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microsoft/checkedc-clang
a173fefde5d7877b7750e7ce96dd08cf18baebf2
lldb/utils/lui/lldbutil.py
python
ChildVisitingFormatter.__init__
(self, indent_child=2)
Default indentation of 2 SPC's for the children.
Default indentation of 2 SPC's for the children.
[ "Default", "indentation", "of", "2", "SPC", "s", "for", "the", "children", "." ]
def __init__(self, indent_child=2): """Default indentation of 2 SPC's for the children.""" self.cindent = indent_child
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https://github.com/microsoft/checkedc-clang/blob/a173fefde5d7877b7750e7ce96dd08cf18baebf2/lldb/utils/lui/lldbutil.py#L989-L991
hanpfei/chromium-net
392cc1fa3a8f92f42e4071ab6e674d8e0482f83f
third_party/catapult/third_party/py_vulcanize/third_party/rjsmin/_setup/py2/ext.py
python
ConfTest.__del__
(self)
Destruction
Destruction
[ "Destruction" ]
def __del__(self): """ Destruction """ self.destroy()
[ "def", "__del__", "(", "self", ")", ":", "self", ".", "destroy", "(", ")" ]
https://github.com/hanpfei/chromium-net/blob/392cc1fa3a8f92f42e4071ab6e674d8e0482f83f/third_party/catapult/third_party/py_vulcanize/third_party/rjsmin/_setup/py2/ext.py#L193-L195
letscontrolit/ESPEasy
acb2c9e695d6f61d8d67adf0fe4037c08d4baedd
lib/IRremoteESP8266/tools/auto_analyse_raw_data.py
python
dump_constants
(message, defines, name="", output=sys.stdout)
Dump the key constants and generate the C++ #defines.
Dump the key constants and generate the C++ #defines.
[ "Dump", "the", "key", "constants", "and", "generate", "the", "C", "++", "#defines", "." ]
def dump_constants(message, defines, name="", output=sys.stdout): """Dump the key constants and generate the C++ #defines.""" ldr_mark = None hdr_mark = 0 hdr_space = 0 if message.ldr_mark is not None: ldr_mark = avg_list(message.mark_buckets[message.ldr_mark]) if message.hdr_mark != 0: hdr_mark = avg_list(message.mark_buckets[message.hdr_mark]) bit_mark = avg_list(message.mark_buckets[message.bit_mark]) if message.hdr_space is not None: hdr_space = avg_list(message.space_buckets[message.hdr_space]) one_space = avg_list(message.space_buckets[message.one_space]) zero_space = avg_list(message.space_buckets[message.zero_space]) output.write("Guessing key value:\n" f"k{name}HdrMark = {hdr_mark}\n" f"k{name}HdrSpace = {hdr_space}\n" f"k{name}BitMark = {bit_mark}\n" f"k{name}OneSpace = {one_space}\n" f"k{name}ZeroSpace = {zero_space}\n") defines.append(f"const uint16_t k{name}HdrMark = {hdr_mark};") defines.append(f"const uint16_t k{name}BitMark = {bit_mark};") defines.append(f"const uint16_t k{name}HdrSpace = {hdr_space};") defines.append(f"const uint16_t k{name}OneSpace = {one_space};") defines.append(f"const uint16_t k{name}ZeroSpace = {zero_space};") if ldr_mark: output.write(f"k{name}LdrMark = {ldr_mark}\n") defines.append(f"const uint16_t k{name}LdrMark = {ldr_mark};") avg_gaps = [avg_list(message.space_buckets[x]) for x in message.gaps] if len(message.gaps) == 1: output.write(f"k{name}SpaceGap = {avg_gaps[0]}\n") defines.append(f"const uint16_t k{name}SpaceGap = {avg_gaps[0]};") else: count = 0 for gap in avg_gaps: # We probably (still) have a gap in the protocol. count = count + 1 output.write(f"k{name}SpaceGap{count} = {gap}\n") defines.append(f"const uint16_t k{name}SpaceGap{count} = {gap};") defines.append(f"const uint16_t k{name}Freq = 38000; " "// Hz. (Guessing the most common frequency.)")
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https://github.com/letscontrolit/ESPEasy/blob/acb2c9e695d6f61d8d67adf0fe4037c08d4baedd/lib/IRremoteESP8266/tools/auto_analyse_raw_data.py#L315-L357
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/pip/_internal/req/req_uninstall.py
python
UninstallPathSet.rollback
(self)
Rollback the changes previously made by remove().
Rollback the changes previously made by remove().
[ "Rollback", "the", "changes", "previously", "made", "by", "remove", "()", "." ]
def rollback(self): # type: () -> None """Rollback the changes previously made by remove().""" if not self._moved_paths.can_rollback: logger.error( "Can't roll back %s; was not uninstalled", self.dist.project_name, ) return logger.info('Rolling back uninstall of %s', self.dist.project_name) self._moved_paths.rollback() for pth in self.pth.values(): pth.rollback()
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/pip/_internal/req/req_uninstall.py#L877-L901
hanpfei/chromium-net
392cc1fa3a8f92f42e4071ab6e674d8e0482f83f
third_party/catapult/third_party/WebOb/webob/cookies.py
python
Base64Serializer.loads
(self, bstruct)
return self.serializer.loads(cstruct)
Given a ``bstruct`` (a bytestring), verify the signature and then deserialize and return the deserialized value. A ``ValueError`` will be raised if the signature fails to validate.
Given a ``bstruct`` (a bytestring), verify the signature and then deserialize and return the deserialized value.
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def loads(self, bstruct): """ Given a ``bstruct`` (a bytestring), verify the signature and then deserialize and return the deserialized value. A ``ValueError`` will be raised if the signature fails to validate. """ try: cstruct = base64.urlsafe_b64decode(bytes_(bstruct)) except (binascii.Error, TypeError) as e: raise ValueError('Badly formed base64 data: %s' % e) return self.serializer.loads(cstruct)
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https://github.com/hanpfei/chromium-net/blob/392cc1fa3a8f92f42e4071ab6e674d8e0482f83f/third_party/catapult/third_party/WebOb/webob/cookies.py#L498-L510
GeometryCollective/boundary-first-flattening
8250e5a0e85980ec50b5e8aa8f49dd6519f915cd
deps/nanogui/docs/exhale.py
python
kindAsBreatheDirective
(kind)
return directive
Returns the appropriate breathe restructured text directive for the specified kind. The output for a given kind is as follows: +-------------+--------------------+ | Input Kind | Output Directive | +=============+====================+ | "class" | "doxygenclass" | +-------------+--------------------+ | "define" | "doxygendefine" | +-------------+--------------------+ | "enum" | "doxygenenum" | +-------------+--------------------+ | "enumvalue" | "doxygenenumvalue" | +-------------+--------------------+ | "file" | "doxygenfile" | +-------------+--------------------+ | "function" | "doxygenfunction" | +-------------+--------------------+ | "group" | "doxygengroup" | +-------------+--------------------+ | "namespace" | "doxygennamespace" | +-------------+--------------------+ | "struct" | "doxygenstruct" | +-------------+--------------------+ | "typedef" | "doxygentypedef" | +-------------+--------------------+ | "union" | "doxygenunion" | +-------------+--------------------+ | "variable" | "doxygenvariable" | +-------------+--------------------+ The following breathe kinds are ignored: - "autodoxygenfile" - "doxygenindex" - "autodoxygenindex" Note also that although a return value is generated, neither "enumvalue" nor "group" are actually used. :Parameters: ``kind`` (str) The kind of the breathe compound / ExhaleNode object (same values). :Return (str): The directive to be used for the given ``kind``. The empty string is returned for both unrecognized and ignored input values.
Returns the appropriate breathe restructured text directive for the specified kind. The output for a given kind is as follows:
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def kindAsBreatheDirective(kind): ''' Returns the appropriate breathe restructured text directive for the specified kind. The output for a given kind is as follows: +-------------+--------------------+ | Input Kind | Output Directive | +=============+====================+ | "class" | "doxygenclass" | +-------------+--------------------+ | "define" | "doxygendefine" | +-------------+--------------------+ | "enum" | "doxygenenum" | +-------------+--------------------+ | "enumvalue" | "doxygenenumvalue" | +-------------+--------------------+ | "file" | "doxygenfile" | +-------------+--------------------+ | "function" | "doxygenfunction" | +-------------+--------------------+ | "group" | "doxygengroup" | +-------------+--------------------+ | "namespace" | "doxygennamespace" | +-------------+--------------------+ | "struct" | "doxygenstruct" | +-------------+--------------------+ | "typedef" | "doxygentypedef" | +-------------+--------------------+ | "union" | "doxygenunion" | +-------------+--------------------+ | "variable" | "doxygenvariable" | +-------------+--------------------+ The following breathe kinds are ignored: - "autodoxygenfile" - "doxygenindex" - "autodoxygenindex" Note also that although a return value is generated, neither "enumvalue" nor "group" are actually used. :Parameters: ``kind`` (str) The kind of the breathe compound / ExhaleNode object (same values). :Return (str): The directive to be used for the given ``kind``. The empty string is returned for both unrecognized and ignored input values. ''' if kind == "class": directive = "doxygenclass" elif kind == "struct": directive = "doxygenstruct" elif kind == "function": directive = "doxygenfunction" elif kind == "enum": directive = "doxygenenum" elif kind == "enumvalue":# unused directive = "doxygenenumvalue" elif kind == "namespace": directive = "doxygennamespace" elif kind == "define": directive = "doxygendefine" elif kind == "typedef": directive = "doxygentypedef" elif kind == "variable": directive = "doxygenvariable" elif kind == "file": directive = "doxygenfile" elif kind == "union": directive = "doxygenunion" elif kind == "group":# unused directive = "doxygengroup" else: directive = "" return directive
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https://github.com/GeometryCollective/boundary-first-flattening/blob/8250e5a0e85980ec50b5e8aa8f49dd6519f915cd/deps/nanogui/docs/exhale.py#L457-L533
apple/swift-lldb
d74be846ef3e62de946df343e8c234bde93a8912
scripts/Python/static-binding/lldb.py
python
SBModule.FindFunctions
(self, *args)
return _lldb.SBModule_FindFunctions(self, *args)
FindFunctions(SBModule self, char const * name, uint32_t name_type_mask) -> SBSymbolContextList FindFunctions(SBModule self, char const * name) -> SBSymbolContextList Find functions by name. @param[in] name The name of the function we are looking for. @param[in] name_type_mask A logical OR of one or more FunctionNameType enum bits that indicate what kind of names should be used when doing the lookup. Bits include fully qualified names, base names, C++ methods, or ObjC selectors. See FunctionNameType for more details. @return A symbol context list that gets filled in with all of the matches.
FindFunctions(SBModule self, char const * name, uint32_t name_type_mask) -> SBSymbolContextList FindFunctions(SBModule self, char const * name) -> SBSymbolContextList
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def FindFunctions(self, *args): """ FindFunctions(SBModule self, char const * name, uint32_t name_type_mask) -> SBSymbolContextList FindFunctions(SBModule self, char const * name) -> SBSymbolContextList Find functions by name. @param[in] name The name of the function we are looking for. @param[in] name_type_mask A logical OR of one or more FunctionNameType enum bits that indicate what kind of names should be used when doing the lookup. Bits include fully qualified names, base names, C++ methods, or ObjC selectors. See FunctionNameType for more details. @return A symbol context list that gets filled in with all of the matches. """ return _lldb.SBModule_FindFunctions(self, *args)
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https://github.com/apple/swift-lldb/blob/d74be846ef3e62de946df343e8c234bde93a8912/scripts/Python/static-binding/lldb.py#L7338-L7360
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/operator.py
python
length_hint
(obj, default=0)
return val
Return an estimate of the number of items in obj. This is useful for presizing containers when building from an iterable. If the object supports len(), the result will be exact. Otherwise, it may over- or under-estimate by an arbitrary amount. The result will be an integer >= 0.
Return an estimate of the number of items in obj. This is useful for presizing containers when building from an iterable.
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def length_hint(obj, default=0): """ Return an estimate of the number of items in obj. This is useful for presizing containers when building from an iterable. If the object supports len(), the result will be exact. Otherwise, it may over- or under-estimate by an arbitrary amount. The result will be an integer >= 0. """ if not isinstance(default, int): msg = ("'%s' object cannot be interpreted as an integer" % type(default).__name__) raise TypeError(msg) try: return len(obj) except TypeError: pass try: hint = type(obj).__length_hint__ except AttributeError: return default try: val = hint(obj) except TypeError: return default if val is NotImplemented: return default if not isinstance(val, int): msg = ('__length_hint__ must be integer, not %s' % type(val).__name__) raise TypeError(msg) if val < 0: msg = '__length_hint__() should return >= 0' raise ValueError(msg) return val
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/operator.py#L185-L222
miyosuda/TensorFlowAndroidDemo
35903e0221aa5f109ea2dbef27f20b52e317f42d
jni-build/jni/include/tensorflow/tensorboard/backend/handler.py
python
TensorboardHandler._send_json_response
(self, obj, code=200)
Writes out the given object as JSON using the given HTTP status code. This also replaces special float values with stringified versions. Args: obj: The object to respond with. code: The numeric HTTP status code to use.
Writes out the given object as JSON using the given HTTP status code.
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def _send_json_response(self, obj, code=200): """Writes out the given object as JSON using the given HTTP status code. This also replaces special float values with stringified versions. Args: obj: The object to respond with. code: The numeric HTTP status code to use. """ content = json.dumps(json_util.WrapSpecialFloats(obj)) self._respond(content, 'application/json', code)
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https://github.com/miyosuda/TensorFlowAndroidDemo/blob/35903e0221aa5f109ea2dbef27f20b52e317f42d/jni-build/jni/include/tensorflow/tensorboard/backend/handler.py#L225-L235
scummvm/scummvm
9c039d027e7ffb9d83ae2e274147e2daf8d57ce2
devtools/dumper-companion.py
python
decode_macjapanese
(text: ByteString)
return res
Decode Mac Japanse Mac OS Japanese https://en.wikipedia.org/wiki/Shift_JIS#MacJapanese https://www.unicode.org/Public/MAPPINGS/VENDORS/APPLE/JAPANESE.TXT
Decode Mac Japanse
[ "Decode", "Mac", "Japanse" ]
def decode_macjapanese(text: ByteString) -> str: """ Decode Mac Japanse Mac OS Japanese https://en.wikipedia.org/wiki/Shift_JIS#MacJapanese https://www.unicode.org/Public/MAPPINGS/VENDORS/APPLE/JAPANESE.TXT """ res = "" i_text = iter(text) hi = next(i_text, None) while hi: if hi <= 0x7F: # ASCII res += chr(hi) elif hi == 0x80: # reverse solidus res += "\u005C" elif (0x81 <= hi <= 0x9F) or (0xE0 <= hi <= 0xFC): # two-byte sequence lo = next(i_text, None) if lo is None: print(f"WARNING: Mac Japanese sequence missing second byte 0x{hi:02x}") return text.decode('mac-roman') hi_key = f'{hi:02x}' lo_key = lo - 0x40 if lo_key < 0: print(f"WARNING: second byte out of range 0x{lo:02x}") return text.decode('mac-roman') elif decode_map.get(hi_key) is None or decode_map[hi_key][lo_key] is None: print(f"WARNING: No mapping for MacJapanese sequence 0x{hi_key}{lo:02x}") return text.decode('mac-roman') assert_tmp = decode_map[hi_key][lo_key] assert assert_tmp # mypy assert res += assert_tmp elif hi == 0xA0: # no-break space res += "\u00A0" elif 0xA1 <= hi <= 0xDF: # Katakana res += chr(hi - 0xA1 + 0xFF61) elif hi == 0xFD: # copyright sign res += "\u00A9" elif hi == 0xFE: # trade mark sign res += "\u2122" elif hi == 0xFF: # halfwidth horizontal ellipsis res += "\u2026\uF87F" else: raise Exception(f"No mapping for MacJapanese sequence 0x{hi:02x}") hi = next(i_text, None) return res
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https://github.com/scummvm/scummvm/blob/9c039d027e7ffb9d83ae2e274147e2daf8d57ce2/devtools/dumper-companion.py#L87-L132
catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/tools/python/src/Lib/xml/sax/_exceptions.py
python
SAXParseException.getSystemId
(self)
return self._systemId
Get the system identifier of the entity where the exception occurred.
Get the system identifier of the entity where the exception occurred.
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def getSystemId(self): "Get the system identifier of the entity where the exception occurred." return self._systemId
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/tools/python/src/Lib/xml/sax/_exceptions.py#L85-L87
HeYijia/PL-VIO
ddec7f3995cae0c90ba216861ad7cd784e7004cf
sim_data_pub/Myimg2bag/img2bag_Mars_imu.py
python
ReadIMU
(filename)
return timestamp,imu_data
return IMU data and timestamp of IMU
return IMU data and timestamp of IMU
[ "return", "IMU", "data", "and", "timestamp", "of", "IMU" ]
def ReadIMU(filename): '''return IMU data and timestamp of IMU''' file = open(filename,'r') all = file.readlines() timestamp = [] imu_data = [] for f in all: line = f.rstrip('\n').split(' ') timestamp.append(line[0]) imu_data.append(line[1:]) return timestamp,imu_data
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https://github.com/HeYijia/PL-VIO/blob/ddec7f3995cae0c90ba216861ad7cd784e7004cf/sim_data_pub/Myimg2bag/img2bag_Mars_imu.py#L38-L48
gemrb/gemrb
730206eed8d1dd358ca5e69a62f9e099aa22ffc6
gemrb/GUIScripts/Actor.py
python
Actor.LevelDiffs
(self)
return [(next-current) for current,next in zip(self.Levels(), self.NextLevels())]
Returns the differences between the current and next classes.
Returns the differences between the current and next classes.
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def LevelDiffs (self): """Returns the differences between the current and next classes.""" return [(next-current) for current,next in zip(self.Levels(), self.NextLevels())]
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https://github.com/gemrb/gemrb/blob/730206eed8d1dd358ca5e69a62f9e099aa22ffc6/gemrb/GUIScripts/Actor.py#L142-L145
krishauser/Klampt
972cc83ea5befac3f653c1ba20f80155768ad519
Python/klampt/robotsim.py
python
Geometry3D.getBBTight
(self)
return _robotsim.Geometry3D_getBBTight(self)
r""" Computes a tighter axis-aligned bounding box of the object than :meth:`Geometry3D.getBB`. Worst case O(n) time.
r""" Computes a tighter axis-aligned bounding box of the object than :meth:`Geometry3D.getBB`. Worst case O(n) time.
