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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_controls.py | python | HelpProvider.RemoveHelp | (*args, **kwargs) | return _controls_.HelpProvider_RemoveHelp(*args, **kwargs) | RemoveHelp(self, Window window)
Removes the association between the window pointer and the help
text. This is called by the wx.Window destructor. Without this, the
table of help strings will fill up and when window pointers are
reused, the wrong help string will be found. | RemoveHelp(self, Window window) | [
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"""
RemoveHelp(self, Window window)
Removes the association between the window pointer and the help
text. This is called by the wx.Window destructor. Without this, the
table of help strings will fill up and when window pointers are
reused, the wrong help string will be found.
"""
return _controls_.HelpProvider_RemoveHelp(*args, **kwargs) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_gdi.py | python | RendererNative.GetDefault | (*args, **kwargs) | return _gdi_.RendererNative_GetDefault(*args, **kwargs) | GetDefault() -> RendererNative
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domino-team/openwrt-cc | 8b181297c34d14d3ca521cc9f31430d561dbc688 | package/gli-pub/openwrt-node-packages-master/node/node-v6.9.1/deps/npm/node_modules/node-gyp/gyp/pylib/gyp/xcode_emulation.py | python | _HasIOSTarget | (targets) | return False | Returns true if any target contains the iOS specific key
IPHONEOS_DEPLOYMENT_TARGET. | Returns true if any target contains the iOS specific key
IPHONEOS_DEPLOYMENT_TARGET. | [
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lammps/lammps | b75c3065430a75b1b5543a10e10f46d9b4c91913 | tools/i-pi/ipi/engine/thermostats.py | python | ThermoPILE_G.__init__ | (self, temp = 1.0, dt = 1.0, tau = 1.0, ethermo=0.0, scale = 1.0) | Initializes ThermoPILE_G.
Args:
temp: The simulation temperature. Defaults to 1.0.
dt: The simulation time step. Defaults to 1.0.
tau: The centroid thermostat damping timescale. Defaults to 1.0.
ethermo: The initial conserved energy quantity. Defaults to 0.0. Will
be non-zero if the thermostat is initialized from a checkpoint file.
scale: A float used to reduce the intensity of the PILE thermostat if
required. | Initializes ThermoPILE_G. | [
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Args:
temp: The simulation temperature. Defaults to 1.0.
dt: The simulation time step. Defaults to 1.0.
tau: The centroid thermostat damping timescale. Defaults to 1.0.
ethermo: The initial conserved energy quantity. Defaults to 0.0. Will
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scale: A float used to reduce the intensity of the PILE thermostat if
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super(ThermoPILE_G,self).__init__(temp,dt,tau,ethermo)
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rrwick/Porechop | 109e437280436d1ec27e5a5b7a34ffb752176390 | porechop/nanopore_read.py | python | NanoporeRead.find_start_trim | (self, adapters, end_size, extra_trim_size, end_threshold,
scoring_scheme_vals, min_trim_size, check_barcodes, forward_or_reverse) | Aligns one or more adapter sequences and possibly adjusts the read's start trim amount based
on the result. | Aligns one or more adapter sequences and possibly adjusts the read's start trim amount based
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"""
Aligns one or more adapter sequences and possibly adjusts the read's start trim amount based
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"""
read_seq_start = self.seq[:end_size]
for adapter in adapters:
if not adapter.start_sequence:
continue
full_score, partial_score, read_start, read_end = \
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if partial_score > end_threshold and read_end != end_size and \
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trim_amount = read_end + extra_trim_size
self.start_trim_amount = max(self.start_trim_amount, trim_amount)
self.start_adapter_alignments.append((adapter, full_score, partial_score,
read_start, read_end))
if check_barcodes and adapter.is_barcode() and \
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self.start_barcode_scores[adapter.get_barcode_name()] = full_score | [
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||
lukasmonk/lucaschess | 13e2e5cb13b38a720ccf897af649054a64bcb914 | Code/QT/Grid.py | python | Grid.mouseDoubleClickEvent | (self, event) | Se gestiona este evento, ante la posibilidad de que la ventana quiera controlar,
cada doble click, llamando a la rutina correspondiente si existe (gridDobleClick)
con el numero de fila y el objeto columna como argumentos | Se gestiona este evento, ante la posibilidad de que la ventana quiera controlar,
cada doble click, llamando a la rutina correspondiente si existe (gridDobleClick)
con el numero de fila y el objeto columna como argumentos | [
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con el numero de fila y el objeto columna como argumentos
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self.wParent.gridDobleClick(self, fil, columna) | [
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||
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/signal.py | python | _enum_to_int | (value) | Convert an IntEnum member to a numeric value.
If it's not an IntEnum member return the value itself. | Convert an IntEnum member to a numeric value.
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"""Convert an IntEnum member to a numeric value.
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||
thalium/icebox | 99d147d5b9269222225443ce171b4fd46d8985d4 | third_party/retdec-3.2/scripts/type_extractor/type_extractor/func_info.py | python | get_declarations | (text) | return re.findall(r'\s?\w+[\w\s\*]*\s+\w+\([\w\s\*\+-/,.()[\]]*?\)\s*;', text) | Extracts all function declarations from text. | Extracts all function declarations from text. | [
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|
mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/ops/_op_impl/cpu/conv3d.py | python | _conv3d_cpu | () | return | Conv3D cpu register | Conv3D cpu register | [
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|
hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/mox3/mox3/mox.py | python | StrContains.__init__ | (self, search_string) | Initialize.
Args:
# search_string: the string you are searching for
search_string: str | Initialize. | [
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openvinotoolkit/openvino | dedcbeafa8b84cccdc55ca64b8da516682b381c7 | tools/mo/openvino/tools/mo/back/OptimizeTransposeReshapeSequence.py | python | set_reshape_new_output_shape | (reshape_node: Node, new_output_shape: np.array) | Updates Reshape node shape to a new output shape. The function updates the second input if the node has it.
:param reshape_node: node to update
:param new_output_shape: new output shape
:return: None | Updates Reshape node shape to a new output shape. The function updates the second input if the node has it.
:param reshape_node: node to update
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if len(in_ports) == 2:
reshape_node.in_port(1).data.set_value(new_output_shape) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/ipython/py3/IPython/core/interactiveshell.py | python | InteractiveShell.register_post_execute | (self, func) | DEPRECATED: Use ip.events.register('post_run_cell', func)
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warn("ip.register_post_execute is deprecated, use "
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] | https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/ipython/py3/IPython/core/interactiveshell.py#L1077-L1084 |
||
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/joblib/joblib/compressor.py | python | BinaryZlibFile.closed | (self) | return self._mode == _MODE_CLOSED | True if this file is closed. | True if this file is closed. | [
"True",
"if",
"this",
"file",
"is",
"closed",
"."
] | def closed(self):
"""True if this file is closed."""
return self._mode == _MODE_CLOSED | [
"def",
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"(",
"self",
")",
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"return",
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] | https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/joblib/joblib/compressor.py#L322-L324 |
|
chromiumembedded/cef | 80caf947f3fe2210e5344713c5281d8af9bdc295 | tools/yapf/yapf/yapflib/format_token.py | python | FormatToken.is_binary_op | (self) | return Subtype.BINARY_OPERATOR in self.subtypes | Token is a binary operator. | Token is a binary operator. | [
"Token",
"is",
"a",
"binary",
"operator",
"."
] | def is_binary_op(self):
"""Token is a binary operator."""
return Subtype.BINARY_OPERATOR in self.subtypes | [
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] | https://github.com/chromiumembedded/cef/blob/80caf947f3fe2210e5344713c5281d8af9bdc295/tools/yapf/yapf/yapflib/format_token.py#L227-L229 |
|
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pyparsing/py3/pyparsing/common.py | python | pyparsing_common.convert_to_datetime | (fmt: str = "%Y-%m-%dT%H:%M:%S.%f") | return cvt_fn | Helper to create a parse action for converting parsed
datetime string to Python datetime.datetime
Params -
- fmt - format to be passed to datetime.strptime (default= ``"%Y-%m-%dT%H:%M:%S.%f"``)
Example::
dt_expr = pyparsing_common.iso8601_datetime.copy()
dt_expr.setParseAction(pyparsing_common.convertToDatetime())
print(dt_expr.parseString("1999-12-31T23:59:59.999"))
prints::
[datetime.datetime(1999, 12, 31, 23, 59, 59, 999000)] | Helper to create a parse action for converting parsed
datetime string to Python datetime.datetime | [
"Helper",
"to",
"create",
"a",
"parse",
"action",
"for",
"converting",
"parsed",
"datetime",
"string",
"to",
"Python",
"datetime",
".",
"datetime"
] | def convert_to_datetime(fmt: str = "%Y-%m-%dT%H:%M:%S.%f"):
"""Helper to create a parse action for converting parsed
datetime string to Python datetime.datetime
Params -
- fmt - format to be passed to datetime.strptime (default= ``"%Y-%m-%dT%H:%M:%S.%f"``)
Example::
dt_expr = pyparsing_common.iso8601_datetime.copy()
dt_expr.setParseAction(pyparsing_common.convertToDatetime())
print(dt_expr.parseString("1999-12-31T23:59:59.999"))
prints::
[datetime.datetime(1999, 12, 31, 23, 59, 59, 999000)]
"""
def cvt_fn(s, l, t):
try:
return datetime.strptime(t[0], fmt)
except ValueError as ve:
raise ParseException(s, l, str(ve))
return cvt_fn | [
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|
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/fsspec/spec.py | python | AbstractBufferedFile.writable | (self) | return self.mode in {"wb", "ab"} and not self.closed | Whether opened for writing | Whether opened for writing | [
"Whether",
"opened",
"for",
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] | def writable(self):
"""Whether opened for writing"""
return self.mode in {"wb", "ab"} and not self.closed | [
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|
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/s3transfer/bandwidth.py | python | BandwidthRateTracker.__init__ | (self, alpha=0.8) | Tracks the rate of bandwidth consumption
:type a: float
:param a: The constant to use in calculating the exponentional moving
average of the bandwidth rate. Specifically it is used in the
following calculation:
current_rate = alpha * new_rate + (1 - alpha) * current_rate
This value of this constant should be between 0 and 1. | Tracks the rate of bandwidth consumption | [
"Tracks",
"the",
"rate",
"of",
"bandwidth",
"consumption"
] | def __init__(self, alpha=0.8):
"""Tracks the rate of bandwidth consumption
:type a: float
:param a: The constant to use in calculating the exponentional moving
average of the bandwidth rate. Specifically it is used in the
following calculation:
current_rate = alpha * new_rate + (1 - alpha) * current_rate
This value of this constant should be between 0 and 1.
"""
self._alpha = alpha
self._last_time = None
self._current_rate = None | [
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||
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py3/pandas/core/indexes/base.py | python | Index.sort_values | (
self,
return_indexer: bool = False,
ascending: bool = True,
na_position: str_t = "last",
key: Callable | None = None,
) | Return a sorted copy of the index.
