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danmar/cppcheck | 78228599da0dfce3763a90a130b14fa2d614ab9f | addons/misra.py | python | MisraChecker.setSeverity | (self, severity) | Set the severity for all errors. | Set the severity for all errors. | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/ultimatelistctrl.py | python | UltimateListMainWindow.GetLine | (self, n) | return self._lines[n] | Returns the line data for the given index.
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/html.py | python | HtmlPrintout.SetFonts | (*args, **kwargs) | return _html.HtmlPrintout_SetFonts(*args, **kwargs) | SetFonts(self, String normal_face, String fixed_face, PyObject sizes=None) | SetFonts(self, String normal_face, String fixed_face, PyObject sizes=None) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/richtext.py | python | RichTextCtrl.IsEmpty | (*args, **kwargs) | return _richtext.RichTextCtrl_IsEmpty(*args, **kwargs) | IsEmpty(self) -> bool
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ceph/ceph | 959663007321a369c83218414a29bd9dbc8bda3a | src/pybind/mgr/cephadm/module.py | python | CephadmOrchestrator.get_facts | (self, hostname: Optional[str] = None) | return [self.cache.get_facts(hostname) for hostname in self.cache.get_hosts()] | Return a list of hosts metadata(gather_facts) managed by the orchestrator.
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msracver/Deep-Image-Analogy | 632b9287b42552e32dad64922967c8c9ec7fc4d3 | examples/pycaffe/tools.py | python | SimpleTransformer.set_scale | (self, scale) | Set the data scaling. | Set the data scaling. | [
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LLNL/lbann | 26083e6c86050302ce33148aea70f62e61cacb92 | applications/graph/communityGAN/model/discriminator.py | python | Discriminator.forward | (self, motif_size, motif_log_embeddings) | return prob, log_not_prob | Predict whether a motif is real.
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"""
# D = 1 - exp(-sum_j(prod_i(d_ij)))
# log(1-D) = -sum_j(exp(sum_i(log(d_ij))))
x = lbann.MatMul(
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x = lbann.Exp(x)
x = lbann.Reduction(x, mode='sum')
x = lbann.Negative(x)
log_not_prob = x
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# Note: D=-expm1(x) is accurate when D~0. When D~1, prefer
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prob = lbann.Negative(lbann.Expm1(log_not_prob))
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microsoft/ivy | 9f3c7ecc0b2383129fdd0953e10890d98d09a82d | ivy/concept.py | python | _union_lists | (lists) | return [seen.setdefault(x, x)
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/AWSPythonSDK/1.5.8/botocore/vendored/requests/packages/urllib3/connectionpool.py | python | HTTPConnectionPool._get_conn | (self, timeout=None) | return conn or self._new_conn() | Get a connection. Will return a pooled connection if one is available.
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:param timeout:
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GeometryCollective/boundary-first-flattening | 8250e5a0e85980ec50b5e8aa8f49dd6519f915cd | deps/nanogui/docs/exhale.py | python | ExhaleRoot.generateNamespaceNodeDocuments | (self) | Generates the reStructuredText document for every namespace, including nested
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The documents generated do not use the Breathe namespace directive, but instead
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llvm/llvm-project | ffa6262cb4e2a335d26416fad39a581b4f98c5f4 | mlir/python/mlir/dialects/linalg/opdsl/ops/core_named_ops.py | python | pooling_nhwc_max_unsigned | (
I=TensorDef(T1, S.N, S.OH * S.SH + S.KH * S.DH, S.OW * S.SW + S.KW * S.DW,
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"""
implements(ConvolutionOpInterface)
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_core.py | python | Image.__init__ | (self, *args, **kwargs) | __init__(self, String name, int type=BITMAP_TYPE_ANY, int index=-1) -> Image
Loads an image from a file. | __init__(self, String name, int type=BITMAP_TYPE_ANY, int index=-1) -> Image | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/lib2to3/pgen2/parse.py | python | Parser.__init__ | (self, grammar, convert=None) | Constructor.
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/media.py | python | MediaCtrl.GetDownloadProgress | (*args, **kwargs) | return _media.MediaCtrl_GetDownloadProgress(*args, **kwargs) | GetDownloadProgress(self) -> wxFileOffset | GetDownloadProgress(self) -> wxFileOffset | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numpy/polynomial/hermite_e.py | python | hermepow | (c, pow, maxpower=16) | return pu._pow(hermemul, c, pow, maxpower) | Raise a Hermite series to a power.
Returns the Hermite series `c` raised to the power `pow`. The
argument `c` is a sequence of coefficients ordered from low to high.
i.e., [1,2,3] is the series ``P_0 + 2*P_1 + 3*P_2.``
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c : array_like
1-D array of Hermite series coefficients ordered from low to
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pow : integer
Power to which the series will be raised
maxpower : integer, optional
Maximum power allowed. This is mainly to limit growth of the series
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Returns
-------
coef : ndarray
Hermite series of power.
See Also
--------
hermeadd, hermesub, hermemulx, hermemul, hermediv
Examples
--------
>>> from numpy.polynomial.hermite_e import hermepow
>>> hermepow([1, 2, 3], 2)
array([23., 28., 46., 12., 9.]) | Raise a Hermite series to a power. | [
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"""Raise a Hermite series to a power.
Returns the Hermite series `c` raised to the power `pow`. The
argument `c` is a sequence of coefficients ordered from low to high.
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1-D array of Hermite series coefficients ordered from low to
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pow : integer
Power to which the series will be raised
maxpower : integer, optional
Maximum power allowed. This is mainly to limit growth of the series
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Hermite series of power.
See Also
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hermeadd, hermesub, hermemulx, hermemul, hermediv
Examples
--------
>>> from numpy.polynomial.hermite_e import hermepow
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array([23., 28., 46., 12., 9.])
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numpy/lib/recfunctions.py | python | join_by | (key, r1, r2, jointype='inner', r1postfix='1', r2postfix='2',
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A string or a sequence of strings corresponding to the fields used
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Structured arrays.
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String appended to the names of the fields of r1 that are present
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String appended to the names of the fields of r2 that are present
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Dictionary mapping field names to the corresponding default values.
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Whether to return a MaskedArray (or MaskedRecords is
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asrecarray : {False, True}, optional
Whether to return a recarray (or MaskedRecords if `usemask==True`)
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"""
Join arrays `r1` and `r2` on key `key`.
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A string or a sequence of strings corresponding to the fields used
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"""
# Check jointype
if jointype not in ('inner', 'outer', 'leftouter'):
raise ValueError(
"The 'jointype' argument should be in 'inner', "
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)
# If we have a single key, put it in a tuple
if isinstance(key, basestring):
key = (key,)
# Check the keys
if len(set(key)) != len(key):
dup = next(x for n,x in enumerate(key) if x in key[n+1:])
raise ValueError("duplicate join key %r" % dup)
for name in key:
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raise ValueError('r1 does not have key field %r' % name)
if name not in r2.dtype.names:
raise ValueError('r2 does not have key field %r' % name)
# Make sure we work with ravelled arrays
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# Fixme: nb2 below is never used. Commenting out for pyflakes.
# (nb1, nb2) = (len(r1), len(r2))
nb1 = len(r1)
(r1names, r2names) = (r1.dtype.names, r2.dtype.names)
# Check the names for collision
collisions = (set(r1names) & set(r2names)) - set(key)
if collisions and not (r1postfix or r2postfix):
msg = "r1 and r2 contain common names, r1postfix and r2postfix "
msg += "can't both be empty"
raise ValueError(msg)
# Make temporary arrays of just the keys
# (use order of keys in `r1` for back-compatibility)
key1 = [ n for n in r1names if n in key ]
r1k = _keep_fields(r1, key1)
r2k = _keep_fields(r2, key1)
# Concatenate the two arrays for comparison
aux = ma.concatenate((r1k, r2k))
idx_sort = aux.argsort(order=key)
aux = aux[idx_sort]
#
# Get the common keys
flag_in = ma.concatenate(([False], aux[1:] == aux[:-1]))
flag_in[:-1] = flag_in[1:] + flag_in[:-1]
idx_in = idx_sort[flag_in]
idx_1 = idx_in[(idx_in < nb1)]
idx_2 = idx_in[(idx_in >= nb1)] - nb1
(r1cmn, r2cmn) = (len(idx_1), len(idx_2))
if jointype == 'inner':
(r1spc, r2spc) = (0, 0)
elif jointype == 'outer':
idx_out = idx_sort[~flag_in]
idx_1 = np.concatenate((idx_1, idx_out[(idx_out < nb1)]))
idx_2 = np.concatenate((idx_2, idx_out[(idx_out >= nb1)] - nb1))
(r1spc, r2spc) = (len(idx_1) - r1cmn, len(idx_2) - r2cmn)
elif jointype == 'leftouter':
idx_out = idx_sort[~flag_in]
idx_1 = np.concatenate((idx_1, idx_out[(idx_out < nb1)]))
(r1spc, r2spc) = (len(idx_1) - r1cmn, 0)
# Select the entries from each input
(s1, s2) = (r1[idx_1], r2[idx_2])
#
# Build the new description of the output array .......
# Start with the key fields
ndtype = _get_fieldspec(r1k.dtype)
# Add the fields from r1
for fname, fdtype in _get_fieldspec(r1.dtype):
if fname not in key:
ndtype.append((fname, fdtype))
# Add the fields from r2
for fname, fdtype in _get_fieldspec(r2.dtype):
# Have we seen the current name already ?
# we need to rebuild this list every time
names = list(name for name, dtype in ndtype)
try:
nameidx = names.index(fname)
except ValueError:
#... we haven't: just add the description to the current list
ndtype.append((fname, fdtype))
else:
# collision
_, cdtype = ndtype[nameidx]
if fname in key:
# The current field is part of the key: take the largest dtype
ndtype[nameidx] = (fname, max(fdtype, cdtype))
else:
# The current field is not part of the key: add the suffixes,
# and place the new field adjacent to the old one
ndtype[nameidx:nameidx + 1] = [
(fname + r1postfix, cdtype),
(fname + r2postfix, fdtype)
]
# Rebuild a dtype from the new fields
ndtype = np.dtype(ndtype)
# Find the largest nb of common fields :
# r1cmn and r2cmn should be equal, but...
cmn = max(r1cmn, r2cmn)
# Construct an empty array
output = ma.masked_all((cmn + r1spc + r2spc,), dtype=ndtype)
names = output.dtype.names
for f in r1names:
selected = s1[f]
if f not in names or (f in r2names and not r2postfix and f not in key):
f += r1postfix
current = output[f]
current[:r1cmn] = selected[:r1cmn]
if jointype in ('outer', 'leftouter'):
current[cmn:cmn + r1spc] = selected[r1cmn:]
for f in r2names:
selected = s2[f]
if f not in names or (f in r1names and not r1postfix and f not in key):
f += r2postfix
current = output[f]
current[:r2cmn] = selected[:r2cmn]
if (jointype == 'outer') and r2spc:
current[-r2spc:] = selected[r2cmn:]
# Sort and finalize the output
output.sort(order=key)
kwargs = dict(usemask=usemask, asrecarray=asrecarray)
return _fix_output(_fix_defaults(output, defaults), **kwargs) | [
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adobe/chromium | cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7 | build/android/android_commands.py | python | AndroidCommands.Adb | (self) | return self._adb | Returns our AdbInterface to avoid us wrapping all its methods. | Returns our AdbInterface to avoid us wrapping all its methods. | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/dashboard/dashboard/bisect_fyi.py | python | BisectFYIHandler.get | (self) | A get request is the same a post request for this endpoint. | A get request is the same a post request for this endpoint. | [
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gromacs/gromacs | 7dec3a3f99993cf5687a122de3e12de31c21c399 | python_packaging/src/gmxapi/simulation/mdrun.py | python | ResourceManager.update_output | (self) | Override gmxapi.operation.ResourceManager.update_output because we handle paralellism as 0.0.7. | Override gmxapi.operation.ResourceManager.update_output because we handle paralellism as 0.0.7. | [
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error_message = 'Got {} while executing {} for operation {}.'
