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wlanjie/AndroidFFmpeg
|
7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf
|
tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/distutils/archive_util.py
|
python
|
_get_uid
|
(name)
|
return None
|
Returns an uid, given a user name.
|
Returns an uid, given a user name.
|
[
"Returns",
"an",
"uid",
"given",
"a",
"user",
"name",
"."
] |
def _get_uid(name):
"""Returns an uid, given a user name."""
if getpwnam is None or name is None:
return None
try:
result = getpwnam(name)
except KeyError:
result = None
if result is not None:
return result[2]
return None
|
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https://github.com/wlanjie/AndroidFFmpeg/blob/7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf/tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/distutils/archive_util.py#L39-L49
|
|
catboost/catboost
|
167f64f237114a4d10b2b4ee42adb4569137debe
|
contrib/python/scipy/scipy/stats/stats.py
|
python
|
ranksums
|
(x, y)
|
return RanksumsResult(z, prob)
|
Compute the Wilcoxon rank-sum statistic for two samples.
The Wilcoxon rank-sum test tests the null hypothesis that two sets
of measurements are drawn from the same distribution. The alternative
hypothesis is that values in one sample are more likely to be
larger than the values in the other sample.
This test should be used to compare two samples from continuous
distributions. It does not handle ties between measurements
in x and y. For tie-handling and an optional continuity correction
see `scipy.stats.mannwhitneyu`.
Parameters
----------
x,y : array_like
The data from the two samples
Returns
-------
statistic : float
The test statistic under the large-sample approximation that the
rank sum statistic is normally distributed
pvalue : float
The two-sided p-value of the test
References
----------
.. [1] http://en.wikipedia.org/wiki/Wilcoxon_rank-sum_test
|
Compute the Wilcoxon rank-sum statistic for two samples.
|
[
"Compute",
"the",
"Wilcoxon",
"rank",
"-",
"sum",
"statistic",
"for",
"two",
"samples",
"."
] |
def ranksums(x, y):
"""
Compute the Wilcoxon rank-sum statistic for two samples.
The Wilcoxon rank-sum test tests the null hypothesis that two sets
of measurements are drawn from the same distribution. The alternative
hypothesis is that values in one sample are more likely to be
larger than the values in the other sample.
This test should be used to compare two samples from continuous
distributions. It does not handle ties between measurements
in x and y. For tie-handling and an optional continuity correction
see `scipy.stats.mannwhitneyu`.
Parameters
----------
x,y : array_like
The data from the two samples
Returns
-------
statistic : float
The test statistic under the large-sample approximation that the
rank sum statistic is normally distributed
pvalue : float
The two-sided p-value of the test
References
----------
.. [1] http://en.wikipedia.org/wiki/Wilcoxon_rank-sum_test
"""
x, y = map(np.asarray, (x, y))
n1 = len(x)
n2 = len(y)
alldata = np.concatenate((x, y))
ranked = rankdata(alldata)
x = ranked[:n1]
s = np.sum(x, axis=0)
expected = n1 * (n1+n2+1) / 2.0
z = (s - expected) / np.sqrt(n1*n2*(n1+n2+1)/12.0)
prob = 2 * distributions.norm.sf(abs(z))
return RanksumsResult(z, prob)
|
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/scipy/scipy/stats/stats.py#L4705-L4748
|
|
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Tools/AWSPythonSDK/1.5.8/docutils/utils/math/math2html.py
|
python
|
FilePosition.current
|
(self)
|
return self.reader.currentline()[self.pos]
|
Return the current character, assuming we are not out.
|
Return the current character, assuming we are not out.
|
[
"Return",
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"out",
"."
] |
def current(self):
"Return the current character, assuming we are not out."
if self.pos == len(self.reader.currentline()):
return '\n'
if self.pos > len(self.reader.currentline()):
Trace.error('Out of the line ' + self.reader.currentline() + ': ' + unicode(self.pos))
return '*'
return self.reader.currentline()[self.pos]
|
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/AWSPythonSDK/1.5.8/docutils/utils/math/math2html.py#L2156-L2163
|
|
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Tools/Python/3.7.10/windows/Lib/pickletools.py
|
python
|
read_bytes8
|
(f)
|
r"""
>>> import io, struct, sys
>>> read_bytes8(io.BytesIO(b"\x00\x00\x00\x00\x00\x00\x00\x00abc"))
b''
>>> read_bytes8(io.BytesIO(b"\x03\x00\x00\x00\x00\x00\x00\x00abcdef"))
b'abc'
>>> bigsize8 = struct.pack("<Q", sys.maxsize//3)
>>> read_bytes8(io.BytesIO(bigsize8 + b"abcdef")) #doctest: +ELLIPSIS
Traceback (most recent call last):
...
ValueError: expected ... bytes in a bytes8, but only 6 remain
|
r"""
>>> import io, struct, sys
>>> read_bytes8(io.BytesIO(b"\x00\x00\x00\x00\x00\x00\x00\x00abc"))
b''
>>> read_bytes8(io.BytesIO(b"\x03\x00\x00\x00\x00\x00\x00\x00abcdef"))
b'abc'
>>> bigsize8 = struct.pack("<Q", sys.maxsize//3)
>>> read_bytes8(io.BytesIO(bigsize8 + b"abcdef")) #doctest: +ELLIPSIS
Traceback (most recent call last):
...
ValueError: expected ... bytes in a bytes8, but only 6 remain
|
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"but",
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"6",
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] |
def read_bytes8(f):
r"""
>>> import io, struct, sys
>>> read_bytes8(io.BytesIO(b"\x00\x00\x00\x00\x00\x00\x00\x00abc"))
b''
>>> read_bytes8(io.BytesIO(b"\x03\x00\x00\x00\x00\x00\x00\x00abcdef"))
b'abc'
>>> bigsize8 = struct.pack("<Q", sys.maxsize//3)
>>> read_bytes8(io.BytesIO(bigsize8 + b"abcdef")) #doctest: +ELLIPSIS
Traceback (most recent call last):
...
ValueError: expected ... bytes in a bytes8, but only 6 remain
"""
n = read_uint8(f)
assert n >= 0
if n > sys.maxsize:
raise ValueError("bytes8 byte count > sys.maxsize: %d" % n)
data = f.read(n)
if len(data) == n:
return data
raise ValueError("expected %d bytes in a bytes8, but only %d remain" %
(n, len(data)))
|
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https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/windows/Lib/pickletools.py#L534-L556
|
||
yyzybb537/libgo
|
4af17b7c67643c4d54aa354dcc77963ea07847d0
|
third_party/boost.context/tools/build/src/build/generators.py
|
python
|
Generator.clone
|
(self, new_id, new_toolset_properties)
|
return self.__class__ (new_id,
self.composing_,
self.source_types_,
self.target_types_and_names_,
# Note: this does not remove any subfeatures of <toolset>
# which might cause problems
property.change (self.requirements_, '<toolset>') + new_toolset_properties)
|
Returns another generator which differers from $(self) in
- id
- value to <toolset> feature in properties
|
Returns another generator which differers from $(self) in
- id
- value to <toolset> feature in properties
|
[
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"-",
"id",
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"value",
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"<toolset",
">",
"feature",
"in",
"properties"
] |
def clone (self, new_id, new_toolset_properties):
""" Returns another generator which differers from $(self) in
- id
- value to <toolset> feature in properties
"""
assert isinstance(new_id, basestring)
assert is_iterable_typed(new_toolset_properties, basestring)
return self.__class__ (new_id,
self.composing_,
self.source_types_,
self.target_types_and_names_,
# Note: this does not remove any subfeatures of <toolset>
# which might cause problems
property.change (self.requirements_, '<toolset>') + new_toolset_properties)
|
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https://github.com/yyzybb537/libgo/blob/4af17b7c67643c4d54aa354dcc77963ea07847d0/third_party/boost.context/tools/build/src/build/generators.py#L230-L243
|
|
pytorch/pytorch
|
7176c92687d3cc847cc046bf002269c6949a21c2
|
benchmarks/distributed/rpc/rl/agent.py
|
python
|
Policy.__init__
|
(self, in_features, nlayers, out_features)
|
r"""
Inits policy class
Args:
in_features (int): Number of input features the model takes
nlayers (int): Number of layers in the model
out_features (int): Number of features the model outputs
|
r"""
Inits policy class
Args:
in_features (int): Number of input features the model takes
nlayers (int): Number of layers in the model
out_features (int): Number of features the model outputs
|
[
"r",
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] |
def __init__(self, in_features, nlayers, out_features):
r"""
Inits policy class
Args:
in_features (int): Number of input features the model takes
nlayers (int): Number of layers in the model
out_features (int): Number of features the model outputs
"""
super(Policy, self).__init__()
self.model = nn.Sequential(
nn.Flatten(1, -1),
nn.Linear(in_features, out_features),
* [nn.Linear(out_features, out_features) for _ in range(nlayers)]
)
self.dim = 0
|
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https://github.com/pytorch/pytorch/blob/7176c92687d3cc847cc046bf002269c6949a21c2/benchmarks/distributed/rpc/rl/agent.py#L17-L32
|
||
Tencent/CMONGO
|
c40380caa14e05509f46993aa8b8da966b09b0b5
|
src/third_party/scons-2.5.0/scons-local-2.5.0/SCons/Subst.py
|
python
|
scons_subst
|
(strSubst, env, mode=SUBST_RAW, target=None, source=None, gvars={}, lvars={}, conv=None)
|
return result
|
Expand a string or list containing construction variable
substitutions.
This is the work-horse function for substitutions in file names
and the like. The companion scons_subst_list() function (below)
handles separating command lines into lists of arguments, so see
that function if that's what you're looking for.
|
Expand a string or list containing construction variable
substitutions.
|
[
"Expand",
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def scons_subst(strSubst, env, mode=SUBST_RAW, target=None, source=None, gvars={}, lvars={}, conv=None):
"""Expand a string or list containing construction variable
substitutions.
This is the work-horse function for substitutions in file names
and the like. The companion scons_subst_list() function (below)
handles separating command lines into lists of arguments, so see
that function if that's what you're looking for.
"""
if isinstance(strSubst, str) and strSubst.find('$') < 0:
return strSubst
class StringSubber(object):
"""A class to construct the results of a scons_subst() call.
This binds a specific construction environment, mode, target and
source with two methods (substitute() and expand()) that handle
the expansion.
"""
def __init__(self, env, mode, conv, gvars):
self.env = env
self.mode = mode
self.conv = conv
self.gvars = gvars
def expand(self, s, lvars):
"""Expand a single "token" as necessary, returning an
appropriate string containing the expansion.
This handles expanding different types of things (strings,
lists, callables) appropriately. It calls the wrapper
substitute() method to re-expand things as necessary, so that
the results of expansions of side-by-side strings still get
re-evaluated separately, not smushed together.
"""
if is_String(s):
try:
s0, s1 = s[:2]
except (IndexError, ValueError):
return s
if s0 != '$':
return s
if s1 == '$':
return '$'
elif s1 in '()':
return s
else:
key = s[1:]
if key[0] == '{' or key.find('.') >= 0:
if key[0] == '{':
key = key[1:-1]
try:
s = eval(key, self.gvars, lvars)
except KeyboardInterrupt:
raise
except Exception, e:
if e.__class__ in AllowableExceptions:
return ''
raise_exception(e, lvars['TARGETS'], s)
else:
if key in lvars:
s = lvars[key]
elif key in self.gvars:
s = self.gvars[key]
elif not NameError in AllowableExceptions:
raise_exception(NameError(key), lvars['TARGETS'], s)
else:
return ''
# Before re-expanding the result, handle
# recursive expansion by copying the local
# variable dictionary and overwriting a null
# string for the value of the variable name
# we just expanded.
#
# This could potentially be optimized by only
# copying lvars when s contains more expansions,
# but lvars is usually supposed to be pretty
# small, and deeply nested variable expansions
# are probably more the exception than the norm,
# so it should be tolerable for now.
lv = lvars.copy()
var = key.split('.')[0]
lv[var] = ''
return self.substitute(s, lv)
elif is_Sequence(s):
def func(l, conv=self.conv, substitute=self.substitute, lvars=lvars):
return conv(substitute(l, lvars))
return list(map(func, s))
elif callable(s):
try:
s = s(target=lvars['TARGETS'],
source=lvars['SOURCES'],
env=self.env,
for_signature=(self.mode != SUBST_CMD))
except TypeError:
# This probably indicates that it's a callable
# object that doesn't match our calling arguments
# (like an Action).
if self.mode == SUBST_RAW:
return s
s = self.conv(s)
return self.substitute(s, lvars)
elif s is None:
return ''
else:
return s
def substitute(self, args, lvars):
"""Substitute expansions in an argument or list of arguments.
This serves as a wrapper for splitting up a string into
separate tokens.
"""
if is_String(args) and not isinstance(args, CmdStringHolder):
args = str(args) # In case it's a UserString.
try:
def sub_match(match):
return self.conv(self.expand(match.group(1), lvars))
result = _dollar_exps.sub(sub_match, args)
except TypeError:
# If the internal conversion routine doesn't return
# strings (it could be overridden to return Nodes, for
# example), then the 1.5.2 re module will throw this
# exception. Back off to a slower, general-purpose
# algorithm that works for all data types.
args = _separate_args.findall(args)
result = []
for a in args:
result.append(self.conv(self.expand(a, lvars)))
if len(result) == 1:
result = result[0]
else:
result = ''.join(map(str, result))
return result
else:
return self.expand(args, lvars)
if conv is None:
conv = _strconv[mode]
# Doing this every time is a bit of a waste, since the Executor
# has typically already populated the OverrideEnvironment with
# $TARGET/$SOURCE variables. We're keeping this (for now), though,
# because it supports existing behavior that allows us to call
# an Action directly with an arbitrary target+source pair, which
# we use in Tool/tex.py to handle calling $BIBTEX when necessary.
# If we dropped that behavior (or found another way to cover it),
# we could get rid of this call completely and just rely on the
# Executor setting the variables.
if 'TARGET' not in lvars:
d = subst_dict(target, source)
if d:
lvars = lvars.copy()
lvars.update(d)
# We're (most likely) going to eval() things. If Python doesn't
# find a __builtins__ value in the global dictionary used for eval(),
# it copies the current global values for you. Avoid this by
# setting it explicitly and then deleting, so we don't pollute the
# construction environment Dictionary(ies) that are typically used
# for expansion.
gvars['__builtins__'] = __builtins__
ss = StringSubber(env, mode, conv, gvars)
result = ss.substitute(strSubst, lvars)
try:
del gvars['__builtins__']
except KeyError:
pass
if is_String(result):
# Remove $(-$) pairs and any stuff in between,
# if that's appropriate.
remove = _regex_remove[mode]
if remove:
result = remove.sub('', result)
if mode != SUBST_RAW:
# Compress strings of white space characters into
# a single space.
result = _space_sep.sub(' ', result).strip()
elif is_Sequence(result):
remove = _list_remove[mode]
if remove:
result = remove(result)
return result
|
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] |
https://github.com/Tencent/CMONGO/blob/c40380caa14e05509f46993aa8b8da966b09b0b5/src/third_party/scons-2.5.0/scons-local-2.5.0/SCons/Subst.py#L393-L580
|
|
Tencent/CMONGO
|
c40380caa14e05509f46993aa8b8da966b09b0b5
|
buildscripts/resmokelib/core/programs.py
|
python
|
mongod_program
|
(logger, executable=None, process_kwargs=None, **kwargs)
|
return _process.Process(logger, args, **process_kwargs)
|
Returns a Process instance that starts a mongod executable with
arguments constructed from 'kwargs'.
|
Returns a Process instance that starts a mongod executable with
arguments constructed from 'kwargs'.
|
[
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"instance",
"that",
"starts",
"a",
"mongod",
"executable",
"with",
"arguments",
"constructed",
"from",
"kwargs",
"."
] |
def mongod_program(logger, executable=None, process_kwargs=None, **kwargs):
"""
Returns a Process instance that starts a mongod executable with
arguments constructed from 'kwargs'.
"""
executable = utils.default_if_none(executable, config.DEFAULT_MONGOD_EXECUTABLE)
args = [executable]
# Apply the --setParameter command line argument. Command line options to resmoke.py override
# the YAML configuration.
suite_set_parameters = kwargs.pop("set_parameters", {})
if config.MONGOD_SET_PARAMETERS is not None:
suite_set_parameters.update(utils.load_yaml(config.MONGOD_SET_PARAMETERS))
_apply_set_parameters(args, suite_set_parameters)
shortcut_opts = {
"nojournal": config.NO_JOURNAL,
"nopreallocj": config.NO_PREALLOC_JOURNAL,
"storageEngine": config.STORAGE_ENGINE,
"wiredTigerCollectionConfigString": config.WT_COLL_CONFIG,
"wiredTigerEngineConfigString": config.WT_ENGINE_CONFIG,
"wiredTigerIndexConfigString": config.WT_INDEX_CONFIG,
}
if config.STORAGE_ENGINE == "rocksdb":
shortcut_opts["rocksdbCacheSizeGB"] = config.STORAGE_ENGINE_CACHE_SIZE
elif config.STORAGE_ENGINE == "wiredTiger" or config.STORAGE_ENGINE is None:
shortcut_opts["wiredTigerCacheSizeGB"] = config.STORAGE_ENGINE_CACHE_SIZE
# These options are just flags, so they should not take a value.
opts_without_vals = ("nojournal", "nopreallocj")
# Have the --nojournal command line argument to resmoke.py unset the journal option.
if shortcut_opts["nojournal"] and "journal" in kwargs:
del kwargs["journal"]
# Ensure that config servers run with journaling enabled.
if "configsvr" in kwargs:
shortcut_opts["nojournal"] = False
kwargs["journal"] = ""
# Command line options override the YAML configuration.
for opt_name in shortcut_opts:
opt_value = shortcut_opts[opt_name]
if opt_name in opts_without_vals:
# Options that are specified as --flag on the command line are represented by a boolean
# value where True indicates that the flag should be included in 'kwargs'.
if opt_value:
kwargs[opt_name] = ""
else:
# Options that are specified as --key=value on the command line are represented by a
# value where None indicates that the key-value pair shouldn't be included in 'kwargs'.
if opt_value is not None:
kwargs[opt_name] = opt_value
# Override the storage engine specified on the command line with "wiredTiger" if running a
# config server replica set.
if "replSet" in kwargs and "configsvr" in kwargs:
kwargs["storageEngine"] = "wiredTiger"
# Apply the rest of the command line arguments.
_apply_kwargs(args, kwargs)
_set_keyfile_permissions(kwargs)
process_kwargs = utils.default_if_none(process_kwargs, {})
return _process.Process(logger, args, **process_kwargs)
|
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",",
"*",
"*",
"process_kwargs",
")"
] |
https://github.com/Tencent/CMONGO/blob/c40380caa14e05509f46993aa8b8da966b09b0b5/buildscripts/resmokelib/core/programs.py#L19-L88
|
|
thalium/icebox
|
99d147d5b9269222225443ce171b4fd46d8985d4
|
third_party/virtualbox/src/libs/libxml2-2.9.4/python/libxml2class.py
|
python
|
UTF8Strndup
|
(utf, len)
|
return ret
|
a strndup for array of UTF8's
|
a strndup for array of UTF8's
|
[
"a",
"strndup",
"for",
"array",
"of",
"UTF8",
"s"
] |
def UTF8Strndup(utf, len):
"""a strndup for array of UTF8's """
ret = libxml2mod.xmlUTF8Strndup(utf, len)
return ret
|
[
"def",
"UTF8Strndup",
"(",
"utf",
",",
"len",
")",
":",
"ret",
"=",
"libxml2mod",
".",
"xmlUTF8Strndup",
"(",
"utf",
",",
"len",
")",
"return",
"ret"
] |
https://github.com/thalium/icebox/blob/99d147d5b9269222225443ce171b4fd46d8985d4/third_party/virtualbox/src/libs/libxml2-2.9.4/python/libxml2class.py#L1269-L1272
|
|
metashell/metashell
|
f4177e4854ea00c8dbc722cadab26ef413d798ea
|
3rd/templight/clang/bindings/python/clang/cindex.py
|
python
|
Cursor.location
|
(self)
|
return self._loc
|
Return the source location (the starting character) of the entity
pointed at by the cursor.
|
Return the source location (the starting character) of the entity
pointed at by the cursor.
|
[
"Return",
"the",
"source",
"location",
"(",
"the",
"starting",
"character",
")",
"of",
"the",
"entity",
"pointed",
"at",
"by",
"the",
"cursor",
"."
] |
def location(self):
"""
Return the source location (the starting character) of the entity
pointed at by the cursor.
"""
if not hasattr(self, '_loc'):
self._loc = conf.lib.clang_getCursorLocation(self)
return self._loc
|
[
"def",
"location",
"(",
"self",
")",
":",
"if",
"not",
"hasattr",
"(",
"self",
",",
"'_loc'",
")",
":",
"self",
".",
"_loc",
"=",
"conf",
".",
"lib",
".",
"clang_getCursorLocation",
"(",
"self",
")",
"return",
"self",
".",
"_loc"
] |
https://github.com/metashell/metashell/blob/f4177e4854ea00c8dbc722cadab26ef413d798ea/3rd/templight/clang/bindings/python/clang/cindex.py#L1574-L1582
|
|
wxWidgets/wxPython-Classic
|
19571e1ae65f1ac445f5491474121998c97a1bf0
|
src/gtk/grid.py
|
python
|
PyGridCellAttrProvider.GetAttr
|
(*args, **kwargs)
|
return _grid.PyGridCellAttrProvider_GetAttr(*args, **kwargs)
|
GetAttr(self, int row, int col, int kind) -> GridCellAttr
|
GetAttr(self, int row, int col, int kind) -> GridCellAttr
|
[
"GetAttr",
"(",
"self",
"int",
"row",
"int",
"col",
"int",
"kind",
")",
"-",
">",
"GridCellAttr"
] |
def GetAttr(*args, **kwargs):
"""GetAttr(self, int row, int col, int kind) -> GridCellAttr"""
return _grid.PyGridCellAttrProvider_GetAttr(*args, **kwargs)
|
[
"def",
"GetAttr",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"return",
"_grid",
".",
"PyGridCellAttrProvider_GetAttr",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")"
] |
https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/gtk/grid.py#L725-L727
|
|
apple/swift-lldb
|
d74be846ef3e62de946df343e8c234bde93a8912
|
scripts/Python/static-binding/lldb.py
|
python
|
SBFrame.GetPC
|
(self)
|
return _lldb.SBFrame_GetPC(self)
|
GetPC(SBFrame self) -> lldb::addr_t
|
GetPC(SBFrame self) -> lldb::addr_t
|
[
"GetPC",
"(",
"SBFrame",
"self",
")",
"-",
">",
"lldb",
"::",
"addr_t"
] |
def GetPC(self):
"""GetPC(SBFrame self) -> lldb::addr_t"""
return _lldb.SBFrame_GetPC(self)
|
[
"def",
"GetPC",
"(",
"self",
")",
":",
"return",
"_lldb",
".",
"SBFrame_GetPC",
"(",
"self",
")"
] |
https://github.com/apple/swift-lldb/blob/d74be846ef3e62de946df343e8c234bde93a8912/scripts/Python/static-binding/lldb.py#L5477-L5479
|
|
OSGeo/gdal
|
3748fc4ba4fba727492774b2b908a2130c864a83
|
swig/python/osgeo/osr.py
|
python
|
OSRAreaOfUse_east_lon_degree_get
|
(*args)
|
return _osr.OSRAreaOfUse_east_lon_degree_get(*args)
|
r"""OSRAreaOfUse_east_lon_degree_get(AreaOfUse area) -> double
|
r"""OSRAreaOfUse_east_lon_degree_get(AreaOfUse area) -> double
|
[
"r",
"OSRAreaOfUse_east_lon_degree_get",
"(",
"AreaOfUse",
"area",
")",
"-",
">",
"double"
] |
def OSRAreaOfUse_east_lon_degree_get(*args):
r"""OSRAreaOfUse_east_lon_degree_get(AreaOfUse area) -> double"""
return _osr.OSRAreaOfUse_east_lon_degree_get(*args)
|
[
"def",
"OSRAreaOfUse_east_lon_degree_get",
"(",
"*",
"args",
")",
":",
"return",
"_osr",
".",
"OSRAreaOfUse_east_lon_degree_get",
"(",
"*",
"args",
")"
] |
https://github.com/OSGeo/gdal/blob/3748fc4ba4fba727492774b2b908a2130c864a83/swig/python/osgeo/osr.py#L304-L306
|
|
blackberry/Boost
|
fc90c3fde129c62565c023f091eddc4a7ed9902b
|
tools/build/v2/build/property_set.py
|
python
|
PropertySet.add_raw
|
(self, properties)
|
return self.add (create (properties))
|
Creates a new property set containing the properties in this one,
plus the ones passed as argument.
|
Creates a new property set containing the properties in this one,
plus the ones passed as argument.
|
[
"Creates",
"a",
"new",
"property",
"set",
"containing",
"the",
"properties",
"in",
"this",
"one",
"plus",
"the",
"ones",
"passed",
"as",
"argument",
"."
