute the generalized (associated) Laguerre polynomial of degree n and order k.
The polynomial :math:`L^{(k)}_n(x)` is orthogonal over ``[0, inf)``,
with weighting function ``exp(-x) * x**k`` with ``k > -1``.
Notes
-----
`assoc_laguerre` is a simple wrapper around `eval_genlaguerre`, with
reversed argument order ``(x, n, k=0.0) --> (n, k, x)``.
|
Compute the generalized (associated) Laguerre polynomial of degree n and order k.
|
[
"Compute",
"the",
"generalized",
"(",
"associated",
")",
"Laguerre",
"polynomial",
"of",
"degree",
"n",
"and",
"order",
"k",
"."
] |
def assoc_laguerre(x, n, k=0.0):
"""Compute the generalized (associated) Laguerre polynomial of degree n and order k.
The polynomial :math:`L^{(k)}_n(x)` is orthogonal over ``[0, inf)``,
with weighting function ``exp(-x) * x**k`` with ``k > -1``.
Notes
-----
`assoc_laguerre` is a simple wrapper around `eval_genlaguerre`, with
reversed argument order ``(x, n, k=0.0) --> (n, k, x)``.
"""
return orthogonal.eval_genlaguerre(n, k, x)
|
[
"def",
"assoc_laguerre",
"(",
"x",
",",
"n",
",",
"k",
"=",
"0.0",
")",
":",
"return",
"orthogonal",
".",
"eval_genlaguerre",
"(",
"n",
",",
"k",
",",
"x",
")"
] |
https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/scipy/py3/scipy/special/basic.py#L922-L934
|
|
wxWidgets/wxPython-Classic
|
19571e1ae65f1ac445f5491474121998c97a1bf0
|
src/osx_carbon/grid.py
|
python
|
GridSizeEvent.__init__
|
(self, *args, **kwargs)
|
__init__(self, int id, EventType type, Grid obj, int rowOrCol=-1,
int x=-1, int y=-1, bool control=False, bool shift=False,
bool alt=False, bool meta=False) -> GridSizeEvent
|
__init__(self, int id, EventType type, Grid obj, int rowOrCol=-1,
int x=-1, int y=-1, bool control=False, bool shift=False,
bool alt=False, bool meta=False) -> GridSizeEvent
|
[
"__init__",
"(",
"self",
"int",
"id",
"EventType",
"type",
"Grid",
"obj",
"int",
"rowOrCol",
"=",
"-",
"1",
"int",
"x",
"=",
"-",
"1",
"int",
"y",
"=",
"-",
"1",
"bool",
"control",
"=",
"False",
"bool",
"shift",
"=",
"False",
"bool",
"alt",
"=",
"False",
"bool",
"meta",
"=",
"False",
")",
"-",
">",
"GridSizeEvent"
] |
def __init__(self, *args, **kwargs):
"""
__init__(self, int id, EventType type, Grid obj, int rowOrCol=-1,
int x=-1, int y=-1, bool control=False, bool shift=False,
bool alt=False, bool meta=False) -> GridSizeEvent
"""
_grid.GridSizeEvent_swiginit(self,_grid.new_GridSizeEvent(*args, **kwargs))
|
[
"def",
"__init__",
"(",
"self",
",",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"_grid",
".",
"GridSizeEvent_swiginit",
"(",
"self",
",",
"_grid",
".",
"new_GridSizeEvent",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
")"
] |
https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_carbon/grid.py#L2350-L2356
|
||
Cantera/cantera
|
0119484b261967ccb55a0066c020599cacc312e4
|
interfaces/cython/cantera/examples/thermo/isentropic.py
|
python
|
soundspeed
|
(gas)
|
return math.sqrt(gamma * ct.gas_constant
* gas.T / gas.mean_molecular_weight)
|
The speed of sound. Assumes an ideal gas.
|
The speed of sound. Assumes an ideal gas.
|
[
"The",
"speed",
"of",
"sound",
".",
"Assumes",
"an",
"ideal",
"gas",
"."
] |
def soundspeed(gas):
"""The speed of sound. Assumes an ideal gas."""
gamma = gas.cp / gas.cv
return math.sqrt(gamma * ct.gas_constant
* gas.T / gas.mean_molecular_weight)
|
[
"def",
"soundspeed",
"(",
"gas",
")",
":",
"gamma",
"=",
"gas",
".",
"cp",
"/",
"gas",
".",
"cv",
"return",
"math",
".",
"sqrt",
"(",
"gamma",
"*",
"ct",
".",
"gas_constant",
"*",
"gas",
".",
"T",
"/",
"gas",
".",
"mean_molecular_weight",
")"
] |
https://github.com/Cantera/cantera/blob/0119484b261967ccb55a0066c020599cacc312e4/interfaces/cython/cantera/examples/thermo/isentropic.py#L12-L17
|
|
christinaa/LLVM-VideoCore4
|
7773c3c9e5d22b785d4b96ed0acea37c8aa9c183
|
examples/Kaleidoscope/MCJIT/complete/genk-timing.py
|
python
|
KScriptGenerator.updateFunctionCallMap
|
(self, caller, callee)
|
Maintains a map of functions that are called from other functions
|
Maintains a map of functions that are called from other functions
|
[
"Maintains",
"a",
"map",
"of",
"functions",
"that",
"are",
"called",
"from",
"other",
"functions"
] |
def updateFunctionCallMap(self, caller, callee):
"""Maintains a map of functions that are called from other functions"""
if not caller in self.calledFunctionTable:
self.calledFunctionTable[caller] = []
if not callee in self.calledFunctionTable[caller]:
self.calledFunctionTable[caller].append(callee)
if not caller in self.comprehensiveCalledFunctionTable:
self.comprehensiveCalledFunctionTable[caller] = []
self.comprehensiveCalledFunctionTable[caller].append(callee)
|
[
"def",
"updateFunctionCallMap",
"(",
"self",
",",
"caller",
",",
"callee",
")",
":",
"if",
"not",
"caller",
"in",
"self",
".",
"calledFunctionTable",
":",
"self",
".",
"calledFunctionTable",
"[",
"caller",
"]",
"=",
"[",
"]",
"if",
"not",
"callee",
"in",
"self",
".",
"calledFunctionTable",
"[",
"caller",
"]",
":",
"self",
".",
"calledFunctionTable",
"[",
"caller",
"]",
".",
"append",
"(",
"callee",
")",
"if",
"not",
"caller",
"in",
"self",
".",
"comprehensiveCalledFunctionTable",
":",
"self",
".",
"comprehensiveCalledFunctionTable",
"[",
"caller",
"]",
"=",
"[",
"]",
"self",
".",
"comprehensiveCalledFunctionTable",
"[",
"caller",
"]",
".",
"append",
"(",
"callee",
")"
] |
https://github.com/christinaa/LLVM-VideoCore4/blob/7773c3c9e5d22b785d4b96ed0acea37c8aa9c183/examples/Kaleidoscope/MCJIT/complete/genk-timing.py#L61-L69
|
||
gem5/gem5
|
141cc37c2d4b93959d4c249b8f7e6a8b2ef75338
|
src/python/m5/ext/pyfdt/pyfdt.py
|
python
|
FdtPropertyStrings.__getitem__
|
(self, index)
|
return self.strings[index]
|
Get strings, returns a string
|
Get strings, returns a string
|
[
"Get",
"strings",
"returns",
"a",
"string"
] |
def __getitem__(self, index):
"""Get strings, returns a string"""
return self.strings[index]
|
[
"def",
"__getitem__",
"(",
"self",
",",
"index",
")",
":",
"return",
"self",
".",
"strings",
"[",
"index",
"]"
] |
https://github.com/gem5/gem5/blob/141cc37c2d4b93959d4c249b8f7e6a8b2ef75338/src/python/m5/ext/pyfdt/pyfdt.py#L226-L228
|
|
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/site-packages/pip/_vendor/resolvelib/resolvers.py
|
python
|
Resolution._backtrack
|
(self)
|
return False
|
Perform backtracking.
