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apache/parquet-cpp | 642da055adf009652689b20e68a198cffb857651 | build-support/cpplint.py | python | _CppLintState.BackupFilters | (self) | Saves the current filter list to backup storage. | Saves the current filter list to backup storage. | [
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""" Saves the current filter list to backup storage."""
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msftguy/ssh-rd | a5f3a79daeac5844edebf01916c9613563f1c390 | _3rd/boost_1_48_0/tools/build/v2/build/generators.py | python | override | (overrider_id, overridee_id) | Make generator 'overrider-id' be preferred to
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both those generators are amoung viable generators,
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_controls.py | python | TreeCtrl.IsBold | (*args, **kwargs) | return _controls_.TreeCtrl_IsBold(*args, **kwargs) | IsBold(self, TreeItemId item) -> bool | IsBold(self, TreeItemId item) -> bool | [
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glotzerlab/hoomd-blue | f7f97abfa3fcc2522fa8d458d65d0aeca7ba781a | hoomd/data/syncedlist.py | python | identity | (obj) | return obj | Returns obj. | Returns obj. | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/core/indexes/base.py | python | Index.set_names | (self, names, level=None, inplace=False) | Set Index or MultiIndex name.
Able to set new names partially and by level.
Parameters
----------
names : label or list of label
Name(s) to set.
level : int, label or list of int or label, optional
If the index is a MultiIndex, level(s) to set (None for all
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inplace : bool, default False
Modifies the object directly, instead of creating a new Index or
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Returns
-------
Index
The same type as the caller or None if inplace is True.
See Also
--------
Index.rename : Able to set new names without level.
Examples
--------
>>> idx = pd.Index([1, 2, 3, 4])
>>> idx
Int64Index([1, 2, 3, 4], dtype='int64')
>>> idx.set_names('quarter')
Int64Index([1, 2, 3, 4], dtype='int64', name='quarter')
>>> idx = pd.MultiIndex.from_product([['python', 'cobra'],
... [2018, 2019]])
>>> idx
MultiIndex([('python', 2018),
('python', 2019),
( 'cobra', 2018),
( 'cobra', 2019)],
)
>>> idx.set_names(['kind', 'year'], inplace=True)
>>> idx
MultiIndex([('python', 2018),
('python', 2019),
( 'cobra', 2018),
( 'cobra', 2019)],
names=['kind', 'year'])
>>> idx.set_names('species', level=0)
MultiIndex([('python', 2018),
('python', 2019),
( 'cobra', 2018),
( 'cobra', 2019)],
names=['species', 'year']) | Set Index or MultiIndex name. | [
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"""
Set Index or MultiIndex name.
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Parameters
----------
names : label or list of label
Name(s) to set.
level : int, label or list of int or label, optional
If the index is a MultiIndex, level(s) to set (None for all
levels). Otherwise level must be None.
inplace : bool, default False
Modifies the object directly, instead of creating a new Index or
MultiIndex.
Returns
-------
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The same type as the caller or None if inplace is True.
See Also
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Index.rename : Able to set new names without level.
Examples
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>>> idx
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>>> idx.set_names('quarter')
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>>> idx
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>>> idx.set_names('species', level=0)
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('python', 2019),
( 'cobra', 2018),
( 'cobra', 2019)],
names=['species', 'year'])
"""
if level is not None and not isinstance(self, ABCMultiIndex):
raise ValueError("Level must be None for non-MultiIndex")
if level is not None and not is_list_like(level) and is_list_like(names):
raise TypeError("Names must be a string when a single level is provided.")
if not is_list_like(names) and level is None and self.nlevels > 1:
raise TypeError("Must pass list-like as `names`.")
if not is_list_like(names):
names = [names]
if level is not None and not is_list_like(level):
level = [level]
if inplace:
idx = self
else:
idx = self._shallow_copy()
idx._set_names(names, level=level)
if not inplace:
return idx | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/functools.py | python | _c3_mro | (cls, abcs=None) | return _c3_merge(
[[cls]] +
explicit_c3_mros + abstract_c3_mros + other_c3_mros +
[explicit_bases] + [abstract_bases] + [other_bases]
) | Computes the method resolution order using extended C3 linearization.
If no *abcs* are given, the algorithm works exactly like the built-in C3
linearization used for method resolution.
If given, *abcs* is a list of abstract base classes that should be inserted
into the resulting MRO. Unrelated ABCs are ignored and don't end up in the
result. The algorithm inserts ABCs where their functionality is introduced,
i.e. issubclass(cls, abc) returns True for the class itself but returns
False for all its direct base classes. Implicit ABCs for a given class
(either registered or inferred from the presence of a special method like
__len__) are inserted directly after the last ABC explicitly listed in the
MRO of said class. If two implicit ABCs end up next to each other in the
resulting MRO, their ordering depends on the order of types in *abcs*. | Computes the method resolution order using extended C3 linearization. | [
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"""Computes the method resolution order using extended C3 linearization.
If no *abcs* are given, the algorithm works exactly like the built-in C3
linearization used for method resolution.
If given, *abcs* is a list of abstract base classes that should be inserted
into the resulting MRO. Unrelated ABCs are ignored and don't end up in the
result. The algorithm inserts ABCs where their functionality is introduced,
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__len__) are inserted directly after the last ABC explicitly listed in the
MRO of said class. If two implicit ABCs end up next to each other in the
resulting MRO, their ordering depends on the order of types in *abcs*.
"""
for i, base in enumerate(reversed(cls.__bases__)):
if hasattr(base, '__abstractmethods__'):
boundary = len(cls.__bases__) - i
break # Bases up to the last explicit ABC are considered first.
else:
boundary = 0
abcs = list(abcs) if abcs else []
explicit_bases = list(cls.__bases__[:boundary])
abstract_bases = []
other_bases = list(cls.__bases__[boundary:])
for base in abcs:
if issubclass(cls, base) and not any(
issubclass(b, base) for b in cls.__bases__
):
# If *cls* is the class that introduces behaviour described by
# an ABC *base*, insert said ABC to its MRO.
abstract_bases.append(base)
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abcs.remove(base)
explicit_c3_mros = [_c3_mro(base, abcs=abcs) for base in explicit_bases]
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other_c3_mros = [_c3_mro(base, abcs=abcs) for base in other_bases]
return _c3_merge(
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/CGIHTTPServer.py | python | executable | (path) | return st.st_mode & 0111 != 0 | Test for executable file. | Test for executable file. | [
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] | def executable(path):
"""Test for executable file."""
try:
st = os.stat(path)
except os.error:
return False
return st.st_mode & 0111 != 0 | [
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hughperkins/tf-coriander | 970d3df6c11400ad68405f22b0c42a52374e94ca | tensorflow/python/debug/local_cli.py | python | LocalCLIDebugWrapperSession._on_run_start_step_handler | (self, args, screen_info=None) | return debugger_cli_common.RichTextLines([], annotations=annotations) | Command handler for "invoke_stepper" command during on-run-start. | Command handler for "invoke_stepper" command during on-run-start. | [
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_ = screen_info # Currently unused.
# No parsing is currently necessary for invoke_stepper. This may change
# in the future when the command has arguments.
action = framework.OnRunStartAction.INVOKE_STEPPER
annotations = {
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py3/sklearn/neural_network/_multilayer_perceptron.py | python | MLPClassifier.predict | (self, X) | return self._label_binarizer.inverse_transform(y_pred) | Predict using the multi-layer perceptron classifier
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input data.
Returns
-------
y : ndarray, shape (n_samples,) or (n_samples, n_classes)
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"""Predict using the multi-layer perceptron classifier
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
The input data.
Returns
-------
y : ndarray, shape (n_samples,) or (n_samples, n_classes)
The predicted classes.
"""
check_is_fitted(self)
y_pred = self._predict(X)
if self.n_outputs_ == 1:
y_pred = y_pred.ravel()
return self._label_binarizer.inverse_transform(y_pred) | [
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tensorflow/deepmath | b5b721f54de1d5d6a02d78f5da5995237f9995f9 | deepmath/deephol/theorem_fingerprint.py | python | ToTacticArgument | (theorem) | return 'THM %d' % Fingerprint(theorem) | Return a representation of the theorem as a tactic argument label.
Args:
theorem: proof_assistant_pb2.Theorem object
Returns:
String that can be used as a tactic argument. | Return a representation of the theorem as a tactic argument label. | [
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"""Return a representation of the theorem as a tactic argument label.
Args:
theorem: proof_assistant_pb2.Theorem object
Returns:
String that can be used as a tactic argument.
"""
return 'THM %d' % Fingerprint(theorem) | [
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apiaryio/drafter | 4634ebd07f6c6f257cc656598ccd535492fdfb55 | tools/gyp/pylib/gyp/msvs_emulation.py | python | ExpandMacros | (string, expansions) | return string | Expand $(Variable) per expansions dict. See MsvsSettings.GetVSMacroEnv
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BitMEX/api-connectors | 37a3a5b806ad5d0e0fc975ab86d9ed43c3bcd812 | auto-generated/python/swagger_client/models/position.py | python | Position.opening_qty | (self) | return self._opening_qty | Gets the opening_qty of this Position. # noqa: E501
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/AWS/resource-manager-code/lib/setuptools/package_index.py | python | parse_bdist_wininst | (name) | return base, py_ver, plat | Return (base,pyversion) or (None,None) for possible .exe name | Return (base,pyversion) or (None,None) for possible .exe name | [
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lower = name.lower()
base, py_ver, plat = None, None, None
if lower.endswith('.exe'):
if lower.endswith('.win32.exe'):
base = name[:-10]
plat = 'win32'
elif lower.startswith('.win32-py', -16):
py_ver = name[-7:-4]
base = name[:-16]
plat = 'win32'
elif lower.endswith('.win-amd64.exe'):
base = name[:-14]
plat = 'win-amd64'
elif lower.startswith('.win-amd64-py', -20):
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base = name[:-20]
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/urllib/request.py | python | URLopener.open_data | (self, url, data=None) | return addinfourl(f, headers, url) | Use "data" URL. | Use "data" URL. | [
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"""Use "data" URL."""
if not isinstance(url, str):
raise URLError('data error: proxy support for data protocol currently not implemented')
# ignore POSTed data
#
# syntax of data URLs:
# dataurl := "data:" [ mediatype ] [ ";base64" ] "," data
# mediatype := [ type "/" subtype ] *( ";" parameter )
# data := *urlchar
# parameter := attribute "=" value
try:
[type, data] = url.split(',', 1)
except ValueError:
raise OSError('data error', 'bad data URL')
if not type:
type = 'text/plain;charset=US-ASCII'
semi = type.rfind(';')
if semi >= 0 and '=' not in type[semi:]:
encoding = type[semi+1:]
type = type[:semi]
else:
encoding = ''
msg = []
msg.append('Date: %s'%time.strftime('%a, %d %b %Y %H:%M:%S GMT',
time.gmtime(time.time())))
msg.append('Content-type: %s' % type)
if encoding == 'base64':
# XXX is this encoding/decoding ok?
data = base64.decodebytes(data.encode('ascii')).decode('latin-1')
else:
data = unquote(data)
msg.append('Content-Length: %d' % len(data))
msg.append('')
msg.append(data)
msg = '\n'.join(msg)
headers = email.message_from_string(msg)
f = io.StringIO(msg)
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r45635/HVAC-IR-Control | 4b6b7944b28ce78247f19744c272a36935bbb305 | python/hvac_ircontrol/mitsubishi.py | python | Mitsubishi.power_off | (self) | power_off | power_off | [
"power_off"
] | def power_off(self):
"""
power_off
"""
self.__send_command(
ClimateMode.Auto,
21,
FanMode.Auto,
VanneVerticalMode.Auto,
VanneHorizontalMode.Swing,
ISeeMode.ISeeOff,
AreaMode.NotSet,
None,
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | tools/valgrind/gdb_helper.py | python | AddressTable.Add | (self, binary, address) | Register a lookup request. | Register a lookup request. | [
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if binary == '':
logging.warn('adding address %s in empty binary?' % address)
if binary in self._binaries:
self._binaries[binary].append(address)
else:
self._binaries[binary] = [address]
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/email/generator.py | python | DecodedGenerator.__init__ | (self, outfp, mangle_from_=True, maxheaderlen=78, fmt=None) | Like Generator.__init__() except that an additional optional
argument is allowed.
Walks through all subparts of a message. If the subpart is of main
type `text', then it prints the decoded payload of the subpart.
Otherwise, fmt is a format string that is used instead of the message
payload. fmt is expanded with the following keywords (in
%(keyword)s format):
type : Full MIME type of the non-text part
maintype : Main MIME type of the non-text part
subtype : Sub-MIME type of the non-text part
filename : Filename of the non-text part
description: Description associated with the non-text part
encoding : Content transfer encoding of the non-text part
The default value for fmt is None, meaning
[Non-text (%(type)s) part of message omitted, filename %(filename)s] | Like Generator.__init__() except that an additional optional
argument is allowed. | [
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] | def __init__(self, outfp, mangle_from_=True, maxheaderlen=78, fmt=None):
"""Like Generator.__init__() except that an additional optional
argument is allowed.
Walks through all subparts of a message. If the subpart is of main
type `text', then it prints the decoded payload of the subpart.
