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greenheartgames/greenworks | 3ea4ab490b56676de3f0a237c74bcfdb17323e60 | deps/cpplint/cpplint.py | python | FileInfo.BaseName | (self) | return self.Split()[1] | File base name - text after the final slash, before the final period. | File base name - text after the final slash, before the final period. | [
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NERSC/timemory | 431912b360ff50d1a160d7826e2eea04fbd1037f | scripts/gprof2dot.py | python | Theme.hsl_to_rgb | (self, h, s, l) | return (r, g, b) | Convert a color from HSL color-model to RGB.
See also:
- http://www.w3.org/TR/css3-color/#hsl-color | Convert a color from HSL color-model to RGB. | [
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"""Convert a color from HSL color-model to RGB.
See also:
- http://www.w3.org/TR/css3-color/#hsl-color
"""
h = h % 1.0
s = min(max(s, 0.0), 1.0)
l = min(max(l, 0.0), 1.0)
if l <= 0.5:
m2 = l*(s + 1.0)
else:
m2 = l + s - l*s
m1 = l*2.0 - m2
r = self._hue_to_rgb(m1, m2, h + 1.0/3.0)
g = self._hue_to_rgb(m1, m2, h)
b = self._hue_to_rgb(m1, m2, h - 1.0/3.0)
# Apply gamma correction
r **= self.gamma
g **= self.gamma
b **= self.gamma
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y123456yz/reading-and-annotate-mongodb-3.6 | 93280293672ca7586dc24af18132aa61e4ed7fcf | mongo/src/third_party/scons-2.5.0/scons-local-2.5.0/SCons/Node/__init__.py | python | Node.select_scanner | (self, scanner) | return scanner.select(self) | Selects a scanner for this Node.
This is a separate method so it can be overridden by Node
subclasses (specifically, Node.FS.Dir) that *must* use their
own Scanner and don't select one the Scanner.Selector that's
configured for the target. | Selects a scanner for this Node. | [
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"""Selects a scanner for this Node.
This is a separate method so it can be overridden by Node
subclasses (specifically, Node.FS.Dir) that *must* use their
own Scanner and don't select one the Scanner.Selector that's
configured for the target.
"""
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/_gdi.py | python | RegionIterator.GetW | (*args, **kwargs) | return _gdi_.RegionIterator_GetW(*args, **kwargs) | GetW(self) -> int | GetW(self) -> int | [
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moderngl/moderngl | 32fe79927e02b0fa893b3603d677bdae39771e14 | moderngl/texture.py | python | Texture.dtype | (self) | return self._dtype | str: Data type. | str: Data type. | [
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weolar/miniblink49 | 1c4678db0594a4abde23d3ebbcc7cd13c3170777 | third_party/WebKit/Tools/Scripts/webkitpy/thirdparty/mod_pywebsocket/standalone.py | python | _StandaloneConnection.__init__ | (self, request_handler) | Construct an instance.
Args:
request_handler: A WebSocketRequestHandler instance. | Construct an instance. | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/Jinja2/py2/jinja2/runtime.py | python | Context.call | (__self, __obj, *args, **kwargs) | Call the callable with the arguments and keyword arguments
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | tools/code_coverage/croc.py | python | Coverage.ParseConfig | (self, filename, **kwargs) | Parses a configuration file.
Args:
filename: Config filename.
kwargs: Additional parameters to pass to AddConfig(). | Parses a configuration file. | [
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"""Parses a configuration file.
Args:
filename: Config filename.
kwargs: Additional parameters to pass to AddConfig().
"""
# TODO: All manner of error checking
f = None
try:
f = open(filename, 'rt')
# Need to strip CR's from CRLF-terminated lines or posix systems can't
# eval the data.
config_data = f.read().replace('\r\n', '\n')
# TODO: some sort of include syntax.
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#
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self.AddConfig(config_data, **kwargs)
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f.close() | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/flatmenu.py | python | FlatMenu.SendOverItem | (self, itemIdx, over) | Sends the ``EVT_FLAT_MENU_ITEM_MOUSE_OVER`` and ``EVT_FLAT_MENU_ITEM_MOUSE_OUT``
events.
:param integer `itemIdx`: the menu item index for which we want to send an event;
:param bool `over`: ``True`` to send a ``EVT_FLAT_MENU_ITEM_MOUSE_OVER`` event, ``False`` to
send a ``EVT_FLAT_MENU_ITEM_MOUSE_OUT`` event. | Sends the ``EVT_FLAT_MENU_ITEM_MOUSE_OVER`` and ``EVT_FLAT_MENU_ITEM_MOUSE_OUT``
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:param integer `itemIdx`: the menu item index for which we want to send an event;
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item = self._itemsArr[itemIdx]
# Create the event
event = FlatMenuEvent((over and [wxEVT_FLAT_MENU_ITEM_MOUSE_OVER] or [wxEVT_FLAT_MENU_ITEM_MOUSE_OUT])[0], item.GetId())
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event.SetEventObject(self)
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Optimizer/vai_p_pytorch/cifar10/analyse.py | python | accuracy | (output, target, topk=(1,)) | Computes the accuracy over the k top predictions
for the specified values of k | Computes the accuracy over the k top predictions
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maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].flatten().float().sum(0, keepdim=True)
res.append(correct_k.mul_(100.0 / batch_size))
return res | [
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pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | torch/multiprocessing/__init__.py | python | set_sharing_strategy | (new_strategy) | Sets the strategy for sharing CPU tensors.
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new_strategy (str): Name of the selected strategy. Should be one of
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"strategy",
"for",
"sharing",
"CPU",
"tensors",
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] | def set_sharing_strategy(new_strategy):
"""Sets the strategy for sharing CPU tensors.
Args:
new_strategy (str): Name of the selected strategy. Should be one of
the values returned by :func:`get_all_sharing_strategies()`.
"""
global _sharing_strategy
assert new_strategy in _all_sharing_strategies
_sharing_strategy = new_strategy | [
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etotheipi/BitcoinArmory | 2a6fc5355bb0c6fe26e387ccba30a5baafe8cd98 | armoryengine/ArmoryUtils.py | python | SettingsFile.extend | (self, name, value) | Adds/converts setting to list, appends value to the end of it | Adds/converts setting to list, appends value to the end of it | [
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self.settingsMap[name] = origVal
self.writeSettingsFile() | [
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TimoSaemann/caffe-segnet-cudnn5 | abcf30dca449245e101bf4ced519f716177f0885 | scripts/cpp_lint.py | python | CheckForHeaderGuard | (filename, lines, error) | Checks that the file contains a header guard.
Logs an error if no #ifndef header guard is present. For other
headers, checks that the full pathname is used.
Args:
filename: The name of the C++ header file.
lines: An array of strings, each representing a line of the file.
error: The function to call with any errors found. | Checks that the file contains a header guard. | [
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] | def CheckForHeaderGuard(filename, lines, error):
"""Checks that the file contains a header guard.
Logs an error if no #ifndef header guard is present. For other
headers, checks that the full pathname is used.
Args:
filename: The name of the C++ header file.
lines: An array of strings, each representing a line of the file.
error: The function to call with any errors found.
"""
cppvar = GetHeaderGuardCPPVariable(filename)
ifndef = None
ifndef_linenum = 0
define = None
endif = None
endif_linenum = 0
for linenum, line in enumerate(lines):
linesplit = line.split()
if len(linesplit) >= 2:
# find the first occurrence of #ifndef and #define, save arg
if not ifndef and linesplit[0] == '#ifndef':
# set ifndef to the header guard presented on the #ifndef line.
ifndef = linesplit[1]
ifndef_linenum = linenum
if not define and linesplit[0] == '#define':
define = linesplit[1]
# find the last occurrence of #endif, save entire line
if line.startswith('#endif'):
endif = line
endif_linenum = linenum
if not ifndef:
error(filename, 0, 'build/header_guard', 5,
'No #ifndef header guard found, suggested CPP variable is: %s' %
cppvar)
return
if not define:
error(filename, 0, 'build/header_guard', 5,
'No #define header guard found, suggested CPP variable is: %s' %
cppvar)
return
# The guard should be PATH_FILE_H_, but we also allow PATH_FILE_H__
# for backward compatibility.
if ifndef != cppvar:
error_level = 0
if ifndef != cppvar + '_':
error_level = 5
ParseNolintSuppressions(filename, lines[ifndef_linenum], ifndef_linenum,
error)
error(filename, ifndef_linenum, 'build/header_guard', error_level,
'#ifndef header guard has wrong style, please use: %s' % cppvar)
if define != ifndef:
error(filename, 0, 'build/header_guard', 5,
'#ifndef and #define don\'t match, suggested CPP variable is: %s' %
cppvar)
return
if endif != ('#endif // %s' % cppvar):
error_level = 0
if endif != ('#endif // %s' % (cppvar + '_')):
error_level = 5
ParseNolintSuppressions(filename, lines[endif_linenum], endif_linenum,
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error(filename, endif_linenum, 'build/header_guard', error_level,
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seqan/seqan | f5f658343c366c9c3d44ba358ffc9317e78a09ed | util/py_lib/seqan/dox/proc_doc.py | python | EntryConverter.bodyToTextNode | (self, raw_body) | return res | Convert a RawBody to a TextNode. | Convert a RawBody to a TextNode. | [
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"TextNode",
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] | def bodyToTextNode(self, raw_body):
"""Convert a RawBody to a TextNode."""
res = TextNode(type='div')
for p in raw_body.paragraphs:
try:
if p.getType() == 'paragraph':
if not p.text.text.strip():
continue # Skip whitespace
p = self.rawTextToTextNode(p.text)
p.type = 'p'
res.addChild(p)
elif p.getType() == 'section':
h = self.rawTextToTextNode(p.heading)
h.type = 'h%d' % (p.level + 1)
res.addChild(h)
elif p.getType() == 'include':
# Including a whole file.
ftype = os.path.splitext(p.path.text)[1]
code_text = self.doc_proc.include_mgr.loadFile(p.path.text)
proc_include = TextNode(type='dox:code', attrs={'type': ftype, 'source': 'include', 'path': p.path.text})
proc_include.addChild(TextNode(text=code_text, verbatim=True))
res.addChild(proc_include)
elif p.getType() == 'snippet':
# Including a snippet file.
ftype = os.path.splitext(p.path.text)[1]
code_text = self.doc_proc.include_mgr.loadSnippet(p.path.text, p.name.text)
proc_snippet = TextNode(type='dox:code', attrs={'type': ftype, 'source': 'snippet', 'path': p.path.text})
proc_snippet.addChild(TextNode(text=code_text, verbatim=True))
res.addChild(proc_snippet)
elif p.getType() == 'code':
code_text = p.text.text
type = '.txt'
m = re.match(r'^{[^}]+}', code_text)
if m:
type = m.group(0)[1:-1]
code_text = code_text[len(type) + 2:].strip()
#print [repr(t.val) for t in p.text.tokens]
x = TextNode(type='dox:code', attrs={'type': type})
x.addChild(TextNode(text=code_text, verbatim=True))
res.addChild(x)
elif p.getType() == 'htmlonly':
res.addChild(TextNode(text=p.text.text, verbatim=True))
except inc_mgr.IncludeException, e:
e2 = dox_parser.ParserError(msg=str(e), token=p.text.tokens[0])
self.doc_proc.msg_printer.printParserError(e2)
n = TextNode(type='div', attrs={'class': 'note warning'})
n.children.append(TextNode(text=str(e)))
res.addChild(n)
return res | [
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yuxng/DA-RNN | 77fbb50b4272514588a10a9f90b7d5f8d46974fb | lib/datasets/rgbd_scene.py | python | rgbd_scene.depth_path_from_index | (self, index) | return depth_path | Construct an depth path from the image's "index" identifier. | Construct an depth path from the image's "index" identifier. | [
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"identifier",
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] | def depth_path_from_index(self, index):
"""
Construct an depth path from the image's "index" identifier.
"""
depth_path = os.path.join(self._data_path, index + '-depth' + self._image_ext)
assert os.path.exists(depth_path), \
'Path does not exist: {}'.format(depth_path)
return depth_path | [
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PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | python/paddle/dataset/wmt16.py | python | get_dict | (lang, dict_size, reverse=False) | return __load_dict(tar_file, dict_size, lang, reverse) | return the word dictionary for the specified language.
Args:
lang(string): A string indicating which language is the source
language. Available options are: "en" for English
and "de" for Germany.
dict_size(int): Size of the specified language dictionary.
reverse(bool): If reverse is set to False, the returned python
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dict: The word dictionary for the specific language. | return the word dictionary for the specified language. | [
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] | def get_dict(lang, dict_size, reverse=False):
"""
return the word dictionary for the specified language.
Args:
lang(string): A string indicating which language is the source
language. Available options are: "en" for English
and "de" for Germany.
dict_size(int): Size of the specified language dictionary.
reverse(bool): If reverse is set to False, the returned python
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Returns:
dict: The word dictionary for the specific language.
"""
if lang == "en": dict_size = min(dict_size, TOTAL_EN_WORDS)
else: dict_size = min(dict_size, TOTAL_DE_WORDS)
dict_path = os.path.join(paddle.dataset.common.DATA_HOME,
"wmt16/%s_%d.dict" % (lang, dict_size))
assert os.path.exists(dict_path), "Word dictionary does not exist. "
"Please invoke paddle.dataset.wmt16.train/test/validation first "
"to build the dictionary."
tar_file = os.path.join(paddle.dataset.common.DATA_HOME, "wmt16.tar.gz")
return __load_dict(tar_file, dict_size, lang, reverse) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/joblib/joblib/externals/cloudpickle/cloudpickle.py | python | _make_skeleton_enum | (bases, name, qualname, members, module,
class_tracker_id, extra) | return _lookup_class_or_track(class_tracker_id, enum_class) | Build dynamic enum with an empty __dict__ to be filled once memoized
The creation of the enum class is inspired by the code of
EnumMeta._create_.
