nwo stringlengths 5 86 | sha stringlengths 40 40 | path stringlengths 4 189 | language stringclasses 1 value | identifier stringlengths 1 94 | parameters stringlengths 2 4.03k | argument_list stringclasses 1 value | return_statement stringlengths 0 11.5k | docstring stringlengths 1 33.2k | docstring_summary stringlengths 0 5.15k | docstring_tokens list | function stringlengths 34 151k | function_tokens list | url stringlengths 90 278 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
commaai/openpilot | 4416c21b1e738ab7d04147c5ae52b5135e0cdb40 | pyextra/acados_template/acados_ocp.py | python | AcadosOcpCost.Zu_e | (self) | return self.__Zu_e | :math:`Z_u^e` - diagonal of Hessian wrt upper slack at terminal shooting node (N).
Default: :code:`np.array([])`. | :math:`Z_u^e` - diagonal of Hessian wrt upper slack at terminal shooting node (N).
Default: :code:`np.array([])`. | [
":",
"math",
":",
"Z_u^e",
"-",
"diagonal",
"of",
"Hessian",
"wrt",
"upper",
"slack",
"at",
"terminal",
"shooting",
"node",
"(",
"N",
")",
".",
"Default",
":",
":",
"code",
":",
"np",
".",
"array",
"(",
"[]",
")",
"."
] | def Zu_e(self):
""":math:`Z_u^e` - diagonal of Hessian wrt upper slack at terminal shooting node (N).
Default: :code:`np.array([])`.
"""
return self.__Zu_e | [
"def",
"Zu_e",
"(",
"self",
")",
":",
"return",
"self",
".",
"__Zu_e"
] | https://github.com/commaai/openpilot/blob/4416c21b1e738ab7d04147c5ae52b5135e0cdb40/pyextra/acados_template/acados_ocp.py#L862-L866 | |
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_misc.py | python | FileType.SetDefaultIcon | (*args, **kwargs) | return _misc_.FileType_SetDefaultIcon(*args, **kwargs) | SetDefaultIcon(self, String cmd=EmptyString, int index=0) -> bool | SetDefaultIcon(self, String cmd=EmptyString, int index=0) -> bool | [
"SetDefaultIcon",
"(",
"self",
"String",
"cmd",
"=",
"EmptyString",
"int",
"index",
"=",
"0",
")",
"-",
">",
"bool"
] | def SetDefaultIcon(*args, **kwargs):
"""SetDefaultIcon(self, String cmd=EmptyString, int index=0) -> bool"""
return _misc_.FileType_SetDefaultIcon(*args, **kwargs) | [
"def",
"SetDefaultIcon",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"return",
"_misc_",
".",
"FileType_SetDefaultIcon",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")"
] | https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/gtk/_misc.py#L2621-L2623 | |
wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/plat-mac/lib-scriptpackages/Netscape/WorldWideWeb_suite.py | python | WorldWideWeb_suite_Events.list_windows | (self, _no_object=None, _attributes={}, **_arguments) | list windows: Lists the IDs of all the hypertext windows
Keyword argument _attributes: AppleEvent attribute dictionary
Returns: List of unique IDs of all the hypertext windows | list windows: Lists the IDs of all the hypertext windows
Keyword argument _attributes: AppleEvent attribute dictionary
Returns: List of unique IDs of all the hypertext windows | [
"list",
"windows",
":",
"Lists",
"the",
"IDs",
"of",
"all",
"the",
"hypertext",
"windows",
"Keyword",
"argument",
"_attributes",
":",
"AppleEvent",
"attribute",
"dictionary",
"Returns",
":",
"List",
"of",
"unique",
"IDs",
"of",
"all",
"the",
"hypertext",
"windows"
] | def list_windows(self, _no_object=None, _attributes={}, **_arguments):
"""list windows: Lists the IDs of all the hypertext windows
Keyword argument _attributes: AppleEvent attribute dictionary
Returns: List of unique IDs of all the hypertext windows
"""
_code = 'WWW!'
_subcode = 'LSTW'
if _arguments: raise TypeError, 'No optional args expected'
if _no_object is not None: raise TypeError, 'No direct arg expected'
_reply, _arguments, _attributes = self.send(_code, _subcode,
_arguments, _attributes)
if _arguments.get('errn', 0):
raise aetools.Error, aetools.decodeerror(_arguments)
# XXXX Optionally decode result
if _arguments.has_key('----'):
return _arguments['----'] | [
"def",
"list_windows",
"(",
"self",
",",
"_no_object",
"=",
"None",
",",
"_attributes",
"=",
"{",
"}",
",",
"*",
"*",
"_arguments",
")",
":",
"_code",
"=",
"'WWW!'",
"_subcode",
"=",
"'LSTW'",
"if",
"_arguments",
":",
"raise",
"TypeError",
",",
"'No optional args expected'",
"if",
"_no_object",
"is",
"not",
"None",
":",
"raise",
"TypeError",
",",
"'No direct arg expected'",
"_reply",
",",
"_arguments",
",",
"_attributes",
"=",
"self",
".",
"send",
"(",
"_code",
",",
"_subcode",
",",
"_arguments",
",",
"_attributes",
")",
"if",
"_arguments",
".",
"get",
"(",
"'errn'",
",",
"0",
")",
":",
"raise",
"aetools",
".",
"Error",
",",
"aetools",
".",
"decodeerror",
"(",
"_arguments",
")",
"# XXXX Optionally decode result",
"if",
"_arguments",
".",
"has_key",
"(",
"'----'",
")",
":",
"return",
"_arguments",
"[",
"'----'",
"]"
] | https://github.com/wlanjie/AndroidFFmpeg/blob/7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf/tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/plat-mac/lib-scriptpackages/Netscape/WorldWideWeb_suite.py#L148-L166 | ||
Polidea/SiriusObfuscator | b0e590d8130e97856afe578869b83a209e2b19be | SymbolExtractorAndRenamer/lldb/scripts/Python/static-binding/lldb.py | python | SBModule.FindTypes | (self, *args) | return _lldb.SBModule_FindTypes(self, *args) | FindTypes(self, str type) -> SBTypeList | FindTypes(self, str type) -> SBTypeList | [
"FindTypes",
"(",
"self",
"str",
"type",
")",
"-",
">",
"SBTypeList"
] | def FindTypes(self, *args):
"""FindTypes(self, str type) -> SBTypeList"""
return _lldb.SBModule_FindTypes(self, *args) | [
"def",
"FindTypes",
"(",
"self",
",",
"*",
"args",
")",
":",
"return",
"_lldb",
".",
"SBModule_FindTypes",
"(",
"self",
",",
"*",
"args",
")"
] | https://github.com/Polidea/SiriusObfuscator/blob/b0e590d8130e97856afe578869b83a209e2b19be/SymbolExtractorAndRenamer/lldb/scripts/Python/static-binding/lldb.py#L6170-L6172 | |
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_misc.py | python | DateTime.Now | (*args, **kwargs) | return _misc_.DateTime_Now(*args, **kwargs) | Now() -> DateTime | Now() -> DateTime | [
"Now",
"()",
"-",
">",
"DateTime"
] | def Now(*args, **kwargs):
"""Now() -> DateTime"""
return _misc_.DateTime_Now(*args, **kwargs) | [
"def",
"Now",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"return",
"_misc_",
".",
"DateTime_Now",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")"
] | https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_carbon/_misc.py#L3766-L3768 | |
wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/compileall.py | python | main | () | return success | Script main program. | Script main program. | [
"Script",
"main",
"program",
"."
] | def main():
"""Script main program."""
import getopt
try:
opts, args = getopt.getopt(sys.argv[1:], 'lfqd:x:i:')
except getopt.error, msg:
print msg
print "usage: python compileall.py [-l] [-f] [-q] [-d destdir] " \
"[-x regexp] [-i list] [directory|file ...]"
print
print "arguments: zero or more file and directory names to compile; " \
"if no arguments given, "
print " defaults to the equivalent of -l sys.path"
print
print "options:"
print "-l: don't recurse into subdirectories"
print "-f: force rebuild even if timestamps are up-to-date"
print "-q: output only error messages"
print "-d destdir: directory to prepend to file paths for use in " \
"compile-time tracebacks and in"
print " runtime tracebacks in cases where the source " \
"file is unavailable"
print "-x regexp: skip files matching the regular expression regexp; " \
"the regexp is searched for"
print " in the full path of each file considered for " \
"compilation"
print "-i file: add all the files and directories listed in file to " \
"the list considered for"
print ' compilation; if "-", names are read from stdin'
sys.exit(2)
maxlevels = 10
ddir = None
force = 0
quiet = 0
rx = None
flist = None
for o, a in opts:
if o == '-l': maxlevels = 0
if o == '-d': ddir = a
if o == '-f': force = 1
if o == '-q': quiet = 1
if o == '-x':
import re
rx = re.compile(a)
if o == '-i': flist = a
if ddir:
if len(args) != 1 and not os.path.isdir(args[0]):
print "-d destdir require exactly one directory argument"
sys.exit(2)
success = 1
try:
if args or flist:
try:
if flist:
args = expand_args(args, flist)
except IOError:
success = 0
if success:
for arg in args:
if os.path.isdir(arg):
if not compile_dir(arg, maxlevels, ddir,
force, rx, quiet):
success = 0
else:
if not compile_file(arg, ddir, force, rx, quiet):
success = 0
else:
success = compile_path()
except KeyboardInterrupt:
print "\n[interrupted]"
success = 0
return success | [
"def",
"main",
"(",
")",
":",
"import",
"getopt",
"try",
":",
"opts",
",",
"args",
"=",
"getopt",
".",
"getopt",
"(",
"sys",
".",
"argv",
"[",
"1",
":",
"]",
",",
"'lfqd:x:i:'",
")",
"except",
"getopt",
".",
"error",
",",
"msg",
":",
"print",
"msg",
"print",
"\"usage: python compileall.py [-l] [-f] [-q] [-d destdir] \"",
"\"[-x regexp] [-i list] [directory|file ...]\"",
"print",
"print",
"\"arguments: zero or more file and directory names to compile; \"",
"\"if no arguments given, \"",
"print",
"\" defaults to the equivalent of -l sys.path\"",
"print",
"print",
"\"options:\"",
"print",
"\"-l: don't recurse into subdirectories\"",
"print",
"\"-f: force rebuild even if timestamps are up-to-date\"",
"print",
"\"-q: output only error messages\"",
"print",
"\"-d destdir: directory to prepend to file paths for use in \"",
"\"compile-time tracebacks and in\"",
"print",
"\" runtime tracebacks in cases where the source \"",
"\"file is unavailable\"",
"print",
"\"-x regexp: skip files matching the regular expression regexp; \"",
"\"the regexp is searched for\"",
"print",
"\" in the full path of each file considered for \"",
"\"compilation\"",
"print",
"\"-i file: add all the files and directories listed in file to \"",
"\"the list considered for\"",
"print",
"' compilation; if \"-\", names are read from stdin'",
"sys",
".",
"exit",
"(",
"2",
")",
"maxlevels",
"=",
"10",
"ddir",
"=",
"None",
"force",
"=",
"0",
"quiet",
"=",
"0",
"rx",
"=",
"None",
"flist",
"=",
"None",
"for",
"o",
",",
"a",
"in",
"opts",
":",
"if",
"o",
"==",
"'-l'",
":",
"maxlevels",
"=",
"0",
"if",
"o",
"==",
"'-d'",
":",
"ddir",
"=",
"a",
"if",
"o",
"==",
"'-f'",
":",
"force",
"=",
"1",
"if",
"o",
"==",
"'-q'",
":",
"quiet",
"=",
"1",
"if",
"o",
"==",
"'-x'",
":",
"import",
"re",
"rx",
"=",
"re",
".",
"compile",
"(",
"a",
")",
"if",
"o",
"==",
"'-i'",
":",
"flist",
"=",
"a",
"if",
"ddir",
":",
"if",
"len",
"(",
"args",
")",
"!=",
"1",
"and",
"not",
"os",
".",
"path",
".",
"isdir",
"(",
"args",
"[",
"0",
"]",
")",
":",
"print",
"\"-d destdir require exactly one directory argument\"",
"sys",
".",
"exit",
"(",
"2",
")",
"success",
"=",
"1",
"try",
":",
"if",
"args",
"or",
"flist",
":",
"try",
":",
"if",
"flist",
":",
"args",
"=",
"expand_args",
"(",
"args",
",",
"flist",
")",
"except",
"IOError",
":",
"success",
"=",
"0",
"if",
"success",
":",
"for",
"arg",
"in",
"args",
":",
"if",
"os",
".",
"path",
".",
"isdir",
"(",
"arg",
")",
":",
"if",
"not",
"compile_dir",
"(",
"arg",
",",
"maxlevels",
",",
"ddir",
",",
"force",
",",
"rx",
",",
"quiet",
")",
":",
"success",
"=",
"0",
"else",
":",
"if",
"not",
"compile_file",
"(",
"arg",
",",
"ddir",
",",
"force",
",",
"rx",
",",
"quiet",
")",
":",
"success",
"=",
"0",
"else",
":",
"success",
"=",
"compile_path",
"(",
")",
"except",
"KeyboardInterrupt",
":",
"print",
"\"\\n[interrupted]\"",
"success",
"=",
"0",
"return",
"success"
] | https://github.com/wlanjie/AndroidFFmpeg/blob/7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf/tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/compileall.py#L153-L225 | |
MegEngine/MegEngine | ce9ad07a27ec909fb8db4dd67943d24ba98fb93a | imperative/python/megengine/distributed/helper.py | python | synchronized | (func: Callable) | return wrapper | r"""Decorator. Decorated function will synchronize when finished.
Specifically, we use this to prevent data race during hub.load | r"""Decorator. Decorated function will synchronize when finished.
Specifically, we use this to prevent data race during hub.load | [
"r",
"Decorator",
".",
"Decorated",
"function",
"will",
"synchronize",
"when",
"finished",
".",
"Specifically",
"we",
"use",
"this",
"to",
"prevent",
"data",
"race",
"during",
"hub",
".",
"load"
] | def synchronized(func: Callable):
r"""Decorator. Decorated function will synchronize when finished.
Specifically, we use this to prevent data race during hub.load
"""
@functools.wraps(func)
def wrapper(*args, **kwargs):
if not is_distributed():
return func(*args, **kwargs)
ret = func(*args, **kwargs)
group_barrier()
return ret
return wrapper | [
"def",
"synchronized",
"(",
"func",
":",
"Callable",
")",
":",
"@",
"functools",
".",
"wraps",
"(",
"func",
")",
"def",
"wrapper",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"if",
"not",
"is_distributed",
"(",
")",
":",
"return",
"func",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
"ret",
"=",
"func",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
"group_barrier",
"(",
")",
"return",
"ret",
"return",
"wrapper"
] | https://github.com/MegEngine/MegEngine/blob/ce9ad07a27ec909fb8db4dd67943d24ba98fb93a/imperative/python/megengine/distributed/helper.py#L166-L180 | |
kamyu104/LeetCode-Solutions | 77605708a927ea3b85aee5a479db733938c7c211 | Python/max-stack.py | python | MaxStack.popMax | (self) | return val | :rtype: int | :rtype: int | [
":",
"rtype",
":",
"int"
] | def popMax(self):
"""
:rtype: int
"""
val = self.__max
self.__remove(val)
return val | [
"def",
"popMax",
"(",
"self",
")",
":",
"val",
"=",
"self",
".",
"__max",
"self",
".",
"__remove",
"(",
"val",
")",
"return",
"val"
] | https://github.com/kamyu104/LeetCode-Solutions/blob/77605708a927ea3b85aee5a479db733938c7c211/Python/max-stack.py#L58-L64 | |
OSGeo/gdal | 3748fc4ba4fba727492774b2b908a2130c864a83 | swig/python/osgeo/gdal.py | python | RasterAttributeTable.DumpReadable | (self, *args) | return _gdal.RasterAttributeTable_DumpReadable(self, *args) | r"""DumpReadable(RasterAttributeTable self) | r"""DumpReadable(RasterAttributeTable self) | [
"r",
"DumpReadable",
"(",
"RasterAttributeTable",
"self",
")"
] | def DumpReadable(self, *args):
r"""DumpReadable(RasterAttributeTable self)"""
return _gdal.RasterAttributeTable_DumpReadable(self, *args) | [
"def",
"DumpReadable",
"(",
"self",
",",
"*",
"args",
")",
":",
"return",
"_gdal",
".",
"RasterAttributeTable_DumpReadable",
"(",
"self",
",",
"*",
"args",
")"
] | https://github.com/OSGeo/gdal/blob/3748fc4ba4fba727492774b2b908a2130c864a83/swig/python/osgeo/gdal.py#L3886-L3888 | |
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/mimetypes.py | python | add_type | (type, ext, strict=True) | return _db.add_type(type, ext, strict) | Add a mapping between a type and an extension.
When the extension is already known, the new
type will replace the old one. When the type
is already known the extension will be added
to the list of known extensions.
If strict is true, information will be added to
list of standard types, else to the list of non-standard
types. | Add a mapping between a type and an extension. | [
"Add",
"a",
"mapping",
"between",
"a",
"type",
"and",
"an",
"extension",
"."
] | def add_type(type, ext, strict=True):
"""Add a mapping between a type and an extension.
When the extension is already known, the new
type will replace the old one. When the type
is already known the extension will be added
to the list of known extensions.
If strict is true, information will be added to
list of standard types, else to the list of non-standard
types.
"""
if _db is None:
init()
return _db.add_type(type, ext, strict) | [
"def",
"add_type",
"(",
"type",
",",
"ext",
",",
"strict",
"=",
"True",
")",
":",
"if",
"_db",
"is",
"None",
":",
"init",
"(",
")",
"return",
"_db",
".",
"add_type",
"(",
"type",
",",
"ext",
",",
"strict",
")"
] | https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/tools/python/src/Lib/mimetypes.py#L330-L344 | |
okex/V3-Open-API-SDK | c5abb0db7e2287718e0055e17e57672ce0ec7fd9 | okex-python-sdk-api/venv/Lib/site-packages/pip-19.0.3-py3.8.egg/pip/_internal/req/req_file.py | python | preprocess | (content, options) | return lines_enum | Split, filter, and join lines, and return a line iterator
:param content: the content of the requirements file
:param options: cli options | Split, filter, and join lines, and return a line iterator | [
"Split",
"filter",
"and",
"join",
"lines",
"and",
"return",
"a",
"line",
"iterator"
] | def preprocess(content, options):
# type: (Text, Optional[optparse.Values]) -> ReqFileLines
"""Split, filter, and join lines, and return a line iterator
:param content: the content of the requirements file
:param options: cli options
"""
lines_enum = enumerate(content.splitlines(), start=1) # type: ReqFileLines
lines_enum = join_lines(lines_enum)
lines_enum = ignore_comments(lines_enum)
lines_enum = skip_regex(lines_enum, options)
lines_enum = expand_env_variables(lines_enum)
return lines_enum | [
"def",
"preprocess",
"(",
"content",
",",
"options",
")",
":",
"# type: (Text, Optional[optparse.Values]) -> ReqFileLines",
"lines_enum",
"=",
"enumerate",
"(",
"content",
".",
"splitlines",
"(",
")",
",",
"start",
"=",
"1",
")",
"# type: ReqFileLines",
"lines_enum",
"=",
"join_lines",
"(",
"lines_enum",
")",
"lines_enum",
"=",
"ignore_comments",
"(",
"lines_enum",
")",
"lines_enum",
"=",
"skip_regex",
"(",
"lines_enum",
",",
"options",
")",
"lines_enum",
"=",
"expand_env_variables",
"(",
"lines_enum",
")",
"return",
"lines_enum"
] | https://github.com/okex/V3-Open-API-SDK/blob/c5abb0db7e2287718e0055e17e57672ce0ec7fd9/okex-python-sdk-api/venv/Lib/site-packages/pip-19.0.3-py3.8.egg/pip/_internal/req/req_file.py#L116-L128 | |
quantOS-org/DataCore | e2ef9bd2c22ee9e2845675b6435a14fa607f3551 | dataserver/api/py/data_api.py | python | DataApi.query | (self, view, filter="", fields="", data_format="", **kwargs) | return self._call_rpc("jset.query",
self._get_format(data_format, "pandas"),
"JSetData",
view=view,
fields=fields,
filter=filter,
**kwargs) | Get various reference data.
Parameters
----------
view : str
data source.
fields : str
Separated by ','
filter : str
filter expressions.
kwargs
Returns
-------
df : pd.DataFrame
msg : str
error code and error message, joined by ','
Examples
--------
res3, msg3 = ds.query("lb.secDailyIndicator", fields="price_level,high_52w_adj,low_52w_adj",\
filter="start_date=20170907&end_date=20170907",\
data_format='pandas')
view does not change. fileds can be any field predefined in reference data api. | Get various reference data.
Parameters
----------
view : str
data source.
fields : str
Separated by ','
filter : str
filter expressions.
kwargs | [
"Get",
"various",
"reference",
"data",
".",
"Parameters",
"----------",
"view",
":",
"str",
"data",
"source",
".",
"fields",
":",
"str",
"Separated",
"by",
"filter",
":",
"str",
"filter",
"expressions",
".",
"kwargs"
] | def query(self, view, filter="", fields="", data_format="", **kwargs):
"""
Get various reference data.
Parameters
----------
view : str
data source.
fields : str
Separated by ','
filter : str
filter expressions.
kwargs
Returns
-------
df : pd.DataFrame
msg : str
error code and error message, joined by ','
Examples
--------
res3, msg3 = ds.query("lb.secDailyIndicator", fields="price_level,high_52w_adj,low_52w_adj",\
filter="start_date=20170907&end_date=20170907",\
data_format='pandas')
view does not change. fileds can be any field predefined in reference data api.
"""
return self._call_rpc("jset.query",
self._get_format(data_format, "pandas"),
"JSetData",
view=view,
fields=fields,
filter=filter,
**kwargs) | [
"def",
"query",
"(",
"self",
",",
"view",
",",
"filter",
"=",
"\"\"",
",",
"fields",
"=",
"\"\"",
",",
"data_format",
"=",
"\"\"",
",",
"*",
"*",
"kwargs",
")",
":",
"return",
"self",
".",
"_call_rpc",
"(",
"\"jset.query\"",
",",
"self",
".",
"_get_format",
"(",
"data_format",
",",
"\"pandas\"",
")",
",",
"\"JSetData\"",
",",
"view",
"=",
"view",
",",
"fields",
"=",
"fields",
",",
"filter",
"=",
"filter",
",",
"*",
"*",
"kwargs",
")"
] | https://github.com/quantOS-org/DataCore/blob/e2ef9bd2c22ee9e2845675b6435a14fa607f3551/dataserver/api/py/data_api.py#L353-L387 | |
LiXizhi/NPLRuntime | a42720e5fe9a6960e0a9ce40bbbcd809192906be | Client/trunk/externals/assimp-4.0.0/port/PyAssimp/scripts/transformations.py | python | inverse_matrix | (matrix) | return numpy.linalg.inv(matrix) | Return inverse of square transformation matrix.
>>> M0 = random_rotation_matrix()
>>> M1 = inverse_matrix(M0.T)
>>> numpy.allclose(M1, numpy.linalg.inv(M0.T))
True
>>> for size in range(1, 7):
... M0 = numpy.random.rand(size, size)
... M1 = inverse_matrix(M0)
... if not numpy.allclose(M1, numpy.linalg.inv(M0)): print size | Return inverse of square transformation matrix. | [
"Return",
"inverse",
"of",
"square",
"transformation",
"matrix",
"."
] | def inverse_matrix(matrix):
"""Return inverse of square transformation matrix.
>>> M0 = random_rotation_matrix()
>>> M1 = inverse_matrix(M0.T)
>>> numpy.allclose(M1, numpy.linalg.inv(M0.T))
True
>>> for size in range(1, 7):
... M0 = numpy.random.rand(size, size)
... M1 = inverse_matrix(M0)
... if not numpy.allclose(M1, numpy.linalg.inv(M0)): print size
"""
return numpy.linalg.inv(matrix) | [
"def",
"inverse_matrix",
"(",
"matrix",
")",
":",
"return",
"numpy",
".",
"linalg",
".",
"inv",
"(",
"matrix",
")"
] | https://github.com/LiXizhi/NPLRuntime/blob/a42720e5fe9a6960e0a9ce40bbbcd809192906be/Client/trunk/externals/assimp-4.0.0/port/PyAssimp/scripts/transformations.py#L1633-L1646 | |
trilinos/Trilinos | 6168be6dd51e35e1cd681e9c4b24433e709df140 | packages/seacas/scripts/exodus3.in.py | python | exodus.put_set_params | (self, object_type, object_id, numSetEntity, numSetDistFacts=None) | initialize a new set of the specified type
>>> exo.put_set_params('EX_NODE_SET', node_set_id,
... num_ns_nodes, num_ns_dist_facts)
Parameters
----------
set_id : int
set *ID* (not *INDEX*)
num_set_entity : int
number of nodes/edges/faces/elements to be added to set
num_dist_facts : int, optional
number of distribution factors (e.g. nodal 'weights') --
must be equal to zero or num_set_entity | initialize a new set of the specified type | [
"initialize",
"a",
"new",
"set",
"of",
"the",
"specified",
"type"
] | def put_set_params(self, object_type, object_id, numSetEntity, numSetDistFacts=None):
"""
initialize a new set of the specified type
>>> exo.put_set_params('EX_NODE_SET', node_set_id,
... num_ns_nodes, num_ns_dist_facts)
Parameters
----------
set_id : int
set *ID* (not *INDEX*)
num_set_entity : int
number of nodes/edges/faces/elements to be added to set
num_dist_facts : int, optional
number of distribution factors (e.g. nodal 'weights') --
must be equal to zero or num_set_entity
"""
if numSetDistFacts is None:
numSetDistFacts = numSetEntity
assert numSetDistFacts in (0, numSetEntity)
self.__ex_put_set_param(object_type, object_id, numSetEntity, numSetDistFacts) | [
"def",
"put_set_params",
"(",
"self",
",",
"object_type",
",",
"object_id",
",",
"numSetEntity",
",",
"numSetDistFacts",
"=",
"None",
")",
":",
"if",
"numSetDistFacts",
"is",
"None",
":",
"numSetDistFacts",
"=",
"numSetEntity",
"assert",
"numSetDistFacts",
"in",
"(",
"0",
",",
"numSetEntity",
")",
"self",
".",
"__ex_put_set_param",
"(",
"object_type",
",",
"object_id",
",",
"numSetEntity",
",",
"numSetDistFacts",
")"
] | https://github.com/trilinos/Trilinos/blob/6168be6dd51e35e1cd681e9c4b24433e709df140/packages/seacas/scripts/exodus3.in.py#L3602-L3622 | ||
intel/caffe | 3f494b442ee3f9d17a07b09ecbd5fa2bbda00836 | tools/extra/plot_loss_trends.py | python | TrainLog.lrs | (self) | return self._multi_lrs | get lrs data showed within log file | get lrs data showed within log file | [
"get",
"lrs",
"data",
"showed",
"within",
"log",
"file"
] | def lrs(self):
'''get lrs data showed within log file'''
return self._multi_lrs | [
"def",
"lrs",
"(",
"self",
")",
":",
"return",
"self",
".",
"_multi_lrs"
] | https://github.com/intel/caffe/blob/3f494b442ee3f9d17a07b09ecbd5fa2bbda00836/tools/extra/plot_loss_trends.py#L112-L114 | |
panda3d/panda3d | 833ad89ebad58395d0af0b7ec08538e5e4308265 | direct/src/distributed/DistributedObjectOV.py | python | DistributedObjectOV.generateInit | (self) | This method is called when the DistributedObjectOV is first introduced
to the world... Not when it is pulled from the cache. | This method is called when the DistributedObjectOV is first introduced
to the world... Not when it is pulled from the cache. | [
"This",
"method",
"is",
"called",
"when",
"the",
"DistributedObjectOV",
"is",
"first",
"introduced",
"to",
"the",
"world",
"...",
"Not",
"when",
"it",
"is",
"pulled",
"from",
"the",
"cache",
"."
] | def generateInit(self):
"""
This method is called when the DistributedObjectOV is first introduced
to the world... Not when it is pulled from the cache.
"""
self.activeState = ESGenerating | [
"def",
"generateInit",
"(",
"self",
")",
":",
"self",
".",
"activeState",
"=",
"ESGenerating"
] | https://github.com/panda3d/panda3d/blob/833ad89ebad58395d0af0b7ec08538e5e4308265/direct/src/distributed/DistributedObjectOV.py#L134-L139 | ||
hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/telemetry/third_party/pyfakefs/pyfakefs/fake_filesystem.py | python | FakeOsModule._fdopen | (self, *args, **kwargs) | return FakeFileOpen(self.filesystem)(*args, **kwargs) | Redirector to open() builtin function.
Args:
*args: pass through args
**kwargs: pass through kwargs
Returns:
File object corresponding to file_des.
Raises:
TypeError: if file descriptor is not an integer. | Redirector to open() builtin function. | [
"Redirector",
"to",
"open",
"()",
"builtin",
"function",
"."
] | def _fdopen(self, *args, **kwargs):
"""Redirector to open() builtin function.
Args:
*args: pass through args
**kwargs: pass through kwargs
Returns:
File object corresponding to file_des.
Raises:
TypeError: if file descriptor is not an integer.
"""
if not isinstance(args[0], int):
raise TypeError('an integer is required')
return FakeFileOpen(self.filesystem)(*args, **kwargs) | [
"def",
"_fdopen",
"(",
"self",
",",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"if",
"not",
"isinstance",
"(",
"args",
"[",
"0",
"]",
",",
"int",
")",
":",
"raise",
"TypeError",
"(",
"'an integer is required'",
")",
"return",
"FakeFileOpen",
"(",
"self",
".",
"filesystem",
")",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")"
] | https://github.com/hanpfei/chromium-net/blob/392cc1fa3a8f92f42e4071ab6e674d8e0482f83f/third_party/catapult/telemetry/third_party/pyfakefs/pyfakefs/fake_filesystem.py#L1173-L1188 | |
natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/contrib/learn/python/learn/monitors.py | python | BaseMonitor.post_step | (self, step, session) | Callback after the step is finished.
Called after step_end and receives session to perform extra session.run
calls. If failure occurred in the process, will be called as well.
Args:
step: `int`, global step of the model.
session: `Session` object. | Callback after the step is finished. | [
"Callback",
"after",
"the",
"step",
"is",
"finished",
"."
] | def post_step(self, step, session): # pylint: disable=unused-argument
"""Callback after the step is finished.
