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tensorflow/tensorflow
419e3a6b650ea4bd1b0cba23c4348f8a69f3272e
tensorflow/python/ops/structured/structured_tensor.py
python
StructuredTensor._from_pyval
(cls, pyval, typespec, path_so_far)
Helper function for from_pyval. Args: pyval: The nested Python structure that should be used to create the new `StructuredTensor`. typespec: A `StructuredTensorSpec` specifying the expected type for each field. If not specified, then all nested dictionaries are turned into StructuredTensors, and all nested lists are turned into Tensors (if rank<2) or RaggedTensors (if rank>=2). path_so_far: the path of fields that led here (for error messages). Returns: A `StructuredTensor`.
Helper function for from_pyval.
[ "Helper", "function", "for", "from_pyval", "." ]
def _from_pyval(cls, pyval, typespec, path_so_far): """Helper function for from_pyval. Args: pyval: The nested Python structure that should be used to create the new `StructuredTensor`. typespec: A `StructuredTensorSpec` specifying the expected type for each field. If not specified, then all nested dictionaries are turned into StructuredTensors, and all nested lists are turned into Tensors (if rank<2) or RaggedTensors (if rank>=2). path_so_far: the path of fields that led here (for error messages). Returns: A `StructuredTensor`. """ if isinstance(pyval, dict): return cls._from_pydict(pyval, typespec, path_so_far) elif isinstance(pyval, (list, tuple)): keys = set() rank = _pyval_find_struct_keys_and_depth(pyval, keys) if rank is not None: return cls._from_pylist_of_dict(pyval, keys, rank, typespec, path_so_far) else: return cls._from_pylist_of_value(pyval, typespec, path_so_far) else: return cls._from_pyscalar(pyval, typespec, path_so_far)
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https://github.com/tensorflow/tensorflow/blob/419e3a6b650ea4bd1b0cba23c4348f8a69f3272e/tensorflow/python/ops/structured/structured_tensor.py#L888-L915
gimli-org/gimli
17aa2160de9b15ababd9ef99e89b1bc3277bbb23
pygimli/frameworks/methodManager.py
python
ParameterInversionManager.__init__
(self, funct=None, fop=None, **kwargs)
Constructor.
Constructor.
[ "Constructor", "." ]
def __init__(self, funct=None, fop=None, **kwargs): """Constructor.""" if fop is not None: if not isinstance(fop, pg.frameworks.ParameterModelling): pg.critical("We need a fop if type ", pg.frameworks.ParameterModelling) elif funct is not None: fop = pg.frameworks.ParameterModelling(funct) else: pg.critical("you should either give a valid fop or a function so " "I can create the fop for you") super(ParameterInversionManager, self).__init__(fop, **kwargs)
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PlatformLab/RAMCloud
b1866af19124325a6dfd8cbc267e2e3ef1f965d1
scripts/cluster.py
python
server_locator
(transport, host, port=server_port)
return locator
Generate a service locator for a master/backup process. @param transport: A transport name (e.g. infrc, basic+infud, tcp, ...) @type transport: C{str} @param host: A 3-tuple of (hostname, ip, id). @type host: C{(str, str, int)} @param port: Port which should be part of the locator (if any). Allows multiple services to be started on the same host. @type port: C{int} @return: A service locator. @rtype: C{str}
Generate a service locator for a master/backup process.
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def server_locator(transport, host, port=server_port): """Generate a service locator for a master/backup process. @param transport: A transport name (e.g. infrc, basic+infud, tcp, ...) @type transport: C{str} @param host: A 3-tuple of (hostname, ip, id). @type host: C{(str, str, int)} @param port: Port which should be part of the locator (if any). Allows multiple services to be started on the same host. @type port: C{int} @return: A service locator. @rtype: C{str} """ locator = (server_locator_templates[transport] % {'host': host[1], 'host1g': host[0], 'port': port, 'id': host[2]}) return locator
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https://github.com/PlatformLab/RAMCloud/blob/b1866af19124325a6dfd8cbc267e2e3ef1f965d1/scripts/cluster.py#L85-L106
generalized-intelligence/GAAS
29ab17d3e8a4ba18edef3a57c36d8db6329fac73
deprecated/algorithms/sfm/OpenSfM/opensfm/reconstruction.py
python
bundle
(graph, reconstruction, gcp, config)
return report
Bundle adjust a reconstruction.
Bundle adjust a reconstruction.
[ "Bundle", "adjust", "a", "reconstruction", "." ]
def bundle(graph, reconstruction, gcp, config): """Bundle adjust a reconstruction.""" fix_cameras = not config['optimize_camera_parameters'] chrono = Chronometer() ba = csfm.BundleAdjuster() for camera in reconstruction.cameras.values(): _add_camera_to_bundle(ba, camera, fix_cameras) for shot in reconstruction.shots.values(): r = shot.pose.rotation t = shot.pose.translation ba.add_shot( shot.id, shot.camera.id, r[0], r[1], r[2], t[0], t[1], t[2], False ) for point in reconstruction.points.values(): x = point.coordinates ba.add_point(point.id, x[0], x[1], x[2], False) for shot_id in reconstruction.shots: if shot_id in graph: for track in graph[shot_id]: if track in reconstruction.points: ba.add_observation(shot_id, track, *graph[shot_id][track]['feature']) if config['bundle_use_gps']: for shot in reconstruction.shots.values(): g = shot.metadata.gps_position ba.add_position_prior(shot.id, g[0], g[1], g[2], shot.metadata.gps_dop) if config['bundle_use_gcp'] and gcp: for observation in gcp: if observation.shot_id in reconstruction.shots: ba.add_ground_control_point_observation( observation.shot_id, observation.coordinates[0], observation.coordinates[1], observation.coordinates[2], observation.shot_coordinates[0], observation.shot_coordinates[1]) ba.set_loss_function(config['loss_function'], config['loss_function_threshold']) ba.set_reprojection_error_sd(config['reprojection_error_sd']) ba.set_internal_parameters_prior_sd( config['exif_focal_sd'], config['principal_point_sd'], config['radial_distorsion_k1_sd'], config['radial_distorsion_k2_sd'], config['radial_distorsion_p1_sd'], config['radial_distorsion_p2_sd'], config['radial_distorsion_k3_sd']) ba.set_num_threads(config['processes']) ba.set_max_num_iterations(50) ba.set_linear_solver_type("SPARSE_SCHUR") chrono.lap('setup') ba.run() chrono.lap('run') for camera in reconstruction.cameras.values(): _get_camera_from_bundle(ba, camera) for shot in reconstruction.shots.values(): s = ba.get_shot(shot.id) shot.pose.rotation = [s.rx, s.ry, s.rz] shot.pose.translation = [s.tx, s.ty, s.tz] for point in reconstruction.points.values(): p = ba.get_point(point.id) point.coordinates = [p.x, p.y, p.z] point.reprojection_error = p.reprojection_error chrono.lap('teardown') logger.debug(ba.brief_report()) report = { 'wall_times': dict(chrono.lap_times()), 'brief_report': ba.brief_report(), } return report
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https://github.com/generalized-intelligence/GAAS/blob/29ab17d3e8a4ba18edef3a57c36d8db6329fac73/deprecated/algorithms/sfm/OpenSfM/opensfm/reconstruction.py#L92-L179
facebookincubator/BOLT
88c70afe9d388ad430cc150cc158641701397f70
lldb/third_party/Python/module/pexpect-4.6/pexpect/fdpexpect.py
python
fdspawn.read_nonblocking
(self, size=1, timeout=-1)
return super(fdspawn, self).read_nonblocking(size)
Read from the file descriptor and return the result as a string. The read_nonblocking method of :class:`SpawnBase` assumes that a call to os.read will not block (timeout parameter is ignored). This is not the case for POSIX file-like objects such as sockets and serial ports. Use :func:`select.select`, timeout is implemented conditionally for POSIX systems. :param int size: Read at most *size* bytes. :param int timeout: Wait timeout seconds for file descriptor to be ready to read. When -1 (default), use self.timeout. When 0, poll. :return: String containing the bytes read
Read from the file descriptor and return the result as a string.
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def read_nonblocking(self, size=1, timeout=-1): """ Read from the file descriptor and return the result as a string. The read_nonblocking method of :class:`SpawnBase` assumes that a call to os.read will not block (timeout parameter is ignored). This is not the case for POSIX file-like objects such as sockets and serial ports. Use :func:`select.select`, timeout is implemented conditionally for POSIX systems. :param int size: Read at most *size* bytes. :param int timeout: Wait timeout seconds for file descriptor to be ready to read. When -1 (default), use self.timeout. When 0, poll. :return: String containing the bytes read """ if os.name == 'posix': if timeout == -1: timeout = self.timeout rlist = [self.child_fd] wlist = [] xlist = [] if self.use_poll: rlist = poll_ignore_interrupts(rlist, timeout) else: rlist, wlist, xlist = select_ignore_interrupts( rlist, wlist, xlist, timeout ) if self.child_fd not in rlist: raise TIMEOUT('Timeout exceeded.') return super(fdspawn, self).read_nonblocking(size)
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zlgopen/awtk
2c49e854a78749d9092907c027a7fba9062be549
3rd/mbedtls/scripts/mbedtls_dev/c_build_helper.py
python
create_c_file
(file_label)
return c_file, c_name, exe_name
Create a temporary C file. * ``file_label``: a string that will be included in the file name. Return ```(c_file, c_name, exe_name)``` where ``c_file`` is a Python stream open for writing to the file, ``c_name`` is the name of the file and ``exe_name`` is the name of the executable that will be produced by compiling the file.
Create a temporary C file.
[ "Create", "a", "temporary", "C", "file", "." ]
def create_c_file(file_label): """Create a temporary C file. * ``file_label``: a string that will be included in the file name. Return ```(c_file, c_name, exe_name)``` where ``c_file`` is a Python stream open for writing to the file, ``c_name`` is the name of the file and ``exe_name`` is the name of the executable that will be produced by compiling the file. """ c_fd, c_name = tempfile.mkstemp(prefix='tmp-{}-'.format(file_label), suffix='.c') exe_suffix = '.exe' if platform.system() == 'Windows' else '' exe_name = c_name[:-2] + exe_suffix remove_file_if_exists(exe_name) c_file = os.fdopen(c_fd, 'w', encoding='ascii') return c_file, c_name, exe_name
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catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/python/scikit-learn/py3/sklearn/mixture/_base.py
python
BaseMixture._print_verbose_msg_init_beg
(self, n_init)
Print verbose message on initialization.
Print verbose message on initialization.
[ "Print", "verbose", "message", "on", "initialization", "." ]
def _print_verbose_msg_init_beg(self, n_init): """Print verbose message on initialization.""" if self.verbose == 1: print("Initialization %d" % n_init) elif self.verbose >= 2: print("Initialization %d" % n_init) self._init_prev_time = time() self._iter_prev_time = self._init_prev_time
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/scikit-learn/py3/sklearn/mixture/_base.py#L508-L515
MythTV/mythtv
d282a209cb8be85d036f85a62a8ec971b67d45f4
mythtv/bindings/python/MythTV/dataheap.py
python
Artist.fromSong
(cls, song, db=None)
return cls(artist, db)
Returns the artist for the given song.
Returns the artist for the given song.
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def fromSong(cls, song, db=None): """Returns the artist for the given song.""" try: artist = song.artist_id db = song._db except AttributeError: db = DBCache(db) artist = Song(song, db).artist_id return cls(artist, db)
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https://github.com/MythTV/mythtv/blob/d282a209cb8be85d036f85a62a8ec971b67d45f4/mythtv/bindings/python/MythTV/dataheap.py#L1343-L1351
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/bdb.py
python
Bdb.run
(self, cmd, globals=None, locals=None)
Debug a statement executed via the exec() function. globals defaults to __main__.dict; locals defaults to globals.
Debug a statement executed via the exec() function.
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def run(self, cmd, globals=None, locals=None): """Debug a statement executed via the exec() function. globals defaults to __main__.dict; locals defaults to globals. """ if globals is None: import __main__ globals = __main__.__dict__ if locals is None: locals = globals self.reset() if isinstance(cmd, str): cmd = compile(cmd, "<string>", "exec") sys.settrace(self.trace_dispatch) try: exec(cmd, globals, locals) except BdbQuit: pass finally: self.quitting = True sys.settrace(None)
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SonarOpenCommunity/sonar-cxx
6e1d456fdcd45d35bcdc61c980e34d85fe88971e
cxx-squid/dox/tools/grammar_parser/grammar_parser.py
python
GrammarParser.__sort_attachments
(sequences)
return sequences
Move all [[xxx]] to the end.
Move all [[xxx]] to the end.
[ "Move", "all", "[[", "xxx", "]]", "to", "the", "end", "." ]
def __sort_attachments(sequences): """ Move all [[xxx]] to the end. """ for i, sequence in enumerate(sequences): expressions = [] attachments = [] for expression in sequence: if expression.startswith('[['): attachments.append(expression) else: expressions.append(expression) attachments.reverse() expressions.extend(attachments) sequences[i] = expressions return sequences
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krishauser/Klampt
972cc83ea5befac3f653c1ba20f80155768ad519
Python/python2_version/klampt/io/loader.py
python
writeMatrix3
(x)
return writeSo3(so3.from_matrix(text))
Writes a 3x3 matrix to a string
Writes a 3x3 matrix to a string
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def writeMatrix3(x): """Writes a 3x3 matrix to a string""" return writeSo3(so3.from_matrix(text))
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https://github.com/krishauser/Klampt/blob/972cc83ea5befac3f653c1ba20f80155768ad519/Python/python2_version/klampt/io/loader.py#L192-L194
krishauser/Klampt
972cc83ea5befac3f653c1ba20f80155768ad519
Python/klampt/robotsim.py
python
SimBody.getSurface
(self)
return _robotsim.SimBody_getSurface(self)
r""" Gets (a copy of) the surface properties.
r""" Gets (a copy of) the surface properties.
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def getSurface(self) -> "ContactParameters": r""" Gets (a copy of) the surface properties. """ return _robotsim.SimBody_getSurface(self)
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eventql/eventql
7ca0dbb2e683b525620ea30dc40540a22d5eb227
deps/3rdparty/spidermonkey/mozjs/python/mozbuild/mozbuild/virtualenv.py
python
VirtualenvManager.__init__
(self, topsrcdir, topobjdir, virtualenv_path, log_handle, manifest_path)
Create a new manager. Each manager is associated with a source directory, a path where you want the virtualenv to be created, and a handle to write output to.
Create a new manager.
