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cms-sw/cmssw | fd9de012d503d3405420bcbeec0ec879baa57cf2 | Configuration/DataProcessing/python/Utils.py | python | stepSKIMPRODUCER | (PhysicsSkims) | return step | _stepSKIMPRODUCER_
Creates and returns the configuration string for the SKIM step
starting from the list of skims to be run. | _stepSKIMPRODUCER_ | [
"_stepSKIMPRODUCER_"
] | def stepSKIMPRODUCER(PhysicsSkims):
"""
_stepSKIMPRODUCER_
Creates and returns the configuration string for the SKIM step
starting from the list of skims to be run.
"""
step = ''
if len(PhysicsSkims) >0 :
step = ',SKIM:'+('+'.join(PhysicsSkims))
return step | [
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google/tink | 59bb34495d1cb8f9d9dbc0f0a52c4f9e21491a14 | python/tink/jwt/_raw_jwt.py | python | raw_jwt_from_json | (type_header: Optional[str], payload: str) | return RawJwt._from_json(type_header, payload) | Internal function used to verify JWT token. | Internal function used to verify JWT token. | [
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"""Internal function used to verify JWT token."""
return RawJwt._from_json(type_header, payload) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/psutil/_psosx.py | python | net_if_stats | () | return ret | Get NIC stats (isup, duplex, speed, mtu). | Get NIC stats (isup, duplex, speed, mtu). | [
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"""Get NIC stats (isup, duplex, speed, mtu)."""
names = net_io_counters().keys()
ret = {}
for name in names:
try:
mtu = cext_posix.net_if_mtu(name)
isup = cext_posix.net_if_flags(name)
duplex, speed = cext_posix.net_if_duplex_speed(name)
except OSError as err:
# https://github.com/giampaolo/psutil/issues/1279
if err.errno != errno.ENODEV:
raise
else:
if hasattr(_common, 'NicDuplex'):
duplex = _common.NicDuplex(duplex)
ret[name] = _common.snicstats(isup, duplex, speed, mtu)
return ret | [
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pmq20/node-packer | 12c46c6e44fbc14d9ee645ebd17d5296b324f7e0 | lts/deps/npm/node_modules/node-gyp/gyp/pylib/gyp/generator/ninja.py | python | NinjaWriter.WriteMacInfoPlist | (self, partial_info_plist, bundle_depends) | Write build rules for bundle Info.plist files. | Write build rules for bundle Info.plist files. | [
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] | def WriteMacInfoPlist(self, partial_info_plist, bundle_depends):
"""Write build rules for bundle Info.plist files."""
info_plist, out, defines, extra_env = gyp.xcode_emulation.GetMacInfoPlist(
generator_default_variables['PRODUCT_DIR'],
self.xcode_settings, self.GypPathToNinja)
if not info_plist:
return
out = self.ExpandSpecial(out)
if defines:
# Create an intermediate file to store preprocessed results.
intermediate_plist = self.GypPathToUniqueOutput(
os.path.basename(info_plist))
defines = ' '.join([Define(d, self.flavor) for d in defines])
info_plist = self.ninja.build(
intermediate_plist, 'preprocess_infoplist', info_plist,
variables=[('defines',defines)])
env = self.GetSortedXcodeEnv(additional_settings=extra_env)
env = self.ComputeExportEnvString(env)
if partial_info_plist:
intermediate_plist = self.GypPathToUniqueOutput('merged_info.plist')
info_plist = self.ninja.build(
intermediate_plist, 'merge_infoplist',
[partial_info_plist, info_plist])
keys = self.xcode_settings.GetExtraPlistItems(self.config_name)
keys = QuoteShellArgument(json.dumps(keys), self.flavor)
isBinary = self.xcode_settings.IsBinaryOutputFormat(self.config_name)
self.ninja.build(out, 'copy_infoplist', info_plist,
variables=[('env', env), ('keys', keys),
('binary', isBinary)])
bundle_depends.append(out) | [
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LiXizhi/NPLRuntime | a42720e5fe9a6960e0a9ce40bbbcd809192906be | Client/trunk/externals/assimp-4.0.0/port/PyAssimp/scripts/transformations.py | python | unit_vector | (data, axis=None, out=None) | Return ndarray normalized by length, i.e. eucledian norm, along axis.
>>> v0 = numpy.random.random(3)
>>> v1 = unit_vector(v0)
>>> numpy.allclose(v1, v0 / numpy.linalg.norm(v0))
True
>>> v0 = numpy.random.rand(5, 4, 3)
>>> v1 = unit_vector(v0, axis=-1)
>>> v2 = v0 / numpy.expand_dims(numpy.sqrt(numpy.sum(v0*v0, axis=2)), 2)
>>> numpy.allclose(v1, v2)
True
>>> v1 = unit_vector(v0, axis=1)
>>> v2 = v0 / numpy.expand_dims(numpy.sqrt(numpy.sum(v0*v0, axis=1)), 1)
>>> numpy.allclose(v1, v2)
True
>>> v1 = numpy.empty((5, 4, 3), dtype=numpy.float64)
>>> unit_vector(v0, axis=1, out=v1)
>>> numpy.allclose(v1, v2)
True
>>> list(unit_vector([]))
[]
>>> list(unit_vector([1.0]))
[1.0] | Return ndarray normalized by length, i.e. eucledian norm, along axis. | [
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] | def unit_vector(data, axis=None, out=None):
"""Return ndarray normalized by length, i.e. eucledian norm, along axis.
>>> v0 = numpy.random.random(3)
>>> v1 = unit_vector(v0)
>>> numpy.allclose(v1, v0 / numpy.linalg.norm(v0))
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>>> v0 = numpy.random.rand(5, 4, 3)
>>> v1 = unit_vector(v0, axis=-1)
>>> v2 = v0 / numpy.expand_dims(numpy.sqrt(numpy.sum(v0*v0, axis=2)), 2)
>>> numpy.allclose(v1, v2)
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>>> v1 = unit_vector(v0, axis=1)
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>>> unit_vector(v0, axis=1, out=v1)
>>> numpy.allclose(v1, v2)
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>>> list(unit_vector([]))
[]
>>> list(unit_vector([1.0]))
[1.0]
"""
if out is None:
data = numpy.array(data, dtype=numpy.float64, copy=True)
if data.ndim == 1:
data /= math.sqrt(numpy.dot(data, data))
return data
else:
if out is not data:
out[:] = numpy.array(data, copy=False)
data = out
length = numpy.atleast_1d(numpy.sum(data*data, axis))
numpy.sqrt(length, length)
if axis is not None:
length = numpy.expand_dims(length, axis)
data /= length
if out is None:
return data | [
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miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/tensorflow/python/ops/tensor_array_ops.py | python | TensorArray.write | (self, index, value, name=None) | Write `value` into index `index` of the TensorArray.
Args:
index: 0-D. int32 scalar with the index to write to.
value: N-D. Tensor of type `dtype`. The Tensor to write to this index.
name: A name for the operation (optional).
Returns:
A new TensorArray object with flow that ensures the write occurs.
Use this object all for subsequent operations.
Raises:
ValueError: if there are more writers than specified. | Write `value` into index `index` of the TensorArray. | [
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] | def write(self, index, value, name=None):
"""Write `value` into index `index` of the TensorArray.
Args:
index: 0-D. int32 scalar with the index to write to.
value: N-D. Tensor of type `dtype`. The Tensor to write to this index.
name: A name for the operation (optional).
Returns:
A new TensorArray object with flow that ensures the write occurs.
Use this object all for subsequent operations.
Raises:
ValueError: if there are more writers than specified.
"""
with ops.colocate_with(self._handle):
flow_out = gen_data_flow_ops._tensor_array_write(
handle=self._handle, index=index, value=value, flow_in=self._flow,
name=name)
ta = TensorArray(dtype=self._dtype, handle=self._handle)
ta._flow = flow_out
ta._infer_shape = self._infer_shape
ta._elem_shape = self._elem_shape
if ta._infer_shape:
val_shape = flow_out.op.inputs[2].get_shape()
if ta._elem_shape:
if not val_shape == ta._elem_shape[0]:
raise ValueError(
"Inconsistent shapes: saw %s but expected %s "
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else:
ta._elem_shape.append(val_shape)
return ta | [
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/klampt/model/contact.py | python | ContactPoint.fromlist | (self,v) | Reads the values x,n, and kFriction from the 7-list v. | Reads the values x,n, and kFriction from the 7-list v. | [
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"""Reads the values x,n, and kFriction from the 7-list v."""
if len(v) != 7: raise ValueError("ContactPoint can only be converted from a 7-element list")
self.x = v[0:3]
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self.kFriction = v[6] | [
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openvinotoolkit/openvino | dedcbeafa8b84cccdc55ca64b8da516682b381c7 | tools/mo/openvino/tools/mo/middle/InterpolateSequenceToInterpolate.py | python | replace_sequence | (seq: List[Node], graph: Graph) | This function replaces a sequence of consecutive Interpolate layers with one Interpolate layer,
if modes of all nodes of a sequence are the same.
:param seq: sequence of Interpolate layers
:param graph: graph to which nodes of seq belong
:return: Nothing | This function replaces a sequence of consecutive Interpolate layers with one Interpolate layer,
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:param seq: sequence of Interpolate layers
:param graph: graph to which nodes of seq belong
:return: Nothing | [
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"""
This function replaces a sequence of consecutive Interpolate layers with one Interpolate layer,
if modes of all nodes of a sequence are the same.
:param seq: sequence of Interpolate layers
:param graph: graph to which nodes of seq belong
:return: Nothing
"""
if not seq:
return
if len(seq) == 1:
return
modes = set([n.mode for n in seq])
if len(modes) != 1:
return
dims_and_scales_ = []
# Each element of the list dims_and_scales_ is a pair
# (axis, output size for this axis) (opset1)
# or
# (axis, output size for this axis, output scales for this axis) (opset4)
if seq[0].get_opset() == 'opset1':
for interp in seq:
dims_and_scales_.extend(zip(Interpolate.get_axes(interp),
interp.in_port(1).get_connection().get_source().data.get_value()))
axis_to_size = sorted(list(dict(dims_and_scales_).items()), key=lambda x: x[0])
axes_of_node = int64_array([z[0] for z in axis_to_size])
sizes = shape_array([z[1] for z in axis_to_size])
scales = np.ones(len(axis_to_size), dtype=np.float32)
else:
for interp in seq:
dims_and_scales_.extend(zip(Interpolate.get_axes(interp),
interp.in_port(1).get_connection().get_source().data.get_value(),
interp.in_port(2).get_connection().get_source().data.get_value()))
axis_to_size = sorted(dims_and_scales_, key=lambda x: x[0])
axes_of_node = int64_array([z[0] for z in axis_to_size])
sizes = shape_array([z[1] for z in axis_to_size])
scales = mo_array([z[2] for z in axis_to_size])
fst_interp_node = seq[0]
last_interp_node = seq[-1]
last_interp_node_name = last_interp_node.soft_get('name', last_interp_node.id)
attributes = get_interpolate_attributes(fst_interp_node)
opset = fst_interp_node.get_opset()
if opset == 'opset1':
attributes['axes'] = axes_of_node
interp_node = create_op_with_const_inputs(graph, Interpolate, {1: sizes}, attributes)
fst_interp_connection = fst_interp_node.in_port(0).get_connection()
fst_interp_connection.set_destination(interp_node.in_port(0))
last_interp_node.out_port(0).get_connection().set_source(interp_node.out_port(0))
else:
attributes['in_ports_count'] = 4
interp_node = create_op_with_const_inputs(graph, Interpolate,
{1: sizes, 2: scales, 3: axes_of_node},
attributes)
fst_interp_connection = fst_interp_node.in_port(0).get_connection()
fst_interp_connection.set_destination(interp_node.in_port(0))
last_interp_node.out_port(0).get_connection().set_source(interp_node.out_port(0))
rename_nodes([(last_interp_node, last_interp_node_name + '/delete'), (interp_node, last_interp_node_name)]) | [
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yuxng/DA-RNN | 77fbb50b4272514588a10a9f90b7d5f8d46974fb | lib/datasets/shapenet_single.py | python | shapenet_single._get_default_path | (self) | return os.path.join(datasets.ROOT_DIR, 'data', 'ShapeNetSingle') | Return the default path where KITTI is expected to be installed. | Return the default path where KITTI is expected to be installed. | [
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Return the default path where KITTI is expected to be installed.
"""
return os.path.join(datasets.ROOT_DIR, 'data', 'ShapeNetSingle') | [
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H-uru/Plasma | c2140ea046e82e9c199e257a7f2e7edb42602871 | Scripts/Python/xSitAugment.py | python | xSitAugment.OnControlKeyEvent | (self,controlKey,activeFlag) | Control key events... anything we're interested in? | Control key events... anything we're interested in? | [
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PtDebugPrint("Got controlKey event %d and its activeFlage is %d" % (controlKey,activeFlag))
if controlKey == PlasmaControlKeys.kKeyExitMode:
self.IQuitDialog() | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/closure_linter/closure_linter/statetracker.py | python | DocComment.IsInvalidated | (self) | return self.invalidated | Test whether Invalidate() has been called. | Test whether Invalidate() has been called. | [
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quantOS-org/DataCore | e2ef9bd2c22ee9e2845675b6435a14fa607f3551 | mdlink/deps/windows/protobuf-2.5.0/python/google/protobuf/message.py | python | Message.ListFields | (self) | Returns a list of (FieldDescriptor, value) tuples for all
fields in the message which are not empty. A singular field is non-empty
if HasField() would return true, and a repeated field is non-empty if
it contains at least one element. The fields are ordered by field
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cathywu/Sentiment-Analysis | eb501fd1375c0c3f3ab430f963255f1bb858e659 | PyML-0.7.9/PyML/containers/ker.py | python | expandKernel | (inKernelFile, referenceKernelFile, outKernelFile, **args) | Given a kernel matrix that might have missing entries, fill those as 0
on the basis of the patterns in a reference kernel (it is checked that
the reference kernel is sorted).