Called after step_end and receives session to perform extra session.run
calls. If failure occurred in the process, will be called as well.
Args:
step: `int`, global step of the model.
session: `Session` object.
"""
_ = step, session | [
"def",
"post_step",
"(",
"self",
",",
"step",
",",
"session",
")",
":",
"# pylint: disable=unused-argument",
"_",
"=",
"step",
",",
"session"
] | https://github.com/natanielruiz/android-yolo/blob/1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f/jni-build/jni/include/tensorflow/contrib/learn/python/learn/monitors.py#L250-L260 | ||
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/site-packages/botocore/configprovider.py | python | ChainProvider.provide | (self) | return None | Provide the value from the first provider to return non-None.
Each provider in the chain has its provide method called. The first
one in the chain to return a non-None value is the returned from the
ChainProvider. When no non-None value is found, None is returned. | Provide the value from the first provider to return non-None. | [
"Provide",
"the",
"value",
"from",
"the",
"first",
"provider",
"to",
"return",
"non",
"-",
"None",
"."
] | def provide(self):
"""Provide the value from the first provider to return non-None.
Each provider in the chain has its provide method called. The first
one in the chain to return a non-None value is the returned from the
ChainProvider. When no non-None value is found, None is returned.
"""
for provider in self._providers:
value = provider.provide()
if value is not None:
return self._convert_type(value)
return None | [
"def",
"provide",
"(",
"self",
")",
":",
"for",
"provider",
"in",
"self",
".",
"_providers",
":",
"value",
"=",
"provider",
".",
"provide",
"(",
")",
"if",
"value",
"is",
"not",
"None",
":",
"return",
"self",
".",
"_convert_type",
"(",
"value",
")",
"return",
"None"
] | https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/windows/Lib/site-packages/botocore/configprovider.py#L382-L393 | |
baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/training/python/training/sequence_queueing_state_saver.py | python | SequenceQueueingStateSaver.prefetch_op | (self) | return self._prefetch_op | The op used to prefetch new data into the state saver.
Running it once enqueues one new input example into the state saver.
The first time this gets called, it additionally creates the prefetch_op.
Subsequent calls simply return the previously created `prefetch_op`.
It should be run in a separate thread via e.g. a `QueueRunner`.
Returns:
An `Operation` that performs prefetching. | The op used to prefetch new data into the state saver. | [
"The",
"op",
"used",
"to",
"prefetch",
"new",
"data",
"into",
"the",
"state",
"saver",
"."
] | def prefetch_op(self):
"""The op used to prefetch new data into the state saver.
Running it once enqueues one new input example into the state saver.
The first time this gets called, it additionally creates the prefetch_op.
Subsequent calls simply return the previously created `prefetch_op`.
It should be run in a separate thread via e.g. a `QueueRunner`.
Returns:
An `Operation` that performs prefetching.
"""
if not self._prefetch_op:
with ops.name_scope(None), ops.name_scope(
self._scope, values=[self._barrier.barrier_ref]):
self._create_prefetch_op()
return self._prefetch_op | [
"def",
"prefetch_op",
"(",
"self",
")",
":",
"if",
"not",
"self",
".",
"_prefetch_op",
":",
"with",
"ops",
".",
"name_scope",
"(",
"None",
")",
",",
"ops",
".",
"name_scope",
"(",
"self",
".",
"_scope",
",",
"values",
"=",
"[",
"self",
".",
"_barrier",
".",
"barrier_ref",
"]",
")",
":",
"self",
".",
"_create_prefetch_op",
"(",
")",
"return",
"self",
".",
"_prefetch_op"
] | https://github.com/baidu-research/tensorflow-allreduce/blob/66d5b855e90b0949e9fa5cca5599fd729a70e874/tensorflow/contrib/training/python/training/sequence_queueing_state_saver.py#L904-L920 | |
PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | tools/check_op_benchmark_result.py | python | check_accuracy_result | (case_name, pr_result) | return not pr_result.get("consistent") | Check accuracy result. | Check accuracy result. | [
"Check",
"accuracy",
"result",
"."
] | def check_accuracy_result(case_name, pr_result):
"""Check accuracy result.
"""
logging.info("------ OP: %s ------" % case_name)
logging.info("Accuracy diff: %s" % pr_result.get("diff"))
logging.info("backward: %s" % pr_result.get("backward"))
logging.info("parameters:")
for line in pr_result.get("parameters").strip().split("\n"):
logging.info("\t%s" % line)
return not pr_result.get("consistent") | [
"def",
"check_accuracy_result",
"(",
"case_name",
",",
"pr_result",
")",
":",
"logging",
".",
"info",
"(",
"\"------ OP: %s ------\"",
"%",
"case_name",
")",
"logging",
".",
"info",
"(",
"\"Accuracy diff: %s\"",
"%",
"pr_result",
".",
"get",
"(",
"\"diff\"",
")",
")",
"logging",
".",
"info",
"(",
"\"backward: %s\"",
"%",
"pr_result",
".",
"get",
"(",
"\"backward\"",
")",
")",
"logging",
".",
"info",
"(",
"\"parameters:\"",
")",
"for",
"line",
"in",
"pr_result",
".",
"get",
"(",
"\"parameters\"",
")",
".",
"strip",
"(",
")",
".",
"split",
"(",
"\"\\n\"",
")",
":",
"logging",
".",
"info",
"(",
"\"\\t%s\"",
"%",
"line",
")",
"return",
"not",
"pr_result",
".",
"get",
"(",
"\"consistent\"",
")"
] | https://github.com/PaddlePaddle/Paddle/blob/1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c/tools/check_op_benchmark_result.py#L93-L103 | |
simsong/bulk_extractor | 738911df22b7066ca9e1662f4131fb44090a4196 | python/bulk_extractor_reader.py | python | BulkReport.margin_size | (self) | return int((self.xmldoc.getElementsByTagName("configuration")[0]
.getElementsByTagName("marginsize")[0].firstChild.wholeText)) | Returns the size of the overlapping margins around each page. | Returns the size of the overlapping margins around each page. | [
"Returns",
"the",
"size",
"of",
"the",
"overlapping",
"margins",
"around",
"each",
"page",
"."
] | def margin_size(self):
"""Returns the size of the overlapping margins around each page."""
return int((self.xmldoc.getElementsByTagName("configuration")[0]
.getElementsByTagName("marginsize")[0].firstChild.wholeText)) | [
"def",
"margin_size",
"(",
"self",
")",
":",
"return",
"int",
"(",
"(",
"self",
".",
"xmldoc",
".",
"getElementsByTagName",
"(",
"\"configuration\"",
")",
"[",
"0",
"]",
".",
"getElementsByTagName",
"(",
"\"marginsize\"",
")",
"[",
"0",
"]",
".",
"firstChild",
".",
"wholeText",
")",
")"
] | https://github.com/simsong/bulk_extractor/blob/738911df22b7066ca9e1662f4131fb44090a4196/python/bulk_extractor_reader.py#L241-L244 | |
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_core.py | python | Menu.FindItemByPosition | (*args, **kwargs) | return _core_.Menu_FindItemByPosition(*args, **kwargs) | FindItemByPosition(self, size_t position) -> MenuItem | FindItemByPosition(self, size_t position) -> MenuItem | [
"FindItemByPosition",
"(",
"self",
"size_t",
"position",
")",
"-",
">",
"MenuItem"
] | def FindItemByPosition(*args, **kwargs):
"""FindItemByPosition(self, size_t position) -> MenuItem"""
return _core_.Menu_FindItemByPosition(*args, **kwargs) | [
"def",
"FindItemByPosition",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"return",
"_core_",
".",
"Menu_FindItemByPosition",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")"
] | https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/gtk/_core.py#L12146-L12148 | |
ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/ros_comm/rosbag/src/rosbag/rosbag_main.py | python | ProgressMeter.terminal_width | () | return width | Estimate the width of the terminal | Estimate the width of the terminal | [
"Estimate",
"the",
"width",
"of",
"the",
"terminal"
] | def terminal_width():
"""Estimate the width of the terminal"""
width = 0
try:
import struct, fcntl, termios
s = struct.pack('HHHH', 0, 0, 0, 0)
x = fcntl.ioctl(1, termios.TIOCGWINSZ, s)
width = struct.unpack('HHHH', x)[1]
except (IOError, ImportError):
pass
if width <= 0:
try:
width = int(os.environ['COLUMNS'])
except:
pass
if width <= 0:
width = 80
return width | [
"def",
"terminal_width",
"(",
")",
":",
"width",
"=",
"0",
"try",
":",
"import",
"struct",
",",
"fcntl",
",",
"termios",
"s",
"=",
"struct",
".",
"pack",
"(",
"'HHHH'",
",",
"0",
",",
"0",
",",
"0",
",",
"0",
")",
"x",
"=",
"fcntl",
".",
"ioctl",
"(",
"1",
",",
"termios",
".",
"TIOCGWINSZ",
",",
"s",
")",
"width",
"=",
"struct",
".",
"unpack",
"(",
"'HHHH'",
",",
"x",
")",
"[",
"1",
"]",
"except",
"(",
"IOError",
",",
"ImportError",
")",
":",
"pass",
"if",
"width",
"<=",
"0",
":",
"try",
":",
"width",
"=",
"int",
"(",
"os",
".",
"environ",
"[",
"'COLUMNS'",
"]",
")",
"except",
":",
"pass",
"if",
"width",
"<=",
"0",
":",
"width",
"=",
"80",
"return",
"width"
] | https://github.com/ApolloAuto/apollo-platform/blob/86d9dc6743b496ead18d597748ebabd34a513289/ros/ros_comm/rosbag/src/rosbag/rosbag_main.py#L837-L855 | |
krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/klampt/control/cartesian_drive.py | python | normalize_rotation | (R) | return so3.from_quaternion(q) | Given a matrix close to a proper (orthogonal) rotation matrix,
returns a true orthogonal matrix. | Given a matrix close to a proper (orthogonal) rotation matrix,
returns a true orthogonal matrix. | [
"Given",
"a",
"matrix",
"close",
"to",
"a",
"proper",
"(",
"orthogonal",
")",
"rotation",
"matrix",
"returns",
"a",
"true",
"orthogonal",
"matrix",
"."
] | def normalize_rotation(R):
"""Given a matrix close to a proper (orthogonal) rotation matrix,
returns a true orthogonal matrix."""
q = so3.quaternion(R) #normalizes
return so3.from_quaternion(q) | [
"def",
"normalize_rotation",
"(",
"R",
")",
":",
"q",
"=",
"so3",
".",
"quaternion",
"(",
"R",
")",
"#normalizes",
"return",
"so3",
".",
"from_quaternion",
"(",
"q",
")"
] | https://github.com/krishauser/Klampt/blob/972cc83ea5befac3f653c1ba20f80155768ad519/Python/klampt/control/cartesian_drive.py#L424-L428 | |
apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | src/python/turicreate/toolkits/recommender/util.py | python | compare_models | (
dataset,
models,
model_names=None,
user_sample=1.0,
metric="auto",
target=None,
exclude_known_for_precision_recall=True,
make_plot=False,
verbose=True,
**kwargs
) | return results | Compare the prediction or recommendation performance of recommender models
on a common test dataset.
Models that are trained to predict ratings are compared separately from
models that are trained without target ratings. The ratings prediction
models are compared on root-mean-squared error, and the rest are compared on
precision-recall.
Parameters
----------
dataset : SFrame
The dataset to use for model evaluation.
models : list[recommender models]
List of trained recommender models.
model_names : list[str], optional
List of model name strings for display.
user_sample : float, optional
Sampling proportion of unique users to use in estimating model
performance. Defaults to 1.0, i.e. use all users in the dataset.
metric : str, {'auto', 'rmse', 'precision_recall'}, optional
Metric for the evaluation. The default automatically splits
models into two groups with their default evaluation metric respectively:
'rmse' for models trained with a target, and 'precision_recall'
otherwise.
target : str, optional
The name of the target column for evaluating rmse. If the model is
trained with a target column, the default is to using the same column.
If the model is trained without a target column and `metric='rmse'`,
then this option must be provided by user.
exclude_known_for_precision_recall : bool, optional
A useful option when `metric='precision_recall'`. Recommender models
automatically exclude items seen in the training data from the
final recommendation list. If the input evaluation `dataset` is the
same as the data used for training the models, set this option to False.
verbose : bool, optional
If true, print the progress.
Returns
-------
out : list[SFrame]
A list of results where each one is an sframe of evaluation results of
the respective model on the given dataset
Examples
--------
If you have created two ItemSimilarityRecommenders ``m1`` and ``m2`` and have
an :class:`~turicreate.SFrame` ``test_data``, then you may compare the
performance of the two models on test data using:
>>> import turicreate
>>> train_data = turicreate.SFrame({'user_id': ["0", "0", "0", "1", "1", "2", "2", "2"],
... 'item_id': ["a", "c", "e", "b", "f", "b", "c", "d"]})
>>> test_data = turicreate.SFrame({'user_id': ["0", "0", "1", "1", "1", "2", "2"],
... 'item_id': ["b", "d", "a", "c", "e", "a", "e"]})
>>> m1 = turicreate.item_similarity_recommender.create(train_data)
>>> m2 = turicreate.item_similarity_recommender.create(train_data, only_top_k=1)
>>> turicreate.recommender.util.compare_models(test_data, [m1, m2], model_names=["m1", "m2"])
The evaluation metric is automatically set to 'precision_recall', and the
evaluation will be based on recommendations that exclude items seen in the
training data.
If you want to evaluate on the original training set:
>>> turicreate.recommender.util.compare_models(train_data, [m1, m2],
... exclude_known_for_precision_recall=False)
Suppose you have four models, two trained with a target rating column, and
the other two trained without a target. By default, the models are put into
two different groups with "rmse", and "precision-recall" as the evaluation
metric respectively.
>>> train_data2 = turicreate.SFrame({'user_id': ["0", "0", "0", "1", "1", "2", "2", "2"],
... 'item_id': ["a", "c", "e", "b", "f", "b", "c", "d"],
... 'rating': [1, 3, 4, 5, 3, 4, 2, 5]})
>>> test_data2 = turicreate.SFrame({'user_id': ["0", "0", "1", "1", "1", "2", "2"],
... 'item_id': ["b", "d", "a", "c", "e", "a", "e"],
... 'rating': [3, 5, 4, 4, 3, 5, 2]})
>>> m3 = turicreate.factorization_recommender.create(train_data2, target='rating')
>>> m4 = turicreate.factorization_recommender.create(train_data2, target='rating')
>>> turicreate.recommender.util.compare_models(test_data2, [m3, m4])
To compare all four models using the same 'precision_recall' metric, you can
do:
>>> turicreate.recommender.util.compare_models(test_data2, [m1, m2, m3, m4],
... metric='precision_recall') | Compare the prediction or recommendation performance of recommender models
on a common test dataset. | [
"Compare",
"the",
"prediction",
"or",
"recommendation",
"performance",
"of",
"recommender",
"models",
"on",
"a",
"common",
"test",
"dataset",
"."
] | def compare_models(
dataset,
models,
model_names=None,
user_sample=1.0,
metric="auto",
target=None,
exclude_known_for_precision_recall=True,
make_plot=False,
verbose=True,
**kwargs
):
"""
Compare the prediction or recommendation performance of recommender models
on a common test dataset.
Models that are trained to predict ratings are compared separately from
models that are trained without target ratings. The ratings prediction
models are compared on root-mean-squared error, and the rest are compared on
precision-recall.
Parameters
----------
dataset : SFrame
The dataset to use for model evaluation.
models : list[recommender models]
List of trained recommender models.
model_names : list[str], optional
List of model name strings for display.
user_sample : float, optional
Sampling proportion of unique users to use in estimating model
performance. Defaults to 1.0, i.e. use all users in the dataset.
metric : str, {'auto', 'rmse', 'precision_recall'}, optional
Metric for the evaluation. The default automatically splits
models into two groups with their default evaluation metric respectively:
'rmse' for models trained with a target, and 'precision_recall'
otherwise.
target : str, optional
The name of the target column for evaluating rmse. If the model is
trained with a target column, the default is to using the same column.
If the model is trained without a target column and `metric='rmse'`,
then this option must be provided by user.
exclude_known_for_precision_recall : bool, optional
A useful option when `metric='precision_recall'`. Recommender models
automatically exclude items seen in the training data from the
final recommendation list. If the input evaluation `dataset` is the
same as the data used for training the models, set this option to False.
verbose : bool, optional
If true, print the progress.
Returns
-------
out : list[SFrame]
A list of results where each one is an sframe of evaluation results of
the respective model on the given dataset
Examples
--------
If you have created two ItemSimilarityRecommenders ``m1`` and ``m2`` and have
an :class:`~turicreate.SFrame` ``test_data``, then you may compare the
performance of the two models on test data using:
>>> import turicreate
>>> train_data = turicreate.SFrame({'user_id': ["0", "0", "0", "1", "1", "2", "2", "2"],
... 'item_id': ["a", "c", "e", "b", "f", "b", "c", "d"]})
>>> test_data = turicreate.SFrame({'user_id': ["0", "0", "1", "1", "1", "2", "2"],
... 'item_id': ["b", "d", "a", "c", "e", "a", "e"]})
>>> m1 = turicreate.item_similarity_recommender.create(train_data)
>>> m2 = turicreate.item_similarity_recommender.create(train_data, only_top_k=1)
>>> turicreate.recommender.util.compare_models(test_data, [m1, m2], model_names=["m1", "m2"])
The evaluation metric is automatically set to 'precision_recall', and the
evaluation will be based on recommendations that exclude items seen in the
training data.
If you want to evaluate on the original training set:
>>> turicreate.recommender.util.compare_models(train_data, [m1, m2],
... exclude_known_for_precision_recall=False)
Suppose you have four models, two trained with a target rating column, and
the other two trained without a target. By default, the models are put into
two different groups with "rmse", and "precision-recall" as the evaluation
metric respectively.
>>> train_data2 = turicreate.SFrame({'user_id': ["0", "0", "0", "1", "1", "2", "2", "2"],
... 'item_id': ["a", "c", "e", "b", "f", "b", "c", "d"],
... 'rating': [1, 3, 4, 5, 3, 4, 2, 5]})
>>> test_data2 = turicreate.SFrame({'user_id': ["0", "0", "1", "1", "1", "2", "2"],
... 'item_id': ["b", "d", "a", "c", "e", "a", "e"],
... 'rating': [3, 5, 4, 4, 3, 5, 2]})
>>> m3 = turicreate.factorization_recommender.create(train_data2, target='rating')
>>> m4 = turicreate.factorization_recommender.create(train_data2, target='rating')
>>> turicreate.recommender.util.compare_models(test_data2, [m3, m4])
To compare all four models using the same 'precision_recall' metric, you can
do:
>>> turicreate.recommender.util.compare_models(test_data2, [m1, m2, m3, m4],
... metric='precision_recall')
"""
from turicreate.toolkits.recommender.util import _Recommender as BaseRecommender
if not isinstance(dataset, _SFrame):
raise TypeError('"dataset" must be of type SFrame.')
if len(dataset) == 0:
raise _ToolkitError("Unable to test on an empty dataset.")
if any(map(lambda m: not issubclass(type(m), BaseRecommender), models)):
raise _ToolkitError("All models must be recommender models.")
num_models = len(models)
if model_names is None:
model_names = ["M" + str(i) for i in range(len(models))]
if num_models < 1:
raise ValueError(
"Must pass in at least one recommender model to \
evaluate"
)
if model_names is not None and len(model_names) != num_models:
raise ValueError(
"Must pass in the same number of model names as \
models"
)
# if we are asked to sample the users, come up with a list of unique users
if user_sample < 1.0:
user_id_name = models[0].user_id
if user_id_name is None:
raise ValueError("user_id not set in model(s)")
user_sa = dataset[user_id_name]
unique_users = list(user_sa.unique())
nusers = len(unique_users)
ntake = int(round(user_sample * nusers))
_random.shuffle(unique_users)
users = unique_users[:ntake]
print("compare_models: using", ntake, "users to estimate model performance")
users = frozenset(users)
ix = [u in users for u in dataset[user_id_name]]
dataset_subset = dataset[_SArray(ix) == True]
else:
dataset_subset = dataset
results = []
for (m, mname) in zip(models, model_names):
if verbose:
print("PROGRESS: Evaluate model %s" % mname)
r = m.evaluate(
dataset_subset,
metric,
exclude_known_for_precision_recall,
target,
verbose=verbose,
cutoffs=list(range(1, 11, 1)) + list(range(11, 50, 5)),
**kwargs
)
results.append(r)
return results | [
"def",
"compare_models",
"(",
"dataset",
",",
"models",
",",
"model_names",
"=",
"None",
",",
"user_sample",
"=",
"1.0",
",",
"metric",
"=",
"\"auto\"",
",",
"target",
"=",
"None",
",",
"exclude_known_for_precision_recall",
"=",
"True",
",",
"make_plot",
"=",
"False",
",",
"verbose",
"=",
"True",
",",
"*",
"*",
"kwargs",
")",
":",
"from",
"turicreate",
".",
"toolkits",
".",
"recommender",
".",
"util",
"import",
"_Recommender",
"as",
"BaseRecommender",
"if",
"not",
"isinstance",
"(",
"dataset",
",",
"_SFrame",
")",
":",
"raise",
"TypeError",
"(",
"'\"dataset\" must be of type SFrame.'",
")",
"if",
"len",
"(",
"dataset",
")",
"==",
"0",
":",
"raise",
"_ToolkitError",
"(",
"\"Unable to test on an empty dataset.\"",
")",
"if",
"any",
"(",
"map",
"(",
"lambda",
"m",
":",
"not",
"issubclass",
"(",
"type",
"(",
"m",
")",
",",
"BaseRecommender",
")",
",",
"models",
")",
")",
":",
"raise",
"_ToolkitError",
"(",
"\"All models must be recommender models.\"",
")",
"num_models",
"=",
"len",
"(",
"models",
")",
"if",
"model_names",
"is",
"None",
":",
"model_names",
"=",
"[",
"\"M\"",
"+",
"str",
"(",
"i",
")",
"for",
"i",
"in",
"range",
"(",
"len",
"(",
"models",
")",
")",
"]",
"if",
"num_models",
"<",
"1",
":",
"raise",
"ValueError",
"(",
"\"Must pass in at least one recommender model to \\\n evaluate\"",
")",
"if",
"model_names",
"is",
"not",
"None",
"and",
"len",
"(",
"model_names",
")",
"!=",
"num_models",
":",
"raise",
"ValueError",
"(",
"\"Must pass in the same number of model names as \\\n models\"",
")",
"# if we are asked to sample the users, come up with a list of unique users",
"if",
"user_sample",
"<",
"1.0",
":",
"user_id_name",
"=",
"models",
"[",
"0",
"]",
".",
"user_id",
"if",
"user_id_name",
"is",
"None",
":",
"raise",
"ValueError",
"(",
"\"user_id not set in model(s)\"",
")",
"user_sa",
"=",
"dataset",
"[",
"user_id_name",
"]",
"unique_users",
"=",
"list",
"(",
"user_sa",
".",
"unique",
"(",
")",
")",
"nusers",
"=",
"len",
"(",
"unique_users",
")",
"ntake",
"=",
"int",
"(",
"round",
"(",
"user_sample",
"*",
"nusers",
")",
")",
"_random",
".",
"shuffle",
"(",
"unique_users",
")",
"users",
"=",
"unique_users",
"[",
":",
"ntake",
"]",
"print",
"(",
"\"compare_models: using\"",
",",
"ntake",
",",
"\"users to estimate model performance\"",
")",
"users",
"=",
"frozenset",
"(",
"users",
")",
"ix",
"=",
"[",
"u",
"in",
"users",
"for",
"u",
"in",
"dataset",
"[",
"user_id_name",
"]",
"]",
"dataset_subset",
"=",
"dataset",
"[",
"_SArray",
"(",
"ix",
")",
"==",
"True",
"]",
"else",
":",
"dataset_subset",
"=",
"dataset",
"results",
"=",
"[",
"]",
"for",
"(",
"m",
",",
"mname",
")",
"in",
"zip",
"(",
"models",
",",
"model_names",
")",
":",
"if",
"verbose",
":",
"print",
"(",
"\"PROGRESS: Evaluate model %s\"",
"%",
"mname",
")",
"r",
"=",
"m",
".",
"evaluate",
"(",
"dataset_subset",
",",
"metric",
",",
"exclude_known_for_precision_recall",
",",
"target",
",",
"verbose",
"=",
"verbose",
",",
"cutoffs",
"=",
"list",
"(",
"range",
"(",
"1",
",",
"11",
",",
"1",
")",
")",
"+",
"list",
"(",
"range",
"(",
"11",
",",
"50",
",",
"5",
")",
")",
",",
"*",
"*",
"kwargs",
")",
"results",
".",
"append",
"(",
"r",
")",
"return",
"results"
] | https://github.com/apple/turicreate/blob/cce55aa5311300e3ce6af93cb45ba791fd1bdf49/src/python/turicreate/toolkits/recommender/util.py#L185-L354 | |
FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Fem/femtools/femutils.py | python | get_refshape_type | (fem_doc_object) | Return shape type the constraints references.
Determine single shape type of references of *fem_doc_object* which must be
a constraint (=have a *References* property). All references must be of the
same type which is than returned as a string. A type can be "Vertex",
"Edge", "Face" or "Solid".
:param fem_doc_object:
A constraint object with a *References* property.
:returns:
A string representing the shape type ("Vertex", "Edge", "Face" or
"Solid"). If *fem_doc_object* isn't a constraint ``""`` is returned.
:note:
Undefined behaviour if the type of the references of one object are
not all the same.
:note:
Undefined behaviour if constraint contains no references (empty list). | Return shape type the constraints references. | [
"Return",
"shape",
"type",
"the",
"constraints",
"references",
"."
] | def get_refshape_type(fem_doc_object):
""" Return shape type the constraints references.
Determine single shape type of references of *fem_doc_object* which must be
a constraint (=have a *References* property). All references must be of the
same type which is than returned as a string. A type can be "Vertex",
"Edge", "Face" or "Solid".
:param fem_doc_object:
A constraint object with a *References* property.
:returns:
A string representing the shape type ("Vertex", "Edge", "Face" or
"Solid"). If *fem_doc_object* isn't a constraint ``""`` is returned.
:note:
Undefined behaviour if the type of the references of one object are
not all the same.
:note:
Undefined behaviour if constraint contains no references (empty list).
"""
from femtools.geomtools import get_element
if hasattr(fem_doc_object, "References") and fem_doc_object.References:
first_ref_obj = fem_doc_object.References[0]
first_ref_shape = get_element(first_ref_obj[0], first_ref_obj[1][0])
st = first_ref_shape.ShapeType
FreeCAD.Console.PrintLog(
"References: {} in {}, {}\n"
. format(st, fem_doc_object.Name, fem_doc_object.Label)
)
return st
else:
FreeCAD.Console.PrintLog(
"References: empty in {}, {}\n"
. format(fem_doc_object.Name, fem_doc_object.Label)
)
return "" | [
"def",
"get_refshape_type",
"(",
"fem_doc_object",
")",
":",
"from",
"femtools",
".",
"geomtools",
"import",
"get_element",
"if",
"hasattr",
"(",
"fem_doc_object",
",",
"\"References\"",
")",
"and",
"fem_doc_object",
".",
"References",
":",
"first_ref_obj",
"=",
"fem_doc_object",
".",
"References",
"[",
"0",
"]",
"first_ref_shape",
"=",
"get_element",
"(",
"first_ref_obj",
"[",
"0",
"]",
",",
"first_ref_obj",
"[",
"1",
"]",
"[",
"0",
"]",
")",
"st",
"=",
"first_ref_shape",
".",
"ShapeType",
"FreeCAD",
".",
"Console",
".",
"PrintLog",
"(",
"\"References: {} in {}, {}\\n\"",
".",
"format",
"(",
"st",
",",
"fem_doc_object",
".",
"Name",
",",
"fem_doc_object",
".",
"Label",
")",
")",
"return",
"st",
"else",
":",
"FreeCAD",
".",
"Console",
".",
"PrintLog",
"(",
"\"References: empty in {}, {}\\n\"",
".",
"format",
"(",
"fem_doc_object",
".",
"Name",
",",
"fem_doc_object",
".",
"Label",
")",
")",
"return",
"\"\""
] | https://github.com/FreeCAD/FreeCAD/blob/ba42231b9c6889b89e064d6d563448ed81e376ec/src/Mod/Fem/femtools/femutils.py#L339-L376 | ||
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numba/typing/npydecl.py | python | MatMulTyperMixin.matmul_typer | (self, a, b, out=None) | Typer function for Numpy matrix multiplication. | Typer function for Numpy matrix multiplication. | [
"Typer",
"function",
"for",
"Numpy",
"matrix",
"multiplication",
"."
] | def matmul_typer(self, a, b, out=None):
"""
Typer function for Numpy matrix multiplication.