[ "Create", "a", "new", "manager", "." ]
def __init__(self, topsrcdir, topobjdir, virtualenv_path, log_handle, manifest_path): """Create a new manager. Each manager is associated with a source directory, a path where you want the virtualenv to be created, and a handle to write output to. """ assert os.path.isabs(manifest_path), "manifest_path must be an absolute path: %s" % (manifest_path) self.topsrcdir = topsrcdir self.topobjdir = topobjdir self.virtualenv_root = virtualenv_path self.log_handle = log_handle self.manifest_path = manifest_path
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trailofbits/llvm-sanitizer-tutorial
d29dfeec7f51fbf234fd0080f28f2b30cd0b6e99
llvm/tools/clang/tools/scan-build-py/libscanbuild/analyze.py
python
dispatch_ctu
(opts, continuation=run_analyzer)
return continuation(opts)
Execute only one phase of 2 phases of CTU if needed.
Execute only one phase of 2 phases of CTU if needed.
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def dispatch_ctu(opts, continuation=run_analyzer): """ Execute only one phase of 2 phases of CTU if needed. """ ctu_config = opts['ctu'] if ctu_config.collect or ctu_config.analyze: assert ctu_config.collect != ctu_config.analyze if ctu_config.collect: return ctu_collect_phase(opts) if ctu_config.analyze: cwd = opts['directory'] cmd = [opts['clang'], '--analyze'] + opts['direct_args'] \ + opts['flags'] + [opts['file']] triarch = get_triple_arch(cmd, cwd) ctu_options = ['ctu-dir=' + os.path.join(ctu_config.dir, triarch), 'experimental-enable-naive-ctu-analysis=true'] analyzer_options = prefix_with('-analyzer-config', ctu_options) direct_options = prefix_with('-Xanalyzer', analyzer_options) opts['direct_args'].extend(direct_options) return continuation(opts)
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catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/tools/python3/src/Lib/turtle.py
python
RawTurtle._getshapepoly
(self, polygon, compound=False)
return tuple((t11*x + t12*y, t21*x + t22*y) for (x, y) in polygon)
Calculate transformed shape polygon according to resizemode and shapetransform.
Calculate transformed shape polygon according to resizemode and shapetransform.
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def _getshapepoly(self, polygon, compound=False): """Calculate transformed shape polygon according to resizemode and shapetransform. """ if self._resizemode == "user" or compound: t11, t12, t21, t22 = self._shapetrafo elif self._resizemode == "auto": l = max(1, self._pensize/5.0) t11, t12, t21, t22 = l, 0, 0, l elif self._resizemode == "noresize": return polygon return tuple((t11*x + t12*y, t21*x + t22*y) for (x, y) in polygon)
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klzgrad/naiveproxy
ed2c513637c77b18721fe428d7ed395b4d284c83
src/build/fuchsia/binary_sizes.py
python
GetSdkModules
()
return lib_names
Finds shared objects (.so) under the Fuchsia SDK arch directory in dist or lib subdirectories. Returns a set of shared objects' filenames.
Finds shared objects (.so) under the Fuchsia SDK arch directory in dist or lib subdirectories.
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def GetSdkModules(): """Finds shared objects (.so) under the Fuchsia SDK arch directory in dist or lib subdirectories. Returns a set of shared objects' filenames. """ # Fuchsia SDK arch directory path (contains all shared object files). sdk_arch_dir = os.path.join(SDK_ROOT, 'arch') # Leaf subdirectories containing shared object files. sdk_so_leaf_dirs = ['dist', 'lib'] # Match a shared object file name. sdk_so_filename_re = r'\.so(\.\d+)?$' lib_names = set() for dirpath, _, file_names in os.walk(sdk_arch_dir): if os.path.basename(dirpath) in sdk_so_leaf_dirs: for name in file_names: if SO_FILENAME_REGEXP.search(name): lib_names.add(name) return lib_names
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https://github.com/klzgrad/naiveproxy/blob/ed2c513637c77b18721fe428d7ed395b4d284c83/src/build/fuchsia/binary_sizes.py#L313-L333
ChromiumWebApps/chromium
c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7
gpu/command_buffer/build_gles2_cmd_buffer.py
python
ManualHandler.WriteBucketServiceImplementation
(self, func, file)
Overrriden from TypeHandler.
Overrriden from TypeHandler.
[ "Overrriden", "from", "TypeHandler", "." ]
def WriteBucketServiceImplementation(self, func, file): """Overrriden from TypeHandler.""" pass
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https://github.com/ChromiumWebApps/chromium/blob/c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7/gpu/command_buffer/build_gles2_cmd_buffer.py#L3609-L3611
catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/python/pandas/py2/pandas/core/dtypes/missing.py
python
_infer_fill_value
(val)
return np.nan
infer the fill value for the nan/NaT from the provided scalar/ndarray/list-like if we are a NaT, return the correct dtyped element to provide proper block construction
infer the fill value for the nan/NaT from the provided scalar/ndarray/list-like if we are a NaT, return the correct dtyped element to provide proper block construction
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def _infer_fill_value(val): """ infer the fill value for the nan/NaT from the provided scalar/ndarray/list-like if we are a NaT, return the correct dtyped element to provide proper block construction """ if not is_list_like(val): val = [val] val = np.array(val, copy=False) if is_datetimelike(val): return np.array('NaT', dtype=val.dtype) elif is_object_dtype(val.dtype): dtype = lib.infer_dtype(ensure_object(val), skipna=False) if dtype in ['datetime', 'datetime64']: return np.array('NaT', dtype=_NS_DTYPE) elif dtype in ['timedelta', 'timedelta64']: return np.array('NaT', dtype=_TD_DTYPE) return np.nan
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/pandas/py2/pandas/core/dtypes/missing.py#L448-L466
thalium/icebox
99d147d5b9269222225443ce171b4fd46d8985d4
third_party/virtualbox/src/VBox/Devices/EFI/Firmware/AppPkg/Applications/Python/PyMod-2.7.2/Lib/pydoc.py
python
getpager
()
Decide what method to use for paging through text.
Decide what method to use for paging through text.
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def getpager(): """Decide what method to use for paging through text.""" if type(sys.stdout) is not types.FileType: return plainpager if not sys.stdin.isatty() or not sys.stdout.isatty(): return plainpager if 'PAGER' in os.environ: if sys.platform == 'win32': # pipes completely broken in Windows return lambda text: tempfilepager(plain(text), os.environ['PAGER']) elif sys.platform == 'uefi': return lambda text: tempfilepager(plain(text), os.environ['PAGER']) elif os.environ.get('TERM') in ('dumb', 'emacs'): return lambda text: pipepager(plain(text), os.environ['PAGER']) else: return lambda text: pipepager(text, os.environ['PAGER']) if os.environ.get('TERM') in ('dumb', 'emacs'): return plainpager if sys.platform == 'uefi': return plainpager if sys.platform == 'win32' or sys.platform.startswith('os2'): return lambda text: tempfilepager(plain(text), 'more <') if hasattr(os, 'system') and os.system('(less) 2>/dev/null') == 0: return lambda text: pipepager(text, 'less') import tempfile (fd, filename) = tempfile.mkstemp() os.close(fd) try: if hasattr(os, 'system') and os.system('more "%s"' % filename) == 0: return lambda text: pipepager(text, 'more') else: return ttypager finally: os.unlink(filename)
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https://github.com/thalium/icebox/blob/99d147d5b9269222225443ce171b4fd46d8985d4/third_party/virtualbox/src/VBox/Devices/EFI/Firmware/AppPkg/Applications/Python/PyMod-2.7.2/Lib/pydoc.py#L1320-L1353
tensorflow/tensorflow
419e3a6b650ea4bd1b0cba23c4348f8a69f3272e
tensorflow/python/training/queue_runner_impl.py
python
add_queue_runner
(qr, collection=ops.GraphKeys.QUEUE_RUNNERS)
Adds a `QueueRunner` to a collection in the graph. When building a complex model that uses many queues it is often difficult to gather all the queue runners that need to be run. This convenience function allows you to add a queue runner to a well known collection in the graph. The companion method `start_queue_runners()` can be used to start threads for all the collected queue runners. @compatibility(TF2) QueueRunners are not compatible with eager execution. Instead, please use [tf.data](https://www.tensorflow.org/guide/data) to get data into your model. @end_compatibility Args: qr: A `QueueRunner`. collection: A `GraphKey` specifying the graph collection to add the queue runner to. Defaults to `GraphKeys.QUEUE_RUNNERS`.
Adds a `QueueRunner` to a collection in the graph.
[ "Adds", "a", "QueueRunner", "to", "a", "collection", "in", "the", "graph", "." ]
def add_queue_runner(qr, collection=ops.GraphKeys.QUEUE_RUNNERS): """Adds a `QueueRunner` to a collection in the graph. When building a complex model that uses many queues it is often difficult to gather all the queue runners that need to be run. This convenience function allows you to add a queue runner to a well known collection in the graph. The companion method `start_queue_runners()` can be used to start threads for all the collected queue runners. @compatibility(TF2) QueueRunners are not compatible with eager execution. Instead, please use [tf.data](https://www.tensorflow.org/guide/data) to get data into your model. @end_compatibility Args: qr: A `QueueRunner`. collection: A `GraphKey` specifying the graph collection to add the queue runner to. Defaults to `GraphKeys.QUEUE_RUNNERS`. """ ops.add_to_collection(collection, qr)
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https://github.com/tensorflow/tensorflow/blob/419e3a6b650ea4bd1b0cba23c4348f8a69f3272e/tensorflow/python/training/queue_runner_impl.py#L393-L414
tensorflow/tensorflow
419e3a6b650ea4bd1b0cba23c4348f8a69f3272e
tensorflow/lite/python/convert_phase.py
python
ConverterError._parse_error_message
(self, message)
If the message matches a pattern, assigns the associated error code. It is difficult to assign an error code to some errrors in MLIR side, Ex: errors thrown by other components than TFLite or not using mlir::emitError. This function try to detect them by the error message and assign the corresponding error code. Args: message: The error message of this exception.
If the message matches a pattern, assigns the associated error code.
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def _parse_error_message(self, message): """If the message matches a pattern, assigns the associated error code. It is difficult to assign an error code to some errrors in MLIR side, Ex: errors thrown by other components than TFLite or not using mlir::emitError. This function try to detect them by the error message and assign the corresponding error code. Args: message: The error message of this exception. """ error_code_mapping = { "Failed to functionalize Control Flow V1 ops. Consider using Control " "Flow V2 ops instead. See https://www.tensorflow.org/api_docs/python/" "tf/compat/v1/enable_control_flow_v2.": converter_error_data_pb2.ConverterErrorData .ERROR_UNSUPPORTED_CONTROL_FLOW_V1, } for pattern, error_code in error_code_mapping.items(): if pattern in message: error_data = converter_error_data_pb2.ConverterErrorData() error_data.error_message = message error_data.error_code = error_code self.append_error(error_data) return
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https://github.com/tensorflow/tensorflow/blob/419e3a6b650ea4bd1b0cba23c4348f8a69f3272e/tensorflow/lite/python/convert_phase.py#L139-L163
trailofbits/llvm-sanitizer-tutorial
d29dfeec7f51fbf234fd0080f28f2b30cd0b6e99
llvm/utils/benchmark/mingw.py
python
download
(url, location, log = EmptyLogger())
return possible
Downloads and unpacks a mingw-builds archive
Downloads and unpacks a mingw-builds archive
[ "Downloads", "and", "unpacks", "a", "mingw", "-", "builds", "archive" ]
def download(url, location, log = EmptyLogger()): ''' Downloads and unpacks a mingw-builds archive ''' log.info('downloading MinGW') log.debug(' - url: %s', url) log.debug(' - location: %s', location) re_content = re.compile(r'attachment;[ \t]*filename=(")?([^"]*)(")?[\r\n]*') stream = request.urlopen(url) try: content = stream.getheader('Content-Disposition') or '' except AttributeError: content = stream.headers.getheader('Content-Disposition') or '' matches = re_content.match(content) if matches: filename = matches.group(2) else: parsed = parse.urlparse(stream.geturl()) filename = os.path.basename(parsed.path) try: os.makedirs(location) except OSError as e: if e.errno == errno.EEXIST and os.path.isdir(location): pass else: raise archive = os.path.join(location, filename) with open(archive, 'wb') as out: while True: buf = stream.read(1024) if not buf: break out.write(buf) unpack(archive, location, log = log) os.remove(archive) possible = os.path.join(location, 'mingw64') if not os.path.exists(possible): possible = os.path.join(location, 'mingw32') if not os.path.exists(possible): raise ValueError('Failed to find unpacked MinGW: ' + possible) return possible
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https://github.com/trailofbits/llvm-sanitizer-tutorial/blob/d29dfeec7f51fbf234fd0080f28f2b30cd0b6e99/llvm/utils/benchmark/mingw.py#L125-L170
Z3Prover/z3
d745d03afdfdf638d66093e2bfbacaf87187f35b
src/api/python/z3/z3.py
python
CharSort
(ctx=None)
return CharSortRef(Z3_mk_char_sort(ctx.ref()), ctx)
Create a character sort >>> ch = CharSort() >>> print(ch) Char
Create a character sort >>> ch = CharSort() >>> print(ch) Char
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def CharSort(ctx=None): """Create a character sort >>> ch = CharSort() >>> print(ch) Char """ ctx = _get_ctx(ctx) return CharSortRef(Z3_mk_char_sort(ctx.ref()), ctx)
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https://github.com/Z3Prover/z3/blob/d745d03afdfdf638d66093e2bfbacaf87187f35b/src/api/python/z3/z3.py#L10625-L10632
tfwu/FaceDetection-ConvNet-3D
f9251c48eb40c5aec8fba7455115c355466555be
python/mxnet/io.py
python
DataIter.iter_next
(self)
Iterate to next batch. Returns ------- has_next : boolean Whether the move is successful.
Iterate to next batch. Returns ------- has_next : boolean Whether the move is successful.
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def iter_next(self): """Iterate to next batch. Returns ------- has_next : boolean Whether the move is successful. """ pass
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https://github.com/tfwu/FaceDetection-ConvNet-3D/blob/f9251c48eb40c5aec8fba7455115c355466555be/python/mxnet/io.py#L66-L73
ChromiumWebApps/chromium
c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7
tools/telemetry/telemetry/core/backends/adb_commands.py
python
AdbCommands.KillAll
(self, process)
return self._adb.KillAll(process)
Android version of killall, connected via adb. Args: process: name of the process to kill off Returns: the number of processess killed
Android version of killall, connected via adb.
