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google/filament | d21f092645b8e1e312307cbf89f1484891347c63 | third_party/libassimp/port/PyAssimp/scripts/transformations.py | python | quaternion_slerp | (quat0, quat1, fraction, spin=0, shortestpath=True) | return q0 | Return spherical linear interpolation between two quaternions.
>>> q0 = random_quaternion()
>>> q1 = random_quaternion()
>>> q = quaternion_slerp(q0, q1, 0.0)
>>> numpy.allclose(q, q0)
True
>>> q = quaternion_slerp(q0, q1, 1.0, 1)
>>> numpy.allclose(q, q1)
True
>>> q = quaternion_slerp(q0, q1, 0.5)
>>> angle = math.acos(numpy.dot(q0, q))
>>> numpy.allclose(2.0, math.acos(numpy.dot(q0, q1)) / angle) or \
numpy.allclose(2.0, math.acos(-numpy.dot(q0, q1)) / angle)
True | Return spherical linear interpolation between two quaternions. | [
"Return",
"spherical",
"linear",
"interpolation",
"between",
"two",
"quaternions",
"."
] | def quaternion_slerp(quat0, quat1, fraction, spin=0, shortestpath=True):
"""Return spherical linear interpolation between two quaternions.
>>> q0 = random_quaternion()
>>> q1 = random_quaternion()
>>> q = quaternion_slerp(q0, q1, 0.0)
>>> numpy.allclose(q, q0)
True
>>> q = quaternion_slerp(q0, q1, 1.0, 1)
>>> numpy.allclose(q, q1)
True
>>> q = quaternion_slerp(q0, q1, 0.5)
>>> angle = math.acos(numpy.dot(q0, q))
>>> numpy.allclose(2.0, math.acos(numpy.dot(q0, q1)) / angle) or \
numpy.allclose(2.0, math.acos(-numpy.dot(q0, q1)) / angle)
True
"""
q0 = unit_vector(quat0[:4])
q1 = unit_vector(quat1[:4])
if fraction == 0.0:
return q0
elif fraction == 1.0:
return q1
d = numpy.dot(q0, q1)
if abs(abs(d) - 1.0) < _EPS:
return q0
if shortestpath and d < 0.0:
# invert rotation
d = -d
q1 *= -1.0
angle = math.acos(d) + spin * math.pi
if abs(angle) < _EPS:
return q0
isin = 1.0 / math.sin(angle)
q0 *= math.sin((1.0 - fraction) * angle) * isin
q1 *= math.sin(fraction * angle) * isin
q0 += q1
return q0 | [
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Illumina/strelka | d7377443b62319f7c7bd70c241c4b2df3459e29a | src/python/scoringModelTraining/somatic/lib/evs/strelka_rf_indel.py | python | StrelkaRFIndel.train | (self, tp, fp, columns, *args, **kwargs) | Train model from sets of TPs and FPs
:param tp: data frame with rows of TP instances.
:type tp: pandas.DataFrame
:param fp: data frame with rows of FP instances.
:type fp: pandas.DataFrame
:param columns: the feature columns to use | Train model from sets of TPs and FPs | [
"Train",
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""" Train model from sets of TPs and FPs
:param tp: data frame with rows of TP instances.
:type tp: pandas.DataFrame
:param fp: data frame with rows of FP instances.
:type fp: pandas.DataFrame
:param columns: the feature columns to use
"""
# import pdb; pdb.set_trace()
tdf = pandas.DataFrame(tp[tp["NT"] == "ref"])
fdf = pandas.DataFrame(fp[fp["NT"] == "ref"])
tdf["tag"] = "TP"
fdf["tag"] = "FP"
allrows = pandas.concat([tdf, fdf])
# TODO: parameters to try
# {'max_depth' : range(1,20,1), 'n_estimators' : range(5, 30+1,5)},
if not kwargs:
kwargs = {"n_jobs": 8,
"max_depth": 4,
"n_estimators": 50,
"max_features": "log2"
}
allrows["INDELTYPE"] = allrows["INDELTYPE"].astype(float).round().astype(int)
self.clf = {}
for it in self.itypes:
self.clf[it] = RandomForestClassifier(**kwargs)
irows = allrows[allrows["INDELTYPE"] == it]
self.clf[it].fit(irows[columns].values, irows["tag"].values) | [
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intel/caffe | 3f494b442ee3f9d17a07b09ecbd5fa2bbda00836 | examples/pycaffe/layers/pascal_multilabel_datalayers.py | python | print_info | (name, params) | Ouput some info regarding the class | Ouput some info regarding the class | [
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"regarding",
"the",
"class"
] | def print_info(name, params):
"""
Ouput some info regarding the class
"""
print "{} initialized for split: {}, with bs: {}, im_shape: {}.".format(
name,
params['split'],
params['batch_size'],
params['im_shape']) | [
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Illumina/strelka | d7377443b62319f7c7bd70c241c4b2df3459e29a | src/python/lib/strelkaSequenceErrorEstimation.py | python | getSequenceErrorEstimatesForSample | (self, estimationIntervals, sampleIndex, taskPrefix="", dependencies=None) | return nextStepWait | Count sequencing errors in one sample and use these to estimate sample error parameters | Count sequencing errors in one sample and use these to estimate sample error parameters | [
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] | def getSequenceErrorEstimatesForSample(self, estimationIntervals, sampleIndex, taskPrefix="", dependencies=None):
"""
Count sequencing errors in one sample and use these to estimate sample error parameters
"""
segmentTasks = set()
segFiles = TempSequenceAlleleCountsSegmentFiles()
if self.params.isErrorEstimationFromAllData :
# get error counts from full data set:
segmentTasks |= countAllEligibleSequenceEvidence(self, estimationIntervals, sampleIndex, segFiles, taskPrefix, dependencies)
else :
# Launch tasks until the required counts are found
segmentTasks |= countSequenceEvidenceUntilTargetIsReached(self, estimationIntervals, sampleIndex, segFiles, taskPrefix, dependencies)
# create a checkpoint for all segments:
completeSegmentsTask = self.addTask(preJoin(taskPrefix,"completedAllGenomeSegments"),dependencies=segmentTasks)
# merge segment stats:
mergeCountsTask = mergeSequenceAlleleCounts(self, sampleIndex, segFiles.counts,
taskPrefix=taskPrefix, dependencies=completeSegmentsTask)
# get error parameters:
estimateTask = estimateParametersFromAlleleCounts(self, sampleIndex,
taskPrefix=taskPrefix, dependencies=mergeCountsTask)
nextStepWait = set()
nextStepWait.add(estimateTask)
return nextStepWait | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/telemetry/telemetry/core/cros_interface.py | python | GetAllCmdOutput | (args, cwd=None, quiet=False) | Open a subprocess to execute a program and returns its output.
Args:
args: A string or a sequence of program arguments. The program to execute is
the string or the first item in the args sequence.
cwd: If not None, the subprocess's current directory will be changed to
|cwd| before it's executed.
Returns:
Captures and returns the command's stdout.
Prints the command's stderr to logger (which defaults to stdout). | Open a subprocess to execute a program and returns its output. | [
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"returns",
"its",
"output",
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] | def GetAllCmdOutput(args, cwd=None, quiet=False):
"""Open a subprocess to execute a program and returns its output.
Args:
args: A string or a sequence of program arguments. The program to execute is
the string or the first item in the args sequence.
cwd: If not None, the subprocess's current directory will be changed to
|cwd| before it's executed.
Returns:
Captures and returns the command's stdout.
Prints the command's stderr to logger (which defaults to stdout).
"""
if not quiet:
logging.debug(' '.join(args) + ' ' + (cwd or ''))
with open(os.devnull, 'w') as devnull:
p = subprocess.Popen(args=args,
cwd=cwd,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
stdin=devnull)
stdout, stderr = p.communicate()
if not quiet:
logging.debug(' > stdout=[%s], stderr=[%s]', stdout, stderr)
return stdout, stderr | [
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Caffe-MPI/Caffe-MPI.github.io | df5992af571a2a19981b69635115c393f18d1c76 | scripts/cpp_lint.py | python | RemoveMultiLineCommentsFromRange | (lines, begin, end) | Clears a range of lines for multi-line comments. | Clears a range of lines for multi-line comments. | [
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"for",
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"-",
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"comments",
"."
] | def RemoveMultiLineCommentsFromRange(lines, begin, end):
"""Clears a range of lines for multi-line comments."""
# Having // dummy comments makes the lines non-empty, so we will not get
# unnecessary blank line warnings later in the code.
for i in range(begin, end):
lines[i] = '// dummy' | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/logging/handlers.py | python | QueueListener.__init__ | (self, queue, *handlers, respect_handler_level=False) | Initialise an instance with the specified queue and
handlers. | Initialise an instance with the specified queue and
handlers. | [
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"""
Initialise an instance with the specified queue and
handlers.
"""
self.queue = queue
self.handlers = handlers
self._thread = None
self.respect_handler_level = respect_handler_level | [
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ros-planning/moveit2 | dd240ef6fd8b9932a7a53964140f2952786187a9 | moveit_commander/src/moveit_commander/planning_scene_interface.py | python | PlanningSceneInterface.add_object | (self, collision_object) | Add an object to the planning scene | Add an object to the planning scene | [
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""" Add an object to the planning scene """
self.__submit(collision_object, attach=False) | [
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SIPp/sipp | f44d0cf5dec0013eff8fd7b4da885d455aa82e0e | cpplint.py | python | IsBlankLine | (line) | return not line or line.isspace() | Returns true if the given line is blank.
We consider a line to be blank if the line is empty or consists of
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Args:
line: A line of a string.
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"""Returns true if the given line is blank.
We consider a line to be blank if the line is empty or consists of
only white spaces.
Args:
line: A line of a string.
Returns:
True, if the given line is blank.
"""
return not line or line.isspace() | [
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pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | caffe2/python/model_helper.py | python | ModelHelper.get_param_to_grad | (self, params) | return param_to_grad | Given a list of parameters returns a dict from a parameter
to a corresponding gradient | Given a list of parameters returns a dict from a parameter
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'''
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raise RuntimeError("You need to run AddGradientOperators first.")
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for p in params:
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/syntax/_html.py | python | SyntaxData.GetKeywords | (self) | Returns Specified Keywords List | Returns Specified Keywords List | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/pip/_internal/index/collector.py | python | _get_html_response | (url, session) | return resp | Access an HTML page with GET, and return the response.
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2. Actually perform the request. Raise HTTP exceptions on network failures.
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# type: (str, PipSession) -> Response
"""Access an HTML page with GET, and return the response.
This consists of three parts:
1. If the URL looks suspiciously like an archive, send a HEAD first to
check the Content-Type is HTML, to avoid downloading a large file.
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2. Actually perform the request. Raise HTTP exceptions on network failures.
3. Check the Content-Type header to make sure we got HTML, and raise
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"""
if is_archive_file(Link(url).filename):
_ensure_html_response(url, session=session)
logger.debug('Getting page %s', redact_auth_from_url(url))
resp = session.get(
url,
headers={
"Accept": "text/html",
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# minimize traffic sent in cases where the page hasn't
# changed at all, we will just always incur the round
# trip for the conditional GET now instead of only
# once per 10 minutes.
# For more information, please see pypa/pip#5670.
"Cache-Control": "max-age=0",
},
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raise_for_status(resp)
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pmq20/node-packer | 12c46c6e44fbc14d9ee645ebd17d5296b324f7e0 | current/deps/v8/third_party/jinja2/compiler.py | python | CodeGenerator.writeline | (self, x, node=None, extra=0) | Combination of newline and write. | Combination of newline and write. | [
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"""Combination of newline and write."""
self.newline(node, extra)
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/locale.py | python | atoi | (string) | return int(delocalize(string)) | Converts a string to an integer according to the locale settings. | Converts a string to an integer according to the locale settings. | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/site-packages/pip/_vendor/requests/hooks.py | python | dispatch_hook | (key, hooks, hook_data, **kwargs) | return hook_data | Dispatches a hook dictionary on a given piece of data. | Dispatches a hook dictionary on a given piece of data. | [
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citizenfx/fivem | 88276d40cc7baf8285d02754cc5ae42ec7a8563f | vendor/chromium/mojo/public/tools/bindings/pylib/mojom/generate/translate.py | python | _Kind | (kinds, spec, scope) | return kind | Convert a type name into a mojom.Kind object.
As a side-effect this function adds the result to 'kinds'.
Args:
kinds: {Dict[str, mojom.Kind]} All known kinds up to this point, indexed by
their names.
spec: {str} A name uniquely identifying a type.
scope: {Tuple[str, str]} A tuple that looks like (namespace,
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"""Convert a type name into a mojom.Kind object.
As a side-effect this function adds the result to 'kinds'.
Args:
kinds: {Dict[str, mojom.Kind]} All known kinds up to this point, indexed by
their names.
spec: {str} A name uniquely identifying a type.
scope: {Tuple[str, str]} A tuple that looks like (namespace,
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referenced.
Returns:
{mojom.Kind} The type corresponding to 'spec'.
"""
kind = _LookupKind(kinds, spec, scope)
if kind:
return kind
if spec.startswith('?'):
kind = _Kind(kinds, spec[1:], scope).MakeNullableKind()
elif spec.startswith('a:'):
kind = mojom.Array(_Kind(kinds, spec[2:], scope))
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inner_kind = _Kind(kinds, spec[5:], scope)
if isinstance(inner_kind, mojom.InterfaceRequest):
kind = mojom.AssociatedInterfaceRequest(inner_kind)
else:
kind = mojom.AssociatedInterface(inner_kind)
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colon = spec.find(':')
length = int(spec[1:colon])
kind = mojom.Array(_Kind(kinds, spec[colon+1:], scope), length)
elif spec.startswith('r:'):
kind = mojom.InterfaceRequest(_Kind(kinds, spec[2:], scope))
elif spec.startswith('rmt:'):
kind = mojom.PendingRemote(_Kind(kinds, spec[4:], scope))
elif spec.startswith('rcv:'):
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second_kind = spec[key_end+2:-1]
kind = mojom.Map(_Kind(kinds, first_kind, scope),
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kinds[spec] = kind
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baidu-research/persistent-rnn | dcb55b7bc4669021a9da82a3e847c7fe1377ef87 | site_scons/site_init.py | python | getLibCXXPaths | () | return (inc_path, lib_path) | Determines libc++ path
returns (inc_path, lib_path) | Determines libc++ path | [
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"""Determines libc++ path
returns (inc_path, lib_path)
"""
# determine defaults
if os.name == 'posix':
inc_path = '/usr/include'
lib_path = '/usr/lib/libc++.so'
else:
raise ValueError, 'Error: unknown OS. Where is libc++ installed?'