] |
def add_raw (self, properties):
""" Creates a new property set containing the properties in this one,
plus the ones passed as argument.
"""
return self.add (create (properties))
|
[
"def",
"add_raw",
"(",
"self",
",",
"properties",
")",
":",
"return",
"self",
".",
"add",
"(",
"create",
"(",
"properties",
")",
")"
] |
https://github.com/blackberry/Boost/blob/fc90c3fde129c62565c023f091eddc4a7ed9902b/tools/build/v2/build/property_set.py#L409-L413
|
|
miyosuda/TensorFlowAndroidMNIST
|
7b5a4603d2780a8a2834575706e9001977524007
|
jni-build/jni/include/tensorflow/python/training/summary_io.py
|
python
|
_EventLoggerThread.__init__
|
(self, queue, ev_writer, flush_secs)
|
Creates an _EventLoggerThread.
Args:
queue: A Queue from which to dequeue events.
ev_writer: An event writer. Used to log brain events for
the visualizer.
flush_secs: How often, in seconds, to flush the
pending file to disk.
|
Creates an _EventLoggerThread.
|
[
"Creates",
"an",
"_EventLoggerThread",
"."
] |
def __init__(self, queue, ev_writer, flush_secs):
"""Creates an _EventLoggerThread.
Args:
queue: A Queue from which to dequeue events.
ev_writer: An event writer. Used to log brain events for
the visualizer.
flush_secs: How often, in seconds, to flush the
pending file to disk.
"""
threading.Thread.__init__(self)
self.daemon = True
self._queue = queue
self._ev_writer = ev_writer
self._flush_secs = flush_secs
# The first event will be flushed immediately.
self._next_event_flush_time = 0
|
[
"def",
"__init__",
"(",
"self",
",",
"queue",
",",
"ev_writer",
",",
"flush_secs",
")",
":",
"threading",
".",
"Thread",
".",
"__init__",
"(",
"self",
")",
"self",
".",
"daemon",
"=",
"True",
"self",
".",
"_queue",
"=",
"queue",
"self",
".",
"_ev_writer",
"=",
"ev_writer",
"self",
".",
"_flush_secs",
"=",
"flush_secs",
"# The first event will be flushed immediately.",
"self",
".",
"_next_event_flush_time",
"=",
"0"
] |
https://github.com/miyosuda/TensorFlowAndroidMNIST/blob/7b5a4603d2780a8a2834575706e9001977524007/jni-build/jni/include/tensorflow/python/training/summary_io.py#L287-L303
|
||
hanpfei/chromium-net
|
392cc1fa3a8f92f42e4071ab6e674d8e0482f83f
|
third_party/catapult/third_party/WebOb/webob/datetime_utils.py
|
python
|
timedelta_to_seconds
|
(td)
|
return td.seconds + (td.days*24*60*60)
|
Converts a timedelta instance to seconds.
|
Converts a timedelta instance to seconds.
|
[
"Converts",
"a",
"timedelta",
"instance",
"to",
"seconds",
"."
] |
def timedelta_to_seconds(td):
"""
Converts a timedelta instance to seconds.
"""
return td.seconds + (td.days*24*60*60)
|
[
"def",
"timedelta_to_seconds",
"(",
"td",
")",
":",
"return",
"td",
".",
"seconds",
"+",
"(",
"td",
".",
"days",
"*",
"24",
"*",
"60",
"*",
"60",
")"
] |
https://github.com/hanpfei/chromium-net/blob/392cc1fa3a8f92f42e4071ab6e674d8e0482f83f/third_party/catapult/third_party/WebOb/webob/datetime_utils.py#L48-L52
|
|
Xilinx/Vitis-AI
|
fc74d404563d9951b57245443c73bef389f3657f
|
tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/data/python/ops/enumerate_ops.py
|
python
|
enumerate_dataset
|
(start=0)
|
return enumerate_ops.enumerate_dataset(start)
|
A transformation that enumerate the elements of a dataset.
It is Similar to python's `enumerate`.
For example:
```python
# NOTE: The following examples use `{ ... }` to represent the
# contents of a dataset.
a = { 1, 2, 3 }
b = { (7, 8), (9, 10) }
# The nested structure of the `datasets` argument determines the
# structure of elements in the resulting dataset.
a.apply(tf.contrib.data.enumerate(start=5)) == { (5, 1), (6, 2), (7, 3) }
b.apply(tf.contrib.data.enumerate()) == { (0, (7, 8)), (1, (9, 10)) }
```
Args:
start: A `tf.int64` scalar `tf.Tensor`, representing the start
value for enumeration.
Returns:
A `Dataset` transformation function, which can be passed to
`tf.data.Dataset.apply`.
|
A transformation that enumerate the elements of a dataset.
|
[
"A",
"transformation",
"that",
"enumerate",
"the",
"elements",
"of",
"a",
"dataset",
"."
] |
def enumerate_dataset(start=0):
"""A transformation that enumerate the elements of a dataset.
It is Similar to python's `enumerate`.
For example:
```python
# NOTE: The following examples use `{ ... }` to represent the
# contents of a dataset.
a = { 1, 2, 3 }
b = { (7, 8), (9, 10) }
# The nested structure of the `datasets` argument determines the
# structure of elements in the resulting dataset.
a.apply(tf.contrib.data.enumerate(start=5)) == { (5, 1), (6, 2), (7, 3) }
b.apply(tf.contrib.data.enumerate()) == { (0, (7, 8)), (1, (9, 10)) }
```
Args:
start: A `tf.int64` scalar `tf.Tensor`, representing the start
value for enumeration.
Returns:
A `Dataset` transformation function, which can be passed to
`tf.data.Dataset.apply`.
"""
return enumerate_ops.enumerate_dataset(start)
|
[
"def",
"enumerate_dataset",
"(",
"start",
"=",
"0",
")",
":",
"return",
"enumerate_ops",
".",
"enumerate_dataset",
"(",
"start",
")"
] |
https://github.com/Xilinx/Vitis-AI/blob/fc74d404563d9951b57245443c73bef389f3657f/tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/data/python/ops/enumerate_ops.py#L27-L53
|
|
domino-team/openwrt-cc
|
8b181297c34d14d3ca521cc9f31430d561dbc688
|
package/gli-pub/openwrt-node-packages-master/node/node-v6.9.1/deps/v8_inspector/third_party/jinja2/jinja2/runtime.py
|
python
|
Context.super
|
(self, name, current)
|
return BlockReference(name, self, blocks, index)
|
Render a parent block.
|
Render a parent block.
|
[
"Render",
"a",
"parent",
"block",
"."
] |
def super(self, name, current):
"""Render a parent block."""
try:
blocks = self.blocks[name]
index = blocks.index(current) + 1
blocks[index]
except LookupError:
return self.environment.undefined('there is no parent block '
'called %r.' % name,
name='super')
return BlockReference(name, self, blocks, index)
|
[
"def",
"super",
"(",
"self",
",",
"name",
",",
"current",
")",
":",
"try",
":",
"blocks",
"=",
"self",
".",
"blocks",
"[",
"name",
"]",
"index",
"=",
"blocks",
".",
"index",
"(",
"current",
")",
"+",
"1",
"blocks",
"[",
"index",
"]",
"except",
"LookupError",
":",
"return",
"self",
".",
"environment",
".",
"undefined",
"(",
"'there is no parent block '",
"'called %r.'",
"%",
"name",
",",
"name",
"=",
"'super'",
")",
"return",
"BlockReference",
"(",
"name",
",",
"self",
",",
"blocks",
",",
"index",
")"
] |
https://github.com/domino-team/openwrt-cc/blob/8b181297c34d14d3ca521cc9f31430d561dbc688/package/gli-pub/openwrt-node-packages-master/node/node-v6.9.1/deps/v8_inspector/third_party/jinja2/jinja2/runtime.py#L128-L138
|
|
tum-vision/fusenet
|
a1451be2971b348a01b0f525c2a3a7a0e215a591
|
examples/pycaffe/tools.py
|
python
|
SimpleTransformer.deprocess
|
(self, im)
|
return np.uint8(im)
|
inverse of preprocess()
|
inverse of preprocess()
|
[
"inverse",
"of",
"preprocess",
"()"
] |
def deprocess(self, im):
"""
inverse of preprocess()
"""
im = im.transpose(1, 2, 0)
im /= self.scale
im += self.mean
im = im[:, :, ::-1] # change to RGB
return np.uint8(im)
|
[
"def",
"deprocess",
"(",
"self",
",",
"im",
")",
":",
"im",
"=",
"im",
".",
"transpose",
"(",
"1",
",",
"2",
",",
"0",
")",
"im",
"/=",
"self",
".",
"scale",
"im",
"+=",
"self",
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"mean",
"im",
"=",
"im",
"[",
":",
",",
":",
",",
":",
":",
"-",
"1",
"]",
"# change to RGB",
"return",
"np",
".",
"uint8",
"(",
"im",
")"
] |
https://github.com/tum-vision/fusenet/blob/a1451be2971b348a01b0f525c2a3a7a0e215a591/examples/pycaffe/tools.py#L41-L50
|
|
benoitsteiner/tensorflow-opencl
|
cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5
|
tensorflow/python/framework/ops.py
|
python
|
Graph.get_collection
|
(self, name, scope=None)
|
Returns a list of values in the collection with the given `name`.
This is different from `get_collection_ref()` which always returns the
actual collection list if it exists in that it returns a new list each time
it is called.
Args:
name: The key for the collection. For example, the `GraphKeys` class
contains many standard names for collections.
scope: (Optional.) A string. If supplied, the resulting list is filtered
to include only items whose `name` attribute matches `scope` using
`re.match`. Items without a `name` attribute are never returned if a
scope is supplied. The choice of `re.match` means that a `scope` without
special tokens filters by prefix.
Returns:
The list of values in the collection with the given `name`, or
an empty list if no value has been added to that collection. The
list contains the values in the order under which they were
collected.
|
Returns a list of values in the collection with the given `name`.
|
[
"Returns",
"a",
"list",
"of",
"values",
"in",
"the",
"collection",
"with",
"the",
"given",
"name",
"."
] |
def get_collection(self, name, scope=None):
"""Returns a list of values in the collection with the given `name`.
This is different from `get_collection_ref()` which always returns the
actual collection list if it exists in that it returns a new list each time
it is called.
Args:
name: The key for the collection. For example, the `GraphKeys` class
contains many standard names for collections.
scope: (Optional.) A string. If supplied, the resulting list is filtered
to include only items whose `name` attribute matches `scope` using
`re.match`. Items without a `name` attribute are never returned if a
scope is supplied. The choice of `re.match` means that a `scope` without
special tokens filters by prefix.
Returns:
The list of values in the collection with the given `name`, or
an empty list if no value has been added to that collection. The
list contains the values in the order under which they were
collected.
""" # pylint: disable=g-doc-exception
_assert_collection_is_ok(name)
with self._lock:
collection = self._collections.get(name, None)
if collection is None:
return []
if scope is None:
return list(collection)
else:
c = []
regex = re.compile(scope)
for item in collection:
if hasattr(item, "name") and regex.match(item.name):
c.append(item)
return c
|
[
"def",
"get_collection",
"(",
"self",
",",
"name",
",",
"scope",
"=",
"None",
")",
":",
"# pylint: disable=g-doc-exception",
"_assert_collection_is_ok",
"(",
"name",
")",
"with",
"self",
".",
"_lock",
":",
"collection",
"=",
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",",
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"item",
".",
"name",
")",
":",
"c",
".",
"append",
"(",
"item",
")",
"return",
"c"
] |
https://github.com/benoitsteiner/tensorflow-opencl/blob/cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5/tensorflow/python/framework/ops.py#L3392-L3427
|
||
mantidproject/mantid
|
03deeb89254ec4289edb8771e0188c2090a02f32
|
scripts/Interface/ui/sans_isis/sans_data_processor_gui.py
|
python
|
SANSDataProcessorGui._check_output_mode
|
(self, value)
|
Set the output mode radio button from a SANS enum.
This method is called when:
1. The gui is launched
2. Via the presenter, from state
:param value: An OutputMode (SANS enum) object
|
Set the output mode radio button from a SANS enum.
This method is called when:
1. The gui is launched
2. Via the presenter, from state
:param value: An OutputMode (SANS enum) object
|
[
"Set",
"the",
"output",
"mode",
"radio",
"button",
"from",
"a",
"SANS",
"enum",
".",
"This",
"method",
"is",
"called",
"when",
":",
"1",
".",
"The",
"gui",
"is",
"launched",
"2",
".",
"Via",
"the",
"presenter",
"from",
"state",
":",
"param",
"value",
":",
"An",
"OutputMode",
"(",
"SANS",
"enum",
")",
"object"
] |
def _check_output_mode(self, value):
"""
Set the output mode radio button from a SANS enum.
This method is called when:
1. The gui is launched
2. Via the presenter, from state
:param value: An OutputMode (SANS enum) object
"""
if value is OutputMode.PUBLISH_TO_ADS:
self.output_mode_memory_radio_button.setChecked(True)
elif value is OutputMode.SAVE_TO_FILE:
self.output_mode_file_radio_button.setChecked(True)
elif value is OutputMode.BOTH:
self.output_mode_both_radio_button.setChecked(True)
# Notify the presenter
self._call_settings_listeners(lambda listener: listener.on_output_mode_changed())
|
[
"def",
"_check_output_mode",
"(",
"self",
",",
"value",
")",
":",
"if",
"value",
"is",
"OutputMode",
".",
"PUBLISH_TO_ADS",
":",
"self",
".",
"output_mode_memory_radio_button",
".",
"setChecked",
"(",
"True",
")",
"elif",
"value",
"is",
"OutputMode",
".",
"SAVE_TO_FILE",
":",
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"(",
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"# Notify the presenter",
"self",
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"_call_settings_listeners",
"(",
"lambda",
"listener",
":",
"listener",
".",
"on_output_mode_changed",
"(",
")",
")"
] |
https://github.com/mantidproject/mantid/blob/03deeb89254ec4289edb8771e0188c2090a02f32/scripts/Interface/ui/sans_isis/sans_data_processor_gui.py#L609-L625
|
||
Illumina/hap.py
|
84011695b2ff2406c16a335106db6831fb67fdfe
|
src/python/Tools/__init__.py
|
python
|
BGZipFile.__init__
|
(self, filename, force=False)
|
Make a subprocess for bgzip
:param filename: name of the output file
:param force: true to overwrite if file exists
|
Make a subprocess for bgzip
:param filename: name of the output file
:param force: true to overwrite if file exists
|
[
"Make",
"a",
"subprocess",
"for",
"bgzip",
":",
"param",
"filename",
":",
"name",
"of",
"the",
"output",
"file",
":",
"param",
"force",
":",
"true",
"to",
"overwrite",
"if",
"file",
"exists"
] |
def __init__(self, filename, force=False):
""" Make a subprocess for bgzip
:param filename: name of the output file
:param force: true to overwrite if file exists
"""
if os.path.exists(filename) and not force:
raise Exception("File %s exists, use force=True to overwrite" % filename)
self.write_file = open(filename, "wb")
zip_pipe = subprocess.Popen(["bgzip", "-f"],
stdin=subprocess.PIPE,
stdout=self.write_file,
stderr=subprocess.PIPE,
shell=True)
self.zip_pipe = zip_pipe
self.name = filename
|
[
"def",
"__init__",
"(",
"self",
",",
"filename",
",",
"force",
"=",
"False",
")",
":",
"if",
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"path",
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"\"wb\"",
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"zip_pipe",
"=",
"zip_pipe",
"self",
".",
"name",
"=",
"filename"
] |
https://github.com/Illumina/hap.py/blob/84011695b2ff2406c16a335106db6831fb67fdfe/src/python/Tools/__init__.py#L179-L194
|
||
krishauser/Klampt
|
972cc83ea5befac3f653c1ba20f80155768ad519
|
Python/python2_version/klampt/src/robotsim.py
|
python
|
SimBody.enableDynamics
|
(self, enabled=True)
|
return _robotsim.SimBody_enableDynamics(self, enabled)
|
enableDynamics(SimBody self, bool enabled=True)
enableDynamics(SimBody self)
Sets the dynamic simulation of the body on/off. If false, velocities will simply
be integrated forward, and forces will not affect velocity i.e., it will be pure
kinematic simulation.
|
enableDynamics(SimBody self, bool enabled=True)
enableDynamics(SimBody self)
|
[
"enableDynamics",
"(",
"SimBody",
"self",
"bool",
"enabled",
"=",
"True",
")",
"enableDynamics",
"(",
"SimBody",
"self",
")"
] |
def enableDynamics(self, enabled=True):
"""
enableDynamics(SimBody self, bool enabled=True)
enableDynamics(SimBody self)
Sets the dynamic simulation of the body on/off. If false, velocities will simply
be integrated forward, and forces will not affect velocity i.e., it will be pure
kinematic simulation.
"""
return _robotsim.SimBody_enableDynamics(self, enabled)
|
[
"def",
"enableDynamics",
"(",
"self",
",",
"enabled",
"=",
"True",
")",
":",
"return",
"_robotsim",
".",
"SimBody_enableDynamics",
"(",
"self",
",",
"enabled",
")"
] |
https://github.com/krishauser/Klampt/blob/972cc83ea5befac3f653c1ba20f80155768ad519/Python/python2_version/klampt/src/robotsim.py#L7879-L7891
|
|
wxWidgets/wxPython-Classic
|
19571e1ae65f1ac445f5491474121998c97a1bf0
|
src/gtk/_windows.py
|
python
|
Frame.GetStatusBar
|
(*args, **kwargs)
|
return _windows_.Frame_GetStatusBar(*args, **kwargs)
|
GetStatusBar(self) -> StatusBar
|
GetStatusBar(self) -> StatusBar
|
[
"GetStatusBar",
"(",
"self",
")",
"-",
">",
"StatusBar"
] |
def GetStatusBar(*args, **kwargs):
"""GetStatusBar(self) -> StatusBar"""
return _windows_.Frame_GetStatusBar(*args, **kwargs)
|
[
"def",
"GetStatusBar",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"return",
"_windows_",
".",
"Frame_GetStatusBar",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")"
] |
https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/gtk/_windows.py#L617-L619
|
|
wlanjie/AndroidFFmpeg
|
7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf
|
tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/multiprocessing/__init__.py
|
python
|
Semaphore
|
(value=1)
|
return Semaphore(value)
|
Returns a semaphore object
|
Returns a semaphore object
|
[
"Returns",
"a",
"semaphore",
"object"
] |
def Semaphore(value=1):
'''
Returns a semaphore object
'''
from multiprocessing.synchronize import Semaphore
return Semaphore(value)
|
[
"def",
"Semaphore",
"(",
"value",
"=",
"1",
")",
":",
"from",
"multiprocessing",
".",
"synchronize",
"import",
"Semaphore",
"return",
"Semaphore",
"(",
"value",
")"
] |
https://github.com/wlanjie/AndroidFFmpeg/blob/7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf/tools/fdk-aac-build/armeabi-v7a/toolchain/lib/python2.7/multiprocessing/__init__.py#L192-L197
|
|
kushview/Element
|
1cc16380caa2ab79461246ba758b9de1f46db2a5
|
waflib/Utils.py
|
python
|
lru_cache.__init__
|
(self, maxlen=100)
|
Maximum amount of elements in the cache
|
Maximum amount of elements in the cache
|
[
"Maximum",
"amount",
"of",
"elements",
"in",
"the",
"cache"
] |
def __init__(self, maxlen=100):
self.maxlen = maxlen
"""
Maximum amount of elements in the cache
"""
self.table = {}
"""
Mapping key-value
"""
self.head = lru_node()
self.head.next = self.head
self.head.prev = self.head
|
[
"def",
"__init__",
"(",
"self",
",",
"maxlen",
"=",
"100",
")",
":",
"self",
".",
"maxlen",
"=",
"maxlen",
"self",
".",
"table",
"=",
"{",
"}",
"\"\"\"\n\t\tMapping key-value\n\t\t\"\"\"",
"self",
".",
"head",
"=",
"lru_node",
"(",
")",
"self",
".",
"head",
".",
"next",
"=",
"self",
".",
"head",
"self",
".",
"head",
".",
"prev",
"=",
"self",
".",
"head"
] |
https://github.com/kushview/Element/blob/1cc16380caa2ab79461246ba758b9de1f46db2a5/waflib/Utils.py#L139-L150
|
||
mantidproject/mantid
|
03deeb89254ec4289edb8771e0188c2090a02f32
|
scripts/SANS/SANSUtility.py
|
python
|
is_valid_ws_for_removing_zero_errors
|
(input_workspace_name)
|
return message, isValid
|
Check if a workspace has been created via Q1D or Qxy.
@param ws :: The input workspace
|
Check if a workspace has been created via Q1D or Qxy.
|
[
"Check",
"if",
"a",
"workspace",
"has",
"been",
"created",
"via",
"Q1D",
"or",
"Qxy",
"."
] |
def is_valid_ws_for_removing_zero_errors(input_workspace_name):
'''
Check if a workspace has been created via Q1D or Qxy.
@param ws :: The input workspace
'''
isValid = False
message = ""
ws = mtd[input_workspace_name]
workspaceHistory= ws.getHistory()
histories = workspaceHistory.getAlgorithmHistories()
for history in histories:
name = history.name()
if name == 'Q1D' or name == 'Qxy':
isValid = True
break
if not isValid:
message = ("Workspace does not seem valid for zero error removal."