[ "Android", "version", "of", "killall", "connected", "via", "adb", "." ]
def KillAll(self, process): """Android version of killall, connected via adb. Args: process: name of the process to kill off Returns: the number of processess killed """ return self._adb.KillAll(process)
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https://github.com/ChromiumWebApps/chromium/blob/c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7/tools/telemetry/telemetry/core/backends/adb_commands.py#L105-L114
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/msw/_gdi.py
python
Region.UnionRegion
(*args, **kwargs)
return _gdi_.Region_UnionRegion(*args, **kwargs)
UnionRegion(self, Region region) -> bool
UnionRegion(self, Region region) -> bool
[ "UnionRegion", "(", "self", "Region", "region", ")", "-", ">", "bool" ]
def UnionRegion(*args, **kwargs): """UnionRegion(self, Region region) -> bool""" return _gdi_.Region_UnionRegion(*args, **kwargs)
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/msw/_gdi.py#L1692-L1694
macchina-io/macchina.io
ef24ba0e18379c3dd48fb84e6dbf991101cb8db0
platform/JS/V8/tools/gyp/pylib/gyp/msvs_emulation.py
python
PrecompiledHeader.GetFlagsModifications
(self, input, output, implicit, command, cflags_c, cflags_cc, expand_special)
return [], output, implicit
Get the modified cflags and implicit dependencies that should be used for the pch compilation step.
Get the modified cflags and implicit dependencies that should be used for the pch compilation step.
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def GetFlagsModifications(self, input, output, implicit, command, cflags_c, cflags_cc, expand_special): """Get the modified cflags and implicit dependencies that should be used for the pch compilation step.""" if input == self.pch_source: pch_output = ['/Yc' + self._PchHeader()] if command == 'cxx': return ([('cflags_cc', map(expand_special, cflags_cc + pch_output))], self.output_obj, []) elif command == 'cc': return ([('cflags_c', map(expand_special, cflags_c + pch_output))], self.output_obj, []) return [], output, implicit
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https://github.com/macchina-io/macchina.io/blob/ef24ba0e18379c3dd48fb84e6dbf991101cb8db0/platform/JS/V8/tools/gyp/pylib/gyp/msvs_emulation.py#L924-L936
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/msw/_misc.py
python
PyTipProvider.__init__
(self, *args, **kwargs)
__init__(self, size_t currentTip) -> PyTipProvider
__init__(self, size_t currentTip) -> PyTipProvider
[ "__init__", "(", "self", "size_t", "currentTip", ")", "-", ">", "PyTipProvider" ]
def __init__(self, *args, **kwargs): """__init__(self, size_t currentTip) -> PyTipProvider""" _misc_.PyTipProvider_swiginit(self,_misc_.new_PyTipProvider(*args, **kwargs)) PyTipProvider._setCallbackInfo(self, self, PyTipProvider)
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/msw/_misc.py#L1275-L1278
apache/incubator-mxnet
f03fb23f1d103fec9541b5ae59ee06b1734a51d9
python/mxnet/gluon/block.py
python
HybridBlock.export
(self, path, epoch=0, remove_amp_cast=True)
return sym, arg_dict
Export HybridBlock to json format that can be loaded by `gluon.SymbolBlock.imports` or the C++ interface. .. note:: When there are only one input, it will have name `data`. When there Are more than one inputs, they will be named as `data0`, `data1`, etc. Parameters ---------- path : str or None Path to save model. Two files `path-symbol.json` and `path-xxxx.params` will be created, where xxxx is the 4 digits epoch number. If None, do not export to file but return Python Symbol object and corresponding dictionary of parameters. epoch : int Epoch number of saved model. remove_amp_cast : bool, optional Whether to remove the amp_cast and amp_multicast operators, before saving the model. Returns ------- symbol_filename : str Filename to which model symbols were saved, including `path` prefix. params_filename : str Filename to which model parameters were saved, including `path` prefix.
Export HybridBlock to json format that can be loaded by `gluon.SymbolBlock.imports` or the C++ interface.
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def export(self, path, epoch=0, remove_amp_cast=True): """Export HybridBlock to json format that can be loaded by `gluon.SymbolBlock.imports` or the C++ interface. .. note:: When there are only one input, it will have name `data`. When there Are more than one inputs, they will be named as `data0`, `data1`, etc. Parameters ---------- path : str or None Path to save model. Two files `path-symbol.json` and `path-xxxx.params` will be created, where xxxx is the 4 digits epoch number. If None, do not export to file but return Python Symbol object and corresponding dictionary of parameters. epoch : int Epoch number of saved model. remove_amp_cast : bool, optional Whether to remove the amp_cast and amp_multicast operators, before saving the model. Returns ------- symbol_filename : str Filename to which model symbols were saved, including `path` prefix. params_filename : str Filename to which model parameters were saved, including `path` prefix. """ if not self._cached_graph: raise RuntimeError( "Please first call block.hybridize() and then run forward with " "this block at least once before calling export.") sym = copy.copy(self._cached_graph[1]) # Deduplicate params (shared parameters use the same input symbol) reverse_params = {v: k for k, v in self.collect_params().items()} params = {v: k for k, v in reverse_params.items()} # In export we have global information on the structure of the graph # can rename the symbol inputs to human-readable, deterministic names. # That's not true in general, which is why internally random unique identifiers are used. rename_map = {param.var().name: name for name, param in params.items()} for var in sym.get_inputs(): if var.name in rename_map: var._set_attr(name=rename_map[var.name]) sym_filename = '%s-symbol.json' % (path if path is not None else "") if path is not None: sym.save(sym_filename, remove_amp_cast=remove_amp_cast) arg_names = set(sym.list_arguments()) aux_names = set(sym.list_auxiliary_states()) arg_dict = {} for is_arg, name, param in self._cached_op_args: if not is_arg: if name in arg_names: arg_dict['arg:{}'.format(name)] = param._reduce() else: if name not in aux_names: warnings.warn('Parameter "{name}" is not found in the graph. ' .format(name=name), stacklevel=3) else: arg_dict['aux:%s'%name] = param._reduce() params_filename = '%s-%04d.params'%((path if path is not None else ""), epoch) if path is not None: if is_np_array(): _mx_npx.savez(params_filename, **arg_dict) else: ndarray.save(params_filename, arg_dict) return (sym_filename, params_filename if arg_dict else None) if remove_amp_cast: handle = SymbolHandle() import ctypes check_call(_LIB.MXSymbolRemoveAmpCast(sym.handle, ctypes.byref(handle))) sym = type(sym)(handle) return sym, arg_dict
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https://github.com/apache/incubator-mxnet/blob/f03fb23f1d103fec9541b5ae59ee06b1734a51d9/python/mxnet/gluon/block.py#L1480-L1555
ChromiumWebApps/chromium
c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7
tools/json_schema_compiler/cc_generator.py
python
_Generator.Generate
(self)
return c
Generates a Code object with the .cc for a single namespace.
Generates a Code object with the .cc for a single namespace.
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def Generate(self): """Generates a Code object with the .cc for a single namespace. """ c = Code() (c.Append(cpp_util.CHROMIUM_LICENSE) .Append() .Append(cpp_util.GENERATED_FILE_MESSAGE % self._namespace.source_file) .Append() .Append(self._util_cc_helper.GetIncludePath()) .Append('#include "base/logging.h"') .Append('#include "base/strings/string_number_conversions.h"') .Append('#include "base/strings/utf_string_conversions.h"') .Append('#include "%s/%s.h"' % (self._namespace.source_file_dir, self._namespace.short_filename)) .Cblock(self._type_helper.GenerateIncludes(include_soft=True)) .Append() .Concat(cpp_util.OpenNamespace(self._cpp_namespace)) .Cblock(self._type_helper.GetNamespaceStart()) ) if self._namespace.properties: (c.Append('//') .Append('// Properties') .Append('//') .Append() ) for property in self._namespace.properties.values(): property_code = self._type_helper.GeneratePropertyValues( property, 'const %(type)s %(name)s = %(value)s;', nodoc=True) if property_code: c.Cblock(property_code) if self._namespace.types: (c.Append('//') .Append('// Types') .Append('//') .Append() .Cblock(self._GenerateTypes(None, self._namespace.types.values())) ) if self._namespace.functions: (c.Append('//') .Append('// Functions') .Append('//') .Append() ) for function in self._namespace.functions.values(): c.Cblock(self._GenerateFunction(function)) if self._namespace.events: (c.Append('//') .Append('// Events') .Append('//') .Append() ) for event in self._namespace.events.values(): c.Cblock(self._GenerateEvent(event)) (c.Concat(self._type_helper.GetNamespaceEnd()) .Cblock(cpp_util.CloseNamespace(self._cpp_namespace)) ) c.Append() return c
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https://github.com/ChromiumWebApps/chromium/blob/c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7/tools/json_schema_compiler/cc_generator.py#L36-L95
mcneel/rhino-developer-samples
995d8bb985da7080cb8df3b94326165d9eaf58cc
rhinocommon/snippets/py/custom-undo.py
python
OnUndoFavoriteNumber
(sender, e)
!!!!!!!!!! NEVER change any setting in the Rhino document or application. Rhino handles ALL changes to the application and document and you will break the Undo/Redo commands if you make any changes to the application or document. This is meant only for your own private plugin data !!!!!!!!!! This function can be called either by undo or redo In order to get redo to work, add another custom undo event with the current value. If you don't want redo to work, just skip adding a custom undo event here
!!!!!!!!!! NEVER change any setting in the Rhino document or application. Rhino handles ALL changes to the application and document and you will break the Undo/Redo commands if you make any changes to the application or document. This is meant only for your own private plugin data !!!!!!!!!!
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def OnUndoFavoriteNumber(sender, e): """!!!!!!!!!! NEVER change any setting in the Rhino document or application. Rhino handles ALL changes to the application and document and you will break the Undo/Redo commands if you make any changes to the application or document. This is meant only for your own private plugin data !!!!!!!!!! This function can be called either by undo or redo In order to get redo to work, add another custom undo event with the current value. If you don't want redo to work, just skip adding a custom undo event here """ current_value = scriptcontext.sticky["FavoriteNumber"] e.Document.AddCustomUndoEvent("Favorite Number", OnUndoFavoriteNumber, current_value) old_value = e.Tag print "Going back to your favorite =", old_value scriptcontext.sticky["FavoriteNumber"]= old_value;
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https://github.com/mcneel/rhino-developer-samples/blob/995d8bb985da7080cb8df3b94326165d9eaf58cc/rhinocommon/snippets/py/custom-undo.py#L5-L23
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/concurrent/futures/_base.py
python
Future.running
(self)
Return True if the future is currently executing.
Return True if the future is currently executing.
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def running(self): """Return True if the future is currently executing.""" with self._condition: return self._state == RUNNING
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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/concurrent/futures/_base.py#L372-L375
krishauser/Klampt
972cc83ea5befac3f653c1ba20f80155768ad519
Python/klampt/src/robotsim.py
python
Mass.getInertia
(self)
return _robotsim.Mass_getInertia(self)
r""" getInertia(Mass self) Returns the inertia matrix as a list of 3 floats or 9 floats.
r""" getInertia(Mass self)
[ "r", "getInertia", "(", "Mass", "self", ")" ]
def getInertia(self) -> "void": r""" getInertia(Mass self) Returns the inertia matrix as a list of 3 floats or 9 floats. """ return _robotsim.Mass_getInertia(self)
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https://github.com/krishauser/Klampt/blob/972cc83ea5befac3f653c1ba20f80155768ad519/Python/klampt/src/robotsim.py#L3934-L3942
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Tools/Python/3.7.10/windows/Lib/site-packages/botocore/docs/bcdoc/style.py
python
ReSTStyle.codeblock
(self, code)
Literal code blocks are introduced by ending a paragraph with the special marker ::. The literal block must be indented (and, like all paragraphs, separated from the surrounding ones by blank lines).
Literal code blocks are introduced by ending a paragraph with the special marker ::. The literal block must be indented (and, like all paragraphs, separated from the surrounding ones by blank lines).
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def codeblock(self, code): """ Literal code blocks are introduced by ending a paragraph with the special marker ::. The literal block must be indented (and, like all paragraphs, separated from the surrounding ones by blank lines). """ self.start_codeblock() self.doc.writeln(code) self.end_codeblock()
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/windows/Lib/site-packages/botocore/docs/bcdoc/style.py#L325-L334
KhronosGroup/Vulkan-Headers
b32da5329b50e3cb96229aaecba9ded032fe29cc
registry/reg.py
python
apiNameMatch
(str, supported)
return False
Return whether a required api name matches a pattern specified for an XML <feature> 'api' attribute or <extension> 'supported' attribute. - str - API name such as 'vulkan' or 'openxr'. May be None, in which case it never matches (this should not happen). - supported - comma-separated list of XML API names. May be None, in which case str always matches (this is the usual case).
Return whether a required api name matches a pattern specified for an XML <feature> 'api' attribute or <extension> 'supported' attribute.
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def apiNameMatch(str, supported): """Return whether a required api name matches a pattern specified for an XML <feature> 'api' attribute or <extension> 'supported' attribute. - str - API name such as 'vulkan' or 'openxr'. May be None, in which case it never matches (this should not happen). - supported - comma-separated list of XML API names. May be None, in which case str always matches (this is the usual case).""" if str is not None: return supported is None or str in supported.split(',') # Fallthrough case - either str is None or the test failed return False
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https://github.com/KhronosGroup/Vulkan-Headers/blob/b32da5329b50e3cb96229aaecba9ded032fe29cc/registry/reg.py#L17-L30
mantidproject/mantid
03deeb89254ec4289edb8771e0188c2090a02f32
scripts/abins/sdata.py
python
SData.add_dict
(self, data: dict)
Add data in dict form to existing values. These atoms/orders must already be present; use self.update() to add new data.
Add data in dict form to existing values.
[ "Add", "data", "in", "dict", "form", "to", "existing", "values", "." ]
def add_dict(self, data: dict) -> None: """Add data in dict form to existing values. These atoms/orders must already be present; use self.update() to add new data. """ for atom_key, atom_data in data.items(): for order, order_data in atom_data['s'].items(): self._data[atom_key]['s'][order] += order_data
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https://github.com/mantidproject/mantid/blob/03deeb89254ec4289edb8771e0188c2090a02f32/scripts/abins/sdata.py#L83-L91
hakuna-m/wubiuefi
caec1af0a09c78fd5a345180ada1fe45e0c63493
src/openpgp/sap/crypto.py
python
verify_ElGamal
(msg, sig_tuple, key_tuple)
return elg.verify(msg, sig_tuple)
Verify an ElGamal signature. :Parameters: - `msg`: string of data signature applies to - `sig_tuple`: tuple of ElGamal signature integers (a, b) (see `ElGamal signature tuple`_) - `key_tuple`: tuple of ElGamal key integers (p, g, y) (see `ElGamal key tuple`_) :Returns: tuple (integer, None) where integer == 1 or 0, verification true or false .. _ElGamal signature tuple: ElGamal signature tuple: - `a`: integer ElGamal "a" - `b`: integer ElGamal "b" .. _ElGamal key tuple: ElGamal key tuple: - `p`: integer ElGamal prime - `g`: integer ElGamal group - `y`: integer ElGamal public key
Verify an ElGamal signature.