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def getBBTight(self) ->None: r""" Computes a tighter axis-aligned bounding box of the object than :meth:`Geometry3D.getBB`. Worst case O(n) time. """ return _robotsim.Geometry3D_getBBTight(self)
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https://github.com/krishauser/Klampt/blob/972cc83ea5befac3f653c1ba20f80155768ad519/Python/klampt/robotsim.py#L2337-L2343
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/distutils/dist.py
python
Distribution.parse_command_line
(self)
return True
Parse the setup script's command line, taken from the 'script_args' instance attribute (which defaults to 'sys.argv[1:]' -- see 'setup()' in core.py). This list is first processed for "global options" -- options that set attributes of the Distribution instance. Then, it is alternately scanned for Distutils commands and options for that command. Each new command terminates the options for the previous command. The allowed options for a command are determined by the 'user_options' attribute of the command class -- thus, we have to be able to load command classes in order to parse the command line. Any error in that 'options' attribute raises DistutilsGetoptError; any error on the command-line raises DistutilsArgError. If no Distutils commands were found on the command line, raises DistutilsArgError. Return true if command-line was successfully parsed and we should carry on with executing commands; false if no errors but we shouldn't execute commands (currently, this only happens if user asks for help).
Parse the setup script's command line, taken from the 'script_args' instance attribute (which defaults to 'sys.argv[1:]' -- see 'setup()' in core.py). This list is first processed for "global options" -- options that set attributes of the Distribution instance. Then, it is alternately scanned for Distutils commands and options for that command. Each new command terminates the options for the previous command. The allowed options for a command are determined by the 'user_options' attribute of the command class -- thus, we have to be able to load command classes in order to parse the command line. Any error in that 'options' attribute raises DistutilsGetoptError; any error on the command-line raises DistutilsArgError. If no Distutils commands were found on the command line, raises DistutilsArgError. Return true if command-line was successfully parsed and we should carry on with executing commands; false if no errors but we shouldn't execute commands (currently, this only happens if user asks for help).
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def parse_command_line(self): """Parse the setup script's command line, taken from the 'script_args' instance attribute (which defaults to 'sys.argv[1:]' -- see 'setup()' in core.py). This list is first processed for "global options" -- options that set attributes of the Distribution instance. Then, it is alternately scanned for Distutils commands and options for that command. Each new command terminates the options for the previous command. The allowed options for a command are determined by the 'user_options' attribute of the command class -- thus, we have to be able to load command classes in order to parse the command line. Any error in that 'options' attribute raises DistutilsGetoptError; any error on the command-line raises DistutilsArgError. If no Distutils commands were found on the command line, raises DistutilsArgError. Return true if command-line was successfully parsed and we should carry on with executing commands; false if no errors but we shouldn't execute commands (currently, this only happens if user asks for help). """ # # We now have enough information to show the Macintosh dialog # that allows the user to interactively specify the "command line". # toplevel_options = self._get_toplevel_options() # We have to parse the command line a bit at a time -- global # options, then the first command, then its options, and so on -- # because each command will be handled by a different class, and # the options that are valid for a particular class aren't known # until we have loaded the command class, which doesn't happen # until we know what the command is. self.commands = [] parser = FancyGetopt(toplevel_options + self.display_options) parser.set_negative_aliases(self.negative_opt) parser.set_aliases({'licence': 'license'}) args = parser.getopt(args=self.script_args, object=self) option_order = parser.get_option_order() log.set_verbosity(self.verbose) # for display options we return immediately if self.handle_display_options(option_order): return while args: args = self._parse_command_opts(parser, args) if args is None: # user asked for help (and got it) return # Handle the cases of --help as a "global" option, ie. # "setup.py --help" and "setup.py --help command ...". For the # former, we show global options (--verbose, --dry-run, etc.) # and display-only options (--name, --version, etc.); for the # latter, we omit the display-only options and show help for # each command listed on the command line. if self.help: self._show_help(parser, display_options=len(self.commands) == 0, commands=self.commands) return # Oops, no commands found -- an end-user error if not self.commands: raise DistutilsArgError("no commands supplied") # All is well: return true return True
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/distutils/dist.py#L439-L504
microsoft/TSS.MSR
0f2516fca2cd9929c31d5450e39301c9bde43688
TSS.Py/src/TpmTypes.py
python
TPM2_ChangePPS_REQUEST.fromTpm
(buf)
return buf.createObj(TPM2_ChangePPS_REQUEST)
Returns new TPM2_ChangePPS_REQUEST object constructed from its marshaled representation in the given TpmBuffer buffer
Returns new TPM2_ChangePPS_REQUEST object constructed from its marshaled representation in the given TpmBuffer buffer
[ "Returns", "new", "TPM2_ChangePPS_REQUEST", "object", "constructed", "from", "its", "marshaled", "representation", "in", "the", "given", "TpmBuffer", "buffer" ]
def fromTpm(buf): """ Returns new TPM2_ChangePPS_REQUEST object constructed from its marshaled representation in the given TpmBuffer buffer """ return buf.createObj(TPM2_ChangePPS_REQUEST)
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https://github.com/microsoft/TSS.MSR/blob/0f2516fca2cd9929c31d5450e39301c9bde43688/TSS.Py/src/TpmTypes.py#L15485-L15489
microsoft/ELL
a1d6bacc37a14879cc025d9be2ba40b1a0632315
tools/utilities/pythonlibs/vision/tiled_image.py
python
TiledImage.__init__
(self, num_images=2, output_shape=(600, 800))
Helper class to create a tiled image out of many smaller images. The class calculates how many horizontal and vertical blocks are needed to fit the requested number of images and fills in unused blocks as blank. For example, to fit 4 images, the number of tiles is 2x2, to fit 5 images, the number of tiles is 3x2, with the last tile being blank. num_images - the maximum number of images that need to be composed into the tiled image. Note that the actual number of tiles is equal to or larger than this number. output_shape - a tuple contaiing rows and columns of the output image. The output tiled image is a composition of sub images.
Helper class to create a tiled image out of many smaller images. The class calculates how many horizontal and vertical blocks are needed to fit the requested number of images and fills in unused blocks as blank. For example, to fit 4 images, the number of tiles is 2x2, to fit 5 images, the number of tiles is 3x2, with the last tile being blank. num_images - the maximum number of images that need to be composed into the tiled image. Note that the actual number of tiles is equal to or larger than this number. output_shape - a tuple contaiing rows and columns of the output image. The output tiled image is a composition of sub images.
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def __init__(self, num_images=2, output_shape=(600, 800)): """ Helper class to create a tiled image out of many smaller images. The class calculates how many horizontal and vertical blocks are needed to fit the requested number of images and fills in unused blocks as blank. For example, to fit 4 images, the number of tiles is 2x2, to fit 5 images, the number of tiles is 3x2, with the last tile being blank. num_images - the maximum number of images that need to be composed into the tiled image. Note that the actual number of tiles is equal to or larger than this number. output_shape - a tuple contaiing rows and columns of the output image. The output tiled image is a composition of sub images. """ self.composed_image_shape = self.get_composed_image_shape(num_images) self.number_of_tiles = self.composed_image_shape[0] * self.composed_image_shape[1] self.output_height_and_width = output_shape self.images = None self.window_name = 'ELL side by side' cv2.namedWindow(self.window_name, cv2.WINDOW_NORMAL) # Ensure the window is resizable # The aspect ratio of the composed image is now self.composed_image_shape[0] : self.composed_image_shape[1] # Adjust the height of the window to account for this, else images will look distorted height = int(output_shape[0] * (self.composed_image_shape[0] / self.composed_image_shape[1])) cv2.resizeWindow(self.window_name, output_shape[1], height)
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https://github.com/microsoft/ELL/blob/a1d6bacc37a14879cc025d9be2ba40b1a0632315/tools/utilities/pythonlibs/vision/tiled_image.py#L18-L37
Vipermdl/OCR_detection_IC15
8eebd353d6fac97f5832a138d7af3bd3071670db
data_loader/datautils.py
python
rectangle_from_parallelogram
(poly)
fit a rectangle from a parallelogram :param poly: :return:
fit a rectangle from a parallelogram :param poly: :return:
[ "fit", "a", "rectangle", "from", "a", "parallelogram", ":", "param", "poly", ":", ":", "return", ":" ]
def rectangle_from_parallelogram(poly): ''' fit a rectangle from a parallelogram :param poly: :return: ''' p0, p1, p2, p3 = poly angle_p0 = np.arccos(np.dot(p1 - p0, p3 - p0) / (np.linalg.norm(p0 - p1) * np.linalg.norm(p3 - p0))) if angle_p0 < 0.5 * np.pi: if np.linalg.norm(p0 - p1) > np.linalg.norm(p0 - p3): # p0 and p2 ## p0 p2p3 = fit_line([p2[0], p3[0]], [p2[1], p3[1]]) p2p3_verticle = line_verticle(p2p3, p0) new_p3 = line_cross_point(p2p3, p2p3_verticle) ## p2 p0p1 = fit_line([p0[0], p1[0]], [p0[1], p1[1]]) p0p1_verticle = line_verticle(p0p1, p2) new_p1 = line_cross_point(p0p1, p0p1_verticle) return np.array([p0, new_p1, p2, new_p3], dtype = np.float32) else: p1p2 = fit_line([p1[0], p2[0]], [p1[1], p2[1]]) p1p2_verticle = line_verticle(p1p2, p0) new_p1 = line_cross_point(p1p2, p1p2_verticle) p0p3 = fit_line([p0[0], p3[0]], [p0[1], p3[1]]) p0p3_verticle = line_verticle(p0p3, p2) new_p3 = line_cross_point(p0p3, p0p3_verticle) return np.array([p0, new_p1, p2, new_p3], dtype = np.float32) else: if np.linalg.norm(p0 - p1) > np.linalg.norm(p0 - p3): # p1 and p3 ## p1 p2p3 = fit_line([p2[0], p3[0]], [p2[1], p3[1]]) p2p3_verticle = line_verticle(p2p3, p1) new_p2 = line_cross_point(p2p3, p2p3_verticle) ## p3 p0p1 = fit_line([p0[0], p1[0]], [p0[1], p1[1]]) p0p1_verticle = line_verticle(p0p1, p3) new_p0 = line_cross_point(p0p1, p0p1_verticle) return np.array([new_p0, p1, new_p2, p3], dtype = np.float32) else: p0p3 = fit_line([p0[0], p3[0]], [p0[1], p3[1]]) p0p3_verticle = line_verticle(p0p3, p1) new_p0 = line_cross_point(p0p3, p0p3_verticle) p1p2 = fit_line([p1[0], p2[0]], [p1[1], p2[1]]) p1p2_verticle = line_verticle(p1p2, p3) new_p2 = line_cross_point(p1p2, p1p2_verticle) return np.array([new_p0, p1, new_p2, p3], dtype = np.float32)
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https://github.com/Vipermdl/OCR_detection_IC15/blob/8eebd353d6fac97f5832a138d7af3bd3071670db/data_loader/datautils.py#L276-L331
pytorch/pytorch
7176c92687d3cc847cc046bf002269c6949a21c2
torch/distributed/_shard/sharded_tensor/_ops/embedding_bag.py
python
_handle_row_wise_sharding
( input, world_size, weight, local_shard, offsets, per_sample_weights, mode, max_norm, norm_type, padding_idx, rank, pg, )
return gathered_output
Entry-point function to handle the logic of row-wise sharding of weight for embeddingBag. (Detailed explanations of the logic can be found in the comment for sharded_embedding_bag.) Args: input: list of ID used for lookup and aggregation. world_size: number of ranks. weight: shareded weight tensor. local_shard: row-wise shared local weight used for lookup. offsets: list of start positions of each bag for 1D input. per_sample_weights: weights for weighted sum mode. mode: aggregation method of each bag. max_norm: If given, each embedding vector with norm larger than max_norm is renormalized to have norm max_norm. Note: this will modify weight in-place. norm_type: The p in the p-norm to compute for the max_norm option. padding_idx: If specified, the entries at padding_idx do not contribute to the gradient; therefore, the embedding vector at padding_idx is not updated during training, i.e. it remains as a fixed “pad”. Note that the embedding vector at padding_idx is excluded from the reduction. rank: # of cuda process. pg: process group. Returns: gathered_output: final result of lookup and aggregation.
Entry-point function to handle the logic of row-wise sharding of weight for embeddingBag. (Detailed explanations of the logic can be found in the comment for sharded_embedding_bag.)
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def _handle_row_wise_sharding( input, world_size, weight, local_shard, offsets, per_sample_weights, mode, max_norm, norm_type, padding_idx, rank, pg, ): """ Entry-point function to handle the logic of row-wise sharding of weight for embeddingBag. (Detailed explanations of the logic can be found in the comment for sharded_embedding_bag.) Args: input: list of ID used for lookup and aggregation. world_size: number of ranks. weight: shareded weight tensor. local_shard: row-wise shared local weight used for lookup. offsets: list of start positions of each bag for 1D input. per_sample_weights: weights for weighted sum mode. mode: aggregation method of each bag. max_norm: If given, each embedding vector with norm larger than max_norm is renormalized to have norm max_norm. Note: this will modify weight in-place. norm_type: The p in the p-norm to compute for the max_norm option. padding_idx: If specified, the entries at padding_idx do not contribute to the gradient; therefore, the embedding vector at padding_idx is not updated during training, i.e. it remains as a fixed “pad”. Note that the embedding vector at padding_idx is excluded from the reduction. rank: # of cuda process. pg: process group. Returns: gathered_output: final result of lookup and aggregation. """ # We sort each interval defined by offset. If 2D, each interval is a row. input_size = input.size() ( input_split_sorted_list, input_split_sorted_indices, split_sizes_1d, split_sizes_1d_with_padding, ) = _input_split_sort(input, offsets, padding_idx) # Within each interval of the sorted list, we first need to distribute # each ID to different bucket(rank) and also ensure the rearrangement # has been done in case the placement idx not equal to rank. # We then perform some simple stats on each interval for the next step # If user specifies per_sample_weights we need to rearrange them # to be sync with IDs and then distribute them to each rank ( input_combined, input_combined_split_sizes, offsets_rearrange_list, offsets_rearrange_sizes, per_sample_weights, sharded_dim_size_max, padding_idx, ) = _sorted_input_distribute_prepare( input_split_sorted_list, input_split_sorted_indices, world_size, input, weight, per_sample_weights, rank, padding_idx, ) # Send ID/offsets/per_sample_weights to different bucket(rank). ( gathered_input, output_offsets_tensor_list, output_split_sizes, gathered_per_sample_weights, ) = _distribute_input( input_combined, input_combined_split_sizes, offsets_rearrange_list, offsets_rearrange_sizes, sharded_dim_size_max, world_size, input, per_sample_weights, pg, ) # Perform the embedding bag look-up and aggregation results = [] for i, inp in enumerate(gathered_input): per_sample_weights = ( gathered_per_sample_weights[i] if gathered_per_sample_weights is not None else None ) # If input is None, passing in max_norm causes # errors in CUDA. if max_norm is not None and inp.size(0) == 0: max_norm = None # Perform local embedding look up and aggregation. result = torch.nn.functional.embedding_bag( inp, local_shard, offsets=output_offsets_tensor_list[i], mode=mode if mode != "mean" else "sum", per_sample_weights=per_sample_weights, max_norm=max_norm, norm_type=norm_type, padding_idx=padding_idx, ) if mode != "max": results.append(result) # For max case, it there is no look-up from some ranks # it will return all zero for that. For that case, we need # to set the row to neg inf; otherwise, in the final # aggregation negative values will be rounded up to zero. elif inp.size(0) == 0: result[:] = -float("Inf") results.append(result) else: for idx, current_offset in enumerate(output_offsets_tensor_list[i]): next_offset = current_offset if idx == len(output_offsets_tensor_list[i]) - 1: next_offset = output_split_sizes[i] else: next_offset = output_offsets_tensor_list[i][idx + 1] # When there is no interval in the current rank or all IDs # are equal to padding_idx, we then need to ensure they # don't contribute to the final result. if (current_offset == next_offset) or ( padding_idx is not None and not torch.any( torch.ne(inp[current_offset:next_offset], padding_idx) ) ): result[idx] = -float("Inf") results.append(result) # Gather all the aggregated results appropriately by using reduce_scatter. row_size = input.size(0) if len(input_size) > 1 else len(split_sizes_1d) gathered_output = torch.empty(row_size, weight.size(1), device=input.device) op = ReduceOp.SUM if mode != "max" else ReduceOp.MAX dist.reduce_scatter(gathered_output, results, op=op, group=pg) # For Mean, we cannot do the division until very end because the sum of means # not equal to the mean of sum. (Divisor is different) if mode == "mean": split_sizes_1d_tensor = torch.tensor( split_sizes_1d_with_padding, dtype=torch.float, device=input.device ) # Make sure divisor is not zero. split_sizes_1d_tensor[split_sizes_1d_tensor == 0.0] = 1.0 return ( torch.div(gathered_output.t().contiguous(), split_sizes_1d_tensor) .t() .contiguous() ) # Return the appropriate local result. return gathered_output
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https://github.com/pytorch/pytorch/blob/7176c92687d3cc847cc046bf002269c6949a21c2/torch/distributed/_shard/sharded_tensor/_ops/embedding_bag.py#L337-L505
swift/swift
12d031cf8177fdec0137f9aa7e2912fa23c4416b
3rdParty/SCons/scons-3.0.1/engine/SCons/Tool/msginit.py
python
exists
(env)
Check if the tool exists
Check if the tool exists
[ "Check", "if", "the", "tool", "exists" ]
def exists(env): """ Check if the tool exists """ from SCons.Tool.GettextCommon import _msginit_exists try: return _msginit_exists(env) except: return False
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https://github.com/swift/swift/blob/12d031cf8177fdec0137f9aa7e2912fa23c4416b/3rdParty/SCons/scons-3.0.1/engine/SCons/Tool/msginit.py#L107-L113
benoitsteiner/tensorflow-opencl
cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5
tensorflow/python/summary/summary_iterator.py
python
SummaryWriter.add_summary
(self, summary, global_step=None)
Adds a `Summary` protocol buffer to the event file. This method wraps the provided summary in an `Event` protocol buffer and adds it to the event file. You can pass the result of evaluating any summary op, using @{tf.Session.run} or @{tf.Tensor.eval}, to this function. Alternatively, you can pass a `tf.Summary` protocol buffer that you populate with your own data. The latter is commonly done to report evaluation results in event files. Args: summary: A `Summary` protocol buffer, optionally serialized as a string. global_step: Number. Optional global step value to record with the summary.
Adds a `Summary` protocol buffer to the event file.