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self._fmt = _FMT
else:
self._fmt = fmt | [
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htcondor/htcondor | 4829724575176d1d6c936e4693dfd78a728569b0 | bindings/python/htcondor/htchirp/htchirp.py | python | HTChirp.whoareyou | (self, remote_host) | return result | Get the server's identity with respect to the remote host.
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"""
length = int(
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vgteam/vg | cf4d516a5e9ee5163c783e4437ddf16b18a4b561 | scripts/giraffe-facts.py | python | Table.box | (self, part) | return skin[part] | Return the box-drawing character to draw the given part of a box.
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_windows.py | python | PrintPreview.GetCurrentPage | (*args, **kwargs) | return _windows_.PrintPreview_GetCurrentPage(*args, **kwargs) | GetCurrentPage(self) -> int | GetCurrentPage(self) -> int | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | tools/android/loading/user_satisfied_lens.py | python | _FirstEventLens.PostloadTimeMsec | (self) | return self._postload_msec | Override. | Override. | [
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FEniCS/dolfinx | 3dfdf038cccdb70962865b58a63bf29c2e55ec6e | python/dolfinx/io.py | python | XDMFFile.write_mesh | (self, mesh: Mesh) | Write mesh to file for a given time (default 0.0) | Write mesh to file for a given time (default 0.0) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/site-packages/pip/_vendor/distlib/_backport/tarfile.py | python | is_tarfile | (name) | Return True if name points to a tar archive that we
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adobe/chromium | cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7 | chrome/app/policy/syntax_check_policy_template_json.py | python | PolicyTemplateChecker._CheckPolicyIDs | (self, policy_ids) | Checks a set of policy_ids to make sure it contains a continuous range
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ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/third_party/lib_aarch64/python2.7/dist-packages/catkin_pkg/changelog.py | python | BulletList.__init__ | (self, bullets=None, bullet_type=None) | :param bullets: ``list(MixedText)`` list of text bullets
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kushview/Element | 1cc16380caa2ab79461246ba758b9de1f46db2a5 | waflib/extras/clang_cross_common.py | python | clang_modifier_msvc | (conf) | Really basic setup to use clang in msvc mode.
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del(v.AR)
conf.find_program(['llvm-lib', 'lib'], var='AR')
v.ARFLAGS = ['-nologo']
v.AR_TGT_F = ['-out:']
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v.LINK_CC = v.CC + ['-fuse-ld=lld', '-nostdlib']
v.CCLNK_TGT_F = ['-o']
v.def_PATTERN = '-Wl,-def:%s'
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v.LIBPATH_ST = '-Wl,-LIBPATH:%s'
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v.STLIBPATH_ST = '-Wl,-LIBPATH:%s'
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tomahawk-player/tomahawk-resolvers | 7f827bbe410ccfdb0446f7d6a91acc2199c9cc8d | archive/spotify/breakpad/third_party/protobuf/protobuf/python/mox.py | python | Reset | (*args) | Reset mocks.
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ricardoquesada/Spidermonkey | 4a75ea2543408bd1b2c515aa95901523eeef7858 | media/webrtc/trunk/tools/gyp/pylib/gyp/common.py | python | AllTargets | (target_list, target_dicts, build_file) | return bftargets + deptargets | Returns all targets (direct and dependencies) for the specified build_file. | Returns all targets (direct and dependencies) for the specified build_file. | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/lib2to3/pytree.py | python | BasePattern.generate_matches | (self, nodes) | Generator yielding all matches for this pattern.
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pmq20/node-packer | 12c46c6e44fbc14d9ee645ebd17d5296b324f7e0 | lts/deps/v8/third_party/jinja2/nodes.py | python | Const.from_untrusted | (cls, value, lineno=None, environment=None) | return cls(value, lineno=lineno, environment=environment) | Return a const object if the value is representable as
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"""Return a const object if the value is representable as
constant value in the generated code, otherwise it will raise
an `Impossible` exception.
"""
from .compiler import has_safe_repr
if not has_safe_repr(value):
raise Impossible()
return cls(value, lineno=lineno, environment=environment) | [
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polyworld/polyworld | eb7e6bbc82fe77ba79e3bc48c3da2ad8c8238c26 | scripts/plotNeuralRhythms.py | python | next_line | (the_file) | return line | Return next line from the brainFunction file, formatted. | Return next line from the brainFunction file, formatted. | [
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] | def next_line(the_file):
"""Return next line from the brainFunction file, formatted."""
line = the_file.readline()
line = line.replace("/", "\n")
return line | [
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RapidsAtHKUST/CommunityDetectionCodes | 23dbafd2e57ab0f5f0528b1322c4a409f21e5892 | Algorithms/2012-DEMON/Demon.py | python | Demon.__overlapping_label_propagation | (self, ego_minus_ego, ego, max_iteration=100) | return community_to_nodes | :param max_iteration: number of desired iteration for the label propagation
:param ego_minus_ego: ego network minus its center
:param ego: ego network center | [] | def __overlapping_label_propagation(self, ego_minus_ego, ego, max_iteration=100):
"""
:param max_iteration: number of desired iteration for the label propagation
:param ego_minus_ego: ego network minus its center
:param ego: ego network center
"""
t = 0
old_node_to_coms = {}
while t < max_iteration:
t += 1
node_to_coms = {}
nodes = list(nx.nodes(ego_minus_ego))
random.shuffle(nodes)
count = -len(nodes)
for n in nodes:
label_freq = {}
n_neighbors = list(nx.neighbors(ego_minus_ego, n))
if len(n_neighbors) < 1:
continue
if count == 0:
t += 1
# compute the frequency of the labels
for nn in n_neighbors:
communities_nn = [nn]
if nn in old_node_to_coms:
communities_nn = old_node_to_coms[nn]
for nn_c in communities_nn:
if nn_c in label_freq:
v = label_freq.get(nn_c)
# case of weighted graph
if self.weighted:
label_freq[nn_c] = v + ego_minus_ego.edge[nn][n]['weight']
else:
label_freq[nn_c] = v + 1
else:
# case of weighted graph
if self.weighted:
label_freq[nn_c] = ego_minus_ego.edge[nn][n]['weight']
else:
label_freq[nn_c] = 1
# first run, random choosing of the communities among the neighbors labels
if t == 1:
if not len(n_neighbors) == 0:
r_label = random.sample(label_freq.keys(), 1)
ego_minus_ego.node[n]['communities'] = r_label
old_node_to_coms[n] = r_label
count += 1
continue
# choose the majority
else:
labels = []
max_freq = -1
for l, c in label_freq.items():
if c > max_freq:
max_freq = c
labels = [l]
elif c == max_freq:
labels.append(l)
node_to_coms[n] = labels
if not n in old_node_to_coms or not set(node_to_coms[n]) == set(old_node_to_coms[n]):
old_node_to_coms[n] = node_to_coms[n]
ego_minus_ego.node[n]['communities'] = labels
t += 1
# build the communities reintroducing the ego
community_to_nodes = {}
for n in nx.nodes(ego_minus_ego):
if len(list(nx.neighbors(ego_minus_ego, n))) == 0:
ego_minus_ego.node[n]['communities'] = [n]
c_n = ego_minus_ego.node[n]['communities']
for c in c_n:
if c in community_to_nodes:
com = community_to_nodes.get(c)
com.append(n)
else:
nodes = [n, ego]
community_to_nodes[c] = nodes
return community_to_nodes | [
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] | https://github.com/RapidsAtHKUST/CommunityDetectionCodes/blob/23dbafd2e57ab0f5f0528b1322c4a409f21e5892/Algorithms/2012-DEMON/Demon.py#L74-L175 | ||
windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/distutils/ccompiler.py | python | CCompiler.set_runtime_library_dirs | (self, dirs) | Set the list of directories to search for shared libraries at
runtime to 'dirs' (a list of strings). This does not affect any
standard search path that the runtime linker may search by
default. | Set the list of directories to search for shared libraries at
runtime to 'dirs' (a list of strings). This does not affect any
standard search path that the runtime linker may search by
default. | [
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] | def set_runtime_library_dirs(self, dirs):
"""Set the list of directories to search for shared libraries at
runtime to 'dirs' (a list of strings). This does not affect any
standard search path that the runtime linker may search by
default.
"""
self.runtime_library_dirs = dirs[:] | [
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/python2_version/klampt/math/symbolic_io.py | python | exprToStr | (expr,parseCompatible=True,expandSubexprs='auto') | Converts an Expression to a printable or parseable string.
Args:
expr (Expression): the Expression to convert
parseCompatible (bool, optional): if True, the result is readable via exprFromStr()
expandSubexprs (str or bool, optional): whether to expand subexpressions. Can be:
* 'auto': if parseCompatible, equivalent to False.
if parseCompatible=False, equivalent to True.
* True: expands all common subexpressions
* False: does not expand common subexpressions.
* 'show': Internally used.
Returns:
(str): a printable or parsable string representing expr. | Converts an Expression to a printable or parseable string. | [
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] | def exprToStr(expr,parseCompatible=True,expandSubexprs='auto'):
"""Converts an Expression to a printable or parseable string.
Args:
expr (Expression): the Expression to convert
parseCompatible (bool, optional): if True, the result is readable via exprFromStr()
expandSubexprs (str or bool, optional): whether to expand subexpressions. Can be:
* 'auto': if parseCompatible, equivalent to False.
if parseCompatible=False, equivalent to True.
* True: expands all common subexpressions
* False: does not expand common subexpressions.
* 'show': Internally used.
Returns:
(str): a printable or parsable string representing expr.
"""
if isinstance(expr,ConstantExpression):
if isinstance(expr.value,slice):
start,stop,step = expr.value.start,expr.value.stop,expr.value.step
return "%s:%s%s"%(str(start),"" if stop > 900000000000 else str(stop),"" if step is None else ":"+str(step))
try:
jsonval = _to_jsonobj(expr.value)
except:
return str(expr.value)
if parseCompatible:
return json.dumps(jsonval)
else:
original_float_repr = encoder.FLOAT_REPR
encoder.FLOAT_REPR = lambda o:format(o,'.14g')
if _json_complex(jsonval):
res = json.dumps(jsonval,sort_keys=True, indent=4, separators=(',', ': '))
else:
res = json.dumps(jsonval,sort_keys=True)
encoder.FLOAT_REPR = original_float_repr
return res
elif isinstance(expr,VariableExpression):
if parseCompatible:
return VAR_PREFIX+expr.var.name
else:
return str(expr.var)
elif isinstance(expr,UserDataExpression):
return USER_DATA_PREFIX+expr.name
elif isinstance(expr,OperatorExpression):
if expandSubexprs == 'auto':
expandSubexprs = not parseCompatible
if expandSubexprs:
astr = []
for i,a in enumerate(expr.args):
a._parent = (weakref.ref(expr),i)
astr.append(exprToStr(a,parseCompatible,expandSubexprs))
if not isinstance(a,OperatorExpression) and expandSubexprs == 'show' and ('id' in a._cache or 'name' in a._cache):
#tagged subexprs need parenthesies
if astr[-1][-1] != ')':
astr[-1] = '('+astr[-1]+')'
astr[-1] = astr[-1] + NAMED_EXPRESSION_TAG + a._cache.get('id',a._cache.get('name'))
a._parent = None
res = _prettyPrintExpr(expr,astr,parseCompatible)
if expandSubexprs == 'show' and ('id' in expr._cache or 'name' in expr._cache):
#tagged subexprs need parenthesies
if res[-1] != ')':
res = '('+res+')'
return res + NAMED_EXPRESSION_TAG + expr._cache.get('id',expr._cache.get('name'))
oldparent = expr._parent
iscomplex = expr.depth() >= 0 and (expr.functionInfo.name in _operator_precedence)
expr._parent = oldparent
if iscomplex and (expr._parent is not None and not isinstance(expr._parent,str)):
if parseCompatible:
return '(' + res + ')'
else:
parent = expr._parent[0]()
if parent.functionInfo.name in _operator_precedence:
expr_precedence = _operator_precedence[expr.functionInfo.name]
parent_precedence = _operator_precedence[parent.functionInfo.name]
#if - is the first in a summation, don't parenthesize it
if expr._parent[1] == 0 and expr.functionInfo.name == 'neg' and parent.functionInfo.name in ['sum','add','sub']:
return res
if expr_precedence > parent_precedence:
return '(' + res + ')'
if expr_precedence == parent_precedence:
if expr.functionInfo is parent.functionInfo and expr.functionInfo.properties.get('associative',False):
return res
else:
return '(' + res + ')'
return res
else:
if not parseCompatible:
taggedexpr = _make_tagged(expr,"")
else:
taggedexpr = _make_tagged(expr)
res = exprToStr(taggedexpr,parseCompatible,'show')
if taggedexpr is not expr:
expr._clearCache('id',deep=True)
return res
elif isinstance(expr,_TaggedExpression):
return NAMED_EXPRESSION_PREFIX+expr.name
elif is_const(expr):
return str(expr)
else:
raise ValueError("Unknown type "+expr.__class__.__name__) | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/learn/python/learn/dataframe/tensorflow_dataframe.py | python | TensorFlowDataFrame.from_examples | (cls,
filepatterns,
features,
reader_cls=io_ops.TFRecordReader,
num_threads=1,
enqueue_size=None,
batch_size=32,
queue_capacity=None,
min_after_dequeue=None,
shuffle=True,
seed=None) | return dataframe | Create a `DataFrame` from `tensorflow.Example`s.
