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CRYTEK/CRYENGINE | 232227c59a220cbbd311576f0fbeba7bb53b2a8c | Code/Tools/waf-1.7.13/crywaflib/compile_settings_gcc.py | python | load_gcc_common_settings | (conf) | Setup all compiler/linker flags with are shared over all targets using the gcc compiler
!!! But not the actual compiler, since the compiler depends on the target !!! | Setup all compiler/linker flags with are shared over all targets using the gcc compiler
!!! But not the actual compiler, since the compiler depends on the target !!! | [
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"""
Setup all compiler/linker flags with are shared over all targets using the gcc compiler
!!! But not the actual compiler, since the compiler depends on the target !!!
"""
v = conf.env
# Figure out GCC compiler version
try:
conf.get_cc_version( [ v['CC'] ], gcc=True)
except: # Can happen if we don't have an GCC installed (code drop stripped of GCC cross compiler)
conf.env.CC_VERSION = (0,0,0)
# AR Tools
v['ARFLAGS'] = 'rcs'
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v['CC_NAME'] = v['CXX_NAME'] = 'gcc'
v['CC_SRC_F'] = v['CXX_SRC_F'] = []
v['CC_TGT_F'] = v['CXX_TGT_F'] = ['-c', '-o']
v['CPPPATH_SYSTEM_ST'] = '-isystem%s'
v['CPPPATH_ST'] = '-I%s'
v['DEFINES_ST'] = '-D%s'
# Linker
v['CCLNK_SRC_F'] = v['CXXLNK_SRC_F'] = []
v['CCLNK_TGT_F'] = v['CXXLNK_TGT_F'] = '-o'
v['LIB_ST'] = '-l%s'
v['LIBPATH_ST'] = '-L%s'
v['STLIB_ST'] = '-l%s'
v['STLIBPATH_ST'] = '-L%s'
# shared library settings
v['CFLAGS_cshlib'] = v['CFLAGS_cxxshlib'] = ['-fpic']
v['CXXFLAGS_cshlib'] = v['CXXFLAGS_cxxshlib'] = ['-fpic']
v['LINKFLAGS_cshlib'] = ['-shared']
v['LINKFLAGS_cxxshlib'] = ['-shared']
# static library settings
v['CFLAGS_cstlib'] = v['CFLAGS_cxxstlib'] = ['-fpic']
v['CXXFLAGS_cstlib'] = v['CXXFLAGS_cxxstlib'] = ['-fpic']
v['LINKFLAGS_cxxstlib'] = ['-Wl,-Bstatic']
v['LINKFLAGS_cxxshtib'] = ['-Wl,-Bstatic']
# Set common compiler flags
COMMON_COMPILER_FLAGS = [
'-Wall', # Generate more warnings
'-Werror', # Tread Warnings as Errors
'-ffast-math', # Enable fast math
'-flax-vector-conversions', # Enable automatic casting between SIMD vector types
'-fvisibility=hidden',
# Disable some warnings
'-Wno-char-subscripts',
'-Wno-unknown-pragmas',
'-Wno-unused-variable',
'-Wno-unused-value',
'-Wno-parentheses',
'-Wno-switch',
'-Wno-unused-function',
'-Wno-unused-result',
'-Wno-multichar',
'-Wno-format-security',
'-Wno-empty-body',
'-Wno-comment',
'-Wno-char-subscripts',
'-Wno-sign-compare',
'-Wno-narrowing',
'-Wno-write-strings',
'-Wno-format',
'-Wno-strict-aliasing',
'-Wno-unused-but-set-variable',
'-Wno-maybe-uninitialized',
'-Wno-strict-overflow',
'-Wno-uninitialized',
'-Wno-unused-local-typedefs',
'-Wno-deprecated',
]
if conf.env.CC_VERSION[0] >= '4' and conf.env.CC_VERSION[1] >= '8' and conf.env.CC_VERSION[2] >= '0':
COMMON_COMPILER_FLAGS += [
'-Wno-unused-result',
'-Wno-sizeof-pointer-memaccess',
'-Wno-array-bounds',
]
# Copy common flags to prevent modifing references
v['CFLAGS'] += COMMON_COMPILER_FLAGS[:]
v['CXXFLAGS'] += COMMON_COMPILER_FLAGS[:] + [
'-fno-rtti', # Disable RTTI
'-fno-exceptions', # Disable Exceptions
'-fvisibility-inlines-hidden',
'-std=c++14', # Enable c++14 features
# Disable some C++ specific warnings
'-Wno-invalid-offsetof',
'-Wno-reorder',
'-Wno-conversion-null',
'-Wno-overloaded-virtual',
]
# Linker Flags
v['LINKFLAGS'] += ['-Wl,--rpath=$ORIGIN']
v['SHLIB_MARKER'] = '-Wl,-Bdynamic'
v['SONAME_ST'] = '-Wl,-h,%s'
v['STLIB_MARKER'] = '-Wl,-Bstatic'
# Compile options appended if compiler optimization is disabled
v['COMPILER_FLAGS_DisableOptimization'] = [ '-O0', '-fno-inline' ]
# Compile options appended if debug symbols are generated
v['COMPILER_FLAGS_DebugSymbols'] = [ '-g2', '-gdwarf-2' ]
# Linker flags when building with debug symbols
v['LINKFLAGS_DebugSymbols'] = []
# Store settings for show includes option
v['SHOWINCLUDES_cflags'] = ['-H']
v['SHOWINCLUDES_cxxflags'] = ['-H']
# Store settings for preprocess to file option
v['PREPROCESS_cflags'] = ['-E', '-dD']
v['PREPROCESS_cxxflags'] = ['-E', '-dD']
v['PREPROCESS_cc_tgt_f'] = ['-o']
v['PREPROCESS_cxx_tgt_f'] = ['-o']
# Store settings for preprocess to file option
v['DISASSEMBLY_cflags'] = ['-S', '-fverbose-asm']
v['DISASSEMBLY_cxxflags'] = ['-S', '-fverbose-asm']
v['DISASSEMBLY_cc_tgt_f'] = ['-o']
v['DISASSEMBLY_cxx_tgt_f'] = ['-o']
# Store setting for static code analyzer
always_disabled_flags = ['-Wno-unknown-pragmas']
disabled_flags = [
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, '-Wno-unused-value'
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# '-Wno-subscripts',
#, '-Wno-address'
#, '-Wno-self-assign'
#, '-Wno-unneeded-internal-declaration'
, '-Wno-strict-aliasing'
]
force_enable_flags = []
cxx_only_flag = ['-Wno-overloaded-virtual', '-Wno-reorder']
analyzer_flags = ['-Wall'] + always_disabled_flags + disabled_flags + force_enable_flags
v['STATIC_CODE_ANALYZE_cflags'] = list(analyzer_flags)
v['STATIC_CODE_ANALYZE_cxxflags'] = list(analyzer_flags + cxx_only_flag) | [
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google/shaka-packager | e1b0c7c45431327fd3ce193514a5407d07b39b22 | packager/third_party/protobuf/python/google/protobuf/internal/containers.py | python | RepeatedScalarFieldContainer.__getslice__ | (self, start, stop) | return self._values[start:stop] | Retrieves the subset of items from between the specified indices. | Retrieves the subset of items from between the specified indices. | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/imaplib.py | python | ParseFlags | (resp) | return tuple(mo.group('flags').split()) | Convert IMAP4 flags response to python tuple. | Convert IMAP4 flags response to python tuple. | [
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wujixiu/helmet-detection | 8eff5c59ddfba5a29e0b76aeb48babcb49246178 | hardhat-wearing-detection/SSD-RPA/scripts/cpp_lint.py | python | _IsTestFilename | (filename) | Determines if the given filename has a suffix that identifies it as a test.
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pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | caffe2/python/task.py | python | Task.__init__ | (
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num_instances=None):
"""
Instantiate a Task and add it to the current TaskGroup and Node.
Args:
step: If provided, this task will run this ExecutionStep.
outputs: If provided, the task will return the provided outputs
to the client at completion time.
node: If provided, force task execution on the given node.
name: Name of the Task.
num_instances: If provided, this task will be cloned num_instances
times at runtime, and all instances will run
concurrently.
"""
if not name and isinstance(step, core.ExecutionStep):
name = step.Proto().name
if not name:
name = 'task'
# register this node name with active context
self.node = str(Node.current(None if node is None else Node(node)))
self.group = TaskGroup.current(group, required=False)
self.name = Task._get_next_name(self.node, self.group, name)
# may need to be temporarily removed later if Task used as a context
if self.group is not None:
self.group._tasks_to_add.append(self)
self._already_used = False
self._step = None
self._step_with_setup = None
self._outputs = []
if step is not None:
self.set_step(step)
if outputs is not None:
self.add_outputs(outputs)
self._pipeline = None
self._is_pipeline_context = False
self._workspace_type = workspace_type
self._report_net = None
self._num_instances = num_instances | [
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] | https://github.com/pytorch/pytorch/blob/7176c92687d3cc847cc046bf002269c6949a21c2/caffe2/python/task.py#L492-L536 | ||
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/fractions.py | python | Fraction.__trunc__ | (a) | trunc(a) | trunc(a) | [
"trunc",
"(",
"a",
")"
] | def __trunc__(a):
"""trunc(a)"""
if a._numerator < 0:
return -(-a._numerator // a._denominator)
else:
return a._numerator // a._denominator | [
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forkineye/ESPixelStick | 22926f1c0d1131f1369fc7cad405689a095ae3cb | dist/bin/pyserial/serial/urlhandler/protocol_alt.py | python | serial_class_for_url | (url) | return (''.join([parts.netloc, parts.path]), cls) | extract host and port from an URL string | extract host and port from an URL string | [
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"URL",
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] | def serial_class_for_url(url):
"""extract host and port from an URL string"""
parts = urlparse.urlsplit(url)
if parts.scheme != 'alt':
raise serial.SerialException(
'expected a string in the form "alt://port[?option[=value][&option[=value]]]": '
'not starting with alt:// ({!r})'.format(parts.scheme))
class_name = 'Serial'
try:
for option, values in urlparse.parse_qs(parts.query, True).items():
if option == 'class':
class_name = values[0]
else:
raise ValueError('unknown option: {!r}'.format(option))
except ValueError as e:
raise serial.SerialException(
'expected a string in the form '
'"alt://port[?option[=value][&option[=value]]]": {!r}'.format(e))
if not hasattr(serial, class_name):
raise ValueError('unknown class: {!r}'.format(class_name))
cls = getattr(serial, class_name)
if not issubclass(cls, serial.Serial):
raise ValueError('class {!r} is not an instance of Serial'.format(class_name))
return (''.join([parts.netloc, parts.path]), cls) | [
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/control-examples/baxterserialrelay.py | python | mainRosControllerToKlamptRobot | (klampt_robot_model_fn,klampt_serial_port) | Relays ROS Baxter controller messages to and from Klamp't simulated robot | Relays ROS Baxter controller messages to and from Klamp't simulated robot | [
"Relays",
"ROS",
"Baxter",
"controller",
"messages",
"to",
"and",
"from",
"Klamp",
"t",
"simulated",
"robot"
] | def mainRosControllerToKlamptRobot(klampt_robot_model_fn,klampt_serial_port):
"""Relays ROS Baxter controller messages to and from Klamp't simulated robot"""
rospy.init_node('klampt_sim')
#load robot file
world = WorldModel()
world.enableGeometryLoading(False)
res = world.readFile(klampt_robot_model_fn)
if not res:
print 'Error, could not load klampt model from',klampt_robot_model_fn
exit(1)
if world.numRobots()==0:
print 'Error, klampt model',klampt_robot_model_fn,'did not contain a robot'
exit(1)
klampt_robot_model = world.robot(0)
print "Load successful"
#print some info
robotName = klampt_robot_model.getName()
linkNames = [klampt_robot_model.link(i).getName() for i in range(klampt_robot_model.numLinks())]
print "Running controller listening on topic /%s/limb/right/joint_command and"%(robotName,)
print "and /%s/limb/left/joint_command andd publishing on topic"%(robotName,)
print "/%s/joint_states"%(robotName,)
print "Klamp't link names are:",linkNames
#advertise version 1.0.0 of the Baxter software
print "Emulating ROS Baxter API version 1.0.0"
rospy.set_param('/rethink/software_version', '1.0.0')
#create the ROS controller
c = rosbaxtercontroller.make(klampt_robot_model)
#launch the serial client to connect to a given host and relay messages from the socket to/from ROS
host = 'localhost'
port = klampt_serial_port
s = ControllerClient((host,port),c)
print "Running Baxter controller -> Klamp't robot relay..."
try:
asyncore.loop()
except KeyboardInterrupt:
print "Ctrl+C pressed, exiting..." | [
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] | https://github.com/krishauser/Klampt/blob/972cc83ea5befac3f653c1ba20f80155768ad519/Python/control-examples/baxterserialrelay.py#L33-L74 | ||
gnuradio/gnuradio | 09c3c4fa4bfb1a02caac74cb5334dfe065391e3b | gr-utils/modtool/tools/grc_yaml_generator.py | python | dict_constructor | (loader, node) | return OrderedDict(loader.construct_pairs(node)) | Construct an OrderedDict for dumping | Construct an OrderedDict for dumping | [
"Construct",
"an",
"OrderedDict",
"for",
"dumping"
] | def dict_constructor(loader, node):
""" Construct an OrderedDict for dumping """
return OrderedDict(loader.construct_pairs(node)) | [
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Ewenwan/MVision | 97b394dfa48cb21c82cd003b1a952745e413a17f | vSLAM/矩阵变换python函数.py | python | superimposition_matrix | (v0, v1, scaling=False, usesvd=True) | return M | Return matrix to transform given vector set into second vector set.
v0 and v1 are shape (3, \*) or (4, \*) arrays of at least 3 vectors.
If usesvd is True, the weighted sum of squared deviations (RMSD) is
minimized according to the algorithm by W. Kabsch [8]. Otherwise the
quaternion based algorithm by B. Horn [9] is used (slower when using
this Python implementation).
The returned matrix performs rotation, translation and uniform scaling
(if specified).
>>> v0 = numpy.random.rand(3, 10)
>>> M = superimposition_matrix(v0, v0)
>>> numpy.allclose(M, numpy.identity(4))
True
>>> R = random_rotation_matrix(numpy.random.random(3))
>>> v0 = ((1,0,0), (0,1,0), (0,0,1), (1,1,1))
>>> v1 = numpy.dot(R, v0)
>>> M = superimposition_matrix(v0, v1)
>>> numpy.allclose(v1, numpy.dot(M, v0))
True
>>> v0 = (numpy.random.rand(4, 100) - 0.5) * 20.0
>>> v0[3] = 1.0
>>> v1 = numpy.dot(R, v0)
>>> M = superimposition_matrix(v0, v1)
>>> numpy.allclose(v1, numpy.dot(M, v0))
True
>>> S = scale_matrix(random.random())
>>> T = translation_matrix(numpy.random.random(3)-0.5)
>>> M = concatenate_matrices(T, R, S)
>>> v1 = numpy.dot(M, v0)
>>> v0[:3] += numpy.random.normal(0.0, 1e-9, 300).reshape(3, -1)
>>> M = superimposition_matrix(v0, v1, scaling=True)
>>> numpy.allclose(v1, numpy.dot(M, v0))
True
>>> M = superimposition_matrix(v0, v1, scaling=True, usesvd=False)
>>> numpy.allclose(v1, numpy.dot(M, v0))
True
>>> v = numpy.empty((4, 100, 3), dtype=numpy.float64)
>>> v[:, :, 0] = v0
>>> M = superimposition_matrix(v0, v1, scaling=True, usesvd=False)
>>> numpy.allclose(v1, numpy.dot(M, v[:, :, 0]))
True | Return matrix to transform given vector set into second vector set.
v0 and v1 are shape (3, \*) or (4, \*) arrays of at least 3 vectors.
If usesvd is True, the weighted sum of squared deviations (RMSD) is
minimized according to the algorithm by W. Kabsch [8]. Otherwise the
quaternion based algorithm by B. Horn [9] is used (slower when using
this Python implementation).
The returned matrix performs rotation, translation and uniform scaling
(if specified).
>>> v0 = numpy.random.rand(3, 10)
>>> M = superimposition_matrix(v0, v0)
>>> numpy.allclose(M, numpy.identity(4))
True
>>> R = random_rotation_matrix(numpy.random.random(3))
>>> v0 = ((1,0,0), (0,1,0), (0,0,1), (1,1,1))
>>> v1 = numpy.dot(R, v0)
>>> M = superimposition_matrix(v0, v1)
>>> numpy.allclose(v1, numpy.dot(M, v0))
True
>>> v0 = (numpy.random.rand(4, 100) - 0.5) * 20.0
>>> v0[3] = 1.0
>>> v1 = numpy.dot(R, v0)
>>> M = superimposition_matrix(v0, v1)
>>> numpy.allclose(v1, numpy.dot(M, v0))
True
>>> S = scale_matrix(random.random())
>>> T = translation_matrix(numpy.random.random(3)-0.5)
>>> M = concatenate_matrices(T, R, S)
>>> v1 = numpy.dot(M, v0)
>>> v0[:3] += numpy.random.normal(0.0, 1e-9, 300).reshape(3, -1)
>>> M = superimposition_matrix(v0, v1, scaling=True)
>>> numpy.allclose(v1, numpy.dot(M, v0))
True
>>> M = superimposition_matrix(v0, v1, scaling=True, usesvd=False)
>>> numpy.allclose(v1, numpy.dot(M, v0))
True
>>> v = numpy.empty((4, 100, 3), dtype=numpy.float64)
>>> v[:, :, 0] = v0
>>> M = superimposition_matrix(v0, v1, scaling=True, usesvd=False)
>>> numpy.allclose(v1, numpy.dot(M, v[:, :, 0]))
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] | def superimposition_matrix(v0, v1, scaling=False, usesvd=True):
"""Return matrix to transform given vector set into second vector set.
v0 and v1 are shape (3, \*) or (4, \*) arrays of at least 3 vectors.