[ "Adds", "a", "Summary", "protocol", "buffer", "to", "the", "event", "file", "." ]
def add_summary(self, summary, global_step=None): """Adds a `Summary` protocol buffer to the event file. This method wraps the provided summary in an `Event` protocol buffer and adds it to the event file. You can pass the result of evaluating any summary op, using @{tf.Session.run} or @{tf.Tensor.eval}, to this function. Alternatively, you can pass a `tf.Summary` protocol buffer that you populate with your own data. The latter is commonly done to report evaluation results in event files. Args: summary: A `Summary` protocol buffer, optionally serialized as a string. global_step: Number. Optional global step value to record with the summary. """ if isinstance(summary, bytes): summ = summary_pb2.Summary() summ.ParseFromString(summary) summary = summ event = event_pb2.Event(wall_time=time.time(), summary=summary) if global_step is not None: event.step = int(global_step) self.add_event(event)
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https://github.com/benoitsteiner/tensorflow-opencl/blob/cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5/tensorflow/python/summary/summary_iterator.py#L120-L145
Xilinx/Vitis-AI
fc74d404563d9951b57245443c73bef389f3657f
tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/third_party/mlir/utils/spirv/gen_spirv_dialect.py
python
gen_opcode
(instructions)
return opcode_str + '\n\n' + enum_attr
Generates the TableGen definition to map opname to opcode Returns: - A string containing the TableGen SPV_OpCode definition
Generates the TableGen definition to map opname to opcode
[ "Generates", "the", "TableGen", "definition", "to", "map", "opname", "to", "opcode" ]
def gen_opcode(instructions): """ Generates the TableGen definition to map opname to opcode Returns: - A string containing the TableGen SPV_OpCode definition """ max_len = max([len(inst['opname']) for inst in instructions]) def_fmt_str = 'def SPV_OC_{name} {colon:>{offset}} '\ 'I32EnumAttrCase<"{name}", {value}>;' opcode_defs = [ def_fmt_str.format( name=inst['opname'], value=inst['opcode'], colon=':', offset=(max_len + 1 - len(inst['opname']))) for inst in instructions ] opcode_str = '\n'.join(opcode_defs) decl_fmt_str = 'SPV_OC_{name}' opcode_list = [ decl_fmt_str.format(name=inst['opname']) for inst in instructions ] opcode_list = split_list_into_sublists(opcode_list, 6) opcode_list = [ '{:6}'.format('') + ', '.join(sublist) for sublist in opcode_list ] opcode_list = ',\n'.join(opcode_list) enum_attr = 'def SPV_OpcodeAttr :\n'\ ' I32EnumAttr<"{name}", "valid SPIR-V instructions", [\n'\ '{lst}\n'\ ' ]> {{\n'\ ' let returnType = "::mlir::spirv::{name}";\n'\ ' let convertFromStorage = '\ '"static_cast<::mlir::spirv::{name}>($_self.getInt())";\n'\ ' let cppNamespace = "::mlir::spirv";\n}}'.format( name='Opcode', lst=opcode_list) return opcode_str + '\n\n' + enum_attr
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https://github.com/Xilinx/Vitis-AI/blob/fc74d404563d9951b57245443c73bef389f3657f/tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/third_party/mlir/utils/spirv/gen_spirv_dialect.py#L186-L223
twtygqyy/caffe-augmentation
c76600d247e5132fa5bd89d87bb5df458341fa84
scripts/cpp_lint.py
python
CleanseComments
(line)
return _RE_PATTERN_CLEANSE_LINE_C_COMMENTS.sub('', line)
Removes //-comments and single-line C-style /* */ comments. Args: line: A line of C++ source. Returns: The line with single-line comments removed.
Removes //-comments and single-line C-style /* */ comments.
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def CleanseComments(line): """Removes //-comments and single-line C-style /* */ comments. Args: line: A line of C++ source. Returns: The line with single-line comments removed. """ commentpos = line.find('//') if commentpos != -1 and not IsCppString(line[:commentpos]): line = line[:commentpos].rstrip() # get rid of /* ... */ return _RE_PATTERN_CLEANSE_LINE_C_COMMENTS.sub('', line)
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https://github.com/twtygqyy/caffe-augmentation/blob/c76600d247e5132fa5bd89d87bb5df458341fa84/scripts/cpp_lint.py#L1171-L1184
tensorflow/tensorflow
419e3a6b650ea4bd1b0cba23c4348f8a69f3272e
tensorflow/python/util/nest.py
python
flatten_with_joined_string_paths
(structure, separator="/", expand_composites=False)
return list(zip(flat_string_paths, flatten(structure, expand_composites=expand_composites)))
Returns a list of (string path, atom) tuples. The order of tuples produced matches that of `nest.flatten`. This allows you to flatten a nested structure while keeping information about where in the structure each atom was located. See `nest.yield_flat_paths` for more information. Args: structure: the nested structure to flatten. separator: string to separate levels of hierarchy in the results, defaults to '/'. expand_composites: If true, then composite tensors such as `tf.sparse.SparseTensor` and `tf.RaggedTensor` are expanded into their component tensors. Returns: A list of (string, atom) tuples.
Returns a list of (string path, atom) tuples.
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def flatten_with_joined_string_paths(structure, separator="/", expand_composites=False): """Returns a list of (string path, atom) tuples. The order of tuples produced matches that of `nest.flatten`. This allows you to flatten a nested structure while keeping information about where in the structure each atom was located. See `nest.yield_flat_paths` for more information. Args: structure: the nested structure to flatten. separator: string to separate levels of hierarchy in the results, defaults to '/'. expand_composites: If true, then composite tensors such as `tf.sparse.SparseTensor` and `tf.RaggedTensor` are expanded into their component tensors. Returns: A list of (string, atom) tuples. """ flat_paths = yield_flat_paths(structure, expand_composites=expand_composites) def stringify_and_join(path_elements): return separator.join(str(path_element) for path_element in path_elements) flat_string_paths = (stringify_and_join(path) for path in flat_paths) return list(zip(flat_string_paths, flatten(structure, expand_composites=expand_composites)))
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https://github.com/tensorflow/tensorflow/blob/419e3a6b650ea4bd1b0cba23c4348f8a69f3272e/tensorflow/python/util/nest.py#L1648-L1674
openvinotoolkit/openvino
dedcbeafa8b84cccdc55ca64b8da516682b381c7
tools/mo/openvino/tools/mo/utils/versions_checker.py
python
parse_and_filter_versions_list
(required_fw_versions, version_list, env_setup)
return version_list
Please do not add parameter type annotations (param:type). Because we import this file while checking Python version. Python 2.x will fail with no clear message on type annotations. Parsing requirements versions for a dependency and filtering out requirements that satisfy environment setup such as python version. if environment version (python_version, etc.) is satisfied :param required_fw_versions: String with fw versions from requirements file :param version_list: List for append :param env_setup: a dictionary with environment setup :return: list of tuples of strings like (name_of_module, sign, version) Examples of required_fw_versions: 'tensorflow>=1.15.2,<2.0; python_version < "3.8"' 'tensorflow>=2.0' Returned object is: [('tensorflow', '>=', '1.2.0'), ('networkx', '==', '2.1'), ('numpy', None, None)]
Please do not add parameter type annotations (param:type). Because we import this file while checking Python version. Python 2.x will fail with no clear message on type annotations.
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def parse_and_filter_versions_list(required_fw_versions, version_list, env_setup): """ Please do not add parameter type annotations (param:type). Because we import this file while checking Python version. Python 2.x will fail with no clear message on type annotations. Parsing requirements versions for a dependency and filtering out requirements that satisfy environment setup such as python version. if environment version (python_version, etc.) is satisfied :param required_fw_versions: String with fw versions from requirements file :param version_list: List for append :param env_setup: a dictionary with environment setup :return: list of tuples of strings like (name_of_module, sign, version) Examples of required_fw_versions: 'tensorflow>=1.15.2,<2.0; python_version < "3.8"' 'tensorflow>=2.0' Returned object is: [('tensorflow', '>=', '1.2.0'), ('networkx', '==', '2.1'), ('numpy', None, None)] """ line = required_fw_versions.strip('\n') line = line.strip(' ') if line == '': return version_list split_requirement = line.split(";") # check environment marker if len(split_requirement) > 1: env_req = split_requirement[1] if any([x in split_requirement[1] for x in [' and ', ' or ']]): log.error("The version checker doesn't support environment marker combination and it will be ignored: {}" "".format(split_requirement[1]), extra={'is_warning': True}) return version_list split_env_req = re.split(r"==|>=|<=|>|<|~=|!=", env_req) split_env_req = [l.strip(',') for l in split_env_req] env_marker = split_env_req[0].strip(' ') if env_marker == 'python_version' and env_marker in env_setup: installed_python_version = env_setup['python_version'] env_req_version_list = [] split_required_versions = re.split(r",", env_req) for i, l in enumerate(split_required_versions): for comparison in ['==', '>=', '<=', '<', '>', '~=']: if comparison in l: required_version = split_env_req[i + 1].strip(' ').replace("'", "").replace('"', '') env_req_version_list.append((env_marker, comparison, required_version)) break not_satisfied_list = [] for name, key, required_version in env_req_version_list: version_check(name, installed_python_version, required_version, key, not_satisfied_list) if len(not_satisfied_list) > 0: # this python_version requirement is not satisfied to required environment # and requirement for a dependency will be skipped return version_list elif env_marker == 'sys_platform' and env_marker in env_setup: split_env_req[1] = split_env_req[1].strip(' ').replace("'", "").replace('"', '') if '==' in env_req: if env_setup['sys_platform'] != split_env_req[1]: # this sys_platform requirement is not satisfied to required environment # and requirement for a dependency will be skipped return version_list elif '!=' in env_req: if env_setup['sys_platform'] == split_env_req[1]: # this sys_platform requirement is not satisfied to required environment # and requirement for a dependency will be skipped return version_list else: log.error("Error during platform version check, line: {}".format(line)) else: log.error("{} is unsupported environment marker and it will be ignored".format(env_marker), extra={'is_warning': True}) # parse a requirement for a dependency requirement = split_requirement[0] split_versions_by_conditions = re.split(r"==|>=|<=|>|<|~=", requirement) split_versions_by_conditions = [l.strip(',').strip(' ') for l in split_versions_by_conditions] if len(split_versions_by_conditions) == 0: return version_list if len(split_versions_by_conditions) == 1: version_list.append((split_versions_by_conditions[0], None, None)) else: split_required_versions= re.split(r",", requirement) for i, l in enumerate(split_required_versions): for comparison in ['==', '>=', '<=', '<', '>', '~=']: if comparison in l: version_list.append((split_versions_by_conditions[0], comparison, split_versions_by_conditions[i + 1])) break return version_list
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https://github.com/openvinotoolkit/openvino/blob/dedcbeafa8b84cccdc55ca64b8da516682b381c7/tools/mo/openvino/tools/mo/utils/versions_checker.py#L45-L135
catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/python/scikit-learn/py2/sklearn/gaussian_process/kernels.py
python
Sum.diag
(self, X)
return self.k1.diag(X) + self.k2.diag(X)
Returns the diagonal of the kernel k(X, X). The result of this method is identical to np.diag(self(X)); however, it can be evaluated more efficiently since only the diagonal is evaluated. Parameters ---------- X : array, shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y) Returns ------- K_diag : array, shape (n_samples_X,) Diagonal of kernel k(X, X)
Returns the diagonal of the kernel k(X, X).
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def diag(self, X): """Returns the diagonal of the kernel k(X, X). The result of this method is identical to np.diag(self(X)); however, it can be evaluated more efficiently since only the diagonal is evaluated. Parameters ---------- X : array, shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y) Returns ------- K_diag : array, shape (n_samples_X,) Diagonal of kernel k(X, X) """ return self.k1.diag(X) + self.k2.diag(X)
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/scikit-learn/py2/sklearn/gaussian_process/kernels.py#L685-L702
wlanjie/AndroidFFmpeg
7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf
tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/lib-tk/ttk.py
python
Panedwindow.sashpos
(self, index, newpos=None)
return self.tk.call(self._w, "sashpos", index, newpos)
If newpos is specified, sets the position of sash number index. May adjust the positions of adjacent sashes to ensure that positions are monotonically increasing. Sash positions are further constrained to be between 0 and the total size of the widget. Returns the new position of sash number index.
If newpos is specified, sets the position of sash number index.
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def sashpos(self, index, newpos=None): """If newpos is specified, sets the position of sash number index. May adjust the positions of adjacent sashes to ensure that positions are monotonically increasing. Sash positions are further constrained to be between 0 and the total size of the widget. Returns the new position of sash number index.""" return self.tk.call(self._w, "sashpos", index, newpos)
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https://github.com/wlanjie/AndroidFFmpeg/blob/7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf/tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/lib-tk/ttk.py#L976-L984
FreeCAD/FreeCAD
ba42231b9c6889b89e064d6d563448ed81e376ec
src/Mod/Arch/ArchSchedule.py
python
ArchScheduleTaskPanel.add
(self)
Adds a new row below the last one
Adds a new row below the last one
[ "Adds", "a", "new", "row", "below", "the", "last", "one" ]
def add(self): """Adds a new row below the last one""" self.form.list.insertRow(self.form.list.currentRow()+1)
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https://github.com/FreeCAD/FreeCAD/blob/ba42231b9c6889b89e064d6d563448ed81e376ec/src/Mod/Arch/ArchSchedule.py#L480-L484
bingwin/MicroChat
81d9a71a212c1cbca5bba497ec42659a7d25dccf
mars/lint/cpplint.py
python
GetLineWidth
(line)
Determines the width of the line in column positions. Args: line: A string, which may be a Unicode string. Returns: The width of the line in column positions, accounting for Unicode combining characters and wide characters.
Determines the width of the line in column positions.
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def GetLineWidth(line): """Determines the width of the line in column positions. Args: line: A string, which may be a Unicode string. Returns: The width of the line in column positions, accounting for Unicode combining characters and wide characters. """ if isinstance(line, unicode): width = 0 for uc in unicodedata.normalize('NFC', line): if unicodedata.east_asian_width(uc) in ('W', 'F'): width += 2 elif not unicodedata.combining(uc): width += 1 return width else: return len(line)
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https://github.com/bingwin/MicroChat/blob/81d9a71a212c1cbca5bba497ec42659a7d25dccf/mars/lint/cpplint.py#L4356-L4375
hughperkins/tf-coriander
970d3df6c11400ad68405f22b0c42a52374e94ca
tensorflow/models/rnn/translate/translate.py
python
create_model
(session, forward_only)
return model
Create translation model and initialize or load parameters in session.
Create translation model and initialize or load parameters in session.
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def create_model(session, forward_only): """Create translation model and initialize or load parameters in session.""" dtype = tf.float16 if FLAGS.use_fp16 else tf.float32 model = seq2seq_model.Seq2SeqModel( FLAGS.en_vocab_size, FLAGS.fr_vocab_size, _buckets, FLAGS.size, FLAGS.num_layers, FLAGS.max_gradient_norm, FLAGS.batch_size, FLAGS.learning_rate, FLAGS.learning_rate_decay_factor, forward_only=forward_only, dtype=dtype) ckpt = tf.train.get_checkpoint_state(FLAGS.train_dir) if ckpt and tf.gfile.Exists(ckpt.model_checkpoint_path): print("Reading model parameters from %s" % ckpt.model_checkpoint_path) model.saver.restore(session, ckpt.model_checkpoint_path) else: print("Created model with fresh parameters.") session.run(tf.initialize_all_variables()) return model
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https://github.com/hughperkins/tf-coriander/blob/970d3df6c11400ad68405f22b0c42a52374e94ca/tensorflow/models/rnn/translate/translate.py#L117-L139
quarnster/boxeebox-xbmc
7209547d3d247a4082de956f4d9a765086d96985
tools/EventClients/lib/python/xbmcclient.py
python
PacketHELO.__init__
(self, devicename=None, icon_type=ICON_NONE, icon_file=None)
Keyword arguments: devicename -- the string that identifies the client icon_type -- one of ICON_NONE, ICON_JPEG, ICON_PNG, ICON_GIF icon_file -- location of icon file with respect to current working directory if icon_type is not ICON_NONE
Keyword arguments: devicename -- the string that identifies the client icon_type -- one of ICON_NONE, ICON_JPEG, ICON_PNG, ICON_GIF icon_file -- location of icon file with respect to current working directory if icon_type is not ICON_NONE
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def __init__(self, devicename=None, icon_type=ICON_NONE, icon_file=None): """ Keyword arguments: devicename -- the string that identifies the client icon_type -- one of ICON_NONE, ICON_JPEG, ICON_PNG, ICON_GIF icon_file -- location of icon file with respect to current working directory if icon_type is not ICON_NONE """ Packet.__init__(self) self.packettype = PT_HELO self.icontype = icon_type self.set_payload ( format_string(devicename)[0:128] ) self.append_payload( chr (icon_type) ) self.append_payload( format_uint16 (0) ) # port no self.append_payload( format_uint32 (0) ) # reserved1 self.append_payload( format_uint32 (0) ) # reserved2 if icon_type != ICON_NONE and icon_file: self.append_payload( file(icon_file).read() )
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https://github.com/quarnster/boxeebox-xbmc/blob/7209547d3d247a4082de956f4d9a765086d96985/tools/EventClients/lib/python/xbmcclient.py#L265-L282
devpack/android-python27
d42dd67565e104cf7b0b50eb473f615db3e69901
python-build-with-qt/sip-4.11.2/siputils.py
python
SIPModuleMakefile.finalise
(self)
Finalise the macros for a SIP generated module Makefile.
Finalise the macros for a SIP generated module Makefile.
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def finalise(self): """Finalise the macros for a SIP generated module Makefile. """ if self._prot_is_public: self.DEFINES.append('SIP_PROTECTED_IS_PUBLIC') self.DEFINES.append('protected=public') self.INCDIR.append(self.config.sip_inc_dir) ModuleMakefile.finalise(self)
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https://github.com/devpack/android-python27/blob/d42dd67565e104cf7b0b50eb473f615db3e69901/python-build-with-qt/sip-4.11.2/siputils.py#L1665-L1674
yshshrm/Data-Structures-And-Algorithms-Hacktoberfest18
ce2807facfb817d1303d2ac9f1b2d90955e29a18
python/algorithms/linear_hashing.py
python
LinearHashing.insert
(self, value, print_status=0)
return True
:param value: value to be inserted :param print_status: set to 1 if :return:
[]
def insert(self, value, print_status=0): """ :param value: value to be inserted :param print_status: set to 1 if :return: """ self.set_index_counter_if() if self.threshold_outbound: # buffer to be extend self.buffer[len(self.buffer)] = [] buffer_index = self.hash_function(value) self.buffer[buffer_index] = self.buffer.setdefault(buffer_index, []) + [value] # bucket to be split bucket_to_split = self.buffer[self.index_counter] self.buffer[self.index_counter] = [] for data in bucket_to_split: buffer_idx = self.hash_function(data, flag=1) self.buffer[buffer_idx] = self.buffer.setdefault(buffer_idx, []) + [data] self.index_counter += 1 else: buffer_index = self.hash_function(value) # self.buffer[buffer_index].append(value) self.buffer[buffer_index] = self.buffer.setdefault(buffer_index, []) + [value] self.total_data += 1 if print_status: print(self.buffer) print("INDEX: {}\nCURRENT PHASE: {} \t PREVIOUS PHASE: {}".format(self.index_counter, self.current_phase, self.previous_phase)) return True
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https://github.com/yshshrm/Data-Structures-And-Algorithms-Hacktoberfest18/blob/ce2807facfb817d1303d2ac9f1b2d90955e29a18/python/algorithms/linear_hashing.py#L54-L84
natanielruiz/android-yolo
1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f
jni-build/jni/include/tensorflow/contrib/learn/python/learn/monitors.py
python
BaseMonitor.epoch_begin
(self, epoch)
Begin epoch. Args: epoch: `int`, the epoch number. Raises: ValueError: if we've already begun an epoch, or `epoch` < 0.