Args:
filepatterns: a list of file patterns containing `tensorflow.Example`s.
features: a dict mapping feature names to `VarLenFeature` or
`FixedLenFeature`.
reader_cls: a subclass of `tensorflow.ReaderBase` that will be used to
read the `Example`s.
num_threads: the number of readers that will work in parallel.
enqueue_size: block size for each read operation.
batch_size: desired batch size.
queue_capacity: capacity of the queue that will store parsed `Example`s
min_after_dequeue: minimum number of elements that can be left by a
dequeue operation. Only used if `shuffle` is true.
shuffle: whether records should be shuffled. Defaults to true.
seed: passed to random shuffle operations. Only used if `shuffle` is true.
Returns:
A `DataFrame` that has columns corresponding to `features` and is filled
with `Example`s from `filepatterns`.
Raises:
ValueError: no files match `filepatterns`.
ValueError: `features` contains the reserved name 'index'. | Create a `DataFrame` from `tensorflow.Example`s. | [
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filepatterns,
features,
reader_cls=io_ops.TFRecordReader,
num_threads=1,
enqueue_size=None,
batch_size=32,
queue_capacity=None,
min_after_dequeue=None,
shuffle=True,
seed=None):
"""Create a `DataFrame` from `tensorflow.Example`s.
Args:
filepatterns: a list of file patterns containing `tensorflow.Example`s.
features: a dict mapping feature names to `VarLenFeature` or
`FixedLenFeature`.
reader_cls: a subclass of `tensorflow.ReaderBase` that will be used to
read the `Example`s.
num_threads: the number of readers that will work in parallel.
enqueue_size: block size for each read operation.
batch_size: desired batch size.
queue_capacity: capacity of the queue that will store parsed `Example`s
min_after_dequeue: minimum number of elements that can be left by a
dequeue operation. Only used if `shuffle` is true.
shuffle: whether records should be shuffled. Defaults to true.
seed: passed to random shuffle operations. Only used if `shuffle` is true.
Returns:
A `DataFrame` that has columns corresponding to `features` and is filled
with `Example`s from `filepatterns`.
Raises:
ValueError: no files match `filepatterns`.
ValueError: `features` contains the reserved name 'index'.
"""
filenames = _expand_file_names(filepatterns)
if not filenames:
raise ValueError("No matching file names.")
if "index" in features:
raise ValueError(
"'index' is reserved and can not be used for a feature name.")
index, record = reader_source.ReaderSource(
reader_cls,
filenames,
enqueue_size=enqueue_size,
batch_size=batch_size,
queue_capacity=queue_capacity,
shuffle=shuffle,
min_after_dequeue=min_after_dequeue,
num_threads=num_threads,
seed=seed)()
parser = example_parser.ExampleParser(features)
parsed = parser(record)
column_dict = parsed._asdict()
column_dict["index"] = index
dataframe = cls()
dataframe.assign(**column_dict)
return dataframe | [
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CRYTEK/CRYENGINE | 232227c59a220cbbd311576f0fbeba7bb53b2a8c | Code/Tools/waf-1.7.13/waflib/Node.py | python | Node.find_or_declare | (self, lst) | return node | if 'self' is in build directory, try to return an existing node
if no node is found, go to the source directory
try to find an existing node in the source directory
if no node is found, create it in the build directory
:param lst: path
:type lst: string or list of string | if 'self' is in build directory, try to return an existing node
if no node is found, go to the source directory
try to find an existing node in the source directory
if no node is found, create it in the build directory | [
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"""
if 'self' is in build directory, try to return an existing node
if no node is found, go to the source directory
try to find an existing node in the source directory
if no node is found, create it in the build directory
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if node:
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return node
self = self.get_src()
node = self.find_node(lst)
if node:
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node.sig = None
node.parent.mkdir()
return node
node = self.get_bld().make_node(lst)
node.parent.mkdir()
return node | [
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kevin-ssy/Optical-Flow-Guided-Feature | 07d4501a29002ee7821c38c1820e4a64c1acf6e8 | lib/caffe-action/tools/extra/parse_log.py | python | parse_log | (path_to_log) | return train_dict_list, test_dict_list | Parse log file
Returns (train_dict_list, train_dict_names, test_dict_list, test_dict_names)
train_dict_list and test_dict_list are lists of dicts that define the table
rows
train_dict_names and test_dict_names are ordered tuples of the column names
for the two dict_lists | Parse log file
Returns (train_dict_list, train_dict_names, test_dict_list, test_dict_names) | [
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"""Parse log file
Returns (train_dict_list, train_dict_names, test_dict_list, test_dict_names)
train_dict_list and test_dict_list are lists of dicts that define the table
rows
train_dict_names and test_dict_names are ordered tuples of the column names
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"""
regex_iteration = re.compile('Iteration (\d+)')
regex_train_output = re.compile('Train net output #(\d+): (\S+) = ([\.\deE+-]+)')
regex_test_output = re.compile('Test net output #(\d+): (\S+) = ([\.\deE+-]+)')
regex_learning_rate = re.compile('lr = ([\.\d]+)')
# Pick out lines of interest
iteration = -1
learning_rate = float('NaN')
train_dict_list = []
test_dict_list = []
train_row = None
test_row = None
logfile_year = extract_seconds.get_log_created_year(path_to_log)
with open(path_to_log) as f:
start_time = extract_seconds.get_start_time(f, logfile_year)
for line in f:
iteration_match = regex_iteration.search(line)
if iteration_match:
iteration = float(iteration_match.group(1))
if iteration == -1:
# Only start parsing for other stuff if we've found the first
# iteration
continue
time = extract_seconds.extract_datetime_from_line(line,
logfile_year)
seconds = (time - start_time).total_seconds()
learning_rate_match = regex_learning_rate.search(line)
if learning_rate_match:
learning_rate = float(learning_rate_match.group(1))
train_dict_list, train_row = parse_line_for_net_output(
regex_train_output, train_row, train_dict_list,
line, iteration, seconds, learning_rate
)
test_dict_list, test_row = parse_line_for_net_output(
regex_test_output, test_row, test_dict_list,
line, iteration, seconds, learning_rate
)
fix_initial_nan_learning_rate(train_dict_list)
fix_initial_nan_learning_rate(test_dict_list)
return train_dict_list, test_dict_list | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/contrib/distributions/python/ops/wishart.py | python | _WishartLinearOperator.log_normalization | (self, name="log_normalization") | Computes the log normalizing constant, log(Z). | Computes the log normalizing constant, log(Z). | [
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] | def log_normalization(self, name="log_normalization"):
"""Computes the log normalizing constant, log(Z)."""
with self._name_scope(name):
return (self.df * self.scale_operator.log_abs_determinant() +
0.5 * self.df * self.dimension * math.log(2.) +
self._multi_lgamma(0.5 * self.df, self.dimension)) | [
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sfzhang15/FaceBoxes | b52cc92f9362d3adc08d54666aeb9ebb62fdb7da | scripts/cpp_lint.py | python | _GetTextInside | (text, start_pattern) | return text[start_position:position - 1] | r"""Retrieves all the text between matching open and close parentheses.
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r"""Retrieves all the text between matching open and close parentheses.
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(, [, or {, and the matching close-punctuation symbol. This properly nested
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"""
# TODO(sugawarayu): Audit cpplint.py to see what places could be profitably
# rewritten to use _GetTextInside (and use inferior regexp matching today).
# Give opening punctuations to get the matching close-punctuations.
matching_punctuation = {'(': ')', '{': '}', '[': ']'}
closing_punctuation = set(matching_punctuation.itervalues())
# Find the position to start extracting text.
match = re.search(start_pattern, text, re.M)
if not match: # start_pattern not found in text.
return None
start_position = match.end(0)
assert start_position > 0, (
'start_pattern must ends with an opening punctuation.')
assert text[start_position - 1] in matching_punctuation, (
'start_pattern must ends with an opening punctuation.')
# Stack of closing punctuations we expect to have in text after position.
punctuation_stack = [matching_punctuation[text[start_position - 1]]]
position = start_position
while punctuation_stack and position < len(text):
if text[position] == punctuation_stack[-1]:
punctuation_stack.pop()
elif text[position] in closing_punctuation:
# A closing punctuation without matching opening punctuations.
return None
elif text[position] in matching_punctuation:
punctuation_stack.append(matching_punctuation[text[position]])
position += 1
if punctuation_stack:
# Opening punctuations left without matching close-punctuations.
return None
# punctuations match.
return text[start_position:position - 1] | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/site-packages/pip/_internal/network/lazy_wheel.py | python | dist_from_wheel_url | (name, url, session) | Return a pkg_resources.Distribution from the given wheel URL.
This uses HTTP range requests to only fetch the potion of the wheel
containing metadata, just enough for the object to be constructed.
If such requests are not supported, HTTPRangeRequestUnsupported
is raised. | Return a pkg_resources.Distribution from the given wheel URL. | [
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] | def dist_from_wheel_url(name, url, session):
# type: (str, str, PipSession) -> Distribution
"""Return a pkg_resources.Distribution from the given wheel URL.
This uses HTTP range requests to only fetch the potion of the wheel
containing metadata, just enough for the object to be constructed.
If such requests are not supported, HTTPRangeRequestUnsupported
is raised.
"""
with LazyZipOverHTTP(url, session) as wheel:
# For read-only ZIP files, ZipFile only needs methods read,
# seek, seekable and tell, not the whole IO protocol.
zip_file = ZipFile(wheel) # type: ignore
# After context manager exit, wheel.name
# is an invalid file by intention.
return pkg_resources_distribution_for_wheel(zip_file, name, wheel.name) | [
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weolar/miniblink49 | 1c4678db0594a4abde23d3ebbcc7cd13c3170777 | third_party/WebKit/Tools/Scripts/webkitpy/thirdparty/coverage/parser.py | python | ByteParser._find_statements | (self) | return stmts | Find the statements in `self.code`.
Return a set of line numbers that start statements. Recurses into all
code objects reachable from `self.code`. | Find the statements in `self.code`. | [
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"""Find the statements in `self.code`.
Return a set of line numbers that start statements. Recurses into all
code objects reachable from `self.code`.
"""
stmts = set()
for bp in self.child_parsers():
# Get all of the lineno information from this code.
for _, l in bp._bytes_lines():
stmts.add(l)
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cms-sw/cmssw | fd9de012d503d3405420bcbeec0ec879baa57cf2 | DQM/Integration/scripts/harvesting_tools/cmsHarvester.py | python | CMSHarvester.build_dataset_list | (self, input_method, input_name) | return dataset_names | Build a list of all datasets to be processed. | Build a list of all datasets to be processed. | [
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] | def build_dataset_list(self, input_method, input_name):
"""Build a list of all datasets to be processed.
"""
dataset_names = []
# It may be, but only for the list of datasets to ignore, that
# the input method and name are None because nothing was
# specified. In that case just an empty list is returned.
if input_method is None:
pass
elif input_method == "dataset":
# Input comes from a dataset name directly on the command
# line. But, this can also contain wildcards so we need
# DBS to translate it conclusively into a list of explicit
# dataset names.
self.logger.info("Asking DBS for dataset names")
dataset_names = self.dbs_resolve_dataset_name(input_name)
elif input_method == "datasetfile":
# In this case a file containing a list of dataset names
# is specified. Still, each line may contain wildcards so
# this step also needs help from DBS.
# NOTE: Lines starting with a `#' are ignored.
self.logger.info("Reading input from list file `%s'" % \
input_name)
try:
listfile = open("/afs/cern.ch/cms/CAF/CMSCOMM/COMM_DQM/harvesting/bin/%s" %input_name, "r")
print("open listfile")
for dataset in listfile:
# Skip empty lines.
dataset_stripped = dataset.strip()
if len(dataset_stripped) < 1:
continue
# Skip lines starting with a `#'.
if dataset_stripped[0] != "#":
dataset_names.extend(self. \
dbs_resolve_dataset_name(dataset_stripped))
listfile.close()
except IOError:
msg = "ERROR: Could not open input list file `%s'" % \
input_name
self.logger.fatal(msg)
raise Error(msg)
else:
# DEBUG DEBUG DEBUG
# We should never get here.
assert False, "Unknown input method `%s'" % input_method
# DEBUG DEBUG DEBUG end
# Remove duplicates from the dataset list.
# NOTE: There should not be any duplicates in any list coming
# from DBS, but maybe the user provided a list file with less
# care.
# Store for later use.
dataset_names = sorted(set(dataset_names))
# End of build_dataset_list.
return dataset_names | [
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thalium/icebox | 99d147d5b9269222225443ce171b4fd46d8985d4 | third_party/virtualbox/src/libs/libxml2-2.9.4/python/libxml2class.py | python | nanoFTPScanProxy | (URL) | (Re)Initialize the FTP Proxy context by parsing the URL and
finding the protocol host port it indicates. Should be like
ftp://myproxy/ or ftp://myproxy:3128/ A None URL cleans up
proxy informations. | (Re)Initialize the FTP Proxy context by parsing the URL and
finding the protocol host port it indicates. Should be like
ftp://myproxy/ or ftp://myproxy:3128/ A None URL cleans up
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eclipse/sumo | 7132a9b8b6eea734bdec38479026b4d8c4336d03 | tools/simpla/_platoon.py | python | Platoon.getMode | (self) | return mode | getMode() -> PlatoonMode
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'''getMode() -> PlatoonMode
Returns the platoon leader's desired PlatoonMode (may return LEADER if current mode is FOLLOWER).
