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physercoe/starquant | c00cad64d1de2da05081b3dc320ef264c6295e08 | cppsrc/fmt-5.3.0/support/docopt.py | python | transform | (pattern) | return Either(*[Required(*e) for e in result]) | Expand pattern into an (almost) equivalent one, but with single Either.
Example: ((-a | -b) (-c | -d)) => (-a -c | -a -d | -b -c | -b -d)
Quirks: [-a] => (-a), (-a...) => (-a -a) | Expand pattern into an (almost) equivalent one, but with single Either. | [
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] | def transform(pattern):
"""Expand pattern into an (almost) equivalent one, but with single Either.
Example: ((-a | -b) (-c | -d)) => (-a -c | -a -d | -b -c | -b -d)
Quirks: [-a] => (-a), (-a...) => (-a -a)
"""
result = []
groups = [[pattern]]
while groups:
children = groups.pop(0)
parents = [Required, Optional, OptionsShortcut, Either, OneOrMore]
if any(t in map(type, children) for t in parents):
child = [c for c in children if type(c) in parents][0]
children.remove(child)
if type(child) is Either:
for c in child.children:
groups.append([c] + children)
elif type(child) is OneOrMore:
groups.append(child.children * 2 + children)
else:
groups.append(child.children + children)
else:
result.append(children)
return Either(*[Required(*e) for e in result]) | [
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BlzFans/wke | b0fa21158312e40c5fbd84682d643022b6c34a93 | cygwin/lib/python2.6/smtplib.py | python | SMTP.quit | (self) | return res | Terminate the SMTP session. | Terminate the SMTP session. | [
"Terminate",
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] | def quit(self):
"""Terminate the SMTP session."""
res = self.docmd("quit")
self.close()
return res | [
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smilehao/xlua-framework | a03801538be2b0e92d39332d445b22caca1ef61f | ConfigData/trunk/tools/protobuf-2.5.0/protobuf-2.5.0/python/build/lib/google/protobuf/internal/encoder.py | python | _SignedVarintSize | (value) | return 10 | Compute the size of a signed varint value. | Compute the size of a signed varint value. | [
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] | def _SignedVarintSize(value):
"""Compute the size of a signed varint value."""
if value < 0: return 10
if value <= 0x7f: return 1
if value <= 0x3fff: return 2
if value <= 0x1fffff: return 3
if value <= 0xfffffff: return 4
if value <= 0x7ffffffff: return 5
if value <= 0x3ffffffffff: return 6
if value <= 0x1ffffffffffff: return 7
if value <= 0xffffffffffffff: return 8
if value <= 0x7fffffffffffffff: return 9
return 10 | [
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macchina-io/macchina.io | ef24ba0e18379c3dd48fb84e6dbf991101cb8db0 | platform/JS/V8/v8/include/PRESUBMIT.py | python | PostUploadHook | (cl, change, output_api) | return output_api.EnsureCQIncludeTrybotsAreAdded(
cl,
[
'master.tryserver.chromium.linux:linux_chromium_rel_ng'
],
'Automatically added layout test trybots to run tests on CQ.') | git cl upload will call this hook after the issue is created/modified.
This hook adds extra try bots to the CL description in order to run layout
tests in addition to CQ try bots. | git cl upload will call this hook after the issue is created/modified. | [
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"""git cl upload will call this hook after the issue is created/modified.
This hook adds extra try bots to the CL description in order to run layout
tests in addition to CQ try bots.
"""
def header_filter(f):
return '.h' in os.path.split(f.LocalPath())[1]
if not change.AffectedFiles(file_filter=header_filter):
return []
return output_api.EnsureCQIncludeTrybotsAreAdded(
cl,
[
'master.tryserver.chromium.linux:linux_chromium_rel_ng'
],
'Automatically added layout test trybots to run tests on CQ.') | [
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ApolloAuto/apollo-platform | 86d9dc6743b496ead18d597748ebabd34a513289 | ros/ros_comm/rosmaster/src/rosmaster/threadpool.py | python | MarkedThreadPool.__init__ | (self, numThreads) | Initialize the thread pool with numThreads workers. | Initialize the thread pool with numThreads workers. | [
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] | def __init__(self, numThreads):
"""Initialize the thread pool with numThreads workers."""
self.__threads = []
self.__resizeLock = threading.Condition(threading.Lock())
self.__taskLock = threading.Condition(threading.Lock())
self.__tasks = []
self.__markers = set()
self.__isJoining = False
self.set_thread_count(numThreads) | [
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MegEngine/MegEngine | ce9ad07a27ec909fb8db4dd67943d24ba98fb93a | imperative/python/megengine/optimizer/clip_grad.py | python | clip_grad_norm | (
tensors: Union[Tensor, Iterable[Tensor]], max_norm: float, ord: float = 2.0,
) | return norm_ | r"""Clips gradient norm of an iterable of parameters.
The norm is computed over all gradients together, as if they were
concatenated into a single vector. Gradients are modified in-place.
Args:
tensors: an iterable of Tensors or a single Tensor.
max_norm: max norm of the gradients.
ord: type of the used p-norm. Can be ``'inf'`` for infinity norm.
Returns:
total norm of the parameters (viewed as a single vector). | r"""Clips gradient norm of an iterable of parameters.
The norm is computed over all gradients together, as if they were
concatenated into a single vector. Gradients are modified in-place. | [
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tensors: Union[Tensor, Iterable[Tensor]], max_norm: float, ord: float = 2.0,
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r"""Clips gradient norm of an iterable of parameters.
The norm is computed over all gradients together, as if they were
concatenated into a single vector. Gradients are modified in-place.
Args:
tensors: an iterable of Tensors or a single Tensor.
max_norm: max norm of the gradients.
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push_scope("clip_grad_norm")
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tensors = [tensors]
tensors = [t for t in tensors if t.grad is not None]
if len(tensors) == 0:
pop_scope("clip_grad_norm")
return Tensor(0.0)
norm_ = [norm(t.grad.flatten(), ord=ord) for t in tensors]
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norm_ = norm_[0]
scale = max_norm / (norm_ + 1e-6)
scale = minimum(scale, 1)
for tensor in tensors:
tensor.grad._reset(tensor.grad * scale)
pop_scope("clip_grad_norm")
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/ipython/py3/IPython/core/completer.py | python | expand_user | (path:str) | return newpath, tilde_expand, tilde_val | Expand ``~``-style usernames in strings.
This is similar to :func:`os.path.expanduser`, but it computes and returns
extra information that will be useful if the input was being used in
computing completions, and you wish to return the completions with the
original '~' instead of its expanded value.
Parameters
----------
path : str
String to be expanded. If no ~ is present, the output is the same as the
input.
Returns
-------
newpath : str
Result of ~ expansion in the input path.
tilde_expand : bool
Whether any expansion was performed or not.
tilde_val : str
The value that ~ was replaced with. | Expand ``~``-style usernames in strings. | [
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"-",
"style",
"usernames",
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"strings",
"."
] | def expand_user(path:str) -> Tuple[str, bool, str]:
"""Expand ``~``-style usernames in strings.
This is similar to :func:`os.path.expanduser`, but it computes and returns
extra information that will be useful if the input was being used in
computing completions, and you wish to return the completions with the
original '~' instead of its expanded value.
Parameters
----------
path : str
String to be expanded. If no ~ is present, the output is the same as the
input.
Returns
-------
newpath : str
Result of ~ expansion in the input path.
tilde_expand : bool
Whether any expansion was performed or not.
tilde_val : str
The value that ~ was replaced with.
"""
# Default values
tilde_expand = False
tilde_val = ''
newpath = path
if path.startswith('~'):
tilde_expand = True
rest = len(path)-1
newpath = os.path.expanduser(path)
if rest:
tilde_val = newpath[:-rest]
else:
tilde_val = newpath
return newpath, tilde_expand, tilde_val | [
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BlzFans/wke | b0fa21158312e40c5fbd84682d643022b6c34a93 | cygwin/lib/python2.6/urllib.py | python | FancyURLopener.http_error_407 | (self, url, fp, errcode, errmsg, headers, data=None) | Error 407 -- proxy authentication required.
This function supports Basic authentication only. | Error 407 -- proxy authentication required.
This function supports Basic authentication only. | [
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"""Error 407 -- proxy authentication required.
This function supports Basic authentication only."""
if not 'proxy-authenticate' in headers:
URLopener.http_error_default(self, url, fp,
errcode, errmsg, headers)
stuff = headers['proxy-authenticate']
import re
match = re.match('[ \t]*([^ \t]+)[ \t]+realm="([^"]*)"', stuff)
if not match:
URLopener.http_error_default(self, url, fp,
errcode, errmsg, headers)
scheme, realm = match.groups()
if scheme.lower() != 'basic':
URLopener.http_error_default(self, url, fp,
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name = 'retry_proxy_' + self.type + '_basic_auth'
if data is None:
return getattr(self,name)(url, realm)
else:
return getattr(self,name)(url, realm, data) | [
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VelsonWang/HmiFuncDesigner | 439265da17bd3424e678932cbfbc0237b52630f3 | HmiFuncDesigner/libs/qscintilla/Python/configure.py | python | ModuleConfiguration.get_mac_wrapped_library_file | (target_configuration) | return os.path.join(lib_dir,
'libqscintilla2_qt%s%s.%s.dylib' % (
target_configuration.qt_version_str[0], debug,
QSCI_API_MAJOR)) | Return the full pathname of the file that implements the library
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set. If it is None or an empty string then the default is used.
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being wrapped by the module as it would be called on OS/X so that the
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set. If it is None or an empty string then the default is used.
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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/distlib/_backport/shutil.py | python | _ensure_directory | (path) | Ensure that the parent directory of `path` exists | Ensure that the parent directory of `path` exists | [
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dirname = os.path.dirname(path)
if not os.path.isdir(dirname):
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/osx_carbon/_gdi.py | python | ColourDatabase.Append | (*args, **kwargs) | return _gdi_.ColourDatabase_Append(*args, **kwargs) | Append(self, String name, int red, int green, int blue) | Append(self, String name, int red, int green, int blue) | [
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return _gdi_.ColourDatabase_Append(*args, **kwargs) | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/idlelib/autocomplete.py | python | AutoComplete.force_open_completions_event | (self, event) | return "break" | (^space) Open completion list, even if a function call is needed. | (^space) Open completion list, even if a function call is needed. | [
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/scipy/cluster/hierarchy.py | python | single | (y) | return linkage(y, method='single', metric='euclidean') | Performs single/min/nearest linkage on the condensed distance matrix ``y``
Parameters
----------
y : ndarray
The upper triangular of the distance matrix. The result of
``pdist`` is returned in this form.
Returns
-------
Z : ndarray
The linkage matrix.
See Also
--------
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"""
Performs single/min/nearest linkage on the condensed distance matrix ``y``
Parameters
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y : ndarray
The upper triangular of the distance matrix. The result of
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return linkage(y, method='single', metric='euclidean') | [
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MythTV/mythtv | d282a209cb8be85d036f85a62a8ec971b67d45f4 | mythtv/contrib/imports/mirobridge/mirobridge/mirobridge_interpreter_3_5_0.py | python | MiroInterpreter.do_mythtv_update_autodownload | (self, line) | Update feeds and auto-download | Update feeds and auto-download | [
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logging.info("Starting auto downloader...")
autodler.start_downloader()
feed.expire_items()
logging.info("Starting video data updates")
#item.update_incomplete_movie_data() # Miro Bridge ignores this data anyway
moviedata.movie_data_updater.start_thread()
commandline.startup()
#autoupdate.check_for_updates() # I think this is autoupdate for the Miro code not videos
# Wait a bit before starting the downloader daemon. It can cause a bunch
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eventloop.add_timeout(5, downloader.startup_downloader,
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# ditto for feed updates
eventloop.add_timeout(30, feed.start_updates, "start feed updates")
# ditto for clearing stale icon cache files, except it's the very lowest
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eventloop.add_timeout(10, clear_icon_cache_orphans, "clear orphans") | [
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oracle/graaljs | 36a56e8e993d45fc40939a3a4d9c0c24990720f1 | graal-nodejs/tools/gyp/pylib/gyp/xcode_emulation.py | python | XcodeSettings.AddImplicitPostbuilds | (
self, configname, output, output_binary, postbuilds=[], quiet=False
) | return pre + postbuilds + post | Returns a list of shell commands that should run before and after
|postbuilds|. | Returns a list of shell commands that should run before and after
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"""Returns a list of shell commands that should run before and after
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assert output_binary is not None
pre = self._GetTargetPostbuilds(configname, output, output_binary, quiet)
post = self._GetIOSPostbuilds(configname, output_binary)
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domino-team/openwrt-cc | 8b181297c34d14d3ca521cc9f31430d561dbc688 | package/gli-pub/openwrt-node-packages-master/node/node-v6.9.1/deps/npm/node_modules/node-gyp/gyp/pylib/gyp/generator/ninja.py | python | CalculateVariables | (default_variables, params) | Calculate additional variables for use in the build (called by gyp). | Calculate additional variables for use in the build (called by gyp). | [
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"""Calculate additional variables for use in the build (called by gyp)."""
global generator_additional_non_configuration_keys
global generator_additional_path_sections
flavor = gyp.common.GetFlavor(params)
if flavor == 'mac':
default_variables.setdefault('OS', 'mac')
default_variables.setdefault('SHARED_LIB_SUFFIX', '.dylib')
default_variables.setdefault('SHARED_LIB_DIR',
generator_default_variables['PRODUCT_DIR'])
default_variables.setdefault('LIB_DIR',
generator_default_variables['PRODUCT_DIR'])
# Copy additional generator configuration data from Xcode, which is shared
# by the Mac Ninja generator.
import gyp.generator.xcode as xcode_generator
generator_additional_non_configuration_keys = getattr(xcode_generator,
'generator_additional_non_configuration_keys', [])
generator_additional_path_sections = getattr(xcode_generator,
'generator_additional_path_sections', [])
global generator_extra_sources_for_rules
generator_extra_sources_for_rules = getattr(xcode_generator,
'generator_extra_sources_for_rules', [])
elif flavor == 'win':
exts = gyp.MSVSUtil.TARGET_TYPE_EXT
default_variables.setdefault('OS', 'win')
default_variables['EXECUTABLE_SUFFIX'] = '.' + exts['executable']
default_variables['STATIC_LIB_PREFIX'] = ''
default_variables['STATIC_LIB_SUFFIX'] = '.' + exts['static_library']
default_variables['SHARED_LIB_PREFIX'] = ''
default_variables['SHARED_LIB_SUFFIX'] = '.' + exts['shared_library']
# Copy additional generator configuration data from VS, which is shared
# by the Windows Ninja generator.
import gyp.generator.msvs as msvs_generator
generator_additional_non_configuration_keys = getattr(msvs_generator,
'generator_additional_non_configuration_keys', [])
generator_additional_path_sections = getattr(msvs_generator,
'generator_additional_path_sections', [])
gyp.msvs_emulation.CalculateCommonVariables(default_variables, params)
else:
operating_system = flavor
if flavor == 'android':
operating_system = 'linux' # Keep this legacy behavior for now.
default_variables.setdefault('OS', operating_system)
default_variables.setdefault('SHARED_LIB_SUFFIX', '.so')
default_variables.setdefault('SHARED_LIB_DIR',
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default_variables.setdefault('LIB_DIR',
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/msw/html.py | python | HtmlPrintout.SetFooter | (*args, **kwargs) | return _html.HtmlPrintout_SetFooter(*args, **kwargs) | SetFooter(self, String footer, int pg=PAGE_ALL) | SetFooter(self, String footer, int pg=PAGE_ALL) | [
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return _html.HtmlPrintout_SetFooter(*args, **kwargs) | [
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yuxng/DA-RNN | 77fbb50b4272514588a10a9f90b7d5f8d46974fb | lib/datasets/gmu_scene.py | python | gmu_scene.label_path_at | (self, i) | return self.label_path_from_index(self.image_index[i]) | Return the absolute path to metadata i in the image sequence. | Return the absolute path to metadata i in the image sequence. | [
"Return",
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"absolute",
"path",
"to",
"metadata",
"i",
"in",
"the",
"image",
"sequence",
"."