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except exceptions.TypeError as e:
message = error_message.format(e,
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runner = self._runner_director(resources)
try:
runner()
except Exception as e:
message = error_message.format(e, runner, self.operation_id)
raise exceptions.ApiError(message) from e
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message = f'update_output implementation failed to update all outputs for {self.operation_id}.'
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llvm/llvm-project | ffa6262cb4e2a335d26416fad39a581b4f98c5f4 | clang/tools/scan-build-py/lib/libscanbuild/arguments.py | python | parse_args_for_analyze_build | () | return args | Parse and validate command-line arguments for analyze-build. | Parse and validate command-line arguments for analyze-build. | [
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""" Parse and validate command-line arguments for analyze-build. """
from_build_command = False
parser = create_analyze_parser(from_build_command)
args = parser.parse_args()
reconfigure_logging(args.verbose)
logging.debug('Raw arguments %s', sys.argv)
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validate_args_for_analyze(parser, args, from_build_command)
logging.debug('Parsed arguments: %s', args)
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_controls.py | python | TreeCtrl.GetPrevVisible | (*args, **kwargs) | return _controls_.TreeCtrl_GetPrevVisible(*args, **kwargs) | GetPrevVisible(self, TreeItemId item) -> TreeItemId | GetPrevVisible(self, TreeItemId item) -> TreeItemId | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/stc.py | python | StyledTextCtrl.GetMarginType | (*args, **kwargs) | return _stc.StyledTextCtrl_GetMarginType(*args, **kwargs) | GetMarginType(self, int margin) -> int
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hughperkins/tf-coriander | 970d3df6c11400ad68405f22b0c42a52374e94ca | tensorflow/python/training/session_manager.py | python | SessionManager.prepare_session | (self, master, init_op=None, saver=None,
checkpoint_dir=None, wait_for_checkpoint=False,
max_wait_secs=7200, config=None, init_feed_dict=None,
init_fn=None) | return sess | Creates a `Session`. Makes sure the model is ready to be used.
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Args:
master: `String` representation of the TensorFlow master to use.
init_op: Optional `Operation` used to initialize the model.
saver: A `Saver` object used to restore a model.
checkpoint_dir: Path to the checkpoint files.
wait_for_checkpoint: Whether to wait for checkpoint to become available.
max_wait_secs: Maximum time to wait for checkpoints to become available.
config: Optional `ConfigProto` proto used to configure the session.
init_feed_dict: Optional dictionary that maps `Tensor` objects to feed
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init_fn: Optional callable used to initialize the model. Called after the
optional `init_op` is called. The callable must accept one argument,
the session being initialized.
Returns:
A `Session` object that can be used to drive the model.
Raises:
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checkpoint_dir=None, wait_for_checkpoint=False,
max_wait_secs=7200, config=None, init_feed_dict=None,
init_fn=None):
"""Creates a `Session`. Makes sure the model is ready to be used.
Creates a `Session` on 'master'. If a `saver` object is passed in, and
`checkpoint_dir` points to a directory containing valid checkpoint
files, then it will try to recover the model from checkpoint. If
no checkpoint files are available, and `wait_for_checkpoint` is
`True`, then the process would check every `recovery_wait_secs`,
up to `max_wait_secs`, for recovery to succeed.
If the model cannot be recovered successfully then it is initialized by
either running the provided `init_op`, or calling the provided `init_fn`.
The local_init_op is also run after init_op and init_fn, regardless of
whether the model was recovered successfully, but only if
ready_for_local_init_op passes.
It is an error if the model cannot be recovered and no `init_op`
or `init_fn` or `local_init_op` are passed.
Args:
master: `String` representation of the TensorFlow master to use.
init_op: Optional `Operation` used to initialize the model.
saver: A `Saver` object used to restore a model.
checkpoint_dir: Path to the checkpoint files.
wait_for_checkpoint: Whether to wait for checkpoint to become available.
max_wait_secs: Maximum time to wait for checkpoints to become available.
config: Optional `ConfigProto` proto used to configure the session.
init_feed_dict: Optional dictionary that maps `Tensor` objects to feed
values. This feed dictionary is passed to the session `run()` call when
running the init op.
init_fn: Optional callable used to initialize the model. Called after the
optional `init_op` is called. The callable must accept one argument,
the session being initialized.
Returns:
A `Session` object that can be used to drive the model.
Raises:
RuntimeError: If the model cannot be initialized or recovered.
"""
sess, is_loaded_from_checkpoint = self._restore_checkpoint(
master,
saver,
checkpoint_dir=checkpoint_dir,
wait_for_checkpoint=wait_for_checkpoint,
max_wait_secs=max_wait_secs,
config=config)
if not is_loaded_from_checkpoint:
if init_op is None and not init_fn and self._local_init_op is None:
raise RuntimeError("Model is not initialized and no init_op or "
"init_fn or local_init_op was given")
if init_op is not None:
sess.run(init_op, feed_dict=init_feed_dict)
if init_fn:
init_fn(sess)
local_init_success, msg = self._try_run_local_init_op(sess)
if not local_init_success:
raise RuntimeError(
"Init operations did not make model ready for local_init. "
"Init op: %s, init fn: %s, error: %s" % ("None" if init_op is None
else init_op.name, init_fn,
msg))
is_ready, msg = self._model_ready(sess)
if not is_ready:
raise RuntimeError(
"Init operations did not make model ready. "
"Init op: %s, init fn: %s, local_init_op: %s, error: %s" %
(None if init_op is None else init_op.name, init_fn,
self._local_init_op, msg))
return sess | [
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nasa/fprime | 595cf3682d8365943d86c1a6fe7c78f0a116acf0 | Autocoders/Python/src/fprime_ac/parsers/XmlPortsParser.py | python | Interface.__init__ | (self, namespace, name, comment=None) | Constructor | Constructor | [
"Constructor"
] | def __init__(self, namespace, name, comment=None):
"""
Constructor
"""
self.__namespace = namespace
self.__name = name
self.__comment = comment
self.__return_type = None
self.__return_modifier = None | [
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openmm/openmm | cb293447c4fc8b03976dfe11399f107bab70f3d9 | wrappers/python/openmm/app/pdbfile.py | python | _format_83 | (f) | Format a single float into a string of width 8, with ideally 3 decimal
places of precision. If the number is a little too large, we can
gracefully degrade the precision by lopping off some of the decimal
places. If it's much too large, we throw a ValueError | Format a single float into a string of width 8, with ideally 3 decimal
places of precision. If the number is a little too large, we can
gracefully degrade the precision by lopping off some of the decimal
places. If it's much too large, we throw a ValueError | [
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gracefully degrade the precision by lopping off some of the decimal
places. If it's much too large, we throw a ValueError"""
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raise ValueError('coordinate "%s" could not be represented '
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/site-packages/pip/_vendor/urllib3/util/request.py | python | set_file_position | (body, pos) | return pos | If a position is provided, move file to that point.
Otherwise, we'll attempt to record a position for future use. | If a position is provided, move file to that point.
Otherwise, we'll attempt to record a position for future use. | [
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"""
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"""
if pos is not None:
rewind_body(body, pos)
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try:
pos = body.tell()
except (IOError, OSError):
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kamyu104/LeetCode-Solutions | 77605708a927ea3b85aee5a479db733938c7c211 | Python/flatten-nested-list-iterator.py | python | NestedIterator.next | (self) | return nestedList[i].getInteger() | :rtype: int | :rtype: int | [
":",
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] | def next(self):
"""
:rtype: int
"""
nestedList, i = self.__depth[-1]
self.__depth[-1][1] += 1
return nestedList[i].getInteger() | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/contrib/learn/python/learn/utils/saved_model_export_utils.py | python | _default_compare_fn | (curr_best_eval_result, cand_eval_result) | return curr_best_eval_result[default_key] > cand_eval_result[default_key] | Compares two evaluation results and returns true if the 2nd one is better.
Both evaluation results should have the values for MetricKey.LOSS, which are
used for comparison.
Args:
curr_best_eval_result: current best eval metrics.
cand_eval_result: candidate eval metrics.
Returns:
True if cand_eval_result is better.
Raises:
ValueError: If input eval result is None or no loss is available. | Compares two evaluation results and returns true if the 2nd one is better. | [
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] | def _default_compare_fn(curr_best_eval_result, cand_eval_result):
"""Compares two evaluation results and returns true if the 2nd one is better.
Both evaluation results should have the values for MetricKey.LOSS, which are
used for comparison.
Args:
curr_best_eval_result: current best eval metrics.
cand_eval_result: candidate eval metrics.
Returns:
True if cand_eval_result is better.
Raises:
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"""
default_key = metric_key.MetricKey.LOSS
if not curr_best_eval_result or default_key not in curr_best_eval_result:
raise ValueError(
'curr_best_eval_result cannot be empty or no loss is found in it.')
if not cand_eval_result or default_key not in cand_eval_result:
raise ValueError(
'cand_eval_result cannot be empty or no loss is found in it.')
return curr_best_eval_result[default_key] > cand_eval_result[default_key] | [
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kamyu104/LeetCode-Solutions | 77605708a927ea3b85aee5a479db733938c7c211 | Python/1-bit-and-2-bit-characters.py | python | Solution.isOneBitCharacter | (self, bits) | return parity == 0 | :type bits: List[int]
:rtype: bool | :type bits: List[int]
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"""
:type bits: List[int]
:rtype: bool
"""
parity = 0
for i in reversed(xrange(len(bits)-1)):
if bits[i] == 0:
break
parity ^= bits[i]
return parity == 0 | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/cgi.py | python | FieldStorage.getlist | (self, key) | Return list of received values. | Return list of received values. | [
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else:
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/HFIR_4Circle_Reduction/reduce4circleGUI.py | python | MainWindow.add_scans_ub_table | (self, scan_list) | add scans to UB matrix construction table
:param scan_list:
:return: | add scans to UB matrix construction table
:param scan_list:
:return: | [
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""" add scans to UB matrix construction table
:param scan_list:
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"""
# TODO/FIXME/ISSUE/NOW - consider to refactor with do_add_peaks_for_ub() and
# get experiment number
status, exp_number = gutil.parse_integers_editors(self.ui.lineEdit_exp)
if not status:
self.pop_one_button_dialog('Unable to get experiment number\n due to %s.' % str(exp_number))
return
# switch to tab-3
# self.ui.tabWidget.setCurrentIndex(MainWindow.TabPage['Calculate UB'])
# prototype for a new thread
self.ui.progressBar_add_ub_peaks.setRange(0, len(scan_list))
self._addUBPeaksThread = thread_pool.AddPeaksThread(self, exp_number, scan_list)
self._addUBPeaksThread.start()
# set the flag/notification where the indexing (HKL) from
self.ui.lineEdit_peaksIndexedBy.setText(IndexFromSpice) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/nntplib.py | python | NNTP.body | (self, id, file=None) | return self.artcmd('BODY ' + id, file) | Process a BODY command. Argument:
- id: article number or message id
- file: Filename string or file object to store the article in
Returns:
- resp: server response if successful
- nr: article number
- id: message id
- list: the lines of the article's body or an empty list
if file was used | Process a BODY command. Argument:
- id: article number or message id
- file: Filename string or file object to store the article in
Returns:
- resp: server response if successful
- nr: article number
- id: message id
- list: the lines of the article's body or an empty list
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- nr: article number
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return self.artcmd('BODY ' + id, file) | [
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potassco/clingo | e0c91d8f95cc28de1c480a871f9c97c30de83d40 | .github/manylinux.py | python | compile_wheels | (idx) | Compile binary wheels for different python versions. | Compile binary wheels for different python versions. | [
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'''
Compile binary wheels for different python versions.
