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limbo018/DREAMPlace | 146c3b9fd003d1acd52c96d9fd02e3f0a05154e4 | dreamplace/ops/dct/dct.py | python | dct | (x, expk, algorithm) | return output.view(x.size()) | compute discrete cosine transformation, DCT II, using N-FFT or 2N-FFT
yk = \sum_{n=0}^{N-1} x_n cos(pi/N*n*(k+1/2))
@param x sequence
@param expk coefficients for post-processing
@param algorithm algorithm type N | 2N | compute discrete cosine transformation, DCT II, using N-FFT or 2N-FFT
yk = \sum_{n=0}^{N-1} x_n cos(pi/N*n*(k+1/2)) | [
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"""compute discrete cosine transformation, DCT II, using N-FFT or 2N-FFT
yk = \sum_{n=0}^{N-1} x_n cos(pi/N*n*(k+1/2))
@param x sequence
@param expk coefficients for post-processing
@param algorithm algorithm type N | 2N
"""
if x.is_cuda:
if algorithm == 'N':
output = dct_cuda.dct(x.view([-1, x.size(-1)]), expk)
elif algorithm == '2N':
output = dct_cuda.dct_2N(x.view([-1, x.size(-1)]), expk)
else:
if algorithm == 'N':
output = dct_cpp.dct(x.view([-1, x.size(-1)]), expk, torch.get_num_threads())
elif algorithm == '2N':
output = dct_cpp.dct_2N(x.view([-1, x.size(-1)]), expk, torch.get_num_threads())
return output.view(x.size()) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py2/pandas/core/generic.py | python | NDFrame._convert | (self, datetime=False, numeric=False, timedelta=False,
coerce=False, copy=True) | return self._constructor(
self._data.convert(datetime=datetime, numeric=numeric,
timedelta=timedelta, coerce=coerce,
copy=copy)).__finalize__(self) | Attempt to infer better dtype for object columns
Parameters
----------
datetime : boolean, default False
If True, convert to date where possible.
numeric : boolean, default False
If True, attempt to convert to numbers (including strings), with
unconvertible values becoming NaN.
timedelta : boolean, default False
If True, convert to timedelta where possible.
coerce : boolean, default False
If True, force conversion with unconvertible values converted to
nulls (NaN or NaT)
copy : boolean, default True
If True, return a copy even if no copy is necessary (e.g. no
conversion was done). Note: This is meant for internal use, and
should not be confused with inplace.
Returns
-------
converted : same as input object | Attempt to infer better dtype for object columns | [
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coerce=False, copy=True):
"""
Attempt to infer better dtype for object columns
Parameters
----------
datetime : boolean, default False
If True, convert to date where possible.
numeric : boolean, default False
If True, attempt to convert to numbers (including strings), with
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timedelta : boolean, default False
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Returns
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converted : same as input object
"""
return self._constructor(
self._data.convert(datetime=datetime, numeric=numeric,
timedelta=timedelta, coerce=coerce,
copy=copy)).__finalize__(self) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/idlelib/config.py | python | IdleConf.GetExtensions | (self, active_only=True,
editor_only=False, shell_only=False) | Return extensions in default and user config-extensions files.
If active_only True, only return active (enabled) extensions
and optionally only editor or shell extensions.
If active_only False, return all extensions. | Return extensions in default and user config-extensions files. | [
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editor_only=False, shell_only=False):
"""Return extensions in default and user config-extensions files.
If active_only True, only return active (enabled) extensions
and optionally only editor or shell extensions.
If active_only False, return all extensions.
"""
extns = self.RemoveKeyBindNames(
self.GetSectionList('default', 'extensions'))
userExtns = self.RemoveKeyBindNames(
self.GetSectionList('user', 'extensions'))
for extn in userExtns:
if extn not in extns: #user has added own extension
extns.append(extn)
for extn in ('AutoComplete','CodeContext',
'FormatParagraph','ParenMatch'):
extns.remove(extn)
# specific exclusions because we are storing config for mainlined old
# extensions in config-extensions.def for backward compatibility
if active_only:
activeExtns = []
for extn in extns:
if self.GetOption('extensions', extn, 'enable', default=True,
type='bool'):
#the extension is enabled
if editor_only or shell_only: # TODO both True contradict
if editor_only:
option = "enable_editor"
else:
option = "enable_shell"
if self.GetOption('extensions', extn,option,
default=True, type='bool',
warn_on_default=False):
activeExtns.append(extn)
else:
activeExtns.append(extn)
return activeExtns
else:
return extns | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py2/scipy/cluster/hierarchy.py | python | from_mlab_linkage | (Z) | return np.hstack([Zpart, CS.reshape(Zs[0], 1)]) | Convert a linkage matrix generated by MATLAB(TM) to a new
linkage matrix compatible with this module.
The conversion does two things:
* the indices are converted from ``1..N`` to ``0..(N-1)`` form,
and
* a fourth column ``Z[:,3]`` is added where ``Z[i,3]`` represents the
number of original observations (leaves) in the non-singleton
cluster ``i``.
This function is useful when loading in linkages from legacy data
files generated by MATLAB.
Parameters
----------
Z : ndarray
A linkage matrix generated by MATLAB(TM).
Returns
-------
ZS : ndarray
A linkage matrix compatible with ``scipy.cluster.hierarchy``.
See Also
--------
linkage: for a description of what a linkage matrix is.
to_mlab_linkage: transform from Scipy to MATLAB format.
Examples
--------
>>> import numpy as np
>>> from scipy.cluster.hierarchy import ward, from_mlab_linkage
Given a linkage matrix in MATLAB format ``mZ``, we can use
`scipy.cluster.hierarchy.from_mlab_linkage` to import
it into Scipy format:
>>> mZ = np.array([[1, 2, 1], [4, 5, 1], [7, 8, 1],
... [10, 11, 1], [3, 13, 1.29099445],
... [6, 14, 1.29099445],
... [9, 15, 1.29099445],
... [12, 16, 1.29099445],
... [17, 18, 5.77350269],
... [19, 20, 5.77350269],
... [21, 22, 8.16496581]])
>>> Z = from_mlab_linkage(mZ)
>>> Z
array([[ 0. , 1. , 1. , 2. ],
[ 3. , 4. , 1. , 2. ],
[ 6. , 7. , 1. , 2. ],
[ 9. , 10. , 1. , 2. ],
[ 2. , 12. , 1.29099445, 3. ],
[ 5. , 13. , 1.29099445, 3. ],
[ 8. , 14. , 1.29099445, 3. ],
[ 11. , 15. , 1.29099445, 3. ],
[ 16. , 17. , 5.77350269, 6. ],
[ 18. , 19. , 5.77350269, 6. ],
[ 20. , 21. , 8.16496581, 12. ]])
As expected, the linkage matrix ``Z`` returned includes an
additional column counting the number of original samples in
each cluster. Also, all cluster indexes are reduced by 1
(MATLAB format uses 1-indexing, whereas Scipy uses 0-indexing). | Convert a linkage matrix generated by MATLAB(TM) to a new
linkage matrix compatible with this module. | [
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"""
Convert a linkage matrix generated by MATLAB(TM) to a new
linkage matrix compatible with this module.
The conversion does two things:
* the indices are converted from ``1..N`` to ``0..(N-1)`` form,
and
* a fourth column ``Z[:,3]`` is added where ``Z[i,3]`` represents the
number of original observations (leaves) in the non-singleton
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This function is useful when loading in linkages from legacy data
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Parameters
----------
Z : ndarray
A linkage matrix generated by MATLAB(TM).
Returns
-------
ZS : ndarray
A linkage matrix compatible with ``scipy.cluster.hierarchy``.
See Also
--------
linkage: for a description of what a linkage matrix is.
to_mlab_linkage: transform from Scipy to MATLAB format.
Examples
--------
>>> import numpy as np
>>> from scipy.cluster.hierarchy import ward, from_mlab_linkage
Given a linkage matrix in MATLAB format ``mZ``, we can use
`scipy.cluster.hierarchy.from_mlab_linkage` to import
it into Scipy format:
>>> mZ = np.array([[1, 2, 1], [4, 5, 1], [7, 8, 1],
... [10, 11, 1], [3, 13, 1.29099445],
... [6, 14, 1.29099445],
... [9, 15, 1.29099445],
... [12, 16, 1.29099445],
... [17, 18, 5.77350269],
... [19, 20, 5.77350269],
... [21, 22, 8.16496581]])
>>> Z = from_mlab_linkage(mZ)
>>> Z
array([[ 0. , 1. , 1. , 2. ],
[ 3. , 4. , 1. , 2. ],
[ 6. , 7. , 1. , 2. ],
[ 9. , 10. , 1. , 2. ],
[ 2. , 12. , 1.29099445, 3. ],
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[ 16. , 17. , 5.77350269, 6. ],
[ 18. , 19. , 5.77350269, 6. ],
[ 20. , 21. , 8.16496581, 12. ]])
As expected, the linkage matrix ``Z`` returned includes an
additional column counting the number of original samples in
each cluster. Also, all cluster indexes are reduced by 1
(MATLAB format uses 1-indexing, whereas Scipy uses 0-indexing).
"""
Z = np.asarray(Z, dtype=np.double, order='c')
Zs = Z.shape
# If it's empty, return it.
if len(Zs) == 0 or (len(Zs) == 1 and Zs[0] == 0):
return Z.copy()
if len(Zs) != 2:
raise ValueError("The linkage array must be rectangular.")
# If it contains no rows, return it.
if Zs[0] == 0:
return Z.copy()
Zpart = Z.copy()
if Zpart[:, 0:2].min() != 1.0 and Zpart[:, 0:2].max() != 2 * Zs[0]:
raise ValueError('The format of the indices is not 1..N')
Zpart[:, 0:2] -= 1.0
CS = np.zeros((Zs[0],), dtype=np.double)
_hierarchy.calculate_cluster_sizes(Zpart, CS, int(Zs[0]) + 1)
return np.hstack([Zpart, CS.reshape(Zs[0], 1)]) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/dataview.py | python | DataViewEvent.__init__ | (self, *args, **kwargs) | __init__(self, EventType commandType=wxEVT_NULL, int winid=0) -> DataViewEvent | __init__(self, EventType commandType=wxEVT_NULL, int winid=0) -> DataViewEvent | [
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chuckcho/video-caffe | fc232b3e3a90ea22dd041b9fc5c542f170581f20 | scripts/cpp_lint.py | python | _NestingState.InNamespaceBody | (self) | return self.stack and isinstance(self.stack[-1], _NamespaceInfo) | Check if we are currently one level inside a namespace body.
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/fsspec/implementations/webhdfs.py | python | WebHDFile._initiate_upload | (self) | Create remote file/upload | Create remote file/upload | [
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arangodb/arangodb | 0d658689c7d1b721b314fa3ca27d38303e1570c8 | 3rdParty/V8/v7.9.317/third_party/jinja2/bccache.py | python | BytecodeCache.dump_bytecode | (self, bucket) | Subclasses have to override this method to write the bytecode
from a bucket back to the cache. If it unable to do so it must not
fail silently but raise an exception. | Subclasses have to override this method to write the bytecode
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/richtext.py | python | RichTextCtrl.MoveHome | (*args, **kwargs) | return _richtext.RichTextCtrl_MoveHome(*args, **kwargs) | MoveHome(self, int flags=0) -> bool
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_windows.py | python | PageSetupDialog.GetPageSetupData | (*args, **kwargs) | return _windows_.PageSetupDialog_GetPageSetupData(*args, **kwargs) | GetPageSetupData(self) -> PageSetupDialogData | GetPageSetupData(self) -> PageSetupDialogData | [
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keyboardio/Kaleidoscope | d59604e98b2439d108647f15be52984a6837d360 | bin/cpplint.py | python | FindNextMultiLineCommentEnd | (lines, lineix) | return len(lines) | We are inside a comment, find the end marker. | We are inside a comment, find the end marker. | [
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FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/App/FreeCADInit.py | python | FCADLogger.isEnabledFor | (self,level) | return self._isEnabledFor(level) | To check for an integer or text log level.
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zhuli19901106/leetcode-zhuli | 0f8fc29ccb8c33ea91149ecb2d4e961024c11db7 | algorithms/0501-1000/0981_time-based-key-value-store_1_AC.py | python | TimeMap.__init__ | (self) | Initialize your data structure here. | Initialize your data structure here. | [
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BTCPrivate/BTCP-Rebase | c8c7fe6ac26b6fba71eae1c89cdc0d924f5c6d82 | contrib/devtools/update-translations.py | python | sanitize_string | (s) | return s.replace('\n',' ') | Sanitize string for printing | Sanitize string for printing | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/plat-mac/pimp.py | python | main | () | Minimal commandline tool to drive pimp. | Minimal commandline tool to drive pimp. | [
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"""Minimal commandline tool to drive pimp."""
import getopt
def _help():
print "Usage: pimp [options] -s [package ...] List installed status"
print " pimp [options] -l [package ...] Show package information"
print " pimp [options] -i package ... Install packages"
print " pimp -d Dump database to stdout"
print " pimp -V Print version number"
print "Options:"
print " -v Verbose"
print " -f Force installation"
print " -D dir Set destination directory"
print " (default: %s)" % DEFAULT_INSTALLDIR
print " -u url URL for database"
sys.exit(1)
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def update(self, msg):
sys.stderr.write(msg + '\r')
return 1
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prefargs['installDir'] = a
if o == '-u':
prefargs['pimpDatabase'] = a
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print 'Pimp version %s; module name is %s' % (PIMP_VERSION, __name__)
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klzgrad/naiveproxy | ed2c513637c77b18721fe428d7ed395b4d284c83 | src/build/config/apple/sdk_info.py | python | FillXcodeVersion | (settings, developer_dir) | Fills the Xcode version and build number into |settings|. | Fills the Xcode version and build number into |settings|. | [
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if developer_dir:
xcode_version_plist_path = os.path.join(developer_dir,
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version_plist = LoadPList(xcode_version_plist_path)
settings['xcode_version'] = FormatVersion(
version_plist['CFBundleShortVersionString'])
settings['xcode_version_int'] = int(settings['xcode_version'], 10)
settings['xcode_build'] = version_plist['ProductBuildVersion']
return
lines = subprocess.check_output(['xcodebuild',
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settings['xcode_version'] = FormatVersion(lines[0].split()[-1])
settings['xcode_version_int'] = int(settings['xcode_version'], 10)
settings['xcode_build'] = lines[-1].split()[-1] | [
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apple/swift-lldb | d74be846ef3e62de946df343e8c234bde93a8912 | scripts/Python/static-binding/lldb.py | python | SBType.GetVectorElementType | (self) | return _lldb.SBType_GetVectorElementType(self) | GetVectorElementType(SBType self) -> SBType | GetVectorElementType(SBType self) -> SBType | [
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"""GetVectorElementType(SBType self) -> SBType"""
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/pandas/io/pytables.py | python | read_hdf | (
path_or_buf,
key=None,
mode: str = "r",
errors: str = "strict",
where=None,
start: Optional[int] = None,
stop: Optional[int] = None,
columns=None,
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Retrieve pandas object stored in file, optionally based on where
criteria
Parameters
----------
path_or_buf : str, path object, pandas.HDFStore or file-like object
Any valid string path is acceptable. The string could be a URL. Valid
URL schemes include http, ftp, s3, and file. For file URLs, a host is
expected. A local file could be: ``file://localhost/path/to/table.h5``.
