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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py2/scipy/stats/stats.py | python | _iqr_nanpercentile | (x, q, axis=None, interpolation='linear', keepdims=False,
contains_nan=False) | return result | Private wrapper that works around the following:
1. A bug in `np.nanpercentile` that was around until numpy version
1.11.0.
2. A bug in `np.percentile` NaN handling that was fixed in numpy
version 1.10.0.
3. The non-existence of `np.nanpercentile` before numpy version
1.9.0.
While this function is pretty much necessary for the moment, it
should be removed as soon as the minimum supported numpy version
allows. | Private wrapper that works around the following: | [
"Private",
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"that",
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"following",
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] | def _iqr_nanpercentile(x, q, axis=None, interpolation='linear', keepdims=False,
contains_nan=False):
"""
Private wrapper that works around the following:
1. A bug in `np.nanpercentile` that was around until numpy version
1.11.0.
2. A bug in `np.percentile` NaN handling that was fixed in numpy
version 1.10.0.
3. The non-existence of `np.nanpercentile` before numpy version
1.9.0.
While this function is pretty much necessary for the moment, it
should be removed as soon as the minimum supported numpy version
allows.
"""
if hasattr(np, 'nanpercentile'):
# At time or writing, this means np.__version__ < 1.9.0
result = np.nanpercentile(x, q, axis=axis,
interpolation=interpolation,
keepdims=keepdims)
# If non-scalar result and nanpercentile does not do proper axis roll.
# I see no way of avoiding the version test since dimensions may just
# happen to match in the data.
if result.ndim > 1 and NumpyVersion(np.__version__) < '1.11.0a':
axis = np.asarray(axis)
if axis.size == 1:
# If only one axis specified, reduction happens along that dimension
if axis.ndim == 0:
axis = axis[None]
result = np.rollaxis(result, axis[0])
else:
# If multiple axes, reduced dimeision is last
result = np.rollaxis(result, -1)
else:
msg = "Keyword nan_policy='omit' not correctly supported for numpy " \
"versions < 1.9.x. The default behavior of numpy.percentile " \
"will be used."
warnings.warn(msg, RuntimeWarning)
result = _iqr_percentile(x, q, axis=axis)
return result | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numba/dispatcher.py | python | _DispatcherBase._compile_for_args | (self, *args, **kws) | For internal use. Compile a specialized version of the function
for the given *args* and *kws*, and return the resulting callable. | For internal use. Compile a specialized version of the function
for the given *args* and *kws*, and return the resulting callable. | [
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"""
For internal use. Compile a specialized version of the function
for the given *args* and *kws*, and return the resulting callable.
"""
assert not kws
def error_rewrite(e, issue_type):
"""
Rewrite and raise Exception `e` with help supplied based on the
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"""
if config.SHOW_HELP:
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e.patch_message('\n'.join((str(e).rstrip(), help_msg)))
if config.FULL_TRACEBACKS:
raise e
else:
reraise(type(e), e, None)
argtypes = []
for a in args:
if isinstance(a, OmittedArg):
argtypes.append(types.Omitted(a.value))
else:
argtypes.append(self.typeof_pyval(a))
try:
return self.compile(tuple(argtypes))
except errors.ForceLiteralArg as e:
# Received request for compiler re-entry with the list of arguments
# indicated by e.requested_args.
# First, check if any of these args are already Literal-ized
already_lit_pos = [i for i in e.requested_args
if isinstance(args[i], types.Literal)]
if already_lit_pos:
# Abort compilation if any argument is already a Literal.
# Letting this continue will cause infinite compilation loop.
m = ("Repeated literal typing request.\n"
"{}.\n"
"This is likely caused by an error in typing. "
"Please see nested and suppressed exceptions.")
info = ', '.join('Arg #{} is {}'.format(i, args[i])
for i in sorted(already_lit_pos))
raise errors.CompilerError(m.format(info))
# Convert requested arguments into a Literal.
args = [(types.literal
if i in e.requested_args
else lambda x: x)(args[i])
for i, v in enumerate(args)]
# Re-enter compilation with the Literal-ized arguments
return self._compile_for_args(*args)
except errors.TypingError as e:
# Intercept typing error that may be due to an argument
# that failed inferencing as a Numba type
failed_args = []
for i, arg in enumerate(args):
val = arg.value if isinstance(arg, OmittedArg) else arg
try:
tp = typeof(val, Purpose.argument)
except ValueError as typeof_exc:
failed_args.append((i, str(typeof_exc)))
else:
if tp is None:
failed_args.append(
(i,
"cannot determine Numba type of value %r" % (val,)))
if failed_args:
# Patch error message to ease debugging
msg = str(e).rstrip() + (
"\n\nThis error may have been caused by the following argument(s):\n%s\n"
% "\n".join("- argument %d: %s" % (i, err)
for i, err in failed_args))
e.patch_message(msg)
error_rewrite(e, 'typing')
except errors.UnsupportedError as e:
# Something unsupported is present in the user code, add help info
error_rewrite(e, 'unsupported_error')
except (errors.NotDefinedError, errors.RedefinedError,
errors.VerificationError) as e:
# These errors are probably from an issue with either the code supplied
# being syntactically or otherwise invalid
error_rewrite(e, 'interpreter')
except errors.ConstantInferenceError as e:
# this is from trying to infer something as constant when it isn't
# or isn't supported as a constant
error_rewrite(e, 'constant_inference')
except Exception as e:
if config.SHOW_HELP:
if hasattr(e, 'patch_message'):
help_msg = errors.error_extras['reportable']
e.patch_message('\n'.join((str(e).rstrip(), help_msg)))
# ignore the FULL_TRACEBACKS config, this needs reporting!
raise e | [
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] | https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numba/dispatcher.py#L326-L420 | ||
arangodb/arangodb | 0d658689c7d1b721b314fa3ca27d38303e1570c8 | 3rdParty/rocksdb/6.29/tools/block_cache_analyzer/block_cache_pysim.py | python | PQTable.pqinsert | (self, entry) | return removed_entry | Add a new key or update the priority of an existing key | Add a new key or update the priority of an existing key | [
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"Add a new key or update the priority of an existing key"
# Remove the entry from the table first.
removed_entry = self.table.pop(entry.key, None)
if removed_entry:
# Mark as removed since there is no 'remove' API in heappq.
# Instead, an entry in pq is removed lazily when calling pop.
removed_entry.is_removed = True
self.table[entry.key] = entry
heapq.heappush(self.pq, entry)
return removed_entry | [
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cvxpy/cvxpy | 5165b4fb750dfd237de8659383ef24b4b2e33aaf | cvxpy/atoms/affine/unary_operators.py | python | NegExpression.graph_implementation | (
self, arg_objs, shape: Tuple[int, ...], data=None
) | return (lu.neg_expr(arg_objs[0]), []) | Negate the affine objective.
Parameters
----------
arg_objs : list
LinExpr for each argument.
shape : tuple
The shape of the resulting expression.
data :
Additional data required by the atom.
Returns
-------
tuple
(LinOp for objective, list of constraints) | Negate the affine objective. | [
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] | def graph_implementation(
self, arg_objs, shape: Tuple[int, ...], data=None
) -> Tuple[lo.LinOp, List[Constraint]]:
"""Negate the affine objective.
Parameters
----------
arg_objs : list
LinExpr for each argument.
shape : tuple
The shape of the resulting expression.
data :
Additional data required by the atom.
Returns
-------
tuple
(LinOp for objective, list of constraints)
"""
return (lu.neg_expr(arg_objs[0]), []) | [
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FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Path/PathScripts/PathUtils.py | python | findToolController | (obj, proxy, name=None) | return tc | returns a tool controller with a given name.
If no name is specified, returns the first controller.
if no controller is found, returns None | returns a tool controller with a given name.
If no name is specified, returns the first controller.
if no controller is found, returns None | [
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"""returns a tool controller with a given name.
If no name is specified, returns the first controller.
if no controller is found, returns None"""
PathLog.track("name: {}".format(name))
c = None
if UserInput:
c = UserInput.selectedToolController()
if c is not None:
return c
controllers = getToolControllers(obj, proxy)
if len(controllers) == 0:
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# If there's only one in the job, use it.
if len(controllers) == 1:
if name is None or name == controllers[0].Label:
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else:
tc = None
elif name is not None: # More than one, make the user choose.
tc = [i for i in controllers if i.Label == name][0]
elif UserInput:
tc = UserInput.chooseToolController(controllers)
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/numpy/py2/numpy/lib/nanfunctions.py | python | _nanquantile_1d | (arr1d, q, overwrite_input=False, interpolation='linear') | return function_base._quantile_unchecked(
arr1d, q, overwrite_input=overwrite_input, interpolation=interpolation) | Private function for rank 1 arrays. Compute quantile ignoring NaNs.
See nanpercentile for parameter usage | Private function for rank 1 arrays. Compute quantile ignoring NaNs.
See nanpercentile for parameter usage | [
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] | def _nanquantile_1d(arr1d, q, overwrite_input=False, interpolation='linear'):
"""
Private function for rank 1 arrays. Compute quantile ignoring NaNs.
See nanpercentile for parameter usage
"""
arr1d, overwrite_input = _remove_nan_1d(arr1d,
overwrite_input=overwrite_input)
if arr1d.size == 0:
return np.full(q.shape, np.nan)[()] # convert to scalar
return function_base._quantile_unchecked(
arr1d, q, overwrite_input=overwrite_input, interpolation=interpolation) | [
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gimli-org/gimli | 17aa2160de9b15ababd9ef99e89b1bc3277bbb23 | pygimli/utils/cache.py | python | CacheManager.cache | (self, funct, *args, **kwargs) | return cached | Create a unique cache | Create a unique cache | [
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] | def cache(self, funct, *args, **kwargs):
""" Create a unique cache """
hashVal = self.hash(funct, *args, **kwargs)
cached = Cache(hashVal)
cached.info['codeinfo'] = self.functInfo(funct)
cached.info['version'] = pg.versionStr()
cached.info['args'] = str(args)
cached.info['kwargs'] = str(kwargs)
return cached | [
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natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/python/ops/nn_grad.py | python | _BiasAddGradV1 | (unused_bias_op, received_grad) | return (received_grad, math_ops.reduce_sum(received_grad,
reduction_dim_tensor)) | Return the gradients for the 2 inputs of bias_op.
The first input of unused_bias_op is the tensor t, and its gradient is
just the gradient the unused_bias_op received.
The second input of unused_bias_op is the bias vector which has one fewer
dimension than "received_grad" (the batch dimension.) Its gradient is the
received gradient Summed on the batch dimension, which is the first dimension.
Args:
unused_bias_op: The BiasOp for which we need to generate gradients.
received_grad: Tensor. The gradients passed to the BiasOp.
Returns:
Two tensors, the first one for the "tensor" input of the BiasOp,
the second one for the "bias" input of the BiasOp. | Return the gradients for the 2 inputs of bias_op. | [
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] | def _BiasAddGradV1(unused_bias_op, received_grad):
"""Return the gradients for the 2 inputs of bias_op.
The first input of unused_bias_op is the tensor t, and its gradient is
just the gradient the unused_bias_op received.
The second input of unused_bias_op is the bias vector which has one fewer
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received gradient Summed on the batch dimension, which is the first dimension.
Args:
unused_bias_op: The BiasOp for which we need to generate gradients.
received_grad: Tensor. The gradients passed to the BiasOp.
Returns:
Two tensors, the first one for the "tensor" input of the BiasOp,
the second one for the "bias" input of the BiasOp.
"""
reduction_dim_tensor = math_ops.range(array_ops.rank(received_grad) - 1)
return (received_grad, math_ops.reduce_sum(received_grad,
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hpi-xnor/BMXNet-v2 | af2b1859eafc5c721b1397cef02f946aaf2ce20d | python/mxnet/ndarray/ndarray.py | python | greater | (lhs, rhs) | return _ufunc_helper(
lhs,
rhs,
op.broadcast_greater,
lambda x, y: 1 if x > y else 0,
_internal._greater_scalar,
_internal._lesser_scalar) | Returns the result of element-wise **greater than** (>) comparison operation
with broadcasting.
For each element in input arrays, return 1(true) if lhs elements are greater than rhs,
otherwise return 0(false).
Equivalent to ``lhs > rhs`` and ``mx.nd.broadcast_greater(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (x > 1).asnumpy()
array([[ 0., 0., 0.],
[ 0., 0., 0.]], dtype=float32)
>>> (x > y).asnumpy()
array([[ 1., 1., 1.],
[ 0., 0., 0.]], dtype=float32)
>>> mx.nd.greater(x, y).asnumpy()
array([[ 1., 1., 1.],
[ 0., 0., 0.]], dtype=float32)
>>> (z > y).asnumpy()
array([[ 0., 1.],
[ 0., 0.]], dtype=float32) | Returns the result of element-wise **greater than** (>) comparison operation
with broadcasting. | [
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"""Returns the result of element-wise **greater than** (>) comparison operation
with broadcasting.
