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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/layers/python/ops/sparse_ops.py | python | sparse_row_envelope | (sparse_input, row_axis=0, col_axis=1, name=None) | Returns the length of each 'row' in a `SparseTensor`.
For example, if `sparse_input` has indices `[[0,0], [2, 0], [2, 1], [2, 2]]`
and shape `[3, 3]`, this function will return `[1, 0, 3]`.
Args:
sparse_input: a `SparseTensor` of rank at least 2.
row_axis: An integer. The axis for the row of the envelope matrix. Default
is 0.
col_axis: An integer. The axis for the col of the envelope matrix. Default
is 1.
name: A name for the operation (optional).
Returns:
A one-dimensional `Tensor` whose entries correspond to the length of each
row of `SparseTensor`.
Raises:
ValueError: If row_axis and col_axis are the same axis or they are not
integers. | Returns the length of each 'row' in a `SparseTensor`. | [
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] | def sparse_row_envelope(sparse_input, row_axis=0, col_axis=1, name=None):
"""Returns the length of each 'row' in a `SparseTensor`.
For example, if `sparse_input` has indices `[[0,0], [2, 0], [2, 1], [2, 2]]`
and shape `[3, 3]`, this function will return `[1, 0, 3]`.
Args:
sparse_input: a `SparseTensor` of rank at least 2.
row_axis: An integer. The axis for the row of the envelope matrix. Default
is 0.
col_axis: An integer. The axis for the col of the envelope matrix. Default
is 1.
name: A name for the operation (optional).
Returns:
A one-dimensional `Tensor` whose entries correspond to the length of each
row of `SparseTensor`.
Raises:
ValueError: If row_axis and col_axis are the same axis or they are not
integers.
"""
if not (isinstance(row_axis, compat.integral_types) and
isinstance(col_axis, compat.integral_types)):
raise ValueError("`row_axis` and `col_axis` must be integers.")
if row_axis == col_axis:
raise ValueError("Row and column can not be the same axis.")
with ops.name_scope(name, "sparse_row_envelope", [sparse_input]):
indices = sparse_input.indices
row_indices = indices[:, row_axis]
col_indices = indices[:, col_axis]
num_rows = math_ops.cast(sparse_input.dense_shape[row_axis], dtypes.int32)
row_envelope = math_ops.unsorted_segment_max(
col_indices + 1, row_indices, num_rows, name=name)
zeros = array_ops.zeros_like(row_envelope)
return array_ops.where(row_envelope > zeros, row_envelope, zeros) | [
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||
kamyu104/LeetCode-Solutions | 77605708a927ea3b85aee5a479db733938c7c211 | Python/count-nodes-with-the-highest-score.py | python | Solution.countHighestScoreNodes | (self, parents) | return iter_dfs(adj) | :type parents: List[int]
:rtype: int | :type parents: List[int]
:rtype: int | [
":",
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] | def countHighestScoreNodes(self, parents):
"""
:type parents: List[int]
:rtype: int
"""
def iter_dfs(adj):
result = [0]*2
stk = [(1, (0, [0]))]
while stk:
step, args = stk.pop()
if step == 1:
i, ret = args
cnts = [[0] for _ in xrange(len(adj[i]))]
stk.append((2, (cnts, ret)))
for j, child in enumerate(adj[i]):
stk.append((1, (child, cnts[j])))
elif step == 2:
cnts, ret = args
ret[0] = sum(cnt[0] for cnt in cnts)+1
score = max((len(adj)-ret[0]), 1)*reduce(lambda x, y: x*y[0], cnts, 1)
if score > result[0]:
result[:] = [score, 1]
elif score == result[0]:
result[1] += 1
return result[1]
adj = [[] for _ in xrange(len(parents))] # Space: O(n)
for i in xrange(1, len(parents)):
adj[parents[i]].append(i)
return iter_dfs(adj) | [
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|
wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/plat-mac/aepack.py | python | pack | (x, forcetype = None) | return AE.AECreateDesc('TEXT', repr(x)) | Pack a python object into an AE descriptor | Pack a python object into an AE descriptor | [
"Pack",
"a",
"python",
"object",
"into",
"an",
"AE",
"descriptor"
] | def pack(x, forcetype = None):
"""Pack a python object into an AE descriptor"""
if forcetype:
if type(x) is StringType:
return AE.AECreateDesc(forcetype, x)
else:
return pack(x).AECoerceDesc(forcetype)
if x is None:
return AE.AECreateDesc('null', '')
if isinstance(x, AEDescType):
return x
if isinstance(x, FSSType):
return AE.AECreateDesc('fss ', x.data)
if isinstance(x, FSRefType):
return AE.AECreateDesc('fsrf', x.data)
if isinstance(x, AliasType):
return AE.AECreateDesc('alis', x.data)
if isinstance(x, IntType):
return AE.AECreateDesc('long', struct.pack('l', x))
if isinstance(x, FloatType):
return AE.AECreateDesc('doub', struct.pack('d', x))
if isinstance(x, StringType):
return AE.AECreateDesc('TEXT', x)
if isinstance(x, UnicodeType):
data = x.encode('utf16')
if data[:2] == '\xfe\xff':
data = data[2:]
return AE.AECreateDesc('utxt', data)
if isinstance(x, ListType):
list = AE.AECreateList('', 0)
for item in x:
list.AEPutDesc(0, pack(item))
return list
if isinstance(x, DictionaryType):
record = AE.AECreateList('', 1)
for key, value in x.items():
packkey(record, key, value)
#record.AEPutParamDesc(key, pack(value))
return record
if type(x) == types.ClassType and issubclass(x, ObjectSpecifier):
# Note: we are getting a class object here, not an instance
return AE.AECreateDesc('type', x.want)
if hasattr(x, '__aepack__'):
return x.__aepack__()
if hasattr(x, 'which'):
return AE.AECreateDesc('TEXT', x.which)
if hasattr(x, 'want'):
return AE.AECreateDesc('TEXT', x.want)
return AE.AECreateDesc('TEXT', repr(x)) | [
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|
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_core.py | python | Validator.TransferFromWindow | (*args, **kwargs) | return _core_.Validator_TransferFromWindow(*args, **kwargs) | TransferFromWindow(self) -> bool | TransferFromWindow(self) -> bool | [
"TransferFromWindow",
"(",
"self",
")",
"-",
">",
"bool"
] | def TransferFromWindow(*args, **kwargs):
"""TransferFromWindow(self) -> bool"""
return _core_.Validator_TransferFromWindow(*args, **kwargs) | [
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|
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_core.py | python | HeaderColumn.IsSortKey | (*args, **kwargs) | return _core_.HeaderColumn_IsSortKey(*args, **kwargs) | IsSortKey(self) -> bool | IsSortKey(self) -> bool | [
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"""IsSortKey(self) -> bool"""
return _core_.HeaderColumn_IsSortKey(*args, **kwargs) | [
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|
gem5/gem5 | 141cc37c2d4b93959d4c249b8f7e6a8b2ef75338 | src/python/m5/util/fdthelper.py | python | Fdt.writeDtsFile | (self, filename) | Convert the device tree to DTS and write to a file. | Convert the device tree to DTS and write to a file. | [
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] | def writeDtsFile(self, filename):
"""Convert the device tree to DTS and write to a file."""
filename = os.path.realpath(filename)
try:
with open(filename, 'w') as f:
f.write(self.to_dts())
return filename
except IOError:
raise RuntimeError("Failed to open DTS output file") | [
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||
albertz/openlierox | d316c14a8eb57848ef56e9bfa7b23a56f694a51b | tools/DedicatedServerVideo/gdata/Crypto/PublicKey/pubkey.py | python | pubkey.verify | (self, M, signature) | return self._verify(M, signature) | verify(M:string|long, signature:tuple) : bool
Verify that the signature is valid for the message M;
returns true if the signature checks out. | verify(M:string|long, signature:tuple) : bool
Verify that the signature is valid for the message M;
returns true if the signature checks out. | [
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"""verify(M:string|long, signature:tuple) : bool
Verify that the signature is valid for the message M;
returns true if the signature checks out.
"""
if isinstance(M, types.StringType): M=bytes_to_long(M)
return self._verify(M, signature) | [
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|
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/_pydecimal.py | python | Decimal.copy_sign | (self, other, context=None) | return _dec_from_triple(other._sign, self._int,
self._exp, self._is_special) | Returns self with the sign of other. | Returns self with the sign of other. | [
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] | def copy_sign(self, other, context=None):
"""Returns self with the sign of other."""
other = _convert_other(other, raiseit=True)
return _dec_from_triple(other._sign, self._int,
self._exp, self._is_special) | [
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|
ceph/ceph | 959663007321a369c83218414a29bd9dbc8bda3a | src/pybind/mgr/mgr_module.py | python | MgrModule.get_mgr_id | (self) | return self._ceph_get_mgr_id() | Retrieve the name of the manager daemon where this plugin
is currently being executed (i.e. the active manager).
:return: str | Retrieve the name of the manager daemon where this plugin
is currently being executed (i.e. the active manager). | [
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:return: str
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return self._ceph_get_mgr_id() | [
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|
plaidml/plaidml | f3c6681db21460e5fdc11ae651d6d7b6c27f8262 | plaidml/edsl/__init__.py | python | pow | (x, y) | return intrinsic('pow', x, y) | Computes the elementwise ``y``th power of ``x``.
Args:
x (Tensor): The base Tensor.
y (Tensor): The exponent Tensor.
Returns:
Tensor: The resultant tensor. | Computes the elementwise ``y``th power of ``x``. | [
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Args:
x (Tensor): The base Tensor.
y (Tensor): The exponent Tensor.
Returns:
Tensor: The resultant tensor.
"""
return intrinsic('pow', x, y) | [
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|
JumpingYang001/webrtc | c03d6e965e1f54aeadd670e491eabe5fdb8db968 | rtc_tools/py_event_log_analyzer/rtp_analyzer.py | python | RTPStatistics.ComputeBandwidth | (self) | Computes bandwidth averaged over several consecutive packets.
The number of consecutive packets used in the average is
BANDWIDTH_SMOOTHING_WINDOW_SIZE. Averaging is done with
numpy.correlate. | Computes bandwidth averaged over several consecutive packets. | [
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"""Computes bandwidth averaged over several consecutive packets.
The number of consecutive packets used in the average is
BANDWIDTH_SMOOTHING_WINDOW_SIZE. Averaging is done with
numpy.correlate.
"""
start_ms = self.data_points[0].real_send_time_ms
stop_ms = self.data_points[-1].real_send_time_ms
(self.bandwidth_kbps, _) = numpy.histogram(
[point.real_send_time_ms for point in self.data_points],
bins=numpy.arange(start_ms, stop_ms,
RTPStatistics.PLOT_RESOLUTION_MS),
weights=[
point.size * 8 / RTPStatistics.PLOT_RESOLUTION_MS
for point in self.data_points
])
correlate_filter = (
numpy.ones(RTPStatistics.BANDWIDTH_SMOOTHING_WINDOW_SIZE) /
RTPStatistics.BANDWIDTH_SMOOTHING_WINDOW_SIZE)
self.smooth_bw_kbps = numpy.correlate(self.bandwidth_kbps,
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||
nasa/fprime | 595cf3682d8365943d86c1a6fe7c78f0a116acf0 | Autocoders/Python/src/fprime_ac/generators/visitors/TopologyCppVisitor.py | python | TopologyCppVisitor.includes2Visit | (self, obj) | Defined to generate internal includes within a file.
Usually used for data type includes and system includes.
@param args: the instance of the concrete element to operation on. | Defined to generate internal includes within a file.
Usually used for data type includes and system includes. | [
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] | def includes2Visit(self, obj):
"""
Defined to generate internal includes within a file.
Usually used for data type includes and system includes.
@param args: the instance of the concrete element to operation on.
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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/requests/models.py | python | Response.apparent_encoding | (self) | return chardet.detect(self.content)['encoding'] | The apparent encoding, provided by the chardet library. | The apparent encoding, provided by the chardet library. | [
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] | def apparent_encoding(self):
"""The apparent encoding, provided by the chardet library."""
return chardet.detect(self.content)['encoding'] | [
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|
pyne/pyne | 0c2714d7c0d1b5e20be6ae6527da2c660dd6b1b3 | pyne/dbgen/ndsfpy.py | python | readtable | (i, spdat) | return dfinal | Reads in a set of 5 html tables and returns corresponding yield data | Reads in a set of 5 html tables and returns corresponding yield data | [
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"""
Reads in a set of 5 html tables and returns corresponding yield data
"""
parent = getdata(i, spdat)[0]
pfinal = (parent.split('<strong>')[1]).split('</strong>')[0]
pid = conv_to_id(pfinal)
fpdata = getdata(i + 1, spdat)
dt = np.dtype([('from_nuc', 'i4'), ('to_nuc', 'i4'),
('yield_thermal', float), ('yield_thermal_err', float),
('yield_fast', float), ('yield_fast_err', float),
('yield_14MeV', float), ('yield_14MeV_err', float)
])
dfinal = np.zeros((len(fpdata),), dtype=dt)
for index, item in enumerate(fpdata):
dfinal[index]['from_nuc'] = pid
dfinal[index]['to_nuc'] = conv_to_id(item)
thermaldata = getdata(i + 2, spdat)
for index, item in enumerate(thermaldata):
dat, err = conv_to_num(item)
dfinal[index]['yield_thermal'] = dat
dfinal[index]['yield_thermal_err'] = err
fastdata = getdata(i + 3, spdat)
for index, item in enumerate(fastdata):
dat, err = conv_to_num(item)
dfinal[index]['yield_fast'] = dat
dfinal[index]['yield_fast_err'] = err
dtdata = getdata(i + 4, spdat)
for index, item in enumerate(dtdata):
dat, err = conv_to_num(item)
dfinal[index]['yield_14MeV'] = dat
dfinal[index]['yield_14MeV_err'] = err
return dfinal | [
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|
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/Jinja2/py2/jinja2/compiler.py | python | CodeGenerator.pull_dependencies | (self, nodes) | Pull all the dependencies. | Pull all the dependencies. | [
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"all",
"the",
"dependencies",
"."
] | def pull_dependencies(self, nodes):
"""Pull all the dependencies."""
visitor = DependencyFinderVisitor()
for node in nodes:
visitor.visit(node)
for dependency in "filters", "tests":
mapping = getattr(self, dependency)
for name in getattr(visitor, dependency):
if name not in mapping:
mapping[name] = self.temporary_identifier()
self.writeline(
"%s = environment.%s[%r]" % (mapping[name], dependency, name)
) | [
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||
idaholab/moose | 9eeebc65e098b4c30f8205fb41591fd5b61eb6ff | python/MooseDocs/base/renderers.py | python | MaterializeRenderer._method | (self, component) | Fallback to the HTMLRenderer method if the MaterializeRenderer method is not located.
Inputs:
component[RenderComponent]: Object to use for locating desired method for renderering. | Fallback to the HTMLRenderer method if the MaterializeRenderer method is not located. | [
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"""
Fallback to the HTMLRenderer method if the MaterializeRenderer method is not located.
Inputs:
component[RenderComponent]: Object to use for locating desired method for renderering.
"""
if hasattr(component, self.METHOD):
return getattr(component, self.METHOD)
elif hasattr(component, HTMLRenderer.METHOD):
return getattr(component, HTMLRenderer.METHOD)
else:
msg = "The component object {} does not have a {} method."
raise exceptions.MooseDocsException(msg, type(component), self.METHOD) | [
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||
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/sheet.py | python | CSheet.OnRightClick | (self, event) | Move grid cursor when a cell is right-clicked | Move grid cursor when a cell is right-clicked | [
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] | def OnRightClick(self, event):
""" Move grid cursor when a cell is right-clicked """
self.SetGridCursor( event.GetRow(), event.GetCol() )
event.Skip() | [
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||
Tencent/CMONGO | c40380caa14e05509f46993aa8b8da966b09b0b5 | src/third_party/scons-2.5.0/scons-local-2.5.0/SCons/Util.py | python | PrependPath | (oldpath, newpath, sep = os.pathsep,
delete_existing=1, canonicalize=None) | This prepends newpath elements to the given oldpath. Will only
add any particular path once (leaving the first one it encounters
and ignoring the rest, to preserve path order), and will
os.path.normpath and os.path.normcase all paths to help assure
this. This can also handle the case where the given old path
variable is a list instead of a string, in which case a list will
be returned instead of a string.