:Parameters:
- `inKernelFile` - input kernel file name
- `referenceKernelFile` - file name for the reference kernel
- `outKernelFile` - file name to output expanded kernel | Given a kernel matrix that might have missing entries, fill those as 0
on the basis of the patterns in a reference kernel (it is checked that
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- `referenceKernelFile` - file name for the reference kernel
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"""
if 'format' in args :
format = args['format']
else :
format = 'gist'
delim = '\t'
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import misc
import numpy
inKernel = KernelData(inKernelFile)
refKernel = KernelData(referenceKernelFile)
print 'loaded data'
ids = refKernel.labels.patternID[:]
ids.sort()
if ids != refKernel.labels.patternID :
raise ValueError, 'reference kernel not sorted'
idDict = misc.list2dict(inKernel.labels.patternID)
outKernel = open(outKernelFile, 'w')
if format == 'gist' :
outKernel.write(outKernelFile + delim)
outKernel.write(delim.join(ids) + '\n')
for i in range(len(refKernel)) :
outKernel.write(id1 + delim)
for j in range(len(refKernel)) :
values = numpy.zeros(len(refKernel), numpy.float_)
if ids[i] in idDict and ids[j] in idDict :
values[j] = inKernel.kernel.eval(inKernel,
idDict[ids[i]],idDict[ids[j]])
tokens = [str(value) for value in values]
outKernel.write(delim.join(tokens) + '\n') | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/lib-tk/Tkinter.py | python | Misc.winfo_reqheight | (self) | return getint(
self.tk.call('winfo', 'reqheight', self._w)) | Return requested height of this widget. | Return requested height of this widget. | [
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] | def winfo_reqheight(self):
"""Return requested height of this widget."""
return getint(
self.tk.call('winfo', 'reqheight', self._w)) | [
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ptrkrysik/gr-gsm | 2de47e28ce1fb9a518337bfc0add36c8e3cff5eb | python/receiver/chirpz.py | python | CZT.__call__ | (self, x, axis=-1) | return y.transpose(*trnsp) | Parameters:
----------
x: array
The signal to transform.
axis: int
Array dimension to operate over. The default is the final
dimension.
Returns:
-------
An array of the same dimensions as x, but with the length of the
transformed axis set to m. Note that this is a view on a much
larger array. To save space, you may want to call it as
y = czt(x).copy() | Parameters:
----------
x: array
The signal to transform.
axis: int
Array dimension to operate over. The default is the final
dimension. | [
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The signal to transform.
axis: int
Array dimension to operate over. The default is the final
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Returns:
-------
An array of the same dimensions as x, but with the length of the
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y = czt(x).copy()
"""
x = np.asarray(x)
if x.shape[axis] != self.n:
raise ValueError("CZT defined for length %d, not %d" %
(self.n, x.shape[axis]))
# Calculate transpose coordinates, to allow operation on any given axis
trnsp = np.arange(x.ndim)
trnsp[[axis, -1]] = [-1, axis]
x = x.transpose(*trnsp)
y = ifft(self._Fwk2 * fft(x*self._Awk2, self._nfft))
y = y[..., self._yidx] * self._wk2
return y.transpose(*trnsp) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/scipy/stats/_multivariate.py | python | matrix_normal_frozen.__init__ | (self, mean=None, rowcov=1, colcov=1, seed=None) | Create a frozen matrix normal distribution.
Parameters
----------
%(_matnorm_doc_default_callparams)s
seed : None or int or np.random.RandomState instance, optional
If int or RandomState, use it for drawing the random variates.
If None (or np.random), the global np.random state is used.
Default is None.
Examples
--------
>>> from scipy.stats import matrix_normal
>>> distn = matrix_normal(mean=np.zeros((3,3)))
>>> X = distn.rvs(); X
array([[-0.02976962, 0.93339138, -0.09663178],
[ 0.67405524, 0.28250467, -0.93308929],
[-0.31144782, 0.74535536, 1.30412916]])
>>> distn.pdf(X)
2.5160642368346784e-05
>>> distn.logpdf(X)
-10.590229595124615 | Create a frozen matrix normal distribution. | [
"Create",
"a",
"frozen",
"matrix",
"normal",
"distribution",
"."
] | def __init__(self, mean=None, rowcov=1, colcov=1, seed=None):
"""
Create a frozen matrix normal distribution.
Parameters
----------
%(_matnorm_doc_default_callparams)s
seed : None or int or np.random.RandomState instance, optional
If int or RandomState, use it for drawing the random variates.
If None (or np.random), the global np.random state is used.
Default is None.
Examples
--------
>>> from scipy.stats import matrix_normal
>>> distn = matrix_normal(mean=np.zeros((3,3)))
>>> X = distn.rvs(); X
array([[-0.02976962, 0.93339138, -0.09663178],
[ 0.67405524, 0.28250467, -0.93308929],
[-0.31144782, 0.74535536, 1.30412916]])
>>> distn.pdf(X)
2.5160642368346784e-05
>>> distn.logpdf(X)
-10.590229595124615
"""
self._dist = matrix_normal_gen(seed)
self.dims, self.mean, self.rowcov, self.colcov = \
self._dist._process_parameters(mean, rowcov, colcov)
self.rowpsd = _PSD(self.rowcov, allow_singular=False)
self.colpsd = _PSD(self.colcov, allow_singular=False) | [
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taichi-dev/taichi | 973c04d6ba40f34e9e3bd5a28ae0ee0802f136a6 | python/taichi/_snode/fields_builder.py | python | FieldsBuilder.finalized_roots | (cls) | return roots_ptr | Gets all the roots of the finalized SNodeTree.
Returns:
A list of the roots of the finalized SNodeTree. | Gets all the roots of the finalized SNodeTree. | [
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] | def finalized_roots(cls):
"""Gets all the roots of the finalized SNodeTree.
Returns:
A list of the roots of the finalized SNodeTree.
"""
roots_ptr = []
size = impl.get_runtime().prog.get_snode_tree_size()
for i in range(size):
res = impl.get_runtime().prog.get_snode_root(i)
roots_ptr.append(snode.SNode(res))
return roots_ptr | [
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sonyxperiadev/WebGL | 0299b38196f78c6d5f74bcf6fa312a3daee6de60 | Tools/Scripts/webkitpy/thirdparty/simplejson/encoder.py | python | JSONEncoder.encode | (self, o) | return ''.join(chunks) | Return a JSON string representation of a Python data structure.
>>> JSONEncoder().encode({"foo": ["bar", "baz"]})
'{"foo":["bar", "baz"]}' | Return a JSON string representation of a Python data structure. | [
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] | def encode(self, o):
"""
Return a JSON string representation of a Python data structure.
>>> JSONEncoder().encode({"foo": ["bar", "baz"]})
'{"foo":["bar", "baz"]}'
"""
# This is for extremely simple cases and benchmarks...
if isinstance(o, basestring):
if isinstance(o, str):
_encoding = self.encoding
if (_encoding is not None
and not (_encoding == 'utf-8' and _need_utf8)):
o = o.decode(_encoding)
return encode_basestring_ascii(o)
# This doesn't pass the iterator directly to ''.join() because it
# sucks at reporting exceptions. It's going to do this internally
# anyway because it uses PySequence_Fast or similar.
chunks = list(self.iterencode(o))
return ''.join(chunks) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py3/pandas/io/pytables.py | python | GenericFixed.validate_read | (self, columns, where) | raise if any keywords are passed which are not-None | raise if any keywords are passed which are not-None | [
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"""
raise if any keywords are passed which are not-None
"""
if columns is not None:
raise TypeError(
"cannot pass a column specification when reading "
"a Fixed format store. this store must be selected in its entirety"
)
if where is not None:
raise TypeError(
"cannot pass a where specification when reading "
"from a Fixed format store. this store must be selected in its entirety"
) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/flatmenu.py | python | FlatMenu.DrawSelection | (self, dc, oldSelection=-1) | Redraws the menu.
:param `dc`: an instance of :class:`DC`;
:param integer `oldSelection`: if non-negative, the index representing the previous selected
menu item. | Redraws the menu. | [
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] | def DrawSelection(self, dc, oldSelection=-1):
"""
Redraws the menu.
:param `dc`: an instance of :class:`DC`;
:param integer `oldSelection`: if non-negative, the index representing the previous selected
menu item.
"""
self.Refresh() | [
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stellar-deprecated/stellard | 67eabb2217bdfa9a6ea317f62338fb6bca458c90 | src/protobuf/python/google/protobuf/internal/encoder.py | python | MessageEncoder | (field_number, is_repeated, is_packed) | Returns an encoder for a message field. | Returns an encoder for a message field. | [
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"""Returns an encoder for a message field."""
tag = TagBytes(field_number, wire_format.WIRETYPE_LENGTH_DELIMITED)
local_EncodeVarint = _EncodeVarint
assert not is_packed
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def EncodeRepeatedField(write, value):
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local_EncodeVarint(write, element.ByteSize())
element._InternalSerialize(write)
return EncodeRepeatedField
else:
def EncodeField(write, value):
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local_EncodeVarint(write, value.ByteSize())
return value._InternalSerialize(write)
return EncodeField | [
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pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | caffe2/experiments/python/net_construct_bench.py | python | AddMomentumParameterUpdate | (train_model, LR) | Add the momentum-SGD update. | Add the momentum-SGD update. | [
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"the",
"momentum",
"-",
"SGD",
"update",
"."
] | def AddMomentumParameterUpdate(train_model, LR):
'''
Add the momentum-SGD update.
'''
params = train_model.GetParams()
assert(len(params) > 0)
ONE = train_model.param_init_net.ConstantFill(
[], "ONE", shape=[1], value=1.0,
)
NEGONE = train_model.param_init_net.ConstantFill(
[], 'NEGONE', shape=[1], value=-1.0,
)
for param in params:
param_grad = train_model.param_to_grad[param]
param_momentum = train_model.param_init_net.ConstantFill(
[param], param + '_momentum', value=0.0
)
# Update param_grad and param_momentum in place
train_model.net.MomentumSGD(
[param_grad, param_momentum, LR],
[param_grad, param_momentum],
momentum=0.9,
nesterov=1
)
# Update parameters by applying the moment-adjusted gradient
train_model.WeightedSum(
[param, ONE, param_grad, NEGONE],
param
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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/urllib3/packages/six.py | python | python_2_unicode_compatible | (klass) | return klass | A decorator that defines __unicode__ and __str__ methods under Python 2.
Under Python 3 it does nothing.
To support Python 2 and 3 with a single code base, define a __str__ method
returning text and apply this decorator to the class. | A decorator that defines __unicode__ and __str__ methods under Python 2.
Under Python 3 it does nothing. | [
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] | def python_2_unicode_compatible(klass):
"""
A decorator that defines __unicode__ and __str__ methods under Python 2.
Under Python 3 it does nothing.
To support Python 2 and 3 with a single code base, define a __str__ method
returning text and apply this decorator to the class.
"""
if PY2:
if "__str__" not in klass.__dict__:
raise ValueError(
"@python_2_unicode_compatible cannot be applied "
"to %s because it doesn't define __str__()." % klass.__name__
)
klass.__unicode__ = klass.__str__
klass.__str__ = lambda self: self.__unicode__().encode("utf-8")
return klass | [
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pmq20/node-packer | 12c46c6e44fbc14d9ee645ebd17d5296b324f7e0 | current/deps/v8/tools/grokdump.py | python | InspectionShell.do_search | (self, word) | Search for a given word in available memory regions.
The given word is expanded to full pointer size and searched at aligned
as well as un-aligned memory locations. Use 'sa' to search aligned locations
only. | Search for a given word in available memory regions. | [
"Search",
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] | def do_search(self, word):
"""
Search for a given word in available memory regions.
The given word is expanded to full pointer size and searched at aligned
as well as un-aligned memory locations. Use 'sa' to search aligned locations
only.
"""
try:
word = self.ParseAddressExpr(word)
except ValueError:
print("Malformed word, prefix with '0x' to use hexadecimal format.")
return
print(
"Searching for word %d/0x%s:" % (word, self.reader.FormatIntPtr(word)))
self.reader.FindWord(word) | [
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Z3Prover/z3 | d745d03afdfdf638d66093e2bfbacaf87187f35b | src/api/python/z3/z3.py | python | If | (a, b, c, ctx=None) | Create a Z3 if-then-else expression.
>>> x = Int('x')
>>> y = Int('y')
>>> max = If(x > y, x, y)
>>> max
If(x > y, x, y)
>>> simplify(max)
If(x <= y, y, x) | Create a Z3 if-then-else expression. | [
"Create",
"a",
"Z3",
"if",
"-",
"then",
"-",
"else",
"expression",
"."
] | def If(a, b, c, ctx=None):
"""Create a Z3 if-then-else expression.
>>> x = Int('x')
>>> y = Int('y')
>>> max = If(x > y, x, y)
>>> max
If(x > y, x, y)
>>> simplify(max)
If(x <= y, y, x)
"""
if isinstance(a, Probe) or isinstance(b, Tactic) or isinstance(c, Tactic):
return Cond(a, b, c, ctx)
else:
ctx = _get_ctx(_ctx_from_ast_arg_list([a, b, c], ctx))
s = BoolSort(ctx)
a = s.cast(a)
b, c = _coerce_exprs(b, c, ctx)
if z3_debug():
_z3_assert(a.ctx == b.ctx, "Context mismatch")
return _to_expr_ref(Z3_mk_ite(ctx.ref(), a.as_ast(), b.as_ast(), c.as_ast()), ctx) | [
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infinit/memo | 3a8394d0f647efe03ccb8bfe885a7279cb8be8a6 | elle/drake/src/drake/go/__init__.py | python | Toolkit.__init__ | (self,
tk = None,
path = None,
root = None,
os = None,
arch = None,
cxx_toolkit = drake.cxx.Toolkit()) | Create a toolkit or clone an existing one.
:param tk: The toolkit to clone. If specified, other arguments must be None
:type tk: Toolkit
:param path: The home of your go environment (override GOPATH).