If usesvd is True, the weighted sum of squared deviations (RMSD) is
minimized according to the algorithm by W. Kabsch [8]. Otherwise the
quaternion based algorithm by B. Horn [9] is used (slower when using
this Python implementation).
The returned matrix performs rotation, translation and uniform scaling
(if specified).
>>> v0 = numpy.random.rand(3, 10)
>>> M = superimposition_matrix(v0, v0)
>>> numpy.allclose(M, numpy.identity(4))
True
>>> R = random_rotation_matrix(numpy.random.random(3))
>>> v0 = ((1,0,0), (0,1,0), (0,0,1), (1,1,1))
>>> v1 = numpy.dot(R, v0)
>>> M = superimposition_matrix(v0, v1)
>>> numpy.allclose(v1, numpy.dot(M, v0))
True
>>> v0 = (numpy.random.rand(4, 100) - 0.5) * 20.0
>>> v0[3] = 1.0
>>> v1 = numpy.dot(R, v0)
>>> M = superimposition_matrix(v0, v1)
>>> numpy.allclose(v1, numpy.dot(M, v0))
True
>>> S = scale_matrix(random.random())
>>> T = translation_matrix(numpy.random.random(3)-0.5)
>>> M = concatenate_matrices(T, R, S)
>>> v1 = numpy.dot(M, v0)
>>> v0[:3] += numpy.random.normal(0.0, 1e-9, 300).reshape(3, -1)
>>> M = superimposition_matrix(v0, v1, scaling=True)
>>> numpy.allclose(v1, numpy.dot(M, v0))
True
>>> M = superimposition_matrix(v0, v1, scaling=True, usesvd=False)
>>> numpy.allclose(v1, numpy.dot(M, v0))
True
>>> v = numpy.empty((4, 100, 3), dtype=numpy.float64)
>>> v[:, :, 0] = v0
>>> M = superimposition_matrix(v0, v1, scaling=True, usesvd=False)
>>> numpy.allclose(v1, numpy.dot(M, v[:, :, 0]))
True
"""
v0 = numpy.array(v0, dtype=numpy.float64, copy=False)[:3]
v1 = numpy.array(v1, dtype=numpy.float64, copy=False)[:3]
if v0.shape != v1.shape or v0.shape[1] < 3:
raise ValueError("Vector sets are of wrong shape or type.")
# move centroids to origin
t0 = numpy.mean(v0, axis=1)
t1 = numpy.mean(v1, axis=1)
v0 = v0 - t0.reshape(3, 1)
v1 = v1 - t1.reshape(3, 1)
if usesvd:
# Singular Value Decomposition of covariance matrix
u, s, vh = numpy.linalg.svd(numpy.dot(v1, v0.T))
# rotation matrix from SVD orthonormal bases
R = numpy.dot(u, vh)
if numpy.linalg.det(R) < 0.0:
# R does not constitute right handed system
R -= numpy.outer(u[:, 2], vh[2, :]*2.0)
s[-1] *= -1.0
# homogeneous transformation matrix
M = numpy.identity(4)
M[:3, :3] = R
else:
# compute symmetric matrix N
xx, yy, zz = numpy.sum(v0 * v1, axis=1)
xy, yz, zx = numpy.sum(v0 * numpy.roll(v1, -1, axis=0), axis=1)
xz, yx, zy = numpy.sum(v0 * numpy.roll(v1, -2, axis=0), axis=1)
N = ((xx+yy+zz, yz-zy, zx-xz, xy-yx),
(yz-zy, xx-yy-zz, xy+yx, zx+xz),
(zx-xz, xy+yx, -xx+yy-zz, yz+zy),
(xy-yx, zx+xz, yz+zy, -xx-yy+zz))
# quaternion: eigenvector corresponding to most positive eigenvalue
l, V = numpy.linalg.eig(N)
q = V[:, numpy.argmax(l)]
q /= vector_norm(q) # unit quaternion
q = numpy.roll(q, -1) # move w component to end
# homogeneous transformation matrix
M = quaternion_matrix(q)
# scale: ratio of rms deviations from centroid
if scaling:
v0 *= v0
v1 *= v1
M[:3, :3] *= math.sqrt(numpy.sum(v1) / numpy.sum(v0))
# translation
M[:3, 3] = t1
T = numpy.identity(4)
T[:3, 3] = -t0
M = numpy.dot(M, T)
return M | [
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] | https://github.com/Ewenwan/MVision/blob/97b394dfa48cb21c82cd003b1a952745e413a17f/vSLAM/矩阵变换python函数.py#L794-L888 | |
Kitware/ParaView | f760af9124ff4634b23ebbeab95a4f56e0261955 | ThirdParty/cinema/paraview/tpl/cinema_python/images/layer_rasters.py | python | LayerRasters.addToBaseQuery | (self, query) | add queries that together define the layer | add queries that together define the layer | [
"add",
"queries",
"that",
"together",
"define",
"the",
"layer"
] | def addToBaseQuery(self, query):
""" add queries that together define the layer """
self.dict.update(query) | [
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cluebotng/cluebotng | 2ed38a518c1019f6b7b03e33b487f96f8df617b0 | fabfile.py | python | _update_utils | () | Clone or pull the utils git repo into the apps path | Clone or pull the utils git repo into the apps path | [
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"pull",
"the",
"utils",
"git",
"repo",
"into",
"the",
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] | def _update_utils():
'''
Clone or pull the utils git repo into the apps path
'''
print('Resetting local changes')
sudo('cd "%(dir)s" && git reset --hard && git clean -fd' %
{'dir': os.path.join(TOOL_DIR, 'apps', 'utils')})
print('Updating code')
sudo('cd "%(dir)s" && git pull origin master' % {'dir': os.path.join(TOOL_DIR, 'apps', 'utils')}) | [
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microsoft/checkedc-clang | a173fefde5d7877b7750e7ce96dd08cf18baebf2 | compiler-rt/lib/sanitizer_common/scripts/cpplint.py | python | CheckForNonConstReference | (filename, clean_lines, linenum,
nesting_state, error) | Check for non-const references.
Separate from CheckLanguage since it scans backwards from current
line, instead of scanning forward.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
nesting_state: A NestingState instance which maintains information about
the current stack of nested blocks being parsed.
error: The function to call with any errors found. | Check for non-const references. | [
"Check",
"for",
"non",
"-",
"const",
"references",
"."
] | def CheckForNonConstReference(filename, clean_lines, linenum,
nesting_state, error):
"""Check for non-const references.
Separate from CheckLanguage since it scans backwards from current
line, instead of scanning forward.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
nesting_state: A NestingState instance which maintains information about
the current stack of nested blocks being parsed.
error: The function to call with any errors found.
"""
# Do nothing if there is no '&' on current line.
line = clean_lines.elided[linenum]
if '&' not in line:
return
# If a function is inherited, current function doesn't have much of
# a choice, so any non-const references should not be blamed on
# derived function.
if IsDerivedFunction(clean_lines, linenum):
return
# Don't warn on out-of-line method definitions, as we would warn on the
# in-line declaration, if it isn't marked with 'override'.
if IsOutOfLineMethodDefinition(clean_lines, linenum):
return
# Long type names may be broken across multiple lines, usually in one
# of these forms:
# LongType
# ::LongTypeContinued &identifier
# LongType::
# LongTypeContinued &identifier
# LongType<
# ...>::LongTypeContinued &identifier
#
# If we detected a type split across two lines, join the previous
# line to current line so that we can match const references
# accordingly.
#
# Note that this only scans back one line, since scanning back
# arbitrary number of lines would be expensive. If you have a type
# that spans more than 2 lines, please use a typedef.
if linenum > 1:
previous = None
if Match(r'\s*::(?:[\w<>]|::)+\s*&\s*\S', line):
# previous_line\n + ::current_line
previous = Search(r'\b((?:const\s*)?(?:[\w<>]|::)+[\w<>])\s*$',
clean_lines.elided[linenum - 1])
elif Match(r'\s*[a-zA-Z_]([\w<>]|::)+\s*&\s*\S', line):
# previous_line::\n + current_line
previous = Search(r'\b((?:const\s*)?(?:[\w<>]|::)+::)\s*$',
clean_lines.elided[linenum - 1])
if previous:
line = previous.group(1) + line.lstrip()
else:
# Check for templated parameter that is split across multiple lines
endpos = line.rfind('>')
if endpos > -1:
(_, startline, startpos) = ReverseCloseExpression(
clean_lines, linenum, endpos)
if startpos > -1 and startline < linenum:
# Found the matching < on an earlier line, collect all
# pieces up to current line.
line = ''
for i in xrange(startline, linenum + 1):
line += clean_lines.elided[i].strip()
# Check for non-const references in function parameters. A single '&' may
# found in the following places:
# inside expression: binary & for bitwise AND
# inside expression: unary & for taking the address of something
# inside declarators: reference parameter
# We will exclude the first two cases by checking that we are not inside a
# function body, including one that was just introduced by a trailing '{'.
# TODO(unknown): Doesn't account for 'catch(Exception& e)' [rare].
if (nesting_state.previous_stack_top and
not (isinstance(nesting_state.previous_stack_top, _ClassInfo) or
isinstance(nesting_state.previous_stack_top, _NamespaceInfo))):
# Not at toplevel, not within a class, and not within a namespace
return
# Avoid initializer lists. We only need to scan back from the
# current line for something that starts with ':'.
#
# We don't need to check the current line, since the '&' would
# appear inside the second set of parentheses on the current line as
# opposed to the first set.
if linenum > 0:
for i in xrange(linenum - 1, max(0, linenum - 10), -1):
previous_line = clean_lines.elided[i]
if not Search(r'[),]\s*$', previous_line):
break
if Match(r'^\s*:\s+\S', previous_line):
return
# Avoid preprocessors
if Search(r'\\\s*$', line):
return
# Avoid constructor initializer lists
if IsInitializerList(clean_lines, linenum):
return
# We allow non-const references in a few standard places, like functions
# called "swap()" or iostream operators like "<<" or ">>". Do not check
# those function parameters.
#
# We also accept & in static_assert, which looks like a function but
# it's actually a declaration expression.
allowed_functions = (r'(?:[sS]wap(?:<\w:+>)?|'
r'operator\s*[<>][<>]|'
r'static_assert|COMPILE_ASSERT'
r')\s*\(')
if Search(allowed_functions, line):
return
elif not Search(r'\S+\([^)]*$', line):
# Don't see an allowed function entry on this line. Actually we
# didn't see any function name on this line, so this is likely a
# multi-line parameter list. Try a bit harder to catch this case.
for i in xrange(2):
if (linenum > i and
Search(allowed_functions, clean_lines.elided[linenum - i - 1])):
return
decls = ReplaceAll(r'{[^}]*}', ' ', line) # exclude function body
for parameter in re.findall(_RE_PATTERN_REF_PARAM, decls):
if (not Match(_RE_PATTERN_CONST_REF_PARAM, parameter) and
not Match(_RE_PATTERN_REF_STREAM_PARAM, parameter)):
error(filename, linenum, 'runtime/references', 2,
'Is this a non-const reference? '
'If so, make const or use a pointer: ' +
ReplaceAll(' *<', '<', parameter)) | [
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LiquidPlayer/LiquidCore | 9405979363f2353ac9a71ad8ab59685dd7f919c9 | deps/node-10.15.3/tools/gyp/pylib/gyp/ordered_dict.py | python | OrderedDict.__reversed__ | (self) | od.__reversed__() <==> reversed(od) | od.__reversed__() <==> reversed(od) | [
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gimli-org/gimli | 17aa2160de9b15ababd9ef99e89b1bc3277bbb23 | pygimli/physics/ert/ves.py | python | VESModelling.drawData | (self, ax, data, error=None, label=None, **kwargs) | r"""Draw modeled apparent resistivity data.
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Matplotlib axes object to draw into.
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Apparent resistivity values to draw.
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Adds an error bar if you have error values.
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Set legend label for the amplitude.
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Override ab2 that fits data size.
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PX4/PX4-Autopilot | 0b9f60a0370be53d683352c63fd92db3d6586e18 | Tools/mavlink_px4.py | python | MAVLink.param_value_encode | (self, param_id, param_value, param_type, param_count, param_index) | return msg | Emit the value of a onboard parameter. The inclusion of param_count
and param_index in the message allows the recipient to
keep track of received parameters and allows him to
re-request missing parameters after a loss or timeout.
param_id : Onboard parameter id, terminated by NULL if the length is less than 16 human-readable chars and WITHOUT null termination (NULL) byte if the length is exactly 16 chars - applications have to provide 16+1 bytes storage if the ID is stored as string (char)
param_value : Onboard parameter value (float)
param_type : Onboard parameter type: see the MAV_PARAM_TYPE enum for supported data types. (uint8_t)
param_count : Total number of onboard parameters (uint16_t)
param_index : Index of this onboard parameter (uint16_t) | Emit the value of a onboard parameter. The inclusion of param_count
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keep track of received parameters and allows him to
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'''
Emit the value of a onboard parameter. The inclusion of param_count
and param_index in the message allows the recipient to
keep track of received parameters and allows him to
re-request missing parameters after a loss or timeout.
param_id : Onboard parameter id, terminated by NULL if the length is less than 16 human-readable chars and WITHOUT null termination (NULL) byte if the length is exactly 16 chars - applications have to provide 16+1 bytes storage if the ID is stored as string (char)
param_value : Onboard parameter value (float)
param_type : Onboard parameter type: see the MAV_PARAM_TYPE enum for supported data types. (uint8_t)
param_count : Total number of onboard parameters (uint16_t)
param_index : Index of this onboard parameter (uint16_t)
'''
msg = MAVLink_param_value_message(param_id, param_value, param_type, param_count, param_index)
msg.pack(self)
return msg | [
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KratosMultiphysics/Kratos | 0000833054ed0503424eb28205d6508d9ca6cbbc | applications/MultilevelMonteCarloApplication/external_libraries/XMC/xmc/methodDefs_xmcAlgorithm/checkInitialisation.py | python | checkInitialisationMLMC | (XMCAlgorithm) | Method checking all attributes of different classes are correctly set to run Multilevel Monte Carlo algorithm (both standard and asynchronous). | Method checking all attributes of different classes are correctly set to run Multilevel Monte Carlo algorithm (both standard and asynchronous). | [
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"""
Method checking all attributes of different classes are correctly set to run Multilevel Monte Carlo algorithm (both standard and asynchronous).
"""
solverWrapperDictionary = XMCAlgorithm.monteCarloSampler.indexConstructorDictionary[
"samplerInputDictionary"]["solverWrapperInputDictionary"]
positionMaxNumberIterationsCriterion=XMCAlgorithm.positionMaxNumberIterationsCriterion
tolerances=XMCAlgorithm.stoppingCriterion.tolerances()
# perform checks
checkInitialisationSolverWrapper(solverWrapperDictionary)
if ("asynchronous" in solverWrapperDictionary):
if (solverWrapperDictionary["asynchronous"] is True):
checkMaxNumberIterationsCriterion(positionMaxNumberIterationsCriterion,tolerances) | [
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lukasmonk/lucaschess | 13e2e5cb13b38a720ccf897af649054a64bcb914 | Code/QT/Grid.py | python | Grid.__init__ | (self, wParent, oColumnas, dicVideo=None, altoFila=20, siSelecFilas=False, siSeleccionMultiple=False,
siLineas=True, siEditable=False, siCabeceraMovible=True, xid=None, background="",
siCabeceraVisible=True, altoCabecera=None) | @param wParent: ventana propietaria
@param oColumnas: configuracion de las columnas.
@param altoFila: altura de todas las filas. | [] | def __init__(self, wParent, oColumnas, dicVideo=None, altoFila=20, siSelecFilas=False, siSeleccionMultiple=False,
siLineas=True, siEditable=False, siCabeceraMovible=True, xid=None, background="",
siCabeceraVisible=True, altoCabecera=None):
"""
@param wParent: ventana propietaria
@param oColumnas: configuracion de las columnas.
@param altoFila: altura de todas las filas.
