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pandas-dev/pandas | 5ba7d714014ae8feaccc0dd4a98890828cf2832d | pandas/core/internals/managers.py | python | SingleBlockManager.internal_values | (self) | return self._block.values | The array that Series._values returns | The array that Series._values returns | [
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kanzure/nanoengineer | 874e4c9f8a9190f093625b267f9767e19f82e6c4 | cad/src/foundation/whatsthis_utilities.py | python | refix_whatsthis_text_and_links | ( ) | return | [public] | [public] | [
"[",
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] | def refix_whatsthis_text_and_links( ): #bruce 060319 part of fixing bug 1421
"""
[public]
"""
win = env.mainwindow()
fix_QAction_whatsthis(win.editUndoAction)
fix_QAction_whatsthis(win.editRedoAction)
return | [
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buke/GreenOdoo | 3d8c55d426fb41fdb3f2f5a1533cfe05983ba1df | runtime/python/lib/python2.7/mailbox.py | python | _ProxyFile._read | (self, size, read_method) | return result | Read size bytes using read_method. | Read size bytes using read_method. | [
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osmr/imgclsmob | f2993d3ce73a2f7ddba05da3891defb08547d504 | pytorch/pytorchcv/models/resnet.py | python | resnetbc26b | (**kwargs) | return get_resnet(blocks=26, bottleneck=True, conv1_stride=False, model_name="resnetbc26b", **kwargs) | ResNet-BC-26b model from 'Deep Residual Learning for Image Recognition,' https://arxiv.org/abs/1512.03385.
It's an experimental model (bottleneck compressed).
Parameters:
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pretrained : bool, default False
Whether to load the pretrained weights for model.
root : str, default '~/.torch/models'
Location for keeping the model parameters. | ResNet-BC-26b model from 'Deep Residual Learning for Image Recognition,' https://arxiv.org/abs/1512.03385.
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Parameters:
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bhoov/exbert | d27b6236aa51b185f7d3fed904f25cabe3baeb1a | server/transformers/src/transformers/tokenization_gpt2.py | python | GPT2Tokenizer._convert_token_to_id | (self, token) | return self.encoder.get(token, self.encoder.get(self.unk_token)) | Converts a token (str) in an id using the vocab. | Converts a token (str) in an id using the vocab. | [
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CarlosGS/Cyclone-PCB-Factory | 2d3136de424a94ea3579a24caf167e540daf0cad | Software/PythonScripts/Replath/pyRepRap/reprap/snap.py | python | _breakHDB1 | (HDB1) | return NDB | Decode Header Byte 1 (HDB1) | Decode Header Byte 1 (HDB1) | [
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chribsen/simple-machine-learning-examples | dc94e52a4cebdc8bb959ff88b81ff8cfeca25022 | venv/lib/python2.7/site-packages/numpy/lib/npyio.py | python | mafromtxt | (fname, **kwargs) | return genfromtxt(fname, **kwargs) | Load ASCII data stored in a text file and return a masked array.
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fname, kwargs : For a description of input parameters, see `genfromtxt`.
See Also
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hatRiot/zarp | 2e772350a01c2aeed3f4da9685cd0cc5d6b3ecad | src/core/config.py | python | get | (key) | Fetch a config value
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python-diamond/Diamond | 7000e16cfdf4508ed9291fc4b3800592557b2431 | src/collectors/sidekiq/sidekiq.py | python | SidekiqCollector.__publish | (self, port, db, queue, queue_length) | :param port: Redis port
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misterch0c/shadowbroker | e3a069bea47a2c1009697941ac214adc6f90aa8d | windows/Resources/Python/Core/Lib/heapq.py | python | merge | (*iterables) | Merge multiple sorted inputs into a single sorted output.
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GetStream/stream-python | 142b5b43c0a60a96c36f25b6fc5a224dd2e418cc | stream/client.py | python | StreamClient.track_impressions | (self, impressions) | Creates a list of impressions
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auth_token = self.create_jwt_token("*", "*", feed_id="*")
self.post("impression/", auth_token, data=impressions, service_name="analytics") | [
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vlachoudis/bCNC | 67126b4894dabf6579baf47af8d0f9b7de35e6e3 | bCNC/lib/tkExtra.py | python | ProgressBar.autoText | (self, tmsg) | [] | def autoText(self, tmsg):
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IronLanguages/main | a949455434b1fda8c783289e897e78a9a0caabb5 | External.LCA_RESTRICTED/Languages/IronPython/repackage/pip/pip/_vendor/distlib/_backport/shutil.py | python | _get_uid | (name) | return None | Returns an uid, given a user name. | Returns an uid, given a user name. | [
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ARM-DOE/pyart | 72affe5b669f1996cd3cc39ec7d8dd29b838bd48 | pyart/util/circular_stats.py | python | mean_of_two_angles_deg | (angle1, angle2) | return np.rad2deg(
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Parameters
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angle1 : array
First set of angles in degrees.
angle2 : array
Second set of angles in degrees.
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angle1 : array
First set of angles in degrees.
angle2 : array
Second set of angles in degrees.
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return np.rad2deg(
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debian-calibre/calibre | 020fc81d3936a64b2ac51459ecb796666ab6a051 | src/calibre/gui2/dialogs/quickview.py | python | Quickview.book_was_changed | (self, mi) | Called when book information is changed in the library view. Make that
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'''
Called when book information is changed in the library view. Make that
book info current. This means that prev and next in edit metadata will move
the current book and change quickview
'''
if self.is_closed or self.current_column is None or not self.follow_library_view:
return
# There is an ordering problem when libraries are changed. The library
# view is changed, triggering a book_was_changed signal. Unfortunately
# this happens before the library_changed actions are run, meaning we
# still have the old database. To avoid the problem we just ignore the
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try:
self.refresh(self.view.model().index(self.db.row(mi.id), self.current_column))
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windelbouwman/ppci | 915c069e0667042c085ec42c78e9e3c9a5295324 | ppci/ir.py | python | SubRoutine.add_parameter | (self, parameter) | Add an argument to this function | Add an argument to this function | [
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stellargraph/stellargraph | 3c2c8c18ab4c5c16660f350d8e23d7dc39e738de | stellargraph/interpretability/saliency_maps/integrated_gradients.py | python | IntegratedGradients.__init__ | (self, model, generator) | Args:
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- adj: The placeholder of the adjacency matrix.
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- adj_values: The placeholder of the adjacency matrix.
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- adj: The placeholder of the adjacency matrix.
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- adj_index: The placeholder of the adjacency matrix.
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bokeh/bokeh | a00e59da76beb7b9f83613533cfd3aced1df5f06 | bokeh/model/util.py | python | visit_immediate_value_references | (value: Unknown, visitor: Callable[[Model], None]) | Visit all references to another Model without recursing into any
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QCoDeS/Qcodes | 3cda2cef44812e2aa4672781f2423bf5f816f9f9 | qcodes/instrument_drivers/tektronix/AWG5014.py | python | Tektronix_AWG5014.stop | (self) | This command stops the output of a waveform or a sequence. | This command stops the output of a waveform or a sequence. | [
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"""This command stops the output of a waveform or a sequence."""
self.write('AWGControl:STOP') | [
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lovelylain/pyctp | fd304de4b50c4ddc31a4190b1caaeb5dec66bc5d | example/ctp/futures/ApiStruct.py | python | ExchangeMarginRate.__init__ | (self, BrokerID='', InstrumentID='', HedgeFlag=HF_Speculation, LongMarginRatioByMoney=0.0, LongMarginRatioByVolume=0.0, ShortMarginRatioByMoney=0.0, ShortMarginRatioByVolume=0.0) | [] | def __init__(self, BrokerID='', InstrumentID='', HedgeFlag=HF_Speculation, LongMarginRatioByMoney=0.0, LongMarginRatioByVolume=0.0, ShortMarginRatioByMoney=0.0, ShortMarginRatioByVolume=0.0):
self.BrokerID = '' #经纪公司代码, char[11]
self.InstrumentID = '' #合约代码, char[31]
self.HedgeFlag = '' #投机套保标志, char
self.LongMarginRatioByMoney = 'Ratio' #多头保证金率, double
self.LongMarginRatioByVolume = 'Money' #多头保证金费, double
self.ShortMarginRatioByMoney = 'Ratio' #空头保证金率, double
self.ShortMarginRatioByVolume = 'Money' | [
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electricitymap/electricitymap-contrib | 3099e873e1da4c95c7c86f7b14a4e2ac13094cc8 | parsers/US_HI.py | python | fetch_production | (zone_key='US-HI-OA', session=None,
target_datetime: datetime.datetime = None,
logger: logging.Logger = logging.getLogger(__name__)) | return data | Requests the last known production mix (in MW) of a given country. | Requests the last known production mix (in MW) of a given country. | [
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logger: logging.Logger = logging.getLogger(__name__)) -> dict:
"""Requests the last known production mix (in MW) of a given country."""
r = session or requests.session()
if target_datetime is None:
request_dt = arrow.now("Pacific/Honolulu")
res = r.get(BASE_URL + 'limit=1').json()
raw_data = res[0]
# the first entry returned by the API always has a UTC datetime, but any additional entries are in HST (local time)
raw_data['dateTime'] = arrow.get(raw_data['dateTime']).to(tz="Pacific/Honolulu").datetime
else:
request_dt = arrow.get(target_datetime).to(tz="Pacific/Honolulu")
raw_data = get_historical_prod(r, request_dt)
if raw_data is None:
return None
energy_dt = arrow.get(raw_data['dateTime'], tzinfo='Pacific/Honolulu')
if validate_prod_timestamp(logger, energy_dt, request_dt) is False:
return None
production = {
'biomass': float(raw_data['Waste2Energy'] + raw_data['BioFuel']),
'coal': float(raw_data['Coal']),
'oil': float(raw_data['Fossil_Fuel']),
'solar': float(raw_data['Solar']),
'wind': float(raw_data['WindFarm'])
}
data = {
'zoneKey': zone_key,
'production': production,
'datetime': energy_dt.datetime,
'storage': {},
'source': 'islandpulse.org'
}
return data | [
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Calysto/calysto_scheme | 15bf81987870bcae1264e5a0a06feb9a8ee12b8b | calysto_scheme/scheme.py | python | b_cont2_78_d | (k) | [] | def b_cont2_78_d(k):
GLOBALS['value1_reg'] = binding_docstring(value1_reg)
GLOBALS['k_reg'] = k
GLOBALS['pc'] = apply_cont2 | [
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ktbyers/pynet | f01ca44afe1db1e64828fc93028f67410174719e | netmiko/load_bgp_config_part3.py | python | check_bgp | (net_connect, cmd='show run | inc router bgp') | return 'bgp' in output | Check whether BGP is currently configured on device. Return boolean | Check whether BGP is currently configured on device. Return boolean | [
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output = net_connect.send_command_expect(cmd)
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krintoxi/NoobSec-Toolkit | 38738541cbc03cedb9a3b3ed13b629f781ad64f6 | NoobSecToolkit /tools/inject/plugins/dbms/postgresql/takeover.py | python | Takeover.udfSetRemotePath | (self) | [] | def udfSetRemotePath(self):
# On Windows
if Backend.isOs(OS.WINDOWS):
# The DLL can be in any folder where postgres user has
# read/write/execute access is valid
# NOTE: by not specifing any path, it will save into the
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# C:\Program Files\PostgreSQL\8.3\data.
self.udfRemoteFile = "%s.%s" % (self.udfSharedLibName, self.udfSharedLibExt)
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# The SO can be in any folder where postgres user has
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self.udfRemoteFile = "/tmp/%s.%s" % (self.udfSharedLibName, self.udfSharedLibExt) | [
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cloudera/hue | 23f02102d4547c17c32bd5ea0eb24e9eadd657a4 | desktop/core/ext-py/python-openid-2.2.5/openid/extensions/ax.py | python | FetchResponse.__init__ | (self, request=None, update_url=None) | @param request: When supplied, I will use namespace aliases
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projecthamster/hamster | 19d160090de30e756bdc3122ff935bdaa86e2843 | waflib/Logs.py | python | log_handler.emit | (self, record) | Delegates the functionality to :py:meth:`waflib.Log.log_handler.emit_override` | Delegates the functionality to :py:meth:`waflib.Log.log_handler.emit_override` | [
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# default implementation
try:
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self.stream = record.stream
except AttributeError:
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record.stream = self.stream = sys.stderr
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record.stream = self.stream = sys.stdout
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self.flush()
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self.handleError(record) | [
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Tautulli/Tautulli | 2410eb33805aaac4bd1c5dad0f71e4f15afaf742 | lib/packaging/version.py | python | _legacy_cmpkey | (version: str) | return epoch, tuple(parts) | [] | def _legacy_cmpkey(version: str) -> LegacyCmpKey:
# We hardcode an epoch of -1 here. A PEP 440 version can only have a epoch
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parts: List[str] = []
for part in _parse_version_parts(version.lower()):
if part.startswith("*"):
# remove "-" before a prerelease tag
if part < "*final":
while parts and parts[-1] == "*final-":
parts.pop()
# remove trailing zeros from each series of numeric parts
while parts and parts[-1] == "00000000":
parts.pop()
parts.append(part)
return epoch, tuple(parts) | [
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buke/GreenOdoo | 3d8c55d426fb41fdb3f2f5a1533cfe05983ba1df | runtime/python/lib/python2.7/lib2to3/pgen2/driver.py | python | Driver.parse_stream_raw | (self, stream, debug=False) | return self.parse_tokens(tokens, debug) | Parse a stream and return the syntax tree. | Parse a stream and return the syntax tree. | [
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timknip/pyswf | 3740cc80d7650156831e728ea0d408819e5671eb | swf/movie.py | python | SWF.parse | (self, data) | Parses the SWF.