For each element in input arrays, return 1(true) if lhs elements are greater than rhs,
otherwise return 0(false).
Equivalent to ``lhs > rhs`` and ``mx.nd.broadcast_greater(lhs, rhs)``.
.. note::
If the corresponding dimensions of two arrays have the same size or one of them has size 1,
then the arrays are broadcastable to a common shape.
Parameters
----------
lhs : scalar or mxnet.ndarray.array
First array to be compared.
rhs : scalar or mxnet.ndarray.array
Second array to be compared. If ``lhs.shape != rhs.shape``, they must be
broadcastable to a common shape.
Returns
-------
NDArray
Output array of boolean values.
Examples
--------
>>> x = mx.nd.ones((2,3))
>>> y = mx.nd.arange(2).reshape((2,1))
>>> z = mx.nd.arange(2).reshape((1,2))
>>> x.asnumpy()
array([[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=float32)
>>> y.asnumpy()
array([[ 0.],
[ 1.]], dtype=float32)
>>> z.asnumpy()
array([[ 0., 1.]], dtype=float32)
>>> (x > 1).asnumpy()
array([[ 0., 0., 0.],
[ 0., 0., 0.]], dtype=float32)
>>> (x > y).asnumpy()
array([[ 1., 1., 1.],
[ 0., 0., 0.]], dtype=float32)
>>> mx.nd.greater(x, y).asnumpy()
array([[ 1., 1., 1.],
[ 0., 0., 0.]], dtype=float32)
>>> (z > y).asnumpy()
array([[ 0., 1.],
[ 0., 0.]], dtype=float32)
"""
# pylint: disable= no-member, protected-access
return _ufunc_helper(
lhs,
rhs,
op.broadcast_greater,
lambda x, y: 1 if x > y else 0,
_internal._greater_scalar,
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/py/sliceshell.py | python | SlicesShell.OnCallTipAutoCompleteManually | (self, shiftDown) | AutoComplete and Calltips manually. | AutoComplete and Calltips manually. | [
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] | def OnCallTipAutoCompleteManually (self, shiftDown):
"""AutoComplete and Calltips manually."""
if self.AutoCompActive():
self.AutoCompCancel()
currpos = self.GetCurrentPos()
stoppos = self.PositionFromLine(self.GetIOSlice()[0])
cpos = currpos
#go back until '.' is found
pointavailpos = -1
while cpos >= stoppos:
if self.GetCharAt(cpos) == ord ('.'):
pointavailpos = cpos
break
cpos -= 1
#word from non whitespace until '.'
if pointavailpos != -1:
#look backward for first whitespace char
textbehind = self.GetTextRange (pointavailpos + 1, currpos)
pointavailpos += 1
if not shiftDown:
#call AutoComplete
stoppos = self.PositionFromLine(self.GetIOSlice()[0])
textbefore = self.GetTextRange(stoppos, pointavailpos)
self.autoCompleteShow(textbefore, len (textbehind))
else:
#call CallTips
cpos = pointavailpos
begpos = -1
while cpos > stoppos:
if chr(self.GetCharAt(cpos)).isspace():
begpos = cpos
break
cpos -= 1
if begpos == -1:
begpos = cpos
ctips = self.GetTextRange (begpos, currpos)
ctindex = ctips.find ('(')
if ctindex != -1 and not self.CallTipActive():
#insert calltip, if current pos is '(', otherwise show it only
self.autoCallTipShow( ctips[:ctindex + 1],
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/propgrid.py | python | PGProperty.GetColumnEditor | (*args, **kwargs) | return _propgrid.PGProperty_GetColumnEditor(*args, **kwargs) | GetColumnEditor(self, int column) -> PGEditor | GetColumnEditor(self, int column) -> PGEditor | [
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"""GetColumnEditor(self, int column) -> PGEditor"""
return _propgrid.PGProperty_GetColumnEditor(*args, **kwargs) | [
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nasa/fprime | 595cf3682d8365943d86c1a6fe7c78f0a116acf0 | Autocoders/Python/src/fprime_ac/generators/ChannelHeader.py | python | ChannelHeader.__call__ | (self, args) | Main execution point.
Calls the accept method on each visitor to generate the code. | Main execution point.
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Main execution point.
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# Note that name handling for params goes
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self.__obj = args
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kismetwireless/kismet | a7c0dc270c960fb1f58bd9cec4601c201885fd4e | capture_sdr_rtl433/KismetCaptureRtl433/kismetexternal/__init__.py | python | Datasource.send_datasource_data_report | (self, message=None, warning=None, full_gps=None, full_signal=None, full_packet=None,
full_spectrum=None, full_json=None, full_buffer=None, **kwargs) | When operating as a Kismet datasource, send a data frame
:param message: Optional message
:param warning: Optional warning to be included in the datasource details
:param full_gps: Optional full datasource_pb2.SubGps record
:param full_signal: Optional full datasource_pb2.SubSignal record
:param full_spectrum: Optional full datasource_pb2 SubSpectrum record
:param full_packet: Optional full datasource_pb2.SubPacket record
:param full_json: Optional JSON record
:param full_buffer: Optional protobuf packed buffer
:return: None | When operating as a Kismet datasource, send a data frame | [
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full_spectrum=None, full_json=None, full_buffer=None, **kwargs):
"""
When operating as a Kismet datasource, send a data frame
:param message: Optional message
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:param full_gps: Optional full datasource_pb2.SubGps record
:param full_signal: Optional full datasource_pb2.SubSignal record
:param full_spectrum: Optional full datasource_pb2 SubSpectrum record
:param full_packet: Optional full datasource_pb2.SubPacket record
:param full_json: Optional JSON record
:param full_buffer: Optional protobuf packed buffer
:return: None
"""
report = datasource_pb2.DataReport()
if message is not None:
report.message.msgtext = message
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deepmodeling/deepmd-kit | 159e45d248b0429844fb6a8cb3b3a201987c8d79 | deepmd/utils/path.py | python | DPH5Path._load_h5py | (cls, path: str) | return h5py.File(path, 'r') | Load hdf5 file.
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/py_compile.py | python | wr_long | (f, x) | Internal; write a 32-bit int to a file in little-endian order. | Internal; write a 32-bit int to a file in little-endian order. | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/tools/saved_model_cli.py | python | get_signature_def_map | (saved_model_dir, tag_set) | return meta_graph.signature_def | Gets SignatureDef map from a MetaGraphDef in a SavedModel.
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directory.
Args:
saved_model_dir: Directory containing the SavedModel to inspect or execute.
tag_set: Group of tag(s) of the MetaGraphDef with the SignatureDef map, in
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meta_graph = saved_model_utils.get_meta_graph_def(saved_model_dir, tag_set)
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apache/impala | 8ddac48f3428c86f2cbd037ced89cfb903298b12 | bin/collect_minidumps.py | python | FileArchiver.make_tarball | (self) | Make a tarball with the maximum number of files such that the size of the tarball
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return status, msg.format(self.source_dir, self.resulting_sizes[1])
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self._tar_files(max_num_files)
status = 0
msg = 'Success. Archived {0} out of {1} files in "{2}".'
return status, msg.format(max_num_files, len(self.file_list), self.source_dir) | [
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eventql/eventql | 7ca0dbb2e683b525620ea30dc40540a22d5eb227 | deps/3rdparty/spidermonkey/mozjs/config/configobj.py | python | ConfigObj._parse | (self, infile) | Actually parse the config file. | Actually parse the config file. | [
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"""Actually parse the config file."""
temp_list_values = self.list_values
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comment_list = []
done_start = False
this_section = self
maxline = len(infile) - 1
cur_index = -1
reset_comment = False
while cur_index < maxline:
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comment_list = []
cur_index += 1
line = infile[cur_index]
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# do we have anything on the line ?
if not sline or sline.startswith('#') or sline.startswith(';'):
reset_comment = False
comment_list.append(line)
continue
if not done_start:
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comment_list = []
done_start = True
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#
if cur_depth < this_section.depth:
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try:
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msg = 'Parse error in value at line %s.'
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continue
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try:
value = unrepr(value)
except Exception, e:
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msg = 'Parse error in value at line %s.'
self._handle_error(msg, UnreprError, infile,
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continue
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try:
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#
key = self._unquote(key)
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RamadhanAmizudin/malware | 2c6c53c8b0d556f5d8078d6ca0fc4448f4697cf1 | Fuzzbunch/fuzzbunch/pyreadline/modes/notemacs.py | python | NotEmacsMode.copy_forward_word | (self, e) | Copy the word following point to the kill buffer. The word
boundaries are the same as forward-word. By default, this command is
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kushview/Element | 1cc16380caa2ab79461246ba758b9de1f46db2a5 | waflib/TaskGen.py | python | task_gen.clone | (self, env) | return newobj | Makes a copy of a task generator. Once the copy is made, it is necessary to ensure that the
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chuckcho/video-caffe | fc232b3e3a90ea22dd041b9fc5c542f170581f20 | tools/extra/summarize.py | python | print_table | (table, max_width) | Print a simple nicely-aligned table.
table must be a list of (equal-length) lists. Columns are space-separated,
and as narrow as possible, but no wider than max_width. Text may overflow
columns; note that unlike string.format, this will not affect subsequent
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"""Print a simple nicely-aligned table.
table must be a list of (equal-length) lists. Columns are space-separated,
and as narrow as possible, but no wider than max_width. Text may overflow
columns; note that unlike string.format, this will not affect subsequent
columns, if possible."""
max_widths = [max_width] * len(table[0])
column_widths = [max(printed_len(row[j]) + 1 for row in table)
for j in range(len(table[0]))]
column_widths = [min(w, max_w) for w, max_w in zip(column_widths, max_widths)]
for row in table:
row_str = ''
right_col = 0
for cell, width in zip(row, column_widths):
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row_str += cell + ' '
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PixarAnimationStudios/USD | faed18ce62c8736b02413635b584a2f637156bad | pxr/usdImaging/usdviewq/appController.py | python | AppController._currentPathChanged | (self) | Called when the currentPathWidget text is changed | Called when the currentPathWidget text is changed | [
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"""Called when the currentPathWidget text is changed"""
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pathList = re.split(", ?", newPaths)
pathList = [path for path in pathList if len(path) != 0]
try:
prims = self._getPrimsFromPaths(pathList)
except PrimNotFoundException as ex:
# _getPrimsFromPaths couldn't find one of the prims
sys.stderr.write("ERROR: %s\n" % str(ex))
self._updatePrimPathText()
return
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if len(prims) == 1 and not explicitProps:
self._dataModel.selection.switchToPrimPath(prims[0].GetPath())
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self._dataModel.selection.clearPrims()
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self._dataModel.selection.clearProps()
for path, prim in zip(pathList, prims):
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if sdfPath.IsPropertyPath():
self._dataModel.selection.addPropPath(path)
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netket/netket | 0d534e54ecbf25b677ea72af6b85947979420652 | netket/experimental/driver/tdvp.py | python | qgt_norm | (driver: TDVP, x: PyTree) | return jnp.sqrt(jnp.real(xc_dot_y)) | Computes the norm induced by the QGT :math:`S`, i.e, :math:`x^\\dagger S x`. | Computes the norm induced by the QGT :math:`S`, i.e, :math:`x^\\dagger S x`. | [
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Computes the norm induced by the QGT :math:`S`, i.e, :math:`x^\\dagger S x`.
"""
y = driver._last_qgt @ x # pylint: disable=protected-access
xc_dot_y = nk.jax.tree_dot(nk.jax.tree_conj(x), y)
return jnp.sqrt(jnp.real(xc_dot_y)) | [
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gklz1982/caffe-yolov2 | ebb27029db4ddc0d40e520634633b0fa9cdcc10d | scripts/cpp_lint.py | python | ReverseCloseExpression | (clean_lines, linenum, pos) | return (line, 0, -1) | If input points to ) or } or ] or >, finds the position that opens it.
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"""If input points to ) or } or ] or >, finds the position that opens it.
If lines[linenum][pos] points to a ')' or '}' or ']' or '>', finds the
linenum/pos that correspond to the opening of the expression.
Args:
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
pos: A position on the line.
Returns:
A tuple (line, linenum, pos) pointer *at* the opening brace, or
(line, 0, -1) if we never find the matching opening brace. Note
we ignore strings and comments when matching; and the line we
return is the 'cleansed' line at linenum.