[ "Verify", "an", "ElGamal", "signature", "." ]
def verify_ElGamal(msg, sig_tuple, key_tuple): """Verify an ElGamal signature. :Parameters: - `msg`: string of data signature applies to - `sig_tuple`: tuple of ElGamal signature integers (a, b) (see `ElGamal signature tuple`_) - `key_tuple`: tuple of ElGamal key integers (p, g, y) (see `ElGamal key tuple`_) :Returns: tuple (integer, None) where integer == 1 or 0, verification true or false .. _ElGamal signature tuple: ElGamal signature tuple: - `a`: integer ElGamal "a" - `b`: integer ElGamal "b" .. _ElGamal key tuple: ElGamal key tuple: - `p`: integer ElGamal prime - `g`: integer ElGamal group - `y`: integer ElGamal public key """ import Crypto.PublicKey.ElGamal as ELG elg = ELG.construct(key_tuple) # note change in ordering return elg.verify(msg, sig_tuple)
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https://github.com/hakuna-m/wubiuefi/blob/caec1af0a09c78fd5a345180ada1fe45e0c63493/src/openpgp/sap/crypto.py#L533-L563
catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/python/scikit-learn/py2/sklearn/ensemble/gradient_boosting.py
python
BinomialDeviance._update_terminal_region
(self, tree, terminal_regions, leaf, X, y, residual, pred, sample_weight)
Make a single Newton-Raphson step. our node estimate is given by: sum(w * (y - prob)) / sum(w * prob * (1 - prob)) we take advantage that: y - prob = residual
Make a single Newton-Raphson step.
[ "Make", "a", "single", "Newton", "-", "Raphson", "step", "." ]
def _update_terminal_region(self, tree, terminal_regions, leaf, X, y, residual, pred, sample_weight): """Make a single Newton-Raphson step. our node estimate is given by: sum(w * (y - prob)) / sum(w * prob * (1 - prob)) we take advantage that: y - prob = residual """ terminal_region = np.where(terminal_regions == leaf)[0] residual = residual.take(terminal_region, axis=0) y = y.take(terminal_region, axis=0) sample_weight = sample_weight.take(terminal_region, axis=0) numerator = np.sum(sample_weight * residual) denominator = np.sum(sample_weight * (y - residual) * (1 - y + residual)) if denominator == 0.0: tree.value[leaf, 0, 0] = 0.0 else: tree.value[leaf, 0, 0] = numerator / denominator
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/scikit-learn/py2/sklearn/ensemble/gradient_boosting.py#L496-L517
catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/python/scipy/py2/scipy/stats/mstats_basic.py
python
skew
(a, axis=0, bias=True)
return vals
Computes the skewness of a data set. Parameters ---------- a : ndarray data axis : int or None, optional Axis along which skewness is calculated. Default is 0. If None, compute over the whole array `a`. bias : bool, optional If False, then the calculations are corrected for statistical bias. Returns ------- skewness : ndarray The skewness of values along an axis, returning 0 where all values are equal. Notes ----- For more details about `skew`, see `stats.skew`.
Computes the skewness of a data set.
[ "Computes", "the", "skewness", "of", "a", "data", "set", "." ]
def skew(a, axis=0, bias=True): """ Computes the skewness of a data set. Parameters ---------- a : ndarray data axis : int or None, optional Axis along which skewness is calculated. Default is 0. If None, compute over the whole array `a`. bias : bool, optional If False, then the calculations are corrected for statistical bias. Returns ------- skewness : ndarray The skewness of values along an axis, returning 0 where all values are equal. Notes ----- For more details about `skew`, see `stats.skew`. """ a, axis = _chk_asarray(a,axis) n = a.count(axis) m2 = moment(a, 2, axis) m3 = moment(a, 3, axis) olderr = np.seterr(all='ignore') try: vals = ma.where(m2 == 0, 0, m3 / m2**1.5) finally: np.seterr(**olderr) if not bias: can_correct = (n > 2) & (m2 > 0) if can_correct.any(): m2 = np.extract(can_correct, m2) m3 = np.extract(can_correct, m3) nval = ma.sqrt((n-1.0)*n)/(n-2.0)*m3/m2**1.5 np.place(vals, can_correct, nval) return vals
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/scipy/py2/scipy/stats/mstats_basic.py#L2135-L2177
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/osx_cocoa/_core.py
python
Window.CanScroll
(*args, **kwargs)
return _core_.Window_CanScroll(*args, **kwargs)
CanScroll(self, int orient) -> bool Can the window have the scrollbar in this orientation?
CanScroll(self, int orient) -> bool
[ "CanScroll", "(", "self", "int", "orient", ")", "-", ">", "bool" ]
def CanScroll(*args, **kwargs): """ CanScroll(self, int orient) -> bool Can the window have the scrollbar in this orientation? """ return _core_.Window_CanScroll(*args, **kwargs)
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_cocoa/_core.py#L11203-L11209
tensorflow/tensorflow
419e3a6b650ea4bd1b0cba23c4348f8a69f3272e
tensorflow/python/data/ops/dataset_ops.py
python
SkipDataset.__init__
(self, input_dataset, count, name=None)
See `Dataset.skip()` for details.
See `Dataset.skip()` for details.
[ "See", "Dataset", ".", "skip", "()", "for", "details", "." ]
def __init__(self, input_dataset, count, name=None): """See `Dataset.skip()` for details.""" self._input_dataset = input_dataset self._count = ops.convert_to_tensor(count, dtype=dtypes.int64, name="count") self._name = name variant_tensor = gen_dataset_ops.skip_dataset( input_dataset._variant_tensor, # pylint: disable=protected-access count=self._count, **self._common_args) super(SkipDataset, self).__init__(input_dataset, variant_tensor)
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https://github.com/tensorflow/tensorflow/blob/419e3a6b650ea4bd1b0cba23c4348f8a69f3272e/tensorflow/python/data/ops/dataset_ops.py#L4850-L4859
deepmind/open_spiel
4ca53bea32bb2875c7385d215424048ae92f78c8
open_spiel/python/algorithms/adidas_utils/solvers/nonsymmetric/ate_regmatch.py
python
Solver.__init__
(self, p=1., lrs=(1e-2,), optimism=True, discount=False, rnd_init=False, seed=None, **kwargs)
Ctor.
Ctor.
[ "Ctor", "." ]
def __init__(self, p=1., lrs=(1e-2,), optimism=True, discount=False, rnd_init=False, seed=None, **kwargs): """Ctor.""" del kwargs if (p < 0.) or (p > 1.): raise ValueError('p must be in [0, 1]') self.num_players = None self.p = p self.rnd_init = rnd_init self.lrs = lrs self.optimism = optimism self.discount = discount self.has_aux = True self.aux_errors = [] self.seed = seed self.random = np.random.RandomState(seed)
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https://github.com/deepmind/open_spiel/blob/4ca53bea32bb2875c7385d215424048ae92f78c8/open_spiel/python/algorithms/adidas_utils/solvers/nonsymmetric/ate_regmatch.py#L29-L45
echronos/echronos
c996f1d2c8af6c6536205eb319c1bf1d4d84569c
external_tools/ply_info/example/ansic/clex.py
python
t_preprocessor
(t)
r'\#(.)*?\n
r'\#(.)*?\n
[ "r", "\\", "#", "(", ".", ")", "*", "?", "\\", "n" ]
def t_preprocessor(t): r'\#(.)*?\n' t.lexer.lineno += 1
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https://github.com/echronos/echronos/blob/c996f1d2c8af6c6536205eb319c1bf1d4d84569c/external_tools/ply_info/example/ansic/clex.py#L149-L151
microsoft/TSS.MSR
0f2516fca2cd9929c31d5450e39301c9bde43688
TSS.Py/src/TpmTypes.py
python
TPMS_ECC_POINT.GetUnionSelector
(self)
return TPM_ALG_ID.ECC
TpmUnion method
TpmUnion method
[ "TpmUnion", "method" ]
def GetUnionSelector(self): # TPM_ALG_ID """ TpmUnion method """ return TPM_ALG_ID.ECC
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https://github.com/microsoft/TSS.MSR/blob/0f2516fca2cd9929c31d5450e39301c9bde43688/TSS.Py/src/TpmTypes.py#L7256-L7258
pmq20/node-packer
12c46c6e44fbc14d9ee645ebd17d5296b324f7e0
current/tools/gyp/pylib/gyp/MSVSSettings.py
python
_ValidateSettings
(validators, settings, stderr)
Validates that the settings are valid for MSBuild or MSVS. We currently only validate the names of the settings, not their values. Args: validators: A dictionary of tools and their validators. settings: A dictionary. The key is the tool name. The values are themselves dictionaries of settings and their values. stderr: The stream receiving the error messages.
Validates that the settings are valid for MSBuild or MSVS.
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def _ValidateSettings(validators, settings, stderr): """Validates that the settings are valid for MSBuild or MSVS. We currently only validate the names of the settings, not their values. Args: validators: A dictionary of tools and their validators. settings: A dictionary. The key is the tool name. The values are themselves dictionaries of settings and their values. stderr: The stream receiving the error messages. """ for tool_name in settings: if tool_name in validators: tool_validators = validators[tool_name] for setting, value in settings[tool_name].items(): if setting in tool_validators: try: tool_validators[setting](value) except ValueError as e: print('Warning: for %s/%s, %s' % (tool_name, setting, e), file=stderr) else: _ValidateExclusionSetting(setting, tool_validators, ('Warning: unrecognized setting %s/%s' % (tool_name, setting)), stderr) else: print('Warning: unrecognized tool %s' % (tool_name), file=stderr)
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https://github.com/pmq20/node-packer/blob/12c46c6e44fbc14d9ee645ebd17d5296b324f7e0/current/tools/gyp/pylib/gyp/MSVSSettings.py#L506-L535
synfig/synfig
a5ec91db5b751dc12e4400ccfb5c063fd6d2d928
synfig-studio/plugins/lottie-exporter/common/Param.py
python
Param.getparent
(self)
return self.parent
Returns the parent of this parameter
Returns the parent of this parameter
[ "Returns", "the", "parent", "of", "this", "parameter" ]
def getparent(self): """ Returns the parent of this parameter """ return self.parent
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https://github.com/synfig/synfig/blob/a5ec91db5b751dc12e4400ccfb5c063fd6d2d928/synfig-studio/plugins/lottie-exporter/common/Param.py#L135-L139
kamyu104/LeetCode-Solutions
77605708a927ea3b85aee5a479db733938c7c211
Python/magnetic-force-between-two-balls.py
python
Solution.maxDistance
(self, position, m)
return right
:type position: List[int] :type m: int :rtype: int
:type position: List[int] :type m: int :rtype: int
[ ":", "type", "position", ":", "List", "[", "int", "]", ":", "type", "m", ":", "int", ":", "rtype", ":", "int" ]
def maxDistance(self, position, m): """ :type position: List[int] :type m: int :rtype: int """ def check(position, m, x): count, prev = 1, position[0] for i in xrange(1, len(position)): if position[i]-prev >= x: count += 1 prev = position[i] return count >= m position.sort() left, right = 1, position[-1]-position[0] while left <= right: mid = left + (right-left)//2 if not check(position, m, mid): right = mid-1 else: left = mid+1 return right
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https://github.com/kamyu104/LeetCode-Solutions/blob/77605708a927ea3b85aee5a479db733938c7c211/Python/magnetic-force-between-two-balls.py#L5-L27
PlatformLab/RAMCloud
b1866af19124325a6dfd8cbc267e2e3ef1f965d1
cpplint.py
python
Error
(filename, linenum, category, confidence, message)
Logs the fact we've found a lint error. We log where the error was found, and also our confidence in the error, that is, how certain we are this is a legitimate style regression, and not a misidentification or a use that's sometimes justified. Args: filename: The name of the file containing the error. linenum: The number of the line containing the error. category: A string used to describe the "category" this bug falls under: "whitespace", say, or "runtime". Categories may have a hierarchy separated by slashes: "whitespace/indent". confidence: A number from 1-5 representing a confidence score for the error, with 5 meaning that we are certain of the problem, and 1 meaning that it could be a legitimate construct. message: The error message.
Logs the fact we've found a lint error.
[ "Logs", "the", "fact", "we", "ve", "found", "a", "lint", "error", "." ]
def Error(filename, linenum, category, confidence, message): """Logs the fact we've found a lint error. We log where the error was found, and also our confidence in the error, that is, how certain we are this is a legitimate style regression, and not a misidentification or a use that's sometimes justified. Args: filename: The name of the file containing the error. linenum: The number of the line containing the error. category: A string used to describe the "category" this bug falls under: "whitespace", say, or "runtime". Categories may have a hierarchy separated by slashes: "whitespace/indent". confidence: A number from 1-5 representing a confidence score for the error, with 5 meaning that we are certain of the problem, and 1 meaning that it could be a legitimate construct. message: The error message. """ # There are two ways we might decide not to print an error message: # the verbosity level isn't high enough, or the filters filter it out. if _ShouldPrintError(category, confidence): _cpplint_state.IncrementErrorCount(category) if _cpplint_state.output_format == 'vs7': sys.stderr.write('%s(%s): %s [%s] [%d]\n' % ( filename, linenum, message, category, confidence)) else: sys.stderr.write('%s:%s: %s [%s] [%d]\n' % ( filename, linenum, message, category, confidence))
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https://github.com/PlatformLab/RAMCloud/blob/b1866af19124325a6dfd8cbc267e2e3ef1f965d1/cpplint.py#L734-L761
OSGeo/gdal
3748fc4ba4fba727492774b2b908a2130c864a83
swig/python/gdal-utils/osgeo_utils/gdal2tiles.py
python
GlobalGeodetic.TileLatLonBounds
(self, tx, ty, zoom)
return (b[1], b[0], b[3], b[2])
Returns bounds of the given tile in the SWNE form
Returns bounds of the given tile in the SWNE form
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def TileLatLonBounds(self, tx, ty, zoom): "Returns bounds of the given tile in the SWNE form" b = self.TileBounds(tx, ty, zoom) return (b[1], b[0], b[3], b[2])
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https://github.com/OSGeo/gdal/blob/3748fc4ba4fba727492774b2b908a2130c864a83/swig/python/gdal-utils/osgeo_utils/gdal2tiles.py#L551-L554
catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/python/scipy/py2/scipy/stats/mstats_extras.py
python
trimmed_mean_ci
(data, limits=(0.2,0.2), inclusive=(True,True), alpha=0.05, axis=None)
return np.array((tmean - tppf*tstde, tmean+tppf*tstde))
Selected confidence interval of the trimmed mean along the given axis. Parameters ---------- data : array_like Input data. limits : {None, tuple}, optional None or a two item tuple. Tuple of the percentages to cut on each side of the array, with respect to the number of unmasked data, as floats between 0. and 1. If ``n`` is the number of unmasked data before trimming, then (``n * limits[0]``)th smallest data and (``n * limits[1]``)th largest data are masked. The total number of unmasked data after trimming is ``n * (1. - sum(limits))``. The value of one limit can be set to None to indicate an open interval. Defaults to (0.2, 0.2). inclusive : (2,) tuple of boolean, optional If relative==False, tuple indicating whether values exactly equal to the absolute limits are allowed. If relative==True, tuple indicating whether the number of data being masked on each side should be rounded (True) or truncated (False). Defaults to (True, True). alpha : float, optional Confidence level of the intervals. Defaults to 0.05. axis : int, optional Axis along which to cut. If None, uses a flattened version of `data`. Defaults to None. Returns ------- trimmed_mean_ci : (2,) ndarray The lower and upper confidence intervals of the trimmed data.