Return a sorted copy of the index, and optionally return the indices
that sorted the index itself.
Parameters
----------
return_indexer : bool, default False
Should the indices that would sort the index be returned.
ascending : bool, default True
Should the index values be sorted in an ascending order.
na_position : {'first' or 'last'}, default 'last'
Argument 'first' puts NaNs at the beginning, 'last' puts NaNs at
the end.
.. versionadded:: 1.2.0
key : callable, optional
If not None, apply the key function to the index values
before sorting. This is similar to the `key` argument in the
builtin :meth:`sorted` function, with the notable difference that
this `key` function should be *vectorized*. It should expect an
``Index`` and return an ``Index`` of the same shape.
.. versionadded:: 1.1.0
Returns
-------
sorted_index : pandas.Index
Sorted copy of the index.
indexer : numpy.ndarray, optional
The indices that the index itself was sorted by.
See Also
--------
Series.sort_values : Sort values of a Series.
DataFrame.sort_values : Sort values in a DataFrame.
Examples
--------
>>> idx = pd.Index([10, 100, 1, 1000])
>>> idx
Int64Index([10, 100, 1, 1000], dtype='int64')
Sort values in ascending order (default behavior).
>>> idx.sort_values()
Int64Index([1, 10, 100, 1000], dtype='int64')
Sort values in descending order, and also get the indices `idx` was
sorted by.
>>> idx.sort_values(ascending=False, return_indexer=True)
(Int64Index([1000, 100, 10, 1], dtype='int64'), array([3, 1, 0, 2])) | Return a sorted copy of the index. | [
"Return",
"a",
"sorted",
"copy",
"of",
"the",
"index",
"."
] | def sort_values(
self,
return_indexer: bool = False,
ascending: bool = True,
na_position: str_t = "last",
key: Callable | None = None,
):
"""
Return a sorted copy of the index.
Return a sorted copy of the index, and optionally return the indices
that sorted the index itself.
Parameters
----------
return_indexer : bool, default False
Should the indices that would sort the index be returned.
ascending : bool, default True
Should the index values be sorted in an ascending order.
na_position : {'first' or 'last'}, default 'last'
Argument 'first' puts NaNs at the beginning, 'last' puts NaNs at
the end.
.. versionadded:: 1.2.0
key : callable, optional
If not None, apply the key function to the index values
before sorting. This is similar to the `key` argument in the
builtin :meth:`sorted` function, with the notable difference that
this `key` function should be *vectorized*. It should expect an
``Index`` and return an ``Index`` of the same shape.
.. versionadded:: 1.1.0
Returns
-------
sorted_index : pandas.Index
Sorted copy of the index.
indexer : numpy.ndarray, optional
The indices that the index itself was sorted by.
See Also
--------
Series.sort_values : Sort values of a Series.
DataFrame.sort_values : Sort values in a DataFrame.
Examples
--------
>>> idx = pd.Index([10, 100, 1, 1000])
>>> idx
Int64Index([10, 100, 1, 1000], dtype='int64')
Sort values in ascending order (default behavior).
>>> idx.sort_values()
Int64Index([1, 10, 100, 1000], dtype='int64')
Sort values in descending order, and also get the indices `idx` was
sorted by.
>>> idx.sort_values(ascending=False, return_indexer=True)
(Int64Index([1000, 100, 10, 1], dtype='int64'), array([3, 1, 0, 2]))
"""
idx = ensure_key_mapped(self, key)
# GH 35584. Sort missing values according to na_position kwarg
# ignore na_position for MultiIndex
if not isinstance(self, ABCMultiIndex):
_as = nargsort(
items=idx, ascending=ascending, na_position=na_position, key=key
)
else:
_as = idx.argsort()
if not ascending:
_as = _as[::-1]
sorted_index = self.take(_as)
if return_indexer:
return sorted_index, _as
else:
return sorted_index | [
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||
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/calendar.py | python | CalendarDateAttr.HasBackgroundColour | (*args, **kwargs) | return _calendar.CalendarDateAttr_HasBackgroundColour(*args, **kwargs) | HasBackgroundColour(self) -> bool | HasBackgroundColour(self) -> bool | [
"HasBackgroundColour",
"(",
"self",
")",
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">",
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] | def HasBackgroundColour(*args, **kwargs):
"""HasBackgroundColour(self) -> bool"""
return _calendar.CalendarDateAttr_HasBackgroundColour(*args, **kwargs) | [
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|
domino-team/openwrt-cc | 8b181297c34d14d3ca521cc9f31430d561dbc688 | package/gli-pub/openwrt-node-packages-master/node/node-v6.9.1/deps/v8_inspector/third_party/jinja2/jinja2/filters.py | python | make_attrgetter | (environment, attribute) | return attrgetter | Returns a callable that looks up the given attribute from a
passed object with the rules of the environment. Dots are allowed
to access attributes of attributes. Integer parts in paths are
looked up as integers. | Returns a callable that looks up the given attribute from a
passed object with the rules of the environment. Dots are allowed
to access attributes of attributes. Integer parts in paths are
looked up as integers. | [
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] | def make_attrgetter(environment, attribute):
"""Returns a callable that looks up the given attribute from a
passed object with the rules of the environment. Dots are allowed
to access attributes of attributes. Integer parts in paths are
looked up as integers.
"""
if not isinstance(attribute, string_types) \
or ('.' not in attribute and not attribute.isdigit()):
return lambda x: environment.getitem(x, attribute)
attribute = attribute.split('.')
def attrgetter(item):
for part in attribute:
if part.isdigit():
part = int(part)
item = environment.getitem(item, part)
return item
return attrgetter | [
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|
pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | caffe2/python/rnn_cell.py | python | RNNCell._prepare_output_sequence | (self, model, state_outputs) | return state_outputs[output_sequence_index] | Allows arbitrary post-processing of primary sequence output.
(Note that state_outputs alternates between full-sequence and final
output for each state, thus the index multiplier 2.) | Allows arbitrary post-processing of primary sequence output. | [
"Allows",
"arbitrary",
"post",
"-",
"processing",
"of",
"primary",
"sequence",
"output",
"."
] | def _prepare_output_sequence(self, model, state_outputs):
'''
Allows arbitrary post-processing of primary sequence output.
(Note that state_outputs alternates between full-sequence and final
output for each state, thus the index multiplier 2.)
'''
output_sequence_index = 2 * self.get_output_state_index()
return state_outputs[output_sequence_index] | [
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|
facebook/ThreatExchange | 31914a51820c73c8a0daffe62ccca29a6e3d359e | api-reference-examples/python/pytx/pytx/batch.py | python | Batch.prepare_single_request | (cls, request, name=None) | return d | Prepare a single request to be included in batch.
:param request: A dictionary in the format required by Batch.submit().
:type request: dict
:param name: A name to give this request.
:type name: str
:returns: dict | Prepare a single request to be included in batch. | [
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"to",
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"."
] | def prepare_single_request(cls, request, name=None):
"""
Prepare a single request to be included in batch.
:param request: A dictionary in the format required by Batch.submit().
:type request: dict
:param name: A name to give this request.
:type name: str
:returns: dict
"""
d = {b.METHOD: request.get('type',
request.get('method', 'GET')),
b.RELATIVE_URL: Batch.get_relative(request.get('url',
request.get('relative_url', '')))}
body = request.get('body', None)
if body:
d[b.BODY] = body
if name:
d['name'] = name
return d | [
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|
protocolbuffers/protobuf | b5ab0b7a18b7336c60130f4ddb2d97c51792f896 | python/mox.py | python | SameElementsAs.__init__ | (self, expected_seq) | Initialize.
Args:
expected_seq: a sequence | Initialize. | [
"Initialize",
"."
] | def __init__(self, expected_seq):
"""Initialize.
Args:
expected_seq: a sequence
"""
self._expected_seq = expected_seq | [
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||
NREL/EnergyPlus | fadc5973b85c70e8cc923efb69c144e808a26078 | src/EnergyPlus/api/datatransfer.py | python | DataExchange.actual_time | (self, state: c_void_p) | return self.api.actualTime(state) | Gets a simple sum of the values of the time part of the date/time function. Could be used in random seeding.
:param state: An active EnergyPlus "state" that is returned from a call to `api.state_manager.new_state()`.
:return: Integer value of time portion of the date/time function. | Gets a simple sum of the values of the time part of the date/time function. Could be used in random seeding. | [
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] | def actual_time(self, state: c_void_p) -> int:
"""
Gets a simple sum of the values of the time part of the date/time function. Could be used in random seeding.
:param state: An active EnergyPlus "state" that is returned from a call to `api.state_manager.new_state()`.
:return: Integer value of time portion of the date/time function.
"""
return self.api.actualTime(state) | [
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|
natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/python/ops/data_flow_ops.py | python | QueueBase.close | (self, cancel_pending_enqueues=False, name=None) | return gen_data_flow_ops._queue_close(
self._queue_ref, cancel_pending_enqueues=cancel_pending_enqueues,
name=name) | Closes this queue.
This operation signals that no more elements will be enqueued in
the given queue. Subsequent `enqueue` and `enqueue_many`
operations will fail. Subsequent `dequeue` and `dequeue_many`
operations will continue to succeed if sufficient elements remain
in the queue. Subsequent `dequeue` and `dequeue_many` operations
that would block will fail immediately.
If `cancel_pending_enqueues` is `True`, all pending requests will also
be cancelled.
Args:
cancel_pending_enqueues: (Optional.) A boolean, defaulting to
`False` (described above).
name: A name for the operation (optional).
Returns:
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] | def close(self, cancel_pending_enqueues=False, name=None):
"""Closes this queue.
This operation signals that no more elements will be enqueued in
the given queue. Subsequent `enqueue` and `enqueue_many`
operations will fail. Subsequent `dequeue` and `dequeue_many`
operations will continue to succeed if sufficient elements remain
in the queue. Subsequent `dequeue` and `dequeue_many` operations
that would block will fail immediately.
If `cancel_pending_enqueues` is `True`, all pending requests will also
be cancelled.
Args:
cancel_pending_enqueues: (Optional.) A boolean, defaulting to
`False` (described above).
name: A name for the operation (optional).
Returns:
The operation that closes the queue.
