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BlzFans/wke | b0fa21158312e40c5fbd84682d643022b6c34a93 | cygwin/lib/python2.6/zipfile.py | python | ZipFile._extract_member | (self, member, targetpath, pwd) | return targetpath | Extract the ZipInfo object 'member' to a physical
file on the path targetpath. | Extract the ZipInfo object 'member' to a physical
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"""Extract the ZipInfo object 'member' to a physical
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"""
# build the destination pathname, replacing
# forward slashes to platform specific separators.
# Strip trailing path separator, unless it represents the root.
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# don't include leading "/" from file name if present
if member.filename[0] == '/':
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target.close()
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/third_party/examples/eager/spinn/spinn.py | python | Tracker.__init__ | (self, tracker_size, predict) | Constructor of Tracker.
Args:
tracker_size: Number of dimensions of the underlying `LSTMCell`.
predict: (`bool`) Whether prediction mode is enabled. | Constructor of Tracker. | [
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Args:
tracker_size: Number of dimensions of the underlying `LSTMCell`.
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"""
super(Tracker, self).__init__()
self._rnn = tf.nn.rnn_cell.LSTMCell(tracker_size)
self._state_size = tracker_size
if predict:
self._transition = layers.Dense(4)
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self._transition = None | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/_extends/parallel_compile/tbe_compiler/tbe_helper.py | python | get_fuzz_build_info | (job_content) | return fuzz_build_info | Get fuzz build info from job content info
:param job_content: job content info
:return: fuzz build info | Get fuzz build info from job content info
:param job_content: job content info
:return: fuzz build info | [
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"""
Get fuzz build info from job content info
:param job_content: job content info
:return: fuzz build info
"""
op_compute_info = get_compute_op_list(job_content)[0]
fuzz_build_info = dict()
fuzz_build_info["compile_type"] = "fuzzily_build" if op_compute_info["build_type"] == BuildType.FUZZILY.value \
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fuzz_build_info["miss_support_info"] = op_compute_info["miss_support_info"]
fuzz_build_info["max_kernel_id"] = op_compute_info["max_kernel_id"]
fuzz_build_info["incremental_link"] = os.path.realpath(
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eventql/eventql | 7ca0dbb2e683b525620ea30dc40540a22d5eb227 | deps/3rdparty/spidermonkey/mozjs/media/webrtc/trunk/tools/gyp/pylib/gyp/generator/msvs.py | python | _ShardName | (name, number) | return '#'.join(parts) | Add a shard number to the end of a target.
Arguments:
name: name of the target (foo#target)
number: shard number
Returns:
Target name with shard added (foo_1#target) | Add a shard number to the end of a target. | [
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"""Add a shard number to the end of a target.
Arguments:
name: name of the target (foo#target)
number: shard number
Returns:
Target name with shard added (foo_1#target)
"""
parts = name.rsplit('#', 1)
parts[0] = '%s_%d' % (parts[0], number)
return '#'.join(parts) | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow2.x/tensorflow_model_optimization/python/core/internal/tensor_encoding/core/core_encoder.py | python | Encoder._commuting_structure_impl | (self, previous) | return commuting_structure | Implementation for the `commuting_structure` property. | Implementation for the `commuting_structure` property. | [
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"""Implementation for the `commuting_structure` property."""
current = previous & self.stage.commutes_with_sum
commuting_structure = {
EncoderKeys.COMMUTE: current,
EncoderKeys.CHILDREN: {}
}
for key, encoder in six.iteritems(self.children):
commuting_structure[EncoderKeys.CHILDREN][key] = (
encoder._commuting_structure_impl(current)) # pylint: disable=protected-access
return commuting_structure | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/py_vulcanize/third_party/rcssmin/_setup/py2/data.py | python | Documentation.flatten | (self, installer) | return Data.flatten(self, installer) | Check if docs should be installed at all | Check if docs should be installed at all | [
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""" Check if docs should be installed at all """
if installer.without_docs:
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microsoft/TSS.MSR | 0f2516fca2cd9929c31d5450e39301c9bde43688 | TSS.Py/src/TpmTypes.py | python | TPM2_CertifyX509_REQUEST.toTpm | (self, buf) | TpmMarshaller method | TpmMarshaller method | [
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""" TpmMarshaller method """
buf.writeSizedByteBuf(self.reserved)
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self.inScheme.toTpm(buf)
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CGRU/cgru | 1881a4128530e3d31ac6c25314c18314fc50c2c7 | afanasy/python/parsers/parser.py | python | parser.tagHTML | (self, i_line) | return i_line | Convert line to HTML.
Designed for GUIs for escape sequences, errors highlighting.
Function designed to be implemented in child classes, if special highlinghting needed.
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:return: converted line | Convert line to HTML.
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hpi-xnor/BMXNet-v2 | af2b1859eafc5c721b1397cef02f946aaf2ce20d | python/mxnet/symbol/random.py | python | multinomial | (data, shape=_Null, get_prob=True, dtype='int32', **kwargs) | return _internal._sample_multinomial(data, shape, get_prob, dtype=dtype, **kwargs) | Concurrent sampling from multiple multinomial distributions.
.. note:: The input distribution must be normalized, i.e. `data` must sum to
1 along its last dimension.
Parameters
----------
data : Symbol
An *n* dimensional array whose last dimension has length `k`, where
`k` is the number of possible outcomes of each multinomial distribution.
For example, data with shape `(m, n, k)` specifies `m*n` multinomial
distributions each with `k` possible outcomes.
shape : int or tuple of ints, optional
The number of samples to draw from each distribution. If shape is empty
one sample will be drawn from each distribution.
get_prob : bool, optional
If true, a second array containing log likelihood of the drawn
samples will also be returned.
This is usually used for reinforcement learning, where you can provide
reward as head gradient w.r.t. this array to estimate gradient.
dtype : str or numpy.dtype, optional
Data type of the sample output array. The default is int32.
Note that the data type of the log likelihood array is the same with that of `data`.
Returns
-------
Symbol
For input `data` with `n` dimensions and shape `(d1, d2, ..., dn-1, k)`, and input
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1 along its last dimension.
Parameters
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data : Symbol
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"""
return _internal._sample_multinomial(data, shape, get_prob, dtype=dtype, **kwargs) | [
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ideawu/ssdb | f229ba277c7f7d0ca5a441c0c6fb3d1209af68e4 | deps/cpy/antlr3/tree.py | python | TreeNodeStream.setUniqueNavigationNodes | (self, uniqueNavigationNodes) | As we flatten the tree, we use UP, DOWN nodes to represent
the tree structure. When debugging we need unique nodes
so we have to instantiate new ones. When doing normal tree
parsing, it's slow and a waste of memory to create unique
navigation nodes. Default should be false; | As we flatten the tree, we use UP, DOWN nodes to represent
the tree structure. When debugging we need unique nodes
so we have to instantiate new ones. When doing normal tree
parsing, it's slow and a waste of memory to create unique
navigation nodes. Default should be false; | [
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"""
As we flatten the tree, we use UP, DOWN nodes to represent
the tree structure. When debugging we need unique nodes
so we have to instantiate new ones. When doing normal tree
parsing, it's slow and a waste of memory to create unique
navigation nodes. Default should be false;
"""
raise NotImplementedError | [
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facebook/watchman | 0917460c71b000b96be9b9575d77f06f2f6053bb | watchman/python/pywatchman_aio/__init__.py | python | AIOClient.pop_log | (self) | return res | Get one log from the log queue. | Get one log from the log queue. | [
"Get",
"one",
"log",
"from",
"the",
"log",
"queue",
"."
] | async def pop_log(self):
"""Get one log from the log queue."""
self._check_receive_loop()
res = self.log_queue.get()
self._check_error(res)
return res | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_controls.py | python | TreeItemId.__init__ | (self, *args, **kwargs) | __init__(self) -> TreeItemId | __init__(self) -> TreeItemId | [
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"(",
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")",
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"""__init__(self) -> TreeItemId"""
_controls_.TreeItemId_swiginit(self,_controls_.new_TreeItemId(*args, **kwargs)) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/combo.py | python | ComboCtrl.UseAltPopupWindow | (*args, **kwargs) | return _combo.ComboCtrl_UseAltPopupWindow(*args, **kwargs) | UseAltPopupWindow(self, bool enable=True)
Enable or disable usage of an alternative popup window, which
guarantees ability to focus the popup control, and allows common
native controls to function normally. This alternative popup window is
usually a wxDialog, and as such, when it is shown, its parent
top-level window will appear as if the focus has been lost from it. | UseAltPopupWindow(self, bool enable=True) | [
"UseAltPopupWindow",
"(",
"self",
"bool",
"enable",
"=",
"True",
")"
] | def UseAltPopupWindow(*args, **kwargs):
"""
UseAltPopupWindow(self, bool enable=True)
Enable or disable usage of an alternative popup window, which
guarantees ability to focus the popup control, and allows common
native controls to function normally. This alternative popup window is
usually a wxDialog, and as such, when it is shown, its parent
top-level window will appear as if the focus has been lost from it.
"""
return _combo.ComboCtrl_UseAltPopupWindow(*args, **kwargs) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | tools/json_to_struct/struct_generator.py | python | GenerateField | (field_info) | Generate a string defining a field of the type specified by
field_info['type'] in a C structure. | Generate a string defining a field of the type specified by
field_info['type'] in a C structure. | [
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"]",
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"C",
"structure",
"."
] | def GenerateField(field_info):
"""Generate a string defining a field of the type specified by
field_info['type'] in a C structure.
"""
field = field_info['field']
type = field_info['type']
if type == 'int':
return 'const int %s' % field
elif type == 'string':
return 'const char* const %s' % field
elif type == 'string16':
return 'const wchar_t* const %s' % field
elif type == 'enum':
return 'const %s %s' % (field_info['ctype'], field)
elif type == 'array':
return _GenerateArrayField(field_info)
elif type == 'struct':
return 'const %s %s' % (field_info['type_name'], field)
else:
raise RuntimeError('Unknown field type "%s"' % type) | [
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facebookincubator/BOLT | 88c70afe9d388ad430cc150cc158641701397f70 | llvm/bindings/python/llvm/object.py | python | Symbol.section | (self) | return Section(sections) | The Section to which this Symbol belongs.
The returned Section instance does not expire, unlike Sections that are
commonly obtained through iteration.
Because this obtains a new section iterator each time it is accessed,
calling this on a number of Symbol instances could be expensive. | The Section to which this Symbol belongs. | [
"The",
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"to",
"which",
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"Symbol",
"belongs",
"."
] | def section(self):
"""The Section to which this Symbol belongs.
The returned Section instance does not expire, unlike Sections that are
commonly obtained through iteration.
Because this obtains a new section iterator each time it is accessed,
calling this on a number of Symbol instances could be expensive.
"""
sections = lib.LLVMGetSections(self._object_file)
lib.LLVMMoveToContainingSection(sections, self)
return Section(sections) | [
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shedskin/shedskin | ae88dbca7b1d9671cd8be448cb0b497122758936 | examples/sha.py | python | _rotateLeft | (x, n) | return (x << n) | (x >> (32-n)) | Rotate x (32 bit) left n bits circularly. | Rotate x (32 bit) left n bits circularly. | [
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"x",
"(",
"32",
"bit",
")",
"left",
"n",
"bits",
"circularly",
"."
] | def _rotateLeft(x, n):
"Rotate x (32 bit) left n bits circularly."
return (x << n) | (x >> (32-n)) | [
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/klampt/robotsim.py | python | Appearance.setTexture2D_b | (self, format: str, np_array2: "ndarray", topdown: bool=True) | return _robotsim.Appearance_setTexture2D_b(self, format, np_array2, topdown) | r"""
Sets a 2D texture of the given width/height. See :func:`setTexture1D_b` for
valid format strings.
Args:
format (str)
np_array2 (:obj:`unsigned char *`)
topdown (bool, optional): default value True
The array is given in top to bottom order if `topdown==True`. Otherwise, it is
given in order bottom to top. | r"""
Sets a 2D texture of the given width/height. See :func:`setTexture1D_b` for
valid format strings. | [
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] | def setTexture2D_b(self, format: str, np_array2: "ndarray", topdown: bool=True) ->None:
r"""
Sets a 2D texture of the given width/height. See :func:`setTexture1D_b` for
valid format strings.
