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hudora/huBarcode | e89cc42a4325aaab20297fecd33a785061633bed | hubarcode/datamatrix/textencoder.py | python | TextEncoder.pad | (self) | Pad out the encoded text to the correct word length | Pad out the encoded text to the correct word length | [
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] | def pad(self):
"""Pad out the encoded text to the correct word length"""
unpadded_len = len(self.codewords)
log.debug("Unpadded data size: %d bytes", unpadded_len)
# Work out how big the matrix needs to be
for self.size_index, length in enumerate(data_word_length):
if length >= unpadded_len:
break
if self.size_index > 8:
log.error("Data too big")
sys.exit(0)
# Number of characters with which the data will be padded
padsize = length - unpadded_len
log.debug("Pad size: %d bytes", padsize)
# First pad character is 129
if padsize:
self.codewords += chr(129)
def rand253(pos):
"""generate the next padding character"""
rnd = ((149 * pos) % 253) + 1
return (129 + rnd) % 254
# Remaining pad characters generated by 253-state algorithm
for i in range(1, padsize):
self.codewords += chr(rand253(unpadded_len + i + 1))
log.debug("Word size after padding: %d bytes", len(self.codewords)) | [
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algolia/color-extractor | 3f1d5753cb418a3461d9b350e907c4d1d693d590 | color_extractor/name.py | python | Name._knn_args | () | return {
'n_neighbors': 50,
'weights': 'distance',
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'n_jobs': -1,
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jacoxu/encoder_decoder | 6829c3bc42105aedfd1b8a81c81be19df4326c0a | keras/models.py | python | Sequential._check_generator_output | (self, generator_output, stop) | return X, y, sample_weight | Validates the output of a generator. On error, calls
stop.set().
# Arguments
generator_output: output of a data generator.
stop: threading event to be called to
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'''Validates the output of a generator. On error, calls
stop.set().
# Arguments
generator_output: output of a data generator.
stop: threading event to be called to
interrupt training/evaluation.
'''
if not hasattr(generator_output, '__len__'):
stop.set()
raise Exception('The generator output must be a tuple. Found: ' +
str(type(generator_output)))
if len(generator_output) == 2:
X, y = generator_output
if type(X) == list:
assert len(set([len(a) for a in X] + [len(y)])) == 1
else:
assert len(X) == len(y)
X = [X]
sample_weight = None
elif len(generator_output) == 3:
X, y, sample_weight = generator_output
if type(X) == list:
assert len(set([len(a) for a in X] +
[len(y), len(sample_weight)])) == 1
else:
assert len(X) == len(y) == len(sample_weight)
X = [X]
else:
stop.set()
raise Exception('The generator output tuple must have '
'2 or 3 elements.')
sample_weight = standardize_weights(y, sample_weight=sample_weight,
sample_weight_mode=self.sample_weight_mode)
return X, y, sample_weight | [
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OUCMachineLearning/OUCML | 5b54337d7c0316084cb1a74befda2bba96137d4a | GAN/人脸还原SCFEGAN/ui/ui.py | python | Ui_Form.setupUi | (self, Form) | [] | def setupUi(self, Form):
Form.setObjectName("Form")
Form.resize(1200, 660)
self.pushButton = QtWidgets.QPushButton(Form)
self.pushButton.setGeometry(QtCore.QRect(10, 10, 97, 27))
self.pushButton.setObjectName("pushButton")
self.pushButton_2 = QtWidgets.QPushButton(Form)
self.pushButton_2.setGeometry(QtCore.QRect(130, 10, 97, 27))
self.pushButton_2.setObjectName("pushButton_2")
self.pushButton_3 = QtWidgets.QPushButton(Form)
self.pushButton_3.setGeometry(QtCore.QRect(250, 10, 97, 27))
self.pushButton_3.setObjectName("pushButton_3")
self.pushButton_4 = QtWidgets.QPushButton(Form)
self.pushButton_4.setGeometry(QtCore.QRect(370, 10, 97, 27))
self.pushButton_4.setObjectName("pushButton_4")
self.pushButton_5 = QtWidgets.QPushButton(Form)
self.pushButton_5.setGeometry(QtCore.QRect(560, 360, 81, 27))
self.pushButton_5.setObjectName("pushButton_5")
self.pushButton_6 = QtWidgets.QPushButton(Form)
self.pushButton_6.setGeometry(QtCore.QRect(490, 40, 97, 27))
self.pushButton_6.setObjectName("pushButton_6")
self.pushButton_7 = QtWidgets.QPushButton(Form)
self.pushButton_7.setGeometry(QtCore.QRect(490, 10, 97, 27))
self.pushButton_7.setObjectName("pushButton_7")
self.pushButton_8 = QtWidgets.QPushButton(Form)
self.pushButton_8.setGeometry(QtCore.QRect(370, 40, 97, 27))
self.pushButton_8.setObjectName("pushButton_8")
self.graphicsView = QtWidgets.QGraphicsView(Form)
self.graphicsView.setGeometry(QtCore.QRect(20, 120, 512, 512))
self.graphicsView.setObjectName("graphicsView")
self.graphicsView_2 = QtWidgets.QGraphicsView(Form)
self.graphicsView_2.setGeometry(QtCore.QRect(660, 120, 512, 512))
self.graphicsView_2.setObjectName("graphicsView_2")
self.saveImg = QtWidgets.QPushButton(Form)
self.saveImg.setGeometry(QtCore.QRect(610, 10, 97, 27))
self.saveImg.setObjectName("saveImg")
self.arrangement = QtWidgets.QPushButton(Form)
self.arrangement.setGeometry(QtCore.QRect(610, 40, 97, 27))
self.arrangement.setObjectName("arrangement")
self.retranslateUi(Form)
self.pushButton.clicked.connect(Form.open)
self.pushButton_2.clicked.connect(Form.mask_mode)
self.pushButton_3.clicked.connect(Form.sketch_mode)
self.pushButton_4.clicked.connect(Form.stroke_mode)
self.pushButton_5.clicked.connect(Form.complete)
self.pushButton_6.clicked.connect(Form.undo)
self.pushButton_7.clicked.connect(Form.color_change_mode)
self.pushButton_8.clicked.connect(Form.clear)
self.saveImg.clicked.connect(Form.save_img)
self.arrangement.clicked.connect(Form.arrange)
QtCore.QMetaObject.connectSlotsByName(Form) | [
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seann999/ssd_tensorflow | f381dba71029a329a2b23763b804289eb8069b4b | vgg/utils.py | python | load_image | (path, size=224) | return resized_img | [] | def load_image(path, size=224):
# load image
img = skimage.io.imread(path)
img = img / 255.0
assert (0 <= img).all() and (img <= 1.0).all()
# print "Original Image Shape: ", img.shape
# we crop image from center
short_edge = min(img.shape[:2])
yy = int((img.shape[0] - short_edge) / 2)
xx = int((img.shape[1] - short_edge) / 2)
crop_img = img[yy: yy + short_edge, xx: xx + short_edge]
# resize to 224, 224
resized_img = skimage.transform.resize(crop_img, (size, size))
return resized_img | [
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ckiplab/ckiptagger | 50add4192087927342f0bc19137a169c608f8ebe | src/model_ws.py | python | Model.predict_label_for_a_batch | (self, sample_list) | return prediction_list | Generate token label sequence for each sample in the mini-batch.
prediction_list: [prediction]
prediction: [token_label] | Generate token label sequence for each sample in the mini-batch.
prediction_list: [prediction]
prediction: [token_label] | [
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"""Generate token label sequence for each sample in the mini-batch.
prediction_list: [prediction]
prediction: [token_label]
"""
(
input_length,
w_k, w_v,
_, _,
) = self.get_formatted_input(sample_list)
logits = self.sess.run( # [batch, length, output_d]
self.logits,
feed_dict = {
self.bilstm.kr: 1.0,
self.input_length: input_length,
self.w_k: w_k,
self.w_v: w_v,
}
)
labelindex_sequence_list = np.argmax(logits, axis=2) # [batch, length]
prediction_list = []
for b, labelindex_sequence in enumerate(labelindex_sequence_list):
prediction = []
for label_index in labelindex_sequence[:input_length[b]]:
token_label = self.tokenlabel_list[label_index]
prediction.append(token_label)
prediction[0] = "B"
prediction_list.append(prediction)
return prediction_list | [
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nltk/nltk | 3f74ac55681667d7ef78b664557487145f51eb02 | nltk/inference/api.py | python | BaseProverCommand.prove | (self, verbose=False) | return self._result | Perform the actual proof. Store the result to prevent unnecessary
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Perform the actual proof. Store the result to prevent unnecessary
re-proving.
"""
if self._result is None:
self._result, self._proof = self._prover._prove(
self.goal(), self.assumptions(), verbose
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pilotmoon/PopClip-Extensions | 29fc472befc09ee350092ac70283bd9fdb456cb6 | source/PushbulletPython/requests/api.py | python | head | (url, **kwargs) | return request('head', url, **kwargs) | Sends a HEAD request. Returns :class:`Response` object.
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:param \*\*kwargs: Optional arguments that ``request`` takes.
"""
kwargs.setdefault('allow_redirects', False)
return request('head', url, **kwargs) | [
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optuna/optuna | 2c44c1a405ba059efd53f4b9c8e849d20fb95c0a | optuna/_transform.py | python | _SearchSpaceTransform.transform | (self, params: Dict[str, Any]) | return trans_params | Transform a parameter configuration from actual values to continuous space.
Args:
params:
A parameter configuration to transform.
Returns:
A 1-dimensional ``numpy.ndarray`` holding the transformed parameters in the
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"""Transform a parameter configuration from actual values to continuous space.
Args:
params:
A parameter configuration to transform.
Returns:
A 1-dimensional ``numpy.ndarray`` holding the transformed parameters in the
configuration.
"""
trans_params = numpy.zeros(self._bounds.shape[0], dtype=numpy.float64)
bound_idx = 0
for name, distribution in self._search_space.items():
assert name in params, "Parameter configuration must contain all distributions."
param = params[name]
if isinstance(distribution, CategoricalDistribution):
choice_idx = distribution.to_internal_repr(param)
trans_params[bound_idx + choice_idx] = 1
bound_idx += len(distribution.choices)
else:
trans_params[bound_idx] = _transform_numerical_param(
param, distribution, self._transform_log
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bound_idx += 1
return trans_params | [
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psychopy/psychopy | 01b674094f38d0e0bd51c45a6f66f671d7041696 | psychopy/app/builder/builder.py | python | RoutineCanvas.getMaxTime | (self) | return maxTime | Return the max time to be drawn in the window | Return the max time to be drawn in the window | [
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"""
maxTime, nonSlip = self.routine.getMaxTime()
if self.routine.hasOnlyStaticComp():
maxTime = int(maxTime) + 1.0
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linxid/Machine_Learning_Study_Path | 558e82d13237114bbb8152483977806fc0c222af | Machine Learning In Action/Chapter4-NaiveBayes/venv/Lib/site-packages/pip/_vendor/html5lib/_inputstream.py | python | EncodingBytes.previous | (self) | return self[p:p + 1] | [] | def previous(self):
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mitmproxy/mitmproxy | 1abb8f69217910c8623bd1339da2502aed98ff0d | mitmproxy/contrib/urwid/raw_display.py | python | Screen._setup_G1 | (self) | Initialize the G1 character set to graphics mode if required. | Initialize the G1 character set to graphics mode if required. | [
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self._setup_G1_done = True | [
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saltstack/salt | fae5bc757ad0f1716483ce7ae180b451545c2058 | salt/states/dvs.py | python | mod_init | (low) | return True | Init function | Init function | [
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ajinabraham/OWASP-Xenotix-XSS-Exploit-Framework | cb692f527e4e819b6c228187c5702d990a180043 | bin/x86/Debug/scripting_engine/Lib/site-packages/Crypto/Util/asn1.py | python | DerInteger.decode | (self, derEle, noLeftOvers=0) | return tlvLength | Decode a complete INTEGER DER element, and re-initializes this
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@param noLeftOvers Indicate whether it is acceptable to complete the
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@return Index of the first unused byte in the given DER element.
Raises a ValueError exception if the DER element is not a
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Raises an IndexError exception if the DER element is too short. | Decode a complete INTEGER DER element, and re-initializes this
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@param derEle A complete INTEGER DER element. It must start with a DER
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@param noLeftOvers Indicate whether it is acceptable to complete the
parsing of the DER element and find that not all
bytes in derEle have been used.
@return Index of the first unused byte in the given DER element.
Raises a ValueError exception if the DER element is not a
valid non-negative INTEGER.
Raises an IndexError exception if the DER element is too short.
