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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | third_party/protobuf/python/google/protobuf/internal/cpp_message.py | python | InitMessage | (message_descriptor, cls) | Constructs a new message instance (called before instance's __init__). | Constructs a new message instance (called before instance's __init__). | [
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cls._extensions_by_name = {}
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/ipython/py3/IPython/core/inputtransformer.py | python | _strip_prompts | (prompt_re, initial_re=None, turnoff_re=None) | Remove matching input prompts from a block of input.
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bryanyzhu/Hidden-Two-Stream | f7f684adbdacb6df6b1cf196c3a476cd23484a0f | scripts/cpp_lint.py | python | GetLineWidth | (line) | Determines the width of the line in column positions.
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Returns:
The width of the line in column positions, accounting for Unicode
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_core.py | python | ChildFocusEvent.__init__ | (self, *args, **kwargs) | __init__(self, Window win=None) -> ChildFocusEvent
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RegrowthStudios/SoACode-Public | c3ddd69355b534d5e70e2e6d0c489b4e93ab1ffe | utils/git-hooks/cpplint/cpplint.py | python | FindNextMultiLineCommentStart | (lines, lineix) | return len(lines) | Find the beginning marker for a multiline comment. | Find the beginning marker for a multiline comment. | [
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# Only return this marker if the comment goes beyond this line
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apache/thrift | 0b29261a4f3c6882ef3b09aae47914f0012b0472 | lib/py/src/Thrift.py | python | TProcessor.process | (self, iprot, oprot) | Process a request. The normal behvaior is to have the
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cmu-db/noisepage | 79276e68fe83322f1249e8a8be96bd63c583ae56 | script/self_driving/forecasting/forecaster.py | python | Forecaster.__init__ | (
self,
trace_file: str,
trace_sequence: List,
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seq_len: int,
horizon_len: int) | Initializer
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:param seq_len: Length of a sequence
:param horizon_len: Horizon length | Initializer
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self._seq_len = seq_len
self._horizon_len = horizon_len
self._test_mode = test_mode
self._eval_data_size = eval_size
self._data_loader = DataLoader(
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adobe/chromium | cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7 | build/android/android_commands.py | python | AndroidCommands.SetupPerformanceTest | (self) | Sets up performance tests. | Sets up performance tests. | [
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"""Sets up performance tests."""
# Disable CPU scaling to reduce noise in tests
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google/shaka-packager | e1b0c7c45431327fd3ce193514a5407d07b39b22 | packager/third_party/protobuf/python/google/protobuf/internal/well_known_types.py | python | _FieldMaskTree.AddPath | (self, path) | Adds a field path into the tree.
If the field path to add is a sub-path of an existing field path
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the given path so nothing will be added to the tree. If the path
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path: The field path to add.
"""
node = self._root
for name in path.split('.'):
if name not in node:
node[name] = {}
elif not node[name]:
# Pre-existing empty node implies we already have this entire tree.
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node = node[name]
# Remove any sub-trees we might have had.
node.clear() | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/pandas/core/frame.py | python | DataFrame.to_feather | (self, path) | Write out the binary feather-format for DataFrames.
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"""
Write out the binary feather-format for DataFrames.
Parameters
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path : str
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larroy/clearskies_core | 3574ddf0edc8555454c7044126e786a6c29444dc | tools/gyp/pylib/gyp/generator/msvs.py | python | _InitNinjaFlavor | (options, target_list, target_dicts) | Initialize targets for the ninja flavor.
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Arguments:
options: Options provided to the generator.
target_list: List of target pairs: 'base/base.gyp:base'.
target_dicts: Dict of target properties keyed on target pair.
"""
for qualified_target in target_list:
spec = target_dicts[qualified_target]
if spec.get('msvs_external_builder'):
# The spec explicitly defined an external builder, so don't change it.
continue
path_to_ninja = spec.get('msvs_path_to_ninja', 'ninja.exe')
spec['msvs_external_builder'] = 'ninja'
if not spec.get('msvs_external_builder_out_dir'):
spec['msvs_external_builder_out_dir'] = \
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/site-packages/pip/_internal/index/package_finder.py | python | PackageFinder._sort_links | (self, links) | return no_eggs + eggs | Returns elements of links in order, non-egg links first, egg links
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/difflib.py | python | IS_CHARACTER_JUNK | (ch, ws=" \t") | return ch in ws | r"""
Return 1 for ignorable character: iff `ch` is a space or tab.
Examples:
>>> IS_CHARACTER_JUNK(' ')
True
>>> IS_CHARACTER_JUNK('\t')
True
>>> IS_CHARACTER_JUNK('\n')
False
>>> IS_CHARACTER_JUNK('x')
False | r"""
Return 1 for ignorable character: iff `ch` is a space or tab. | [
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] | def IS_CHARACTER_JUNK(ch, ws=" \t"):
r"""
Return 1 for ignorable character: iff `ch` is a space or tab.
Examples:
>>> IS_CHARACTER_JUNK(' ')
True
>>> IS_CHARACTER_JUNK('\t')
True
>>> IS_CHARACTER_JUNK('\n')
False
>>> IS_CHARACTER_JUNK('x')
False
"""
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/mixins/listctrl.py | python | ListCtrlSelectionManagerMix.setPopupMenu | (self, menu) | Must be set for default behaviour | Must be set for default behaviour | [
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""" Must be set for default behaviour """
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dmlc/treelite | df56babb6a4a2d7c29d719c28ce53acfa7dbab3c | python/treelite/contrib/__init__.py | python | create_shared | (toolchain, dirpath, *, nthread=None, verbose=False, options=None,
long_build_time_warning=True) | return libpath | Create shared library.
Parameters
----------
toolchain : :py:class:`str <python:str>`
which toolchain to use. You may choose one of 'msvc', 'clang', and 'gcc'.
You may also specify a specific variation of clang or gcc (e.g. 'gcc-7')
dirpath : :py:class:`str <python:str>`
directory containing the header and source files previously generated
by :py:meth:`Model.compile`. The directory must contain recipe.json
which specifies build dependencies.
nthread : :py:class:`int <python:int>`, optional
number of threads to use in creating the shared library.
Defaults to the number of cores in the system.
verbose : :py:class:`bool <python:bool>`, optional
whether to produce extra messages
options : :py:class:`list <python:list>` of :py:class:`str <python:str>`, \
optional
Additional options to pass to toolchain
long_build_time_warning : :py:class:`bool <python:bool>`, optional
If set to False, suppress the warning about potentially long build time
Returns
-------
libpath : :py:class:`str <python:str>`
absolute path of created shared library
Example
-------
The following command uses Visual C++ toolchain to generate
``./my/model/model.dll``:
.. code-block:: python
model.compile(dirpath='./my/model', params={}, verbose=True)
create_shared(toolchain='msvc', dirpath='./my/model', verbose=True)
Later, the shared library can be referred to by its directory name:
.. code-block:: python
predictor = Predictor(libpath='./my/model', verbose=True)
# looks for ./my/model/model.dll
Alternatively, one may specify the library down to its file name:
.. code-block:: python
predictor = Predictor(libpath='./my/model/model.dll', verbose=True) | Create shared library. | [
"Create",
"shared",
"library",
"."
] | def create_shared(toolchain, dirpath, *, nthread=None, verbose=False, options=None,
long_build_time_warning=True):
"""Create shared library.
Parameters
----------
toolchain : :py:class:`str <python:str>`
which toolchain to use. You may choose one of 'msvc', 'clang', and 'gcc'.
You may also specify a specific variation of clang or gcc (e.g. 'gcc-7')
dirpath : :py:class:`str <python:str>`
directory containing the header and source files previously generated
by :py:meth:`Model.compile`. The directory must contain recipe.json
which specifies build dependencies.
nthread : :py:class:`int <python:int>`, optional
number of threads to use in creating the shared library.
Defaults to the number of cores in the system.
verbose : :py:class:`bool <python:bool>`, optional
whether to produce extra messages
options : :py:class:`list <python:list>` of :py:class:`str <python:str>`, \
optional
Additional options to pass to toolchain
long_build_time_warning : :py:class:`bool <python:bool>`, optional
If set to False, suppress the warning about potentially long build time
Returns
-------
libpath : :py:class:`str <python:str>`
absolute path of created shared library
Example
-------
The following command uses Visual C++ toolchain to generate
``./my/model/model.dll``:
.. code-block:: python
model.compile(dirpath='./my/model', params={}, verbose=True)
create_shared(toolchain='msvc', dirpath='./my/model', verbose=True)
Later, the shared library can be referred to by its directory name:
.. code-block:: python
predictor = Predictor(libpath='./my/model', verbose=True)
# looks for ./my/model/model.dll
Alternatively, one may specify the library down to its file name:
.. code-block:: python
predictor = Predictor(libpath='./my/model/model.dll', verbose=True)
"""
# pylint: disable=R0912
if nthread is not None and nthread <= 0:
raise TreeliteError('nthread must be positive integer')
dirpath = expand_windows_path(dirpath)
if not os.path.isdir(dirpath):
raise TreeliteError('Directory {} does not exist'.format(dirpath))
try:
with open(os.path.join(dirpath, 'recipe.json'), 'r', encoding='UTF-8') as f:
recipe = json.load(f)
except IOError as e:
raise TreeliteError('Failed to open recipe.json') from e
if 'sources' not in recipe or 'target' not in recipe:
raise TreeliteError('Malformed recipe.json')
if options is not None:
try:
_ = iter(options)
options = [str(x) for x in options]
except TypeError as e:
raise TreeliteError('options must be a list of string') from e
else:
options = []
# Write warning for potentially long compile time
if long_build_time_warning:
warn = False
for source in recipe['sources']:
if int(source['length']) > 10000:
warn = True
break
if warn:
log_info(__file__, lineno(),
'\033[1;31mWARNING: some of the source files are long. ' + \
'Expect long build time.\u001B[0m ' + \
'You may want to adjust the parameter ' + \
'\x1B[33mparallel_comp\u001B[0m.\n')
tstart = time.time()
_toolchain_exist_check(toolchain)
if toolchain == 'msvc':
from .msvc import _create_shared
else:
from .gcc import _create_shared
libpath = \
_create_shared(dirpath, toolchain, recipe, nthread, options, verbose)
if verbose:
log_info(__file__, lineno(),
'Generated shared library in ' + \
'{0:.2f} seconds'.format(time.time() - tstart))
return libpath | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/wsgiref/handlers.py | python | BaseHandler.client_is_modern | (self) | return self.environ['SERVER_PROTOCOL'].upper() != 'HTTP/0.9' | True if client can accept status and headers | True if client can accept status and headers | [
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] | def client_is_modern(self):
"""True if client can accept status and headers"""
return self.environ['SERVER_PROTOCOL'].upper() != 'HTTP/0.9' | [
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pytorch/ELF | e851e786ced8d26cf470f08a6b9bf7e413fc63f7 | src_py/elf/utils_elf.py | python | Batch.transfer_cpu2cpu | (self, batch_dst, non_blocking=True) | transfer batch data to cpu | transfer batch data to cpu | [
"transfer",
"batch",
"data",
"to",
"cpu"
] | def transfer_cpu2cpu(self, batch_dst, non_blocking=True):
''' transfer batch data to cpu '''
# For each time step
for k, v in self.batch.items():
batch_dst[k].copy_(v) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/numpy/py2/numpy/polynomial/_polybase.py | python | ABCPolyBase.degree | (self) | return len(self) - 1 | The degree of the series.
.. versionadded:: 1.5.0
Returns
-------
degree : int
Degree of the series, one less than the number of coefficients. | The degree of the series. | [
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] | def degree(self):
"""The degree of the series.
.. versionadded:: 1.5.0
Returns
-------
degree : int
Degree of the series, one less than the number of coefficients.
"""
return len(self) - 1 | [
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mamedev/mame | 02cd26d37ee11191f3e311e19e805d872cb1e3a4 | 3rdparty/portmidi/pm_python/pyportmidi/midi.py | python | Input.__init__ | (self, device_id, buffer_size=4096) | The buffer_size specifies the number of input events to be buffered
waiting to be read using Input.read(). | The buffer_size specifies the number of input events to be buffered
waiting to be read using Input.read(). | [
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"""
The buffer_size specifies the number of input events to be buffered
waiting to be read using Input.read().
"""
_check_init()
if device_id == -1:
raise MidiException("Device id is -1, not a valid output id. -1 usually means there were no default Output devices.")
try:
r = get_device_info(device_id)
except TypeError:
raise TypeError("an integer is required")
except OverflowError:
raise OverflowError("long int too large to convert to int")
# and now some nasty looking error checking, to provide nice error
# messages to the kind, lovely, midi using people of whereever.
if r:
interf, name, input, output, opened = r
if input:
try:
self._input = _pypm.Input(device_id, buffer_size)
except TypeError:
raise TypeError("an integer is required")
self.device_id = device_id
elif output:
raise MidiException("Device id given is not a valid input id, it is an output id.")
else:
raise MidiException("Device id given is not a valid input id.")
else:
raise MidiException("Device id invalid, out of range.") | [
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pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | torch/utils/data/gen_pyi.py | python | extract_method_name | (line: str) | return line[start:end] | Extracts method name from decorator in the form of "@functional_datapipe({method_name})" | Extracts method name from decorator in the form of " | [
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"""
Extracts method name from decorator in the form of "@functional_datapipe({method_name})"
"""
if "(\"" in line:
start_token, end_token = "(\"", "\")"
elif "(\'" in line:
start_token, end_token = "(\'", "\')"
else:
raise RuntimeError(f"Unable to find appropriate method name within line:\n{line}")
start, end = line.find(start_token) + len(start_token), line.find(end_token)
return line[start:end] | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/distribute/distribute_lib.py | python | ReplicaContextBase._merge_call | (self, merge_fn, args, kwargs) | Default implementation for single replica. | Default implementation for single replica. | [
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] | def _merge_call(self, merge_fn, args, kwargs):
"""Default implementation for single replica."""