The @data parameter can be a file object or a SWFStream | Parses the SWF.
The | [
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] | def parse(self, data):
"""
Parses the SWF.
The @data parameter can be a file object or a SWFStream
"""
self._data = data = data if isinstance(data, SWFStream) else SWFStream(data)
self._header = SWFHeader(self._data)
if self._header.compressed:
temp = BytesIO()
if self._header.compressed_zlib:
import zlib
data = data.f.read()
zip = zlib.decompressobj()
temp.write(zip.decompress(data))
else:
import pylzma
data.readUI32() #consume compressed length
data = data.f.read()
temp.write(pylzma.decompress(data))
temp.seek(0)
data = SWFStream(temp)
self._header._frame_size = data.readRECT()
self._header._frame_rate = data.readFIXED8()
self._header._frame_count = data.readUI16()
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pypa/pip | 7f8a6844037fb7255cfd0d34ff8e8cf44f2598d4 | src/pip/_vendor/pkg_resources/__init__.py | python | ResourceManager.resource_filename | (self, package_or_requirement, resource_name) | return get_provider(package_or_requirement).get_resource_filename(
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wistbean/learn_python3_spider | 73c873f4845f4385f097e5057407d03dd37a117b | stackoverflow/venv/lib/python3.6/site-packages/zope/interface/interfaces.py | python | IDeclaration.__iter__ | () | Return an iterator for the interfaces in the specification | Return an iterator for the interfaces in the specification | [
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modflowpy/flopy | eecd1ad193c5972093c9712e5c4b7a83284f0688 | flopy/modpath/mp7particlegroup.py | python | ParticleGroupLRCTemplate.write | (self, fp=None, ws=".") | return | Parameters
----------
fp : fileobject
Fileobject that is open with write access
ws : str
Workspace for particle data
Returns
------- | [] | def write(self, fp=None, ws="."):
"""
Parameters
----------
fp : fileobject
Fileobject that is open with write access
ws : str
Workspace for particle data
Returns
-------
"""
# validate that a valid file object was passed
if not hasattr(fp, "write"):
raise ValueError(
"{}: cannot write data for template without passing a valid "
"file object ({}) open for writing".format(self.name, fp)
)
# call base class write method to write common data
_Modpath7ParticleGroup.write(self, fp, ws)
# open external file if required
if self.external:
fpth = os.path.join(ws, self.filename)
f = open(fpth, "w")
else:
f = fp
# item 1
f.write(f"{self.inputstyle}\n")
# items 2, 3, 4 or 5, and 6
self.particledata.write(f)
# close the external file
if self.external:
f.close()
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accel-brain/accel-brain-code | 86f489dc9be001a3bae6d053f48d6b57c0bedb95 | Algorithm-Wars/algowars/noisesampler/volatility_conditional_noise_sampler.py | python | VolatilityConditionalNoiseSampler.__init__ | (
self,
extractable_historical_data,
ticker_list,
start_date,
end_date,
batch_size=20,
seq_len=10,
channel=3,
target_features_list=None,
diff_mode=False,
log_mode=False,
lstm_mode=True,
ctx=mx.gpu()
) | Init.
Args:
extractable_historical_data: is-a `ExtractableHistoricalData`.
ticker_list: `list` of tickers.
start_date: `str` of start date.
end_date: `str` of end date.
batch_size: Batch size.
seq_len: The length of sequneces.
channel: Channel.
target_features_list: `list` of target feature list.
diff_mode: `bool`. If `True`, this class outputs difference sequences.
log_mode: `bool`. If `True`, this class outputs logarithmic rates of change.
lstm_mode: `bool`. If `True`, this class converts data for LSTM model.
ctx: `mx.gpu()` or `mx.cpu()`. | Init. | [
"Init",
"."
] | def __init__(
self,
extractable_historical_data,
ticker_list,
start_date,
end_date,
batch_size=20,
seq_len=10,
channel=3,
target_features_list=None,
diff_mode=False,
log_mode=False,
lstm_mode=True,
ctx=mx.gpu()
):
'''
Init.
Args:
extractable_historical_data: is-a `ExtractableHistoricalData`.
ticker_list: `list` of tickers.
start_date: `str` of start date.
end_date: `str` of end date.
batch_size: Batch size.
seq_len: The length of sequneces.
channel: Channel.
target_features_list: `list` of target feature list.
diff_mode: `bool`. If `True`, this class outputs difference sequences.
log_mode: `bool`. If `True`, this class outputs logarithmic rates of change.
lstm_mode: `bool`. If `True`, this class converts data for LSTM model.
ctx: `mx.gpu()` or `mx.cpu()`.
'''
if isinstance(extractable_historical_data, ExtractableHistoricalData) is False:
raise TypeError()
self.__extractable_historical_data = extractable_historical_data
self.__batch_size = batch_size
self.__seq_len = seq_len
self.__channel = channel
self.__ticker_list = ticker_list
self.__start_date = start_date
self.__end_date = end_date
if target_features_list is not None:
self.__target_features_list = target_features_list
self.__dim = len(self.__target_features_list)
if lstm_mode is True and len(target_features_list) > 1:
raise ValueError()
self.__lstm_mode = lstm_mode
self.__diff_mode = diff_mode
self.__normlized_flag = False
self.__log_mode = log_mode
self.setup_data()
self.__ctx = ctx | [
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pyparallel/pyparallel | 11e8c6072d48c8f13641925d17b147bf36ee0ba3 | Lib/site-packages/numpy-1.10.0.dev0_046311a-py3.3-win-amd64.egg/numpy/ma/extras.py | python | notmasked_edges | (a, axis=None) | return [tuple([idx[i].min(axis).compressed() for i in range(a.ndim)]),
tuple([idx[i].max(axis).compressed() for i in range(a.ndim)]), ] | Find the indices of the first and last unmasked values along an axis.
If all values are masked, return None. Otherwise, return a list
of two tuples, corresponding to the indices of the first and last
unmasked values respectively.
Parameters
----------
a : array_like
The input array.
axis : int, optional
Axis along which to perform the operation.
If None (default), applies to a flattened version of the array.
Returns
-------
edges : ndarray or list
An array of start and end indexes if there are any masked data in
the array. If there are no masked data in the array, `edges` is a
list of the first and last index.
See Also
--------
flatnotmasked_contiguous, flatnotmasked_edges, notmasked_contiguous,
clump_masked, clump_unmasked
Examples
--------
>>> a = np.arange(9).reshape((3, 3))
>>> m = np.zeros_like(a)
>>> m[1:, 1:] = 1
>>> am = np.ma.array(a, mask=m)
>>> np.array(am[~am.mask])
array([0, 1, 2, 3, 6])
>>> np.ma.notmasked_edges(ma)
array([0, 6]) | Find the indices of the first and last unmasked values along an axis. | [
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"""
Find the indices of the first and last unmasked values along an axis.
If all values are masked, return None. Otherwise, return a list
of two tuples, corresponding to the indices of the first and last
unmasked values respectively.
Parameters
----------
a : array_like
The input array.
axis : int, optional
Axis along which to perform the operation.
If None (default), applies to a flattened version of the array.
Returns
-------
edges : ndarray or list
An array of start and end indexes if there are any masked data in
the array. If there are no masked data in the array, `edges` is a
list of the first and last index.
See Also
--------
flatnotmasked_contiguous, flatnotmasked_edges, notmasked_contiguous,
clump_masked, clump_unmasked
Examples
--------
>>> a = np.arange(9).reshape((3, 3))
>>> m = np.zeros_like(a)
>>> m[1:, 1:] = 1
>>> am = np.ma.array(a, mask=m)
>>> np.array(am[~am.mask])
array([0, 1, 2, 3, 6])
>>> np.ma.notmasked_edges(ma)
array([0, 6])
"""
a = asarray(a)
if axis is None or a.ndim == 1:
return flatnotmasked_edges(a)
m = getmaskarray(a)
idx = array(np.indices(a.shape), mask=np.asarray([m] * a.ndim))
return [tuple([idx[i].min(axis).compressed() for i in range(a.ndim)]),
tuple([idx[i].max(axis).compressed() for i in range(a.ndim)]), ] | [
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freedombox/FreedomBox | 335a7f92cc08f27981f838a7cddfc67740598e54 | plinth/modules/backups/repository.py | python | BaseBorgRepository._run | (self, cmd, arguments, superuser=True, **kwargs) | Run a backups or sshfs action script command. | Run a backups or sshfs action script command. | [
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openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | openshift/installer/vendored/openshift-ansible-3.10.0-0.29.0/roles/lib_vendored_deps/library/oc_serviceaccount.py | python | ServiceAccount.secrets | (self) | return self._secrets | property for secrets | property for secrets | [
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''' property for secrets '''
if not self._secrets:
self._secrets = self.get(ServiceAccount.secrets_path) or []
return self._secrets | [
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oilshell/oil | 94388e7d44a9ad879b12615f6203b38596b5a2d3 | Python-2.7.13/Lib/urllib2.py | python | CacheFTPHandler.setMaxConns | (self, m) | [] | def setMaxConns(self, m):
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prlz77/ResNeXt.pytorch | 39fb8d03847f26ec02fb9b880ecaaa88db7a7d16 | models/model.py | python | ResNeXtBottleneck.__init__ | (self, in_channels, out_channels, stride, cardinality, base_width, widen_factor) | Constructor
Args:
in_channels: input channel dimensionality
out_channels: output channel dimensionality
stride: conv stride. Replaces pooling layer.
cardinality: num of convolution groups.
base_width: base number of channels in each group.
widen_factor: factor to reduce the input dimensionality before convolution. | Constructor | [
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] | def __init__(self, in_channels, out_channels, stride, cardinality, base_width, widen_factor):
""" Constructor
Args:
in_channels: input channel dimensionality
out_channels: output channel dimensionality
stride: conv stride. Replaces pooling layer.
cardinality: num of convolution groups.
base_width: base number of channels in each group.
widen_factor: factor to reduce the input dimensionality before convolution.
"""
super(ResNeXtBottleneck, self).__init__()
width_ratio = out_channels / (widen_factor * 64.)
D = cardinality * int(base_width * width_ratio)
self.conv_reduce = nn.Conv2d(in_channels, D, kernel_size=1, stride=1, padding=0, bias=False)
self.bn_reduce = nn.BatchNorm2d(D)
self.conv_conv = nn.Conv2d(D, D, kernel_size=3, stride=stride, padding=1, groups=cardinality, bias=False)
self.bn = nn.BatchNorm2d(D)
self.conv_expand = nn.Conv2d(D, out_channels, kernel_size=1, stride=1, padding=0, bias=False)
self.bn_expand = nn.BatchNorm2d(out_channels)
self.shortcut = nn.Sequential()
if in_channels != out_channels:
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self.shortcut.add_module('shortcut_bn', nn.BatchNorm2d(out_channels)) | [
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spender-sandbox/cuckoo-modified | eb93ef3d41b8fee51b4330306dcd315d8101e021 | lib/cuckoo/common/peepdf/PDFCore.py | python | PDFDictionary.getURLs | (self) | return self.urlsFound | Gets the URLs of the object
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NVlabs/STEP | 59da38af240869fa6f1bc565803cff34aafdaa99 | external/ActivityNet/Evaluation/ava/np_box_mask_list_ops.py | python | intersection | (box_mask_list1, box_mask_list2) | return np_mask_ops.intersection(box_mask_list1.get_masks(),
box_mask_list2.get_masks()) | Compute pairwise intersection areas between masks.