Begin epoch.
[ "Begin", "epoch", "." ]
def epoch_begin(self, epoch): """Begin epoch. Args: epoch: `int`, the epoch number. Raises: ValueError: if we've already begun an epoch, or `epoch` < 0. """ if self._current_epoch is not None: raise ValueError("epoch_begin called twice without epoch_end.") if epoch < 0: raise ValueError("Invalid epoch %s." % epoch) self._current_epoch = epoch
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https://github.com/natanielruiz/android-yolo/blob/1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f/jni-build/jni/include/tensorflow/contrib/learn/python/learn/monitors.py#L168-L181
thalium/icebox
99d147d5b9269222225443ce171b4fd46d8985d4
third_party/virtualbox/src/libs/libxml2-2.9.4/python/libxml2class.py
python
newText
(content)
return xmlNode(_obj=ret)
Creation of a new text node.
Creation of a new text node.
[ "Creation", "of", "a", "new", "text", "node", "." ]
def newText(content): """Creation of a new text node. """ ret = libxml2mod.xmlNewText(content) if ret is None:raise treeError('xmlNewText() failed') return xmlNode(_obj=ret)
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https://github.com/thalium/icebox/blob/99d147d5b9269222225443ce171b4fd46d8985d4/third_party/virtualbox/src/libs/libxml2-2.9.4/python/libxml2class.py#L910-L914
tensorflow/tensorflow
419e3a6b650ea4bd1b0cba23c4348f8a69f3272e
tensorflow/python/ops/metrics_impl.py
python
recall_at_top_k
(labels, predictions_idx, k=None, class_id=None, weights=None, metrics_collections=None, updates_collections=None, name=None)
Computes recall@k of top-k predictions with respect to sparse labels. Differs from `recall_at_k` in that predictions must be in the form of top `k` class indices, whereas `recall_at_k` expects logits. Refer to `recall_at_k` for more details. Args: labels: `int64` `Tensor` or `SparseTensor` with shape [D1, ... DN, num_labels] or [D1, ... DN], where the latter implies num_labels=1. N >= 1 and num_labels is the number of target classes for the associated prediction. Commonly, N=1 and `labels` has shape [batch_size, num_labels]. [D1, ... DN] must match `predictions`. Values should be in range [0, num_classes), where num_classes is the last dimension of `predictions`. Values outside this range always count towards `false_negative_at_<k>`. predictions_idx: Integer `Tensor` with shape [D1, ... DN, k] where N >= 1. Commonly, N=1 and predictions has shape [batch size, k]. The final dimension contains the top `k` predicted class indices. [D1, ... DN] must match `labels`. k: Integer, k for @k metric. Only used for the default op name. class_id: Integer class ID for which we want binary metrics. This should be in range [0, num_classes), where num_classes is the last dimension of `predictions`. If class_id is outside this range, the method returns NAN. weights: `Tensor` whose rank is either 0, or n-1, where n is the rank of `labels`. If the latter, it must be broadcastable to `labels` (i.e., all dimensions must be either `1`, or the same as the corresponding `labels` dimension). metrics_collections: An optional list of collections that values should be added to. updates_collections: An optional list of collections that updates should be added to. name: Name of new update operation, and namespace for other dependent ops. Returns: recall: Scalar `float64` `Tensor` with the value of `true_positives` divided by the sum of `true_positives` and `false_negatives`. update_op: `Operation` that increments `true_positives` and `false_negatives` variables appropriately, and whose value matches `recall`. Raises: ValueError: If `weights` is not `None` and its shape doesn't match `predictions`, or if either `metrics_collections` or `updates_collections` are not a list or tuple.
Computes recall@k of top-k predictions with respect to sparse labels.
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def recall_at_top_k(labels, predictions_idx, k=None, class_id=None, weights=None, metrics_collections=None, updates_collections=None, name=None): """Computes recall@k of top-k predictions with respect to sparse labels. Differs from `recall_at_k` in that predictions must be in the form of top `k` class indices, whereas `recall_at_k` expects logits. Refer to `recall_at_k` for more details. Args: labels: `int64` `Tensor` or `SparseTensor` with shape [D1, ... DN, num_labels] or [D1, ... DN], where the latter implies num_labels=1. N >= 1 and num_labels is the number of target classes for the associated prediction. Commonly, N=1 and `labels` has shape [batch_size, num_labels]. [D1, ... DN] must match `predictions`. Values should be in range [0, num_classes), where num_classes is the last dimension of `predictions`. Values outside this range always count towards `false_negative_at_<k>`. predictions_idx: Integer `Tensor` with shape [D1, ... DN, k] where N >= 1. Commonly, N=1 and predictions has shape [batch size, k]. The final dimension contains the top `k` predicted class indices. [D1, ... DN] must match `labels`. k: Integer, k for @k metric. Only used for the default op name. class_id: Integer class ID for which we want binary metrics. This should be in range [0, num_classes), where num_classes is the last dimension of `predictions`. If class_id is outside this range, the method returns NAN. weights: `Tensor` whose rank is either 0, or n-1, where n is the rank of `labels`. If the latter, it must be broadcastable to `labels` (i.e., all dimensions must be either `1`, or the same as the corresponding `labels` dimension). metrics_collections: An optional list of collections that values should be added to. updates_collections: An optional list of collections that updates should be added to. name: Name of new update operation, and namespace for other dependent ops. Returns: recall: Scalar `float64` `Tensor` with the value of `true_positives` divided by the sum of `true_positives` and `false_negatives`. update_op: `Operation` that increments `true_positives` and `false_negatives` variables appropriately, and whose value matches `recall`. Raises: ValueError: If `weights` is not `None` and its shape doesn't match `predictions`, or if either `metrics_collections` or `updates_collections` are not a list or tuple. """ with ops.name_scope(name, _at_k_name('recall', k, class_id=class_id), (predictions_idx, labels, weights)) as scope: labels = _maybe_expand_labels(labels, predictions_idx) top_k_idx = math_ops.cast(predictions_idx, dtypes.int64) tp, tp_update = _streaming_sparse_true_positive_at_k( predictions_idx=top_k_idx, labels=labels, k=k, class_id=class_id, weights=weights) fn, fn_update = _streaming_sparse_false_negative_at_k( predictions_idx=top_k_idx, labels=labels, k=k, class_id=class_id, weights=weights) def compute_recall(_, tp, fn): return math_ops.divide(tp, math_ops.add(tp, fn), name=scope) metric = _aggregate_across_replicas( metrics_collections, compute_recall, tp, fn) update = math_ops.divide( tp_update, math_ops.add(tp_update, fn_update), name='update') if updates_collections: ops.add_to_collections(updates_collections, update) return metric, update
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https://github.com/tensorflow/tensorflow/blob/419e3a6b650ea4bd1b0cba23c4348f8a69f3272e/tensorflow/python/ops/metrics_impl.py#L2754-L2834
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/psutil/_pswindows.py
python
Process._proc_info
(self)
return ret
Return multiple information about this process as a raw tuple.
Return multiple information about this process as a raw tuple.
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def _proc_info(self): """Return multiple information about this process as a raw tuple. """ ret = cext.proc_info(self.pid) assert len(ret) == len(pinfo_map) return ret
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/psutil/_pswindows.py#L732-L738
idaholab/moose
9eeebc65e098b4c30f8205fb41591fd5b61eb6ff
tutorials/darcy_thermo_mech/step07_adaptivity/problems/step7abc.py
python
frames
()
Render frames
Render frames
[ "Render", "frames" ]
def frames(): """Render frames""" camera = vtk.vtkCamera() camera.SetViewUp(0.0000000000, 1.0000000000, 0.0000000000) camera.SetPosition(0.1520000000, 0.0128500000, 0.1198046424) camera.SetFocalPoint(0.1520000000, 0.0128500000, 0.0000000000) reader_a = chigger.exodus.ExodusReader('step7a_coarse_out.e') reader_b = chigger.exodus.ExodusReader('step7b_fine_out.e') reader_c = chigger.exodus.ExodusReader('step7c_adapt_out.e') temp_a = chigger.exodus.ExodusResult(reader_a, camera=camera, variable='temperature', viewport=[0,0,0.9,0.333], edges=True, edge_color=EDGE_COLOR, range=[300, 350], cmap='viridis') temp_b = chigger.exodus.ExodusResult(reader_b, camera=camera, variable='temperature', viewport=[0,0.333,0.9,0.666], edges=True, edge_color=EDGE_COLOR, range=[300, 350], cmap='viridis') temp_c = chigger.exodus.ExodusResult(reader_c, camera=camera, variable='temperature', viewport=[0,0.666,0.9,1], edges=True, edge_color=EDGE_COLOR, range=[300, 350], cmap='viridis') cbar = chigger.exodus.ExodusColorBar(temp_a, viewport=[0.9,0,1,1], length=0.8, width=0.3, colorbar_origin=[0.1, 0.1]) cbar.setOptions('primary', title='Temperature (K)', font_size=28, font_color=[0,0,0]) time = chigger.annotations.TextAnnotation(position=[0.45,0.3], font_size=48, text_color=[0,0,0], viewport=[0,0,1,1], justification='center') window = chigger.RenderWindow(temp_a, temp_b, temp_c, cbar, time, size=[1920, 1080], background=[1,1,1], motion_factor=0.24) for i, t in enumerate(reader_a.getTimes()): reader_a.setOptions(timestep=i) reader_b.setOptions(timestep=i) reader_c.setOptions(timestep=i) time.setOptions(text='Time = {:.1f} sec.'.format(t)) filename = 'output/step07abc_{:05d}.png'.format(i) window.write(filename) window.start()
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https://github.com/idaholab/moose/blob/9eeebc65e098b4c30f8205fb41591fd5b61eb6ff/tutorials/darcy_thermo_mech/step07_adaptivity/problems/step7abc.py#L18-L66
bulletphysics/bullet3
f0f2a952e146f016096db6f85cf0c44ed75b0b9a
examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_four_leg_stand_env.py
python
MinitaurFourLegStandEnv._get_observation
(self)
return self._observation
Get the true observations of this environment. It includes the roll, pitch, roll dot, pitch dot of the base, and the motor angles. Returns: The observation list.
Get the true observations of this environment.
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def _get_observation(self): """Get the true observations of this environment. It includes the roll, pitch, roll dot, pitch dot of the base, and the motor angles. Returns: The observation list. """ roll, pitch, _ = self.minitaur.GetBaseRollPitchYaw() observation = [roll, pitch] if self._use_angular_velocity_in_observation: roll_rate, pitch_rate, _ = self.minitaur.GetBaseRollPitchYawRate() observation.extend([roll_rate, pitch_rate]) if self._use_motor_angle_in_observation: observation.extend(self.minitaur.GetMotorAngles().tolist()) self._observation = np.array(observation) return self._observation
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https://github.com/bulletphysics/bullet3/blob/f0f2a952e146f016096db6f85cf0c44ed75b0b9a/examples/pybullet/gym/pybullet_envs/minitaur/envs/minitaur_four_leg_stand_env.py#L268-L285
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/pip/_internal/req/req_file.py
python
parse_requirements
( filename, # type: str session, # type: PipSession finder=None, # type: Optional[PackageFinder] options=None, # type: Optional[optparse.Values] constraint=False, # type: bool )
Parse a requirements file and yield ParsedRequirement instances. :param filename: Path or url of requirements file. :param session: PipSession instance. :param finder: Instance of pip.index.PackageFinder. :param options: cli options. :param constraint: If true, parsing a constraint file rather than requirements file.
Parse a requirements file and yield ParsedRequirement instances.
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def parse_requirements( filename, # type: str session, # type: PipSession finder=None, # type: Optional[PackageFinder] options=None, # type: Optional[optparse.Values] constraint=False, # type: bool ): # type: (...) -> Iterator[ParsedRequirement] """Parse a requirements file and yield ParsedRequirement instances. :param filename: Path or url of requirements file. :param session: PipSession instance. :param finder: Instance of pip.index.PackageFinder. :param options: cli options. :param constraint: If true, parsing a constraint file rather than requirements file. """ line_parser = get_line_parser(finder) parser = RequirementsFileParser(session, line_parser) for parsed_line in parser.parse(filename, constraint): parsed_req = handle_line( parsed_line, options=options, finder=finder, session=session ) if parsed_req is not None: yield parsed_req
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/pip/_internal/req/req_file.py#L253-L309
bryanyzhu/Hidden-Two-Stream
f7f684adbdacb6df6b1cf196c3a476cd23484a0f
scripts/cpp_lint.py
python
CheckForNonStandardConstructs
(filename, clean_lines, linenum, nesting_state, error)
r"""Logs an error if we see certain non-ANSI constructs ignored by gcc-2. Complain about several constructs which gcc-2 accepts, but which are not standard C++. Warning about these in lint is one way to ease the transition to new compilers. - put storage class first (e.g. "static const" instead of "const static"). - "%lld" instead of %qd" in printf-type functions. - "%1$d" is non-standard in printf-type functions. - "\%" is an undefined character escape sequence. - text after #endif is not allowed. - invalid inner-style forward declaration. - >? and <? operators, and their >?= and <?= cousins. Additionally, check for constructor/destructor style violations and reference members, as it is very convenient to do so while checking for gcc-2 compliance. Args: filename: The name of the current file. clean_lines: A CleansedLines instance containing the file. linenum: The number of the line to check. nesting_state: A _NestingState instance which maintains information about the current stack of nested blocks being parsed. error: A callable to which errors are reported, which takes 4 arguments: filename, line number, error level, and message
r"""Logs an error if we see certain non-ANSI constructs ignored by gcc-2.
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def CheckForNonStandardConstructs(filename, clean_lines, linenum, nesting_state, error): r"""Logs an error if we see certain non-ANSI constructs ignored by gcc-2. Complain about several constructs which gcc-2 accepts, but which are not standard C++. Warning about these in lint is one way to ease the transition to new compilers. - put storage class first (e.g. "static const" instead of "const static"). - "%lld" instead of %qd" in printf-type functions. - "%1$d" is non-standard in printf-type functions. - "\%" is an undefined character escape sequence. - text after #endif is not allowed. - invalid inner-style forward declaration. - >? and <? operators, and their >?= and <?= cousins. Additionally, check for constructor/destructor style violations and reference members, as it is very convenient to do so while checking for gcc-2 compliance. Args: filename: The name of the current file. clean_lines: A CleansedLines instance containing the file. linenum: The number of the line to check. nesting_state: A _NestingState instance which maintains information about the current stack of nested blocks being parsed. error: A callable to which errors are reported, which takes 4 arguments: filename, line number, error level, and message """ # Remove comments from the line, but leave in strings for now. line = clean_lines.lines[linenum] if Search(r'printf\s*\(.*".*%[-+ ]?\d*q', line): error(filename, linenum, 'runtime/printf_format', 3, '%q in format strings is deprecated. Use %ll instead.') if Search(r'printf\s*\(.*".*%\d+\$', line): error(filename, linenum, 'runtime/printf_format', 2, '%N$ formats are unconventional. Try rewriting to avoid them.') # Remove escaped backslashes before looking for undefined escapes. line = line.replace('\\\\', '') if Search(r'("|\').*\\(%|\[|\(|{)', line): error(filename, linenum, 'build/printf_format', 3, '%, [, (, and { are undefined character escapes. Unescape them.') # For the rest, work with both comments and strings removed. line = clean_lines.elided[linenum] if Search(r'\b(const|volatile|void|char|short|int|long' r'|float|double|signed|unsigned' r'|schar|u?int8|u?int16|u?int32|u?int64)' r'\s+(register|static|extern|typedef)\b', line): error(filename, linenum, 'build/storage_class', 5, 'Storage class (static, extern, typedef, etc) should be first.') if Match(r'\s*#\s*endif\s*[^/\s]+', line): error(filename, linenum, 'build/endif_comment', 5, 'Uncommented text after #endif is non-standard. Use a comment.') if Match(r'\s*class\s+(\w+\s*::\s*)+\w+\s*;', line): error(filename, linenum, 'build/forward_decl', 5, 'Inner-style forward declarations are invalid. Remove this line.') if Search(r'(\w+|[+-]?\d+(\.\d*)?)\s*(<|>)\?=?\s*(\w+|[+-]?\d+)(\.\d*)?', line): error(filename, linenum, 'build/deprecated', 3, '>? and <? (max and min) operators are non-standard and deprecated.') if Search(r'^\s*const\s*string\s*&\s*\w+\s*;', line): # TODO(unknown): Could it be expanded safely to arbitrary references, # without triggering too many false positives? The first # attempt triggered 5 warnings for mostly benign code in the regtest, hence # the restriction. # Here's the original regexp, for the reference: # type_name = r'\w+((\s*::\s*\w+)|(\s*<\s*\w+?\s*>))?' # r'\s*const\s*' + type_name + '\s*&\s*\w+\s*;' error(filename, linenum, 'runtime/member_string_references', 2, 'const string& members are dangerous. It is much better to use ' 'alternatives, such as pointers or simple constants.') # Everything else in this function operates on class declarations. # Return early if the top of the nesting stack is not a class, or if # the class head is not completed yet. classinfo = nesting_state.InnermostClass() if not classinfo or not classinfo.seen_open_brace: return # The class may have been declared with namespace or classname qualifiers. # The constructor and destructor will not have those qualifiers. base_classname = classinfo.name.split('::')[-1] # Look for single-argument constructors that aren't marked explicit. # Technically a valid construct, but against style. args = Match(r'\s+(?:inline\s+)?%s\s*\(([^,()]+)\)' % re.escape(base_classname), line) if (args and args.group(1) != 'void' and not Match(r'(const\s+)?%s(\s+const)?\s*(?:<\w+>\s*)?&' % re.escape(base_classname), args.group(1).strip())): error(filename, linenum, 'runtime/explicit', 5, 'Single-argument constructors should be marked explicit.')
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https://github.com/bryanyzhu/Hidden-Two-Stream/blob/f7f684adbdacb6df6b1cf196c3a476cd23484a0f/scripts/cpp_lint.py#L2194-L2298
wlanjie/AndroidFFmpeg
7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf
tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/ntpath.py
python
dirname
(p)
return split(p)[0]
Returns the directory component of a pathname
Returns the directory component of a pathname
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def dirname(p): """Returns the directory component of a pathname""" return split(p)[0]
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https://github.com/wlanjie/AndroidFFmpeg/blob/7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf/tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/ntpath.py#L203-L205
hpi-xnor/BMXNet
ed0b201da6667887222b8e4b5f997c4f6b61943d
python/mxnet/gluon/block.py
python
HybridBlock.forward
(self, x, *args)
Defines the forward computation. Arguments can be either :py:class:`NDArray` or :py:class:`Symbol`.
Defines the forward computation. Arguments can be either :py:class:`NDArray` or :py:class:`Symbol`.