:type path: str
:param root: The root of your go installation (override GOROOT).
:type root: str
:param os: The target os for cross-compilation (override GOOS).
:type os: str
:param arch: The target arch for cross-compilation (override GOARCH).
:type arch: str
Example:
t = Toolkit(os = "windows", arch = "amd64")
print(t.os)
> "windows"
print(t.env)
{"GOOS": "windows", "GOARCH": "amd64"}
t2 = Toolkit(t)
print(t2.arch)
> "amd64" | Create a toolkit or clone an existing one. | [
"Create",
"a",
"toolkit",
"or",
"clone",
"an",
"existing",
"one",
"."
] | def __init__(self,
tk = None,
path = None,
root = None,
os = None,
arch = None,
cxx_toolkit = drake.cxx.Toolkit()):
"""
Create a toolkit or clone an existing one.
:param tk: The toolkit to clone. If specified, other arguments must be None
:type tk: Toolkit
:param path: The home of your go environment (override GOPATH).
:type path: str
:param root: The root of your go installation (override GOROOT).
:type root: str
:param os: The target os for cross-compilation (override GOOS).
:type os: str
:param arch: The target arch for cross-compilation (override GOARCH).
:type arch: str
Example:
t = Toolkit(os = "windows", arch = "amd64")
print(t.os)
> "windows"
print(t.env)
{"GOOS": "windows", "GOARCH": "amd64"}
t2 = Toolkit(t)
print(t2.arch)
> "amd64"
"""
if isinstance(tk, Toolkit):
assert all(a is None for a in [path, root, os, arch])
return super().__init__(path = tk.path,
root = tk.root,
os = tk.os,
arch = tk.arch)
else:
self.__arch = arch
self.__go = tk or which('go')
self.__path = path
self.__os = os
self.__root = root
self.__version = None
self.__env = False
self.__cxx_toolkit = cxx_toolkit
if self.go is None:
raise Exception('go executable is undefined. Check its installation')
try:
self.run(['help'])
except FileNotFoundError:
raise Exception('go executable not found') | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | tools/perf/metrics/speedindex.py | python | SpeedIndexImpl.CalculateSpeedIndex | (self, tab) | return int(speed_index) | Calculate the speed index.
The speed index number conceptually represents the number of milliseconds
that the page was "visually incomplete". If the page were 0% complete for
1000 ms, then the score would be 1000; if it were 0% complete for 100 ms
then 90% complete (ie 10% incomplete) for 900 ms, then the score would be
1.0*100 + 0.1*900 = 190.
Returns:
A single number, milliseconds of visual incompleteness. | Calculate the speed index. | [
"Calculate",
"the",
"speed",
"index",
"."
] | def CalculateSpeedIndex(self, tab):
"""Calculate the speed index.
The speed index number conceptually represents the number of milliseconds
that the page was "visually incomplete". If the page were 0% complete for
1000 ms, then the score would be 1000; if it were 0% complete for 100 ms
then 90% complete (ie 10% incomplete) for 900 ms, then the score would be
1.0*100 + 0.1*900 = 190.
Returns:
A single number, milliseconds of visual incompleteness.
"""
time_completeness_list = self.GetTimeCompletenessList(tab)
prev_completeness = 0.0
speed_index = 0.0
prev_time = time_completeness_list[0][0]
for time, completeness in time_completeness_list:
# Add the incremental value for the interval just before this event.
elapsed_time = time - prev_time
incompleteness = (1.0 - prev_completeness)
speed_index += elapsed_time * incompleteness
# Update variables for next iteration.
prev_completeness = completeness
prev_time = time
return int(speed_index) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py3/scipy/stats/stats.py | python | tvar | (a, limits=None, inclusive=(True, True), axis=0, ddof=1) | return np.ma.var(am, ddof=ddof, axis=axis) | Compute the trimmed variance.
This function computes the sample variance of an array of values,
while ignoring values which are outside of given `limits`.
Parameters
----------
a : array_like
Array of values.
limits : None or (lower limit, upper limit), optional
Values in the input array less than the lower limit or greater than the
upper limit will be ignored. When limits is None, then all values are
used. Either of the limit values in the tuple can also be None
representing a half-open interval. The default value is None.
inclusive : (bool, bool), optional
A tuple consisting of the (lower flag, upper flag). These flags
determine whether values exactly equal to the lower or upper limits
are included. The default value is (True, True).
axis : int or None, optional
Axis along which to operate. Default is 0. If None, compute over the
whole array `a`.
ddof : int, optional
Delta degrees of freedom. Default is 1.
Returns
-------
tvar : float
Trimmed variance.
Notes
-----
`tvar` computes the unbiased sample variance, i.e. it uses a correction
factor ``n / (n - 1)``.
Examples
--------
>>> from scipy import stats
>>> x = np.arange(20)
>>> stats.tvar(x)
35.0
>>> stats.tvar(x, (3,17))
20.0 | Compute the trimmed variance. | [
"Compute",
"the",
"trimmed",
"variance",
"."
] | def tvar(a, limits=None, inclusive=(True, True), axis=0, ddof=1):
"""
Compute the trimmed variance.
This function computes the sample variance of an array of values,
while ignoring values which are outside of given `limits`.
Parameters
----------
a : array_like
Array of values.
limits : None or (lower limit, upper limit), optional
Values in the input array less than the lower limit or greater than the
upper limit will be ignored. When limits is None, then all values are
used. Either of the limit values in the tuple can also be None
representing a half-open interval. The default value is None.
inclusive : (bool, bool), optional
A tuple consisting of the (lower flag, upper flag). These flags
determine whether values exactly equal to the lower or upper limits
are included. The default value is (True, True).
axis : int or None, optional
Axis along which to operate. Default is 0. If None, compute over the
whole array `a`.
ddof : int, optional
Delta degrees of freedom. Default is 1.
Returns
-------
tvar : float
Trimmed variance.
Notes
-----
`tvar` computes the unbiased sample variance, i.e. it uses a correction
factor ``n / (n - 1)``.
Examples
--------
>>> from scipy import stats
>>> x = np.arange(20)
>>> stats.tvar(x)
35.0
>>> stats.tvar(x, (3,17))
20.0
"""
a = asarray(a)
a = a.astype(float).ravel()
if limits is None:
n = len(a)
return a.var() * n / (n - 1.)
am = _mask_to_limits(a, limits, inclusive)
return np.ma.var(am, ddof=ddof, axis=axis) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_misc.py | python | BusyCursor.__init__ | (self, *args, **kwargs) | __init__(self, Cursor cursor=wxHOURGLASS_CURSOR) -> BusyCursor | __init__(self, Cursor cursor=wxHOURGLASS_CURSOR) -> BusyCursor | [
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] | def __init__(self, *args, **kwargs):
"""__init__(self, Cursor cursor=wxHOURGLASS_CURSOR) -> BusyCursor"""
_misc_.BusyCursor_swiginit(self,_misc_.new_BusyCursor(*args, **kwargs)) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numba/transforms.py | python | _cfg_nodes_in_region | (cfg, region_begin, region_end) | return region_nodes | Find the set of CFG nodes that are in the given region | Find the set of CFG nodes that are in the given region | [
"Find",
"the",
"set",
"of",
"CFG",
"nodes",
"that",
"are",
"in",
"the",
"given",
"region"
] | def _cfg_nodes_in_region(cfg, region_begin, region_end):
"""Find the set of CFG nodes that are in the given region
"""
region_nodes = set()
stack = [region_begin]
while stack:
tos = stack.pop()
succs, _ = zip(*cfg.successors(tos))
nodes = set([node for node in succs
if node not in region_nodes and
node != region_end])
stack.extend(nodes)
region_nodes |= nodes
return region_nodes | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_core.py | python | WithImages.AssignImageList | (*args, **kwargs) | return _core_.WithImages_AssignImageList(*args, **kwargs) | AssignImageList(self, ImageList imageList) | AssignImageList(self, ImageList imageList) | [
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pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | benchmarks/functional_autograd_benchmark/torchvision_models.py | python | accuracy | (output, target, topk=(1,)) | return res | Computes the precision@k for the specified values of k | Computes the precision | [
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correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0)
res.append(correct_k.mul_(100.0 / batch_size))
return res | [
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Yaafe/Yaafe | f5ed847bdbf540b47e8fe1980dddfb5509ae7f9d | src_python/yaafelib/dataflow.py | python | DataFlow.loads | (self, buf) | return False | Build DataFlow from buf read from a :ref:`dataflow file
<dataflow-file>`.
:param buf: buffer read from a dataflow file
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:return: True on success, False on fail. | Build DataFlow from buf read from a :ref:`dataflow file
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"""
Build DataFlow from buf read from a :ref:`dataflow file
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:param buf: buffer read from a dataflow file
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gnuradio/gnuradio | 09c3c4fa4bfb1a02caac74cb5334dfe065391e3b | gr-utils/modtool/templates/gr-newmod/docs/doxygen/other/doxypy.py | python | Doxypy.__docstringSummaryToBrief | (self, line) | Adds \\brief to the docstrings summary line.
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A \\brief is prepended, provided no other doxygen command is at the
start of the line.
"""
stripped = line.strip()
if stripped and not stripped[0] in ('@', '\\'):
return "\\brief " + line
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_misc.py | python | SaveFileSelector | (*args, **kwargs) | return _misc_.SaveFileSelector(*args, **kwargs) | SaveFileSelector(String what, String extension, String default_name=EmptyString,
Window parent=None) -> String | SaveFileSelector(String what, String extension, String default_name=EmptyString,
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"""
SaveFileSelector(String what, String extension, String default_name=EmptyString,
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/propgrid.py | python | PGProperty.GetMainParent | (*args, **kwargs) | return _propgrid.PGProperty_GetMainParent(*args, **kwargs) | GetMainParent(self) -> PGProperty | GetMainParent(self) -> PGProperty | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/boto/boto/cognito/identity/__init__.py | python | regions | () | return get_regions('cognito-identity',
connection_cls=CognitoIdentityConnection) | Get all available regions for the Amazon Cognito Identity service.
:rtype: list
:return: A list of :class:`boto.regioninfo.RegionInfo` | Get all available regions for the Amazon Cognito Identity service. | [
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"""
Get all available regions for the Amazon Cognito Identity service.
:rtype: list
:return: A list of :class:`boto.regioninfo.RegionInfo`
"""
from boto.cognito.identity.layer1 import CognitoIdentityConnection
return get_regions('cognito-identity',
connection_cls=CognitoIdentityConnection) | [
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hpi-xnor/BMXNet-v2 | af2b1859eafc5c721b1397cef02f946aaf2ce20d | python/mxnet/module/module.py | python | Module._sync_params_from_devices | (self) | Synchronizes parameters from devices to CPU. This function should be called after
calling `update` that updates the parameters on the devices, before one can read the
latest parameters from ``self._arg_params`` and ``self._aux_params``.
For row_sparse parameters on devices, ther are pulled from KVStore with all row ids. | Synchronizes parameters from devices to CPU. This function should be called after
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latest parameters from ``self._arg_params`` and ``self._aux_params``.
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self._kvstore.row_sparse_pull(param_name, param_val, row_ids=row_ids)
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apache/incubator-mxnet | f03fb23f1d103fec9541b5ae59ee06b1734a51d9 | python/mxnet/model.py | python | _update_params_on_kvstore | (param_arrays, grad_arrays, kvstore, param_names) | Perform update of param_arrays from grad_arrays on kvstore. | Perform update of param_arrays from grad_arrays on kvstore. | [
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"""Perform update of param_arrays from grad_arrays on kvstore."""
for index, pair in enumerate(zip(param_arrays, grad_arrays)):
arg_list, grad_list = pair
if grad_list[0] is None:
continue
name = param_names[index]
# push gradient, priority is negative index
# pull back the weights
if grad_list[0].stype == 'default' and arg_list[0].stype == 'default':
kvstore.pushpull(name, grad_list, out=arg_list, priority=-index)
else:
kvstore.push(name, grad_list, priority=-index)
kvstore.pull(name, out=arg_list, priority=-index) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/richtext.py | python | RichTextCtrl.CalcScrolledPosition | (*args) | return _richtext.RichTextCtrl_CalcScrolledPosition(*args) | CalcScrolledPosition(self, Point pt) -> Point
CalcScrolledPosition(int x, int y) -> (sx, sy)
Translate between scrolled and unscrolled coordinates. | CalcScrolledPosition(self, Point pt) -> Point
CalcScrolledPosition(int x, int y) -> (sx, sy) | [
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"""
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"""
return _richtext.RichTextCtrl_CalcScrolledPosition(*args) | [
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Tencent/ncnn | 6f824c57a1f8ee6dd3902fb13bef947cf4e6a73f | python/ncnn/utils/functional.py | python | nms | (boxes, scores, iou_threshold, top_k=-1, candidate_size=200) | return picked | Args:
box_scores (N, 5): boxes in corner-form(x1, y1, x2, y2) and probabilities.
iou_threshold: intersection over union threshold.
top_k: keep top_k results. If k <= 0, keep all the results.
candidate_size: only consider the candidates with the highest scores.
Returns:
picked: a list of indexes of the kept boxes | [] | def nms(boxes, scores, iou_threshold, top_k=-1, candidate_size=200):
"""
Args:
box_scores (N, 5): boxes in corner-form(x1, y1, x2, y2) and probabilities.
iou_threshold: intersection over union threshold.
top_k: keep top_k results. If k <= 0, keep all the results.
candidate_size: only consider the candidates with the highest scores.
Returns:
picked: a list of indexes of the kept boxes
"""
picked = []
indexes = np.argsort(scores)
indexes = indexes[-candidate_size:]
while len(indexes) > 0:
current = indexes[-1]
picked.append(current)
if 0 < top_k == len(picked) or len(indexes) == 1:
break
current_box = boxes[current, :]
indexes = indexes[:-1]
rest_boxes = boxes[indexes, :]
iou = iou_of(
rest_boxes,
np.expand_dims(current_box, axis=0),
)
indexes = indexes[iou <= iou_threshold]
return picked | [
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nest/nest-simulator | f2623eb78518cdbd55e77e0ed486bf1111bcb62f | pynest/nest/lib/hl_api_spatial.py | python | DumpLayerNodes | (layer, outname) | Write `node ID` and position data of `layer` to file.