When we enter here, the stack is like this::
[ state Z ]
[ state Y ]
[ state X ]
.... earlier states are irrelevant.
1. No pins worked for Z, so it does not have a pin.
2. We want to reset state Y to unpinned, and pin another candidate.
3. State X holds what state Y was before the pin, but does not
have the incompatibility information gathered in state Y.
Each iteration of the loop will:
1. Discard Z.
2. Discard Y but remember its incompatibility information gathered
previously, and the failure we're dealing with right now.
3. Push a new state Y' based on X, and apply the incompatibility
information from Y to Y'.
4a. If this causes Y' to conflict, we need to backtrack again. Make Y'
the new Z and go back to step 2.
4b. If the incompatibilities apply cleanly, end backtracking.
|
Perform backtracking.
|
[
"Perform",
"backtracking",
"."
] |
def _backtrack(self):
"""Perform backtracking.
When we enter here, the stack is like this::
[ state Z ]
[ state Y ]
[ state X ]
.... earlier states are irrelevant.
1. No pins worked for Z, so it does not have a pin.
2. We want to reset state Y to unpinned, and pin another candidate.
3. State X holds what state Y was before the pin, but does not
have the incompatibility information gathered in state Y.
Each iteration of the loop will:
1. Discard Z.
2. Discard Y but remember its incompatibility information gathered
previously, and the failure we're dealing with right now.
3. Push a new state Y' based on X, and apply the incompatibility
information from Y to Y'.
4a. If this causes Y' to conflict, we need to backtrack again. Make Y'
the new Z and go back to step 2.
4b. If the incompatibilities apply cleanly, end backtracking.
"""
while len(self._states) >= 3:
# Remove the state that triggered backtracking.
del self._states[-1]
# Retrieve the last candidate pin and known incompatibilities.
broken_state = self._states.pop()
name, candidate = broken_state.mapping.popitem()
incompatibilities_from_broken = [
(k, v.incompatibilities)
for k, v in broken_state.criteria.items()
]
# Also mark the newly known incompatibility.
incompatibilities_from_broken.append((name, [candidate]))
self._r.backtracking(candidate)
# Create a new state from the last known-to-work one, and apply
# the previously gathered incompatibility information.
def _patch_criteria():
for k, incompatibilities in incompatibilities_from_broken:
if not incompatibilities:
continue
try:
criterion = self.state.criteria[k]
except KeyError:
continue
criterion = criterion.excluded_of(incompatibilities)
if criterion is None:
return False
self.state.criteria[k] = criterion
return True
self._push_new_state()
success = _patch_criteria()
# It works! Let's work on this new state.
if success:
return True
# State does not work after applying known incompatibilities.
# Try the still previous state.
# No way to backtrack anymore.
return False
|
[
"def",
"_backtrack",
"(",
"self",
")",
":",
"while",
"len",
"(",
"self",
".",
"_states",
")",
">=",
"3",
":",
"# Remove the state that triggered backtracking.",
"del",
"self",
".",
"_states",
"[",
"-",
"1",
"]",
"# Retrieve the last candidate pin and known incompatibilities.",
"broken_state",
"=",
"self",
".",
"_states",
".",
"pop",
"(",
")",
"name",
",",
"candidate",
"=",
"broken_state",
".",
"mapping",
".",
"popitem",
"(",
")",
"incompatibilities_from_broken",
"=",
"[",
"(",
"k",
",",
"v",
".",
"incompatibilities",
")",
"for",
"k",
",",
"v",
"in",
"broken_state",
".",
"criteria",
".",
"items",
"(",
")",
"]",
"# Also mark the newly known incompatibility.",
"incompatibilities_from_broken",
".",
"append",
"(",
"(",
"name",
",",
"[",
"candidate",
"]",
")",
")",
"self",
".",
"_r",
".",
"backtracking",
"(",
"candidate",
")",
"# Create a new state from the last known-to-work one, and apply",
"# the previously gathered incompatibility information.",
"def",
"_patch_criteria",
"(",
")",
":",
"for",
"k",
",",
"incompatibilities",
"in",
"incompatibilities_from_broken",
":",
"if",
"not",
"incompatibilities",
":",
"continue",
"try",
":",
"criterion",
"=",
"self",
".",
"state",
".",
"criteria",
"[",
"k",
"]",
"except",
"KeyError",
":",
"continue",
"criterion",
"=",
"criterion",
".",
"excluded_of",
"(",
"incompatibilities",
")",
"if",
"criterion",
"is",
"None",
":",
"return",
"False",
"self",
".",
"state",
".",
"criteria",
"[",
"k",
"]",
"=",
"criterion",
"return",
"True",
"self",
".",
"_push_new_state",
"(",
")",
"success",
"=",
"_patch_criteria",
"(",
")",
"# It works! Let's work on this new state.",
"if",
"success",
":",
"return",
"True",
"# State does not work after applying known incompatibilities.",
"# Try the still previous state.",
"# No way to backtrack anymore.",
"return",
"False"
] |
https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/site-packages/pip/_vendor/resolvelib/resolvers.py#L236-L306
|
|
wlanjie/AndroidFFmpeg
|
7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf
|
tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/plistlib.py
|
python
|
writePlist
|
(rootObject, pathOrFile)
|
Write 'rootObject' to a .plist file. 'pathOrFile' may either be a
file name or a (writable) file object.