Args:
format (str)
np_array2 (:obj:`unsigned char *`)
topdown (bool, optional): default value True
The array is given in top to bottom order if `topdown==True`. Otherwise, it is
given in order bottom to top.
"""
return _robotsim.Appearance_setTexture2D_b(self, format, np_array2, topdown) | [
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OSGeo/gdal | 3748fc4ba4fba727492774b2b908a2130c864a83 | swig/python/gdal-utils/osgeo_utils/gdal2tiles.py | python | GDAL2Tiles.open_input | (self) | Initialization of the input raster, reprojection if necessary | Initialization of the input raster, reprojection if necessary | [
"Initialization",
"of",
"the",
"input",
"raster",
"reprojection",
"if",
"necessary"
] | def open_input(self) -> None:
"""Initialization of the input raster, reprojection if necessary"""
gdal.AllRegister()
self.out_drv = gdal.GetDriverByName(self.tiledriver)
self.mem_drv = gdal.GetDriverByName('MEM')
if not self.out_drv:
raise Exception("The '%s' driver was not found, is it available in this GDAL build?" %
self.tiledriver)
if not self.mem_drv:
raise Exception("The 'MEM' driver was not found, is it available in this GDAL build?")
# Open the input file
if self.input_file:
input_dataset: gdal.Dataset = gdal.Open(self.input_file, gdal.GA_ReadOnly)
else:
raise Exception("No input file was specified")
if self.options.verbose:
print("Input file:",
"( %sP x %sL - %s bands)" % (input_dataset.RasterXSize,
input_dataset.RasterYSize,
input_dataset.RasterCount))
if not input_dataset:
# Note: GDAL prints the ERROR message too
exit_with_error("It is not possible to open the input file '%s'." % self.input_file)
# Read metadata from the input file
if input_dataset.RasterCount == 0:
exit_with_error("Input file '%s' has no raster band" % self.input_file)
if input_dataset.GetRasterBand(1).GetRasterColorTable():
exit_with_error(
"Please convert this file to RGB/RGBA and run gdal2tiles on the result.",
"From paletted file you can create RGBA file (temp.vrt) by:\n"
"gdal_translate -of vrt -expand rgba %s temp.vrt\n"
"then run:\n"
"gdal2tiles temp.vrt" % self.input_file
)
if input_dataset.GetRasterBand(1).DataType != gdal.GDT_Byte:
exit_with_error(
"Please convert this file to 8-bit and run gdal2tiles on the result.",
"To scale pixel values you can use:\n"
"gdal_translate -of VRT -ot Byte -scale %s temp.vrt\n"
"then run:\n"
"gdal2tiles temp.vrt" % self.input_file
)
in_nodata = setup_no_data_values(input_dataset, self.options)
if self.options.verbose:
print("Preprocessed file:",
"( %sP x %sL - %s bands)" % (input_dataset.RasterXSize,
input_dataset.RasterYSize,
input_dataset.RasterCount))
self.in_srs, self.in_srs_wkt = setup_input_srs(input_dataset, self.options)
self.out_srs = setup_output_srs(self.in_srs, self.options)
# If input and output reference systems are different, we reproject the input dataset into
# the output reference system for easier manipulation
self.warped_input_dataset = None
if self.options.profile != 'raster':
if not self.in_srs:
exit_with_error(
"Input file has unknown SRS.",
"Use --s_srs EPSG:xyz (or similar) to provide source reference system.")
if not has_georeference(input_dataset):
exit_with_error(
"There is no georeference - neither affine transformation (worldfile) "
"nor GCPs. You can generate only 'raster' profile tiles.",
"Either gdal2tiles with parameter -p 'raster' or use another GIS "
"software for georeference e.g. gdal_transform -gcp / -a_ullr / -a_srs"
)
if ((self.in_srs.ExportToProj4() != self.out_srs.ExportToProj4()) or
(input_dataset.GetGCPCount() != 0)):
self.warped_input_dataset = reproject_dataset(
input_dataset, self.in_srs, self.out_srs)
if in_nodata:
self.warped_input_dataset = update_no_data_values(
self.warped_input_dataset, in_nodata, options=self.options)
else:
self.warped_input_dataset = update_alpha_value_for_non_alpha_inputs(
self.warped_input_dataset, options=self.options)
if self.warped_input_dataset and self.options.verbose:
print("Projected file:", "tiles.vrt", "( %sP x %sL - %s bands)" % (
self.warped_input_dataset.RasterXSize,
self.warped_input_dataset.RasterYSize,
self.warped_input_dataset.RasterCount))
if not self.warped_input_dataset:
self.warped_input_dataset = input_dataset
gdal.GetDriverByName('VRT').CreateCopy(self.tmp_vrt_filename,
self.warped_input_dataset)
# Get alpha band (either directly or from NODATA value)
self.alphaband = self.warped_input_dataset.GetRasterBand(1).GetMaskBand()
self.dataBandsCount = nb_data_bands(self.warped_input_dataset)
# KML test
self.isepsg4326 = False
srs4326 = osr.SpatialReference()
srs4326.ImportFromEPSG(4326)
srs4326.SetAxisMappingStrategy(osr.OAMS_TRADITIONAL_GIS_ORDER)
if self.out_srs and srs4326.ExportToProj4() == self.out_srs.ExportToProj4():
self.isepsg4326 = True
if self.kml is None:
self.kml = True
if self.kml and self.options.verbose:
print("KML autotest OK!")
if self.kml is None:
self.kml = False
# Read the georeference
self.out_gt = self.warped_input_dataset.GetGeoTransform()
# Test the size of the pixel
# Report error in case rotation/skew is in geotransform (possible only in 'raster' profile)
if (self.out_gt[2], self.out_gt[4]) != (0, 0):
exit_with_error("Georeference of the raster contains rotation or skew. "
"Such raster is not supported. Please use gdalwarp first.")
# Here we expect: pixel is square, no rotation on the raster
# Output Bounds - coordinates in the output SRS
self.ominx = self.out_gt[0]
self.omaxx = self.out_gt[0] + self.warped_input_dataset.RasterXSize * self.out_gt[1]
self.omaxy = self.out_gt[3]
self.ominy = self.out_gt[3] - self.warped_input_dataset.RasterYSize * self.out_gt[1]
# Note: maybe round(x, 14) to avoid the gdal_translate behavior, when 0 becomes -1e-15
if self.options.verbose:
print("Bounds (output srs):", round(self.ominx, 13), self.ominy, self.omaxx, self.omaxy)
# Calculating ranges for tiles in different zoom levels
if self.options.profile == 'mercator':
self.mercator = GlobalMercator(tile_size=self.tile_size)
# Function which generates SWNE in LatLong for given tile
self.tileswne = self.mercator.TileLatLonBounds
# Generate table with min max tile coordinates for all zoomlevels
self.tminmax = list(range(0, MAXZOOMLEVEL))
for tz in range(0, MAXZOOMLEVEL):
tminx, tminy = self.mercator.MetersToTile(self.ominx, self.ominy, tz)
tmaxx, tmaxy = self.mercator.MetersToTile(self.omaxx, self.omaxy, tz)
# crop tiles extending world limits (+-180,+-90)
tminx, tminy = max(0, tminx), max(0, tminy)
tmaxx, tmaxy = min(2**tz - 1, tmaxx), min(2**tz - 1, tmaxy)
self.tminmax[tz] = (tminx, tminy, tmaxx, tmaxy)
# TODO: Maps crossing 180E (Alaska?)
# Get the minimal zoom level (map covers area equivalent to one tile)
if self.tminz is None:
self.tminz = self.mercator.ZoomForPixelSize(
self.out_gt[1] *
max(self.warped_input_dataset.RasterXSize,
self.warped_input_dataset.RasterYSize) /
float(self.tile_size))
# Get the maximal zoom level
# (closest possible zoom level up on the resolution of raster)
if self.tmaxz is None:
self.tmaxz = self.mercator.ZoomForPixelSize(self.out_gt[1])
self.tmaxz = max(self.tminz, self.tmaxz)
self.tminz = min(self.tminz, self.tmaxz)
if self.options.verbose:
print("Bounds (latlong):",
self.mercator.MetersToLatLon(self.ominx, self.ominy),
self.mercator.MetersToLatLon(self.omaxx, self.omaxy))
print('MinZoomLevel:', self.tminz)
print("MaxZoomLevel:",
self.tmaxz,
"(",
self.mercator.Resolution(self.tmaxz),
")")
elif self.options.profile == 'geodetic':
self.geodetic = GlobalGeodetic(self.options.tmscompatible, tile_size=self.tile_size)
# Function which generates SWNE in LatLong for given tile
self.tileswne = self.geodetic.TileLatLonBounds
# Generate table with min max tile coordinates for all zoomlevels
self.tminmax = list(range(0, MAXZOOMLEVEL))
for tz in range(0, MAXZOOMLEVEL):
tminx, tminy = self.geodetic.LonLatToTile(self.ominx, self.ominy, tz)
tmaxx, tmaxy = self.geodetic.LonLatToTile(self.omaxx, self.omaxy, tz)
# crop tiles extending world limits (+-180,+-90)
tminx, tminy = max(0, tminx), max(0, tminy)
tmaxx, tmaxy = min(2**(tz + 1) - 1, tmaxx), min(2**tz - 1, tmaxy)
self.tminmax[tz] = (tminx, tminy, tmaxx, tmaxy)