If class_tracker_id is not None, try to lookup an existing enum definition
matching that id. If none is found, track a newly reconstructed enum
definition under that id so that other instances stemming from the same
class id will also reuse this enum definition.
The "extra" variable is meant to be a dict (or None) that can be used for
forward compatibility shall the need arise. | Build dynamic enum with an empty __dict__ to be filled once memoized | [
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class_tracker_id, extra):
"""Build dynamic enum with an empty __dict__ to be filled once memoized
The creation of the enum class is inspired by the code of
EnumMeta._create_.
If class_tracker_id is not None, try to lookup an existing enum definition
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definition under that id so that other instances stemming from the same
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The "extra" variable is meant to be a dict (or None) that can be used for
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"""
# enums always inherit from their base Enum class at the last position in
# the list of base classes:
enum_base = bases[-1]
metacls = enum_base.__class__
classdict = metacls.__prepare__(name, bases)
for member_name, member_value in members.items():
classdict[member_name] = member_value
enum_class = metacls.__new__(metacls, name, bases, classdict)
enum_class.__module__ = module
enum_class.__qualname__ = qualname
return _lookup_class_or_track(class_tracker_id, enum_class) | [
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CNevd/Difacto_DMLC | f16862e35062707b1cf7e37d04d9b6ae34bbfd28 | dmlc-core/tracker/dmlc_sge.py | python | sge_submit | (nworker, nserver, pass_envs) | customized submit script, that submit nslave jobs, each must contain args as parameter
note this can be a lambda function containing additional parameters in input
Parameters
nworker number of slave process to start up
nserver number of server nodes to start up
pass_envs enviroment variables to be added to the starting programs | customized submit script, that submit nslave jobs, each must contain args as parameter
note this can be a lambda function containing additional parameters in input
Parameters
nworker number of slave process to start up
nserver number of server nodes to start up
pass_envs enviroment variables to be added to the starting programs | [
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note this can be a lambda function containing additional parameters in input
Parameters
nworker number of slave process to start up
nserver number of server nodes to start up
pass_envs enviroment variables to be added to the starting programs
"""
env_arg = ','.join(['%s=\"%s\"' % (k, str(v)) for k, v in pass_envs.items()])
cmd = 'qsub -cwd -t 1-%d -S /bin/bash' % (nworker + nserver)
if args.queue != 'default':
cmd += '-q %s' % args.queue
cmd += ' -N %s ' % args.jobname
cmd += ' -e %s -o %s' % (args.logdir, args.logdir)
cmd += ' -pe orte %d' % (args.vcores)
cmd += ' -v %s,PATH=${PATH}:.' % env_arg
cmd += ' %s %s' % (runscript, ' '.join(args.command) + ' ' + ' '.join(unknown))
print cmd
subprocess.check_call(cmd, shell = True)
print 'Waiting for the jobs to get up...' | [
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Illumina/strelka | d7377443b62319f7c7bd70c241c4b2df3459e29a | src/python/lib/workflowUtil.py | python | cleanId | (input_id) | return re.sub(r'([^a-zA-Z0-9_\-])', "_", input_id) | filter id so that it's safe to use as a pyflow indentifier | filter id so that it's safe to use as a pyflow indentifier | [
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] | def cleanId(input_id) :
"""
filter id so that it's safe to use as a pyflow indentifier
"""
import re
return re.sub(r'([^a-zA-Z0-9_\-])', "_", input_id) | [
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emsesp/EMS-ESP | 65c4a381bf8df61d1e18ba00223b1a55933fc547 | scripts/esptool.py | python | timeout_per_mb | (seconds_per_mb, size_bytes) | return result | Scales timeouts which are size-specific | Scales timeouts which are size-specific | [
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] | def timeout_per_mb(seconds_per_mb, size_bytes):
""" Scales timeouts which are size-specific """
result = seconds_per_mb * (size_bytes / 1e6)
if result < DEFAULT_TIMEOUT:
return DEFAULT_TIMEOUT
return result | [
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FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Draft/draftobjects/layer.py | python | Layer.addObject | (self, obj, child) | Add an object to this object if not in the Group property. | Add an object to this object if not in the Group property. | [
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] | def addObject(self, obj, child):
"""Add an object to this object if not in the Group property."""
group = obj.Group
if child not in group:
group.append(child)
obj.Group = group | [
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bulletphysics/bullet3 | f0f2a952e146f016096db6f85cf0c44ed75b0b9a | examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/sensors/sensor.py | python | newer_obs_blender | (low_value: Any, unused_high_value: Any,
unused_coeff: float) | return low_value | Always choosing low value, which is the newer value between low/high. | Always choosing low value, which is the newer value between low/high. | [
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] | def newer_obs_blender(low_value: Any, unused_high_value: Any,
unused_coeff: float):
"""Always choosing low value, which is the newer value between low/high."""
return low_value | [
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AcademySoftwareFoundation/OpenColorIO | 73508eb5230374df8d96147a0627c015d359a641 | share/docs/frozendoc.py | python | backup_frozen | (app) | Duplicate existing frozen RST files for comparison with newly
frozen RST in ``compare_frozen()`` after docs are built. | Duplicate existing frozen RST files for comparison with newly
frozen RST in ``compare_frozen()`` after docs are built. | [
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"""
Duplicate existing frozen RST files for comparison with newly
frozen RST in ``compare_frozen()`` after docs are built.
"""
if app.config.frozendoc_build or app.config.frozendoc_compare:
# Apply monkeypatch, enabling frozen RST generation
patch()
if not app.config.frozendoc_compare:
return
logger.info("Backing up frozen RST: {src} -> {dst}".format(
src=PYTHON_FROZEN_DIR,
dst=PYTHON_BACKUP_DIR
))
if os.path.exists(PYTHON_FROZEN_DIR) and os.listdir(PYTHON_FROZEN_DIR):
if os.path.exists(PYTHON_BACKUP_DIR):
shutil.rmtree(PYTHON_BACKUP_DIR)
shutil.move(PYTHON_FROZEN_DIR, PYTHON_BACKUP_DIR)
else:
raise ExtensionError(
"No frozen RST found! Build OpenColorIO with CMake option "
"'-DOCIO_BUILD_FROZEN_DOCS=ON' to generate required frozen "
"documentation source files in: {dir}".format(dir=PYTHON_FROZEN_DIR)
)
logger.info("Backup complete.") | [
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okex/V3-Open-API-SDK | c5abb0db7e2287718e0055e17e57672ce0ec7fd9 | okex-python-sdk-api/venv/Lib/site-packages/pip-19.0.3-py3.8.egg/pip/_vendor/requests/cookies.py | python | remove_cookie_by_name | (cookiejar, name, domain=None, path=None) | Unsets a cookie by name, by default over all domains and paths.
Wraps CookieJar.clear(), is O(n). | Unsets a cookie by name, by default over all domains and paths. | [
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] | def remove_cookie_by_name(cookiejar, name, domain=None, path=None):
"""Unsets a cookie by name, by default over all domains and paths.
Wraps CookieJar.clear(), is O(n).
"""
clearables = []
for cookie in cookiejar:
if cookie.name != name:
continue
if domain is not None and domain != cookie.domain:
continue
if path is not None and path != cookie.path:
continue
clearables.append((cookie.domain, cookie.path, cookie.name))
for domain, path, name in clearables:
cookiejar.clear(domain, path, name) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py3/pandas/core/internals/blocks.py | python | Block.putmask | (self, mask, new) | putmask the data to the block; it is possible that we may create a
new dtype of block
Return the resulting block(s).
Parameters
----------
mask : np.ndarray[bool], SparseArray[bool], or BooleanArray
new : a ndarray/object
Returns
-------
List[Block] | putmask the data to the block; it is possible that we may create a
new dtype of block | [
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"""
putmask the data to the block; it is possible that we may create a
new dtype of block
Return the resulting block(s).
Parameters
----------
mask : np.ndarray[bool], SparseArray[bool], or BooleanArray
new : a ndarray/object
Returns
-------
List[Block]
"""
orig_mask = mask
mask, noop = validate_putmask(self.values.T, mask)
assert not isinstance(new, (ABCIndex, ABCSeries, ABCDataFrame))
# if we are passed a scalar None, convert it here
if not self.is_object and is_valid_na_for_dtype(new, self.dtype):
new = self.fill_value
if self._can_hold_element(new):
# error: Argument 1 to "putmask_without_repeat" has incompatible type
# "Union[ndarray, ExtensionArray]"; expected "ndarray"
putmask_without_repeat(self.values.T, mask, new) # type: ignore[arg-type]
return [self]
elif noop:
return [self]
dtype, _ = infer_dtype_from(new)
if dtype.kind in ["m", "M"]:
# using putmask with object dtype will incorrectly cast to object
# Having excluded self._can_hold_element, we know we cannot operate
# in-place, so we are safe using `where`
return self.where(new, ~mask)
elif self.ndim == 1 or self.shape[0] == 1:
# no need to split columns
# error: Argument 1 to "putmask_smart" has incompatible type "Union[ndarray,
# ExtensionArray]"; expected "ndarray"
nv = putmask_smart(self.values.T, mask, new).T # type: ignore[arg-type]
return [self.make_block(nv)]
else:
is_array = isinstance(new, np.ndarray)
res_blocks = []
nbs = self._split()
for i, nb in enumerate(nbs):
n = new
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# we have a different value per-column
n = new[:, i : i + 1]
submask = orig_mask[:, i : i + 1]
rbs = nb.putmask(submask, n)
res_blocks.extend(rbs)
return res_blocks | [
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hakuna-m/wubiuefi | caec1af0a09c78fd5a345180ada1fe45e0c63493 | src/urlgrabber/mirror.py | python | MirrorGroup.increment_mirror | (self, gr, action={}) | Tell the mirror object increment the mirror index
This increments the mirror index, which amounts to telling the
mirror object to use a different mirror (for this and future
downloads).
This is a SEMI-public method. It will be called internally,
and you may never need to call it. However, it is provided
(and is made public) so that the calling program can increment
the mirror choice for methods like urlopen. For example, with
urlopen, there's no good way for the mirror group to know that
an error occurs mid-download (it's already returned and given
you the file object).
remove --- can have several values
0 do not remove the mirror from the list
1 remove the mirror for this download only
2 remove the mirror permanently
beware of remove=0 as it can lead to infinite loops | Tell the mirror object increment the mirror index | [
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"object",
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] | def increment_mirror(self, gr, action={}):
"""Tell the mirror object increment the mirror index
This increments the mirror index, which amounts to telling the
mirror object to use a different mirror (for this and future
downloads).
This is a SEMI-public method. It will be called internally,
and you may never need to call it. However, it is provided
(and is made public) so that the calling program can increment
the mirror choice for methods like urlopen. For example, with
urlopen, there's no good way for the mirror group to know that
an error occurs mid-download (it's already returned and given
you the file object).
remove --- can have several values
0 do not remove the mirror from the list
1 remove the mirror for this download only
2 remove the mirror permanently
beware of remove=0 as it can lead to infinite loops
"""
badmirror = gr.mirrors[gr._next]
self._lock.acquire()
try:
ind = self.mirrors.index(badmirror)
except ValueError:
pass
else:
if action.get('remove_master', 0):
del self.mirrors[ind]
elif self._next == ind and action.get('increment_master', 1):
self._next += 1
if self._next >= len(self.mirrors): self._next = 0
self._lock.release()
if action.get('remove', 1):
del gr.mirrors[gr._next]
elif action.get('increment', 1):
gr._next += 1
if gr._next >= len(gr.mirrors): gr._next = 0
if DEBUG:
grm = [m['mirror'] for m in gr.mirrors]
DEBUG.info('GR mirrors: [%s] %i', ' '.join(grm), gr._next)
selfm = [m['mirror'] for m in self.mirrors]
DEBUG.info('MAIN mirrors: [%s] %i', ' '.join(selfm), self._next) | [
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tangzhenyu/Scene-Text-Understanding | 0f7ffc7aea5971a50cdc03d33d0a41075285948b | SynthText_Chinese/ransac.py | python | fit_plane_ransac | (pts, neighbors=None,z_pos=None, dist_inlier=0.05,
min_inlier_frac=0.60, nsample=3, max_iter=100) | Fits a 3D plane model using RANSAC.
pts : (nx3 array) of point coordinates | Fits a 3D plane model using RANSAC.
pts : (nx3 array) of point coordinates | [
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] | def fit_plane_ransac(pts, neighbors=None,z_pos=None, dist_inlier=0.05,
min_inlier_frac=0.60, nsample=3, max_iter=100):
"""
Fits a 3D plane model using RANSAC.
pts : (nx3 array) of point coordinates
"""
n,_ = pts.shape
ninlier,models = [],[]
for i in xrange(max_iter):
if neighbors is None:
p = pts[np.random.choice(pts.shape[0],nsample,replace=False),:]
else:
p = pts[neighbors[:,i],:]
m = fit_plane(p,z_pos)
ds = np.abs(pts.dot(m[:3])+m[3])
nin = np.sum(ds < dist_inlier)
if nin/pts.shape[0] >= min_inlier_frac:
ninlier.append(nin)
models.append(m)
if models == []:
print "RANSAC plane fitting failed!"
return #None
else: #refit the model to inliers:
ninlier = np.array(ninlier)
best_model_idx = np.argsort(-ninlier)
n_refit, m_refit, inliers = [],[],[]
for idx in best_model_idx[:min(10,len(best_model_idx))]:
# re-estimate the model based on inliers:
dists = np.abs(pts.dot(models[idx][:3])+models[idx][3])
inlier = dists < dist_inlier
m = fit_plane(pts[inlier,:],z_pos)
# compute new inliers:
d = np.abs(pts.dot(m[:3])+m[3])
inlier = d < dist_inlier/2 # heuristic
n_refit.append(np.sum(inlier))
m_refit.append(m)
inliers.append(inlier)
best_plane = np.argmax(n_refit)
return m_refit[best_plane],inliers[best_plane] | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/ipython/py2/IPython/core/builtin_trap.py | python | BuiltinTrap.add_builtin | (self, key, value) | Add a builtin and save the original. | Add a builtin and save the original. | [
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"builtin",
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"save",
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"original",
"."