"""
tlvLength = DerObject.decode(self, derEle, noLeftOvers)
if self.typeTag!=self.typeTags['INTEGER']:
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if bord(self.payload[0])>127:
raise ValueError ("Negative INTEGER.")
self.value = bytes_to_long(self.payload)
return tlvLength | [
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HSLCY/ABSA-BERT-pair | 7d238eb8c772946b9e572373c144b11151e4187f | optimization.py | python | BERTAdam.step | (self, closure=None) | return loss | Performs a single optimization step.
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"""
loss = None
if closure is not None:
loss = closure()
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continue
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if grad.is_sparse:
raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')
state = self.state[p]
# State initialization
if len(state) == 0:
state['step'] = 0
# Exponential moving average of gradient values
state['next_m'] = torch.zeros_like(p.data)
# Exponential moving average of squared gradient values
state['next_v'] = torch.zeros_like(p.data)
next_m, next_v = state['next_m'], state['next_v']
beta1, beta2 = group['b1'], group['b2']
# Add grad clipping
if group['max_grad_norm'] > 0:
clip_grad_norm_(p, group['max_grad_norm'])
# Decay the first and second moment running average coefficient
# In-place operations to update the averages at the same time
next_m.mul_(beta1).add_(1 - beta1, grad)
next_v.mul_(beta2).addcmul_(1 - beta2, grad, grad)
update = next_m / (next_v.sqrt() + group['e'])
# Just adding the square of the weights to the loss function is *not*
# the correct way of using L2 regularization/weight decay with Adam,
# since that will interact with the m and v parameters in strange ways.
#
# Instead we want ot decay the weights in a manner that doesn't interact
# with the m/v parameters. This is equivalent to adding the square
# of the weights to the loss with plain (non-momentum) SGD.
if group['weight_decay_rate'] > 0.0:
update += group['weight_decay_rate'] * p.data
if group['t_total'] != -1:
schedule_fct = SCHEDULES[group['schedule']]
lr_scheduled = group['lr'] * schedule_fct(state['step']/group['t_total'], group['warmup'])
else:
lr_scheduled = group['lr']
update_with_lr = lr_scheduled * update
p.data.add_(-update_with_lr)
state['step'] += 1
# step_size = lr_scheduled * math.sqrt(bias_correction2) / bias_correction1
# bias_correction1 = 1 - beta1 ** state['step']
# bias_correction2 = 1 - beta2 ** state['step']
return loss | [
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KhronosGroup/NNEF-Tools | c913758ca687dab8cb7b49e8f1556819a2d0ca25 | nnef_tools/conversion/converter.py | python | Converter._check_conditions | (self, op, transform) | return error | [] | def _check_conditions(self, op, transform):
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return error | [
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lovelylain/pyctp | fd304de4b50c4ddc31a4190b1caaeb5dec66bc5d | lts/ctp/ApiStruct.py | python | QryTradingAccount.__init__ | (self, BrokerID='', InvestorID='') | [] | def __init__(self, BrokerID='', InvestorID=''):
self.BrokerID = '' #经纪公司代码, char[11]
self.InvestorID = '' | [
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ethereum/trinity | 6383280c5044feb06695ac2f7bc1100b7bcf4fe0 | p2p/discovery.py | python | DiscoveryService.bootstrap | (self) | [] | async def bootstrap(self) -> None:
bonding_queries = []
for node in self.bootstrap_nodes:
uri = node.uri()
pubkey, _, uri_tail = uri.partition('@')
pubkey_head = pubkey[:16]
pubkey_tail = pubkey[-8:]
self.logger.debug("full-bootnode: %s", uri)
self.logger.debug("bootnode: %s...%s@%s", pubkey_head, pubkey_tail, uri_tail)
# Upon startup we want to force a new bond with our bootnodes. That ensures we only
# query the ones that are still reachable and that we tell them about our latest ENR,
# which might have changed.
self.invalidate_bond(node.id)
bonding_queries.append((self.bond, node.id))
bonded = await trio_utils.gather(*bonding_queries)
successful_bonds = len([item for item in bonded if item is True])
if not successful_bonds:
self.logger.warning("Failed to bond with any bootstrap nodes %s", self.bootstrap_nodes)
return
else:
self.logger.info(
"Bonded with %d bootstrap nodes, performing initial lookup", successful_bonds)
await self.lookup_random() | [
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chribsen/simple-machine-learning-examples | dc94e52a4cebdc8bb959ff88b81ff8cfeca25022 | venv/lib/python2.7/site-packages/sklearn/utils/__init__.py | python | indices_to_mask | (indices, mask_length) | return mask | Convert list of indices to boolean mask.
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----------
indices : list-like
List of integers treated as indices.
mask_length : int
Length of boolean mask to be generated.
Returns
-------
mask : 1d boolean nd-array
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"""Convert list of indices to boolean mask.
Parameters
----------
indices : list-like
List of integers treated as indices.
mask_length : int
Length of boolean mask to be generated.
Returns
-------
mask : 1d boolean nd-array
Boolean array that is True where indices are present, else False.
"""
if mask_length <= np.max(indices):
raise ValueError("mask_length must be greater than max(indices)")
mask = np.zeros(mask_length, dtype=np.bool)
mask[indices] = True
return mask | [
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lxdock/lxdock | f71006d130bc8b53603eea36a546003495437493 | lxdock/cli/main.py | python | LXDock.status | (self, args) | [] | def status(self, args):
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vmware/pyvcloud | d72c615fa41b8ea5ab049a929e18d8ba6460fc59 | pyvcloud/vcd/vm.py | python | VM.get_memory | (self) | return int(
self.resource.VmSpecSection.MemoryResourceMb.Configured.text) | Returns the amount of memory in MB.
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"""Returns the amount of memory in MB.
:return: amount of memory in MB.
:rtype: int
"""
self.get_resource()
return int(
self.resource.VmSpecSection.MemoryResourceMb.Configured.text) | [
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AndroBugs/AndroBugs_Framework | 7fd3a2cb1cf65a9af10b7ed2129701d4451493fe | tools/modified/androguard/core/bytecodes/dvm.py | python | Instruction.get_output | (self, idx=-1) | Return an additional output of the instruction
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/lib/python2.7/site-packages/pip/_vendor/urllib3/poolmanager.py | python | PoolManager.connection_from_host | (self, host, port=None, scheme='http', pool_kwargs=None) | return self.connection_from_context(request_context) | Get a :class:`ConnectionPool` based on the host, port, and scheme.
If ``port`` isn't given, it will be derived from the ``scheme`` using
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needed. | Get a :class:`ConnectionPool` based on the host, port, and scheme. | [
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"""
Get a :class:`ConnectionPool` based on the host, port, and scheme.
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request_context['scheme'] = scheme or 'http'
if not port:
port = port_by_scheme.get(request_context['scheme'].lower(), 80)
request_context['port'] = port
request_context['host'] = host
return self.connection_from_context(request_context) | [
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pybliometrics-dev/pybliometrics | 26ad9656e5a1d4c80774937706a0df85776f07d0 | pybliometrics/scopus/abstract_retrieval.py | python | AbstractRetrieval.refcount | (self) | Number of references of an article.
Note: Requires either the FULL view or REF view. | Number of references of an article.
Note: Requires either the FULL view or REF view. | [
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try: # REF view
return int(self._ref['@total-references'])
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try:
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limodou/uliweb | 8bc827fa6bf7bf58aa8136b6c920fe2650c52422 | uliweb/utils/date.py | python | to_timezone | (dt, tzinfo=None) | Convert a datetime to timezone | Convert a datetime to timezone | [
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"""
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if not tz:
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pantsbuild/pex | 473c6ac732ed4bc338b4b20a9ec930d1d722c9b4 | pex/vendor/_vendored/pip/pip/_vendor/pep517/_in_process.py | python | get_requires_for_build_sdist | (config_settings) | Invoke the optional get_requires_for_build_wheel hook
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backend = _build_backend()
try:
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tendenci/tendenci | 0f2c348cc0e7d41bc56f50b00ce05544b083bf1d | tendenci/apps/files/models.py | python | File.icon | (self) | return static('images/icons/'+icons[self.type()]) | [] | def icon(self):
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# map file-type to image file
icons = {
'text': 'icon-ms-word-2007.gif',
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'image': 'icon-ms-image-2007.png',
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'video': 'icon-wmv.png',
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jborean93/pypsrp | 678df470648e9b82a1be82d6a08e056b2d3c413c | pypsrp/host.py | python | PSHostRawUserInterface.SetBufferContents2 | (self, runspace, pipeline, origin, contents) | MI: 48
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x and y are extended properties of this object
origin.extended_properties['x']
origin.extended_properties['y']
:param contents: A rectangle of BufferCell objects to be copied to the
screen buffer. This is also a GenericComplexObject which is a multi
dimensional array.
https://msdn.microsoft.com/en-us/library/dd340684.aspx
# number of elements in each row
contents.extended_properties['mal']
# each BufferCell is in this list, use mal to determine what
# rows they are in
contents.extended_properties['mae']
"""
pass | [
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home-assistant/core | 265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1 | homeassistant/components/zwave_js/api.py | python | async_get_node | (orig_func: Callable) | return async_get_node_func | Decorate async function to get node. | Decorate async function to get node. | [
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@wraps(orig_func)
async def async_get_node_func(
hass: HomeAssistant,
connection: ActiveConnection,
msg: dict,
entry: ConfigEntry,
client: Client,
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"""Provide user specific data and store to function."""
node_id = msg[NODE_ID]
node = client.driver.controller.nodes.get(node_id)
if node is None:
connection.send_error(msg[ID], ERR_NOT_FOUND, f"Node {node_id} not found")
return
await orig_func(hass, connection, msg, node)
return async_get_node_func | [
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hzy46/Deep-Learning-21-Examples | 15c2d9edccad090cd67b033f24a43c544e5cba3e | chapter_5/research/object_detection/core/anchor_generator.py | python | AnchorGenerator.num_anchors_per_location | (self) | Returns the number of anchors per spatial location.
Returns:
a list of integers, one for each expected feature map to be passed to
the `generate` function. | Returns the number of anchors per spatial location. | [
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] | def num_anchors_per_location(self):
"""Returns the number of anchors per spatial location.
Returns:
a list of integers, one for each expected feature map to be passed to
the `generate` function.
"""
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timbrel/GitSavvy | c0a627c30da79cb10a5a33f30d92d177f78da0ab | core/git_mixins/stage_unstage.py | python | StageUnstageMixin.stage_file | (self, *fpath, force=True) | Given an absolute path or path relative to the repo's root, stage
the file. | Given an absolute path or path relative to the repo's root, stage
the file. | [
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] | def stage_file(self, *fpath, force=True):
# type: (str, bool) -> None
"""
Given an absolute path or path relative to the repo's root, stage
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"""
self.git(
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chb/indivo_server | 9826c67ab17d7fc0df935db327344fb0c7d237e5 | indivo/views/reports/measurement.py | python | carenet_measurement_list | (*args, **kwargs) | return _measurement_list(*args, **kwargs) | List the measurement data for a given carenet.
For 1:1 mapping of URLs to views. Just calls
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] | def carenet_measurement_list(*args, **kwargs):
""" List the measurement data for a given carenet.
For 1:1 mapping of URLs to views. Just calls
:py:meth:`~indivo.views.reports.measurement._measurement_list`.
"""
return _measurement_list(*args, **kwargs) | [
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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_adm_registry.py | python | Registry.create | (self) | return {'returncode': rval, 'results': results} | Create a registry | Create a registry | [
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'''Create a registry'''
results = []
self.needs_update()
# if the object is none, then we need to create it
# if the object needs an update, then we should call replace
# Handle the deploymentconfig
if self.deploymentconfig is None:
results.append(self._create(self.prepared_registry['deployment_file']))
elif self.prepared_registry['deployment_update']:
results.append(self._replace(self.prepared_registry['deployment_file']))
# Handle the service
if self.service is None:
results.append(self._create(self.prepared_registry['service_file']))
elif self.prepared_registry['service_update']:
results.append(self._replace(self.prepared_registry['service_file']))
# Clean up returned results
rval = 0
for result in results:
# pylint: disable=invalid-sequence-index
if 'returncode' in result and result['returncode'] != 0:
rval = result['returncode']
return {'returncode': rval, 'results': results} | [
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gaphor/gaphor | dfe8df33ef3b884afdadf7c91fc7740f8d3c2e88 | gaphor/UML/umloverrides.py | python | property_navigability | (self: uml.Property) | Get navigability of an association end.
If no association is related to the property, then unknown
navigability is assumed. | Get navigability of an association end. | [
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] | def property_navigability(self: uml.Property) -> list[bool | None]:
"""Get navigability of an association end.
If no association is related to the property, then unknown
navigability is assumed.