"""
if name is None:
name = "%s_Close" % self._name
return gen_data_flow_ops._queue_close(
self._queue_ref, cancel_pending_enqueues=cancel_pending_enqueues,
name=name) | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/ops/operations/math_ops.py | python | Addcmul.__init__ | (self) | Initialize Addcmul | Initialize Addcmul | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_windows.py | python | PrintDialogData.GetFromPage | (*args, **kwargs) | return _windows_.PrintDialogData_GetFromPage(*args, **kwargs) | GetFromPage(self) -> int | GetFromPage(self) -> int | [
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|
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/prompt-toolkit/py2/prompt_toolkit/eventloop/asyncio_base.py | python | AsyncioTimeout.reset | (self) | Reset the timeout. Starts a new timer. | Reset the timeout. Starts a new timer. | [
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eric612/MobileNet-YOLO | 69b4441cb3ec8d553fbdef788ad033e246f901bd | scripts/cpp_lint.py | python | FileInfo.IsSource | (self) | return self.Extension()[1:] in ('c', 'cc', 'cpp', 'cxx') | File has a source file extension. | File has a source file extension. | [
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OSGeo/gdal | 3748fc4ba4fba727492774b2b908a2130c864a83 | swig/python/osgeo/ogr.py | python | FeatureDefn.GetFieldDefn | (self, *args) | return _ogr.FeatureDefn_GetFieldDefn(self, *args) | r"""
GetFieldDefn(FeatureDefn self, int i) -> FieldDefn
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hDefn: handle to the feature definition to get the field definition
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iField: the field to fetch, between 0 and GetFieldCount()-1.
a handle to an internal field definition object or NULL if invalid
index. This object should not be modified or freed by the application. | r"""
GetFieldDefn(FeatureDefn self, int i) -> FieldDefn
OGRFieldDefnH
OGR_FD_GetFieldDefn(OGRFeatureDefnH hDefn, int iField) | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | native_client_sdk/src/build_tools/update_nacl_manifest.py | python | VersionFinder.GetAvailablePlatformArchivesFor | (self, version, allow_trunk_revisions) | return expected_archive_urls, missing_archives | Returns a sequence of archives that exist for a given version, on the
given platforms.
The second element of the returned tuple is a list of all platforms that do
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Args:
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Returns:
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The second element of the returned tuple is a list of all platforms that do
not have an archive for the given version.
Args:
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"""
archive_urls = self._GetAvailableArchivesFor(version)
platform_archives = set(GetPlatformArchiveName(p) for p in self.platforms)
expected_archives = platform_archives
if self.extra_archives:
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missing_archives.discard(trunk_archive)
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|
benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/contrib/factorization/python/ops/clustering_ops.py | python | KMeans._mini_batch_training_op | (self, inputs, cluster_idx_list, cluster_centers,
total_counts) | return control_flow_ops.group(*update_ops) | Creates an op for training for mini batch case.
Args:
inputs: list of input Tensors.
cluster_idx_list: A vector (or list of vectors). Each element in the
vector corresponds to an input row in 'inp' and specifies the cluster id
corresponding to the input.
cluster_centers: Tensor Ref of cluster centers.
total_counts: Tensor Ref of cluster counts.
Returns:
An op for doing an update of mini-batch k-means. | Creates an op for training for mini batch case. | [
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"""Creates an op for training for mini batch case.
Args:
inputs: list of input Tensors.
cluster_idx_list: A vector (or list of vectors). Each element in the
vector corresponds to an input row in 'inp' and specifies the cluster id
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cluster_centers: Tensor Ref of cluster centers.
total_counts: Tensor Ref of cluster counts.
Returns:
An op for doing an update of mini-batch k-means.
"""
update_ops = []
for inp, cluster_idx in zip(inputs, cluster_idx_list):
with ops.colocate_with(inp, ignore_existing=True):
assert total_counts is not None
cluster_idx = array_ops.reshape(cluster_idx, [-1])
# Dedupe the unique ids of cluster_centers being updated so that updates
# can be locally aggregated.
unique_ids, unique_idx = array_ops.unique(cluster_idx)
num_unique_cluster_idx = array_ops.size(unique_ids)
# Fetch the old values of counts and cluster_centers.
with ops.colocate_with(total_counts, ignore_existing=True):
old_counts = array_ops.gather(total_counts, unique_ids)
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with ops.colocate_with(cluster_centers, ignore_existing=True):
old_cluster_centers = array_ops.gather(cluster_centers, unique_ids)
# Locally aggregate the increment to counts.
count_updates = math_ops.unsorted_segment_sum(
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unique_idx, num_unique_cluster_idx)
# Locally compute the sum of inputs mapped to each id.
# For a cluster with old cluster value x, old count n, and with data
# d_1,...d_k newly assigned to it, we recompute the new value as
# x += (sum_i(d_i) - k * x) / (n + k).
# Compute sum_i(d_i), see comment above.
cluster_center_updates = math_ops.unsorted_segment_sum(
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# Shape to enable broadcasting count_updates and learning_rate to inp.
# It extends the shape with 1's to match the rank of inp.
broadcast_shape = array_ops.concat([
array_ops.reshape(num_unique_cluster_idx, [1]),
array_ops.ones(
array_ops.reshape(array_ops.rank(inp) - 1, [1]),
dtype=dtypes.int32)
], 0)
# Subtract k * x, see comment above.
cluster_center_updates -= math_ops.cast(
array_ops.reshape(count_updates, broadcast_shape),
inp.dtype) * old_cluster_centers
learning_rate = math_ops.reciprocal(
math_ops.cast(old_counts + count_updates, inp.dtype))
learning_rate = array_ops.reshape(learning_rate, broadcast_shape)
# scale by 1 / (n + k), see comment above.
cluster_center_updates *= learning_rate
# Apply the updates.
update_counts = state_ops.scatter_add(total_counts, unique_ids,
count_updates)
update_cluster_centers = state_ops.scatter_add(
cluster_centers, unique_ids, cluster_center_updates)
update_ops.extend([update_counts, update_cluster_centers])
return control_flow_ops.group(*update_ops) | [
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|
mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/parallel/_utils.py | python | _check_same_layout | (tensor_layout1, tensor_layout2) | return tensor_layout1[0] == tensor_layout2[0] and tensor_layout1[1] == tensor_layout2[1] | check if two tensor layouts are same | check if two tensor layouts are same | [
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] | def _check_same_layout(tensor_layout1, tensor_layout2):
"""check if two tensor layouts are same"""
return tensor_layout1[0] == tensor_layout2[0] and tensor_layout1[1] == tensor_layout2[1] | [
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|
hpi-xnor/BMXNet-v2 | af2b1859eafc5c721b1397cef02f946aaf2ce20d | example/svrg_module/api_usage_example/example_api_train.py | python | create_network | (batch_size, update_freq) | return di, mod | Create a linear regression network for performing SVRG optimization.
Parameters
----------
batch_size: int
Size of data split
update_freq: int
Update Frequency for calculating full gradients
Returns
----------
di: mx.io.NDArrayIter
Data iterator
update_freq: SVRGModule
An instance of SVRGModule for performing SVRG optimization | Create a linear regression network for performing SVRG optimization.
Parameters
----------
batch_size: int
Size of data split
update_freq: int
Update Frequency for calculating full gradients | [
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"""Create a linear regression network for performing SVRG optimization.
Parameters
----------
batch_size: int
Size of data split
update_freq: int
Update Frequency for calculating full gradients
Returns
----------
di: mx.io.NDArrayIter
Data iterator
update_freq: SVRGModule
An instance of SVRGModule for performing SVRG optimization
"""
import logging
head = '%(asctime)-15s %(message)s'
logging.basicConfig(level=logging.INFO, format=head)
train_data = np.random.randint(1, 5, [1000, 2])
weights = np.array([1.0, 2.0])
train_label = train_data.dot(weights)
di = mx.io.NDArrayIter(train_data, train_label, batch_size=batch_size, shuffle=True, label_name='lin_reg_label')
X = mx.sym.Variable('data')
Y = mx.symbol.Variable('lin_reg_label')
fully_connected_layer = mx.sym.FullyConnected(data=X, name='fc1', num_hidden=1)
lro = mx.sym.LinearRegressionOutput(data=fully_connected_layer, label=Y, name="lro")
mod = SVRGModule(
symbol=lro,
data_names=['data'],
label_names=['lin_reg_label'], update_freq=update_freq, logger=logging
)
return di, mod | [
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|
mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/profiler/parser/integrator.py | python | AscendTimelineGenerator._load_timeline_data | (self, all_reduce_names=None) | return timeline_list | Load timeline data from file. | Load timeline data from file. | [
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] | def _load_timeline_data(self, all_reduce_names=None):
"""Load timeline data from file."""
all_reduce_names = all_reduce_names or []
file_path = os.path.join(
self._profiling_dir,
self._output_timeline_data_file_path.format(self._rank_id)
)
file_path = validate_and_normalize_path(file_path)
if not os.path.exists(file_path):
logger.critical("Failed to find parsed timeline file.")
raise ProfilerFileNotFoundException('parsed timeline file')
timeline_list = []
try:
with open(file_path, 'r') as f_obj:
for line in f_obj:
line_list = line.strip('\n').split(',')
if line_list[0] == 'op_name' or line_list[0] in all_reduce_names:
continue
line_list[self._tid_idx] = f"Stream #{line_list[self._tid_idx]}"
timeline_list.append(line_list)
except (IOError, OSError) as err:
logger.critical('Error occurred when read timeline intermediate file: %s', err)
raise ProfilerIOException()
return timeline_list | [
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|
cms-sw/cmssw | fd9de012d503d3405420bcbeec0ec879baa57cf2 | RecoVertex/BeamSpotProducer/scripts/getBeamSpotDB.py | python | nonzero | (self) | return False | True if options were given | True if options were given | [
"True",
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"options",
"were",
"given"
] | def nonzero(self): # will become the nonzero method of optparse.Values
"True if options were given"
for v in self.__dict__.values():
if v is not None: return True
return False | [
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|
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/site-packages/s3transfer/bandwidth.py | python | BandwidthLimiter.__init__ | (self, leaky_bucket, time_utils=None) | Limits bandwidth for shared S3 transfers
:type leaky_bucket: LeakyBucket
:param leaky_bucket: The leaky bucket to use limit bandwidth
:type time_utils: TimeUtils
:param time_utils: Time utility to use for interacting with time. | Limits bandwidth for shared S3 transfers | [
"Limits",
"bandwidth",
"for",
"shared",
"S3",
"transfers"
] | def __init__(self, leaky_bucket, time_utils=None):
"""Limits bandwidth for shared S3 transfers
:type leaky_bucket: LeakyBucket
:param leaky_bucket: The leaky bucket to use limit bandwidth
:type time_utils: TimeUtils
:param time_utils: Time utility to use for interacting with time.