] | def add_builtin(self, key, value):
"""Add a builtin and save the original."""
bdict = builtin_mod.__dict__
orig = bdict.get(key, BuiltinUndefined)
if value is HideBuiltin:
if orig is not BuiltinUndefined: #same as 'key in bdict'
self._orig_builtins[key] = orig
del bdict[key]
else:
self._orig_builtins[key] = orig
bdict[key] = value | [
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bulletphysics/bullet3 | f0f2a952e146f016096db6f85cf0c44ed75b0b9a | examples/pybullet/gym/pybullet_utils/transformations.py | python | arcball_constrain_to_axis | (point, axis) | return unit_vector([-a[1], a[0], 0]) | Return sphere point perpendicular to axis. | Return sphere point perpendicular to axis. | [
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"axis",
"."
] | def arcball_constrain_to_axis(point, axis):
"""Return sphere point perpendicular to axis."""
v = numpy.array(point, dtype=numpy.float64, copy=True)
a = numpy.array(axis, dtype=numpy.float64, copy=True)
v -= a * numpy.dot(a, v) # on plane
n = vector_norm(v)
if n > _EPS:
if v[2] < 0.0:
v *= -1.0
v /= n
return v
if a[2] == 1.0:
return numpy.array([1, 0, 0], dtype=numpy.float64)
return unit_vector([-a[1], a[0], 0]) | [
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] | https://github.com/bulletphysics/bullet3/blob/f0f2a952e146f016096db6f85cf0c44ed75b0b9a/examples/pybullet/gym/pybullet_utils/transformations.py#L1485-L1498 | |
DaFuCoding/MTCNN_Caffe | 09c30c3ff391bd9cb6b249c1910afaf147767ab3 | scripts/cpp_lint.py | python | CheckAccess | (filename, clean_lines, linenum, nesting_state, error) | Checks for improper use of DISALLOW* macros.
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. | Checks for improper use of DISALLOW* macros. | [
"Checks",
"for",
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"use",
"of",
"DISALLOW",
"*",
"macros",
"."
] | def CheckAccess(filename, clean_lines, linenum, nesting_state, error):
"""Checks for improper use of DISALLOW* macros.
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.
"""
line = clean_lines.elided[linenum] # get rid of comments and strings
matched = Match((r'\s*(DISALLOW_COPY_AND_ASSIGN|'
r'DISALLOW_EVIL_CONSTRUCTORS|'
r'DISALLOW_IMPLICIT_CONSTRUCTORS)'), line)
if not matched:
return
if nesting_state.stack and isinstance(nesting_state.stack[-1], _ClassInfo):
if nesting_state.stack[-1].access != 'private':
error(filename, linenum, 'readability/constructors', 3,
'%s must be in the private: section' % matched.group(1))
else:
# Found DISALLOW* macro outside a class declaration, or perhaps it
# was used inside a function when it should have been part of the
# class declaration. We could issue a warning here, but it
# probably resulted in a compiler error already.
pass | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/site-packages/sipconfig.py | python | ProgramMakefile.generate_target_default | (self, mfile) | Generate the default target.
mfile is the file object. | Generate the default target. | [
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"default",
"target",
"."
] | def generate_target_default(self, mfile):
"""Generate the default target.
mfile is the file object.
"""
# Do these first so that it's safe for a sub-class to append additional
# commands to the real target, but make sure the default is correct.
mfile.write("\nall: $(TARGET)\n")
mfile.write("\n$(OFILES): $(HFILES)\n")
for mf in self._build["moc_headers"].split():
root, _ = os.path.splitext(mf)
cpp = "moc_" + root + ".cpp"
if self._src_dir != self.dir:
mf = os.path.join(self._src_dir, mf)
mfile.write("\n%s: %s\n" % (cpp, mf))
mfile.write("\t$(MOC) -o %s %s\n" % (cpp, mf))
mfile.write("\n$(TARGET): $(OFILES)\n")
if self.generator in ("MSVC", "MSVC.NET", "MSBUILD"):
mfile.write("\t$(LINK) $(LFLAGS) /OUT:$(TARGET) @<<\n")
mfile.write("\t $(OFILES) $(LIBS)\n")
mfile.write("<<\n")
elif self.generator == "BMAKE":
mfile.write("\t$(LINK) @&&|\n")
mfile.write("\t$(LFLAGS) $(OFILES) ,$(TARGET),,$(LIBS),,\n")
mfile.write("|\n")
else:
mfile.write("\t$(LINK) $(LFLAGS) -o $(TARGET) $(OFILES) $(LIBS)\n")
if self._manifest:
mfile.write("\tmt -nologo -manifest $(TARGET).manifest -outputresource:$(TARGET);1\n") | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/tensor_forest/client/random_forest.py | python | _assert_float32 | (tensors) | Assert all tensors are float32.
Args:
tensors: `Tensor` or `dict` of `Tensor` objects.
Raises:
TypeError: if any tensor is not float32. | Assert all tensors are float32. | [
"Assert",
"all",
"tensors",
"are",
"float32",
"."
] | def _assert_float32(tensors):
"""Assert all tensors are float32.
Args:
tensors: `Tensor` or `dict` of `Tensor` objects.
Raises:
TypeError: if any tensor is not float32.
"""
if not isinstance(tensors, dict):
tensors = [tensors]
else:
tensors = tensors.values()
for tensor in tensors:
if tensor.dtype.base_dtype != dtypes.float32:
raise TypeError('Expected dtype=float32, %s.' % tensor) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/difflib.py | python | IS_LINE_JUNK | (line, pat=re.compile(r"\s*(?:#\s*)?$").match) | return pat(line) is not None | r"""
Return True for ignorable line: iff `line` is blank or contains a single '#'.
Examples:
>>> IS_LINE_JUNK('\n')
True
>>> IS_LINE_JUNK(' # \n')
True
>>> IS_LINE_JUNK('hello\n')
False | r"""
Return True for ignorable line: iff `line` is blank or contains a single '#'. | [
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"."
] | def IS_LINE_JUNK(line, pat=re.compile(r"\s*(?:#\s*)?$").match):
r"""
Return True for ignorable line: iff `line` is blank or contains a single '#'.
Examples:
>>> IS_LINE_JUNK('\n')
True
>>> IS_LINE_JUNK(' # \n')
True
>>> IS_LINE_JUNK('hello\n')
False
"""
return pat(line) is not None | [
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milvus-io/milvus | 3b1030de2b6c39e3512833e97f6044d63eb24237 | internal/core/build-support/cpplint.py | python | FilesBelongToSameModule | (filename_cc, filename_h) | return files_belong_to_same_module, common_path | Check if these two filenames belong to the same module.
The concept of a 'module' here is as follows:
foo.h, foo-inl.h, foo.cc, foo_test.cc and foo_unittest.cc belong to the
same 'module' if they are in the same directory.
some/path/public/xyzzy and some/path/internal/xyzzy are also considered
to belong to the same module here.
If the filename_cc contains a longer path than the filename_h, for example,
'/absolute/path/to/base/sysinfo.cc', and this file would include
'base/sysinfo.h', this function also produces the prefix needed to open the
header. This is used by the caller of this function to more robustly open the
header file. We don't have access to the real include paths in this context,
so we need this guesswork here.
Known bugs: tools/base/bar.cc and base/bar.h belong to the same module
according to this implementation. Because of this, this function gives
some false positives. This should be sufficiently rare in practice.
Args:
filename_cc: is the path for the source (e.g. .cc) file
filename_h: is the path for the header path
Returns:
Tuple with a bool and a string:
bool: True if filename_cc and filename_h belong to the same module.
string: the additional prefix needed to open the header file. | Check if these two filenames belong to the same module. | [
"Check",
"if",
"these",
"two",
"filenames",
"belong",
"to",
"the",
"same",
"module",
"."
] | def FilesBelongToSameModule(filename_cc, filename_h):
"""Check if these two filenames belong to the same module.
The concept of a 'module' here is as follows:
foo.h, foo-inl.h, foo.cc, foo_test.cc and foo_unittest.cc belong to the
same 'module' if they are in the same directory.
some/path/public/xyzzy and some/path/internal/xyzzy are also considered
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If the filename_cc contains a longer path than the filename_h, for example,
'/absolute/path/to/base/sysinfo.cc', and this file would include
'base/sysinfo.h', this function also produces the prefix needed to open the
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Args:
filename_cc: is the path for the source (e.g. .cc) file
filename_h: is the path for the header path
Returns:
Tuple with a bool and a string:
bool: True if filename_cc and filename_h belong to the same module.
string: the additional prefix needed to open the header file.
"""
fileinfo_cc = FileInfo(filename_cc)
if not fileinfo_cc.Extension().lstrip('.') in GetNonHeaderExtensions():
return (False, '')
fileinfo_h = FileInfo(filename_h)
if not IsHeaderExtension(fileinfo_h.Extension().lstrip('.')):
return (False, '')
filename_cc = filename_cc[:-(len(fileinfo_cc.Extension()))]
matched_test_suffix = Search(_TEST_FILE_SUFFIX, fileinfo_cc.BaseName())
if matched_test_suffix:
filename_cc = filename_cc[:-len(matched_test_suffix.group(1))]
filename_cc = filename_cc.replace('/public/', '/')
filename_cc = filename_cc.replace('/internal/', '/')
filename_h = filename_h[:-(len(fileinfo_h.Extension()))]
if filename_h.endswith('-inl'):
filename_h = filename_h[:-len('-inl')]
filename_h = filename_h.replace('/public/', '/')
filename_h = filename_h.replace('/internal/', '/')
files_belong_to_same_module = filename_cc.endswith(filename_h)
common_path = ''
if files_belong_to_same_module:
common_path = filename_cc[:-len(filename_h)]
return files_belong_to_same_module, common_path | [
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wallix/redemption | fb4ceefb39e11e1ae250bce17e878e1dc7d195d2 | tools/sesman/sesmanworker/challenge.py | python | md_to_challenge | (md) | return Challenge(
challenge_type=auth_type,
title=title,
message=message,
fields=[],
echos=[],
username=md.get("username"),
challenge=md,
token=None,
recall=True,
) | Convert new Challenge from bastion to internal Challenge
param challenge: Challenge from bastion
:rtype: Challenge
:return: a converted Challenge | Convert new Challenge from bastion to internal Challenge | [
"Convert",
"new",
"Challenge",
"from",
"bastion",
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"Challenge"
] | def md_to_challenge(md):
""" Convert new Challenge from bastion to internal Challenge
param challenge: Challenge from bastion
:rtype: Challenge
:return: a converted Challenge
"""
auth_type = md.get("auth_type")
title = "= MOBILE DEVICE ="
message = md.get("prompt", "")
return Challenge(
challenge_type=auth_type,
title=title,
message=message,
fields=[],
echos=[],
username=md.get("username"),
challenge=md,
token=None,
recall=True,
) | [
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y123456yz/reading-and-annotate-mongodb-3.6 | 93280293672ca7586dc24af18132aa61e4ed7fcf | mongo/src/third_party/scons-2.5.0/scons-local-2.5.0/SCons/Environment.py | python | Base.PrependENVPath | (self, name, newpath, envname = 'ENV', sep = os.pathsep,
delete_existing=1) | Prepend path elements to the path 'name' in the 'ENV'
dictionary for this environment. Will only add any particular
path once, and will normpath and normcase all paths to help
assure this. This can also handle the case where the env
variable is a list instead of a string.
If delete_existing is 0, a newpath which is already in the path
will not be moved to the front (it will be left where it is). | Prepend path elements to the path 'name' in the 'ENV'
dictionary for this environment. Will only add any particular
path once, and will normpath and normcase all paths to help
assure this. This can also handle the case where the env
variable is a list instead of a string. | [
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] | def PrependENVPath(self, name, newpath, envname = 'ENV', sep = os.pathsep,
delete_existing=1):
"""Prepend path elements to the path 'name' in the 'ENV'
dictionary for this environment. Will only add any particular
path once, and will normpath and normcase all paths to help
assure this. This can also handle the case where the env
variable is a list instead of a string.
If delete_existing is 0, a newpath which is already in the path
will not be moved to the front (it will be left where it is).
"""
orig = ''
if envname in self._dict and name in self._dict[envname]:
orig = self._dict[envname][name]
nv = SCons.Util.PrependPath(orig, newpath, sep, delete_existing,
canonicalize=self._canonicalize)
if envname not in self._dict:
self._dict[envname] = {}
self._dict[envname][name] = nv | [
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KratosMultiphysics/Kratos | 0000833054ed0503424eb28205d6508d9ca6cbbc | applications/FSIApplication/python_scripts/trilinos_partitioned_fsi_base_solver.py | python | TrilinosPartitionedFSIBaseSolver.GetDefaultParameters | (cls) | return this_defaults | This function returns the default-settings used by this class | This function returns the default-settings used by this class | [
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"""This function returns the default-settings used by this class
"""
this_defaults = KratosMultiphysics.Parameters("""{
"parallel_type": "MPI"
}""")
this_defaults.AddMissingParameters(super().GetDefaultParameters())
return this_defaults | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_cocoa/propgrid.py | python | PGProperty.OnEvent | (*args, **kwargs) | return _propgrid.PGProperty_OnEvent(*args, **kwargs) | OnEvent(self, PropertyGrid propgrid, Window wnd_primary, Event event) -> bool | OnEvent(self, PropertyGrid propgrid, Window wnd_primary, Event event) -> bool | [
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"""OnEvent(self, PropertyGrid propgrid, Window wnd_primary, Event event) -> bool"""
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SOUI2/soui | 774e5566b2d3254a94f4b3efd55b982e7c665434 | third-part/jsoncpp/makerelease.py | python | svn_export | (tag_url, export_dir) | Exports the tag_url revision to export_dir.
