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giuspen/cherrytree | 84712f206478fcf9acf30174009ad28c648c6344 | pygtk2/modules/core.py | python | CherryTree.links_entries_reset | (self) | Reset Global Links Variables | Reset Global Links Variables | [
"Reset",
"Global",
"Links",
"Variables"
] | def links_entries_reset(self):
"""Reset Global Links Variables"""
self.link_node_id = None
self.links_entries['webs'] = ""
self.links_entries['file'] = ""
self.links_entries['fold'] = ""
self.links_entries['anch'] = "" | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_gdi.py | python | Locale.GetLanguage | (*args, **kwargs) | return _gdi_.Locale_GetLanguage(*args, **kwargs) | GetLanguage(self) -> int | GetLanguage(self) -> int | [
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"""GetLanguage(self) -> int"""
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/AWS/common-code/lib/OpenSSL/crypto.py | python | NetscapeSPKI.get_pubkey | (self) | return pkey | Get the public key of this certificate.
:return: The public key.
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"""
Get the public key of this certificate.
:return: The public key.
:rtype: :py:class:`PKey`
"""
pkey = PKey.__new__(PKey)
pkey._pkey = _lib.NETSCAPE_SPKI_get_pubkey(self._spki)
_openssl_assert(pkey._pkey != _ffi.NULL)
pkey._pkey = _ffi.gc(pkey._pkey, _lib.EVP_PKEY_free)
pkey._only_public = True
return pkey | [
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xhzdeng/crpn | a5aef0f80dbe486103123f740c634fb01e6cc9a1 | lib/datasets/imdb.py | python | imdb.evaluate_recall | (self, candidate_boxes=None, thresholds=None,
area='all', limit=None) | return {'ar': ar, 'recalls': recalls, 'thresholds': thresholds,
'gt_overlaps': gt_overlaps} | Evaluate detection proposal recall metrics.
Returns:
results: dictionary of results with keys
'ar': average recall
'recalls': vector recalls at each IoU overlap threshold
'thresholds': vector of IoU overlap thresholds
'gt_overlaps': vector of all ground-truth overlaps | Evaluate detection proposal recall metrics. | [
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"metrics",
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] | def evaluate_recall(self, candidate_boxes=None, thresholds=None,
area='all', limit=None):
"""Evaluate detection proposal recall metrics.
Returns:
results: dictionary of results with keys
'ar': average recall
'recalls': vector recalls at each IoU overlap threshold
'thresholds': vector of IoU overlap thresholds
'gt_overlaps': vector of all ground-truth overlaps
"""
# Record max overlap value for each gt box
# Return vector of overlap values
areas = { 'all': 0, 'small': 1, 'medium': 2, 'large': 3,
'96-128': 4, '128-256': 5, '256-512': 6, '512-inf': 7}
area_ranges = [ [0**2, 1e5**2], # all
[0**2, 32**2], # small
[32**2, 96**2], # medium
[96**2, 1e5**2], # large
[96**2, 128**2], # 96-128
[128**2, 256**2], # 128-256
[256**2, 512**2], # 256-512
[512**2, 1e5**2], # 512-inf
]
assert areas.has_key(area), 'unknown area range: {}'.format(area)
area_range = area_ranges[areas[area]]
gt_overlaps = np.zeros(0)
num_pos = 0
for i in xrange(self.num_images):
# Checking for max_overlaps == 1 avoids including crowd annotations
# (...pretty hacking :/)
max_gt_overlaps = self.roidb[i]['gt_overlaps'].toarray().max(axis=1)
gt_inds = np.where((self.roidb[i]['gt_classes'] > 0) &
(max_gt_overlaps == 1))[0]
gt_boxes = self.roidb[i]['boxes'][gt_inds, :]
gt_areas = self.roidb[i]['seg_areas'][gt_inds]
valid_gt_inds = np.where((gt_areas >= area_range[0]) &
(gt_areas <= area_range[1]))[0]
gt_boxes = gt_boxes[valid_gt_inds, :]
num_pos += len(valid_gt_inds)
if candidate_boxes is None:
# If candidate_boxes is not supplied, the default is to use the
# non-ground-truth boxes from this roidb
non_gt_inds = np.where(self.roidb[i]['gt_classes'] == 0)[0]
boxes = self.roidb[i]['boxes'][non_gt_inds, :]
else:
boxes = candidate_boxes[i]
if boxes.shape[0] == 0:
continue
if limit is not None and boxes.shape[0] > limit:
boxes = boxes[:limit, :]
overlaps = bbox_overlaps(boxes.astype(np.float),
gt_boxes.astype(np.float))
_gt_overlaps = np.zeros((gt_boxes.shape[0]))
for j in xrange(gt_boxes.shape[0]):
# find which proposal box maximally covers each gt box
argmax_overlaps = overlaps.argmax(axis=0)
# and get the iou amount of coverage for each gt box
max_overlaps = overlaps.max(axis=0)
# find which gt box is 'best' covered (i.e. 'best' = most iou)
gt_ind = max_overlaps.argmax()
gt_ovr = max_overlaps.max()
assert(gt_ovr >= 0)
# find the proposal box that covers the best covered gt box
box_ind = argmax_overlaps[gt_ind]
# record the iou coverage of this gt box
_gt_overlaps[j] = overlaps[box_ind, gt_ind]
assert(_gt_overlaps[j] == gt_ovr)
# mark the proposal box and the gt box as used
overlaps[box_ind, :] = -1
overlaps[:, gt_ind] = -1
# append recorded iou coverage level
gt_overlaps = np.hstack((gt_overlaps, _gt_overlaps))
gt_overlaps = np.sort(gt_overlaps)
if thresholds is None:
step = 0.05
thresholds = np.arange(0.5, 0.95 + 1e-5, step)
recalls = np.zeros_like(thresholds)
# compute recall for each iou threshold
for i, t in enumerate(thresholds):
recalls[i] = (gt_overlaps >= t).sum() / float(num_pos)
# ar = 2 * np.trapz(recalls, thresholds)
ar = recalls.mean()
return {'ar': ar, 'recalls': recalls, 'thresholds': thresholds,
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mongodb/mongo | d8ff665343ad29cf286ee2cf4a1960d29371937b | buildscripts/resmokelib/hang_analyzer/dumper.py | python | LLDBDumper._find_debugger | (debugger) | return find_program(debugger, ['/usr/bin']) | Find the installed debugger. | Find the installed debugger. | [
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intel/llvm | e6d0547e9d99b5a56430c4749f6c7e328bf221ab | lldb/third_party/Python/module/pexpect-4.6/pexpect/screen.py | python | constrain | (n, min, max) | return n | This returns a number, n constrained to the min and max bounds. | This returns a number, n constrained to the min and max bounds. | [
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rapidsai/cudf | d5b2448fc69f17509304d594f029d0df56984962 | python/cudf/cudf/core/column/column.py | python | to_arrow | (self) | return libcudf.interop.to_arrow(
cudf.core.frame.Frame(
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[["None"]],
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Examples
--------
>>> import cudf
>>> col = cudf.core.column.as_column([1, 2, 3, 4])
>>> col.to_arrow()
<pyarrow.lib.Int64Array object at 0x7f886547f830>
[
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"""Convert to PyArrow Array
Examples
--------
>>> import cudf
>>> col = cudf.core.column.as_column([1, 2, 3, 4])
>>> col.to_arrow()
<pyarrow.lib.Int64Array object at 0x7f886547f830>
[
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"""
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pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | torch/utils/data/_typing.py | python | issubtype | (left, right, recursive=True) | return all(_issubtype_with_constraints(variant, constraints, recursive) for variant in variants) | r"""
Check if the left-side type is a subtype of the right-side type.
If any of type is a composite type like `Union` and `TypeVar` with
bounds, it would be expanded into a list of types and check all
of left-side types are subtypes of either one from right-side types. | r"""
Check if the left-side type is a subtype of the right-side type.
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/site-packages/boto3/resources/factory.py | python | ResourceFactory._create_waiter | (factory_self, resource_waiter_model, resource_name,
service_context) | return do_waiter | Creates a new wait method for each resource where both a waiter and
resource model is defined. | Creates a new wait method for each resource where both a waiter and
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"""
waiter = WaiterAction(resource_waiter_model,
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google/earthenterprise | 0fe84e29be470cd857e3a0e52e5d0afd5bb8cee9 | earth_enterprise/src/fusion/portableglobe/servers/postgres_search.py | python | PostgresDatabase.KmlSearch | (self, search_term) | return self.Search(search_term, kml_start_template,
kml_end_template, self.kml_placemark_) | Search all tables and return resuls as kml. | Search all tables and return resuls as kml. | [
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"""Search all tables and return resuls as kml."""
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_windows.py | python | VarScrollHelperBase.GetNonOrientationTargetSize | (*args, **kwargs) | return _windows_.VarScrollHelperBase_GetNonOrientationTargetSize(*args, **kwargs) | GetNonOrientationTargetSize(self) -> int | GetNonOrientationTargetSize(self) -> int | [
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"""GetNonOrientationTargetSize(self) -> int"""
return _windows_.VarScrollHelperBase_GetNonOrientationTargetSize(*args, **kwargs) | [
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adobe/chromium | cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7 | tools/playback_benchmark/playback_driver.py | python | PlaybackRequestHandler._GetBenchmarkResource | (self, resource, handler) | Sends requested resource to browser. | Sends requested resource to browser. | [
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handler.send_response(404)
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balloonwj/TeamTalk | dc79c40687e4c9d7bec07ff5c9782be586fd9b41 | win-client/3rdParty/src/json/devtools/tarball.py | python | decompress | ( tarball_path, base_dir ) | Decompress the gzipped tarball into directory base_dir. | Decompress the gzipped tarball into directory base_dir. | [
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] | def decompress( tarball_path, base_dir ):
"""Decompress the gzipped tarball into directory base_dir.
"""
# !!! This class method is not documented in the online doc
# nor is bz2open!
tar = tarfile.TarFile.gzopen(tarball_path, mode='r')
try:
tar.extractall( base_dir )
finally:
tar.close() | [
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gem5/gem5 | 141cc37c2d4b93959d4c249b8f7e6a8b2ef75338 | util/gerrit-bot/gerrit.py | python | GerritRestAPI.get_account | (self, account_id="self") | return self._get(f"/accounts/{account_id}") | get an account detail from an account_id | get an account detail from an account_id | [
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citizenfx/fivem | 88276d40cc7baf8285d02754cc5ae42ec7a8563f | vendor/chromium/mojo/public/tools/bindings/pylib/mojom/parse/parser.py | python | Parser.p_attribute_2 | (self, p) | attribute : NAME | attribute : NAME | [
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apache/mesos | 97d9a4063332aae3825d78de71611657e05cf5e2 | support/cpplint.py | python | CleansedLines._CollapseStrings | (elided) | return collapsed | Collapses strings and chars on a line to simple "" or '' blocks.
We nix strings first so we're not fooled by text like '"http://"'
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"""Collapses strings and chars on a line to simple "" or '' blocks.
We nix strings first so we're not fooled by text like '"http://"'
Args:
elided: The line being processed.
Returns:
The line with collapsed strings.
"""
if _RE_PATTERN_INCLUDE.match(elided):
return elided
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# basic. Things that look like escaped characters shouldn't occur
# outside of strings and chars.
elided = _RE_PATTERN_CLEANSE_LINE_ESCAPES.sub('', elided)
# Replace quoted strings and digit separators. Both single quotes
# and double quotes are processed in the same loop, otherwise
# nested quotes wouldn't work.
collapsed = ''
while True:
# Find the first quote character
match = Match(r'^([^\'"]*)([\'"])(.*)$', elided)
if not match:
collapsed += elided
break
head, quote, tail = match.groups()
if quote == '"':
# Collapse double quoted strings
second_quote = tail.find('"')
if second_quote >= 0:
collapsed += head + '""'
elided = tail[second_quote + 1:]
else:
# Unmatched double quote, don't bother processing the rest
# of the line since this is probably a multiline string.
collapsed += elided
break
else:
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break
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cocos2d/cocos2d-js | f0a7d4fa454de7076c054b677a36c5968a160901 | build/android-build.py | python | check_environment_variables_sdk | () | return SDK_ROOT | Checking the environment ANDROID_SDK_ROOT, which will be used for building | Checking the environment ANDROID_SDK_ROOT, which will be used for building | [
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''' Checking the environment ANDROID_SDK_ROOT, which will be used for building
'''
try:
SDK_ROOT = os.environ['ANDROID_SDK_ROOT']
except Exception:
print "ANDROID_SDK_ROOT not defined. Please define ANDROID_SDK_ROOT in your environment"
sys.exit(1)
return SDK_ROOT | [
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gem5/gem5 | 141cc37c2d4b93959d4c249b8f7e6a8b2ef75338 | configs/example/arm/ruby_fs.py | python | create | (args) | return system | Create and configure the system object. | Create and configure the system object. | [
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"system",
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"."