] | def label_path_at(self, i):
"""
Return the absolute path to metadata i in the image sequence.
"""
return self.label_path_from_index(self.image_index[i]) | [
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OPAE/opae-sdk | 221124343c8275243a249eb72d69e0ea2d568d1b | python/opae.admin/opae/admin/tools/ihex2ipmi.py | python | bmc_convert | (ctype, ifile, ofile) | return 0 | Main conversion. Validate after writing. | Main conversion. Validate after writing. | [
"Main",
"conversion",
".",
"Validate",
"after",
"writing",
"."
] | def bmc_convert(ctype, ifile, ofile):
""" Main conversion. Validate after writing. """
bw_bmc = BittwareBmc(None, ifile)
bw_bmc.set_ofile(ofile)
if ctype == 'bmc_bl':
bw_bmc.write_partitions(bw_bmc.BW_ACT_APP_BL)
elif ctype == 'bmc_app':
bw_bmc.write_partitions(bw_bmc.BW_ACT_APP_MAIN)
else:
raise Exception("unknown ctype: %s" % (ctype))
pad_bytes = 1024 - (ofile.tell() - ((ofile.tell() / 1024) * 1024))
if pad_bytes > 0:
padding = [0x0 for _ in range(pad_bytes)]
ofile.write(bytearray(padding))
print("Validating")
bw_bmc.validate(ctype)
return 0 | [
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CNevd/Difacto_DMLC | f16862e35062707b1cf7e37d04d9b6ae34bbfd28 | dmlc-core/tracker/tracker.py | python | RabitTracker.get_ring | (self, tree_map, parent_map) | return ring_map | get a ring connection used to recover local data | get a ring connection used to recover local data | [
"get",
"a",
"ring",
"connection",
"used",
"to",
"recover",
"local",
"data"
] | def get_ring(self, tree_map, parent_map):
"""
get a ring connection used to recover local data
"""
assert parent_map[0] == -1
rlst = self.find_share_ring(tree_map, parent_map, 0)
assert len(rlst) == len(tree_map)
ring_map = {}
nslave = len(tree_map)
for r in range(nslave):
rprev = (r + nslave - 1) % nslave
rnext = (r + 1) % nslave
ring_map[rlst[r]] = (rlst[rprev], rlst[rnext])
return ring_map | [
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adobe/chromium | cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7 | native_client_sdk/src/project_templates/init_project.py | python | GetTargetFileName | (source_file_name, project_name) | return target_file_name | Converts a source file name into a project file name.
Args:
source_file_name: The name of a file that is to be included in the project
stub, as it appears at the source location.
project_name: The name of the project that is being generated.
Returns:
The target file name for a given source file. All project files are run
through this filter and it modifies them as needed. | Converts a source file name into a project file name. | [
"Converts",
"a",
"source",
"file",
"name",
"into",
"a",
"project",
"file",
"name",
"."
] | def GetTargetFileName(source_file_name, project_name):
"""Converts a source file name into a project file name.
Args:
source_file_name: The name of a file that is to be included in the project
stub, as it appears at the source location.
project_name: The name of the project that is being generated.
Returns:
The target file name for a given source file. All project files are run
through this filter and it modifies them as needed.
"""
target_file_name = ''
if source_file_name.startswith(PROJECT_FILE_NAME):
target_file_name = source_file_name.replace(PROJECT_FILE_NAME,
project_name)
else:
target_file_name = source_file_name
return target_file_name | [
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tensorflow/tensorflow | 419e3a6b650ea4bd1b0cba23c4348f8a69f3272e | tensorflow/python/ops/math_grad.py | python | _FresnelCosGrad | (op, grad) | Compute gradient of fresnel_cos(x) with respect to its argument. | Compute gradient of fresnel_cos(x) with respect to its argument. | [
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"(",
"x",
")",
"with",
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"argument",
"."
] | def _FresnelCosGrad(op, grad):
"""Compute gradient of fresnel_cos(x) with respect to its argument."""
x = op.inputs[0]
with ops.control_dependencies([grad]):
return grad * math_ops.cos((np.pi / 2.) * math_ops.square(x)) | [
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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_tensorflow1.x/tensorflow/python/ops/metrics_impl.py | python | auc | (labels,
predictions,
weights=None,
num_thresholds=200,
metrics_collections=None,
updates_collections=None,
curve='ROC',
name=None,
summation_method='trapezoidal',
thresholds=None) | Computes the approximate AUC via a Riemann sum.
The `auc` function creates four local variables, `true_positives`,
`true_negatives`, `false_positives` and `false_negatives` that are used to
compute the AUC. To discretize the AUC curve, a linearly spaced set of
thresholds is used to compute pairs of recall and precision values. The area
under the ROC-curve is therefore computed using the height of the recall
values by the false positive rate, while the area under the PR-curve is the
computed using the height of the precision values by the recall.
This value is ultimately returned as `auc`, an idempotent operation that
computes the area under a discretized curve of precision versus recall values
(computed using the aforementioned variables). The `num_thresholds` variable
controls the degree of discretization with larger numbers of thresholds more
closely approximating the true AUC. The quality of the approximation may vary
dramatically depending on `num_thresholds`.
For best results, `predictions` should be distributed approximately uniformly
in the range [0, 1] and not peaked around 0 or 1. The quality of the AUC
approximation may be poor if this is not the case. Setting `summation_method`
to 'minoring' or 'majoring' can help quantify the error in the approximation
by providing lower or upper bound estimate of the AUC. The `thresholds`
parameter can be used to manually specify thresholds which split the
predictions more evenly.
For estimation of the metric over a stream of data, the function creates an
`update_op` operation that updates these variables and returns the `auc`.
If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
Args:
labels: A `Tensor` whose shape matches `predictions`. Will be cast to
`bool`.
predictions: A floating point `Tensor` of arbitrary shape and whose values
are in the range `[0, 1]`.
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).
num_thresholds: The number of thresholds to use when discretizing the roc
curve.
metrics_collections: An optional list of collections that `auc` should be
added to.
updates_collections: An optional list of collections that `update_op` should
be added to.
curve: Specifies the name of the curve to be computed, 'ROC' [default] or
'PR' for the Precision-Recall-curve.
name: An optional variable_scope name.
summation_method: Specifies the Riemann summation method used
(https://en.wikipedia.org/wiki/Riemann_sum): 'trapezoidal' [default] that
applies the trapezoidal rule; 'careful_interpolation', a variant of it
differing only by a more correct interpolation scheme for PR-AUC -
interpolating (true/false) positives but not the ratio that is precision;
'minoring' that applies left summation for increasing intervals and right
summation for decreasing intervals; 'majoring' that does the opposite.
Note that 'careful_interpolation' is strictly preferred to 'trapezoidal'
(to be deprecated soon) as it applies the same method for ROC, and a
better one (see Davis & Goadrich 2006 for details) for the PR curve.
thresholds: An optional list of floating point values to use as the
thresholds for discretizing the curve. If set, the `num_thresholds`
parameter is ignored. Values should be in [0, 1]. Endpoint thresholds
equal to {-epsilon, 1+epsilon} for a small positive epsilon value will be
automatically included with these to correctly handle predictions equal to
exactly 0 or 1.
Returns:
auc: A scalar `Tensor` representing the current area-under-curve.
update_op: An operation that increments the `true_positives`,
`true_negatives`, `false_positives` and `false_negatives` variables
appropriately and whose value matches `auc`.
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.
RuntimeError: If eager execution is enabled. | Computes the approximate AUC via a Riemann sum. | [
"Computes",
"the",
"approximate",
"AUC",
"via",
"a",
"Riemann",
"sum",
"."
] | def auc(labels,
predictions,
weights=None,
num_thresholds=200,
metrics_collections=None,
updates_collections=None,
curve='ROC',
name=None,
summation_method='trapezoidal',
thresholds=None):
"""Computes the approximate AUC via a Riemann sum.
The `auc` function creates four local variables, `true_positives`,
`true_negatives`, `false_positives` and `false_negatives` that are used to
compute the AUC. To discretize the AUC curve, a linearly spaced set of
thresholds is used to compute pairs of recall and precision values. The area
under the ROC-curve is therefore computed using the height of the recall
values by the false positive rate, while the area under the PR-curve is the
computed using the height of the precision values by the recall.
This value is ultimately returned as `auc`, an idempotent operation that
computes the area under a discretized curve of precision versus recall values
(computed using the aforementioned variables). The `num_thresholds` variable
controls the degree of discretization with larger numbers of thresholds more
closely approximating the true AUC. The quality of the approximation may vary
dramatically depending on `num_thresholds`.
For best results, `predictions` should be distributed approximately uniformly
in the range [0, 1] and not peaked around 0 or 1. The quality of the AUC
approximation may be poor if this is not the case. Setting `summation_method`
to 'minoring' or 'majoring' can help quantify the error in the approximation
by providing lower or upper bound estimate of the AUC. The `thresholds`
parameter can be used to manually specify thresholds which split the
predictions more evenly.
For estimation of the metric over a stream of data, the function creates an
`update_op` operation that updates these variables and returns the `auc`.
If `weights` is `None`, weights default to 1. Use weights of 0 to mask values.
Args:
labels: A `Tensor` whose shape matches `predictions`. Will be cast to
`bool`.
predictions: A floating point `Tensor` of arbitrary shape and whose values
are in the range `[0, 1]`.
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).
num_thresholds: The number of thresholds to use when discretizing the roc
curve.
metrics_collections: An optional list of collections that `auc` should be
added to.
updates_collections: An optional list of collections that `update_op` should
be added to.
curve: Specifies the name of the curve to be computed, 'ROC' [default] or
'PR' for the Precision-Recall-curve.
name: An optional variable_scope name.
summation_method: Specifies the Riemann summation method used
(https://en.wikipedia.org/wiki/Riemann_sum): 'trapezoidal' [default] that
applies the trapezoidal rule; 'careful_interpolation', a variant of it
differing only by a more correct interpolation scheme for PR-AUC -
interpolating (true/false) positives but not the ratio that is precision;
'minoring' that applies left summation for increasing intervals and right
summation for decreasing intervals; 'majoring' that does the opposite.
Note that 'careful_interpolation' is strictly preferred to 'trapezoidal'
(to be deprecated soon) as it applies the same method for ROC, and a
better one (see Davis & Goadrich 2006 for details) for the PR curve.
thresholds: An optional list of floating point values to use as the
thresholds for discretizing the curve. If set, the `num_thresholds`
parameter is ignored. Values should be in [0, 1]. Endpoint thresholds
equal to {-epsilon, 1+epsilon} for a small positive epsilon value will be
automatically included with these to correctly handle predictions equal to
exactly 0 or 1.
Returns:
auc: A scalar `Tensor` representing the current area-under-curve.
update_op: An operation that increments the `true_positives`,
`true_negatives`, `false_positives` and `false_negatives` variables
appropriately and whose value matches `auc`.
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.
RuntimeError: If eager execution is enabled.
"""
if context.executing_eagerly():
raise RuntimeError('tf.metrics.auc is not supported when eager execution '
'is enabled.')
with variable_scope.variable_scope(name, 'auc',
(labels, predictions, weights)):
if curve != 'ROC' and curve != 'PR':
raise ValueError('curve must be either ROC or PR, %s unknown' % (curve))
kepsilon = 1e-7 # To account for floating point imprecisions.
if thresholds is not None:
# If specified, use the supplied thresholds.
thresholds = sorted(thresholds)
num_thresholds = len(thresholds) + 2
else:
# Otherwise, linearly interpolate (num_thresholds - 2) thresholds in
# (0, 1).
thresholds = [(i + 1) * 1.0 / (num_thresholds - 1)
for i in range(num_thresholds - 2)]
# Add an endpoint "threshold" below zero and above one for either threshold
# method.
thresholds = [0.0 - kepsilon] + thresholds + [1.0 + kepsilon]
values, update_ops = _confusion_matrix_at_thresholds(
labels, predictions, thresholds, weights)
# Add epsilons to avoid dividing by 0.
epsilon = 1.0e-6
def interpolate_pr_auc(tp, fp, fn):
"""Interpolation formula inspired by section 4 of Davis & Goadrich 2006.
Note here we derive & use a closed formula not present in the paper
- as follows:
Modeling all of TP (true positive weight),
FP (false positive weight) and their sum P = TP + FP (positive weight)
as varying linearly within each interval [A, B] between successive
thresholds, we get
Precision = (TP_A + slope * (P - P_A)) / P
with slope = dTP / dP = (TP_B - TP_A) / (P_B - P_A).
The area within the interval is thus (slope / total_pos_weight) times
int_A^B{Precision.dP} = int_A^B{(TP_A + slope * (P - P_A)) * dP / P}
int_A^B{Precision.dP} = int_A^B{slope * dP + intercept * dP / P}
where intercept = TP_A - slope * P_A = TP_B - slope * P_B, resulting in
int_A^B{Precision.dP} = TP_B - TP_A + intercept * log(P_B / P_A)
Bringing back the factor (slope / total_pos_weight) we'd put aside, we get
slope * [dTP + intercept * log(P_B / P_A)] / total_pos_weight
where dTP == TP_B - TP_A.
Note that when P_A == 0 the above calculation simplifies into
int_A^B{Precision.dTP} = int_A^B{slope * dTP} = slope * (TP_B - TP_A)
which is really equivalent to imputing constant precision throughout the
first bucket having >0 true positives.
Args:
tp: true positive counts
fp: false positive counts
fn: false negative counts
Returns:
pr_auc: an approximation of the area under the P-R curve.
"""
dtp = tp[:num_thresholds - 1] - tp[1:]
p = tp + fp
prec_slope = math_ops.div_no_nan(
dtp,
math_ops.maximum(p[:num_thresholds - 1] - p[1:], 0),
name='prec_slope')
intercept = tp[1:] - math_ops.multiply(prec_slope, p[1:])
safe_p_ratio = array_ops.where(
math_ops.logical_and(p[:num_thresholds - 1] > 0, p[1:] > 0),
math_ops.div_no_nan(
p[:num_thresholds - 1],
math_ops.maximum(p[1:], 0),
name='recall_relative_ratio'), array_ops.ones_like(p[1:]))
return math_ops.reduce_sum(
math_ops.div_no_nan(
prec_slope * (dtp + intercept * math_ops.log(safe_p_ratio)),
math_ops.maximum(tp[1:] + fn[1:], 0),
name='pr_auc_increment'),
name='interpolate_pr_auc')
def compute_auc(tp, fn, tn, fp, name):
"""Computes the roc-auc or pr-auc based on confusion counts."""
if curve == 'PR':
if summation_method == 'trapezoidal':
logging.warning(
'Trapezoidal rule is known to produce incorrect PR-AUCs; '
'please switch to "careful_interpolation" instead.')
elif summation_method == 'careful_interpolation':
# This one is a bit tricky and is handled separately.
return interpolate_pr_auc(tp, fp, fn)
rec = math_ops.div(tp + epsilon, tp + fn + epsilon)
if curve == 'ROC':
fp_rate = math_ops.div(fp, fp + tn + epsilon)
x = fp_rate
y = rec
else: # curve == 'PR'.
prec = math_ops.div(tp + epsilon, tp + fp + epsilon)
x = rec
y = prec
if summation_method in ('trapezoidal', 'careful_interpolation'):
# Note that the case ('PR', 'careful_interpolation') has been handled
# above.
return math_ops.reduce_sum(
math_ops.multiply(x[:num_thresholds - 1] - x[1:],
(y[:num_thresholds - 1] + y[1:]) / 2.),
name=name)
elif summation_method == 'minoring':
return math_ops.reduce_sum(
math_ops.multiply(x[:num_thresholds - 1] - x[1:],
math_ops.minimum(y[:num_thresholds - 1], y[1:])),
name=name)
elif summation_method == 'majoring':
return math_ops.reduce_sum(
math_ops.multiply(x[:num_thresholds - 1] - x[1:],
math_ops.maximum(y[:num_thresholds - 1], y[1:])),
name=name)
else:
raise ValueError('Invalid summation_method: %s' % summation_method)
# sum up the areas of all the trapeziums
def compute_auc_value(_, values):
return compute_auc(values['tp'], values['fn'], values['tn'], values['fp'],
'value')
auc_value = _aggregate_across_replicas(
metrics_collections, compute_auc_value, values)
update_op = compute_auc(update_ops['tp'], update_ops['fn'],
update_ops['tn'], update_ops['fp'], 'update_op')
if updates_collections:
ops.add_to_collections(updates_collections, update_op)
return auc_value, update_op | [
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ceph/ceph | 959663007321a369c83218414a29bd9dbc8bda3a | src/pybind/mgr/rook/rook_cluster.py | python | KubernetesResource.items | (self) | return self._items.values() | Returns the items of the request.