'''
for pybin in glob('/opt/python/*/bin'):
# Requires Py3.6 or greater - on the docker image 3.5 is cp35-cp35m
if "35" not in pybin:
check_call(['rm', '-rf', '_skbuild'])
args = [path.join(pybin, 'pip'), 'wheel', '--verbose', '--no-deps', '-w', 'wheelhouse/']
if idx is not None:
args.extend(['--extra-index-url', idx])
args.extend(['./'])
check_call(args) | [
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timi-liuliang/echo | 40a5a24d430eee4118314459ab7e03afcb3b8719 | thirdparty/protobuf/python/google/protobuf/internal/python_message.py | python | _ExtensionDict.__init__ | (self, extended_message) | extended_message: Message instance for which we are the Extensions dict. | extended_message: Message instance for which we are the Extensions dict. | [
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"""extended_message: Message instance for which we are the Extensions dict.
"""
self._extended_message = extended_message | [
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eclipse/sumo | 7132a9b8b6eea734bdec38479026b4d8c4336d03 | tools/traci/_vehicle.py | python | VehicleDomain.replaceStop | (self, vehID, nextStopIndex, edgeID, pos=1., laneIndex=0, duration=tc.INVALID_DOUBLE_VALUE,
flags=tc.STOP_DEFAULT, startPos=tc.INVALID_DOUBLE_VALUE,
until=tc.INVALID_DOUBLE_VALUE, teleport=0) | replaceStop(string, int, string, double, integer, double, integer, double, double) -> None
Replaces stop at the given index with a new stop. Automatically modifies
the route if the replacement stop is at another location.
For edgeID a stopping place id may be given if the flag marks this
stop as stopping on busStop, parkingArea, containerStop etc.
If edgeID is "", the stop at the given index will be removed without
replacement and the route will not be modified.
If teleport is set to 1, the route to the replacement stop will be
disconnected (forcing a teleport).
If stopIndex is 0 the gap will be between the current
edge and the new stop. Otherwise the gap will be between the stop edge for
nextStopIndex - 1 and the new stop. | replaceStop(string, int, string, double, integer, double, integer, double, double) -> None | [
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flags=tc.STOP_DEFAULT, startPos=tc.INVALID_DOUBLE_VALUE,
until=tc.INVALID_DOUBLE_VALUE, teleport=0):
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"""
self._setCmd(tc.CMD_REPLACE_STOP, vehID, "tsdbdiddib", 9, edgeID, pos,
laneIndex, duration, flags, startPos, until, nextStopIndex, teleport) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_windows.py | python | SplitterWindow.IsSplit | (*args, **kwargs) | return _windows_.SplitterWindow_IsSplit(*args, **kwargs) | IsSplit(self) -> bool
Is the window split? | IsSplit(self) -> bool | [
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"""
IsSplit(self) -> bool
Is the window split?
"""
return _windows_.SplitterWindow_IsSplit(*args, **kwargs) | [
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adobe/chromium | cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7 | chrome/common/extensions/docs/build/build.py | python | RenderPages | (names, dump_render_tree) | return changed_files | Calls DumpRenderTree .../generator.html?<names> and writes the
results to .../docs/<name>.html | Calls DumpRenderTree .../generator.html?<names> and writes the
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"""
Calls DumpRenderTree .../generator.html?<names> and writes the
results to .../docs/<name>.html
"""
if not names:
raise Exception("RenderPage called with empty names param")
generator_url = "file:" + urllib.pathname2url(_generator_html)
generator_url += "?" + ",".join(names)
# Start with a fresh copy of page shell for each file.
# Save the current contents so that we can look for changes later.
originals = {}
for name in names:
input_file = _base_dir + "/" + name + ".html"
if (os.path.isfile(input_file)):
originals[name] = open(input_file, 'rb').read()
os.remove(input_file)
else:
originals[name] = ""
shutil.copy(_page_shell_html, input_file)
# Run DumpRenderTree and capture result
dump_render_tree_timeout = 1000 * 60 * 5 # five minutes
p = Popen(
[dump_render_tree, "--test-shell",
"%s %s" % (generator_url, dump_render_tree_timeout)],
stdout=PIPE)
# The remaining output will be the content of the generated pages.
output = p.stdout.read()
# Parse out just the JSON part.
begin = output.find(_expected_output_preamble)
end = output.rfind(_expected_output_postamble)
if (begin < 0 or end < 0):
raise Exception("%s returned invalid output:\n\n%s" %
(dump_render_tree, output))
begin += len(_expected_output_preamble)
try:
output_parsed = json.loads(output[begin:end])
except ValueError, msg:
raise Exception("Could not parse DumpRenderTree output as JSON. Error: " +
msg + "\n\nOutput was:\n" + output)
changed_files = []
for name in names:
result = output_parsed[name].encode("utf8") + '\n'
# Remove CRs that are appearing from captured DumpRenderTree output.
result = result.replace('\r', '')
# Remove empty style attributes.
result = result.replace(' style=""', '')
# Remove page_shell
input_file = _base_dir + "/" + name + ".html"
os.remove(input_file)
# Write output
open(input_file, 'wb').write(result)
if (originals[name] and result != originals[name]):
changed_files.append(input_file)
return changed_files | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py | python | StreamingDataFeeder.__init__ | (self, x, y, n_classes, batch_size) | Initializes a StreamingDataFeeder instance.
Args:
x: iterator each element of which returns one feature sample. Sample can
be a Nd numpy matrix or dictionary of Nd numpy matrices.
y: iterator each element of which returns one label sample. Sample can be
a Nd numpy matrix or dictionary of Nd numpy matrices with 1 or many
classes regression values.
n_classes: indicator of how many classes the corresponding label sample
has for the purposes of one-hot conversion of label. In case where `y`
is a dictionary, `n_classes` must be dictionary (with same keys as `y`)
of how many classes there are in each label in `y`. If key is
present in `y` and missing in `n_classes`, the value is assumed `None`
and no one-hot conversion will be applied to the label with that key.
batch_size: Mini batch size to accumulate samples in one batch. If set
`None`, then assumes that iterator to return already batched element.
Attributes:
x: input features (or dictionary of input features).
y: input label (or dictionary of output features).
n_classes: number of classes.
batch_size: mini batch size to accumulate.
input_shape: shape of the input (can be dictionary depending on `x`).
output_shape: shape of the output (can be dictionary depending on `y`).
input_dtype: dtype of input (can be dictionary depending on `x`).
output_dtype: dtype of output (can be dictionary depending on `y`). | Initializes a StreamingDataFeeder instance. | [
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"""Initializes a StreamingDataFeeder instance.
Args:
x: iterator each element of which returns one feature sample. Sample can
be a Nd numpy matrix or dictionary of Nd numpy matrices.
y: iterator each element of which returns one label sample. Sample can be
a Nd numpy matrix or dictionary of Nd numpy matrices with 1 or many
classes regression values.
n_classes: indicator of how many classes the corresponding label sample
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output_shape: shape of the output (can be dictionary depending on `y`).
input_dtype: dtype of input (can be dictionary depending on `x`).
output_dtype: dtype of output (can be dictionary depending on `y`).
"""
# pylint: disable=invalid-name,super-init-not-called
x_first_el = six.next(x)
self._x = itertools.chain([x_first_el], x)
if y is not None:
y_first_el = six.next(y)
self._y = itertools.chain([y_first_el], y)
else:
y_first_el = None
self._y = None
self.n_classes = n_classes
x_is_dict = isinstance(x_first_el, dict)
y_is_dict = y is not None and isinstance(y_first_el, dict)
if y_is_dict and n_classes is not None:
assert isinstance(n_classes, dict)
# extract shapes for first_elements
if x_is_dict:
x_first_el_shape = dict(
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else:
x_first_el_shape = [1] + list(x_first_el.shape)
if y_is_dict:
y_first_el_shape = dict(
[(k, [1] + list(v.shape)) for k, v in list(y_first_el.items())])
elif y is None:
y_first_el_shape = None
else:
y_first_el_shape = (
[1] + list(y_first_el[0].shape
if isinstance(y_first_el, list) else y_first_el.shape))
self.input_shape, self.output_shape, self._batch_size = _get_in_out_shape(
x_first_el_shape, y_first_el_shape, n_classes, batch_size)
# Input dtype of x_first_el.
if x_is_dict:
self._input_dtype = dict(
[(k, _check_dtype(v.dtype)) for k, v in list(x_first_el.items())])
else:
self._input_dtype = _check_dtype(x_first_el.dtype)
# Output dtype of y_first_el.
def check_y_dtype(el):
if isinstance(el, np.ndarray):
return el.dtype
elif isinstance(el, list):
return check_y_dtype(el[0])
else:
return _check_dtype(np.dtype(type(el)))
# Output types are floats, due to both softmaxes and regression req.
if n_classes is not None and (y is None or not y_is_dict) and n_classes > 0:
self._output_dtype = np.float32
elif y_is_dict:
self._output_dtype = dict(
[(k, check_y_dtype(v)) for k, v in list(y_first_el.items())])
elif y is None:
self._output_dtype = None
else:
self._output_dtype = check_y_dtype(y_first_el) | [
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xenia-project/xenia | 9b1fdac98665ac091b9660a5d0fbb259ed79e578 | third_party/google-styleguide/cpplint/cpplint.py | python | PrintCategories | () | Prints a list of all the error-categories used by error messages.