"""
line = clean_lines.elided[linenum]
endchar = line[pos]
if endchar not in ')}]>':
return (line, 0, -1)
if endchar == ')': startchar = '('
if endchar == ']': startchar = '['
if endchar == '}': startchar = '{'
if endchar == '>': startchar = '<'
# Check last line
(start_pos, num_open) = FindStartOfExpressionInLine(
line, pos, 0, startchar, endchar)
if start_pos > -1:
return (line, linenum, start_pos)
# Continue scanning backward
while linenum > 0:
linenum -= 1
line = clean_lines.elided[linenum]
(start_pos, num_open) = FindStartOfExpressionInLine(
line, len(line) - 1, num_open, startchar, endchar)
if start_pos > -1:
return (line, linenum, start_pos)
# Did not find startchar before beginning of file, give up
return (line, 0, -1) | [
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echronos/echronos | c996f1d2c8af6c6536205eb319c1bf1d4d84569c | external_tools/ply_info/example/ansic/cparse.py | python | p_translation_unit_2 | (t) | translation_unit : translation_unit external_declaration | translation_unit : translation_unit external_declaration | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/pip/_vendor/distlib/locators.py | python | Locator._get_project | (self, name) | For a given project, get a dictionary mapping available versions to Distribution
instances.
This should be implemented in subclasses.
If called from a locate() request, self.matcher will be set to a
matcher for the requirement to satisfy, otherwise it will be None. | [] | def _get_project(self, name):
"""
For a given project, get a dictionary mapping available versions to Distribution
instances.
This should be implemented in subclasses.
If called from a locate() request, self.matcher will be set to a
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/customtreectrl.py | python | GenericTreeItem.GetCheckedImage | (self, which=TreeItemIcon_Checked) | return self._checkedimages[which] | Returns the item check image.
:param integer `which`: can be one of the following bits:
================================= ========================
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``TreeItemIcon_Checked`` To get the checkbox checked item image
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``TreeItemIcon_NotFlagged`` To get the radiobutton unchecked image
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Returns the item check image.
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================================= ========================
Item State Description
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``TreeItemIcon_Checked`` To get the checkbox checked item image
``TreeItemIcon_NotChecked`` To get the checkbox unchecked item image
``TreeItemIcon_Undetermined`` To get the checkbox undetermined state item image
``TreeItemIcon_Flagged`` To get the radiobutton checked image
``TreeItemIcon_NotFlagged`` To get the radiobutton unchecked image
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/logging/__init__.py | python | Filterer.__init__ | (self) | Initialize the list of filters to be an empty list. | Initialize the list of filters to be an empty list. | [
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apple/swift-lldb | d74be846ef3e62de946df343e8c234bde93a8912 | scripts/Python/static-binding/lldb.py | python | SBDebugger.SetScriptLanguage | (self, script_lang) | return _lldb.SBDebugger_SetScriptLanguage(self, script_lang) | SetScriptLanguage(SBDebugger self, lldb::ScriptLanguage script_lang) | SetScriptLanguage(SBDebugger self, lldb::ScriptLanguage script_lang) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | tools/auto_bisect/bisect_perf_regression.py | python | RemoveDirectoryTree | (path_to_dir) | Removes a directory tree. Returns True if successful or False otherwise. | Removes a directory tree. Returns True if successful or False otherwise. | [
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logging.info('REMOVING FILE %s' % path_to_dir)
os.remove(path_to_dir)
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if os.path.exists(path_to_dir):
shutil.rmtree(path_to_dir)
except OSError, e:
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Polidea/SiriusObfuscator | b0e590d8130e97856afe578869b83a209e2b19be | SymbolExtractorAndRenamer/lldb/scripts/Python/static-binding/lldb.py | python | SBValue.GetPreferSyntheticValue | (self) | return _lldb.SBValue_GetPreferSyntheticValue(self) | GetPreferSyntheticValue(self) -> bool | GetPreferSyntheticValue(self) -> bool | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/shutil.py | python | chown | (path, user=None, group=None) | Change owner user and group of the given path.
user and group can be the uid/gid or the user/group names, and in that case,
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"""Change owner user and group of the given path.
user and group can be the uid/gid or the user/group names, and in that case,
they are converted to their respective uid/gid.
"""
if user is None and group is None:
raise ValueError("user and/or group must be set")
_user = user
_group = group
# -1 means don't change it
if user is None:
_user = -1
# user can either be an int (the uid) or a string (the system username)
elif isinstance(user, str):
_user = _get_uid(user)
if _user is None:
raise LookupError("no such user: {!r}".format(user))
if group is None:
_group = -1
elif not isinstance(group, int):
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if _group is None:
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_gdi.py | python | GraphicsPath.AddArcToPoint | (*args, **kwargs) | return _gdi_.GraphicsPath_AddArcToPoint(*args, **kwargs) | AddArcToPoint(self, Double x1, Double y1, Double x2, Double y2, Double r)
Appends an arc to two tangents connecting (current) to (x1,y1) and
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seqan/seqan | f5f658343c366c9c3d44ba358ffc9317e78a09ed | util/py_lib/seqan/dox/inc_mgr.py | python | IncludeManager._loadSnippets | (self, path) | Load snippet
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aimerykong/Low-Rank-Bilinear-Pooling | 487eb2c857fd9c95357a5166b0c15ad0fe135b28 | caffe-20160312/python/caffe/io.py | python | Transformer.set_channel_swap | (self, in_, order) | Set the input channel order for e.g. RGB to BGR conversion
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N.B. this assumes the channels are the first dimension AFTER transpose.
Parameters
----------
in_ : which input to assign this channel order
order : the order to take the channels.
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miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/tensorflow/models/embedding/word2vec.py | python | Word2Vec.forward | (self, examples, labels) | return true_logits, sampled_logits | Build the graph for the forward pass. | Build the graph for the forward pass. | [
"Build",
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"pass",
"."
] | def forward(self, examples, labels):
"""Build the graph for the forward pass."""
opts = self._options
# Declare all variables we need.
# Embedding: [vocab_size, emb_dim]
init_width = 0.5 / opts.emb_dim
emb = tf.Variable(
tf.random_uniform(
[opts.vocab_size, opts.emb_dim], -init_width, init_width),
name="emb")
self._emb = emb
# Softmax weight: [vocab_size, emb_dim]. Transposed.
sm_w_t = tf.Variable(
tf.zeros([opts.vocab_size, opts.emb_dim]),
name="sm_w_t")
# Softmax bias: [emb_dim].
sm_b = tf.Variable(tf.zeros([opts.vocab_size]), name="sm_b")
# Global step: scalar, i.e., shape [].
self.global_step = tf.Variable(0, name="global_step")
# Nodes to compute the nce loss w/ candidate sampling.
labels_matrix = tf.reshape(
tf.cast(labels,
dtype=tf.int64),
[opts.batch_size, 1])
# Negative sampling.
sampled_ids, _, _ = (tf.nn.fixed_unigram_candidate_sampler(
true_classes=labels_matrix,
num_true=1,
num_sampled=opts.num_samples,
unique=True,
range_max=opts.vocab_size,
distortion=0.75,
unigrams=opts.vocab_counts.tolist()))
# Embeddings for examples: [batch_size, emb_dim]
example_emb = tf.nn.embedding_lookup(emb, examples)
# Weights for labels: [batch_size, emb_dim]
true_w = tf.nn.embedding_lookup(sm_w_t, labels)
# Biases for labels: [batch_size, 1]
true_b = tf.nn.embedding_lookup(sm_b, labels)
# Weights for sampled ids: [num_sampled, emb_dim]
sampled_w = tf.nn.embedding_lookup(sm_w_t, sampled_ids)
# Biases for sampled ids: [num_sampled, 1]
sampled_b = tf.nn.embedding_lookup(sm_b, sampled_ids)
# True logits: [batch_size, 1]
true_logits = tf.reduce_sum(tf.mul(example_emb, true_w), 1) + true_b
# Sampled logits: [batch_size, num_sampled]
# We replicate sampled noise labels for all examples in the batch
# using the matmul.
sampled_b_vec = tf.reshape(sampled_b, [opts.num_samples])
sampled_logits = tf.matmul(example_emb,
sampled_w,
transpose_b=True) + sampled_b_vec
return true_logits, sampled_logits | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | tools/mac/symbolicate_crash.py | python | CrashReport.Symbolicate | (self, symbol_path) | Symbolicates a crash report stack trace. | Symbolicates a crash report stack trace. | [
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"a",
"crash",
"report",
"stack",
"trace",
"."
] | def Symbolicate(self, symbol_path):
"""Symbolicates a crash report stack trace."""
# In order to be efficient, collect all the offsets that will be passed to
# atos by the image name.
offsets_by_image = self._CollectAddressesForImages(SYMBOL_IMAGE_MAP.keys())
# For each image, run atos with the list of addresses.
for image_name, addresses in offsets_by_image.items():
# If this image was not loaded or is in no stacks, skip.
if image_name not in self._binary_images or not len(addresses):
continue
# Combine the |image_name| and |symbol_path| into the path of the dSYM.
dsym_file = self._GetDSymPath(symbol_path, image_name)
# From the list of 2-Tuples of (frame, address), create a list of just
# addresses.
address_list = map(lambda x: x[1], addresses)
# Look up the load address of the image.
binary_base = self._binary_images[image_name][0]
# This returns a list of just symbols. The indices will match up with the
# list of |addresses|.
symbol_names = self._RunAtos(binary_base, dsym_file, address_list)
if not symbol_names:
print 'Error loading symbols for ' + image_name
continue
# Attaches a list of symbol names to stack frames. This assumes that the
# order of |addresses| has stayed the same as |symbol_names|.
self._AddSymbolsToFrames(symbol_names, addresses) | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/python/ops/control_flow_ops.py | python | ControlFlowContext.GetControlPivot | (self) | return None | Returns the pivot node for this context, or None. | Returns the pivot node for this context, or None. | [
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] | def GetControlPivot(self):
"""Returns the pivot node for this context, or None."""
return None | [
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google/syzygy | 8164b24ebde9c5649c9a09e88a7fc0b0fcbd1bc5 | third_party/numpy/files/numpy/core/defchararray.py | python | startswith | (a, prefix, start=0, end=None) | return _vec_string(
a, bool_, 'startswith', [prefix, start] + _clean_args(end)) | Returns a boolean array which is `True` where the string element
in `a` starts with `prefix`, otherwise `False`.
Calls `str.startswith` element-wise.
Parameters
----------
a : array_like of str or unicode
suffix : str
start, end : int, optional
With optional `start`, test beginning at that position. With
optional `end`, stop comparing at that position.
Returns
-------
out : ndarray
Array of booleans
See also
--------
str.startswith | Returns a boolean array which is `True` where the string element
in `a` starts with `prefix`, otherwise `False`. | [
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"""
Returns a boolean array which is `True` where the string element
in `a` starts with `prefix`, otherwise `False`.
Calls `str.startswith` element-wise.
Parameters
----------
a : array_like of str or unicode
suffix : str
start, end : int, optional
With optional `start`, test beginning at that position. With
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Returns
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return _vec_string(
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AcademySoftwareFoundation/OpenColorIO | 73508eb5230374df8d96147a0627c015d359a641 | src/apps/pyociodisplay/pyociodisplay.py | python | ImagePlane.paintGL | (self) | Called whenever a repaint is needed. Calling ``update()`` will
schedule a repaint. | Called whenever a repaint is needed. Calling ``update()`` will
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"""
Called whenever a repaint is needed. Calling ``update()`` will
schedule a repaint.