|
Write 'rootObject' to a .plist file. 'pathOrFile' may either be a
file name or a (writable) file object.
|
[
"Write",
"rootObject",
"to",
"a",
".",
"plist",
"file",
".",
"pathOrFile",
"may",
"either",
"be",
"a",
"file",
"name",
"or",
"a",
"(",
"writable",
")",
"file",
"object",
"."
] |
def writePlist(rootObject, pathOrFile):
"""Write 'rootObject' to a .plist file. 'pathOrFile' may either be a
file name or a (writable) file object.
"""
didOpen = 0
if isinstance(pathOrFile, (str, unicode)):
pathOrFile = open(pathOrFile, "w")
didOpen = 1
writer = PlistWriter(pathOrFile)
writer.writeln("<plist version=\"1.0\">")
writer.writeValue(rootObject)
writer.writeln("</plist>")
if didOpen:
pathOrFile.close()
|
[
"def",
"writePlist",
"(",
"rootObject",
",",
"pathOrFile",
")",
":",
"didOpen",
"=",
"0",
"if",
"isinstance",
"(",
"pathOrFile",
",",
"(",
"str",
",",
"unicode",
")",
")",
":",
"pathOrFile",
"=",
"open",
"(",
"pathOrFile",
",",
"\"w\"",
")",
"didOpen",
"=",
"1",
"writer",
"=",
"PlistWriter",
"(",
"pathOrFile",
")",
"writer",
".",
"writeln",
"(",
"\"<plist version=\\\"1.0\\\">\"",
")",
"writer",
".",
"writeValue",
"(",
"rootObject",
")",
"writer",
".",
"writeln",
"(",
"\"</plist>\"",
")",
"if",
"didOpen",
":",
"pathOrFile",
".",
"close",
"(",
")"
] |
https://github.com/wlanjie/AndroidFFmpeg/blob/7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf/tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/plistlib.py#L84-L97
|
||
pmq20/node-packer
|
12c46c6e44fbc14d9ee645ebd17d5296b324f7e0
|
lts/tools/gyp/pylib/gyp/MSVSVersion.py
|
python
|
_RegistryGetValue
|
(key, value)
|
return match.group(1)
|
Use _winreg or reg.exe to obtain the value of a registry key.
Using _winreg is preferable because it solves an issue on some corporate
environments where access to reg.exe is locked down. However, we still need
to fallback to reg.exe for the case where the _winreg module is not available
(for example in cygwin python).
Args:
key: The registry key.
value: The particular registry value to read.
Return:
contents of the registry key's value, or None on failure.
|
Use _winreg or reg.exe to obtain the value of a registry key.
|
[
"Use",
"_winreg",
"or",
"reg",
".",
"exe",
"to",
"obtain",
"the",
"value",
"of",
"a",
"registry",
"key",
"."
] |
def _RegistryGetValue(key, value):
"""Use _winreg or reg.exe to obtain the value of a registry key.
Using _winreg is preferable because it solves an issue on some corporate
environments where access to reg.exe is locked down. However, we still need
to fallback to reg.exe for the case where the _winreg module is not available
(for example in cygwin python).
Args:
key: The registry key.
value: The particular registry value to read.
Return:
contents of the registry key's value, or None on failure.
"""
try:
return _RegistryGetValueUsingWinReg(key, value)
except ImportError:
pass
# Fallback to reg.exe if we fail to import _winreg.
text = _RegistryQuery(key, value)
if not text:
return None
# Extract value.
match = re.search(r'REG_\w+\s+([^\r]+)\r\n', text)
if not match:
return None
return match.group(1)
|
[
"def",
"_RegistryGetValue",
"(",
"key",
",",
"value",
")",
":",
"try",
":",
"return",
"_RegistryGetValueUsingWinReg",
"(",
"key",
",",
"value",
")",
"except",
"ImportError",
":",
"pass",
"# Fallback to reg.exe if we fail to import _winreg.",
"text",
"=",
"_RegistryQuery",
"(",
"key",
",",
"value",
")",
"if",
"not",
"text",
":",
"return",
"None",
"# Extract value.",
"match",
"=",
"re",
".",
"search",
"(",
"r'REG_\\w+\\s+([^\\r]+)\\r\\n'",
",",
"text",
")",
"if",
"not",
"match",
":",
"return",
"None",
"return",
"match",
".",
"group",
"(",
"1",
")"
] |
https://github.com/pmq20/node-packer/blob/12c46c6e44fbc14d9ee645ebd17d5296b324f7e0/lts/tools/gyp/pylib/gyp/MSVSVersion.py#L230-L257
|
|
snap-stanford/snap-python
|
d53c51b0a26aa7e3e7400b014cdf728948fde80a
|
setup/snap.py
|
python
|
TPrGraph
|
(*args)
|
return _snap.TPrGraph(*args)
|
TPrGraph(PUNGraph G) -> TUNGraph
Parameters:
G: PUNGraph
|
TPrGraph(PUNGraph G) -> TUNGraph
|
[
"TPrGraph",
"(",
"PUNGraph",
"G",
")",
"-",
">",
"TUNGraph"
] |
def TPrGraph(*args):
"""
TPrGraph(PUNGraph G) -> TUNGraph
Parameters:
G: PUNGraph
"""
return _snap.TPrGraph(*args)
|
[
"def",
"TPrGraph",
"(",
"*",
"args",
")",
":",
"return",
"_snap",
".",
"TPrGraph",
"(",
"*",
"args",
")"
] |
https://github.com/snap-stanford/snap-python/blob/d53c51b0a26aa7e3e7400b014cdf728948fde80a/setup/snap.py#L21203-L21211
|
|
wxWidgets/wxPython-Classic
|
19571e1ae65f1ac445f5491474121998c97a1bf0
|
src/msw/_core.py
|
python
|
Menu.UpdateUI
|
(*args, **kwargs)
|
return _core_.Menu_UpdateUI(*args, **kwargs)
|
UpdateUI(self, EvtHandler source=None)
|
UpdateUI(self, EvtHandler source=None)
|
[
"UpdateUI",
"(",
"self",
"EvtHandler",
"source",
"=",
"None",
")"
] |
def UpdateUI(*args, **kwargs):
"""UpdateUI(self, EvtHandler source=None)"""
return _core_.Menu_UpdateUI(*args, **kwargs)
|
[
"def",
"UpdateUI",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"return",
"_core_",
".",
"Menu_UpdateUI",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")"
] |
https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/msw/_core.py#L12222-L12224
|
|
runtimejs/runtime
|
0a6e84c30823d35a4548d6634166784260ae7b74
|
deps/v8/gypfiles/vs_toolchain.py
|
python
|
GetVisualStudioVersion
|
()
|
return os.environ.get('GYP_MSVS_VERSION', CURRENT_DEFAULT_TOOLCHAIN_VERSION)
|
Return GYP_MSVS_VERSION of Visual Studio.