_push_per_thread_mode( # thread-local, so not needed with multiple threads
distribution_strategy_context._CrossReplicaThreadMode(self._strategy)) # pylint: disable=protected-access
try:
return merge_fn(self._strategy, *args, **kwargs)
finally:
_pop_per_thread_mode() | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/site-packages/botocore/signers.py | python | CloudFrontSigner.build_policy | (self, resource, date_less_than,
date_greater_than=None, ip_address=None) | return json.dumps(custom_policy, separators=(',', ':')) | A helper to build policy.
:type resource: str
:param resource: The URL or the stream filename of the protected object
:type date_less_than: datetime
:param date_less_than: The URL will expire after the time has passed
:type date_greater_than: datetime
:param date_greater_than: The URL will not be valid until this time
:type ip_address: str
:param ip_address: Use 'x.x.x.x' for an IP, or 'x.x.x.x/x' for a subnet
:rtype: str
:return: The policy in a compact string. | A helper to build policy. | [
"A",
"helper",
"to",
"build",
"policy",
"."
] | def build_policy(self, resource, date_less_than,
date_greater_than=None, ip_address=None):
"""A helper to build policy.
:type resource: str
:param resource: The URL or the stream filename of the protected object
:type date_less_than: datetime
:param date_less_than: The URL will expire after the time has passed
:type date_greater_than: datetime
:param date_greater_than: The URL will not be valid until this time
:type ip_address: str
:param ip_address: Use 'x.x.x.x' for an IP, or 'x.x.x.x/x' for a subnet
:rtype: str
:return: The policy in a compact string.
"""
# Note:
# 1. Order in canned policy is significant. Special care has been taken
# to ensure the output will match the order defined by the document.
# There is also a test case to ensure that order.
# SEE: http://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/private-content-creating-signed-url-canned-policy.html#private-content-canned-policy-creating-policy-statement
# 2. Albeit the order in custom policy is not required by CloudFront,
# we still use OrderedDict internally to ensure the result is stable
# and also matches canned policy requirement.
# SEE: http://docs.aws.amazon.com/AmazonCloudFront/latest/DeveloperGuide/private-content-creating-signed-url-custom-policy.html
moment = int(datetime2timestamp(date_less_than))
condition = OrderedDict({"DateLessThan": {"AWS:EpochTime": moment}})
if ip_address:
if '/' not in ip_address:
ip_address += '/32'
condition["IpAddress"] = {"AWS:SourceIp": ip_address}
if date_greater_than:
moment = int(datetime2timestamp(date_greater_than))
condition["DateGreaterThan"] = {"AWS:EpochTime": moment}
ordered_payload = [('Resource', resource), ('Condition', condition)]
custom_policy = {"Statement": [OrderedDict(ordered_payload)]}
return json.dumps(custom_policy, separators=(',', ':')) | [
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Cantera/cantera | 0119484b261967ccb55a0066c020599cacc312e4 | platform/posix/coverage.py | python | test | () | Run the full test suite. | Run the full test suite. | [
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"""
Run the full test suite.
"""
subprocess.call(['scons', 'test-reset'])
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apache/incubator-mxnet | f03fb23f1d103fec9541b5ae59ee06b1734a51d9 | python/mxnet/numpy/linalg.py | python | cholesky | (a, upper=False) | return _mx_nd_np.linalg.cholesky(a, upper) | r"""
Cholesky decomposition.
Notes
-----
`upper` param is requested by API standardization in
https://data-apis.org/array-api/latest/extensions/generated/signatures.linalg.cholesky.html
instead of parameter in official NumPy operator.
Return the Cholesky decomposition, `L * L.T`, of the square matrix `a`,
where `L` is lower-triangular and .T is the transpose operator. `a` must be
symmetric and positive-definite. Only `L` is actually returned. Complex-valued
input is currently not supported.
Parameters
----------
a : (..., M, M) ndarray
Symmetric, positive-definite input matrix.
upper : bool
If `True`, the result must be the upper-triangular Cholesky factor.
If `False`, the result must be the lower-triangular Cholesky factor.
Default: `False`.
Returns
-------
L : (..., M, M) ndarray
Lower-triangular Cholesky factor of `a`.
Raises
------
MXNetError
If the decomposition fails, for example, if `a` is not positive-definite.
Notes
-----
Broadcasting rules apply.
The Cholesky decomposition is often used as a fast way of solving
.. math:: A \mathbf{x} = \mathbf{b}
(when `A` is both symmetric and positive-definite).
First, we solve for :math:`\mathbf{y}` in
.. math:: L \mathbf{y} = \mathbf{b},
and then for :math:`\mathbf{x}` in
.. math:: L.T \mathbf{x} = \mathbf{y}.
Examples
--------
>>> A = np.array([[16, 4], [4, 10]])
>>> A
array([[16., 4.],
[ 4., 10.]])
>>> L = np.linalg.cholesky(A)
>>> L
array([[4., 0.],
[1., 3.]])
>>> np.dot(L, L.T)
array([[16., 4.],
[ 4., 10.]]) | r"""
Cholesky decomposition. | [
"r",
"Cholesky",
"decomposition",
"."
] | def cholesky(a, upper=False):
r"""
Cholesky decomposition.
Notes
-----
`upper` param is requested by API standardization in
https://data-apis.org/array-api/latest/extensions/generated/signatures.linalg.cholesky.html
instead of parameter in official NumPy operator.
Return the Cholesky decomposition, `L * L.T`, of the square matrix `a`,
where `L` is lower-triangular and .T is the transpose operator. `a` must be
symmetric and positive-definite. Only `L` is actually returned. Complex-valued
input is currently not supported.
Parameters
----------
a : (..., M, M) ndarray
Symmetric, positive-definite input matrix.
upper : bool
If `True`, the result must be the upper-triangular Cholesky factor.
If `False`, the result must be the lower-triangular Cholesky factor.
Default: `False`.
Returns
-------
L : (..., M, M) ndarray
Lower-triangular Cholesky factor of `a`.
Raises
------
MXNetError
If the decomposition fails, for example, if `a` is not positive-definite.
Notes
-----
Broadcasting rules apply.
The Cholesky decomposition is often used as a fast way of solving
.. math:: A \mathbf{x} = \mathbf{b}
(when `A` is both symmetric and positive-definite).
First, we solve for :math:`\mathbf{y}` in
.. math:: L \mathbf{y} = \mathbf{b},
and then for :math:`\mathbf{x}` in
.. math:: L.T \mathbf{x} = \mathbf{y}.
Examples
--------
>>> A = np.array([[16, 4], [4, 10]])
>>> A
array([[16., 4.],
[ 4., 10.]])
>>> L = np.linalg.cholesky(A)
>>> L
array([[4., 0.],
[1., 3.]])
>>> np.dot(L, L.T)
array([[16., 4.],
[ 4., 10.]])
"""
return _mx_nd_np.linalg.cholesky(a, upper) | [
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praydog/REFramework | c12cdd921e4beb5e3398f8afe0376e446be91344 | reversing/rsz/emulation-dumper.py | python | hook_code | (emu, address, size, frame) | try:
emu.mem_read(address, 4)
except unicorn.UcError as e:
#frame["call_stack"].pop()
return False | try:
emu.mem_read(address, 4)
except unicorn.UcError as e:
#frame["call_stack"].pop()
return False | [
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frame["context"] = pickle.dumps(emu.context_save())
cs = frame["cs"]
deserialize_arg = frame["deserialize_arg"]
# We don't want to do this. We manually call each parent deserializer to mark where in the structure they start.
# It's also easier to manage this way, we don't have to worry about nested shit.
if len(frame["call_stack"]) > 1 and address in frame["deserializers"]:
print("STOPPING EXECUTION!!!!")
invalidate_and_return_call(emu, frame)
emu.emu_stop()
return
# Upon first address in a call
if len(frame["call_stack"]) > 0 and frame["call_stack"][-1]["first"] == True:
frame["call_stack"][-1]["first"] = False
# When the deserialize function calls another function,
# We only care when it calls a function that reads the stream for deserialization
# Any other function is irrelevant to us
if emu.reg_read(UC_X86_REG_RCX) != deserialize_arg:
invalidate_and_return_call(emu, frame)
emu.emu_stop()
return
'''
try:
emu.mem_read(address, 4)
except unicorn.UcError as e:
#frame["call_stack"].pop()
return False
'''
# print("%X %i" % (address, size))
try:
dis = next(cs.disasm(emu.mem_read(address, size), address, 1))
except Exception as e:
print(traceback.format_exc(), "EXCEPTION 0x%X" % address)
print(" ", emu.mem_read(address, 0x100).hex())
print("%X" % frame["call_stack"][-1]["last_executed_addr"])
os.system("pause")
# print("0x%x: %s %s" % (address, dis.mnemonic, dis.op_str))
lex = 0
if len(frame["call_stack"]) > 0:
lex = frame["call_stack"][-1]["last_executed_addr"]
if len(frame["call_stack"]) == 1:
cur_hist = frame["call_stack"][0]["history"]
# We just left a loop
if address not in cur_hist.keys() and lex in cur_hist.keys() and cur_hist[lex] > 1:
list_size = cur_hist[lex] - 1
# Loop count matches the integer we filled the whole buffer with
if list_size == FILL_BYTE:
try:
element_layout = frame["layout"][-1]
except IndexError as e:
cs.detail = True
dis_g = cs.disasm(emu.mem_read(lex, 0x100), address, 1)
dis = next(dis_g)
print("LEX: 0x%x" % lex)
print("0x%x: %s %s" % (address, dis.mnemonic, dis.op_str))
print("Instruction at %X didn't read bytes from stream?" % address)
os.system("pause")
return
# Erase the elements that were added to the layout, keep list only
frame["layout"] = frame["layout"][0:len(frame["layout"]) - FILL_BYTE]
frame["was_string"] = False
list_layout = frame["layout"][-1]
list_layout["list"] = True
list_layout["element"] = element_layout
list_layout["element_size"] = int((element_layout["offset"] - list_layout["offset"]) / FILL_BYTE)
# print("LIST DETECTED")
# easy way to wait until after insn executes to read stuff
if lex > 0:
cs.detail = True
try:
last_dis_g = cs.disasm(emu.mem_read(lex, 0x100), address, 1)
except Exception as e:
print(traceback.format_exc(), "LEX EXCEPTION 0x%X 0x%X" % (address, lex))
for i in range(0, len(frame["call_stack"])):
print("0x%X" % frame["call_stack"][i]["last_executed_addr"])
print(" ", emu.mem_read(address, 0x100).hex())
print("%X" % frame["call_stack"][-1]["last_executed_addr"])
os.system("pause")
last_dis = next(last_dis_g)
deserialize_cur = int.from_bytes(emu.mem_read(frame["deserialize_arg"] + 0x8, 8), sys.byteorder)
has_operands = len(last_dis.operands) > 0
if last_dis.mnemonic == "mov" and has_operands and last_dis.operands[0].type == X86_OP_REG:
val = emu.reg_read(last_dis.operands[0].reg)
if val == deserialize_cur:
# print("0x%X" % val)
frame["last_deserialize_reg"] = last_dis.operands[0].reg
frame["last_deserialize_reg_val"] = val
# print("0x%x: %s %s" % (address, last_dis.mnemonic, last_dis.op_str))
# print("0x%x: %s %s %s" % (address, dis.mnemonic, dis.op_str, dis.reg_name(dis.operands[0].reg)))
elif frame["last_deserialize_reg"] != -1:
if deserialize_cur != frame["last_deserialize_cur"]:
frame["last_deserialize_reg"] = -1
frame["last_deserialize_reg_val"] = 0
# print("0x%x: %s %s" % (address, last_dis.mnemonic, last_dis.op_str))
elif has_operands and last_dis.operands[0].type == X86_OP_REG:
val = emu.reg_read(last_dis.operands[0].reg)
if val != frame["last_deserialize_reg_val"]:
delta = val - frame["last_deserialize_reg_val"]
if abs(delta) > 0x10000:
print("Huge delta detected. Register overwritten? 0x%X" % lex)
# frame["last_deserialize_reg"] = -1
# frame["last_deserialize_reg_val"] = 0
# invalidate_and_return_call(emu, frame)
# os.system("pause")
if last_dis.mnemonic == "and" and last_dis.operands[1].type == X86_OP_IMM and last_dis.operands[0].reg == frame["last_deserialize_reg"]:
# print("0x%X alignment detected" % (~last_dis.operands[1].imm + 1))
# Set this because we don't want the actual byte count screwing up
frame["last_deserialize_cur"] = val
frame["last_alignment"] = (~last_dis.operands[1].imm + 1)
frame["last_deserialize_reg_val"] = val
elif frame["last_alignment"] == 4 and last_dis.group(X86_GRP_BRANCH_RELATIVE):
frame["was_string"] = True
elif frame["last_alignment"] == 4 and last_dis.bytes == bytearray(b"\x4B\x8D\x0C\x41"): # this means "lea rcx, [r9+r8*2]", e.g. reading a wide string
frame["was_string"] = True
#print("String or list detected")
cs.detail = False
# Keep track of how many times we've executed the instruction at this address
if dis.mnemonic != "ret":