Selected confidence interval of the trimmed mean along the given axis.
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def trimmed_mean_ci(data, limits=(0.2,0.2), inclusive=(True,True), alpha=0.05, axis=None): """ Selected confidence interval of the trimmed mean along the given axis. Parameters ---------- data : array_like Input data. limits : {None, tuple}, optional None or a two item tuple. Tuple of the percentages to cut on each side of the array, with respect to the number of unmasked data, as floats between 0. and 1. If ``n`` is the number of unmasked data before trimming, then (``n * limits[0]``)th smallest data and (``n * limits[1]``)th largest data are masked. The total number of unmasked data after trimming is ``n * (1. - sum(limits))``. The value of one limit can be set to None to indicate an open interval. Defaults to (0.2, 0.2). inclusive : (2,) tuple of boolean, optional If relative==False, tuple indicating whether values exactly equal to the absolute limits are allowed. If relative==True, tuple indicating whether the number of data being masked on each side should be rounded (True) or truncated (False). Defaults to (True, True). alpha : float, optional Confidence level of the intervals. Defaults to 0.05. axis : int, optional Axis along which to cut. If None, uses a flattened version of `data`. Defaults to None. Returns ------- trimmed_mean_ci : (2,) ndarray The lower and upper confidence intervals of the trimmed data. """ data = ma.array(data, copy=False) trimmed = mstats.trimr(data, limits=limits, inclusive=inclusive, axis=axis) tmean = trimmed.mean(axis) tstde = mstats.trimmed_stde(data,limits=limits,inclusive=inclusive,axis=axis) df = trimmed.count(axis) - 1 tppf = t.ppf(1-alpha/2.,df) return np.array((tmean - tppf*tstde, tmean+tppf*tstde))
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/scipy/py2/scipy/stats/mstats_extras.py#L193-L241
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/osx_cocoa/aui.py
python
AuiDockArt.DrawSash
(*args, **kwargs)
return _aui.AuiDockArt_DrawSash(*args, **kwargs)
DrawSash(self, DC dc, Window window, int orientation, Rect rect)
DrawSash(self, DC dc, Window window, int orientation, Rect rect)
[ "DrawSash", "(", "self", "DC", "dc", "Window", "window", "int", "orientation", "Rect", "rect", ")" ]
def DrawSash(*args, **kwargs): """DrawSash(self, DC dc, Window window, int orientation, Rect rect)""" return _aui.AuiDockArt_DrawSash(*args, **kwargs)
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_cocoa/aui.py#L998-L1000
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/share/doc/python3.7/examples/Tools/iobench/iobench.py
python
write_small_chunks
(f, source)
write 20 units at a time
write 20 units at a time
[ "write", "20", "units", "at", "a", "time" ]
def write_small_chunks(f, source): """ write 20 units at a time """ for i in xrange(0, len(source), 20): f.write(source[i:i+20])
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/share/doc/python3.7/examples/Tools/iobench/iobench.py#L145-L148
NVIDIA/TensorRT
42805f078052daad1a98bc5965974fcffaad0960
demo/BERT/builder_utils.py
python
onnx_to_trt_name
(onnx_name)
return parsed
Converting variables in the onnx checkpoint to names corresponding to the naming convention used in the TF version, expected by the builder
Converting variables in the onnx checkpoint to names corresponding to the naming convention used in the TF version, expected by the builder
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def onnx_to_trt_name(onnx_name): """ Converting variables in the onnx checkpoint to names corresponding to the naming convention used in the TF version, expected by the builder """ qkv_strings = {'key', 'value', 'query', 'query_key_value'} onnx_name = onnx_name.lower() toks = [t.strip('_') for t in onnx_name.split('.')] if toks[0] == 'bert': #embeddings or encoder if toks[1] == 'encoder': #transformer # Token conversions for sparse checkpoints if toks[-2] == 'dense_act': toks[-2] = 'dense' elif toks[-3] == 'dense_act': if toks[-2] == 'input_quantizer': toks[-2] = 'input' elif toks[-2] == 'weight_quantizer': toks[-2] = 'kernel' toks[-3] = 'dense' elif toks[-2].startswith('matmul'): toks[-2] = { 'matmul_q_quantizer': 'qv_a_input_quantizer', 'matmul_k_quantizer': 'qv_b_input_quantizer', 'matmul_v_quantizer': 'av_b_input_quantizer', 'matmul_a_quantizer': 'av_a_input_quantizer', }[toks[-2].replace('input_', '')] # Token conversions for all checkpoints if toks[-2] == 'layernorm': #bias->beta, weight->gamma toks[-1] = 'beta' if toks[-1] == 'bias' else 'gamma' elif (toks[-2] == 'dense' or toks[-2] in qkv_strings) and toks[-1] == 'weight': toks[-1] = 'kernel' elif (toks[-3] == 'dense' or toks[-3] in qkv_strings) and toks[-1] == 'amax': if toks[-2] == 'weight_quantizer': toks[-2] = 'kernel' elif toks[-2] == 'input_quantizer': toks[-2] = 'input' if 'final_input_quantizer' not in toks[2]: ind = toks.index('layers')+1 if 'layers' in toks else 3 toks = toks[ind:] toks[0] = 'l{}'.format(int(toks[0])) else: if toks[-2] == 'layernorm': #bias->beta, weight->gamma toks[-1] = 'beta' if toks[-1] == 'bias' else 'gamma' else: #embeddings: drop "_weight" suffix if toks[-1] == 'amax': toks[-2] = 'amax' toks = toks[:-1] elif 'qa' in onnx_name: name = 'cls_squad_output_bias' if toks[-1] == 'bias' else 'cls_squad_output_weights' return name else: print("Encountered unknown case:", onnx_name) assert(False) parsed = '_'.join(toks) return parsed
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https://github.com/NVIDIA/TensorRT/blob/42805f078052daad1a98bc5965974fcffaad0960/demo/BERT/builder_utils.py#L136-L191
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/osx_cocoa/propgrid.py
python
EnumProperty.GetItemCount
(*args, **kwargs)
return _propgrid.EnumProperty_GetItemCount(*args, **kwargs)
GetItemCount(self) -> size_t
GetItemCount(self) -> size_t
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def GetItemCount(*args, **kwargs): """GetItemCount(self) -> size_t""" return _propgrid.EnumProperty_GetItemCount(*args, **kwargs)
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_cocoa/propgrid.py#L3001-L3003
deepmind/open_spiel
4ca53bea32bb2875c7385d215424048ae92f78c8
open_spiel/python/mfg/algorithms/nash_conv.py
python
NashConv.nash_conv
(self)
return sum([ self._br_value.eval_state(state) - self._pi_value.eval_state(state) for state in self._root_states ])
Returns the nash conv. Returns: A float representing the nash conv for the policy.
Returns the nash conv.
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def nash_conv(self): """Returns the nash conv. Returns: A float representing the nash conv for the policy. """ return sum([ self._br_value.eval_state(state) - self._pi_value.eval_state(state) for state in self._root_states ])
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https://github.com/deepmind/open_spiel/blob/4ca53bea32bb2875c7385d215424048ae92f78c8/open_spiel/python/mfg/algorithms/nash_conv.py#L61-L70
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
wx/lib/agw/flatmenu.py
python
FlatMenuButton.SetSize
(self, input1, input2=None)
Sets the size for :class:`FlatMenuButton`. :param `input1`: if it is an instance of :class:`Size`, it represents the :class:`FlatMenuButton` size and the `input2` parameter is not used. Otherwise it is an integer representing the button width; :param `input2`: if not ``None``, it is an integer representing the button height.
Sets the size for :class:`FlatMenuButton`.
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def SetSize(self, input1, input2=None): """ Sets the size for :class:`FlatMenuButton`. :param `input1`: if it is an instance of :class:`Size`, it represents the :class:`FlatMenuButton` size and the `input2` parameter is not used. Otherwise it is an integer representing the button width; :param `input2`: if not ``None``, it is an integer representing the button height. """ if type(input) == type(1): self._size = wx.Size(input1, input2) else: self._size = input1
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/wx/lib/agw/flatmenu.py#L4098-L4111
Xilinx/Vitis-AI
fc74d404563d9951b57245443c73bef389f3657f
tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/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.div(tp, math_ops.add(tp, fn), name=scope) metric = _aggregate_across_replicas( metrics_collections, compute_recall, tp, fn) update = math_ops.div( 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/Xilinx/Vitis-AI/blob/fc74d404563d9951b57245443c73bef389f3657f/tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/ops/metrics_impl.py#L2568-L2648
CGRU/cgru
1881a4128530e3d31ac6c25314c18314fc50c2c7
lib/python/filelock.py
python
FileLock.__enter__
(self)
return self
Activated when used in the with statement. Should automatically acquire a lock to be used in the with block.
Activated when used in the with statement. Should automatically acquire a lock to be used in the with block.
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def __enter__(self): """Activated when used in the with statement. Should automatically acquire a lock to be used in the with block. """ if not self.is_locked: self.acquire() return self
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https://github.com/CGRU/cgru/blob/1881a4128530e3d31ac6c25314c18314fc50c2c7/lib/python/filelock.py#L90-L96
catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/python/numpy/py2/numpy/polynomial/legendre.py
python
leggauss
(deg)
return x, w
Gauss-Legendre quadrature. Computes the sample points and weights for Gauss-Legendre quadrature. These sample points and weights will correctly integrate polynomials of degree :math:`2*deg - 1` or less over the interval :math:`[-1, 1]` with the weight function :math:`f(x) = 1`. Parameters ---------- deg : int Number of sample points and weights. It must be >= 1. Returns ------- x : ndarray 1-D ndarray containing the sample points. y : ndarray 1-D ndarray containing the weights. Notes ----- .. versionadded:: 1.7.0 The results have only been tested up to degree 100, higher degrees may be problematic. The weights are determined by using the fact that .. math:: w_k = c / (L'_n(x_k) * L_{n-1}(x_k)) where :math:`c` is a constant independent of :math:`k` and :math:`x_k` is the k'th root of :math:`L_n`, and then scaling the results to get the right value when integrating 1.
Gauss-Legendre quadrature.
[ "Gauss", "-", "Legendre", "quadrature", "." ]
def leggauss(deg): """ Gauss-Legendre quadrature. Computes the sample points and weights for Gauss-Legendre quadrature. These sample points and weights will correctly integrate polynomials of degree :math:`2*deg - 1` or less over the interval :math:`[-1, 1]` with the weight function :math:`f(x) = 1`. Parameters ---------- deg : int Number of sample points and weights. It must be >= 1. Returns ------- x : ndarray 1-D ndarray containing the sample points. y : ndarray 1-D ndarray containing the weights. Notes ----- .. versionadded:: 1.7.0 The results have only been tested up to degree 100, higher degrees may be problematic. The weights are determined by using the fact that .. math:: w_k = c / (L'_n(x_k) * L_{n-1}(x_k)) where :math:`c` is a constant independent of :math:`k` and :math:`x_k` is the k'th root of :math:`L_n`, and then scaling the results to get the right value when integrating 1. """ ideg = int(deg) if ideg != deg or ideg < 1: raise ValueError("deg must be a non-negative integer") # first approximation of roots. We use the fact that the companion # matrix is symmetric in this case in order to obtain better zeros. c = np.array([0]*deg + [1]) m = legcompanion(c) x = la.eigvalsh(m) # improve roots by one application of Newton dy = legval(x, c) df = legval(x, legder(c)) x -= dy/df # compute the weights. We scale the factor to avoid possible numerical # overflow. fm = legval(x, c[1:]) fm /= np.abs(fm).max() df /= np.abs(df).max() w = 1/(fm * df) # for Legendre we can also symmetrize w = (w + w[::-1])/2 x = (x - x[::-1])/2 # scale w to get the right value w *= 2. / w.sum() return x, w
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/numpy/py2/numpy/polynomial/legendre.py#L1705-L1770
hpi-xnor/BMXNet-v2
af2b1859eafc5c721b1397cef02f946aaf2ce20d
python/mxnet/notebook/callback.py
python
PandasLogger.elapsed
(self)
return datetime.datetime.now() - self.start_time
Calcaulate the elapsed time from training starting.
Calcaulate the elapsed time from training starting.
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def elapsed(self): """Calcaulate the elapsed time from training starting. """ return datetime.datetime.now() - self.start_time
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https://github.com/hpi-xnor/BMXNet-v2/blob/af2b1859eafc5c721b1397cef02f946aaf2ce20d/python/mxnet/notebook/callback.py#L125-L128
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/gtk/_misc.py
python
TimeSpan.__lt__
(*args, **kwargs)
return _misc_.TimeSpan___lt__(*args, **kwargs)
__lt__(self, TimeSpan other) -> bool
__lt__(self, TimeSpan other) -> bool
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def __lt__(*args, **kwargs): """__lt__(self, TimeSpan other) -> bool""" return _misc_.TimeSpan___lt__(*args, **kwargs)
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/gtk/_misc.py#L4466-L4468
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/osx_cocoa/_core.py
python
OutputStream.close
(*args, **kwargs)
return _core_.OutputStream_close(*args, **kwargs)
close(self)
close(self)
[ "close", "(", "self", ")" ]
def close(*args, **kwargs): """close(self)""" return _core_.OutputStream_close(*args, **kwargs)
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_cocoa/_core.py#L2235-L2237
tensorflow/tensorflow
419e3a6b650ea4bd1b0cba23c4348f8a69f3272e
tensorflow/python/keras/layers/advanced_activations.py
python
_large_compatible_negative
(tensor_type)
return -1e9
Large negative number as Tensor. This function is necessary because the standard value for epsilon in this module (-1e9) cannot be represented using tf.float16 Args: tensor_type: a dtype to determine the type. Returns: a large negative number.
Large negative number as Tensor.