"It must have been reduced with Q1D or Qxy.")
return message, isValid
|
[
"def",
"is_valid_ws_for_removing_zero_errors",
"(",
"input_workspace_name",
")",
":",
"isValid",
"=",
"False",
"message",
"=",
"\"\"",
"ws",
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"mtd",
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"input_workspace_name",
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"workspaceHistory",
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"\"Workspace does not seem valid for zero error removal.\"",
"\"It must have been reduced with Q1D or Qxy.\"",
")",
"return",
"message",
",",
"isValid"
] |
https://github.com/mantidproject/mantid/blob/03deeb89254ec4289edb8771e0188c2090a02f32/scripts/SANS/SANSUtility.py#L941-L962
|
|
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Tools/Python/3.7.10/windows/Lib/site-packages/pip/_vendor/distro.py
|
python
|
LinuxDistribution.minor_version
|
(self, best=False)
|
return self.version_parts(best)[1]
|
Return the minor version number of the current distribution.
For details, see :func:`distro.minor_version`.
|
Return the minor version number of the current distribution.
|
[
"Return",
"the",
"minor",
"version",
"number",
"of",
"the",
"current",
"distribution",
"."
] |
def minor_version(self, best=False):
"""
Return the minor version number of the current distribution.
For details, see :func:`distro.minor_version`.
"""
return self.version_parts(best)[1]
|
[
"def",
"minor_version",
"(",
"self",
",",
"best",
"=",
"False",
")",
":",
"return",
"self",
".",
"version_parts",
"(",
"best",
")",
"[",
"1",
"]"
] |
https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/windows/Lib/site-packages/pip/_vendor/distro.py#L791-L797
|
|
windystrife/UnrealEngine_NVIDIAGameWorks
|
b50e6338a7c5b26374d66306ebc7807541ff815e
|
Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/rlcompleter.py
|
python
|
Completer.complete
|
(self, text, state)
|
Return the next possible completion for 'text'.
This is called successively with state == 0, 1, 2, ... until it
returns None. The completion should begin with 'text'.
|
Return the next possible completion for 'text'.
|
[
"Return",
"the",
"next",
"possible",
"completion",
"for",
"text",
"."
] |
def complete(self, text, state):
"""Return the next possible completion for 'text'.
This is called successively with state == 0, 1, 2, ... until it
returns None. The completion should begin with 'text'.
"""
if self.use_main_ns:
self.namespace = __main__.__dict__
if state == 0:
if "." in text:
self.matches = self.attr_matches(text)
else:
self.matches = self.global_matches(text)
try:
return self.matches[state]
except IndexError:
return None
|
[
"def",
"complete",
"(",
"self",
",",
"text",
",",
"state",
")",
":",
"if",
"self",
".",
"use_main_ns",
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"self",
".",
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"=",
"__main__",
".",
"__dict__",
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"return",
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".",
"matches",
"[",
"state",
"]",
"except",
"IndexError",
":",
"return",
"None"
] |
https://github.com/windystrife/UnrealEngine_NVIDIAGameWorks/blob/b50e6338a7c5b26374d66306ebc7807541ff815e/Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/rlcompleter.py#L71-L89
|
||
Xilinx/Vitis-AI
|
fc74d404563d9951b57245443c73bef389f3657f
|
tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/opt/python/training/elastic_average_optimizer.py
|
python
|
ElasticAverageOptimizer.apply_gradients
|
(self, grads_and_vars, global_step=None, name=None)
|
return conditional_update
|
Apply gradients to global variables.
This is the second part of `minimize()`. It returns an `Operation` that
applies gradients.
Args:
grads_and_vars: List of (gradient, variable) pairs as returned by
`compute_gradients()`.
global_step: Optional `Variable` to increment by one after the variables
have been updated.
name: Optional name for the returned operation. Default to the name
passed to the `Optimizer` constructor.
Returns:
An `Operation` that applies the specified gradients. If `global_step`
was not None, that operation also increments `global_step`.
Raises:
TypeError: If `grads_and_vars` is malformed.
ValueError: If none of the variables have gradients.
|
Apply gradients to global variables.
|
[
"Apply",
"gradients",
"to",
"global",
"variables",
"."
] |
def apply_gradients(self, grads_and_vars, global_step=None, name=None):
"""Apply gradients to global variables.
This is the second part of `minimize()`. It returns an `Operation` that
applies gradients.
Args:
grads_and_vars: List of (gradient, variable) pairs as returned by
`compute_gradients()`.
global_step: Optional `Variable` to increment by one after the variables
have been updated.
name: Optional name for the returned operation. Default to the name
passed to the `Optimizer` constructor.
Returns:
An `Operation` that applies the specified gradients. If `global_step`
was not None, that operation also increments `global_step`.
Raises:
TypeError: If `grads_and_vars` is malformed.
ValueError: If none of the variables have gradients.
"""
global_old = set(n.op.name for n in variables.global_variables())
apply_updates = self._opt.apply_gradients(grads_and_vars)
global_new = set(n.op.name for n in variables.global_variables())
with ops.control_dependencies([apply_updates]):
local_update = state_ops.assign_add(
self._local_step, 1, name='local_step_update').op
# this is for place the variables created by optimizer to local collection
# e.g., AdamOptimizer will create beta as global variables
def _adjust_optimizer_variable_collection(opt_vars):
g = ops.get_default_graph()
idx = 0
for _ in range(len(g._collections[ops.GraphKeys.GLOBAL_VARIABLES])):
var = g.get_collection_ref(ops.GraphKeys.GLOBAL_VARIABLES)[idx]
name = var.op.name
if name in opt_vars:
ops.add_to_collection(ops.GraphKeys.LOCAL_VARIABLES, var)
del g.get_collection_ref(ops.GraphKeys.GLOBAL_VARIABLES)[idx]
else:
idx += 1
_adjust_optimizer_variable_collection(global_new - global_old)
# update global variables.
def _Update_global_variables():
local_vars = [v for g, v in grads_and_vars if g is not None]
global_center_vars = [self._global_map[var] for var in local_vars]
local_center_vars = [self._local_map[var] for var in local_vars]
local_center_vars_update = []
for lvar, var in zip(local_center_vars, global_center_vars):
local_center_vars_update.append(lvar.assign(var))
update_ops = []
differences = []
with ops.control_dependencies(local_center_vars_update):
for v, lv in zip(local_vars, local_center_vars):
with ops.device(v.device):
differences.append(math_ops.subtract(v, lv))
for lvar, diff in zip(local_vars, differences):
with ops.device(lvar.device):
update_ops.append(
state_ops.assign_sub(lvar,
math_ops.multiply(self._moving_rate,
diff)))
for var, diff in zip(global_center_vars, differences):
with ops.device(var.device):
update_ops.append(
state_ops.assign_add(var,
math_ops.multiply(self._moving_rate,
diff)))
if global_step:
with ops.colocate_with(global_step):
update_ops.append(state_ops.assign_add(global_step, 1))
variable_update = control_flow_ops.group(*(update_ops))
return variable_update
with ops.control_dependencies([local_update]):
condition = math_ops.equal(
math_ops.mod(self._local_step, self._period), 0)
conditional_update = control_flow_ops.cond(condition,
_Update_global_variables,
control_flow_ops.no_op)
return conditional_update
|
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",",
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",",
"_Update_global_variables",
",",
"control_flow_ops",
".",
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")",
"return",
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] |
https://github.com/Xilinx/Vitis-AI/blob/fc74d404563d9951b57245443c73bef389f3657f/tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/opt/python/training/elastic_average_optimizer.py#L268-L351
|
|
wxWidgets/wxPython-Classic
|
19571e1ae65f1ac445f5491474121998c97a1bf0
|
src/msw/_misc.py
|
python
|
DateTime.GetNextWeekDay
|
(*args, **kwargs)
|
return _misc_.DateTime_GetNextWeekDay(*args, **kwargs)
|
GetNextWeekDay(self, int weekday) -> DateTime
|
GetNextWeekDay(self, int weekday) -> DateTime
|
[
"GetNextWeekDay",
"(",
"self",
"int",
"weekday",
")",
"-",
">",
"DateTime"
] |
def GetNextWeekDay(*args, **kwargs):
"""GetNextWeekDay(self, int weekday) -> DateTime"""
return _misc_.DateTime_GetNextWeekDay(*args, **kwargs)
|
[
"def",
"GetNextWeekDay",
"(",
"*",
"args",
",",
"*",
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"kwargs",
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"_misc_",
".",
"DateTime_GetNextWeekDay",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")"
] |
https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/msw/_misc.py#L3857-L3859
|
|
wxWidgets/wxPython-Classic
|
19571e1ae65f1ac445f5491474121998c97a1bf0
|
src/gtk/html.py
|
python
|
HtmlWinParser.SetFontFixed
|
(*args, **kwargs)
|
return _html.HtmlWinParser_SetFontFixed(*args, **kwargs)
|
SetFontFixed(self, int x)
|
SetFontFixed(self, int x)
|
[
"SetFontFixed",
"(",
"self",
"int",
"x",
")"
] |
def SetFontFixed(*args, **kwargs):
"""SetFontFixed(self, int x)"""
return _html.HtmlWinParser_SetFontFixed(*args, **kwargs)
|
[
"def",
"SetFontFixed",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"return",
"_html",
".",
"HtmlWinParser_SetFontFixed",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")"
] |
https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/gtk/html.py#L328-L330
|
|
catboost/catboost
|
167f64f237114a4d10b2b4ee42adb4569137debe
|
contrib/python/pandas/py2/pandas/core/resample.py
|
python
|
Resampler.quantile
|
(self, q=0.5, **kwargs)
|
return self._downsample('quantile', q=q, **kwargs)
|
Return value at the given quantile.
.. versionadded:: 0.24.0
Parameters
----------
q : float or array-like, default 0.5 (50% quantile)
See Also
--------
Series.quantile
DataFrame.quantile
DataFrameGroupBy.quantile
|
Return value at the given quantile.
|
[
"Return",
"value",
"at",
"the",
"given",
"quantile",
"."
] |
def quantile(self, q=0.5, **kwargs):
"""
Return value at the given quantile.
.. versionadded:: 0.24.0
Parameters
----------
q : float or array-like, default 0.5 (50% quantile)
See Also
--------
Series.quantile
DataFrame.quantile
DataFrameGroupBy.quantile
"""
return self._downsample('quantile', q=q, **kwargs)
|
[
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https://github.com/catboost/catboost/blob/167f64f237114a4d10b2b4ee42adb4569137debe/contrib/python/pandas/py2/pandas/core/resample.py#L830-L846
|
|
etternagame/etterna
|
8775f74ac9c353320128609d4b4150672e9a6d04
|
extern/crashpad/buildtools/checkdeps/results.py
|
python
|
DependeeStatus.HasViolations
|
(self)
|
return not not self.violations
|
Returns True if this dependee is violating one or more rules.
|
Returns True if this dependee is violating one or more rules.
|
[
"Returns",
"True",
"if",
"this",
"dependee",
"is",
"violating",
"one",
"or",
"more",
"rules",
"."
] |
def HasViolations(self):
"""Returns True if this dependee is violating one or more rules."""
return not not self.violations
|
[
"def",
"HasViolations",
"(",
"self",
")",
":",
"return",
"not",
"not",
"self",
".",
"violations"
] |
https://github.com/etternagame/etterna/blob/8775f74ac9c353320128609d4b4150672e9a6d04/extern/crashpad/buildtools/checkdeps/results.py#L42-L44
|
|
SpenceKonde/megaTinyCore
|
1c4a70b18a149fe6bcb551dfa6db11ca50b8997b
|
megaavr/tools/libs/pyedbglib/protocols/avr8protocol.py
|
python
|
Avr8Protocol.program_counter_write
|
(self, program_counter)
|
Writes the program counter
:param program_counter:
|
Writes the program counter
|
[
"Writes",
"the",
"program",
"counter"
] |
def program_counter_write(self, program_counter):
"""
Writes the program counter
:param program_counter:
"""
self.check_response(self.jtagice3_command_response(
bytearray([self.CMD_AVR8_PC_WRITE, self.CMD_VERSION0]) + binary.pack_le32(program_counter)))
|
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https://github.com/SpenceKonde/megaTinyCore/blob/1c4a70b18a149fe6bcb551dfa6db11ca50b8997b/megaavr/tools/libs/pyedbglib/protocols/avr8protocol.py#L424-L431
|
||
microsoft/checkedc-clang
|
a173fefde5d7877b7750e7ce96dd08cf18baebf2
|
clang/bindings/python/clang/cindex.py
|
python
|
Type.kind
|
(self)
|
return TypeKind.from_id(self._kind_id)
|
Return the kind of this type.
|
Return the kind of this type.
|
[
"Return",
"the",
"kind",
"of",
"this",
"type",
"."
] |
def kind(self):
"""Return the kind of this type."""
return TypeKind.from_id(self._kind_id)
|
[
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"self",
")",
":",
"return",
"TypeKind",
".",
"from_id",
"(",
"self",
".",
"_kind_id",
")"
] |
https://github.com/microsoft/checkedc-clang/blob/a173fefde5d7877b7750e7ce96dd08cf18baebf2/clang/bindings/python/clang/cindex.py#L2187-L2189
|
|
swift/swift
|
12d031cf8177fdec0137f9aa7e2912fa23c4416b
|
3rdParty/SCons/scons-3.0.1/engine/SCons/SConf.py
|
python
|
SConfBase.AddTests
|
(self, tests)
|
Adds all the tests given in the tests dictionary to this SConf
instance
|
Adds all the tests given in the tests dictionary to this SConf
instance
|
[
"Adds",
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"the",
"tests",
"given",
"in",
"the",
"tests",
"dictionary",
"to",
"this",
"SConf",
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] |
def AddTests(self, tests):
"""Adds all the tests given in the tests dictionary to this SConf
instance
"""
for name in list(tests.keys()):
self.AddTest(name, tests[name])
|
[
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",",
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https://github.com/swift/swift/blob/12d031cf8177fdec0137f9aa7e2912fa23c4416b/3rdParty/SCons/scons-3.0.1/engine/SCons/SConf.py#L670-L675
|
||
Illumina/hap.py
|
84011695b2ff2406c16a335106db6831fb67fdfe
|
src/python/Tools/vcfcallerinfo.py
|
python
|
CallerInfo.addVCF
|
(self, vcfname)
|
Add caller versions from a VCF
:param vcfname: VCF file name
|
Add caller versions from a VCF
:param vcfname: VCF file name
|
[
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":",
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] |
def addVCF(self, vcfname):
""" Add caller versions from a VCF
:param vcfname: VCF file name
"""
tf = tempfile.NamedTemporaryFile(delete=False)
tf.close()
vfh = {}
try:
sp = subprocess.Popen("vcfhdr2json '%s' '%s'" % (vcfname, tf.name),
shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
o, e = sp.communicate()
if sp.returncode != 0:
raise Exception("vcfhdr2json call failed: %s / %s" % (o, e))
vfh = json.load(open(tf.name))
finally:
try:
os.unlink(tf.name)
except:
pass
cp = ['unknown', 'unknown', '']
gatk_callers = ["haplotypecaller", "unifiedgenotyper", "mutect"]
sent_callers = ["haplotyper"]
source_found = False
for hf in vfh["fields"]:
try:
k = hf["key"]
if k == "source":
try:
cp[0] = str(hf["values"])
except:
cp[0] = hf["value"]
if cp[0].startswith("Platypus_Version_"):
cp[1] = cp[0][len("Platypus_Version_"):]
cp[0] = "Platypus"
source_found = True
elif k == "source_version":
try:
cp[1] = str(hf["values"])
except:
cp[1] = hf["value"]
source_found = True
elif k == "cmdline":
try:
cp[2] = str(hf["values"])
except:
cp[2] = hf["value"]
source_found = True
elif k == "platypusOptions":
try:
cp[2] = str(hf["values"])
except:
cp[2] = hf["value"]
source_found = True
elif k == "octopus":
# octopus doesn't add a version
self.callers.append(["octopus", "unknown", str(hf["values"])])
elif k.startswith("GATKCommandLine"):
caller = "GATK"
try:
caller += "-" + hf["values"]["ID"]
except:
pass
version = "unknown"
try:
version = hf["values"]["Version"]
except:
pass
options = ""
try:
options = hf["values"]["CommandLineOptions"]
except:
pass
if any(g in caller.lower() for g in gatk_callers):
self.callers.append([caller, version, options])
elif k.startswith("SentieonCommandLine"):
caller = "Sentieon"
try:
caller += "-" + hf["values"]["ID"]
except:
pass
version = "unknown"
try:
version = hf["values"]["Version"]
except:
pass
options = ""
if any(s in caller.lower() for s in sent_callers):
self.callers.append([caller, version])
except:
pass
if source_found:
self.callers.append(cp)
|
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https://github.com/Illumina/hap.py/blob/84011695b2ff2406c16a335106db6831fb67fdfe/src/python/Tools/vcfcallerinfo.py#L39-L135
|
||
Xilinx/Vitis-AI
|
fc74d404563d9951b57245443c73bef389f3657f
|
tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/autograph/operators/symbols.py
|
python
|
AttributeAccessSymbol.maybe_compute_value
|
(self)
|
Compute the value corresponding to the attribute access or `Undefined`.
This will be `Undefined` if no such value exists either because there is no
such attribute or if the base is itself undefined.
Returns:
value corresponding to the attribute access or `Undefined`
|
Compute the value corresponding to the attribute access or `Undefined`.
|
[
"Compute",
"the",
"value",
"corresponding",
"to",
"the",
"attribute",
"access",
"or",
"Undefined",
"."
] |
def maybe_compute_value(self):
"""Compute the value corresponding to the attribute access or `Undefined`.
This will be `Undefined` if no such value exists either because there is no
such attribute or if the base is itself undefined.
Returns:
value corresponding to the attribute access or `Undefined`
"""
parent_value = self.parent_symbol.maybe_compute_value()
if (is_undefined(parent_value) or
getattr(parent_value, self.attr_name, None) is None):
return Undefined(self.name)
else:
return parent_value.__getattribute__(self.attr_name)
|
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https://github.com/Xilinx/Vitis-AI/blob/fc74d404563d9951b57245443c73bef389f3657f/tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/autograph/operators/symbols.py#L69-L83
|
||
apple/turicreate
|
cce55aa5311300e3ce6af93cb45ba791fd1bdf49
|
deps/src/libxml2-2.9.1/python/libxml2class.py
|
python
|
uCSIsSpecials
|
(code)
|
return ret
|
Check whether the character is part of Specials UCS Block
|
Check whether the character is part of Specials UCS Block
|
[
"Check",
"whether",
"the",
"character",
"is",
"part",
"of",
"Specials",
"UCS",
"Block"
] |
def uCSIsSpecials(code):
"""Check whether the character is part of Specials UCS Block """
ret = libxml2mod.xmlUCSIsSpecials(code)
return ret
|
[
"def",
"uCSIsSpecials",
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"=",
"libxml2mod",
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"xmlUCSIsSpecials",
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")",
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https://github.com/apple/turicreate/blob/cce55aa5311300e3ce6af93cb45ba791fd1bdf49/deps/src/libxml2-2.9.1/python/libxml2class.py#L2073-L2076
|
|
NVIDIA/thrust
|
627dccb359a635afdd69e95a6cc59698f23f70e2
|
internal/benchmark/compare_benchmark_results.py
|
python
|
int_or_float
|
(x)
|
Convert `x` to either `int` or `float`, preferring `int`.
Raises:
ValueError : If `x` is not convertible to either `int` or `float`
|
Convert `x` to either `int` or `float`, preferring `int`.
|
[
"Convert",
"x",
"to",
"either",
"int",
"or",
"float",
"preferring",
"int",
"."
] |
def int_or_float(x):
"""Convert `x` to either `int` or `float`, preferring `int`.
Raises:
ValueError : If `x` is not convertible to either `int` or `float`
"""
try:
return int(x)
except ValueError:
return float(x)
|
[
"def",
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"x",
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":",
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"int",
"(",
"x",
")",
"except",
"ValueError",
":",
"return",
"float",
"(",
"x",
")"
] |
https://github.com/NVIDIA/thrust/blob/627dccb359a635afdd69e95a6cc59698f23f70e2/internal/benchmark/compare_benchmark_results.py#L107-L116
|
||
wlanjie/AndroidFFmpeg
|
7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf
|
tools/fdk-aac-build/x86/toolchain/lib/python2.7/decimal.py
|
python
|
Decimal.next_toward
|
(self, other, context=None)
|
return ans
|
Returns the number closest to self, in the direction towards other.
The result is the closest representable number to self
(excluding self) that is in the direction towards other,
unless both have the same value. If the two operands are
numerically equal, then the result is a copy of self with the
sign set to be the same as the sign of other.
|
Returns the number closest to self, in the direction towards other.
|
[
"Returns",
"the",
"number",
"closest",
"to",
"self",
"in",
"the",
"direction",
"towards",
"other",
"."
] |
def next_toward(self, other, context=None):
"""Returns the number closest to self, in the direction towards other.
The result is the closest representable number to self
(excluding self) that is in the direction towards other,
unless both have the same value. If the two operands are
numerically equal, then the result is a copy of self with the
sign set to be the same as the sign of other.
"""
other = _convert_other(other, raiseit=True)
if context is None:
context = getcontext()
ans = self._check_nans(other, context)
if ans:
return ans
comparison = self._cmp(other)
if comparison == 0:
return self.copy_sign(other)
if comparison == -1:
ans = self.next_plus(context)
else: # comparison == 1
ans = self.next_minus(context)
# decide which flags to raise using value of ans
if ans._isinfinity():
context._raise_error(Overflow,
'Infinite result from next_toward',
ans._sign)
context._raise_error(Inexact)
context._raise_error(Rounded)
elif ans.adjusted() < context.Emin:
context._raise_error(Underflow)
context._raise_error(Subnormal)
context._raise_error(Inexact)
context._raise_error(Rounded)
# if precision == 1 then we don't raise Clamped for a
# result 0E-Etiny.
if not ans:
context._raise_error(Clamped)
return ans
|
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https://github.com/wlanjie/AndroidFFmpeg/blob/7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf/tools/fdk-aac-build/x86/toolchain/lib/python2.7/decimal.py#L3440-L3484
|
|
wy1iu/LargeMargin_Softmax_Loss
|
c3e9f20e4f16e2b4daf7d358a614366b9b39a6ec
|
python/caffe/classifier.py
|
python
|
Classifier.predict
|
(self, inputs, oversample=True)
|
return predictions
|
Predict classification probabilities of inputs.
Parameters
----------
inputs : iterable of (H x W x K) input ndarrays.
oversample : boolean
average predictions across center, corners, and mirrors
when True (default). Center-only prediction when False.
Returns
-------
predictions: (N x C) ndarray of class probabilities for N images and C
classes.
|
Predict classification probabilities of inputs.
|
[
"Predict",
"classification",
"probabilities",
"of",
"inputs",
"."
] |
def predict(self, inputs, oversample=True):
"""
Predict classification probabilities of inputs.
Parameters
----------
inputs : iterable of (H x W x K) input ndarrays.
oversample : boolean
average predictions across center, corners, and mirrors
when True (default). Center-only prediction when False.
Returns
-------
predictions: (N x C) ndarray of class probabilities for N images and C
classes.