counter = 0
# SNEAKY!
# unicorn runs this instruction multiple times through the callback to emulate it
if dis.mnemonic == "rep movsb":
counter = emu.reg_read(UC_X86_REG_ECX)
if len(frame["call_stack"]) > 0:
# Keep track of all the bytes of the instruction because we NOP them out sometimes
for i in range(address, address + dis.size):
history = frame["call_stack"][-1]["history"]
if i not in history:
history[i] = 0
if counter == 0:
history[i] = history[i] + 1
if len(frame["call_stack"]) == 1 and counter > FILL_BYTE:
print("YUP", history[i])
# if dis.mnemonic == "rep movsb":
# print("YUP", history[i])
if dis.mnemonic == "call":
is_normal_call = dis.bytes[0] == 0xE8
frame["call_stack"].append({
"addr": address + dis.size,
"context": pickle.dumps(emu.context_save()),
"history": {},
"last_executed_addr": 0,
"first": is_normal_call
})
# Potential return from read func
if dis.mnemonic == "ret":
frame["last_return_val"] = emu.reg_read(UC_X86_REG_RAX)
if len(frame["call_stack"]) > 0:
frame["call_stack"].pop()
deserialize_cur = int.from_bytes(emu.mem_read(deserialize_arg + 0x8, 8), sys.byteorder)
if deserialize_cur != frame["last_deserialize_cur"]:
delta = deserialize_cur - frame["last_deserialize_cur"]
# print("0x%X bytes, 0x%X alignment" % (delta, frame["last_alignment"]))
frame["layout"].append({
"size": delta,
"element_size": delta,
"element": None,
"align": frame["last_alignment"],
"string": frame["was_string"],
"list": False,
"offset": deserialize_cur
})
frame["last_layout_size"] = len(frame["layout"])
frame["was_string"] = False
frame["last_deserialize_reg"] = -1
frame["last_deserialize_reg_val"] = 0
frame["last_deserialize_cur"] = deserialize_cur
frame["last_alignment"] = 1
if len(frame["call_stack"]) == 0:
# print("Reached end of function call")
frame["start"] = EMU_END
emu.emu_stop()
else:
print("Reached end of function call in a BAD WAY")
frame["start"] = EMU_END
emu.emu_stop()
if len(frame["call_stack"]) > 0:
frame["call_stack"][-1]["last_executed_addr"] = address
frame["last_disasm"] = dis | [
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cms-sw/cmssw | fd9de012d503d3405420bcbeec0ec879baa57cf2 | DQM/SiStripMonitorClient/scripts/submitDQMOfflineCAF.py | python | Func_MkDir | (str_path) | Function Func_MkDir():
Create new directory | Function Func_MkDir():
Create new directory | [
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""" Function Func_MkDir():
Create new directory
"""
shutil.rmtree(str_path, True)
os.mkdir(str_path) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/io/pytables.py | python | Selection.generate | (self, where) | where can be a : dict,list,tuple,string | where can be a : dict,list,tuple,string | [
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":",
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] | def generate(self, where):
""" where can be a : dict,list,tuple,string """
if where is None:
return None
q = self.table.queryables()
try:
return PyTablesExpr(where, queryables=q, encoding=self.table.encoding)
except NameError:
# raise a nice message, suggesting that the user should use
# data_columns
qkeys = ",".join(q.keys())
raise ValueError(
f"The passed where expression: {where}\n"
" contains an invalid variable reference\n"
" all of the variable references must be a "
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f" The currently defined references are: {qkeys}\n"
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ceph/ceph | 959663007321a369c83218414a29bd9dbc8bda3a | qa/tasks/ceph_manager.py | python | CephManager.remove_pool | (self, pool_name) | Remove the indicated pool
:param pool_name: Pool to be removed | Remove the indicated pool
:param pool_name: Pool to be removed | [
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":",
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] | def remove_pool(self, pool_name):
"""
Remove the indicated pool
:param pool_name: Pool to be removed
"""
with self.lock:
assert isinstance(pool_name, str)
assert pool_name in self.pools
self.log("removing pool_name %s" % (pool_name,))
del self.pools[pool_name]
self.raw_cluster_cmd('osd', 'pool', 'rm', pool_name, pool_name,
"--yes-i-really-really-mean-it") | [
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y123456yz/reading-and-annotate-mongodb-3.6 | 93280293672ca7586dc24af18132aa61e4ed7fcf | mongo/buildscripts/resmokelib/parser.py | python | _expand_user | (pathname) | return os.path.expanduser(pathname) | Wrapper around os.path.expanduser() to do nothing when given None. | Wrapper around os.path.expanduser() to do nothing when given None. | [
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] | def _expand_user(pathname):
"""
Wrapper around os.path.expanduser() to do nothing when given None.
"""
if pathname is None:
return None
return os.path.expanduser(pathname) | [
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idaholab/moose | 9eeebc65e098b4c30f8205fb41591fd5b61eb6ff | python/MooseDocs/tree/base.py | python | NodeBase.__repr__ | (self) | return mooseutils.colorText(moosetree.Node.__repr__(self), self.COLOR) | Prints the name of the token, this works in union with __str__ to print
the tree structure of any given node. | Prints the name of the token, this works in union with __str__ to print
the tree structure of any given node. | [
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"""
Prints the name of the token, this works in union with __str__ to print
the tree structure of any given node.
"""
return mooseutils.colorText(moosetree.Node.__repr__(self), self.COLOR) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_misc.py | python | AboutBox | (*args, **kwargs) | return _misc_.AboutBox(*args, **kwargs) | AboutBox(AboutDialogInfo info, Window parent=None)
This function shows the standard about dialog containing the
information specified in ``info``. If the current platform has a
native about dialog which is capable of showing all the fields in
`wx.AboutDialogInfo`, the native dialog is used, otherwise the
function falls back to the generic wxWidgets version of the dialog. | AboutBox(AboutDialogInfo info, Window parent=None) | [
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] | def AboutBox(*args, **kwargs):
"""
AboutBox(AboutDialogInfo info, Window parent=None)
This function shows the standard about dialog containing the
information specified in ``info``. If the current platform has a
native about dialog which is capable of showing all the fields in
`wx.AboutDialogInfo`, the native dialog is used, otherwise the
function falls back to the generic wxWidgets version of the dialog.
"""
return _misc_.AboutBox(*args, **kwargs) | [
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PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | python/paddle/fluid/dygraph/amp/auto_cast.py | python | amp_decorate | (models,
optimizers=None,
level='O1',
master_weight=None,
save_dtype=None) | Decorate models and optimizers for auto-mixed-precision. When level is O1(amp), the decorate will do nothing.
When level is O2(pure fp16), the decorate will cast all parameters of models to FP16, except BatchNorm and LayerNorm.
Commonly, it is used together with `amp_guard` to achieve Pure fp16 in imperative mode.
Args:
models(Layer|list of Layer, optional): The defined models by user, models must be either a single model or a list of models. Default is None.
optimizers(Optimizer|list of Optimizer, optional): The defined optimizers by user, optimizers must be either a single optimizer or a list of optimizers. Default is None.
level(str, optional): Auto mixed precision level. Accepted values are "O1" and "O2": O1 represent mixed precision, the decorator will do nothing;
O2 represent Pure fp16, the decorator will cast all parameters of models to FP16, except BatchNorm and LayerNorm. Default is O1(amp)
master_weight(bool, optinal): For level='O2', whether to use multi-precision during weight updating. If master_weight is None, in O2 level optimizer will use multi-precision. Default is None.
save_dtype(float, optional): The save model parameter dtype when use `paddle.save` or `paddle.jit.save`,it should be float16, float32, float64 or None.
The save_dtype will not change model parameters dtype, it just change the state_dict dtype. When save_dtype is None, the save dtype is same as model dtype. Default is None.
Examples:
.. code-block:: python
# required: gpu
# Demo1: single model and optimizer:
import paddle
model = paddle.nn.Conv2D(3, 2, 3, bias_attr=False)
optimzier = paddle.optimizer.SGD(parameters=model.parameters())
model, optimizer = paddle.fluid.dygraph.amp_decorate(models=model, optimizers=optimzier, level='O2')
data = paddle.rand([10, 3, 32, 32])
with paddle.fluid.dygraph.amp_guard(enable=True, custom_white_list=None, custom_black_list=None, level='O2'):
output = model(data)
print(output.dtype) # FP16
# required: gpu
# Demo2: multi models and optimizers:
model2 = paddle.nn.Conv2D(3, 2, 3, bias_attr=False)
optimizer2 = paddle.optimizer.Adam(parameters=model2.parameters())
models, optimizers = paddle.fluid.dygraph.amp_decorate(models=[model, model2], optimizers=[optimzier, optimizer2], level='O2')
data = paddle.rand([10, 3, 32, 32])
with paddle.fluid.dygraph.amp_guard(enable=True, custom_white_list=None, custom_black_list=None, level='O2'):
output = models[0](data)
output2 = models[1](data)
print(output.dtype) # FP16
print(output2.dtype) # FP16
# required: gpu
# Demo3: optimizers is None:
model3 = paddle.nn.Conv2D(3, 2, 3, bias_attr=False)
optimizer3 = paddle.optimizer.Adam(parameters=model2.parameters())
model = paddle.fluid.dygraph.amp_decorate(models=model3, level='O2')
data = paddle.rand([10, 3, 32, 32])
with paddle.fluid.dygraph.amp_guard(enable=True, custom_white_list=None, custom_black_list=None, level='O2'):
output = model(data)
print(output.dtype) # FP16 | Decorate models and optimizers for auto-mixed-precision. When level is O1(amp), the decorate will do nothing.
When level is O2(pure fp16), the decorate will cast all parameters of models to FP16, except BatchNorm and LayerNorm.
Commonly, it is used together with `amp_guard` to achieve Pure fp16 in imperative mode. | [
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optimizers=None,
level='O1',
master_weight=None,
save_dtype=None):
"""
Decorate models and optimizers for auto-mixed-precision. When level is O1(amp), the decorate will do nothing.
When level is O2(pure fp16), the decorate will cast all parameters of models to FP16, except BatchNorm and LayerNorm.
Commonly, it is used together with `amp_guard` to achieve Pure fp16 in imperative mode.
Args:
models(Layer|list of Layer, optional): The defined models by user, models must be either a single model or a list of models. Default is None.
optimizers(Optimizer|list of Optimizer, optional): The defined optimizers by user, optimizers must be either a single optimizer or a list of optimizers. Default is None.
level(str, optional): Auto mixed precision level. Accepted values are "O1" and "O2": O1 represent mixed precision, the decorator will do nothing;
O2 represent Pure fp16, the decorator will cast all parameters of models to FP16, except BatchNorm and LayerNorm. Default is O1(amp)
master_weight(bool, optinal): For level='O2', whether to use multi-precision during weight updating. If master_weight is None, in O2 level optimizer will use multi-precision. Default is None.
save_dtype(float, optional): The save model parameter dtype when use `paddle.save` or `paddle.jit.save`,it should be float16, float32, float64 or None.
The save_dtype will not change model parameters dtype, it just change the state_dict dtype. When save_dtype is None, the save dtype is same as model dtype. Default is None.
Examples:
.. code-block:: python
# required: gpu
# Demo1: single model and optimizer:
import paddle
model = paddle.nn.Conv2D(3, 2, 3, bias_attr=False)
optimzier = paddle.optimizer.SGD(parameters=model.parameters())
model, optimizer = paddle.fluid.dygraph.amp_decorate(models=model, optimizers=optimzier, level='O2')
data = paddle.rand([10, 3, 32, 32])
with paddle.fluid.dygraph.amp_guard(enable=True, custom_white_list=None, custom_black_list=None, level='O2'):
output = model(data)
print(output.dtype) # FP16
# required: gpu
# Demo2: multi models and optimizers:
model2 = paddle.nn.Conv2D(3, 2, 3, bias_attr=False)
optimizer2 = paddle.optimizer.Adam(parameters=model2.parameters())
models, optimizers = paddle.fluid.dygraph.amp_decorate(models=[model, model2], optimizers=[optimzier, optimizer2], level='O2')
data = paddle.rand([10, 3, 32, 32])
with paddle.fluid.dygraph.amp_guard(enable=True, custom_white_list=None, custom_black_list=None, level='O2'):
output = models[0](data)
output2 = models[1](data)
print(output.dtype) # FP16
print(output2.dtype) # FP16
# required: gpu
# Demo3: optimizers is None:
model3 = paddle.nn.Conv2D(3, 2, 3, bias_attr=False)
optimizer3 = paddle.optimizer.Adam(parameters=model2.parameters())
model = paddle.fluid.dygraph.amp_decorate(models=model3, level='O2')
data = paddle.rand([10, 3, 32, 32])
with paddle.fluid.dygraph.amp_guard(enable=True, custom_white_list=None, custom_black_list=None, level='O2'):
output = model(data)
print(output.dtype) # FP16
"""
if not (level in ['O1', 'O2']):
raise ValueError(
"level should be O1 or O2, O1 represent AMP train mode, O2 represent Pure fp16 train mode."