"""
self._leaky_bucket = leaky_bucket
self._time_utils = time_utils
if time_utils is None:
self._time_utils = TimeUtils() | [
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||
tensorflow/minigo | 6d89c202cdceaf449aefc3149ab2110d44f1a6a4 | oneoffs/sharp_positions.py | python | grouper | (n, iterable) | return (iterable[i:i + n] for i in range(0, len(iterable), n)) | Itertools recipe
>>> list(grouper(3, iter('ABCDEFG')))
[['A', 'B', 'C'], ['D', 'E', 'F'], ['G']] | Itertools recipe
>>> list(grouper(3, iter('ABCDEFG')))
[['A', 'B', 'C'], ['D', 'E', 'F'], ['G']] | [
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"""Itertools recipe
>>> list(grouper(3, iter('ABCDEFG')))
[['A', 'B', 'C'], ['D', 'E', 'F'], ['G']]
"""
return (iterable[i:i + n] for i in range(0, len(iterable), n)) | [
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|
scribusproject/scribus | 41ec7c775a060912cf251682a8b1437f753f80f4 | codegen/cheetah/Cheetah/CacheRegion.py | python | CacheRegion.clear | (self) | drop all the caches stored in this cache region | drop all the caches stored in this cache region | [
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" drop all the caches stored in this cache region "
for cacheItemId in self._cacheItems.keys():
cacheItem = self._cacheItems[cacheItemId]
cacheItem.clear()
del self._cacheItems[cacheItemId] | [
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||
baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/python/training/session_run_hook.py | python | SessionRunContext.stop_requested | (self) | return self._stop_requested | Returns whether a stop is requested or not.
If true, `MonitoredSession` stops iterations.
Returns:
A `bool` | Returns whether a stop is requested or not. | [
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"""Returns whether a stop is requested or not.
If true, `MonitoredSession` stops iterations.
Returns:
A `bool`
"""
return self._stop_requested | [
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|
rsms/immutable-cpp | a4a32022d895dd0d3c03547a2b2a2b03face01eb | misc/ninja_syntax.py | python | Writer._count_dollars_before_index | (self, s, i) | return dollar_count | Returns the number of '$' characters right in front of s[i]. | Returns the number of '$' characters right in front of s[i]. | [
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"""Returns the number of '$' characters right in front of s[i]."""
dollar_count = 0
dollar_index = i - 1
while dollar_index > 0 and s[dollar_index] == '$':
dollar_count += 1
dollar_index -= 1
return dollar_count | [
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|
CRYTEK/CRYENGINE | 232227c59a220cbbd311576f0fbeba7bb53b2a8c | Editor/Python/windows/Lib/site-packages/setuptools/_vendor/pyparsing.py | python | ParseResults.append | ( self, item ) | Add single element to end of ParseResults list of elements.
Example::
print(OneOrMore(Word(nums)).parseString("0 123 321")) # -> ['0', '123', '321']
# use a parse action to compute the sum of the parsed integers, and add it to the end
def append_sum(tokens):
tokens.append(sum(map(int, tokens)))
print(OneOrMore(Word(nums)).addParseAction(append_sum).parseString("0 123 321")) # -> ['0', '123', '321', 444] | Add single element to end of ParseResults list of elements. | [
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] | def append( self, item ):
"""
Add single element to end of ParseResults list of elements.
Example::
print(OneOrMore(Word(nums)).parseString("0 123 321")) # -> ['0', '123', '321']
# use a parse action to compute the sum of the parsed integers, and add it to the end
def append_sum(tokens):
tokens.append(sum(map(int, tokens)))
print(OneOrMore(Word(nums)).addParseAction(append_sum).parseString("0 123 321")) # -> ['0', '123', '321', 444]
"""
self.__toklist.append(item) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/site-packages/pip/_internal/utils/misc.py | python | get_installed_distributions | (
local_only=True, # type: bool
skip=stdlib_pkgs, # type: Container[str]
include_editables=True, # type: bool
editables_only=False, # type: bool
user_only=False, # type: bool
paths=None # type: Optional[List[str]]
) | return [d for d in working_set
if local_test(d) and
d.key not in skip and
editable_test(d) and
editables_only_test(d) and
user_test(d)
] | Return a list of installed Distribution objects.
If ``local_only`` is True (default), only return installations
local to the current virtualenv, if in a virtualenv.
``skip`` argument is an iterable of lower-case project names to
ignore; defaults to stdlib_pkgs
If ``include_editables`` is False, don't report editables.
If ``editables_only`` is True , only report editables.
If ``user_only`` is True , only report installations in the user
site directory.
If ``paths`` is set, only report the distributions present at the
specified list of locations. | Return a list of installed Distribution objects. | [
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] | def get_installed_distributions(
local_only=True, # type: bool
skip=stdlib_pkgs, # type: Container[str]
include_editables=True, # type: bool
editables_only=False, # type: bool
user_only=False, # type: bool
paths=None # type: Optional[List[str]]
):
# type: (...) -> List[Distribution]
"""
Return a list of installed Distribution objects.
If ``local_only`` is True (default), only return installations
local to the current virtualenv, if in a virtualenv.
``skip`` argument is an iterable of lower-case project names to
ignore; defaults to stdlib_pkgs
If ``include_editables`` is False, don't report editables.
If ``editables_only`` is True , only report editables.
If ``user_only`` is True , only report installations in the user
site directory.
If ``paths`` is set, only report the distributions present at the
specified list of locations.
"""
if paths:
working_set = pkg_resources.WorkingSet(paths)
else:
working_set = pkg_resources.working_set
if local_only:
local_test = dist_is_local
else:
def local_test(d):
return True
if include_editables:
def editable_test(d):
return True
else:
def editable_test(d):
return not dist_is_editable(d)
if editables_only:
def editables_only_test(d):
return dist_is_editable(d)
else:
def editables_only_test(d):
return True
if user_only:
user_test = dist_in_usersite
else:
def user_test(d):
return True
return [d for d in working_set
if local_test(d) and
d.key not in skip and
editable_test(d) and
editables_only_test(d) and
user_test(d)
] | [
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|
mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | scripts/SANS/isis_reduction_steps.py | python | ConvertToQISIS.set_output_type | (self, descript) | Requests the given output from the Q conversion, either 1D or 2D. For
the 1D calculation it asks the reducer to keep a workspace for error
estimates
@param descript: 1D or 2D | Requests the given output from the Q conversion, either 1D or 2D. For
the 1D calculation it asks the reducer to keep a workspace for error
estimates | [
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"""
Requests the given output from the Q conversion, either 1D or 2D. For
the 1D calculation it asks the reducer to keep a workspace for error
estimates
@param descript: 1D or 2D
"""
self._Q_alg = self._OUTPUT_TYPES[descript]
self._output_type = descript | [
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||
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/propgrid.py | python | PGTypeOperationFailed | (*args, **kwargs) | return _propgrid.PGTypeOperationFailed(*args, **kwargs) | PGTypeOperationFailed(PGProperty p, String typestr, String op) | PGTypeOperationFailed(PGProperty p, String typestr, String op) | [
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"""PGTypeOperationFailed(PGProperty p, String typestr, String op)"""
return _propgrid.PGTypeOperationFailed(*args, **kwargs) | [
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|
apache/singa | 93fd9da72694e68bfe3fb29d0183a65263d238a1 | python/singa/autograd.py | python | _xor | (a, b) | return Xor()(a, b)[0] | Return `np.logical_xor(a,b)`, where a and b are Tensor. | Return `np.logical_xor(a,b)`, where a and b are Tensor. | [
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] | def _xor(a, b):
"""
Return `np.logical_xor(a,b)`, where a and b are Tensor.
"""
return Xor()(a, b)[0] | [
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|
mongodb/mongo | d8ff665343ad29cf286ee2cf4a1960d29371937b | buildscripts/resmokelib/utils/history.py | python | HistoryDict.write_equals | (self, other_dict) | return True | Compare two dicts for write equality. | Compare two dicts for write equality. | [
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"write",
"equality",
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] | def write_equals(self, other_dict):
"""Compare two dicts for write equality."""
if not len(other_dict._value_store) == len(self._value_store): # pylint: disable=protected-access
return False
for key in self._value_store:
our_writes = [
access.value_written for access in self._history_store[key]
if access.type == AccessType.WRITE
]
their_writes = [
access.value_written for access in other_dict._history_store[key] # pylint: disable=protected-access
if access.type == AccessType.WRITE
]
if not our_writes == their_writes:
return False
return True | [
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|
baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/python/ops/summary_op_util.py | python | summary_scope | (name, family=None, default_name=None, values=None) | Enters a scope used for the summary and yields both the name and tag.
To ensure that the summary tag name is always unique, we create a name scope
based on `name` and use the full scope name in the tag.
If `family` is set, then the tag name will be '<family>/<scope_name>', where
`scope_name` is `<outer_scope>/<family>/<name>`. This ensures that `family`
is always the prefix of the tag (and unmodified), while ensuring the scope
respects the outer scope from this summary was created.
Args:
name: A name for the generated summary node.
family: Optional; if provided, used as the prefix of the summary tag name.
default_name: Optional; if provided, used as default name of the summary.
values: Optional; passed as `values` parameter to name_scope.
Yields:
A tuple `(tag, scope)`, both of which are unique and should be used for the
tag and the scope for the summary to output. | Enters a scope used for the summary and yields both the name and tag. | [
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] | def summary_scope(name, family=None, default_name=None, values=None):
"""Enters a scope used for the summary and yields both the name and tag.
To ensure that the summary tag name is always unique, we create a name scope
based on `name` and use the full scope name in the tag.
If `family` is set, then the tag name will be '<family>/<scope_name>', where
`scope_name` is `<outer_scope>/<family>/<name>`. This ensures that `family`
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respects the outer scope from this summary was created.
Args:
name: A name for the generated summary node.
family: Optional; if provided, used as the prefix of the summary tag name.
default_name: Optional; if provided, used as default name of the summary.
values: Optional; passed as `values` parameter to name_scope.
Yields:
A tuple `(tag, scope)`, both of which are unique and should be used for the
tag and the scope for the summary to output.
"""
name = clean_tag(name)
family = clean_tag(family)
# Use family name in the scope to ensure uniqueness of scope/tag.
scope_base_name = name if family is None else '{}/{}'.format(family, name)
with ops.name_scope(scope_base_name, default_name, values=values) as scope:
if family is None:
tag = scope.rstrip('/')
else:
# Prefix our scope with family again so it displays in the right tab.
tag = '{}/{}'.format(family, scope.rstrip('/'))
# Note: tag is not 100% unique if the user explicitly enters a scope with
# the same name as family, then later enter it again before summaries.
# This is very contrived though, and we opt here to let it be a runtime
# exception if tags do indeed collide.
yield (tag, scope) | [
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apiaryio/drafter | 4634ebd07f6c6f257cc656598ccd535492fdfb55 | tools/gyp/pylib/gyp/ninja_syntax.py | python | Writer._count_dollars_before_index | (self, s, i) | return dollar_count | Returns the number of '$' characters right in front of s[i]. | Returns the number of '$' characters right in front of s[i]. | [
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"""Returns the number of '$' characters right in front of s[i]."""
dollar_count = 0
dollar_index = i - 1
while dollar_index > 0 and s[dollar_index] == '$':
dollar_count += 1
dollar_index -= 1
return dollar_count | [
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|
eclipse/sumo | 7132a9b8b6eea734bdec38479026b4d8c4336d03 | tools/contributed/sumopy/agilepy/lib_wx/toolbox.py | python | BaseTool.force_deactivation | (self) | Explicit call to deactivate this tool in the tools panel. | Explicit call to deactivate this tool in the tools panel. | [
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"""
Explicit call to deactivate this tool in the tools panel.