|
Return GYP_MSVS_VERSION of Visual Studio.
|
[
"Return",
"GYP_MSVS_VERSION",
"of",
"Visual",
"Studio",
"."
] |
def GetVisualStudioVersion():
"""Return GYP_MSVS_VERSION of Visual Studio.
"""
return os.environ.get('GYP_MSVS_VERSION', CURRENT_DEFAULT_TOOLCHAIN_VERSION)
|
[
"def",
"GetVisualStudioVersion",
"(",
")",
":",
"return",
"os",
".",
"environ",
".",
"get",
"(",
"'GYP_MSVS_VERSION'",
",",
"CURRENT_DEFAULT_TOOLCHAIN_VERSION",
")"
] |
https://github.com/runtimejs/runtime/blob/0a6e84c30823d35a4548d6634166784260ae7b74/deps/v8/gypfiles/vs_toolchain.py#L110-L113
|
|
hpi-xnor/BMXNet
|
ed0b201da6667887222b8e4b5f997c4f6b61943d
|
python/mxnet/image/detection.py
|
python
|
DetAugmenter.__call__
|
(self, src, label)
|
Abstract implementation body
|
Abstract implementation body
|
[
"Abstract",
"implementation",
"body"
] |
def __call__(self, src, label):
"""Abstract implementation body"""
raise NotImplementedError("Must override implementation.")
|
[
"def",
"__call__",
"(",
"self",
",",
"src",
",",
"label",
")",
":",
"raise",
"NotImplementedError",
"(",
"\"Must override implementation.\"",
")"
] |
https://github.com/hpi-xnor/BMXNet/blob/ed0b201da6667887222b8e4b5f997c4f6b61943d/python/mxnet/image/detection.py#L58-L60
|
||
catboost/catboost
|
167f64f237114a4d10b2b4ee42adb4569137debe
|
contrib/python/traitlets/py2/traitlets/traitlets.py
|
python
|
HasTraits.on_trait_change
|
(self, handler=None, name=None, remove=False)
|
DEPRECATED: Setup a handler to be called when a trait changes.
This is used to setup dynamic notifications of trait changes.
Static handlers can be created by creating methods on a HasTraits
subclass with the naming convention '_[traitname]_changed'. Thus,
to create static handler for the trait 'a', create the method
_a_changed(self, name, old, new) (fewer arguments can be used, see
below).
If `remove` is True and `handler` is not specified, all change
handlers for the specified name are uninstalled.
Parameters
----------
handler : callable, None
A callable that is called when a trait changes. Its
signature can be handler(), handler(name), handler(name, new),
handler(name, old, new), or handler(name, old, new, self).
name : list, str, None
If None, the handler will apply to all traits. If a list
of str, handler will apply to all names in the list. If a
str, the handler will apply just to that name.
remove : bool
If False (the default), then install the handler. If True
then unintall it.
|
DEPRECATED: Setup a handler to be called when a trait changes.
|
[
"DEPRECATED",
":",
"Setup",
"a",
"handler",
"to",
"be",
"called",
"when",
"a",
"trait",
"changes",
"."
] |
def on_trait_change(self, handler=None, name=None, remove=False):
"""DEPRECATED: Setup a handler to be called when a trait changes.
This is used to setup dynamic notifications of trait changes.
Static handlers can be created by creating methods on a HasTraits
subclass with the naming convention '_[traitname]_changed'. Thus,
to create static handler for the trait 'a', create the method
_a_changed(self, name, old, new) (fewer arguments can be used, see
below).
If `remove` is True and `handler` is not specified, all change
handlers for the specified name are uninstalled.
Parameters
----------
handler : callable, None
A callable that is called when a trait changes. Its
signature can be handler(), handler(name), handler(name, new),
handler(name, old, new), or handler(name, old, new, self).
name : list, str, None
If None, the handler will apply to all traits. If a list
of str, handler will apply to all names in the list. If a
str, the handler will apply just to that name.
remove : bool
If False (the default), then install the handler. If True
then unintall it.
"""
warn("on_trait_change is deprecated in traitlets 4.1: use observe instead",
DeprecationWarning, stacklevel=2)
if name is None:
name = All
if remove:
self.unobserve(_callback_wrapper(handler), names=name)
else:
self.observe(_callback_wrapper(handler), names=name)
|
[
"def",
"on_trait_change",
"(",
"self",
",",
"handler",
"=",
"None",
",",
"name",
"=",
"None",
",",
"remove",
"=",
"False",
")",
":",
"warn",
"(",
"\"on_trait_change is deprecated in traitlets 4.1: use observe instead\"",
",",
"DeprecationWarning",
",",
"stacklevel",
"=",
"2",
")",
"if",
"name",
"is",
"None",
":",
"name",
"=",
"All",
"if",
"remove",
":",
"self",
".",
"unobserve",
"(",
"_callback_wrapper",
"(",
"handler",
")",
",",
"names",
"=",
"name",
")",
"else",
":",
"self",
".",
"observe",
"(",
"_callback_wrapper",
"(",
"handler",
")",
",",
"names",
"=",
"name",
")"
] |
https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/traitlets/py2/traitlets/traitlets.py#L1200-L1235
|
||
krishauser/Klampt
|
972cc83ea5befac3f653c1ba20f80155768ad519
|
Python/python2_version/klampt/robotsim.py
|
python
|
IKObjective.setFreePosition
|
(self)
|
return _robotsim.IKObjective_setFreePosition(self)
|
setFreePosition(IKObjective self)
Manual: Sets a free position constraint.
|
setFreePosition(IKObjective self)
|
[
"setFreePosition",
"(",
"IKObjective",
"self",
")"
] |
def setFreePosition(self):
"""
setFreePosition(IKObjective self)
Manual: Sets a free position constraint.
