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jeog/TDAmeritradeAPI
|
91c738afd7d57b54f6231170bd64c2550fafd34d
|
python/tdma_api/auth.py
|
python
|
set_certificate_bundle_path
|
(path)
|
Set certificate bundle file(.pem) path for ssl/tls host authentication.
If library is built against default ssl/tls library the default certificate
store should be used. If not(a connection error is returned) you'll have to
provide a certificate bundle to the connection libraries.
def set_certificate_bundle_path(path);
path :: str :: path to the certificate bundle file(.pem)
returns -> None
throws -> LibraryNotLoaded, CLibException
|
Set certificate bundle file(.pem) path for ssl/tls host authentication.
If library is built against default ssl/tls library the default certificate
store should be used. If not(a connection error is returned) you'll have to
provide a certificate bundle to the connection libraries.
def set_certificate_bundle_path(path);
path :: str :: path to the certificate bundle file(.pem)
returns -> None
throws -> LibraryNotLoaded, CLibException
|
[
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def set_certificate_bundle_path(path):
"""Set certificate bundle file(.pem) path for ssl/tls host authentication.
If library is built against default ssl/tls library the default certificate
store should be used. If not(a connection error is returned) you'll have to
provide a certificate bundle to the connection libraries.
def set_certificate_bundle_path(path);
path :: str :: path to the certificate bundle file(.pem)
returns -> None
throws -> LibraryNotLoaded, CLibException
"""
clib.set_str('SetCertificateBundlePath_ABI', path)
|
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"'SetCertificateBundlePath_ABI'",
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https://github.com/jeog/TDAmeritradeAPI/blob/91c738afd7d57b54f6231170bd64c2550fafd34d/python/tdma_api/auth.py#L145-L160
|
||
su2code/SU2
|
72b2fa977b64b9683a388920f05298a40d39e5c5
|
SU2_PY/SU2/opt/scipy_tools.py
|
python
|
obj_df
|
(x,project)
|
return dobj
|
dobj = obj_df(x,project)
Objective Function Gradients
SU2 Project interface to scipy.fmin_slsqp
su2: df(x), list[nobj x dim]
scipy_slsqp: df(x), ndarray[dim]
|
dobj = obj_df(x,project)
Objective Function Gradients
SU2 Project interface to scipy.fmin_slsqp
su2: df(x), list[nobj x dim]
scipy_slsqp: df(x), ndarray[dim]
|
[
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")",
"ndarray",
"[",
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def obj_df(x,project):
""" dobj = obj_df(x,project)
Objective Function Gradients
SU2 Project interface to scipy.fmin_slsqp
su2: df(x), list[nobj x dim]
scipy_slsqp: df(x), ndarray[dim]
"""
dobj_list = project.obj_df(x)
dobj=[0.0]*len(dobj_list[0])
for this_dobj in dobj_list:
idv=0
for this_dv_dobj in this_dobj:
dobj[idv] = dobj[idv]+this_dv_dobj;
idv+=1
dobj = array( dobj )
return dobj
|
[
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https://github.com/su2code/SU2/blob/72b2fa977b64b9683a388920f05298a40d39e5c5/SU2_PY/SU2/opt/scipy_tools.py#L390-L410
|
|
hanpfei/chromium-net
|
392cc1fa3a8f92f42e4071ab6e674d8e0482f83f
|
third_party/catapult/third_party/apiclient/googleapiclient/model.py
|
python
|
BaseModel.deserialize
|
(self, content)
|
Perform the actual deserialization from response string to Python
object.
Args:
content: string, the body of the HTTP response
Returns:
The body de-serialized as a Python object.
|
Perform the actual deserialization from response string to Python
object.
|
[
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"deserialization",
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"response",
"string",
"to",
"Python",
"object",
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] |
def deserialize(self, content):
"""Perform the actual deserialization from response string to Python
object.
Args:
content: string, the body of the HTTP response
Returns:
The body de-serialized as a Python object.
"""
_abstract()
|
[
"def",
"deserialize",
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"self",
",",
"content",
")",
":",
"_abstract",
"(",
")"
] |
https://github.com/hanpfei/chromium-net/blob/392cc1fa3a8f92f42e4071ab6e674d8e0482f83f/third_party/catapult/third_party/apiclient/googleapiclient/model.py#L223-L233
|
||
nucleic/atom
|
9f0cb2a8101dd63c354a98ebc7489b2c616dc82a
|
examples/tutorial/person.py
|
python
|
Person.debug_print
|
(self, change)
|
Prints out a debug message whenever the person's age changes.
|
Prints out a debug message whenever the person's age changes.
|
[
"Prints",
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"a",
"debug",
"message",
"whenever",
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"s",
"age",
"changes",
"."
] |
def debug_print(self, change):
"""Prints out a debug message whenever the person's age changes."""
if self.debug:
templ = "{first} {last} is {age} years old."
s = templ.format(
first=self.first_name,
last=self.last_name,
age=self.age,
)
print(s)
|
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https://github.com/nucleic/atom/blob/9f0cb2a8101dd63c354a98ebc7489b2c616dc82a/examples/tutorial/person.py#L26-L35
|
||
catboost/catboost
|
167f64f237114a4d10b2b4ee42adb4569137debe
|
contrib/python/jedi/jedi/evaluate/arguments.py
|
python
|
try_iter_content
|
(types, depth=0)
|
Helper method for static analysis.
|
Helper method for static analysis.
|
[
"Helper",
"method",
"for",
"static",
"analysis",
"."
] |
def try_iter_content(types, depth=0):
"""Helper method for static analysis."""
if depth > 10:
# It's possible that a loop has references on itself (especially with
# CompiledObject). Therefore don't loop infinitely.
return
for typ in types:
try:
f = typ.py__iter__
except AttributeError:
pass
else:
for lazy_context in f():
try_iter_content(lazy_context.infer(), depth + 1)
|
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/jedi/jedi/evaluate/arguments.py#L16-L30
|
||
wxWidgets/wxPython-Classic
|
19571e1ae65f1ac445f5491474121998c97a1bf0
|
src/osx_carbon/_misc.py
|
python
|
DateSpan.SetWeeks
|
(*args, **kwargs)
|
return _misc_.DateSpan_SetWeeks(*args, **kwargs)
|
SetWeeks(self, int n) -> DateSpan
|
SetWeeks(self, int n) -> DateSpan
|
[
"SetWeeks",
"(",
"self",
"int",
"n",
")",
"-",
">",
"DateSpan"
] |
def SetWeeks(*args, **kwargs):
"""SetWeeks(self, int n) -> DateSpan"""
return _misc_.DateSpan_SetWeeks(*args, **kwargs)
|
[
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",",
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"*",
"kwargs",
")"
] |
https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_carbon/_misc.py#L4661-L4663
|
|
hanpfei/chromium-net
|
392cc1fa3a8f92f42e4071ab6e674d8e0482f83f
|
third_party/catapult/third_party/gsutil/third_party/boto/boto/swf/layer1.py
|
python
|
Layer1.describe_activity_type
|
(self, domain, activity_name, activity_version)
|
return self.json_request('DescribeActivityType', {
'domain': domain,
'activityType': {'name': activity_name,
'version': activity_version}
})
|
Returns information about the specified activity type. This
includes configuration settings provided at registration time
as well as other general information about the type.
:type domain: string
:param domain: The name of the domain in which the activity
type is registered.
:type activity_name: string
:param activity_name: The name of this activity.
:type activity_version: string
:param activity_version: The version of this activity.
:raises: UnknownResourceFault, SWFOperationNotPermittedError
|
Returns information about the specified activity type. This
includes configuration settings provided at registration time
as well as other general information about the type.
|
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"the",
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] |
def describe_activity_type(self, domain, activity_name, activity_version):
"""
Returns information about the specified activity type. This
includes configuration settings provided at registration time
as well as other general information about the type.
:type domain: string
:param domain: The name of the domain in which the activity
type is registered.
:type activity_name: string
:param activity_name: The name of this activity.
:type activity_version: string
:param activity_version: The version of this activity.
:raises: UnknownResourceFault, SWFOperationNotPermittedError
"""
return self.json_request('DescribeActivityType', {
'domain': domain,
'activityType': {'name': activity_name,
'version': activity_version}
})
|
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"}",
"}",
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] |
https://github.com/hanpfei/chromium-net/blob/392cc1fa3a8f92f42e4071ab6e674d8e0482f83f/third_party/catapult/third_party/gsutil/third_party/boto/boto/swf/layer1.py#L909-L931
|
|
wxWidgets/wxPython-Classic
|
19571e1ae65f1ac445f5491474121998c97a1bf0
|
src/osx_cocoa/wizard.py
|
python
|
PyWizardPage.DoGetSize
|
(*args, **kwargs)
|
return _wizard.PyWizardPage_DoGetSize(*args, **kwargs)
|
DoGetSize() -> (width, height)
|
DoGetSize() -> (width, height)
|
[
"DoGetSize",
"()",
"-",
">",
"(",
"width",
"height",
")"
] |
def DoGetSize(*args, **kwargs):
"""DoGetSize() -> (width, height)"""
return _wizard.PyWizardPage_DoGetSize(*args, **kwargs)
|
[
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_cocoa/wizard.py#L167-L169
|
|
hanpfei/chromium-net
|
392cc1fa3a8f92f42e4071ab6e674d8e0482f83f
|
third_party/catapult/third_party/closure_linter/closure_linter/statetracker.py
|
python
|
DocComment.GetTargetToken
|
(self)
|
Get this comment's target token.
Returns:
The token that is the target of this comment, or None if there isn't one.
|
Get this comment's target token.
|
[
"Get",
"this",
"comment",
"s",
"target",
"token",
"."
] |
def GetTargetToken(self):
"""Get this comment's target token.
Returns:
The token that is the target of this comment, or None if there isn't one.
"""
# File overviews describe the file, not a token.
if self.HasFlag('fileoverview'):
return
skip_types = frozenset([
Type.WHITESPACE,
Type.BLANK_LINE,
Type.START_PAREN])
target_types = frozenset([
Type.FUNCTION_NAME,
Type.IDENTIFIER,
Type.SIMPLE_LVALUE])
token = self.end_token.next
while token:
if token.type in target_types:
return token
# Handles the case of a comment on "var foo = ...'
if token.IsKeyword('var'):
next_code_token = tokenutil.CustomSearch(
token,
lambda t: t.type not in Type.NON_CODE_TYPES)
if (next_code_token and
next_code_token.IsType(Type.SIMPLE_LVALUE)):
return next_code_token
return
# Handles the case of a comment on "function foo () {}"
if token.type is Type.FUNCTION_DECLARATION:
next_code_token = tokenutil.CustomSearch(
token,
lambda t: t.type not in Type.NON_CODE_TYPES)
if next_code_token.IsType(Type.FUNCTION_NAME):
return next_code_token
return
# Skip types will end the search.
if token.type not in skip_types:
return
token = token.next
|
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https://github.com/hanpfei/chromium-net/blob/392cc1fa3a8f92f42e4071ab6e674d8e0482f83f/third_party/catapult/third_party/closure_linter/closure_linter/statetracker.py#L452-L505
|
||
nlohmann/json
|
eb2182414749825be086c825edb5229e5c28503d
|
third_party/cpplint/cpplint.py
|
python
|
IsBlankLine
|
(line)
|
return not line or line.isspace()
|
Returns true if the given line is blank.
We consider a line to be blank if the line is empty or consists of
only white spaces.
Args:
line: A line of a string.
Returns:
True, if the given line is blank.
|
Returns true if the given line is blank.
|
[
"Returns",
"true",
"if",
"the",
"given",
"line",
"is",
"blank",
"."
] |
def IsBlankLine(line):
"""Returns true if the given line is blank.
We consider a line to be blank if the line is empty or consists of
only white spaces.
Args:
line: A line of a string.
Returns:
True, if the given line is blank.
"""
return not line or line.isspace()
|
[
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"line",
")",
":",
"return",
"not",
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"or",
"line",
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"isspace",
"(",
")"
] |
https://github.com/nlohmann/json/blob/eb2182414749825be086c825edb5229e5c28503d/third_party/cpplint/cpplint.py#L3513-L3525
|
|
ricardoquesada/Spidermonkey
|
4a75ea2543408bd1b2c515aa95901523eeef7858
|
dom/bindings/parser/WebIDL.py
|
python
|
Parser.p_Ellipsis
|
(self, p)
|
Ellipsis : ELLIPSIS
|
Ellipsis : ELLIPSIS
|
[
"Ellipsis",
":",
"ELLIPSIS"
] |
def p_Ellipsis(self, p):
"""
Ellipsis : ELLIPSIS
"""
p[0] = True
|
[
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"p_Ellipsis",
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"self",
",",
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"True"
] |
https://github.com/ricardoquesada/Spidermonkey/blob/4a75ea2543408bd1b2c515aa95901523eeef7858/dom/bindings/parser/WebIDL.py#L5016-L5020
|
||
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Gems/CloudGemDefectReporter/v1/AWS/common-code/Lib/requests_toolbelt/multipart/encoder.py
|
python
|
Part.from_field
|
(cls, field, encoding)
|
return cls(headers, body)
|
Create a part from a Request Field generated by urllib3.
|
Create a part from a Request Field generated by urllib3.
|
[
"Create",
"a",
"part",
"from",
"a",
"Request",
"Field",
"generated",
"by",
"urllib3",
"."
] |
def from_field(cls, field, encoding):
"""Create a part from a Request Field generated by urllib3."""
headers = encode_with(field.render_headers(), encoding)
body = coerce_data(field.data, encoding)
return cls(headers, body)
|
[
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"data",
",",
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")",
"return",
"cls",
"(",
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",",
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")"
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemDefectReporter/v1/AWS/common-code/Lib/requests_toolbelt/multipart/encoder.py#L485-L489
|
|
Illumina/strelka
|
d7377443b62319f7c7bd70c241c4b2df3459e29a
|
src/python/lib/configureUtil.py
|
python
|
validateFixExistingDirArg
|
(argDir,label)
|
return _validateFixArgHelper(argDir,label,os.path.isdir)
|
convert directory arg to absolute path and check that it exists
|
convert directory arg to absolute path and check that it exists
|
[
"convert",
"directory",
"arg",
"to",
"absolute",
"path",
"and",
"check",
"that",
"it",
"exists"
] |
def validateFixExistingDirArg(argDir,label) :
"""
convert directory arg to absolute path and check that it exists
"""
return _validateFixArgHelper(argDir,label,os.path.isdir)
|
[
"def",
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"(",
"argDir",
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"label",
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".",
"isdir",
")"
] |
https://github.com/Illumina/strelka/blob/d7377443b62319f7c7bd70c241c4b2df3459e29a/src/python/lib/configureUtil.py#L186-L190
|
|
ApolloAuto/apollo
|
463fb82f9e979d02dcb25044e60931293ab2dba0
|
scripts/record_map_data.py
|
python
|
ArgManager.args
|
(self)
|
return self._args
|
Get parsed args.
|
Get parsed args.
|
[
"Get",
"parsed",
"args",
"."
] |
def args(self):
"""Get parsed args."""
if self._args is None:
self._args = self.parser.parse_args()
return self._args
|
[
"def",
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"(",
")",
"return",
"self",
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https://github.com/ApolloAuto/apollo/blob/463fb82f9e979d02dcb25044e60931293ab2dba0/scripts/record_map_data.py#L94-L98
|
|
OSGeo/gdal
|
3748fc4ba4fba727492774b2b908a2130c864a83
|
swig/python/osgeo/ogr.py
|
python
|
MajorObject.SetMetadata
|
(self, *args)
|
return _ogr.MajorObject_SetMetadata(self, *args)
|
r"""
SetMetadata(MajorObject self, char ** papszMetadata, char const * pszDomain="") -> CPLErr
SetMetadata(MajorObject self, char * pszMetadataString, char const * pszDomain="") -> CPLErr
|
r"""
SetMetadata(MajorObject self, char ** papszMetadata, char const * pszDomain="") -> CPLErr
SetMetadata(MajorObject self, char * pszMetadataString, char const * pszDomain="") -> CPLErr
|
[
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")",
"-",
">",
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] |
def SetMetadata(self, *args):
r"""
SetMetadata(MajorObject self, char ** papszMetadata, char const * pszDomain="") -> CPLErr
SetMetadata(MajorObject self, char * pszMetadataString, char const * pszDomain="") -> CPLErr
"""
return _ogr.MajorObject_SetMetadata(self, *args)
|
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https://github.com/OSGeo/gdal/blob/3748fc4ba4fba727492774b2b908a2130c864a83/swig/python/osgeo/ogr.py#L436-L441
|
|
panda3d/panda3d
|
833ad89ebad58395d0af0b7ec08538e5e4308265
|
samples/networking/03-distributed-node/AIDGameObject.py
|
python
|
AIDGameObject.announceGenerate
|
(self)
|
The AI has created this object, so we send it's distributed object ID
over to the client. That way the client can actually grab the object
and use it to communicate with the AI. Alternatively store it in the
Client Repository in self.cr
|
The AI has created this object, so we send it's distributed object ID
over to the client. That way the client can actually grab the object
and use it to communicate with the AI. Alternatively store it in the
Client Repository in self.cr
|
[
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def announceGenerate(self):
""" The AI has created this object, so we send it's distributed object ID
over to the client. That way the client can actually grab the object
and use it to communicate with the AI. Alternatively store it in the
Client Repository in self.cr """
base.messenger.send(self.cr.uniqueName('AIDGameObjectGenerated'), [self.doId])
# call the base class method
DistributedObject.announceGenerate(self)
|
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https://github.com/panda3d/panda3d/blob/833ad89ebad58395d0af0b7ec08538e5e4308265/samples/networking/03-distributed-node/AIDGameObject.py#L10-L17
|
||
ChromiumWebApps/chromium
|
c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7
|
tools/clang/scripts/run_tool.py
|
python
|
_CompilerDispatcher.__ProcessResult
|
(self, result)
|
Handles result processing.
Args:
result: The result dictionary returned by _ExecuteTool.
|
Handles result processing.
|
[
"Handles",
"result",
"processing",
"."
] |
def __ProcessResult(self, result):
"""Handles result processing.
Args:
result: The result dictionary returned by _ExecuteTool.
"""
if result['status']:
self.__success_count += 1
for k, v in result['edits'].iteritems():
self.__edits[k].extend(v)
else:
self.__failed_count += 1
sys.stdout.write('\nFailed to process %s\n' % result['filename'])
sys.stdout.write(result['stderr'])
sys.stdout.write('\n')
percentage = (
float(self.__success_count + self.__failed_count) /
len(self.__filenames)) * 100
sys.stdout.write('Succeeded: %d, Failed: %d [%.2f%%]\r' % (
self.__success_count, self.__failed_count, percentage))
sys.stdout.flush()
|
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https://github.com/ChromiumWebApps/chromium/blob/c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7/tools/clang/scripts/run_tool.py#L163-L183
|
||
BlzFans/wke
|
b0fa21158312e40c5fbd84682d643022b6c34a93
|
cygwin/lib/python2.6/distutils/command/build_py.py
|
python
|
build_py.find_data_files
|
(self, package, src_dir)
|
return files
|
Return filenames for package's data files in 'src_dir
|
Return filenames for package's data files in 'src_dir
|
[
"Return",
"filenames",
"for",
"package",
"s",
"data",
"files",
"in",
"src_dir"
] |
def find_data_files (self, package, src_dir):
"""Return filenames for package's data files in 'src_dir'"""
globs = (self.package_data.get('', [])
+ self.package_data.get(package, []))
files = []
for pattern in globs:
# Each pattern has to be converted to a platform-specific path
filelist = glob(os.path.join(src_dir, convert_path(pattern)))
# Files that match more than one pattern are only added once
files.extend([fn for fn in filelist if fn not in files])
return files
|
[
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https://github.com/BlzFans/wke/blob/b0fa21158312e40c5fbd84682d643022b6c34a93/cygwin/lib/python2.6/distutils/command/build_py.py#L129-L139
|
|
kichik/nsis
|
e39fe70400b823ac3d00321e338cf3410634b10a
|
SCons/Tools/mstoolkit.py
|
python
|
pch_emitter
|
(target, source, env)
|
return (target, source)
|
Sets up the PDB dependencies for a pch file, and adds the object
file target.
|
Sets up the PDB dependencies for a pch file, and adds the object
file target.
|
[
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"up",
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"dependencies",
"for",
"a",
"pch",
"file",
"and",
"adds",
"the",
"object",
"file",
"target",
"."
] |
def pch_emitter(target, source, env):
"""Sets up the PDB dependencies for a pch file, and adds the object
file target."""
validate_vars(env)
pch = None
obj = None
for t in target:
if SCons.Util.splitext(str(t))[1] == '.pch':
pch = t
if SCons.Util.splitext(str(t))[1] == '.obj':
obj = t
if not obj:
obj = SCons.Util.splitext(str(pch))[0]+'.obj'
target = [pch, obj] # pch must be first, and obj second for the PCHCOM to work
if 'PDB' in env and env['PDB']:
env.SideEffect(env['PDB'], target)
env.Precious(env['PDB'])
return (target, source)
|
[
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https://github.com/kichik/nsis/blob/e39fe70400b823ac3d00321e338cf3410634b10a/SCons/Tools/mstoolkit.py#L104-L128
|
|
wxWidgets/wxPython-Classic
|
19571e1ae65f1ac445f5491474121998c97a1bf0
|
src/osx_carbon/_core.py
|
python
|
WindowCreateEvent.__init__
|
(self, *args, **kwargs)
|
__init__(self, Window win=None) -> WindowCreateEvent
The EVT_WINDOW_CREATE event is sent as soon as the window object (the
underlying GUI object) exists.
|
__init__(self, Window win=None) -> WindowCreateEvent
|
[
"__init__",
"(",
"self",
"Window",
"win",
"=",
"None",
")",
"-",
">",
"WindowCreateEvent"
] |
def __init__(self, *args, **kwargs):
"""
__init__(self, Window win=None) -> WindowCreateEvent
The EVT_WINDOW_CREATE event is sent as soon as the window object (the
underlying GUI object) exists.