"""
GL.glClearColor(0.0, 0.0, 0.0, 1.0)
GL.glClear(GL.GL_COLOR_BUFFER_BIT)
if self._shader_program is not None:
GL.glUseProgram(self._shader_program)
self._use_ocio_tex()
self._use_ocio_uniforms()
# Set uniforms
mvp_mat = self._proj_mat * self._model_view_mat
mvp_mat_loc = GL.glGetUniformLocation(
self._shader_program,
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GL.glUniformMatrix4fv(
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image_tex_loc = GL.glGetUniformLocation(
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GL.glUniform1i(image_tex_loc, 0)
# Bind texture, VAO, and draw
GL.glActiveTexture(GL.GL_TEXTURE0 + 0)
GL.glBindTexture(GL.GL_TEXTURE_2D, self._image_tex)
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GL.glDrawElements(
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GL.glBindVertexArray(0) | [
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microsoft/CNTK | e9396480025b9ca457d26b6f33dd07c474c6aa04 | Examples/Image/Detection/utils/od_utils.py | python | filter_results | (regressed_rois, cls_probs, cfg) | return filtered_bboxes, filtered_labels, filtered_scores | Filters the provided results by performing NMS (non maximum suppression)
:param regressed_rois: the predicted bounding boxes
:param cls_probs: class probabilities per bounding box
:param cfg: the configuration
:return:
bboxes - the filtered list of bounding boxes
labels - the single class label per bounding box
scores - the probability for the assigned class label per bounding box | Filters the provided results by performing NMS (non maximum suppression)
:param regressed_rois: the predicted bounding boxes
:param cls_probs: class probabilities per bounding box
:param cfg: the configuration
:return:
bboxes - the filtered list of bounding boxes
labels - the single class label per bounding box
scores - the probability for the assigned class label per bounding box | [
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:param cfg: the configuration
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"""
labels = cls_probs.argmax(axis=1)
scores = cls_probs.max(axis=1)
nmsKeepIndices = apply_nms_to_single_image_results(
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use_gpu_nms=cfg.USE_GPU_NMS,
device_id=cfg.GPU_ID,
nms_threshold=cfg.RESULTS_NMS_THRESHOLD,
conf_threshold=cfg.RESULTS_NMS_CONF_THRESHOLD)
filtered_bboxes = regressed_rois[nmsKeepIndices]
filtered_labels = labels[nmsKeepIndices]
filtered_scores = scores[nmsKeepIndices]
return filtered_bboxes, filtered_labels, filtered_scores | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/prompt-toolkit/py3/prompt_toolkit/key_binding/bindings/named_commands.py | python | emacs_editing_mode | (event: E) | Switch to Emacs editing mode. | Switch to Emacs editing mode. | [
"Switch",
"to",
"Emacs",
"editing",
"mode",
"."
] | def emacs_editing_mode(event: E) -> None:
"""
Switch to Emacs editing mode.
"""
event.app.editing_mode = EditingMode.EMACS | [
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facebook/openr | ed38bdfd6bf290084bfab4821b59f83e7b59315d | openr/py/openr/cli/utils/utils.py | python | adj_dbs_to_dict | (resp, nodes, bidir, iter_func) | return adjs_map | get parsed adjacency db
:param resp kv_store_types.Publication, or decision_types.adjDbs
:param nodes set: the set of the nodes to print prefixes for
:param bidir bool: only dump bidirectional adjacencies
:return map(node, map(adjacency_keys, (adjacency_values)): the parsed
adjacency DB in a map with keys and values in strings | get parsed adjacency db | [
"get",
"parsed",
"adjacency",
"db"
] | def adj_dbs_to_dict(resp, nodes, bidir, iter_func):
"""get parsed adjacency db
:param resp kv_store_types.Publication, or decision_types.adjDbs
:param nodes set: the set of the nodes to print prefixes for
:param bidir bool: only dump bidirectional adjacencies
:return map(node, map(adjacency_keys, (adjacency_values)): the parsed
adjacency DB in a map with keys and values in strings
"""
adj_dbs = resp
if isinstance(adj_dbs, kv_store_types.Publication):
adj_dbs = build_global_adj_db(resp)
def _parse_adj(adjs_map, adj_db):
version = None
if isinstance(adj_db, kv_store_types.Value):
version = adj_db.version
adj_db = deserialize_thrift_object(
adj_db.value, openr_types.AdjacencyDatabase
)
adj_db_to_dict(adjs_map, adj_dbs, adj_db, bidir, version)
adjs_map = {}
iter_func(adjs_map, resp, nodes, _parse_adj)
return adjs_map | [
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SoarGroup/Soar | a1c5e249499137a27da60533c72969eef3b8ab6b | scons/scons-local-4.1.0/SCons/Node/FS.py | python | RootDir._lookup_abs | (self, p, klass, create=1) | return result | Fast (?) lookup of a *normalized* absolute path.
This method is intended for use by internal lookups with
already-normalized path data. For general-purpose lookups,
use the FS.Entry(), FS.Dir() or FS.File() methods.
The caller is responsible for making sure we're passed a
normalized absolute path; we merely let Python's dictionary look
up and return the One True Node.FS object for the path.
If a Node for the specified "p" doesn't already exist, and
"create" is specified, the Node may be created after recursive
invocation to find or create the parent directory or directories. | Fast (?) lookup of a *normalized* absolute path. | [
"Fast",
"(",
"?",
")",
"lookup",
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"a",
"*",
"normalized",
"*",
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"."
] | def _lookup_abs(self, p, klass, create=1):
"""
Fast (?) lookup of a *normalized* absolute path.
This method is intended for use by internal lookups with
already-normalized path data. For general-purpose lookups,
use the FS.Entry(), FS.Dir() or FS.File() methods.
The caller is responsible for making sure we're passed a
normalized absolute path; we merely let Python's dictionary look
up and return the One True Node.FS object for the path.
If a Node for the specified "p" doesn't already exist, and
"create" is specified, the Node may be created after recursive
invocation to find or create the parent directory or directories.
"""
k = _my_normcase(p)
try:
result = self._lookupDict[k]
except KeyError:
if not create:
msg = "No such file or directory: '%s' in '%s' (and create is False)" % (p, str(self))
raise SCons.Errors.UserError(msg)
# There is no Node for this path name, and we're allowed
# to create it.
dir_name, file_name = p.rsplit('/',1)
dir_node = self._lookup_abs(dir_name, Dir)
result = klass(file_name, dir_node, self.fs)
# Double-check on disk (as configured) that the Node we
# created matches whatever is out there in the real world.
result.diskcheck_match()
self._lookupDict[k] = result
dir_node.entries[_my_normcase(file_name)] = result
dir_node.implicit = None
else:
# There is already a Node for this path name. Allow it to
# complain if we were looking for an inappropriate type.
result.must_be_same(klass)
return result | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/boto/boto/ec2/autoscale/group.py | python | AutoScalingGroup.put_notification_configuration | (self, topic, notification_types) | return self.connection.put_notification_configuration(self,
topic,
notification_types) | Configures an Auto Scaling group to send notifications when
specified events take place. Valid notification types are:
'autoscaling:EC2_INSTANCE_LAUNCH',
'autoscaling:EC2_INSTANCE_LAUNCH_ERROR',
'autoscaling:EC2_INSTANCE_TERMINATE',
'autoscaling:EC2_INSTANCE_TERMINATE_ERROR',
'autoscaling:TEST_NOTIFICATION' | Configures an Auto Scaling group to send notifications when
specified events take place. Valid notification types are:
'autoscaling:EC2_INSTANCE_LAUNCH',
'autoscaling:EC2_INSTANCE_LAUNCH_ERROR',
'autoscaling:EC2_INSTANCE_TERMINATE',
'autoscaling:EC2_INSTANCE_TERMINATE_ERROR',
'autoscaling:TEST_NOTIFICATION' | [
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] | def put_notification_configuration(self, topic, notification_types):
"""
Configures an Auto Scaling group to send notifications when
specified events take place. Valid notification types are:
'autoscaling:EC2_INSTANCE_LAUNCH',
'autoscaling:EC2_INSTANCE_LAUNCH_ERROR',
'autoscaling:EC2_INSTANCE_TERMINATE',
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"""
return self.connection.put_notification_configuration(self,
topic,
notification_types) | [
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llvm-mirror/libcxx | 78d6a7767ed57b50122a161b91f59f19c9bd0d19 | utils/gdb/libcxx/printers.py | python | _typename_with_n_generic_arguments | (gdb_type, n) | return result | Return a string for the type with the first n (1, ...) generic args. | Return a string for the type with the first n (1, ...) generic args. | [
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] | def _typename_with_n_generic_arguments(gdb_type, n):
"""Return a string for the type with the first n (1, ...) generic args."""
base_type = _remove_generics(_prettify_typename(gdb_type))
arg_list = [base_type]
template = "%s<"
for i in range(n):
arg_list.append(_typename_for_nth_generic_argument(gdb_type, i))
template += "%s, "
result = (template[:-2] + ">") % tuple(arg_list)
return result | [
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rapidsai/cudf | d5b2448fc69f17509304d594f029d0df56984962 | python/cudf/cudf/core/indexed_frame.py | python | IndexedFrame.sort_index | (
self,
axis=0,
level=None,
ascending=True,
inplace=False,
kind=None,
na_position="last",
sort_remaining=True,
ignore_index=False,
key=None,
) | return self._mimic_inplace(out, inplace=inplace) | Sort object by labels (along an axis).
Parameters
----------
axis : {0 or ‘index’, 1 or ‘columns’}, default 0
The axis along which to sort. The value 0 identifies the rows,
and 1 identifies the columns.
level : int or level name or list of ints or list of level names
If not None, sort on values in specified index level(s).
This is only useful in the case of MultiIndex.
ascending : bool, default True
Sort ascending vs. descending.
inplace : bool, default False
If True, perform operation in-place.
kind : sorting method such as `quick sort` and others.
Not yet supported.
na_position : {‘first’, ‘last’}, default ‘last’
Puts NaNs at the beginning if first; last puts NaNs at the end.
sort_remaining : bool, default True
Not yet supported
ignore_index : bool, default False
if True, index will be replaced with RangeIndex.
key : callable, optional
If not None, apply the key function to the index values before
sorting. This is similar to the key argument in the builtin
sorted() function, with the notable difference that this key
function should be vectorized. It should expect an Index and return
an Index of the same shape. For MultiIndex inputs, the key is
applied per level.
Returns
-------
Frame or None
Notes
-----
Difference from pandas:
* Not supporting: kind, sort_remaining=False
Examples
--------
**Series**
>>> import cudf
>>> series = cudf.Series(['a', 'b', 'c', 'd'], index=[3, 2, 1, 4])
>>> series
3 a
2 b
1 c
4 d
dtype: object
>>> series.sort_index()
1 c
2 b
3 a
4 d
dtype: object
Sort Descending
>>> series.sort_index(ascending=False)
4 d
3 a
2 b
1 c
dtype: object
**DataFrame**
>>> df = cudf.DataFrame(
... {"b":[3, 2, 1], "a":[2, 1, 3]}, index=[1, 3, 2])
>>> df.sort_index(axis=0)
b a
1 3 2
2 1 3
3 2 1
>>> df.sort_index(axis=1)
a b
1 2 3
3 1 2
2 3 1 | Sort object by labels (along an axis). | [
"Sort",
"object",
"by",
"labels",
"(",
"along",
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"axis",
")",
"."
] | def sort_index(
self,
axis=0,
level=None,
ascending=True,
inplace=False,
kind=None,
na_position="last",
sort_remaining=True,
ignore_index=False,
key=None,
):
"""Sort object by labels (along an axis).
Parameters
----------
axis : {0 or ‘index’, 1 or ‘columns’}, default 0
The axis along which to sort. The value 0 identifies the rows,
and 1 identifies the columns.
level : int or level name or list of ints or list of level names
If not None, sort on values in specified index level(s).
This is only useful in the case of MultiIndex.
ascending : bool, default True
Sort ascending vs. descending.
inplace : bool, default False
If True, perform operation in-place.
kind : sorting method such as `quick sort` and others.
Not yet supported.
na_position : {‘first’, ‘last’}, default ‘last’
Puts NaNs at the beginning if first; last puts NaNs at the end.
sort_remaining : bool, default True
Not yet supported
ignore_index : bool, default False
if True, index will be replaced with RangeIndex.
key : callable, optional
If not None, apply the key function to the index values before
sorting. This is similar to the key argument in the builtin
sorted() function, with the notable difference that this key
function should be vectorized. It should expect an Index and return
an Index of the same shape. For MultiIndex inputs, the key is
applied per level.