"""
if not isinstance(a, types.Array) or not isinstance(b, types.Array):
return
if not all(x.ndim in (1, 2) for x in (a, b)):
raise TypingError("%s only supported on 1-D and 2-D arrays"
% (self.func_name, ))
# Output dimensionality
ndims = set([a.ndim, b.ndim])
if ndims == set([2]):
# M * M
out_ndim = 2
elif ndims == set([1, 2]):
# M* V and V * M
out_ndim = 1
elif ndims == set([1]):
# V * V
out_ndim = 0
if out is not None:
if out_ndim == 0:
raise TypeError("explicit output unsupported for vector * vector")
elif out.ndim != out_ndim:
raise TypeError("explicit output has incorrect dimensionality")
if not isinstance(out, types.Array) or out.layout != 'C':
raise TypeError("output must be a C-contiguous array")
all_args = (a, b, out)
else:
all_args = (a, b)
if not (config.DISABLE_PERFORMANCE_WARNINGS or
all(x.layout in 'CF' for x in (a, b))):
msg = ("%s is faster on contiguous arrays, called on %s" %
(self.func_name, (a, b)))
warnings.warn(NumbaPerformanceWarning(msg))
if not all(x.dtype == a.dtype for x in all_args):
raise TypingError("%s arguments must all have "
"the same dtype" % (self.func_name,))
if not isinstance(a.dtype, (types.Float, types.Complex)):
raise TypingError("%s only supported on "
"float and complex arrays"
% (self.func_name,))
if out:
return out
elif out_ndim > 0:
return types.Array(a.dtype, out_ndim, 'C')
else:
return a.dtype | [
"def",
"matmul_typer",
"(",
"self",
",",
"a",
",",
"b",
",",
"out",
"=",
"None",
")",
":",
"if",
"not",
"isinstance",
"(",
"a",
",",
"types",
".",
"Array",
")",
"or",
"not",
"isinstance",
"(",
"b",
",",
"types",
".",
"Array",
")",
":",
"return",
"if",
"not",
"all",
"(",
"x",
".",
"ndim",
"in",
"(",
"1",
",",
"2",
")",
"for",
"x",
"in",
"(",
"a",
",",
"b",
")",
")",
":",
"raise",
"TypingError",
"(",
"\"%s only supported on 1-D and 2-D arrays\"",
"%",
"(",
"self",
".",
"func_name",
",",
")",
")",
"# Output dimensionality",
"ndims",
"=",
"set",
"(",
"[",
"a",
".",
"ndim",
",",
"b",
".",
"ndim",
"]",
")",
"if",
"ndims",
"==",
"set",
"(",
"[",
"2",
"]",
")",
":",
"# M * M",
"out_ndim",
"=",
"2",
"elif",
"ndims",
"==",
"set",
"(",
"[",
"1",
",",
"2",
"]",
")",
":",
"# M* V and V * M",
"out_ndim",
"=",
"1",
"elif",
"ndims",
"==",
"set",
"(",
"[",
"1",
"]",
")",
":",
"# V * V",
"out_ndim",
"=",
"0",
"if",
"out",
"is",
"not",
"None",
":",
"if",
"out_ndim",
"==",
"0",
":",
"raise",
"TypeError",
"(",
"\"explicit output unsupported for vector * vector\"",
")",
"elif",
"out",
".",
"ndim",
"!=",
"out_ndim",
":",
"raise",
"TypeError",
"(",
"\"explicit output has incorrect dimensionality\"",
")",
"if",
"not",
"isinstance",
"(",
"out",
",",
"types",
".",
"Array",
")",
"or",
"out",
".",
"layout",
"!=",
"'C'",
":",
"raise",
"TypeError",
"(",
"\"output must be a C-contiguous array\"",
")",
"all_args",
"=",
"(",
"a",
",",
"b",
",",
"out",
")",
"else",
":",
"all_args",
"=",
"(",
"a",
",",
"b",
")",
"if",
"not",
"(",
"config",
".",
"DISABLE_PERFORMANCE_WARNINGS",
"or",
"all",
"(",
"x",
".",
"layout",
"in",
"'CF'",
"for",
"x",
"in",
"(",
"a",
",",
"b",
")",
")",
")",
":",
"msg",
"=",
"(",
"\"%s is faster on contiguous arrays, called on %s\"",
"%",
"(",
"self",
".",
"func_name",
",",
"(",
"a",
",",
"b",
")",
")",
")",
"warnings",
".",
"warn",
"(",
"NumbaPerformanceWarning",
"(",
"msg",
")",
")",
"if",
"not",
"all",
"(",
"x",
".",
"dtype",
"==",
"a",
".",
"dtype",
"for",
"x",
"in",
"all_args",
")",
":",
"raise",
"TypingError",
"(",
"\"%s arguments must all have \"",
"\"the same dtype\"",
"%",
"(",
"self",
".",
"func_name",
",",
")",
")",
"if",
"not",
"isinstance",
"(",
"a",
".",
"dtype",
",",
"(",
"types",
".",
"Float",
",",
"types",
".",
"Complex",
")",
")",
":",
"raise",
"TypingError",
"(",
"\"%s only supported on \"",
"\"float and complex arrays\"",
"%",
"(",
"self",
".",
"func_name",
",",
")",
")",
"if",
"out",
":",
"return",
"out",
"elif",
"out_ndim",
">",
"0",
":",
"return",
"types",
".",
"Array",
"(",
"a",
".",
"dtype",
",",
"out_ndim",
",",
"'C'",
")",
"else",
":",
"return",
"a",
".",
"dtype"
] | https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numba/typing/npydecl.py#L922-L971 | ||
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py3/scipy/stats/_multivariate.py | python | multivariate_normal_frozen.entropy | (self) | return 0.5 * (rank * (_LOG_2PI + 1) + log_pdet) | Computes the differential entropy of the multivariate normal.
Returns
-------
h : scalar
Entropy of the multivariate normal distribution | Computes the differential entropy of the multivariate normal. | [
"Computes",
"the",
"differential",
"entropy",
"of",
"the",
"multivariate",
"normal",
"."
] | def entropy(self):
"""
Computes the differential entropy of the multivariate normal.
Returns
-------
h : scalar
Entropy of the multivariate normal distribution
"""
log_pdet = self.cov_info.log_pdet
rank = self.cov_info.rank
return 0.5 * (rank * (_LOG_2PI + 1) + log_pdet) | [
"def",
"entropy",
"(",
"self",
")",
":",
"log_pdet",
"=",
"self",
".",
"cov_info",
".",
"log_pdet",
"rank",
"=",
"self",
".",
"cov_info",
".",
"rank",
"return",
"0.5",
"*",
"(",
"rank",
"*",
"(",
"_LOG_2PI",
"+",
"1",
")",
"+",
"log_pdet",
")"
] | https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/scipy/py3/scipy/stats/_multivariate.py#L764-L776 | |
miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/tensorflow/python/ops/variables.py | python | Variable._OverloadAllOperators | () | Register overloads for all operators. | Register overloads for all operators. | [
"Register",
"overloads",
"for",
"all",
"operators",
"."
] | def _OverloadAllOperators():
"""Register overloads for all operators."""
for operator in ops.Tensor.OVERLOADABLE_OPERATORS:
Variable._OverloadOperator(operator) | [
"def",
"_OverloadAllOperators",
"(",
")",
":",
"for",
"operator",
"in",
"ops",
".",
"Tensor",
".",
"OVERLOADABLE_OPERATORS",
":",
"Variable",
".",
"_OverloadOperator",
"(",
"operator",
")"
] | https://github.com/miyosuda/TensorFlowAndroidDemo/blob/35903e0221aa5f109ea2dbef27f20b52e317f42d/jni-build/jni/include/tensorflow/python/ops/variables.py#L600-L603 | ||
tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/ops/stateful_random_ops.py | python | create_rng_state | (seed, alg) | return _make_state_from_seed(seed, alg) | Creates a RNG state from an integer or a vector.
Example:
>>> tf.random.create_rng_state(
... 1234, "philox")
<tf.Tensor: shape=(3,), dtype=int64, numpy=array([1234, 0, 0])>
>>> tf.random.create_rng_state(
... [12, 34], "threefry")
<tf.Tensor: shape=(2,), dtype=int64, numpy=array([12, 34])>
Args:
seed: an integer or 1-D numpy array.
alg: the RNG algorithm. Can be a string, an `Algorithm` or an integer.
Returns:
a 1-D numpy array whose size depends on the algorithm. | Creates a RNG state from an integer or a vector. | [
"Creates",
"a",
"RNG",
"state",
"from",
"an",
"integer",
"or",
"a",
"vector",
"."
] | def create_rng_state(seed, alg):
"""Creates a RNG state from an integer or a vector.
Example:
>>> tf.random.create_rng_state(
... 1234, "philox")
<tf.Tensor: shape=(3,), dtype=int64, numpy=array([1234, 0, 0])>
>>> tf.random.create_rng_state(
... [12, 34], "threefry")
<tf.Tensor: shape=(2,), dtype=int64, numpy=array([12, 34])>
Args:
seed: an integer or 1-D numpy array.
alg: the RNG algorithm. Can be a string, an `Algorithm` or an integer.
Returns:
a 1-D numpy array whose size depends on the algorithm.
"""
alg = stateless_random_ops.convert_alg_to_int(alg)
return _make_state_from_seed(seed, alg) | [
"def",
"create_rng_state",
"(",
"seed",
",",
"alg",
")",
":",
"alg",
"=",
"stateless_random_ops",
".",
"convert_alg_to_int",
"(",
"alg",
")",
"return",
"_make_state_from_seed",
"(",
"seed",
",",
"alg",
")"
] | https://github.com/tensorflow/tensorflow/blob/419e3a6b650ea4bd1b0cba23c4348f8a69f3272e/tensorflow/python/ops/stateful_random_ops.py#L163-L183 | |
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/stc.py | python | StyledTextCtrl.SetRectangularSelectionAnchor | (*args, **kwargs) | return _stc.StyledTextCtrl_SetRectangularSelectionAnchor(*args, **kwargs) | SetRectangularSelectionAnchor(self, int posAnchor) | SetRectangularSelectionAnchor(self, int posAnchor) | [
"SetRectangularSelectionAnchor",
"(",
"self",
"int",
"posAnchor",
")"
] | def SetRectangularSelectionAnchor(*args, **kwargs):
"""SetRectangularSelectionAnchor(self, int posAnchor)"""
return _stc.StyledTextCtrl_SetRectangularSelectionAnchor(*args, **kwargs) | [
"def",
"SetRectangularSelectionAnchor",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"return",
"_stc",
".",
"StyledTextCtrl_SetRectangularSelectionAnchor",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")"
] | https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/msw/stc.py#L6216-L6218 | |
wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/idlelib/configHandler.py | python | IdleConf.SaveUserCfgFiles | (self) | write all loaded user configuration files back to disk | write all loaded user configuration files back to disk | [
"write",
"all",
"loaded",
"user",
"configuration",
"files",
"back",
"to",
"disk"
] | def SaveUserCfgFiles(self):
"""
write all loaded user configuration files back to disk
"""
for key in self.userCfg.keys():
self.userCfg[key].Save() | [
"def",
"SaveUserCfgFiles",
"(",
"self",
")",
":",
"for",
"key",
"in",
"self",
".",
"userCfg",
".",
"keys",
"(",
")",
":",
"self",
".",
"userCfg",
"[",
"key",
"]",
".",
"Save",
"(",
")"
] | https://github.com/wlanjie/AndroidFFmpeg/blob/7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf/tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/idlelib/configHandler.py#L693-L698 | ||
BitMEX/api-connectors | 37a3a5b806ad5d0e0fc975ab86d9ed43c3bcd812 | auto-generated/python/swagger_client/models/position.py | python | Position.pos_loss | (self, pos_loss) | Sets the pos_loss of this Position.
:param pos_loss: The pos_loss of this Position. # noqa: E501
:type: float | Sets the pos_loss of this Position. | [
"Sets",
"the",
"pos_loss",
"of",
"this",
"Position",
"."
] | def pos_loss(self, pos_loss):
"""Sets the pos_loss of this Position.
:param pos_loss: The pos_loss of this Position. # noqa: E501
:type: float
"""
self._pos_loss = pos_loss | [
"def",
"pos_loss",
"(",
"self",
",",
"pos_loss",
")",
":",
"self",
".",
"_pos_loss",
"=",
"pos_loss"
] | https://github.com/BitMEX/api-connectors/blob/37a3a5b806ad5d0e0fc975ab86d9ed43c3bcd812/auto-generated/python/swagger_client/models/position.py#L1648-L1656 | ||
oracle/graaljs | 36a56e8e993d45fc40939a3a4d9c0c24990720f1 | graal-nodejs/deps/npm/node_modules/node-gyp/gyp/pylib/gyp/MSVSVersion.py | python | _DetectVisualStudioVersions | (versions_to_check, force_express) | return versions | Collect the list of installed visual studio versions.
Returns:
A list of visual studio versions installed in descending order of
usage preference.
Base this on the registry and a quick check if devenv.exe exists.
Possibilities are:
2005(e) - Visual Studio 2005 (8)
2008(e) - Visual Studio 2008 (9)
2010(e) - Visual Studio 2010 (10)
2012(e) - Visual Studio 2012 (11)
2013(e) - Visual Studio 2013 (12)
2015 - Visual Studio 2015 (14)
2017 - Visual Studio 2017 (15)
2019 - Visual Studio 2019 (16)
2022 - Visual Studio 2022 (17)
Where (e) is e for express editions of MSVS and blank otherwise. | Collect the list of installed visual studio versions. | [
"Collect",
"the",
"list",
"of",
"installed",
"visual",
"studio",
"versions",
"."
] | def _DetectVisualStudioVersions(versions_to_check, force_express):
"""Collect the list of installed visual studio versions.
Returns:
A list of visual studio versions installed in descending order of
usage preference.
Base this on the registry and a quick check if devenv.exe exists.
Possibilities are:
2005(e) - Visual Studio 2005 (8)
2008(e) - Visual Studio 2008 (9)
2010(e) - Visual Studio 2010 (10)
2012(e) - Visual Studio 2012 (11)
2013(e) - Visual Studio 2013 (12)
2015 - Visual Studio 2015 (14)
2017 - Visual Studio 2017 (15)
2019 - Visual Studio 2019 (16)
2022 - Visual Studio 2022 (17)
Where (e) is e for express editions of MSVS and blank otherwise.
"""
version_to_year = {
"8.0": "2005",
"9.0": "2008",
"10.0": "2010",
"11.0": "2012",
"12.0": "2013",
"14.0": "2015",
"15.0": "2017",
"16.0": "2019",
"17.0": "2022",
}
versions = []
for version in versions_to_check:
# Old method of searching for which VS version is installed
# We don't use the 2010-encouraged-way because we also want to get the
# path to the binaries, which it doesn't offer.
keys = [
r"HKLM\Software\Microsoft\VisualStudio\%s" % version,
r"HKLM\Software\Wow6432Node\Microsoft\VisualStudio\%s" % version,
r"HKLM\Software\Microsoft\VCExpress\%s" % version,
r"HKLM\Software\Wow6432Node\Microsoft\VCExpress\%s" % version,
]
for index in range(len(keys)):
path = _RegistryGetValue(keys[index], "InstallDir")
if not path:
continue
path = _ConvertToCygpath(path)
# Check for full.
full_path = os.path.join(path, "devenv.exe")
express_path = os.path.join(path, "*express.exe")
if not force_express and os.path.exists(full_path):
# Add this one.
versions.append(
_CreateVersion(
version_to_year[version], os.path.join(path, "..", "..")
)
)
# Check for express.
elif glob.glob(express_path):
# Add this one.
versions.append(
_CreateVersion(
version_to_year[version] + "e", os.path.join(path, "..", "..")
)
)
# The old method above does not work when only SDK is installed.
keys = [
r"HKLM\Software\Microsoft\VisualStudio\SxS\VC7",
r"HKLM\Software\Wow6432Node\Microsoft\VisualStudio\SxS\VC7",
r"HKLM\Software\Microsoft\VisualStudio\SxS\VS7",
r"HKLM\Software\Wow6432Node\Microsoft\VisualStudio\SxS\VS7",
]
for index in range(len(keys)):
path = _RegistryGetValue(keys[index], version)
if not path:
continue
path = _ConvertToCygpath(path)
if version == "15.0":
if os.path.exists(path):
versions.append(_CreateVersion("2017", path))
elif version != "14.0": # There is no Express edition for 2015.
versions.append(
_CreateVersion(
version_to_year[version] + "e",
os.path.join(path, ".."),
sdk_based=True,
)
)
return versions | [
"def",
"_DetectVisualStudioVersions",
"(",
"versions_to_check",
",",
"force_express",
")",
":",
"version_to_year",
"=",
"{",
"\"8.0\"",
":",
"\"2005\"",
",",
"\"9.0\"",
":",
"\"2008\"",
",",
"\"10.0\"",
":",
"\"2010\"",
",",
"\"11.0\"",
":",
"\"2012\"",
",",
"\"12.0\"",
":",
"\"2013\"",
",",
"\"14.0\"",
":",
"\"2015\"",
",",
"\"15.0\"",
":",
"\"2017\"",
",",
"\"16.0\"",
":",
"\"2019\"",
",",
"\"17.0\"",
":",
"\"2022\"",
",",
"}",
"versions",
"=",
"[",
"]",
"for",
"version",
"in",
"versions_to_check",
":",
"# Old method of searching for which VS version is installed",
"# We don't use the 2010-encouraged-way because we also want to get the",
"# path to the binaries, which it doesn't offer.",
"keys",
"=",
"[",
"r\"HKLM\\Software\\Microsoft\\VisualStudio\\%s\"",
"%",
"version",
",",
"r\"HKLM\\Software\\Wow6432Node\\Microsoft\\VisualStudio\\%s\"",
"%",
"version",
",",
"r\"HKLM\\Software\\Microsoft\\VCExpress\\%s\"",
"%",
"version",
",",
"r\"HKLM\\Software\\Wow6432Node\\Microsoft\\VCExpress\\%s\"",
"%",
"version",
",",
"]",
"for",
"index",
"in",
"range",
"(",
"len",
"(",
"keys",
")",
")",
":",
"path",
"=",
"_RegistryGetValue",
"(",
"keys",
"[",
"index",
"]",
",",
"\"InstallDir\"",
")",
"if",
"not",
"path",
":",
"continue",
"path",
"=",
"_ConvertToCygpath",
"(",
"path",
")",
"# Check for full.",
"full_path",
"=",
"os",
".",
"path",
".",
"join",
"(",
"path",
",",
"\"devenv.exe\"",
")",
"express_path",
"=",
"os",
".",
"path",
".",
"join",
"(",
"path",
",",
"\"*express.exe\"",
")",
"if",
"not",
"force_express",
"and",
"os",
".",
"path",
".",
"exists",
"(",
"full_path",
")",
":",
"# Add this one.",
"versions",
".",
"append",
"(",
"_CreateVersion",
"(",
"version_to_year",
"[",
"version",
"]",
",",
"os",
".",
"path",
".",
"join",
"(",
"path",
",",
"\"..\"",
",",
"\"..\"",
")",
")",
")",
"# Check for express.",
"elif",
"glob",
".",
"glob",
"(",
"express_path",
")",
":",
"# Add this one.",
"versions",
".",
"append",
"(",
"_CreateVersion",
"(",
"version_to_year",
"[",
"version",
"]",
"+",
"\"e\"",
",",
"os",
".",
"path",
".",
"join",
"(",
"path",
",",
"\"..\"",
",",
"\"..\"",
")",
")",
")",
"# The old method above does not work when only SDK is installed.",
"keys",
"=",
"[",
"r\"HKLM\\Software\\Microsoft\\VisualStudio\\SxS\\VC7\"",
",",
"r\"HKLM\\Software\\Wow6432Node\\Microsoft\\VisualStudio\\SxS\\VC7\"",
",",
"r\"HKLM\\Software\\Microsoft\\VisualStudio\\SxS\\VS7\"",
",",
"r\"HKLM\\Software\\Wow6432Node\\Microsoft\\VisualStudio\\SxS\\VS7\"",
",",
"]",
"for",
"index",
"in",
"range",
"(",
"len",
"(",
"keys",
")",
")",
":",
"path",
"=",
"_RegistryGetValue",
"(",
"keys",
"[",
"index",
"]",
",",
"version",
")",
"if",
"not",
"path",
":",
"continue",
"path",
"=",
"_ConvertToCygpath",
"(",
"path",
")",
"if",
"version",
"==",
"\"15.0\"",
":",
"if",
"os",
".",
"path",
".",
"exists",
"(",
"path",
")",
":",
"versions",
".",
"append",
"(",
"_CreateVersion",
"(",
"\"2017\"",
",",
"path",
")",
")",
"elif",
"version",
"!=",
"\"14.0\"",
":",
"# There is no Express edition for 2015.",
"versions",
".",
"append",
"(",
"_CreateVersion",
"(",
"version_to_year",
"[",
"version",
"]",
"+",
"\"e\"",
",",
"os",
".",
"path",
".",
"join",
"(",
"path",
",",
"\"..\"",
")",
",",
"sdk_based",
"=",
"True",
",",
")",
")",
"return",
"versions"
] | https://github.com/oracle/graaljs/blob/36a56e8e993d45fc40939a3a4d9c0c24990720f1/graal-nodejs/deps/npm/node_modules/node-gyp/gyp/pylib/gyp/MSVSVersion.py#L435-L524 | |
wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/distutils/ccompiler.py | python | CCompiler.add_library_dir | (self, dir) | Add 'dir' to the list of directories that will be searched for
libraries specified to 'add_library()' and 'set_libraries()'. The
linker will be instructed to search for libraries in the order they
are supplied to 'add_library_dir()' and/or 'set_library_dirs()'. | Add 'dir' to the list of directories that will be searched for
libraries specified to 'add_library()' and 'set_libraries()'. The
linker will be instructed to search for libraries in the order they
are supplied to 'add_library_dir()' and/or 'set_library_dirs()'. | [
"Add",
"dir",
"to",
"the",
"list",
"of",
"directories",
"that",
"will",
"be",
"searched",
"for",
"libraries",
"specified",
"to",
"add_library",
"()",
"and",
"set_libraries",
"()",
".",
"The",
"linker",
"will",
"be",
"instructed",
"to",
"search",
"for",
"libraries",
"in",
"the",
"order",
"they",
"are",
"supplied",
"to",
"add_library_dir",
"()",
"and",
"/",
"or",
"set_library_dirs",
"()",
"."
] | def add_library_dir(self, dir):
"""Add 'dir' to the list of directories that will be searched for
libraries specified to 'add_library()' and 'set_libraries()'. The
linker will be instructed to search for libraries in the order they
are supplied to 'add_library_dir()' and/or 'set_library_dirs()'.
"""
self.library_dirs.append(dir) | [
"def",
"add_library_dir",
"(",
"self",
",",
"dir",
")",
":",
"self",
".",
"library_dirs",
".",
"append",
"(",
"dir",
")"
] | https://github.com/wlanjie/AndroidFFmpeg/blob/7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf/tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/distutils/ccompiler.py#L272-L278 | ||
natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/contrib/distributions/python/ops/mvn.py | python | MultivariateNormalOperatorPD.sample_n | (self, n, seed=None, name="sample_n") | Sample `n` observations from the Multivariate Normal Distributions.
Args:
n: `Scalar`, type int32, the number of observations to sample.
seed: Python integer, the random seed.
name: The name to give this op.
Returns:
samples: `[n, ...]`, a `Tensor` of `n` samples for each
of the distributions determined by broadcasting the hyperparameters. | Sample `n` observations from the Multivariate Normal Distributions. | [
"Sample",
"n",
"observations",
"from",
"the",
"Multivariate",
"Normal",
"Distributions",
"."
] | def sample_n(self, n, seed=None, name="sample_n"):
"""Sample `n` observations from the Multivariate Normal Distributions.
Args:
n: `Scalar`, type int32, the number of observations to sample.
seed: Python integer, the random seed.
name: The name to give this op.
Returns:
samples: `[n, ...]`, a `Tensor` of `n` samples for each
of the distributions determined by broadcasting the hyperparameters.
"""
with ops.name_scope(self.name):
with ops.op_scope([self._mu, n] + self._cov.inputs, name):
# Recall _check_mu ensures mu and self._cov have same batch shape.
broadcast_shape = self.mu.get_shape()
n = ops.convert_to_tensor(n)
shape = array_ops.concat(0, [self._cov.vector_shape(), [n]])
white_samples = random_ops.random_normal(shape=shape,
mean=0,
stddev=1,
dtype=self.dtype,
seed=seed)
correlated_samples = self._cov.sqrt_matmul(white_samples)
# Move the last dimension to the front
perm = array_ops.concat(0, (
array_ops.pack([array_ops.rank(correlated_samples) - 1]),
math_ops.range(0, array_ops.rank(correlated_samples) - 1)))
# TODO(ebrevdo): Once we get a proper tensor contraction op,
# perform the inner product using that instead of batch_matmul
# and this slow transpose can go away!
correlated_samples = array_ops.transpose(correlated_samples, perm)
samples = correlated_samples + self.mu
# Provide some hints to shape inference
n_val = tensor_util.constant_value(n)
final_shape = tensor_shape.vector(n_val).concatenate(broadcast_shape)
samples.set_shape(final_shape)
return samples | [
"def",
"sample_n",
"(",
"self",
",",
"n",
",",
"seed",
"=",
"None",
",",
"name",
"=",
"\"sample_n\"",
")",
":",
"with",
"ops",
".",
"name_scope",
"(",
"self",
".",
"name",
")",
":",
"with",
"ops",
".",
"op_scope",
"(",
"[",
"self",
".",
"_mu",
",",
"n",
"]",
"+",
"self",
".",
"_cov",
".",
"inputs",
",",
"name",
")",
":",
"# Recall _check_mu ensures mu and self._cov have same batch shape.",
"broadcast_shape",
"=",
"self",
".",
"mu",
".",
"get_shape",
"(",
")",
"n",
"=",
"ops",
".",
"convert_to_tensor",
"(",
"n",
")",
"shape",
"=",
"array_ops",
".",
"concat",
"(",
"0",
",",
"[",
"self",
".",
"_cov",
".",
"vector_shape",
"(",
")",
",",
"[",
"n",
"]",
"]",
")",
"white_samples",
"=",
"random_ops",
".",
"random_normal",
"(",
"shape",
"=",
"shape",
",",
"mean",
"=",
"0",
",",
"stddev",
"=",
"1",
",",
"dtype",
"=",
"self",
".",
"dtype",
",",
"seed",
"=",
"seed",
")",
"correlated_samples",
"=",
"self",
".",
"_cov",
".",
"sqrt_matmul",
"(",
"white_samples",
")",
"# Move the last dimension to the front",
"perm",
"=",
"array_ops",
".",
"concat",
"(",
"0",
",",
"(",
"array_ops",
".",
"pack",
"(",
"[",
"array_ops",
".",
"rank",
"(",
"correlated_samples",
")",
"-",
"1",
"]",
")",
",",
"math_ops",
".",
"range",
"(",
"0",
",",
"array_ops",
".",
"rank",
"(",
"correlated_samples",
")",
"-",
"1",
")",
")",
")",
"# TODO(ebrevdo): Once we get a proper tensor contraction op,",
"# perform the inner product using that instead of batch_matmul",
"# and this slow transpose can go away!",
"correlated_samples",
"=",
"array_ops",
".",
"transpose",
"(",
"correlated_samples",
",",
"perm",
")",
"samples",
"=",
"correlated_samples",
"+",
"self",
".",
"mu",
"# Provide some hints to shape inference",
"n_val",
"=",
"tensor_util",
".",
"constant_value",
"(",
"n",
")",
"final_shape",
"=",
"tensor_shape",
".",
"vector",
"(",
"n_val",
")",
".",
"concatenate",
"(",
"broadcast_shape",
")",
"samples",
".",
"set_shape",
"(",
"final_shape",
")",
"return",
"samples"
] | https://github.com/natanielruiz/android-yolo/blob/1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f/jni-build/jni/include/tensorflow/contrib/distributions/python/ops/mvn.py#L347-L391 | ||
bumptop/BumpTop | 466d23597a07ae738f4265262fa01087fc6e257c | trunk/win/Source/bin/jinja2/compiler.py | python | UndeclaredNameVisitor.visit_Block | (self, node) | Stop visiting a blocks. | Stop visiting a blocks. | [
"Stop",
"visiting",
"a",
"blocks",
"."
] | def visit_Block(self, node):
"""Stop visiting a blocks.""" | [
"def",
"visit_Block",
"(",
"self",
",",
"node",
")",
":"
] | https://github.com/bumptop/BumpTop/blob/466d23597a07ae738f4265262fa01087fc6e257c/trunk/win/Source/bin/jinja2/compiler.py#L247-L248 | ||
tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/distribute/input_lib.py | python | DistributedDataset.__init__ | (self,
input_workers,
strategy,
dataset=None,
num_replicas_in_sync=None,
input_context=None,
components=None,
element_spec=None,
enable_get_next_as_optional=None,
build=True,
options=None) | Distribute the dataset on all workers.
If `num_replicas_in_sync` is not None, we split each batch of the dataset
into `num_replicas_in_sync` smaller batches, to be distributed among that
worker's replicas, so that the batch size for a global step (across all
workers and replicas) is as expected.
Args:
input_workers: an `InputWorkers` object.
strategy: a `tf.distribute.Strategy` object, used to run all-reduce to
handle last partial batch.
dataset: `tf.data.Dataset` that will be used as the input source. Either
dataset or components field should be passed when constructing
DistributedDataset. Use this when contructing DistributedDataset from a
new `tf.data.Dataset`. Use components when constructing using
DistributedDatasetSpec.
num_replicas_in_sync: Optional integer. If this is not None, the value
is used to decide how to rebatch datasets into smaller batches so that
the total batch size for each step (across all workers and replicas)
adds up to `dataset`'s batch size.
input_context: `InputContext` for sharding. Only pass this in for between
graph multi-worker cases where there is only one `input_worker`. In
these cases, we will shard based on the `input_pipeline_id` and
`num_input_pipelines` in the `InputContext`.
components: datasets when DistributedDataset is constructed from
DistributedDatasetSpec. Either field dataset or components should be
passed.
element_spec: element spec for DistributedDataset when constructing from
DistributedDatasetSpec. This will be used to set the element_spec for
DistributedDataset and verified against element_spec from components.
enable_get_next_as_optional: this is required when components is passed
instead of dataset.
build: whether to build underlying datasets when this object is created.
This is only useful for `ParameterServerStrategy` now.
options: `tf.distribute.InputOptions` used to control options on how this
dataset is distributed. | Distribute the dataset on all workers. | [
"Distribute",
"the",
"dataset",
"on",
"all",
"workers",
"."
] | def __init__(self,
input_workers,
strategy,
dataset=None,
num_replicas_in_sync=None,
input_context=None,
components=None,
element_spec=None,
enable_get_next_as_optional=None,
build=True,
options=None):
"""Distribute the dataset on all workers.
If `num_replicas_in_sync` is not None, we split each batch of the dataset
into `num_replicas_in_sync` smaller batches, to be distributed among that
worker's replicas, so that the batch size for a global step (across all
workers and replicas) is as expected.
Args:
input_workers: an `InputWorkers` object.
strategy: a `tf.distribute.Strategy` object, used to run all-reduce to
handle last partial batch.
dataset: `tf.data.Dataset` that will be used as the input source. Either
dataset or components field should be passed when constructing
DistributedDataset. Use this when contructing DistributedDataset from a
new `tf.data.Dataset`. Use components when constructing using
DistributedDatasetSpec.
num_replicas_in_sync: Optional integer. If this is not None, the value
is used to decide how to rebatch datasets into smaller batches so that
the total batch size for each step (across all workers and replicas)
adds up to `dataset`'s batch size.
input_context: `InputContext` for sharding. Only pass this in for between
graph multi-worker cases where there is only one `input_worker`. In
these cases, we will shard based on the `input_pipeline_id` and
`num_input_pipelines` in the `InputContext`.
components: datasets when DistributedDataset is constructed from
DistributedDatasetSpec. Either field dataset or components should be
passed.
element_spec: element spec for DistributedDataset when constructing from
DistributedDatasetSpec. This will be used to set the element_spec for
DistributedDataset and verified against element_spec from components.
enable_get_next_as_optional: this is required when components is passed
instead of dataset.
build: whether to build underlying datasets when this object is created.