"""
_core_.WindowCreateEvent_swiginit(self,_core_.new_WindowCreateEvent(*args, **kwargs))
|
[
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_carbon/_core.py#L7332-L7339
|
||
Polidea/SiriusObfuscator
|
b0e590d8130e97856afe578869b83a209e2b19be
|
SymbolExtractorAndRenamer/lldb/scripts/Python/static-binding/lldb.py
|
python
|
SBValue.__eol_test__
|
(val)
|
Default function for end of list test takes an SBValue object.
Return True if val is invalid or it corresponds to a null pointer.
Otherwise, return False.
|
Default function for end of list test takes an SBValue object.
|
[
"Default",
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"for",
"end",
"of",
"list",
"test",
"takes",
"an",
"SBValue",
"object",
"."
] |
def __eol_test__(val):
"""Default function for end of list test takes an SBValue object.
Return True if val is invalid or it corresponds to a null pointer.
Otherwise, return False.
"""
if not val or val.GetValueAsUnsigned() == 0:
return True
else:
return False
|
[
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"(",
")",
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"0",
":",
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"True",
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":",
"return",
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] |
https://github.com/Polidea/SiriusObfuscator/blob/b0e590d8130e97856afe578869b83a209e2b19be/SymbolExtractorAndRenamer/lldb/scripts/Python/static-binding/lldb.py#L11724-L11733
|
||
apple/swift-lldb
|
d74be846ef3e62de946df343e8c234bde93a8912
|
scripts/Python/static-binding/lldb.py
|
python
|
SBDebugger.SetErrorFileHandle
|
(self, f, transfer_ownership)
|
return _lldb.SBDebugger_SetErrorFileHandle(self, f, transfer_ownership)
|
SetErrorFileHandle(SBDebugger self, FILE * f, bool transfer_ownership)
|
SetErrorFileHandle(SBDebugger self, FILE * f, bool transfer_ownership)
|
[
"SetErrorFileHandle",
"(",
"SBDebugger",
"self",
"FILE",
"*",
"f",
"bool",
"transfer_ownership",
")"
] |
def SetErrorFileHandle(self, f, transfer_ownership):
"""SetErrorFileHandle(SBDebugger self, FILE * f, bool transfer_ownership)"""
return _lldb.SBDebugger_SetErrorFileHandle(self, f, transfer_ownership)
|
[
"def",
"SetErrorFileHandle",
"(",
"self",
",",
"f",
",",
"transfer_ownership",
")",
":",
"return",
"_lldb",
".",
"SBDebugger_SetErrorFileHandle",
"(",
"self",
",",
"f",
",",
"transfer_ownership",
")"
] |
https://github.com/apple/swift-lldb/blob/d74be846ef3e62de946df343e8c234bde93a8912/scripts/Python/static-binding/lldb.py#L3887-L3889
|
|
miyosuda/TensorFlowAndroidMNIST
|
7b5a4603d2780a8a2834575706e9001977524007
|
jni-build/jni/include/tensorflow/contrib/learn/python/learn/dataframe/transforms/reader_source.py
|
python
|
ReaderSource.__init__
|
(self,
reader_cls,
work_units,
reader_kwargs=None,
enqueue_size=None,
batch_size=1,
queue_capacity=None,
shuffle=False,
min_after_dequeue=None,
num_threads=1,
seed=None)
|
Initializes a ReaderSource.
Args:
reader_cls: A subclass of `tesorflow.ReaderBase` that will be used to read
from `work_units`.
work_units: A list that describes the source(s) of data to read.
Typically, this is a list of filenames.
reader_kwargs: A dictionary of kwargs to be passed to `reader_cls` when it
is constructed.
enqueue_size: block size for each read operation.
batch_size: The desired batch size of output. Defaults to 1.
queue_capacity: Capacity of the queue. Defaults to 10 * `batch_size`.
shuffle: Whether records will be shuffled before returning. Defaults to
false.
min_after_dequeue: Minimum number of elements in the queue to allow a
dequeue operation. Only used when `shuffle` is true. Defaults to
`queue_capacity` / 4.
num_threads: Number of threads that will be used for reading. Each thread
has its own instance of `reader_cls`.
seed: A seed used for shuffling. Only used if `shuffle` is true.
|
Initializes a ReaderSource.
|
[
"Initializes",
"a",
"ReaderSource",
"."
] |
def __init__(self,
reader_cls,
work_units,
reader_kwargs=None,
enqueue_size=None,
batch_size=1,
queue_capacity=None,
shuffle=False,
min_after_dequeue=None,
num_threads=1,
seed=None):
"""Initializes a ReaderSource.
Args:
reader_cls: A subclass of `tesorflow.ReaderBase` that will be used to read
from `work_units`.
work_units: A list that describes the source(s) of data to read.
Typically, this is a list of filenames.
reader_kwargs: A dictionary of kwargs to be passed to `reader_cls` when it
is constructed.
enqueue_size: block size for each read operation.
batch_size: The desired batch size of output. Defaults to 1.
queue_capacity: Capacity of the queue. Defaults to 10 * `batch_size`.
shuffle: Whether records will be shuffled before returning. Defaults to
false.
min_after_dequeue: Minimum number of elements in the queue to allow a
dequeue operation. Only used when `shuffle` is true. Defaults to
`queue_capacity` / 4.
num_threads: Number of threads that will be used for reading. Each thread
has its own instance of `reader_cls`.
seed: A seed used for shuffling. Only used if `shuffle` is true.
"""
super(ReaderSource, self).__init__()
self._reader_cls = reader_cls
self._reader_kwargs = reader_kwargs
self._work_units = work_units
self._reader_kwargs = {} if reader_kwargs is None else reader_kwargs
if enqueue_size is None:
enqueue_size = max(1, int(batch_size / num_threads))
self._enqueue_size = enqueue_size
self._batch_size = batch_size
self._queue_capacity = (batch_size * 10 if queue_capacity is None else
queue_capacity)
self._shuffle = shuffle
self._min_after_dequeue = int(self.queue_capacity / 4 if min_after_dequeue
is None else min_after_dequeue)
self._num_threads = num_threads
self._seed = seed
|
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"=",
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https://github.com/miyosuda/TensorFlowAndroidMNIST/blob/7b5a4603d2780a8a2834575706e9001977524007/jni-build/jni/include/tensorflow/contrib/learn/python/learn/dataframe/transforms/reader_source.py#L29-L76
|
||
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Tools/Python/3.7.10/windows/Lib/email/_policybase.py
|
python
|
Policy.header_fetch_parse
|
(self, name, value)
|
Given the header name and the value from the model, return the value
to be returned to the application program that is requesting that
header. The value passed in by the email package may contain
surrogateescaped binary data if the lines were parsed by a BytesParser.
The returned value should not contain any surrogateescaped data.
|
Given the header name and the value from the model, return the value
to be returned to the application program that is requesting that
header. The value passed in by the email package may contain
surrogateescaped binary data if the lines were parsed by a BytesParser.
The returned value should not contain any surrogateescaped data.
|
[
"Given",
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"returned",
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"should",
"not",
"contain",
"any",
"surrogateescaped",
"data",
"."
] |
def header_fetch_parse(self, name, value):
"""Given the header name and the value from the model, return the value
to be returned to the application program that is requesting that
header. The value passed in by the email package may contain
surrogateescaped binary data if the lines were parsed by a BytesParser.
The returned value should not contain any surrogateescaped data.
"""
raise NotImplementedError
|
[
"def",
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"(",
"self",
",",
"name",
",",
"value",
")",
":",
"raise",
"NotImplementedError"
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/windows/Lib/email/_policybase.py#L238-L246
|
||
zju3dv/clean-pvnet
|
5870c509e3cc205e1bb28910a7b1a9a3c8add9a8
|
lib/utils/meshrenderer/pysixd/transform.py
|
python
|
is_same_transform
|
(matrix0, matrix1)
|
return numpy.allclose(matrix0, matrix1)
|
Return True if two matrices perform same transformation.
>>> is_same_transform(numpy.identity(4), numpy.identity(4))
True
>>> is_same_transform(numpy.identity(4), random_rotation_matrix())
False
|
Return True if two matrices perform same transformation.
|
[
"Return",
"True",
"if",
"two",
"matrices",
"perform",
"same",
"transformation",
"."
] |
def is_same_transform(matrix0, matrix1):
"""Return True if two matrices perform same transformation.
>>> is_same_transform(numpy.identity(4), numpy.identity(4))
True
>>> is_same_transform(numpy.identity(4), random_rotation_matrix())
False
"""
matrix0 = numpy.array(matrix0, dtype=numpy.float64, copy=True)
matrix0 /= matrix0[3, 3]
matrix1 = numpy.array(matrix1, dtype=numpy.float64, copy=True)
matrix1 /= matrix1[3, 3]
return numpy.allclose(matrix0, matrix1)
|
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https://github.com/zju3dv/clean-pvnet/blob/5870c509e3cc205e1bb28910a7b1a9a3c8add9a8/lib/utils/meshrenderer/pysixd/transform.py#L1861-L1874
|
|
flink-extended/dl-on-flink
|
60646aa9520f49619b64e9ff03ce73959e8a3858
|
flink-ml-tensorflow/python/flink_ml_tensorflow/gpu_info.py
|
python
|
_get_free_gpu
|
(max_gpu_utilization=40, min_free_memory=0.5, num_gpu=1)
|
return gpus_to_use, free_memory
|
Get available GPUs according to utilization thresholds.
Args:
:max_gpu_utilization: percent utilization threshold to consider a GPU "free"
:min_free_memory: percent free memory to consider a GPU "free"
:num_gpu: number of requested GPUs
Returns:
A tuple of (available_gpus, minimum_free_memory), where available_gpus is a comma-delimited string of GPU ids, and minimum_free_memory
is the lowest amount of free memory available on the available_gpus.
|
Get available GPUs according to utilization thresholds.
|
[
"Get",
"available",
"GPUs",
"according",
"to",
"utilization",
"thresholds",
"."
] |
def _get_free_gpu(max_gpu_utilization=40, min_free_memory=0.5, num_gpu=1):
"""Get available GPUs according to utilization thresholds.
Args:
:max_gpu_utilization: percent utilization threshold to consider a GPU "free"
:min_free_memory: percent free memory to consider a GPU "free"
:num_gpu: number of requested GPUs
Returns:
A tuple of (available_gpus, minimum_free_memory), where available_gpus is a comma-delimited string of GPU ids, and minimum_free_memory
is the lowest amount of free memory available on the available_gpus.
"""
def get_gpu_info():
# Get the gpu information
gpu_info = subprocess.check_output(["nvidia-smi", "--format=csv,noheader,nounits",
"--query-gpu=index,memory.total,memory.free,memory.used,utilization.gpu"]).decode()
gpu_info = gpu_info.split('\n')
gpu_info_array = []
# Check each gpu
for line in gpu_info:
if len(line) > 0:
gpu_id, total_memory, free_memory, used_memory, gpu_util = line.split(',')
gpu_memory_util = float(used_memory) / float(total_memory)
gpu_info_array.append((float(gpu_util), gpu_memory_util, gpu_id))
return (gpu_info_array)
# Read the gpu information multiple times
num_times_to_average = 5
current_array = []
for ind in range(num_times_to_average):
current_array.append(get_gpu_info())
time.sleep(1)
# Get number of gpus
num_gpus = len(current_array[0])
# Average the gpu information
avg_array = [(0, 0, str(x)) for x in range(num_gpus)]
for ind in range(num_times_to_average):
for gpu_ind in range(num_gpus):
avg_array[gpu_ind] = (avg_array[gpu_ind][0] + current_array[ind][gpu_ind][0],
avg_array[gpu_ind][1] + current_array[ind][gpu_ind][1], avg_array[gpu_ind][2])
for gpu_ind in range(num_gpus):
avg_array[gpu_ind] = (
float(avg_array[gpu_ind][0]) / num_times_to_average, float(avg_array[gpu_ind][1]) / num_times_to_average,
avg_array[gpu_ind][2])
avg_array.sort()
gpus_found = 0
gpus_to_use = ""
free_memory = 1.0
# Return the least utilized GPUs if it's utilized less than max_gpu_utilization and amount of free memory is at least min_free_memory
# Otherwise, run in cpu only mode
for current_gpu in avg_array:
if current_gpu[0] < max_gpu_utilization and (1 - current_gpu[1]) > min_free_memory:
if gpus_found == 0:
gpus_to_use = current_gpu[2]
free_memory = 1 - current_gpu[1]
else:
gpus_to_use = gpus_to_use + "," + current_gpu[2]
free_memory = min(free_memory, 1 - current_gpu[1])
gpus_found = gpus_found + 1
if gpus_found == num_gpu:
break
return gpus_to_use, free_memory
|
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https://github.com/flink-extended/dl-on-flink/blob/60646aa9520f49619b64e9ff03ce73959e8a3858/flink-ml-tensorflow/python/flink_ml_tensorflow/gpu_info.py#L128-L202
|
|
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/pandas/compat/numpy/function.py
|
python
|
validate_argsort_with_ascending
|
(ascending, args, kwargs)
|
return ascending
|
If 'Categorical.argsort' is called via the 'numpy' library, the
first parameter in its signature is 'axis', which takes either
an integer or 'None', so check if the 'ascending' parameter has
either integer type or is None, since 'ascending' itself should
be a boolean
|
If 'Categorical.argsort' is called via the 'numpy' library, the
first parameter in its signature is 'axis', which takes either
an integer or 'None', so check if the 'ascending' parameter has
either integer type or is None, since 'ascending' itself should
be a boolean
|
[
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] |
def validate_argsort_with_ascending(ascending, args, kwargs):
"""
If 'Categorical.argsort' is called via the 'numpy' library, the
first parameter in its signature is 'axis', which takes either
an integer or 'None', so check if the 'ascending' parameter has
either integer type or is None, since 'ascending' itself should
be a boolean
"""
if is_integer(ascending) or ascending is None:
args = (ascending,) + args
ascending = True
validate_argsort_kind(args, kwargs, max_fname_arg_count=3)
return ascending
|
[
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/pandas/compat/numpy/function.py#L133-L147
|
|
miyosuda/TensorFlowAndroidDemo
|
35903e0221aa5f109ea2dbef27f20b52e317f42d
|
jni-build/jni/include/tensorflow/contrib/graph_editor/util.py
|
python
|
get_tensors
|
(graph)
|
return ts
|
get all the tensors which are input or output of an op in the graph.
Args:
graph: a tf.Graph.
Returns:
A list of tf.Tensor.
Raises:
TypeError: if graph is not a tf.Graph.
|
get all the tensors which are input or output of an op in the graph.
|
[
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"tensors",
"which",
"are",
"input",
"or",
"output",
"of",
"an",
"op",
"in",
"the",
"graph",
"."
] |
def get_tensors(graph):
"""get all the tensors which are input or output of an op in the graph.
Args:
graph: a tf.Graph.
Returns:
A list of tf.Tensor.
Raises:
TypeError: if graph is not a tf.Graph.
"""
if not isinstance(graph, tf_ops.Graph):
raise TypeError("Expected a graph, got: {}".format(type(graph)))
ts = []
for op in graph.get_operations():
concatenate_unique(ts, op.inputs)
concatenate_unique(ts, op.outputs)
return ts
|
[
"def",
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https://github.com/miyosuda/TensorFlowAndroidDemo/blob/35903e0221aa5f109ea2dbef27f20b52e317f42d/jni-build/jni/include/tensorflow/contrib/graph_editor/util.py#L152-L168
|
|
pytorch/pytorch
|
7176c92687d3cc847cc046bf002269c6949a21c2
|
torch/ao/ns/fx/graph_passes.py
|
python
|
create_a_shadows_b
|
(
name_a: str,
gm_a: GraphModule,
name_b: str,
gm_b: GraphModule,
matched_subgraph_pairs: Dict[str, Tuple[NSSubgraph, NSSubgraph]],
logger_cls: Callable,
should_log_inputs: bool,
node_type_to_io_type_map: Optional[Dict[str, Set[NSNodeTargetType]]] = None,
)
|
return gm_c
|
Creates a new GraphModule consisting of the graph of C, with the meaningful
nodes of A shadowing the corresponding nodes of B. For example,
Graph A:
a0 -> op0_fp32 -> a1 -> op1_fp32 -> a2
Graph B:
b0 -> op0_int8 -> b1 -> op1_int8 -> b2
matched_node_pairs: {'op0': (op0_fp32, op0_int8), 'op1': (op1_fp32, op1_int8)}
Graph C (A shadows B):
/ dequant0 -> op0_fp32 -> logger_a_0 / dequant_1 -> op1_fp32 -> logger_a_1
/ /
b0 -------------> op0_int8 -> logger_b_0 --------------> op1_int8 -> logger_b_1
In a nutshell, this function does the following for each node pair:
* copies the necessary attributes and modules from gm_a to gm_b,
keeping names unique
* adds a dtype cast op (dequant, quant, etc)
* adds a copy of node_a in gm_b's graph
* adds loggers to the outputs of node_a and node_b
|
Creates a new GraphModule consisting of the graph of C, with the meaningful
nodes of A shadowing the corresponding nodes of B. For example,
|
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"shadowing",
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def create_a_shadows_b(
name_a: str,
gm_a: GraphModule,
name_b: str,
gm_b: GraphModule,
matched_subgraph_pairs: Dict[str, Tuple[NSSubgraph, NSSubgraph]],
logger_cls: Callable,
should_log_inputs: bool,
node_type_to_io_type_map: Optional[Dict[str, Set[NSNodeTargetType]]] = None,
) -> GraphModule:
"""
Creates a new GraphModule consisting of the graph of C, with the meaningful
nodes of A shadowing the corresponding nodes of B. For example,
Graph A:
a0 -> op0_fp32 -> a1 -> op1_fp32 -> a2
Graph B:
b0 -> op0_int8 -> b1 -> op1_int8 -> b2
matched_node_pairs: {'op0': (op0_fp32, op0_int8), 'op1': (op1_fp32, op1_int8)}
Graph C (A shadows B):
/ dequant0 -> op0_fp32 -> logger_a_0 / dequant_1 -> op1_fp32 -> logger_a_1
/ /
b0 -------------> op0_int8 -> logger_b_0 --------------> op1_int8 -> logger_b_1
In a nutshell, this function does the following for each node pair:
* copies the necessary attributes and modules from gm_a to gm_b,
keeping names unique
* adds a dtype cast op (dequant, quant, etc)
* adds a copy of node_a in gm_b's graph
* adds loggers to the outputs of node_a and node_b
"""
if node_type_to_io_type_map is None:
node_type_to_io_type_map = get_node_type_to_io_type_map()
# graph_c is the graph created from copying the nodes of graph_b and inserting
# the shadows with the nodes copied from graph_a
graph_c = Graph()
env_c: Dict[str, Any] = {}
modules = dict(gm_b.named_modules())
def load_arg(a):
return map_arg(a, lambda node: env_c[node.name])
start_node_b_to_matched_subgraph_a_and_name = {}
end_node_b_to_matched_subgraph_a_and_name = {}
for match_name, match in matched_subgraph_pairs.items():
subgraph_a, subgraph_b = match
ref_node_type_a = get_target_type_str(subgraph_a.base_op_node, gm_a)
ref_node_type_b = get_target_type_str(subgraph_b.base_op_node, gm_b)
start_node_b_to_matched_subgraph_a_and_name[subgraph_b.start_node] = \
(subgraph_a, match_name, ref_node_type_a, ref_node_type_b)
end_node_b_to_matched_subgraph_a_and_name[subgraph_b.end_node] = \
(subgraph_a, match_name, ref_node_type_a, ref_node_type_b)
for node_b in gm_b.graph.nodes:
if node_b.op == 'output':
graph_c.output(map_arg(node_b.args[0], load_arg))
continue
# calculate the flags to determine what to do with this node
node_b_is_start_node = node_b in start_node_b_to_matched_subgraph_a_and_name
node_b_is_end_node = node_b in end_node_b_to_matched_subgraph_a_and_name
if (node_b_is_start_node or node_b_is_end_node):
if node_b_is_start_node:
subgraph_a, ref_name, ref_node_type_a, ref_node_type_b = \
start_node_b_to_matched_subgraph_a_and_name[node_b]
else:
assert node_b_is_end_node
subgraph_a, ref_name, ref_node_type_a, ref_node_type_b = \
end_node_b_to_matched_subgraph_a_and_name[node_b]
# For both start_node and end_node verify that we know how to do
# the dtype cast. If we do not, skip.
node_input_type_a, node_output_type_a = \
get_node_first_input_and_output_type(
subgraph_a.start_node, gm_a, logger_cls,
node_type_to_io_type_map)
node_input_type_b, node_output_type_b = \
get_node_first_input_and_output_type(
node_b, gm_b, logger_cls,
node_type_to_io_type_map)
node_io_types_known_a_and_b = (
node_input_type_a != NodeInputOrOutputType.UNKNOWN and
node_output_type_a != NodeInputOrOutputType.UNKNOWN and
node_input_type_b != NodeInputOrOutputType.UNKNOWN and
node_output_type_b != NodeInputOrOutputType.UNKNOWN
)
if not node_io_types_known_a_and_b:
print(
f'skipping shadow loggers for node_b: {get_target_type_str(node_b, gm_b)}' +
f', start_node_a: {get_target_type_str(subgraph_a.start_node, gm_a)}' +
', unknown dtype cast')
env_c[node_b.name] = graph_c.node_copy(node_b, load_arg)