# override with environement variables
if 'LIBCXX_INC_PATH' in os.environ:
inc_path = os.path.abspath(os.environ['LIBCXX_INC_PATH'])
if 'LIBCXX_LIB_PATH' in os.environ:
lib_path = os.path.abspath(os.environ['LIBCXX_LIB_PATH'])
return (inc_path, lib_path) | [
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amd/OpenCL-caffe | 638543108517265366c18ae5821f3096cf5cf34a | python/caffe/pycaffe.py | python | _Net_batch | (self, blobs) | Batch blob lists according to net's batch size.
Parameters
----------
blobs: Keys blob names and values are lists of blobs (of any length).
Naturally, all the lists should have the same length.
Yields
------
batch: {blob name: list of blobs} dict for a single batch. | Batch blob lists according to net's batch size. | [
"Batch",
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"lists",
"according",
"to",
"net",
"s",
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"size",
"."
] | def _Net_batch(self, blobs):
"""
Batch blob lists according to net's batch size.
Parameters
----------
blobs: Keys blob names and values are lists of blobs (of any length).
Naturally, all the lists should have the same length.
Yields
------
batch: {blob name: list of blobs} dict for a single batch.
"""
num = len(blobs.itervalues().next())
batch_size = self.blobs.itervalues().next().num
remainder = num % batch_size
num_batches = num / batch_size
# Yield full batches.
for b in range(num_batches):
i = b * batch_size
yield {name: blobs[name][i:i + batch_size] for name in blobs}
# Yield last padded batch, if any.
if remainder > 0:
padded_batch = {}
for name in blobs:
padding = np.zeros((batch_size - remainder,)
+ blobs[name].shape[1:])
padded_batch[name] = np.concatenate([blobs[name][-remainder:],
padding])
yield padded_batch | [
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FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/AddonManager/package_list.py | python | PackageListItemModel.setData | (self, index: QModelIndex, value, role=Qt.EditRole) | Set the data for this row. The column of the index is ignored. | Set the data for this row. The column of the index is ignored. | [
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] | def setData(self, index: QModelIndex, value, role=Qt.EditRole) -> None:
"""Set the data for this row. The column of the index is ignored."""
row = index.row()
self.write_lock.acquire()
if role == PackageListItemModel.StatusUpdateRole:
self.repos[row].set_status(value)
self.dataChanged.emit(
self.index(row, 2),
self.index(row, 2),
[PackageListItemModel.StatusUpdateRole],
)
elif role == PackageListItemModel.IconUpdateRole:
self.repos[row].icon = value
self.dataChanged.emit(
self.index(row, 0),
self.index(row, 0),
[PackageListItemModel.IconUpdateRole],
)
self.write_lock.release() | [
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microsoft/TSS.MSR | 0f2516fca2cd9929c31d5450e39301c9bde43688 | TSS.Py/src/TpmTypes.py | python | ObjectChangeAuthResponse.__init__ | (self, outPrivate = None) | This command is used to change the authorization secret for a
TPM-resident object.
Attributes:
outPrivate (TPM2B_PRIVATE): Private area containing the new
authorization value | This command is used to change the authorization secret for a
TPM-resident object. | [
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] | def __init__(self, outPrivate = None):
""" This command is used to change the authorization secret for a
TPM-resident object.
Attributes:
outPrivate (TPM2B_PRIVATE): Private area containing the new
authorization value
"""
self.outPrivate = outPrivate | [
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GoSSIP-SJTU/TripleDoggy | 03648d6b19c812504b14e8b98c8c7b3f443f4e54 | tools/clang/tools/scan-build-py/libscanbuild/analyze.py | python | ctu_collect_phase | (opts) | Preprocess source by generating all data needed by CTU analysis. | Preprocess source by generating all data needed by CTU analysis. | [
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] | def ctu_collect_phase(opts):
""" Preprocess source by generating all data needed by CTU analysis. """
def generate_ast(triple_arch):
""" Generates ASTs for the current compilation command. """
args = opts['direct_args'] + opts['flags']
ast_joined_path = os.path.join(opts['ctu'].dir, triple_arch, 'ast',
os.path.realpath(opts['file'])[1:] +
'.ast')
ast_path = os.path.abspath(ast_joined_path)
ast_dir = os.path.dirname(ast_path)
if not os.path.isdir(ast_dir):
try:
os.makedirs(ast_dir)
except OSError:
# In case an other process already created it.
pass
ast_command = [opts['clang'], '-emit-ast']
ast_command.extend(args)
ast_command.append('-w')
ast_command.append(opts['file'])
ast_command.append('-o')
ast_command.append(ast_path)
logging.debug("Generating AST using '%s'", ast_command)
run_command(ast_command, cwd=opts['directory'])
def map_functions(triple_arch):
""" Generate function map file for the current source. """
args = opts['direct_args'] + opts['flags']
funcmap_command = [opts['ctu'].func_map_cmd]
funcmap_command.append(opts['file'])
funcmap_command.append('--')
funcmap_command.extend(args)
logging.debug("Generating function map using '%s'", funcmap_command)
func_src_list = run_command(funcmap_command, cwd=opts['directory'])
func_ast_list = func_map_list_src_to_ast(func_src_list)
extern_fns_map_folder = os.path.join(opts['ctu'].dir, triple_arch,
CTU_TEMP_FNMAP_FOLDER)
if not os.path.isdir(extern_fns_map_folder):
try:
os.makedirs(extern_fns_map_folder)
except OSError:
# In case an other process already created it.
pass
if func_ast_list:
with tempfile.NamedTemporaryFile(mode='w',
dir=extern_fns_map_folder,
delete=False) as out_file:
out_file.write("\n".join(func_ast_list) + "\n")
cwd = opts['directory']
cmd = [opts['clang'], '--analyze'] + opts['direct_args'] + opts['flags'] \
+ [opts['file']]
triple_arch = get_triple_arch(cmd, cwd)
generate_ast(triple_arch)
map_functions(triple_arch) | [
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apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | src/external/coremltools_wrap/coremltools/coremltools/converters/mil/frontend/torch/converter.py | python | TorchConverter._create_placeholder | (_input) | return mb.placeholder(shape, dtype=dtype) | Converts an InputType torch.Tensor into a Placeholder. | Converts an InputType torch.Tensor into a Placeholder. | [
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"an",
"InputType",
"torch",
".",
"Tensor",
"into",
"a",
"Placeholder",
"."
] | def _create_placeholder(_input):
"""Converts an InputType torch.Tensor into a Placeholder.
"""
shape = _input.shape.shape
dtype = _input.dtype
return mb.placeholder(shape, dtype=dtype) | [
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gnuradio/gnuradio | 09c3c4fa4bfb1a02caac74cb5334dfe065391e3b | gr-blocks/python/blocks/qa_multiply_matrix_xx.py | python | test_multiply_matrix_xx.test_005_t | (self) | Tags | Tags | [
"Tags"
] | def test_005_t(self):
""" Tags """
X_in = (
(1, 2, 3, 4),
(5, 6, 7, 8),
)
A = (
(0, 1), # Flip them round
(1, 0),
)
tag1 = gr.tag_t()
tag1.offset = 0
tag1.key = pmt.intern("in1")
tag1.value = pmt.PMT_T
tag2 = gr.tag_t()
tag2.offset = 0
tag2.key = pmt.intern("in2")
tag2.value = pmt.PMT_T
self.run_once(X_in, A, tpp=gr.TPP_ONE_TO_ONE, tags=(tag1, tag2))
self.assertTrue(pmt.equal(tag1.key, self.the_tags[0][0].key))
self.assertTrue(pmt.equal(tag2.key, self.the_tags[1][0].key)) | [
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kushview/Element | 1cc16380caa2ab79461246ba758b9de1f46db2a5 | waflib/extras/codelite.py | python | vsnode.ptype | (self) | Return a special uuid for projects written in the solution file | Return a special uuid for projects written in the solution file | [
"Return",
"a",
"special",
"uuid",
"for",
"projects",
"written",
"in",
"the",
"solution",
"file"
] | def ptype(self):
"""
Return a special uuid for projects written in the solution file
"""
pass | [
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"pass"
] | https://github.com/kushview/Element/blob/1cc16380caa2ab79461246ba758b9de1f46db2a5/waflib/extras/codelite.py#L409-L413 | ||
zhaoweicai/hwgq | ebc706bee3e2d145de1da4be446ce8de8740738f | scripts/cpp_lint.py | python | CheckMakePairUsesDeduction | (filename, clean_lines, linenum, error) | Check that make_pair's template arguments are deduced.
G++ 4.6 in C++0x mode fails badly if make_pair's template arguments are
specified explicitly, and such use isn't intended in any case.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found. | Check that make_pair's template arguments are deduced. | [
"Check",
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"make_pair",
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"template",
"arguments",
"are",
"deduced",
"."
] | def CheckMakePairUsesDeduction(filename, clean_lines, linenum, error):
"""Check that make_pair's template arguments are deduced.
G++ 4.6 in C++0x mode fails badly if make_pair's template arguments are
specified explicitly, and such use isn't intended in any case.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
match = _RE_PATTERN_EXPLICIT_MAKEPAIR.search(line)
if match:
error(filename, linenum, 'build/explicit_make_pair',
4, # 4 = high confidence
'For C++11-compatibility, omit template arguments from make_pair'
' OR use pair directly OR if appropriate, construct a pair directly') | [
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facebook/watchman | 0917460c71b000b96be9b9575d77f06f2f6053bb | build/fbcode_builder/getdeps/platform.py | python | get_available_ram | () | Returns a platform-appropriate available RAM metric in MiB. | Returns a platform-appropriate available RAM metric in MiB. | [
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"available",
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"."
] | def get_available_ram() -> int:
"""
Returns a platform-appropriate available RAM metric in MiB.
"""
if sys.platform == "linux":
return _get_available_ram_linux()
elif sys.platform == "darwin":
return _get_available_ram_macos()
elif sys.platform == "win32":
return _get_available_ram_windows()
elif sys.platform.startswith("freebsd"):
return _get_available_ram_freebsd()
else:
raise NotImplementedError(
f"platform {sys.platform} does not have an implementation of get_available_ram"
) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/telemetry/telemetry/internal/backends/chrome_inspector/devtools_http.py | python | DevToolsHttp._Connect | (self, timeout) | Attempts to establish a connection to Chrome devtools. | Attempts to establish a connection to Chrome devtools. | [
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"connection",
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] | def _Connect(self, timeout):
"""Attempts to establish a connection to Chrome devtools."""
assert not self._conn
try:
host_port = '127.0.0.1:%i' % self._devtools_port
self._conn = httplib.HTTPConnection(host_port, timeout=timeout)
except (socket.error, httplib.HTTPException) as e:
raise DevToolsClientConnectionError, (e,), sys.exc_info()[2] | [
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hpi-xnor/BMXNet-v2 | af2b1859eafc5c721b1397cef02f946aaf2ce20d | python/mxnet/torch.py | python | _make_torch_function | (handle) | return ret_function | Create a Torch function from the FunctionHandle. | Create a Torch function from the FunctionHandle. | [
"Create",
"a",
"Torch",
"function",
"from",
"the",
"FunctionHandle",
"."
] | def _make_torch_function(handle):
"""Create a Torch function from the FunctionHandle."""
# Get the property of function
n_used_vars = mx_uint()
n_scalars = mx_uint()
n_mutate_vars = mx_uint()
type_mask = ctypes.c_int()
check_call(_LIB.MXFuncDescribe(
handle,
ctypes.byref(n_used_vars),
ctypes.byref(n_scalars),
ctypes.byref(n_mutate_vars),
ctypes.byref(type_mask)))
n_mutate_vars = n_mutate_vars.value
n_used_vars = n_used_vars.value
n_scalars = n_scalars.value
type_mask = type_mask.value
# Get the information from the function
name = ctypes.c_char_p()
desc = ctypes.c_char_p()
num_args = mx_uint()
arg_names = ctypes.POINTER(ctypes.c_char_p)()
arg_types = ctypes.POINTER(ctypes.c_char_p)()
arg_descs = ctypes.POINTER(ctypes.c_char_p)()
ret_type = ctypes.c_char_p()
check_call(_LIB.MXFuncGetInfo(
handle, ctypes.byref(name), ctypes.byref(desc),
ctypes.byref(num_args),
ctypes.byref(arg_names),
ctypes.byref(arg_types),
ctypes.byref(arg_descs),
ctypes.byref(ret_type)))
func_name = py_str(name.value)
if not func_name.startswith('_th_'):
return None
narg = int(num_args.value)
param_str = _build_param_doc(
[py_str(arg_names[i]) for i in range(narg)],
[py_str(arg_types[i]) for i in range(narg)],
[py_str(arg_descs[i]) for i in range(narg)])
if n_mutate_vars > 1:
res = ','.join(['res%d '%i for i in range(n_mutate_vars)])
else:
res = 'res '
doc_str = (('Interface for Torch function {name}.\n' +
'Invoke with\n{res}= mxnet.th.{name}(Parameters)\nor\n'+
'mxnet.th.{name}({res}, Parameters).\n\n' +
'{param_str}\n' +
'References: ' +
'https://github.com/torch/torch7/blob/master/doc/maths.md\n').format(
name=func_name[4:], param_str=param_str,
res=res))
def generic_torch_function(*args, **kwargs):
"""Invoke this function by passing in parameters.