Example:
Old Path: "/foo/bar:/foo"
New Path: "/biz/boom:/foo"
Result: "/biz/boom:/foo:/foo/bar"
If delete_existing is 0, then adding a path that exists will
not move it to the beginning; it will stay where it is in the
list.
If canonicalize is not None, it is applied to each element of
newpath before use. | This prepends newpath elements to the given oldpath. Will only
add any particular path once (leaving the first one it encounters
and ignoring the rest, to preserve path order), and will
os.path.normpath and os.path.normcase all paths to help assure
this. This can also handle the case where the given old path
variable is a list instead of a string, in which case a list will
be returned instead of a string. | [
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] | def PrependPath(oldpath, newpath, sep = os.pathsep,
delete_existing=1, canonicalize=None):
"""This prepends newpath elements to the given oldpath. Will only
add any particular path once (leaving the first one it encounters
and ignoring the rest, to preserve path order), and will
os.path.normpath and os.path.normcase all paths to help assure
this. This can also handle the case where the given old path
variable is a list instead of a string, in which case a list will
be returned instead of a string.
Example:
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New Path: "/biz/boom:/foo"
Result: "/biz/boom:/foo:/foo/bar"
If delete_existing is 0, then adding a path that exists will
not move it to the beginning; it will stay where it is in the
list.
If canonicalize is not None, it is applied to each element of
newpath before use.
"""
orig = oldpath
is_list = 1
paths = orig
if not is_List(orig) and not is_Tuple(orig):
paths = paths.split(sep)
is_list = 0
if is_String(newpath):
newpaths = newpath.split(sep)
elif not is_List(newpath) and not is_Tuple(newpath):
newpaths = [ newpath ] # might be a Dir
else:
newpaths = newpath
if canonicalize:
newpaths=list(map(canonicalize, newpaths))
if not delete_existing:
# First uniquify the old paths, making sure to
# preserve the first instance (in Unix/Linux,
# the first one wins), and remembering them in normpaths.
# Then insert the new paths at the head of the list
# if they're not already in the normpaths list.
result = []
normpaths = []
for path in paths:
if not path:
continue
normpath = os.path.normpath(os.path.normcase(path))
if normpath not in normpaths:
result.append(path)
normpaths.append(normpath)
newpaths.reverse() # since we're inserting at the head
for path in newpaths:
if not path:
continue
normpath = os.path.normpath(os.path.normcase(path))
if normpath not in normpaths:
result.insert(0, path)
normpaths.append(normpath)
paths = result
else:
newpaths = newpaths + paths # prepend new paths
normpaths = []
paths = []
# now we add them only if they are unique
for path in newpaths:
normpath = os.path.normpath(os.path.normcase(path))
if path and not normpath in normpaths:
paths.append(path)
normpaths.append(normpath)
if is_list:
return paths
else:
return sep.join(paths) | [
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||
sdhash/sdhash | b9eff63e4e5867e910f41fd69032bbb1c94a2a5e | sdhash-ui/jinja2/utils.py | python | LRUCache.itervalue | (self) | return iter(self.values()) | Iterate over all values. | Iterate over all values. | [
"Iterate",
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"all",
"values",
"."
] | def itervalue(self):
"""Iterate over all values."""
return iter(self.values()) | [
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|
benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/contrib/distributions/python/ops/vector_student_t.py | python | _VectorStudentT.__init__ | (self,
df,
loc=None,
scale_identity_multiplier=None,
scale_diag=None,
scale_tril=None,
scale_perturb_factor=None,
scale_perturb_diag=None,
validate_args=False,
allow_nan_stats=True,
name="VectorStudentT") | Instantiates the vector Student's t-distributions on `R^k`.
The `batch_shape` is the broadcast between `df.batch_shape` and
`Affine.batch_shape` where `Affine` is constructed from `loc` and
`scale_*` arguments.
The `event_shape` is the event shape of `Affine.event_shape`.
Args:
df: Floating-point `Tensor`. The degrees of freedom of the
distribution(s). `df` must contain only positive values. Must be
scalar if `loc`, `scale_*` imply non-scalar batch_shape or must have the
same `batch_shape` implied by `loc`, `scale_*`.
loc: Floating-point `Tensor`. If this is set to `None`, no `loc` is
applied.
scale_identity_multiplier: floating point rank 0 `Tensor` representing a
scaling done to the identity matrix. When `scale_identity_multiplier =
scale_diag=scale_tril = None` then `scale += IdentityMatrix`. Otherwise
no scaled-identity-matrix is added to `scale`.
scale_diag: Floating-point `Tensor` representing the diagonal matrix.
`scale_diag` has shape [N1, N2, ..., k], which represents a k x k
diagonal matrix. When `None` no diagonal term is added to `scale`.
scale_tril: Floating-point `Tensor` representing the diagonal matrix.
`scale_diag` has shape [N1, N2, ..., k, k], which represents a k x k
lower triangular matrix. When `None` no `scale_tril` term is added to
`scale`. The upper triangular elements above the diagonal are ignored.
scale_perturb_factor: Floating-point `Tensor` representing factor matrix
with last two dimensions of shape `(k, r)`. When `None`, no rank-r
update is added to `scale`.
scale_perturb_diag: Floating-point `Tensor` representing the diagonal
matrix. `scale_perturb_diag` has shape [N1, N2, ..., r], which
represents an r x r Diagonal matrix. When `None` low rank updates will
take the form `scale_perturb_factor * scale_perturb_factor.T`.
validate_args: Python `bool`, default `False`. When `True` distribution
parameters are checked for validity despite possibly degrading runtime
performance. When `False` invalid inputs may silently render incorrect
outputs.
allow_nan_stats: Python `bool`, default `True`. When `True`,
statistics (e.g., mean, mode, variance) use the value "`NaN`" to
indicate the result is undefined. When `False`, an exception is raised
if one or more of the statistic's batch members are undefined.
name: Python `str` name prefixed to Ops created by this class. | Instantiates the vector Student's t-distributions on `R^k`. | [
"Instantiates",
"the",
"vector",
"Student",
"s",
"t",
"-",
"distributions",
"on",
"R^k",
"."
] | def __init__(self,
df,
loc=None,
scale_identity_multiplier=None,
scale_diag=None,
scale_tril=None,
scale_perturb_factor=None,
scale_perturb_diag=None,
validate_args=False,
allow_nan_stats=True,
name="VectorStudentT"):
"""Instantiates the vector Student's t-distributions on `R^k`.
The `batch_shape` is the broadcast between `df.batch_shape` and
`Affine.batch_shape` where `Affine` is constructed from `loc` and
`scale_*` arguments.
The `event_shape` is the event shape of `Affine.event_shape`.
Args:
df: Floating-point `Tensor`. The degrees of freedom of the
distribution(s). `df` must contain only positive values. Must be
scalar if `loc`, `scale_*` imply non-scalar batch_shape or must have the
same `batch_shape` implied by `loc`, `scale_*`.
loc: Floating-point `Tensor`. If this is set to `None`, no `loc` is
applied.
scale_identity_multiplier: floating point rank 0 `Tensor` representing a
scaling done to the identity matrix. When `scale_identity_multiplier =
scale_diag=scale_tril = None` then `scale += IdentityMatrix`. Otherwise
no scaled-identity-matrix is added to `scale`.
scale_diag: Floating-point `Tensor` representing the diagonal matrix.
`scale_diag` has shape [N1, N2, ..., k], which represents a k x k
diagonal matrix. When `None` no diagonal term is added to `scale`.
scale_tril: Floating-point `Tensor` representing the diagonal matrix.
`scale_diag` has shape [N1, N2, ..., k, k], which represents a k x k
lower triangular matrix. When `None` no `scale_tril` term is added to
`scale`. The upper triangular elements above the diagonal are ignored.
scale_perturb_factor: Floating-point `Tensor` representing factor matrix
with last two dimensions of shape `(k, r)`. When `None`, no rank-r
update is added to `scale`.
scale_perturb_diag: Floating-point `Tensor` representing the diagonal
matrix. `scale_perturb_diag` has shape [N1, N2, ..., r], which
represents an r x r Diagonal matrix. When `None` low rank updates will
take the form `scale_perturb_factor * scale_perturb_factor.T`.
validate_args: Python `bool`, default `False`. When `True` distribution
parameters are checked for validity despite possibly degrading runtime
performance. When `False` invalid inputs may silently render incorrect
outputs.
allow_nan_stats: Python `bool`, default `True`. When `True`,
statistics (e.g., mean, mode, variance) use the value "`NaN`" to
indicate the result is undefined. When `False`, an exception is raised
if one or more of the statistic's batch members are undefined.
name: Python `str` name prefixed to Ops created by this class.
"""
parameters = locals()
graph_parents = [df, loc, scale_identity_multiplier, scale_diag,
scale_tril, scale_perturb_factor, scale_perturb_diag]
with ops.name_scope(name):
with ops.name_scope("init", values=graph_parents):
# The shape of the _VectorStudentT distribution is governed by the
# relationship between df.batch_shape and affine.batch_shape. In
# pseudocode the basic procedure is:
# if df.batch_shape is scalar:
# if affine.batch_shape is not scalar:
# # broadcast distribution.sample so
# # it has affine.batch_shape.
# self.batch_shape = affine.batch_shape
# else:
# if affine.batch_shape is scalar:
# # let affine broadcasting do its thing.
# self.batch_shape = df.batch_shape
# All of the above magic is actually handled by TransformedDistribution.
# Here we really only need to collect the affine.batch_shape and decide
# what we're going to pass in to TransformedDistribution's
# (override) batch_shape arg.
affine = bijectors.Affine(
shift=loc,
scale_identity_multiplier=scale_identity_multiplier,
scale_diag=scale_diag,
scale_tril=scale_tril,
scale_perturb_factor=scale_perturb_factor,
scale_perturb_diag=scale_perturb_diag,
validate_args=validate_args)
distribution = student_t.StudentT(
df=df,
loc=array_ops.zeros([], dtype=affine.dtype),
scale=array_ops.ones([], dtype=affine.dtype))
batch_shape, override_event_shape = (
distribution_util.shapes_from_loc_and_scale(
affine.shift, affine.scale))
override_batch_shape = distribution_util.pick_vector(
distribution.is_scalar_batch(),
batch_shape,
constant_op.constant([], dtype=dtypes.int32))
super(_VectorStudentT, self).__init__(
distribution=distribution,
bijector=affine,
batch_shape=override_batch_shape,
event_shape=override_event_shape,
validate_args=validate_args,
name=name)
self._parameters = parameters | [
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||
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/email/utils.py | python | decode_rfc2231 | (s) | return parts | Decode string according to RFC 2231 | Decode string according to RFC 2231 | [
"Decode",
"string",
"according",
"to",
"RFC",
"2231"
] | def decode_rfc2231(s):
"""Decode string according to RFC 2231"""
parts = s.split(TICK, 2)
if len(parts) <= 2:
return None, None, s
return parts | [
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|
SequoiaDB/SequoiaDB | 2894ed7e5bd6fe57330afc900cf76d0ff0df9f64 | tools/server/php_linux/libxml2/lib/python2.4/site-packages/libxml2.py | python | parserCtxt.parseQuotedString | (self) | return ret | Parse and return a string between quotes or doublequotes
TODO: Deprecated, to be removed at next drop of binary
compatibility | Parse and return a string between quotes or doublequotes
TODO: Deprecated, to be removed at next drop of binary
compatibility | [
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] | def parseQuotedString(self):
"""Parse and return a string between quotes or doublequotes
TODO: Deprecated, to be removed at next drop of binary
compatibility """
ret = libxml2mod.xmlParseQuotedString(self._o)
return ret | [
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] | https://github.com/SequoiaDB/SequoiaDB/blob/2894ed7e5bd6fe57330afc900cf76d0ff0df9f64/tools/server/php_linux/libxml2/lib/python2.4/site-packages/libxml2.py#L5353-L5358 |
|
apache/incubator-weex | 5c25f0b59f7ac90703c363e7261f60bd06356dbe | weex_core/tools/cpplint.py | python | UpdateIncludeState | (filename, include_dict, io=codecs) | return True | Fill up the include_dict with new includes found from the file.
Args:
filename: the name of the header to read.
include_dict: a dictionary in which the headers are inserted.
io: The io factory to use to read the file. Provided for testability.
Returns:
True if a header was successfully added. False otherwise. | Fill up the include_dict with new includes found from the file. | [
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"the",
"include_dict",
"with",
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"found",
"from",
"the",
"file",
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] | def UpdateIncludeState(filename, include_dict, io=codecs):
"""Fill up the include_dict with new includes found from the file.
Args:
filename: the name of the header to read.
include_dict: a dictionary in which the headers are inserted.
io: The io factory to use to read the file. Provided for testability.
Returns:
True if a header was successfully added. False otherwise.
"""
headerfile = None
try:
headerfile = io.open(filename, 'r', 'utf8', 'replace')
except IOError:
return False
linenum = 0
for line in headerfile:
linenum += 1
clean_line = CleanseComments(line)
match = _RE_PATTERN_INCLUDE.search(clean_line)
if match:
include = match.group(2)
include_dict.setdefault(include, linenum)
return True | [
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|
clementine-player/Clementine | 111379dfd027802b59125829fcf87e3e1d0ad73b | dist/cpplint.py | python | CheckForFunctionLengths | (filename, clean_lines, linenum,
function_state, error) | Reports for long function bodies.
For an overview why this is done, see:
http://google-styleguide.googlecode.com/svn/trunk/cppguide.xml#Write_Short_Functions
Uses a simplistic algorithm assuming other style guidelines
(especially spacing) are followed.
Only checks unindented functions, so class members are unchecked.
Trivial bodies are unchecked, so constructors with huge initializer lists
may be missed.
Blank/comment lines are not counted so as to avoid encouraging the removal
of vertical space and comments just to get through a lint check.
NOLINT *on the last line of a function* disables this check.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
function_state: Current function name and lines in body so far.
error: The function to call with any errors found. | Reports for long function bodies. | [
"Reports",
"for",
"long",
"function",
"bodies",
"."
] | def CheckForFunctionLengths(filename, clean_lines, linenum,
function_state, error):
"""Reports for long function bodies.
For an overview why this is done, see:
http://google-styleguide.googlecode.com/svn/trunk/cppguide.xml#Write_Short_Functions
Uses a simplistic algorithm assuming other style guidelines
(especially spacing) are followed.
Only checks unindented functions, so class members are unchecked.
Trivial bodies are unchecked, so constructors with huge initializer lists
may be missed.
Blank/comment lines are not counted so as to avoid encouraging the removal
of vertical space and comments just to get through a lint check.
NOLINT *on the last line of a function* disables this check.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
function_state: Current function name and lines in body so far.
error: The function to call with any errors found.