This is only useful for `ParameterServerStrategy` now.
options: `tf.distribute.InputOptions` used to control options on how this
dataset is distributed.
"""
super(DistributedDataset, self).__init__(input_workers=input_workers)
if input_workers is None or strategy is None:
raise ValueError("input_workers and strategy are required arguments")
if dataset is not None and components is not None:
raise ValueError("Only one of dataset or components should be present")
if dataset is None and components is None:
raise ValueError("At least one of dataset or components should be passed")
self._input_workers = input_workers
self._strategy = strategy
self._options = options
self._input_context = input_context
self._num_replicas_in_sync = num_replicas_in_sync
if dataset is not None:
self._original_dataset = dataset
self._built = False
if build:
self.build()
else:
if not build:
raise ValueError(
"When constructing DistributedDataset with components, build "
"should not be False. This is an internal error. Please file a "
"bug.")
if enable_get_next_as_optional is None:
raise ValueError(
"When constructing DistributedDataset with components, " +
"enable_get_next_as_optional should also be passed")
self._cloned_datasets = components
self._cardinality = _cardinality(self._cloned_datasets[0])
self._enable_get_next_as_optional = enable_get_next_as_optional
assert element_spec is not None
if element_spec != _create_distributed_tensor_spec(
self._strategy, self._cloned_datasets[0].element_spec):
raise ValueError("Mismatched element_spec from the passed components")
self._element_spec = element_spec
self._built = True | [
"def",
"__init__",
"(",
"self",
",",
"input_workers",
",",
"strategy",
",",
"dataset",
"=",
"None",
",",
"num_replicas_in_sync",
"=",
"None",
",",
"input_context",
"=",
"None",
",",
"components",
"=",
"None",
",",
"element_spec",
"=",
"None",
",",
"enable_get_next_as_optional",
"=",
"None",
",",
"build",
"=",
"True",
",",
"options",
"=",
"None",
")",
":",
"super",
"(",
"DistributedDataset",
",",
"self",
")",
".",
"__init__",
"(",
"input_workers",
"=",
"input_workers",
")",
"if",
"input_workers",
"is",
"None",
"or",
"strategy",
"is",
"None",
":",
"raise",
"ValueError",
"(",
"\"input_workers and strategy are required arguments\"",
")",
"if",
"dataset",
"is",
"not",
"None",
"and",
"components",
"is",
"not",
"None",
":",
"raise",
"ValueError",
"(",
"\"Only one of dataset or components should be present\"",
")",
"if",
"dataset",
"is",
"None",
"and",
"components",
"is",
"None",
":",
"raise",
"ValueError",
"(",
"\"At least one of dataset or components should be passed\"",
")",
"self",
".",
"_input_workers",
"=",
"input_workers",
"self",
".",
"_strategy",
"=",
"strategy",
"self",
".",
"_options",
"=",
"options",
"self",
".",
"_input_context",
"=",
"input_context",
"self",
".",
"_num_replicas_in_sync",
"=",
"num_replicas_in_sync",
"if",
"dataset",
"is",
"not",
"None",
":",
"self",
".",
"_original_dataset",
"=",
"dataset",
"self",
".",
"_built",
"=",
"False",
"if",
"build",
":",
"self",
".",
"build",
"(",
")",
"else",
":",
"if",
"not",
"build",
":",
"raise",
"ValueError",
"(",
"\"When constructing DistributedDataset with components, build \"",
"\"should not be False. This is an internal error. Please file a \"",
"\"bug.\"",
")",
"if",
"enable_get_next_as_optional",
"is",
"None",
":",
"raise",
"ValueError",
"(",
"\"When constructing DistributedDataset with components, \"",
"+",
"\"enable_get_next_as_optional should also be passed\"",
")",
"self",
".",
"_cloned_datasets",
"=",
"components",
"self",
".",
"_cardinality",
"=",
"_cardinality",
"(",
"self",
".",
"_cloned_datasets",
"[",
"0",
"]",
")",
"self",
".",
"_enable_get_next_as_optional",
"=",
"enable_get_next_as_optional",
"assert",
"element_spec",
"is",
"not",
"None",
"if",
"element_spec",
"!=",
"_create_distributed_tensor_spec",
"(",
"self",
".",
"_strategy",
",",
"self",
".",
"_cloned_datasets",
"[",
"0",
"]",
".",
"element_spec",
")",
":",
"raise",
"ValueError",
"(",
"\"Mismatched element_spec from the passed components\"",
")",
"self",
".",
"_element_spec",
"=",
"element_spec",
"self",
".",
"_built",
"=",
"True"
] | https://github.com/tensorflow/tensorflow/blob/419e3a6b650ea4bd1b0cba23c4348f8a69f3272e/tensorflow/python/distribute/input_lib.py#L960-L1047 | ||
strukturag/libheif | 0082fea96ee70a20c8906a0373bedec0c01777bc | scripts/cpplint.py | python | _IncludeState.CheckNextIncludeOrder | (self, header_type) | return '' | Returns a non-empty error message if the next header is out of order.
This function also updates the internal state to be ready to check
the next include.
Args:
header_type: One of the _XXX_HEADER constants defined above.
Returns:
The empty string if the header is in the right order, or an
error message describing what's wrong. | Returns a non-empty error message if the next header is out of order. | [
"Returns",
"a",
"non",
"-",
"empty",
"error",
"message",
"if",
"the",
"next",
"header",
"is",
"out",
"of",
"order",
"."
] | def CheckNextIncludeOrder(self, header_type):
"""Returns a non-empty error message if the next header is out of order.
This function also updates the internal state to be ready to check
the next include.
Args:
header_type: One of the _XXX_HEADER constants defined above.
Returns:
The empty string if the header is in the right order, or an
error message describing what's wrong.
"""
error_message = ('Found %s after %s' %
(self._TYPE_NAMES[header_type],
self._SECTION_NAMES[self._section]))
last_section = self._section
if header_type == _C_SYS_HEADER:
if self._section <= self._C_SECTION:
self._section = self._C_SECTION
else:
self._last_header = ''
return error_message
elif header_type == _CPP_SYS_HEADER:
if self._section <= self._CPP_SECTION:
self._section = self._CPP_SECTION
else:
self._last_header = ''
return error_message
elif header_type == _LIKELY_MY_HEADER:
if self._section <= self._MY_H_SECTION:
self._section = self._MY_H_SECTION
else:
self._section = self._OTHER_H_SECTION
elif header_type == _POSSIBLE_MY_HEADER:
if self._section <= self._MY_H_SECTION:
self._section = self._MY_H_SECTION
else:
# This will always be the fallback because we're not sure
# enough that the header is associated with this file.
self._section = self._OTHER_H_SECTION
else:
assert header_type == _OTHER_HEADER
self._section = self._OTHER_H_SECTION
if last_section != self._section:
self._last_header = ''
return '' | [
"def",
"CheckNextIncludeOrder",
"(",
"self",
",",
"header_type",
")",
":",
"error_message",
"=",
"(",
"'Found %s after %s'",
"%",
"(",
"self",
".",
"_TYPE_NAMES",
"[",
"header_type",
"]",
",",
"self",
".",
"_SECTION_NAMES",
"[",
"self",
".",
"_section",
"]",
")",
")",
"last_section",
"=",
"self",
".",
"_section",
"if",
"header_type",
"==",
"_C_SYS_HEADER",
":",
"if",
"self",
".",
"_section",
"<=",
"self",
".",
"_C_SECTION",
":",
"self",
".",
"_section",
"=",
"self",
".",
"_C_SECTION",
"else",
":",
"self",
".",
"_last_header",
"=",
"''",
"return",
"error_message",
"elif",
"header_type",
"==",
"_CPP_SYS_HEADER",
":",
"if",
"self",
".",
"_section",
"<=",
"self",
".",
"_CPP_SECTION",
":",
"self",
".",
"_section",
"=",
"self",
".",
"_CPP_SECTION",
"else",
":",
"self",
".",
"_last_header",
"=",
"''",
"return",
"error_message",
"elif",
"header_type",
"==",
"_LIKELY_MY_HEADER",
":",
"if",
"self",
".",
"_section",
"<=",
"self",
".",
"_MY_H_SECTION",
":",
"self",
".",
"_section",
"=",
"self",
".",
"_MY_H_SECTION",
"else",
":",
"self",
".",
"_section",
"=",
"self",
".",
"_OTHER_H_SECTION",
"elif",
"header_type",
"==",
"_POSSIBLE_MY_HEADER",
":",
"if",
"self",
".",
"_section",
"<=",
"self",
".",
"_MY_H_SECTION",
":",
"self",
".",
"_section",
"=",
"self",
".",
"_MY_H_SECTION",
"else",
":",
"# This will always be the fallback because we're not sure",
"# enough that the header is associated with this file.",
"self",
".",
"_section",
"=",
"self",
".",
"_OTHER_H_SECTION",
"else",
":",
"assert",
"header_type",
"==",
"_OTHER_HEADER",
"self",
".",
"_section",
"=",
"self",
".",
"_OTHER_H_SECTION",
"if",
"last_section",
"!=",
"self",
".",
"_section",
":",
"self",
".",
"_last_header",
"=",
"''",
"return",
"''"
] | https://github.com/strukturag/libheif/blob/0082fea96ee70a20c8906a0373bedec0c01777bc/scripts/cpplint.py#L773-L824 | |
emsesp/EMS-ESP | 65c4a381bf8df61d1e18ba00223b1a55933fc547 | scripts/esptool.py | python | ESP32FirmwareImage.default_output_name | (self, input_file) | return "%s.bin" % (os.path.splitext(input_file)[0]) | Derive a default output name from the ELF name. | Derive a default output name from the ELF name. | [
"Derive",
"a",
"default",
"output",
"name",
"from",
"the",
"ELF",
"name",
"."
] | def default_output_name(self, input_file):
""" Derive a default output name from the ELF name. """
return "%s.bin" % (os.path.splitext(input_file)[0]) | [
"def",
"default_output_name",
"(",
"self",
",",
"input_file",
")",
":",
"return",
"\"%s.bin\"",
"%",
"(",
"os",
".",
"path",
".",
"splitext",
"(",
"input_file",
")",
"[",
"0",
"]",
")"
] | https://github.com/emsesp/EMS-ESP/blob/65c4a381bf8df61d1e18ba00223b1a55933fc547/scripts/esptool.py#L1606-L1608 | |
papyrussolution/OpenPapyrus | bbfb5ec2ea2109b8e2f125edd838e12eaf7b8b91 | Src/OSF/protobuf-3.19.1/python/mox.py | python | Regex.equals | (self, rhs) | return self.regex.search(rhs) is not None | Check to see if rhs matches regular expression pattern.
Returns:
bool | Check to see if rhs matches regular expression pattern. | [
"Check",
"to",
"see",
"if",
"rhs",
"matches",
"regular",
"expression",
"pattern",
"."
] | def equals(self, rhs):
"""Check to see if rhs matches regular expression pattern.
Returns:
bool
"""
return self.regex.search(rhs) is not None | [
"def",
"equals",
"(",
"self",
",",
"rhs",
")",
":",
"return",
"self",
".",
"regex",
".",
"search",
"(",
"rhs",
")",
"is",
"not",
"None"
] | https://github.com/papyrussolution/OpenPapyrus/blob/bbfb5ec2ea2109b8e2f125edd838e12eaf7b8b91/Src/OSF/protobuf-3.19.1/python/mox.py#L922-L929 | |
greatscottgadgets/gr-bluetooth | c2a7d7d810e047f8a18902a4e3d1a152420655bb | docs/doxygen/doxyxml/base.py | python | Base._get_dict_members | (self, cat=None) | return self._dict_members[cat] | For given category a dictionary is returned mapping member names to
members of that category. For names that are duplicated the name is
mapped to None. | For given category a dictionary is returned mapping member names to
members of that category. For names that are duplicated the name is
mapped to None. | [
"For",
"given",
"category",
"a",
"dictionary",
"is",
"returned",
"mapping",
"member",
"names",
"to",
"members",
"of",
"that",
"category",
".",
"For",
"names",
"that",
"are",
"duplicated",
"the",
"name",
"is",
"mapped",
"to",
"None",
"."
] | def _get_dict_members(self, cat=None):
"""
For given category a dictionary is returned mapping member names to
members of that category. For names that are duplicated the name is
mapped to None.
"""
self.confirm_no_error()
if cat not in self._dict_members:
new_dict = {}
for mem in self.in_category(cat):
if mem.name() not in new_dict:
new_dict[mem.name()] = mem
else:
new_dict[mem.name()] = self.Duplicate
self._dict_members[cat] = new_dict
return self._dict_members[cat] | [
"def",
"_get_dict_members",
"(",
"self",
",",
"cat",
"=",
"None",
")",
":",
"self",
".",
"confirm_no_error",
"(",
")",
"if",
"cat",
"not",
"in",
"self",
".",
"_dict_members",
":",
"new_dict",
"=",
"{",
"}",
"for",
"mem",
"in",
"self",
".",
"in_category",
"(",
"cat",
")",
":",
"if",
"mem",
".",
"name",
"(",
")",
"not",
"in",
"new_dict",
":",
"new_dict",
"[",
"mem",
".",
"name",
"(",
")",
"]",
"=",
"mem",
"else",
":",
"new_dict",
"[",
"mem",
".",
"name",
"(",
")",
"]",
"=",
"self",
".",
"Duplicate",
"self",
".",
"_dict_members",
"[",
"cat",
"]",
"=",
"new_dict",
"return",
"self",
".",
"_dict_members",
"[",
"cat",
"]"
] | https://github.com/greatscottgadgets/gr-bluetooth/blob/c2a7d7d810e047f8a18902a4e3d1a152420655bb/docs/doxygen/doxyxml/base.py#L122-L137 | |
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/build/waf-1.7.13/waflib/extras/pgicc.py | python | get_pgi_version | (conf, cc) | return version[0] | Find the version of a pgi compiler. | Find the version of a pgi compiler. | [
"Find",
"the",
"version",
"of",
"a",
"pgi",
"compiler",
"."
] | def get_pgi_version(conf, cc):
"""Find the version of a pgi compiler."""
version_re = re.compile(r"The Portland Group", re.I).search
cmd = cc + ['-V', '-E'] # Issue 1078, prevent wrappers from linking
try:
out, err = conf.cmd_and_log(cmd, output=0)
except Exception:
conf.fatal('Could not find pgi compiler %r' % cmd)
if out: match = version_re(out)
else: match = version_re(err)
if not match:
conf.fatal('Could not verify PGI signature')
cmd = cc + ['-help=variable']
try:
out, err = conf.cmd_and_log(cmd, output=0)
except Exception:
conf.fatal('Could not find pgi compiler %r' % cmd)
version = re.findall('^COMPVER\s*=(.*)', out, re.M)
if len(version) != 1:
conf.fatal('Could not determine the compiler version')
return version[0] | [
"def",
"get_pgi_version",
"(",
"conf",
",",
"cc",
")",
":",
"version_re",
"=",
"re",
".",
"compile",
"(",
"r\"The Portland Group\"",
",",
"re",
".",
"I",
")",
".",
"search",
"cmd",
"=",
"cc",
"+",
"[",
"'-V'",
",",
"'-E'",
"]",
"# Issue 1078, prevent wrappers from linking",
"try",
":",
"out",
",",
"err",
"=",
"conf",
".",
"cmd_and_log",
"(",
"cmd",
",",
"output",
"=",
"0",
")",
"except",
"Exception",
":",
"conf",
".",
"fatal",
"(",
"'Could not find pgi compiler %r'",
"%",
"cmd",
")",
"if",
"out",
":",
"match",
"=",
"version_re",
"(",
"out",
")",
"else",
":",
"match",
"=",
"version_re",
"(",
"err",
")",
"if",
"not",
"match",
":",
"conf",
".",
"fatal",
"(",
"'Could not verify PGI signature'",
")",
"cmd",
"=",
"cc",
"+",
"[",
"'-help=variable'",
"]",
"try",
":",
"out",
",",
"err",
"=",
"conf",
".",
"cmd_and_log",
"(",
"cmd",
",",
"output",
"=",
"0",
")",
"except",
"Exception",
":",
"conf",
".",
"fatal",
"(",
"'Could not find pgi compiler %r'",
"%",
"cmd",
")",
"version",
"=",
"re",
".",
"findall",
"(",
"'^COMPVER\\s*=(.*)'",
",",
"out",
",",
"re",
".",
"M",
")",
"if",
"len",
"(",
"version",
")",
"!=",
"1",
":",
"conf",
".",
"fatal",
"(",
"'Could not determine the compiler version'",
")",
"return",
"version",
"[",
"0",
"]"
] | https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/build/waf-1.7.13/waflib/extras/pgicc.py#L35-L60 | |
Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/keras/engine/data_adapter.py | python | _type_name | (x) | return str(type(x)) | Generates a description of the type of an object. | Generates a description of the type of an object. | [
"Generates",
"a",
"description",
"of",
"the",
"type",
"of",
"an",
"object",
"."
] | def _type_name(x):
"""Generates a description of the type of an object."""
if isinstance(x, dict):
key_types = set(_type_name(key) for key in x.keys())
val_types = set(_type_name(key) for key in x.values())
return "({} containing {} keys and {} values)".format(
type(x), key_types, val_types)
if isinstance(x, (list, tuple)):
types = set(_type_name(val) for val in x)
return "({} containing values of types {})".format(
type(x), types)
return str(type(x)) | [
"def",
"_type_name",
"(",
"x",
")",
":",
"if",
"isinstance",
"(",
"x",
",",
"dict",
")",
":",
"key_types",
"=",
"set",
"(",
"_type_name",
"(",
"key",
")",
"for",
"key",
"in",
"x",
".",
"keys",
"(",
")",
")",
"val_types",
"=",
"set",
"(",
"_type_name",
"(",
"key",
")",
"for",
"key",
"in",
"x",
".",
"values",
"(",
")",
")",
"return",
"\"({} containing {} keys and {} values)\"",
".",
"format",
"(",
"type",
"(",
"x",
")",
",",
"key_types",
",",
"val_types",
")",
"if",
"isinstance",
"(",
"x",
",",
"(",
"list",
",",
"tuple",
")",
")",
":",
"types",
"=",
"set",
"(",
"_type_name",
"(",
"val",
")",
"for",
"val",
"in",
"x",
")",
"return",
"\"({} containing values of types {})\"",
".",
"format",
"(",
"type",
"(",
"x",
")",
",",
"types",
")",
"return",
"str",
"(",
"type",
"(",
"x",
")",
")"
] | https://github.com/Xilinx/Vitis-AI/blob/fc74d404563d9951b57245443c73bef389f3657f/tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/keras/engine/data_adapter.py#L641-L652 | |
pmq20/node-packer | 12c46c6e44fbc14d9ee645ebd17d5296b324f7e0 | current/deps/v8/tools/run_perf.py | python | ResultTracker.HasEnoughRuns | (self, graph_config, confidence_level) | return confidence_level * mean_stderr < mean / 1000.0 | Checks if the mean of the results for a given trace config is within
0.1% of the true value with the specified confidence level.
This assumes Gaussian distribution of the noise and based on
https://en.wikipedia.org/wiki/68%E2%80%9395%E2%80%9399.7_rule.
Args:
graph_config: An instance of GraphConfig.
confidence_level: Number of standard deviations from the mean that all
values must lie within. Typical values are 1, 2 and 3 and correspond
to 68%, 95% and 99.7% probability that the measured value is within
0.1% of the true value.
Returns:
True if specified confidence level have been achieved. | Checks if the mean of the results for a given trace config is within
0.1% of the true value with the specified confidence level. | [
"Checks",
"if",
"the",
"mean",
"of",
"the",
"results",
"for",
"a",
"given",
"trace",
"config",
"is",
"within",
"0",
".",
"1%",
"of",
"the",
"true",
"value",
"with",
"the",
"specified",
"confidence",
"level",
"."
] | def HasEnoughRuns(self, graph_config, confidence_level):
"""Checks if the mean of the results for a given trace config is within
0.1% of the true value with the specified confidence level.
This assumes Gaussian distribution of the noise and based on
https://en.wikipedia.org/wiki/68%E2%80%9395%E2%80%9399.7_rule.
Args:
graph_config: An instance of GraphConfig.
confidence_level: Number of standard deviations from the mean that all
values must lie within. Typical values are 1, 2 and 3 and correspond
to 68%, 95% and 99.7% probability that the measured value is within
0.1% of the true value.
Returns:
True if specified confidence level have been achieved.
"""
if not isinstance(graph_config, TraceConfig):
return all(self.HasEnoughRuns(child, confidence_level)
for child in graph_config.children)
trace = self.traces.get(graph_config.name, {})
results = trace.get('results', [])
logging.debug('HasEnoughRuns for %s', graph_config.name)
if len(results) < MIN_RUNS_FOR_CONFIDENCE:
logging.debug(' Ran %d times, need at least %d',
len(results), MIN_RUNS_FOR_CONFIDENCE)
return False
logging.debug(' Results: %d entries', len(results))
mean = numpy.mean(results)
mean_stderr = numpy.std(results) / numpy.sqrt(len(results))
logging.debug(' Mean: %.2f, mean_stderr: %.2f', mean, mean_stderr)
logging.info('>>> Confidence level is %.2f', mean / (1000.0 * mean_stderr))
return confidence_level * mean_stderr < mean / 1000.0 | [
"def",
"HasEnoughRuns",
"(",
"self",
",",
"graph_config",
",",
"confidence_level",
")",
":",
"if",
"not",
"isinstance",
"(",
"graph_config",
",",
"TraceConfig",
")",
":",
"return",
"all",
"(",
"self",
".",
"HasEnoughRuns",
"(",
"child",
",",
"confidence_level",
")",
"for",
"child",
"in",
"graph_config",
".",
"children",
")",
"trace",
"=",
"self",
".",
"traces",
".",
"get",
"(",
"graph_config",
".",
"name",
",",
"{",
"}",
")",
"results",
"=",
"trace",
".",
"get",
"(",
"'results'",
",",
"[",
"]",
")",
"logging",
".",
"debug",
"(",
"'HasEnoughRuns for %s'",
",",
"graph_config",
".",
"name",
")",
"if",
"len",
"(",
"results",
")",
"<",
"MIN_RUNS_FOR_CONFIDENCE",
":",
"logging",
".",
"debug",
"(",
"' Ran %d times, need at least %d'",
",",
"len",
"(",
"results",
")",
",",
"MIN_RUNS_FOR_CONFIDENCE",
")",
"return",
"False",
"logging",
".",
"debug",
"(",
"' Results: %d entries'",
",",
"len",
"(",
"results",
")",
")",
"mean",
"=",
"numpy",
".",
"mean",
"(",
"results",
")",
"mean_stderr",
"=",
"numpy",
".",
"std",
"(",
"results",
")",
"/",
"numpy",
".",
"sqrt",
"(",
"len",
"(",
"results",
")",
")",
"logging",
".",
"debug",
"(",
"' Mean: %.2f, mean_stderr: %.2f'",
",",
"mean",
",",
"mean_stderr",
")",
"logging",
".",
"info",
"(",
"'>>> Confidence level is %.2f'",
",",
"mean",
"/",
"(",
"1000.0",
"*",
"mean_stderr",
")",
")",
"return",
"confidence_level",
"*",
"mean_stderr",
"<",
"mean",
"/",
"1000.0"
] | https://github.com/pmq20/node-packer/blob/12c46c6e44fbc14d9ee645ebd17d5296b324f7e0/current/deps/v8/tools/run_perf.py#L235-L270 | |
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemDefectReporter/v1/AWS/common-code/Lib/PIL/ImageFilter.py | python | Color3DLUT.transform | (self, callback, with_normals=False, channels=None, target_mode=None) | return type(self)(
self.size,
table,
channels=ch_out,
target_mode=target_mode or self.mode,
_copy_table=False,
) | Transforms the table values using provided callback and returns
a new LUT with altered values.
:param callback: A function which takes old lookup table values
and returns a new set of values. The number
of arguments which function should take is
``self.channels`` or ``3 + self.channels``
if ``with_normals`` flag is set.
Should return a tuple of ``self.channels`` or
``channels`` elements if it is set.
:param with_normals: If true, ``callback`` will be called with
coordinates in the color cube as the first
three arguments. Otherwise, ``callback``
will be called only with actual color values.
:param channels: The number of channels in the resulting lookup table.
:param target_mode: Passed to the constructor of the resulting
lookup table. | Transforms the table values using provided callback and returns
a new LUT with altered values. | [
"Transforms",
"the",
"table",
"values",
"using",
"provided",
"callback",
"and",
"returns",
"a",
"new",
"LUT",
"with",
"altered",
"values",
"."
] | def transform(self, callback, with_normals=False, channels=None, target_mode=None):
"""Transforms the table values using provided callback and returns
a new LUT with altered values.
:param callback: A function which takes old lookup table values
and returns a new set of values. The number
of arguments which function should take is
``self.channels`` or ``3 + self.channels``
if ``with_normals`` flag is set.
Should return a tuple of ``self.channels`` or
``channels`` elements if it is set.
:param with_normals: If true, ``callback`` will be called with
coordinates in the color cube as the first
three arguments. Otherwise, ``callback``
will be called only with actual color values.
:param channels: The number of channels in the resulting lookup table.
:param target_mode: Passed to the constructor of the resulting
lookup table.
"""
if channels not in (None, 3, 4):
raise ValueError("Only 3 or 4 output channels are supported")
ch_in = self.channels
ch_out = channels or ch_in
size1D, size2D, size3D = self.size
table = [0] * (size1D * size2D * size3D * ch_out)
idx_in = 0
idx_out = 0
for b in range(size3D):
for g in range(size2D):
for r in range(size1D):
values = self.table[idx_in : idx_in + ch_in]
if with_normals:
values = callback(
r / (size1D - 1),
g / (size2D - 1),
b / (size3D - 1),
*values,
)
else:
values = callback(*values)
table[idx_out : idx_out + ch_out] = values
idx_in += ch_in
idx_out += ch_out
return type(self)(
self.size,
table,
channels=ch_out,
target_mode=target_mode or self.mode,
_copy_table=False,
) | [
"def",
"transform",
"(",
"self",
",",
"callback",
",",
"with_normals",
"=",
"False",
",",
"channels",
"=",
"None",
",",
"target_mode",
"=",
"None",
")",
":",
"if",
"channels",
"not",
"in",
"(",
"None",
",",
"3",
",",
"4",
")",
":",
"raise",
"ValueError",
"(",
"\"Only 3 or 4 output channels are supported\"",
")",
"ch_in",
"=",
"self",
".",
"channels",
"ch_out",
"=",
"channels",
"or",
"ch_in",
"size1D",
",",
"size2D",
",",
"size3D",
"=",
"self",
".",
"size",
"table",
"=",
"[",
"0",
"]",
"*",
"(",
"size1D",
"*",
"size2D",
"*",
"size3D",
"*",
"ch_out",
")",
"idx_in",
"=",
"0",
"idx_out",
"=",
"0",
"for",
"b",
"in",
"range",
"(",
"size3D",
")",
":",
"for",
"g",
"in",
"range",
"(",
"size2D",
")",
":",
"for",
"r",
"in",
"range",
"(",
"size1D",
")",
":",
"values",
"=",
"self",
".",
"table",
"[",
"idx_in",
":",
"idx_in",
"+",
"ch_in",
"]",
"if",
"with_normals",
":",
"values",
"=",
"callback",
"(",
"r",
"/",
"(",
"size1D",
"-",
"1",
")",
",",
"g",
"/",
"(",
"size2D",
"-",
"1",
")",
",",
"b",
"/",
"(",
"size3D",
"-",
"1",
")",
",",
"*",
"values",
",",
")",
"else",
":",
"values",
"=",
"callback",
"(",
"*",
"values",
")",
"table",
"[",
"idx_out",
":",
"idx_out",
"+",
"ch_out",
"]",
"=",
"values",
"idx_in",
"+=",
"ch_in",
"idx_out",
"+=",
"ch_out",
"return",
"type",
"(",
"self",
")",
"(",
"self",
".",
"size",
",",
"table",
",",
"channels",
"=",
"ch_out",
",",
"target_mode",
"=",
"target_mode",
"or",
"self",
".",
"mode",
",",
"_copy_table",
"=",
"False",
",",
")"
] | https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemDefectReporter/v1/AWS/common-code/Lib/PIL/ImageFilter.py#L461-L512 | |
rrwick/Unicycler | 96ffea71e3a78d63ade19d6124946773e65cf129 | unicycler/assembly_graph.py | python | AssemblyGraph.find_all_simple_loops | (self) | return simple_loops | This function finds all cases of a simple loop in the graph: A->B->C->B->D.
It returns them as a list of 4-tuples of segment numbers in this order:
(start, end, middle, repeat). | This function finds all cases of a simple loop in the graph: A->B->C->B->D.
It returns them as a list of 4-tuples of segment numbers in this order:
(start, end, middle, repeat). | [
"This",
"function",
"finds",
"all",
"cases",
"of",
"a",
"simple",
"loop",
"in",
"the",
"graph",
":",
"A",
"-",
">",
"B",
"-",
">",
"C",
"-",
">",
"B",
"-",
">",
"D",
".",
"It",
"returns",
"them",
"as",
"a",
"list",
"of",
"4",
"-",
"tuples",
"of",
"segment",
"numbers",
"in",
"this",
"order",
":",
"(",
"start",
"end",
"middle",
"repeat",
")",
"."
] | def find_all_simple_loops(self):
"""
This function finds all cases of a simple loop in the graph: A->B->C->B->D.
It returns them as a list of 4-tuples of segment numbers in this order:
(start, end, middle, repeat).