"""
assert wParent is not None
QtGui.QTableView.__init__(self)
if VarGen.configuracion.tablaSelBackground:
p = self.palette()
p.setBrush(QtGui.QPalette.Inactive, QtGui.QPalette.Highlight, QtGui.QBrush(QtGui.QColor(VarGen.configuracion.tablaSelBackground)))
p.setBrush(QtGui.QPalette.Active, QtGui.QPalette.Highlight, QtGui.QBrush(QtGui.QColor(VarGen.configuracion.tablaSelBackground)))
self.setPalette(p)
self.wParent = wParent
self.id = xid
self.oColumnas = oColumnas
if dicVideo:
self.recuperarVideo(dicVideo)
self.oColumnasR = self.oColumnas.columnasMostrables() # Necesario tras recuperar video
self.cg = ControlGrid(self, wParent, self.oColumnasR)
self.setModel(self.cg)
self.setShowGrid(siLineas)
if background == "":
self.setStyleSheet("QTableView {background: %s;}" % QTUtil.backgroundGUI())
elif background is not None:
self.setStyleSheet("QTableView {background: %s;}" % background)
self.coloresAlternados()
if altoCabecera:
hh = CabeceraHeight(self, siCabeceraMovible, altoCabecera)
else:
hh = Cabecera(self, siCabeceraMovible)
self.setHorizontalHeader(hh)
if not siCabeceraVisible:
hh.setVisible(False)
vh = self.verticalHeader()
vh.setResizeMode(QtGui.QHeaderView.Fixed)
vh.setDefaultSectionSize(altoFila)
vh.setVisible(False)
self.seleccionaFilas(siSelecFilas, siSeleccionMultiple)
self.ponAnchosColumnas() # es necesario llamarlo desde aqui
self.siEditable = siEditable | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_windows.py | python | StatusBar.GetFields | (self) | return [self.GetStatusText(i) for i in range(self.GetFieldsCount())] | Return a list of field values in the status bar. | Return a list of field values in the status bar. | [
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] | def GetFields(self):
"""Return a list of field values in the status bar. """
return [self.GetStatusText(i) for i in range(self.GetFieldsCount())] | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/ultimatelistctrl.py | python | UltimateListItemData.GetY | (self) | return self._rect.y | Returns the item `y` position. | Returns the item `y` position. | [
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rampageX/firmware-mod-kit | c94cd6aeee50d92ec5280a6dba6d74828fd3606b | src/binwalk-2.1.1/src/binwalk/modules/hashmatch.py | python | HashMatch.run | (self) | return True | Main module method. | Main module method. | [
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"method",
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] | def run(self):
'''
Main module method.
'''
# Access the raw self.config.files list directly here, since we accept both
# files and directories and self.next_file only works for files.
needle = self.config.files[0]
haystack = self.config.files[1:]
self.header()
if os.path.isfile(needle):
if os.path.isfile(haystack[0]):
self.hash_files(needle, haystack)
else:
self.hash_file(needle, haystack)
else:
self.hash_directories(needle, haystack)
self.footer()
return True | [
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bigartm/bigartm | 47e37f982de87aa67bfd475ff1f39da696b181b3 | 3rdparty/protobuf-3.0.0/python/google/protobuf/descriptor.py | python | ServiceDescriptor.CopyToProto | (self, proto) | Copies this to a descriptor_pb2.ServiceDescriptorProto.
Args:
proto: An empty descriptor_pb2.ServiceDescriptorProto. | Copies this to a descriptor_pb2.ServiceDescriptorProto. | [
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] | def CopyToProto(self, proto):
"""Copies this to a descriptor_pb2.ServiceDescriptorProto.
Args:
proto: An empty descriptor_pb2.ServiceDescriptorProto.
"""
# This function is overridden to give a better doc comment.
super(ServiceDescriptor, self).CopyToProto(proto) | [
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facebookarchive/LogDevice | ce7726050edc49a1e15d9160e81c890736b779e2 | logdevice/ops/ldshell/logdevice_context.py | python | LDShellContext.on_connected | (self, *args, **kwargs) | Gets called after a connect() command is executed | Gets called after a connect() command is executed | [
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"""
Gets called after a connect() command is executed
"""
with self._lock:
self._reset_cache()
if not self._should_we_be_connected():
cprint(self._get_disconnected_warning(), "yellow", file=sys.stderr)
self._is_connected = False
else:
self._is_connected = True
# Fetch config, and cluster name.
try:
self._initialize_after_connected()
except Exception as e:
cprint("{}".format(e), "red", file=sys.stderr)
self._reset()
self._is_connected = False | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/site-packages/pip/_vendor/distlib/locators.py | python | Locator.get_errors | (self) | return result | Return any errors which have occurred. | Return any errors which have occurred. | [
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] | def get_errors(self):
"""
Return any errors which have occurred.
"""
result = []
while not self.errors.empty(): # pragma: no cover
try:
e = self.errors.get(False)
result.append(e)
except self.errors.Empty:
continue
self.errors.task_done()
return result | [
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pyne/pyne | 0c2714d7c0d1b5e20be6ae6527da2c660dd6b1b3 | pyne/mesh.py | python | Mesh.structured_iterate_hex_volumes | (self, order="zyx", **kw) | Get an iterator over the volumes of the mesh hexahedra
See structured_iterate_hex() for an explanation of the order argument
and the available keyword arguments. | Get an iterator over the volumes of the mesh hexahedra | [
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"""Get an iterator over the volumes of the mesh hexahedra
See structured_iterate_hex() for an explanation of the order argument
and the available keyword arguments.
"""
self._structured_check()
indices, _ = _structured_iter_setup(self.dims, order, **kw)
# Use an inefficient but simple approach: call structured_hex_volume()
# on each required i,j,k pair.
# A better implementation would only make one call to getVtxCoords.
for A in itertools.product(*indices):
# the ordmap returned from _structured_iter_setup maps to kji/zyx
# ordering, but we want ijk/xyz ordering, so create the ordmap
# differently.
ordmap = [order.find(L) for L in "xyz"]
ijk = [A[ordmap[x]] for x in range(3)]
yield self.structured_hex_volume(*ijk) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numpy/ma/core.py | python | concatenate | (arrays, axis=0) | return data | Concatenate a sequence of arrays along the given axis.
Parameters
----------
arrays : sequence of array_like
The arrays must have the same shape, except in the dimension
corresponding to `axis` (the first, by default).
axis : int, optional
The axis along which the arrays will be joined. Default is 0.
Returns
-------
result : MaskedArray
The concatenated array with any masked entries preserved.
See Also
--------
numpy.concatenate : Equivalent function in the top-level NumPy module.
Examples
--------
>>> import numpy.ma as ma
>>> a = ma.arange(3)
>>> a[1] = ma.masked
>>> b = ma.arange(2, 5)
>>> a
masked_array(data=[0, --, 2],
mask=[False, True, False],
fill_value=999999)
>>> b
masked_array(data=[2, 3, 4],
mask=False,
fill_value=999999)
>>> ma.concatenate([a, b])
masked_array(data=[0, --, 2, 2, 3, 4],
mask=[False, True, False, False, False, False],
fill_value=999999) | Concatenate a sequence of arrays along the given axis. | [
"Concatenate",
"a",
"sequence",
"of",
"arrays",
"along",
"the",
"given",
"axis",
"."
] | def concatenate(arrays, axis=0):
"""
Concatenate a sequence of arrays along the given axis.
Parameters
----------
arrays : sequence of array_like
The arrays must have the same shape, except in the dimension
corresponding to `axis` (the first, by default).
axis : int, optional
The axis along which the arrays will be joined. Default is 0.
Returns
-------
result : MaskedArray
The concatenated array with any masked entries preserved.
See Also
--------
numpy.concatenate : Equivalent function in the top-level NumPy module.
Examples
--------
>>> import numpy.ma as ma
>>> a = ma.arange(3)
>>> a[1] = ma.masked
>>> b = ma.arange(2, 5)
>>> a
masked_array(data=[0, --, 2],
mask=[False, True, False],
fill_value=999999)
>>> b
masked_array(data=[2, 3, 4],
mask=False,
fill_value=999999)
>>> ma.concatenate([a, b])
masked_array(data=[0, --, 2, 2, 3, 4],
mask=[False, True, False, False, False, False],
fill_value=999999)
"""
d = np.concatenate([getdata(a) for a in arrays], axis)
rcls = get_masked_subclass(*arrays)
data = d.view(rcls)
# Check whether one of the arrays has a non-empty mask.
for x in arrays:
if getmask(x) is not nomask:
break
else:
return data
# OK, so we have to concatenate the masks
dm = np.concatenate([getmaskarray(a) for a in arrays], axis)
dm = dm.reshape(d.shape)
# If we decide to keep a '_shrinkmask' option, we want to check that
# all of them are True, and then check for dm.any()
data._mask = _shrink_mask(dm)
return data | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/site-packages/pip/vendor/distlib/version.py | python | suggest_normalized_version | (s) | return rs | Suggest a normalized version close to the given version string.
If you have a version string that isn't rational (i.e. NormalizedVersion
doesn't like it) then you might be able to get an equivalent (or close)
rational version from this function.
This does a number of simple normalizations to the given string, based
on observation of versions currently in use on PyPI. Given a dump of
those version during PyCon 2009, 4287 of them:
- 2312 (53.93%) match NormalizedVersion without change
with the automatic suggestion
- 3474 (81.04%) match when using this suggestion method
@param s {str} An irrational version string.
@returns A rational version string, or None, if couldn't determine one. | Suggest a normalized version close to the given version string. | [
"Suggest",
"a",
"normalized",
"version",
"close",
"to",
"the",
"given",
"version",
"string",
"."
] | def suggest_normalized_version(s):
"""Suggest a normalized version close to the given version string.
If you have a version string that isn't rational (i.e. NormalizedVersion
doesn't like it) then you might be able to get an equivalent (or close)
rational version from this function.
This does a number of simple normalizations to the given string, based
on observation of versions currently in use on PyPI. Given a dump of
those version during PyCon 2009, 4287 of them:
- 2312 (53.93%) match NormalizedVersion without change
with the automatic suggestion
- 3474 (81.04%) match when using this suggestion method
@param s {str} An irrational version string.
@returns A rational version string, or None, if couldn't determine one.
"""
try:
normalized_key(s)
return s # already rational
except UnsupportedVersionError:
pass
rs = s.lower()
# part of this could use maketrans
for orig, repl in (('-alpha', 'a'), ('-beta', 'b'), ('alpha', 'a'),
('beta', 'b'), ('rc', 'c'), ('-final', ''),
('-pre', 'c'),
('-release', ''), ('.release', ''), ('-stable', ''),
('+', '.'), ('_', '.'), (' ', ''), ('.final', ''),
('final', '')):
rs = rs.replace(orig, repl)
# if something ends with dev or pre, we add a 0
rs = re.sub(r"pre$", r"pre0", rs)
rs = re.sub(r"dev$", r"dev0", rs)
# if we have something like "b-2" or "a.2" at the end of the
# version, that is pobably beta, alpha, etc
# let's remove the dash or dot
rs = re.sub(r"([abc]|rc)[\-\.](\d+)$", r"\1\2", rs)
# 1.0-dev-r371 -> 1.0.dev371
# 0.1-dev-r79 -> 0.1.dev79
rs = re.sub(r"[\-\.](dev)[\-\.]?r?(\d+)$", r".\1\2", rs)
# Clean: 2.0.a.3, 2.0.b1, 0.9.0~c1
rs = re.sub(r"[.~]?([abc])\.?", r"\1", rs)
# Clean: v0.3, v1.0
if rs.startswith('v'):
rs = rs[1:]
# Clean leading '0's on numbers.
#TODO: unintended side-effect on, e.g., "2003.05.09"
# PyPI stats: 77 (~2%) better
rs = re.sub(r"\b0+(\d+)(?!\d)", r"\1", rs)
# Clean a/b/c with no version. E.g. "1.0a" -> "1.0a0". Setuptools infers
# zero.
# PyPI stats: 245 (7.56%) better
rs = re.sub(r"(\d+[abc])$", r"\g<1>0", rs)
# the 'dev-rNNN' tag is a dev tag
rs = re.sub(r"\.?(dev-r|dev\.r)\.?(\d+)$", r".dev\2", rs)
# clean the - when used as a pre delimiter
rs = re.sub(r"-(a|b|c)(\d+)$", r"\1\2", rs)
# a terminal "dev" or "devel" can be changed into ".dev0"
rs = re.sub(r"[\.\-](dev|devel)$", r".dev0", rs)
# a terminal "dev" can be changed into ".dev0"
rs = re.sub(r"(?![\.\-])dev$", r".dev0", rs)
# a terminal "final" or "stable" can be removed
rs = re.sub(r"(final|stable)$", "", rs)
# The 'r' and the '-' tags are post release tags
# 0.4a1.r10 -> 0.4a1.post10
# 0.9.33-17222 -> 0.9.33.post17222
# 0.9.33-r17222 -> 0.9.33.post17222
rs = re.sub(r"\.?(r|-|-r)\.?(\d+)$", r".post\2", rs)
# Clean 'r' instead of 'dev' usage:
# 0.9.33+r17222 -> 0.9.33.dev17222
# 1.0dev123 -> 1.0.dev123
# 1.0.git123 -> 1.0.dev123
# 1.0.bzr123 -> 1.0.dev123
# 0.1a0dev.123 -> 0.1a0.dev123
# PyPI stats: ~150 (~4%) better
rs = re.sub(r"\.?(dev|git|bzr)\.?(\d+)$", r".dev\2", rs)
# Clean '.pre' (normalized from '-pre' above) instead of 'c' usage:
# 0.2.pre1 -> 0.2c1
# 0.2-c1 -> 0.2c1
# 1.0preview123 -> 1.0c123
# PyPI stats: ~21 (0.62%) better
rs = re.sub(r"\.?(pre|preview|-c)(\d+)$", r"c\g<2>", rs)
# Tcl/Tk uses "px" for their post release markers
rs = re.sub(r"p(\d+)$", r".post\1", rs)
try:
normalized_key(rs)
except UnsupportedVersionError:
rs = None
return rs | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/grid.py | python | Grid.GetSelectionBackground | (*args, **kwargs) | return _grid.Grid_GetSelectionBackground(*args, **kwargs) | GetSelectionBackground(self) -> Colour | GetSelectionBackground(self) -> Colour | [
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"""GetSelectionBackground(self) -> Colour"""
return _grid.Grid_GetSelectionBackground(*args, **kwargs) | [
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pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | torch/nn/utils/prune.py | python | global_unstructured | (parameters, pruning_method, importance_scores=None, **kwargs) | r"""
Globally prunes tensors corresponding to all parameters in ``parameters``
by applying the specified ``pruning_method``.
Modifies modules in place by:
1) adding a named buffer called ``name+'_mask'`` corresponding to the
binary mask applied to the parameter ``name`` by the pruning method.
2) replacing the parameter ``name`` by its pruned version, while the
original (unpruned) parameter is stored in a new parameter named
``name+'_orig'``.
Args:
parameters (Iterable of (module, name) tuples): parameters of
the model to prune in a global fashion, i.e. by aggregating all
weights prior to deciding which ones to prune. module must be of
type :class:`nn.Module`, and name must be a string.
pruning_method (function): a valid pruning function from this module,
or a custom one implemented by the user that satisfies the
implementation guidelines and has ``PRUNING_TYPE='unstructured'``.
importance_scores (dict): a dictionary mapping (module, name) tuples to
the corresponding parameter's importance scores tensor. The tensor
should be the same shape as the parameter, and is used for computing
mask for pruning.
If unspecified or None, the parameter will be used in place of its
importance scores.
kwargs: other keyword arguments such as:
amount (int or float): quantity of parameters to prune across the
specified parameters.
If ``float``, should be between 0.0 and 1.0 and represent the
fraction of parameters to prune. If ``int``, it represents the
absolute number of parameters to prune.
Raises:
TypeError: if ``PRUNING_TYPE != 'unstructured'``
Note:
Since global structured pruning doesn't make much sense unless the
norm is normalized by the size of the parameter, we now limit the
scope of global pruning to unstructured methods.
Examples:
>>> net = nn.Sequential(OrderedDict([
('first', nn.Linear(10, 4)),
('second', nn.Linear(4, 1)),
]))
>>> parameters_to_prune = (
(net.first, 'weight'),
(net.second, 'weight'),
)
>>> prune.global_unstructured(
parameters_to_prune,
pruning_method=prune.L1Unstructured,
amount=10,
)
>>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0))
tensor(10, dtype=torch.uint8) | r"""
Globally prunes tensors corresponding to all parameters in ``parameters``
by applying the specified ``pruning_method``.
Modifies modules in place by: | [
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r"""
Globally prunes tensors corresponding to all parameters in ``parameters``
by applying the specified ``pruning_method``.
Modifies modules in place by:
1) adding a named buffer called ``name+'_mask'`` corresponding to the
binary mask applied to the parameter ``name`` by the pruning method.
2) replacing the parameter ``name`` by its pruned version, while the
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parameters (Iterable of (module, name) tuples): parameters of
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pruning_method (function): a valid pruning function from this module,
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implementation guidelines and has ``PRUNING_TYPE='unstructured'``.
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should be the same shape as the parameter, and is used for computing
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If unspecified or None, the parameter will be used in place of its
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kwargs: other keyword arguments such as:
amount (int or float): quantity of parameters to prune across the
specified parameters.
If ``float``, should be between 0.0 and 1.0 and represent the
fraction of parameters to prune. If ``int``, it represents the
absolute number of parameters to prune.
Raises:
TypeError: if ``PRUNING_TYPE != 'unstructured'``
Note:
Since global structured pruning doesn't make much sense unless the
norm is normalized by the size of the parameter, we now limit the
scope of global pruning to unstructured methods.