Args:
box_mask_list1: BoxMaskList holding N boxes and masks
box_mask_list2: BoxMaskList holding M boxes and masks
Returns:
a numpy array with shape [N*M] representing pairwise intersection area | Compute pairwise intersection areas between masks. | [
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Args:
box_mask_list1: BoxMaskList holding N boxes and masks
box_mask_list2: BoxMaskList holding M boxes and masks
Returns:
a numpy array with shape [N*M] representing pairwise intersection area
"""
return np_mask_ops.intersection(box_mask_list1.get_masks(),
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larryhastings/gilectomy | 4315ec3f1d6d4f813cc82ce27a24e7f784dbfc1a | Lib/macpath.py | python | expandvars | (path) | return path | Dummy to retain interface-compatibility with other operating systems. | Dummy to retain interface-compatibility with other operating systems. | [
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mrlesmithjr/Ansible | d44f0dc0d942bdf3bf7334b307e6048f0ee16e36 | roles/ansible-vsphere-management/scripts/pdns/lib/python2.7/site-packages/setuptools/command/easy_install.py | python | samefile | (p1, p2) | return norm_p1 == norm_p2 | Determine if two paths reference the same file.
Augments os.path.samefile to work on Windows and
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"""
Determine if two paths reference the same file.
Augments os.path.samefile to work on Windows and
suppresses errors if the path doesn't exist.
"""
both_exist = os.path.exists(p1) and os.path.exists(p2)
use_samefile = hasattr(os.path, 'samefile') and both_exist
if use_samefile:
return os.path.samefile(p1, p2)
norm_p1 = os.path.normpath(os.path.normcase(p1))
norm_p2 = os.path.normpath(os.path.normcase(p2))
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larryhastings/gilectomy | 4315ec3f1d6d4f813cc82ce27a24e7f784dbfc1a | Lib/asyncio/selector_events.py | python | BaseSelectorEventLoop.remove_reader | (self, fd) | Remove a reader callback. | Remove a reader callback. | [
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] | def remove_reader(self, fd):
"""Remove a reader callback."""
if self.is_closed():
return False
try:
key = self._selector.get_key(fd)
except KeyError:
return False
else:
mask, (reader, writer) = key.events, key.data
mask &= ~selectors.EVENT_READ
if not mask:
self._selector.unregister(fd)
else:
self._selector.modify(fd, mask, (None, writer))
if reader is not None:
reader.cancel()
return True
else:
return False | [
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natashamjaques/neural_chat | ddb977bb4602a67c460d02231e7bbf7b2cb49a97 | ParlAI/parlai/mturk/core/worlds.py | python | MTurkTaskWorld.review_work | (self) | Programmatically approve/reject the turker's work. Doing this now
(if possible) means that you don't need to do the work of reviewing
later on.
For example:
.. code-block:: python
if self.turker_response == '0':
self.mturk_agent.reject_work(
'You rated our model's response as a 0/10 but we '
'know we\'re better than that'
)
else:
if self.turker_response == '10':
self.mturk_agent.pay_bonus(1, 'Thanks for a great rating!')
self.mturk_agent.approve_work() | Programmatically approve/reject the turker's work. Doing this now
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] | def review_work(self):
"""Programmatically approve/reject the turker's work. Doing this now
(if possible) means that you don't need to do the work of reviewing
later on.
For example:
.. code-block:: python
if self.turker_response == '0':
self.mturk_agent.reject_work(
'You rated our model's response as a 0/10 but we '
'know we\'re better than that'
)
else:
if self.turker_response == '10':
self.mturk_agent.pay_bonus(1, 'Thanks for a great rating!')
self.mturk_agent.approve_work()
"""
# self.mturk_agent.approve_work()
# self.mturk_agent.reject_work()
# self.mturk_agent.pay_bonus(1000) # Pay $1000 as bonus
# self.mturk_agent.block_worker() # Block this worker from future HITs
pass | [
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"# self.mturk_agent.block_worker() # Block this worker from future HITs",
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jbjorne/TEES | caf19a4a1352ac59f5dc13a8684cc42ce4342d9d | Utils/Download.py | python | SizeReportingFile.read | (self, size) | return file.read(self, size) | [] | def read(self, size):
global pbar
#self.readSize += size
if pbar != None:
percent = int(self.tell() * 100 / self.totalSize)
percent = max(0, min(percent, 100)) # clamp
pbar.update(percent)
return file.read(self, size) | [
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slackapi/python-slack-sdk | 2dee6656ffacb7de0c29bb2a6c2b51ec6b5dbce7 | slack_sdk/web/legacy_client.py | python | LegacyWebClient.admin_teams_admins_list | (
self,
*,
team_id: str,
cursor: Optional[str] = None,
limit: Optional[int] = None,
**kwargs,
) | return self.api_call("admin.teams.admins.list", http_verb="GET", params=kwargs) | List all of the admins on a given workspace.
https://api.slack.com/methods/admin.inviteRequests.list | List all of the admins on a given workspace.
https://api.slack.com/methods/admin.inviteRequests.list | [
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self,
*,
team_id: str,
cursor: Optional[str] = None,
limit: Optional[int] = None,
**kwargs,
) -> Union[Future, SlackResponse]:
"""List all of the admins on a given workspace.
https://api.slack.com/methods/admin.inviteRequests.list
"""
kwargs.update(
{
"cursor": cursor,
"limit": limit,
"team_id": team_id,
}
)
return self.api_call("admin.teams.admins.list", http_verb="GET", params=kwargs) | [
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HonglinChu/SiamTrackers | 8471660b14f970578a43f077b28207d44a27e867 | SiamRPN/SiamRPN/siamrpn/bbox_util.py | python | clip_box | (bbox, clip_box, alpha) | return bbox | Clip the bounding boxes to the borders of an image
Parameters
----------
bbox: numpy.ndarray
Numpy array containing bounding boxes of shape `N X 4` where N is the
number of bounding boxes and the bounding boxes are represented in the
format `x1 y1 x2 y2`
clip_box: numpy.ndarray
An array of shape (4,) specifying the diagonal co-ordinates of the image
The coordinates are represented in the format `x1 y1 x2 y2`
alpha: float
If the fraction of a bounding box left in the image after being clipped is
less than `alpha` the bounding box is dropped.
Returns
-------
numpy.ndarray
Numpy array containing **clipped** bounding boxes of shape `N X 4` where N is the
number of bounding boxes left are being clipped and the bounding boxes are represented in the
format `x1 y1 x2 y2` | Clip the bounding boxes to the borders of an image
Parameters
----------
bbox: numpy.ndarray
Numpy array containing bounding boxes of shape `N X 4` where N is the
number of bounding boxes and the bounding boxes are represented in the
format `x1 y1 x2 y2`
clip_box: numpy.ndarray
An array of shape (4,) specifying the diagonal co-ordinates of the image
The coordinates are represented in the format `x1 y1 x2 y2`
alpha: float
If the fraction of a bounding box left in the image after being clipped is
less than `alpha` the bounding box is dropped.
Returns
-------
numpy.ndarray
Numpy array containing **clipped** bounding boxes of shape `N X 4` where N is the
number of bounding boxes left are being clipped and the bounding boxes are represented in the
format `x1 y1 x2 y2` | [
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"""Clip the bounding boxes to the borders of an image
Parameters
----------
bbox: numpy.ndarray
Numpy array containing bounding boxes of shape `N X 4` where N is the
number of bounding boxes and the bounding boxes are represented in the
format `x1 y1 x2 y2`
clip_box: numpy.ndarray
An array of shape (4,) specifying the diagonal co-ordinates of the image
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alpha: float
If the fraction of a bounding box left in the image after being clipped is
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Returns
-------
numpy.ndarray
Numpy array containing **clipped** bounding boxes of shape `N X 4` where N is the
number of bounding boxes left are being clipped and the bounding boxes are represented in the
format `x1 y1 x2 y2`
"""
ar_ = (bbox_area(bbox))
x_min = np.maximum(bbox[:,0], clip_box[0]).reshape(-1,1)
y_min = np.maximum(bbox[:,1], clip_box[1]).reshape(-1,1)
x_max = np.minimum(bbox[:,2], clip_box[2]).reshape(-1,1)
y_max = np.minimum(bbox[:,3], clip_box[3]).reshape(-1,1)
bbox = np.hstack((x_min, y_min, x_max, y_max, bbox[:,4:]))
delta_area = ((ar_ - bbox_area(bbox))/ar_)
mask = (delta_area < (1 - alpha)).astype(int)
bbox = bbox[mask == 1,:]
return bbox | [
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glumpy/glumpy | 46a7635c08d3a200478397edbe0371a6c59cd9d7 | glumpy/ext/png.py | python | _add_common_options | (parser) | return parser | Call *parser.add_option* for each of the options that are
common between this PNG--PNM conversion tool and the gen
tool. | Call *parser.add_option* for each of the options that are
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] | def _add_common_options(parser):
"""Call *parser.add_option* for each of the options that are
common between this PNG--PNM conversion tool and the gen
tool.
"""
parser.add_option("-i", "--interlace",
default=False, action="store_true",
help="create an interlaced PNG file (Adam7)")
parser.add_option("-t", "--transparent",
action="store", type="string", metavar="#RRGGBB",
help="mark the specified colour as transparent")
parser.add_option("-b", "--background",
action="store", type="string", metavar="#RRGGBB",
help="save the specified background colour")
parser.add_option("-g", "--gamma",
action="store", type="float", metavar="value",
help="save the specified gamma value")
parser.add_option("-c", "--compression",
action="store", type="int", metavar="level",
help="zlib compression level (0-9)")
return parser | [
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xtiankisutsa/MARA_Framework | ac4ac88bfd38f33ae8780a606ed09ab97177c562 | tools/lobotomy/core/logging/logger.py | python | Logger.binja_log | (self, t, m) | Log a message to the console.
Args:
param1: Type of log {info, warn, critical}
param2: Log message
Returns:
None | Log a message to the console. | [
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] | def binja_log(self, t, m):
"""
Log a message to the console.
Args:
param1: Type of log {info, warn, critical}
param2: Log message
Returns:
None
"""
if t == "info":
print(self.t.cyan("[{}] ".format(datetime.now())) + "{}".format(m))
elif t == "critical":
print(self.t.red("[{}] ".format(datetime.now())) + self.t.white("{}".format(m))) | [
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bbfamily/abu | 2de85ae57923a720dac99a545b4f856f6b87304b | abupy/CoreBu/ABuBase.py | python | PickleStateMixin.__setstate__ | (self, state) | 开始从本地序列化文件转换为python对象，即unpick | 开始从本地序列化文件转换为python对象，即unpick | [
"开始从本地序列化文件转换为python对象，即unpick"
] | def __setstate__(self, state):
"""开始从本地序列化文件转换为python对象，即unpick"""
# 从本地序列化文件中读取的pickle的最高支持版本, 默认0
pickle_highest_protocol = state.pop("_pickle_highest_protocol", 0)
# 从本地序列化文件中读取的abupy的版本号, 默认0.0.1
old_abupy_version = state.pop("_abupy_version", '0.0.1')
# 从本地序列化文件中读取的python版本号, 默认2.7.0
python_version = state.pop("_python_version", '2.7.0')
# 从本地序列化文件中读取的平台信息, 默认False，即windows
platform_version = state.pop("_is_mac_os", False)
if self.skip_abupy_version:
# 忽略abupy的版本号
_abupy_version = old_abupy_version
else:
from .. import __version__
_abupy_version = __version__
if self._pickle_highest_protocol != pickle_highest_protocol \
or _abupy_version != old_abupy_version or self._python_version != python_version \
or self._is_mac_os != platform_version:
"""只要有一个信息不一致，打印info，即有序列化读取失败的可能"""
logging.info(
"unpickle {} : "
"old pickle_highest_protocol={},"
"now pickle_highest_protocol={}, "
"old abupy_version={}, "
"now abupy_version={}, "
"old python_version={}, "
"now python_version={}, "
"old platform_version={}, "
"now platform_version={}, ".format(
self.__class__.__name__,
pickle_highest_protocol, self._pickle_highest_protocol,
old_abupy_version, _abupy_version,
python_version, self._python_version,
platform_version, self._is_mac_os))
self.__dict__.update(state)
# 混入对象可覆盖unpick_extend_work方法，完成对象特有的unpick工作
self.unpick_extend_work(state) | [
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smart-mobile-software/gitstack | d9fee8f414f202143eb6e620529e8e5539a2af56 | python/Lib/sets.py | python | BaseSet.union | (self, other) | return result | Return the union of two sets as a new set.