"""
simple_loops = []
# We'll search specifically for the middle segments as they should be easy to spot.
for middle in self.segments:
if self.segments[middle].get_length() > settings.MAX_SIMPLE_LOOP_SIZE:
continue
# A middle segment will always have exactly one connection on each end which connect
# to the same segment (the repeat segment).
if middle not in self.forward_links or middle not in self.reverse_links:
continue
if len(self.forward_links[middle]) != 1 or len(self.reverse_links[middle]) != 1:
continue
if self.forward_links[middle][0] != self.reverse_links[middle][0]:
continue
repeat = self.forward_links[middle][0]
# The repeat segment should have exactly two connections on each end. If less, then we
# have a simple path which can be merged. If more, it's a more complex loop.
if len(self.forward_links[repeat]) != 2 or len(self.reverse_links[repeat]) != 2:
continue
# Find the start and end segment numbers. It's okay if the start and the end are the
# same, but we exclude any other screwy cases where the start or end is the middle or
# repeat segment.
start = self.reverse_links[repeat][0]
if abs(start) == abs(middle):
start = self.reverse_links[repeat][1]
if abs(start) == abs(middle) or abs(start) == abs(repeat):
continue
end = self.forward_links[repeat][0]
if abs(end) == abs(middle):
end = self.forward_links[repeat][1]
if abs(end) == abs(middle) or abs(end) == abs(repeat):
continue
simple_loops.append((start, end, middle, repeat))
# Simple loops may just have the repeat node loop back to itself (i.e. no separate middle
# segment). Look for these structures.
for repeat in self.segments:
if repeat not in self.forward_links or repeat not in self.reverse_links or \
len(self.forward_links[repeat]) != 2 or len(self.reverse_links[repeat]) != 2:
continue
# Make sure that the repeat loops back to itself.
if repeat not in self.forward_links[repeat] or repeat not in self.reverse_links[repeat]:
continue
start_segs = list(self.reverse_links[repeat])
start_segs.remove(repeat)
end_segs = list(self.forward_links[repeat])
end_segs.remove(repeat)
if len(start_segs) != 1 or len(end_segs) != 1:
continue
start = start_segs[0]
end = end_segs[0]
if abs(start) == abs(repeat) or abs(end) == abs(repeat):
continue
simple_loops.append((start, end, None, repeat))
return simple_loops | [
"def",
"find_all_simple_loops",
"(",
"self",
")",
":",
"simple_loops",
"=",
"[",
"]",
"# We'll search specifically for the middle segments as they should be easy to spot.",
"for",
"middle",
"in",
"self",
".",
"segments",
":",
"if",
"self",
".",
"segments",
"[",
"middle",
"]",
".",
"get_length",
"(",
")",
">",
"settings",
".",
"MAX_SIMPLE_LOOP_SIZE",
":",
"continue",
"# A middle segment will always have exactly one connection on each end which connect",
"# to the same segment (the repeat segment).",
"if",
"middle",
"not",
"in",
"self",
".",
"forward_links",
"or",
"middle",
"not",
"in",
"self",
".",
"reverse_links",
":",
"continue",
"if",
"len",
"(",
"self",
".",
"forward_links",
"[",
"middle",
"]",
")",
"!=",
"1",
"or",
"len",
"(",
"self",
".",
"reverse_links",
"[",
"middle",
"]",
")",
"!=",
"1",
":",
"continue",
"if",
"self",
".",
"forward_links",
"[",
"middle",
"]",
"[",
"0",
"]",
"!=",
"self",
".",
"reverse_links",
"[",
"middle",
"]",
"[",
"0",
"]",
":",
"continue",
"repeat",
"=",
"self",
".",
"forward_links",
"[",
"middle",
"]",
"[",
"0",
"]",
"# The repeat segment should have exactly two connections on each end. If less, then we",
"# have a simple path which can be merged. If more, it's a more complex loop.",
"if",
"len",
"(",
"self",
".",
"forward_links",
"[",
"repeat",
"]",
")",
"!=",
"2",
"or",
"len",
"(",
"self",
".",
"reverse_links",
"[",
"repeat",
"]",
")",
"!=",
"2",
":",
"continue",
"# Find the start and end segment numbers. It's okay if the start and the end are the",
"# same, but we exclude any other screwy cases where the start or end is the middle or",
"# repeat segment.",
"start",
"=",
"self",
".",
"reverse_links",
"[",
"repeat",
"]",
"[",
"0",
"]",
"if",
"abs",
"(",
"start",
")",
"==",
"abs",
"(",
"middle",
")",
":",
"start",
"=",
"self",
".",
"reverse_links",
"[",
"repeat",
"]",
"[",
"1",
"]",
"if",
"abs",
"(",
"start",
")",
"==",
"abs",
"(",
"middle",
")",
"or",
"abs",
"(",
"start",
")",
"==",
"abs",
"(",
"repeat",
")",
":",
"continue",
"end",
"=",
"self",
".",
"forward_links",
"[",
"repeat",
"]",
"[",
"0",
"]",
"if",
"abs",
"(",
"end",
")",
"==",
"abs",
"(",
"middle",
")",
":",
"end",
"=",
"self",
".",
"forward_links",
"[",
"repeat",
"]",
"[",
"1",
"]",
"if",
"abs",
"(",
"end",
")",
"==",
"abs",
"(",
"middle",
")",
"or",
"abs",
"(",
"end",
")",
"==",
"abs",
"(",
"repeat",
")",
":",
"continue",
"simple_loops",
".",
"append",
"(",
"(",
"start",
",",
"end",
",",
"middle",
",",
"repeat",
")",
")",
"# Simple loops may just have the repeat node loop back to itself (i.e. no separate middle",
"# segment). Look for these structures.",
"for",
"repeat",
"in",
"self",
".",
"segments",
":",
"if",
"repeat",
"not",
"in",
"self",
".",
"forward_links",
"or",
"repeat",
"not",
"in",
"self",
".",
"reverse_links",
"or",
"len",
"(",
"self",
".",
"forward_links",
"[",
"repeat",
"]",
")",
"!=",
"2",
"or",
"len",
"(",
"self",
".",
"reverse_links",
"[",
"repeat",
"]",
")",
"!=",
"2",
":",
"continue",
"# Make sure that the repeat loops back to itself.",
"if",
"repeat",
"not",
"in",
"self",
".",
"forward_links",
"[",
"repeat",
"]",
"or",
"repeat",
"not",
"in",
"self",
".",
"reverse_links",
"[",
"repeat",
"]",
":",
"continue",
"start_segs",
"=",
"list",
"(",
"self",
".",
"reverse_links",
"[",
"repeat",
"]",
")",
"start_segs",
".",
"remove",
"(",
"repeat",
")",
"end_segs",
"=",
"list",
"(",
"self",
".",
"forward_links",
"[",
"repeat",
"]",
")",
"end_segs",
".",
"remove",
"(",
"repeat",
")",
"if",
"len",
"(",
"start_segs",
")",
"!=",
"1",
"or",
"len",
"(",
"end_segs",
")",
"!=",
"1",
":",
"continue",
"start",
"=",
"start_segs",
"[",
"0",
"]",
"end",
"=",
"end_segs",
"[",
"0",
"]",
"if",
"abs",
"(",
"start",
")",
"==",
"abs",
"(",
"repeat",
")",
"or",
"abs",
"(",
"end",
")",
"==",
"abs",
"(",
"repeat",
")",
":",
"continue",
"simple_loops",
".",
"append",
"(",
"(",
"start",
",",
"end",
",",
"None",
",",
"repeat",
")",
")",
"return",
"simple_loops"
] | https://github.com/rrwick/Unicycler/blob/96ffea71e3a78d63ade19d6124946773e65cf129/unicycler/assembly_graph.py#L1535-L1604 | |
opengauss-mirror/openGauss-server | e383f1b77720a00ddbe4c0655bc85914d9b02a2b | src/gausskernel/dbmind/tools/index_advisor/index_advisor_workload.py | python | read_input_from_pipe | () | return input_str | Read stdin input if there is "echo 'str1 str2' | python xx.py",
return the input string | Read stdin input if there is "echo 'str1 str2' | python xx.py",
return the input string | [
"Read",
"stdin",
"input",
"if",
"there",
"is",
"echo",
"str1",
"str2",
"|",
"python",
"xx",
".",
"py",
"return",
"the",
"input",
"string"
] | def read_input_from_pipe():
"""
Read stdin input if there is "echo 'str1 str2' | python xx.py",
return the input string
"""
input_str = ""
r_handle, _, _ = select.select([sys.stdin], [], [], 0)
if not r_handle:
return ""
for item in r_handle:
if item == sys.stdin:
input_str = sys.stdin.read().strip()
return input_str | [
"def",
"read_input_from_pipe",
"(",
")",
":",
"input_str",
"=",
"\"\"",
"r_handle",
",",
"_",
",",
"_",
"=",
"select",
".",
"select",
"(",
"[",
"sys",
".",
"stdin",
"]",
",",
"[",
"]",
",",
"[",
"]",
",",
"0",
")",
"if",
"not",
"r_handle",
":",
"return",
"\"\"",
"for",
"item",
"in",
"r_handle",
":",
"if",
"item",
"==",
"sys",
".",
"stdin",
":",
"input_str",
"=",
"sys",
".",
"stdin",
".",
"read",
"(",
")",
".",
"strip",
"(",
")",
"return",
"input_str"
] | https://github.com/opengauss-mirror/openGauss-server/blob/e383f1b77720a00ddbe4c0655bc85914d9b02a2b/src/gausskernel/dbmind/tools/index_advisor/index_advisor_workload.py#L59-L72 | |
PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | python/paddle/distributed/fleet/data_generator/data_generator.py | python | DataGenerator.run_from_stdin | (self) | This function reads the data row from stdin, parses it with the
process function, and further parses the return value of the
process function with the _gen_str function. The parsed data will
be wrote to stdout and the corresponding protofile will be
generated.
Example:
.. code-block:: python
import paddle.distributed.fleet.data_generator as dg
class MyData(dg.DataGenerator):
def generate_sample(self, line):
def local_iter():
int_words = [int(x) for x in line.split()]
yield ("words", [int_words])
return local_iter
mydata = MyData()
mydata.run_from_stdin() | This function reads the data row from stdin, parses it with the
process function, and further parses the return value of the
process function with the _gen_str function. The parsed data will
be wrote to stdout and the corresponding protofile will be
generated. | [
"This",
"function",
"reads",
"the",
"data",
"row",
"from",
"stdin",
"parses",
"it",
"with",
"the",
"process",
"function",
"and",
"further",
"parses",
"the",
"return",
"value",
"of",
"the",
"process",
"function",
"with",
"the",
"_gen_str",
"function",
".",
"The",
"parsed",
"data",
"will",
"be",
"wrote",
"to",
"stdout",
"and",
"the",
"corresponding",
"protofile",
"will",
"be",
"generated",
"."
] | def run_from_stdin(self):
'''
This function reads the data row from stdin, parses it with the
process function, and further parses the return value of the
process function with the _gen_str function. The parsed data will
be wrote to stdout and the corresponding protofile will be
generated.
Example:
.. code-block:: python
import paddle.distributed.fleet.data_generator as dg
class MyData(dg.DataGenerator):
def generate_sample(self, line):
def local_iter():
int_words = [int(x) for x in line.split()]
yield ("words", [int_words])
return local_iter
mydata = MyData()
mydata.run_from_stdin()
'''
batch_samples = []
for line in sys.stdin:
line_iter = self.generate_sample(line)
for user_parsed_line in line_iter():
if user_parsed_line == None:
continue
batch_samples.append(user_parsed_line)
if len(batch_samples) == self.batch_size_:
batch_iter = self.generate_batch(batch_samples)
for sample in batch_iter():
sys.stdout.write(self._gen_str(sample))
batch_samples = []
if len(batch_samples) > 0:
batch_iter = self.generate_batch(batch_samples)
for sample in batch_iter():
sys.stdout.write(self._gen_str(sample)) | [
"def",
"run_from_stdin",
"(",
"self",
")",
":",
"batch_samples",
"=",
"[",
"]",
"for",
"line",
"in",
"sys",
".",
"stdin",
":",
"line_iter",
"=",
"self",
".",
"generate_sample",
"(",
"line",
")",
"for",
"user_parsed_line",
"in",
"line_iter",
"(",
")",
":",
"if",
"user_parsed_line",
"==",
"None",
":",
"continue",
"batch_samples",
".",
"append",
"(",
"user_parsed_line",
")",
"if",
"len",
"(",
"batch_samples",
")",
"==",
"self",
".",
"batch_size_",
":",
"batch_iter",
"=",
"self",
".",
"generate_batch",
"(",
"batch_samples",
")",
"for",
"sample",
"in",
"batch_iter",
"(",
")",
":",
"sys",
".",
"stdout",
".",
"write",
"(",
"self",
".",
"_gen_str",
"(",
"sample",
")",
")",
"batch_samples",
"=",
"[",
"]",
"if",
"len",
"(",
"batch_samples",
")",
">",
"0",
":",
"batch_iter",
"=",
"self",
".",
"generate_batch",
"(",
"batch_samples",
")",
"for",
"sample",
"in",
"batch_iter",
"(",
")",
":",
"sys",
".",
"stdout",
".",
"write",
"(",
"self",
".",
"_gen_str",
"(",
"sample",
")",
")"
] | https://github.com/PaddlePaddle/Paddle/blob/1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c/python/paddle/distributed/fleet/data_generator/data_generator.py#L96-L136 | ||
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numpy/distutils/lib2def.py | python | parse_cmd | () | return libfile, deffile | Parses the command-line arguments.
libfile, deffile = parse_cmd() | Parses the command-line arguments. | [
"Parses",
"the",
"command",
"-",
"line",
"arguments",
"."
] | def parse_cmd():
"""Parses the command-line arguments.
libfile, deffile = parse_cmd()"""
if len(sys.argv) == 3:
if sys.argv[1][-4:] == '.lib' and sys.argv[2][-4:] == '.def':
libfile, deffile = sys.argv[1:]
elif sys.argv[1][-4:] == '.def' and sys.argv[2][-4:] == '.lib':
deffile, libfile = sys.argv[1:]
else:
print("I'm assuming that your first argument is the library")
print("and the second is the DEF file.")
elif len(sys.argv) == 2:
if sys.argv[1][-4:] == '.def':
deffile = sys.argv[1]
libfile = 'python%s.lib' % py_ver
elif sys.argv[1][-4:] == '.lib':
deffile = None
libfile = sys.argv[1]
else:
libfile = 'python%s.lib' % py_ver
deffile = None
return libfile, deffile | [
"def",
"parse_cmd",
"(",
")",
":",
"if",
"len",
"(",
"sys",
".",
"argv",
")",
"==",
"3",
":",
"if",
"sys",
".",
"argv",
"[",
"1",
"]",
"[",
"-",
"4",
":",
"]",
"==",
"'.lib'",
"and",
"sys",
".",
"argv",
"[",
"2",
"]",
"[",
"-",
"4",
":",
"]",
"==",
"'.def'",
":",
"libfile",
",",
"deffile",
"=",
"sys",
".",
"argv",
"[",
"1",
":",
"]",
"elif",
"sys",
".",
"argv",
"[",
"1",
"]",
"[",
"-",
"4",
":",
"]",
"==",
"'.def'",
"and",
"sys",
".",
"argv",
"[",
"2",
"]",
"[",
"-",
"4",
":",
"]",
"==",
"'.lib'",
":",
"deffile",
",",
"libfile",
"=",
"sys",
".",
"argv",
"[",
"1",
":",
"]",
"else",
":",
"print",
"(",
"\"I'm assuming that your first argument is the library\"",
")",
"print",
"(",
"\"and the second is the DEF file.\"",
")",
"elif",
"len",
"(",
"sys",
".",
"argv",
")",
"==",
"2",
":",
"if",
"sys",
".",
"argv",
"[",
"1",
"]",
"[",
"-",
"4",
":",
"]",
"==",
"'.def'",
":",
"deffile",
"=",
"sys",
".",
"argv",
"[",
"1",
"]",
"libfile",
"=",
"'python%s.lib'",
"%",
"py_ver",
"elif",
"sys",
".",
"argv",
"[",
"1",
"]",
"[",
"-",
"4",
":",
"]",
"==",
"'.lib'",
":",
"deffile",
"=",
"None",
"libfile",
"=",
"sys",
".",
"argv",
"[",
"1",
"]",
"else",
":",
"libfile",
"=",
"'python%s.lib'",
"%",
"py_ver",
"deffile",
"=",
"None",
"return",
"libfile",
",",
"deffile"
] | https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numpy/distutils/lib2def.py#L40-L62 | |
vtraag/louvain-igraph | 124ea1be49ee74eec2eaca8006599d7fc5560db6 | src/louvain/VertexPartition.py | python | MutableVertexPartition.total_weight_to_comm | (self, comm) | return _c_louvain._MutableVertexPartition_total_weight_to_comm(self._partition, comm) | The total weight (i.e. number of edges) to a community.
Parameters
----------
comm
Community
Notes
-----
This includes all edges, also the ones that are internal to a community.
Sometimes this is also referred to as the community (in)degree.
See Also
--------
:func:`~VertexPartition.MutableVertexPartition.total_weight_from_comm`
:func:`~VertexPartition.MutableVertexPartition.total_weight_in_comm`
:func:`~VertexPartition.MutableVertexPartition.total_weight_in_all_comms` | The total weight (i.e. number of edges) to a community. | [
"The",
"total",
"weight",
"(",
"i",
".",
"e",
".",
"number",
"of",
"edges",
")",
"to",
"a",
"community",
"."
] | def total_weight_to_comm(self, comm):
""" The total weight (i.e. number of edges) to a community.
Parameters
----------
comm
Community
Notes
-----
This includes all edges, also the ones that are internal to a community.
Sometimes this is also referred to as the community (in)degree.
See Also
--------
:func:`~VertexPartition.MutableVertexPartition.total_weight_from_comm`
:func:`~VertexPartition.MutableVertexPartition.total_weight_in_comm`
:func:`~VertexPartition.MutableVertexPartition.total_weight_in_all_comms`
"""
return _c_louvain._MutableVertexPartition_total_weight_to_comm(self._partition, comm) | [
"def",
"total_weight_to_comm",
"(",
"self",
",",
"comm",
")",
":",
"return",
"_c_louvain",
".",
"_MutableVertexPartition_total_weight_to_comm",
"(",
"self",
".",
"_partition",
",",
"comm",
")"
] | https://github.com/vtraag/louvain-igraph/blob/124ea1be49ee74eec2eaca8006599d7fc5560db6/src/louvain/VertexPartition.py#L311-L332 | |
pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | torch/nn/modules/container.py | python | ParameterDict.items | (self) | return self._parameters.items() | r"""Return an iterable of the ParameterDict key/value pairs. | r"""Return an iterable of the ParameterDict key/value pairs. | [
"r",
"Return",
"an",
"iterable",
"of",
"the",
"ParameterDict",
"key",
"/",
"value",
"pairs",
"."
] | def items(self) -> Iterable[Tuple[str, 'Parameter']]:
r"""Return an iterable of the ParameterDict key/value pairs.
"""
return self._parameters.items() | [
"def",
"items",
"(",
"self",
")",
"->",
"Iterable",
"[",
"Tuple",
"[",
"str",
",",
"'Parameter'",
"]",
"]",
":",
"return",
"self",
".",
"_parameters",
".",
"items",
"(",
")"
] | https://github.com/pytorch/pytorch/blob/7176c92687d3cc847cc046bf002269c6949a21c2/torch/nn/modules/container.py#L691-L694 | |
ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | build/android/adb_install_apk.py | python | AddInstallAPKOption | (option_parser) | Adds apk option used to install the APK to the OptionParser. | Adds apk option used to install the APK to the OptionParser. | [
"Adds",
"apk",
"option",
"used",
"to",
"install",
"the",
"APK",
"to",
"the",
"OptionParser",
"."
] | def AddInstallAPKOption(option_parser):
"""Adds apk option used to install the APK to the OptionParser."""
test_options_parser.AddBuildTypeOption(option_parser)
option_parser.add_option('--apk',
help=('The name of the apk containing the '
' application (with the .apk extension).'))
option_parser.add_option('--apk_package',
help=('The package name used by the apk containing '
'the application.'))
option_parser.add_option('--keep_data',
action='store_true',
default=False,
help=('Keep the package data when installing '
'the application.')) | [
"def",
"AddInstallAPKOption",
"(",
"option_parser",
")",
":",
"test_options_parser",
".",
"AddBuildTypeOption",
"(",
"option_parser",
")",
"option_parser",
".",
"add_option",
"(",
"'--apk'",
",",
"help",
"=",
"(",
"'The name of the apk containing the '",
"' application (with the .apk extension).'",
")",
")",
"option_parser",
".",
"add_option",
"(",
"'--apk_package'",
",",
"help",
"=",
"(",
"'The package name used by the apk containing '",
"'the application.'",
")",
")",
"option_parser",
".",
"add_option",
"(",
"'--keep_data'",
",",
"action",
"=",
"'store_true'",
",",
"default",
"=",
"False",
",",
"help",
"=",
"(",
"'Keep the package data when installing '",
"'the application.'",
")",
")"
] | https://github.com/ChromiumWebApps/chromium/blob/c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7/build/android/adb_install_apk.py#L20-L33 | ||
3drobotics/ardupilot-solo | 05a123b002c11dccc905d4d7703a38e5f36ee723 | mk/PX4/Tools/genmsg/src/genmsg/msg_loader.py | python | load_msg_from_file | (msg_context, file_path, full_name) | Convert the .msg representation in the file to a :class:`MsgSpec` instance.
NOTE: this will register the message in the *msg_context*.
:param file_path: path of file to load from, ``str``
:returns: :class:`MsgSpec` instance
:raises: :exc:`InvalidMsgSpec`: if syntax errors or other problems are detected in file | Convert the .msg representation in the file to a :class:`MsgSpec` instance. | [
"Convert",
"the",
".",
"msg",
"representation",
"in",
"the",
"file",
"to",
"a",
":",
"class",
":",
"MsgSpec",
"instance",
"."
] | def load_msg_from_file(msg_context, file_path, full_name):
"""
Convert the .msg representation in the file to a :class:`MsgSpec` instance.
NOTE: this will register the message in the *msg_context*.
:param file_path: path of file to load from, ``str``
:returns: :class:`MsgSpec` instance
:raises: :exc:`InvalidMsgSpec`: if syntax errors or other problems are detected in file
"""
log("Load spec from", file_path)
with open(file_path, 'r') as f:
text = f.read()
try:
return load_msg_from_string(msg_context, text, full_name)
except InvalidMsgSpec as e:
raise InvalidMsgSpec('%s: %s'%(file_path, e)) | [
"def",
"load_msg_from_file",
"(",
"msg_context",
",",
"file_path",
",",
"full_name",
")",
":",
"log",
"(",
"\"Load spec from\"",
",",
"file_path",
")",
"with",
"open",
"(",
"file_path",
",",
"'r'",
")",
"as",
"f",
":",
"text",
"=",
"f",
".",
"read",
"(",
")",
"try",
":",
"return",
"load_msg_from_string",
"(",
"msg_context",
",",
"text",
",",
"full_name",
")",
"except",
"InvalidMsgSpec",
"as",
"e",
":",
"raise",
"InvalidMsgSpec",
"(",
"'%s: %s'",
"%",
"(",
"file_path",
",",
"e",
")",
")"
] | https://github.com/3drobotics/ardupilot-solo/blob/05a123b002c11dccc905d4d7703a38e5f36ee723/mk/PX4/Tools/genmsg/src/genmsg/msg_loader.py#L268-L284 | ||
google/sling | f408a148a06bc2d62e853a292a8ba7266c642839 | python/task/workflow.py | python | Scope.prefix | (self) | return '/'.join(reversed(parts)) | Returns the name prefix defined in the scope by concatenating all nested
name spaces. | Returns the name prefix defined in the scope by concatenating all nested
name spaces. | [
"Returns",
"the",
"name",
"prefix",
"defined",
"in",
"the",
"scope",
"by",
"concatenating",
"all",
"nested",
"name",
"spaces",
"."
] | def prefix(self):
"""Returns the name prefix defined in the scope by concatenating all nested
name spaces."""
parts = []
s = self
while s != None:
parts.append(s.name)
s = s.prev
return '/'.join(reversed(parts)) | [
"def",
"prefix",
"(",
"self",
")",
":",
"parts",
"=",
"[",
"]",
"s",
"=",
"self",
"while",
"s",
"!=",
"None",
":",
"parts",
".",
"append",
"(",
"s",
".",
"name",
")",
"s",
"=",
"s",
".",
"prev",
"return",
"'/'",
".",
"join",
"(",
"reversed",
"(",
"parts",
")",
")"
] | https://github.com/google/sling/blob/f408a148a06bc2d62e853a292a8ba7266c642839/python/task/workflow.py#L266-L274 | |
snap-stanford/snap-python | d53c51b0a26aa7e3e7400b014cdf728948fde80a | setup/snap.py | python | TMemIn.New | (*args) | return _snap.TMemIn_New(*args) | New(TMem Mem) -> PSIn
Parameters:
Mem: TMem const &
New(PMem const & Mem) -> PSIn
Parameters:
Mem: PMem const & | New(TMem Mem) -> PSIn | [
"New",
"(",
"TMem",
"Mem",
")",
"-",
">",
"PSIn"
] | def New(*args):
"""
New(TMem Mem) -> PSIn
Parameters:
Mem: TMem const &
New(PMem const & Mem) -> PSIn
Parameters:
Mem: PMem const &
"""
return _snap.TMemIn_New(*args) | [
"def",
"New",
"(",
"*",
"args",
")",
":",
"return",
"_snap",
".",
"TMemIn_New",
"(",
"*",
"args",
")"
] | https://github.com/snap-stanford/snap-python/blob/d53c51b0a26aa7e3e7400b014cdf728948fde80a/setup/snap.py#L8361-L8374 | |
KhronosGroup/SPIR | f33c27876d9f3d5810162b60fa89cc13d2b55725 | bindings/python/clang/cindex.py | python | Config.set_library_file | (file) | Set the exact location of libclang from | Set the exact location of libclang from | [
"Set",
"the",
"exact",
"location",
"of",
"libclang",
"from"
] | def set_library_file(file):
"""Set the exact location of libclang from"""
if Config.loaded:
raise Exception("library file must be set before before using " \
"any other functionalities in libclang.")
Config.library_file = path | [
"def",
"set_library_file",
"(",
"file",
")",
":",
"if",
"Config",
".",
"loaded",
":",
"raise",
"Exception",
"(",
"\"library file must be set before before using \"",
"\"any other functionalities in libclang.\"",
")",
"Config",
".",
"library_file",
"=",
"path"
] | https://github.com/KhronosGroup/SPIR/blob/f33c27876d9f3d5810162b60fa89cc13d2b55725/bindings/python/clang/cindex.py#L3049-L3055 | ||
verilog-to-routing/vtr-verilog-to-routing | d9719cf7374821156c3cee31d66991cb85578562 | vtr_flow/scripts/python_libs/vtr/parse_vtr_task.py | python | calculate_individual_geo_mean | (lines, index, geo_mean, num) | return geo_mean, num, previous_value | Calculate an individual line of parse results goe_mean | Calculate an individual line of parse results goe_mean | [
"Calculate",
"an",
"individual",
"line",
"of",
"parse",
"results",
"goe_mean"
] | def calculate_individual_geo_mean(lines, index, geo_mean, num):
"""Calculate an individual line of parse results goe_mean"""
previous_value = None
for line in lines:
line = line.split("\t")[4:]
current_value = line[index]
try:
if float(current_value) > 0:
geo_mean *= float(current_value)
num += 1
except ValueError:
if not previous_value:
previous_value = current_value
elif current_value != previous_value:
previous_value = "-1"
return geo_mean, num, previous_value | [
"def",
"calculate_individual_geo_mean",
"(",
"lines",
",",
"index",
",",
"geo_mean",
",",
"num",
")",
":",
"previous_value",
"=",
"None",
"for",
"line",
"in",
"lines",
":",
"line",
"=",
"line",
".",
"split",
"(",
"\"\\t\"",
")",
"[",
"4",
":",
"]",
"current_value",
"=",
"line",
"[",
"index",
"]",
"try",
":",
"if",
"float",
"(",
"current_value",
")",
">",
"0",
":",
"geo_mean",
"*=",
"float",
"(",
"current_value",
")",
"num",
"+=",
"1",
"except",
"ValueError",
":",
"if",
"not",
"previous_value",
":",
"previous_value",
"=",
"current_value",
"elif",
"current_value",
"!=",
"previous_value",
":",
"previous_value",
"=",
"\"-1\"",
"return",
"geo_mean",
",",
"num",
",",
"previous_value"
] | https://github.com/verilog-to-routing/vtr-verilog-to-routing/blob/d9719cf7374821156c3cee31d66991cb85578562/vtr_flow/scripts/python_libs/vtr/parse_vtr_task.py#L535-L550 | |
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_gdi.py | python | Colour.Green | (*args, **kwargs) | return _gdi_.Colour_Green(*args, **kwargs) | Green(self) -> byte
Returns the green intensity. | Green(self) -> byte | [
"Green",
"(",
"self",
")",
"-",
">",
"byte"
] | def Green(*args, **kwargs):
"""
Green(self) -> byte
Returns the green intensity.
"""
return _gdi_.Colour_Green(*args, **kwargs) | [
"def",
"Green",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"return",
"_gdi_",
".",
"Colour_Green",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")"
] | https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/msw/_gdi.py#L141-L147 | |
hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/telemetry/telemetry/util/statistics.py | python | StandardDeviation | (data) | return std_dev | Calculates the standard deviation.
Args:
data: A list of samples.
Returns:
The standard deviation of the samples provided. | Calculates the standard deviation. | [
"Calculates",
"the",
"standard",
"deviation",
"."
] | def StandardDeviation(data):
"""Calculates the standard deviation.
Args:
data: A list of samples.
Returns:
The standard deviation of the samples provided.
"""
if len(data) == 1:
return 0.0
mean = ArithmeticMean(data)
variances = [float(x) - mean for x in data]
variances = [x * x for x in variances]
std_dev = math.sqrt(ArithmeticMean(variances))
return std_dev | [
"def",
"StandardDeviation",
"(",
"data",
")",
":",
"if",
"len",
"(",
"data",
")",
"==",
"1",
":",
"return",
"0.0",
"mean",
"=",
"ArithmeticMean",
"(",
"data",
")",
"variances",
"=",
"[",
"float",
"(",
"x",
")",
"-",
"mean",
"for",
"x",
"in",
"data",
"]",
"variances",
"=",
"[",
"x",
"*",
"x",
"for",
"x",
"in",
"variances",
"]",
"std_dev",
"=",
"math",
".",
"sqrt",
"(",
"ArithmeticMean",
"(",
"variances",
")",
")",
"return",
"std_dev"
] | https://github.com/hanpfei/chromium-net/blob/392cc1fa3a8f92f42e4071ab6e674d8e0482f83f/third_party/catapult/telemetry/telemetry/util/statistics.py#L218-L235 | |
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py2/sklearn/gaussian_process/kernels.py | python | Kernel.n_dims | (self) | return self.theta.shape[0] | Returns the number of non-fixed hyperparameters of the kernel. | Returns the number of non-fixed hyperparameters of the kernel. | [
"Returns",
"the",
"number",
"of",
"non",
"-",
"fixed",
"hyperparameters",
"of",
"the",
"kernel",
"."
