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myriadrf/LoRa-SDR | c545c51e5e37284363a971ec298f72255646a6fa | RN2483.py | python | RN2483.enableCW | (self) | Enable CW, remeber to reset after to use LoRa again. | Enable CW, remeber to reset after to use LoRa again. | [
"Enable",
"CW",
"remeber",
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"after",
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"."
] | def enableCW(self):
"""
Enable CW, remeber to reset after to use LoRa again.
"""
self.command('radio cw on') | [
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||
hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | tools/idl_parser/idl_parser.py | python | IDLParser.p_AttributeNameKeyword | (self, p) | AttributeNameKeyword : REQUIRED | AttributeNameKeyword : REQUIRED | [
"AttributeNameKeyword",
":",
"REQUIRED"
] | def p_AttributeNameKeyword(self, p):
"""AttributeNameKeyword : REQUIRED"""
p[0] = p[1] | [
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||
facebook/openr | ed38bdfd6bf290084bfab4821b59f83e7b59315d | openr/py/openr/cli/clis/fib.py | python | FibSnoopCli.snoop | (
cli_opts: Bunch, # noqa: B902
duration: int,
initial_dump: bool,
prefixes: List[str],
) | Snoop on fib streaming updates. | Snoop on fib streaming updates. | [
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] | def snoop(
cli_opts: Bunch, # noqa: B902
duration: int,
initial_dump: bool,
prefixes: List[str],
):
"""Snoop on fib streaming updates."""
fib.FibSnoopCmd(cli_opts).run(duration, initial_dump, prefixes) | [
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rapidsai/cudf | d5b2448fc69f17509304d594f029d0df56984962 | python/dask_cudf/dask_cudf/accessors.py | python | ListMethods.len | (self) | return self.d_series.map_partitions(
lambda s: s.list.len(), meta=self.d_series._meta
) | Computes the length of each element in the Series/Index.
Returns
-------
Series or Index
Examples
--------
>>> s = cudf.Series([[1, 2, 3], None, [4, 5]])
>>> ds = dask_cudf.from_cudf(s, 2)
>>> ds
0 [1, 2, 3]
1 None
2 [4, 5]
dtype: list
>>> ds.list.len().compute()
0 3
1 <NA>
2 2
dtype: int32 | Computes the length of each element in the Series/Index. | [
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"""
Computes the length of each element in the Series/Index.
Returns
-------
Series or Index
Examples
--------
>>> s = cudf.Series([[1, 2, 3], None, [4, 5]])
>>> ds = dask_cudf.from_cudf(s, 2)
>>> ds
0 [1, 2, 3]
1 None
2 [4, 5]
dtype: list
>>> ds.list.len().compute()
0 3
1 <NA>
2 2
dtype: int32
"""
return self.d_series.map_partitions(
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baidu/AnyQ | d94d450d2aaa5f7ed73424b10aa4539835b97527 | tools/simnet/train/tf/layers/tf_layers.py | python | AttentionLayer.ops | (self, input_x) | return output | operation | operation | [
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"""
operation
"""
input_shape = input_x.shape
sequence_length = input_shape[1].value
# suppose input_x is not time major
v = tf.tanh(tf.matmul(tf.reshape(input_x,
[-1, self.hidden_size]), self.W) + tf.reshape(self.b, [1, -1]))
vu = tf.matmul(v, tf.reshape(self.u, [-1, 1]))
exps = tf.reshape(tf.exp(vu), [-1, sequence_length])
alphas = exps / tf.reshape(tf.reduce_sum(exps, 1), [-1, 1])
# Output of Bi-RNN is reduced with attention vector
output = tf.reduce_sum(
input_x * tf.reshape(alphas, [-1, sequence_length, 1]), 1)
return output | [
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|
hpi-xnor/BMXNet-v2 | af2b1859eafc5c721b1397cef02f946aaf2ce20d | python/mxnet/ndarray/ndarray.py | python | NDArray.context | (self) | return Context(Context.devtype2str[dev_typeid.value], dev_id.value) | Device context of the array.
Examples
--------
>>> x = mx.nd.array([1, 2, 3, 4])
>>> x.context
cpu(0)
>>> type(x.context)
<class 'mxnet.context.Context'>
>>> y = mx.nd.zeros((2,3), mx.gpu(0))
>>> y.context
gpu(0) | Device context of the array. | [
"Device",
"context",
"of",
"the",
"array",
"."
] | def context(self):
"""Device context of the array.
Examples
--------
>>> x = mx.nd.array([1, 2, 3, 4])
>>> x.context
cpu(0)
>>> type(x.context)
<class 'mxnet.context.Context'>
>>> y = mx.nd.zeros((2,3), mx.gpu(0))
>>> y.context
gpu(0)
"""
dev_typeid = ctypes.c_int()
dev_id = ctypes.c_int()
check_call(_LIB.MXNDArrayGetContext(
self.handle, ctypes.byref(dev_typeid), ctypes.byref(dev_id)))
return Context(Context.devtype2str[dev_typeid.value], dev_id.value) | [
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|
bundy-dns/bundy | 3d41934996b82b0cd2fe22dd74d2abc1daba835d | src/lib/python/bundy/server_common/tsig_keyring.py | python | Updater.__update | (self, value=None, module_cfg=None) | Update the key ring by the configuration.
Note that this function is used as a callback, but can raise
on bad data. The bad data is expected to be handled by the
configuration plugin and not be allowed as far as here.
The parameters are there just to match the signature which
the callback should have (i.e. they are ignored). | Update the key ring by the configuration. | [
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] | def __update(self, value=None, module_cfg=None):
"""
Update the key ring by the configuration.
Note that this function is used as a callback, but can raise
on bad data. The bad data is expected to be handled by the
configuration plugin and not be allowed as far as here.
The parameters are there just to match the signature which
the callback should have (i.e. they are ignored).
"""
logger.debug(logger.DBGLVL_TRACE_BASIC,
PYSERVER_COMMON_TSIG_KEYRING_UPDATE)
(data, _) = self.__session.get_remote_config_value('tsig_keys', 'keys')
if data is not None: # There's an update
keyring = bundy.dns.TSIGKeyRing()
for key_data in data:
key = bundy.dns.TSIGKey(key_data)
if keyring.add(key) != bundy.dns.TSIGKeyRing.SUCCESS:
raise AddError("Can't add key " + str(key))
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||
FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Draft/draftfunctions/extrude.py | python | extrude | (obj, vector, solid=False) | return newobj | extrude(object, vector, [solid])
Create a Part::Extrusion object from a given object.
Parameters
----------
obj :
vector : Base.Vector
The extrusion direction and module.
solid : bool
TODO: describe. | extrude(object, vector, [solid])
Create a Part::Extrusion object from a given object. | [
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"""extrude(object, vector, [solid])
Create a Part::Extrusion object from a given object.
Parameters
----------
obj :
vector : Base.Vector
The extrusion direction and module.
solid : bool
TODO: describe.
"""
if not App.ActiveDocument:
App.Console.PrintError("No active document. Aborting\n")
return
newobj = App.ActiveDocument.addObject("Part::Extrusion", "Extrusion")
newobj.Base = obj
newobj.Dir = vector
newobj.Solid = solid
if App.GuiUp:
obj.ViewObject.Visibility = False
gui_utils.format_object(newobj,obj)
gui_utils.select(newobj)
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|
apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | deps/src/libxml2-2.9.1/python/libxml2.py | python | buildQName | (ncname, prefix, memory, len) | return ret | Builds the QName @prefix:@ncname in @memory if there is
enough space and prefix is not None nor empty, otherwise
allocate a new string. If prefix is None or empty it
returns ncname. | Builds the QName | [
"Builds",
"the",
"QName"
] | def buildQName(ncname, prefix, memory, len):
"""Builds the QName @prefix:@ncname in @memory if there is
enough space and prefix is not None nor empty, otherwise
allocate a new string. If prefix is None or empty it
returns ncname. """
ret = libxml2mod.xmlBuildQName(ncname, prefix, memory, len)
return ret | [
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|
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/AWSPythonSDK/1.5.8/botocore/hooks.py | python | BaseEventHooks.register | (self, event_name, handler, unique_id=None,
unique_id_uses_count=False) | Register an event handler for a given event.
If a ``unique_id`` is given, the handler will not be registered
if a handler with the ``unique_id`` has already been registered.
Handlers are called in the order they have been registered.
Note handlers can also be registered with ``register_first()``
and ``register_last()``. All handlers registered with
``register_first()`` are called before handlers registered
with ``register()`` which are called before handlers registered
with ``register_last()``. | Register an event handler for a given event. | [
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] | def register(self, event_name, handler, unique_id=None,
unique_id_uses_count=False):
"""Register an event handler for a given event.
If a ``unique_id`` is given, the handler will not be registered
if a handler with the ``unique_id`` has already been registered.
Handlers are called in the order they have been registered.
Note handlers can also be registered with ``register_first()``
and ``register_last()``. All handlers registered with
``register_first()`` are called before handlers registered
with ``register()`` which are called before handlers registered
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"""
self._verify_and_register(event_name, handler, unique_id,
register_method=self._register,
unique_id_uses_count=unique_id_uses_count) | [
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apache/incubator-mxnet | f03fb23f1d103fec9541b5ae59ee06b1734a51d9 | python/mxnet/ndarray/utils.py | python | array | (source_array, ctx=None, dtype=None) | Creates an array from any object exposing the array interface.
Parameters
----------
source_array : array_like
An object exposing the array interface, an object whose `__array__`
method returns an array, or any (nested) sequence.
ctx : Context, optional
Device context (default is the current default context).
dtype : str or numpy.dtype, optional
The data type of the output array. The default dtype is ``source_array.dtype``
if `source_array` is an `NDArray`, `float32` otherwise.
Returns
-------
NDArray, RowSparseNDArray or CSRNDArray
An array with the same contents as the `source_array`.
Examples
--------
>>> import numpy as np
>>> mx.nd.array([1, 2, 3])
<NDArray 3 @cpu(0)>
>>> mx.nd.array([[1, 2], [3, 4]])
<NDArray 2x2 @cpu(0)>
>>> mx.nd.array(np.zeros((3, 2)))
<NDArray 3x2 @cpu(0)>
>>> mx.nd.array(np.zeros((3, 2)), mx.gpu(0))
<NDArray 3x2 @gpu(0)>
>>> mx.nd.array(mx.nd.zeros((3, 2), stype='row_sparse'))
<RowSparseNDArray 3x2 @cpu(0)> | Creates an array from any object exposing the array interface. | [
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"interface",
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] | def array(source_array, ctx=None, dtype=None):
"""Creates an array from any object exposing the array interface.
Parameters
----------
source_array : array_like
An object exposing the array interface, an object whose `__array__`
method returns an array, or any (nested) sequence.
ctx : Context, optional
Device context (default is the current default context).
dtype : str or numpy.dtype, optional
The data type of the output array. The default dtype is ``source_array.dtype``
if `source_array` is an `NDArray`, `float32` otherwise.
Returns
-------
NDArray, RowSparseNDArray or CSRNDArray
An array with the same contents as the `source_array`.
Examples
--------
>>> import numpy as np
>>> mx.nd.array([1, 2, 3])
<NDArray 3 @cpu(0)>
>>> mx.nd.array([[1, 2], [3, 4]])
<NDArray 2x2 @cpu(0)>
>>> mx.nd.array(np.zeros((3, 2)))
<NDArray 3x2 @cpu(0)>
>>> mx.nd.array(np.zeros((3, 2)), mx.gpu(0))
<NDArray 3x2 @gpu(0)>
>>> mx.nd.array(mx.nd.zeros((3, 2), stype='row_sparse'))
<RowSparseNDArray 3x2 @cpu(0)>
"""
if spsp is not None and isinstance(source_array, spsp.csr.csr_matrix):
return _sparse_array(source_array, ctx=ctx, dtype=dtype)
elif isinstance(source_array, NDArray) and source_array.stype != 'default':
return _sparse_array(source_array, ctx=ctx, dtype=dtype)
else:
return _array(source_array, ctx=ctx, dtype=dtype) | [
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||
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/scipy/io/idl.py | python | _read_array | (f, typecode, array_desc) | return array | Read an array of type `typecode`, with the array descriptor given as
`array_desc`. | Read an array of type `typecode`, with the array descriptor given as
`array_desc`. | [
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] | def _read_array(f, typecode, array_desc):
'''
Read an array of type `typecode`, with the array descriptor given as
`array_desc`.
'''
if typecode in [1, 3, 4, 5, 6, 9, 13, 14, 15]:
if typecode == 1:
nbytes = _read_int32(f)
if nbytes != array_desc['nbytes']:
warnings.warn("Not able to verify number of bytes from header")
# Read bytes as numpy array
array = np.fromstring(f.read(array_desc['nbytes']),
dtype=DTYPE_DICT[typecode])
elif typecode in [2, 12]:
# These are 2 byte types, need to skip every two as they are not packed
array = np.fromstring(f.read(array_desc['nbytes']*2),
dtype=DTYPE_DICT[typecode])[1::2]
else:
# Read bytes into list
array = []
for i in range(array_desc['nelements']):
dtype = typecode
data = _read_data(f, dtype)
array.append(data)
array = np.array(array, dtype=np.object_)
# Reshape array if needed
if array_desc['ndims'] > 1:
dims = array_desc['dims'][:int(array_desc['ndims'])]
dims.reverse()
array = array.reshape(dims)
# Go to next alignment position
_align_32(f)
return array | [
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|
Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/debug/cli/curses_ui.py | python | CursesUI._screen_color_init | (self) | Initialization of screen colors. | Initialization of screen colors. | [
"Initialization",
"of",
"screen",
"colors",
"."