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/telemetry/third_party/pyserial/serial/serialposix.py | python | PosixSerial.flush | (self) | Flush of file like objects. In this case, wait until all data
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_gdi.py | python | NativeFontInfo.GetEncoding | (*args, **kwargs) | return _gdi_.NativeFontInfo_GetEncoding(*args, **kwargs) | GetEncoding(self) -> int | GetEncoding(self) -> int | [
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etternagame/etterna | 8775f74ac9c353320128609d4b4150672e9a6d04 | extern/crashpad/crashpad/third_party/mini_chromium/mini_chromium/build/win_helper.py | python | WinTool.Dispatch | (self, args) | return getattr(self, method)(*args[1:]) | Dispatches a string command to a method. | Dispatches a string command to a method. | [
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mongodb/mongo | d8ff665343ad29cf286ee2cf4a1960d29371937b | src/third_party/scons-3.1.2/scons-local-3.1.2/SCons/SConf.py | python | SConfBase.AddTest | (self, test_name, test_instance) | Adds test_class to this SConf instance. It can be called with
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/third_party/httplib2/upload-diffs.py | python | LoadSubversionAutoProperties | () | Returns the content of [auto-props] section of Subversion's config file as
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deepmodeling/deepmd-kit | 159e45d248b0429844fb6a8cb3b3a201987c8d79 | deepmd/descriptor/descriptor.py | python | Descriptor.compute_input_stats | (self,
data_coord: List[np.ndarray],
data_box: List[np.ndarray],
data_atype: List[np.ndarray],
natoms_vec: List[np.ndarray],
mesh: List[np.ndarray],
input_dict: Dict[str, List[np.ndarray]]
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normalized by the statistics.
Parameters
----------
data_coord : list[np.ndarray]
The coordinates. Can be generated by
:meth:`deepmd.model.model_stat.make_stat_input`
data_box : list[np.ndarray]
The box. Can be generated by
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data_atype : list[np.ndarray]
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natoms_vec : list[np.ndarray]
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mesh : list[np.ndarray]
The mesh for neighbor searching. Can be generated by
:meth:`deepmd.model.model_stat.make_stat_input`
input_dict : dict[str, list[np.ndarray]]
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thalium/icebox | 99d147d5b9269222225443ce171b4fd46d8985d4 | third_party/retdec-3.2/scripts/type_extractor/type_extractor/header_text_filters.py | python | filter_whitespaces | (text) | return text | Filters redundant whitespaces.
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text = re.sub(r'\s*\(\s*', '(', text)
text = re.sub(r'\s*\{\s*', '{ ', text)
text = re.sub(r'\s*\}\s*', ' }', text)
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nest/nest-simulator | f2623eb78518cdbd55e77e0ed486bf1111bcb62f | pynest/nest/lib/hl_api_types.py | python | NodeCollection.__array__ | (self, dtype=None) | return numpy.array(self.tolist(), dtype=dtype) | Convert the NodeCollection to a NumPy array. | Convert the NodeCollection to a NumPy array. | [
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"""Convert the NodeCollection to a NumPy array."""
return numpy.array(self.tolist(), dtype=dtype) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | build/ymake_conf.py | python | Platform.__init__ | (self, name, os, arch) | :type name: str
:type os: str
:type arch: str | :type name: str
:type os: str
:type arch: str | [
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"""
:type name: str
:type os: str
:type arch: str
"""
self.name = name
self.os = self._parse_os(os)
self.arch = arch.lower()
self.is_i386 = self.arch in ('i386', 'x86')
self.is_i686 = self.arch == 'i686'
self.is_x86 = self.is_i386 or self.is_i686
self.is_x86_64 = self.arch in ('x86_64', 'amd64')
self.is_intel = self.is_x86 or self.is_x86_64
self.is_armv7 = self.arch in ('armv7', 'armv7a', 'armv7a_neon', 'arm', 'armv7a_cortex_a9', 'armv7ahf_cortex_a35', 'armv7ahf_cortex_a53')
self.is_armv8 = self.arch in ('armv8', 'armv8a', 'arm64', 'aarch64', 'armv8a_cortex_a35', 'armv8a_cortex_a53')
self.is_armv8m = self.arch in ('armv8m_cortex_m33',)
self.is_arm64 = self.arch in ('arm64',)
self.is_arm = self.is_armv7 or self.is_armv8 or self.is_armv8m
self.is_armv7_neon = self.arch in ('armv7a_neon', 'armv7a_cortex_a9', 'armv7ahf_cortex_a35', 'armv7ahf_cortex_a53')
self.is_armv7hf = self.arch in ('armv7ahf_cortex_a35', 'armv7ahf_cortex_a53')
self.armv7_float_abi = None
if self.is_armv7:
if self.is_armv7hf:
self.armv7_float_abi = 'hard'
else:
self.armv7_float_abi = 'softfp'
self.is_cortex_a9 = self.arch in ('armv7a_cortex_a9',)
self.is_cortex_a35 = self.arch in ('armv7ahf_cortex_a35', 'armv8a_cortex_a35')
self.is_cortex_a53 = self.arch in ('armv7ahf_cortex_a53', 'armv8a_cortex_a53')
self.is_cortex_m33 = self.arch in ('armv8m_cortex_m33',)
self.is_power8le = self.arch == 'ppc64le'
self.is_power9le = self.arch == 'power9le'
self.is_powerpc = self.is_power8le or self.is_power9le
self.is_32_bit = self.is_x86 or self.is_armv7 or self.is_armv8m
self.is_64_bit = self.is_x86_64 or self.is_armv8 or self.is_powerpc
assert self.is_32_bit or self.is_64_bit
assert not (self.is_32_bit and self.is_64_bit)
self.is_linux = self.os == 'linux' or 'yocto' in self.os
self.is_linux_x86_64 = self.is_linux and self.is_x86_64
self.is_linux_armv8 = self.is_linux and self.is_armv8
self.is_linux_armv7 = self.is_linux and self.is_armv7
self.is_linux_power8le = self.is_linux and self.is_power8le
self.is_linux_power9le = self.is_linux and self.is_power9le
self.is_linux_powerpc = self.is_linux_power8le or self.is_linux_power9le
self.is_macos = self.os == 'macos'
self.is_macos_x86_64 = self.is_macos and self.is_x86_64
self.is_macos_arm64 = self.is_macos and self.is_arm64
self.is_iossim = self.os == 'iossim' or (self.os == 'ios' and self.is_intel)
self.is_ios = self.os == 'ios' or self.is_iossim
self.is_apple = self.is_macos or self.is_ios
self.is_windows = self.os == 'windows'
self.is_windows_x86_64 = self.is_windows and self.is_x86_64
self.is_android = self.os == 'android'
if self.is_android:
# This is default Android API level unless `ANDROID_API` is specified
# 18 is the smallest level with OpenGL support
# 21 is the smallest level for 64-bit platforms
default_android_api = 21 if self.is_64_bit else 18
self.android_api = int(preset('ANDROID_API', default_android_api))
self.is_cygwin = self.os == 'cygwin'
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self.is_none = self.os == 'none'
self.is_posix = self.is_linux or self.is_apple or self.is_android or self.is_cygwin or self.is_yocto | [
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alibaba/weex_js_engine | 2bdf4b6f020c1fc99c63f649718f6faf7e27fdde | jni/v8core/v8/build/gyp/pylib/gyp/generator/android.py | python | WriteAutoRegenerationRule | (params, root_makefile, makefile_name,
build_files) | Write the target to regenerate the Makefile. | Write the target to regenerate the Makefile. | [
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] | def WriteAutoRegenerationRule(params, root_makefile, makefile_name,
build_files):
"""Write the target to regenerate the Makefile."""
options = params['options']
# Sort to avoid non-functional changes to makefile.
build_files = sorted([os.path.join('$(LOCAL_PATH)', f) for f in build_files])
build_files_args = [gyp.common.RelativePath(filename, options.toplevel_dir)
for filename in params['build_files_arg']]
build_files_args = [os.path.join('$(PRIVATE_LOCAL_PATH)', f)
for f in build_files_args]
gyp_binary = gyp.common.FixIfRelativePath(params['gyp_binary'],
options.toplevel_dir)
makefile_path = os.path.join('$(LOCAL_PATH)', makefile_name)
if not gyp_binary.startswith(os.sep):
gyp_binary = os.path.join('.', gyp_binary)
root_makefile.write('GYP_FILES := \\\n %s\n\n' %
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root_makefile.write('%s: PRIVATE_LOCAL_PATH := $(LOCAL_PATH)\n' %
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root_makefile.write('%s: $(GYP_FILES)\n' % makefile_path)
root_makefile.write('\techo ACTION Regenerating $@\n\t%s\n\n' %
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gyp.RegenerateFlags(options) +
build_files_args)) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/AWS/resource-manager-code/lib/setuptools/archive_util.py | python | default_filter | (src, dst) | return dst | The default progress/filter callback; returns True for all files | The default progress/filter callback; returns True for all files | [
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"""The default progress/filter callback; returns True for all files"""
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/data/python/ops/dataset_ops.py | python | Iterator.__init__ | (self, iterator_resource, initializer, output_types,
output_shapes) | Creates a new iterator from the given iterator resource.
NOTE(mrry): Most users will not call this initializer directly, and will
instead use `Iterator.from_dataset()` or `Dataset.make_one_shot_iterator()`.
Args:
iterator_resource: A `tf.resource` scalar `tf.Tensor` representing the
iterator.
initializer: A `tf.Operation` that should be run to initialize this
iterator.
output_types: A nested structure of `tf.DType` objects corresponding to
each component of an element of this iterator.
output_shapes: A nested structure of `tf.TensorShape` objects
corresponding to each component of an element of this dataset. | Creates a new iterator from the given iterator resource. | [
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output_shapes):
"""Creates a new iterator from the given iterator resource.
NOTE(mrry): Most users will not call this initializer directly, and will
instead use `Iterator.from_dataset()` or `Dataset.make_one_shot_iterator()`.
Args:
iterator_resource: A `tf.resource` scalar `tf.Tensor` representing the
iterator.
initializer: A `tf.Operation` that should be run to initialize this
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output_types: A nested structure of `tf.DType` objects corresponding to
each component of an element of this iterator.
output_shapes: A nested structure of `tf.TensorShape` objects
corresponding to each component of an element of this dataset.
"""
self._iterator_resource = iterator_resource
self._initializer = initializer
self._output_types = output_types
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py3/pandas/core/indexes/range.py | python | RangeIndex.stop | (self) | return self._range.stop | The value of the `stop` parameter. | The value of the `stop` parameter. | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/difflib.py | python | IS_CHARACTER_JUNK | (ch, ws=" \t") | return ch in ws | r"""
Return True for ignorable character: iff `ch` is a space or tab.
Examples:
>>> IS_CHARACTER_JUNK(' ')
True
>>> IS_CHARACTER_JUNK('\t')
True
>>> IS_CHARACTER_JUNK('\n')
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>>> IS_CHARACTER_JUNK('x')
False | r"""
Return True for ignorable character: iff `ch` is a space or tab. | [
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Return True for ignorable character: iff `ch` is a space or tab.
Examples:
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True
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/flatnotebook.py | python | FlatNotebook.HasAGWFlag | (self, flag) | return res | Returns whether a flag is present in the :class:`FlatNotebook` style.
:param `flag`: one of the possible :class:`FlatNotebook` window styles.
:see: :meth:`~FlatNotebook.SetAGWWindowStyleFlag` for a list of possible window style flags. | Returns whether a flag is present in the :class:`FlatNotebook` style. | [
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Returns whether a flag is present in the :class:`FlatNotebook` style.
:param `flag`: one of the possible :class:`FlatNotebook` window styles.