Returns
-------
Frame or None
Notes
-----
Difference from pandas:
* Not supporting: kind, sort_remaining=False
Examples
--------
**Series**
>>> import cudf
>>> series = cudf.Series(['a', 'b', 'c', 'd'], index=[3, 2, 1, 4])
>>> series
3 a
2 b
1 c
4 d
dtype: object
>>> series.sort_index()
1 c
2 b
3 a
4 d
dtype: object
Sort Descending
>>> series.sort_index(ascending=False)
4 d
3 a
2 b
1 c
dtype: object
**DataFrame**
>>> df = cudf.DataFrame(
... {"b":[3, 2, 1], "a":[2, 1, 3]}, index=[1, 3, 2])
>>> df.sort_index(axis=0)
b a
1 3 2
2 1 3
3 2 1
>>> df.sort_index(axis=1)
a b
1 2 3
3 1 2
2 3 1
"""
if kind is not None:
raise NotImplementedError("kind is not yet supported")
if not sort_remaining:
raise NotImplementedError(
"sort_remaining == False is not yet supported"
)
if key is not None:
raise NotImplementedError("key is not yet supported.")
if na_position not in {"first", "last"}:
raise ValueError(f"invalid na_position: {na_position}")
if axis in (0, "index"):
idx = self.index
if isinstance(idx, MultiIndex):
if level is not None:
# Pandas doesn't handle na_position in case of MultiIndex.
na_position = "first" if ascending is True else "last"
labels = [
idx._get_level_label(lvl)
for lvl in (level if is_list_like(level) else (level,))
]
# Explicitly construct a Frame rather than using type(self)
# to avoid constructing a SingleColumnFrame (e.g. Series).
idx = Frame._from_data(idx._data.select_by_label(labels))
inds = idx._get_sorted_inds(
ascending=ascending, na_position=na_position
)
out = self._gather(inds)
# TODO: frame factory function should handle multilevel column
# names
if isinstance(
self, cudf.core.dataframe.DataFrame
) and isinstance(
self.columns, pd.core.indexes.multi.MultiIndex
):
out.columns = self.columns
elif (ascending and idx.is_monotonic_increasing) or (
not ascending and idx.is_monotonic_decreasing
):
out = self.copy()
else:
inds = idx.argsort(
ascending=ascending, na_position=na_position
)
out = self._gather(inds)
if isinstance(
self, cudf.core.dataframe.DataFrame
) and isinstance(
self.columns, pd.core.indexes.multi.MultiIndex
):
out.columns = self.columns
else:
labels = sorted(self._data.names, reverse=not ascending)
out = self[labels]
if ignore_index is True:
out = out.reset_index(drop=True)
return self._mimic_inplace(out, inplace=inplace) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/extern/aui/framemanager.py | python | AuiManager.GetAllPanes | (self) | return self._panes | Returns a reference to all the pane info structures. | Returns a reference to all the pane info structures. | [
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""" Returns a reference to all the pane info structures. """
return self._panes | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/pandas/core/frame.py | python | DataFrame._set_value | (self, index, col, value, takeable: bool = False) | Put single value at passed column and index.
Parameters
----------
index : row label
col : column label
value : scalar
takeable : interpret the index/col as indexers, default False
Returns
-------
DataFrame
If label pair is contained, will be reference to calling DataFrame,
otherwise a new object. | Put single value at passed column and index. | [
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"""
Put single value at passed column and index.
Parameters
----------
index : row label
col : column label
value : scalar
takeable : interpret the index/col as indexers, default False
Returns
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DataFrame
If label pair is contained, will be reference to calling DataFrame,
otherwise a new object.
"""
try:
if takeable is True:
series = self._iget_item_cache(col)
return series._set_value(index, value, takeable=True)
series = self._get_item_cache(col)
engine = self.index._engine
engine.set_value(series._values, index, value)
return self
except (KeyError, TypeError):
# set using a non-recursive method & reset the cache
if takeable:
self.iloc[index, col] = value
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self._item_cache.pop(col, None)
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow2.x/tensorflow_model_optimization/python/core/sparsity/keras/pruning_impl.py | python | Pruning._maybe_update_block_mask | (self, weights) | return new_threshold, tf.reshape(sliced_mask, tf.shape(weights)) | Performs block-granular masking of the weights.
Block pruning occurs only if the block_height or block_width is > 1 and
if the weight tensor, when squeezed, has ndims = 2. Otherwise, elementwise
pruning occurs.
Args:
weights: The weight tensor that needs to be masked.
Returns:
new_threshold: The new value of the threshold based on weights, and
sparsity at the current global_step
new_mask: A numpy array of the same size and shape as weights containing
0 or 1 to indicate which of the values in weights falls below
the threshold
Raises:
ValueError: if block pooling function is not AVG or MAX | Performs block-granular masking of the weights. | [
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"""Performs block-granular masking of the weights.
Block pruning occurs only if the block_height or block_width is > 1 and
if the weight tensor, when squeezed, has ndims = 2. Otherwise, elementwise
pruning occurs.
Args:
weights: The weight tensor that needs to be masked.
Returns:
new_threshold: The new value of the threshold based on weights, and
sparsity at the current global_step
new_mask: A numpy array of the same size and shape as weights containing
0 or 1 to indicate which of the values in weights falls below
the threshold
Raises:
ValueError: if block pooling function is not AVG or MAX
"""
if self._block_size == [1, 1]:
return self._update_mask(weights)
# TODO(pulkitb): Check if squeeze operations should now be removed since
# we are only accepting 2-D weights.
squeezed_weights = tf.squeeze(weights)
abs_weights = tf.math.abs(squeezed_weights)
pooled_weights = pruning_utils.factorized_pool(
abs_weights,
window_shape=self._block_size,
pooling_type=self._block_pooling_type,
strides=self._block_size,
padding='SAME')
if pooled_weights.get_shape().ndims != 2:
pooled_weights = tf.squeeze(pooled_weights)
new_threshold, new_mask = self._update_mask(pooled_weights)
updated_mask = pruning_utils.expand_tensor(new_mask, self._block_size)
sliced_mask = tf.slice(
updated_mask, [0, 0],
[squeezed_weights.get_shape()[0],
squeezed_weights.get_shape()[1]])
return new_threshold, tf.reshape(sliced_mask, tf.shape(weights)) | [
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ZintrulCre/LeetCode_Archiver | de23e16ead29336b5ee7aa1898a392a5d6463d27 | LeetCode/python/1020.py | python | Solution.canThreePartsEqualSum | (self, A) | return False | :type A: List[int]
:rtype: bool | :type A: List[int]
:rtype: bool | [
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"""
:type A: List[int]
:rtype: bool
"""
s = sum(A)
if s % 3 != 0:
return False
s //= 3
cum = 0
total = 0
for a in A:
cum += a
if cum == s:
total += 1
cum = 0
if total == 3 and cum == 0:
return True
return False | [
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blitzpp/blitz | 39f885951a9b8b11f931f917935a16066a945056 | blitz/generate/makeloops.py | python | genf77 | (loop) | Generate the fortran code from loop data. | Generate the fortran code from loop data. | [
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] | def genf77(loop):
"""Generate the fortran code from loop data."""
f77expr = indexexpr(loop,"i")
# see if we must continue line
maxlen=60
if len(f77expr)>maxlen:
f77expr=f77expr[:maxlen]+"\n !"+f77expr[maxlen:]
subs=[
("loopname",loopname(loop)),
("f77args", cc([", %s"%n for n in looparrays(loop)])+
cc([", %s"%n for n in loopscalars(loop)])),
("f77decls", "%s(N)"%looparrays(loop)[0] +
cc([", %s(N)"%n for n in looparrays(loop)[1:]])+
cc([", %s"%n for n in loopscalars(loop)])),
("numtypesize",`loopnumtype(loop)[1]`),
("f77loopexpr", f77expr)
]
f77 = sub_skeleton(f77_skeleton, subs)
f=open("%sf.f"%loopname(loop),"w")
f.write(f77) | [
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kushview/Element | 1cc16380caa2ab79461246ba758b9de1f46db2a5 | waflib/Scripting.py | python | Dist.get_arch_name | (self) | return self.arch_name | Returns the archive file name.
Set the attribute *arch_name* to change the default value::
def dist(ctx):
ctx.arch_name = 'ctx.tar.bz2'
:rtype: string | Returns the archive file name.
Set the attribute *arch_name* to change the default value:: | [
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"""
Returns the archive file name.
Set the attribute *arch_name* to change the default value::
def dist(ctx):
ctx.arch_name = 'ctx.tar.bz2'
:rtype: string
"""
try:
self.arch_name
except AttributeError:
self.arch_name = self.get_base_name() + '.' + self.ext_algo.get(self.algo, self.algo)
return self.arch_name | [
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happynear/caffe-windows | 967eedf25009e334b7f6f933bb5e17aaaff5bef6 | examples/pycaffe/tools.py | python | SimpleTransformer.deprocess | (self, im) | return np.uint8(im) | inverse of preprocess() | inverse of preprocess() | [
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] | def deprocess(self, im):
"""
inverse of preprocess()
"""
im = im.transpose(1, 2, 0)
im /= self.scale
im += self.mean
im = im[:, :, ::-1] # change to RGB
return np.uint8(im) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_core.py | python | Image.Resize | (*args, **kwargs) | return _core_.Image_Resize(*args, **kwargs) | Resize(self, Size size, Point pos, int r=-1, int g=-1, int b=-1) -> Image
Changes the size of the image in-place without scaling it, by adding
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image. If red = green = blue = -1 then use either the current mask
colour if set or find, use, and set a suitable mask colour for any
newly exposed areas.
Returns the (modified) image itself. | Resize(self, Size size, Point pos, int r=-1, int g=-1, int b=-1) -> Image | [
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"""
Resize(self, Size size, Point pos, int r=-1, int g=-1, int b=-1) -> Image
Changes the size of the image in-place without scaling it, by adding
either a border with the given colour or cropping as necessary. The
image is pasted into a new image with the given size and background
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"""
return _core_.Image_Resize(*args, **kwargs) | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/mindrecord/mindpage.py | python | MindPage.read_at_page_by_name | (self, category_name, page, num_row) | return self._segment.read_at_page_by_name(category_name, page, num_row) | Query by category name in pagination.
Args:
category_name (str): String of category field's value,
referred to the return of `read_category_info`.
page (int): Index of page.
num_row (int): Number of row in a page.
Returns:
list[dict], data queried by category name. | Query by category name in pagination. | [
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"""
Query by category name in pagination.
Args:
category_name (str): String of category field's value,
referred to the return of `read_category_info`.
page (int): Index of page.
num_row (int): Number of row in a page.
Returns:
list[dict], data queried by category name.
"""
if not isinstance(category_name, str):
raise ParamValueError("Category name should be str.")
if not isinstance(page, int) or page < 0:
raise ParamValueError("Page should be int and greater than or equal to 0.")
if not isinstance(num_row, int) or num_row <= 0:
raise ParamValueError("num_row should be int and greater than 0.")
return self._segment.read_at_page_by_name(category_name, page, num_row) | [
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luliyucoordinate/Leetcode | 96afcdc54807d1d184e881a075d1dbf3371e31fb | src/0392-Is-Subsequence/0392.py | python | Solution.isSubsequence | (self, s, t) | return True | :type s: str
:type t: str
:rtype: bool | :type s: str
:type t: str
:rtype: bool | [
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"""
:type s: str
:type t: str
:rtype: bool
"""
for per_s in s:
s_index_t = t.find(per_s)
if s_index_t == -1:
return False
if s_index_t == len(t) - 1:
t = str()
else:
t = t[s_index_t+1:]
return True | [
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TGAC/KAT | e8870331de2b4bb0a1b3b91c6afb8fb9d59e9216 | deps/boost/tools/build/src/build/property.py | python | take | (attributes, properties) | return result | Returns a property set which include all
properties in 'properties' that have any of 'attributes'. | Returns a property set which include all
properties in 'properties' that have any of 'attributes'. | [
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"""Returns a property set which include all
properties in 'properties' that have any of 'attributes'."""
assert is_iterable_typed(attributes, basestring)
assert is_iterable_typed(properties, basestring)
result = []
for e in properties:
if b2.util.set.intersection(attributes, feature.attributes(get_grist(e))):
result.append(e)
return result | [
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taichi-dev/taichi | 973c04d6ba40f34e9e3bd5a28ae0ee0802f136a6 | python/taichi/lang/_ndarray.py | python | Ndarray.fill | (self, val) | Fills ndarray with a specific scalar value.
Args:
val (Union[int, float]): Value to fill. | Fills ndarray with a specific scalar value. | [
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] | def fill(self, val):
"""Fills ndarray with a specific scalar value.
Args:
val (Union[int, float]): Value to fill.
"""
if self.ndarray_use_torch:
self.arr.fill_(val)
elif impl.current_cfg(
).arch != _ti_core.Arch.cuda and impl.current_cfg(
).arch != _ti_core.Arch.x64:
self.fill_by_kernel(val)
elif self.dtype == primitive_types.f32:
self.arr.fill_float(val)
elif self.dtype == primitive_types.i32:
self.arr.fill_int(val)
elif self.dtype == primitive_types.u32:
self.arr.fill_uint(val)
else:
self.fill_by_kernel(val) | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/textwrap.py | python | fill | (text, width=70, **kwargs) | return w.fill(text) | Fill a single paragraph of text, returning a new string.
Reformat the single paragraph in 'text' to fit in lines of no more
than 'width' columns, and return a new string containing the entire
wrapped paragraph. As with wrap(), tabs are expanded and other
whitespace characters converted to space. See TextWrapper class for
available keyword args to customize wrapping behaviour. | Fill a single paragraph of text, returning a new string. | [
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] | def fill(text, width=70, **kwargs):
"""Fill a single paragraph of text, returning a new string.