] | def _screen_color_init(self):
"""Initialization of screen colors."""
curses.start_color()
curses.use_default_colors()
self._color_pairs = {}
color_index = 0
# Prepare color pairs.
for fg_color in self._FOREGROUND_COLORS:
for bg_color in self._BACKGROUND_COLORS:
color_index += 1
curses.init_pair(color_index, self._FOREGROUND_COLORS[fg_color],
self._BACKGROUND_COLORS[bg_color])
color_name = fg_color
if bg_color != "transparent":
color_name += "_on_" + bg_color
self._color_pairs[color_name] = curses.color_pair(color_index)
# Try getting color(s) available only under 256-color support.
try:
color_index += 1
curses.init_pair(color_index, 245, -1)
self._color_pairs[cli_shared.COLOR_GRAY] = curses.color_pair(color_index)
except curses.error:
# Use fall-back color(s):
self._color_pairs[cli_shared.COLOR_GRAY] = (
self._color_pairs[cli_shared.COLOR_GREEN])
# A_BOLD or A_BLINK is not really a "color". But place it here for
# convenience.
self._color_pairs["bold"] = curses.A_BOLD
self._color_pairs["blink"] = curses.A_BLINK
self._color_pairs["underline"] = curses.A_UNDERLINE
# Default color pair to use when a specified color pair does not exist.
self._default_color_pair = self._color_pairs[cli_shared.COLOR_WHITE] | [
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] | https://github.com/Xilinx/Vitis-AI/blob/fc74d404563d9951b57245443c73bef389f3657f/tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/debug/cli/curses_ui.py#L406-L443 |
||
mamedev/mame | 02cd26d37ee11191f3e311e19e805d872cb1e3a4 | scripts/build/png.py | python | color_triple | (color) | Convert a command line colour value to a RGB triple of integers.
FIXME: Somewhere we need support for greyscale backgrounds etc. | Convert a command line colour value to a RGB triple of integers.
FIXME: Somewhere we need support for greyscale backgrounds etc. | [
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"FIXME",
":",
"Somewhere",
"we",
"need",
"support",
"for",
"greyscale",
"backgrounds",
"etc",
"."
] | def color_triple(color):
"""
Convert a command line colour value to a RGB triple of integers.
FIXME: Somewhere we need support for greyscale backgrounds etc.
"""
if color.startswith('#') and len(color) == 4:
return (int(color[1], 16),
int(color[2], 16),
int(color[3], 16))
if color.startswith('#') and len(color) == 7:
return (int(color[1:3], 16),
int(color[3:5], 16),
int(color[5:7], 16))
elif color.startswith('#') and len(color) == 13:
return (int(color[1:5], 16),
int(color[5:9], 16),
int(color[9:13], 16)) | [
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||
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_gdi.py | python | ImageList.AddIcon | (*args, **kwargs) | return _gdi_.ImageList_AddIcon(*args, **kwargs) | AddIcon(self, Icon icon) -> int | AddIcon(self, Icon icon) -> int | [
"AddIcon",
"(",
"self",
"Icon",
"icon",
")",
"-",
">",
"int"
] | def AddIcon(*args, **kwargs):
"""AddIcon(self, Icon icon) -> int"""
return _gdi_.ImageList_AddIcon(*args, **kwargs) | [
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|
miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/tensorflow/python/client/timeline.py | python | _TensorTracker.__init__ | (self, name, object_id, timestamp, pid, allocator, num_bytes) | Creates an object to track tensor references.
This class is not thread safe and is intended only for internal use by
the 'Timeline' class in this file.
Args:
name: The name of the Tensor as a string.
object_id: Chrome Trace object identifier assigned for this Tensor.
timestamp: The creation timestamp of this event as a long integer.
pid: Process identifier of the assicaiated device, as an integer.
allocator: Name of the allocator used to create the Tensor.
num_bytes: Number of bytes allocated (long integer).
Returns:
A 'TensorTracker' object. | Creates an object to track tensor references. | [
"Creates",
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"object",
"to",
"track",
"tensor",
"references",
"."
] | def __init__(self, name, object_id, timestamp, pid, allocator, num_bytes):
"""Creates an object to track tensor references.
This class is not thread safe and is intended only for internal use by
the 'Timeline' class in this file.
Args:
name: The name of the Tensor as a string.
object_id: Chrome Trace object identifier assigned for this Tensor.
timestamp: The creation timestamp of this event as a long integer.
pid: Process identifier of the assicaiated device, as an integer.
allocator: Name of the allocator used to create the Tensor.
num_bytes: Number of bytes allocated (long integer).
Returns:
A 'TensorTracker' object.
"""
self._name = name
self._pid = pid
self._object_id = object_id
self._create_time = timestamp
self._allocator = allocator
self._num_bytes = num_bytes
self._ref_times = []
self._unref_times = [] | [
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||
cvxpy/cvxpy | 5165b4fb750dfd237de8659383ef24b4b2e33aaf | cvxpy/lin_ops/lin_utils.py | python | is_const | (operator) | return operator.type in [lo.SCALAR_CONST,
lo.SPARSE_CONST,
lo.DENSE_CONST,
lo.PARAM] | Returns whether a LinOp is constant.
Parameters
----------
operator : LinOp
The LinOp to test.
Returns
-------
True if the LinOp is a constant, False otherwise. | Returns whether a LinOp is constant. | [
"Returns",
"whether",
"a",
"LinOp",
"is",
"constant",
"."
] | def is_const(operator) -> bool:
"""Returns whether a LinOp is constant.
Parameters
----------
operator : LinOp
The LinOp to test.
Returns
-------
True if the LinOp is a constant, False otherwise.
"""
return operator.type in [lo.SCALAR_CONST,
lo.SPARSE_CONST,
lo.DENSE_CONST,
lo.PARAM] | [
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|
okex/V3-Open-API-SDK | c5abb0db7e2287718e0055e17e57672ce0ec7fd9 | okex-python-sdk-api/venv/Lib/site-packages/pip-19.0.3-py3.8.egg/pip/_vendor/idna/core.py | python | uts46_remap | (domain, std3_rules=True, transitional=False) | Re-map the characters in the string according to UTS46 processing. | Re-map the characters in the string according to UTS46 processing. | [
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"-",
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"the",
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"in",
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"UTS46",
"processing",
"."
] | def uts46_remap(domain, std3_rules=True, transitional=False):
"""Re-map the characters in the string according to UTS46 processing."""
from .uts46data import uts46data
output = u""
try:
for pos, char in enumerate(domain):
code_point = ord(char)
uts46row = uts46data[code_point if code_point < 256 else
bisect.bisect_left(uts46data, (code_point, "Z")) - 1]
status = uts46row[1]
replacement = uts46row[2] if len(uts46row) == 3 else None
if (status == "V" or
(status == "D" and not transitional) or
(status == "3" and not std3_rules and replacement is None)):
output += char
elif replacement is not None and (status == "M" or
(status == "3" and not std3_rules) or
(status == "D" and transitional)):
output += replacement
elif status != "I":
raise IndexError()
return unicodedata.normalize("NFC", output)
except IndexError:
raise InvalidCodepoint(
"Codepoint {0} not allowed at position {1} in {2}".format(
_unot(code_point), pos + 1, repr(domain))) | [
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||
openmm/openmm | cb293447c4fc8b03976dfe11399f107bab70f3d9 | wrappers/python/openmm/app/internal/pdbx/reader/PdbxReader.py | python | PdbxReader.__getContainerName | (self,inWord) | return str(inWord[5:]).strip() | Returns the name of the data_ or save_ container | Returns the name of the data_ or save_ container | [
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"""
return str(inWord[5:]).strip() | [
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|
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/pip/_internal/index/package_finder.py | python | LinkEvaluator.evaluate_link | (self, link) | return (True, version) | Determine whether a link is a candidate for installation.
:return: A tuple (is_candidate, result), where `result` is (1) a
version string if `is_candidate` is True, and (2) if
`is_candidate` is False, an optional string to log the reason
the link fails to qualify. | [] | def evaluate_link(self, link):
# type: (Link) -> Tuple[bool, Optional[str]]
"""
Determine whether a link is a candidate for installation.
:return: A tuple (is_candidate, result), where `result` is (1) a
version string if `is_candidate` is True, and (2) if
`is_candidate` is False, an optional string to log the reason
the link fails to qualify.
"""
version = None
if link.is_yanked and not self._allow_yanked:
reason = link.yanked_reason or '<none given>'
return (False, f'yanked for reason: {reason}')
if link.egg_fragment:
egg_info = link.egg_fragment
ext = link.ext
else:
egg_info, ext = link.splitext()
if not ext:
return (False, 'not a file')
if ext not in SUPPORTED_EXTENSIONS:
return (False, f'unsupported archive format: {ext}')
if "binary" not in self._formats and ext == WHEEL_EXTENSION:
reason = 'No binaries permitted for {}'.format(
self.project_name)
return (False, reason)
if "macosx10" in link.path and ext == '.zip':
return (False, 'macosx10 one')
if ext == WHEEL_EXTENSION:
try:
wheel = Wheel(link.filename)
except InvalidWheelFilename:
return (False, 'invalid wheel filename')
if canonicalize_name(wheel.name) != self._canonical_name:
reason = 'wrong project name (not {})'.format(
self.project_name)
return (False, reason)
supported_tags = self._target_python.get_tags()
if not wheel.supported(supported_tags):
# Include the wheel's tags in the reason string to
# simplify troubleshooting compatibility issues.
file_tags = wheel.get_formatted_file_tags()
reason = (
"none of the wheel's tags match: {}".format(
', '.join(file_tags)
)
)
return (False, reason)
version = wheel.version
# This should be up by the self.ok_binary check, but see issue 2700.
if "source" not in self._formats and ext != WHEEL_EXTENSION:
reason = f'No sources permitted for {self.project_name}'
return (False, reason)
if not version:
version = _extract_version_from_fragment(
egg_info, self._canonical_name,
)
if not version:
reason = f'Missing project version for {self.project_name}'
return (False, reason)
match = self._py_version_re.search(version)
if match:
version = version[:match.start()]
py_version = match.group(1)
if py_version != self._target_python.py_version:
return (False, 'Python version is incorrect')
supports_python = _check_link_requires_python(
link, version_info=self._target_python.py_version_info,
ignore_requires_python=self._ignore_requires_python,
)
if not supports_python:
# Return None for the reason text to suppress calling
# _log_skipped_link().
return (False, None)
logger.debug('Found link %s, version: %s', link, version)
return (True, version) | [
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||
tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/distribute/coordinator/values.py | python | PerWorkerDistributedIterator.get_next | (self, name=None) | Returns the next input from the iterator for all replicas. | Returns the next input from the iterator for all replicas. | [
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] | def get_next(self, name=None):
"""Returns the next input from the iterator for all replicas."""
raise NotImplementedError("Iterating over an `AsyncDistributedIterator` "
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||
fengbingchun/NN_Test | d6305825d5273e4569ccd1eda9ffa2a9c72e18d2 | src/tiny-dnn/third_party/cpplint.py | python | _SetFilters | (filters) | Sets the module's error-message filters.
These filters are applied when deciding whether to emit a given
error message.
Args:
filters: A string of comma-separated filters (eg "whitespace/indent").
Each filter should start with + or -; else we die. | Sets the module's error-message filters. | [
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These filters are applied when deciding whether to emit a given
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filters: A string of comma-separated filters (eg "whitespace/indent").
Each filter should start with + or -; else we die.
"""
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cvmfs/cvmfs | 4637bdb5153178eadf885c1acf37bdc5c685bf8a | cpplint.py | python | CheckRedundantOverrideOrFinal | (filename, clean_lines, linenum, error) | Check if line contains a redundant "override" or "final" virt-specifier.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found. | Check if line contains a redundant "override" or "final" virt-specifier. | [
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"""Check if line contains a redundant "override" or "final" virt-specifier.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
# Look for closing parenthesis nearby. We need one to confirm where
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line = clean_lines.elided[linenum]
declarator_end = line.rfind(')')
if declarator_end >= 0:
fragment = line[declarator_end:]
else:
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fragment = line
else:
return
# Check that at most one of "override" or "final" is present, not both
if Search(r'\boverride\b', fragment) and Search(r'\bfinal\b', fragment):
error(filename, linenum, 'readability/inheritance', 4,
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emscripten-core/emscripten | 0d413d3c5af8b28349682496edc14656f5700c2f | third_party/ply/example/GardenSnake/GardenSnake.py | python | p_funcdef | (p) | funcdef : DEF NAME parameters COLON suite | funcdef : DEF NAME parameters COLON suite | [
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p[0] = ast.Function(None, p[2], tuple(p[3]), (), 0, None, p[5]) | [
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Kitware/ParaView | f760af9124ff4634b23ebbeab95a4f56e0261955 | Wrapping/Python/paraview/servermanager.py | python | ProxyManager.__init__ | (self, session=None) | Constructor. Assigned self.SMProxyManager to
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session = ActiveConnection.Session
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baoboa/pyqt5 | 11d5f43bc6f213d9d60272f3954a0048569cfc7c | configure.py | python | check_5_4_modules | (target_config, disabled_modules, verbose) | Check which modules introduced in Qt v5.4 can be built and update the
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] | https://github.com/baoboa/pyqt5/blob/11d5f43bc6f213d9d60272f3954a0048569cfc7c/configure.py#L1421-L1431 |
||
takemaru/graphillion | 51879f92bb96b53ef8f914ef37a05252ce383617 | graphillion/graphset.py | python | GraphSet.cycles | (is_hamilton=False, graphset=None) | return GraphSet.graphs(vertex_groups=[[]], degree_constraints=dc,
graphset=graphset) | Returns a GraphSet of cycles.
This method can be parallelized with OpenMP by specifying the
environmental variable `OMP_NUM_THREADS`:
`$ OMP_NUM_THREADS=4 python your_graphillion_script.py`
Examples:
>>> GraphSet.cycles(is_hamilton=True)
GraphSet([[(1, 2), (1, 4), (2, 3), (3, 6), (4, 5), (5, 6)]])
Args:
is_hamilton: Optional. True or False. If true, cycles must
be composed of all vertices.
graphset: Optional. A GraphSet object. Cycles to be stored
are selected from this object.
Returns:
A new GraphSet object.
See Also:
graphs() | Returns a GraphSet of cycles. | [
"Returns",
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"GraphSet",
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"."
] | def cycles(is_hamilton=False, graphset=None):
"""Returns a GraphSet of cycles.