If you want to pass in a path object, pandas accepts any
``os.PathLike``.
Alternatively, pandas accepts an open :class:`pandas.HDFStore` object.
By file-like object, we refer to objects with a ``read()`` method,
such as a file handler (e.g. via builtin ``open`` function)
or ``StringIO``.
.. versionadded:: 0.21.0 support for __fspath__ protocol.
key : object, optional
The group identifier in the store. Can be omitted if the HDF file
contains a single pandas object.
mode : {'r', 'r+', 'a'}, default 'r'
Mode to use when opening the file. Ignored if path_or_buf is a
:class:`pandas.HDFStore`. Default is 'r'.
where : list, optional
A list of Term (or convertible) objects.
start : int, optional
Row number to start selection.
stop : int, optional
Row number to stop selection.
columns : list, optional
A list of columns names to return.
iterator : bool, optional
Return an iterator object.
chunksize : int, optional
Number of rows to include in an iteration when using an iterator.
errors : str, default 'strict'
Specifies how encoding and decoding errors are to be handled.
See the errors argument for :func:`open` for a full list
of options.
**kwargs
Additional keyword arguments passed to HDFStore.
Returns
-------
item : object
The selected object. Return type depends on the object stored.
See Also
--------
DataFrame.to_hdf : Write a HDF file from a DataFrame.
HDFStore : Low-level access to HDF files.
Examples
--------
>>> df = pd.DataFrame([[1, 1.0, 'a']], columns=['x', 'y', 'z'])
>>> df.to_hdf('./store.h5', 'data')
>>> reread = pd.read_hdf('./store.h5') | Read from the store, close it if we opened it. | [
"Read",
"from",
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"store",
"close",
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"if",
"we",
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] | def read_hdf(
path_or_buf,
key=None,
mode: str = "r",
errors: str = "strict",
where=None,
start: Optional[int] = None,
stop: Optional[int] = None,
columns=None,
iterator=False,
chunksize: Optional[int] = None,
**kwargs,
):
"""
Read from the store, close it if we opened it.
Retrieve pandas object stored in file, optionally based on where
criteria
Parameters
----------
path_or_buf : str, path object, pandas.HDFStore or file-like object
Any valid string path is acceptable. The string could be a URL. Valid
URL schemes include http, ftp, s3, and file. For file URLs, a host is
expected. A local file could be: ``file://localhost/path/to/table.h5``.
If you want to pass in a path object, pandas accepts any
``os.PathLike``.
Alternatively, pandas accepts an open :class:`pandas.HDFStore` object.
By file-like object, we refer to objects with a ``read()`` method,
such as a file handler (e.g. via builtin ``open`` function)
or ``StringIO``.
.. versionadded:: 0.21.0 support for __fspath__ protocol.
key : object, optional
The group identifier in the store. Can be omitted if the HDF file
contains a single pandas object.
mode : {'r', 'r+', 'a'}, default 'r'
Mode to use when opening the file. Ignored if path_or_buf is a
:class:`pandas.HDFStore`. Default is 'r'.
where : list, optional
A list of Term (or convertible) objects.
start : int, optional
Row number to start selection.
stop : int, optional
Row number to stop selection.
columns : list, optional
A list of columns names to return.
iterator : bool, optional
Return an iterator object.
chunksize : int, optional
Number of rows to include in an iteration when using an iterator.
errors : str, default 'strict'
Specifies how encoding and decoding errors are to be handled.
See the errors argument for :func:`open` for a full list
of options.
**kwargs
Additional keyword arguments passed to HDFStore.
Returns
-------
item : object
The selected object. Return type depends on the object stored.
See Also
--------
DataFrame.to_hdf : Write a HDF file from a DataFrame.
HDFStore : Low-level access to HDF files.
Examples
--------
>>> df = pd.DataFrame([[1, 1.0, 'a']], columns=['x', 'y', 'z'])
>>> df.to_hdf('./store.h5', 'data')
>>> reread = pd.read_hdf('./store.h5')
"""
if mode not in ["r", "r+", "a"]:
raise ValueError(
f"mode {mode} is not allowed while performing a read. "
f"Allowed modes are r, r+ and a."
)
# grab the scope
if where is not None:
where = _ensure_term(where, scope_level=1)
if isinstance(path_or_buf, HDFStore):
if not path_or_buf.is_open:
raise IOError("The HDFStore must be open for reading.")
store = path_or_buf
auto_close = False
else:
path_or_buf = stringify_path(path_or_buf)
if not isinstance(path_or_buf, str):
raise NotImplementedError(
"Support for generic buffers has not been implemented."
)
try:
exists = os.path.exists(path_or_buf)
# if filepath is too long
except (TypeError, ValueError):
exists = False
if not exists:
raise FileNotFoundError(f"File {path_or_buf} does not exist")
store = HDFStore(path_or_buf, mode=mode, errors=errors, **kwargs)
# can't auto open/close if we are using an iterator
# so delegate to the iterator
auto_close = True
try:
if key is None:
groups = store.groups()
if len(groups) == 0:
raise ValueError("No dataset in HDF5 file.")
candidate_only_group = groups[0]
# For the HDF file to have only one dataset, all other groups
# should then be metadata groups for that candidate group. (This
# assumes that the groups() method enumerates parent groups
# before their children.)
for group_to_check in groups[1:]:
if not _is_metadata_of(group_to_check, candidate_only_group):
raise ValueError(
"key must be provided when HDF5 file "
"contains multiple datasets."
)
key = candidate_only_group._v_pathname
return store.select(
key,
where=where,
start=start,
stop=stop,
columns=columns,
iterator=iterator,
chunksize=chunksize,
auto_close=auto_close,
)
except (ValueError, TypeError, KeyError):
if not isinstance(path_or_buf, HDFStore):
# if there is an error, close the store if we opened it.
try:
store.close()
except AttributeError:
pass
raise | [
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htcondor/htcondor | 4829724575176d1d6c936e4693dfd78a728569b0 | bindings/python/htcondor/compat_enum/__init__.py | python | unique | (enumeration) | return enumeration | Class decorator that ensures only unique members exist in an enumeration. | Class decorator that ensures only unique members exist in an enumeration. | [
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] | def unique(enumeration):
"""Class decorator that ensures only unique members exist in an enumeration."""
duplicates = []
for name, member in enumeration.__members__.items():
if name != member.name:
duplicates.append((name, member.name))
if duplicates:
duplicate_names = ', '.join(
["%s -> %s" % (alias, name) for (alias, name) in duplicates]
)
raise ValueError('duplicate names found in %r: %s' %
(enumeration, duplicate_names)
)
return enumeration | [
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moflow/moflow | 2dfb27c799c90c6caf1477508eca3eec616ef7d2 | bap/libtracewrap/libtrace/protobuf/python/google/protobuf/internal/decoder.py | python | MessageDecoder | (field_number, is_repeated, is_packed, key, new_default) | Returns a decoder for a message field. | Returns a decoder for a message field. | [
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] | def MessageDecoder(field_number, is_repeated, is_packed, key, new_default):
"""Returns a decoder for a message field."""
local_DecodeVarint = _DecodeVarint
assert not is_packed
if is_repeated:
tag_bytes = encoder.TagBytes(field_number,
wire_format.WIRETYPE_LENGTH_DELIMITED)
tag_len = len(tag_bytes)
def DecodeRepeatedField(buffer, pos, end, message, field_dict):
value = field_dict.get(key)
if value is None:
value = field_dict.setdefault(key, new_default(message))
while 1:
value = field_dict.get(key)
if value is None:
value = field_dict.setdefault(key, new_default(message))
# Read length.
(size, pos) = local_DecodeVarint(buffer, pos)
new_pos = pos + size
if new_pos > end:
raise _DecodeError('Truncated message.')
# Read sub-message.
if value.add()._InternalParse(buffer, pos, new_pos) != new_pos:
# The only reason _InternalParse would return early is if it
# encountered an end-group tag.
raise _DecodeError('Unexpected end-group tag.')
# Predict that the next tag is another copy of the same repeated field.
pos = new_pos + tag_len
if buffer[new_pos:pos] != tag_bytes or new_pos == end:
# Prediction failed. Return.
return new_pos
return DecodeRepeatedField
else:
def DecodeField(buffer, pos, end, message, field_dict):
value = field_dict.get(key)
if value is None:
value = field_dict.setdefault(key, new_default(message))
# Read length.
(size, pos) = local_DecodeVarint(buffer, pos)
new_pos = pos + size
if new_pos > end:
raise _DecodeError('Truncated message.')
# Read sub-message.
if value._InternalParse(buffer, pos, new_pos) != new_pos:
# The only reason _InternalParse would return early is if it encountered
# an end-group tag.
raise _DecodeError('Unexpected end-group tag.')
return new_pos
return DecodeField | [
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Slicer/Slicer | ba9fadf332cb0303515b68d8d06a344c82e3e3e5 | Modules/Loadable/Annotations/SubjectHierarchyPlugins/AnnotationsSubjectHierarchyPlugin.py | python | AnnotationsSubjectHierarchyPlugin.viewContextMenuActions | (self) | return [] | Important note:
In order to use view menus in scripted plugins, it needs to be registered differently,
so that the Python API can be fully built by the time this function is called.
The following changes are necessary:
1. Remove or comment out the following line from constructor
AbstractScriptedSubjectHierarchyPlugin.__init__(self, scriptedPlugin)
2. In addition to the initialization where the scripted plugin is instantialized and
the source set, the plugin also needs to be registered manually:
pluginHandler = slicer.qSlicerSubjectHierarchyPluginHandler.instance()
pluginHandler.registerPlugin(scriptedPlugin) | Important note:
In order to use view menus in scripted plugins, it needs to be registered differently,
so that the Python API can be fully built by the time this function is called. | [
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] | def viewContextMenuActions(self):
""" Important note:
In order to use view menus in scripted plugins, it needs to be registered differently,
so that the Python API can be fully built by the time this function is called.
The following changes are necessary:
1. Remove or comment out the following line from constructor
AbstractScriptedSubjectHierarchyPlugin.__init__(self, scriptedPlugin)
2. In addition to the initialization where the scripted plugin is instantialized and
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pluginHandler.registerPlugin(scriptedPlugin)
"""
return [] | [
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turi-code/SFrame | 796b9bdfb2fa1b881d82080754643c7e68629cd2 | oss_src/unity/python/sframe/data_structures/image.py | python | Image.height | (self) | return self._height | Returns the height of the image stored in the Image object.
Returns
-------
out : int
The height of the image stored in the Image object.
See Also
--------
width, channels, pixel_data
Examples
--------
>>> img = graphlab.Image('https://static.turi.com/datasets/images/sample.jpg')
>>> img.height | Returns the height of the image stored in the Image object. | [
"Returns",
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"height",
"of",
"the",
"image",
"stored",
"in",
"the",
"Image",
"object",
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] | def height(self):
"""
Returns the height of the image stored in the Image object.
Returns
-------
out : int
The height of the image stored in the Image object.
See Also
--------
width, channels, pixel_data
Examples
--------
>>> img = graphlab.Image('https://static.turi.com/datasets/images/sample.jpg')
>>> img.height
"""
return self._height | [
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eclipse/sumo | 7132a9b8b6eea734bdec38479026b4d8c4336d03 | tools/traci/_busstop.py | python | BusStopDomain.getName | (self, stopID) | return self._getUniversal(tc.VAR_NAME, stopID) | getName(string) -> string
Returns the name of this stop | getName(string) -> string | [
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"(",
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"""getName(string) -> string
Returns the name of this stop
"""
return self._getUniversal(tc.VAR_NAME, stopID) | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | chrome/common/extensions/docs/server2/availability_finder.py | python | AvailabilityFinder._GetApiSchema | (self, api_name, file_system, version) | return matching_schemas or None | Searches |file_system| for |api_name|'s API schema data, and processes
and returns it if found. | Searches |file_system| for |api_name|'s API schema data, and processes
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] | def _GetApiSchema(self, api_name, file_system, version):
'''Searches |file_system| for |api_name|'s API schema data, and processes
and returns it if found.
'''
api_filename = self._GetApiSchemaFilename(api_name, file_system, version)
if api_filename is None:
# No file for the API could be found in the given |file_system|.
return None
schema_fs = self._compiled_fs_factory.ForApiSchema(file_system)
api_schemas = schema_fs.GetFromFile(api_filename).Get()
matching_schemas = [api for api in api_schemas
if api['namespace'] == api_name]
# There should only be a single matching schema per file, or zero in the
# case of no API data being found in _EXTENSION_API.
assert len(matching_schemas) <= 1
return matching_schemas or None | [
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pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | torch/multiprocessing/pool.py | python | Pool._repopulate_pool | (self) | Bring the number of pool processes up to the specified number,
for use after reaping workers which have exited. | Bring the number of pool processes up to the specified number,
for use after reaping workers which have exited. | [
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] | def _repopulate_pool(self):
"""Bring the number of pool processes up to the specified number,
for use after reaping workers which have exited.