] | def create(args):
''' Create and configure the system object. '''
if args.script and not os.path.isfile(args.script):
print("Error: Bootscript %s does not exist" % args.script)
sys.exit(1)
cpu_class = cpu_types[args.cpu][0]
mem_mode = cpu_class.memory_mode()
system = devices.ArmRubySystem(args.mem_size,
mem_mode=mem_mode,
workload=ArmFsLinux(
object_file=
SysPaths.binary(args.kernel)),
readfile=args.script)
# Add CPU clusters to the system
system.cpu_cluster = [
devices.CpuCluster(system,
args.num_cpus,
args.cpu_freq, "1.0V",
*cpu_types[args.cpu]),
]
# Add the PCI devices we need for this system. The base system
# doesn't have any PCI devices by default since they are assumed
# to be added by the configuration scripts needing them.
system.pci_devices = [
# Create a VirtIO block device for the system's boot
# disk. Attach the disk image using gem5's Copy-on-Write
# functionality to avoid writing changes to the stored copy of
# the disk image.
PciVirtIO(vio=VirtIOBlock(image=create_cow_image(args.disk_image))),
]
# Attach the PCI devices to the system. The helper method in the
# system assigns a unique PCI bus ID to each of the devices and
# connects them to the IO bus.
for dev in system.pci_devices:
system.attach_pci(dev)
config_ruby(system, args)
# Wire up the system's memory system
system.connect()
# Setup gem5's minimal Linux boot loader.
system.realview.setupBootLoader(system, SysPaths.binary)
if args.dtb:
system.workload.dtb_filename = args.dtb
else:
# No DTB specified: autogenerate DTB
system.workload.dtb_filename = \
os.path.join(m5.options.outdir, 'system.dtb')
system.generateDtb(system.workload.dtb_filename)
# Linux boot command flags
kernel_cmd = [
# Tell Linux to use the simulated serial port as a console
"console=ttyAMA0",
# Hard-code timi
"lpj=19988480",
# Disable address space randomisation to get a consistent
# memory layout.
"norandmaps",
# Tell Linux where to find the root disk image.
"root=%s" % args.root_device,
# Mount the root disk read-write by default.
"rw",
# Tell Linux about the amount of physical memory present.
"mem=%s" % args.mem_size,
]
system.workload.command_line = " ".join(kernel_cmd)
return system | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/pandas/core/internals/blocks.py | python | Block.coerce_to_target_dtype | (self, other) | coerce the current block to a dtype compat for other
we will return a block, possibly object, and not raise
we can also safely try to coerce to the same dtype
and will receive the same block | coerce the current block to a dtype compat for other
we will return a block, possibly object, and not raise | [
"coerce",
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] | def coerce_to_target_dtype(self, other):
"""
coerce the current block to a dtype compat for other
we will return a block, possibly object, and not raise
we can also safely try to coerce to the same dtype
and will receive the same block
"""
# if we cannot then coerce to object
dtype, _ = infer_dtype_from(other, pandas_dtype=True)
if is_dtype_equal(self.dtype, dtype):
return self
if self.is_bool or is_object_dtype(dtype) or is_bool_dtype(dtype):
# we don't upcast to bool
return self.astype(object)
elif (self.is_float or self.is_complex) and (
is_integer_dtype(dtype) or is_float_dtype(dtype)
):
# don't coerce float/complex to int
return self
elif (
self.is_datetime
or is_datetime64_dtype(dtype)
or is_datetime64tz_dtype(dtype)
):
# not a datetime
if not (
(is_datetime64_dtype(dtype) or is_datetime64tz_dtype(dtype))
and self.is_datetime
):
return self.astype(object)
# don't upcast timezone with different timezone or no timezone
mytz = getattr(self.dtype, "tz", None)
othertz = getattr(dtype, "tz", None)
if not tz_compare(mytz, othertz):
return self.astype(object)
raise AssertionError(
f"possible recursion in coerce_to_target_dtype: {self} {other}"
)
elif self.is_timedelta or is_timedelta64_dtype(dtype):
# not a timedelta
if not (is_timedelta64_dtype(dtype) and self.is_timedelta):
return self.astype(object)
raise AssertionError(
f"possible recursion in coerce_to_target_dtype: {self} {other}"
)
try:
return self.astype(dtype)
except (ValueError, TypeError, OverflowError):
return self.astype(object) | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/framework/python/framework/tensor_util.py | python | with_shape | (expected_shape, tensor) | return tensor | Asserts tensor has expected shape.
If tensor shape and expected_shape, are fully defined, assert they match.
Otherwise, add assert op that will validate the shape when tensor is
evaluated, and set shape on tensor.
Args:
expected_shape: Expected shape to assert, as a 1D array of ints, or tensor
of same.
tensor: Tensor whose shape we're validating.
Returns:
tensor, perhaps with a dependent assert operation.
Raises:
ValueError: if tensor has an invalid shape. | Asserts tensor has expected shape. | [
"Asserts",
"tensor",
"has",
"expected",
"shape",
"."
] | def with_shape(expected_shape, tensor):
"""Asserts tensor has expected shape.
If tensor shape and expected_shape, are fully defined, assert they match.
Otherwise, add assert op that will validate the shape when tensor is
evaluated, and set shape on tensor.
Args:
expected_shape: Expected shape to assert, as a 1D array of ints, or tensor
of same.
tensor: Tensor whose shape we're validating.
Returns:
tensor, perhaps with a dependent assert operation.
Raises:
ValueError: if tensor has an invalid shape.
"""
if isinstance(tensor, sparse_tensor.SparseTensor):
raise ValueError('SparseTensor not supported.')
# Shape type must be 1D int32.
if tensor_util.is_tensor(expected_shape):
if expected_shape.dtype.base_dtype != dtypes.int32:
raise ValueError(
'Invalid dtype %s for shape %s expected of tensor %s.' % (
expected_shape.dtype, expected_shape, tensor.name))
if isinstance(expected_shape, (list, tuple)):
if not expected_shape:
expected_shape = np.asarray([], dtype=np.int32)
else:
np_expected_shape = np.asarray(expected_shape)
expected_shape = (
np.asarray(expected_shape, dtype=np.int32)
if np_expected_shape.dtype == np.int64 else np_expected_shape)
if isinstance(expected_shape, np.ndarray):
if expected_shape.ndim > 1:
raise ValueError(
'Invalid rank %s for shape %s expected of tensor %s.' % (
expected_shape.ndim, expected_shape, tensor.name))
if expected_shape.dtype != np.int32:
raise ValueError(
'Invalid dtype %s for shape %s expected of tensor %s.' % (
expected_shape.dtype, expected_shape, tensor.name))
actual_shape = tensor.get_shape()
if (not actual_shape.is_fully_defined()
or tensor_util.is_tensor(expected_shape)):
with ops.name_scope('%s/' % tensor.op.name, values=[tensor]):
if (not tensor_util.is_tensor(expected_shape)
and (len(expected_shape) < 1)):
# TODO(irving): Remove scalar special case
return array_ops.reshape(tensor, [])
with ops.control_dependencies([_assert_shape_op(expected_shape, tensor)]):
result = array_ops.identity(tensor)
if not tensor_util.is_tensor(expected_shape):
result.set_shape(expected_shape)
return result
if (not tensor_util.is_tensor(expected_shape) and
not actual_shape.is_compatible_with(expected_shape)):
if (len(expected_shape) < 1) and actual_shape.is_compatible_with([1]):
# TODO(irving): Remove scalar special case.
with ops.name_scope('%s/' % tensor.op.name, values=[tensor]):
return array_ops.reshape(tensor, [])
raise ValueError('Invalid shape for tensor %s, expected %s, got %s.' % (
tensor.name, expected_shape, actual_shape))
return tensor | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/grid.py | python | PyGridCellAttrProvider._setCallbackInfo | (*args, **kwargs) | return _grid.PyGridCellAttrProvider__setCallbackInfo(*args, **kwargs) | _setCallbackInfo(self, PyObject self, PyObject _class) | _setCallbackInfo(self, PyObject self, PyObject _class) | [
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OSGeo/gdal | 3748fc4ba4fba727492774b2b908a2130c864a83 | swig/python/osgeo/ogr.py | python | FieldDomain.SetSplitPolicy | (self, *args) | return _ogr.FieldDomain_SetSplitPolicy(self, *args) | r"""SetSplitPolicy(FieldDomain self, OGRFieldDomainSplitPolicy policy) | r"""SetSplitPolicy(FieldDomain self, OGRFieldDomainSplitPolicy policy) | [
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cvmfs/cvmfs | 4637bdb5153178eadf885c1acf37bdc5c685bf8a | cpplint.py | python | _OutputFormat | () | return _cpplint_state.output_format | Gets the module's output format. | Gets the module's output format. | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/lib2to3/pytree.py | python | WildcardPattern._iterative_matches | (self, nodes) | Helper to iteratively yield the matches. | Helper to iteratively yield the matches. | [
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"""Helper to iteratively yield the matches."""
nodelen = len(nodes)
if 0 >= self.min:
yield 0, {}
results = []
# generate matches that use just one alt from self.content
for alt in self.content:
for c, r in generate_matches(alt, nodes):
yield c, r
results.append((c, r))
# for each match, iterate down the nodes
while results:
new_results = []
for c0, r0 in results:
# stop if the entire set of nodes has been matched
if c0 < nodelen and c0 <= self.max:
for alt in self.content:
for c1, r1 in generate_matches(alt, nodes[c0:]):
if c1 > 0:
r = {}
r.update(r0)
r.update(r1)
yield c0 + c1, r
new_results.append((c0 + c1, r))
results = new_results | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/platform/tf_logging.py | python | log_if | (level, msg, condition, *args) | Log 'msg % args' at level 'level' only if condition is fulfilled. | Log 'msg % args' at level 'level' only if condition is fulfilled. | [
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"""Log 'msg % args' at level 'level' only if condition is fulfilled."""
if condition:
vlog(level, msg, *args) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/ipython/py3/IPython/utils/io.py | python | Tee.close | (self) | Close the file and restore the channel. | Close the file and restore the channel. | [
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"""Close the file and restore the channel."""
self.flush()
setattr(sys, self.channel, self.ostream)
self.file.close()
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RoboJackets/robocup-software | bce13ce53ddb2ecb9696266d980722c34617dc15 | rj_gameplay/rj_gameplay/play/restart.py | python | DirectRestartPlay.__init__ | (self) | self.pass_tactic = pass_tactic.Pass(
self.target_point, pass_tactic.PasserCost(self.target_point),
pass_tactic.PassToClosestReceiver(self.target_point))
self.seek_tactic = pass_seek.Seek(
self.target_point, pass_seek.restart_seek,
pass_seek.SeekCost(self.target_point)) | self.pass_tactic = pass_tactic.Pass(
self.target_point, pass_tactic.PasserCost(self.target_point),
pass_tactic.PassToClosestReceiver(self.target_point))
self.seek_tactic = pass_seek.Seek(
self.target_point, pass_seek.restart_seek,
pass_seek.SeekCost(self.target_point)) | [
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self.target_point = np.array([1.0, 4.0])
# TODO: simplify tactic with list (see basic_defense.py)
self.goalie_tactic = goalie_tactic.GoalieTactic()
self.clear_tactic = clear_tactic.Clear(
np.array([0.0, 9.0]), chip=False, kick_speed=5.5
)
# TODO: make it pass
"""
self.pass_tactic = pass_tactic.Pass(
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self.seek_tactic = pass_seek.Seek(
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"""
self.wall_tactic_1 = wall_tactic.WallTactic(role.Priority.LOW, cost_scale=0.1)
self.wall_tactic_2 = wall_tactic.WallTactic(role.Priority.LOW, cost_scale=0.1)
# might need to change to for-loop
self.num_wallers = 2
left_pt = np.array([1.5, 7.5])
self.seek_left = pass_seek.Seek(
left_pt,
pass_seek.build_seek_function(left_pt),
pass_seek.SeekCost(left_pt),
)
right_pt = np.array([-1.5, 7.5])
self.seek_right = pass_seek.Seek(
right_pt,
pass_seek.build_seek_function(right_pt),
pass_seek.SeekCost(right_pt),
)
self.role_assigner = NaiveRoleAssignment() | [
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fatih/subvim | 241b6d170597857105da219c9b7d36059e9f11fb | vim/base/YouCompleteMe/third_party/bottle/bottle.py | python | _re_flatten | (p) | return re.sub(r'(\\*)(\(\?P<[^>]+>|\((?!\?))',
lambda m: m.group(0) if len(m.group(1)) % 2 else m.group(1) + '(?:', p) | Turn all capturing groups in a regular expression pattern into
non-capturing groups. | Turn all capturing groups in a regular expression pattern into
non-capturing groups. | [
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] | def _re_flatten(p):
''' Turn all capturing groups in a regular expression pattern into
non-capturing groups. '''
if '(' not in p: return p
return re.sub(r'(\\*)(\(\?P<[^>]+>|\((?!\?))',
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/pickletools.py | python | genops | (pickle) | return _genops(pickle) | Generate all the opcodes in a pickle.
