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mcordts/cityscapesScripts | aeb7b82531f86185ce287705be28f452ba3ddbb8 | cityscapesscripts/helpers/annotation.py | python | CsBbox2d.bbox_modal | (self) | return [
self.bbox_modal_xywh[0],
self.bbox_modal_xywh[1],
self.bbox_modal_xywh[0] + self.bbox_modal_xywh[2],
self.bbox_modal_xywh[1] + self.bbox_modal_xywh[3]
] | Returns the 2d box as [xmin, ymin, xmax, ymax] | Returns the 2d box as [xmin, ymin, xmax, ymax] | [
"Returns",
"the",
"2d",
"box",
"as",
"[",
"xmin",
"ymin",
"xmax",
"ymax",
"]"
] | def bbox_modal(self):
"""Returns the 2d box as [xmin, ymin, xmax, ymax]"""
return [
self.bbox_modal_xywh[0],
self.bbox_modal_xywh[1],
self.bbox_modal_xywh[0] + self.bbox_modal_xywh[2],
self.bbox_modal_xywh[1] + self.bbox_modal_xywh[3]
] | [
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wucng/TensorExpand | 4ea58f64f5c5082b278229b799c9f679536510b7 | TensorExpand/Object detection/SSD/balancap-SSD-Tensorflow/nets/ssd_vgg_512.py | python | ssd_arg_scope_caffe | (caffe_scope) | Caffe scope definition.
Args:
caffe_scope: Caffe scope object with loaded weights.
Returns:
An arg_scope. | Caffe scope definition. | [
"Caffe",
"scope",
"definition",
"."
] | def ssd_arg_scope_caffe(caffe_scope):
"""Caffe scope definition.
Args:
caffe_scope: Caffe scope object with loaded weights.
Returns:
An arg_scope.
"""
# Default network arg scope.
with slim.arg_scope([slim.conv2d],
activation_fn=tf.nn.relu,
weights_initializer=caffe_scope.conv_weights_init(),
biases_initializer=caffe_scope.conv_biases_init()):
with slim.arg_scope([slim.fully_connected],
activation_fn=tf.nn.relu):
with slim.arg_scope([custom_layers.l2_normalization],
scale_initializer=caffe_scope.l2_norm_scale_init()):
with slim.arg_scope([slim.conv2d, slim.max_pool2d],
padding='SAME') as sc:
return sc | [
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nedbat/coveragepy | d004b18a1ad59ec89b89c96c03a789a55cc51693 | coverage/misc.py | python | human_sorted_items | (items, reverse=False) | return sorted(items, key=lambda pair: (human_key(pair[0]), pair[1]), reverse=reverse) | Sort the (string, value) items the way humans expect.
Returns the sorted list of items. | Sort the (string, value) items the way humans expect. | [
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] | def human_sorted_items(items, reverse=False):
"""Sort the (string, value) items the way humans expect.
Returns the sorted list of items.
"""
return sorted(items, key=lambda pair: (human_key(pair[0]), pair[1]), reverse=reverse) | [
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microsoft/TextWorld | c419bb63a92c7f6960aa004a367fb18894043e7f | textworld/core.py | python | GameState.copy | (self) | return state | Returns a deepcopy of this game state. | Returns a deepcopy of this game state. | [
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] | def copy(self) -> "GameState":
""" Returns a deepcopy of this game state. """
state = GameState(self)
for key in self:
state[key] = deepcopy(self[key])
return state | [
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] | https://github.com/microsoft/TextWorld/blob/c419bb63a92c7f6960aa004a367fb18894043e7f/textworld/core.py#L133-L139 | |
AutodeskRoboticsLab/Mimic | 85447f0d346be66988303a6a054473d92f1ed6f4 | mimic/scripts/extern/pyqtgraph_0_11_0_dev0/pyqtgraph/widgets/SpinBox.py | python | SpinBox.selectNumber | (self) | Select the numerical portion of the text to allow quick editing by the user. | Select the numerical portion of the text to allow quick editing by the user. | [
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] | def selectNumber(self):
"""
Select the numerical portion of the text to allow quick editing by the user.
"""
le = self.lineEdit()
text = asUnicode(le.text())
m = self.opts['regex'].match(text)
if m is None:
return
s,e = m.start('number'), m.end('number')
le.setSelection(s, e-s) | [
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/pandas-0.24.2-py3.7-macosx-10.9-x86_64.egg/pandas/core/generic.py | python | NDFrame.clip_upper | (self, threshold, axis=None, inplace=False) | return self._clip_with_one_bound(threshold, method=self.le,
axis=axis, inplace=inplace) | Trim values above a given threshold.
.. deprecated:: 0.24.0
Use clip(upper=threshold) instead.
Elements above the `threshold` will be changed to match the
`threshold` value(s). Threshold can be a single value or an array,
in the latter case it performs the truncation element-wise.
Parameters
----------
threshold : numeric or array-like
Maximum value allowed. All values above threshold will be set to
this value.
* float : every value is compared to `threshold`.