Creates the watcher as a side effect.
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | Framework/PythonInterface/plugins/algorithms/EnggCalibrateFull.py | python | EnggCalibrateFull._prepare_ws_for_fitting | (self, ws, bin_width) | return result | Rebins the workspace and converts it to distribution | Rebins the workspace and converts it to distribution | [
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Rebins the workspace and converts it to distribution
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rebin_alg = self.createChildAlgorithm('Rebin')
rebin_alg.setProperty('InputWorkspace', ws)
rebin_alg.setProperty('Params', bin_width)
rebin_alg.execute()
result = rebin_alg.getProperty('OutputWorkspace').value
if not result.isDistribution():
convert_alg = self.createChildAlgorithm('ConvertToDistribution')
convert_alg.setProperty('Workspace', result)
convert_alg.execute()
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/py3/scipy/signal/bsplines.py | python | gauss_spline | (x, n) | return 1 / sqrt(2 * pi * signsq) * exp(-x ** 2 / 2 / signsq) | Gaussian approximation to B-spline basis function of order n.
Parameters
----------
n : int
The order of the spline. Must be nonnegative, i.e. n >= 0
References
----------
.. [1] Bouma H., Vilanova A., Bescos J.O., ter Haar Romeny B.M., Gerritsen
F.A. (2007) Fast and Accurate Gaussian Derivatives Based on B-Splines. In:
Sgallari F., Murli A., Paragios N. (eds) Scale Space and Variational
Methods in Computer Vision. SSVM 2007. Lecture Notes in Computer
Science, vol 4485. Springer, Berlin, Heidelberg | Gaussian approximation to B-spline basis function of order n. | [
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"""Gaussian approximation to B-spline basis function of order n.
Parameters
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The order of the spline. Must be nonnegative, i.e. n >= 0
References
----------
.. [1] Bouma H., Vilanova A., Bescos J.O., ter Haar Romeny B.M., Gerritsen
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Sgallari F., Murli A., Paragios N. (eds) Scale Space and Variational
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signsq = (n + 1) / 12.0
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microsoft/checkedc-clang | a173fefde5d7877b7750e7ce96dd08cf18baebf2 | clang/bindings/python/clang/cindex.py | python | SourceLocation.file | (self) | return self._get_instantiation()[0] | Get the file represented by this source location. | Get the file represented by this source location. | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/slim/python/slim/evaluation.py | python | evaluation_loop | (master,
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initial_op=None,
initial_op_feed_dict=None,
init_fn=None,
eval_op=None,
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max_number_of_evaluations=None,
session_config=None,
timeout=None,
hooks=None) | return evaluation.evaluate_repeatedly(
checkpoint_dir,
master=master,
scaffold=monitored_session.Scaffold(
init_op=initial_op, init_feed_dict=initial_op_feed_dict,
init_fn=init_fn, saver=saver),
eval_ops=eval_op,
feed_dict=eval_op_feed_dict,
final_ops=final_op,
final_ops_feed_dict=final_op_feed_dict,
eval_interval_secs=eval_interval_secs,
hooks=all_hooks,
config=session_config,
max_number_of_evaluations=max_number_of_evaluations,
timeout=timeout) | Runs TF-Slim's Evaluation Loop.
Args:
master: The BNS address of the TensorFlow master.
checkpoint_dir: The directory where checkpoints are stored.
logdir: The directory where the TensorFlow summaries are written to.
num_evals: The number of times to run `eval_op`.
initial_op: An operation run at the beginning of evaluation.
initial_op_feed_dict: A feed dictionary to use when executing `initial_op`.
init_fn: An optional callable to be executed after `init_op` is called. The
callable must accept one argument, the session being initialized.
eval_op: A operation run `num_evals` times.
eval_op_feed_dict: The feed dictionary to use when executing the `eval_op`.
final_op: An operation to execute after all of the `eval_op` executions. The
value of `final_op` is returned.
final_op_feed_dict: A feed dictionary to use when executing `final_op`.
summary_op: The summary_op to evaluate after running TF-Slims metric ops. By
default the summary_op is set to tf.summary.merge_all().
summary_op_feed_dict: An optional feed dictionary to use when running the
`summary_op`.
variables_to_restore: A list of TensorFlow variables to restore during
evaluation. If the argument is left as `None` then
slim.variables.GetVariablesToRestore() is used.
eval_interval_secs: The minimum number of seconds between evaluations.
max_number_of_evaluations: the max number of iterations of the evaluation.
If the value is left as 'None', the evaluation continues indefinitely.
session_config: An instance of `tf.ConfigProto` that will be used to
configure the `Session`. If left as `None`, the default will be used.
timeout: The maximum amount of time to wait between checkpoints. If left as
`None`, then the process will wait indefinitely.
hooks: A list of additional SessionRunHook objects to pass during
repeated evaluations.
Returns:
The value of `final_op` or `None` if `final_op` is `None`. | Runs TF-Slim's Evaluation Loop. | [
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] | def evaluation_loop(master,
checkpoint_dir,
logdir,
num_evals=1,
initial_op=None,
initial_op_feed_dict=None,
init_fn=None,
eval_op=None,
eval_op_feed_dict=None,
final_op=None,
final_op_feed_dict=None,
summary_op=_USE_DEFAULT,
summary_op_feed_dict=None,
variables_to_restore=None,
eval_interval_secs=60,
max_number_of_evaluations=None,
session_config=None,
timeout=None,
hooks=None):
"""Runs TF-Slim's Evaluation Loop.
Args:
master: The BNS address of the TensorFlow master.
checkpoint_dir: The directory where checkpoints are stored.
logdir: The directory where the TensorFlow summaries are written to.
num_evals: The number of times to run `eval_op`.
initial_op: An operation run at the beginning of evaluation.
initial_op_feed_dict: A feed dictionary to use when executing `initial_op`.
init_fn: An optional callable to be executed after `init_op` is called. The
callable must accept one argument, the session being initialized.
eval_op: A operation run `num_evals` times.
eval_op_feed_dict: The feed dictionary to use when executing the `eval_op`.
final_op: An operation to execute after all of the `eval_op` executions. The
value of `final_op` is returned.
final_op_feed_dict: A feed dictionary to use when executing `final_op`.
summary_op: The summary_op to evaluate after running TF-Slims metric ops. By
default the summary_op is set to tf.summary.merge_all().
summary_op_feed_dict: An optional feed dictionary to use when running the
`summary_op`.
variables_to_restore: A list of TensorFlow variables to restore during
evaluation. If the argument is left as `None` then
slim.variables.GetVariablesToRestore() is used.
eval_interval_secs: The minimum number of seconds between evaluations.
max_number_of_evaluations: the max number of iterations of the evaluation.
If the value is left as 'None', the evaluation continues indefinitely.
session_config: An instance of `tf.ConfigProto` that will be used to
configure the `Session`. If left as `None`, the default will be used.
timeout: The maximum amount of time to wait between checkpoints. If left as
`None`, then the process will wait indefinitely.
hooks: A list of additional SessionRunHook objects to pass during
repeated evaluations.
Returns:
The value of `final_op` or `None` if `final_op` is `None`.
"""
if summary_op == _USE_DEFAULT:
summary_op = summary.merge_all()
all_hooks = [evaluation.StopAfterNEvalsHook(num_evals),]
if summary_op is not None:
all_hooks.append(evaluation.SummaryAtEndHook(
log_dir=logdir, summary_op=summary_op, feed_dict=summary_op_feed_dict))
if hooks is not None:
# Add custom hooks if provided.
all_hooks.extend(hooks)
saver = None
if variables_to_restore is not None:
saver = tf_saver.Saver(variables_to_restore)
return evaluation.evaluate_repeatedly(
checkpoint_dir,
master=master,
scaffold=monitored_session.Scaffold(
init_op=initial_op, init_feed_dict=initial_op_feed_dict,
init_fn=init_fn, saver=saver),
eval_ops=eval_op,
feed_dict=eval_op_feed_dict,
final_ops=final_op,
final_ops_feed_dict=final_op_feed_dict,
eval_interval_secs=eval_interval_secs,
hooks=all_hooks,
config=session_config,
max_number_of_evaluations=max_number_of_evaluations,
timeout=timeout) | [
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psi4/psi4 | be533f7f426b6ccc263904e55122899b16663395 | psi4/driver/qcdb/molecule.py | python | Molecule.__getattr__ | (self, name) | Function to overload accessing attribute contents to allow
retrival of geometry variable values as if member data. | Function to overload accessing attribute contents to allow
retrival of geometry variable values as if member data. | [
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] | def __getattr__(self, name):
"""Function to overload accessing attribute contents to allow
retrival of geometry variable values as if member data.
"""
if 'all_variables' in self.__dict__ and name.upper() in self.__dict__['all_variables']:
return self.get_variable(name)
else:
raise AttributeError | [
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FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Arch/importIFCHelper.py | python | getIfcPsetProperties | (ifcfile, pid) | return getIfcProperties(ifcfile, pid, getIfcPropertySets(ifcfile, pid), {}) | directly build the property table from pid and ifcfile for FreeCAD | directly build the property table from pid and ifcfile for FreeCAD | [
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] | def getIfcPsetProperties(ifcfile, pid):
""" directly build the property table from pid and ifcfile for FreeCAD"""
return getIfcProperties(ifcfile, pid, getIfcPropertySets(ifcfile, pid), {}) | [
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google/llvm-propeller | 45c226984fe8377ebfb2ad7713c680d652ba678d | lldb/examples/python/mach_o.py | python | TerminalColors.magenta | (self, fg=True) | return '' | Set the foreground or background color to magenta.
The foreground color will be set if "fg" tests True. The background color will be set if "fg" tests False. | Set the foreground or background color to magenta.
The foreground color will be set if "fg" tests True. The background color will be set if "fg" tests False. | [
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'''Set the foreground or background color to magenta.
The foreground color will be set if "fg" tests True. The background color will be set if "fg" tests False.'''
if self.enabled:
if fg:
return "\x1b[35m"
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/ptyprocess/ptyprocess/ptyprocess.py | python | PtyProcessUnicode.read | (self, size=1024) | return self.decoder.decode(b, final=False) | Read at most ``size`` bytes from the pty, return them as unicode.
Can block if there is nothing to read. Raises :exc:`EOFError` if the
terminal was closed.
The size argument still refers to bytes, not unicode code points. | Read at most ``size`` bytes from the pty, return them as unicode. | [
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] | def read(self, size=1024):
"""Read at most ``size`` bytes from the pty, return them as unicode.
Can block if there is nothing to read. Raises :exc:`EOFError` if the
terminal was closed.
The size argument still refers to bytes, not unicode code points.
"""
b = super(PtyProcessUnicode, self).read(size)
return self.decoder.decode(b, final=False) | [
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facebook/fboss | 60063db1df37c2ec0e7dcd0955c54885ea9bf7f0 | fboss/py/fboss/cli/cli.py | python | PortState._show | (cli_opts, ports, all) | Show port state for given [port(s)] | Show port state for given [port(s)] | [
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] | def _show(cli_opts, ports, all): # noqa: B902
"""Show port state for given [port(s)]"""
port.PortStatusCmd(cli_opts).run(
detail=False, ports=ports, verbose=False, internal=True, all=all
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/ipython/py2/IPython/core/inputtransformer.py | python | has_comment | (src) | return (tokenize2.COMMENT in _line_tokens(src)) | Indicate whether an input line has (i.e. ends in, or is) a comment.
This uses tokenize, so it can distinguish comments from # inside strings.
Parameters
----------
src : string
A single line input string.
Returns
-------
comment : bool
True if source has a comment. | Indicate whether an input line has (i.e. ends in, or is) a comment. | [
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"""Indicate whether an input line has (i.e. ends in, or is) a comment.
This uses tokenize, so it can distinguish comments from # inside strings.
Parameters
----------
src : string
A single line input string.
Returns
-------
comment : bool
True if source has a comment.
"""
return (tokenize2.COMMENT in _line_tokens(src)) | [
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moderngl/moderngl | 32fe79927e02b0fa893b3603d677bdae39771e14 | moderngl/texture_cube.py | python | TextureCube.filter | (self) | return self.mglo.filter | tuple: The minification and magnification filter for the texture.
(Default ``(moderngl.LINEAR. moderngl.LINEAR)``)
Example::
texture.filter == (moderngl.NEAREST, moderngl.NEAREST)
texture.filter == (moderngl.LINEAR_MIPMAP_LINEAR, moderngl.LINEAR)
texture.filter == (moderngl.NEAREST_MIPMAP_LINEAR, moderngl.NEAREST)
texture.filter == (moderngl.LINEAR_MIPMAP_NEAREST, moderngl.NEAREST) | tuple: The minification and magnification filter for the texture.
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'''
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texture.filter == (moderngl.NEAREST, moderngl.NEAREST)
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/numba/targets/cmathimpl.py | python | log_impl | (x, y, x_is_finite, y_is_finite) | return complex(a, b) | cmath.log(x + y j) | cmath.log(x + y j) | [
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"""cmath.log(x + y j)"""
a = math.log(math.hypot(x, y))
b = math.atan2(y, x)
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/windows/Lib/site-packages/botocore/handlers.py | python | set_operation_specific_signer | (context, signing_name, **kwargs) | Choose the operation-specific signer.
Individual operations may have a different auth type than the service as a
whole. This will most often manifest as operations that should not be
authenticated at all, but can include other auth modes such as sigv4
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""" Choose the operation-specific signer.
Individual operations may have a different auth type than the service as a
whole. This will most often manifest as operations that should not be
authenticated at all, but can include other auth modes such as sigv4
without body signing.
"""
auth_type = context.get('auth_type')
# Auth type will be None if the operation doesn't have a configured auth
# type.
if not auth_type:
return
# Auth type will be the string value 'none' if the operation should not
# be signed at all.
if auth_type == 'none':
return botocore.UNSIGNED
if auth_type.startswith('v4'):
signature_version = 'v4'
if signing_name == 's3':
signature_version = 's3v4'
# If the operation needs an unsigned body, we set additional context
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if auth_type == 'v4-unsigned-body':
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LiquidPlayer/LiquidCore | 9405979363f2353ac9a71ad8ab59685dd7f919c9 | deps/node-10.15.3/tools/jinja2/bccache.py | python | BytecodeCache.clear | (self) | Clears the cache. This method is not used by Jinja2 but should be
implemented to allow applications to clear the bytecode cache used
by a particular environment. | Clears the cache. This method is not used by Jinja2 but should be
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"""Clears the cache. This method is not used by Jinja2 but should be
implemented to allow applications to clear the bytecode cache used
by a particular environment.
""" | [
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pgRouting/osm2pgrouting | 8491929fc4037d308f271e84d59bb96da3c28aa2 | tools/cpplint.py | python | FindStartOfExpressionInLine | (line, endpos, stack) | return (-1, stack) | Find position at the matching start of current expression.