This method can be parallelized with OpenMP by specifying the
environmental variable `OMP_NUM_THREADS`:
`$ OMP_NUM_THREADS=4 python your_graphillion_script.py`
Examples:
>>> GraphSet.cycles(is_hamilton=True)
GraphSet([[(1, 2), (1, 4), (2, 3), (3, 6), (4, 5), (5, 6)]])
Args:
is_hamilton: Optional. True or False. If true, cycles must
be composed of all vertices.
graphset: Optional. A GraphSet object. Cycles to be stored
are selected from this object.
Returns:
A new GraphSet object.
See Also:
graphs()
"""
dc = {}
for v in GraphSet._vertices:
dc[v] = 2 if is_hamilton else range(0, 3, 2)
return GraphSet.graphs(vertex_groups=[[]], degree_constraints=dc,
graphset=graphset) | [
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|
openvinotoolkit/openvino | dedcbeafa8b84cccdc55ca64b8da516682b381c7 | tools/pot/openvino/tools/pot/statistics/functions/weights.py | python | calculate_per_filter_stats | (weights, fn, transpose=False) | return fn(t, axis=1) | Calculates per-filter statistics for weights using a specific function
:param weights: model layer weights
:param fn: function to calculate per-filter statistics
:param transpose: transpose weights data from IOHW to OIHW to collect stats
:return statistics generated by fn | Calculates per-filter statistics for weights using a specific function
:param weights: model layer weights
:param fn: function to calculate per-filter statistics
:param transpose: transpose weights data from IOHW to OIHW to collect stats
:return statistics generated by fn | [
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:param weights: model layer weights
:param fn: function to calculate per-filter statistics
:param transpose: transpose weights data from IOHW to OIHW to collect stats
:return statistics generated by fn
"""
if transpose:
weights_shape = [1, 0]
original_axes = np.array(range(len(weights.shape)))
weights_shape.extend(original_axes[2:])
weights = np.transpose(weights, weights_shape)
if not weights.shape:
return fn(weights)
t = np.reshape(weights, (weights.shape[0], -1))
return fn(t, axis=1) | [
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|
visionworkbench/visionworkbench | eff1ee8f0efd70565292031d12c4b960db80f48f | graveyard/Plate/plate2kml.py | python | TileRegion.project_to_level | (self, level) | return TileRegion( level, proj_bbox ) | Return a TileRegion representing the extent of this TileRegion,
projected onto a different level of the tile pyramid. | Return a TileRegion representing the extent of this TileRegion,
projected onto a different level of the tile pyramid. | [
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"""
Return a TileRegion representing the extent of this TileRegion,
projected onto a different level of the tile pyramid.
"""
level_delta = level - self.level
if level_delta == 0:
return self
scale_factor = 2 ** level_delta
proj_bbox = BBox(
self.minx * scale_factor,
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self.width * scale_factor,
self.height * scale_factor
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if level_delta < 0:
# Ensure that region bounds are still integers
for prop in (proj_bbox.minx, proj_bbox.miny, proj_bbox.width, proj_bbox.height):
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return TileRegion( level, proj_bbox ) | [
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|
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py2/pandas/core/generic.py | python | NDFrame._add_series_or_dataframe_operations | (cls) | Add the series or dataframe only operations to the cls; evaluate
the doc strings again. | Add the series or dataframe only operations to the cls; evaluate
the doc strings again. | [
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] | def _add_series_or_dataframe_operations(cls):
"""
Add the series or dataframe only operations to the cls; evaluate
the doc strings again.
"""
from pandas.core import window as rwindow
@Appender(rwindow.rolling.__doc__)
def rolling(self, window, min_periods=None, center=False,
win_type=None, on=None, axis=0, closed=None):
axis = self._get_axis_number(axis)
return rwindow.rolling(self, window=window,
min_periods=min_periods,
center=center, win_type=win_type,
on=on, axis=axis, closed=closed)
cls.rolling = rolling
@Appender(rwindow.expanding.__doc__)
def expanding(self, min_periods=1, center=False, axis=0):
axis = self._get_axis_number(axis)
return rwindow.expanding(self, min_periods=min_periods,
center=center, axis=axis)
cls.expanding = expanding
@Appender(rwindow.ewm.__doc__)
def ewm(self, com=None, span=None, halflife=None, alpha=None,
min_periods=0, adjust=True, ignore_na=False,
axis=0):
axis = self._get_axis_number(axis)
return rwindow.ewm(self, com=com, span=span, halflife=halflife,
alpha=alpha, min_periods=min_periods,
adjust=adjust, ignore_na=ignore_na, axis=axis)
cls.ewm = ewm | [
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||
epiqc/ScaffCC | 66a79944ee4cd116b27bc1a69137276885461db8 | llvm/bindings/python/llvm/object.py | python | Symbol.expire | (self) | Mark the object as expired to prevent future API accesses.
This is called internally by this module and it is unlikely that
external callers have a legitimate reason for using it. | Mark the object as expired to prevent future API accesses. | [
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] | def expire(self):
"""Mark the object as expired to prevent future API accesses.
This is called internally by this module and it is unlikely that
external callers have a legitimate reason for using it.
"""
self.expired = True | [
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||
ProgerXP/Notepad2e | 71585758099ec07d61dd14ba806068c0d937efd3 | scintilla/scripts/Dependencies.py | python | ExtractDependencies | (input) | return deps | Create a list of dependencies from input list of lines
Each element contains the name of the object and a list of
files that it depends on.
Dependencies that contain "/usr/" are removed as they are system headers. | Create a list of dependencies from input list of lines
Each element contains the name of the object and a list of
files that it depends on.
Dependencies that contain "/usr/" are removed as they are system headers. | [
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] | def ExtractDependencies(input):
""" Create a list of dependencies from input list of lines
Each element contains the name of the object and a list of
files that it depends on.
Dependencies that contain "/usr/" are removed as they are system headers. """
deps = []
for line in input:
headersLine = line.startswith(" ") or line.startswith("\t")
line = line.strip()
isContinued = line.endswith("\\")
line = line.rstrip("\\ ")
fileNames = line.strip().split(" ")
if not headersLine:
# its a source file line, there may be headers too
sourceLine = fileNames[0].rstrip(":")
fileNames = fileNames[1:]
deps.append([sourceLine, []])
deps[-1][1].extend(header for header in fileNames if "/usr/" not in header)
return deps | [
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|
acbull/Unbiased_LambdaMart | 7c39abe5caa18ca07df2d23c2db392916d92956c | Unbias_LightGBM/python-package/lightgbm/basic.py | python | cint32_array_to_numpy | (cptr, length) | Convert a ctypes float pointer array to a numpy array. | Convert a ctypes float pointer array to a numpy array. | [
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"float",
"pointer",
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"array",
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] | def cint32_array_to_numpy(cptr, length):
"""Convert a ctypes float pointer array to a numpy array.
"""
if isinstance(cptr, ctypes.POINTER(ctypes.c_int32)):
return np.fromiter(cptr, dtype=np.int32, count=length)
else:
raise RuntimeError('Expected int pointer') | [
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||
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py2/sklearn/datasets/lfw.py | python | load_lfw_pairs | (download_if_missing=False, **kwargs) | return fetch_lfw_pairs(download_if_missing=download_if_missing, **kwargs) | Alias for fetch_lfw_pairs(download_if_missing=False)
.. deprecated:: 0.17
This function will be removed in 0.19.
Use :func:`sklearn.datasets.fetch_lfw_pairs` with parameter
``download_if_missing=False`` instead.
Check fetch_lfw_pairs.__doc__ for the documentation and parameter list. | Alias for fetch_lfw_pairs(download_if_missing=False) | [
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] | def load_lfw_pairs(download_if_missing=False, **kwargs):
"""
Alias for fetch_lfw_pairs(download_if_missing=False)
.. deprecated:: 0.17
This function will be removed in 0.19.
Use :func:`sklearn.datasets.fetch_lfw_pairs` with parameter
``download_if_missing=False`` instead.
Check fetch_lfw_pairs.__doc__ for the documentation and parameter list.
"""
return fetch_lfw_pairs(download_if_missing=download_if_missing, **kwargs) | [
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] | https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/scikit-learn/py2/sklearn/datasets/lfw.py#L517-L528 |
|
cvxpy/cvxpy | 5165b4fb750dfd237de8659383ef24b4b2e33aaf | cvxpy/interface/matrix_utilities.py | python | is_sparse_symmetric | (m, complex: bool = False) | return check | Check if a sparse matrix is symmetric
Parameters
----------
m : array or sparse matrix
A square matrix.
Returns
-------
check : bool
The check result. | Check if a sparse matrix is symmetric | [
"Check",
"if",
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] | def is_sparse_symmetric(m, complex: bool = False) -> bool:
"""Check if a sparse matrix is symmetric
Parameters
----------
m : array or sparse matrix
A square matrix.
Returns
-------
check : bool
The check result.
"""
# https://mail.scipy.org/pipermail/scipy-dev/2014-October/020101.html
if m.shape[0] != m.shape[1]:
raise ValueError('m must be a square matrix')
if not isinstance(m, sp.coo_matrix):
m = sp.coo_matrix(m)
r, c, v = m.row, m.col, m.data
tril_no_diag = r > c
triu_no_diag = c > r
if triu_no_diag.sum() != tril_no_diag.sum():
return False
rl = r[tril_no_diag]
cl = c[tril_no_diag]
vl = v[tril_no_diag]
ru = r[triu_no_diag]
cu = c[triu_no_diag]
vu = v[triu_no_diag]
sortl = np.lexsort((cl, rl))
sortu = np.lexsort((ru, cu))
vl = vl[sortl]
vu = vu[sortu]
if complex:
check = np.allclose(vl, np.conj(vu))
else:
check = np.allclose(vl, vu)
return check | [
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|
plumonito/dtslam | 5994bb9cf7a11981b830370db206bceb654c085d | 3rdparty/opencv-git/samples/python2/video.py | python | create_capture | (source = 0, fallback = presets['chess']) | return cap | source: <int> or '<int>|<filename>|synth [:<param_name>=<value> [:...]]' | source: <int> or '<int>|<filename>|synth [:<param_name>=<value> [:...]]' | [
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'''source: <int> or '<int>|<filename>|synth [:<param_name>=<value> [:...]]'
'''
source = str(source).strip()
chunks = source.split(':')
# handle drive letter ('c:', ...)
if len(chunks) > 1 and len(chunks[0]) == 1 and chunks[0].isalpha():
chunks[1] = chunks[0] + ':' + chunks[1]
del chunks[0]
source = chunks[0]
try: source = int(source)
except ValueError: pass
params = dict( s.split('=') for s in chunks[1:] )
cap = None
if source == 'synth':
Class = classes.get(params.get('class', None), VideoSynthBase)
try: cap = Class(**params)
except: pass
else:
cap = cv2.VideoCapture(source)
if 'size' in params:
w, h = map(int, params['size'].split('x'))
cap.set(cv2.CAP_PROP_FRAME_WIDTH, w)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, h)
if cap is None or not cap.isOpened():
print 'Warning: unable to open video source: ', source
if fallback is not None:
return create_capture(fallback, None)
return cap | [
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|
francinexue/xuefu | b6ff79747a42e020588c0c0a921048e08fe4680c | ctpx/ctp3/ctptd.py | python | CtpTd.onFrontDisconnected | (self, reasonCode) | 0x2003 收到错误报文 | 0x2003 收到错误报文 | [
"0x2003",
"收到错误报文"
] | def onFrontDisconnected(self, reasonCode):
"""0x2003 收到错误报文"""
pass | [
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||
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/aui.py | python | AuiToolBar.ClearTools | (*args, **kwargs) | return _aui.AuiToolBar_ClearTools(*args, **kwargs) | ClearTools(self) | ClearTools(self) | [
"ClearTools",
"(",
"self",
")"
] | def ClearTools(*args, **kwargs):
"""ClearTools(self)"""
return _aui.AuiToolBar_ClearTools(*args, **kwargs) | [
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|
natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/contrib/learn/python/learn/estimators/linear.py | python | LinearRegressor._get_predict_ops | (self, features) | return super(LinearRegressor, self)._get_predict_ops(features) | See base class. | See base class. | [
"See",
"base",
"class",
"."
] | def _get_predict_ops(self, features):
"""See base class."""
self._validate_linear_feature_columns(features)
return super(LinearRegressor, self)._get_predict_ops(features) | [
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|
apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | src/python/turicreate/toolkits/_tf_utils.py | python | convert_conv1d_coreml_to_tf | (conv_weights) | return np.squeeze(conv_weights, axis=3) | The Convolutional weights in CoreML specification converted to
the TensorFlow format for training in TensorFlow.
Parameters
----------
conv_weights: 4d numpy array of shape
[outputChannels, kernelChannels, kernelHeight, kernelWidth]
Returns
-------
return: 3d numpy array of shape
[kernelWidth, kernelChannels, outputChannels]
since kernelHeight = 1 for conv1d | The Convolutional weights in CoreML specification converted to
the TensorFlow format for training in TensorFlow. | [
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"specification",
"converted",
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"TensorFlow",
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"."
] | def convert_conv1d_coreml_to_tf(conv_weights):
"""
The Convolutional weights in CoreML specification converted to
the TensorFlow format for training in TensorFlow.
Parameters
----------
conv_weights: 4d numpy array of shape
[outputChannels, kernelChannels, kernelHeight, kernelWidth]
Returns
-------
return: 3d numpy array of shape
[kernelWidth, kernelChannels, outputChannels]
since kernelHeight = 1 for conv1d
"""
conv_weights = np.transpose(conv_weights, (3, 1, 0, 2))
return np.squeeze(conv_weights, axis=3) | [
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|
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/scipy/sparse/linalg/eigen/arpack/arpack.py | python | svds | (A, k=6, ncv=None, tol=0, which='LM', v0=None,
maxiter=None, return_singular_vectors=True) | return u, s, vh | Compute the largest k singular values/vectors for a sparse matrix.
Parameters
----------
A : {sparse matrix, LinearOperator}
Array to compute the SVD on, of shape (M, N)
k : int, optional
Number of singular values and vectors to compute.