'pickle' is a file-like object, or string, containing the pickle.
Each opcode in the pickle is generated, from the current pickle position,
stopping after a STOP opcode is delivered. A triple is generated for
each opcode:
opcode, arg, pos
opcode is an OpcodeInfo record, describing the current opcode.
If the opcode has an argument embedded in the pickle, arg is its decoded
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is None.
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it's wrapped in a BytesIO object, and the latter's tell() result is
used. Else (the pickle doesn't have a tell(), and it's not obvious how
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"""Generate all the opcodes in a pickle.
'pickle' is a file-like object, or string, containing the pickle.
Each opcode in the pickle is generated, from the current pickle position,
stopping after a STOP opcode is delivered. A triple is generated for
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opcode, arg, pos
opcode is an OpcodeInfo record, describing the current opcode.
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return _genops(pickle) | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/debug/lib/grpc_debug_server.py | python | EventListenerBaseServicer.SendSourceFiles | (self, request, context) | return debug_service_pb2.EventReply() | Base implementation of the handling of SendSourceFiles calls.
The base implementation does nothing with the incoming request.
Override in an implementation of the server if necessary.
Args:
request: A `DebuggedSourceFiles` proto, containing the path, content, size
and last-modified timestamp of source files.
context: Server context.
Returns:
A `EventReply` proto. | Base implementation of the handling of SendSourceFiles calls. | [
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"""Base implementation of the handling of SendSourceFiles calls.
The base implementation does nothing with the incoming request.
Override in an implementation of the server if necessary.
Args:
request: A `DebuggedSourceFiles` proto, containing the path, content, size
and last-modified timestamp of source files.
context: Server context.
Returns:
A `EventReply` proto.
"""
return debug_service_pb2.EventReply() | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py3/pandas/core/frame.py | python | DataFrame.assign | (self, **kwargs) | return data | r"""
Assign new columns to a DataFrame.
Returns a new object with all original columns in addition to new ones.
Existing columns that are re-assigned will be overwritten.
Parameters
----------
**kwargs : dict of {str: callable or Series}
The column names are keywords. If the values are
callable, they are computed on the DataFrame and
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If the values are not callable, (e.g. a Series, scalar, or array),
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Returns
-------
DataFrame
A new DataFrame with the new columns in addition to
all the existing columns.
Notes
-----
Assigning multiple columns within the same ``assign`` is possible.
Later items in '\*\*kwargs' may refer to newly created or modified
columns in 'df'; items are computed and assigned into 'df' in order.
Examples
--------
>>> df = pd.DataFrame({'temp_c': [17.0, 25.0]},
... index=['Portland', 'Berkeley'])
>>> df
temp_c
Portland 17.0
Berkeley 25.0
Where the value is a callable, evaluated on `df`:
>>> df.assign(temp_f=lambda x: x.temp_c * 9 / 5 + 32)
temp_c temp_f
Portland 17.0 62.6
Berkeley 25.0 77.0
Alternatively, the same behavior can be achieved by directly
referencing an existing Series or sequence:
>>> df.assign(temp_f=df['temp_c'] * 9 / 5 + 32)
temp_c temp_f
Portland 17.0 62.6
Berkeley 25.0 77.0
You can create multiple columns within the same assign where one
of the columns depends on another one defined within the same assign:
>>> df.assign(temp_f=lambda x: x['temp_c'] * 9 / 5 + 32,
... temp_k=lambda x: (x['temp_f'] + 459.67) * 5 / 9)
temp_c temp_f temp_k
Portland 17.0 62.6 290.15
Berkeley 25.0 77.0 298.15 | r"""
Assign new columns to a DataFrame. | [
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r"""
Assign new columns to a DataFrame.
Returns a new object with all original columns in addition to new ones.
Existing columns that are re-assigned will be overwritten.
Parameters
----------
**kwargs : dict of {str: callable or Series}
The column names are keywords. If the values are
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If the values are not callable, (e.g. a Series, scalar, or array),
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Returns
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DataFrame
A new DataFrame with the new columns in addition to
all the existing columns.
Notes
-----
Assigning multiple columns within the same ``assign`` is possible.
Later items in '\*\*kwargs' may refer to newly created or modified
columns in 'df'; items are computed and assigned into 'df' in order.
Examples
--------
>>> df = pd.DataFrame({'temp_c': [17.0, 25.0]},
... index=['Portland', 'Berkeley'])
>>> df
temp_c
Portland 17.0
Berkeley 25.0
Where the value is a callable, evaluated on `df`:
>>> df.assign(temp_f=lambda x: x.temp_c * 9 / 5 + 32)
temp_c temp_f
Portland 17.0 62.6
Berkeley 25.0 77.0
Alternatively, the same behavior can be achieved by directly
referencing an existing Series or sequence:
>>> df.assign(temp_f=df['temp_c'] * 9 / 5 + 32)
temp_c temp_f
Portland 17.0 62.6
Berkeley 25.0 77.0
You can create multiple columns within the same assign where one
of the columns depends on another one defined within the same assign:
>>> df.assign(temp_f=lambda x: x['temp_c'] * 9 / 5 + 32,
... temp_k=lambda x: (x['temp_f'] + 459.67) * 5 / 9)
temp_c temp_f temp_k
Portland 17.0 62.6 290.15
Berkeley 25.0 77.0 298.15
"""
data = self.copy()
for k, v in kwargs.items():
data[k] = com.apply_if_callable(v, data)
return data | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | tools/ipc_messages_log.py | python | _ReadLines | (f) | Read from file f and generate right-stripped lines. | Read from file f and generate right-stripped lines. | [
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] | def _ReadLines(f):
"""Read from file f and generate right-stripped lines."""
for line in f:
yield line.rstrip() | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/boringssl/src/util/bot/go/bootstrap.py | python | get_toolset_url | () | return '%s/%s.%s' % (DOWNLOAD_URL_PREFIX, TOOLSET_VERSION, variant) | URL of a platform specific Go toolset archive. | URL of a platform specific Go toolset archive. | [
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] | def get_toolset_url():
"""URL of a platform specific Go toolset archive."""
# TODO(vadimsh): Support toolset for cross-compilation.
arch = {
'amd64': 'x86-64',
'x86_64': 'x86-64',
'i386': 'x86-32',
'x86': 'x86-32',
}.get(platform.machine().lower())
variant = TOOLSET_VARIANTS.get((sys.platform, arch))
if not variant:
# TODO(vadimsh): Compile go lang from source.
raise Failure('Unrecognized platform')
return '%s/%s.%s' % (DOWNLOAD_URL_PREFIX, TOOLSET_VERSION, variant) | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/ops/nn_impl.py | python | batch_norm_with_global_normalization_v2 | (input,
mean,
variance,
beta,
gamma,
variance_epsilon,
scale_after_normalization,
name=None) | return batch_norm_with_global_normalization(t=input,
m=mean,
v=variance,
beta=beta,
gamma=gamma,
variance_epsilon=variance_epsilon,
scale_after_normalization=scale_after_normalization,
name=name) | Batch normalization.
This op is deprecated. See `tf.nn.batch_normalization`.
Args:
input: A 4D input Tensor.
mean: A 1D mean Tensor with size matching the last dimension of t.
This is the first output from tf.nn.moments,
or a saved moving average thereof.
variance: A 1D variance Tensor with size matching the last dimension of t.
This is the second output from tf.nn.moments,
or a saved moving average thereof.
beta: A 1D beta Tensor with size matching the last dimension of t.
An offset to be added to the normalized tensor.
gamma: A 1D gamma Tensor with size matching the last dimension of t.
If "scale_after_normalization" is true, this tensor will be multiplied
with the normalized tensor.
variance_epsilon: A small float number to avoid dividing by 0.
scale_after_normalization: A bool indicating whether the resulted tensor
needs to be multiplied with gamma.
name: A name for this operation (optional).
Returns:
A batch-normalized `t`.
References:
Batch Normalization - Accelerating Deep Network Training by Reducing Internal Covariate Shift:
[Ioffe et al., 2015](http://proceedings.mlr.press/v37/ioffe15.html)
([pdf](http://proceedings.mlr.press/v37/ioffe15.pdf)) | Batch normalization. | [
"Batch",
"normalization",
"."
] | def batch_norm_with_global_normalization_v2(input,
mean,
variance,
beta,
gamma,
variance_epsilon,
scale_after_normalization,
name=None):
"""Batch normalization.
This op is deprecated. See `tf.nn.batch_normalization`.
Args:
input: A 4D input Tensor.
mean: A 1D mean Tensor with size matching the last dimension of t.
This is the first output from tf.nn.moments,
or a saved moving average thereof.
variance: A 1D variance Tensor with size matching the last dimension of t.
This is the second output from tf.nn.moments,
or a saved moving average thereof.
beta: A 1D beta Tensor with size matching the last dimension of t.
An offset to be added to the normalized tensor.
gamma: A 1D gamma Tensor with size matching the last dimension of t.
If "scale_after_normalization" is true, this tensor will be multiplied
with the normalized tensor.
variance_epsilon: A small float number to avoid dividing by 0.
scale_after_normalization: A bool indicating whether the resulted tensor
needs to be multiplied with gamma.
name: A name for this operation (optional).
Returns:
A batch-normalized `t`.
References:
Batch Normalization - Accelerating Deep Network Training by Reducing Internal Covariate Shift:
[Ioffe et al., 2015](http://proceedings.mlr.press/v37/ioffe15.html)
([pdf](http://proceedings.mlr.press/v37/ioffe15.pdf))
"""
return batch_norm_with_global_normalization(t=input,
m=mean,
v=variance,
beta=beta,
gamma=gamma,
variance_epsilon=variance_epsilon,
scale_after_normalization=scale_after_normalization,
name=name) | [
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miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/tensorflow/python/training/slot_creator.py | python | _create_slot_var | (primary, val, scope) | return slot | Helper function for creating a slot variable. | Helper function for creating a slot variable. | [
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"creating",
"a",
"slot",
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] | def _create_slot_var(primary, val, scope):
"""Helper function for creating a slot variable."""
slot = variables.Variable(val, name=scope, trainable=False)
# pylint: disable=protected-access
if isinstance(primary, variables.Variable) and primary._save_slice_info:
# Primary is a partitioned variable, so we need to also indicate that
# the slot is a partitioned variable. Slots have the same partitioning
# as their primaries.
real_slot_name = scope[len(primary.op.name + "/"):-1]
slice_info = primary._save_slice_info
slot._set_save_slice_info(variables.Variable.SaveSliceInfo(
slice_info.full_name + "/" + real_slot_name,
slice_info.full_shape[:],
slice_info.var_offset[:],
slice_info.var_shape[:]))
# pylint: enable=protected-access
return slot | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/eager/function.py | python | defun | (func) | return tf_decorator.make_decorator(func, named_defun(func, func.__name__)) | Decorator to compile func into graph_mode.