This is almost the reverse of FindEndOfExpressionInLine, but note
that the input position and returned position differs by 1.
Args:
line: a CleansedLines line.
endpos: start searching at this position.
stack: nesting stack at endpos.
Returns:
On finding matching start: (index at matching start, None)
On finding an unclosed expression: (-1, None)
Otherwise: (-1, new stack at beginning of this line) | Find position at the matching start of current expression. | [
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] | def FindStartOfExpressionInLine(line, endpos, stack):
"""Find position at the matching start of current expression.
This is almost the reverse of FindEndOfExpressionInLine, but note
that the input position and returned position differs by 1.
Args:
line: a CleansedLines line.
endpos: start searching at this position.
stack: nesting stack at endpos.
Returns:
On finding matching start: (index at matching start, None)
On finding an unclosed expression: (-1, None)
Otherwise: (-1, new stack at beginning of this line)
"""
i = endpos
while i >= 0:
char = line[i]
if char in ')]}':
# Found end of expression, push to expression stack
stack.append(char)
elif char == '>':
# Found potential end of template argument list.
#
# Ignore it if it's a "->" or ">=" or "operator>"
if (i > 0 and
(line[i - 1] == '-' or
Match(r'\s>=\s', line[i - 1:]) or
Search(r'\boperator\s*$', line[0:i]))):
i -= 1
else:
stack.append('>')
elif char == '<':
# Found potential start of template argument list
if i > 0 and line[i - 1] == '<':
# Left shift operator
i -= 1
else:
# If there is a matching '>', we can pop the expression stack.
# Otherwise, ignore this '<' since it must be an operator.
if stack and stack[-1] == '>':
stack.pop()
if not stack:
return (i, None)
elif char in '([{':
# Found start of expression.
#
# If there are any unmatched '>' on the stack, they must be
# operators. Remove those.
while stack and stack[-1] == '>':
stack.pop()
if not stack:
return (-1, None)
if ((char == '(' and stack[-1] == ')') or
(char == '[' and stack[-1] == ']') or
(char == '{' and stack[-1] == '}')):
stack.pop()
if not stack:
return (i, None)
else:
# Mismatched parentheses
return (-1, None)
elif char == ';':
# Found something that look like end of statements. If we are currently
# expecting a '<', the matching '>' must have been an operator, since
# template argument list should not contain statements.
while stack and stack[-1] == '>':
stack.pop()
if not stack:
return (-1, None)
i -= 1
return (-1, stack) | [
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natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/python/ops/tensor_array_ops.py | python | TensorArray.__init__ | (self, dtype, size=None, dynamic_size=None,
clear_after_read=None, tensor_array_name=None, handle=None,
flow=None, infer_shape=True, name=None) | Construct a new TensorArray or wrap an existing TensorArray handle.
A note about the parameter `name`:
The name of the `TensorArray` (even if passed in) is uniquified: each time
a new `TensorArray` is created at runtime it is assigned its own name for
the duration of the run. This avoids name collissions if a `TensorArray`
is created within a `while_loop`.
Args:
dtype: (required) data type of the TensorArray.
size: (optional) int32 scalar `Tensor`: the size of the TensorArray.
Required if handle is not provided.
dynamic_size: (optional) Python bool: If true, writes to the TensorArray
can grow the TensorArray past its initial size. Default: False.
clear_after_read: Boolean (optional, default: True). If True, clear
TensorArray values after reading them. This disables read-many
semantics, but allows early release of memory.
tensor_array_name: (optional) Python string: the name of the TensorArray.
This is used when creating the TensorArray handle. If this value is
set, handle should be None.
handle: (optional) A `Tensor` handle to an existing TensorArray. If this
is set, tensor_array_name should be None.
flow: (optional) A float `Tensor` scalar coming from an existing
`TensorArray.flow`.
infer_shape: (optional, default: True) If True, shape inference
is enabled. In this case, all elements must have the same shape.
name: A name for the operation (optional).
Raises:
ValueError: if both handle and tensor_array_name are provided.
TypeError: if handle is provided but is not a Tensor. | Construct a new TensorArray or wrap an existing TensorArray handle. | [
"Construct",
"a",
"new",
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"or",
"wrap",
"an",
"existing",
"TensorArray",
"handle",
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] | def __init__(self, dtype, size=None, dynamic_size=None,
clear_after_read=None, tensor_array_name=None, handle=None,
flow=None, infer_shape=True, name=None):
"""Construct a new TensorArray or wrap an existing TensorArray handle.
A note about the parameter `name`:
The name of the `TensorArray` (even if passed in) is uniquified: each time
a new `TensorArray` is created at runtime it is assigned its own name for
the duration of the run. This avoids name collissions if a `TensorArray`
is created within a `while_loop`.
Args:
dtype: (required) data type of the TensorArray.
size: (optional) int32 scalar `Tensor`: the size of the TensorArray.
Required if handle is not provided.
dynamic_size: (optional) Python bool: If true, writes to the TensorArray
can grow the TensorArray past its initial size. Default: False.
clear_after_read: Boolean (optional, default: True). If True, clear
TensorArray values after reading them. This disables read-many
semantics, but allows early release of memory.
tensor_array_name: (optional) Python string: the name of the TensorArray.
This is used when creating the TensorArray handle. If this value is
set, handle should be None.
handle: (optional) A `Tensor` handle to an existing TensorArray. If this
is set, tensor_array_name should be None.
flow: (optional) A float `Tensor` scalar coming from an existing
`TensorArray.flow`.
infer_shape: (optional, default: True) If True, shape inference
is enabled. In this case, all elements must have the same shape.
name: A name for the operation (optional).
Raises:
ValueError: if both handle and tensor_array_name are provided.
TypeError: if handle is provided but is not a Tensor.
"""
if handle is not None and tensor_array_name:
raise ValueError(
"Cannot construct with both handle and tensor_array_name")
if handle is not None and not isinstance(handle, ops.Tensor):
raise TypeError("Handle must be a Tensor")
if handle is None and size is None:
raise ValueError("Size must be provided if handle is not provided")
if handle is not None and size is not None:
raise ValueError("Cannot provide both a handle and size "
"at the same time")
if handle is not None and dynamic_size is not None:
raise ValueError("Cannot provide both a handle and dynamic_size "
"at the same time")
if handle is not None and clear_after_read is not None:
raise ValueError("Cannot provide both a handle and clear_after_read "
"at the same time")
if clear_after_read is None:
clear_after_read = True
dynamic_size = dynamic_size or False
self._dtype = dtype
self._infer_shape = infer_shape
# Record the current static shape for the array elements. The first
# write adds the shape of the tensor it writes, and all subsequent
# writes checks for shape equality.
self._elem_shape = []
with ops.op_scope([handle, size, flow], name, "TensorArray") as scope:
if handle is not None:
self._handle = handle
else:
if flow is not None:
with ops.colocate_with(flow):
self._handle = gen_data_flow_ops._tensor_array(
dtype=dtype, size=size, dynamic_size=dynamic_size,
clear_after_read=clear_after_read,
tensor_array_name=tensor_array_name, name=scope)
else:
self._handle = gen_data_flow_ops._tensor_array(
dtype=dtype, size=size, dynamic_size=dynamic_size,
clear_after_read=clear_after_read,
tensor_array_name=tensor_array_name, name=scope)
if flow is not None:
self._flow = flow
else:
with ops.colocate_with(self._handle):
self._flow = constant_op.constant(0, dtype=_dtypes.float32) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | wx/lib/agw/aui/auibar.py | python | AuiToolBar.FindControl | (self, id) | return wnd | Returns a pointer to the control identified by `id` or ``None`` if no corresponding control is found.
:param integer `id`: the control identifier. | Returns a pointer to the control identified by `id` or ``None`` if no corresponding control is found. | [
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"or",
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"is",
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] | def FindControl(self, id):
"""
Returns a pointer to the control identified by `id` or ``None`` if no corresponding control is found.
:param integer `id`: the control identifier.
"""
wnd = self.FindWindow(id)
return wnd | [
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KratosMultiphysics/Kratos | 0000833054ed0503424eb28205d6508d9ca6cbbc | applications/ChimeraApplication/python_scripts/rotate_region_process.py | python | ApplyRotateRegionProcess.ExecuteFinalizeSolutionStep | (self) | This method is executed in order to finalize the current step
Keyword arguments:
self -- It signifies an instance of a class. | This method is executed in order to finalize the current step | [
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""" This method is executed in order to finalize the current step
Keyword arguments:
self -- It signifies an instance of a class.
"""
current_time = self.model_part.ProcessInfo[KratosMultiphysics.TIME]
if self.interval.IsInInterval(current_time):
self.rotate_region_process.ExecuteFinalizeSolutionStep() | [
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miyosuda/TensorFlowAndroidDemo | 35903e0221aa5f109ea2dbef27f20b52e317f42d | jni-build/jni/include/tensorflow/contrib/learn/python/learn/estimators/classifier.py | python | Classifier.predict_proba | (
self, x=None, input_fn=None, batch_size=None, as_iterable=False) | Returns predicted probabilty distributions for given features.
Args:
x: Matrix of shape [n_samples, n_features...]. Can be iterator that
returns arrays of features. The training input samples for fitting the
model. If set, `input_fn` must be `None`.
input_fn: Input function. If set, `x` and 'batch_size' must be `None`.
batch_size: Override default batch size. If set, 'input_fn' must be
'None'.
as_iterable: If True, return an iterable which keeps yielding predictions
for each example until inputs are exhausted. Note: The inputs must
terminate if you want the iterable to terminate (e.g. be sure to pass
num_epochs=1 if you are using something like read_batch_features).
Returns:
A numpy array of predicted probability distributions (or an iterable of
predicted probability distributions if as_iterable is True).
Raises:
ValueError: If x and input_fn are both provided or both `None`. | Returns predicted probabilty distributions for given features. | [
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"predicted",
"probabilty",
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"."
] | def predict_proba(
self, x=None, input_fn=None, batch_size=None, as_iterable=False):
"""Returns predicted probabilty distributions for given features.
Args:
x: Matrix of shape [n_samples, n_features...]. Can be iterator that
returns arrays of features. The training input samples for fitting the
model. If set, `input_fn` must be `None`.
input_fn: Input function. If set, `x` and 'batch_size' must be `None`.
batch_size: Override default batch size. If set, 'input_fn' must be
'None'.
as_iterable: If True, return an iterable which keeps yielding predictions
for each example until inputs are exhausted. Note: The inputs must
terminate if you want the iterable to terminate (e.g. be sure to pass
num_epochs=1 if you are using something like read_batch_features).
Returns:
A numpy array of predicted probability distributions (or an iterable of
predicted probability distributions if as_iterable is True).
Raises:
ValueError: If x and input_fn are both provided or both `None`.
"""
predictions = super(Classifier, self).predict(
x=x, input_fn=input_fn, batch_size=batch_size, as_iterable=as_iterable,
outputs=[self.PROBABILITY_OUTPUT])
if as_iterable:
return (p[self.PROBABILITY_OUTPUT] for p in predictions)
else:
return predictions[self.PROBABILITY_OUTPUT] | [
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hanpfei/chromium-net | 392cc1fa3a8f92f42e4071ab6e674d8e0482f83f | third_party/catapult/telemetry/telemetry/internal/backends/chrome_inspector/devtools_client_backend.py | python | DevToolsClientBackend.CloseTab | (self, tab_id, timeout) | Closes the tab with the given id.
Raises:
devtools_http.DevToolsClientConnectionError
TabNotFoundError | Closes the tab with the given id. | [
"Closes",
"the",
"tab",
"with",
"the",
"given",
"id",
"."
] | def CloseTab(self, tab_id, timeout):
"""Closes the tab with the given id.
Raises:
devtools_http.DevToolsClientConnectionError
TabNotFoundError
"""
try:
return self._devtools_http.Request('close/%s' % tab_id,
timeout=timeout)
except devtools_http.DevToolsClientUrlError:
error = TabNotFoundError(
'Unable to close tab, tab id not found: %s' % tab_id)
raise error, None, sys.exc_info()[2] | [
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sdhash/sdhash | b9eff63e4e5867e910f41fd69032bbb1c94a2a5e | sdhash-ui/cherrypy/wsgiserver/wsgiserver2.py | python | CP_fileobject.sendall | (self, data) | Sendall for non-blocking sockets. | Sendall for non-blocking sockets. | [
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] | def sendall(self, data):
"""Sendall for non-blocking sockets."""
while data:
try:
bytes_sent = self.send(data)
data = data[bytes_sent:]
except socket.error, e:
if e.args[0] not in socket_errors_nonblocking:
raise | [
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acado/acado | b4e28f3131f79cadfd1a001e9fff061f361d3a0f | misc/cpplint.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. | [
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"of",
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"*",
"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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Xilinx/Vitis-AI | fc74d404563d9951b57245443c73bef389f3657f | tools/Vitis-AI-Quantizer/vai_q_pytorch/nndct_shared/nndct_graph/base_graph.py | python | GraphBase.nodes | (self) | Yield node in graph according to topo order
Returns:
generator: yiled a node when tranverse graph | Yield node in graph according to topo order | [
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"in",
"graph",
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] | def nodes(self):
"""Yield node in graph according to topo order
Returns:
generator: yiled a node when tranverse graph
""" | [
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"self",
")",
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] | https://github.com/Xilinx/Vitis-AI/blob/fc74d404563d9951b57245443c73bef389f3657f/tools/Vitis-AI-Quantizer/vai_q_pytorch/nndct_shared/nndct_graph/base_graph.py#L43-L48 | ||
mhammond/pywin32 | 44afd86ba8485194df93234639243252deeb40d5 | win32/Demos/win32netdemo.py | python | GroupEnum | () | Enumerates all the domain groups | Enumerates all the domain groups | [
"Enumerates",
"all",
"the",
"domain",
"groups"
] | def GroupEnum():
"Enumerates all the domain groups"
nmembers = 0
resume = 0
while 1:
data, total, resume = win32net.NetGroupEnum(server, 1, resume)
# print "Call to NetGroupEnum obtained %d entries of %d total" % (len(data), total)
for group in data:
verbose("Found group %(name)s:%(comment)s " % group)
memberresume = 0
while 1:
memberdata, total, memberresume = win32net.NetGroupGetUsers(
server, group["name"], 0, resume
)
for member in memberdata:
verbose(" Member %(name)s" % member)
nmembers = nmembers + 1
if memberresume == 0:
break
if not resume:
break
assert nmembers, "Couldnt find a single member in a single group!"
print("Enumerated all the groups") | [
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ablab/quast | 5f6709528129a6ad266a6b24ef3f40b88f0fe04b | quast_libs/busco/BuscoConfig.py | python | BuscoConfig.nice_path | (path) | :param path: a path to check
:type path: str
:return: the same but cleaned path
:rtype str: | :param path: a path to check
:type path: str
:return: the same but cleaned path
:rtype str: | [
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"""
:param path: a path to check
:type path: str
:return: the same but cleaned path
:rtype str:
"""
try:
if path[-1] != '/':
path += '/'
return path
except TypeError:
return None | [
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h2oai/deepwater | 80e345c582e6ef912a31f42707a2f31c01b064da | tensorflow/src/main/resources/deepwater/train.py | python | ImageClassificationTrainStrategy.loss | (self) | return self._loss | Returns the tensor containing the loss value | Returns the tensor containing the loss value | [
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""" Returns the tensor containing the loss value """
return self._loss | [
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JoseExposito/touchegg | 1f3fda214358d071c05da4bf17c070c33d67b5eb | cmake/cpplint.py | python | ProcessFile | (filename, vlevel, extra_check_functions=[]) | Does google-lint on a single file.
Args:
filename: The name of the file to parse.
vlevel: The level of errors to report. Every error of confidence
>= verbose_level will be reported. 0 is a good default.
extra_check_functions: An array of additional check functions that will be
run on each source line. Each function takes 4
arguments: filename, clean_lines, line, error | Does google-lint on a single file. | [
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"-",
"lint",
"on",
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"file",
"."