)
if level == 'O1':
if optimizers is None:
return models
else:
return models, optimizers
models_is_list = False
if isinstance(models, paddle.nn.Layer):
models_is_list = False
models = [models]
check_models(models)
elif isinstance(models, list):
check_models(models)
models_is_list = True
else:
raise TypeError(
"models must be either a single model or a list of models.")
models = pure_fp16_initialize(models=models)
if optimizers is not None:
# check optimizers
optimizers_is_list = False
if isinstance(optimizers, (paddle.optimizer.Optimizer,
paddle.fluid.optimizer.Optimizer)):
optimizers_is_list = False
optimizers = [optimizers]
check_optimizers(optimizers)
elif isinstance(optimizers, list):
check_optimizers(optimizers)
optimizers_is_list = True
else:
raise TypeError(
"optimizers must be either a single optimizer or a list of optimizers."
)
# supprot master_weight
for idx_opt in range(len(optimizers)):
if hasattr(optimizers[idx_opt], '_multi_precision'):
if master_weight is False:
optimizers[idx_opt]._multi_precision = False
else:
optimizers[idx_opt]._multi_precision = True
if save_dtype is not None:
if not (save_dtype in ['float16', 'float32', 'float64']):
raise ValueError(
"save_dtype can only be float16 float32 or float64, but your input save_dtype is %s."
% save_dtype)
for idx in range(len(models)):
for layer in models[idx].sublayers(include_self=True):
layer.register_state_dict_hook(StateDictHook(save_dtype))
if models_is_list:
if optimizers is not None:
if optimizers_is_list:
return models, optimizers
else:
return models, optimizers[0]
else:
return models
else:
if optimizers is not None:
if optimizers_is_list:
return models[0], optimizers
else:
return models[0], optimizers[0]
else:
return models[0] | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/FilterEvents/eventFilterGUI.py | python | MainWindow.move_rightSlider | (self) | Re-setup left range line in figure.
Triggered by a change in Qt Widget. NO EVENT is required. | Re-setup left range line in figure.
Triggered by a change in Qt Widget. NO EVENT is required. | [
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] | def move_rightSlider(self):
""" Re-setup left range line in figure.
Triggered by a change in Qt Widget. NO EVENT is required.
"""
newx = self.ui.horizontalSlider_2.value()
if newx >= self._leftSlideValue and newx != self._rightSlideValue:
# Allowed value: move the value bar
self._rightSlideValue = newx
xlim = self.ui.mainplot.get_xlim()
if self.ui.lineEdit_3.text():
# that is not entirely fool proof, as the user could still remove the value in the field after putting
# a non round percent, but this a) is unlikely and b) will not crash mantid, only show an artifact
newx = max(xlim[0] + newx*(xlim[1] - xlim[0])*0.01, float(self.ui.lineEdit_3.text()))
else:
newx = xlim[0] + newx*(xlim[1] - xlim[0])*0.01
leftx = [newx, newx]
lefty = self.ui.mainplot.get_ylim()
setp(self.rightslideline, xdata=leftx, ydata=lefty)
self.canvas.draw()
# Change value
self.ui.lineEdit_4.setText(str(newx))
else:
# Reset the value
self.ui.horizontalSlider_2.setValue(self._rightSlideValue) | [
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htcondor/htcondor | 4829724575176d1d6c936e4693dfd78a728569b0 | src/condor_contrib/condor_pigeon/src/condor_pigeon_client/skype_linux_tools/Skype4Py/conversion.py | python | IConversion.ChatMessageStatusToText | (self, Status) | return self._ToText('cms', Status) | Returns message status as text.
@param Status: Chat message status.
@type Status: L{Chat message status<enums.cmsUnknown>}
@return: Text describing the chat message status.
@rtype: unicode | Returns message status as text. | [
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] | def ChatMessageStatusToText(self, Status):
'''Returns message status as text.
@param Status: Chat message status.
@type Status: L{Chat message status<enums.cmsUnknown>}
@return: Text describing the chat message status.
@rtype: unicode
'''
return self._ToText('cms', Status) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_windows.py | python | PreHVScrolledWindow | (*args, **kwargs) | return val | PreHVScrolledWindow() -> HVScrolledWindow | PreHVScrolledWindow() -> HVScrolledWindow | [
"PreHVScrolledWindow",
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"-",
">",
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] | def PreHVScrolledWindow(*args, **kwargs):
"""PreHVScrolledWindow() -> HVScrolledWindow"""
val = _windows_.new_PreHVScrolledWindow(*args, **kwargs)
return val | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | tools/deep_memory_profiler/lib/policy.py | python | Policy.find_mmap | (self, region, bucket_set,
pageframe=None, group_pfn_counts=None) | Finds a matching component which a given mmap |region| belongs to.
It uses |bucket_set| to match with backtraces. If |pageframe| is given,
it considers memory sharing among processes.
NOTE: Don't use Bucket's |component_cache| for mmap regions because they're
classified not only with bucket information (mappedpathname for example).
Args:
region: A tuple representing a memory region.
bucket_set: A BucketSet object to look up backtraces.
pageframe: A PageFrame object representing a pageframe maybe including
a pagecount.
group_pfn_counts: A dict mapping a PFN to the number of times the
the pageframe is mapped by the known "group (Chrome)" processes.
Returns:
A string representing a component name. | Finds a matching component which a given mmap |region| belongs to. | [
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"component",
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"|region|",
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] | def find_mmap(self, region, bucket_set,
pageframe=None, group_pfn_counts=None):
"""Finds a matching component which a given mmap |region| belongs to.
It uses |bucket_set| to match with backtraces. If |pageframe| is given,
it considers memory sharing among processes.
NOTE: Don't use Bucket's |component_cache| for mmap regions because they're
classified not only with bucket information (mappedpathname for example).
Args:
region: A tuple representing a memory region.
bucket_set: A BucketSet object to look up backtraces.
pageframe: A PageFrame object representing a pageframe maybe including
a pagecount.
group_pfn_counts: A dict mapping a PFN to the number of times the
the pageframe is mapped by the known "group (Chrome)" processes.
Returns:
A string representing a component name.
"""
assert region[0] == 'hooked'
bucket = bucket_set.get(region[1]['bucket_id'])
assert not bucket or bucket.allocator_type == 'mmap'
if not bucket:
return 'no-bucket', None
stackfunction = bucket.symbolized_joined_stackfunction
stacksourcefile = bucket.symbolized_joined_stacksourcefile
sharedwith = self._categorize_pageframe(pageframe, group_pfn_counts)
for rule in self._rules:
if (rule.allocator_type == 'mmap' and
(not rule.stackfunction_pattern or
rule.stackfunction_pattern.match(stackfunction)) and
(not rule.stacksourcefile_pattern or
rule.stacksourcefile_pattern.match(stacksourcefile)) and
(not rule.mappedpathname_pattern or
rule.mappedpathname_pattern.match(region[1]['vma']['name'])) and
(not rule.mappedpermission_pattern or
rule.mappedpermission_pattern.match(
region[1]['vma']['readable'] +
region[1]['vma']['writable'] +
region[1]['vma']['executable'] +
region[1]['vma']['private'])) and
(not rule.sharedwith or
not pageframe or sharedwith in rule.sharedwith)):
return rule.name, bucket
assert False | [
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NVIDIAGameWorks/kaolin | e5148d05e9c1e2ce92a07881ce3593b1c5c3f166 | kaolin/ops/batch.py | python | padded_to_packed | (padded_tensor, shape_per_tensor) | return torch.cat([t.reshape(-1, padded_tensor.shape[-1])
for t in padded_to_list(padded_tensor, shape_per_tensor)], dim=0) | Converts a single padded tensor into a packed tensor.
Args:
padded_tensor (torch.Tensor): a :ref:`padded tensor<padded>`.
shape_per_tensor (torch.LongTensor): the :ref:`shape_per_tensor<padded_shape_per_tensor>`
tensor associated to the padded tensor.
Returns:
(torch.Tensor): the :ref:`packed tensor<packed>`. | Converts a single padded tensor into a packed tensor. | [
"Converts",
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] | def padded_to_packed(padded_tensor, shape_per_tensor):
"""Converts a single padded tensor into a packed tensor.
Args:
padded_tensor (torch.Tensor): a :ref:`padded tensor<padded>`.
shape_per_tensor (torch.LongTensor): the :ref:`shape_per_tensor<padded_shape_per_tensor>`
tensor associated to the padded tensor.
Returns:
(torch.Tensor): the :ref:`packed tensor<packed>`.
"""
return torch.cat([t.reshape(-1, padded_tensor.shape[-1])
for t in padded_to_list(padded_tensor, shape_per_tensor)], dim=0) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/psutil/_pssunos.py | python | cpu_count_logical | () | Return the number of logical CPUs in the system. | Return the number of logical CPUs in the system. | [
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] | def cpu_count_logical():
"""Return the number of logical CPUs in the system."""
try:
return os.sysconf("SC_NPROCESSORS_ONLN")
except ValueError:
# mimic os.cpu_count() behavior
return None | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/ribbon/gallery.py | python | RibbonGallery.SetItemClientData | (self, item, data) | Set the client data associated with a gallery item.
:param `item`: an instance of :class:`RibbonGalleryItem`;
:param `data`: any Python object. | Set the client data associated with a gallery item.
:param `item`: an instance of :class:`RibbonGalleryItem`;
:param `data`: any Python object. | [
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"""
Set the client data associated with a gallery item.
:param `item`: an instance of :class:`RibbonGalleryItem`;
:param `data`: any Python object.
"""
item.SetClientData(data) | [
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FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Plot/Plot.py | python | Plot.__init__ | (self,
winTitle="plot",
parent=None,
flags=PySide.QtCore.Qt.WindowFlags(0)) | Construct a new plot widget.
Keyword arguments:
winTitle -- Tab title.
parent -- Widget parent.
flags -- QWidget flags | Construct a new plot widget. | [
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] | def __init__(self,
winTitle="plot",
parent=None,
flags=PySide.QtCore.Qt.WindowFlags(0)):
"""Construct a new plot widget.
Keyword arguments:
winTitle -- Tab title.
parent -- Widget parent.
flags -- QWidget flags
"""
PySide.QtGui.QWidget.__init__(self, parent, flags)
self.setWindowTitle(winTitle)
# Create matplotlib canvas
self.fig = Figure()
self.canvas = FigureCanvas(self.fig)
self.canvas.setParent(self)
# Get axes
self.axes = self.fig.add_subplot(111)
self.axesList = [self.axes]
self.axes.xaxis.set_ticks_position('bottom')
self.axes.spines['top'].set_color('none')
self.axes.yaxis.set_ticks_position('left')
self.axes.spines['right'].set_color('none')
# Add the navigation toolbar by default
self.mpl_toolbar = NavigationToolbar(self.canvas, self)
# Setup layout
vbox = PySide.QtGui.QVBoxLayout()
vbox.addWidget(self.canvas)
vbox.addWidget(self.mpl_toolbar)
self.setLayout(vbox)
# Active series
self.series = []
# Indicators
self.skip = False
self.legend = False
self.legPos = (1.0, 1.0)
self.legSiz = 14
self.grid = False | [
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danxuhk/ContinuousCRF-CNN | 2b6dcaf179620f118b225ed12c890414ca828e21 | python/caffe/MultilabelDataLayer.py | python | print_info | (name, params) | Output some info regarding the class | Output some info regarding the class | [
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"the",
"class"
] | def print_info(name, params):
"""
Output some info regarding the class
"""
print "{} initialized for split: {}, with bs: {}, image cropped to size: {}.".format(
name,
params['split'],
params['batch_size'],
params['crop_size']) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/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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nasa/fprime | 595cf3682d8365943d86c1a6fe7c78f0a116acf0 | Autocoders/Python/src/fprime_ac/generators/visitors/PortCppVisitor.py | python | PortCppVisitor.__init__ | (self) | Constructor. | Constructor. | [
"Constructor",
"."
] | def __init__(self):
"""
Constructor.
"""
super().__init__()
self.__config = ConfigManager.ConfigManager.getInstance()
self.__form = formatters.Formatters()
self.__form_comment = formatters.CommentFormatters()
DEBUG.info("PortCppVisitor: Instanced.")
self.bodytext = ""
self.prototypetext = "" | [
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... | https://github.com/nasa/fprime/blob/595cf3682d8365943d86c1a6fe7c78f0a116acf0/Autocoders/Python/src/fprime_ac/generators/visitors/PortCppVisitor.py#L66-L76 | ||
hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/telemetry/third_party/pyfakefs/pyfakefs/fake_filesystem.py | python | FakeFilesystem.JoinPaths | (self, *paths) | return ''.join(joined_path_segments) | Mimics os.path.join using the specified path_separator.
Mimics os.path.join using the path_separator that was specified
for this FakeFilesystem.
Args:
*paths: (str) Zero or more paths to join.
Returns:
(str) The paths joined by the path separator, starting with the last
absolute path in paths. | Mimics os.path.join using the specified path_separator. | [
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"os",
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"using",
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"path_separator",
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] | def JoinPaths(self, *paths):
"""Mimics os.path.join using the specified path_separator.
Mimics os.path.join using the path_separator that was specified
for this FakeFilesystem.
Args:
*paths: (str) Zero or more paths to join.
Returns:
(str) The paths joined by the path separator, starting with the last
absolute path in paths.