`defun` converts a function that constructs a TensorFlow graph into a function
that executes the graph. TensorFlow graphs typically execute faster and with a
lower memory-footprint than executing each of the operations that make up the
function individually as the TensorFlow runtime can optimize the graph and
execute sub-operations in parallel.
func must be a Python function that constructs a TensorFlow graph,
typically using functions in the tensorflow module.
Arguments to func can be either Tensor objects or Python
objects. Non-Tensor python objects are treated as constants, and new function
definitions are created internally based on their values.
func must return a tf.Tensor (NOT a Tensor) or a list of tf.Tensor (NOT a
Tensor).
Control flow constructs (e.g., `if`, `while`) are not yet compatible with
`defun`.
Example:
```python
def f(x, y):
return tf.reduce_mean(tf.multiply(x ** 2, 3) + y)
@tfe.defun
def g(x, y):
return tf.reduce_mean(tf.multiply(x ** 2, 3) + y)
x = tf.constant([[2.0, 3.0]])
y = tf.constant([[3.0, -2.0]])
# The plain function and defun-compiled function should return the same value.
assert f(x, y).numpy() == g(x, y).numpy()
# After the first invocation, the defun-compiled (graph) function runs faster
# than the plain function because the defun-compiled function does not involve
# Python interpreter overhead during the execution.
%time print(f(x, y))
%time print(g(x, y))
```
Args:
func: function to be compiled.
Returns:
A callable that will execute the compiled function (and return zero
or more Tensor objects). | Decorator to compile func into graph_mode. | [
"Decorator",
"to",
"compile",
"func",
"into",
"graph_mode",
"."
] | def defun(func):
"""Decorator to compile func into graph_mode.
`defun` converts a function that constructs a TensorFlow graph into a function
that executes the graph. TensorFlow graphs typically execute faster and with a
lower memory-footprint than executing each of the operations that make up the
function individually as the TensorFlow runtime can optimize the graph and
execute sub-operations in parallel.
func must be a Python function that constructs a TensorFlow graph,
typically using functions in the tensorflow module.
Arguments to func can be either Tensor objects or Python
objects. Non-Tensor python objects are treated as constants, and new function
definitions are created internally based on their values.
func must return a tf.Tensor (NOT a Tensor) or a list of tf.Tensor (NOT a
Tensor).
Control flow constructs (e.g., `if`, `while`) are not yet compatible with
`defun`.
Example:
```python
def f(x, y):
return tf.reduce_mean(tf.multiply(x ** 2, 3) + y)
@tfe.defun
def g(x, y):
return tf.reduce_mean(tf.multiply(x ** 2, 3) + y)
x = tf.constant([[2.0, 3.0]])
y = tf.constant([[3.0, -2.0]])
# The plain function and defun-compiled function should return the same value.
assert f(x, y).numpy() == g(x, y).numpy()
# After the first invocation, the defun-compiled (graph) function runs faster
# than the plain function because the defun-compiled function does not involve
# Python interpreter overhead during the execution.
%time print(f(x, y))
%time print(g(x, y))
```
Args:
func: function to be compiled.
Returns:
A callable that will execute the compiled function (and return zero
or more Tensor objects).
"""
# TODO(apassos): deal with captured global state. Deal with control flow.
return tf_decorator.make_decorator(func, named_defun(func, func.__name__)) | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/ops/_op_impl/tbe/scatter_nd_update.py | python | _scatter_nd_update_tbe | () | return | ScatterNdUpdate TBE register | ScatterNdUpdate TBE register | [
"ScatterNdUpdate",
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] | def _scatter_nd_update_tbe():
"""ScatterNdUpdate TBE register"""
return | [
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arangodb/arangodb | 0d658689c7d1b721b314fa3ca27d38303e1570c8 | 3rdParty/V8/v7.9.317/tools/sanitizers/sancov_formatter.py | python | merge | (options) | Implements the 'merge' action of this tool. | Implements the 'merge' action of this tool. | [
"Implements",
"the",
"merge",
"action",
"of",
"this",
"tool",
"."
] | def merge(options):
"""Implements the 'merge' action of this tool."""
# Check if folder with coverage output exists.
assert (os.path.exists(options.coverage_dir) and
os.path.isdir(options.coverage_dir))
# Inputs for multiprocessing. List of tuples of:
# Coverage dir, absoluate path to executable, sancov file name.
inputs = []
for sancov_file in os.listdir(options.coverage_dir):
match = SANCOV_FILE_RE.match(sancov_file)
if match:
inputs.append((
options.coverage_dir,
os.path.join(options.build_dir, match.group(1)),
sancov_file,
))
logging.info('Merging %d sancov files into %s',
len(inputs), options.json_input)
# Post-process covered lines in parallel.
pool = Pool(CPUS)
try:
results = pool.imap_unordered(get_covered_lines, inputs)
finally:
pool.close()
# Load existing json data file for merging the results.
with open(options.json_input, 'r') as f:
data = json.load(f)
# Merge muliprocessing results. Mutates data.
merge_covered_line_results(data, results)
logging.info('Merged data from %d executables, which covers %d files.',
len(data['tests']), len(data['files']))
logging.info('Writing results to %s', options.json_output)
# Write merged results to file.
with open(options.json_output, 'w') as f:
json.dump(data, f, sort_keys=True) | [
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tenzir/vast | ff450d61f35c721db1760339869085a89222386f | pyvast/pyvast/vast.py | python | VAST.test_connection | (self) | return proc.returncode == 0 | Checks if the configured endpoint is reachable | Checks if the configured endpoint is reachable | [
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] | async def test_connection(self):
"""Checks if the configured endpoint is reachable"""
proc = await self.__spawn("status")
await proc.wait()
return proc.returncode == 0 | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | scripts/abins/input/vasploader.py | python | _to_text | (item: ElementTree.Element) | return item.text | Get an Element.text property, avoiding confusing type errors | Get an Element.text property, avoiding confusing type errors | [
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"""Get an Element.text property, avoiding confusing type errors"""
if item.text is None:
raise ValueError(f"XML Element {item} didn't contain expected text")
return item.text | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/AWSPythonSDK/1.5.8/docutils/utils/math/math2html.py | python | PositionEnding.checkin | (self, pos) | return pos.checkfor(self.ending) | Check for the ending | Check for the ending | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/ipython/py2/IPython/core/inputtransformer.py | python | _tr_system2 | (line_info) | return '%sget_ipython().getoutput(%r)' % (line_info.pre, cmd) | Translate lines escaped with: !! | Translate lines escaped with: !! | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numba/targets/base.py | python | BaseContext.pair_second | (self, builder, val, ty) | return pair.second | Extract the second element of a heterogeneous pair. | Extract the second element of a heterogeneous pair. | [
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su2code/SU2 | 72b2fa977b64b9683a388920f05298a40d39e5c5 | SU2_PY/SU2/eval/gradients.py | python | findiff | ( config, state=None ) | return grads | vals = SU2.eval.findiff(config,state=None)
Evaluates the aerodynamics gradients using
finite differencing with:
SU2.eval.func()
SU2.run.deform()
SU2.run.direct()
Assumptions:
Config is already setup for deformation.
Mesh may or may not be deformed.
Updates config and state by reference.
Gradient Redundancy if state.GRADIENTS has the key func_name.
Direct Redundancy if state.FUNCTIONS has key func_name.
Executes in:
./FINDIFF
Inputs:
config - an SU2 config
state - optional, an SU2 state
Outputs:
A Bunch() with keys of objective function names
and values of list of floats of gradient values | vals = SU2.eval.findiff(config,state=None) | [
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""" vals = SU2.eval.findiff(config,state=None)
Evaluates the aerodynamics gradients using
finite differencing with:
SU2.eval.func()
SU2.run.deform()
SU2.run.direct()
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Updates config and state by reference.
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Direct Redundancy if state.FUNCTIONS has key func_name.
Executes in:
./FINDIFF
Inputs:
config - an SU2 config
state - optional, an SU2 state
Outputs:
A Bunch() with keys of objective function names
and values of list of floats of gradient values
"""
# ----------------------------------------------------
# Initialize
# ----------------------------------------------------
# initialize
state = su2io.State(state)
special_cases = su2io.get_specialCases(config)
Definition_DV = config['DEFINITION_DV']
# console output
if config.get('CONSOLE','VERBOSE') in ['QUIET','CONCISE']:
log_findiff = 'log_FinDiff.out'
else:
log_findiff = None
# evaluate step length or set default value
if 'FIN_DIFF_STEP' in config:
step = float(config.FIN_DIFF_STEP)
else:
step = 0.001
opt_names = []
for i in range(config['NZONES']):
for key in sorted(su2io.historyOutFields):
if su2io.historyOutFields[key]['TYPE'] == 'COEFFICIENT':
if (config['NZONES'] == 1):
opt_names.append(key)
else:
opt_names.append(key + '[' + str(i) + ']')
# ----------------------------------------------------
# Redundancy Check
# ----------------------------------------------------
# master redundancy check
findiff_todo = all([key in state.GRADIENTS for key in opt_names])
if findiff_todo:
grads = state['GRADIENTS']
return copy.deepcopy(grads)
# ----------------------------------------------------
# Zero Step
# ----------------------------------------------------
# run
func_base = function( 'ALL', config, state )
# ----------------------------------------------------
# Plot Setup
# ----------------------------------------------------
grad_filename = config['GRAD_OBJFUNC_FILENAME']
grad_filename = os.path.splitext( grad_filename )[0]
output_format = config['TABULAR_FORMAT']
plot_extension = su2io.get_extension(output_format)
grad_filename = grad_filename + '_findiff' + plot_extension
# ----------------------------------------------------
# Finite Difference Steps
# ----------------------------------------------------
# local config
konfig = copy.deepcopy(config)
# check deformation setup
n_dv = sum(Definition_DV['SIZE'])
deform_set = konfig['DV_KIND'] == Definition_DV['KIND']
if not deform_set:
dvs_base = [0.0] * n_dv
konfig.unpack_dvs(dvs_base,dvs_base)
else:
dvs_base = konfig['DV_VALUE_NEW']
# initialize gradients
func_keys = ['VARIABLE'] + opt_names + ['FINDIFF_STEP']
grads = su2util.ordered_bunch.fromkeys(func_keys)
for key in grads.keys(): grads[key] = []
# step vector
if isinstance(step,list):
assert n_dv == len(step) , 'unexpected step vector length'
else:
step = [step] * n_dv
# files to pull
files = state['FILES']
pull = []; link = []
pull.extend(config.get('CONFIG_LIST',[]))
# files: mesh
name = files['MESH']
name = su2io.expand_part(name,konfig)
link.extend(name)
# files: direct solution
if 'DIRECT' in files:
name = files['DIRECT']
name = su2io.expand_time(name,config)
link.extend(name)
# files: restart solution for dual-time stepping first and second order
if 'RESTART_FILE_1' in files:
name = files['RESTART_FILE_1']
pull.append(name)
if 'RESTART_FILE_2' in files:
name = files['RESTART_FILE_2']
pull.append(name)
# files: target equivarea distribution
if 'EQUIV_AREA' in special_cases and 'TARGET_EA' in files:
pull.append(files['TARGET_EA'])
# files: target pressure distribution
if 'INV_DESIGN_CP' in special_cases and 'TARGET_CP' in files:
pull.append(files['TARGET_CP'])
# files: target heat flux distribution
if 'INV_DESIGN_HEATFLUX' in special_cases and 'TARGET_HEATFLUX' in files:
pull.append(files['TARGET_HEATFLUX'])
# output redirection
with redirect_folder('FINDIFF',pull,link) as push:
with redirect_output(log_findiff):
# iterate each dv
for i_dv in range(n_dv):
this_step = step[i_dv]
temp_config_name = 'config_FINDIFF_%i.cfg' % i_dv
this_dvs = copy.deepcopy(dvs_base)
this_konfig = copy.deepcopy(konfig)
this_dvs[i_dv] = this_dvs[i_dv] + this_step
this_state = su2io.State()
this_state.FILES = copy.deepcopy( state.FILES )
this_konfig.unpack_dvs(this_dvs,dvs_base)
this_konfig.dump(temp_config_name)
# Direct Solution, findiff step
func_step = function( 'ALL', this_konfig, this_state )
# remove deform step files
meshfiles = this_state.FILES.MESH
meshfiles = su2io.expand_part(meshfiles,this_konfig)
for name in meshfiles: os.remove(name)
for key in grads.keys():
if key == 'VARIABLE' or key == 'FINDIFF_STEP':
pass
elif not key in func_step:
del grads[key]
# calc finite difference and store
for key in grads.keys():
if key == 'VARIABLE':
grads[key].append(i_dv)
elif key == 'FINDIFF_STEP':
grads[key].append(this_step)
else:
this_grad = ( func_step[key] - func_base[key] ) / this_step
grads[key].append(this_grad)
#: for each grad name
su2util.write_plot(grad_filename,output_format,grads)
os.remove(temp_config_name)
#: for each dv
#: with output redirection
# remove plot items
del grads['VARIABLE']
del grads['FINDIFF_STEP']
state.GRADIENTS.update(grads)
# return results
grads = copy.deepcopy(grads)
return grads | [
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pmq20/node-packer | 12c46c6e44fbc14d9ee645ebd17d5296b324f7e0 | lts/deps/npm/node_modules/node-gyp/gyp/pylib/gyp/generator/msvs.py | python | _FixPaths | (paths) | return [_FixPath(i) for i in paths] | Fix each of the paths of the list. | Fix each of the paths of the list. | [
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] | def _FixPaths(paths):
"""Fix each of the paths of the list."""
