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pyne/pyne | 0c2714d7c0d1b5e20be6ae6527da2c660dd6b1b3 | pyne/cccc.py | python | Isotxs._read_nuclide_scatter | (self, nuc, block, subBlock) | Read nuclide scattering matrix.
In some versions of the specification, the written description of the
scattering matrix is wrong! The person who was typing that version had
shifted their right hand one key to the right on the keyboard resulting
in gibberish. The CCCC-IV pdf has the correct specification. | Read nuclide scattering matrix. | [
"Read",
"nuclide",
"scattering",
"matrix",
"."
] | def _read_nuclide_scatter(self, nuc, block, subBlock):
"""Read nuclide scattering matrix.
In some versions of the specification, the written description of the
scattering matrix is wrong! The person who was typing that version had
shifted their right hand one key to the right on the keyboard resulting
in gibberish. The CCCC-IV pdf has the correct specification.
"""
# Get record
r = self.get_fortran_record()
# Copy values for number of groups and number of subblocks
ng = self.fc['ngroup']
nsblok = self.fc['nsblok']
# Make sure blocks and subblocks are indexed starting from 1
m = subBlock + 1
n = block + 1
# Determine number of scattering orders in this block
lordn = nuc.libParams['ords'][block]
# This is basically how many scattering cross sections there are for
# this scatter type for this nuclide
jl = (m - 1)*((ng - 1)//nsblok + 1) + 1
jup = m*((ng - 1)//nsblok + 1)
ju = min(ng, jup)
# Figure out kmax for this sub-block.
kmax = 0
for j in range(jl, ju+1):
g = j - 1 # convert to groups starting at 0
kmax += nuc.libParams['jband'][g, block]
# scattering from group j
for order in range(lordn):
# for k in range(kmax):
for j in range(jl, ju+1):
# There are JBAND values for scattering into group j listed in
# order of the "from" group as from j+jup to j, from j+jup-1 to
# j, ...,from j to j, from j-1 to j, j-2 to j, ... , j-down to j
# anything listed to the left of j represents
# upscatter. anything to the right is downscatter. n,2n on
# MC**2-2 ISOTXS scatter matrix are reaction based and need to
# be multiplied by 2 to get the correct neutron balance.
g = j-1
assert g >= 0, "loading negative group in ISOTXS."
jup = nuc.libParams['jj'][g, block] - 1
jdown = nuc.libParams['jband'][g, block] - \
nuc.libParams['jj'][g, block]
fromgroups = list(range(j-jdown, j+jup+1))
fromgroups.reverse()
for k in fromgroups:
fromg = k-1
nuc.micros['scat', block, g, fromg, order] = r.get_float()[
0] | [
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pmq20/node-packer | 12c46c6e44fbc14d9ee645ebd17d5296b324f7e0 | lts/tools/gyp/pylib/gyp/generator/cmake.py | python | SetTargetProperty | (output, target_name, property_name, values, sep='') | Given a target, sets the given property. | Given a target, sets the given property. | [
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output.write('set_target_properties(')
output.write(target_name)
output.write(' PROPERTIES ')
output.write(property_name)
output.write(' "')
for value in values:
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intel/caffe | 3f494b442ee3f9d17a07b09ecbd5fa2bbda00836 | examples/rfcn/lib/rpn/proposal_target_layer.py | python | _compute_targets | (ex_rois, gt_rois, labels) | return np.hstack(
(labels[:, np.newaxis], targets)).astype(np.float32, copy=False) | Compute bounding-box regression targets for an image. | Compute bounding-box regression targets for an image. | [
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] | def _compute_targets(ex_rois, gt_rois, labels):
"""Compute bounding-box regression targets for an image."""
assert ex_rois.shape[0] == gt_rois.shape[0]
assert ex_rois.shape[1] == 4
assert gt_rois.shape[1] == 4
targets = bbox_transform(ex_rois, gt_rois)
if cfg.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED:
# Optionally normalize targets by a precomputed mean and stdev
targets = ((targets - np.array(cfg.TRAIN.BBOX_NORMALIZE_MEANS))
/ np.array(cfg.TRAIN.BBOX_NORMALIZE_STDS))
return np.hstack(
(labels[:, np.newaxis], targets)).astype(np.float32, copy=False) | [
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apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | deps/src/libxml2-2.9.1/python/libxml2.py | python | outputBuffer.nodeDumpOutput | (self, doc, cur, level, format, encoding) | Dump an XML node, recursive behaviour, children are printed
too. Note that @format = 1 provide node indenting only if
xmlIndentTreeOutput = 1 or xmlKeepBlanksDefault(0) was
called | Dump an XML node, recursive behaviour, children are printed
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"""Dump an XML node, recursive behaviour, children are printed
too. Note that @format = 1 provide node indenting only if
xmlIndentTreeOutput = 1 or xmlKeepBlanksDefault(0) was
called """
if doc is None: doc__o = None
else: doc__o = doc._o
if cur is None: cur__o = None
else: cur__o = cur._o
libxml2mod.xmlNodeDumpOutput(self._o, doc__o, cur__o, level, format, encoding) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/extern/aui/framemanager.py | python | ShowDockingGuides | (guides, show) | Shows or hide the docking guide windows.
:param `guides`: a list of :class:`AuiDockingGuide` classes;
:param bool `show`: whether to show or hide the docking guide windows. | Shows or hide the docking guide windows. | [
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] | def ShowDockingGuides(guides, show):
"""
Shows or hide the docking guide windows.
:param `guides`: a list of :class:`AuiDockingGuide` classes;
:param bool `show`: whether to show or hide the docking guide windows.
"""
for target in guides:
if show and not target.host.IsShown():
target.host.Show()
target.host.Update()
elif not show and target.host.IsShown():
target.host.Hide() | [
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ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/third_party/lib_x86_64/python2.7/dist-packages/numpy/lib/index_tricks.py | python | fill_diagonal | (a, val, wrap=False) | Fill the main diagonal of the given array of any dimensionality.
For an array `a` with ``a.ndim > 2``, the diagonal is the list of
locations with indices ``a[i, i, ..., i]`` all identical. This function
modifies the input array in-place, it does not return a value.
Parameters
----------
a : array, at least 2-D.
Array whose diagonal is to be filled, it gets modified in-place.
val : scalar
Value to be written on the diagonal, its type must be compatible with
that of the array a.
wrap : bool
For tall matrices in NumPy version up to 1.6.2, the
diagonal "wrapped" after N columns. You can have this behavior
with this option. This affect only tall matrices.
See also
--------
diag_indices, diag_indices_from
Notes
-----
.. versionadded:: 1.4.0
This functionality can be obtained via `diag_indices`, but internally
this version uses a much faster implementation that never constructs the
indices and uses simple slicing.
Examples
--------
>>> a = np.zeros((3, 3), int)
>>> np.fill_diagonal(a, 5)
>>> a
array([[5, 0, 0],
[0, 5, 0],
[0, 0, 5]])
The same function can operate on a 4-D array:
>>> a = np.zeros((3, 3, 3, 3), int)
>>> np.fill_diagonal(a, 4)
We only show a few blocks for clarity:
>>> a[0, 0]
array([[4, 0, 0],
[0, 0, 0],
[0, 0, 0]])
>>> a[1, 1]
array([[0, 0, 0],
[0, 4, 0],
[0, 0, 0]])
>>> a[2, 2]
array([[0, 0, 0],
[0, 0, 0],
[0, 0, 4]])
# tall matrices no wrap
>>> a = np.zeros((5, 3),int)
>>> fill_diagonal(a, 4)
array([[4, 0, 0],
[0, 4, 0],
[0, 0, 4],
[0, 0, 0],
[0, 0, 0]])
# tall matrices wrap
>>> a = np.zeros((5, 3),int)
>>> fill_diagonal(a, 4)
array([[4, 0, 0],
[0, 4, 0],
[0, 0, 4],
[0, 0, 0],
[4, 0, 0]])
# wide matrices
>>> a = np.zeros((3, 5),int)
>>> fill_diagonal(a, 4)
array([[4, 0, 0, 0, 0],
[0, 4, 0, 0, 0],
[0, 0, 4, 0, 0]]) | Fill the main diagonal of the given array of any dimensionality. | [
"Fill",
"the",
"main",
"diagonal",
"of",
"the",
"given",
"array",
"of",
"any",
"dimensionality",
"."
] | def fill_diagonal(a, val, wrap=False):
"""Fill the main diagonal of the given array of any dimensionality.
For an array `a` with ``a.ndim > 2``, the diagonal is the list of
locations with indices ``a[i, i, ..., i]`` all identical. This function
modifies the input array in-place, it does not return a value.
Parameters
----------
a : array, at least 2-D.
Array whose diagonal is to be filled, it gets modified in-place.
val : scalar
Value to be written on the diagonal, its type must be compatible with
that of the array a.
wrap : bool
For tall matrices in NumPy version up to 1.6.2, the
diagonal "wrapped" after N columns. You can have this behavior
with this option. This affect only tall matrices.
See also
--------
diag_indices, diag_indices_from
Notes
-----
.. versionadded:: 1.4.0
This functionality can be obtained via `diag_indices`, but internally
this version uses a much faster implementation that never constructs the
indices and uses simple slicing.
Examples
--------
>>> a = np.zeros((3, 3), int)
>>> np.fill_diagonal(a, 5)
>>> a
array([[5, 0, 0],
[0, 5, 0],
[0, 0, 5]])
The same function can operate on a 4-D array:
>>> a = np.zeros((3, 3, 3, 3), int)
>>> np.fill_diagonal(a, 4)
We only show a few blocks for clarity:
>>> a[0, 0]
array([[4, 0, 0],
[0, 0, 0],
[0, 0, 0]])
>>> a[1, 1]
array([[0, 0, 0],
[0, 4, 0],
[0, 0, 0]])
>>> a[2, 2]
array([[0, 0, 0],
[0, 0, 0],
[0, 0, 4]])
# tall matrices no wrap
>>> a = np.zeros((5, 3),int)
>>> fill_diagonal(a, 4)
array([[4, 0, 0],
[0, 4, 0],
[0, 0, 4],
[0, 0, 0],
[0, 0, 0]])
# tall matrices wrap
>>> a = np.zeros((5, 3),int)
>>> fill_diagonal(a, 4)
array([[4, 0, 0],
[0, 4, 0],
[0, 0, 4],
[0, 0, 0],
[4, 0, 0]])
# wide matrices
>>> a = np.zeros((3, 5),int)
>>> fill_diagonal(a, 4)
array([[4, 0, 0, 0, 0],
[0, 4, 0, 0, 0],
[0, 0, 4, 0, 0]])
"""
if a.ndim < 2:
raise ValueError("array must be at least 2-d")
end = None
if a.ndim == 2:
# Explicit, fast formula for the common case. For 2-d arrays, we
# accept rectangular ones.
step = a.shape[1] + 1
#This is needed to don't have tall matrix have the diagonal wrap.
if not wrap:
end = a.shape[1] * a.shape[1]
else:
# For more than d=2, the strided formula is only valid for arrays with
# all dimensions equal, so we check first.
if not alltrue(diff(a.shape)==0):
raise ValueError("All dimensions of input must be of equal length")
step = 1 + (cumprod(a.shape[:-1])).sum()
# Write the value out into the diagonal.
a.flat[:end:step] = val | [
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Polidea/SiriusObfuscator | b0e590d8130e97856afe578869b83a209e2b19be | SymbolExtractorAndRenamer/lldb/scripts/Python/static-binding/lldb.py | python | SBWatchpoint_GetWatchpointFromEvent | (*args) | return _lldb.SBWatchpoint_GetWatchpointFromEvent(*args) | SBWatchpoint_GetWatchpointFromEvent(SBEvent event) -> SBWatchpoint | SBWatchpoint_GetWatchpointFromEvent(SBEvent event) -> SBWatchpoint | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/devil/devil/android/device_utils.py | python | DeviceUtils.FileExists | (self, device_path, timeout=None, retries=None) | return self.PathExists(device_path, timeout=timeout, retries=retries) | Checks whether the given file exists on the device.
Arguments are the same as PathExists. | Checks whether the given file exists on the device. | [
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"""Checks whether the given file exists on the device.
Arguments are the same as PathExists.
"""
return self.PathExists(device_path, timeout=timeout, retries=retries) | [
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freesurfer/freesurfer | 6dbe527d43ffa611acb2cd112e9469f9bfec8e36 | python/freesurfer/ndarray.py | python | Volume.resample_like | (self, target, interp_method='linear', fill=0) | return resampled | Returns a resampled image in the target space. | Returns a resampled image in the target space. | [
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'''
Returns a resampled image in the target space.