"""
assoc = self.association
if not (assoc and self.opposite):
return [None] # assume unknown
owner = self.opposite.type
if (
isinstance(owner, (uml.Class, uml.DataType, uml.Interface))
and isinstance(self.type, (uml.Class, uml.DataType, uml.Interface))
and (self in owner.ownedAttribute)
or self in assoc.navigableOwnedEnd
):
return [True]
elif isinstance(assoc.ownedEnd, uml.ExtensionEnd):
return [self is assoc.ownedEnd]
elif assoc.ownedEnd is None or self in assoc.ownedEnd:
return [None]
else:
return [False] | [
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SpaceNetChallenge/SpaceNet_Off_Nadir_Solutions | 014c4ca27a70b5907a183e942228004c989dcbe4 | number13/number13/src/create_patches_all.py | python | patches_and_cocoann | (gt, outdir_rgb, outdir_mpan, count=1,create_anns=True) | return annotations, images, counter | [] | def patches_and_cocoann(gt, outdir_rgb, outdir_mpan, count=1,create_anns=True):
gt.index = gt.ImageId
gt_flt = gt[gt.PolygonWKT_Geo != 'POLYGON EMPTY']
gv = gt_flt.ImageId.value_counts()
annotations = []
images = []
counter = count
for u in gt_flt.ImageId.unique():
try:
pan_sharpen_dir = ''.join([RAW_DIR, gt.name, '/Pan-Sharpen/'])
image_file = ''.join([pan_sharpen_dir, 'Pan-Sharpen', '_', u, '.tif'])
img_rgb = tifffile.imread(image_file)
if np.argmin(img_rgb.shape) == 0:
img_rgb = np.transpose(img_rgb, (1, 2, 0))
img_rgb = img_rgb[:, :, [3, 2, 1, 0]]
img_mpan = get_pan_sharpend(''.join([RAW_DIR, gt.name, '/']), u)
except:
print('load error..', u)
continue
if gv[u] > 1:
poly = gt.loc[u].PolygonWKT_Pix.apply(lambda x: loads(x)).values.tolist()
else:
poly = [loads(gt.loc[u].PolygonWKT_Pix)]
mask = util.mask_for_polygons(poly, im_size=imsize)
img_patches_rgb, mask_patches, kp = patch_creator.create(img=img_rgb, mask=mask, nonzero=True)
img_patches_mpan, _, _ = patch_creator.create(img=img_mpan, mask=mask, nonzero=True)
for i, k in enumerate(kp.keys()):
file_name_rgb = os.path.join(outdir_rgb, ''.join([u, '_', str(k), '.tif']))
file_name_mpan = os.path.join(outdir_mpan, ''.join([u, '_', str(k), '.tif']))
if create_anns:
anns, images_d, counter = create_coco_anns(file_name_rgb, counter, mask_patches[i].squeeze())
annotations.extend(anns)
images.extend(images_d)
tifffile.imsave(file_name_mpan, img_patches_mpan[i].astype('uint16'))
tifffile.imsave(file_name_rgb, img_patches_rgb[i].astype('uint16'))
if DEBUG:
break
return annotations, images, counter | [
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lawsie/guizero | 1c3011a3b0930175beaec3aa2f5503bf9339a0a3 | guizero/utilities.py | python | AnimationPlayer.stop | (self) | Stops the animation | Stops the animation | [
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"""
Stops the animation
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sigmavirus24/github3.py | f642a27833d6bef93daf5bb27b35e5ddbcfbb773 | src/github3/github.py | python | GitHub.create_key | (self, title, key, read_only=False) | return self._instance_or_null(users.Key, json) | Create a new key for the authenticated user.
:param str title:
(required), key title
:param str key:
(required), actual key contents, accepts path
as a string or file-like object
:param bool read_only:
(optional), restrict key access to read-only, default to False
:returns:
created key
:rtype:
:class:`~github3.users.Key` | Create a new key for the authenticated user. | [
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] | def create_key(self, title, key, read_only=False):
"""Create a new key for the authenticated user.
:param str title:
(required), key title
:param str key:
(required), actual key contents, accepts path
as a string or file-like object
:param bool read_only:
(optional), restrict key access to read-only, default to False
:returns:
created key
:rtype:
:class:`~github3.users.Key`
"""
json = None
if title and key:
data = {"title": title, "key": key, "read_only": read_only}
url = self._build_url("user", "keys")
req = self._post(url, data=data)
json = self._json(req, 201)
return self._instance_or_null(users.Key, json) | [
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triaquae/triaquae | bbabf736b3ba56a0c6498e7f04e16c13b8b8f2b9 | TriAquae/hosts/views_auth.py | python | load_batch_auth | (request) | [] | def load_batch_auth(request):
if 'buf' in request.POST:
buf = request.POST['buf']
list_buf = buf.splitlines()
list_buf = [l for l in list_buf if l and not re.match('#', l)]
num = 0
for l in list_buf:
logger.error('save %s' % l)
try:
(ip, remoteuser, authtype, password) = re.split('\s+', l)[:4]
ip, created = models.IP.objects.get_or_create(ip=ip)
logger.error('ip [%s] created[%s]' %(ip, created))
remoteuser, created = models.RemoteUser.objects.get_or_create(name=remoteuser)
logger.error('remoteuser [%s] created[%s]' %(remoteuser, created))
obj_auth = models.AuthByIpAndRemoteUser(ip=ip, remoteUser=remoteuser, authtype=authtype, password=password)
obj_auth.validate_unique()
obj_auth.save()
num += 1
except:
logger.error('%s' % traceback.format_exc())
messages.info(request, 'load %s auths, filter %s ones' % (num, len(list_buf) - num))
return HttpResponseRedirect('/admin/hosts/authbyipandremoteuser/')
elif 'buf' in request.GET:
logger.error(request.GET)
return HttpResponseRedirect('/admin/hosts/authbyipandremoteuser/') | [
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timkpaine/pyEX | 254acd2b0cf7cb7183100106f4ecc11d1860c46a | pyEX/stocks/iex.py | python | iexOfficialPrice | (symbol=None, token="", version="stable", format="json") | return _get("deep/official-price", token=token, version=version, format=format) | The Official Price message is used to disseminate the IEX Official Opening and Closing Prices.
These messages will be provided only for IEX Listed Securities.
https://iexcloud.io/docs/api/#deep-official-price
Args:
symbol (str): Ticker to request
token (str): Access token
version (str): API version
format (str): return format, defaults to json
Returns:
dict: result | The Official Price message is used to disseminate the IEX Official Opening and Closing Prices. | [
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] | def iexOfficialPrice(symbol=None, token="", version="stable", format="json"):
"""The Official Price message is used to disseminate the IEX Official Opening and Closing Prices.
These messages will be provided only for IEX Listed Securities.
https://iexcloud.io/docs/api/#deep-official-price
Args:
symbol (str): Ticker to request
token (str): Access token
version (str): API version
format (str): return format, defaults to json
Returns:
dict: result
"""
_raiseIfNotStr(symbol)
if symbol:
return _get(
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token=token,
version=version,
format=format,
)
return _get("deep/official-price", token=token, version=version, format=format) | [
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Calysto/calysto_scheme | 15bf81987870bcae1264e5a0a06feb9a8ee12b8b | calysto_scheme/scheme.py | python | try_body_hat | (x) | return cadr_hat(x) | [] | def try_body_hat(x):
return cadr_hat(x) | [
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qxf2/qxf2-page-object-model | 8b2978e5bd2a5413d581f9e29a2098e5f5e742fd | utils/BrowserStack_Library.py | python | BrowserStack_Library.get_session_url | (self) | return session_url | Get the session URL | Get the session URL | [
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"the",
"session",
"URL"
] | def get_session_url(self):
"Get the session URL"
build_id = self.get_build_id()
session_id = self.get_active_session_id()
session_url = self.browserstack_url + 'builds/%s/sessions/%s'%(build_id,session_id)
return session_url | [
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CvvT/dumpDex | 92ab3b7e996194a06bf1dd5538a4954e8a5ee9c1 | python/idaapi.py | python | cinsnptrvec_t.at | (self, *args) | return _idaapi.cinsnptrvec_t_at(self, *args) | at(self, n) -> cinsn_t | at(self, n) -> cinsn_t | [
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"""
at(self, n) -> cinsn_t
"""
return _idaapi.cinsnptrvec_t_at(self, *args) | [
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freelawproject/courtlistener | ab3ae7bb6e5e836b286749113e7dbb403d470912 | cl/search/views.py | python | check_pagination_depth | (page_number) | Check if the pagination is too deep (indicating a crawler) | Check if the pagination is too deep (indicating a crawler) | [
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")"
] | def check_pagination_depth(page_number):
"""Check if the pagination is too deep (indicating a crawler)"""
max_search_pagination_depth = 100
if page_number > max_search_pagination_depth:
logger.warning(
"Query depth of %s denied access (probably a crawler)",
page_number,
)
raise PermissionDenied | [
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WebwareForPython/DBUtils | 798d5ad3bdfccdebc22fd3d7b16c8b9e86bdcaf7 | dbutils/steady_db.py | python | SteadyDBCursor.__iter__ | (self) | Make cursor compatible to the iteration protocol. | Make cursor compatible to the iteration protocol. | [
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"""Make cursor compatible to the iteration protocol."""
cursor = self._cursor
try: # use iterator provided by original cursor
return iter(cursor)
except TypeError: # create iterator if not provided
return iter(cursor.fetchone, None) | [
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AppScale/gts | 46f909cf5dc5ba81faf9d81dc9af598dcf8a82a9 | AppServer/lib/django-1.4/django/forms/forms.py | python | BoundField.as_textarea | (self, attrs=None, **kwargs) | return self.as_widget(Textarea(), attrs, **kwargs) | Returns a string of HTML for representing this as a <textarea>. | Returns a string of HTML for representing this as a <textarea>. | [
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] | def as_textarea(self, attrs=None, **kwargs):
"Returns a string of HTML for representing this as a <textarea>."
return self.as_widget(Textarea(), attrs, **kwargs) | [
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Finance-Hub/FinanceHub | 3968d9965e8e2c3b5850f1852b56c485859a9c89 | dataapi/AWS/getb3derivatives.py | python | DI1.dv01 | (self, code, t) | return dv01 | compute the dv01 based on the last quote of date t.
:param code: contract code
:param t: reference date | compute the dv01 based on the last quote of date t.
:param code: contract code
:param t: reference date | [
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"""
compute the dv01 based on the last quote of date t.
:param code: contract code
:param t: reference date
"""
du = self.du2maturity(t, code)
y = self.implied_yield(code, t)
pu = self.theoretical_price(code, t)
dPdy = pu * (-du/252) / (1 + y)
dv01 = dPdy/10000 # changes to 1bp move
return dv01 | [
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mpenning/ciscoconfparse | a6a176e6ceac7c5f3e974272fa70273476ba84a3 | ciscoconfparse/models_nxos.py | python | NXOSvPCLine.has_layer3_peer_router | (self) | return retval | [] | def has_layer3_peer_router(self):
retval = self.re_match_iter_typed(
r"^\s+(layer3\s+peer-router)", result_type=bool, default=False
)
return retval | [
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krintoxi/NoobSec-Toolkit | 38738541cbc03cedb9a3b3ed13b629f781ad64f6 | NoobSecToolkit /tools/inject/thirdparty/bottle/bottle.py | python | Route.call | (self) | return self._make_callback() | The route callback with all plugins applied. This property is
created on demand and then cached to speed up subsequent requests. | The route callback with all plugins applied. This property is
created on demand and then cached to speed up subsequent requests. | [
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] | def call(self):
''' The route callback with all plugins applied. This property is
created on demand and then cached to speed up subsequent requests.'''
return self._make_callback() | [
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d2l-ai/d2l-zh | 1c2e25a557db446b5691c18e595e5664cc254730 | d2l/torch.py | python | grad_clipping | (net, theta) | 裁剪梯度
Defined in :numref:`sec_rnn_scratch` | 裁剪梯度 | [
"裁剪梯度"
] | def grad_clipping(net, theta):
"""裁剪梯度
Defined in :numref:`sec_rnn_scratch`"""
if isinstance(net, nn.Module):
params = [p for p in net.parameters() if p.requires_grad]
else:
params = net.params
norm = torch.sqrt(sum(torch.sum((p.grad ** 2)) for p in params))
if norm > theta:
for param in params:
param.grad[:] *= theta / norm | [
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sahana/eden | 1696fa50e90ce967df69f66b571af45356cc18da | modules/s3cfg.py | python | S3Config.get_gis_poi_create_resources | (self) | return self.gis.get("poi_create_resources",
[{"c": "gis", # Controller
"f": "poi", # Function
"table": "gis_poi", # For permissions check
# Default:
#"type": "point", # Feature Type: point, line or polygon
"label": T("Add PoI"), # Label
#"tooltip": T("Add PoI"), # Tooltip
"layer": "PoIs", # Layer Name to refresh
"location": "button", # Location to access from
},
]
) | List of resources which can be directly added to the main map.