] | def n_dims(self):
"""Returns the number of non-fixed hyperparameters of the kernel."""
return self.theta.shape[0] | [
"def",
"n_dims",
"(",
"self",
")",
":",
"return",
"self",
".",
"theta",
".",
"shape",
"[",
"0",
"]"
] | https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/scikit-learn/py2/sklearn/gaussian_process/kernels.py#L208-L210 | |
cms-sw/cmssw | fd9de012d503d3405420bcbeec0ec879baa57cf2 | Validation/RecoTrack/python/plotting/ntupleDataFormat.py | python | TrackingVertex.__init__ | (self, tree, index) | Constructor.
Arguments:
tree -- TTree object
index -- Index of the TrackingVertex | Constructor. | [
"Constructor",
"."
] | def __init__(self, tree, index):
"""Constructor.
Arguments:
tree -- TTree object
index -- Index of the TrackingVertex
"""
super(TrackingVertex, self).__init__(tree, index, "simvtx") | [
"def",
"__init__",
"(",
"self",
",",
"tree",
",",
"index",
")",
":",
"super",
"(",
"TrackingVertex",
",",
"self",
")",
".",
"__init__",
"(",
"tree",
",",
"index",
",",
"\"simvtx\"",
")"
] | https://github.com/cms-sw/cmssw/blob/fd9de012d503d3405420bcbeec0ec879baa57cf2/Validation/RecoTrack/python/plotting/ntupleDataFormat.py#L1135-L1142 | ||
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/pandas/io/sas/sas_xport.py | python | _parse_float_vec | (vec) | return ieee | Parse a vector of float values representing IBM 8 byte floats into
native 8 byte floats. | Parse a vector of float values representing IBM 8 byte floats into
native 8 byte floats. | [
"Parse",
"a",
"vector",
"of",
"float",
"values",
"representing",
"IBM",
"8",
"byte",
"floats",
"into",
"native",
"8",
"byte",
"floats",
"."
] | def _parse_float_vec(vec):
"""
Parse a vector of float values representing IBM 8 byte floats into
native 8 byte floats.
"""
dtype = np.dtype(">u4,>u4")
vec1 = vec.view(dtype=dtype)
xport1 = vec1["f0"]
xport2 = vec1["f1"]
# Start by setting first half of ieee number to first half of IBM
# number sans exponent
ieee1 = xport1 & 0x00FFFFFF
# The fraction bit to the left of the binary point in the ieee
# format was set and the number was shifted 0, 1, 2, or 3
# places. This will tell us how to adjust the ibm exponent to be a
# power of 2 ieee exponent and how to shift the fraction bits to
# restore the correct magnitude.
shift = np.zeros(len(vec), dtype=np.uint8)
shift[np.where(xport1 & 0x00200000)] = 1
shift[np.where(xport1 & 0x00400000)] = 2
shift[np.where(xport1 & 0x00800000)] = 3
# shift the ieee number down the correct number of places then
# set the second half of the ieee number to be the second half
# of the ibm number shifted appropriately, ored with the bits
# from the first half that would have been shifted in if we
# could shift a double. All we are worried about are the low
# order 3 bits of the first half since we're only shifting by
# 1, 2, or 3.
ieee1 >>= shift
ieee2 = (xport2 >> shift) | ((xport1 & 0x00000007) << (29 + (3 - shift)))
# clear the 1 bit to the left of the binary point
ieee1 &= 0xFFEFFFFF
# set the exponent of the ieee number to be the actual exponent
# plus the shift count + 1023. Or this into the first half of the
# ieee number. The ibm exponent is excess 64 but is adjusted by 65
# since during conversion to ibm format the exponent is
# incremented by 1 and the fraction bits left 4 positions to the
# right of the radix point. (had to add >> 24 because C treats &
# 0x7f as 0x7f000000 and Python doesn't)
ieee1 |= ((((((xport1 >> 24) & 0x7F) - 65) << 2) + shift + 1023) << 20) | (
xport1 & 0x80000000
)
ieee = np.empty((len(ieee1),), dtype=">u4,>u4")
ieee["f0"] = ieee1
ieee["f1"] = ieee2
ieee = ieee.view(dtype=">f8")
ieee = ieee.astype("f8")
return ieee | [
"def",
"_parse_float_vec",
"(",
"vec",
")",
":",
"dtype",
"=",
"np",
".",
"dtype",
"(",
"\">u4,>u4\"",
")",
"vec1",
"=",
"vec",
".",
"view",
"(",
"dtype",
"=",
"dtype",
")",
"xport1",
"=",
"vec1",
"[",
"\"f0\"",
"]",
"xport2",
"=",
"vec1",
"[",
"\"f1\"",
"]",
"# Start by setting first half of ieee number to first half of IBM",
"# number sans exponent",
"ieee1",
"=",
"xport1",
"&",
"0x00FFFFFF",
"# The fraction bit to the left of the binary point in the ieee",
"# format was set and the number was shifted 0, 1, 2, or 3",
"# places. This will tell us how to adjust the ibm exponent to be a",
"# power of 2 ieee exponent and how to shift the fraction bits to",
"# restore the correct magnitude.",
"shift",
"=",
"np",
".",
"zeros",
"(",
"len",
"(",
"vec",
")",
",",
"dtype",
"=",
"np",
".",
"uint8",
")",
"shift",
"[",
"np",
".",
"where",
"(",
"xport1",
"&",
"0x00200000",
")",
"]",
"=",
"1",
"shift",
"[",
"np",
".",
"where",
"(",
"xport1",
"&",
"0x00400000",
")",
"]",
"=",
"2",
"shift",
"[",
"np",
".",
"where",
"(",
"xport1",
"&",
"0x00800000",
")",
"]",
"=",
"3",
"# shift the ieee number down the correct number of places then",
"# set the second half of the ieee number to be the second half",
"# of the ibm number shifted appropriately, ored with the bits",
"# from the first half that would have been shifted in if we",
"# could shift a double. All we are worried about are the low",
"# order 3 bits of the first half since we're only shifting by",
"# 1, 2, or 3.",
"ieee1",
">>=",
"shift",
"ieee2",
"=",
"(",
"xport2",
">>",
"shift",
")",
"|",
"(",
"(",
"xport1",
"&",
"0x00000007",
")",
"<<",
"(",
"29",
"+",
"(",
"3",
"-",
"shift",
")",
")",
")",
"# clear the 1 bit to the left of the binary point",
"ieee1",
"&=",
"0xFFEFFFFF",
"# set the exponent of the ieee number to be the actual exponent",
"# plus the shift count + 1023. Or this into the first half of the",
"# ieee number. The ibm exponent is excess 64 but is adjusted by 65",
"# since during conversion to ibm format the exponent is",
"# incremented by 1 and the fraction bits left 4 positions to the",
"# right of the radix point. (had to add >> 24 because C treats &",
"# 0x7f as 0x7f000000 and Python doesn't)",
"ieee1",
"|=",
"(",
"(",
"(",
"(",
"(",
"(",
"xport1",
">>",
"24",
")",
"&",
"0x7F",
")",
"-",
"65",
")",
"<<",
"2",
")",
"+",
"shift",
"+",
"1023",
")",
"<<",
"20",
")",
"|",
"(",
"xport1",
"&",
"0x80000000",
")",
"ieee",
"=",
"np",
".",
"empty",
"(",
"(",
"len",
"(",
"ieee1",
")",
",",
")",
",",
"dtype",
"=",
"\">u4,>u4\"",
")",
"ieee",
"[",
"\"f0\"",
"]",
"=",
"ieee1",
"ieee",
"[",
"\"f1\"",
"]",
"=",
"ieee2",
"ieee",
"=",
"ieee",
".",
"view",
"(",
"dtype",
"=",
"\">f8\"",
")",
"ieee",
"=",
"ieee",
".",
"astype",
"(",
"\"f8\"",
")",
"return",
"ieee"
] | https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/pandas/io/sas/sas_xport.py#L196-L251 | |
mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/_checkparam.py | python | check_number_range | (arg_value, lower_limit, upper_limit, rel, value_type, arg_name=None, prim_name=None) | return arg_value | Method for checking whether an int value is in some range.
Usage:
- number = check_number_range(number, 0.0, 1.0, Rel.INC_NEITHER, "number", float) # number in [0.0, 1.0]
- number = check_number_range(number, 0, 1, Rel.INC_NEITHER, "number", int) # number in [0, 1] | Method for checking whether an int value is in some range. | [
"Method",
"for",
"checking",
"whether",
"an",
"int",
"value",
"is",
"in",
"some",
"range",
"."
] | def check_number_range(arg_value, lower_limit, upper_limit, rel, value_type, arg_name=None, prim_name=None):
"""
Method for checking whether an int value is in some range.
Usage:
- number = check_number_range(number, 0.0, 1.0, Rel.INC_NEITHER, "number", float) # number in [0.0, 1.0]
- number = check_number_range(number, 0, 1, Rel.INC_NEITHER, "number", int) # number in [0, 1]
"""
rel_fn = Rel.get_fns(rel)
prim_name = f'in `{prim_name}`' if prim_name else ''
arg_name = f'`{arg_name}`' if arg_name else ''
type_mismatch = not isinstance(arg_value, (np.ndarray, np.generic, value_type)) or isinstance(arg_value, bool)
if type_mismatch:
raise TypeError("{} {} must be `{}`, but got `{}`.".format(
arg_name, prim_name, value_type.__name__, type(arg_value).__name__))
if not rel_fn(arg_value, lower_limit, upper_limit):
rel_str = Rel.get_strs(rel).format(lower_limit, upper_limit)
raise ValueError("{} {} should be in range of {}, but got {} with type `{}`.".format(
arg_name, prim_name, rel_str, arg_value, type(arg_value).__name__))
return arg_value | [
"def",
"check_number_range",
"(",
"arg_value",
",",
"lower_limit",
",",
"upper_limit",
",",
"rel",
",",
"value_type",
",",
"arg_name",
"=",
"None",
",",
"prim_name",
"=",
"None",
")",
":",
"rel_fn",
"=",
"Rel",
".",
"get_fns",
"(",
"rel",
")",
"prim_name",
"=",
"f'in `{prim_name}`'",
"if",
"prim_name",
"else",
"''",
"arg_name",
"=",
"f'`{arg_name}`'",
"if",
"arg_name",
"else",
"''",
"type_mismatch",
"=",
"not",
"isinstance",
"(",
"arg_value",
",",
"(",
"np",
".",
"ndarray",
",",
"np",
".",
"generic",
",",
"value_type",
")",
")",
"or",
"isinstance",
"(",
"arg_value",
",",
"bool",
")",
"if",
"type_mismatch",
":",
"raise",
"TypeError",
"(",
"\"{} {} must be `{}`, but got `{}`.\"",
".",
"format",
"(",
"arg_name",
",",
"prim_name",
",",
"value_type",
".",
"__name__",
",",
"type",
"(",
"arg_value",
")",
".",
"__name__",
")",
")",
"if",
"not",
"rel_fn",
"(",
"arg_value",
",",
"lower_limit",
",",
"upper_limit",
")",
":",
"rel_str",
"=",
"Rel",
".",
"get_strs",
"(",
"rel",
")",
".",
"format",
"(",
"lower_limit",
",",
"upper_limit",
")",
"raise",
"ValueError",
"(",
"\"{} {} should be in range of {}, but got {} with type `{}`.\"",
".",
"format",
"(",
"arg_name",
",",
"prim_name",
",",
"rel_str",
",",
"arg_value",
",",
"type",
"(",
"arg_value",
")",
".",
"__name__",
")",
")",
"return",
"arg_value"
] | https://github.com/mindspore-ai/mindspore/blob/fb8fd3338605bb34fa5cea054e535a8b1d753fab/mindspore/python/mindspore/_checkparam.py#L192-L211 | |
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/numpy/py3/numpy/core/numerictypes.py | python | maximum_sctype | (t) | Return the scalar type of highest precision of the same kind as the input.
Parameters
----------
t : dtype or dtype specifier
The input data type. This can be a `dtype` object or an object that
is convertible to a `dtype`.
Returns
-------
out : dtype
The highest precision data type of the same kind (`dtype.kind`) as `t`.
See Also
--------
obj2sctype, mintypecode, sctype2char
dtype
Examples
--------
>>> np.maximum_sctype(int)
<class 'numpy.int64'>
>>> np.maximum_sctype(np.uint8)
<class 'numpy.uint64'>
>>> np.maximum_sctype(complex)
<class 'numpy.complex256'> # may vary
>>> np.maximum_sctype(str)
<class 'numpy.str_'>
>>> np.maximum_sctype('i2')
<class 'numpy.int64'>
>>> np.maximum_sctype('f4')
<class 'numpy.float128'> # may vary | Return the scalar type of highest precision of the same kind as the input. | [
"Return",
"the",
"scalar",
"type",
"of",
"highest",
"precision",
"of",
"the",
"same",
"kind",
"as",
"the",
"input",
"."
] | def maximum_sctype(t):
"""
Return the scalar type of highest precision of the same kind as the input.
Parameters
----------
t : dtype or dtype specifier
The input data type. This can be a `dtype` object or an object that
is convertible to a `dtype`.
Returns
-------
out : dtype
The highest precision data type of the same kind (`dtype.kind`) as `t`.
See Also
--------
obj2sctype, mintypecode, sctype2char
dtype
Examples
--------
>>> np.maximum_sctype(int)
<class 'numpy.int64'>
>>> np.maximum_sctype(np.uint8)
<class 'numpy.uint64'>
>>> np.maximum_sctype(complex)
<class 'numpy.complex256'> # may vary
>>> np.maximum_sctype(str)
<class 'numpy.str_'>
>>> np.maximum_sctype('i2')
<class 'numpy.int64'>
>>> np.maximum_sctype('f4')
<class 'numpy.float128'> # may vary
"""
g = obj2sctype(t)
if g is None:
return t
t = g
base = _kind_name(dtype(t))
if base in sctypes:
return sctypes[base][-1]
else:
return t | [
"def",
"maximum_sctype",
"(",
"t",
")",
":",
"g",
"=",
"obj2sctype",
"(",
"t",
")",
"if",
"g",
"is",
"None",
":",
"return",
"t",
"t",
"=",
"g",
"base",
"=",
"_kind_name",
"(",
"dtype",
"(",
"t",
")",
")",
"if",
"base",
"in",
"sctypes",
":",
"return",
"sctypes",
"[",
"base",
"]",
"[",
"-",
"1",
"]",
"else",
":",
"return",
"t"
] | https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/numpy/py3/numpy/core/numerictypes.py#L135-L181 | ||
kamyu104/LeetCode-Solutions | 77605708a927ea3b85aee5a479db733938c7c211 | Python/design-bitset.py | python | Bitset.__init__ | (self, size) | :type size: int | :type size: int | [
":",
"type",
"size",
":",
"int"
] | def __init__(self, size):
"""
:type size: int
"""
self.__lookup = [False]*size
self.__flip = False
self.__cnt = 0 | [
"def",
"__init__",
"(",
"self",
",",
"size",
")",
":",
"self",
".",
"__lookup",
"=",
"[",
"False",
"]",
"*",
"size",
"self",
".",
"__flip",
"=",
"False",
"self",
".",
"__cnt",
"=",
"0"
] | https://github.com/kamyu104/LeetCode-Solutions/blob/77605708a927ea3b85aee5a479db733938c7c211/Python/design-bitset.py#L14-L20 | ||
natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/python/ops/nn_ops.py | python | _DepthwiseConv2dNativeBackpropInputShape | (op) | Shape function for the DepthwiseConv2dNativeBackpropInput op. | Shape function for the DepthwiseConv2dNativeBackpropInput op. | [
"Shape",
"function",
"for",
"the",
"DepthwiseConv2dNativeBackpropInput",
"op",
"."
] | def _DepthwiseConv2dNativeBackpropInputShape(op):
"""Shape function for the DepthwiseConv2dNativeBackpropInput op."""
input_shape = tensor_util.constant_value(op.inputs[0])
if input_shape is not None:
return [tensor_shape.TensorShape(input_shape.tolist())]
else:
return [tensor_shape.unknown_shape(ndims=4)] | [
"def",
"_DepthwiseConv2dNativeBackpropInputShape",
"(",
"op",
")",
":",
"input_shape",
"=",
"tensor_util",
".",
"constant_value",
"(",
"op",
".",
"inputs",
"[",
"0",
"]",
")",
"if",
"input_shape",
"is",
"not",
"None",
":",
"return",
"[",
"tensor_shape",
".",
"TensorShape",
"(",
"input_shape",
".",
"tolist",
"(",
")",
")",
"]",
"else",
":",
"return",
"[",
"tensor_shape",
".",
"unknown_shape",
"(",
"ndims",
"=",
"4",
")",
"]"
] | https://github.com/natanielruiz/android-yolo/blob/1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f/jni-build/jni/include/tensorflow/python/ops/nn_ops.py#L834-L840 | ||
cms-sw/cmssw | fd9de012d503d3405420bcbeec0ec879baa57cf2 | Utilities/RelMon/python/dqm_interfaces.py | python | DirWalkerFile.ls | (self,directory_name="") | return contents | Return common objects to the 2 files. | Return common objects to the 2 files. | [
"Return",
"common",
"objects",
"to",
"the",
"2",
"files",
"."
] | def ls(self,directory_name=""):
"""Return common objects to the 2 files.
"""
contents1=self.dqmrootfile1.ls(directory_name)
contents2=self.dqmrootfile2.ls(directory_name)
#print "cont1: %s"%(contents1)
#print "cont2: %s"%(contents2)
contents={}
self.different_histograms['file1']= {}
self.different_histograms['file2']= {}
keys = [key for key in contents2.keys() if key in contents1] #set of all possible contents from both files
#print " ## keys: %s" %(keys)
for key in keys: #iterate on all unique keys
if contents1[key]!=contents2[key]:
diff_file1 = set(contents1.keys()) - set(contents2.keys()) #set of contents that file1 is missing
diff_file2 = set(contents2.keys()) - set(contents1.keys()) #--'-- that file2 is missing
for key1 in diff_file1:
obj_type = contents1[key1]
if obj_type == "TDirectoryFile":
self.different_histograms['file1'][key1] = contents1[key1] #if direcory
#print "\n Missing inside a dir: ", self.ls(key1)
#contents[key] = contents1[key1]
if obj_type[:2]!="TH" and obj_type[:3]!="TPr" : #if histogram
continue
self.different_histograms['file1'][key1] = contents1[key1]
for key1 in diff_file2:
obj_type = contents2[key1]
if obj_type == "TDirectoryFile":
self.different_histograms['file2'][key1] = contents2[key1] #if direcory
#print "\n Missing inside a dir: ", self.ls(key1)
#contents[key] = contents2[key1]
if obj_type[:2]!="TH" and obj_type[:3]!="TPr" : #if histogram
continue
self.different_histograms['file2'][key1] = contents2[key1]
contents[key]=contents1[key]
return contents | [
"def",
"ls",
"(",
"self",
",",
"directory_name",
"=",
"\"\"",
")",
":",
"contents1",
"=",
"self",
".",
"dqmrootfile1",
".",
"ls",
"(",
"directory_name",
")",
"contents2",
"=",
"self",
".",
"dqmrootfile2",
".",
"ls",
"(",
"directory_name",
")",
"#print \"cont1: %s\"%(contents1)",
"#print \"cont2: %s\"%(contents2)",
"contents",
"=",
"{",
"}",
"self",
".",
"different_histograms",
"[",
"'file1'",
"]",
"=",
"{",
"}",
"self",
".",
"different_histograms",
"[",
"'file2'",
"]",
"=",
"{",
"}",
"keys",
"=",
"[",
"key",
"for",
"key",
"in",
"contents2",
".",
"keys",
"(",
")",
"if",
"key",
"in",
"contents1",
"]",
"#set of all possible contents from both files",
"#print \" ## keys: %s\" %(keys)",
"for",
"key",
"in",
"keys",
":",
"#iterate on all unique keys",
"if",
"contents1",
"[",
"key",
"]",
"!=",
"contents2",
"[",
"key",
"]",
":",
"diff_file1",
"=",
"set",
"(",
"contents1",
".",
"keys",
"(",
")",
")",
"-",
"set",
"(",
"contents2",
".",
"keys",
"(",
")",
")",
"#set of contents that file1 is missing",
"diff_file2",
"=",
"set",
"(",
"contents2",
".",
"keys",
"(",
")",
")",
"-",
"set",
"(",
"contents1",
".",
"keys",
"(",
")",
")",
"#--'-- that file2 is missing",
"for",
"key1",
"in",
"diff_file1",
":",
"obj_type",
"=",
"contents1",
"[",
"key1",
"]",
"if",
"obj_type",
"==",
"\"TDirectoryFile\"",
":",
"self",
".",
"different_histograms",
"[",
"'file1'",
"]",
"[",
"key1",
"]",
"=",
"contents1",
"[",
"key1",
"]",
"#if direcory",
"#print \"\\n Missing inside a dir: \", self.ls(key1)",
"#contents[key] = contents1[key1]",
"if",
"obj_type",
"[",
":",
"2",
"]",
"!=",
"\"TH\"",
"and",
"obj_type",
"[",
":",
"3",
"]",
"!=",
"\"TPr\"",
":",
"#if histogram",
"continue",
"self",
".",
"different_histograms",
"[",
"'file1'",
"]",
"[",
"key1",
"]",
"=",
"contents1",
"[",
"key1",
"]",
"for",
"key1",
"in",
"diff_file2",
":",
"obj_type",
"=",
"contents2",
"[",
"key1",
"]",
"if",
"obj_type",
"==",
"\"TDirectoryFile\"",
":",
"self",
".",
"different_histograms",
"[",
"'file2'",
"]",
"[",
"key1",
"]",
"=",
"contents2",
"[",
"key1",
"]",
"#if direcory",
"#print \"\\n Missing inside a dir: \", self.ls(key1)",
"#contents[key] = contents2[key1]",
"if",
"obj_type",
"[",
":",
"2",
"]",
"!=",
"\"TH\"",
"and",
"obj_type",
"[",
":",
"3",
"]",
"!=",
"\"TPr\"",
":",
"#if histogram",
"continue",
"self",
".",
"different_histograms",
"[",
"'file2'",
"]",
"[",
"key1",
"]",
"=",
"contents2",
"[",
"key1",
"]",
"contents",
"[",
"key",
"]",
"=",
"contents1",
"[",
"key",
"]",
"return",
"contents"
] | https://github.com/cms-sw/cmssw/blob/fd9de012d503d3405420bcbeec0ec879baa57cf2/Utilities/RelMon/python/dqm_interfaces.py#L592-L627 | |
KratosMultiphysics/Kratos | 0000833054ed0503424eb28205d6508d9ca6cbbc | applications/ConvectionDiffusionApplication/python_scripts/convection_diffusion_solver.py | python | ConvectionDiffusionSolver.ImportModelPart | (self) | This function imports the ModelPart | This function imports the ModelPart | [
"This",
"function",
"imports",
"the",
"ModelPart"
] | def ImportModelPart(self):
"""This function imports the ModelPart"""
if self.solver_imports_model_part:
if not _CheckIsDistributed():
self._ImportModelPart(self.main_model_part, self.settings["model_import_settings"])
else:
self.distributed_model_part_importer = distributed_import_model_part_utility.DistributedImportModelPartUtility(
self.main_model_part,
self.settings)
self.distributed_model_part_importer.ImportModelPart() | [
"def",
"ImportModelPart",
"(",
"self",
")",
":",
"if",
"self",
".",
"solver_imports_model_part",
":",
"if",
"not",
"_CheckIsDistributed",
"(",
")",
":",
"self",
".",
"_ImportModelPart",
"(",
"self",
".",
"main_model_part",
",",
"self",
".",
"settings",
"[",
"\"model_import_settings\"",
"]",
")",
"else",
":",
"self",
".",
"distributed_model_part_importer",
"=",
"distributed_import_model_part_utility",
".",
"DistributedImportModelPartUtility",
"(",
"self",
".",
"main_model_part",
",",
"self",
".",
"settings",
")",
"self",
".",
"distributed_model_part_importer",
".",
"ImportModelPart",
"(",
")"
] | https://github.com/KratosMultiphysics/Kratos/blob/0000833054ed0503424eb28205d6508d9ca6cbbc/applications/ConvectionDiffusionApplication/python_scripts/convection_diffusion_solver.py#L298-L307 | ||
keyboardio/Kaleidoscope | d59604e98b2439d108647f15be52984a6837d360 | bin/cpplint.py | python | _FilterExcludedFiles | (filenames) | return [f for f in filenames if os.path.abspath(f) not in exclude_paths] | Filters out files listed in the --exclude command line switch. File paths
in the switch are evaluated relative to the current working directory | Filters out files listed in the --exclude command line switch. File paths
in the switch are evaluated relative to the current working directory | [
"Filters",
"out",
"files",
"listed",
"in",
"the",
"--",
"exclude",
"command",
"line",
"switch",
".",
"File",
"paths",
"in",
"the",
"switch",
"are",
"evaluated",
"relative",
"to",
"the",
"current",
"working",
"directory"
] | def _FilterExcludedFiles(filenames):
"""Filters out files listed in the --exclude command line switch. File paths
in the switch are evaluated relative to the current working directory
"""
exclude_paths = [os.path.abspath(f) for f in _excludes]
return [f for f in filenames if os.path.abspath(f) not in exclude_paths] | [
"def",
"_FilterExcludedFiles",
"(",
"filenames",
")",
":",
"exclude_paths",
"=",
"[",
"os",
".",
"path",
".",
"abspath",
"(",
"f",
")",
"for",
"f",
"in",
"_excludes",
"]",
"return",
"[",
"f",
"for",
"f",
"in",
"filenames",
"if",
"os",
".",
"path",
".",
"abspath",
"(",
"f",
")",
"not",
"in",
"exclude_paths",
"]"
] | https://github.com/keyboardio/Kaleidoscope/blob/d59604e98b2439d108647f15be52984a6837d360/bin/cpplint.py#L6551-L6556 | |
mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/_extends/parallel_compile/tbe_compiler/tbe_adapter.py | python | parallel_pre_compile_op | (job: TbeJob) | return True | Parallel pre compile op
:param job:
:return: | Parallel pre compile op
:param job:
:return: | [
"Parallel",
"pre",
"compile",
"op",
":",
"param",
"job",
":",
":",
"return",
":"
] | def parallel_pre_compile_op(job: TbeJob):
"""
Parallel pre compile op
:param job:
:return:
"""
compute_op_info_list = get_compute_op_list(job.content)
if len(compute_op_info_list) != 1:
job.error("Invalid op compute num ({}) in pre compile op".format(len(compute_op_info_list)))
return False
compute_op_info = compute_op_info_list[0]
adjust_custom_op_info(compute_op_info)
_pre_build_compute_op_info(compute_op_info, job)
return True | [
"def",
"parallel_pre_compile_op",
"(",
"job",
":",
"TbeJob",
")",
":",
"compute_op_info_list",
"=",
"get_compute_op_list",
"(",
"job",
".",
"content",
")",
"if",
"len",
"(",
"compute_op_info_list",
")",
"!=",
"1",
":",
"job",
".",
"error",
"(",
"\"Invalid op compute num ({}) in pre compile op\"",
".",
"format",
"(",
"len",
"(",
"compute_op_info_list",
")",
")",
")",
"return",
"False",
"compute_op_info",
"=",
"compute_op_info_list",
"[",
"0",
"]",
"adjust_custom_op_info",
"(",
"compute_op_info",
")",
"_pre_build_compute_op_info",
"(",
"compute_op_info",
",",
"job",
")",
"return",
"True"
] | https://github.com/mindspore-ai/mindspore/blob/fb8fd3338605bb34fa5cea054e535a8b1d753fab/mindspore/python/mindspore/_extends/parallel_compile/tbe_compiler/tbe_adapter.py#L368-L381 | |
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/idlelib/config.py | python | IdleUserConfParser.Save | (self) | Update user configuration file.
If self not empty after removing empty sections, write the file
to disk. Otherwise, remove the file from disk if it exists. | Update user configuration file. | [
"Update",
"user",
"configuration",
"file",
"."
] | def Save(self):
"""Update user configuration file.
If self not empty after removing empty sections, write the file
to disk. Otherwise, remove the file from disk if it exists.
"""
fname = self.file
if fname and fname[0] != '#':
if not self.IsEmpty():
try:
cfgFile = open(fname, 'w')
except OSError:
os.unlink(fname)
cfgFile = open(fname, 'w')
with cfgFile:
self.write(cfgFile)
elif os.path.exists(self.file):
os.remove(self.file) | [
"def",
"Save",
"(",
"self",
")",
":",
"fname",
"=",
"self",
".",
"file",
"if",
"fname",
"and",
"fname",
"[",
"0",
"]",
"!=",
"'#'",
":",
"if",
"not",
"self",
".",
"IsEmpty",
"(",
")",
":",
"try",
":",
"cfgFile",
"=",
"open",
"(",
"fname",
",",
"'w'",
")",
"except",
"OSError",
":",
"os",
".",
"unlink",
"(",
"fname",
")",
"cfgFile",
"=",
"open",
"(",
"fname",
",",
"'w'",
")",
"with",
"cfgFile",
":",
"self",
".",
"write",
"(",
"cfgFile",
")",
"elif",
"os",
".",
"path",
".",
"exists",
"(",
"self",
".",
"file",
")",
":",
"os",
".",
"remove",
"(",
"self",
".",
"file",
")"
] | https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/idlelib/config.py#L126-L143 | ||
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/email/mime/base.py | python | MIMEBase.__init__ | (self, _maintype, _subtype, **_params) | This constructor adds a Content-Type: and a MIME-Version: header.
The Content-Type: header is taken from the _maintype and _subtype
arguments. Additional parameters for this header are taken from the
keyword arguments. | This constructor adds a Content-Type: and a MIME-Version: header. | [
"This",
"constructor",
"adds",
"a",
"Content",
"-",
"Type",
":",
"and",
"a",
"MIME",
"-",
"Version",
":",
"header",
"."
] | def __init__(self, _maintype, _subtype, **_params):
"""This constructor adds a Content-Type: and a MIME-Version: header.
The Content-Type: header is taken from the _maintype and _subtype
arguments. Additional parameters for this header are taken from the
keyword arguments.