return [_FixPath(i) for i in paths] | [
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hughperkins/tf-coriander | 970d3df6c11400ad68405f22b0c42a52374e94ca | tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py | python | _get_in_out_shape | (x_shape, y_shape, n_classes, batch_size=None) | return input_shape, output_shape, batch_size | Returns shape for input and output of the data feeder. | Returns shape for input and output of the data feeder. | [
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] | def _get_in_out_shape(x_shape, y_shape, n_classes, batch_size=None):
"""Returns shape for input and output of the data feeder."""
if batch_size is None:
batch_size = x_shape[0]
elif batch_size <= 0:
raise ValueError('Invalid batch_size %d.' % batch_size)
x_shape = list(x_shape[1:]) if len(x_shape) > 1 else [1]
input_shape = [batch_size] + x_shape
if y_shape is None:
return input_shape, None, batch_size
y_shape = list(y_shape[1:]) if len(y_shape) > 1 else []
# Skip first dimension if it is 1.
if y_shape and y_shape[0] == 1:
y_shape = y_shape[1:]
if n_classes is not None and n_classes > 1:
output_shape = [batch_size] + y_shape + [n_classes]
else:
output_shape = [batch_size] + y_shape
return input_shape, output_shape, batch_size | [
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ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/third_party/lib_aarch64/python2.7/dist-packages/rospkg/stack.py | python | parse_stack | (string, filename) | return s | Parse stack.xml string contents.
:param string: stack.xml contents, ``str``
:param filename: full file path for debugging, ``str``
:returns: return parsed :class:`Stack` | Parse stack.xml string contents. | [
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] | def parse_stack(string, filename):
"""
Parse stack.xml string contents.
:param string: stack.xml contents, ``str``
:param filename: full file path for debugging, ``str``
:returns: return parsed :class:`Stack`
"""
# Create some classes to hold some members
new_tuples = {}
for key, members in LISTED_ATTRIBUTES.items():
new_tuples[key] = collections.namedtuple(key, members)
try:
d = dom.parseString(string)
except Exception as e:
raise InvalidStack("[%s] invalid XML: %s"%(filename, e))
s = Stack()
p = _get_nodes_by_name(d, 'stack')
if len(p) != 1:
raise InvalidStack("stack.xml [%s] must have a single 'stack' element"%(filename))
p = p[0]
for attr in [ 'name', 'version', 'description',
'license', 'copyright', 'url', 'build_type', 'message_generator' ]:
val = _check(attr)(p, filename)
if val:
setattr(s, attr, val)
try:
tag = _get_nodes_by_name(p, 'description')[0]
s.description_brief = tag.getAttribute('brief') or ''
except:
# means that 'description' tag is missing
pass
s.authors = _build_listed_attributes(p, 'author', new_tuples['Author'])
s.maintainers = _build_listed_attributes(p, 'maintainer', new_tuples['Maintainer'])
s.depends = _build_listed_attributes(p, 'depends', new_tuples['Depend'])
s.build_depends = _build_listed_attributes(p, 'build_depends', new_tuples['Depend'])
try:
tag = _get_nodes_by_name(p, 'review')[0]
s.review_status = tag.getAttribute('status') or ''
except:
pass #stack.xml is missing optional 'review status' tag
try:
tag = _get_nodes_by_name(p, 'review')[0]
s.review_notes = tag.getAttribute('notes') or ''
except:
pass #stack.xml is missing optional 'review notes' tag
try:
tag = _get_nodes_by_name(p, 'build_type')[0]
s.build_type_file = tag.getAttribute('file') or ''
except:
pass #stack.xml is missing optional 'build_type file' tag
# store unrecognized tags
s.unknown_tags = [e.nodeName for e in p.childNodes if e.nodeType == e.ELEMENT_NODE and e.tagName not in VALID]
if s.unknown_tags:
raise InvalidStack("stack.xml [%s] must be cleaned up from %s"%(filename, str(s.unknown_tags)))
return s | [
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include-what-you-use/include-what-you-use | 208fbfffa5d69364b9f78e427caa443441279283 | iwyu-check-license-header.py | python | format_file_error | (filename, *lines) | return os.linesep.join(lines) | Format an error message from filename and lines | Format an error message from filename and lines | [
"Format",
"an",
"error",
"message",
"from",
"filename",
"and",
"lines"
] | def format_file_error(filename, *lines):
""" Format an error message from filename and lines """
lines = list(lines)
lines[0] = '%s: %s' % (filename, lines[0])
return os.linesep.join(lines) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py3/pandas/io/formats/style_render.py | python | Tooltips._pseudo_css | (self, uuid: str, name: str, row: int, col: int, text: str) | return [
{
"selector": selector_id + f":hover .{name}",
"props": [("visibility", "visible")],
},
{
"selector": selector_id + f" .{name}::after",
"props": [("content", f'"{text}"')],
},
] | For every table data-cell that has a valid tooltip (not None, NaN or
empty string) must create two pseudo CSS entries for the specific
<td> element id which are added to overall table styles:
an on hover visibility change and a content change
dependent upon the user's chosen display string.
For example:
[{"selector": "T__row1_col1:hover .pd-t",
"props": [("visibility", "visible")]},
{"selector": "T__row1_col1 .pd-t::after",
"props": [("content", "Some Valid Text String")]}]
Parameters
----------
uuid: str
The uuid of the Styler instance
name: str
The css-name of the class used for styling tooltips
row : int
The row index of the specified tooltip string data
col : int
The col index of the specified tooltip string data
text : str
The textual content of the tooltip to be displayed in HTML.
Returns
-------
pseudo_css : List | For every table data-cell that has a valid tooltip (not None, NaN or
empty string) must create two pseudo CSS entries for the specific
<td> element id which are added to overall table styles:
an on hover visibility change and a content change
dependent upon the user's chosen display string. | [
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"""
For every table data-cell that has a valid tooltip (not None, NaN or
empty string) must create two pseudo CSS entries for the specific
<td> element id which are added to overall table styles:
an on hover visibility change and a content change
dependent upon the user's chosen display string.
For example:
[{"selector": "T__row1_col1:hover .pd-t",
"props": [("visibility", "visible")]},
{"selector": "T__row1_col1 .pd-t::after",
"props": [("content", "Some Valid Text String")]}]
Parameters
----------
uuid: str
The uuid of the Styler instance
name: str
The css-name of the class used for styling tooltips
row : int
The row index of the specified tooltip string data
col : int
The col index of the specified tooltip string data
text : str
The textual content of the tooltip to be displayed in HTML.
Returns
-------
pseudo_css : List
"""
selector_id = "#T_" + uuid + "row" + str(row) + "_col" + str(col)
return [
{
"selector": selector_id + f":hover .{name}",
"props": [("visibility", "visible")],
},
{
"selector": selector_id + f" .{name}::after",
"props": [("content", f'"{text}"')],
},
] | [
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SoarGroup/Soar | a1c5e249499137a27da60533c72969eef3b8ab6b | scons/scons-local-4.1.0/SCons/Script/Interactive.py | python | SConsInteractiveCmd.do_shell | (self, argv) | \
shell [COMMANDLINE] Execute COMMANDLINE in a subshell. 'sh' and
'!' are synonyms. | \
shell [COMMANDLINE] Execute COMMANDLINE in a subshell. 'sh' and
'!' are synonyms. | [
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] | def do_shell(self, argv):
"""\
shell [COMMANDLINE] Execute COMMANDLINE in a subshell. 'sh' and
'!' are synonyms.
"""
import subprocess
argv = argv[1:]
if not argv:
argv = os.environ[self.shell_variable]
try:
# Per "[Python-Dev] subprocess insufficiently platform-independent?"
# http://mail.python.org/pipermail/python-dev/2008-August/081979.html "+
# Doing the right thing with an argument list currently
# requires different shell= values on Windows and Linux.
p = subprocess.Popen(argv, shell=(sys.platform=='win32'))
except EnvironmentError as e:
sys.stderr.write('scons: %s: %s\n' % (argv[0], e.strerror))
else:
p.wait() | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py2/scipy/optimize/_basinhopping.py | python | Metropolis.accept_reject | (self, energy_new, energy_old) | return w >= rand | If new energy is lower than old, it will always be accepted.
If new is higher than old, there is a chance it will be accepted,
less likely for larger differences. | If new energy is lower than old, it will always be accepted.
If new is higher than old, there is a chance it will be accepted,
less likely for larger differences. | [
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redpony/cdec | f7c4899b174d86bc70b40b1cae68dcad364615cb | python/cdec/configobj.py | python | ConfigObj.__init__ | (self, infile=None, options=None, configspec=None, encoding=None,
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# TODO: check the values too.
for entry in options:
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self._original_configspec = configspec
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/learn/python/learn/models.py | python | logistic_regression_zero_init | (x, y) | return logistic_regression(x, y, init_mean=0.0, init_stddev=0.0) | Logistic regression subgraph with zero-value initial weights and bias.
Args:
x: tensor or placeholder for input features.
y: tensor or placeholder for labels.
Returns:
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return logistic_regression(x, y, init_mean=0.0, init_stddev=0.0) | [
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turi-code/SFrame | 796b9bdfb2fa1b881d82080754643c7e68629cd2 | oss_src/unity/python/sframe/util/queue_channel.py | python | QueueListener.dequeue | (self, block) | return self.queue.get(block) | Dequeue a record and return it, optionally blocking.
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hpi-xnor/BMXNet-v2 | af2b1859eafc5c721b1397cef02f946aaf2ce20d | example/gluon/lipnet/utils/multi.py | python | test_worker | (from_idx, to_idx, params) | return (succ, fail) | the worker to test multi-process | the worker to test multi-process | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/runpy.py | python | _run_module_code | (code, init_globals=None,
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echronos/echronos | c996f1d2c8af6c6536205eb319c1bf1d4d84569c | pylib/utils.py | python | top_path | (topdir, *path) | return os.path.join(topdir, *path) | Return a path relative to the directory in which the x tool or wrapper was invoked.
This function is equivalent to base_path(), except when the x tool is invoked in a client repository through a
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/psutil/_pswindows.py | python | net_if_addrs | () | return ret | Return the addresses associated to each NIC. | Return the addresses associated to each NIC. | [
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JumpingYang001/webrtc | c03d6e965e1f54aeadd670e491eabe5fdb8db968 | tools_webrtc/autoroller/roll_deps.py | python | BuildDepsentryDict | (deps_dict) | return result | Builds a dict of paths to DepsEntry objects from a raw parsed deps dict. | Builds a dict of paths to DepsEntry objects from a raw parsed deps dict. | [
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y123456yz/reading-and-annotate-mongodb-3.6 | 93280293672ca7586dc24af18132aa61e4ed7fcf | mongo/buildscripts/idl/idl/generator.py | python | _CppSourceFileWriter.gen_op_msg_request_deserializer_methods | (self, struct) | Generate the C++ deserializer method definitions from OpMsgRequest. | Generate the C++ deserializer method definitions from OpMsgRequest. | [
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"OpMsgRequest",
"."