"""
for i in range(self._processes - len(self._pool)):
# changed worker -> clean_worker
args = (self._inqueue, self._outqueue,
self._initializer,
self._initargs, self._maxtasksperchild)
if hasattr(self, '_wrap_exception'):
args += (self._wrap_exception,)
w = self.Process(target=clean_worker, args=args)
self._pool.append(w)
w.name = w.name.replace('Process', 'PoolWorker')
w.daemon = True
w.start()
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/_windows.py | python | VarScrollHelperBase.EnablePhysicalScrolling | (*args, **kwargs) | return _windows_.VarScrollHelperBase_EnablePhysicalScrolling(*args, **kwargs) | EnablePhysicalScrolling(self, bool scrolling=True) | EnablePhysicalScrolling(self, bool scrolling=True) | [
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] | def EnablePhysicalScrolling(*args, **kwargs):
"""EnablePhysicalScrolling(self, bool scrolling=True)"""
return _windows_.VarScrollHelperBase_EnablePhysicalScrolling(*args, **kwargs) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_core.py | python | MenuBar.IsAttached | (*args, **kwargs) | return _core_.MenuBar_IsAttached(*args, **kwargs) | IsAttached(self) -> bool | IsAttached(self) -> bool | [
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"""IsAttached(self) -> bool"""
return _core_.MenuBar_IsAttached(*args, **kwargs) | [
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apple/swift-clang | d7403439fc6641751840b723e7165fb02f52db95 | tools/scan-build-py/libscanbuild/intercept.py | python | format_entry | (exec_trace) | Generate the desired fields for compilation database entries. | Generate the desired fields for compilation database entries. | [
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] | def format_entry(exec_trace):
""" Generate the desired fields for compilation database entries. """
def abspath(cwd, name):
""" Create normalized absolute path from input filename. """
fullname = name if os.path.isabs(name) else os.path.join(cwd, name)
return os.path.normpath(fullname)
logging.debug('format this command: %s', exec_trace['command'])
compilation = split_command(exec_trace['command'])
if compilation:
for source in compilation.files:
compiler = 'c++' if compilation.compiler == 'c++' else 'cc'
command = [compiler, '-c'] + compilation.flags + [source]
logging.debug('formated as: %s', command)
yield {
'directory': exec_trace['directory'],
'command': encode(command),
'file': abspath(exec_trace['directory'], source)
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GJDuck/LowFat | ecf6a0f0fa1b73a27a626cf493cc39e477b6faea | llvm-4.0.0.src/examples/Kaleidoscope/MCJIT/complete/genk-timing.py | python | TimingScriptGenerator.writeTimingCall | (self, filename, numFuncs, funcsCalled, totalCalls) | Echo some comments and invoke both versions of toy | Echo some comments and invoke both versions of toy | [
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"comments",
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"invoke",
"both",
"versions",
"of",
"toy"
] | def writeTimingCall(self, filename, numFuncs, funcsCalled, totalCalls):
"""Echo some comments and invoke both versions of toy"""
rootname = filename
if '.' in filename:
rootname = filename[:filename.rfind('.')]
self.shfile.write("echo \"%s: Calls %d of %d functions, %d total\" >> %s\n" % (filename, funcsCalled, numFuncs, totalCalls, self.timeFile))
self.shfile.write("echo \"\" >> %s\n" % self.timeFile)
self.shfile.write("echo \"With MCJIT (original)\" >> %s\n" % self.timeFile)
self.shfile.write("/usr/bin/time -f \"Command %C\\n\\tuser time: %U s\\n\\tsytem time: %S s\\n\\tmax set: %M kb\"")
self.shfile.write(" -o %s -a " % self.timeFile)
self.shfile.write("./toy -suppress-prompts -use-mcjit=true -enable-lazy-compilation=false < %s > %s-mcjit.out 2> %s-mcjit.err\n" % (filename, rootname, rootname))
self.shfile.write("echo \"\" >> %s\n" % self.timeFile)
self.shfile.write("echo \"With MCJIT (lazy)\" >> %s\n" % self.timeFile)
self.shfile.write("/usr/bin/time -f \"Command %C\\n\\tuser time: %U s\\n\\tsytem time: %S s\\n\\tmax set: %M kb\"")
self.shfile.write(" -o %s -a " % self.timeFile)
self.shfile.write("./toy -suppress-prompts -use-mcjit=true -enable-lazy-compilation=true < %s > %s-mcjit-lazy.out 2> %s-mcjit-lazy.err\n" % (filename, rootname, rootname))
self.shfile.write("echo \"\" >> %s\n" % self.timeFile)
self.shfile.write("echo \"With JIT\" >> %s\n" % self.timeFile)
self.shfile.write("/usr/bin/time -f \"Command %C\\n\\tuser time: %U s\\n\\tsytem time: %S s\\n\\tmax set: %M kb\"")
self.shfile.write(" -o %s -a " % self.timeFile)
self.shfile.write("./toy -suppress-prompts -use-mcjit=false < %s > %s-jit.out 2> %s-jit.err\n" % (filename, rootname, rootname))
self.shfile.write("echo \"\" >> %s\n" % self.timeFile)
self.shfile.write("echo \"\" >> %s\n" % self.timeFile) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py3/pandas/io/common.py | python | stringify_path | (
filepath_or_buffer: FilePathOrBuffer[AnyStr],
convert_file_like: bool = False,
) | return _expand_user(filepath_or_buffer) | Attempt to convert a path-like object to a string.
Parameters
----------
filepath_or_buffer : object to be converted
Returns
-------
str_filepath_or_buffer : maybe a string version of the object
Notes
-----
Objects supporting the fspath protocol (python 3.6+) are coerced
according to its __fspath__ method.
Any other object is passed through unchanged, which includes bytes,
strings, buffers, or anything else that's not even path-like. | Attempt to convert a path-like object to a string. | [
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"to",
"convert",
"a",
"path",
"-",
"like",
"object",
"to",
"a",
"string",
"."
] | def stringify_path(
filepath_or_buffer: FilePathOrBuffer[AnyStr],
convert_file_like: bool = False,
) -> FileOrBuffer[AnyStr]:
"""
Attempt to convert a path-like object to a string.
Parameters
----------
filepath_or_buffer : object to be converted
Returns
-------
str_filepath_or_buffer : maybe a string version of the object
Notes
-----
Objects supporting the fspath protocol (python 3.6+) are coerced
according to its __fspath__ method.
Any other object is passed through unchanged, which includes bytes,
strings, buffers, or anything else that's not even path-like.
"""
if not convert_file_like and is_file_like(filepath_or_buffer):
# GH 38125: some fsspec objects implement os.PathLike but have already opened a
# file. This prevents opening the file a second time. infer_compression calls
# this function with convert_file_like=True to infer the compression.
return cast(FileOrBuffer[AnyStr], filepath_or_buffer)
if isinstance(filepath_or_buffer, os.PathLike):
filepath_or_buffer = filepath_or_buffer.__fspath__()
return _expand_user(filepath_or_buffer) | [
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/klampt/src/robotsim.py | python | RobotModelDriver.getVelocity | (self) | return _robotsim.RobotModelDriver_getVelocity(self) | r"""
getVelocity(RobotModelDriver self) -> double
Returns the current driver velocity value from the robot's velocity. | r"""
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/aui.py | python | PreAuiMDIClientWindow | (*args, **kwargs) | return val | PreAuiMDIClientWindow() -> AuiMDIClientWindow | PreAuiMDIClientWindow() -> AuiMDIClientWindow | [
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SpaceNetChallenge/BuildingDetectors | 3def3c44b5847c744cd2f3356182892d92496579 | qinhaifang/src/caffe-mnc/scripts/cpp_lint.py | python | FileInfo.IsSource | (self) | return self.Extension()[1:] in ('c', 'cc', 'cpp', 'cxx') | File has a source file extension. | File has a source file extension. | [
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manutdzou/KITTI_SSD | 5b620c2f291d36a0fe14489214f22a992f173f44 | examples/pycaffe/layers/pascal_multilabel_datalayers.py | python | PascalMultilabelDataLayerSync.backward | (self, top, propagate_down, bottom) | These layers does not back propagate | These layers does not back propagate | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/perf_insights/third_party/cloudstorage/common.py | python | validate_bucket_name | (name) | Validate a Google Storage bucket name.
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vmware/concord-bft | ec036a384b4c81be0423d4b429bd37900b13b864 | util/pyclient/bft_client.py | python | BftClient.__enter__ | (self) | Context manager method for 'with' statements | Context manager method for 'with' statements | [
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happynear/caffe-windows | 967eedf25009e334b7f6f933bb5e17aaaff5bef6 | examples/pycaffe/tools.py | python | CaffeSolver.add_from_file | (self, filepath) | Reads a caffe solver prototxt file and updates the Caffesolver
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vicaya/hypertable | e7386f799c238c109ae47973417c2a2c7f750825 | src/py/ThriftClient/gen-py/hyperthrift/gen/ClientService.py | python | Iface.get_row_as_arrays | (self, name, row) | Alternative interface using array as cell
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numpy/core/defchararray.py | python | isalnum | (a) | return _vec_string(a, bool_, 'isalnum') | Returns true for each element if all characters in the string are
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intel/llvm | e6d0547e9d99b5a56430c4749f6c7e328bf221ab | clang/bindings/python/clang/cindex.py | python | Cursor.result_type | (self) | return self._result_type | Retrieve the Type of the result for this Cursor. | Retrieve the Type of the result for this Cursor. | [
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toggl-open-source/toggldesktop | 91865205885531cc8fd9e8d613dad49d625d56e7 | third_party/cpplint/cpplint.py | python | GetIndentLevel | (line) | Return the number of leading spaces in line.
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espressomd/espresso | 7e29f9052e710fe1ebf0f5d2a8076b32921fbc6a | src/python/espressomd/rotation.py | python | diagonalized_inertia_tensor | (positions, masses) | return eig, eigv | Calculate the diagonalized inertia tensor
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Parameters
----------
positions : (N,3) array_like of :obj:`float`
Positions of the masses.
masses : (N,) array_like of :obj:`float`
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-------
(3,) array_like of :obj:`float`
Principal moments of inertia.
(3,3) array_like of :obj:`float`
Principal axes of inertia.
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/keras/utils/conv_utils.py | python | conv_input_length | (output_length, filter_size, padding, stride) | return (output_length - 1) * stride - 2 * pad + filter_size | Determines input length of a convolution given output length.
Args:
output_length: integer.
filter_size: integer.
padding: one of "same", "valid", "full".
stride: integer.
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Args:
output_length: integer.
filter_size: integer.
padding: one of "same", "valid", "full".
stride: integer.
Returns:
The input length (integer).
"""
if output_length is None:
return None
assert padding in {'same', 'valid', 'full'}
if padding == 'same':
pad = filter_size // 2
elif padding == 'valid':
pad = 0
elif padding == 'full':
pad = filter_size - 1
return (output_length - 1) * stride - 2 * pad + filter_size | [
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snap-stanford/snap-python | d53c51b0a26aa7e3e7400b014cdf728948fde80a | setup/snap.py | python | TIntIntVH.BegI | (self) | return _snap.TIntIntVH_BegI(self) | BegI(TIntIntVH self) -> THash< TInt,TVec< TInt,int > >::TIter
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/python/framework/ops.py | python | Graph.unique_name | (self, name, mark_as_used=True) | return name | Return a unique operation name for `name`.
Note: You rarely need to call `unique_name()` directly. Most of
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Note: You rarely need to call `unique_name()` directly. Most of
the time you just need to create `with g.name_scope()` blocks to
generate structured names.
`unique_name` is used to generate structured names, separated by
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Operation names are displayed in error messages reported by the
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TensorBoard.
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i += 1
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PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | python/paddle/tensor/search.py | python | index_sample | (x, index) | return out | **IndexSample Layer**
IndexSample OP returns the element of the specified location of X,
and the location is specified by Index.
.. code-block:: text
Given:
X = [[1, 2, 3, 4, 5],
[6, 7, 8, 9, 10]]
Index = [[0, 1, 3],
[0, 2, 4]]
Then:
Out = [[1, 2, 4],
[6, 8, 10]]
Args:
x (Tensor): The source input tensor with 2-D shape. Supported data type is
int32, int64, float32, float64.
index (Tensor): The index input tensor with 2-D shape, first dimension should be same with X.
Data type is int32 or int64.
Returns:
output (Tensor): The output is a tensor with the same shape as index.
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([[1.0, 2.0, 3.0, 4.0],
[5.0, 6.0, 7.0, 8.0],
[9.0, 10.0, 11.0, 12.0]], dtype='float32')
index = paddle.to_tensor([[0, 1, 2],
[1, 2, 3],
[0, 0, 0]], dtype='int32')
target = paddle.to_tensor([[100, 200, 300, 400],
[500, 600, 700, 800],
[900, 1000, 1100, 1200]], dtype='int32')
out_z1 = paddle.index_sample(x, index)
print(out_z1)
#[[1. 2. 3.]
# [6. 7. 8.]
# [9. 9. 9.]]
# Use the index of the maximum value by topk op
# get the value of the element of the corresponding index in other tensors
top_value, top_index = paddle.topk(x, k=2)
out_z2 = paddle.index_sample(target, top_index)
print(top_value)
#[[ 4. 3.]
# [ 8. 7.]
# [12. 11.]]
print(top_index)
#[[3 2]
# [3 2]
# [3 2]]
print(out_z2)
#[[ 400 300]
# [ 800 700]
# [1200 1100]] | **IndexSample Layer** | [
"**",
"IndexSample",
"Layer",
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] | def index_sample(x, index):
"""
**IndexSample Layer**
IndexSample OP returns the element of the specified location of X,
and the location is specified by Index.
.. code-block:: text
Given:
X = [[1, 2, 3, 4, 5],
[6, 7, 8, 9, 10]]
Index = [[0, 1, 3],
[0, 2, 4]]
Then:
Out = [[1, 2, 4],
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x (Tensor): The source input tensor with 2-D shape. Supported data type is
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index (Tensor): The index input tensor with 2-D shape, first dimension should be same with X.
Data type is int32 or int64.
Returns:
output (Tensor): The output is a tensor with the same shape as index.
Examples:
.. code-block:: python
import paddle
x = paddle.to_tensor([[1.0, 2.0, 3.0, 4.0],
[5.0, 6.0, 7.0, 8.0],
[9.0, 10.0, 11.0, 12.0]], dtype='float32')
index = paddle.to_tensor([[0, 1, 2],
[1, 2, 3],
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target = paddle.to_tensor([[100, 200, 300, 400],
[500, 600, 700, 800],
[900, 1000, 1100, 1200]], dtype='int32')
out_z1 = paddle.index_sample(x, index)
print(out_z1)
#[[1. 2. 3.]
# [6. 7. 8.]
# [9. 9. 9.]]
# Use the index of the maximum value by topk op
# get the value of the element of the corresponding index in other tensors
top_value, top_index = paddle.topk(x, k=2)
out_z2 = paddle.index_sample(target, top_index)
print(top_value)
#[[ 4. 3.]
# [ 8. 7.]
# [12. 11.]]
print(top_index)
#[[3 2]
# [3 2]
# [3 2]]
print(out_z2)
#[[ 400 300]
# [ 800 700]
# [1200 1100]]
"""
if in_dygraph_mode():
return _C_ops.index_sample(x, index)
helper = LayerHelper("index_sample", **locals())
check_variable_and_dtype(x, 'x', ['float32', 'float64', 'int32', 'int64'],
'paddle.tensor.search.index_sample')
check_variable_and_dtype(index, 'index', ['int32', 'int64'],
'paddle.tensor.search.index_sample')
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='index_sample',
inputs={'X': x,
'Index': index},
outputs={'Out': out})
return out | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/inspect.py | python | ismethod | (object) | return isinstance(object, types.MethodType) | Return true if the object is an instance method.