"""
message.Message.__init__(self)
ctype = '%s/%s' % (_maintype, _subtype)
self.add_header('Content-Type', ctype, **_params)
self['MIME-Version'] = '1.0' | [
"def",
"__init__",
"(",
"self",
",",
"_maintype",
",",
"_subtype",
",",
"*",
"*",
"_params",
")",
":",
"message",
".",
"Message",
".",
"__init__",
"(",
"self",
")",
"ctype",
"=",
"'%s/%s'",
"%",
"(",
"_maintype",
",",
"_subtype",
")",
"self",
".",
"add_header",
"(",
"'Content-Type'",
",",
"ctype",
",",
"*",
"*",
"_params",
")",
"self",
"[",
"'MIME-Version'",
"]",
"=",
"'1.0'"
] | https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/tools/python/src/Lib/email/mime/base.py#L16-L26 | ||
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | contrib/gizmos/osx_carbon/gizmos.py | python | EditableListBox.SetStrings | (*args, **kwargs) | return _gizmos.EditableListBox_SetStrings(*args, **kwargs) | SetStrings(self, wxArrayString strings) | SetStrings(self, wxArrayString strings) | [
"SetStrings",
"(",
"self",
"wxArrayString",
"strings",
")"
] | def SetStrings(*args, **kwargs):
"""SetStrings(self, wxArrayString strings)"""
return _gizmos.EditableListBox_SetStrings(*args, **kwargs) | [
"def",
"SetStrings",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"return",
"_gizmos",
".",
"EditableListBox_SetStrings",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")"
] | https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/contrib/gizmos/osx_carbon/gizmos.py#L155-L157 | |
Illumina/strelka | d7377443b62319f7c7bd70c241c4b2df3459e29a | src/python/lib/sharedWorkflow.py | python | _getDepthShared | (self,taskPrefix, dependencies, bamList, outputPath, depthFunc) | return nextStepWait | estimate chrom depth using the specified depthFunc to compute per-sample depth | estimate chrom depth using the specified depthFunc to compute per-sample depth | [
"estimate",
"chrom",
"depth",
"using",
"the",
"specified",
"depthFunc",
"to",
"compute",
"per",
"-",
"sample",
"depth"
] | def _getDepthShared(self,taskPrefix, dependencies, bamList, outputPath, depthFunc) :
"""
estimate chrom depth using the specified depthFunc to compute per-sample depth
"""
outputFilename=os.path.basename(outputPath)
tmpDir=outputPath+".tmpdir"
makeTmpDirCmd = getMkdirCmd() + [tmpDir]
dirTask=self.addTask(preJoin(taskPrefix,"makeTmpDir"), makeTmpDirCmd, dependencies=dependencies, isForceLocal=True)
tmpFiles = []
scatterTasks = set()
for (bamIndex, bamFile) in enumerate(bamList) :
indexStr = str(bamIndex).zfill(3)
tmpFiles.append(os.path.join(tmpDir,outputFilename+"."+ indexStr +".txt"))
scatterTasks |= setzer(depthFunc(self,taskPrefix+"_sample"+indexStr,dirTask,bamFile,tmpFiles[-1]))
cmd = [ self.params.mergeChromDepth ]
cmd.extend(["--out",outputPath])
for tmpFile in tmpFiles :
cmd.extend(["--in",tmpFile])
mergeTask = self.addTask(preJoin(taskPrefix,"mergeChromDepth"),cmd,dependencies=scatterTasks,isForceLocal=True)
nextStepWait = set()
nextStepWait.add(mergeTask)
if not self.params.isRetainTempFiles :
rmTmpCmd = getRmdirCmd() + [tmpDir]
rmTask=self.addTask(preJoin(taskPrefix,"removeTmpDir"),rmTmpCmd,dependencies=mergeTask, isForceLocal=True)
return nextStepWait | [
"def",
"_getDepthShared",
"(",
"self",
",",
"taskPrefix",
",",
"dependencies",
",",
"bamList",
",",
"outputPath",
",",
"depthFunc",
")",
":",
"outputFilename",
"=",
"os",
".",
"path",
".",
"basename",
"(",
"outputPath",
")",
"tmpDir",
"=",
"outputPath",
"+",
"\".tmpdir\"",
"makeTmpDirCmd",
"=",
"getMkdirCmd",
"(",
")",
"+",
"[",
"tmpDir",
"]",
"dirTask",
"=",
"self",
".",
"addTask",
"(",
"preJoin",
"(",
"taskPrefix",
",",
"\"makeTmpDir\"",
")",
",",
"makeTmpDirCmd",
",",
"dependencies",
"=",
"dependencies",
",",
"isForceLocal",
"=",
"True",
")",
"tmpFiles",
"=",
"[",
"]",
"scatterTasks",
"=",
"set",
"(",
")",
"for",
"(",
"bamIndex",
",",
"bamFile",
")",
"in",
"enumerate",
"(",
"bamList",
")",
":",
"indexStr",
"=",
"str",
"(",
"bamIndex",
")",
".",
"zfill",
"(",
"3",
")",
"tmpFiles",
".",
"append",
"(",
"os",
".",
"path",
".",
"join",
"(",
"tmpDir",
",",
"outputFilename",
"+",
"\".\"",
"+",
"indexStr",
"+",
"\".txt\"",
")",
")",
"scatterTasks",
"|=",
"setzer",
"(",
"depthFunc",
"(",
"self",
",",
"taskPrefix",
"+",
"\"_sample\"",
"+",
"indexStr",
",",
"dirTask",
",",
"bamFile",
",",
"tmpFiles",
"[",
"-",
"1",
"]",
")",
")",
"cmd",
"=",
"[",
"self",
".",
"params",
".",
"mergeChromDepth",
"]",
"cmd",
".",
"extend",
"(",
"[",
"\"--out\"",
",",
"outputPath",
"]",
")",
"for",
"tmpFile",
"in",
"tmpFiles",
":",
"cmd",
".",
"extend",
"(",
"[",
"\"--in\"",
",",
"tmpFile",
"]",
")",
"mergeTask",
"=",
"self",
".",
"addTask",
"(",
"preJoin",
"(",
"taskPrefix",
",",
"\"mergeChromDepth\"",
")",
",",
"cmd",
",",
"dependencies",
"=",
"scatterTasks",
",",
"isForceLocal",
"=",
"True",
")",
"nextStepWait",
"=",
"set",
"(",
")",
"nextStepWait",
".",
"add",
"(",
"mergeTask",
")",
"if",
"not",
"self",
".",
"params",
".",
"isRetainTempFiles",
":",
"rmTmpCmd",
"=",
"getRmdirCmd",
"(",
")",
"+",
"[",
"tmpDir",
"]",
"rmTask",
"=",
"self",
".",
"addTask",
"(",
"preJoin",
"(",
"taskPrefix",
",",
"\"removeTmpDir\"",
")",
",",
"rmTmpCmd",
",",
"dependencies",
"=",
"mergeTask",
",",
"isForceLocal",
"=",
"True",
")",
"return",
"nextStepWait"
] | https://github.com/Illumina/strelka/blob/d7377443b62319f7c7bd70c241c4b2df3459e29a/src/python/lib/sharedWorkflow.py#L96-L129 | |
trilinos/Trilinos | 6168be6dd51e35e1cd681e9c4b24433e709df140 | packages/seacas/scripts/exomerge3.py | python | ExodusModel.delete_element_field | (self,
element_field_names,
element_block_ids='all') | Delete one or more element fields.
Examples:
>>> model.delete_element_field('eqps')
>>> model.delete_element_field('all') | Delete one or more element fields. | [
"Delete",
"one",
"or",
"more",
"element",
"fields",
"."
] | def delete_element_field(self,
element_field_names,
element_block_ids='all'):
"""
Delete one or more element fields.
Examples:
>>> model.delete_element_field('eqps')
>>> model.delete_element_field('all')
"""
element_block_ids = self._format_element_block_id_list(
element_block_ids)
element_field_names = self._format_id_list(
element_field_names, self.get_element_field_names(),
'element field')
# for each field
for element_field_name in element_field_names:
any_deleted = False
# for each element block
for element_block_id in element_block_ids:
fields = self._get_element_block_fields(element_block_id)
if element_field_name in fields:
any_deleted = True
del fields[element_field_name]
if not any_deleted:
self._warning(
'Element field not defined.',
'The element field "%s" was not defined on any '
'of the given element blocks. It cannot be '
'deleted.' % element_field_name) | [
"def",
"delete_element_field",
"(",
"self",
",",
"element_field_names",
",",
"element_block_ids",
"=",
"'all'",
")",
":",
"element_block_ids",
"=",
"self",
".",
"_format_element_block_id_list",
"(",
"element_block_ids",
")",
"element_field_names",
"=",
"self",
".",
"_format_id_list",
"(",
"element_field_names",
",",
"self",
".",
"get_element_field_names",
"(",
")",
",",
"'element field'",
")",
"# for each field",
"for",
"element_field_name",
"in",
"element_field_names",
":",
"any_deleted",
"=",
"False",
"# for each element block",
"for",
"element_block_id",
"in",
"element_block_ids",
":",
"fields",
"=",
"self",
".",
"_get_element_block_fields",
"(",
"element_block_id",
")",
"if",
"element_field_name",
"in",
"fields",
":",
"any_deleted",
"=",
"True",
"del",
"fields",
"[",
"element_field_name",
"]",
"if",
"not",
"any_deleted",
":",
"self",
".",
"_warning",
"(",
"'Element field not defined.'",
",",
"'The element field \"%s\" was not defined on any '",
"'of the given element blocks. It cannot be '",
"'deleted.'",
"%",
"element_field_name",
")"
] | https://github.com/trilinos/Trilinos/blob/6168be6dd51e35e1cd681e9c4b24433e709df140/packages/seacas/scripts/exomerge3.py#L4625-L4655 | ||
gem5/gem5 | 141cc37c2d4b93959d4c249b8f7e6a8b2ef75338 | src/mem/slicc/parser.py | python | SLICC.p_statement__if | (self, p) | statement : if_statement | statement : if_statement | [
"statement",
":",
"if_statement"
] | def p_statement__if(self, p):
"statement : if_statement"
p[0] = p[1] | [
"def",
"p_statement__if",
"(",
"self",
",",
"p",
")",
":",
"p",
"[",
"0",
"]",
"=",
"p",
"[",
"1",
"]"
] | https://github.com/gem5/gem5/blob/141cc37c2d4b93959d4c249b8f7e6a8b2ef75338/src/mem/slicc/parser.py#L648-L650 | ||
baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/python/feature_column/feature_column.py | python | _check_default_value | (shape, default_value, dtype, key) | Returns default value as tuple if it's valid, otherwise raises errors.
This function verifies that `default_value` is compatible with both `shape`
and `dtype`. If it is not compatible, it raises an error. If it is compatible,
it casts default_value to a tuple and returns it. `key` is used only
for error message.
Args:
shape: An iterable of integers specifies the shape of the `Tensor`.
default_value: If a single value is provided, the same value will be applied
as the default value for every item. If an iterable of values is
provided, the shape of the `default_value` should be equal to the given
`shape`.
dtype: defines the type of values. Default value is `tf.float32`. Must be a
non-quantized, real integer or floating point type.
key: Column name, used only for error messages.
Returns:
A tuple which will be used as default value.
Raises:
TypeError: if `default_value` is an iterable but not compatible with `shape`
TypeError: if `default_value` is not compatible with `dtype`.
ValueError: if `dtype` is not convertible to `tf.float32`. | Returns default value as tuple if it's valid, otherwise raises errors. | [
"Returns",
"default",
"value",
"as",
"tuple",
"if",
"it",
"s",
"valid",
"otherwise",
"raises",
"errors",
"."
] | def _check_default_value(shape, default_value, dtype, key):
"""Returns default value as tuple if it's valid, otherwise raises errors.
This function verifies that `default_value` is compatible with both `shape`
and `dtype`. If it is not compatible, it raises an error. If it is compatible,
it casts default_value to a tuple and returns it. `key` is used only
for error message.
Args:
shape: An iterable of integers specifies the shape of the `Tensor`.
default_value: If a single value is provided, the same value will be applied
as the default value for every item. If an iterable of values is
provided, the shape of the `default_value` should be equal to the given
`shape`.
dtype: defines the type of values. Default value is `tf.float32`. Must be a
non-quantized, real integer or floating point type.
key: Column name, used only for error messages.
Returns:
A tuple which will be used as default value.
Raises:
TypeError: if `default_value` is an iterable but not compatible with `shape`
TypeError: if `default_value` is not compatible with `dtype`.
ValueError: if `dtype` is not convertible to `tf.float32`.
"""
if default_value is None:
return None
if isinstance(default_value, int):
return _create_tuple(shape, default_value)
if isinstance(default_value, float) and dtype.is_floating:
return _create_tuple(shape, default_value)
if callable(getattr(default_value, 'tolist', None)): # Handles numpy arrays
default_value = default_value.tolist()
if nest.is_sequence(default_value):
if not _is_shape_and_default_value_compatible(default_value, shape):
raise ValueError(
'The shape of default_value must be equal to given shape. '
'default_value: {}, shape: {}, key: {}'.format(
default_value, shape, key))
# Check if the values in the list are all integers or are convertible to
# floats.
is_list_all_int = all(
isinstance(v, int) for v in nest.flatten(default_value))
is_list_has_float = any(
isinstance(v, float) for v in nest.flatten(default_value))
if is_list_all_int:
return _as_tuple(default_value)
if is_list_has_float and dtype.is_floating:
return _as_tuple(default_value)
raise TypeError('default_value must be compatible with dtype. '
'default_value: {}, dtype: {}, key: {}'.format(
default_value, dtype, key)) | [
"def",
"_check_default_value",
"(",
"shape",
",",
"default_value",
",",
"dtype",
",",
"key",
")",
":",
"if",
"default_value",
"is",
"None",
":",
"return",
"None",
"if",
"isinstance",
"(",
"default_value",
",",
"int",
")",
":",
"return",
"_create_tuple",
"(",
"shape",
",",
"default_value",
")",
"if",
"isinstance",
"(",
"default_value",
",",
"float",
")",
"and",
"dtype",
".",
"is_floating",
":",
"return",
"_create_tuple",
"(",
"shape",
",",
"default_value",
")",
"if",
"callable",
"(",
"getattr",
"(",
"default_value",
",",
"'tolist'",
",",
"None",
")",
")",
":",
"# Handles numpy arrays",
"default_value",
"=",
"default_value",
".",
"tolist",
"(",
")",
"if",
"nest",
".",
"is_sequence",
"(",
"default_value",
")",
":",
"if",
"not",
"_is_shape_and_default_value_compatible",
"(",
"default_value",
",",
"shape",
")",
":",
"raise",
"ValueError",
"(",
"'The shape of default_value must be equal to given shape. '",
"'default_value: {}, shape: {}, key: {}'",
".",
"format",
"(",
"default_value",
",",
"shape",
",",
"key",
")",
")",
"# Check if the values in the list are all integers or are convertible to",
"# floats.",
"is_list_all_int",
"=",
"all",
"(",
"isinstance",
"(",
"v",
",",
"int",
")",
"for",
"v",
"in",
"nest",
".",
"flatten",
"(",
"default_value",
")",
")",
"is_list_has_float",
"=",
"any",
"(",
"isinstance",
"(",
"v",
",",
"float",
")",
"for",
"v",
"in",
"nest",
".",
"flatten",
"(",
"default_value",
")",
")",
"if",
"is_list_all_int",
":",
"return",
"_as_tuple",
"(",
"default_value",
")",
"if",
"is_list_has_float",
"and",
"dtype",
".",
"is_floating",
":",
"return",
"_as_tuple",
"(",
"default_value",
")",
"raise",
"TypeError",
"(",
"'default_value must be compatible with dtype. '",
"'default_value: {}, dtype: {}, key: {}'",
".",
"format",
"(",
"default_value",
",",
"dtype",
",",
"key",
")",
")"
] | https://github.com/baidu-research/tensorflow-allreduce/blob/66d5b855e90b0949e9fa5cca5599fd729a70e874/tensorflow/python/feature_column/feature_column.py#L1901-L1957 | ||
GJDuck/LowFat | ecf6a0f0fa1b73a27a626cf493cc39e477b6faea | llvm-4.0.0.src/tools/clang/bindings/python/clang/cindex.py | python | SourceLocation.from_position | (tu, file, line, column) | return conf.lib.clang_getLocation(tu, file, line, column) | Retrieve the source location associated with a given file/line/column in
a particular translation unit. | Retrieve the source location associated with a given file/line/column in
a particular translation unit. | [
"Retrieve",
"the",
"source",
"location",
"associated",
"with",
"a",
"given",
"file",
"/",
"line",
"/",
"column",
"in",
"a",
"particular",
"translation",
"unit",
"."
] | def from_position(tu, file, line, column):
"""
Retrieve the source location associated with a given file/line/column in
a particular translation unit.
"""
return conf.lib.clang_getLocation(tu, file, line, column) | [
"def",
"from_position",
"(",
"tu",
",",
"file",
",",
"line",
",",
"column",
")",
":",
"return",
"conf",
".",
"lib",
".",
"clang_getLocation",
"(",
"tu",
",",
"file",
",",
"line",
",",
"column",
")"
] | https://github.com/GJDuck/LowFat/blob/ecf6a0f0fa1b73a27a626cf493cc39e477b6faea/llvm-4.0.0.src/tools/clang/bindings/python/clang/cindex.py#L180-L185 | |
plumonito/dtslam | 5994bb9cf7a11981b830370db206bceb654c085d | 3rdparty/opencv-git/doc/pattern_tools/svgfig.py | python | funcRtoR | (expr, var="x", globals=None, locals=None) | return output | Converts a "f(x)" string to a function acceptable for Curve.
expr required string in the form "f(x)"
var default="x" name of the independent variable
globals default=None dict of global variables used in the expression;
you may want to use Python's builtin globals()
locals default=None dict of local variables | Converts a "f(x)" string to a function acceptable for Curve. | [
"Converts",
"a",
"f",
"(",
"x",
")",
"string",
"to",
"a",
"function",
"acceptable",
"for",
"Curve",
"."
] | def funcRtoR(expr, var="x", globals=None, locals=None):
"""Converts a "f(x)" string to a function acceptable for Curve.
expr required string in the form "f(x)"
var default="x" name of the independent variable
globals default=None dict of global variables used in the expression;
you may want to use Python's builtin globals()
locals default=None dict of local variables
"""
if locals is None:
locals = {} # python 2.3's eval() won't accept None
g = math.__dict__
if globals is not None:
g.update(globals)
output = eval("lambda %s: (%s, %s)" % (var, var, expr), g, locals)
set_func_name(output, "%s -> %s" % (var, expr))
return output | [
"def",
"funcRtoR",
"(",
"expr",
",",
"var",
"=",
"\"x\"",
",",
"globals",
"=",
"None",
",",
"locals",
"=",
"None",
")",
":",
"if",
"locals",
"is",
"None",
":",
"locals",
"=",
"{",
"}",
"# python 2.3's eval() won't accept None",
"g",
"=",
"math",
".",
"__dict__",
"if",
"globals",
"is",
"not",
"None",
":",
"g",
".",
"update",
"(",
"globals",
")",
"output",
"=",
"eval",
"(",
"\"lambda %s: (%s, %s)\"",
"%",
"(",
"var",
",",
"var",
",",
"expr",
")",
",",
"g",
",",
"locals",
")",
"set_func_name",
"(",
"output",
",",
"\"%s -> %s\"",
"%",
"(",
"var",
",",
"expr",
")",
")",
"return",
"output"
] | https://github.com/plumonito/dtslam/blob/5994bb9cf7a11981b830370db206bceb654c085d/3rdparty/opencv-git/doc/pattern_tools/svgfig.py#L1610-L1626 | |
SFTtech/openage | d6a08c53c48dc1e157807471df92197f6ca9e04d | buildsystem/cythonize.py | python | main | () | CLI entry point | CLI entry point | [
"CLI",
"entry",
"point"
] | def main():
""" CLI entry point """
cli = argparse.ArgumentParser()
cli.add_argument("module_list", help=(
"Module list file (semicolon-separated)."
))
cli.add_argument("embedded_module_list", help=(
"Embedded module list file (semicolon-separated).\n"
"Modules in this list are compiled with the --embed option."
))
cli.add_argument("depends_list", help=(
"Dependency list file (semicolon-separated).\n"
"Contains all .pxd and other files that may get included.\n"
"Used to verify that all dependencies are properly listed "
"in the CMake build configuration."
))
cli.add_argument("--clean", action="store_true", help=(
"Clean compilation results and exit."
))
cli.add_argument("--build-dir", help=(
"Build output directory to generate the cpp files in."
"note: this is also added for module search path."
))
cli.add_argument("--memcleanup", type=int, default=0, help=(
"Generate memory cleanup code to make valgrind happy:\n"
"0: nothing, 1+: interned objects,\n"
"2+: cdef globals, 3+: types objects"
))
cli.add_argument("--threads", type=int, default=cpu_count(),
help="number of compilation threads to use")
args = cli.parse_args()
# cython emits warnings on using absolute paths to modules
# https://github.com/cython/cython/issues/2323
modules = read_list_from_file(args.module_list)
embedded_modules = read_list_from_file(args.embedded_module_list)
depends = set(read_list_from_file(args.depends_list))
if args.clean:
for module in modules + embedded_modules:
rel_module = module.relative_to(Path.cwd())
build_module = args.build_dir / rel_module
remove_if_exists(build_module.with_suffix('.cpp'))
remove_if_exists(build_module.with_suffix('.html'))
sys.exit(0)
from Cython.Compiler import Options
Options.annotate = True
Options.fast_fail = True
Options.generate_cleanup_code = args.memcleanup
Options.cplus = 1
# build cython modules (emits shared libraries)
cythonize_args = {
'compiler_directives': {'language_level': 3},
'build_dir': args.build_dir,
'include_path': [args.build_dir],
'nthreads': args.threads
}
# this is deprecated, but still better than
# writing funny lines at the head of each file.
cythonize_args['language'] = 'c++'
cythonize_wrapper(modules, **cythonize_args)
# build standalone executables that embed the py interpreter
Options.embed = "main"
cythonize_wrapper(embedded_modules, **cythonize_args)
# verify depends
from Cython.Build.Dependencies import _dep_tree
depend_failed = False
# TODO figure out a less hacky way of getting the depends out of Cython
# pylint: disable=no-member, protected-access
for module, files in _dep_tree.__cimported_files_cache.items():
for filename in files:
if not filename.startswith('.'):
# system include starts with /
continue
if os.path.realpath(os.path.abspath(filename)) not in depends:
print("\x1b[31mERR\x1b[m unlisted dependency: " + filename)
depend_failed = True
if depend_failed:
sys.exit(1) | [
"def",
"main",
"(",
")",
":",
"cli",
"=",
"argparse",
".",
"ArgumentParser",
"(",
")",
"cli",
".",
"add_argument",
"(",
"\"module_list\"",
",",
"help",
"=",
"(",
"\"Module list file (semicolon-separated).\"",
")",
")",
"cli",
".",
"add_argument",
"(",
"\"embedded_module_list\"",
",",
"help",
"=",
"(",
"\"Embedded module list file (semicolon-separated).\\n\"",
"\"Modules in this list are compiled with the --embed option.\"",
")",
")",
"cli",
".",
"add_argument",
"(",
"\"depends_list\"",
",",
"help",
"=",
"(",
"\"Dependency list file (semicolon-separated).\\n\"",
"\"Contains all .pxd and other files that may get included.\\n\"",
"\"Used to verify that all dependencies are properly listed \"",
"\"in the CMake build configuration.\"",
")",
")",
"cli",
".",
"add_argument",
"(",
"\"--clean\"",
",",
"action",
"=",
"\"store_true\"",
",",
"help",
"=",
"(",
"\"Clean compilation results and exit.\"",
")",
")",
"cli",
".",
"add_argument",
"(",
"\"--build-dir\"",
",",
"help",
"=",
"(",
"\"Build output directory to generate the cpp files in.\"",
"\"note: this is also added for module search path.\"",
")",
")",
"cli",
".",
"add_argument",
"(",
"\"--memcleanup\"",
",",
"type",
"=",
"int",
",",
"default",
"=",
"0",
",",
"help",
"=",
"(",
"\"Generate memory cleanup code to make valgrind happy:\\n\"",
"\"0: nothing, 1+: interned objects,\\n\"",
"\"2+: cdef globals, 3+: types objects\"",
")",
")",
"cli",
".",
"add_argument",
"(",
"\"--threads\"",
",",
"type",
"=",
"int",
",",
"default",
"=",
"cpu_count",
"(",
")",
",",
"help",
"=",
"\"number of compilation threads to use\"",
")",
"args",
"=",
"cli",
".",
"parse_args",
"(",
")",
"# cython emits warnings on using absolute paths to modules",
"# https://github.com/cython/cython/issues/2323",
"modules",
"=",
"read_list_from_file",
"(",
"args",
".",
"module_list",
")",
"embedded_modules",
"=",
"read_list_from_file",
"(",
"args",
".",
"embedded_module_list",
")",
"depends",
"=",
"set",
"(",
"read_list_from_file",
"(",
"args",
".",
"depends_list",
")",
")",
"if",
"args",
".",
"clean",
":",
"for",
"module",
"in",
"modules",
"+",
"embedded_modules",
":",
"rel_module",
"=",
"module",
".",
"relative_to",
"(",
"Path",
".",
"cwd",
"(",
")",
")",
"build_module",
"=",
"args",
".",
"build_dir",
"/",
"rel_module",
"remove_if_exists",
"(",
"build_module",
".",
"with_suffix",
"(",
"'.cpp'",
")",
")",
"remove_if_exists",
"(",
"build_module",
".",
"with_suffix",
"(",
"'.html'",
")",
")",
"sys",
".",
"exit",
"(",
"0",
")",
"from",
"Cython",
".",
"Compiler",
"import",
"Options",
"Options",
".",
"annotate",
"=",
"True",
"Options",
".",
"fast_fail",
"=",
"True",
"Options",
".",
"generate_cleanup_code",
"=",
"args",
".",
"memcleanup",
"Options",
".",
"cplus",
"=",
"1",
"# build cython modules (emits shared libraries)",
"cythonize_args",
"=",
"{",
"'compiler_directives'",
":",
"{",
"'language_level'",
":",
"3",
"}",
",",
"'build_dir'",
":",
"args",
".",
"build_dir",
",",
"'include_path'",
":",
"[",
"args",
".",
"build_dir",
"]",
",",
"'nthreads'",
":",
"args",
".",
"threads",
"}",
"# this is deprecated, but still better than",
"# writing funny lines at the head of each file.",
"cythonize_args",
"[",
"'language'",
"]",
"=",
"'c++'",
"cythonize_wrapper",
"(",
"modules",
",",
"*",
"*",
"cythonize_args",
")",
"# build standalone executables that embed the py interpreter",
"Options",
".",
"embed",
"=",
"\"main\"",
"cythonize_wrapper",
"(",
"embedded_modules",
",",
"*",
"*",
"cythonize_args",
")",
"# verify depends",
"from",
"Cython",
".",
"Build",
".",
"Dependencies",
"import",
"_dep_tree",
"depend_failed",
"=",
"False",
"# TODO figure out a less hacky way of getting the depends out of Cython",
"# pylint: disable=no-member, protected-access",
"for",
"module",
",",
"files",
"in",
"_dep_tree",
".",
"__cimported_files_cache",
".",
"items",
"(",
")",
":",
"for",
"filename",
"in",
"files",
":",
"if",
"not",
"filename",
".",
"startswith",
"(",
"'.'",
")",
":",
"# system include starts with /",
"continue",
"if",
"os",
".",
"path",
".",
"realpath",
"(",
"os",
".",
"path",
".",
"abspath",
"(",
"filename",
")",
")",
"not",
"in",
"depends",
":",
"print",
"(",
"\"\\x1b[31mERR\\x1b[m unlisted dependency: \"",
"+",
"filename",
")",
"depend_failed",
"=",
"True",
"if",
"depend_failed",
":",
"sys",
".",
"exit",
"(",
"1",
")"
] | https://github.com/SFTtech/openage/blob/d6a08c53c48dc1e157807471df92197f6ca9e04d/buildsystem/cythonize.py#L98-L186 | ||
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numba/cuda/cudadrv/driver.py | python | _PendingDeallocs.clear | (self) | Flush any pending deallocations unless it is disabled.
Do nothing if disabled. | Flush any pending deallocations unless it is disabled.
Do nothing if disabled. | [
"Flush",
"any",
"pending",
"deallocations",
"unless",
"it",
"is",
"disabled",
".",
"Do",
"nothing",
"if",
"disabled",
"."
] | def clear(self):
"""
Flush any pending deallocations unless it is disabled.
Do nothing if disabled.