'''
if target.affine is None or self.affine is None:
raise ValueError("Can't resample volume without geometry information.")
vox2vox = LinearTransform.matmul(self.ras2vox(), target.vox2ras())
resampled_data = resample(self.data, target.shape, vox2vox, interp_method=interp_method, fill=fill)
resampled = Volume(resampled_data)
resampled.copy_geometry(target)
resampled.copy_metadata(self)
return resampled | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/llvmlite/ir/builder.py | python | IRBuilder.cmpxchg | (self, ptr, cmp, val, ordering, failordering=None, name='') | return inst | Atomic compared-and-set:
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old = *ptr
success = (old == cmp)
if (success)
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name = { old, success }
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"""
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atomic {
old = *ptr
success = (old == cmp)
if (success)
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name = { old, success }
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"""
failordering = ordering if failordering is None else failordering
inst = instructions.CmpXchg(self.block, ptr, cmp, val, ordering,
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self._insert(inst)
return inst | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py2/scipy/stats/_multivariate.py | python | multivariate_normal_gen.rvs | (self, mean=None, cov=1, size=1, random_state=None) | return _squeeze_output(out) | Draw random samples from a multivariate normal distribution.
Parameters
----------
%(_mvn_doc_default_callparams)s
size : integer, optional
Number of samples to draw (default 1).
%(_doc_random_state)s
Returns
-------
rvs : ndarray or scalar
Random variates of size (`size`, `N`), where `N` is the
dimension of the random variable.
Notes
-----
%(_mvn_doc_callparams_note)s | Draw random samples from a multivariate normal distribution. | [
"Draw",
"random",
"samples",
"from",
"a",
"multivariate",
"normal",
"distribution",
"."
] | def rvs(self, mean=None, cov=1, size=1, random_state=None):
"""
Draw random samples from a multivariate normal distribution.
Parameters
----------
%(_mvn_doc_default_callparams)s
size : integer, optional
Number of samples to draw (default 1).
%(_doc_random_state)s
Returns
-------
rvs : ndarray or scalar
Random variates of size (`size`, `N`), where `N` is the
dimension of the random variable.
Notes
-----
%(_mvn_doc_callparams_note)s
"""
dim, mean, cov = self._process_parameters(None, mean, cov)
random_state = self._get_random_state(random_state)
out = random_state.multivariate_normal(mean, cov, size)
return _squeeze_output(out) | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/site-packages/pip/__init__.py | python | autocomplete | () | Command and option completion for the main option parser (and options)
and its subcommands (and options).
Enable by sourcing one of the completion shell scripts (bash or zsh). | Command and option completion for the main option parser (and options)
and its subcommands (and options). | [
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] | def autocomplete():
"""Command and option completion for the main option parser (and options)
and its subcommands (and options).
Enable by sourcing one of the completion shell scripts (bash or zsh).
"""
# Don't complete if user hasn't sourced bash_completion file.
if 'PIP_AUTO_COMPLETE' not in os.environ:
return
cwords = os.environ['COMP_WORDS'].split()[1:]
cword = int(os.environ['COMP_CWORD'])
try:
current = cwords[cword - 1]
except IndexError:
current = ''
subcommands = [cmd for cmd, summary in get_summaries()]
options = []
# subcommand
try:
subcommand_name = [w for w in cwords if w in subcommands][0]
except IndexError:
subcommand_name = None
parser = create_main_parser()
# subcommand options
if subcommand_name:
# special case: 'help' subcommand has no options
if subcommand_name == 'help':
sys.exit(1)
# special case: list locally installed dists for uninstall command
if subcommand_name == 'uninstall' and not current.startswith('-'):
installed = []
lc = current.lower()
for dist in get_installed_distributions(local_only=True):
if dist.key.startswith(lc) and dist.key not in cwords[1:]:
installed.append(dist.key)
# if there are no dists installed, fall back to option completion
if installed:
for dist in installed:
print(dist)
sys.exit(1)
subcommand = commands[subcommand_name](parser)
options += [(opt.get_opt_string(), opt.nargs)
for opt in subcommand.parser.option_list_all
if opt.help != optparse.SUPPRESS_HELP]
# filter out previously specified options from available options
prev_opts = [x.split('=')[0] for x in cwords[1:cword - 1]]
options = [(x, v) for (x, v) in options if x not in prev_opts]
# filter options by current input
options = [(k, v) for k, v in options if k.startswith(current)]
for option in options:
opt_label = option[0]
# append '=' to options which require args
if option[1]:
opt_label += '='
print(opt_label)
else:
# show main parser options only when necessary
if current.startswith('-') or current.startswith('--'):
opts = [i.option_list for i in parser.option_groups]
opts.append(parser.option_list)
opts = (o for it in opts for o in it)
subcommands += [i.get_opt_string() for i in opts
if i.help != optparse.SUPPRESS_HELP]
print(' '.join([x for x in subcommands if x.startswith(current)]))
sys.exit(1) | [
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baidu/AnyQ | d94d450d2aaa5f7ed73424b10aa4539835b97527 | tools/simnet/train/tf/layers/tf_layers.py | python | GRULayer.ops | (self, input_emb) | return rnn_outputs | operation | operation | [
"operation"
] | def ops(self, input_emb):
"""
operation
"""
rnn_outputs, _ = rnn(GRUCell(self.hidden_size), inputs=input_emb,
dtype=tf.float32)
return rnn_outputs | [
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forkineye/ESPixelStick | 22926f1c0d1131f1369fc7cad405689a095ae3cb | dist/bin/pyserial/serial/serialjava.py | python | Serial.close | (self) | Close port | Close port | [
"Close",
"port"
] | def close(self):
"""Close port"""
if self.is_open:
if self.sPort:
self._instream.close()
self._outstream.close()
self.sPort.close()
self.sPort = None
self.is_open = False | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/gslib/command_argument.py | python | CommandArgument.MakeZeroOrMoreCloudBucketURLsArgument | () | return CommandArgument(
'file', nargs='*', completer=CompleterType.CLOUD_BUCKET) | Constructs an argument that takes 0+ Cloud bucket URLs as parameters. | Constructs an argument that takes 0+ Cloud bucket URLs as parameters. | [
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] | def MakeZeroOrMoreCloudBucketURLsArgument():
"""Constructs an argument that takes 0+ Cloud bucket URLs as parameters."""
return CommandArgument(
'file', nargs='*', completer=CompleterType.CLOUD_BUCKET) | [
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eventql/eventql | 7ca0dbb2e683b525620ea30dc40540a22d5eb227 | deps/3rdparty/spidermonkey/mozjs/python/mozboot/mozboot/android.py | python | ensure_android_sdk_and_ndk | (path, sdk_path, sdk_url, ndk_path, ndk_url) | Ensure the Android SDK and NDK are found at the given paths. If not, fetch
and unpack the SDK and/or NDK from the given URLs into |path|. | Ensure the Android SDK and NDK are found at the given paths. If not, fetch
and unpack the SDK and/or NDK from the given URLs into |path|. | [
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] | def ensure_android_sdk_and_ndk(path, sdk_path, sdk_url, ndk_path, ndk_url):
'''
Ensure the Android SDK and NDK are found at the given paths. If not, fetch
and unpack the SDK and/or NDK from the given URLs into |path|.
'''
# It's not particularyl bad to overwrite the NDK toolchain, but it does take
# a while to unpack, so let's avoid the disk activity if possible. The SDK
# may prompt about licensing, so we do this first.
if os.path.isdir(ndk_path):
print(ANDROID_NDK_EXISTS % ndk_path)
else:
install_mobile_android_sdk_or_ndk(ndk_url, path)
# We don't want to blindly overwrite, since we use the |android| tool to
# install additional parts of the Android toolchain. If we overwrite,
# we lose whatever Android packages the user may have already installed.
if os.path.isdir(sdk_path):
print(ANDROID_SDK_EXISTS % sdk_path)
else:
install_mobile_android_sdk_or_ndk(sdk_url, path) | [
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turi-code/SFrame | 796b9bdfb2fa1b881d82080754643c7e68629cd2 | oss_src/unity/python/sframe/data_structures/sketch.py | python | Sketch.sketch_ready | (self) | Returns True if the sketch has been executed on all the data.
If the sketch is created with background == False (default), this will
always return True. Otherwise, this will return False until the sketch
is ready. | Returns True if the sketch has been executed on all the data.
If the sketch is created with background == False (default), this will
always return True. Otherwise, this will return False until the sketch
is ready. | [
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"""
Returns True if the sketch has been executed on all the data.
If the sketch is created with background == False (default), this will
always return True. Otherwise, this will return False until the sketch
is ready.
"""
with cython_context():
return self.__proxy__.sketch_ready() | [
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apache/incubator-mxnet | f03fb23f1d103fec9541b5ae59ee06b1734a51d9 | python/mxnet/image/image.py | python | SequentialAug.dumps | (self) | return [self.__class__.__name__.lower(), [x.dumps() for x in self.ts]] | Override the default to avoid duplicate dump. | Override the default to avoid duplicate dump. | [
"Override",
"the",
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"."
] | def dumps(self):
"""Override the default to avoid duplicate dump."""
return [self.__class__.__name__.lower(), [x.dumps() for x in self.ts]] | [
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google-coral/edgetpu | 5020de9386ff370dcc1f63291a2d0f98eeb98adb | benchmarks/classification_benchmarks.py | python | run_benchmark | (model, image) | return result | Returns average inference time in ms on specified model and image. | Returns average inference time in ms on specified model and image. | [
"Returns",
"average",
"inference",
"time",
"in",
"ms",
"on",
"specified",
"model",
"and",
"image",
"."
] | def run_benchmark(model, image):
"""Returns average inference time in ms on specified model and image."""
print('Benchmark for [%s] on %s' % (model, image))
engine = ClassificationEngine(test_utils.test_data_path(model))
iterations = 200 if 'edgetpu' in model else 10
with test_utils.test_image(image) as img:
result = 1000 * timeit.timeit(
lambda: engine.classify_with_image(img, threshold=0.4, top_k=10),
number=iterations) / iterations
print('%.2f ms (iterations = %d)' % (result, iterations))
return result | [
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opengauss-mirror/openGauss-server | e383f1b77720a00ddbe4c0655bc85914d9b02a2b | src/gausskernel/dbmind/tools/xtuner/tuner/knob.py | python | RecommendedKnobs.__init__ | (self) | Package tuned knobs.
Record recommendation results and final tuning results and outputs formatted results text. | Package tuned knobs.
Record recommendation results and final tuning results and outputs formatted results text. | [
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"results",
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"."
] | def __init__(self):
"""
Package tuned knobs.
Record recommendation results and final tuning results and outputs formatted results text.
"""
self._need_tune_knobs = list()
self._only_report_knobs = list()
self._tbl = dict()
self.report = 'There is no report because the current mode specifies the list of tuning knobs ' \
'through the configuration file.' | [
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panda3d/panda3d | 833ad89ebad58395d0af0b7ec08538e5e4308265 | direct/src/showutil/TexMemWatcher.py | python | TexPlacement.clearBitmasks | (self, bitmasks) | Clears all of the appropriate bits to indicate this region
is available. | Clears all of the appropriate bits to indicate this region
is available. | [
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] | def clearBitmasks(self, bitmasks):
""" Clears all of the appropriate bits to indicate this region
is available. """
l, r, b, t = self.p
mask = ~BitArray.range(l, r - l)
for yi in range(b, t):
assert (bitmasks[yi] | mask).isAllOn()
bitmasks[yi] &= mask | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_windows.py | python | FontData.GetChosenFont | (*args, **kwargs) | return _windows_.FontData_GetChosenFont(*args, **kwargs) | GetChosenFont(self) -> Font
Gets the font chosen by the user. | GetChosenFont(self) -> Font | [
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"(",
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")",
"-",
">",
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] | def GetChosenFont(*args, **kwargs):
"""
GetChosenFont(self) -> Font
Gets the font chosen by the user.
"""
return _windows_.FontData_GetChosenFont(*args, **kwargs) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/propgrid.py | python | SystemColourProperty.GetCustomColourIndex | (*args, **kwargs) | return _propgrid.SystemColourProperty_GetCustomColourIndex(*args, **kwargs) | GetCustomColourIndex(self) -> int | GetCustomColourIndex(self) -> int | [
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] | def GetCustomColourIndex(*args, **kwargs):
"""GetCustomColourIndex(self) -> int"""
return _propgrid.SystemColourProperty_GetCustomColourIndex(*args, **kwargs) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/idlelib/autocomplete_w.py | python | AutoCompleteWindow.show_window | (self, comp_lists, index, complete, mode, userWantsWin) | return None | Show the autocomplete list, bind events.