Parameters
----------
*args
Positional arguments of inputs (both scalar and `NDArray`).
Returns
-------
out : NDArray
The result NDArray(tuple) of result of computation.
"""
ndargs = []
arg_format = ''
value = ''
for arg in args:
if isinstance(arg, NDArray):
ndargs.append(arg)
arg_format += 'n'
value += ','
elif isinstance(arg, int):
arg_format += 'i'
value += str(arg) + ','
elif isinstance(arg, str):
arg_format += 's'
value += str(arg) + ','
elif isinstance(arg, float):
arg_format += 'f'
value += str(arg) + ','
elif isinstance(arg, bool):
arg_format += 'b'
value += str(arg) + ','
value = value[:-1]
if len(ndargs) == n_used_vars:
ndargs = [NDArray(_new_empty_handle()) for _ in range(n_mutate_vars)] + ndargs
arg_format = 'n'*n_mutate_vars + arg_format
value = ','*n_mutate_vars + value
elif len(ndargs) == n_mutate_vars + n_used_vars:
pass
else:
raise AssertionError(('Incorrect number of input NDArrays. ' +
'Need to be either %d (inputs) or %d ' +
'(output buffer) + %d (input)') %
(n_used_vars, n_mutate_vars, n_used_vars))
kwargs['format'] = arg_format
kwargs['args'] = value
for k in kwargs:
kwargs[k] = str(kwargs[k])
check_call(_LIB.MXFuncInvokeEx(
handle,
c_handle_array(ndargs[n_mutate_vars:]), # pylint: disable=invalid-slice-index
c_array(mx_float, []),
c_handle_array(ndargs[:n_mutate_vars]), # pylint: disable=invalid-slice-index
ctypes.c_int(len(kwargs)),
c_str_array(kwargs.keys()),
c_str_array(kwargs.values())))
if n_mutate_vars == 1:
return ndargs[0]
else:
return ndargs[:n_mutate_vars] # pylint: disable=invalid-slice-index
# End of function declaration
ret_function = generic_torch_function
ret_function.__name__ = func_name[4:]
ret_function.__doc__ = doc_str
return ret_function | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/datetime.py | python | timedelta.days | (self) | return self._days | days | days | [
"days"
] | def days(self):
"""days"""
return self._days | [
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deepmodeling/deepmd-kit | 159e45d248b0429844fb6a8cb3b3a201987c8d79 | deepmd/fit/ener.py | python | EnerFitting.compute_input_stats | (self,
all_stat : dict,
protection : float = 1e-2) | Compute the input statistics
Parameters
----------
all_stat
if numb_fparam > 0 must have all_stat['fparam']
if numb_aparam > 0 must have all_stat['aparam']
can be prepared by model.make_stat_input
protection
Divided-by-zero protection | Compute the input statistics | [
"Compute",
"the",
"input",
"statistics"
] | def compute_input_stats(self,
all_stat : dict,
protection : float = 1e-2) -> None:
"""
Compute the input statistics
Parameters
----------
all_stat
if numb_fparam > 0 must have all_stat['fparam']
if numb_aparam > 0 must have all_stat['aparam']
can be prepared by model.make_stat_input
protection
Divided-by-zero protection
"""
# stat fparam
if self.numb_fparam > 0:
cat_data = np.concatenate(all_stat['fparam'], axis = 0)
cat_data = np.reshape(cat_data, [-1, self.numb_fparam])
self.fparam_avg = np.average(cat_data, axis = 0)
self.fparam_std = np.std(cat_data, axis = 0)
for ii in range(self.fparam_std.size):
if self.fparam_std[ii] < protection:
self.fparam_std[ii] = protection
self.fparam_inv_std = 1./self.fparam_std
# stat aparam
if self.numb_aparam > 0:
sys_sumv = []
sys_sumv2 = []
sys_sumn = []
for ss_ in all_stat['aparam'] :
ss = np.reshape(ss_, [-1, self.numb_aparam])
sys_sumv.append(np.sum(ss, axis = 0))
sys_sumv2.append(np.sum(np.multiply(ss, ss), axis = 0))
sys_sumn.append(ss.shape[0])
sumv = np.sum(sys_sumv, axis = 0)
sumv2 = np.sum(sys_sumv2, axis = 0)
sumn = np.sum(sys_sumn)
self.aparam_avg = (sumv)/sumn
self.aparam_std = self._compute_std(sumv2, sumv, sumn)
for ii in range(self.aparam_std.size):
if self.aparam_std[ii] < protection:
self.aparam_std[ii] = protection
self.aparam_inv_std = 1./self.aparam_std | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/core/arrays/datetimelike.py | python | validate_inferred_freq | (freq, inferred_freq, freq_infer) | return freq, freq_infer | If the user passes a freq and another freq is inferred from passed data,
require that they match.
Parameters
----------
freq : DateOffset or None
inferred_freq : DateOffset or None
freq_infer : bool
Returns
-------
freq : DateOffset or None
freq_infer : bool
Notes
-----
We assume at this point that `maybe_infer_freq` has been called, so
`freq` is either a DateOffset object or None. | If the user passes a freq and another freq is inferred from passed data,
require that they match. | [
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] | def validate_inferred_freq(freq, inferred_freq, freq_infer):
"""
If the user passes a freq and another freq is inferred from passed data,
require that they match.
Parameters
----------
freq : DateOffset or None
inferred_freq : DateOffset or None
freq_infer : bool
Returns
-------
freq : DateOffset or None
freq_infer : bool
Notes
-----
We assume at this point that `maybe_infer_freq` has been called, so
`freq` is either a DateOffset object or None.
"""
if inferred_freq is not None:
if freq is not None and freq != inferred_freq:
raise ValueError(
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f"{freq.freqstr}"
)
elif freq is None:
freq = inferred_freq
freq_infer = False
return freq, freq_infer | [
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potassco/clingo | e0c91d8f95cc28de1c480a871f9c97c30de83d40 | .github/conda.py | python | get_build_number | (channels, version) | return build_number + 1 | Get the next build number. | Get the next build number. | [
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] | def get_build_number(channels, version):
'''
Get the next build number.
'''
try:
pkgs = json.loads(subprocess.check_output(['conda', 'search', '--json', '-c', channels[0], NAME]))
except subprocess.CalledProcessError:
pkgs = {NAME: []}
build_number = -1
for pkg in pkgs.get(NAME, []):
if pkg['channel'].find(channels[0]) >= 0 and pkg["version"] == version:
build_number = max(build_number, pkg['build_number'])
return build_number + 1 | [
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Z3Prover/z3 | d745d03afdfdf638d66093e2bfbacaf87187f35b | src/api/python/z3/z3.py | python | ExprRef.num_args | (self) | return int(Z3_get_app_num_args(self.ctx_ref(), self.as_ast())) | Return the number of arguments of a Z3 application.
>>> a = Int('a')
>>> b = Int('b')
>>> (a + b).num_args()
2
>>> f = Function('f', IntSort(), IntSort(), IntSort(), IntSort())
>>> t = f(a, b, 0)
>>> t.num_args()
3 | Return the number of arguments of a Z3 application. | [
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] | def num_args(self):
"""Return the number of arguments of a Z3 application.
>>> a = Int('a')
>>> b = Int('b')
>>> (a + b).num_args()
2
>>> f = Function('f', IntSort(), IntSort(), IntSort(), IntSort())
>>> t = f(a, b, 0)
>>> t.num_args()
3
"""
if z3_debug():
_z3_assert(is_app(self), "Z3 application expected")
return int(Z3_get_app_num_args(self.ctx_ref(), self.as_ast())) | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/ops/gradients_impl.py | python | _IndexedSlicesToTensor | (value, dtype=None, name=None, as_ref=False) | return math_ops.unsorted_segment_sum(
value.values, value.indices, value.dense_shape[0], name=name) | Converts an IndexedSlices object `value` to a Tensor.
NOTE(mrry): This function is potentially expensive.
Args:
value: An ops.IndexedSlices object.
dtype: The dtype of the Tensor to be returned.
name: Optional name to use for the returned Tensor.
as_ref: True if a ref is requested.
Returns:
A dense Tensor representing the values in the given IndexedSlices.
Raises:
ValueError: If the IndexedSlices does not have the same dtype. | Converts an IndexedSlices object `value` to a Tensor. | [
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] | def _IndexedSlicesToTensor(value, dtype=None, name=None, as_ref=False):
"""Converts an IndexedSlices object `value` to a Tensor.
NOTE(mrry): This function is potentially expensive.
Args:
value: An ops.IndexedSlices object.
dtype: The dtype of the Tensor to be returned.
name: Optional name to use for the returned Tensor.
as_ref: True if a ref is requested.
Returns:
A dense Tensor representing the values in the given IndexedSlices.
Raises:
ValueError: If the IndexedSlices does not have the same dtype.
"""
_ = as_ref
if dtype and not dtype.is_compatible_with(value.dtype):
raise ValueError(
"Tensor conversion requested dtype %s for IndexedSlices with dtype %s" %
(dtype.name, value.dtype.name))
if value.dense_shape is None:
raise ValueError(
"Tensor conversion requested for IndexedSlices without dense_shape: %s"
% str(value))
# TODO(mrry): Consider adding static shape information to
# IndexedSlices, to avoid using numpy here.
dense_shape_value = tensor_util.constant_value(value.dense_shape)
if dense_shape_value is not None:
num_elements = np.prod(dense_shape_value)
if num_elements >= _LARGE_SPARSE_NUM_ELEMENTS:
warnings.warn(
"Converting sparse IndexedSlices to a dense Tensor with %d elements. "
"This may consume a large amount of memory." % num_elements)
else:
warnings.warn(
"Converting sparse IndexedSlices to a dense Tensor of unknown shape. "
"This may consume a large amount of memory.")
return math_ops.unsorted_segment_sum(
value.values, value.indices, value.dense_shape[0], name=name) | [
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FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Draft/draftutils/init_tools.py | python | init_toolbar | (workbench, toolbar, cmd_list) | Initialize a toolbar.
Parameters
----------
workbench: Gui.Workbench
The workbench. The commands from cmd_list must be available.
toolbar: string
The name of the toolbar.
cmd_list: list of strings or list of strings and tuples
See f.e. the return value of get_draft_drawing_commands. | Initialize a toolbar. | [
"Initialize",
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"toolbar",
"."
] | def init_toolbar(workbench, toolbar, cmd_list):
"""Initialize a toolbar.
Parameters
----------
workbench: Gui.Workbench
The workbench. The commands from cmd_list must be available.
toolbar: string
The name of the toolbar.
cmd_list: list of strings or list of strings and tuples
See f.e. the return value of get_draft_drawing_commands.
"""
for cmd in cmd_list:
if isinstance(cmd, tuple):
if len(cmd) == 1:
workbench.appendToolbar(toolbar, [cmd[0]])
else:
workbench.appendToolbar(toolbar, [cmd]) | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/data/util/options.py | python | merge_options | (*options_list) | return result | Merges the given options, returning the result as a new options object.
The input arguments are expected to have a matching type that derives from
`OptionsBase` (and thus each represent a set of options). The method outputs
an object of the same type created by merging the sets of options represented
by the input arguments.
The sets of options can be merged as long as there does not exist an option
with different non-default values.
If an option is an instance of `OptionsBase` itself, then this method is
applied recursively to the set of options represented by this option.
Args:
*options_list: options to merge
Raises:
TypeError: if the input arguments are incompatible or not derived from
`OptionsBase`
ValueError: if the given options cannot be merged
Returns:
A new options object which is the result of merging the given options. | Merges the given options, returning the result as a new options object. | [
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] | def merge_options(*options_list):
"""Merges the given options, returning the result as a new options object.
The input arguments are expected to have a matching type that derives from
`OptionsBase` (and thus each represent a set of options). The method outputs
an object of the same type created by merging the sets of options represented
by the input arguments.
The sets of options can be merged as long as there does not exist an option
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If an option is an instance of `OptionsBase` itself, then this method is
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Args:
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Raises:
TypeError: if the input arguments are incompatible or not derived from
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ValueError: if the given options cannot be merged
Returns:
A new options object which is the result of merging the given options.
"""
if len(options_list) < 1:
raise ValueError("At least one options should be provided")
result_type = type(options_list[0])
for options in options_list:
if not isinstance(options, result_type):
raise TypeError("Incompatible options type: %r vs %r" % (type(options),
result_type))
if not isinstance(options_list[0], OptionsBase):
raise TypeError("The inputs should inherit from `OptionsBase`")
default_options = result_type()
result = result_type()
for options in options_list:
# Iterate over all set options and merge the into the result.
for name in options._options: # pylint: disable=protected-access
this = getattr(result, name)
that = getattr(options, name)
default = getattr(default_options, name)
if that == default:
continue
elif this == default:
setattr(result, name, that)
elif isinstance(this, OptionsBase):
setattr(result, name, merge_options(this, that))
elif this != that:
raise ValueError(
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return result | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/model_pruning/python/pruning_utils.py | python | factorized_pool | (input_tensor,
window_shape,
pooling_type,
strides,
padding,
name=None) | return array_ops.squeeze(
array_ops.transpose(width_pooling, perm=[0, 1, 3, 2]), axis=[0, 1]) | Performs m x n pooling through a combination of 1xm and 1xn pooling.
Args:
input_tensor: Input tensor. Must be rank 2
window_shape: Pooling window shape
pooling_type: Either 'MAX' or 'AVG'
strides: The stride of the pooling window
padding: 'SAME' or 'VALID'.
name: Name of the op
Returns:
A rank 2 tensor containing the pooled output
Raises:
ValueError: if the input tensor is not rank 2 | Performs m x n pooling through a combination of 1xm and 1xn pooling. | [
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"""Performs m x n pooling through a combination of 1xm and 1xn pooling.