Reformat the single paragraph in 'text' to fit in lines of no more
than 'width' columns, and return a new string containing the entire
wrapped paragraph. As with wrap(), tabs are expanded and other
whitespace characters converted to space. See TextWrapper class for
available keyword args to customize wrapping behaviour.
"""
w = TextWrapper(width=width, **kwargs)
return w.fill(text) | [
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SFTtech/openage | d6a08c53c48dc1e157807471df92197f6ca9e04d | openage/convert/processor/conversion/ror/nyan_subprocessor.py | python | RoRNyanSubprocessor.unit_line_to_game_entity | (unit_line) | Creates raw API objects for a unit line.
:param unit_line: Unit line that gets converted to a game entity.
:type unit_line: ..dataformat.converter_object.ConverterObjectGroup | Creates raw API objects for a unit line. | [
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] | def unit_line_to_game_entity(unit_line):
"""
Creates raw API objects for a unit line.
:param unit_line: Unit line that gets converted to a game entity.
:type unit_line: ..dataformat.converter_object.ConverterObjectGroup
"""
current_unit = unit_line.get_head_unit()
current_unit_id = unit_line.get_head_unit_id()
dataset = unit_line.data
name_lookup_dict = internal_name_lookups.get_entity_lookups(dataset.game_version)
class_lookup_dict = internal_name_lookups.get_class_lookups(dataset.game_version)
# Start with the generic GameEntity
game_entity_name = name_lookup_dict[current_unit_id][0]
obj_location = f"data/game_entity/generic/{name_lookup_dict[current_unit_id][1]}/"
raw_api_object = RawAPIObject(game_entity_name, game_entity_name,
dataset.nyan_api_objects)
raw_api_object.add_raw_parent("engine.util.game_entity.GameEntity")
raw_api_object.set_location(obj_location)
raw_api_object.set_filename(name_lookup_dict[current_unit_id][1])
unit_line.add_raw_api_object(raw_api_object)
# =======================================================================
# Game Entity Types
# =======================================================================
# we give a unit two types
# - util.game_entity_type.types.Unit (if unit_type >= 70)
# - util.game_entity_type.types.<Class> (depending on the class)
# =======================================================================
# Create or use existing auxiliary types
types_set = []
unit_type = current_unit["unit_type"].get_value()
if unit_type >= 70:
type_obj = dataset.pregen_nyan_objects["util.game_entity_type.types.Unit"].get_nyan_object()
types_set.append(type_obj)
unit_class = current_unit["unit_class"].get_value()
class_name = class_lookup_dict[unit_class]
class_obj_name = f"util.game_entity_type.types.{class_name}"
type_obj = dataset.pregen_nyan_objects[class_obj_name].get_nyan_object()
types_set.append(type_obj)
raw_api_object.add_raw_member("types", types_set, "engine.util.game_entity.GameEntity")
# =======================================================================
# Abilities
# =======================================================================
abilities_set = []
abilities_set.append(AoCAbilitySubprocessor.death_ability(unit_line))
abilities_set.append(AoCAbilitySubprocessor.delete_ability(unit_line))
abilities_set.append(AoCAbilitySubprocessor.despawn_ability(unit_line))
abilities_set.append(AoCAbilitySubprocessor.idle_ability(unit_line))
abilities_set.append(AoCAbilitySubprocessor.hitbox_ability(unit_line))
abilities_set.append(AoCAbilitySubprocessor.live_ability(unit_line))
abilities_set.append(AoCAbilitySubprocessor.los_ability(unit_line))
abilities_set.append(AoCAbilitySubprocessor.move_ability(unit_line))
abilities_set.append(AoCAbilitySubprocessor.named_ability(unit_line))
abilities_set.append(RoRAbilitySubprocessor.resistance_ability(unit_line))
abilities_set.extend(AoCAbilitySubprocessor.selectable_ability(unit_line))
abilities_set.append(AoCAbilitySubprocessor.stop_ability(unit_line))
abilities_set.append(AoCAbilitySubprocessor.terrain_requirement_ability(unit_line))
abilities_set.append(AoCAbilitySubprocessor.turn_ability(unit_line))
abilities_set.append(AoCAbilitySubprocessor.visibility_ability(unit_line))
# Creation
if len(unit_line.creates) > 0:
abilities_set.append(AoCAbilitySubprocessor.create_ability(unit_line))
# Config
ability = AoCAbilitySubprocessor.use_contingent_ability(unit_line)
if ability:
abilities_set.append(ability)
if unit_line.has_command(104):
# Recharging attribute points (priests)
abilities_set.extend(AoCAbilitySubprocessor.regenerate_attribute_ability(unit_line))
# Applying effects and shooting projectiles
if unit_line.is_projectile_shooter():
abilities_set.append(RoRAbilitySubprocessor.shoot_projectile_ability(unit_line, 7))
RoRNyanSubprocessor.projectiles_from_line(unit_line)
elif unit_line.is_melee() or unit_line.is_ranged():
if unit_line.has_command(7):
# Attack
abilities_set.append(AoCAbilitySubprocessor.apply_discrete_effect_ability(unit_line,
7,
unit_line.is_ranged()))
if unit_line.has_command(101):
# Build
abilities_set.append(AoCAbilitySubprocessor.apply_continuous_effect_ability(unit_line,
101,
unit_line.is_ranged()))
if unit_line.has_command(104):
# TODO: Success chance is not a resource in RoR
# Convert
abilities_set.append(RoRAbilitySubprocessor.apply_discrete_effect_ability(unit_line,
104,
unit_line.is_ranged()))
if unit_line.has_command(105):
# Heal
abilities_set.append(AoCAbilitySubprocessor.apply_continuous_effect_ability(unit_line,
105,
unit_line.is_ranged()))
if unit_line.has_command(106):
# Repair
abilities_set.append(AoCAbilitySubprocessor.apply_continuous_effect_ability(unit_line,
106,
unit_line.is_ranged()))
# Formation/Stance
if not isinstance(unit_line, GenieVillagerGroup):
abilities_set.append(RoRAbilitySubprocessor.game_entity_stance_ability(unit_line))
# Storage abilities
if unit_line.is_garrison():
abilities_set.append(AoCAbilitySubprocessor.storage_ability(unit_line))
abilities_set.append(AoCAbilitySubprocessor.remove_storage_ability(unit_line))
if len(unit_line.garrison_locations) > 0:
ability = AoCAbilitySubprocessor.enter_container_ability(unit_line)
if ability:
abilities_set.append(ability)
ability = AoCAbilitySubprocessor.exit_container_ability(unit_line)
if ability:
abilities_set.append(ability)
# Resource abilities
if unit_line.is_gatherer():
abilities_set.append(AoCAbilitySubprocessor.drop_resources_ability(unit_line))
abilities_set.extend(AoCAbilitySubprocessor.gather_ability(unit_line))
# Resource storage
if unit_line.is_gatherer() or unit_line.has_command(111):
abilities_set.append(AoCAbilitySubprocessor.resource_storage_ability(unit_line))
if unit_line.is_harvestable():
abilities_set.append(AoCAbilitySubprocessor.harvestable_ability(unit_line))
# Trade abilities
if unit_line.has_command(111):
abilities_set.append(AoCAbilitySubprocessor.trade_ability(unit_line))
raw_api_object.add_raw_member("abilities", abilities_set,
"engine.util.game_entity.GameEntity")
# =======================================================================
# Modifiers
# =======================================================================
modifiers_set = []
if unit_line.is_gatherer():
modifiers_set.extend(AoCModifierSubprocessor.gather_rate_modifier(unit_line))
# TODO: Other modifiers?
raw_api_object.add_raw_member("modifiers", modifiers_set,
"engine.util.game_entity.GameEntity")
# =======================================================================
# TODO: Variants
# =======================================================================
variants_set = []
raw_api_object.add_raw_member("variants", variants_set,
"engine.util.game_entity.GameEntity")
# =======================================================================
# Misc (Objects that are not used by the unit line itself, but use its values)
# =======================================================================
if unit_line.is_creatable():
RoRAuxiliarySubprocessor.get_creatable_game_entity(unit_line) | [
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ricardoquesada/Spidermonkey | 4a75ea2543408bd1b2c515aa95901523eeef7858 | python/configobj/configobj.py | python | Section.dict | (self) | return newdict | Return a deepcopy of self as a dictionary.
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_controls.py | python | AnyButton.SetBitmapMargins | (*args) | return _controls_.AnyButton_SetBitmapMargins(*args) | SetBitmapMargins(self, int x, int y)
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facebook/ThreatExchange | 31914a51820c73c8a0daffe62ccca29a6e3d359e | python-threatexchange/threatexchange/signal_type/pdq_index.py | python | PDQIndex.query | (self, hash: str) | return matches | Look up entries against the index, up to the max supported distance. | Look up entries against the index, up to the max supported distance. | [
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apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | src/external/coremltools_wrap/coremltools/deps/protobuf/python/google/protobuf/internal/well_known_types.py | python | FieldMask.ToJsonString | (self) | return ','.join(camelcase_paths) | Converts FieldMask to string according to proto3 JSON spec. | Converts FieldMask to string according to proto3 JSON spec. | [
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zeakey/DeepSkeleton | dc70170f8fd2ec8ca1157484ce66129981104486 | scripts/cpp_lint.py | python | GetLineWidth | (line) | Determines the width of the line in column positions.
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py2/sklearn/externals/joblib/memory.py | python | Memory.eval | (self, func, *args, **kwargs) | return self.cache(func)(*args, **kwargs) | Eval function func with arguments `*args` and `**kwargs`,
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in the context of the memory.
This method works similarly to the builtin `apply`, except
that the function is called only if the cache is not
up to date.
"""
if self.cachedir is None:
return func(*args, **kwargs)
return self.cache(func)(*args, **kwargs) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py2/scipy/optimize/_shgo.py | python | SHGO.surface_topo_ref | (self) | Find the BD and FD finite differences along each component vector. | Find the BD and FD finite differences along each component vector. | [
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"""
Find the BD and FD finite differences along each component vector.
"""
# Replace numpy inf, -inf and nan objects with floating point numbers
# nan --> float
self.F[np.isnan(self.F)] = np.inf
# inf, -inf --> floats
self.F = np.nan_to_num(self.F)
self.Ft = self.F[self.Ind_sorted]
self.Ftp = np.diff(self.Ft, axis=0) # FD
self.Ftm = np.diff(self.Ft[::-1], axis=0)[::-1] | [
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apache/qpid-proton | 6bcdfebb55ea3554bc29b1901422532db331a591 | python/proton/_endpoints.py | python | Terminus.capabilities | (self) | return Data(pn_terminus_capabilities(self._impl)) | Capabilities of the source or target.
:type: :class:`Data` containing an array of :class:`symbol`. | Capabilities of the source or target. | [
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"""
Capabilities of the source or target.
:type: :class:`Data` containing an array of :class:`symbol`.
"""
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FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Draft/draftviewproviders/view_dimension.py | python | ViewProviderLinearDimension.is_linked_to_circle | (self) | return False | Return true if the dimension measures a circular edge. | Return true if the dimension measures a circular edge. | [
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return True
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/build/waf-1.7.13/lmbrwaflib/build_configurations.py | python | PlatformDetail.__init__ | (self, platform, enabled, has_server, has_tests, is_monolithic_value, output_folder, aliases, attributes, needs_java, platform_env_dict) | Initialize
:param platform: The base name of this platform. (The concrete platform name may be extended with a server suffix if the right options are set)
:param enabled: Flag: Is this platform enabled or not
:param has_server: Flag: Are server platforms/configurations supported at all
:param has_tests: Flag: Does this platform support test configurations?
:param is_monolithic_value: Flag: Is this a monolithic platform? (or a list of configurations to force monolithic builds on that configuration.)
:param output_folder: The output folder base name that is used as the base for each of the platform configurations output folder
:param aliases: Optional aliases to add this platform to
:param attributes: Optional platform/specific attributes needed for custom platform processing
:param needs_java: Flag to indicate if we need java to be loaded (the javaw module)
:param platform_env_dict: Optional env map to apply to the env for this platform during configure | Initialize | [
"Initialize"
] | def __init__(self, platform, enabled, has_server, has_tests, is_monolithic_value, output_folder, aliases, attributes, needs_java, platform_env_dict):
"""
Initialize
:param platform: The base name of this platform. (The concrete platform name may be extended with a server suffix if the right options are set)
:param enabled: Flag: Is this platform enabled or not
:param has_server: Flag: Are server platforms/configurations supported at all
:param has_tests: Flag: Does this platform support test configurations?