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def forward(self, x, *args): """Defines the forward computation. Arguments can be either :py:class:`NDArray` or :py:class:`Symbol`.""" if isinstance(x, NDArray): with x.context as ctx: try: if self._active: return self._call_cached_op(x, *args) params = {i: j.data(ctx) for i, j in self._reg_params.items()} except DeferredInitializationError: self._finish_deferred_init(self._active, x, *args) if self._active: return self._call_cached_op(x, *args) params = {i: j.data(ctx) for i, j in self._reg_params.items()} return self.hybrid_forward(ndarray, x, *args, **params) assert isinstance(x, Symbol), \ "HybridBlock requires the first argument to forward be either " \ "Symbol or NDArray, but got %s"%type(x) params = {i: j.var() for i, j in self._reg_params.items()} with self.name_scope(): return self.hybrid_forward(symbol, x, *args, **params)
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mapnik/mapnik
f3da900c355e1d15059c4a91b00203dcc9d9f0ef
scons/scons-local-4.1.0/SCons/Tool/docbook/__init__.py
python
DocbookHtmlhelp
(env, target, source=None, *args, **kw)
return result
A pseudo-Builder, providing a Docbook toolchain for HTMLHELP output.
A pseudo-Builder, providing a Docbook toolchain for HTMLHELP output.
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def DocbookHtmlhelp(env, target, source=None, *args, **kw): """ A pseudo-Builder, providing a Docbook toolchain for HTMLHELP output. """ # Init target/source if not SCons.Util.is_List(target): target = [target] if not source: source = target target = ['index.html'] elif not SCons.Util.is_List(source): source = [source] # Init XSL stylesheet __init_xsl_stylesheet(kw, env, '$DOCBOOK_DEFAULT_XSL_HTMLHELP', ['htmlhelp','htmlhelp.xsl']) # Setup builder __builder = __select_builder(__lxml_noresult_builder, __xsltproc_nobase_builder) # Detect base dir base_dir = kw.get('base_dir', '') if base_dir: __create_output_dir(base_dir) # Create targets result = [] r = __builder.__call__(env, __ensure_suffix(str(target[0]), '.html'), source[0], **kw) env.Depends(r, kw['DOCBOOK_XSL']) result.extend(r) # Add supporting files for cleanup env.Clean(r, ['toc.hhc', 'htmlhelp.hhp', 'index.hhk'] + glob.glob(os.path.join(base_dir, '[ar|bk|ch]*.html'))) return result
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https://github.com/mapnik/mapnik/blob/f3da900c355e1d15059c4a91b00203dcc9d9f0ef/scons/scons-local-4.1.0/SCons/Tool/docbook/__init__.py#L555-L588
msitt/blpapi-python
bebcf43668c9e5f5467b1f685f9baebbfc45bc87
src/blpapi/constant.py
python
Constant.status
(self)
return internals.blpapi_Constant_status(self.__handle)
Returns: int: Status of this :class:`Constant`. The possible return values are enumerated in :class:`SchemaStatus`.
Returns: int: Status of this :class:`Constant`.
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def status(self): """ Returns: int: Status of this :class:`Constant`. The possible return values are enumerated in :class:`SchemaStatus`. """ return internals.blpapi_Constant_status(self.__handle)
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wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
samples/doodle/doodle.py
python
DoodleWindow.OnPaint
(self, event)
Called when the window is exposed.
Called when the window is exposed.
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def OnPaint(self, event): """ Called when the window is exposed. """ # Create a buffered paint DC. It will create the real # wx.PaintDC and then blit the bitmap to it when dc is # deleted. Since we don't need to draw anything else # here that's all there is to it. dc = wx.BufferedPaintDC(self, self.buffer)
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/samples/doodle/doodle.py#L204-L212
mindspore-ai/mindspore
fb8fd3338605bb34fa5cea054e535a8b1d753fab
mindspore/python/mindspore/nn/cell.py
python
Cell._set_mixed_precision_type_recursive
(self, mixed_type)
Set mixed precision type to each cell
Set mixed precision type to each cell
[ "Set", "mixed", "precision", "type", "to", "each", "cell" ]
def _set_mixed_precision_type_recursive(self, mixed_type): """Set mixed precision type to each cell""" Cell_.set_mixed_precision_type(self, mixed_type) for cell in self.cells(): cell._set_mixed_precision_type_recursive(mixed_type)
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https://github.com/mindspore-ai/mindspore/blob/fb8fd3338605bb34fa5cea054e535a8b1d753fab/mindspore/python/mindspore/nn/cell.py#L1317-L1321
sdhash/sdhash
b9eff63e4e5867e910f41fd69032bbb1c94a2a5e
sdhash-ui/cherrypy/process/plugins.py
python
SignalHandler.subscribe
(self)
Subscribe self.handlers to signals.
Subscribe self.handlers to signals.
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def subscribe(self): """Subscribe self.handlers to signals.""" for sig, func in self.handlers.items(): try: self.set_handler(sig, func) except ValueError: pass
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https://github.com/sdhash/sdhash/blob/b9eff63e4e5867e910f41fd69032bbb1c94a2a5e/sdhash-ui/cherrypy/process/plugins.py#L115-L121
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/msw/stc.py
python
StyledTextCtrl.SetWhitespaceBackground
(*args, **kwargs)
return _stc.StyledTextCtrl_SetWhitespaceBackground(*args, **kwargs)
SetWhitespaceBackground(self, bool useSetting, Colour back) Set the background colour of all whitespace and whether to use this setting.
SetWhitespaceBackground(self, bool useSetting, Colour back)
[ "SetWhitespaceBackground", "(", "self", "bool", "useSetting", "Colour", "back", ")" ]
def SetWhitespaceBackground(*args, **kwargs): """ SetWhitespaceBackground(self, bool useSetting, Colour back) Set the background colour of all whitespace and whether to use this setting. """ return _stc.StyledTextCtrl_SetWhitespaceBackground(*args, **kwargs)
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/msw/stc.py#L2921-L2927
thalium/icebox
99d147d5b9269222225443ce171b4fd46d8985d4
third_party/virtualbox/src/libs/libxml2-2.9.4/python/libxml2class.py
python
xmlAttr.removeProp
(self)
return ret
Unlink and free one attribute, all the content is freed too Note this doesn't work for namespace definition attributes
Unlink and free one attribute, all the content is freed too Note this doesn't work for namespace definition attributes
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def removeProp(self): """Unlink and free one attribute, all the content is freed too Note this doesn't work for namespace definition attributes """ ret = libxml2mod.xmlRemoveProp(self._o) return ret
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https://github.com/thalium/icebox/blob/99d147d5b9269222225443ce171b4fd46d8985d4/third_party/virtualbox/src/libs/libxml2-2.9.4/python/libxml2class.py#L4828-L4832
catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/tools/python3/src/Lib/email/_header_value_parser.py
python
get_extended_attrtext
(value)
return attrtext, value
attrtext = 1*(any non-ATTRIBUTE_ENDS character plus '%') This is a special parsing routine so that we get a value that includes % escapes as a single string (which we decode as a single string later).
attrtext = 1*(any non-ATTRIBUTE_ENDS character plus '%')
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def get_extended_attrtext(value): """attrtext = 1*(any non-ATTRIBUTE_ENDS character plus '%') This is a special parsing routine so that we get a value that includes % escapes as a single string (which we decode as a single string later). """ m = _non_extended_attribute_end_matcher(value) if not m: raise errors.HeaderParseError( "expected extended attrtext but found {!r}".format(value)) attrtext = m.group() value = value[len(attrtext):] attrtext = ValueTerminal(attrtext, 'extended-attrtext') _validate_xtext(attrtext) return attrtext, value
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/tools/python3/src/Lib/email/_header_value_parser.py#L2319-L2335
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
wx/lib/agw/hypertreelist.py
python
EditCtrl.AcceptChanges
(self)
Accepts/refuses the changes made by the user.
Accepts/refuses the changes made by the user.
[ "Accepts", "/", "refuses", "the", "changes", "made", "by", "the", "user", "." ]
def AcceptChanges(self): """Accepts/refuses the changes made by the user.""" value = self.GetValue() if value == self._startValue: # nothing changed, always accept # when an item remains unchanged, the owner # needs to be notified that the user decided # not to change the tree item label, and that # the edit has been cancelled self._owner.OnCancelEdit() return True else: return self._owner.OnAcceptEdit(value)
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/wx/lib/agw/hypertreelist.py#L1896-L1910
tensorflow/deepmath
b5b721f54de1d5d6a02d78f5da5995237f9995f9
deepmath/deephol/utilities/proof_checker_lib.py
python
proof_log_as_dict
(log: deephol_pb2.ProofLog )
return d
Turns proof log into a dictionary.
Turns proof log into a dictionary.
[ "Turns", "proof", "log", "into", "a", "dictionary", "." ]
def proof_log_as_dict(log: deephol_pb2.ProofLog ) -> Dict[int, deephol_pb2.ProofNode]: """Turns proof log into a dictionary.""" d = {} for node in log.nodes: fingerprint = theorem_fingerprint.Fingerprint(node.goal) if fingerprint in d: raise ValueError('Duplicate subgoal in fingerprint. Ignoring') d[fingerprint] = node return d
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https://github.com/tensorflow/deepmath/blob/b5b721f54de1d5d6a02d78f5da5995237f9995f9/deepmath/deephol/utilities/proof_checker_lib.py#L76-L85
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Tools/AWSPythonSDK/1.5.8/docutils/utils/math/math2html.py
python
LineWriter.writestring
(self, string)
Write a string
Write a string
[ "Write", "a", "string" ]
def writestring(self, string): "Write a string" if not self.file: self.file = codecs.open(self.filename, 'w', "utf-8") if self.file == sys.stdout and sys.version_info < (3,0): string = string.encode('utf-8') self.file.write(string)
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/AWSPythonSDK/1.5.8/docutils/utils/math/math2html.py#L1799-L1805
bareos/bareos
56a10bb368b0a81e977bb51304033fe49d59efb0
core/src/plugins/filed/python/mariabackup/BareosFdPluginMariabackup.py
python
BareosFdMariabackup.create_file
(self, restorepkt)
return bRC_OK
On restore we create a subdirectory for the first base backup and each incremental backup. Because mariabackup expects an empty directory, we create a tree starting with jobId/ of restore job
On restore we create a subdirectory for the first base backup and each incremental backup. Because mariabackup expects an empty directory, we create a tree starting with jobId/ of restore job
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def create_file(self, restorepkt): """ On restore we create a subdirectory for the first base backup and each incremental backup. Because mariabackup expects an empty directory, we create a tree starting with jobId/ of restore job """ FNAME = restorepkt.ofname DebugMessage(100, "create file with %s called\n" % FNAME) self.writeDir = "%s/%d/" % (os.path.dirname(FNAME), self.jobId) # FNAME contains originating jobId after last . origJobId = int(FNAME.rpartition(".")[-1]) if origJobId in self.rop_data: rop_from_lsn = int(self.rop_data[origJobId]["from_lsn"]) rop_to_lsn = int(self.rop_data[origJobId]["to_lsn"]) self.writeDir += "/%020d_" % rop_from_lsn self.writeDir += "%020d_" % rop_to_lsn self.writeDir += "%010d" % origJobId else: JobMessage( M_ERROR, "No lsn information found in restore object for file %s from job %d\n" % (FNAME, origJobId), ) # Create restore directory, if not existent if not os.path.exists(self.writeDir): bareosfd.DebugMessage( 200, "Directory %s does not exist, creating it now\n" % self.writeDir, ) os.makedirs(self.writeDir) # Mariabackup requires empty directory if os.listdir(self.writeDir): JobMessage( M_FATAL, "Restore with xbstream needs empty directory: %s\n" % self.writeDir, ) return bRC_Error self.restorecommand += self.writeDir DebugMessage( 100, 'Restore using xbstream to extract files with "%s"\n' % self.restorecommand, ) restorepkt.create_status = CF_EXTRACT return bRC_OK
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https://github.com/bareos/bareos/blob/56a10bb368b0a81e977bb51304033fe49d59efb0/core/src/plugins/filed/python/mariabackup/BareosFdPluginMariabackup.py#L135-L178
catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/tools/python3/src/Lib/_pydecimal.py
python
Decimal.__rsub__
(self, other, context=None)
return other.__sub__(self, context=context)
Return other - self
Return other - self
[ "Return", "other", "-", "self" ]
def __rsub__(self, other, context=None): """Return other - self""" other = _convert_other(other) if other is NotImplemented: return other return other.__sub__(self, context=context)
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/tools/python3/src/Lib/_pydecimal.py#L1259-L1265
benoitsteiner/tensorflow-opencl
cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5
tensorflow/python/keras/_impl/keras/backend.py
python
switch
(condition, then_expression, else_expression)
return x
Switches between two operations depending on a scalar value. Note that both `then_expression` and `else_expression` should be symbolic tensors of the *same shape*. Arguments: condition: scalar tensor (`int` or `bool`). then_expression: either a tensor, or a callable that returns a tensor. else_expression: either a tensor, or a callable that returns a tensor. Returns: The selected tensor.
Switches between two operations depending on a scalar value.
[ "Switches", "between", "two", "operations", "depending", "on", "a", "scalar", "value", "." ]
def switch(condition, then_expression, else_expression): """Switches between two operations depending on a scalar value. Note that both `then_expression` and `else_expression` should be symbolic tensors of the *same shape*. Arguments: condition: scalar tensor (`int` or `bool`). then_expression: either a tensor, or a callable that returns a tensor. else_expression: either a tensor, or a callable that returns a tensor. Returns: The selected tensor. """ if condition.dtype != dtypes_module.bool: condition = math_ops.cast(condition, 'bool') if not callable(then_expression): def then_expression_fn(): return then_expression else: then_expression_fn = then_expression if not callable(else_expression): def else_expression_fn(): return else_expression else: else_expression_fn = else_expression x = control_flow_ops.cond(condition, then_expression_fn, else_expression_fn) return x
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https://github.com/benoitsteiner/tensorflow-opencl/blob/cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5/tensorflow/python/keras/_impl/keras/backend.py#L2769-L2798
catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/python/scipy/scipy/stats/morestats.py
python
bayes_mvs
(data, alpha=0.90)
return m_res, v_res, s_res
r""" Bayesian confidence intervals for the mean, var, and std. Parameters ---------- data : array_like Input data, if multi-dimensional it is flattened to 1-D by `bayes_mvs`. Requires 2 or more data points. alpha : float, optional Probability that the returned confidence interval contains the true parameter. Returns ------- mean_cntr, var_cntr, std_cntr : tuple The three results are for the mean, variance and standard deviation, respectively. Each result is a tuple of the form:: (center, (lower, upper)) with `center` the mean of the conditional pdf of the value given the data, and `(lower, upper)` a confidence interval, centered on the median, containing the estimate to a probability ``alpha``. See Also -------- mvsdist Notes ----- Each tuple of mean, variance, and standard deviation estimates represent the (center, (lower, upper)) with center the mean of the conditional pdf of the value given the data and (lower, upper) is a confidence interval centered on the median, containing the estimate to a probability ``alpha``. Converts data to 1-D and assumes all data has the same mean and variance. Uses Jeffrey's prior for variance and std. Equivalent to ``tuple((x.mean(), x.interval(alpha)) for x in mvsdist(dat))`` References ---------- T.E. Oliphant, "A Bayesian perspective on estimating mean, variance, and standard-deviation from data", http://scholarsarchive.byu.edu/facpub/278, 2006. Examples -------- First a basic example to demonstrate the outputs: >>> from scipy import stats >>> data = [6, 9, 12, 7, 8, 8, 13] >>> mean, var, std = stats.bayes_mvs(data) >>> mean Mean(statistic=9.0, minmax=(7.1036502226125329, 10.896349777387467)) >>> var Variance(statistic=10.0, minmax=(3.176724206..., 24.45910382...)) >>> std Std_dev(statistic=2.9724954732045084, minmax=(1.7823367265645143, 4.9456146050146295)) Now we generate some normally distributed random data, and get estimates of mean and standard deviation with 95% confidence intervals for those estimates: >>> n_samples = 100000 >>> data = stats.norm.rvs(size=n_samples) >>> res_mean, res_var, res_std = stats.bayes_mvs(data, alpha=0.95) >>> import matplotlib.pyplot as plt >>> fig = plt.figure() >>> ax = fig.add_subplot(111) >>> ax.hist(data, bins=100, normed=True, label='Histogram of data') >>> ax.vlines(res_mean.statistic, 0, 0.5, colors='r', label='Estimated mean') >>> ax.axvspan(res_mean.minmax[0],res_mean.minmax[1], facecolor='r', ... alpha=0.2, label=r'Estimated mean (95% limits)') >>> ax.vlines(res_std.statistic, 0, 0.5, colors='g', label='Estimated scale') >>> ax.axvspan(res_std.minmax[0],res_std.minmax[1], facecolor='g', alpha=0.2, ... label=r'Estimated scale (95% limits)') >>> ax.legend(fontsize=10) >>> ax.set_xlim([-4, 4]) >>> ax.set_ylim([0, 0.5]) >>> plt.show()
r""" Bayesian confidence intervals for the mean, var, and std.