"""
lines = clean_lines.lines
line = lines[linenum]
joined_line = ''
starting_func = False
regexp = r'(\w(\w|::|\*|\&|\s)*)\(' # decls * & space::name( ...
match_result = Match(regexp, line)
if match_result:
# If the name is all caps and underscores, figure it's a macro and
# ignore it, unless it's TEST or TEST_F.
function_name = match_result.group(1).split()[-1]
if function_name == 'TEST' or function_name == 'TEST_F' or (
not Match(r'[A-Z_]+$', function_name)):
starting_func = True
if starting_func:
body_found = False
for start_linenum in xrange(linenum, clean_lines.NumLines()):
start_line = lines[start_linenum]
joined_line += ' ' + start_line.lstrip()
if Search(r'(;|})', start_line): # Declarations and trivial functions
body_found = True
break # ... ignore
elif Search(r'{', start_line):
body_found = True
function = Search(r'((\w|:)*)\(', line).group(1)
if Match(r'TEST', function): # Handle TEST... macros
parameter_regexp = Search(r'(\(.*\))', joined_line)
if parameter_regexp: # Ignore bad syntax
function += parameter_regexp.group(1)
else:
function += '()'
function_state.Begin(function)
break
if not body_found:
# No body for the function (or evidence of a non-function) was found.
error(filename, linenum, 'readability/fn_size', 5,
'Lint failed to find start of function body.')
elif Match(r'^\}\s*$', line): # function end
function_state.Check(error, filename, linenum)
function_state.End()
elif not Match(r'^\s*$', line):
function_state.Count() | [
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||
Kitware/ParaView | f760af9124ff4634b23ebbeab95a4f56e0261955 | Plugins/pvblot/blotish.py | python | tmin | (value=None) | Set deltime | Set deltime | [
"Set",
"deltime"
] | def tmin(value=None):
"""Set deltime"""
state.time_selection.set_tmin(_maybe_convert(value, float))
state.time_selection.print_show() | [
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||
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py3/pandas/io/formats/latex.py | python | LatexFormatter.column_format | (self) | return self._column_format | Column format. | Column format. | [
"Column",
"format",
"."
] | def column_format(self) -> str | None:
"""Column format."""
return self._column_format | [
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|
pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | torch/distributed/fsdp/fully_sharded_data_parallel.py | python | FullyShardedDataParallel._lazy_init | (self) | Initialization steps that should happen lazily, typically right
before the first forward pass. | Initialization steps that should happen lazily, typically right
before the first forward pass. | [
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"."
] | def _lazy_init(self) -> None:
"""Initialization steps that should happen lazily, typically right
before the first forward pass.
"""
# Initialize param attributes lazily, in case the param's dtype or
# device changes after __init__.
for p in self.params:
self._init_param_attributes(p)
# Initialize _is_root and setup streams. These steps would ideally
# happen in __init__, but _is_root can only be determined after the
# entire model hierarchy is setup, thus we run it lazily.
if self._is_root is None:
# _is_root means that we are in the outermost module's forward.
self._set_is_root()
self._setup_streams()
if self._is_root:
# Buffers stay on GPU, and don't get sharded. Since _cast_buffers
# applies recursively, we only call this from the root instance.
self._cast_buffers()
# Don't free the full params for the outer-most (root) instance,
# In most cases, root instance contains params in the last layers
# or has no params. In these cases, those params will be needed
# immediately after for the backward pass. Note that this only
# applies currently when freeing parameters at end of layer's
# forward pass.
self.reshard_after_forward = False
# Due to the use of streams, we need to make sure the previous
# ``optim.step()`` is done before we all-gather parameters.
self._wait_for_previous_optim_step() | [
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||
LiquidPlayer/LiquidCore | 9405979363f2353ac9a71ad8ab59685dd7f919c9 | deps/boost_1_66_0/tools/build/src/build/targets.py | python | ProjectTarget.mark_targets_as_explicit | (self, target_names) | Add 'target' to the list of targets in this project
that should be build only by explicit request. | Add 'target' to the list of targets in this project
that should be build only by explicit request. | [
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] | def mark_targets_as_explicit (self, target_names):
"""Add 'target' to the list of targets in this project
that should be build only by explicit request."""
# Record the name of the target, not instance, since this
# rule is called before main target instaces are created.
assert is_iterable_typed(target_names, basestring)
self.explicit_targets_.update(target_names) | [
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||
cathywu/Sentiment-Analysis | eb501fd1375c0c3f3ab430f963255f1bb858e659 | PyML-0.7.9/PyML/utils/myio.py | python | csvread | (fileName, delim = ',') | return data | read a character array from a file in csv format | read a character array from a file in csv format | [
"read",
"a",
"character",
"array",
"from",
"a",
"file",
"in",
"csv",
"format"
] | def csvread(fileName, delim = ',') :
'''read a character array from a file in csv format'''
import misc
fileHandle = open(fileName, "r")
line = fileHandle.readline()
if delim == ' ' : delim = None
data = misc.emptyLOL(len(line.split(delim)))
dim = len(data)
while 1 :
line = line[:-1]
fields = line.split(delim)
if len(fields) != dim :
print 'badline:', line
for i in range(dim) :
data[i].append(fields[i])
line=fileHandle.readline()
if not line : break
if len(data) == 1 : data=data[0]
return data | [
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|
google/earthenterprise | 0fe84e29be470cd857e3a0e52e5d0afd5bb8cee9 | earth_enterprise/src/google/protobuf-py/mox.py | python | Mox.CreateMockAnything | (self) | return new_mock | Create a mock that will accept any method calls.
This does not enforce an interface. | Create a mock that will accept any method calls. | [
"Create",
"a",
"mock",
"that",
"will",
"accept",
"any",
"method",
"calls",
"."
] | def CreateMockAnything(self):
"""Create a mock that will accept any method calls.
This does not enforce an interface.
"""
new_mock = MockAnything()
self._mock_objects.append(new_mock)
return new_mock | [
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|
wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/decimal.py | python | Context.quantize | (self, a, b) | return a.quantize(b, context=self) | Returns a value equal to 'a' (rounded), having the exponent of 'b'.
The coefficient of the result is derived from that of the left-hand
operand. It may be rounded using the current rounding setting (if the
exponent is being increased), multiplied by a positive power of ten (if
the exponent is being decreased), or is unchanged (if the exponent is
already equal to that of the right-hand operand).
Unlike other operations, if the length of the coefficient after the
quantize operation would be greater than precision then an Invalid
operation condition is raised. This guarantees that, unless there is
an error condition, the exponent of the result of a quantize is always
equal to that of the right-hand operand.
Also unlike other operations, quantize will never raise Underflow, even
if the result is subnormal and inexact.
>>> ExtendedContext.quantize(Decimal('2.17'), Decimal('0.001'))
Decimal('2.170')
>>> ExtendedContext.quantize(Decimal('2.17'), Decimal('0.01'))
Decimal('2.17')
>>> ExtendedContext.quantize(Decimal('2.17'), Decimal('0.1'))
Decimal('2.2')
>>> ExtendedContext.quantize(Decimal('2.17'), Decimal('1e+0'))
Decimal('2')
>>> ExtendedContext.quantize(Decimal('2.17'), Decimal('1e+1'))
Decimal('0E+1')
>>> ExtendedContext.quantize(Decimal('-Inf'), Decimal('Infinity'))
Decimal('-Infinity')
>>> ExtendedContext.quantize(Decimal('2'), Decimal('Infinity'))
Decimal('NaN')
>>> ExtendedContext.quantize(Decimal('-0.1'), Decimal('1'))
Decimal('-0')
>>> ExtendedContext.quantize(Decimal('-0'), Decimal('1e+5'))
Decimal('-0E+5')
>>> ExtendedContext.quantize(Decimal('+35236450.6'), Decimal('1e-2'))
Decimal('NaN')
>>> ExtendedContext.quantize(Decimal('-35236450.6'), Decimal('1e-2'))
Decimal('NaN')
>>> ExtendedContext.quantize(Decimal('217'), Decimal('1e-1'))
Decimal('217.0')
>>> ExtendedContext.quantize(Decimal('217'), Decimal('1e-0'))
Decimal('217')
>>> ExtendedContext.quantize(Decimal('217'), Decimal('1e+1'))
Decimal('2.2E+2')
>>> ExtendedContext.quantize(Decimal('217'), Decimal('1e+2'))
Decimal('2E+2')
>>> ExtendedContext.quantize(1, 2)
Decimal('1')
>>> ExtendedContext.quantize(Decimal(1), 2)
Decimal('1')
>>> ExtendedContext.quantize(1, Decimal(2))
Decimal('1') | Returns a value equal to 'a' (rounded), having the exponent of 'b'. | [
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The coefficient of the result is derived from that of the left-hand
operand. It may be rounded using the current rounding setting (if the
exponent is being increased), multiplied by a positive power of ten (if
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Decimal('NaN')
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Decimal('-0')
>>> ExtendedContext.quantize(Decimal('-0'), Decimal('1e+5'))
Decimal('-0E+5')
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Decimal('NaN')
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Decimal('NaN')
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Decimal('217.0')
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Decimal('217')
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Decimal('2.2E+2')
>>> ExtendedContext.quantize(Decimal('217'), Decimal('1e+2'))
Decimal('2E+2')
>>> ExtendedContext.quantize(1, 2)
Decimal('1')
>>> ExtendedContext.quantize(Decimal(1), 2)
Decimal('1')
>>> ExtendedContext.quantize(1, Decimal(2))
Decimal('1')
"""
a = _convert_other(a, raiseit=True)
return a.quantize(b, context=self) | [
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|
Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_pytorch/nndct_shared/utils/registry.py | python | Registry.register | (self, obj, name=None) | Registers a Python object "obj" for the given "name".
Args:
obj: The object to add to the registry.
name: An optional string specifying the registry key for the obj.
If None, obj.__name__ will be used.
Raises:
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"""Registers a Python object "obj" for the given "name".
Args:
obj: The object to add to the registry.
name: An optional string specifying the registry key for the obj.
If None, obj.__name__ will be used.
Raises:
KeyError: If same name is registered twice.
"""
if not name:
name = obj.__name__
if name in self._registry:
raise KeyError("Name '%s' has been registered in '%s'!" %
(name, self._name))
# logging.vlog(1, "Registering %s (%s) in %s.", name, obj, self._name)
self._registry[name] = obj | [
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||
ceph/ceph | 959663007321a369c83218414a29bd9dbc8bda3a | src/ceph-volume/ceph_volume/devices/lvm/prepare.py | python | Prepare.prepare_data_device | (self, device_type, osd_uuid) | Check if ``arg`` is a device or partition to create an LV out of it
with a distinct volume group name, assigning LV tags on it and
ultimately, returning the logical volume object. Failing to detect
a device or partition will result in error.
:param arg: The value of ``--data`` when parsing args
:param device_type: Usually, either ``data`` or ``block`` (filestore vs. bluestore)
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ultimately, returning the logical volume object. Failing to detect
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"""
device = self.args.data
if disk.is_partition(device) or disk.is_device(device):
# we must create a vg, and then a single lv
lv_name_prefix = "osd-{}".format(device_type)
kwargs = {'device': device,
'tags': {'ceph.type': device_type},
'slots': self.args.data_slots,
}
logger.debug('data device size: {}'.format(self.args.data_size))
if self.args.data_size != 0:
kwargs['size'] = self.args.data_size
return api.create_lv(
lv_name_prefix,
osd_uuid,
**kwargs)
else:
error = [
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raise RuntimeError(' '.join(error))
raise RuntimeError('no data logical volume found with: {}'.format(device)) | [
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||
hughperkins/tf-coriander | 970d3df6c11400ad68405f22b0c42a52374e94ca | tensorflow/python/ops/sparse_ops.py | python | sparse_to_dense | (sparse_indices,
output_shape,
sparse_values,
default_value=0,
validate_indices=True,
name=None) | return gen_sparse_ops._sparse_to_dense(
sparse_indices,
output_shape,
sparse_values,
default_value=default_value,
validate_indices=validate_indices,
name=name) | Converts a sparse representation into a dense tensor.
Builds an array `dense` with shape `output_shape` such that
```python
# If sparse_indices is scalar
dense[i] = (i == sparse_indices ? sparse_values : default_value)
# If sparse_indices is a vector, then for each i
dense[sparse_indices[i]] = sparse_values[i]
# If sparse_indices is an n by d matrix, then for each i in [0, n)
dense[sparse_indices[i][0], ..., sparse_indices[i][d-1]] = sparse_values[i]
```
All other values in `dense` are set to `default_value`. If `sparse_values`
is a scalar, all sparse indices are set to this single value.
Indices should be sorted in lexicographic order, and indices must not
contain any repeats. If `validate_indices` is True, these properties
are checked during execution.
Args:
sparse_indices: A 0-D, 1-D, or 2-D `Tensor` of type `int32` or `int64`.
`sparse_indices[i]` contains the complete index where `sparse_values[i]`
will be placed.
output_shape: A 1-D `Tensor` of the same type as `sparse_indices`. Shape
of the dense output tensor.
sparse_values: A 0-D or 1-D `Tensor`. Values corresponding to each row of
`sparse_indices`, or a scalar value to be used for all sparse indices.
default_value: A 0-D `Tensor` of the same type as `sparse_values`. Value
to set for indices not specified in `sparse_indices`. Defaults to zero.
validate_indices: A boolean value. If True, indices are checked to make
sure they are sorted in lexicographic order and that there are no repeats.
name: A name for the operation (optional).
Returns:
Dense `Tensor` of shape `output_shape`. Has the same type as
`sparse_values`. | Converts a sparse representation into a dense tensor. | [
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] | def sparse_to_dense(sparse_indices,
output_shape,
sparse_values,
default_value=0,
validate_indices=True,
name=None):
"""Converts a sparse representation into a dense tensor.
Builds an array `dense` with shape `output_shape` such that
```python
# If sparse_indices is scalar
dense[i] = (i == sparse_indices ? sparse_values : default_value)
# If sparse_indices is a vector, then for each i
dense[sparse_indices[i]] = sparse_values[i]
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dense[sparse_indices[i][0], ..., sparse_indices[i][d-1]] = sparse_values[i]
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output_shape: A 1-D `Tensor` of the same type as `sparse_indices`. Shape
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sparse_values: A 0-D or 1-D `Tensor`. Values corresponding to each row of
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default_value: A 0-D `Tensor` of the same type as `sparse_values`. Value
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validate_indices: A boolean value. If True, indices are checked to make
sure they are sorted in lexicographic order and that there are no repeats.
name: A name for the operation (optional).
Returns:
Dense `Tensor` of shape `output_shape`. Has the same type as
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"""
return gen_sparse_ops._sparse_to_dense(
sparse_indices,
output_shape,
sparse_values,
default_value=default_value,
validate_indices=validate_indices,
name=name) | [
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|
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/flatmenu.py | python | FlatMenuBar.SetOptions | (self, options) | Sets the :class:`FlatMenuBar` options, whether to show a toolbar, to use LCD screen settings etc...
:param integer `options`: a combination of the following bits:
========================= ========= =============================
`options` Bit Hex Value Description
========================= ========= =============================
``FM_OPT_IS_LCD`` 0x1 Use this style if your computer uses a LCD screen
``FM_OPT_MINIBAR`` 0x2 Use this if you plan to use toolbar only
``FM_OPT_SHOW_CUSTOMIZE`` 0x4 Show "customize link" in more menus, you will need to write your own handler. See demo.
``FM_OPT_SHOW_TOOLBAR`` 0x8 Set this option is you are planing to use the toolbar
========================= ========= ============================= | Sets the :class:`FlatMenuBar` options, whether to show a toolbar, to use LCD screen settings etc... | [
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"""
Sets the :class:`FlatMenuBar` options, whether to show a toolbar, to use LCD screen settings etc...
:param integer `options`: a combination of the following bits:
========================= ========= =============================
`options` Bit Hex Value Description
========================= ========= =============================
``FM_OPT_IS_LCD`` 0x1 Use this style if your computer uses a LCD screen
``FM_OPT_MINIBAR`` 0x2 Use this if you plan to use toolbar only
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self._options = options
self._showToolbar = options & FM_OPT_SHOW_TOOLBAR
self._showCustomize = options & FM_OPT_SHOW_CUSTOMIZE
self._isLCD = options & FM_OPT_IS_LCD
self._isMinibar = options & FM_OPT_MINIBAR
self.SetBarHeight()
self.Refresh()
self.Update() | [
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||
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/importlib-metadata/py3/importlib_metadata/__init__.py | python | DistributionFinder.find_distributions | (self, context=Context()) | Find distributions.