(I.e. all elements that are in either set.) | Return the union of two sets as a new set. | [
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] | def union(self, other):
"""Return the union of two sets as a new set.
(I.e. all elements that are in either set.)
"""
result = self.__class__(self)
result._update(other)
return result | [
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brutasse/graphite-api | 0886b7adcf985a1e8bcb084f6dd1dc166a3f3dff | graphite_api/functions.py | python | highestAverage | (requestContext, seriesList, n=1) | return sorted(seriesList, key=safeAvg)[-n:] | Takes one metric or a wildcard seriesList followed by an integer N.
Out of all metrics passed, draws only the top N metrics with the highest
average value for the time period specified.
Example::
&target=highestAverage(server*.instance*.threads.busy,5)
Draws the top 5 servers with the highest average value. | Takes one metric or a wildcard seriesList followed by an integer N.
Out of all metrics passed, draws only the top N metrics with the highest
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"""
Takes one metric or a wildcard seriesList followed by an integer N.
Out of all metrics passed, draws only the top N metrics with the highest
average value for the time period specified.
Example::
&target=highestAverage(server*.instance*.threads.busy,5)
Draws the top 5 servers with the highest average value.
"""
return sorted(seriesList, key=safeAvg)[-n:] | [
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SciTools/cartopy | 591fb5450e11b42b6de1cebe4f240112f915bd52 | lib/cartopy/mpl/geoaxes.py | python | GeoAxes.cla | (self) | return result | Clear the current axes and adds boundary lines. | Clear the current axes and adds boundary lines. | [
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] | def cla(self):
"""Clear the current axes and adds boundary lines."""
result = super().cla()
self.xaxis.set_visible(False)
self.yaxis.set_visible(False)
# Enable tight autoscaling.
self._tight = True
self.set_aspect('equal')
self._boundary()
# XXX consider a margin - but only when the map is not global...
# self._xmargin = 0.15
# self._ymargin = 0.15
self.dataLim.intervalx = self.projection.x_limits
self.dataLim.intervaly = self.projection.y_limits
return result | [
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dimagi/commcare-hq | d67ff1d3b4c51fa050c19e60c3253a79d3452a39 | corehq/apps/app_manager/views/forms.py | python | get_form_questions | (request, domain, app_id) | return json_response(xform_questions) | [] | def get_form_questions(request, domain, app_id):
form_unique_id = request.GET.get('form_unique_id')
module_id_temp = request.GET.get('module_id')
form_id_temp = request.GET.get('form_id')
try:
app = get_app(domain, app_id)
if module_id_temp is not None and form_id_temp is not None:
# temporary fallback
form = app.get_module(module_id_temp).get_form(form_id_temp)
else:
form = app.get_form(form_unique_id)
lang, langs = get_langs(request, app)
except FormNotFoundException:
raise Http404()
xform_questions = form.get_questions(langs, include_triggers=True)
return json_response(xform_questions) | [
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/combinat/root_system/ambient_space.py | python | AmbientSpaceElement.is_positive_root | (self) | return self.parent().rho().scalar(self) > 0 | EXAMPLES::
sage: R = RootSystem(['A',3]).ambient_space()
sage: r=R.simple_root(1)+R.simple_root(2)
sage: r.is_positive_root()
True
sage: r=R.simple_root(1)-R.simple_root(2)
sage: r.is_positive_root()
False | EXAMPLES:: | [
"EXAMPLES",
"::"
] | def is_positive_root(self):
"""
EXAMPLES::
sage: R = RootSystem(['A',3]).ambient_space()
sage: r=R.simple_root(1)+R.simple_root(2)
sage: r.is_positive_root()
True
sage: r=R.simple_root(1)-R.simple_root(2)
sage: r.is_positive_root()
False
"""
return self.parent().rho().scalar(self) > 0 | [
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BigBrotherBot/big-brother-bot | 848823c71413c86e7f1ff9584f43e08d40a7f2c0 | b3/tools/debug/statlib/stats.py | python | lbetai | (a,b,x) | Returns the incomplete beta function:
I-sub-x(a,b) = 1/B(a,b)*(Integral(0,x) of t^(a-1)(1-t)^(b-1) dt)
where a,b>0 and B(a,b) = G(a)*G(b)/(G(a+b)) where G(a) is the gamma
function of a. The continued fraction formulation is implemented here,
using the betacf function. (Adapted from: Numerical Recipes in C.)
Usage: lbetai(a,b,x) | Returns the incomplete beta function: | [
"Returns",
"the",
"incomplete",
"beta",
"function",
":"
] | def lbetai(a,b,x):
"""
Returns the incomplete beta function:
I-sub-x(a,b) = 1/B(a,b)*(Integral(0,x) of t^(a-1)(1-t)^(b-1) dt)
where a,b>0 and B(a,b) = G(a)*G(b)/(G(a+b)) where G(a) is the gamma
function of a. The continued fraction formulation is implemented here,
using the betacf function. (Adapted from: Numerical Recipes in C.)
Usage: lbetai(a,b,x)
"""
if (x<0.0 or x>1.0):
raise ValueError, 'Bad x in lbetai'
if (x==0.0 or x==1.0):
bt = 0.0
else:
bt = math.exp(gammln(a+b)-gammln(a)-gammln(b)+a*math.log(x)+b*
math.log(1.0-x))
if (x<(a+1.0)/(a+b+2.0)):
return bt*betacf(a,b,x)/float(a)
else:
return 1.0-bt*betacf(b,a,1.0-x)/float(b) | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/Python-2.7.9/Lib/bsddb/dbobj.py | python | DBEnv.dbremove | (self, *args, **kwargs) | return self._cobj.dbremove(*args, **kwargs) | [] | def dbremove(self, *args, **kwargs):
return self._cobj.dbremove(*args, **kwargs) | [
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google-research/language | 61fa7260ac7d690d11ef72ca863e45a37c0bdc80 | language/labs/drkit/run_multihop_follow.py | python | validate_flags_or_throw | () | Validate the input FLAGS or throw an exception. | Validate the input FLAGS or throw an exception. | [
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"FLAGS",
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] | def validate_flags_or_throw():
"""Validate the input FLAGS or throw an exception."""
if (not FLAGS.do_train and not FLAGS.do_predict and not FLAGS.do_test):
raise ValueError("At least one of `do_train`, `do_predict` or "
"`do_test` must be True.")
if FLAGS.do_train:
if not FLAGS.train_file:
raise ValueError(
"If `do_train` is True, then `train_file` must be specified.")
if FLAGS.do_predict:
if not FLAGS.predict_file:
raise ValueError(
"If `do_predict` is True, then `predict_file` must be specified.") | [
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LiyuanLucasLiu/LD-Net | f9489b6e7d436b7e3ed6447b797fb6ce9a886483 | model_word_ada/utils.py | python | init_lstm | (input_lstm) | random initialize lstms | random initialize lstms | [
"random",
"initialize",
"lstms"
] | def init_lstm(input_lstm):
"""
random initialize lstms
"""
for ind in range(0, input_lstm.num_layers):
weight = eval('input_lstm.weight_ih_l'+str(ind))
bias = np.sqrt(6.0 / (weight.size(0)/4 + weight.size(1)))
nn.init.uniform_(weight, -bias, bias)
weight = eval('input_lstm.weight_hh_l'+str(ind))
bias = np.sqrt(6.0 / (weight.size(0)/4 + weight.size(1)))
nn.init.uniform_(weight, -bias, bias)
if input_lstm.bias:
for ind in range(0, input_lstm.num_layers):
weight = eval('input_lstm.bias_ih_l'+str(ind))
weight.data.zero_()
weight.data[input_lstm.hidden_size: 2 * input_lstm.hidden_size] = 1
weight = eval('input_lstm.bias_hh_l'+str(ind))
weight.data.zero_()
weight.data[input_lstm.hidden_size: 2 * input_lstm.hidden_size] = 1 | [
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gmate/gmate | 83312e64e0c115a9842500e4eb8617d3f5f4025b | plugins/gedit3/zencoding/zencoding/zencoding/zen_actions.py | python | unindent | (editor, text) | return unindent_text(text, get_current_line_padding(editor)) | Unindent content, thus preparing text for tag wrapping
@param editor: Editor instance
@type editor: ZenEditor
@param text: str
@return str | Unindent content, thus preparing text for tag wrapping | [
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"""
Unindent content, thus preparing text for tag wrapping
@param editor: Editor instance
@type editor: ZenEditor
@param text: str
@return str
"""
return unindent_text(text, get_current_line_padding(editor)) | [
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aplanas/kmanga | 61162f09e8c61fa22831aea101b2899a28bf01fc | scraper/scraper/spiders/mangadex.py | python | MangaDex.parse_collection | (self, response, manga=None) | return response.follow(url, self._parse_issues, meta=meta) | Generate the list of issues for a manga
@url https://mangadex.org/manga/39/one-piece
@returns items 0
@returns request 1 | Generate the list of issues for a manga | [
"Generate",
"the",
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"of",
"issues",
"for",
"a",
"manga"
] | def parse_collection(self, response, manga=None):
"""Generate the list of issues for a manga
@url https://mangadex.org/manga/39/one-piece
@returns items 0
@returns request 1
"""
if 'manga' in response.meta:
manga = response.meta['manga']
else:
manga = Manga(url=response.url)
# URL
manga['url'] = response.url
# Name
xp = '//h3[@class="panel-title"]/text()'
manga['name'] = response.xpath(xp).extract()
# Alternate name
xp = '//th[contains(text(),"%s")]' \
'/following-sibling::td/descendant-or-self::*/text()'
manga['alt_name'] = response.xpath(xp % 'Alt name(s):').extract()
# Author
manga['author'] = response.xpath(xp % 'Author:').re(r'([^,]+),?')
# Artist
manga['artist'] = response.xpath(xp % 'Artist:').re(r'([^,]+),?')
# Reading direction
xp = '//h3[@class="panel-title"]/img/@alt'
manga['reading_direction'] = response.xpath(xp).extract_first()
# Status
xp = '//th[contains(text(),"%s")]' \
'/following-sibling::td/descendant-or-self::*/text()'
manga['status'] = response.xpath(xp % 'Pub. status:').extract_first()
# Genres
demographic = response.xpath(xp % 'Demographic:').extract()
genres = response.xpath(xp % 'Genres:').extract()
manga['genres'] = demographic + genres
# Rank
rank = response.xpath(xp % 'Rating:').extract_first()
manga['rank'] = 100 * convert_to_number(rank)
# Rank order
manga['rank_order'] = 'DESC'
# Description
manga['description'] = response.xpath(xp % 'Description:').extract()
# Cover image
xp = '//img[@class="border-radius"]/@src'
url = response.xpath(xp).extract_first()
manga['image_urls'] = [response.urljoin(url)]
# Information needed to deduce the issue order
xp = '//p[@class="text-center"]/text()'
chapters = response.xpath(xp).re_first(r'of (.*) chapters')
if chapters:
chapters = convert_to_number(chapters, as_int=True)
else:
xp = '//tr[contains(@id,"chapter_")]'
chapters = len(response.xpath(xp))
# If the manga is empty (is frequent in MangaDex), end the
# processing
if not chapters:
return
# Parse the manga issues list
manga['issues'] = []
meta = {
'manga': manga,
'chapters': chapters,
}
url = response.url + '/chapters/1'
return response.follow(url, self._parse_issues, meta=meta) | [
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rwth-i6/returnn | f2d718a197a280b0d5f0fd91a7fcb8658560dddb | returnn/tf/util/data.py | python | Data.set_dynamic_size | (self, axis, sizes) | :param int axis: counted with batch-dim
:param tf.Tensor sizes: shape [B] | :param int axis: counted with batch-dim
:param tf.Tensor sizes: shape [B] | [
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"[",
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"]"
] | def set_dynamic_size(self, axis, sizes):
"""
:param int axis: counted with batch-dim
:param tf.Tensor sizes: shape [B]
"""