* array-like : The shape of `threshold` should match the object
it's compared to. When `self` is a Series, `threshold` should be
the length. When `self` is a DataFrame, `threshold` should 2-D
and the same shape as `self` for ``axis=None``, or 1-D and the
same length as the axis being compared.
axis : {0 or 'index', 1 or 'columns'}, default 0
Align object with `threshold` along the given axis.
inplace : boolean, default False
Whether to perform the operation in place on the data.
.. versionadded:: 0.21.0
Returns
-------
Series or DataFrame
Original data with values trimmed.
See Also
--------
Series.clip : General purpose method to trim Series values to given
threshold(s).
DataFrame.clip : General purpose method to trim DataFrame values to
given threshold(s).
Examples
--------
>>> s = pd.Series([1, 2, 3, 4, 5])
>>> s
0 1
1 2
2 3
3 4
4 5
dtype: int64
>>> s.clip(upper=3)
0 1
1 2
2 3
3 3
4 3
dtype: int64
>>> elemwise_thresholds = [5, 4, 3, 2, 1]
>>> elemwise_thresholds
[5, 4, 3, 2, 1]
>>> s.clip(upper=elemwise_thresholds)
0 1
1 2
2 3
3 2
4 1
dtype: int64 | Trim values above a given threshold. | [
"Trim",
"values",
"above",
"a",
"given",
"threshold",
"."
] | def clip_upper(self, threshold, axis=None, inplace=False):
"""
Trim values above a given threshold.
.. deprecated:: 0.24.0
Use clip(upper=threshold) instead.
Elements above the `threshold` will be changed to match the
`threshold` value(s). Threshold can be a single value or an array,
in the latter case it performs the truncation element-wise.
Parameters
----------
threshold : numeric or array-like
Maximum value allowed. All values above threshold will be set to
this value.
* float : every value is compared to `threshold`.
* array-like : The shape of `threshold` should match the object
it's compared to. When `self` is a Series, `threshold` should be
the length. When `self` is a DataFrame, `threshold` should 2-D
and the same shape as `self` for ``axis=None``, or 1-D and the
same length as the axis being compared.
axis : {0 or 'index', 1 or 'columns'}, default 0
Align object with `threshold` along the given axis.
inplace : boolean, default False
Whether to perform the operation in place on the data.
.. versionadded:: 0.21.0
Returns
-------
Series or DataFrame
Original data with values trimmed.
See Also
--------
Series.clip : General purpose method to trim Series values to given
threshold(s).
DataFrame.clip : General purpose method to trim DataFrame values to
given threshold(s).
Examples
--------
>>> s = pd.Series([1, 2, 3, 4, 5])
>>> s
0 1
1 2
2 3
3 4
4 5
dtype: int64
>>> s.clip(upper=3)
0 1
1 2
2 3
3 3
4 3
dtype: int64
>>> elemwise_thresholds = [5, 4, 3, 2, 1]
>>> elemwise_thresholds
[5, 4, 3, 2, 1]
>>> s.clip(upper=elemwise_thresholds)
0 1
1 2
2 3
3 2
4 1
dtype: int64
"""
warnings.warn('clip_upper(threshold) is deprecated, '
'use clip(upper=threshold) instead',
FutureWarning, stacklevel=2)
return self._clip_with_one_bound(threshold, method=self.le,
axis=axis, inplace=inplace) | [
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aws/Trusted-Advisor-Tools | 09c353c78810f534ee22bd28254d56f0ab7166c9 | ExposedAccessKeys/lambda_functions/notify_security.py | python | publish_msg | (subject, message) | Publishes message to SNS topic.
Args:
subject (string): Subject of message to be published to topic.
message (string): Content of message to be published to topic.
Returns:
(None) | Publishes message to SNS topic. | [
"Publishes",
"message",
"to",
"SNS",
"topic",
"."
] | def publish_msg(subject, message):
""" Publishes message to SNS topic.
Args:
subject (string): Subject of message to be published to topic.
message (string): Content of message to be published to topic.
Returns:
(None)
"""
try:
sns.publish(
TopicArn=TOPIC_ARN,
Message=message,
Subject=subject,
MessageStructure='string'
)
except Exception as e:
print(e)
print('Could not publish message to SNS topic "{}"'.format(TOPIC_ARN))
raise e | [
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dipy/dipy | be956a529465b28085f8fc435a756947ddee1c89 | dipy/reconst/csdeconv.py | python | AxSymShResponse.basis | (self, sphere) | return real_sh_descoteaux_from_index(self.m, self.n, theta, phi) | A basis that maps the response coefficients onto a sphere. | A basis that maps the response coefficients onto a sphere. | [
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] | def basis(self, sphere):
"""A basis that maps the response coefficients onto a sphere."""
theta = sphere.theta[:, None]
phi = sphere.phi[:, None]
return real_sh_descoteaux_from_index(self.m, self.n, theta, phi) | [
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pypa/pipenv | b21baade71a86ab3ee1429f71fbc14d4f95fb75d | pipenv/patched/notpip/_vendor/pkg_resources/__init__.py | python | EntryPoint.parse_map | (cls, data, dist=None) | return maps | Parse a map of entry point groups | Parse a map of entry point groups | [
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] | def parse_map(cls, data, dist=None):
"""Parse a map of entry point groups"""
if isinstance(data, dict):
data = data.items()
else:
data = split_sections(data)
maps = {}
for group, lines in data:
if group is None:
if not lines:
continue
raise ValueError("Entry points must be listed in groups")
group = group.strip()
if group in maps:
raise ValueError("Duplicate group name", group)
maps[group] = cls.parse_group(group, lines, dist)
return maps | [
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python/cpython | e13cdca0f5224ec4e23bdd04bb3120506964bc8b | Lib/pathlib.py | python | Path.is_dir | (self) | Whether this path is a directory. | Whether this path is a directory. | [
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"""
Whether this path is a directory.