"""
if not self.is_disabled:
while self._cons:
[dtor, handle, size] = self._cons.popleft()
_logger.info('dealloc: %s %s bytes', dtor.__name__, size)
dtor(handle)
self._size = 0 | [
"def",
"clear",
"(",
"self",
")",
":",
"if",
"not",
"self",
".",
"is_disabled",
":",
"while",
"self",
".",
"_cons",
":",
"[",
"dtor",
",",
"handle",
",",
"size",
"]",
"=",
"self",
".",
"_cons",
".",
"popleft",
"(",
")",
"_logger",
".",
"info",
"(",
"'dealloc: %s %s bytes'",
",",
"dtor",
".",
"__name__",
",",
"size",
")",
"dtor",
"(",
"handle",
")",
"self",
".",
"_size",
"=",
"0"
] | https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numba/cuda/cudadrv/driver.py#L601-L611 | ||
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/richtext.py | python | RichTextBuffer.DeleteRangeWithUndo | (*args, **kwargs) | return _richtext.RichTextBuffer_DeleteRangeWithUndo(*args, **kwargs) | DeleteRangeWithUndo(self, RichTextRange range, RichTextCtrl ctrl) -> bool | DeleteRangeWithUndo(self, RichTextRange range, RichTextCtrl ctrl) -> bool | [
"DeleteRangeWithUndo",
"(",
"self",
"RichTextRange",
"range",
"RichTextCtrl",
"ctrl",
")",
"-",
">",
"bool"
] | def DeleteRangeWithUndo(*args, **kwargs):
"""DeleteRangeWithUndo(self, RichTextRange range, RichTextCtrl ctrl) -> bool"""
return _richtext.RichTextBuffer_DeleteRangeWithUndo(*args, **kwargs) | [
"def",
"DeleteRangeWithUndo",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"return",
"_richtext",
".",
"RichTextBuffer_DeleteRangeWithUndo",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")"
] | https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_cocoa/richtext.py#L2525-L2527 | |
0vercl0k/rp | 5fe693c26d76b514efaedb4084f6e37d820db023 | src/third_party/fmt/support/docopt.py | python | parse_expr | (tokens, options) | return [Either(*result)] if len(result) > 1 else result | expr ::= seq ( '|' seq )* ; | expr ::= seq ( '|' seq )* ; | [
"expr",
"::",
"=",
"seq",
"(",
"|",
"seq",
")",
"*",
";"
] | def parse_expr(tokens, options):
"""expr ::= seq ( '|' seq )* ;"""
seq = parse_seq(tokens, options)
if tokens.current() != '|':
return seq
result = [Required(*seq)] if len(seq) > 1 else seq
while tokens.current() == '|':
tokens.move()
seq = parse_seq(tokens, options)
result += [Required(*seq)] if len(seq) > 1 else seq
return [Either(*result)] if len(result) > 1 else result | [
"def",
"parse_expr",
"(",
"tokens",
",",
"options",
")",
":",
"seq",
"=",
"parse_seq",
"(",
"tokens",
",",
"options",
")",
"if",
"tokens",
".",
"current",
"(",
")",
"!=",
"'|'",
":",
"return",
"seq",
"result",
"=",
"[",
"Required",
"(",
"*",
"seq",
")",
"]",
"if",
"len",
"(",
"seq",
")",
">",
"1",
"else",
"seq",
"while",
"tokens",
".",
"current",
"(",
")",
"==",
"'|'",
":",
"tokens",
".",
"move",
"(",
")",
"seq",
"=",
"parse_seq",
"(",
"tokens",
",",
"options",
")",
"result",
"+=",
"[",
"Required",
"(",
"*",
"seq",
")",
"]",
"if",
"len",
"(",
"seq",
")",
">",
"1",
"else",
"seq",
"return",
"[",
"Either",
"(",
"*",
"result",
")",
"]",
"if",
"len",
"(",
"result",
")",
">",
"1",
"else",
"result"
] | https://github.com/0vercl0k/rp/blob/5fe693c26d76b514efaedb4084f6e37d820db023/src/third_party/fmt/support/docopt.py#L377-L387 | |
PixarAnimationStudios/USD | faed18ce62c8736b02413635b584a2f637156bad | pxr/usdImaging/usdviewq/__init__.py | python | Launcher.__LaunchProcess | (self, arg_parse_result) | after the arguments have been parsed, launch the UI in a forked process | after the arguments have been parsed, launch the UI in a forked process | [
"after",
"the",
"arguments",
"have",
"been",
"parsed",
"launch",
"the",
"UI",
"in",
"a",
"forked",
"process"
] | def __LaunchProcess(self, arg_parse_result):
'''
after the arguments have been parsed, launch the UI in a forked process
'''
# Initialize concurrency limit as early as possible so that it is
# respected by subsequent imports.
(app, appController) = self.LaunchPreamble(arg_parse_result)
if arg_parse_result.quitAfterStartup:
# Enqueue event to shutdown application. We don't use quit() because
# it doesn't trigger the closeEvent() on the main window which is
# used to orchestrate the shutdown.
QtCore.QTimer.singleShot(0, app.instance().closeAllWindows)
app.exec_() | [
"def",
"__LaunchProcess",
"(",
"self",
",",
"arg_parse_result",
")",
":",
"# Initialize concurrency limit as early as possible so that it is",
"# respected by subsequent imports.",
"(",
"app",
",",
"appController",
")",
"=",
"self",
".",
"LaunchPreamble",
"(",
"arg_parse_result",
")",
"if",
"arg_parse_result",
".",
"quitAfterStartup",
":",
"# Enqueue event to shutdown application. We don't use quit() because",
"# it doesn't trigger the closeEvent() on the main window which is",
"# used to orchestrate the shutdown.",
"QtCore",
".",
"QTimer",
".",
"singleShot",
"(",
"0",
",",
"app",
".",
"instance",
"(",
")",
".",
"closeAllWindows",
")",
"app",
".",
"exec_",
"(",
")"
] | https://github.com/PixarAnimationStudios/USD/blob/faed18ce62c8736b02413635b584a2f637156bad/pxr/usdImaging/usdviewq/__init__.py#L338-L352 | ||
timi-liuliang/echo | 40a5a24d430eee4118314459ab7e03afcb3b8719 | thirdparty/protobuf/python/google/protobuf/internal/containers.py | python | RepeatedScalarFieldContainer.__getslice__ | (self, start, stop) | return self._values[start:stop] | Retrieves the subset of items from between the specified indices. | Retrieves the subset of items from between the specified indices. | [
"Retrieves",
"the",
"subset",
"of",
"items",
"from",
"between",
"the",
"specified",
"indices",
"."
] | def __getslice__(self, start, stop):
"""Retrieves the subset of items from between the specified indices."""
return self._values[start:stop] | [
"def",
"__getslice__",
"(",
"self",
",",
"start",
",",
"stop",
")",
":",
"return",
"self",
".",
"_values",
"[",
"start",
":",
"stop",
"]"
] | https://github.com/timi-liuliang/echo/blob/40a5a24d430eee4118314459ab7e03afcb3b8719/thirdparty/protobuf/python/google/protobuf/internal/containers.py#L154-L156 | |
tpfister/caffe-heatmap | 4db69ef53e6b8a0b3b4ebb29328b0ab3dbf67c4e | python/caffe/detector.py | python | Detector.detect_windows | (self, images_windows) | return detections | Do windowed detection over given images and windows. Windows are
extracted then warped to the input dimensions of the net.
Parameters
----------
images_windows: (image filename, window list) iterable.
context_crop: size of context border to crop in pixels.
Returns
-------
detections: list of {filename: image filename, window: crop coordinates,
predictions: prediction vector} dicts. | Do windowed detection over given images and windows. Windows are
extracted then warped to the input dimensions of the net. | [
"Do",
"windowed",
"detection",
"over",
"given",
"images",
"and",
"windows",
".",
"Windows",
"are",
"extracted",
"then",
"warped",
"to",
"the",
"input",
"dimensions",
"of",
"the",
"net",
"."
] | def detect_windows(self, images_windows):
"""
Do windowed detection over given images and windows. Windows are
extracted then warped to the input dimensions of the net.
Parameters
----------
images_windows: (image filename, window list) iterable.
context_crop: size of context border to crop in pixels.
Returns
-------
detections: list of {filename: image filename, window: crop coordinates,
predictions: prediction vector} dicts.
"""
# Extract windows.
window_inputs = []
for image_fname, windows in images_windows:
image = caffe.io.load_image(image_fname).astype(np.float32)
for window in windows:
window_inputs.append(self.crop(image, window))
# Run through the net (warping windows to input dimensions).
in_ = self.inputs[0]
caffe_in = np.zeros((len(window_inputs), window_inputs[0].shape[2])
+ self.blobs[in_].data.shape[2:],
dtype=np.float32)
for ix, window_in in enumerate(window_inputs):
caffe_in[ix] = self.transformer.preprocess(in_, window_in)
out = self.forward_all(**{in_: caffe_in})
predictions = out[self.outputs[0]].squeeze(axis=(2, 3))
# Package predictions with images and windows.
detections = []
ix = 0
for image_fname, windows in images_windows:
for window in windows:
detections.append({
'window': window,
'prediction': predictions[ix],
'filename': image_fname
})
ix += 1
return detections | [
"def",
"detect_windows",
"(",
"self",
",",
"images_windows",
")",
":",
"# Extract windows.",
"window_inputs",
"=",
"[",
"]",
"for",
"image_fname",
",",
"windows",
"in",
"images_windows",
":",
"image",
"=",
"caffe",
".",
"io",
".",
"load_image",
"(",
"image_fname",
")",
".",
"astype",
"(",
"np",
".",
"float32",
")",
"for",
"window",
"in",
"windows",
":",
"window_inputs",
".",
"append",
"(",
"self",
".",
"crop",
"(",
"image",
",",
"window",
")",
")",
"# Run through the net (warping windows to input dimensions).",
"in_",
"=",
"self",
".",
"inputs",
"[",
"0",
"]",
"caffe_in",
"=",
"np",
".",
"zeros",
"(",
"(",
"len",
"(",
"window_inputs",
")",
",",
"window_inputs",
"[",
"0",
"]",
".",
"shape",
"[",
"2",
"]",
")",
"+",
"self",
".",
"blobs",
"[",
"in_",
"]",
".",
"data",
".",
"shape",
"[",
"2",
":",
"]",
",",
"dtype",
"=",
"np",
".",
"float32",
")",
"for",
"ix",
",",
"window_in",
"in",
"enumerate",
"(",
"window_inputs",
")",
":",
"caffe_in",
"[",
"ix",
"]",
"=",
"self",
".",
"transformer",
".",
"preprocess",
"(",
"in_",
",",
"window_in",
")",
"out",
"=",
"self",
".",
"forward_all",
"(",
"*",
"*",
"{",
"in_",
":",
"caffe_in",
"}",
")",
"predictions",
"=",
"out",
"[",
"self",
".",
"outputs",
"[",
"0",
"]",
"]",
".",
"squeeze",
"(",
"axis",
"=",
"(",
"2",
",",
"3",
")",
")",
"# Package predictions with images and windows.",
"detections",
"=",
"[",
"]",
"ix",
"=",
"0",
"for",
"image_fname",
",",
"windows",
"in",
"images_windows",
":",
"for",
"window",
"in",
"windows",
":",
"detections",
".",
"append",
"(",
"{",
"'window'",
":",
"window",
",",
"'prediction'",
":",
"predictions",
"[",
"ix",
"]",
",",
"'filename'",
":",
"image_fname",
"}",
")",
"ix",
"+=",
"1",
"return",
"detections"
] | https://github.com/tpfister/caffe-heatmap/blob/4db69ef53e6b8a0b3b4ebb29328b0ab3dbf67c4e/python/caffe/detector.py#L56-L99 | |
priyankchheda/algorithms | c361aa9071573fa9966d5b02d05e524815abcf2b | ternary_search_tries/ternary_search_tries.py | python | TernarySearchTries.put | (self, key, value) | inserts new key-value pair into the ternary search tries | inserts new key-value pair into the ternary search tries | [
"inserts",
"new",
"key",
"-",
"value",
"pair",
"into",
"the",
"ternary",
"search",
"tries"
] | def put(self, key, value):
""" inserts new key-value pair into the ternary search tries """
def _put(node, key, value, depth):
""" recursive internal method which works on node level """
char = key[depth]
if node is None:
node = Node(char)
if char < node.character:
node.left = _put(node.left, key, value, depth)
elif char > node.character:
node.right = _put(node.right, key, value, depth)
elif depth < len(key) - 1:
node.middle = _put(node.middle, key, value, depth + 1)
else:
node.value = value
return node
self.root = _put(self.root, key, value, 0) | [
"def",
"put",
"(",
"self",
",",
"key",
",",
"value",
")",
":",
"def",
"_put",
"(",
"node",
",",
"key",
",",
"value",
",",
"depth",
")",
":",
"\"\"\" recursive internal method which works on node level \"\"\"",
"char",
"=",
"key",
"[",
"depth",
"]",
"if",
"node",
"is",
"None",
":",
"node",
"=",
"Node",
"(",
"char",
")",
"if",
"char",
"<",
"node",
".",
"character",
":",
"node",
".",
"left",
"=",
"_put",
"(",
"node",
".",
"left",
",",
"key",
",",
"value",
",",
"depth",
")",
"elif",
"char",
">",
"node",
".",
"character",
":",
"node",
".",
"right",
"=",
"_put",
"(",
"node",
".",
"right",
",",
"key",
",",
"value",
",",
"depth",
")",
"elif",
"depth",
"<",
"len",
"(",
"key",
")",
"-",
"1",
":",
"node",
".",
"middle",
"=",
"_put",
"(",
"node",
".",
"middle",
",",
"key",
",",
"value",
",",
"depth",
"+",
"1",
")",
"else",
":",
"node",
".",
"value",
"=",
"value",
"return",
"node",
"self",
".",
"root",
"=",
"_put",
"(",
"self",
".",
"root",
",",
"key",
",",
"value",
",",
"0",
")"
] | https://github.com/priyankchheda/algorithms/blob/c361aa9071573fa9966d5b02d05e524815abcf2b/ternary_search_tries/ternary_search_tries.py#L32-L50 | ||
schwehr/libais | 1e19605942c8e155cd02fde6d1acde75ecd15d75 | ais/tag_block.py | python | DecodeTagMultiple | (tag_block_message) | return msg['decoded'] | Decode a TAG block message that spans multiple lines. | Decode a TAG block message that spans multiple lines. | [
"Decode",
"a",
"TAG",
"block",
"message",
"that",
"spans",
"multiple",
"lines",
"."
] | def DecodeTagMultiple(tag_block_message):
"""Decode a TAG block message that spans multiple lines."""
payloads = [msg['payload'] for msg in tag_block_message['matches']]
q = vdm.BareQueue()
for line in vdm.VdmLines(payloads):
q.put(line)
if q.qsize() != 1:
logger.info('Error: Should get just one message decoded from this: %s',
tag_block_message)
return
msg = q.get()
return msg['decoded'] | [
"def",
"DecodeTagMultiple",
"(",
"tag_block_message",
")",
":",
"payloads",
"=",
"[",
"msg",
"[",
"'payload'",
"]",
"for",
"msg",
"in",
"tag_block_message",
"[",
"'matches'",
"]",
"]",
"q",
"=",
"vdm",
".",
"BareQueue",
"(",
")",
"for",
"line",
"in",
"vdm",
".",
"VdmLines",
"(",
"payloads",
")",
":",
"q",
".",
"put",
"(",
"line",
")",
"if",
"q",
".",
"qsize",
"(",
")",
"!=",
"1",
":",
"logger",
".",
"info",
"(",
"'Error: Should get just one message decoded from this: %s'",
",",
"tag_block_message",
")",
"return",
"msg",
"=",
"q",
".",
"get",
"(",
")",
"return",
"msg",
"[",
"'decoded'",
"]"
] | https://github.com/schwehr/libais/blob/1e19605942c8e155cd02fde6d1acde75ecd15d75/ais/tag_block.py#L213-L225 | |
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/stc.py | python | StyledTextCtrl.ShowLines | (*args, **kwargs) | return _stc.StyledTextCtrl_ShowLines(*args, **kwargs) | ShowLines(self, int lineStart, int lineEnd)
Make a range of lines visible. | ShowLines(self, int lineStart, int lineEnd) | [
"ShowLines",
"(",
"self",
"int",
"lineStart",
"int",
"lineEnd",
")"
] | def ShowLines(*args, **kwargs):
"""
ShowLines(self, int lineStart, int lineEnd)
Make a range of lines visible.
"""
return _stc.StyledTextCtrl_ShowLines(*args, **kwargs) | [
"def",
"ShowLines",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"return",
"_stc",
".",
"StyledTextCtrl_ShowLines",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")"
] | https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_cocoa/stc.py#L3930-L3936 | |
weolar/miniblink49 | 1c4678db0594a4abde23d3ebbcc7cd13c3170777 | third_party/WebKit/Tools/Scripts/webkitpy/thirdparty/autopep8.py | python | FixPEP8.fix_w291 | (self, result) | Remove trailing whitespace. | Remove trailing whitespace. | [
"Remove",
"trailing",
"whitespace",
"."
] | def fix_w291(self, result):
"""Remove trailing whitespace."""
fixed_line = self.source[result['line'] - 1].rstrip()
self.source[result['line'] - 1] = fixed_line + '\n' | [
"def",
"fix_w291",
"(",
"self",
",",
"result",
")",
":",
"fixed_line",
"=",
"self",
".",
"source",
"[",
"result",
"[",
"'line'",
"]",
"-",
"1",
"]",
".",
"rstrip",
"(",
")",
"self",
".",
"source",
"[",
"result",
"[",
"'line'",
"]",
"-",
"1",
"]",
"=",
"fixed_line",
"+",
"'\\n'"
] | https://github.com/weolar/miniblink49/blob/1c4678db0594a4abde23d3ebbcc7cd13c3170777/third_party/WebKit/Tools/Scripts/webkitpy/thirdparty/autopep8.py#L975-L978 | ||
ricardoquesada/Spidermonkey | 4a75ea2543408bd1b2c515aa95901523eeef7858 | media/webrtc/trunk/tools/gyp/pylib/gyp/generator/ninja.py | python | NinjaWriter.WriteMacBundleResources | (self, resources, bundle_depends) | Writes ninja edges for 'mac_bundle_resources'. | Writes ninja edges for 'mac_bundle_resources'. | [
"Writes",
"ninja",
"edges",
"for",
"mac_bundle_resources",
"."
] | def WriteMacBundleResources(self, resources, bundle_depends):
"""Writes ninja edges for 'mac_bundle_resources'."""
for output, res in gyp.xcode_emulation.GetMacBundleResources(
self.ExpandSpecial(generator_default_variables['PRODUCT_DIR']),
self.xcode_settings, map(self.GypPathToNinja, resources)):
self.ninja.build(output, 'mac_tool', res,
variables=[('mactool_cmd', 'copy-bundle-resource')])
bundle_depends.append(output) | [
"def",
"WriteMacBundleResources",
"(",
"self",
",",
"resources",
",",
"bundle_depends",
")",
":",
"for",
"output",
",",
"res",
"in",
"gyp",
".",
"xcode_emulation",
".",
"GetMacBundleResources",
"(",
"self",
".",
"ExpandSpecial",
"(",
"generator_default_variables",
"[",
"'PRODUCT_DIR'",
"]",
")",
",",
"self",
".",
"xcode_settings",
",",
"map",
"(",
"self",
".",
"GypPathToNinja",
",",
"resources",
")",
")",
":",
"self",
".",
"ninja",
".",
"build",
"(",
"output",
",",
"'mac_tool'",
",",
"res",
",",
"variables",
"=",
"[",
"(",
"'mactool_cmd'",
",",
"'copy-bundle-resource'",
")",
"]",
")",
"bundle_depends",
".",
"append",
"(",
"output",
")"
] | https://github.com/ricardoquesada/Spidermonkey/blob/4a75ea2543408bd1b2c515aa95901523eeef7858/media/webrtc/trunk/tools/gyp/pylib/gyp/generator/ninja.py#L680-L687 | ||
mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/Muon/GUI/Common/contexts/fitting_contexts/tf_asymmetry_fitting_context.py | python | TFAsymmetryFittingContext.remove_workspace_by_name | (self, workspace_name: str) | Remove a Fit from the history when an ADS delete event happens on one of its output workspaces. | Remove a Fit from the history when an ADS delete event happens on one of its output workspaces. | [
"Remove",
"a",
"Fit",
"from",
"the",
"history",
"when",
"an",
"ADS",
"delete",
"event",
"happens",
"on",
"one",
"of",
"its",
"output",
"workspaces",
"."
] | def remove_workspace_by_name(self, workspace_name: str) -> None:
"""Remove a Fit from the history when an ADS delete event happens on one of its output workspaces."""
self.remove_fit_by_name(self._fit_history[TF_SINGLE_FITS_KEY], workspace_name)
self.remove_fit_by_name(self._fit_history[TF_SIMULTANEOUS_FITS_KEY], workspace_name)
super().remove_workspace_by_name(workspace_name) | [
"def",
"remove_workspace_by_name",
"(",
"self",
",",
"workspace_name",
":",
"str",
")",
"->",
"None",
":",
"self",
".",
"remove_fit_by_name",
"(",
"self",
".",
"_fit_history",
"[",
"TF_SINGLE_FITS_KEY",
"]",
",",
"workspace_name",
")",
"self",
".",
"remove_fit_by_name",
"(",
"self",
".",
"_fit_history",
"[",
"TF_SIMULTANEOUS_FITS_KEY",
"]",
",",
"workspace_name",
")",
"super",
"(",
")",
".",
"remove_workspace_by_name",
"(",
"workspace_name",
")"
] | https://github.com/mantidproject/mantid/blob/03deeb89254ec4289edb8771e0188c2090a02f32/qt/python/mantidqtinterfaces/mantidqtinterfaces/Muon/GUI/Common/contexts/fitting_contexts/tf_asymmetry_fitting_context.py#L70-L74 | ||
apache/incubator-mxnet | f03fb23f1d103fec9541b5ae59ee06b1734a51d9 | python/mxnet/notebook/callback.py | python | LiveBokehChart.interval_elapsed | (self) | return time.time() - self.last_update > self.display_freq | Check whether it is time to update plot.
Returns
-------
Boolean value of whethe to update now | Check whether it is time to update plot.
Returns
-------
Boolean value of whethe to update now | [
"Check",
"whether",
"it",
"is",
"time",
"to",
"update",
"plot",
".",
"Returns",
"-------",
"Boolean",
"value",
"of",
"whethe",
"to",
"update",
"now"
] | def interval_elapsed(self):
"""Check whether it is time to update plot.
Returns
-------
Boolean value of whethe to update now
"""
return time.time() - self.last_update > self.display_freq | [
"def",
"interval_elapsed",
"(",
"self",
")",
":",
"return",
"time",
".",
"time",
"(",
")",
"-",
"self",
".",
"last_update",
">",
"self",
".",
"display_freq"
] | https://github.com/apache/incubator-mxnet/blob/f03fb23f1d103fec9541b5ae59ee06b1734a51d9/python/mxnet/notebook/callback.py#L235-L241 | |
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/build/waf-1.7.13/waflib/extras/review.py | python | ReviewContext.load_review_set | (self) | return ConfigSet.ConfigSet() | Load and return the review set from the cache if it exists.
Otherwise, return an empty set. | Load and return the review set from the cache if it exists.
Otherwise, return an empty set. | [
"Load",
"and",
"return",
"the",
"review",
"set",
"from",
"the",
"cache",
"if",
"it",
"exists",
".",
"Otherwise",
"return",
"an",
"empty",
"set",
"."
] | def load_review_set(self):
"""
Load and return the review set from the cache if it exists.
Otherwise, return an empty set.
"""
if os.path.isfile(self.review_path):
return ConfigSet.ConfigSet(self.review_path)
return ConfigSet.ConfigSet() | [
"def",
"load_review_set",
"(",
"self",
")",
":",
"if",
"os",
".",
"path",
".",
"isfile",
"(",
"self",
".",
"review_path",
")",
":",
"return",
"ConfigSet",
".",
"ConfigSet",
"(",
"self",
".",
"review_path",
")",
"return",
"ConfigSet",
".",
"ConfigSet",
"(",
")"
] | https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/build/waf-1.7.13/waflib/extras/review.py#L189-L196 | |
SpenceKonde/megaTinyCore | 1c4a70b18a149fe6bcb551dfa6db11ca50b8997b | megaavr/tools/libs/intelhex/__init__.py | python | IntelHex.__delitem__ | (self, addr) | Delete byte at address. | Delete byte at address. | [
"Delete",
"byte",
"at",
"address",
"."
] | def __delitem__(self, addr):
"""Delete byte at address."""
t = type(addr)
if t in IntTypes:
if addr < 0:
raise TypeError('Address should be >= 0.')
del self._buf[addr]
elif t == slice:
addresses = dict_keys(self._buf)
if addresses:
addresses.sort()
start = addr.start or addresses[0]
stop = addr.stop or (addresses[-1]+1)
step = addr.step or 1
for i in range_g(start, stop, step):
x = self._buf.get(i)
if x is not None:
del self._buf[i]
else:
raise TypeError('Address has unsupported type: %s' % t) | [
"def",
"__delitem__",
"(",
"self",
",",
"addr",
")",
":",
"t",
"=",
"type",
"(",
"addr",
")",
"if",
"t",
"in",
"IntTypes",
":",
"if",
"addr",
"<",
"0",
":",
"raise",
"TypeError",
"(",
"'Address should be >= 0.'",
")",
"del",
"self",
".",
"_buf",
"[",
"addr",
"]",
"elif",
"t",
"==",
"slice",
":",
"addresses",
"=",
"dict_keys",
"(",
"self",
".",
"_buf",
")",
"if",
"addresses",
":",
"addresses",
".",
"sort",
"(",
")",
"start",
"=",
"addr",
".",
"start",
"or",
"addresses",
"[",
"0",
"]",
"stop",
"=",
"addr",
".",
"stop",
"or",
"(",
"addresses",
"[",
"-",
"1",
"]",
"+",
"1",
")",
"step",
"=",
"addr",
".",
"step",
"or",
"1",
"for",
"i",
"in",
"range_g",
"(",
"start",
",",
"stop",
",",
"step",
")",
":",
"x",
"=",
"self",
".",
"_buf",
".",
"get",
"(",
"i",
")",
"if",
"x",
"is",
"not",
"None",
":",
"del",
"self",
".",
"_buf",
"[",
"i",
"]",
"else",
":",
"raise",
"TypeError",
"(",
"'Address has unsupported type: %s'",
"%",
"t",
")"
] | https://github.com/SpenceKonde/megaTinyCore/blob/1c4a70b18a149fe6bcb551dfa6db11ca50b8997b/megaavr/tools/libs/intelhex/__init__.py#L510-L529 | ||
versatica/mediasoup | 36155116316782ba747caa93f41fefb4ed1c33c8 | worker/scripts/clang-tidy.py | python | get_tidy_invocation | (f, clang_tidy_binary, checks, tmpdir, build_path,
header_filter, extra_arg, extra_arg_before, quiet,
config) | return start | Gets a command line for clang-tidy. | Gets a command line for clang-tidy. | [
"Gets",
"a",
"command",
"line",
"for",
"clang",
"-",
"tidy",
"."
] | def get_tidy_invocation(f, clang_tidy_binary, checks, tmpdir, build_path,
header_filter, extra_arg, extra_arg_before, quiet,
config):
"""Gets a command line for clang-tidy."""
start = [clang_tidy_binary]
if header_filter is not None:
start.append('-header-filter=' + header_filter)
else:
# Show warnings in all in-project headers by default.
start.append('-header-filter=^' + build_path + '/.*')
if checks:
start.append('-checks=' + checks)
if tmpdir is not None:
start.append('-export-fixes')
# Get a temporary file. We immediately close the handle so clang-tidy can
# overwrite it.
(handle, name) = tempfile.mkstemp(suffix='.yaml', dir=tmpdir)
os.close(handle)
start.append(name)
for arg in extra_arg:
start.append('-extra-arg=%s' % arg)
for arg in extra_arg_before:
start.append('-extra-arg-before=%s' % arg)
start.append('-p=' + build_path)
if quiet:
start.append('-quiet')
if config:
start.append('-config=' + config)
start.append(f)
return start | [
"def",
"get_tidy_invocation",
"(",
"f",
",",
"clang_tidy_binary",
",",
"checks",
",",
"tmpdir",
",",
"build_path",
",",
"header_filter",
",",
"extra_arg",
",",
"extra_arg_before",
",",
"quiet",
",",
"config",
")",
":",
"start",
"=",
"[",
"clang_tidy_binary",
"]",
"if",
"header_filter",
"is",
"not",
"None",
":",
"start",
".",
"append",
"(",
"'-header-filter='",
"+",
"header_filter",
")",
"else",
":",
"# Show warnings in all in-project headers by default.",
"start",
".",
"append",
"(",
"'-header-filter=^'",
"+",
"build_path",
"+",
"'/.*'",
")",
"if",
"checks",
":",
"start",
".",
"append",
"(",
"'-checks='",
"+",
"checks",
")",
"if",
"tmpdir",
"is",
"not",
"None",
":",
"start",
".",
"append",
"(",
"'-export-fixes'",
")",
"# Get a temporary file. We immediately close the handle so clang-tidy can",
"# overwrite it.",
"(",
"handle",
",",
"name",
")",
"=",
"tempfile",
".",
"mkstemp",
"(",
"suffix",
"=",
"'.yaml'",
",",
"dir",
"=",
"tmpdir",
")",
"os",
".",
"close",
"(",
"handle",
")",
"start",
".",
"append",
"(",
"name",
")",
"for",
"arg",
"in",
"extra_arg",
":",
"start",
".",
"append",
"(",
"'-extra-arg=%s'",
"%",
"arg",
")",
"for",
"arg",
"in",
"extra_arg_before",
":",
"start",
".",
"append",
"(",
"'-extra-arg-before=%s'",
"%",
"arg",
")",
"start",
".",
"append",
"(",
"'-p='",
"+",
"build_path",
")",
"if",
"quiet",
":",
"start",
".",
"append",
"(",
"'-quiet'",
")",
"if",
"config",
":",
"start",
".",
"append",
"(",
"'-config='",
"+",
"config",
")",
"start",
".",
"append",
"(",
"f",
")",
"return",
"start"
] | https://github.com/versatica/mediasoup/blob/36155116316782ba747caa93f41fefb4ed1c33c8/worker/scripts/clang-tidy.py#L76-L105 | |
pmq20/node-packer | 12c46c6e44fbc14d9ee645ebd17d5296b324f7e0 | lts/deps/npm/node_modules/node-gyp/gyp/pylib/gyp/generator/make.py | python | StringToMakefileVariable | (string) | return re.sub('[^a-zA-Z0-9_]', '_', string) | Convert a string to a value that is acceptable as a make variable name. | Convert a string to a value that is acceptable as a make variable name. | [
"Convert",
"a",
"string",
"to",
"a",
"value",
"that",
"is",
"acceptable",
"as",
"a",
"make",
"variable",
"name",
"."
] | def StringToMakefileVariable(string):
"""Convert a string to a value that is acceptable as a make variable name."""
return re.sub('[^a-zA-Z0-9_]', '_', string) | [
"def",
"StringToMakefileVariable",
"(",
"string",
")",
":",
"return",
"re",
".",
"sub",
"(",
"'[^a-zA-Z0-9_]'",
",",
"'_'",
",",
"string",
")"
] | https://github.com/pmq20/node-packer/blob/12c46c6e44fbc14d9ee645ebd17d5296b324f7e0/lts/deps/npm/node_modules/node-gyp/gyp/pylib/gyp/generator/make.py#L634-L636 | |
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_controls.py | python | TreeCtrl.SetQuickBestSize | (*args, **kwargs) | return _controls_.TreeCtrl_SetQuickBestSize(*args, **kwargs) | SetQuickBestSize(self, bool q) | SetQuickBestSize(self, bool q) | [
"SetQuickBestSize",
"(",
"self",
"bool",
"q",
")"
] | def SetQuickBestSize(*args, **kwargs):
"""SetQuickBestSize(self, bool q)"""
return _controls_.TreeCtrl_SetQuickBestSize(*args, **kwargs) | [
"def",
"SetQuickBestSize",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"return",
"_controls_",
".",
"TreeCtrl_SetQuickBestSize",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")"
] | https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_cocoa/_controls.py#L5572-L5574 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.