"""
self.parent.unselect_tool() | [
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||
mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqt/mantidqt/widgets/fitpropertybrowser/mouse_state_machine.py | python | MoveMarkersState.motion_notify_callback | (self, event) | Override base class method | Override base class method | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/ipython/py3/IPython/utils/path.py | python | compress_user | (path) | return path | Reverse of :func:`os.path.expanduser` | Reverse of :func:`os.path.expanduser` | [
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"""Reverse of :func:`os.path.expanduser`
"""
home = os.path.expanduser('~')
if path.startswith(home):
path = "~" + path[len(home):]
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|
protocolbuffers/protobuf | b5ab0b7a18b7336c60130f4ddb2d97c51792f896 | python/google/protobuf/text_format.py | python | Merge | (text,
message,
allow_unknown_extension=False,
allow_field_number=False,
descriptor_pool=None,
allow_unknown_field=False) | return MergeLines(
text.split(b'\n' if isinstance(text, bytes) else u'\n'),
message,
allow_unknown_extension,
allow_field_number,
descriptor_pool=descriptor_pool,
allow_unknown_field=allow_unknown_field) | Parses a text representation of a protocol message into a message.
Like Parse(), but allows repeated values for a non-repeated field, and uses
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Args:
text (str): Message text representation.
message (Message): A protocol buffer message to merge into.
allow_unknown_extension: if True, skip over missing extensions and keep
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allow_field_number: if True, both field number and field name are allowed.
descriptor_pool (DescriptorPool): Descriptor pool used to resolve Any types.
allow_unknown_field: if True, skip over unknown field and keep
parsing. Avoid to use this option if possible. It may hide some
errors (e.g. spelling error on field name)
Returns:
Message: The same message passed as argument.
Raises:
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message,
allow_unknown_extension=False,
allow_field_number=False,
descriptor_pool=None,
allow_unknown_field=False):
"""Parses a text representation of a protocol message into a message.
Like Parse(), but allows repeated values for a non-repeated field, and uses
the last one. This means any non-repeated, top-level fields specified in text
replace those in the message.
Args:
text (str): Message text representation.
message (Message): A protocol buffer message to merge into.
allow_unknown_extension: if True, skip over missing extensions and keep
parsing
allow_field_number: if True, both field number and field name are allowed.
descriptor_pool (DescriptorPool): Descriptor pool used to resolve Any types.
allow_unknown_field: if True, skip over unknown field and keep
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Returns:
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Raises:
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"""
return MergeLines(
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] | https://github.com/protocolbuffers/protobuf/blob/b5ab0b7a18b7336c60130f4ddb2d97c51792f896/python/google/protobuf/text_format.py#L690-L725 |
|
mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/ops/operations/array_ops.py | python | Concat.__init__ | (self, axis=0) | Initialize Concat | Initialize Concat | [
"Initialize",
"Concat"
] | def __init__(self, axis=0):
"""Initialize Concat"""
validator.check_value_type("axis", axis, [int], self.name) | [
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||
facebookincubator/BOLT | 88c70afe9d388ad430cc150cc158641701397f70 | lldb/third_party/Python/module/pexpect-4.6/pexpect/pxssh.py | python | pxssh.levenshtein_distance | (self, a, b) | return current[n] | This calculates the Levenshtein distance between a and b. | This calculates the Levenshtein distance between a and b. | [
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] | def levenshtein_distance(self, a, b):
'''This calculates the Levenshtein distance between a and b.
'''
n, m = len(a), len(b)
if n > m:
a,b = b,a
n,m = m,n
current = range(n+1)
for i in range(1,m+1):
previous, current = current, [i]+[0]*n
for j in range(1,n+1):
add, delete = previous[j]+1, current[j-1]+1
change = previous[j-1]
if a[j-1] != b[i-1]:
change = change + 1
current[j] = min(add, delete, change)
return current[n] | [
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|
whai362/PSENet | 4d95395658662f2223805c36dcd573d9e190ce26 | eval/ic15_rec/rrc_evaluation_funcs_1_1.py | python | main_validation | (default_evaluation_params_fn,validate_data_fn) | This process validates a method
Params:
default_evaluation_params_fn: points to a function that returns a dictionary with the default parameters used for the evaluation
validate_data_fn: points to a method that validates the corrct format of the submission | This process validates a method
Params:
default_evaluation_params_fn: points to a function that returns a dictionary with the default parameters used for the evaluation
validate_data_fn: points to a method that validates the corrct format of the submission | [
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"""
This process validates a method
Params:
default_evaluation_params_fn: points to a function that returns a dictionary with the default parameters used for the evaluation
validate_data_fn: points to a method that validates the corrct format of the submission
"""
try:
p = dict([s[1:].split('=') for s in sys.argv[1:]])
evalParams = default_evaluation_params_fn()
if 'p' in p.keys():
evalParams.update( p['p'] if isinstance(p['p'], dict) else json.loads(p['p']) )
validate_data_fn(p['g'], p['s'], evalParams)
print ('SUCCESS')
sys.exit(0)
except Exception as e:
print (str(e))
sys.exit(101) | [
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||
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_core.py | python | Control.GetAlignment | (*args, **kwargs) | return _core_.Control_GetAlignment(*args, **kwargs) | GetAlignment(self) -> int
Get the control alignment (left/right/centre, top/bottom/centre) | GetAlignment(self) -> int | [
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] | def GetAlignment(*args, **kwargs):
"""
GetAlignment(self) -> int
Get the control alignment (left/right/centre, top/bottom/centre)
"""
return _core_.Control_GetAlignment(*args, **kwargs) | [
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|
wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/nntplib.py | python | NNTP.putline | (self, line) | Internal: send one line to the server, appending CRLF. | Internal: send one line to the server, appending CRLF. | [
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"""Internal: send one line to the server, appending CRLF."""
line = line + CRLF
if self.debugging > 1: print '*put*', repr(line)
self.sock.sendall(line) | [
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||
pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | torch/cuda/__init__.py | python | set_sync_debug_mode | (debug_mode: Union[int, str]) | r"""Sets the debug mode for cuda synchronizing operations.
Args:
debug_mode(str or int): if "default" or 0, don't error or warn on synchronizing operations,
if "warn" or 1, warn on synchronizing operations, if "error" or 2, error out synchronizing operations.
Warning:
This is an experimental feature, and not all synchronizing operations will trigger warning or error. In
particular, operations in torch.distributed and torch.sparse namespaces are not covered yet. | r"""Sets the debug mode for cuda synchronizing operations. | [
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] | def set_sync_debug_mode(debug_mode: Union[int, str]) -> None:
r"""Sets the debug mode for cuda synchronizing operations.
Args:
debug_mode(str or int): if "default" or 0, don't error or warn on synchronizing operations,
if "warn" or 1, warn on synchronizing operations, if "error" or 2, error out synchronizing operations.
Warning:
This is an experimental feature, and not all synchronizing operations will trigger warning or error. In
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"""
_lazy_init()
if isinstance(debug_mode, str):
if debug_mode == "default":
debug_mode = 0
elif debug_mode == "warn":
debug_mode = 1
elif debug_mode == "error":
debug_mode = 2
else:
raise RuntimeError("invalid value of debug_mode, expected one of `default`, `warn`, `error`")
torch._C._cuda_set_sync_debug_mode(debug_mode) | [
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||
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/grid.py | python | GridEvent.ControlDown | (*args, **kwargs) | return _grid.GridEvent_ControlDown(*args, **kwargs) | ControlDown(self) -> bool | ControlDown(self) -> bool | [
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"""ControlDown(self) -> bool"""
return _grid.GridEvent_ControlDown(*args, **kwargs) | [
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|
ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/genpy/src/genpy/generator.py | python | Special.get_post_deserialize | (self, varname) | :returns: Post-deserialization code to executed (unindented) or
``None`` if no post-deserialization is required, ``str`` | :returns: Post-deserialization code to executed (unindented) or
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if self.post_deserialize:
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||
ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | chrome/common/extensions/docs/server2/features_utility.py | python | Parse | (features_json) | return features | Process JSON from a _features.json file, standardizing it into a dictionary
of Features. | Process JSON from a _features.json file, standardizing it into a dictionary
of Features. | [
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'''Process JSON from a _features.json file, standardizing it into a dictionary
of Features.
'''
features = {}
def ignore_feature(name, value):
'''Returns true if this feature should be ignored. This is defined by the
presence of a 'whitelist' property for non-private APIs. Private APIs
shouldn't have whitelisted features ignored since they're inherently
private. Logic elsewhere makes sure not to list private APIs.
'''
return 'whitelist' in value and not name.endswith('Private')
for name, value in deepcopy(features_json).iteritems():
# Some feature names correspond to a list, typically because they're
# whitelisted in stable for certain extensions and available in dev for
# everybody else. Force a list down to a single feature by attempting to
# remove the entries that don't affect the typical usage of an API.
if isinstance(value, list):
available_values = [subvalue for subvalue in value
if not ignore_feature(name, subvalue)]
if len(available_values) == 0:
logging.warning('No available values for feature "%s"' % name)
value = value[0]
elif len(available_values) == 1:
value = available_values[0]
else:
# Multiple available values probably implies different feature
# configurations for apps vs extensions. Currently, this is 'commands'.
# To get the ball rolling, add a hack to combine the extension types.
# See http://crbug.com/316194.
extension_types = set()
for value in available_values:
extension_types.update(value['extension_types'])
value = [subvalue for subvalue in available_values
if subvalue['channel'] == 'stable'][0]
value['extension_types'] = list(extension_types)
if ignore_feature(name, value):
continue
features[name] = { 'platforms': [] }
extension_types = value.pop('extension_types', None)
if extension_types is not None:
features[name]['platforms'] = _GetPlatformsForExtensionTypes(
extension_types)
features[name]['name'] = name
features[name].update(value)
return features | [
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|
mapnik/mapnik | f3da900c355e1d15059c4a91b00203dcc9d9f0ef | scons/scons-local-4.1.0/SCons/Environment.py | python | SubstitutionEnvironment.RemoveMethod | (self, function) | Removes the specified function's MethodWrapper from the
added_methods list, so we don't re-bind it when making a clone. | Removes the specified function's MethodWrapper from the
added_methods list, so we don't re-bind it when making a clone. | [
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"""
Removes the specified function's MethodWrapper from the
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self.added_methods = [dm for dm in self.added_methods if dm.method is not function] | [
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||
LiquidPlayer/LiquidCore | 9405979363f2353ac9a71ad8ab59685dd7f919c9 | deps/node-10.15.3/tools/gyp/pylib/gyp/xcodeproj_file.py | python | XCObject.Children | (self) | return children | Returns a list of all of this object's owned (strong) children. | Returns a list of all of this object's owned (strong) children. | [
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"""Returns a list of all of this object's owned (strong) children."""