Instance method objects provide these attributes:
__doc__ documentation string
__name__ name with which this method was defined
im_class class object in which this method belongs
im_func function object containing implementation of method
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__doc__ documentation string
__name__ name with which this method was defined
im_class class object in which this method belongs
im_func function object containing implementation of method
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/lib-tk/Tkinter.py | python | Misc.after_cancel | (self, id) | Cancel scheduling of function identified with ID.
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# In Tk 8.3, splitlist returns: (script, type)
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script = self.tk.splitlist(data)[0]
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pass
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cvxpy/cvxpy | 5165b4fb750dfd237de8659383ef24b4b2e33aaf | cvxpy/atoms/mixed_norm.py | python | mixed_norm | (X, p: Union[int, str] = 2, q: Union[int, str] = 1) | return norm(vecnorms, q) | Lp,q norm; :math:`(\\sum_k (\\sum_l \\lvert x_{k,l} \\rvert^p)^{q/p})^{1/q}`.
Parameters
----------
X : Expression or numeric constant
The matrix to take the l_{p,q} norm of.
p : int or str, optional
The type of inner norm.
q : int or str, optional
The type of outer norm.
Returns
-------
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An Expression representing the mixed norm. | Lp,q norm; :math:`(\\sum_k (\\sum_l \\lvert x_{k,l} \\rvert^p)^{q/p})^{1/q}`. | [
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X = Expression.cast_to_const(X)
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vecnorms = norm(X, p, axis=1)
# outer norm
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nasa/astrobee | 9241e67e6692810d6e275abb3165b6d02f4ca5ef | scripts/git/cpplint.py | python | _BlockInfo.CheckBegin | (self, filename, clean_lines, linenum, error) | Run checks that applies to text up to the opening brace.
This is mostly for checking the text after the class identifier
and the "{", usually where the base class is specified. For other
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filename: The name of the current file.
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"""Run checks that applies to text up to the opening brace.
This is mostly for checking the text after the class identifier
and the "{", usually where the base class is specified. For other
blocks, there isn't much to check, so we always pass.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
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apple/swift-lldb | d74be846ef3e62de946df343e8c234bde93a8912 | scripts/Python/static-binding/lldb.py | python | SBSymbolContext.SetCompileUnit | (self, compile_unit) | return _lldb.SBSymbolContext_SetCompileUnit(self, compile_unit) | SetCompileUnit(SBSymbolContext self, SBCompileUnit compile_unit) | SetCompileUnit(SBSymbolContext self, SBCompileUnit compile_unit) | [
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LiquidPlayer/LiquidCore | 9405979363f2353ac9a71ad8ab59685dd7f919c9 | deps/node-10.15.3/tools/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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apple/foundationdb | f7118ad406f44ab7a33970fc8370647ed0085e18 | layers/containers/vector.py | python | Vector.empty | (self, tr=None) | return self._size(self._to_transaction(tr)) == 0 | Test whether the Vector is empty. | Test whether the Vector is empty. | [
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ceph/ceph | 959663007321a369c83218414a29bd9dbc8bda3a | qa/tasks/ceph_manager.py | python | CephManager.do_rados | (self, cmd, pool=None, namespace=None, remote=None, **kwargs) | return proc | Execute a remote rados command. | Execute a remote rados command. | [
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google/filament | d21f092645b8e1e312307cbf89f1484891347c63 | third_party/glslang/update_glslang_sources.py | python | GoodCommit.GetUrl | (self) | return '{host}{subrepo}'.format(
host=host,
subrepo=self.subrepo) | Returns the URL for the repository. | Returns the URL for the repository. | [
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"""Returns the URL for the repository."""
host = SITE_TO_HOST[self.site]
return '{host}{subrepo}'.format(
host=host,
subrepo=self.subrepo) | [
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yue/yue | 619d62c191b13c51c01be451dc48917c34a5aefc | building/tools/cpplint.py | python | _SetFilters | (filters) | Sets the module's error-message filters.
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yrnkrn/zapcc | c6a8aa30006d997eff0d60fd37b0e62b8aa0ea50 | tools/clang/tools/scan-build-py/libscanbuild/analyze.py | python | require | (required) | return decorator | Decorator for checking the required values in state.
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def decorator(function):
@functools.wraps(function)
def wrapper(*args, **kwargs):
for key in required:
if key not in args[0]:
raise KeyError('{0} not passed to {1}'.format(
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return wrapper
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/site-packages/win32/lib/win32pdhquery.py | python | Query.addperfcounter | (self, object, counter, machine=None) | A "Performance Counter" is a stable, known, common counter,
such as Memory, or Processor. The use of addperfcounter by
end-users is deprecated, since the use of
addcounterbybrowsing is considerably more flexible and general.
It is provided here to allow the easy development of scripts
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usage:
query.addperfcounter('Memory', 'Available Bytes')
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query.addcounter('Memory', 'Available Bytes') | A "Performance Counter" is a stable, known, common counter,
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addcounterbybrowsing is considerably more flexible and general.
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query.addperfcounter('Memory', 'Available Bytes')
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'''
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end-users is deprecated, since the use of
addcounterbybrowsing is considerably more flexible and general.
It is provided here to allow the easy development of scripts
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usage:
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query.addcounter('Memory', 'Available Bytes')
'''
BaseQuery.addcounter(self, object=object, counter=counter, machine=machine) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/AWSPythonSDK/1.5.8/dateutil/tz/tz.py | python | tzfile.fromutc | (self, dt) | return enfold(dt_out, fold=int(fold)) | The ``tzfile`` implementation of :py:func:`datetime.tzinfo.fromutc`.
:param dt:
A :py:class:`datetime.datetime` object.
:raises TypeError:
Raised if ``dt`` is not a :py:class:`datetime.datetime` object.
:raises ValueError:
Raised if this is called with a ``dt`` which does not have this
``tzinfo`` attached.
:return:
Returns a :py:class:`datetime.datetime` object representing the
wall time in ``self``'s time zone. | The ``tzfile`` implementation of :py:func:`datetime.tzinfo.fromutc`. | [
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The ``tzfile`` implementation of :py:func:`datetime.tzinfo.fromutc`.
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# These isinstance checks are in datetime.tzinfo, so we'll preserve
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idx = self._find_last_transition(dt, in_utc=True)
tti = self._get_ttinfo(idx)
dt_out = dt + datetime.timedelta(seconds=tti.offset)
fold = self.is_ambiguous(dt_out, idx=idx)
return enfold(dt_out, fold=int(fold)) | [
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SequoiaDB/SequoiaDB | 2894ed7e5bd6fe57330afc900cf76d0ff0df9f64 | tools/server/php_linux/libxml2/lib/python2.4/site-packages/libxml2.py | python | parserCtxt.htmlCtxtReadFd | (self, fd, URL, encoding, options) | return __tmp | parse an XML from a file descriptor and build a tree. This
reuses the existing @ctxt parser context | parse an XML from a file descriptor and build a tree. This
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"""parse an XML from a file descriptor and build a tree. This
reuses the existing @ctxt parser context """
ret = libxml2mod.htmlCtxtReadFd(self._o, fd, URL, encoding, options)
if ret is None:raise treeError('htmlCtxtReadFd() failed')
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fatih/subvim | 241b6d170597857105da219c9b7d36059e9f11fb | vim/base/YouCompleteMe/third_party/jedi/jedi/parser/representation.py | python | Scope.get_code | (self, first_indent=False, indention=' ') | return string | :return: Returns the code of the current scope.
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"""
:return: Returns the code of the current scope.
:rtype: str
"""
string = ""
if len(self.docstr) > 0:
string += '"""' + self.docstr + '"""\n'
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/decimal.py | python | Context.to_integral_exact | (self, a) | return a.to_integral_exact(context=self) | Rounds to an integer.
When the operand has a negative exponent, the result is the same
as using the quantize() operation using the given operand as the
left-hand-operand, 1E+0 as the right-hand-operand, and the precision
of the operand as the precision setting; Inexact and Rounded flags
are allowed in this operation. The rounding mode is taken from the
context.
>>> ExtendedContext.to_integral_exact(Decimal('2.1'))
Decimal('2')
>>> ExtendedContext.to_integral_exact(Decimal('100'))
Decimal('100')
>>> ExtendedContext.to_integral_exact(Decimal('100.0'))
Decimal('100')
>>> ExtendedContext.to_integral_exact(Decimal('101.5'))
Decimal('102')
>>> ExtendedContext.to_integral_exact(Decimal('-101.5'))
Decimal('-102')
>>> ExtendedContext.to_integral_exact(Decimal('10E+5'))
Decimal('1.0E+6')
>>> ExtendedContext.to_integral_exact(Decimal('7.89E+77'))
Decimal('7.89E+77')
>>> ExtendedContext.to_integral_exact(Decimal('-Inf'))
Decimal('-Infinity') | Rounds to an integer. | [
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"""Rounds to an integer.
When the operand has a negative exponent, the result is the same
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left-hand-operand, 1E+0 as the right-hand-operand, and the precision
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>>> ExtendedContext.to_integral_exact(Decimal('2.1'))
Decimal('2')
>>> ExtendedContext.to_integral_exact(Decimal('100'))
Decimal('100')
>>> ExtendedContext.to_integral_exact(Decimal('100.0'))
Decimal('100')
>>> ExtendedContext.to_integral_exact(Decimal('101.5'))
Decimal('102')
>>> ExtendedContext.to_integral_exact(Decimal('-101.5'))
Decimal('-102')
>>> ExtendedContext.to_integral_exact(Decimal('10E+5'))
Decimal('1.0E+6')
>>> ExtendedContext.to_integral_exact(Decimal('7.89E+77'))
Decimal('7.89E+77')
>>> ExtendedContext.to_integral_exact(Decimal('-Inf'))
Decimal('-Infinity')
"""
a = _convert_other(a, raiseit=True)
return a.to_integral_exact(context=self) | [
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scylladb/scylla | 00a6fda7b98438184024fc4683b0accf1852e30c | scylla-gdb.py | python | histogram.__init__ | (self, counts = None, print_indicators = True, formatter=None) | Constructor.
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* print_indicators: print the '+' characters to illustrate relative
count. Can be turned off when the item names are very long and would
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* formatter: a callable that receives the item as its argument and is
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By default, items are printed verbatim. | Constructor. | [
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* counts: initial counts (default to empty).
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count. Can be turned off when the item names are very long and would
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* formatter: a callable that receives the item as its argument and is
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if counts is None:
self._counts = defaultdict(int)
else:
self._counts = counts
self._print_indicators = print_indicators
def default_formatter(value):
return str(value)
if formatter is None:
self._formatter = default_formatter
else:
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numba/targets/linalg.py | python | call_xxdot | (context, builder, conjugate, dtype,
n, a_data, b_data, out_data) | Call the BLAS vector * vector product function for the given arguments. | Call the BLAS vector * vector product function for the given arguments. | [
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"""
Call the BLAS vector * vector product function for the given arguments.
"""
fnty = ir.FunctionType(ir.IntType(32),
[ll_char, ll_char, intp_t, # kind, conjugate, n
ll_void_p, ll_void_p, ll_void_p, # a, b, out
])
fn = builder.module.get_or_insert_function(fnty, name="numba_xxdot")
kind = get_blas_kind(dtype)
kind_val = ir.Constant(ll_char, ord(kind))
conjugate = ir.Constant(ll_char, int(conjugate))
res = builder.call(fn, (kind_val, conjugate, n,
builder.bitcast(a_data, ll_void_p),
builder.bitcast(b_data, ll_void_p),
builder.bitcast(out_data, ll_void_p)))
check_blas_return(context, builder, res) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/numpy/polynomial/legendre.py | python | legfit | (x, y, deg, rcond=None, full=False, w=None) | return pu._fit(legvander, x, y, deg, rcond, full, w) | Least squares fit of Legendre series to data.
Return the coefficients of a Legendre series of degree `deg` that is the
least squares fit to the data values `y` given at points `x`. If `y` is
1-D the returned coefficients will also be 1-D. If `y` is 2-D multiple
fits are done, one for each column of `y`, and the resulting
coefficients are stored in the corresponding columns of a 2-D return.
The fitted polynomial(s) are in the form
.. math:: p(x) = c_0 + c_1 * L_1(x) + ... + c_n * L_n(x),
where `n` is `deg`.
Parameters
----------
x : array_like, shape (M,)
x-coordinates of the M sample points ``(x[i], y[i])``.
y : array_like, shape (M,) or (M, K)
y-coordinates of the sample points. Several data sets of sample
points sharing the same x-coordinates can be fitted at once by
passing in a 2D-array that contains one dataset per column.
deg : int or 1-D array_like
Degree(s) of the fitting polynomials. If `deg` is a single integer
all terms up to and including the `deg`'th term are included in the
fit. For NumPy versions >= 1.11.0 a list of integers specifying the
degrees of the terms to include may be used instead.
rcond : float, optional
Relative condition number of the fit. Singular values smaller than
this relative to the largest singular value will be ignored. The
default value is len(x)*eps, where eps is the relative precision of
the float type, about 2e-16 in most cases.
full : bool, optional
Switch determining nature of return value. When it is False (the
default) just the coefficients are returned, when True diagnostic
information from the singular value decomposition is also returned.
w : array_like, shape (`M`,), optional
Weights. If not None, the contribution of each point
``(x[i],y[i])`` to the fit is weighted by `w[i]`. Ideally the
weights are chosen so that the errors of the products ``w[i]*y[i]``
all have the same variance. The default value is None.
.. versionadded:: 1.5.0
Returns
-------
coef : ndarray, shape (M,) or (M, K)
Legendre coefficients ordered from low to high. If `y` was
2-D, the coefficients for the data in column k of `y` are in
column `k`. If `deg` is specified as a list, coefficients for
terms not included in the fit are set equal to zero in the
returned `coef`.
[residuals, rank, singular_values, rcond] : list
These values are only returned if `full` = True
resid -- sum of squared residuals of the least squares fit
rank -- the numerical rank of the scaled Vandermonde matrix
sv -- singular values of the scaled Vandermonde matrix
rcond -- value of `rcond`.
For more details, see `linalg.lstsq`.
Warns
-----
RankWarning
The rank of the coefficient matrix in the least-squares fit is
deficient. The warning is only raised if `full` = False. The
warnings can be turned off by
>>> import warnings
>>> warnings.simplefilter('ignore', np.RankWarning)
See Also
--------
chebfit, polyfit, lagfit, hermfit, hermefit
legval : Evaluates a Legendre series.
legvander : Vandermonde matrix of Legendre series.
legweight : Legendre weight function (= 1).
linalg.lstsq : Computes a least-squares fit from the matrix.
scipy.interpolate.UnivariateSpline : Computes spline fits.