"""
# Scale to standardize input dimensions.
input_ = np.zeros((len(inputs),
self.image_dims[0],
self.image_dims[1],
inputs[0].shape[2]),
dtype=np.float32)
for ix, in_ in enumerate(inputs):
input_[ix] = caffe.io.resize_image(in_, self.image_dims)
if oversample:
# Generate center, corner, and mirrored crops.
input_ = caffe.io.oversample(input_, self.crop_dims)
else:
# Take center crop.
center = np.array(self.image_dims) / 2.0
crop = np.tile(center, (1, 2))[0] + np.concatenate([
-self.crop_dims / 2.0,
self.crop_dims / 2.0
])
crop = crop.astype(int)
input_ = input_[:, crop[0]:crop[2], crop[1]:crop[3], :]
# Classify
caffe_in = np.zeros(np.array(input_.shape)[[0, 3, 1, 2]],
dtype=np.float32)
for ix, in_ in enumerate(input_):
caffe_in[ix] = self.transformer.preprocess(self.inputs[0], in_)
out = self.forward_all(**{self.inputs[0]: caffe_in})
predictions = out[self.outputs[0]]
# For oversampling, average predictions across crops.
if oversample:
predictions = predictions.reshape((len(predictions) / 10, 10, -1))
predictions = predictions.mean(1)
return predictions
|
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https://github.com/wy1iu/LargeMargin_Softmax_Loss/blob/c3e9f20e4f16e2b4daf7d358a614366b9b39a6ec/python/caffe/classifier.py#L47-L98
|
|
yuxng/PoseCNN
|
9f3dd7b7bce21dcafc05e8f18ccc90da3caabd04
|
lib/setup.py
|
python
|
locate_cuda
|
()
|
return cudaconfig
|
Locate the CUDA environment on the system
Returns a dict with keys 'home', 'nvcc', 'include', and 'lib64'
and values giving the absolute path to each directory.
Starts by looking for the CUDAHOME env variable. If not found, everything
is based on finding 'nvcc' in the PATH.
|
Locate the CUDA environment on the system
|
[
"Locate",
"the",
"CUDA",
"environment",
"on",
"the",
"system"
] |
def locate_cuda():
"""Locate the CUDA environment on the system
Returns a dict with keys 'home', 'nvcc', 'include', and 'lib64'
and values giving the absolute path to each directory.
Starts by looking for the CUDAHOME env variable. If not found, everything
is based on finding 'nvcc' in the PATH.
"""
# first check if the CUDAHOME env variable is in use
if 'CUDAHOME' in os.environ:
home = os.environ['CUDAHOME']
nvcc = pjoin(home, 'bin', 'nvcc')
else:
# otherwise, search the PATH for NVCC
default_path = pjoin(os.sep, 'usr', 'local', 'cuda', 'bin')
nvcc = find_in_path('nvcc', os.environ['PATH'] + os.pathsep + default_path)
if nvcc is None:
raise EnvironmentError('The nvcc binary could not be '
'located in your $PATH. Either add it to your path, or set $CUDAHOME')
home = os.path.dirname(os.path.dirname(nvcc))
cudaconfig = {'home':home, 'nvcc':nvcc,
'include': pjoin(home, 'include'),
'lib64': pjoin(home, 'lib64')}
for k, v in cudaconfig.iteritems():
if not os.path.exists(v):
raise EnvironmentError('The CUDA %s path could not be located in %s' % (k, v))
return cudaconfig
|
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https://github.com/yuxng/PoseCNN/blob/9f3dd7b7bce21dcafc05e8f18ccc90da3caabd04/lib/setup.py#L24-L54
|
|
daijifeng001/caffe-rfcn
|
543f8f6a4b7c88256ea1445ae951a12d1ad9cffd
|
scripts/cpp_lint.py
|
python
|
CheckForNonConstReference
|
(filename, clean_lines, linenum,
nesting_state, error)
|
Check for non-const references.
Separate from CheckLanguage since it scans backwards from current
line, instead of scanning forward.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
nesting_state: A _NestingState instance which maintains information about
the current stack of nested blocks being parsed.
error: The function to call with any errors found.
|
Check for non-const references.
|
[
"Check",
"for",
"non",
"-",
"const",
"references",
"."
] |
def CheckForNonConstReference(filename, clean_lines, linenum,
nesting_state, error):
"""Check for non-const references.
Separate from CheckLanguage since it scans backwards from current
line, instead of scanning forward.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
nesting_state: A _NestingState instance which maintains information about
the current stack of nested blocks being parsed.
error: The function to call with any errors found.
"""
# Do nothing if there is no '&' on current line.
line = clean_lines.elided[linenum]
if '&' not in line:
return
# Long type names may be broken across multiple lines, usually in one
# of these forms:
# LongType
# ::LongTypeContinued &identifier
# LongType::
# LongTypeContinued &identifier
# LongType<
# ...>::LongTypeContinued &identifier
#
# If we detected a type split across two lines, join the previous
# line to current line so that we can match const references
# accordingly.
#
# Note that this only scans back one line, since scanning back
# arbitrary number of lines would be expensive. If you have a type
# that spans more than 2 lines, please use a typedef.
if linenum > 1:
previous = None
if Match(r'\s*::(?:[\w<>]|::)+\s*&\s*\S', line):
# previous_line\n + ::current_line
previous = Search(r'\b((?:const\s*)?(?:[\w<>]|::)+[\w<>])\s*$',
clean_lines.elided[linenum - 1])
elif Match(r'\s*[a-zA-Z_]([\w<>]|::)+\s*&\s*\S', line):
# previous_line::\n + current_line
previous = Search(r'\b((?:const\s*)?(?:[\w<>]|::)+::)\s*$',
clean_lines.elided[linenum - 1])
if previous:
line = previous.group(1) + line.lstrip()
else:
# Check for templated parameter that is split across multiple lines
endpos = line.rfind('>')
if endpos > -1:
(_, startline, startpos) = ReverseCloseExpression(
clean_lines, linenum, endpos)
if startpos > -1 and startline < linenum:
# Found the matching < on an earlier line, collect all
# pieces up to current line.
line = ''
for i in xrange(startline, linenum + 1):
line += clean_lines.elided[i].strip()
# Check for non-const references in function parameters. A single '&' may
# found in the following places:
# inside expression: binary & for bitwise AND
# inside expression: unary & for taking the address of something
# inside declarators: reference parameter
# We will exclude the first two cases by checking that we are not inside a
# function body, including one that was just introduced by a trailing '{'.
# TODO(unknwon): Doesn't account for preprocessor directives.
# TODO(unknown): Doesn't account for 'catch(Exception& e)' [rare].
check_params = False
if not nesting_state.stack:
check_params = True # top level
elif (isinstance(nesting_state.stack[-1], _ClassInfo) or
isinstance(nesting_state.stack[-1], _NamespaceInfo)):
check_params = True # within class or namespace
elif Match(r'.*{\s*$', line):
if (len(nesting_state.stack) == 1 or
isinstance(nesting_state.stack[-2], _ClassInfo) or
isinstance(nesting_state.stack[-2], _NamespaceInfo)):
check_params = True # just opened global/class/namespace block
# We allow non-const references in a few standard places, like functions
# called "swap()" or iostream operators like "<<" or ">>". Do not check
# those function parameters.
#
# We also accept & in static_assert, which looks like a function but
# it's actually a declaration expression.
whitelisted_functions = (r'(?:[sS]wap(?:<\w:+>)?|'
r'operator\s*[<>][<>]|'
r'static_assert|COMPILE_ASSERT'
r')\s*\(')
if Search(whitelisted_functions, line):
check_params = False
elif not Search(r'\S+\([^)]*$', line):
# Don't see a whitelisted function on this line. Actually we
# didn't see any function name on this line, so this is likely a
# multi-line parameter list. Try a bit harder to catch this case.
for i in xrange(2):
if (linenum > i and
Search(whitelisted_functions, clean_lines.elided[linenum - i - 1])):
check_params = False
break
if check_params:
decls = ReplaceAll(r'{[^}]*}', ' ', line) # exclude function body
for parameter in re.findall(_RE_PATTERN_REF_PARAM, decls):
if not Match(_RE_PATTERN_CONST_REF_PARAM, parameter):
error(filename, linenum, 'runtime/references', 2,
'Is this a non-const reference? '
'If so, make const or use a pointer: ' +
ReplaceAll(' *<', '<', parameter))
|
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"# LongType::",
"# LongTypeContinued &identifier",
"# LongType<",
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"=",
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"+",
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"(",
"' *<'",
",",
"'<'",
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"parameter",
")",
")"
] |
https://github.com/daijifeng001/caffe-rfcn/blob/543f8f6a4b7c88256ea1445ae951a12d1ad9cffd/scripts/cpp_lint.py#L4134-L4244
|
||
CRYTEK/CRYENGINE
|
232227c59a220cbbd311576f0fbeba7bb53b2a8c
|
Editor/Python/windows/Lib/site-packages/pkg_resources/_vendor/pyparsing.py
|
python
|
ParserElement.__invert__
|
( self )
|
return NotAny( self )
|
Implementation of ~ operator - returns C{L{NotAny}}
|
Implementation of ~ operator - returns C{L{NotAny}}
|
[
"Implementation",
"of",
"~",
"operator",
"-",
"returns",
"C",
"{",
"L",
"{",
"NotAny",
"}}"
] |
def __invert__( self ):
"""
Implementation of ~ operator - returns C{L{NotAny}}
"""
return NotAny( self )
|
[
"def",
"__invert__",
"(",
"self",
")",
":",
"return",
"NotAny",
"(",
"self",
")"
] |
https://github.com/CRYTEK/CRYENGINE/blob/232227c59a220cbbd311576f0fbeba7bb53b2a8c/Editor/Python/windows/Lib/site-packages/pkg_resources/_vendor/pyparsing.py#L2020-L2024
|
|
plaidml/plaidml
|
f3c6681db21460e5fdc11ae651d6d7b6c27f8262
|
plaidml/edsl/__init__.py
|
python
|
TensorDim.__mul__
|
(self, other)
|
return TensorDim(_dim_op(lib.PLAIDML_INT_OP_MUL, self, other))
|
Performs a multiplication between a TensorDim and another operand in
a polynomial expression.
Example:
>>> N, M = TensorDims(2)
>>> A = Placeholder(DType.FLOAT32, [3, 3])
>>> A.bind_dims(N, M)
>>> R = Contraction().outShape(N * 5)
|
Performs a multiplication between a TensorDim and another operand in
a polynomial expression.
|
[
"Performs",
"a",
"multiplication",
"between",
"a",
"TensorDim",
"and",
"another",
"operand",
"in",
"a",
"polynomial",
"expression",
"."
] |
def __mul__(self, other):
"""Performs a multiplication between a TensorDim and another operand in
a polynomial expression.
Example:
>>> N, M = TensorDims(2)
>>> A = Placeholder(DType.FLOAT32, [3, 3])
>>> A.bind_dims(N, M)
>>> R = Contraction().outShape(N * 5)
"""
return TensorDim(_dim_op(lib.PLAIDML_INT_OP_MUL, self, other))
|
[
"def",
"__mul__",
"(",
"self",
",",
"other",
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":",
"return",
"TensorDim",
"(",
"_dim_op",
"(",
"lib",
".",
"PLAIDML_INT_OP_MUL",
",",
"self",
",",
"other",
")",
")"
] |
https://github.com/plaidml/plaidml/blob/f3c6681db21460e5fdc11ae651d6d7b6c27f8262/plaidml/edsl/__init__.py#L103-L113
|
|
ChromiumWebApps/chromium
|
c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7
|
third_party/google_appengine_cloudstorage/cloudstorage/cloudstorage_api.py
|
python
|
_Bucket.__iter__
|
(self)
|
Iter over the bucket.
Yields:
GCSFileStat: a GCSFileStat for an object in the bucket.
They are ordered by GCSFileStat.filename.
|
Iter over the bucket.
|
[
"Iter",
"over",
"the",
"bucket",
"."
] |
def __iter__(self):
"""Iter over the bucket.
Yields:
GCSFileStat: a GCSFileStat for an object in the bucket.
They are ordered by GCSFileStat.filename.
"""
total = 0
max_keys = self._options.get('max-keys')
while self._get_bucket_fut:
status, resp_headers, content = self._get_bucket_fut.get_result()
errors.check_status(status, [200], self._path, resp_headers=resp_headers,
extras=self._options)
if self._should_get_another_batch(content):
self._get_bucket_fut = self._api.get_bucket_async(
self._path + '?' + urllib.urlencode(self._options))
else:
self._get_bucket_fut = None
root = ET.fromstring(content)
dirs = self._next_dir_gen(root)
files = self._next_file_gen(root)
next_file = files.next()
next_dir = dirs.next()
while ((max_keys is None or total < max_keys) and
not (next_file is None and next_dir is None)):
total += 1
if next_file is None:
self._last_yield = next_dir
next_dir = dirs.next()
elif next_dir is None:
self._last_yield = next_file
next_file = files.next()
elif next_dir < next_file:
self._last_yield = next_dir
next_dir = dirs.next()
elif next_file < next_dir:
self._last_yield = next_file
next_file = files.next()
else:
logging.error(
'Should never reach. next file is %r. next dir is %r.',
next_file, next_dir)
if self._new_max_keys:
self._new_max_keys -= 1
yield self._last_yield
|
[
"def",
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"self",
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":",
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",",
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"_new_max_keys",
"-=",
"1",
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] |
https://github.com/ChromiumWebApps/chromium/blob/c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7/third_party/google_appengine_cloudstorage/cloudstorage/cloudstorage_api.py#L308-L356
|
||
GeometryCollective/boundary-first-flattening
|
8250e5a0e85980ec50b5e8aa8f49dd6519f915cd
|
deps/nanogui/docs/exhale.py
|
python
|
ExhaleRoot.generateDirectoryView
|
(self, treeView)
|
Generates the file view hierarchy, writing it to ``self.directory_view_file``.
:Parameters:
``treeView`` (bool)
Whether or not to use the collapsibleList version. See the
``createTreeView`` description in :func:`exhale.generate`.
|
Generates the file view hierarchy, writing it to ``self.directory_view_file``.
|
[
"Generates",
"the",
"file",
"view",
"hierarchy",
"writing",
"it",
"to",
"self",
".",
"directory_view_file",
"."
] |
def generateDirectoryView(self, treeView):
'''
Generates the file view hierarchy, writing it to ``self.directory_view_file``.
:Parameters:
``treeView`` (bool)
Whether or not to use the collapsibleList version. See the
``createTreeView`` description in :func:`exhale.generate`.
'''
directory_view_stream = StringIO()
for d in self.dirs:
d.toDirectoryView(0, directory_view_stream, treeView)
# add potential missing files (not sure if this is possible though)
missing = []
for f in sorted(self.files):
if not f.in_directory_view:
missing.append(f)
found_missing = len(missing) > 0
if found_missing:
idx = 0
last_missing_child = len(missing) - 1
for m in missing:
m.toDirectoryView(0, directory_view_stream, treeView, idx == last_missing_child)
idx += 1
elif treeView:
# need to restart since there were no missing children found, otherwise the
# last directory will not correctly have a lastChild
directory_view_stream.close()
directory_view_stream = StringIO()
last_dir_index = len(self.dirs) - 1
for idx in range(last_dir_index + 1):
curr_d = self.dirs[idx]
curr_d.toDirectoryView(0, directory_view_stream, treeView, idx == last_dir_index)
# extract the value from the stream and close it down
directory_view_string = directory_view_stream.getvalue()
directory_view_stream.close()
# inject the raw html for the treeView unordered lists
if treeView:
# we need to indent everything to be under the .. raw:: html directive, add
# indentation so the html is readable while we are at it
indented = re.sub(r'(.+)', r' \1', directory_view_string)
directory_view_string = \
'.. raw:: html\n\n' \
' <ul class="treeView">\n' \
' <li>\n' \
' <ul class="collapsibleList">\n' \
'{}' \
' </ul><!-- collapsibleList -->\n' \
' </li><!-- only tree view element -->\n' \
' </ul><!-- treeView -->\n'.format(indented)
# write everything to file to be included in the root api later
try:
with open(self.directory_view_file, "w") as dvf:
dvf.write("File Hierarchy\n{}\n\n{}\n\n".format(EXHALE_SECTION_HEADING,
directory_view_string))
except Exception as e:
exclaimError("Error writing the directory hierarchy: {}".format(e))
|
[
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",",
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"in",
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":",
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"' <ul class=\"treeView\">\\n'",
"' <li>\\n'",
"' <ul class=\"collapsibleList\">\\n'",
"'{}'",
"' </ul><!-- collapsibleList -->\\n'",
"' </li><!-- only tree view element -->\\n'",
"' </ul><!-- treeView -->\\n'",
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"exclaimError",
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".",
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")"
] |
https://github.com/GeometryCollective/boundary-first-flattening/blob/8250e5a0e85980ec50b5e8aa8f49dd6519f915cd/deps/nanogui/docs/exhale.py#L2749-L2812
|
||
EQEmu/Server
|
4a4158380551c134302a7517f1a59a14d8735d69
|
utils/scripts/opcode_handlers.py
|
python
|
loadclientopcodes
|
()
|
return True
|
Load CLIENT OPCODES into memory
|
Load CLIENT OPCODES into memory
|
[
"Load",
"CLIENT",
"OPCODES",
"into",
"memory"
] |
def loadclientopcodes():
""" Load CLIENT OPCODES into memory """
if VERBOSE:
dprint('entering \'loadclientopcodes()\'\n')
bad_clients = []
for client in client_list:
try:
short_name = '/patch_{0}.conf'.format(client)
file_name = '{0}/utils/patches{1}'.format(
base_path,
short_name
)
vprint(file_name)
with open(file_name, 'r') as data_file:
if VERBOSE:
dprint('->open: \'{0}\' in \'r\' mode\n'.format(file_name))
client_opcodes[client] = {}
line_no = 0
for data_line in data_file:
line_no += 1
key_begin = data_line.find('OP_')
key_end = data_line.find('=', key_begin)
if not key_begin == 0 or key_end < 0:
continue
val_begin = data_line.find('0x', key_end)
val_end = val_begin + 6
if val_begin < 0:
continue
value = int(data_line[(val_begin + 2):val_end].lower(), 16)
if value == 0:
if VERBOSE:
uprint('\nUNDEFINED OPCODE FOUND: ../utils/patches{0}({1}:{2}) [{3}][{4}] = {5}\n'.format(
short_name,
line_no,
key_begin,
client,
data_line[key_begin:key_end],
'0x{0}'.format(hex(value)[2:].zfill(4))
))
continue
client_opcodes[client][data_line[key_begin:key_end]] = '0x{0}'.format(hex(value)[2:].zfill(4))
if VERBOSE:
dprint('../utils/patches{0}({1}:{2}) [{3}][{4}] = {5}\n'.format(
short_name,
line_no,
key_begin,
client,
data_line[key_begin:key_end],
client_opcodes[client][data_line[key_begin:key_end]]
))
data_file.close()
if VERBOSE:
dprint('->close: \'{0}\'\n'.format(file_name))
if not len(client_opcodes[client]) > 0:
bad_clients.append(client)
except:
print('(Exception Error: {0}) loadclientopcodes() [{1}]'.format(
sys.exc_info()[0],
client
))
dprint('<-except: \'{0} [{1}]\'\n'.format(
sys.exc_info()[0],
client
))
bad_clients.append(client)
for bad_client in bad_clients:
vprint('Deleting \'{0}\' client from search criteria...'.format(bad_client))
client_list.remove(bad_client)
dprint('->delete: \'{0}\' client\n'.format(bad_client))
if bad_client in client_opcodes:
vprint('Deleting stale entries for \'{0}\' client...'.format(bad_client))
del client_opcodes[bad_client]
dprint('->delete: \'{0}\' client opcode entries\n'.format(bad_client))
if not len(client_list) > 0:
print('Could not locate valid clients...')
dprint('leaving \'loadclientopcodes(): NO VALID CLIENTS EXIST\'\n\n')
return False
if not len(client_opcodes) > 0:
print('Could not locate client opcode lists...')
dprint('leaving \'loadclientopcodes(): CLIENT OPCODES NOT FOUND\'\n\n')
return False
if VERBOSE:
dprint('leaving \'loadclientopcodes()\'\n\n')
return True
|
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":",
"dprint",
"(",
"'leaving \\'loadclientopcodes()\\'\\n\\n'",
")",
"return",
"True"
] |
https://github.com/EQEmu/Server/blob/4a4158380551c134302a7517f1a59a14d8735d69/utils/scripts/opcode_handlers.py#L262-L380
|
|
wxWidgets/wxPython-Classic
|
19571e1ae65f1ac445f5491474121998c97a1bf0
|
wx/lib/ogl/_drawn.py
|
python
|
DrawnShape.DrawArc
|
(self, centrePt, startPt, endPt)
|
Draw an arc.
|
Draw an arc.
|
[
"Draw",
"an",
"arc",
"."
] |
def DrawArc(self, centrePt, startPt, endPt):
"""Draw an arc."""
self._metafiles[self._currentAngle].DrawArc(centrePt, startPt, endPt)
|
[
"def",
"DrawArc",
"(",
"self",
",",
"centrePt",
",",
"startPt",
",",
"endPt",
")",
":",
"self",
".",
"_metafiles",
"[",
"self",
".",
"_currentAngle",
"]",
".",
"DrawArc",
"(",
"centrePt",
",",
"startPt",
",",
"endPt",
")"
] |
https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/wx/lib/ogl/_drawn.py#L774-L776
|
||
krishauser/Klampt
|
972cc83ea5befac3f653c1ba20f80155768ad519
|
Python/klampt/robotsim.py
|
python
|
WorldModel.loadElement
|
(self, fn: str)
|
return _robotsim.WorldModel_loadElement(self, fn)
|
r"""
Loads some element from a file, automatically detecting its type. Meshes are
interpreted as terrains.
Args:
fn (str)
Returns:
The element's ID, or -1 if loading failed.
|
r"""
Loads some element from a file, automatically detecting its type. Meshes are
interpreted as terrains.
|
[
"r",
"Loads",
"some",
"element",
"from",
"a",
"file",
"automatically",
"detecting",
"its",
"type",
".",
"Meshes",
"are",
"interpreted",
"as",
"terrains",
"."
] |
def loadElement(self, fn: str) ->int:
r"""
Loads some element from a file, automatically detecting its type. Meshes are
interpreted as terrains.
Args:
fn (str)
Returns:
The element's ID, or -1 if loading failed.
"""
return _robotsim.WorldModel_loadElement(self, fn)
|
[
"def",
"loadElement",
"(",
"self",
",",
"fn",
":",
"str",
")",
"->",
"int",
":",
"return",
"_robotsim",
".",
"WorldModel_loadElement",
"(",
"self",
",",
"fn",
")"
] |
https://github.com/krishauser/Klampt/blob/972cc83ea5befac3f653c1ba20f80155768ad519/Python/klampt/robotsim.py#L5865-L5878
|
|
ideawu/ssdb
|
f229ba277c7f7d0ca5a441c0c6fb3d1209af68e4
|
deps/cpy/antlr3/tree.py
|
python
|
TreeParser.getErrorHeader
|
(self, e)
|
return (self.getGrammarFileName() +
": node from %sline %s:%s"
% (['', "after "][e.approximateLineInfo],
e.line,
e.charPositionInLine
)
)
|
Prefix error message with the grammar name because message is
always intended for the programmer because the parser built
the input tree not the user.
|
Prefix error message with the grammar name because message is
always intended for the programmer because the parser built
the input tree not the user.
|
[
"Prefix",
"error",
"message",
"with",
"the",
"grammar",
"name",
"because",
"message",
"is",
"always",
"intended",
"for",
"the",
"programmer",
"because",
"the",
"parser",
"built",
"the",
"input",
"tree",
"not",
"the",
"user",
"."
] |
def getErrorHeader(self, e):
"""
Prefix error message with the grammar name because message is
always intended for the programmer because the parser built
the input tree not the user.