:see: :meth:`~FlatNotebook.SetAGWWindowStyleFlag` for a list of possible window style flags.
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agwStyle = self.GetAGWWindowStyleFlag()
res = (agwStyle & flag and [True] or [False])[0]
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RLBot/RLBot | 34332b12cf158b3ef8dbf174ae67c53683368a9d | src/main/python/rlbot/utils/structures/game_interface.py | python | GameInterface.update_rigid_body_tick | (self, rigid_body_tick: RigidBodyTick) | return rigid_body_tick | Get the most recent state of the physics engine. | Get the most recent state of the physics engine. | [
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rlbot_status = self.game.UpdateRigidBodyTick(rigid_body_tick)
self.game_status(None, rlbot_status)
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/python/debug/cli/analyzer_cli.py | python | DebugAnalyzer.add_tensor_filter | (self, filter_name, filter_callable) | Add a tensor filter.
A tensor filter is a named callable of the signature:
filter_callable(dump_datum, tensor),
wherein dump_datum is an instance of debug_data.DebugTensorDatum carrying
metadata about the dumped tensor, including tensor name, timestamps, etc.
tensor is the value of the dumped tensor as an numpy.ndarray object.
The return value of the function is a bool.
This is the same signature as the input argument to
debug_data.DebugDumpDir.find().
Args:
filter_name: (str) name of the filter. Cannot be empty.
filter_callable: (callable) a filter function of the signature described
as above.
Raises:
ValueError: If filter_name is an empty str.
TypeError: If filter_name is not a str.
Or if filter_callable is not callable. | Add a tensor filter. | [
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] | def add_tensor_filter(self, filter_name, filter_callable):
"""Add a tensor filter.
A tensor filter is a named callable of the signature:
filter_callable(dump_datum, tensor),
wherein dump_datum is an instance of debug_data.DebugTensorDatum carrying
metadata about the dumped tensor, including tensor name, timestamps, etc.
tensor is the value of the dumped tensor as an numpy.ndarray object.
The return value of the function is a bool.
This is the same signature as the input argument to
debug_data.DebugDumpDir.find().
Args:
filter_name: (str) name of the filter. Cannot be empty.
filter_callable: (callable) a filter function of the signature described
as above.
Raises:
ValueError: If filter_name is an empty str.
TypeError: If filter_name is not a str.
Or if filter_callable is not callable.
"""
if not isinstance(filter_name, str):
raise TypeError("Input argument filter_name is expected to be str, "
"but is not.")
# Check that filter_name is not an empty str.
if not filter_name:
raise ValueError("Input argument filter_name cannot be empty.")
# Check that filter_callable is callable.
if not callable(filter_callable):
raise TypeError(
"Input argument filter_callable is expected to be callable, "
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self._tensor_filters[filter_name] = filter_callable | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/grid.py | python | Grid.DisableDragColSize | (*args, **kwargs) | return _grid.Grid_DisableDragColSize(*args, **kwargs) | DisableDragColSize(self) | DisableDragColSize(self) | [
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"""DisableDragColSize(self)"""
return _grid.Grid_DisableDragColSize(*args, **kwargs) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/grid.py | python | Grid.GetLabelFont | (*args, **kwargs) | return _grid.Grid_GetLabelFont(*args, **kwargs) | GetLabelFont(self) -> Font | GetLabelFont(self) -> Font | [
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Z3Prover/z3 | d745d03afdfdf638d66093e2bfbacaf87187f35b | src/api/python/z3/z3.py | python | Optimize.help | (self) | Display a string describing all available options. | Display a string describing all available options. | [
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print(Z3_optimize_get_help(self.ctx.ref(), self.optimize)) | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/dataset/engine/validators.py | python | check_filter | (method) | return new_method | check the input arguments of filter. | check the input arguments of filter. | [
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""""check the input arguments of filter."""
@wraps(method)
def new_method(self, *args, **kwargs):
[predicate, input_columns, num_parallel_workers], _ = parse_user_args(method, *args, **kwargs)
if not callable(predicate):
raise TypeError("Predicate should be a Python function or a callable Python object.")
if num_parallel_workers is not None:
check_num_parallel_workers(num_parallel_workers)
if input_columns is not None:
check_columns(input_columns, "input_columns")
return method(self, *args, **kwargs)
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smartdevicelink/sdl_core | 68f082169e0a40fccd9eb0db3c83911c28870f07 | src/3rd_party-static/gmock-1.7.0/scripts/generator/cpp/ast.py | python | PrintIndentifiers | (filename, should_print) | Prints all identifiers for a C++ source file.
Args:
filename: 'file1'
should_print: predicate with signature: bool Function(token) | Prints all identifiers for a C++ source file. | [
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] | def PrintIndentifiers(filename, should_print):
"""Prints all identifiers for a C++ source file.
Args:
filename: 'file1'
should_print: predicate with signature: bool Function(token)
"""
source = utils.ReadFile(filename, False)
if source is None:
sys.stderr.write('Unable to find: %s\n' % filename)
return
#print('Processing %s' % actual_filename)
builder = BuilderFromSource(source, filename)
try:
for node in builder.Generate():
if should_print(node):
print(node.name)
except KeyboardInterrupt:
return
except:
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OSGeo/gdal | 3748fc4ba4fba727492774b2b908a2130c864a83 | swig/python/osgeo/ogr.py | python | Layer.RollbackTransaction | (self, *args) | return _ogr.Layer_RollbackTransaction(self, *args) | r"""
RollbackTransaction(Layer self) -> OGRErr
OGRErr
OGR_L_RollbackTransaction(OGRLayerH hLayer)
For datasources which support transactions, RollbackTransaction will
roll back a datasource to its state before the start of the current
transaction.
If no transaction is active, or the rollback fails, will return
OGRERR_FAILURE. Datasources which do not support transactions will
always return OGRERR_NONE.
This function is the same as the C++ method
OGRLayer::RollbackTransaction().
Parameters:
-----------
hLayer: handle to the layer
OGRERR_NONE on success. | r"""
RollbackTransaction(Layer self) -> OGRErr
OGRErr
OGR_L_RollbackTransaction(OGRLayerH hLayer) | [
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RollbackTransaction(Layer self) -> OGRErr
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hLayer: handle to the layer
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ValveSoftware/source-sdk-2013 | 0d8dceea4310fde5706b3ce1c70609d72a38efdf | sp/src/thirdparty/protobuf-2.3.0/python/google/protobuf/internal/decoder.py | python | _FieldSkipper | () | return SkipField | Constructs the SkipField function. | Constructs the SkipField function. | [
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Returns:
The new position (after the tag value), or -1 if the tag is an end-group
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# The wire type is always in the first byte since varints are little-endian.
wire_type = local_ord(tag_bytes[0]) & wiretype_mask
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/xrc.py | python | XmlProperty.SetName | (*args, **kwargs) | return _xrc.XmlProperty_SetName(*args, **kwargs) | SetName(self, String name) | SetName(self, String name) | [
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"""SetName(self, String name)"""
return _xrc.XmlProperty_SetName(*args, **kwargs) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numba/roc/hsadrv/devicearray.py | python | is_hsa_ndarray | (obj) | return getattr(obj, '__hsa_ndarray__', False) | Check if an object is a HSA ndarray | Check if an object is a HSA ndarray | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/parso/py2/parso/python/tree.py | python | ClassOrFunc.get_decorators | (self) | :rtype: list of :class:`Decorator` | :rtype: list of :class:`Decorator` | [
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decorated = decorated.parent
if decorated.type == 'decorated':
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HKUST-Aerial-Robotics/Teach-Repeat-Replan | 98505a7f74b13c8b501176ff838a38423dbef536 | utils/quadrotor_msgs/src/quadrotor_msgs/msg/_PPROutputData.py | python | PPROutputData.serialize | (self, buff) | serialize message into buffer
:param buff: buffer, ``StringIO`` | serialize message into buffer
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"""
serialize message into buffer
:param buff: buffer, ``StringIO``
"""
try:
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length = len(_x)
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_x = _x.encode('utf-8')
length = len(_x)
if python3:
buff.write(struct.pack('<I%sB'%length, length, *_x))
else:
buff.write(struct.pack('<I%ss'%length, length, _x))
_x = self
buff.write(_struct_H13d.pack(_x.quad_time, _x.des_thrust, _x.des_roll, _x.des_pitch, _x.des_yaw, _x.est_roll, _x.est_pitch, _x.est_yaw, _x.est_angvel_x, _x.est_angvel_y, _x.est_angvel_z, _x.est_acc_x, _x.est_acc_y, _x.est_acc_z))
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except TypeError as te: self._check_types(ValueError("%s: '%s' when writing '%s'" % (type(te), str(te), str(_x)))) | [
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gimli-org/gimli | 17aa2160de9b15ababd9ef99e89b1bc3277bbb23 | pygimli/solver/solver.py | python | parseMarkersDictKey | (key, markers) | return [m for m in mas if m in markers] | Parse dictionary key of type str to marker list.
Utility function to parse a dictionary key string into a valid list of
markers containing in a given markers list.
Parameters
----------
key: str | int
Supported are
- int: single markers
- '*': all markers
- 'm1': Single marker
- 'm1,m2': Comma separated list
- ':': Slice wildcard
- 'start:stop:step': Slice like syntax
markers: [int]
List of integers, e.g., cell or boundary markers
Returns
-------
mas: [int]
List of integers described by key | Parse dictionary key of type str to marker list. | [
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""" Parse dictionary key of type str to marker list.
Utility function to parse a dictionary key string into a valid list of
markers containing in a given markers list.
Parameters
----------
key: str | int
Supported are
- int: single markers
- '*': all markers
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- 'start:stop:step': Slice like syntax
markers: [int]
List of integers, e.g., cell or boundary markers
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List of integers described by key
"""
markers = pg.unique(markers)
mas = None
if isinstance(key, str):
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if len(sse) > 2:
try:
step = int(sse[2])
except BaseException as _:
pass
mas = list(range(start, stop, step))
else:
mas = [int(key)]
else:
mas = [int(key)]
return [m for m in mas if m in markers] | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | tools/bisect-perf-regression.py | python | BisectPerformanceMetrics.TryParseResultValuesFromOutput | (self, metric, text) | return values_list | Attempts to parse a metric in the format RESULT <graph: <trace>.
Args:
metric: The metric as a list of [<trace>, <value>] strings.
text: The text to parse the metric values from.
Returns:
A list of floating point numbers found. | Attempts to parse a metric in the format RESULT <graph: <trace>. | [
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"""Attempts to parse a metric in the format RESULT <graph: <trace>.
Args:
metric: The metric as a list of [<trace>, <value>] strings.
text: The text to parse the metric values from.
Returns:
A list of floating point numbers found.