Includes the type (point, line or polygon) and where they are to be
accessed from (button, menu or popup)
Defaults to the generic 'gis_poi' resource as a point from a button
@ToDo: Complete the button vs menu vs popup
@ToDo: S3PoIWidget() to allow other resources to pickup the passed Lat/Lon/WKT | List of resources which can be directly added to the main map.
Includes the type (point, line or polygon) and where they are to be
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"""
List of resources which can be directly added to the main map.
Includes the type (point, line or polygon) and where they are to be
accessed from (button, menu or popup)
Defaults to the generic 'gis_poi' resource as a point from a button
@ToDo: Complete the button vs menu vs popup
@ToDo: S3PoIWidget() to allow other resources to pickup the passed Lat/Lon/WKT
"""
T = current.T
return self.gis.get("poi_create_resources",
[{"c": "gis", # Controller
"f": "poi", # Function
"table": "gis_poi", # For permissions check
# Default:
#"type": "point", # Feature Type: point, line or polygon
"label": T("Add PoI"), # Label
#"tooltip": T("Add PoI"), # Tooltip
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SteveDoyle2/pyNastran | eda651ac2d4883d95a34951f8a002ff94f642a1a | pyNastran/op2/tables/geom/edt.py | python | EDT._read_aesurfs | (self, data: bytes, n: int) | return n | Word Name Type Description
1 ID I Identification of an aerodynamic trim variable degree
of freedom >0, no default
2 LABEL(2) CHAR4 Control Surface (CS) name, no default
4 LIST1 I Identification of a SET1 that contains the grids ids
associated with this control surface
5 LIST2 I Identification of a SET1 that contains the grids ids
associated with this control surface | Word Name Type Description
1 ID I Identification of an aerodynamic trim variable degree
of freedom >0, no default
2 LABEL(2) CHAR4 Control Surface (CS) name, no default
4 LIST1 I Identification of a SET1 that contains the grids ids
associated with this control surface
5 LIST2 I Identification of a SET1 that contains the grids ids
associated with this control surface | [
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"""
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1 ID I Identification of an aerodynamic trim variable degree
of freedom >0, no default
2 LABEL(2) CHAR4 Control Surface (CS) name, no default
4 LIST1 I Identification of a SET1 that contains the grids ids
associated with this control surface
5 LIST2 I Identification of a SET1 that contains the grids ids
associated with this control surface
"""
op2 = self.op2
ntotal = 20 * self.factor # 4 * 5
ndatai = len(data) - n
ncards = ndatai // ntotal
assert ndatai % ntotal == 0
if self.size == 4:
struct1 = Struct(op2._endian + b'i 8s 2i')
else:
struct1 = Struct(op2._endian + b'i 16s 2i')
for unused_i in range(ncards):
edata = data[n:n + ntotal]
out1 = struct1.unpack(edata)
aesid, label_bytes, list1, list2 = out1
label = reshape_bytes_block_size(label_bytes, self.size)
aesurfs = op2.add_aesurfs(aesid, label, list1, list2)
str(aesurfs)
n += ntotal
return n | [
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googlefonts/nototools | 903a218f62256a286cde48c76b3051703f8a1de5 | nototools/noto_lint.py | python | all_scripts | () | return frozenset(result) | Extends unicode scripts with pseudo-script 'Urdu'. | Extends unicode scripts with pseudo-script 'Urdu'. | [
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] | def all_scripts():
"""Extends unicode scripts with pseudo-script 'Urdu'."""
result = set(unicode_data.all_scripts())
result.add("Urdu")
return frozenset(result) | [
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pycom/pycom-libraries | 75d0e67cb421e0576a3a9677bb0d9d81f27ebdb7 | pymesh/pymesh_frozen/lib/ble_rpc.py | python | RPCHandler.send_message_was_sent | (self, mac, msg_id) | return self.mesh.mesage_was_ack(mac, msg_id) | checks for ACK received for msg_id
returns True if message was delivered to last node connected to BLE mobile | checks for ACK received for msg_id
returns True if message was delivered to last node connected to BLE mobile | [
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] | def send_message_was_sent(self, mac, msg_id):
""" checks for ACK received for msg_id
returns True if message was delivered to last node connected to BLE mobile """
error = False
try:
mac_int = int(mac)
msg_id_int = int(msg_id)
except:
error = True
if error:
return False
# mesh.mesage_was_ack(5, 12345)
return self.mesh.mesage_was_ack(mac, msg_id) | [
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sdaps/sdaps | 51d1072185223f5e48512661e2c1e8399d63e876 | sdaps/paths.py | python | init_gettext | (locale_dir) | Initialize gettext using the given directory containing the l10n data. | Initialize gettext using the given directory containing the l10n data. | [
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] | def init_gettext(locale_dir):
'''Initialize gettext using the given directory containing the l10n data.
'''
gettext.bindtextdomain('sdaps', locale_dir)
if hasattr(gettext, 'bind_textdomain_codeset'):
gettext.bind_textdomain_codeset('sdaps', 'UTF-8')
gettext.textdomain('sdaps')
if hasattr(locale, 'bind_textdomain_codeset'):
locale.bindtextdomain('sdaps', locale_dir)
locale.bind_textdomain_codeset('sdaps', 'UTF-8')
locale.textdomain('sdaps') | [
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... | https://github.com/sdaps/sdaps/blob/51d1072185223f5e48512661e2c1e8399d63e876/sdaps/paths.py#L89-L100 | ||
twisted/twisted | dee676b040dd38b847ea6fb112a712cb5e119490 | src/twisted/internet/process.py | python | ProcessWriter.fileno | (self) | return self.fd | Return the fileno() of my process's stdin. | Return the fileno() of my process's stdin. | [
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] | def fileno(self):
"""
Return the fileno() of my process's stdin.
"""
return self.fd | [
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HewlettPackard/dlcookbook-dlbs | 863ac1d7e72ad2fcafc78d8a13f67d35bc00c235 | python/dlbs/data/imagenet/tensorflow_build_imagenet_data.py | python | _process_image_files_batch | (coder, thread_index, ranges, name, filenames,
synsets, labels, humans, bboxes, num_shards) | Processes and saves list of images as TFRecord in 1 thread.
Args:
coder: instance of ImageCoder to provide TensorFlow image coding utils.
thread_index: integer, unique batch to run index is within [0, len(ranges)).
ranges: list of pairs of integers specifying ranges of each batches to
analyze in parallel.
name: string, unique identifier specifying the data set
filenames: list of strings; each string is a path to an image file
synsets: list of strings; each string is a unique WordNet ID
labels: list of integer; each integer identifies the ground truth
humans: list of strings; each string is a human-readable label
bboxes: list of bounding boxes for each image. Note that each entry in this
list might contain from 0+ entries corresponding to the number of bounding
box annotations for the image.
num_shards: integer number of shards for this data set. | Processes and saves list of images as TFRecord in 1 thread. | [
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] | def _process_image_files_batch(coder, thread_index, ranges, name, filenames,
synsets, labels, humans, bboxes, num_shards):
"""Processes and saves list of images as TFRecord in 1 thread.
Args:
coder: instance of ImageCoder to provide TensorFlow image coding utils.
thread_index: integer, unique batch to run index is within [0, len(ranges)).
ranges: list of pairs of integers specifying ranges of each batches to
analyze in parallel.
name: string, unique identifier specifying the data set
filenames: list of strings; each string is a path to an image file
synsets: list of strings; each string is a unique WordNet ID
labels: list of integer; each integer identifies the ground truth
humans: list of strings; each string is a human-readable label
bboxes: list of bounding boxes for each image. Note that each entry in this
list might contain from 0+ entries corresponding to the number of bounding
box annotations for the image.
num_shards: integer number of shards for this data set.
"""
# Each thread produces N shards where N = int(num_shards / num_threads).
# For instance, if num_shards = 128, and the num_threads = 2, then the first
# thread would produce shards [0, 64).
num_threads = len(ranges)
assert not num_shards % num_threads
num_shards_per_batch = int(num_shards / num_threads)
shard_ranges = np.linspace(ranges[thread_index][0],
ranges[thread_index][1],
num_shards_per_batch + 1).astype(int)
num_files_in_thread = ranges[thread_index][1] - ranges[thread_index][0]
counter = 0
for s in range(num_shards_per_batch):
# Generate a sharded version of the file name, e.g. 'train-00002-of-00010'
shard = thread_index * num_shards_per_batch + s
output_filename = '%s-%.5d-of-%.5d' % (name, shard, num_shards)
output_file = os.path.join(FLAGS.output_directory, output_filename)
writer = tf.python_io.TFRecordWriter(output_file)
shard_counter = 0
files_in_shard = np.arange(shard_ranges[s], shard_ranges[s + 1], dtype=int)
for i in files_in_shard:
filename = filenames[i]
label = labels[i]
synset = synsets[i]
human = humans[i]
bbox = bboxes[i]
image_buffer, height, width = _process_image(filename, coder)
example = _convert_to_example(filename, image_buffer, label,
synset, human, bbox,
height, width)
writer.write(example.SerializeToString())
shard_counter += 1
counter += 1
if not counter % 1000:
print('%s [thread %d]: Processed %d of %d images in thread batch.' %
(datetime.now(), thread_index, counter, num_files_in_thread))
sys.stdout.flush()
writer.close()
print('%s [thread %d]: Wrote %d images to %s' %
(datetime.now(), thread_index, shard_counter, output_file))
sys.stdout.flush()
shard_counter = 0
print('%s [thread %d]: Wrote %d images to %d shards.' %
(datetime.now(), thread_index, counter, num_files_in_thread))
sys.stdout.flush() | [
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theotherp/nzbhydra | 4b03d7f769384b97dfc60dade4806c0fc987514e | libs/flask_cache/__init__.py | python | Cache.delete_memoized | (self, f, *args, **kwargs) | Deletes the specified functions caches, based by given parameters.
If parameters are given, only the functions that were memoized with them
will be erased. Otherwise all versions of the caches will be forgotten.
Example::
@cache.memoize(50)
def random_func():
return random.randrange(1, 50)
@cache.memoize()
def param_func(a, b):
return a+b+random.randrange(1, 50)
.. code-block:: pycon
>>> random_func()
43
>>> random_func()
43
>>> cache.delete_memoized('random_func')
>>> random_func()
16
>>> param_func(1, 2)
32
>>> param_func(1, 2)
32
>>> param_func(2, 2)
47
>>> cache.delete_memoized('param_func', 1, 2)
>>> param_func(1, 2)
13
>>> param_func(2, 2)
47
Delete memoized is also smart about instance methods vs class methods.
When passing a instancemethod, it will only clear the cache related
to that instance of that object. (object uniqueness can be overridden
by defining the __repr__ method, such as user id).
When passing a classmethod, it will clear all caches related across
all instances of that class.
Example::
class Adder(object):
@cache.memoize()
def add(self, b):
return b + random.random()
.. code-block:: pycon
>>> adder1 = Adder()
>>> adder2 = Adder()
>>> adder1.add(3)
3.23214234
>>> adder2.add(3)
3.60898509
>>> cache.delete_memoized(adder.add)
>>> adder1.add(3)
3.01348673
>>> adder2.add(3)
3.60898509
>>> cache.delete_memoized(Adder.add)
>>> adder1.add(3)
3.53235667
>>> adder2.add(3)
3.72341788
:param fname: Name of the memoized function, or a reference to the function.
:param \*args: A list of positional parameters used with memoized function.
:param \**kwargs: A dict of named parameters used with memoized function.
.. note::
Flask-Cache uses inspect to order kwargs into positional args when
the function is memoized. If you pass a function reference into ``fname``
instead of the function name, Flask-Cache will be able to place
the args/kwargs in the proper order, and delete the positional cache.
However, if ``delete_memoized`` is just called with the name of the
function, be sure to pass in potential arguments in the same order
as defined in your function as args only, otherwise Flask-Cache
will not be able to compute the same cache key.
.. note::
Flask-Cache maintains an internal random version hash for the function.
Using delete_memoized will only swap out the version hash, causing
the memoize function to recompute results and put them into another key.
This leaves any computed caches for this memoized function within the
caching backend.
It is recommended to use a very high timeout with memoize if using
this function, so that when the version has is swapped, the old cached
results would eventually be reclaimed by the caching backend. | Deletes the specified functions caches, based by given parameters.
If parameters are given, only the functions that were memoized with them
will be erased. Otherwise all versions of the caches will be forgotten. | [
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"""
Deletes the specified functions caches, based by given parameters.
If parameters are given, only the functions that were memoized with them
will be erased. Otherwise all versions of the caches will be forgotten.