Examples:
>>> net = nn.Sequential(OrderedDict([
('first', nn.Linear(10, 4)),
('second', nn.Linear(4, 1)),
]))
>>> parameters_to_prune = (
(net.first, 'weight'),
(net.second, 'weight'),
)
>>> prune.global_unstructured(
parameters_to_prune,
pruning_method=prune.L1Unstructured,
amount=10,
)
>>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0))
tensor(10, dtype=torch.uint8)
"""
# ensure parameters is a list or generator of tuples
if not isinstance(parameters, Iterable):
raise TypeError("global_unstructured(): parameters is not an Iterable")
importance_scores = importance_scores if importance_scores is not None else {}
if not isinstance(importance_scores, dict):
raise TypeError("global_unstructured(): importance_scores must be of type dict")
# flatten importance scores to consider them all at once in global pruning
relevant_importance_scores = torch.nn.utils.parameters_to_vector(
[
importance_scores.get((module, name), getattr(module, name))
for (module, name) in parameters
]
)
# similarly, flatten the masks (if they exist), or use a flattened vector
# of 1s of the same dimensions as t
default_mask = torch.nn.utils.parameters_to_vector(
[
getattr(module, name + "_mask", torch.ones_like(getattr(module, name)))
for (module, name) in parameters
]
)
# use the canonical pruning methods to compute the new mask, even if the
# parameter is now a flattened out version of `parameters`
container = PruningContainer()
container._tensor_name = "temp" # to make it match that of `method`
method = pruning_method(**kwargs)
method._tensor_name = "temp" # to make it match that of `container`
if method.PRUNING_TYPE != "unstructured":
raise TypeError(
'Only "unstructured" PRUNING_TYPE supported for '
"the `pruning_method`. Found method {} of type {}".format(
pruning_method, method.PRUNING_TYPE
)
)
container.add_pruning_method(method)
# use the `compute_mask` method from `PruningContainer` to combine the
# mask computed by the new method with the pre-existing mask
final_mask = container.compute_mask(relevant_importance_scores, default_mask)
# Pointer for slicing the mask to match the shape of each parameter
pointer = 0
for module, name in parameters:
param = getattr(module, name)
# The length of the parameter
num_param = param.numel()
# Slice the mask, reshape it
param_mask = final_mask[pointer : pointer + num_param].view_as(param)
# Assign the correct pre-computed mask to each parameter and add it
# to the forward_pre_hooks like any other pruning method
custom_from_mask(module, name, mask=param_mask)
# Increment the pointer to continue slicing the final_mask
pointer += num_param | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/tornado/tornado-6/tornado/iostream.py | python | BaseIOStream.read_until_close | (self) | return future | Asynchronously reads all data from the socket until it is closed.
This will buffer all available data until ``max_buffer_size``
is reached. If flow control or cancellation are desired, use a
loop with `read_bytes(partial=True) <.read_bytes>` instead.
.. versionchanged:: 4.0
The callback argument is now optional and a `.Future` will
be returned if it is omitted.
.. versionchanged:: 6.0
The ``callback`` and ``streaming_callback`` arguments have
been removed. Use the returned `.Future` (and `read_bytes`
with ``partial=True`` for ``streaming_callback``) instead. | Asynchronously reads all data from the socket until it is closed. | [
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] | def read_until_close(self) -> Awaitable[bytes]:
"""Asynchronously reads all data from the socket until it is closed.
This will buffer all available data until ``max_buffer_size``
is reached. If flow control or cancellation are desired, use a
loop with `read_bytes(partial=True) <.read_bytes>` instead.
.. versionchanged:: 4.0
The callback argument is now optional and a `.Future` will
be returned if it is omitted.
.. versionchanged:: 6.0
The ``callback`` and ``streaming_callback`` arguments have
been removed. Use the returned `.Future` (and `read_bytes`
with ``partial=True`` for ``streaming_callback``) instead.
"""
future = self._start_read()
if self.closed():
self._finish_read(self._read_buffer_size, False)
return future
self._read_until_close = True
try:
self._try_inline_read()
except:
future.add_done_callback(lambda f: f.exception())
raise
return future | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/telemetry/third_party/web-page-replay/net_configs.py | python | GetNetConfig | (key) | return _NET_CONFIGS[key] | Returns the NetConfig object corresponding to the given |key|. | Returns the NetConfig object corresponding to the given |key|. | [
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"""Returns the NetConfig object corresponding to the given |key|."""
if key not in _NET_CONFIGS:
raise KeyError('No net config with key: %s' % key)
return _NET_CONFIGS[key] | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/prompt-toolkit/py2/prompt_toolkit/output.py | python | Output.encoding | (self) | Return the encoding for this output, e.g. 'utf-8'.
(This is used mainly to know which characters are supported by the
output the data, so that the UI can provide alternatives, when
required.) | Return the encoding for this output, e.g. 'utf-8'.
(This is used mainly to know which characters are supported by the
output the data, so that the UI can provide alternatives, when
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"""
Return the encoding for this output, e.g. 'utf-8'.
(This is used mainly to know which characters are supported by the
output the data, so that the UI can provide alternatives, when
required.)
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microsoft/ivy | 9f3c7ecc0b2383129fdd0953e10890d98d09a82d | ivy/ivy_concept_space.py | python | p_terms_terms_term | (p) | terms : terms COMMA term | terms : terms COMMA term | [
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] | def p_terms_terms_term(p):
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/email/_parseaddr.py | python | AddrlistClass.getaddrlist | (self) | return result | Parse all addresses.
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"""Parse all addresses.
Returns a list containing all of the addresses.
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result = []
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/dataset/vision/py_transforms_util.py | python | horizontal_flip | (img) | return img.transpose(Image.FLIP_LEFT_RIGHT) | Flip the input image horizontally.
Args:
img (PIL image): Image to be flipped horizontally.
Returns:
img (PIL image), Horizontally flipped image. | Flip the input image horizontally. | [
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"""
Flip the input image horizontally.
Args:
img (PIL image): Image to be flipped horizontally.
Returns:
img (PIL image), Horizontally flipped image.
"""
if not is_pil(img):
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hfinkel/llvm-project-cxxjit | 91084ef018240bbb8e24235ff5cd8c355a9c1a1e | lldb/third_party/Python/module/pexpect-2.4/examples/rippy.py | python | extract_audio | (
video_source_filename,
audio_id=128,
verbose_flag=0,
dry_run_flag=0) | This extracts the given audio_id track as raw uncompressed PCM from the given source video.
Note that mplayer always saves this to audiodump.wav.
At this time there is no way to set the output audio name. | This extracts the given audio_id track as raw uncompressed PCM from the given source video.
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audio_id=128,
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"""This extracts the given audio_id track as raw uncompressed PCM from the given source video.
Note that mplayer always saves this to audiodump.wav.
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#cmd = "mplayer %(video_source_filename)s -vc null -vo null -aid %(audio_id)s -ao pcm:fast -noframedrop" % locals()
cmd = "mplayer -quiet '%(video_source_filename)s' -vc dummy -vo null -aid %(audio_id)s -ao pcm:fast -noframedrop" % locals()
if verbose_flag:
print cmd
if not dry_run_flag:
run(cmd)
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemDefectReporter/v1/AWS/common-code/Lib/PIL/PSDraw.py | python | PSDraw.setfont | (self, font, size) | Selects which font to use.
:param font: A Postscript font name
:param size: Size in points. | Selects which font to use. | [
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] | def setfont(self, font, size):
"""
Selects which font to use.
:param font: A Postscript font name
:param size: Size in points.
"""
if font not in self.isofont:
# reencode font
self._fp_write("/PSDraw-{} ISOLatin1Encoding /{} E\n".format(font, font))
self.isofont[font] = 1
# rough
self._fp_write("/F0 %d /PSDraw-%s F\n" % (size, font)) | [
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y123456yz/reading-and-annotate-mongodb-3.6 | 93280293672ca7586dc24af18132aa61e4ed7fcf | mongo/src/third_party/scons-2.5.0/scons-local-2.5.0/SCons/Tool/packaging/rpm.py | python | build_specfile | (target, source, env) | Builds a RPM specfile from a dictionary with string metadata and
by analyzing a tree of nodes. | Builds a RPM specfile from a dictionary with string metadata and
by analyzing a tree of nodes. | [
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] | def build_specfile(target, source, env):
""" Builds a RPM specfile from a dictionary with string metadata and
by analyzing a tree of nodes.
"""
file = open(target[0].get_abspath(), 'w')
try:
file.write( build_specfile_header(env) )
file.write( build_specfile_sections(env) )
file.write( build_specfile_filesection(env, source) )
file.close()
# call a user specified function
if 'CHANGE_SPECFILE' in env:
env['CHANGE_SPECFILE'](target, source)
except KeyError, e:
raise SCons.Errors.UserError( '"%s" package field for RPM is missing.' % e.args[0] ) | [
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projectchrono/chrono | 92015a8a6f84ef63ac8206a74e54a676251dcc89 | src/demos/python/chrono-tensorflow/PPO/utils.py | python | Scaler.__init__ | (self, obs_dim, env_name) | Args:
obs_dim: dimension of axis=1 | Args:
obs_dim: dimension of axis=1 | [
"Args",
":",
"obs_dim",
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"dimension",
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] | def __init__(self, obs_dim, env_name):
"""
Args:
obs_dim: dimension of axis=1
"""
self.env_name = env_name
self.vars = np.zeros(obs_dim)
self.means = np.zeros(obs_dim)
self.m = 0
self.n = 0
self.first_pass = True | [
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google/llvm-propeller | 45c226984fe8377ebfb2ad7713c680d652ba678d | compiler-rt/lib/sanitizer_common/scripts/cpplint.py | python | RemoveMultiLineCommentsFromRange | (lines, begin, end) | Clears a range of lines for multi-line comments. | Clears a range of lines for multi-line comments. | [
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"of",
"lines",
"for",
"multi",
"-",
"line",
"comments",
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] | def RemoveMultiLineCommentsFromRange(lines, begin, end):
"""Clears a range of lines for multi-line comments."""
# Having // dummy comments makes the lines non-empty, so we will not get
# unnecessary blank line warnings later in the code.
for i in range(begin, end):
lines[i] = '/**/' | [
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hfinkel/llvm-project-cxxjit | 91084ef018240bbb8e24235ff5cd8c355a9c1a1e | llvm/bindings/python/llvm/object.py | python | Section.address | (self) | return lib.LLVMGetSectionAddress(self) | The address of this section, in long bytes. | The address of this section, in long bytes. | [
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] | def address(self):
"""The address of this section, in long bytes."""
if self.expired:
raise Exception('Section instance has expired.')
return lib.LLVMGetSectionAddress(self) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/ed_vim.py | python | EditraCommander.JoinLines | (self, repeat) | Join lines into a single line.
@param repeat: number of lines below the current line to join with | Join lines into a single line.
@param repeat: number of lines below the current line to join with | [
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] | def JoinLines(self, repeat):
"""Join lines into a single line.
@param repeat: number of lines below the current line to join with
"""
self.SelectLines(repeat)
self.stc.LinesJoinSelected() | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/AWS/resource-manager-code/lib/pkg_resources/_vendor/pyparsing.py | python | Regex.__init__ | ( self, pattern, flags=0) | The parameters C{pattern} and C{flags} are passed to the C{re.compile()} function as-is. See the Python C{re} module for an explanation of the acceptable patterns and flags. | The parameters C{pattern} and C{flags} are passed to the C{re.compile()} function as-is. See the Python C{re} module for an explanation of the acceptable patterns and flags. | [
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] | def __init__( self, pattern, flags=0):
"""The parameters C{pattern} and C{flags} are passed to the C{re.compile()} function as-is. See the Python C{re} module for an explanation of the acceptable patterns and flags."""
super(Regex,self).__init__()
if isinstance(pattern, basestring):
if not pattern:
warnings.warn("null string passed to Regex; use Empty() instead",
SyntaxWarning, stacklevel=2)
self.pattern = pattern
self.flags = flags
try:
self.re = re.compile(self.pattern, self.flags)
self.reString = self.pattern
except sre_constants.error:
warnings.warn("invalid pattern (%s) passed to Regex" % pattern,
SyntaxWarning, stacklevel=2)
raise
elif isinstance(pattern, Regex.compiledREtype):
self.re = pattern
self.pattern = \
self.reString = str(pattern)
self.flags = flags
else:
raise ValueError("Regex may only be constructed with a string or a compiled RE object")
self.name = _ustr(self)
self.errmsg = "Expected " + self.name
self.mayIndexError = False
self.mayReturnEmpty = True | [
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abyzovlab/CNVnator | c73786d6160f17b020feae928148533ca036fad2 | pytools/io.py | python | IO.signal_name | (self, chr, bin_size, signal, flags=FLAG_USEMASK | FLAG_GC_CORR) | return self.signals[signal] % {"chr": chr, "bin_size": bin_size, "rd_flag": self.sufix_rd_flag(flags),
"snp_flag": self.sufix_snp_flag(flags), "flag": self.sufix_flag(flags)} | Returns TH1 or TH2 name for signal | Returns TH1 or TH2 name for signal | [
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"TH1",
"or",
"TH2",
"name",
"for",
"signal"
] | def signal_name(self, chr, bin_size, signal, flags=FLAG_USEMASK | FLAG_GC_CORR):
"""Returns TH1 or TH2 name for signal"""
return self.signals[signal] % {"chr": chr, "bin_size": bin_size, "rd_flag": self.sufix_rd_flag(flags),
"snp_flag": self.sufix_snp_flag(flags), "flag": self.sufix_flag(flags)} | [
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mongodb/mongo | d8ff665343ad29cf286ee2cf4a1960d29371937b | buildscripts/ciconfig/evergreen.py | python | Task.generated_task_name | (self) | return self.name[:-4] | Get basename of the tasks generated by this _gen task.
:return: Basename of the generated tasks. | Get basename of the tasks generated by this _gen task. | [
"Get",
"basename",
"of",
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"tasks",
"generated",
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"_gen",
"task",
"."
] | def generated_task_name(self):
"""
Get basename of the tasks generated by this _gen task.
:return: Basename of the generated tasks.
"""
if not self.is_generate_resmoke_task:
raise TypeError("Only _gen tasks can have generated task names")
return self.name[:-4] | [
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google/clif | cab24d6a105609a65c95a36a1712ae3c20c7b5df | clif/python/gen.py | python | FunctionCall | (pyname, wrapper, doc, catch, call, postcall_init,
typepostconversion, func_ast, lineno, prepend_self=None) | Generate PyCFunction wrapper from AST.FuncDecl func_ast.
Args:
pyname: str - Python function name (may be special: ends with @)
wrapper: str - generated function name
doc: str - C++ signature
catch: bool - catch C++ exceptions
call: str | [str] - C++ command(s) to call the wrapped function
(without "(params);" part).
postcall_init: str - C++ command; to (re)set ret0.
typepostconversion: dict(pytype, index) to convert to pytype
func_ast: AST.FuncDecl protobuf
lineno: int - .clif line number where func_ast defined
prepend_self: AST.Param - Use self as 1st parameter.
Yields:
Source code for wrapped function.
Raises:
ValueError: for non-supported default arguments | Generate PyCFunction wrapper from AST.FuncDecl func_ast. | [
"Generate",
"PyCFunction",
"wrapper",
"from",
"AST",
".",
"FuncDecl",
"func_ast",
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] | def FunctionCall(pyname, wrapper, doc, catch, call, postcall_init,
typepostconversion, func_ast, lineno, prepend_self=None):
"""Generate PyCFunction wrapper from AST.FuncDecl func_ast.
Args:
pyname: str - Python function name (may be special: ends with @)
wrapper: str - generated function name
doc: str - C++ signature
catch: bool - catch C++ exceptions
call: str | [str] - C++ command(s) to call the wrapped function
(without "(params);" part).
postcall_init: str - C++ command; to (re)set ret0.
typepostconversion: dict(pytype, index) to convert to pytype
func_ast: AST.FuncDecl protobuf
lineno: int - .clif line number where func_ast defined
prepend_self: AST.Param - Use self as 1st parameter.
Yields:
Source code for wrapped function.
Raises:
ValueError: for non-supported default arguments
"""
ctxmgr = pyname.endswith('@')
if ctxmgr:
ctxmgr = pyname
assert ctxmgr in ('__enter__@', '__exit__@'), (
'Invalid context manager name ' + pyname)
pyname = pyname.rstrip('@')
nret = len(func_ast.returns)
return_type = astutils.FuncReturnType(func_ast) # Can't use cpp_exact_type.