"""
if len(paths) == 1:
return paths[0]
joined_path_segments = []
for path_segment in paths:
if path_segment.startswith(self.path_separator):
# An absolute path
joined_path_segments = [path_segment]
else:
if (joined_path_segments and
not joined_path_segments[-1].endswith(self.path_separator)):
joined_path_segments.append(self.path_separator)
if path_segment:
joined_path_segments.append(path_segment)
return ''.join(joined_path_segments) | [
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openthread/openthread | 9fcdbed9c526c70f1556d1ed84099c1535c7cd32 | tools/harness-thci/OpenThread_WpanCtl.py | python | OpenThread_WpanCtl.removeRouterPrefix | (self, prefixEntry) | remove the configured prefix on a border router
Args:
prefixEntry: a on-mesh prefix entry
Returns:
True: successful to remove the prefix entry from border router
False: fail to remove the prefix entry from border router
@todo: required if as reference device | remove the configured prefix on a border router | [
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] | def removeRouterPrefix(self, prefixEntry):
"""remove the configured prefix on a border router
Args:
prefixEntry: a on-mesh prefix entry
Returns:
True: successful to remove the prefix entry from border router
False: fail to remove the prefix entry from border router
@todo: required if as reference device
""" | [
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microsoft/ivy | 9f3c7ecc0b2383129fdd0953e10890d98d09a82d | ivy/ivy_cpp.py | python | CppType.instantiate | (self,name,initializer=None) | return '{} {};'.format(self.short_name(),name) | Returns a declaration for a symbol of this type | Returns a declaration for a symbol of this type | [
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""" Returns a declaration for a symbol of this type """
if initializer:
return '{} {}={};'.format(self.short_name(),name,initializer.get())
return '{} {};'.format(self.short_name(),name) | [
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PaddlePaddle/PaddleOCR | b756bf5f8c90142e0d89d3db0163965c686b6ffe | ppocr/data/imaug/sast_process.py | python | SASTProcessTrain.shrink_poly_along_width | (self,
quads,
shrink_ratio_of_width,
expand_height_ratio=1.0) | return np.array(out_quad_list), list(range(left_idx, right_idx + 1)) | shrink poly with given length. | shrink poly with given length. | [
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"given",
"length",
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] | def shrink_poly_along_width(self,
quads,
shrink_ratio_of_width,
expand_height_ratio=1.0):
"""
shrink poly with given length.
"""
upper_edge_list = []
def get_cut_info(edge_len_list, cut_len):
for idx, edge_len in enumerate(edge_len_list):
cut_len -= edge_len
if cut_len <= 0.000001:
ratio = (cut_len + edge_len_list[idx]) / edge_len_list[idx]
return idx, ratio
for quad in quads:
upper_edge_len = np.linalg.norm(quad[0] - quad[1])
upper_edge_list.append(upper_edge_len)
# length of left edge and right edge.
left_length = np.linalg.norm(quads[0][0] - quads[0][
3]) * expand_height_ratio
right_length = np.linalg.norm(quads[-1][1] - quads[-1][
2]) * expand_height_ratio
shrink_length = min(left_length, right_length,
sum(upper_edge_list)) * shrink_ratio_of_width
# shrinking length
upper_len_left = shrink_length
upper_len_right = sum(upper_edge_list) - shrink_length
left_idx, left_ratio = get_cut_info(upper_edge_list, upper_len_left)
left_quad = self.shrink_quad_along_width(
quads[left_idx], begin_width_ratio=left_ratio, end_width_ratio=1)
right_idx, right_ratio = get_cut_info(upper_edge_list, upper_len_right)
right_quad = self.shrink_quad_along_width(
quads[right_idx], begin_width_ratio=0, end_width_ratio=right_ratio)
out_quad_list = []
if left_idx == right_idx:
out_quad_list.append(
[left_quad[0], right_quad[1], right_quad[2], left_quad[3]])
else:
out_quad_list.append(left_quad)
for idx in range(left_idx + 1, right_idx):
out_quad_list.append(quads[idx])
out_quad_list.append(right_quad)
return np.array(out_quad_list), list(range(left_idx, right_idx + 1)) | [
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KratosMultiphysics/Kratos | 0000833054ed0503424eb28205d6508d9ca6cbbc | kratos/python_scripts/sympy_fe_utilities.py | python | DefineVector | ( name, m) | return Matrix( m,1, lambda i,j: var(name+'_%d' % (i)) ) | This method defines a symbolic vector
Keyword arguments:
name -- Name of variables.
m -- Number of components. | This method defines a symbolic vector | [
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] | def DefineVector( name, m):
""" This method defines a symbolic vector
Keyword arguments:
name -- Name of variables.
m -- Number of components.
"""
return Matrix( m,1, lambda i,j: var(name+'_%d' % (i)) ) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/build/waf-1.7.13/lmbrwaflib/gems.py | python | load_gems_from_gem_spec | (ctx, gem_spec_file, add_to_manager=False) | return gems_list | Load gems from a specific gem-spec file.
:param ctx:
:param gem_spec_file: The path of the gem spec file to read
:param add_to_manager: Option to add any missing gem that is discovered in the spec file but not in the manager
:return: List of gems from the gem spec | Load gems from a specific gem-spec file.
:param ctx:
:param gem_spec_file: The path of the gem spec file to read
:param add_to_manager: Option to add any missing gem that is discovered in the spec file but not in the manager
:return: List of gems from the gem spec | [
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"""
Load gems from a specific gem-spec file.
:param ctx:
:param gem_spec_file: The path of the gem spec file to read
:param add_to_manager: Option to add any missing gem that is discovered in the spec file but not in the manager
:return: List of gems from the gem spec
"""
if not gem_spec_file or not os.path.exists(gem_spec_file):
raise Errors.WafError('Invalid empty gem_spec_file {}'.format(gem_spec_file or ""))
# Read the gem spec file
gem_info_list = utils.parse_json_file(gem_spec_file)
list_reader = _create_field_reader(ctx, gem_info_list, 'Gems from {}'.format(gem_spec_file))
# Verify that the project file is an up-to-date format
gem_format_version = list_reader.field_int('GemListFormatVersion')
if gem_format_version != GEMS_FORMAT_VERSION:
raise Errors.WafError('Gems list file at {} is of version {}, not expected version {}. Please update your project file.'.format(gem_spec_file, gem_format_version, GEMS_FORMAT_VERSION))
manager = GemManager.GetInstance(ctx)
gems_list = list()
for idx, gem_info_obj in enumerate(list_reader.field_req('Gems')):
# String for error reporting.
gem_context_msg = 'Gem {} from gems spec {}'.format(idx, gem_spec_file)
reader = _create_field_reader(ctx, gem_info_obj, gem_context_msg)
gem_id = reader.uuid()
version = reader.version()
path = os.path.normpath(reader.field_req('Path'))
gem = manager.get_gem_by_spec(gem_id, version, path)
if not gem:
if add_to_manager:
gems_list_context_msg = 'Gems list {}'.format(gem_spec_file)
manager.load_gem_from_disk(gem_id, version, path, gems_list_context_msg)
else:
raise Errors.WafError('Invalid gem {}'.format(gem_id))
gems_list.append(gem)
return gems_list | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/common/tensor.py | python | Tensor.argmax | (self, axis=None) | return tensor_operator_registry.get('argmax')(axis)(a) | Return the indices of the maximum values along an axis.
Args:
axis (int, optional): By default, the index is into
the flattened tensor, otherwise along the specified axis. Default: None.
Returns:
Tensor, indices into the input tensor. It has the same
shape as self.shape with the dimension along axis removed.
Raises:
ValueError: If the axis is out of range.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
See also:
:func:`mindspore.Tensor.argmin`: Return the indices of the minimum values along an axis.
:func:`mindspore.Tensor.min`: Return the minimum of a tensor or minimum along an axis.
:func:`mindspore.Tensor.max`: Return the maximum of a tensor or maximum along an axis.
Examples:
>>> import numpy as np
>>> from mindspore import Tensor
>>> a = Tensor(np.arange(10, 16).reshape(2, 3).astype("float32"))
>>> print(a.argmax())
5 | Return the indices of the maximum values along an axis. | [
"Return",
"the",
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] | def argmax(self, axis=None):
"""
Return the indices of the maximum values along an axis.
Args:
axis (int, optional): By default, the index is into
the flattened tensor, otherwise along the specified axis. Default: None.
Returns:
Tensor, indices into the input tensor. It has the same
shape as self.shape with the dimension along axis removed.
Raises:
ValueError: If the axis is out of range.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
See also:
:func:`mindspore.Tensor.argmin`: Return the indices of the minimum values along an axis.
:func:`mindspore.Tensor.min`: Return the minimum of a tensor or minimum along an axis.
:func:`mindspore.Tensor.max`: Return the maximum of a tensor or maximum along an axis.
Examples:
>>> import numpy as np
>>> from mindspore import Tensor
>>> a = Tensor(np.arange(10, 16).reshape(2, 3).astype("float32"))
>>> print(a.argmax())
5
"""
# P.Argmax only supports float
a = self.astype(mstype.float32)
if axis is None:
a = a.ravel()
axis = 0
else:
axis = validator.check_axis_in_range(axis, a.ndim)
return tensor_operator_registry.get('argmax')(axis)(a) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/mailbox.py | python | Babyl.__init__ | (self, path, factory=None, create=True) | Initialize a Babyl mailbox. | Initialize a Babyl mailbox. | [
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] | def __init__(self, path, factory=None, create=True):
"""Initialize a Babyl mailbox."""
_singlefileMailbox.__init__(self, path, factory, create)
self._labels = {} | [
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apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | deps/src/libxml2-2.9.1/python/libxml2class.py | python | inputBuffer.grow | (self, len) | return ret | Grow up the content of the input buffer, the old data are
preserved This routine handle the I18N transcoding to
internal UTF-8 This routine is used when operating the
parser in normal (pull) mode TODO: one should be able to
remove one extra copy by copying directly onto in->buffer
or in->raw | Grow up the content of the input buffer, the old data are
preserved This routine handle the I18N transcoding to
internal UTF-8 This routine is used when operating the
parser in normal (pull) mode TODO: one should be able to
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"""Grow up the content of the input buffer, the old data are
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internal UTF-8 This routine is used when operating the
parser in normal (pull) mode TODO: one should be able to
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ret = libxml2mod.xmlParserInputBufferGrow(self._o, len)
return ret | [
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Cantera/cantera | 0119484b261967ccb55a0066c020599cacc312e4 | interfaces/cython/cantera/composite.py | python | SolutionArray.write_hdf | (self, filename, *args, cols=None, group=None, subgroup=None,
attrs={}, mode='a', append=False,
compression=None, compression_opts=None, **kwargs) | return group | Write data specified by *cols* to a HDF container file named *filename*.
Note that it is possible to write multiple data entries to a single HDF
container file, where *group* is used to differentiate data.
An example for the default HDF file structure is:::
/ Group
/group0 Group
/group0/some_attr Attribute
...
/group0/T Dataset
...
/group0/phase Group
/group0/phase/name Attribute
/group0/phase/source Attribute
where ``group0`` is the default name for the top level HDF entry. In
addition to datasets, information stored in `SolutionArray.meta` is
saved in form of HDF attributes. An additional intermediate layer may
be created using the *subgroup* argument.
:param filename:
Name of the HDF container file; typical file extensions are
``.hdf``, ``.hdf5`` or ``.h5``.
:param cols:
A list of any properties of the solution being exported.
:param group:
Identifier for the group in the container file. If no subgroup is
specified, a group represents a `SolutionArray`. If 'None', group
names default to 'groupN', with N being the number of pre-existing
groups within the HDF container file.
:param subgroup:
Name identifier for an optional subgroup, with subgroups
representing individual `SolutionArray` objects. If 'None', no
subgroup is created.
:param attrs:
Dictionary of user-defined attributes added at the group level
(typically used in conjunction with a subgroup argument).
: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 append:
If False, the content of a pre-existing group is deleted before
writing the `SolutionArray` in the first position. If True, the
current `SolutionArray` objects is appended to the group.
: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}.
:return:
Group identifier used for storing HDF data.
Arguments *compression*, and *compression_opts* are mapped to parameters
for `h5py.create_dataset`; in both cases, the choices of `None` results
in default values set by h5py.
Additional arguments (i.e. *args* and *kwargs*) are passed on to
`collect_data`; see `collect_data` for further information. This method
requires a working installation of h5py (`h5py` can be installed using
pip or conda). | Write data specified by *cols* to a HDF container file named *filename*.
Note that it is possible to write multiple data entries to a single HDF
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attrs={}, mode='a', append=False,
compression=None, compression_opts=None, **kwargs):
"""
Write data specified by *cols* to a HDF container file named *filename*.
Note that it is possible to write multiple data entries to a single HDF
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saved in form of HDF attributes. An additional intermediate layer may
be created using the *subgroup* argument.
:param filename:
Name of the HDF container file; typical file extensions are
``.hdf``, ``.hdf5`` or ``.h5``.
:param cols:
A list of any properties of the solution being exported.
:param group:
Identifier for the group in the container file. If no subgroup is
specified, a group represents a `SolutionArray`. If 'None', group
names default to 'groupN', with N being the number of pre-existing
groups within the HDF container file.
:param subgroup:
Name identifier for an optional subgroup, with subgroups
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subgroup is created.
:param attrs:
Dictionary of user-defined attributes added at the group level
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:param mode:
Mode h5py uses to open the output file {'a' to read/write if file
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:param append:
If False, the content of a pre-existing group is deleted before
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current `SolutionArray` objects is appended to the group.
: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}.
:return:
Group identifier used for storing HDF data.
Arguments *compression*, and *compression_opts* are mapped to parameters
for `h5py.create_dataset`; in both cases, the choices of `None` results
in default values set by h5py.
Additional arguments (i.e. *args* and *kwargs*) are passed on to
`collect_data`; see `collect_data` for further information. This method
requires a working installation of h5py (`h5py` can be installed using
pip or conda).