Example::
@cache.memoize(50)
def random_func():
return random.randrange(1, 50)
@cache.memoize()
def param_func(a, b):
return a+b+random.randrange(1, 50)
.. code-block:: pycon
>>> random_func()
43
>>> random_func()
43
>>> cache.delete_memoized('random_func')
>>> random_func()
16
>>> param_func(1, 2)
32
>>> param_func(1, 2)
32
>>> param_func(2, 2)
47
>>> cache.delete_memoized('param_func', 1, 2)
>>> param_func(1, 2)
13
>>> param_func(2, 2)
47
Delete memoized is also smart about instance methods vs class methods.
When passing a instancemethod, it will only clear the cache related
to that instance of that object. (object uniqueness can be overridden
by defining the __repr__ method, such as user id).
When passing a classmethod, it will clear all caches related across
all instances of that class.
Example::
class Adder(object):
@cache.memoize()
def add(self, b):
return b + random.random()
.. code-block:: pycon
>>> adder1 = Adder()
>>> adder2 = Adder()
>>> adder1.add(3)
3.23214234
>>> adder2.add(3)
3.60898509
>>> cache.delete_memoized(adder.add)
>>> adder1.add(3)
3.01348673
>>> adder2.add(3)
3.60898509
>>> cache.delete_memoized(Adder.add)
>>> adder1.add(3)
3.53235667
>>> adder2.add(3)
3.72341788
:param fname: Name of the memoized function, or a reference to the function.
:param \*args: A list of positional parameters used with memoized function.
:param \**kwargs: A dict of named parameters used with memoized function.
.. note::
Flask-Cache uses inspect to order kwargs into positional args when
the function is memoized. If you pass a function reference into ``fname``
instead of the function name, Flask-Cache will be able to place
the args/kwargs in the proper order, and delete the positional cache.
However, if ``delete_memoized`` is just called with the name of the
function, be sure to pass in potential arguments in the same order
as defined in your function as args only, otherwise Flask-Cache
will not be able to compute the same cache key.
.. note::
Flask-Cache maintains an internal random version hash for the function.
Using delete_memoized will only swap out the version hash, causing
the memoize function to recompute results and put them into another key.
This leaves any computed caches for this memoized function within the
caching backend.
It is recommended to use a very high timeout with memoize if using
this function, so that when the version has is swapped, the old cached
results would eventually be reclaimed by the caching backend.
"""
if not callable(f):
raise DeprecationWarning("Deleting messages by relative name is no longer"
" reliable, please switch to a function reference")
try:
if not args and not kwargs:
self._memoize_version(f, reset=True)
else:
cache_key = f.make_cache_key(f.uncached, *args, **kwargs)
self.cache.delete(cache_key)
except Exception:
if current_app.debug:
raise
logger.exception("Exception possibly due to cache backend.") | [
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rembo10/headphones | b3199605be1ebc83a7a8feab6b1e99b64014187c | lib/cherrypy/_cpcompat_subprocess.py | python | check_output | (*popenargs, **kwargs) | return output | r"""Run command with arguments and return its output as a byte string.
If the exit code was non-zero it raises a CalledProcessError. The
CalledProcessError object will have the return code in the returncode
attribute and output in the output attribute.
The arguments are the same as for the Popen constructor. Example:
>>> check_output(["ls", "-l", "/dev/null"])
'crw-rw-rw- 1 root root 1, 3 Oct 18 2007 /dev/null\n'
The stdout argument is not allowed as it is used internally.
To capture standard error in the result, use stderr=STDOUT.
>>> check_output(["/bin/sh", "-c",
... "ls -l non_existent_file ; exit 0"],
... stderr=STDOUT)
'ls: non_existent_file: No such file or directory\n' | r"""Run command with arguments and return its output as a byte string. | [
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"return",
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"output",
"as",
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] | def check_output(*popenargs, **kwargs):
r"""Run command with arguments and return its output as a byte string.
If the exit code was non-zero it raises a CalledProcessError. The
CalledProcessError object will have the return code in the returncode
attribute and output in the output attribute.
The arguments are the same as for the Popen constructor. Example:
>>> check_output(["ls", "-l", "/dev/null"])
'crw-rw-rw- 1 root root 1, 3 Oct 18 2007 /dev/null\n'
The stdout argument is not allowed as it is used internally.
To capture standard error in the result, use stderr=STDOUT.
>>> check_output(["/bin/sh", "-c",
... "ls -l non_existent_file ; exit 0"],
... stderr=STDOUT)
'ls: non_existent_file: No such file or directory\n'
"""
if 'stdout' in kwargs:
raise ValueError('stdout argument not allowed, it will be overridden.')
process = Popen(stdout=PIPE, *popenargs, **kwargs)
output, unused_err = process.communicate()
retcode = process.poll()
if retcode:
cmd = kwargs.get("args")
if cmd is None:
cmd = popenargs[0]
raise CalledProcessError(retcode, cmd, output=output)
return output | [
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cogeotiff/rio-cogeo | a07d914e2d898878417638bbc089179f01eb5b28 | rio_cogeo/scripts/cli.py | python | create_tag_table | (tags: typing.Dict, sep: int) | return table | Helper method to create a table from dictionary of image tags -- used by info cli | Helper method to create a table from dictionary of image tags -- used by info cli | [
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"a",
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"image",
"tags",
"--",
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"info",
"cli"
] | def create_tag_table(tags: typing.Dict, sep: int) -> str:
"""Helper method to create a table from dictionary of image tags -- used by info cli"""
table = ""
for idx, (k, v) in enumerate(tags.items()):
name = f"{k}:"
row = f"{click.style(name, bold=True):<{sep}} {v}"
if idx + 1 != len(tags):
row += "\n "
table += row
return table | [
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TeaPearce/precipitation-prediction-convLSTM-keras | 03d3492f113637395cbdebbe5adf2a3461dbc0b4 | precip_v09.py | python | fn_get_model_2D_timeD | () | return model | [] | def fn_get_model_2D_timeD():
t_frames, rows, cols = 15, 101, 101
input_shape = (t_frames, rows, cols, 1)
inp = Input(shape=input_shape)
inpN = BatchNormalization()(inp)
c1 = Conv3D(filters=8, kernel_size= (5,5,5), strides=(1,1,1), activation='relu',
kernel_initializer='glorot_uniform', padding='same')(inpN)
c1 = BatchNormalization()(c1)
c1 = Conv3D(filters=8, kernel_size= (5,5,5), strides=(1,1,1), activation='relu',
kernel_initializer='glorot_uniform', padding='same')(c1)
c1 = BatchNormalization()(c1)
pool_1 = MaxPooling3D(pool_size=(2,3,3))(c1)
drop_1 = Dropout(0.25)(pool_1)
c2 = Conv3D(filters=16, kernel_size= (3,3,3), strides=(1,1,1), activation='relu',
kernel_initializer='glorot_uniform', padding='same')(drop_1)
c2 = BatchNormalization()(c2)
c2 = Conv3D(filters=16, kernel_size= (3,3,3), strides=(1,1,1), activation='relu',
kernel_initializer='glorot_uniform', padding='same')(c2)
c2 = BatchNormalization()(c2)
pool_2 = MaxPooling3D(pool_size=(1,3,3))(c2)
drop_1 = Dropout(0.25)(pool_2)
c3 = Conv3D(filters=32, kernel_size= (3,3,3), strides=(1,1,1), activation='relu',
kernel_initializer='glorot_uniform', padding='same')(drop_1)
c3 = BatchNormalization()(c3)
c3 = Conv3D(filters=32, kernel_size= (3,3,3), strides=(1,1,1), activation='relu',
kernel_initializer='glorot_uniform', padding='same')(c3)
c3 = BatchNormalization()(c3)
pool_3 = MaxPooling3D(pool_size=(1,2,2))(c3)
drop_3 = Dropout(0.25)(pool_3)
flat = Flatten()(drop_3)
hidden_1 = Dense(1024, kernel_initializer='glorot_uniform', activation='relu')(flat)
hidden_1 = BatchNormalization()(hidden_1)
hidden_1 = Dropout(0.3)(hidden_1)
hidden_2 = Dense(512, kernel_initializer='glorot_uniform', activation='relu')(hidden_1)
hidden_2 = BatchNormalization()(hidden_2)
hidden_2 = Dropout(0.3)(hidden_2)
hidden_3 = Dense(124, kernel_initializer='glorot_uniform', activation='relu')(hidden_2)
hidden_3 = BatchNormalization()(hidden_3)
hidden_3 = Dropout(0.3)(hidden_3)
hidden_4 = Dense(8, kernel_initializer='glorot_uniform', activation='relu')(hidden_3)
hidden_4 = BatchNormalization()(hidden_4)
hidden_4 = Dropout(0.3)(hidden_4)
out = Dense(1, activation='relu')(hidden_4)
model = Model(outputs=out, inputs=inp)
print(model.summary())
return model | [
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/combinat/sf/sfa.py | python | SymmetricFunctionAlgebra_generic_Element.restrict_degree | (self, d, exact = True) | return self.parent()._from_dict(res) | r"""
Return the degree ``d`` component of ``self``.
INPUT:
- ``d`` -- positive integer, degree of the terms to be returned
- ``exact`` -- boolean, if ``True``, returns the terms of degree
exactly ``d``, otherwise returns all terms of degree less than
or equal to ``d``
OUTPUT:
- the homogeneous component of ``self`` of degree ``d``
EXAMPLES::
sage: s = SymmetricFunctions(QQ).s()
sage: z = s([4]) + s([2,1]) + s([1,1,1]) + s([1])
sage: z.restrict_degree(2)
0
sage: z.restrict_degree(1)
s[1]
sage: z.restrict_degree(3)
s[1, 1, 1] + s[2, 1]
sage: z.restrict_degree(3, exact=False)
s[1] + s[1, 1, 1] + s[2, 1]
sage: z.restrict_degree(0)
0 | r"""
Return the degree ``d`` component of ``self``. | [
"r",
"Return",
"the",
"degree",
"d",
"component",
"of",
"self",
"."
] | def restrict_degree(self, d, exact = True):
r"""
Return the degree ``d`` component of ``self``.
INPUT:
- ``d`` -- positive integer, degree of the terms to be returned
- ``exact`` -- boolean, if ``True``, returns the terms of degree
exactly ``d``, otherwise returns all terms of degree less than
or equal to ``d``
OUTPUT:
- the homogeneous component of ``self`` of degree ``d``
EXAMPLES::
sage: s = SymmetricFunctions(QQ).s()
sage: z = s([4]) + s([2,1]) + s([1,1,1]) + s([1])
sage: z.restrict_degree(2)
0
sage: z.restrict_degree(1)
s[1]
sage: z.restrict_degree(3)
s[1, 1, 1] + s[2, 1]
sage: z.restrict_degree(3, exact=False)
s[1] + s[1, 1, 1] + s[2, 1]
sage: z.restrict_degree(0)
0
"""
if exact:
res = dict(x for x in self._monomial_coefficients.items() if sum(x[0]) == d)
else:
res = dict(x for x in self._monomial_coefficients.items() if sum(x[0]) <= d)
return self.parent()._from_dict(res) | [
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/urllib/parse.py | python | to_bytes | (url) | return url | to_bytes(u"URL") --> 'URL'. | to_bytes(u"URL") --> 'URL'. | [
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] | def to_bytes(url):
"""to_bytes(u"URL") --> 'URL'."""
# Most URL schemes require ASCII. If that changes, the conversion
# can be relaxed.
# XXX get rid of to_bytes()
if isinstance(url, str):
try:
url = url.encode("ASCII").decode()
except UnicodeError:
raise UnicodeError("URL " + repr(url) +
" contains non-ASCII characters")
return url | [
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nosmokingbandit/watcher | dadacd21a5790ee609058a98a17fcc8954d24439 | lib/sqlalchemy/sql/selectable.py | python | FromClause.replace_selectable | (self, sqlutil, old, alias) | return sqlutil.ClauseAdapter(alias).traverse(self) | replace all occurrences of FromClause 'old' with the given Alias
object, returning a copy of this :class:`.FromClause`. | replace all occurrences of FromClause 'old' with the given Alias
object, returning a copy of this :class:`.FromClause`. | [
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"""replace all occurrences of FromClause 'old' with the given Alias
object, returning a copy of this :class:`.FromClause`.
"""
return sqlutil.ClauseAdapter(alias).traverse(self) | [
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JaniceWuo/MovieRecommend | 4c86db64ca45598917d304f535413df3bc9fea65 | movierecommend/venv1/Lib/site-packages/pip-9.0.1-py3.6.egg/pip/_vendor/ipaddress.py | python | IPv4Address.is_reserved | (self) | return self in self._constants._reserved_network | Test if the address is otherwise IETF reserved.