Must be 1 <= k < min(A.shape).
ncv : int, optional
The number of Lanczos vectors generated
ncv must be greater than k+1 and smaller than n;
it is recommended that ncv > 2*k
Default: ``min(n, max(2*k + 1, 20))``
tol : float, optional
Tolerance for singular values. Zero (default) means machine precision.
which : str, ['LM' | 'SM'], optional
Which `k` singular values to find:
- 'LM' : largest singular values
- 'SM' : smallest singular values
.. versionadded:: 0.12.0
v0 : ndarray, optional
Starting vector for iteration, of length min(A.shape). Should be an
(approximate) left singular vector if N > M and a right singular
vector otherwise.
Default: random
.. versionadded:: 0.12.0
maxiter : int, optional
Maximum number of iterations.
.. versionadded:: 0.12.0
return_singular_vectors : bool or str, optional
- True: return singular vectors (True) in addition to singular values.
.. versionadded:: 0.12.0
- "u": only return the u matrix, without computing vh (if N > M).
- "vh": only return the vh matrix, without computing u (if N <= M).
.. versionadded:: 0.16.0
Returns
-------
u : ndarray, shape=(M, k)
Unitary matrix having left singular vectors as columns.
If `return_singular_vectors` is "vh", this variable is not computed,
and None is returned instead.
s : ndarray, shape=(k,)
The singular values.
vt : ndarray, shape=(k, N)
Unitary matrix having right singular vectors as rows.
If `return_singular_vectors` is "u", this variable is not computed,
and None is returned instead.
Notes
-----
This is a naive implementation using ARPACK as an eigensolver
on A.H * A or A * A.H, depending on which one is more efficient. | Compute the largest k singular values/vectors for a sparse matrix. | [
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] | def svds(A, k=6, ncv=None, tol=0, which='LM', v0=None,
maxiter=None, return_singular_vectors=True):
"""Compute the largest k singular values/vectors for a sparse matrix.
Parameters
----------
A : {sparse matrix, LinearOperator}
Array to compute the SVD on, of shape (M, N)
k : int, optional
Number of singular values and vectors to compute.
Must be 1 <= k < min(A.shape).
ncv : int, optional
The number of Lanczos vectors generated
ncv must be greater than k+1 and smaller than n;
it is recommended that ncv > 2*k
Default: ``min(n, max(2*k + 1, 20))``
tol : float, optional
Tolerance for singular values. Zero (default) means machine precision.
which : str, ['LM' | 'SM'], optional
Which `k` singular values to find:
- 'LM' : largest singular values
- 'SM' : smallest singular values
.. versionadded:: 0.12.0
v0 : ndarray, optional
Starting vector for iteration, of length min(A.shape). Should be an
(approximate) left singular vector if N > M and a right singular
vector otherwise.
Default: random
.. versionadded:: 0.12.0
maxiter : int, optional
Maximum number of iterations.
.. versionadded:: 0.12.0
return_singular_vectors : bool or str, optional
- True: return singular vectors (True) in addition to singular values.
.. versionadded:: 0.12.0
- "u": only return the u matrix, without computing vh (if N > M).
- "vh": only return the vh matrix, without computing u (if N <= M).
.. versionadded:: 0.16.0
Returns
-------
u : ndarray, shape=(M, k)
Unitary matrix having left singular vectors as columns.
If `return_singular_vectors` is "vh", this variable is not computed,
and None is returned instead.
s : ndarray, shape=(k,)
The singular values.
vt : ndarray, shape=(k, N)
Unitary matrix having right singular vectors as rows.
If `return_singular_vectors` is "u", this variable is not computed,
and None is returned instead.
Notes
-----
This is a naive implementation using ARPACK as an eigensolver
on A.H * A or A * A.H, depending on which one is more efficient.
"""
if not (isinstance(A, LinearOperator) or isspmatrix(A)):
A = np.asarray(A)
n, m = A.shape
if k <= 0 or k >= min(n, m):
raise ValueError("k must be between 1 and min(A.shape), k=%d" % k)
if isinstance(A, LinearOperator):
if n > m:
X_dot = A.matvec
X_matmat = A.matmat
XH_dot = A.rmatvec
else:
X_dot = A.rmatvec
XH_dot = A.matvec
dtype = getattr(A, 'dtype', None)
if dtype is None:
dtype = A.dot(np.zeros([m,1])).dtype
# A^H * V; works around lack of LinearOperator.adjoint.
# XXX This can be slow!
def X_matmat(V):
out = np.empty((V.shape[1], m), dtype=dtype)
for i, col in enumerate(V.T):
out[i, :] = A.rmatvec(col.reshape(-1, 1)).T
return out.T
else:
if n > m:
X_dot = X_matmat = A.dot
XH_dot = _herm(A).dot
else:
XH_dot = A.dot
X_dot = X_matmat = _herm(A).dot
def matvec_XH_X(x):
return XH_dot(X_dot(x))
XH_X = LinearOperator(matvec=matvec_XH_X, dtype=A.dtype,
shape=(min(A.shape), min(A.shape)))
# Get a low rank approximation of the implicitly defined gramian matrix.
# This is not a stable way to approach the problem.
eigvals, eigvec = eigsh(XH_X, k=k, tol=tol ** 2, maxiter=maxiter,
ncv=ncv, which=which, v0=v0)
# In 'LM' mode try to be clever about small eigenvalues.
# Otherwise in 'SM' mode do not try to be clever.
if which == 'LM':
# Gramian matrices have real non-negative eigenvalues.
eigvals = np.maximum(eigvals.real, 0)
# Use the sophisticated detection of small eigenvalues from pinvh.
t = eigvec.dtype.char.lower()
factor = {'f': 1E3, 'd': 1E6}
cond = factor[t] * np.finfo(t).eps
cutoff = cond * np.max(eigvals)
# Get a mask indicating which eigenpairs are not degenerately tiny,
# and create the re-ordered array of thresholded singular values.
above_cutoff = (eigvals > cutoff)
nlarge = above_cutoff.sum()
nsmall = k - nlarge
slarge = np.sqrt(eigvals[above_cutoff])
s = np.zeros_like(eigvals)
s[:nlarge] = slarge
if not return_singular_vectors:
return s
if n > m:
vlarge = eigvec[:, above_cutoff]
ularge = X_matmat(vlarge) / slarge if return_singular_vectors != 'vh' else None
vhlarge = _herm(vlarge)
else:
ularge = eigvec[:, above_cutoff]
vhlarge = _herm(X_matmat(ularge) / slarge) if return_singular_vectors != 'u' else None
u = _augmented_orthonormal_cols(ularge, nsmall) if ularge is not None else None
vh = _augmented_orthonormal_rows(vhlarge, nsmall) if vhlarge is not None else None
elif which == 'SM':
s = np.sqrt(eigvals)
if not return_singular_vectors:
return s
if n > m:
v = eigvec
u = X_matmat(v) / s if return_singular_vectors != 'vh' else None
vh = _herm(v)
else:
u = eigvec
vh = _herm(X_matmat(u) / s) if return_singular_vectors != 'u' else None
else:
raise ValueError("which must be either 'LM' or 'SM'.")
return u, s, vh | [
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|
apache/incubator-mxnet | f03fb23f1d103fec9541b5ae59ee06b1734a51d9 | python/mxnet/symbol/numpy/_symbol.py | python | bitwise_not | (x, out=None, **kwargs) | return _unary_func_helper(x, _npi.bitwise_not, _np.bitwise_not, out=out, **kwargs) | r"""
Compute bit-wise inversion, or bit-wise NOT, element-wise.
Computes the bit-wise NOT of the underlying binary representation of
the integers in the input arrays. This ufunc implements the C/Python
operator ``~``.
Parameters
----------
x : array_like
Only integer and boolean types are handled.
out : ndarray, None, or tuple of ndarray and None, optional
A location into which the result is stored. If provided, it must have
a shape that the inputs broadcast to. If not provided or `None`,
a freshly-allocated array is returned. A tuple (possible only as a
keyword argument) must have length equal to the number of outputs.
Returns
-------
out : ndarray or scalar
Result.
This is a scalar if `x` is a scalar.
See Also
--------
bitwise_and, bitwise_or, bitwise_xor
logical_not
binary_repr :
Return the binary representation of the input number as a string.
Examples
--------
We've seen that 13 is represented by ``00001101``.
The invert or bit-wise NOT of 13 is then:
>>> x = np.invert(np.array(13, dtype=np.uint8))
>>> x
242
>>> np.binary_repr(x, width=8)
'11110010'
Notes
-----
`bitwise_not` is an alias for `invert`:
>>> np.bitwise_not is np.invert
True | r"""
Compute bit-wise inversion, or bit-wise NOT, element-wise.
Computes the bit-wise NOT of the underlying binary representation of
the integers in the input arrays. This ufunc implements the C/Python
operator ``~``.
Parameters
----------
x : array_like
Only integer and boolean types are handled.
out : ndarray, None, or tuple of ndarray and None, optional
A location into which the result is stored. If provided, it must have
a shape that the inputs broadcast to. If not provided or `None`,
a freshly-allocated array is returned. A tuple (possible only as a
keyword argument) must have length equal to the number of outputs.
Returns
-------
out : ndarray or scalar
Result.
This is a scalar if `x` is a scalar.
See Also
--------
bitwise_and, bitwise_or, bitwise_xor
logical_not
binary_repr :
Return the binary representation of the input number as a string.
Examples
--------
We've seen that 13 is represented by ``00001101``.
The invert or bit-wise NOT of 13 is then:
>>> x = np.invert(np.array(13, dtype=np.uint8))
>>> x
242
>>> np.binary_repr(x, width=8)
'11110010'
Notes
-----
`bitwise_not` is an alias for `invert`:
>>> np.bitwise_not is np.invert
True | [
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r"""
Compute bit-wise inversion, or bit-wise NOT, element-wise.
Computes the bit-wise NOT of the underlying binary representation of
the integers in the input arrays. This ufunc implements the C/Python
operator ``~``.
Parameters
----------
x : array_like
Only integer and boolean types are handled.
out : ndarray, None, or tuple of ndarray and None, optional
A location into which the result is stored. If provided, it must have
a shape that the inputs broadcast to. If not provided or `None`,
a freshly-allocated array is returned. A tuple (possible only as a
keyword argument) must have length equal to the number of outputs.
Returns
-------
out : ndarray or scalar
Result.
This is a scalar if `x` is a scalar.
See Also
--------
bitwise_and, bitwise_or, bitwise_xor
logical_not
binary_repr :
Return the binary representation of the input number as a string.
Examples
--------
We've seen that 13 is represented by ``00001101``.
The invert or bit-wise NOT of 13 is then:
>>> x = np.invert(np.array(13, dtype=np.uint8))
>>> x
242
>>> np.binary_repr(x, width=8)
'11110010'
Notes
-----
`bitwise_not` is an alias for `invert`:
>>> np.bitwise_not is np.invert
True
"""
return _unary_func_helper(x, _npi.bitwise_not, _np.bitwise_not, out=out, **kwargs) | [
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|
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_gdi.py | python | GraphicsPath.Transform | (*args, **kwargs) | return _gdi_.GraphicsPath_Transform(*args, **kwargs) | Transform(self, GraphicsMatrix matrix)
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Transform(self, GraphicsMatrix matrix)
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py3/pandas/core/arrays/interval.py | python | _maybe_convert_platform_interval | (values) | return values | Try to do platform conversion, with special casing for IntervalArray.
Wrapper around maybe_convert_platform that alters the default return
dtype in certain cases to be compatible with IntervalArray. For example,
empty lists return with integer dtype instead of object dtype, which is
prohibited for IntervalArray.
Parameters
----------
values : array-like
Returns
-------
array | Try to do platform conversion, with special casing for IntervalArray.
Wrapper around maybe_convert_platform that alters the default return
dtype in certain cases to be compatible with IntervalArray. For example,
empty lists return with integer dtype instead of object dtype, which is
prohibited for IntervalArray. | [
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Wrapper around maybe_convert_platform that alters the default return
dtype in certain cases to be compatible with IntervalArray. For example,
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Parameters
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values : array-like
Returns
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array
"""
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# GH 19016
# empty lists/tuples get object dtype by default, but this is
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return np.array([], dtype=np.int64)
elif not is_list_like(values) or isinstance(values, ABCDataFrame):
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return values
elif is_categorical_dtype(values):
values = np.asarray(values)
elif not hasattr(values, "dtype") and not isinstance(values, (list, tuple, range)):
# TODO: should we just cast these to list?
return values
else:
values = extract_array(values, extract_numpy=True)
if not hasattr(values, "dtype"):
return np.asarray(values)
return values | [
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|
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/ResourceManager/lib/Crypto/Cipher/_mode_ccm.py | python | CcmMode.hexdigest | (self) | return "".join(["%02x" % bord(x) for x in self.digest()]) | Compute the *printable* MAC tag.
This method is like `digest`.
:Return: the MAC, as a hexadecimal string. | Compute the *printable* MAC tag. | [
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"""Compute the *printable* MAC tag.
This method is like `digest`.
:Return: the MAC, as a hexadecimal string.
"""
return "".join(["%02x" % bord(x) for x in self.digest()]) | [
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|
benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/contrib/tensor_forest/python/tensor_forest.py | python | RandomTreeGraphs.inference_graph | (self, input_data, data_spec, sparse_features=None) | return model_ops.tree_predictions_v4(
self.variables.tree,
input_data,
sparse_indices,
sparse_values,
sparse_shape,
input_spec=data_spec.SerializeToString(),
params=self.params.serialized_params_proto) | Constructs a TF graph for evaluating a random tree.
Args:
input_data: A tensor or placeholder for input data.
data_spec: A TensorForestDataSpec proto specifying the original
input columns.
sparse_features: A tf.SparseTensor for sparse input data.
Returns:
A tuple of (probabilities, tree_paths). | Constructs a TF graph for evaluating a random tree. | [
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] | def inference_graph(self, input_data, data_spec, sparse_features=None):
"""Constructs a TF graph for evaluating a random tree.