Write `node ID` and position data to `outname` file. For each node in `layer`,
a line with the following information is written:
::
node ID x-position y-position [z-position]
If `layer` contains several `node IDs`, data for all nodes in `layer` will be written to a
single file.
Parameters
----------
layer : NodeCollection
`NodeCollection` of spatially distributed node IDs
outname : str
Name of file to write to (existing files are overwritten)
See also
--------
DumpLayerConnections: Write connectivity information to file.
GetPosition: Return the spatial locations of nodes.
Notes
-----
* If calling this function from a distributed simulation, this function
will write to one file per MPI rank.
* File names are formed by adding the MPI Rank into the file name before
the file name suffix.
* Each file stores data for nodes local to that file.
Example
-------
::
import nest
# create a spatial population
s_nodes = nest.Create('iaf_psc_alpha', positions=nest.spatial.grid(shape=[5, 5]))
# write layer node positions to file
nest.DumpLayerNodes(s_nodes, 'positions.txt') | Write `node ID` and position data of `layer` to file. | [
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"""
Write `node ID` and position data of `layer` to file.
Write `node ID` and position data to `outname` file. For each node in `layer`,
a line with the following information is written:
::
node ID x-position y-position [z-position]
If `layer` contains several `node IDs`, data for all nodes in `layer` will be written to a
single file.
Parameters
----------
layer : NodeCollection
`NodeCollection` of spatially distributed node IDs
outname : str
Name of file to write to (existing files are overwritten)
See also
--------
DumpLayerConnections: Write connectivity information to file.
GetPosition: Return the spatial locations of nodes.
Notes
-----
* If calling this function from a distributed simulation, this function
will write to one file per MPI rank.
* File names are formed by adding the MPI Rank into the file name before
the file name suffix.
* Each file stores data for nodes local to that file.
Example
-------
::
import nest
# create a spatial population
s_nodes = nest.Create('iaf_psc_alpha', positions=nest.spatial.grid(shape=[5, 5]))
# write layer node positions to file
nest.DumpLayerNodes(s_nodes, 'positions.txt')
"""
if not isinstance(layer, NodeCollection):
raise TypeError("layer must be a NodeCollection")
sli_func("""
(w) file exch DumpLayerNodes close
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | Framework/PythonInterface/plugins/algorithms/WorkflowAlgorithms/TimeSlice.py | python | _count_monitors | (raw_file) | Returns the number of monitors and if they're at the start or end of the file | Returns the number of monitors and if they're at the start or end of the file | [
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"""
Returns the number of monitors and if they're at the start or end of the file
"""
raw_file = mtd[raw_file]
num_hist = raw_file.getNumberHistograms()
mon_count = 1
spectrumInfo = raw_file.spectrumInfo()
if spectrumInfo.isMonitor(0):
# Monitors are at the start
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break
return mon_count, True
else:
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for i in range(num_hist, 0, -1):
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gimli-org/gimli | 17aa2160de9b15ababd9ef99e89b1bc3277bbb23 | pygimli/_version.py | python | run_command | (commands, args, cwd=None, verbose=False, hide_stderr=False,
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assert isinstance(commands, list)
p = None
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try:
dispcmd = str([c] + args)
# remember shell=False, so use git.cmd on windows, not just git
p = subprocess.Popen([c] + args, cwd=cwd, env=env,
stdout=subprocess.PIPE,
stderr=(subprocess.PIPE if hide_stderr
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break
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if p.returncode != 0:
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print("stdout was %s" % stdout)
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google/flatbuffers | b3006913369e0a7550795e477011ac5bebb93497 | python/flatbuffers/builder.py | python | Builder.assertStructIsInline | (self, obj) | Structs are always stored inline, so need to be created right
where they are used. You'll get this error if you created it
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N.enforce_number(obj, N.UOffsetTFlags)
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/stc.py | python | StyledTextCtrl.GetTextRange | (*args, **kwargs) | return _stc.StyledTextCtrl_GetTextRange(*args, **kwargs) | GetTextRange(self, int startPos, int endPos) -> String
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_gdi.py | python | Locale.GetLanguage | (*args, **kwargs) | return _gdi_.Locale_GetLanguage(*args, **kwargs) | GetLanguage(self) -> int | GetLanguage(self) -> int | [
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ros-drivers/rosserial | c169ae2173dcfda7cee567d64beae45198459400 | rosserial_python/src/rosserial_python/SerialClient.py | python | SerialClient.setupSubscriber | (self, data) | Register a new subscriber. | Register a new subscriber. | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/AWSPythonSDK/1.5.8/docutils/core.py | python | publish_parts | (source, source_path=None, source_class=io.StringInput,
destination_path=None,
reader=None, reader_name='standalone',
parser=None, parser_name='restructuredtext',
writer=None, writer_name='pseudoxml',
settings=None, settings_spec=None,
settings_overrides=None, config_section=None,
enable_exit_status=False) | return pub.writer.parts | Set up & run a `Publisher`, and return a dictionary of document parts.
Dictionary keys are the names of parts, and values are Unicode strings;
encoding is up to the client. For programmatic use with string I/O.
For encoded string input, be sure to set the 'input_encoding' setting to
the desired encoding. Set it to 'unicode' for unencoded Unicode string
input. Here's how::
publish_parts(..., settings_overrides={'input_encoding': 'unicode'})
Parameters: see `publish_programmatically`. | Set up & run a `Publisher`, and return a dictionary of document parts.
Dictionary keys are the names of parts, and values are Unicode strings;
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writer=None, writer_name='pseudoxml',
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"""
output, pub = publish_programmatically(
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destination=None, destination_path=destination_path,
reader=reader, reader_name=reader_name,
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writer=writer, writer_name=writer_name,
settings=settings, settings_spec=settings_spec,
settings_overrides=settings_overrides,
config_section=config_section,
enable_exit_status=enable_exit_status)
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Z3Prover/z3 | d745d03afdfdf638d66093e2bfbacaf87187f35b | src/api/python/z3/z3.py | python | Solver.help | (self) | Display a string describing all available options. | Display a string describing all available options. | [
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natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/python/framework/ops.py | python | SparseTensor.shape | (self) | return self._shape | A 1-D Tensor of int64 representing the shape of the dense tensor. | A 1-D Tensor of int64 representing the shape of the dense tensor. | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_controls.py | python | ComboBox.__init__ | (self, *args, **kwargs) | __init__(Window parent, int id=-1, String value=EmptyString,
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zachriggle/ida-splode | a4aee3be415b318a0e051a523ebd0a8d6d5e0026 | py/idasplode/color.py | python | ColorHit | (IP, Count=-1) | Color a single instruction | Color a single instruction | [
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if Enhanced: SetColor(IP, settings.COLOR_INS_ENHANCED)
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/ops/_op_impl/aicpu/topk.py | python | _top_k_aicpu | () | return | TopK aicpu register | TopK aicpu register | [
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"""TopK aicpu register"""
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/compiler/pycodegen.py | python | findOp | (node) | return v.op | Find the op (DELETE, LOAD, STORE) in an AssTuple tree | Find the op (DELETE, LOAD, STORE) in an AssTuple tree | [
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"""Find the op (DELETE, LOAD, STORE) in an AssTuple tree"""
v = OpFinder()
walk(node, v, verbose=0)
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/ops/template.py | python | Template.global_variables | (self) | Returns the list of global variables created by the Template. | Returns the list of global variables created by the Template. | [
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"""Returns the list of global variables created by the Template."""
if self._variables_created:
return ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES,
self.variable_scope_name)
else:
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microsoft/onnxruntime | f92e47e95b13a240e37caf7b36577983544f98fc | onnxruntime/python/tools/quantization/quantize.py | python | optimize_model | (model_path: Path) | return optimized_model | Generate model that applies graph optimization (constant folding,etc.)
parameter model_path: path to the original onnx model
return: optimized onnx model | Generate model that applies graph optimization (constant folding,etc.)
parameter model_path: path to the original onnx model
return: optimized onnx model | [
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'''
Generate model that applies graph optimization (constant folding,etc.)
parameter model_path: path to the original onnx model
return: optimized onnx model
'''
opt_model_path = generate_identified_filename(model_path, "-opt")
sess_option = SessionOptions()
sess_option.optimized_model_filepath = opt_model_path.as_posix()
sess_option.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_BASIC
_ = InferenceSession(model_path.as_posix(), sess_option, providers=['CPUExecutionProvider'])
optimized_model = onnx.load(opt_model_path.as_posix())
return optimized_model | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_gdi.py | python | RegionFromBitmapColour | (*args, **kwargs) | return val | RegionFromBitmapColour(Bitmap bmp, Colour transColour, int tolerance=0) -> Region | RegionFromBitmapColour(Bitmap bmp, Colour transColour, int tolerance=0) -> Region | [
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"""RegionFromBitmapColour(Bitmap bmp, Colour transColour, int tolerance=0) -> Region"""
val = _gdi_.new_RegionFromBitmapColour(*args, **kwargs)
return val | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/mailbox.py | python | MaildirMessage.__init__ | (self, message=None) | Initialize a MaildirMessage instance. | Initialize a MaildirMessage instance. | [
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"""Initialize a MaildirMessage instance."""
self._subdir = 'new'
self._info = ''
self._date = time.time()
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weolar/miniblink49 | 1c4678db0594a4abde23d3ebbcc7cd13c3170777 | third_party/WebKit/Tools/Scripts/webkitpy/thirdparty/pep8.py | python | Checker.report_invalid_syntax | (self) | Check if the syntax is valid. | Check if the syntax is valid. | [
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"""Check if the syntax is valid."""
(exc_type, exc) = sys.exc_info()[:2]
if len(exc.args) > 1:
offset = exc.args[1]
if len(offset) > 2:
offset = offset[1:3]
else:
offset = (1, 0)
self.report_error(offset[0], offset[1] or 0,
'E901 %s: %s' % (exc_type.__name__, exc.args[0]),
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/random.py | python | Random.randint | (self, a, b) | return self.randrange(a, b+1) | Return random integer in range [a, b], including both end points. | Return random integer in range [a, b], including both end points. | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py3/scipy/stats/_multivariate.py | python | invwishart_gen.var | (self, df, scale) | return _squeeze_output(out) if out is not None else out | Variance of the inverse Wishart distribution
Only valid if the degrees of freedom are greater than the dimension of
the scale matrix plus three.
Parameters
----------
%(_doc_default_callparams)s
Returns
-------
var : float
The variance of the distribution | Variance of the inverse Wishart distribution | [
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"""
Variance of the inverse Wishart distribution
Only valid if the degrees of freedom are greater than the dimension of
the scale matrix plus three.
Parameters
----------
%(_doc_default_callparams)s
Returns
-------
var : float
The variance of the distribution
"""
dim, df, scale = self._process_parameters(df, scale)
out = self._var(dim, df, scale)
return _squeeze_output(out) if out is not None else out | [
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magazino/move_base_flex | a8ca484e354a0196b5e9fbda5f0eae311d9b72d3 | git-clang-format.py | python | temporary_index_file | (tree=None) | Context manager for setting GIT_INDEX_FILE to a temporary file and deleting
the file afterward. | Context manager for setting GIT_INDEX_FILE to a temporary file and deleting
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"""Context manager for setting GIT_INDEX_FILE to a temporary file and deleting
the file afterward."""
index_path = create_temporary_index(tree)
old_index_path = os.environ.get('GIT_INDEX_FILE')
os.environ['GIT_INDEX_FILE'] = index_path
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else:
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google/llvm-propeller | 45c226984fe8377ebfb2ad7713c680d652ba678d | compiler-rt/lib/sanitizer_common/scripts/cpplint.py | python | IsErrorSuppressedByNolint | (category, linenum) | return (_global_error_suppressions.get(category, False) or
linenum in _error_suppressions.get(category, set()) or
linenum in _error_suppressions.get(None, set())) | Returns true if the specified error category is suppressed on this line.
Consults the global error_suppressions map populated by
ParseNolintSuppressions/ProcessGlobalSuppresions/ResetNolintSuppressions.
Args:
category: str, the category of the error.
linenum: int, the current line number.
Returns:
bool, True iff the error should be suppressed due to a NOLINT comment or
global suppression. | Returns true if the specified error category is suppressed on this line. | [
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"""Returns true if the specified error category is suppressed on this line.
Consults the global error_suppressions map populated by
ParseNolintSuppressions/ProcessGlobalSuppresions/ResetNolintSuppressions.
Args:
category: str, the category of the error.
linenum: int, the current line number.
Returns:
bool, True iff the error should be suppressed due to a NOLINT comment or
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"""
return (_global_error_suppressions.get(category, False) or
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linenum in _error_suppressions.get(None, set())) | [
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rootm0s/Protectors | 5b3f4d11687a5955caf9c3af30666c4bfc2c19ab | OWASP-ZSC/module/readline_windows/pyreadline/lineeditor/history.py | python | LineHistory.write_history_file | (self, filename=None) | Save a readline history file. | Save a readline history file. | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/sysconfig.py | python | get_platform | () | return "%s-%s-%s" % (osname, release, machine) | Return a string that identifies the current platform.
This is used mainly to distinguish platform-specific build directories and
platform-specific built distributions. Typically includes the OS name and
version and the architecture (as supplied by 'os.uname()'), although the
exact information included depends on the OS; on Linux, the kernel version
isn't particularly important.
Examples of returned values:
linux-i586
linux-alpha (?)
solaris-2.6-sun4u
Windows will return one of:
win-amd64 (64bit Windows on AMD64 (aka x86_64, Intel64, EM64T, etc)
win32 (all others - specifically, sys.platform is returned)
For other non-POSIX platforms, currently just returns 'sys.platform'. | Return a string that identifies the current platform. | [
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"""Return a string that identifies the current platform.