Notes
-----
The solution is the coefficients of the Legendre series `p` that
minimizes the sum of the weighted squared errors
.. math:: E = \\sum_j w_j^2 * |y_j - p(x_j)|^2,
where :math:`w_j` are the weights. This problem is solved by setting up
as the (typically) overdetermined matrix equation
.. math:: V(x) * c = w * y,
where `V` is the weighted pseudo Vandermonde matrix of `x`, `c` are the
coefficients to be solved for, `w` are the weights, and `y` are the
observed values. This equation is then solved using the singular value
decomposition of `V`.
If some of the singular values of `V` are so small that they are
neglected, then a `RankWarning` will be issued. This means that the
coefficient values may be poorly determined. Using a lower order fit
will usually get rid of the warning. The `rcond` parameter can also be
set to a value smaller than its default, but the resulting fit may be
spurious and have large contributions from roundoff error.
Fits using Legendre series are usually better conditioned than fits
using power series, but much can depend on the distribution of the
sample points and the smoothness of the data. If the quality of the fit
is inadequate splines may be a good alternative.
References
----------
.. [1] Wikipedia, "Curve fitting",
https://en.wikipedia.org/wiki/Curve_fitting
Examples
-------- | Least squares fit of Legendre series to data. | [
"Least",
"squares",
"fit",
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"Legendre",
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"to",
"data",
"."
] | def legfit(x, y, deg, rcond=None, full=False, w=None):
"""
Least squares fit of Legendre series to data.
Return the coefficients of a Legendre series of degree `deg` that is the
least squares fit to the data values `y` given at points `x`. If `y` is
1-D the returned coefficients will also be 1-D. If `y` is 2-D multiple
fits are done, one for each column of `y`, and the resulting
coefficients are stored in the corresponding columns of a 2-D return.
The fitted polynomial(s) are in the form
.. math:: p(x) = c_0 + c_1 * L_1(x) + ... + c_n * L_n(x),
where `n` is `deg`.
Parameters
----------
x : array_like, shape (M,)
x-coordinates of the M sample points ``(x[i], y[i])``.
y : array_like, shape (M,) or (M, K)
y-coordinates of the sample points. Several data sets of sample
points sharing the same x-coordinates can be fitted at once by
passing in a 2D-array that contains one dataset per column.
deg : int or 1-D array_like
Degree(s) of the fitting polynomials. If `deg` is a single integer
all terms up to and including the `deg`'th term are included in the
fit. For NumPy versions >= 1.11.0 a list of integers specifying the
degrees of the terms to include may be used instead.
rcond : float, optional
Relative condition number of the fit. Singular values smaller than
this relative to the largest singular value will be ignored. The
default value is len(x)*eps, where eps is the relative precision of
the float type, about 2e-16 in most cases.
full : bool, optional
Switch determining nature of return value. When it is False (the
default) just the coefficients are returned, when True diagnostic
information from the singular value decomposition is also returned.
w : array_like, shape (`M`,), optional
Weights. If not None, the contribution of each point
``(x[i],y[i])`` to the fit is weighted by `w[i]`. Ideally the
weights are chosen so that the errors of the products ``w[i]*y[i]``
all have the same variance. The default value is None.
.. versionadded:: 1.5.0
Returns
-------
coef : ndarray, shape (M,) or (M, K)
Legendre coefficients ordered from low to high. If `y` was
2-D, the coefficients for the data in column k of `y` are in
column `k`. If `deg` is specified as a list, coefficients for
terms not included in the fit are set equal to zero in the
returned `coef`.
[residuals, rank, singular_values, rcond] : list
These values are only returned if `full` = True
resid -- sum of squared residuals of the least squares fit
rank -- the numerical rank of the scaled Vandermonde matrix
sv -- singular values of the scaled Vandermonde matrix
rcond -- value of `rcond`.
For more details, see `linalg.lstsq`.
Warns
-----
RankWarning
The rank of the coefficient matrix in the least-squares fit is
deficient. The warning is only raised if `full` = False. The
warnings can be turned off by
>>> import warnings
>>> warnings.simplefilter('ignore', np.RankWarning)
See Also
--------
chebfit, polyfit, lagfit, hermfit, hermefit
legval : Evaluates a Legendre series.
legvander : Vandermonde matrix of Legendre series.
legweight : Legendre weight function (= 1).
linalg.lstsq : Computes a least-squares fit from the matrix.
scipy.interpolate.UnivariateSpline : Computes spline fits.
Notes
-----
The solution is the coefficients of the Legendre series `p` that
minimizes the sum of the weighted squared errors
.. math:: E = \\sum_j w_j^2 * |y_j - p(x_j)|^2,
where :math:`w_j` are the weights. This problem is solved by setting up
as the (typically) overdetermined matrix equation
.. math:: V(x) * c = w * y,
where `V` is the weighted pseudo Vandermonde matrix of `x`, `c` are the
coefficients to be solved for, `w` are the weights, and `y` are the
observed values. This equation is then solved using the singular value
decomposition of `V`.
If some of the singular values of `V` are so small that they are
neglected, then a `RankWarning` will be issued. This means that the
coefficient values may be poorly determined. Using a lower order fit
will usually get rid of the warning. The `rcond` parameter can also be
set to a value smaller than its default, but the resulting fit may be
spurious and have large contributions from roundoff error.
Fits using Legendre series are usually better conditioned than fits
using power series, but much can depend on the distribution of the
sample points and the smoothness of the data. If the quality of the fit
is inadequate splines may be a good alternative.
References
----------
.. [1] Wikipedia, "Curve fitting",
https://en.wikipedia.org/wiki/Curve_fitting
Examples
--------
"""
return pu._fit(legvander, x, y, deg, rcond, full, w) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/plugins/codebrowser/codebrowser/gentag/verilogtags.py | python | GenerateTags | (buff) | return rtags | Create a DocStruct object that represents a Verilog document
@param buff: a file like buffer object (StringIO)
@todo: add support for parsing module definitions / class variables | Create a DocStruct object that represents a Verilog document
@param buff: a file like buffer object (StringIO)
@todo: add support for parsing module definitions / class variables | [
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rtags.SetElementPriority('class', 3)
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rtags.SetElementPriority('function', 1)
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infunction = False # Inside a function definition
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incomment = True
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lclass = rtags.GetLastClass()
task = taglib.Function(tname, lnum, 'task',
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task = taglib.Function(tname, lnum, 'task')
rtags.AddElement('task', task)
break # goto next line
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weolar/miniblink49 | 1c4678db0594a4abde23d3ebbcc7cd13c3170777 | third_party/WebKit/Tools/Scripts/webkitpy/thirdparty/wpt/wpt/tools/wptserve/wptserve/response.py | python | Response.set_cookie | (self, name, value, path="/", domain=None, max_age=None,
expires=None, secure=False, httponly=False, comment=None) | Set a cookie to be sent with a Set-Cookie header in the
response
:param name: String name of the cookie
:param value: String value of the cookie
:param max_age: datetime.timedelta int representing the time (in seconds)
until the cookie expires
:param path: String path to which the cookie applies
:param domain: String domain to which the cookie applies
:param secure: Boolean indicating whether the cookie is marked as secure
:param httponly: Boolean indicating whether the cookie is marked as
HTTP Only
:param comment: String comment
:param expires: datetime.datetime or datetime.timedelta indicating a
time or interval from now when the cookie expires | Set a cookie to be sent with a Set-Cookie header in the
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expires=None, secure=False, httponly=False, comment=None):
"""Set a cookie to be sent with a Set-Cookie header in the
response
:param name: String name of the cookie
:param value: String value of the cookie
:param max_age: datetime.timedelta int representing the time (in seconds)
until the cookie expires
:param path: String path to which the cookie applies
:param domain: String domain to which the cookie applies
:param secure: Boolean indicating whether the cookie is marked as secure
:param httponly: Boolean indicating whether the cookie is marked as
HTTP Only
:param comment: String comment
:param expires: datetime.datetime or datetime.timedelta indicating a
time or interval from now when the cookie expires
"""
days = dict((i+1, name) for i, name in enumerate(["jan", "feb", "mar",
"apr", "may", "jun",
"jul", "aug", "sep",
"oct", "nov", "dec"]))
if value is None:
value = ''
max_age = 0
expires = timedelta(days=-1)
if isinstance(expires, timedelta):
expires = datetime.utcnow() + expires
if expires is not None:
expires_str = expires.strftime("%d %%s %Y %H:%M:%S GMT")
expires_str = expires_str % days[expires.month]
expires = expires_str
if max_age is not None:
if hasattr(max_age, "total_seconds"):
max_age = int(max_age.total_seconds())
max_age = "%.0d" % max_age
m = Cookie.Morsel()
def maybe_set(key, value):
if value is not None and value is not False:
m[key] = value
m.set(name, value, value)
maybe_set("path", path)
maybe_set("domain", domain)
maybe_set("comment", comment)
maybe_set("expires", expires)
maybe_set("max-age", max_age)
maybe_set("secure", secure)
maybe_set("httponly", httponly)
self.headers.append("Set-Cookie", m.OutputString()) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | tools/code_coverage/croc.py | python | Coverage.ParseLcovFile | (self, input_filename) | Adds coverage data from a .lcov file.
Args:
input_filename: Input filename. | Adds coverage data from a .lcov file. | [
"Adds",
"coverage",
"data",
"from",
"a",
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"lcov",
"file",
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] | def ParseLcovFile(self, input_filename):
"""Adds coverage data from a .lcov file.
Args:
input_filename: Input filename.
"""
# TODO: All manner of error checking
lcov_file = None
try:
lcov_file = open(input_filename, 'rt')
self.ParseLcovData(lcov_file)
finally:
if lcov_file:
lcov_file.close() | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/framework/python/ops/checkpoint_ops.py | python | load_variable_slot_initializer | (ckpt_path,
old_tensor_name,
primary_partition_info,
new_row_vocab_size,
new_col_vocab_size,
old_row_vocab_file=None,
new_row_vocab_file=None,
old_col_vocab_file=None,
new_col_vocab_file=None,
num_row_oov_buckets=0,
num_col_oov_buckets=0,
initializer=None,
max_rows_in_memory=-1) | return _initializer | Loads pre-trained multi-class slots for linear models from checkpoint.
Wrapper around `load_and_remap_matrix_initializer()` specialized for loading
multi-class slots (such as optimizer accumulators) and remapping them
according to the provided vocab files. See docs for
`load_and_remap_matrix_initializer()` for more details. Takes in a
`variable_scope._PartitionInfo` representing the slot's primary `Variable`'s
partitioning. This is necessary since accumulator `Variable` creation ignores
primary scoping and partitioning information.
Args:
ckpt_path: Path to the TensorFlow checkpoint (version 2, `TensorBundle`)
from which the old matrix `Tensor` will be loaded.
old_tensor_name: Name of the 2-D `Tensor` to load from checkpoint.
primary_partition_info: A `variable_scope._PartitionInfo` containing this
slot's primary `Variable`'s partitioning information. This is used to
calculate the offset and override the partition_info passed to the call to
_initialize.
new_row_vocab_size: `int` specifying the number of entries in
`new_row_vocab_file`. If no row remapping is needed (no row vocab
provided), this should be equal to the number of rows to load from the old
matrix (which can theoretically be smaller than the number of rows in the
old matrix).
new_col_vocab_size: `int` specifying the number of entries in
`new_col_vocab_file`. If no column remapping is needed (no column vocab
provided), this should be equal to the number of columns in the old
matrix.
old_row_vocab_file: A scalar `Tensor` of type `string` containing the
path to the old row vocabulary file. Can be None, which represents no
remapping on the row axis.
new_row_vocab_file: A scalar `Tensor` of type `string` containing the path
to the new row vocabulary file. Can be None, which represents no remapping
on the row axis.
old_col_vocab_file: A scalar `Tensor` of type `string` containing the
path to the old column vocabulary file. Can be None, which represents no
remapping on the column axis.
new_col_vocab_file: A scalar `Tensor` of type `string` containing the path
to the new column vocabulary file. Can be None, which represents no
remapping on the column axis.
num_row_oov_buckets: `int` specifying the number of out-of-vocabulary rows
to append. Must be >= 0.
num_col_oov_buckets: `int` specifying the number of out-of-vocabulary
columns to append. Must be >= 0.
initializer: Initializer function to initialize missing values. Accepts a
1-D tensor as the arg to specify the shape of the returned tensor. If
`None`, defaults to using `zeros_initializer()`.
max_rows_in_memory: `int` specifying the maximum number of rows to load from
the checkpoint at once. If less than or equal to 0, the entire matrix will
be loaded into memory. Setting this arg trades increased disk reads for
lower memory usage.
Returns:
A variable initializer function that should be used to initialize a
(potentially partitioned) `Variable` whose complete shape is
`[new_row_vocab_size + num_row_oov_buckets, new_col_vocab_size +
num_col_oov_buckets]`.
Raises:
TypeError: If `initializer` is specified but not callable. | Loads pre-trained multi-class slots for linear models from checkpoint. | [
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"multi",
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] | def load_variable_slot_initializer(ckpt_path,
old_tensor_name,
primary_partition_info,
new_row_vocab_size,
new_col_vocab_size,
old_row_vocab_file=None,
new_row_vocab_file=None,
old_col_vocab_file=None,
new_col_vocab_file=None,
num_row_oov_buckets=0,
num_col_oov_buckets=0,
initializer=None,
max_rows_in_memory=-1):
"""Loads pre-trained multi-class slots for linear models from checkpoint.
Wrapper around `load_and_remap_matrix_initializer()` specialized for loading
multi-class slots (such as optimizer accumulators) and remapping them
according to the provided vocab files. See docs for
`load_and_remap_matrix_initializer()` for more details. Takes in a
`variable_scope._PartitionInfo` representing the slot's primary `Variable`'s
partitioning. This is necessary since accumulator `Variable` creation ignores
primary scoping and partitioning information.