Args:
input_data: A tensor or placeholder for input data.
data_spec: A TensorForestDataSpec proto specifying the original
input columns.
sparse_features: A tf.SparseTensor for sparse input data.
Returns:
A tuple of (probabilities, tree_paths).
"""
sparse_indices = []
sparse_values = []
sparse_shape = []
if sparse_features is not None:
sparse_indices = sparse_features.indices
sparse_values = sparse_features.values
sparse_shape = sparse_features.dense_shape
if input_data is None:
input_data = []
return model_ops.tree_predictions_v4(
self.variables.tree,
input_data,
sparse_indices,
sparse_values,
sparse_shape,
input_spec=data_spec.SerializeToString(),
params=self.params.serialized_params_proto) | [
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|
google/iree | 1224bbdbe65b0d1fdf40e7324f60f68beeaf7c76 | llvm-external-projects/iree-dialects/python/iree/compiler/dialects/iree_pydm/importer/util.py | python | ImportContext.box | (self, value: ir.Value, to_typed: Optional[bool] = True) | Boxes a value if necessary. | Boxes a value if necessary. | [
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"value",
"if",
"necessary",
"."
] | def box(self, value: ir.Value, to_typed: Optional[bool] = True) -> ir.Value:
"""Boxes a value if necessary."""
with self.ip, self.loc:
t = value.type
if d.ObjectType.isinstance(t):
# Already boxed.
return value
boxed_type = d.ObjectType.get_typed(t) if to_typed else d.ObjectType.get()
return d.BoxOp(boxed_type, value).result | [
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||
cvxpy/cvxpy | 5165b4fb750dfd237de8659383ef24b4b2e33aaf | cvxpy/atoms/elementwise/abs.py | python | abs.is_atom_concave | (self) | return False | Is the atom concave? | Is the atom concave? | [
"Is",
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"atom",
"concave?"
] | def is_atom_concave(self) -> bool:
"""Is the atom concave?
"""
return False | [
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|
netket/netket | 0d534e54ecbf25b677ea72af6b85947979420652 | netket/graph/common_lattices.py | python | FCC | (
extent: Sequence[int], *, pbc: Union[bool, Sequence[bool]] = True, **kwargs
) | return Lattice(
basis_vectors=basis, extent=extent, pbc=pbc, point_group=point_group, **kwargs
) | Constructs an FCC lattice of a given spatial extent.
Periodic boundary conditions can also be imposed
Sites are returned at the Bravais lattice points.
Arguments:
extent: Number of primitive unit cells along each direction, needs
to be an array of length 3
pbc: If `True`, the lattice will have periodic boundary conditions (PBC);
if `False`, the lattice will have open boundary conditions (OBC).
This parameter can also be a list of booleans with same length as
the parameter `length`, in which case each dimension will have
PBC/OBC depending on the corresponding entry of `pbc`.
kwargs: Additional keyword arguments are passed on to the constructor of
:ref:`netket.graph.Lattice`.
Example:
Construct an FCC lattice with 3×3×3 primitive unit cells:
>>> from netket.graph import FCC
>>> g = FCC(extent=[3,3,3])
>>> print(g.n_nodes)
27 | Constructs an FCC lattice of a given spatial extent.
Periodic boundary conditions can also be imposed
Sites are returned at the Bravais lattice points. | [
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] | def FCC(
extent: Sequence[int], *, pbc: Union[bool, Sequence[bool]] = True, **kwargs
) -> Lattice:
"""Constructs an FCC lattice of a given spatial extent.
Periodic boundary conditions can also be imposed
Sites are returned at the Bravais lattice points.
Arguments:
extent: Number of primitive unit cells along each direction, needs
to be an array of length 3
pbc: If `True`, the lattice will have periodic boundary conditions (PBC);
if `False`, the lattice will have open boundary conditions (OBC).
This parameter can also be a list of booleans with same length as
the parameter `length`, in which case each dimension will have
PBC/OBC depending on the corresponding entry of `pbc`.
kwargs: Additional keyword arguments are passed on to the constructor of
:ref:`netket.graph.Lattice`.
Example:
Construct an FCC lattice with 3×3×3 primitive unit cells:
>>> from netket.graph import FCC
>>> g = FCC(extent=[3,3,3])
>>> print(g.n_nodes)
27
"""
basis = [[0, 0.5, 0.5], [0.5, 0, 0.5], [0.5, 0.5, 0]]
# determine if full point group is realised by the simulation box
point_group = cubic.Oh() if np.all(pbc) and len(set(extent)) == 1 else None
return Lattice(
basis_vectors=basis, extent=extent, pbc=pbc, point_group=point_group, **kwargs
) | [
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|
benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/framework/tensor_shape.py | python | Dimension.__eq__ | (self, other) | return self._value == other.value | Returns true if `other` has the same known value as this Dimension. | Returns true if `other` has the same known value as this Dimension. | [
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] | def __eq__(self, other):
"""Returns true if `other` has the same known value as this Dimension."""
try:
other = as_dimension(other)
except (TypeError, ValueError):
return NotImplemented
if self._value is None or other.value is None:
return None
return self._value == other.value | [
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|
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numpy/ma/core.py | python | compressed | (x) | return asanyarray(x).compressed() | Return all the non-masked data as a 1-D array.
This function is equivalent to calling the "compressed" method of a
`MaskedArray`, see `MaskedArray.compressed` for details.
See Also
--------
MaskedArray.compressed
Equivalent method. | Return all the non-masked data as a 1-D array. | [
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] | def compressed(x):
"""
Return all the non-masked data as a 1-D array.
This function is equivalent to calling the "compressed" method of a
`MaskedArray`, see `MaskedArray.compressed` for details.
See Also
--------
MaskedArray.compressed
Equivalent method.
"""
return asanyarray(x).compressed() | [
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|
pmq20/node-packer | 12c46c6e44fbc14d9ee645ebd17d5296b324f7e0 | current/tools/gyp/pylib/gyp/xcodeproj_file.py | python | XCObject.EnsureNoIDCollisions | (self) | Verifies that no two objects have the same ID. Checks all descendants. | Verifies that no two objects have the same ID. Checks all descendants. | [
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] | def EnsureNoIDCollisions(self):
"""Verifies that no two objects have the same ID. Checks all descendants.
"""
ids = {}
descendants = self.Descendants()
for descendant in descendants:
if descendant.id in ids:
other = ids[descendant.id]
raise KeyError(
'Duplicate ID %s, objects "%s" and "%s" in "%s"' % \
(descendant.id, str(descendant._properties),
str(other._properties), self._properties['rootObject'].Name()))
ids[descendant.id] = descendant | [
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||
pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | torch/fx/graph.py | python | _Namespace.associate_name_with_obj | (self, name: str, obj: Any) | Associate a unique name with an object.
Neither `name` nor `obj` should be associated already. | Associate a unique name with an object. | [
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] | def associate_name_with_obj(self, name: str, obj: Any):
"""Associate a unique name with an object.
Neither `name` nor `obj` should be associated already.
"""
assert obj not in self._obj_to_name
assert name in self._unassociated_names
self._obj_to_name[obj] = name
self._unassociated_names.remove(name) | [
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||
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/eclib/_filetree.py | python | FileTree.EnableLabelEditing | (self, enable=True) | Enable/Disable label editing. This functionality is
enabled by default.
@keyword enable: bool | Enable/Disable label editing. This functionality is
enabled by default.
@keyword enable: bool | [
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] | def EnableLabelEditing(self, enable=True):
"""Enable/Disable label editing. This functionality is
enabled by default.
@keyword enable: bool
"""
self._editlabels = enable | [
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||
trilinos/Trilinos | 6168be6dd51e35e1cd681e9c4b24433e709df140 | packages/seacas/scripts/exodus2.in.py | python | exodus.put_face_count_per_polyhedra | (self, blkID, entityCounts) | return True | status = exo.put_face_count_per_polyhedra(blkID, entityCounts)
-> put in a count of faces in for each polyhedra in an elem block
input values:
<int> blkID id of the block to be added
if array_type == 'ctype':
<list<float>> entityCounts
if array_type == 'numpy':
<np_array<double>> entityCounts
return value(s):
<bool> status True = successful execution | status = exo.put_face_count_per_polyhedra(blkID, entityCounts) | [
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"""
status = exo.put_face_count_per_polyhedra(blkID, entityCounts)
-> put in a count of faces in for each polyhedra in an elem block
input values:
<int> blkID id of the block to be added
if array_type == 'ctype':
<list<float>> entityCounts
if array_type == 'numpy':
<np_array<double>> entityCounts
return value(s):
<bool> status True = successful execution
"""
ebType = ex_entity_type("EX_ELEM_BLOCK")
entity_counts = (c_int * len(entityCounts))()
entity_counts[:] = entityCounts
EXODUS_LIB.ex_put_entity_count_per_polyhedra(
self.fileId, ebType, c_int(blkID), entity_counts)
return True | [
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|
adobe/chromium | cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7 | chrome/tools/build/win/scan_server_dlls.py | python | ScanServerDlls | (config, distribution, output_dir) | return registered_dll_list | Scans for DLLs in the specified section of config that are in the
subdirectory of output_dir named SERVERS_DIR. Returns a list of only the
filename components of the paths to all matching DLLs. | Scans for DLLs in the specified section of config that are in the
subdirectory of output_dir named SERVERS_DIR. Returns a list of only the
filename components of the paths to all matching DLLs. | [
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] | def ScanServerDlls(config, distribution, output_dir):
"""Scans for DLLs in the specified section of config that are in the
subdirectory of output_dir named SERVERS_DIR. Returns a list of only the
filename components of the paths to all matching DLLs.
"""
print "Scanning for server DLLs in " + output_dir
registered_dll_list = []
ScanDllsInSection(config, 'GENERAL', output_dir, registered_dll_list)
if distribution:
if len(distribution) > 1 and distribution[0] == '_':
distribution = distribution[1:]
ScanDllsInSection(config, distribution.upper(), output_dir,
registered_dll_list)
return registered_dll_list | [
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|
miyosuda/TensorFlowAndroidMNIST | 7b5a4603d2780a8a2834575706e9001977524007 | jni-build/jni/include/tensorflow/python/ops/control_flow_ops.py | python | GradLoopState.AddBackPropAccumulatedValue | (self, history_value, value,
dead_branch=False) | return pop | Add the getter for an accumulated value in the grad context.
This is added to the backprop loop. Called in the grad context to
get the value of an accumulated value. The stack pop op must be guarded
by the pred of the controlling cond.
Args:
history_value: The history (a stack) of a value.
value: The value that is pushed onto the stack.
dead_branch: True iff the tensor is on a dead branch of a cond.
Returns:
The current value (the top of the stack). | Add the getter for an accumulated value in the grad context. | [
"Add",
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"for",
"an",
"accumulated",
"value",
"in",
"the",
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"context",
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] | def AddBackPropAccumulatedValue(self, history_value, value,
dead_branch=False):
"""Add the getter for an accumulated value in the grad context.
This is added to the backprop loop. Called in the grad context to
get the value of an accumulated value. The stack pop op must be guarded
by the pred of the controlling cond.
Args:
history_value: The history (a stack) of a value.
value: The value that is pushed onto the stack.
dead_branch: True iff the tensor is on a dead branch of a cond.
Returns:
The current value (the top of the stack).
"""
history_ctxt = history_value.op._get_control_flow_context()
# Find the cond context that controls history_value.
cond_ctxt = None
value_ctxt = value.op._get_control_flow_context()
while value_ctxt and value_ctxt != history_ctxt:
if isinstance(value_ctxt, CondContext):
cond_ctxt = value_ctxt
break
value_ctxt = value_ctxt.outer_context
with ops.control_dependencies(None):
self.grad_context.Enter()
if cond_ctxt:
# Guard stack pop with a switch if it is controlled by a cond
grad_state = self
pred = None
while pred is None and grad_state:
pred = grad_state.history_map.get(cond_ctxt.pred.name)
grad_state = grad_state.outer_grad_state
branch = (1 - cond_ctxt.branch) if dead_branch else cond_ctxt.branch
history_value = _SwitchRefOrTensor(history_value, pred)[branch]
pop = gen_data_flow_ops._stack_pop(history_value, value.dtype.base_dtype)
self.grad_context.Exit()
parallel_iterations = self.grad_context.parallel_iterations
if parallel_iterations is not None and parallel_iterations > 1:
# All pops are ordered after pivot_for_body and before grad_sync.
self.grad_sync._add_control_input(pop.op)
return pop | [
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|
psi4/psi4 | be533f7f426b6ccc263904e55122899b16663395 | psi4/driver/ipi_broker.py | python | IPIBroker.calculate_gradient | (self, LOT, bypass_scf=False, **kwargs) | Calculate the gradient with @LOT.
When bypass_scf=True a hf energy calculation has been done before. | Calculate the gradient with @LOT. | [
"Calculate",
"the",
"gradient",
"with",
"@LOT",
"."
] | def calculate_gradient(self, LOT, bypass_scf=False, **kwargs):
"""Calculate the gradient with @LOT.
When bypass_scf=True a hf energy calculation has been done before.
"""
start = time.time()
self.grd = psi4.gradient(LOT, bypass_scf=bypass_scf, **kwargs)
time_needed = time.time() - start
self.timing[LOT] = self.timing.get(LOT, []) + [time_needed] | [
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||
generalized-intelligence/GAAS | 29ab17d3e8a4ba18edef3a57c36d8db6329fac73 | deprecated/algorithms/sfm/OpenSfM/opensfm/geo.py | python | TopocentricConverter.__init__ | (self, reflat, reflon, refalt) | Init the converter given the reference origin. | Init the converter given the reference origin. | [
"Init",
"the",
"converter",
"given",
"the",
"reference",
"origin",
"."
] | def __init__(self, reflat, reflon, refalt):
"""Init the converter given the reference origin."""
self.lat = reflat
self.lon = reflon
self.alt = refalt | [
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||
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/autocomp/autocomp.py | python | MetaCompleter.__call__ | (mcs, base, buff) | return obj | Modify the base class with our new methods at time of
instantiation. | Modify the base class with our new methods at time of
instantiation. | [
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] | def __call__(mcs, base, buff):
"""Modify the base class with our new methods at time of
instantiation.