# return_type mangled to FQN and drop &, sadly it also drop const.
void_return_type = 'void' == return_type
# Has extra func parameters for output values.
xouts = nret > (0 if void_return_type else 1)
params = [] # C++ parameter names.
nargs = len(func_ast.params)
is_ternaryfunc_slot = pyname == '__call__'
yield ''
if func_ast.classmethod:
yield '// @classmethod ' + doc
arg0 = 'cls' # Extra protection that generated code does not use 'self'.
else:
yield '// ' + doc
arg0 = 'self'
needs_kw = nargs or is_ternaryfunc_slot
yield 'static PyObject* %s(PyObject* %s%s) {' % (
wrapper, arg0, ', PyObject* args, PyObject* kw' if needs_kw else '')
if is_ternaryfunc_slot and not nargs:
yield I+('if (!ensure_no_args_and_kw_args("%s", args, kw)) return nullptr;'
% pyname)
if prepend_self:
unused_check_nullptr, out = _CreateInputParameter(
pyname+' line %d' % lineno, prepend_self, 'arg0', params)
yield I+out
yield I+'if (!Clif_PyObjAs(self, &arg0)) return nullptr;'
minargs = sum(1 for p in func_ast.params if not p.default_value)
if nargs:
yield I+'PyObject* a[%d]%s;' % (nargs, '' if minargs == nargs else '{}')
yield I+'const char* names[] = {'
for p in func_ast.params:
yield I+I+I+'"%s",' % p.name.native
yield I+I+I+'nullptr'
yield I+'};'
yield I+('if (!PyArg_ParseTupleAndKeywords(args, kw, "%s:%s", '
'const_cast<char**>(names), %s)) '
'return nullptr;' % ('O'*nargs if minargs == nargs else
'O'*minargs+'|'+'O'*(nargs-minargs), pyname,
', '.join('&a[%d]'%i for i in range(nargs))))
if minargs < nargs and not xouts:
yield I+'int nargs; // Find how many args actually passed in.'
yield I+'for (nargs = %d; nargs > %d; --nargs) {' % (nargs, minargs)
yield I+I+'if (a[nargs-1] != nullptr) break;'
yield I+'}'
# Convert input parameters from Python.
for i, p in enumerate(func_ast.params):
n = i+1
arg = 'arg%d' % n
check_nullptr, out = _CreateInputParameter(
pyname+' line %d' % lineno, p, arg, params)
yield I+out
return_arg_err = (
'return ArgError("{func_name}", names[{i}], "{ctype}", a[{i}]);'
).format(i=i, func_name=pyname, ctype=astutils.Type(p))
cvt = ('if (!Clif_PyObjAs(a[{i}], &{cvar}{postconv})) {return_arg_err}'
).format(i=i, cvar=arg, return_arg_err=return_arg_err,
# Add post conversion parameter for std::function.
postconv='' if p.type.cpp_type else ', {%s}' % ', '.join(
postconv.Initializer(t.type, typepostconversion)
for t in p.type.callable.params))
def YieldCheckNullptr(ii):
# pylint: disable=cell-var-from-loop
if check_nullptr:
yield ii+'if (%s == nullptr) {' % arg
yield ii+I+return_arg_err
yield ii+'}'
if i < minargs:
# Non-default parameter.
yield I+cvt
for s in YieldCheckNullptr(I):
yield s
else:
if xouts:
_I = '' # pylint: disable=invalid-name
else:
_I = I # pylint: disable=invalid-name
yield I+'if (nargs > %d) {' % i
# Check if we're passed kw args, skipping some default C++ args.
# In this case we must substitute missed default args with default_value
if (p.default_value == 'default' # Matcher could not find the default.
or 'inf' in p.default_value): # W/A for b/29437257
if xouts:
raise ValueError("Can't supply the default for C++ function"
' argument. Drop =default in def %s(%s).'
% (pyname, p.name.native))
if n < nargs:
if p.type.cpp_type.startswith('::std::unique_ptr'):
yield I+I+'if (!a[%d]) { /* default-constructed smartptr */ }' % i
yield I+I+'else '+cvt
else:
yield I+I+('if (!a[{i}]) return DefaultArgMissedError('
'"{}", names[{i}]);'.format(pyname, i=i))
yield I+I+cvt
else:
yield I+I+cvt
for s in YieldCheckNullptr(I+I):
yield s
elif (p.default_value and
params[-1].startswith('&') and p.type.cpp_raw_pointer):
# Special case for a pointer to an integral type param (like int*).
raise ValueError('A default for integral type pointer argument is '
' not supported. Drop =default in def %s(%s).'
% (pyname, p.name.native))
else:
# C-cast takes care of the case where |arg| is an enum value, while
# the matcher would return an integral literal. Using static_cast
# would be ideal, but its argument should be an expression, which a
# struct value like {1, 2, 3} is not.
yield _I+I+'if (!a[%d]) %s = (%s)%s;' % (i, arg, astutils.Type(p),
p.default_value)
yield _I+I+'else '+cvt
for s in YieldCheckNullptr(_I+I):
yield s
if not xouts:
yield I+'}'
# Create input parameters for extra return values.
for n, p in enumerate(func_ast.returns):
if n or void_return_type:
yield I+'%s ret%d{};' % (astutils.Type(p), n)
params.append('&ret%d' % n)
yield I+'// Call actual C++ method.'
if isinstance(call, list):
for s in call[:-1]:
yield I+s
call = call[-1]
if not func_ast.py_keep_gil:
if nargs:
yield I+'Py_INCREF(args);'
yield I+'Py_XINCREF(kw);'
yield I+'PyThreadState* _save;'
yield I+'Py_UNBLOCK_THREADS'
optional_ret0 = False
convert_ref_to_ptr = False
if (minargs < nargs or catch) and not void_return_type:
if catch and return_type.rstrip().endswith('&'):
convert_ref_to_ptr = True
idx = return_type.rindex('&')
return_type = return_type[:idx] + '*'
if func_ast.returns[0].type.cpp_has_def_ctor:
yield I+return_type+' ret0;'
else:
# Using optional<> requires T be have T(x) and T::op=(x) available.
# While we need only t=x, implementing it will be a pain we skip for now.
yield I+'::absl::optional<%s> ret0;' % return_type
optional_ret0 = True
if catch:
for s in _GenExceptionTry():
yield s
if minargs < nargs and not xouts:
if not void_return_type:
call = 'ret0 = '+call
yield I+'switch (nargs) {'
for n in range(minargs, nargs+1):
yield I+'case %d:' % n
if func_ast.is_extend_method and func_ast.constructor:
call_with_params = call % (func_ast.name.cpp_name,
astutils.TupleStr(params[:n]))
else:
num_params = n
# extended methods need to include `self` as the first parameter, but
# extended constructors do not.
if func_ast.is_extend_method:
num_params += 1
call_with_params = call + astutils.TupleStr(params[:num_params])
yield I+I+'%s; break;' % call_with_params
yield I+'}'
else:
if func_ast.is_extend_method and func_ast.constructor:
call = call % (func_ast.name.cpp_name, astutils.TupleStr(params))
else:
call += astutils.TupleStr(params)
_I = I if catch else '' # pylint: disable=invalid-name
if void_return_type:
yield _I+I+call+';'
elif catch:
if convert_ref_to_ptr:
yield _I+I+'ret0 = &'+call+';'
else:
yield _I+I+'ret0 = '+call+';'
else:
yield _I+I+return_type+' ret0 = '+call+';'
if catch:
for s in _GenExceptionCatch():
yield s
if postcall_init:
if void_return_type:
yield I+postcall_init
else:
yield I+'ret0'+postcall_init
if not func_ast.py_keep_gil:
yield I+'Py_BLOCK_THREADS'
if nargs:
yield I+'Py_DECREF(args);'
yield I+'Py_XDECREF(kw);'
if catch:
for s in _GenExceptionRaise():
yield s
if func_ast.postproc == '->self':
func_ast.postproc = ''
return_self = True
assert nret == 0, '-> self must have no other output parameters'
else:
return_self = False
ret = '*ret' if convert_ref_to_ptr else 'ret'
# If ctxmgr, force return self on enter, None on exit.
if nret > 1 or (func_ast.postproc or ctxmgr) and nret:
yield I+'// Convert return values to Python.'
yield I+'PyObject* p, * result_tuple = PyTuple_New(%d);' % nret
yield I+'if (result_tuple == nullptr) return nullptr;'
for i in range(nret):
yield I+'if ((p=Clif_PyObjFrom(std::move(%s%d), %s)) == nullptr) {' % (
ret, i,
postconv.Initializer(
func_ast.returns[i].type,
typepostconversion,
marked_non_raising=func_ast.marked_non_raising))
yield I+I+'Py_DECREF(result_tuple);'
yield I+I+'return nullptr;'
yield I+'}'
yield I+'PyTuple_SET_ITEM(result_tuple, %d, p);' % i
if func_ast.postproc:
yield I+'PyObject* pyproc = ImportFQName("%s");' % func_ast.postproc
yield I+'if (pyproc == nullptr) {'
yield I+I+'Py_DECREF(result_tuple);'
yield I+I+'return nullptr;'
yield I+'}'
yield I+'p = PyObject_CallObject(pyproc, result_tuple);'
yield I+'Py_DECREF(pyproc);'
yield I+'Py_CLEAR(result_tuple);'
if ctxmgr:
yield I+'if (p == nullptr) return nullptr;'
yield I+'Py_DECREF(p); // Not needed by the context manager.'
else:
yield I+'result_tuple = p;'
if ctxmgr == '__enter__@':
yield I+'Py_XDECREF(result_tuple);'
yield I+'Py_INCREF(self);'
yield I+'return self;'
elif ctxmgr == '__exit__@':
yield I+'Py_XDECREF(result_tuple);'
yield I+'Py_RETURN_NONE;'
else:
yield I+'return result_tuple;'
elif nret:
yield I+'return Clif_PyObjFrom(std::move(%s0%s), %s);' % (
ret, ('.value()' if optional_ret0 else ''),
postconv.Initializer(
func_ast.returns[0].type,
typepostconversion,
marked_non_raising=func_ast.marked_non_raising))
elif return_self or ctxmgr == '__enter__@':
yield I+'Py_INCREF(self);'
yield I+'return self;'
else:
yield I+'Py_RETURN_NONE;'
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/saved_model/function_deserialization.py | python | recreate_function | (saved_function, concrete_functions) | return tf_decorator.make_decorator(
restored_function_body,
restored_function,
decorator_argspec=function_spec.fullargspec) | Creates a `Function` from a `SavedFunction`.
Args:
saved_function: `SavedFunction` proto.
concrete_functions: map from function name to `ConcreteFunction`.
Returns:
A `Function`. | Creates a `Function` from a `SavedFunction`. | [
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] | def recreate_function(saved_function, concrete_functions):
"""Creates a `Function` from a `SavedFunction`.
Args:
saved_function: `SavedFunction` proto.
concrete_functions: map from function name to `ConcreteFunction`.
Returns:
A `Function`.
"""
# TODO(andresp): Construct a `Function` with the cache populated
# instead of creating a new `Function` backed by a Python layer to
# glue things together. Current approach is nesting functions deeper for each
# serialization cycle.
coder = nested_structure_coder.StructureCoder()
# Note: handling method functions is tricky since make_decorator does not
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# not behave as methods i.e. they always use the same captured tensors
# independent of the object they are bound to, there is little value on
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function_spec = _deserialize_function_spec_as_nonmethod(
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def restored_function_body(*args, **kwargs):
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inputs = (args, kwargs)
# First try to find a concrete function that can be called without input
# conversions. This allows one to pick a more specific trace in case there
# was also a more expensive one that supported tensors.
for allow_conversion in [False, True]:
for function_name in saved_function.concrete_functions:
function = concrete_functions[function_name]
if _concrete_function_callable_with(function, inputs, allow_conversion):
return _call_concrete_function(function, inputs)
signature_descriptions = []
def _pretty_format_positional(positional):
return "Positional arguments ({} total):\n * {}".format(
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concrete_function = concrete_functions[function_name]
positional, keyword = concrete_function.structured_input_signature
signature_descriptions.append(
"Option {}:\n {}\n Keyword arguments: {}"
.format(index + 1, _pretty_format_positional(positional), keyword))
raise ValueError(
"Could not find matching function to call loaded from the SavedModel. "
"Got:\n {}\n Keyword arguments: {}\n\nExpected "
"these arguments to match one of the following {} option(s):\n\n{}"
.format(_pretty_format_positional(args), kwargs,
len(saved_function.concrete_functions),
"\n\n".join(signature_descriptions)))
concrete_function_objects = []
for concrete_function_name in saved_function.concrete_functions:
concrete_function_objects.append(concrete_functions[concrete_function_name])
restored_function = RestoredFunction(
restored_function_body,
restored_function_body.__name__,
function_spec,
concrete_function_objects)
return tf_decorator.make_decorator(
restored_function_body,
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_core.py | python | Sizer.RecalcSizes | (*args, **kwargs) | return _core_.Sizer_RecalcSizes(*args, **kwargs) | RecalcSizes(self)
Using the sizes calculated by `CalcMin` reposition and resize all the
items managed by this sizer. You should not need to call this directly as
it is called by `Layout`. | RecalcSizes(self) | [
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"""
RecalcSizes(self)
Using the sizes calculated by `CalcMin` reposition and resize all the
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apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | deps/src/libxml2-2.9.1/python/libxml2class.py | python | xpathContext.xpathRegisteredNsCleanup | (self) | Cleanup the XPath context data associated to registered
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synfig/synfig | a5ec91db5b751dc12e4400ccfb5c063fd6d2d928 | synfig-studio/plugins/lottie-exporter/common/Layer.py | python | Layer.get_param | (self, *keys) | return Param(None, None) | Given a key, this will return the address of the child corresponding to
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cvxpy/cvxpy | 5165b4fb750dfd237de8659383ef24b4b2e33aaf | cvxpy/reductions/complex2real/atom_canonicalizers/matrix_canon.py | python | lambda_sum_largest_canon | (expr, real_args, imag_args, real2imag) | return real, imag | Canonicalize nuclear norm with Hermitian matrix input. | Canonicalize nuclear norm with Hermitian matrix input. | [
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real, imag = hermitian_canon(expr, real_args, imag_args, real2imag)
real.k *= 2
if imag_args[0] is not None:
real /= 2
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wang-bin/QtAV | 3b937991afce248648836ae811324d4051b31def | python/configure.py | python | _TargetConfiguration.get_qt_configuration | (self, opts) | Get the Qt configuration that can be extracted from qmake. opts
are the command line options. | Get the Qt configuration that can be extracted from qmake. opts
are the command line options. | [
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] | def get_qt_configuration(self, opts):
""" Get the Qt configuration that can be extracted from qmake. opts
are the command line options.
"""
# Query qmake.
qt_config = _TargetQtConfiguration(self.qmake)
self.qt_version_str = getattr(qt_config, 'QT_VERSION', '')
self.qt_version = version_from_string(self.qt_version_str)
if self.qt_version is None:
error("Unable to determine the version of Qt.")
# On Windows for Qt versions prior to v5.9.0 we need to be explicit
# about the qmake spec.
if self.qt_version < 0x050900 and self.py_platform == 'win32':
if self.py_version >= 0x030500:
self.qmake_spec = 'win32-msvc2015'
elif self.py_version >= 0x030300:
self.qmake_spec = 'win32-msvc2010'
elif self.py_version >= 0x020600:
self.qmake_spec = 'win32-msvc2008'
elif self.py_version >= 0x020400:
self.qmake_spec = 'win32-msvc.net'
else:
self.qmake_spec = 'win32-msvc'
else:
# Otherwise use the default.
self.qmake_spec = ''
# The binary MacOS/X Qt installer used to default to XCode. If so then
# use macx-clang (Qt v5) or macx-g++ (Qt v4).
if sys.platform == 'darwin':
try:
# Qt v5.
if qt_config.QMAKE_SPEC == 'macx-xcode':
# This will exist (and we can't check anyway).
self.qmake_spec = 'macx-clang'
else:
# No need to explicitly name the default.
self.qmake_spec = ''
except AttributeError:
# Qt v4.
self.qmake_spec = 'macx-g++'
self.api_dir = os.path.join(qt_config.QT_INSTALL_DATA, 'qsci')
self.qt_inc_dir = qt_config.QT_INSTALL_HEADERS
self.qt_lib_dir = qt_config.QT_INSTALL_LIBS
if self.sysroot == '':
self.sysroot = getattr(qt_config, 'QT_SYSROOT', '') | [
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ricardoquesada/Spidermonkey | 4a75ea2543408bd1b2c515aa95901523eeef7858 | python/psutil/psutil/_psbsd.py | python | Process.get_process_uids | (self) | return nt_uids(real, effective, saved) | Return real, effective and saved user ids. | Return real, effective and saved user ids. | [
"Return",
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] | def get_process_uids(self):
"""Return real, effective and saved user ids."""
real, effective, saved = _psutil_bsd.get_process_uids(self.pid)
return nt_uids(real, effective, saved) | [
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/klampt/plan/rigidobjectcspace.py | python | RigidObjectCSpace.__init__ | (self,rigidObject,collider=None,translationDomain=None,rotationDomain=None) | Args:
rigidObject (RigidObjectModel): the object that should move.
collider (:class:`WorldCollider`, optional): a collider instance
containing the world in which the object lives. Any ignored
collisions will be respected in the feasibility test.
translationDomain (list of pairs, optional): a bounding box in
which the translation should be sampled. If None (default),
the improper logarithmic prior is used to sample translations.
rotationDomain (pair, optional): If provided, must be a
(rotation0,rdomain) pair specifying a range in which the
rotation should be sampled. rotation0 must be an SO3 element.
rdomain may be:
* A number: rotation is sampled with absolute angular error
from rotation0 in the range [0,rdomain].