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loading the metadata for packages matching the ``context``,
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Find distributions.
Return an iterable of all Distribution instances capable of
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||
gwaldron/osgearth | 4c521857d59a69743e4a9cedba00afe570f984e8 | src/third_party/tinygltf/deps/cpplint.py | python | NestingState.InExternC | (self) | return self.stack and isinstance(self.stack[-1], _ExternCInfo) | Check if we are currently one level inside an 'extern "C"' block.
Returns:
True if top of the stack is an extern block, False otherwise. | Check if we are currently one level inside an 'extern "C"' block. | [
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|
stan-dev/math | 5fd79f89933269a4ca4d8dd1fde2a36d53d4768c | lib/tbb_2020.3/python/tbb/pool.py | python | ApplyResult.successful | (self) | return self._success | Returns whether the call completed without raising an
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ready. | Returns whether the call completed without raising an
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"""Returns whether the call completed without raising an
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pgRouting/osm2pgrouting | 8491929fc4037d308f271e84d59bb96da3c28aa2 | tools/cpplint.py | python | NestingState.InAsmBlock | (self) | return self.stack and self.stack[-1].inline_asm != _NO_ASM | Check if we are currently one level inside an inline ASM block.
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] | https://github.com/pgRouting/osm2pgrouting/blob/8491929fc4037d308f271e84d59bb96da3c28aa2/tools/cpplint.py#L2256-L2262 |
|
apache/arrow | af33dd1157eb8d7d9bfac25ebf61445b793b7943 | python/pyarrow/__init__.py | python | show_info | () | Print detailed version and platform information, for error reporting | Print detailed version and platform information, for error reporting | [
"Print",
"detailed",
"version",
"and",
"platform",
"information",
"for",
"error",
"reporting"
] | def show_info():
"""
Print detailed version and platform information, for error reporting
"""
show_versions()
def print_entry(label, value):
print(f" {label: <20}: {value: <8}")
print("\nPlatform:")
print_entry("OS / Arch", f"{_platform.system()} {_platform.machine()}")
print_entry("SIMD Level", runtime_info().simd_level)
print_entry("Detected SIMD Level", runtime_info().detected_simd_level)
pool = default_memory_pool()
print("\nMemory:")
print_entry("Default backend", pool.backend_name)
print_entry("Bytes allocated", f"{pool.bytes_allocated()} bytes")
print_entry("Max memory", f"{pool.max_memory()} bytes")
print_entry("Supported Backends", ', '.join(supported_memory_backends()))
print("\nOptional modules:")
modules = ["csv", "cuda", "dataset", "feather", "flight", "fs", "gandiva", "json",
"orc", "parquet", "plasma"]
for module in modules:
status = "Enabled" if _module_is_available(module) else "-"
print(f" {module: <20}: {status: <8}")
print("\nFilesystems:")
filesystems = ["GcsFileSystem", "HadoopFileSystem", "S3FileSystem"]
for fs in filesystems:
status = "Enabled" if _filesystem_is_available(fs) else "-"
print(f" {fs: <20}: {status: <8}")
print("\nCompression Codecs:")
codecs = ["brotli", "bz2", "gzip", "lz4_frame", "lz4", "snappy", "zstd"]
for codec in codecs:
status = "Enabled" if Codec.is_available(codec) else "-"
print(f" {codec: <20}: {status: <8}") | [
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] | https://github.com/apache/arrow/blob/af33dd1157eb8d7d9bfac25ebf61445b793b7943/python/pyarrow/__init__.py#L119-L157 |
||
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/llvmlite/six.py | python | with_metaclass | (meta, *bases) | return type.__new__(metaclass, 'temporary_class', (), {}) | Create a base class with a metaclass. | Create a base class with a metaclass. | [
"Create",
"a",
"base",
"class",
"with",
"a",
"metaclass",
"."
] | def with_metaclass(meta, *bases):
"""Create a base class with a metaclass."""
# This requires a bit of explanation: the basic idea is to make a dummy
# metaclass for one level of class instantiation that replaces itself with
# the actual metaclass.
class metaclass(meta):
def __new__(cls, name, this_bases, d):
return meta(name, bases, d)
return type.__new__(metaclass, 'temporary_class', (), {}) | [
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|
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/AWSPythonSDK/1.5.8/botocore/credentials.py | python | AssumeRoleCredentialFetcher._get_credentials | (self) | return client.assume_role(**kwargs) | Get credentials by calling assume role. | Get credentials by calling assume role. | [
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"role",
"."
] | def _get_credentials(self):
"""Get credentials by calling assume role."""
kwargs = self._assume_role_kwargs()
client = self._create_client()
return client.assume_role(**kwargs) | [
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|
plumonito/dtslam | 5994bb9cf7a11981b830370db206bceb654c085d | 3rdparty/opencv-git/3rdparty/jinja2/environment.py | python | Environment.preprocess | (self, source, name=None, filename=None) | return reduce(lambda s, e: e.preprocess(s, name, filename),
self.iter_extensions(), text_type(source)) | Preprocesses the source with all extensions. This is automatically
called for all parsing and compiling methods but *not* for :meth:`lex`
because there you usually only want the actual source tokenized. | Preprocesses the source with all extensions. This is automatically
called for all parsing and compiling methods but *not* for :meth:`lex`
because there you usually only want the actual source tokenized. | [
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] | def preprocess(self, source, name=None, filename=None):
"""Preprocesses the source with all extensions. This is automatically
called for all parsing and compiling methods but *not* for :meth:`lex`
because there you usually only want the actual source tokenized.
"""
return reduce(lambda s, e: e.preprocess(s, name, filename),
self.iter_extensions(), text_type(source)) | [
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|
mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | Framework/PythonInterface/plugins/algorithms/WorkflowAlgorithms/DirectILLCollectData.py | python | _createFlatBkg | (ws, wsType, windowWidth, wsNames, algorithmLogging) | return bkgWS | Return a flat background workspace. | Return a flat background workspace. | [
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"."
] | def _createFlatBkg(ws, wsType, windowWidth, wsNames, algorithmLogging):
"""Return a flat background workspace."""
if wsType == common.WS_CONTENT_DETS:
bkgWSName = wsNames.withSuffix('flat_bkg_for_detectors')
else:
bkgWSName = wsNames.withSuffix('flat_bkg_for_monitors')
bkgWS = CalculateFlatBackground(InputWorkspace=ws,
OutputWorkspace=bkgWSName,
Mode='Moving Average',
OutputMode='Return Background',
SkipMonitors=False,
NullifyNegativeValues=False,
AveragingWindowWidth=windowWidth,
EnableLogging=algorithmLogging)
firstBinStart = bkgWS.dataX(0)[0]
firstBinEnd = bkgWS.dataX(0)[1]
bkgWS = CropWorkspace(InputWorkspace=bkgWS,
OutputWorkspace=bkgWS,
XMin=firstBinStart,
XMax=firstBinEnd,
EnableLogging=algorithmLogging)
return bkgWS | [
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|
ideawu/ssdb | f229ba277c7f7d0ca5a441c0c6fb3d1209af68e4 | deps/cpy/antlr3/tree.py | python | CommonTreeAdaptor.dupNode | (self, treeNode) | return treeNode.dupNode() | Duplicate a node. This is part of the factory;
override if you want another kind of node to be built.
I could use reflection to prevent having to override this
but reflection is slow. | Duplicate a node. This is part of the factory;
override if you want another kind of node to be built. | [
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] | def dupNode(self, treeNode):
"""
Duplicate a node. This is part of the factory;
override if you want another kind of node to be built.
I could use reflection to prevent having to override this
but reflection is slow.
"""
if treeNode is None:
return None
return treeNode.dupNode() | [
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|
panda3d/panda3d | 833ad89ebad58395d0af0b7ec08538e5e4308265 | direct/src/filter/CommonFilters.py | python | CommonFilters.delSrgbEncode | (self) | return True | Reverses the effects of setSrgbEncode. | Reverses the effects of setSrgbEncode. | [
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] | def delSrgbEncode(self):
""" Reverses the effects of setSrgbEncode. """
if "SrgbEncode" in self.configuration:
old_enable = self.configuration["SrgbEncode"]
del self.configuration["SrgbEncode"]
return self.reconfigure(old_enable, "SrgbEncode")
return True | [
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|
jackaudio/jack2 | 21b293dbc37d42446141a08922cdec0d2550c6a0 | waflib/Logs.py | python | formatter.format | (self, rec) | return logging.Formatter.format(self, rec) | Formats records and adds colors as needed. The records do not get
a leading hour format if the logging level is above *INFO*. | Formats records and adds colors as needed. The records do not get
a leading hour format if the logging level is above *INFO*. | [
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] | def format(self, rec):
"""
Formats records and adds colors as needed. The records do not get
a leading hour format if the logging level is above *INFO*.
"""
try:
msg = rec.msg.decode('utf-8')
except Exception:
msg = rec.msg
use = colors_lst['USE']
if (use == 1 and rec.stream.isatty()) or use == 2:
c1 = getattr(rec, 'c1', None)
if c1 is None:
c1 = ''
if rec.levelno >= logging.ERROR:
c1 = colors.RED
elif rec.levelno >= logging.WARNING:
c1 = colors.YELLOW
elif rec.levelno >= logging.INFO:
c1 = colors.GREEN
c2 = getattr(rec, 'c2', colors.NORMAL)
msg = '%s%s%s' % (c1, msg, c2)
else:
# remove single \r that make long lines in text files
# and other terminal commands
msg = re.sub(r'\r(?!\n)|\x1B\[(K|.*?(m|h|l))', '', msg)
if rec.levelno >= logging.INFO:
# the goal of this is to format without the leading "Logs, hour" prefix
if rec.args:
return msg % rec.args
return msg
rec.msg = msg
rec.c1 = colors.PINK
rec.c2 = colors.NORMAL
return logging.Formatter.format(self, rec) | [
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|
floooh/oryol | eb08cffe1b1cb6b05ed14ec692bca9372cef064e | fips-files/generators/util/png.py | python | read_pnm_header | (infile, supported=('P5','P6')) | return header[0], header[1], header[2], depth, header[3] | Read a PNM header, returning (format,width,height,depth,maxval).
`width` and `height` are in pixels. `depth` is the number of
channels in the image; for PBM and PGM it is synthesized as 1, for
PPM as 3; for PAM images it is read from the header. `maxval` is
synthesized (as 1) for PBM images. | Read a PNM header, returning (format,width,height,depth,maxval).
`width` and `height` are in pixels. `depth` is the number of
channels in the image; for PBM and PGM it is synthesized as 1, for
PPM as 3; for PAM images it is read from the header. `maxval` is
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"""
Read a PNM header, returning (format,width,height,depth,maxval).
`width` and `height` are in pixels. `depth` is the number of
channels in the image; for PBM and PGM it is synthesized as 1, for
PPM as 3; for PAM images it is read from the header. `maxval` is
synthesized (as 1) for PBM images.
"""
# Generally, see http://netpbm.sourceforge.net/doc/ppm.html
# and http://netpbm.sourceforge.net/doc/pam.html
supported = [strtobytes(x) for x in supported]
# Technically 'P7' must be followed by a newline, so by using
# rstrip() we are being liberal in what we accept. I think this
# is acceptable.
type = infile.read(3).rstrip()
if type not in supported:
raise NotImplementedError('file format %s not supported' % type)
if type == strtobytes('P7'):
# PAM header parsing is completely different.
return read_pam_header(infile)
# Expected number of tokens in header (3 for P4, 4 for P6)
expected = 4
pbm = ('P1', 'P4')
if type in pbm:
expected = 3
header = [type]
# We have to read the rest of the header byte by byte because the
# final whitespace character (immediately following the MAXVAL in
# the case of P6) may not be a newline. Of course all PNM files in
# the wild use a newline at this point, so it's tempting to use
# readline; but it would be wrong.
def getc():
c = infile.read(1)
if not c:
raise Error('premature EOF reading PNM header')
return c
c = getc()
while True:
# Skip whitespace that precedes a token.
while c.isspace():
c = getc()
# Skip comments.
while c == '#':
while c not in '\n\r':
c = getc()
if not c.isdigit():
raise Error('unexpected character %s found in header' % c)
# According to the specification it is legal to have comments
# that appear in the middle of a token.
# This is bonkers; I've never seen it; and it's a bit awkward to
# code good lexers in Python (no goto). So we break on such
# cases.
token = strtobytes('')
while c.isdigit():
token += c
c = getc()
# Slight hack. All "tokens" are decimal integers, so convert
# them here.
header.append(int(token))
if len(header) == expected:
break
# Skip comments (again)
while c == '#':
while c not in '\n\r':
c = getc()
if not c.isspace():
raise Error('expected header to end with whitespace, not %s' % c)
if type in pbm:
# synthesize a MAXVAL
header.append(1)
depth = (1,3)[type == strtobytes('P6')]
return header[0], header[1], header[2], depth, header[3] | [
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|
miyosuda/TensorFlowAndroidMNIST | 7b5a4603d2780a8a2834575706e9001977524007 | jni-build/jni/include/tensorflow/python/ops/image_ops.py | python | random_saturation | (image, lower, upper, seed=None) | return adjust_saturation(image, saturation_factor) | Adjust the saturation of an RGB image by a random factor.
Equivalent to `adjust_saturation()` but uses a `saturation_factor` randomly
picked in the interval `[lower, upper]`.
Args:
image: RGB image or images. Size of the last dimension must be 3.
lower: float. Lower bound for the random saturation factor.
upper: float. Upper bound for the random saturation factor.
seed: An operation-specific seed. It will be used in conjunction
with the graph-level seed to determine the real seeds that will be
used in this operation. Please see the documentation of
set_random_seed for its interaction with the graph-level random seed.
Returns:
Adjusted image(s), same shape and DType as `image`.
Raises:
ValueError: if `upper <= lower` or if `lower < 0`. | Adjust the saturation of an RGB image by a random factor. | [
"Adjust",
"the",
"saturation",
"of",
"an",
"RGB",
"image",
"by",
"a",
"random",
"factor",
"."
] | def random_saturation(image, lower, upper, seed=None):
"""Adjust the saturation of an RGB image by a random factor.
Equivalent to `adjust_saturation()` but uses a `saturation_factor` randomly
picked in the interval `[lower, upper]`.
Args:
image: RGB image or images. Size of the last dimension must be 3.
lower: float. Lower bound for the random saturation factor.
upper: float. Upper bound for the random saturation factor.
seed: An operation-specific seed. It will be used in conjunction
with the graph-level seed to determine the real seeds that will be
used in this operation. Please see the documentation of
set_random_seed for its interaction with the graph-level random seed.
Returns:
Adjusted image(s), same shape and DType as `image`.
Raises:
ValueError: if `upper <= lower` or if `lower < 0`.
"""
if upper <= lower:
raise ValueError('upper must be > lower.')
if lower < 0:
raise ValueError('lower must be non-negative.')
# Pick a float in [lower, upper]
saturation_factor = random_ops.random_uniform([], lower, upper, seed=seed)
return adjust_saturation(image, saturation_factor) | [
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|
synfig/synfig | a5ec91db5b751dc12e4400ccfb5c063fd6d2d928 | synfig-studio/plugins/lottie-exporter/common/WidthPointList.py | python | WidthPointList.get_len | (self) | return len(self.entry_list) | Returns the number of entries | Returns the number of entries | [
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"""
Returns the number of entries
"""
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|
NVIDIA/nvvl | a94c7493ec9f309cc54acf81a66c62a068a06962 | examples/pytorch_superres/nvidia/fp16.py | python | FP16_Optimizer.clip_fp32_grads | (self, max_norm, norm_type=2) | Clips fp32 master gradients via torch.nn.utils.clip_grad_norm.