'''
mode = self._vehicles[0].getCurrentPlatoonMode()
if mode == PlatoonMode.FOLLOWER:
# Leader was kept in FOLLOW mode due to safety constraints
mode = PlatoonMode.LEADER
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# Leader was kept in CATCHUP_FOLLOW mode due to safety constraints
mode = PlatoonMode.CATCHUP
return mode | [
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lballabio/quantlib-old | 136336947ed4fea9ecc1da6edad188700e821739 | gensrc/gensrc/serialization/exceptions.py | python | SerializationDictException.__init__ | (self, xmlDocumentName, dictElementName) | Initialize the SerializationDictException object. | Initialize the SerializationDictException object. | [
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] | def __init__(self, xmlDocumentName, dictElementName):
"""Initialize the SerializationDictException object."""
self.value_ = SerializationDictException.EMPTY_DICT_ERROR % {
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'dictElementName' : dictElementName } | [
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sfzhang15/RefineDet | 52b6fe23dc1a160fe710b7734576dca509bf4fae | python/caffe/draw.py | python | get_pooling_types_dict | () | return d | Get dictionary mapping pooling type number to type name | Get dictionary mapping pooling type number to type name | [
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"""Get dictionary mapping pooling type number to type name
"""
desc = caffe_pb2.PoolingParameter.PoolMethod.DESCRIPTOR
d = {}
for k, v in desc.values_by_name.items():
d[v.number] = k
return d | [
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gimli-org/gimli | 17aa2160de9b15ababd9ef99e89b1bc3277bbb23 | doc/tutorials/dev/solverFVM.py | python | findVelocity | (mesh, v, b, c, nc=None) | return vel | Find velocity for boundary b based on vector field v
Parameters
----------
mesh : :gimliapi:`GIMLI::Mesh`
v :
Vector field [[x,y,z]] Cell based or Boundary based
b : :gimliapi:`GIMLI::Boundary`
Boundary
c : :gimliapi:`GIMLI::Cell`
Associated Cell in flow direction
nc : :gimliapi:`GIMLI::Cell`
Associated neighbor cell .. if one given
from flow direction | Find velocity for boundary b based on vector field v | [
"Find",
"velocity",
"for",
"boundary",
"b",
"based",
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"vector",
"field",
"v"
] | def findVelocity(mesh, v, b, c, nc=None):
"""
Find velocity for boundary b based on vector field v
Parameters
----------
mesh : :gimliapi:`GIMLI::Mesh`
v :
Vector field [[x,y,z]] Cell based or Boundary based
b : :gimliapi:`GIMLI::Boundary`
Boundary
c : :gimliapi:`GIMLI::Cell`
Associated Cell in flow direction
nc : :gimliapi:`GIMLI::Cell`
Associated neighbor cell .. if one given
from flow direction
"""
vel = [0.0, 0.0, 0.0]
if hasattr(v, '__len__'):
if len(v) == mesh.cellCount():
if nc:
# mean cell based vector-field v[x,y,z] for cell c and cell nc
vel = (v[c.id()] + v[nc.id()])/2.0
else:
# cell based vector-field v[x,y,z] for cell c
vel = v[c.id()]
elif len(v) == mesh.boundaryCount():
vel = v[b.id()]
else:
# interpolate node based vector-field v[x,y,z] at point b.center()
vel = c.vec(b.center(), v)
return vel | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py3/scipy/sparse/csc.py | python | isspmatrix_csc | (x) | return isinstance(x, csc_matrix) | Is x of csc_matrix type?
Parameters
----------
x
object to check for being a csc matrix
Returns
-------
bool
True if x is a csc matrix, False otherwise
Examples
--------
>>> from scipy.sparse import csc_matrix, isspmatrix_csc
>>> isspmatrix_csc(csc_matrix([[5]]))
True
>>> from scipy.sparse import csc_matrix, csr_matrix, isspmatrix_csc
>>> isspmatrix_csc(csr_matrix([[5]]))
False | Is x of csc_matrix type? | [
"Is",
"x",
"of",
"csc_matrix",
"type?"
] | def isspmatrix_csc(x):
"""Is x of csc_matrix type?
Parameters
----------
x
object to check for being a csc matrix
Returns
-------
bool
True if x is a csc matrix, False otherwise
Examples
--------
>>> from scipy.sparse import csc_matrix, isspmatrix_csc
>>> isspmatrix_csc(csc_matrix([[5]]))
True
>>> from scipy.sparse import csc_matrix, csr_matrix, isspmatrix_csc
>>> isspmatrix_csc(csr_matrix([[5]]))
False
"""
return isinstance(x, csc_matrix) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Tools/scripts/patchcheck.py | python | normalize_whitespace | (file_paths) | return fixed | Make sure that the whitespace for .py files have been normalized. | Make sure that the whitespace for .py files have been normalized. | [
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reindent.makebackup = False # No need to create backups.
fixed = [path for path in file_paths if path.endswith('.py') and
reindent.check(os.path.join(SRCDIR, path))]
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cvxpy/cvxpy | 5165b4fb750dfd237de8659383ef24b4b2e33aaf | cvxpy/atoms/norm_inf.py | python | norm_inf.is_incr | (self, idx) | return self.args[0].is_nonneg() | Is the composition non-decreasing in argument idx? | Is the composition non-decreasing in argument idx? | [
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"""
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/ipython/py3/IPython/core/completer.py | python | cursor_to_position | (text:str, line:int, column:int) | return sum(len(l) + 1 for l in lines[:line]) + column | Convert the (line,column) position of the cursor in text to an offset in a
string.
Parameters
----------
text : str
The text in which to calculate the cursor offset
line : int
Line of the cursor; 0-indexed
column : int
Column of the cursor 0-indexed
Return
------
Position of the cursor in ``text``, 0-indexed.
See Also
--------
position_to_cursor: reciprocal of this function | [] | def cursor_to_position(text:str, line:int, column:int)->int:
"""
Convert the (line,column) position of the cursor in text to an offset in a
string.
Parameters
----------
text : str
The text in which to calculate the cursor offset
line : int
Line of the cursor; 0-indexed
column : int
Column of the cursor 0-indexed
Return
------
Position of the cursor in ``text``, 0-indexed.
See Also
--------
position_to_cursor: reciprocal of this function
"""
lines = text.split('\n')
assert line <= len(lines), '{} <= {}'.format(str(line), str(len(lines)))
return sum(len(l) + 1 for l in lines[:line]) + column | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/propgrid.py | python | PropertyGrid.AutoGetTranslation | (*args, **kwargs) | return _propgrid.PropertyGrid_AutoGetTranslation(*args, **kwargs) | AutoGetTranslation(bool enable) | AutoGetTranslation(bool enable) | [
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"""AutoGetTranslation(bool enable)"""
return _propgrid.PropertyGrid_AutoGetTranslation(*args, **kwargs) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/protobuf/py3/google/protobuf/internal/python_message.py | python | _AddClearFieldMethod | (message_descriptor, cls) | Helper for _AddMessageMethods(). | Helper for _AddMessageMethods(). | [
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] | def _AddClearFieldMethod(message_descriptor, cls):
"""Helper for _AddMessageMethods()."""
def ClearField(self, field_name):
try:
field = message_descriptor.fields_by_name[field_name]
except KeyError:
try:
field = message_descriptor.oneofs_by_name[field_name]
if field in self._oneofs:
field = self._oneofs[field]
else:
return
except KeyError:
raise ValueError('Protocol message %s has no "%s" field.' %
(message_descriptor.name, field_name))
if field in self._fields:
# To match the C++ implementation, we need to invalidate iterators
# for map fields when ClearField() happens.
if hasattr(self._fields[field], 'InvalidateIterators'):
self._fields[field].InvalidateIterators()
# Note: If the field is a sub-message, its listener will still point
# at us. That's fine, because the worst than can happen is that it
# will call _Modified() and invalidate our byte size. Big deal.
del self._fields[field]
if self._oneofs.get(field.containing_oneof, None) is field:
del self._oneofs[field.containing_oneof]
# Always call _Modified() -- even if nothing was changed, this is
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# present in the parent message.
self._Modified()
cls.ClearField = ClearField | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_core.py | python | InputStream.close | (*args, **kwargs) | return _core_.InputStream_close(*args, **kwargs) | close(self) | close(self) | [
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return _core_.InputStream_close(*args, **kwargs) | [
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domino-team/openwrt-cc | 8b181297c34d14d3ca521cc9f31430d561dbc688 | package/gli-pub/openwrt-node-packages-master/node/node-v6.9.1/deps/npm/node_modules/node-gyp/gyp/pylib/gyp/input.py | python | Unify | (l) | return [seen.setdefault(e, e) for e in l if e not in seen] | Removes duplicate elements from l, keeping the first element. | Removes duplicate elements from l, keeping the first element. | [
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] | def Unify(l):
"""Removes duplicate elements from l, keeping the first element."""
seen = {}
return [seen.setdefault(e, e) for e in l if e not in seen] | [
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/python2_version/klampt/robotsim.py | python | RobotModel.getConfig | (self) | return _robotsim.RobotModel_getConfig(self) | getConfig(RobotModel self)
Retrieves the current configuration of the robot model. | getConfig(RobotModel self) | [
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] | def getConfig(self):
"""
getConfig(RobotModel self)
Retrieves the current configuration of the robot model.
"""
return _robotsim.RobotModel_getConfig(self) | [
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apache/incubator-mxnet | f03fb23f1d103fec9541b5ae59ee06b1734a51d9 | benchmark/opperf/utils/op_registry_utils.py | python | get_all_broadcast_binary_operators | () | return binary_broadcast_mx_operators | Gets all binary broadcast operators registered with MXNet.
Returns
-------
{"operator_name": {"has_backward", "nd_op_handle", "params"}} | Gets all binary broadcast operators registered with MXNet. | [
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] | def get_all_broadcast_binary_operators():
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Returns
-------
{"operator_name": {"has_backward", "nd_op_handle", "params"}}
"""
# Get all mxnet operators
mx_operators = _get_all_mxnet_operators()
# Filter for binary broadcast operators
binary_broadcast_mx_operators = {}
for op_name, op_params in mx_operators.items():
if op_name.startswith("broadcast_") and op_params["params"]["narg"] == 2 and \
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binary_broadcast_mx_operators[op_name] = mx_operators[op_name]
return binary_broadcast_mx_operators | [
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GoSSIP-SJTU/TripleDoggy | 03648d6b19c812504b14e8b98c8c7b3f443f4e54 | utils/lit/lit/llvm/config.py | python | LLVMConfig.use_clang | (self, required=True) | Configure the test suite to be able to invoke clang.
Sets up some environment variables important to clang, locates a
just-built or installed clang, and add a set of standard
substitutions useful to any test suite that makes use of clang. | Configure the test suite to be able to invoke clang. | [
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] | def use_clang(self, required=True):
"""Configure the test suite to be able to invoke clang.
Sets up some environment variables important to clang, locates a
just-built or installed clang, and add a set of standard
substitutions useful to any test suite that makes use of clang.
"""
# Clear some environment variables that might affect Clang.
#
# This first set of vars are read by Clang, but shouldn't affect tests
# that aren't specifically looking for these features, or are required
# simply to run the tests at all.
#
# FIXME: Should we have a tool that enforces this?
# safe_env_vars = ('TMPDIR', 'TEMP', 'TMP', 'USERPROFILE', 'PWD',
# 'MACOSX_DEPLOYMENT_TARGET', 'IPHONEOS_DEPLOYMENT_TARGET',
# 'VCINSTALLDIR', 'VC100COMNTOOLS', 'VC90COMNTOOLS',
# 'VC80COMNTOOLS')
possibly_dangerous_env_vars = ['COMPILER_PATH', 'RC_DEBUG_OPTIONS',
'CINDEXTEST_PREAMBLE_FILE', 'LIBRARY_PATH',
'CPATH', 'C_INCLUDE_PATH', 'CPLUS_INCLUDE_PATH',
'OBJC_INCLUDE_PATH', 'OBJCPLUS_INCLUDE_PATH',
'LIBCLANG_TIMING', 'LIBCLANG_OBJTRACKING',
'LIBCLANG_LOGGING', 'LIBCLANG_BGPRIO_INDEX',
'LIBCLANG_BGPRIO_EDIT', 'LIBCLANG_NOTHREADS',
'LIBCLANG_RESOURCE_USAGE',
'LIBCLANG_CODE_COMPLETION_LOGGING']
# Clang/Win32 may refer to %INCLUDE%. vsvarsall.bat sets it.
if platform.system() != 'Windows':
possibly_dangerous_env_vars.append('INCLUDE')
self.clear_environment(possibly_dangerous_env_vars)