"""
return (self.getGrammarFileName() +
": node from %sline %s:%s"
% (['', "after "][e.approximateLineInfo],
e.line,
e.charPositionInLine
)
)
|
[
"def",
"getErrorHeader",
"(",
"self",
",",
"e",
")",
":",
"return",
"(",
"self",
".",
"getGrammarFileName",
"(",
")",
"+",
"\": node from %sline %s:%s\"",
"%",
"(",
"[",
"''",
",",
"\"after \"",
"]",
"[",
"e",
".",
"approximateLineInfo",
"]",
",",
"e",
".",
"line",
",",
"e",
".",
"charPositionInLine",
")",
")"
] |
https://github.com/ideawu/ssdb/blob/f229ba277c7f7d0ca5a441c0c6fb3d1209af68e4/deps/cpy/antlr3/tree.py#L2123-L2136
|
|
ChromiumWebApps/chromium
|
c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7
|
third_party/tlslite/tlslite/integration/AsyncStateMachine.py
|
python
|
AsyncStateMachine.inReadEvent
|
(self)
|
Tell the state machine it can read from the socket.
|
Tell the state machine it can read from the socket.
|
[
"Tell",
"the",
"state",
"machine",
"it",
"can",
"read",
"from",
"the",
"socket",
"."
] |
def inReadEvent(self):
"""Tell the state machine it can read from the socket."""
try:
self._checkAssert()
if self.handshaker:
self._doHandshakeOp()
elif self.closer:
self._doCloseOp()
elif self.reader:
self._doReadOp()
elif self.writer:
self._doWriteOp()
else:
self.reader = self.tlsConnection.readAsync(16384)
self._doReadOp()
except:
self._clear()
raise
|
[
"def",
"inReadEvent",
"(",
"self",
")",
":",
"try",
":",
"self",
".",
"_checkAssert",
"(",
")",
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".",
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")",
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"(",
")",
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"self",
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"(",
")",
"raise"
] |
https://github.com/ChromiumWebApps/chromium/blob/c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7/third_party/tlslite/tlslite/integration/AsyncStateMachine.py#L118-L135
|
||
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Tools/Python/3.7.10/windows/Lib/tkinter/__init__.py
|
python
|
Listbox.activate
|
(self, index)
|
Activate item identified by INDEX.
|
Activate item identified by INDEX.
|
[
"Activate",
"item",
"identified",
"by",
"INDEX",
"."
] |
def activate(self, index):
"""Activate item identified by INDEX."""
self.tk.call(self._w, 'activate', index)
|
[
"def",
"activate",
"(",
"self",
",",
"index",
")",
":",
"self",
".",
"tk",
".",
"call",
"(",
"self",
".",
"_w",
",",
"'activate'",
",",
"index",
")"
] |
https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/windows/Lib/tkinter/__init__.py#L2779-L2781
|
||
hanpfei/chromium-net
|
392cc1fa3a8f92f42e4071ab6e674d8e0482f83f
|
tools/vim/chromium.ycm_extra_conf.py
|
python
|
GetClangCommandLineForNinjaOutput
|
(out_dir, build_target)
|
return None
|
Returns the Clang command line for building |build_target|
Asks ninja for the list of commands used to build |filename| and returns the
final Clang invocation.
Args:
out_dir: (String) Absolute path to ninja build output directory.
build_target: (String) A build target understood by ninja
Returns:
(String or None) Clang command line or None if a Clang command line couldn't
be determined.
|
Returns the Clang command line for building |build_target|
|
[
"Returns",
"the",
"Clang",
"command",
"line",
"for",
"building",
"|build_target|"
] |
def GetClangCommandLineForNinjaOutput(out_dir, build_target):
"""Returns the Clang command line for building |build_target|
Asks ninja for the list of commands used to build |filename| and returns the
final Clang invocation.
Args:
out_dir: (String) Absolute path to ninja build output directory.
build_target: (String) A build target understood by ninja
Returns:
(String or None) Clang command line or None if a Clang command line couldn't
be determined.
"""
p = subprocess.Popen(['ninja', '-v', '-C', out_dir,
'-t', 'commands', build_target],
stdout=subprocess.PIPE, universal_newlines=True)
stdout, stderr = p.communicate()
if p.returncode != 0:
return None
# Ninja will return multiple build steps for all dependencies up to
# |build_target|. The build step we want is the last Clang invocation, which
# is expected to be the one that outputs |build_target|.
for line in reversed(stdout.split('\n')):
if 'clang' in line:
return line
return None
|
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] |
https://github.com/hanpfei/chromium-net/blob/392cc1fa3a8f92f42e4071ab6e674d8e0482f83f/tools/vim/chromium.ycm_extra_conf.py#L184-L211
|
|
tensorflow/tensorflow
|
419e3a6b650ea4bd1b0cba23c4348f8a69f3272e
|
tensorflow/python/distribute/sharded_variable.py
|
python
|
ShardedVariable._overload_operator
|
(cls, operator)
|
Delegate an operator overload to `ops.Tensor`.
|
Delegate an operator overload to `ops.Tensor`.
|
[
"Delegate",
"an",
"operator",
"overload",
"to",
"ops",
".",
"Tensor",
"."
] |
def _overload_operator(cls, operator):
"""Delegate an operator overload to `ops.Tensor`."""
tensor_operator = getattr(ops.Tensor, operator)
def _operator(v, *args, **kwargs):
return tensor_operator(_var_to_tensor(v), *args, **kwargs)
setattr(cls, operator, _operator)
|
[
"def",
"_overload_operator",
"(",
"cls",
",",
"operator",
")",
":",
"tensor_operator",
"=",
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",",
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"(",
"cls",
",",
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",",
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")"
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https://github.com/tensorflow/tensorflow/blob/419e3a6b650ea4bd1b0cba23c4348f8a69f3272e/tensorflow/python/distribute/sharded_variable.py#L811-L818
|
||
OAID/Caffe-HRT
|
aae71e498ab842c6f92bcc23fc668423615a4d65
|
examples/pycaffe/layers/pascal_multilabel_datalayers.py
|
python
|
PascalMultilabelDataLayerSync.forward
|
(self, bottom, top)
|
Load data.
|
Load data.
|
[
"Load",
"data",
"."
] |
def forward(self, bottom, top):
"""
Load data.
"""
for itt in range(self.batch_size):
# Use the batch loader to load the next image.
im, multilabel = self.batch_loader.load_next_image()
# Add directly to the caffe data layer
top[0].data[itt, ...] = im
top[1].data[itt, ...] = multilabel
|
[
"def",
"forward",
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"self",
",",
"bottom",
",",
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"]",
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https://github.com/OAID/Caffe-HRT/blob/aae71e498ab842c6f92bcc23fc668423615a4d65/examples/pycaffe/layers/pascal_multilabel_datalayers.py#L55-L65
|
||
eranif/codelite
|
076eb332d6d2b7ea9a7654afa0461a01d91543aa
|
Runtime/gdb_printers/libstdcxx/v6/printers.py
|
python
|
TemplateTypePrinter.instantiate
|
(self)
|
return self._recognizer(self.name, self.defargs)
|
Return a recognizer object for this type printer.
|
Return a recognizer object for this type printer.
|
[
"Return",
"a",
"recognizer",
"object",
"for",
"this",
"type",
"printer",
"."
] |
def instantiate(self):
"Return a recognizer object for this type printer."
return self._recognizer(self.name, self.defargs)
|
[
"def",
"instantiate",
"(",
"self",
")",
":",
"return",
"self",
".",
"_recognizer",
"(",
"self",
".",
"name",
",",
"self",
".",
"defargs",
")"
] |
https://github.com/eranif/codelite/blob/076eb332d6d2b7ea9a7654afa0461a01d91543aa/Runtime/gdb_printers/libstdcxx/v6/printers.py#L1459-L1461
|
|
Xilinx/Vitis-AI
|
fc74d404563d9951b57245443c73bef389f3657f
|
tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/data/experimental/ops/batching.py
|
python
|
map_and_batch
|
(map_func,
batch_size,
num_parallel_batches=None,
drop_remainder=False,
num_parallel_calls=None)
|
return _apply_fn
|
Fused implementation of `map` and `batch`.
Maps `map_func` across `batch_size` consecutive elements of this dataset
and then combines them into a batch. Functionally, it is equivalent to `map`
followed by `batch`. However, by fusing the two transformations together, the
implementation can be more efficient. Surfacing this transformation in the API
is temporary. Once automatic input pipeline optimization is implemented,
the fusing of `map` and `batch` will happen automatically and this API will be
deprecated.
Args:
map_func: A function mapping a nested structure of tensors to another
nested structure of tensors.
batch_size: A `tf.int64` scalar `tf.Tensor`, representing the number of
consecutive elements of this dataset to combine in a single batch.
num_parallel_batches: (Optional.) A `tf.int64` scalar `tf.Tensor`,
representing the number of batches to create in parallel. On one hand,
higher values can help mitigate the effect of stragglers. On the other
hand, higher values can increase contention if CPU is scarce.
drop_remainder: (Optional.) A `tf.bool` scalar `tf.Tensor`, representing
whether the last batch should be dropped in case its size is smaller than
desired; the default behavior is not to drop the smaller batch.
num_parallel_calls: (Optional.) A `tf.int32` scalar `tf.Tensor`,
representing the number of elements to process in parallel. If not
specified, `batch_size * num_parallel_batches` elements will be processed
in parallel. If the value `tf.data.experimental.AUTOTUNE` is used, then
the number of parallel calls is set dynamically based on available CPU.
Returns:
A `Dataset` transformation function, which can be passed to
`tf.data.Dataset.apply`.
Raises:
ValueError: If both `num_parallel_batches` and `num_parallel_calls` are
specified.
|
Fused implementation of `map` and `batch`.
|
[
"Fused",
"implementation",
"of",
"map",
"and",
"batch",
"."
] |
def map_and_batch(map_func,
batch_size,
num_parallel_batches=None,
drop_remainder=False,
num_parallel_calls=None):
"""Fused implementation of `map` and `batch`.
Maps `map_func` across `batch_size` consecutive elements of this dataset
and then combines them into a batch. Functionally, it is equivalent to `map`
followed by `batch`. However, by fusing the two transformations together, the
implementation can be more efficient. Surfacing this transformation in the API
is temporary. Once automatic input pipeline optimization is implemented,
the fusing of `map` and `batch` will happen automatically and this API will be
deprecated.
Args:
map_func: A function mapping a nested structure of tensors to another
nested structure of tensors.
batch_size: A `tf.int64` scalar `tf.Tensor`, representing the number of
consecutive elements of this dataset to combine in a single batch.
num_parallel_batches: (Optional.) A `tf.int64` scalar `tf.Tensor`,
representing the number of batches to create in parallel. On one hand,
higher values can help mitigate the effect of stragglers. On the other
hand, higher values can increase contention if CPU is scarce.
drop_remainder: (Optional.) A `tf.bool` scalar `tf.Tensor`, representing
whether the last batch should be dropped in case its size is smaller than
desired; the default behavior is not to drop the smaller batch.
num_parallel_calls: (Optional.) A `tf.int32` scalar `tf.Tensor`,
representing the number of elements to process in parallel. If not
specified, `batch_size * num_parallel_batches` elements will be processed
in parallel. If the value `tf.data.experimental.AUTOTUNE` is used, then
the number of parallel calls is set dynamically based on available CPU.
Returns:
A `Dataset` transformation function, which can be passed to
`tf.data.Dataset.apply`.
Raises:
ValueError: If both `num_parallel_batches` and `num_parallel_calls` are
specified.
"""
if num_parallel_batches is None and num_parallel_calls is None:
num_parallel_calls = batch_size
elif num_parallel_batches is not None and num_parallel_calls is None:
num_parallel_calls = batch_size * num_parallel_batches
elif num_parallel_batches is not None and num_parallel_calls is not None:
raise ValueError("The `num_parallel_batches` and `num_parallel_calls` "
"arguments are mutually exclusive.")
def _apply_fn(dataset):
return _MapAndBatchDataset(dataset, map_func, batch_size,
num_parallel_calls, drop_remainder)
return _apply_fn
|
[
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",",
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",",
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")",
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] |
https://github.com/Xilinx/Vitis-AI/blob/fc74d404563d9951b57245443c73bef389f3657f/tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/data/experimental/ops/batching.py#L145-L199
|
|
turi-code/SFrame
|
796b9bdfb2fa1b881d82080754643c7e68629cd2
|
oss_src/unity/python/sframe/meta/__init__.py
|
python
|
decompile
|
(code, mode='exec')
|
Decompile a code object into python ast.
:param mode: must be 'exec' to compile a module or 'eval' to compile an expression.
|
Decompile a code object into python ast.
:param mode: must be 'exec' to compile a module or 'eval' to compile an expression.
|
[
"Decompile",
"a",
"code",
"object",
"into",
"python",
"ast",
".",
":",
"param",
"mode",
":",
"must",
"be",
"exec",
"to",
"compile",
"a",
"module",
"or",
"eval",
"to",
"compile",
"an",
"expression",
"."
] |
def decompile(code, mode='exec'):
'''
Decompile a code object into python ast.
:param mode: must be 'exec' to compile a module or 'eval' to compile an expression.
'''
if mode == 'exec':
return make_module(code)
else:
raise Exception("can not handle mode %r yet" % mode)
|
[
"def",
"decompile",
"(",
"code",
",",
"mode",
"=",
"'exec'",
")",
":",
"if",
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"code",
")",
"else",
":",
"raise",
"Exception",
"(",
"\"can not handle mode %r yet\"",
"%",
"mode",
")"
] |
https://github.com/turi-code/SFrame/blob/796b9bdfb2fa1b881d82080754643c7e68629cd2/oss_src/unity/python/sframe/meta/__init__.py#L5-L15
|
||
wxWidgets/wxPython-Classic
|
19571e1ae65f1ac445f5491474121998c97a1bf0
|
src/msw/html.py
|
python
|
HtmlTag.GetEndPos1
|
(*args, **kwargs)
|
return _html.HtmlTag_GetEndPos1(*args, **kwargs)
|
GetEndPos1(self) -> int
|
GetEndPos1(self) -> int
|
[
"GetEndPos1",
"(",
"self",
")",
"-",
">",
"int"
] |
def GetEndPos1(*args, **kwargs):
"""GetEndPos1(self) -> int"""
return _html.HtmlTag_GetEndPos1(*args, **kwargs)
|
[
"def",
"GetEndPos1",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"return",
"_html",
".",
"HtmlTag_GetEndPos1",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")"
] |
https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/msw/html.py#L165-L167
|
|
wxWidgets/wxPython-Classic
|
19571e1ae65f1ac445f5491474121998c97a1bf0
|
wx/tools/Editra/src/eclib/finddlg.py
|
python
|
FindPanel.SetFlag
|
(self, flag)
|
Set a search flag
@param flag: AFR_* flag value
|
Set a search flag
@param flag: AFR_* flag value
|
[
"Set",
"a",
"search",
"flag",
"@param",
"flag",
":",
"AFR_",
"*",
"flag",
"value"
] |
def SetFlag(self, flag):
"""Set a search flag
@param flag: AFR_* flag value
"""
flags = self._fdata.GetFlags()
flags |= flag
self.SetFlags(flags)
|
[
"def",
"SetFlag",
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",",
"flag",
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"_fdata",
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"GetFlags",
"(",
")",
"flags",
"|=",
"flag",
"self",
".",
"SetFlags",
"(",
"flags",
")"
] |
https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/wx/tools/Editra/src/eclib/finddlg.py#L1246-L1253
|
||
cmu-db/noisepage
|
79276e68fe83322f1249e8a8be96bd63c583ae56
|
script/self_driving/forecasting/data_loader.py
|
python
|
DataLoader._to_timeseries
|
(self, data: np.ndarray)
|
Convert the 2D array with query id and timestamps into a map of time-series for each query id
:param data: Loaded 2D numpy array of [query_id, timestamp]
:return: None
|
Convert the 2D array with query id and timestamps into a map of time-series for each query id
:param data: Loaded 2D numpy array of [query_id, timestamp]
:return: None
|
[
"Convert",
"the",
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"timestamps",
"into",
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"-",
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"query",
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"numpy",
"array",
"of",
"[",
"query_id",
"timestamp",
"]",
":",
"return",
":",
"None"
] |
def _to_timeseries(self, data: np.ndarray) -> None:
"""
Convert the 2D array with query id and timestamps into a map of time-series for each query id
:param data: Loaded 2D numpy array of [query_id, timestamp]
:return: None
"""
# Query trace file is sorted by timestamps
start_timestamp = data[0][self.TS_IDX]
end_timestamp = data[-1][self.TS_IDX]
if end_timestamp - start_timestamp <= 1:
raise ValueError(
"Empty data set with start timestamp >= end timestamp.")
# Number of data points in the new time-series
num_buckets = (end_timestamp - start_timestamp -
1) // self._interval_us + 1
# Iterate through the timestamps
self._ts_data = {}
for i in range(len(data)):
t = data[i][self.TS_IDX]
qid = data[i][self.QID_IDX]
# Initialize a new query's time-series
if self._ts_data.get(qid) is None:
self._ts_data[qid] = np.zeros(num_buckets)
# Bucket index
bi = (t - start_timestamp) // self._interval_us
self._ts_data[qid][bi] += 1
|
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"[",
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"]",
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] |
https://github.com/cmu-db/noisepage/blob/79276e68fe83322f1249e8a8be96bd63c583ae56/script/self_driving/forecasting/data_loader.py#L60-L90
|
||
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/site-packages/pip/_vendor/html5lib/treewalkers/base.py
|
python
|
TreeWalker.doctype
|
(self, name, publicId=None, systemId=None)
|
return {"type": "Doctype",
"name": name,
"publicId": publicId,
"systemId": systemId}
|
Generates a Doctype token
:arg name:
:arg publicId:
:arg systemId:
:returns: the Doctype token
|
Generates a Doctype token
|
[
"Generates",
"a",
"Doctype",
"token"
] |
def doctype(self, name, publicId=None, systemId=None):
"""Generates a Doctype token
:arg name:
:arg publicId:
:arg systemId:
:returns: the Doctype token
"""
return {"type": "Doctype",
"name": name,
"publicId": publicId,
"systemId": systemId}
|
[
"def",
"doctype",
"(",
"self",
",",
"name",
",",
"publicId",
"=",
"None",
",",
"systemId",
"=",
"None",
")",
":",
"return",
"{",
"\"type\"",
":",
"\"Doctype\"",
",",
"\"name\"",
":",
"name",
",",
"\"publicId\"",
":",
"publicId",
",",
"\"systemId\"",
":",
"systemId",
"}"
] |
https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/site-packages/pip/_vendor/html5lib/treewalkers/base.py#L148-L163
|
|
shedskin/shedskin
|
ae88dbca7b1d9671cd8be448cb0b497122758936
|
examples/linalg.py
|
python
|
inner_prod
|
(v1, v2)
|
return sum
|
inner production of two vectors.
|
inner production of two vectors.
|
[
"inner",
"production",
"of",
"two",
"vectors",
"."
] |
def inner_prod(v1, v2):
'inner production of two vectors.'
sum = 0
for i in xrange(len(v1)):
sum += v1[i] * v2[i]
return sum
|
[
"def",
"inner_prod",
"(",
"v1",
",",
"v2",
")",
":",
"sum",
"=",
"0",
"for",
"i",
"in",
"xrange",
"(",
"len",
"(",
"v1",
")",
")",
":",
"sum",
"+=",
"v1",
"[",
"i",
"]",
"*",
"v2",
"[",
"i",
"]",
"return",
"sum"
] |
https://github.com/shedskin/shedskin/blob/ae88dbca7b1d9671cd8be448cb0b497122758936/examples/linalg.py#L7-L12
|
|
kevinlin311tw/caffe-cvprw15
|
45c2a1bf0368569c54e0be4edf8d34285cf79e70
|
scripts/cpp_lint.py
|
python
|
Search
|
(pattern, s)
|
return _regexp_compile_cache[pattern].search(s)
|
Searches the string for the pattern, caching the compiled regexp.
|
Searches the string for the pattern, caching the compiled regexp.
|
[
"Searches",
"the",
"string",
"for",
"the",
"pattern",
"caching",
"the",
"compiled",
"regexp",
"."
] |
def Search(pattern, s):
"""Searches the string for the pattern, caching the compiled regexp."""
if pattern not in _regexp_compile_cache:
_regexp_compile_cache[pattern] = sre_compile.compile(pattern)
return _regexp_compile_cache[pattern].search(s)
|
[
"def",
"Search",
"(",
"pattern",
",",
"s",
")",
":",
"if",
"pattern",
"not",
"in",
"_regexp_compile_cache",
":",
"_regexp_compile_cache",
"[",
"pattern",
"]",
"=",
"sre_compile",
".",
"compile",
"(",
"pattern",
")",
"return",
"_regexp_compile_cache",
"[",
"pattern",
"]",
".",
"search",
"(",
"s",
")"
] |
https://github.com/kevinlin311tw/caffe-cvprw15/blob/45c2a1bf0368569c54e0be4edf8d34285cf79e70/scripts/cpp_lint.py#L543-L547
|
|
intel/llvm
|
e6d0547e9d99b5a56430c4749f6c7e328bf221ab
|
clang/docs/tools/dump_ast_matchers.py
|
python
|
act_on_decl
|
(declaration, comment, allowed_types)
|
Parse the matcher out of the given declaration and comment.
If 'allowed_types' is set, it contains a list of node types the matcher
can match on, as extracted from the static type asserts in the matcher
definition.
|
Parse the matcher out of the given declaration and comment.
|
[
"Parse",
"the",
"matcher",
"out",
"of",
"the",
"given",
"declaration",
"and",
"comment",
"."
] |
def act_on_decl(declaration, comment, allowed_types):
"""Parse the matcher out of the given declaration and comment.
If 'allowed_types' is set, it contains a list of node types the matcher
can match on, as extracted from the static type asserts in the matcher
definition.
"""
if declaration.strip():
if re.match(r'^\s?(#|namespace|using)', declaration): return
# Node matchers are defined by writing:
# VariadicDynCastAllOfMatcher<ResultType, ArgumentType> name;
m = re.match(r""".*Variadic(?:DynCast)?AllOfMatcher\s*<
\s*([^\s,]+)\s*(?:,
\s*([^\s>]+)\s*)?>
\s*([^\s;]+)\s*;\s*$""", declaration, flags=re.X)
if m:
result, inner, name = m.groups()
if not inner:
inner = result
add_matcher(result, name, 'Matcher<%s>...' % inner,
comment, is_dyncast=True)
return
# Special case of type matchers:
# AstTypeMatcher<ArgumentType> name
m = re.match(r""".*AstTypeMatcher\s*<
\s*([^\s>]+)\s*>
\s*([^\s;]+)\s*;\s*$""", declaration, flags=re.X)
if m:
inner, name = m.groups()
add_matcher('Type', name, 'Matcher<%s>...' % inner,
comment, is_dyncast=True)
# FIXME: re-enable once we have implemented casting on the TypeLoc
# hierarchy.
# add_matcher('TypeLoc', '%sLoc' % name, 'Matcher<%sLoc>...' % inner,
# comment, is_dyncast=True)
return
# Parse the various matcher definition macros.
m = re.match(""".*AST_TYPE(LOC)?_TRAVERSE_MATCHER(?:_DECL)?\(
\s*([^\s,]+\s*),
\s*(?:[^\s,]+\s*),
\s*AST_POLYMORPHIC_SUPPORTED_TYPES\(([^)]*)\)
\)\s*;\s*$""", declaration, flags=re.X)
if m:
loc, name, results = m.groups()[0:3]
result_types = [r.strip() for r in results.split(',')]
comment_result_types = extract_result_types(comment)
if (comment_result_types and
sorted(result_types) != sorted(comment_result_types)):
raise Exception('Inconsistent documentation for: %s' % name)
for result_type in result_types:
add_matcher(result_type, name, 'Matcher<Type>', comment)
# if loc:
# add_matcher('%sLoc' % result_type, '%sLoc' % name, 'Matcher<TypeLoc>',
# comment)
return
m = re.match(r"""^\s*AST_POLYMORPHIC_MATCHER(_P)?(.?)(?:_OVERLOAD)?\(
\s*([^\s,]+)\s*,
\s*AST_POLYMORPHIC_SUPPORTED_TYPES\(([^)]*)\)
(?:,\s*([^\s,]+)\s*
,\s*([^\s,]+)\s*)?