"""
if isinstance(_h5py, ImportError):
raise _h5py
# collect data
data = self.collect_data(*args, cols=cols, **kwargs)
hdf_kwargs = {'compression': compression,
'compression_opts': compression_opts}
hdf_kwargs = {k: v for k, v in hdf_kwargs.items() if v is not None}
# save to container file
with _h5py.File(filename, mode) as hdf:
# check existence of tagged item
if not group:
# add group with default name
group = 'group{}'.format(len(hdf.keys()))
root = hdf.create_group(group)
elif group not in hdf.keys():
# add group with custom name
root = hdf.create_group(group)
elif append and subgroup is not None:
# add subgroup to existing subgroup(s)
root = hdf[group]
else:
# reset data in existing group
root = hdf[group]
for sub in root.keys():
del root[sub]
# save attributes
for attr, value in attrs.items():
root.attrs[attr] = value
# add subgroup if specified
if subgroup is not None:
dgroup = root.create_group(subgroup)
else:
dgroup = root
# add subgroup containing information on gas
sol = dgroup.create_group('phase')
sol.attrs['name'] = self.name
sol.attrs['source'] = self.source
# store SolutionArray data
for key, val in self._meta.items():
dgroup.attrs[key] = val
for header, value in data.items():
if value.dtype.type == np.str_:
dgroup.create_dataset(header, data=value.astype('S'),
**hdf_kwargs)
else:
dgroup.create_dataset(header, data=value, **hdf_kwargs)
return group | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/dummy_thread.py | python | LockType.acquire | (self, waitflag=None) | Dummy implementation of acquire().
For blocking calls, self.locked_status is automatically set to
True and returned appropriately based on value of
``waitflag``. If it is non-blocking, then the value is
actually checked and not set if it is already acquired. This
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"""Dummy implementation of acquire().
For blocking calls, self.locked_status is automatically set to
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``waitflag``. If it is non-blocking, then the value is
actually checked and not set if it is already acquired. This
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"""
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zerollzeng/tiny-tensorrt | e7bdb8f82934342a0f22ce68dfefdb8e15eb72b2 | third_party/pybind11/tools/clang/cindex.py | python | TranslationUnit.reparse | (self, unsaved_files=None, options=0) | Reparse an already parsed translation unit.
In-memory contents for files can be provided by passing a list of pairs
as unsaved_files, the first items should be the filenames to be mapped
and the second should be the contents to be substituted for the
file. The contents may be passed as strings or file objects. | Reparse an already parsed translation unit. | [
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] | def reparse(self, unsaved_files=None, options=0):
"""
Reparse an already parsed translation unit.
In-memory contents for files can be provided by passing a list of pairs
as unsaved_files, the first items should be the filenames to be mapped
and the second should be the contents to be substituted for the
file. The contents may be passed as strings or file objects.
"""
if unsaved_files is None:
unsaved_files = []
unsaved_files_array = 0
if len(unsaved_files):
unsaved_files_array = (_CXUnsavedFile * len(unsaved_files))()
for i,(name,value) in enumerate(unsaved_files):
if not isinstance(value, str):
# FIXME: It would be great to support an efficient version
# of this, one day.
value = value.read()
print(value)
if not isinstance(value, str):
raise TypeError('Unexpected unsaved file contents.')
unsaved_files_array[i].name = name
unsaved_files_array[i].contents = value
unsaved_files_array[i].length = len(value)
ptr = conf.lib.clang_reparseTranslationUnit(self, len(unsaved_files),
unsaved_files_array, options) | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Source/ThirdParty/CEF3/cef_source/tools/cef_parser.py | python | obj_argument.get_types | (self, list) | Return a dictionary mapping data types to analyzed values. | Return a dictionary mapping data types to analyzed values. | [
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name = self.type.get_type()
if not name in list:
list[name] = self.type | [
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RegrowthStudios/SoACode-Public | c3ddd69355b534d5e70e2e6d0c489b4e93ab1ffe | utils/git-hooks/pep8.py | python | explicit_line_join | (logical_line, tokens) | r"""
Avoid explicit line join between brackets.
The preferred way of wrapping long lines is by using Python's implied line
continuation inside parentheses, brackets and braces. Long lines can be
broken over multiple lines by wrapping expressions in parentheses. These
should be used in preference to using a backslash for line continuation.
E502: aaa = [123, \\n 123]
E502: aaa = ("bbb " \\n "ccc")
Okay: aaa = [123,\n 123]
Okay: aaa = ("bbb "\n "ccc")
Okay: aaa = "bbb " \\n "ccc" | r"""
Avoid explicit line join between brackets. | [
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"Avoid",
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"brackets",
"."
] | def explicit_line_join(logical_line, tokens):
r"""
Avoid explicit line join between brackets.
The preferred way of wrapping long lines is by using Python's implied line
continuation inside parentheses, brackets and braces. Long lines can be
broken over multiple lines by wrapping expressions in parentheses. These
should be used in preference to using a backslash for line continuation.
E502: aaa = [123, \\n 123]
E502: aaa = ("bbb " \\n "ccc")
Okay: aaa = [123,\n 123]
Okay: aaa = ("bbb "\n "ccc")
Okay: aaa = "bbb " \\n "ccc"
"""
prev_start = prev_end = parens = 0
for token_type, text, start, end, line in tokens:
if start[0] != prev_start and parens and backslash:
yield backslash, "E502 the backslash is redundant between brackets"
if end[0] != prev_end:
if line.rstrip('\r\n').endswith('\\'):
backslash = (end[0], len(line.splitlines()[-1]) - 1)
else:
backslash = None
prev_start = prev_end = end[0]
else:
prev_start = start[0]
if token_type == tokenize.OP:
if text in '([{':
parens += 1
elif text in ')]}':
parens -= 1 | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/dataview.py | python | DataViewCtrl.GetItemRect | (*args, **kwargs) | return _dataview.DataViewCtrl_GetItemRect(*args, **kwargs) | GetItemRect(self, DataViewItem item, DataViewColumn column=None) -> Rect | GetItemRect(self, DataViewItem item, DataViewColumn column=None) -> Rect | [
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CNugteren/CLBlast | 4500a03440e2cc54998c0edab366babf5e504d67 | scripts/generator/generator/routine.py | python | Routine.options_def_wrapper_cblas | (self) | return [] | As above, but now using CBLAS data-types | As above, but now using CBLAS data-types | [
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"""As above, but now using CBLAS data-types"""
if self.options:
definitions = ["const " + convert.option_to_cblas(o) + " " + o for o in self.options]
return [", ".join(definitions)]
return [] | [
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apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | src/external/coremltools_wrap/coremltools/coremltools/converters/mil/frontend/tensorflow/converter.py | python | TFConverter.__init__ | (self, tfssa, inputs=None, outputs=None, **kwargs) | tfssa: TensorFlow IR.
inputs: list of TensorType or ImageType, optional, defaults to None.
outputs: list of str or str, optional, defaults to None.
A list of names of the output nodes or a str for single output name.
If None, the converter will try to extract the output information from
TensorFlow model. | tfssa: TensorFlow IR.
inputs: list of TensorType or ImageType, optional, defaults to None.
outputs: list of str or str, optional, defaults to None.
A list of names of the output nodes or a str for single output name.
If None, the converter will try to extract the output information from
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"""
tfssa: TensorFlow IR.
inputs: list of TensorType or ImageType, optional, defaults to None.
outputs: list of str or str, optional, defaults to None.
A list of names of the output nodes or a str for single output name.
If None, the converter will try to extract the output information from
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"""
self.tfssa = tfssa
self.global_type = {}
self.inputs = None
main_func = tfssa.functions["main"]
graph = main_func.graph
# Filter the inputs to only Placeholder names
tf_placeholder_names = [n for n in graph if graph[n].op == "Placeholder"]
placeholder_names = []
if inputs is not None:
# Check inputs format
if not isinstance(inputs, (list, tuple)):
raise ValueError(
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type(inputs)
)
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if not all([isinstance(i, InputType) for i in inputs]):
raise ValueError(
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)
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# Special case: if there's only 1 input and 1 placeholder, we match them.
if len(tf_placeholder_names) == 1 and len(inputs) == 1:
if inputs[0].name is None:
inputs[0].name = tf_placeholder_names[0]
# filter out those inputs which is not in tf_placeholder_names
inputs = [x for x in inputs if x.name in tf_placeholder_names]
# We fill in shapes for user-specified input that doesn't have shape
for inp in inputs:
# Check inputs existence
if inp.name is None:
raise ValueError(
"Unable to infer input's name or input name was not provided"
)
if inp.name not in tf_placeholder_names:
raise ValueError(
"Input ({}) provided is not found in given tensorflow graph. Placeholders in graph are: {}".format(
inp.name, tf_placeholder_names
)
)
if inp.shape is None:
if graph[inp.name].attr.get("_output_shapes", None) is not None:
shape = graph[inp.name].attr["_output_shapes"][0]
if shape is None:
# Scalar is given as None
shape = []
elif graph[inp.name].attr.get("shape", None) is not None:
shape = graph[inp.name].attr["shape"]
else:
raise ValueError(
"Can't extract shape from attribute of ({})".format(
inp.name
)
)
inp.shape = _get_shaping_class(shape)
# Extract placeholders that users didn't specify.
user_input_names = [inp.name for inp in inputs]
for name in tf_placeholder_names:
if name not in user_input_names:
placeholder_names.append(name)
else:
inputs = []
placeholder_names = tf_placeholder_names
placeholder_inputs = {}
for inp in main_func.inputs:
if inp not in placeholder_names:
continue
if graph[inp].attr.get("_output_shapes", None) is not None:
placeholder_inputs.update({inp: graph[inp].attr["_output_shapes"][0]})
elif graph[inp].attr.get("shape", None) is not None:
placeholder_inputs.update({inp: graph[inp].attr["shape"]})
else:
raise ValueError("Can't find input shape for ({})".format(inp))
if len(placeholder_inputs) > 0:
logging.info(
"Adding Input not specified by users: '{}'".format(placeholder_inputs)
)
for k, v in placeholder_inputs.items():
inputs.append(TensorType(name=k, shape=v))
for idx, inp in enumerate(inputs):
# We set the default image format in TF as NHWC, since NHWC is used
# for TF unless GPU is specified as device.
if isinstance(inp, ImageType) and inputs[idx].channel_first is None:
inputs[idx].channel_first = False
self.inputs = tuple(inputs)
for inputtype in self.inputs:
if not isinstance(inputtype.shape, InputShape):
continue
if any([isinstance(s, RangeDim) for s in inputtype.shape.shape]):
continue
node = graph[inputtype.name]
shape = [-1 if is_symbolic(s) else s for s in inputtype.shape.shape]
node.attr["_output_shapes"] = [shape] # list of length 1
# infer outputs if not provided
self._validate_outputs(tfssa, outputs)
outputs = main_func.outputs if outputs is None else outputs
outputs = outputs if isinstance(outputs, (tuple, list)) else [outputs]
outputs = [x if isinstance(x, six.string_types) else x.name for x in outputs]
self.outputs = outputs
# We would like a stack so that we run conversion sequentially.
self.graph_stack = self._get_stack(tfssa, root="main")
self.context = TranscriptionContext()
self.tensorflow_passes = tensorflow_passes | [
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KratosMultiphysics/Kratos | 0000833054ed0503424eb28205d6508d9ca6cbbc | applications/WindEngineeringApplication/python_scripts/compute_level_force_process.py | python | ComputeLevelForceProcess.__init__ | (self, model: KratosMultiphysics.Model, parameters: KratosMultiphysics.Parameters) | Reduce nodal reaction forces and torques on stacked slab domains.
A region of space between 'bottom_point' and 'top_point' is subdivided into 'number_of_slabs'
parallel slabs. Then, nodes from the specified model part are sorted into sub model parts
based on which slab they are located in. Finally, for each sub model part, the reaction forces
are summed up, and their torque (plus MOMENT if applicable) is reduced to 'moment_reference_point'.
The reduced values are written to output files for each sub model part.
Default parameters:
{
"model_part_name" : "",
"moment_reference_point" : [0.0, 0.0, 0.0],
"bottom_point" : [0.0, 0.0, 0.0],
"top_point" : [0.0, 0.0, 0.0],
"number_of_slabs" : 1,
"open_domain" : false,
"time_domain" : [0.0, 1e100],
"output_name_stub" : "slab_"
} | Reduce nodal reaction forces and torques on stacked slab domains.
A region of space between 'bottom_point' and 'top_point' is subdivided into 'number_of_slabs'
parallel slabs. Then, nodes from the specified model part are sorted into sub model parts
based on which slab they are located in. Finally, for each sub model part, the reaction forces
are summed up, and their torque (plus MOMENT if applicable) is reduced to 'moment_reference_point'.
The reduced values are written to output files for each sub model part.
Default parameters:
{
"model_part_name" : "",
"moment_reference_point" : [0.0, 0.0, 0.0],
"bottom_point" : [0.0, 0.0, 0.0],
"top_point" : [0.0, 0.0, 0.0],
"number_of_slabs" : 1,
"open_domain" : false,
"time_domain" : [0.0, 1e100],
"output_name_stub" : "slab_"
} | [
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"""Reduce nodal reaction forces and torques on stacked slab domains.