continue
# If we are shadowing from fp32 to int8, we need to insert
# quantize_per_tensor call with qparams from the previous node.
# Only do this if we are able to infer these qparams from the graph.
if (
node_input_type_a == NodeInputOrOutputType.INT8 and
node_input_type_b == NodeInputOrOutputType.FP32
):
node_a_input_qparams = get_node_input_qparams(
subgraph_a.start_node, gm_a, node_type_to_io_type_map)
if not node_a_input_qparams:
print(
f'skipping shadow loggers for node_b: {get_target_type_str(node_b, gm_b)}' +
f', start_node_a: {get_target_type_str(subgraph_a.start_node, gm_a)}' +
', unknown input qparams')
env_c[node_b.name] = graph_c.node_copy(node_b, load_arg)
continue
fqn_base_a = _maybe_get_fqn(subgraph_a.base_op_node, gm_a)
fqn_base_b = _maybe_get_fqn(subgraph_b.base_op_node, gm_b)
if node_b_is_start_node:
# if necessary, log the input of node_c
if should_log_inputs:
if isinstance(node_b.args[0], Node):
prev_node_c = env_c[node_b.args[0].name]
env_c[prev_node_c.name] = _insert_logger_after_node(
prev_node_c, gm_b, logger_cls, '_ns_logger_b_inp_',
node_b.name, name_b, ref_name, ref_node_type_b,
NSSingleResultValuesType.NODE_INPUT.value,
index_within_arg=0, index_of_arg=0,
fqn=fqn_base_b)
elif isinstance(node_b.args[0], list):
# first, save the prev_node instances, because they
# will be overwritten in the env after the first logger
# is added
prev_node_c_list = [env_c[arg.name] for arg in node_b.args[0]]
for arg_idx, arg in enumerate(node_b.args[0]):
prev_node_c = prev_node_c_list[arg_idx]
env_c[prev_node_c.name] = _insert_logger_after_node(
prev_node_c, gm_b, logger_cls, '_ns_logger_b_inp_',
node_b.name, name_b, ref_name, ref_node_type_b,
NSSingleResultValuesType.NODE_INPUT.value,
index_within_arg=arg_idx, index_of_arg=0,
fqn=fqn_base_b)
else:
# logging of inputs which are not lists is not supported yet
raise AssertionError(f"type {type(node_b.args[0])} is not handled yet")
# subgraph so far:
#
# (prev_node_c)+ -> (logger_c_input)?
# Note: this if statement is always True, spelling it out to clarify code
# intent.
if node_b_is_start_node or node_b_is_end_node:
# ensure env_c is populated with base node
env_c[node_b.name] = graph_c.node_copy(node_b, load_arg)
node_c = env_c[node_b.name]
# after this point,
#
# node_a is the original node from graph_a, with parent module gm_a
# node_b is the original node from graph_b, with parent module gm_b
# node_c is the copy of node_b in graph_c
#
# subgraph so far:
#
# (prev_node_c)+ -> (logger_c_input)? -> node_start_c
if node_b_is_start_node:
# cast dtype from the dtype of node_c's input to the dtype of
# node_a's input (dequant, etc)
prev_node_c = node_c.args[0]
if should_log_inputs:
# skip the input logger when inserting a dtype cast
if isinstance(prev_node_c, Node):
prev_node_c = prev_node_c.args[0]
elif isinstance(prev_node_c, list):
prev_node_c = [arg.args[0] for arg in prev_node_c]
dtype_cast_node = _insert_dtype_cast_after_node(
subgraph_a.start_node, node_c, prev_node_c, gm_a, gm_b, graph_c,
node_b.name + '_dtype_cast_', logger_cls,
node_type_to_io_type_map)
# note: not inserting to env_c because all nodes which use the dtype
# casts are copied from graph_a
#
# subgraph so far:
#
# (dtype_cast_node)+
# /
# (prev_node_c)+ -> (logger_c_input)? -> node_start_c
# if input logging is enabled, log the input to the subgraph
if should_log_inputs:
# TODO: explain this
ref_node_name = ''
if isinstance(dtype_cast_node, Node):
dtype_cast_node = _insert_logger_after_node(
dtype_cast_node, gm_b, logger_cls, '_ns_logger_a_inp_',
ref_node_name, name_a, ref_name, ref_node_type_a,
NSSingleResultValuesType.NODE_INPUT.value,
index_within_arg=0, index_of_arg=0,
fqn=fqn_base_a)
input_logger: Union[Node, List[Node]] = dtype_cast_node
else:
assert isinstance(dtype_cast_node, list)
new_loggers = []
for dtype_cast_idx, dtype_cast_node_inner in enumerate(dtype_cast_node):
dtype_cast_logger = _insert_logger_after_node(
dtype_cast_node_inner, gm_b, logger_cls, '_ns_logger_a_inp_',
ref_node_name, name_a, ref_name, ref_node_type_a,
NSSingleResultValuesType.NODE_INPUT.value,
index_within_arg=dtype_cast_idx,
index_of_arg=0,
fqn=fqn_base_a)
new_loggers.append(dtype_cast_logger)
dtype_cast_node = new_loggers
input_logger = dtype_cast_node
# subgraph so far:
#
# (dtype_cast_node)+ -> (logger_a_input)?
# /
# prev_node_c -> (logger_c_input)? -> node_start_c
# hook up the new mod_a copy to be in the graph, receiving the
# same inputs as mod_b does, with dtype cast to match a
# Some ops, such as LSTMs, have two non-param inputs. If we have
# such an op, pass the second param as well. Note: dtype casting
# for the second param is not implemented yet, it can be added
# later if there is a use case.
node_c_second_non_param_arg = None
num_non_param_args_node_a = get_number_of_non_param_args(subgraph_a.start_node, gm_a)
if num_non_param_args_node_a == 2:
node_c_second_non_param_arg = node_c.args[1]
node_a_shadows_c = _insert_copy_of_subgraph_a_after_input_node_c(
dtype_cast_node, node_c_second_non_param_arg,
subgraph_a, gm_a, gm_b, node_c.name + '_shadow_copy_')
env_c[node_a_shadows_c.name] = node_a_shadows_c
# subgraph so far:
#
# dtype_cast_node -> (logger_a_input)? -> subgraph_a_copy(args/kwargs not shown)
# /
# (prev_node_c)+ -> (logger_c_input)? -> node_start_c
if should_log_inputs:
# When we created the input logger, we left the ref_node_name
# as an empty string, because the subgraph copy did not exist
# yet. Now that the subgraph copy exists, we modify this name
# to its true value.
# Note: the alternative to this is to create the input logger
# after creating the subgraph, which is slightly more
# complicated. This is the lesser of two evils.
# input_logger = env_c[dtype_cast_node.name]
# Find the first node in the subgraph
cur_node = node_a_shadows_c
while cur_node.args[0] != input_logger:
cur_node = cur_node.args[0] # type: ignore[assignment]
if isinstance(input_logger, Node):
input_logger_mod = getattr(gm_b, input_logger.name)
input_logger_mod.ref_node_name = cur_node.name
else:
assert isinstance(input_logger, list)
for input_logger_inner in input_logger:
input_logger_mod = getattr(gm_b, input_logger_inner.name)
input_logger_mod.ref_node_name = cur_node.name
# hook up a logger to the mod_a copy
env_c[node_a_shadows_c.name] = _insert_logger_after_node(
env_c[node_a_shadows_c.name], gm_b, logger_cls, '_ns_logger_a_',
node_a_shadows_c.name, name_a, ref_name, ref_node_type_a,
NSSingleResultValuesType.NODE_OUTPUT.value,
index_within_arg=0, index_of_arg=0,
fqn=fqn_base_a)
# subgraph so far:
#
# dtype_cast_node -> (logger_a_input)? -> subgraph_a_copy -> logger_a
# /
# (prev_node_c)+ -> (logger_c_input)? -> node_start_c
if node_b_is_end_node:
# hook up a logger to the mod_b copy
env_c[node_b.name] = _insert_logger_after_node(
env_c[node_b.name], gm_b, logger_cls, '_ns_logger_b_',
node_b.name, name_b, ref_name, ref_node_type_b,
NSSingleResultValuesType.NODE_OUTPUT.value,
index_within_arg=0, index_of_arg=0,
fqn=fqn_base_b)
# subgraph so far:
#
# dtype_cast_node -> (logger_a_input)? -> subgraph_a_copy -> logger_a
# /
# (prev_node_c+) -> (logger_c_input)? -> node_start_c -> ... -> node_end_c -> logger_c
#
# Note: node_start_c may be the same node as node_end_c, or they
# may have nodes inbetween.
else:
env_c[node_b.name] = graph_c.node_copy(node_b, load_arg)
gm_c = GraphModule(gm_b, graph_c)
return gm_c
|
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https://github.com/pytorch/pytorch/blob/7176c92687d3cc847cc046bf002269c6949a21c2/torch/ao/ns/fx/graph_passes.py#L514-L819
|
|
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/core/indexes/multi.py
|
python
|
MultiIndex._hashed_values
|
(self)
|
return hash_tuples(self)
|
return a uint64 ndarray of my hashed values
|
return a uint64 ndarray of my hashed values
|
[
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] |
def _hashed_values(self):
""" return a uint64 ndarray of my hashed values """
return hash_tuples(self)
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[
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"return",
"hash_tuples",
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"self",
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] |
https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/core/indexes/multi.py#L1408-L1410
|
|
bigartm/bigartm
|
47e37f982de87aa67bfd475ff1f39da696b181b3
|
utils/cpplint.py
|
python
|
FileInfo.NoExtension
|
(self)
|
return '/'.join(self.Split()[0:2])
|
File has no source file extension.
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File has no source file extension.
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[
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def NoExtension(self):
"""File has no source file extension."""
return '/'.join(self.Split()[0:2])
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https://github.com/bigartm/bigartm/blob/47e37f982de87aa67bfd475ff1f39da696b181b3/utils/cpplint.py#L1055-L1057
|
|
neoml-lib/neoml
|
a0d370fba05269a1b2258cef126f77bbd2054a3e
|
NeoML/Python/neoml/Dnn/Dnn.py
|
python
|
Dnn.initializer
|
(self, new_initializer)
|
Sets the initializer that will fill in the weight values
before training starts.
|
Sets the initializer that will fill in the weight values
before training starts.
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def initializer(self, new_initializer):
"""Sets the initializer that will fill in the weight values
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self.set_initializer(new_initializer._internal)
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https://github.com/neoml-lib/neoml/blob/a0d370fba05269a1b2258cef126f77bbd2054a3e/NeoML/Python/neoml/Dnn/Dnn.py#L106-L110
|
||
llvm/llvm-project
|
ffa6262cb4e2a335d26416fad39a581b4f98c5f4
|
llvm/utils/lit/lit/LitConfig.py
|
python
|
LitConfig.maxIndividualTestTimeIsSupported
|
(self)
|
return lit.util.killProcessAndChildrenIsSupported()
|
Returns a tuple (<supported> , <error message>)
where
`<supported>` is True if setting maxIndividualTestTime is supported
on the current host, returns False otherwise.
`<error message>` is an empty string if `<supported>` is True,
otherwise is contains a string describing why setting
maxIndividualTestTime is not supported.
|
Returns a tuple (<supported> , <error message>)
where
`<supported>` is True if setting maxIndividualTestTime is supported
on the current host, returns False otherwise.
`<error message>` is an empty string if `<supported>` is True,
otherwise is contains a string describing why setting
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def maxIndividualTestTimeIsSupported(self):
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`<error message>` is an empty string if `<supported>` is True,
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maxIndividualTestTime is not supported.
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return lit.util.killProcessAndChildrenIsSupported()
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https://github.com/llvm/llvm-project/blob/ffa6262cb4e2a335d26416fad39a581b4f98c5f4/llvm/utils/lit/lit/LitConfig.py#L79-L89
|
|
miyosuda/TensorFlowAndroidMNIST
|
7b5a4603d2780a8a2834575706e9001977524007
|
jni-build/jni/include/tensorflow/python/ops/control_flow_ops.py
|
python
|
CondContext.BuildCondBranch
|
(self, fn)
|
return original_r, result
|
Add the subgraph defined by fn() to the graph.
|
Add the subgraph defined by fn() to the graph.
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def BuildCondBranch(self, fn):
"""Add the subgraph defined by fn() to the graph."""
r = fn()
original_r = r
result = []
if r is not None:
if not isinstance(r, list) and not isinstance(r, _basetuple):
r = [r]
original_r = [original_r]
r = _convert_tensorarrays_to_flows(r)
for v in r:
real_v = v
if isinstance(v, ops.Operation):
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real_v = with_dependencies([v], self._pivot)
elif v.name not in self._values:
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if self._outer_context:
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self._values.add(real_v.name)
real_v = _SwitchRefOrTensor(real_v, self._pred)[self._branch]
self._external_values[v.name] = real_v
else:
external_v = self._external_values.get(v.name)
if external_v is not None:
real_v = external_v
result.append(real_v)
return original_r, result
|
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] |
https://github.com/miyosuda/TensorFlowAndroidMNIST/blob/7b5a4603d2780a8a2834575706e9001977524007/jni-build/jni/include/tensorflow/python/ops/control_flow_ops.py#L1244-L1272
|
|
Tencent/CMONGO
|
c40380caa14e05509f46993aa8b8da966b09b0b5
|
buildscripts/packager.py
|
python
|
Distro.repo_os_version
|
(self, build_os)
|
Return an OS version suitable for package repo directory
naming - e.g. 5, 6 or 7 for redhat/centos, "precise," "wheezy," etc.
for Ubuntu/Debian, 11 for suse, "2013.03" for amazon
|
Return an OS version suitable for package repo directory
naming - e.g. 5, 6 or 7 for redhat/centos, "precise," "wheezy," etc.
for Ubuntu/Debian, 11 for suse, "2013.03" for amazon
|
[
"Return",
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"repo",
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"/",
"Debian",
"11",
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"2013",
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"03",
"for",
"amazon"
] |
def repo_os_version(self, build_os):
"""Return an OS version suitable for package repo directory
naming - e.g. 5, 6 or 7 for redhat/centos, "precise," "wheezy," etc.
for Ubuntu/Debian, 11 for suse, "2013.03" for amazon"""
if self.n == 'suse':
return re.sub(r'^suse(\d+)$', r'\1', build_os)
if self.n == 'redhat':
return re.sub(r'^rhel(\d).*$', r'\1', build_os)
if self.n == 'amazon':
return "2013.03"
elif self.n == 'ubuntu':
if build_os == 'ubuntu1204':
return "precise"
elif build_os == 'ubuntu1404':
return "trusty"
elif build_os == 'ubuntu1604':
return "xenial"
else:
raise Exception("unsupported build_os: %s" % build_os)
elif self.n == 'debian':
if build_os == 'debian71':
return 'wheezy'
elif build_os == 'debian81':
return 'jessie'
else:
raise Exception("unsupported build_os: %s" % build_os)
else:
raise Exception("unsupported distro: %s" % self.n)
|
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")"
] |
https://github.com/Tencent/CMONGO/blob/c40380caa14e05509f46993aa8b8da966b09b0b5/buildscripts/packager.py#L197-L224
|
||
infinit/memo
|
3a8394d0f647efe03ccb8bfe885a7279cb8be8a6
|
elle/drake/src/drake/__init__.py
|
python
|
BaseNode.drake_type
|
(self)
|
return '%s.%s' % (self.__module__, self.__name__)
|
The qualified name of this type.
|
The qualified name of this type.
|
[
"The",
"qualified",
"name",
"of",
"this",
"type",
"."
] |
def drake_type(self):
"""The qualified name of this type."""
return '%s.%s' % (self.__module__, self.__name__)
|
[
"def",
"drake_type",
"(",
"self",
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"return",
"'%s.%s'",
"%",
"(",
"self",
".",
"__module__",
",",
"self",
".",
"__name__",
")"
] |
https://github.com/infinit/memo/blob/3a8394d0f647efe03ccb8bfe885a7279cb8be8a6/elle/drake/src/drake/__init__.py#L1391-L1393
|
|
benoitsteiner/tensorflow-opencl
|
cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5
|
tensorflow/contrib/distributions/python/ops/binomial.py
|
python
|
Binomial.probs
|
(self)
|
return self._probs
|
Probability of drawing a `1`.
|
Probability of drawing a `1`.
|
[
"Probability",
"of",
"drawing",
"a",
"1",
"."
] |
def probs(self):
"""Probability of drawing a `1`."""
return self._probs
|
[
"def",
"probs",
"(",
"self",
")",
":",
"return",
"self",
".",
"_probs"
] |
https://github.com/benoitsteiner/tensorflow-opencl/blob/cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5/tensorflow/contrib/distributions/python/ops/binomial.py#L198-L200
|
|
Kitware/kwiver
|
7ed70308905698b6e88d27ae3dc028c9b016ca0a
|
python/kwiver/sprokit/processes/kwiver_process.py
|
python
|
KwiverProcess.config_value_using_trait
|
(self, name)
|
return self.config_value(ct.name)
|
Get value from config using trait.
An exception will be thrown if the config trait has not been registered
with the process.
:param name: Name of the configuration trait.
|
Get value from config using trait.
An exception will be thrown if the config trait has not been registered
with the process.
|
[
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"trait",
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"."
] |
def config_value_using_trait(self, name):
"""
Get value from config using trait.
An exception will be thrown if the config trait has not been registered
with the process.
:param name: Name of the configuration trait.
"""
ct = self._config_trait_set.get(name, None)
if ct is None:
raise ValueError('config trait name \"%s\" not registered' % name)
return self.config_value(ct.name)
|
[
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")"
] |
https://github.com/Kitware/kwiver/blob/7ed70308905698b6e88d27ae3dc028c9b016ca0a/python/kwiver/sprokit/processes/kwiver_process.py#L369-L382
|
|
luliyucoordinate/Leetcode
|
96afcdc54807d1d184e881a075d1dbf3371e31fb
|
src/0019-Remove-Nth-Node-From-End-of-List/0019.py
|
python
|
Solution.removeNthFromEnd
|
(self, head, n)
|
return h.next
|
:type head: ListNode
:type n: int
:rtype: ListNode
|
:type head: ListNode
:type n: int
:rtype: ListNode
|
[
":",
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":",
"ListNode",
":",
"type",
"n",
":",
"int",
":",
"rtype",
":",
"ListNode"
] |
def removeNthFromEnd(self, head, n):
"""
:type head: ListNode
:type n: int
:rtype: ListNode
"""
h = ListNode(-1)
h.next = head
p, q = h, h
for _ in range(n + 1):
assert (q)
q = q.next
while q != None:
p = p.next
q = q.next
p.next = p.next.next
return h.next
|
[
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https://github.com/luliyucoordinate/Leetcode/blob/96afcdc54807d1d184e881a075d1dbf3371e31fb/src/0019-Remove-Nth-Node-From-End-of-List/0019.py#L8-L27
|
|
stulp/dmpbbo
|
ca900e3b851d25faaf59ea296650370c70ed7d0f
|
python/bbo/updaters.py
|
python
|
costsToWeights
|
(costs, weighting_method, eliteness)
|
return weights
|
Convert costs to weights using different weighting methods.
\param[in] costs A vector of costs.
\param[in] weighting_method The weighting method ('PI-BB','CMA-ES','CEM')
\param[in] eliteness The eliteness parameter (h in PI-BB, mu in CMA-ES)
\return A vector of weights (they always sum to 1).
|
Convert costs to weights using different weighting methods.
\param[in] costs A vector of costs.
\param[in] weighting_method The weighting method ('PI-BB','CMA-ES','CEM')
\param[in] eliteness The eliteness parameter (h in PI-BB, mu in CMA-ES)
\return A vector of weights (they always sum to 1).
|
[
"Convert",
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def costsToWeights(costs, weighting_method, eliteness):
""" Convert costs to weights using different weighting methods.
\param[in] costs A vector of costs.
\param[in] weighting_method The weighting method ('PI-BB','CMA-ES','CEM')
\param[in] eliteness The eliteness parameter (h in PI-BB, mu in CMA-ES)
\return A vector of weights (they always sum to 1).
"""
# Costs can be a 2D array or a list of lists. In this case, the first
# column is the sum of the other columns (which contain the different cost
# components). In this case, we should use only the first column.
costs = np.asarray([np.atleast_1d(x)[0] for x in costs])
#np.set_printoptions(precision=4, suppress=True)
if weighting_method == 'PI-BB':
# PI^2 style weighting: continuous, cost exponention
h = eliteness # In PI^2, eliteness parameter is known as "h"
costs_range = max(costs)-min(costs)
if costs_range==0:
weights = np.full(costs.shape,1.0)
else:
costs_norm = np.asarray([-h*(x-min(costs))/costs_range for x in costs])
weights = np.exp(costs_norm)
elif weighting_method=='CEM' or weighting_method=='CMA-ES':
# CEM/CMA-ES style weights: rank-based, uses defaults
mu = eliteness # In CMA-ES, eliteness parameter is known as "mu"
indices = np.argsort(costs)
weights = np.full(costs.size,0.0)
if weighting_method=='CEM':
# CEM
weights[indices[0:mu]] = 1.0/mu
else:
# CMA-ES
for ii in range(mu):
weights[indices[ii]] = np.log(mu+0.5)-np.log(ii+1)
else:
print("WARNING: Unknown weighting method '", weighting_method, "'. Calling with PI-BB weighting.");
return costsToWeights(costs, 'PI-BB', eliteness);
#// Relative standard deviation of total costs
#double mean = weights.mean();
#double std = sqrt((weights.array()-mean).pow(2).mean());
#double rel_std = std/mean;
#if (rel_std<1e-10)
#{
# // Special case: all costs are the same
# // Set same weights for all.
# weights.fill(1);
#}
# Normalize weights
weights = weights/sum(weights)
return weights
|
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"#if (rel_std<1e-10)",
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https://github.com/stulp/dmpbbo/blob/ca900e3b851d25faaf59ea296650370c70ed7d0f/python/bbo/updaters.py#L199-L254
|
|
apple/turicreate
|
cce55aa5311300e3ce6af93cb45ba791fd1bdf49
|
src/external/coremltools_wrap/coremltools/coremltools/converters/mil/frontend/torch/ops.py
|
python
|
convert_block
|
(context, block, inputs)
|
return outputs
|
Convert a block (sub-graph) to MIL. Conversion happens within a new
context frame.
Arguments:
context: A TranscriptionContext object to pull node inputs and
assign node outputs.
block: An InternalTorchIRBlock object.
inputs: List of Vars from the outer context that map to the block's
expected inputs. The number of inputs provided must match the
number expected by the block.
|
Convert a block (sub-graph) to MIL. Conversion happens within a new
context frame.
|
[
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"sub",
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"new",
"context",
"frame",
"."