# TODO: Maps crossing 180E (Alaska?)
# Get the maximal zoom level
# (closest possible zoom level up on the resolution of raster)
if self.tminz is None:
self.tminz = self.geodetic.ZoomForPixelSize(
self.out_gt[1] *
max(self.warped_input_dataset.RasterXSize,
self.warped_input_dataset.RasterYSize) /
float(self.tile_size))
# Get the maximal zoom level
# (closest possible zoom level up on the resolution of raster)
if self.tmaxz is None:
self.tmaxz = self.geodetic.ZoomForPixelSize(self.out_gt[1])
self.tmaxz = max(self.tminz, self.tmaxz)
self.tminz = min(self.tminz, self.tmaxz)
if self.options.verbose:
print("Bounds (latlong):", self.ominx, self.ominy, self.omaxx, self.omaxy)
elif self.options.profile == 'raster':
def log2(x):
return math.log10(x) / math.log10(2)
self.nativezoom = max(0, int(
max(math.ceil(log2(self.warped_input_dataset.RasterXSize / float(self.tile_size))),
math.ceil(log2(self.warped_input_dataset.RasterYSize / float(self.tile_size))))))
if self.options.verbose:
print("Native zoom of the raster:", self.nativezoom)
# Get the minimal zoom level (whole raster in one tile)
if self.tminz is None:
self.tminz = 0
# Get the maximal zoom level (native resolution of the raster)
if self.tmaxz is None:
self.tmaxz = self.nativezoom
self.tmaxz = max(self.tminz, self.tmaxz)
elif self.tmaxz > self.nativezoom:
print('Clamping max zoom level to %d' % self.nativezoom)
self.tmaxz = self.nativezoom
# Generate table with min max tile coordinates for all zoomlevels
self.tminmax = list(range(0, self.tmaxz + 1))
self.tsize = list(range(0, self.tmaxz + 1))
for tz in range(0, self.tmaxz + 1):
tsize = 2.0**(self.nativezoom - tz) * self.tile_size
tminx, tminy = 0, 0
tmaxx = int(math.ceil(self.warped_input_dataset.RasterXSize / tsize)) - 1
tmaxy = int(math.ceil(self.warped_input_dataset.RasterYSize / tsize)) - 1
self.tsize[tz] = math.ceil(tsize)
self.tminmax[tz] = (tminx, tminy, tmaxx, tmaxy)
# Function which generates SWNE in LatLong for given tile
if self.kml and self.in_srs_wkt:
ct = osr.CoordinateTransformation(self.in_srs, srs4326)
def rastertileswne(x, y, z):
pixelsizex = (2**(self.tmaxz - z) * self.out_gt[1]) # X-pixel size in level
west = self.out_gt[0] + x * self.tile_size * pixelsizex
east = west + self.tile_size * pixelsizex
if self.options.xyz:
north = self.omaxy - y * self.tile_size * pixelsizex
south = north - self.tile_size * pixelsizex
else:
south = self.ominy + y * self.tile_size * pixelsizex
north = south + self.tile_size * pixelsizex
if not self.isepsg4326:
# Transformation to EPSG:4326 (WGS84 datum)
west, south = ct.TransformPoint(west, south)[:2]
east, north = ct.TransformPoint(east, north)[:2]
return south, west, north, east
self.tileswne = rastertileswne
else:
self.tileswne = lambda x, y, z: (0, 0, 0, 0) # noqa
else:
tms = tmsMap[self.options.profile]
# Function which generates SWNE in LatLong for given tile
self.tileswne = None # not implemented
# Generate table with min max tile coordinates for all zoomlevels
self.tminmax = list(range(0, tms.level_count+1))
for tz in range(0, tms.level_count+1):
tminx, tminy = tms.GeorefCoordToTileCoord(self.ominx, self.ominy, tz, self.tile_size)
tmaxx, tmaxy = tms.GeorefCoordToTileCoord(self.omaxx, self.omaxy, tz, self.tile_size)
tminx, tminy = max(0, tminx), max(0, tminy)
tmaxx, tmaxy = min(tms.matrix_width * 2**tz - 1, tmaxx), min(tms.matrix_height * 2**tz - 1, tmaxy)
self.tminmax[tz] = (tminx, tminy, tmaxx, tmaxy)
# Get the minimal zoom level (map covers area equivalent to one tile)
if self.tminz is None:
self.tminz = tms.ZoomForPixelSize(
self.out_gt[1] *
max(self.warped_input_dataset.RasterXSize,
self.warped_input_dataset.RasterYSize) /
float(self.tile_size), self.tile_size)
# Get the maximal zoom level
# (closest possible zoom level up on the resolution of raster)
if self.tmaxz is None:
self.tmaxz = tms.ZoomForPixelSize(self.out_gt[1], self.tile_size)
self.tmaxz = max(self.tminz, self.tmaxz)
self.tminz = min(self.tminz, self.tmaxz)
if self.options.verbose:
print("Bounds (georef):", self.ominx, self.ominy, self.omaxx, self.omaxy)
print('MinZoomLevel:', self.tminz)
print("MaxZoomLevel:", self.tmaxz) | [
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/python2_version/klampt/robotsim.py | python | Geometry3D.withinDistance | (self, other, tol) | return _robotsim.Geometry3D_withinDistance(self, other, tol) | withinDistance(Geometry3D self, Geometry3D other, double tol) -> bool
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swift/swift | 12d031cf8177fdec0137f9aa7e2912fa23c4416b | 3rdParty/SCons/scons-3.0.1/engine/SCons/Node/__init__.py | python | Node.reset_executor | (self) | Remove cached executor; forces recompute when needed. | Remove cached executor; forces recompute when needed. | [
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OSGeo/gdal | 3748fc4ba4fba727492774b2b908a2130c864a83 | swig/python/osgeo/gdal.py | python | RasterAttributeTable.GetLinearBinning | (self, *args) | return _gdal.RasterAttributeTable_GetLinearBinning(self, *args) | r"""GetLinearBinning(RasterAttributeTable self) -> bool | r"""GetLinearBinning(RasterAttributeTable self) -> bool | [
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hpi-xnor/BMXNet | ed0b201da6667887222b8e4b5f997c4f6b61943d | python/mxnet/image/image.py | python | ImageIter.postprocess_data | (self, datum) | return nd.transpose(datum, axes=(2, 0, 1)) | Final postprocessing step before image is loaded into the batch. | Final postprocessing step before image is loaded into the batch. | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/urllib.py | python | splitattr | (url) | return words[0], words[1:] | splitattr('/path;attr1=value1;attr2=value2;...') ->
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google/shaka-packager | e1b0c7c45431327fd3ce193514a5407d07b39b22 | packager/third_party/protobuf/python/google/protobuf/internal/well_known_types.py | python | Duration.ToTimedelta | (self) | return timedelta(
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Manu343726/siplasplas | 9fae7559f87087cf8ef34f04bd1e774b84b2ea9c | reference/cindex.py | python | SourceRange.__contains__ | (self, other) | return False | Useful to detect the Token/Lexer bug | Useful to detect the Token/Lexer bug | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/stc.py | python | StyledTextCtrl.SetFoldExpanded | (*args, **kwargs) | return _stc.StyledTextCtrl_SetFoldExpanded(*args, **kwargs) | SetFoldExpanded(self, int line, bool expanded)
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py2/pandas/io/pytables.py | python | Table.get_attrs | (self) | retrieve our attributes | retrieve our attributes | [
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miyosuda/TensorFlowAndroidMNIST | 7b5a4603d2780a8a2834575706e9001977524007 | jni-build/jni/include/tensorflow/python/framework/ops.py | python | reset_default_graph | () | Clears the default graph stack and resets the global default graph.
NOTE: The default graph is a property of the current thread. This
function applies only to the current thread. Calling this function while
a `tf.Session` or `tf.InteractiveSession` is active will result in undefined
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a `tf.Session` or `tf.InteractiveSession` is active will result in undefined
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miyosuda/TensorFlowAndroidMNIST | 7b5a4603d2780a8a2834575706e9001977524007 | jni-build/jni/include/tensorflow/python/framework/tensor_shape.py | python | TensorShape.assert_is_compatible_with | (self, other) | Raises exception if `self` and `other` do not represent the same shape.
This method can be used to assert that there exists a shape that both
`self` and `other` represent.
Args:
other: Another TensorShape.
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rdkit/rdkit | ede860ae316d12d8568daf5ee800921c3389c84e | rdkit/Chem/MolStandardize/standardize.py | python | enumerate_tautomers_smiles | (smiles) | return {Chem.MolToSmiles(m, isomericSmiles=True) for m in tautomers} | Return a set of tautomers as SMILES strings, given a SMILES string.
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_core.py | python | FileSystemHandler.GetProtocol | (*args, **kwargs) | return _core_.FileSystemHandler_GetProtocol(*args, **kwargs) | GetProtocol(String location) -> String | GetProtocol(String location) -> String | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/pip/_internal/utils/misc.py | python | dist_in_usersite | (dist) | return dist_location(dist).startswith(normalize_path(user_site)) | Return True if given Distribution is installed in user site. | [] | def dist_in_usersite(dist):
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ricardoquesada/Spidermonkey | 4a75ea2543408bd1b2c515aa95901523eeef7858 | media/webrtc/trunk/tools/gyp/pylib/gyp/generator/msvs.py | python | _AppendFiltersForMSBuild | (parent_filter_name, sources,
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sources: The hierarchy of filters and sources to process.
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | tools/grit/grit/util.py | python | Substituter._SubFragment | (self, fragment) | return fragment | Utility function for Substitute.
Performs a simple substitution if the fragment is exactly of the form
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Args:
fragment: A simple string.
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Performs a simple substitution if the fragment is exactly of the form
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Args:
fragment: A simple string.
Returns:
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microsoft/onnxruntime | f92e47e95b13a240e37caf7b36577983544f98fc | onnxruntime/python/tools/quantization/calibrate.py | python | PercentileCalibrater.__init__ | (self,
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:param augmented_model_path: save augmented model to this path.
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num_bins=2048,
percentile=99.999):
'''
:param model: ONNX model to calibrate. It can be a ModelProto or a model path
:param op_types_to_calibrate: operator types to calibrate. By default, calibrate all the float32/float16 tensors.
:param augmented_model_path: save augmented model to this path.
:param method: A string. One of ['entropy', 'percentile'].
:param num_quantized_bins: number of quantized bins. Default 128.
:param percentile: A float number between [0, 100]. Default 99.99.
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super(PercentileCalibrater, self).__init__(model, op_types_to_calibrate, augmented_model_path,
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panda3d/panda3d | 833ad89ebad58395d0af0b7ec08538e5e4308265 | samples/networking/05-small-chat/message.py | python | Message.b_sendText | (self, messageText) | Function which combines the local and distributed functionality,
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KratosMultiphysics/Kratos | 0000833054ed0503424eb28205d6508d9ca6cbbc | applications/CoSimulationApplication/python_scripts/factories/coupling_operation_factory.py | python | CreateCouplingOperation | (coupling_operation_settings, *args) | return base_factory.Create(coupling_operation_settings, [*args], "KratosMultiphysics.CoSimulationApplication.coupling_operations") | This function creates and returns the Coupling Operation used for CoSimulation | This function creates and returns the Coupling Operation used for CoSimulation | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/site-packages/win32com/server/dispatcher.py | python | DispatcherWin32dbg._HandleException_ | (self) | Invoke the debugger post mortem capability | Invoke the debugger post mortem capability | [
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hakuna-m/wubiuefi | caec1af0a09c78fd5a345180ada1fe45e0c63493 | src/pypack/modulegraph/pkg_resources.py | python | EntryPoint.parse_map | (cls, data, dist=None) | return maps | Parse a map of entry point groups | Parse a map of entry point groups | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/compat/__init__.py | python | is_platform_little_endian | () | return sys.byteorder == "little" | Checking if the running platform is little endian.
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"""
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_core.py | python | SizerItemList.__iter__ | (*args, **kwargs) | return _core_.SizerItemList___iter__(*args, **kwargs) | __iter__(self) -> SizerItemList_iterator | __iter__(self) -> SizerItemList_iterator | [
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gwaldron/osgearth | 4c521857d59a69743e4a9cedba00afe570f984e8 | src/third_party/tinygltf/deps/cpplint.py | python | CleanseRawStrings | (raw_lines) | return lines_without_raw_strings | Removes C++11 raw strings from lines.
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if end >= 0:
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weolar/miniblink49 | 1c4678db0594a4abde23d3ebbcc7cd13c3170777 | third_party/WebKit/Tools/Scripts/webkitpy/thirdparty/BeautifulSoup.py | python | buildTagMap | (default, *args) | return built | Turns a list of maps, lists, or scalars into a single map.
Used to build the SELF_CLOSING_TAGS, NESTABLE_TAGS, and
NESTING_RESET_TAGS maps out of lists and partial maps. | Turns a list of maps, lists, or scalars into a single map.
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/distribute/cross_device_ops.py | python | AllReduceCrossDeviceOps._batch_all_reduce | (self, reduce_op, per_replica_values) | return cross_device_utils.stitch_values(((dense_results, dense_indices),
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if dense_values:
dense_results = self._do_batch_all_reduce(reduce_op, dense_values)
else:
dense_results = []
if sparse_values:
sparse_results = self._do_batch_all_reduce_sparse(reduce_op,
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/email/_encoded_words.py | python | decode | (ew) | return string, charset, lang, defects | Decode encoded word and return (string, charset, lang, defects) tuple.
An RFC 2047/2243 encoded word has the form:
=?charset*lang?cte?encoded_string?=
where '*lang' may be omitted but the other parts may not be.
This function expects exactly such a string (that is, it does not check the
syntax and may raise errors if the string is not well formed), and returns
the encoded_string decoded first from its Content Transfer Encoding and
then from the resulting bytes into unicode using the specified charset. If
the cte-decoded string does not successfully decode using the specified
character set, a defect is added to the defects list and the unknown octets
are replaced by the unicode 'unknown' character \\uFDFF.
The specified charset and language are returned. The default for language,
which is rarely if ever encountered, is the empty string. | Decode encoded word and return (string, charset, lang, defects) tuple. | [
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"""Decode encoded word and return (string, charset, lang, defects) tuple.
An RFC 2047/2243 encoded word has the form:
=?charset*lang?cte?encoded_string?=
where '*lang' may be omitted but the other parts may not be.