] | def ProcessFile(filename, vlevel, extra_check_functions=[]):
"""Does google-lint on a single file.
Args:
filename: The name of the file to parse.
vlevel: The level of errors to report. Every error of confidence
>= verbose_level will be reported. 0 is a good default.
extra_check_functions: An array of additional check functions that will be
run on each source line. Each function takes 4
arguments: filename, clean_lines, line, error
"""
_SetVerboseLevel(vlevel)
_BackupFilters()
old_errors = _cpplint_state.error_count
if not ProcessConfigOverrides(filename):
_RestoreFilters()
return
lf_lines = []
crlf_lines = []
try:
# Support the UNIX convention of using "-" for stdin. Note that
# we are not opening the file with universal newline support
# (which codecs doesn't support anyway), so the resulting lines do
# contain trailing '\r' characters if we are reading a file that
# has CRLF endings.
# If after the split a trailing '\r' is present, it is removed
# below.
if filename == '-':
lines = codecs.StreamReaderWriter(sys.stdin,
codecs.getreader('utf8'),
codecs.getwriter('utf8'),
'replace').read().split('\n')
else:
lines = codecs.open(filename, 'r', 'utf8', 'replace').read().split('\n')
# Remove trailing '\r'.
# The -1 accounts for the extra trailing blank line we get from split()
for linenum in range(len(lines) - 1):
if lines[linenum].endswith('\r'):
lines[linenum] = lines[linenum].rstrip('\r')
crlf_lines.append(linenum + 1)
else:
lf_lines.append(linenum + 1)
except IOError:
sys.stderr.write(
"Skipping input '%s': Can't open for reading\n" % filename)
_RestoreFilters()
return
# Note, if no dot is found, this will give the entire filename as the ext.
file_extension = filename[filename.rfind('.') + 1:]
# When reading from stdin, the extension is unknown, so no cpplint tests
# should rely on the extension.
if filename != '-' and file_extension not in _valid_extensions:
sys.stderr.write('Ignoring %s; not a valid file name '
'(%s)\n' % (filename, ', '.join(_valid_extensions)))
else:
ProcessFileData(filename, file_extension, lines, Error,
extra_check_functions)
# If end-of-line sequences are a mix of LF and CR-LF, issue
# warnings on the lines with CR.
#
# Don't issue any warnings if all lines are uniformly LF or CR-LF,
# since critique can handle these just fine, and the style guide
# doesn't dictate a particular end of line sequence.
#
# We can't depend on os.linesep to determine what the desired
# end-of-line sequence should be, since that will return the
# server-side end-of-line sequence.
if lf_lines and crlf_lines:
# Warn on every line with CR. An alternative approach might be to
# check whether the file is mostly CRLF or just LF, and warn on the
# minority, we bias toward LF here since most tools prefer LF.
for linenum in crlf_lines:
Error(filename, linenum, 'whitespace/newline', 1,
'Unexpected \\r (^M) found; better to use only \\n')
# Suppress printing anything if --quiet was passed unless the error
# count has increased after processing this file.
if not _cpplint_state.quiet or old_errors != _cpplint_state.error_count:
sys.stdout.write('Done processing %s\n' % filename)
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/python/windows/Lib/pandas/core/arrays/timedeltas.py | python | TimedeltaArray.components | (self) | return result | Return a dataframe of the components (days, hours, minutes,
seconds, milliseconds, microseconds, nanoseconds) of the Timedeltas.
Returns
-------
a DataFrame | Return a dataframe of the components (days, hours, minutes,
seconds, milliseconds, microseconds, nanoseconds) of the Timedeltas. | [
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"""
Return a dataframe of the components (days, hours, minutes,
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"""
from pandas import DataFrame
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hasnans = self._hasnans
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yyzybb537/libgo | 4af17b7c67643c4d54aa354dcc77963ea07847d0 | third_party/boost.context/tools/build/src/build/targets.py | python | BasicTarget.compute_usage_requirements | (self, subvariant) | return result | Given the set of generated targets, and refined build
properties, determines and sets appripriate usage requirements
on those targets. | Given the set of generated targets, and refined build
properties, determines and sets appripriate usage requirements
on those targets. | [
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""" Given the set of generated targets, and refined build
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"""
assert isinstance(subvariant, virtual_target.Subvariant)
rproperties = subvariant.build_properties ()
xusage_requirements =self.evaluate_requirements(
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# We generate all dependency properties and add them,
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(r1, r2) = self.generate_dependency_properties(xusage_requirements.dependency (), rproperties)
extra = r1 + r2
result = property_set.create (xusage_requirements.non_dependency () + extra)
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# Essentially, when those two features are in usage requirements,
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removed_pch = filter(lambda prop: prop.feature().name() not in ['<pch-header>', '<pch-file>'], subvariant.sources_usage_requirements().all())
result = result.add(property_set.PropertySet(removed_pch))
return result | [
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SequoiaDB/SequoiaDB | 2894ed7e5bd6fe57330afc900cf76d0ff0df9f64 | tools/server/php_linux/libxml2/lib/python2.4/site-packages/libxml2.py | python | parserCtxt.validate | (self, validate) | Switch the parser to validation mode. | Switch the parser to validation mode. | [
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"""Switch the parser to validation mode. """
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NVIDIA/DALI | bf16cc86ba8f091b145f91962f21fe1b6aff243d | dali/python/nvidia/dali/pipeline.py | python | Pipeline.feed_input | (self, data_node, data, layout = None, cuda_stream = None, use_copy_kernel = False) | Pass a mutlidimensional array or DLPack (or a list thereof) to an output of ExternalSource.
In the case of the GPU input, the data must be modified on the same stream as the one
used by feed_input. See ``cuda_stream`` parameter for details.
Parameters
----------
data_node : :class:`DataNode` or str
The name of the :class:`nvidia.dali.fn.external_source` node or a :class:`DataNode`
object returned by a call to that ExternalSource.
data : an ndarray or DLPack or a list thereof
The array(s) may be one of:
* NumPy ndarray (CPU)
* MXNet ndarray (CPU)
* PyTorch tensor (CPU or GPU)
* CuPy array (GPU)
* objects implementing ``__cuda_array_interface__``
* DALI `TensorList` or list of DALI `Tensor` objects
The data to be used as the output of the ExternalSource referred to by `data_node`.
layout : str or None
The description of the data layout (or empty string, if not specified).
It should be a string of the length that matches the dimensionality of the data, batch
dimension excluded. For a batch of channel-first images, this should be "CHW", for
channel-last video it's "FHWC" and so on.
If ``data`` is a DALI `TensorList` or a list of DALI `Tensor` objects and ``layout``
is ``None``, the layout is taken from ``data``.
cuda_stream : optional, `cudaStream_t` or an object convertible to `cudaStream_t`, e.g. `cupy.cuda.Stream`, `torch.cuda.Stream`
The CUDA stream, which is going to be used for copying data to GPU or from a GPU
source. If not set, best effort will be taken to maintain correctness - i.e. if the data
is provided as a tensor/array from a recognized library (CuPy, PyTorch), the library's
current stream is used. This should work in typical scenarios, but advanced use cases
(and code using unsupported libraries) may still need to supply the stream handle
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Special values:
* 0 - use default CUDA stream
* -1 - use DALI's internal stream
If internal stream is used, the call to ``feed_input`` will block until the copy to
internal buffer is complete, since there's no way to synchronize with this stream to
prevent overwriting the array with new data in another stream.
use_copy_kernel : optional, `bool`
If set to True, DALI will use a CUDA kernel to feed the data (only applicable when copying
data to/from GPU memory) instead of cudaMemcpyAsync (default). | Pass a mutlidimensional array or DLPack (or a list thereof) to an output of ExternalSource.
In the case of the GPU input, the data must be modified on the same stream as the one
used by feed_input. See ``cuda_stream`` parameter for details. | [
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"""Pass a mutlidimensional array or DLPack (or a list thereof) to an output of ExternalSource.
In the case of the GPU input, the data must be modified on the same stream as the one
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Parameters
----------
data_node : :class:`DataNode` or str
The name of the :class:`nvidia.dali.fn.external_source` node or a :class:`DataNode`
object returned by a call to that ExternalSource.
data : an ndarray or DLPack or a list thereof
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layout : str or None
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channel-last video it's "FHWC" and so on.
If ``data`` is a DALI `TensorList` or a list of DALI `Tensor` objects and ``layout``
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current stream is used. This should work in typical scenarios, but advanced use cases
(and code using unsupported libraries) may still need to supply the stream handle
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Special values:
* 0 - use default CUDA stream
* -1 - use DALI's internal stream
If internal stream is used, the call to ``feed_input`` will block until the copy to
internal buffer is complete, since there's no way to synchronize with this stream to
prevent overwriting the array with new data in another stream.
use_copy_kernel : optional, `bool`
If set to True, DALI will use a CUDA kernel to feed the data (only applicable when copying
data to/from GPU memory) instead of cudaMemcpyAsync (default).
"""
if not self._built:
raise RuntimeError("Pipeline must be built first.")
if isinstance(data_node, str):
name = data_node
else:
_data_node._check(data_node)
name = data_node.name
# Check for use of feed_input on an external_source operator that was initialized with 'source'.
if next((op._callback is not None for op in self._ops if op.name == name), False):
raise RuntimeError(f"Cannot use `feed_input` on the external source '{name}' with a `source`"
" argument specified.")
self._feed_input(name, data, layout, cuda_stream, use_copy_kernel) | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | src/gtk/_windows.py | python | PrintDialogData.EnableSelection | (*args, **kwargs) | return _windows_.PrintDialogData_EnableSelection(*args, **kwargs) | EnableSelection(self, bool flag) | EnableSelection(self, bool flag) | [
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"""EnableSelection(self, bool flag)"""
return _windows_.PrintDialogData_EnableSelection(*args, **kwargs) | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | tools/telemetry/third_party/pyserial/serial/serialutil.py | python | SerialBase.getInterCharTimeout | (self) | return self._interCharTimeout | Get the current inter-character timeout setting. | Get the current inter-character timeout setting. | [
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facebookresearch/habitat-sim | 63b6c71d9ca8adaefb140b198196f5d0ca1f1e34 | examples/instance_segmentation/common.py | python | area_filter | (mask, bounding_box, img_height, img_width, size_tol=0.05) | return not_sparse and big_enough | Function to filter out masks that contain sparse instances
for example:
0 0 0 0 0 0
1 0 0 0 0 0
1 0 0 0 1 0 This is a sparse mask
0 0 0 0 1 0
0 0 0 0 0 0
0 0 0 0 0 0
1 1 1 1 1 0
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0 0 0 0 0 0 | Function to filter out masks that contain sparse instances
for example: | [
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"""
Function to filter out masks that contain sparse instances
for example:
0 0 0 0 0 0
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1 0 0 0 1 0 This is a sparse mask
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xmin, ymin, xmax, ymax = bounding_box
num_positive_pixels = np.sum(mask[ymin:ymax, xmin:xmax])
num_total_pixels = (xmax - xmin) * (ymax - ymin)
not_sparse = num_positive_pixels / num_total_pixels >= 0.3
big_enough = (xmax - xmin) >= size_tol * img_width and (
ymax - ymin
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return not_sparse and big_enough | [
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intel/llvm | e6d0547e9d99b5a56430c4749f6c7e328bf221ab | mlir/python/mlir/dialects/linalg/opdsl/ops/core_named_ops.py | python | depthwise_conv_1d_nwc_wc | (
I=TensorDef(T1, S.N, S.OW * S.SW + S.KW * S.DW, S.IC),
K=TensorDef(T2, S.KW, S.IC),
O=TensorDef(U, S.N, S.OW, S.IC, output=True),
strides=IndexAttrDef(S.SW),
dilations=IndexAttrDef(S.DW)) | Performs depth-wise 1-D convolution.
Numeric casting is performed on the operands to the inner multiply, promoting
them to the same data type as the accumulator/output. Multiplier is set to 1
which is a special case for most depthwise convolutions. | Performs depth-wise 1-D convolution. | [
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] | def depthwise_conv_1d_nwc_wc(
I=TensorDef(T1, S.N, S.OW * S.SW + S.KW * S.DW, S.IC),
K=TensorDef(T2, S.KW, S.IC),
O=TensorDef(U, S.N, S.OW, S.IC, output=True),
strides=IndexAttrDef(S.SW),
dilations=IndexAttrDef(S.DW)):
"""Performs depth-wise 1-D convolution.
Numeric casting is performed on the operands to the inner multiply, promoting
them to the same data type as the accumulator/output. Multiplier is set to 1
which is a special case for most depthwise convolutions.
"""
implements(ConvolutionOpInterface)
domain(D.n, D.ow, D.ic, D.kw)
O[D.n, D.ow, D.ic] += \
TypeFn.cast(U, I[D.n, D.ow * S.SW + D.kw * S.DW, D.ic]) * \
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mindspore-ai/mindspore | fb8fd3338605bb34fa5cea054e535a8b1d753fab | mindspore/python/mindspore/run_check/run_check.py | python | _check_mul | () | Define the mul method. | Define the mul method. | [
"Define",
"the",
"mul",
"method",
"."
] | def _check_mul():
"""
Define the mul method.
"""
from importlib import import_module
import numpy as np
try:
ms = import_module("mindspore")
except ModuleNotFoundError:
ms = None
finally:
pass
print(f"MindSpore version: ", ms.__version__)
input_x = ms.Tensor(np.array([1.0, 2.0, 3.0]), ms.float32)
input_y = ms.Tensor(np.array([4.0, 5.0, 6.0]), ms.float32)
mul = ms.ops.Mul()
mul(input_x, input_y)
print(f"The result of multiplication calculation is correct, MindSpore has been installed successfully!") | [
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raymondlu/super-animation-samples | 04234269112ff0dc32447f27a761dbbb00b8ba17 | samples/cocos2d-x-3.1/CocosLuaGame2/frameworks/cocos2d-x/tools/bindings-generator/clang/cindex.py | python | Type.is_function_variadic | (self) | return conf.lib.clang_isFunctionTypeVariadic(self) | Determine whether this function Type is a variadic function type. | Determine whether this function Type is a variadic function type. | [
"Determine",
"whether",
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"Type",
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"variadic",
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] | def is_function_variadic(self):
"""Determine whether this function Type is a variadic function type."""
assert self.kind == TypeKind.FUNCTIONPROTO
return conf.lib.clang_isFunctionTypeVariadic(self) | [
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epam/Indigo | 30e40b4b1eb9bae0207435a26cfcb81ddcc42be1 | api/python/indigo/__init__.py | python | IndigoObject.cmlFooter | (self) | return self.dispatcher._checkResult(
Indigo._lib.indigoCmlFooter(self.id)
) | CML builder adds footer information
Returns:
int: 1 if there are no errors | CML builder adds footer information | [
"CML",
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] | def cmlFooter(self):
"""CML builder adds footer information
Returns:
int: 1 if there are no errors
"""
self.dispatcher._setSessionId()
return self.dispatcher._checkResult(
Indigo._lib.indigoCmlFooter(self.id)
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemMetric/v1/AWS/common-code/Lib/pandas/core/arrays/boolean.py | python | BooleanArray.any | (self, skipna: bool = True, **kwargs) | Return whether any element is True.
Returns False unless there is at least one element that is True.
By default, NAs are skipped. If ``skipna=False`` is specified and
missing values are present, similar :ref:`Kleene logic <boolean.kleene>`
is used as for logical operations.
Parameters
----------
skipna : bool, default True
Exclude NA values. If the entire array is NA and `skipna` is
True, then the result will be False, as for an empty array.
If `skipna` is False, the result will still be True if there is
at least one element that is True, otherwise NA will be returned
if there are NA's present.
**kwargs : any, default None
Additional keywords have no effect but might be accepted for
compatibility with NumPy.