# Note: The following code is somewhat ugly patchwork
# to fix some other currently incomplete or buggy behavior of some layers
# which introduce sizes without correctly setting the dim tag.
# The beam information is also missing currently.
# We make the ugly assumption that when it is unset,
# the first usage should hopefully define the correct beam.
if getattr(sizes, "_RETURNN_dyn_size_beam", NotSpecified) is NotSpecified:
sizes._RETURNN_dyn_size_beam = self.beam
if self.beam and getattr(sizes, "_RETURNN_dyn_size_beam", None) != self.beam:
tag = Dim.get_tag_from_size_tensor(sizes)
assert tag and self.batch
tag = tag.get_for_batch_ctx(batch=self.batch, ctx=self.control_flow_ctx)
assert tag.dyn_size is not None
sizes = tag.dyn_size
sizes_tag = Dim.get_tag_from_size_tensor(sizes)
if sizes_tag:
assert sizes_tag.is_same_size_tensor(sizes)
tag = self.dim_tags[axis]
assert tag.dimension is None # dynamic axis
if tag.is_same_size_tensor(sizes):
return # nothing to do
if tag.dyn_size is None:
if sizes_tag: # special rule for older code: overtake previous existing
assert sizes_tag.is_same_size_tensor(sizes)
self._dim_tags = self.dim_tags[:axis] + (sizes_tag,) + self.dim_tags[axis + 1:]
# Also assume the existing dim tag should be expected as equal.
# Likely there is anyway no reference so this does not matter.
tag.declare_same_as(sizes_tag)
else:
# Assign now. This should also set the dim tag on sizes.
new_tag = tag.set_tag_on_size_tensor(sizes, batch=self.batch)
if new_tag is not tag:
self._dim_tags = self.dim_tags[:axis] + (new_tag,) + self.dim_tags[axis + 1:]
else:
# Reset to some new size.
# Use new dim tag, or previous existing attached to size.
assert sizes_tag, "%s: assign dyn sizes %s without defined dim tag" % (self, sizes)
self._dim_tags = self.dim_tags[:axis] + (sizes_tag,) + self.dim_tags[axis + 1:] | [
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CedricGuillemet/Imogen | ee417b42747ed5b46cb11b02ef0c3630000085b3 | bin/Lib/turtle.py | python | TurtleScreenBase._drawimage | (self, item, pos, image) | Configure image item as to draw image object
at position (x,y) on canvas) | Configure image item as to draw image object
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"""Configure image item as to draw image object
at position (x,y) on canvas)
"""
x, y = pos
self.cv.coords(item, (x * self.xscale, -y * self.yscale))
self.cv.itemconfig(item, image=image) | [
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tobegit3hub/deep_image_model | 8a53edecd9e00678b278bb10f6fb4bdb1e4ee25e | java_predict_client/src/main/proto/tensorflow/contrib/layers/python/layers/layers.py | python | max_pool2d | (inputs,
kernel_size,
stride=2,
padding='VALID',
data_format=DATA_FORMAT_NHWC,
outputs_collections=None,
scope=None) | Adds a 2D Max Pooling op.
It is assumed that the pooling is done per image but not in batch or channels.
Args:
inputs: A 4-D tensor of shape `[batch_size, height, width, channels]` if
`data_format` is `NHWC`, and `[batch_size, channels, height, width]` if
`data_format` is `NCHW`.
kernel_size: A list of length 2: [kernel_height, kernel_width] of the
pooling kernel over which the op is computed. Can be an int if both
values are the same.
stride: A list of length 2: [stride_height, stride_width].
Can be an int if both strides are the same. Note that presently
both strides must have the same value.
padding: The padding method, either 'VALID' or 'SAME'.
data_format: A string. `NHWC` (default) and `NCHW` are supported.
outputs_collections: The collections to which the outputs are added.
scope: Optional scope for name_scope.
Returns:
A `Tensor` representing the results of the pooling operation.
Raises:
ValueError: if `data_format` is neither `NHWC` nor `NCHW`.
ValueError: If 'kernel_size' is not a 2-D list | Adds a 2D Max Pooling op. | [
"Adds",
"a",
"2D",
"Max",
"Pooling",
"op",
"."
] | def max_pool2d(inputs,
kernel_size,
stride=2,
padding='VALID',
data_format=DATA_FORMAT_NHWC,
outputs_collections=None,
scope=None):
"""Adds a 2D Max Pooling op.
It is assumed that the pooling is done per image but not in batch or channels.
Args:
inputs: A 4-D tensor of shape `[batch_size, height, width, channels]` if
`data_format` is `NHWC`, and `[batch_size, channels, height, width]` if
`data_format` is `NCHW`.
kernel_size: A list of length 2: [kernel_height, kernel_width] of the
pooling kernel over which the op is computed. Can be an int if both
values are the same.
stride: A list of length 2: [stride_height, stride_width].
Can be an int if both strides are the same. Note that presently
both strides must have the same value.
padding: The padding method, either 'VALID' or 'SAME'.
data_format: A string. `NHWC` (default) and `NCHW` are supported.
outputs_collections: The collections to which the outputs are added.
scope: Optional scope for name_scope.
Returns:
A `Tensor` representing the results of the pooling operation.
Raises:
ValueError: if `data_format` is neither `NHWC` nor `NCHW`.
ValueError: If 'kernel_size' is not a 2-D list
"""
if data_format not in (DATA_FORMAT_NCHW, DATA_FORMAT_NHWC):
raise ValueError('data_format has to be either NCHW or NHWC.')
with ops.name_scope(scope, 'MaxPool2D', [inputs]) as sc:
inputs = ops.convert_to_tensor(inputs)
kernel_h, kernel_w = utils.two_element_tuple(kernel_size)
stride_h, stride_w = utils.two_element_tuple(stride)
if data_format == DATA_FORMAT_NHWC:
ksize = [1, kernel_h, kernel_w, 1]
strides = [1, stride_h, stride_w, 1]
else:
ksize = [1, 1, kernel_h, kernel_w]
strides = [1, 1, stride_h, stride_w]
outputs = nn.max_pool(inputs,
ksize=ksize,
strides=strides,
padding=padding,
data_format=data_format)
return utils.collect_named_outputs(outputs_collections, sc, outputs) | [
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ReactionMechanismGenerator/RMG-Py | 2b7baf51febf27157def58fb3f6cee03fb6a684c | rmgpy/reaction.py | python | Reaction.is_isomerization | (self) | return len(self.reactants) == 1 and len(self.products) == 1 | Return ``True`` if the reaction represents an isomerization reaction
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"""
Return ``True`` if the reaction represents an isomerization reaction
:math:`\\ce{A <=> B}` or ``False`` if not.
"""
return len(self.reactants) == 1 and len(self.products) == 1 | [
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wxWidgets/Phoenix | b2199e299a6ca6d866aa6f3d0888499136ead9d6 | wx/lib/agw/ultimatelistctrl.py | python | UltimateListItemData.GetX | (self) | return self._rect.x | Returns the item `x` position. | Returns the item `x` position. | [
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nathanlopez/Stitch | 8e22e91c94237959c02d521aab58dc7e3d994cea | Application/stitch_help.py | python | usage_screenshot | () | [] | def usage_screenshot(): st_print('[*] Usage: screenshot\n') | [
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realpython/book2-exercises | cde325eac8e6d8cff2316601c2e5b36bb46af7d0 | web2py/venv/lib/python2.7/site-packages/pip/_vendor/distlib/markers.py | python | Evaluator.get_handler | (self, node_type) | return getattr(self, 'do_%s' % node_type, None) | Get a handler for the specified AST node type. | Get a handler for the specified AST node type. | [
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Get a handler for the specified AST node type.
"""
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jaraco/irc | 19859340b2ad3bffc58d842a7657fae9ec3563f8 | irc/server.py | python | IRCClient.handle_dump | (self, params) | Dump internal server information for debugging purposes. | Dump internal server information for debugging purposes. | [
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"""
Dump internal server information for debugging purposes.
"""
print("Clients:", self.server.clients)
for client in self.server.clients.values():
print(" ", client)
for channel in client.channels.values():
print(" ", channel.name)
print("Channels:", self.server.channels)
for channel in self.server.channels.values():
print(" ", channel.name, channel)
for client in channel.clients:
print(" ", client.nick, client) | [
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TencentCloud/tencentcloud-sdk-python | 3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2 | tencentcloud/iottid/v20190411/models.py | python | UploadDeviceUniqueCodeRequest.__init__ | (self) | r"""
:param CodeSet: 硬件唯一标识码
:type CodeSet: list of str
:param OrderId: 硬件标识码绑定的申请编号
:type OrderId: str | r"""
:param CodeSet: 硬件唯一标识码
:type CodeSet: list of str
:param OrderId: 硬件标识码绑定的申请编号
:type OrderId: str | [
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r"""
:param CodeSet: 硬件唯一标识码
:type CodeSet: list of str
:param OrderId: 硬件标识码绑定的申请编号
:type OrderId: str
"""
self.CodeSet = None
self.OrderId = None | [
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toxinu/Sublimall | 58eb7bc624234720003ad4df82d13ce23d70e4e1 | sublimall/requests/api.py | python | patch | (url, data=None, **kwargs) | return request('patch', url, data=data, **kwargs) | Sends a PATCH request. Returns :class:`Response` object.
:param url: URL for the new :class:`Request` object.
:param data: (optional) Dictionary, bytes, or file-like object to send in the body of the :class:`Request`.
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"""Sends a PATCH request. Returns :class:`Response` object.
:param url: URL for the new :class:`Request` object.
:param data: (optional) Dictionary, bytes, or file-like object to send in the body of the :class:`Request`.
:param \*\*kwargs: Optional arguments that ``request`` takes.
"""
return request('patch', url, data=data, **kwargs) | [
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Azure/azure-devops-cli-extension | 11334cd55806bef0b99c3bee5a438eed71e44037 | azure-devops/azext_devops/devops_sdk/v6_0/client_factory.py | python | ClientFactoryV6_0.get_release_client | (self) | return self._connection.get_client('azure.devops.v6_0.release.release_client.ReleaseClient') | get_release_client.
Gets the 6.0 version of the ReleaseClient
:rtype: :class:`<ReleaseClient> <azure.devops.v6_0.release.release_client.ReleaseClient>` | get_release_client.
Gets the 6.0 version of the ReleaseClient
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"""get_release_client.
Gets the 6.0 version of the ReleaseClient
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return self._connection.get_client('azure.devops.v6_0.release.release_client.ReleaseClient') | [
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/combinat/crystals/affine.py | python | AffineCrystalFromClassicalAndPromotion.__init__ | (self, cartan_type, classical_crystal, p_automorphism, p_inverse_automorphism, dynkin_node, category=None) | Input is an affine Cartan type ``cartan_type``, a classical crystal
``classical_crystal``, and promotion automorphism and its inverse
``p_automorphism`` and ``p_inverse_automorphism``, and the Dynkin
node ``dynkin_node``.
EXAMPLES::
sage: n = 1
sage: C = crystals.Tableaux(['A',n],shape=[1])
sage: pr = attrcall("promotion")
sage: pr_inverse = attrcall("promotion_inverse")
sage: A = crystals.AffineFromClassicalAndPromotion(['A',n,1],C,pr,pr_inverse,1)
sage: A.list()
[[[1]], [[2]]]
sage: A.cartan_type()
['A', 1, 1]
sage: A.index_set()
(0, 1)
TESTS::
sage: TestSuite(A).run() | Input is an affine Cartan type ``cartan_type``, a classical crystal
``classical_crystal``, and promotion automorphism and its inverse
``p_automorphism`` and ``p_inverse_automorphism``, and the Dynkin
node ``dynkin_node``. | [
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"""
Input is an affine Cartan type ``cartan_type``, a classical crystal
``classical_crystal``, and promotion automorphism and its inverse
``p_automorphism`` and ``p_inverse_automorphism``, and the Dynkin
node ``dynkin_node``.
EXAMPLES::
sage: n = 1
sage: C = crystals.Tableaux(['A',n],shape=[1])
sage: pr = attrcall("promotion")
sage: pr_inverse = attrcall("promotion_inverse")
sage: A = crystals.AffineFromClassicalAndPromotion(['A',n,1],C,pr,pr_inverse,1)
sage: A.list()
[[[1]], [[2]]]
sage: A.cartan_type()
['A', 1, 1]
sage: A.index_set()
(0, 1)
TESTS::
sage: TestSuite(A).run()
"""
AffineCrystalFromClassical.__init__(self, cartan_type, classical_crystal, category)
self.p_automorphism = p_automorphism
self.p_inverse_automorphism = p_inverse_automorphism
self.dynkin_node = dynkin_node | [
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dropbox/dropbox-sdk-python | 015437429be224732990041164a21a0501235db1 | dropbox/base.py | python | DropboxBase.paper_docs_list_continue | (self,
cursor) | return r | Once a cursor has been retrieved from :meth:`paper_docs_list`, use this
to paginate through all Paper doc. Note that this endpoint will continue
to work for content created by users on the older version of Paper. To
check which version of Paper a user is on, use
/users/features/get_values. If the paper_as_files feature is enabled,
then the user is running the new version of Paper. Refer to the `Paper
Migration Guide
<https://www.dropbox.com/lp/developers/reference/paper-migration-guide>`_
for migration information.