Target directory, including its parent is created if it does not exist.
If the directory export_dir exist, it is deleted before export proceed. | Exports the tag_url revision to export_dir.
Target directory, including its parent is created if it does not exist.
If the directory export_dir exist, it is deleted before export proceed. | [
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] | def svn_export(tag_url, export_dir):
"""Exports the tag_url revision to export_dir.
Target directory, including its parent is created if it does not exist.
If the directory export_dir exist, it is deleted before export proceed.
"""
rmdir_if_exist(export_dir)
svn_command('export', tag_url, export_dir) | [
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quantOS-org/DataCore | e2ef9bd2c22ee9e2845675b6435a14fa607f3551 | mdlink/deps/windows/protobuf-2.5.0/python/google/protobuf/internal/python_message.py | python | _AddPropertiesForField | (field, cls) | Adds a public property for a protocol message field.
Clients can use this property to get and (in the case
of non-repeated scalar fields) directly set the value
of a protocol message field.
Args:
field: A FieldDescriptor for this field.
cls: The class we're constructing. | Adds a public property for a protocol message field.
Clients can use this property to get and (in the case
of non-repeated scalar fields) directly set the value
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] | def _AddPropertiesForField(field, cls):
"""Adds a public property for a protocol message field.
Clients can use this property to get and (in the case
of non-repeated scalar fields) directly set the value
of a protocol message field.
Args:
field: A FieldDescriptor for this field.
cls: The class we're constructing.
"""
# Catch it if we add other types that we should
# handle specially here.
assert _FieldDescriptor.MAX_CPPTYPE == 10
constant_name = field.name.upper() + "_FIELD_NUMBER"
setattr(cls, constant_name, field.number)
if field.label == _FieldDescriptor.LABEL_REPEATED:
_AddPropertiesForRepeatedField(field, cls)
elif field.cpp_type == _FieldDescriptor.CPPTYPE_MESSAGE:
_AddPropertiesForNonRepeatedCompositeField(field, cls)
else:
_AddPropertiesForNonRepeatedScalarField(field, cls) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/site-packages/pip/_internal/cli/req_command.py | python | with_cleanup | (func) | return wrapper | Decorator for common logic related to managing temporary
directories. | Decorator for common logic related to managing temporary
directories. | [
"Decorator",
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"logic",
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] | def with_cleanup(func):
# type: (Any) -> Any
"""Decorator for common logic related to managing temporary
directories.
"""
def configure_tempdir_registry(registry):
# type: (TempDirectoryTypeRegistry) -> None
for t in KEEPABLE_TEMPDIR_TYPES:
registry.set_delete(t, False)
def wrapper(self, options, args):
# type: (RequirementCommand, Values, List[Any]) -> Optional[int]
assert self.tempdir_registry is not None
if options.no_clean:
configure_tempdir_registry(self.tempdir_registry)
try:
return func(self, options, args)
except PreviousBuildDirError:
# This kind of conflict can occur when the user passes an explicit
# build directory with a pre-existing folder. In that case we do
# not want to accidentally remove it.
configure_tempdir_registry(self.tempdir_registry)
raise
return wrapper | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/plugins/Launch/launch/handlers.py | python | _OpenToLine | (fname, line, mainw) | Open the given filename to the given line number
@param fname: File name to open, relative paths will be converted to abs
paths.
@param line: Line number to set the cursor to after opening the file
@param mainw: MainWindow instance to open the file in | Open the given filename to the given line number
@param fname: File name to open, relative paths will be converted to abs
paths.
@param line: Line number to set the cursor to after opening the file
@param mainw: MainWindow instance to open the file in | [
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"""Open the given filename to the given line number
@param fname: File name to open, relative paths will be converted to abs
paths.
@param line: Line number to set the cursor to after opening the file
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"""
fname = os.path.abspath(fname) # Normalize path
nbook = mainw.GetNotebook()
buffers = [ page.GetFileName() for page in nbook.GetTextControls() ]
for page, name in enumerate(buffers):
if ebmlib.ComparePaths(fname, name):
nbook.ChangePage(page)
nbook.GetPage(page).GotoLine(line)
break
else:
nbook.OnDrop([fname])
nbook.GetPage(nbook.GetSelection()).GotoLine(line) | [
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pytorch/pytorch | 7176c92687d3cc847cc046bf002269c6949a21c2 | torch/fx/passes/operator_support.py | python | create_op_support | (is_node_supported: IsNodeSupported) | return FunctionalOperatorSupport() | Wraps a `IsNodeSupported` function into an `OperatorSupportBase` instance
`IsNodeSupported` has the same call signature as
`OperatorSupportBase.is_node_supported` | Wraps a `IsNodeSupported` function into an `OperatorSupportBase` instance | [
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"instance"
] | def create_op_support(is_node_supported: IsNodeSupported) -> OperatorSupportBase:
"""Wraps a `IsNodeSupported` function into an `OperatorSupportBase` instance
`IsNodeSupported` has the same call signature as
`OperatorSupportBase.is_node_supported`
"""
class FunctionalOperatorSupport(OperatorSupportBase):
def is_node_supported(
self, submodules: t.Mapping[str, torch.nn.Module], node: torch.fx.Node
) -> bool:
return is_node_supported(submodules, node)
return FunctionalOperatorSupport() | [
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klzgrad/naiveproxy | ed2c513637c77b18721fe428d7ed395b4d284c83 | src/base/android/jni_generator/jni_registration_generator.py | python | CreateFromDict | (registration_dict, use_hash) | return template.substitute(registration_dict) | Returns the content of the header file. | Returns the content of the header file. | [
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] | def CreateFromDict(registration_dict, use_hash):
"""Returns the content of the header file."""
template = string.Template("""\
// Copyright 2017 The Chromium Authors. All rights reserved.
// Use of this source code is governed by a BSD-style license that can be
// found in the LICENSE file.
// This file is autogenerated by
// base/android/jni_generator/jni_registration_generator.py
// Please do not change its content.
#ifndef ${HEADER_GUARD}
#define ${HEADER_GUARD}
#include <jni.h>
#include "base/android/jni_generator/jni_generator_helper.h"
#include "base/android/jni_int_wrapper.h"
#include "base/cxx17_backports.h" // For base::size().
// Step 1: Forward declarations (classes).
${CLASS_PATH_DECLARATIONS}
// Step 2: Forward declarations (methods).
${FORWARD_DECLARATIONS}
// Step 3: Method declarations.
${JNI_NATIVE_METHOD_ARRAY}\
${PROXY_NATIVE_METHOD_ARRAY}\
${JNI_NATIVE_METHOD}
// Step 4: Main dex and non-main dex registration functions.
namespace ${NAMESPACE} {
bool RegisterMainDexNatives(JNIEnv* env) {\
${REGISTER_MAIN_DEX_PROXY_NATIVES}
${REGISTER_MAIN_DEX_NATIVES}
return true;
}
bool RegisterNonMainDexNatives(JNIEnv* env) {\
${REGISTER_PROXY_NATIVES}
${REGISTER_NON_MAIN_DEX_NATIVES}
return true;
}
} // namespace ${NAMESPACE}
#endif // ${HEADER_GUARD}
""")
_SetProxyRegistrationFields(registration_dict, use_hash)
if len(registration_dict['FORWARD_DECLARATIONS']) == 0:
return ''
return template.substitute(registration_dict) | [
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Tencent/CMONGO | c40380caa14e05509f46993aa8b8da966b09b0b5 | buildscripts/cpplint.py | python | _Filters | () | return _cpplint_state.filters | Returns the module's list of output filters, as a list. | Returns the module's list of output filters, as a list. | [
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] | def _Filters():
"""Returns the module's list of output filters, as a list."""
return _cpplint_state.filters | [
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vicaya/hypertable | e7386f799c238c109ae47973417c2a2c7f750825 | src/py/ThriftClient/gen-py/hyperthrift/gen/ClientService.py | python | Client.open_mutator | (self, name, flags, flush_interval) | return self.recv_open_mutator() | Open a table mutator
@param name - table name
@param flags - mutator flags
@param flush_interval - auto-flush interval in milliseconds; 0 disables it.
@return mutator id
Parameters:
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- flags
- flush_interval | Open a table mutator | [
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"""
Open a table mutator
@param name - table name
@param flags - mutator flags
@param flush_interval - auto-flush interval in milliseconds; 0 disables it.
@return mutator id
Parameters:
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- flags
- flush_interval
"""
self.send_open_mutator(name, flags, flush_interval)
return self.recv_open_mutator() | [
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krishauser/Klampt | 972cc83ea5befac3f653c1ba20f80155768ad519 | Python/klampt/robotsim.py | python | SimRobotController.getCommandedTorque | (self) | return _robotsim.SimRobotController_getCommandedTorque(self) | r"""
Returns The current commanded (feedforward) torque (size model().numDrivers()) | r"""
Returns The current commanded (feedforward) torque (size model().numDrivers()) | [
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naver/sling | 5671cd445a2caae0b4dd0332299e4cfede05062c | webkit/Tools/Scripts/webkitpy/common/system/path.py | python | cygpath | (path) | return path | Converts an absolute cygwin path to an absolute Windows path. | Converts an absolute cygwin path to an absolute Windows path. | [
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if sys.platform == 'cygwin':
return _CygPath.convert_using_singleton(path)
return path | [
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/_extends/graph_kernel/splitter.py | python | split_with_json | (json_str, flags_str) | Call cost model to split GraphKernel | Call cost model to split GraphKernel | [
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"""Call cost model to split GraphKernel"""
try:
graph_desc = json.loads(json_str)
flags = json.loads(flags_str)
target = graph_desc['process']
comp = model.load_composite(graph_desc)
graph_split, graph_mode = model.split(comp.graph, target, flags)
is_multi_graph = len(graph_split) > 1
graph_list = list(map(comp.dump, graph_split))
_reset_graphmode_for_inplaceassign(graph_list, graph_mode)
result = {"multi_graph": is_multi_graph,
"graph_desc": graph_list,
"graph_mode": graph_mode}
_dump_split_info(flags, json_str, comp.graph, graph_split, graph_mode)
return json.dumps(result)
except jd.JSONDecodeError:
logger.error(traceback.format_exc())
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/aui/auibook.py | python | AuiNotebook.OnRenameAccept | (self, page_index, value) | return not self.GetEventHandler().ProcessEvent(evt) or evt.IsAllowed() | Called by :class:`TabTextCtrl`, to accept the changes and to send the
``EVT_AUINOTEBOOK_END_LABEL_EDIT`` event.
:param integer `page_index`: the page index in the notebook;
:param string `value`: the new label for the tab. | Called by :class:`TabTextCtrl`, to accept the changes and to send the
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"""
Called by :class:`TabTextCtrl`, to accept the changes and to send the
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:param integer `page_index`: the page index in the notebook;
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facebook/fbthrift | fb9c8562aba04c4fd9b17716eb5d970cc88a75bb | thrift/lib/py/util/__init__.py | python | __list_to_dict | (alist, type_args, defaults: bool=False) | Given a python list-like collection, potentially containing Thrift Structs,
convert it into a dict
:param alist: a list or set
:param defaults: return default values
:return: List | Given a python list-like collection, potentially containing Thrift Structs,
convert it into a dict
:param alist: a list or set
:param defaults: return default values
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"""
Given a python list-like collection, potentially containing Thrift Structs,
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:param alist: a list or set
:param defaults: return default values
:return: List
"""
if not alist:
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apple/swift-lldb | d74be846ef3e62de946df343e8c234bde93a8912 | scripts/Python/static-binding/lldb.py | python | SBPlatformShellCommand.SetWorkingDirectory | (self, path) | return _lldb.SBPlatformShellCommand_SetWorkingDirectory(self, path) | SetWorkingDirectory(SBPlatformShellCommand self, char const * path) | SetWorkingDirectory(SBPlatformShellCommand self, char const * path) | [
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"""SetWorkingDirectory(SBPlatformShellCommand self, char const * path)"""
return _lldb.SBPlatformShellCommand_SetWorkingDirectory(self, path) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/linux_x64/lib/python3.7/site-packages/urllib3/contrib/_appengine_environ.py | python | is_appengine_sandbox | () | return is_appengine() and os.environ["APPENGINE_RUNTIME"] == "python27" | Reports if the app is running in the first generation sandbox.
The second generation runtimes are technically still in a sandbox, but it
is much less restrictive, so generally you shouldn't need to check for it.
see https://cloud.google.com/appengine/docs/standard/runtimes | Reports if the app is running in the first generation sandbox. | [
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"""Reports if the app is running in the first generation sandbox.
The second generation runtimes are technically still in a sandbox, but it
is much less restrictive, so generally you shouldn't need to check for it.
see https://cloud.google.com/appengine/docs/standard/runtimes
"""
return is_appengine() and os.environ["APPENGINE_RUNTIME"] == "python27" | [
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google/mediapipe | e6c19885c6d3c6f410c730952aeed2852790d306 | mediapipe/python/solutions/face_detection.py | python | get_key_point | (
detection: detection_pb2.Detection, key_point_enum: 'FaceKeyPoint'
) | return detection.location_data.relative_keypoints[key_point_enum] | A convenience method to return a face key point by the FaceKeyPoint type.
Args:
detection: A detection proto message that contains face key points.
key_point_enum: A FaceKeyPoint type.
Returns:
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detection: detection_pb2.Detection, key_point_enum: 'FaceKeyPoint'
) -> Union[None, location_data_pb2.LocationData.RelativeKeypoint]:
"""A convenience method to return a face key point by the FaceKeyPoint type.
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detection: A detection proto message that contains face key points.
key_point_enum: A FaceKeyPoint type.