(?:,\s*([^\s,]+)\s*
,\s*([^\s,]+)\s*)?
(?:,\s*\d+\s*)?
\)\s*{\s*$""", declaration, flags=re.X)
if m:
p, n, name, results = m.groups()[0:4]
args = m.groups()[4:]
result_types = [r.strip() for r in results.split(',')]
if allowed_types and allowed_types != result_types:
raise Exception('Inconsistent documentation for: %s' % name)
if n not in ['', '2']:
raise Exception('Cannot parse "%s"' % declaration)
args = ', '.join('%s %s' % (args[i], args[i+1])
for i in range(0, len(args), 2) if args[i])
for result_type in result_types:
add_matcher(result_type, name, args, comment)
return
m = re.match(r"""^\s*AST_POLYMORPHIC_MATCHER_REGEX(?:_OVERLOAD)?\(
\s*([^\s,]+)\s*,
\s*AST_POLYMORPHIC_SUPPORTED_TYPES\(([^)]*)\),
\s*([^\s,]+)\s*
(?:,\s*\d+\s*)?
\)\s*{\s*$""", declaration, flags=re.X)
if m:
name, results, arg_name = m.groups()[0:3]
result_types = [r.strip() for r in results.split(',')]
if allowed_types and allowed_types != result_types:
raise Exception('Inconsistent documentation for: %s' % name)
arg = "StringRef %s, Regex::RegexFlags Flags = NoFlags" % arg_name
comment += """
If the matcher is used in clang-query, RegexFlags parameter
should be passed as a quoted string. e.g: "NoFlags".
Flags can be combined with '|' example \"IgnoreCase | BasicRegex\"
"""
for result_type in result_types:
add_matcher(result_type, name, arg, comment)
return
m = re.match(r"""^\s*AST_MATCHER_FUNCTION(_P)?(.?)(?:_OVERLOAD)?\(
(?:\s*([^\s,]+)\s*,)?
\s*([^\s,]+)\s*
(?:,\s*([^\s,]+)\s*
,\s*([^\s,]+)\s*)?
(?:,\s*([^\s,]+)\s*
,\s*([^\s,]+)\s*)?
(?:,\s*\d+\s*)?
\)\s*{\s*$""", declaration, flags=re.X)
if m:
p, n, result, name = m.groups()[0:4]
args = m.groups()[4:]
if n not in ['', '2']:
raise Exception('Cannot parse "%s"' % declaration)
args = ', '.join('%s %s' % (args[i], args[i+1])
for i in range(0, len(args), 2) if args[i])
add_matcher(result, name, args, comment)
return
m = re.match(r"""^\s*AST_MATCHER(_P)?(.?)(?:_OVERLOAD)?\(
(?:\s*([^\s,]+)\s*,)?
\s*([^\s,]+)\s*
(?:,\s*([^,]+)\s*
,\s*([^\s,]+)\s*)?
(?:,\s*([^\s,]+)\s*
,\s*([^\s,]+)\s*)?
(?:,\s*\d+\s*)?
\)\s*{""", declaration, flags=re.X)
if m:
p, n, result, name = m.groups()[0:4]
args = m.groups()[4:]
if not result:
if not allowed_types:
raise Exception('Did not find allowed result types for: %s' % name)
result_types = allowed_types
else:
result_types = [result]
if n not in ['', '2']:
raise Exception('Cannot parse "%s"' % declaration)
args = ', '.join('%s %s' % (args[i], args[i+1])
for i in range(0, len(args), 2) if args[i])
for result_type in result_types:
add_matcher(result_type, name, args, comment)
return
m = re.match(r"""^\s*AST_MATCHER_REGEX(?:_OVERLOAD)?\(
\s*([^\s,]+)\s*,
\s*([^\s,]+)\s*,
\s*([^\s,]+)\s*
(?:,\s*\d+\s*)?
\)\s*{""", declaration, flags=re.X)
if m:
result, name, arg_name = m.groups()[0:3]
if not result:
if not allowed_types:
raise Exception('Did not find allowed result types for: %s' % name)
result_types = allowed_types
else:
result_types = [result]
arg = "StringRef %s, Regex::RegexFlags Flags = NoFlags" % arg_name
comment += """
If the matcher is used in clang-query, RegexFlags parameter
should be passed as a quoted string. e.g: "NoFlags".
Flags can be combined with '|' example \"IgnoreCase | BasicRegex\"
"""
for result_type in result_types:
add_matcher(result_type, name, arg, comment)
return
# Parse ArgumentAdapting matchers.
m = re.match(
r"""^.*ArgumentAdaptingMatcherFunc<.*>\s*
([a-zA-Z]*);$""",
declaration, flags=re.X)
if m:
name = m.groups()[0]
add_matcher('*', name, 'Matcher<*>', comment)
return
# Parse Variadic functions.
m = re.match(
r"""^.*internal::VariadicFunction\s*<\s*([^,]+),\s*([^,]+),\s*[^>]+>\s*
([a-zA-Z]*);$""",
declaration, flags=re.X)
if m:
result, arg, name = m.groups()[:3]
add_matcher(result, name, '%s, ..., %s' % (arg, arg), comment)
return
m = re.match(
r"""^.*internal::VariadicFunction\s*<\s*
internal::PolymorphicMatcher<[\S\s]+
AST_POLYMORPHIC_SUPPORTED_TYPES\(([^)]*)\),\s*(.*);$""",
declaration, flags=re.X)
if m:
results, trailing = m.groups()
trailing, name = trailing.rsplit(">", 1)
name = name.strip()
trailing, _ = trailing.rsplit(",", 1)
_, arg = trailing.rsplit(",", 1)
arg = arg.strip()
result_types = [r.strip() for r in results.split(',')]
for result_type in result_types:
add_matcher(result_type, name, '%s, ..., %s' % (arg, arg), comment)
return
# Parse Variadic operator matchers.
m = re.match(
r"""^.*VariadicOperatorMatcherFunc\s*<\s*([^,]+),\s*([^\s]+)\s*>\s*
([a-zA-Z]*);$""",
declaration, flags=re.X)
if m:
min_args, max_args, name = m.groups()[:3]
if max_args == '1':
add_matcher('*', name, 'Matcher<*>', comment)
return
elif max_args == 'std::numeric_limits<unsigned>::max()':
add_matcher('*', name, 'Matcher<*>, ..., Matcher<*>', comment)
return
m = re.match(
r"""^.*MapAnyOfMatcher<.*>\s*
([a-zA-Z]*);$""",
declaration, flags=re.X)
if m:
name = m.groups()[0]
add_matcher('*', name, 'Matcher<*>...Matcher<*>', comment)
return
# Parse free standing matcher functions, like:
# Matcher<ResultType> Name(Matcher<ArgumentType> InnerMatcher) {
m = re.match(r"""^\s*(?:template\s+<\s*(?:class|typename)\s+(.+)\s*>\s+)?
(.*)\s+
([^\s\(]+)\s*\(
(.*)
\)\s*{""", declaration, re.X)
if m:
template_name, result, name, args = m.groups()
if template_name:
matcherTemplateArgs = re.findall(r'Matcher<\s*(%s)\s*>' % template_name, args)
templateArgs = re.findall(r'(?:^|[\s,<])(%s)(?:$|[\s,>])' % template_name, args)
if len(matcherTemplateArgs) < len(templateArgs):
# The template name is used naked, so don't replace with `*`` later on
template_name = None
else :
args = re.sub(r'(^|[\s,<])%s($|[\s,>])' % template_name, r'\1*\2', args)
args = ', '.join(p.strip() for p in args.split(','))
m = re.match(r'(?:^|.*\s+)internal::(?:Bindable)?Matcher<([^>]+)>$', result)
if m:
result_types = [m.group(1)]
if template_name and len(result_types) is 1 and result_types[0] == template_name:
result_types = ['*']
else:
result_types = extract_result_types(comment)
if not result_types:
if not comment:
# Only overloads don't have their own doxygen comments; ignore those.
print('Ignoring "%s"' % name)
else:
print('Cannot determine result type for "%s"' % name)
else:
for result_type in result_types:
add_matcher(result_type, name, args, comment)
else:
print('*** Unparsable: "' + declaration + '" ***')
|
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] |
https://github.com/intel/llvm/blob/e6d0547e9d99b5a56430c4749f6c7e328bf221ab/clang/docs/tools/dump_ast_matchers.py#L164-L439
|
||
rootm0s/Protectors
|
5b3f4d11687a5955caf9c3af30666c4bfc2c19ab
|
OWASP-ZSC/module/readline_windows/pyreadline/modes/notemacs.py
|
python
|
NotEmacsMode.next_history
|
(self, e)
|
Move forward through the history list, fetching the next command.
|
Move forward through the history list, fetching the next command.
|
[
"Move",
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"the",
"history",
"list",
"fetching",
"the",
"next",
"command",
"."
] |
def next_history(self, e): # (C-n)
'''Move forward through the history list, fetching the next command. '''
self._history.next_history(self.l_buffer)
|
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] |
https://github.com/rootm0s/Protectors/blob/5b3f4d11687a5955caf9c3af30666c4bfc2c19ab/OWASP-ZSC/module/readline_windows/pyreadline/modes/notemacs.py#L146-L148
|
||
hanpfei/chromium-net
|
392cc1fa3a8f92f42e4071ab6e674d8e0482f83f
|
third_party/catapult/third_party/py_vulcanize/third_party/rcssmin/_setup/py3/ext.py
|
python
|
ConfTest.destroy
|
(self)
|
Destroy the conftest leftovers on disk
|
Destroy the conftest leftovers on disk
|
[
"Destroy",
"the",
"conftest",
"leftovers",
"on",
"disk"
] |
def destroy(self):
""" Destroy the conftest leftovers on disk """
tempdir, self._tempdir = self._tempdir, None
if tempdir is not None:
_shutil.rmtree(tempdir)
|
[
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] |
https://github.com/hanpfei/chromium-net/blob/392cc1fa3a8f92f42e4071ab6e674d8e0482f83f/third_party/catapult/third_party/py_vulcanize/third_party/rcssmin/_setup/py3/ext.py#L196-L200
|
||
Polidea/SiriusObfuscator
|
b0e590d8130e97856afe578869b83a209e2b19be
|
SymbolExtractorAndRenamer/lldb/scripts/Python/static-binding/lldb.py
|
python
|
SBHostOS.__init__
|
(self)
|
__init__(self) -> SBHostOS
|
__init__(self) -> SBHostOS
|
[
"__init__",
"(",
"self",
")",
"-",
">",
"SBHostOS"
] |
def __init__(self):
"""__init__(self) -> SBHostOS"""
this = _lldb.new_SBHostOS()
try: self.this.append(this)
except: self.this = this
|
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] |
https://github.com/Polidea/SiriusObfuscator/blob/b0e590d8130e97856afe578869b83a209e2b19be/SymbolExtractorAndRenamer/lldb/scripts/Python/static-binding/lldb.py#L5115-L5119
|
||
pytorch/pytorch
|
7176c92687d3cc847cc046bf002269c6949a21c2
|
benchmarks/operator_benchmark/benchmark_caffe2.py
|
python
|
Caffe2BenchmarkBase._device_option
|
(self, device)
|
return self.dev
|
This method is used to set device option.
|
This method is used to set device option.
|
[
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"used",
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"set",
"device",
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"."
] |
def _device_option(self, device):
""" This method is used to set device option.
"""
if device not in ['cuda', 'cpu']:
raise ValueError("Missing attrs in configs")
if 'cuda' in device:
self.dev = core.DeviceOption(caffe2_pb2.CUDA, 0)
else:
self.dev = core.DeviceOption(caffe2_pb2.CPU)
return self.dev
|
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https://github.com/pytorch/pytorch/blob/7176c92687d3cc847cc046bf002269c6949a21c2/benchmarks/operator_benchmark/benchmark_caffe2.py#L30-L40
|
|
twhui/LiteFlowNet
|
00925aebf2db9ac50f4b1666f718688b10dd10d1
|
scripts/cpp_lint.py
|
python
|
CheckEmptyBlockBody
|
(filename, clean_lines, linenum, error)
|
Look for empty loop/conditional body with only a single semicolon.
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.
|
Look for empty loop/conditional body with only a single semicolon.
|
[
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"empty",
"loop",
"/",
"conditional",
"body",
"with",
"only",
"a",
"single",
"semicolon",
"."
] |
def CheckEmptyBlockBody(filename, clean_lines, linenum, error):
"""Look for empty loop/conditional body with only a single semicolon.
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.
"""
# Search for loop keywords at the beginning of the line. Because only
# whitespaces are allowed before the keywords, this will also ignore most
# do-while-loops, since those lines should start with closing brace.
#
# We also check "if" blocks here, since an empty conditional block
# is likely an error.
line = clean_lines.elided[linenum]
matched = Match(r'\s*(for|while|if)\s*\(', line)
if matched:
# Find the end of the conditional expression
(end_line, end_linenum, end_pos) = CloseExpression(
clean_lines, linenum, line.find('('))
# Output warning if what follows the condition expression is a semicolon.
# No warning for all other cases, including whitespace or newline, since we
# have a separate check for semicolons preceded by whitespace.
if end_pos >= 0 and Match(r';', end_line[end_pos:]):
if matched.group(1) == 'if':
error(filename, end_linenum, 'whitespace/empty_conditional_body', 5,
'Empty conditional bodies should use {}')
else:
error(filename, end_linenum, 'whitespace/empty_loop_body', 5,
'Empty loop bodies should use {} or continue')
|
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"clean_lines",
".",
"elided",
"[",
"linenum",
"]",
"matched",
"=",
"Match",
"(",
"r'\\s*(for|while|if)\\s*\\('",
",",
"line",
")",
"if",
"matched",
":",
"# Find the end of the conditional expression",
"(",
"end_line",
",",
"end_linenum",
",",
"end_pos",
")",
"=",
"CloseExpression",
"(",
"clean_lines",
",",
"linenum",
",",
"line",
".",
"find",
"(",
"'('",
")",
")",
"# Output warning if what follows the condition expression is a semicolon.",
"# No warning for all other cases, including whitespace or newline, since we",
"# have a separate check for semicolons preceded by whitespace.",
"if",
"end_pos",
">=",
"0",
"and",
"Match",
"(",
"r';'",
",",
"end_line",
"[",
"end_pos",
":",
"]",
")",
":",
"if",
"matched",
".",
"group",
"(",
"1",
")",
"==",
"'if'",
":",
"error",
"(",
"filename",
",",
"end_linenum",
",",
"'whitespace/empty_conditional_body'",
",",
"5",
",",
"'Empty conditional bodies should use {}'",
")",
"else",
":",
"error",
"(",
"filename",
",",
"end_linenum",
",",
"'whitespace/empty_loop_body'",
",",
"5",
",",
"'Empty loop bodies should use {} or continue'",
")"
] |
https://github.com/twhui/LiteFlowNet/blob/00925aebf2db9ac50f4b1666f718688b10dd10d1/scripts/cpp_lint.py#L3243-L3275
|
||
Slicer/Slicer
|
ba9fadf332cb0303515b68d8d06a344c82e3e3e5
|
Utilities/Scripts/SlicerWizard/Utilities.py
|
python
|
inquire
|
(msg, choices=_yesno)
|
Get multiple-choice input from the user.
:param msg:
Text of the prompt which the user will be shown.
:type msg:
:class:`str`
:param choices:
Map of possible choices to their respective return values.
:type choices:
:class:`dict`
:returns:
Value of the selected choice.
This function presents a question (``msg``) to the user and asks them to
select an option from a list of choices, which are presented in the manner of
'git add --patch' (i.e. the possible choices are shown between the prompt
text and the final '?'). The prompt is repeated indefinitely until a valid
selection is made.
The ``choices`` are a :class:`dict`, with each key being a possible choice
(using a single letter is recommended). The value for the selected key is
returned to the caller.
The default ``choices`` provides a yes/no prompt with a :class:`bool` return
value.
|
Get multiple-choice input from the user.
|
[
"Get",
"multiple",
"-",
"choice",
"input",
"from",
"the",
"user",
"."
] |
def inquire(msg, choices=_yesno):
"""Get multiple-choice input from the user.
:param msg:
Text of the prompt which the user will be shown.
:type msg:
:class:`str`
:param choices:
Map of possible choices to their respective return values.
:type choices:
:class:`dict`
:returns:
Value of the selected choice.
This function presents a question (``msg``) to the user and asks them to
select an option from a list of choices, which are presented in the manner of
'git add --patch' (i.e. the possible choices are shown between the prompt
text and the final '?'). The prompt is repeated indefinitely until a valid
selection is made.
The ``choices`` are a :class:`dict`, with each key being a possible choice
(using a single letter is recommended). The value for the selected key is
returned to the caller.
The default ``choices`` provides a yes/no prompt with a :class:`bool` return
value.
"""
choiceKeys = list(choices.keys())
msg = "{} {}? ".format(msg, ",".join(choiceKeys))
def throw(*args):
raise ValueError()
parser = argparse.ArgumentParser()
parser.add_argument("choice", choices=choiceKeys)
parser.error = throw
while True:
try:
args = parser.parse_args(input(msg))
if args.choice in choices:
return choices[args.choice]
except:
pass
|
[
"def",
"inquire",
"(",
"msg",
",",
"choices",
"=",
"_yesno",
")",
":",
"choiceKeys",
"=",
"list",
"(",
"choices",
".",
"keys",
"(",
")",
")",
"msg",
"=",
"\"{} {}? \"",
".",
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"(",
"msg",
",",
"\",\"",
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"join",
"(",
"choiceKeys",
")",
")",
"def",
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"*",
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"parser",
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"(",
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"msg",
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"choice",
"in",
"choices",
":",
"return",
"choices",
"[",
"args",
".",
"choice",
"]",
"except",
":",
"pass"
] |
https://github.com/Slicer/Slicer/blob/ba9fadf332cb0303515b68d8d06a344c82e3e3e5/Utilities/Scripts/SlicerWizard/Utilities.py#L132-L178
|
||
miyosuda/TensorFlowAndroidDemo
|
35903e0221aa5f109ea2dbef27f20b52e317f42d
|
jni-build/jni/include/tensorflow/python/framework/ops.py
|
python
|
IndexedSlices.dtype
|
(self)
|
return self.values.dtype
|
The `DType` of elements in this tensor.
|
The `DType` of elements in this tensor.
|
[
"The",
"DType",
"of",
"elements",
"in",
"this",
"tensor",
"."
] |
def dtype(self):
"""The `DType` of elements in this tensor."""
return self.values.dtype
|
[
"def",
"dtype",
"(",
"self",
")",
":",
"return",
"self",
".",
"values",
".",
"dtype"
] |
https://github.com/miyosuda/TensorFlowAndroidDemo/blob/35903e0221aa5f109ea2dbef27f20b52e317f42d/jni-build/jni/include/tensorflow/python/framework/ops.py#L879-L881
|
|
hanpfei/chromium-net
|
392cc1fa3a8f92f42e4071ab6e674d8e0482f83f
|
tools/android/loading/cloud/common/clovis_task.py
|
python
|
ClovisTask.__init__
|
(self, action, action_params, backend_params)
|
See tools/android/loading/cloud/frontend/README.md for a specification
of the parameters.
Args:
action(str): Action accomplished by this task.
action_params(dict): Parameters of task.
backend_params(dict): Parameters of the instances running the task.
If this is None, no instances are created. If this dictionary has no
'tag' key, a unique tag will be generated.
|
See tools/android/loading/cloud/frontend/README.md for a specification
of the parameters.
|
[
"See",
"tools",
"/",
"android",
"/",
"loading",
"/",
"cloud",
"/",
"frontend",
"/",
"README",
".",
"md",
"for",
"a",
"specification",
"of",
"the",
"parameters",
"."
] |
def __init__(self, action, action_params, backend_params):
""" See tools/android/loading/cloud/frontend/README.md for a specification
of the parameters.
Args:
action(str): Action accomplished by this task.
action_params(dict): Parameters of task.
backend_params(dict): Parameters of the instances running the task.
If this is None, no instances are created. If this dictionary has no
'tag' key, a unique tag will be generated.
"""
self._action = action
self._action_params = action_params or {}
self._backend_params = backend_params or {}
# If no tag is specified, generate a unique tag.
if not self._backend_params.get('tag'):
self._backend_params.update({'tag': str(uuid.uuid1())})
|
[
"def",
"__init__",
"(",
"self",
",",
"action",
",",
"action_params",
",",
"backend_params",
")",
":",
"self",
".",
"_action",
"=",
"action",
"self",
".",
"_action_params",
"=",
"action_params",
"or",
"{",
"}",
"self",
".",
"_backend_params",
"=",
"backend_params",
"or",
"{",
"}",
"# If no tag is specified, generate a unique tag.",
"if",
"not",
"self",
".",
"_backend_params",
".",
"get",
"(",
"'tag'",
")",
":",
"self",
".",
"_backend_params",
".",
"update",
"(",
"{",
"'tag'",
":",
"str",
"(",
"uuid",
".",
"uuid1",
"(",
")",
")",
"}",
")"
] |
https://github.com/hanpfei/chromium-net/blob/392cc1fa3a8f92f42e4071ab6e674d8e0482f83f/tools/android/loading/cloud/common/clovis_task.py#L14-L30
|
||
trilinos/Trilinos
|
6168be6dd51e35e1cd681e9c4b24433e709df140
|
packages/seacas/scripts/exomerge3.py
|
python
|
ExodusModel._distance_squared_between
|
(point_one, point_two)
|
return ((point_two[0] - point_one[0])**2 +
(point_two[1] - point_one[1])**2 +
(point_two[2] - point_one[2])**2)
|
Return the distance squared between two three-dimensional points.
|
Return the distance squared between two three-dimensional points.
|
[
"Return",
"the",
"distance",
"squared",
"between",
"two",
"three",
"-",
"dimensional",
"points",
"."