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"""
_, charset, cte, cte_string, _ = ew.split('?')
charset, _, lang = charset.partition('*')
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bstring = cte_string.encode('ascii', 'surrogateescape')
bstring, defects = _cte_decoders[cte](bstring)
# Turn the CTE decoded bytes into unicode.
try:
string = bstring.decode(charset)
except UnicodeError:
defects.append(errors.UndecodableBytesDefect("Encoded word "
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string = bstring.decode(charset, 'surrogateescape')
except LookupError:
string = bstring.decode('ascii', 'surrogateescape')
if charset.lower() != 'unknown-8bit':
defects.append(errors.CharsetError("Unknown charset {} "
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return string, charset, lang, defects | [
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apple/swift-lldb | d74be846ef3e62de946df343e8c234bde93a8912 | scripts/Python/static-binding/lldb.py | python | SBUnixSignals.SetShouldNotify | (self, signo, value) | return _lldb.SBUnixSignals_SetShouldNotify(self, signo, value) | SetShouldNotify(SBUnixSignals self, int32_t signo, bool value) -> bool | SetShouldNotify(SBUnixSignals self, int32_t signo, bool value) -> bool | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/lite/tools/visualize.py | python | CamelCaseToSnakeCase | (camel_case_input) | return re.sub("([a-z0-9])([A-Z])", r"\1_\2", s1).lower() | Converts an identifier in CamelCase to snake_case. | Converts an identifier in CamelCase to snake_case. | [
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"""Converts an identifier in CamelCase to snake_case."""
s1 = re.sub("(.)([A-Z][a-z]+)", r"\1_\2", camel_case_input)
return re.sub("([a-z0-9])([A-Z])", r"\1_\2", s1).lower() | [
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vesoft-inc/nebula | 25a06217ebaf169e1f0e5ff6a797ba6f0c41fc35 | .linters/cpp/cpplint.py | python | CheckPrintf | (filename, clean_lines, linenum, error) | Check for printf related issues.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
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Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
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match = Search(r'snprintf\s*\(([^,]*),\s*([0-9]*)\s*,', line)
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error(filename, linenum, 'runtime/printf', 3,
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# Check if some verboten C functions are being used.
if Search(r'\bsprintf\s*\(', line):
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match = Search(r'\b(strcpy|strcat)\s*\(', line)
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BlzFans/wke | b0fa21158312e40c5fbd84682d643022b6c34a93 | cygwin/lib/python2.6/distutils/cygwinccompiler.py | python | get_msvcr | () | Include the appropriate MSVC runtime library if Python was built
with MSVC 7.0 or later. | Include the appropriate MSVC runtime library if Python was built
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"""Include the appropriate MSVC runtime library if Python was built
with MSVC 7.0 or later.
"""
msc_pos = sys.version.find('MSC v.')
if msc_pos != -1:
msc_ver = sys.version[msc_pos+6:msc_pos+10]
if msc_ver == '1300':
# MSVC 7.0
return ['msvcr70']
elif msc_ver == '1310':
# MSVC 7.1
return ['msvcr71']
elif msc_ver == '1400':
# VS2005 / MSVC 8.0
return ['msvcr80']
elif msc_ver == '1500':
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pmq20/node-packer | 12c46c6e44fbc14d9ee645ebd17d5296b324f7e0 | lts/deps/v8/third_party/jinja2/compiler.py | python | CodeGenerator.visit_FromImport | (self, node, frame) | Visit named imports. | Visit named imports. | [
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"""Visit named imports."""
self.newline(node)
self.write('included_template = %senvironment.get_template('
% (self.environment.is_async and 'await ' or ''))
self.visit(node.template, frame)
self.write(', %r).' % self.name)
if node.with_context:
self.write('make_module%s(context.get_all(), True, %s)'
% (self.environment.is_async and '_async' or '',
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self.write('_get_default_module()')
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self.writeline('context.vars[%r] = %s' %
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self.writeline('context.vars.update({%s})' % ', '.join(
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if discarded_names:
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self.writeline('context.exported_vars.discard(%r)' %
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/site-packages/pip/_internal/operations/install/wheel.py | python | get_csv_rows_for_installed | (
old_csv_rows, # type: List[List[str]]
installed, # type: Dict[RecordPath, RecordPath]
changed, # type: Set[RecordPath]
generated, # type: List[str]
lib_dir, # type: str
) | return installed_rows | :param installed: A map from archive RECORD path to installation RECORD
path. | :param installed: A map from archive RECORD path to installation RECORD
path. | [
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old_csv_rows, # type: List[List[str]]
installed, # type: Dict[RecordPath, RecordPath]
changed, # type: Set[RecordPath]
generated, # type: List[str]
lib_dir, # type: str
):
# type: (...) -> List[InstalledCSVRow]
"""
:param installed: A map from archive RECORD path to installation RECORD
path.
"""
installed_rows = [] # type: List[InstalledCSVRow]
for row in old_csv_rows:
if len(row) > 3:
logger.warning('RECORD line has more than three elements: %s', row)
old_record_path = _parse_record_path(row[0])
new_record_path = installed.pop(old_record_path, old_record_path)
if new_record_path in changed:
digest, length = rehash(_record_to_fs_path(new_record_path))
else:
digest = row[1] if len(row) > 1 else ''
length = row[2] if len(row) > 2 else ''
installed_rows.append((new_record_path, digest, length))
for f in generated:
path = _fs_to_record_path(f, lib_dir)
digest, length = rehash(f)
installed_rows.append((path, digest, length))
for installed_record_path in installed.values():
installed_rows.append((installed_record_path, '', ''))
return installed_rows | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/mailbox.py | python | mbox._post_message_hook | (self, f) | Called after writing each message to file f. | Called after writing each message to file f. | [
"Called",
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] | def _post_message_hook(self, f):
"""Called after writing each message to file f."""
f.write(linesep) | [
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miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/tensorflow/python/ops/variable_scope.py | python | variable_scope | (name_or_scope,
reuse=None,
initializer=None,
regularizer=None,
caching_device=None,
partitioner=None,
custom_getter=None,
dtype=None) | Returns a context for variable scope.
Variable scope allows to create new variables and to share already created
ones while providing checks to not create or share by accident. For details,
see the [Variable Scope How To](../../how_tos/variable_scope/index.md),
here we present only a few basic examples.
Simple example of how to create a new variable:
```python
with tf.variable_scope("foo"):
with tf.variable_scope("bar"):
v = tf.get_variable("v", [1])
assert v.name == "foo/bar/v:0"
```
Basic example of sharing a variable:
```python
with tf.variable_scope("foo"):
v = tf.get_variable("v", [1])
with tf.variable_scope("foo", reuse=True):
v1 = tf.get_variable("v", [1])
assert v1 == v
```
Sharing a variable by capturing a scope and setting reuse:
```python
with tf.variable_scope("foo") as scope:
v = tf.get_variable("v", [1])
scope.reuse_variables()
v1 = tf.get_variable("v", [1])
assert v1 == v
```
To prevent accidental sharing of variables, we raise an exception when
getting an existing variable in a non-reusing scope.
```python
with tf.variable_scope("foo"):
v = tf.get_variable("v", [1])
v1 = tf.get_variable("v", [1])
# Raises ValueError("... v already exists ...").
```
Similarly, we raise an exception when trying to get a variable that
does not exist in reuse mode.
```python
with tf.variable_scope("foo", reuse=True):
v = tf.get_variable("v", [1])
# Raises ValueError("... v does not exists ...").
```
Note that the `reuse` flag is inherited: if we open a reusing scope,
then all its sub-scopes become reusing as well.
Args:
name_or_scope: `string` or `VariableScope`: the scope to open.
reuse: `True` or `None`; if `True`, we go into reuse mode for this scope as
well as all sub-scopes; if `None`, we just inherit the parent scope reuse.
initializer: default initializer for variables within this scope.
regularizer: default regularizer for variables within this scope.
caching_device: default caching device for variables within this scope.
partitioner: default partitioner for variables within this scope.
custom_getter: default custom getter for variables within this scope.
dtype: type of variables created in this scope (defaults to the type
in the passed scope, or inherited from parent scope).
Returns:
A scope that can be to captured and reused.
Raises:
ValueError: when trying to reuse within a create scope, or create within
a reuse scope, or if reuse is not `None` or `True`.
TypeError: when the types of some arguments are not appropriate. | Returns a context for variable scope. | [
"Returns",
"a",
"context",
"for",
"variable",
"scope",
"."
] | def variable_scope(name_or_scope,
reuse=None,
initializer=None,
regularizer=None,
caching_device=None,
partitioner=None,
custom_getter=None,
dtype=None):
"""Returns a context for variable scope.
Variable scope allows to create new variables and to share already created
ones while providing checks to not create or share by accident. For details,
see the [Variable Scope How To](../../how_tos/variable_scope/index.md),
here we present only a few basic examples.
Simple example of how to create a new variable:
```python
with tf.variable_scope("foo"):
with tf.variable_scope("bar"):
v = tf.get_variable("v", [1])
assert v.name == "foo/bar/v:0"
```
Basic example of sharing a variable:
```python
with tf.variable_scope("foo"):
v = tf.get_variable("v", [1])
with tf.variable_scope("foo", reuse=True):
v1 = tf.get_variable("v", [1])
assert v1 == v
```
Sharing a variable by capturing a scope and setting reuse:
```python
with tf.variable_scope("foo") as scope:
v = tf.get_variable("v", [1])
scope.reuse_variables()
v1 = tf.get_variable("v", [1])
assert v1 == v
```
To prevent accidental sharing of variables, we raise an exception when
getting an existing variable in a non-reusing scope.
```python
with tf.variable_scope("foo"):
v = tf.get_variable("v", [1])
v1 = tf.get_variable("v", [1])
# Raises ValueError("... v already exists ...").
```
Similarly, we raise an exception when trying to get a variable that
does not exist in reuse mode.
```python
with tf.variable_scope("foo", reuse=True):
v = tf.get_variable("v", [1])
# Raises ValueError("... v does not exists ...").
```
Note that the `reuse` flag is inherited: if we open a reusing scope,
then all its sub-scopes become reusing as well.
Args:
name_or_scope: `string` or `VariableScope`: the scope to open.
reuse: `True` or `None`; if `True`, we go into reuse mode for this scope as
well as all sub-scopes; if `None`, we just inherit the parent scope reuse.
initializer: default initializer for variables within this scope.
regularizer: default regularizer for variables within this scope.
caching_device: default caching device for variables within this scope.
partitioner: default partitioner for variables within this scope.
custom_getter: default custom getter for variables within this scope.
dtype: type of variables created in this scope (defaults to the type
in the passed scope, or inherited from parent scope).
Returns:
A scope that can be to captured and reused.
Raises:
ValueError: when trying to reuse within a create scope, or create within
a reuse scope, or if reuse is not `None` or `True`.
TypeError: when the types of some arguments are not appropriate.
"""
if not isinstance(name_or_scope, (VariableScope,) + six.string_types):
raise TypeError("VariableScope: name_or_scope must be a string or "
"VariableScope.")
if isinstance(name_or_scope, six.string_types):
name_scope = name_or_scope
else:
name_scope = name_or_scope.name.split("/")[-1]
if name_scope:
with ops.name_scope(name_scope) as cur_name_scope:
if isinstance(name_or_scope, six.string_types):
old_name_scope = cur_name_scope
else:
old_name_scope = name_or_scope.original_name_scope
with _pure_variable_scope(
name_or_scope,
reuse=reuse,
initializer=initializer,
regularizer=regularizer,
caching_device=caching_device,
partitioner=partitioner,
custom_getter=custom_getter,
old_name_scope=old_name_scope,
dtype=dtype) as vs:
yield vs
else:
# This can only happen if someone is entering the root variable scope.
with _pure_variable_scope(
name_or_scope,
reuse=reuse,
initializer=initializer,
regularizer=regularizer,
caching_device=caching_device,
partitioner=partitioner,
custom_getter=custom_getter,
dtype=dtype) as vs:
yield vs | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/build/waf-1.7.13/lmbrwaflib/lumberyard.py | python | update_module_definition | (ctx, module_type, build_type, kw) | Update the module definition file for the current module
:param ctx: Configuration context
:param module_type: The module type (from cryengine_modules)
:param build_type: The WAF type (shlib, stlib, program)
:param kw: The keyword dictionary for the current module | Update the module definition file for the current module | [
"Update",
"the",
"module",
"definition",
"file",
"for",
"the",
"current",
"module"
] | def update_module_definition(ctx, module_type, build_type, kw):
"""
Update the module definition file for the current module
:param ctx: Configuration context
:param module_type: The module type (from cryengine_modules)
:param build_type: The WAF type (shlib, stlib, program)
:param kw: The keyword dictionary for the current module
"""
def _to_set(input):
if isinstance(input, list):
return set(input)
else:
return set([input])
target = kw.get('target', '')
if len(target) == 0:
raise Errors.WafError("Missing/invalid 'target' keyword in {}/".format(ctx.path.abspath()))
platforms = list(_to_set(kw.get('platforms',['all'])))
configurations = list(_to_set(kw.get('configurations',['all'])))
# Attempt to collect all permutations of the 'use' keyword so we can build the full 'use' dependencies for all cases
use_related_keywords = ctx.get_all_eligible_use_keywords()
# Include 'uselib' as a module use
append_to_unique_list(use_related_keywords, 'uselib')
uses = []
for use_keyword in use_related_keywords:
append_to_unique_list(uses, kw.get(use_keyword, []))
path = ctx.path.path_from(ctx.engine_node)
module_def = {
'target': target,
'type': build_type,
'module_type': module_type,
'path': path,
'platforms': sorted(platforms),
'configurations': sorted(configurations),
'uses': sorted(uses)
}
module_def_folder = os.path.join(ctx.engine_path, BINTEMP_FOLDER, BINTEMP_MODULE_DEF)
if not ctx.cached_does_path_exist(module_def_folder):
os.makedirs(module_def_folder)
ctx.cached_does_path_exist(module_def_folder, True)
module_def_file = os.path.join(module_def_folder, _get_module_def_filename(target))
write_json_file(module_def, module_def_file) | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/ops/composite/multitype_ops/_compile_utils.py | python | tensor_in_sequence | (x, y) | return result | Assigns whether a sequence contains the given tensor | Assigns whether a sequence contains the given tensor | [
"Assigns",
"whether",
"a",
"sequence",
"contains",
"the",
"given",
"tensor"
] | def tensor_in_sequence(x, y):
"""Assigns whether a sequence contains the given tensor"""
result = const_utils.scalar_to_tensor(False)
for i in y:
if isinstance(i, Tensor) and x.shape == i.shape and x.dtype == i.dtype:
result = F.logical_or(F.equal(x, i).all(), result)
return result | [
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] | https://github.com/mindspore-ai/mindspore/blob/fb8fd3338605bb34fa5cea054e535a8b1d753fab/mindspore/python/mindspore/ops/composite/multitype_ops/_compile_utils.py#L990-L996 | |
GJDuck/LowFat | ecf6a0f0fa1b73a27a626cf493cc39e477b6faea | llvm-4.0.0.src/tools/clang/docs/tools/dump_ast_matchers.py | python | act_on_decl | (declaration, comment, allowed_types) | Parse the matcher out of the given declaration and comment.
If 'allowed_types' is set, it contains a list of node types the matcher
can match on, as extracted from the static type asserts in the matcher
definition. | Parse the matcher out of the given declaration and comment. | [
"Parse",
"the",
"matcher",
"out",
"of",
"the",
"given",
"declaration",
"and",
"comment",
"."
] | def act_on_decl(declaration, comment, allowed_types):
"""Parse the matcher out of the given declaration and comment.
If 'allowed_types' is set, it contains a list of node types the matcher
can match on, as extracted from the static type asserts in the matcher
definition.
"""
if declaration.strip():
# Node matchers are defined by writing:
# VariadicDynCastAllOfMatcher<ResultType, ArgumentType> name;
m = re.match(r""".*Variadic(?:DynCast)?AllOfMatcher\s*<
\s*([^\s,]+)\s*(?:,
\s*([^\s>]+)\s*)?>
\s*([^\s;]+)\s*;\s*$""", declaration, flags=re.X)
if m:
result, inner, name = m.groups()
if not inner:
inner = result
add_matcher(result, name, 'Matcher<%s>...' % inner,
comment, is_dyncast=True)
return
# Parse the various matcher definition macros.
m = re.match(""".*AST_TYPE_MATCHER\(
\s*([^\s,]+\s*),
\s*([^\s,]+\s*)
\)\s*;\s*$""", declaration, flags=re.X)
if m:
inner, name = m.groups()
add_matcher('Type', name, 'Matcher<%s>...' % inner,
comment, is_dyncast=True)
# FIXME: re-enable once we have implemented casting on the TypeLoc
# hierarchy.
# add_matcher('TypeLoc', '%sLoc' % name, 'Matcher<%sLoc>...' % inner,
# comment, is_dyncast=True)
return
m = re.match(""".*AST_TYPE(LOC)?_TRAVERSE_MATCHER\(
\s*([^\s,]+\s*),
\s*(?:[^\s,]+\s*),
\s*AST_POLYMORPHIC_SUPPORTED_TYPES\(([^)]*)\)
\)\s*;\s*$""", declaration, flags=re.X)
if m:
loc, name, results = m.groups()[0:3]
result_types = [r.strip() for r in results.split(',')]
comment_result_types = extract_result_types(comment)
if (comment_result_types and
sorted(result_types) != sorted(comment_result_types)):
raise Exception('Inconsistent documentation for: %s' % name)
for result_type in result_types:
add_matcher(result_type, name, 'Matcher<Type>', comment)
if loc:
add_matcher('%sLoc' % result_type, '%sLoc' % name, 'Matcher<TypeLoc>',
comment)
return
m = re.match(r"""^\s*AST_POLYMORPHIC_MATCHER(_P)?(.?)(?:_OVERLOAD)?\(
\s*([^\s,]+)\s*,
\s*AST_POLYMORPHIC_SUPPORTED_TYPES\(([^)]*)\)
(?:,\s*([^\s,]+)\s*
,\s*([^\s,]+)\s*)?
(?:,\s*([^\s,]+)\s*
,\s*([^\s,]+)\s*)?
(?:,\s*\d+\s*)?
\)\s*{\s*$""", declaration, flags=re.X)
if m:
p, n, name, results = m.groups()[0:4]
args = m.groups()[4:]
result_types = [r.strip() for r in results.split(',')]
if allowed_types and allowed_types != result_types:
raise Exception('Inconsistent documentation for: %s' % name)
if n not in ['', '2']:
raise Exception('Cannot parse "%s"' % declaration)
args = ', '.join('%s %s' % (args[i], args[i+1])
for i in range(0, len(args), 2) if args[i])
for result_type in result_types:
add_matcher(result_type, name, args, comment)
return
m = re.match(r"""^\s*AST_MATCHER_FUNCTION(_P)?(.?)(?:_OVERLOAD)?\(
(?:\s*([^\s,]+)\s*,)?
\s*([^\s,]+)\s*
(?:,\s*([^\s,]+)\s*
,\s*([^\s,]+)\s*)?
(?:,\s*([^\s,]+)\s*
,\s*([^\s,]+)\s*)?
(?:,\s*\d+\s*)?
\)\s*{\s*$""", declaration, flags=re.X)
if m:
p, n, result, name = m.groups()[0:4]
args = m.groups()[4:]
if n not in ['', '2']:
raise Exception('Cannot parse "%s"' % declaration)
args = ', '.join('%s %s' % (args[i], args[i+1])
for i in range(0, len(args), 2) if args[i])
add_matcher(result, name, args, comment)
return
m = re.match(r"""^\s*AST_MATCHER(_P)?(.?)(?:_OVERLOAD)?\(
(?:\s*([^\s,]+)\s*,)?
\s*([^\s,]+)\s*
(?:,\s*([^,]+)\s*
,\s*([^\s,]+)\s*)?
(?:,\s*([^\s,]+)\s*
,\s*([^\s,]+)\s*)?
(?:,\s*\d+\s*)?
\)\s*{\s*$""", declaration, flags=re.X)
if m:
p, n, result, name = m.groups()[0:4]
args = m.groups()[4:]
if not result:
if not allowed_types:
raise Exception('Did not find allowed result types for: %s' % name)
result_types = allowed_types
else:
result_types = [result]
if n not in ['', '2']:
raise Exception('Cannot parse "%s"' % declaration)
args = ', '.join('%s %s' % (args[i], args[i+1])
for i in range(0, len(args), 2) if args[i])
for result_type in result_types:
add_matcher(result_type, name, args, comment)
return
# Parse ArgumentAdapting matchers.
m = re.match(
r"""^.*ArgumentAdaptingMatcherFunc<.*>\s*(?:LLVM_ATTRIBUTE_UNUSED\s*)
([a-zA-Z]*)\s*=\s*{};$""",
declaration, flags=re.X)
if m:
name = m.groups()[0]
add_matcher('*', name, 'Matcher<*>', comment)
return
# Parse Variadic functions.
m = re.match(
r"""^.*internal::VariadicFunction\s*<\s*([^,]+),\s*([^,]+),\s*[^>]+>\s*
([a-zA-Z]*)\s*=\s*{.*};$""",
declaration, flags=re.X)
if m:
result, arg, name = m.groups()[:3]
add_matcher(result, name, '%s, ..., %s' % (arg, arg), comment)
return
# Parse Variadic operator matchers.
m = re.match(
r"""^.*VariadicOperatorMatcherFunc\s*<\s*([^,]+),\s*([^\s>]+)\s*>\s*
([a-zA-Z]*)\s*=\s*{.*};$""",
declaration, flags=re.X)
if m:
min_args, max_args, name = m.groups()[:3]
if max_args == '1':
add_matcher('*', name, 'Matcher<*>', comment)
return
elif max_args == 'UINT_MAX':
add_matcher('*', name, 'Matcher<*>, ..., Matcher<*>', comment)
return
# Parse free standing matcher functions, like:
# Matcher<ResultType> Name(Matcher<ArgumentType> InnerMatcher) {
m = re.match(r"""^\s*(.*)\s+
([^\s\(]+)\s*\(
(.*)
\)\s*{""", declaration, re.X)
if m:
result, name, args = m.groups()
args = ', '.join(p.strip() for p in args.split(','))
m = re.match(r'.*\s+internal::(Bindable)?Matcher<([^>]+)>$', result)
if m:
result_types = [m.group(2)]
else:
result_types = extract_result_types(comment)
if not result_types:
if not comment:
# Only overloads don't have their own doxygen comments; ignore those.
print 'Ignoring "%s"' % name
else:
print 'Cannot determine result type for "%s"' % name
else:
for result_type in result_types:
add_matcher(result_type, name, args, comment)
else:
print '*** Unparsable: "' + declaration + '" ***' | [
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] | https://github.com/GJDuck/LowFat/blob/ecf6a0f0fa1b73a27a626cf493cc39e477b6faea/llvm-4.0.0.src/tools/clang/docs/tools/dump_ast_matchers.py#L131-L316 | ||
NeoGeographyToolkit/StereoPipeline | eedf54a919fb5cce1ab0e280bb0df4050763aa11 | src/asp/IceBridge/pleiades_manager.py | python | getEmailAddress | (userName) | Return the email address to use for a user | Return the email address to use for a user | [
"Return",
"the",
"email",
"address",
"to",
"use",
"for",
"a",
"user"
] | def getEmailAddress(userName):
'''Return the email address to use for a user'''
if userName == 'smcmich1':
return 'scott.mcmichael@gmail.com'
if userName == 'oalexan1':
return 'oleg.alexandrov@nasa.gov' | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | tools/copyright_scanner/copyright_scanner.py | python | ScanAtPresubmit | (input_api, output_api) | return results | Invoked at change presubmit time. Verifies that updated non third-party
code doesn't contain external copyrighted code.
input_api: InputAPI of presubmit scripts.
output_api: OutputAPI of presubmit scripts. | Invoked at change presubmit time. Verifies that updated non third-party
code doesn't contain external copyrighted code.
input_api: InputAPI of presubmit scripts.
output_api: OutputAPI of presubmit scripts. | [
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] | def ScanAtPresubmit(input_api, output_api):
"""Invoked at change presubmit time. Verifies that updated non third-party
code doesn't contain external copyrighted code.
input_api: InputAPI of presubmit scripts.
output_api: OutputAPI of presubmit scripts.
"""
files_to_check = set([])
deleted_files = set([])
whitelist_contents_changed = False
for f in input_api.AffectedFiles():
if f.LocalPath() == _GetWhitelistFileName(input_api):
whitelist_contents_changed = True
deleted_files |= set(_ProcessWhitelistedFilesList(
input_api, _GetDeletedContents(f)))
continue
if f.Action() != 'D':
files_to_check.add(f.LocalPath())
else:
deleted_files.add(f.LocalPath())
whitelisted_files = set(LoadWhitelistedFilesList(input_api))
if not whitelist_contents_changed:
whitelisted_files &= files_to_check | deleted_files
else:
# Need to re-check the entire contents of the whitelist file.
# Also add files removed from the whitelist. If the file has indeed been
# deleted, the scanner will not complain.
files_to_check |= whitelisted_files | deleted_files
(unknown_files, missing_files, stale_files) = _DoScanAtPresubmit(
input_api, list(whitelisted_files), list(files_to_check))
results = []
if unknown_files:
results.append(output_api.PresubmitError(
'The following files contain a third-party license but are not in ' \
'a listed third-party directory and are not whitelisted. You must ' \
'add the following files to the whitelist file %s\n' \
'(Note that if the code you are adding does not actually contain ' \
'any third-party code, it may contain the word "copyright", which ' \
'should be masked out, e.g. by writing it as "copy-right"):' \
'' % _GetWhitelistFileName(input_api),
sorted(unknown_files)))
if missing_files:
results.append(output_api.PresubmitPromptWarning(
'The following files are whitelisted in %s, ' \
'but do not exist or not files:' % _GetWhitelistFileName(input_api),
sorted(missing_files)))
if stale_files:
results.append(output_api.PresubmitPromptWarning(
'The following files are whitelisted unnecessarily. You must ' \
'remove the following files from the whitelist file ' \
'%s:' % _GetWhitelistFileName(input_api),
sorted(stale_files)))
return results | [
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raspberrypi/tools | 13474ee775d0c5ec8a7da4fb0a9fa84187abfc87 | arm-bcm2708/gcc-linaro-arm-linux-gnueabihf-raspbian-x64/share/gdb/python/gdb/command/explore.py | python | Explorer.explore_type | (name, datatype, is_child) | Main function to explore a data type.