] | def gen_op_msg_request_deserializer_methods(self, struct):
# type: (ast.Struct) -> None
"""Generate the C++ deserializer method definitions from OpMsgRequest."""
# pylint: disable=invalid-name
# Commands that have concatentate_with_db namespaces require db name as a parameter
if not isinstance(struct, ast.Command):
return
struct_type_info = struct_types.get_struct_info(struct)
self.get_bson_deserializer_static_common(
struct,
struct_type_info.get_op_msg_request_deserializer_static_method(),
struct_type_info.get_op_msg_request_deserializer_method())
func_def = struct_type_info.get_op_msg_request_deserializer_method().get_definition()
with self._block('%s {' % (func_def), '}'):
# Deserialize all the fields
field_usage_check = self._gen_fields_deserializer_common(struct, "request.body")
# Iterate through the document sequences if we have any
has_doc_sequence = len(
[field for field in struct.fields if field.supports_doc_sequence])
if has_doc_sequence:
with self._block('for (auto&& sequence : request.sequences) {', '}'):
field_usage_check.add_store("sequence.name")
self._writer.write_empty_line()
first_field = True
for field in struct.fields:
# Only parse document sequence fields here
if not field.supports_doc_sequence:
continue
field_predicate = 'sequence.name == %s' % (_get_field_constant_name(field))
with self._predicate(field_predicate, not first_field):
field_usage_check.add(field, "sequence.name")
if _is_required_serializer_field(field):
self._writer.write_line('%s = true;' %
(_get_has_field_member_name(field)))
self.gen_doc_sequence_deserializer(field)
if first_field:
first_field = False
# End of for fields
# Generate strict check for extranous fields
if struct.strict:
with self._block('else {', '}'):
self._writer.write_line('ctxt.throwUnknownField(sequence.name);')
self._writer.write_empty_line()
# Check for required fields
field_usage_check.add_final_checks()
self._writer.write_empty_line()
# Generate namespace check now that "$db" has been read or defaulted
struct_type_info.gen_namespace_check(self._writer, "_dbName", "commandElement") | [
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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/pkg_resources/__init__.py | python | WorkingSet.resolve | (self, requirements, env=None, installer=None,
replace_conflicting=False, extras=None) | return to_activate | List all distributions needed to (recursively) meet `requirements`
`requirements` must be a sequence of ``Requirement`` objects. `env`,
if supplied, should be an ``Environment`` instance. If
not supplied, it defaults to all distributions available within any
entry or distribution in the working set. `installer`, if supplied,
will be invoked with each requirement that cannot be met by an
already-installed distribution; it should return a ``Distribution`` or
``None``.
Unless `replace_conflicting=True`, raises a VersionConflict exception
if
any requirements are found on the path that have the correct name but
the wrong version. Otherwise, if an `installer` is supplied it will be
invoked to obtain the correct version of the requirement and activate
it.
`extras` is a list of the extras to be used with these requirements.
This is important because extra requirements may look like `my_req;
extra = "my_extra"`, which would otherwise be interpreted as a purely
optional requirement. Instead, we want to be able to assert that these
requirements are truly required. | List all distributions needed to (recursively) meet `requirements` | [
"List",
"all",
"distributions",
"needed",
"to",
"(",
"recursively",
")",
"meet",
"requirements"
] | def resolve(self, requirements, env=None, installer=None,
replace_conflicting=False, extras=None):
"""List all distributions needed to (recursively) meet `requirements`
`requirements` must be a sequence of ``Requirement`` objects. `env`,
if supplied, should be an ``Environment`` instance. If
not supplied, it defaults to all distributions available within any
entry or distribution in the working set. `installer`, if supplied,
will be invoked with each requirement that cannot be met by an
already-installed distribution; it should return a ``Distribution`` or
``None``.
Unless `replace_conflicting=True`, raises a VersionConflict exception
if
any requirements are found on the path that have the correct name but
the wrong version. Otherwise, if an `installer` is supplied it will be
invoked to obtain the correct version of the requirement and activate
it.
`extras` is a list of the extras to be used with these requirements.
This is important because extra requirements may look like `my_req;
extra = "my_extra"`, which would otherwise be interpreted as a purely
optional requirement. Instead, we want to be able to assert that these
requirements are truly required.
"""
# set up the stack
requirements = list(requirements)[::-1]
# set of processed requirements
processed = {}
# key -> dist
best = {}
to_activate = []
req_extras = _ReqExtras()
# Mapping of requirement to set of distributions that required it;
# useful for reporting info about conflicts.
required_by = collections.defaultdict(set)
while requirements:
# process dependencies breadth-first
req = requirements.pop(0)
if req in processed:
# Ignore cyclic or redundant dependencies
continue
if not req_extras.markers_pass(req, extras):
continue
dist = best.get(req.key)
if dist is None:
# Find the best distribution and add it to the map
dist = self.by_key.get(req.key)
if dist is None or (dist not in req and replace_conflicting):
ws = self
if env is None:
if dist is None:
env = Environment(self.entries)
else:
# Use an empty environment and workingset to avoid
# any further conflicts with the conflicting
# distribution
env = Environment([])
ws = WorkingSet([])
dist = best[req.key] = env.best_match(
req, ws, installer,
replace_conflicting=replace_conflicting
)
if dist is None:
requirers = required_by.get(req, None)
raise DistributionNotFound(req, requirers)
to_activate.append(dist)
if dist not in req:
# Oops, the "best" so far conflicts with a dependency
dependent_req = required_by[req]
raise VersionConflict(dist, req).with_context(dependent_req)
# push the new requirements onto the stack
new_requirements = dist.requires(req.extras)[::-1]
requirements.extend(new_requirements)
# Register the new requirements needed by req
for new_requirement in new_requirements:
required_by[new_requirement].add(req.project_name)
req_extras[new_requirement] = req.extras
processed[req] = True
# return list of distros to activate
return to_activate | [
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apple/swift | 469f72fdae2ea828b3b6c0d7d62d7e4cf98c4893 | utils/swift_build_support/swift_build_support/products/libdispatch.py | python | LibDispatch.is_before_build_script_impl_product | (cls) | return False | is_before_build_script_impl_product -> bool
Whether this product is built before any build-script-impl products. | is_before_build_script_impl_product -> bool | [
"is_before_build_script_impl_product",
"-",
">",
"bool"
] | def is_before_build_script_impl_product(cls):
"""is_before_build_script_impl_product -> bool
Whether this product is built before any build-script-impl products.
"""
return False | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/distutils/util.py | python | split_quoted | (s) | return words | Split a string up according to Unix shell-like rules for quotes and
backslashes. In short: words are delimited by spaces, as long as those
spaces are not escaped by a backslash, or inside a quoted string.
Single and double quotes are equivalent, and the quote characters can
be backslash-escaped. The backslash is stripped from any two-character
escape sequence, leaving only the escaped character. The quote
characters are stripped from any quoted string. Returns a list of
words. | Split a string up according to Unix shell-like rules for quotes and
backslashes. In short: words are delimited by spaces, as long as those
spaces are not escaped by a backslash, or inside a quoted string.
Single and double quotes are equivalent, and the quote characters can
be backslash-escaped. The backslash is stripped from any two-character
escape sequence, leaving only the escaped character. The quote
characters are stripped from any quoted string. Returns a list of
words. | [
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"""Split a string up according to Unix shell-like rules for quotes and
backslashes. In short: words are delimited by spaces, as long as those
spaces are not escaped by a backslash, or inside a quoted string.
Single and double quotes are equivalent, and the quote characters can
be backslash-escaped. The backslash is stripped from any two-character
escape sequence, leaving only the escaped character. The quote
characters are stripped from any quoted string. Returns a list of
words.
"""
# This is a nice algorithm for splitting up a single string, since it
# doesn't require character-by-character examination. It was a little
# bit of a brain-bender to get it working right, though...
if _wordchars_re is None: _init_regex()
s = string.strip(s)
words = []
pos = 0
while s:
m = _wordchars_re.match(s, pos)
end = m.end()
if end == len(s):
words.append(s[:end])
break
if s[end] in string.whitespace: # unescaped, unquoted whitespace: now
words.append(s[:end]) # we definitely have a word delimiter
s = string.lstrip(s[end:])
pos = 0
elif s[end] == '\\': # preserve whatever is being escaped;
# will become part of the current word
s = s[:end] + s[end+1:]
pos = end+1
else:
if s[end] == "'": # slurp singly-quoted string
m = _squote_re.match(s, end)
elif s[end] == '"': # slurp doubly-quoted string
m = _dquote_re.match(s, end)
else:
raise RuntimeError, \
"this can't happen (bad char '%c')" % s[end]
if m is None:
raise ValueError, \
"bad string (mismatched %s quotes?)" % s[end]
(beg, end) = m.span()
s = s[:beg] + s[beg+1:end-1] + s[end:]
pos = m.end() - 2
if pos >= len(s):
words.append(s)
break
return words | [
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miyosuda/TensorFlowAndroidMNIST | 7b5a4603d2780a8a2834575706e9001977524007 | jni-build/jni/include/tensorflow/python/platform/gfile.py | python | _GFileBase.read | (self, n=-1) | return self._fp.read(n) | Read n bytes from the underlying file handle.
Args:
n: Number of bytes to read (if negative, read to end of file handle.)
Returns:
A string of the bytes read, up to the end of file. | Read n bytes from the underlying file handle. | [
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"""Read n bytes from the underlying file handle.
Args:
n: Number of bytes to read (if negative, read to end of file handle.)
Returns:
A string of the bytes read, up to the end of file.
"""
return self._fp.read(n) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_gdi.py | python | PseudoDC.SetBrush | (*args, **kwargs) | return _gdi_.PseudoDC_SetBrush(*args, **kwargs) | SetBrush(self, Brush brush)
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mapequation/infomap | 5f56b94fe0f956483f61a03ef07e94d8def2205e | interfaces/python/infomap.py | python | Infomap.names | (self) | return super().getNames() | Get all node names.
Short-hand for ``get_names``.
See Also
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get_names
get_name
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Short-hand for ``get_names``.
See Also
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PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | python/paddle/fluid/contrib/slim/quantization/quantization_pass.py | python | QuantizationTransformPass._insert_channel_dequant_op | (self, graph, var_node, scale_var_nodes,
quant_bits, quant_axis) | return dequant_var_node | Insert fake_channel_wise_dequantize_max_abs in the graph. | Insert fake_channel_wise_dequantize_max_abs in the graph. | [
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quant_bits, quant_axis):
"""
Insert fake_channel_wise_dequantize_max_abs in the graph.
"""
assert var_node.is_var(), '{} is not a var'.format(var_node.name())
dequant_var_node = graph.create_var_node(
name=self._dequantized_var_name(var_node.name()),
var_type=var_node.type(),
shape=var_node.shape(),
var_dtype=var_node.dtype())
dequant_op_node = graph.create_op_node(
op_type='fake_channel_wise_dequantize_max_abs',
attrs={
'quant_bits': quant_bits,
'quant_axis': quant_axis,
'op_role': core.op_proto_and_checker_maker.OpRole.Forward
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inputs={'X': var_node,
'Scales': scale_var_nodes},
outputs={'Out': dequant_var_node})
graph.link_to(var_node, dequant_op_node)
for scale_n in scale_var_nodes:
graph.link_to(scale_n, dequant_op_node)
graph.link_to(dequant_op_node, dequant_var_node)
return dequant_var_node | [
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BlzFans/wke | b0fa21158312e40c5fbd84682d643022b6c34a93 | cygwin/lib/python2.6/codecs.py | python | StreamReaderWriter.__getattr__ | (self, name,
getattr=getattr) | return getattr(self.stream, name) | Inherit all other methods from the underlying stream. | Inherit all other methods from the underlying stream. | [
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] | def __getattr__(self, name,
getattr=getattr):
""" Inherit all other methods from the underlying stream.
"""
return getattr(self.stream, name) | [
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sfzhang15/RefineDet | 52b6fe23dc1a160fe710b7734576dca509bf4fae | scripts/cpp_lint.py | python | _NestingState.CheckCompletedBlocks | (self, filename, error) | Checks that all classes and namespaces have been completely parsed.