children = []
for property, attributes in self._schema.iteritems():
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|
benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/contrib/layers/python/layers/target_column.py | python | _TargetColumn.get_eval_ops | (self, features, logits, labels, metrics=None) | Returns eval op. | Returns eval op. | [
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||
LiquidPlayer/LiquidCore | 9405979363f2353ac9a71ad8ab59685dd7f919c9 | deps/node-10.15.3/deps/v8/third_party/binutils/detect_v8_host_arch.py | python | DetectHostArch | () | return host_arch | Hook to be called from gyp without starting a separate python
interpreter. | Hook to be called from gyp without starting a separate python
interpreter. | [
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] | def DetectHostArch():
"""Hook to be called from gyp without starting a separate python
interpreter."""
host_arch = platform.machine()
host_system = platform.system();
# Convert machine type to format recognized by gyp.
if re.match(r'i.86', host_arch) or host_arch == 'i86pc':
host_arch = 'ia32'
elif host_arch in ['x86_64', 'amd64']:
host_arch = 'x64'
elif host_arch.startswith('arm'):
host_arch = 'arm'
elif host_arch == 'aarch64':
host_arch = 'arm64'
elif host_arch == 'mips64':
host_arch = 'mips64el'
elif host_arch.startswith('mips'):
host_arch = 'mipsel'
# Under AIX the value returned by platform.machine is not
# the best indicator of the host architecture
# AIX 6.1 which is the lowest level supported only provides
# a 64 bit kernel
if host_system == 'AIX':
host_arch = 'ppc64'
# platform.machine is based on running kernel. It's possible to use 64-bit
# kernel with 32-bit userland, e.g. to give linker slightly more memory.
# Distinguish between different userland bitness by querying
# the python binary.
if host_arch == 'x64' and platform.architecture()[0] == '32bit':
host_arch = 'ia32'
return host_arch | [
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|
wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/lib-tk/Tkinter.py | python | Tk.__init__ | (self, screenName=None, baseName=None, className='Tk',
useTk=1, sync=0, use=None) | Return a new Toplevel widget on screen SCREENNAME. A new Tcl interpreter will
be created. BASENAME will be used for the identification of the profile file (see
readprofile).
It is constructed from sys.argv[0] without extensions if None is given. CLASSNAME
is the name of the widget class. | Return a new Toplevel widget on screen SCREENNAME. A new Tcl interpreter will
be created. BASENAME will be used for the identification of the profile file (see
readprofile).
It is constructed from sys.argv[0] without extensions if None is given. CLASSNAME
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"""Return a new Toplevel widget on screen SCREENNAME. A new Tcl interpreter will
be created. BASENAME will be used for the identification of the profile file (see
readprofile).
It is constructed from sys.argv[0] without extensions if None is given. CLASSNAME
is the name of the widget class."""
self.master = None
self.children = {}
self._tkloaded = 0
# to avoid recursions in the getattr code in case of failure, we
# ensure that self.tk is always _something_.
self.tk = None
if baseName is None:
import sys, os
baseName = os.path.basename(sys.argv[0])
baseName, ext = os.path.splitext(baseName)
if ext not in ('.py', '.pyc', '.pyo'):
baseName = baseName + ext
interactive = 0
self.tk = _tkinter.create(screenName, baseName, className, interactive, wantobjects, useTk, sync, use)
if useTk:
self._loadtk()
if not sys.flags.ignore_environment:
# Issue #16248: Honor the -E flag to avoid code injection.
self.readprofile(baseName, className) | [
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||
apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | deps/src/libxml2-2.9.1/python/libxml2class.py | python | relaxNgValidCtxt.relaxNGValidateFullElement | (self, doc, elem) | return ret | Validate a full subtree when
xmlRelaxNGValidatePushElement() returned 0 and the content
of the node has been expanded. | Validate a full subtree when
xmlRelaxNGValidatePushElement() returned 0 and the content
of the node has been expanded. | [
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] | def relaxNGValidateFullElement(self, doc, elem):
"""Validate a full subtree when
xmlRelaxNGValidatePushElement() returned 0 and the content
of the node has been expanded. """
if doc is None: doc__o = None
else: doc__o = doc._o
if elem is None: elem__o = None
else: elem__o = elem._o
ret = libxml2mod.xmlRelaxNGValidateFullElement(self._o, doc__o, elem__o)
return ret | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/build/waf-1.7.13/waflib/Tools/python.py | python | check_python_module | (conf, module_name, condition='') | Check if the selected python interpreter can import the given python module::
def configure(conf):
conf.check_python_module('pygccxml')
conf.check_python_module('re', condition="ver > num(2, 0, 4) and ver <= num(3, 0, 0)")
:param module_name: module
:type module_name: string | Check if the selected python interpreter can import the given python module:: | [
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"""
Check if the selected python interpreter can import the given python module::
def configure(conf):
conf.check_python_module('pygccxml')
conf.check_python_module('re', condition="ver > num(2, 0, 4) and ver <= num(3, 0, 0)")
:param module_name: module
:type module_name: string
"""
msg = 'Python module %s' % module_name
if condition:
msg = '%s (%s)' % (msg, condition)
conf.start_msg(msg)
try:
ret = conf.cmd_and_log(conf.env['PYTHON'] + ['-c', PYTHON_MODULE_TEMPLATE % module_name])
except Exception:
conf.end_msg(False)
conf.fatal('Could not find the python module %r' % module_name)
ret = ret.strip()
if condition:
conf.end_msg(ret)
if ret == 'unknown version':
conf.fatal('Could not check the %s version' % module_name)
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def num(*k):
if isinstance(k[0], int):
return LooseVersion('.'.join([str(x) for x in k]))
else:
return LooseVersion(k[0])
d = {'num': num, 'ver': LooseVersion(ret)}
ev = eval(condition, {}, d)
if not ev:
conf.fatal('The %s version does not satisfy the requirements' % module_name)
else:
if ret == 'unknown version':
conf.end_msg(True)
else:
conf.end_msg(ret) | [
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||
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/idlelib/textview.py | python | ScrollableTextFrame.__init__ | (self, master, wrap=NONE, **kwargs) | Create a frame for Textview.
master - master widget for this frame
wrap - type of text wrapping to use ('word', 'char' or 'none')
All parameters except for 'wrap' are passed to Frame.__init__().
The Text widget is accessible via the 'text' attribute.
Note: Changing the wrapping mode of the text widget after
instantiation is not supported. | Create a frame for Textview. | [
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] | def __init__(self, master, wrap=NONE, **kwargs):
"""Create a frame for Textview.
master - master widget for this frame
wrap - type of text wrapping to use ('word', 'char' or 'none')
All parameters except for 'wrap' are passed to Frame.__init__().
The Text widget is accessible via the 'text' attribute.
Note: Changing the wrapping mode of the text widget after
instantiation is not supported.
"""
super().__init__(master, **kwargs)
text = self.text = Text(self, wrap=wrap)
text.grid(row=0, column=0, sticky=NSEW)
self.grid_rowconfigure(0, weight=1)
self.grid_columnconfigure(0, weight=1)
# vertical scrollbar
self.yscroll = AutoHideScrollbar(self, orient=VERTICAL,
takefocus=False,
command=text.yview)
self.yscroll.grid(row=0, column=1, sticky=NS)
text['yscrollcommand'] = self.yscroll.set
# horizontal scrollbar - only when wrap is set to NONE
if wrap == NONE:
self.xscroll = AutoHideScrollbar(self, orient=HORIZONTAL,
takefocus=False,
command=text.xview)
self.xscroll.grid(row=1, column=0, sticky=EW)
text['xscrollcommand'] = self.xscroll.set
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self.xscroll = None | [
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pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | torch/distributed/optim/zero_redundancy_optimizer.py | python | ZeroRedundancyOptimizer._verify_and_init_params | (self, params: Any) | r"""
Verifies the type of ``params`` and initializes ``self._all_params``
if ``params`` is valid.
While :class:`optim.Optimizer <torch.optim.Optimizer>` allows
``params`` to be an iterable of :class:`dict` s, currently
``ZeroRedundancyOptimizer`` strictly requires ``params`` to be an
iterable of :class:`torch.Tensor` s.
Raises:
TypeError: ``params`` has an invalid type.
ValueError: ``params`` is empty. | r"""
Verifies the type of ``params`` and initializes ``self._all_params``
if ``params`` is valid. | [
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Verifies the type of ``params`` and initializes ``self._all_params``
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While :class:`optim.Optimizer <torch.optim.Optimizer>` allows
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arangodb/arangodb | 0d658689c7d1b721b314fa3ca27d38303e1570c8 | 3rdParty/V8/v7.9.317/tools/run-clang-tidy.py | python | CheckCompDB | (build_folder) | return os.path.isfile(os.path.join(build_folder, 'compile_commands.json')) | Checks if a compilation database exists in the build_folder. | Checks if a compilation database exists in the build_folder. | [
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"""
Checks if a compilation database exists in the build_folder.
"""
return os.path.isfile(os.path.join(build_folder, 'compile_commands.json')) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py2/scipy/optimize/_tstutils.py | python | cplx01_f | (z, n, a) | return z**n - a | r"""z**n-a: Use to find the n-th root of a | r"""z**n-a: Use to find the n-th root of a | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py2/sklearn/discriminant_analysis.py | python | _class_cov | (X, y, priors=None, shrinkage=None) | return np.average(covs, axis=0, weights=priors) | Compute class covariance matrix.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Input data.
y : array-like, shape (n_samples,) or (n_samples, n_targets)
Target values.
priors : array-like, shape (n_classes,)
Class priors.
shrinkage : string or float, optional
Shrinkage parameter, possible values:
- None: no shrinkage (default).
- 'auto': automatic shrinkage using the Ledoit-Wolf lemma.
- float between 0 and 1: fixed shrinkage parameter.
Returns
-------
cov : array-like, shape (n_features, n_features)
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"""Compute class covariance matrix.
Parameters
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Input data.
y : array-like, shape (n_samples,) or (n_samples, n_targets)
Target values.
priors : array-like, shape (n_classes,)
Class priors.
shrinkage : string or float, optional
Shrinkage parameter, possible values:
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cov : array-like, shape (n_features, n_features)
Class covariance matrix.