[ "Large", "negative", "number", "as", "Tensor", "." ]
def _large_compatible_negative(tensor_type): """Large negative number as Tensor. This function is necessary because the standard value for epsilon in this module (-1e9) cannot be represented using tf.float16 Args: tensor_type: a dtype to determine the type. Returns: a large negative number. """ if tensor_type == dtypes.float16: return dtypes.float16.min return -1e9
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https://github.com/tensorflow/tensorflow/blob/419e3a6b650ea4bd1b0cba23c4348f8a69f3272e/tensorflow/python/keras/layers/advanced_activations.py#L277-L291
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/osx_cocoa/richtext.py
python
RichTextBuffer.BeginSymbolBullet
(*args, **kwargs)
return _richtext.RichTextBuffer_BeginSymbolBullet(*args, **kwargs)
BeginSymbolBullet(self, String symbol, int leftIndent, int leftSubIndent, int bulletStyle=TEXT_ATTR_BULLET_STYLE_SYMBOL) -> bool
BeginSymbolBullet(self, String symbol, int leftIndent, int leftSubIndent, int bulletStyle=TEXT_ATTR_BULLET_STYLE_SYMBOL) -> bool
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def BeginSymbolBullet(*args, **kwargs): """BeginSymbolBullet(self, String symbol, int leftIndent, int leftSubIndent, int bulletStyle=TEXT_ATTR_BULLET_STYLE_SYMBOL) -> bool""" return _richtext.RichTextBuffer_BeginSymbolBullet(*args, **kwargs)
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_cocoa/richtext.py#L2432-L2434
tensorflow/tensorflow
419e3a6b650ea4bd1b0cba23c4348f8a69f3272e
tensorflow/python/ops/resource_variable_ops.py
python
BaseResourceVariable.assign_sub
(self, delta, use_locking=None, name=None, read_value=True)
return assign_sub_op
Subtracts a value from this variable. Args: delta: A `Tensor`. The value to subtract from this variable. use_locking: If `True`, use locking during the operation. name: The name to use for the operation. read_value: A `bool`. Whether to read and return the new value of the variable or not. Returns: If `read_value` is `True`, this method will return the new value of the variable after the assignment has completed. Otherwise, when in graph mode it will return the `Operation` that does the assignment, and when in eager mode it will return `None`.
Subtracts a value from this variable.
[ "Subtracts", "a", "value", "from", "this", "variable", "." ]
def assign_sub(self, delta, use_locking=None, name=None, read_value=True): """Subtracts a value from this variable. Args: delta: A `Tensor`. The value to subtract from this variable. use_locking: If `True`, use locking during the operation. name: The name to use for the operation. read_value: A `bool`. Whether to read and return the new value of the variable or not. Returns: If `read_value` is `True`, this method will return the new value of the variable after the assignment has completed. Otherwise, when in graph mode it will return the `Operation` that does the assignment, and when in eager mode it will return `None`. """ # TODO(apassos): this here and below is not atomic. Consider making it # atomic if there's a way to do so without a performance cost for those who # don't need it. with _handle_graph(self.handle), self._assign_dependencies(): assign_sub_op = gen_resource_variable_ops.assign_sub_variable_op( self.handle, ops.convert_to_tensor(delta, dtype=self.dtype), name=name) if read_value: return self._lazy_read(assign_sub_op) return assign_sub_op
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https://github.com/tensorflow/tensorflow/blob/419e3a6b650ea4bd1b0cba23c4348f8a69f3272e/tensorflow/python/ops/resource_variable_ops.py#L842-L868
greenheartgames/greenworks
3ea4ab490b56676de3f0a237c74bcfdb17323e60
deps/cpplint/cpplint.py
python
ProcessGlobalSuppresions
(lines)
Updates the list of global error suppressions. Parses any lint directives in the file that have global effect. Args: lines: An array of strings, each representing a line of the file, with the last element being empty if the file is terminated with a newline.
Updates the list of global error suppressions.
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def ProcessGlobalSuppresions(lines): """Updates the list of global error suppressions. Parses any lint directives in the file that have global effect. Args: lines: An array of strings, each representing a line of the file, with the last element being empty if the file is terminated with a newline. """ for line in lines: if _SEARCH_C_FILE.search(line): for category in _DEFAULT_C_SUPPRESSED_CATEGORIES: _global_error_suppressions[category] = True if _SEARCH_KERNEL_FILE.search(line): for category in _DEFAULT_KERNEL_SUPPRESSED_CATEGORIES: _global_error_suppressions[category] = True
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https://github.com/greenheartgames/greenworks/blob/3ea4ab490b56676de3f0a237c74bcfdb17323e60/deps/cpplint/cpplint.py#L603-L618
SpenceKonde/megaTinyCore
1c4a70b18a149fe6bcb551dfa6db11ca50b8997b
megaavr/tools/libs/pymcuprog/backend.py
python
Backend._is_connected_to_serialport
(self)
return self.connected_to_tool and isinstance(self.transport, str)
Check if a connection to a Serial port is active
Check if a connection to a Serial port is active
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def _is_connected_to_serialport(self): """ Check if a connection to a Serial port is active """ # For Serial port communication transport is only set to a string with the name of the serial port # to use (e.g. 'COM1'). return self.connected_to_tool and isinstance(self.transport, str)
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https://github.com/SpenceKonde/megaTinyCore/blob/1c4a70b18a149fe6bcb551dfa6db11ca50b8997b/megaavr/tools/libs/pymcuprog/backend.py#L643-L649
indutny/candor
48e7260618f5091c80a3416828e2808cad3ea22e
tools/gyp/pylib/gyp/win_tool.py
python
WinTool.ExecLinkWrapper
(self, arch, *args)
Filter diagnostic output from link that looks like: ' Creating library ui.dll.lib and object ui.dll.exp' This happens when there are exports from the dll or exe.
Filter diagnostic output from link that looks like: ' Creating library ui.dll.lib and object ui.dll.exp' This happens when there are exports from the dll or exe.
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def ExecLinkWrapper(self, arch, *args): """Filter diagnostic output from link that looks like: ' Creating library ui.dll.lib and object ui.dll.exp' This happens when there are exports from the dll or exe. """ with LinkLock(): env = self._GetEnv(arch) popen = subprocess.Popen(args, shell=True, env=env, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) out, _ = popen.communicate() for line in out.splitlines(): if not line.startswith(' Creating library '): print line return popen.returncode
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https://github.com/indutny/candor/blob/48e7260618f5091c80a3416828e2808cad3ea22e/tools/gyp/pylib/gyp/win_tool.py#L85-L98
krishauser/Klampt
972cc83ea5befac3f653c1ba20f80155768ad519
Python/klampt/control/robotinterface.py
python
RobotInterfaceBase.robotToPartConfig
(self, robotConfig: Vector, part: str, joint_idx: Optional[int] = None )
return [robotConfig[i] for i in pindices]
Retrieves a part's configuration from a robot configuration
Retrieves a part's configuration from a robot configuration
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def robotToPartConfig(self, robotConfig: Vector, part: str, joint_idx: Optional[int] = None ) -> Vector: """Retrieves a part's configuration from a robot configuration""" pindices = self.indices(part,joint_idx) return [robotConfig[i] for i in pindices]
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https://github.com/krishauser/Klampt/blob/972cc83ea5befac3f653c1ba20f80155768ad519/Python/klampt/control/robotinterface.py#L844-L851
Xilinx/Vitis-AI
fc74d404563d9951b57245443c73bef389f3657f
tools/RNN/rnn_quantizer/pytorch_binding/pytorch_nndct/utils/jit_utils.py
python
get_attr_chains
(root_getattr_node)
return get_use_chains(root_getattr_node, terminate)
Returns chains of attribute access starting from root_getattr_node For example, given attribute "block", as in "self.block" when "self" points to the top level torch.nn.Module, it returns lists of attribute "chains", e.g. ['block', '2'], ['block', '1'], ['block', '0', '_packed_params'] These sets of attributes form full attribute accessors. For example, "self.block.1", "self.block.2" will return the second and third submodule, and "self.block.0._packed_params" will return the parameters of the first submodule.
Returns chains of attribute access starting from root_getattr_node
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def get_attr_chains(root_getattr_node): """Returns chains of attribute access starting from root_getattr_node For example, given attribute "block", as in "self.block" when "self" points to the top level torch.nn.Module, it returns lists of attribute "chains", e.g. ['block', '2'], ['block', '1'], ['block', '0', '_packed_params'] These sets of attributes form full attribute accessors. For example, "self.block.1", "self.block.2" will return the second and third submodule, and "self.block.0._packed_params" will return the parameters of the first submodule. """ def terminate(users): next_attrs = [user for user in users if user.kind() == "prim::GetAttr"] return len(next_attrs) == 0 return get_use_chains(root_getattr_node, terminate)
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https://github.com/Xilinx/Vitis-AI/blob/fc74d404563d9951b57245443c73bef389f3657f/tools/RNN/rnn_quantizer/pytorch_binding/pytorch_nndct/utils/jit_utils.py#L425-L442
arangodb/arangodb
0d658689c7d1b721b314fa3ca27d38303e1570c8
3rdParty/V8/v7.9.317/third_party/jinja2/utils.py
python
LRUCache.__setitem__
(self, key, value)
Sets the value for an item. Moves the item up so that it has the highest priority then.
Sets the value for an item. Moves the item up so that it has the highest priority then.
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def __setitem__(self, key, value): """Sets the value for an item. Moves the item up so that it has the highest priority then. """ self._wlock.acquire() try: if key in self._mapping: self._remove(key) elif len(self._mapping) == self.capacity: del self._mapping[self._popleft()] self._append(key) self._mapping[key] = value finally: self._wlock.release()
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https://github.com/arangodb/arangodb/blob/0d658689c7d1b721b314fa3ca27d38303e1570c8/3rdParty/V8/v7.9.317/third_party/jinja2/utils.py#L414-L427
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/osx_cocoa/grid.py
python
Grid.GetSelectionBlockBottomRight
(*args, **kwargs)
return _grid.Grid_GetSelectionBlockBottomRight(*args, **kwargs)
GetSelectionBlockBottomRight(self) -> wxGridCellCoordsArray
GetSelectionBlockBottomRight(self) -> wxGridCellCoordsArray
[ "GetSelectionBlockBottomRight", "(", "self", ")", "-", ">", "wxGridCellCoordsArray" ]
def GetSelectionBlockBottomRight(*args, **kwargs): """GetSelectionBlockBottomRight(self) -> wxGridCellCoordsArray""" return _grid.Grid_GetSelectionBlockBottomRight(*args, **kwargs)
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_cocoa/grid.py#L2065-L2067
mongodb/mongo
d8ff665343ad29cf286ee2cf4a1960d29371937b
src/third_party/scons-3.1.2/scons-local-3.1.2/SCons/Memoize.py
python
CountMethodCall
(fn)
Decorator for counting memoizer hits/misses while retrieving a simple value in a class method. It wraps the given method fn and uses a CountValue object to keep track of the caching statistics. Wrapping gets enabled by calling EnableMemoization().
Decorator for counting memoizer hits/misses while retrieving a simple value in a class method. It wraps the given method fn and uses a CountValue object to keep track of the caching statistics. Wrapping gets enabled by calling EnableMemoization().
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def CountMethodCall(fn): """ Decorator for counting memoizer hits/misses while retrieving a simple value in a class method. It wraps the given method fn and uses a CountValue object to keep track of the caching statistics. Wrapping gets enabled by calling EnableMemoization(). """ if use_memoizer: def wrapper(self, *args, **kwargs): global CounterList key = self.__class__.__name__+'.'+fn.__name__ if key not in CounterList: CounterList[key] = CountValue(self.__class__.__name__, fn.__name__) CounterList[key].count(self, *args, **kwargs) return fn(self, *args, **kwargs) wrapper.__name__= fn.__name__ return wrapper else: return fn
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google/llvm-propeller
45c226984fe8377ebfb2ad7713c680d652ba678d
clang/utils/check_cfc/obj_diff.py
python
first_diff
(a, b, fromfile, tofile)
return difference
Returns the first few lines of a difference, if there is one. Python diff can be very slow with large objects and the most interesting changes are the first ones. Truncate data before sending to difflib. Returns None is there is no difference.
Returns the first few lines of a difference, if there is one. Python diff can be very slow with large objects and the most interesting changes are the first ones. Truncate data before sending to difflib. Returns None is there is no difference.
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def first_diff(a, b, fromfile, tofile): """Returns the first few lines of a difference, if there is one. Python diff can be very slow with large objects and the most interesting changes are the first ones. Truncate data before sending to difflib. Returns None is there is no difference.""" # Find first diff first_diff_idx = None for idx, val in enumerate(a): if val != b[idx]: first_diff_idx = idx break if first_diff_idx == None: # No difference return None # Diff to first line of diff plus some lines context = 3 diff = difflib.unified_diff(a[:first_diff_idx+context], b[:first_diff_idx+context], fromfile, tofile) difference = "\n".join(diff) if first_diff_idx + context < len(a): difference += "\n*** Diff truncated ***" return difference
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wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/msw/richtext.py
python
RichTextPlainText.GetText
(*args, **kwargs)
return _richtext.RichTextPlainText_GetText(*args, **kwargs)
GetText(self) -> String
GetText(self) -> String
[ "GetText", "(", "self", ")", "-", ">", "String" ]
def GetText(*args, **kwargs): """GetText(self) -> String""" return _richtext.RichTextPlainText_GetText(*args, **kwargs)
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/msw/richtext.py#L2096-L2098
kamyu104/LeetCode-Solutions
77605708a927ea3b85aee5a479db733938c7c211
Python/index-pairs-of-a-string.py
python
Solution.indexPairs
(self, text, words)
return result
:type text: str :type words: List[str] :rtype: List[List[int]]
:type text: str :type words: List[str] :rtype: List[List[int]]
[ ":", "type", "text", ":", "str", ":", "type", "words", ":", "List", "[", "str", "]", ":", "rtype", ":", "List", "[", "List", "[", "int", "]]" ]
def indexPairs(self, text, words): """ :type text: str :type words: List[str] :rtype: List[List[int]] """ result = [] reversed_words = [w[::-1] for w in words] trie = AhoTrie(reversed_words) for i in reversed(xrange(len(text))): for j in trie.step(text[i]): result.append([i, i+len(reversed_words[j])-1]) result.reverse() return result
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https://github.com/kamyu104/LeetCode-Solutions/blob/77605708a927ea3b85aee5a479db733938c7c211/Python/index-pairs-of-a-string.py#L69-L82
fatih/subvim
241b6d170597857105da219c9b7d36059e9f11fb
vim/base/YouCompleteMe/third_party/requests/requests/utils.py
python
except_on_missing_scheme
(url)
Given a URL, raise a MissingSchema exception if the scheme is missing.
Given a URL, raise a MissingSchema exception if the scheme is missing.
[ "Given", "a", "URL", "raise", "a", "MissingSchema", "exception", "if", "the", "scheme", "is", "missing", "." ]
def except_on_missing_scheme(url): """Given a URL, raise a MissingSchema exception if the scheme is missing. """ scheme, netloc, path, params, query, fragment = urlparse(url) if not scheme: raise MissingSchema('Proxy URLs must have explicit schemes.')