[ "r", "Bayesian", "confidence", "intervals", "for", "the", "mean", "var", "and", "std", "." ]
def bayes_mvs(data, alpha=0.90): r""" Bayesian confidence intervals for the mean, var, and std. Parameters ---------- data : array_like Input data, if multi-dimensional it is flattened to 1-D by `bayes_mvs`. Requires 2 or more data points. alpha : float, optional Probability that the returned confidence interval contains the true parameter. Returns ------- mean_cntr, var_cntr, std_cntr : tuple The three results are for the mean, variance and standard deviation, respectively. Each result is a tuple of the form:: (center, (lower, upper)) with `center` the mean of the conditional pdf of the value given the data, and `(lower, upper)` a confidence interval, centered on the median, containing the estimate to a probability ``alpha``. See Also -------- mvsdist Notes ----- Each tuple of mean, variance, and standard deviation estimates represent the (center, (lower, upper)) with center the mean of the conditional pdf of the value given the data and (lower, upper) is a confidence interval centered on the median, containing the estimate to a probability ``alpha``. Converts data to 1-D and assumes all data has the same mean and variance. Uses Jeffrey's prior for variance and std. Equivalent to ``tuple((x.mean(), x.interval(alpha)) for x in mvsdist(dat))`` References ---------- T.E. Oliphant, "A Bayesian perspective on estimating mean, variance, and standard-deviation from data", http://scholarsarchive.byu.edu/facpub/278, 2006. Examples -------- First a basic example to demonstrate the outputs: >>> from scipy import stats >>> data = [6, 9, 12, 7, 8, 8, 13] >>> mean, var, std = stats.bayes_mvs(data) >>> mean Mean(statistic=9.0, minmax=(7.1036502226125329, 10.896349777387467)) >>> var Variance(statistic=10.0, minmax=(3.176724206..., 24.45910382...)) >>> std Std_dev(statistic=2.9724954732045084, minmax=(1.7823367265645143, 4.9456146050146295)) Now we generate some normally distributed random data, and get estimates of mean and standard deviation with 95% confidence intervals for those estimates: >>> n_samples = 100000 >>> data = stats.norm.rvs(size=n_samples) >>> res_mean, res_var, res_std = stats.bayes_mvs(data, alpha=0.95) >>> import matplotlib.pyplot as plt >>> fig = plt.figure() >>> ax = fig.add_subplot(111) >>> ax.hist(data, bins=100, normed=True, label='Histogram of data') >>> ax.vlines(res_mean.statistic, 0, 0.5, colors='r', label='Estimated mean') >>> ax.axvspan(res_mean.minmax[0],res_mean.minmax[1], facecolor='r', ... alpha=0.2, label=r'Estimated mean (95% limits)') >>> ax.vlines(res_std.statistic, 0, 0.5, colors='g', label='Estimated scale') >>> ax.axvspan(res_std.minmax[0],res_std.minmax[1], facecolor='g', alpha=0.2, ... label=r'Estimated scale (95% limits)') >>> ax.legend(fontsize=10) >>> ax.set_xlim([-4, 4]) >>> ax.set_ylim([0, 0.5]) >>> plt.show() """ m, v, s = mvsdist(data) if alpha >= 1 or alpha <= 0: raise ValueError("0 < alpha < 1 is required, but alpha=%s was given." % alpha) m_res = Mean(m.mean(), m.interval(alpha)) v_res = Variance(v.mean(), v.interval(alpha)) s_res = Std_dev(s.mean(), s.interval(alpha)) return m_res, v_res, s_res
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/scipy/scipy/stats/morestats.py#L43-L139
domino-team/openwrt-cc
8b181297c34d14d3ca521cc9f31430d561dbc688
package/gli-pub/openwrt-node-packages-master/node/node-v6.9.1/deps/npm/node_modules/node-gyp/gyp/pylib/gyp/xcodeproj_file.py
python
PBXProject.RootGroupForPath
(self, path)
return (self.SourceGroup(), True)
Returns a PBXGroup child of this object to which path should be added. This method is intended to choose between SourceGroup and IntermediatesGroup on the basis of whether path is present in a source directory or an intermediates directory. For the purposes of this determination, any path located within a derived file directory such as PROJECT_DERIVED_FILE_DIR is treated as being in an intermediates directory. The returned value is a two-element tuple. The first element is the PBXGroup, and the second element specifies whether that group should be organized hierarchically (True) or as a single flat list (False).
Returns a PBXGroup child of this object to which path should be added.
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def RootGroupForPath(self, path): """Returns a PBXGroup child of this object to which path should be added. This method is intended to choose between SourceGroup and IntermediatesGroup on the basis of whether path is present in a source directory or an intermediates directory. For the purposes of this determination, any path located within a derived file directory such as PROJECT_DERIVED_FILE_DIR is treated as being in an intermediates directory. The returned value is a two-element tuple. The first element is the PBXGroup, and the second element specifies whether that group should be organized hierarchically (True) or as a single flat list (False). """ # TODO(mark): make this a class variable and bind to self on call? # Also, this list is nowhere near exhaustive. # INTERMEDIATE_DIR and SHARED_INTERMEDIATE_DIR are used by # gyp.generator.xcode. There should probably be some way for that module # to push the names in, rather than having to hard-code them here. source_tree_groups = { 'DERIVED_FILE_DIR': (self.IntermediatesGroup, True), 'INTERMEDIATE_DIR': (self.IntermediatesGroup, True), 'PROJECT_DERIVED_FILE_DIR': (self.IntermediatesGroup, True), 'SHARED_INTERMEDIATE_DIR': (self.IntermediatesGroup, True), } (source_tree, path) = SourceTreeAndPathFromPath(path) if source_tree != None and source_tree in source_tree_groups: (group_func, hierarchical) = source_tree_groups[source_tree] group = group_func() return (group, hierarchical) # TODO(mark): make additional choices based on file extension. return (self.SourceGroup(), True)
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https://github.com/domino-team/openwrt-cc/blob/8b181297c34d14d3ca521cc9f31430d561dbc688/package/gli-pub/openwrt-node-packages-master/node/node-v6.9.1/deps/npm/node_modules/node-gyp/gyp/pylib/gyp/xcodeproj_file.py#L2584-L2619
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/osx_cocoa/_misc.py
python
ClipboardLocker.__init__
(self, *args, **kwargs)
__init__(self, Clipboard clipboard=None) -> ClipboardLocker A helpful class for opening the clipboard and automatically closing it when the locker is destroyed.
__init__(self, Clipboard clipboard=None) -> ClipboardLocker
[ "__init__", "(", "self", "Clipboard", "clipboard", "=", "None", ")", "-", ">", "ClipboardLocker" ]
def __init__(self, *args, **kwargs): """ __init__(self, Clipboard clipboard=None) -> ClipboardLocker A helpful class for opening the clipboard and automatically closing it when the locker is destroyed. """ _misc_.ClipboardLocker_swiginit(self,_misc_.new_ClipboardLocker(*args, **kwargs))
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_cocoa/_misc.py#L5955-L5962
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/osx_cocoa/_gdi.py
python
NativePixelData.__init__
(self, *args)
__init__(self, Bitmap bmp) -> NativePixelData __init__(self, Bitmap bmp, Rect rect) -> NativePixelData __init__(self, Bitmap bmp, Point pt, Size sz) -> NativePixelData
__init__(self, Bitmap bmp) -> NativePixelData __init__(self, Bitmap bmp, Rect rect) -> NativePixelData __init__(self, Bitmap bmp, Point pt, Size sz) -> NativePixelData
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def __init__(self, *args): """ __init__(self, Bitmap bmp) -> NativePixelData __init__(self, Bitmap bmp, Rect rect) -> NativePixelData __init__(self, Bitmap bmp, Point pt, Size sz) -> NativePixelData """ _gdi_.NativePixelData_swiginit(self,_gdi_.new_NativePixelData(*args))
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_cocoa/_gdi.py#L1017-L1023
mindspore-ai/mindspore
fb8fd3338605bb34fa5cea054e535a8b1d753fab
mindspore/python/mindspore/train/summary/_summary_adapter.py
python
_fill_image_summary
(tag: str, np_value, summary_image, input_format='NCHW')
return True
Package the image summary. Args: tag (str): Summary tag describe. np_value (Type): Summary data type. summary_image (Tensor): The tensor of summary. input_format (str): Data sort order index. Default: 'NCHW'. Returns: Summary, return image summary content.
Package the image summary.
[ "Package", "the", "image", "summary", "." ]
def _fill_image_summary(tag: str, np_value, summary_image, input_format='NCHW'): """ Package the image summary. Args: tag (str): Summary tag describe. np_value (Type): Summary data type. summary_image (Tensor): The tensor of summary. input_format (str): Data sort order index. Default: 'NCHW'. Returns: Summary, return image summary content. """ logger.debug(f"Set({tag}) the image summary value") if np_value.ndim != 4 or np_value.shape[1] not in (1, 3): logger.error(f"The value is not Image, tag = {tag}, ndim = {np_value.ndim}, shape={np_value.shape}") return False if np_value.ndim != len(input_format): logger.error( f"The tensor with dim({np_value.ndim}) can't convert the format({input_format}) because dim not same") return False if 0 in np_value.shape: logger.error( f"The tensor with shape({np_value.shape}) is not a valid image because the shape contains zero.") return False # convert the tensor format tensor = _convert_image_format(np_value, input_format) # convert the tensor dtype # Do not assume that user passes in values in [0, 255], use data type to detect scale_factor = 1 if tensor.dtype == np.uint8: scale_factor = 1 elif np.max(tensor) <= 1 and np.min(tensor) >= 0: scale_factor = 255 tensor = tensor.astype(np.float32) tensor = (tensor * scale_factor).astype(np.uint8) # create the image summary height, width, channel, image_string = _make_image(tensor) summary_image.height = height summary_image.width = width summary_image.colorspace = channel summary_image.encoded_image = image_string return True
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https://github.com/mindspore-ai/mindspore/blob/fb8fd3338605bb34fa5cea054e535a8b1d753fab/mindspore/python/mindspore/train/summary/_summary_adapter.py#L342-L389
FreeCAD/FreeCAD
ba42231b9c6889b89e064d6d563448ed81e376ec
src/Mod/Draft/importDXF.py
python
drawCircle
(circle, forceShape=False)
return None
Return a Part shape (Circle, Edge) from a DXF circle. Parameters ---------- circle : drawing.entities The DXF object of type `'circle'`. The `'circle'` object is different from an `'arc'` because the circle forms a full circumference. forceShape : bool, optional It defaults to `False`. If it is `True` it will try to produce a `Part.Edge`, otherwise it tries to produce a `Draft Circle`. Returns ------- Part::Part2DObject or Part::TopoShape ('Edge') The returned object is normally a `Draft Circle` with no face, if the global variables `dxfCreateDraft` or `dxfCreateSketch` are set, and `forceShape` is `False`. Otherwise it produces a `Part.Edge`. It returns `None` if it fails producing a shape. See also -------- drawArc, drawBlock To do ----- Use local variables, not global variables.
Return a Part shape (Circle, Edge) from a DXF circle.
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def drawCircle(circle, forceShape=False): """Return a Part shape (Circle, Edge) from a DXF circle. Parameters ---------- circle : drawing.entities The DXF object of type `'circle'`. The `'circle'` object is different from an `'arc'` because the circle forms a full circumference. forceShape : bool, optional It defaults to `False`. If it is `True` it will try to produce a `Part.Edge`, otherwise it tries to produce a `Draft Circle`. Returns ------- Part::Part2DObject or Part::TopoShape ('Edge') The returned object is normally a `Draft Circle` with no face, if the global variables `dxfCreateDraft` or `dxfCreateSketch` are set, and `forceShape` is `False`. Otherwise it produces a `Part.Edge`. It returns `None` if it fails producing a shape. See also -------- drawArc, drawBlock To do ----- Use local variables, not global variables. """ v = vec(circle.loc) curve = Part.Circle() curve.Radius = vec(circle.radius) curve.Center = v try: if (dxfCreateDraft or dxfCreateSketch) and (not forceShape): pl = placementFromDXFOCS(circle) return Draft.makeCircle(circle.radius, pl) else: return curve.toShape() except Part.OCCError: warn(circle) return None
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https://github.com/FreeCAD/FreeCAD/blob/ba42231b9c6889b89e064d6d563448ed81e376ec/src/Mod/Draft/importDXF.py#L1061-L1104
apple/swift-lldb
d74be846ef3e62de946df343e8c234bde93a8912
third_party/Python/module/pexpect-4.6/pexpect/FSM.py
python
FSM.add_transition_any
(self, state, action=None, next_state=None)
This adds a transition that associates: (current_state) --> (action, next_state) That is, any input symbol will match the current state. The process() method checks the "any" state associations after it first checks for an exact match of (input_symbol, current_state). The action may be set to None in which case the process() method will ignore the action and only set the next_state. The next_state may be set to None in which case the current state will be unchanged.
This adds a transition that associates:
[ "This", "adds", "a", "transition", "that", "associates", ":" ]
def add_transition_any (self, state, action=None, next_state=None): '''This adds a transition that associates: (current_state) --> (action, next_state) That is, any input symbol will match the current state. The process() method checks the "any" state associations after it first checks for an exact match of (input_symbol, current_state). The action may be set to None in which case the process() method will ignore the action and only set the next_state. The next_state may be set to None in which case the current state will be unchanged. ''' if next_state is None: next_state = state self.state_transitions_any [state] = (action, next_state)
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https://github.com/apple/swift-lldb/blob/d74be846ef3e62de946df343e8c234bde93a8912/third_party/Python/module/pexpect-4.6/pexpect/FSM.py#L164-L180
sailing-pmls/pmls-caffe
49e98bced9c6d5af7cd701d18ab235b5fd0e4b3a
scripts/cpp_lint.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.
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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 ''
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https://github.com/sailing-pmls/pmls-caffe/blob/49e98bced9c6d5af7cd701d18ab235b5fd0e4b3a/scripts/cpp_lint.py#L633-L684
pmq20/node-packer
12c46c6e44fbc14d9ee645ebd17d5296b324f7e0
lts/tools/inspector_protocol/jinja2/environment.py
python
Template.make_module_async
(self, vars=None, shared=False, locals=None)
As template module creation can invoke template code for asynchronous exections this method must be used instead of the normal :meth:`make_module` one. Likewise the module attribute becomes unavailable in async mode.
As template module creation can invoke template code for asynchronous exections this method must be used instead of the normal :meth:`make_module` one. Likewise the module attribute becomes unavailable in async mode.
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def make_module_async(self, vars=None, shared=False, locals=None): """As template module creation can invoke template code for asynchronous exections this method must be used instead of the normal :meth:`make_module` one. Likewise the module attribute becomes unavailable in async mode. """ # see asyncsupport for the actual implementation raise NotImplementedError('This feature is not available for this ' 'version of Python')
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https://github.com/pmq20/node-packer/blob/12c46c6e44fbc14d9ee645ebd17d5296b324f7e0/lts/tools/inspector_protocol/jinja2/environment.py#L1075-L1083
mongodb/mongo
d8ff665343ad29cf286ee2cf4a1960d29371937b
src/third_party/scons-3.1.2/scons-local-3.1.2/SCons/Defaults.py
python
_stripixes
(prefix, itms, suffix, stripprefixes, stripsuffixes, env, c=None)
return c(prefix, stripped, suffix, env)
This is a wrapper around _concat()/_concat_ixes() that checks for the existence of prefixes or suffixes on list items and strips them where it finds them. This is used by tools (like the GNU linker) that need to turn something like 'libfoo.a' into '-lfoo'.
This is a wrapper around _concat()/_concat_ixes() that checks for the existence of prefixes or suffixes on list items and strips them where it finds them. This is used by tools (like the GNU linker) that need to turn something like 'libfoo.a' into '-lfoo'.
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def _stripixes(prefix, itms, suffix, stripprefixes, stripsuffixes, env, c=None): """ This is a wrapper around _concat()/_concat_ixes() that checks for the existence of prefixes or suffixes on list items and strips them where it finds them. This is used by tools (like the GNU linker) that need to turn something like 'libfoo.a' into '-lfoo'. """ if not itms: return itms if not callable(c): env_c = env['_concat'] if env_c != _concat and callable(env_c): # There's a custom _concat() method in the construction # environment, and we've allowed people to set that in # the past (see test/custom-concat.py), so preserve the # backwards compatibility. c = env_c else: c = _concat_ixes stripprefixes = list(map(env.subst, SCons.Util.flatten(stripprefixes))) stripsuffixes = list(map(env.subst, SCons.Util.flatten(stripsuffixes))) stripped = [] for l in SCons.PathList.PathList(itms).subst_path(env, None, None): if isinstance(l, SCons.Node.FS.File): stripped.append(l) continue if not SCons.Util.is_String(l): l = str(l) for stripprefix in stripprefixes: lsp = len(stripprefix) if l[:lsp] == stripprefix: l = l[lsp:] # Do not strip more than one prefix break for stripsuffix in stripsuffixes: lss = len(stripsuffix) if l[-lss:] == stripsuffix: l = l[:-lss] # Do not strip more than one suffix break stripped.append(l) return c(prefix, stripped, suffix, env)
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https://github.com/mongodb/mongo/blob/d8ff665343ad29cf286ee2cf4a1960d29371937b/src/third_party/scons-3.1.2/scons-local-3.1.2/SCons/Defaults.py#L401-L451
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/core/computation/scope.py
python
_get_pretty_string
(obj)
return sio.getvalue()
Return a prettier version of obj. Parameters ---------- obj : object Object to pretty print Returns ------- str Pretty print object repr
Return a prettier version of obj.
[ "Return", "a", "prettier", "version", "of", "obj", "." ]
def _get_pretty_string(obj) -> str: """ Return a prettier version of obj. Parameters ---------- obj : object Object to pretty print Returns ------- str Pretty print object repr """ sio = StringIO() pprint.pprint(obj, stream=sio) return sio.getvalue()
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/core/computation/scope.py#L67-L83
miyosuda/TensorFlowAndroidDemo
35903e0221aa5f109ea2dbef27f20b52e317f42d
jni-build/jni/include/tensorflow/python/ops/array_ops.py
python
ones
(shape, dtype=dtypes.float32, name=None)
return output
Creates a tensor with all elements set to 1. This operation returns a tensor of type `dtype` with shape `shape` and all elements set to 1. For example: ```python tf.ones([2, 3], int32) ==> [[1, 1, 1], [1, 1, 1]] ``` Args: shape: Either a list of integers, or a 1-D `Tensor` of type `int32`. dtype: The type of an element in the resulting `Tensor`. name: A name for the operation (optional). Returns: A `Tensor` with all elements set to 1.
Creates a tensor with all elements set to 1.
[ "Creates", "a", "tensor", "with", "all", "elements", "set", "to", "1", "." ]
def ones(shape, dtype=dtypes.float32, name=None): """Creates a tensor with all elements set to 1. This operation returns a tensor of type `dtype` with shape `shape` and all elements set to 1. For example: ```python tf.ones([2, 3], int32) ==> [[1, 1, 1], [1, 1, 1]] ``` Args: shape: Either a list of integers, or a 1-D `Tensor` of type `int32`. dtype: The type of an element in the resulting `Tensor`. name: A name for the operation (optional). Returns: A `Tensor` with all elements set to 1. """ with ops.op_scope([shape], name, "ones") as name: try: shape = tensor_shape.as_shape(shape) output = constant(1, shape=shape, dtype=dtype, name=name) except (TypeError, ValueError): shape = ops.convert_to_tensor(shape, dtype=dtypes.int32, name="shape") output = fill(shape, constant(1, dtype=dtype), name=name) assert output.dtype.base_dtype == dtypes.as_dtype(dtype).base_dtype return output
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https://github.com/miyosuda/TensorFlowAndroidDemo/blob/35903e0221aa5f109ea2dbef27f20b52e317f42d/jni-build/jni/include/tensorflow/python/ops/array_ops.py#L1143-L1171
giuspen/cherrytree
84712f206478fcf9acf30174009ad28c648c6344
pygtk2/modules/core.py
python
CherryTree.dialog_preferences
(self, *args)
Opens the Preferences Dialog
Opens the Preferences Dialog
[ "Opens", "the", "Preferences", "Dialog" ]
def dialog_preferences(self, *args): """Opens the Preferences Dialog""" self.combobox_country_lang_init() self.combobox_style_scheme_init() if self.spell_check_init: self.combobox_spell_check_lang_init() config.dialog_preferences(self) config.config_file_save(self)
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https://github.com/giuspen/cherrytree/blob/84712f206478fcf9acf30174009ad28c648c6344/pygtk2/modules/core.py#L1340-L1346
domino-team/openwrt-cc
8b181297c34d14d3ca521cc9f31430d561dbc688
package/gli-pub/openwrt-node-packages-master/node/node-v6.9.1/deps/npm/node_modules/node-gyp/gyp/pylib/gyp/generator/msvs.py
python
_FilterActionsFromExcluded
(excluded_sources, actions_to_add)
return [s for s in excluded_sources if s not in must_keep]
Take inputs with actions attached out of the list of exclusions. Arguments: excluded_sources: list of source files not to be built. actions_to_add: dict of actions keyed on source file they're attached to. Returns: excluded_sources with files that have actions attached removed.