A region of space between 'bottom_point' and 'top_point' is subdivided into 'number_of_slabs'
parallel slabs. Then, nodes from the specified model part are sorted into sub model parts
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Default parameters:
{
"model_part_name" : "",
"moment_reference_point" : [0.0, 0.0, 0.0],
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"output_name_stub" : "slab_"
}"""
KratosMultiphysics.Process.__init__(self)
self.model_part = model[parameters["model_part_name"].GetString()]
parameters.ValidateAndAssignDefaults(self.GetDefaultParameters())
self.moment_reference_point = parameters["moment_reference_point"].GetVector()
self.bottom_point = parameters["bottom_point"].GetVector()
self.top_point = parameters["top_point"].GetVector()
self.time_domain = parameters["time_domain"].GetVector()
self.number_of_slabs = parameters["number_of_slabs"].GetInt()
self.is_open_domain = parameters["open_domain"].GetBool()
self.output_name_stub = pathlib.Path(parameters["output_name_stub"].GetString()) | [
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cms-sw/cmssw | fd9de012d503d3405420bcbeec0ec879baa57cf2 | PhysicsTools/HeppyCore/python/utils/dataset.py | python | Dataset.extractFileSizes | (self) | Get the file size for each file, from the eos ls -l command. | Get the file size for each file, from the eos ls -l command. | [
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trailofbits/llvm-sanitizer-tutorial | d29dfeec7f51fbf234fd0080f28f2b30cd0b6e99 | llvm/tools/clang/tools/scan-view/share/ScanView.py | python | ScanViewRequestHandler.do_POST | (self) | Serve a POST request. | Serve a POST request. | [
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kushview/Element | 1cc16380caa2ab79461246ba758b9de1f46db2a5 | waflib/Build.py | python | inst.get_install_path | (self, destdir=True) | return dest | Returns the destination path where files will be installed, pre-pending `destdir`.
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/multiprocessing/connection.py | python | _ConnectionBase.close | (self) | Close the connection | Close the connection | [
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macchina-io/macchina.io | ef24ba0e18379c3dd48fb84e6dbf991101cb8db0 | platform/JS/V8/tools/gyp/pylib/gyp/msvs_emulation.py | python | MsvsSettings._ConfigAttrib | (self, path, config,
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numba/targets/base.py | python | BaseContext.active_code_library | (self) | return self._codelib_stack[-1] | Get the active code library | Get the active code library | [
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domino-team/openwrt-cc | 8b181297c34d14d3ca521cc9f31430d561dbc688 | package/gli-pub/openwrt-node-packages-master/node/node-v6.9.1/deps/v8/tools/stats-viewer.py | python | Counter.Value | (self) | return self.data.IntAt(self.offset) | Return the integer value of this counter. | Return the integer value of this counter. | [
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hpi-xnor/BMXNet | ed0b201da6667887222b8e4b5f997c4f6b61943d | python/mxnet/gluon/block.py | python | Block.save_params | (self, filename) | Save parameters to file.
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hpi-xnor/BMXNet | ed0b201da6667887222b8e4b5f997c4f6b61943d | python/mxnet/gluon/block.py | python | Block.register_child | (self, block) | Registers block as a child of self. :py:class:`Block` s assigned to self as
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eventql/eventql | 7ca0dbb2e683b525620ea30dc40540a22d5eb227 | deps/3rdparty/spidermonkey/mozjs/media/webrtc/trunk/tools/gyp/pylib/gyp/sun_tool.py | python | SunTool.Dispatch | (self, args) | Dispatches a string command to a method. | Dispatches a string command to a method. | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/context.py | python | BaseContext.Semaphore | (self, value=1) | return Semaphore(value, ctx=self.get_context()) | Returns a semaphore object | Returns a semaphore object | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_controls.py | python | TreeCtrl.SetIndent | (*args, **kwargs) | return _controls_.TreeCtrl_SetIndent(*args, **kwargs) | SetIndent(self, unsigned int indent) | SetIndent(self, unsigned int indent) | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | scripts/Inelastic/CrystalField/fitting.py | python | CrystalField.Symmetry | (self) | return self.crystalFieldFunction.getAttributeValue('Symmetry') | Get value of Symmetry attribute. For example:
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symm = cf.Symmetry | Get value of Symmetry attribute. For example: | [
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ukoethe/vigra | 093d57d15c8c237adf1704d96daa6393158ce299 | vigranumpy/lib/pyqt/imagewindow.py | python | ImageViewer.contextMenuEvent | (self, e) | handles pop-up menu | handles pop-up menu | [
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miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/tensorflow/contrib/slim/python/slim/data/tfexample_decoder.py | python | TFExampleDecoder.__init__ | (self, keys_to_features, items_to_handlers) | Constructs the decoder.
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keys_to_features: a dictionary from TF-Example keys to either
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self._keys_to_features = keys_to_features
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/propgrid.py | python | PyComboBoxEditor._SetSelf | (*args, **kwargs) | return _propgrid.PyComboBoxEditor__SetSelf(*args, **kwargs) | _SetSelf(self, PyObject self) | _SetSelf(self, PyObject self) | [
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/klampt/robotsim.py | python | Geometry3D.rayCast | (self, s: Point, d: Point) | return _robotsim.Geometry3D_rayCast(self, s, d) | r"""
Performs a ray cast.
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_controls.py | python | ListView.Focus | (*args, **kwargs) | return _controls_.ListView_Focus(*args, **kwargs) | Focus(self, long index) | Focus(self, long index) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/webkit.py | python | WebKitNewWindowEvent.__init__ | (self, *args, **kwargs) | __init__(self, Window win=None) -> WebKitNewWindowEvent | __init__(self, Window win=None) -> WebKitNewWindowEvent | [
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"""__init__(self, Window win=None) -> WebKitNewWindowEvent"""
_webkit.WebKitNewWindowEvent_swiginit(self,_webkit.new_WebKitNewWindowEvent(*args, **kwargs)) | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/Muon/GUI/Common/corrections_tab_widget/dead_time_corrections_presenter.py | python | DeadTimeCorrectionsPresenter._handle_selected_table_is_invalid | (self) | Handles when the selected dead time table workspace is invalid. | Handles when the selected dead time table workspace is invalid. | [
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"""Handles when the selected dead time table workspace is invalid."""
# Triggers handle_dead_time_from_selector_changed
self.view.set_dead_time_from_data_file_selected() | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_core.py | python | GBSizerItem.SetGBSizer | (*args, **kwargs) | return _core_.GBSizerItem_SetGBSizer(*args, **kwargs) | SetGBSizer(self, GridBagSizer sizer)
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"""
SetGBSizer(self, GridBagSizer sizer)
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/distutils/text_file.py | python | TextFile.unreadline | (self, line) | Push 'line' (a string) onto an internal buffer that will be
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eclipse/sumo | 7132a9b8b6eea734bdec38479026b4d8c4336d03 | tools/traci/_vehicle.py | python | VehicleDomain.getMaxSpeed | (self, vehID) | return self._getUniversal(tc.VAR_MAXSPEED, vehID) | getMaxSpeed(string) -> double
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"""getMaxSpeed(string) -> double
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"""
return self._getUniversal(tc.VAR_MAXSPEED, vehID) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_core.py | python | Size.Get | (*args, **kwargs) | return _core_.Size_Get(*args, **kwargs) | Get() -> (width,height)
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BitMEX/api-connectors | 37a3a5b806ad5d0e0fc975ab86d9ed43c3bcd812 | auto-generated/python/swagger_client/models/execution.py | python | Execution.exec_inst | (self) | return self._exec_inst | Gets the exec_inst of this Execution. # noqa: E501
:return: The exec_inst of this Execution. # noqa: E501
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"""Gets the exec_inst of this Execution. # noqa: E501
:return: The exec_inst of this Execution. # noqa: E501
:rtype: str
"""
return self._exec_inst | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/plat-mac/lib-scriptpackages/StdSuites/Standard_Suite.py | python | Standard_Suite_Events.count | (self, _object, _attributes={}, **_arguments) | count: Return the number of elements of an object
Required argument: the object whose elements are to be counted
Keyword argument each: if specified, restricts counting to objects of this class
Keyword argument _attributes: AppleEvent attribute dictionary
Returns: the number of elements | count: Return the number of elements of an object
Required argument: the object whose elements are to be counted
Keyword argument each: if specified, restricts counting to objects of this class
Keyword argument _attributes: AppleEvent attribute dictionary
Returns: the number of elements | [
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"""count: Return the number of elements of an object
Required argument: the object whose elements are to be counted
Keyword argument each: if specified, restricts counting to objects of this class
Keyword argument _attributes: AppleEvent attribute dictionary
Returns: the number of elements
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/pandas/io/formats/format.py | python | Datetime64Formatter._format_strings | (self) | return fmt_values.tolist() | we by definition have DO NOT have a TZ | we by definition have DO NOT have a TZ | [
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values = self.values
if not isinstance(values, DatetimeIndex):
values = DatetimeIndex(values)
if self.formatter is not None and callable(self.formatter):
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fmt_values = format_array_from_datetime(
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return fmt_values.tolist() | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/ops/bincount_ops.py | python | bincount_v1 | (arr,
weights=None,
minlength=None,
maxlength=None,
dtype=dtypes.int32) | return bincount(arr, weights, minlength, maxlength, dtype) | Counts the number of occurrences of each value in an integer array.
If `minlength` and `maxlength` are not given, returns a vector with length
`tf.reduce_max(arr) + 1` if `arr` is non-empty, and length 0 otherwise.
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arr: An int32 tensor of non-negative values.
weights: If non-None, must be the same shape as arr. For each value in
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minlength: If given, ensures the output has length at least `minlength`,
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maxlength: If given, skips values in `arr` that are equal or greater than
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dtype: If `weights` is None, determines the type of the output bins.
Returns:
A vector with the same dtype as `weights` or the given `dtype`. The bin
values. | Counts the number of occurrences of each value in an integer array. | [
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weights=None,
minlength=None,
maxlength=None,
dtype=dtypes.int32):
"""Counts the number of occurrences of each value in an integer array.
If `minlength` and `maxlength` are not given, returns a vector with length
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dtype: If `weights` is None, determines the type of the output bins.
Returns:
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"""
return bincount(arr, weights, minlength, maxlength, dtype) | [
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INK-USC/USC-DS-RelationExtraction | eebcfa7fd2eda5bba92f3ef8158797cdf91e6981 | code/Classifier/Logistic.py | python | Logistic.fit | (self, train_x, train_y) | train_x: list of feature ids
train_y: list of [labels] | train_x: list of feature ids
train_y: list of [labels] | [
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"""
train_x: list of feature ids
train_y: list of [labels]
"""
assert len(train_x) == len(train_y)
y = []
x = []
for i in range(len(train_x)):
feature = {}
for fid in train_x[i]:
feature[fid + 1] = 1.0
for j in range(len(train_y[i])):
y.append(float(train_y[i][j]))
x.append(feature)
prob = problem(y, x)
param = parameter('-s 0 -c 1 -n 35 -q')
self.model = train(prob, param) # L2-Logistic
print('Finish training.') | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/ops/resource_variable_ops.py | python | ResourceVariable._init_from_proto | (self, variable_def, import_scope=None) | Initializes from `VariableDef` proto. | Initializes from `VariableDef` proto. | [
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"""Initializes from `VariableDef` proto."""
# Note that init_from_proto is currently not supported in Eager mode.
assert not context.executing_eagerly()
self._in_graph_mode = True
assert isinstance(variable_def, variable_pb2.VariableDef)
if not variable_def.is_resource:
raise ValueError("Trying to restore Variable as ResourceVariable.")
# Create from variable_def.
g = ops.get_default_graph()
self._handle = g.as_graph_element(
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self._shape = tensor_shape.TensorShape(
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self._handle_name = self._handle.name
self._unique_id = self._handle_name
self._initializer_op = g.as_graph_element(
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# Check whether initial_value_name exists for backwards compatibility.
if (hasattr(variable_def, "initial_value_name") and
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self._initial_value = g.as_graph_element(
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else:
self._initial_value = None
synchronization, aggregation, trainable = (
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variable_def.synchronization,
variable_def.aggregation,
variable_def.trainable,
variable_def.variable_name))
self._synchronization = synchronization
self._aggregation = aggregation
self._trainable = trainable
if variable_def.snapshot_name:
snapshot = g.as_graph_element(
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variable_def.snapshot_name, import_scope=import_scope))
if snapshot.op.type != "ReadVariableOp":
self._cached_value = snapshot
else:
self._cached_value = None
while snapshot.op.type != "ReadVariableOp":
snapshot = snapshot.op.inputs[0]
self._graph_element = snapshot
else:
self._cached_value = None
# Legacy case for protos without the snapshot name; assume it's the
# following.
self._graph_element = g.get_tensor_by_name(
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if variable_def.HasField("save_slice_info_def"):
self._save_slice_info = variables.Variable.SaveSliceInfo(
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import_scope=import_scope)
else:
self._save_slice_info = None
self._caching_device = None
self._dtype = dtypes.as_dtype(self._handle.op.get_attr("dtype"))
self._constraint = None | [
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google/mozc | 7329757e1ad30e327c1ae823a8302c79482d6b9c | src/prediction/gen_zero_query_data.py | python | ReadSymbolTsv | (stream) | return zero_query_dict | Reads emoji data from stream and returns zero query data. | Reads emoji data from stream and returns zero query data. | [
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"""Reads emoji data from stream and returns zero query data."""