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if not detection or not detection.location_data:
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microsoft/ELL | a1d6bacc37a14879cc025d9be2ba40b1a0632315 | tools/importers/CNTK/lib/cntk_layers.py | python | DenseLayer.process | (self, ellLayers) | Appends the ELL equivalent of the current layer to ellLayers. | Appends the ELL equivalent of the current layer to ellLayers. | [
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"""Appends the ELL equivalent of the current layer to ellLayers."""
# Note that a single CNTK Dense function block is equivalent to the following 3 ELL layers:
# - FullyConnectedLayer
# - BiasLayer
# - ActivationLayer. This layer is sometimes missing, depending on activation type.
#
# Therefore, make sure the output padding characteristics of the last layer reflect the next layer's
# padding requirements.
weightsParameter = utilities.find_parameter_by_name(
self.layer.parameters, 'W', 0)
biasParameter = utilities.find_parameter_by_name(
self.layer.parameters, 'b', 1)
weightsTensor = converters.get_tensor_from_cntk_dense_weight_parameter(
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biasVector = converters.get_vector_from_cntk_trainable_parameter(
biasParameter)
# Create the ell.neural.LayerParameters for the various ELL layers
firstLayerParameters = ell.neural.LayerParameters(
self.layer.ell_inputShape, self.layer.ell_inputPaddingParameters, self.layer.ell_outputShapeMinusPadding,
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middleLayerParameters = ell.neural.LayerParameters(
self.layer.ell_outputShapeMinusPadding, ell.neural.NoPadding(),
self.layer.ell_outputShapeMinusPadding, ell.neural.NoPadding(), ell.nodes.PortType.smallReal)
lastLayerParameters = ell.neural.LayerParameters(
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self.layer.ell_outputShape, self.layer.ell_outputPaddingParameters, ell.nodes.PortType.smallReal)
layerParameters = firstLayerParameters
internalNodes = utilities.get_model_layers(self.layer.block_root)
activationType = utilities.get_ell_activation_type(internalNodes)
# Create the ELL fully connected layer
ellLayers.append(ell.neural.FullyConnectedLayer(
layerParameters, weightsTensor))
# Create the ELL bias layer
if (utilities.is_softmax_activation(internalNodes) or activationType is not None):
layerParameters = middleLayerParameters
else:
layerParameters = lastLayerParameters
ellLayers.append(ell.neural.BiasLayer(layerParameters, biasVector))
# Create the ELL activation layer
if (utilities.is_softmax_activation(internalNodes) or activationType is not None):
layerParameters = lastLayerParameters
# Special case: if this is softmax activation, create an ELL Softmax layer.
# Else, insert an ELL ActivationLayer
if (utilities.is_softmax_activation(internalNodes)):
ellLayers.append(ell.neural.SoftmaxLayer(layerParameters))
else:
if (activationType is not None):
ellLayers.append(ell.neural.ActivationLayer(
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OAID/Caffe-HRT | aae71e498ab842c6f92bcc23fc668423615a4d65 | tools/extra/summarize.py | python | print_table | (table, max_width) | Print a simple nicely-aligned table.
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nyuwireless-unipd/ns3-mmwave | 4ff9e87e8079764e04cbeccd8e85bff15ae16fb3 | waf-tools/cflags.py | python | CompilerTraits.get_debug_flags | (self, level) | get_debug_flags(level) -> (list of cflags, list of cppdefines) | get_debug_flags(level) -> (list of cflags, list of cppdefines) | [
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Atarity/Lightpack | 4dee73a443cba4c4073291febe450e6c1941f3af | Software/apiexamples/liOSC/OSC.py | python | OSCServer.__ne__ | (self, other) | return not self.__eq__(other) | Compare function. | Compare function. | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/site-packages/boto3/s3/inject.py | python | bucket_download_fileobj | (self, Key, Fileobj, ExtraArgs=None,
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_core.py | python | EvtHandler.ProcessEvent | (*args, **kwargs) | return _core_.EvtHandler_ProcessEvent(*args, **kwargs) | ProcessEvent(self, Event event) -> bool | ProcessEvent(self, Event event) -> bool | [
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klzgrad/naiveproxy | ed2c513637c77b18721fe428d7ed395b4d284c83 | src/build/android/gyp/javac_output_processor.py | python | JavacOutputProcessor._ElaborateLinesForUnknownSymbol | (self, lines) | Elaborates passed-in javac output for unresolved symbols.
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apple/swift-lldb | d74be846ef3e62de946df343e8c234bde93a8912 | scripts/Python/static-binding/lldb.py | python | SBFrame.GetModule | (self) | return _lldb.SBFrame_GetModule(self) | GetModule(SBFrame self) -> SBModule | GetModule(SBFrame self) -> SBModule | [
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JoseExposito/touchegg | 1f3fda214358d071c05da4bf17c070c33d67b5eb | cmake/cpplint.py | python | CheckForNonConstReference | (filename, clean_lines, linenum,
nesting_state, error) | Check for non-const references.
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filename: The name of the current file.
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Args:
filename: The name of the current file.
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deepmodeling/deepmd-kit | 159e45d248b0429844fb6a8cb3b3a201987c8d79 | deepmd/utils/pair_tab.py | python | PairTab.reinit | (self,
filename : str
) | Initialize the tabulated interaction
Parameters
----------
filename
File name for the short-range tabulated potential.
The table is a text data file with (N_t + 1) * N_t / 2 + 1 columes.
The first colume is the distance between atoms.
The second to the last columes are energies for pairs of certain types.
For example we have two atom types, 0 and 1.
The columes from 2nd to 4th are for 0-0, 0-1 and 1-1 correspondingly. | Initialize the tabulated interaction | [
"Initialize",
"the",
"tabulated",
"interaction"
] | def reinit(self,
filename : str
) -> None:
"""
Initialize the tabulated interaction
Parameters
----------
filename
File name for the short-range tabulated potential.
The table is a text data file with (N_t + 1) * N_t / 2 + 1 columes.
The first colume is the distance between atoms.
The second to the last columes are energies for pairs of certain types.
For example we have two atom types, 0 and 1.
The columes from 2nd to 4th are for 0-0, 0-1 and 1-1 correspondingly.
"""
self.vdata = np.loadtxt(filename)
self.rmin = self.vdata[0][0]
self.hh = self.vdata[1][0] - self.vdata[0][0]
self.nspline = self.vdata.shape[0] - 1
ncol = self.vdata.shape[1] - 1
n0 = (-1 + np.sqrt(1 + 8 * ncol)) * 0.5
self.ntypes = int(n0 + 0.1)
assert(self.ntypes * (self.ntypes+1) // 2 == ncol),\
"number of volumes provided in %s does not match guessed number of types %d" % (filename, self.ntypes)
self.tab_info = np.array([self.rmin, self.hh, self.nspline, self.ntypes])
self.tab_data = self._make_data() | [
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windystrife/UnrealEngine_NVIDIAGameWorks | b50e6338a7c5b26374d66306ebc7807541ff815e | Engine/Extras/ThirdPartyNotUE/emsdk/Win64/python/2.7.5.3_64bit/Lib/site-packages/setuptools/command/build_py.py | python | build_py.exclude_data_files | (self, package, src_dir, files) | return [
f for f in files if f not in bad
and f not in seen and seen.setdefault(f,1) # ditch dupes
] | Filter filenames for package's data files in 'src_dir | Filter filenames for package's data files in 'src_dir | [
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"""Filter filenames for package's data files in 'src_dir'"""
globs = (self.exclude_package_data.get('', [])
+ self.exclude_package_data.get(package, []))
bad = []
for pattern in globs:
bad.extend(
fnmatch.filter(
files, os.path.join(src_dir, convert_path(pattern))
)
)
bad = dict.fromkeys(bad)
seen = {}
return [
f for f in files if f not in bad
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_misc.py | python | DateTime.MakeTimezone | (*args, **kwargs) | return _misc_.DateTime_MakeTimezone(*args, **kwargs) | MakeTimezone(self, wxDateTime::TimeZone tz, bool noDST=False) -> DateTime | MakeTimezone(self, wxDateTime::TimeZone tz, bool noDST=False) -> DateTime | [
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return _misc_.DateTime_MakeTimezone(*args, **kwargs) | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/pandas/py2/pandas/io/pytables.py | python | HDFStore.select_as_multiple | (self, keys, where=None, selector=None, columns=None,
start=None, stop=None, iterator=False,
chunksize=None, auto_close=False, **kwargs) | return it.get_result(coordinates=True) | Retrieve pandas objects from multiple tables
Parameters
----------
keys : a list of the tables
selector : the table to apply the where criteria (defaults to keys[0]
if not supplied)
columns : the columns I want back
start : integer (defaults to None), row number to start selection
stop : integer (defaults to None), row number to stop selection
iterator : boolean, return an iterator, default False
chunksize : nrows to include in iteration, return an iterator
Exceptions
----------
raises KeyError if keys or selector is not found or keys is empty
raises TypeError if keys is not a list or tuple
raises ValueError if the tables are not ALL THE SAME DIMENSIONS | Retrieve pandas objects from multiple tables | [
"Retrieve",
"pandas",
"objects",
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] | def select_as_multiple(self, keys, where=None, selector=None, columns=None,
start=None, stop=None, iterator=False,
chunksize=None, auto_close=False, **kwargs):
""" Retrieve pandas objects from multiple tables
Parameters
----------
keys : a list of the tables
selector : the table to apply the where criteria (defaults to keys[0]
if not supplied)
columns : the columns I want back
start : integer (defaults to None), row number to start selection
stop : integer (defaults to None), row number to stop selection
iterator : boolean, return an iterator, default False
chunksize : nrows to include in iteration, return an iterator
Exceptions
----------
raises KeyError if keys or selector is not found or keys is empty
raises TypeError if keys is not a list or tuple
raises ValueError if the tables are not ALL THE SAME DIMENSIONS
"""
# default to single select
where = _ensure_term(where, scope_level=1)
if isinstance(keys, (list, tuple)) and len(keys) == 1:
keys = keys[0]
if isinstance(keys, string_types):
return self.select(key=keys, where=where, columns=columns,
start=start, stop=stop, iterator=iterator,
chunksize=chunksize, **kwargs)
if not isinstance(keys, (list, tuple)):
raise TypeError("keys must be a list/tuple")
if not len(keys):
raise ValueError("keys must have a non-zero length")
if selector is None:
selector = keys[0]
# collect the tables
tbls = [self.get_storer(k) for k in keys]
s = self.get_storer(selector)
# validate rows
nrows = None
for t, k in itertools.chain([(s, selector)], zip(tbls, keys)):
if t is None:
raise KeyError("Invalid table [{key}]".format(key=k))
if not t.is_table:
raise TypeError(
"object [{obj}] is not a table, and cannot be used in all "
"select as multiple".format(obj=t.pathname)
)
if nrows is None:
nrows = t.nrows
elif t.nrows != nrows:
raise ValueError(
"all tables must have exactly the same nrows!")
# axis is the concentation axes
axis = list({t.non_index_axes[0][0] for t in tbls})[0]
def func(_start, _stop, _where):
# retrieve the objs, _where is always passed as a set of
# coordinates here
objs = [t.read(where=_where, columns=columns, start=_start,
stop=_stop, **kwargs) for t in tbls]
# concat and return
return concat(objs, axis=axis,
verify_integrity=False)._consolidate()
# create the iterator
it = TableIterator(self, s, func, where=where, nrows=nrows,
start=start, stop=stop, iterator=iterator,
chunksize=chunksize, auto_close=auto_close)
return it.get_result(coordinates=True) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/gsutil/gslib/progress_callback.py | python | FileProgressCallbackHandler.call | (self, # pylint: disable=invalid-name
last_byte_processed,
total_size) | Prints an overwriting line to stderr describing the operation progress.
Args:
last_byte_processed: The last byte processed in the file. For file
components, this number should be in the range
[start_byte:start_byte + override_total_size].
total_size: Total size of the ongoing operation. | Prints an overwriting line to stderr describing the operation progress. | [
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] | def call(self, # pylint: disable=invalid-name
last_byte_processed,
total_size):
"""Prints an overwriting line to stderr describing the operation progress.
Args:
last_byte_processed: The last byte processed in the file. For file
components, this number should be in the range
[start_byte:start_byte + override_total_size].
total_size: Total size of the ongoing operation.
"""
if not self._logger.isEnabledFor(logging.INFO) or self._last_byte_written:
return
if self._override_total_size:
total_size = self._override_total_size
if total_size:
total_size_string = '/%s' % MakeHumanReadable(total_size)
else:
total_size_string = ''
# Use sys.stderr.write instead of self.logger.info so progress messages
# output on a single continuously overwriting line.
# TODO: Make this work with logging.Logger.
sys.stderr.write('%s%s%s \r' % (
self._announce_text,
MakeHumanReadable(last_byte_processed - self._start_byte),
total_size_string))
if total_size and last_byte_processed - self._start_byte == total_size:
self._last_byte_written = True
sys.stderr.write('\n') | [
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savoirfairelinux/jami-daemon | 7634487e9f568ae727f2d4cffbb735d23fa0324c | tools/jamictrl/controller.py | python | DRingCtrl.Call | (self, dest, account=None) | return callid | Start a call and return a CallID
Use the current account previously set using setAccount().
If no account specified, first registered one in account list is used.
return callID Newly generated callidentifier for this call | Start a call and return a CallID | [
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"""Start a call and return a CallID
Use the current account previously set using setAccount().