If complete is True, complete the text, and if there is exactly
one matching completion, don't open a list. | Show the autocomplete list, bind events. | [
"Show",
"the",
"autocomplete",
"list",
"bind",
"events",
"."
] | def show_window(self, comp_lists, index, complete, mode, userWantsWin):
"""Show the autocomplete list, bind events.
If complete is True, complete the text, and if there is exactly
one matching completion, don't open a list.
"""
# Handle the start we already have
self.completions, self.morecompletions = comp_lists
self.mode = mode
self.startindex = self.widget.index(index)
self.start = self.widget.get(self.startindex, "insert")
if complete:
completed = self._complete_string(self.start)
start = self.start
self._change_start(completed)
i = self._binary_search(completed)
if self.completions[i] == completed and \
(i == len(self.completions)-1 or
self.completions[i+1][:len(completed)] != completed):
# There is exactly one matching completion
return completed == start
self.userwantswindow = userWantsWin
self.lasttypedstart = self.start
# Put widgets in place
self.autocompletewindow = acw = Toplevel(self.widget)
# Put it in a position so that it is not seen.
acw.wm_geometry("+10000+10000")
# Make it float
acw.wm_overrideredirect(1)
try:
# This command is only needed and available on Tk >= 8.4.0 for OSX
# Without it, call tips intrude on the typing process by grabbing
# the focus.
acw.tk.call("::tk::unsupported::MacWindowStyle", "style", acw._w,
"help", "noActivates")
except TclError:
pass
self.scrollbar = scrollbar = Scrollbar(acw, orient=VERTICAL)
self.listbox = listbox = Listbox(acw, yscrollcommand=scrollbar.set,
exportselection=False)
for item in self.completions:
listbox.insert(END, item)
self.origselforeground = listbox.cget("selectforeground")
self.origselbackground = listbox.cget("selectbackground")
scrollbar.config(command=listbox.yview)
scrollbar.pack(side=RIGHT, fill=Y)
listbox.pack(side=LEFT, fill=BOTH, expand=True)
acw.lift() # work around bug in Tk 8.5.18+ (issue #24570)
# Initialize the listbox selection
self.listbox.select_set(self._binary_search(self.start))
self._selection_changed()
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self.hideaid = acw.bind(HIDE_VIRTUAL_EVENT_NAME, self.hide_event)
self.hidewid = self.widget.bind(HIDE_VIRTUAL_EVENT_NAME, self.hide_event)
acw.event_add(HIDE_VIRTUAL_EVENT_NAME, HIDE_FOCUS_OUT_SEQUENCE)
for seq in HIDE_SEQUENCES:
self.widget.event_add(HIDE_VIRTUAL_EVENT_NAME, seq)
self.keypressid = self.widget.bind(KEYPRESS_VIRTUAL_EVENT_NAME,
self.keypress_event)
for seq in KEYPRESS_SEQUENCES:
self.widget.event_add(KEYPRESS_VIRTUAL_EVENT_NAME, seq)
self.keyreleaseid = self.widget.bind(KEYRELEASE_VIRTUAL_EVENT_NAME,
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self.widget.event_add(KEYRELEASE_VIRTUAL_EVENT_NAME,KEYRELEASE_SEQUENCE)
self.listupdateid = listbox.bind(LISTUPDATE_SEQUENCE,
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self.is_configuring = False
self.winconfigid = acw.bind(WINCONFIG_SEQUENCE, self.winconfig_event)
self.doubleclickid = listbox.bind(DOUBLECLICK_SEQUENCE,
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/contrib/graph_editor/util.py | python | ControlOutputs.update | (self) | return self | Update the control outputs if the graph has changed. | Update the control outputs if the graph has changed. | [
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root-project/root | fcd3583bb14852bf2e8cd2415717cbaac0e75896 | bindings/experimental/distrdf/python/DistRDF/HeadNode.py | python | TreeHeadNode.__init__ | (self, npartitions, *args) | Creates a new RDataFrame instance for the given arguments.
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super(TreeHeadNode, self).__init__(None, None)
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self.tree = args[1].Get(args[0])
elif isinstance(args[1], (str, ROOT.std.string_view)):
# RDataFrame(treeName, filenameglob, defaultBranches = {})
self.tree = ROOT.TChain(args[0])
self.tree.Add(str(args[1]))
elif isinstance(args[1], (list, ROOT.std.vector[ROOT.std.string])):
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self.tree = ROOT.TChain(args[0])
for filename in args[1]:
self.tree.Add(str(filename))
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self.inputfiles = [str(filename) for filename in ROOT.Internal.TreeUtils.GetFileNamesFromTree(self.tree)] | [
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SFTtech/openage | d6a08c53c48dc1e157807471df92197f6ca9e04d | openage/convert/value_object/init/game_version.py | python | GameExpansion.__init__ | (self, name, game_id, support, game_hashes, media_paths,
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crosslife/OpenBird | 9e0198a1a2295f03fa1e8676e216e22c9c7d380b | cocos2d/tools/bindings-generator/backup/clang-llvm-3.3-pybinding/cindex.py | python | CursorKind.name | (self) | return self._name_map[self] | Get the enumeration name of this cursor kind. | Get the enumeration name of this cursor kind. | [
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CRYTEK/CRYENGINE | 232227c59a220cbbd311576f0fbeba7bb53b2a8c | Editor/Python/windows/Lib/site-packages/pip/_vendor/distlib/_backport/shutil.py | python | _make_zipfile | (base_name, base_dir, verbose=0, dry_run=0, logger=None) | return zip_filename | Create a zip file from all the files under 'base_dir'.
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"""
zip_filename = base_name + ".zip"
archive_dir = os.path.dirname(base_name)
if not os.path.exists(archive_dir):
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zip = zipfile.ZipFile(zip_filename, "w",
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for dirpath, dirnames, filenames in os.walk(base_dir):
for name in filenames:
path = os.path.normpath(os.path.join(dirpath, name))
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if logger is not None:
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adobe/chromium | cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7 | tools/python/google/process_utils.py | python | RunCommandFull | (command, verbose=True, collect_output=False,
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Prints the given command (which should be a list of one or more strings).
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print '\n' + subprocess.list2cmdline(command).replace('\\', '/') + '\n', ###
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err = subprocess.STDOUT
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out = file(os.devnull, 'w')
err = subprocess.PIPE
try:
proc = subprocess.Popen(command, stdout=out, stderr=err, bufsize=1)
except OSError, e:
if e.errno == errno.ENOENT:
raise CommandNotFound('Unable to find "%s"' % command[0])
raise
output = []
if verbose:
read_from = proc.stdout
else:
read_from = proc.stderr
line = read_from.readline()
while line:
line = line.rstrip()
if collect_output:
output.append(line)
if print_output:
# Windows Python converts \n to \r\n automatically whenever it
# encounters it written to a text file (including stdout). The only
# way around it is to write to a binary file, which isn't feasible for
# stdout. So we end up with \r\n here even though we explicitly write
# \n. (We could write \r instead, which doesn't get converted to \r\n,
# but that's probably more troublesome for people trying to read the
# files.)
print line + '\n',
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sys.stdout.flush()
line = read_from.readline()
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proc.wait()
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microsoft/onnxruntime | f92e47e95b13a240e37caf7b36577983544f98fc | onnxruntime/python/onnxruntime_inference_collection.py | python | SparseTensor.__init__ | (self, sparse_tensor) | Internal constructor | Internal constructor | [
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LisaAnne/lisa-caffe-public | 49b8643ddef23a4f6120017968de30c45e693f59 | scripts/cpp_lint.py | python | _CppLintState.SetFilters | (self, filters) | Sets the error-message filters.
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baidu/bigflow | 449245016c0df7d1252e85581e588bfc60cefad3 | bigflow_python/python/bigflow/transform_impls/processor.py | python | AbstractProcessor.begin | (self, keys, inputs, emitter) | Indicate the beginning of a group
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papyrussolution/OpenPapyrus | bbfb5ec2ea2109b8e2f125edd838e12eaf7b8b91 | Src/OSF/protobuf-3.19.1/python/google/protobuf/internal/wire_format.py | python | IsTypePackable | (field_type) | return field_type not in NON_PACKABLE_TYPES | Return true iff packable = true is valid for fields of this type.
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/lib-tk/Tkinter.py | python | Wm.wm_group | (self, pathName=None) | return self.tk.call('wm', 'group', self._w, pathName) | Set the group leader widgets for related widgets to PATHNAME. Return
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BitMEX/api-connectors | 37a3a5b806ad5d0e0fc975ab86d9ed43c3bcd812 | auto-generated/python/swagger_client/models/trade.py | python | Trade.__repr__ | (self) | return self.to_str() | For `print` and `pprint` | For `print` and `pprint` | [
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"""For `print` and `pprint`"""
return self.to_str() | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/plat-mac/lib-scriptpackages/CodeWarrior/Metrowerks_Shell_Suite.py | python | Metrowerks_Shell_Suite_Events.Get_Segments | (self, _no_object=None, _attributes={}, **_arguments) | Get Segments: Returns a description of each segment in the project.
Keyword argument _attributes: AppleEvent attribute dictionary
Returns: undocumented, typecode 'Seg ' | Get Segments: Returns a description of each segment in the project.
Keyword argument _attributes: AppleEvent attribute dictionary
Returns: undocumented, typecode 'Seg ' | [
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"""Get Segments: Returns a description of each segment in the project.
Keyword argument _attributes: AppleEvent attribute dictionary
Returns: undocumented, typecode 'Seg '
"""
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_subcode = 'GSeg'
if _arguments: raise TypeError, 'No optional args expected'
if _no_object is not None: raise TypeError, 'No direct arg expected'
_reply, _arguments, _attributes = self.send(_code, _subcode,
_arguments, _attributes)
if _arguments.get('errn', 0):
raise aetools.Error, aetools.decodeerror(_arguments)
# XXXX Optionally decode result
if _arguments.has_key('----'):
return _arguments['----'] | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/profiler/parser/integrator.py | python | BaseTimelineGenerator.get_thread_label_name | (self) | return [
{"name": "process_labels", "ph": "M", "pid": self._device_id, "args": {"labels": "AI Core Op"}},
{"name": "process_labels", "ph": "M", "pid": self._AI_CPU_PID, "args": {"labels": "AI CPU Op"}},
{"name": "process_labels", "ph": "M", "pid": self._COMMUNICATION_OP_PID,
"args": {"labels": "Communication Op"}},
{"name": "process_labels", "ph": "M", "pid": self._HOST_CPU_PID, "args": {"labels": "Host CPU Op"}},
{"name": "process_labels", "ph": "M", "pid": self._OP_OVERLAP_PID,
"args": {"labels": "Op Overlap Analyse"}},
{"name": "process_sort_index", "ph": "M", "pid": self._device_id, "args": {"sort_index": 0}},
{"name": "process_sort_index", "ph": "M", "pid": self._AI_CPU_PID, "args": {"sort_index": 10}},
{"name": "process_sort_index", "ph": "M", "pid": self._COMMUNICATION_OP_PID, "args": {"sort_index": 20}},
{"name": "process_sort_index", "ph": "M", "pid": self._HOST_CPU_PID, "args": {"sort_index": 30}},
{"name": "process_sort_index", "ph": "M", "pid": self._OP_OVERLAP_PID, "args": {"sort_index": 40}},
{"name": "thread_name", "ph": "M", "pid": self._HOST_CPU_PID, "tid": self._HOST_CPU_OP_TID,
"args": {"name": "Host CPU Op"}},
{"name": "thread_name", "ph": "M", "pid": self._OP_OVERLAP_PID, "tid": self._MERGED_COMPUTATION_TID,
"args": {"name": "Merged Computation Op"}},
{"name": "thread_name", "ph": "M", "pid": self._OP_OVERLAP_PID, "tid": self._PURE_COMMUNICATION_TID,
"args": {"name": "Pure Communication Op"}},
{"name": "thread_name", "ph": "M", "pid": self._OP_OVERLAP_PID, "tid": self._MERGED_COMMUNICATION_TID,
"args": {"name": "Merged Communication Op"}},
{"name": "thread_name", "ph": "M", "pid": self._OP_OVERLAP_PID, "tid": self._FREE_TIME_TID,
"args": {"name": "Free Time"}},
{"name": "thread_sort_index", "ph": "M", "pid": self._OP_OVERLAP_PID, "tid": self._MERGED_COMPUTATION_TID,
"args": {"sort_index": self._MERGED_COMPUTATION_TID}},
{"name": "thread_sort_index", "ph": "M", "pid": self._OP_OVERLAP_PID, "tid": self._PURE_COMMUNICATION_TID,
"args": {"sort_index": self._PURE_COMMUNICATION_TID}},
{"name": "thread_sort_index", "ph": "M", "pid": self._OP_OVERLAP_PID, "tid": self._MERGED_COMMUNICATION_TID,
"args": {"sort_index": self._MERGED_COMMUNICATION_TID}},
{"name": "thread_sort_index", "ph": "M", "pid": self._OP_OVERLAP_PID, "tid": self._FREE_TIME_TID,
"args": {"sort_index": self._FREE_TIME_TID}}
] | Get process and thread config. | Get process and thread config. | [
"Get",
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"."