# Tweak the PATH to include the tools dir and the scripts dir.
# Put Clang first to avoid LLVM from overriding out-of-tree clang builds.
possible_paths = ['clang_tools_dir', 'llvm_tools_dir']
paths = [getattr(self.config, pp) for pp in possible_paths
if getattr(self.config, pp, None)]
self.with_environment('PATH', paths, append_path=True)
paths = [self.config.llvm_shlib_dir, self.config.llvm_libs_dir]
self.with_environment('LD_LIBRARY_PATH', paths, append_path=True)
# Discover the 'clang' and 'clangcc' to use.
self.config.clang = self.use_llvm_tool(
'clang', search_env='CLANG', required=required)
self.config.substitutions.append(
('%llvmshlibdir', self.config.llvm_shlib_dir))
self.config.substitutions.append(
('%pluginext', self.config.llvm_plugin_ext))
builtin_include_dir = self.get_clang_builtin_include_dir(self.config.clang)
tool_substitutions = [
ToolSubst('%clang', command=self.config.clang),
ToolSubst('%clang_analyze_cc1', command='%clang_cc1', extra_args=['-analyze']),
ToolSubst('%clang_cc1', command=self.config.clang, extra_args=['-cc1', '-internal-isystem', builtin_include_dir, '-nostdsysteminc']),
ToolSubst('%clang_cpp', command=self.config.clang, extra_args=['--driver-mode=cpp']),
ToolSubst('%clang_cl', command=self.config.clang, extra_args=['--driver-mode=cl']),
ToolSubst('%clangxx', command=self.config.clang, extra_args=['--driver-mode=g++']),
]
self.add_tool_substitutions(tool_substitutions)
self.config.substitutions.append(('%itanium_abi_triple',
self.make_itanium_abi_triple(self.config.target_triple)))
self.config.substitutions.append(('%ms_abi_triple',
self.make_msabi_triple(self.config.target_triple)))
self.config.substitutions.append(
('%resource_dir', builtin_include_dir))
# The host triple might not be set, at least if we're compiling clang from
# an already installed llvm.
if self.config.host_triple and self.config.host_triple != '@LLVM_HOST_TRIPLE@':
self.config.substitutions.append(('%target_itanium_abi_host_triple',
'--target=%s' % self.make_itanium_abi_triple(self.config.host_triple)))
else:
self.config.substitutions.append(
('%target_itanium_abi_host_triple', ''))
self.config.substitutions.append(
('%src_include_dir', self.config.clang_src_dir + '/include'))
# FIXME: Find nicer way to prohibit this.
self.config.substitutions.append(
(' clang ', """*** Do not use 'clang' in tests, use '%clang'. ***"""))
self.config.substitutions.append(
(' clang\+\+ ', """*** Do not use 'clang++' in tests, use '%clangxx'. ***"""))
self.config.substitutions.append(
(' clang-cc ',
"""*** Do not use 'clang-cc' in tests, use '%clang_cc1'. ***"""))
self.config.substitutions.append(
(' clang -cc1 -analyze ',
"""*** Do not use 'clang -cc1 -analyze' in tests, use '%clang_analyze_cc1'. ***"""))
self.config.substitutions.append(
(' clang -cc1 ',
"""*** Do not use 'clang -cc1' in tests, use '%clang_cc1'. ***"""))
self.config.substitutions.append(
(' %clang-cc1 ',
"""*** invalid substitution, use '%clang_cc1'. ***"""))
self.config.substitutions.append(
(' %clang-cpp ',
"""*** invalid substitution, use '%clang_cpp'. ***"""))
self.config.substitutions.append(
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/HFIR_4Circle_Reduction/hfctables.py | python | SinglePtIntegrationTable.__init__ | (self, parent) | return | initialization
:param parent: | initialization
:param parent: | [
"initialization",
":",
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] | def __init__(self, parent):
"""
initialization
:param parent:
"""
super(SinglePtIntegrationTable, self).__init__(parent)
# class variables
self._scan_index = None
self._pt_index = None
self._hkl_index = None
self._height_index = None
self._2theta_index = None
self._fwhm_index = None
self._intensity_index = None
self._2thta_scans_index = None
self._ref_scans_index = None
self._roi_index = None
self._pt_row_dict = dict()
return | [
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Samsung/veles | 95ed733c2e49bc011ad98ccf2416ecec23fbf352 | veles/external/prettytable.py | python | PrettyTable._get_horizontal_char | (self) | return self._horizontal_char | The charcter used when printing table borders to draw horizontal lines
Arguments:
horizontal_char - single character string used to draw horizontal lines | The charcter used when printing table borders to draw horizontal lines | [
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] | def _get_horizontal_char(self):
"""The charcter used when printing table borders to draw horizontal lines
Arguments:
horizontal_char - single character string used to draw horizontal lines"""
return self._horizontal_char | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/multiprocessing/__init__.py | python | Lock | () | return Lock() | Returns a non-recursive lock object | Returns a non-recursive lock object | [
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'''
Returns a non-recursive lock object
'''
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rootm0s/Protectors | 5b3f4d11687a5955caf9c3af30666c4bfc2c19ab | OWASP-ZSC/module/readline_windows/pyreadline/modes/notemacs.py | python | NotEmacsMode.dump_functions | (self, e) | Print all of the functions and their key bindings to the Readline
output stream. If a numeric argument is supplied, the output is
formatted in such a way that it can be made part of an inputrc
file. This command is unbound by default. | Print all of the functions and their key bindings to the Readline
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py3/pandas/core/dtypes/common.py | python | is_1d_only_ea_dtype | (dtype: Optional[DtypeObj]) | return isinstance(dtype, ExtensionDtype) and not isinstance(dtype, DatetimeTZDtype) | Analogue to is_extension_array_dtype but excluding DatetimeTZDtype. | Analogue to is_extension_array_dtype but excluding DatetimeTZDtype. | [
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"""
Analogue to is_extension_array_dtype but excluding DatetimeTZDtype.
"""
# Note: if other EA dtypes are ever held in HybridBlock, exclude those
# here too.
# NB: need to check DatetimeTZDtype and not is_datetime64tz_dtype
# to exclude ArrowTimestampUSDtype
return isinstance(dtype, ExtensionDtype) and not isinstance(dtype, DatetimeTZDtype) | [
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deepmind/open_spiel | 4ca53bea32bb2875c7385d215424048ae92f78c8 | open_spiel/python/games/dynamic_routing.py | python | DynamicRoutingGameState.assert_valid_player | (self, vehicle: int) | Assert that a vehicle as a int between 0 and num_players. | Assert that a vehicle as a int between 0 and num_players. | [
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"""Assert that a vehicle as a int between 0 and num_players."""
assert isinstance(vehicle, int), f"{vehicle} is not a int."
assert vehicle >= 0, f"player: {vehicle}<0."
assert vehicle < self.get_game().num_players(), (
f"player: {vehicle} >= num_players: {self.get_game().num_players()}") | [
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ucla-vision/xivo | 1b97bf5a3124e62bf4920429af5bb91c7a6de876 | thirdparty/jsoncpp/devtools/batchbuild.py | python | fix_eol | (stdout) | return re.sub('\r*\n', os.linesep, stdout) | Fixes wrong EOL produced by cmake --build on Windows (\r\r\n instead of \r\n). | Fixes wrong EOL produced by cmake --build on Windows (\r\r\n instead of \r\n). | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numba/targets/optional.py | python | optional_to_optional | (context, builder, fromty, toty, val) | return outoptval._getvalue() | The handling of optional->optional cast must be special cased for
correct propagation of None value. Given type T and U. casting of
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is None, the None value the casted value (U?) should be None; otherwise,
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which requires the from-value must not be None. | [
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"""
The handling of optional->optional cast must be special cased for
correct propagation of None value. Given type T and U. casting of
T? to U? (? denotes optional) should always succeed. If the from-value
is None, the None value the casted value (U?) should be None; otherwise,
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"""
optval = context.make_helper(builder, fromty, value=val)
validbit = cgutils.as_bool_bit(builder, optval.valid)
# Create uninitialized optional value
outoptval = context.make_helper(builder, toty)
with builder.if_else(validbit) as (is_valid, is_not_valid):
with is_valid:
# Cast internal value
outoptval.valid = cgutils.true_bit
outoptval.data = context.cast(builder, optval.data,
fromty.type, toty.type)
with is_not_valid:
# Store None to result
outoptval.valid = cgutils.false_bit
outoptval.data = cgutils.get_null_value(
outoptval.data.type)
return outoptval._getvalue() | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/pandas/core/frame.py | python | DataFrame.dot | (self, other) | Compute the matrix multiplication between the DataFrame and other.
This method computes the matrix product between the DataFrame and the
values of an other Series, DataFrame or a numpy array.
It can also be called using ``self @ other`` in Python >= 3.5.
Parameters
----------
other : Series, DataFrame or array-like
The other object to compute the matrix product with.
Returns
-------
Series or DataFrame
If other is a Series, return the matrix product between self and
other as a Serie. If other is a DataFrame or a numpy.array, return
the matrix product of self and other in a DataFrame of a np.array.
See Also
--------
Series.dot: Similar method for Series.
Notes
-----
The dimensions of DataFrame and other must be compatible in order to
compute the matrix multiplication. In addition, the column names of
DataFrame and the index of other must contain the same values, as they
will be aligned prior to the multiplication.
The dot method for Series computes the inner product, instead of the
matrix product here.
Examples
--------
Here we multiply a DataFrame with a Series.
>>> df = pd.DataFrame([[0, 1, -2, -1], [1, 1, 1, 1]])
>>> s = pd.Series([1, 1, 2, 1])
>>> df.dot(s)
0 -4
1 5
dtype: int64
Here we multiply a DataFrame with another DataFrame.
>>> other = pd.DataFrame([[0, 1], [1, 2], [-1, -1], [2, 0]])
>>> df.dot(other)
0 1
0 1 4
1 2 2
Note that the dot method give the same result as @
>>> df @ other
0 1
0 1 4
1 2 2
The dot method works also if other is an np.array.
>>> arr = np.array([[0, 1], [1, 2], [-1, -1], [2, 0]])
>>> df.dot(arr)
0 1
0 1 4
1 2 2
Note how shuffling of the objects does not change the result.
>>> s2 = s.reindex([1, 0, 2, 3])
>>> df.dot(s2)
0 -4
1 5
dtype: int64 | Compute the matrix multiplication between the DataFrame and other. | [
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"""
Compute the matrix multiplication between the DataFrame and other.
This method computes the matrix product between the DataFrame and the
values of an other Series, DataFrame or a numpy array.
It can also be called using ``self @ other`` in Python >= 3.5.
Parameters
----------
other : Series, DataFrame or array-like
The other object to compute the matrix product with.
Returns
-------
Series or DataFrame
If other is a Series, return the matrix product between self and
other as a Serie. If other is a DataFrame or a numpy.array, return
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See Also
--------
Series.dot: Similar method for Series.
Notes
-----
The dimensions of DataFrame and other must be compatible in order to
compute the matrix multiplication. In addition, the column names of
DataFrame and the index of other must contain the same values, as they
will be aligned prior to the multiplication.
The dot method for Series computes the inner product, instead of the
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Examples
--------
Here we multiply a DataFrame with a Series.
>>> df = pd.DataFrame([[0, 1, -2, -1], [1, 1, 1, 1]])
>>> s = pd.Series([1, 1, 2, 1])
>>> df.dot(s)
0 -4
1 5
dtype: int64
Here we multiply a DataFrame with another DataFrame.
>>> other = pd.DataFrame([[0, 1], [1, 2], [-1, -1], [2, 0]])
>>> df.dot(other)
0 1
0 1 4
1 2 2
Note that the dot method give the same result as @
>>> df @ other
0 1
0 1 4
1 2 2
The dot method works also if other is an np.array.
>>> arr = np.array([[0, 1], [1, 2], [-1, -1], [2, 0]])
>>> df.dot(arr)
0 1
0 1 4
1 2 2
Note how shuffling of the objects does not change the result.
>>> s2 = s.reindex([1, 0, 2, 3])
>>> df.dot(s2)
0 -4
1 5
dtype: int64
"""
if isinstance(other, (Series, DataFrame)):
common = self.columns.union(other.index)
if len(common) > len(self.columns) or len(common) > len(other.index):
raise ValueError("matrices are not aligned")
left = self.reindex(columns=common, copy=False)
right = other.reindex(index=common, copy=False)
lvals = left.values
rvals = right.values
else:
left = self
lvals = self.values
rvals = np.asarray(other)
if lvals.shape[1] != rvals.shape[0]:
raise ValueError(
f"Dot product shape mismatch, {lvals.shape} vs {rvals.shape}"
)
if isinstance(other, DataFrame):
return self._constructor(
np.dot(lvals, rvals), index=left.index, columns=other.columns
)
elif isinstance(other, Series):
return Series(np.dot(lvals, rvals), index=left.index)
elif isinstance(rvals, (np.ndarray, Index)):
result = np.dot(lvals, rvals)
if result.ndim == 2:
return self._constructor(result, index=left.index)
else:
return Series(result, index=left.index)
else: # pragma: no cover
raise TypeError(f"unsupported type: {type(other)}") | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/ultimatelistctrl.py | python | GeometryInfo.ExtendWidth | (self, w) | Extends all our rectangles to be centered inside the one of given width.
:param `w`: the given width. | Extends all our rectangles to be centered inside the one of given width. | [
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] | def ExtendWidth(self, w):
"""
Extends all our rectangles to be centered inside the one of given width.
:param `w`: the given width.
"""
if self._rectAll.width > w:
raise Exception("width can only be increased")
self._rectAll.width = w
self._rectLabel.x = self._rectAll.x + (w - self._rectLabel.width)/2
self._rectIcon.x = self._rectAll.x + (w - self._rectIcon.width)/2
self._rectHighlight.x = self._rectAll.x + (w - self._rectHighlight.width)/2 | [
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idaholab/moose | 9eeebc65e098b4c30f8205fb41591fd5b61eb6ff | python/peacock/Input/InputFileEditorPlugin.py | python | InputFileEditorPlugin.addToMenu | (self, menu) | Register the menus specific to the InputTab.