] |
def convert_block(context, block, inputs):
"""Convert a block (sub-graph) to MIL. Conversion happens within a new
context frame.
Arguments:
context: A TranscriptionContext object to pull node inputs and
assign node outputs.
block: An InternalTorchIRBlock object.
inputs: List of Vars from the outer context that map to the block's
expected inputs. The number of inputs provided must match the
number expected by the block.
"""
assert len(block.inputs) == len(inputs)
# Start a new context frame.
context.push((block.inputs, inputs))
# Add the block ops.
convert_nodes(context, block)
# Collect the block outputs.
outputs = [context[outp] for outp in block.outputs]
# Return to the previous context frame.
context.pop()
return outputs
|
[
"def",
"convert_block",
"(",
"context",
",",
"block",
",",
"inputs",
")",
":",
"assert",
"len",
"(",
"block",
".",
"inputs",
")",
"==",
"len",
"(",
"inputs",
")",
"# Start a new context frame.",
"context",
".",
"push",
"(",
"(",
"block",
".",
"inputs",
",",
"inputs",
")",
")",
"# Add the block ops.",
"convert_nodes",
"(",
"context",
",",
"block",
")",
"# Collect the block outputs.",
"outputs",
"=",
"[",
"context",
"[",
"outp",
"]",
"for",
"outp",
"in",
"block",
".",
"outputs",
"]",
"# Return to the previous context frame.",
"context",
".",
"pop",
"(",
")",
"return",
"outputs"
] |
https://github.com/apple/turicreate/blob/cce55aa5311300e3ce6af93cb45ba791fd1bdf49/src/external/coremltools_wrap/coremltools/coremltools/converters/mil/frontend/torch/ops.py#L62-L88
|
|
google/llvm-propeller
|
45c226984fe8377ebfb2ad7713c680d652ba678d
|
clang/bindings/python/clang/cindex.py
|
python
|
Type.get_ref_qualifier
|
(self)
|
return RefQualifierKind.from_id(
conf.lib.clang_Type_getCXXRefQualifier(self))
|
Retrieve the ref-qualifier of the type.
|
Retrieve the ref-qualifier of the type.
|
[
"Retrieve",
"the",
"ref",
"-",
"qualifier",
"of",
"the",
"type",
"."
] |
def get_ref_qualifier(self):
"""
Retrieve the ref-qualifier of the type.
"""
return RefQualifierKind.from_id(
conf.lib.clang_Type_getCXXRefQualifier(self))
|
[
"def",
"get_ref_qualifier",
"(",
"self",
")",
":",
"return",
"RefQualifierKind",
".",
"from_id",
"(",
"conf",
".",
"lib",
".",
"clang_Type_getCXXRefQualifier",
"(",
"self",
")",
")"
] |
https://github.com/google/llvm-propeller/blob/45c226984fe8377ebfb2ad7713c680d652ba678d/clang/bindings/python/clang/cindex.py#L2396-L2401
|
|
MegEngine/MegEngine
|
ce9ad07a27ec909fb8db4dd67943d24ba98fb93a
|
imperative/python/megengine/functional/math.py
|
python
|
dot
|
(inp1: Tensor, inp2: Tensor)
|
return result
|
r"""Computes dot-product of two vectors ``inp1`` and ``inp2``.
inputs must be 1-dimensional or scalar. A scalar input is automatically broadcasted.
Refer to :func:`~.matmul` for more general usage.
Args:
inp1: first vector.
inp2: second vector.
Returns:
output value.
Examples:
.. testcode::
import numpy as np
from megengine import tensor
import megengine.functional as F
data1 = tensor(np.arange(0, 6, dtype=np.float32))
data2 = tensor(np.arange(0, 6, dtype=np.float32))
out = F.dot(data1, data2)
print(out.numpy())
Outputs:
.. testoutput::
55.
|
r"""Computes dot-product of two vectors ``inp1`` and ``inp2``.
inputs must be 1-dimensional or scalar. A scalar input is automatically broadcasted.
Refer to :func:`~.matmul` for more general usage.
|
[
"r",
"Computes",
"dot",
"-",
"product",
"of",
"two",
"vectors",
"inp1",
"and",
"inp2",
".",
"inputs",
"must",
"be",
"1",
"-",
"dimensional",
"or",
"scalar",
".",
"A",
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"input",
"is",
"automatically",
"broadcasted",
".",
"Refer",
"to",
":",
"func",
":",
"~",
".",
"matmul",
"for",
"more",
"general",
"usage",
"."
] |
def dot(inp1: Tensor, inp2: Tensor) -> Tensor:
r"""Computes dot-product of two vectors ``inp1`` and ``inp2``.
inputs must be 1-dimensional or scalar. A scalar input is automatically broadcasted.
Refer to :func:`~.matmul` for more general usage.
Args:
inp1: first vector.
inp2: second vector.
Returns:
output value.
Examples:
.. testcode::
import numpy as np
from megengine import tensor
import megengine.functional as F
data1 = tensor(np.arange(0, 6, dtype=np.float32))
data2 = tensor(np.arange(0, 6, dtype=np.float32))
out = F.dot(data1, data2)
print(out.numpy())
Outputs:
.. testoutput::
55.
"""
op = builtin.Dot()
assert (
inp1.ndim <= 1 and inp2.ndim <= 1
), "Input tensors for dot must be 1-dimensional or scalar"
(result,) = apply(op, inp1, inp2)
return result
|
[
"def",
"dot",
"(",
"inp1",
":",
"Tensor",
",",
"inp2",
":",
"Tensor",
")",
"->",
"Tensor",
":",
"op",
"=",
"builtin",
".",
"Dot",
"(",
")",
"assert",
"(",
"inp1",
".",
"ndim",
"<=",
"1",
"and",
"inp2",
".",
"ndim",
"<=",
"1",
")",
",",
"\"Input tensors for dot must be 1-dimensional or scalar\"",
"(",
"result",
",",
")",
"=",
"apply",
"(",
"op",
",",
"inp1",
",",
"inp2",
")",
"return",
"result"
] |
https://github.com/MegEngine/MegEngine/blob/ce9ad07a27ec909fb8db4dd67943d24ba98fb93a/imperative/python/megengine/functional/math.py#L1116-L1152
|
|
microsoft/DirectXShaderCompiler
|
8348ff8d9e0287610ba05d3a828e10af981a1c05
|
tools/clang/bindings/python/clang/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/microsoft/DirectXShaderCompiler/blob/8348ff8d9e0287610ba05d3a828e10af981a1c05/tools/clang/bindings/python/clang/cindex.py#L213-L215
|
|
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/distutils/command/build_py.py
|
python
|
build_py.build_package_data
|
(self)
|
Copy data files into build directory
|
Copy data files into build directory
|
[
"Copy",
"data",
"files",
"into",
"build",
"directory"
] |
def build_package_data(self):
"""Copy data files into build directory"""
lastdir = None
for package, src_dir, build_dir, filenames in self.data_files:
for filename in filenames:
target = os.path.join(build_dir, filename)
self.mkpath(os.path.dirname(target))
self.copy_file(os.path.join(src_dir, filename), target,
preserve_mode=False)
|
[
"def",
"build_package_data",
"(",
"self",
")",
":",
"lastdir",
"=",
"None",
"for",
"package",
",",
"src_dir",
",",
"build_dir",
",",
"filenames",
"in",
"self",
".",
"data_files",
":",
"for",
"filename",
"in",
"filenames",
":",
"target",
"=",
"os",
".",
"path",
".",
"join",
"(",
"build_dir",
",",
"filename",
")",
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"mkpath",
"(",
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".",
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".",
"dirname",
"(",
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")",
")",
"self",
".",
"copy_file",
"(",
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"(",
"src_dir",
",",
"filename",
")",
",",
"target",
",",
"preserve_mode",
"=",
"False",
")"
] |
https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/distutils/command/build_py.py#L134-L142
|
||
google/brunsli
|
e811197ab1ad8ddde3e3cf444548e42e2bdacf92
|
contrib/py/jxl_library_patches/jxl_utils.py
|
python
|
is_jpegxl_recompressed_jpeg_file
|
(filename)
|
Returns True iff the given filename is a genuine JPEG-XL file.
|
Returns True iff the given filename is a genuine JPEG-XL file.
|
[
"Returns",
"True",
"iff",
"the",
"given",
"filename",
"is",
"a",
"genuine",
"JPEG",
"-",
"XL",
"file",
"."
] |
def is_jpegxl_recompressed_jpeg_file(filename):
"""Returns True iff the given filename is a genuine JPEG-XL file."""
try:
with open(filename, 'rb') as h:
header = h.read(len(JPEGXL_RECOMPRESSED_JPEG_HEADER))
# Cf. https://arxiv.org/pdf/1908.03565.pdf, section 9.1,
# on recompressed-JPEG header.
return header == JPEGXL_RECOMPRESSED_JPEG_HEADER
except: # pylint:disable=bare-except
# If anything failed, this means that we cannot establish that the file
# has the expected header, so we return False.
return False
|
[
"def",
"is_jpegxl_recompressed_jpeg_file",
"(",
"filename",
")",
":",
"try",
":",
"with",
"open",
"(",
"filename",
",",
"'rb'",
")",
"as",
"h",
":",
"header",
"=",
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"read",
"(",
"len",
"(",
"JPEGXL_RECOMPRESSED_JPEG_HEADER",
")",
")",
"# Cf. https://arxiv.org/pdf/1908.03565.pdf, section 9.1,",
"# on recompressed-JPEG header.",
"return",
"header",
"==",
"JPEGXL_RECOMPRESSED_JPEG_HEADER",
"except",
":",
"# pylint:disable=bare-except",
"# If anything failed, this means that we cannot establish that the file",
"# has the expected header, so we return False.",
"return",
"False"
] |
https://github.com/google/brunsli/blob/e811197ab1ad8ddde3e3cf444548e42e2bdacf92/contrib/py/jxl_library_patches/jxl_utils.py#L44-L55
|
||
sdhash/sdhash
|
b9eff63e4e5867e910f41fd69032bbb1c94a2a5e
|
sdhash-ui/cherrypy/process/servers.py
|
python
|
client_host
|
(server_host)
|
return server_host
|
Return the host on which a client can connect to the given listener.
|
Return the host on which a client can connect to the given listener.
|
[
"Return",
"the",
"host",
"on",
"which",
"a",
"client",
"can",
"connect",
"to",
"the",
"given",
"listener",
"."
] |
def client_host(server_host):
"""Return the host on which a client can connect to the given listener."""
if server_host == '0.0.0.0':
# 0.0.0.0 is INADDR_ANY, which should answer on localhost.
return '127.0.0.1'
if server_host in ('::', '::0', '::0.0.0.0'):
# :: is IN6ADDR_ANY, which should answer on localhost.
# ::0 and ::0.0.0.0 are non-canonical but common ways to write IN6ADDR_ANY.
return '::1'
return server_host
|
[
"def",
"client_host",
"(",
"server_host",
")",
":",
"if",
"server_host",
"==",
"'0.0.0.0'",
":",
"# 0.0.0.0 is INADDR_ANY, which should answer on localhost.",
"return",
"'127.0.0.1'",
"if",
"server_host",
"in",
"(",
"'::'",
",",
"'::0'",
",",
"'::0.0.0.0'",
")",
":",
"# :: is IN6ADDR_ANY, which should answer on localhost.",
"# ::0 and ::0.0.0.0 are non-canonical but common ways to write IN6ADDR_ANY.",
"return",
"'::1'",
"return",
"server_host"
] |
https://github.com/sdhash/sdhash/blob/b9eff63e4e5867e910f41fd69032bbb1c94a2a5e/sdhash-ui/cherrypy/process/servers.py#L340-L349
|
|
catboost/catboost
|
167f64f237114a4d10b2b4ee42adb4569137debe
|
contrib/python/scipy/py2/scipy/signal/waveforms.py
|
python
|
unit_impulse
|
(shape, idx=None, dtype=float)
|
return out
|
Unit impulse signal (discrete delta function) or unit basis vector.
Parameters
----------
shape : int or tuple of int
Number of samples in the output (1-D), or a tuple that represents the
shape of the output (N-D).
idx : None or int or tuple of int or 'mid', optional
Index at which the value is 1. If None, defaults to the 0th element.
If ``idx='mid'``, the impulse will be centered at ``shape // 2`` in
all dimensions. If an int, the impulse will be at `idx` in all
dimensions.
dtype : data-type, optional
The desired data-type for the array, e.g., `numpy.int8`. Default is
`numpy.float64`.
Returns
-------
y : ndarray
Output array containing an impulse signal.
Notes
-----
The 1D case is also known as the Kronecker delta.
.. versionadded:: 0.19.0
Examples
--------
An impulse at the 0th element (:math:`\\delta[n]`):
>>> from scipy import signal
>>> signal.unit_impulse(8)
array([ 1., 0., 0., 0., 0., 0., 0., 0.])
Impulse offset by 2 samples (:math:`\\delta[n-2]`):
>>> signal.unit_impulse(7, 2)
array([ 0., 0., 1., 0., 0., 0., 0.])
2-dimensional impulse, centered:
>>> signal.unit_impulse((3, 3), 'mid')
array([[ 0., 0., 0.],
[ 0., 1., 0.],
[ 0., 0., 0.]])
Impulse at (2, 2), using broadcasting:
>>> signal.unit_impulse((4, 4), 2)
array([[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 1., 0.],
[ 0., 0., 0., 0.]])
Plot the impulse response of a 4th-order Butterworth lowpass filter:
>>> imp = signal.unit_impulse(100, 'mid')
>>> b, a = signal.butter(4, 0.2)
>>> response = signal.lfilter(b, a, imp)
>>> import matplotlib.pyplot as plt
>>> plt.plot(np.arange(-50, 50), imp)
>>> plt.plot(np.arange(-50, 50), response)
>>> plt.margins(0.1, 0.1)
>>> plt.xlabel('Time [samples]')
>>> plt.ylabel('Amplitude')
>>> plt.grid(True)
>>> plt.show()
|
Unit impulse signal (discrete delta function) or unit basis vector.
|
[
"Unit",
"impulse",
"signal",
"(",
"discrete",
"delta",
"function",
")",
"or",
"unit",
"basis",
"vector",
"."
] |
def unit_impulse(shape, idx=None, dtype=float):
"""
Unit impulse signal (discrete delta function) or unit basis vector.
Parameters
----------
shape : int or tuple of int
Number of samples in the output (1-D), or a tuple that represents the
shape of the output (N-D).
idx : None or int or tuple of int or 'mid', optional
Index at which the value is 1. If None, defaults to the 0th element.
If ``idx='mid'``, the impulse will be centered at ``shape // 2`` in
all dimensions. If an int, the impulse will be at `idx` in all
dimensions.
dtype : data-type, optional
The desired data-type for the array, e.g., `numpy.int8`. Default is
`numpy.float64`.
Returns
-------
y : ndarray
Output array containing an impulse signal.
Notes
-----
The 1D case is also known as the Kronecker delta.
.. versionadded:: 0.19.0
Examples
--------
An impulse at the 0th element (:math:`\\delta[n]`):
>>> from scipy import signal
>>> signal.unit_impulse(8)
array([ 1., 0., 0., 0., 0., 0., 0., 0.])
Impulse offset by 2 samples (:math:`\\delta[n-2]`):
>>> signal.unit_impulse(7, 2)
array([ 0., 0., 1., 0., 0., 0., 0.])
2-dimensional impulse, centered:
>>> signal.unit_impulse((3, 3), 'mid')
array([[ 0., 0., 0.],
[ 0., 1., 0.],
[ 0., 0., 0.]])
Impulse at (2, 2), using broadcasting:
>>> signal.unit_impulse((4, 4), 2)
array([[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 1., 0.],
[ 0., 0., 0., 0.]])
Plot the impulse response of a 4th-order Butterworth lowpass filter:
>>> imp = signal.unit_impulse(100, 'mid')
>>> b, a = signal.butter(4, 0.2)
>>> response = signal.lfilter(b, a, imp)
>>> import matplotlib.pyplot as plt
>>> plt.plot(np.arange(-50, 50), imp)
>>> plt.plot(np.arange(-50, 50), response)
>>> plt.margins(0.1, 0.1)
>>> plt.xlabel('Time [samples]')
>>> plt.ylabel('Amplitude')
>>> plt.grid(True)
>>> plt.show()
"""
out = zeros(shape, dtype)
shape = np.atleast_1d(shape)
if idx is None:
idx = (0,) * len(shape)
elif idx == 'mid':
idx = tuple(shape // 2)
elif not hasattr(idx, "__iter__"):
idx = (idx,) * len(shape)
out[idx] = 1
return out
|
[
"def",
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"(",
"shape",
",",
"idx",
"=",
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",",
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"*",
"len",
"(",
"shape",
")",
"out",
"[",
"idx",
"]",
"=",
"1",
"return",
"out"
] |
https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/scipy/py2/scipy/signal/waveforms.py#L596-L681
|
|
psnonis/FinBERT
|
c0c555d833a14e2316a3701e59c0b5156f804b4e
|
bert/optimization.py
|
python
|
AdamWeightDecayOptimizer._do_use_weight_decay
|
(self, param_name)
|
return True
|
Whether to use L2 weight decay for `param_name`.
|
Whether to use L2 weight decay for `param_name`.
|
[
"Whether",
"to",
"use",
"L2",
"weight",
"decay",
"for",
"param_name",
"."
] |
def _do_use_weight_decay(self, param_name):
"""Whether to use L2 weight decay for `param_name`."""
if not self.weight_decay_rate:
return False
if self.exclude_from_weight_decay:
for r in self.exclude_from_weight_decay:
if re.search(r, param_name) is not None:
return False
return True
|
[
"def",
"_do_use_weight_decay",
"(",
"self",
",",
"param_name",
")",
":",
"if",
"not",
"self",
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"weight_decay_rate",
":",
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"False",
"if",
"self",
".",
"exclude_from_weight_decay",
":",
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"in",
"self",
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"exclude_from_weight_decay",
":",
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".",
"search",
"(",
"r",
",",
"param_name",
")",
"is",
"not",
"None",
":",
"return",
"False",
"return",
"True"
] |
https://github.com/psnonis/FinBERT/blob/c0c555d833a14e2316a3701e59c0b5156f804b4e/bert/optimization.py#L159-L167
|
|
Xilinx/Vitis-AI
|
fc74d404563d9951b57245443c73bef389f3657f
|
tools/Vitis-AI-Quantizer/vai_q_pytorch/nndct_shared/quantization/quant_strategy.py
|
python
|
QuantStrategyBase._get_default_quant_config
|
(self,
quant_info_mgr,
lstm=False)
|
return config
|
1. unified activation bits
2 .mixed bits for lstm
|
1. unified activation bits
2 .mixed bits for lstm
|
[
"1",
".",
"unified",
"activation",
"bits",
"2",
".",
"mixed",
"bits",
"for",
"lstm"
] |
def _get_default_quant_config(self,
quant_info_mgr,
lstm=False):
"""
1. unified activation bits
2 .mixed bits for lstm
"""
# import ipdb
# ipdb.set_trace()
config = {'param': {}, 'output': {}, 'input': {}}
for node in quant_info_mgr.Nndctgraph.nodes:
# print('---- Handling node %s type: %s' % (node.name, node.op.type))
if quant_info_mgr.is_node_quantizable(node, lstm):
# parameters
for k in quant_info_mgr.quant_node_params(node).keys():
p = quant_info_mgr.quant_node_params(node)[k]
# for mix precision quantization
bw = self._bits_act
if (node.has_bound_params() and
(hasattr(node.op.ParamName, 'WEIGHTS') and k == node.op.ParamName.WEIGHTS or
hasattr(node.op.ParamName, 'GAMMA') and k == node.op.ParamName.GAMMA)):
bw = self._bits_weight
config['param'][p.name] = [bw, None]
# print('---- Add fix of param %s' % p.name)
# output blobs
end = quant_info_mgr.quant_output(node.name).name
if end not in config['output']:
config['output'][end] = [self._bits_act, None]
# print('---- Add fix of output blob %s' % end)
# input blobs (for mix precision quantization)
if self._bits_weight != self._bits_act:
if node.op.type in [NNDCT_OP.DENSE, NNDCT_OP.CONV2D]:
config['input'][node.name] = [self._bits_weight, None]
# print('---- Add fix of input blob %s' % end)
elif (lstm and (node in quant_info_mgr.Nndctgraph.inputs)):
# print('---- Handling input node %s' % (node.name))
# this path is only for quantizing a whole graph without quant stub OP
# for lstm, check the following node type
if (node.in_quant_part or (any(
(quant_info_mgr.is_node_quantizable(c, lstm) and
c.op.type is not NNDCT_OP.QUANT_STUB)
for c in quant_info_mgr.Nndctgraph.children(node.name)))):
end = quant_info_mgr.quant_output(node.name).name
if end not in config['output']:
config['output'][end] = [self._bits_act, None]
# print('---- Add fix of quant net input blob %s' % end)
# check the input fix of all quantized ops
# import ipdb
# ipdb.set_trace()
if not lstm:
for node in quant_info_mgr.Nndctgraph.nodes:
if quant_info_mgr.is_node_quantizable(node, lstm):
#print('---- Check input of node %s type: %s' % (node.name, node.op.type))
if node.op.type not in [NNDCT_OP.INPUT, NNDCT_OP.QUANT_STUB, NNDCT_OP.CONCAT]:
for p_n in quant_info_mgr.Nndctgraph.parents(node):
# if not quant_info_mgr.op_unquantizable(p_n.op.type):
end = quant_info_mgr.quant_output(p_n.name).name
end_node = quant_info_mgr.Nndctgraph.node(end)
out_is_tensor = True
for tensor in end_node.out_tensors:
if tensor.shape == None:
out_is_tensor = False
if end not in config['output'] and out_is_tensor:
config['output'][end] = [self._bits_act, None]
#print('---- Add fix of output blob %s type: %s' % (end, end_node.op.type))
elif node.op.type in [NNDCT_OP.INPUT]:
cn_nodes = quant_info_mgr.Nndctgraph.children(node)
if len(cn_nodes) == 1 and cn_nodes[0].op.is_custom_op:
end = quant_info_mgr.quant_output(node.name).name
if end in config['output']:
del config['output'][end]
node.in_quant_part = False
return config
|
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")",
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"(",
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")",
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"[",
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"[",
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"]",
"[",
"end",
"]",
"node",
".",
"in_quant_part",
"=",
"False",
"return",
"config"
] |
https://github.com/Xilinx/Vitis-AI/blob/fc74d404563d9951b57245443c73bef389f3657f/tools/Vitis-AI-Quantizer/vai_q_pytorch/nndct_shared/quantization/quant_strategy.py#L56-L132
|
|
mantidproject/mantid
|
03deeb89254ec4289edb8771e0188c2090a02f32
|
scripts/reduction_gui/reduction/scripter.py
|
python
|
BaseScriptElement.apply
|
(self)
|
return NotImplemented
|
Method called to apply the reduction script element
to a Mantid Reducer
|
Method called to apply the reduction script element
to a Mantid Reducer
|
[
"Method",
"called",
"to",
"apply",
"the",
"reduction",
"script",
"element",
"to",
"a",
"Mantid",
"Reducer"
] |
def apply(self):
"""
Method called to apply the reduction script element
to a Mantid Reducer
"""
return NotImplemented
|
[
"def",
"apply",
"(",
"self",
")",
":",
"return",
"NotImplemented"
] |
https://github.com/mantidproject/mantid/blob/03deeb89254ec4289edb8771e0188c2090a02f32/scripts/reduction_gui/reduction/scripter.py#L66-L71
|
|
ycm-core/ycmd
|
fc0fb7e5e15176cc5a2a30c80956335988c6b59a
|
ycmd/completers/language_server/language_server_completer.py
|
python
|
LanguageServerCompleter.WorkspaceConfigurationResponse
|
( self, request )
|
return None
|
If the concrete completer wants to respond to workspace/configuration
requests, it should override this method.
|
If the concrete completer wants to respond to workspace/configuration
requests, it should override this method.
|
[
"If",
"the",
"concrete",
"completer",
"wants",
"to",
"respond",
"to",
"workspace",
"/",
"configuration",
"requests",
"it",
"should",
"override",
"this",
"method",
"."