This is used mainly to distinguish platform-specific build directories and
platform-specific built distributions. Typically includes the OS name and
version and the architecture (as supplied by 'os.uname()'), although the
exact information included depends on the OS; on Linux, the kernel version
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solaris-2.6-sun4u
Windows will return one of:
win-amd64 (64bit Windows on AMD64 (aka x86_64, Intel64, EM64T, etc)
win32 (all others - specifically, sys.platform is returned)
For other non-POSIX platforms, currently just returns 'sys.platform'.
"""
if os.name == 'nt':
if 'amd64' in sys.version.lower():
return 'win-amd64'
return sys.platform
if os.name != "posix" or not hasattr(os, 'uname'):
# XXX what about the architecture? NT is Intel or Alpha
return sys.platform
# Set for cross builds explicitly
if "_PYTHON_HOST_PLATFORM" in os.environ:
return os.environ["_PYTHON_HOST_PLATFORM"]
# Try to distinguish various flavours of Unix
osname, host, release, version, machine = os.uname()
# Convert the OS name to lowercase, remove '/' characters, and translate
# spaces (for "Power Macintosh")
osname = osname.lower().replace('/', '')
machine = machine.replace(' ', '_')
machine = machine.replace('/', '-')
if osname[:5] == "linux":
# At least on Linux/Intel, 'machine' is the processor --
# i386, etc.
# XXX what about Alpha, SPARC, etc?
return "%s-%s" % (osname, machine)
elif osname[:5] == "sunos":
if release[0] >= "5": # SunOS 5 == Solaris 2
osname = "solaris"
release = "%d.%s" % (int(release[0]) - 3, release[2:])
# We can't use "platform.architecture()[0]" because a
# bootstrap problem. We use a dict to get an error
# if some suspicious happens.
bitness = {2147483647:"32bit", 9223372036854775807:"64bit"}
machine += ".%s" % bitness[sys.maxsize]
# fall through to standard osname-release-machine representation
elif osname[:3] == "aix":
return "%s-%s.%s" % (osname, version, release)
elif osname[:6] == "cygwin":
osname = "cygwin"
import re
rel_re = re.compile(r'[\d.]+')
m = rel_re.match(release)
if m:
release = m.group()
elif osname[:6] == "darwin":
import _osx_support
osname, release, machine = _osx_support.get_platform_osx(
get_config_vars(),
osname, release, machine)
return "%s-%s-%s" % (osname, release, machine) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pluggy/py2/pluggy/manager.py | python | PluginManager.enable_tracing | (self) | return self.add_hookcall_monitoring(before, after) | enable tracing of hook calls and return an undo function. | enable tracing of hook calls and return an undo function. | [
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""" enable tracing of hook calls and return an undo function. """
hooktrace = self.trace.root.get("hook")
def before(hook_name, methods, kwargs):
hooktrace.root.indent += 1
hooktrace(hook_name, kwargs)
def after(outcome, hook_name, methods, kwargs):
if outcome.excinfo is None:
hooktrace("finish", hook_name, "-->", outcome.get_result())
hooktrace.root.indent -= 1
return self.add_hookcall_monitoring(before, after) | [
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/python2_version/klampt/robotsim.py | python | GeneralizedIKSolver.setTolerance | (self, res) | return _robotsim.GeneralizedIKSolver_setTolerance(self, res) | setTolerance(GeneralizedIKSolver self, double res)
Sets the constraint solve tolerance (default 1e-3) | setTolerance(GeneralizedIKSolver self, double res) | [
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"""
setTolerance(GeneralizedIKSolver self, double res)
Sets the constraint solve tolerance (default 1e-3)
"""
return _robotsim.GeneralizedIKSolver_setTolerance(self, res) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/closure_linter/closure_linter/typeannotation.py | python | TypeAnnotation.IterIdentifiers | (self) | Iterates over all identifiers in this type and its subtypes. | Iterates over all identifiers in this type and its subtypes. | [
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"""Iterates over all identifiers in this type and its subtypes."""
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apache/incubator-mxnet | f03fb23f1d103fec9541b5ae59ee06b1734a51d9 | python/mxnet/numpy/multiarray.py | python | trace | (a, offset=0, axis1=0, axis2=1, out=None) | return _mx_nd_np.trace(a, offset, axis1, axis2, out) | Return the sum along diagonals of the array.
If `a` is 2-D, the sum along its diagonal with the given offset
is returned, i.e., the sum of elements ``a[i,i+offset]`` for all i.
If `a` has more than two dimensions, then the axes specified by axis1 and
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Parameters
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a : ndarray
Input array, from which the diagonals are taken.
offset : int, optional
Offset of the diagonal from the main diagonal. Can be both positive
and negative. Defaults to 0.
axis1, axis2 : int, optional
Axes to be used as the first and second axis of the 2-D sub-arrays
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out : ndarray, optional
Array into which the output is placed. It must be of the right shape
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Returns
-------
sum_along_diagonals : ndarray
If `a` is 2-D, the sum along the diagonal is returned. If `a` has
larger dimensions, then an array of sums along diagonals is returned.
Examples
--------
>>> a = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
>>> np.trace(a)
array(3.)
>>> a = np.arange(8).reshape((2, 2, 2))
>>> np.trace(a)
array([6., 8.])
>>> a = np.arange(24).reshape((2, 2, 2, 3))
>>> np.trace(a).shape
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Examples
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>>> a = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
>>> np.trace(a)
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>>> a = np.arange(8).reshape((2, 2, 2))
>>> np.trace(a)
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>>> a = np.arange(24).reshape((2, 2, 2, 3))
>>> np.trace(a).shape
(2, 3)
"""
return _mx_nd_np.trace(a, offset, axis1, axis2, out) | [
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ArmageddonGames/ZeldaClassic | c244ae6c1d361d24a5529b1c0394e656f1f5d965 | allegro/demos/skater/blender/ademo_export.py | python | upper | (i1, i2) | return v | Given two vertex ids, determine if the edge is an upper face. | Given two vertex ids, determine if the edge is an upper face. | [
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"""
Given two vertex ids, determine if the edge is an upper face.
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v = V[i2][0] - V[i1][0]
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_controls.py | python | ListCtrl.Create | (*args, **kwargs) | return _controls_.ListCtrl_Create(*args, **kwargs) | Create(self, Window parent, int id=-1, Point pos=DefaultPosition,
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Validator validator=DefaultValidator, String name=ListCtrlNameStr) -> bool
Do the 2nd phase and create the GUI control. | Create(self, Window parent, int id=-1, Point pos=DefaultPosition,
Size size=DefaultSize, long style=LC_ICON,
Validator validator=DefaultValidator, String name=ListCtrlNameStr) -> bool | [
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"""
Create(self, Window parent, int id=-1, Point pos=DefaultPosition,
Size size=DefaultSize, long style=LC_ICON,
Validator validator=DefaultValidator, String name=ListCtrlNameStr) -> bool
Do the 2nd phase and create the GUI control.
"""
return _controls_.ListCtrl_Create(*args, **kwargs) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/difflib.py | python | restore | (delta, which) | r"""
Generate one of the two sequences that generated a delta.
Given a `delta` produced by `Differ.compare()` or `ndiff()`, extract
lines originating from file 1 or 2 (parameter `which`), stripping off line
prefixes.
Examples:
>>> diff = ndiff('one\ntwo\nthree\n'.splitlines(1),
... 'ore\ntree\nemu\n'.splitlines(1))
>>> diff = list(diff)
>>> print ''.join(restore(diff, 1)),
one
two
three
>>> print ''.join(restore(diff, 2)),
ore
tree
emu | r"""
Generate one of the two sequences that generated a delta. | [
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] | def restore(delta, which):
r"""
Generate one of the two sequences that generated a delta.
Given a `delta` produced by `Differ.compare()` or `ndiff()`, extract
lines originating from file 1 or 2 (parameter `which`), stripping off line
prefixes.
Examples:
>>> diff = ndiff('one\ntwo\nthree\n'.splitlines(1),
... 'ore\ntree\nemu\n'.splitlines(1))
>>> diff = list(diff)
>>> print ''.join(restore(diff, 1)),
one
two
three
>>> print ''.join(restore(diff, 2)),
ore
tree
emu
"""
try:
tag = {1: "- ", 2: "+ "}[int(which)]
except KeyError:
raise ValueError, ('unknown delta choice (must be 1 or 2): %r'
% which)
prefixes = (" ", tag)
for line in delta:
if line[:2] in prefixes:
yield line[2:] | [
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jsupancic/deep_hand_pose | 22cbeae1a8410ff5d37c060c7315719d0a5d608f | scripts/cpp_lint.py | python | _VerboseLevel | () | return _cpplint_state.verbose_level | Returns the module's verbosity setting. | Returns the module's verbosity setting. | [
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"""Returns the module's verbosity setting."""
return _cpplint_state.verbose_level | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_windows.py | python | TopLevelWindow.Maximize | (*args, **kwargs) | return _windows_.TopLevelWindow_Maximize(*args, **kwargs) | Maximize(self, bool maximize=True) | Maximize(self, bool maximize=True) | [
"Maximize",
"(",
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"bool",
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] | def Maximize(*args, **kwargs):
"""Maximize(self, bool maximize=True)"""
return _windows_.TopLevelWindow_Maximize(*args, **kwargs) | [
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yuxng/DA-RNN | 77fbb50b4272514588a10a9f90b7d5f8d46974fb | lib/datasets/shapenet_scene.py | python | shapenet_scene.label_path_at | (self, i) | return self.label_path_from_index(self.image_index[i]) | Return the absolute path to metadata i in the image sequence. | Return the absolute path to metadata i in the image sequence. | [
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"""
Return the absolute path to metadata i in the image sequence.
"""
return self.label_path_from_index(self.image_index[i]) | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | Framework/PythonInterface/plugins/algorithms/WorkflowAlgorithms/IndirectFlatPlateAbsorption2.py | python | IndirectFlatPlateAbsorption.validateInputs | (self) | return issues | Validate algorithm options. | Validate algorithm options. | [
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] | def validateInputs(self):
"""
Validate algorithm options.
"""
self._setup()
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if self._use_can_corrections and self._can_chemical_formula == '':
issues['CanChemicalFormula'] = 'Must be set to use can corrections'
if self._use_can_corrections and self._can_ws_name is None:
issues['CanWorkspace'] = 'Must specify a can workspace to use can corrections'
return issues | [
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SequoiaDB/SequoiaDB | 2894ed7e5bd6fe57330afc900cf76d0ff0df9f64 | tools/server/php_linux/libxml2/lib/python2.4/site-packages/libxml2.py | python | xmlTextReader.Close | (self) | return ret | This method releases any resources allocated by the current
instance changes the state to Closed and close any
underlying input. | This method releases any resources allocated by the current
instance changes the state to Closed and close any
underlying input. | [
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"""This method releases any resources allocated by the current
instance changes the state to Closed and close any
underlying input. """
ret = libxml2mod.xmlTextReaderClose(self._o)
return ret | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/scipy/special/basic.py | python | lpn | (n, z) | return pn[:(n+1)], pd[:(n+1)] | Legendre functions of the first kind, Pn(z).
Compute sequence of Legendre functions of the first kind (polynomials),
Pn(z) and derivatives for all degrees from 0 to n (inclusive).
See also special.legendre for polynomial class.
References
----------
.. [1] Zhang, Shanjie and Jin, Jianming. "Computation of Special
Functions", John Wiley and Sons, 1996.
http://jin.ece.illinois.edu/specfunc.html | Legendre functions of the first kind, Pn(z). | [
"Legendre",
"functions",
"of",
"the",
"first",
"kind",
"Pn",
"(",
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] | def lpn(n, z):
"""Legendre functions of the first kind, Pn(z).
Compute sequence of Legendre functions of the first kind (polynomials),
Pn(z) and derivatives for all degrees from 0 to n (inclusive).
See also special.legendre for polynomial class.
References
----------
.. [1] Zhang, Shanjie and Jin, Jianming. "Computation of Special
Functions", John Wiley and Sons, 1996.
http://jin.ece.illinois.edu/specfunc.html
"""
if not (isscalar(n) and isscalar(z)):
raise ValueError("arguments must be scalars.")
if (n != floor(n)) or (n < 0):
raise ValueError("n must be a non-negative integer.")
if (n < 1):
n1 = 1
else:
n1 = n
if iscomplex(z):
pn, pd = specfun.clpn(n1, z)
else:
pn, pd = specfun.lpn(n1, z)
return pn[:(n+1)], pd[:(n+1)] | [
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natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/contrib/learn/python/learn/datasets/text_datasets.py | python | maybe_download_dbpedia | (data_dir) | Download if DBpedia data is not present. | Download if DBpedia data is not present. | [
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"data",
"is",
"not",
"present",
"."
] | def maybe_download_dbpedia(data_dir):
"""Download if DBpedia data is not present."""
train_path = os.path.join(data_dir, 'dbpedia_csv/train.csv')
test_path = os.path.join(data_dir, 'dbpedia_csv/test.csv')
if not (gfile.Exists(train_path) and gfile.Exists(test_path)):
archive_path = base.maybe_download(
'dbpedia_csv.tar.gz', data_dir, DBPEDIA_URL)
tfile = tarfile.open(archive_path, 'r:*')
tfile.extractall(data_dir) | [
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LLNL/lbann | 26083e6c86050302ce33148aea70f62e61cacb92 | applications/nlp/transformer/subgraph/dataset.py | python | detokenize | (indices) | return text | Convert token indices to string.
Stops at the first EOS token. All other special tokens are
ignored. | Convert token indices to string. | [
"Convert",
"token",
"indices",
"to",
"string",
"."
] | def detokenize(indices):
"""Convert token indices to string.
Stops at the first EOS token. All other special tokens are
ignored.