"""
# Format is: RESULT <graph>: <trace>= <value> <units>
metric_formatted = re.escape('RESULT %s: %s=' % (metric[0], metric[1]))
text_lines = text.split('\n')
values_list = []
for current_line in text_lines:
# Parse the output from the performance test for the metric we're
# interested in.
metric_re = metric_formatted +\
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metric_re = re.compile(metric_re)
regex_results = metric_re.search(current_line)
if not regex_results is None:
values_list += [regex_results.group('values')]
else:
metric_re = metric_formatted +\
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metric_re = re.compile(metric_re)
regex_results = metric_re.search(current_line)
if not regex_results is None:
metric_values = regex_results.group('values')
values_list += metric_values.split(',')
values_list = [float(v) for v in values_list if IsStringFloat(v)]
# If the metric is times/t, we need to sum the timings in order to get
# similar regression results as the try-bots.
metrics_to_sum = [['times', 't'], ['times', 'page_load_time'],
['cold_times', 'page_load_time'], ['warm_times', 'page_load_time']]
if metric in metrics_to_sum:
if values_list:
values_list = [reduce(lambda x, y: float(x) + float(y), values_list)]
return values_list | [
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floatlazer/semantic_slam | 657814a1ba484de6b7f6f9d07c564566c8121f13 | semantic_cloud/src/semantic_cloud.py | python | SemanticCloud.predict | (self, img) | Do semantic segmantation
\param img: (numpy array bgr8) The input cv image | Do semantic segmantation
\param img: (numpy array bgr8) The input cv image | [
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"""
Do semantic segmantation
\param img: (numpy array bgr8) The input cv image
"""
img = img.copy() # Make a copy of image because the method will modify the image
#orig_size = (img.shape[0], img.shape[1]) # Original image size
# Prepare image: first resize to CNN input size then extract the mean value of SUNRGBD dataset. No normalization
img = resize(img, self.cnn_input_size, mode = 'reflect', anti_aliasing=True, preserve_range = True) # Give float64
img = img.astype(np.float32)
img -= self.mean
# Convert HWC -> CHW
img = img.transpose(2, 0, 1)
# Convert to tensor
img = torch.tensor(img, dtype = torch.float32)
img = img.unsqueeze(0) # Add batch dimension required by CNN
with torch.no_grad():
img = img.to(self.device)
# Do inference
since = time.time()
outputs = self.model(img) #N,C,W,H
# Apply softmax to obtain normalized probabilities
outputs = torch.nn.functional.softmax(outputs, 1)
return outputs | [
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gimli-org/gimli | 17aa2160de9b15ababd9ef99e89b1bc3277bbb23 | pygimli/physics/em/fdem.py | python | FDEM2dFOPold.__init__ | (self, data, nlay=2, verbose=False) | constructor with data and (optionally) number of layers | constructor with data and (optionally) number of layers | [
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""" constructor with data and (optionally) number of layers """
pg.core.ModellingBase.__init__(self, verbose)
self.nlay = nlay
self.FOP1d = data.FOP(nlay)
self.nx = len(data.x)
self.nf = len(data.freq())
self.mesh_ = pg.meshtools.createMesh1D(self.nx, 2 * nlay - 1)
self.setMesh(self.mesh_) | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/plat-mac/aetools.py | python | decodeerror | (arguments) | return (err_a1, err_a2, err_a3) | Create the 'best' argument for a raise MacOS.Error | Create the 'best' argument for a raise MacOS.Error | [
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"""Create the 'best' argument for a raise MacOS.Error"""
errn = arguments['errn']
err_a1 = errn
if 'errs' in arguments:
err_a2 = arguments['errs']
else:
err_a2 = MacOS.GetErrorString(errn)
if 'erob' in arguments:
err_a3 = arguments['erob']
else:
err_a3 = None
return (err_a1, err_a2, err_a3) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/site-packages/requests/cookies.py | python | RequestsCookieJar.get_policy | (self) | return self._policy | Return the CookiePolicy instance used. | Return the CookiePolicy instance used. | [
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"""Return the CookiePolicy instance used."""
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hakuna-m/wubiuefi | caec1af0a09c78fd5a345180ada1fe45e0c63493 | src/pypack/altgraph/GraphUtil.py | python | generate_scale_free_graph | (steps, growth_num, self_loops=False, multi_edges=False) | return graph | Generates and returns a L{Graph.Graph} instance that will have C{steps*growth_num} nodes
and a scale free (powerlaw) connectivity. Starting with a fully connected graph with C{growth_num} nodes
at every step C{growth_num} nodes are added to the graph and are connected to existing nodes with
a probability proportional to the degree of these existing nodes. | Generates and returns a L{Graph.Graph} instance that will have C{steps*growth_num} nodes
and a scale free (powerlaw) connectivity. Starting with a fully connected graph with C{growth_num} nodes
at every step C{growth_num} nodes are added to the graph and are connected to existing nodes with
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'''
Generates and returns a L{Graph.Graph} instance that will have C{steps*growth_num} nodes
and a scale free (powerlaw) connectivity. Starting with a fully connected graph with C{growth_num} nodes
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'''
graph = Graph.Graph()
# initialize the graph
store = []
for i in range(growth_num):
store += [ i ] * (growth_num - 1)
for j in range(i + 1, growth_num):
graph.add_edge(i,j)
# generate
for node in range(growth_num, (steps-1) * growth_num):
graph.add_node(node)
while ( graph.out_degree(node) < growth_num ):
nbr = random.choice(store)
# loop defense
if node == nbr and not self_loops:
continue
# multi edge defense
if graph.edge_by_node(node, nbr) and not multi_edges:
continue
graph.add_edge(node, nbr)
for nbr in graph.out_nbrs(node):
store.append(node)
store.append(nbr)
return graph | [
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cvxpy/cvxpy | 5165b4fb750dfd237de8659383ef24b4b2e33aaf | cvxpy/reductions/solvers/conic_solvers/scip_conif.py | python | SCIP._solve | (
self,
model: ScipModel,
variables: List,
constraints: List,
data: Dict[str, Any],
dims: Dict[str, Union[int, List]],
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self,
model: ScipModel,
variables: List,
constraints: List,
data: Dict[str, Any],
dims: Dict[str, Union[int, List]],
) -> Dict[str, Any]:
"""Solve and return a solution if one exists."""
solution = {}
try:
model.optimize()
solution["value"] = model.getObjVal()
sol = model.getBestSol()
solution["primal"] = array([sol[v] for v in variables])
if not (data[s.BOOL_IDX] or data[s.INT_IDX]):
# Not the following code calculating the dual values does not
# always return the correct values, see tests `test_scip_lp_2`
# and `test_scip_socp_1`.
vals = []
# Get linear duals.
for lc in constraints:
if lc is not None and lc.isLinear():
dual = model.getDualsolLinear(lc)
vals.append(dual)
# Get non-linear duals.
if len(dims[s.SOC_DIM]) > 1:
for row in model.getNlRows():
vals.append(row.getDualsol())
solution["y"] = -array(vals)
solution[s.EQ_DUAL] = solution["y"][0:dims[s.EQ_DIM]]
solution[s.INEQ_DUAL] = solution["y"][dims[s.EQ_DIM]:]
except Exception as e:
log.warning("Error encountered when optimising %s: %s", model, e)
solution[s.SOLVE_TIME] = model.getSolvingTime()
solution['status'] = STATUS_MAP[model.getStatus()]
if solution["status"] == s.SOLVER_ERROR and model.getNCountedSols() > 0:
solution["status"] = s.OPTIMAL_INACCURATE
return solution | [
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oracle/graaljs | 36a56e8e993d45fc40939a3a4d9c0c24990720f1 | graal-nodejs/deps/v8/tools/stats-viewer.py | python | ChromeCounterCollection.CountersInUse | (self) | return self.max_counters | Return the number of counters in active use. | Return the number of counters in active use. | [
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"""Return the number of counters in active use."""
for i in range(self.max_counters):
name_offset = self.counter_names_offset + i * self._COUNTER_NAME_SIZE
if self.data.ByteAt(name_offset) == 0:
return i
return self.max_counters | [
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adobe/chromium | cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7 | tools/coverity/coverity.py | python | _ReadPassword | (pwfilename) | return password.rstrip() | Reads the coverity password in from a file where it was stashed | Reads the coverity password in from a file where it was stashed | [
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"""Reads the coverity password in from a file where it was stashed"""
pwfile = open(pwfilename, 'r')
password = pwfile.readline()
pwfile.close()
return password.rstrip() | [
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funnyzhou/Adaptive_Feeding | 9c78182331d8c0ea28de47226e805776c638d46f | python/caffe/pycaffe.py | python | _Net_set_input_arrays | (self, data, labels) | return self._set_input_arrays(data, labels) | Set input arrays of the in-memory MemoryDataLayer.
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"""
Set input arrays of the in-memory MemoryDataLayer.
(Note: this is only for networks declared with the memory data layer.)
"""
if labels.ndim == 1:
labels = np.ascontiguousarray(labels[:, np.newaxis, np.newaxis,
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return self._set_input_arrays(data, labels) | [
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InsightSoftwareConsortium/ITK | 87acfce9a93d928311c38bc371b666b515b9f19d | Modules/ThirdParty/pygccxml/src/pygccxml/declarations/type_traits.py | python | is_calldef_pointer | (type_) | return isinstance(nake_type, cpptypes.compound_t) \
and isinstance(nake_type.base, cpptypes.calldef_type_t) | returns True, if type represents pointer to free/member function,
False otherwise | returns True, if type represents pointer to free/member function,
False otherwise | [
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"""returns True, if type represents pointer to free/member function,
False otherwise"""
if not is_pointer(type_):
return False
nake_type = remove_alias(type_)
nake_type = remove_cv(nake_type)
return isinstance(nake_type, cpptypes.compound_t) \
and isinstance(nake_type.base, cpptypes.calldef_type_t) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numpy/ma/core.py | python | resize | (x, new_shape) | return result | Return a new masked array with the specified size and shape.
This is the masked equivalent of the `numpy.resize` function. The new
array is filled with repeated copies of `x` (in the order that the
data are stored in memory). If `x` is masked, the new array will be
masked, and the new mask will be a repetition of the old one.
See Also
--------
numpy.resize : Equivalent function in the top level NumPy module.
Examples
--------
>>> import numpy.ma as ma
>>> a = ma.array([[1, 2] ,[3, 4]])
>>> a[0, 1] = ma.masked
>>> a
masked_array(
data=[[1, --],
[3, 4]],
mask=[[False, True],
[False, False]],
fill_value=999999)
>>> np.resize(a, (3, 3))
masked_array(
data=[[1, 2, 3],
[4, 1, 2],
[3, 4, 1]],
mask=False,
fill_value=999999)
>>> ma.resize(a, (3, 3))
masked_array(
data=[[1, --, 3],
[4, 1, --],
[3, 4, 1]],
mask=[[False, True, False],
[False, False, True],
[False, False, False]],
fill_value=999999)
A MaskedArray is always returned, regardless of the input type.
>>> a = np.array([[1, 2] ,[3, 4]])
>>> ma.resize(a, (3, 3))
masked_array(
data=[[1, 2, 3],
[4, 1, 2],
[3, 4, 1]],
mask=False,
fill_value=999999) | Return a new masked array with the specified size and shape. | [
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"""
Return a new masked array with the specified size and shape.
This is the masked equivalent of the `numpy.resize` function. The new
array is filled with repeated copies of `x` (in the order that the
data are stored in memory). If `x` is masked, the new array will be
masked, and the new mask will be a repetition of the old one.
See Also
--------
numpy.resize : Equivalent function in the top level NumPy module.