] |
def WorkspaceConfigurationResponse( self, request ):
"""If the concrete completer wants to respond to workspace/configuration
requests, it should override this method."""
return None
|
[
"def",
"WorkspaceConfigurationResponse",
"(",
"self",
",",
"request",
")",
":",
"return",
"None"
] |
https://github.com/ycm-core/ycmd/blob/fc0fb7e5e15176cc5a2a30c80956335988c6b59a/ycmd/completers/language_server/language_server_completer.py#L1597-L1600
|
|
echronos/echronos
|
c996f1d2c8af6c6536205eb319c1bf1d4d84569c
|
external_tools/pystache/renderer.py
|
python
|
Renderer._bytes_to_str
|
(self, _bytes)
|
return str(_bytes, self.string_encoding, self.decode_errors)
|
Convert a byte string to str, using string_encoding and decode_errors.
|
Convert a byte string to str, using string_encoding and decode_errors.
|
[
"Convert",
"a",
"byte",
"string",
"to",
"str",
"using",
"string_encoding",
"and",
"decode_errors",
"."
] |
def _bytes_to_str(self, _bytes):
"""Convert a byte string to str, using string_encoding and decode_errors.
"""
assert type(_bytes) == bytes
return str(_bytes, self.string_encoding, self.decode_errors)
|
[
"def",
"_bytes_to_str",
"(",
"self",
",",
"_bytes",
")",
":",
"assert",
"type",
"(",
"_bytes",
")",
"==",
"bytes",
"return",
"str",
"(",
"_bytes",
",",
"self",
".",
"string_encoding",
",",
"self",
".",
"decode_errors",
")"
] |
https://github.com/echronos/echronos/blob/c996f1d2c8af6c6536205eb319c1bf1d4d84569c/external_tools/pystache/renderer.py#L182-L187
|
|
catboost/catboost
|
167f64f237114a4d10b2b4ee42adb4569137debe
|
contrib/python/pexpect/pexpect/screen.py
|
python
|
screen._decode
|
(self, s)
|
This converts from the external coding system (as passed to
the constructor) to the internal one (unicode).
|
This converts from the external coding system (as passed to
the constructor) to the internal one (unicode).
|
[
"This",
"converts",
"from",
"the",
"external",
"coding",
"system",
"(",
"as",
"passed",
"to",
"the",
"constructor",
")",
"to",
"the",
"internal",
"one",
"(",
"unicode",
")",
"."
] |
def _decode(self, s):
'''This converts from the external coding system (as passed to
the constructor) to the internal one (unicode). '''
if self.decoder is not None:
return self.decoder.decode(s)
else:
raise TypeError("This screen was constructed with encoding=None, "
"so it does not handle bytes.")
|
[
"def",
"_decode",
"(",
"self",
",",
"s",
")",
":",
"if",
"self",
".",
"decoder",
"is",
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")",
"else",
":",
"raise",
"TypeError",
"(",
"\"This screen was constructed with encoding=None, \"",
"\"so it does not handle bytes.\"",
")"
] |
https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/pexpect/pexpect/screen.py#L104-L111
|
||
trilinos/Trilinos
|
6168be6dd51e35e1cd681e9c4b24433e709df140
|
packages/muelu/utils/analysis/tableau.py
|
python
|
tableau10
|
()
|
return rgb2float(colors)
|
Tableau 10' colors as RGB
|
Tableau 10' colors as RGB
|
[
"Tableau",
"10",
"colors",
"as",
"RGB"
] |
def tableau10():
"""'Tableau 10' colors as RGB"""
colors = [
( 31, 119, 180), (255, 127, 14), ( 44, 160, 44), (214, 39, 40),
(148, 103, 189), (140, 86, 75), (227, 119, 194), (127, 127, 127),
(188, 189, 34), ( 23, 190, 207)
]
return rgb2float(colors)
|
[
"def",
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https://github.com/trilinos/Trilinos/blob/6168be6dd51e35e1cd681e9c4b24433e709df140/packages/muelu/utils/analysis/tableau.py#L10-L17
|
|
takemaru/graphillion
|
51879f92bb96b53ef8f914ef37a05252ce383617
|
graphillion/graphset.py
|
python
|
GraphSet.paths
|
(terminal1, terminal2, is_hamilton=False, graphset=None)
|
return GraphSet.graphs(vertex_groups=[[terminal1, terminal2]],
degree_constraints=dc,
no_loop=True, graphset=graphset)
|
Returns a GraphSet of paths.
This method can be parallelized with OpenMP by specifying the
environmental variable `OMP_NUM_THREADS`:
`$ OMP_NUM_THREADS=4 python your_graphillion_script.py`
Examples:
>>> GraphSet.paths(1, 6)
GraphSet([[(1, 2), (2, 3), (3, 6)], [(1, 2), (2, 5), (5, 6)], [(1, 4), (4, 5 ...
Args:
terminal1 and terminal2: Both end vertices of a paths.
graphset: Optional. A GraphSet object. Paths to be stored
are selected from this object.
Returns:
A new GraphSet object.
See Also:
graphs()
|
Returns a GraphSet of paths.
|
[
"Returns",
"a",
"GraphSet",
"of",
"paths",
"."
] |
def paths(terminal1, terminal2, is_hamilton=False, graphset=None):
"""Returns a GraphSet of paths.
This method can be parallelized with OpenMP by specifying the
environmental variable `OMP_NUM_THREADS`:
`$ OMP_NUM_THREADS=4 python your_graphillion_script.py`
Examples:
>>> GraphSet.paths(1, 6)
GraphSet([[(1, 2), (2, 3), (3, 6)], [(1, 2), (2, 5), (5, 6)], [(1, 4), (4, 5 ...
Args:
terminal1 and terminal2: Both end vertices of a paths.
graphset: Optional. A GraphSet object. Paths to be stored
are selected from this object.
Returns:
A new GraphSet object.
See Also:
graphs()
"""
dc = {}
for v in GraphSet._vertices:
if v in (terminal1, terminal2):
dc[v] = 1
else:
dc[v] = 2 if is_hamilton else range(0, 3, 2)
return GraphSet.graphs(vertex_groups=[[terminal1, terminal2]],
degree_constraints=dc,
no_loop=True, graphset=graphset)
|
[
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] |
https://github.com/takemaru/graphillion/blob/51879f92bb96b53ef8f914ef37a05252ce383617/graphillion/graphset.py#L1941-L1973
|
|
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/plistlib.py
|
python
|
_BinaryPlistParser._get_size
|
(self, tokenL)
|
return tokenL
|
return the size of the next object.
|
return the size of the next object.
|
[
"return",
"the",
"size",
"of",
"the",
"next",
"object",
"."
] |
def _get_size(self, tokenL):
""" return the size of the next object."""
if tokenL == 0xF:
m = self._fp.read(1)[0] & 0x3
s = 1 << m
f = '>' + _BINARY_FORMAT[s]
return struct.unpack(f, self._fp.read(s))[0]
return tokenL
|
[
"def",
"_get_size",
"(",
"self",
",",
"tokenL",
")",
":",
"if",
"tokenL",
"==",
"0xF",
":",
"m",
"=",
"self",
".",
"_fp",
".",
"read",
"(",
"1",
")",
"[",
"0",
"]",
"&",
"0x3",
"s",
"=",
"1",
"<<",
"m",
"f",
"=",
"'>'",
"+",
"_BINARY_FORMAT",
"[",
"s",
"]",
"return",
"struct",
".",
"unpack",
"(",
"f",
",",
"self",
".",
"_fp",
".",
"read",
"(",
"s",
")",
")",
"[",
"0",
"]",
"return",
"tokenL"
] |
https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/plistlib.py#L574-L582
|
|
catboost/catboost
|
167f64f237114a4d10b2b4ee42adb4569137debe
|
contrib/python/prompt-toolkit/py3/prompt_toolkit/input/win32_pipe.py
|
python
|
Win32PipeInput.send_bytes
|
(self, data: bytes)
|
Send bytes to the input.
|
Send bytes to the input.
|
[
"Send",
"bytes",
"to",
"the",
"input",
"."
] |
def send_bytes(self, data: bytes) -> None:
"Send bytes to the input."
self.send_text(data.decode("utf-8", "ignore"))
|
[
"def",
"send_bytes",
"(",
"self",
",",
"data",
":",
"bytes",
")",
"->",
"None",
":",
"self",
".",
"send_text",
"(",
"data",
".",
"decode",
"(",
"\"utf-8\"",
",",
"\"ignore\"",
")",
")"
] |
https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/prompt-toolkit/py3/prompt_toolkit/input/win32_pipe.py#L108-L110
|
||
baidu-research/tensorflow-allreduce
|
66d5b855e90b0949e9fa5cca5599fd729a70e874
|
tensorflow/contrib/tensor_forest/client/eval_metrics.py
|
python
|
get_metric
|
(metric_name)
|
return _EVAL_METRICS[metric_name]
|
Given a metric name, return the corresponding metric function.
|
Given a metric name, return the corresponding metric function.
|
[
"Given",
"a",
"metric",
"name",
"return",
"the",
"corresponding",
"metric",
"function",
"."
] |
def get_metric(metric_name):
"""Given a metric name, return the corresponding metric function."""
return _EVAL_METRICS[metric_name]
|
[
"def",
"get_metric",
"(",
"metric_name",
")",
":",
"return",
"_EVAL_METRICS",
"[",
"metric_name",
"]"
] |
https://github.com/baidu-research/tensorflow-allreduce/blob/66d5b855e90b0949e9fa5cca5599fd729a70e874/tensorflow/contrib/tensor_forest/client/eval_metrics.py#L154-L156
|
|
pytorch/pytorch
|
7176c92687d3cc847cc046bf002269c6949a21c2
|
torch/distributed/pipeline/sync/phony.py
|
python
|
get_phony
|
(device: torch.device, *, requires_grad: bool)
|
return phony
|
Gets a phony. Phony is tensor without space. It is useful to make
arbitrary dependency in a autograd graph because it doesn't require any
gradient accumulation.
.. note::
Phonies for each device are cached. If an autograd function gets a phony
internally, the phony must be detached to be returned. Otherwise, the
autograd engine will mutate the cached phony in-place::
class Phonify(torch.autograd.Function):
@staticmethod
def forward(ctx, input):
phony = get_phony(input.device, requires_grad=False)
return phony.detach() # detach() is necessary.
|
Gets a phony. Phony is tensor without space. It is useful to make
arbitrary dependency in a autograd graph because it doesn't require any
gradient accumulation.
|
[
"Gets",
"a",
"phony",
".",
"Phony",
"is",
"tensor",
"without",
"space",
".",
"It",
"is",
"useful",
"to",
"make",
"arbitrary",
"dependency",
"in",
"a",
"autograd",
"graph",
"because",
"it",
"doesn",
"t",
"require",
"any",
"gradient",
"accumulation",
"."
] |
def get_phony(device: torch.device, *, requires_grad: bool) -> Tensor:
"""Gets a phony. Phony is tensor without space. It is useful to make
arbitrary dependency in a autograd graph because it doesn't require any
gradient accumulation.
.. note::
Phonies for each device are cached. If an autograd function gets a phony
internally, the phony must be detached to be returned. Otherwise, the
autograd engine will mutate the cached phony in-place::
class Phonify(torch.autograd.Function):
@staticmethod
def forward(ctx, input):
phony = get_phony(input.device, requires_grad=False)
return phony.detach() # detach() is necessary.
"""
key = (device, requires_grad)
try:
phony = _phonies[key]
except KeyError:
with use_stream(default_stream(device)):
phony = torch.empty(0, device=device, requires_grad=requires_grad)
_phonies[key] = phony
return phony
|
[
"def",
"get_phony",
"(",
"device",
":",
"torch",
".",
"device",
",",
"*",
",",
"requires_grad",
":",
"bool",
")",
"->",
"Tensor",
":",
"key",
"=",
"(",
"device",
",",
"requires_grad",
")",
"try",
":",
"phony",
"=",
"_phonies",
"[",
"key",
"]",
"except",
"KeyError",
":",
"with",
"use_stream",
"(",
"default_stream",
"(",
"device",
")",
")",
":",
"phony",
"=",
"torch",
".",
"empty",
"(",
"0",
",",
"device",
"=",
"device",
",",
"requires_grad",
"=",
"requires_grad",
")",
"_phonies",
"[",
"key",
"]",
"=",
"phony",
"return",
"phony"
] |
https://github.com/pytorch/pytorch/blob/7176c92687d3cc847cc046bf002269c6949a21c2/torch/distributed/pipeline/sync/phony.py#L21-L49
|
|
wxWidgets/wxPython-Classic
|
19571e1ae65f1ac445f5491474121998c97a1bf0
|
src/gtk/_core.py
|
python
|
Control_RemoveMnemonics
|
(*args, **kwargs)
|
return _core_.Control_RemoveMnemonics(*args, **kwargs)
|
Control_RemoveMnemonics(String str) -> String
removes the mnemonics characters
|
Control_RemoveMnemonics(String str) -> String
|
[
"Control_RemoveMnemonics",
"(",
"String",
"str",
")",
"-",
">",
"String"
] |
def Control_RemoveMnemonics(*args, **kwargs):
"""
Control_RemoveMnemonics(String str) -> String
removes the mnemonics characters
"""
return _core_.Control_RemoveMnemonics(*args, **kwargs)
|
[
"def",
"Control_RemoveMnemonics",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"return",
"_core_",
".",
"Control_RemoveMnemonics",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")"
] |
https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/gtk/_core.py#L12781-L12787
|
|
Xilinx/Vitis-AI
|
fc74d404563d9951b57245443c73bef389f3657f
|
tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/training/input.py
|
python
|
slice_input_producer
|
(tensor_list, num_epochs=None, shuffle=True, seed=None,
capacity=32, shared_name=None, name=None)
|
Produces a slice of each `Tensor` in `tensor_list`.
Implemented using a Queue -- a `QueueRunner` for the Queue
is added to the current `Graph`'s `QUEUE_RUNNER` collection.
Args:
tensor_list: A list of `Tensor` objects. Every `Tensor` in
`tensor_list` must have the same size in the first dimension.
num_epochs: An integer (optional). If specified, `slice_input_producer`
produces each slice `num_epochs` times before generating
an `OutOfRange` error. If not specified, `slice_input_producer` can cycle
through the slices an unlimited number of times.
shuffle: Boolean. If true, the integers are randomly shuffled within each
epoch.
seed: An integer (optional). Seed used if shuffle == True.
capacity: An integer. Sets the queue capacity.
shared_name: (optional). If set, this queue will be shared under the given
name across multiple sessions.
name: A name for the operations (optional).
Returns:
A list of tensors, one for each element of `tensor_list`. If the tensor
in `tensor_list` has shape `[N, a, b, .., z]`, then the corresponding output
tensor will have shape `[a, b, ..., z]`.
Raises:
ValueError: if `slice_input_producer` produces nothing from `tensor_list`.
@compatibility(eager)
Input pipelines based on Queues are not supported when eager execution is
enabled. Please use the `tf.data` API to ingest data under eager execution.
@end_compatibility
|
Produces a slice of each `Tensor` in `tensor_list`.
|
[
"Produces",
"a",
"slice",
"of",
"each",
"Tensor",
"in",
"tensor_list",
"."
] |
def slice_input_producer(tensor_list, num_epochs=None, shuffle=True, seed=None,
capacity=32, shared_name=None, name=None):
"""Produces a slice of each `Tensor` in `tensor_list`.
Implemented using a Queue -- a `QueueRunner` for the Queue
is added to the current `Graph`'s `QUEUE_RUNNER` collection.
Args:
tensor_list: A list of `Tensor` objects. Every `Tensor` in
`tensor_list` must have the same size in the first dimension.
num_epochs: An integer (optional). If specified, `slice_input_producer`
produces each slice `num_epochs` times before generating
an `OutOfRange` error. If not specified, `slice_input_producer` can cycle
through the slices an unlimited number of times.
shuffle: Boolean. If true, the integers are randomly shuffled within each
epoch.
seed: An integer (optional). Seed used if shuffle == True.
capacity: An integer. Sets the queue capacity.
shared_name: (optional). If set, this queue will be shared under the given
name across multiple sessions.
name: A name for the operations (optional).
Returns:
A list of tensors, one for each element of `tensor_list`. If the tensor
in `tensor_list` has shape `[N, a, b, .., z]`, then the corresponding output
tensor will have shape `[a, b, ..., z]`.
Raises:
ValueError: if `slice_input_producer` produces nothing from `tensor_list`.
@compatibility(eager)
Input pipelines based on Queues are not supported when eager execution is
enabled. Please use the `tf.data` API to ingest data under eager execution.
@end_compatibility
"""
with ops.name_scope(name, "input_producer", tensor_list):
tensor_list = ops.convert_n_to_tensor_or_indexed_slices(tensor_list)
if not tensor_list:
raise ValueError(
"Expected at least one tensor in slice_input_producer().")
range_size = array_ops.shape(tensor_list[0])[0]
# TODO(josh11b): Add an assertion that the first dimension of
# everything in TensorList matches. Maybe just check the inferred shapes?
queue = range_input_producer(range_size, num_epochs=num_epochs,
shuffle=shuffle, seed=seed, capacity=capacity,
shared_name=shared_name)
index = queue.dequeue()
output = [array_ops.gather(t, index) for t in tensor_list]
return output
|
[
"def",
"slice_input_producer",
"(",
"tensor_list",
",",
"num_epochs",
"=",
"None",
",",
"shuffle",
"=",
"True",
",",
"seed",
"=",
"None",
",",
"capacity",
"=",
"32",
",",
"shared_name",
"=",
"None",
",",
"name",
"=",
"None",
")",
":",
"with",
"ops",
".",
"name_scope",
"(",
"name",
",",
"\"input_producer\"",
",",
"tensor_list",
")",
":",
"tensor_list",
"=",
"ops",
".",
"convert_n_to_tensor_or_indexed_slices",
"(",
"tensor_list",
")",
"if",
"not",
"tensor_list",
":",
"raise",
"ValueError",
"(",
"\"Expected at least one tensor in slice_input_producer().\"",
")",
"range_size",
"=",
"array_ops",
".",
"shape",
"(",
"tensor_list",
"[",
"0",
"]",
")",
"[",
"0",
"]",
"# TODO(josh11b): Add an assertion that the first dimension of",
"# everything in TensorList matches. Maybe just check the inferred shapes?",
"queue",
"=",
"range_input_producer",
"(",
"range_size",
",",
"num_epochs",
"=",
"num_epochs",
",",
"shuffle",
"=",
"shuffle",
",",
"seed",
"=",
"seed",
",",
"capacity",
"=",
"capacity",
",",
"shared_name",
"=",
"shared_name",
")",
"index",
"=",
"queue",
".",
"dequeue",
"(",
")",
"output",
"=",
"[",
"array_ops",
".",
"gather",
"(",
"t",
",",
"index",
")",
"for",
"t",
"in",
"tensor_list",
"]",
"return",
"output"
] |
https://github.com/Xilinx/Vitis-AI/blob/fc74d404563d9951b57245443c73bef389f3657f/tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/training/input.py#L328-L376
|
||
wxWidgets/wxPython-Classic
|
19571e1ae65f1ac445f5491474121998c97a1bf0
|
src/osx_cocoa/_misc.py
|
python
|
DataObjectComposite.GetObject
|
(*args, **kwargs)
|
return _misc_.DataObjectComposite_GetObject(*args, **kwargs)
|
GetObject(self, DataFormat format, wxDataObjectBase::Direction dir=Get) -> DataObjectSimple
Returns the pointer to the object which supports this format or None.