Args:
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"""
if input_tensor.get_shape().ndims != 2:
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[height, width] = input_tensor.get_shape()
with ops.name_scope(name, 'factorized_pool'):
input_tensor_aligned = array_ops.reshape(
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name=input_tensor.op.name + '_aligned')
height_pooling = nn_ops.pool(
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strides=[1, strides[0]],
padding=padding)
swap_height_width = array_ops.transpose(height_pooling, perm=[0, 1, 3, 2])
width_pooling = nn_ops.pool(
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/tkinter/__init__.py | python | Canvas.tag_raise | (self, *args) | Raise an item TAGORID given in ARGS
(optional above another item). | Raise an item TAGORID given in ARGS
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idaholab/moose | 9eeebc65e098b4c30f8205fb41591fd5b61eb6ff | python/MooseDocs/extensions/floats.py | python | caption_settings | () | return settings | Return settings necessary for captions. | Return settings necessary for captions. | [
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settings = dict()
settings['caption'] = (None, "The caption text for the float object.")
settings['prefix'] = (None, "The numbered caption label to include prior to the caption text.")
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/lmbr_aws/cleanup_utils/cleanup_utils.py | python | wait_for | (fn, attempts, interval, timeout_exception=None) | A custom waiter for AWS services that do not provide their own waiter. Uses a default attempt and interval which
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:param fn: The target function to execute
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:param timeout_exception: The exception to raise. An assertion error is raise by default
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can be overridden. Continues to execute a given function until the function returns True. Raises an exception if
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:param fn: The target function to execute
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:param timeout_exception: The exception to raise. An assertion error is raise by default
:param interval: The time to wait between subsequent function calls | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/ipaddress.py | python | _IPAddressBase.exploded | (self) | return self._explode_shorthand_ip_string() | Return the longhand version of the IP address as a string. | Return the longhand version of the IP address as a string. | [
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bumptop/BumpTop | 466d23597a07ae738f4265262fa01087fc6e257c | trunk/win/Source/Includes/QtIncludes/src/3rdparty/freetype/src/tools/docmaker/content.py | python | ContentProcessor.set_section | ( self, section_name ) | set current section during parsing | set current section during parsing | [
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self.sections[section_name] = section
self.section = section
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fatih/subvim | 241b6d170597857105da219c9b7d36059e9f11fb | vim/base/YouCompleteMe/third_party/jedi/jedi/parser/representation.py | python | Name.get_code | (self) | return ".".join(self.names) | Returns the names in a full string format | Returns the names in a full string format | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/scipy/sparse/coo.py | python | coo_matrix._check | (self) | Checks data structure for consistency | Checks data structure for consistency | [
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""" Checks data structure for consistency """
# index arrays should have integer data types
if self.row.dtype.kind != 'i':
warn("row index array has non-integer dtype (%s) "
% self.row.dtype.name)
if self.col.dtype.kind != 'i':
warn("col index array has non-integer dtype (%s) "
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idx_dtype = get_index_dtype(maxval=max(self.shape))
self.row = np.asarray(self.row, dtype=idx_dtype)
self.col = np.asarray(self.col, dtype=idx_dtype)
self.data = to_native(self.data)
if self.nnz > 0:
if self.row.max() >= self.shape[0]:
raise ValueError('row index exceeds matrix dimensions')
if self.col.max() >= self.shape[1]:
raise ValueError('column index exceeds matrix dimensions')
if self.row.min() < 0:
raise ValueError('negative row index found')
if self.col.min() < 0:
raise ValueError('negative column index found') | [
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Tencent/CMONGO | c40380caa14e05509f46993aa8b8da966b09b0b5 | buildscripts/resmoke.py | python | _dump_suite_config | (suite, logging_config) | return "\n".join(sb) | Returns a string that represents the YAML configuration of a suite.
TODO: include the "options" key in the result | Returns a string that represents the YAML configuration of a suite. | [
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"""
Returns a string that represents the YAML configuration of a suite.
TODO: include the "options" key in the result
"""
sb = []
sb.append("YAML configuration of suite %s" % (suite.get_name()))
sb.append(resmokelib.utils.dump_yaml({"selector": suite.get_selector_config()}))
sb.append("")
sb.append(resmokelib.utils.dump_yaml({"executor": suite.get_executor_config()}))
sb.append("")
sb.append(resmokelib.utils.dump_yaml({"logging": logging_config}))
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/email/_parseaddr.py | python | AddrlistClass.getquote | (self) | return self.getdelimited('"', '"\r', False) | Get a quote-delimited fragment from self's field. | Get a quote-delimited fragment from self's field. | [
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"""Get a quote-delimited fragment from self's field."""
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google/syzygy | 8164b24ebde9c5649c9a09e88a7fc0b0fcbd1bc5 | third_party/numpy/files/numpy/lib/_iotools.py | python | LineSplitter.autostrip | (self, method) | return lambda input: [_.strip() for _ in method(input)] | Wrapper to strip each member of the output of `method`.
Parameters
----------
method : function
Function that takes a single argument and returns a sequence of
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Returns
-------
wrapped : function
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"""
Wrapper to strip each member of the output of `method`.
Parameters
----------
method : function
Function that takes a single argument and returns a sequence of
strings.
Returns
-------
wrapped : function
The result of wrapping `method`. `wrapped` takes a single input
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"""
return lambda input: [_.strip() for _ in method(input)] | [
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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/pep517/wrappers.py | python | Pep517HookCaller.prepare_metadata_for_build_wheel | (
self, metadata_directory, config_settings=None) | return self._call_hook('prepare_metadata_for_build_wheel', {
'metadata_directory': abspath(metadata_directory),
'config_settings': config_settings,
}) | Prepare a *.dist-info folder with metadata for this project.
Returns the name of the newly created folder.
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"""Prepare a *.dist-info folder with metadata for this project.
Returns the name of the newly created folder.
If the build backend defines a hook with this name, it will be called
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and the dist-info extracted from that.
"""
return self._call_hook('prepare_metadata_for_build_wheel', {
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/Pygments/py3/pygments/lexers/textfmts.py | python | HttpLexer.get_tokens_unprocessed | (self, text, stack=('root',)) | return RegexLexer.get_tokens_unprocessed(self, text, stack) | Reset the content-type state. | Reset the content-type state. | [
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"""Reset the content-type state."""
self.content_type = None
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gimli-org/gimli | 17aa2160de9b15ababd9ef99e89b1bc3277bbb23 | pygimli/viewer/pv/utils.py | python | pgMesh2pvMesh | (mesh, data=None, label=None) | return grid | pyGIMLi's mesh format is different from pyvista's needs,
some preparation is necessary.
Parameters
----------
mesh: pg.Mesh
Structure generated by pyGIMLi to display.
data: iterable
Parameter to distribute to cells/nodes. | pyGIMLi's mesh format is different from pyvista's needs,
some preparation is necessary. | [
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"""
pyGIMLi's mesh format is different from pyvista's needs,
some preparation is necessary.
Parameters
----------
mesh: pg.Mesh
Structure generated by pyGIMLi to display.
data: iterable
Parameter to distribute to cells/nodes.
"""
_, tmp = tempfile.mkstemp(suffix=".vtk")
# export given mesh temporarily is the easiest and fastest option ATM
mesh.exportVTK(tmp)
grid = pv.read(tmp)
# check for parameters inside the pg.Mesh
for key, values in mesh.dataMap():
if len(values) == mesh.cellCount():
grid.cell_arrays[key] = np.asarray(values)
elif len(values) == mesh.nodeCount():
grid.point_arrays[key] = np.asarray(values)
# check the given data as well
try:
if data is not None:
if len(data) == mesh.cellCount():
grid.cell_arrays[label] = np.asarray(data)
elif len(data) == mesh.nodeCount():
grid.point_arrays[label] = np.asarray(data)
else:
pg.warn("Given data fits neither cell count nor node count:")
pg.warn("{} vs. {} vs. {}".format(len(data), mesh.cellCount(),
mesh.nodeCount()))
except Exception as e:
print(e)
pg.error("fix pyvista bindings")
if label is None:
# last data that was added
label = grid.array_names[-1]
elif label not in grid.array_names:
pg.warn("Given label '{}' was not found.".format(label))
label = grid.array_names[-1]
grid.set_active_scalars(label)
return grid | [
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ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/third_party/lib_x86_64/python2.7/dist-packages/numpy/lib/nanfunctions.py | python | nanstd | (a, axis=None, dtype=None, out=None, ddof=0, keepdims=False) | return std | Compute the standard deviation along the specified axis, while
ignoring NaNs.
Returns the standard deviation, a measure of the spread of a
distribution, of the non-NaN array elements. The standard deviation is
computed for the flattened array by default, otherwise over the
specified axis.
For all-NaN slices or slices with zero degrees of freedom, NaN is
returned and a `RuntimeWarning` is raised.
.. versionadded:: 1.8.0
Parameters
----------
a : array_like
Calculate the standard deviation of the non-NaN values.
axis : int, optional
Axis along which the standard deviation is computed. The default is
to compute the standard deviation of the flattened array.
dtype : dtype, optional
Type to use in computing the standard deviation. For arrays of
integer type the default is float64, for arrays of float types it
is the same as the array type.
out : ndarray, optional
Alternative output array in which to place the result. It must have
the same shape as the expected output but the type (of the
calculated values) will be cast if necessary.
ddof : int, optional
Means Delta Degrees of Freedom. The divisor used in calculations
is ``N - ddof``, where ``N`` represents the number of non-NaN
elements. By default `ddof` is zero.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the original `arr`.
Returns
-------
standard_deviation : ndarray, see dtype parameter above.
If `out` is None, return a new array containing the standard
deviation, otherwise return a reference to the output array. If
ddof is >= the number of non-NaN elements in a slice or the slice
contains only NaNs, then the result for that slice is NaN.
See Also
--------
var, mean, std
nanvar, nanmean
numpy.doc.ufuncs : Section "Output arguments"
Notes
-----
The standard deviation is the square root of the average of the squared
deviations from the mean: ``std = sqrt(mean(abs(x - x.mean())**2))``.
The average squared deviation is normally calculated as
``x.sum() / N``, where ``N = len(x)``. If, however, `ddof` is
specified, the divisor ``N - ddof`` is used instead. In standard
statistical practice, ``ddof=1`` provides an unbiased estimator of the
variance of the infinite population. ``ddof=0`` provides a maximum
likelihood estimate of the variance for normally distributed variables.
The standard deviation computed in this function is the square root of
the estimated variance, so even with ``ddof=1``, it will not be an
unbiased estimate of the standard deviation per se.
Note that, for complex numbers, `std` takes the absolute value before
squaring, so that the result is always real and nonnegative.
For floating-point input, the *std* is computed using the same
precision the input has. Depending on the input data, this can cause
the results to be inaccurate, especially for float32 (see example
below). Specifying a higher-accuracy accumulator using the `dtype`
keyword can alleviate this issue.
Examples
--------
>>> a = np.array([[1, np.nan], [3, 4]])
>>> np.nanstd(a)
1.247219128924647
>>> np.nanstd(a, axis=0)
array([ 1., 0.])
>>> np.nanstd(a, axis=1)
array([ 0., 0.5]) | Compute the standard deviation along the specified axis, while
ignoring NaNs. | [
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"""
Compute the standard deviation along the specified axis, while
ignoring NaNs.
Returns the standard deviation, a measure of the spread of a
distribution, of the non-NaN array elements. The standard deviation is
computed for the flattened array by default, otherwise over the
specified axis.
For all-NaN slices or slices with zero degrees of freedom, NaN is
returned and a `RuntimeWarning` is raised.
.. versionadded:: 1.8.0
Parameters
----------
a : array_like
Calculate the standard deviation of the non-NaN values.
axis : int, optional
Axis along which the standard deviation is computed. The default is
to compute the standard deviation of the flattened array.
dtype : dtype, optional
Type to use in computing the standard deviation. For arrays of
integer type the default is float64, for arrays of float types it
is the same as the array type.
out : ndarray, optional
Alternative output array in which to place the result. It must have
the same shape as the expected output but the type (of the
calculated values) will be cast if necessary.
ddof : int, optional
Means Delta Degrees of Freedom. The divisor used in calculations
is ``N - ddof``, where ``N`` represents the number of non-NaN
elements. By default `ddof` is zero.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the original `arr`.
Returns
-------
standard_deviation : ndarray, see dtype parameter above.
If `out` is None, return a new array containing the standard
deviation, otherwise return a reference to the output array. If
ddof is >= the number of non-NaN elements in a slice or the slice
contains only NaNs, then the result for that slice is NaN.
See Also
--------
var, mean, std
nanvar, nanmean
numpy.doc.ufuncs : Section "Output arguments"
Notes
-----
The standard deviation is the square root of the average of the squared
deviations from the mean: ``std = sqrt(mean(abs(x - x.mean())**2))``.
The average squared deviation is normally calculated as
``x.sum() / N``, where ``N = len(x)``. If, however, `ddof` is
specified, the divisor ``N - ddof`` is used instead. In standard
statistical practice, ``ddof=1`` provides an unbiased estimator of the
variance of the infinite population. ``ddof=0`` provides a maximum
likelihood estimate of the variance for normally distributed variables.
The standard deviation computed in this function is the square root of
the estimated variance, so even with ``ddof=1``, it will not be an
unbiased estimate of the standard deviation per se.
Note that, for complex numbers, `std` takes the absolute value before
squaring, so that the result is always real and nonnegative.
For floating-point input, the *std* is computed using the same
precision the input has. Depending on the input data, this can cause
the results to be inaccurate, especially for float32 (see example
below). Specifying a higher-accuracy accumulator using the `dtype`
keyword can alleviate this issue.
Examples
--------
>>> a = np.array([[1, np.nan], [3, 4]])
>>> np.nanstd(a)
1.247219128924647
>>> np.nanstd(a, axis=0)
array([ 1., 0.])