* A triple: rotation is sampled with euler angles with roll
in the range [-rdomain[0],rdomain[0]], pitch in the range
[-rdomain[1],rdomain[1]], and yaw in the range
[-rdomain[2],rdomain[2]]. The sampled rotation is then
multiplied by rotation0. | Args:
rigidObject (RigidObjectModel): the object that should move.
collider (:class:`WorldCollider`, optional): a collider instance
containing the world in which the object lives. Any ignored
collisions will be respected in the feasibility test.
translationDomain (list of pairs, optional): a bounding box in
which the translation should be sampled. If None (default),
the improper logarithmic prior is used to sample translations.
rotationDomain (pair, optional): If provided, must be a
(rotation0,rdomain) pair specifying a range in which the
rotation should be sampled. rotation0 must be an SO3 element.
rdomain may be: | [
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"""
Args:
rigidObject (RigidObjectModel): the object that should move.
collider (:class:`WorldCollider`, optional): a collider instance
containing the world in which the object lives. Any ignored
collisions will be respected in the feasibility test.
translationDomain (list of pairs, optional): a bounding box in
which the translation should be sampled. If None (default),
the improper logarithmic prior is used to sample translations.
rotationDomain (pair, optional): If provided, must be a
(rotation0,rdomain) pair specifying a range in which the
rotation should be sampled. rotation0 must be an SO3 element.
rdomain may be:
* A number: rotation is sampled with absolute angular error
from rotation0 in the range [0,rdomain].
* A triple: rotation is sampled with euler angles with roll
in the range [-rdomain[0],rdomain[0]], pitch in the range
[-rdomain[1],rdomain[1]], and yaw in the range
[-rdomain[2],rdomain[2]]. The sampled rotation is then
multiplied by rotation0.
"""
CSpace.__init__(self)
self.rigidObject = rigidObject
if translationDomain is None:
translationDomain = [(-float('inf'),float('inf'))]*3
self.bound = translationDomain + [(-math.pi,math.pi)]*3
self.rotationDomain = rotationDomain
self.collider = collider
self.rotationWeight = 1.0/math.pi
if collider:
def robCollide(r):
return any(True for _ in self.collider.robotObjectCollisions(r,self.rigidObject.index))
def objCollide(o):
return any(True for _ in self.collider.objectObjectCollisions(self.rigidObject.index,o))
def terrCollide(o):
return any(True for _ in self.collider.objectTerrainCollisions(self.rigidObject.index,o))
self.addFeasibilityTest(self.setConfig,"setconfig")
for o in range(self.collider.world.numRobots()):
self.addFeasibilityTest((lambda x,o=o: not robCollide(o)),"robot collision "+str(o)+" "+self.collider.world.robot(o).getName(),dependencies="setconfig")
for o in range(self.collider.world.numRigidObjects()):
if o != self.rigidObject.index:
self.addFeasibilityTest((lambda x,o=o: not objCollide(o)),"obj collision "+str(o)+" "+self.collider.world.rigidObject(o).getName(),dependencies="setconfig")
for o in range(self.collider.world.numTerrains()):
self.addFeasibilityTest((lambda x,o=o: not terrCollide(o)),"terrain collision "+str(o)+" "+self.collider.world.terrain(o).getName(),dependencies="setconfig")
else:
self.addFeasibilityTest(self.setConfig,"setconfig")
self.properties['geodesic'] = 1 | [
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intel/llvm | e6d0547e9d99b5a56430c4749f6c7e328bf221ab | lldb/utils/lui/lldbutil.py | python | run_break_set_by_regexp | (
test,
regexp,
extra_options=None,
num_expected_locations=-1) | return get_bpno_from_match(break_results) | Set a breakpoint by regular expression match on symbol name. Common options are the same as run_break_set_by_file_and_line. | Set a breakpoint by regular expression match on symbol name. Common options are the same as run_break_set_by_file_and_line. | [
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] | def run_break_set_by_regexp(
test,
regexp,
extra_options=None,
num_expected_locations=-1):
"""Set a breakpoint by regular expression match on symbol name. Common options are the same as run_break_set_by_file_and_line."""
command = 'breakpoint set -r "%s"' % (regexp)
if extra_options:
command += " " + extra_options
break_results = run_break_set_command(test, command)
check_breakpoint_result(
test,
break_results,
num_locations=num_expected_locations)
return get_bpno_from_match(break_results) | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/contrib/signal/python/ops/mel_ops.py | python | _validate_arguments | (num_mel_bins, num_spectrogram_bins, sample_rate,
lower_edge_hertz, upper_edge_hertz, dtype) | Checks the inputs to linear_to_mel_weight_matrix. | Checks the inputs to linear_to_mel_weight_matrix. | [
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] | def _validate_arguments(num_mel_bins, num_spectrogram_bins, sample_rate,
lower_edge_hertz, upper_edge_hertz, dtype):
"""Checks the inputs to linear_to_mel_weight_matrix."""
if num_mel_bins <= 0:
raise ValueError('num_mel_bins must be positive. Got: %s' % num_mel_bins)
if num_spectrogram_bins <= 0:
raise ValueError('num_spectrogram_bins must be positive. Got: %s' %
num_spectrogram_bins)
if sample_rate <= 0.0:
raise ValueError('sample_rate must be positive. Got: %s' % sample_rate)
if lower_edge_hertz < 0.0:
raise ValueError('lower_edge_hertz must be non-negative. Got: %s' %
lower_edge_hertz)
if lower_edge_hertz >= upper_edge_hertz:
raise ValueError('lower_edge_hertz %.1f >= upper_edge_hertz %.1f' %
(lower_edge_hertz, upper_edge_hertz))
if not dtype.is_floating:
raise ValueError('dtype must be a floating point type. Got: %s' % dtype) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numba/six.py | python | add_move | (move) | Add an item to six.moves. | Add an item to six.moves. | [
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klzgrad/naiveproxy | ed2c513637c77b18721fe428d7ed395b4d284c83 | src/tools/grit/grit/tclib.py | python | BaseMessage.GetContent | (self) | return self.parts | Returns the parts of the message. You may modify parts if you wish.
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google/iree | 1224bbdbe65b0d1fdf40e7324f60f68beeaf7c76 | integrations/tensorflow/python_projects/iree_tf/iree/tf/support/trace_utils.py | python | ModuleCall.get_tolerances | (self) | return self.rtol, self.atol | Gets the floating point tolerances associated with this call. | Gets the floating point tolerances associated with this call. | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemDefectReporter/v1/AWS/common-code/Lib/setuptools/msvc.py | python | RegistryInfo.windows_kits_roots | (self) | return r'Windows Kits\Installed Roots' | Microsoft Windows Kits Roots registry key. | Microsoft Windows Kits Roots registry key. | [
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natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/contrib/ffmpeg/ffmpeg_ops.py | python | _decode_audio_shape | (op) | return [tensor_shape.TensorShape([None, channels])] | Computes the shape of a DecodeAudio operation.
Args:
op: A DecodeAudio operation.
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This is a rank 2 tensor with an unknown number of samples and a
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bulletphysics/bullet3 | f0f2a952e146f016096db6f85cf0c44ed75b0b9a | examples/pybullet/gym/pybullet_envs/minitaur/agents/baseline_controller/leg_controller.py | python | LegController.get_action | (self) | Gets the control signal e.g. torques/positions for the leg. | Gets the control signal e.g. torques/positions for the leg. | [
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intel/llvm | e6d0547e9d99b5a56430c4749f6c7e328bf221ab | llvm/utils/lint/common_lint.py | python | VerifyTrailingWhitespace | (filename, lines) | return lint | Checks to make sure the file has no lines with trailing whitespace.
Args:
filename: the file under consideration as string
lines: contents of the file as string array
Returns:
A list of tuples with format [(filename, line number, msg), ...] with any
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filename: the file under consideration as string
lines: contents of the file as string array
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A list of tuples with format [(filename, line number, msg), ...] with any
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lint = []
trailing_whitespace_re = re.compile(r'\s+$')
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commaai/openpilot | 4416c21b1e738ab7d04147c5ae52b5135e0cdb40 | pyextra/acados_template/acados_ocp.py | python | AcadosOcpOptions.hessian_approx | (self) | return self.__hessian_approx | Hessian approximation.
String in ('GAUSS_NEWTON', 'EXACT').
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smilehao/xlua-framework | a03801538be2b0e92d39332d445b22caca1ef61f | ConfigData/trunk/tools/protobuf-2.5.0/protobuf-2.5.0/python/google/protobuf/internal/containers.py | python | RepeatedScalarFieldContainer.__getslice__ | (self, start, stop) | return self._values[start:stop] | Retrieves the subset of items from between the specified indices. | Retrieves the subset of items from between the specified indices. | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/tkinter/__init__.py | python | PanedWindow.remove | (self, child) | Remove the pane containing child from the panedwindow
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/ops/data_flow_ops.py | python | Barrier.barrier_ref | (self) | return self._barrier_ref | Get the underlying barrier reference. | Get the underlying barrier reference. | [
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ukoethe/vigra | 093d57d15c8c237adf1704d96daa6393158ce299 | vigranumpy/lib/arraytypes.py | python | ImagePyramid.__getitem__ | (self, level) | return list.__getitem__(self, level - self.lowestLevel) | Get the image at 'level'.
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/feature_column/feature_column_v2.py | python | categorical_column_with_vocabulary_file | (key,
vocabulary_file,
vocabulary_size=None,
num_oov_buckets=0,
default_value=None,
dtype=dtypes.string) | return categorical_column_with_vocabulary_file_v2(
key, vocabulary_file, vocabulary_size,
dtype, default_value,
num_oov_buckets) | A `CategoricalColumn` with a vocabulary file.
Use this when your inputs are in string or integer format, and you have a
vocabulary file that maps each value to an integer ID. By default,
out-of-vocabulary values are ignored. Use either (but not both) of
`num_oov_buckets` and `default_value` to specify how to include
out-of-vocabulary values.
For input dictionary `features`, `features[key]` is either `Tensor` or
`SparseTensor`. If `Tensor`, missing values can be represented by `-1` for int
and `''` for string, which will be dropped by this feature column.
Example with `num_oov_buckets`:
File '/us/states.txt' contains 50 lines, each with a 2-character U.S. state
abbreviation. All inputs with values in that file are assigned an ID 0-49,
corresponding to its line number. All other values are hashed and assigned an
ID 50-54.
```python
states = categorical_column_with_vocabulary_file(
key='states', vocabulary_file='/us/states.txt', vocabulary_size=50,
num_oov_buckets=5)
columns = [states, ...]
features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
linear_prediction = linear_model(features, columns)
```
Example with `default_value`:
File '/us/states.txt' contains 51 lines - the first line is 'XX', and the
other 50 each have a 2-character U.S. state abbreviation. Both a literal 'XX'
in input, and other values missing from the file, will be assigned ID 0. All
others are assigned the corresponding line number 1-50.
```python
states = categorical_column_with_vocabulary_file(
key='states', vocabulary_file='/us/states.txt', vocabulary_size=51,
default_value=0)
columns = [states, ...]
features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
linear_prediction, _, _ = linear_model(features, columns)
```
And to make an embedding with either:
```python
columns = [embedding_column(states, 3),...]
features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
dense_tensor = input_layer(features, columns)
```
Args:
key: A unique string identifying the input feature. It is used as the
column name and the dictionary key for feature parsing configs, feature
`Tensor` objects, and feature columns.
vocabulary_file: The vocabulary file name.
vocabulary_size: Number of the elements in the vocabulary. This must be no
greater than length of `vocabulary_file`, if less than length, later
values are ignored. If None, it is set to the length of `vocabulary_file`.
num_oov_buckets: Non-negative integer, the number of out-of-vocabulary
buckets. All out-of-vocabulary inputs will be assigned IDs in the range
`[vocabulary_size, vocabulary_size+num_oov_buckets)` based on a hash of
the input value. A positive `num_oov_buckets` can not be specified with
`default_value`.
default_value: The integer ID value to return for out-of-vocabulary feature
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`num_oov_buckets`.
dtype: The type of features. Only string and integer types are supported.
Returns:
A `CategoricalColumn` with a vocabulary file.
Raises:
ValueError: `vocabulary_file` is missing or cannot be opened.
ValueError: `vocabulary_size` is missing or < 1.
ValueError: `num_oov_buckets` is a negative integer.
ValueError: `num_oov_buckets` and `default_value` are both specified.
ValueError: `dtype` is neither string nor integer. | A `CategoricalColumn` with a vocabulary file. | [
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vocabulary_file,
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num_oov_buckets=0,
default_value=None,
dtype=dtypes.string):
"""A `CategoricalColumn` with a vocabulary file.
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For input dictionary `features`, `features[key]` is either `Tensor` or
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File '/us/states.txt' contains 50 lines, each with a 2-character U.S. state
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corresponding to its line number. All other values are hashed and assigned an
ID 50-54.
```python
states = categorical_column_with_vocabulary_file(
key='states', vocabulary_file='/us/states.txt', vocabulary_size=50,
num_oov_buckets=5)
columns = [states, ...]
features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
linear_prediction = linear_model(features, columns)
```
Example with `default_value`:
File '/us/states.txt' contains 51 lines - the first line is 'XX', and the
other 50 each have a 2-character U.S. state abbreviation. Both a literal 'XX'
in input, and other values missing from the file, will be assigned ID 0. All
others are assigned the corresponding line number 1-50.
```python
states = categorical_column_with_vocabulary_file(
key='states', vocabulary_file='/us/states.txt', vocabulary_size=51,
default_value=0)
columns = [states, ...]
features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
linear_prediction, _, _ = linear_model(features, columns)
```
And to make an embedding with either:
```python
columns = [embedding_column(states, 3),...]
features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
dense_tensor = input_layer(features, columns)
```
Args:
key: A unique string identifying the input feature. It is used as the
column name and the dictionary key for feature parsing configs, feature
`Tensor` objects, and feature columns.
vocabulary_file: The vocabulary file name.
vocabulary_size: Number of the elements in the vocabulary. This must be no
greater than length of `vocabulary_file`, if less than length, later
values are ignored. If None, it is set to the length of `vocabulary_file`.
num_oov_buckets: Non-negative integer, the number of out-of-vocabulary
buckets. All out-of-vocabulary inputs will be assigned IDs in the range
`[vocabulary_size, vocabulary_size+num_oov_buckets)` based on a hash of
the input value. A positive `num_oov_buckets` can not be specified with
`default_value`.
default_value: The integer ID value to return for out-of-vocabulary feature
values, defaults to `-1`. This can not be specified with a positive
`num_oov_buckets`.
dtype: The type of features. Only string and integer types are supported.
Returns:
A `CategoricalColumn` with a vocabulary file.
Raises:
ValueError: `vocabulary_file` is missing or cannot be opened.
ValueError: `vocabulary_size` is missing or < 1.
ValueError: `num_oov_buckets` is a negative integer.
ValueError: `num_oov_buckets` and `default_value` are both specified.
ValueError: `dtype` is neither string nor integer.
"""
return categorical_column_with_vocabulary_file_v2(
key, vocabulary_file, vocabulary_size,
dtype, default_value,
num_oov_buckets) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/site-packages/botocore/vendored/six.py | python | with_metaclass | (meta, *bases) | return type.__new__(metaclass, 'temporary_class', (), {}) | Create a base class with a metaclass. | Create a base class with a metaclass. | [
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] | def with_metaclass(meta, *bases):
"""Create a base class with a metaclass."""
# This requires a bit of explanation: the basic idea is to make a dummy
# metaclass for one level of class instantiation that replaces itself with
# the actual metaclass.
class metaclass(meta):
def __new__(cls, name, this_bases, d):
return meta(name, bases, d)
return type.__new__(metaclass, 'temporary_class', (), {}) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py2/pandas/io/stata.py | python | StataReader.value_labels | (self) | return self.value_label_dict | Returns a dict, associating each variable name a dict, associating
each value its corresponding label | Returns a dict, associating each variable name a dict, associating
each value its corresponding label | [
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"""Returns a dict, associating each variable name a dict, associating
each value its corresponding label
"""
if not self._value_labels_read:
self._read_value_labels()
return self.value_label_dict | [
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apache/madlib | be297fe6beada0640f93317e8948834032718e32 | src/madpack/madpack.py | python | _get_relative_maddir | (maddir, port) | Return a relative path version of maddir
GPDB installations have a symlink outside of GPHOME that
links to the current GPHOME. After a DB upgrade, this symlink is updated to
the new GPHOME.
'maddir_lib', which uses the absolute path of GPHOME, is hardcoded into each
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relative path makes it simpler to perform DB upgrades without breaking MADlib. | Return a relative path version of maddir | [
"Return",
"a",
"relative",
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"version",
"of",
"maddir"
] | def _get_relative_maddir(maddir, port):
""" Return a relative path version of maddir
GPDB installations have a symlink outside of GPHOME that
links to the current GPHOME. After a DB upgrade, this symlink is updated to
the new GPHOME.
'maddir_lib', which uses the absolute path of GPHOME, is hardcoded into each
madlib function definition. Replacing the GPHOME path with the equivalent
relative path makes it simpler to perform DB upgrades without breaking MADlib.