Examples
--------
>>> import numpy.ma as ma
>>> a = ma.array([[1, 2] ,[3, 4]])
>>> a[0, 1] = ma.masked
>>> a
masked_array(
data=[[1, --],
[3, 4]],
mask=[[False, True],
[False, False]],
fill_value=999999)
>>> np.resize(a, (3, 3))
masked_array(
data=[[1, 2, 3],
[4, 1, 2],
[3, 4, 1]],
mask=False,
fill_value=999999)
>>> ma.resize(a, (3, 3))
masked_array(
data=[[1, --, 3],
[4, 1, --],
[3, 4, 1]],
mask=[[False, True, False],
[False, False, True],
[False, False, False]],
fill_value=999999)
A MaskedArray is always returned, regardless of the input type.
>>> a = np.array([[1, 2] ,[3, 4]])
>>> ma.resize(a, (3, 3))
masked_array(
data=[[1, 2, 3],
[4, 1, 2],
[3, 4, 1]],
mask=False,
fill_value=999999)
"""
# We can't use _frommethods here, as N.resize is notoriously whiny.
m = getmask(x)
if m is not nomask:
m = np.resize(m, new_shape)
result = np.resize(x, new_shape).view(get_masked_subclass(x))
if result.ndim:
result._mask = m
return result | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/_collections_abc.py | python | Set.isdisjoint | (self, other) | return True | Return True if two sets have a null intersection. | Return True if two sets have a null intersection. | [
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'Return True if two sets have a null intersection.'
for value in other:
if value in self:
return False
return True | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/importlib/_bootstrap.py | python | _gcd_import | (name, package=None, level=0) | return _find_and_load(name, _gcd_import) | Import and return the module based on its name, the package the call is
being made from, and the level adjustment.
This function represents the greatest common denominator of functionality
between import_module and __import__. This includes setting __package__ if
the loader did not. | Import and return the module based on its name, the package the call is
being made from, and the level adjustment. | [
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"""Import and return the module based on its name, the package the call is
being made from, and the level adjustment.
This function represents the greatest common denominator of functionality
between import_module and __import__. This includes setting __package__ if
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"""
_sanity_check(name, package, level)
if level > 0:
name = _resolve_name(name, package, level)
return _find_and_load(name, _gcd_import) | [
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tensorflow/deepmath | b5b721f54de1d5d6a02d78f5da5995237f9995f9 | deepmath/guidance/train.py | python | general_train | (make_loss, hparams, make_hooks=None) | Trains a general model with a loss.
Args:
make_loss: Function which creates loss (and possibly registers accuracy
summaries and other features).
hparams: Hyperparameters (see default_hparams() for details).
make_hooks: Optional, function which creates additional hooks for training.
Returns:
Final loss.
Raises:
ValueError: If flags are missing or invalid. | Trains a general model with a loss. | [
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] | def general_train(make_loss, hparams, make_hooks=None):
"""Trains a general model with a loss.
Args:
make_loss: Function which creates loss (and possibly registers accuracy
summaries and other features).
hparams: Hyperparameters (see default_hparams() for details).
make_hooks: Optional, function which creates additional hooks for training.
Returns:
Final loss.
Raises:
ValueError: If flags are missing or invalid.
"""
train_dir = mode_dir('train')
if not tf.gfile.Exists(train_dir):
tf.gfile.MakeDirs(train_dir)
if hparams.seed:
tf.set_random_seed(hparams.seed)
# Configure keras
keras.backend.set_learning_phase(1)
keras.backend.manual_variable_initialization(True)
with tf.device(tf.train.replica_device_setter(FLAGS.ps_tasks,
merge_devices=True)):
# Set the caching device to prevent hangs during distributed training
vs = tf.get_variable_scope()
if vs.caching_device is None:
vs.set_caching_device(lambda op: op.device)
# Grab loss and global step
total_loss = make_loss()
global_step = slim.get_or_create_global_step()
# Set up Polyak averaging if desired
if hparams.use_averages:
moving_average_variables = tf.trainable_variables()
moving_average_variables.extend(slim.losses.get_losses())
moving_average_variables.append(total_loss)
variable_averages = tf.train.ExponentialMovingAverage(
hparams.moving_average_decay, global_step)
# For sync_replicas, averaging happens in the chief queue runner
if not hparams.sync_replicas:
tf.add_to_collection(tf.GraphKeys.UPDATE_OPS,
variable_averages.apply(moving_average_variables))
else:
variable_averages = None
moving_average_variables = None
# Decay learning rate exponentially
learning_rate = tf.train.exponential_decay(
hparams.learning_rate,
global_step,
hparams.decay_steps,
hparams.learning_rate_decay_factor,
staircase=True)
tf.contrib.deprecated.scalar_summary('learning rate', learning_rate)
# Create optimizer
if hparams.optimizer == 'adam':
optimizer = tf.train.AdamOptimizer(learning_rate, epsilon=1e-3)
elif hparams.optimizer == 'rmsprop':
optimizer = tf.train.RMSPropOptimizer(
learning_rate=learning_rate, decay=0.9, momentum=0.9,
epsilon=1e-5)
else:
raise ValueError('Unknown optimizer %s' % hparams.optimizer)
is_chief = FLAGS.task == 0
chief_only_hooks = []
hooks = [tf.train.LoggingTensorHook({
'global_step': global_step,
'total_loss': total_loss
}, every_n_iter=FLAGS.log_every_n_iter),
tf.train.NanTensorHook(total_loss),
tf.train.StopAtStepHook(hparams.max_steps),
]
if make_hooks is not None:
hooks.extend(make_hooks())
# If desired, optimize synchronously
if hparams.sync_replicas:
optimizer = tf.SyncReplicasOptimizer(
optimizer=optimizer,
replicas_to_aggregate=FLAGS.worker_replicas - hparams.backup_replicas,
variable_averages=variable_averages,
variables_to_average=moving_average_variables,
replica_id=FLAGS.task,
total_num_replicas=FLAGS.worker_replicas)
sync_replicas_hook = optimizer.make_session_run_hook(is_chief)
hooks.append(sync_replicas_hook)
# Train
train_tensor = slim.learning.create_train_op(
total_loss, optimizer,
clip_gradient_norm=hparams.gradient_clipping_norm)
saver = tf.train.Saver(keep_checkpoint_every_n_hours=2)
scaffold = tf.train.Scaffold(saver=saver)
if FLAGS.save_summaries_secs > 0:
save_summaries_secs = FLAGS.save_summaries_secs
save_summaries_steps = None
else:
save_summaries_steps = FLAGS.save_summaries_steps
save_summaries_secs = None
with tf.train.MonitoredTrainingSession(
master=FLAGS.super_master,
is_chief=is_chief,
hooks=hooks,
chief_only_hooks=chief_only_hooks,
checkpoint_dir=train_dir,
scaffold=scaffold,
save_checkpoint_secs=FLAGS.save_checkpoint_secs,
save_summaries_secs=save_summaries_secs,
save_summaries_steps=save_summaries_steps) as mon_sess:
while not mon_sess.should_stop():
mon_sess.run(train_tensor) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/traitlets/py3/traitlets/config/manager.py | python | recursive_update | (target, new) | Recursively update one dictionary using another.
None values will delete their keys. | Recursively update one dictionary using another. | [
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"""Recursively update one dictionary using another.
None values will delete their keys.
"""
for k, v in new.items():
if isinstance(v, dict):
if k not in target:
target[k] = {}
recursive_update(target[k], v)
if not target[k]:
# Prune empty subdicts
del target[k]
elif v is None:
target.pop(k, None)
else:
target[k] = v | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/site-packages/botocore/utils.py | python | percent_encode_sequence | (mapping, safe=SAFE_CHARS) | return '&'.join(encoded_pairs) | Urlencode a dict or list into a string.
This is similar to urllib.urlencode except that:
* It uses quote, and not quote_plus
* It has a default list of safe chars that don't need
to be encoded, which matches what AWS services expect.
If any value in the input ``mapping`` is a list type,
then each list element wil be serialized. This is the equivalent
to ``urlencode``'s ``doseq=True`` argument.
This function should be preferred over the stdlib
``urlencode()`` function.
:param mapping: Either a dict to urlencode or a list of
``(key, value)`` pairs. | Urlencode a dict or list into a string. | [
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"a",
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] | def percent_encode_sequence(mapping, safe=SAFE_CHARS):
"""Urlencode a dict or list into a string.
This is similar to urllib.urlencode except that:
* It uses quote, and not quote_plus
* It has a default list of safe chars that don't need
to be encoded, which matches what AWS services expect.
If any value in the input ``mapping`` is a list type,
then each list element wil be serialized. This is the equivalent
to ``urlencode``'s ``doseq=True`` argument.
This function should be preferred over the stdlib
``urlencode()`` function.
:param mapping: Either a dict to urlencode or a list of
``(key, value)`` pairs.
"""
encoded_pairs = []
if hasattr(mapping, 'items'):
pairs = mapping.items()
else:
pairs = mapping
for key, value in pairs:
if isinstance(value, list):
for element in value:
encoded_pairs.append('%s=%s' % (percent_encode(key),
percent_encode(element)))
else:
encoded_pairs.append('%s=%s' % (percent_encode(key),
percent_encode(value)))
return '&'.join(encoded_pairs) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numba/types/functions.py | python | Dispatcher.get_impl_key | (self, sig) | return self.get_overload(sig) | Get the implementation key for the given signature. | Get the implementation key for the given signature. | [
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"""
Get the implementation key for the given signature.
"""
return self.get_overload(sig) | [
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moderngl/moderngl | 32fe79927e02b0fa893b3603d677bdae39771e14 | moderngl/context.py | python | Context.scope | (self, framebuffer=None, enable_only=None, *, textures=(),
uniform_buffers=(), storage_buffers=(), samplers=(), enable=None) | return res | Create a :py:class:`Scope` object.
Args:
framebuffer (Framebuffer): The framebuffer to use when entering.
enable_only (int): The enable_only flags to set when entering.
Keyword Args:
textures (list): List of (texture, binding) tuples.
uniform_buffers (list): List of (buffer, binding) tuples.
storage_buffers (list): List of (buffer, binding) tuples.
samplers (list): List of sampler bindings
enable (int): Flags to enable for this vao such as depth testing and blending | Create a :py:class:`Scope` object. | [
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] | def scope(self, framebuffer=None, enable_only=None, *, textures=(),
uniform_buffers=(), storage_buffers=(), samplers=(), enable=None) -> 'Scope':
'''
Create a :py:class:`Scope` object.
Args:
framebuffer (Framebuffer): The framebuffer to use when entering.
enable_only (int): The enable_only flags to set when entering.