"""
obj = type.__call__(mcs, base, buff)
# Set/override attributes on the new completer object.
setattr(obj, 'BaseGetAutoCompList', obj.GetAutoCompList)
setattr(obj, 'GetAutoCompList', lambda cmd: GetAutoCompList(obj, cmd))
setattr(obj, 'scomp', simplecomp.Completer(buff))
# Return the new augmented completer
return obj | [
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|
mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | scripts/Inelastic/Direct/DirectEnergyConversion.py | python | DirectEnergyConversion.prop_man | (self,value) | Assign new instance of direct property manager to provide DirectEnergyConversion parameters | Assign new instance of direct property manager to provide DirectEnergyConversion parameters | [
"Assign",
"new",
"instance",
"of",
"direct",
"property",
"manager",
"to",
"provide",
"DirectEnergyConversion",
"parameters"
] | def prop_man(self,value):
""" Assign new instance of direct property manager to provide DirectEnergyConversion parameters """
if isinstance(value,PropertyManager):
self._propMan = value
else:
raise KeyError("Property manager can be initialized by an instance of ProperyManager only") | [
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||
learnforpractice/pyeos | 4f04eb982c86c1fdb413084af77c713a6fda3070 | libraries/vm/vm_cpython_ss/lib/codecs.py | python | StreamReaderWriter.__next__ | (self) | return next(self.reader) | Return the next decoded line from the input stream. | Return the next decoded line from the input stream. | [
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] | def __next__(self):
""" Return the next decoded line from the input stream."""
return next(self.reader) | [
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|
panda3d/panda3d | 833ad89ebad58395d0af0b7ec08538e5e4308265 | samples/networking/03-distributed-node/ClientRepository.py | python | GameClientRepository.lostConnection | (self) | This should be overridden by a derived class to handle an
unexpectedly lost connection to the gameserver. | This should be overridden by a derived class to handle an
unexpectedly lost connection to the gameserver. | [
"This",
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] | def lostConnection(self):
""" This should be overridden by a derived class to handle an
unexpectedly lost connection to the gameserver. """
# Handle the disconnection from the server. This can be a reconnect,
# simply exiting the application or anything else.
exit() | [
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||
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/ResourceManager/lib/Crypto/Hash/CMAC.py | python | CMAC.update | (self, msg) | return self | Authenticate the next chunk of message.
Args:
data (byte string/byte array/memoryview): The next chunk of data | Authenticate the next chunk of message. | [
"Authenticate",
"the",
"next",
"chunk",
"of",
"message",
"."
] | def update(self, msg):
"""Authenticate the next chunk of message.
Args:
data (byte string/byte array/memoryview): The next chunk of data
"""
if self._mac_tag is not None and not self._update_after_digest:
raise TypeError("update() cannot be called after digest() or verify()")
self._data_size += len(msg)
bs = self._block_size
if self._cache_n > 0:
filler = min(bs - self._cache_n, len(msg))
self._cache[self._cache_n:self._cache_n+filler] = msg[:filler]
self._cache_n += filler
if self._cache_n < bs:
return self
msg = memoryview(msg)[filler:]
self._update(self._cache)
self._cache_n = 0
remain = len(msg) % bs
if remain > 0:
self._update(msg[:-remain])
self._cache[:remain] = msg[-remain:]
else:
self._update(msg)
self._cache_n = remain
return self | [
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|
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/ResourceManager/lib/Crypto/Protocol/KDF.py | python | PBKDF1 | (password, salt, dkLen, count=1000, hashAlgo=None) | return pHash.digest()[:dkLen] | Derive one key from a password (or passphrase).
This function performs key derivation according to an old version of
the PKCS#5 standard (v1.5) or `RFC2898
<https://www.ietf.org/rfc/rfc2898.txt>`_.
Args:
password (string):
The secret password to generate the key from.
salt (byte string):
An 8 byte string to use for better protection from dictionary attacks.
This value does not need to be kept secret, but it should be randomly
chosen for each derivation.
dkLen (integer):
The length of the desired key. The default is 16 bytes, suitable for
instance for :mod:`Crypto.Cipher.AES`.
count (integer):
The number of iterations to carry out. The recommendation is 1000 or
more.
hashAlgo (module):
The hash algorithm to use, as a module or an object from the :mod:`Crypto.Hash` package.
The digest length must be no shorter than ``dkLen``.
The default algorithm is :mod:`Crypto.Hash.SHA1`.
Return:
A byte string of length ``dkLen`` that can be used as key. | Derive one key from a password (or passphrase). | [
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"from",
"a",
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] | def PBKDF1(password, salt, dkLen, count=1000, hashAlgo=None):
"""Derive one key from a password (or passphrase).
This function performs key derivation according to an old version of
the PKCS#5 standard (v1.5) or `RFC2898
<https://www.ietf.org/rfc/rfc2898.txt>`_.
Args:
password (string):
The secret password to generate the key from.
salt (byte string):
An 8 byte string to use for better protection from dictionary attacks.
This value does not need to be kept secret, but it should be randomly
chosen for each derivation.
dkLen (integer):
The length of the desired key. The default is 16 bytes, suitable for
instance for :mod:`Crypto.Cipher.AES`.
count (integer):
The number of iterations to carry out. The recommendation is 1000 or
more.
hashAlgo (module):
The hash algorithm to use, as a module or an object from the :mod:`Crypto.Hash` package.
The digest length must be no shorter than ``dkLen``.
The default algorithm is :mod:`Crypto.Hash.SHA1`.
Return:
A byte string of length ``dkLen`` that can be used as key.
"""
if not hashAlgo:
hashAlgo = SHA1
password = tobytes(password)
pHash = hashAlgo.new(password+salt)
digest = pHash.digest_size
if dkLen > digest:
raise TypeError("Selected hash algorithm has a too short digest (%d bytes)." % digest)
if len(salt) != 8:
raise ValueError("Salt is not 8 bytes long (%d bytes instead)." % len(salt))
for i in iter_range(count-1):
pHash = pHash.new(pHash.digest())
return pHash.digest()[:dkLen] | [
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|
SFTtech/openage | d6a08c53c48dc1e157807471df92197f6ca9e04d | openage/util/fslike/path.py | python | Path.with_suffix | (self, suffix) | return self.parent.joinpath(self.stem + suffix) | Returns path for different suffix (same parent and stem). | Returns path for different suffix (same parent and stem). | [
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] | def with_suffix(self, suffix):
""" Returns path for different suffix (same parent and stem). """
if isinstance(suffix, bytes):
suffix = suffix.decode()
return self.parent.joinpath(self.stem + suffix) | [
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|
metashell/metashell | f4177e4854ea00c8dbc722cadab26ef413d798ea | 3rd/templight/llvm/utils/docker/scripts/llvm_checksum/llvm_checksum.py | python | ReadLLVMChecksums | (f) | return checksums | Reads checksums from a text file, produced by WriteLLVMChecksums.
Returns:
A dict, mapping from project name to project checksum. | Reads checksums from a text file, produced by WriteLLVMChecksums. | [
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] | def ReadLLVMChecksums(f):
"""Reads checksums from a text file, produced by WriteLLVMChecksums.
Returns:
A dict, mapping from project name to project checksum.
"""
checksums = {}
while True:
line = f.readline()
if line == "":
break
checksum, proj = line.split()
checksums[proj] = checksum
return checksums | [
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|
mongodb/mongo | d8ff665343ad29cf286ee2cf4a1960d29371937b | buildscripts/task_generation/suite_split.py | python | GeneratedSuite.sub_suite_task_name | (self, index: Optional[int] = None) | return taskname.name_generated_task(self.task_name, index, len(self.sub_suites),
self.build_variant) | Get the name of the task that runs one of the generated sub-suites.
:param index: Index of suite or None for '_misc' suite.
:return: Name of generated Evergreen task. | Get the name of the task that runs one of the generated sub-suites. | [
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] | def sub_suite_task_name(self, index: Optional[int] = None) -> str:
"""
Get the name of the task that runs one of the generated sub-suites.
:param index: Index of suite or None for '_misc' suite.
:return: Name of generated Evergreen task.
"""
return taskname.name_generated_task(self.task_name, index, len(self.sub_suites),
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|
dicecco1/fpga_caffe | 7a191704efd7873071cfef35772d7e7bf3e3cfd6 | scripts/cpp_lint.py | python | _BlockInfo.CheckBegin | (self, filename, clean_lines, linenum, error) | Run checks that applies to text up to the opening brace.
This is mostly for checking the text after the class identifier
and the "{", usually where the base class is specified. For other
blocks, there isn't much to check, so we always pass.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found. | Run checks that applies to text up to the opening brace. | [
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] | def CheckBegin(self, filename, clean_lines, linenum, error):
"""Run checks that applies to text up to the opening brace.
This is mostly for checking the text after the class identifier
and the "{", usually where the base class is specified. For other
blocks, there isn't much to check, so we always pass.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
pass | [
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||
argman/EAST | dca414de39a3a4915a019c9a02c1832a31cdd0ca | icdar.py | python | check_and_validate_polys | (polys, tags, xxx_todo_changeme) | return np.array(validated_polys), np.array(validated_tags) | check so that the text poly is in the same direction,
and also filter some invalid polygons
:param polys:
:param tags:
:return: | check so that the text poly is in the same direction,
and also filter some invalid polygons
:param polys:
:param tags:
:return: | [
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'''
check so that the text poly is in the same direction,
and also filter some invalid polygons
:param polys:
:param tags:
:return:
'''
(h, w) = xxx_todo_changeme
if polys.shape[0] == 0:
return polys
polys[:, :, 0] = np.clip(polys[:, :, 0], 0, w-1)
polys[:, :, 1] = np.clip(polys[:, :, 1], 0, h-1)
validated_polys = []
validated_tags = []
for poly, tag in zip(polys, tags):
p_area = polygon_area(poly)
if abs(p_area) < 1:
# print poly
print('invalid poly')
continue
if p_area > 0:
print('poly in wrong direction')
poly = poly[(0, 3, 2, 1), :]
validated_polys.append(poly)
validated_tags.append(tag)
return np.array(validated_polys), np.array(validated_tags) | [
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|
panda3d/panda3d | 833ad89ebad58395d0af0b7ec08538e5e4308265 | makepanda/installpanda.py | python | GetDebLibDir | () | return "lib" | Returns the lib dir according to the debian system. | Returns the lib dir according to the debian system. | [
"Returns",
"the",
"lib",
"dir",
"according",
"to",
"the",
"debian",
"system",
"."
] | def GetDebLibDir():
""" Returns the lib dir according to the debian system. """
# We're on Debian or Ubuntu, which use multiarch directories.
# Call dpkg-architecture to get the multiarch libdir.
handle = os.popen("dpkg-architecture -qDEB_HOST_MULTIARCH")
multiarch = handle.read().strip()
if handle.close():
# It failed. Old Debian/Ubuntu version?
pass
elif len(multiarch) > 0:
return "lib/" + multiarch
return "lib" | [
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|
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py2/sklearn/datasets/samples_generator.py | python | make_friedman2 | (n_samples=100, noise=0.0, random_state=None) | return X, y | Generate the "Friedman \#2" regression problem
This dataset is described in Friedman [1] and Breiman [2].
Inputs `X` are 4 independent features uniformly distributed on the
intervals::
0 <= X[:, 0] <= 100,
40 * pi <= X[:, 1] <= 560 * pi,
0 <= X[:, 2] <= 1,
1 <= X[:, 3] <= 11.
The output `y` is created according to the formula::
y(X) = (X[:, 0] ** 2 + (X[:, 1] * X[:, 2] \
- 1 / (X[:, 1] * X[:, 3])) ** 2) ** 0.5 + noise * N(0, 1).
Read more in the :ref:`User Guide <sample_generators>`.
Parameters
----------
n_samples : int, optional (default=100)
The number of samples.
noise : float, optional (default=0.0)
The standard deviation of the gaussian noise applied to the output.
random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
Returns
-------
X : array of shape [n_samples, 4]
The input samples.
y : array of shape [n_samples]
The output values.
References
----------
.. [1] J. Friedman, "Multivariate adaptive regression splines", The Annals
of Statistics 19 (1), pages 1-67, 1991.
.. [2] L. Breiman, "Bagging predictors", Machine Learning 24,
pages 123-140, 1996. | Generate the "Friedman \#2" regression problem | [
"Generate",
"the",
"Friedman",
"\\",
"#2",
"regression",
"problem"
] | def make_friedman2(n_samples=100, noise=0.0, random_state=None):
"""Generate the "Friedman \#2" regression problem
This dataset is described in Friedman [1] and Breiman [2].
Inputs `X` are 4 independent features uniformly distributed on the
intervals::
0 <= X[:, 0] <= 100,
40 * pi <= X[:, 1] <= 560 * pi,
0 <= X[:, 2] <= 1,
1 <= X[:, 3] <= 11.
The output `y` is created according to the formula::
y(X) = (X[:, 0] ** 2 + (X[:, 1] * X[:, 2] \
- 1 / (X[:, 1] * X[:, 3])) ** 2) ** 0.5 + noise * N(0, 1).
Read more in the :ref:`User Guide <sample_generators>`.
Parameters
----------
n_samples : int, optional (default=100)
The number of samples.
noise : float, optional (default=0.0)
The standard deviation of the gaussian noise applied to the output.
random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
Returns
-------
X : array of shape [n_samples, 4]
The input samples.
y : array of shape [n_samples]
The output values.
References
----------
.. [1] J. Friedman, "Multivariate adaptive regression splines", The Annals
of Statistics 19 (1), pages 1-67, 1991.
.. [2] L. Breiman, "Bagging predictors", Machine Learning 24,
pages 123-140, 1996.