] |
def _distance_squared_between(point_one, point_two):
"""Return the distance squared between two three-dimensional points."""
return ((point_two[0] - point_one[0])**2 +
(point_two[1] - point_one[1])**2 +
(point_two[2] - point_one[2])**2)
|
[
"def",
"_distance_squared_between",
"(",
"point_one",
",",
"point_two",
")",
":",
"return",
"(",
"(",
"point_two",
"[",
"0",
"]",
"-",
"point_one",
"[",
"0",
"]",
")",
"**",
"2",
"+",
"(",
"point_two",
"[",
"1",
"]",
"-",
"point_one",
"[",
"1",
"]",
")",
"**",
"2",
"+",
"(",
"point_two",
"[",
"2",
"]",
"-",
"point_one",
"[",
"2",
"]",
")",
"**",
"2",
")"
] |
https://github.com/trilinos/Trilinos/blob/6168be6dd51e35e1cd681e9c4b24433e709df140/packages/seacas/scripts/exomerge3.py#L6813-L6817
|
|
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Tools/Python/3.7.10/windows/Tools/pynche/ColorDB.py
|
python
|
ColorDB.find_byrgb
|
(self, rgbtuple)
|
Return name for rgbtuple
|
Return name for rgbtuple
|
[
"Return",
"name",
"for",
"rgbtuple"
] |
def find_byrgb(self, rgbtuple):
"""Return name for rgbtuple"""
try:
return self.__byrgb[rgbtuple]
except KeyError:
raise BadColor(rgbtuple) from None
|
[
"def",
"find_byrgb",
"(",
"self",
",",
"rgbtuple",
")",
":",
"try",
":",
"return",
"self",
".",
"__byrgb",
"[",
"rgbtuple",
"]",
"except",
"KeyError",
":",
"raise",
"BadColor",
"(",
"rgbtuple",
")",
"from",
"None"
] |
https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/windows/Tools/pynche/ColorDB.py#L85-L90
|
||
intel/ros_object_analytics
|
eb0208edbb6da67e5d5c4092fd2964a2c8d9838e
|
object_analytics_visualization/scripts/marker_publisher.py
|
python
|
ObjectItem.__init__
|
(self, header, roi, track_id, detected_object, min, max)
|
Build an instance from message header, region of interest, tracking id, detected object and 3d min max.
Args:
header (std_msgs.msg.Header): Message header
roi (RegionOfInterest): Region of interest
track_id (int): Tracking id
detected_object (Object): Instance of object_msgs.msg.Object
min (geometry_msgs.msg.Point32): Min position in 3d space
max (geometry_msgs.msg.Point32): Max position in 3d space
|
Build an instance from message header, region of interest, tracking id, detected object and 3d min max.
|
[
"Build",
"an",
"instance",
"from",
"message",
"header",
"region",
"of",
"interest",
"tracking",
"id",
"detected",
"object",
"and",
"3d",
"min",
"max",
"."
] |
def __init__(self, header, roi, track_id, detected_object, min, max):
"""Build an instance from message header, region of interest, tracking id, detected object and 3d min max.
Args:
header (std_msgs.msg.Header): Message header
roi (RegionOfInterest): Region of interest
track_id (int): Tracking id
detected_object (Object): Instance of object_msgs.msg.Object
min (geometry_msgs.msg.Point32): Min position in 3d space
max (geometry_msgs.msg.Point32): Max position in 3d space
"""
self._header = header
self._roi = roi
self._track_id = track_id
self._object = detected_object
self._p1 = deepcopy(min)
self._p2 = deepcopy(self._p1)
self._p2.x = max.x
self._p3 = deepcopy(self._p2)
self._p3.z = max.z
self._p4 = deepcopy(self._p3)
self._p4.x = min.x
self._p5 = deepcopy(min)
self._p5.y = max.y
self._p6 = deepcopy(self._p5)
self._p6.x = max.x
self._p7 = deepcopy(self._p6)
self._p7.z = max.z
self._p8 = deepcopy(self._p7)
self._p8.x = min.x
|
[
"def",
"__init__",
"(",
"self",
",",
"header",
",",
"roi",
",",
"track_id",
",",
"detected_object",
",",
"min",
",",
"max",
")",
":",
"self",
".",
"_header",
"=",
"header",
"self",
".",
"_roi",
"=",
"roi",
"self",
".",
"_track_id",
"=",
"track_id",
"self",
".",
"_object",
"=",
"detected_object",
"self",
".",
"_p1",
"=",
"deepcopy",
"(",
"min",
")",
"self",
".",
"_p2",
"=",
"deepcopy",
"(",
"self",
".",
"_p1",
")",
"self",
".",
"_p2",
".",
"x",
"=",
"max",
".",
"x",
"self",
".",
"_p3",
"=",
"deepcopy",
"(",
"self",
".",
"_p2",
")",
"self",
".",
"_p3",
".",
"z",
"=",
"max",
".",
"z",
"self",
".",
"_p4",
"=",
"deepcopy",
"(",
"self",
".",
"_p3",
")",
"self",
".",
"_p4",
".",
"x",
"=",
"min",
".",
"x",
"self",
".",
"_p5",
"=",
"deepcopy",
"(",
"min",
")",
"self",
".",
"_p5",
".",
"y",
"=",
"max",
".",
"y",
"self",
".",
"_p6",
"=",
"deepcopy",
"(",
"self",
".",
"_p5",
")",
"self",
".",
"_p6",
".",
"x",
"=",
"max",
".",
"x",
"self",
".",
"_p7",
"=",
"deepcopy",
"(",
"self",
".",
"_p6",
")",
"self",
".",
"_p7",
".",
"z",
"=",
"max",
".",
"z",
"self",
".",
"_p8",
"=",
"deepcopy",
"(",
"self",
".",
"_p7",
")",
"self",
".",
"_p8",
".",
"x",
"=",
"min",
".",
"x"
] |
https://github.com/intel/ros_object_analytics/blob/eb0208edbb6da67e5d5c4092fd2964a2c8d9838e/object_analytics_visualization/scripts/marker_publisher.py#L64-L101
|
||
LARG/HFO
|
b8b2a1d462823c6732f4d5581aa7fe2e371d55cb
|
hfo/hfo.py
|
python
|
HFOEnvironment.getState
|
(self, state_data=None)
|
return state_data
|
Returns the current state features
|
Returns the current state features
|
[
"Returns",
"the",
"current",
"state",
"features"
] |
def getState(self, state_data=None):
""" Returns the current state features """
if state_data is None:
state_data = np.zeros(self.getStateSize(), dtype=np.float32)
hfo_lib.getState(self.obj, as_ctypes(state_data))
return state_data
|
[
"def",
"getState",
"(",
"self",
",",
"state_data",
"=",
"None",
")",
":",
"if",
"state_data",
"is",
"None",
":",
"state_data",
"=",
"np",
".",
"zeros",
"(",
"self",
".",
"getStateSize",
"(",
")",
",",
"dtype",
"=",
"np",
".",
"float32",
")",
"hfo_lib",
".",
"getState",
"(",
"self",
".",
"obj",
",",
"as_ctypes",
"(",
"state_data",
")",
")",
"return",
"state_data"
] |
https://github.com/LARG/HFO/blob/b8b2a1d462823c6732f4d5581aa7fe2e371d55cb/hfo/hfo.py#L150-L155
|
|
wxWidgets/wxPython-Classic
|
19571e1ae65f1ac445f5491474121998c97a1bf0
|
src/gtk/_controls.py
|
python
|
HelpProvider_Set
|
(*args, **kwargs)
|
return _controls_.HelpProvider_Set(*args, **kwargs)
|
HelpProvider_Set(HelpProvider helpProvider) -> HelpProvider
Sset the current, application-wide help provider. Returns the previous
one. Unlike some other classes, the help provider is not created on
demand. This must be explicitly done by the application.
|
HelpProvider_Set(HelpProvider helpProvider) -> HelpProvider
|
[
"HelpProvider_Set",
"(",
"HelpProvider",
"helpProvider",
")",
"-",
">",
"HelpProvider"
] |
def HelpProvider_Set(*args, **kwargs):
"""
HelpProvider_Set(HelpProvider helpProvider) -> HelpProvider
Sset the current, application-wide help provider. Returns the previous
one. Unlike some other classes, the help provider is not created on
demand. This must be explicitly done by the application.
"""
return _controls_.HelpProvider_Set(*args, **kwargs)
|
[
"def",
"HelpProvider_Set",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")",
":",
"return",
"_controls_",
".",
"HelpProvider_Set",
"(",
"*",
"args",
",",
"*",
"*",
"kwargs",
")"
] |
https://github.com/wxWidgets/wxPython-Classic/blob/19571e1ae65f1ac445f5491474121998c97a1bf0/src/gtk/_controls.py#L6301-L6309
|
|
CRYTEK/CRYENGINE
|
232227c59a220cbbd311576f0fbeba7bb53b2a8c
|
Code/Tools/waf-1.7.13/waflib/Tools/gcc.py
|
python
|
find_gcc
|
(conf)
|
Find the program gcc, and if present, try to detect its version number
|
Find the program gcc, and if present, try to detect its version number
|
[
"Find",
"the",
"program",
"gcc",
"and",
"if",
"present",
"try",
"to",
"detect",
"its",
"version",
"number"
] |
def find_gcc(conf):
"""
Find the program gcc, and if present, try to detect its version number
"""
cc = conf.find_program(['gcc', 'cc'], var='CC')
cc = conf.cmd_to_list(cc)
conf.get_cc_version(cc, gcc=True)
conf.env.CC_NAME = 'gcc'
conf.env.CC = cc
|
[
"def",
"find_gcc",
"(",
"conf",
")",
":",
"cc",
"=",
"conf",
".",
"find_program",
"(",
"[",
"'gcc'",
",",
"'cc'",
"]",
",",
"var",
"=",
"'CC'",
")",
"cc",
"=",
"conf",
".",
"cmd_to_list",
"(",
"cc",
")",
"conf",
".",
"get_cc_version",
"(",
"cc",
",",
"gcc",
"=",
"True",
")",
"conf",
".",
"env",
".",
"CC_NAME",
"=",
"'gcc'",
"conf",
".",
"env",
".",
"CC",
"=",
"cc"
] |
https://github.com/CRYTEK/CRYENGINE/blob/232227c59a220cbbd311576f0fbeba7bb53b2a8c/Code/Tools/waf-1.7.13/waflib/Tools/gcc.py#L15-L23
|
||
include-what-you-use/include-what-you-use
|
208fbfffa5d69364b9f78e427caa443441279283
|
fix_includes.py
|
python
|
_WriteFile
|
(filename, fileinfo, file_lines)
|
Write the given file-lines to the file.
|
Write the given file-lines to the file.
|
[
"Write",
"the",
"given",
"file",
"-",
"lines",
"to",
"the",
"file",
"."
] |
def _WriteFile(filename, fileinfo, file_lines):
"""Write the given file-lines to the file."""
try:
with open(filename, 'wb') as f:
# file_lines already have line endings, so join with ''.
content = ''.join(file_lines)
content = content.encode(fileinfo.encoding)
f.write(content)
except (IOError, OSError) as why:
print("Error writing '%s': %s" % (filename, why))
|
[
"def",
"_WriteFile",
"(",
"filename",
",",
"fileinfo",
",",
"file_lines",
")",
":",
"try",
":",
"with",
"open",
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",",
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",",
"why",
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")"
] |
https://github.com/include-what-you-use/include-what-you-use/blob/208fbfffa5d69364b9f78e427caa443441279283/fix_includes.py#L606-L615
|
||
ChromiumWebApps/chromium
|
c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7
|
gpu/command_buffer/build_gles2_cmd_buffer.py
|
python
|
BucketFunction.WriteCommandDescription
|
(self, file)
|
Overridden from Function
|
Overridden from Function
|
[
"Overridden",
"from",
"Function"
] |
def WriteCommandDescription(self, file):
"""Overridden from Function"""
file.Write("//! Bucket version of command that corresponds to gl%s.\n" %
self.original_name)
|
[
"def",
"WriteCommandDescription",
"(",
"self",
",",
"file",
")",
":",
"file",
".",
"Write",
"(",
"\"//! Bucket version of command that corresponds to gl%s.\\n\"",
"%",
"self",
".",
"original_name",
")"
] |
https://github.com/ChromiumWebApps/chromium/blob/c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7/gpu/command_buffer/build_gles2_cmd_buffer.py#L6793-L6796
|
||
NVIDIA/TensorRT
|
42805f078052daad1a98bc5965974fcffaad0960
|
demo/HuggingFace/NNDF/models.py
|
python
|
ModelFileConverter.onnx_to_torch
|
(
self, output_fpath: str, input_fpath: str, network_metadata: NetworkMetadata
)
|
Converts ONNX file into torch.Model which is written to disk.
Arg:
output_fpath (str): File location of the generated ONNX file.
input_fpath (str): Input file location of the generated ONNX file.
network_metadata (NetworkMetadata): Network metadata of the network being converted.
Returns:
TorchModelFile: Newly generated TorchModelFile
|
Converts ONNX file into torch.Model which is written to disk.
|
[
"Converts",
"ONNX",
"file",
"into",
"torch",
".",
"Model",
"which",
"is",
"written",
"to",
"disk",
"."
] |
def onnx_to_torch(
self, output_fpath: str, input_fpath: str, network_metadata: NetworkMetadata
):
"""
Converts ONNX file into torch.Model which is written to disk.
Arg:
output_fpath (str): File location of the generated ONNX file.
input_fpath (str): Input file location of the generated ONNX file.
network_metadata (NetworkMetadata): Network metadata of the network being converted.
Returns:
TorchModelFile: Newly generated TorchModelFile
"""
raise NotImplementedError(
"Current model does not support exporting to torch model."
)
|
[
"def",
"onnx_to_torch",
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"self",
",",
"output_fpath",
":",
"str",
",",
"input_fpath",
":",
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"network_metadata",
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"NetworkMetadata",
")",
":",
"raise",
"NotImplementedError",
"(",
"\"Current model does not support exporting to torch model.\"",
")"
] |
https://github.com/NVIDIA/TensorRT/blob/42805f078052daad1a98bc5965974fcffaad0960/demo/HuggingFace/NNDF/models.py#L71-L87
|
||
miyosuda/TensorFlowAndroidMNIST
|
7b5a4603d2780a8a2834575706e9001977524007
|
jni-build/jni/include/tensorflow/python/training/input.py
|
python
|
shuffle_batch_join
|
(tensors_list, batch_size, capacity,
min_after_dequeue, seed=None, enqueue_many=False,
shapes=None, allow_smaller_final_batch=False,
shared_name=None, name=None)
|
Create batches by randomly shuffling tensors.
The `tensors_list` argument is a list of tuples of tensors, or a list of
dictionaries of tensors. Each element in the list is treated similarily
to the `tensors` argument of `tf.train.shuffle_batch()`.
This version enqueues a different list of tensors in different threads.
It adds the following to the current `Graph`:
* A shuffling queue into which tensors from `tensors_list` are enqueued.
* A `dequeue_many` operation to create batches from the queue.
* A `QueueRunner` to `QUEUE_RUNNER` collection, to enqueue the tensors
from `tensors_list`.
`len(tensors_list)` threads will be started, with thread `i` enqueuing
the tensors from `tensors_list[i]`. `tensors_list[i1][j]` must match
`tensors_list[i2][j]` in type and shape, except in the first dimension if
`enqueue_many` is true.
If `enqueue_many` is `False`, each `tensors_list[i]` is assumed
to represent a single example. An input tensor with shape `[x, y, z]`
will be output as a tensor with shape `[batch_size, x, y, z]`.
If `enqueue_many` is `True`, `tensors_list[i]` is assumed to
represent a batch of examples, where the first dimension is indexed
by example, and all members of `tensors_list[i]` should have the
same size in the first dimension. If an input tensor has shape `[*, x,
y, z]`, the output will have shape `[batch_size, x, y, z]`.
The `capacity` argument controls the how long the prefetching is allowed to
grow the queues.
The returned operation is a dequeue operation and will throw
`tf.errors.OutOfRangeError` if the input queue is exhausted. If this
operation is feeding another input queue, its queue runner will catch
this exception, however, if this operation is used in your main thread
you are responsible for catching this yourself.
If `allow_smaller_final_batch` is `True`, a smaller batch value than
`batch_size` is returned when the queue is closed and there are not enough
elements to fill the batch, otherwise the pending elements are discarded.
In addition, all output tensors' static shapes, as accessed via the
`get_shape` method will have a first `Dimension` value of `None`, and
operations that depend on fixed batch_size would fail.
Args:
tensors_list: A list of tuples or dictionaries of tensors to enqueue.
batch_size: An integer. The new batch size pulled from the queue.
capacity: An integer. The maximum number of elements in the queue.
min_after_dequeue: Minimum number elements in the queue after a
dequeue, used to ensure a level of mixing of elements.
seed: Seed for the random shuffling within the queue.
enqueue_many: Whether each tensor in `tensor_list_list` is a single
example.
shapes: (Optional) The shapes for each example. Defaults to the
inferred shapes for `tensors_list[i]`.
allow_smaller_final_batch: (Optional) Boolean. If `True`, allow the final
batch to be smaller if there are insufficient items left in the queue.
shared_name: (optional). If set, this queue will be shared under the given
name across multiple sessions.
name: (Optional) A name for the operations.
Returns:
A list or dictionary of tensors with the same number and types as
`tensors_list[i]`.
Raises:
ValueError: If the `shapes` are not specified, and cannot be
inferred from the elements of `tensors_list`.
|
Create batches by randomly shuffling tensors.
|
[
"Create",
"batches",
"by",
"randomly",
"shuffling",
"tensors",
"."
] |
def shuffle_batch_join(tensors_list, batch_size, capacity,
min_after_dequeue, seed=None, enqueue_many=False,
shapes=None, allow_smaller_final_batch=False,
shared_name=None, name=None):
"""Create batches by randomly shuffling tensors.
The `tensors_list` argument is a list of tuples of tensors, or a list of
dictionaries of tensors. Each element in the list is treated similarily
to the `tensors` argument of `tf.train.shuffle_batch()`.
This version enqueues a different list of tensors in different threads.
It adds the following to the current `Graph`:
* A shuffling queue into which tensors from `tensors_list` are enqueued.
* A `dequeue_many` operation to create batches from the queue.
* A `QueueRunner` to `QUEUE_RUNNER` collection, to enqueue the tensors
from `tensors_list`.
`len(tensors_list)` threads will be started, with thread `i` enqueuing
the tensors from `tensors_list[i]`. `tensors_list[i1][j]` must match
`tensors_list[i2][j]` in type and shape, except in the first dimension if
`enqueue_many` is true.
If `enqueue_many` is `False`, each `tensors_list[i]` is assumed
to represent a single example. An input tensor with shape `[x, y, z]`
will be output as a tensor with shape `[batch_size, x, y, z]`.
If `enqueue_many` is `True`, `tensors_list[i]` is assumed to
represent a batch of examples, where the first dimension is indexed
by example, and all members of `tensors_list[i]` should have the
same size in the first dimension. If an input tensor has shape `[*, x,
y, z]`, the output will have shape `[batch_size, x, y, z]`.
The `capacity` argument controls the how long the prefetching is allowed to
grow the queues.
The returned operation is a dequeue operation and will throw
`tf.errors.OutOfRangeError` if the input queue is exhausted. If this
operation is feeding another input queue, its queue runner will catch
this exception, however, if this operation is used in your main thread
you are responsible for catching this yourself.
If `allow_smaller_final_batch` is `True`, a smaller batch value than
`batch_size` is returned when the queue is closed and there are not enough
elements to fill the batch, otherwise the pending elements are discarded.
In addition, all output tensors' static shapes, as accessed via the
`get_shape` method will have a first `Dimension` value of `None`, and
operations that depend on fixed batch_size would fail.
Args:
tensors_list: A list of tuples or dictionaries of tensors to enqueue.
batch_size: An integer. The new batch size pulled from the queue.
capacity: An integer. The maximum number of elements in the queue.
min_after_dequeue: Minimum number elements in the queue after a
dequeue, used to ensure a level of mixing of elements.
seed: Seed for the random shuffling within the queue.
enqueue_many: Whether each tensor in `tensor_list_list` is a single
example.
shapes: (Optional) The shapes for each example. Defaults to the
inferred shapes for `tensors_list[i]`.
allow_smaller_final_batch: (Optional) Boolean. If `True`, allow the final
batch to be smaller if there are insufficient items left in the queue.
shared_name: (optional). If set, this queue will be shared under the given
name across multiple sessions.
name: (Optional) A name for the operations.
Returns:
A list or dictionary of tensors with the same number and types as
`tensors_list[i]`.
Raises:
ValueError: If the `shapes` are not specified, and cannot be
inferred from the elements of `tensors_list`.
"""
tensor_list_list = _as_tensor_list_list(tensors_list)
with ops.op_scope(
_flatten(tensor_list_list), name, "shuffle_batch_join") as name:
tensor_list_list = _validate_join(tensor_list_list)
tensor_list_list, sparse_info = _serialize_sparse_tensors_join(
tensor_list_list, enqueue_many)
types = _dtypes(tensor_list_list)
shapes = _shapes(tensor_list_list, shapes, enqueue_many)
queue = data_flow_ops.RandomShuffleQueue(
capacity=capacity, min_after_dequeue=min_after_dequeue, seed=seed,
dtypes=types, shapes=shapes, shared_name=shared_name)
_enqueue_join(queue, tensor_list_list, enqueue_many)
full = (math_ops.cast(math_ops.maximum(0, queue.size() - min_after_dequeue),
dtypes.float32) *
(1. / (capacity - min_after_dequeue)))
# Note that name contains a '/' at the end so we intentionally do not place
# a '/' after %s below.
summary_name = (
"queue/%sfraction_over_%d_of_%d_full" %
(name, min_after_dequeue, capacity - min_after_dequeue))
logging_ops.scalar_summary(summary_name, full)
if allow_smaller_final_batch:
dequeued = queue.dequeue_up_to(batch_size, name=name)
else:
dequeued = queue.dequeue_many(batch_size, name=name)
dequeued = _deserialize_sparse_tensors(dequeued, sparse_info)
# tensors_list was validated to not be empty.
return _as_original_type(tensors_list[0], dequeued)
|
[
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"_as_original_type",
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"[",
"0",
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")"
] |
https://github.com/miyosuda/TensorFlowAndroidMNIST/blob/7b5a4603d2780a8a2834575706e9001977524007/jni-build/jni/include/tensorflow/python/training/input.py#L822-L924
|
||
esa/pykep
|
b410363653623730b577de257c04b0e0289f2014
|
pykep/trajopt/gym/_cassini1.py
|
python
|
_cassini1_udp.__init__
|
(self)
|
Write Me
|
Write Me
|
[
"Write",
"Me"
] |
def __init__(self):
"""
Write Me
"""
super().__init__(
seq=_seq_cassini1,
t0=[-1000., 0.],
tof=[[30, 400], [100, 470], [30, 400], [400, 2000], [1000, 6000]],
vinf=3.,
tof_encoding='direct',
orbit_insertion=True,
e_target=0.98,
rp_target=108950000)
|
[
"def",
"__init__",
"(",
"self",
")",
":",
"super",
"(",
")",
".",
"__init__",
"(",
"seq",
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"_seq_cassini1",
",",
"t0",
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"[",
"-",
"1000.",
",",
"0.",
"]",
",",
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"[",
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",",
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"=",
"True",
",",
"e_target",
"=",
"0.98",
",",
"rp_target",
"=",
"108950000",
")"
] |
https://github.com/esa/pykep/blob/b410363653623730b577de257c04b0e0289f2014/pykep/trajopt/gym/_cassini1.py#L17-L29
|
||
MythTV/mythtv
|
d282a209cb8be85d036f85a62a8ec971b67d45f4
|
mythtv/programs/scripts/internetcontent/nv_python_libs/dailymotion/dailymotion_api.py
|
python
|
OutStreamEncoder.write
|
(self, obj)
|
Wraps the output stream, encoding Unicode strings with the specified encoding
|
Wraps the output stream, encoding Unicode strings with the specified encoding
|
[
"Wraps",
"the",
"output",
"stream",
"encoding",
"Unicode",
"strings",
"with",
"the",
"specified",
"encoding"
] |
def write(self, obj):
"""Wraps the output stream, encoding Unicode strings with the specified encoding"""
if isinstance(obj, str):
obj = obj.encode(self.encoding)
self.out.buffer.write(obj)
|
[
"def",
"write",
"(",
"self",
",",
"obj",
")",
":",
"if",
"isinstance",
"(",
"obj",
",",
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".",
"encoding",
")",
"self",
".",
"out",
".",
"buffer",
".",
"write",
"(",
"obj",
")"
] |
https://github.com/MythTV/mythtv/blob/d282a209cb8be85d036f85a62a8ec971b67d45f4/mythtv/programs/scripts/internetcontent/nv_python_libs/dailymotion/dailymotion_api.py#L59-L63
|
||
wlanjie/AndroidFFmpeg
|
7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf
|
tools/fdk-aac-build/x86/toolchain/lib/python2.7/mhlib.py
|
python
|
Message.__init__
|
(self, f, n, fp = None)
|
Constructor.
|
Constructor.
|
[
"Constructor",
"."