>>> np.nanstd(a, axis=1)
array([ 0., 0.5])
"""
var = nanvar(a, axis=axis, dtype=dtype, out=out, ddof=ddof,
keepdims=keepdims)
if isinstance(var, np.ndarray):
std = np.sqrt(var, out=var)
else:
std = var.dtype.type(np.sqrt(var))
return std | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqt/mantidqt/widgets/sliceviewer/lineplots.py | python | LinePlots.plot_y_line | (self, x: np.array, y: np.array) | Plots cut parallel to the Y axis of the image.
:param x: Array of values for X axis
:param y: Array of values for Y axis | Plots cut parallel to the Y axis of the image.
:param x: Array of values for X axis
:param y: Array of values for Y axis | [
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"""
Plots cut parallel to the Y axis of the image.
:param x: Array of values for X axis
:param y: Array of values for Y axis
"""
try:
self._yfig.set_data(y, x)
except (AttributeError, IndexError):
self._axy.clear()
self._yfig = self._axy.plot(y, x, scaley=False)[0]
self._yfig.set_linewidth(0.5)
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] | https://github.com/mantidproject/mantid/blob/03deeb89254ec4289edb8771e0188c2090a02f32/qt/python/mantidqt/mantidqt/widgets/sliceviewer/lineplots.py#L98-L110 | ||
OSGeo/gdal | 3748fc4ba4fba727492774b2b908a2130c864a83 | swig/python/osgeo/ogr.py | python | Feature.ExportToJson | (self, as_object=False, options=None) | return output | Exports a GeoJSON object which represents the Feature. The
as_object parameter determines whether the returned value
should be a Python object instead of a string. Defaults to False.
The options parameter is passed to Geometry.ExportToJson() | Exports a GeoJSON object which represents the Feature. The
as_object parameter determines whether the returned value
should be a Python object instead of a string. Defaults to False.
The options parameter is passed to Geometry.ExportToJson() | [
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] | def ExportToJson(self, as_object=False, options=None):
"""Exports a GeoJSON object which represents the Feature. The
as_object parameter determines whether the returned value
should be a Python object instead of a string. Defaults to False.
The options parameter is passed to Geometry.ExportToJson()"""
try:
import simplejson
except ImportError:
try:
import json as simplejson
except ImportError:
raise ImportError("Unable to import simplejson or json, needed for ExportToJson.")
geom = self.GetGeometryRef()
if geom is not None:
if options is None:
options = []
geom_json_string = geom.ExportToJson(options=options)
geom_json_object = simplejson.loads(geom_json_string)
else:
geom_json_object = None
output = {'type':'Feature',
'geometry': geom_json_object,
'properties': {}
}
fid = self.GetFID()
if fid != NullFID:
output['id'] = fid
for key in self.keys():
fld_defn = self.GetFieldDefnRef(self.GetFieldIndex(key))
if fld_defn.GetType() == _ogr.OFTInteger and fld_defn.GetSubType() == _ogr.OFSTBoolean:
output['properties'][key] = bool(self.GetField(key))
else:
output['properties'][key] = self.GetField(key)
if not as_object:
output = simplejson.dumps(output)
return output | [
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] | https://github.com/OSGeo/gdal/blob/3748fc4ba4fba727492774b2b908a2130c864a83/swig/python/osgeo/ogr.py#L4431-L4473 | |
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/datetime.py | python | datetime.fromtimestamp | (cls, t, tz=None) | return cls._fromtimestamp(t, tz is not None, tz) | Construct a datetime from a POSIX timestamp (like time.time()).
A timezone info object may be passed in as well. | Construct a datetime from a POSIX timestamp (like time.time()). | [
"Construct",
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"datetime",
"from",
"a",
"POSIX",
"timestamp",
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] | def fromtimestamp(cls, t, tz=None):
"""Construct a datetime from a POSIX timestamp (like time.time()).
A timezone info object may be passed in as well.
"""
_check_tzinfo_arg(tz)
return cls._fromtimestamp(t, tz is not None, tz) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/nntplib.py | python | _NNTPBase.newnews | (self, group, date, *, file=None) | return self._longcmdstring(cmd, file) | Process a NEWNEWS command. Arguments:
- group: group name or '*'
- date: a date or datetime object
Return:
- resp: server response if successful
- list: list of message ids | Process a NEWNEWS command. Arguments:
- group: group name or '*'
- date: a date or datetime object
Return:
- resp: server response if successful
- list: list of message ids | [
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] | def newnews(self, group, date, *, file=None):
"""Process a NEWNEWS command. Arguments:
- group: group name or '*'
- date: a date or datetime object
Return:
- resp: server response if successful
- list: list of message ids
"""
if not isinstance(date, (datetime.date, datetime.date)):
raise TypeError(
"the date parameter must be a date or datetime object, "
"not '{:40}'".format(date.__class__.__name__))
date_str, time_str = _unparse_datetime(date, self.nntp_version < 2)
cmd = 'NEWNEWS {0} {1} {2}'.format(group, date_str, time_str)
return self._longcmdstring(cmd, file) | [
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FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Arch/importIFClegacy.py | python | IfcEntity.getProperty | (self,propName) | return None | finds the value of the given property or quantity in this object, if exists | finds the value of the given property or quantity in this object, if exists | [
"finds",
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"value",
"of",
"the",
"given",
"property",
"or",
"quantity",
"in",
"this",
"object",
"if",
"exists"
] | def getProperty(self,propName):
"finds the value of the given property or quantity in this object, if exists"
propsets = self.doc.find('IFCRELDEFINESBYPROPERTIES','RelatedObjects',self)
if not propsets: return None
propset = []
for p in propsets:
if hasattr(p.RelatingPropertyDefinition,"HasProperties"):
propset.extend(p.RelatingPropertyDefinition.HasProperties)
elif hasattr(p.RelatingPropertyDefinition,"Quantities"):
propset.extend(p.RelatingPropertyDefinition.Quantities)
for prop in propset:
if prop.Name == propName:
print("found valid",prop)
if hasattr(prop,"LengthValue"):
return prop.LengthValue
elif hasattr(prop,"AreaValue"):
return prop.AreaValue
elif hasattr(prop,"VolumeValue"):
return prop.VolumeValue
elif hasattr(prop,"NominalValue"):
return prop.NominalValue
return None | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/training/python/training/hparam.py | python | HParams._init_from_proto | (self, hparam_def) | Creates a new HParams from `HParamDef` protocol buffer.
Args:
hparam_def: `HParamDef` protocol buffer. | Creates a new HParams from `HParamDef` protocol buffer. | [
"Creates",
"a",
"new",
"HParams",
"from",
"HParamDef",
"protocol",
"buffer",
"."
] | def _init_from_proto(self, hparam_def):
"""Creates a new HParams from `HParamDef` protocol buffer.
Args:
hparam_def: `HParamDef` protocol buffer.
"""
assert isinstance(hparam_def, hparam_pb2.HParamDef)
for name, value in hparam_def.hparam.items():
kind = value.WhichOneof('kind')
if kind.endswith('_value'):
# Single value.
if kind.startswith('int64'):
# Setting attribute value to be 'int' to ensure the type is compatible
# with both Python2 and Python3.
self.add_hparam(name, int(getattr(value, kind)))
elif kind.startswith('bytes'):
# Setting attribute value to be 'str' to ensure the type is compatible
# with both Python2 and Python3. UTF-8 encoding is assumed.
self.add_hparam(name, compat.as_str(getattr(value, kind)))
else:
self.add_hparam(name, getattr(value, kind))
else:
# List of values.
if kind.startswith('int64'):
# Setting attribute value to be 'int' to ensure the type is compatible
# with both Python2 and Python3.
self.add_hparam(name, [int(v) for v in getattr(value, kind).value])
elif kind.startswith('bytes'):
# Setting attribute value to be 'str' to ensure the type is compatible
# with both Python2 and Python3. UTF-8 encoding is assumed.
self.add_hparam(name, [compat.as_str(v)
for v in getattr(value, kind).value])
else:
self.add_hparam(name, [v for v in getattr(value, kind).value]) | [
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] | https://github.com/baidu-research/tensorflow-allreduce/blob/66d5b855e90b0949e9fa5cca5599fd729a70e874/tensorflow/contrib/training/python/training/hparam.py#L246-L279 | ||
pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | torch/utils/tensorboard/writer.py | python | SummaryWriter.add_embedding | (self, mat, metadata=None, label_img=None, global_step=None, tag='default', metadata_header=None) | Add embedding projector data to summary.
Args:
mat (torch.Tensor or numpy.array): A matrix which each row is the feature vector of the data point
metadata (list): A list of labels, each element will be convert to string
label_img (torch.Tensor): Images correspond to each data point
global_step (int): Global step value to record
tag (string): Name for the embedding
Shape:
mat: :math:`(N, D)`, where N is number of data and D is feature dimension
label_img: :math:`(N, C, H, W)`
Examples::
import keyword
import torch
meta = []
while len(meta)<100:
meta = meta+keyword.kwlist # get some strings
meta = meta[:100]
for i, v in enumerate(meta):
meta[i] = v+str(i)
label_img = torch.rand(100, 3, 10, 32)
for i in range(100):
label_img[i]*=i/100.0
writer.add_embedding(torch.randn(100, 5), metadata=meta, label_img=label_img)
writer.add_embedding(torch.randn(100, 5), label_img=label_img)
writer.add_embedding(torch.randn(100, 5), metadata=meta) | Add embedding projector data to summary. | [
"Add",
"embedding",
"projector",
"data",
"to",
"summary",
"."
] | def add_embedding(self, mat, metadata=None, label_img=None, global_step=None, tag='default', metadata_header=None):
"""Add embedding projector data to summary.
Args:
mat (torch.Tensor or numpy.array): A matrix which each row is the feature vector of the data point
metadata (list): A list of labels, each element will be convert to string
label_img (torch.Tensor): Images correspond to each data point
global_step (int): Global step value to record
tag (string): Name for the embedding
Shape:
mat: :math:`(N, D)`, where N is number of data and D is feature dimension
label_img: :math:`(N, C, H, W)`
Examples::
import keyword
import torch
meta = []
while len(meta)<100:
meta = meta+keyword.kwlist # get some strings
meta = meta[:100]
for i, v in enumerate(meta):
meta[i] = v+str(i)
label_img = torch.rand(100, 3, 10, 32)
for i in range(100):
label_img[i]*=i/100.0
writer.add_embedding(torch.randn(100, 5), metadata=meta, label_img=label_img)
writer.add_embedding(torch.randn(100, 5), label_img=label_img)
writer.add_embedding(torch.randn(100, 5), metadata=meta)
"""
torch._C._log_api_usage_once("tensorboard.logging.add_embedding")
mat = make_np(mat)
if global_step is None:
global_step = 0
# clear pbtxt?
# Maybe we should encode the tag so slashes don't trip us up?
# I don't think this will mess us up, but better safe than sorry.
subdir = "%s/%s" % (str(global_step).zfill(5), self._encode(tag))
save_path = os.path.join(self._get_file_writer().get_logdir(), subdir)
fs = tf.io.gfile.get_filesystem(save_path)
if fs.exists(save_path):
if fs.isdir(save_path):
print(
'warning: Embedding dir exists, did you set global_step for add_embedding()?')
else:
raise Exception("Path: `%s` exists, but is a file. Cannot proceed." % save_path)
else:
fs.makedirs(save_path)
if metadata is not None:
assert mat.shape[0] == len(
metadata), '#labels should equal with #data points'
make_tsv(metadata, save_path, metadata_header=metadata_header)
if label_img is not None:
assert mat.shape[0] == label_img.shape[0], '#images should equal with #data points'
make_sprite(label_img, save_path)
assert mat.ndim == 2, 'mat should be 2D, where mat.size(0) is the number of data points'
make_mat(mat, save_path)
# Filesystem doesn't necessarily have append semantics, so we store an
# internal buffer to append to and re-write whole file after each
# embedding is added
if not hasattr(self, "_projector_config"):
self._projector_config = ProjectorConfig()
embedding_info = get_embedding_info(
metadata, label_img, fs, subdir, global_step, tag)
self._projector_config.embeddings.extend([embedding_info])
from google.protobuf import text_format
config_pbtxt = text_format.MessageToString(self._projector_config)
write_pbtxt(self._get_file_writer().get_logdir(), config_pbtxt) | [
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miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/tensorflow/python/framework/dtypes.py | python | DType.is_unsigned | (self) | Returns whether this type is unsigned.
Non-numeric, unordered, and quantized types are not considered unsigned, and
this function returns `False`.
Returns:
Whether a `DType` is unsigned. | Returns whether this type is unsigned. | [
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] | def is_unsigned(self):
"""Returns whether this type is unsigned.
Non-numeric, unordered, and quantized types are not considered unsigned, and
this function returns `False`.
Returns:
Whether a `DType` is unsigned.