Args:
max_norm (float or int): max norm of the gradients
norm_type (float or int): type of the used p-norm. Can be ``'inf'`` for
infinity norm.
Returns:
Total norm of the current fp32 gradients (viewed as a single vector).
.. warning::
Returns -1 if the most recently computed fp16 gradients overflowed (that is, if self.overflow is True). | Clips fp32 master gradients via torch.nn.utils.clip_grad_norm. | [
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] | def clip_fp32_grads(self, max_norm, norm_type=2):
"""
Clips fp32 master gradients via torch.nn.utils.clip_grad_norm.
Args:
max_norm (float or int): max norm of the gradients
norm_type (float or int): type of the used p-norm. Can be ``'inf'`` for
infinity norm.
Returns:
Total norm of the current fp32 gradients (viewed as a single vector).
.. warning::
Returns -1 if the most recently computed fp16 gradients overflowed (that is, if self.overflow is True).
"""
if not self.overflow:
fp32_params = []
for param_group in self.optimizer.param_groups:
for param in param_group['params']:
fp32_params.append(param)
return torch.nn.utils.clip_grad_norm(fp32_params, max_norm, norm_type)
else:
return -1 | [
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||
nodejs/nan | 8db8c8f544f2b6ce1b0859ef6ecdd0a3873a9e62 | cpplint.py | python | CheckForFunctionLengths | (filename, clean_lines, linenum,
function_state, error) | Reports for long function bodies.
For an overview why this is done, see:
https://google-styleguide.googlecode.com/svn/trunk/cppguide.xml#Write_Short_Functions
Uses a simplistic algorithm assuming other style guidelines
(especially spacing) are followed.
Only checks unindented functions, so class members are unchecked.
Trivial bodies are unchecked, so constructors with huge initializer lists
may be missed.
Blank/comment lines are not counted so as to avoid encouraging the removal
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NOLINT *on the last line of a function* disables this check.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
function_state: Current function name and lines in body so far.
error: The function to call with any errors found. | Reports for long function bodies. | [
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] | def CheckForFunctionLengths(filename, clean_lines, linenum,
function_state, error):
"""Reports for long function bodies.
For an overview why this is done, see:
https://google-styleguide.googlecode.com/svn/trunk/cppguide.xml#Write_Short_Functions
Uses a simplistic algorithm assuming other style guidelines
(especially spacing) are followed.
Only checks unindented functions, so class members are unchecked.
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may be missed.
Blank/comment lines are not counted so as to avoid encouraging the removal
of vertical space and comments just to get through a lint check.
NOLINT *on the last line of a function* disables this check.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
function_state: Current function name and lines in body so far.
error: The function to call with any errors found.
"""
lines = clean_lines.lines
line = lines[linenum]
joined_line = ''
starting_func = False
regexp = r'(\w(\w|::|\*|\&|\s)*)\(' # decls * & space::name( ...
match_result = Match(regexp, line)
if match_result:
# If the name is all caps and underscores, figure it's a macro and
# ignore it, unless it's TEST or TEST_F.
function_name = match_result.group(1).split()[-1]
if function_name == 'TEST' or function_name == 'TEST_F' or (
not Match(r'[A-Z_]+$', function_name)):
starting_func = True
if starting_func:
body_found = False
for start_linenum in xrange(linenum, clean_lines.NumLines()):
start_line = lines[start_linenum]
joined_line += ' ' + start_line.lstrip()
if Search(r'(;|})', start_line): # Declarations and trivial functions
body_found = True
break # ... ignore
if Search(r'{', start_line):
body_found = True
function = Search(r'((\w|:)*)\(', line).group(1)
if Match(r'TEST', function): # Handle TEST... macros
parameter_regexp = Search(r'(\(.*\))', joined_line)
if parameter_regexp: # Ignore bad syntax
function += parameter_regexp.group(1)
else:
function += '()'
function_state.Begin(function)
break
if not body_found:
# No body for the function (or evidence of a non-function) was found.
error(filename, linenum, 'readability/fn_size', 5,
'Lint failed to find start of function body.')
elif Match(r'^\}\s*$', line): # function end
function_state.Check(error, filename, linenum)
function_state.End()
elif not Match(r'^\s*$', line):
function_state.Count() | [
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||
rapidsai/cudf | d5b2448fc69f17509304d594f029d0df56984962 | python/cudf/cudf/core/series.py | python | DatetimeProperties.is_month_start | (self) | return (self.day == 1).fillna(False) | Booleans indicating if dates are the first day of the month. | Booleans indicating if dates are the first day of the month. | [
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] | def is_month_start(self):
"""
Booleans indicating if dates are the first day of the month.
"""
return (self.day == 1).fillna(False) | [
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cliqz-oss/keyvi | 957cb7197b2bff78d3afb22ad95e092f89afb0a6 | keyvi/3rdparty/tpie/scripts/stream_header.py | python | read | (file_name, output_file) | Read the TPIE stream in `file_name` (str),
and print its header as JSON to `output_file` (file). | Read the TPIE stream in `file_name` (str),
and print its header as JSON to `output_file` (file). | [
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"""Read the TPIE stream in `file_name` (str),
and print its header as JSON to `output_file` (file)."""
with open(file_name, 'rb') as stream:
values = stream_header.unpack_from(stream.read(stream_header.size))
header_data = dict(zip(field_names, values))
json.dump(header_data, output_file, indent=0, sort_keys=True, separators=(',', ': '))
output_file.write('\n')
output_file.flush() | [
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||
microsoft/checkedc-clang | a173fefde5d7877b7750e7ce96dd08cf18baebf2 | lldb/examples/python/mach_o.py | python | TerminalColors.white | (self, fg=True) | return '' | Set the foreground or background color to white.
The foreground color will be set if "fg" tests True. The background color will be set if "fg" tests False. | Set the foreground or background color to white.
The foreground color will be set if "fg" tests True. The background color will be set if "fg" tests False. | [
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'''Set the foreground or background color to white.
The foreground color will be set if "fg" tests True. The background color will be set if "fg" tests False.'''
if self.enabled:
if fg:
return "\x1b[37m"
else:
return "\x1b[47m"
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|
naver/sling | 5671cd445a2caae0b4dd0332299e4cfede05062c | webkit/Tools/Scripts/webkitpy/xcode/simulator.py | python | Simulator.lookup_or_create_device | (self, name, device_type, runtime) | return testing_device | Returns an available iOS Simulator device for testing.
This function will create a new simulator device with the specified name,
device type and runtime if one does not already exist.
:param name: The name of the simulator device to lookup or create.
:type name: str
:param device_type: The CoreSimulator device type.
:type device_type: DeviceType
:param runtime: The CoreSimulator runtime.
:type runtime: Runtime
:return: A dictionary describing the device.
:rtype: Device | Returns an available iOS Simulator device for testing. | [
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] | def lookup_or_create_device(self, name, device_type, runtime):
"""
Returns an available iOS Simulator device for testing.
This function will create a new simulator device with the specified name,
device type and runtime if one does not already exist.
:param name: The name of the simulator device to lookup or create.
:type name: str
:param device_type: The CoreSimulator device type.
:type device_type: DeviceType
:param runtime: The CoreSimulator runtime.
:type runtime: Runtime
:return: A dictionary describing the device.
:rtype: Device
"""
assert(runtime.available)
testing_device = self.device(name=name, runtime=runtime, should_ignore_unavailable_devices=True)
if testing_device:
return testing_device
testing_device = Device.create(name, device_type, runtime)
assert(testing_device.available)
return testing_device | [
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|
hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/boto/boto/glacier/utils.py | python | tree_hash_from_str | (str_as_bytes) | return bytes_to_hex(tree_hash(chunk_hashes(str_as_bytes))) | :type str_as_bytes: str
:param str_as_bytes: The string for which to compute the tree hash.
:rtype: str
:return: The computed tree hash, returned as hex. | [] | def tree_hash_from_str(str_as_bytes):
"""
:type str_as_bytes: str
:param str_as_bytes: The string for which to compute the tree hash.
:rtype: str
:return: The computed tree hash, returned as hex.
"""
return bytes_to_hex(tree_hash(chunk_hashes(str_as_bytes))) | [
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||
hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/coverage/coverage/html.py | python | HtmlStatus.reset | (self) | Initialize to empty. | Initialize to empty. | [
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"""Initialize to empty."""
self.settings = ''
self.files = {} | [
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||
mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/ops/_op_impl/_custom_op/fake_learned_scale_quant_perchannel_grad.py | python | fake_learned_scale_quant_perchannel_grad_d_compute | (dout, input_data, alpha_data, quant_max_data, neg_trunc,
kernel_name="fake_learned_scale_quant_perchannel_grad_d") | return [dx, dalpha_each] | FakeLearnedScaleQuantPerChannelGradD | FakeLearnedScaleQuantPerChannelGradD | [
"FakeLearnedScaleQuantPerChannelGradD"
] | def fake_learned_scale_quant_perchannel_grad_d_compute(dout, input_data, alpha_data, quant_max_data, neg_trunc,
kernel_name="fake_learned_scale_quant_perchannel_grad_d"):
"""FakeLearnedScaleQuantPerChannelGradD"""
input_shape = te.lang.cce.util.shape_to_list(input_data.shape)
eps = tvm.const(1e-6, input_data.dtype)
alpha_data = te.lang.cce.vcmpsel(te.lang.cce.vabs(alpha_data), eps, 'ge', alpha_data, eps)
alpha_data = te.lang.cce.broadcast(alpha_data, input_shape, input_data.dtype)
quant_max_data = te.lang.cce.broadcast(quant_max_data, input_shape, input_data.dtype)
input_x = te.lang.cce.vdiv(input_data, alpha_data)
input_div_alpha = input_x
if neg_trunc:
input_x = te.lang.cce.round_to(input_x, 1.0, 0.0)
else:
input_x = te.lang.cce.round_to(input_x, 1.0, -1.0)
nudge_input = te.lang.cce.floor(te.lang.cce.vadds(te.lang.cce.vmul(input_x, quant_max_data), 0.5))
input_quant = te.lang.cce.vdiv(nudge_input, quant_max_data)
dtype = input_div_alpha.dtype.lower()
shape = te.lang.cce.util.shape_to_list(input_div_alpha.shape)
dx = dout
tensor_one = tvm.const(1.0, input_div_alpha.dtype)
tensor_one = te.lang.cce.broadcast(tensor_one, shape)
out_of_upper_bounds = te.lang.cce.vcmpsel(input_div_alpha, 1.0, 'gt', 1.0, 0.0)
if neg_trunc:
out_of_lower_bounds = te.lang.cce.vcmpsel(input_div_alpha, 0.0, 'lt', 1.0, 0.0)
else:
out_of_lower_bounds = te.lang.cce.vcmpsel(input_div_alpha, -1.0, 'lt', 1.0, 0.0)
out_of_bounds = te.lang.cce.vadd(out_of_lower_bounds, out_of_upper_bounds)
dx = te.lang.cce.vmul(dx, te.lang.cce.vsub(tensor_one, out_of_bounds))
sign = _sign_function(dtype, input_div_alpha)
# The following lines are equivalent to :
# dalpha_each = dout * sign if out of bounds
# dout * (input_quant - input_div_alpha) if within bounds
quant_error = te.lang.cce.vsub(input_quant, input_div_alpha)
within_bounds = te.lang.cce.vsub(tensor_one, out_of_bounds)
error_within_bounds = te.lang.cce.vmul(quant_error, within_bounds)
grad_range = te.lang.cce.vmadd(sign, error_within_bounds, out_of_bounds)
dalpha_each = te.lang.cce.vmul(dout, grad_range)
return [dx, dalpha_each] | [
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|
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/pandas/core/frame.py | python | DataFrame.rename | (
self,
mapper: Optional[Renamer] = None,
*,
index: Optional[Renamer] = None,
columns: Optional[Renamer] = None,
axis: Optional[Axis] = None,
copy: bool = True,
inplace: bool = False,
level: Optional[Level] = None,
errors: str = "ignore",
) | return super().rename(
mapper=mapper,
index=index,
columns=columns,
axis=axis,
copy=copy,
inplace=inplace,
level=level,
errors=errors,
) | Alter axes labels.
Function / dict values must be unique (1-to-1). Labels not contained in
a dict / Series will be left as-is. Extra labels listed don't throw an
error.
See the :ref:`user guide <basics.rename>` for more.
Parameters
----------
mapper : dict-like or function
Dict-like or functions transformations to apply to
that axis' values. Use either ``mapper`` and ``axis`` to
specify the axis to target with ``mapper``, or ``index`` and
``columns``.
index : dict-like or function
Alternative to specifying axis (``mapper, axis=0``
is equivalent to ``index=mapper``).
columns : dict-like or function
Alternative to specifying axis (``mapper, axis=1``
is equivalent to ``columns=mapper``).
axis : int or str
Axis to target with ``mapper``. Can be either the axis name
('index', 'columns') or number (0, 1). The default is 'index'.
copy : bool, default True
Also copy underlying data.
inplace : bool, default False
Whether to return a new DataFrame. If True then value of copy is
ignored.
level : int or level name, default None
In case of a MultiIndex, only rename labels in the specified
level.
errors : {'ignore', 'raise'}, default 'ignore'
If 'raise', raise a `KeyError` when a dict-like `mapper`, `index`,
or `columns` contains labels that are not present in the Index
being transformed.
If 'ignore', existing keys will be renamed and extra keys will be
ignored.
Returns
-------
DataFrame
DataFrame with the renamed axis labels.
Raises
------
KeyError
If any of the labels is not found in the selected axis and
"errors='raise'".
See Also
--------
DataFrame.rename_axis : Set the name of the axis.
Examples
--------
``DataFrame.rename`` supports two calling conventions
* ``(index=index_mapper, columns=columns_mapper, ...)``
* ``(mapper, axis={'index', 'columns'}, ...)``
We *highly* recommend using keyword arguments to clarify your
intent.
Rename columns using a mapping:
>>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
>>> df.rename(columns={"A": "a", "B": "c"})
a c
0 1 4
1 2 5
2 3 6
Rename index using a mapping:
>>> df.rename(index={0: "x", 1: "y", 2: "z"})
A B
x 1 4
y 2 5
z 3 6
Cast index labels to a different type:
>>> df.index
RangeIndex(start=0, stop=3, step=1)
>>> df.rename(index=str).index
Index(['0', '1', '2'], dtype='object')
>>> df.rename(columns={"A": "a", "B": "b", "C": "c"}, errors="raise")
Traceback (most recent call last):
KeyError: ['C'] not found in axis
Using axis-style parameters
>>> df.rename(str.lower, axis='columns')
a b
0 1 4
1 2 5
2 3 6
>>> df.rename({1: 2, 2: 4}, axis='index')
A B
0 1 4
2 2 5
4 3 6 | Alter axes labels. | [
"Alter",
"axes",
"labels",
"."
] | def rename(
self,
mapper: Optional[Renamer] = None,
*,
index: Optional[Renamer] = None,
columns: Optional[Renamer] = None,
axis: Optional[Axis] = None,
copy: bool = True,
inplace: bool = False,
level: Optional[Level] = None,
errors: str = "ignore",
) -> Optional["DataFrame"]:
"""
Alter axes labels.
Function / dict values must be unique (1-to-1). Labels not contained in
a dict / Series will be left as-is. Extra labels listed don't throw an
error.
See the :ref:`user guide <basics.rename>` for more.