"""
return _robotsim.IKObjective_setFreePosition(self)
|
[
"def",
"setFreePosition",
"(",
"self",
")",
":",
"return",
"_robotsim",
".",
"IKObjective_setFreePosition",
"(",
"self",
")"
] |
https://github.com/krishauser/Klampt/blob/972cc83ea5befac3f653c1ba20f80155768ad519/Python/python2_version/klampt/robotsim.py#L6317-L6326
|
|
Xilinx/Vitis-AI
|
fc74d404563d9951b57245443c73bef389f3657f
|
tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py
|
python
|
GradientBoostedDecisionTreeModel.make_update_ensemble_fn
|
(self, ensemble_stamp, training_state,
dropout_seed, class_id)
|
return _update_ensemble
|
A method to create the function which updates the tree ensemble.
|
A method to create the function which updates the tree ensemble.
|
[
"A",
"method",
"to",
"create",
"the",
"function",
"which",
"updates",
"the",
"tree",
"ensemble",
"."
] |
def make_update_ensemble_fn(self, ensemble_stamp, training_state,
dropout_seed, class_id):
"""A method to create the function which updates the tree ensemble."""
# Determine learning rate.
learning_rate_tuner = self._learner_config.learning_rate_tuner.WhichOneof(
"tuner")
if learning_rate_tuner == "fixed" or learning_rate_tuner == "dropout":
tuner = getattr(self._learner_config.learning_rate_tuner,
learning_rate_tuner)
learning_rate = tuner.learning_rate
else:
# TODO(nponomareva, soroush) do the line search.
raise ValueError("Line search learning rate is not yet supported.")
def _update_ensemble():
"""A method to update the tree ensemble."""
# Get next stamp token.
next_ensemble_stamp = ensemble_stamp + 1
# Finalize bias stats.
_, _, _, bias_grads, bias_hess = (
training_state.bias_stats_accumulator.flush(ensemble_stamp,
next_ensemble_stamp))
# Finalize handler splits.
are_splits_ready_list = []
partition_ids_list = []
gains_list = []
split_info_list = []
for handler in training_state.handlers:
(are_splits_ready,
partition_ids, gains, split_info) = handler.make_splits(
ensemble_stamp, next_ensemble_stamp, class_id)
are_splits_ready_list.append(are_splits_ready)
partition_ids_list.append(partition_ids)
gains_list.append(gains)
split_info_list.append(split_info)
# Stack all the inputs to one tensor per type.
# This is a workaround for the slowness of graph building in tf.cond.
# See (b/36554864).
split_sizes = array_ops.reshape(
array_ops.shape_n(partition_ids_list), [len(partition_ids_list)])
partition_ids = array_ops.concat(partition_ids_list, axis=0)
gains = array_ops.concat(gains_list, axis=0)
split_infos = array_ops.concat(split_info_list, axis=0)
# Determine if all splits are ready.
are_all_splits_ready = math_ops.reduce_all(
array_ops.stack(
are_splits_ready_list, axis=0, name="stack_handler_readiness"))
# Define bias centering update operation.
def _center_bias_fn():
# Center tree ensemble bias.
delta_updates = array_ops.where(bias_hess > 0, -bias_grads / bias_hess,
array_ops.zeros_like(bias_grads))
center_bias = training_ops.center_tree_ensemble_bias(
tree_ensemble_handle=self._ensemble_handle,
stamp_token=ensemble_stamp,
next_stamp_token=next_ensemble_stamp,
delta_updates=delta_updates,
learner_config=self._learner_config_serialized)
return training_state.continue_centering.assign(center_bias)
# Define ensemble growing operations.
def _grow_ensemble_ready_fn():
# Grow the ensemble given the current candidates.
sizes = array_ops.unstack(split_sizes)
partition_ids_list = list(array_ops.split(partition_ids, sizes, axis=0))
# When using the oblivious decision tree as weak learner, it produces
# one gain and one split per handler and not number of partitions.
if self._learner_config.weak_learner_type == (
learner_pb2.LearnerConfig.OBLIVIOUS_DECISION_TREE):
sizes = len(training_state.handlers)
gains_list = list(array_ops.split(gains, sizes, axis=0))
split_info_list = list(array_ops.split(split_infos, sizes, axis=0))
return training_ops.grow_tree_ensemble(
tree_ensemble_handle=self._ensemble_handle,
stamp_token=ensemble_stamp,
next_stamp_token=next_ensemble_stamp,
learning_rate=learning_rate,
partition_ids=partition_ids_list,
gains=gains_list,
splits=split_info_list,
learner_config=self._learner_config_serialized,
dropout_seed=dropout_seed,
center_bias=self._center_bias,
max_tree_depth=self._max_tree_depth,
weak_learner_type=self._learner_config.weak_learner_type)
def _grow_ensemble_not_ready_fn():
# Don't grow the ensemble, just update the stamp.
return training_ops.grow_tree_ensemble(
tree_ensemble_handle=self._ensemble_handle,
stamp_token=ensemble_stamp,
next_stamp_token=next_ensemble_stamp,
learning_rate=0,
partition_ids=[],
gains=[],
splits=[],
learner_config=self._learner_config_serialized,
dropout_seed=dropout_seed,
center_bias=self._center_bias,
max_tree_depth=self._max_tree_depth,
weak_learner_type=self._learner_config.weak_learner_type)
def _grow_ensemble_fn():
# Conditionally grow an ensemble depending on whether the splits
# from all the handlers are ready.
return control_flow_ops.cond(are_all_splits_ready,
_grow_ensemble_ready_fn,
_grow_ensemble_not_ready_fn)