:param str cursor: The cursor obtained from :meth:`paper_docs_list` or
:meth:`paper_docs_list_continue`. Allows for pagination.
:rtype: :class:`dropbox.paper.ListPaperDocsResponse`
:raises: :class:`.exceptions.ApiError`
If this raises, ApiError will contain:
:class:`dropbox.paper.ListDocsCursorError` | Once a cursor has been retrieved from :meth:`paper_docs_list`, use this
to paginate through all Paper doc. Note that this endpoint will continue
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check which version of Paper a user is on, use
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<https://www.dropbox.com/lp/developers/reference/paper-migration-guide>`_
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"""
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to paginate through all Paper doc. Note that this endpoint will continue
to work for content created by users on the older version of Paper. To
check which version of Paper a user is on, use
/users/features/get_values. If the paper_as_files feature is enabled,
then the user is running the new version of Paper. Refer to the `Paper
Migration Guide
<https://www.dropbox.com/lp/developers/reference/paper-migration-guide>`_
for migration information.
:param str cursor: The cursor obtained from :meth:`paper_docs_list` or
:meth:`paper_docs_list_continue`. Allows for pagination.
:rtype: :class:`dropbox.paper.ListPaperDocsResponse`
:raises: :class:`.exceptions.ApiError`
If this raises, ApiError will contain:
:class:`dropbox.paper.ListDocsCursorError`
"""
warnings.warn(
'docs/list/continue is deprecated.',
DeprecationWarning,
)
arg = paper.ListPaperDocsContinueArgs(cursor)
r = self.request(
paper.docs_list_continue,
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arg,
None,
)
return r | [
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hyperspy/hyperspy | 1ffb3fab33e607045a37f30c1463350b72617e10 | hyperspy/io_plugins/sur.py | python | DigitalSurfHandler._build_1D_series | (self,) | Build a series of 1D objects. The T axis is navigation and set from
the first object | Build a series of 1D objects. The T axis is navigation and set from
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] | def _build_1D_series(self,):
"""Build a series of 1D objects. The T axis is navigation and set from
the first object"""
#First object dictionary
hypdic = self._list_sur_file_content[0]
#Metadata are set from first dictionary
self._set_metadata_and_original_metadata(hypdic)
#Add the series-axis to the signal dict
self.signal_dict['axes'].append(\
self._build_Tax(hypdic,'_03_Number_of_Objects',ind=0,nav=True))
#All objects must share the same signal axis
self.signal_dict['axes'].append(\
self._build_Xax(hypdic,ind=1,nav=False))
#We put all the data together
data = []
for obj in self._list_sur_file_content:
data.append(obj['_62_points'])
self.signal_dict['data'] = np.stack(data) | [
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Eugeny/reconfigure | ff1115dede4b80222a2618d0e7657cafa36a2573 | reconfigure/parsers/ini.py | python | IniFileParser.stringify | (self, tree) | return data | [] | def stringify(self, tree):
cp = INIConfig()
for section in tree.children:
if self.sectionless and section.name is None:
sectionname = self.nullsection
else:
sectionname = section.name
cp._new_namespace(sectionname)
for option in section.children:
if not isinstance(option, PropertyNode):
raise TypeError('Third level nodes should be PropertyNodes')
cp[sectionname][option.name] = option.value
if option.comment:
self._set_comment(cp[sectionname]._options[option.name], option.comment)
if section._extra_content:
for k, v in section._extra_content.items():
cp[sectionname][k] = v
if hasattr(cp[sectionname], '_lines'):
self._set_comment(cp[sectionname]._lines[0], section.comment)
data = (str if sys.version_info[0] >= 3 else unicode)(cp) + u'\n'
if self.sectionless:
data = data.replace('[' + self.nullsection + ']\n', '')
return data | [
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"s... | https://github.com/Eugeny/reconfigure/blob/ff1115dede4b80222a2618d0e7657cafa36a2573/reconfigure/parsers/ini.py#L56-L81 | |||
krintoxi/NoobSec-Toolkit | 38738541cbc03cedb9a3b3ed13b629f781ad64f6 | NoobSecToolkit - MAC OSX/scripts/sshbackdoors/backdoors/shell/pupy/pupy/packages/windows/amd64/psutil/_psosx.py | python | swap_memory | () | return _common.sswap(total, used, free, percent, sin, sout) | Swap system memory as a (total, used, free, sin, sout) tuple. | Swap system memory as a (total, used, free, sin, sout) tuple. | [
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"""Swap system memory as a (total, used, free, sin, sout) tuple."""
total, used, free, sin, sout = cext.swap_mem()
percent = usage_percent(used, total, _round=1)
return _common.sswap(total, used, free, percent, sin, sout) | [
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replit-archive/empythoned | 977ec10ced29a3541a4973dc2b59910805695752 | cpython/Lib/idlelib/configHandler.py | python | IdleConf.GetExtraHelpSourceList | (self,configSet) | return helpSources | Fetch list of extra help sources from a given configSet.
Valid configSets are 'user' or 'default'. Return a list of tuples of
the form (menu_item , path_to_help_file , option), or return the empty
list. 'option' is the sequence number of the help resource. 'option'
values determine the position of the menu items on the Help menu,
therefore the returned list must be sorted by 'option'. | Fetch list of extra help sources from a given configSet. | [
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] | def GetExtraHelpSourceList(self,configSet):
"""Fetch list of extra help sources from a given configSet.
Valid configSets are 'user' or 'default'. Return a list of tuples of
the form (menu_item , path_to_help_file , option), or return the empty
list. 'option' is the sequence number of the help resource. 'option'
values determine the position of the menu items on the Help menu,
therefore the returned list must be sorted by 'option'.
"""
helpSources=[]
if configSet=='user':
cfgParser=self.userCfg['main']
elif configSet=='default':
cfgParser=self.defaultCfg['main']
else:
raise InvalidConfigSet, 'Invalid configSet specified'
options=cfgParser.GetOptionList('HelpFiles')
for option in options:
value=cfgParser.Get('HelpFiles',option,default=';')
if value.find(';')==-1: #malformed config entry with no ';'
menuItem='' #make these empty
helpPath='' #so value won't be added to list
else: #config entry contains ';' as expected
value=string.split(value,';')
menuItem=value[0].strip()
helpPath=value[1].strip()
if menuItem and helpPath: #neither are empty strings
helpSources.append( (menuItem,helpPath,option) )
helpSources.sort(key=lambda x: int(x[2]))
return helpSources | [
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playframework/play1 | 0ecac3bc2421ae2dbec27a368bf671eda1c9cba5 | python/Lib/site-packages/win32/lib/win32serviceutil.py | python | RestartService | (serviceName, args = None, waitSeconds = 30, machine = None) | Stop the service, and then start it again (with some tolerance for allowing it to stop.) | Stop the service, and then start it again (with some tolerance for allowing it to stop.) | [
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"Stop the service, and then start it again (with some tolerance for allowing it to stop.)"
try:
StopService(serviceName, machine)
except pywintypes.error, exc:
# Allow only "service not running" error
if exc.winerror!=winerror.ERROR_SERVICE_NOT_ACTIVE:
raise
# Give it a few goes, as the service may take time to stop
for i in range(waitSeconds):
try:
StartService(serviceName, args, machine)
break
except pywintypes.error, exc:
if exc.winerror!=winerror.ERROR_SERVICE_ALREADY_RUNNING:
raise
win32api.Sleep(1000)
else:
print "Gave up waiting for the old service to stop!" | [
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algorhythms/LeetCode | 3fb14aeea62a960442e47dfde9f964c7ffce32be | 1000 Minimum Cost to Merge Stones.py | python | Solution.mergeStones | (self, stones: List[int], K: int) | return ret if ret != float("inf") else -1 | Mergeable? K -> 1. Reduction size (K - 1)
N - (K - 1) * m = 1
mergeable: (N - 1) % (K - 1) = 0
K consecutive
every piles involves at least once
Non-consecutive: priority queue merge the least first
But here it is consecutive, need to search, cannot gready
* Merge the piles cost the same as merge individual ones.
Attemp 1:
F[i] = cost to merge A[:i] into 1
F[i] = F[i-3] + A[i-1] + A[i-2] + A[i-3] ??
Attemp 2:
F[i][j] = cost of merge A[i:j] into 1
F[i][j] = F[i][k] + F[k][j] ??
Answer:
F[i][j][m] = cost of merge A[i:j] into m piles
F[i][j][1] = F[i][j][k] + sum(stones[i:j]) # merge
F[i][j][m] = min F[i][mid][1] + F[mid][j][m-1] # add
initial:
F[i][i+1][1] = 0
F[i][i+1][m] = inf
since the mid goes through the middle in the i, j.
Use memoization rather than dp | Mergeable? K -> 1. Reduction size (K - 1)
N - (K - 1) * m = 1
mergeable: (N - 1) % (K - 1) = 0 | [
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"""
Mergeable? K -> 1. Reduction size (K - 1)
N - (K - 1) * m = 1
mergeable: (N - 1) % (K - 1) = 0
K consecutive
every piles involves at least once
Non-consecutive: priority queue merge the least first
But here it is consecutive, need to search, cannot gready
* Merge the piles cost the same as merge individual ones.
Attemp 1:
F[i] = cost to merge A[:i] into 1
F[i] = F[i-3] + A[i-1] + A[i-2] + A[i-3] ??
Attemp 2:
F[i][j] = cost of merge A[i:j] into 1
F[i][j] = F[i][k] + F[k][j] ??
Answer:
F[i][j][m] = cost of merge A[i:j] into m piles
F[i][j][1] = F[i][j][k] + sum(stones[i:j]) # merge
F[i][j][m] = min F[i][mid][1] + F[mid][j][m-1] # add
initial:
F[i][i+1][1] = 0
F[i][i+1][m] = inf
since the mid goes through the middle in the i, j.
Use memoization rather than dp
"""
N = len(stones)
sums = [0]
for s in stones:
sums.append(sums[-1] + s)
@lru_cache(None)
def F(i, j, m):
if i >= j or m < 1:
return float("inf")
n = j - i
if (n - m) % (K - 1) != 0:
return float("inf")
if j == i + 1:
if m == 1:
return 0
return float("inf")
if m == 1:
return F(i, j, K) + sums[j] - sums[i]
ret = min(
F(i, mid, 1) + F(mid, j, m - 1)
for mid in range(i + 1, j, K - 1)
)
return ret
ret = F(0, N, 1)
return ret if ret != float("inf") else -1 | [
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scikit-fuzzy/scikit-fuzzy | 92ad3c382ac19707086204ac6cdf6e81353345a7 | skfuzzy/fuzzymath/fuzzy_ops.py | python | fuzzy_sub | (x, a, y, b) | return fuzzy_op(x, a, y, b, op=np.subtract) | Subtract fuzzy set ``b`` from fuzzy set ``a``.
Parameters
----------
x : 1d array, length N
Universe variable for fuzzy set ``a``.
A : 1d array, length N
Fuzzy set for universe ``x``.
y : 1d array, length M
Universe variable for fuzzy set ``b``.
b : 1d array, length M
Fuzzy set for universe ``y``.
Returns
-------
z : 1d array
Output variable.
mfz : 1d array
Fuzzy membership set for variable z.
Notes
-----
Uses Zadeh's Extension Principle from Ross, Fuzzy Logic w/Engineering
Applications, (2010), pp.414, Eq. 12.17.
If these results are unexpected and your membership functions are convex,
consider trying the ``skfuzzy.dsw_*`` functions for fuzzy mathematics
using interval arithmetic via the restricted Dong, Shah, and Wong method. | Subtract fuzzy set ``b`` from fuzzy set ``a``. | [
"Subtract",
"fuzzy",
"set",
"b",
"from",
"fuzzy",
"set",
"a",
"."
] | def fuzzy_sub(x, a, y, b):
"""
Subtract fuzzy set ``b`` from fuzzy set ``a``.
Parameters
----------
x : 1d array, length N
Universe variable for fuzzy set ``a``.
A : 1d array, length N
Fuzzy set for universe ``x``.
y : 1d array, length M
Universe variable for fuzzy set ``b``.
b : 1d array, length M
Fuzzy set for universe ``y``.
Returns
-------
z : 1d array
Output variable.
mfz : 1d array
Fuzzy membership set for variable z.