Arguments:
name: The string representing the path to the data type being
explored.
datatype: The gdb.Type value of the data type being explored.
is_child: Boolean value to indicate if the name is a child.
A name is a child if it is derived from the main name
entered by the user. For example, if the user entered
the name of struct type, then when exploring the fields
of the struct, is_child is set to True internally.
Returns:
No return value. | Main function to explore a data type. | [
"Main",
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"to",
"explore",
"a",
"data",
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"."
] | def explore_type(name, datatype, is_child):
"""Main function to explore a data type.
Arguments:
name: The string representing the path to the data type being
explored.
datatype: The gdb.Type value of the data type being explored.
is_child: Boolean value to indicate if the name is a child.
A name is a child if it is derived from the main name
entered by the user. For example, if the user entered
the name of struct type, then when exploring the fields
of the struct, is_child is set to True internally.
Returns:
No return value.
"""
type_code = datatype.code
if type_code in Explorer.type_code_to_explorer_map:
explorer_class = Explorer.type_code_to_explorer_map[type_code]
while explorer_class.explore_type(name, datatype, is_child):
pass
else:
print ("Explorer for type '%s' not yet available.\n" %
str(datatype)) | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/saved_model/function_deserialization.py | python | _list_function_deps | (fdef, library_function_names, library_gradient_names) | return deps | Find functions referenced in `fdef`. | Find functions referenced in `fdef`. | [
"Find",
"functions",
"referenced",
"in",
"fdef",
"."
] | def _list_function_deps(fdef, library_function_names, library_gradient_names):
"""Find functions referenced in `fdef`."""
# TODO(b/205023953): Recurse into list attributes and into NameAttrList attrs
# both when listing deps and when fixing them. `function_def_to_graph` also
# requires fixes.
deps = set()
for node_def in fdef.node_def:
grad_op_type = _get_gradient_op_type(node_def)
if node_def.op in library_function_names:
deps.add(node_def.op)
elif grad_op_type and grad_op_type in library_gradient_names:
deps.add(library_gradient_names[grad_op_type])
else:
for _, attr_value in node_def.attr.items():
if attr_value.WhichOneof("value") == "func":
deps.add(attr_value.func.name)
elif attr_value.WhichOneof("value") == "list":
for fn in attr_value.list.func:
deps.add(fn.name)
return deps | [
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cvxpy/cvxpy | 5165b4fb750dfd237de8659383ef24b4b2e33aaf | cvxpy/atoms/elementwise/minimum.py | python | minimum.is_atom_convex | (self) | return False | Is the atom convex? | Is the atom convex? | [
"Is",
"the",
"atom",
"convex?"
] | def is_atom_convex(self) -> bool:
"""Is the atom convex?
"""
return False | [
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] | https://github.com/cvxpy/cvxpy/blob/5165b4fb750dfd237de8659383ef24b4b2e33aaf/cvxpy/atoms/elementwise/minimum.py#L49-L52 | |
krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/python2_version/klampt/src/robotsim.py | python | IKObjective.setFixedPoint | (self, link, plocal, pworld) | return _robotsim.IKObjective_setFixedPoint(self, link, plocal, pworld) | setFixedPoint(IKObjective self, int link, double const [3] plocal, double const [3] pworld)
Sets a fixed-point constraint. | setFixedPoint(IKObjective self, int link, double const [3] plocal, double const [3] pworld) | [
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] | def setFixedPoint(self, link, plocal, pworld):
"""
setFixedPoint(IKObjective self, int link, double const [3] plocal, double const [3] pworld)
Sets a fixed-point constraint.
"""
return _robotsim.IKObjective_setFixedPoint(self, link, plocal, pworld) | [
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johmathe/shotdetect | 1ecf93a695c96fd7601a41ab5834f1117b9d7d50 | tools/cpplint.py | python | CheckSectionSpacing | (filename, clean_lines, class_info, linenum, error) | Checks for additional blank line issues related to sections.
Currently the only thing checked here is blank line before protected/private.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
class_info: A _ClassInfo objects.
linenum: The number of the line to check.
error: The function to call with any errors found. | Checks for additional blank line issues related to sections. | [
"Checks",
"for",
"additional",
"blank",
"line",
"issues",
"related",
"to",
"sections",
"."
] | def CheckSectionSpacing(filename, clean_lines, class_info, linenum, error):
"""Checks for additional blank line issues related to sections.
Currently the only thing checked here is blank line before protected/private.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
class_info: A _ClassInfo objects.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
# Skip checks if the class is small, where small means 25 lines or less.
# 25 lines seems like a good cutoff since that's the usual height of
# terminals, and any class that can't fit in one screen can't really
# be considered "small".
#
# Also skip checks if we are on the first line. This accounts for
# classes that look like
# class Foo { public: ... };
#
# If we didn't find the end of the class, last_line would be zero,
# and the check will be skipped by the first condition.
if (class_info.last_line - class_info.linenum <= 24 or
linenum <= class_info.linenum):
return
matched = Match(r'\s*(public|protected|private):', clean_lines.lines[linenum])
if matched:
# Issue warning if the line before public/protected/private was
# not a blank line, but don't do this if the previous line contains
# "class" or "struct". This can happen two ways:
# - We are at the beginning of the class.
# - We are forward-declaring an inner class that is semantically
# private, but needed to be public for implementation reasons.
prev_line = clean_lines.lines[linenum - 1]
if (not IsBlankLine(prev_line) and
not Search(r'\b(class|struct)\b', prev_line)):
# Try a bit harder to find the beginning of the class. This is to
# account for multi-line base-specifier lists, e.g.:
# class Derived
# : public Base {
end_class_head = class_info.linenum
for i in range(class_info.linenum, linenum):
if Search(r'\{\s*$', clean_lines.lines[i]):
end_class_head = i
break
if end_class_head < linenum - 1:
error(filename, linenum, 'whitespace/blank_line', 3,
'"%s:" should be preceded by a blank line' % matched.group(1)) | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/HFIR_4Circle_Reduction/hfctables.py | python | PeakIntegrationTableWidget.simple_integrate_peak | (self, background) | return sum_intensity | Integrate peak in a simple way. Refer to documentation of this interface
:param background:
:return: | Integrate peak in a simple way. Refer to documentation of this interface
:param background:
:return: | [
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assert self.rowCount() > 0, 'Table is empty!'
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return sum_intensity | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/ast.py | python | walk | (node) | Recursively yield all descendant nodes in the tree starting at *node*
(including *node* itself), in no specified order. This is useful if you
only want to modify nodes in place and don't care about the context. | Recursively yield all descendant nodes in the tree starting at *node*
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | tools/grit/grit/node/variant.py | python | SkeletonNode.DefaultAttributes | (self) | return {'encoding' : ''} | If not specified, 'encoding' will actually default to the parent node's
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sonyxperiadev/WebGL | 0299b38196f78c6d5f74bcf6fa312a3daee6de60 | Tools/CodeCoverage/cov.py | python | parse_source_file | (file) | return f_source_list | Parse one source file and return a list of lines | Parse one source file and return a list of lines | [
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f_source_list = []
init_state = STATE_NOT_SEEN
in_test_code = False
nesting = 0
for line in open(file, "r"):
code = line.split(":", 2)[-1]
if not in_test_code and code.startswith("#ifdef BUILD_UNIT_TESTS"):
in_test_code = 1
if in_test_code and code.startswith("#if"):
nesting += 1
if in_test_code and code.startswith("#endif"):
nesting -= 1
if not nesting:
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/eager/python/examples/revnet/main_estimator.py | python | model_fn | (features, labels, mode, params) | Function specifying the model that is required by the `tf.estimator` API.
Args:
features: Input images
labels: Labels of images
mode: One of `ModeKeys.TRAIN`, `ModeKeys.EVAL` or 'ModeKeys.PREDICT'
params: A dictionary of extra parameter that might be passed
Returns:
An instance of `tf.estimator.EstimatorSpec` | Function specifying the model that is required by the `tf.estimator` API. | [
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"""Function specifying the model that is required by the `tf.estimator` API.
Args:
features: Input images
labels: Labels of images
mode: One of `ModeKeys.TRAIN`, `ModeKeys.EVAL` or 'ModeKeys.PREDICT'
params: A dictionary of extra parameter that might be passed
Returns:
An instance of `tf.estimator.EstimatorSpec`
"""
inputs = features
if isinstance(inputs, dict):
inputs = features["image"]
config = params["config"]
model = revnet.RevNet(config=config)
if mode == tf.estimator.ModeKeys.TRAIN:
global_step = tf.train.get_or_create_global_step()
learning_rate = tf.train.piecewise_constant(
global_step, config.lr_decay_steps, config.lr_list)
optimizer = tf.train.MomentumOptimizer(
learning_rate, momentum=config.momentum)
logits, saved_hidden = model(inputs, training=True)
grads, loss = model.compute_gradients(saved_hidden, labels, training=True)
with tf.control_dependencies(model.get_updates_for(inputs)):
train_op = optimizer.apply_gradients(
zip(grads, model.trainable_variables), global_step=global_step)
return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)
else:
logits, _ = model(inputs, training=False)
predictions = tf.argmax(logits, axis=1)
probabilities = tf.nn.softmax(logits)
if mode == tf.estimator.ModeKeys.EVAL:
loss = model.compute_loss(labels=labels, logits=logits)
return tf.estimator.EstimatorSpec(
mode=mode,
loss=loss,
eval_metric_ops={
"accuracy":
tf.metrics.accuracy(labels=labels, predictions=predictions)
})
else: # mode == tf.estimator.ModeKeys.PREDICT
result = {
"classes": predictions,
"probabilities": probabilities,
}
return tf.estimator.EstimatorSpec(
mode=mode,
predictions=predictions,
export_outputs={
"classify": tf.estimator.export.PredictOutput(result)
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py3/sklearn/feature_selection/_mutual_info.py | python | mutual_info_classif | (X, y, discrete_features='auto', n_neighbors=3,
copy=True, random_state=None) | return _estimate_mi(X, y, discrete_features, True, n_neighbors,
copy, random_state) | Estimate mutual information for a discrete target variable.
Mutual information (MI) [1]_ between two random variables is a non-negative
value, which measures the dependency between the variables. It is equal
to zero if and only if two random variables are independent, and higher
values mean higher dependency.
The function relies on nonparametric methods based on entropy estimation
from k-nearest neighbors distances as described in [2]_ and [3]_. Both
methods are based on the idea originally proposed in [4]_.
It can be used for univariate features selection, read more in the
:ref:`User Guide <univariate_feature_selection>`.
Parameters
----------
X : array_like or sparse matrix, shape (n_samples, n_features)
Feature matrix.
y : array_like, shape (n_samples,)
Target vector.
discrete_features : {'auto', bool, array_like}, default 'auto'
If bool, then determines whether to consider all features discrete
or continuous. If array, then it should be either a boolean mask
with shape (n_features,) or array with indices of discrete features.
If 'auto', it is assigned to False for dense `X` and to True for
sparse `X`.
n_neighbors : int, default 3
Number of neighbors to use for MI estimation for continuous variables,
see [2]_ and [3]_. Higher values reduce variance of the estimation, but
could introduce a bias.
copy : bool, default True
Whether to make a copy of the given data. If set to False, the initial
data will be overwritten.
random_state : int, RandomState instance or None, optional, default None
The seed of the pseudo random number generator for adding small noise
to continuous variables in order to remove repeated values. If int,
random_state is the seed used by the random number generator; If
RandomState instance, random_state is the random number generator; If
None, the random number generator is the RandomState instance used by
`np.random`.