Input:
menu[QMenu]: The menu to add the items to. | Register the menus specific to the InputTab.
Input:
menu[QMenu]: The menu to add the items to. | [
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"""
Register the menus specific to the InputTab.
Input:
menu[QMenu]: The menu to add the items to.
"""
self._open_action = WidgetUtils.addAction(menu, "Open", self._openInputFile, "Ctrl+O")
recentMenu = menu.addMenu("Recently opened")
self._recently_used_menu = RecentlyUsedMenu(recentMenu,
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)
self._recently_used_menu.selected.connect(self.setInputFile)
self._save_action = WidgetUtils.addAction(menu, "Save", self._saveInputFile)
self._save_as_action = WidgetUtils.addAction(menu, "Save As", self._saveInputFileAs)
self._clear_action = WidgetUtils.addAction(menu, "Clear", self._clearInputFile)
self._check_action = WidgetUtils.addAction(menu, "Check", self._checkInputFile, "Ctrl+K")
self._view_file = WidgetUtils.addAction(menu, "View current input file", self._viewInputFile, "Ctrl+V", True)
self._menus_initialized = True
self._setMenuStatus() | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py3/pandas/core/common.py | python | count_not_none | (*args) | return sum(x is not None for x in args) | Returns the count of arguments that are not None. | Returns the count of arguments that are not None. | [
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"""
Returns the count of arguments that are not None.
"""
return sum(x is not None for x in args) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/scipy/fftpack/realtransforms.py | python | dst | (x, type=2, n=None, axis=-1, norm=None, overwrite_x=False) | return _dst(x, type, n, axis, normalize=norm, overwrite_x=overwrite_x) | Return the Discrete Sine Transform of arbitrary type sequence x.
Parameters
----------
x : array_like
The input array.
type : {1, 2, 3}, optional
Type of the DST (see Notes). Default type is 2.
n : int, optional
Length of the transform. If ``n < x.shape[axis]``, `x` is
truncated. If ``n > x.shape[axis]``, `x` is zero-padded. The
default results in ``n = x.shape[axis]``.
axis : int, optional
Axis along which the dst is computed; the default is over the
last axis (i.e., ``axis=-1``).
norm : {None, 'ortho'}, optional
Normalization mode (see Notes). Default is None.
overwrite_x : bool, optional
If True, the contents of `x` can be destroyed; the default is False.
Returns
-------
dst : ndarray of reals
The transformed input array.
See Also
--------
idst : Inverse DST
Notes
-----
For a single dimension array ``x``.
There are theoretically 8 types of the DST for different combinations of
even/odd boundary conditions and boundary off sets [1]_, only the first
3 types are implemented in scipy.
**Type I**
There are several definitions of the DST-I; we use the following
for ``norm=None``. DST-I assumes the input is odd around n=-1 and n=N. ::
N-1
y[k] = 2 * sum x[n]*sin(pi*(k+1)*(n+1)/(N+1))
n=0
Only None is supported as normalization mode for DCT-I. Note also that the
DCT-I is only supported for input size > 1
The (unnormalized) DCT-I is its own inverse, up to a factor `2(N+1)`.
**Type II**
There are several definitions of the DST-II; we use the following
for ``norm=None``. DST-II assumes the input is odd around n=-1/2 and
n=N-1/2; the output is odd around k=-1 and even around k=N-1 ::
N-1
y[k] = 2* sum x[n]*sin(pi*(k+1)*(n+0.5)/N), 0 <= k < N.
n=0
if ``norm='ortho'``, ``y[k]`` is multiplied by a scaling factor `f` ::
f = sqrt(1/(4*N)) if k == 0
f = sqrt(1/(2*N)) otherwise.
**Type III**
There are several definitions of the DST-III, we use the following
(for ``norm=None``). DST-III assumes the input is odd around n=-1
and even around n=N-1 ::
N-2
y[k] = x[N-1]*(-1)**k + 2* sum x[n]*sin(pi*(k+0.5)*(n+1)/N), 0 <= k < N.
n=0
The (unnormalized) DCT-III is the inverse of the (unnormalized) DCT-II, up
to a factor `2N`. The orthonormalized DST-III is exactly the inverse of
the orthonormalized DST-II.
.. versionadded:: 0.11.0
References
----------
.. [1] Wikipedia, "Discrete sine transform",
http://en.wikipedia.org/wiki/Discrete_sine_transform | Return the Discrete Sine Transform of arbitrary type sequence x. | [
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"""
Return the Discrete Sine Transform of arbitrary type sequence x.
Parameters
----------
x : array_like
The input array.
type : {1, 2, 3}, optional
Type of the DST (see Notes). Default type is 2.
n : int, optional
Length of the transform. If ``n < x.shape[axis]``, `x` is
truncated. If ``n > x.shape[axis]``, `x` is zero-padded. The
default results in ``n = x.shape[axis]``.
axis : int, optional
Axis along which the dst is computed; the default is over the
last axis (i.e., ``axis=-1``).
norm : {None, 'ortho'}, optional
Normalization mode (see Notes). Default is None.
overwrite_x : bool, optional
If True, the contents of `x` can be destroyed; the default is False.
Returns
-------
dst : ndarray of reals
The transformed input array.
See Also
--------
idst : Inverse DST
Notes
-----
For a single dimension array ``x``.
There are theoretically 8 types of the DST for different combinations of
even/odd boundary conditions and boundary off sets [1]_, only the first
3 types are implemented in scipy.
**Type I**
There are several definitions of the DST-I; we use the following
for ``norm=None``. DST-I assumes the input is odd around n=-1 and n=N. ::
N-1
y[k] = 2 * sum x[n]*sin(pi*(k+1)*(n+1)/(N+1))
n=0
Only None is supported as normalization mode for DCT-I. Note also that the
DCT-I is only supported for input size > 1
The (unnormalized) DCT-I is its own inverse, up to a factor `2(N+1)`.
**Type II**
There are several definitions of the DST-II; we use the following
for ``norm=None``. DST-II assumes the input is odd around n=-1/2 and
n=N-1/2; the output is odd around k=-1 and even around k=N-1 ::
N-1
y[k] = 2* sum x[n]*sin(pi*(k+1)*(n+0.5)/N), 0 <= k < N.
n=0
if ``norm='ortho'``, ``y[k]`` is multiplied by a scaling factor `f` ::
f = sqrt(1/(4*N)) if k == 0
f = sqrt(1/(2*N)) otherwise.
**Type III**
There are several definitions of the DST-III, we use the following
(for ``norm=None``). DST-III assumes the input is odd around n=-1
and even around n=N-1 ::
N-2
y[k] = x[N-1]*(-1)**k + 2* sum x[n]*sin(pi*(k+0.5)*(n+1)/N), 0 <= k < N.
n=0
The (unnormalized) DCT-III is the inverse of the (unnormalized) DCT-II, up
to a factor `2N`. The orthonormalized DST-III is exactly the inverse of
the orthonormalized DST-II.
.. versionadded:: 0.11.0
References
----------
.. [1] Wikipedia, "Discrete sine transform",
http://en.wikipedia.org/wiki/Discrete_sine_transform
"""
if type == 1 and norm is not None:
raise NotImplementedError(
"Orthonormalization not yet supported for IDCT-I")
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/xmlrpc/server.py | python | SimpleXMLRPCDispatcher.register_multicall_functions | (self) | Registers the XML-RPC multicall method in the system
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"""Registers the XML-RPC multicall method in the system
namespace.
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/idlelib/CallTips.py | python | get_arg_text | (ob) | return arg_text | Get a string describing the arguments for the given object,
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items = map(lambda arg, dflt: arg + dflt, real_args, defaults)
if fob.func_code.co_flags & 0x4:
items.append("...")
if fob.func_code.co_flags & 0x8:
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arg_text = ", ".join(items)
arg_text = "(%s)" % re.sub("\.\d+", "<tuple>", arg_text)
# See if we can use the docstring
doc = getattr(ob, "__doc__", "")
if doc:
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pos = doc.find("\n")
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deepmind/open_spiel | 4ca53bea32bb2875c7385d215424048ae92f78c8 | open_spiel/python/algorithms/adidas_utils/solvers/nonsymmetric/ate_poly.py | python | Solver.init_polymatrix | (self, num_strats, num_players) | return init_pm | Initialize all pairwise bimatrix games to zero and return as dict. | Initialize all pairwise bimatrix games to zero and return as dict. | [
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"""Initialize all pairwise bimatrix games to zero and return as dict."""
init_pm = dict()
for i, j in itertools.combinations(range(num_players), 2):
init_pm[(i, j)] = np.zeros((2, num_strats[i], num_strats[j])) # i < j
return init_pm | [
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HKUST-Aerial-Robotics/Teach-Repeat-Replan | 98505a7f74b13c8b501176ff838a38423dbef536 | utils/quadrotor_msgs/src/quadrotor_msgs/msg/_Corrections.py | python | Corrections._get_types | (self) | return self._slot_types | internal API method | internal API method | [
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internal API method
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domino-team/openwrt-cc | 8b181297c34d14d3ca521cc9f31430d561dbc688 | package/gli-pub/openwrt-node-packages-master/node/node-v6.9.1/deps/v8_inspector/third_party/jinja2/jinja2/environment.py | python | Environment._parse | (self, source, name, filename) | return Parser(self, source, name, encode_filename(filename)).parse() | Internal parsing function used by `parse` and `compile`. | Internal parsing function used by `parse` and `compile`. | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/pip/_vendor/distlib/locators.py | python | DependencyFinder.find_providers | (self, reqt) | return result | Find the distributions which can fulfill a requirement.
:param reqt: The requirement.
:type reqt: str
:return: A set of distribution which can fulfill the requirement. | [] | def find_providers(self, reqt):
"""
Find the distributions which can fulfill a requirement.
:param reqt: The requirement.
:type reqt: str
:return: A set of distribution which can fulfill the requirement.
"""
matcher = self.get_matcher(reqt)
name = matcher.key # case-insensitive
result = set()
provided = self.provided
if name in provided:
for version, provider in provided[name]:
try:
match = matcher.match(version)
except UnsupportedVersionError:
match = False
if match:
result.add(provider)
break
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facebookresearch/ELF | 1f790173095cd910976d9f651b80beb872ec5d12 | vendor/pybind11/tools/clang/cindex.py | python | Token.cursor | (self) | return cursor | The Cursor this Token corresponds to. | The Cursor this Token corresponds to. | [
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mhammond/pywin32 | 44afd86ba8485194df93234639243252deeb40d5 | com/win32comext/mapi/demos/mapisend.py | python | SendEMAPIMail | (
Subject="", Message="", SendTo=None, SendCC=None, SendBCC=None, MAPIProfile=None
) | Sends an email to the recipient using the extended MAPI interface
Subject and Message are strings
Send{To,CC,BCC} are comma-separated address lists
MAPIProfile is the name of the MAPI profile | Sends an email to the recipient using the extended MAPI interface
Subject and Message are strings
Send{To,CC,BCC} are comma-separated address lists
MAPIProfile is the name of the MAPI profile | [
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Subject="", Message="", SendTo=None, SendCC=None, SendBCC=None, MAPIProfile=None
):
"""Sends an email to the recipient using the extended MAPI interface
Subject and Message are strings
Send{To,CC,BCC} are comma-separated address lists
MAPIProfile is the name of the MAPI profile"""
# initialize and log on
mapi.MAPIInitialize(None)
session = mapi.MAPILogonEx(
0, MAPIProfile, None, mapi.MAPI_EXTENDED | mapi.MAPI_USE_DEFAULT
)
messagestorestable = session.GetMsgStoresTable(0)
messagestorestable.SetColumns(
(mapitags.PR_ENTRYID, mapitags.PR_DISPLAY_NAME_A, mapitags.PR_DEFAULT_STORE), 0
)
while True:
rows = messagestorestable.QueryRows(1, 0)
# if this is the last row then stop
if len(rows) != 1:
break
row = rows[0]
# if this is the default store then stop
if (mapitags.PR_DEFAULT_STORE, True) in row:
break
# unpack the row and open the message store
(eid_tag, eid), (name_tag, name), (def_store_tag, def_store) = row
msgstore = session.OpenMsgStore(
0, eid, None, mapi.MDB_NO_DIALOG | mapi.MAPI_BEST_ACCESS
)
# get the outbox
hr, props = msgstore.GetProps((mapitags.PR_IPM_OUTBOX_ENTRYID), 0)
(tag, eid) = props[0]
# check for errors
if mapitags.PROP_TYPE(tag) == mapitags.PT_ERROR:
raise TypeError("got PT_ERROR instead of PT_BINARY: %s" % eid)
outboxfolder = msgstore.OpenEntry(eid, None, mapi.MAPI_BEST_ACCESS)
# create the message and the addrlist
message = outboxfolder.CreateMessage(None, 0)
# note: you can use the resolveaddress functions for this. but you may get headaches
pal = []
def makeentry(recipient, recipienttype):
return (
(mapitags.PR_RECIPIENT_TYPE, recipienttype),
(mapitags.PR_SEND_RICH_INFO, False),
(mapitags.PR_DISPLAY_TYPE, 0),
(mapitags.PR_OBJECT_TYPE, 6),
(mapitags.PR_EMAIL_ADDRESS_A, recipient),
(mapitags.PR_ADDRTYPE_A, "SMTP"),
(mapitags.PR_DISPLAY_NAME_A, recipient),
)
if SendTo:
pal.extend(
[makeentry(recipient, mapi.MAPI_TO) for recipient in SendTo.split(",")]
)
if SendCC:
pal.extend(
[makeentry(recipient, mapi.MAPI_CC) for recipient in SendCC.split(",")]
)
if SendBCC:
pal.extend(
[makeentry(recipient, mapi.MAPI_BCC) for recipient in SendBCC.split(",")]
)
# add the resolved recipients to the message
message.ModifyRecipients(mapi.MODRECIP_ADD, pal)
message.SetProps([(mapitags.PR_BODY_A, Message), (mapitags.PR_SUBJECT_A, Subject)])
# save changes and submit
outboxfolder.SaveChanges(0)
message.SubmitMessage(0) | [
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] | https://github.com/mhammond/pywin32/blob/44afd86ba8485194df93234639243252deeb40d5/com/win32comext/mapi/demos/mapisend.py#L13-L90 | ||
Slicer/SlicerGitSVNArchive | 65e92bb16c2b32ea47a1a66bee71f238891ee1ca | Base/Python/slicer/util.py | python | findChildren | (widget=None, name="", text="", title="", className="") | return children | Return a list of child widgets that meet all the given criteria.