Notes
-----
Uses Zadeh's Extension Principle from Ross, Fuzzy Logic w/Engineering
Applications, (2010), pp.414, Eq. 12.17.
If these results are unexpected and your membership functions are convex,
consider trying the ``skfuzzy.dsw_*`` functions for fuzzy mathematics
using interval arithmetic via the restricted Dong, Shah, and Wong method.
"""
return fuzzy_op(x, a, y, b, op=np.subtract) | [
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NetManAIOps/donut | c8a44d91f102f36ba712e1e896408337501bce9b | donut/model.py | python | Donut.get_score | (self, x, y=None, n_z=None, mcmc_iteration=None,
last_point_only=True) | Get the reconstruction probability for `x` and `y`.
The larger `reconstruction probability`, the less likely a point
is anomaly. You may take the negative of the score, if you want
something to directly indicate the severity of anomaly.
Args:
x (tf.Tensor): 2-D `float32` :class:`tf.Tensor`, the windows of
KPI observations in a mini-batch.
y (tf.Tensor): 2-D `int32` :class:`tf.Tensor`, the windows of
missing point indicators in a mini-batch.
n_z (int or None): Number of `z` samples to take for each `x`.
(default :obj:`None`, one sample without explicit sampling
dimension)
mcmc_iteration (int or tf.Tensor): Iteration count for MCMC
missing data imputation. (default :obj:`None`, no iteration)
last_point_only (bool): Whether to obtain the reconstruction
probability of only the last point in each window?
(default :obj:`True`)
Returns:
tf.Tensor: The reconstruction probability, with the shape
``(len(x) - self.x_dims + 1,)`` if `last_point_only` is
:obj:`True`, or ``(len(x) - self.x_dims + 1, self.x_dims)``
if `last_point_only` is :obj:`False`. This is because the
first ``self.x_dims - 1`` points are not the last point of
any window. | Get the reconstruction probability for `x` and `y`. | [
"Get",
"the",
"reconstruction",
"probability",
"for",
"x",
"and",
"y",
"."
] | def get_score(self, x, y=None, n_z=None, mcmc_iteration=None,
last_point_only=True):
"""
Get the reconstruction probability for `x` and `y`.
The larger `reconstruction probability`, the less likely a point
is anomaly. You may take the negative of the score, if you want
something to directly indicate the severity of anomaly.
Args:
x (tf.Tensor): 2-D `float32` :class:`tf.Tensor`, the windows of
KPI observations in a mini-batch.
y (tf.Tensor): 2-D `int32` :class:`tf.Tensor`, the windows of
missing point indicators in a mini-batch.
n_z (int or None): Number of `z` samples to take for each `x`.
(default :obj:`None`, one sample without explicit sampling
dimension)
mcmc_iteration (int or tf.Tensor): Iteration count for MCMC
missing data imputation. (default :obj:`None`, no iteration)
last_point_only (bool): Whether to obtain the reconstruction
probability of only the last point in each window?
(default :obj:`True`)
Returns:
tf.Tensor: The reconstruction probability, with the shape
``(len(x) - self.x_dims + 1,)`` if `last_point_only` is
:obj:`True`, or ``(len(x) - self.x_dims + 1, self.x_dims)``
if `last_point_only` is :obj:`False`. This is because the
first ``self.x_dims - 1`` points are not the last point of
any window.
"""
with tf.name_scope('Donut.get_score'):
# MCMC missing data imputation
if y is not None and mcmc_iteration:
x_r = iterative_masked_reconstruct(
reconstruct=self.vae.reconstruct,
x=x,
mask=y,
iter_count=mcmc_iteration,
back_prop=False,
)
else:
x_r = x
# get the reconstruction probability
q_net = self.vae.variational(x=x_r, n_z=n_z) # notice: x=x_r
p_net = self.vae.model(z=q_net['z'], x=x, n_z=n_z) # notice: x=x
r_prob = p_net['x'].log_prob(group_ndims=0)
if n_z is not None:
n_z = validate_n_samples(n_z, 'n_z')
assert_shape_op = tf.assert_equal(
tf.shape(r_prob),
tf.stack([n_z, tf.shape(x)[0], self.x_dims]),
message='Unexpected shape of reconstruction prob'
)
with tf.control_dependencies([assert_shape_op]):
r_prob = tf.reduce_mean(r_prob, axis=0)
if last_point_only:
r_prob = r_prob[:, -1]
return r_prob | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/Python-2.7.9/Lib/plat-freebsd5/IN.py | python | IN6_IS_ADDR_MC_SITELOCAL | (a) | return | [] | def IN6_IS_ADDR_MC_SITELOCAL(a): return | [
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CalebBell/thermo | 572a47d1b03d49fe609b8d5f826fa6a7cde00828 | thermo/phases/iapws_phase.py | python | IAPWS97.d2A_d2tau | (self) | return d2A_d2tau | [] | def d2A_d2tau(self):
try:
return self._d2A_d2tau
except:
pass
self._d2A_d2tau = d2A_d2tau = iapws.iapws97_d2A_d2tau_region3(self.tau, self.delta)
return d2A_d2tau | [
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DataDog/integrations-core | 934674b29d94b70ccc008f76ea172d0cdae05e1e | etcd/datadog_checks/etcd/config_models/defaults.py | python | instance_kerberos_keytab | (field, value) | return get_default_field_value(field, value) | [] | def instance_kerberos_keytab(field, value):
return get_default_field_value(field, value) | [
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man-group/mdf | 4b2c78084467791ad883c0b4c53832ad70fc96ef | mdf/builders/basic.py | python | NodeTypeHandler.get_dataframe | (self, dtype=object) | return df | Returns a DataFrame containing the values accumulated
for each column for a node. | Returns a DataFrame containing the values accumulated
for each column for a node. | [
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"""
Returns a DataFrame containing the values accumulated
for each column for a node.
"""
columns = self.get_columns()
df = pa.DataFrame(data={}, index=self._index, columns=columns, dtype=dtype)
for (d, l), value in self._data.items():
df[l][d] = value
return df | [
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pengchenglin/ATX-Test | fb3354b210934726af6a369746d6bdf6359f268d | Public/report.py | python | _get_report_info | (run) | return result | 获取每个设备报告的参数 | 获取每个设备报告的参数 | [
"获取每个设备报告的参数"
] | def _get_report_info(run):
'''获取每个设备报告的参数'''
report = run.test_report_path + '/TestReport.html'
result = {}
with open(report, 'r', encoding='utf-8') as f:
res_str = re.findall("测试结果(.+%)", f.read())
if res_str:
res = re.findall(r"\d+", res_str[0])
result["sum"] = res[0]
result["pass"] = res[1]
result['fail'] = res[2]
result['error'] = res[3]
result['passrate'] = re.findall('通过率 = (.+%)', res_str[0])[0]
else:
raise Exception("The TestReport.html in %s has no string'测试结果',please check out!!!" % run.get_path())
f.close()
with open(report, 'r', encoding='utf-8') as f:
result['duration'] = re.findall("合计耗时 : </strong> (.+)</p>", f.read())[0].split('.')[0]
f.close()
return result | [
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ogrisel/pygbm | 686495bedd7d495bcab1d66eb74309de48d3f3c1 | pygbm/loss.py | python | BaseLoss.get_baseline_prediction | (self, y_train, prediction_dim) | Return initial predictions (before the first iteration).
Parameters
----------
y_train : array-like, shape=(n_samples,)
The target training values.
prediction_dim : int
The dimension of one prediction: 1 for binary classification and
regression, n_classes for multiclass classification.
Returns
-------
baseline_prediction: float or array of shape (1, prediction_dim)
The baseline prediction. | Return initial predictions (before the first iteration). | [
"Return",
"initial",
"predictions",
"(",
"before",
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"first",
"iteration",
")",
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] | def get_baseline_prediction(self, y_train, prediction_dim):
"""Return initial predictions (before the first iteration).
Parameters
----------
y_train : array-like, shape=(n_samples,)
The target training values.
prediction_dim : int
The dimension of one prediction: 1 for binary classification and
regression, n_classes for multiclass classification.
Returns
-------
baseline_prediction: float or array of shape (1, prediction_dim)
The baseline prediction.
"""
pass | [
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googleads/google-ads-python | 2a1d6062221f6aad1992a6bcca0e7e4a93d2db86 | google/ads/googleads/v7/services/services/location_view_service/client.py | python | LocationViewServiceClient.from_service_account_file | (cls, filename: str, *args, **kwargs) | return cls(*args, **kwargs) | Creates an instance of this client using the provided credentials
file.
Args:
filename (str): The path to the service account private key json
file.
args: Additional arguments to pass to the constructor.
kwargs: Additional arguments to pass to the constructor.
Returns:
LocationViewServiceClient: The constructed client. | Creates an instance of this client using the provided credentials
file. | [
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"provided",
"credentials",
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] | def from_service_account_file(cls, filename: str, *args, **kwargs):
"""Creates an instance of this client using the provided credentials
file.
Args:
filename (str): The path to the service account private key json
file.
args: Additional arguments to pass to the constructor.
kwargs: Additional arguments to pass to the constructor.
Returns:
LocationViewServiceClient: The constructed client.
"""
credentials = service_account.Credentials.from_service_account_file(
filename
)
kwargs["credentials"] = credentials
return cls(*args, **kwargs) | [
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... | https://github.com/googleads/google-ads-python/blob/2a1d6062221f6aad1992a6bcca0e7e4a93d2db86/google/ads/googleads/v7/services/services/location_view_service/client.py#L128-L145 | |
jesseweisberg/moveo_ros | b9282bdadbf2505a26d3b94b91e60a98d86efa34 | object_detector_app/object_detection/meta_architectures/ssd_meta_arch.py | python | SSDMetaArch._get_feature_map_spatial_dims | (self, feature_maps) | return [(shape[1], shape[2]) for shape in feature_map_shapes] | Return list of spatial dimensions for each feature map in a list.
Args:
feature_maps: a list of tensors where the ith tensor has shape
[batch, height_i, width_i, depth_i].
Returns:
a list of pairs (height, width) for each feature map in feature_maps | Return list of spatial dimensions for each feature map in a list. | [
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] | def _get_feature_map_spatial_dims(self, feature_maps):
"""Return list of spatial dimensions for each feature map in a list.
Args:
feature_maps: a list of tensors where the ith tensor has shape
[batch, height_i, width_i, depth_i].
Returns:
a list of pairs (height, width) for each feature map in feature_maps
"""
feature_map_shapes = [
feature_map.get_shape().as_list() for feature_map in feature_maps
]
return [(shape[1], shape[2]) for shape in feature_map_shapes] | [
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vlachoudis/bCNC | 67126b4894dabf6579baf47af8d0f9b7de35e6e3 | bCNC/CNC.py | python | CNC.toolChange | (self, tool=None) | return lines | [] | def toolChange(self, tool=None):
if tool is not None:
# Force a change
self.tool = tool
self._lastTool = None
# check if it is the same tool
if self.tool is None or self.tool == self._lastTool: return []
# create the necessary code
lines = []
lines.append("$g") # remember state and populate variables, FIXME: move to ./controllers/_GenericController.py
lines.append("m5") # stop spindle
lines.append("%wait")
lines.append("%_x,_y,_z = wx,wy,wz") # remember position
lines.append("g53 g0 z[toolchangez]")
lines.append("g53 g0 x[toolchangex] y[toolchangey]")
lines.append("%wait")
if CNC.comment:
lines.append("%%msg Tool change T%02d (%s)"%(self.tool,CNC.comment))
else:
lines.append("%%msg Tool change T%02d"%(self.tool))
lines.append("m0") # feed hold
if CNC.toolPolicy < 4:
lines.append("g53 g0 x[toolprobex] y[toolprobey]")
lines.append("g53 g0 z[toolprobez]")
# fixed WCS
if CNC.vars["fastprbfeed"]:
prb_reverse = {"2": "4", "3": "5", "4": "2", "5": "3"}
CNC.vars["prbcmdreverse"] = (CNC.vars["prbcmd"][:-1] +
prb_reverse[CNC.vars["prbcmd"][-1]])
currentFeedrate = CNC.vars["fastprbfeed"]
while currentFeedrate > CNC.vars["prbfeed"]:
lines.append("%wait")
lines.append("g91 [prbcmd] %s z[toolprobez-mz-tooldistance]" \
% CNC.fmt('f',currentFeedrate))
lines.append("%wait")
lines.append("[prbcmdreverse] %s z[toolprobez-mz]" \
% CNC.fmt('f',currentFeedrate))
currentFeedrate /= 10
lines.append("%wait")
lines.append("g91 [prbcmd] f[prbfeed] z[toolprobez-mz-tooldistance]")
if CNC.toolPolicy==2:
# Adjust the current WCS to fit to the tool
# FIXME could be done dynamically in the code
p = WCS.index(CNC.vars["WCS"])+1
lines.append("g10l20p%d z[toolheight]"%(p))
lines.append("%wait")
elif CNC.toolPolicy==3:
# Modify the tool length, update the TLO
lines.append("g4 p1") # wait a sec to get the probe info
lines.append("%wait")
lines.append("%global TLO; TLO=prbz-toolmz")
lines.append("g43.1z[TLO]")
lines.append("%update TLO")
lines.append("g53 g0 z[toolchangez]")
lines.append("g53 g0 x[toolchangex] y[toolchangey]")
if CNC.toolWaitAfterProbe:
lines.append("%wait")
lines.append("%msg Restart spindle")
lines.append("m0") # feed hold
# restore state
lines.append("g90") # restore mode
lines.append("g0 x[_x] y[_y]") # ... x,y position
lines.append("g0 z[_z]") # ... z position
lines.append("f[feed] [spindle]")# ... feed and spindle
lines.append("g4 p5") # wait 5s for spindle to speed up
# remember present tool
self._lastTool = self.tool
return lines | [
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evennia/evennia | fa79110ba6b219932f22297838e8ac72ebc0be0e | evennia/utils/batchprocessors.py | python | tb_filename | (tb) | return tb.tb_frame.f_code.co_filename | Helper to get filename from traceback | Helper to get filename from traceback | [
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"""Helper to get filename from traceback"""
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largelymfs/topical_word_embeddings | 1ae3d15d0afcd3fcd39cc81eec4ad9463413a9f6 | TWE-1/gensim/corpora/wikicorpus.py | python | get_namespace | (tag) | return namespace | Returns the namespace of tag. | Returns the namespace of tag. | [
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] | def get_namespace(tag):
"""Returns the namespace of tag."""