Args:
ckpt_path: Path to the TensorFlow checkpoint (version 2, `TensorBundle`)
from which the old matrix `Tensor` will be loaded.
old_tensor_name: Name of the 2-D `Tensor` to load from checkpoint.
primary_partition_info: A `variable_scope._PartitionInfo` containing this
slot's primary `Variable`'s partitioning information. This is used to
calculate the offset and override the partition_info passed to the call to
_initialize.
new_row_vocab_size: `int` specifying the number of entries in
`new_row_vocab_file`. If no row remapping is needed (no row vocab
provided), this should be equal to the number of rows to load from the old
matrix (which can theoretically be smaller than the number of rows in the
old matrix).
new_col_vocab_size: `int` specifying the number of entries in
`new_col_vocab_file`. If no column remapping is needed (no column vocab
provided), this should be equal to the number of columns in the old
matrix.
old_row_vocab_file: A scalar `Tensor` of type `string` containing the
path to the old row vocabulary file. Can be None, which represents no
remapping on the row axis.
new_row_vocab_file: A scalar `Tensor` of type `string` containing the path
to the new row vocabulary file. Can be None, which represents no remapping
on the row axis.
old_col_vocab_file: A scalar `Tensor` of type `string` containing the
path to the old column vocabulary file. Can be None, which represents no
remapping on the column axis.
new_col_vocab_file: A scalar `Tensor` of type `string` containing the path
to the new column vocabulary file. Can be None, which represents no
remapping on the column axis.
num_row_oov_buckets: `int` specifying the number of out-of-vocabulary rows
to append. Must be >= 0.
num_col_oov_buckets: `int` specifying the number of out-of-vocabulary
columns to append. Must be >= 0.
initializer: Initializer function to initialize missing values. Accepts a
1-D tensor as the arg to specify the shape of the returned tensor. If
`None`, defaults to using `zeros_initializer()`.
max_rows_in_memory: `int` specifying the maximum number of rows to load from
the checkpoint at once. If less than or equal to 0, the entire matrix will
be loaded into memory. Setting this arg trades increased disk reads for
lower memory usage.
Returns:
A variable initializer function that should be used to initialize a
(potentially partitioned) `Variable` whose complete shape is
`[new_row_vocab_size + num_row_oov_buckets, new_col_vocab_size +
num_col_oov_buckets]`.
Raises:
TypeError: If `initializer` is specified but not callable.
"""
initializer_fn = load_and_remap_matrix_initializer(
ckpt_path=ckpt_path,
old_tensor_name=old_tensor_name,
new_row_vocab_size=new_row_vocab_size,
new_col_vocab_size=new_col_vocab_size,
old_row_vocab_file=old_row_vocab_file,
new_row_vocab_file=new_row_vocab_file,
old_col_vocab_file=old_col_vocab_file,
new_col_vocab_file=new_col_vocab_file,
num_row_oov_buckets=num_row_oov_buckets,
num_col_oov_buckets=num_col_oov_buckets,
initializer=initializer,
max_rows_in_memory=max_rows_in_memory)
def _initializer(shape, dtype=dtypes.float32, partition_info=None):
del partition_info # Unused by this override.
return initializer_fn(shape, dtype, partition_info=primary_partition_info)
return _initializer | [
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openvinotoolkit/openvino | dedcbeafa8b84cccdc55ca64b8da516682b381c7 | tools/pot/openvino/tools/pot/engines/utils.py | python | cast_friendly_names | (nodes) | Process nGraph nodes and sets POT-friendly tensor name
based on friendly_name | Process nGraph nodes and sets POT-friendly tensor name
based on friendly_name | [
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"tensor",
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"friendly_name"
] | def cast_friendly_names(nodes):
""" Process nGraph nodes and sets POT-friendly tensor name
based on friendly_name
"""
for ng_node in nodes:
names = ng_node.get_tensor().get_names()
names.add(ng_node.get_node().friendly_name)
ng_node.get_tensor().set_names(names) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/ed_stc.py | python | EditraStc.GotoBraceMatch | (self) | Jump the caret to the brace opposite of the one the caret is
currently at. If there is no match or the caret currently is not next
to a brace no action is taken.
@return: bool | Jump the caret to the brace opposite of the one the caret is
currently at. If there is no match or the caret currently is not next
to a brace no action is taken.
@return: bool | [
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"""Jump the caret to the brace opposite of the one the caret is
currently at. If there is no match or the caret currently is not next
to a brace no action is taken.
@return: bool
"""
cbrace, brace_opposite = self.GetBracePair()
if -1 in (cbrace, brace_opposite):
return False
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self.GotoPos(brace_opposite)
return True | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/Paste/paste/request.py | python | path_info_split | (path_info) | Splits off the first segment of the path. Returns (first_part,
rest_of_path). first_part can be None (if PATH_INFO is empty), ''
(if PATH_INFO is '/'), or a name without any /'s. rest_of_path
can be '' or a string starting with /. | Splits off the first segment of the path. Returns (first_part,
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(if PATH_INFO is '/'), or a name without any /'s. rest_of_path
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first, rest = path_info.split('/', 1)
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Polidea/SiriusObfuscator | b0e590d8130e97856afe578869b83a209e2b19be | SymbolExtractorAndRenamer/lldb/scripts/Python/static-binding/lldb.py | python | SBTypeSynthetic.IsValid | (self) | return _lldb.SBTypeSynthetic_IsValid(self) | IsValid(self) -> bool | IsValid(self) -> bool | [
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google/nucleus | 68d3947fafba1337f294c0668a6e1c7f3f1273e3 | nucleus/io/vcf.py | python | _create_get_fn_cache | (fields) | return {
field.id: vcf_constants.create_get_fn(field.type, field.number)
for field in fields
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return {
field.id: vcf_constants.create_get_fn(field.type, field.number)
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PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | python/paddle/distributed/auto_parallel/process_mesh.py | python | ProcessMesh.ndim | (self) | return len(self._topology) | r"""
Get the number of dimension of ProcessMesh. | r"""
Get the number of dimension of ProcessMesh. | [
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r"""
Get the number of dimension of ProcessMesh.
"""
return len(self._topology) | [
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microsoft/CNTK | e9396480025b9ca457d26b6f33dd07c474c6aa04 | bindings/python/cntk/io/transforms.py | python | color | (brightness_radius=0.0, contrast_radius=0.0, saturation_radius=0.0) | return cntk_py.reader_color(brightness_radius, contrast_radius, saturation_radius) | Color transform that can be used to pass to `map_features` for data augmentation.
Args:
brightness_radius (float, default 0.0): Radius for brightness change. Must be
set within [0.0, 1.0]. For example, assume brightness_radius = 0.2, a random
number `x` is uniformly drawn from [-0.2, 0.2], and every pixel's value is
added by `x*meanVal`, where meanVal is the mean of the image pixel intensity
combining all color channels.
contrast_radius (float, default 0.0): Radius for contrast change. Must be
set within [0.0, 1.0]. For example, assume contrast_radius = 0.2, a random
number `x` is uniformly drawn from [-0.2, 0.2], and every pixel's value is
multiplied by `1+x`.
saturation_radius (float, default 0.0): Radius for saturation change. Only for
color images and must be set within [0.0, 1.0]. For example, assume
saturation_radius = 0.2, a random number `x` is uniformly drawn from [-0.2, 0.2],
and every pixel's saturation is multiplied by `1+x`.
Returns:
A dictionary-like object describing the mean transform | Color transform that can be used to pass to `map_features` for data augmentation. | [
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'''
Color transform that can be used to pass to `map_features` for data augmentation.
Args:
brightness_radius (float, default 0.0): Radius for brightness change. Must be
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Returns:
A dictionary-like object describing the mean transform
'''
return cntk_py.reader_color(brightness_radius, contrast_radius, saturation_radius) | [
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OSGeo/gdal | 3748fc4ba4fba727492774b2b908a2130c864a83 | swig/python/osgeo/gdal.py | python | MDArray.GetStatistics | (self, *args, **kwargs) | return _gdal.MDArray_GetStatistics(self, *args, **kwargs) | r"""GetStatistics(MDArray self, bool approx_ok=FALSE, bool force=TRUE, GDALProgressFunc callback=0, void * callback_data=None) -> Statistics | r"""GetStatistics(MDArray self, bool approx_ok=FALSE, bool force=TRUE, GDALProgressFunc callback=0, void * callback_data=None) -> Statistics | [
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r"""GetStatistics(MDArray self, bool approx_ok=FALSE, bool force=TRUE, GDALProgressFunc callback=0, void * callback_data=None) -> Statistics"""
return _gdal.MDArray_GetStatistics(self, *args, **kwargs) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/site-packages/boto3/resources/collection.py | python | ResourceCollection.page_size | (self, count) | return self._clone(page_size=count) | Fetch at most this many resources per service request.
>>> for obj in s3.Bucket('boto3').objects.page_size(100):
... print(obj.key)
:type count: int
:param count: Fetch this many items per request
:rtype: :py:class:`ResourceCollection` | Fetch at most this many resources per service request. | [
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"""
Fetch at most this many resources per service request.
>>> for obj in s3.Bucket('boto3').objects.page_size(100):
... print(obj.key)
:type count: int
:param count: Fetch this many items per request
:rtype: :py:class:`ResourceCollection`
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return self._clone(page_size=count) | [
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PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | python/paddle/fluid/incubate/fleet/collective/__init__.py | python | CollectiveOptimizer.minimize | (self,
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startup_program=None,
parameter_list=None,
no_grad_set=None) | return optimize_ops, param_grads | minimize a program through loss
Args:
loss (Variable|Variable List): loss variable or loss variable list to run optimization.
startup_program (Program): startup_program for initializing parameters
in `parameter_list`.
parameter_list (list): list of Variables to update.
no_grad_set (set|None): set of Variables should be ignored.
Returns:
tuple: (optimize_ops, params_grads) which are, list of operators appended;
and list of (param, grad) Variables pair for optimization.
Note that in parameter server mode, a worker will not get anything about optimize_os
Because optimizer algorithms run on pserver side. We will make this usable in pserver
process, but currently the optimization part is written into Fleet(). A user does not
need to care about how to startup a pserver node. | minimize a program through loss
Args:
loss (Variable|Variable List): loss variable or loss variable list to run optimization.
startup_program (Program): startup_program for initializing parameters
in `parameter_list`.
parameter_list (list): list of Variables to update.
no_grad_set (set|None): set of Variables should be ignored.
Returns:
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Note that in parameter server mode, a worker will not get anything about optimize_os
Because optimizer algorithms run on pserver side. We will make this usable in pserver
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parameter_list=None,
no_grad_set=None):
"""
minimize a program through loss
Args:
loss (Variable|Variable List): loss variable or loss variable list to run optimization.
startup_program (Program): startup_program for initializing parameters
in `parameter_list`.
parameter_list (list): list of Variables to update.
no_grad_set (set|None): set of Variables should be ignored.
Returns:
tuple: (optimize_ops, params_grads) which are, list of operators appended;
and list of (param, grad) Variables pair for optimization.
Note that in parameter server mode, a worker will not get anything about optimize_os
Because optimizer algorithms run on pserver side. We will make this usable in pserver
process, but currently the optimization part is written into Fleet(). A user does not
need to care about how to startup a pserver node.
"""
# check optimizer conflicts
if self._forward_recompute:
if self._recompute_checkpoints == []:
raise ValueError("please set strategy.recompute_checkpoints"
"when set strategy.forward_recompute as True")
if self._optimizer.__class__.__name__ in [
"RecomputeOptimizer", "OptimizerWithMixedPrecision"
]:
self.raiseOptimizeError("forward_recompute",
self._optimizer.__class__.__name__)
self._optimizer = \
fluid.optimizer.RecomputeOptimizer(self._optimizer)
self._optimizer._set_checkpoints(self._recompute_checkpoints)
if self._use_amp:
if self._optimizer.__class__.__name__ in [
"OptimizerWithMixedPrecision", "DGCMomentumOptimizer"
]:
self.raiseOptimizeError("mixed_precision",
self._optimizer.__class__.__name__)
self._optimizer = fluid.contrib.mixed_precision.decorate(
self._optimizer,
init_loss_scaling=self._amp_loss_scaling,
use_dynamic_loss_scaling=True)
main_program = loss.block.program
if startup_program is None:
startup_program = fluid.default_startup_program()
fleet.startup_program = startup_program
self._loss = loss
self._check_collective_mode(main_program, self._optimizer,
self._strategy)
optimize_ops, param_grads = self._optimizer.minimize(
loss, startup_program, parameter_list, no_grad_set=no_grad_set)
fleet._origin_program = main_program.clone(for_test=False)
fleet._transpiled_program = main_program
fleet.main_program = self._try_to_compile(startup_program, main_program)
return optimize_ops, param_grads | [
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PyMesh/PyMesh | 384ba882b7558ba6e8653ed263c419226c22bddf | python/pymesh/snap_rounding.py | python | snap_rounding | (points, segments, pixel_size, use_iterative=True) | return vertices, edges | 2D snap rounding.
Args:
points (``numpy.ndarray``): Input points.
segments (``numpy.ndarray``): Input segments.
pixel_size (``float``): Pixel size.
use_iterative (``bool``): Whether to use iterative snap rounding.
Returns:
2 values are returned.
* ``vertices``: Snap rounded vertices.
* ``edges``: Edge connecting vertices. | 2D snap rounding. | [
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""" 2D snap rounding.
Args:
points (``numpy.ndarray``): Input points.
segments (``numpy.ndarray``): Input segments.
pixel_size (``float``): Pixel size.
use_iterative (``bool``): Whether to use iterative snap rounding.
Returns:
2 values are returned.
* ``vertices``: Snap rounded vertices.
* ``edges``: Edge connecting vertices.
"""
engine = PyMesh.SnapRounding2()
engine.points = points
engine.segments = segments
engine.run(pixel_size, use_iterative)
vertices = engine.vertices
edges = engine.edges
vertices, edges, __ = remove_duplicated_vertices_raw(
vertices, edges, tol=pixel_size/2)
return vertices, edges | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/pdb.py | python | Pdb.lookupmodule | (self, filename) | return None | Helper function for break/clear parsing -- may be overridden.
lookupmodule() translates (possibly incomplete) file or module name
into an absolute file name. | Helper function for break/clear parsing -- may be overridden. | [
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"""Helper function for break/clear parsing -- may be overridden.
lookupmodule() translates (possibly incomplete) file or module name
into an absolute file name.
"""
if os.path.isabs(filename) and os.path.exists(filename):
return filename
f = os.path.join(sys.path[0], filename)
if os.path.exists(f) and self.canonic(f) == self.mainpyfile:
return f
root, ext = os.path.splitext(filename)
if ext == '':
filename = filename + '.py'
if os.path.isabs(filename):
return filename
for dirname in sys.path:
while os.path.islink(dirname):
dirname = os.readlink(dirname)
fullname = os.path.join(dirname, filename)
if os.path.exists(fullname):
return fullname
return None | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/lib2to3/pytree.py | python | Base._eq | (self, other) | Compare two nodes for equality.
This is called by __eq__ and __ne__. It is only called if the two nodes
have the same type. This must be implemented by the concrete subclass.
Nodes should be considered equal if they have the same structure,
ignoring the prefix string and other context information. | Compare two nodes for equality. | [
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"""
Compare two nodes for equality.
This is called by __eq__ and __ne__. It is only called if the two nodes
have the same type. This must be implemented by the concrete subclass.
Nodes should be considered equal if they have the same structure,
ignoring the prefix string and other context information.