If no account specified, first registered one in account list is used.
return callID Newly generated callidentifier for this call
"""
if dest is None or dest == "":
raise DRingCtrlError("Invalid call destination")
# Set the account to be used for this call
if not self.account:
self.setFirstRegisteredAccount()
if self.account != "IP2IP" and not self.isAccountRegistered():
raise DRingCtrlAccountError("Can't place a call without a registered account")
# Send the request to the CallManager
callid = self.callmanager.placeCall(self.account, dest)
if callid:
# Add the call to the list of active calls and set status to SENT
self.activeCalls[callid] = {'Account': self.account, 'To': dest, 'State': 'SENT' }
return callid | [
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swift/swift | 12d031cf8177fdec0137f9aa7e2912fa23c4416b | 3rdParty/SCons/scons-3.0.1/engine/SCons/Taskmaster.py | python | Task.get_target | (self) | return self.node | Fetch the target being built or updated by this task. | Fetch the target being built or updated by this task. | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/tools/python3/src/Lib/mailbox.py | python | MH.lock | (self) | Lock the mailbox. | Lock the mailbox. | [
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"""Lock the mailbox."""
if not self._locked:
self._file = open(os.path.join(self._path, '.mh_sequences'), 'rb+')
_lock_file(self._file)
self._locked = True | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/graphy/graphy/bar_chart.py | python | BarChart.GetMinMaxValues | (self) | return min_value, max_value | Get the largest & smallest bar values as (min_value, max_value). | Get the largest & smallest bar values as (min_value, max_value). | [
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"""Get the largest & smallest bar values as (min_value, max_value)."""
if not self.stacked:
return super(BarChart, self).GetMinMaxValues()
if not self.data:
return None, None # No data, nothing to do.
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return min_value, max_value | [
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manutdzou/KITTI_SSD | 5b620c2f291d36a0fe14489214f22a992f173f44 | python/caffe/pycaffe.py | python | _Net_forward_backward_all | (self, blobs=None, diffs=None, **kwargs) | return all_outs, all_diffs | Run net forward + backward in batches.
Parameters
----------
blobs: list of blobs to extract as in forward()
diffs: list of diffs to extract as in backward()
kwargs: Keys are input (for forward) and output (for backward) blob names
and values are ndarrays. Refer to forward() and backward().
Prefilled variants are called for lack of input or output blobs.
Returns
-------
all_blobs: {blob name: blob ndarray} dict.
all_diffs: {blob name: diff ndarray} dict. | Run net forward + backward in batches. | [
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"""
Run net forward + backward in batches.
Parameters
----------
blobs: list of blobs to extract as in forward()
diffs: list of diffs to extract as in backward()
kwargs: Keys are input (for forward) and output (for backward) blob names
and values are ndarrays. Refer to forward() and backward().
Prefilled variants are called for lack of input or output blobs.
Returns
-------
all_blobs: {blob name: blob ndarray} dict.
all_diffs: {blob name: diff ndarray} dict.
"""
# Batch blobs and diffs.
all_outs = {out: [] for out in set(self.outputs + (blobs or []))}
all_diffs = {diff: [] for diff in set(self.inputs + (diffs or []))}
forward_batches = self._batch({in_: kwargs[in_]
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backward_batches = self._batch({out: kwargs[out]
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# Collect outputs from batches (and heed lack of forward/backward batches).
for fb, bb in izip_longest(forward_batches, backward_batches, fillvalue={}):
batch_blobs = self.forward(blobs=blobs, **fb)
batch_diffs = self.backward(diffs=diffs, **bb)
for out, out_blobs in six.iteritems(batch_blobs):
all_outs[out].extend(out_blobs.copy())
for diff, out_diffs in six.iteritems(batch_diffs):
all_diffs[diff].extend(out_diffs.copy())
# Package in ndarray.
for out, diff in zip(all_outs, all_diffs):
all_outs[out] = np.asarray(all_outs[out])
all_diffs[diff] = np.asarray(all_diffs[diff])
# Discard padding at the end and package in ndarray.
pad = len(six.next(six.itervalues(all_outs))) - len(six.next(six.itervalues(kwargs)))
if pad:
for out, diff in zip(all_outs, all_diffs):
all_outs[out] = all_outs[out][:-pad]
all_diffs[diff] = all_diffs[diff][:-pad]
return all_outs, all_diffs | [
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ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/ros_comm/rospy/src/rospy/rostime.py | python | Time.from_sec | (float_secs) | return Time(secs, nsecs) | Create new Time instance from a float seconds representation
(e.g. time.time()).
@param float_secs: time value in time.time() format
@type float_secs: float
@return: Time instance for specified time
@rtype: L{Time} | Create new Time instance from a float seconds representation
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Create new Time instance from a float seconds representation
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@type float_secs: float
@return: Time instance for specified time
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secs = int(float_secs)
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return Time(secs, nsecs) | [
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Kitware/ParaView | f760af9124ff4634b23ebbeab95a4f56e0261955 | ThirdParty/cinema/paraview/tpl/cinema_python/images/compositor.py | python | Compositor_SpecB.ambient | (self, rgb) | return np.dstack((rgb[:, :, 0], rgb[:, :, 0], rgb[:, :, 0])) | Returns the ambient contribution in an RGB luminance image. | Returns the ambient contribution in an RGB luminance image. | [
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] | def ambient(self, rgb):
""" Returns the ambient contribution in an RGB luminance image. """
return np.dstack((rgb[:, :, 0], rgb[:, :, 0], rgb[:, :, 0])) | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/third_party/closure_linter/closure_linter/error_fixer.py | python | ErrorFixer.FinishFile | (self) | Called when the current file has finished style checking.
Used to go back and fix any errors in the file. It currently supports both
js and html files. For js files it does a simple dump of all tokens, but in
order to support html file, we need to merge the original file with the new
token set back together. This works because the tokenized html file is the
original html file with all non js lines kept but blanked out with one blank
line token per line of html. | Called when the current file has finished style checking. | [
"Called",
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] | def FinishFile(self):
"""Called when the current file has finished style checking.
Used to go back and fix any errors in the file. It currently supports both
js and html files. For js files it does a simple dump of all tokens, but in
order to support html file, we need to merge the original file with the new
token set back together. This works because the tokenized html file is the
original html file with all non js lines kept but blanked out with one blank
line token per line of html.
"""
if self._file_fix_count:
# Get the original file content for html.
if self._file_is_html:
f = open(self._file_name, 'r')
original_lines = f.readlines()
f.close()
f = self._external_file
if not f:
error_noun = 'error' if self._file_fix_count == 1 else 'errors'
print 'Fixed %d %s in %s' % (
self._file_fix_count, error_noun, self._file_name)
f = open(self._file_name, 'w')
token = self._file_token
# Finding the first not deleted token.
while token.is_deleted:
token = token.next
# If something got inserted before first token (e.g. due to sorting)
# then move to start. Bug 8398202.
while token.previous:
token = token.previous
char_count = 0
line = ''
while token:
line += token.string
char_count += len(token.string)
if token.IsLastInLine():
# We distinguish if a blank line in html was from stripped original
# file or newly added error fix by looking at the "org_line_number"
# field on the token. It is only set in the tokenizer, so for all
# error fixes, the value should be None.
if (line or not self._file_is_html or
token.orig_line_number is None):
f.write(line)
f.write('\n')
else:
f.write(original_lines[token.orig_line_number - 1])
line = ''
if char_count > 80 and token.line_number in self._file_changed_lines:
print 'WARNING: Line %d of %s is now longer than 80 characters.' % (
token.line_number, self._file_name)
char_count = 0
token = token.next
if not self._external_file:
# Close the file if we created it
f.close() | [
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NervanaSystems/ngraph | f677a119765ca30636cf407009dabd118664951f | python/src/ngraph/ops.py | python | max_pool | (
data: NodeInput,
strides: List[int],
pads_begin: List[int],
pads_end: List[int],
kernel_shape: TensorShape,
rounding_type: str = "floor",
auto_pad: Optional[str] = None,
name: Optional[str] = None,
) | return _get_node_factory().create(
"MaxPool",
[as_node(data)],
{
"strides": strides,
"pads_begin": pads_begin,
"pads_end": pads_end,
"kernel": kernel_shape,
"rounding_type": rounding_type.upper(),
"auto_pad": auto_pad.upper(),
},
) | Perform max pooling operation with given parameters on provided data.
:param data: The node providing input data.
:param strides: The distance (in pixels) to slide the filter on the feature map
over the axes.
:param pads_begin: The number of pixels to add at the beginning along each axis.
:param pads_end: The number of pixels to add at the end along each axis.
:param kernel_shape: The pooling operation kernel shape.
:param rounding_type: Determines used rounding schema when computing output shape. Acceptable
values are: ['floor', 'ceil']
:param auto_pad: Determines how the padding is calculated. Acceptable values:
[None, 'same_upper', 'same_lower', 'valid']
:param name: The optional name for the created output node.
:returns: The new node performing max pooling operation. | Perform max pooling operation with given parameters on provided data. | [
"Perform",
"max",
"pooling",
"operation",
"with",
"given",
"parameters",
"on",
"provided",
"data",
"."
] | def max_pool(
data: NodeInput,
strides: List[int],
pads_begin: List[int],
pads_end: List[int],
kernel_shape: TensorShape,
rounding_type: str = "floor",
auto_pad: Optional[str] = None,
name: Optional[str] = None,
) -> Node:
"""Perform max pooling operation with given parameters on provided data.
:param data: The node providing input data.
:param strides: The distance (in pixels) to slide the filter on the feature map
over the axes.
:param pads_begin: The number of pixels to add at the beginning along each axis.
:param pads_end: The number of pixels to add at the end along each axis.
:param kernel_shape: The pooling operation kernel shape.
:param rounding_type: Determines used rounding schema when computing output shape. Acceptable
values are: ['floor', 'ceil']
:param auto_pad: Determines how the padding is calculated. Acceptable values:
[None, 'same_upper', 'same_lower', 'valid']
:param name: The optional name for the created output node.
:returns: The new node performing max pooling operation.
"""
if auto_pad is None:
auto_pad = "explicit"
return _get_node_factory().create(
"MaxPool",
[as_node(data)],
{
"strides": strides,
"pads_begin": pads_begin,
"pads_end": pads_end,
"kernel": kernel_shape,
"rounding_type": rounding_type.upper(),
"auto_pad": auto_pad.upper(),
},
) | [
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thalium/icebox | 99d147d5b9269222225443ce171b4fd46d8985d4 | third_party/virtualbox/src/libs/libxml2-2.9.4/python/libxml2class.py | python | xmlTextReader.NodeType | (self) | return ret | Get the node type of the current node Reference:
http://www.gnu.org/software/dotgnu/pnetlib-doc/System/Xml/Xm
lNodeType.html | Get the node type of the current node Reference:
http://www.gnu.org/software/dotgnu/pnetlib-doc/System/Xml/Xm
lNodeType.html | [
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"/",
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] | def NodeType(self):
"""Get the node type of the current node Reference:
http://www.gnu.org/software/dotgnu/pnetlib-doc/System/Xml/Xm
lNodeType.html """
ret = libxml2mod.xmlTextReaderNodeType(self._o)
return ret | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/contrib/metrics/python/ops/metric_ops.py | python | streaming_false_positives | (predictions,
labels,
weights=None,
metrics_collections=None,
updates_collections=None,
name=None) | return metrics.false_positives(
predictions=predictions,
labels=labels,
weights=weights,
metrics_collections=metrics_collections,
updates_collections=updates_collections,
name=name) | Sum the weights of false positives.
If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
Args:
predictions: The predicted values, a `Tensor` of arbitrary dimensions. Will
be cast to `bool`.
labels: The ground truth values, a `Tensor` whose dimensions must match
`predictions`. Will be cast to `bool`.
weights: Optional `Tensor` whose rank is either 0, or the same rank as
`labels`, and must be broadcastable to `labels` (i.e., all dimensions must
be either `1`, or the same as the corresponding `labels` dimension).
metrics_collections: An optional list of collections that the metric value
variable should be added to.
updates_collections: An optional list of collections that the metric update
ops should be added to.
name: An optional variable_scope name.
Returns:
value_tensor: A `Tensor` representing the current value of the metric.
update_op: An operation that accumulates the error from a batch of data.
Raises:
ValueError: If `predictions` and `labels` have mismatched shapes, or if
`weights` is not `None` and its shape doesn't match `predictions`, or if
either `metrics_collections` or `updates_collections` are not a list or
tuple. | Sum the weights of false positives. | [
"Sum",
"the",
"weights",
"of",
"false",
"positives",
"."
] | def streaming_false_positives(predictions,
labels,
weights=None,
metrics_collections=None,
updates_collections=None,
name=None):
"""Sum the weights of false positives.
If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
Args:
predictions: The predicted values, a `Tensor` of arbitrary dimensions. Will
be cast to `bool`.
labels: The ground truth values, a `Tensor` whose dimensions must match
`predictions`. Will be cast to `bool`.
weights: Optional `Tensor` whose rank is either 0, or the same rank as
`labels`, and must be broadcastable to `labels` (i.e., all dimensions must
be either `1`, or the same as the corresponding `labels` dimension).
metrics_collections: An optional list of collections that the metric value
variable should be added to.
updates_collections: An optional list of collections that the metric update
ops should be added to.
name: An optional variable_scope name.
Returns:
value_tensor: A `Tensor` representing the current value of the metric.
update_op: An operation that accumulates the error from a batch of data.
Raises:
ValueError: If `predictions` and `labels` have mismatched shapes, or if
`weights` is not `None` and its shape doesn't match `predictions`, or if
either `metrics_collections` or `updates_collections` are not a list or
tuple.
"""
return metrics.false_positives(
predictions=predictions,
labels=labels,
weights=weights,
metrics_collections=metrics_collections,
updates_collections=updates_collections,
name=name) | [
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mongodb/mongo | d8ff665343ad29cf286ee2cf4a1960d29371937b | buildscripts/gdb/mongo.py | python | get_session_kv_pairs | () | return list(absl_get_nodes(session_catalog["_sessions"])) | Return the SessionRuntimeInfoMap stored in the global SessionCatalog object.