"""
text = ''
for index in indices:
if index == eos_index:
break
elif index in (unk_index, bos_index, pad_index):
continue
else:
text += f' {tokens[index]}'
return text | [
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floooh/oryol | eb08cffe1b1cb6b05ed14ec692bca9372cef064e | fips-files/generators/util/png.py | python | Test.testPNMsbit | (self) | Test that PNM files can generates sBIT chunk. | Test that PNM files can generates sBIT chunk. | [
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] | def testPNMsbit(self):
"""Test that PNM files can generates sBIT chunk."""
def do():
return _main(['testPNMsbit'])
s = BytesIO()
s.write(strtobytes('P6 8 1 1\n'))
for pixel in range(8):
s.write(struct.pack('<I', (0x4081*pixel)&0x10101)[:3])
s.flush()
s.seek(0)
o = BytesIO()
testWithIO(s, o, do)
r = Reader(bytes=o.getvalue())
sbit = r.chunk('sBIT')[1]
self.assertEqual(sbit, strtobytes('\x01\x01\x01')) | [
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MichalBusta/E2E-MLT | 2f0b54e31ebb414cd2daad824d7d474062ebe834 | data_gen.py | python | random_rotation | (img, word_gto) | return dst | for i in range(0, len(word_gto) - 1):
draw_box_points(dst, word_gto[i])
cv2.imshow('dst', dst)
cv2.waitKey(0) | for i in range(0, len(word_gto) - 1):
draw_box_points(dst, word_gto[i])
cv2.imshow('dst', dst)
cv2.waitKey(0) | [
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center = (img.shape[1] / 2, img.shape[0] / 2)
angle = random.uniform(-1900, 1900) / 10
M = cv2.getRotationMatrix2D(center, angle, 1)
dst_size = (img.shape[1], img.shape[0])
dst = cv2.warpAffine(img, M, dst_size)
angle_rad = - angle * math.pi / 180
wor = np.copy(word_gto)
word_gto[:, 0, 0] = ((wor[:, 0, 0] - center[0]) * math.cos(angle_rad)) - ((wor[:, 0, 1] - center[1]) * math.sin(angle_rad)) + center[0]
word_gto[:, 0, 1] = ((wor[:, 0, 0] - center[0]) * math.sin(angle_rad)) + ((wor[:, 0, 1] - center[1]) * math.cos(angle_rad)) + center[1]
word_gto[:, 1, 0] = ((wor[:, 1, 0] - center[0]) * math.cos(angle_rad)) - ((wor[:, 1, 1] - center[1]) * math.sin(angle_rad)) + center[0]
word_gto[:, 1, 1] = ((wor[:, 1, 0] - center[0]) * math.sin(angle_rad)) + ((wor[:, 1, 1] - center[1]) * math.cos(angle_rad)) + center[1]
word_gto[:, 2, 0] = ((wor[:, 2, 0] - center[0]) * math.cos(angle_rad)) - ((wor[:, 2, 1] - center[1]) * math.sin(angle_rad)) + center[0]
word_gto[:, 2, 1] = ((wor[:, 2, 0] - center[0]) * math.sin(angle_rad)) + ((wor[:, 2, 1] - center[1]) * math.cos(angle_rad)) + center[1]
word_gto[:, 3, 0] = ((wor[:, 3, 0] - center[0]) * math.cos(angle_rad)) - ((wor[:, 3, 1] - center[1]) * math.sin(angle_rad)) + center[0]
word_gto[:, 3, 1] = ((wor[:, 3, 0] - center[0]) * math.sin(angle_rad)) + ((wor[:, 3, 1] - center[1]) * math.cos(angle_rad)) + center[1]
'''
for i in range(0, len(word_gto) - 1):
draw_box_points(dst, word_gto[i])
cv2.imshow('dst', dst)
cv2.waitKey(0)
'''
return dst | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/AWS/common-code/lib/cffi/api.py | python | FFI.string | (self, cdata, maxlen=-1) | return self._backend.string(cdata, maxlen) | Return a Python string (or unicode string) from the 'cdata'.
If 'cdata' is a pointer or array of characters or bytes, returns
the null-terminated string. The returned string extends until
the first null character, or at most 'maxlen' characters. If
'cdata' is an array then 'maxlen' defaults to its length.
If 'cdata' is a pointer or array of wchar_t, returns a unicode
string following the same rules.
If 'cdata' is a single character or byte or a wchar_t, returns
it as a string or unicode string.
If 'cdata' is an enum, returns the value of the enumerator as a
string, or 'NUMBER' if the value is out of range. | Return a Python string (or unicode string) from the 'cdata'.
If 'cdata' is a pointer or array of characters or bytes, returns
the null-terminated string. The returned string extends until
the first null character, or at most 'maxlen' characters. If
'cdata' is an array then 'maxlen' defaults to its length. | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/numpy/py3/numpy/polynomial/legendre.py | python | leg2poly | (c) | Convert a Legendre series to a polynomial.
Convert an array representing the coefficients of a Legendre series,
ordered from lowest degree to highest, to an array of the coefficients
of the equivalent polynomial (relative to the "standard" basis) ordered
from lowest to highest degree.
Parameters
----------
c : array_like
1-D array containing the Legendre series coefficients, ordered
from lowest order term to highest.
Returns
-------
pol : ndarray
1-D array containing the coefficients of the equivalent polynomial
(relative to the "standard" basis) ordered from lowest order term
to highest.
See Also
--------
poly2leg
Notes
-----
The easy way to do conversions between polynomial basis sets
is to use the convert method of a class instance.
Examples
--------
>>> from numpy import polynomial as P
>>> c = P.Legendre(range(4))
>>> c
Legendre([0., 1., 2., 3.], domain=[-1, 1], window=[-1, 1])
>>> p = c.convert(kind=P.Polynomial)
>>> p
Polynomial([-1. , -3.5, 3. , 7.5], domain=[-1., 1.], window=[-1., 1.])
>>> P.leg2poly(range(4))
array([-1. , -3.5, 3. , 7.5]) | Convert a Legendre series to a polynomial. | [
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"""
Convert a Legendre series to a polynomial.
Convert an array representing the coefficients of a Legendre series,
ordered from lowest degree to highest, to an array of the coefficients
of the equivalent polynomial (relative to the "standard" basis) ordered
from lowest to highest degree.
Parameters
----------
c : array_like
1-D array containing the Legendre series coefficients, ordered
from lowest order term to highest.
Returns
-------
pol : ndarray
1-D array containing the coefficients of the equivalent polynomial
(relative to the "standard" basis) ordered from lowest order term
to highest.
See Also
--------
poly2leg
Notes
-----
The easy way to do conversions between polynomial basis sets
is to use the convert method of a class instance.
Examples
--------
>>> from numpy import polynomial as P
>>> c = P.Legendre(range(4))
>>> c
Legendre([0., 1., 2., 3.], domain=[-1, 1], window=[-1, 1])
>>> p = c.convert(kind=P.Polynomial)
>>> p
Polynomial([-1. , -3.5, 3. , 7.5], domain=[-1., 1.], window=[-1., 1.])
>>> P.leg2poly(range(4))
array([-1. , -3.5, 3. , 7.5])
"""
from .polynomial import polyadd, polysub, polymulx
[c] = pu.as_series([c])
n = len(c)
if n < 3:
return c
else:
c0 = c[-2]
c1 = c[-1]
# i is the current degree of c1
for i in range(n - 1, 1, -1):
tmp = c0
c0 = polysub(c[i - 2], (c1*(i - 1))/i)
c1 = polyadd(tmp, (polymulx(c1)*(2*i - 1))/i)
return polyadd(c0, polymulx(c1)) | [
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thalium/icebox | 99d147d5b9269222225443ce171b4fd46d8985d4 | third_party/virtualbox/src/VBox/ValidationKit/common/utils.py | python | _sudoFixArguments | (aPositionalArgs, dKeywordArgs, fInitialEnv = True) | return None | Adds 'sudo' (or similar) to the args parameter, whereever it is. | Adds 'sudo' (or similar) to the args parameter, whereever it is. | [
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"""
Adds 'sudo' (or similar) to the args parameter, whereever it is.
"""
# Are we root?
fIsRoot = True;
try:
fIsRoot = os.getuid() == 0; # pylint: disable=E1101
except:
pass;
# If not, prepend sudo (non-interactive, simulate initial login).
if fIsRoot is not True:
asArgs = dKeywordArgs.get('args');
if asArgs is None:
asArgs = aPositionalArgs[0];
# Detect old sudo.
global g_fOldSudo;
if g_fOldSudo is None:
try:
sVersion = processOutputChecked(['sudo', '-V']);
except:
sVersion = '1.7.0';
sVersion = sVersion.strip().split('\n')[0];
sVersion = sVersion.replace('Sudo version', '').strip();
g_fOldSudo = len(sVersion) >= 4 \
and sVersion[0] == '1' \
and sVersion[1] == '.' \
and sVersion[2] <= '6' \
and sVersion[3] == '.';
asArgs.insert(0, 'sudo');
if not g_fOldSudo:
asArgs.insert(1, '-n');
if fInitialEnv and not g_fOldSudo:
asArgs.insert(1, '-i');
# paranoia...
if dKeywordArgs.get('args') is not None:
dKeywordArgs['args'] = asArgs;
else:
aPositionalArgs = (asArgs,) + aPositionalArgs[1:];
return None; | [
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alexozer/jankdrone | c4b403eb254b41b832ab2bdfade12ba59c99e5dc | drone/lib/nanopb/generator/nanopb_generator.py | python | ExtensionField.tags | (self) | return '#define %-40s %d\n' % (identifier, self.tag) | Return the #define for the tag number of this field. | Return the #define for the tag number of this field. | [
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'''Return the #define for the tag number of this field.'''
identifier = '%s_tag' % self.fullname
return '#define %-40s %d\n' % (identifier, self.tag) | [
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apache/incubator-mxnet | f03fb23f1d103fec9541b5ae59ee06b1734a51d9 | python/mxnet/ndarray/random.py | python | multinomial | (n=[1], p=[[1.0]], shape=_Null, dtype='float32', ctx=None, out=None, **kwargs) | return _internal._sample_multinomial(n, p, shape=shape, out=out, ctx=ctx, dtype=dtype, **kwargs) | Concurrent sampling from multiple multinomial distributions.
.. note:: The input distribution must be normalized, i.e. `p` must sum to
1 along its last dimension.
Parameters
----------
n : NDArray
An *n* dimensional array containing the number of trials of each
multinomial distribution.
p : NDArray
An *n+1* dimensional array containing the probabilities of each multinomial
distribution. Its last dimension has length `k`, where `k` is the number
of possible outcomes of each multinomial distribution.
For example, p with shape `(m, n, k)` specifies `m*n` multinomial
distributions each with `k` possible outcomes.
shape : int or tuple of ints, optional
The number of samples to draw from each distribution. If shape is empty
one sample will be drawn from each distribution.
out : NDArray, optional
Store output to an existing NDArray.
ctx : Context, optional
Device context of output. Default is current context. Overridden by
`n.context` when `n` is an NDArray.
dtype : {'float16', 'float32', 'float64'}, optional
Data type of output samples. Default is 'float32'
Returns
-------
NDArray
If input `shape` has shape, e.g., `(m, n)` and `n` and `p` are a scalar and an array of length k
respectively, output shape will be `(m, n, k)`. If `n` and `p` are NDArrays with shape, e.g.,
`(x, y)` and `(x, y, k)`, then output will have shape `(x, y, m, n, k)`, where `m*n`
samples are drawn for each `[n, p)` pair.
Examples
--------
>>> mx.nd.random.multinomial(mx.nd.array([10]), mx.nd.array([[0.1, 0.9]]))
[[ 1. 9.]]
<NDArray 1x2 @cpu(0)>
>>> mx.nd.random.multinomial(mx.nd.array([10]), mx.nd.array([[0.6, 0.4]]), shape=(2,))
[[[ 5. 5.]
[ 6. 4.]]]
<NDArray 1x2x2 @cpu(0)>
>>> n = mx.nd.array([10, 2, 3])
>>> p = mx.nd.array([[0.2, 0.8], [0.3, 0.7], [0.4, 0.6]])
>>> mx.nd.random.binomial(n, p)
[[ 2. 8.]
[ 1. 1.]
[ 1. 2.]]
<NDArray 3x2 @cpu(0)> | Concurrent sampling from multiple multinomial distributions. | [
"Concurrent",
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"distributions",
"."
] | def multinomial(n=[1], p=[[1.0]], shape=_Null, dtype='float32', ctx=None, out=None, **kwargs):
"""Concurrent sampling from multiple multinomial distributions.
.. note:: The input distribution must be normalized, i.e. `p` must sum to
1 along its last dimension.
Parameters
----------
n : NDArray
An *n* dimensional array containing the number of trials of each
multinomial distribution.
p : NDArray
An *n+1* dimensional array containing the probabilities of each multinomial
distribution. Its last dimension has length `k`, where `k` is the number
of possible outcomes of each multinomial distribution.
For example, p with shape `(m, n, k)` specifies `m*n` multinomial
distributions each with `k` possible outcomes.
shape : int or tuple of ints, optional
The number of samples to draw from each distribution. If shape is empty
one sample will be drawn from each distribution.
out : NDArray, optional
Store output to an existing NDArray.
ctx : Context, optional
Device context of output. Default is current context. Overridden by
`n.context` when `n` is an NDArray.
dtype : {'float16', 'float32', 'float64'}, optional
Data type of output samples. Default is 'float32'
Returns
-------
NDArray
If input `shape` has shape, e.g., `(m, n)` and `n` and `p` are a scalar and an array of length k
respectively, output shape will be `(m, n, k)`. If `n` and `p` are NDArrays with shape, e.g.,
`(x, y)` and `(x, y, k)`, then output will have shape `(x, y, m, n, k)`, where `m*n`
samples are drawn for each `[n, p)` pair.
Examples
--------
>>> mx.nd.random.multinomial(mx.nd.array([10]), mx.nd.array([[0.1, 0.9]]))
[[ 1. 9.]]
<NDArray 1x2 @cpu(0)>
>>> mx.nd.random.multinomial(mx.nd.array([10]), mx.nd.array([[0.6, 0.4]]), shape=(2,))
[[[ 5. 5.]
[ 6. 4.]]]