Returns
-------
bool or :attr:`pandas.NA`
See Also
--------
numpy.any : Numpy version of this method.
BooleanArray.all : Return whether all elements are True.
Examples
--------
The result indicates whether any element is True (and by default
skips NAs):
>>> pd.array([True, False, True]).any()
True
>>> pd.array([True, False, pd.NA]).any()
True
>>> pd.array([False, False, pd.NA]).any()
False
>>> pd.array([], dtype="boolean").any()
False
>>> pd.array([pd.NA], dtype="boolean").any()
False
With ``skipna=False``, the result can be NA if this is logically
required (whether ``pd.NA`` is True or False influences the result):
>>> pd.array([True, False, pd.NA]).any(skipna=False)
True
>>> pd.array([False, False, pd.NA]).any(skipna=False)
<NA> | Return whether any element is True. | [
"Return",
"whether",
"any",
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"True",
"."
] | def any(self, skipna: bool = True, **kwargs):
"""
Return whether any element is True.
Returns False unless there is at least one element that is True.
By default, NAs are skipped. If ``skipna=False`` is specified and
missing values are present, similar :ref:`Kleene logic <boolean.kleene>`
is used as for logical operations.
Parameters
----------
skipna : bool, default True
Exclude NA values. If the entire array is NA and `skipna` is
True, then the result will be False, as for an empty array.
If `skipna` is False, the result will still be True if there is
at least one element that is True, otherwise NA will be returned
if there are NA's present.
**kwargs : any, default None
Additional keywords have no effect but might be accepted for
compatibility with NumPy.
Returns
-------
bool or :attr:`pandas.NA`
See Also
--------
numpy.any : Numpy version of this method.
BooleanArray.all : Return whether all elements are True.
Examples
--------
The result indicates whether any element is True (and by default
skips NAs):
>>> pd.array([True, False, True]).any()
True
>>> pd.array([True, False, pd.NA]).any()
True
>>> pd.array([False, False, pd.NA]).any()
False
>>> pd.array([], dtype="boolean").any()
False
>>> pd.array([pd.NA], dtype="boolean").any()
False
With ``skipna=False``, the result can be NA if this is logically
required (whether ``pd.NA`` is True or False influences the result):
>>> pd.array([True, False, pd.NA]).any(skipna=False)
True
>>> pd.array([False, False, pd.NA]).any(skipna=False)
<NA>
"""
kwargs.pop("axis", None)
nv.validate_any((), kwargs)
values = self._data.copy()
np.putmask(values, self._mask, False)
result = values.any()
if skipna:
return result
else:
if result or len(self) == 0:
return result
else:
return self.dtype.na_value | [
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mantidproject/mantid | 03deeb89254ec4289edb8771e0188c2090a02f32 | qt/python/mantidqtinterfaces/mantidqtinterfaces/Muon/GUI/Common/results_tab_widget/results_tab_model.py | python | ResultsTabModel.on_new_fit_performed | (self) | Called when a new fit has been added to the context.
The function name is set to the name fit if it is the first time | Called when a new fit has been added to the context.
The function name is set to the name fit if it is the first time | [
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] | def on_new_fit_performed(self):
"""Called when a new fit has been added to the context.
The function name is set to the name fit if it is the first time"""
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idaholab/moose | 9eeebc65e098b4c30f8205fb41591fd5b61eb6ff | python/chigger/geometric/CylinderSource.py | python | CylinderSource.update | (self, **kwargs) | Set the options for this cylinder. (public) | Set the options for this cylinder. (public) | [
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] | def update(self, **kwargs):
"""
Set the options for this cylinder. (public)
"""
super(CylinderSource, self).update(**kwargs)
if self.isOptionValid('height'):
self._vtksource.SetHeight(self.getOption('height'))
if self.isOptionValid('radius'):
self._vtksource.SetRadius(self.getOption('radius'))
if self.isOptionValid('resolution'):
self._vtksource.SetResolution(self.getOption('resolution'))
if self.isOptionValid('capping'):
self._vtksource.CappingOn() | [
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llvm-mirror/lldb | d01083a850f577b85501a0902b52fd0930de72c7 | utils/vim-lldb/python-vim-lldb/vim_panes.py | python | PaneLayout.prepare | (self, panes=[]) | Draw panes on screen. If empty list is provided, show all. | Draw panes on screen. If empty list is provided, show all. | [
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""" Draw panes on screen. If empty list is provided, show all. """
# If we can't select a window contained in the layout, we are doing a
# first draw
first_draw = not self.selectWindow(True)
did_first_draw = False
# Prepare each registered pane
for name in self.panes:
if name in panes or len(panes) == 0:
if first_draw:
# First window in layout will be created with :vsp, and
# closed later
vim.command(":vsp")
first_draw = False
did_first_draw = True
self.panes[name].prepare()
if did_first_draw:
# Close the split window
vim.command(":q")
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NicknineTheEagle/TF2-Base | 20459c5a7fbc995b6bf54fa85c2f62a101e9fb64 | src/thirdparty/protobuf-2.3.0/python/google/protobuf/descriptor.py | python | FileDescriptor.__init__ | (self, name, package, options=None, serialized_pb=None) | Constructor. | Constructor. | [
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] | def __init__(self, name, package, options=None, serialized_pb=None):
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super(FileDescriptor, self).__init__(options, 'FileOptions')
self.name = name
self.package = package
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FreeCAD/FreeCAD | ba42231b9c6889b89e064d6d563448ed81e376ec | src/Mod/Draft/draftguitools/gui_rotate.py | python | Rotate.action | (self, arg) | Handle the 3D scene events.
This is installed as an EventCallback in the Inventor view.
Parameters
----------
arg: dict
Dictionary with strings that indicates the type of event received
from the 3D view. | Handle the 3D scene events. | [
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] | def action(self, arg):
"""Handle the 3D scene events.
This is installed as an EventCallback in the Inventor view.
Parameters
----------
arg: dict
Dictionary with strings that indicates the type of event received
from the 3D view.
"""
if arg["Type"] == "SoKeyboardEvent" and arg["Key"] == "ESCAPE":
self.finish()
elif arg["Type"] == "SoLocation2Event":
self.handle_mouse_move_event(arg)
elif (arg["Type"] == "SoMouseButtonEvent"
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and arg["Button"] == "BUTTON1"):
self.handle_mouse_click_event(arg) | [
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freeorion/freeorion | c266a40eccd3a99a17de8fe57c36ef6ba3771665 | default/python/AI/AIstate.py | python | AIstate.log_alliance_request | (self, initiating_empire_id, recipient_empire_id) | Keep a record of alliance requests made or received by this empire. | Keep a record of alliance requests made or received by this empire. | [
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] | def log_alliance_request(self, initiating_empire_id, recipient_empire_id):
"""Keep a record of alliance requests made or received by this empire."""
alliance_requests = self.diplomatic_logs.setdefault("alliance_requests", {})
log_index = (initiating_empire_id, recipient_empire_id)
alliance_requests.setdefault(log_index, []).append(fo.currentTurn()) | [
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snap-stanford/snap-python | d53c51b0a26aa7e3e7400b014cdf728948fde80a | setup/snap.py | python | TStrUtil_IsLatinStr | (*args) | return _snap.TStrUtil_IsLatinStr(*args) | TStrUtil_IsLatinStr(TChA Str, double const & MinAlFrac) -> bool
Parameters:
Str: TChA const &
MinAlFrac: double const & | TStrUtil_IsLatinStr(TChA Str, double const & MinAlFrac) -> bool | [
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"(",
"TChA",
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] | def TStrUtil_IsLatinStr(*args):
"""
TStrUtil_IsLatinStr(TChA Str, double const & MinAlFrac) -> bool
Parameters:
Str: TChA const &
MinAlFrac: double const &
"""
return _snap.TStrUtil_IsLatinStr(*args) | [
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/slim/python/slim/data/data_provider.py | python | DataProvider.num_samples | (self) | return self._num_samples | Returns the number of data samples in the dataset.
Returns:
a positive whole number. | Returns the number of data samples in the dataset. | [
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] | def num_samples(self):
"""Returns the number of data samples in the dataset.
Returns:
a positive whole number.
"""
return self._num_samples | [
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chromiumembedded/cef | 80caf947f3fe2210e5344713c5281d8af9bdc295 | tools/make_distrib.py | python | combine_libs | (platform, build_dir, libs, dest_lib) | Combine multiple static libraries into a single static library. | Combine multiple static libraries into a single static library. | [
"Combine",
"multiple",
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"libraries",
"into",
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"single",
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] | def combine_libs(platform, build_dir, libs, dest_lib):
""" Combine multiple static libraries into a single static library. """
intermediate_obj = None
if platform == 'windows':
cmdline = 'msvs_env.bat win%s "%s" combine_libs.py -o "%s"' % (
platform_arch, sys.executable, dest_lib)
elif platform == 'mac':
# Find CEF_EXPORT symbols from libcef_sandbox.a (include/cef_sandbox_mac.h)
# Export only symbols that include these strings.
symbol_match = [
'_cef_', # C symbols
'Cef', # C++ symbols
]
print('Finding exported symbols...')
assert 'libcef_sandbox.a' in libs[0], libs[0]
symbols = []
for symbol in get_exported_symbols(os.path.join(build_dir, libs[0])):
for match in symbol_match:
if symbol.find(match) >= 0:
symbols.append(symbol)
break
assert len(symbols) > 0
# Create an intermediate object file that combines all other object files.
# Symbols not identified above will be made private (local).
intermediate_obj = os.path.splitext(dest_lib)[0] + '.o'
arch = 'arm64' if options.arm64build else 'x86_64'
cmdline = 'ld -arch %s -r -o "%s"' % (arch, intermediate_obj)
for symbol in symbols:
cmdline += ' -exported_symbol %s' % symbol
for lib in libs:
lib_path = os.path.join(build_dir, lib)
for path in get_files(lib_path): # Expand wildcards in |lib_path|.
if not path_exists(path):
raise Exception('File not found: ' + path)
cmdline += ' "%s"' % path
run(cmdline, os.path.join(cef_dir, 'tools'))
if not intermediate_obj is None:
# Create an archive file containing the new object file.
cmdline = 'libtool -static -o "%s" "%s"' % (dest_lib, intermediate_obj)
run(cmdline, os.path.join(cef_dir, 'tools'))
remove_file(intermediate_obj)
# Verify that only the expected symbols are exported from the archive file.
print('Verifying exported symbols...')
result_symbols = get_exported_symbols(dest_lib)
if set(symbols) != set(result_symbols):
print('Expected', symbols)
print('Got', result_symbols)
raise Exception('Failure verifying exported symbols')
# Verify that no C++ symbols are imported by the archive file. If the
# archive imports C++ symbols and the client app links an incompatible C++
# library, the result will be undefined behavior.
# For example, to avoid importing libc++ symbols the cef_sandbox target
# should have a dependency on libc++abi. This dependency can be verified
# with the following command:
# gn path out/[config] //cef:cef_sandbox //buildtools/third_party/libc++abi
print('Verifying imported (undefined) symbols...')
undefined_symbols = get_undefined_symbols(dest_lib)
cpp_symbols = list(
filter(lambda symbol: symbol.startswith('__Z'), undefined_symbols))
if cpp_symbols:
print('Found C++ symbols:', cpp_symbols)
raise Exception('Failure verifying imported (undefined) symbols') | [
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] | https://github.com/chromiumembedded/cef/blob/80caf947f3fe2210e5344713c5281d8af9bdc295/tools/make_distrib.py#L355-L422 | ||
catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scikit-learn/py2/sklearn/metrics/regression.py | python | mean_absolute_error | (y_true, y_pred,
sample_weight=None,
multioutput='uniform_average') | return np.average(output_errors, weights=multioutput) | Mean absolute error regression loss
Read more in the :ref:`User Guide <mean_absolute_error>`.
Parameters
----------
y_true : array-like of shape = (n_samples) or (n_samples, n_outputs)
Ground truth (correct) target values.
y_pred : array-like of shape = (n_samples) or (n_samples, n_outputs)
Estimated target values.
sample_weight : array-like of shape = (n_samples), optional
Sample weights.
multioutput : string in ['raw_values', 'uniform_average']
or array-like of shape (n_outputs)
Defines aggregating of multiple output values.
Array-like value defines weights used to average errors.
'raw_values' :
Returns a full set of errors in case of multioutput input.
'uniform_average' :
Errors of all outputs are averaged with uniform weight.
Returns
-------
loss : float or ndarray of floats
If multioutput is 'raw_values', then mean absolute error is returned
for each output separately.
If multioutput is 'uniform_average' or an ndarray of weights, then the
weighted average of all output errors is returned.
MAE output is non-negative floating point. The best value is 0.0.
Examples
--------
>>> from sklearn.metrics import mean_absolute_error
>>> y_true = [3, -0.5, 2, 7]
>>> y_pred = [2.5, 0.0, 2, 8]
>>> mean_absolute_error(y_true, y_pred)
0.5
>>> y_true = [[0.5, 1], [-1, 1], [7, -6]]
>>> y_pred = [[0, 2], [-1, 2], [8, -5]]
>>> mean_absolute_error(y_true, y_pred)
0.75
>>> mean_absolute_error(y_true, y_pred, multioutput='raw_values')
array([ 0.5, 1. ])
>>> mean_absolute_error(y_true, y_pred, multioutput=[0.3, 0.7])
... # doctest: +ELLIPSIS
0.849... | Mean absolute error regression loss | [
"Mean",
"absolute",
"error",
"regression",
"loss"
] | def mean_absolute_error(y_true, y_pred,
sample_weight=None,
multioutput='uniform_average'):
"""Mean absolute error regression loss
Read more in the :ref:`User Guide <mean_absolute_error>`.
Parameters
----------
y_true : array-like of shape = (n_samples) or (n_samples, n_outputs)
Ground truth (correct) target values.
y_pred : array-like of shape = (n_samples) or (n_samples, n_outputs)
Estimated target values.
sample_weight : array-like of shape = (n_samples), optional
Sample weights.
multioutput : string in ['raw_values', 'uniform_average']
or array-like of shape (n_outputs)
Defines aggregating of multiple output values.
Array-like value defines weights used to average errors.
'raw_values' :
Returns a full set of errors in case of multioutput input.
'uniform_average' :
Errors of all outputs are averaged with uniform weight.
Returns
-------
loss : float or ndarray of floats
If multioutput is 'raw_values', then mean absolute error is returned
for each output separately.
If multioutput is 'uniform_average' or an ndarray of weights, then the
weighted average of all output errors is returned.
MAE output is non-negative floating point. The best value is 0.0.
Examples
--------
>>> from sklearn.metrics import mean_absolute_error
>>> y_true = [3, -0.5, 2, 7]
>>> y_pred = [2.5, 0.0, 2, 8]
>>> mean_absolute_error(y_true, y_pred)
0.5
>>> y_true = [[0.5, 1], [-1, 1], [7, -6]]
>>> y_pred = [[0, 2], [-1, 2], [8, -5]]
>>> mean_absolute_error(y_true, y_pred)
0.75
>>> mean_absolute_error(y_true, y_pred, multioutput='raw_values')
array([ 0.5, 1. ])
>>> mean_absolute_error(y_true, y_pred, multioutput=[0.3, 0.7])
... # doctest: +ELLIPSIS
0.849...
"""
y_type, y_true, y_pred, multioutput = _check_reg_targets(
y_true, y_pred, multioutput)
output_errors = np.average(np.abs(y_pred - y_true),
weights=sample_weight, axis=0)
if isinstance(multioutput, string_types):
if multioutput == 'raw_values':
return output_errors
elif multioutput == 'uniform_average':
# pass None as weights to np.average: uniform mean
multioutput = None
return np.average(output_errors, weights=multioutput) | [
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apple/turicreate | cce55aa5311300e3ce6af93cb45ba791fd1bdf49 | src/python/turicreate/toolkits/image_analysis/image_analysis.py | python | resize | (image, width, height, channels=None, decode=False, resample="nearest") | Resizes the image or SArray of Images to a specific width, height, and
number of channels.