"""
classes = np.unique(y)
covs = []
for group in classes:
Xg = X[y == group, :]
covs.append(np.atleast_2d(_cov(Xg, shrinkage)))
return np.average(covs, axis=0, weights=priors) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py2/pandas/core/base.py | python | SelectionMixin._is_builtin_func | (self, arg) | return self._builtin_table.get(arg, arg) | if we define an builtin function for this argument, return it,
otherwise return the arg | if we define an builtin function for this argument, return it,
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return self._builtin_table.get(arg, arg) | [
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|
tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/keras/engine/training_v1.py | python | Model._set_input_attrs | (self, inputs) | return inputs | Sets attributes related to the inputs of the Model. | Sets attributes related to the inputs of the Model. | [
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"""Sets attributes related to the inputs of the Model."""
if self.inputs:
raise ValueError('Model inputs are already set.')
if self.__class__.__name__ == 'Sequential' and not self.built:
if tensor_util.is_tf_type(inputs):
input_shape = (None,) + tuple(inputs.shape.as_list()[1:])
elif isinstance(inputs, tensor_shape.TensorShape):
input_shape = (None,) + tuple(inputs.as_list()[1:])
elif isinstance(inputs, dict):
# We assert that the first layer is a FeatureLayer.
if not training_utils_v1.is_feature_layer(self.layers[0]):
raise ValueError('Passing a dictionary input to a Sequential Model '
'which doesn\'t have FeatureLayer as the first layer'
' is an error.')
input_shape = (None,)
else:
input_shape = (None,) + tuple(inputs.shape[1:])
self._build_input_shape = input_shape
# Cast inputs to the compute dtype. This is primarily used
# when saving to determine the correct dtype in the input signature.
inputs = self._maybe_cast_inputs(inputs)
# On-the-fly setting of symbolic model inputs (either by using the tensor
# provided, or by creating a placeholder if Numpy data was provided).
model_inputs = training_utils_v1.ModelInputs(inputs)
inputs = model_inputs.get_symbolic_inputs()
self.inputs = model_inputs.get_symbolic_inputs(return_single_as_list=True)
self.input_names = model_inputs.get_input_names()
self._feed_inputs = []
self._feed_input_names = []
self._feed_input_shapes = []
for k, v in model_inputs.as_dict():
if backend.is_placeholder(v):
self._feed_input_names.append(k)
self._feed_inputs.append(v)
self._feed_input_shapes.append(backend.int_shape(v))
return inputs | [
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|
alexozer/jankdrone | c4b403eb254b41b832ab2bdfade12ba59c99e5dc | shm/lib/pyratemp/pyratemp.py | python | TemplateParseError.__init__ | (self, err, errpos) | :Parameters:
- `err`: error-message or exception to wrap
- `errpos`: ``(filename,row,col)`` where the error occured. | :Parameters:
- `err`: error-message or exception to wrap
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self.err = err
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ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/third_party/lib_aarch64/python2.7/dist-packages/geodesy/wu_point.py | python | WuPoint.toPoint | (self) | return self.utm.toPoint() | :returns: Corresponding `geometry_msgs/Point`_ message. | :returns: Corresponding `geometry_msgs/Point`_ message. | [
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citizenfx/fivem | 88276d40cc7baf8285d02754cc5ae42ec7a8563f | vendor/chromium/mojo/public/tools/bindings/pylib/mojom/generate/translate.py | python | _EnumField | (module, enum, parsed_field, parent_kind) | return field | Args:
module: {mojom.Module} Module currently being constructed.
enum: {mojom.Enum} Enum this field belongs to.
parsed_field: {ast.EnumValue} Parsed enum value.
parent_kind: {mojom.Kind} The enclosing type.
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module: {mojom.Module} Module currently being constructed.
enum: {mojom.Enum} Enum this field belongs to.
parsed_field: {ast.EnumValue} Parsed enum value.
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field.mojom_name = parsed_field.mojom_name
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|
tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/ops/parallel_for/pfor.py | python | WhileOp.inputs | (self) | return [x.op.inputs[0] for x in self._enters + self._direct_enters] | Input to all the Enter nodes. | Input to all the Enter nodes. | [
"Input",
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] | def inputs(self):
"""Input to all the Enter nodes."""
return [x.op.inputs[0] for x in self._enters + self._direct_enters] | [
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|
pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | torch/_lobpcg.py | python | _matrix_polynomial_value | (poly, x, zero_power=None) | return _polynomial_value(poly, x, zero_power, transition) | Evaluates `poly(x)` for the (batched) matrix input `x`.
Check out `_polynomial_value` function for more details. | Evaluates `poly(x)` for the (batched) matrix input `x`.
Check out `_polynomial_value` function for more details. | [
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] | def _matrix_polynomial_value(poly, x, zero_power=None):
"""
Evaluates `poly(x)` for the (batched) matrix input `x`.
Check out `_polynomial_value` function for more details.
"""
# matrix-aware Horner's rule iteration
def transition(curr_poly_val, x, poly_coeff):
res = x.matmul(curr_poly_val)
res.diagonal(dim1=-2, dim2=-1).add_(poly_coeff.unsqueeze(-1))
return res
if zero_power is None:
zero_power = torch.eye(x.size(-1), x.size(-1), dtype=x.dtype, device=x.device) \
.view(*([1] * len(list(x.shape[:-2]))), x.size(-1), x.size(-1))
return _polynomial_value(poly, x, zero_power, transition) | [
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|
moderngl/moderngl | 32fe79927e02b0fa893b3603d677bdae39771e14 | examples/growing_buffers.py | python | Points.__init__ | (self, ctx, num_points) | Args:
ctx: moderngl context
num_points: Initial number of points to allocate | Args:
ctx: moderngl context
num_points: Initial number of points to allocate | [
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] | def __init__(self, ctx, num_points):
"""
Args:
ctx: moderngl context
num_points: Initial number of points to allocate
"""
self.points = []
self.ctx = ctx
self.buffer = self.ctx.buffer(reserve=num_points * 12) # 12 bytes for a 3f
self.program = self.ctx.program(
vertex_shader="""
#version 330
in vec3 in_position;
uniform mat4 model_matrix;
void main() {
gl_Position = model_matrix * vec4(in_position, 1.0);
}
""",
fragment_shader="""
#version 330
out vec4 outColor;
void main() {
outColor = vec4(1.0);
}
""",
)
self.vao = self.ctx.vertex_array(
self.program,
[(self.buffer, '3f', 'in_position')],
) | [
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||
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/poplib.py | python | POP3.rpop | (self, user) | return self._shortcmd('RPOP %s' % user) | Not sure what this does. | Not sure what this does. | [
"Not",
"sure",
"what",
"this",
"does",
"."
] | def rpop(self, user):
"""Not sure what this does."""
return self._shortcmd('RPOP %s' % user) | [
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|
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_core.py | python | PyApp_GetTraitsIfExists | (*args) | return _core_.PyApp_GetTraitsIfExists(*args) | PyApp_GetTraitsIfExists() -> wxAppTraits
This function provides safer access to traits object than
wx.GetApp().GetTraits() during startup or termination when the global
application object itself may be unavailable. | PyApp_GetTraitsIfExists() -> wxAppTraits | [
"PyApp_GetTraitsIfExists",
"()",
"-",
">",
"wxAppTraits"
] | def PyApp_GetTraitsIfExists(*args):
"""
PyApp_GetTraitsIfExists() -> wxAppTraits
This function provides safer access to traits object than
wx.GetApp().GetTraits() during startup or termination when the global
application object itself may be unavailable.
"""
return _core_.PyApp_GetTraitsIfExists(*args) | [
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|
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/site-packages/setuptools/_vendor/pyparsing.py | python | ParseResults.pprint | (self, *args, **kwargs) | Pretty-printer for parsed results as a list, using the C{pprint} module.
Accepts additional positional or keyword args as defined for the
C{pprint.pprint} method. (U{http://docs.python.org/3/library/pprint.html#pprint.pprint})
Example::
ident = Word(alphas, alphanums)
num = Word(nums)
func = Forward()
term = ident | num | Group('(' + func + ')')
func <<= ident + Group(Optional(delimitedList(term)))
result = func.parseString("fna a,b,(fnb c,d,200),100")
result.pprint(width=40)
prints::
['fna',
['a',
'b',
['(', 'fnb', ['c', 'd', '200'], ')'],
'100']] | Pretty-printer for parsed results as a list, using the C{pprint} module.
Accepts additional positional or keyword args as defined for the
C{pprint.pprint} method. (U{http://docs.python.org/3/library/pprint.html#pprint.pprint}) | [
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"""
Pretty-printer for parsed results as a list, using the C{pprint} module.
Accepts additional positional or keyword args as defined for the
C{pprint.pprint} method. (U{http://docs.python.org/3/library/pprint.html#pprint.pprint})
Example::
ident = Word(alphas, alphanums)
num = Word(nums)
func = Forward()
term = ident | num | Group('(' + func + ')')
func <<= ident + Group(Optional(delimitedList(term)))
result = func.parseString("fna a,b,(fnb c,d,200),100")
result.pprint(width=40)
prints::
['fna',
['a',
'b',
['(', 'fnb', ['c', 'd', '200'], ')'],
'100']]
"""
pprint.pprint(self.asList(), *args, **kwargs) | [
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||
glotzerlab/hoomd-blue | f7f97abfa3fcc2522fa8d458d65d0aeca7ba781a | hoomd/tune/attr_tuner.py | python | ManualTuneDefinition.__hash__ | (self) | return hash((self._user_get_x, self._user_set_x, self._user_get_y,
self._target)) | Compute a hash of the tune definition. | Compute a hash of the tune definition. | [
"Compute",
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"hash",
"of",
"the",
"tune",
"definition",
"."
] | def __hash__(self):
"""Compute a hash of the tune definition."""
return hash((self._user_get_x, self._user_set_x, self._user_get_y,
self._target)) | [
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|
krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/klampt/control/blocks/state_machine.py | python | StateMachineBase.next_state | (self,state,*args,**kwargs) | return state | Subclasses should override this to implement the transitions | Subclasses should override this to implement the transitions | [
"Subclasses",
"should",
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"this",
"to",
"implement",
"the",
"transitions"
] | def next_state(self,state,*args,**kwargs):
"""Subclasses should override this to implement the transitions"""
return state | [
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|
mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | Framework/PythonInterface/plugins/algorithms/WorkflowAlgorithms/ILL_utilities.py | python | NameSource.__init__ | (self, prefix, cleanupMode) | Initialize an instance of the class. | Initialize an instance of the class. | [
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] | def __init__(self, prefix, cleanupMode):
"""Initialize an instance of the class."""
self._names = set()
self._prefix = '__' + prefix if cleanupMode == Cleanup.ON else prefix | [
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||
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/random.py | python | Random._randbelow | (self, n, _log=_log, _int=int, _maxwidth=1L<<BPF,
_Method=_MethodType, _BuiltinMethod=_BuiltinMethodType) | return _int(self.random() * n) | Return a random int in the range [0,n)
Handles the case where n has more bits than returned
by a single call to the underlying generator. | Return a random int in the range [0,n) | [
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"int",
"in",
"the",
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")"
] | def _randbelow(self, n, _log=_log, _int=int, _maxwidth=1L<<BPF,
_Method=_MethodType, _BuiltinMethod=_BuiltinMethodType):
"""Return a random int in the range [0,n)
Handles the case where n has more bits than returned
by a single call to the underlying generator.