Parameters
----------
mapper : dict-like or function
Dict-like or functions transformations to apply to
that axis' values. Use either ``mapper`` and ``axis`` to
specify the axis to target with ``mapper``, or ``index`` and
``columns``.
index : dict-like or function
Alternative to specifying axis (``mapper, axis=0``
is equivalent to ``index=mapper``).
columns : dict-like or function
Alternative to specifying axis (``mapper, axis=1``
is equivalent to ``columns=mapper``).
axis : int or str
Axis to target with ``mapper``. Can be either the axis name
('index', 'columns') or number (0, 1). The default is 'index'.
copy : bool, default True
Also copy underlying data.
inplace : bool, default False
Whether to return a new DataFrame. If True then value of copy is
ignored.
level : int or level name, default None
In case of a MultiIndex, only rename labels in the specified
level.
errors : {'ignore', 'raise'}, default 'ignore'
If 'raise', raise a `KeyError` when a dict-like `mapper`, `index`,
or `columns` contains labels that are not present in the Index
being transformed.
If 'ignore', existing keys will be renamed and extra keys will be
ignored.
Returns
-------
DataFrame
DataFrame with the renamed axis labels.
Raises
------
KeyError
If any of the labels is not found in the selected axis and
"errors='raise'".
See Also
--------
DataFrame.rename_axis : Set the name of the axis.
Examples
--------
``DataFrame.rename`` supports two calling conventions
* ``(index=index_mapper, columns=columns_mapper, ...)``
* ``(mapper, axis={'index', 'columns'}, ...)``
We *highly* recommend using keyword arguments to clarify your
intent.
Rename columns using a mapping:
>>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
>>> df.rename(columns={"A": "a", "B": "c"})
a c
0 1 4
1 2 5
2 3 6
Rename index using a mapping:
>>> df.rename(index={0: "x", 1: "y", 2: "z"})
A B
x 1 4
y 2 5
z 3 6
Cast index labels to a different type:
>>> df.index
RangeIndex(start=0, stop=3, step=1)
>>> df.rename(index=str).index
Index(['0', '1', '2'], dtype='object')
>>> df.rename(columns={"A": "a", "B": "b", "C": "c"}, errors="raise")
Traceback (most recent call last):
KeyError: ['C'] not found in axis
Using axis-style parameters
>>> df.rename(str.lower, axis='columns')
a b
0 1 4
1 2 5
2 3 6
>>> df.rename({1: 2, 2: 4}, axis='index')
A B
0 1 4
2 2 5
4 3 6
"""
return super().rename(
mapper=mapper,
index=index,
columns=columns,
axis=axis,
copy=copy,
inplace=inplace,
level=level,
errors=errors,
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|
y123456yz/reading-and-annotate-mongodb-3.6 | 93280293672ca7586dc24af18132aa61e4ed7fcf | mongo/src/third_party/scons-2.5.0/scons-local-2.5.0/SCons/Node/Alias.py | python | Alias.sconsign | (self) | An Alias is not recorded in .sconsign files | An Alias is not recorded in .sconsign files | [
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"""An Alias is not recorded in .sconsign files"""
pass | [
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||
baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/python/platform/tf_logging.py | python | _get_thread_id | () | return thread_id & _THREAD_ID_MASK | Get id of current thread, suitable for logging as an unsigned quantity. | Get id of current thread, suitable for logging as an unsigned quantity. | [
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] | def _get_thread_id():
"""Get id of current thread, suitable for logging as an unsigned quantity."""
# pylint: disable=protected-access
thread_id = six.moves._thread.get_ident()
# pylint:enable=protected-access
return thread_id & _THREAD_ID_MASK | [
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|
y123456yz/reading-and-annotate-mongodb-3.6 | 93280293672ca7586dc24af18132aa61e4ed7fcf | mongo/buildscripts/resmokelib/logging/loggers.py | python | BaseLogger.__init__ | (self, name, logging_config=None, build_logger_server=None, parent=None) | Initialize a BaseLogger.
:param name: the logger name.
:param logging_config: the logging configuration.
:param build_logger_server: the build logger server (e.g. logkeeper).
:param parent: the parent logger. | Initialize a BaseLogger. | [
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] | def __init__(self, name, logging_config=None, build_logger_server=None, parent=None):
"""Initialize a BaseLogger.
:param name: the logger name.
:param logging_config: the logging configuration.
:param build_logger_server: the build logger server (e.g. logkeeper).
:param parent: the parent logger.
"""
logging.Logger.__init__(self, name, level=logging.DEBUG)
self._logging_config = logging_config
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self.parent = parent
self.propagate = True | [
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||
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py3/sklearn/covariance/_shrunk_covariance.py | python | shrunk_covariance | (emp_cov, shrinkage=0.1) | return shrunk_cov | Calculates a covariance matrix shrunk on the diagonal
Read more in the :ref:`User Guide <shrunk_covariance>`.
Parameters
----------
emp_cov : array-like, shape (n_features, n_features)
Covariance matrix to be shrunk
shrinkage : float, 0 <= shrinkage <= 1
Coefficient in the convex combination used for the computation
of the shrunk estimate.
Returns
-------
shrunk_cov : array-like
Shrunk covariance.
Notes
-----
The regularized (shrunk) covariance is given by:
(1 - shrinkage) * cov + shrinkage * mu * np.identity(n_features)
where mu = trace(cov) / n_features | Calculates a covariance matrix shrunk on the diagonal | [
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] | def shrunk_covariance(emp_cov, shrinkage=0.1):
"""Calculates a covariance matrix shrunk on the diagonal
Read more in the :ref:`User Guide <shrunk_covariance>`.
Parameters
----------
emp_cov : array-like, shape (n_features, n_features)
Covariance matrix to be shrunk
shrinkage : float, 0 <= shrinkage <= 1
Coefficient in the convex combination used for the computation
of the shrunk estimate.
Returns
-------
shrunk_cov : array-like
Shrunk covariance.
Notes
-----
The regularized (shrunk) covariance is given by:
(1 - shrinkage) * cov + shrinkage * mu * np.identity(n_features)
where mu = trace(cov) / n_features
"""
emp_cov = check_array(emp_cov)
n_features = emp_cov.shape[0]
mu = np.trace(emp_cov) / n_features
shrunk_cov = (1. - shrinkage) * emp_cov
shrunk_cov.flat[::n_features + 1] += shrinkage * mu
return shrunk_cov | [
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|
RoboJackets/robocup-software | bce13ce53ddb2ecb9696266d980722c34617dc15 | rj_gameplay/stp/role/constraint.py | python | has_ball | () | return constraint_fn | Creates a constraint function that returns true if the current robot has the
ball.
:return: Constraint function for current robot having the ball. | Creates a constraint function that returns true if the current robot has the
ball.
:return: Constraint function for current robot having the ball. | [
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"""Creates a constraint function that returns true if the current robot has the
ball.
:return: Constraint function for current robot having the ball.
"""
def constraint_fn(
robot: rc.Robot,
prev_result: Optional[role.RoleResult],
world_state: rc.WorldState,
) -> bool:
return robot.has_ball_sense
return constraint_fn | [
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|
Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | demo/DPU-for-RNN/rnnt_asr_vck5000/model_rnnt.py | python | RNNT.predict | (self, y, state=None, add_sos=True) | return g, hid | B - batch size
U - label length
H - Hidden dimension size
L - Number of decoder layers = 2
Args:
y: (B, U)
Returns:
Tuple (g, hid) where:
g: (B, U + 1, H)
hid: (h, c) where h is the final sequence hidden state and c is
the final cell state:
h (tensor), shape (L, B, H)
c (tensor), shape (L, B, H) | B - batch size
U - label length
H - Hidden dimension size
L - Number of decoder layers = 2 | [
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"""
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H - Hidden dimension size
L - Number of decoder layers = 2
Args:
y: (B, U)
Returns:
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c (tensor), shape (L, B, H)
"""
if isinstance(y, torch.Tensor):
y = self.prediction["embed"](y)
elif isinstance(y, torch.nn.utils.rnn.PackedSequence):
# Teacher-forced training mode
# (B, U) -> (B, U, H)
y._replace(data=self.prediction["embed"](y.data))
else:
# inference mode
B = 1 if state is None else state[0].size(1)
y = torch.zeros((B, 1, self.pred_n_hidden)).to(
device=self.joint_net[0].weight.device,
dtype=self.joint_net[0].weight.dtype
)
# preprend blank "start of sequence" symbol
if add_sos:
B, U, H = y.shape
start = torch.zeros((B, 1, H)).to(device=y.device, dtype=y.dtype)
y = torch.cat([start, y], dim=1).contiguous() # (B, U + 1, H)
else:
start = None # makes del call later easier
# if state is None:
# batch = y.size(0)
# state = [
# (torch.zeros(batch, self.pred_n_hidden, dtype=y.dtype, device=y.device),
# torch.zeros(batch, self.pred_n_hidden, dtype=y.dtype, device=y.device))
# for _ in range(self.pred_rnn_layers)
# ]
y = y.transpose(0, 1) # .contiguous() # (U + 1, B, H)
g, hid = self.prediction["dec_rnn"](y, state)
g = g.transpose(0, 1) # .contiguous() # (B, U + 1, H)
del y, start, state
return g, hid | [
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|
baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/python/ops/resource_variable_ops.py | python | ResourceVariable.value | (self) | A cached operation which reads the value of this variable. | A cached operation which reads the value of this variable. | [
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"""A cached operation which reads the value of this variable."""
if self._cached_value is not None:
return self._cached_value
with ops.colocate_with(None, ignore_existing=True):
with ops.device(self._handle.device):
return gen_resource_variable_ops.read_variable_op(
self._handle, dtype=self._dtype) | [
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||
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_core.py | python | EvtHandler.Disconnect | (*args, **kwargs) | return _core_.EvtHandler_Disconnect(*args, **kwargs) | Disconnect(self, int id, int lastId=-1, EventType eventType=wxEVT_NULL,
PyObject func=None) -> bool | Disconnect(self, int id, int lastId=-1, EventType eventType=wxEVT_NULL,
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"""
Disconnect(self, int id, int lastId=-1, EventType eventType=wxEVT_NULL,
PyObject func=None) -> bool
"""
return _core_.EvtHandler_Disconnect(*args, **kwargs) | [
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|
miyosuda/TensorFlowAndroidMNIST | 7b5a4603d2780a8a2834575706e9001977524007 | jni-build/jni/include/tensorflow/python/framework/common_shapes.py | python | bias_add_grad_shape | (op) | return [output_shape] | Shape function for a BiasAddGrad op. | Shape function for a BiasAddGrad op. | [
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"""Shape function for a BiasAddGrad op."""
input_shape = op.inputs[0].get_shape().with_rank_at_least(2)
try:
data_format = op.get_attr("data_format")
except ValueError:
data_format = None
if data_format == b"NCHW":
output_shape = input_shape[-3]
else:
output_shape = input_shape[-1]
return [output_shape] | [
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|
yuxng/PoseCNN | 9f3dd7b7bce21dcafc05e8f18ccc90da3caabd04 | lib/datasets/ycb.py | python | ycb.image_path_from_index | (self, index) | return image_path | Construct an image path from the image's "index" identifier. | Construct an image path from the image's "index" identifier. | [
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"""
Construct an image path from the image's "index" identifier.
"""
image_path = os.path.join(self._data_path, index + self._image_ext)
assert os.path.exists(image_path), \
'Path does not exist: {}'.format(image_path)
return image_path | [
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|
naver/sling | 5671cd445a2caae0b4dd0332299e4cfede05062c | webkit/Tools/Scripts/webkitpy/thirdparty/mod_pywebsocket/util.py | python | prepend_message_to_exception | (message, exc) | return | Prepend message to the exception. | Prepend message to the exception. | [
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"exception",
"."
] | def prepend_message_to_exception(message, exc):
"""Prepend message to the exception."""
exc.args = (message + str(exc),)
return | [
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|
google/syzygy | 8164b24ebde9c5649c9a09e88a7fc0b0fcbd1bc5 | syzygy/py/etw_db/etw_db/process.py | python | ProcessThreadDatabase.__init__ | (self, no_pruning=False) | Initializes a new database.
Args:
no_pruning: if true, process and thread information is maintained past
process/thread end events. | Initializes a new database. | [
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"database",
"."
] | def __init__(self, no_pruning=False):
"""Initializes a new database.
Args:
no_pruning: if true, process and thread information is maintained past
process/thread end events.
"""
EventConsumer.__init__(self)
self._no_pruning = no_pruning
# Processes by id maps from process ID to command line.
self._processes_by_id = {}
# Threads by id maps from thread ID to owning process ID.
self._threads_by_id = {} | [
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||
wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/logging/handlers.py | python | SMTPHandler.getSubject | (self, record) | return self.subject | Determine the subject for the email.
If you want to specify a subject line which is record-dependent,
override this method. | Determine the subject for the email. | [
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] | def getSubject(self, record):
"""
Determine the subject for the email.
If you want to specify a subject line which is record-dependent,
override this method.
"""
return self.subject | [
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|
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/pip/_internal/index/package_finder.py | python | CandidatePreferences.__init__ | (
self,
prefer_binary=False, # type: bool
allow_all_prereleases=False, # type: bool
) | :param allow_all_prereleases: Whether to allow all pre-releases. | [] | def __init__(
self,
prefer_binary=False, # type: bool
allow_all_prereleases=False, # type: bool
):
# type: (...) -> None
"""
:param allow_all_prereleases: Whether to allow all pre-releases.
"""
self.allow_all_prereleases = allow_all_prereleases
self.prefer_binary = prefer_binary | [
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|||
baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/python/ops/distributions/transformed_distribution.py | python | _logical_not | (x) | return constant_op.constant(np.logical_not(x_)) | Convenience function which attempts to statically apply `logical_not`. | Convenience function which attempts to statically apply `logical_not`. | [
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] | def _logical_not(x):
"""Convenience function which attempts to statically apply `logical_not`."""
x_ = _static_value(x)
if x_ is None:
return math_ops.logical_not(x)
return constant_op.constant(np.logical_not(x_)) | [
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|
Samsung/veles | 95ed733c2e49bc011ad98ccf2416ecec23fbf352 | veles/external/pydot.py | python | Graph.set_simplify | (self, simplify) | Set whether to simplify or not.
If True it will avoid displaying equal edges, i.e.
only one edge between two nodes. removing the
duplicated ones. | Set whether to simplify or not.
If True it will avoid displaying equal edges, i.e.
only one edge between two nodes. removing the
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"""Set whether to simplify or not.
If True it will avoid displaying equal edges, i.e.
only one edge between two nodes. removing the
duplicated ones.
"""
self.obj_dict['simplify'] = simplify | [
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||
scribusproject/scribus | 41ec7c775a060912cf251682a8b1437f753f80f4 | codegen/cheetah/Cheetah/Template.py | python | Template.webInput | (self, names, namesMulti=(), default='', src='f',
defaultInt=0, defaultFloat=0.00, badInt=0, badFloat=0.00, debug=False) | return dic | Method for importing web transaction variables in bulk.
This works for GET/POST fields both in Webware servlets and in CGI
scripts, and for cookies and session variables in Webware servlets. If
you try to read a cookie or session variable in a CGI script, you'll get
a RuntimeError. 'In a CGI script' here means 'not running as a Webware
servlet'. If the CGI environment is not properly set up, Cheetah will
act like there's no input.
The public method provided is:
def webInput(self, names, namesMulti=(), default='', src='f',
defaultInt=0, defaultFloat=0.00, badInt=0, badFloat=0.00, debug=False):
This method places the specified GET/POST fields, cookies or session
variables into a dictionary, which is both returned and put at the
beginning of the searchList. It handles:
* single vs multiple values
* conversion to integer or float for specified names
* default values/exceptions for missing or bad values
* printing a snapshot of all values retrieved for debugging
All the 'default*' and 'bad*' arguments have 'use or raise' behavior,
meaning that if they're a subclass of Exception, they're raised. If
they're anything else, that value is substituted for the missing/bad
value.