] | def get_thread_label_name(self):
"""Get process and thread config."""
return [
{"name": "process_labels", "ph": "M", "pid": self._device_id, "args": {"labels": "AI Core Op"}},
{"name": "process_labels", "ph": "M", "pid": self._AI_CPU_PID, "args": {"labels": "AI CPU Op"}},
{"name": "process_labels", "ph": "M", "pid": self._COMMUNICATION_OP_PID,
"args": {"labels": "Communication Op"}},
{"name": "process_labels", "ph": "M", "pid": self._HOST_CPU_PID, "args": {"labels": "Host CPU Op"}},
{"name": "process_labels", "ph": "M", "pid": self._OP_OVERLAP_PID,
"args": {"labels": "Op Overlap Analyse"}},
{"name": "process_sort_index", "ph": "M", "pid": self._device_id, "args": {"sort_index": 0}},
{"name": "process_sort_index", "ph": "M", "pid": self._AI_CPU_PID, "args": {"sort_index": 10}},
{"name": "process_sort_index", "ph": "M", "pid": self._COMMUNICATION_OP_PID, "args": {"sort_index": 20}},
{"name": "process_sort_index", "ph": "M", "pid": self._HOST_CPU_PID, "args": {"sort_index": 30}},
{"name": "process_sort_index", "ph": "M", "pid": self._OP_OVERLAP_PID, "args": {"sort_index": 40}},
{"name": "thread_name", "ph": "M", "pid": self._HOST_CPU_PID, "tid": self._HOST_CPU_OP_TID,
"args": {"name": "Host CPU Op"}},
{"name": "thread_name", "ph": "M", "pid": self._OP_OVERLAP_PID, "tid": self._MERGED_COMPUTATION_TID,
"args": {"name": "Merged Computation Op"}},
{"name": "thread_name", "ph": "M", "pid": self._OP_OVERLAP_PID, "tid": self._PURE_COMMUNICATION_TID,
"args": {"name": "Pure Communication Op"}},
{"name": "thread_name", "ph": "M", "pid": self._OP_OVERLAP_PID, "tid": self._MERGED_COMMUNICATION_TID,
"args": {"name": "Merged Communication Op"}},
{"name": "thread_name", "ph": "M", "pid": self._OP_OVERLAP_PID, "tid": self._FREE_TIME_TID,
"args": {"name": "Free Time"}},
{"name": "thread_sort_index", "ph": "M", "pid": self._OP_OVERLAP_PID, "tid": self._MERGED_COMPUTATION_TID,
"args": {"sort_index": self._MERGED_COMPUTATION_TID}},
{"name": "thread_sort_index", "ph": "M", "pid": self._OP_OVERLAP_PID, "tid": self._PURE_COMMUNICATION_TID,
"args": {"sort_index": self._PURE_COMMUNICATION_TID}},
{"name": "thread_sort_index", "ph": "M", "pid": self._OP_OVERLAP_PID, "tid": self._MERGED_COMMUNICATION_TID,
"args": {"sort_index": self._MERGED_COMMUNICATION_TID}},
{"name": "thread_sort_index", "ph": "M", "pid": self._OP_OVERLAP_PID, "tid": self._FREE_TIME_TID,
"args": {"sort_index": self._FREE_TIME_TID}}
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/site-packages/s3transfer/download.py | python | DownloadOutputManager.get_io_write_task | (self, fileobj, data, offset) | return IOWriteTask(
self._transfer_coordinator,
main_kwargs={
'fileobj': fileobj,
'data': data,
'offset': offset,
}
) | Get an IO write task for the requested set of data
This task can be ran immediately or be submitted to the IO executor
for it to run.
:type fileobj: file-like object
:param fileobj: The file-like object to write to
:type data: bytes
:param data: The data to write out
:type offset: integer
:param offset: The offset to write the data to in the file-like object
:returns: An IO task to be used to write data to a file-like object | Get an IO write task for the requested set of data | [
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] | def get_io_write_task(self, fileobj, data, offset):
"""Get an IO write task for the requested set of data
This task can be ran immediately or be submitted to the IO executor
for it to run.
:type fileobj: file-like object
:param fileobj: The file-like object to write to
:type data: bytes
:param data: The data to write out
:type offset: integer
:param offset: The offset to write the data to in the file-like object
:returns: An IO task to be used to write data to a file-like object
"""
return IOWriteTask(
self._transfer_coordinator,
main_kwargs={
'fileobj': fileobj,
'data': data,
'offset': offset,
}
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FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Fem/ObjectsFem.py | python | makeEquationElectricforce | (
doc,
base_solver=None,
name="Electricforce"
) | return obj | makeEquationElectricforce(document, [base_solver], [name]):
creates a FEM Electricforce equation for a solver | makeEquationElectricforce(document, [base_solver], [name]):
creates a FEM Electricforce equation for a solver | [
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] | def makeEquationElectricforce(
doc,
base_solver=None,
name="Electricforce"
):
"""makeEquationElectricforce(document, [base_solver], [name]):
creates a FEM Electricforce equation for a solver"""
from femsolver.elmer.equations import electricforce
obj = electricforce.create(doc, name)
if base_solver:
base_solver.addObject(obj)
return obj | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/dataset/engine/datasets.py | python | Dataset.__init_size_getter | (self) | return getter, runtime_context, api_tree | Get pipeline information. | Get pipeline information. | [
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] | def __init_size_getter(self):
"""
Get pipeline information.
"""
ir_tree, api_tree = self.create_ir_tree()
runtime_context = cde.PythonRuntimeContext()
runtime_context.Init()
getter = cde.DatasetSizeGetters()
getter.Init(ir_tree)
runtime_context.AssignConsumer(getter)
return getter, runtime_context, api_tree | [
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apache/incubator-mxnet | f03fb23f1d103fec9541b5ae59ee06b1734a51d9 | python/mxnet/numpy/multiarray.py | python | isnan | (x, out=None, **kwargs) | return _mx_nd_np.isnan(x, out=out, **kwargs) | Test element-wise for NaN and return result as a boolean array.
Parameters
----------
x : ndarray
Input array.
out : ndarray or None, optional
A location into which the result is stored.
If provided, it must have the same shape and dtype as input ndarray.
If not provided or `None`, a freshly-allocated array is returned.
Returns
-------
y : ndarray or bool
True where x is NaN, false otherwise.
This is a scalar if x is a scalar.
Notes
-----
NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754).
.. note::
This function differs from the original `numpy.isinf
<https://docs.scipy.org/doc/numpy/reference/generated/numpy.isnan.html>`_ in
the following aspects:
* Does not support complex number for now
* Input type does not support Python native iterables(list, tuple, ...).
* ``out`` param: cannot perform auto broadcasting. ``out`` ndarray's shape must be
the same as the expected output.
* ``out`` param: cannot perform auto type cast. ``out`` ndarray's dtype must be the
same as the expected output.
* ``out`` param does not support scalar input case.
Examples
--------
>>> np.isnan(np.nan)
True
>>> np.isnan(np.inf)
False
>>> np.isnan(np.array([np.log(-1.),1.,np.log(0)]))
array([ True, False, False]) | Test element-wise for NaN and return result as a boolean array. | [
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] | def isnan(x, out=None, **kwargs):
"""
Test element-wise for NaN and return result as a boolean array.
Parameters
----------
x : ndarray
Input array.
out : ndarray or None, optional
A location into which the result is stored.
If provided, it must have the same shape and dtype as input ndarray.
If not provided or `None`, a freshly-allocated array is returned.
Returns
-------
y : ndarray or bool
True where x is NaN, false otherwise.
This is a scalar if x is a scalar.
Notes
-----
NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754).
.. note::
This function differs from the original `numpy.isinf
<https://docs.scipy.org/doc/numpy/reference/generated/numpy.isnan.html>`_ in
the following aspects:
* Does not support complex number for now
* Input type does not support Python native iterables(list, tuple, ...).
* ``out`` param: cannot perform auto broadcasting. ``out`` ndarray's shape must be
the same as the expected output.
* ``out`` param: cannot perform auto type cast. ``out`` ndarray's dtype must be the
same as the expected output.
* ``out`` param does not support scalar input case.
Examples
--------
>>> np.isnan(np.nan)
True
>>> np.isnan(np.inf)
False
>>> np.isnan(np.array([np.log(-1.),1.,np.log(0)]))
array([ True, False, False])
"""
return _mx_nd_np.isnan(x, out=out, **kwargs) | [
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Ifsttar/I-Simpa | 2283385f4cac769a92e265edabb9c79cb6c42d03 | currentRelease/SystemScript/graphy/common.py | python | Marker.__init__ | (self, shape, color, size) | Construct a Marker. See class docstring for details on args. | Construct a Marker. See class docstring for details on args. | [
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] | def __init__(self, shape, color, size):
"""Construct a Marker. See class docstring for details on args."""
# TODO: Shapes 'r' and 'b' would be much easier to use if they had a
# special-purpose API (instead of trying to fake it with markers)
self.shape = shape
self.color = color
self.size = size | [
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okex/V3-Open-API-SDK | c5abb0db7e2287718e0055e17e57672ce0ec7fd9 | okex-python-sdk-api/venv/Lib/site-packages/pip-19.0.3-py3.8.egg/pip/_vendor/appdirs.py | python | _get_win_folder_from_registry | (csidl_name) | return dir | This is a fallback technique at best. I'm not sure if using the
registry for this guarantees us the correct answer for all CSIDL_*
names. | This is a fallback technique at best. I'm not sure if using the
registry for this guarantees us the correct answer for all CSIDL_*
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"""This is a fallback technique at best. I'm not sure if using the
registry for this guarantees us the correct answer for all CSIDL_*
names.
"""
if PY3:
import winreg as _winreg
else:
import _winreg
shell_folder_name = {
"CSIDL_APPDATA": "AppData",
"CSIDL_COMMON_APPDATA": "Common AppData",
"CSIDL_LOCAL_APPDATA": "Local AppData",
}[csidl_name]
key = _winreg.OpenKey(
_winreg.HKEY_CURRENT_USER,
r"Software\Microsoft\Windows\CurrentVersion\Explorer\Shell Folders"
)
dir, type = _winreg.QueryValueEx(key, shell_folder_name)
return dir | [
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google/mysql-protobuf | 467cda676afaa49e762c5c9164a43f6ad31a1fbf | storage/ndb/mcc/util.py | python | ConvertToPython.endElement | (self, name) | Pops the element stack and inserts the popped element as a
child of the new top | Pops the element stack and inserts the popped element as a
child of the new top | [
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"""Pops the element stack and inserts the popped element as a
child of the new top"""
#print 'end(', name, ')'
if len(self._estack) > 1:
e = self._estack.pop()
self._estack[-1].append(e) | [
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carla-simulator/carla | 8854804f4d7748e14d937ec763a2912823a7e5f5 | PythonAPI/carla/agents/tools/misc.py | python | vector | (location_1, location_2) | return [x / norm, y / norm, z / norm] | Returns the unit vector from location_1 to location_2
:param location_1, location_2: carla.Location objects | Returns the unit vector from location_1 to location_2 | [
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"""
Returns the unit vector from location_1 to location_2
:param location_1, location_2: carla.Location objects
"""
x = location_2.x - location_1.x
y = location_2.y - location_1.y
z = location_2.z - location_1.z
norm = np.linalg.norm([x, y, z]) + np.finfo(float).eps
return [x / norm, y / norm, z / norm] | [
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tensorflow/minigo | 6d89c202cdceaf449aefc3149ab2110d44f1a6a4 | cluster/evaluator/launch_eval.py | python | add_top_pairs | (dry_run=False, pair_now=False) | Pairs up the top twenty models against each other.
#1 plays 2,3,4,5, #2 plays 3,4,5,6 etc. for a total of 15*4 matches.
Default behavior is to add the pairs to the working pairlist.
`pair_now` will immediately create the pairings on the cluster.
`dry_run` makes it only print the pairings that would be added | Pairs up the top twenty models against each other.
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""" Pairs up the top twenty models against each other.
#1 plays 2,3,4,5, #2 plays 3,4,5,6 etc. for a total of 15*4 matches.