"""
if port == 'postgres':
# do nothing for postgres
return maddir
# e.g. maddir_lib = $GPHOME/madlib/Versions/1.9/lib/libmadlib.so
# 'madlib' is supposed to be in this path, which is the default folder
# used by GPPKG to install madlib
try:
abs_gphome, tail = maddir.split('madlib/')
except ValueError:
return maddir
# Check outside $GPHOME if there is a symlink to this absolute path
# os.pardir is equivalent to ..
# os.path.normpath removes the extraneous .. from that path
rel_gphome = os.path.normpath(os.path.join(abs_gphome, os.pardir, 'greenplum-db'))
if (os.path.islink(rel_gphome) and
os.path.realpath(rel_gphome) == os.path.realpath(abs_gphome)):
# if the relative link exists and is pointing to current location
return os.path.join(rel_gphome, 'madlib', tail)
else:
return maddir | [
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apache/incubator-mxnet | f03fb23f1d103fec9541b5ae59ee06b1734a51d9 | python/mxnet/onnx/mx2onnx/_op_translations/_op_translations_opset12.py | python | convert_slice_axis | (node, **kwargs) | return nodes | Map MXNet's slice_axis operator attributes to onnx's Slice operator
and return the created node. | Map MXNet's slice_axis operator attributes to onnx's Slice operator
and return the created node. | [
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] | def convert_slice_axis(node, **kwargs):
"""Map MXNet's slice_axis operator attributes to onnx's Slice operator
and return the created node.
"""
from onnx.helper import make_node
name, input_nodes, attrs = get_inputs(node, kwargs)
axis = int(attrs.get("axis"))
begin = int(attrs.get("begin"))
end = attrs.get("end", None)
nodes = []
create_tensor([axis], name+'_axis', kwargs["initializer"])
create_tensor([begin], name+'_begin', kwargs["initializer"])
if not end or end == 'None':
# ONNX doesn't support None for ends. Since ends=None depicts
# length of dimension, passing dimension in this case.
nodes += [
make_node('Shape', [input_nodes[0]], [name+"_data_shape"])
]
# corner case when end = None and axis = -1
if axis == -1:
create_tensor([-1], name+'_-1', kwargs["initializer"])
nodes += [
make_node('Shape', [name+'_data_shape'], [name+'_data_dim']),
make_node('Add', [name+'_data_dim', name+'_-1'], [name+'_axis_max']),
make_node('Slice', [name+'_data_shape', name+'_axis_max', name+'_data_dim'], [name+'_end']),
]
else:
create_tensor([axis+1], name+"_axis_plus_1", kwargs["initializer"])
nodes += [
make_node('Slice', [name+'_data_shape', name+'_axis', name+'_axis_plus_1'],
[name+"_end"])
]
else:
create_tensor([int(end)], name+'_end', kwargs["initializer"])
nodes += [
make_node('Slice', [input_nodes[0], name+'_begin', name+'_end', name+'_axis'],
[name], name=name)
]
return nodes | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/logging/handlers.py | python | BufferingHandler.close | (self) | Close the handler.
This version just flushes and chains to the parent class' close(). | Close the handler. | [
"Close",
"the",
"handler",
"."
] | def close(self):
"""
Close the handler.
This version just flushes and chains to the parent class' close().
"""
try:
self.flush()
finally:
logging.Handler.close(self) | [
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/python2_version/klampt/robotsim.py | python | VolumeGrid.set | (self, *args) | return _robotsim.VolumeGrid_set(self, *args) | set(VolumeGrid self, double value)
set(VolumeGrid self, int i, int j, int k, double value) | set(VolumeGrid self, double value)
set(VolumeGrid self, int i, int j, int k, double value) | [
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"""
set(VolumeGrid self, double value)
set(VolumeGrid self, int i, int j, int k, double value)
"""
return _robotsim.VolumeGrid_set(self, *args) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py3/sklearn/preprocessing/_data.py | python | MaxAbsScaler.transform | (self, X) | return X | Scale the data
Parameters
----------
X : {array-like, sparse matrix}
The data that should be scaled. | Scale the data | [
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] | def transform(self, X):
"""Scale the data
Parameters
----------
X : {array-like, sparse matrix}
The data that should be scaled.
"""
check_is_fitted(self)
X = check_array(X, accept_sparse=('csr', 'csc'), copy=self.copy,
estimator=self, dtype=FLOAT_DTYPES,
force_all_finite='allow-nan')
if sparse.issparse(X):
inplace_column_scale(X, 1.0 / self.scale_)
else:
X /= self.scale_
return X | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/ResourceManager/lib/Crypto/Hash/CMAC.py | python | CMAC._update | (self, data_block) | Update a block aligned to the block boundary | Update a block aligned to the block boundary | [
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] | def _update(self, data_block):
"""Update a block aligned to the block boundary"""
bs = self._block_size
assert len(data_block) % bs == 0
if len(data_block) == 0:
return
ct = self._cbc.encrypt(data_block)
if len(data_block) == bs:
second_last = self._last_ct
else:
second_last = ct[-bs*2:-bs]
self._last_ct = ct[-bs:]
self._last_pt = strxor(second_last, data_block[-bs:]) | [
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FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Import/App/config_control_design.py | python | gbsf_check_curve | (cv,) | return FALSE | :param cv
:type cv:curve | :param cv
:type cv:curve | [
":",
"param",
"cv",
":",
"type",
"cv",
":",
"curve"
] | def gbsf_check_curve(cv,):
'''
:param cv
:type cv:curve
'''
if (SIZEOF(['CONFIG_CONTROL_DESIGN.BOUNDED_CURVE','CONFIG_CONTROL_DESIGN.CONIC','CONFIG_CONTROL_DESIGN.CURVE_REPLICA','CONFIG_CONTROL_DESIGN.LINE','CONFIG_CONTROL_DESIGN.OFFSET_CURVE_3D'] * TYPEOF(cv)) > 1):
return FALSE
else:
if (SIZEOF(['CONFIG_CONTROL_DESIGN.CIRCLE','CONFIG_CONTROL_DESIGN.ELLIPSE'] * TYPEOF(cv)) == 1):
return TRUE
else:
if ((('CONFIG_CONTROL_DESIGN.B_SPLINE_CURVE' == TYPEOF(cv)) and (cv.b_spline_curve.self_intersect == FALSE)) or (cv.b_spline_curve.self_intersect == UNKNOWN)):
return TRUE
else:
if ((('CONFIG_CONTROL_DESIGN.COMPOSITE_CURVE' == TYPEOF(cv)) and (cv.composite_curve.self_intersect == FALSE)) or (cv.composite_curve.self_intersect == UNKNOWN)):
return SIZEOF(None) == 0
else:
if ('CONFIG_CONTROL_DESIGN.CURVE_REPLICA' == TYPEOF(cv)):
return gbsf_check_curve(cv.curve_replica.parent_curve)
else:
if ((('CONFIG_CONTROL_DESIGN.OFFSET_CURVE_3D' == TYPEOF(cv)) and ((cv.offset_curve_3d.self_intersect == FALSE) or (cv.offset_curve_3d.self_intersect == UNKNOWN))) and ( not ('CONFIG_CONTROL_DESIGN.POLYLINE' == TYPEOF(cv.basis_curve)))):
return gbsf_check_curve(cv.offset_curve_3d.basis_curve)
else:
if ('CONFIG_CONTROL_DESIGN.PCURVE' == TYPEOF(cv)):
return gbsf_check_curve(cv.pcurve.reference_to_curve.representation.items[1]) and gbsf_check_surface(cv.pcurve.basis_surface)
else:
if ('CONFIG_CONTROL_DESIGN.POLYLINE' == TYPEOF(cv)):
if (SIZEOF(cv.polyline.points) >= 3):
return TRUE
else:
if ('CONFIG_CONTROL_DESIGN.SURFACE_CURVE' == TYPEOF(cv)):
if (gbsf_check_curve(cv.surface_curve.curve_3d)):
for i in range(1,SIZEOF(cv.surface_curve.associated_geometry),1):
if ('CONFIG_CONTROL_DESIGN.SURFACE' == TYPEOF(cv.surface_curve.associated_geometry[i])):
if ( not gbsf_check_surface(cv.surface_curve.associated_geometry[i])):
return FALSE
else:
if ('CONFIG_CONTROL_DESIGN.PCURVE' == TYPEOF(cv.surface_curve.associated_geometry[i])):
if ( not gbsf_check_curve(cv.surface_curve.associated_geometry[i])):
return FALSE
return TRUE
else:
if ('CONFIG_CONTROL_DESIGN.TRIMMED_CURVE' == TYPEOF(cv)):
if (SIZEOF(['CONFIG_CONTROL_DESIGN.LINE','CONFIG_CONTROL_DESIGN.PARABOLA','CONFIG_CONTROL_DESIGN.HYPERBOLA'] * TYPEOF(cv.trimmed_curve.basis_curve)) == 1):
return TRUE
else:
return gbsf_check_curve(cv.trimmed_curve.basis_curve)
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/keras/_impl/keras/backend.py | python | batch_normalization | (x, mean, var, beta, gamma, epsilon=1e-3) | return nn.batch_normalization(x, mean, var, beta, gamma, epsilon) | Applies batch normalization on x given mean, var, beta and gamma.
I.e. returns:
`output = (x - mean) / (sqrt(var) + epsilon) * gamma + beta`
Arguments:
x: Input tensor or variable.
mean: Mean of batch.
var: Variance of batch.
beta: Tensor with which to center the input.
gamma: Tensor by which to scale the input.
epsilon: Fuzz factor.
Returns:
A tensor. | Applies batch normalization on x given mean, var, beta and gamma. | [
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] | def batch_normalization(x, mean, var, beta, gamma, epsilon=1e-3):
"""Applies batch normalization on x given mean, var, beta and gamma.
I.e. returns:
`output = (x - mean) / (sqrt(var) + epsilon) * gamma + beta`
Arguments:
x: Input tensor or variable.
mean: Mean of batch.
var: Variance of batch.
beta: Tensor with which to center the input.
gamma: Tensor by which to scale the input.
epsilon: Fuzz factor.
Returns:
A tensor.
"""
return nn.batch_normalization(x, mean, var, beta, gamma, epsilon) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/richtext.py | python | RichTextObject_GetTotalMargin | (*args, **kwargs) | return _richtext.RichTextObject_GetTotalMargin(*args, **kwargs) | RichTextObject_GetTotalMargin(DC dc, RichTextBuffer buffer, RichTextAttr attr, int leftMargin,
int rightMargin, int topMargin,
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miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/tensorflow/contrib/rnn/python/ops/rnn_cell.py | python | _get_sharded_variable | (name, shape, dtype, num_shards) | return shards | Get a list of sharded variables with the given dtype. | Get a list of sharded variables with the given dtype. | [
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"""Get a list of sharded variables with the given dtype."""
if num_shards > shape[0]:
raise ValueError("Too many shards: shape=%s, num_shards=%d" %
(shape, num_shards))
unit_shard_size = int(math.floor(shape[0] / num_shards))
remaining_rows = shape[0] - unit_shard_size * num_shards
shards = []
for i in range(num_shards):
current_size = unit_shard_size
if i < remaining_rows:
current_size += 1
shards.append(vs.get_variable(name + "_%d" % i, [current_size] + shape[1:],
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return shards | [
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psi4/psi4 | be533f7f426b6ccc263904e55122899b16663395 | psi4/driver/qcdb/libmintscoordentry.py | python | VariableValue.type | (self) | return 'VariableType' | Gets specialization type of CoordValue | Gets specialization type of CoordValue | [
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return 'VariableType' | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_misc.py | python | AboutDialogInfo.HasLicence | (*args, **kwargs) | return _misc_.AboutDialogInfo_HasLicence(*args, **kwargs) | HasLicence(self) -> bool
Returns ``True`` if the licence property has been set. | HasLicence(self) -> bool | [
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"""
HasLicence(self) -> bool
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return _misc_.AboutDialogInfo_HasLicence(*args, **kwargs) | [
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hughperkins/tf-coriander | 970d3df6c11400ad68405f22b0c42a52374e94ca | tensorflow/contrib/layers/python/layers/feature_column_ops.py | python | _check_forbidden_sequence_columns | (feature_columns) | Recursively cecks `feature_columns` for `_FORBIDDEN_SEQUENCE_COLUMNS`. | Recursively cecks `feature_columns` for `_FORBIDDEN_SEQUENCE_COLUMNS`. | [
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"""Recursively cecks `feature_columns` for `_FORBIDDEN_SEQUENCE_COLUMNS`."""
all_feature_columns = _gather_feature_columns(feature_columns)
for feature_column in all_feature_columns:
if isinstance(feature_column, _FORBIDDEN_SEQUENCE_COLUMNS):
raise ValueError(
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type(feature_column).__name__)) | [
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nvdla/sw | 79538ba1b52b040a4a4645f630e457fa01839e90 | umd/external/protobuf-2.6/python/google/protobuf/internal/python_message.py | python | _IsPresent | (item) | Given a (FieldDescriptor, value) tuple from _fields, return true if the
value should be included in the list returned by ListFields(). | Given a (FieldDescriptor, value) tuple from _fields, return true if the
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if item[0].label == _FieldDescriptor.LABEL_REPEATED:
return bool(item[1])
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/codecs.py | python | IncrementalEncoder.getstate | (self) | return 0 | Return the current state of the encoder. | Return the current state of the encoder. | [
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ceph/ceph | 959663007321a369c83218414a29bd9dbc8bda3a | src/pybind/mgr/crash/module.py | python | Module.timestamp_filter | (self, f: Callable[[datetime.datetime], bool]) | return filter(inner, self.crashes.items()) | Filter crash reports by timestamp.
:param f: f(time) return true to keep crash report
:returns: crash reports for which f(time) returns true | Filter crash reports by timestamp. | [
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"""
Filter crash reports by timestamp.
:param f: f(time) return true to keep crash report
:returns: crash reports for which f(time) returns true
"""
def inner(pair: Tuple[str, CrashT]) -> bool:
_, crash = pair
time = self.time_from_string(cast(str, crash["timestamp"]))
return f(time)
assert self.crashes is not None
return filter(inner, self.crashes.items()) | [
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BlzFans/wke | b0fa21158312e40c5fbd84682d643022b6c34a93 | cygwin/lib/python2.6/logging/handlers.py | python | SocketHandler.emit | (self, record) | Emit a record.
Pickles the record and writes it to the socket in binary format.
If there is an error with the socket, silently drop the packet.
If there was a problem with the socket, re-establishes the
socket. | Emit a record. | [
"Emit",
"a",
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] | def emit(self, record):
"""
Emit a record.
Pickles the record and writes it to the socket in binary format.
If there is an error with the socket, silently drop the packet.
If there was a problem with the socket, re-establishes the
socket.
"""
try:
s = self.makePickle(record)
self.send(s)
except (KeyboardInterrupt, SystemExit):
raise
except:
self.handleError(record) | [
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microsoft/CNTK | e9396480025b9ca457d26b6f33dd07c474c6aa04 | bindings/python/cntk/logging/graph.py | python | plot | (root, filename=None) | return model | Walks through every node of the graph starting at ``root``,
creates a network graph, and returns a network description. If ``filename`` is
specified, it outputs a DOT, PNG, PDF, or SVG file depending on the file name's suffix.
Requirements:
* for DOT output: `pydot_ng <https://pypi.python.org/pypi/pydot-ng>`__
* for PNG, PDF, and SVG output: `pydot_ng <https://pypi.python.org/pypi/pydot-ng>`__
and `graphviz <http://graphviz.org>`__ (GraphViz executable has to be in the system's PATH).
Args:
node (graph node): the node to start the journey from
filename (`str`, default None): file with extension '.dot', 'png', 'pdf', or 'svg'
to denote what format should be written. If `None` then nothing
will be plotted, and the returned string can be used to debug the graph.
Returns:
`str` describing the graph | Walks through every node of the graph starting at ``root``,
creates a network graph, and returns a network description. If ``filename`` is
specified, it outputs a DOT, PNG, PDF, or SVG file depending on the file name's suffix. | [
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'''
Walks through every node of the graph starting at ``root``,
creates a network graph, and returns a network description. If ``filename`` is
specified, it outputs a DOT, PNG, PDF, or SVG file depending on the file name's suffix.
Requirements:
* for DOT output: `pydot_ng <https://pypi.python.org/pypi/pydot-ng>`__
* for PNG, PDF, and SVG output: `pydot_ng <https://pypi.python.org/pypi/pydot-ng>`__
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Args:
node (graph node): the node to start the journey from
filename (`str`, default None): file with extension '.dot', 'png', 'pdf', or 'svg'
to denote what format should be written. If `None` then nothing
will be plotted, and the returned string can be used to debug the graph.
Returns:
`str` describing the graph
'''
if filename:
suffix = os.path.splitext(filename)[1].lower()
if suffix not in ('.svg', '.pdf', '.png', '.dot'):
raise ValueError('only file extensions ".svg", ".pdf", ".png", and ".dot" are supported')
else:
suffix = None
if filename:
try:
import pydot_ng as pydot
except ImportError:
raise ImportError("Unable to import pydot_ng, which is required to output SVG, PDF, PNG, and DOT format.")