The simplest usage is:
#silent $webInput(['choice'])
$choice
dic = self.webInput(['choice'])
write(dic['choice'])
Both these examples retrieves the GET/POST field 'choice' and print it.
If you leave off the'#silent', all the values would be printed too. But
a better way to preview the values is
#silent $webInput(['name'], $debug=1)
because this pretty-prints all the values inside HTML <PRE> tags.
** KLUDGE: 'debug' is supposed to insert into the template output, but it
wasn't working so I changed it to a'print' statement. So the debugging
output will appear wherever standard output is pointed, whether at the
terminal, in a Webware log file, or whatever. ***
Since we didn't specify any coversions, the value is a string. It's a
'single' value because we specified it in 'names' rather than
'namesMulti'. Single values work like this:
* If one value is found, take it.
* If several values are found, choose one arbitrarily and ignore the rest.
* If no values are found, use or raise the appropriate 'default*' value.
Multi values work like this:
* If one value is found, put it in a list.
* If several values are found, leave them in a list.
* If no values are found, use the empty list ([]). The 'default*'
arguments are *not* consulted in this case.
Example: assume 'days' came from a set of checkboxes or a multiple combo
box on a form, and the user chose'Monday', 'Tuesday' and 'Thursday'.
#silent $webInput([], ['days'])
The days you chose are: #slurp
#for $day in $days
$day #slurp
#end for
dic = self.webInput([], ['days'])
write('The days you chose are: ')
for day in dic['days']:
write(day + ' ')
Both these examples print: 'The days you chose are: Monday Tuesday Thursday'.
By default, missing strings are replaced by '' and missing/bad numbers
by zero. (A'bad number' means the converter raised an exception for
it, usually because of non-numeric characters in the value.) This
mimics Perl/PHP behavior, and simplifies coding for many applications
where missing/bad values *should* be blank/zero. In those relatively
few cases where you must distinguish between empty-string/zero on the
one hand and missing/bad on the other, change the appropriate
'default*' and 'bad*' arguments to something like:
* None
* another constant value
* $NonNumericInputError/self.NonNumericInputError
* $ValueError/ValueError
(NonNumericInputError is defined in this class and is useful for
distinguishing between bad input vs a TypeError/ValueError thrown for
some other rason.)
Here's an example using multiple values to schedule newspaper
deliveries. 'checkboxes' comes from a form with checkboxes for all the
days of the week. The days the user previously chose are preselected.
The user checks/unchecks boxes as desired and presses Submit. The value
of 'checkboxes' is a list of checkboxes that were checked when Submit
was pressed. Our task now is to turn on the days the user checked, turn
off the days he unchecked, and leave on or off the days he didn't
change.
dic = self.webInput([], ['dayCheckboxes'])
wantedDays = dic['dayCheckboxes'] # The days the user checked.
for day, on in self.getAllValues():
if not on and wantedDays.has_key(day):
self.TurnOn(day)
# ... Set a flag or insert a database record ...
elif on and not wantedDays.has_key(day):
self.TurnOff(day)
# ... Unset a flag or delete a database record ...
'source' allows you to look up the variables from a number of different
sources:
'f' fields (CGI GET/POST parameters)
'c' cookies
's' session variables
'v' 'values', meaning fields or cookies
In many forms, you're dealing only with strings, which is why the
'default' argument is third and the numeric arguments are banished to
the end. But sometimes you want automatic number conversion, so that
you can do numeric comparisions in your templates without having to
write a bunch of conversion/exception handling code. Example:
#silent $webInput(['name', 'height:int'])
$name is $height cm tall.
#if $height >= 300
Wow, you're tall!
#else
Pshaw, you're short.
#end if
dic = self.webInput(['name', 'height:int'])
name = dic[name]
height = dic[height]
write('%s is %s cm tall.' % (name, height))
if height > 300:
write('Wow, you're tall!')
else:
write('Pshaw, you're short.')
To convert a value to a number, suffix ':int' or ':float' to the name.
The method will search first for a 'height:int' variable and then for a
'height' variable. (It will be called 'height' in the final
dictionary.) If a numeric conversion fails, use or raise 'badInt' or
'badFloat'. Missing values work the same way as for strings, except the
default is 'defaultInt' or 'defaultFloat' instead of 'default'.
If a name represents an uploaded file, the entire file will be read into
memory. For more sophistocated file-upload handling, leave that name
out of the list and do your own handling, or wait for
Cheetah.Utils.UploadFileMixin.
This only in a subclass that also inherits from Webware's Servlet or
HTTPServlet. Otherwise you'll get an AttributeError on 'self.request'.
EXCEPTIONS: ValueError if 'source' is not one of the stated characters.
TypeError if a conversion suffix is not ':int' or ':float'.
FUTURE EXPANSION: a future version of this method may allow source
cascading; e.g., 'vs' would look first in 'values' and then in session
variables.
Meta-Data
================================================================================
Author: Mike Orr <[email protected]>
License: This software is released for unlimited distribution under the
terms of the MIT license. See the LICENSE file.
Version: $Revision: 1.186 $
Start Date: 2002/03/17
Last Revision Date: $Date: 2008/03/10 04:48:11 $ | Method for importing web transaction variables in bulk. | [
"Method",
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"."
] | def webInput(self, names, namesMulti=(), default='', src='f',
defaultInt=0, defaultFloat=0.00, badInt=0, badFloat=0.00, debug=False):
"""Method for importing web transaction variables in bulk.
This works for GET/POST fields both in Webware servlets and in CGI
scripts, and for cookies and session variables in Webware servlets. If
you try to read a cookie or session variable in a CGI script, you'll get
a RuntimeError. 'In a CGI script' here means 'not running as a Webware
servlet'. If the CGI environment is not properly set up, Cheetah will
act like there's no input.
The public method provided is:
def webInput(self, names, namesMulti=(), default='', src='f',
defaultInt=0, defaultFloat=0.00, badInt=0, badFloat=0.00, debug=False):
This method places the specified GET/POST fields, cookies or session
variables into a dictionary, which is both returned and put at the
beginning of the searchList. It handles:
* single vs multiple values
* conversion to integer or float for specified names
* default values/exceptions for missing or bad values
* printing a snapshot of all values retrieved for debugging
All the 'default*' and 'bad*' arguments have 'use or raise' behavior,
meaning that if they're a subclass of Exception, they're raised. If
they're anything else, that value is substituted for the missing/bad
value.
The simplest usage is:
#silent $webInput(['choice'])
$choice
dic = self.webInput(['choice'])
write(dic['choice'])
Both these examples retrieves the GET/POST field 'choice' and print it.
If you leave off the'#silent', all the values would be printed too. But
a better way to preview the values is
#silent $webInput(['name'], $debug=1)
because this pretty-prints all the values inside HTML <PRE> tags.
** KLUDGE: 'debug' is supposed to insert into the template output, but it
wasn't working so I changed it to a'print' statement. So the debugging
output will appear wherever standard output is pointed, whether at the
terminal, in a Webware log file, or whatever. ***
Since we didn't specify any coversions, the value is a string. It's a
'single' value because we specified it in 'names' rather than
'namesMulti'. Single values work like this:
* If one value is found, take it.
* If several values are found, choose one arbitrarily and ignore the rest.
* If no values are found, use or raise the appropriate 'default*' value.
Multi values work like this:
* If one value is found, put it in a list.
* If several values are found, leave them in a list.
* If no values are found, use the empty list ([]). The 'default*'
arguments are *not* consulted in this case.
Example: assume 'days' came from a set of checkboxes or a multiple combo
box on a form, and the user chose'Monday', 'Tuesday' and 'Thursday'.
#silent $webInput([], ['days'])
The days you chose are: #slurp
#for $day in $days
$day #slurp
#end for
dic = self.webInput([], ['days'])
write('The days you chose are: ')
for day in dic['days']:
write(day + ' ')
Both these examples print: 'The days you chose are: Monday Tuesday Thursday'.
By default, missing strings are replaced by '' and missing/bad numbers
by zero. (A'bad number' means the converter raised an exception for
it, usually because of non-numeric characters in the value.) This
mimics Perl/PHP behavior, and simplifies coding for many applications
where missing/bad values *should* be blank/zero. In those relatively
few cases where you must distinguish between empty-string/zero on the
one hand and missing/bad on the other, change the appropriate
'default*' and 'bad*' arguments to something like:
* None
* another constant value
* $NonNumericInputError/self.NonNumericInputError
* $ValueError/ValueError
(NonNumericInputError is defined in this class and is useful for
distinguishing between bad input vs a TypeError/ValueError thrown for
some other rason.)
Here's an example using multiple values to schedule newspaper
deliveries. 'checkboxes' comes from a form with checkboxes for all the
days of the week. The days the user previously chose are preselected.
The user checks/unchecks boxes as desired and presses Submit. The value
of 'checkboxes' is a list of checkboxes that were checked when Submit
was pressed. Our task now is to turn on the days the user checked, turn
off the days he unchecked, and leave on or off the days he didn't
change.
dic = self.webInput([], ['dayCheckboxes'])
wantedDays = dic['dayCheckboxes'] # The days the user checked.
for day, on in self.getAllValues():
if not on and wantedDays.has_key(day):
self.TurnOn(day)
# ... Set a flag or insert a database record ...
elif on and not wantedDays.has_key(day):
self.TurnOff(day)
# ... Unset a flag or delete a database record ...
'source' allows you to look up the variables from a number of different
sources:
'f' fields (CGI GET/POST parameters)
'c' cookies
's' session variables
'v' 'values', meaning fields or cookies
In many forms, you're dealing only with strings, which is why the
'default' argument is third and the numeric arguments are banished to
the end. But sometimes you want automatic number conversion, so that
you can do numeric comparisions in your templates without having to
write a bunch of conversion/exception handling code. Example:
#silent $webInput(['name', 'height:int'])
$name is $height cm tall.
#if $height >= 300
Wow, you're tall!
#else
Pshaw, you're short.
#end if
dic = self.webInput(['name', 'height:int'])
name = dic[name]
height = dic[height]
write('%s is %s cm tall.' % (name, height))
if height > 300:
write('Wow, you're tall!')
else:
write('Pshaw, you're short.')
To convert a value to a number, suffix ':int' or ':float' to the name.
The method will search first for a 'height:int' variable and then for a
'height' variable. (It will be called 'height' in the final
dictionary.) If a numeric conversion fails, use or raise 'badInt' or
'badFloat'. Missing values work the same way as for strings, except the
default is 'defaultInt' or 'defaultFloat' instead of 'default'.
If a name represents an uploaded file, the entire file will be read into
memory. For more sophistocated file-upload handling, leave that name
out of the list and do your own handling, or wait for
Cheetah.Utils.UploadFileMixin.
This only in a subclass that also inherits from Webware's Servlet or
HTTPServlet. Otherwise you'll get an AttributeError on 'self.request'.
EXCEPTIONS: ValueError if 'source' is not one of the stated characters.
TypeError if a conversion suffix is not ':int' or ':float'.
FUTURE EXPANSION: a future version of this method may allow source
cascading; e.g., 'vs' would look first in 'values' and then in session
variables.
Meta-Data
================================================================================
Author: Mike Orr <[email protected]>
License: This software is released for unlimited distribution under the
terms of the MIT license. See the LICENSE file.
Version: $Revision: 1.186 $
Start Date: 2002/03/17
Last Revision Date: $Date: 2008/03/10 04:48:11 $
"""
src = src.lower()
isCgi = not self._CHEETAH__isControlledByWebKit
if isCgi and src in ('f', 'v'):
global _formUsedByWebInput
if _formUsedByWebInput is None:
_formUsedByWebInput = cgi.FieldStorage()
source, func = 'field', _formUsedByWebInput.getvalue
elif isCgi and src == 'c':
raise RuntimeError("can't get cookies from a CGI script")
elif isCgi and src == 's':
raise RuntimeError("can't get session variables from a CGI script")
elif isCgi and src == 'v':
source, func = 'value', self.request().value
elif isCgi and src == 's':
source, func = 'session', self.request().session().value
elif src == 'f':
source, func = 'field', self.request().field
elif src == 'c':
source, func = 'cookie', self.request().cookie
elif src == 'v':
source, func = 'value', self.request().value
elif src == 's':
source, func = 'session', self.request().session().value
else:
raise TypeError("arg 'src' invalid")
sources = source + 's'
converters = {
'': _Converter('string', None, default, default ),
'int': _Converter('int', int, defaultInt, badInt ),
'float': _Converter('float', float, defaultFloat, badFloat), }
#pprint.pprint(locals()); return {}
dic = {} # Destination.
for name in names:
k, v = _lookup(name, func, False, converters)
dic[k] = v
for name in namesMulti:
k, v = _lookup(name, func, True, converters)
dic[k] = v
# At this point, 'dic' contains all the keys/values we want to keep.
# We could split the method into a superclass
# method for Webware/WebwareExperimental and a subclass for Cheetah.
# The superclass would merely 'return dic'. The subclass would
# 'dic = super(ThisClass, self).webInput(names, namesMulti, ...)'
# and then the code below.
if debug:
print("<PRE>\n" + pprint.pformat(dic) + "\n</PRE>\n\n")
self.searchList().insert(0, dic)
return dic | [
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|
baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/specs/python/specs_lib.py | python | check_keywords | (spec) | Check for common Python keywords in spec.
This function discourages the use of complex constructs
in TensorFlow specs; it doesn't completely prohibit them
(if necessary, we could check the AST).
Args:
spec: spec string
Raises:
ValueError: raised if spec contains a prohibited keyword. | Check for common Python keywords in spec. | [
"Check",
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"common",
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"keywords",
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"."
] | def check_keywords(spec):
"""Check for common Python keywords in spec.
This function discourages the use of complex constructs
in TensorFlow specs; it doesn't completely prohibit them
(if necessary, we could check the AST).
Args:
spec: spec string
Raises:
ValueError: raised if spec contains a prohibited keyword.
"""
spec = re.sub(QUOTED, "", spec)
match = re.search(KEYWORDS, spec)
if match:
raise ValueError("keyword '%s' found in spec" % match.group(1)) | [
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||
ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/ros_comm/roslaunch/src/roslaunch/server.py | python | ROSLaunchChildHandler.shutdown | (self) | return 1, "success", 1 | @return: code, msg, ignore
@rtype: int, str, int | [] | def shutdown(self):
"""
@return: code, msg, ignore
@rtype: int, str, int
"""
self._shutdown("external call")
return 1, "success", 1 | [
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||
tensorflow/deepmath | b5b721f54de1d5d6a02d78f5da5995237f9995f9 | deepmath/deephol/utilities/normalization_lib.py | python | extend_context | (context: List[Callable[[Text], Text]], old_name: Text,
new_name: Text) | return new_context | Extends the stack of variable renamings. | Extends the stack of variable renamings. | [
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"."
] | def extend_context(context: List[Callable[[Text], Text]], old_name: Text,
new_name: Text):
"""Extends the stack of variable renamings."""
new_context = list(context)
new_context.append(lambda s: new_name if s == old_name else s)
return new_context | [
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|
FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Draft/draftutils/init_tools.py | python | get_draft_utility_commands_menu | () | return ["Draft_SetStyle",
"Draft_ApplyStyle",
"Separator",
"Draft_Layer",
"Draft_AddNamedGroup",
"Draft_AddToGroup",
"Draft_SelectGroup",
"Draft_ToggleConstructionMode",
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"Separator",
"Draft_Heal",
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] | def get_draft_utility_commands_menu():
"""Return the utility commands list for the menu."""
return ["Draft_SetStyle",
"Draft_ApplyStyle",
"Separator",
"Draft_Layer",
"Draft_AddNamedGroup",
"Draft_AddToGroup",
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|
wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/logging/__init__.py | python | FileHandler._open | (self) | return stream | Open the current base file with the (original) mode and encoding.