Returns
-------
mi : ndarray, shape (n_features,)
Estimated mutual information between each feature and the target.
Notes
-----
1. The term "discrete features" is used instead of naming them
"categorical", because it describes the essence more accurately.
For example, pixel intensities of an image are discrete features
(but hardly categorical) and you will get better results if mark them
as such. Also note, that treating a continuous variable as discrete and
vice versa will usually give incorrect results, so be attentive about that.
2. True mutual information can't be negative. If its estimate turns out
to be negative, it is replaced by zero.
References
----------
.. [1] `Mutual Information <https://en.wikipedia.org/wiki/Mutual_information>`_
on Wikipedia.
.. [2] A. Kraskov, H. Stogbauer and P. Grassberger, "Estimating mutual
information". Phys. Rev. E 69, 2004.
.. [3] B. C. Ross "Mutual Information between Discrete and Continuous
Data Sets". PLoS ONE 9(2), 2014.
.. [4] L. F. Kozachenko, N. N. Leonenko, "Sample Estimate of the Entropy
of a Random Vector:, Probl. Peredachi Inf., 23:2 (1987), 9-16 | Estimate mutual information for a discrete target variable. | [
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copy=True, random_state=None):
"""Estimate mutual information for a discrete target variable.
Mutual information (MI) [1]_ between two random variables is a non-negative
value, which measures the dependency between the variables. It is equal
to zero if and only if two random variables are independent, and higher
values mean higher dependency.
The function relies on nonparametric methods based on entropy estimation
from k-nearest neighbors distances as described in [2]_ and [3]_. Both
methods are based on the idea originally proposed in [4]_.
It can be used for univariate features selection, read more in the
:ref:`User Guide <univariate_feature_selection>`.
Parameters
----------
X : array_like or sparse matrix, shape (n_samples, n_features)
Feature matrix.
y : array_like, shape (n_samples,)
Target vector.
discrete_features : {'auto', bool, array_like}, default 'auto'
If bool, then determines whether to consider all features discrete
or continuous. If array, then it should be either a boolean mask
with shape (n_features,) or array with indices of discrete features.
If 'auto', it is assigned to False for dense `X` and to True for
sparse `X`.
n_neighbors : int, default 3
Number of neighbors to use for MI estimation for continuous variables,
see [2]_ and [3]_. Higher values reduce variance of the estimation, but
could introduce a bias.
copy : bool, default True
Whether to make a copy of the given data. If set to False, the initial
data will be overwritten.
random_state : int, RandomState instance or None, optional, default None
The seed of the pseudo random number generator for adding small noise
to continuous variables in order to remove repeated values. If int,
random_state is the seed used by the random number generator; If
RandomState instance, random_state is the random number generator; If
None, the random number generator is the RandomState instance used by
`np.random`.
Returns
-------
mi : ndarray, shape (n_features,)
Estimated mutual information between each feature and the target.
Notes
-----
1. The term "discrete features" is used instead of naming them
"categorical", because it describes the essence more accurately.
For example, pixel intensities of an image are discrete features
(but hardly categorical) and you will get better results if mark them
as such. Also note, that treating a continuous variable as discrete and
vice versa will usually give incorrect results, so be attentive about that.
2. True mutual information can't be negative. If its estimate turns out
to be negative, it is replaced by zero.
References
----------
.. [1] `Mutual Information <https://en.wikipedia.org/wiki/Mutual_information>`_
on Wikipedia.
.. [2] A. Kraskov, H. Stogbauer and P. Grassberger, "Estimating mutual
information". Phys. Rev. E 69, 2004.
.. [3] B. C. Ross "Mutual Information between Discrete and Continuous
Data Sets". PLoS ONE 9(2), 2014.
.. [4] L. F. Kozachenko, N. N. Leonenko, "Sample Estimate of the Entropy
of a Random Vector:, Probl. Peredachi Inf., 23:2 (1987), 9-16
"""
check_classification_targets(y)
return _estimate_mi(X, y, discrete_features, True, n_neighbors,
copy, random_state) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/pickletools.py | python | read_uint1 | (f) | r"""
>>> import StringIO
>>> read_uint1(StringIO.StringIO('\xff'))
255 | r"""
>>> import StringIO
>>> read_uint1(StringIO.StringIO('\xff'))
255 | [
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r"""
>>> import StringIO
>>> read_uint1(StringIO.StringIO('\xff'))
255
"""
data = f.read(1)
if data:
return ord(data)
raise ValueError("not enough data in stream to read uint1") | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/contrib/ndlstm/python/lstm1d.py | python | sequence_softmax | (inputs, noutput, scope=None, name=None, linear_name=None) | return outputs | Run a softmax layer over all the time steps of an input sequence.
Args:
inputs: (length, batch_size, depth) tensor
noutput: output depth
scope: optional scope name
name: optional name for output tensor
linear_name: name for linear (pre-softmax) output
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] | def sequence_softmax(inputs, noutput, scope=None, name=None, linear_name=None):
"""Run a softmax layer over all the time steps of an input sequence.
Args:
inputs: (length, batch_size, depth) tensor
noutput: output depth
scope: optional scope name
name: optional name for output tensor
linear_name: name for linear (pre-softmax) output
Returns:
A tensor of size (length, batch_size, noutput).
"""
length, _, ninputs = _shape(inputs)
inputs_u = array_ops.unstack(inputs)
output_u = []
with variable_scope.variable_scope(scope, "SequenceSoftmax", [inputs]):
initial_w = random_ops.truncated_normal([0 + ninputs, noutput], stddev=0.1)
initial_b = constant_op.constant(0.1, shape=[noutput])
w = variables.model_variable("weights", initializer=initial_w)
b = variables.model_variable("biases", initializer=initial_b)
for i in xrange(length):
with variable_scope.variable_scope(scope, "SequenceSoftmaxStep",
[inputs_u[i]]):
# TODO(tmb) consider using slim.fully_connected(...,
# activation_fn=tf.nn.softmax)
linear = nn_ops.xw_plus_b(inputs_u[i], w, b, name=linear_name)
output = nn_ops.softmax(linear)
output_u += [output]
outputs = array_ops.stack(output_u, name=name)
return outputs | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/share/doc/python3.7/examples/Tools/scripts/patchcheck.py | python | reported_news | (file_paths) | return any(p.startswith(os.path.join('Misc', 'NEWS.d', 'next'))
for p in file_paths) | Check if Misc/NEWS.d has been changed. | Check if Misc/NEWS.d has been changed. | [
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"""Check if Misc/NEWS.d has been changed."""
return any(p.startswith(os.path.join('Misc', 'NEWS.d', 'next'))
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_controls.py | python | FileCtrlEvent.GetDirectory | (*args, **kwargs) | return _controls_.FileCtrlEvent_GetDirectory(*args, **kwargs) | GetDirectory(self) -> String | GetDirectory(self) -> String | [
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return _controls_.FileCtrlEvent_GetDirectory(*args, **kwargs) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/bdb.py | python | _set_stopinfo | (self, stopframe, returnframe, stoplineno=0) | Set the attributes for stopping.
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"""Set the attributes for stopping.
If stoplineno is greater than or equal to 0, then stop at line
greater than or equal to the stopline. If stoplineno is -1, then
don't stop at all.
"""
self.stopframe = stopframe
self.returnframe = returnframe
self.quitting = False
# stoplineno >= 0 means: stop at line >= the stoplineno
# stoplineno -1 means: don't stop at all
self.stoplineno = stoplineno | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/quopri.py | python | encode | (input, output, quotetabs, header = 0) | Read 'input', apply quoted-printable encoding, and write to 'output'.
'input' and 'output' are files with readline() and write() methods.
The 'quotetabs' flag indicates whether embedded tabs and spaces should be
quoted. Note that line-ending tabs and spaces are always encoded, as per
RFC 1521.
The 'header' flag indicates whether we are encoding spaces as _ as per
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] | def encode(input, output, quotetabs, header = 0):
"""Read 'input', apply quoted-printable encoding, and write to 'output'.
'input' and 'output' are files with readline() and write() methods.
The 'quotetabs' flag indicates whether embedded tabs and spaces should be
quoted. Note that line-ending tabs and spaces are always encoded, as per
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The 'header' flag indicates whether we are encoding spaces as _ as per
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"""
if b2a_qp is not None:
data = input.read()
odata = b2a_qp(data, quotetabs = quotetabs, header = header)
output.write(odata)
return
def write(s, output=output, lineEnd='\n'):
# RFC 1521 requires that the line ending in a space or tab must have
# that trailing character encoded.
if s and s[-1:] in ' \t':
output.write(s[:-1] + quote(s[-1]) + lineEnd)
elif s == '.':
output.write(quote(s) + lineEnd)
else:
output.write(s + lineEnd)
prevline = None
while 1:
line = input.readline()
if not line:
break
outline = []
# Strip off any readline induced trailing newline
stripped = ''
if line[-1:] == '\n':
line = line[:-1]
stripped = '\n'
# Calculate the un-length-limited encoded line
for c in line:
if needsquoting(c, quotetabs, header):
c = quote(c)
if header and c == ' ':
outline.append('_')
else:
outline.append(c)
# First, write out the previous line
if prevline is not None:
write(prevline)
# Now see if we need any soft line breaks because of RFC-imposed
# length limitations. Then do the thisline->prevline dance.
thisline = EMPTYSTRING.join(outline)
while len(thisline) > MAXLINESIZE:
# Don't forget to include the soft line break `=' sign in the
# length calculation!
write(thisline[:MAXLINESIZE-1], lineEnd='=\n')
thisline = thisline[MAXLINESIZE-1:]
# Write out the current line
prevline = thisline
# Write out the last line, without a trailing newline
if prevline is not None:
write(prevline, lineEnd=stripped) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/Pygments/py2/pygments/util.py | python | format_lines | (var_name, seq, raw=False, indent_level=0) | return '\n'.join(lines) | Formats a sequence of strings for output. | Formats a sequence of strings for output. | [
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] | def format_lines(var_name, seq, raw=False, indent_level=0):
"""Formats a sequence of strings for output."""
lines = []
base_indent = ' ' * indent_level * 4
inner_indent = ' ' * (indent_level + 1) * 4
lines.append(base_indent + var_name + ' = (')
if raw:
# These should be preformatted reprs of, say, tuples.
for i in seq:
lines.append(inner_indent + i + ',')
else:
for i in seq:
# Force use of single quotes
r = repr(i + '"')
lines.append(inner_indent + r[:-2] + r[-1] + ',')
lines.append(base_indent + ')')
return '\n'.join(lines) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/richtext.py | python | TextBoxAttr.GetBottomMargin | (*args) | return _richtext.TextBoxAttr_GetBottomMargin(*args) | GetBottomMargin(self) -> TextAttrDimension
GetBottomMargin(self) -> TextAttrDimension | GetBottomMargin(self) -> TextAttrDimension
GetBottomMargin(self) -> TextAttrDimension | [
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"""
GetBottomMargin(self) -> TextAttrDimension
GetBottomMargin(self) -> TextAttrDimension
"""
return _richtext.TextBoxAttr_GetBottomMargin(*args) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/dashboard/dashboard/report.py | python | ReportHandler._GetQueryStringForOldUri | (self) | return query_string | Gets a new query string if old URI parameters are present.
SID is a hash string generated from a page state dictionary which is
created here from old URI request parameters.
Returns:
A query string if request parameters are from old URI, otherwise None. | Gets a new query string if old URI parameters are present. | [
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"""Gets a new query string if old URI parameters are present.
SID is a hash string generated from a page state dictionary which is
created here from old URI request parameters.
Returns:
A query string if request parameters are from old URI, otherwise None.