"""
try:
return S_ISDIR(self.stat().st_mode)
except OSError as e:
if not _ignore_error(e):
raise
# Path doesn't exist or is a broken symlink
# (see http://web.archive.org/web/20200623061726/https://bitbucket.org/pitrou/pathlib/issues/12/ )
return False
except ValueError:
# Non-encodable path
return False | [
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openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | openshift/installer/vendored/openshift-ansible-3.10.0-0.29.0/roles/lib_vendored_deps/filter_plugins/openshift_master.py | python | OpenIDIdentityProvider.validate | (self) | validate this idp instance | validate this idp instance | [
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] | def validate(self):
''' validate this idp instance '''
if not isinstance(self.provider['claims'], dict):
raise errors.AnsibleFilterError("|failed claims for provider {0} "
"must be a dictionary".format(self.__class__.__name__))
for var, var_type in (('extraScopes', list), ('extraAuthorizeParameters', dict)):
if var in self.provider and not isinstance(self.provider[var], var_type):
raise errors.AnsibleFilterError("|failed {1} for provider "
"{0} must be a {2}".format(self.__class__.__name__,
var,
var_type.__class__.__name__))
required_claims = ['id']
optional_claims = ['email', 'name', 'preferredUsername']
all_claims = required_claims + optional_claims
for claim in required_claims:
if claim in required_claims and claim not in self.provider['claims']:
raise errors.AnsibleFilterError("|failed {0} claim missing "
"for provider {1}".format(claim, self.__class__.__name__))
for claim in all_claims:
if claim in self.provider['claims'] and not isinstance(self.provider['claims'][claim], list):
raise errors.AnsibleFilterError("|failed {0} claims for "
"provider {1} must be a list".format(claim, self.__class__.__name__))
unknown_claims = set(self.provider['claims'].keys()) - set(all_claims)
if len(unknown_claims) > 0:
raise errors.AnsibleFilterError("|failed provider {0} has unknown "
"claims: {1}".format(self.__class__.__name__, ', '.join(unknown_claims)))
if not isinstance(self.provider['urls'], dict):
raise errors.AnsibleFilterError("|failed urls for provider {0} "
"must be a dictionary".format(self.__class__.__name__))
required_urls = ['authorize', 'token']
optional_urls = ['userInfo']
all_urls = required_urls + optional_urls
for url in required_urls:
if url not in self.provider['urls']:
raise errors.AnsibleFilterError("|failed {0} url missing for "
"provider {1}".format(url, self.__class__.__name__))
unknown_urls = set(self.provider['urls'].keys()) - set(all_urls)
if len(unknown_urls) > 0:
raise errors.AnsibleFilterError("|failed provider {0} has unknown "
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Trusted-AI/AIX360 | 36459f2a585d0e2a2e8582562bf226d4402b57d6 | aix360/algorithms/lime/lime_wrapper.py | python | LimeTabularExplainer.__init__ | (self, *argv, **kwargs) | Initialize lime Tabular Explainer object | Initialize lime Tabular Explainer object | [
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"Explainer",
"object"
] | def __init__(self, *argv, **kwargs):
"""
Initialize lime Tabular Explainer object
"""
super(LimeTabularExplainer, self).__init__(*argv, **kwargs)
self.explainer = lime_tabular.LimeTabularExplainer(*argv, **kwargs) | [
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oilshell/oil | 94388e7d44a9ad879b12615f6203b38596b5a2d3 | Python-2.7.13/Lib/symtable.py | python | SymbolTable.get_type | (self) | [] | def get_type(self):
if self._table.type == _symtable.TYPE_MODULE:
return "module"
if self._table.type == _symtable.TYPE_FUNCTION:
return "function"
if self._table.type == _symtable.TYPE_CLASS:
return "class"
assert self._table.type in (1, 2, 3), \
"unexpected type: {0}".format(self._table.type) | [
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accel-brain/accel-brain-code | 86f489dc9be001a3bae6d053f48d6b57c0bedb95 | Accel-Brain-Base/accelbrainbase/iteratabledata/_mxnet/unlabeled_t_hot_txt_iterator.py | python | UnlabeledTHotTXTIterator.generate_learned_samples | (self) | Draw and generate data.
Returns:
`Tuple` data. The shape is ...
- `mxnet.ndarray` of observed data points in training.
- `mxnet.ndarray` of supervised data in training.
- `mxnet.ndarray` of observed data points in test.
- `mxnet.ndarray` of supervised data in test. | Draw and generate data. | [
"Draw",
"and",
"generate",
"data",
"."
] | def generate_learned_samples(self):
'''
Draw and generate data.
Returns:
`Tuple` data. The shape is ...