<NDArray 1x2x2 @cpu(0)>
>>> n = mx.nd.array([10, 2, 3])
>>> p = mx.nd.array([[0.2, 0.8], [0.3, 0.7], [0.4, 0.6]])
>>> mx.nd.random.binomial(n, p)
[[ 2. 8.]
[ 1. 1.]
[ 1. 2.]]
<NDArray 3x2 @cpu(0)>
"""
return _internal._sample_multinomial(n, p, shape=shape, out=out, ctx=ctx, dtype=dtype, **kwargs) | [
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ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/third_party/lib_x86_64/python2.7/dist-packages/numpy/lib/function_base.py | python | trim_zeros | (filt, trim='fb') | return filt[first:last] | Trim the leading and/or trailing zeros from a 1-D array or sequence.
Parameters
----------
filt : 1-D array or sequence
Input array.
trim : str, optional
A string with 'f' representing trim from front and 'b' to trim from
back. Default is 'fb', trim zeros from both front and back of the
array.
Returns
-------
trimmed : 1-D array or sequence
The result of trimming the input. The input data type is preserved.
Examples
--------
>>> a = np.array((0, 0, 0, 1, 2, 3, 0, 2, 1, 0))
>>> np.trim_zeros(a)
array([1, 2, 3, 0, 2, 1])
>>> np.trim_zeros(a, 'b')
array([0, 0, 0, 1, 2, 3, 0, 2, 1])
The input data type is preserved, list/tuple in means list/tuple out.
>>> np.trim_zeros([0, 1, 2, 0])
[1, 2] | Trim the leading and/or trailing zeros from a 1-D array or sequence. | [
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Trim the leading and/or trailing zeros from a 1-D array or sequence.
Parameters
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filt : 1-D array or sequence
Input array.
trim : str, optional
A string with 'f' representing trim from front and 'b' to trim from
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Returns
-------
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The result of trimming the input. The input data type is preserved.
Examples
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>>> a = np.array((0, 0, 0, 1, 2, 3, 0, 2, 1, 0))
>>> np.trim_zeros(a)
array([1, 2, 3, 0, 2, 1])
>>> np.trim_zeros(a, 'b')
array([0, 0, 0, 1, 2, 3, 0, 2, 1])
The input data type is preserved, list/tuple in means list/tuple out.
>>> np.trim_zeros([0, 1, 2, 0])
[1, 2]
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adobe/chromium | cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7 | native_client_sdk/src/build_tools/nacl_sdk_scons/site_tools/nacl_tools.py | python | NaClProgram | (env, target, sources, variant_dir='obj') | return env.Program(target, program_objects) | Add a Program to env that builds its objects in the directory specified
by |variant_dir|.
This is slightly different than VariantDir() in that the sources can live in
the same directory as the calling SConscript file.
Args:
env: Environment to modify.
target: The target name that depends on the object files. E.g.
"hello_world_x86_32.nexe"
sources: The list of source files that are used to build the objects.
variant_dir: The built object files are put in this directory. Default
value is "obj".
Returns:
The Program Node. | Add a Program to env that builds its objects in the directory specified
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'''Add a Program to env that builds its objects in the directory specified
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This is slightly different than VariantDir() in that the sources can live in
the same directory as the calling SConscript file.
Args:
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sources: The list of source files that are used to build the objects.
variant_dir: The built object files are put in this directory. Default
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'''
program_objects = []
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obj_file = os.path.splitext(src_file)[0] + env.get('OBJSUFFIX', '.o')
program_objects.append(env.StaticObject(
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/setuptools/py2/pkg_resources/__init__.py | python | IResourceProvider.resource_listdir | (resource_name) | List of resource names in the directory (like ``os.listdir()``) | List of resource names in the directory (like ``os.listdir()``) | [
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"""List of resource names in the directory (like ``os.listdir()``)""" | [
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gimli-org/gimli | 17aa2160de9b15ababd9ef99e89b1bc3277bbb23 | pygimli/physics/sNMR/mrsprofile.py | python | MRSprofile.showModel | (self, showFit=0, cmap=Spectral, figsize=(13, 12),
wlim=(0, 0.5), tlim=(0.05, 0.5)) | return fig, ax | Show 2d model as stitched 1d models along with fit. | Show 2d model as stitched 1d models along with fit. | [
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] | def showModel(self, showFit=0, cmap=Spectral, figsize=(13, 12),
wlim=(0, 0.5), tlim=(0.05, 0.5)):
"""Show 2d model as stitched 1d models along with fit."""
fig, ax = plt.subplots(nrows=2+showFit, figsize=figsize, sharex=True)
self.showWC(wlim, ax=ax[-2], cmap=cmap)
self.showT2(tlim, ax=ax[-1], cmap=cmap)
xl = ax[-1].get_xlim()
ax[0].set_xlabel('x (m)')
ax[0].xaxis.set_label_position('top')
ax[0].xaxis.tick_top()
ax[0].set_ylabel(r'$\chi^2$ (-)')
for axi in ax[-2:]:
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root-project/root | fcd3583bb14852bf2e8cd2415717cbaac0e75896 | interpreter/llvm/src/tools/clang/bindings/python/clang/cindex.py | python | CompileCommand.directory | (self) | return conf.lib.clang_CompileCommand_getDirectory(self.cmd) | Get the working directory for this CompileCommand | Get the working directory for this CompileCommand | [
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"""Get the working directory for this CompileCommand"""
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/metrics/python/ops/histogram_ops.py | python | _strict_conv1d | (x, h) | Return x * h for rank 1 tensors x and h. | Return x * h for rank 1 tensors x and h. | [
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return array_ops.reshape(result, [-1]) | [
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OAID/Caffe-HRT | aae71e498ab842c6f92bcc23fc668423615a4d65 | scripts/cpp_lint.py | python | _NestingState.UpdatePreprocessor | (self, line) | Update preprocessor stack.
We need to handle preprocessors due to classes like this:
#ifdef SWIG
struct ResultDetailsPageElementExtensionPoint {
#else
struct ResultDetailsPageElementExtensionPoint : public Extension {
#endif
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- Preprocessor condition evaluates to false from #else/#elif up
to #endif. We still perform lint checks on these lines, but
these do not affect nesting stack.
Args:
line: current line to check. | Update preprocessor stack. | [
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"""Update preprocessor stack.
We need to handle preprocessors due to classes like this:
#ifdef SWIG
struct ResultDetailsPageElementExtensionPoint {
#else
struct ResultDetailsPageElementExtensionPoint : public Extension {
#endif
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Args:
line: current line to check.
"""
if Match(r'^\s*#\s*(if|ifdef|ifndef)\b', line):
# Beginning of #if block, save the nesting stack here. The saved
# stack will allow us to restore the parsing state in the #else case.
self.pp_stack.append(_PreprocessorInfo(copy.deepcopy(self.stack)))
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self.pp_stack[-1].seen_else = True
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"pass"
] | https://github.com/OAID/Caffe-HRT/blob/aae71e498ab842c6f92bcc23fc668423615a4d65/scripts/cpp_lint.py#L1948-L2002 | ||
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/distutils/msvc9compiler.py | python | Reg.read_values | (cls, base, key) | return d | Return dict of registry keys and values.
All names are converted to lowercase. | Return dict of registry keys and values. | [
"Return",
"dict",
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"registry",
"keys",
"and",
"values",
"."
] | def read_values(cls, base, key):
"""Return dict of registry keys and values.
All names are converted to lowercase.
"""
try:
handle = RegOpenKeyEx(base, key)
except RegError:
return None
d = {}
i = 0
while True:
try:
name, value, type = RegEnumValue(handle, i)
except RegError:
break
name = name.lower()
d[cls.convert_mbcs(name)] = cls.convert_mbcs(value)
i += 1
return d | [
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apple/swift-lldb | d74be846ef3e62de946df343e8c234bde93a8912 | utils/vim-lldb/python-vim-lldb/import_lldb.py | python | import_lldb | () | return False | Find and import the lldb modules. This function tries to find the lldb module by:
1. Simply by doing "import lldb" in case the system python installation is aware of lldb. If that fails,
2. Executes the lldb executable pointed to by the LLDB environment variable (or if unset, the first lldb
on PATH") with the -P flag to determine the PYTHONPATH to set. If the lldb executable returns a valid
path, it is added to sys.path and the import is attempted again. If that fails, 3. On Mac OS X the
default Xcode 4.5 installation path. | Find and import the lldb modules. This function tries to find the lldb module by:
1. Simply by doing "import lldb" in case the system python installation is aware of lldb. If that fails,
2. Executes the lldb executable pointed to by the LLDB environment variable (or if unset, the first lldb
on PATH") with the -P flag to determine the PYTHONPATH to set. If the lldb executable returns a valid
path, it is added to sys.path and the import is attempted again. If that fails, 3. On Mac OS X the
default Xcode 4.5 installation path. | [
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""" Find and import the lldb modules. This function tries to find the lldb module by:
1. Simply by doing "import lldb" in case the system python installation is aware of lldb. If that fails,
2. Executes the lldb executable pointed to by the LLDB environment variable (or if unset, the first lldb
on PATH") with the -P flag to determine the PYTHONPATH to set. If the lldb executable returns a valid
path, it is added to sys.path and the import is attempted again. If that fails, 3. On Mac OS X the
default Xcode 4.5 installation path.
"""
# Try simple 'import lldb', in case of a system-wide install or a
# pre-configured PYTHONPATH
try:
import lldb
return True
except ImportError:
pass
# Allow overriding default path to lldb executable with the LLDB
# environment variable
lldb_executable = 'lldb'
if 'LLDB' in os.environ and os.path.exists(os.environ['LLDB']):
lldb_executable = os.environ['LLDB']
# Try using builtin module location support ('lldb -P')
from subprocess import check_output, CalledProcessError
try:
with open(os.devnull, 'w') as fnull:
lldb_minus_p_path = check_output(
"%s -P" %
lldb_executable,
shell=True,
stderr=fnull).strip()
if not os.path.exists(lldb_minus_p_path):
# lldb -P returned invalid path, probably too old
pass
else:
sys.path.append(lldb_minus_p_path)
import lldb
return True
except CalledProcessError:
# Cannot run 'lldb -P' to determine location of lldb python module
pass
except ImportError:
# Unable to import lldb module from path returned by `lldb -P`
pass
# On Mac OS X, use the try the default path to XCode lldb module
if "darwin" in sys.platform:
xcode_python_path = "/Applications/Xcode.app/Contents/SharedFrameworks/LLDB.framework/Versions/Current/Resources/Python/"
sys.path.append(xcode_python_path)
try:
import lldb
return True
except ImportError:
# Unable to import lldb module from default Xcode python path
pass
return False | [
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apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | src/external/coremltools_wrap/coremltools/coremltools/converters/_converters_entry.py | python | convert | (
model,
source="auto",
inputs=None,
outputs=None,
classifier_config=None,
minimum_deployment_target=None,
**kwargs
) | return model | Convert TensorFlow or Pytorch models to Core ML model format. Whether a
parameter is required may differ between frameworks (see below). Note that
this function is aliased as `ct.convert` in the tutorials.
Parameters
----------
model:
TensorFlow 1, TensorFlow 2 or Pytorch model in one of the following
format:
For TensorFlow versions 1.x:
- Frozen `tf.Graph <https://www.tensorflow.org/api_docs/python/tf/Graph>`_
- Frozen graph (`.pb`) file path
- `tf.keras.Model <https://www.tensorflow.org/api_docs/python/tf/keras>`_
- `HDF5 <https://keras.io/api/models/model_saving_apis/>`_ file path (`.h5`)
- `SavedModel <https://www.tensorflow.org/guide/saved_model>`_ directory path
For TensorFlow versions 2.x:
- `tf.keras.Model <https://www.tensorflow.org/api_docs/python/tf/keras>`_
- `HDF5 file path <https://keras.io/api/models/model_saving_apis/>`_ (`.h5`)
- `SavedModel <https://www.tensorflow.org/guide/saved_model>`_ directory path
- A `concrete function <https://www.tensorflow.org/guide/concrete_function>`_
For Pytorch:
- A `TorchScript <https://pytorch.org/docs/stable/jit.html>`_ object
- Path to a `.pt` file
source: str (optional)
One of `auto`, `tensorflow`, or `pytorch`. `auto` determines the
framework automatically for most cases. Raise ValueError if it fails
to determine the source framework.
inputs: list of `TensorType` or `ImageType`
- Inputs are required for PyTorch model, but optional for TensorFlow.
- For PyTorch models, the inputs may be nested list or tuple, but for
TensorFlow models it must be a flat list.
- For TensorFlow, if inputs is `None`, the inputs are `Placeholder`
nodes in the model (if model is frozen graph) or function inputs (if
model is tf function).
- For TensorFlow, if inputs is not `None`, inputs may contain only a
subset of all Placeholder in the TF model.
outputs: list[str] (optional)
TensorFlow 1 and 2:
- `outputs` are optional.
- If specified, `outputs` is a list of string representing node
names.
- If `outputs` are not specified, converter infers outputs as all
terminal identity nodes.
PyTorch:
- `outputs` must not be specified.
classifier_config: ClassifierConfig class (optional)
The configuration if the mlmodel is intended to be a classifier.
minimum_deployment_target: coremltools.target enumeration (optional)
- one of the members of enum "coremltools.target."