Return the resulting stream. | Open the current base file with the (original) mode and encoding.
Return the resulting stream. | [
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"""
Open the current base file with the (original) mode and encoding.
Return the resulting stream.
"""
if self.encoding is None:
stream = open(self.baseFilename, self.mode)
else:
stream = codecs.open(self.baseFilename, self.mode, self.encoding)
return stream | [
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|
google-ar/WebARonTango | e86965d2cbc652156b480e0fcf77c716745578cd | chromium/src/gpu/command_buffer/build_gles2_cmd_buffer.py | python | Function.WriteGLES2Header | (self, f) | Writes the GLES2 Implemention unit test. | Writes the GLES2 Implemention unit test. | [
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] | def WriteGLES2Header(self, f):
"""Writes the GLES2 Implemention unit test."""
self.type_handler.WriteGLES2Header(self, f) | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/factorization/examples/mnist.py | python | placeholder_inputs | () | return images_placeholder, labels_placeholder | Generate placeholder variables to represent the input tensors.
Returns:
images_placeholder: Images placeholder.
labels_placeholder: Labels placeholder. | Generate placeholder variables to represent the input tensors. | [
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] | def placeholder_inputs():
"""Generate placeholder variables to represent the input tensors.
Returns:
images_placeholder: Images placeholder.
labels_placeholder: Labels placeholder.
"""
images_placeholder = tf.placeholder(tf.float32, shape=(None,
mnist.IMAGE_PIXELS))
labels_placeholder = tf.placeholder(tf.int32, shape=(None))
return images_placeholder, labels_placeholder | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/site-packages/pip/_vendor/six.py | python | _add_doc | (func, doc) | Add documentation to a function. | Add documentation to a function. | [
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"""Add documentation to a function."""
func.__doc__ = doc | [
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||
kevinlin311tw/cvpr16-deepbit | c60fb3233d7d534cfcee9d3ed47d77af437ee32a | scripts/cpp_lint.py | python | _NestingState.Update | (self, filename, clean_lines, linenum, error) | Update nesting state with current line.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found. | Update nesting state with current line. | [
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"""Update nesting state with current line.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
# Update pp_stack first
self.UpdatePreprocessor(line)
# Count parentheses. This is to avoid adding struct arguments to
# the nesting stack.
if self.stack:
inner_block = self.stack[-1]
depth_change = line.count('(') - line.count(')')
inner_block.open_parentheses += depth_change
# Also check if we are starting or ending an inline assembly block.
if inner_block.inline_asm in (_NO_ASM, _END_ASM):
if (depth_change != 0 and
inner_block.open_parentheses == 1 and
_MATCH_ASM.match(line)):
# Enter assembly block
inner_block.inline_asm = _INSIDE_ASM
else:
# Not entering assembly block. If previous line was _END_ASM,
# we will now shift to _NO_ASM state.
inner_block.inline_asm = _NO_ASM
elif (inner_block.inline_asm == _INSIDE_ASM and
inner_block.open_parentheses == 0):
# Exit assembly block
inner_block.inline_asm = _END_ASM
# Consume namespace declaration at the beginning of the line. Do
# this in a loop so that we catch same line declarations like this:
# namespace proto2 { namespace bridge { class MessageSet; } }
while True:
# Match start of namespace. The "\b\s*" below catches namespace
# declarations even if it weren't followed by a whitespace, this
# is so that we don't confuse our namespace checker. The
# missing spaces will be flagged by CheckSpacing.
namespace_decl_match = Match(r'^\s*namespace\b\s*([:\w]+)?(.*)$', line)
if not namespace_decl_match:
break
new_namespace = _NamespaceInfo(namespace_decl_match.group(1), linenum)
self.stack.append(new_namespace)
line = namespace_decl_match.group(2)
if line.find('{') != -1:
new_namespace.seen_open_brace = True
line = line[line.find('{') + 1:]
# Look for a class declaration in whatever is left of the line
# after parsing namespaces. The regexp accounts for decorated classes
# such as in:
# class LOCKABLE API Object {
# };
#
# Templates with class arguments may confuse the parser, for example:
# template <class T
# class Comparator = less<T>,
# class Vector = vector<T> >
# class HeapQueue {
#
# Because this parser has no nesting state about templates, by the
# time it saw "class Comparator", it may think that it's a new class.
# Nested templates have a similar problem:
# template <
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domino-team/openwrt-cc | 8b181297c34d14d3ca521cc9f31430d561dbc688 | package/gli-pub/openwrt-node-packages-master/node/node-v6.9.1/tools/gyp/tools/pretty_vcproj.py | python | FlattenFilter | (node) | return node_list | Returns a list of all the node and sub nodes. | Returns a list of all the node and sub nodes. | [
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DaFuCoding/MTCNN_Caffe | 09c30c3ff391bd9cb6b249c1910afaf147767ab3 | scripts/cpp_lint.py | python | PrintCategories | () | Prints a list of all the error-categories used by error messages.
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tomahawk-player/tomahawk-resolvers | 7f827bbe410ccfdb0446f7d6a91acc2199c9cc8d | archive/spotify/breakpad/third_party/protobuf/protobuf/python/mox.py | python | In.equals | (self, rhs) | return self._key in rhs | Check to see whether key is in rhs.
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esphome/esphome | 40e06c9819f17409615d4f4eec5cfe4dc9a3776d | esphome/cpp_generator.py | python | MockObj.template | (self, *args: SafeExpType) | return MockObj(f"{self.base}{args}") | Apply template parameters to this object. | Apply template parameters to this object. | [
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CRYTEK/CRYENGINE | 232227c59a220cbbd311576f0fbeba7bb53b2a8c | Code/Tools/waf-1.7.13/waflib/Configure.py | python | conf | (f) | return f | Decorator: attach new configuration functions to :py:class:`waflib.Build.BuildContext` and
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|
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/numpy/py3/numpy/core/defchararray.py | python | translate | (a, table, deletechars=None) | For each element in `a`, return a copy of the string where all
characters occurring in the optional argument `deletechars` are
removed, and the remaining characters have been mapped through the
given translation table.
Calls `str.translate` element-wise.
Parameters
----------
a : array-like of str or unicode
table : str of length 256
deletechars : str
Returns
-------
out : ndarray
Output array of str or unicode, depending on input type
See Also
--------
str.translate | For each element in `a`, return a copy of the string where all
characters occurring in the optional argument `deletechars` are
removed, and the remaining characters have been mapped through the
given translation table. | [
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"""
For each element in `a`, return a copy of the string where all
characters occurring in the optional argument `deletechars` are
removed, and the remaining characters have been mapped through the
given translation table.
Calls `str.translate` element-wise.
Parameters
----------
a : array-like of str or unicode
table : str of length 256
deletechars : str
Returns
-------
out : ndarray
Output array of str or unicode, depending on input type
See Also
--------
str.translate
"""
a_arr = numpy.asarray(a)
if issubclass(a_arr.dtype.type, unicode_):
return _vec_string(
a_arr, a_arr.dtype, 'translate', (table,))
else:
return _vec_string(
a_arr, a_arr.dtype, 'translate', [table] + _clean_args(deletechars)) | [
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||
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_core.py | python | SettableHeaderColumn.UnsetAsSortKey | (*args, **kwargs) | return _core_.SettableHeaderColumn_UnsetAsSortKey(*args, **kwargs) | UnsetAsSortKey(self) | UnsetAsSortKey(self) | [
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"""UnsetAsSortKey(self)"""
return _core_.SettableHeaderColumn_UnsetAsSortKey(*args, **kwargs) | [
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|
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_core.py | python | MenuItem.GetMenu | (*args, **kwargs) | return _core_.MenuItem_GetMenu(*args, **kwargs) | GetMenu(self) -> Menu | GetMenu(self) -> Menu | [
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|
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/numpy/py3/numpy/polynomial/legendre.py | python | leggrid3d | (x, y, z, c) | return pu._gridnd(legval, c, x, y, z) | Evaluate a 3-D Legendre series on the Cartesian product of x, y, and z.
This function returns the values:
.. math:: p(a,b,c) = \\sum_{i,j,k} c_{i,j,k} * L_i(a) * L_j(b) * L_k(c)
where the points `(a, b, c)` consist of all triples formed by taking
`a` from `x`, `b` from `y`, and `c` from `z`. The resulting points form
a grid with `x` in the first dimension, `y` in the second, and `z` in
the third.
The parameters `x`, `y`, and `z` are converted to arrays only if they
are tuples or a lists, otherwise they are treated as a scalars. In
either case, either `x`, `y`, and `z` or their elements must support
multiplication and addition both with themselves and with the elements
of `c`.
If `c` has fewer than three dimensions, ones are implicitly appended to
its shape to make it 3-D. The shape of the result will be c.shape[3:] +
x.shape + y.shape + z.shape.
Parameters
----------
x, y, z : array_like, compatible objects
The three dimensional series is evaluated at the points in the
Cartesian product of `x`, `y`, and `z`. If `x`,`y`, or `z` is a
list or tuple, it is first converted to an ndarray, otherwise it is
left unchanged and, if it isn't an ndarray, it is treated as a
scalar.
c : array_like
Array of coefficients ordered so that the coefficients for terms of
degree i,j are contained in ``c[i,j]``. If `c` has dimension
greater than two the remaining indices enumerate multiple sets of
coefficients.
Returns
-------
values : ndarray, compatible object
The values of the two dimensional polynomial at points in the Cartesian
product of `x` and `y`.
See Also
--------
legval, legval2d, leggrid2d, legval3d
Notes
-----
.. versionadded:: 1.7.0 | Evaluate a 3-D Legendre series on the Cartesian product of x, y, and z. | [
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"""
Evaluate a 3-D Legendre series on the Cartesian product of x, y, and z.
This function returns the values:
.. math:: p(a,b,c) = \\sum_{i,j,k} c_{i,j,k} * L_i(a) * L_j(b) * L_k(c)
where the points `(a, b, c)` consist of all triples formed by taking
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Parameters
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x, y, z : array_like, compatible objects
The three dimensional series is evaluated at the points in the
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list or tuple, it is first converted to an ndarray, otherwise it is
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c : array_like
Array of coefficients ordered so that the coefficients for terms of
degree i,j are contained in ``c[i,j]``. If `c` has dimension
greater than two the remaining indices enumerate multiple sets of
coefficients.
Returns
-------
values : ndarray, compatible object
The values of the two dimensional polynomial at points in the Cartesian
product of `x` and `y`.
See Also
--------
legval, legval2d, leggrid2d, legval3d
Notes
-----
.. versionadded:: 1.7.0
"""
return pu._gridnd(legval, c, x, y, z) | [
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|
daijifeng001/caffe-rfcn | 543f8f6a4b7c88256ea1445ae951a12d1ad9cffd | python/caffe/net_spec.py | python | assign_proto | (proto, name, val) | Assign a Python object to a protobuf message, based on the Python
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repeated fields whose values are not lists are converted to single-element
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repeated fields whose values are not lists are converted to single-element
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repeated fields whose values are not lists are converted to single-element
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is_repeated_field = hasattr(getattr(proto, name), 'extend')
if is_repeated_field and not isinstance(val, list):
val = [val]
if isinstance(val, list):
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for item in val:
proto_item = getattr(proto, name).add()
for k, v in six.iteritems(item):
assign_proto(proto_item, k, v)
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getattr(proto, name).extend(val)
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for k, v in six.iteritems(val):
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||
jubatus/jubatus | 1251ce551bac980488a6313728e72b3fe0b79a9f | tools/codestyle/cpplint/cpplint.py | python | ProcessLine | (filename, file_extension,
clean_lines, line, include_state, function_state,
class_state, error, extra_check_functions=[]) | Processes a single line in the file.
Args:
filename: Filename of the file that is being processed.
file_extension: The extension (dot not included) of the file.
clean_lines: An array of strings, each representing a line of the file,
with comments stripped.
line: Number of line being processed.
include_state: An _IncludeState instance in which the headers are inserted.
function_state: A _FunctionState instance which counts function lines, etc.
class_state: A _ClassState instance which maintains information about
the current stack of nested class declarations being parsed.
error: A callable to which errors are reported, which takes 4 arguments:
filename, line number, error level, and message
extra_check_functions: An array of additional check functions that will be
run on each source line. Each function takes 4
arguments: filename, clean_lines, line, error | Processes a single line in the file. | [
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clean_lines, line, include_state, function_state,
class_state, error, extra_check_functions=[]):
"""Processes a single line in the file.
Args:
filename: Filename of the file that is being processed.
file_extension: The extension (dot not included) of the file.
clean_lines: An array of strings, each representing a line of the file,
with comments stripped.
line: Number of line being processed.
include_state: An _IncludeState instance in which the headers are inserted.
function_state: A _FunctionState instance which counts function lines, etc.
class_state: A _ClassState instance which maintains information about
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error: A callable to which errors are reported, which takes 4 arguments:
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"""
raw_lines = clean_lines.raw_lines
ParseNolintSuppressions(filename, raw_lines[line], line, error)
CheckForFunctionLengths(filename, clean_lines, line, function_state, error)
CheckForMultilineCommentsAndStrings(filename, clean_lines, line, error)
CheckStyle(filename, clean_lines, line, file_extension, class_state, error)
CheckLanguage(filename, clean_lines, line, file_extension, include_state,
error)
CheckForNonStandardConstructs(filename, clean_lines, line,
class_state, error)
CheckPosixThreading(filename, clean_lines, line, error)
CheckInvalidIncrement(filename, clean_lines, line, error)
CheckMakePairUsesDeduction(filename, clean_lines, line, error)
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||
lukasmonk/lucaschess | 13e2e5cb13b38a720ccf897af649054a64bcb914 | Code/QT/Grid.py | python | Grid.mouseCabecera | (self, numColumna) | Se gestiona este evento, ante la posibilidad de que la ventana quiera controlar,
los doble clicks sobre la cabecera , normalmente para cambiar el orden de la columna,
llamando a la rutina correspondiente si existe (gridDobleClickCabecera) y con el
argumento del objeto columna | Se gestiona este evento, ante la posibilidad de que la ventana quiera controlar,
los doble clicks sobre la cabecera , normalmente para cambiar el orden de la columna,
llamando a la rutina correspondiente si existe (gridDobleClickCabecera) y con el
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if hasattr(self.wParent, "gridMouseCabecera"):
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thalium/icebox | 99d147d5b9269222225443ce171b4fd46d8985d4 | third_party/retdec-3.2/scripts/type_extractor/type_extractor/io.py | python | load_json_file | (json_file) | Loads the data from the given json file, returns them as dict. | Loads the data from the given json file, returns them as dict. | [
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with open(json_file, 'r') as j_file:
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] | https://github.com/thalium/icebox/blob/99d147d5b9269222225443ce171b4fd46d8985d4/third_party/retdec-3.2/scripts/type_extractor/type_extractor/io.py#L22-L25 |
||
cms-sw/cmssw | fd9de012d503d3405420bcbeec0ec879baa57cf2 | Validation/RecoTrack/python/plotting/ntupleDataFormat.py | python | _TrackingParticleMatchAdaptor.bestMatchingTrackingParticleFromFirstHitShareFrac | (self) | return self.bestFromFirstHitSimTrkShareFrac() | Fraction of shared hits with reco hits as denominator for best-matching TrackingParticle starting from the first hit of a track. | Fraction of shared hits with reco hits as denominator for best-matching TrackingParticle starting from the first hit of a track. | [
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] | def bestMatchingTrackingParticleFromFirstHitShareFrac(self):
"""Fraction of shared hits with reco hits as denominator for best-matching TrackingParticle starting from the first hit of a track."""
return self.bestFromFirstHitSimTrkShareFrac() | [
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