- `mxnet.ndarray` of observed data points in training.
- `mxnet.ndarray` of supervised data in training.
- `mxnet.ndarray` of observed data points in test.
- `mxnet.ndarray` of supervised data in test.
'''
for _ in range(self.iter_n):
training_batch_arr = np.zeros(
(
self.batch_size,
1,
self.seq_len,
len(self.__token_list)
),
)
test_batch_arr = np.zeros(
(
self.batch_size,
1,
self.seq_len,
len(self.__token_list)
),
)
for i in range(self.batch_size):
file_key = np.random.randint(low=0, high=len(self.__train_txt_path_list))
with open(self.__train_txt_path_list[file_key]) as f:
train_txt = f.read()
test_file_key = np.random.randint(low=0, high=len(self.__test_txt_path_list))
with open(self.__test_txt_path_list[test_file_key]) as f:
test_txt = f.read()
if self.__pre_txt_arr is None:
start_row = np.random.randint(low=0, high=len(train_txt) - self.seq_len)
test_start_row = np.random.randint(low=0, high=len(test_txt) - self.seq_len)
train_txt = train_txt[start_row:start_row+self.seq_len]
test_txt = test_txt[test_start_row:test_start_row+self.seq_len]
else:
if train_txt.index(self.__pre_txt_arr[i]) + (self.seq_len * 2) < len(train_txt):
start_row = train_txt.index(self.__pre_txt_arr[i]) + self.seq_len
else:
start_row = np.random.randint(low=0, high=len(train_txt) - self.seq_len)
train_txt = train_txt[start_row:start_row+self.seq_len]
test_start_row = np.random.randint(low=0, high=len(test_txt) - self.seq_len)
test_txt = test_txt[test_start_row:test_start_row+self.seq_len]
for seq in range(self.seq_len):
training_batch_arr[i, 0, seq, self.__token_list.index(train_txt[seq])] = 1.0
test_batch_arr[i, 0, seq, self.__token_list.index(test_txt[seq])] = 1.0
training_batch_arr = nd.ndarray.array(training_batch_arr, ctx=self.__ctx)
test_batch_arr = nd.ndarray.array(test_batch_arr, ctx=self.__ctx)
training_batch_arr = self.pre_normalize(training_batch_arr)
test_batch_arr = self.pre_normalize(test_batch_arr)
if self.__noiseable_data is not None:
training_batch_arr = self.__noiseable_data.noise(training_batch_arr)
yield training_batch_arr, training_batch_arr, test_batch_arr, test_batch_arr | [
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pyqt/examples | 843bb982917cecb2350b5f6d7f42c9b7fb142ec1 | src/pyqt-official/designer/plugins/widgets/datetimeedit.py | python | PyDateEdit.mousePressEvent | (self, event) | [] | def mousePressEvent(self, event):
super(PyDateEdit, self).mousePressEvent(event)
if not self.__cw:
self.__cw = self.findChild(QCalendarWidget)
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self.__cw.setFirstDayOfWeek(self.__firstDayOfWeek)
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uqfoundation/multiprocess | 028cc73f02655e6451d92e5147d19d8c10aebe50 | pypy3.7/multiprocess/pool.py | python | Pool._map_async | (self, func, iterable, mapper, chunksize=None, callback=None,
error_callback=None) | return result | Helper function to implement map, starmap and their async counterparts. | Helper function to implement map, starmap and their async counterparts. | [
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] | def _map_async(self, func, iterable, mapper, chunksize=None, callback=None,
error_callback=None):
'''
Helper function to implement map, starmap and their async counterparts.
'''
if self._state != RUN:
raise ValueError("Pool not running")
if not hasattr(iterable, '__len__'):
iterable = list(iterable)
if chunksize is None:
chunksize, extra = divmod(len(iterable), len(self._pool) * 4)
if extra:
chunksize += 1
if len(iterable) == 0:
chunksize = 0
task_batches = Pool._get_tasks(func, iterable, chunksize)
result = MapResult(self._cache, chunksize, len(iterable), callback,
error_callback=error_callback)
self._taskqueue.put(
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self._guarded_task_generation(result._job,
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return result | [
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SALib/SALib | b6b6b5cab3388f3b80590c98d66aca7dc784d894 | src/SALib/analyze/fast.py | python | compute_orders | (outputs, N, M, omega) | return (D1 / V), (1.0 - Dt / V) | [] | def compute_orders(outputs, N, M, omega):
f = np.fft.fft(outputs)
Sp = np.power(np.absolute(f[np.arange(1, int((N + 1) / 2))]) / N, 2)
V = 2.0 * np.sum(Sp)
# Calculate first and total order
D1 = 2.0 * np.sum(Sp[np.arange(1, M + 1) * int(omega) - 1])
Dt = 2.0 * np.sum(Sp[np.arange(int(omega / 2.0))])
return (D1 / V), (1.0 - Dt / V) | [
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huggingface/transformers | 623b4f7c63f60cce917677ee704d6c93ee960b4b | src/transformers/models/marian/convert_marian_to_pytorch.py | python | unzip | (zip_path: str, dest_dir: str) | [] | def unzip(zip_path: str, dest_dir: str) -> None:
with ZipFile(zip_path, "r") as zipObj:
zipObj.extractall(dest_dir) | [
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weinbe58/QuSpin | 5bbc3204dbf5c227a87a44f0dacf39509cba580c | quspin/basis/photon.py | python | photon_basis.__init__ | (self,basis_constructor,*constructor_args,**blocks) | Initialises the `photon_basis` object.