Parameters
----------
image : turicreate.Image | SArray
The image or SArray of images to be resized.
width : int
The width the image is resized to.
height : int
The height the image is resized to.
channels : int, optional
The number of channels the image is resized to. 1 channel
corresponds to grayscale, 3 channels corresponds to RGB, and 4
channels corresponds to RGBA images.
decode : bool, optional
Whether to store the resized image in decoded format. Decoded takes
more space, but makes the resize and future operations on the image faster.
resample : 'nearest' or 'bilinear'
Specify the resampling filter:
- ``'nearest'``: Nearest neigbhor, extremely fast
- ``'bilinear'``: Bilinear, fast and with less aliasing artifacts
Returns
-------
out : turicreate.Image
Returns a resized Image object.
Notes
-----
Grayscale Images -> Images with one channel, representing a scale from
white to black
RGB Images -> Images with 3 channels, with each pixel having Green, Red,
and Blue values.
RGBA Images -> An RGB image with an opacity channel.
Examples
--------
Resize a single image
>>> img = turicreate.Image('https://static.turi.com/datasets/images/sample.jpg')
>>> resized_img = turicreate.image_analysis.resize(img,100,100,1)
Resize an SArray of images
>>> url ='https://static.turi.com/datasets/images/nested'
>>> image_sframe = turicreate.image_analysis.load_images(url, "auto", with_path=False,
... recursive=True)
>>> image_sarray = image_sframe["image"]
>>> resized_images = turicreate.image_analysis.resize(image_sarray, 100, 100, 1) | Resizes the image or SArray of Images to a specific width, height, and
number of channels. | [
"Resizes",
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"of",
"Images",
"to",
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"width",
"height",
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"number",
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"."
] | def resize(image, width, height, channels=None, decode=False, resample="nearest"):
"""
Resizes the image or SArray of Images to a specific width, height, and
number of channels.
Parameters
----------
image : turicreate.Image | SArray
The image or SArray of images to be resized.
width : int
The width the image is resized to.
height : int
The height the image is resized to.
channels : int, optional
The number of channels the image is resized to. 1 channel
corresponds to grayscale, 3 channels corresponds to RGB, and 4
channels corresponds to RGBA images.
decode : bool, optional
Whether to store the resized image in decoded format. Decoded takes
more space, but makes the resize and future operations on the image faster.
resample : 'nearest' or 'bilinear'
Specify the resampling filter:
- ``'nearest'``: Nearest neigbhor, extremely fast
- ``'bilinear'``: Bilinear, fast and with less aliasing artifacts
Returns
-------
out : turicreate.Image
Returns a resized Image object.
Notes
-----
Grayscale Images -> Images with one channel, representing a scale from
white to black
RGB Images -> Images with 3 channels, with each pixel having Green, Red,
and Blue values.
RGBA Images -> An RGB image with an opacity channel.
Examples
--------
Resize a single image
>>> img = turicreate.Image('https://static.turi.com/datasets/images/sample.jpg')
>>> resized_img = turicreate.image_analysis.resize(img,100,100,1)
Resize an SArray of images
>>> url ='https://static.turi.com/datasets/images/nested'
>>> image_sframe = turicreate.image_analysis.load_images(url, "auto", with_path=False,
... recursive=True)
>>> image_sarray = image_sframe["image"]
>>> resized_images = turicreate.image_analysis.resize(image_sarray, 100, 100, 1)
"""
if height < 0 or width < 0:
raise ValueError("Cannot resize to negative sizes")
if resample not in ("nearest", "bilinear"):
raise ValueError("Unknown resample option: '%s'" % resample)
from ...data_structures.sarray import SArray as _SArray
from ... import extensions as _extensions
import turicreate as _tc
if type(image) is _Image:
assert resample in ("nearest", "bilinear")
resample_method = 0 if resample == "nearest" else 1
if channels is None:
channels = image.channels
if channels <= 0:
raise ValueError("cannot resize images to 0 or fewer channels")
return _extensions.resize_image(
image, width, height, channels, decode, resample_method
)
elif type(image) is _SArray:
if channels is None:
channels = 3
if channels <= 0:
raise ValueError("cannot resize images to 0 or fewer channels")
return image.apply(
lambda x: _tc.image_analysis.resize(
x, width, height, channels, decode, resample
)
)
else:
raise ValueError(
"Cannot call 'resize' on objects that are not either an Image or SArray of Images"
) | [
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ZhouWeikuan/DouDiZhu | 0d84ff6c0bc54dba6ae37955de9ae9307513dc99 | code/frameworks/cocos2d-x/tools/bindings-generator/backup/clang-llvm-3.3-pybinding/cindex.py | python | FileInclusion.is_input_file | (self) | return self.depth == 0 | True if the included file is the input file. | True if the included file is the input file. | [
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ricardoquesada/Spidermonkey | 4a75ea2543408bd1b2c515aa95901523eeef7858 | python/configobj/validate.py | python | is_integer | (value, min=None, max=None) | return value | A check that tests that a given value is an integer (int, or long)
and optionally, between bounds. A negative value is accepted, while
a float will fail.
If the value is a string, then the conversion is done - if possible.
Otherwise a VdtError is raised.
>>> vtor.check('integer', '-1')
-1
>>> vtor.check('integer', '0')
0
>>> vtor.check('integer', 9)
9
>>> vtor.check('integer', 'a')
Traceback (most recent call last):
VdtTypeError: the value "a" is of the wrong type.
>>> vtor.check('integer', '2.2')
Traceback (most recent call last):
VdtTypeError: the value "2.2" is of the wrong type.
>>> vtor.check('integer(10)', '20')
20
>>> vtor.check('integer(max=20)', '15')
15
>>> vtor.check('integer(10)', '9')
Traceback (most recent call last):
VdtValueTooSmallError: the value "9" is too small.
>>> vtor.check('integer(10)', 9)
Traceback (most recent call last):
VdtValueTooSmallError: the value "9" is too small.
>>> vtor.check('integer(max=20)', '35')
Traceback (most recent call last):
VdtValueTooBigError: the value "35" is too big.
>>> vtor.check('integer(max=20)', 35)
Traceback (most recent call last):
VdtValueTooBigError: the value "35" is too big.
>>> vtor.check('integer(0, 9)', False)
0 | A check that tests that a given value is an integer (int, or long)
and optionally, between bounds. A negative value is accepted, while
a float will fail.
If the value is a string, then the conversion is done - if possible.
Otherwise a VdtError is raised.
>>> vtor.check('integer', '-1')
-1
>>> vtor.check('integer', '0')
0
>>> vtor.check('integer', 9)
9
>>> vtor.check('integer', 'a')
Traceback (most recent call last):
VdtTypeError: the value "a" is of the wrong type.
>>> vtor.check('integer', '2.2')
Traceback (most recent call last):
VdtTypeError: the value "2.2" is of the wrong type.
>>> vtor.check('integer(10)', '20')
20
>>> vtor.check('integer(max=20)', '15')
15
>>> vtor.check('integer(10)', '9')
Traceback (most recent call last):
VdtValueTooSmallError: the value "9" is too small.
>>> vtor.check('integer(10)', 9)
Traceback (most recent call last):
VdtValueTooSmallError: the value "9" is too small.
>>> vtor.check('integer(max=20)', '35')
Traceback (most recent call last):
VdtValueTooBigError: the value "35" is too big.
>>> vtor.check('integer(max=20)', 35)
Traceback (most recent call last):
VdtValueTooBigError: the value "35" is too big.
>>> vtor.check('integer(0, 9)', False)
0 | [
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"""
A check that tests that a given value is an integer (int, or long)
and optionally, between bounds. A negative value is accepted, while
a float will fail.
If the value is a string, then the conversion is done - if possible.
Otherwise a VdtError is raised.
>>> vtor.check('integer', '-1')
-1
>>> vtor.check('integer', '0')
0
>>> vtor.check('integer', 9)
9
>>> vtor.check('integer', 'a')
Traceback (most recent call last):
VdtTypeError: the value "a" is of the wrong type.
>>> vtor.check('integer', '2.2')
Traceback (most recent call last):
VdtTypeError: the value "2.2" is of the wrong type.
>>> vtor.check('integer(10)', '20')
20
>>> vtor.check('integer(max=20)', '15')
15
>>> vtor.check('integer(10)', '9')
Traceback (most recent call last):
VdtValueTooSmallError: the value "9" is too small.
>>> vtor.check('integer(10)', 9)
Traceback (most recent call last):
VdtValueTooSmallError: the value "9" is too small.
>>> vtor.check('integer(max=20)', '35')
Traceback (most recent call last):
VdtValueTooBigError: the value "35" is too big.
>>> vtor.check('integer(max=20)', 35)
Traceback (most recent call last):
VdtValueTooBigError: the value "35" is too big.
>>> vtor.check('integer(0, 9)', False)
0
"""
(min_val, max_val) = _is_num_param(('min', 'max'), (min, max))
if not isinstance(value, (int, long, basestring)):
raise VdtTypeError(value)
if isinstance(value, basestring):
# if it's a string - does it represent an integer ?
try:
value = int(value)
except ValueError:
raise VdtTypeError(value)
if (min_val is not None) and (value < min_val):
raise VdtValueTooSmallError(value)
if (max_val is not None) and (value > max_val):
raise VdtValueTooBigError(value)
return value | [
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InsightSoftwareConsortium/ITK | 87acfce9a93d928311c38bc371b666b515b9f19d | Modules/ThirdParty/pygccxml/src/pygccxml/declarations/calldef.py | python | calldef_t._get__cmp__call_items | (self) | Implementation detail. | Implementation detail. | [
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PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | python/paddle/nn/layer/transformer.py | python | TransformerEncoder.gen_cache | (self, src) | return cache | r"""
Generates cache for `forward` usage. The generated cache is a list, and
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`TransformerEncoderLayer.gen_cache`. See `TransformerEncoderLayer.gen_cache`
for more details.
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src (Tensor): The input of Transformer encoder. It is a tensor
with shape `[batch_size, source_length, d_model]`. The data type
should be float32 or float64.
Returns:
list: It is a list, and each element in the list is `incremental_cache`
produced by `TransformerEncoderLayer.gen_cache`. See
`TransformerEncoderLayer.gen_cache` for more details. | r"""
Generates cache for `forward` usage. The generated cache is a list, and
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ap--/python-seabreeze | 86e9145edf7a30cedd4dffd4658a142aeab2d2fc | src/seabreeze/pyseabreeze/devices.py | python | SeaBreezeDevice.is_open | (self) | return self._transport.is_open | returns if the spectrometer device usb connection is opened
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SpenceKonde/megaTinyCore | 1c4a70b18a149fe6bcb551dfa6db11ca50b8997b | megaavr/tools/libs/pyedbglib/protocols/avr8protocol.py | python | Avr8Protocol.leave_progmode | (self) | Exits programming mode | Exits programming mode | [
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natanielruiz/android-yolo | 1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f | jni-build/jni/include/tensorflow/python/training/supervisor.py | python | Supervisor.start_standard_services | (self, sess) | return threads | Start the standard services for 'sess'.
This starts services in the background. The services started depend
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- A Summary thread computing summaries every save_summaries_secs.
- A Checkpoint thread saving the model every save_model_secs.
- A StepCounter thread measure step time.
Args:
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- A Summary thread computing summaries every save_summaries_secs.
- A Checkpoint thread saving the model every save_model_secs.
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] | https://github.com/natanielruiz/android-yolo/blob/1ebb54f96a67a20ff83ddfc823ed83a13dc3a47f/jni-build/jni/include/tensorflow/python/training/supervisor.py#L587-L638 | |
PaddlePaddle/Paddle | 1252f4bb3e574df80aa6d18c7ddae1b3a90bd81c | python/paddle/fluid/incubate/fleet/base/role_maker.py | python | GeneralRoleMaker._server_num | (self) | return len(self._server_endpoints) | return the current number of server | return the current number of server | [
"return",
"the",
"current",
"number",
"of",
"server"
] | def _server_num(self):
"""
return the current number of server
"""
if not self._role_is_generated:
self.generate_role()
return len(self._server_endpoints) | [
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p4lang/behavioral-model | 81ce0163f0770c6b9d6056a28ce2e0cc035bb6e9 | tools/runtime_CLI.py | python | RuntimeAPI.do_mc_node_dissociate | (self, line) | Dissociate node from multicast group: mc_node_associate <group handle> <node handle> | Dissociate node from multicast group: mc_node_associate <group handle> <node handle> | [
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">"
] | def do_mc_node_dissociate(self, line):
"Dissociate node from multicast group: mc_node_associate <group handle> <node handle>"
self.check_has_pre()
args = line.split()
self.exactly_n_args(args, 2)
mgrp = self.get_mgrp(args[0])
l1_hdl = self.get_node_handle(args[1])
print("Dissociating node", l1_hdl, "from multicast group", mgrp)
self.mc_client.bm_mc_node_dissociate(0, mgrp, l1_hdl) | [
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mhammond/pywin32 | 44afd86ba8485194df93234639243252deeb40d5 | Pythonwin/pywin/framework/scriptutils.py | python | GetActiveView | () | Gets the edit control (eg, EditView) with the focus, or None | Gets the edit control (eg, EditView) with the focus, or None | [
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"the",
"edit",
"control",
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")",
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] | def GetActiveView():
"""Gets the edit control (eg, EditView) with the focus, or None"""
try:
childFrame, bIsMaximised = win32ui.GetMainFrame().MDIGetActive()
return childFrame.GetActiveView()
except win32ui.error:
return None | [
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wlanjie/AndroidFFmpeg | 7baf9122f4b8e1c74e7baf4be5c422c7a5ba5aaf | tools/fdk-aac-build/x86/toolchain/lib/python2.7/threading.py | python | _Timer.cancel | (self) | Stop the timer if it hasn't finished yet | Stop the timer if it hasn't finished yet | [
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] | def cancel(self):
"""Stop the timer if it hasn't finished yet"""
self.finished.set() | [
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aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Gems/CloudGemFramework/v1/AWS/resource-manager-code/lib/setuptools/command/easy_install.py | python | _update_zipimporter_cache | (normalized_path, cache, updater=None) | Update zipimporter cache data for a given normalized path.
Any sub-path entries are processed as well, i.e. those corresponding to zip
archives embedded in other zip archives.
Given updater is a callable taking a cache entry key and the original entry
(after already removing the entry from the cache), and expected to update
the entry and possibly return a new one to be inserted in its place.
Returning None indicates that the entry should not be replaced with a new
one. If no updater is given, the cache entries are simply removed without
any additional processing, the same as if the updater simply returned None. | Update zipimporter cache data for a given normalized path. | [
"Update",
"zipimporter",
"cache",
"data",
"for",
"a",
"given",
"normalized",
"path",
"."
] | def _update_zipimporter_cache(normalized_path, cache, updater=None):
"""
Update zipimporter cache data for a given normalized path.
Any sub-path entries are processed as well, i.e. those corresponding to zip
archives embedded in other zip archives.
Given updater is a callable taking a cache entry key and the original entry
(after already removing the entry from the cache), and expected to update
the entry and possibly return a new one to be inserted in its place.
Returning None indicates that the entry should not be replaced with a new
one. If no updater is given, the cache entries are simply removed without
any additional processing, the same as if the updater simply returned None.
"""
for p in _collect_zipimporter_cache_entries(normalized_path, cache):
# N.B. pypy's custom zipimport._zip_directory_cache implementation does
# not support the complete dict interface:
# * Does not support item assignment, thus not allowing this function
# to be used only for removing existing cache entries.
# * Does not support the dict.pop() method, forcing us to use the
# get/del patterns instead. For more detailed information see the
# following links:
# https://github.com/pypa/setuptools/issues/202#issuecomment-202913420
# http://bit.ly/2h9itJX
old_entry = cache[p]
del cache[p]
new_entry = updater and updater(p, old_entry)
if new_entry is not None:
cache[p] = new_entry | [
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ChromiumWebApps/chromium | c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7 | third_party/jinja2/compiler.py | python | CodeGenerator.indent | (self) | Indent by one. | Indent by one. | [
"Indent",
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"."