Returns a list of (LogicalSessionId, std::unique_ptr<SessionRuntimeInfo>) key-value pairs. For
key-value pair 'session_kv', access the key with 'session_kv["first"]' and access the value with
'session_kv["second"]'. | Return the SessionRuntimeInfoMap stored in the global SessionCatalog object. | [
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"SessionRuntimeInfoMap",
"stored",
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"the",
"global",
"SessionCatalog",
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"."
] | def get_session_kv_pairs():
"""Return the SessionRuntimeInfoMap stored in the global SessionCatalog object.
Returns a list of (LogicalSessionId, std::unique_ptr<SessionRuntimeInfo>) key-value pairs. For
key-value pair 'session_kv', access the key with 'session_kv["first"]' and access the value with
'session_kv["second"]'.
"""
session_catalog = get_session_catalog()
if session_catalog is None:
return list()
return list(absl_get_nodes(session_catalog["_sessions"])) | [
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eventql/eventql | 7ca0dbb2e683b525620ea30dc40540a22d5eb227 | deps/3rdparty/spidermonkey/mozjs/python/bitstring/bitstring.py | python | ConstBitStream._setbytepos | (self, bytepos) | Move to absolute byte-aligned position in stream. | Move to absolute byte-aligned position in stream. | [
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] | def _setbytepos(self, bytepos):
"""Move to absolute byte-aligned position in stream."""
self._setbitpos(bytepos * 8) | [
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domino-team/openwrt-cc | 8b181297c34d14d3ca521cc9f31430d561dbc688 | package/gli-pub/openwrt-node-packages-master/node/node-v6.9.1/deps/v8_inspector/third_party/jinja2/jinja2/runtime.py | python | LoopContext.cycle | (self, *args) | return args[self.index0 % len(args)] | Cycles among the arguments with the current loop index. | Cycles among the arguments with the current loop index. | [
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"index",
"."
] | def cycle(self, *args):
"""Cycles among the arguments with the current loop index."""
if not args:
raise TypeError('no items for cycling given')
return args[self.index0 % len(args)] | [
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networkit/networkit | 695b7a786a894a303fa8587597d5ef916e797729 | networkit/algebraic.py | python | symmetricEigenvectors | (matrix, cutoff=-1, reverse=False) | return (orderedW, orderedV) | symmetricEigenvectors(matrix, cutoff=-1, reverse=False)
Computes eigenvectors and -values of symmetric matrices.
Parameters
----------
matrix : sparse matrix
The matrix to compute the eigenvectors of
cutoff : int, optional
The maximum (or minimum) magnitude of the eigenvectors needed
reverse : boolean, optional
If set to true, the smaller eigenvalues will be computed before the larger ones
Returns
-------
( [ float ], [ ndarray ] )
A tuple of ordered lists, the first containing the eigenvalues in descending (ascending) magnitude, the
second one holding the corresponding eigenvectors. | symmetricEigenvectors(matrix, cutoff=-1, reverse=False) | [
"symmetricEigenvectors",
"(",
"matrix",
"cutoff",
"=",
"-",
"1",
"reverse",
"=",
"False",
")"
] | def symmetricEigenvectors(matrix, cutoff=-1, reverse=False):
"""
symmetricEigenvectors(matrix, cutoff=-1, reverse=False)
Computes eigenvectors and -values of symmetric matrices.
Parameters
----------
matrix : sparse matrix
The matrix to compute the eigenvectors of
cutoff : int, optional
The maximum (or minimum) magnitude of the eigenvectors needed
reverse : boolean, optional
If set to true, the smaller eigenvalues will be computed before the larger ones
Returns
-------
( [ float ], [ ndarray ] )
A tuple of ordered lists, the first containing the eigenvalues in descending (ascending) magnitude, the
second one holding the corresponding eigenvectors.
"""
if cutoff == -1:
cutoff = matrix.shape[0] - 3
if reverse:
mode = "SA"
else:
mode = "LA"
w, v = scipy.sparse.linalg.eigsh(matrix, cutoff + 1, which=mode)
orderlist = zip(w, range(0, len(w)))
orderlist = sorted(orderlist)
orderedW = column(orderlist, 0)
orderedV = [v[:,i] for i in column(orderlist, 1)]
return (orderedW, orderedV) | [
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PlatformLab/Arachne | e67391471007174dd4002dc2c160628e19c284e8 | scripts/cpplint.py | python | CheckCStyleCast | (filename, clean_lines, linenum, cast_type, pattern, error) | return True | Checks for a C-style cast by looking for the pattern.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
cast_type: The string for the C++ cast to recommend. This is either
reinterpret_cast, static_cast, or const_cast, depending.
pattern: The regular expression used to find C-style casts.
error: The function to call with any errors found.
Returns:
True if an error was emitted.
False otherwise. | Checks for a C-style cast by looking for the pattern. | [
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"""Checks for a C-style cast by looking for the pattern.
Args:
filename: The name of the current file.
clean_lines: A CleansedLines instance containing the file.
linenum: The number of the line to check.
cast_type: The string for the C++ cast to recommend. This is either
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pattern: The regular expression used to find C-style casts.
error: The function to call with any errors found.
Returns:
True if an error was emitted.
False otherwise.
"""
line = clean_lines.elided[linenum]
match = Search(pattern, line)
if not match:
return False
# Exclude lines with keywords that tend to look like casts
context = line[0:match.start(1) - 1]
if Match(r'.*\b(?:sizeof|alignof|alignas|[_A-Z][_A-Z0-9]*)\s*$', context):
return False
# Try expanding current context to see if we one level of
# parentheses inside a macro.
if linenum > 0:
for i in xrange(linenum - 1, max(0, linenum - 5), -1):
context = clean_lines.elided[i] + context
if Match(r'.*\b[_A-Z][_A-Z0-9]*\s*\((?:\([^()]*\)|[^()])*$', context):
return False
# operator++(int) and operator--(int)
if context.endswith(' operator++') or context.endswith(' operator--'):
return False
# A single unnamed argument for a function tends to look like old style cast.
# If we see those, don't issue warnings for deprecated casts.
remainder = line[match.end(0):]
if Match(r'^\s*(?:;|const\b|throw\b|final\b|override\b|[=>{),]|->)',
remainder):
return False
# At this point, all that should be left is actual casts.
error(filename, linenum, 'readability/casting', 4,
'Using C-style cast. Use %s<%s>(...) instead' %
(cast_type, match.group(1)))
return True | [
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kevinlin311tw/caffe-cvprw15 | 45c2a1bf0368569c54e0be4edf8d34285cf79e70 | scripts/cpp_lint.py | python | CheckCStyleCast | (filename, linenum, line, raw_line, cast_type, pattern,
error) | return True | Checks for a C-style cast by looking for the pattern.
Args:
filename: The name of the current file.
linenum: The number of the line to check.
line: The line of code to check.
raw_line: The raw line of code to check, with comments.
cast_type: The string for the C++ cast to recommend. This is either
reinterpret_cast, static_cast, or const_cast, depending.
pattern: The regular expression used to find C-style casts.
error: The function to call with any errors found.
Returns:
True if an error was emitted.
False otherwise. | Checks for a C-style cast by looking for the pattern. | [
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] | def CheckCStyleCast(filename, linenum, line, raw_line, cast_type, pattern,
error):
"""Checks for a C-style cast by looking for the pattern.
Args:
filename: The name of the current file.
linenum: The number of the line to check.
line: The line of code to check.
raw_line: The raw line of code to check, with comments.
cast_type: The string for the C++ cast to recommend. This is either
reinterpret_cast, static_cast, or const_cast, depending.
pattern: The regular expression used to find C-style casts.
error: The function to call with any errors found.
Returns:
True if an error was emitted.
False otherwise.
"""
match = Search(pattern, line)
if not match:
return False
# Exclude lines with sizeof, since sizeof looks like a cast.
sizeof_match = Match(r'.*sizeof\s*$', line[0:match.start(1) - 1])
if sizeof_match:
return False
# operator++(int) and operator--(int)
if (line[0:match.start(1) - 1].endswith(' operator++') or
line[0:match.start(1) - 1].endswith(' operator--')):
return False
# A single unnamed argument for a function tends to look like old
# style cast. If we see those, don't issue warnings for deprecated
# casts, instead issue warnings for unnamed arguments where
# appropriate.
#
# These are things that we want warnings for, since the style guide
# explicitly require all parameters to be named:
# Function(int);
# Function(int) {
# ConstMember(int) const;
# ConstMember(int) const {
# ExceptionMember(int) throw (...);
# ExceptionMember(int) throw (...) {
# PureVirtual(int) = 0;
#
# These are functions of some sort, where the compiler would be fine
# if they had named parameters, but people often omit those
# identifiers to reduce clutter:
# (FunctionPointer)(int);
# (FunctionPointer)(int) = value;
# Function((function_pointer_arg)(int))
# <TemplateArgument(int)>;
# <(FunctionPointerTemplateArgument)(int)>;
remainder = line[match.end(0):]
if Match(r'^\s*(?:;|const\b|throw\b|=|>|\{|\))', remainder):
# Looks like an unnamed parameter.
# Don't warn on any kind of template arguments.
if Match(r'^\s*>', remainder):
return False
# Don't warn on assignments to function pointers, but keep warnings for
# unnamed parameters to pure virtual functions. Note that this pattern
# will also pass on assignments of "0" to function pointers, but the
# preferred values for those would be "nullptr" or "NULL".
matched_zero = Match(r'^\s=\s*(\S+)\s*;', remainder)
if matched_zero and matched_zero.group(1) != '0':
return False
# Don't warn on function pointer declarations. For this we need
# to check what came before the "(type)" string.
if Match(r'.*\)\s*$', line[0:match.start(0)]):
return False
# Don't warn if the parameter is named with block comments, e.g.:
# Function(int /*unused_param*/);
if '/*' in raw_line:
return False
# Passed all filters, issue warning here.
error(filename, linenum, 'readability/function', 3,
'All parameters should be named in a function')
return True
# At this point, all that should be left is actual casts.
error(filename, linenum, 'readability/casting', 4,
'Using C-style cast. Use %s<%s>(...) instead' %
(cast_type, match.group(1)))
return True | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/io/formats/style.py | python | _get_level_lengths | (index, hidden_elements=None) | return non_zero_lengths | Given an index, find the level length for each element.
Optional argument is a list of index positions which
should not be visible.
Result is a dictionary of (level, initial_position): span | Given an index, find the level length for each element. | [
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"""
Given an index, find the level length for each element.
Optional argument is a list of index positions which
should not be visible.
Result is a dictionary of (level, initial_position): span
"""
levels = index.format(sparsify=lib.no_default, adjoin=False, names=False)
if hidden_elements is None:
hidden_elements = []
lengths = {}
if index.nlevels == 1:
for i, value in enumerate(levels):
if i not in hidden_elements:
lengths[(0, i)] = 1
return lengths
for i, lvl in enumerate(levels):
for j, row in enumerate(lvl):
if not get_option("display.multi_sparse"):
lengths[(i, j)] = 1
elif (row is not lib.no_default) and (j not in hidden_elements):
last_label = j
lengths[(i, last_label)] = 1
elif row is not lib.no_default:
# even if its hidden, keep track of it in case
# length >1 and later elements are visible
last_label = j
lengths[(i, last_label)] = 0
elif j not in hidden_elements:
lengths[(i, last_label)] += 1
non_zero_lengths = {
element: length for element, length in lengths.items() if length >= 1
}
return non_zero_lengths | [
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bakwc/JamSpell | ab5ade201df3e52d99c3a1d38ec422cf4ded1795 | evaluate/context_spell_prototype.py | python | edits1 | (word) | return set(deletes + transposes + replaces + inserts) | All edits that are one edit away from `word`. | All edits that are one edit away from `word`. | [
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splits = [(word[:i], word[i:]) for i in range(len(word) + 1)]
deletes = [L + R[1:] for L, R in splits if R]
transposes = [L + R[1] + R[0] + R[2:] for L, R in splits if len(R)>1]
replaces = [L + c + R[1:] for L, R in splits if R for c in letters]
inserts = [L + c + R for L, R in splits for c in letters]
return set(deletes + transposes + replaces + inserts) | [
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MythTV/mythtv | d282a209cb8be85d036f85a62a8ec971b67d45f4 | mythtv/programs/scripts/internetcontent/nv_python_libs/mtv/mtv_api.py | python | Videos.makeURL | (self, URL) | Form a URL to search for videos
return a URL | Form a URL to search for videos
return a URL | [
"Form",
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"URL",
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"search",
"for",
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"return",
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] | def makeURL(self, URL):
'''Form a URL to search for videos
return a URL
'''
additions = dict(self.tree_customize[self.tree_key]['__default__']) # Set defaults
# Add customizations
if self.feed in list(self.tree_customize[self.tree_key].keys()):
for element in list(self.tree_customize[self.tree_key][self.feed].keys()):
additions[element] = self.tree_customize[self.tree_key][self.feed][element]
# Make the search extension string that is added to the URL
addition = ''
for ky in list(additions.keys()):
if ky.startswith('add_'):
addition+='/%s' % additions[ky]
else:
addition+='&%s=%s' % (ky, additions[ky])
index = URL.find('%')
if index == -1:
return (URL+addition)
else:
return (URL+addition) % self.feed | [
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ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/ros/rosunit/src/rosunit/xmlrunner.py | python | XMLTestRunnerTest._try_test_run | (self, test_class, expected) | Run the test suite against the supplied test class and compare the
XML result against the expected XML string. Fail if the expected
string doesn't match the actual string. All time attribute in the
expected string should have the value "0.000". All error and failure
messages are reduced to "Foobar". | Run the test suite against the supplied test class and compare the
XML result against the expected XML string. Fail if the expected
string doesn't match the actual string. All time attribute in the
expected string should have the value "0.000". All error and failure
messages are reduced to "Foobar". | [
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"""Run the test suite against the supplied test class and compare the
XML result against the expected XML string. Fail if the expected
string doesn't match the actual string. All time attribute in the
expected string should have the value "0.000". All error and failure
messages are reduced to "Foobar".