If no criteria are provided, the function will return all widgets descendants.
If no widget is provided, slicer.util.mainWindow() is used.
:param widget: parent widget where the widgets will be searched
:param name: name attribute of the widget
:param text: text attribute of the widget
:param title: title attribute of the widget
:param className: className() attribute of the widget
:return: list with all the widgets that meet all the given criteria. | Return a list of child widgets that meet all the given criteria.
If no criteria are provided, the function will return all widgets descendants.
If no widget is provided, slicer.util.mainWindow() is used.
:param widget: parent widget where the widgets will be searched
:param name: name attribute of the widget
:param text: text attribute of the widget
:param title: title attribute of the widget
:param className: className() attribute of the widget
:return: list with all the widgets that meet all the given criteria. | [
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""" Return a list of child widgets that meet all the given criteria.
If no criteria are provided, the function will return all widgets descendants.
If no widget is provided, slicer.util.mainWindow() is used.
:param widget: parent widget where the widgets will be searched
:param name: name attribute of the widget
:param text: text attribute of the widget
:param title: title attribute of the widget
:param className: className() attribute of the widget
:return: list with all the widgets that meet all the given criteria.
"""
# TODO: figure out why the native QWidget.findChildren method does not seem to work from PythonQt
import slicer, fnmatch
if not widget:
widget = mainWindow()
if not widget:
return []
children = []
parents = [widget]
kwargs = {'name': name, 'text': text, 'title': title, 'className': className}
expected_matches = []
for kwarg in kwargs.keys():
if kwargs[kwarg]:
expected_matches.append(kwarg)
while parents:
p = parents.pop()
# sometimes, p is null, f.e. when using --python-script or --python-code
if not p:
continue
if not hasattr(p,'children'):
continue
parents += p.children()
matched_filter_criteria = 0
for attribute in expected_matches:
if hasattr(p, attribute):
attr_name = getattr(p, attribute)
if attribute == 'className':
# className is a method, not a direct attribute. Invoke the method
attr_name = attr_name()
# Objects may have text attributes with non-string value (for example,
# QUndoStack objects have text attribute of 'builtin_qt_slot' type.
# We only consider string type attributes.
if isinstance(attr_name, str):
if fnmatch.fnmatchcase(attr_name, kwargs[attribute]):
matched_filter_criteria = matched_filter_criteria + 1
if matched_filter_criteria == len(expected_matches):
children.append(p)
return children | [
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Slicer/SlicerGitSVNArchive | 65e92bb16c2b32ea47a1a66bee71f238891ee1ca | Base/Python/slicer/util.py | python | updateVolumeFromArray | (volumeNode, narray) | Sets voxels of a volume node from a numpy array.
Voxels values are deep-copied, therefore if the numpy array
is modified after calling this method, voxel values in the volume node will not change.
Dimensions and data size of the source numpy array does not have to match the current
content of the volume node. | Sets voxels of a volume node from a numpy array.
Voxels values are deep-copied, therefore if the numpy array
is modified after calling this method, voxel values in the volume node will not change.
Dimensions and data size of the source numpy array does not have to match the current
content of the volume node. | [
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"""Sets voxels of a volume node from a numpy array.
Voxels values are deep-copied, therefore if the numpy array
is modified after calling this method, voxel values in the volume node will not change.
Dimensions and data size of the source numpy array does not have to match the current
content of the volume node.
"""
vshape = tuple(reversed(narray.shape))
if len(vshape) == 3:
# Scalar volume
vcomponents = 1
elif len(vshape) == 4:
# Vector volume
vcomponents = vshape[0]
vshape = vshape[1:4]
else:
# TODO: add support for tensor volumes
raise RuntimeError("Unsupported numpy array shape: "+str(narray.shape))
vimage = volumeNode.GetImageData()
if not vimage:
import vtk
vimage = vtk.vtkImageData()
volumeNode.SetAndObserveImageData(vimage)
import vtk.util.numpy_support
vtype = vtk.util.numpy_support.get_vtk_array_type(narray.dtype)
# Volumes with "long long" scalar type are not rendered corectly.
# Probably this could be fixed in VTK or Slicer but for now just reject it.
if vtype == vtk.VTK_LONG_LONG:
raise RuntimeError("Unsupported numpy array type: long long")
vimage.SetDimensions(vshape)
vimage.AllocateScalars(vtype, vcomponents)
narrayTarget = arrayFromVolume(volumeNode)
narrayTarget[:] = narray
# Notify the application that image data is changed
# (same notifications as in vtkMRMLVolumeNode.SetImageDataConnection)
import slicer
volumeNode.StorableModified()
volumeNode.Modified()
volumeNode.InvokeEvent(slicer.vtkMRMLVolumeNode.ImageDataModifiedEvent, volumeNode) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_controls.py | python | PreToggleButton | (*args, **kwargs) | return val | PreToggleButton() -> ToggleButton | PreToggleButton() -> ToggleButton | [
"PreToggleButton",
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"-",
">",
"ToggleButton"
] | def PreToggleButton(*args, **kwargs):
"""PreToggleButton() -> ToggleButton"""
val = _controls_.new_PreToggleButton(*args, **kwargs)
return val | [
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SFTtech/openage | d6a08c53c48dc1e157807471df92197f6ca9e04d | openage/convert/entity_object/conversion/modpack.py | python | Modpack.get_data_files | (self) | return self.data_export_files | Returns the data files for exporting. | Returns the data files for exporting. | [
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"""
Returns the data files for exporting.
"""
return self.data_export_files | [
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linyouhappy/kongkongxiyou | 7a69b2913eb29f4be77f9a62fb90cdd72c4160f1 | cocosjs/frameworks/cocos2d-x/build/android-build.py | python | caculate_built_samples | (args) | return list(targets) | Compute the sampels to be built
'cpp' for short of all cpp tests
'lua' for short of all lua tests | Compute the sampels to be built
'cpp' for short of all cpp tests
'lua' for short of all lua tests | [
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] | def caculate_built_samples(args):
''' Compute the sampels to be built
'cpp' for short of all cpp tests
'lua' for short of all lua tests
'''
if 'all' in args:
return ALL_SAMPLES
targets = []
if 'cpp' in args:
targets += CPP_SAMPLES
args.remove('cpp')
if 'lua' in args:
targets += LUA_SAMPLES
args.remove('lua')
if 'js' in args:
targets += JS_SAMPLES
args.remove('js')
targets += args
# remove duplicate elements, for example
# python android-build.py cpp hellocpp
targets = set(targets)
return list(targets) | [
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microsoft/CNTK | e9396480025b9ca457d26b6f33dd07c474c6aa04 | bindings/python/cntk/debugging/__init__.py | python | force_deterministic | (seed) | Force most of the computation nodes to run deterministically.
Args:
seed (int): set the random seed for all random ops in the graph and readers. | Force most of the computation nodes to run deterministically.
Args:
seed (int): set the random seed for all random ops in the graph and readers. | [
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] | def force_deterministic(seed):
'''
Force most of the computation nodes to run deterministically.
Args:
seed (int): set the random seed for all random ops in the graph and readers.
'''
from _cntk_py import set_fixed_random_seed, force_deterministic_algorithms
import warnings
warnings.warn("RNN based nodes don't run deterministically yet.", Warning)
set_fixed_random_seed(seed)
force_deterministic_algorithms() | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/numbers.py | python | Integral.numerator | (self) | return +self | Integers are their own numerators. | Integers are their own numerators. | [
"Integers",
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] | def numerator(self):
"""Integers are their own numerators."""
return +self | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/ops/composite/multitype_ops/pow_impl.py | python | _tuple_pow_tensor | (x, y) | return F.tensor_pow(x, y) | Returns x ** y where x is a tuple and y is a tensor. | Returns x ** y where x is a tuple and y is a tensor. | [
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] | def _tuple_pow_tensor(x, y):
"""Returns x ** y where x is a tuple and y is a tensor. """
x = utils.sequence_to_tensor(x, y.dtype)
return F.tensor_pow(x, y) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_controls.py | python | SpinButton.Create | (*args, **kwargs) | return _controls_.SpinButton_Create(*args, **kwargs) | Create(self, Window parent, int id=-1, Point pos=DefaultPosition,
Size size=DefaultSize, long style=SP_HORIZONTAL,
String name=SPIN_BUTTON_NAME) -> bool | Create(self, Window parent, int id=-1, Point pos=DefaultPosition,
Size size=DefaultSize, long style=SP_HORIZONTAL,
String name=SPIN_BUTTON_NAME) -> bool | [
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"""
Create(self, Window parent, int id=-1, Point pos=DefaultPosition,
Size size=DefaultSize, long style=SP_HORIZONTAL,
String name=SPIN_BUTTON_NAME) -> bool
"""
return _controls_.SpinButton_Create(*args, **kwargs) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py3/sklearn/ensemble/partial_dependence.py | python | plot_partial_dependence | (gbrt, X, features, feature_names=None,
label=None, n_cols=3, grid_resolution=100,
percentiles=(0.05, 0.95), n_jobs=None,
verbose=0, ax=None, line_kw=None,
contour_kw=None, **fig_kw) | return fig, axs | Partial dependence plots for ``features``.
The ``len(features)`` plots are arranged in a grid with ``n_cols``
columns. Two-way partial dependence plots are plotted as contour
plots.
Read more in the :ref:`User Guide <partial_dependence>`.
.. deprecated:: 0.21
This function was deprecated in version 0.21 in favor of
:func:`sklearn.inspection.plot_partial_dependence` and will be
removed in 0.23.
Parameters
----------
gbrt : BaseGradientBoosting
A fitted gradient boosting model.
X : array-like of shape (n_samples, n_features)
The data on which ``gbrt`` was trained.
features : seq of ints, strings, or tuples of ints or strings
If seq[i] is an int or a tuple with one int value, a one-way
PDP is created; if seq[i] is a tuple of two ints, a two-way
PDP is created.
If feature_names is specified and seq[i] is an int, seq[i]
must be < len(feature_names).
If seq[i] is a string, feature_names must be specified, and
seq[i] must be in feature_names.
feature_names : seq of str
Name of each feature; feature_names[i] holds
the name of the feature with index i.
label : object
The class label for which the PDPs should be computed.
Only if gbrt is a multi-class model. Must be in ``gbrt.classes_``.
n_cols : int
The number of columns in the grid plot (default: 3).
grid_resolution : int, default=100
The number of equally spaced points on the axes.
percentiles : (low, high), default=(0.05, 0.95)
The lower and upper percentile used to create the extreme values
for the PDP axes.
n_jobs : int or None, optional (default=None)
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.
verbose : int
Verbose output during PD computations. Defaults to 0.
ax : Matplotlib axis object, default None
An axis object onto which the plots will be drawn.
line_kw : dict
Dict with keywords passed to the ``matplotlib.pyplot.plot`` call.
For one-way partial dependence plots.
contour_kw : dict
Dict with keywords passed to the ``matplotlib.pyplot.plot`` call.
For two-way partial dependence plots.
``**fig_kw`` : dict
Dict with keywords passed to the figure() call.
Note that all keywords not recognized above will be automatically
included here.
Returns
-------
fig : figure
The Matplotlib Figure object.
axs : seq of Axis objects
A seq of Axis objects, one for each subplot.
Examples
--------
>>> from sklearn.datasets import make_friedman1
>>> from sklearn.ensemble import GradientBoostingRegressor
>>> X, y = make_friedman1()
>>> clf = GradientBoostingRegressor(n_estimators=10).fit(X, y)
>>> fig, axs = plot_partial_dependence(clf, X, [0, (0, 1)]) #doctest: +SKIP
... | Partial dependence plots for ``features``. | [
"Partial",
"dependence",
"plots",
"for",
"features",
"."
] | def plot_partial_dependence(gbrt, X, features, feature_names=None,
label=None, n_cols=3, grid_resolution=100,
percentiles=(0.05, 0.95), n_jobs=None,
verbose=0, ax=None, line_kw=None,
contour_kw=None, **fig_kw):
"""Partial dependence plots for ``features``.