:param is_monolithic_value: Flag: Is this a monolithic platform? (or a list of configurations to force monolithic builds on that configuration.)
:param output_folder: The output folder base name that is used as the base for each of the platform configurations output folder
:param aliases: Optional aliases to add this platform to
:param attributes: Optional platform/specific attributes needed for custom platform processing
:param needs_java: Flag to indicate if we need java to be loaded (the javaw module)
:param platform_env_dict: Optional env map to apply to the env for this platform during configure
"""
self.platform = platform
self.enabled = enabled
self.has_server = has_server
self.has_tests = has_tests
# Handle the fact that 'is_monolithic' can be represented in multiple ways
if not isinstance(is_monolithic_value, bool) and not isinstance(is_monolithic_value, list) and not isinstance(is_monolithic_value, str):
raise Errors.WafError("Invalid settings value type for 'is_platform' for platform '{}'. Expected a boolean, string, or list of strings")
if isinstance(is_monolithic_value, str):
self.is_monolithic = [is_monolithic_value]
else:
self.is_monolithic = is_monolithic_value
self.output_folder = output_folder
self.aliases = aliases
self.attributes = attributes
self.needs_java = needs_java
self.platform_env_dict = platform_env_dict
# Initialize all the platform configurations based on the parameters and settings
self.platform_configs = {}
for config_settings in settings_manager.LUMBERYARD_SETTINGS.get_build_configuration_settings():
# Determine if the configuration is monolithic or not
if isinstance(self.is_monolithic, bool):
# The 'is_monolithic' flag was set to a boolean value in the platform's configuration file
if self.is_monolithic:
# The 'is_monolithic' flag was set to True, then set (override) all configurations to be monolithic
is_config_monolithic = True
else:
# The 'is_monolithic' flag was set to False, then use the default configuration specific setting to
# control whether its monolithic or not. We will never override 'performance' or 'release' monolithic
# settings
is_config_monolithic = config_settings.is_monolithic
elif isinstance(self.is_monolithic, list):
# Special case: If the 'is_monolithic' is a list, then evaluate based on the contents of the list
if len(self.is_monolithic) > 0:
# If there is at least one configuration specified in the list, then match the current config to any of the
# configurations to determine if its monolithic or not. This mechanism can only enable configurations
# that by default are non-monolithic. If the default value is monolithic, it cannot be turned off
is_config_monolithic = config_settings.name in self.is_monolithic or config_settings.is_monolithic
else:
# If the list is empty, then that implies we do not override any of the default configuration's
# monolithic flag (same behavior as setting 'is_monolithic' to False
is_config_monolithic = config_settings.is_monolithic
else:
raise Errors.WafError("Invalid type for 'is_monolithic' in platform settings for {}".format(platform))
# Add the base configuration
base_config = PlatformConfiguration(platform=self,
settings=config_settings,
platform_output_folder=output_folder,
is_test=False,
is_server=False,
is_monolithic=is_config_monolithic)
self.platform_configs[base_config.config_name()] = base_config
if has_server and ENABLE_SERVER:
# Add the optional server configuration if available(has_server) and enabled(enable_server)
non_test_server_config = PlatformConfiguration(platform=self,
settings=config_settings,
platform_output_folder=output_folder,
is_test=False,
is_server=True,
is_monolithic=is_config_monolithic)
self.platform_configs[non_test_server_config.config_name()] = non_test_server_config
if self.has_tests and config_settings.has_test and ENABLE_TEST_CONFIGURATIONS:
# Add the optional test configuration(s) if available (config_detail.has_test) and enabled (enable_test_configurations)
test_non_server_config = PlatformConfiguration(platform=self,
settings=config_settings,
platform_output_folder=output_folder,
is_test=True,
is_server=False,
is_monolithic=is_config_monolithic)
self.platform_configs[test_non_server_config.config_name()] = test_non_server_config
if has_server and ENABLE_SERVER:
# Add the optional server configuration if available(has_server) and enabled(enable_server)
test_server_config = PlatformConfiguration(platform=self,
settings=config_settings,
platform_output_folder=output_folder,
is_test=True,
is_server=True,
is_monolithic=is_config_monolithic)
self.platform_configs[test_server_config.config_name()] = test_server_config
# Check if there is a 3rd party platform alias override, otherwise use the platform name
config_third_party_platform_alias = self.attributes.get('third_party_alias_platform', None)
if config_third_party_platform_alias:
try:
settings_manager.LUMBERYARD_SETTINGS.get_platform_settings(config_third_party_platform_alias)
except Errors.WafError:
raise Errors.WafError("Invalid 'third_party_alias_platform' attribute ({}) for platform {}".format(config_third_party_platform_alias, platform))
self.third_party_platform_key = config_third_party_platform_alias
else:
self.third_party_platform_key = platform | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/tkinter/__init__.py | python | Canvas.create_text | (self, *args, **kw) | return self._create('text', args, kw) | Create text with coordinates x1,y1. | Create text with coordinates x1,y1. | [
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] | def create_text(self, *args, **kw):
"""Create text with coordinates x1,y1."""
return self._create('text', args, kw) | [
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google/or-tools | 2cb85b4eead4c38e1c54b48044f92087cf165bce | ortools/sat/python/cp_model.py | python | CpModel.AddNoOverlap | (self, interval_vars) | return ct | Adds NoOverlap(interval_vars).
A NoOverlap constraint ensures that all present intervals do not overlap
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Args:
interval_vars: The list of interval variables to constrain.
Returns:
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"""Adds NoOverlap(interval_vars).
A NoOverlap constraint ensures that all present intervals do not overlap
in time.
Args:
interval_vars: The list of interval variables to constrain.
Returns:
An instance of the `Constraint` class.
"""
ct = Constraint(self.__model.constraints)
model_ct = self.__model.constraints[ct.Index()]
model_ct.no_overlap.intervals.extend(
[self.GetIntervalIndex(x) for x in interval_vars])
return ct | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | scripts/reduction_workflow/command_interface.py | python | ClearDataFiles | () | Empty the list of data files to be processed while keeping
all other reduction options. | Empty the list of data files to be processed while keeping
all other reduction options. | [
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Empty the list of data files to be processed while keeping
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/xml/etree/ElementTree.py | python | ElementTree._setroot | (self, element) | Replace root element of this tree.
This will discard the current contents of the tree and replace it
with the given element. Use with care! | Replace root element of this tree. | [
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"""Replace root element of this tree.
This will discard the current contents of the tree and replace it
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"""
# assert iselement(element)
self._root = element | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py2/scipy/signal/filter_design.py | python | buttap | (N) | return z, p, k | Return (z,p,k) for analog prototype of Nth-order Butterworth filter.
The filter will have an angular (e.g. rad/s) cutoff frequency of 1.
See Also
--------
butter : Filter design function using this prototype | Return (z,p,k) for analog prototype of Nth-order Butterworth filter. | [
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"""Return (z,p,k) for analog prototype of Nth-order Butterworth filter.
The filter will have an angular (e.g. rad/s) cutoff frequency of 1.
See Also
--------
butter : Filter design function using this prototype
"""
if abs(int(N)) != N:
raise ValueError("Filter order must be a nonnegative integer")
z = numpy.array([])
m = numpy.arange(-N+1, N, 2)
# Middle value is 0 to ensure an exactly real pole
p = -numpy.exp(1j * pi * m / (2 * N))
k = 1
return z, p, k | [
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regomne/chinesize | 2ae555445046cd28d60a514e30ac1d6eca1c442a | N2System/nsbparser/nsbParser.py | python | NsbParser.pa5 | (self) | 加法 | 加法 | [
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] | def pa5(self):
'加法'
var1=self.stack.pop()
self.stack.append('('+self.stack.pop()+' + '+var1+')') | [
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apache/incubator-weex | 5c25f0b59f7ac90703c363e7261f60bd06356dbe | weex_core/tools/cpplint.py | python | _AddFilters | (filters) | Adds more filter overrides.
Unlike _SetFilters, this function does not reset the current list of filters
available.
Args:
filters: A string of comma-separated filters (eg "whitespace/indent").
Each filter should start with + or -; else we die. | Adds more filter overrides. | [
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] | def _AddFilters(filters):
"""Adds more filter overrides.
Unlike _SetFilters, this function does not reset the current list of filters
available.
Args:
filters: A string of comma-separated filters (eg "whitespace/indent").
Each filter should start with + or -; else we die.
"""
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/distutils/config.py | python | PyPIRCCommand._store_pypirc | (self, username, password) | Creates a default .pypirc file. | Creates a default .pypirc file. | [
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] | def _store_pypirc(self, username, password):
"""Creates a default .pypirc file."""
rc = self._get_rc_file()
with os.fdopen(os.open(rc, os.O_CREAT | os.O_WRONLY, 0o600), 'w') as f:
f.write(DEFAULT_PYPIRC % (username, password)) | [
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klzgrad/naiveproxy | ed2c513637c77b18721fe428d7ed395b4d284c83 | src/build/toolchain/win/setup_toolchain.py | python | _DetectVisualStudioPath | () | return vs_toolchain.DetectVisualStudioPath() | Return path to the installed Visual Studio. | Return path to the installed Visual Studio. | [
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] | def _DetectVisualStudioPath():
"""Return path to the installed Visual Studio.
"""
# Use the code in build/vs_toolchain.py to avoid duplicating code.
chromium_dir = os.path.abspath(os.path.join(SCRIPT_DIR, '..', '..', '..'))
sys.path.append(os.path.join(chromium_dir, 'build'))
import vs_toolchain
return vs_toolchain.DetectVisualStudioPath() | [
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hpi-xnor/BMXNet-v2 | af2b1859eafc5c721b1397cef02f946aaf2ce20d | python/mxnet/contrib/onnx/mx2onnx/_op_translations.py | python | convert_expand_dims | (node, **kwargs) | return [node] | Map MXNet's expand_dims operator attributes to onnx's Unsqueeze operator
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"""Map MXNet's expand_dims operator attributes to onnx's Unsqueeze operator
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"""
name, input_nodes, attrs = get_inputs(node, kwargs)
axis = int(attrs.get("axis"))
node = onnx.helper.make_node(
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return [node] | [
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google/syzygy | 8164b24ebde9c5649c9a09e88a7fc0b0fcbd1bc5 | third_party/numpy/files/numpy/distutils/fcompiler/__init__.py | python | FCompiler.update_executables | (elf) | Called at the beginning of customisation. Subclasses should
override this if they need to set up the executables dictionary.
Note that self.find_executables() is run afterwards, so the
self.executables dictionary values can contain <F77> or <F90> as
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"""Called at the beginning of customisation. Subclasses should
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Note that self.find_executables() is run afterwards, so the
self.executables dictionary values can contain <F77> or <F90> as
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"""
pass | [
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freesurfer/freesurfer | 6dbe527d43ffa611acb2cd112e9469f9bfec8e36 | cnn_sphere_register/ext/neuron/neuron/plot.py | python | slices | (slices_in, # the 2D slices
titles=None, # list of titles
cmaps=None, # list of colormaps
norms=None, # list of normalizations
do_colorbars=False, # option to show colorbars on each slice
grid=False, # option to plot the images in a grid or a single row
width=15, # width in in
show=True, # option to actually show the plot (plt.show())
imshow_args=None) | return (fig, axs) | plot a grid of slices (2d images) | plot a grid of slices (2d images) | [
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titles=None, # list of titles
cmaps=None, # list of colormaps
norms=None, # list of normalizations
do_colorbars=False, # option to show colorbars on each slice
grid=False, # option to plot the images in a grid or a single row
width=15, # width in in
show=True, # option to actually show the plot (plt.show())
imshow_args=None):
''' plot a grid of slices (2d images) '''
# input processing
nb_plots = len(slices_in)
def input_check(inputs, nb_plots, name):
''' change input from None/single-link '''
assert (inputs is None) or (len(inputs) == nb_plots) or (len(inputs) == 1), \
'number of %s is incorrect' % name
if inputs is None:
inputs = [None]
if len(inputs) == 1:
inputs = [inputs[0] for i in range(nb_plots)]
return inputs
titles = input_check(titles, nb_plots, 'titles')
cmaps = input_check(cmaps, nb_plots, 'cmaps')
norms = input_check(norms, nb_plots, 'norms')
imshow_args = input_check(imshow_args, nb_plots, 'imshow_args')
for idx, ia in enumerate(imshow_args):
imshow_args[idx] = {} if ia is None else ia
# figure out the number of rows and columns
if grid:
if isinstance(grid, bool):
rows = np.floor(np.sqrt(nb_plots)).astype(int)
cols = np.ceil(nb_plots/rows).astype(int)
else:
assert isinstance(grid, (list, tuple)), \
"grid should either be bool or [rows,cols]"
rows, cols = grid
else:
rows = 1
cols = nb_plots
# prepare the subplot
fig, axs = plt.subplots(rows, cols)
if rows == 1 and cols == 1:
axs = [axs]
for i in range(nb_plots):
col = np.remainder(i, cols)
row = np.floor(i/cols).astype(int)
# get row and column axes
row_axs = axs if rows == 1 else axs[row]
ax = row_axs[col]
# turn off axis
ax.axis('off')
# some cleanup
if titles is not None:
ax.title.set_text(titles[i])
# show figure
im_ax = ax.imshow(slices_in[i], cmap=cmaps[i], interpolation="nearest", norm=norms[i], **imshow_args[i])
# colorbars
# http://stackoverflow.com/questions/18195758/set-matplotlib-colorbar-size-to-match-graph
if do_colorbars and cmaps[i] is not None:
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
fig.colorbar(im_ax, cax=cax)
# show the plots
fig.set_size_inches(width, rows/cols*width)
plt.tight_layout()
if show:
plt.show()
return (fig, axs) | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/email/charset.py | python | add_alias | (alias, canonical) | Add a character set alias.
alias is the alias name, e.g. latin-1
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"""Add a character set alias.