Call this when all lines in a file have been processed.
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filename: The name of the current file.
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"""Checks that all classes and namespaces have been completely parsed.
Call this when all lines in a file have been processed.
Args:
filename: The name of the current file.
error: The function to call with any errors found.
"""
# Note: This test can result in false positives if #ifdef constructs
# get in the way of brace matching. See the testBuildClass test in
# cpplint_unittest.py for an example of this.
for obj in self.stack:
if isinstance(obj, _ClassInfo):
error(filename, obj.starting_linenum, 'build/class', 5,
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obj.name)
elif isinstance(obj, _NamespaceInfo):
error(filename, obj.starting_linenum, 'build/namespaces', 5,
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sdhash/sdhash | b9eff63e4e5867e910f41fd69032bbb1c94a2a5e | sdhash-server/gen-py/sdhashsrv/sdhashsrv.py | python | Iface.displaySet | (self, num1) | Parameters:
- num1 | Parameters:
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numba/array_analysis.py | python | SymbolicEquivSet.define | (self, var, redefined, func_ir=None, typ=None) | Besides incrementing the definition count of the given variable
name, it will also retrieve and simplify its definition from func_ir,
and remember the result for later equivalence comparison. Supported
operations are:
1. arithmetic plus and minus with constants
2. wrap_index (relative to some given size) | Besides incrementing the definition count of the given variable
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operations are:
1. arithmetic plus and minus with constants
2. wrap_index (relative to some given size) | [
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if isinstance(var, ir.Var):
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if isinstance(value, int):
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ind = self._get_or_add_ind(name)
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self.obj_to_ind[name] = ind
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else:
self.ind_to_var[ind] = [var] | [
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nest/nest-simulator | f2623eb78518cdbd55e77e0ed486bf1111bcb62f | doc/slihelp_generator/writers.py | python | write_helpindex | (helpdir) | Writes helpindex.html and helpindex.hlp
--------------------------------------- | Writes helpindex.html and helpindex.hlp
--------------------------------------- | [
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"---------------------------------------"
] | def write_helpindex(helpdir):
"""
Writes helpindex.html and helpindex.hlp
---------------------------------------
"""
# We only have to generate a helpindex if the help directory exists
if not os.path.exists(helpdir):
print("Error: Help directory not found: " + helpdir)
return
filelist = glob.glob(os.path.join(helpdir, '*', '*.hlp'))
html_list = []
hlp_list = []
# Loading Template for helpindex.html
ftemplate = io.open(os.path.join('templates', 'helpindex.tpl.html'),
encoding='utf-8')
templ = ftemplate.read()
ftemplate.close()
# Loading Template for CSS
cssf = io.open(os.path.join('templates', 'nest.tpl.css'),
encoding='utf-8')
csstempl = cssf.read()
cssf.close()
# Loading Template for footer
footerf = io.open(os.path.join('templates', 'footer.tpl.html'),
encoding='utf-8')
footertempl = footerf.read()
footerf.close()
s = Template(templ)
alpha = [('A', 'a'), ('B', 'b'), ('C', 'c'), ('D', 'd'), ('E', 'e'),
('F', 'f'), ('G', 'g'), ('H', 'h'), ('I', 'i'), ('J', 'j'),
('K', 'k'), ('L', 'l'), ('M', 'm'), ('N', 'n'), ('O', 'o'),
('P', 'p'), ('Q', 'q'), ('R', 'r'), ('S', 's'), ('T', 't'),
('U', 'u'), ('V', 'v'), ('W', 'w'), ('X', 'x'), ('Z', 'z'), '-',
':', '<', '=']
for doubles in alpha:
html_list.append('<center><table class="alpha">')
html_list.append('<table class="letteridx"><tr>')
for x in alpha:
html_list.append('<td><a href="#%s">%s</a></td>' % (x[0], x[0]))
html_list.append('</tr></table></center>')
html_list.append('<center><table class="commands" id="%s">'
% doubles[0])
for item in sorted(filelist,
key=lambda name: name.lower().rsplit('/', 1)[1]):
fitem = io.open(item, encoding='utf-8')
itemtext = fitem.read()
fitem.close()
# only the basename of the file
name = os.path.basename(item)[:-4]
# only the first line of itemtext
name_line = itemtext.splitlines()[0]
#
if name_line.rsplit(' - ')[0] == 'Name: ' + name:
fullname = name_line.rsplit(' - ')[1]
else:
fullname = name
# file extension
itemext = item.rsplit('/')[-2]
if name.startswith(doubles) and os.path.isfile(item):
# check if 'name' is available in folder with os.path.isfile(
# checkfile)
html_list.append('<tr><td class="left">')
html_list.append('<a href="%s/%s.html">%s</a></td>' %
(itemext, name, name))
html_list.append('<td>%s</td></tr>' % fullname)
# Better Format for the index.hlp
c = len(name)
hlp_list.append(name + '\t' * (16 - min(c, 60) // 4) +
fullname)
elif not os.path.isfile(item):
print('WARNING: Checkfile ' + item + ' not exist.')
html_list.append('</table></center>')
html_list.append('</table></center>')
# html_list.append(footer)
htmlstring = (u'\n'.join(html_list))
indexstring = s.substitute(indexbody=htmlstring, css=csstempl,
footer=footertempl)
f_helpindex = io.open(os.path.join(helpdir, 'helpindex.html'), mode='w',
encoding='utf-8')
f_helpindex.write(indexstring)
f_helpindex.write(u'\n')
f_helpindex.close()
# Todo: using string template for .hlp
f_helphlpindex = io.open(os.path.join(helpdir, 'helpindex.hlp'), mode='w',
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f_helphlpindex.write(u'\n'.join(hlp_list))
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macchina-io/macchina.io | ef24ba0e18379c3dd48fb84e6dbf991101cb8db0 | platform/JS/V8/tools/gyp/pylib/gyp/generator/ninja.py | python | NinjaWriter.WriteMacXCassets | (self, xcassets, bundle_depends) | return partial_info_plist | Writes ninja edges for 'mac_bundle_resources' .xcassets files.
This add an invocation of 'actool' via the 'mac_tool.py' helper script.
It assumes that the assets catalogs define at least one imageset and
thus an Assets.car file will be generated in the application resources
directory. If this is not the case, then the build will probably be done
at each invocation of ninja. | Writes ninja edges for 'mac_bundle_resources' .xcassets files. | [
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"""Writes ninja edges for 'mac_bundle_resources' .xcassets files.
This add an invocation of 'actool' via the 'mac_tool.py' helper script.
It assumes that the assets catalogs define at least one imageset and
thus an Assets.car file will be generated in the application resources
directory. If this is not the case, then the build will probably be done
at each invocation of ninja."""
if not xcassets:
return
extra_arguments = {}
settings_to_arg = {
'XCASSETS_APP_ICON': 'app-icon',
'XCASSETS_LAUNCH_IMAGE': 'launch-image',
}
settings = self.xcode_settings.xcode_settings[self.config_name]
for settings_key, arg_name in settings_to_arg.iteritems():
value = settings.get(settings_key)
if value:
extra_arguments[arg_name] = value
partial_info_plist = None
if extra_arguments:
partial_info_plist = self.GypPathToUniqueOutput(
'assetcatalog_generated_info.plist')
extra_arguments['output-partial-info-plist'] = partial_info_plist
outputs = []
outputs.append(
os.path.join(
self.xcode_settings.GetBundleResourceFolder(),
'Assets.car'))
if partial_info_plist:
outputs.append(partial_info_plist)
keys = QuoteShellArgument(json.dumps(extra_arguments), self.flavor)
extra_env = self.xcode_settings.GetPerTargetSettings()
env = self.GetSortedXcodeEnv(additional_settings=extra_env)
env = self.ComputeExportEnvString(env)
bundle_depends.extend(self.ninja.build(
outputs, 'compile_xcassets', xcassets,
variables=[('env', env), ('keys', keys)]))
return partial_info_plist | [
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microsoft/AirSim | 8057725712c0cd46979135396381784075ffc0f3 | PythonClient/airsim/client.py | python | VehicleClient.simListAssets | (self) | return self.client.call('simListAssets') | Lists all the assets present in the Asset Registry
Returns:
list[str]: Names of all the assets | Lists all the assets present in the Asset Registry | [
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"""
Lists all the assets present in the Asset Registry
Returns:
list[str]: Names of all the assets
"""
return self.client.call('simListAssets') | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_controls.py | python | TreeCtrl.AppendItem | (*args, **kwargs) | return _controls_.TreeCtrl_AppendItem(*args, **kwargs) | AppendItem(self, TreeItemId parent, String text, int image=-1, int selectedImage=-1,
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miyosuda/TensorFlowAndroidMNIST | 7b5a4603d2780a8a2834575706e9001977524007 | jni-build/jni/include/tensorflow/python/ops/variable_scope.py | python | VariableScope.reuse_variables | (self) | Reuse variables in this scope. | Reuse variables in this scope. | [
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hszhao/PSPNet | cf7e5a99ba37e46118026e96be5821a9bc63bde0 | scripts/cpp_lint.py | python | CheckForIncludeWhatYouUse | (filename, clean_lines, include_state, error,
io=codecs) | Reports for missing stl includes.
This function will output warnings to make sure you are including the headers
necessary for the stl containers and functions that you use. We only give one
reason to include a header. For example, if you use both equal_to<> and
less<> in a .h file, only one (the latter in the file) of these will be
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filename: The name of the current file.
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include_state: An _IncludeState instance.
error: The function to call with any errors found.
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io=codecs):
"""Reports for missing stl includes.
This function will output warnings to make sure you are including the headers
necessary for the stl containers and functions that you use. We only give one
reason to include a header. For example, if you use both equal_to<> and
less<> in a .h file, only one (the latter in the file) of these will be
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Args:
filename: The name of the current file.
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include_state: An _IncludeState instance.
error: The function to call with any errors found.
io: The IO factory to use to read the header file. Provided for unittest
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required = {} # A map of header name to linenumber and the template entity.
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continue
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continue
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# Let's copy the include_state so it is only messed up within this function.
include_state = include_state.copy()
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header_found = False
# Use the absolute path so that matching works properly.
abs_filename = FileInfo(filename).FullName()
# For Emacs's flymake.
# If cpplint is invoked from Emacs's flymake, a temporary file is generated
# by flymake and that file name might end with '_flymake.cc'. In that case,
# restore original file name here so that the corresponding header file can be
# found.
# e.g. If the file name is 'foo_flymake.cc', we should search for 'foo.h'
# instead of 'foo_flymake.h'
abs_filename = re.sub(r'_flymake\.cc$', '.cc', abs_filename)
# include_state is modified during iteration, so we iterate over a copy of
# the keys.
header_keys = include_state.keys()
for header in header_keys:
(same_module, common_path) = FilesBelongToSameModule(abs_filename, header)
fullpath = common_path + header
if same_module and UpdateIncludeState(fullpath, include_state, io):
header_found = True
# If we can't find the header file for a .cc, assume it's because we don't
# know where to look. In that case we'll give up as we're not sure they
# didn't include it in the .h file.
# TODO(unknown): Do a better job of finding .h files so we are confident that
# not having the .h file means there isn't one.
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return
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template = required[required_header_unstripped][1]
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/autograph/impl/conversion.py | python | convert_func_to_ast | (f, program_ctx, do_rename=True) | return (node,), new_name, entity_info | Specialization of `convert_entity_to_ast` for callable functions. | Specialization of `convert_entity_to_ast` for callable functions. | [
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"""Specialization of `convert_entity_to_ast` for callable functions."""