Default behavior is to add the pairs to the working pairlist.
`pair_now` will immediately create the pairings on the cluster.
`dry_run` makes it only print the pairings that would be added
"""
top = ratings.top_n(15)
new_pairs = []
for idx, t in enumerate(top[:10]):
new_pairs += [[t[0], o[0]] for o in top[idx+1:idx+5]]
if dry_run:
print(new_pairs)
return
if pair_now:
maybe_enqueue(new_pairs)
else:
_append_pairs(new_pairs) | [
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rdkit/rdkit | ede860ae316d12d8568daf5ee800921c3389c84e | rdkit/utils/chemutils.py | python | SplitComposition | (compStr) | return res | Takes a simple chemical composition and turns into a list of element,# pairs.
i.e. 'Fe3Al' -> [('Fe',3),('Al',1)]
**Arguments**
- compStr: the composition string to be processed
**Returns**
- the *composVect* corresponding to _compStr_
**Note**
-this isn't smart enough by half to deal with anything even
remotely subtle, so be gentle. | Takes a simple chemical composition and turns into a list of element,# pairs. | [
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""" Takes a simple chemical composition and turns into a list of element,# pairs.
i.e. 'Fe3Al' -> [('Fe',3),('Al',1)]
**Arguments**
- compStr: the composition string to be processed
**Returns**
- the *composVect* corresponding to _compStr_
**Note**
-this isn't smart enough by half to deal with anything even
remotely subtle, so be gentle.
"""
target = r'([A-Z][a-z]?)([0-9\.]*)'
theExpr = re.compile(target)
matches = theExpr.findall(compStr)
res = []
for match in matches:
if len(match[1]) > 0:
res.append((match[0], float(match[1])))
else:
res.append((match[0], 1))
return res | [
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BlzFans/wke | b0fa21158312e40c5fbd84682d643022b6c34a93 | cygwin/lib/python2.6/urllib.py | python | FancyURLopener.http_error_307 | (self, url, fp, errcode, errmsg, headers, data=None) | Error 307 -- relocated, but turn POST into error. | Error 307 -- relocated, but turn POST into error. | [
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"""Error 307 -- relocated, but turn POST into error."""
if data is None:
return self.http_error_302(url, fp, errcode, errmsg, headers, data)
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | tools/json_schema_compiler/memoize.py | python | memoize | (fn) | return impl | Decorates |fn| to memoize. | Decorates |fn| to memoize. | [
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'''Decorates |fn| to memoize.
'''
memory = {}
def impl(*args, **optargs):
full_args = args + tuple(optargs.iteritems())
if full_args not in memory:
memory[full_args] = fn(*args, **optargs)
return memory[full_args]
return impl | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/aui.py | python | AuiManager_GetManager | (*args, **kwargs) | return _aui.AuiManager_GetManager(*args, **kwargs) | AuiManager_GetManager(Window window) -> AuiManager | AuiManager_GetManager(Window window) -> AuiManager | [
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"""AuiManager_GetManager(Window window) -> AuiManager"""
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klzgrad/naiveproxy | ed2c513637c77b18721fe428d7ed395b4d284c83 | src/build/apple/plist_util.py | python | MergePList | (plist1, plist2) | return result | Merges |plist1| with |plist2| recursively.
Creates a new dictionary representing a Property List (.plist) files by
merging the two dictionary |plist1| and |plist2| recursively (only for
dictionary values). List value will be concatenated.
Args:
plist1: a dictionary representing a Property List (.plist) file
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"""Merges |plist1| with |plist2| recursively.
Creates a new dictionary representing a Property List (.plist) files by
merging the two dictionary |plist1| and |plist2| recursively (only for
dictionary values). List value will be concatenated.
Args:
plist1: a dictionary representing a Property List (.plist) file
plist2: a dictionary representing a Property List (.plist) file
Returns:
A new dictionary representing a Property List (.plist) file by merging
|plist1| with |plist2|. If any value is a dictionary, they are merged
recursively, otherwise |plist2| value is used. If values are list, they
are concatenated.
"""
result = plist1.copy()
for key, value in plist2.items():
if isinstance(value, dict):
old_value = result.get(key)
if isinstance(old_value, dict):
value = MergePList(old_value, value)
if isinstance(value, list):
value = plist1.get(key, []) + plist2.get(key, [])
result[key] = value
return result | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/ResourceManager/resource_manager/service.py | python | BaseService._get_stack_resource | (self, resource_name) | return self._context.stack.get_physical_resource_id(self._stack_id, resource_name) | Gets the list of outputs in given stack | Gets the list of outputs in given stack | [
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Gets the list of outputs in given stack
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/learn/python/learn/estimators/head.py | python | _BinarySvmHead._transform_labels | (self, mode, labels) | return labels_tensor | Applies transformations to labels tensor. | Applies transformations to labels tensor. | [
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] | def _transform_labels(self, mode, labels):
"""Applies transformations to labels tensor."""
if (mode == model_fn.ModeKeys.INFER) or (labels is None):
return None
labels_tensor = _to_labels_tensor(labels, self._label_name)
_check_no_sparse_tensor(labels_tensor)
return labels_tensor | [
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yyzybb537/libgo | 4af17b7c67643c4d54aa354dcc77963ea07847d0 | third_party/boost.context/tools/build/src/tools/builtin.py | python | reset | () | Clear the module state. This is mainly for testing purposes. | Clear the module state. This is mainly for testing purposes. | [
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""" Clear the module state. This is mainly for testing purposes.
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global __variant_explicit_properties
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/site-packages/win32com/client/__init__.py | python | GetObject | (Pathname = None, Class = None, clsctx = None) | Mimic VB's GetObject() function.
ob = GetObject(Class = "ProgID") or GetObject(Class = clsid) will
connect to an already running instance of the COM object.
ob = GetObject(r"c:\blah\blah\foo.xls") (aka the COM moniker syntax)
will return a ready to use Python wrapping of the required COM object.
Note: You must specifiy one or the other of these arguments. I know
this isn't pretty, but it is what VB does. Blech. If you don't
I'll throw ValueError at you. :)
This will most likely throw pythoncom.com_error if anything fails. | Mimic VB's GetObject() function. | [
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"""
Mimic VB's GetObject() function.
ob = GetObject(Class = "ProgID") or GetObject(Class = clsid) will
connect to an already running instance of the COM object.
ob = GetObject(r"c:\blah\blah\foo.xls") (aka the COM moniker syntax)
will return a ready to use Python wrapping of the required COM object.
Note: You must specifiy one or the other of these arguments. I know
this isn't pretty, but it is what VB does. Blech. If you don't
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This will most likely throw pythoncom.com_error if anything fails.
"""
if clsctx is None:
clsctx = pythoncom.CLSCTX_ALL
if (Pathname is None and Class is None) or \
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raise ValueError("You must specify a value for Pathname or Class, but not both.")
if Class is not None:
return GetActiveObject(Class, clsctx)
else:
return Moniker(Pathname, clsctx) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/setuptools/py3/setuptools/_vendor/pyparsing.py | python | ParseResults.haskeys | ( self ) | return bool(self.__tokdict) | Since keys() returns an iterator, this method is helpful in bypassing
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/aui/auibar.py | python | CommandToolBarEvent.GetToolId | (self) | return self.tool_id | Returns the :class:`AuiToolBarItem` identifier. | Returns the :class:`AuiToolBarItem` identifier. | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/python/ops/math_ops.py | python | log_sigmoid | (x, name=None) | Computes log sigmoid of `x` element-wise.
Specifically, `y = log(1 / (1 + exp(-x)))`. For numerical stability,
we use `y = -tf.nn.softplus(-x)`.
Args:
x: A Tensor with type `float32` or `float64`.
name: A name for the operation (optional).
Returns:
A Tensor with the same type as `x`. | Computes log sigmoid of `x` element-wise. | [
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"""Computes log sigmoid of `x` element-wise.
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we use `y = -tf.nn.softplus(-x)`.
Args:
x: A Tensor with type `float32` or `float64`.
name: A name for the operation (optional).
Returns:
A Tensor with the same type as `x`.
"""
with ops.name_scope(name, "LogSigmoid", [x]) as name:
x = ops.convert_to_tensor(x, name="x")
return gen_math_ops._neg(gen_nn_ops.softplus(-x), name=name) | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/HFIR_4Circle_Reduction/FindUBUtility.py | python | AddScansForUBDialog.__init__ | (self, parent) | initialization
:param parent: main GUI, reductionControl | initialization
:param parent: main GUI, reductionControl | [
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"""
initialization
:param parent: main GUI, reductionControl
"""
super(AddScansForUBDialog, self).__init__(parent)
self._myParent = parent
# set up UI
ui_path = "AddUBPeaksDialog.ui"
self.ui = load_ui(__file__, ui_path, baseinstance=self)
# initialize widgets
self.ui.checkBox_loadHKLfromFile.setChecked(True)
self.ui.pushButton_findPeak.clicked.connect(self.do_find_peak)
self.ui.pushButton_addPeakToCalUB.clicked.connect(self.do_add_single_scan)
self.ui.pushButton_loadScans.clicked.connect(self.do_load_scans)
self.ui.pushButton_addScans.clicked.connect(self.do_add_scans)
self.ui.pushButton_quit.clicked.connect(self.do_quit) | [
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LiquidPlayer/LiquidCore | 9405979363f2353ac9a71ad8ab59685dd7f919c9 | deps/node-10.15.3/tools/jinja2/compiler.py | python | generate | (node, environment, name, filename, stream=None,
defer_init=False, optimized=True) | Generate the python source for a node tree. | Generate the python source for a node tree. | [
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"""Generate the python source for a node tree."""
if not isinstance(node, nodes.Template):
raise TypeError('Can\'t compile non template nodes')
generator = environment.code_generator_class(environment, name, filename,
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bigtreetech/BIGTREETECH-SKR-mini-E3 | 221247c12502ff92d071c701ea63cf3aa9bb3b29 | firmware/V2.0/Marlin-2.0.8.2.x-SKR-mini-E3-V2.0/Marlin/src/lcd/extui/ftdi_eve_touch_ui/ftdi_eve_lib/scripts/bitmap2cpp.py | python | pack_rle | (data) | return rle | Use run-length encoding to pack the bytes | Use run-length encoding to pack the bytes | [
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rle.append(count)
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rle.append(count)
rle.append(value)
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pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | caffe2/python/memonger.py | python | get_updated_ranges | (ranges, max_live=None) | return ranges | Set LiveRange.defined = -1 if it is None
Set LiveRange.used = max_live if it is None
Set LiveRanee.size = 1 if it is None | Set LiveRange.defined = -1 if it is None
Set LiveRange.used = max_live if it is None
Set LiveRanee.size = 1 if it is None | [
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''' Set LiveRange.defined = -1 if it is None
Set LiveRange.used = max_live if it is None
Set LiveRanee.size = 1 if it is None
'''
def _get_max_live(ranges):
max_live = max(x[1].used for x in ranges if x[1].used) + 1
return max_live
def _update_range(x, max_live, size):
cx = x
if x[1].defined is None:
cx = (cx[0], cx[1]._replace(defined=-1))
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cx = (cx[0], cx[1]._replace(used=max_live))
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cx = (cx[0], cx[1]._replace(size=size))
return cx
if max_live is None:
max_live = _get_max_live(ranges)
ranges = [_update_range(x, max_live, 1) for x in ranges]
return ranges | [
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CRYTEK/CRYENGINE | 232227c59a220cbbd311576f0fbeba7bb53b2a8c | Code/Tools/waf-1.7.13/waflib/TaskGen.py | python | task_gen.__str__ | (self) | return "<task_gen %r declared in %s>" % (self.name, self.path.abspath()) | for debugging purposes | for debugging purposes | [
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"""for debugging purposes"""
return "<task_gen %r declared in %s>" % (self.name, self.path.abspath()) | [
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eomahony/Numberjack | 53fa9e994a36f881ffd320d8d04158097190aad8 | Numberjack/XCSPOut.py | python | XCSPOutput.output_expressions | (self, outfile) | Print all the constraints and predicates to the file | Print all the constraints and predicates to the file | [
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Print all the constraints and predicates to the file
"""
if len(self.__predicates)>0:
outfile.write("\n<predicates nbPredicates=\"%d\">" % len(self.__predicates))
for pred in self.__predicates:
outfile.write(pred)
outfile.write("</predicates>\n")
outfile.write("\n<constraints nbConstraints=\"%d\">" % len(self.__constraints))
for con in self.__constraints:
outfile.write(con)
outfile.write("</constraints>\n") | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/ops/histogram_ops.py | python | histogram_fixed_width | (values,
value_range,
nbins=100,
dtype=dtypes.int32,
name=None) | Return histogram of values.