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https://github.com/fatih/subvim/blob/241b6d170597857105da219c9b7d36059e9f11fb/vim/base/YouCompleteMe/third_party/requests/requests/utils.py#L536-L542
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/gtk/_windows.py
python
StatusBar.GetBorders
(*args, **kwargs)
return _windows_.StatusBar_GetBorders(*args, **kwargs)
GetBorders(self) -> Size
GetBorders(self) -> Size
[ "GetBorders", "(", "self", ")", "-", ">", "Size" ]
def GetBorders(*args, **kwargs): """GetBorders(self) -> Size""" return _windows_.StatusBar_GetBorders(*args, **kwargs)
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/gtk/_windows.py#L1295-L1297
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/gtk/_core.py
python
DropFilesEvent.GetFiles
(*args, **kwargs)
return _core_.DropFilesEvent_GetFiles(*args, **kwargs)
GetFiles(self) -> PyObject Returns a list of the filenames that were dropped.
GetFiles(self) -> PyObject
[ "GetFiles", "(", "self", ")", "-", ">", "PyObject" ]
def GetFiles(*args, **kwargs): """ GetFiles(self) -> PyObject Returns a list of the filenames that were dropped. """ return _core_.DropFilesEvent_GetFiles(*args, **kwargs)
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/gtk/_core.py#L6670-L6676
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/osx_carbon/grid.py
python
Grid.AutoSizeColLabelSize
(*args, **kwargs)
return _grid.Grid_AutoSizeColLabelSize(*args, **kwargs)
AutoSizeColLabelSize(self, int col)
AutoSizeColLabelSize(self, int col)
[ "AutoSizeColLabelSize", "(", "self", "int", "col", ")" ]
def AutoSizeColLabelSize(*args, **kwargs): """AutoSizeColLabelSize(self, int col)""" return _grid.Grid_AutoSizeColLabelSize(*args, **kwargs)
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_carbon/grid.py#L1906-L1908
catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/python/pandas/py2/pandas/core/sparse/series.py
python
SparseSeries.sparse_reindex
(self, new_index)
return self._constructor(values, index=self.index).__finalize__(self)
Conform sparse values to new SparseIndex Parameters ---------- new_index : {BlockIndex, IntIndex} Returns ------- reindexed : SparseSeries
Conform sparse values to new SparseIndex
[ "Conform", "sparse", "values", "to", "new", "SparseIndex" ]
def sparse_reindex(self, new_index): """ Conform sparse values to new SparseIndex Parameters ---------- new_index : {BlockIndex, IntIndex} Returns ------- reindexed : SparseSeries """ if not isinstance(new_index, splib.SparseIndex): raise TypeError("new index must be a SparseIndex") values = self.values values = values.sp_index.to_int_index().reindex( values.sp_values.astype('float64'), values.fill_value, new_index) values = SparseArray(values, sparse_index=new_index, fill_value=self.values.fill_value) return self._constructor(values, index=self.index).__finalize__(self)
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/pandas/py2/pandas/core/sparse/series.py#L474-L494
mongodb/mongo
d8ff665343ad29cf286ee2cf4a1960d29371937b
buildscripts/patch_builds/change_data.py
python
generate_revision_map
(repos: List[Repo], revisions_data: Dict[str, str])
return {k: v for k, v in revision_map.items() if v}
Generate a revision map for the given repositories using the revisions in the given file. :param repos: Repositories to generate map for. :param revisions_data: Dictionary of revisions to use for repositories. :return: Map of repositories to revisions
Generate a revision map for the given repositories using the revisions in the given file.
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def generate_revision_map(repos: List[Repo], revisions_data: Dict[str, str]) -> RevisionMap: """ Generate a revision map for the given repositories using the revisions in the given file. :param repos: Repositories to generate map for. :param revisions_data: Dictionary of revisions to use for repositories. :return: Map of repositories to revisions """ revision_map = {repo.git_dir: revisions_data.get(_get_id_from_repo(repo)) for repo in repos} return {k: v for k, v in revision_map.items() if v}
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https://github.com/mongodb/mongo/blob/d8ff665343ad29cf286ee2cf4a1960d29371937b/buildscripts/patch_builds/change_data.py#L26-L35
daijifeng001/caffe-rfcn
543f8f6a4b7c88256ea1445ae951a12d1ad9cffd
tools/extra/extract_seconds.py
python
get_start_time
(line_iterable, year)
return start_datetime
Find start time from group of lines
Find start time from group of lines
[ "Find", "start", "time", "from", "group", "of", "lines" ]
def get_start_time(line_iterable, year): """Find start time from group of lines """ start_datetime = None for line in line_iterable: line = line.strip() if line.find('Solving') != -1: start_datetime = extract_datetime_from_line(line, year) break return start_datetime
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https://github.com/daijifeng001/caffe-rfcn/blob/543f8f6a4b7c88256ea1445ae951a12d1ad9cffd/tools/extra/extract_seconds.py#L31-L41
miyosuda/TensorFlowAndroidDemo
35903e0221aa5f109ea2dbef27f20b52e317f42d
jni-build/jni/include/tensorflow/python/framework/graph_util.py
python
tensor_shape_from_node_def_name
(graph, input_name)
return shape
Convenience function to get a shape from a NodeDef's input string.
Convenience function to get a shape from a NodeDef's input string.
[ "Convenience", "function", "to", "get", "a", "shape", "from", "a", "NodeDef", "s", "input", "string", "." ]
def tensor_shape_from_node_def_name(graph, input_name): """Convenience function to get a shape from a NodeDef's input string.""" # To get a tensor, the name must be in the form <input>:<port>, for example # 'Mul:0'. The GraphDef input strings don't always have the port specified # though, so if there isn't a colon we need to add a default ':0' to the end. if ":" not in input_name: canonical_name = input_name + ":0" else: canonical_name = input_name tensor = graph.get_tensor_by_name(canonical_name) shape = tensor.get_shape() return shape
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https://github.com/miyosuda/TensorFlowAndroidDemo/blob/35903e0221aa5f109ea2dbef27f20b52e317f42d/jni-build/jni/include/tensorflow/python/framework/graph_util.py#L179-L190
wlanjie/AndroidFFmpeg
7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf
tools/fdk-aac-build/x86/toolchain/lib/python2.7/distutils/util.py
python
check_environ
()
Ensure that 'os.environ' has all the environment variables we guarantee that users can use in config files, command-line options, etc. Currently this includes: HOME - user's home directory (Unix only) PLAT - description of the current platform, including hardware and OS (see 'get_platform()')
Ensure that 'os.environ' has all the environment variables we guarantee that users can use in config files, command-line options, etc. Currently this includes: HOME - user's home directory (Unix only) PLAT - description of the current platform, including hardware and OS (see 'get_platform()')
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def check_environ (): """Ensure that 'os.environ' has all the environment variables we guarantee that users can use in config files, command-line options, etc. Currently this includes: HOME - user's home directory (Unix only) PLAT - description of the current platform, including hardware and OS (see 'get_platform()') """ global _environ_checked if _environ_checked: return if os.name == 'posix' and 'HOME' not in os.environ: import pwd os.environ['HOME'] = pwd.getpwuid(os.getuid())[5] if 'PLAT' not in os.environ: os.environ['PLAT'] = get_platform() _environ_checked = 1
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https://github.com/wlanjie/AndroidFFmpeg/blob/7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf/tools/fdk-aac-build/x86/toolchain/lib/python2.7/distutils/util.py#L175-L194
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
src/osx_cocoa/_windows.py
python
HScrolledWindow.__init__
(self, *args, **kwargs)
__init__(self, Window parent, int id=ID_ANY, Point pos=DefaultPosition, Size size=DefaultSize, long style=0, String name=PanelNameStr) -> HScrolledWindow
__init__(self, Window parent, int id=ID_ANY, Point pos=DefaultPosition, Size size=DefaultSize, long style=0, String name=PanelNameStr) -> HScrolledWindow
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def __init__(self, *args, **kwargs): """ __init__(self, Window parent, int id=ID_ANY, Point pos=DefaultPosition, Size size=DefaultSize, long style=0, String name=PanelNameStr) -> HScrolledWindow """ _windows_.HScrolledWindow_swiginit(self,_windows_.new_HScrolledWindow(*args, **kwargs)) self._setOORInfo(self);HScrolledWindow._setCallbackInfo(self, self, HScrolledWindow)
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_cocoa/_windows.py#L2501-L2507
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Gems/CloudGemDefectReporter/v1/AWS/common-code/Lib/PIL/TiffImagePlugin.py
python
TiffImageFile._setup
(self)
Setup this image object based on current tags
Setup this image object based on current tags
[ "Setup", "this", "image", "object", "based", "on", "current", "tags" ]
def _setup(self): """Setup this image object based on current tags""" if 0xBC01 in self.tag_v2: raise OSError("Windows Media Photo files not yet supported") # extract relevant tags self._compression = COMPRESSION_INFO[self.tag_v2.get(COMPRESSION, 1)] self._planar_configuration = self.tag_v2.get(PLANAR_CONFIGURATION, 1) # photometric is a required tag, but not everyone is reading # the specification photo = self.tag_v2.get(PHOTOMETRIC_INTERPRETATION, 0) # old style jpeg compression images most certainly are YCbCr if self._compression == "tiff_jpeg": photo = 6 fillorder = self.tag_v2.get(FILLORDER, 1) if DEBUG: print("*** Summary ***") print("- compression:", self._compression) print("- photometric_interpretation:", photo) print("- planar_configuration:", self._planar_configuration) print("- fill_order:", fillorder) print("- YCbCr subsampling:", self.tag.get(530)) # size xsize = int(self.tag_v2.get(IMAGEWIDTH)) ysize = int(self.tag_v2.get(IMAGELENGTH)) self._size = xsize, ysize if DEBUG: print("- size:", self.size) sampleFormat = self.tag_v2.get(SAMPLEFORMAT, (1,)) if len(sampleFormat) > 1 and max(sampleFormat) == min(sampleFormat) == 1: # SAMPLEFORMAT is properly per band, so an RGB image will # be (1,1,1). But, we don't support per band pixel types, # and anything more than one band is a uint8. So, just # take the first element. Revisit this if adding support # for more exotic images. sampleFormat = (1,) bps_tuple = self.tag_v2.get(BITSPERSAMPLE, (1,)) extra_tuple = self.tag_v2.get(EXTRASAMPLES, ()) if photo in (2, 6, 8): # RGB, YCbCr, LAB bps_count = 3 elif photo == 5: # CMYK bps_count = 4 else: bps_count = 1 bps_count += len(extra_tuple) # Some files have only one value in bps_tuple, # while should have more. Fix it if bps_count > len(bps_tuple) and len(bps_tuple) == 1: bps_tuple = bps_tuple * bps_count # mode: check photometric interpretation and bits per pixel key = ( self.tag_v2.prefix, photo, sampleFormat, fillorder, bps_tuple, extra_tuple, ) if DEBUG: print("format key:", key) try: self.mode, rawmode = OPEN_INFO[key] except KeyError: if DEBUG: print("- unsupported format") raise SyntaxError("unknown pixel mode") if DEBUG: print("- raw mode:", rawmode) print("- pil mode:", self.mode) self.info["compression"] = self._compression xres = self.tag_v2.get(X_RESOLUTION, 1) yres = self.tag_v2.get(Y_RESOLUTION, 1) if xres and yres: resunit = self.tag_v2.get(RESOLUTION_UNIT) if resunit == 2: # dots per inch self.info["dpi"] = int(xres + 0.5), int(yres + 0.5) elif resunit == 3: # dots per centimeter. convert to dpi self.info["dpi"] = int(xres * 2.54 + 0.5), int(yres * 2.54 + 0.5) elif resunit is None: # used to default to 1, but now 2) self.info["dpi"] = int(xres + 0.5), int(yres + 0.5) # For backward compatibility, # we also preserve the old behavior self.info["resolution"] = xres, yres else: # No absolute unit of measurement self.info["resolution"] = xres, yres # build tile descriptors x = y = layer = 0 self.tile = [] self.use_load_libtiff = READ_LIBTIFF or self._compression != "raw" if self.use_load_libtiff: # Decoder expects entire file as one tile. # There's a buffer size limit in load (64k) # so large g4 images will fail if we use that # function. # # Setup the one tile for the whole image, then # use the _load_libtiff function. # libtiff handles the fillmode for us, so 1;IR should # actually be 1;I. Including the R double reverses the # bits, so stripes of the image are reversed. See # https://github.com/python-pillow/Pillow/issues/279 if fillorder == 2: # Replace fillorder with fillorder=1 key = key[:3] + (1,) + key[4:] if DEBUG: print("format key:", key) # this should always work, since all the # fillorder==2 modes have a corresponding # fillorder=1 mode self.mode, rawmode = OPEN_INFO[key] # libtiff always returns the bytes in native order. # we're expecting image byte order. So, if the rawmode # contains I;16, we need to convert from native to image # byte order. if rawmode == "I;16": rawmode = "I;16N" if ";16B" in rawmode: rawmode = rawmode.replace(";16B", ";16N") if ";16L" in rawmode: rawmode = rawmode.replace(";16L", ";16N") # Offset in the tile tuple is 0, we go from 0,0 to # w,h, and we only do this once -- eds a = (rawmode, self._compression, False, self.tag_v2.offset) self.tile.append(("libtiff", (0, 0, xsize, ysize), 0, a)) elif STRIPOFFSETS in self.tag_v2 or TILEOFFSETS in self.tag_v2: # striped image if STRIPOFFSETS in self.tag_v2: offsets = self.tag_v2[STRIPOFFSETS] h = self.tag_v2.get(ROWSPERSTRIP, ysize) w = self.size[0] else: # tiled image offsets = self.tag_v2[TILEOFFSETS] w = self.tag_v2.get(322) h = self.tag_v2.get(323) for offset in offsets: if x + w > xsize: stride = w * sum(bps_tuple) / 8 # bytes per line else: stride = 0 tile_rawmode = rawmode if self._planar_configuration == 2: # each band on it's own layer tile_rawmode = rawmode[layer] # adjust stride width accordingly stride /= bps_count a = (tile_rawmode, int(stride), 1) self.tile.append( ( self._compression, (x, y, min(x + w, xsize), min(y + h, ysize)), offset, a, ) ) x = x + w if x >= self.size[0]: x, y = 0, y + h if y >= self.size[1]: x = y = 0 layer += 1 else: if DEBUG: print("- unsupported data organization") raise SyntaxError("unknown data organization") # Fix up info. if ICCPROFILE in self.tag_v2: self.info["icc_profile"] = self.tag_v2[ICCPROFILE] # fixup palette descriptor if self.mode in ["P", "PA"]: palette = [o8(b // 256) for b in self.tag_v2[COLORMAP]] self.palette = ImagePalette.raw("RGB;L", b"".join(palette)) self._tile_orientation = self.tag_v2.get(0x0112)
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemDefectReporter/v1/AWS/common-code/Lib/PIL/TiffImagePlugin.py#L1186-L1383
wxWidgets/wxPython-Classic
19571e1ae65f1ac445f5491474121998c97a1bf0
wx/lib/agw/knobctrl.py
python
KnobCtrl.DrawTags
(self, dc, size)
Draws the tags. :param `dc`: an instance of :class:`DC`; :param `size`: the control size.