TODO: Fix this to use OOR and return the right object type.
|
GetObject(self, DataFormat format, wxDataObjectBase::Direction dir=Get) -> DataObjectSimple
|
[
"GetObject",
"(",
"self",
"DataFormat",
"format",
"wxDataObjectBase",
"::",
"Direction",
"dir",
"=",
"Get",
")",
"-",
">",
"DataObjectSimple"
] |
def GetObject(*args, **kwargs):
"""
GetObject(self, DataFormat format, wxDataObjectBase::Direction dir=Get) -> DataObjectSimple
Returns the pointer to the object which supports this format or None.
TODO: Fix this to use OOR and return the right object type.
"""
return _misc_.DataObjectComposite_GetObject(*args, **kwargs)
|
[
"def",
"GetObject",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"return",
"_misc_",
".",
"DataObjectComposite_GetObject",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")"
] |
https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_cocoa/_misc.py#L5154-L5161
|
|
baidu-research/tensorflow-allreduce
|
66d5b855e90b0949e9fa5cca5599fd729a70e874
|
tensorflow/contrib/data/python/ops/dataset_ops.py
|
python
|
ZipDataset.__init__
|
(self, datasets)
|
See `Dataset.zip()` for details.
|
See `Dataset.zip()` for details.
|
[
"See",
"Dataset",
".",
"zip",
"()",
"for",
"details",
"."
] |
def __init__(self, datasets):
"""See `Dataset.zip()` for details."""
super(ZipDataset, self).__init__()
self._datasets = datasets
|
[
"def",
"__init__",
"(",
"self",
",",
"datasets",
")",
":",
"super",
"(",
"ZipDataset",
",",
"self",
")",
".",
"__init__",
"(",
")",
"self",
".",
"_datasets",
"=",
"datasets"
] |
https://github.com/baidu-research/tensorflow-allreduce/blob/66d5b855e90b0949e9fa5cca5599fd729a70e874/tensorflow/contrib/data/python/ops/dataset_ops.py#L1171-L1174
|
||
hfinkel/llvm-project-cxxjit
|
91084ef018240bbb8e24235ff5cd8c355a9c1a1e
|
clang/bindings/python/clang/cindex.py
|
python
|
Type.get_offset
|
(self, fieldname)
|
return conf.lib.clang_Type_getOffsetOf(self, fieldname)
|
Retrieve the offset of a field in the record.
|
Retrieve the offset of a field in the record.
|
[
"Retrieve",
"the",
"offset",
"of",
"a",
"field",
"in",
"the",
"record",
"."
] |
def get_offset(self, fieldname):
"""
Retrieve the offset of a field in the record.
"""
return conf.lib.clang_Type_getOffsetOf(self, fieldname)
|
[
"def",
"get_offset",
"(",
"self",
",",
"fieldname",
")",
":",
"return",
"conf",
".",
"lib",
".",
"clang_Type_getOffsetOf",
"(",
"self",
",",
"fieldname",
")"
] |
https://github.com/hfinkel/llvm-project-cxxjit/blob/91084ef018240bbb8e24235ff5cd8c355a9c1a1e/clang/bindings/python/clang/cindex.py#L2384-L2388
|
|
strasdat/Sophus
|
36b08885e094fda63e92ad89d65be380c288265a
|
sympy/sophus/complex.py
|
python
|
Complex.Da_a_mul_b
|
(a, b)
|
return sympy.Matrix([[b.real, -b.imag],
[b.imag, b.real]])
|
derivatice of complex muliplication wrt left multiplier a
|
derivatice of complex muliplication wrt left multiplier a
|
[
"derivatice",
"of",
"complex",
"muliplication",
"wrt",
"left",
"multiplier",
"a"
] |
def Da_a_mul_b(a, b):
""" derivatice of complex muliplication wrt left multiplier a """
return sympy.Matrix([[b.real, -b.imag],
[b.imag, b.real]])
|
[
"def",
"Da_a_mul_b",
"(",
"a",
",",
"b",
")",
":",
"return",
"sympy",
".",
"Matrix",
"(",
"[",
"[",
"b",
".",
"real",
",",
"-",
"b",
".",
"imag",
"]",
",",
"[",
"b",
".",
"imag",
",",
"b",
".",
"real",
"]",
"]",
")"
] |
https://github.com/strasdat/Sophus/blob/36b08885e094fda63e92ad89d65be380c288265a/sympy/sophus/complex.py#L72-L75
|
|
BlzFans/wke
|
b0fa21158312e40c5fbd84682d643022b6c34a93
|
cygwin/lib/python2.6/platform.py
|
python
|
linux_distribution
|
(distname='', version='', id='',
supported_dists=_supported_dists,
full_distribution_name=1)
|
return distname, version, id
|
Tries to determine the name of the Linux OS distribution name.
The function first looks for a distribution release file in
/etc and then reverts to _dist_try_harder() in case no
suitable files are found.
supported_dists may be given to define the set of Linux
distributions to look for. It defaults to a list of currently
supported Linux distributions identified by their release file
name.
If full_distribution_name is true (default), the full
distribution read from the OS is returned. Otherwise the short
name taken from supported_dists is used.
Returns a tuple (distname,version,id) which default to the
args given as parameters.
|
Tries to determine the name of the Linux OS distribution name.
|
[
"Tries",
"to",
"determine",
"the",
"name",
"of",
"the",
"Linux",
"OS",
"distribution",
"name",
"."
] |
def linux_distribution(distname='', version='', id='',
supported_dists=_supported_dists,
full_distribution_name=1):
""" Tries to determine the name of the Linux OS distribution name.
The function first looks for a distribution release file in
/etc and then reverts to _dist_try_harder() in case no
suitable files are found.
supported_dists may be given to define the set of Linux
distributions to look for. It defaults to a list of currently
supported Linux distributions identified by their release file
name.
If full_distribution_name is true (default), the full
distribution read from the OS is returned. Otherwise the short
name taken from supported_dists is used.
Returns a tuple (distname,version,id) which default to the
args given as parameters.
"""
try:
etc = os.listdir('/etc')
except os.error:
# Probably not a Unix system
return distname,version,id
etc.sort()
for file in etc:
m = _release_filename.match(file)
if m is not None:
_distname,dummy = m.groups()
if _distname in supported_dists:
distname = _distname
break
else:
return _dist_try_harder(distname,version,id)
# Read the first line
f = open('/etc/'+file, 'r')
firstline = f.readline()
f.close()
_distname, _version, _id = _parse_release_file(firstline)
if _distname and full_distribution_name:
distname = _distname
if _version:
version = _version
if _id:
id = _id
return distname, version, id
|
[
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"''",
",",
"version",
"=",
"''",
",",
"id",
"=",
"''",
",",
"supported_dists",
"=",
"_supported_dists",
",",
"full_distribution_name",
"=",
"1",
")",
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"try",
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"os",
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"'/etc'",
")",
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":",
"distname",
"=",
"_distname",
"break",
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":",
"return",
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":",
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"distname",
",",
"version",
",",
"id"
] |
https://github.com/BlzFans/wke/blob/b0fa21158312e40c5fbd84682d643022b6c34a93/cygwin/lib/python2.6/platform.py#L293-L345
|
|
apple/swift
|
469f72fdae2ea828b3b6c0d7d62d7e4cf98c4893
|
utils/swift_build_support/swift_build_support/products/swiftsyntax.py
|
python
|
SwiftSyntax.product_source_name
|
(cls)
|
return "swift-syntax"
|
product_source_name() -> str
The name of the source code directory of this product.
|
product_source_name() -> str
|
[
"product_source_name",
"()",
"-",
">",
"str"
] |
def product_source_name(cls):
"""product_source_name() -> str
The name of the source code directory of this product.
"""
return "swift-syntax"
|
[
"def",
"product_source_name",
"(",
"cls",
")",
":",
"return",
"\"swift-syntax\""
] |
https://github.com/apple/swift/blob/469f72fdae2ea828b3b6c0d7d62d7e4cf98c4893/utils/swift_build_support/swift_build_support/products/swiftsyntax.py#L33-L38
|
|
krishauser/Klampt
|
972cc83ea5befac3f653c1ba20f80155768ad519
|
Python/control-examples/system_id.py
|
python
|
LinearSystemID.fixC
|
(self,i,value)
|
Sets the i'th entry of the C vector to a fixed value
|
Sets the i'th entry of the C vector to a fixed value
|
[
"Sets",
"the",
"i",
"th",
"entry",
"of",
"the",
"C",
"vector",
"to",
"a",
"fixed",
"value"
] |
def fixC(self,i,value):
"""Sets the i'th entry of the C vector to a fixed value"""
if self.coeffPattern[2] == None:
m,n=self.m,self.n
self.coeffPattern[2] = [None]*m
self.coeffPattern[2][i]=value
self._updateEstimatorSize(i)
|
[
"def",
"fixC",
"(",
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",",
"i",
",",
"value",
")",
":",
"if",
"self",
".",
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"[",
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"_updateEstimatorSize",
"(",
"i",
")"
] |
https://github.com/krishauser/Klampt/blob/972cc83ea5befac3f653c1ba20f80155768ad519/Python/control-examples/system_id.py#L39-L45
|
||
facebook/proxygen
|
a9ca025af207787815cb01eee1971cd572c7a81e
|
build/fbcode_builder/shell_quoting.py
|
python
|
ShellQuoted.__new__
|
(cls, s)
|
return super(ShellQuoted, cls).__new__(
cls, s.do_not_use_raw_str if isinstance(s, ShellQuoted) else s
)
|
No need to nest ShellQuoted.
|
No need to nest ShellQuoted.
|
[
"No",
"need",
"to",
"nest",
"ShellQuoted",
"."
] |
def __new__(cls, s):
"No need to nest ShellQuoted."
return super(ShellQuoted, cls).__new__(
cls, s.do_not_use_raw_str if isinstance(s, ShellQuoted) else s
)
|
[
"def",
"__new__",
"(",
"cls",
",",
"s",
")",
":",
"return",
"super",
"(",
"ShellQuoted",
",",
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".",
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"(",
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",",
"s",
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"if",
"isinstance",
"(",
"s",
",",
"ShellQuoted",
")",
"else",
"s",
")"
] |
https://github.com/facebook/proxygen/blob/a9ca025af207787815cb01eee1971cd572c7a81e/build/fbcode_builder/shell_quoting.py#L34-L38
|
|
Polidea/SiriusObfuscator
|
b0e590d8130e97856afe578869b83a209e2b19be
|
SymbolExtractorAndRenamer/lldb/third_party/Python/module/pexpect-2.4/screen.py
|
python
|
screen.cr
|
(self)
|
This moves the cursor to the beginning (col 1) of the current row.
|
This moves the cursor to the beginning (col 1) of the current row.
|
[
"This",
"moves",
"the",
"cursor",
"to",
"the",
"beginning",
"(",
"col",
"1",
")",
"of",
"the",
"current",
"row",
"."
] |
def cr(self):
"""This moves the cursor to the beginning (col 1) of the current row.
"""
self.cursor_home(self.cur_r, 1)
|
[
"def",
"cr",
"(",
"self",
")",
":",
"self",
".",
"cursor_home",
"(",
"self",
".",
"cur_r",
",",
"1",
")"
] |
https://github.com/Polidea/SiriusObfuscator/blob/b0e590d8130e97856afe578869b83a209e2b19be/SymbolExtractorAndRenamer/lldb/third_party/Python/module/pexpect-2.4/screen.py#L101-L105
|
||
zju3dv/clean-pvnet
|
5870c509e3cc205e1bb28910a7b1a9a3c8add9a8
|
lib/utils/meshrenderer/gl_utils/camera.py
|
python
|
Camera.setIntrinsic
|
(self, I, W, H, near, far, scale=1.0, originIsInTopLeft=True)
|
Args:
I: 3x3 intrinsic camera matrix from real camera (without any OpenGL stuff)
W: Width of the camera image
H: Height of the camera image
near: Near plane
far: Far plane
originIsInTopLeft: If True then the image origin is in top left
if False the image origin is in image center
Source: http://ksimek.github.io/2013/06/03/calibrated_cameras_in_opengl/
|
Args:
I: 3x3 intrinsic camera matrix from real camera (without any OpenGL stuff)
W: Width of the camera image
H: Height of the camera image
near: Near plane
far: Far plane
originIsInTopLeft: If True then the image origin is in top left
if False the image origin is in image center
Source: http://ksimek.github.io/2013/06/03/calibrated_cameras_in_opengl/
|
[
"Args",
":",
"I",
":",
"3x3",
"intrinsic",
"camera",
"matrix",
"from",
"real",
"camera",
"(",
"without",
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"OpenGL",
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"2013",
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"06",
"/",
"03",
"/",
"calibrated_cameras_in_opengl",
"/"
] |
def setIntrinsic(self, I, W, H, near, far, scale=1.0, originIsInTopLeft=True):
'''
Args:
I: 3x3 intrinsic camera matrix from real camera (without any OpenGL stuff)
W: Width of the camera image
H: Height of the camera image
near: Near plane
far: Far plane
originIsInTopLeft: If True then the image origin is in top left
if False the image origin is in image center
Source: http://ksimek.github.io/2013/06/03/calibrated_cameras_in_opengl/
'''
Camera.__check_matrix__(I)
A = near + far
B = near * far
persp = np.array( [ [ I[0,0]*scale, I[0,1]*scale, -I[0,2]*scale, 0 ],
[ 0 , I[1,1]*scale, -I[1,2]*scale, 0 ],
[ 0 , 0 , A , B ],
[ 0 , 0 , -1 , 0 ] ] , dtype=np.float64)
ortho = Camera.__glOrtho__(0, W, H, 0, near, far) if originIsInTopLeft else\
Camera.__glOrtho__(-W/2., W/2., -H/2., H/2., near, far)
self.__T_proj_view[:] = np.dot( ortho, persp ).astype(np.float32)
self.__T_view_proj[:] = np.linalg.inv(self.__T_proj_view)
self.__T_proj_world[:] = np.dot(self.__T_proj_view, self.__T_view_world)
self.dirty = True
|
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",",
"self",
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"__T_view_world",
")",
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"dirty",
"=",
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] |
https://github.com/zju3dv/clean-pvnet/blob/5870c509e3cc205e1bb28910a7b1a9a3c8add9a8/lib/utils/meshrenderer/gl_utils/camera.py#L139-L166
|
||
wxWidgets/wxPython-Classic
|
19571e1ae65f1ac445f5491474121998c97a1bf0
|
wx/py/pseudo.py
|
python
|
PseudoFile.__init__
|
(self)
|
Create a file-like object.
|
Create a file-like object.
|
[
"Create",
"a",
"file",
"-",
"like",
"object",
"."
] |
def __init__(self):
"""Create a file-like object."""
pass
|
[
"def",
"__init__",
"(",
"self",
")",
":",
"pass"
] |
https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/wx/py/pseudo.py#L46-L48
|
||
catboost/catboost
|
167f64f237114a4d10b2b4ee42adb4569137debe
|
contrib/python/ipython/py2/IPython/utils/text.py
|
python
|
strip_ansi
|
(source)
|
return re.sub(r'\033\[(\d|;)+?m', '', source)
|
Remove ansi escape codes from text.
Parameters
----------
source : str
Source to remove the ansi from
|
Remove ansi escape codes from text.
Parameters
----------
source : str
Source to remove the ansi from
|
[
"Remove",
"ansi",
"escape",
"codes",
"from",
"text",
".",
"Parameters",
"----------",
"source",
":",
"str",
"Source",
"to",
"remove",
"the",
"ansi",
"from"
] |
def strip_ansi(source):
"""
Remove ansi escape codes from text.
Parameters
----------
source : str
Source to remove the ansi from
"""
return re.sub(r'\033\[(\d|;)+?m', '', source)
|
[
"def",
"strip_ansi",
"(",
"source",
")",
":",
"return",
"re",
".",
"sub",
"(",
"r'\\033\\[(\\d|;)+?m'",
",",
"''",
",",
"source",
")"
] |
https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/ipython/py2/IPython/utils/text.py#L479-L488
|
|
ApolloAuto/apollo
|
463fb82f9e979d02dcb25044e60931293ab2dba0
|
modules/tools/sensor_calibration/extract_data.py
|
python
|
Extractor.generate_compressed_file
|
(input_path,
input_name,
output_path,
compressed_file='sensor_data')
|
Compress data extraction directory as a single tar.gz archive
|
Compress data extraction directory as a single tar.gz archive
|
[
"Compress",
"data",
"extraction",
"directory",
"as",
"a",
"single",
"tar",
".",
"gz",
"archive"
] |
def generate_compressed_file(input_path,
input_name,
output_path,
compressed_file='sensor_data'):
"""
Compress data extraction directory as a single tar.gz archive
"""
cwd_path = os.getcwd()
os.chdir(input_path)
shutil.make_archive(base_name=os.path.join(output_path,
compressed_file),
format='gztar',
root_dir=input_path,
base_dir=input_name)
os.chdir(cwd_path)
|
[
"def",
"generate_compressed_file",
"(",
"input_path",
",",
"input_name",
",",
"output_path",
",",
"compressed_file",
"=",
"'sensor_data'",
")",
":",
"cwd_path",
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",",
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",",
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"=",
"input_name",
")",
"os",
".",
"chdir",
"(",
"cwd_path",
")"
] |
https://github.com/ApolloAuto/apollo/blob/463fb82f9e979d02dcb25044e60931293ab2dba0/modules/tools/sensor_calibration/extract_data.py#L323-L337
|
||
DmitryKoterov/dklab_realplexor
|
01281d42fddcf7b9efe763b3ab50191c4429debc
|
api/python/Dklab/realplexor.py
|
python
|
Dklab_Realplexor.send
|
(self, idsAndCursors, data, showOnlyForIds=None)
|
Send data to realplexor.
Throw Dklab_Realplexor_Exception in case of error.
idsAndCursors -- Target IDs in form of: dictionary(id1 => cursor1, id2 => cursor2, ...)
of dictionary(id1, id2, id3, ...). If sending to a single ID,
you may pass it as a plain string, not dictionary.
data -- Data to be sent (any format, e.g. nested dictionaries are OK).
showOnlyForIds -- Send this message to only those who also listen any of these IDs.
This parameter may be used to limit the visibility to a closed
number of cliens: give each client an unique ID and enumerate
client IDs in $showOnlyForIds to not to send messages to others.
|
Send data to realplexor.
Throw Dklab_Realplexor_Exception in case of error.
|
[
"Send",
"data",
"to",
"realplexor",
".",
"Throw",
"Dklab_Realplexor_Exception",
"in",
"case",
"of",
"error",
"."
] |
def send(self, idsAndCursors, data, showOnlyForIds=None):
"""
Send data to realplexor.
Throw Dklab_Realplexor_Exception in case of error.
idsAndCursors -- Target IDs in form of: dictionary(id1 => cursor1, id2 => cursor2, ...)
of dictionary(id1, id2, id3, ...). If sending to a single ID,
you may pass it as a plain string, not dictionary.
data -- Data to be sent (any format, e.g. nested dictionaries are OK).
showOnlyForIds -- Send this message to only those who also listen any of these IDs.
This parameter may be used to limit the visibility to a closed
number of cliens: give each client an unique ID and enumerate
client IDs in $showOnlyForIds to not to send messages to others.
"""
data = json.dumps(data)
pairs = []
for id in idsAndCursors:
if type(id) == type(1):
id = cursor # this is NOT cursor, but ID!
cursor = None
if re.search('^\w+$', id) is None:
raise Dklab_Realplexor_Exception("Identifier must be alphanumeric, \"%s\" given" % id)
try:
cursor = idsAndCursors[id]
except:
cursor = None
id = (self._namespace or '') + id
if cursor is not None:
try:
i = float(cursor)
except ValueError:
raise Dklab_Realplexor_Exception("Cursor must be numeric, \"%s\" given" % cursor)
pairs.append("%s:%s" % (cursor,id))
else:
pairs.append(id)
if isinstance(showOnlyForIds, (list, tuple)):
for id in showOnlyForIds:
pairs.append("*" + (self._namespace or '') + id)
self._send(",".join(pairs), data)
|
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")"
] |
https://github.com/DmitryKoterov/dklab_realplexor/blob/01281d42fddcf7b9efe763b3ab50191c4429debc/api/python/Dklab/realplexor.py#L36-L74
|
||
catboost/catboost
|
167f64f237114a4d10b2b4ee42adb4569137debe
|
contrib/python/pandas/py2/pandas/core/dtypes/cast.py
|
python
|
construct_1d_ndarray_preserving_na
|
(values, dtype=None, copy=False)
|
return subarr
|
Construct a new ndarray, coercing `values` to `dtype`, preserving NA.
Parameters
----------
values : Sequence
dtype : numpy.dtype, optional
copy : bool, default False
Note that copies may still be made with ``copy=False`` if casting
is required.
Returns
-------
arr : ndarray[dtype]
Examples
--------
>>> np.array([1.0, 2.0, None], dtype='str')
array(['1.0', '2.0', 'None'], dtype='<U4')
>>> construct_1d_ndarray_preserving_na([1.0, 2.0, None], dtype='str')
|
Construct a new ndarray, coercing `values` to `dtype`, preserving NA.
|
[
"Construct",
"a",
"new",
"ndarray",
"coercing",
"values",
"to",
"dtype",
"preserving",
"NA",
"."
] |
def construct_1d_ndarray_preserving_na(values, dtype=None, copy=False):
"""
Construct a new ndarray, coercing `values` to `dtype`, preserving NA.
Parameters
----------
values : Sequence
dtype : numpy.dtype, optional
copy : bool, default False
Note that copies may still be made with ``copy=False`` if casting
is required.