- When not-specified or None, converter aims for as minimum of a deployment target as possible
Returns
-------
model: MLModel
A Core ML MLModel object
Examples
--------
TensorFlow 1, 2 (`model` is a frozen graph):
>>> with tf.Graph().as_default() as graph:
>>> x = tf.placeholder(tf.float32, shape=(1, 2, 3), name="input")
>>> y = tf.nn.relu(x, name="output")
# Automatically infer inputs and outputs
>>> mlmodel = ct.convert(graph)
>>> test_input = np.random.rand(1, 2, 3) - 0.5
>>> results = mlmodel.predict({"input": test_input})
>>> print(results['output'])
TensorFlow 2 (`model` is tf.Keras model path):
>>> x = tf.keras.Input(shape=(32,), name='input')
>>> y = tf.keras.layers.Dense(16, activation='softmax')(x)
>>> keras_model = tf.keras.Model(x, y)
>>> keras_model.save(h5_path)
>>> mlmodel = ct.convert(h5_path)
>>> test_input = np.random.rand(2, 32)
>>> results = mlmodel.predict({'input': test_input})
>>> print(results['Identity'])
Pytorch:
>>> model = torchvision.models.mobilenet_v2()
>>> model.eval()
>>> example_input = torch.rand(1, 3, 256, 256)
>>> traced_model = torch.jit.trace(model, example_input)
>>> input = ct.TensorType(name='input_name', shape=(1, 3, 256, 256))
>>> mlmodel = ct.convert(traced_model, inputs=[input])
>>> results = mlmodel.predict({"input": example_input.numpy()})
>>> print(results['1651']) # 1651 is the node name given by PyTorch's JIT
See `here <https://coremltools.readme.io/docs/neural-network-conversion>`_ for
more advanced options | Convert TensorFlow or Pytorch models to Core ML model format. Whether a
parameter is required may differ between frameworks (see below). Note that
this function is aliased as `ct.convert` in the tutorials. | [
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] | def convert(
model,
source="auto",
inputs=None,
outputs=None,
classifier_config=None,
minimum_deployment_target=None,
**kwargs
):
"""
Convert TensorFlow or Pytorch models to Core ML model format. Whether a
parameter is required may differ between frameworks (see below). Note that
this function is aliased as `ct.convert` in the tutorials.
Parameters
----------
model:
TensorFlow 1, TensorFlow 2 or Pytorch model in one of the following
format:
For TensorFlow versions 1.x:
- Frozen `tf.Graph <https://www.tensorflow.org/api_docs/python/tf/Graph>`_
- Frozen graph (`.pb`) file path
- `tf.keras.Model <https://www.tensorflow.org/api_docs/python/tf/keras>`_
- `HDF5 <https://keras.io/api/models/model_saving_apis/>`_ file path (`.h5`)
- `SavedModel <https://www.tensorflow.org/guide/saved_model>`_ directory path
For TensorFlow versions 2.x:
- `tf.keras.Model <https://www.tensorflow.org/api_docs/python/tf/keras>`_
- `HDF5 file path <https://keras.io/api/models/model_saving_apis/>`_ (`.h5`)
- `SavedModel <https://www.tensorflow.org/guide/saved_model>`_ directory path
- A `concrete function <https://www.tensorflow.org/guide/concrete_function>`_
For Pytorch:
- A `TorchScript <https://pytorch.org/docs/stable/jit.html>`_ object
- Path to a `.pt` file
source: str (optional)
One of `auto`, `tensorflow`, or `pytorch`. `auto` determines the
framework automatically for most cases. Raise ValueError if it fails
to determine the source framework.
inputs: list of `TensorType` or `ImageType`
- Inputs are required for PyTorch model, but optional for TensorFlow.
- For PyTorch models, the inputs may be nested list or tuple, but for
TensorFlow models it must be a flat list.
- For TensorFlow, if inputs is `None`, the inputs are `Placeholder`
nodes in the model (if model is frozen graph) or function inputs (if
model is tf function).
- For TensorFlow, if inputs is not `None`, inputs may contain only a
subset of all Placeholder in the TF model.
outputs: list[str] (optional)
TensorFlow 1 and 2:
- `outputs` are optional.
- If specified, `outputs` is a list of string representing node
names.
- If `outputs` are not specified, converter infers outputs as all
terminal identity nodes.
PyTorch:
- `outputs` must not be specified.
classifier_config: ClassifierConfig class (optional)
The configuration if the mlmodel is intended to be a classifier.
minimum_deployment_target: coremltools.target enumeration (optional)
- one of the members of enum "coremltools.target."
- When not-specified or None, converter aims for as minimum of a deployment target as possible
Returns
-------
model: MLModel
A Core ML MLModel object
Examples
--------
TensorFlow 1, 2 (`model` is a frozen graph):
>>> with tf.Graph().as_default() as graph:
>>> x = tf.placeholder(tf.float32, shape=(1, 2, 3), name="input")
>>> y = tf.nn.relu(x, name="output")
# Automatically infer inputs and outputs
>>> mlmodel = ct.convert(graph)
>>> test_input = np.random.rand(1, 2, 3) - 0.5
>>> results = mlmodel.predict({"input": test_input})
>>> print(results['output'])
TensorFlow 2 (`model` is tf.Keras model path):
>>> x = tf.keras.Input(shape=(32,), name='input')
>>> y = tf.keras.layers.Dense(16, activation='softmax')(x)
>>> keras_model = tf.keras.Model(x, y)
>>> keras_model.save(h5_path)
>>> mlmodel = ct.convert(h5_path)
>>> test_input = np.random.rand(2, 32)
>>> results = mlmodel.predict({'input': test_input})
>>> print(results['Identity'])
Pytorch:
>>> model = torchvision.models.mobilenet_v2()
>>> model.eval()
>>> example_input = torch.rand(1, 3, 256, 256)
>>> traced_model = torch.jit.trace(model, example_input)
>>> input = ct.TensorType(name='input_name', shape=(1, 3, 256, 256))
>>> mlmodel = ct.convert(traced_model, inputs=[input])
>>> results = mlmodel.predict({"input": example_input.numpy()})
>>> print(results['1651']) # 1651 is the node name given by PyTorch's JIT
See `here <https://coremltools.readme.io/docs/neural-network-conversion>`_ for
more advanced options
"""
if minimum_deployment_target is not None and not isinstance(
minimum_deployment_target, AvailableTarget
):
msg = (
"Unrecognized value of argument 'minimum_deployment_target': {}. "
"It needs to be a member of 'coremltools.target' enumeration. "
"For example, coremltools.target.iOS13"
)
raise TypeError(msg.format(minimum_deployment_target))
source = source.lower()
if source not in {"auto", "tensorflow", "pytorch"}:
msg = (
'Unrecognized value of argument "source": {}. '
'It must be one of ["auto", "tensorflow", "pytorch"].'
)
raise ValueError(msg.format(source))
def raise_if_duplicated(input_list):
# Detect duplicated inputs
input_names = [t.name for t in input_list if t.name is not None]
dups = [
item
for item, count in collections.Counter(input_names).items()
if count > 1
]
if len(dups) > 0:
raise ValueError("Duplicated inputs: {}".format(dups))
if inputs is not None:
if not isinstance(inputs, list):
msg = '"inputs" must be of type list'
raise ValueError(msg)
if classifier_config is not None:
if not isinstance(classifier_config, ClassifierConfig):
msg = '"classifier_config" must be of type ClassifierConfig'
raise ValueError(msg)
if source == "tensorflow" and _HAS_TF_2:
source = "tensorflow2"
if source == "auto" and _HAS_TF_1:
try:
loader = TF1Loader(model, outputs=outputs)
loader._graph_def_from_model(outputs=outputs)
source = "tensorflow"
except:
pass
if source == "auto" and _HAS_TF_2:
try:
loader = TF2Loader(model, outputs=outputs)
loader._graph_def_from_model(outputs=outputs)
source = "tensorflow2"
except:
pass
if source == "auto" and _HAS_TORCH:
try:
pytorch_load(model)
source = "pytorch"
except:
pass
if source == "auto" and isinstance(model, Program):
source = "mil"
convert_to = kwargs.get("convert_to", "nn_proto")
kwargs.pop("convert_to", None)
if source == "auto":
msg = (
"Unable to determine the type of the model, i.e. the source framework. "
'Please provide the value of argument "source", from one of '
'["tensorflow", "pytorch"]. Note that model conversion requires the '
"source package that generates the model. Please make sure you have "
"the appropriate version of source package installed. E.g., if you're "
"converting model originally trained with TensorFlow 1.14, make sure "
"you have `tensorflow==1.14` installed."
)
raise ValueError(msg)
elif source in {"tensorflow", "tensorflow2"}:
if source == "tensorflow" and not _HAS_TF_1:
raise ValueError(
'Converter was called with source="tensorflow", but missing tensorflow package'
)
if inputs is not None:
raise_if_duplicated(inputs)
if inputs is not None and not all(
[isinstance(_input, InputType) for _input in inputs]
):
raise ValueError("Input should be a list of TensorType or ImageType")
proto_spec = _convert(
model,
convert_from=source,
convert_to=convert_to,
inputs=inputs,
outputs=outputs,
classifier_config=classifier_config,
**kwargs
)
elif source == "pytorch":
if "example_inputs" in kwargs:
msg = 'Unexpected argument "example_inputs" found'
raise ValueError(msg)
def _flatten_list(_inputs):
ret = []
for _input in _inputs:
if isinstance(_input, (list, tuple)):
ret.extend(_flatten_list(_input))
elif isinstance(_input, InputType):
ret.append(_input)
else:
raise ValueError(
"Unknown type {} for flattening into InputType.".format(
type(_input)
)
)
return ret
flat_inputs = _flatten_list(inputs)
raise_if_duplicated(flat_inputs)
if inputs is not None and not all(
[isinstance(_input, InputType) for _input in flat_inputs]
):
raise ValueError(
"Input should be a list/tuple (or nested lists/tuples) of TensorType or ImageType"
)
if outputs is not None:
raise ValueError("outputs must not be specified for PyTorch")
proto_spec = _convert(
model,
convert_from="torch",
convert_to=convert_to,
inputs=inputs,
outputs=outputs,
classifier_config=classifier_config,
**kwargs
)
elif source == "mil":
if not isinstance(model, Program):
msg = "Converter was asked to convert MIL input, but input is not a MIL program!"
raise ValueError(msg)
proto_spec = _convert(
model,
convert_from="mil",
convert_to=convert_to,
example_inputs=inputs,
classifier_config=classifier_config,
**kwargs
)
model = coremltools.models.MLModel(proto_spec, useCPUOnly=True)
if minimum_deployment_target is not None:
check_deployment_compatibility(
spec=proto_spec,
representation=convert_to,
deployment_target=minimum_deployment_target,
)
del proto_spec
gc.collect()
# recording metadata: coremltools version, source framework and version
if source in {"tensorflow", "tensorflow2"} and (_HAS_TF_1 or _HAS_TF_2):
src_pkg_version = "tensorflow=={0}".format(tf.__version__)
elif source == "pytorch" and _HAS_TORCH:
src_pkg_version = "torch=={0}".format(torch.__version__)
else:
src_pkg_version = "unknown"
model.user_defined_metadata[_METADATA_VERSION] = ct_version
model.user_defined_metadata[_METADATA_SOURCE] = src_pkg_version
return model | [
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] | https://github.com/apple/turicreate/blob/cce55aa5311300e3ce6af93cb45ba791fd1bdf49/src/external/coremltools_wrap/coremltools/coremltools/converters/_converters_entry.py#L35-L339 | |
netket/netket | 0d534e54ecbf25b677ea72af6b85947979420652 | netket/hilbert/tensor_hilbert.py | python | TensorHilbert.__init__ | (self, *hilb_spaces: DiscreteHilbert) | r"""Constructs a tensor Hilbert space
Args:
*hilb: An iterable object containing at least 1 hilbert space. | r"""Constructs a tensor Hilbert space | [
"r",
"Constructs",
"a",
"tensor",
"Hilbert",
"space"
] | def __init__(self, *hilb_spaces: DiscreteHilbert):
r"""Constructs a tensor Hilbert space
Args:
*hilb: An iterable object containing at least 1 hilbert space.
"""
self._hilbert_spaces = hilb_spaces
self._n_hilbert_spaces = len(hilb_spaces)
self._hilbert_i = np.concatenate(
[[i for _ in range(hi.size)] for (i, hi) in enumerate(hilb_spaces)]
)
self._sizes = tuple([hi.size for hi in hilb_spaces])
shape = np.concatenate([hi.shape for hi in hilb_spaces])
self._cum_sizes = np.cumsum(self._sizes)
self._cum_indices = np.concatenate([[0], self._cum_sizes])
self._size = sum(self._sizes)
self._delta_indices_i = np.array(
[self._cum_indices[i] for i in self._hilbert_i]
)
# pre-compute indexing data iff the tensor space is still indexable
if all(hi.is_indexable for hi in hilb_spaces) and _is_indexable(shape):
self._ns_states = [hi.n_states for hi in self._hilbert_spaces]
self._ns_states_r = np.flip(self._ns_states)
self._cum_ns_states = np.concatenate([[0], np.cumprod(self._ns_states)])
self._cum_ns_states_r = np.flip(
np.cumprod(np.concatenate([[1], np.flip(self._ns_states)]))[:-1]
)
self._n_states = np.prod(self._ns_states)
super().__init__(shape=shape) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numpy/lib/histograms.py | python | _get_outer_edges | (a, range) | return first_edge, last_edge | Determine the outer bin edges to use, from either the data or the range
argument | Determine the outer bin edges to use, from either the data or the range
argument | [
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"edges",
"to",
"use",
"from",
"either",
"the",
"data",
"or",
"the",
"range",
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] | def _get_outer_edges(a, range):
"""
Determine the outer bin edges to use, from either the data or the range
argument
"""
if range is not None:
first_edge, last_edge = range
if first_edge > last_edge:
raise ValueError(
'max must be larger than min in range parameter.')
if not (np.isfinite(first_edge) and np.isfinite(last_edge)):
raise ValueError(
"supplied range of [{}, {}] is not finite".format(first_edge, last_edge))
elif a.size == 0:
# handle empty arrays. Can't determine range, so use 0-1.
first_edge, last_edge = 0, 1
else:
first_edge, last_edge = a.min(), a.max()
if not (np.isfinite(first_edge) and np.isfinite(last_edge)):
raise ValueError(
"autodetected range of [{}, {}] is not finite".format(first_edge, last_edge))
# expand empty range to avoid divide by zero
if first_edge == last_edge:
first_edge = first_edge - 0.5
last_edge = last_edge + 0.5
return first_edge, last_edge | [
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] | https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numpy/lib/histograms.py#L307-L334 |
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