"""
raise NotImplementedError | [
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uzh-rpg/rpg_svo | d6161063b47f36ce78252ee4c4fedf3f6d8f2898 | svo_analysis/src/svo_analysis/tum_benchmark_tools/evaluate_rpe.py | python | percentile | (seq,q) | return seq_sorted[int((len(seq_sorted)-1)*q)] | Return the q-percentile of a list | Return the q-percentile of a list | [
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"""
Return the q-percentile of a list
"""
seq_sorted = list(seq)
seq_sorted.sort()
return seq_sorted[int((len(seq_sorted)-1)*q)] | [
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cluebotng/cluebotng | 2ed38a518c1019f6b7b03e33b487f96f8df617b0 | fabfile.py | python | _stop | () | Internal function, calls jstop on the grid jobs | Internal function, calls jstop on the grid jobs | [
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'''
Internal function, calls jstop on the grid jobs
'''
sudo('jstop cbng_bot | true')
sudo('jstop cbng_core | true') | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/keras/engine/functional.py | python | _map_graph_network | (inputs, outputs) | return network_nodes, nodes_by_depth, layers, layers_by_depth | Validates a network's topology and gather its layers and nodes.
Args:
inputs: List of input tensors.
outputs: List of outputs tensors.
Returns:
A tuple `(nodes, nodes_by_depth, layers, layers_by_depth)`.
- nodes: list of Node instances.
- nodes_by_depth: dict mapping ints (depth) to lists of node instances.
- layers: list of Layer instances.
- layers_by_depth: dict mapping ints (depth) to lists of layer instances.
Raises:
ValueError: In case the network is not valid (e.g. disconnected graph). | Validates a network's topology and gather its layers and nodes. | [
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"""Validates a network's topology and gather its layers and nodes.
Args:
inputs: List of input tensors.
outputs: List of outputs tensors.
Returns:
A tuple `(nodes, nodes_by_depth, layers, layers_by_depth)`.
- nodes: list of Node instances.
- nodes_by_depth: dict mapping ints (depth) to lists of node instances.
- layers: list of Layer instances.
- layers_by_depth: dict mapping ints (depth) to lists of layer instances.
Raises:
ValueError: In case the network is not valid (e.g. disconnected graph).
"""
# "depth" is number of layers between output Node and the Node.
# Nodes are ordered from inputs -> outputs.
nodes_in_decreasing_depth, layer_indices = _build_map(outputs)
network_nodes = {
_make_node_key(node.layer.name, node.layer._inbound_nodes.index(node))
for node in nodes_in_decreasing_depth
}
nodes_depths = {} # dict {node: depth value}
layers_depths = {} # dict {layer: depth value}
for node in reversed(nodes_in_decreasing_depth):
# If the depth is not set, the node has no outbound nodes (depth 0).
depth = nodes_depths.setdefault(node, 0)
# Update the depth of the corresponding layer
previous_depth = layers_depths.get(node.layer, 0)
# If we've seen this layer before at a higher depth,
# we should use that depth instead of the node depth.
# This is necessary for shared layers that have inputs at different
# depth levels in the graph.
depth = max(depth, previous_depth)
layers_depths[node.layer] = depth
nodes_depths[node] = depth
# Update the depth of inbound nodes.
# The "depth" of a node is the max of the depths
# of all nodes it is connected to + 1.
for node_dep in node.parent_nodes:
previous_depth = nodes_depths.get(node_dep, 0)
nodes_depths[node_dep] = max(depth + 1, previous_depth)
# Handle inputs that are not connected to outputs.
# We do not error out here because the inputs may be used to compute losses
# and metrics.
for input_t in inputs:
input_layer = input_t._keras_history[0]
if input_layer not in layers_depths:
layers_depths[input_layer] = 0
layer_indices[input_layer] = -1
nodes_depths[input_layer._inbound_nodes[0]] = 0
network_nodes.add(_make_node_key(input_layer.name, 0))
# Build a dict {depth: list of nodes with this depth}
nodes_by_depth = collections.defaultdict(list)
for node, depth in nodes_depths.items():
nodes_by_depth[depth].append(node)
# Build a dict {depth: list of layers with this depth}
layers_by_depth = collections.defaultdict(list)
for layer, depth in layers_depths.items():
layers_by_depth[depth].append(layer)
# Get sorted list of layer depths.
depth_keys = list(layers_by_depth.keys())
depth_keys.sort(reverse=True)
# Set self.layers ordered by depth.
layers = []
for depth in depth_keys:
layers_for_depth = layers_by_depth[depth]
# Network.layers needs to have a deterministic order:
# here we order them by traversal order.
layers_for_depth.sort(key=lambda x: layer_indices[x])
layers.extend(layers_for_depth)
# Get sorted list of node depths.
depth_keys = list(nodes_by_depth.keys())
depth_keys.sort(reverse=True)
# Check that all tensors required are computable.
# computable_tensors: all tensors in the graph
# that can be computed from the inputs provided.
computable_tensors = set()
for x in inputs:
computable_tensors.add(id(x))
layers_with_complete_input = [] # To provide a better error msg.
for depth in depth_keys:
for node in nodes_by_depth[depth]:
layer = node.layer
if layer and not node.is_input:
for x in nest.flatten(node.keras_inputs):
if id(x) not in computable_tensors:
raise ValueError('Graph disconnected: '
'cannot obtain value for tensor ' + str(x) +
' at layer "' + layer.name + '". '
'The following previous layers '
'were accessed without issue: ' +
str(layers_with_complete_input))
for x in nest.flatten(node.outputs):
computable_tensors.add(id(x))
layers_with_complete_input.append(layer.name)
# Ensure name unicity, which will be crucial for serialization
# (since serialized nodes refer to layers by their name).
all_names = [layer.name for layer in layers]
for name in all_names:
if all_names.count(name) != 1:
raise ValueError('The name "' + name + '" is used ' +
str(all_names.count(name)) + ' times in the model. '
'All layer names should be unique.')
return network_nodes, nodes_by_depth, layers, layers_by_depth | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/distutils/dist.py | python | Distribution.get_command_class | (self, command) | Return the class that implements the Distutils command named by
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command is mentioned there, we fetch the class object from the
dictionary and return it. Otherwise we load the command module
("distutils.command." + command) and fetch the command class from
the module. The loaded class is also stored in 'cmdclass'
to speed future calls to 'get_command_class()'.
Raises DistutilsModuleError if the expected module could not be
found, or if that module does not define the expected class. | Return the class that implements the Distutils command named by
'command'. First we check the 'cmdclass' dictionary; if the
command is mentioned there, we fetch the class object from the
dictionary and return it. Otherwise we load the command module
("distutils.command." + command) and fetch the command class from
the module. The loaded class is also stored in 'cmdclass'
to speed future calls to 'get_command_class()'. | [
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dictionary and return it. Otherwise we load the command module
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Raises DistutilsModuleError if the expected module could not be
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/combo.py | python | ComboCtrl.GetBitmapNormal | (*args, **kwargs) | return _combo.ComboCtrl_GetBitmapNormal(*args, **kwargs) | GetBitmapNormal(self) -> Bitmap | GetBitmapNormal(self) -> Bitmap | [
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funnyzhou/Adaptive_Feeding | 9c78182331d8c0ea28de47226e805776c638d46f | lib/datasets/pascal_voc.py | python | pascal_voc.image_path_from_index | (self, index) | return image_path | Construct an image path from the image's "index" identifier. | Construct an image path from the image's "index" identifier. | [
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natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/external/bazel_tools/third_party/py/gflags/__init__.py | python | FloatParser.Convert | (self, argument) | return float(argument) | Converts argument to a float; raises ValueError on errors. | Converts argument to a float; raises ValueError on errors. | [
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/python2_version/klampt/robotsim.py | python | TerrainModel.getName | (self) | return _robotsim.TerrainModel_getName(self) | getName(TerrainModel self) -> char const * | getName(TerrainModel self) -> char const * | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_core.py | python | Image.IsTransparent | (*args, **kwargs) | return _core_.Image_IsTransparent(*args, **kwargs) | IsTransparent(self, int x, int y, byte threshold=IMAGE_ALPHA_THRESHOLD) -> bool
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Cisco-Talos/moflow | ed71dfb0540d9e0d7a4c72f0881b58958d573728 | BAP-0.7-moflow/libtracewrap/libtrace/protobuf/python/google/protobuf/internal/python_message.py | python | _AddSlots | (message_descriptor, dictionary) | Adds a __slots__ entry to dictionary, containing the names of all valid
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PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | python/paddle/fluid/layers/sequence_lod.py | python | sequence_expand | (x, y, ref_level=-1, name=None) | return tmp | r"""
:api_attr: Static Graph
Sequence Expand Layer. This layer will expand the input variable ``x`` \
according to specified level ``ref_level`` lod of ``y``. Please note that \
the lod level of ``x`` is at most 1. If the lod level of ``x`` is 1, than \
the size of lod of ``x`` must be equal to the length of ``ref_level`` lod \
of ``y``. If the lod level of ``x`` is 0, then the first dim of ``x`` should \
be equal to the size of ``ref_level`` of ``y``. The rank of **x** is at least 2. \
When rank of ``x`` is greater than 2, then it would be viewed as a 2-D tensor.
Please note that the input ``x`` should be LodTensor or Tensor, \
and input ``y`` must be LodTensor.
Following examples will explain how sequence_expand works:
.. code-block:: text
Case 1
Consider 2 sequences [a][b] and [c][d], now we want to expand them to [a][b], [a][b], [c][d] and [c][d].
Sequence [a][b] expand twice and [c][d] expands twice, so the lod which according to is [2, 2].
Input x is a 1-level LoDTensor:
x.lod = [[2, 2]] #lod based on length may be easier to understand
x.data = [[a], [b], [c], [d]]
x.dims = [4, 1]
input y is a LoDTensor:
y.lod = [[2, 2], #the 0th level lod, according to this level
[3, 3, 1, 1]] #the 1st level lod, it has nothing to do with this level
ref_level: 0
then output is a 1-level LoDTensor out:
out.lod = [[2, 2, 2, 2]] #lod based on offset
out.data = [[a], [b], [a], [b], [c], [d], [c], [d]]
out.dims = [8, 1]
Case 2
Consider 3 sequences [a], [b], [c], now we want to expand them to [a][a], [c][c][c].
It's obvious that the lod info of expanded sequences is [2, 0, 3].
x is a Tensor:
x.data = [[a], [b], [c]]
x.dims = [3, 1]
y is a LoDTensor:
y.lod = [[2, 0, 3]]
ref_level: -1
then output is a 1-level LodTensor:
out.data = [[a], [a], [c], [c], [c]]
out.dims = [5, 1]
Args:
x (Variable): The input variable which is a Tensor or LoDTensor, with the \
dims ``[M, K]``. The lod level is at most 1. The data type should be \
float32, float64, int32 or int64.
y (Variable): The input variable which is a LoDTensor, the lod level is \
at least 1.
ref_level (int): Lod level of ``y`` to be referred by ``x``. If set to -1, \
refer the last level of lod.
name(str, optional): For detailed information, please refer \
to :ref:`api_guide_Name`. Usually name is no need to set and \
None by default.
Returns: The expanded variable which is a LoDTensor, with dims ``[N, K]``. \
``N`` depends on the lod info of ``x`` and ``y``. \
The data type is same as input.
Return Type: Variable
Examples:
.. code-block:: python
import paddle
from paddle import fluid
paddle.enable_static()
import numpy as np
x = paddle.static.data(name='x', shape=[4, 1], dtype='float32')
y = paddle.static.data(name='y', shape=[8, 1],
dtype='float32', lod_level=1)
out = paddle.static.nn.sequence_expand(x=x, y=y, ref_level=0)
exe = paddle.static.Executor(fluid.CPUPlace())
place = paddle.CPUPlace()
np_data = np.array([[1], [2], [3], [4]]).astype('float32')
x_lod_tensor = fluid.create_lod_tensor(np_data, [[2, 2]], place)
print(x_lod_tensor)
#lod: [[0, 2, 4]]
# dim: 4, 1
# layout: NCHW
# dtype: float
# data: [1 2 3 4]
np_data = np.array([[1], [2], [3], [4], [5], [6], [7], [8]]).astype('float32')
y_lod_tensor = fluid.create_lod_tensor(np_data, [[2, 2], [3,3,1,1]], place)
print(y_lod_tensor)
#lod: [[0, 2, 4][0, 3, 6, 7, 8]]
# dim: 8, 1
# layout: NCHW
# dtype: int64_t
# data: [0 0 1 1 1 1 1 0]
out_main = exe.run(fluid.default_main_program(),
feed={'x': x_lod_tensor, 'y': y_lod_tensor},
fetch_list=[out], return_numpy=False)
print(out_main[0])
#lod: [[0, 2, 4, 6, 8]]
# dim: 8, 1
# layout: NCHW
# dtype: float
# data: [1 2 1 2 3 4 3 4] | r"""
:api_attr: Static Graph | [
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r"""
:api_attr: Static Graph
Sequence Expand Layer. This layer will expand the input variable ``x`` \
according to specified level ``ref_level`` lod of ``y``. Please note that \
the lod level of ``x`` is at most 1. If the lod level of ``x`` is 1, than \
the size of lod of ``x`` must be equal to the length of ``ref_level`` lod \
of ``y``. If the lod level of ``x`` is 0, then the first dim of ``x`` should \
be equal to the size of ``ref_level`` of ``y``. The rank of **x** is at least 2. \
When rank of ``x`` is greater than 2, then it would be viewed as a 2-D tensor.
Please note that the input ``x`` should be LodTensor or Tensor, \
and input ``y`` must be LodTensor.
Following examples will explain how sequence_expand works:
.. code-block:: text
Case 1
Consider 2 sequences [a][b] and [c][d], now we want to expand them to [a][b], [a][b], [c][d] and [c][d].
Sequence [a][b] expand twice and [c][d] expands twice, so the lod which according to is [2, 2].
Input x is a 1-level LoDTensor:
x.lod = [[2, 2]] #lod based on length may be easier to understand
x.data = [[a], [b], [c], [d]]
x.dims = [4, 1]
input y is a LoDTensor:
y.lod = [[2, 2], #the 0th level lod, according to this level
[3, 3, 1, 1]] #the 1st level lod, it has nothing to do with this level
ref_level: 0
then output is a 1-level LoDTensor out:
out.lod = [[2, 2, 2, 2]] #lod based on offset
out.data = [[a], [b], [a], [b], [c], [d], [c], [d]]
out.dims = [8, 1]
Case 2
Consider 3 sequences [a], [b], [c], now we want to expand them to [a][a], [c][c][c].