Keyword Args:
textures (list): List of (texture, binding) tuples.
uniform_buffers (list): List of (buffer, binding) tuples.
storage_buffers (list): List of (buffer, binding) tuples.
samplers (list): List of sampler bindings
enable (int): Flags to enable for this vao such as depth testing and blending
'''
if enable is not None:
enable_only = enable
if framebuffer is None:
framebuffer = self.screen
mgl_textures = tuple((tex.mglo, idx) for tex, idx in textures)
mgl_uniform_buffers = tuple((buf.mglo, idx) for buf, idx in uniform_buffers)
mgl_storage_buffers = tuple((buf.mglo, idx) for buf, idx in storage_buffers)
res = Scope.__new__(Scope)
res.mglo = self.mglo.scope(framebuffer.mglo, enable_only, mgl_textures,
mgl_uniform_buffers, mgl_storage_buffers, samplers)
res.ctx = self
res._framebuffer = framebuffer
res._textures = textures
res._uniform_buffers = uniform_buffers
res._storage_buffers = storage_buffers
res._samplers = samplers
res.extra = None
return res | [
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Polidea/SiriusObfuscator | b0e590d8130e97856afe578869b83a209e2b19be | SymbolExtractorAndRenamer/lldb/scripts/Python/static-binding/lldb.py | python | SBMemoryRegionInfo.IsReadable | (self) | return _lldb.SBMemoryRegionInfo_IsReadable(self) | IsReadable(self) -> bool | IsReadable(self) -> bool | [
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"""IsReadable(self) -> bool"""
return _lldb.SBMemoryRegionInfo_IsReadable(self) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/cgitb.py | python | scanvars | (reader, frame, locals) | return vars | Scan one logical line of Python and look up values of variables used. | Scan one logical line of Python and look up values of variables used. | [
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] | def scanvars(reader, frame, locals):
"""Scan one logical line of Python and look up values of variables used."""
vars, lasttoken, parent, prefix, value = [], None, None, '', __UNDEF__
for ttype, token, start, end, line in tokenize.generate_tokens(reader):
if ttype == tokenize.NEWLINE: break
if ttype == tokenize.NAME and token not in keyword.kwlist:
if lasttoken == '.':
if parent is not __UNDEF__:
value = getattr(parent, token, __UNDEF__)
vars.append((prefix + token, prefix, value))
else:
where, value = lookup(token, frame, locals)
vars.append((token, where, value))
elif token == '.':
prefix += lasttoken + '.'
parent = value
else:
parent, prefix = None, ''
lasttoken = token
return vars | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/ipython/py2/IPython/utils/io.py | python | Tee.close | (self) | Close the file and restore the channel. | Close the file and restore the channel. | [
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] | def close(self):
"""Close the file and restore the channel."""
self.flush()
setattr(sys, self.channel, self.ostream)
self.file.close()
self._closed = True | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/eager/python/evaluator.py | python | Evaluator.track_metric | (self, metric) | return metric | Add a Metric to be tracked.
Metrics can only be tracked by one `Evaluator`. Metrics must be
tracked or they will not appear in `all_metric_results()`.
Args:
metric: A `Metric` object.
Returns:
The `metric` passed into this function.
Raises:
RuntimeError: If called before __init__.
TypeError: If `metric` is not of the correct type.
ValueError: If there is a name collision between Metrics or `metric`
has already been added to another `Evaluator`. | Add a Metric to be tracked. | [
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] | def track_metric(self, metric):
"""Add a Metric to be tracked.
Metrics can only be tracked by one `Evaluator`. Metrics must be
tracked or they will not appear in `all_metric_results()`.
Args:
metric: A `Metric` object.
Returns:
The `metric` passed into this function.
Raises:
RuntimeError: If called before __init__.
TypeError: If `metric` is not of the correct type.
ValueError: If there is a name collision between Metrics or `metric`
has already been added to another `Evaluator`.
"""
if not hasattr(self, "_metrics"):
raise RuntimeError(
"Need to call Evaluator.__init__ before adding metrics")
if not isinstance(metric, metrics.Metric):
raise TypeError(
"Evaluator.track_metric() passed type %s, not a tfe.metrics.Metric" %
(type(metric),))
if metric.name in self._metrics:
if metric is self._metrics[metric.name]:
return metric
raise ValueError(
"Attempt to add two Metrics with the name '%s' to the same Evaluator "
"'%s'" % (metric.name, self.name))
# pylint: disable=protected-access
if hasattr(metric, "_added_to_an_evaluator"):
raise ValueError("Metric %s already added to Evaluator %s" %
(metric.name, metric._added_to_an_evaluator))
metric._added_to_an_evaluator = self.__class__.__name__
# pylint: enable=protected-access
self._metrics[metric.name] = metric
return metric | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/learn/python/learn/estimators/run_config.py | python | _get_master | (cluster_spec, task_type, task_id) | return '' | Returns the appropriate string for the TensorFlow master. | Returns the appropriate string for the TensorFlow master. | [
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] | def _get_master(cluster_spec, task_type, task_id):
"""Returns the appropriate string for the TensorFlow master."""
if not cluster_spec:
return ''
# If there is only one node in the cluster, do things locally.
jobs = cluster_spec.jobs
if len(jobs) == 1 and len(cluster_spec.job_tasks(jobs[0])) == 1:
return ''
# Lookup the master in cluster_spec using task_type and task_id,
# if possible.
if task_type:
if task_type not in jobs:
raise ValueError(
'%s is not a valid task_type in the cluster_spec:\n'
'%s\n\n'
'Note that these values may be coming from the TF_CONFIG environment '
'variable.' % (task_type, cluster_spec))
addresses = cluster_spec.job_tasks(task_type)
if task_id >= len(addresses) or task_id < 0:
raise ValueError(
'%d is not a valid task_id for task_type %s in the '
'cluster_spec:\n'
'%s\n\n'
'Note that these value may be coming from the TF_CONFIG environment '
'variable.' % (task_id, task_type, cluster_spec))
return 'grpc://' + addresses[task_id]
# For backwards compatibility, we return empty string if task_type was
# not set (task_type did not previously exist).
return '' | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/ipython/py3/IPython/core/history.py | python | HistoryAccessor.__init__ | (self, profile='default', hist_file=u'', **traits) | Create a new history accessor.
Parameters
----------
profile : str
The name of the profile from which to open history.
hist_file : str
Path to an SQLite history database stored by IPython. If specified,
hist_file overrides profile.
config : :class:`~traitlets.config.loader.Config`
Config object. hist_file can also be set through this. | Create a new history accessor.
Parameters
----------
profile : str
The name of the profile from which to open history.
hist_file : str
Path to an SQLite history database stored by IPython. If specified,
hist_file overrides profile.
config : :class:`~traitlets.config.loader.Config`
Config object. hist_file can also be set through this. | [
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"""Create a new history accessor.
Parameters
----------
profile : str
The name of the profile from which to open history.
hist_file : str
Path to an SQLite history database stored by IPython. If specified,
hist_file overrides profile.
config : :class:`~traitlets.config.loader.Config`
Config object. hist_file can also be set through this.
"""
# We need a pointer back to the shell for various tasks.
super(HistoryAccessor, self).__init__(**traits)
# defer setting hist_file from kwarg until after init,
# otherwise the default kwarg value would clobber any value
# set by config
if hist_file:
self.hist_file = hist_file
if self.hist_file == u'':
# No one has set the hist_file, yet.
self.hist_file = self._get_hist_file_name(profile)
if sqlite3 is None and self.enabled:
warn("IPython History requires SQLite, your history will not be saved")
self.enabled = False
self.init_db() | [
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SFTtech/openage | d6a08c53c48dc1e157807471df92197f6ca9e04d | openage/convert/processor/conversion/aoc/upgrade_resource_subprocessor.py | python | AoCUpgradeResourceSubprocessor.starting_stone_upgrade | (converter_group, value, operator, team=False) | return patches | Creates a patch for the starting stone modify effect (ID: 93).
:param converter_group: Tech/Civ that gets the patch.
:type converter_group: ...dataformat.converter_object.ConverterObjectGroup
:param value: Value used for patching the member.
:type value: MemberOperator
:param operator: Operator used for patching the member.
:type operator: MemberOperator
:returns: The forward references for the generated patches.
:rtype: list | Creates a patch for the starting stone modify effect (ID: 93). | [
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"."
] | def starting_stone_upgrade(converter_group, value, operator, team=False):
"""
Creates a patch for the starting stone modify effect (ID: 93).
:param converter_group: Tech/Civ that gets the patch.
:type converter_group: ...dataformat.converter_object.ConverterObjectGroup
:param value: Value used for patching the member.
:type value: MemberOperator
:param operator: Operator used for patching the member.
:type operator: MemberOperator
:returns: The forward references for the generated patches.
:rtype: list
"""
patches = []
# TODO: Implement
return patches | [
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stan-dev/math | 5fd79f89933269a4ca4d8dd1fde2a36d53d4768c | lib/boost_1.75.0/tools/build/src/build/virtual_target.py | python | NotFileTarget.path | (self) | return None | Returns nothing, to indicate that target path is not known. | Returns nothing, to indicate that target path is not known. | [
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"""Returns nothing, to indicate that target path is not known."""
return None | [
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emscripten-core/emscripten | 0d413d3c5af8b28349682496edc14656f5700c2f | third_party/ply/example/ansic/cparse.py | python | p_type_name | (t) | type_name : specifier_qualifier_list abstract_declarator_opt | type_name : specifier_qualifier_list abstract_declarator_opt | [
"type_name",
":",
"specifier_qualifier_list",
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] | def p_type_name(t):
'type_name : specifier_qualifier_list abstract_declarator_opt'
pass | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/ipython/py3/IPython/core/displaypub.py | python | DisplayPublisher.clear_output | (self, wait=False) | Clear the output of the cell receiving output. | Clear the output of the cell receiving output. | [
"Clear",
"the",
"output",
"of",
"the",
"cell",
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] | def clear_output(self, wait=False):
"""Clear the output of the cell receiving output."""
print('\033[2K\r', end='')
sys.stdout.flush()
print('\033[2K\r', end='')
sys.stderr.flush() | [
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ampl/mp | cad8d370089a76507cb9c5518c21a1097f4a504b | support/build-docs.py | python | copy_content | (src_dir, dst_dir) | Copy content of the src_dir to dst_dir recursively. | Copy content of the src_dir to dst_dir recursively. | [
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] | def copy_content(src_dir, dst_dir):
"Copy content of the src_dir to dst_dir recursively."
for entry in os.listdir(src_dir):
src = os.path.join(src_dir, entry)
dst = os.path.join(dst_dir, entry)
if os.path.isdir(src):
fileutil.rmtree_if_exists(dst)
shutil.copytree(src, dst)
else:
shutil.copyfile(src, dst) | [
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mkeeter/antimony | ee525bbdad34ae94879fd055821f92bcef74e83f | py/fab/shapes.py | python | revolve_xy_y | (a, x) | return move(revolve_y(move(a, -x, 0)), x, 0) | Revolves the given shape about the y-axis
(offset by the given x value) | Revolves the given shape about the y-axis
(offset by the given x value) | [
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] | def revolve_xy_y(a, x):
""" Revolves the given shape about the y-axis
(offset by the given x value)
"""
return move(revolve_y(move(a, -x, 0)), x, 0) | [
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PrincetonUniversity/athena-public-version | 9c266692b9423743d8e23509b3ab266a232a92d2 | tst/regression/scripts/utils/RiemannSolver/riemann.py | python | StateVector.ram | (self) | return self.p + self.rho * self.u ** 2 | Computes ram pressure. | Computes ram pressure. | [
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] | def ram(self):
"""Computes ram pressure."""
return self.p + self.rho * self.u ** 2 | [
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