"""
generator = check_random_state(random_state)
X = generator.rand(n_samples, 4)
X[:, 0] *= 100
X[:, 1] *= 520 * np.pi
X[:, 1] += 40 * np.pi
X[:, 3] *= 10
X[:, 3] += 1
y = (X[:, 0] ** 2
+ (X[:, 1] * X[:, 2] - 1 / (X[:, 1] * X[:, 3])) ** 2) ** 0.5 \
+ noise * generator.randn(n_samples)
return X, y | [
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|
hughperkins/tf-coriander | 970d3df6c11400ad68405f22b0c42a52374e94ca | tensorflow/python/training/basic_session_run_hooks.py | python | SummarySaverHook.__init__ | (self,
save_steps=100,
output_dir=None,
summary_writer=None,
scaffold=None,
summary_op=None) | Initializes a `SummarySaver` monitor.
Args:
save_steps: `int`, save summaries every N steps. See `EveryN`.
output_dir: `string`, the directory to save the summaries to. Only used
if no `summary_writer` is supplied.
summary_writer: `SummaryWriter`. If `None` and an `output_dir` was passed,
one will be created accordingly.
scaffold: `Scaffold` to get summary_op if it's not provided.
summary_op: `Tensor` of type `string`. A serialized `Summary` protocol
buffer, as output by TF summary methods like `scalar_summary` or
`merge_all_summaries`. | Initializes a `SummarySaver` monitor. | [
"Initializes",
"a",
"SummarySaver",
"monitor",
"."
] | def __init__(self,
save_steps=100,
output_dir=None,
summary_writer=None,
scaffold=None,
summary_op=None):
"""Initializes a `SummarySaver` monitor.
Args:
save_steps: `int`, save summaries every N steps. See `EveryN`.
output_dir: `string`, the directory to save the summaries to. Only used
if no `summary_writer` is supplied.
summary_writer: `SummaryWriter`. If `None` and an `output_dir` was passed,
one will be created accordingly.
scaffold: `Scaffold` to get summary_op if it's not provided.
summary_op: `Tensor` of type `string`. A serialized `Summary` protocol
buffer, as output by TF summary methods like `scalar_summary` or
`merge_all_summaries`.
"""
# TODO(ipolosukhin): Implement every N seconds.
self._summary_op = summary_op
self._summary_writer = summary_writer
if summary_writer is None and output_dir:
self._summary_writer = SummaryWriterCache.get(output_dir)
self._scaffold = scaffold
self._save_steps = save_steps | [
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||
koth/kcws | 88efbd36a7022de4e6e90f5a1fb880cf87cfae9f | third_party/python/cpplint/cpplint.py | python | FindNextMultiLineCommentStart | (lines, lineix) | return len(lines) | Find the beginning marker for a multiline comment. | Find the beginning marker for a multiline comment. | [
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] | def FindNextMultiLineCommentStart(lines, lineix):
"""Find the beginning marker for a multiline comment."""
while lineix < len(lines):
if lines[lineix].strip().startswith('/*'):
# Only return this marker if the comment goes beyond this line
if lines[lineix].strip().find('*/', 2) < 0:
return lineix
lineix += 1
return len(lines) | [
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|
trilinos/Trilinos | 6168be6dd51e35e1cd681e9c4b24433e709df140 | packages/seacas/scripts/exomerge2.py | python | ExodusModel.get_node_field_values | (self, node_field_name, timestep='last') | return self.node_fields[node_field_name][timestep_index] | Return the list of node field values for the given field and timestep.
This returns the actual list of values, so any modifications to the
list will be retained in the model.
Examples:
>>> model.get_node_field_values('disp_x')
>>> model.get_node_field_values('disp_x', 0.0) | Return the list of node field values for the given field and timestep. | [
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] | def get_node_field_values(self, node_field_name, timestep='last'):
"""
Return the list of node field values for the given field and timestep.
This returns the actual list of values, so any modifications to the
list will be retained in the model.
Examples:
>>> model.get_node_field_values('disp_x')
>>> model.get_node_field_values('disp_x', 0.0)
"""
[node_field_name] = self._format_id_list(
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timestep_index = self._get_internal_timestep_index(timestep)
return self.node_fields[node_field_name][timestep_index] | [
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|
Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/compiler/tf2xla/python/xla.py | python | reduce_window | (operand,
init,
reducer,
window_dimensions,
window_strides=None,
base_dilations=None,
window_dilations=None,
padding=None,
name=None) | return gen_xla_ops.xla_reduce_window(
input=operand,
init_value=init,
window_dimensions=window_dimensions,
window_strides=window_strides,
base_dilations=base_dilations,
window_dilations=window_dilations,
padding=padding,
computation=reducer,
name=name) | Wraps the XLA ReduceWindow operator.
ReduceWindow is documented at
https://www.tensorflow.org/performance/xla/operation_semantics#reducewindow .
Args:
operand: the input tensor
init: a scalar tensor representing the initial value for the reduction
reducer: a reduction function that combines a pair of scalars.
window_dimensions: shape of the window, as a list of integers
window_strides: inter-window strides, as a list of integers. Optional; if
omitted, defaults to strides of 1.
padding: padding to apply to 'operand'. List of (low, high) pairs of
integers that specify the padding to apply before and after each
dimension. Optional; if omitted, defaults to no padding.
name: the operator name, or None.
Returns:
A tensor that represents the output of the reduce_window operator. | Wraps the XLA ReduceWindow operator. | [
"Wraps",
"the",
"XLA",
"ReduceWindow",
"operator",
"."
] | def reduce_window(operand,
init,
reducer,
window_dimensions,
window_strides=None,
base_dilations=None,
window_dilations=None,
padding=None,
name=None):
"""Wraps the XLA ReduceWindow operator.
ReduceWindow is documented at
https://www.tensorflow.org/performance/xla/operation_semantics#reducewindow .
Args:
operand: the input tensor
init: a scalar tensor representing the initial value for the reduction
reducer: a reduction function that combines a pair of scalars.
window_dimensions: shape of the window, as a list of integers
window_strides: inter-window strides, as a list of integers. Optional; if
omitted, defaults to strides of 1.
padding: padding to apply to 'operand'. List of (low, high) pairs of
integers that specify the padding to apply before and after each
dimension. Optional; if omitted, defaults to no padding.
name: the operator name, or None.
Returns:
A tensor that represents the output of the reduce_window operator.
"""
window_strides = window_strides or [1] * len(window_dimensions)
base_dilations = base_dilations or [1] * len(window_dimensions)
window_dilations = window_dilations or [1] * len(window_dimensions)
padding = padding or [(0, 0)] * len(window_dimensions)
return gen_xla_ops.xla_reduce_window(
input=operand,
init_value=init,
window_dimensions=window_dimensions,
window_strides=window_strides,
base_dilations=base_dilations,
window_dilations=window_dilations,
padding=padding,
computation=reducer,
name=name) | [
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|
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/protobuf/py3/google/protobuf/symbol_database.py | python | SymbolDatabase.RegisterMessage | (self, message) | return message | Registers the given message type in the local database.
Calls to GetSymbol() and GetMessages() will return messages registered here.
Args:
message: A :class:`google.protobuf.message.Message` subclass (or
instance); its descriptor will be registered.
Returns:
The provided message. | Registers the given message type in the local database. | [
"Registers",
"the",
"given",
"message",
"type",
"in",
"the",
"local",
"database",
"."
] | def RegisterMessage(self, message):
"""Registers the given message type in the local database.
Calls to GetSymbol() and GetMessages() will return messages registered here.
Args:
message: A :class:`google.protobuf.message.Message` subclass (or
instance); its descriptor will be registered.
Returns:
The provided message.
"""
desc = message.DESCRIPTOR
self._classes[desc] = message
self.RegisterMessageDescriptor(desc)
return message | [
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|
ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | tools/perf/benchmarks/indexeddb_perf.py | python | _IndexedDbMeasurement.DidStartBrowser | (self, browser) | Initialize metrics once right after the browser has been launched. | Initialize metrics once right after the browser has been launched. | [
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"launched",
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] | def DidStartBrowser(self, browser):
"""Initialize metrics once right after the browser has been launched."""
self._memory_metric = memory.MemoryMetric(browser)
self._v8_object_stats_metric = (
v8_object_stats.V8ObjectStatsMetric(_V8_COUNTER_NAMES)) | [
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||
thalium/icebox | 99d147d5b9269222225443ce171b4fd46d8985d4 | third_party/virtualbox/src/libs/libxml2-2.9.4/python/libxml2class.py | python | parserCtxt.lineNumbers | (self, linenumbers) | Switch on the generation of line number for elements nodes. | Switch on the generation of line number for elements nodes. | [
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"number",
"for",
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] | def lineNumbers(self, linenumbers):
"""Switch on the generation of line number for elements nodes. """
libxml2mod.xmlParserSetLineNumbers(self._o, linenumbers) | [
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||
google/clif | cab24d6a105609a65c95a36a1712ae3c20c7b5df | clif/python/ast_manipulations.py | python | MoveExtendPropertiesBackIntoClassesInPlace | (ast, class_decls_by_fq_native) | See module docstring. | See module docstring. | [
"See",
"module",
"docstring",
"."
] | def MoveExtendPropertiesBackIntoClassesInPlace(ast, class_decls_by_fq_native):
"""See module docstring."""
extend_properties_orig_decl_indices = []
for orig_decl_index, decl in enumerate(ast.decls):
if decl.decltype != ast_pb2.Decl.Type.VAR:
continue
if not decl.var.is_extend_variable:
continue
fq_native_from_var, property_name_from_var = decl.var.name.native.split(
EXTEND_INFIX, 1)
target_class_decl = class_decls_by_fq_native[fq_native_from_var]
property_decl = ast_pb2.Decl()
property_decl.CopyFrom(decl)
property_decl.var.name.native = property_name_from_var
if property_decl.var.HasField('cpp_set'):
del property_decl.var.cpp_set.params[0]
target_class_decl.members.append(property_decl)
extend_properties_orig_decl_indices.append(orig_decl_index)
for orig_decl_index in reversed(extend_properties_orig_decl_indices):
del ast.decls[orig_decl_index] | [
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||
LLNL/lbann | 26083e6c86050302ce33148aea70f62e61cacb92 | applications/ATOM/train_atom_char_rnn.py | python | construct_data_reader | (run_args) | return message | Construct Protobuf message for Python data reader.
The Python data reader will import this Python file to access the
sample access functions. | Construct Protobuf message for Python data reader. | [
"Construct",
"Protobuf",
"message",
"for",
"Python",
"data",
"reader",
"."
] | def construct_data_reader(run_args):
"""
Construct Protobuf message for Python data reader.
The Python data reader will import this Python file to access the
sample access functions.
"""
module_file = os.path.abspath(run_args.data_module_file)
os.environ["DATA_CONFIG"] = os.path.abspath(run_args.data_config)
module_name = os.path.splitext(os.path.basename(module_file))[0]
module_dir = os.path.dirname(module_file)
print("module_name: {}\tmodule_dir: {}".format(module_name, module_dir))
# Base data reader message
message = lbann.reader_pb2.DataReader()
# Training set data reader
data_reader = message.reader.add()
data_reader.name = "python"
data_reader.role = "train"
data_reader.shuffle = True
data_reader.percent_of_data_to_use = 1.0
data_reader.python.module = module_name
data_reader.python.module_dir = module_dir
data_reader.python.sample_function = "get_sample"
data_reader.python.num_samples_function = "num_samples"
data_reader.python.sample_dims_function = "sample_dims"
return message | [
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|
amrayn/easyloggingpp | 8489989bb26c6371df103f6cbced3fbee1bc3c2f | tools/cpplint.py | python | CheckPosixThreading | (filename, clean_lines, linenum, error) | Checks for calls to thread-unsafe functions.
Much code has been originally written without consideration of
multi-threading. Also, engineers are relying on their old experience;
they have learned posix before threading extensions were added. These
tests guide the engineers to use thread-safe functions (when using
posix directly).
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found. | Checks for calls to thread-unsafe functions. | [
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"for",
"calls",
"to",
"thread",
"-",
"unsafe",
"functions",
"."
] | def CheckPosixThreading(filename, clean_lines, linenum, error):
"""Checks for calls to thread-unsafe functions.
Much code has been originally written without consideration of
multi-threading. Also, engineers are relying on their old experience;
they have learned posix before threading extensions were added. These
tests guide the engineers to use thread-safe functions (when using
posix directly).
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
for single_thread_function, multithread_safe_function in threading_list:
ix = line.find(single_thread_function)
# Comparisons made explicit for clarity -- pylint: disable=g-explicit-bool-comparison
if ix >= 0 and (ix == 0 or (not line[ix - 1].isalnum() and
line[ix - 1] not in ('_', '.', '>'))):
error(filename, linenum, 'runtime/threadsafe_fn', 2,
'Consider using ' + multithread_safe_function +
'...) instead of ' + single_thread_function +
'...) for improved thread safety.') | [
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||
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/richtext.py | python | RichTextCtrl.GetDefaultStyle | (*args, **kwargs) | return _richtext.RichTextCtrl_GetDefaultStyle(*args, **kwargs) | GetDefaultStyle(self) -> RichTextAttr
Retrieves a copy of the default style object. | GetDefaultStyle(self) -> RichTextAttr | [
"GetDefaultStyle",
"(",
"self",
")",
"-",
">",
"RichTextAttr"
] | def GetDefaultStyle(*args, **kwargs):
"""
GetDefaultStyle(self) -> RichTextAttr
Retrieves a copy of the default style object.
"""
return _richtext.RichTextCtrl_GetDefaultStyle(*args, **kwargs) | [
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|
Z3Prover/z3 | d745d03afdfdf638d66093e2bfbacaf87187f35b | src/api/python/z3/z3.py | python | Solver.reset | (self) | Remove all asserted constraints and backtracking points created using `push()`.
>>> x = Int('x')
>>> s = Solver()
>>> s.add(x > 0)
>>> s
[x > 0]
>>> s.reset()
>>> s
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] | def reset(self):
"""Remove all asserted constraints and backtracking points created using `push()`.