Given the tensor `values`, this operation returns a rank 1 histogram counting
the number of entries in `values` that fell into every bin. The bins are
equal width and determined by the arguments `value_range` and `nbins`.
Args:
values: Numeric `Tensor`.
value_range: Shape [2] `Tensor` of same `dtype` as `values`.
values <= value_range[0] will be mapped to hist[0],
values >= value_range[1] will be mapped to hist[-1].
nbins: Scalar `int32 Tensor`. Number of histogram bins.
dtype: dtype for returned histogram.
name: A name for this operation (defaults to 'histogram_fixed_width').
Returns:
A 1-D `Tensor` holding histogram of values.
Raises:
TypeError: If any unsupported dtype is provided.
tf.errors.InvalidArgumentError: If value_range does not
satisfy value_range[0] < value_range[1].
Examples:
```python
# Bins will be: (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf)
nbins = 5
value_range = [0.0, 5.0]
new_values = [-1.0, 0.0, 1.5, 2.0, 5.0, 15]
with tf.compat.v1.get_default_session() as sess:
hist = tf.histogram_fixed_width(new_values, value_range, nbins=5)
variables.global_variables_initializer().run()
sess.run(hist) => [2, 1, 1, 0, 2]
``` | Return histogram of values. | [
"Return",
"histogram",
"of",
"values",
"."
] | def histogram_fixed_width(values,
value_range,
nbins=100,
dtype=dtypes.int32,
name=None):
"""Return histogram of values.
Given the tensor `values`, this operation returns a rank 1 histogram counting
the number of entries in `values` that fell into every bin. The bins are
equal width and determined by the arguments `value_range` and `nbins`.
Args:
values: Numeric `Tensor`.
value_range: Shape [2] `Tensor` of same `dtype` as `values`.
values <= value_range[0] will be mapped to hist[0],
values >= value_range[1] will be mapped to hist[-1].
nbins: Scalar `int32 Tensor`. Number of histogram bins.
dtype: dtype for returned histogram.
name: A name for this operation (defaults to 'histogram_fixed_width').
Returns:
A 1-D `Tensor` holding histogram of values.
Raises:
TypeError: If any unsupported dtype is provided.
tf.errors.InvalidArgumentError: If value_range does not
satisfy value_range[0] < value_range[1].
Examples:
```python
# Bins will be: (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf)
nbins = 5
value_range = [0.0, 5.0]
new_values = [-1.0, 0.0, 1.5, 2.0, 5.0, 15]
with tf.compat.v1.get_default_session() as sess:
hist = tf.histogram_fixed_width(new_values, value_range, nbins=5)
variables.global_variables_initializer().run()
sess.run(hist) => [2, 1, 1, 0, 2]
```
"""
with ops.name_scope(name, 'histogram_fixed_width',
[values, value_range, nbins]) as name:
# pylint: disable=protected-access
return gen_math_ops._histogram_fixed_width(
values, value_range, nbins, dtype=dtype, name=name) | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/FilterEvents/eventFilterGUI.py | python | MainWindow.filterByLogValue | (self) | Filter by log value | Filter by log value | [
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"log",
"value"
] | def filterByLogValue(self):
""" Filter by log value
"""
# Generate event filter
kwargs = {}
samplelog = str(self.ui.comboBox_2.currentText())
if len(samplelog) == 0:
error_msg = "No sample log is selected!"
Logger("Filter_Events").error(error_msg)
return
if self.ui.lineEdit_3.text() != "":
rel_starttime = float(self.ui.lineEdit_3.text())
kwargs["StartTime"] = str(rel_starttime)
if self.ui.lineEdit_4.text() != "":
rel_stoptime = float(self.ui.lineEdit_4.text())
kwargs["StopTime"] = str(rel_stoptime)
if self.ui.lineEdit_5.text() != "":
minlogvalue = float(self.ui.lineEdit_5.text())
kwargs["MinimumLogValue"] = minlogvalue
if self.ui.lineEdit_6.text() != "":
maxlogvalue = float(self.ui.lineEdit_6.text())
kwargs["MaximumLogValue"] = maxlogvalue
if self.ui.lineEdit_7.text() != "":
logvalueintv = float(self.ui.lineEdit_7.text())
kwargs["LogValueInterval"] = logvalueintv
logvalchangedir = str(self.ui.comboBox_4.currentText())
kwargs["FilterLogValueByChangingDirection"] = logvalchangedir
if self.ui.lineEdit_9.text() != "":
logvalueintv = float(self.ui.lineEdit_9.text())
kwargs["TimeTolerance"] = logvalueintv
logboundtype = str(self.ui.comboBox_5.currentText())
kwargs["LogBoundary"] = logboundtype
if self.ui.lineEdit_8.text() != "":
logvaluetol = float(self.ui.lineEdit_8.text())
kwargs["LogValueTolerance"] = logvaluetol
splitwsname = str(self._dataWS) + "_splitters"
splitinfowsname = str(self._dataWS) + "_info"
fastLog = self.ui.checkBox_fastLog.isChecked()
title = str(self.ui.lineEdit_title.text())
try:
splitws, infows = api.GenerateEventsFilter(InputWorkspace=self._dataWS,
UnitOfTime="Seconds",
TitleOfSplitters=title,
OutputWorkspace=splitwsname,
LogName=samplelog,
FastLog=fastLog,
InformationWorkspace=splitinfowsname,
**kwargs)
self.splitWksp(splitws, infows)
except RuntimeError as e:
self._setErrorMsg("Splitting Failed!\n %s" % (str(e))) | [
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PaddlePaddle/Anakin | 5fd68a6cc4c4620cd1a30794c1bf06eebd3f4730 | tools/external_converter_v2/parser/graph_io.py | python | GraphProtoIO.add_out_edge | (self, node_name_0, node_name_1, scale=None) | add_out_edge is directive from node_name_0 to node_name_1 | add_out_edge is directive from node_name_0 to node_name_1 | [
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"""
add_out_edge is directive from node_name_0 to node_name_1
"""
edges_out = self.graph_proto.edges_out
nexts = list()
for target in edges_out[node_name_0].target:
nexts.append(target.node)
if node_name_1 not in nexts:
target = edges_out[node_name_0].target.add()
if scale is not None:
target.scale.append(scale)
target.node = node_name_1 | [
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miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/tensorflow/contrib/distributions/python/ops/inverse_gamma.py | python | InverseGamma.batch_shape | (self, name="batch_shape") | Batch dimensions of this instance as a 1-D int32 `Tensor`.
The product of the dimensions of the `batch_shape` is the number of
independent distributions of this kind the instance represents.
Args:
name: name to give to the op
Returns:
`Tensor` `batch_shape` | Batch dimensions of this instance as a 1-D int32 `Tensor`. | [
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"""Batch dimensions of this instance as a 1-D int32 `Tensor`.
The product of the dimensions of the `batch_shape` is the number of
independent distributions of this kind the instance represents.
Args:
name: name to give to the op
Returns:
`Tensor` `batch_shape`
"""
with ops.name_scope(self.name):
with ops.op_scope([self._broadcast_tensor], name):
return array_ops.shape(self._broadcast_tensor) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/grid.py | python | GridTableBase.InsertCols | (*args, **kwargs) | return _grid.GridTableBase_InsertCols(*args, **kwargs) | InsertCols(self, size_t pos=0, size_t numCols=1) -> bool | InsertCols(self, size_t pos=0, size_t numCols=1) -> bool | [
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return _grid.GridTableBase_InsertCols(*args, **kwargs) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/numpy/py2/numpy/ma/extras.py | python | notmasked_edges | (a, axis=None) | return [tuple([idx[i].min(axis).compressed() for i in range(a.ndim)]),
tuple([idx[i].max(axis).compressed() for i in range(a.ndim)]), ] | Find the indices of the first and last unmasked values along an axis.
If all values are masked, return None. Otherwise, return a list
of two tuples, corresponding to the indices of the first and last
unmasked values respectively.
Parameters
----------
a : array_like
The input array.
axis : int, optional
Axis along which to perform the operation.
If None (default), applies to a flattened version of the array.
Returns
-------
edges : ndarray or list
An array of start and end indexes if there are any masked data in
the array. If there are no masked data in the array, `edges` is a
list of the first and last index.
See Also
--------
flatnotmasked_contiguous, flatnotmasked_edges, notmasked_contiguous,
clump_masked, clump_unmasked
Examples
--------
>>> a = np.arange(9).reshape((3, 3))
>>> m = np.zeros_like(a)
>>> m[1:, 1:] = 1
>>> am = np.ma.array(a, mask=m)
>>> np.array(am[~am.mask])
array([0, 1, 2, 3, 6])
>>> np.ma.notmasked_edges(ma)
array([0, 6]) | Find the indices of the first and last unmasked values along an axis. | [
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"first",
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] | def notmasked_edges(a, axis=None):
"""
Find the indices of the first and last unmasked values along an axis.
If all values are masked, return None. Otherwise, return a list
of two tuples, corresponding to the indices of the first and last
unmasked values respectively.
Parameters
----------
a : array_like
The input array.
axis : int, optional
Axis along which to perform the operation.
If None (default), applies to a flattened version of the array.
Returns
-------
edges : ndarray or list
An array of start and end indexes if there are any masked data in
the array. If there are no masked data in the array, `edges` is a
list of the first and last index.
See Also
--------
flatnotmasked_contiguous, flatnotmasked_edges, notmasked_contiguous,
clump_masked, clump_unmasked
Examples
--------
>>> a = np.arange(9).reshape((3, 3))
>>> m = np.zeros_like(a)
>>> m[1:, 1:] = 1
>>> am = np.ma.array(a, mask=m)
>>> np.array(am[~am.mask])
array([0, 1, 2, 3, 6])
>>> np.ma.notmasked_edges(ma)
array([0, 6])
"""
a = asarray(a)
if axis is None or a.ndim == 1:
return flatnotmasked_edges(a)
m = getmaskarray(a)
idx = array(np.indices(a.shape), mask=np.asarray([m] * a.ndim))
return [tuple([idx[i].min(axis).compressed() for i in range(a.ndim)]),
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intel-iot-devkit/how-to-code-samples | b4ea616f36bbfa2e042beb1698f968cfd651d79f | range-finder-scanner/python/iot_range_finder_scanner/runner.py | python | Runner.serve_css | (self) | return static_file(resource_path, root=package_root) | Serve the 'styles.css' file. | Serve the 'styles.css' file. | [
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Slicer/Slicer | ba9fadf332cb0303515b68d8d06a344c82e3e3e5 | Modules/Scripted/DICOMLib/DICOMRecentActivityWidget.py | python | DICOMRecentActivityWidget.__init__ | (self, parent, dicomDatabase=None, browserWidget=None) | If browserWidget is specified (e.g., set to slicer.modules.DICOMInstance.browserWidget)
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google/or-tools | 2cb85b4eead4c38e1c54b48044f92087cf165bce | examples/python/linear_programming.py | python | RunLinearExampleNaturalLanguageAPI | (optimization_problem_type) | Example of simple linear program with natural language API. | Example of simple linear program with natural language API. | [
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] | def RunLinearExampleNaturalLanguageAPI(optimization_problem_type):
"""Example of simple linear program with natural language API."""
solver = pywraplp.Solver.CreateSolver(optimization_problem_type)
if not solver:
return
Announce(optimization_problem_type, 'natural language API')
infinity = solver.infinity()
# x1, x2 and x3 are continuous non-negative variables.
x1 = solver.NumVar(0.0, infinity, 'x1')
x2 = solver.NumVar(0.0, infinity, 'x2')
x3 = solver.NumVar(0.0, infinity, 'x3')
solver.Maximize(10 * x1 + 6 * x2 + 4 * x3)
c0 = solver.Add(10 * x1 + 4 * x2 + 5 * x3 <= 600, 'ConstraintName0')
c1 = solver.Add(2 * x1 + 2 * x2 + 6 * x3 <= 300)
sum_of_vars = sum([x1, x2, x3])
c2 = solver.Add(sum_of_vars <= 100.0, 'OtherConstraintName')
SolveAndPrint(solver, [x1, x2, x3], [c0, c1, c2])
# Print a linear expression's solution value.
print('Sum of vars: %s = %s' % (sum_of_vars, sum_of_vars.solution_value())) | [
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hpi-xnor/BMXNet | ed0b201da6667887222b8e4b5f997c4f6b61943d | example/nce-loss/wordvec_subwords.py | python | get_subword_units | (token, gram=GRAMS) | return [t[i:i + gram] for i in range(0, len(t) - gram + 1)] | Return subword-units presentation, given a word/token. | Return subword-units presentation, given a word/token. | [
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"""
if token == '</s>': # special token for padding purpose.
return [token]
t = '#' + token + '#'
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eomahony/Numberjack | 53fa9e994a36f881ffd320d8d04158097190aad8 | Numberjack/__init__.py | python | Expression.initial | (self) | return output | Returns a string representing the initial domain of the expression. For
example:
.. code-block:: python
var1 = Variable(0, 10)
print var1.initial()
>>> x0 in {0..10}
:return: A String representation of original expression definition
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.. code-block:: python
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print var1.initial()
>>> x0 in {0..10}
:return: A String representation of original expression definition
:rtype: str
"""
output = self.name()
if self.domain_ is None:
output += ' in ' + str(Domain(self.lb, self.ub))
else:
output += ' in ' + str(Domain(self.domain_))
return output | [
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MythTV/mythtv | d282a209cb8be85d036f85a62a8ec971b67d45f4 | mythtv/bindings/python/MythTV/services_api/utilities.py | python | url_encode | (value=None) | return quote(value) | This is really unnecessary. It's more of a reminder about how to
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Output: The URL encoded string. E.g. the DEGREE SIGN becomes: %C2%B0
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percent encoded text. E.g. don't use it. How show titles with
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/bayesflow/python/ops/stochastic_gradient_estimators.py | python | get_mean_baseline | (ema_decay=0.99, name=None) | return mean_baseline | ExponentialMovingAverage baseline.