# Update ensemble.
update_ops = [are_all_splits_ready]
if self._center_bias:
update_model = control_flow_ops.cond(training_state.continue_centering,
_center_bias_fn, _grow_ensemble_fn)
else:
update_model = _grow_ensemble_fn()
update_ops.append(update_model)
# Update ensemble stats.
with ops.control_dependencies([update_model]):
stats = training_ops.tree_ensemble_stats(
self._ensemble_handle, stamp_token=next_ensemble_stamp)
update_ops.append(self._finalized_trees.assign(stats.num_trees))
update_ops.append(self._attempted_trees.assign(stats.attempted_trees))
update_ops.append(training_state.num_layers.assign(stats.num_layers))
update_ops.append(training_state.active_tree.assign(stats.active_tree))
update_ops.append(
training_state.active_layer.assign(stats.active_layer))
# Flush step stats.
update_ops.extend(
training_state.steps_accumulator.flush(ensemble_stamp,
next_ensemble_stamp))
return control_flow_ops.group(*update_ops, name="update_ensemble")
return _update_ensemble
|
[
"def",
"make_update_ensemble_fn",
"(",
"self",
",",
"ensemble_stamp",
",",
"training_state",
",",
"dropout_seed",
",",
"class_id",
")",
":",
"# Determine learning rate.",
"learning_rate_tuner",
"=",
"self",
".",
"_learner_config",
".",
"learning_rate_tuner",
".",
"WhichOneof",
"(",
"\"tuner\"",
")",
"if",
"learning_rate_tuner",
"==",
"\"fixed\"",
"or",
"learning_rate_tuner",
"==",
"\"dropout\"",
":",
"tuner",
"=",
"getattr",
"(",
"self",
".",
"_learner_config",
".",
"learning_rate_tuner",
",",
"learning_rate_tuner",
")",
"learning_rate",
"=",
"tuner",
".",
"learning_rate",
"else",
":",
"# TODO(nponomareva, soroush) do the line search.",
"raise",
"ValueError",
"(",
"\"Line search learning rate is not yet supported.\"",
")",
"def",
"_update_ensemble",
"(",
")",
":",
"\"\"\"A method to update the tree ensemble.\"\"\"",
"# Get next stamp token.",
"next_ensemble_stamp",
"=",
"ensemble_stamp",
"+",
"1",
"# Finalize bias stats.",
"_",
",",
"_",
",",
"_",
",",
"bias_grads",
",",
"bias_hess",
"=",
"(",
"training_state",
".",
"bias_stats_accumulator",
".",
"flush",
"(",
"ensemble_stamp",
",",
"next_ensemble_stamp",
")",
")",
"# Finalize handler splits.",
"are_splits_ready_list",
"=",
"[",
"]",
"partition_ids_list",
"=",
"[",
"]",
"gains_list",
"=",
"[",
"]",
"split_info_list",
"=",
"[",
"]",
"for",
"handler",
"in",
"training_state",
".",
"handlers",
":",
"(",
"are_splits_ready",
",",
"partition_ids",
",",
"gains",
",",
"split_info",
")",
"=",
"handler",
".",
"make_splits",
"(",
"ensemble_stamp",
",",
"next_ensemble_stamp",
",",
"class_id",
")",
"are_splits_ready_list",
".",
"append",
"(",
"are_splits_ready",
")",
"partition_ids_list",
".",
"append",
"(",
"partition_ids",
")",
"gains_list",
".",
"append",
"(",
"gains",
")",
"split_info_list",
".",
"append",
"(",
"split_info",
")",
"# Stack all the inputs to one tensor per type.",
"# This is a workaround for the slowness of graph building in tf.cond.",
"# See (b/36554864).",
"split_sizes",
"=",
"array_ops",
".",
"reshape",
"(",
"array_ops",
".",
"shape_n",
"(",
"partition_ids_list",
")",
",",
"[",
"len",
"(",
"partition_ids_list",
")",
"]",
")",
"partition_ids",
"=",
"array_ops",
".",
"concat",
"(",
"partition_ids_list",
",",
"axis",
"=",
"0",
")",
"gains",
"=",
"array_ops",
".",
"concat",
"(",
"gains_list",
",",
"axis",
"=",
"0",
")",
"split_infos",
"=",
"array_ops",
".",
"concat",
"(",
"split_info_list",
",",
"axis",
"=",
"0",
")",
"# Determine if all splits are ready.",
"are_all_splits_ready",
"=",
"math_ops",
".",
"reduce_all",
"(",
"array_ops",
".",
"stack",
"(",
"are_splits_ready_list",
",",
"axis",
"=",
"0",
",",
"name",
"=",
"\"stack_handler_readiness\"",
")",
")",
"# Define bias centering update operation.",
"def",
"_center_bias_fn",
"(",
")",
":",
"# Center tree ensemble bias.",
"delta_updates",
"=",
"array_ops",
".",
"where",
"(",
"bias_hess",
">",
"0",
",",
"-",
"bias_grads",
"/",
"bias_hess",
",",
"array_ops",
".",
"zeros_like",
"(",
"bias_grads",
")",
")",
"center_bias",
"=",
"training_ops",
".",
"center_tree_ensemble_bias",
"(",
"tree_ensemble_handle",
"=",
"self",
".",
"_ensemble_handle",
",",
"stamp_token",
"=",
"ensemble_stamp",
",",
"next_stamp_token",
"=",
"next_ensemble_stamp",
",",
"delta_updates",
"=",
"delta_updates",
",",
"learner_config",
"=",
"self",
".",
"_learner_config_serialized",
")",
"return",
"training_state",
".",
"continue_centering",
".",
"assign",
"(",
"center_bias",
")",
"# Define ensemble growing operations.",
"def",
"_grow_ensemble_ready_fn",
"(",
")",
":",
"# Grow the ensemble given the current candidates.",
"sizes",
"=",
"array_ops",
".",
"unstack",
"(",
"split_sizes",
")",
"partition_ids_list",
"=",
"list",
"(",
"array_ops",
".",
"split",
"(",
"partition_ids",
",",
"sizes",
",",
"axis",
"=",
"0",
")",
")",
"# When using the oblivious decision tree as weak learner, it produces",
"# one gain and one split per handler and not number of partitions.",
"if",
"self",
".",
"_learner_config",
".",
"weak_learner_type",
"==",
"(",
"learner_pb2",
".",
"LearnerConfig",
".",
"OBLIVIOUS_DECISION_TREE",
")",
":",
"sizes",
"=",
"len",
"(",
"training_state",
".",
"handlers",
")",
"gains_list",
"=",
"list",
"(",
"array_ops",
".",
"split",
"(",
"gains",
",",
"sizes",
",",
"axis",
"=",
"0",
")",
")",
"split_info_list",
"=",
"list",
"(",
"array_ops",
".",
"split",
"(",
"split_infos",
",",
"sizes",
",",
"axis",