>>> x = Int('x')
>>> s = Solver()
>>> s.add(x > 0)
>>> s
[x > 0]
>>> s.reset()
>>> s
[]
"""
Z3_solver_reset(self.ctx.ref(), self.solver) | [
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||
SonarOpenCommunity/sonar-cxx | 6e1d456fdcd45d35bcdc61c980e34d85fe88971e | cxx-sensors/src/tools/vc_createrules.py | python | read_warnings | () | return warnings | Read warnings from HTML pages.
- root pages are defined in URLS
- special property values are defined in RULE_MAP | Read warnings from HTML pages.
- root pages are defined in URLS
- special property values are defined in RULE_MAP | [
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] | def read_warnings():
"""
Read warnings from HTML pages.
- root pages are defined in URLS
- special property values are defined in RULE_MAP
"""
# page contains JavaScript. Use Firefox to create HTML page
# you have to download and install https://github.com/mozilla/geckodriver/releases
browser = webdriver.Firefox(executable_path=r'C:\Program Files\geckodriver\geckodriver.exe')
# read links to warning pages from menu of overview pages
warnings = {}
for url, properties in URLS.items():
page_source = read_page_source(browser, url)
parse_warning_hrefs(page_source, warnings)
for key, warning in warnings.items():
assign_warning_properties(warning, properties, False)
# warnings = dict(list(warnings.items())[:1]) # for testing only
# sort warnings ascending by message number
warnings = dict(sorted(warnings.items(), key=sorter))
# read cotent of warning pages
read_warning_pages(browser, warnings)
# override defaults
for key, defaults in RULE_MAP.items():
if key in warnings:
warning = warnings[key]
assign_warning_properties(warning, defaults, True)
# close browser
browser.quit()
return warnings | [
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|
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/pydoc.py | python | locate | (path, forceload=0) | return object | Locate an object by name or dotted path, importing as necessary. | Locate an object by name or dotted path, importing as necessary. | [
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"object",
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"dotted",
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"as",
"necessary",
"."
] | def locate(path, forceload=0):
"""Locate an object by name or dotted path, importing as necessary."""
parts = [part for part in path.split('.') if part]
module, n = None, 0
while n < len(parts):
nextmodule = safeimport('.'.join(parts[:n+1]), forceload)
if nextmodule: module, n = nextmodule, n + 1
else: break
if module:
object = module
else:
object = builtins
for part in parts[n:]:
try:
object = getattr(object, part)
except AttributeError:
return None
return object | [
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|
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | contrib/gizmos/msw/gizmos.py | python | TreeListCtrl.AssignButtonsImageList | (*args, **kwargs) | return _gizmos.TreeListCtrl_AssignButtonsImageList(*args, **kwargs) | AssignButtonsImageList(self, ImageList imageList) | AssignButtonsImageList(self, ImageList imageList) | [
"AssignButtonsImageList",
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] | def AssignButtonsImageList(*args, **kwargs):
"""AssignButtonsImageList(self, ImageList imageList)"""
return _gizmos.TreeListCtrl_AssignButtonsImageList(*args, **kwargs) | [
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|
hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/webapp2/webapp2.py | python | _to_utf8 | (value) | return value.encode('utf-8') | Encodes a unicode value to UTF-8 if not yet encoded. | Encodes a unicode value to UTF-8 if not yet encoded. | [
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] | def _to_utf8(value):
"""Encodes a unicode value to UTF-8 if not yet encoded."""
if isinstance(value, str):
return value
return value.encode('utf-8') | [
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|
quantOS-org/DataCore | e2ef9bd2c22ee9e2845675b6435a14fa607f3551 | mdlink/deps/windows/protobuf-2.5.0/python/google/protobuf/message_factory.py | python | _GetAllDescriptors | (desc_protos, package) | Gets all levels of nested message types as a flattened list of descriptors.
Args:
desc_protos: The descriptor protos to process.
package: The package where the protos are defined.
Yields:
Each message descriptor for each nested type. | Gets all levels of nested message types as a flattened list of descriptors. | [
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] | def _GetAllDescriptors(desc_protos, package):
"""Gets all levels of nested message types as a flattened list of descriptors.
Args:
desc_protos: The descriptor protos to process.
package: The package where the protos are defined.
Yields:
Each message descriptor for each nested type.
"""
for desc_proto in desc_protos:
name = '.'.join((package, desc_proto.name))
yield _POOL.FindMessageTypeByName(name)
for nested_desc in _GetAllDescriptors(desc_proto.nested_type, name):
yield nested_desc | [
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||
google/sentencepiece | 8420f2179007c398c8b70f63cb12d8aec827397c | python/src/sentencepiece/__init__.py | python | _batchnize | (classname, name) | Enables batch request for the method classname.name. | Enables batch request for the method classname.name. | [
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] | def _batchnize(classname, name):
"""Enables batch request for the method classname.name."""
func = getattr(classname, name, None)
def _func(v, n):
if type(n) is int and (n < 0 or n >= v.piece_size()):
raise IndexError('piece id is out of range.')
return func(v, n)
def _batched_func(self, arg):
if type(arg) is list:
return [_func(self, n) for n in arg]
else:
return _func(self, arg)
setattr(classname, name, _batched_func) | [
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||
baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/keras/python/keras/backend.py | python | mean | (x, axis=None, keepdims=False) | return math_ops.reduce_mean(x, axis=axis, keep_dims=keepdims) | Mean of a tensor, alongside the specified axis.
Arguments:
x: A tensor or variable.
axis: A list of integer. Axes to compute the mean.
keepdims: A boolean, whether to keep the dimensions or not.
If `keepdims` is `False`, the rank of the tensor is reduced
by 1 for each entry in `axis`. If `keep_dims` is `True`,
the reduced dimensions are retained with length 1.
Returns:
A tensor with the mean of elements of `x`. | Mean of a tensor, alongside the specified axis. | [
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] | def mean(x, axis=None, keepdims=False):
"""Mean of a tensor, alongside the specified axis.
Arguments:
x: A tensor or variable.
axis: A list of integer. Axes to compute the mean.
keepdims: A boolean, whether to keep the dimensions or not.
If `keepdims` is `False`, the rank of the tensor is reduced
by 1 for each entry in `axis`. If `keep_dims` is `True`,
the reduced dimensions are retained with length 1.
Returns:
A tensor with the mean of elements of `x`.
"""
axis = _normalize_axis(axis, ndim(x))
if x.dtype.base_dtype == dtypes_module.bool:
x = math_ops.cast(x, floatx())
return math_ops.reduce_mean(x, axis=axis, keep_dims=keepdims) | [
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|
isl-org/Open3D | 79aec3ddde6a571ce2f28e4096477e52ec465244 | examples/python/visualization/online_processing.py | python | PipelineController.on_toggle_capture | (self, is_enabled) | Callback to toggle capture. | Callback to toggle capture. | [
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"."
] | def on_toggle_capture(self, is_enabled):
"""Callback to toggle capture."""
self.pipeline_model.flag_capture = is_enabled
if not is_enabled:
self.on_toggle_record(False)
if self.pipeline_view.toggle_record is not None:
self.pipeline_view.toggle_record.is_on = False
else:
with self.pipeline_model.cv_capture:
self.pipeline_model.cv_capture.notify() | [
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||
ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | build/android/pylib/android_commands.py | python | AndroidCommands.GetBuildType | (self) | return build_type | Returns the build type of the system (e.g. eng). | Returns the build type of the system (e.g. eng). | [
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] | def GetBuildType(self):
"""Returns the build type of the system (e.g. eng)."""
build_type = self.system_properties['ro.build.type']
assert build_type
return build_type | [
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|
s9xie/DSN | 065e49898d239f5c96be558616b2556eabc50351 | scripts/cpp_lint.py | python | CloseExpression | (clean_lines, linenum, pos) | return (line, clean_lines.NumLines(), -1) | If input points to ( or { or [ or <, finds the position that closes it.
If lines[linenum][pos] points to a '(' or '{' or '[' or '<', finds the
linenum/pos that correspond to the closing 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 *past* the closing brace, or
(line, len(lines), -1) if we never find a close. Note we ignore
strings and comments when matching; and the line we return is the
'cleansed' line at linenum. | If input points to ( or { or [ or <, finds the position that closes it. | [
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"it",
"."
] | def CloseExpression(clean_lines, linenum, pos):
"""If input points to ( or { or [ or <, finds the position that closes it.
If lines[linenum][pos] points to a '(' or '{' or '[' or '<', finds the
linenum/pos that correspond to the closing 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 *past* the closing brace, or
(line, len(lines), -1) if we never find a close. Note we ignore
strings and comments when matching; and the line we return is the
'cleansed' line at linenum.
"""
line = clean_lines.elided[linenum]
startchar = line[pos]
if startchar not in '({[<':
return (line, clean_lines.NumLines(), -1)
if startchar == '(': endchar = ')'
if startchar == '[': endchar = ']'
if startchar == '{': endchar = '}'
if startchar == '<': endchar = '>'
# Check first line
(end_pos, num_open) = FindEndOfExpressionInLine(
line, pos, 0, startchar, endchar)
if end_pos > -1:
return (line, linenum, end_pos)
# Continue scanning forward
while linenum < clean_lines.NumLines() - 1:
linenum += 1
line = clean_lines.elided[linenum]
(end_pos, num_open) = FindEndOfExpressionInLine(
line, 0, num_open, startchar, endchar)
if end_pos > -1:
return (line, linenum, end_pos)
# Did not find endchar before end of file, give up
return (line, clean_lines.NumLines(), -1) | [
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|
microsoft/checkedc-clang | a173fefde5d7877b7750e7ce96dd08cf18baebf2 | lldb/examples/python/gdbremote.py | python | TerminalColors.bold | (self, on=True) | return '' | Enable or disable bold depending on the "on" parameter. | Enable or disable bold depending on the "on" parameter. | [
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] | def bold(self, on=True):
'''Enable or disable bold depending on the "on" parameter.'''
if self.enabled:
if on:
return "\x1b[1m"
else:
return "\x1b[22m"
return '' | [
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|
eclipse/sumo | 7132a9b8b6eea734bdec38479026b4d8c4336d03 | tools/traci/_simulation.py | python | SimulationDomain.getNetBoundary | (self) | return self._getUniversal(tc.VAR_NET_BOUNDING_BOX) | getNetBoundary() -> ((double, double), (double, double))
The boundary box of the simulation network. | getNetBoundary() -> ((double, double), (double, double)) | [
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] | def getNetBoundary(self):
"""getNetBoundary() -> ((double, double), (double, double))
The boundary box of the simulation network.
"""
return self._getUniversal(tc.VAR_NET_BOUNDING_BOX) | [
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|
TGAC/KAT | e8870331de2b4bb0a1b3b91c6afb8fb9d59e9216 | deps/boost/tools/build/src/build/property.py | python | evaluate_conditionals_in_context | (properties, context) | return result | Removes all conditional properties which conditions are not met
For those with met conditions, removes the condition. Properies
in conditions are looked up in 'context' | Removes all conditional properties which conditions are not met
For those with met conditions, removes the condition. Properies
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""" Removes all conditional properties which conditions are not met
For those with met conditions, removes the condition. Properies
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"""
if __debug__:
from .property_set import PropertySet
assert is_iterable_typed(properties, Property)
assert isinstance(context, PropertySet)
base = []
conditional = []
for p in properties:
if p.condition:
conditional.append (p)
else:
base.append (p)
result = base[:]
for p in conditional:
# Evaluate condition
# FIXME: probably inefficient
if all(x in context for x in p.condition):
result.append(Property(p.feature, p.value))
return result | [
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|
Studio3T/robomongo | 2411cd032e2e69b968dadda13ac91ca4ef3483b0 | src/third-party/qscintilla-2.8.4/sources/Python/configure.py | python | ModuleConfiguration.get_sip_installs | (self, target_configuration) | return path, files | Return a tuple of the installation directory of the module's .sip
files and a sequence of the names of each of the .sip files relative to
the directory containing this configuration script. None is returned
if the module's .sip files are not to be installed.
target_configuration is the target configuration. | Return a tuple of the installation directory of the module's .sip
files and a sequence of the names of each of the .sip files relative to
the directory containing this configuration script. None is returned
if the module's .sip files are not to be installed.
target_configuration is the target configuration. | [
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""" Return a tuple of the installation directory of the module's .sip
files and a sequence of the names of each of the .sip files relative to
the directory containing this configuration script. None is returned
if the module's .sip files are not to be installed.
target_configuration is the target configuration.
"""
if target_configuration.qsci_sip_dir == '':
return None
path = os.path.join(target_configuration.qsci_sip_dir, 'Qsci')
files = glob.glob('sip/*.sip')
return path, files | [
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|
hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/dashboard/dashboard/graph_json.py | python | _GetSubTestDict | (test_paths) | return subtests | Gets a dict of test suite path to sub test dict.
Args:
test_paths: List of test paths.
Returns:
Dictionary of test suite path to sub-test tree (see
list_tests.GetSubTests). | Gets a dict of test suite path to sub test dict. | [
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] | def _GetSubTestDict(test_paths):
"""Gets a dict of test suite path to sub test dict.
Args:
test_paths: List of test paths.
Returns:
Dictionary of test suite path to sub-test tree (see
list_tests.GetSubTests).
"""
subtests = {}
for test_path in test_paths:
path_parts = test_path.split('/')
bot_path = '/'.join(path_parts[0:2])
test_suite_path = '/'.join(path_parts[0:3])
test_suite = path_parts[2]
if test_suite_path not in subtests:
subtests[test_suite_path] = {}
subtests[test_suite_path] = list_tests.GetSubTests(test_suite, [bot_path])
return subtests | [
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|
PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | python/paddle/fluid/layers/control_flow.py | python | StaticRNN.step | (self) | return BlockGuardWithCompletion(self) | Define operators in each step. step is used in :code:`with` block, OP in :code:`with` block
will be executed sequence_len times (sequence_len is the length of input) | Define operators in each step. step is used in :code:`with` block, OP in :code:`with` block
will be executed sequence_len times (sequence_len is the length of input) | [
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] | def step(self):
"""
Define operators in each step. step is used in :code:`with` block, OP in :code:`with` block
will be executed sequence_len times (sequence_len is the length of input)
"""
return BlockGuardWithCompletion(self) | [
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