The ``len(features)`` plots are arranged in a grid with ``n_cols``
columns. Two-way partial dependence plots are plotted as contour
plots.
Read more in the :ref:`User Guide <partial_dependence>`.
.. deprecated:: 0.21
This function was deprecated in version 0.21 in favor of
:func:`sklearn.inspection.plot_partial_dependence` and will be
removed in 0.23.
Parameters
----------
gbrt : BaseGradientBoosting
A fitted gradient boosting model.
X : array-like of shape (n_samples, n_features)
The data on which ``gbrt`` was trained.
features : seq of ints, strings, or tuples of ints or strings
If seq[i] is an int or a tuple with one int value, a one-way
PDP is created; if seq[i] is a tuple of two ints, a two-way
PDP is created.
If feature_names is specified and seq[i] is an int, seq[i]
must be < len(feature_names).
If seq[i] is a string, feature_names must be specified, and
seq[i] must be in feature_names.
feature_names : seq of str
Name of each feature; feature_names[i] holds
the name of the feature with index i.
label : object
The class label for which the PDPs should be computed.
Only if gbrt is a multi-class model. Must be in ``gbrt.classes_``.
n_cols : int
The number of columns in the grid plot (default: 3).
grid_resolution : int, default=100
The number of equally spaced points on the axes.
percentiles : (low, high), default=(0.05, 0.95)
The lower and upper percentile used to create the extreme values
for the PDP axes.
n_jobs : int or None, optional (default=None)
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.
verbose : int
Verbose output during PD computations. Defaults to 0.
ax : Matplotlib axis object, default None
An axis object onto which the plots will be drawn.
line_kw : dict
Dict with keywords passed to the ``matplotlib.pyplot.plot`` call.
For one-way partial dependence plots.
contour_kw : dict
Dict with keywords passed to the ``matplotlib.pyplot.plot`` call.
For two-way partial dependence plots.
``**fig_kw`` : dict
Dict with keywords passed to the figure() call.
Note that all keywords not recognized above will be automatically
included here.
Returns
-------
fig : figure
The Matplotlib Figure object.
axs : seq of Axis objects
A seq of Axis objects, one for each subplot.
Examples
--------
>>> from sklearn.datasets import make_friedman1
>>> from sklearn.ensemble import GradientBoostingRegressor
>>> X, y = make_friedman1()
>>> clf = GradientBoostingRegressor(n_estimators=10).fit(X, y)
>>> fig, axs = plot_partial_dependence(clf, X, [0, (0, 1)]) #doctest: +SKIP
...
"""
import matplotlib.pyplot as plt
from matplotlib import transforms
from matplotlib.ticker import MaxNLocator
from matplotlib.ticker import ScalarFormatter
if not isinstance(gbrt, BaseGradientBoosting):
raise ValueError('gbrt has to be an instance of BaseGradientBoosting')
check_is_fitted(gbrt)
# set label_idx for multi-class GBRT
if hasattr(gbrt, 'classes_') and np.size(gbrt.classes_) > 2:
if label is None:
raise ValueError('label is not given for multi-class PDP')
label_idx = np.searchsorted(gbrt.classes_, label)
if gbrt.classes_[label_idx] != label:
raise ValueError('label %s not in ``gbrt.classes_``' % str(label))
else:
# regression and binary classification
label_idx = 0
X = check_array(X, dtype=DTYPE, order='C')
if gbrt.n_features_ != X.shape[1]:
raise ValueError('X.shape[1] does not match gbrt.n_features_')
if line_kw is None:
line_kw = {'color': 'green'}
if contour_kw is None:
contour_kw = {}
# convert feature_names to list
if feature_names is None:
# if not feature_names use fx indices as name
feature_names = [str(i) for i in range(gbrt.n_features_)]
elif isinstance(feature_names, np.ndarray):
feature_names = feature_names.tolist()
def convert_feature(fx):
if isinstance(fx, str):
try:
fx = feature_names.index(fx)
except ValueError:
raise ValueError('Feature %s not in feature_names' % fx)
return fx
# convert features into a seq of int tuples
tmp_features = []
for fxs in features:
if isinstance(fxs, (numbers.Integral, str)):
fxs = (fxs,)
try:
fxs = np.array([convert_feature(fx) for fx in fxs], dtype=np.int32)
except TypeError:
raise ValueError('features must be either int, str, or tuple '
'of int/str')
if not (1 <= np.size(fxs) <= 2):
raise ValueError('target features must be either one or two')
tmp_features.append(fxs)
features = tmp_features
names = []
try:
for fxs in features:
l = []
# explicit loop so "i" is bound for exception below
for i in fxs:
l.append(feature_names[i])
names.append(l)
except IndexError:
raise ValueError('All entries of features must be less than '
'len(feature_names) = {0}, got {1}.'
.format(len(feature_names), i))
# compute PD functions
pd_result = Parallel(n_jobs=n_jobs, verbose=verbose)(
delayed(partial_dependence)(gbrt, fxs, X=X,
grid_resolution=grid_resolution,
percentiles=percentiles)
for fxs in features)
# get global min and max values of PD grouped by plot type
pdp_lim = {}
for pdp, axes in pd_result:
min_pd, max_pd = pdp[label_idx].min(), pdp[label_idx].max()
n_fx = len(axes)
old_min_pd, old_max_pd = pdp_lim.get(n_fx, (min_pd, max_pd))
min_pd = min(min_pd, old_min_pd)
max_pd = max(max_pd, old_max_pd)
pdp_lim[n_fx] = (min_pd, max_pd)
# create contour levels for two-way plots
if 2 in pdp_lim:
Z_level = np.linspace(*pdp_lim[2], num=8)
if ax is None:
fig = plt.figure(**fig_kw)
else:
fig = ax.get_figure()
fig.clear()
n_cols = min(n_cols, len(features))
n_rows = int(np.ceil(len(features) / float(n_cols)))
axs = []
for i, fx, name, (pdp, axes) in zip(count(), features, names,
pd_result):
ax = fig.add_subplot(n_rows, n_cols, i + 1)
if len(axes) == 1:
ax.plot(axes[0], pdp[label_idx].ravel(), **line_kw)
else:
# make contour plot
assert len(axes) == 2
XX, YY = np.meshgrid(axes[0], axes[1])
Z = pdp[label_idx].reshape(list(map(np.size, axes))).T
CS = ax.contour(XX, YY, Z, levels=Z_level, linewidths=0.5,
colors='k')
ax.contourf(XX, YY, Z, levels=Z_level, vmax=Z_level[-1],
vmin=Z_level[0], alpha=0.75, **contour_kw)
ax.clabel(CS, fmt='%2.2f', colors='k', fontsize=10, inline=True)
# plot data deciles + axes labels
deciles = mquantiles(X[:, fx[0]], prob=np.arange(0.1, 1.0, 0.1))
trans = transforms.blended_transform_factory(ax.transData,
ax.transAxes)
ylim = ax.get_ylim()
ax.vlines(deciles, [0], 0.05, transform=trans, color='k')
ax.set_xlabel(name[0])
ax.set_ylim(ylim)
# prevent x-axis ticks from overlapping
ax.xaxis.set_major_locator(MaxNLocator(nbins=6, prune='lower'))
tick_formatter = ScalarFormatter()
tick_formatter.set_powerlimits((-3, 4))
ax.xaxis.set_major_formatter(tick_formatter)
if len(axes) > 1:
# two-way PDP - y-axis deciles + labels
deciles = mquantiles(X[:, fx[1]], prob=np.arange(0.1, 1.0, 0.1))
trans = transforms.blended_transform_factory(ax.transAxes,
ax.transData)
xlim = ax.get_xlim()
ax.hlines(deciles, [0], 0.05, transform=trans, color='k')
ax.set_ylabel(name[1])
# hline erases xlim
ax.set_xlim(xlim)
else:
ax.set_ylabel('Partial dependence')
if len(axes) == 1:
ax.set_ylim(pdp_lim[1])
axs.append(ax)
fig.subplots_adjust(bottom=0.15, top=0.7, left=0.1, right=0.95, wspace=0.4,
hspace=0.3)
return fig, axs | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_core.py | python | PyApp.IsDisplayAvailable | (*args, **kwargs) | return _core_.PyApp_IsDisplayAvailable(*args, **kwargs) | IsDisplayAvailable() -> bool
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python/src/Lib/cmd.py | python | Cmd.cmdloop | (self, intro=None) | Repeatedly issue a prompt, accept input, parse an initial prefix
off the received input, and dispatch to action methods, passing them
the remainder of the line as argument. | Repeatedly issue a prompt, accept input, parse an initial prefix
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"""
self.preloop()
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self.postloop()
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readline.set_completer(self.old_completer)
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KratosMultiphysics/Kratos | 0000833054ed0503424eb28205d6508d9ca6cbbc | applications/ShallowWaterApplication/python_scripts/postprocess/convergence_plotter.py | python | ConvergencePlotter.__init__ | (self, file_name, analysis_name='analysis_000', area=1.0) | Construct the plotter, read the file and initialize the variables.
Parameters
----------
file_name : str
The file name without extension
analysis_name : str
The name of the dataset inside the file
area : float
The area of the domain. It is used to compute the average element size | Construct the plotter, read the file and initialize the variables. | [
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] | def __init__(self, file_name, analysis_name='analysis_000', area=1.0):
'''Construct the plotter, read the file and initialize the variables.
Parameters
----------
file_name : str
The file name without extension
analysis_name : str
The name of the dataset inside the file
area : float
The area of the domain. It is used to compute the average element size
'''
# Read the file
file_name = file_name + '.hdf5'
f = h5.File(file_name)
self.ds = f[analysis_name]
# General data
self.dset_time_steps = self.ds["time_step"]
self.dset_num_elems = self.ds["num_elems"]
self.dset_num_nodes = self.ds["num_nodes"]
self.dset_elem_sizes = np.sqrt(area / self.dset_num_elems)
# Initialize the filter
self.filter = self._TrueFilter() | [
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cms-sw/cmssw | fd9de012d503d3405420bcbeec0ec879baa57cf2 | DQM/Integration/scripts/harvesting_tools/cmsHarvester.py | python | CMSHarvester.option_handler_force | (self, option, opt_str, value, parser) | Switch on `force mode' in which case we don't brake for nobody.
In so-called `force mode' all sanity checks are performed but
we don't halt on failure. Of course this requires some care
from the user. | Switch on `force mode' in which case we don't brake for nobody. | [
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] | def option_handler_force(self, option, opt_str, value, parser):
"""Switch on `force mode' in which case we don't brake for nobody.
In so-called `force mode' all sanity checks are performed but
we don't halt on failure. Of course this requires some care
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"""
self.logger.debug("Switching on `force mode'.")
self.force_running = True | [
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asLody/whale | 6a661b27cc4cf83b7b5a3b02451597ee1ac7f264 | whale/cpplint.py | python | IsErrorSuppressedByNolint | (category, linenum) | return (_global_error_suppressions.get(category, False) or
linenum in _error_suppressions.get(category, set()) or
linenum in _error_suppressions.get(None, set())) | Returns true if the specified error category is suppressed on this line.
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category: str, the category of the error.
linenum: int, the current line number.
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"""Returns true if the specified error category is suppressed on this line.
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Args:
category: str, the category of the error.
linenum: int, the current line number.
Returns:
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"""
return (_global_error_suppressions.get(category, False) or
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_misc.py | python | LogWindow.GetFrame | (*args, **kwargs) | return _misc_.LogWindow_GetFrame(*args, **kwargs) | GetFrame(self) -> wxFrame | GetFrame(self) -> wxFrame | [
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"""GetFrame(self) -> wxFrame"""
return _misc_.LogWindow_GetFrame(*args, **kwargs) | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/distribute/distribute_lib.py | python | get_update_replica_id | () | Get the current device if in a `tf.distribute.Strategy.update()` call. | Get the current device if in a `tf.distribute.Strategy.update()` call. | [
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"""Get the current device if in a `tf.distribute.Strategy.update()` call."""
try:
return _update_replica_id.current
except AttributeError:
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | catboost/benchmarks/ranking/ndcg_kaggle.py | python | dcg_score | (y_true, y_score, k=5) | return np.sum(gain / discounts) | Discounted cumulative gain (DCG) at rank K.
Parameters
----------
y_true : array, shape = [n_samples]
Ground truth (true relevance labels).
y_score : array, shape = [n_samples, n_classes]
Predicted scores.
k : int
Rank.
Returns
-------
score : float | Discounted cumulative gain (DCG) at rank K. | [
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"""Discounted cumulative gain (DCG) at rank K.
Parameters
----------
y_true : array, shape = [n_samples]
Ground truth (true relevance labels).
y_score : array, shape = [n_samples, n_classes]
Predicted scores.
k : int
Rank.
Returns
-------
score : float
"""
order = np.argsort(y_score)[::-1]
y_true = np.take(y_true, order[:k])
gain = 2 ** y_true - 1
discounts = np.log2(np.arange(len(y_true)) + 2)
return np.sum(gain / discounts) | [
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OGRECave/ogre-next | 287307980e6de8910f04f3cc0994451b075071fd | Tools/BlenderExport/ogrepkg/armatureexport.py | python | ArmatureExporter.getActions | (self) | return self.actionManager.getActions() | Returns list of available actions. | Returns list of available actions. | [
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