] |
def __init__(self, f, n, fp = None):
"""Constructor."""
self.folder = f
self.number = n
if fp is None:
path = f.getmessagefilename(n)
fp = open(path, 'r')
mimetools.Message.__init__(self, fp)
|
[
"def",
"__init__",
"(",
"self",
",",
"f",
",",
"n",
",",
"fp",
"=",
"None",
")",
":",
"self",
".",
"folder",
"=",
"f",
"self",
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"(",
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",",
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")"
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https://github.com/wlanjie/AndroidFFmpeg/blob/7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf/tools/fdk-aac-build/x86/toolchain/lib/python2.7/mhlib.py#L665-L672
|
||
flann-lib/flann
|
1d04523268c388dabf1c0865d69e1b638c8c7d9d
|
src/python/pyflann/index.py
|
python
|
FLANN.save_index
|
(self, filename)
|
This saves the index to a disk file.
|
This saves the index to a disk file.
|
[
"This",
"saves",
"the",
"index",
"to",
"a",
"disk",
"file",
"."
] |
def save_index(self, filename):
"""
This saves the index to a disk file.
"""
if self.__curindex is not None:
flann.save_index[self.__curindex_type](
self.__curindex, c_char_p(to_bytes(filename)))
|
[
"def",
"save_index",
"(",
"self",
",",
"filename",
")",
":",
"if",
"self",
".",
"__curindex",
"is",
"not",
"None",
":",
"flann",
".",
"save_index",
"[",
"self",
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"__curindex_type",
"]",
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"self",
".",
"__curindex",
",",
"c_char_p",
"(",
"to_bytes",
"(",
"filename",
")",
")",
")"
] |
https://github.com/flann-lib/flann/blob/1d04523268c388dabf1c0865d69e1b638c8c7d9d/src/python/pyflann/index.py#L184-L190
|
||
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Tools/Python/3.7.10/windows/Lib/idlelib/searchengine.py
|
python
|
search_reverse
|
(prog, chars, col)
|
return found
|
Search backwards and return an re match object or None.
This is done by searching forwards until there is no match.
Prog: compiled re object with a search method returning a match.
Chars: line of text, without \\n.
Col: stop index for the search; the limit for match.end().
|
Search backwards and return an re match object or None.
|
[
"Search",
"backwards",
"and",
"return",
"an",
"re",
"match",
"object",
"or",
"None",
"."
] |
def search_reverse(prog, chars, col):
'''Search backwards and return an re match object or None.
This is done by searching forwards until there is no match.
Prog: compiled re object with a search method returning a match.
Chars: line of text, without \\n.
Col: stop index for the search; the limit for match.end().
'''
m = prog.search(chars)
if not m:
return None
found = None
i, j = m.span() # m.start(), m.end() == match slice indexes
while i < col and j <= col:
found = m
if i == j:
j = j+1
m = prog.search(chars, j)
if not m:
break
i, j = m.span()
return found
|
[
"def",
"search_reverse",
"(",
"prog",
",",
"chars",
",",
"col",
")",
":",
"m",
"=",
"prog",
".",
"search",
"(",
"chars",
")",
"if",
"not",
"m",
":",
"return",
"None",
"found",
"=",
"None",
"i",
",",
"j",
"=",
"m",
".",
"span",
"(",
")",
"# m.start(), m.end() == match slice indexes",
"while",
"i",
"<",
"col",
"and",
"j",
"<=",
"col",
":",
"found",
"=",
"m",
"if",
"i",
"==",
"j",
":",
"j",
"=",
"j",
"+",
"1",
"m",
"=",
"prog",
".",
"search",
"(",
"chars",
",",
"j",
")",
"if",
"not",
"m",
":",
"break",
"i",
",",
"j",
"=",
"m",
".",
"span",
"(",
")",
"return",
"found"
] |
https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/windows/Lib/idlelib/searchengine.py#L192-L213
|
|
hanpfei/chromium-net
|
392cc1fa3a8f92f42e4071ab6e674d8e0482f83f
|
third_party/catapult/third_party/gsutil/third_party/boto/boto/codedeploy/layer1.py
|
python
|
CodeDeployConnection.batch_get_deployments
|
(self, deployment_ids=None)
|
return self.make_request(action='BatchGetDeployments',
body=json.dumps(params))
|
Gets information about one or more deployments.
:type deployment_ids: list
:param deployment_ids: A list of deployment IDs, with multiple
deployment IDs separated by spaces.
|
Gets information about one or more deployments.
|
[
"Gets",
"information",
"about",
"one",
"or",
"more",
"deployments",
"."
] |
def batch_get_deployments(self, deployment_ids=None):
"""
Gets information about one or more deployments.
:type deployment_ids: list
:param deployment_ids: A list of deployment IDs, with multiple
deployment IDs separated by spaces.
"""
params = {}
if deployment_ids is not None:
params['deploymentIds'] = deployment_ids
return self.make_request(action='BatchGetDeployments',
body=json.dumps(params))
|
[
"def",
"batch_get_deployments",
"(",
"self",
",",
"deployment_ids",
"=",
"None",
")",
":",
"params",
"=",
"{",
"}",
"if",
"deployment_ids",
"is",
"not",
"None",
":",
"params",
"[",
"'deploymentIds'",
"]",
"=",
"deployment_ids",
"return",
"self",
".",
"make_request",
"(",
"action",
"=",
"'BatchGetDeployments'",
",",
"body",
"=",
"json",
".",
"dumps",
"(",
"params",
")",
")"
] |
https://github.com/hanpfei/chromium-net/blob/392cc1fa3a8f92f42e4071ab6e674d8e0482f83f/third_party/catapult/third_party/gsutil/third_party/boto/boto/codedeploy/layer1.py#L178-L191
|
|
aws/lumberyard
|
f85344403c1c2e77ec8c75deb2c116e97b713217
|
dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/lib2to3/btm_utils.py
|
python
|
rec_test
|
(sequence, test_func)
|
Tests test_func on all items of sequence and items of included
sub-iterables
|
Tests test_func on all items of sequence and items of included
sub-iterables
|
[
"Tests",
"test_func",
"on",
"all",
"items",
"of",
"sequence",
"and",
"items",
"of",
"included",
"sub",
"-",
"iterables"
] |
def rec_test(sequence, test_func):
"""Tests test_func on all items of sequence and items of included
sub-iterables"""
for x in sequence:
if isinstance(x, (list, tuple)):
yield from rec_test(x, test_func)
else:
yield test_func(x)
|
[
"def",
"rec_test",
"(",
"sequence",
",",
"test_func",
")",
":",
"for",
"x",
"in",
"sequence",
":",
"if",
"isinstance",
"(",
"x",
",",
"(",
"list",
",",
"tuple",
")",
")",
":",
"yield",
"from",
"rec_test",
"(",
"x",
",",
"test_func",
")",
"else",
":",
"yield",
"test_func",
"(",
"x",
")"
] |
https://github.com/aws/lumberyard/blob/f85344403c1c2e77ec8c75deb2c116e97b713217/dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/lib2to3/btm_utils.py#L274-L281
|
||
ApolloAuto/apollo-platform
|
86d9dc6743b496ead18d597748ebabd34a513289
|
ros/third_party/lib_x86_64/python2.7/dist-packages/numpy/ma/core.py
|
python
|
MaskedArray.__iadd__
|
(self, other)
|
return self
|
Add other to self in-place.
|
Add other to self in-place.
|
[
"Add",
"other",
"to",
"self",
"in",
"-",
"place",
"."
] |
def __iadd__(self, other):
"Add other to self in-place."
m = getmask(other)
if self._mask is nomask:
if m is not nomask and m.any():
self._mask = make_mask_none(self.shape, self.dtype)
self._mask += m
else:
if m is not nomask:
self._mask += m
ndarray.__iadd__(self._data, np.where(self._mask, 0, getdata(other)))
return self
|
[
"def",
"__iadd__",
"(",
"self",
",",
"other",
")",
":",
"m",
"=",
"getmask",
"(",
"other",
")",
"if",
"self",
".",
"_mask",
"is",
"nomask",
":",
"if",
"m",
"is",
"not",
"nomask",
"and",
"m",
".",
"any",
"(",
")",
":",
"self",
".",
"_mask",
"=",
"make_mask_none",
"(",
"self",
".",
"shape",
",",
"self",
".",
"dtype",
")",
"self",
".",
"_mask",
"+=",
"m",
"else",
":",
"if",
"m",
"is",
"not",
"nomask",
":",
"self",
".",
"_mask",
"+=",
"m",
"ndarray",
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"__iadd__",
"(",
"self",
".",
"_data",
",",
"np",
".",
"where",
"(",
"self",
".",
"_mask",
",",
"0",
",",
"getdata",
"(",
"other",
")",
")",
")",
"return",
"self"
] |
https://github.com/ApolloAuto/apollo-platform/blob/86d9dc6743b496ead18d597748ebabd34a513289/ros/third_party/lib_x86_64/python2.7/dist-packages/numpy/ma/core.py#L3741-L3752
|
|
baidu-research/tensorflow-allreduce
|
66d5b855e90b0949e9fa5cca5599fd729a70e874
|
tensorflow/python/ops/data_flow_ops.py
|
python
|
MapStagingArea._pop
|
(self, key, indices=None, name=None)
|
return key, self._get_return_value(result, indices)
|
Remove and return the associated (key, value)
is returned from the staging area. If the key is not
in the staging area, this method will block until
the associated (key, value) is inserted.
Args:
key: Key associated with the required data
indices: Partial list of tensors to retrieve (optional).
A list of integer or string indices.
String indices are only valid if the Staging Area
has names associated with it.
name: A name for the operation (optional)
Returns:
The created op
|
Remove and return the associated (key, value)
is returned from the staging area. If the key is not
in the staging area, this method will block until
the associated (key, value) is inserted.
|
[
"Remove",
"and",
"return",
"the",
"associated",
"(",
"key",
"value",
")",
"is",
"returned",
"from",
"the",
"staging",
"area",
".",
"If",
"the",
"key",
"is",
"not",
"in",
"the",
"staging",
"area",
"this",
"method",
"will",
"block",
"until",
"the",
"associated",
"(",
"key",
"value",
")",
"is",
"inserted",
"."
] |
def _pop(self, key, indices=None, name=None):
"""
Remove and return the associated (key, value)
is returned from the staging area. If the key is not
in the staging area, this method will block until
the associated (key, value) is inserted.
Args:
key: Key associated with the required data
indices: Partial list of tensors to retrieve (optional).
A list of integer or string indices.
String indices are only valid if the Staging Area
has names associated with it.
name: A name for the operation (optional)
Returns:
The created op
"""
if name is None:
name = "%s_get" % self._name
indices, dtypes = self._get_indices_and_dtypes(indices)
with ops.colocate_with(self._coloc_op):
result = self._pop_fn(key, shared_name=self._name,
indices=indices,
dtypes=dtypes,
name=name,
capacity=self._capacity,
memory_limit=self._memory_limit)
return key, self._get_return_value(result, indices)
|
[
"def",
"_pop",
"(",
"self",
",",
"key",
",",
"indices",
"=",
"None",
",",
"name",
"=",
"None",
")",
":",
"if",
"name",
"is",
"None",
":",
"name",
"=",
"\"%s_get\"",
"%",
"self",
".",
"_name",
"indices",
",",
"dtypes",
"=",
"self",
".",
"_get_indices_and_dtypes",
"(",
"indices",
")",
"with",
"ops",
".",
"colocate_with",
"(",
"self",
".",
"_coloc_op",
")",
":",
"result",
"=",
"self",
".",
"_pop_fn",
"(",
"key",
",",
"shared_name",
"=",
"self",
".",
"_name",
",",
"indices",
"=",
"indices",
",",
"dtypes",
"=",
"dtypes",
",",
"name",
"=",
"name",
",",
"capacity",
"=",
"self",
".",
"_capacity",
",",
"memory_limit",
"=",
"self",
".",
"_memory_limit",
")",
"return",
"key",
",",
"self",
".",
"_get_return_value",
"(",
"result",
",",
"indices",
")"
] |
https://github.com/baidu-research/tensorflow-allreduce/blob/66d5b855e90b0949e9fa5cca5599fd729a70e874/tensorflow/python/ops/data_flow_ops.py#L2026-L2057
|
|
envoyproxy/envoy
|
65541accdafe255e72310b4298d646e091da2d80
|
tools/api_proto_plugin/type_context.py
|
python
|
TypeContext.leading_detached_comments
|
(self)
|
return self.source_code_info.leading_detached_comments_path_lookup(self.path)
|
Leading detached comments for type context.
|
Leading detached comments for type context.
|
[
"Leading",
"detached",
"comments",
"for",
"type",
"context",
"."
] |
def leading_detached_comments(self):
"""Leading detached comments for type context."""
return self.source_code_info.leading_detached_comments_path_lookup(self.path)
|
[
"def",
"leading_detached_comments",
"(",
"self",
")",
":",
"return",
"self",
".",
"source_code_info",
".",
"leading_detached_comments_path_lookup",
"(",
"self",
".",
"path",
")"
] |
https://github.com/envoyproxy/envoy/blob/65541accdafe255e72310b4298d646e091da2d80/tools/api_proto_plugin/type_context.py#L267-L269
|
|
natanielruiz/android-yolo
|
1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f
|
jni-build/jni/include/tensorflow/python/ops/random_ops.py
|
python
|
random_crop
|
(value, size, seed=None, name=None)
|
Randomly crops a tensor to a given size.
Slices a shape `size` portion out of `value` at a uniformly chosen offset.
Requires `value.shape >= size`.
If a dimension should not be cropped, pass the full size of that dimension.
For example, RGB images can be cropped with
`size = [crop_height, crop_width, 3]`.
Args:
value: Input tensor to crop.
size: 1-D tensor with size the rank of `value`.
seed: Python integer. Used to create a random seed. See
[`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
for behavior.
name: A name for this operation (optional).
Returns:
A cropped tensor of the same rank as `value` and shape `size`.
|
Randomly crops a tensor to a given size.
|
[
"Randomly",
"crops",
"a",
"tensor",
"to",
"a",
"given",
"size",
"."
] |
def random_crop(value, size, seed=None, name=None):
"""Randomly crops a tensor to a given size.
Slices a shape `size` portion out of `value` at a uniformly chosen offset.
Requires `value.shape >= size`.
If a dimension should not be cropped, pass the full size of that dimension.
For example, RGB images can be cropped with
`size = [crop_height, crop_width, 3]`.
Args:
value: Input tensor to crop.
size: 1-D tensor with size the rank of `value`.
seed: Python integer. Used to create a random seed. See
[`set_random_seed`](../../api_docs/python/constant_op.md#set_random_seed)
for behavior.
name: A name for this operation (optional).
Returns:
A cropped tensor of the same rank as `value` and shape `size`.
"""
# TODO(shlens): Implement edge case to guarantee output size dimensions.
# If size > value.shape, zero pad the result so that it always has shape
# exactly size.
with ops.op_scope([value, size], name, "random_crop") as name:
value = ops.convert_to_tensor(value, name="value")
size = ops.convert_to_tensor(size, dtype=dtypes.int32, name="size")
shape = array_ops.shape(value)
check = logging_ops.Assert(
math_ops.reduce_all(shape >= size),
["Need value.shape >= size, got ", shape, size])
shape = control_flow_ops.with_dependencies([check], shape)
limit = shape - size + 1
offset = random_uniform(
array_ops.shape(shape),
dtype=size.dtype,
maxval=size.dtype.max,
seed=seed) % limit
return array_ops.slice(value, offset, size, name=name)
|
[
"def",
"random_crop",
"(",
"value",
",",
"size",
",",
"seed",
"=",
"None",
",",
"name",
"=",
"None",
")",
":",
"# TODO(shlens): Implement edge case to guarantee output size dimensions.",
"# If size > value.shape, zero pad the result so that it always has shape",
"# exactly size.",
"with",
"ops",
".",
"op_scope",
"(",
"[",
"value",
",",
"size",
"]",
",",
"name",
",",
"\"random_crop\"",
")",
"as",
"name",
":",
"value",
"=",
"ops",
".",
"convert_to_tensor",
"(",
"value",
",",
"name",
"=",
"\"value\"",
")",
"size",
"=",
"ops",
".",
"convert_to_tensor",
"(",
"size",
",",
"dtype",
"=",
"dtypes",
".",
"int32",
",",
"name",
"=",
"\"size\"",
")",
"shape",
"=",
"array_ops",
".",
"shape",
"(",
"value",
")",
"check",
"=",
"logging_ops",
".",
"Assert",
"(",
"math_ops",
".",
"reduce_all",
"(",
"shape",
">=",
"size",
")",
",",
"[",
"\"Need value.shape >= size, got \"",
",",
"shape",
",",
"size",
"]",
")",
"shape",
"=",
"control_flow_ops",
".",
"with_dependencies",
"(",
"[",
"check",
"]",
",",
"shape",
")",
"limit",
"=",
"shape",
"-",
"size",
"+",
"1",
"offset",
"=",
"random_uniform",
"(",
"array_ops",
".",
"shape",
"(",
"shape",
")",
",",
"dtype",
"=",
"size",
".",
"dtype",
",",
"maxval",
"=",
"size",
".",
"dtype",
".",
"max",
",",
"seed",
"=",
"seed",
")",
"%",
"limit",
"return",
"array_ops",
".",
"slice",
"(",
"value",
",",
"offset",
",",
"size",
",",
"name",
"=",
"name",
")"
] |
https://github.com/natanielruiz/android-yolo/blob/1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f/jni-build/jni/include/tensorflow/python/ops/random_ops.py#L288-L326
|
||
mapnik/mapnik
|
f3da900c355e1d15059c4a91b00203dcc9d9f0ef
|
scons/scons-local-4.1.0/SCons/Platform/virtualenv.py
|
python
|
_running_in_virtualenv
|
()
|
return (hasattr(sys, 'real_prefix') or
(hasattr(sys, 'base_prefix') and sys.base_prefix != sys.prefix))
|
Returns True if scons is executed within a virtualenv
|
Returns True if scons is executed within a virtualenv
|
[
"Returns",
"True",
"if",
"scons",
"is",
"executed",
"within",
"a",
"virtualenv"
] |
def _running_in_virtualenv():
"""Returns True if scons is executed within a virtualenv"""
# see https://stackoverflow.com/a/42580137
return (hasattr(sys, 'real_prefix') or
(hasattr(sys, 'base_prefix') and sys.base_prefix != sys.prefix))
|
[
"def",
"_running_in_virtualenv",
"(",
")",
":",
"# see https://stackoverflow.com/a/42580137",
"return",
"(",
"hasattr",
"(",
"sys",
",",
"'real_prefix'",
")",
"or",
"(",
"hasattr",
"(",
"sys",
",",
"'base_prefix'",
")",
"and",
"sys",
".",
"base_prefix",
"!=",
"sys",
".",
"prefix",
")",
")"
] |
https://github.com/mapnik/mapnik/blob/f3da900c355e1d15059c4a91b00203dcc9d9f0ef/scons/scons-local-4.1.0/SCons/Platform/virtualenv.py#L47-L51
|
|
llvm/llvm-project
|
ffa6262cb4e2a335d26416fad39a581b4f98c5f4
|
lldb/examples/python/mach_o.py
|
python
|
TerminalColors.magenta
|
(self, fg=True)
|
return ''
|
Set the foreground or background color to magenta.
The foreground color will be set if "fg" tests True. The background color will be set if "fg" tests False.
|
Set the foreground or background color to magenta.
The foreground color will be set if "fg" tests True. The background color will be set if "fg" tests False.
|
[
"Set",
"the",
"foreground",
"or",
"background",
"color",
"to",
"magenta",
".",
"The",
"foreground",
"color",
"will",
"be",
"set",
"if",
"fg",
"tests",
"True",
".",
"The",
"background",
"color",
"will",
"be",
"set",
"if",
"fg",
"tests",
"False",
"."
] |
def magenta(self, fg=True):
'''Set the foreground or background color to magenta.
The foreground color will be set if "fg" tests True. The background color will be set if "fg" tests False.'''
if self.enabled:
if fg:
return "\x1b[35m"
else:
return "\x1b[45m"
return ''
|
[
"def",
"magenta",
"(",
"self",
",",
"fg",
"=",
"True",
")",
":",
"if",
"self",
".",
"enabled",
":",
"if",
"fg",
":",
"return",
"\"\\x1b[35m\"",
"else",
":",
"return",
"\"\\x1b[45m\"",
"return",
"''"
] |
https://github.com/llvm/llvm-project/blob/ffa6262cb4e2a335d26416fad39a581b4f98c5f4/lldb/examples/python/mach_o.py#L321-L329
|
|
zcash/zcash
|
944453065b40f6bed6bd59c4ff01c4d123c6cdb7
|
contrib/devtools/security-check.py
|
python
|
check_PE_DYNAMIC_BASE
|
(executable)
|
return (bits & reqbits) == reqbits
|
PIE: DllCharacteristics bit 0x40 signifies dynamicbase (ASLR)
|
PIE: DllCharacteristics bit 0x40 signifies dynamicbase (ASLR)
|
[
"PIE",
":",
"DllCharacteristics",
"bit",
"0x40",
"signifies",
"dynamicbase",
"(",
"ASLR",
")"
] |
def check_PE_DYNAMIC_BASE(executable):
'''PIE: DllCharacteristics bit 0x40 signifies dynamicbase (ASLR)'''
(arch,bits) = get_PE_dll_characteristics(executable)
reqbits = IMAGE_DLL_CHARACTERISTICS_DYNAMIC_BASE
return (bits & reqbits) == reqbits
|
[
"def",
"check_PE_DYNAMIC_BASE",
"(",
"executable",
")",
":",
"(",
"arch",
",",
"bits",
")",
"=",
"get_PE_dll_characteristics",
"(",
"executable",
")",
"reqbits",
"=",
"IMAGE_DLL_CHARACTERISTICS_DYNAMIC_BASE",
"return",
"(",
"bits",
"&",
"reqbits",
")",
"==",
"reqbits"
] |
https://github.com/zcash/zcash/blob/944453065b40f6bed6bd59c4ff01c4d123c6cdb7/contrib/devtools/security-check.py#L141-L145
|
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