"=",
"0",
")",
")",
"return",
"training_ops",
".",
"grow_tree_ensemble",
"(",
"tree_ensemble_handle",
"=",
"self",
".",
"_ensemble_handle",
",",
"stamp_token",
"=",
"ensemble_stamp",
",",
"next_stamp_token",
"=",
"next_ensemble_stamp",
",",
"learning_rate",
"=",
"learning_rate",
",",
"partition_ids",
"=",
"partition_ids_list",
",",
"gains",
"=",
"gains_list",
",",
"splits",
"=",
"split_info_list",
",",
"learner_config",
"=",
"self",
".",
"_learner_config_serialized",
",",
"dropout_seed",
"=",
"dropout_seed",
",",
"center_bias",
"=",
"self",
".",
"_center_bias",
",",
"max_tree_depth",
"=",
"self",
".",
"_max_tree_depth",
",",
"weak_learner_type",
"=",
"self",
".",
"_learner_config",
".",
"weak_learner_type",
")",
"def",
"_grow_ensemble_not_ready_fn",
"(",
")",
":",
"# Don't grow the ensemble, just update the stamp.",
"return",
"training_ops",
".",
"grow_tree_ensemble",
"(",
"tree_ensemble_handle",
"=",
"self",
".",
"_ensemble_handle",
",",
"stamp_token",
"=",
"ensemble_stamp",
",",
"next_stamp_token",
"=",
"next_ensemble_stamp",
",",
"learning_rate",
"=",
"0",
",",
"partition_ids",
"=",
"[",
"]",
",",
"gains",
"=",
"[",
"]",
",",
"splits",
"=",
"[",
"]",
",",
"learner_config",
"=",
"self",
".",
"_learner_config_serialized",
",",
"dropout_seed",
"=",
"dropout_seed",
",",
"center_bias",
"=",
"self",
".",
"_center_bias",
",",
"max_tree_depth",
"=",
"self",
".",
"_max_tree_depth",
",",
"weak_learner_type",
"=",
"self",
".",
"_learner_config",
".",
"weak_learner_type",
")",
"def",
"_grow_ensemble_fn",
"(",
")",
":",
"# Conditionally grow an ensemble depending on whether the splits",
"# from all the handlers are ready.",
"return",
"control_flow_ops",
".",
"cond",
"(",
"are_all_splits_ready",
",",
"_grow_ensemble_ready_fn",
",",
"_grow_ensemble_not_ready_fn",
")",
"# Update ensemble.",
"update_ops",
"=",
"[",
"are_all_splits_ready",
"]",
"if",
"self",
".",
"_center_bias",
":",
"update_model",
"=",
"control_flow_ops",
".",
"cond",
"(",
"training_state",
".",
"continue_centering",
",",
"_center_bias_fn",
",",
"_grow_ensemble_fn",
")",
"else",
":",
"update_model",
"=",
"_grow_ensemble_fn",
"(",
")",
"update_ops",
".",
"append",
"(",
"update_model",
")",
"# Update ensemble stats.",
"with",
"ops",
".",
"control_dependencies",
"(",
"[",
"update_model",
"]",
")",
":",
"stats",
"=",
"training_ops",
".",
"tree_ensemble_stats",
"(",
"self",
".",
"_ensemble_handle",
",",
"stamp_token",
"=",
"next_ensemble_stamp",
")",
"update_ops",
".",
"append",
"(",
"self",
".",
"_finalized_trees",
".",
"assign",
"(",
"stats",
".",
"num_trees",
")",
")",
"update_ops",
".",
"append",
"(",
"self",
".",
"_attempted_trees",
".",
"assign",
"(",
"stats",
".",
"attempted_trees",
")",
")",
"update_ops",
".",
"append",
"(",
"training_state",
".",
"num_layers",
".",
"assign",
"(",
"stats",
".",
"num_layers",
")",
")",
"update_ops",
".",
"append",
"(",
"training_state",
".",
"active_tree",
".",
"assign",
"(",
"stats",
".",
"active_tree",
")",
")",
"update_ops",
".",
"append",
"(",
"training_state",
".",
"active_layer",
".",
"assign",
"(",
"stats",
".",
"active_layer",
")",
")",
"# Flush step stats.",
"update_ops",
".",
"extend",
"(",
"training_state",
".",
"steps_accumulator",
".",
"flush",
"(",
"ensemble_stamp",
",",
"next_ensemble_stamp",
")",
")",
"return",
"control_flow_ops",
".",
"group",
"(",
"*",
"update_ops",
",",
"name",
"=",
"\"update_ensemble\"",
")",
"return",
"_update_ensemble"
] |
https://github.com/Xilinx/Vitis-AI/blob/fc74d404563d9951b57245443c73bef389f3657f/tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py#L1040-L1180
|
|
kamyu104/LeetCode-Solutions
|
77605708a927ea3b85aee5a479db733938c7c211
|
Python/check-if-a-parentheses-string-can-be-valid.py
|
python
|
Solution.canBeValid
|
(self, s, locked)
|
return True
|
:type s: str
:type locked: str
:rtype: bool
|
:type s: str
:type locked: str
:rtype: bool
|
[
":",
"type",
"s",
":",
"str",
":",
"type",
"locked",
":",
"str",
":",
"rtype",
":",
"bool"
] |
def canBeValid(self, s, locked):
"""
:type s: str
:type locked: str
:rtype: bool
"""
if len(s)%2:
return False
for direction, c in ((lambda x:x, '('), (reversed, ')')):
cnt = bal = 0
for i in direction(xrange(len(s))):
if locked[i] == '0':
cnt += 1
else:
bal += 1 if s[i] == c else -1
if cnt+bal < 0:
return False
return True
|
[
"def",
"canBeValid",
"(",
"self",
",",
"s",
",",
"locked",
")",
":",
"if",
"len",
"(",
"s",
")",
"%",
"2",
":",
"return",
"False",
"for",
"direction",
",",
"c",
"in",
"(",
"(",
"lambda",
"x",
":",
"x",
",",
"'('",
")",
",",
"(",
"reversed",
",",
"')'",
")",
")",
":",
"cnt",
"=",
"bal",
"=",
"0",
"for",
"i",
"in",
"direction",
"(",
"xrange",
"(",
"len",
"(",
"s",
")",
")",
")",
":",
"if",
"locked",
"[",
"i",
"]",
"==",
"'0'",
":",
"cnt",
"+=",
"1",
"else",
":",
"bal",
"+=",
"1",
"if",
"s",
"[",
"i",
"]",
"==",
"c",
"else",
"-",
"1",
"if",
"cnt",
"+",
"bal",
"<",
"0",
":",
"return",
"False",
"return",
"True"
] |
https://github.com/kamyu104/LeetCode-Solutions/blob/77605708a927ea3b85aee5a479db733938c7c211/Python/check-if-a-parentheses-string-can-be-valid.py#L5-L22
|
|
hanpfei/chromium-net
|
392cc1fa3a8f92f42e4071ab6e674d8e0482f83f
|
third_party/catapult/third_party/closure_linter/closure_linter/aliaspass.py
|
python
|
AliasPass._IsTokenInParentBlock
|
(token, parent_block)
|
return False
|
Determines whether the given token is contained by the given block.