Parameters
-----------
basis_constructor: :obj:`basis`
`basis` constructor for the lattice part of the `photon_basis`.
constructor_args: obj
Required arguments required by the specific `basis_constructor`.
blocks: obj
Optional keyword arguments for `basis_constructor` which include (but are not limited to):
**Nph** (*int*) - specify the dimension of photon (HO) Hilbert space.
**Ntot** (*int*) - specify total number of particles (photons + lattice).
**anyblock** (*int*) - specify any lattice symmetry blocks | Initialises the `photon_basis` object. | [
"Initialises",
"the",
"photon_basis",
"object",
"."
] | def __init__(self,basis_constructor,*constructor_args,**blocks):
"""Initialises the `photon_basis` object.
Parameters
-----------
basis_constructor: :obj:`basis`
`basis` constructor for the lattice part of the `photon_basis`.
constructor_args: obj
Required arguments required by the specific `basis_constructor`.
blocks: obj
Optional keyword arguments for `basis_constructor` which include (but are not limited to):
**Nph** (*int*) - specify the dimension of photon (HO) Hilbert space.
**Ntot** (*int*) - specify total number of particles (photons + lattice).
**anyblock** (*int*) - specify any lattice symmetry blocks
"""
Ntot = blocks.get("Ntot")
Nph = blocks.get("Nph")
self._Nph = Nph
self._Ntot = Ntot
if Ntot is not None: blocks.pop("Ntot")
if Nph is not None: blocks.pop("Nph")
if Ntot is None:
if Nph is None: raise TypeError("If Ntot not specified, Nph must specify the cutoff on the number of photon states.")
if type(Nph) is not int: raise TypeError("Nph must be integer")
if Nph < 0: raise ValueError("Nph must be an integer >= 0.")
self._check_pcon=False
b1 = basis_constructor(*constructor_args,_Np=-1,**blocks)
b2 = ho_basis(Nph)
tensor_basis.__init__(self,b1,b2)
else:
if type(Ntot) is not int: raise TypeError("Ntot must be integer")
if Ntot < 0: raise ValueError("Ntot must be an integer >= 0.")
self._check_pcon=True
self._basis_left = basis_constructor(*constructor_args,_Np=Ntot,**blocks)
if isinstance(self._basis_left,tensor_basis): raise TypeError("Can only create photon basis with non-tensor type basis")
if not isinstance(self._basis_left,basis): raise TypeError("Can only create photon basis with basis type")
self._basis_right = ho_basis(Ntot)
self._n = self._basis_left._Np_list
self._n -= Ntot
self._n *= -1
self._blocks = self._basis_left._blocks
self._Ns = self._basis_left._Ns
self._unique_me = self._basis_left._unique_me
self._operators = self._basis_left._operators +"\n"+ self._basis_right._operators
self._sps = self._basis_left.sps | [
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google-research/morph-net | be4d79dea816c473007c5967d45ab4036306c21c | examples/keras/model.py | python | MorphNetModel.initialize_model | (self, input_tensor=None) | Initialize the model.
Args:
input_tensor: Input tensor to the model. tf.placeholder variable or tf.Keras.Input instance. If not provided, use the default inputs from the the base model. | Initialize the model.
Args:
input_tensor: Input tensor to the model. tf.placeholder variable or tf.Keras.Input instance. If not provided, use the default inputs from the the base model. | [
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"""
Initialize the model.
Args:
input_tensor: Input tensor to the model. tf.placeholder variable or tf.Keras.Input instance. If not provided, use the default inputs from the the base model.
"""
with tf.device(self.main_train_device):
base_model = self.base_model(weights=None,
include_top=False,
input_tensor=input_tensor)
x = base_model.output
# Add a global spatial average pooling layer since MorphNet does not support Flatten/Reshape OPs.
x = tf.keras.layers.GlobalAveragePooling2D()(x)
x = tf.keras.layers.Dense(1024, activation="relu")(x)
logits = tf.keras.layers.Dense(self.num_classes)(x)
self.model = tf.keras.Model(inputs=base_model.input, outputs=logits)
self.inputs = self.model.input
self.labels = tf.placeholder(tf.float32, [None, self.num_classes])
self.morphnet_regularization_strength_placeholder = tf.placeholder(
tf.float32, shape=[]) | [
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cleverhans-lab/cleverhans | e5d00e537ce7ad6119ed5a8db1f0e9736d1f6e1d | cleverhans_v3.1.0/cleverhans/model_zoo/madry_lab_challenges/cifar10_model.py | python | Input.fprop | (self, x) | [] | def fprop(self, x):
with tf.variable_scope("input", reuse=tf.AUTO_REUSE):
input_standardized = tf.map_fn(
lambda img: tf.image.per_image_standardization(img), x
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return _conv("init_conv", input_standardized, 3, 3, 16, _stride_arr(1)) | [
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buke/GreenOdoo | 3d8c55d426fb41fdb3f2f5a1533cfe05983ba1df | source/addons/fetchmail/fetchmail.py | python | fetchmail_server.fetch_mail | (self, cr, uid, ids, context=None) | return True | WARNING: meant for cron usage only - will commit() after each email! | WARNING: meant for cron usage only - will commit() after each email! | [
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"""WARNING: meant for cron usage only - will commit() after each email!"""