It's obvious that the lod info of expanded sequences is [2, 0, 3].
x is a Tensor:
x.data = [[a], [b], [c]]
x.dims = [3, 1]
y is a LoDTensor:
y.lod = [[2, 0, 3]]
ref_level: -1
then output is a 1-level LodTensor:
out.data = [[a], [a], [c], [c], [c]]
out.dims = [5, 1]
Args:
x (Variable): The input variable which is a Tensor or LoDTensor, with the \
dims ``[M, K]``. The lod level is at most 1. The data type should be \
float32, float64, int32 or int64.
y (Variable): The input variable which is a LoDTensor, the lod level is \
at least 1.
ref_level (int): Lod level of ``y`` to be referred by ``x``. If set to -1, \
refer the last level of lod.
name(str, optional): For detailed information, please refer \
to :ref:`api_guide_Name`. Usually name is no need to set and \
None by default.
Returns: The expanded variable which is a LoDTensor, with dims ``[N, K]``. \
``N`` depends on the lod info of ``x`` and ``y``. \
The data type is same as input.
Return Type: Variable
Examples:
.. code-block:: python
import paddle
from paddle import fluid
paddle.enable_static()
import numpy as np
x = paddle.static.data(name='x', shape=[4, 1], dtype='float32')
y = paddle.static.data(name='y', shape=[8, 1],
dtype='float32', lod_level=1)
out = paddle.static.nn.sequence_expand(x=x, y=y, ref_level=0)
exe = paddle.static.Executor(fluid.CPUPlace())
place = paddle.CPUPlace()
np_data = np.array([[1], [2], [3], [4]]).astype('float32')
x_lod_tensor = fluid.create_lod_tensor(np_data, [[2, 2]], place)
print(x_lod_tensor)
#lod: [[0, 2, 4]]
# dim: 4, 1
# layout: NCHW
# dtype: float
# data: [1 2 3 4]
np_data = np.array([[1], [2], [3], [4], [5], [6], [7], [8]]).astype('float32')
y_lod_tensor = fluid.create_lod_tensor(np_data, [[2, 2], [3,3,1,1]], place)
print(y_lod_tensor)
#lod: [[0, 2, 4][0, 3, 6, 7, 8]]
# dim: 8, 1
# layout: NCHW
# dtype: int64_t
# data: [0 0 1 1 1 1 1 0]
out_main = exe.run(fluid.default_main_program(),
feed={'x': x_lod_tensor, 'y': y_lod_tensor},
fetch_list=[out], return_numpy=False)
print(out_main[0])
#lod: [[0, 2, 4, 6, 8]]
# dim: 8, 1
# layout: NCHW
# dtype: float
# data: [1 2 1 2 3 4 3 4]
"""
assert not in_dygraph_mode(), (
"sequence layer is not supported in dygraph mode yet.")
check_variable_and_dtype(x, 'x', ['float32', 'float64', 'int32', 'int64'],
'sequence_expand')
helper = LayerHelper('sequence_expand', **locals())
dtype = helper.input_dtype(input_param_name='x')
tmp = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type='sequence_expand',
inputs={'X': x,
'Y': y},
outputs={'Out': tmp},
attrs={'ref_level': ref_level})
return tmp | [
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"""Returns list of paths of files that contain |string_a| or |string_b|.
Uses --name-only to print the file paths. The default behavior of git grep
is to OR together multiple patterns.
Args:
string_a: A string to search for (not a regular expression).
string_b: As above.
Returns:
A list of file paths as strings.
"""
matching_files = subprocess.check_output([
'git', 'grep', '--name-only', '--fixed-strings', '-e', string_a,
'-e', string_b])
files_list = matching_files.split('\n')
# The output ends in a newline, so slice that off.
files_list = files_list[:-1]
return files_list | [
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LiquidPlayer/LiquidCore | 9405979363f2353ac9a71ad8ab59685dd7f919c9 | deps/node-10.15.3/tools/cpplint.py | python | NestingState.Update | (self, filename, clean_lines, linenum, error) | Update nesting state with current line.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found. | Update nesting state with current line. | [
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"""Update nesting state with current line.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
error: The function to call with any errors found.
"""
line = clean_lines.elided[linenum]
# Remember top of the previous nesting stack.
#
# The stack is always pushed/popped and not modified in place, so
# we can just do a shallow copy instead of copy.deepcopy. Using
# deepcopy would slow down cpplint by ~28%.
if self.stack:
self.previous_stack_top = self.stack[-1]
else:
self.previous_stack_top = None
# Update pp_stack
self.UpdatePreprocessor(line)
# Count parentheses. This is to avoid adding struct arguments to
# the nesting stack.
if self.stack:
inner_block = self.stack[-1]
depth_change = line.count('(') - line.count(')')
inner_block.open_parentheses += depth_change
# Also check if we are starting or ending an inline assembly block.
if inner_block.inline_asm in (_NO_ASM, _END_ASM):
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inner_block.inline_asm = _INSIDE_ASM
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# Not entering assembly block. If previous line was _END_ASM,
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inner_block.inline_asm = _NO_ASM
elif (inner_block.inline_asm == _INSIDE_ASM and
inner_block.open_parentheses == 0):
# Exit assembly block
inner_block.inline_asm = _END_ASM
# Consume namespace declaration at the beginning of the line. Do
# this in a loop so that we catch same line declarations like this:
# namespace proto2 { namespace bridge { class MessageSet; } }
while True:
# Match start of namespace. The "\b\s*" below catches namespace
# declarations even if it weren't followed by a whitespace, this
# is so that we don't confuse our namespace checker. The
# missing spaces will be flagged by CheckSpacing.
namespace_decl_match = Match(r'^\s*namespace\b\s*([:\w]+)?(.*)$', line)
if not namespace_decl_match:
break
new_namespace = _NamespaceInfo(namespace_decl_match.group(1), linenum)
self.stack.append(new_namespace)
line = namespace_decl_match.group(2)
if line.find('{') != -1:
new_namespace.seen_open_brace = True
line = line[line.find('{') + 1:]
# Look for a class declaration in whatever is left of the line
# after parsing namespaces. The regexp accounts for decorated classes
# such as in:
# class LOCKABLE API Object {
# };
class_decl_match = Match(
r'^(\s*(?:template\s*<[\w\s<>,:=]*>\s*)?'
r'(class|struct)\s+(?:[A-Z_]+\s+)*(\w+(?:::\w+)*))'
r'(.*)$', line)
if (class_decl_match and
(not self.stack or self.stack[-1].open_parentheses == 0)):
# We do not want to accept classes that are actually template arguments:
# template <class Ignore1,
# class Ignore2 = Default<Args>,
# template <Args> class Ignore3>
# void Function() {};
#
# To avoid template argument cases, we scan forward and look for
# an unmatched '>'. If we see one, assume we are inside a
# template argument list.
end_declaration = len(class_decl_match.group(1))
if not self.InTemplateArgumentList(clean_lines, linenum, end_declaration):
self.stack.append(_ClassInfo(
class_decl_match.group(3), class_decl_match.group(2),
clean_lines, linenum))
line = class_decl_match.group(4)
# If we have not yet seen the opening brace for the innermost block,
# run checks here.
if not self.SeenOpenBrace():
self.stack[-1].CheckBegin(filename, clean_lines, linenum, error)
# Update access control if we are inside a class/struct
if self.stack and isinstance(self.stack[-1], _ClassInfo):
classinfo = self.stack[-1]
access_match = Match(
r'^(.*)\b(public|private|protected|signals)(\s+(?:slots\s*)?)?'
r':(?:[^:]|$)',
line)
if access_match:
classinfo.access = access_match.group(2)
# Check that access keywords are indented +1 space. Skip this
# check if the keywords are not preceded by whitespaces.
indent = access_match.group(1)
if (len(indent) != classinfo.class_indent + 1 and
Match(r'^\s*$', indent)):
if classinfo.is_struct:
parent = 'struct ' + classinfo.name
else:
parent = 'class ' + classinfo.name
slots = ''
if access_match.group(3):
slots = access_match.group(3)
error(filename, linenum, 'whitespace/indent', 3,
'%s%s: should be indented +1 space inside %s' % (
access_match.group(2), slots, parent))
# Consume braces or semicolons from what's left of the line
while True:
# Match first brace, semicolon, or closed parenthesis.
matched = Match(r'^[^{;)}]*([{;)}])(.*)$', line)
if not matched:
break
token = matched.group(1)
if token == '{':
# If namespace or class hasn't seen a opening brace yet, mark
# namespace/class head as complete. Push a new block onto the
# stack otherwise.
if not self.SeenOpenBrace():
self.stack[-1].seen_open_brace = True
elif Match(r'^extern\s*"[^"]*"\s*\{', line):
self.stack.append(_ExternCInfo(linenum))
else:
self.stack.append(_BlockInfo(linenum, True))
if _MATCH_ASM.match(line):
self.stack[-1].inline_asm = _BLOCK_ASM
elif token == ';' or token == ')':
# If we haven't seen an opening brace yet, but we already saw
# a semicolon, this is probably a forward declaration. Pop
# the stack for these.
#
# Similarly, if we haven't seen an opening brace yet, but we
# already saw a closing parenthesis, then these are probably
# function arguments with extra "class" or "struct" keywords.
# Also pop these stack for these.
if not self.SeenOpenBrace():
self.stack.pop()
else: # token == '}'
# Perform end of block checks and pop the stack.
if self.stack:
self.stack[-1].CheckEnd(filename, clean_lines, linenum, error)
self.stack.pop()
line = matched.group(2) | [
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Samsung/veles | 95ed733c2e49bc011ad98ccf2416ecec23fbf352 | libVeles/cpplint.py | python | ReplaceableCheck | (operator, macro, line) | return Match(match_this, line) and not Search(r'NULL|&&|\|\|', line) | Determine whether a basic CHECK can be replaced with a more specific one.
For example suggest using CHECK_EQ instead of CHECK(a == b) and
similarly for CHECK_GE, CHECK_GT, CHECK_LE, CHECK_LT, CHECK_NE.
Args:
operator: The C++ operator used in the CHECK.
macro: The CHECK or EXPECT macro being called.
line: The current source line.
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True if the CHECK can be replaced with a more specific one. | Determine whether a basic CHECK can be replaced with a more specific one. | [
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] | https://github.com/Samsung/veles/blob/95ed733c2e49bc011ad98ccf2416ecec23fbf352/libVeles/cpplint.py#L2672-L2704 | |
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/fileinput.py | python | fileno | () | return _state.fileno() | Return the file number of the current file. When no file is currently
opened, returns -1. | Return the file number of the current file. When no file is currently
opened, returns -1. | [
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"""
Return the file number of the current file. When no file is currently
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"""
if not _state:
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return _state.fileno() | [
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llvm-mirror/lldb | d01083a850f577b85501a0902b52fd0930de72c7 | third_party/Python/module/pexpect-4.6/pexpect/screen.py | python | screen.pretty | (self) | return top_bot + u'\n'.join([u'|'+line+u'|' for line in unicode(self).split(u'\n')]) + u'\n' + top_bot | This returns a copy of the screen as a unicode string with an ASCII
text box around the screen border. This is similar to
__str__/__unicode__ except that it adds a box. | This returns a copy of the screen as a unicode string with an ASCII
text box around the screen border. This is similar to
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'''This returns a copy of the screen as a unicode string with an ASCII
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__str__/__unicode__ except that it adds a box.'''
top_bot = u'+' + u'-'*self.cols + u'+\n'
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shedskin/shedskin | ae88dbca7b1d9671cd8be448cb0b497122758936 | examples/block.py | python | easytest | () | Tests block code with N=3, f=0.01 on a tiny example.
>>> easytest() # doctest:+NORMALIZE_WHITESPACE
#Symbol Count Codeword
000 (0.97) 1
001 (0.0098) 001
010 (0.0098) 010
011 (9.9e-05) 00001
100 (0.0098) 011
101 (9.9e-05) 00010
110 (9.9e-05) 00011
111 (1e-06) 00000
zipped = 1001010000010110111
decoded = ['000', '001', '010', '011', '100', '100', '000']
OK! | Tests block code with N=3, f=0.01 on a tiny example.
>>> easytest() # doctest:+NORMALIZE_WHITESPACE
#Symbol Count Codeword
000 (0.97) 1
001 (0.0098) 001
010 (0.0098) 010
011 (9.9e-05) 00001
100 (0.0098) 011
101 (9.9e-05) 00010
110 (9.9e-05) 00011
111 (1e-06) 00000
zipped = 1001010000010110111
decoded = ['000', '001', '010', '011', '100', '100', '000']
OK! | [
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"""
Tests block code with N=3, f=0.01 on a tiny example.
>>> easytest() # doctest:+NORMALIZE_WHITESPACE
#Symbol Count Codeword
000 (0.97) 1
001 (0.0098) 001
010 (0.0098) 010
011 (9.9e-05) 00001
100 (0.0098) 011
101 (9.9e-05) 00010
110 (9.9e-05) 00011
111 (1e-06) 00000
zipped = 1001010000010110111
decoded = ['000', '001', '010', '011', '100', '100', '000']
OK!
"""
N=3
f=0.01
probs = findprobs(f,N)
# if len(probs) > 999 :
# sys.setrecursionlimit( len(probs)+100 )
symbols = makenodes(probs) # makenodes is defined at the bottom of Huffman3 package
root = iterate(symbols) # make huffman code and put it into the symbols' nodes, and return the root of the decoding tree
symbols.sort(lambda x, y: cmp(x.index, y.index)) # sort by index
for co in symbols : # and write the answer
co.report()
source = ['000','001','010','011','100','100','000']
zipped = encode(source, symbols)
print "zipped =",zipped
answer = decode( zipped, root )
print "decoded =",answer
if ( source != answer ):
print "ERROR"
else:
print "OK!"
pass | [
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] | https://github.com/shedskin/shedskin/blob/ae88dbca7b1d9671cd8be448cb0b497122758936/examples/block.py#L332-L370 | ||
windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/lib-tk/turtle.py | python | TNavigator.back | (self, distance) | Move the turtle backward by distance.
Aliases: back | backward | bk
Argument:
distance -- a number
Move the turtle backward by distance ,opposite to the direction the
turtle is headed. Do not change the turtle's heading.
Example (for a Turtle instance named turtle):
>>> turtle.position()
(0.00, 0.00)
>>> turtle.backward(30)
>>> turtle.position()
(-30.00, 0.00) | Move the turtle backward by distance. | [
"Move",
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"backward",
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] | def back(self, distance):
"""Move the turtle backward by distance.
Aliases: back | backward | bk
Argument:
distance -- a number
Move the turtle backward by distance ,opposite to the direction the
turtle is headed. Do not change the turtle's heading.
Example (for a Turtle instance named turtle):
>>> turtle.position()
(0.00, 0.00)
>>> turtle.backward(30)
>>> turtle.position()
(-30.00, 0.00)
"""
self._go(-distance) | [
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