"""
try:
getrandbits = self.getrandbits
except AttributeError:
pass
else:
# Only call self.getrandbits if the original random() builtin method
# has not been overridden or if a new getrandbits() was supplied.
# This assures that the two methods correspond.
if type(self.random) is _BuiltinMethod or type(getrandbits) is _Method:
k = _int(1.00001 + _log(n-1, 2.0)) # 2**k > n-1 > 2**(k-2)
r = getrandbits(k)
while r >= n:
r = getrandbits(k)
return r
if n >= _maxwidth:
_warn("Underlying random() generator does not supply \n"
"enough bits to choose from a population range this large")
return _int(self.random() * n) | [
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|
wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_core.py | python | Window.SetCursor | (*args, **kwargs) | return _core_.Window_SetCursor(*args, **kwargs) | SetCursor(self, Cursor cursor) -> bool
Sets the window's cursor. Notice that the window cursor also sets it
for the children of the window implicitly.
The cursor may be wx.NullCursor in which case the window cursor will
be reset back to default. | SetCursor(self, Cursor cursor) -> bool | [
"SetCursor",
"(",
"self",
"Cursor",
"cursor",
")",
"-",
">",
"bool"
] | def SetCursor(*args, **kwargs):
"""
SetCursor(self, Cursor cursor) -> bool
Sets the window's cursor. Notice that the window cursor also sets it
for the children of the window implicitly.
The cursor may be wx.NullCursor in which case the window cursor will
be reset back to default.
"""
return _core_.Window_SetCursor(*args, **kwargs) | [
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] | https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/osx_cocoa/_core.py#L10958-L10968 |
|
Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/tpu/python/tpu/keras_support.py | python | TPUDatasetInfeedManager._verify_dataset_shape | (self, dataset) | Verifies a dataset is of an appropriate shape for TPUs. | Verifies a dataset is of an appropriate shape for TPUs. | [
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"appropriate",
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] | def _verify_dataset_shape(self, dataset):
"""Verifies a dataset is of an appropriate shape for TPUs."""
dataset_output_shapes = dataset_ops.get_legacy_output_shapes(dataset)
dataset_output_classes = dataset_ops.get_legacy_output_classes(dataset)
if not isinstance(dataset, dataset_ops.DatasetV2):
raise ValueError('The function passed as the `x` parameter did not '
'return a `tf.data.Dataset`.')
if not isinstance(dataset_output_classes, tuple):
raise ValueError('The dataset must return a tuple of tf.Tensors, '
'instead it returns: %s' % dataset_output_classes)
if len(dataset_output_classes) != 2:
raise ValueError('The dataset must return a 2-element tuple, got '
'%s output classes instead.' % (dataset_output_classes,))
for i, cls in enumerate(dataset_output_classes):
if cls != ops.Tensor:
raise ValueError('The dataset returned a non-Tensor type (%s) at '
'index %d.' % (cls, i))
for i, shape in enumerate(dataset_output_shapes):
if not shape:
raise ValueError('The dataset returns a scalar tensor in '
'tuple index %d. Did you forget to batch? '
'(Output shapes: %s).' % (i, dataset_output_shapes))
for j, dim in enumerate(shape):
if dim.value is None:
if j == 0:
hint = (' Hint: did you use `ds.batch(BATCH_SIZE, '
'drop_remainder=True)`?')
else:
hint = ''
raise ValueError(
'The Keras-TPU integration for `tf.data` '
'currently requires static shapes. The provided '
'dataset only has a partially defined shape. '
'(Dimension %d of output tensor %d is not statically known '
'for output shapes: %s.%s)' % (j, i, dataset_output_shapes, hint)) | [
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||
hughperkins/tf-coriander | 970d3df6c11400ad68405f22b0c42a52374e94ca | tensorflow/models/rnn/translate/data_utils.py | python | maybe_download | (directory, filename, url) | return filepath | Download filename from url unless it's already in directory. | Download filename from url unless it's already in directory. | [
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] | def maybe_download(directory, filename, url):
"""Download filename from url unless it's already in directory."""
if not os.path.exists(directory):
print("Creating directory %s" % directory)
os.mkdir(directory)
filepath = os.path.join(directory, filename)
if not os.path.exists(filepath):
print("Downloading %s to %s" % (url, filepath))
filepath, _ = urllib.request.urlretrieve(url, filepath)
statinfo = os.stat(filepath)
print("Succesfully downloaded", filename, statinfo.st_size, "bytes")
return filepath | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/ops/math_ops.py | python | div | (x, y, name=None) | return _div_python2(x, y, name) | Divides x / y elementwise (using Python 2 division operator semantics).
@compatibility(TF2)
This function is deprecated in TF2. Prefer using the Tensor division operator,
`tf.divide`, or `tf.math.divide`, which obey the Python 3 division operator
semantics.
@end_compatibility
This function divides `x` and `y`, forcing Python 2 semantics. That is, if `x`
and `y` are both integers then the result will be an integer. This is in
contrast to Python 3, where division with `/` is always a float while division
with `//` is always an integer.
Args:
x: `Tensor` numerator of real numeric type.
y: `Tensor` denominator of real numeric type.
name: A name for the operation (optional).
Returns:
`x / y` returns the quotient of x and y. | Divides x / y elementwise (using Python 2 division operator semantics). | [
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"""Divides x / y elementwise (using Python 2 division operator semantics).
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gabyx/ApproxMVBB | 838f3ff7690a938f1e4199a5f41b6feefc32a603 | example/kdTreeFiltering/python/Tools/Transformations/Transformations.py | python | Arcball.drag | (self, point) | Update current cursor window coordinates. | Update current cursor window coordinates. | [
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"""Update current cursor window coordinates."""
vnow = arcball_map_to_sphere(point, self._center, self._radius)
if self._axis is not None:
vnow = arcball_constrain_to_axis(vnow, self._axis)
self._qpre = self._qnow
t = numpy.cross(self._vdown, vnow)
if numpy.dot(t, t) < _EPS:
self._qnow = self._qdown
else:
q = [numpy.dot(self._vdown, vnow), t[0], t[1], t[2]]
self._qnow = quaternion_multiply(q, self._qdown) | [
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infinit/memo | 3a8394d0f647efe03ccb8bfe885a7279cb8be8a6 | elle/drake/src/drake/__init__.py | python | Version.__ge__ | (self, rhs) | Whether a version is greater than another.
>>> Version(1, 2, 3) >= Version(1, 2, 3)
True
>>> Version(1, 2, 4) >= Version(1, 2, 3)
True
>>> Version(1, 3, 2) >= Version(1, 2, 3)
True
>>> Version(2, 0, 0) >= Version(1, 10, 23)
True
>>> Version(1, 2, 3) >= Version(1, 2, 4)
False
>>> Version(1, 2, 3) >= Version(1, 3, 2)
False | Whether a version is greater than another. | [
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"""Whether a version is greater than another.
>>> Version(1, 2, 3) >= Version(1, 2, 3)
True
>>> Version(1, 2, 4) >= Version(1, 2, 3)
True
>>> Version(1, 3, 2) >= Version(1, 2, 3)
True
>>> Version(2, 0, 0) >= Version(1, 10, 23)
True
>>> Version(1, 2, 3) >= Version(1, 2, 4)
False
>>> Version(1, 2, 3) >= Version(1, 3, 2)
False
"""
assert self.__major is not None and rhs.__major is not None
if self.__major == rhs.__major:
minor = self.__minor or 0
rhs_minor = rhs.__minor or 0
if minor == rhs_minor:
subminor = self.__subminor or 0
rhs_subminor = rhs.__subminor or 0
return subminor >= rhs_subminor
else:
return minor > rhs_minor
else:
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Tencent/CMONGO | c40380caa14e05509f46993aa8b8da966b09b0b5 | src/third_party/scons-2.5.0/scons-local-2.5.0/SCons/Tool/ilink.py | python | generate | (env) | Add Builders and construction variables for ilink to an Environment. | Add Builders and construction variables for ilink to an Environment. | [
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] | def generate(env):
"""Add Builders and construction variables for ilink to an Environment."""
SCons.Tool.createProgBuilder(env)
env['LINK'] = 'ilink'
env['LINKFLAGS'] = SCons.Util.CLVar('')
env['LINKCOM'] = '$LINK $LINKFLAGS /O:$TARGET $SOURCES $_LIBDIRFLAGS $_LIBFLAGS'
env['LIBDIRPREFIX']='/LIBPATH:'
env['LIBDIRSUFFIX']=''
env['LIBLINKPREFIX']=''
env['LIBLINKSUFFIX']='$LIBSUFFIX' | [
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||
xiaolonw/caffe-video_triplet | c39ea1ad6e937ccf7deba4510b7e555165abf05f | scripts/cpp_lint.py | python | _CppLintState.ResetErrorCounts | (self) | Sets the module's error statistic back to zero. | Sets the module's error statistic back to zero. | [
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] | def ResetErrorCounts(self):
"""Sets the module's error statistic back to zero."""
self.error_count = 0
self.errors_by_category = {} | [
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eclipse/sumo | 7132a9b8b6eea734bdec38479026b4d8c4336d03 | tools/traci/_overheadwire.py | python | OverheadWireDomain.getVehicleIDs | (self, stopID) | return self._getUniversal(tc.VAR_STOP_STARTING_VEHICLES_IDS, stopID) | getOverheadWireWaiting() -> list(string)
Get the IDs of vehicles stopped at the named overhead wire. | getOverheadWireWaiting() -> list(string)
Get the IDs of vehicles stopped at the named overhead wire. | [
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return self._getUniversal(tc.VAR_STOP_STARTING_VEHICLES_IDS, stopID) | [
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|
arkenthera/electron-vibrancy | 383153ef9ccb23a6c7517150d6bb0794dff3115e | scripts/cpplint.py | python | CleanseComments | (line) | return _RE_PATTERN_CLEANSE_LINE_C_COMMENTS.sub('', line) | Removes //-comments and single-line C-style /* */ comments.
Args:
line: A line of C++ source.
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"""Removes //-comments and single-line C-style /* */ comments.
Args:
line: A line of C++ source.
Returns:
The line with single-line comments removed.
"""
commentpos = line.find('//')
if commentpos != -1 and not IsCppString(line[:commentpos]):
line = line[:commentpos].rstrip()
# get rid of /* ... */
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FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/TemplatePyMod/DocumentObject.py | python | ViewProvider.show | (self) | switches this object to visible | switches this object to visible | [
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"switches this object to visible"
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"(",
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] | https://github.com/FreeCAD/FreeCAD/blob/ba42231b9c6889b89e064d6d563448ed81e376ec/src/Mod/TemplatePyMod/DocumentObject.py#L188-L190 |
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