context = dict(context or {})
context['fetchmail_cron_running'] = True
mail_thread = self.pool.get('mail.thread')
action_pool = self.pool.get('ir.actions.server')
for server in self.browse(cr, uid, ids, context=context):
_logger.info('start checking for new emails on %s server %s', server.type, server.name)
context.update({'fetchmail_server_id': server.id, 'server_type': server.type})
count, failed = 0, 0
imap_server = False
pop_server = False
if server.type == 'imap':
try:
imap_server = server.connect()
imap_server.select()
result, data = imap_server.search(None, '(UNSEEN)')
for num in data[0].split():
res_id = None
result, data = imap_server.fetch(num, '(RFC822)')
imap_server.store(num, '-FLAGS', '\\Seen')
try:
res_id = mail_thread.message_process(cr, uid, server.object_id.model,
data[0][1],
save_original=server.original,
strip_attachments=(not server.attach),
context=context)
except Exception:
_logger.exception('Failed to process mail from %s server %s.', server.type, server.name)
failed += 1
if res_id and server.action_id:
action_pool.run(cr, uid, [server.action_id.id], {'active_id': res_id, 'active_ids': [res_id], 'active_model': context.get("thread_model", server.object_id.model)})
imap_server.store(num, '+FLAGS', '\\Seen')
cr.commit()
count += 1
_logger.info("Fetched %d email(s) on %s server %s; %d succeeded, %d failed.", count, server.type, server.name, (count - failed), failed)
except Exception:
_logger.exception("General failure when trying to fetch mail from %s server %s.", server.type, server.name)
finally:
if imap_server:
imap_server.close()
imap_server.logout()
elif server.type == 'pop':
try:
while True:
pop_server = server.connect()
(numMsgs, totalSize) = pop_server.stat()
pop_server.list()
for num in range(1, min(MAX_POP_MESSAGES, numMsgs) + 1):
(header, msges, octets) = pop_server.retr(num)
msg = '\n'.join(msges)
res_id = None
try:
res_id = mail_thread.message_process(cr, uid, server.object_id.model,
msg,
save_original=server.original,
strip_attachments=(not server.attach),
context=context)
pop_server.dele(num)
except Exception:
_logger.exception('Failed to process mail from %s server %s.', server.type, server.name)
failed += 1
if res_id and server.action_id:
action_pool.run(cr, uid, [server.action_id.id], {'active_id': res_id, 'active_ids': [res_id], 'active_model': context.get("thread_model", server.object_id.model)})
cr.commit()
if numMsgs < MAX_POP_MESSAGES:
break
pop_server.quit()
_logger.info("Fetched %d email(s) on %s server %s; %d succeeded, %d failed.", numMsgs, server.type, server.name, (numMsgs - failed), failed)
except Exception:
_logger.exception("General failure when trying to fetch mail from %s server %s.", server.type, server.name)
finally:
if pop_server:
pop_server.quit()
server.write({'date': time.strftime(tools.DEFAULT_SERVER_DATETIME_FORMAT)})
return True | [
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Mailu/Mailu | 1e53530164e9eaf77a89c322e34bff447ace5a28 | core/admin/mailu/utils.py | python | handle_needs_login | () | return flask.redirect(
flask.url_for('sso.login')
) | redirect unauthorized requests to login page | redirect unauthorized requests to login page | [
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""" redirect unauthorized requests to login page """
return flask.redirect(
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linxid/Machine_Learning_Study_Path | 558e82d13237114bbb8152483977806fc0c222af | Machine Learning In Action/Chapter5-LogisticRegression/venv/Lib/site-packages/setuptools/package_index.py | python | PackageIndex.download | (self, spec, tmpdir) | return getattr(self.fetch_distribution(spec, tmpdir), 'location', None) | Locate and/or download `spec` to `tmpdir`, returning a local path
`spec` may be a ``Requirement`` object, or a string containing a URL,
an existing local filename, or a project/version requirement spec
(i.e. the string form of a ``Requirement`` object). If it is the URL
of a .py file with an unambiguous ``#egg=name-version`` tag (i.e., one
that escapes ``-`` as ``_`` throughout), a trivial ``setup.py`` is
automatically created alongside the downloaded file.
If `spec` is a ``Requirement`` object or a string containing a
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a matching distribution (possibly after downloading it to `tmpdir`).
If `spec` is a locally existing file or directory name, it is simply
returned unchanged. If `spec` is a URL, it is downloaded to a subpath
of `tmpdir`, and the local filename is returned. Various errors may be
raised if a problem occurs during downloading. | Locate and/or download `spec` to `tmpdir`, returning a local path | [
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"""Locate and/or download `spec` to `tmpdir`, returning a local path
`spec` may be a ``Requirement`` object, or a string containing a URL,
an existing local filename, or a project/version requirement spec
(i.e. the string form of a ``Requirement`` object). If it is the URL
of a .py file with an unambiguous ``#egg=name-version`` tag (i.e., one
that escapes ``-`` as ``_`` throughout), a trivial ``setup.py`` is
automatically created alongside the downloaded file.