Args:
ema_decay: decay rate for the ExponentialMovingAverage.
name: name for variable scope of the ExponentialMovingAverage.
Returns:
Callable baseline function that takes the `StochasticTensor` (unused) and
the downstream `loss`, and returns an EMA of the loss. | ExponentialMovingAverage baseline. | [
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] | def get_mean_baseline(ema_decay=0.99, name=None):
"""ExponentialMovingAverage baseline.
Args:
ema_decay: decay rate for the ExponentialMovingAverage.
name: name for variable scope of the ExponentialMovingAverage.
Returns:
Callable baseline function that takes the `StochasticTensor` (unused) and
the downstream `loss`, and returns an EMA of the loss.
"""
def mean_baseline(_, loss):
with vs.variable_scope(name, default_name="MeanBaseline"):
reduced_loss = math_ops.reduce_mean(loss)
ema = training.ExponentialMovingAverage(decay=ema_decay, zero_debias=True)
update_op = ema.apply([reduced_loss])
with ops.control_dependencies([update_op]):
# Using `identity` causes an op to be added in this context, which
# triggers the update. Removing the `identity` means nothing is updated.
baseline = array_ops.identity(ema.average(reduced_loss))
return baseline
return mean_baseline | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/Jinja2/py3/jinja2/filters.py | python | do_attr | (
environment: "Environment", obj: t.Any, name: str
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See :ref:`Notes on subscriptions <notes-on-subscriptions>` for more details. | Get an attribute of an object. ``foo|attr("bar")`` works like
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"""Get an attribute of an object. ``foo|attr("bar")`` works like
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See :ref:`Notes on subscriptions <notes-on-subscriptions>` for more details.
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try:
name = str(name)
except UnicodeError:
pass
else:
try:
value = getattr(obj, name)
except AttributeError:
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else:
if environment.sandboxed:
environment = t.cast("SandboxedEnvironment", environment)
if not environment.is_safe_attribute(obj, name, value):
return environment.unsafe_undefined(obj, name)
return value
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/scipy/special/orthogonal.py | python | sh_legendre | (n, monic=False) | return p | r"""Shifted Legendre polynomial.
Defined as :math:`P^*_n(x) = P_n(2x - 1)` for :math:`P_n` the nth
Legendre polynomial.
Parameters
----------
n : int
Degree of the polynomial.
monic : bool, optional
If `True`, scale the leading coefficient to be 1. Default is
`False`.
Returns
-------
P : orthopoly1d
Shifted Legendre polynomial.
Notes
-----
The polynomials :math:`P^*_n` are orthogonal over :math:`[0, 1]`
with weight function 1. | r"""Shifted Legendre polynomial. | [
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r"""Shifted Legendre polynomial.
Defined as :math:`P^*_n(x) = P_n(2x - 1)` for :math:`P_n` the nth
Legendre polynomial.
Parameters
----------
n : int
Degree of the polynomial.
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If `True`, scale the leading coefficient to be 1. Default is
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Returns
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P : orthopoly1d
Shifted Legendre polynomial.
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-----
The polynomials :math:`P^*_n` are orthogonal over :math:`[0, 1]`
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"""
if n < 0:
raise ValueError("n must be nonnegative.")
wfunc = lambda x: 0.0 * x + 1.0
if n == 0:
return orthopoly1d([], [], 1.0, 1.0, wfunc, (0, 1), monic,
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x, w, mu0 = ps_roots(n, mu=True)
hn = 1.0 / (2 * n + 1.0)
kn = _gam(2 * n + 1) / _gam(n + 1)**2
p = orthopoly1d(x, w, hn, kn, wfunc, limits=(0, 1), monic=monic,
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/training/python/training/evaluation.py | python | evaluate_repeatedly | (checkpoint_dir,
master='',
scaffold=None,
eval_ops=None,
feed_dict=None,
final_ops=None,
final_ops_feed_dict=None,
eval_interval_secs=60,
hooks=None,
config=None,
max_number_of_evaluations=None,
timeout=None,
timeout_fn=None) | return final_ops_hook.final_ops_values | Repeatedly searches for a checkpoint in `checkpoint_dir` and evaluates it.
During a single evaluation, the `eval_ops` is run until the session is
interrupted or requested to finish. This is typically requested via a
`tf.contrib.training.StopAfterNEvalsHook` which results in `eval_ops` running
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Optionally, a user can pass in `final_ops`, a single `Tensor`, a list of
`Tensors` or a dictionary from names to `Tensors`. The `final_ops` is
evaluated a single time after `eval_ops` has finished running and the fetched
values of `final_ops` are returned. If `final_ops` is left as `None`, then
`None` is returned.
One may also consider using a `tf.contrib.training.SummaryAtEndHook` to record
summaries after the `eval_ops` have run. If `eval_ops` is `None`, the
summaries run immediately after the model checkpoint has been restored.
Note that `evaluate_once` creates a local variable used to track the number of
evaluations run via `tf.contrib.training.get_or_create_eval_step`.
Consequently, if a custom local init op is provided via a `scaffold`, the
caller should ensure that the local init op also initializes the eval step.
Args:
checkpoint_dir: The directory where checkpoints are stored.
master: The address of the TensorFlow master.
scaffold: An tf.compat.v1.train.Scaffold instance for initializing variables
and restoring variables. Note that `scaffold.init_fn` is used by the
function to restore the checkpoint. If you supply a custom init_fn, then
it must also take care of restoring the model from its checkpoint.
eval_ops: A single `Tensor`, a list of `Tensors` or a dictionary of names to
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done by a `tf.contrib.training.StopAfterNEvalsHook`.
feed_dict: The feed dictionary to use when executing the `eval_ops`.
final_ops: A single `Tensor`, a list of `Tensors` or a dictionary of names
to `Tensors`.
final_ops_feed_dict: A feed dictionary to use when evaluating `final_ops`.
eval_interval_secs: The minimum number of seconds between evaluations.
hooks: List of `tf.estimator.SessionRunHook` callbacks which are run inside
the evaluation loop.
config: An instance of `tf.compat.v1.ConfigProto` that will be used to
configure the `Session`. If left as `None`, the default will be used.
max_number_of_evaluations: The maximum times to run the evaluation. If left
as `None`, then evaluation runs indefinitely.
timeout: The maximum number of seconds to wait between checkpoints. If left
as `None`, then the process will wait indefinitely.
timeout_fn: Optional function to call after a timeout. If the function
returns True, then it means that no new checkpoints will be generated and
the iterator will exit. The function is called with no arguments.
Returns:
The fetched values of `final_ops` or `None` if `final_ops` is `None`. | Repeatedly searches for a checkpoint in `checkpoint_dir` and evaluates it. | [
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] | def evaluate_repeatedly(checkpoint_dir,
master='',
scaffold=None,
eval_ops=None,
feed_dict=None,
final_ops=None,
final_ops_feed_dict=None,
eval_interval_secs=60,
hooks=None,
config=None,
max_number_of_evaluations=None,
timeout=None,
timeout_fn=None):
"""Repeatedly searches for a checkpoint in `checkpoint_dir` and evaluates it.
During a single evaluation, the `eval_ops` is run until the session is
interrupted or requested to finish. This is typically requested via a
`tf.contrib.training.StopAfterNEvalsHook` which results in `eval_ops` running
the requested number of times.
Optionally, a user can pass in `final_ops`, a single `Tensor`, a list of
`Tensors` or a dictionary from names to `Tensors`. The `final_ops` is
evaluated a single time after `eval_ops` has finished running and the fetched
values of `final_ops` are returned. If `final_ops` is left as `None`, then
`None` is returned.
One may also consider using a `tf.contrib.training.SummaryAtEndHook` to record
summaries after the `eval_ops` have run. If `eval_ops` is `None`, the
summaries run immediately after the model checkpoint has been restored.
Note that `evaluate_once` creates a local variable used to track the number of
evaluations run via `tf.contrib.training.get_or_create_eval_step`.
Consequently, if a custom local init op is provided via a `scaffold`, the
caller should ensure that the local init op also initializes the eval step.
Args:
checkpoint_dir: The directory where checkpoints are stored.
master: The address of the TensorFlow master.
scaffold: An tf.compat.v1.train.Scaffold instance for initializing variables
and restoring variables. Note that `scaffold.init_fn` is used by the
function to restore the checkpoint. If you supply a custom init_fn, then
it must also take care of restoring the model from its checkpoint.
eval_ops: A single `Tensor`, a list of `Tensors` or a dictionary of names to
`Tensors`, which is run until the session is requested to stop, commonly
done by a `tf.contrib.training.StopAfterNEvalsHook`.
feed_dict: The feed dictionary to use when executing the `eval_ops`.
final_ops: A single `Tensor`, a list of `Tensors` or a dictionary of names
to `Tensors`.
final_ops_feed_dict: A feed dictionary to use when evaluating `final_ops`.
eval_interval_secs: The minimum number of seconds between evaluations.
hooks: List of `tf.estimator.SessionRunHook` callbacks which are run inside
the evaluation loop.
config: An instance of `tf.compat.v1.ConfigProto` that will be used to
configure the `Session`. If left as `None`, the default will be used.
max_number_of_evaluations: The maximum times to run the evaluation. If left
as `None`, then evaluation runs indefinitely.
timeout: The maximum number of seconds to wait between checkpoints. If left
as `None`, then the process will wait indefinitely.
timeout_fn: Optional function to call after a timeout. If the function
returns True, then it means that no new checkpoints will be generated and
the iterator will exit. The function is called with no arguments.
Returns:
The fetched values of `final_ops` or `None` if `final_ops` is `None`.
"""
eval_step = get_or_create_eval_step()
# Prepare the run hooks.
hooks = hooks or []
if eval_ops is not None:
update_eval_step = state_ops.assign_add(eval_step, 1)
for h in hooks:
if isinstance(h, StopAfterNEvalsHook):
h._set_evals_completed_tensor(update_eval_step) # pylint: disable=protected-access
if isinstance(eval_ops, dict):
eval_ops['update_eval_step'] = update_eval_step
elif isinstance(eval_ops, (tuple, list)):
eval_ops = list(eval_ops) + [update_eval_step]
else:
eval_ops = [eval_ops, update_eval_step]
final_ops_hook = basic_session_run_hooks.FinalOpsHook(final_ops,
final_ops_feed_dict)
hooks.append(final_ops_hook)
num_evaluations = 0
for checkpoint_path in checkpoints_iterator(
checkpoint_dir,
min_interval_secs=eval_interval_secs,
timeout=timeout,
timeout_fn=timeout_fn):
session_creator = monitored_session.ChiefSessionCreator(
scaffold=scaffold,
checkpoint_filename_with_path=checkpoint_path,
master=master,
config=config)
with monitored_session.MonitoredSession(
session_creator=session_creator, hooks=hooks) as session:
logging.info('Starting evaluation at ' +
time.strftime('%Y-%m-%d-%H:%M:%S', time.gmtime()))
if eval_ops is not None:
while not session.should_stop():
session.run(eval_ops, feed_dict)
logging.info('Finished evaluation at ' +
time.strftime('%Y-%m-%d-%H:%M:%S', time.gmtime()))
num_evaluations += 1
if (max_number_of_evaluations is not None and
num_evaluations >= max_number_of_evaluations):
return final_ops_hook.final_ops_values
return final_ops_hook.final_ops_values | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqt/mantidqt/plotting/markers.py | python | RangeMarker.set_maximum | (self, maximum) | Sets the maximum for the range.