Returns:
A boolean, True if the address is within the
reserved IPv4 Network range. | Test if the address is otherwise IETF reserved. | [
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] | def is_reserved(self):
"""Test if the address is otherwise IETF reserved.
Returns:
A boolean, True if the address is within the
reserved IPv4 Network range.
"""
return self in self._constants._reserved_network | [
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and3rson/clay | c271cecf6b6ea6465abcdd2444171b1a565a60a3 | clay/songlist.py | python | SongListBox.is_context_menu_visible | (self) | return self.contents['body'][0] is self.overlay | Return ``True`` if context menu is currently being shown. | Return ``True`` if context menu is currently being shown. | [
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"""
Return ``True`` if context menu is currently being shown.
"""
return self.contents['body'][0] is self.overlay | [
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"]",
"is",
"self",
".",
"overlay"
] | https://github.com/and3rson/clay/blob/c271cecf6b6ea6465abcdd2444171b1a565a60a3/clay/songlist.py#L588-L592 | |
lisa-lab/pylearn2 | af81e5c362f0df4df85c3e54e23b2adeec026055 | pylearn2/expr/evaluation.py | python | all_pr | (pos_scores, neg_scores) | return precision, recall | Computes all possible precision and recall points given a list of
scores assigned to the positive class and a list of scores assigned
to the negative class.
Parameters
----------
pos_scores : list
List of floats. Higher values = more likely to belong to positive
class.
neg_scores : list
List of floats. Higher values = more likely to belong to negative
class.
Returns
-------
precision : list
List of all possible precision values obtainable by varying the
threshold of the detector.
recall : list
List of all possible recall values obtainable by varing the
threshold of the detector. recall[i] is formed using the same
threshold as precision[i] | Computes all possible precision and recall points given a list of
scores assigned to the positive class and a list of scores assigned
to the negative class. | [
"Computes",
"all",
"possible",
"precision",
"and",
"recall",
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"class",
"and",
"a",
"list",
"of",
"scores",
"assigned",
"to",
"the",
"negative",
"class",
"."
] | def all_pr(pos_scores, neg_scores):
"""
Computes all possible precision and recall points given a list of
scores assigned to the positive class and a list of scores assigned
to the negative class.
Parameters
----------
pos_scores : list
List of floats. Higher values = more likely to belong to positive
class.
neg_scores : list
List of floats. Higher values = more likely to belong to negative
class.
Returns
-------
precision : list
List of all possible precision values obtainable by varying the
threshold of the detector.
recall : list
List of all possible recall values obtainable by varing the
threshold of the detector. recall[i] is formed using the same
threshold as precision[i]
"""
# Attach labels to scores
labeled_neg = [(x, 0) for x in neg_scores]
labeled_pos = [(x, 1) for x in pos_scores]
labeled = labeled_pos + labeled_neg
# Sort labeled scores by descending score
sorted_labeled = sorted(labeled, key=lambda x: -x[0])
label_chunks = []
# Group sorted labels into chunks of equal score
prev_score = None
cur_chunk = []
for score, label in sorted_labeled:
if prev_score is None or prev_score == score:
cur_chunk.append(label)
else:
label_chunks.append(cur_chunk)
cur_chunk = [label]
prev_score = score
label_chunks.append(cur_chunk)
# Initialize with threshold set to label all inputs as negative
precision = [1.]
recall = [0.]
tp = 0
fp = 0
fn = len(pos_scores)
count = fn
# Incrementally raise threshold, with each increment chosen to
# label everything with score >= t as positive, where t is the
# score of the next tied chunk
for label_chunk in label_chunks:
for label in label_chunk:
if label:
fn -= 1
tp += 1
else:
fp += 1
ap = tp + fp
if ap == 0:
precision.append(1.)
else:
precision.append(float(tp)/float(ap))
recall.append(float(tp)/count)
return precision, recall | [
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HonglinChu/SiamTrackers | 8471660b14f970578a43f077b28207d44a27e867 | SiamCAR/SiamCAR/toolkit/datasets/video.py | python | Video.draw_box | (self, roi, img, linewidth, color, name=None) | return img | roi: rectangle or polygon
img: numpy array img
linewith: line width of the bbox | roi: rectangle or polygon
img: numpy array img
linewith: line width of the bbox | [
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":",
"numpy",
"array",
"img",
"linewith",
":",
"line",
"width",
"of",
"the",
"bbox"
] | def draw_box(self, roi, img, linewidth, color, name=None):
"""
roi: rectangle or polygon
img: numpy array img
linewith: line width of the bbox
"""
if len(roi) > 6 and len(roi) % 2 == 0:
pts = np.array(roi, np.int32).reshape(-1, 1, 2)
color = tuple(map(int, color))
img = cv2.polylines(img, [pts], True, color, linewidth)
pt = (pts[0, 0, 0], pts[0, 0, 1]-5)
if name:
img = cv2.putText(img, name, pt, cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, color, 1)
elif len(roi) == 4:
if not np.isnan(roi[0]):
roi = list(map(int, roi))
color = tuple(map(int, color))
img = cv2.rectangle(img, (roi[0], roi[1]), (roi[0]+roi[2], roi[1]+roi[3]),
color, linewidth)
if name:
img = cv2.putText(img, name, (roi[0], roi[1]-5), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, color, 1)
return img | [
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ivre/ivre | 5728855b51c0ae2e59450a1c3a782febcad2128b | ivre/view.py | python | _extract_passive_HONEYPOT_HIT | (rec) | return {
"ports": [
{
"port": -1,
"scripts": [
{
"id": "scanner",
"output": output,
"scanner": structured_output,
}
],
}
]
} | Handle {TCP,UDP}_HONEYPOT_HIT records | Handle {TCP,UDP}_HONEYPOT_HIT records | [
"Handle",
"{",
"TCP",
"UDP",
"}",
"_HONEYPOT_HIT",
"records"
] | def _extract_passive_HONEYPOT_HIT(rec):
"""Handle {TCP,UDP}_HONEYPOT_HIT records"""
try:
scanned_proto, scanned_port = rec["source"].split("/", 1)
except ValueError:
utils.LOGGER.warning("Unknown source in record [%r]", rec)
return {}
scanned_port = int(scanned_port)
output = "Scanned port: %s" % rec["source"].replace("/", ": ")
structured_output = {
"ports": {"count": 1, scanned_proto: {"count": 1, "ports": [scanned_port]}}
}
if rec.get("infos", {}).get("service_name") == "scanner":
structured_output["scanners"] = [
{
"name": rec["infos"]["service_product"],
"probes": [
{"proto": scanned_proto, "name": rec["infos"]["service_extrainfo"]}
],
}
]
output += "\nScanner:\n - %s [%s/%s]" % (
rec["infos"]["service_product"],
rec["infos"]["service_extrainfo"],
scanned_proto,
)
if rec.get("value"):
structured_output["probes"] = [{"proto": scanned_proto, "value": rec["value"]}]
return {
"ports": [
{
"port": -1,
"scripts": [
{
"id": "scanner",
"output": output,
"scanner": structured_output,
}
],
}
]
} | [
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"warnin... | https://github.com/ivre/ivre/blob/5728855b51c0ae2e59450a1c3a782febcad2128b/ivre/view.py#L192-L233 | |
Netflix-Skunkworks/diffy | 259523466c7ad64de33ca125130f871b1e98ce48 | diffy_api/analysis/views.py | python | AnalysisList.get | (self) | return data, 200 | .. http:get:: /analysis
The current list of analysiss
**Example request**:
.. sourcecode:: http
GET /analysis HTTP/1.1
Host: example.com
Accept: application/json, text/javascript
**Example response**:
.. sourcecode:: http
HTTP/1.1 200 OK
Vary: Accept
Content-Type: text/javascript
# TODO
:statuscode 200: no error
:statuscode 403: unauthenticated | .. http:get:: /analysis
The current list of analysiss | [
"..",
"http",
":",
"get",
"::",
"/",
"analysis",
"The",
"current",
"list",
"of",
"analysiss"
] | def get(self):
"""
.. http:get:: /analysis
The current list of analysiss
**Example request**:
.. sourcecode:: http
GET /analysis HTTP/1.1
Host: example.com
Accept: application/json, text/javascript
**Example response**:
.. sourcecode:: http
HTTP/1.1 200 OK
Vary: Accept
Content-Type: text/javascript
# TODO
:statuscode 200: no error
:statuscode 403: unauthenticated
"""
data = plugins.get(current_app.config["DIFFY_PERSISTENCE_PLUGIN"]).get_all(
"analysis"
)
return data, 200 | [
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] | https://github.com/Netflix-Skunkworks/diffy/blob/259523466c7ad64de33ca125130f871b1e98ce48/diffy_api/analysis/views.py#L29-L54 | |
volatilityfoundation/volatility3 | 168b0d0b053ab97a7cb096ef2048795cc54d885f | volatility3/cli/text_renderer.py | python | JsonLinesRenderer.output_result | (self, outfd, result) | Outputs the JSON results as JSON lines | Outputs the JSON results as JSON lines | [
"Outputs",
"the",
"JSON",
"results",
"as",
"JSON",
"lines"
] | def output_result(self, outfd, result):
"""Outputs the JSON results as JSON lines"""
for line in result:
outfd.write(json.dumps(line, sort_keys = True))
outfd.write("\n") | [
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paypal/support | 0c88c3d32644c897c30e12de8c8db00d065a76ea | support/async.py | python | killsock | (sock) | Attempts to cleanly shutdown a socket. Regardless of cleanliness,
ensures that upon return, the socket is fully closed, catching any
exceptions along the way. A safe and prompt way to dispose of the
socket, freeing system resources. | Attempts to cleanly shutdown a socket. Regardless of cleanliness,
ensures that upon return, the socket is fully closed, catching any
exceptions along the way. A safe and prompt way to dispose of the
socket, freeing system resources. | [
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"a",
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"closed",
"catching",
"any",
"exceptions",
"along",
"the",
"way",
".",
"A",
"safe",
"and",
"p... | def killsock(sock):
"""Attempts to cleanly shutdown a socket. Regardless of cleanliness,
ensures that upon return, the socket is fully closed, catching any
exceptions along the way. A safe and prompt way to dispose of the
socket, freeing system resources.
"""
if hasattr(sock, '_sock'):
ml.ld("Killing socket {0}/FD {1}", id(sock), sock._sock.fileno())
else:
ml.ld("Killing socket {0}", id(sock))
try:
# TODO: better ideas for how to get SHUT_RDWR constant?
sock.shutdown(gevent.socket.SHUT_RDWR)
except gevent.socket.error:
pass # just being nice to the server, don't care if it fails
except Exception as e:
log_rec = context.get_context().log.info("SOCKET", "SHUTDOWN")
log_rec.failure('error ({exc}) shutting down socket: {socket}',
socket=sock, exc=e)
try:
sock.close()
except gevent.socket.error:
pass # just being nice to the server, don't care if it fails
except Exception as e:
log_rec = context.get_context().log.info("SOCKET", "CLOSE")
log_rec.failure('error ({exc}) closing socket: {socket}',
socket=sock, exc=e) | [
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"... | https://github.com/paypal/support/blob/0c88c3d32644c897c30e12de8c8db00d065a76ea/support/async.py#L422-L448 | ||
zjhthu/OANet | 51d71ff3f57161e912ec72420cd91cf7db64ab74 | dump_match/transformations.py | python | euler_from_matrix | (matrix, axes='sxyz') | return ax, ay, az | Return Euler angles from rotation matrix for specified axis sequence.
axes : One of 24 axis sequences as string or encoded tuple
Note that many Euler angle triplets can describe one matrix.
>>> R0 = euler_matrix(1, 2, 3, 'syxz')
>>> al, be, ga = euler_from_matrix(R0, 'syxz')
>>> R1 = euler_matrix(al, be, ga, 'syxz')
>>> numpy.allclose(R0, R1)
True
>>> angles = (4*math.pi) * (numpy.random.random(3) - 0.5)
>>> for axes in _AXES2TUPLE.keys():
... R0 = euler_matrix(axes=axes, *angles)
... R1 = euler_matrix(axes=axes, *euler_from_matrix(R0, axes))
... if not numpy.allclose(R0, R1): print(axes, "failed") | Return Euler angles from rotation matrix for specified axis sequence. | [
"Return",
"Euler",
"angles",
"from",
"rotation",
"matrix",
"for",
"specified",
"axis",
"sequence",
"."
] | def euler_from_matrix(matrix, axes='sxyz'):
"""Return Euler angles from rotation matrix for specified axis sequence.
axes : One of 24 axis sequences as string or encoded tuple
Note that many Euler angle triplets can describe one matrix.