If `spec` is a ``Requirement`` object or a string containing a
project/version requirement spec, this method returns the location of
a matching distribution (possibly after downloading it to `tmpdir`).
If `spec` is a locally existing file or directory name, it is simply
returned unchanged. If `spec` is a URL, it is downloaded to a subpath
of `tmpdir`, and the local filename is returned. Various errors may be
raised if a problem occurs during downloading.
"""
if not isinstance(spec, Requirement):
scheme = URL_SCHEME(spec)
if scheme:
# It's a url, download it to tmpdir
found = self._download_url(scheme.group(1), spec, tmpdir)
base, fragment = egg_info_for_url(spec)
if base.endswith('.py'):
found = self.gen_setup(found, fragment, tmpdir)
return found
elif os.path.exists(spec):
# Existing file or directory, just return it
return spec
else:
spec = parse_requirement_arg(spec)
return getattr(self.fetch_distribution(spec, tmpdir), 'location', None) | [
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samuelclay/NewsBlur | 2c45209df01a1566ea105e04d499367f32ac9ad2 | apps/social/views.py | python | mute_story | (request, secret_token, shared_story_id) | return {} | [] | def mute_story(request, secret_token, shared_story_id):
user_profile = Profile.objects.get(secret_token=secret_token)
shared_story = MSharedStory.objects.get(id=shared_story_id)
shared_story.mute_for_user(user_profile.user_id)
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TencentCloud/tencentcloud-sdk-python | 3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2 | tencentcloud/iai/v20200303/models.py | python | Hair.__init__ | (self) | r"""
:param Length: 头发长度信息。
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:type Length: :class:`tencentcloud.iai.v20200303.models.AttributeItem`
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:type Bang: :class:`tencentcloud.iai.v20200303.models.AttributeItem`
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:param Length: 头发长度信息。
AttributeItem对应的Type为 —— 0：光头，1：短发，2：中发，3：长发，4：绑发。
:type Length: :class:`tencentcloud.iai.v20200303.models.AttributeItem`
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:type Color: :class:`tencentcloud.iai.v20200303.models.AttributeItem` | [
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self.Length = None
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pysmt/pysmt | ade4dc2a825727615033a96d31c71e9f53ce4764 | pysmt/smtlib/printers.py | python | SmtPrinter._walk_quantifier | (self, operator, formula) | [] | def _walk_quantifier(self, operator, formula):
assert len(formula.quantifier_vars()) > 0
self.write("(%s (" % operator)
for s in formula.quantifier_vars():
self.write("(")
yield s
self.write(" %s)" % s.symbol_type().as_smtlib(False))
self.write(") ")
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edisonlz/fastor | 342078a18363ac41d3c6b1ab29dbdd44fdb0b7b3 | base/site-packages/django/contrib/auth/models.py | python | AbstractUser.email_user | (self, subject, message, from_email=None) | Sends an email to this User. | Sends an email to this User. | [
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"User",
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] | def email_user(self, subject, message, from_email=None):
"""
Sends an email to this User.
"""
send_mail(subject, message, from_email, [self.email]) | [
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Veil-Framework/Veil | c825577bbc97db04be5c47e004369038491f6b7a | tools/evasion/evasion_common/encryption.py | python | buildAryaLauncher | (raw) | return payload_code | Takes a raw set of bytes and builds a launcher shell to b64decode/decrypt
a string rep of the bytes, and then use reflection to invoke
the original .exe | Takes a raw set of bytes and builds a launcher shell to b64decode/decrypt
a string rep of the bytes, and then use reflection to invoke
the original .exe | [
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] | def buildAryaLauncher(raw):
"""
Takes a raw set of bytes and builds a launcher shell to b64decode/decrypt
a string rep of the bytes, and then use reflection to invoke
the original .exe
"""
# the 'key' is a randomized alpha lookup table [a-zA-Z] used for substitution
key = ''.join(sorted(list(string.ascii_letters), key=lambda *args: random.random()))
base64payload = b64sub(raw, key)
payload_code = "using System; using System.Collections.Generic; using System.Text;"
payload_code += "using System.IO; using System.Reflection; using System.Linq;\n"
decodeFuncName = evasion_helpers.randomString()
baseStringName = evasion_helpers.randomString()
targetStringName = evasion_helpers.randomString()
dictionaryName = evasion_helpers.randomString()
# build out the letter sub decrypt function
payload_code += "namespace %s { class %s { private static string %s(string t, string k) {\n" % (evasion_helpers.randomString(), evasion_helpers.randomString(), decodeFuncName)
payload_code += "string %s = \"abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ\";\n" % (baseStringName)
payload_code += "string %s = \"\"; Dictionary<char, char> %s = new Dictionary<char, char>();\n" % (targetStringName, dictionaryName)
payload_code += "for (int i = 0; i < %s.Length; ++i){ %s.Add(k[i], %s[i]); }\n" % (baseStringName, dictionaryName,baseStringName)
payload_code += "for (int i = 0; i < t.Length; ++i){ if ((t[i] >= 'A' && t[i] <= 'Z') || (t[i] >= 'a' && t[i] <= 'z')) { %s += %s[t[i]];}\n" % (targetStringName, dictionaryName)