Take inputs with actions attached out of the list of exclusions.
[ "Take", "inputs", "with", "actions", "attached", "out", "of", "the", "list", "of", "exclusions", "." ]
def _FilterActionsFromExcluded(excluded_sources, actions_to_add): """Take inputs with actions attached out of the list of exclusions. Arguments: excluded_sources: list of source files not to be built. actions_to_add: dict of actions keyed on source file they're attached to. Returns: excluded_sources with files that have actions attached removed. """ must_keep = OrderedSet(_FixPaths(actions_to_add.keys())) return [s for s in excluded_sources if s not in must_keep]
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https://github.com/domino-team/openwrt-cc/blob/8b181297c34d14d3ca521cc9f31430d561dbc688/package/gli-pub/openwrt-node-packages-master/node/node-v6.9.1/deps/npm/node_modules/node-gyp/gyp/pylib/gyp/generator/msvs.py#L848-L858
benoitsteiner/tensorflow-opencl
cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5
tensorflow/contrib/receptive_field/python/util/receptive_field.py
python
_conv_kernel_size
(node, name_to_order_node)
return kernel_size_x, kernel_size_y
Computes kernel size given a TF convolution or pooling node. Args: node: Tensorflow node (NodeDef proto). name_to_order_node: Map from name to {order, node}. Output of graph_compute_order.get_compute_order(). Returns: kernel_size_x: Kernel size for horizontal direction (integer). kernel_size_y: Kernel size for vertical direction (integer). Raises: ValueError: If the weight layer node is invalid.
Computes kernel size given a TF convolution or pooling node.
[ "Computes", "kernel", "size", "given", "a", "TF", "convolution", "or", "pooling", "node", "." ]
def _conv_kernel_size(node, name_to_order_node): """Computes kernel size given a TF convolution or pooling node. Args: node: Tensorflow node (NodeDef proto). name_to_order_node: Map from name to {order, node}. Output of graph_compute_order.get_compute_order(). Returns: kernel_size_x: Kernel size for horizontal direction (integer). kernel_size_y: Kernel size for vertical direction (integer). Raises: ValueError: If the weight layer node is invalid. """ weights_layer_read_name = node.input[1] if not weights_layer_read_name.endswith("/read"): raise ValueError( "Weight layer's name input to conv layer does not end with '/read'") weights_layer_param_name = weights_layer_read_name[:-5] weights_node = name_to_order_node[weights_layer_param_name].node if weights_node.op != "VariableV2": raise ValueError("Weight layer is not of type VariableV2") shape = weights_node.attr["shape"] logging.vlog(4, "weight shape = %s", shape) kernel_size_y = shape.shape.dim[0].size kernel_size_x = shape.shape.dim[1].size return kernel_size_x, kernel_size_y
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https://github.com/benoitsteiner/tensorflow-opencl/blob/cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5/tensorflow/contrib/receptive_field/python/util/receptive_field.py#L62-L89
tensorflow/tensorflow
419e3a6b650ea4bd1b0cba23c4348f8a69f3272e
tensorflow/python/feature_column/feature_column_v2.py
python
VocabularyListCategoricalColumn._transform_input_tensor
(self, input_tensor, state_manager=None)
return table.lookup(input_tensor)
Creates a lookup table for the vocabulary list.
Creates a lookup table for the vocabulary list.
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def _transform_input_tensor(self, input_tensor, state_manager=None): """Creates a lookup table for the vocabulary list.""" if self.dtype.is_integer != input_tensor.dtype.is_integer: raise ValueError( 'Column dtype and SparseTensors dtype must be compatible. ' 'key: {}, column dtype: {}, tensor dtype: {}'.format( self.key, self.dtype, input_tensor.dtype)) fc_utils.assert_string_or_int( input_tensor.dtype, prefix='column_name: {} input_tensor'.format(self.key)) key_dtype = self.dtype if input_tensor.dtype.is_integer: # `index_table_from_tensor` requires 64-bit integer keys. key_dtype = dtypes.int64 input_tensor = math_ops.cast(input_tensor, dtypes.int64) name = '{}_lookup'.format(self.key) if state_manager is None or not state_manager.has_resource(self, name): with ops.init_scope(): table = lookup_ops.index_table_from_tensor( vocabulary_list=tuple(self.vocabulary_list), default_value=self.default_value, num_oov_buckets=self.num_oov_buckets, dtype=key_dtype, name=name) if state_manager is not None: state_manager.add_resource(self, name, table) else: # Reuse the table from the previous run. table = state_manager.get_resource(self, name) return table.lookup(input_tensor)
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https://github.com/tensorflow/tensorflow/blob/419e3a6b650ea4bd1b0cba23c4348f8a69f3272e/tensorflow/python/feature_column/feature_column_v2.py#L3663-L3695
wlanjie/AndroidFFmpeg
7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf
tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/decimal.py
python
Context._raise_error
(self, condition, explanation = None, *args)
Handles an error If the flag is in _ignored_flags, returns the default response. Otherwise, it sets the flag, then, if the corresponding trap_enabler is set, it reraises the exception. Otherwise, it returns the default value after setting the flag.
Handles an error
[ "Handles", "an", "error" ]
def _raise_error(self, condition, explanation = None, *args): """Handles an error If the flag is in _ignored_flags, returns the default response. Otherwise, it sets the flag, then, if the corresponding trap_enabler is set, it reraises the exception. Otherwise, it returns the default value after setting the flag. """ error = _condition_map.get(condition, condition) if error in self._ignored_flags: # Don't touch the flag return error().handle(self, *args) self.flags[error] = 1 if not self.traps[error]: # The errors define how to handle themselves. return condition().handle(self, *args) # Errors should only be risked on copies of the context # self._ignored_flags = [] raise error(explanation)
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https://github.com/wlanjie/AndroidFFmpeg/blob/7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf/tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/decimal.py#L3852-L3872
thalium/icebox
99d147d5b9269222225443ce171b4fd46d8985d4
third_party/virtualbox/src/libs/libxml2-2.9.4/python/libxml2class.py
python
parserCtxt.parsePEReference
(self)
parse PEReference declarations The entity content is handled directly by pushing it's content as a new input stream. [69] PEReference ::= '%' Name ';' [ WFC: No Recursion ] A parsed entity must not contain a recursive reference to itself, either directly or indirectly. [ WFC: Entity Declared ] In a document without any DTD, a document with only an internal DTD subset which contains no parameter entity references, or a document with "standalone='yes'", ... ... The declaration of a parameter entity must precede any reference to it... [ VC: Entity Declared ] In a document with an external subset or external parameter entities with "standalone='no'", ... ... The declaration of a parameter entity must precede any reference to it... [ WFC: In DTD ] Parameter-entity references may only appear in the DTD. NOTE: misleading but this is handled.
parse PEReference declarations The entity content is handled directly by pushing it's content as a new input stream. [69] PEReference ::= '%' Name ';' [ WFC: No Recursion ] A parsed entity must not contain a recursive reference to itself, either directly or indirectly. [ WFC: Entity Declared ] In a document without any DTD, a document with only an internal DTD subset which contains no parameter entity references, or a document with "standalone='yes'", ... ... The declaration of a parameter entity must precede any reference to it... [ VC: Entity Declared ] In a document with an external subset or external parameter entities with "standalone='no'", ... ... The declaration of a parameter entity must precede any reference to it... [ WFC: In DTD ] Parameter-entity references may only appear in the DTD. NOTE: misleading but this is handled.
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def parsePEReference(self): """parse PEReference declarations The entity content is handled directly by pushing it's content as a new input stream. [69] PEReference ::= '%' Name ';' [ WFC: No Recursion ] A parsed entity must not contain a recursive reference to itself, either directly or indirectly. [ WFC: Entity Declared ] In a document without any DTD, a document with only an internal DTD subset which contains no parameter entity references, or a document with "standalone='yes'", ... ... The declaration of a parameter entity must precede any reference to it... [ VC: Entity Declared ] In a document with an external subset or external parameter entities with "standalone='no'", ... ... The declaration of a parameter entity must precede any reference to it... [ WFC: In DTD ] Parameter-entity references may only appear in the DTD. NOTE: misleading but this is handled. """ libxml2mod.xmlParsePEReference(self._o)
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https://github.com/thalium/icebox/blob/99d147d5b9269222225443ce171b4fd46d8985d4/third_party/virtualbox/src/libs/libxml2-2.9.4/python/libxml2class.py#L4588-L4605
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/site-packages/pip/_vendor/requests/adapters.py
python
HTTPAdapter.proxy_headers
(self, proxy)
return headers
Returns a dictionary of the headers to add to any request sent through a proxy. This works with urllib3 magic to ensure that they are correctly sent to the proxy, rather than in a tunnelled request if CONNECT is being used. This should not be called from user code, and is only exposed for use when subclassing the :class:`HTTPAdapter <requests.adapters.HTTPAdapter>`. :param proxy: The url of the proxy being used for this request. :rtype: dict
Returns a dictionary of the headers to add to any request sent through a proxy. This works with urllib3 magic to ensure that they are correctly sent to the proxy, rather than in a tunnelled request if CONNECT is being used.
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def proxy_headers(self, proxy): """Returns a dictionary of the headers to add to any request sent through a proxy. This works with urllib3 magic to ensure that they are correctly sent to the proxy, rather than in a tunnelled request if CONNECT is being used. This should not be called from user code, and is only exposed for use when subclassing the :class:`HTTPAdapter <requests.adapters.HTTPAdapter>`. :param proxy: The url of the proxy being used for this request. :rtype: dict """ headers = {} username, password = get_auth_from_url(proxy) if username: headers['Proxy-Authorization'] = _basic_auth_str(username, password) return headers
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/site-packages/pip/_vendor/requests/adapters.py#L372-L392
gnuradio/gnuradio
09c3c4fa4bfb1a02caac74cb5334dfe065391e3b
grc/core/utils/backports/chainmap.py
python
ChainMap.__init__
(self, *maps)
Initialize a ChainMap by setting *maps* to the given mappings. If no mappings are provided, a single empty dictionary is used.
Initialize a ChainMap by setting *maps* to the given mappings. If no mappings are provided, a single empty dictionary is used.
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def __init__(self, *maps): """Initialize a ChainMap by setting *maps* to the given mappings. If no mappings are provided, a single empty dictionary is used. """ self.maps = list(maps) or [{}]
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https://github.com/gnuradio/gnuradio/blob/09c3c4fa4bfb1a02caac74cb5334dfe065391e3b/grc/core/utils/backports/chainmap.py#L20-L25
grpc/grpc
27bc6fe7797e43298dc931b96dc57322d0852a9f
src/python/grpcio/grpc/framework/interfaces/base/utilities.py
python
completion
(terminal_metadata, code, message)
return _Completion(terminal_metadata, code, message)
Creates a base.Completion aggregating the given operation values. Args: terminal_metadata: A terminal metadata value for an operaton. code: A code value for an operation. message: A message value for an operation. Returns: A base.Completion aggregating the given operation values.
Creates a base.Completion aggregating the given operation values.
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def completion(terminal_metadata, code, message): """Creates a base.Completion aggregating the given operation values. Args: terminal_metadata: A terminal metadata value for an operaton. code: A code value for an operation. message: A message value for an operation. Returns: A base.Completion aggregating the given operation values. """ return _Completion(terminal_metadata, code, message)
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https://github.com/grpc/grpc/blob/27bc6fe7797e43298dc931b96dc57322d0852a9f/src/python/grpcio/grpc/framework/interfaces/base/utilities.py#L45-L56
catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/tools/python3/src/Lib/ntpath.py
python
_abspath_fallback
(path)
return normpath(path)
Return the absolute version of a path as a fallback function in case `nt._getfullpathname` is not available or raises OSError. See bpo-31047 for more.
Return the absolute version of a path as a fallback function in case `nt._getfullpathname` is not available or raises OSError. See bpo-31047 for more.
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def _abspath_fallback(path): """Return the absolute version of a path as a fallback function in case `nt._getfullpathname` is not available or raises OSError. See bpo-31047 for more. """ path = os.fspath(path) if not isabs(path): if isinstance(path, bytes): cwd = os.getcwdb() else: cwd = os.getcwd() path = join(cwd, path) return normpath(path)
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adobe/chromium
cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7
third_party/tlslite/tlslite/TLSConnection.py
python
TLSConnection.handshakeClientUnknown
(self, srpCallback=None, certCallback=None, session=None, settings=None, checker=None, async=False)
Perform a to-be-determined type of handshake in the role of client. This function performs an SSL or TLS handshake. If the server requests client certificate authentication, the certCallback will be invoked and should return a (certChain, privateKey) pair. If the callback returns None, the library will attempt to proceed without client authentication. The server may or may not allow this. If the server requests SRP authentication, the srpCallback will be invoked and should return a (username, password) pair. If the callback returns None, the local implementation will signal a user_canceled error alert. After the handshake completes, the client can inspect the connection's session attribute to determine what type of authentication was performed. Like any handshake function, this can be called on a closed TLS connection, or on a TLS connection that is already open. If called on an open connection it performs a re-handshake. If the function completes without raising an exception, the TLS connection will be open and available for data transfer. If an exception is raised, the connection will have been automatically closed (if it was ever open). @type srpCallback: callable @param srpCallback: The callback to be used if the server requests SRP authentication. If None, the client will not offer support for SRP ciphersuites. @type certCallback: callable @param certCallback: The callback to be used if the server requests client certificate authentication. @type session: L{tlslite.Session.Session} @param session: A TLS session to attempt to resume. If the resumption does not succeed, a full handshake will be performed. @type settings: L{tlslite.HandshakeSettings.HandshakeSettings} @param settings: Various settings which can be used to control the ciphersuites, certificate types, and SSL/TLS versions offered by the client. @type checker: L{tlslite.Checker.Checker} @param checker: A Checker instance. This instance will be invoked to examine the other party's authentication credentials, if the handshake completes succesfully. @type async: bool @param async: If False, this function will block until the handshake is completed. If True, this function will return a generator. Successive invocations of the generator will return 0 if it is waiting to read from the socket, 1 if it is waiting to write to the socket, or will raise StopIteration if the handshake operation is completed. @rtype: None or an iterable @return: If 'async' is True, a generator object will be returned. @raise socket.error: If a socket error occurs. @raise tlslite.errors.TLSAbruptCloseError: If the socket is closed without a preceding alert. @raise tlslite.errors.TLSAlert: If a TLS alert is signalled. @raise tlslite.errors.TLSAuthenticationError: If the checker doesn't like the other party's authentication credentials.
Perform a to-be-determined type of handshake in the role of client.
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def handshakeClientUnknown(self, srpCallback=None, certCallback=None, session=None, settings=None, checker=None, async=False): """Perform a to-be-determined type of handshake in the role of client. This function performs an SSL or TLS handshake. If the server requests client certificate authentication, the certCallback will be invoked and should return a (certChain, privateKey) pair. If the callback returns None, the library will attempt to proceed without client authentication. The server may or may not allow this. If the server requests SRP authentication, the srpCallback will be invoked and should return a (username, password) pair. If the callback returns None, the local implementation will signal a user_canceled error alert. After the handshake completes, the client can inspect the connection's session attribute to determine what type of authentication was performed. Like any handshake function, this can be called on a closed TLS connection, or on a TLS connection that is already open. If called on an open connection it performs a re-handshake. If the function completes without raising an exception, the TLS connection will be open and available for data transfer. If an exception is raised, the connection will have been automatically closed (if it was ever open). @type srpCallback: callable @param srpCallback: The callback to be used if the server requests SRP authentication. If None, the client will not offer support for SRP ciphersuites. @type certCallback: callable @param certCallback: The callback to be used if the server requests client certificate authentication. @type session: L{tlslite.Session.Session} @param session: A TLS session to attempt to resume. If the resumption does not succeed, a full handshake will be performed. @type settings: L{tlslite.HandshakeSettings.HandshakeSettings} @param settings: Various settings which can be used to control the ciphersuites, certificate types, and SSL/TLS versions offered by the client. @type checker: L{tlslite.Checker.Checker} @param checker: A Checker instance. This instance will be invoked to examine the other party's authentication credentials, if the handshake completes succesfully. @type async: bool @param async: If False, this function will block until the handshake is completed. If True, this function will return a generator. Successive invocations of the generator will return 0 if it is waiting to read from the socket, 1 if it is waiting to write to the socket, or will raise StopIteration if the handshake operation is completed. @rtype: None or an iterable @return: If 'async' is True, a generator object will be returned. @raise socket.error: If a socket error occurs. @raise tlslite.errors.TLSAbruptCloseError: If the socket is closed without a preceding alert. @raise tlslite.errors.TLSAlert: If a TLS alert is signalled. @raise tlslite.errors.TLSAuthenticationError: If the checker doesn't like the other party's authentication credentials. """ handshaker = self._handshakeClientAsync(unknownParams=(srpCallback, certCallback), session=session, settings=settings, checker=checker) if async: return handshaker for result in handshaker: pass
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https://github.com/adobe/chromium/blob/cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7/third_party/tlslite/tlslite/TLSConnection.py#L210-L290
NVIDIA/TensorRT
42805f078052daad1a98bc5965974fcffaad0960
samples/python/uff_ssd/model.py
python
parse_commandline_arguments
()
return args
Parses command line arguments and adjusts internal data structures.
Parses command line arguments and adjusts internal data structures.
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def parse_commandline_arguments(): """Parses command line arguments and adjusts internal data structures.""" # Define script command line arguments parser = argparse.ArgumentParser(description='Run object detection inference on input image.') parser.add_argument('-w', '--workspace_dir', help='sample workspace directory') parser.add_argument('-d', '--data', help="Specify the data directory where it is saved in. $TRT_DATA_DIR will be overwritten by this argument.") args, _ = parser.parse_known_args() data_dir = os.environ.get('TRT_DATA_DIR', None) if args.data is None else args.data if data_dir is None: raise ValueError("Data directory must be specified by either `-d $DATA` or environment variable $TRT_DATA_DIR.") PATHS.set_data_dir_path(data_dir) # Set workspace dir path if passed by user if args.workspace_dir: PATHS.set_workspace_dir_path(args.workspace_dir) try: os.makedirs(PATHS.get_workspace_dir_path()) except: pass # Verify Paths after adjustments. This also exits script if verification fails PATHS.verify_all_paths() return args
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https://github.com/NVIDIA/TensorRT/blob/42805f078052daad1a98bc5965974fcffaad0960/samples/python/uff_ssd/model.py#L26-L55
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/msw/_misc.py
python
SystemOptions.__init__
(self, *args, **kwargs)
__init__(self) -> SystemOptions
__init__(self) -> SystemOptions
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def __init__(self, *args, **kwargs): """__init__(self) -> SystemOptions""" _misc_.SystemOptions_swiginit(self,_misc_.new_SystemOptions(*args, **kwargs))
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/msw/_misc.py#L218-L220
gnuradio/gnuradio
09c3c4fa4bfb1a02caac74cb5334dfe065391e3b
grc/core/FlowGraph.py
python
FlowGraph.new_block
(self, block_id, **kwargs)
return block
Get a new block of the specified key. Add the block to the list of elements. Args: block_id: the block key Returns: the new block or None if not found
Get a new block of the specified key. Add the block to the list of elements.