] | def indent(self):
"""Indent by one."""
self._indentation += 1 | [
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] | https://github.com/ChromiumWebApps/chromium/blob/c7361d39be8abd1574e6ce8957c8dbddd4c6ccf7/third_party/jinja2/compiler.py#L451-L453 | ||
GoSSIP-SJTU/TripleDoggy | 03648d6b19c812504b14e8b98c8c7b3f443f4e54 | utils/lit/lit/util.py | python | to_bytes | (s) | return s.encode('utf-8') | Return the parameter as type 'bytes', possibly encoding it.
In Python2, the 'bytes' type is the same as 'str'. In Python3, they
are distinct. | Return the parameter as type 'bytes', possibly encoding it. | [
"Return",
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"parameter",
"as",
"type",
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] | def to_bytes(s):
"""Return the parameter as type 'bytes', possibly encoding it.
In Python2, the 'bytes' type is the same as 'str'. In Python3, they
are distinct.
"""
if isinstance(s, bytes):
# In Python2, this branch is taken for both 'str' and 'bytes'.
# In Python3, this branch is taken only for 'bytes'.
return s
# In Python2, 's' is a 'unicode' object.
# In Python3, 's' is a 'str' object.
# Encode to UTF-8 to get 'bytes' data.
return s.encode('utf-8') | [
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mysql/mysql-workbench | 2f35f9034f015cbcd22139a60e1baa2e3e8e795c | plugins/wb.admin/backend/wb_admin_perfschema_instrumentation_be.py | python | PSInstrumentGroup.set_initial_states | (self) | Deep first method to set the initial states of
the hierarchy groups based on the status of the
leaf elements | Deep first method to set the initial states of
the hierarchy groups based on the status of the
leaf elements | [
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] | def set_initial_states(self):
"""
Deep first method to set the initial states of
the hierarchy groups based on the status of the
leaf elements
"""
if '_data_' not in self:
for key in list(self.keys()):
self[key].set_initial_states()
self.set_state_from_children('enabled')
self.set_state_from_children('timed') | [
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CRYTEK/CRYENGINE | 232227c59a220cbbd311576f0fbeba7bb53b2a8c | Code/Tools/waf-1.7.13/waflib/Context.py | python | Context.recurse | (self, dirs, name=None, mandatory=True, once=True) | Run user code from the supplied list of directories.
The directories can be either absolute, or relative to the directory
of the wscript file. The methods :py:meth:`waflib.Context.Context.pre_recurse` and :py:meth:`waflib.Context.Context.post_recurse`
are called immediately before and after a script has been executed.
:param dirs: List of directories to visit
:type dirs: list of string or space-separated string
:param name: Name of function to invoke from the wscript
:type name: string
:param mandatory: whether sub wscript files are required to exist
:type mandatory: bool
:param once: read the script file once for a particular context
:type once: bool | Run user code from the supplied list of directories.
The directories can be either absolute, or relative to the directory
of the wscript file. The methods :py:meth:`waflib.Context.Context.pre_recurse` and :py:meth:`waflib.Context.Context.post_recurse`
are called immediately before and after a script has been executed. | [
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"""
Run user code from the supplied list of directories.
The directories can be either absolute, or relative to the directory
of the wscript file. The methods :py:meth:`waflib.Context.Context.pre_recurse` and :py:meth:`waflib.Context.Context.post_recurse`
are called immediately before and after a script has been executed.
:param dirs: List of directories to visit
:type dirs: list of string or space-separated string
:param name: Name of function to invoke from the wscript
:type name: string
:param mandatory: whether sub wscript files are required to exist
:type mandatory: bool
:param once: read the script file once for a particular context
:type once: bool
"""
try:
cache = self.recurse_cache
except AttributeError:
cache = self.recurse_cache = {}
for d in Utils.to_list(dirs):
if not os.path.isabs(d):
# absolute paths only
d = os.path.join(self.path.abspath(), d)
WSCRIPT = os.path.join(d, WSCRIPT_FILE)
WSCRIPT_FUN = WSCRIPT + '_' + (name or self.fun)
node = self.root.find_node(WSCRIPT_FUN)
if node and (not once or node not in cache):
cache[node] = True
self.pre_recurse(node)
try:
function_code = node.read('rU')
exec(compile(function_code, node.abspath(), 'exec'), self.exec_dict)
finally:
self.post_recurse(node)
elif not node:
node = self.root.find_node(WSCRIPT)
tup = (node, name or self.fun)
if node and (not once or tup not in cache):
cache[tup] = True
self.pre_recurse(node)
try:
wscript_module = load_module(node.abspath())
user_function = getattr(wscript_module, (name or self.fun), None)
if not user_function:
user_function = getattr(wscript_module, 'build', None) # Fall back to 'build' function
if not user_function:
if not mandatory:
continue
raise Errors.WafError('No function %s defined in %s' % (name or self.fun, node.abspath()))
self.execute_user_function( user_function, wscript_module )
finally:
self.post_recurse(node)
elif not node:
if not mandatory:
continue
raise Errors.WafError('No wscript file in directory %s' % d) | [
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adobe/chromium | cfe5bf0b51b1f6b9fe239c2a3c2f2364da9967d7 | third_party/closure_linter/closure_linter/indentation.py | python | IndentationRules.__init__ | (self) | Initializes the IndentationRules checker. | Initializes the IndentationRules checker. | [
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baidu/AnyQ | d94d450d2aaa5f7ed73424b10aa4539835b97527 | tools/simnet/train/tf/losses/simnet_loss.py | python | PairwiseHingeLoss.__init__ | (self, config) | init function | init function | [
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albertz/openlierox | d316c14a8eb57848ef56e9bfa7b23a56f694a51b | tools/DedicatedServerVideo/gdata/photos/service.py | python | ConvertAtomTimestampToEpoch | (timestamp) | return time.mktime(time.strptime(timestamp, '%Y-%m-%dT%H:%M:%S.000Z')) | Helper function to convert a timestamp string, for instance
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catboost/catboost | 167f64f237114a4d10b2b4ee42adb4569137debe | contrib/python/scipy/scipy/optimize/_differentialevolution.py | python | DifferentialEvolutionSolver._ensure_constraint | (self, trial) | make sure the parameters lie between the limits | make sure the parameters lie between the limits | [
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benoitsteiner/tensorflow-opencl | cb7cb40a57fde5cfd4731bc551e82a1e2fef43a5 | tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py | python | StreamingDataFeeder.__init__ | (self, x, y, n_classes, batch_size) | Initializes a StreamingDataFeeder instance.
Args:
x: iterator each element of which returns one feature sample. Sample can
be a Nd numpy matrix or dictionary of Nd numpy matrices.
y: iterator each element of which returns one label sample. Sample can be
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batch_size: Mini batch size to accumulate samples in one batch. If set
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x: input features (or dictionary of input features).
y: input label (or dictionary of output features).
n_classes: number of classes.
batch_size: mini batch size to accumulate.
input_shape: shape of the input (can be dictionary depending on `x`).
output_shape: shape of the output (can be dictionary depending on `y`).
input_dtype: dtype of input (can be dictionary depending on `x`).
output_dtype: dtype of output (can be dictionary depending on `y`). | Initializes a StreamingDataFeeder instance. | [
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"""Initializes a StreamingDataFeeder instance.
Args:
x: iterator each element of which returns one feature sample. Sample can
be a Nd numpy matrix or dictionary of Nd numpy matrices.
y: iterator each element of which returns one label sample. Sample can be
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n_classes: indicator of how many classes the corresponding label sample
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y: input label (or dictionary of output features).
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batch_size: mini batch size to accumulate.
input_shape: shape of the input (can be dictionary depending on `x`).
output_shape: shape of the output (can be dictionary depending on `y`).
input_dtype: dtype of input (can be dictionary depending on `x`).
output_dtype: dtype of output (can be dictionary depending on `y`).
"""
# pylint: disable=invalid-name,super-init-not-called
x_first_el = six.next(x)
self._x = itertools.chain([x_first_el], x)
if y is not None:
y_first_el = six.next(y)
self._y = itertools.chain([y_first_el], y)
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y_first_el = None
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x_first_el_shape = [1] + list(x_first_el.shape)
if y_is_dict:
y_first_el_shape = dict(
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elif y is None:
y_first_el_shape = None
else:
y_first_el_shape = ([1] + list(y_first_el[0].shape if isinstance(
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self.input_shape, self.output_shape, self._batch_size = _get_in_out_shape(
x_first_el_shape, y_first_el_shape, n_classes, batch_size)
# Input dtype of x_first_el.
if x_is_dict:
self._input_dtype = dict(
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else:
self._input_dtype = _check_dtype(x_first_el.dtype)
# Output dtype of y_first_el.
def check_y_dtype(el):
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return check_y_dtype(el[0])
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self._output_dtype = np.float32
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self._output_dtype = dict(
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baidu-research/tensorflow-allreduce | 66d5b855e90b0949e9fa5cca5599fd729a70e874 | tensorflow/contrib/rnn/python/ops/rnn_cell.py | python | LayerNormBasicLSTMCell.__init__ | (self, num_units, forget_bias=1.0,
input_size=None, activation=math_ops.tanh,
layer_norm=True, norm_gain=1.0, norm_shift=0.0,
dropout_keep_prob=1.0, dropout_prob_seed=None,
reuse=None) | Initializes the basic LSTM cell.
Args:
num_units: int, The number of units in the LSTM cell.
forget_bias: float, The bias added to forget gates (see above).
input_size: Deprecated and unused.
activation: Activation function of the inner states.
layer_norm: If `True`, layer normalization will be applied.
norm_gain: float, The layer normalization gain initial value. If
`layer_norm` has been set to `False`, this argument will be ignored.
norm_shift: float, The layer normalization shift initial value. If
`layer_norm` has been set to `False`, this argument will be ignored.
dropout_keep_prob: unit Tensor or float between 0 and 1 representing the
recurrent dropout probability value. If float and 1.0, no dropout will
be applied.
dropout_prob_seed: (optional) integer, the randomness seed.
reuse: (optional) Python boolean describing whether to reuse variables
in an existing scope. If not `True`, and the existing scope already has
the given variables, an error is raised. | Initializes the basic LSTM cell. | [
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input_size=None, activation=math_ops.tanh,
layer_norm=True, norm_gain=1.0, norm_shift=0.0,
dropout_keep_prob=1.0, dropout_prob_seed=None,
reuse=None):
"""Initializes the basic LSTM cell.
Args:
num_units: int, The number of units in the LSTM cell.
forget_bias: float, The bias added to forget gates (see above).
input_size: Deprecated and unused.
activation: Activation function of the inner states.
layer_norm: If `True`, layer normalization will be applied.
norm_gain: float, The layer normalization gain initial value. If
`layer_norm` has been set to `False`, this argument will be ignored.
norm_shift: float, The layer normalization shift initial value. If
`layer_norm` has been set to `False`, this argument will be ignored.
dropout_keep_prob: unit Tensor or float between 0 and 1 representing the
recurrent dropout probability value. If float and 1.0, no dropout will
be applied.
dropout_prob_seed: (optional) integer, the randomness seed.
reuse: (optional) Python boolean describing whether to reuse variables
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the given variables, an error is raised.
"""
super(LayerNormBasicLSTMCell, self).__init__(_reuse=reuse)
if input_size is not None:
logging.warn("%s: The input_size parameter is deprecated.", self)
self._num_units = num_units
self._activation = activation
self._forget_bias = forget_bias
self._keep_prob = dropout_keep_prob
self._seed = dropout_prob_seed
self._layer_norm = layer_norm
self._g = norm_gain
self._b = norm_shift
self._reuse = reuse | [
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cornell-zhang/heterocl | 6d9e4b4acc2ee2707b2d25b27298c0335bccedfd | python/heterocl/schedule.py | python | Schedule.reshape | (self, target, shape) | Reshape a Tensor to a specified new shape
Parameters
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target : Tensor
The tensor to be reshaped
shape : tuple of int
The new shape of the tensor | Reshape a Tensor to a specified new shape | [
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"""Reshape a Tensor to a specified new shape
Parameters
----------
target : Tensor
The tensor to be reshaped
shape : tuple of int
The new shape of the tensor
"""
try:
target = target.tensor
except (AttributeError, ValueError):
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target = target._op
except AttributeError:
pass
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] | https://github.com/cornell-zhang/heterocl/blob/6d9e4b4acc2ee2707b2d25b27298c0335bccedfd/python/heterocl/schedule.py#L654-L672 | ||
aws/lumberyard | f85344403c1c2e77ec8c75deb2c116e97b713217 | dev/Tools/Python/3.7.10/mac/Python.framework/Versions/3.7/lib/python3.7/posixpath.py | python | ismount | (path) | return False | Test whether a path is a mount point | Test whether a path is a mount point | [
"Test",
"whether",
"a",
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"is",
"a",
"mount",
"point"
] | def ismount(path):
"""Test whether a path is a mount point"""
try:
s1 = os.lstat(path)
except OSError:
# It doesn't exist -- so not a mount point. :-)
return False
else:
# A symlink can never be a mount point
if stat.S_ISLNK(s1.st_mode):
return False
if isinstance(path, bytes):
parent = join(path, b'..')
else:
parent = join(path, '..')
parent = realpath(parent)
try:
s2 = os.lstat(parent)
except OSError:
return False
dev1 = s1.st_dev
dev2 = s2.st_dev
if dev1 != dev2:
return True # path/.. on a different device as path
ino1 = s1.st_ino
ino2 = s2.st_ino
if ino1 == ino2:
return True # path/.. is the same i-node as path
return False | [
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openvinotoolkit/openvino | dedcbeafa8b84cccdc55ca64b8da516682b381c7 | docs/nbdoc/nbdoc.py | python | NbProcessor.fetch_binder_list | (self, file_format: str = 'txt') | Funtion that fetches list of notebooks with binder buttons
:param file_format: Format of file containing list of notebooks with button. Defaults to 'txt'
:type file_format: str
:return: List of notebooks conaining binder buttons
:rtype: list | Funtion that fetches list of notebooks with binder buttons | [
"Funtion",
"that",
"fetches",
"list",
"of",
"notebooks",
"with",
"binder",
"buttons"
] | def fetch_binder_list(self, file_format: str = 'txt') -> list:
"""Funtion that fetches list of notebooks with binder buttons
:param file_format: Format of file containing list of notebooks with button. Defaults to 'txt'
:type file_format: str
:return: List of notebooks conaining binder buttons
:rtype: list
"""
list_of_buttons = glob(f"{self.nb_path}/*.{file_format}")
if list_of_buttons:
with open(list_of_buttons[0]) as file:
list_of_buttons = file.read().splitlines()
return list_of_buttons
else:
return [] | [
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wxWidgets/wxPython-Classic | 19571e1ae65f1ac445f5491474121998c97a1bf0 | samples/ide/activegrid/tool/SVNService.py | python | ReadSvnUrlList | () | return urlList | Read in list of SNV repository URLs. First in list is the last one path used. | Read in list of SNV repository URLs. First in list is the last one path used. | [
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"URLs",
".",
"First",
"in",
"list",
"is",
"the",
"last",
"one",
"path",
"used",
"."
] | def ReadSvnUrlList():
""" Read in list of SNV repository URLs. First in list is the last one path used. """
config = wx.ConfigBase_Get()
urlStringList = config.Read(SVN_REPOSITORY_URL)
if len(urlStringList):
urlList = eval(urlStringList)
else:
urlList = []
if len(urlList) == 0:
svnService = wx.GetApp().GetService(SVNService)
if svnService and hasattr(svnService, "_defaultURL"):
urlList.append(svnService._defaultURL)
return urlList | [
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