Draws the tags.
[ "Draws", "the", "tags", "." ]
def DrawTags(self, dc, size): """ Draws the tags. :param `dc`: an instance of :class:`DC`; :param `size`: the control size. """ deltarange = abs(self._tags[-1] - self._tags[0]) deltaangle = self._angleend - self._anglestart width = size.x height = size.y xshift = 0 yshift = 0 if width > height: xshift = width - height elif width < height: yshift = height - width coeff = float(deltaangle)/float(deltarange) dcPen = wx.Pen(self._tagscolour, 1) for tags in self._tags: if tags == self._tags[0] or tags == self._tags[-1]: # draw first and last tags bigger dcPen.SetWidth(2) tagLen = 8 else: dcPen.SetWidth(1) tagLen = 6 dc.SetPen(dcPen) tg = tags - self._tags[0] angle = tg*coeff + self._anglestart angle = angle*math.pi/180.0 sxi = math.cos(angle)*(width - xshift + tagLen - 6)/2.0 syi = math.sin(angle)*(height - yshift + tagLen - 6)/2.0 dxi = math.cos(angle)*((width - xshift + tagLen - 6)/2.0 - tagLen) dyi = math.sin(angle)*((height - yshift + tagLen - 6)/2.0 - tagLen) dc.DrawLine(width/2 - sxi, height/2 - syi, width/2 - dxi, height/2 - dyi)
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https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/wx/lib/agw/knobctrl.py#L599-L648
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/llvmlite/ir/builder.py
python
IRBuilder.ret_void
(self)
return self._set_terminator( instructions.Ret(self.block, "ret void"))
Return from function without a value.
Return from function without a value.
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def ret_void(self): """ Return from function without a value. """ return self._set_terminator( instructions.Ret(self.block, "ret void"))
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/llvmlite/ir/builder.py#L811-L816
mickem/nscp
79f89fdbb6da63f91bc9dedb7aea202fe938f237
scripts/python/lib/google/protobuf/internal/python_message.py
python
_ExtensionDict.__setitem__
(self, extension_handle, value)
If extension_handle specifies a non-repeated, scalar extension field, sets the value of that field.
If extension_handle specifies a non-repeated, scalar extension field, sets the value of that field.
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def __setitem__(self, extension_handle, value): """If extension_handle specifies a non-repeated, scalar extension field, sets the value of that field. """ _VerifyExtensionHandle(self._extended_message, extension_handle) if (extension_handle.label == _FieldDescriptor.LABEL_REPEATED or extension_handle.cpp_type == _FieldDescriptor.CPPTYPE_MESSAGE): raise TypeError( 'Cannot assign to extension "%s" because it is a repeated or ' 'composite type.' % extension_handle.full_name) # It's slightly wasteful to lookup the type checker each time, # but we expect this to be a vanishingly uncommon case anyway. type_checker = type_checkers.GetTypeChecker( extension_handle.cpp_type, extension_handle.type) type_checker.CheckValue(value) self._extended_message._fields[extension_handle] = value self._extended_message._Modified()
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https://github.com/mickem/nscp/blob/79f89fdbb6da63f91bc9dedb7aea202fe938f237/scripts/python/lib/google/protobuf/internal/python_message.py#L1068-L1087
mongodb/mongo
d8ff665343ad29cf286ee2cf4a1960d29371937b
buildscripts/resmokelib/powercycle/lib/services.py
python
PosixService.delete
(self)
return 0, None
Simulate delete service. Returns (code, output) tuple.
Simulate delete service. Returns (code, output) tuple.
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def delete(self): # pylint: disable=no-self-use """Simulate delete service. Returns (code, output) tuple.""" return 0, None
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https://github.com/mongodb/mongo/blob/d8ff665343ad29cf286ee2cf4a1960d29371937b/buildscripts/resmokelib/powercycle/lib/services.py#L202-L204
apache/qpid-proton
6bcdfebb55ea3554bc29b1901422532db331a591
python/proton/_reactor.py
python
SenderOption.apply
(self, sender: 'Sender')
Set the option on the sender. :param sender: The sender on which this option is to be applied.
Set the option on the sender.
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def apply(self, sender: 'Sender') -> None: """ Set the option on the sender. :param sender: The sender on which this option is to be applied. """ pass
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https://github.com/apache/qpid-proton/blob/6bcdfebb55ea3554bc29b1901422532db331a591/python/proton/_reactor.py#L711-L717
bh107/bohrium
5b83e7117285fefc7779ed0e9acb0f8e74c7e068
bridge/npbackend/bohrium/reorganization.py
python
gather
(ary, indexes)
return ret
gather(ary, indexes) Gather elements from 'ary' selected by 'indexes'. The values of 'indexes' are absolute indexed into a flatten 'ary' The shape of the returned array equals indexes.shape. Parameters ---------- ary : array_like The array to gather elements from. indexes : array_like, interpreted as integers Array or list of indexes that will be gather from 'array' Returns ------- r : ndarray The gathered array freshly-allocated.
gather(ary, indexes)
[ "gather", "(", "ary", "indexes", ")" ]
def gather(ary, indexes): """ gather(ary, indexes) Gather elements from 'ary' selected by 'indexes'. The values of 'indexes' are absolute indexed into a flatten 'ary' The shape of the returned array equals indexes.shape. Parameters ---------- ary : array_like The array to gather elements from. indexes : array_like, interpreted as integers Array or list of indexes that will be gather from 'array' Returns ------- r : ndarray The gathered array freshly-allocated. """ from . import _bh ary = array_manipulation.flatten(array_create.array(ary)) # Convert a scalar index to a 1-element array if is_scalar(indexes): indexes = [indexes] indexes = array_create.array(indexes, dtype=numpy.uint64, bohrium=True) ret = array_create.empty(indexes.shape, dtype=ary.dtype, bohrium=True) if ary.size == 0 or indexes.size == 0: return array_create.array([]) _bh.ufunc(_info.op['gather']['id'], (ret, ary, indexes)) return ret
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https://github.com/bh107/bohrium/blob/5b83e7117285fefc7779ed0e9acb0f8e74c7e068/bridge/npbackend/bohrium/reorganization.py#L18-L52
francinexue/xuefu
b6ff79747a42e020588c0c0a921048e08fe4680c
cnx/bar.py
python
Bars.__contains__
(self, instrument)
return instrument in self.__barDict
Returns True if a :class:`pyalgotrade.bar.Bar` for the given instrument is available.
Returns True if a :class:`pyalgotrade.bar.Bar` for the given instrument is available.
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def __contains__(self, instrument): """Returns True if a :class:`pyalgotrade.bar.Bar` for the given instrument is available.""" return instrument in self.__barDict
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https://github.com/francinexue/xuefu/blob/b6ff79747a42e020588c0c0a921048e08fe4680c/cnx/bar.py#L280-L282
sandialabs/Albany
e7e05599c47f65dee6f1916b26f49a5b80d39416
PyAlbany/python/Utils.py
python
createAlbanyProblem
(filename, parallelEnv)
return wpa.PyProblem(filename, parallelEnv)
@brief Creates an Albany problem given a yaml file and a parallel environment.
[]
def createAlbanyProblem(filename, parallelEnv): """@brief Creates an Albany problem given a yaml file and a parallel environment.""" return wpa.PyProblem(filename, parallelEnv)
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https://github.com/sandialabs/Albany/blob/e7e05599c47f65dee6f1916b26f49a5b80d39416/PyAlbany/python/Utils.py#L36-L38
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/setuptools/dist.py
python
check_entry_points
(dist, attr, value)
Verify that entry_points map is parseable
Verify that entry_points map is parseable
[ "Verify", "that", "entry_points", "map", "is", "parseable" ]
def check_entry_points(dist, attr, value): """Verify that entry_points map is parseable""" try: pkg_resources.EntryPoint.parse_map(value) except ValueError as e: raise DistutilsSetupError(e)
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/setuptools/dist.py#L298-L303
catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
contrib/python/py/py/_path/svnwc.py
python
SvnWCCommandPath.status
(self, updates=0, rec=0, externals=0)
return rootstatus
return (collective) Status object for this file.
return (collective) Status object for this file.
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def status(self, updates=0, rec=0, externals=0): """ return (collective) Status object for this file. """ # http://svnbook.red-bean.com/book.html#svn-ch-3-sect-4.3.1 # 2201 2192 jum test # XXX if externals: raise ValueError("XXX cannot perform status() " "on external items yet") else: #1.2 supports: externals = '--ignore-externals' externals = '' if rec: rec= '' else: rec = '--non-recursive' # XXX does not work on all subversion versions #if not externals: # externals = '--ignore-externals' if updates: updates = '-u' else: updates = '' try: cmd = 'status -v --xml --no-ignore %s %s %s' % ( updates, rec, externals) out = self._authsvn(cmd) except py.process.cmdexec.Error: cmd = 'status -v --no-ignore %s %s %s' % ( updates, rec, externals) out = self._authsvn(cmd) rootstatus = WCStatus(self).fromstring(out, self) else: rootstatus = XMLWCStatus(self).fromstring(out, self) return rootstatus
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/py/py/_path/svnwc.py#L616-L652
aws/lumberyard
f85344403c1c2e77ec8c75deb2c116e97b713217
dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numpy/lib/npyio.py
python
zipfile_factory
(file, *args, **kwargs)
return zipfile.ZipFile(file, *args, **kwargs)
Create a ZipFile. Allows for Zip64, and the `file` argument can accept file, str, or pathlib.Path objects. `args` and `kwargs` are passed to the zipfile.ZipFile constructor.
Create a ZipFile.
[ "Create", "a", "ZipFile", "." ]
def zipfile_factory(file, *args, **kwargs): """ Create a ZipFile. Allows for Zip64, and the `file` argument can accept file, str, or pathlib.Path objects. `args` and `kwargs` are passed to the zipfile.ZipFile constructor. """ if not hasattr(file, 'read'): file = os_fspath(file) import zipfile kwargs['allowZip64'] = True return zipfile.ZipFile(file, *args, **kwargs)
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numpy/lib/npyio.py#L107-L119
openmm/openmm
cb293447c4fc8b03976dfe11399f107bab70f3d9
wrappers/python/openmm/app/desmonddmsfile.py
python
DesmondDMSFile._addNonbondedForceToSystem
(self, sys, OPLS)
return nb, cnb
Create the nonbonded force
Create the nonbonded force
[ "Create", "the", "nonbonded", "force" ]
def _addNonbondedForceToSystem(self, sys, OPLS): """Create the nonbonded force """ cnb = None nb = mm.NonbondedForce() sys.addForce(nb) if OPLS: cnb = mm.CustomNonbondedForce("4.0*epsilon12*((sigma12/r)^12 - (sigma12/r)^6); sigma12=sqrt(sigma1*sigma2); epsilon12=sqrt(epsilon1*epsilon2)") cnb.addPerParticleParameter("sigma") cnb.addPerParticleParameter("epsilon") sys.addForce(cnb) if OPLS: q = """SELECT sigma, epsilon FROM particle INNER JOIN nonbonded_param ON particle.nbtype=nonbonded_param.id ORDER BY particle.id""" for (fcounter,conn,tables,offset) in self._localVars(): for sigma, epsilon in conn.execute(q): cnb.addParticle([sigma*angstrom, epsilon*kilocalorie_per_mole]) q = """SELECT charge, sigma, epsilon FROM particle INNER JOIN nonbonded_param ON particle.nbtype=nonbonded_param.id ORDER BY particle.id""" for (fcounter,conn,tables,offset) in self._localVars(): for charge, sigma, epsilon in conn.execute(q): if OPLS: epsilon = 0 nb.addParticle(charge, sigma*angstrom, epsilon*kilocalorie_per_mole) for (fcounter,conn,tables,offset) in self._localVars(): for p0, p1 in conn.execute('SELECT p0, p1 FROM exclusion'): p0 += offset p1 += offset nb.addException(p0, p1, 0.0, 1.0, 0.0) if OPLS: cnb.addExclusion(p0, p1) q = """SELECT p0, p1, aij, bij, qij FROM pair_12_6_es_term INNER JOIN pair_12_6_es_param ON pair_12_6_es_term.param=pair_12_6_es_param.id""" for (fcounter,conn,tables,offset) in self._localVars(): for p0, p1, a_ij, b_ij, q_ij in conn.execute(q): p0 += offset p1 += offset a_ij = (a_ij*kilocalorie_per_mole*(angstrom**12)).in_units_of(kilojoule_per_mole*(nanometer**12)) b_ij = (b_ij*kilocalorie_per_mole*(angstrom**6)).in_units_of(kilojoule_per_mole*(nanometer**6)) q_ij = q_ij*elementary_charge**2 if (b_ij._value == 0.0) or (a_ij._value == 0.0): new_epsilon = 0 new_sigma = 1 else: new_epsilon = b_ij**2/(4*a_ij) new_sigma = (a_ij / b_ij)**(1.0/6.0) nb.addException(p0, p1, q_ij, new_sigma, new_epsilon, True) n_total = conn.execute("""SELECT COUNT(*) FROM pair_12_6_es_term""").fetchone() n_in_exclusions = conn.execute("""SELECT COUNT(*) FROM exclusion INNER JOIN pair_12_6_es_term ON ( ( exclusion.p0==pair_12_6_es_term.p0 AND exclusion.p1==pair_12_6_es_term.p1) OR ( exclusion.p0==pair_12_6_es_term.p1 AND exclusion.p1==pair_12_6_es_term.p0) )""").fetchone() if not n_total == n_in_exclusions: raise NotImplementedError('All pair_12_6_es_terms must have a corresponding exclusion') return nb, cnb
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https://github.com/openmm/openmm/blob/cb293447c4fc8b03976dfe11399f107bab70f3d9/wrappers/python/openmm/app/desmonddmsfile.py#L690-L755
catboost/catboost
167f64f237114a4d10b2b4ee42adb4569137debe
catboost/python-package/catboost/eval/evaluation_result.py
python
CaseEvaluationResult.get_best_metric_for_fold
(self, fold)
return self._fold_metric[fold], self._fold_metric_iteration[fold]
:param fold: id of fold to get result :return: best metric value, best metric iteration
[]
def get_best_metric_for_fold(self, fold): """ :param fold: id of fold to get result :return: best metric value, best metric iteration """ return self._fold_metric[fold], self._fold_metric_iteration[fold]
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/catboost/python-package/catboost/eval/evaluation_result.py#L131-L137