Returns
-------
arr : ndarray[dtype]
Examples
--------
>>> np.array([1.0, 2.0, None], dtype='str')
array(['1.0', '2.0', 'None'], dtype='<U4')
>>> construct_1d_ndarray_preserving_na([1.0, 2.0, None], dtype='str')
"""
subarr = np.array(values, dtype=dtype, copy=copy)
if dtype is not None and dtype.kind in ("U", "S"):
# GH-21083
# We can't just return np.array(subarr, dtype='str') since
# NumPy will convert the non-string objects into strings
# Including NA values. Se we have to go
# string -> object -> update NA, which requires an
# additional pass over the data.
na_values = isna(values)
subarr2 = subarr.astype(object)
subarr2[na_values] = np.asarray(values, dtype=object)[na_values]
subarr = subarr2
return subarr
|
[
"def",
"construct_1d_ndarray_preserving_na",
"(",
"values",
",",
"dtype",
"=",
"None",
",",
"copy",
"=",
"False",
")",
":",
"subarr",
"=",
"np",
".",
"array",
"(",
"values",
",",
"dtype",
"=",
"dtype",
",",
"copy",
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"copy",
")",
"if",
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"and",
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".",
"kind",
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"(",
"\"U\"",
",",
"\"S\"",
")",
":",
"# GH-21083",
"# We can't just return np.array(subarr, dtype='str') since",
"# NumPy will convert the non-string objects into strings",
"# Including NA values. Se we have to go",
"# string -> object -> update NA, which requires an",
"# additional pass over the data.",
"na_values",
"=",
"isna",
"(",
"values",
")",
"subarr2",
"=",
"subarr",
".",
"astype",
"(",
"object",
")",
"subarr2",
"[",
"na_values",
"]",
"=",
"np",
".",
"asarray",
"(",
"values",
",",
"dtype",
"=",
"object",
")",
"[",
"na_values",
"]",
"subarr",
"=",
"subarr2",
"return",
"subarr"
] |
https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/pandas/py2/pandas/core/dtypes/cast.py#L1218-L1257
|
|
wxWidgets/wxPython-Classic
|
19571e1ae65f1ac445f5491474121998c97a1bf0
|
wx/tools/Editra/src/extern/aui/framemanager.py
|
python
|
AuiManager.AddPane4
|
(self, window, pane_info, target)
|
return True
|
See comments on :meth:`AddPane`.
|
See comments on :meth:`AddPane`.
|
[
"See",
"comments",
"on",
":",
"meth",
":",
"AddPane",
"."
] |
def AddPane4(self, window, pane_info, target):
""" See comments on :meth:`AddPane`. """
if not self.AddPane(window, pane_info):
return False
paneInfo = self.GetPane(window)
if not paneInfo.IsNotebookDockable():
return self.AddPane1(window, pane_info)
if not target.IsNotebookDockable() and not target.IsNotebookControl():
return self.AddPane1(window, pane_info)
if not target.HasNotebook():
self.CreateNotebookBase(self._panes, target)
# Add new item to notebook
paneInfo.NotebookPage(target.notebook_id)
# we also want to remove our captions sometimes
self.RemoveAutoNBCaption(paneInfo)
self.UpdateNotebook()
return True
|
[
"def",
"AddPane4",
"(",
"self",
",",
"window",
",",
"pane_info",
",",
"target",
")",
":",
"if",
"not",
"self",
".",
"AddPane",
"(",
"window",
",",
"pane_info",
")",
":",
"return",
"False",
"paneInfo",
"=",
"self",
".",
"GetPane",
"(",
"window",
")",
"if",
"not",
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".",
"IsNotebookDockable",
"(",
")",
":",
"return",
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".",
"AddPane1",
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"window",
",",
"pane_info",
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"IsNotebookDockable",
"(",
")",
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"IsNotebookControl",
"(",
")",
":",
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"window",
",",
"pane_info",
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"if",
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"target",
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"HasNotebook",
"(",
")",
":",
"self",
".",
"CreateNotebookBase",
"(",
"self",
".",
"_panes",
",",
"target",
")",
"# Add new item to notebook",
"paneInfo",
".",
"NotebookPage",
"(",
"target",
".",
"notebook_id",
")",
"# we also want to remove our captions sometimes",
"self",
".",
"RemoveAutoNBCaption",
"(",
"paneInfo",
")",
"self",
".",
"UpdateNotebook",
"(",
")",
"return",
"True"
] |
https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/wx/tools/Editra/src/extern/aui/framemanager.py#L4847-L4870
|
|
LiquidPlayer/LiquidCore
|
9405979363f2353ac9a71ad8ab59685dd7f919c9
|
deps/node-10.15.3/tools/gyp/pylib/gyp/xcode_emulation.py
|
python
|
XcodeSettings.GetInstallName
|
(self)
|
return install_name
|
Return LD_DYLIB_INSTALL_NAME for this target.
|
Return LD_DYLIB_INSTALL_NAME for this target.
|
[
"Return",
"LD_DYLIB_INSTALL_NAME",
"for",
"this",
"target",
"."
] |
def GetInstallName(self):
"""Return LD_DYLIB_INSTALL_NAME for this target."""
# Xcode sets this for shared_libraries, and for nonbundled loadable_modules.
if (self.spec['type'] != 'shared_library' and
(self.spec['type'] != 'loadable_module' or self._IsBundle())):
return None
default_install_name = \
'$(DYLIB_INSTALL_NAME_BASE:standardizepath)/$(EXECUTABLE_PATH)'
install_name = self.GetPerTargetSetting(
'LD_DYLIB_INSTALL_NAME', default=default_install_name)
# Hardcode support for the variables used in chromium for now, to
# unblock people using the make build.
if '$' in install_name:
assert install_name in ('$(DYLIB_INSTALL_NAME_BASE:standardizepath)/'
'$(WRAPPER_NAME)/$(PRODUCT_NAME)', default_install_name), (
'Variables in LD_DYLIB_INSTALL_NAME are not generally supported '
'yet in target \'%s\' (got \'%s\')' %
(self.spec['target_name'], install_name))
install_name = install_name.replace(
'$(DYLIB_INSTALL_NAME_BASE:standardizepath)',
self._StandardizePath(self.GetInstallNameBase()))
if self._IsBundle():
# These are only valid for bundles, hence the |if|.
install_name = install_name.replace(
'$(WRAPPER_NAME)', self.GetWrapperName())
install_name = install_name.replace(
'$(PRODUCT_NAME)', self.GetProductName())
else:
assert '$(WRAPPER_NAME)' not in install_name
assert '$(PRODUCT_NAME)' not in install_name
install_name = install_name.replace(
'$(EXECUTABLE_PATH)', self.GetExecutablePath())
return install_name
|
[
"def",
"GetInstallName",
"(",
"self",
")",
":",
"# Xcode sets this for shared_libraries, and for nonbundled loadable_modules.",
"if",
"(",
"self",
".",
"spec",
"[",
"'type'",
"]",
"!=",
"'shared_library'",
"and",
"(",
"self",
".",
"spec",
"[",
"'type'",
"]",
"!=",
"'loadable_module'",
"or",
"self",
".",
"_IsBundle",
"(",
")",
")",
")",
":",
"return",
"None",
"default_install_name",
"=",
"'$(DYLIB_INSTALL_NAME_BASE:standardizepath)/$(EXECUTABLE_PATH)'",
"install_name",
"=",
"self",
".",
"GetPerTargetSetting",
"(",
"'LD_DYLIB_INSTALL_NAME'",
",",
"default",
"=",
"default_install_name",
")",
"# Hardcode support for the variables used in chromium for now, to",
"# unblock people using the make build.",
"if",
"'$'",
"in",
"install_name",
":",
"assert",
"install_name",
"in",
"(",
"'$(DYLIB_INSTALL_NAME_BASE:standardizepath)/'",
"'$(WRAPPER_NAME)/$(PRODUCT_NAME)'",
",",
"default_install_name",
")",
",",
"(",
"'Variables in LD_DYLIB_INSTALL_NAME are not generally supported '",
"'yet in target \\'%s\\' (got \\'%s\\')'",
"%",
"(",
"self",
".",
"spec",
"[",
"'target_name'",
"]",
",",
"install_name",
")",
")",
"install_name",
"=",
"install_name",
".",
"replace",
"(",
"'$(DYLIB_INSTALL_NAME_BASE:standardizepath)'",
",",
"self",
".",
"_StandardizePath",
"(",
"self",
".",
"GetInstallNameBase",
"(",
")",
")",
")",
"if",
"self",
".",
"_IsBundle",
"(",
")",
":",
"# These are only valid for bundles, hence the |if|.",
"install_name",
"=",
"install_name",
".",
"replace",
"(",
"'$(WRAPPER_NAME)'",
",",
"self",
".",
"GetWrapperName",
"(",
")",
")",
"install_name",
"=",
"install_name",
".",
"replace",
"(",
"'$(PRODUCT_NAME)'",
",",
"self",
".",
"GetProductName",
"(",
")",
")",
"else",
":",
"assert",
"'$(WRAPPER_NAME)'",
"not",
"in",
"install_name",
"assert",
"'$(PRODUCT_NAME)'",
"not",
"in",
"install_name",
"install_name",
"=",
"install_name",
".",
"replace",
"(",
"'$(EXECUTABLE_PATH)'",
",",
"self",
".",
"GetExecutablePath",
"(",
")",
")",
"return",
"install_name"
] |
https://github.com/LiquidPlayer/LiquidCore/blob/9405979363f2353ac9a71ad8ab59685dd7f919c9/deps/node-10.15.3/tools/gyp/pylib/gyp/xcode_emulation.py#L779-L815
|
|
wxWidgets/wxPython-Classic
|
19571e1ae65f1ac445f5491474121998c97a1bf0
|
src/osx_cocoa/_core.py
|
python
|
MemoryFSHandler_AddFile
|
(filename, dataItem, imgType=-1)
|
Add 'file' to the memory filesystem. The dataItem parameter can
either be a `wx.Bitmap`, `wx.Image` or a string that can contain
arbitrary data. If a bitmap or image is used then the imgType
parameter should specify what kind of image file it should be
written as, wx.BITMAP_TYPE_PNG, etc.
|
Add 'file' to the memory filesystem. The dataItem parameter can
either be a `wx.Bitmap`, `wx.Image` or a string that can contain
arbitrary data. If a bitmap or image is used then the imgType
parameter should specify what kind of image file it should be
written as, wx.BITMAP_TYPE_PNG, etc.
|
[
"Add",
"file",
"to",
"the",
"memory",
"filesystem",
".",
"The",
"dataItem",
"parameter",
"can",
"either",
"be",
"a",
"wx",
".",
"Bitmap",
"wx",
".",
"Image",
"or",
"a",
"string",
"that",
"can",
"contain",
"arbitrary",
"data",
".",
"If",
"a",
"bitmap",
"or",
"image",
"is",
"used",
"then",
"the",
"imgType",
"parameter",
"should",
"specify",
"what",
"kind",
"of",
"image",
"file",
"it",
"should",
"be",
"written",
"as",
"wx",
".",
"BITMAP_TYPE_PNG",
"etc",
"."
] |
def MemoryFSHandler_AddFile(filename, dataItem, imgType=-1):
"""
Add 'file' to the memory filesystem. The dataItem parameter can
either be a `wx.Bitmap`, `wx.Image` or a string that can contain
arbitrary data. If a bitmap or image is used then the imgType
parameter should specify what kind of image file it should be
written as, wx.BITMAP_TYPE_PNG, etc.
"""
if isinstance(dataItem, wx.Image):
__wxMemoryFSHandler_AddFile_wxImage(filename, dataItem, imgType)
elif isinstance(dataItem, wx.Bitmap):
__wxMemoryFSHandler_AddFile_wxBitmap(filename, dataItem, imgType)
else:
try:
__wxMemoryFSHandler_AddFile_Data(filename, dataItem)
except TypeError:
raise TypeError, 'wx.Image, wx.Bitmap or buffer object expected'
|
[
"def",
"MemoryFSHandler_AddFile",
"(",
"filename",
",",
"dataItem",
",",
"imgType",
"=",
"-",
"1",
")",
":",
"if",
"isinstance",
"(",
"dataItem",
",",
"wx",
".",
"Image",
")",
":",
"__wxMemoryFSHandler_AddFile_wxImage",
"(",
"filename",
",",
"dataItem",
",",
"imgType",
")",
"elif",
"isinstance",
"(",
"dataItem",
",",
"wx",
".",
"Bitmap",
")",
":",
"__wxMemoryFSHandler_AddFile_wxBitmap",
"(",
"filename",
",",
"dataItem",
",",
"imgType",
")",
"else",
":",
"try",
":",
"__wxMemoryFSHandler_AddFile_Data",
"(",
"filename",
",",
"dataItem",
")",
"except",
"TypeError",
":",
"raise",
"TypeError",
",",
"'wx.Image, wx.Bitmap or buffer object expected'"
] |
https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_cocoa/_core.py#L2538-L2554
|
||
BlzFans/wke
|
b0fa21158312e40c5fbd84682d643022b6c34a93
|
cygwin/lib/python2.6/imaplib.py
|
python
|
IMAP4.noop
|
(self)
|
return self._simple_command('NOOP')
|
Send NOOP command.
(typ, [data]) = <instance>.noop()
|
Send NOOP command.
|
[
"Send",
"NOOP",
"command",
"."
] |
def noop(self):
"""Send NOOP command.
(typ, [data]) = <instance>.noop()
"""
if __debug__:
if self.debug >= 3:
self._dump_ur(self.untagged_responses)
return self._simple_command('NOOP')
|
[
"def",
"noop",
"(",
"self",
")",
":",
"if",
"__debug__",
":",
"if",
"self",
".",
"debug",
">=",
"3",
":",
"self",
".",
"_dump_ur",
"(",
"self",
".",
"untagged_responses",
")",
"return",
"self",
".",
"_simple_command",
"(",
"'NOOP'",
")"
] |
https://github.com/BlzFans/wke/blob/b0fa21158312e40c5fbd84682d643022b6c34a93/cygwin/lib/python2.6/imaplib.py#L565-L573
|
|
PX4/PX4-Autopilot
|
0b9f60a0370be53d683352c63fd92db3d6586e18
|
platforms/nuttx/NuttX/tools/kconfiglib.py
|
python
|
Choice.referenced
|
(self)
|
return {item for node in self.nodes for item in node.referenced}
|
See the class documentation.
|
See the class documentation.
|
[
"See",
"the",
"class",
"documentation",
"."
] |
def referenced(self):
"""
See the class documentation.
"""
return {item for node in self.nodes for item in node.referenced}
|
[
"def",
"referenced",
"(",
"self",
")",
":",
"return",
"{",
"item",
"for",
"node",
"in",
"self",
".",
"nodes",
"for",
"item",
"in",
"node",
".",
"referenced",
"}"
] |
https://github.com/PX4/PX4-Autopilot/blob/0b9f60a0370be53d683352c63fd92db3d6586e18/platforms/nuttx/NuttX/tools/kconfiglib.py#L4888-L4892
|
|
gimli-org/gimli
|
17aa2160de9b15ababd9ef99e89b1bc3277bbb23
|
pygimli/physics/petro/resistivity.py
|
python
|
transInvArchiePhi
|
(rFluid=20, m=2)
|
return pg.trans.TransPower(-1/m, rFluid)
|
Inverse Archie transformation function porosity(resistivity).
# rFluid/rho = phi^m ==> phi = (rFluid/rho)^(1/m) = (rho/rFluid)^(-1/m)
See
---
:py:mod:`pygimli.physics.petro.transFwdArchiePhi`
|
Inverse Archie transformation function porosity(resistivity).
|
[
"Inverse",
"Archie",
"transformation",
"function",
"porosity",
"(",
"resistivity",
")",
"."
] |
def transInvArchiePhi(rFluid=20, m=2): # phi(rho)
"""Inverse Archie transformation function porosity(resistivity).
# rFluid/rho = phi^m ==> phi = (rFluid/rho)^(1/m) = (rho/rFluid)^(-1/m)
See
---
:py:mod:`pygimli.physics.petro.transFwdArchiePhi`
"""
return pg.trans.TransPower(-1/m, rFluid)
|
[
"def",
"transInvArchiePhi",
"(",
"rFluid",
"=",
"20",
",",
"m",
"=",
"2",
")",
":",
"# phi(rho)",
"return",
"pg",
".",
"trans",
".",
"TransPower",
"(",
"-",
"1",
"/",
"m",
",",
"rFluid",
")"
] |
https://github.com/gimli-org/gimli/blob/17aa2160de9b15ababd9ef99e89b1bc3277bbb23/pygimli/physics/petro/resistivity.py#L152-L160
|
|
PaddlePaddle/PaddleOCR
|
b756bf5f8c90142e0d89d3db0163965c686b6ffe
|
ppstructure/table/tablepyxl/style.py
|
python
|
Element.get_dimension
|
(self, dimension_key)
|
return dimension
|
Extracts the dimension from the style dict of the Element and returns it as a float.
|
Extracts the dimension from the style dict of the Element and returns it as a float.
|
[
"Extracts",
"the",
"dimension",
"from",
"the",
"style",
"dict",
"of",
"the",
"Element",
"and",
"returns",
"it",
"as",
"a",
"float",
"."
] |
def get_dimension(self, dimension_key):
"""
Extracts the dimension from the style dict of the Element and returns it as a float.
"""
dimension = self.style_dict.get(dimension_key)
if dimension:
if dimension[-2:] in ['px', 'em', 'pt', 'in', 'cm']:
dimension = dimension[:-2]
dimension = float(dimension)
return dimension
|
[
"def",
"get_dimension",
"(",
"self",
",",
"dimension_key",
")",
":",
"dimension",
"=",
"self",
".",
"style_dict",
".",
"get",
"(",
"dimension_key",
")",
"if",
"dimension",
":",
"if",
"dimension",
"[",
"-",
"2",
":",
"]",
"in",
"[",
"'px'",
",",
"'em'",
",",
"'pt'",
",",
"'in'",
",",
"'cm'",
"]",
":",
"dimension",
"=",
"dimension",
"[",
":",
"-",
"2",
"]",
"dimension",
"=",
"float",
"(",
"dimension",
")",
"return",
"dimension"
] |
https://github.com/PaddlePaddle/PaddleOCR/blob/b756bf5f8c90142e0d89d3db0163965c686b6ffe/ppstructure/table/tablepyxl/style.py#L164-L173
|
|
catboost/catboost
|
167f64f237114a4d10b2b4ee42adb4569137debe
|
contrib/tools/python/src/Lib/sets.py
|
python
|
Set.discard
|
(self, element)
|
Remove an element from a set if it is a member.
If the element is not a member, do nothing.
|
Remove an element from a set if it is a member.
|
[
"Remove",
"an",
"element",
"from",
"a",
"set",
"if",
"it",
"is",
"a",
"member",
"."
] |
def discard(self, element):
"""Remove an element from a set if it is a member.
If the element is not a member, do nothing.
"""
try:
self.remove(element)
except KeyError:
pass
|
[
"def",
"discard",
"(",
"self",
",",
"element",
")",
":",
"try",
":",
"self",
".",
"remove",
"(",
"element",
")",
"except",
"KeyError",
":",
"pass"
] |
https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/tools/python/src/Lib/sets.py#L525-L533
|
||
wlanjie/AndroidFFmpeg
|
7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf
|
tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/logging/__init__.py
|
python
|
basicConfig
|
(**kwargs)
|
Do basic configuration for the logging system.
This function does nothing if the root logger already has handlers
configured. It is a convenience method intended for use by simple scripts
to do one-shot configuration of the logging package.
The default behaviour is to create a StreamHandler which writes to
sys.stderr, set a formatter using the BASIC_FORMAT format string, and
add the handler to the root logger.
A number of optional keyword arguments may be specified, which can alter
the default behaviour.
filename Specifies that a FileHandler be created, using the specified
filename, rather than a StreamHandler.
filemode Specifies the mode to open the file, if filename is specified
(if filemode is unspecified, it defaults to 'a').
format Use the specified format string for the handler.
datefmt Use the specified date/time format.
level Set the root logger level to the specified level.
stream Use the specified stream to initialize the StreamHandler. Note
that this argument is incompatible with 'filename' - if both
are present, 'stream' is ignored.
Note that you could specify a stream created using open(filename, mode)
rather than passing the filename and mode in. However, it should be
remembered that StreamHandler does not close its stream (since it may be
using sys.stdout or sys.stderr), whereas FileHandler closes its stream
when the handler is closed.
|
Do basic configuration for the logging system.
|
[
"Do",
"basic",
"configuration",
"for",
"the",
"logging",
"system",
"."
] |
def basicConfig(**kwargs):
"""
Do basic configuration for the logging system.
This function does nothing if the root logger already has handlers
configured. It is a convenience method intended for use by simple scripts
to do one-shot configuration of the logging package.
The default behaviour is to create a StreamHandler which writes to
sys.stderr, set a formatter using the BASIC_FORMAT format string, and
add the handler to the root logger.
A number of optional keyword arguments may be specified, which can alter
the default behaviour.
filename Specifies that a FileHandler be created, using the specified
filename, rather than a StreamHandler.
filemode Specifies the mode to open the file, if filename is specified
(if filemode is unspecified, it defaults to 'a').
format Use the specified format string for the handler.
datefmt Use the specified date/time format.
level Set the root logger level to the specified level.
stream Use the specified stream to initialize the StreamHandler. Note
that this argument is incompatible with 'filename' - if both
are present, 'stream' is ignored.
Note that you could specify a stream created using open(filename, mode)
rather than passing the filename and mode in. However, it should be
remembered that StreamHandler does not close its stream (since it may be
using sys.stdout or sys.stderr), whereas FileHandler closes its stream
when the handler is closed.
"""
# Add thread safety in case someone mistakenly calls
# basicConfig() from multiple threads
_acquireLock()
try:
if len(root.handlers) == 0:
filename = kwargs.get("filename")
if filename:
mode = kwargs.get("filemode", 'a')
hdlr = FileHandler(filename, mode)
else:
stream = kwargs.get("stream")
hdlr = StreamHandler(stream)
fs = kwargs.get("format", BASIC_FORMAT)
dfs = kwargs.get("datefmt", None)
fmt = Formatter(fs, dfs)
hdlr.setFormatter(fmt)
root.addHandler(hdlr)
level = kwargs.get("level")
if level is not None:
root.setLevel(level)
finally:
_releaseLock()
|
[
"def",
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"*",
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"kwargs",
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":",
"# Add thread safety in case someone mistakenly calls",
"# basicConfig() from multiple threads",
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"None",
":",
"root",
".",
"setLevel",
"(",
"level",
")",
"finally",
":",
"_releaseLock",
"(",
")"
] |
https://github.com/wlanjie/AndroidFFmpeg/blob/7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf/tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/logging/__init__.py#L1489-L1542
|
||
taichi-dev/taichi
|
973c04d6ba40f34e9e3bd5a28ae0ee0802f136a6
|
python/taichi/lang/util.py
|
python
|
has_pytorch
|
()
|
return _has_pytorch
|
Whether has pytorch in the current Python environment.