"""
runner = XMLTestRunner(self._stream)
runner.run(unittest.makeSuite(test_class))
got = self._stream.getvalue()
# Replace all time="X.YYY" attributes by time="0.000" to enable a
# simple string comparison.
got = re.sub(r'time="\d+\.\d+"', 'time="0.000"', got)
# Likewise, replace all failure and error messages by a simple "Foobar"
# string.
got = re.sub(r'(?s)<failure (.*?)>.*?</failure>', r'<failure \1>Foobar</failure>', got)
got = re.sub(r'(?s)<error (.*?)>.*?</error>', r'<error \1>Foobar</error>', got)
self.assertEqual(expected, got) | [
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sdhash/sdhash | b9eff63e4e5867e910f41fd69032bbb1c94a2a5e | sdhash-ui/cherrypy/lib/sessions.py | python | PostgresqlSession.setup | (cls, **kwargs) | Set up the storage system for Postgres-based sessions.
This should only be called once per process; this will be done
automatically when using sessions.init (as the built-in Tool does). | Set up the storage system for Postgres-based sessions.
This should only be called once per process; this will be done
automatically when using sessions.init (as the built-in Tool does). | [
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"""Set up the storage system for Postgres-based sessions.
This should only be called once per process; this will be done
automatically when using sessions.init (as the built-in Tool does).
"""
for k, v in kwargs.items():
setattr(cls, k, v)
self.db = self.get_db() | [
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okex/V3-Open-API-SDK | c5abb0db7e2287718e0055e17e57672ce0ec7fd9 | okex-python-sdk-api/venv/Lib/site-packages/pip-19.0.3-py3.8.egg/pip/_vendor/pyparsing.py | python | pyparsing_common.stripHTMLTags | (s, l, tokens) | return pyparsing_common._html_stripper.transformString(tokens[0]) | Parse action to remove HTML tags from web page HTML source
Example::
# strip HTML links from normal text
text = '<td>More info at the <a href="https://github.com/pyparsing/pyparsing/wiki">pyparsing</a> wiki page</td>'
td,td_end = makeHTMLTags("TD")
table_text = td + SkipTo(td_end).setParseAction(pyparsing_common.stripHTMLTags)("body") + td_end
print(table_text.parseString(text).body)
Prints::
More info at the pyparsing wiki page | Parse action to remove HTML tags from web page HTML source | [
"Parse",
"action",
"to",
"remove",
"HTML",
"tags",
"from",
"web",
"page",
"HTML",
"source"
] | def stripHTMLTags(s, l, tokens):
"""Parse action to remove HTML tags from web page HTML source
Example::
# strip HTML links from normal text
text = '<td>More info at the <a href="https://github.com/pyparsing/pyparsing/wiki">pyparsing</a> wiki page</td>'
td,td_end = makeHTMLTags("TD")
table_text = td + SkipTo(td_end).setParseAction(pyparsing_common.stripHTMLTags)("body") + td_end
print(table_text.parseString(text).body)
Prints::
More info at the pyparsing wiki page
"""
return pyparsing_common._html_stripper.transformString(tokens[0]) | [
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y123456yz/reading-and-annotate-mongodb-3.6 | 93280293672ca7586dc24af18132aa61e4ed7fcf | mongo/src/third_party/scons-2.5.0/scons-local-2.5.0/SCons/Tool/GettextCommon.py | python | _POFileBuilder._execute | (self, env, target, source, *args, **kw) | return SCons.Node.NodeList(result) | Execute builder's actions.
Here we append to `target` the languages read from `$LINGUAS_FILE` and
apply `SCons.Builder.BuilderBase._execute()` separatelly to each target.
The arguments and return value are same as for
`SCons.Builder.BuilderBase._execute()`. | Execute builder's actions.
Here we append to `target` the languages read from `$LINGUAS_FILE` and
apply `SCons.Builder.BuilderBase._execute()` separatelly to each target.
The arguments and return value are same as for
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""" Execute builder's actions.
Here we append to `target` the languages read from `$LINGUAS_FILE` and
apply `SCons.Builder.BuilderBase._execute()` separatelly to each target.
The arguments and return value are same as for
`SCons.Builder.BuilderBase._execute()`.
"""
import SCons.Util
import SCons.Node
linguas_files = None
if env.has_key('LINGUAS_FILE') and env['LINGUAS_FILE']:
linguas_files = env['LINGUAS_FILE']
# This prevents endless recursion loop (we'll be invoked once for
# each target appended here, we must not extend the list again).
env['LINGUAS_FILE'] = None
linguas = _read_linguas_from_files(env,linguas_files)
if SCons.Util.is_List(target):
target.extend(linguas)
elif target is not None:
target = [target] + linguas
else:
target = linguas
if not target:
# Let the SCons.BuilderBase to handle this patologic situation
return BuilderBase._execute( self, env, target, source, *args, **kw)
# The rest is ours
if not SCons.Util.is_List(target):
target = [ target ]
result = []
for tgt in target:
r = BuilderBase._execute( self, env, [tgt], source, *args, **kw)
result.extend(r)
if linguas_files is not None:
env['LINGUAS_FILE'] = linguas_files
return SCons.Node.NodeList(result) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/tools/Editra/src/extern/aui/tabart.py | python | FF2TabArt.DrawTabBackground | (self, dc, rect, focus, upperTabs) | Draws the tab background for the Firefox 2 style.
This is more consistent with :class:`~lib.agw.flatnotebook.FlatNotebook` than before.
:param `dc`: a :class:`DC` device context;
:param Rect `rect`: rectangle the tab should be confined to;
:param bool `focus`: whether the tab has focus or not;
:param bool `upperTabs`: whether the style is ``AUI_NB_TOP`` or ``AUI_NB_BOTTOM``. | Draws the tab background for the Firefox 2 style.
This is more consistent with :class:`~lib.agw.flatnotebook.FlatNotebook` than before. | [
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"""
Draws the tab background for the Firefox 2 style.
This is more consistent with :class:`~lib.agw.flatnotebook.FlatNotebook` than before.
:param `dc`: a :class:`DC` device context;
:param Rect `rect`: rectangle the tab should be confined to;
:param bool `focus`: whether the tab has focus or not;
:param bool `upperTabs`: whether the style is ``AUI_NB_TOP`` or ``AUI_NB_BOTTOM``.
"""
# Define the rounded rectangle base on the given rect
# we need an array of 9 points for it
regPts = [wx.Point() for indx in xrange(9)]
if focus:
if upperTabs:
leftPt = wx.Point(rect.x, rect.y + (rect.height / 10)*8)
rightPt = wx.Point(rect.x + rect.width - 2, rect.y + (rect.height / 10)*8)
else:
leftPt = wx.Point(rect.x, rect.y + (rect.height / 10)*5)
rightPt = wx.Point(rect.x + rect.width - 2, rect.y + (rect.height / 10)*5)
else:
leftPt = wx.Point(rect.x, rect.y + (rect.height / 2))
rightPt = wx.Point(rect.x + rect.width - 2, rect.y + (rect.height / 2))
# Define the top region
top = wx.RectPP(rect.GetTopLeft(), rightPt)
bottom = wx.RectPP(leftPt, rect.GetBottomRight())
topStartColour = wx.WHITE
if not focus:
topStartColour = LightColour(wx.SystemSettings_GetColour(wx.SYS_COLOUR_3DFACE), 50)
topEndColour = wx.SystemSettings_GetColour(wx.SYS_COLOUR_3DFACE)
bottomStartColour = topEndColour
bottomEndColour = topEndColour
# Incase we use bottom tabs, switch the colours
if upperTabs:
if focus:
dc.GradientFillLinear(top, topStartColour, topEndColour, wx.SOUTH)
dc.GradientFillLinear(bottom, bottomStartColour, bottomEndColour, wx.SOUTH)
else:
dc.GradientFillLinear(top, topEndColour , topStartColour, wx.SOUTH)
dc.GradientFillLinear(bottom, bottomStartColour, bottomEndColour, wx.SOUTH)
else:
if focus:
dc.GradientFillLinear(bottom, topEndColour, bottomEndColour, wx.SOUTH)
dc.GradientFillLinear(top, topStartColour, topStartColour, wx.SOUTH)
else:
dc.GradientFillLinear(bottom, bottomStartColour, bottomEndColour, wx.SOUTH)
dc.GradientFillLinear(top, topEndColour, topStartColour, wx.SOUTH)
dc.SetBrush(wx.TRANSPARENT_BRUSH) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/site-packages/pip/_vendor/distlib/database.py | python | Distribution.__eq__ | (self, other) | return result | See if this distribution is the same as another.
:param other: The distribution to compare with. To be equal to one
another. distributions must have the same type, name,
version and source_url.
:return: True if it is the same, else False. | See if this distribution is the same as another.
:param other: The distribution to compare with. To be equal to one
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version and source_url.
:return: True if it is the same, else False. | [
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Smorodov/Multitarget-tracker | bee300e8bfd660c86cbeb6892c65a5b7195c9381 | thirdparty/pybind11/tools/clang/cindex.py | python | Cursor.is_static_method | (self) | return conf.lib.clang_CXXMethod_isStatic(self) | Returns True if the cursor refers to a C++ member function or member
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trilinos/Trilinos | 6168be6dd51e35e1cd681e9c4b24433e709df140 | packages/kokkos-kernels/scripts/analysis/batched/pd.py | python | kkt_parse_exespace_re | (ln) | return hits[0] | Parse an ExecSpace line. | Parse an ExecSpace line. | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/armeabi/toolchain/lib/python2.7/lib-tk/ttk.py | python | Scale.configure | (self, cnf=None, **kw) | Modify or query scale options.
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kw.update(cnf)
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miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/tensorflow/contrib/learn/python/learn/monitors.py | python | BaseMonitor.begin | (self, max_steps=None) | Called at the beginning of training.
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neopenx/Dragon | 0e639a7319035ddc81918bd3df059230436ee0a1 | Dragon/python/dragon/core/mpi.py | python | AllowParallel | () | return -1, [] | Whether this node was set for data parallelism.
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apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | src/python/turicreate/toolkits/regression/_regression.py | python | create | (dataset, target, features=None, validation_set="auto", verbose=True) | return _sl.wrap_model_proxy(model_proxy) | Automatically create a suitable regression model based on the provided
training data.
To use specific options of a desired model, use the ``create`` function
of the corresponding model.
Parameters
----------
dataset : SFrame
Dataset for training the model.
target : str
The name of the column in ``dataset`` that is the prediction target.
This column must have a numeric type (int/float).
features : list[string], optional
Names of the columns containing features. 'None' (the default) indicates
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- *Numeric*: values of numeric type integer or float.
- *Categorical*: values of type string.
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- *Dictionary*: key-value pairs with numeric (integer or float) values
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Columns of type *list* are not supported. Convert such feature
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validation_set : SFrame, optional
A dataset for monitoring the model's generalization performance. For
each row of the progress table, the chosen metrics are computed for
both the provided training dataset and the validation_set. The format
of this SFrame must be the same as the training set. By default this
argument is set to 'auto' and a validation set is automatically sampled
and used for progress printing. If validation_set is set to None, then
no additional metrics are computed. The default value is 'auto'.
verbose : boolean, optional
If True, print progress information during training.
Returns
-------
out : A trained regression model.
See Also
--------
turicreate.linear_regression.LinearRegression,
turicreate.boosted_trees_regression.BoostedTreesRegression
Examples
--------
.. sourcecode:: python
# Setup the data
>>> import turicreate as tc
>>> data = tc.SFrame('https://static.turi.com/datasets/regression/houses.csv')
# Selects the best model based on your data.
>>> model = tc.regression.create(data, target='price',
... features=['bath', 'bedroom', 'size'])
# Make predictions and evaluate results.
>>> predictions = model.predict(data)
>>> results = model.evaluate(data)
# Setup the data
>>> import turicreate as tc
>>> data = tc.SFrame('https://static.turi.com/datasets/regression/houses.csv')
# Selects the best model based on your data.
>>> model = tc.regression.create(data, target='price',
... features=['bath', 'bedroom', 'size'])
# Make predictions and evaluate results.
>>> predictions = model.predict(data)
>>> results = model.evaluate(data) | Automatically create a suitable regression model based on the provided
training data. | [
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Automatically create a suitable regression model based on the provided
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To use specific options of a desired model, use the ``create`` function
of the corresponding model.
Parameters
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dataset : SFrame
Dataset for training the model.
target : str
The name of the column in ``dataset`` that is the prediction target.
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Names of the columns containing features. 'None' (the default) indicates
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verbose : boolean, optional
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Returns
-------
out : A trained regression model.
See Also
--------
turicreate.linear_regression.LinearRegression,
turicreate.boosted_trees_regression.BoostedTreesRegression
Examples
--------
.. sourcecode:: python
# Setup the data
>>> import turicreate as tc
>>> data = tc.SFrame('https://static.turi.com/datasets/regression/houses.csv')
# Selects the best model based on your data.
>>> model = tc.regression.create(data, target='price',
... features=['bath', 'bedroom', 'size'])
# Make predictions and evaluate results.
>>> predictions = model.predict(data)
>>> results = model.evaluate(data)
# Setup the data
>>> import turicreate as tc
>>> data = tc.SFrame('https://static.turi.com/datasets/regression/houses.csv')
# Selects the best model based on your data.
>>> model = tc.regression.create(data, target='price',
... features=['bath', 'bedroom', 'size'])
# Make predictions and evaluate results.
>>> predictions = model.predict(data)
>>> results = model.evaluate(data)
"""
dataset, validation_set = _validate_data(dataset, target, features, validation_set)
if validation_set is None:
validation_set = _turicreate.SFrame()
model_proxy = _turicreate.extensions.create_automatic_regression_model(
dataset, target, validation_set, {}
)
return _sl.wrap_model_proxy(model_proxy) | [
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