"""
try:
return self.min == 0
except TypeError:
return False | [
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cms-sw/cmssw | fd9de012d503d3405420bcbeec0ec879baa57cf2 | FWCore/PythonUtilities/python/XML2Python.py | python | xml2obj | (**kwargs) | return builder.topLevel() | Converts XML data into native Python object. Takes either
file handle or string as input. Does NOT fix illegal characters.
input source: Exactly one of the three following is needed
filehandle - input from file handle
contents - input from string
filename - input from filename
options:
filtering - boolean value telling code whether or not to fileter
input selection to remove illegal XML characters
nameChangeDict - dictionaries of names to change in python object | Converts XML data into native Python object. Takes either
file handle or string as input. Does NOT fix illegal characters. | [
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''' Converts XML data into native Python object. Takes either
file handle or string as input. Does NOT fix illegal characters.
input source: Exactly one of the three following is needed
filehandle - input from file handle
contents - input from string
filename - input from filename
options:
filtering - boolean value telling code whether or not to fileter
input selection to remove illegal XML characters
nameChangeDict - dictionaries of names to change in python object'''
# make sure we have exactly 1 input source
filehandle = kwargs.get ('filehandle')
contents = kwargs.get ('contents')
filename = kwargs.get ('filename')
if not filehandle and not contents and not filename:
raise RuntimeError("You must provide 'filehandle', 'contents', or 'filename'")
if filehandle and contents or \
filehandle and filename or \
contents and filename:
raise RuntimeError("You must provide only ONE of 'filehandle', 'contents', or 'filename'")
# are we filtering?
filtering = kwargs.get ('filtering')
if filtering:
# if we are filtering, we need to read in the contents to modify them
if not contents:
if not filehandle:
try:
filehandle = open (filename, 'r')
except:
raise RuntimeError("Failed to open '%s'" % filename)
contents = ''
for line in filehandle:
contents += line
filehandle.close()
filehandle = filename = ''
contents = quoteRE.sub (fixQuoteValue, contents)
ncDict = kwargs.get ('nameChangeDict', {})
builder = TreeBuilder (nameChangeDict = ncDict)
if contents:
xml.sax.parseString(contents, builder)
else:
if not filehandle:
try:
filehandle = open (filename, 'r')
except:
raise RuntimeError("Failed to open '%s'" % filename)
xml.sax.parse(filehandle, builder)
return builder.topLevel() | [
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apache/incubator-mxnet | f03fb23f1d103fec9541b5ae59ee06b1734a51d9 | python/mxnet/onnx/mx2onnx/_op_translations/_op_translations_opset12.py | python | convert_size | (node, **kwargs) | return nodes | Map MXNet's size_array operator attributes to onnx's Size operator
and return the created node. | Map MXNet's size_array operator attributes to onnx's Size operator
and return the created node. | [
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] | def convert_size(node, **kwargs):
"""Map MXNet's size_array operator attributes to onnx's Size operator
and return the created node.
"""
from onnx.helper import make_node
name, input_nodes, _ = get_inputs(node, kwargs)
create_tensor([1], name+'_1', kwargs['initializer'])
nodes = [
make_node('Size', [input_nodes[0]], [name+'_size']),
make_node('Reshape', [name+'_size', name+'_1'], [name], name=name)
]
return nodes | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/numpy/py3/numpy/lib/function_base.py | python | trim_zeros | (filt, trim='fb') | return filt[first:last] | Trim the leading and/or trailing zeros from a 1-D array or sequence.
Parameters
----------
filt : 1-D array or sequence
Input array.
trim : str, optional
A string with 'f' representing trim from front and 'b' to trim from
back. Default is 'fb', trim zeros from both front and back of the
array.
Returns
-------
trimmed : 1-D array or sequence
The result of trimming the input. The input data type is preserved.
Examples
--------
>>> a = np.array((0, 0, 0, 1, 2, 3, 0, 2, 1, 0))
>>> np.trim_zeros(a)
array([1, 2, 3, 0, 2, 1])
>>> np.trim_zeros(a, 'b')
array([0, 0, 0, ..., 0, 2, 1])
The input data type is preserved, list/tuple in means list/tuple out.
>>> np.trim_zeros([0, 1, 2, 0])
[1, 2] | Trim the leading and/or trailing zeros from a 1-D array or sequence. | [
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"""
Trim the leading and/or trailing zeros from a 1-D array or sequence.
Parameters
----------
filt : 1-D array or sequence
Input array.
trim : str, optional
A string with 'f' representing trim from front and 'b' to trim from
back. Default is 'fb', trim zeros from both front and back of the
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Returns
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Examples
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>>> a = np.array((0, 0, 0, 1, 2, 3, 0, 2, 1, 0))
>>> np.trim_zeros(a)
array([1, 2, 3, 0, 2, 1])
>>> np.trim_zeros(a, 'b')
array([0, 0, 0, ..., 0, 2, 1])
The input data type is preserved, list/tuple in means list/tuple out.
>>> np.trim_zeros([0, 1, 2, 0])
[1, 2]
"""
first = 0
trim = trim.upper()
if 'F' in trim:
for i in filt:
if i != 0.:
break
else:
first = first + 1
last = len(filt)
if 'B' in trim:
for i in filt[::-1]:
if i != 0.:
break
else:
last = last - 1
return filt[first:last] | [
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ethereum/solidity | 1210c3e60f2a0bd02ed7fb8f2da2f686ee38e1af | scripts/error_codes.py | python | find_ids_in_source_files | (file_names) | return id_to_file_names | Returns a dictionary with list of source files for every appearance of every id | Returns a dictionary with list of source files for every appearance of every id | [
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"""Returns a dictionary with list of source files for every appearance of every id"""
id_to_file_names = {}
for file_name in file_names:
find_ids_in_source_file(file_name, id_to_file_names)
return id_to_file_names | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py2/pandas/core/base.py | python | IndexOpsMixin.shape | (self) | return self._values.shape | Return a tuple of the shape of the underlying data. | Return a tuple of the shape of the underlying data. | [
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Return a tuple of the shape of the underlying data.
"""
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/shutil.py | python | _get_uid | (name) | return None | Returns an uid, given a user name. | Returns an uid, given a user name. | [
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] | def _get_uid(name):
"""Returns an uid, given a user name."""
if getpwnam is None or name is None:
return None
try:
result = getpwnam(name)
except KeyError:
result = None
if result is not None:
return result[2]
return None | [
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apple/swift-lldb | d74be846ef3e62de946df343e8c234bde93a8912 | scripts/Python/static-binding/lldb.py | python | SBCommunication.SetReadThreadBytesReceivedCallback | (self, callback, callback_baton) | return _lldb.SBCommunication_SetReadThreadBytesReceivedCallback(self, callback, callback_baton) | SetReadThreadBytesReceivedCallback(SBCommunication self, lldb::SBCommunication::ReadThreadBytesReceived callback, void * callback_baton) -> bool | SetReadThreadBytesReceivedCallback(SBCommunication self, lldb::SBCommunication::ReadThreadBytesReceived callback, void * callback_baton) -> bool | [
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"""SetReadThreadBytesReceivedCallback(SBCommunication self, lldb::SBCommunication::ReadThreadBytesReceived callback, void * callback_baton) -> bool"""
return _lldb.SBCommunication_SetReadThreadBytesReceivedCallback(self, callback, callback_baton) | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/python/feature_column/feature_column.py | python | indicator_column | (categorical_column) | return _IndicatorColumn(categorical_column) | Represents multi-hot representation of given categorical column.
Used to wrap any `categorical_column_*` (e.g., to feed to DNN). Use
`embedding_column` if the inputs are sparse.
```python
name = indicator_column(categorical_column_with_vocabulary_list(
'name', ['bob', 'george', 'wanda'])
columns = [name, ...]
features = tf.parse_example(..., features=make_parse_example_spec(columns))
dense_tensor = input_layer(features, columns)
dense_tensor == [[1, 0, 0]] # If "name" bytes_list is ["bob"]
dense_tensor == [[1, 0, 1]] # If "name" bytes_list is ["bob", "wanda"]
dense_tensor == [[2, 0, 0]] # If "name" bytes_list is ["bob", "bob"]
```
Args:
categorical_column: A `_CategoricalColumn` which is created by
`categorical_column_with_*` or `crossed_column` functions.
Returns:
An `_IndicatorColumn`. | Represents multi-hot representation of given categorical column. | [
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] | def indicator_column(categorical_column):
"""Represents multi-hot representation of given categorical column.
Used to wrap any `categorical_column_*` (e.g., to feed to DNN). Use
`embedding_column` if the inputs are sparse.
```python
name = indicator_column(categorical_column_with_vocabulary_list(
'name', ['bob', 'george', 'wanda'])
columns = [name, ...]
features = tf.parse_example(..., features=make_parse_example_spec(columns))
dense_tensor = input_layer(features, columns)
dense_tensor == [[1, 0, 0]] # If "name" bytes_list is ["bob"]
dense_tensor == [[1, 0, 1]] # If "name" bytes_list is ["bob", "wanda"]
dense_tensor == [[2, 0, 0]] # If "name" bytes_list is ["bob", "bob"]
```
Args:
categorical_column: A `_CategoricalColumn` which is created by
`categorical_column_with_*` or `crossed_column` functions.
Returns:
An `_IndicatorColumn`.
"""
return _IndicatorColumn(categorical_column) | [
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francinexue/xuefu | b6ff79747a42e020588c0c0a921048e08fe4680c | api/ctpx/ctpmd.py | python | CtpMd.onHeartBeatWarning | (self, lapsedTime) | @:param lapsedTime 距离上次接收报文的时间 | [] | def onHeartBeatWarning(self, lapsedTime):
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pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | torch/_appdirs.py | python | user_data_dir | (appname=None, appauthor=None, version=None, roaming=False) | return path | r"""Return full path to the user-specific data dir for this application.
"appname" is the name of application.
If None, just the system directory is returned.
"appauthor" (only used on Windows) is the name of the
appauthor or distributing body for this application. Typically
it is the owning company name. This falls back to appname. You may
pass False to disable it.
"version" is an optional version path element to append to the
path. You might want to use this if you want multiple versions
of your app to be able to run independently. If used, this
would typically be "<major>.<minor>".
Only applied when appname is present.
"roaming" (boolean, default False) can be set True to use the Windows
roaming appdata directory. That means that for users on a Windows
network setup for roaming profiles, this user data will be
sync'd on login. See
<http://technet.microsoft.com/en-us/library/cc766489(WS.10).aspx>
for a discussion of issues.
Typical user data directories are:
Mac OS X: ~/Library/Application Support/<AppName>
Unix: ~/.local/share/<AppName> # or in $XDG_DATA_HOME, if defined
Win XP (not roaming): C:\Documents and Settings\<username>\Application Data\<AppAuthor>\<AppName>
Win XP (roaming): C:\Documents and Settings\<username>\Local Settings\Application Data\<AppAuthor>\<AppName>
Win 7 (not roaming): C:\Users\<username>\AppData\Local\<AppAuthor>\<AppName>
Win 7 (roaming): C:\Users\<username>\AppData\Roaming\<AppAuthor>\<AppName>
For Unix, we follow the XDG spec and support $XDG_DATA_HOME.
That means, by default "~/.local/share/<AppName>". | r"""Return full path to the user-specific data dir for this application. | [
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r"""Return full path to the user-specific data dir for this application.
"appname" is the name of application.
If None, just the system directory is returned.
"appauthor" (only used on Windows) is the name of the
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Win 7 (not roaming): C:\Users\<username>\AppData\Local\<AppAuthor>\<AppName>
Win 7 (roaming): C:\Users\<username>\AppData\Roaming\<AppAuthor>\<AppName>
For Unix, we follow the XDG spec and support $XDG_DATA_HOME.
That means, by default "~/.local/share/<AppName>".
"""
if system == "win32":
if appauthor is None:
appauthor = appname
const = roaming and "CSIDL_APPDATA" or "CSIDL_LOCAL_APPDATA"
path = os.path.normpath(_get_win_folder(const))
if appname:
if appauthor is not False:
path = os.path.join(path, appauthor, appname)
else:
path = os.path.join(path, appname)
elif system == 'darwin':
path = os.path.expanduser('~/Library/Application Support/')
if appname:
path = os.path.join(path, appname)
else:
path = os.getenv('XDG_DATA_HOME', os.path.expanduser("~/.local/share"))
if appname:
path = os.path.join(path, appname)
if appname and version:
path = os.path.join(path, version)
return path | [
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CRYTEK/CRYENGINE | 232227c59a220cbbd311576f0fbeba7bb53b2a8c | Editor/Python/windows/Lib/site-packages/pip/_vendor/packaging/specifiers.py | python | BaseSpecifier.__hash__ | (self) | Returns a hash value for this Specifier like object. | Returns a hash value for this Specifier like object. | [
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Returns a hash value for this Specifier like object.
""" | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | scripts/SANS/sans/algorithm_detail/mask_workspace.py | python | mask_beam_stop | (mask_info, workspace) | return workspace | The beam stop is being masked here.
:param mask_info: a SANSStateMask object.
:param workspace: the workspace which is to be masked.
:return: a masked workspace | The beam stop is being masked here. | [
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"""
The beam stop is being masked here.
:param mask_info: a SANSStateMask object.
:param workspace: the workspace which is to be masked.
:return: a masked workspace
"""
beam_stop_arm_width = mask_info.beam_stop_arm_width
beam_stop_arm_angle = mask_info.beam_stop_arm_angle
beam_stop_arm_pos1 = mask_info.beam_stop_arm_pos1
beam_stop_arm_pos2 = mask_info.beam_stop_arm_pos2
if not beam_stop_arm_width or not beam_stop_arm_angle:
return workspace
lab_ipf_key = "low-angle-detector-name"
lab_component_name = workspace.getInstrument().getStringParameter(lab_ipf_key)
if not lab_component_name:
raise KeyError("{0} was not found in the IPF file for this instrument")
lab_component_name = lab_component_name[0]
comp_info = workspace.componentInfo()
detector_index = comp_info.indexOfAny(lab_component_name)
detector_pos = comp_info.position(detector_index)
z_position = detector_pos.getZ()
start_point = [beam_stop_arm_pos1, beam_stop_arm_pos2, z_position]
line_mask = create_line_mask(start_point, 100., beam_stop_arm_width, beam_stop_arm_angle)
mask_name = "MaskDetectorsInShape"
mask_options = {"Workspace": workspace,
"ShapeXML": line_mask}
mask_alg = create_unmanaged_algorithm(mask_name, **mask_options)
mask_alg.execute()
workspace = mask_alg.getProperty("Workspace").value
return workspace | [
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cksystemsgroup/scal | fa2208a97a77d65f4e90f85fef3404c27c1f2ac2 | tools/cpplint.py | python | _AddFilters | (filters) | Adds more filter overrides.
Unlike _SetFilters, this function does not reset the current list of filters
available.
Args:
filters: A string of comma-separated filters (eg "whitespace/indent").
Each filter should start with + or -; else we die. | Adds more filter overrides. | [
"Adds",
"more",
"filter",
"overrides",
"."
] | def _AddFilters(filters):
"""Adds more filter overrides.
Unlike _SetFilters, this function does not reset the current list of filters
available.