Returns:
bool: True if has pytorch else False.
|
Whether has pytorch in the current Python environment.
|
[
"Whether",
"has",
"pytorch",
"in",
"the",
"current",
"Python",
"environment",
"."
] |
def has_pytorch():
"""Whether has pytorch in the current Python environment.
Returns:
bool: True if has pytorch else False.
"""
return _has_pytorch
|
[
"def",
"has_pytorch",
"(",
")",
":",
"return",
"_has_pytorch"
] |
https://github.com/taichi-dev/taichi/blob/973c04d6ba40f34e9e3bd5a28ae0ee0802f136a6/python/taichi/lang/util.py#L23-L30
|
|
kamyu104/LeetCode-Solutions
|
77605708a927ea3b85aee5a479db733938c7c211
|
Python/maximize-distance-to-closest-person.py
|
python
|
Solution.maxDistToClosest
|
(self, seats)
|
return max(result, len(seats)-1-prev)
|
:type seats: List[int]
:rtype: int
|
:type seats: List[int]
:rtype: int
|
[
":",
"type",
"seats",
":",
"List",
"[",
"int",
"]",
":",
"rtype",
":",
"int"
] |
def maxDistToClosest(self, seats):
"""
:type seats: List[int]
:rtype: int
"""
prev, result = -1, 1
for i in xrange(len(seats)):
if seats[i]:
if prev < 0:
result = i
else:
result = max(result, (i-prev)//2)
prev = i
return max(result, len(seats)-1-prev)
|
[
"def",
"maxDistToClosest",
"(",
"self",
",",
"seats",
")",
":",
"prev",
",",
"result",
"=",
"-",
"1",
",",
"1",
"for",
"i",
"in",
"xrange",
"(",
"len",
"(",
"seats",
")",
")",
":",
"if",
"seats",
"[",
"i",
"]",
":",
"if",
"prev",
"<",
"0",
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"result",
"=",
"i",
"else",
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"result",
",",
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"-",
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"max",
"(",
"result",
",",
"len",
"(",
"seats",
")",
"-",
"1",
"-",
"prev",
")"
] |
https://github.com/kamyu104/LeetCode-Solutions/blob/77605708a927ea3b85aee5a479db733938c7c211/Python/maximize-distance-to-closest-person.py#L6-L19
|
|
thalium/icebox
|
99d147d5b9269222225443ce171b4fd46d8985d4
|
third_party/virtualbox/src/libs/libxml2-2.9.4/python/libxml2class.py
|
python
|
catalog.resolve
|
(self, pubID, sysID)
|
return ret
|
Do a complete resolution lookup of an External Identifier
|
Do a complete resolution lookup of an External Identifier
|
[
"Do",
"a",
"complete",
"resolution",
"lookup",
"of",
"an",
"External",
"Identifier"
] |
def resolve(self, pubID, sysID):
"""Do a complete resolution lookup of an External Identifier """
ret = libxml2mod.xmlACatalogResolve(self._o, pubID, sysID)
return ret
|
[
"def",
"resolve",
"(",
"self",
",",
"pubID",
",",
"sysID",
")",
":",
"ret",
"=",
"libxml2mod",
".",
"xmlACatalogResolve",
"(",
"self",
".",
"_o",
",",
"pubID",
",",
"sysID",
")",
"return",
"ret"
] |
https://github.com/thalium/icebox/blob/99d147d5b9269222225443ce171b4fd46d8985d4/third_party/virtualbox/src/libs/libxml2-2.9.4/python/libxml2class.py#L4902-L4905
|
|
wxWidgets/wxPython-Classic
|
19571e1ae65f1ac445f5491474121998c97a1bf0
|
src/osx_cocoa/_gdi.py
|
python
|
PseudoDC.SetPalette
|
(*args, **kwargs)
|
return _gdi_.PseudoDC_SetPalette(*args, **kwargs)
|
SetPalette(self, Palette palette)
If this is a window DC or memory DC, assigns the given palette to the
window or bitmap associated with the DC. If the argument is
``wx.NullPalette``, the current palette is selected out of the device
context, and the original palette restored.
|
SetPalette(self, Palette palette)
|
[
"SetPalette",
"(",
"self",
"Palette",
"palette",
")"
] |
def SetPalette(*args, **kwargs):
"""
SetPalette(self, Palette palette)
If this is a window DC or memory DC, assigns the given palette to the
window or bitmap associated with the DC. If the argument is
``wx.NullPalette``, the current palette is selected out of the device
context, and the original palette restored.
"""
return _gdi_.PseudoDC_SetPalette(*args, **kwargs)
|
[
"def",
"SetPalette",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"return",
"_gdi_",
".",
"PseudoDC_SetPalette",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")"
] |
https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_cocoa/_gdi.py#L8266-L8275
|
|
apple/turicreate
|
cce55aa5311300e3ce6af93cb45ba791fd1bdf49
|
deps/src/libxml2-2.9.1/python/libxml2.py
|
python
|
xmlTextReader.QuoteChar
|
(self)
|
return ret
|
The quotation mark character used to enclose the value of
an attribute.
|
The quotation mark character used to enclose the value of
an attribute.
|
[
"The",
"quotation",
"mark",
"character",
"used",
"to",
"enclose",
"the",
"value",
"of",
"an",
"attribute",
"."
] |
def QuoteChar(self):
"""The quotation mark character used to enclose the value of
an attribute. """
ret = libxml2mod.xmlTextReaderQuoteChar(self._o)
return ret
|
[
"def",
"QuoteChar",
"(",
"self",
")",
":",
"ret",
"=",
"libxml2mod",
".",
"xmlTextReaderQuoteChar",
"(",
"self",
".",
"_o",
")",
"return",
"ret"
] |
https://github.com/apple/turicreate/blob/cce55aa5311300e3ce6af93cb45ba791fd1bdf49/deps/src/libxml2-2.9.1/python/libxml2.py#L6816-L6820
|
|
wxWidgets/wxPython-Classic
|
19571e1ae65f1ac445f5491474121998c97a1bf0
|
wx/tools/Editra/src/ed_msg.py
|
python
|
RegisterCallback
|
(callback, msgtype)
|
Register a callback method for the given message type
@param callback: callable
@param msgtype: message type
|
Register a callback method for the given message type
@param callback: callable
@param msgtype: message type
|
[
"Register",
"a",
"callback",
"method",
"for",
"the",
"given",
"message",
"type",
"@param",
"callback",
":",
"callable",
"@param",
"msgtype",
":",
"message",
"type"
] |
def RegisterCallback(callback, msgtype):
"""Register a callback method for the given message type
@param callback: callable
@param msgtype: message type
"""
if isinstance(msgtype, tuple):
mtype = '.'.join(msgtype)
else:
mtype = msgtype
if mtype not in _CALLBACK_REGISTRY:
_CALLBACK_REGISTRY[mtype] = list()
if callback not in _CALLBACK_REGISTRY[mtype]:
_CALLBACK_REGISTRY[mtype].append(callback)
|
[
"def",
"RegisterCallback",
"(",
"callback",
",",
"msgtype",
")",
":",
"if",
"isinstance",
"(",
"msgtype",
",",
"tuple",
")",
":",
"mtype",
"=",
"'.'",
".",
"join",
"(",
"msgtype",
")",
"else",
":",
"mtype",
"=",
"msgtype",
"if",
"mtype",
"not",
"in",
"_CALLBACK_REGISTRY",
":",
"_CALLBACK_REGISTRY",
"[",
"mtype",
"]",
"=",
"list",
"(",
")",
"if",
"callback",
"not",
"in",
"_CALLBACK_REGISTRY",
"[",
"mtype",
"]",
":",
"_CALLBACK_REGISTRY",
"[",
"mtype",
"]",
".",
"append",
"(",
"callback",
")"
] |
https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/wx/tools/Editra/src/ed_msg.py#L429-L444
|
||
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/botocore/utils.py
|
python
|
deep_merge
|
(base, extra)
|
Deeply two dictionaries, overriding existing keys in the base.
:param base: The base dictionary which will be merged into.
:param extra: The dictionary to merge into the base. Keys from this
dictionary will take precedence.
|
Deeply two dictionaries, overriding existing keys in the base.
|
[
"Deeply",
"two",
"dictionaries",
"overriding",
"existing",
"keys",
"in",
"the",
"base",
"."
] |
def deep_merge(base, extra):
"""Deeply two dictionaries, overriding existing keys in the base.
:param base: The base dictionary which will be merged into.
:param extra: The dictionary to merge into the base. Keys from this
dictionary will take precedence.
"""
for key in extra:
# If the key represents a dict on both given dicts, merge the sub-dicts
if key in base and isinstance(base[key], dict)\
and isinstance(extra[key], dict):
deep_merge(base[key], extra[key])
continue
# Otherwise, set the key on the base to be the value of the extra.
base[key] = extra[key]
|
[
"def",
"deep_merge",
"(",
"base",
",",
"extra",
")",
":",
"for",
"key",
"in",
"extra",
":",
"# If the key represents a dict on both given dicts, merge the sub-dicts",
"if",
"key",
"in",
"base",
"and",
"isinstance",
"(",
"base",
"[",
"key",
"]",
",",
"dict",
")",
"and",
"isinstance",
"(",
"extra",
"[",
"key",
"]",
",",
"dict",
")",
":",
"deep_merge",
"(",
"base",
"[",
"key",
"]",
",",
"extra",
"[",
"key",
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")",
"continue",
"# Otherwise, set the key on the base to be the value of the extra.",
"base",
"[",
"key",
"]",
"=",
"extra",
"[",
"key",
"]"
] |
https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/botocore/utils.py#L1113-L1128
|
||
bh107/bohrium
|
5b83e7117285fefc7779ed0e9acb0f8e74c7e068
|
bridge/npbackend/bohrium/reorganization.py
|
python
|
cond_scatter
|
(ary, indexes, values, mask)
|
scatter(ary, indexes, values, mask)
Scatter 'values' into 'ary' selected by 'indexes' where 'mask' is true.
The values of 'indexes' are absolute indexed into a flatten 'ary'
The shape of 'indexes', 'value', and 'mask' must be equal.
Parameters
----------
ary : array_like
The target array to write the values to.
indexes : array_like, interpreted as integers
Array or list of indexes that will be written to in 'ary'
values : array_like
Values to write into 'ary'
mask : array_like, interpreted as booleans
A mask that specifies which indexes and values to include and exclude
|
scatter(ary, indexes, values, mask)
|
[
"scatter",
"(",
"ary",
"indexes",
"values",
"mask",
")"
] |
def cond_scatter(ary, indexes, values, mask):
"""
scatter(ary, indexes, values, mask)
Scatter 'values' into 'ary' selected by 'indexes' where 'mask' is true.
The values of 'indexes' are absolute indexed into a flatten 'ary'
The shape of 'indexes', 'value', and 'mask' must be equal.
Parameters
----------
ary : array_like
The target array to write the values to.
indexes : array_like, interpreted as integers
Array or list of indexes that will be written to in 'ary'
values : array_like
Values to write into 'ary'
mask : array_like, interpreted as booleans
A mask that specifies which indexes and values to include and exclude
"""
from . import _bh
indexes = array_manipulation.flatten(array_create.array(indexes, dtype=numpy.uint64), always_copy=False)
values = array_manipulation.flatten(array_create.array(values, dtype=ary.dtype), always_copy=False)
mask = array_manipulation.flatten(array_create.array(mask, dtype=numpy.bool), always_copy=False)
assert (indexes.shape == values.shape and values.shape == mask.shape)
if ary.size == 0 or indexes.size == 0:
return
# In order to ensure a contiguous array, we do the scatter on a flatten copy
flat = array_manipulation.flatten(ary, always_copy=True)
_bh.ufunc(_info.op['cond_scatter']['id'], (flat, values, indexes, mask))
ary[...] = flat.reshape(ary.shape)
|
[
"def",
"cond_scatter",
"(",
"ary",
",",
"indexes",
",",
"values",
",",
"mask",
")",
":",
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"[",
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"]",
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"flat",
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"reshape",
"(",
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"shape",
")"
] |
https://github.com/bh107/bohrium/blob/5b83e7117285fefc7779ed0e9acb0f8e74c7e068/bridge/npbackend/bohrium/reorganization.py#L393-L426
|
||
benoitsteiner/tensorflow-opencl
|
cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5
|
tensorflow/tools/docs/doc_generator_visitor.py
|
python
|
DocGeneratorVisitor.__call__
|
(self, parent_name, parent, children)
|
Visitor interface, see `tensorflow/tools/common:traverse` for details.
This method is called for each symbol found in a traversal using
`tensorflow/tools/common:traverse`. It should not be called directly in
user code.
Args:
parent_name: The fully qualified name of a symbol found during traversal.
parent: The Python object referenced by `parent_name`.
children: A list of `(name, py_object)` pairs enumerating, in alphabetical
order, the children (as determined by `tf_inspect.getmembers`) of
`parent`. `name` is the local name of `py_object` in `parent`.
Raises:
RuntimeError: If this visitor is called with a `parent` that is not a
class or module.
|
Visitor interface, see `tensorflow/tools/common:traverse` for details.
|
[
"Visitor",
"interface",
"see",
"tensorflow",
"/",
"tools",
"/",
"common",
":",
"traverse",
"for",
"details",
"."
] |
def __call__(self, parent_name, parent, children):
"""Visitor interface, see `tensorflow/tools/common:traverse` for details.
This method is called for each symbol found in a traversal using
`tensorflow/tools/common:traverse`. It should not be called directly in
user code.
Args:
parent_name: The fully qualified name of a symbol found during traversal.
parent: The Python object referenced by `parent_name`.
children: A list of `(name, py_object)` pairs enumerating, in alphabetical
order, the children (as determined by `tf_inspect.getmembers`) of
`parent`. `name` is the local name of `py_object` in `parent`.
Raises:
RuntimeError: If this visitor is called with a `parent` that is not a
class or module.
"""
parent_name = self._add_prefix(parent_name)
self._index[parent_name] = parent
self._tree[parent_name] = []
if not (tf_inspect.ismodule(parent) or tf_inspect.isclass(parent)):
raise RuntimeError('Unexpected type in visitor -- %s: %r' % (parent_name,
parent))
for i, (name, child) in enumerate(list(children)):
# Don't document __metaclass__
if name in ['__metaclass__']:
del children[i]
continue
full_name = '.'.join([parent_name, name]) if parent_name else name
self._index[full_name] = child
self._tree[parent_name].append(name)
|
[
"def",
"__call__",
"(",
"self",
",",
"parent_name",
",",
"parent",
",",
"children",
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":",
"parent_name",
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"raise",
"RuntimeError",
"(",
"'Unexpected type in visitor -- %s: %r'",
"%",
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"parent_name",
",",
"parent",
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")",
"for",
"i",
",",
"(",
"name",
",",
"child",
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"list",
"(",
"children",
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")",
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"# Don't document __metaclass__",
"if",
"name",
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"'__metaclass__'",
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":",
"del",
"children",
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"continue",
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"parent_name",
",",
"name",
"]",
")",
"if",
"parent_name",
"else",
"name",
"self",
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"_index",
"[",
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"=",
"child",
"self",
".",
"_tree",
"[",
"parent_name",
"]",
".",
"append",
"(",
"name",
")"
] |
https://github.com/benoitsteiner/tensorflow-opencl/blob/cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5/tensorflow/tools/docs/doc_generator_visitor.py#L125-L159
|
||
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/cgi.py
|
python
|
MiniFieldStorage.__init__
|
(self, name, value)
|
Constructor from field name and value.
|
Constructor from field name and value.
|
[
"Constructor",
"from",
"field",
"name",
"and",
"value",
"."
] |
def __init__(self, name, value):
"""Constructor from field name and value."""
self.name = name
self.value = value
|
[
"def",
"__init__",
"(",
"self",
",",
"name",
",",
"value",
")",
":",
"self",
".",
"name",
"=",
"name",
"self",
".",
"value",
"=",
"value"
] |
https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/cgi.py#L282-L285
|
||
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/tkinter/__init__.py
|
python
|
Entry.scan_mark
|
(self, x)
|
Remember the current X, Y coordinates.
|
Remember the current X, Y coordinates.
|
[
"Remember",
"the",
"current",
"X",
"Y",
"coordinates",
"."
] |
def scan_mark(self, x):
"""Remember the current X, Y coordinates."""
self.tk.call(self._w, 'scan', 'mark', x)
|
[
"def",
"scan_mark",
"(",
"self",
",",
"x",
")",
":",
"self",
".",
"tk",
".",
"call",
"(",
"self",
".",
"_w",
",",
"'scan'",
",",
"'mark'",
",",
"x",
")"
] |
https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/tkinter/__init__.py#L2693-L2695
|
||
wxWidgets/wxPython-Classic
|
19571e1ae65f1ac445f5491474121998c97a1bf0
|
src/osx_carbon/_gdi.py
|
python
|
PseudoDC.SetFont
|
(*args, **kwargs)
|
return _gdi_.PseudoDC_SetFont(*args, **kwargs)
|
SetFont(self, Font font)
Sets the current font for the DC. It must be a valid font, in
particular you should not pass ``wx.NullFont`` to this method.
|
SetFont(self, Font font)
|
[
"SetFont",
"(",
"self",
"Font",
"font",
")"
] |
def SetFont(*args, **kwargs):
"""
SetFont(self, Font font)
Sets the current font for the DC. It must be a valid font, in
particular you should not pass ``wx.NullFont`` to this method.
"""
return _gdi_.PseudoDC_SetFont(*args, **kwargs)
|
[
"def",
"SetFont",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"return",
"_gdi_",
".",
"PseudoDC_SetFont",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")"
] |
https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_carbon/_gdi.py#L8216-L8223
|
|
wxWidgets/wxPython-Classic
|
19571e1ae65f1ac445f5491474121998c97a1bf0
|
src/msw/_gdi.py
|
python
|
GraphicsContext.CreateLinearGradientBrush
|
(*args)
|
return _gdi_.GraphicsContext_CreateLinearGradientBrush(*args)
|
CreateLinearGradientBrush(self, Double x1, Double y1, Double x2, Double y2, Colour c1,
Colour c2) -> GraphicsBrush
CreateLinearGradientBrush(self, Double x1, Double y1, Double x2, Double y2, GraphicsGradientStops stops) -> GraphicsBrush
Creates a native brush, having a linear gradient, starting at (x1,y1)
to (x2,y2) with the given boundary colors or the specified stops.
|
CreateLinearGradientBrush(self, Double x1, Double y1, Double x2, Double y2, Colour c1,
Colour c2) -> GraphicsBrush
CreateLinearGradientBrush(self, Double x1, Double y1, Double x2, Double y2, GraphicsGradientStops stops) -> GraphicsBrush
|
[
"CreateLinearGradientBrush",
"(",
"self",
"Double",
"x1",
"Double",
"y1",
"Double",
"x2",
"Double",
"y2",
"Colour",
"c1",
"Colour",
"c2",
")",
"-",
">",
"GraphicsBrush",
"CreateLinearGradientBrush",
"(",
"self",
"Double",
"x1",
"Double",
"y1",
"Double",
"x2",
"Double",
"y2",
"GraphicsGradientStops",
"stops",
")",
"-",
">",
"GraphicsBrush"
] |
def CreateLinearGradientBrush(*args):
"""
CreateLinearGradientBrush(self, Double x1, Double y1, Double x2, Double y2, Colour c1,
Colour c2) -> GraphicsBrush
CreateLinearGradientBrush(self, Double x1, Double y1, Double x2, Double y2, GraphicsGradientStops stops) -> GraphicsBrush
Creates a native brush, having a linear gradient, starting at (x1,y1)
to (x2,y2) with the given boundary colors or the specified stops.
"""
return _gdi_.GraphicsContext_CreateLinearGradientBrush(*args)
|
[
"def",
"CreateLinearGradientBrush",
"(",
"*",
"args",
")",
":",
"return",
"_gdi_",
".",
"GraphicsContext_CreateLinearGradientBrush",
"(",
"*",
"args",
")"
] |
https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/msw/_gdi.py#L6255-L6264
|
|
miyosuda/TensorFlowAndroidDemo
|
35903e0221aa5f109ea2dbef27f20b52e317f42d
|
jni-build/jni/include/tensorflow/contrib/losses/python/losses/loss_ops.py
|
python
|
get_losses
|
(scope=None)
|
return ops.get_collection(ops.GraphKeys.LOSSES, scope)
|
Gets the list of loss variables.
Args:
scope: an optional scope for filtering the losses to return.
Returns:
a list of loss variables.
|
Gets the list of loss variables.
|
[
"Gets",
"the",
"list",
"of",
"loss",
"variables",
"."
] |
def get_losses(scope=None):
"""Gets the list of loss variables.
Args:
scope: an optional scope for filtering the losses to return.
Returns:
a list of loss variables.
"""
return ops.get_collection(ops.GraphKeys.LOSSES, scope)
|
[
"def",
"get_losses",
"(",
"scope",
"=",
"None",
")",
":",
"return",
"ops",
".",
"get_collection",
"(",
"ops",
".",
"GraphKeys",
".",
"LOSSES",
",",
"scope",
")"
] |
https://github.com/miyosuda/TensorFlowAndroidDemo/blob/35903e0221aa5f109ea2dbef27f20b52e317f42d/jni-build/jni/include/tensorflow/contrib/losses/python/losses/loss_ops.py#L204-L213
|
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