:param maximum: The maximum of the range. | Sets the maximum for the range.
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Sets the maximum for the range.
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minimum = min([self.min_marker.get_position(), self.max_marker.get_position()])
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/inspect.py | python | _signature_from_builtin | (cls, func, skip_bound_arg=True) | return _signature_fromstr(cls, func, s, skip_bound_arg) | Private helper function to get signature for
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] | def _signature_from_builtin(cls, func, skip_bound_arg=True):
"""Private helper function to get signature for
builtin callables.
"""
if not _signature_is_builtin(func):
raise TypeError("{!r} is not a Python builtin "
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s = getattr(func, "__text_signature__", None)
if not s:
raise ValueError("no signature found for builtin {!r}".format(func))
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/devil/devil/android/device_utils.py | python | DeviceUtils._EnsureCacheInitialized | (self) | Populates cache token, runs getprop and fetches $EXTERNAL_STORAGE. | Populates cache token, runs getprop and fetches $EXTERNAL_STORAGE. | [
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] | def _EnsureCacheInitialized(self):
"""Populates cache token, runs getprop and fetches $EXTERNAL_STORAGE."""
if self._cache['token']:
return
with self._cache_lock:
if self._cache['token']:
return
# Change the token every time to ensure that it will match only the
# previously dumped cache.
token = str(uuid.uuid1())
cmd = (
'c=/data/local/tmp/cache_token;'
'echo $EXTERNAL_STORAGE;'
'cat $c 2>/dev/null||echo;'
'echo "%s">$c &&' % token +
'getprop'
)
output = self.RunShellCommand(cmd, check_return=True, large_output=True)
# Error-checking for this existing is done in GetExternalStoragePath().
self._cache['external_storage'] = output[0]
self._cache['prev_token'] = output[1]
output = output[2:]
prop_cache = self._cache['getprop']
prop_cache.clear()
for key, value in _GETPROP_RE.findall(''.join(output)):
prop_cache[key] = value
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RamadhanAmizudin/malware | 2c6c53c8b0d556f5d8078d6ca0fc4448f4697cf1 | Fuzzbunch/fuzzbunch/pyreadline/console/ironpython_console.py | python | event.__init__ | (self, console, input) | Initialize an event from the Windows input structure. | Initialize an event from the Windows input structure. | [
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] | def __init__(self, console, input):
'''Initialize an event from the Windows input structure.'''
self.type = '??'
self.serial = console.next_serial()
self.width = 0
self.height = 0
self.x = 0
self.y = 0
self.char = str(input.KeyChar)
self.keycode = input.Key
self.state = input.Modifiers
log_sock("%s,%s,%s"%(input.Modifiers,input.Key,input.KeyChar),"console")
self.type="KeyRelease"
self.keysym = make_keysym(self.keycode)
self.keyinfo = make_KeyPress(self.char, self.state, self.keycode) | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/plat-mac/lib-scriptpackages/Terminal/Standard_Suite.py | python | Standard_Suite_Events.count | (self, _object, _attributes={}, **_arguments) | count: Return the number of elements of a particular class within an object.
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Keyword argument each: The class of objects to be counted.
Keyword argument _attributes: AppleEvent attribute dictionary
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numba/array_analysis.py | python | ShapeEquivSet.intersect | (self, equiv_set) | return newset | Overload the intersect method to handle ind_to_var. | Overload the intersect method to handle ind_to_var. | [
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] | def intersect(self, equiv_set):
"""Overload the intersect method to handle ind_to_var.
"""
newset = super(ShapeEquivSet, self).intersect(equiv_set)
ind_to_var = {}
for i, objs in newset.ind_to_obj.items():
assert(len(objs) > 0)
obj = objs[0]
assert(obj in self.obj_to_ind)
assert(obj in equiv_set.obj_to_ind)
j = self.obj_to_ind[obj]
k = equiv_set.obj_to_ind[obj]
assert(j in self.ind_to_var)
assert(k in equiv_set.ind_to_var)
varlist = []
names = [x.name for x in equiv_set.ind_to_var[k]]
for x in self.ind_to_var[j]:
if x.name in names:
varlist.append(x)
ind_to_var[i] = varlist
newset.ind_to_var = ind_to_var
return newset | [
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microsoft/ivy | 9f3c7ecc0b2383129fdd0953e10890d98d09a82d | ivy/ivy_parser.py | python | p_top_conjecture_labeledfmla | (p) | top : top CONJECTURE labeledfmla | top : top CONJECTURE labeledfmla | [
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":",
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p[0] = p[1]
d = ConjectureDecl(addlabel(p[3],'conj'))
d.lineno = get_lineno(p,2)
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/combo.py | python | ComboPopup_DefaultPaintComboControl | (*args, **kwargs) | return _combo.ComboPopup_DefaultPaintComboControl(*args, **kwargs) | ComboPopup_DefaultPaintComboControl(wxComboCtrlBase combo, DC dc, Rect rect)
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"""
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Default PaintComboControl behaviour
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return _combo.ComboPopup_DefaultPaintComboControl(*args, **kwargs) | [
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microsoft/TSS.MSR | 0f2516fca2cd9929c31d5450e39301c9bde43688 | TSS.Py/src/TpmTypes.py | python | TPM2_NV_GlobalWriteLock_REQUEST.fromBytes | (buffer) | return TpmBuffer(buffer).createObj(TPM2_NV_GlobalWriteLock_REQUEST) | Returns new TPM2_NV_GlobalWriteLock_REQUEST object constructed from
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""" Returns new TPM2_NV_GlobalWriteLock_REQUEST object constructed from
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return TpmBuffer(buffer).createObj(TPM2_NV_GlobalWriteLock_REQUEST) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py2/scipy/special/_generate_pyx.py | python | FusedFunc._get_incallvars | (self, intypes, c) | return incallvars | Generate pure input variables to a specialization,
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"""
incallvars = []
for n, intype in enumerate(intypes):
var = self.invars[n]
if c and intype == "double complex":
var = npy_cdouble_from_double_complex(var)
incallvars.append(var)
return incallvars | [
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microsoft/onnxruntime | f92e47e95b13a240e37caf7b36577983544f98fc | onnxruntime/python/tools/quantization/calibrate.py | python | CalibraterBase._create_inference_session | (self) | create an OnnxRuntime InferenceSession. | create an OnnxRuntime InferenceSession. | [
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'''
create an OnnxRuntime InferenceSession.
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sess_options = onnxruntime.SessionOptions()
sess_options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_DISABLE_ALL
self.infer_session = onnxruntime.InferenceSession(self.augmented_model_path,
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stack-of-tasks/pinocchio | 593d4d43fded997bb9aa2421f4e55294dbd233c4 | bindings/python/pinocchio/robot_wrapper.py | python | RobotWrapper.getViewerNodeName | (self, geometry_object, geometry_type) | return self.viz.getViewerNodeName(geometry_object, geometry_type) | For each geometry object, returns the corresponding name of the node in the display. | For each geometry object, returns the corresponding name of the node in the display. | [
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"""For each geometry object, returns the corresponding name of the node in the display."""
return self.viz.getViewerNodeName(geometry_object, geometry_type) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/flatmenu.py | python | FlatMenuBar.AddControl | (self, control) | Adds any control to the toolbar, typically e.g. a combobox.
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/ops/_grad/grad_base.py | python | get_bprop_fn | (prim) | return bprops.get(prim, None) | get bprop function by primitive obj or prim name for c++ | get bprop function by primitive obj or prim name for c++ | [
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"""get bprop function by primitive obj or prim name for c++"""
out = bprop_getters.get(prim, None)
if out:
return out(prim)
return bprops.get(prim, None) | [
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linyouhappy/kongkongxiyou | 7a69b2913eb29f4be77f9a62fb90cdd72c4160f1 | cocosjs/frameworks/cocos2d-x/tools/bindings-generator/backup/clang-llvm-3.3-pybinding/cindex.py | python | Index.read | (self, path) | return TranslationUnit.from_ast(path, self) | Load a TranslationUnit from the given AST file. | Load a TranslationUnit from the given AST file. | [
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"""Load a TranslationUnit from the given AST file."""
return TranslationUnit.from_ast(path, self) | [
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okex/V3-Open-API-SDK | c5abb0db7e2287718e0055e17e57672ce0ec7fd9 | okex-python-sdk-api/venv/Lib/site-packages/pip-19.0.3-py3.8.egg/pip/_internal/wheel.py | python | message_about_scripts_not_on_PATH | (scripts) | return "\n".join(msg_lines) | Determine if any scripts are not on PATH and format a warning.
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] | def message_about_scripts_not_on_PATH(scripts):
# type: (Sequence[str]) -> Optional[str]
"""Determine if any scripts are not on PATH and format a warning.
Returns a warning message if one or more scripts are not on PATH,
otherwise None.
"""
if not scripts:
return None
# Group scripts by the path they were installed in
grouped_by_dir = collections.defaultdict(set) # type: Dict[str, set]
for destfile in scripts:
parent_dir = os.path.dirname(destfile)
script_name = os.path.basename(destfile)
grouped_by_dir[parent_dir].add(script_name)
# We don't want to warn for directories that are on PATH.
not_warn_dirs = [
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os.environ.get("PATH", "").split(os.pathsep)
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# If an executable sits with sys.executable, we don't warn for it.
# This covers the case of venv invocations without activating the venv.
not_warn_dirs.append(os.path.normcase(os.path.dirname(sys.executable)))
warn_for = {
parent_dir: scripts for parent_dir, scripts in grouped_by_dir.items()
if os.path.normcase(parent_dir) not in not_warn_dirs
}
if not warn_for:
return None
# Format a message
msg_lines = []
for parent_dir, scripts in warn_for.items():
scripts = sorted(scripts)
if len(scripts) == 1:
start_text = "script {} is".format(scripts[0])
else:
start_text = "scripts {} are".format(
", ".join(scripts[:-1]) + " and " + scripts[-1]
)
msg_lines.append(
"The {} installed in '{}' which is not on PATH."
.format(start_text, parent_dir)
)
last_line_fmt = (
"Consider adding {} to PATH or, if you prefer "
"to suppress this warning, use --no-warn-script-location."
)
if len(msg_lines) == 1:
msg_lines.append(last_line_fmt.format("this directory"))
else:
msg_lines.append(last_line_fmt.format("these directories"))
# Returns the formatted multiline message
return "\n".join(msg_lines) | [
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Kitware/ParaView | f760af9124ff4634b23ebbeab95a4f56e0261955 | ThirdParty/cinema/paraview/tpl/cinema_python/adaptors/paraview/pv_introspect.py | python | extend_range | (arrayRanges, name, minmax) | This updates the data ranges in the data base meta file.
Throughout a time varying data export ranges will vary.
Here we accumulate them as we go so that by the end we get
the min and max values over for each array component over
all time.
This version happens in catalyst, where we recreate the
database file every timestep. | This updates the data ranges in the data base meta file.
Throughout a time varying data export ranges will vary.
Here we accumulate them as we go so that by the end we get
the min and max values over for each array component over
all time. | [
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"""
This updates the data ranges in the data base meta file.
Throughout a time varying data export ranges will vary.
Here we accumulate them as we go so that by the end we get
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This version happens in catalyst, where we recreate the
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"""
adjustedMinMax = range_epsilon(minmax)
if name in arrayRanges:
temporalMinMax = list(arrayRanges[name])
if adjustedMinMax[0] < temporalMinMax[0]:
temporalMinMax[0] = adjustedMinMax[0]
if adjustedMinMax[1] > temporalMinMax[1]:
temporalMinMax[1] = adjustedMinMax[1]
arrayRanges[name] = temporalMinMax
else:
arrayRanges[name] = adjustedMinMax | [
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natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/python/client/session.py | python | SessionInterface.sess_str | (self) | The TensorFlow process to which this session will connect. | The TensorFlow process to which this session will connect. | [
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] | def sess_str(self):
"""The TensorFlow process to which this session will connect."""
raise NotImplementedError('sess_str') | [
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neoml-lib/neoml | a0d370fba05269a1b2258cef126f77bbd2054a3e | NeoML/Python/neoml/Dnn/Binarization.py | python | EnumBinarization.enum_size | (self, enum_size) | Sets the number of constants in the enumeration. | Sets the number of constants in the enumeration. | [
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] | def enum_size(self, enum_size):
"""Sets the number of constants in the enumeration.
"""
self._internal.set_enum_size(enum_size) | [
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