>>> R0 = euler_matrix(1, 2, 3, 'syxz')
>>> al, be, ga = euler_from_matrix(R0, 'syxz')
>>> R1 = euler_matrix(al, be, ga, 'syxz')
>>> numpy.allclose(R0, R1)
True
>>> angles = (4*math.pi) * (numpy.random.random(3) - 0.5)
>>> for axes in _AXES2TUPLE.keys():
... R0 = euler_matrix(axes=axes, *angles)
... R1 = euler_matrix(axes=axes, *euler_from_matrix(R0, axes))
... if not numpy.allclose(R0, R1): print(axes, "failed")
"""
try:
firstaxis, parity, repetition, frame = _AXES2TUPLE[axes.lower()]
except (AttributeError, KeyError):
_TUPLE2AXES[axes] # validation
firstaxis, parity, repetition, frame = axes
i = firstaxis
j = _NEXT_AXIS[i+parity]
k = _NEXT_AXIS[i-parity+1]
M = numpy.array(matrix, dtype=numpy.float64, copy=False)[:3, :3]
if repetition:
sy = math.sqrt(M[i, j]*M[i, j] + M[i, k]*M[i, k])
if sy > _EPS:
ax = math.atan2( M[i, j], M[i, k])
ay = math.atan2( sy, M[i, i])
az = math.atan2( M[j, i], -M[k, i])
else:
ax = math.atan2(-M[j, k], M[j, j])
ay = math.atan2( sy, M[i, i])
az = 0.0
else:
cy = math.sqrt(M[i, i]*M[i, i] + M[j, i]*M[j, i])
if cy > _EPS:
ax = math.atan2( M[k, j], M[k, k])
ay = math.atan2(-M[k, i], cy)
az = math.atan2( M[j, i], M[i, i])
else:
ax = math.atan2(-M[j, k], M[j, j])
ay = math.atan2(-M[k, i], cy)
az = 0.0
if parity:
ax, ay, az = -ax, -ay, -az
if frame:
ax, az = az, ax
return ax, ay, az | [
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... | https://github.com/zjhthu/OANet/blob/51d71ff3f57161e912ec72420cd91cf7db64ab74/dump_match/transformations.py#L1112-L1167 | |
GoogleCloudPlatform/PerfKitBenchmarker | 6e3412d7d5e414b8ca30ed5eaf970cef1d919a67 | perfkitbenchmarker/container_service.py | python | BaseContainerRegistry.LocalBuild | (self, image) | Build the image locally.
Args:
image: Instance of _ContainerImage representing the image to build. | Build the image locally. | [
"Build",
"the",
"image",
"locally",
"."
] | def LocalBuild(self, image):
"""Build the image locally.
Args:
image: Instance of _ContainerImage representing the image to build.
"""
build_cmd = [
'docker', 'buildx', 'build',
'--platform', FLAGS.container_cluster_architecture,
'--no-cache', '--load',
'-t', image.name, image.directory
]
vm_util.IssueCommand(build_cmd) | [
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mcahny/vps | 138503edbc9e70de744dc0c9fa4b71c799a94d5d | mmdet/models/plugins/generalized_attention.py | python | GeneralizedAttention.__init__ | (self,
in_dim,
spatial_range=-1,
num_heads=9,
position_embedding_dim=-1,
position_magnitude=1,
kv_stride=2,
q_stride=1,
attention_type='1111') | [] | def __init__(self,
in_dim,
spatial_range=-1,
num_heads=9,
position_embedding_dim=-1,
position_magnitude=1,
kv_stride=2,
q_stride=1,
attention_type='1111'):
super(GeneralizedAttention, self).__init__()
# hard range means local range for non-local operation
self.position_embedding_dim = (
position_embedding_dim if position_embedding_dim > 0 else in_dim)
self.position_magnitude = position_magnitude
self.num_heads = num_heads
self.channel_in = in_dim
self.spatial_range = spatial_range
self.kv_stride = kv_stride
self.q_stride = q_stride
self.attention_type = [bool(int(_)) for _ in attention_type]
self.qk_embed_dim = in_dim // num_heads
out_c = self.qk_embed_dim * num_heads
if self.attention_type[0] or self.attention_type[1]:
self.query_conv = nn.Conv2d(
in_channels=in_dim,
out_channels=out_c,
kernel_size=1,
bias=False)
self.query_conv.kaiming_init = True
if self.attention_type[0] or self.attention_type[2]:
self.key_conv = nn.Conv2d(
in_channels=in_dim,
out_channels=out_c,
kernel_size=1,
bias=False)
self.key_conv.kaiming_init = True
self.v_dim = in_dim // num_heads
self.value_conv = nn.Conv2d(
in_channels=in_dim,
out_channels=self.v_dim * num_heads,
kernel_size=1,
bias=False)
self.value_conv.kaiming_init = True
if self.attention_type[1] or self.attention_type[3]:
self.appr_geom_fc_x = nn.Linear(
self.position_embedding_dim // 2, out_c, bias=False)
self.appr_geom_fc_x.kaiming_init = True
self.appr_geom_fc_y = nn.Linear(
self.position_embedding_dim // 2, out_c, bias=False)
self.appr_geom_fc_y.kaiming_init = True
if self.attention_type[2]:
stdv = 1.0 / math.sqrt(self.qk_embed_dim * 2)
appr_bias_value = -2 * stdv * torch.rand(out_c) + stdv
self.appr_bias = nn.Parameter(appr_bias_value)
if self.attention_type[3]:
stdv = 1.0 / math.sqrt(self.qk_embed_dim * 2)
geom_bias_value = -2 * stdv * torch.rand(out_c) + stdv
self.geom_bias = nn.Parameter(geom_bias_value)
self.proj_conv = nn.Conv2d(
in_channels=self.v_dim * num_heads,
out_channels=in_dim,
kernel_size=1,
bias=True)
self.proj_conv.kaiming_init = True
self.gamma = nn.Parameter(torch.zeros(1))
if self.spatial_range >= 0:
# only works when non local is after 3*3 conv
if in_dim == 256:
max_len = 84
elif in_dim == 512:
max_len = 42
max_len_kv = int((max_len - 1.0) / self.kv_stride + 1)
local_constraint_map = np.ones(
(max_len, max_len, max_len_kv, max_len_kv), dtype=np.int)
for iy in range(max_len):
for ix in range(max_len):
local_constraint_map[iy, ix,
max((iy - self.spatial_range) //
self.kv_stride, 0):min(
(iy + self.spatial_range +
1) // self.kv_stride +
1, max_len),
max((ix - self.spatial_range) //
self.kv_stride, 0):min(
(ix + self.spatial_range +
1) // self.kv_stride +
1, max_len)] = 0
self.local_constraint_map = nn.Parameter(
torch.from_numpy(local_constraint_map).byte(),
requires_grad=False)
if self.q_stride > 1:
self.q_downsample = nn.AvgPool2d(
kernel_size=1, stride=self.q_stride)
else:
self.q_downsample = None
if self.kv_stride > 1:
self.kv_downsample = nn.AvgPool2d(
kernel_size=1, stride=self.kv_stride)
else:
self.kv_downsample = None
self.init_weights() | [
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tao12345666333/tornado-zh | e9e8519beb147d9e1290f6a4fa7d61123d1ecb1c | tornado/gen.py | python | Runner.register_callback | (self, key) | Adds ``key`` to the list of callbacks. | Adds ``key`` to the list of callbacks. | [
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robert/wavefunction-collapse | 093d218e79f01cbb7787b898b458cc5d174ad7cb | main.py | python | render_colors | (matrix, colors) | Render the fully collapsed `matrix` using the given `colors.
Arguments:
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matrix -- 2-D matrix of tiles
colors -- dict of tile -> `colorama` color
"""
for row in matrix:
output_row = []
for val in row:
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output_row.append(color + val + colorama.Style.RESET_ALL)
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zzzeek/sqlalchemy | fc5c54fcd4d868c2a4c7ac19668d72f506fe821e | lib/sqlalchemy/ext/asyncio/engine.py | python | AsyncTransaction.start | (self, is_ctxmanager=False) | return self | Start this :class:`_asyncio.AsyncTransaction` object's context
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JulianEberius/SublimePythonIDE | d70e40abc0c9f347af3204c7b910e0d6bfd6e459 | server/lib/python3/rope/base/resourceobserver.py | python | ResourceObserver.resource_created | (self, resource) | Is called when a new resource is created | Is called when a new resource is created | [
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wangzheng0822/algo | b2c1228ff915287ad7ebeae4355fa26854ea1557 | python/12_sorts/merge_sort.py | python | _merge_sort_between | (a: List[int], low: int, high: int) | [] | def _merge_sort_between(a: List[int], low: int, high: int):
# The indices are inclusive for both low and high.
if low < high:
mid = low + (high - low) // 2
_merge_sort_between(a, low, mid)
_merge_sort_between(a, mid + 1, high)
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playframework/play1 | 0ecac3bc2421ae2dbec27a368bf671eda1c9cba5 | python/Lib/pydoc.py | python | TextDoc.docproperty | (self, object, name=None, mod=None, cl=None) | return self._docdescriptor(name, object, mod) | Produce text documentation for a property. | Produce text documentation for a property. | [
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etetoolkit/ete | 2b207357dc2a40ccad7bfd8f54964472c72e4726 | ete3/tools/ete_build_lib/interface.py | python | clear_env | () | [] | def clear_env():
try:
terminate_job_launcher()
except:
pass
base_dir = GLOBALS["basedir"]
lock_file = pjoin(base_dir, "alive")
try:
os.remove(lock_file)
except Exception:
print("could not remove lock file %s" %lock_file, file=sys.stderr)
clear_tempdir() | [
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Droidtown/ArticutAPI | ee415bb30c9722a85334d54d7015d5ad3870205f | WS_ArticutAPI.py | python | WS_Articut.bulk_getVerbStemLIST | (self, parseResultLIST, indexWithPOS=True) | return resultLIST | 取出斷詞結果中的動詞 (verb)。此處指的是 ACTION_verb 標記的動詞詞彙。
每個句子內的動詞為一個 list。 | 取出斷詞結果中的動詞 (verb)。此處指的是 ACTION_verb 標記的動詞詞彙。
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'''
取出斷詞結果中的動詞 (verb)。此處指的是 ACTION_verb 標記的動詞詞彙。
每個句子內的動詞為一個 list。
'''
resultLIST = [self.POS.getVerbStemLIST(x, indexWithPOS) for x in parseResultLIST]
return resultLIST | [
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bendmorris/static-python | 2e0f8c4d7ed5b359dc7d8a75b6fb37e6b6c5c473 | Lib/venv/__init__.py | python | EnvBuilder.post_setup | (self, context) | Hook for post-setup modification of the venv. Subclasses may install
additional packages or scripts here, add activation shell scripts, etc.
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"""
Hook for post-setup modification of the venv. Subclasses may install
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:param context: The information for the environment creation request
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"""
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oleg-yaroshevskiy/quest_qa_labeling | 730a9632314e54584f69f909d5e2ef74d843e02c | packages/fairseq-hacked/fairseq/models/roberta/model.py | python | RobertaEncoder.max_positions | (self) | return self.args.max_positions | Maximum output length supported by the encoder. | Maximum output length supported by the encoder. | [
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"""Maximum output length supported by the encoder."""
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openstack/heat | ea6633c35b04bb49c4a2858edc9df0a82d039478 | heat/engine/stk_defn.py | python | ResourceProxy.action | (self) | return self._resource_data.action | The current action of the resource. | The current action of the resource. | [
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] | def action(self):
"""The current action of the resource."""
if self._resource_data is None:
return self.INIT
return self._resource_data.action | [
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DonyorM/weresync | 3c0094cb386358589c48bf48cb60f20acf961f9c | src/weresync/daemon/device.py | python | DeviceManager.get_part_uuid | (self, partition_num) | return self._get_blkid_info(partition_num, "PARTUUID") | Gets the PARTUUID for a given partition, if it exists. This is
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microsoft/azure-devops-python-api | 451cade4c475482792cbe9e522c1fee32393139e | azure-devops/azure/devops/v5_1/symbol/symbol_client.py | python | SymbolClient.check_availability | (self) | CheckAvailability.
[Preview API] Check the availability of symbol service. This includes checking for feature flag, and possibly license in future. Note this is NOT an anonymous endpoint, and the caller will be redirected to authentication before hitting it. | CheckAvailability.