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def new_block(self, block_id, **kwargs): """ Get a new block of the specified key. Add the block to the list of elements. Args: block_id: the block key Returns: the new block or None if not found """ if block_id == 'options': return self.options_block try: block = self.parent_platform.make_block(self, block_id, **kwargs) self.blocks.append(block) except KeyError: block = None return block
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llvm/llvm-project
ffa6262cb4e2a335d26416fad39a581b4f98c5f4
lldb/third_party/Python/module/pexpect-4.6/pexpect/spawnbase.py
python
SpawnBase.readline
(self, size=-1)
This reads and returns one entire line. The newline at the end of line is returned as part of the string, unless the file ends without a newline. An empty string is returned if EOF is encountered immediately. This looks for a newline as a CR/LF pair (\\r\\n) even on UNIX because this is what the pseudotty device returns. So contrary to what you may expect you will receive newlines as \\r\\n. If the size argument is 0 then an empty string is returned. In all other cases the size argument is ignored, which is not standard behavior for a file-like object.
This reads and returns one entire line. The newline at the end of line is returned as part of the string, unless the file ends without a newline. An empty string is returned if EOF is encountered immediately. This looks for a newline as a CR/LF pair (\\r\\n) even on UNIX because this is what the pseudotty device returns. So contrary to what you may expect you will receive newlines as \\r\\n.
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def readline(self, size=-1): '''This reads and returns one entire line. The newline at the end of line is returned as part of the string, unless the file ends without a newline. An empty string is returned if EOF is encountered immediately. This looks for a newline as a CR/LF pair (\\r\\n) even on UNIX because this is what the pseudotty device returns. So contrary to what you may expect you will receive newlines as \\r\\n. If the size argument is 0 then an empty string is returned. In all other cases the size argument is ignored, which is not standard behavior for a file-like object. ''' if size == 0: return self.string_type() # delimiter default is EOF index = self.expect([self.crlf, self.delimiter]) if index == 0: return self.before + self.crlf else: return self.before
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https://github.com/llvm/llvm-project/blob/ffa6262cb4e2a335d26416fad39a581b4f98c5f4/lldb/third_party/Python/module/pexpect-4.6/pexpect/spawnbase.py#L459-L478
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Tools/Python/3.7.10/windows/Lib/chunk.py
python
Chunk.seek
(self, pos, whence=0)
Seek to specified position into the chunk. Default position is 0 (start of chunk). If the file is not seekable, this will result in an error.
Seek to specified position into the chunk. Default position is 0 (start of chunk). If the file is not seekable, this will result in an error.
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def seek(self, pos, whence=0): """Seek to specified position into the chunk. Default position is 0 (start of chunk). If the file is not seekable, this will result in an error. """ if self.closed: raise ValueError("I/O operation on closed file") if not self.seekable: raise OSError("cannot seek") if whence == 1: pos = pos + self.size_read elif whence == 2: pos = pos + self.chunksize if pos < 0 or pos > self.chunksize: raise RuntimeError self.file.seek(self.offset + pos, 0) self.size_read = pos
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/windows/Lib/chunk.py#L98-L115
cvxpy/cvxpy
5165b4fb750dfd237de8659383ef24b4b2e33aaf
cvxpy/atoms/atom.py
python
Atom.is_convex
(self)
Is the expression convex?
Is the expression convex?
[ "Is", "the", "expression", "convex?" ]
def is_convex(self) -> bool: """Is the expression convex? """ # Applies DCP composition rule. if self.is_constant(): return True elif self.is_atom_convex(): for idx, arg in enumerate(self.args): if not (arg.is_affine() or (arg.is_convex() and self.is_incr(idx)) or (arg.is_concave() and self.is_decr(idx))): return False return True else: return False
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https://github.com/cvxpy/cvxpy/blob/5165b4fb750dfd237de8659383ef24b4b2e33aaf/cvxpy/atoms/atom.py#L170-L184
catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/tools/python3/src/Lib/base64.py
python
main
()
Small main program
Small main program
[ "Small", "main", "program" ]
def main(): """Small main program""" import sys, getopt try: opts, args = getopt.getopt(sys.argv[1:], 'deut') except getopt.error as msg: sys.stdout = sys.stderr print(msg) print("""usage: %s [-d|-e|-u|-t] [file|-] -d, -u: decode -e: encode (default) -t: encode and decode string 'Aladdin:open sesame'"""%sys.argv[0]) sys.exit(2) func = encode for o, a in opts: if o == '-e': func = encode if o == '-d': func = decode if o == '-u': func = decode if o == '-t': test(); return if args and args[0] != '-': with open(args[0], 'rb') as f: func(f, sys.stdout.buffer) else: func(sys.stdin.buffer, sys.stdout.buffer)
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/tools/python3/src/Lib/base64.py#L542-L565
catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/python/scipy/py2/scipy/odr/odrpack.py
python
Model.__getattr__
(self, attr)
Dispatch attribute access to the metadata.
Dispatch attribute access to the metadata.
[ "Dispatch", "attribute", "access", "to", "the", "metadata", "." ]
def __getattr__(self, attr): """ Dispatch attribute access to the metadata. """ if attr in self.meta: return self.meta[attr] else: raise AttributeError("'%s' not in metadata" % attr)
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/scipy/py2/scipy/odr/odrpack.py#L535-L542
wlanjie/AndroidFFmpeg
7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf
tools/fdk-aac-build/x86/toolchain/lib/python2.7/imaplib.py
python
IMAP4.xatom
(self, name, *args)
return self._simple_command(name, *args)
Allow simple extension commands notified by server in CAPABILITY response. Assumes command is legal in current state. (typ, [data]) = <instance>.xatom(name, arg, ...) Returns response appropriate to extension command `name'.
Allow simple extension commands notified by server in CAPABILITY response.
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def xatom(self, name, *args): """Allow simple extension commands notified by server in CAPABILITY response. Assumes command is legal in current state. (typ, [data]) = <instance>.xatom(name, arg, ...) Returns response appropriate to extension command `name'. """ name = name.upper() #if not name in self.capabilities: # Let the server decide! # raise self.error('unknown extension command: %s' % name) if not name in Commands: Commands[name] = (self.state,) return self._simple_command(name, *args)
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https://github.com/wlanjie/AndroidFFmpeg/blob/7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf/tools/fdk-aac-build/x86/toolchain/lib/python2.7/imaplib.py#L776-L791
BlzFans/wke
b0fa21158312e40c5fbd84682d643022b6c34a93
cygwin/lib/python2.6/uuid.py
python
_ipconfig_getnode
()
Get the hardware address on Windows by running ipconfig.exe.
Get the hardware address on Windows by running ipconfig.exe.
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def _ipconfig_getnode(): """Get the hardware address on Windows by running ipconfig.exe.""" import os, re dirs = ['', r'c:\windows\system32', r'c:\winnt\system32'] try: import ctypes buffer = ctypes.create_string_buffer(300) ctypes.windll.kernel32.GetSystemDirectoryA(buffer, 300) dirs.insert(0, buffer.value.decode('mbcs')) except: pass for dir in dirs: try: pipe = os.popen(os.path.join(dir, 'ipconfig') + ' /all') except IOError: continue for line in pipe: value = line.split(':')[-1].strip().lower() if re.match('([0-9a-f][0-9a-f]-){5}[0-9a-f][0-9a-f]', value): return int(value.replace('-', ''), 16)
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https://github.com/BlzFans/wke/blob/b0fa21158312e40c5fbd84682d643022b6c34a93/cygwin/lib/python2.6/uuid.py#L340-L359
apache/incubator-mxnet
f03fb23f1d103fec9541b5ae59ee06b1734a51d9
python/mxnet/symbol/symbol.py
python
Symbol.arcsin
(self, *args, **kwargs)
return op.arcsin(self, *args, **kwargs)
Convenience fluent method for :py:func:`arcsin`. The arguments are the same as for :py:func:`arcsin`, with this array as data.
Convenience fluent method for :py:func:`arcsin`.
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def arcsin(self, *args, **kwargs): """Convenience fluent method for :py:func:`arcsin`. The arguments are the same as for :py:func:`arcsin`, with this array as data. """ return op.arcsin(self, *args, **kwargs)
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https://github.com/apache/incubator-mxnet/blob/f03fb23f1d103fec9541b5ae59ee06b1734a51d9/python/mxnet/symbol/symbol.py#L2389-L2395
ChromiumWebApps/chromium
c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7
tools/code_coverage/croc.py
python
CoveredFile.UpdateCoverage
(self)
Updates the coverage summary based on covered lines.
Updates the coverage summary based on covered lines.
[ "Updates", "the", "coverage", "summary", "based", "on", "covered", "lines", "." ]
def UpdateCoverage(self): """Updates the coverage summary based on covered lines.""" exe = instr = cov = 0 for l in self.lines.itervalues(): exe += 1 if l is not None: instr += 1 if l == 1: cov += 1 # Add stats that always exist self.stats = CoverageStats(lines_executable=exe, lines_instrumented=instr, lines_covered=cov, files_executable=1) # Add conditional stats if cov: self.stats['files_covered'] = 1 if instr or self.in_lcov: self.stats['files_instrumented'] = 1
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https://github.com/ChromiumWebApps/chromium/blob/c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7/tools/code_coverage/croc.py#L83-L103
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numpy/core/getlimits.py
python
_get_machar
(ftype)
return _discovered_machar(ftype)
Get MachAr instance or MachAr-like instance Get parameters for floating point type, by first trying signatures of various known floating point types, then, if none match, attempting to identify parameters by analysis. Parameters ---------- ftype : class Numpy floating point type class (e.g. ``np.float64``) Returns ------- ma_like : instance of :class:`MachAr` or :class:`MachArLike` Object giving floating point parameters for `ftype`. Warns ----- UserWarning If the binary signature of the float type is not in the dictionary of known float types.
Get MachAr instance or MachAr-like instance
[ "Get", "MachAr", "instance", "or", "MachAr", "-", "like", "instance" ]
def _get_machar(ftype): """ Get MachAr instance or MachAr-like instance Get parameters for floating point type, by first trying signatures of various known floating point types, then, if none match, attempting to identify parameters by analysis. Parameters ---------- ftype : class Numpy floating point type class (e.g. ``np.float64``) Returns ------- ma_like : instance of :class:`MachAr` or :class:`MachArLike` Object giving floating point parameters for `ftype`. Warns ----- UserWarning If the binary signature of the float type is not in the dictionary of known float types. """ params = _MACHAR_PARAMS.get(ftype) if params is None: raise ValueError(repr(ftype)) # Detect known / suspected types key = ftype('-0.1').newbyteorder('<').tobytes() ma_like = _KNOWN_TYPES.get(key) # Could be 80 bit == 10 byte extended precision, where last bytes can be # random garbage. Try comparing first 10 bytes to pattern. if ma_like is None and ftype == ntypes.longdouble: ma_like = _KNOWN_TYPES.get(key[:10]) if ma_like is not None: return ma_like # Fall back to parameter discovery warnings.warn( 'Signature {} for {} does not match any known type: ' 'falling back to type probe function'.format(key, ftype), UserWarning, stacklevel=2) return _discovered_machar(ftype)
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numpy/core/getlimits.py#L239-L279
cms-sw/cmssw
fd9de012d503d3405420bcbeec0ec879baa57cf2
Alignment/MuonAlignment/python/svgfig.py
python
XAxis.SVG
(self, trans=None)
return LineAxis.SVG(self, trans)
Apply the transformation "trans" and return an SVG object.
Apply the transformation "trans" and return an SVG object.
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def SVG(self, trans=None): """Apply the transformation "trans" and return an SVG object.""" self.y1 = self.aty self.y2 = self.aty return LineAxis.SVG(self, trans)
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https://github.com/cms-sw/cmssw/blob/fd9de012d503d3405420bcbeec0ec879baa57cf2/Alignment/MuonAlignment/python/svgfig.py#L2983-L2987
ablab/spades
3a754192b88540524ce6fb69eef5ea9273a38465
assembler/ext/src/python_libs/pyyaml3/__init__.py
python
safe_dump
(data, stream=None, **kwds)
return dump_all([data], stream, Dumper=SafeDumper, **kwds)
Serialize a Python object into a YAML stream. Produce only basic YAML tags. If stream is None, return the produced string instead.
Serialize a Python object into a YAML stream. Produce only basic YAML tags. If stream is None, return the produced string instead.
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def safe_dump(data, stream=None, **kwds): """ Serialize a Python object into a YAML stream. Produce only basic YAML tags. If stream is None, return the produced string instead. """ return dump_all([data], stream, Dumper=SafeDumper, **kwds)
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https://github.com/ablab/spades/blob/3a754192b88540524ce6fb69eef5ea9273a38465/assembler/ext/src/python_libs/pyyaml3/__init__.py#L210-L216
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/msw/grid.py
python
GridCellFloatRenderer.GetFormat
(*args, **kwargs)
return _grid.GridCellFloatRenderer_GetFormat(*args, **kwargs)
GetFormat(self) -> int
GetFormat(self) -> int
[ "GetFormat", "(", "self", ")", "-", ">", "int" ]
def GetFormat(*args, **kwargs): """GetFormat(self) -> int""" return _grid.GridCellFloatRenderer_GetFormat(*args, **kwargs)
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/msw/grid.py#L203-L205
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/msw/richtext.py
python
RichTextCtrl.IsSelectionUnderlined
(*args, **kwargs)
return _richtext.RichTextCtrl_IsSelectionUnderlined(*args, **kwargs)
IsSelectionUnderlined(self) -> bool Is all of the selection underlined?
IsSelectionUnderlined(self) -> bool
[ "IsSelectionUnderlined", "(", "self", ")", "-", ">", "bool" ]
def IsSelectionUnderlined(*args, **kwargs): """ IsSelectionUnderlined(self) -> bool Is all of the selection underlined? """ return _richtext.RichTextCtrl_IsSelectionUnderlined(*args, **kwargs)
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/msw/richtext.py#L3935-L3941
windystrife/UnrealEngine_NVIDIAGameWorks
b50e6338a7c5b26374d66306ebc7807541ff815e
Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/pickle.py
python
Pickler.__init__
(self, file, protocol=None)
This takes a file-like object for writing a pickle data stream. The optional protocol argument tells the pickler to use the given protocol; supported protocols are 0, 1, 2. The default protocol is 0, to be backwards compatible. (Protocol 0 is the only protocol that can be written to a file opened in text mode and read back successfully. When using a protocol higher than 0, make sure the file is opened in binary mode, both when pickling and unpickling.) Protocol 1 is more efficient than protocol 0; protocol 2 is more efficient than protocol 1. Specifying a negative protocol version selects the highest protocol version supported. The higher the protocol used, the more recent the version of Python needed to read the pickle produced. The file parameter must have a write() method that accepts a single string argument. It can thus be an open file object, a StringIO object, or any other custom object that meets this interface.
This takes a file-like object for writing a pickle data stream.
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def __init__(self, file, protocol=None): """This takes a file-like object for writing a pickle data stream. The optional protocol argument tells the pickler to use the given protocol; supported protocols are 0, 1, 2. The default protocol is 0, to be backwards compatible. (Protocol 0 is the only protocol that can be written to a file opened in text mode and read back successfully. When using a protocol higher than 0, make sure the file is opened in binary mode, both when pickling and unpickling.) Protocol 1 is more efficient than protocol 0; protocol 2 is more efficient than protocol 1. Specifying a negative protocol version selects the highest protocol version supported. The higher the protocol used, the more recent the version of Python needed to read the pickle produced. The file parameter must have a write() method that accepts a single string argument. It can thus be an open file object, a StringIO object, or any other custom object that meets this interface. """ if protocol is None: protocol = 0 if protocol < 0: protocol = HIGHEST_PROTOCOL elif not 0 <= protocol <= HIGHEST_PROTOCOL: raise ValueError("pickle protocol must be <= %d" % HIGHEST_PROTOCOL) self.write = file.write self.memo = {} self.proto = int(protocol) self.bin = protocol >= 1 self.fast = 0
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https://github.com/windystrife/UnrealEngine_NVIDIAGameWorks/blob/b50e6338a7c5b26374d66306ebc7807541ff815e/Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/pickle.py#L173-L207
Xilinx/Vitis-AI
fc74d404563d9951b57245443c73bef389f3657f
tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/framework/ops.py
python
prepend_name_scope
(name, import_scope)
Prepends name scope to a name. Args: name: A `string` name. import_scope: Optional `string`. Name scope to add. Returns: Name with name scope added, or the original name if import_scope is None.
Prepends name scope to a name.
[ "Prepends", "name", "scope", "to", "a", "name", "." ]
def prepend_name_scope(name, import_scope): """Prepends name scope to a name. Args: name: A `string` name. import_scope: Optional `string`. Name scope to add. Returns: Name with name scope added, or the original name if import_scope is None. """ if import_scope: if import_scope[-1] == "/": import_scope = import_scope[:-1] try: str_to_replace = r"([\^]|loc:@|^)(.*)" return re.sub(str_to_replace, r"\1" + import_scope + r"/\2", compat.as_str(name)) except TypeError as e: # If the name is not of a type we can process, simply return it. logging.warning(e) return name else: return name
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https://github.com/Xilinx/Vitis-AI/blob/fc74d404563d9951b57245443c73bef389f3657f/tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/framework/ops.py#L6482-L6506
apple/turicreate
cce55aa5311300e3ce6af93cb45ba791fd1bdf49
src/python/turicreate/data_structures/sarray.py
python
SArray.argmin
(self)
return sf_out["minimum_x1"][0]
Get the index of the minimum numeric value in SArray. Returns None on an empty SArray. Raises an exception if called on an SArray with non-numeric type. Returns ------- out : int index of the minimum value of SArray See Also -------- argmax Examples -------- >>> turicreate.SArray([14, 62, 83, 72, 77, 96, 5, 25, 69, 66]).argmin()
Get the index of the minimum numeric value in SArray.
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def argmin(self): """ Get the index of the minimum numeric value in SArray. Returns None on an empty SArray. Raises an exception if called on an SArray with non-numeric type. Returns ------- out : int index of the minimum value of SArray See Also -------- argmax Examples -------- >>> turicreate.SArray([14, 62, 83, 72, 77, 96, 5, 25, 69, 66]).argmin() """ from .sframe import SFrame as _SFrame if len(self) == 0: return None if not any([isinstance(self[0], i) for i in [int, float, long]]): raise TypeError("SArray must be of type 'int', 'long', or 'float'.") sf = _SFrame(self).add_row_number() sf_out = sf.groupby( key_column_names=[], operations={"minimum_x1": _aggregate.ARGMIN("X1", "id")}, ) return sf_out["minimum_x1"][0]
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https://github.com/apple/turicreate/blob/cce55aa5311300e3ce6af93cb45ba791fd1bdf49/src/python/turicreate/data_structures/sarray.py#L2223-L2256