zero_query_dict = collections.defaultdict(list)
stream = code_generator_util.SkipLineComment(stream)
for columns in code_generator_util.ParseColumnStream(stream, delimiter='\t'):
if len(columns) < 3:
logging.warning('format error: %s', '\t'.join(columns))
continue
symbol = columns[1]
readings = columns[2]
symbol_unicode = symbol
if len(symbol_unicode) != 1:
continue
symbol_code_point = ord(symbol_unicode)
# Selects emoji symbols from symbol dictionary.
# TODO(toshiyuki): Update the range if we need.
# from "☀"(black sun with rays) to "❧"(rotated floral heart).
if not (0x2600 <= symbol_code_point and symbol_code_point <= 0x2767):
continue
for reading in re.split(RE_SPLIT, readings.strip()):
if not reading:
continue
zero_query_dict[reading].append(
util.ZeroQueryEntry(util.ZERO_QUERY_TYPE_NONE,
symbol, util.EMOJI_TYPE_NONE, 0))
if len(columns) >= 4 and columns[3]:
# description: "天気", etc.
description = columns[3]
zero_query_dict[description].append(
util.ZeroQueryEntry(util.ZERO_QUERY_TYPE_NONE,
symbol, util.EMOJI_TYPE_NONE, 0))
if len(columns) >= 5 and columns[4]:
# additional_description: "傘", etc.
additional_description = columns[4]
zero_query_dict[additional_description].append(
util.ZeroQueryEntry(util.ZERO_QUERY_TYPE_NONE,
symbol, util.EMOJI_TYPE_NONE, 0))
return zero_query_dict | [
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"P... | https://github.com/google/mozc/blob/7329757e1ad30e327c1ae823a8302c79482d6b9c/src/prediction/gen_zero_query_data.py#L205-L248 | |
eclipse/sumo | 7132a9b8b6eea734bdec38479026b4d8c4336d03 | tools/contributed/sumopy/plugins/mapmatching/mapmatching.py | python | GpsPoints.get_ids_selected | (self) | return self.select_ids(self.parent.trips.are_selected[self.ids_trip.get_value()] ) | Returns point ids of selected traces | Returns point ids of selected traces | [
"Returns",
"point",
"ids",
"of",
"selected",
"traces"
] | def get_ids_selected(self):
"""
Returns point ids of selected traces
"""
#print 'GpsPoints.get_ids_selected'
#print ' ??ids_points = ',self.select_ids(self.parent.trips.are_selected[self.ids_trip.get_value()] )
# TODO: why is this working??? do we need trips.ids_points????
return self.select_ids(self.parent.trips.are_selected[self.ids_trip.get_value()] ) | [
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google/shaka-packager | e1b0c7c45431327fd3ce193514a5407d07b39b22 | packager/third_party/protobuf/python/google/protobuf/internal/decoder.py | python | MapDecoder | (field_descriptor, new_default, is_message_map) | return DecodeMap | Returns a decoder for a map field. | Returns a decoder for a map field. | [
"Returns",
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"decoder",
"for",
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"map",
"field",
"."
] | def MapDecoder(field_descriptor, new_default, is_message_map):
"""Returns a decoder for a map field."""
key = field_descriptor
tag_bytes = encoder.TagBytes(field_descriptor.number,
wire_format.WIRETYPE_LENGTH_DELIMITED)
tag_len = len(tag_bytes)
local_DecodeVarint = _DecodeVarint
# Can't read _concrete_class yet; might not be initialized.
message_type = field_descriptor.message_type
def DecodeMap(buffer, pos, end, message, field_dict):
submsg = message_type._concrete_class()
value = field_dict.get(key)
if value is None:
value = field_dict.setdefault(key, new_default(message))
while 1:
# Read length.
(size, pos) = local_DecodeVarint(buffer, pos)
new_pos = pos + size
if new_pos > end:
raise _DecodeError('Truncated message.')
# Read sub-message.
submsg.Clear()
if submsg._InternalParse(buffer, pos, new_pos) != new_pos:
# The only reason _InternalParse would return early is if it
# encountered an end-group tag.
raise _DecodeError('Unexpected end-group tag.')
if is_message_map:
value[submsg.key].MergeFrom(submsg.value)
else:
value[submsg.key] = submsg.value
# Predict that the next tag is another copy of the same repeated field.
pos = new_pos + tag_len
if buffer[new_pos:pos] != tag_bytes or new_pos == end:
# Prediction failed. Return.
return new_pos
return DecodeMap | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py3/pandas/plotting/_matplotlib/hist.py | python | _grouped_hist | (
data,
column=None,
by=None,
ax=None,
bins=50,
figsize=None,
layout=None,
sharex=False,
sharey=False,
rot=90,
grid=True,
xlabelsize=None,
xrot=None,
ylabelsize=None,
yrot=None,
legend=False,
**kwargs,
) | return axes | Grouped histogram
Parameters
----------
data : Series/DataFrame
column : object, optional
by : object, optional
ax : axes, optional
bins : int, default 50
figsize : tuple, optional
layout : optional
sharex : bool, default False
sharey : bool, default False
rot : int, default 90
grid : bool, default True
legend: : bool, default False
kwargs : dict, keyword arguments passed to matplotlib.Axes.hist
Returns
-------
collection of Matplotlib Axes | Grouped histogram | [
"Grouped",
"histogram"
] | def _grouped_hist(
data,
column=None,
by=None,
ax=None,
bins=50,
figsize=None,
layout=None,
sharex=False,
sharey=False,
rot=90,
grid=True,
xlabelsize=None,
xrot=None,
ylabelsize=None,
yrot=None,
legend=False,
**kwargs,
):
"""
Grouped histogram
Parameters
----------
data : Series/DataFrame
column : object, optional
by : object, optional
ax : axes, optional
bins : int, default 50
figsize : tuple, optional
layout : optional
sharex : bool, default False
sharey : bool, default False
rot : int, default 90
grid : bool, default True
legend: : bool, default False
kwargs : dict, keyword arguments passed to matplotlib.Axes.hist
Returns
-------
collection of Matplotlib Axes
"""
if legend:
assert "label" not in kwargs
if data.ndim == 1:
kwargs["label"] = data.name
elif column is None:
kwargs["label"] = data.columns
else:
kwargs["label"] = column
def plot_group(group, ax):
ax.hist(group.dropna().values, bins=bins, **kwargs)
if legend:
ax.legend()
if xrot is None:
xrot = rot
fig, axes = _grouped_plot(
plot_group,
data,
column=column,
by=by,
sharex=sharex,
sharey=sharey,
ax=ax,
figsize=figsize,
layout=layout,
rot=rot,
)
set_ticks_props(
axes, xlabelsize=xlabelsize, xrot=xrot, ylabelsize=ylabelsize, yrot=yrot
)
maybe_adjust_figure(
fig, bottom=0.15, top=0.9, left=0.1, right=0.9, hspace=0.5, wspace=0.3
)
return axes | [
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Kitware/ParaView | f760af9124ff4634b23ebbeab95a4f56e0261955 | ThirdParty/cinema/paraview/tpl/cinema_python/database/store.py | python | Store.get_parameter_values | (self, name) | return values | Get all values of type value
:param name: Name for the parameter. | Get all values of type value
:param name: Name for the parameter. | [
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"of",
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":",
"param",
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":",
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"for",
"the",
"parameter",
"."
] | def get_parameter_values(self, name):
"""
Get all values of type value
:param name: Name for the parameter.
"""
values = []
if ('values' in self.__parameter_list[name] and
'types' in self.__parameter_list[name]):
for val, typ in zip(self.__parameter_list[name]['values'],
self.__parameter_list[name]['types']):
if typ == 'value':
values.append(val)
return values | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/scipy/special/basic.py | python | assoc_laguerre | (x, n, k=0.0) | return orthogonal.eval_genlaguerre(n, k, x) | Compute the generalized (associated) Laguerre polynomial of degree n and order k.
The polynomial :math:`L^{(k)}_n(x)` is orthogonal over ``[0, inf)``,
with weighting function ``exp(-x) * x**k`` with ``k > -1``.
Notes
-----
`assoc_laguerre` is a simple wrapper around `eval_genlaguerre`, with
reversed argument order ``(x, n, k=0.0) --> (n, k, x)``. | Compute the generalized (associated) Laguerre polynomial of degree n and order k. | [
"Compute",
"the",
"generalized",
"(",
"associated",
")",
"Laguerre",
"polynomial",
"of",
"degree",
"n",
"and",
"order",
"k",
"."
] | def assoc_laguerre(x, n, k=0.0):
"""Compute the generalized (associated) Laguerre polynomial of degree n and order k.
The polynomial :math:`L^{(k)}_n(x)` is orthogonal over ``[0, inf)``,
with weighting function ``exp(-x) * x**k`` with ``k > -1``.
Notes
-----
`assoc_laguerre` is a simple wrapper around `eval_genlaguerre`, with
reversed argument order ``(x, n, k=0.0) --> (n, k, x)``.
"""
return orthogonal.eval_genlaguerre(n, k, x) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/site-packages/pip/_internal/commands/list.py | python | format_for_columns | (pkgs, options) | return data, header | Convert the package data into something usable
by output_package_listing_columns. | Convert the package data into something usable
by output_package_listing_columns. | [
"Convert",
"the",
"package",
"data",
"into",
"something",
"usable",
"by",
"output_package_listing_columns",
"."
] | def format_for_columns(pkgs, options):
# type: (List[Distribution], Values) -> Tuple[List[List[str]], List[str]]
"""
Convert the package data into something usable
by output_package_listing_columns.
"""
running_outdated = options.outdated
# Adjust the header for the `pip list --outdated` case.
if running_outdated:
header = ["Package", "Version", "Latest", "Type"]
else:
header = ["Package", "Version"]
data = []
if options.verbose >= 1 or any(dist_is_editable(x) for x in pkgs):
header.append("Location")
if options.verbose >= 1:
header.append("Installer")
for proj in pkgs:
# if we're working on the 'outdated' list, separate out the
# latest_version and type
row = [proj.project_name, proj.version]
if running_outdated:
row.append(proj.latest_version)
row.append(proj.latest_filetype)
if options.verbose >= 1 or dist_is_editable(proj):
row.append(proj.location)
if options.verbose >= 1:
row.append(get_installer(proj))
data.append(row)
return data, header | [
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... | https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/windows/Lib/site-packages/pip/_internal/commands/list.py#L270-L305 | |
hpi-xnor/BMXNet | ed0b201da6667887222b8e4b5f997c4f6b61943d | python/mxnet/contrib/autograd.py | python | grad | (func, argnum=None) | return wrapped | Return function that computes gradient of arguments.
Parameters
----------
func: a python function
The forward (loss) function.
argnum: an int or a list of int
The index of argument to calculate gradient for.
Returns
-------
grad_func: a python function
A function that would compute the gradient of arguments.
Examples
--------
>>> # autograd supports dynamic graph which is changed
>>> # every instance
>>> def func(x):
>>> r = random.randint(0, 1)
>>> if r % 2:
>>> return x**2
>>> else:
>>> return x/3
>>> # use `grad(func)` to get the gradient function
>>> for x in range(10):
>>> grad_func = grad(func)
>>> inputs = nd.array([[1, 2, 3], [4, 5, 6]])
>>> grad_vals = grad_func(inputs) | Return function that computes gradient of arguments. | [
"Return",
"function",
"that",
"computes",
"gradient",
"of",
"arguments",
"."
] | def grad(func, argnum=None):
"""Return function that computes gradient of arguments.
Parameters
----------
func: a python function
The forward (loss) function.
argnum: an int or a list of int
The index of argument to calculate gradient for.
Returns
-------
grad_func: a python function
A function that would compute the gradient of arguments.
Examples
--------
>>> # autograd supports dynamic graph which is changed
>>> # every instance
>>> def func(x):
>>> r = random.randint(0, 1)
>>> if r % 2:
>>> return x**2
>>> else:
>>> return x/3
>>> # use `grad(func)` to get the gradient function
>>> for x in range(10):
>>> grad_func = grad(func)
>>> inputs = nd.array([[1, 2, 3], [4, 5, 6]])
>>> grad_vals = grad_func(inputs)
"""
grad_with_loss_func = grad_and_loss(func, argnum)
@functools.wraps(grad_with_loss_func)
def wrapped(*args):
return grad_with_loss_func(*args)[0]
return wrapped | [
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bareos/bareos | 56a10bb368b0a81e977bb51304033fe49d59efb0 | contrib/dir-plugins/graphite/BareosDirPluginGraphiteSender.py | python | BareosDirPluginGraphiteSender.parse_plugin_definition | (self, context, plugindef) | return bRCs['bRC_OK'] | Check, if mandatory monitoringHost is set and set default for other unset parameters | Check, if mandatory monitoringHost is set and set default for other unset parameters | [
"Check",
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"mandatory",
"monitoringHost",
"is",
"set",
"and",
"set",
"default",
"for",
"other",
"unset",
"parameters"
] | def parse_plugin_definition(self, context, plugindef):
'''
Check, if mandatory monitoringHost is set and set default for other unset parameters
'''
super(BareosDirPluginGraphiteSender, self).parse_plugin_definition(
context, plugindef)
# monitoring Host is mandatory
if 'collectorHost' not in self.options:
self.collectorHost = "graphite"
else:
self.collectorHost = self.options['collectorHost']
if 'collectorPort' not in self.options:
self.collectorPort = 2003
else:
self.collectorPort = int(self.options['collectorPort'])
if 'metricPrefix' not in self.options:
self.metricPrefix = 'apps'
else:
self.metricPrefix = self.options['metricPrefix']
# we return OK in anyway, we do not want to produce Bareos errors just because of failing
# Nagios notifications
return bRCs['bRC_OK'] | [
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