# initialize a dot object to store vertices and edges
dot_object = pydot.Dot(graph_name="network_graph", rankdir='TB')
dot_object.set_node_defaults(shape='rectangle', fixedsize='false',
style='filled',
fillcolor='lightgray',
height=.85, width=.85, fontsize=12)
dot_object.set_edge_defaults(fontsize=10)
# string to store model
model = []
root = root.root_function
root_uid = root.uid
stack = [root]
visited = set() # [uid] instead of node object itself, as this gives us duplicate entries for nodes with multiple outputs
primitive_op_map = {
'Plus': '+',
'Minus': '-',
'ElementTimes': '*',
'Times': '@',
}
function_nodes = {} # [uid] -> dot node
def node_desc(node):
name = "<font point-size=\"10\" face=\"sans\">'%s'</font> <br/>"%node.name
try:
name += "<b><font point-size=\"14\" face=\"sans\">%s</font></b> <br/>"%node.op_name
except AttributeError:
pass
name += "<font point-size=\"8\" face=\"sans\">%s</font>"%node.uid
return '<' + name + '>'
def shape_desc(node):
dyn_axes = node.dynamic_axes
dyn = '[#' + ',*' * (len(dyn_axes) - 1) + ']' if len(dyn_axes) > 0 else ''
# the '#' indicates the batch axis, while * indicate dynamic axes (which can be sequences)
return dyn + str(node.shape)
static_shape = str(node.shape)
return '"#dyn: %i\nstatic: %s"'%(num_dyn_axes, static_shape)
while stack:
node = stack.pop(0)
if node.uid in visited:
continue
try:
# Function node
node = node.root_function
stack = list(node.root_function.inputs) + stack
# add current Function node
def lazy_create_node(node):
if node.uid in function_nodes: # dot node already exists
return function_nodes[node.uid]
if node.is_primitive and not node.is_block and len(node.outputs) == 1 and node.output.name == node.name: # skip the node name if redundant
op_name = primitive_op_map.get(node.op_name, node.op_name)
render_as_primitive = len(op_name) <= 4
size = 0.4 if render_as_primitive else 0.6
cur_node = pydot.Node(node.uid, label='"' + op_name + '"',
shape='ellipse' if render_as_primitive else 'box',
fixedsize='true' if render_as_primitive else 'false', height=size, width=size,
fontsize=20 if render_as_primitive and len(op_name) == 1 else 12 ,
penwidth=4 if node.op_name != 'Pass' and node.op_name != 'ParameterOrder' else 1)
# TODO: Would be cool, if the user could pass a dictionary with overrides. But maybe for a later version.
else:
f_name = '\n' + node.name + '()' if node.name else ''
cur_node = pydot.Node(node.uid, label='"' + node.op_name + f_name + '"',
fixedsize='true', height=1, width=1.3,
penwidth=4 if node.op_name != 'Pass' and node.op_name != 'ParameterOrder' else 1)
dot_object.add_node(cur_node)
function_nodes[node.uid] = cur_node
return cur_node
# add current node
line = [node.op_name]
line.append('(')
if filename:
cur_node = lazy_create_node(node)
dot_object.add_node(cur_node)
# add node's inputs
for i, input in enumerate(node.inputs):
# Suppress Constants inside BlockFunctions, since those are really private to the BlockFunction.
# Still show Parameters, so users know what parameters it learns, e.g. a layer.
from cntk import cntk_py
if node.is_block and isinstance (input, cntk_py.Variable) and input.is_constant:
continue
line.append(input.uid)
if i != len(node.inputs) - 1:
line.append(', ')
if filename:
if input.is_input:
shape = 'invhouse'
color = 'yellow'
elif input.is_placeholder:
shape = 'invhouse'
color = 'grey'
elif input.is_parameter:
shape = 'diamond'
color = 'green'
elif input.is_constant:
shape = 'rectangle'
color = 'lightblue'
else: # is_output
shape = 'invhouse'
color = 'grey'
if isinstance (input, cntk_py.Variable) and not input.is_output:
name = 'Parameter' if input.is_parameter else 'Constant' if input.is_constant else 'Input' if input.is_input else 'Placeholder'
if input.name:
if name == 'Parameter': # don't say 'Parameter' for named parameters, it's already indicated by being a box
name = input.name
else:
name = name + '\n' + input.name
name += '\n' + shape_desc(input)
if input.is_input or input.is_placeholder: # graph inputs are eggs (since dot has no oval)
input_node = pydot.Node(input.uid, shape='egg', label=name, fixedsize='true', height=1, width=1.3, penwidth=4) # wish it had an oval
elif not input.name and input.is_constant and (input.shape == () or input.shape == (1,)): # unnamed scalar constants are just shown as values
input_node = pydot.Node(input.uid, shape='box', label=str(input.as_constant().value), color='white', fillcolor='white', height=0.3, width=0.4)
else: # parameters and constants are boxes
input_node = pydot.Node(input.uid, shape='box', label=name, height=0.6, width=1)
else: # output variables never get drawn except the final output
assert(isinstance (input, cntk_py.Variable))
input_node = lazy_create_node(input.owner) # connect to where the output comes from directly, no need to draw it
dot_object.add_node(input_node)
label = input.name if input.name else input.uid # the Output variables have no name if the function has none
label += '\n' + shape_desc(input)
dot_object.add_edge(pydot.Edge(input_node, cur_node, label=label))
# add node's output
line.append(') -> ')
line = ''.join(line)
for n in node.outputs:
model.append(line + n.uid + ';\n')
if (filename):
if node.uid == root_uid: # only final network outputs are drawn
for output in node.outputs:
final_node = pydot.Node(output.uid, shape='egg', label=output.name + '\n' + shape_desc(output),
fixedsize='true', height=1, width=1.3, penwidth=4)
dot_object.add_node(final_node)
dot_object.add_edge(pydot.Edge(cur_node, final_node, label=shape_desc(output)))
except AttributeError:
# OutputVariable node
try:
if node.is_output:
stack.insert(0, node.owner)
except AttributeError:
pass
visited.add(node.uid)
if filename:
if suffix == '.svg':
dot_object.write_svg(filename, prog='dot')
elif suffix == '.pdf':
dot_object.write_pdf(filename, prog='dot')
elif suffix == '.png':
dot_object.write_png(filename, prog='dot')
else:
dot_object.write_raw(filename)
model = "\n".join(reversed(model))
return model | [
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BlzFans/wke | b0fa21158312e40c5fbd84682d643022b6c34a93 | cygwin/lib/python2.6/zipfile.py | python | ZipFile.__init__ | (self, file, mode="r", compression=ZIP_STORED, allowZip64=False) | Open the ZIP file with mode read "r", write "w" or append "a". | Open the ZIP file with mode read "r", write "w" or append "a". | [
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CNugteren/CLBlast | 4500a03440e2cc54998c0edab366babf5e504d67 | scripts/generator/generator/routine.py | python | Routine.arguments_def_c | (self, flavour) | return (self.options_def_c() + self.sizes_def() +
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qboticslabs/mastering_ros | d83e78f30acc45b0f18522c1d5fae3a7f52974b9 | chapter_9_codes/chefbot/chefbot/chefbot_bringup/scripts/bkup_working/arduino.py | python | Arduino._WriteSpeedControllerParams | (self, speedControllerParams) | Writes the speed controller parameters (drive gains (PID), and command timeout) to the Arduino controller. | Writes the speed controller parameters (drive gains (PID), and command timeout) to the Arduino controller. | [
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""" Writes the speed controller parameters (drive gains (PID), and command timeout) to the Arduino controller. """
rospy.logdebug("Handling '_WriteSpeedControllerParams'; received parameters " + str(speedControllerParams))
message = 'SpeedCo %d %d %d %d %d %d %d %d %d %d\r' % self._GetBaseAndExponents(speedControllerParams)
message = 'SpeedCo 763 -4 3700 -4 9750\r'
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/json/encoder.py | python | JSONEncoder.default | (self, o) | Implement this method in a subclass such that it returns
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sdhash/sdhash | b9eff63e4e5867e910f41fd69032bbb1c94a2a5e | sdhash-ui/cherrypy/lib/sessions.py | python | RamSession.release_lock | (self) | Release the lock on the currently-loaded session data. | Release the lock on the currently-loaded session data. | [
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Pay20Y/FOTS_TF | c42ea59a20c28d506fee35cfb4c553b0cb20eee8 | nets/resnet_utils.py | python | stack_blocks_dense | (net, blocks, output_stride=None,
outputs_collections=None) | return net | Stacks ResNet `Blocks` and controls output feature density.
First, this function creates scopes for the ResNet in the form of
'block_name/unit_1', 'block_name/unit_2', etc.
Second, this function allows the user to explicitly control the ResNet
output_stride, which is the ratio of the input to output spatial resolution.
This is useful for dense prediction tasks such as semantic segmentation or
object detection.
Most ResNets consist of 4 ResNet blocks and subsample the activations by a
factor of 2 when transitioning between consecutive ResNet blocks. This results
to a nominal ResNet output_stride equal to 8. If we set the output_stride to
half the nominal network stride (e.g., output_stride=4), then we compute
responses twice.
Control of the output feature density is implemented by atrous convolution.
Args:
net: A `Tensor` of size [batch, height, width, channels].
blocks: A list of length equal to the number of ResNet `Blocks`. Each
element is a ResNet `Block` object describing the units in the `Block`.
output_stride: If `None`, then the output will be computed at the nominal
network stride. If output_stride is not `None`, it specifies the requested
ratio of input to output spatial resolution, which needs to be equal to
the product of unit strides from the start up to some level of the ResNet.
For example, if the ResNet employs units with strides 1, 2, 1, 3, 4, 1,
then valid values for the output_stride are 1, 2, 6, 24 or None (which
is equivalent to output_stride=24).
outputs_collections: Collection to add the ResNet block outputs.
Returns:
net: Output tensor with stride equal to the specified output_stride.
Raises:
ValueError: If the target output_stride is not valid. | Stacks ResNet `Blocks` and controls output feature density. | [
"Stacks",
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"and",
"controls",
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"feature",
"density",
"."
] | def stack_blocks_dense(net, blocks, output_stride=None,
outputs_collections=None):
"""Stacks ResNet `Blocks` and controls output feature density.
First, this function creates scopes for the ResNet in the form of
'block_name/unit_1', 'block_name/unit_2', etc.
Second, this function allows the user to explicitly control the ResNet
output_stride, which is the ratio of the input to output spatial resolution.
This is useful for dense prediction tasks such as semantic segmentation or
object detection.
Most ResNets consist of 4 ResNet blocks and subsample the activations by a
factor of 2 when transitioning between consecutive ResNet blocks. This results
to a nominal ResNet output_stride equal to 8. If we set the output_stride to
half the nominal network stride (e.g., output_stride=4), then we compute
responses twice.
Control of the output feature density is implemented by atrous convolution.
Args:
net: A `Tensor` of size [batch, height, width, channels].
blocks: A list of length equal to the number of ResNet `Blocks`. Each
element is a ResNet `Block` object describing the units in the `Block`.
output_stride: If `None`, then the output will be computed at the nominal
network stride. If output_stride is not `None`, it specifies the requested
ratio of input to output spatial resolution, which needs to be equal to
the product of unit strides from the start up to some level of the ResNet.
For example, if the ResNet employs units with strides 1, 2, 1, 3, 4, 1,
then valid values for the output_stride are 1, 2, 6, 24 or None (which
is equivalent to output_stride=24).
outputs_collections: Collection to add the ResNet block outputs.
Returns:
net: Output tensor with stride equal to the specified output_stride.
Raises:
ValueError: If the target output_stride is not valid.
"""
# The current_stride variable keeps track of the effective stride of the
# activations. This allows us to invoke atrous convolution whenever applying
# the next residual unit would result in the activations having stride larger
# than the target output_stride.
current_stride = 1
# The atrous convolution rate parameter.
rate = 1
for block in blocks:
with tf.variable_scope(block.scope, 'block', [net]) as sc:
for i, unit in enumerate(block.args):
if output_stride is not None and current_stride > output_stride:
raise ValueError('The target output_stride cannot be reached.')
with tf.variable_scope('unit_%d' % (i + 1), values=[net]):
unit_depth, unit_depth_bottleneck, unit_stride = unit
# If we have reached the target output_stride, then we need to employ
# atrous convolution with stride=1 and multiply the atrous rate by the
# current unit's stride for use in subsequent layers.
if output_stride is not None and current_stride == output_stride:
net = block.unit_fn(net,
depth=unit_depth,
depth_bottleneck=unit_depth_bottleneck,
stride=1,
rate=rate)
rate *= unit_stride
else:
net = block.unit_fn(net,
depth=unit_depth,
depth_bottleneck=unit_depth_bottleneck,
stride=unit_stride,
rate=1)
current_stride *= unit_stride
print(sc.name, net.shape)
net = slim.utils.collect_named_outputs(outputs_collections, sc.name, net)
if output_stride is not None and current_stride != output_stride:
raise ValueError('The target output_stride cannot be reached.')
return net | [
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] | https://github.com/Pay20Y/FOTS_TF/blob/c42ea59a20c28d506fee35cfb4c553b0cb20eee8/nets/resnet_utils.py#L126-L206 | |
priyankchheda/algorithms | c361aa9071573fa9966d5b02d05e524815abcf2b | red_black_tree/red_black_tree.py | python | RedBlackTree.sibling | (node) | return node.parent.left | returns sibling of given node | returns sibling of given node | [
"returns",
"sibling",
"of",
"given",
"node"
] | def sibling(node):
""" returns sibling of given node """
if node is None or node.parent is None:
return None
if node is node.parent.left:
return node.parent.right
return node.parent.left | [
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baoboa/pyqt5 | 11d5f43bc6f213d9d60272f3954a0048569cfc7c | examples/ipc/sharedmemory/sharedmemory.py | python | Dialog.loadFromFile | (self) | This slot function is called when the "Load Image From File..."
button is pressed on the firs Dialog process. First, it tests whether
the process is already connected to a shared memory segment and, if so,
detaches from that segment. This ensures that we always start the
example from the beginning if we run it multiple times with the same
two Dialog processes. After detaching from an existing shared memory
segment, the user is prompted to select an image file. The selected
file is loaded into a QImage. The QImage is displayed in the Dialog
and streamed into a QBuffer with a QDataStream. Next, it gets a new
shared memory segment from the system big enough to hold the image data
in the QBuffer, and it locks the segment to prevent the second Dialog
process from accessing it. Then it copies the image from the QBuffer
into the shared memory segment. Finally, it unlocks the shared memory
segment so the second Dialog process can access it. After self
function runs, the user is expected to press the "Load Image from
Shared Memory" button on the second Dialog process. | This slot function is called when the "Load Image From File..."
button is pressed on the firs Dialog process. First, it tests whether
the process is already connected to a shared memory segment and, if so,
detaches from that segment. This ensures that we always start the
example from the beginning if we run it multiple times with the same
two Dialog processes. After detaching from an existing shared memory
segment, the user is prompted to select an image file. The selected
file is loaded into a QImage. The QImage is displayed in the Dialog
and streamed into a QBuffer with a QDataStream. Next, it gets a new
shared memory segment from the system big enough to hold the image data
in the QBuffer, and it locks the segment to prevent the second Dialog
process from accessing it. Then it copies the image from the QBuffer
into the shared memory segment. Finally, it unlocks the shared memory
segment so the second Dialog process can access it. After self
function runs, the user is expected to press the "Load Image from
Shared Memory" button on the second Dialog process. | [
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] | def loadFromFile(self):
""" This slot function is called when the "Load Image From File..."
button is pressed on the firs Dialog process. First, it tests whether
the process is already connected to a shared memory segment and, if so,
detaches from that segment. This ensures that we always start the
example from the beginning if we run it multiple times with the same
two Dialog processes. After detaching from an existing shared memory
segment, the user is prompted to select an image file. The selected
file is loaded into a QImage. The QImage is displayed in the Dialog
and streamed into a QBuffer with a QDataStream. Next, it gets a new
shared memory segment from the system big enough to hold the image data
in the QBuffer, and it locks the segment to prevent the second Dialog
process from accessing it. Then it copies the image from the QBuffer
into the shared memory segment. Finally, it unlocks the shared memory
segment so the second Dialog process can access it. After self
function runs, the user is expected to press the "Load Image from
Shared Memory" button on the second Dialog process.
"""
if self.sharedMemory.isAttached():
self.detach()
self.ui.label.setText("Select an image file")
fileName, _ = QFileDialog.getOpenFileName(self, None, None,
"Images (*.png *.xpm *.jpg)")
image = QImage()
if not image.load(fileName):
self.ui.label.setText(
"Selected file is not an image, please select another.")
return
self.ui.label.setPixmap(QPixmap.fromImage(image))
# Load into shared memory.
buf = QBuffer()
buf.open(QBuffer.ReadWrite)
out = QDataStream(buf)
out << image
size = buf.size()
if not self.sharedMemory.create(size):
self.ui.label.setText("Unable to create shared memory segment.")
return
size = min(self.sharedMemory.size(), size)
self.sharedMemory.lock()
# Copy image data from buf into shared memory area.
self.sharedMemory.data()[:size] = buf.data()[:size]
self.sharedMemory.unlock() | [
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] | https://github.com/baoboa/pyqt5/blob/11d5f43bc6f213d9d60272f3954a0048569cfc7c/examples/ipc/sharedmemory/sharedmemory.py#L84-L133 |
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