m = re.match("^{(.*?)}", tag)
namespace = m.group(1) if m else ""
if not namespace.startswith("http://www.mediawiki.org/xml/export-"):
raise ValueError("%s not recognized as MediaWiki dump namespace"
% namespace)
return namespace | [
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igrishaev/f | 9be7113517a4488a0b7150688043d9bbb6be0a73 | f/function.py | python | arr2 | (value, *forms) | return reduce(reducer, forms, value) | Clojure's second threading macro implementation.
The logic is the same as `thread_first`, but puts the value
at the end of each form.
See https://clojuredocs.org/clojure.core/->>
:param value: Initial value to process.
:type value: any
:param forms: A tuple of forms.
:type forms: tuple of func|(func, arg1, arg2, ...)
:return: A value passed through the all forms.
:rtype: any | Clojure's second threading macro implementation. | [
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"."
] | def arr2(value, *forms):
"""
Clojure's second threading macro implementation.
The logic is the same as `thread_first`, but puts the value
at the end of each form.
See https://clojuredocs.org/clojure.core/->>
:param value: Initial value to process.
:type value: any
:param forms: A tuple of forms.
:type forms: tuple of func|(func, arg1, arg2, ...)
:return: A value passed through the all forms.
:rtype: any
"""
def reducer(value, form):
if isinstance(form, (tuple, list)):
func, args = form[0], form[1:]
else:
func, args = form, ()
all_args = tuple(args) + (value, )
return func(*all_args)
return reduce(reducer, forms, value) | [
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aws/sagemaker-python-sdk | 9d259b316f7f43838c16f35c10e98a110b56735b | src/sagemaker/tuner.py | python | HyperparameterTuner._prepare_estimator_for_tuning | (cls, estimator, inputs, job_name, **kwargs) | Prepare one estimator before starting tuning. | Prepare one estimator before starting tuning. | [
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"."
] | def _prepare_estimator_for_tuning(cls, estimator, inputs, job_name, **kwargs):
"""Prepare one estimator before starting tuning."""
if isinstance(inputs, (list, RecordSet, FileSystemRecordSet)):
estimator._prepare_for_training(inputs, **kwargs)
else:
estimator._prepare_for_training(job_name) | [
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rm-hull/luma.lcd | 156d0613ceb973356cea768263f3847d68994bde | luma/lcd/device.py | python | ili9486.contrast | (self, level) | NOT SUPPORTED
:param level: Desired contrast level in the range of 0-255.
:type level: int | NOT SUPPORTED | [
"NOT",
"SUPPORTED"
] | def contrast(self, level):
"""
NOT SUPPORTED
:param level: Desired contrast level in the range of 0-255.
:type level: int
"""
assert(0 <= level <= 255) | [
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PyMVPA/PyMVPA | 76c476b3de8264b0bb849bf226da5674d659564e | mvpa2/mappers/flatten.py | python | ProductFlattenMapper.__init__ | (self, factor_names, factor_values=None, **kwargs) | Parameters
----------
factor_names: iterable
The names for each dimension. If the dataset to
be flattened is shaped ns X nf1 x nf2 x ... x nfN, then
factor_names should have a length of N. Furthermore
when applied to a dataset ds, it should have each
of the factor names factor_names[K] as an attribute and the value
of this attribute should have nfK values.
Applying this mapper to such a dataset yields a new dataset
with size ns X (nf1 * nf2 * ... * nfN) with
feature attributes nameK and nameKindices for each nameK
in the factor names.
factor_values: iterable or None
Optionally the factor values for each dimension. If
not provided or set to None, then it will be inferred
upon training on a dataset. Setting this parameter
explicitly means this instance does not have to be trained. | Parameters
----------
factor_names: iterable
The names for each dimension. If the dataset to
be flattened is shaped ns X nf1 x nf2 x ... x nfN, then
factor_names should have a length of N. Furthermore
when applied to a dataset ds, it should have each
of the factor names factor_names[K] as an attribute and the value
of this attribute should have nfK values.
Applying this mapper to such a dataset yields a new dataset
with size ns X (nf1 * nf2 * ... * nfN) with
feature attributes nameK and nameKindices for each nameK
in the factor names.
factor_values: iterable or None
Optionally the factor values for each dimension. If
not provided or set to None, then it will be inferred
upon training on a dataset. Setting this parameter
explicitly means this instance does not have to be trained. | [
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"x",
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'''
Parameters
----------
factor_names: iterable
The names for each dimension. If the dataset to
be flattened is shaped ns X nf1 x nf2 x ... x nfN, then
factor_names should have a length of N. Furthermore
when applied to a dataset ds, it should have each
of the factor names factor_names[K] as an attribute and the value
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Applying this mapper to such a dataset yields a new dataset
with size ns X (nf1 * nf2 * ... * nfN) with
feature attributes nameK and nameKindices for each nameK
in the factor names.
factor_values: iterable or None
Optionally the factor values for each dimension. If
not provided or set to None, then it will be inferred
upon training on a dataset. Setting this parameter
explicitly means this instance does not have to be trained.
'''
kwargs['auto_train'] = kwargs.get('auto_train', True)
# make sure the factor names and values are properly set
factor_names = list(factor_names)
# override default value for space argument
space = kwargs.get('space', None)
if kwargs.get('space', None) is None:
kwargs['space'] = '_'.join(factor_names) + '_indices'
super(ProductFlattenMapper, self).__init__(**kwargs)
self._factor_names = factor_names
if factor_values is not None:
if len(factor_values) != len(factor_names):
raise ValueError('factor_values must have %d elements, '
'found %d' % (len(factor_names),
len(factor_names)))
self._factor_values = factor_values | [
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tensorflow/tfx | b4a6b83269815ed12ba9df9e9154c7376fef2ea0 | tfx/examples/custom_components/slack/example/taxi_pipeline_slack_kubeflow.py | python | _create_pipeline | () | return pipeline.Pipeline(
pipeline_name=_pipeline_name,
pipeline_root=_pipeline_root,
components=[
example_gen, statistics_gen, schema_gen, example_validator, transform,
trainer, evaluator, model_validator, slack_validator, pusher
],
enable_cache=True,
) | Implements the chicago taxi pipeline with TFX. | Implements the chicago taxi pipeline with TFX. | [
"Implements",
"the",
"chicago",
"taxi",
"pipeline",
"with",
"TFX",
"."
] | def _create_pipeline():
"""Implements the chicago taxi pipeline with TFX."""
examples = csv_input(_data_root)
# Brings data into the pipeline or otherwise joins/converts training data.
example_gen = CsvExampleGen(input=examples)
# Computes statistics over data for visualization and example validation.
statistics_gen = StatisticsGen(examples=example_gen.outputs['examples'])
# Generates schema based on statistics files.
schema_gen = SchemaGen(statistics=statistics_gen.outputs['statistics'])
# Performs anomaly detection based on statistics and data schema.
example_validator = ExampleValidator(
statistics=statistics_gen.outputs['statistics'],
schema=schema_gen.outputs['schema'])
# Performs transformations and feature engineering in training and serving.
transform = Transform(
examples=example_gen.outputs['examples'],
schema=schema_gen.outputs['schema'],
preprocessing_fn=_taxi_transformer_func)
# Uses user-provided Python function that implements a model.
trainer = Trainer(
trainer_fn=_taxi_trainer_func,
examples=transform.outputs['transformed_examples'],
schema=schema_gen.outputs['schema'],
transform_graph=transform.outputs['transform_graph'],
train_args=trainer_pb2.TrainArgs(num_steps=10000),
eval_args=trainer_pb2.EvalArgs(num_steps=5000))
# Uses TFMA to compute a evaluation statistics over features of a model.
evaluator = Evaluator(
examples=example_gen.outputs['examples'],
model=trainer.outputs['model'],
feature_slicing_spec=evaluator_pb2.FeatureSlicingSpec(specs=[
evaluator_pb2.SingleSlicingSpec(
column_for_slicing=['trip_start_hour'])
]))
# Performs quality validation of a candidate model (compared to a baseline).
model_validator = ModelValidator(
examples=example_gen.outputs['examples'], model=trainer.outputs['model'])
# This custom component serves as a bridge between pipeline and human model
# reviewers to enable review-and-push workflow in model development cycle. It
# utilizes Slack API to send message to user-defined Slack channel with model
# URI info and wait for go / no-go decision from the same Slack channel:
# * To approve the model, users need to reply the thread sent out by the bot
# started by SlackComponent with 'lgtm' or 'approve'.
# * To reject the model, users need to reply the thread sent out by the bot
# started by SlackComponent with 'decline' or 'reject'.
slack_validator = SlackComponent(
model=trainer.outputs['model'],
model_blessing=model_validator.outputs['blessing'],
slack_token=_slack_token,
slack_channel_id=_slack_channel_id,
timeout_sec=3600,
)
# Checks whether the model passed the validation steps and pushes the model
# to a file destination if check passed.
pusher = Pusher(
model=trainer.outputs['model'],
model_blessing=slack_validator.outputs['slack_blessing'],
push_destination=pusher_pb2.PushDestination(
filesystem=pusher_pb2.PushDestination.Filesystem(
base_directory=_serving_model_dir)))
return pipeline.Pipeline(
pipeline_name=_pipeline_name,
pipeline_root=_pipeline_root,
components=[
example_gen, statistics_gen, schema_gen, example_validator, transform,
trainer, evaluator, model_validator, slack_validator, pusher
],
enable_cache=True,
) | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/celery/backends/rpc.py | python | RPCBackend.on_out_of_band_result | (self, task_id, message) | [] | def on_out_of_band_result(self, task_id, message):
# Callback called when a reply for a task is received,
# but we have no idea what do do with it.
# Since the result is not pending, we put it in a separate
# buffer: probably it will become pending later.
if self.result_consumer:
self.result_consumer.on_out_of_band_result(message)
self._out_of_band[task_id] = message | [
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home-assistant/core | 265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1 | homeassistant/components/litterrobot/vacuum.py | python | LitterRobotCleaner.async_reset_waste_drawer | (self) | Reset the waste drawer level. | Reset the waste drawer level. | [
"Reset",
"the",
"waste",
"drawer",
"level",
"."
] | async def async_reset_waste_drawer(self) -> None:
"""Reset the waste drawer level."""
# The Litter-Robot reset waste drawer service has been replaced by a
# dedicated button entity and marked as deprecated
_LOGGER.warning(
"The 'litterrobot.reset_waste_drawer' service is deprecated and "
"replaced by a dedicated reset waste drawer button entity; Please "
"use that entity to reset the waste drawer instead"
)
await self.robot.reset_waste_drawer()
self.coordinator.async_set_updated_data(True) | [
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