future_features = inspect_utils.getfutureimports(f)
node, source = parser.parse_entity(f, future_features=future_features)
logging.log(3, 'Source code of %s:\n\n%s\n', f, source)
# Parsed AST should contain future imports and one function def node.
# In general, the output of inspect.getsource is inexact for lambdas because
# it uses regex matching to adjust the exact location around the line number
# that CPython records. Then, the entire containing line is returned, which
# we may have trouble disambiguating. For example:
# x, y = lambda: 1, lambda: 2
if f.__name__ == '<lambda>':
nodes = ast_util.find_matching_definitions(node, f)
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' in a separate expression.'.format(f, source))
node, = nodes
# TODO(znado): Place inside standard_analysis.
origin_info.resolve_entity(node, source, f)
namespace = inspect_utils.getnamespace(f)
_add_self_references(namespace, program_ctx.autograph_module)
namer = naming.Namer(namespace)
if isinstance(node, gast.Lambda):
new_name = namer.new_symbol('tf__lambda', ())
elif do_rename:
new_name = namer.function_name(f.__name__)
else:
new_name = f.__name__
entity_info = transformer.EntityInfo(
source_code=source,
source_file='<fragment>',
future_features=future_features,
namespace=namespace)
context = converter.EntityContext(namer, entity_info, program_ctx, new_name)
node = node_to_graph(node, context)
if isinstance(node, gast.Lambda):
node = gast.Assign(
targets=[gast.Name(new_name, gast.Store(), None)], value=node)
elif do_rename:
node.name = new_name
else:
assert node.name == new_name
return (node,), new_name, entity_info | [
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snap-stanford/snap-python | d53c51b0a26aa7e3e7400b014cdf728948fde80a | setup/snap.py | python | TStrV.Sort | (self, Asc=True) | return _snap.TStrV_Sort(self, Asc) | Sort(TStrV self, bool const & Asc=True)
Parameters:
Asc: bool const &
Sort(TStrV self)
Parameters:
self: TVec< TStr,int > * | Sort(TStrV self, bool const & Asc=True) | [
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"""
Sort(TStrV self, bool const & Asc=True)
Parameters:
Asc: bool const &
Sort(TStrV self)
Parameters:
self: TVec< TStr,int > *
"""
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sdhash/sdhash | b9eff63e4e5867e910f41fd69032bbb1c94a2a5e | external/tools/build/v2/util/path.py | python | glob_in_parents | (dir, patterns, upper_limit=None) | return result | Recursive version of GLOB which glob sall parent directories
of dir until the first match is found. Returns an empty result if no match
is found | Recursive version of GLOB which glob sall parent directories
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"""Recursive version of GLOB which glob sall parent directories
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is found"""
assert(isinstance(dir, str))
assert(isinstance(patterns, list))
result = []
absolute_dir = os.path.join(os.getcwd(), dir)
absolute_dir = os.path.normpath(absolute_dir)
while absolute_dir:
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OpenGenus/quark | 225ad96efdfcc66cb6584a756c17eb3871e6eb62 | code/code/data_structures/src/tree/b_tree/b_tree/b_tree.py | python | BTree.search | (self, k, x=None) | Search the B-Tree for the key k.
args
=====================
k : Key to search for
x : (optional) Node at which to begin search. Can be None, in which case the entire tree is searched. | Search the B-Tree for the key k.
args
=====================
k : Key to search for
x : (optional) Node at which to begin search. Can be None, in which case the entire tree is searched. | [
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"""Search the B-Tree for the key k.
args
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x : (optional) Node at which to begin search. Can be None, in which case the entire tree is searched.
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return self.search(k, self.root) | [
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tensorflow/minigo | 6d89c202cdceaf449aefc3149ab2110d44f1a6a4 | ratings/cbt_ratings.py | python | current_run | (model_ids) | return 'v' + str(max_num) | Return the largest number run.
If you want something different, feel free to extend with fsdb or something. | Return the largest number run. | [
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] | def current_run(model_ids):
"""Return the largest number run.
If you want something different, feel free to extend with fsdb or something.
"""
all_runs = set(run for run, name in model_ids)
# TODO fix before submit
return max(all_runs)
# Assumption of the run format.
assert all(re.match("v[0-9]+", run) for run in all_runs), all_runs
# "Current" is defined as largest numbered run.
max_num = max(int(r[1:] for r in all_runs))
return 'v' + str(max_num) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/sets.py | python | BaseSet.symmetric_difference | (self, other) | return result | Return the symmetric difference of two sets as a new set.
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albertz/openlierox | d316c14a8eb57848ef56e9bfa7b23a56f694a51b | tools/DedicatedServerVideo/gdata/tlslite/VerifierDB.py | python | VerifierDB.makeVerifier | (username, password, bits) | return mathtls.makeVerifier(username, password, bits) | Create a verifier entry which can be stored in a VerifierDB.
@type username: str
@param username: The username for this verifier. Must be less
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@type password: str
@param password: The password for this verifier.
@type bits: int
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@rtype: tuple
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@param username: The username for this verifier. Must be less
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@type password: str
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/AWSPythonSDK/1.5.8/botocore/vendored/requests/packages/urllib3/packages/six.py | python | iterkeys | (d) | return iter(getattr(d, _iterkeys)()) | Return an iterator over the keys of a dictionary. | Return an iterator over the keys of a dictionary. | [
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CRYTEK/CRYENGINE | 232227c59a220cbbd311576f0fbeba7bb53b2a8c | Tools/CryVersionSelector/cryselect.py | python | error_engine_path_not_found | (args, engine_id) | Error to specify that the engine path could not be found,
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martinrotter/textosaurus | 4e2ad75abaf5b7e6a823766a2aa8a30f0c965cb8 | src/libtextosaurus/3rd-party/scintilla-lt/scripts/Dependencies.py | python | InsertSynonym | (dependencies, current, additional) | return result | Insert a copy of one object file with dependencies under a different name.
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jsupancic/deep_hand_pose | 22cbeae1a8410ff5d37c060c7315719d0a5d608f | scripts/cpp_lint.py | python | RemoveMultiLineCommentsFromRange | (lines, begin, end) | Clears a range of lines for multi-line comments. | Clears a range of lines for multi-line comments. | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/keras/_impl/keras/backend.py | python | moving_average_update | (x, value, momentum) | return moving_averages.assign_moving_average(
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momentum: The moving average momentum.
Returns:
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py3/scipy/sparse/compressed.py | python | _cs_matrix.sort_indices | (self) | Sort the indices of this matrix *in place* | Sort the indices of this matrix *in place* | [
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livecode/livecode | 4606a10ea10b16d5071d0f9f263ccdd7ede8b31d | gyp/pylib/gyp/__init__.py | python | RegeneratableOptionParser.add_option | (self, *args, **kw) | Add an option to the parser.
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regenerate: can be set to False to prevent this option from being included
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env_name: name of environment variable that additional values for this
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env_name = kw.pop('env_name', None)
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_gdi.py | python | Font.Smaller | (*args, **kwargs) | return _gdi_.Font_Smaller(*args, **kwargs) | Smaller(self) -> Font | Smaller(self) -> Font | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/pyfakefs/pyfakefs/fake_filesystem.py | python | FakeOsModule.close | (self, file_des) | Closes a file descriptor.
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s9xie/hed | 94fb22f10cbfec8d84fbc0642b224022014b6bd6 | scripts/cpp_lint.py | python | FindNextMatchingAngleBracket | (clean_lines, linenum, init_suffix) | return True | Find the corresponding > to close a template.
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linenum: Current line number.
init_suffix: Remainder of the current line after the initial <.
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] | https://github.com/s9xie/hed/blob/94fb22f10cbfec8d84fbc0642b224022014b6bd6/scripts/cpp_lint.py#L2517-L2583 | |
hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/telemetry/third_party/web-page-replay/platformsettings.py | python | _LinuxPlatformSettings.setup_temporary_loopback_config | (self) | Setup Linux to temporarily use reasonably sized frames.
Linux uses jumbo frames by default (16KB), using the combination
of MTU probing and a base MSS makes it use normal sized packets.
The reason this works is because tcp_base_mss is only used when MTU
probing is enabled. And since we're using the max value, it will
always use the reasonable size. This is relevant for server-side realism.
The client-side will vary depending on the client TCP stack config. | Setup Linux to temporarily use reasonably sized frames. | [
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] | def setup_temporary_loopback_config(self):
"""Setup Linux to temporarily use reasonably sized frames.
Linux uses jumbo frames by default (16KB), using the combination
of MTU probing and a base MSS makes it use normal sized packets.
The reason this works is because tcp_base_mss is only used when MTU
probing is enabled. And since we're using the max value, it will
always use the reasonable size. This is relevant for server-side realism.
The client-side will vary depending on the client TCP stack config.
"""
ENABLE_MTU_PROBING = 2
original_probing = self.get_sysctl(self.TCP_MTU_PROBING)
self.set_sysctl(self.TCP_MTU_PROBING, ENABLE_MTU_PROBING)
atexit.register(self.set_sysctl, self.TCP_MTU_PROBING, original_probing)
TCP_FULL_MSS = 1460
original_mss = self.get_sysctl(self.TCP_BASE_MSS)
self.set_sysctl(self.TCP_BASE_MSS, TCP_FULL_MSS)
atexit.register(self.set_sysctl, self.TCP_BASE_MSS, original_mss) | [
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FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Draft/draftguitools/gui_clone.py | python | Clone.GetResources | (self) | return {'Pixmap': 'Draft_Clone',
'Accel': "C,L",
'MenuText': QT_TRANSLATE_NOOP("Draft_Clone", "Clone"),
'ToolTip': QT_TRANSLATE_NOOP("Draft_Clone", "Creates a clone of the selected objects.\nThe resulting clone can be scaled in each of its three directions.")} | Set icon, menu and tooltip. | Set icon, menu and tooltip. | [
"Set",
"icon",
"menu",
"and",
"tooltip",
"."
] | def GetResources(self):
"""Set icon, menu and tooltip."""
return {'Pixmap': 'Draft_Clone',
'Accel': "C,L",
'MenuText': QT_TRANSLATE_NOOP("Draft_Clone", "Clone"),
'ToolTip': QT_TRANSLATE_NOOP("Draft_Clone", "Creates a clone of the selected objects.\nThe resulting clone can be scaled in each of its three directions.")} | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/html2.py | python | WebViewFactory.Create | (*args) | return _html2.WebViewFactory_Create(*args) | Create(self) -> WebView
Create(self, Window parent, int id, String url=wxWebViewDefaultURLStr,
Point pos=DefaultPosition, Size size=DefaultSize,
long style=0, String name=wxWebViewNameStr) -> WebView | Create(self) -> WebView
Create(self, Window parent, int id, String url=wxWebViewDefaultURLStr,
Point pos=DefaultPosition, Size size=DefaultSize,
long style=0, String name=wxWebViewNameStr) -> WebView | [
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"""
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Point pos=DefaultPosition, Size size=DefaultSize,
long style=0, String name=wxWebViewNameStr) -> WebView
"""
return _html2.WebViewFactory_Create(*args) | [
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rodeofx/OpenWalter | 6116fbe3f04f1146c854afbfbdbe944feaee647e | walter/common/walterWidgets/utils.py | python | toPyObject | (obj) | return obj | Call QVariant.toPyObject on the case we are in PyQt. | Call QVariant.toPyObject on the case we are in PyQt. | [
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# QAbstractItemModel.data() returns QVariant in PyQt and a python type in
# PySide. The result of this function should be the same for all the Qts.
if obj and qtBinding == "PyQt4":
return obj.toPyObject()
return obj | [
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wujian16/Cornell-MOE | df299d1be882d2af9796d7a68b3f9505cac7a53e | moe/optimal_learning/python/cpp_wrappers/domain.py | python | TensorProductDomain.get_bounding_box | (self) | return copy.copy(self._domain_bounds) | Return a list of ClosedIntervals representing a bounding box for this domain. | Return a list of ClosedIntervals representing a bounding box for this domain. | [
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] | def get_bounding_box(self):
"""Return a list of ClosedIntervals representing a bounding box for this domain."""
return copy.copy(self._domain_bounds) | [
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metashell/metashell | f4177e4854ea00c8dbc722cadab26ef413d798ea | 3rd/templight/compiler-rt/lib/sanitizer_common/scripts/cpplint.py | python | _CppLintState.AddFilters | (self, filters) | Adds more filters to the existing list of error-message filters. | Adds more filters to the existing list of error-message filters. | [
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""" Adds more filters to the existing list of error-message filters. """
for filt in filters.split(','):
clean_filt = filt.strip()
if clean_filt:
self.filters.append(clean_filt)
for filt in self.filters:
if not (filt.startswith('+') or filt.startswith('-')):
raise ValueError('Every filter in --filters must start with + or -'
' (%s does not)' % filt) | [
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