Args:
token: A token
parent_block: An EcmaContext.
Returns:
Whether the token is in a context that is or is a child of the given
parent_block context.
|
Determines whether the given token is contained by the given block.
|
[
"Determines",
"whether",
"the",
"given",
"token",
"is",
"contained",
"by",
"the",
"given",
"block",
"."
] |
def _IsTokenInParentBlock(token, parent_block):
"""Determines whether the given token is contained by the given block.
Args:
token: A token
parent_block: An EcmaContext.
Returns:
Whether the token is in a context that is or is a child of the given
parent_block context.
"""
context = token.metadata.context
while context:
if context is parent_block:
return True
context = context.parent
return False
|
[
"def",
"_IsTokenInParentBlock",
"(",
"token",
",",
"parent_block",
")",
":",
"context",
"=",
"token",
".",
"metadata",
".",
"context",
"while",
"context",
":",
"if",
"context",
"is",
"parent_block",
":",
"return",
"True",
"context",
"=",
"context",
".",
"parent",
"return",
"False"
] |
https://github.com/hanpfei/chromium-net/blob/392cc1fa3a8f92f42e4071ab6e674d8e0482f83f/third_party/catapult/third_party/closure_linter/closure_linter/aliaspass.py#L159-L177
|
|
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Tools/Python/3.7.10/windows/Lib/pprint.py
|
python
|
_safe_tuple
|
(t)
|
return _safe_key(t[0]), _safe_key(t[1])
|
Helper function for comparing 2-tuples
|
Helper function for comparing 2-tuples
|
[
"Helper",
"function",
"for",
"comparing",
"2",
"-",
"tuples"
] |
def _safe_tuple(t):
"Helper function for comparing 2-tuples"
return _safe_key(t[0]), _safe_key(t[1])
|
[
"def",
"_safe_tuple",
"(",
"t",
")",
":",
"return",
"_safe_key",
"(",
"t",
"[",
"0",
"]",
")",
",",
"_safe_key",
"(",
"t",
"[",
"1",
"]",
")"
] |
https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/windows/Lib/pprint.py#L94-L96
|
|
wlanjie/AndroidFFmpeg
|
7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf
|
tools/fdk-aac-build/x86/toolchain/lib/python2.7/plat-mac/lib-scriptpackages/StdSuites/AppleScript_Suite.py
|
python
|
AppleScript_Suite_Events._3c_
|
(self, _object, _attributes={}, **_arguments)
|
<: Less than
Required argument: an AE object reference
Keyword argument _attributes: AppleEvent attribute dictionary
Returns: anything
|
<: Less than
Required argument: an AE object reference
Keyword argument _attributes: AppleEvent attribute dictionary
Returns: anything
|
[
"<",
":",
"Less",
"than",
"Required",
"argument",
":",
"an",
"AE",
"object",
"reference",
"Keyword",
"argument",
"_attributes",
":",
"AppleEvent",
"attribute",
"dictionary",
"Returns",
":",
"anything"
] |
def _3c_(self, _object, _attributes={}, **_arguments):
"""<: Less than
Required argument: an AE object reference
Keyword argument _attributes: AppleEvent attribute dictionary
Returns: anything
"""
_code = 'ascr'
_subcode = '< '
if _arguments: raise TypeError, 'No optional args expected'
_arguments['----'] = _object
_reply, _arguments, _attributes = self.send(_code, _subcode,
_arguments, _attributes)
if _arguments.get('errn', 0):
raise aetools.Error, aetools.decodeerror(_arguments)
# XXXX Optionally decode result
if _arguments.has_key('----'):
return _arguments['----']
|
[
"def",
"_3c_",
"(",
"self",
",",
"_object",
",",
"_attributes",
"=",
"{",
"}",
",",
"*",
"*",
"_arguments",
")",
":",
"_code",
"=",
"'ascr'",
"_subcode",
"=",
"'< '",
"if",
"_arguments",
":",
"raise",
"TypeError",
",",
"'No optional args expected'",
"_arguments",
"[",
"'----'",
"]",
"=",
"_object",
"_reply",
",",
"_arguments",
",",
"_attributes",
"=",
"self",
".",
"send",
"(",
"_code",
",",
"_subcode",
",",
"_arguments",
",",
"_attributes",
")",
"if",
"_arguments",
".",
"get",
"(",
"'errn'",
",",
"0",
")",
":",
"raise",
"aetools",
".",
"Error",
",",
"aetools",
".",
"decodeerror",
"(",
"_arguments",
")",
"# XXXX Optionally decode result",
"if",
"_arguments",
".",
"has_key",
"(",
"'----'",
")",
":",
"return",
"_arguments",
"[",
"'----'",
"]"
] |
https://github.com/wlanjie/AndroidFFmpeg/blob/7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf/tools/fdk-aac-build/x86/toolchain/lib/python2.7/plat-mac/lib-scriptpackages/StdSuites/AppleScript_Suite.py#L99-L118
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.