Args:
filters: A string of comma-separated filters (eg "whitespace/indent").
Each filter should start with + or -; else we die.
"""
_cpplint_state.AddFilters(filters) | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/boosted_trees/estimator_batch/trainer_hooks.py | python | SwitchTrainOp.__init__ | (self, first_train_op, train_steps, second_train_op) | Initializes a `SwitchTrainOp`. | Initializes a `SwitchTrainOp`. | [
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"SwitchTrainOp",
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] | def __init__(self, first_train_op, train_steps, second_train_op):
"""Initializes a `SwitchTrainOp`."""
self._first_train_op = first_train_op
self._second_train_op = second_train_op
self._train_steps = train_steps | [
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Cantera/cantera | 0119484b261967ccb55a0066c020599cacc312e4 | interfaces/cython/cantera/onedim.py | python | FlameBase.write_hdf | (self, filename, *args, group=None, species='X', mode='a',
description=None, compression=None, compression_opts=None,
quiet=True, normalize=True, **kwargs) | Write the solution vector to a HDF container file.
The `write_hdf` method preserves the structure of a `FlameBase`-derived
object (such as `FreeFlame`). Each simulation is saved as a *group*,
whereas individual domains are saved as subgroups. In addition to
datasets, information on `Sim1D.settings` and `Domain1D.settings` is
saved in form of HDF attributes. The internal HDF file structure is
illustrated for a `FreeFlame` output as:::
/ Group
/group0 Group
/group0/Sim1D_type Attribute
...
/group0/flame Group
/group0/flame/Domain1D_type Attribute
...
/group0/flame/T Dataset
...
/group0/flame/phase Group
/group0/products Group
/group0/products/Domain1D_type Attribute
...
/group0/products/T Dataset
...
/group0/products/phase Group
/group0/reactants Group
/group0/reactants/Domain1D_type Attribute
...
/group0/reactants/T Dataset
...
/group0/reactants/phase Group
where ``group0`` is the default name for the top level HDF entry, and
``reactants``, ``flame`` and ``products`` correspond to domain names.
Note that it is possible to save multiple solutions to a single HDF
container file.
:param filename:
HDF container file containing data to be restored
:param group:
Identifier for the group in the container file. A group may contain
multiple `SolutionArray` objects.
:param species:
Attribute to use obtaining species profiles, e.g. ``X`` for
mole fractions or ``Y`` for mass fractions.
:param mode:
Mode h5py uses to open the output file {'a' to read/write if file
exists, create otherwise (default); 'w' to create file, truncate if
exists; 'r+' to read/write, file must exist}.
:param description:
Custom comment describing the dataset to be stored.
:param compression:
Pre-defined h5py compression filters {None, 'gzip', 'lzf', 'szip'}
used for data compression.
:param compression_opts:
Options for the h5py compression filter; for 'gzip', this
corresponds to the compression level {None, 0-9}.
:param quiet:
Suppress message confirming successful file output.
:param normalize:
Boolean flag to indicate whether the mole/mass fractions should
be normalized (default is ``True``)
Additional arguments (i.e. *args* and *kwargs*) are passed on to
`SolutionArray.collect_data`. The method exports data using
`SolutionArray.write_hdf` via `to_solution_array` and requires a working
installation of h5py (`h5py` can be installed using pip or conda). | Write the solution vector to a HDF container file. | [
"Write",
"the",
"solution",
"vector",
"to",
"a",
"HDF",
"container",
"file",
"."
] | def write_hdf(self, filename, *args, group=None, species='X', mode='a',
description=None, compression=None, compression_opts=None,
quiet=True, normalize=True, **kwargs):
"""
Write the solution vector to a HDF container file.
The `write_hdf` method preserves the structure of a `FlameBase`-derived
object (such as `FreeFlame`). Each simulation is saved as a *group*,
whereas individual domains are saved as subgroups. In addition to
datasets, information on `Sim1D.settings` and `Domain1D.settings` is
saved in form of HDF attributes. The internal HDF file structure is
illustrated for a `FreeFlame` output as:::
/ Group
/group0 Group
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/group0/products/phase Group
/group0/reactants Group
/group0/reactants/Domain1D_type Attribute
...
/group0/reactants/T Dataset
...
/group0/reactants/phase Group
where ``group0`` is the default name for the top level HDF entry, and
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Note that it is possible to save multiple solutions to a single HDF
container file.
:param filename:
HDF container file containing data to be restored
:param group:
Identifier for the group in the container file. A group may contain
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:param species:
Attribute to use obtaining species profiles, e.g. ``X`` for
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:param mode:
Mode h5py uses to open the output file {'a' to read/write if file
exists, create otherwise (default); 'w' to create file, truncate if
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:param description:
Custom comment describing the dataset to be stored.
:param compression:
Pre-defined h5py compression filters {None, 'gzip', 'lzf', 'szip'}
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:param compression_opts:
Options for the h5py compression filter; for 'gzip', this
corresponds to the compression level {None, 0-9}.
:param quiet:
Suppress message confirming successful file output.
:param normalize:
Boolean flag to indicate whether the mole/mass fractions should
be normalized (default is ``True``)
Additional arguments (i.e. *args* and *kwargs*) are passed on to
`SolutionArray.collect_data`. The method exports data using
`SolutionArray.write_hdf` via `to_solution_array` and requires a working
installation of h5py (`h5py` can be installed using pip or conda).
"""
cols = ('extra', 'T', 'D', species)
meta = self.settings
meta['date'] = formatdate(localtime=True)
meta['cantera_version'] = __version__
meta['git_commit'] = __git_commit__
if description is not None:
meta['description'] = description
for i in range(3):
arr = self.to_solution_array(domain=self.domains[i], normalize=normalize)
group = arr.write_hdf(filename, *args, group=group, cols=cols,
subgroup=self.domains[i].name,
attrs=meta, mode=mode, append=(i > 0),
compression=compression,
compression_opts=compression_opts,
**kwargs)
meta = {}
mode = 'a'
if not quiet:
msg = "Solution saved to '{0}' as group '{1}'."
print(msg.format(filename, group)) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/aui.py | python | AuiPaneInfo.FloatingPosition | (*args, **kwargs) | return _aui.AuiPaneInfo_FloatingPosition(*args, **kwargs) | FloatingPosition(self, Point pos) -> AuiPaneInfo | FloatingPosition(self, Point pos) -> AuiPaneInfo | [
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"""FloatingPosition(self, Point pos) -> AuiPaneInfo"""
return _aui.AuiPaneInfo_FloatingPosition(*args, **kwargs) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/pandas/core/generic.py | python | NDFrame.asfreq | (
self: FrameOrSeries,
freq,
method=None,
how: Optional[str] = None,
normalize: bool_t = False,
fill_value=None,
) | return asfreq(
self,
freq,
method=method,
how=how,
normalize=normalize,
fill_value=fill_value,
) | Convert TimeSeries to specified frequency.
Optionally provide filling method to pad/backfill missing values.
Returns the original data conformed to a new index with the specified
frequency. ``resample`` is more appropriate if an operation, such as
summarization, is necessary to represent the data at the new frequency.
Parameters
----------
freq : DateOffset or str
method : {'backfill'/'bfill', 'pad'/'ffill'}, default None
Method to use for filling holes in reindexed Series (note this
does not fill NaNs that already were present):
* 'pad' / 'ffill': propagate last valid observation forward to next
valid
* 'backfill' / 'bfill': use NEXT valid observation to fill.
how : {'start', 'end'}, default end
For PeriodIndex only (see PeriodIndex.asfreq).
normalize : bool, default False
Whether to reset output index to midnight.
fill_value : scalar, optional
Value to use for missing values, applied during upsampling (note
this does not fill NaNs that already were present).
Returns
-------
converted : same type as caller
See Also
--------
reindex
Notes
-----
To learn more about the frequency strings, please see `this link
<https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__.
Examples
--------
Start by creating a series with 4 one minute timestamps.
>>> index = pd.date_range('1/1/2000', periods=4, freq='T')
>>> series = pd.Series([0.0, None, 2.0, 3.0], index=index)
>>> df = pd.DataFrame({'s':series})
>>> df
s
2000-01-01 00:00:00 0.0
2000-01-01 00:01:00 NaN
2000-01-01 00:02:00 2.0
2000-01-01 00:03:00 3.0
Upsample the series into 30 second bins.
>>> df.asfreq(freq='30S')
s
2000-01-01 00:00:00 0.0
2000-01-01 00:00:30 NaN
2000-01-01 00:01:00 NaN
2000-01-01 00:01:30 NaN
2000-01-01 00:02:00 2.0
2000-01-01 00:02:30 NaN
2000-01-01 00:03:00 3.0
Upsample again, providing a ``fill value``.
>>> df.asfreq(freq='30S', fill_value=9.0)
s
2000-01-01 00:00:00 0.0
2000-01-01 00:00:30 9.0
2000-01-01 00:01:00 NaN
2000-01-01 00:01:30 9.0
2000-01-01 00:02:00 2.0
2000-01-01 00:02:30 9.0
2000-01-01 00:03:00 3.0
Upsample again, providing a ``method``.
>>> df.asfreq(freq='30S', method='bfill')
s
2000-01-01 00:00:00 0.0
2000-01-01 00:00:30 NaN
2000-01-01 00:01:00 NaN
2000-01-01 00:01:30 2.0
2000-01-01 00:02:00 2.0
2000-01-01 00:02:30 3.0
2000-01-01 00:03:00 3.0 | Convert TimeSeries to specified frequency. | [
"Convert",
"TimeSeries",
"to",
"specified",
"frequency",
"."
] | def asfreq(
self: FrameOrSeries,
freq,
method=None,
how: Optional[str] = None,
normalize: bool_t = False,
fill_value=None,
) -> FrameOrSeries:
"""
Convert TimeSeries to specified frequency.
Optionally provide filling method to pad/backfill missing values.
Returns the original data conformed to a new index with the specified
frequency. ``resample`` is more appropriate if an operation, such as
summarization, is necessary to represent the data at the new frequency.
Parameters
----------
freq : DateOffset or str
method : {'backfill'/'bfill', 'pad'/'ffill'}, default None
Method to use for filling holes in reindexed Series (note this
does not fill NaNs that already were present):
* 'pad' / 'ffill': propagate last valid observation forward to next
valid
* 'backfill' / 'bfill': use NEXT valid observation to fill.
how : {'start', 'end'}, default end
For PeriodIndex only (see PeriodIndex.asfreq).
normalize : bool, default False
Whether to reset output index to midnight.
fill_value : scalar, optional
Value to use for missing values, applied during upsampling (note
this does not fill NaNs that already were present).
Returns
-------
converted : same type as caller
See Also
--------
reindex
Notes
-----
To learn more about the frequency strings, please see `this link
<https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__.
Examples
--------
Start by creating a series with 4 one minute timestamps.
>>> index = pd.date_range('1/1/2000', periods=4, freq='T')
>>> series = pd.Series([0.0, None, 2.0, 3.0], index=index)
>>> df = pd.DataFrame({'s':series})
>>> df
s
2000-01-01 00:00:00 0.0
2000-01-01 00:01:00 NaN
2000-01-01 00:02:00 2.0
2000-01-01 00:03:00 3.0
Upsample the series into 30 second bins.
>>> df.asfreq(freq='30S')
s
2000-01-01 00:00:00 0.0
2000-01-01 00:00:30 NaN
2000-01-01 00:01:00 NaN
2000-01-01 00:01:30 NaN
2000-01-01 00:02:00 2.0
2000-01-01 00:02:30 NaN
2000-01-01 00:03:00 3.0
Upsample again, providing a ``fill value``.
>>> df.asfreq(freq='30S', fill_value=9.0)
s
2000-01-01 00:00:00 0.0
2000-01-01 00:00:30 9.0
2000-01-01 00:01:00 NaN
2000-01-01 00:01:30 9.0
2000-01-01 00:02:00 2.0
2000-01-01 00:02:30 9.0
2000-01-01 00:03:00 3.0
Upsample again, providing a ``method``.
>>> df.asfreq(freq='30S', method='bfill')
s
2000-01-01 00:00:00 0.0
2000-01-01 00:00:30 NaN
2000-01-01 00:01:00 NaN
2000-01-01 00:01:30 2.0
2000-01-01 00:02:00 2.0
2000-01-01 00:02:30 3.0
2000-01-01 00:03:00 3.0
"""
from pandas.core.resample import asfreq
return asfreq(
self,
freq,
method=method,
how=how,
normalize=normalize,
fill_value=fill_value,
) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_controls.py | python | ListItemAttr.GetTextColour | (*args, **kwargs) | return _controls_.ListItemAttr_GetTextColour(*args, **kwargs) | GetTextColour(self) -> Colour | GetTextColour(self) -> Colour | [
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return _controls_.ListItemAttr_GetTextColour(*args, **kwargs) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py3/pandas/core/arrays/categorical.py | python | Categorical.itemsize | (self) | return self.categories.itemsize | return the size of a single category | return the size of a single category | [
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return the size of a single category
"""
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DaFuCoding/MTCNN_Caffe | 09c30c3ff391bd9cb6b249c1910afaf147767ab3 | tools/extra/parse_log.py | python | fix_initial_nan_learning_rate | (dict_list) | Correct initial value of learning rate
Learning rate is normally not printed until after the initial test and
training step, which means the initial testing and training rows have
LearningRate = NaN. Fix this by copying over the LearningRate from the
second row, if it exists. | Correct initial value of learning rate | [
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] | def fix_initial_nan_learning_rate(dict_list):
"""Correct initial value of learning rate
Learning rate is normally not printed until after the initial test and
training step, which means the initial testing and training rows have
LearningRate = NaN. Fix this by copying over the LearningRate from the
second row, if it exists.
"""
if len(dict_list) > 1:
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natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/models/image/cifar10/cifar10.py | python | _variable_with_weight_decay | (name, shape, stddev, wd) | return var | Helper to create an initialized Variable with weight decay.
Note that the Variable is initialized with a truncated normal distribution.