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ceph/ceph | 959663007321a369c83218414a29bd9dbc8bda3a | qa/tasks/cephfs/filesystem.py | python | Filesystem.list_dirfrag | (self, dir_ino) | return key_list_str.strip().split("\n") if key_list_str else [] | Read the named object and return the list of omap keys
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zeakey/DeepSkeleton | dc70170f8fd2ec8ca1157484ce66129981104486 | 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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domino-team/openwrt-cc | 8b181297c34d14d3ca521cc9f31430d561dbc688 | package/gli-pub/openwrt-node-packages-master/node/node-v6.9.1/tools/gyp/pylib/gyp/xcodeproj_file.py | python | XCObject._EncodeString | (self, value) | return '"' + _escaped.sub(self._EncodeTransform, value) + '"' | Encodes a string to be placed in the project file output, mimicing
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# Use quotation marks when any character outside of the range A-Z, a-z, 0-9,
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# Escape " (double-quote) and \ (backslash) by preceding them with a
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# All other characters within the ASCII control character range (0 through
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# Characters above the ASCII range are passed through to the output encoded
# as UTF-8 without any escaping. These mappings are contained in the
# class' _encode_transforms list.
if _unquoted.search(value) and not _quoted.search(value):
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return '"' + _escaped.sub(self._EncodeTransform, value) + '"' | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/curses/textpad.py | python | rectangle | (win, uly, ulx, lry, lrx) | Draw a rectangle with corners at the provided upper-left
and lower-right coordinates. | Draw a rectangle with corners at the provided upper-left
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"""Draw a rectangle with corners at the provided upper-left
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"""
win.vline(uly+1, ulx, curses.ACS_VLINE, lry - uly - 1)
win.hline(uly, ulx+1, curses.ACS_HLINE, lrx - ulx - 1)
win.hline(lry, ulx+1, curses.ACS_HLINE, lrx - ulx - 1)
win.vline(uly+1, lrx, curses.ACS_VLINE, lry - uly - 1)
win.addch(uly, ulx, curses.ACS_ULCORNER)
win.addch(uly, lrx, curses.ACS_URCORNER)
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_gdi.py | python | DC.SetUserScale | (*args, **kwargs) | return _gdi_.DC_SetUserScale(*args, **kwargs) | SetUserScale(self, double x, double y)
Sets the user scaling factor, useful for applications which require
'zooming'. | SetUserScale(self, double x, double y) | [
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"""
SetUserScale(self, double x, double y)
Sets the user scaling factor, useful for applications which require
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"""
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llvm/llvm-project | ffa6262cb4e2a335d26416fad39a581b4f98c5f4 | lldb/utils/lui/lldbutil.py | python | value_type_to_str | (enum) | Returns the valueType string given an enum. | Returns the valueType string given an enum. | [
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return "local_variable"
elif enum == lldb.eValueTypeRegister:
return "register"
elif enum == lldb.eValueTypeRegisterSet:
return "register_set"
elif enum == lldb.eValueTypeConstResult:
return "constant_result"
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raise Exception("Unknown ValueType enum") | [
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deepmind/open_spiel | 4ca53bea32bb2875c7385d215424048ae92f78c8 | open_spiel/python/pytorch/rcfr.py | python | ReservoirBuffer.insert | (self, candidate) | Consider this `candidate` for inclusion in this sampling buffer. | Consider this `candidate` for inclusion in this sampling buffer. | [
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"""Consider this `candidate` for inclusion in this sampling buffer."""
self._num_candidates += 1
if self.num_elements < self.size:
self._buffer[self.num_elements] = candidate
self.num_elements += 1
return
idx = np.random.choice(self._num_candidates)
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Samsung/veles | 95ed733c2e49bc011ad98ccf2416ecec23fbf352 | libVeles/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.
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may be missed.
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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. | [
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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:
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]
raw = clean_lines.raw_lines
raw_line = raw[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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natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/contrib/bayesflow/python/ops/stochastic_graph.py | python | DistributionTensor._create_value | (self) | Create the value Tensor based on the value type, store as self._value. | Create the value Tensor based on the value type, store as self._value. | [
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"""Create the value Tensor based on the value type, store as self._value."""
if isinstance(self._value_type, MeanValue):
value_tensor = self._dist.mean()
elif isinstance(self._value_type, SampleValue):
value_tensor = self._dist.sample_n(self._value_type.n)
elif isinstance(self._value_type, SampleAndReshapeValue):
if self._value_type.n == 1:
value_tensor = self._dist.sample()
else:
samples = self._dist.sample_n(self._value_type.n)
samples_shape = array_ops.shape(samples)
samples_static_shape = samples.get_shape()
new_batch_size = samples_shape[0] * samples_shape[1]
value_tensor = array_ops.reshape(
samples, array_ops.concat(0, ([new_batch_size], samples_shape[2:])))
if samples_static_shape.ndims is not None:
# Update the static shape for shape inference purposes
shape_list = samples_static_shape.as_list()
new_shape = tensor_shape.vector(
shape_list[0] * shape_list[1]
if shape_list[0] is not None and shape_list[1] is not None
else None)
new_shape = new_shape.concatenate(samples_static_shape[2:])
value_tensor.set_shape(new_shape)
else:
raise TypeError(
"Unrecognized Distribution Value Type: %s", self._value_type)
if self._value_type.stop_gradient:
# stop_gradient is being enforced by the value type
return array_ops.stop_gradient(value_tensor)
if isinstance(self._value_type, MeanValue):
return value_tensor # Using pathwise-derivative for this one.
if self._dist.is_continuous and self._dist.is_reparameterized:
return value_tensor # Using pathwise-derivative for this one.
else:
# Will have to perform some variant of score function
# estimation. Call stop_gradient on the sampler just in case we
# may accidentally leak some gradient from it.
return array_ops.stop_gradient(value_tensor) | [
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FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Draft/draftutils/utils.py | python | epsilon | () | return 1.0/(10.0**tolerance()) | Return a small number based on the tolerance for use in comparisons.
The epsilon value is used in floating point comparisons. Use with caution.
::
denom = 10**tolerance
num = 1
epsilon = num/denom
Returns
-------
float
1/(10**tolerance) | Return a small number based on the tolerance for use in comparisons. | [
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"""Return a small number based on the tolerance for use in comparisons.
The epsilon value is used in floating point comparisons. Use with caution.
::
denom = 10**tolerance
num = 1
epsilon = num/denom
Returns
-------
float
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"""
return 1.0/(10.0**tolerance()) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_controls.py | python | FileCtrl.Create | (*args, **kwargs) | return _controls_.FileCtrl_Create(*args, **kwargs) | Create(self, Window parent, int id=-1, String defaultDirectory=wxEmptyString,
String defaultFilename=wxEmptyString,
String wildCard=wxFileSelectorDefaultWildcardStr,
long style=FC_DEFAULT_STYLE, Point pos=DefaultPosition,
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String name=FileCtrlNameStr) -> bool | Create(self, Window parent, int id=-1, String defaultDirectory=wxEmptyString,
String defaultFilename=wxEmptyString,
String wildCard=wxFileSelectorDefaultWildcardStr,
long style=FC_DEFAULT_STYLE, Point pos=DefaultPosition,
Size size=DefaultSize,
String name=FileCtrlNameStr) -> bool | [
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"""
Create(self, Window parent, int id=-1, String defaultDirectory=wxEmptyString,
String defaultFilename=wxEmptyString,
String wildCard=wxFileSelectorDefaultWildcardStr,
long style=FC_DEFAULT_STYLE, Point pos=DefaultPosition,
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"""
return _controls_.FileCtrl_Create(*args, **kwargs) | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | Framework/PythonInterface/plugins/algorithms/WorkflowAlgorithms/SANSILLReduction.py | python | SANSILLReduction._split_kinetic_frames | (self, ws) | return frames | Explodes the frames of a kinetic workspace into separate workspaces | Explodes the frames of a kinetic workspace into separate workspaces | [
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'''Explodes the frames of a kinetic workspace into separate workspaces'''
n_frames = mtd[ws].blocksize()
n_hist = mtd[ws].getNumberHistograms()
wavelength = round(mtd[ws].getRun().getLogData('wavelength').value * 100) / 100
wave_bins = [wavelength * 0.9, wavelength * 1.1]
frames = []
for frame_index in range(n_frames):
frame_name = ws + '_t' + str(frame_index)
frames.append(frame_name)
CropWorkspace(InputWorkspace=ws, OutputWorkspace=frame_name, XMin=frame_index, XMax=frame_index)
ConvertToHistogram(InputWorkspace=frame_name, OutputWorkspace=frame_name)
mtd[frame_name].getAxis(0).setUnit('Wavelength')
for s in range(n_hist):
mtd[frame_name].setX(s, wave_bins)
RenameWorkspace(InputWorkspace=ws, OutputWorkspace=ws[2:])
return frames | [
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trailofbits/llvm-sanitizer-tutorial | d29dfeec7f51fbf234fd0080f28f2b30cd0b6e99 | llvm/tools/clang/bindings/python/clang/cindex.py | python | Cursor.access_specifier | (self) | return AccessSpecifier.from_id(self._access_specifier) | Retrieves the access specifier (if any) of the entity pointed at by the
cursor. | Retrieves the access specifier (if any) of the entity pointed at by the
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"""
Retrieves the access specifier (if any) of the entity pointed at by the
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"""
if not hasattr(self, '_access_specifier'):
self._access_specifier = conf.lib.clang_getCXXAccessSpecifier(self)
return AccessSpecifier.from_id(self._access_specifier) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/combo.py | python | ComboCtrl.Popup | (*args, **kwargs) | return _combo.ComboCtrl_Popup(*args, **kwargs) | Popup(self) | Popup(self) | [
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"""Popup(self)"""
return _combo.ComboCtrl_Popup(*args, **kwargs) | [
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stulp/dmpbbo | ca900e3b851d25faaf59ea296650370c70ed7d0f | python/bbo/updaters.py | python | UpdaterCovarDecay.__init__ | (self,eliteness = 10, weighting_method = 'PI-BB', covar_decay_factor = 0.8) | Initialize an UpdaterCovarDecay object.
\param[in] eliteness The eliteness parameter (see costsToWeights(...))
\param[in] weighting_method The weighting method ('PI-BB','CMA-ES','CEM', see costsToWeights(...))
\param[in] covar_decay_factor Factor with which to decay the covariance matrix (i.e. covar_decay_factor*covar_decay_factor*C at each update) | Initialize an UpdaterCovarDecay object.
\param[in] eliteness The eliteness parameter (see costsToWeights(...))
\param[in] weighting_method The weighting method ('PI-BB','CMA-ES','CEM', see costsToWeights(...))
\param[in] covar_decay_factor Factor with which to decay the covariance matrix (i.e. covar_decay_factor*covar_decay_factor*C at each update) | [
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""" Initialize an UpdaterCovarDecay object.
\param[in] eliteness The eliteness parameter (see costsToWeights(...))
\param[in] weighting_method The weighting method ('PI-BB','CMA-ES','CEM', see costsToWeights(...))
\param[in] covar_decay_factor Factor with which to decay the covariance matrix (i.e. covar_decay_factor*covar_decay_factor*C at each update)
"""
self.eliteness = eliteness
self.weighting_method = weighting_method
self.covar_decay_factor = covar_decay_factor | [
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