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self._send(http_method='GET',
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/Python-2.7.9/Lib/plat-mac/lib-scriptpackages/StdSuites/AppleScript_Suite.py | python | AppleScript_Suite_Events.stop_log | (self, _no_object=None, _attributes={}, **_arguments) | stop log: Stop event logging in the script editor
Keyword argument _attributes: AppleEvent attribute dictionary | stop log: Stop event logging in the script editor
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"""stop log: Stop event logging in the script editor
Keyword argument _attributes: AppleEvent attribute dictionary
"""
_code = 'ToyS'
_subcode = 'log0'
if _arguments: raise TypeError, 'No optional args expected'
if _no_object is not None: raise TypeError, 'No direct arg expected'
_reply, _arguments, _attributes = self.send(_code, _subcode,
_arguments, _attributes)
if _arguments.get('errn', 0):
raise aetools.Error, aetools.decodeerror(_arguments)
# XXXX Optionally decode result
if _arguments.has_key('----'):
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_flaskbb/Python-2.7.9/Lib/cmd.py | python | Cmd.complete | (self, text, state) | Return the next possible completion for 'text'.
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If a command has not been entered, then complete against command list.
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"""
if state == 0:
import readline
origline = readline.get_line_buffer()
line = origline.lstrip()
stripped = len(origline) - len(line)
begidx = readline.get_begidx() - stripped
endidx = readline.get_endidx() - stripped
if begidx>0:
cmd, args, foo = self.parseline(line)
if cmd == '':
compfunc = self.completedefault
else:
try:
compfunc = getattr(self, 'complete_' + cmd)
except AttributeError:
compfunc = self.completedefault
else:
compfunc = self.completenames
self.completion_matches = compfunc(text, line, begidx, endidx)
try:
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except IndexError:
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datafolklabs/cement | 2d44d2c1821bda6bdfcfe605d244dc2dfb0b19a6 | cement/cli/contrib/jinja2/filters.py | python | do_format | (value, *args, **kwargs) | return soft_unicode(value) % (kwargs or args) | Apply python string formatting on an object:
.. sourcecode:: jinja
{{ "%s - %s"|format("Hello?", "Foo!") }}
-> Hello? - Foo! | Apply python string formatting on an object: | [
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"""
Apply python string formatting on an object:
.. sourcecode:: jinja
{{ "%s - %s"|format("Hello?", "Foo!") }}
-> Hello? - Foo!
"""
if args and kwargs:
raise FilterArgumentError('can\'t handle positional and keyword '
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return soft_unicode(value) % (kwargs or args) | [
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"soft_unicode",... | https://github.com/datafolklabs/cement/blob/2d44d2c1821bda6bdfcfe605d244dc2dfb0b19a6/cement/cli/contrib/jinja2/filters.py#L673-L685 | |
openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | openshift/installer/vendored/openshift-ansible-3.9.14-1/roles/lib_openshift/src/lib/service.py | python | Service.get_external_ips | (self) | return self.get(Service.external_ips) or [] | get a list of external_ips | get a list of external_ips | [
"get",
"a",
"list",
"of",
"external_ips"
] | def get_external_ips(self):
''' get a list of external_ips '''
return self.get(Service.external_ips) or [] | [
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CvvT/dumpDex | 92ab3b7e996194a06bf1dd5538a4954e8a5ee9c1 | python/idaapi.py | python | boolvec_t.clear | (self, *args) | return _idaapi.boolvec_t_clear(self, *args) | clear(self) | clear(self) | [
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] | def clear(self, *args):
"""
clear(self)
"""
return _idaapi.boolvec_t_clear(self, *args) | [
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] | https://github.com/CvvT/dumpDex/blob/92ab3b7e996194a06bf1dd5538a4954e8a5ee9c1/python/idaapi.py#L1634-L1638 | |
holoviz/datashader | 25578abde75c7fa28c6633b33cb8d8a1e433da67 | datashader/reductions.py | python | FloatingReduction._create | (shape, array_module) | return array_module.full(shape, np.nan, dtype='f8') | [] | def _create(shape, array_module):
return array_module.full(shape, np.nan, dtype='f8') | [
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frePPLe/frepple | 57aa612030b4fcd03cb9c613f83a7dac4f0e8d6d | freppledb/execute/management/commands/scheduletasks.py | python | Command.createScheduledTasks | (self, *args, **options) | Task scheduler that looks at the defined schedule and generates tasks
at the right moment. | Task scheduler that looks at the defined schedule and generates tasks
at the right moment. | [
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] | def createScheduledTasks(self, *args, **options):
"""
Task scheduler that looks at the defined schedule and generates tasks
at the right moment.
"""
database = options["database"]
if database not in settings.DATABASES:
raise CommandError("No database settings known for '%s'" % database)
# Collect tasks
# Note: use transaction and select_for_update to handle concurrent access
now = datetime.now()
created = False
with transaction.atomic(using=database):
for schedule in (
ScheduledTask.objects.all()
.using(database)
.filter(next_run__isnull=False, next_run__lte=now)
.order_by("next_run", "name")
.select_for_update(skip_locked=True)
):
Task(
name="scheduletasks",
submitted=now,
status="Waiting",
user=schedule.user,
arguments="--schedule='%s'" % schedule.name,
).save(using=database)
schedule.computeNextRun(now + timedelta(seconds=1))
schedule.save(using=database)
created = True
# Launch the worker process
if created:
launchWorker(database)
# Reschedule to run this task again at the next date
earliest_next = (
ScheduledTask.objects.using(database)
.filter(next_run__isnull=False, next_run__gt=now)
.aggregate(Min("next_run"))
)["next_run__min"]
if earliest_next:
retcode = 0
if os.name == "nt":
# TODO For multi-tenancy possible we would also need to set the
# FREPPLE_CONFIGDIR environment variable. It seems that isn't
# possible with the Windows task scheduler.
# TODO The task scheduler has a more powerful XML interface that
# allows to define eg wake up of computer, run when not logged on,
# priviliges, etc
if "python" in sys.executable:
# Development layout
cmd = "%s %s scheduletasks --database=%s" % (
sys.executable.replace("python.exe", "pythonw.exe"),
os.path.abspath(sys.argv[0]),
database,
)
else:
# Windows installer
cmd = '"%s" scheduletasks --database=%s' % (
sys.executable.replace("freppleserver.exe", "frepplectl.exe"),
database,
)
retcode = call(
[
"schtasks",
"/create",
"/tn",
"frePPLe scheduler on %s" % database,
"/sc",
"once",
"/st",
earliest_next.strftime("%H:%M"),
"/sd",
earliest_next.strftime("%m/%d/%Y"),
"/f",
"/tr",
cmd,
]
)
else:
my_env = os.environ.copy()
my_env["FREPPLE_CONFIGDIR"] = settings.FREPPLE_CONFIGDIR
try:
if which("frepplectl"):
retcode = call(
"echo frepplectl scheduletasks --database=%s | at %s"
% (database, earliest_next.strftime("%H:%M %y-%m-%d")),
env=my_env,
shell=True,
)
else:
retcode = call(
"echo %s scheduletasks --database=%s | at %s"
% (
os.path.abspath(sys.argv[0]),
database,
earliest_next.strftime("%H:%M %y-%m-%d"),
),
env=my_env,
shell=True,
)
except OSError as e:
raise CommandError("Can't schedule the task: %s" % e)
if retcode < 0:
raise CommandError("Non-zero exit code when scheduling the task") | [
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buke/GreenOdoo | 3d8c55d426fb41fdb3f2f5a1533cfe05983ba1df | runtime/python/lib/python2.7/site-packages/PIL/ImageStat.py | python | Stat._getcount | (self) | return v | Get total number of pixels in each layer | Get total number of pixels in each layer | [
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"in",
"each",
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] | def _getcount(self):
"Get total number of pixels in each layer"
v = []
for i in range(0, len(self.h), 256):
v.append(reduce(operator.add, self.h[i:i+256]))
return v | [
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sugarlabs/sugar | 9fc581968c6a1df042bda29bb64f0c62e4444c78 | src/jarabe/webservice/account.py | python | Account.get_description | (self) | get_description returns a brief description of the online
service. The description is used in palette menuitems and on
the webservices control panel.
:returns: online-account name
:rtype: string | get_description returns a brief description of the online
service. The description is used in palette menuitems and on
the webservices control panel. | [
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"."
] | def get_description(self):
''' get_description returns a brief description of the online
service. The description is used in palette menuitems and on
the webservices control panel.
:returns: online-account name
:rtype: string
'''
raise NotImplementedError | [
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] | https://github.com/sugarlabs/sugar/blob/9fc581968c6a1df042bda29bb64f0c62e4444c78/src/jarabe/webservice/account.py#L29-L37 | ||
LexPredict/lexpredict-contraxsuite | 1d5a2540d31f8f3f1adc442cfa13a7c007319899 | contraxsuite_services/apps/analyze/ml/classify.py | python | ClassifierEngine.__call__ | (self, use_tfidf: bool = True, **options) | return self.model | Just call activated class instance to classify data.
:param use_tfidf: bool - whether to use TF IDF Transformer
:param options: **dict - unpacked classifier algorithm options
:return: ClassifyEngine instance with attributes listed in __init__ | Just call activated class instance to classify data.
:param use_tfidf: bool - whether to use TF IDF Transformer
:param options: **dict - unpacked classifier algorithm options
:return: ClassifyEngine instance with attributes listed in __init__ | [
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"""
Just call activated class instance to classify data.
:param use_tfidf: bool - whether to use TF IDF Transformer
:param options: **dict - unpacked classifier algorithm options
:return: ClassifyEngine instance with attributes listed in __init__
"""
self.use_tfidf = use_tfidf
self.user_options = options
self.model = self.get_model()
return self.model | [
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Mellcap/MellPlayer | 90b3210eaaed675552fd69717b6b953fd0e0b07d | mellplayer/api.py | python | Netease.song_detail | (self, song_ids) | return result | 歌曲详情
http://music.163.com/api/song/detail?ids=[xxx, xxx] | 歌曲详情
http://music.163.com/api/song/detail?ids=[xxx, xxx] | [
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"/",
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"/",
"detail?ids",
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"xxx",
"]"
] | def song_detail(self, song_ids):
'''
歌曲详情
http://music.163.com/api/song/detail?ids=[xxx, xxx]
'''
url = 'http://music.163.com/api/song/detail?ids=%s' % song_ids
result = self._request(url)
return result | [
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tensorflow/ranking | 94cccec8b4e71d2cc4489c61e2623522738c2924 | tensorflow_ranking/python/metrics.py | python | precision_ia | (labels, predictions, weights=None, topn=None, name=None) | return tf.compat.v1.metrics.mean(precision_at_k, per_list_weights) | Computes Intent-Aware Precision as weighted average of relevant examples.
Args:
labels: A `Tensor` with shape [batch_size, list_size, subtopic_size]. A
nonzero value means that the example covers the corresponding subtopic.
predictions: A `Tensor` with shape [batch_size, list_size]. Each value is
the ranking score of the corresponding example.
weights: A `Tensor` of the same shape of predictions or [batch_size, 1]. The
former case is per-example and the latter case is per-list.
topn: A cutoff for how many examples to consider for this metric.
name: A string used as the name for this metric.
Returns:
A metric for the weighted precision of the batch. | Computes Intent-Aware Precision as weighted average of relevant examples. | [
"Computes",
"Intent",
"-",
"Aware",
"Precision",
"as",
"weighted",
"average",
"of",
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] | def precision_ia(labels, predictions, weights=None, topn=None, name=None):
"""Computes Intent-Aware Precision as weighted average of relevant examples.
Args:
labels: A `Tensor` with shape [batch_size, list_size, subtopic_size]. A
nonzero value means that the example covers the corresponding subtopic.
predictions: A `Tensor` with shape [batch_size, list_size]. Each value is
the ranking score of the corresponding example.
weights: A `Tensor` of the same shape of predictions or [batch_size, 1]. The
former case is per-example and the latter case is per-list.
topn: A cutoff for how many examples to consider for this metric.
name: A string used as the name for this metric.
Returns:
A metric for the weighted precision of the batch.
"""
metric = metrics_impl.PrecisionIAMetric(name, topn)
with tf.compat.v1.name_scope(metric.name, 'precision_ia',
(labels, predictions, weights)):
# TODO: Add mask argument for metric.compute() call
precision_at_k, per_list_weights = metric.compute(labels, predictions,
weights)
return tf.compat.v1.metrics.mean(precision_at_k, per_list_weights) | [
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collinsctk/PyQYT | 7af3673955f94ff1b2df2f94220cd2dab2e252af | ExtentionPackages/Crypto/Cipher/XOR.py | python | XORCipher.encrypt | (self, plaintext) | return self._cipher.encrypt(plaintext) | Encrypt a piece of data.
:Parameters:
plaintext : byte string
The piece of data to encrypt. It can be of any size.
:Return: the encrypted data (byte string, as long as the
plaintext). | Encrypt a piece of data. | [
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] | def encrypt(self, plaintext):
"""Encrypt a piece of data.
:Parameters:
plaintext : byte string
The piece of data to encrypt. It can be of any size.
:Return: the encrypted data (byte string, as long as the
plaintext).
"""
return self._cipher.encrypt(plaintext) | [
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