A weight decay is added only if one is specified.
Args:
name: name of the variable
shape: list of ints
stddev: standard deviation of a truncated Gaussian
wd: add L2Loss weight decay multiplied by this float. If None, weight
decay is not added for this Variable.
Returns:
Variable Tensor | Helper to create an initialized Variable with weight decay. | [
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] | def _variable_with_weight_decay(name, shape, stddev, wd):
"""Helper to create an initialized Variable with weight decay.
Note that the Variable is initialized with a truncated normal distribution.
A weight decay is added only if one is specified.
Args:
name: name of the variable
shape: list of ints
stddev: standard deviation of a truncated Gaussian
wd: add L2Loss weight decay multiplied by this float. If None, weight
decay is not added for this Variable.
Returns:
Variable Tensor
"""
dtype = tf.float16 if FLAGS.use_fp16 else tf.float32
var = _variable_on_cpu(
name,
shape,
tf.truncated_normal_initializer(stddev=stddev, dtype=dtype))
if wd is not None:
weight_decay = tf.mul(tf.nn.l2_loss(var), wd, name='weight_loss')
tf.add_to_collection('losses', weight_decay)
return var | [
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miyosuda/TensorFlowAndroidMNIST | 7b5a4603d2780a8a2834575706e9001977524007 | jni-build/jni/include/tensorflow/python/util/compat.py | python | as_str_any | (value) | Converts to `str` as `str(value)`, but use `as_str` for `bytes`.
Args:
value: A object that can be converted to `str`.
Returns:
A `str` object. | Converts to `str` as `str(value)`, but use `as_str` for `bytes`. | [
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"""Converts to `str` as `str(value)`, but use `as_str` for `bytes`.
Args:
value: A object that can be converted to `str`.
Returns:
A `str` object.
"""
if isinstance(value, bytes):
return as_str(value)
else:
return str(value) | [
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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/distlib/_backport/sysconfig.py | python | get_scheme_names | () | return tuple(sorted(_SCHEMES.sections())) | Return a tuple containing the schemes names. | Return a tuple containing the schemes names. | [
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facebookresearch/habitat-sim | 63b6c71d9ca8adaefb140b198196f5d0ca1f1e34 | src_python/habitat_sim/robots/mobile_manipulator.py | python | MobileManipulator.reconfigure | (self) | Instantiates the robot the scene. Loads the URDF, sets initial state of parameters, joints, motors, etc... | Instantiates the robot the scene. Loads the URDF, sets initial state of parameters, joints, motors, etc... | [
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] | def reconfigure(self) -> None:
"""Instantiates the robot the scene. Loads the URDF, sets initial state of parameters, joints, motors, etc..."""
ao_mgr = self._sim.get_articulated_object_manager()
self.sim_obj = ao_mgr.add_articulated_object_from_urdf(
self.urdf_path, fixed_base=self._fixed_base
)
if self._limit_robo_joints:
# automatic joint limit clamping after each call to sim.step_physics()
self.sim_obj.auto_clamp_joint_limits = True
for link_id in self.sim_obj.get_link_ids():
self.joint_pos_indices[link_id] = self.sim_obj.get_link_joint_pos_offset(
link_id
)
self.joint_dof_indices[link_id] = self.sim_obj.get_link_dof_offset(link_id)
self.joint_limits = self.sim_obj.joint_position_limits
# remove any default damping motors
for motor_id in self.sim_obj.existing_joint_motor_ids:
self.sim_obj.remove_joint_motor(motor_id)
# re-generate all joint motors with arm gains.
jms = JointMotorSettings(
0, # position_target
self.params.arm_mtr_pos_gain, # position_gain
0, # velocity_target
self.params.arm_mtr_vel_gain, # velocity_gain
self.params.arm_mtr_max_impulse, # max_impulse
)
self.sim_obj.create_all_motors(jms)
self._update_motor_settings_cache()
# set correct gains for wheels
if self.params.wheel_joints is not None:
jms = JointMotorSettings(
0, # position_target
self.params.wheel_mtr_pos_gain, # position_gain
0, # velocity_target
self.params.wheel_mtr_vel_gain, # velocity_gain
self.params.wheel_mtr_max_impulse, # max_impulse
)
# pylint: disable=not-an-iterable
for i in self.params.wheel_joints:
self.sim_obj.update_joint_motor(self.joint_motors[i][0], jms)
# set initial states and targets
self.arm_joint_pos = self.params.arm_init_params
self.gripper_joint_pos = self.params.gripper_init_params
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google-ar/WebARonTango | e86965d2cbc652156b480e0fcf77c716745578cd | chromium/src/gpu/command_buffer/build_gles2_cmd_buffer.py | python | GENnHandler.WriteImmediateCmdComputeSize | (self, func, f) | Overrriden from TypeHandler. | Overrriden from TypeHandler. | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/distutils/command/install.py | python | install.convert_paths | (self, *names) | Call `convert_path` over `names`. | Call `convert_path` over `names`. | [
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OSGeo/gdal | 3748fc4ba4fba727492774b2b908a2130c864a83 | swig/python/osgeo/gdal.py | python | RegenerateOverview | (*args, **kwargs) | return _gdal.RegenerateOverview(*args, **kwargs) | r"""RegenerateOverview(Band srcBand, Band overviewBand, char const * resampling="average", GDALProgressFunc callback=0, void * callback_data=None) -> int | r"""RegenerateOverview(Band srcBand, Band overviewBand, char const * resampling="average", GDALProgressFunc callback=0, void * callback_data=None) -> int | [
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apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | src/external/coremltools_wrap/coremltools/coremltools/converters/sklearn/_one_hot_encoder.py | python | update_dimension | (model, input_dimension) | return out_dimension | Given a model that takes an array of dimension input_dimension, returns
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BitMEX/api-connectors | 37a3a5b806ad5d0e0fc975ab86d9ed43c3bcd812 | auto-generated/python/swagger_client/api/position_api.py | python | PositionApi.position_update_risk_limit | (self, symbol, risk_limit, **kwargs) | Update your risk limit. # noqa: E501
This method makes a synchronous HTTP request by default. To make an
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>>> thread = api.position_update_risk_limit(symbol, risk_limit, async_req=True)
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/site-packages/pip/_vendor/contextlib2.py | python | ExitStack.callback | (self, callback, *args, **kwds) | return callback | Registers an arbitrary callback and arguments.
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | third_party/closure_linter/closure_linter/indentation.py | python | IndentationRules._AddToEach | (self, original, amount) | return set([x + amount for x in original]) | Returns a new set with the given amount added to each element.
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/ResourceManager/lib/Crypto/Hash/BLAKE2b.py | python | new | (**kwargs) | return BLAKE2b_Hash(data, key, digest_bytes, update_after_digest) | Create a new hash object.
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | scripts/Inelastic/CrystalField/pointcharge.py | python | PointCharge.Charges | (self) | return self._charges | The current list of charges as a dictionary | The current list of charges as a dictionary | [
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"""The current list of charges as a dictionary"""
return self._charges | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/python/framework/common_shapes.py | python | max_pool_shape | (op) | return [tensor_shape.TensorShape(output_shape)] | Shape function for a MaxPool op.
This op has one input:
* input, a 4D tensor with shape = [batch_size, rows, cols, depth_in]
The output is a 4D tensor with shape = [batch_size, out_rows,
out_cols, depth_out], where out_rows, out_cols, and depth_out depend
on the value of the op's "ksize", "strides", and "padding" attrs.
Args:
op: A MaxPool Operation.
Returns:
A single-element list containing the Shape of the MaxPool output.
Raises:
ValueError: If the shape of the input is invalid or incompatible with
the values of the attrs. | Shape function for a MaxPool op. | [
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"""Shape function for a MaxPool op.
This op has one input:
* input, a 4D tensor with shape = [batch_size, rows, cols, depth_in]
The output is a 4D tensor with shape = [batch_size, out_rows,
out_cols, depth_out], where out_rows, out_cols, and depth_out depend
on the value of the op's "ksize", "strides", and "padding" attrs.
Args:
op: A MaxPool Operation.
Returns:
A single-element list containing the Shape of the MaxPool output.
Raises:
ValueError: If the shape of the input is invalid or incompatible with
the values of the attrs.
"""
input_shape = op.inputs[0].get_shape().with_rank(4)
try:
data_format = op.get_attr("data_format")
except ValueError:
data_format = None
if data_format == b"NCHW":
# Convert input shape to the default NHWC for inference.
input_shape = [input_shape[0], input_shape[2], input_shape[3],
input_shape[1]]
if data_format == b"NCHW":
ksize_b, ksize_d, ksize_r, ksize_c = op.get_attr("ksize")
stride_b, stride_d, stride_r, stride_c = op.get_attr("strides")
else:
ksize_b, ksize_r, ksize_c, ksize_d = op.get_attr("ksize")
stride_b, stride_r, stride_c, stride_d = op.get_attr("strides")
batch_size = input_shape[0]
in_rows = input_shape[1]
in_cols = input_shape[2]
depth = input_shape[3]
if ksize_b != 1:
raise ValueError("Current implementation does not support pooling "
"in the batch dimension.")
if stride_b != 1:
raise ValueError("Current implementation does not support strides "
"in the batch dimension.")
if not ((ksize_r == 1 and ksize_c == 1) or ksize_d == 1):
raise ValueError("MaxPooling supports exactly one of pooling across depth "
"or pooling across width/height.")
# TODO(mrry,shlens): Raise an error if the stride would cause
# information in the input to be ignored. This will require a change
# in the kernel implementation.
if ksize_d == 1:
padding = op.get_attr("padding")
out_rows, out_cols = get2d_conv_output_size(in_rows, in_cols, ksize_r,
ksize_c, stride_r, stride_c,
padding)
output_shape = [batch_size, out_rows, out_cols, depth]
else:
if depth % ksize_d > 0:
raise ValueError("Depthwise max pooling requires the depth window "
"to evenly divide the input depth.")
if stride_d != ksize_d:
raise ValueError("Depthwise max pooling requires the depth window "
"to equal the depth stride.")
output_shape = [batch_size, in_rows, in_cols, depth // ksize_d]
if data_format == b"NCHW":
# Convert output shape back to NCHW.
output_shape = [output_shape[0], output_shape[3], output_shape[1],
output_shape[2]]
return [tensor_shape.TensorShape(output_shape)] | [
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google/fhir | d77f57706c1a168529b0b87ca7ccb1c0113e83c2 | py/google/fhir/r4/primitive_handler.py | python | PrimitiveHandler.primitive_wrapper_from_json_value | (
self,
json_value: Optional[Any],
primitive_cls: Type[message.Message],
*,
default_timezone: str = _primitive_time_utils.SIMPLE_ZULU
) | return wrapper_cls.from_json_value(json_value, primitive_cls,
wrapper_context) | See jsonformat PrimitiveHandler.primitive_wrapper_from_json_value. | See jsonformat PrimitiveHandler.primitive_wrapper_from_json_value. | [
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] | def primitive_wrapper_from_json_value(
self,
json_value: Optional[Any],
primitive_cls: Type[message.Message],
*,
default_timezone: str = _primitive_time_utils.SIMPLE_ZULU
) -> _primitive_wrappers.PrimitiveWrapper:
"""See jsonformat PrimitiveHandler.primitive_wrapper_from_json_value."""
if not annotation_utils.is_primitive_type(primitive_cls.DESCRIPTOR):
raise ValueError(
f'Not a primitive type: {primitive_cls.DESCRIPTOR.full_name!r}.')
wrapper_cls = self.get_primitive_wrapper_cls_for_primitive_cls(
primitive_cls)
wrapper_context = _primitive_wrappers.Context(
separator_stride_cls=fhirproto_extensions_pb2
.Base64BinarySeparatorStride,
code_cls=self.code_cls,
default_timezone=default_timezone)
return wrapper_cls.from_json_value(json_value, primitive_cls,
wrapper_context) | [
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moflow/moflow | 2dfb27c799c90c6caf1477508eca3eec616ef7d2 | bap/libtracewrap/libtrace/protobuf/python/google/protobuf/text_format.py | python | _Tokenizer.AtEnd | (self) | return self.token == '' | Checks the end of the text was reached.
Returns:
True iff the end was reached. | Checks the end of the text was reached. | [
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] | def AtEnd(self):
"""Checks the end of the text was reached.
Returns:
True iff the end was reached.
"""
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pytz/tzfile.py | python | _std_string | (s) | return str(s.decode('ASCII')) | Cast a string or byte string to an ASCII string. | Cast a string or byte string to an ASCII string. | [
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natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/python/ops/array_ops.py | python | _ZerosLikeShape | (op) | return [op.inputs[0].get_shape()] | Shape function for the ZerosLike op. | Shape function for the ZerosLike op. | [
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eclipse/sumo | 7132a9b8b6eea734bdec38479026b4d8c4336d03 | tools/contributed/sumopy/coremodules/network/routing.py | python | priorityDictionary.__setitem__ | (self, key, val) | Change value stored in dictionary and add corresponding
pair to heap. Rebuilds the heap if the number of deleted items grows
too large, to avoid memory leakage. | Change value stored in dictionary and add corresponding
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/dataview.py | python | DataViewCtrl.GetCurrentColumn | (*args, **kwargs) | return _dataview.DataViewCtrl_GetCurrentColumn(*args, **kwargs) | GetCurrentColumn(self) -> DataViewColumn | GetCurrentColumn(self) -> DataViewColumn | [
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pmq20/node-packer | 12c46c6e44fbc14d9ee645ebd17d5296b324f7e0 | lts/deps/v8/tools/stats-viewer.py | python | ChromeCounter.Name | (self) | return result | Return the ascii name of this counter. | Return the ascii name of this counter. | [
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"data",
".",
"ByteAt",
"(",
"index",
")",
"return",
"result"
] | https://github.com/pmq20/node-packer/blob/12c46c6e44fbc14d9ee645ebd17d5296b324f7e0/lts/deps/v8/tools/stats-viewer.py#L405-L414 |
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