"""
masters = self.request.get('masters')
bots = self.request.get('bots')
tests = self.request.get('tests')
checked = self.request.get('checked')
if not (masters and bots and tests):
return None
# Page state is a list of chart state. Chart state is
# a list of pair of test path and selected series which is used
# to generate a chart on /report page.
state = _CreatePageState(masters, bots, tests, checked)
# Replace default separators to remove whitespace.
state_json = json.dumps(state, separators=(',', ':'))
state_id = short_uri.GenerateHash(state_json)
# Save page state.
if not ndb.Key(page_state.PageState, state_id).get():
page_state.PageState(id=state_id, value=state_json).put()
query_string = 'sid=' + state_id
if self.request.get('start_rev'):
query_string += '&start_rev=' + self.request.get('start_rev')
if self.request.get('end_rev'):
query_string += '&end_rev=' + self.request.get('end_rev')
if self.request.get('rev'):
query_string += '&rev=' + self.request.get('rev')
return query_string | [
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rdkit/rdkit | ede860ae316d12d8568daf5ee800921c3389c84e | rdkit/sping/pid.py | python | Canvas.flush | (self) | Call this to indicate that any comamnds that have been issued \
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MrMC/mrmc | 5a8e460b2aec44f03eb9604cbd7681d4277dbb81 | tools/EventClients/lib/python/xbmcclient.py | python | XBMCClient.send_keyboard_button | (self, button=None) | return self.send_button(map="KB", button=button) | Send a keyboard event to XBMC
Keyword Arguments:
button -- name of the keyboard button to send (same as in Keymap.xml) | Send a keyboard event to XBMC
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button -- name of the keyboard button to send (same as in Keymap.xml) | [
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snap-stanford/snap-python | d53c51b0a26aa7e3e7400b014cdf728948fde80a | setup/snap.py | python | TFltRect.Save | (self, *args) | return _snap.TFltRect_Save(self, *args) | Save(TFltRect self, TSOut SOut)
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_windows.py | python | ScrollHelper.GetViewStart | (*args, **kwargs) | return _windows_.ScrollHelper_GetViewStart(*args, **kwargs) | GetViewStart(self) -> Point
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hpi-xnor/BMXNet-v2 | af2b1859eafc5c721b1397cef02f946aaf2ce20d | python/mxnet/image/image.py | python | random_size_crop | (src, size, area, ratio, interp=2, **kwargs) | return center_crop(src, size, interp) | Randomly crop src with size. Randomize area and aspect ratio.
Parameters
----------
src : NDArray
Input image
size : tuple of (int, int)
Size of the crop formatted as (width, height).
area : float in (0, 1] or tuple of (float, float)
If tuple, minimum area and maximum area to be maintained after cropping
If float, minimum area to be maintained after cropping, maximum area is set to 1.0
ratio : tuple of (float, float)
Aspect ratio range as (min_aspect_ratio, max_aspect_ratio)
interp: int, optional, default=2
Interpolation method. See resize_short for details.
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"""Randomly crop src with size. Randomize area and aspect ratio.
Parameters
----------
src : NDArray
Input image
size : tuple of (int, int)
Size of the crop formatted as (width, height).
area : float in (0, 1] or tuple of (float, float)
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if isinstance(area, numeric_types):
area = (area, 1.0)
for _ in range(10):
target_area = random.uniform(area[0], area[1]) * src_area
log_ratio = (np.log(ratio[0]), np.log(ratio[1]))
new_ratio = np.exp(random.uniform(*log_ratio))
new_w = int(round(np.sqrt(target_area * new_ratio)))
new_h = int(round(np.sqrt(target_area / new_ratio)))
if new_w <= w and new_h <= h:
x0 = random.randint(0, w - new_w)
y0 = random.randint(0, h - new_h)
out = fixed_crop(src, x0, y0, new_w, new_h, size, interp)
return out, (x0, y0, new_w, new_h)
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return center_crop(src, size, interp) | [
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wyrover/book-code | 7f4883d9030d553bc6bcfa3da685e34789839900 | 3rdparty/protobuf/python/google/protobuf/internal/well_known_types.py | python | FieldMask.FromJsonString | (self, value) | Converts string to FieldMask according to proto3 JSON spec. | Converts string to FieldMask according to proto3 JSON spec. | [
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self.Clear()
for path in value.split(','):
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apiaryio/snowcrash | b5b39faa85f88ee17459edf39fdc6fe4fc70d2e3 | tools/gyp/pylib/gyp/xcodeproj_file.py | python | XCObject._SetDefaultsFromSchema | (self) | Assign object default values according to the schema. This will not
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opencv/opencv | 76aff8478883858f0e46746044348ebb16dc3c67 | samples/dnn/dnn_model_runner/dnn_conversion/paddlepaddle/paddle_resnet50.py | python | export_onnx_resnet50 | (save_path) | export PaddlePaddle model to ONNX format
Args:
save_path(str): Path to save exported ONNX model
Returns:
None | export PaddlePaddle model to ONNX format | [
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''' export PaddlePaddle model to ONNX format
Args:
save_path(str): Path to save exported ONNX model
Returns:
None
'''
model = hub.Module(name="resnet50_vd_imagenet_ssld")
input_spec = paddle.static.InputSpec(
[1, 3, 224, 224], "float32", "image")
paddle.onnx.export(model, save_path,
input_spec=[input_spec],
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FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Arch/importIFClegacy.py | python | IfcFile.parseLine | (self, line) | return {"id": id, "name": name, "attributes": self.parseAttributes(name, attrs)} | Parse a line | Parse a line | [
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"""
Parse a line
"""
m = IFCLINE_RE.search(line) # id,name,attrs
if m:
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id = id.strip()
name = name.strip()
attrs = attrs.strip()
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/collections/__init__.py | python | Counter.update | (*args, **kwds) | Like dict.update() but add counts instead of replacing them.
Source can be an iterable, a dictionary, or another Counter instance.
>>> c = Counter('which')
>>> c.update('witch') # add elements from another iterable
>>> d = Counter('watch')
>>> c.update(d) # add elements from another counter
>>> c['h'] # four 'h' in which, witch, and watch
4 | Like dict.update() but add counts instead of replacing them. | [
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>>> c = Counter('which')
>>> c.update('witch') # add elements from another iterable
>>> d = Counter('watch')
>>> c.update(d) # add elements from another counter
>>> c['h'] # four 'h' in which, witch, and watch
4
'''
# The regular dict.update() operation makes no sense here because the
# replace behavior results in the some of original untouched counts
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# doesn't have a straight-forward interpretation in most counting
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self[elem] = count + self_get(elem, 0)
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super(Counter, self).update(iterable) # fast path when counter is empty
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/logging/handlers.py | python | SysLogHandler.mapPriority | (self, levelName) | return self.priority_map.get(levelName, "warning") | Map a logging level name to a key in the priority_names map.
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/core/indexes/base.py | python | Index._sort_levels_monotonic | (self) | return self | Compat with MultiIndex. | Compat with MultiIndex. | [
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kamyu104/LeetCode-Solutions | 77605708a927ea3b85aee5a479db733938c7c211 | Python/number-of-substrings-containing-all-three-characters.py | python | Solution2.numberOfSubstrings | (self, s) | return result | :type s: str
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/protobuf/py2/google/protobuf/service_reflection.py | python | _ServiceBuilder._CallMethod | (self, srvc, method_descriptor,
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srvc: Instance of the service for which this method is called.
method_descriptor: Descriptor that represent the method to call.
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method_descriptor: Descriptor that represent the method to call.
rpc_controller: RPC controller to use for this method's execution.
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if method_descriptor.containing_service != self.descriptor:
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nileshkulkarni/csm | 0e6e0e7d4f725fd36f2414c0be4b9d83197aa1fc | csm/utils/bird_vis.py | python | VisRenderer.__call__ | (self, verts, cams=None, texture=None, rend_mask=False) | return rend.astype(np.uint8) | verts is |V| x 3 cuda torch Variable
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/boto3/resources/collection.py | python | ResourceCollection.filter | (self, **kwargs) | return self._clone(**kwargs) | Get items from the collection, passing keyword arguments along
as parameters to the underlying service operation, which are
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# Iterate through items
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'https://url1'
'https://url2'
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>>> queues = list(sqs.queues.filter(Param='foo'))
>>> len(queues)
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/site-packages/pip/_vendor/distlib/database.py | python | Distribution.name_and_version | (self) | return '%s (%s)' % (self.name, self.version) | A utility property which displays the name and version in parentheses. | A utility property which displays the name and version in parentheses. | [
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arangodb/arangodb | 0d658689c7d1b721b314fa3ca27d38303e1570c8 | 3rdParty/V8/gyp/msvs_emulation.py | python | MsvsSettings._GetPchFlags | (self, config, extension) | return [] | Get the flags to be added to the cflags for precompiled header support. | Get the flags to be added to the cflags for precompiled header support. | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/estimator/estimator.py | python | Estimator.get_variable_value | (self, name) | return training.load_variable(self.model_dir, name) | Returns value of the variable given by name.
Args:
name: string or a list of string, name of the tensor.
Returns:
Numpy array - value of the tensor.
Raises:
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Raises:
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"""
_check_checkpoint_available(self.model_dir)
return training.load_variable(self.model_dir, name) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py2/sklearn/feature_selection/univariate_selection.py | python | f_regression | (X, y, center=True) | return F, pv | Univariate linear regression tests.
Quick linear model for testing the effect of a single regressor,
sequentially for many regressors.
This is done in 2 steps:
1. The cross correlation between each regressor and the target is computed,
that is, ((X[:, i] - mean(X[:, i])) * (y - mean_y)) / (std(X[:, i]) *
std(y)).
2. It is converted to an F score then to a p-value.
Read more in the :ref:`User Guide <univariate_feature_selection>`.
Parameters
----------
X : {array-like, sparse matrix} shape = (n_samples, n_features)
The set of regressors that will be tested sequentially.
y : array of shape(n_samples).
The data matrix
center : True, bool,
If true, X and y will be centered.
Returns
-------
F : array, shape=(n_features,)
F values of features.
pval : array, shape=(n_features,)
p-values of F-scores.
See also
--------
f_classif: ANOVA F-value between label/feature for classification tasks.
chi2: Chi-squared stats of non-negative features for classification tasks. | Univariate linear regression tests. | [
"Univariate",
"linear",
"regression",
"tests",
"."
] | def f_regression(X, y, center=True):
"""Univariate linear regression tests.
Quick linear model for testing the effect of a single regressor,
sequentially for many regressors.
This is done in 2 steps:
1. The cross correlation between each regressor and the target is computed,
that is, ((X[:, i] - mean(X[:, i])) * (y - mean_y)) / (std(X[:, i]) *
std(y)).
2. It is converted to an F score then to a p-value.
Read more in the :ref:`User Guide <univariate_feature_selection>`.
Parameters
----------
X : {array-like, sparse matrix} shape = (n_samples, n_features)
The set of regressors that will be tested sequentially.
y : array of shape(n_samples).
The data matrix
center : True, bool,
If true, X and y will be centered.
Returns
-------
F : array, shape=(n_features,)
F values of features.
pval : array, shape=(n_features,)
p-values of F-scores.
See also
--------
f_classif: ANOVA F-value between label/feature for classification tasks.
chi2: Chi-squared stats of non-negative features for classification tasks.
"""
if issparse(X) and center:
raise ValueError("center=True only allowed for dense data")
X, y = check_X_y(X, y, ['csr', 'csc', 'coo'], dtype=np.float64)
if center:
y = y - np.mean(y)
X = X.copy('F') # faster in fortran
X -= X.mean(axis=0)
# compute the correlation
corr = safe_sparse_dot(y, X)
corr /= row_norms(X.T)
corr /= norm(y)
# convert to p-value
degrees_of_freedom = y.size - (2 if center else 1)
F = corr ** 2 / (1 - corr ** 2) * degrees_of_freedom
pv = stats.f.sf(F, 1, degrees_of_freedom)
return F, pv | [
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cinder/Cinder | e83f5bb9c01a63eec20168d02953a0879e5100f7 | docs/libs/pystache/context.py | python | ContextStack.pop | (self) | return self._stack.pop() | Pop an item off of the stack, and return it. | Pop an item off of the stack, and return it. | [
"Pop",
"an",
"item",
"off",
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] | def pop(self):
"""
Pop an item off of the stack, and return it.
"""
return self._stack.pop() | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_misc.py | python | VideoMode.IsOk | (*args, **kwargs) | return _misc_.VideoMode_IsOk(*args, **kwargs) | IsOk(self) -> bool
returns true if the object has been initialized | IsOk(self) -> bool | [
"IsOk",
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"""
IsOk(self) -> bool
returns true if the object has been initialized
"""
return _misc_.VideoMode_IsOk(*args, **kwargs) | [
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