payload_code += "else { %s += t[i]; }} return %s; }\n" % (targetStringName, targetStringName)
base64PayloadName = evasion_helpers.randomString()
assemblyName = evasion_helpers.randomString()
# build out Main()
assemblyName = evasion_helpers.randomString()
methodInfoName = evasion_helpers.randomString()
keyName = evasion_helpers.randomString()
payload_code += "static void Main() {\n"
payload_code += "string %s = \"%s\";\n" % (base64PayloadName, base64payload)
payload_code += "string %s = \"%s\";\n" % (keyName, key)
# load up the assembly of the decoded binary
payload_code += "Assembly %s = Assembly.Load(Convert.FromBase64String(%s(%s, %s)));\n" % (assemblyName, decodeFuncName, base64PayloadName, keyName)
payload_code += "MethodInfo %s = %s.EntryPoint;\n" % (methodInfoName, assemblyName)
# use reflection to jump to its entry point
payload_code += "%s.Invoke(%s.CreateInstance(%s.Name), null);\n" % (methodInfoName, assemblyName, methodInfoName)
payload_code += "}}}\n"
return payload_code | [
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donnemartin/gitsome | d7c57abc7cb66e9c910a844f15d4536866da3310 | xonsh/ast.py | python | load_attribute_chain | (name, lineno=None, col=None) | return node | Creates an AST that loads variable name that may (or may not)
have attribute chains. For example, "a.b.c" | Creates an AST that loads variable name that may (or may not)
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] | def load_attribute_chain(name, lineno=None, col=None):
"""Creates an AST that loads variable name that may (or may not)
have attribute chains. For example, "a.b.c"
"""
names = name.split(".")
node = Name(id=names.pop(0), ctx=Load(), lineno=lineno, col_offset=col)
for attr in names:
node = Attribute(
value=node, attr=attr, ctx=Load(), lineno=lineno, col_offset=col
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return node | [
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Fallen-Breath/MCDReforged | fdb1d2520b35f916123f265dbd94603981bb2b0c | mcdreforged/permission/permission_manager.py | python | PermissionManager._pre_save | (self, data) | [] | def _pre_save(self, data):
# Deduplicate the permission data=
for key, value in data.items():
if key in PermissionLevel.NAMES and isinstance(value, list):
data[key] = misc_util.unique_list(data[key])
# Change empty list to None for nicer look in the .yml file
for key, value in data.items():
if key in PermissionLevel.NAMES and value is not None and len(value) == 0:
data[key] = None | [
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wbond/packagecontrol.io | 9f5eb7e3392e6bc2ad979ad32d3dd27ef9c00b20 | app/lib/package_control/deps/asn1crypto/x509.py | python | IPAddress.parse | (self, spec=None, spec_params=None) | This method is not applicable to IP addresses | This method is not applicable to IP addresses | [
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"""
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raise ValueError(unwrap(
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IP address values can not be parsed
'''
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demisto/content | 5c664a65b992ac8ca90ac3f11b1b2cdf11ee9b07 | Packs/epo/Integrations/epoV2/epoV2.py | python | Client.get_system_group_path | (self, group_id: int) | return '' | return the system group path for giving group_id
Args:
group_id (str): the groupID to find
Returns (str): returns the system group path for a giving group id | return the system group path for giving group_id
Args:
group_id (str): the groupID to find
Returns (str): returns the system group path for a giving group id | [
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"""
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Args:
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Returns (str): returns the system group path for a giving group id
"""
response_json, response = self.get_system_tree_groups(search_text='')
if response is None:
return ''
for entry in response_json:
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ajinabraham/OWASP-Xenotix-XSS-Exploit-Framework | cb692f527e4e819b6c228187c5702d990a180043 | external/Scripting Engine/Xenotix Python Scripting Engine/Lib/wsgiref/util.py | python | request_uri | (environ, include_query=1) | return url | Return the full request URI, optionally including the query string | Return the full request URI, optionally including the query string | [
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] | def request_uri(environ, include_query=1):
"""Return the full request URI, optionally including the query string"""
url = application_uri(environ)
from urllib import quote
path_info = quote(environ.get('PATH_INFO',''),safe='/;=,')
if not environ.get('SCRIPT_NAME'):
url += path_info[1:]
else:
url += path_info
if include_query and environ.get('QUERY_STRING'):
url += '?' + environ['QUERY_STRING']
return url | [
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ericgazoni/openpyxl | c55988e4904d4337ce4c35ab8b7dc305bca9de23 | openpyxl/writer/workbook.py | python | write_workbook_rels | (workbook) | return get_document_content(root) | Write the workbook relationships xml. | Write the workbook relationships xml. | [
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"."
] | def write_workbook_rels(workbook):
"""Write the workbook relationships xml."""
root = Element('{%s}Relationships' % PKG_REL_NS)
for i in range(1, len(workbook.worksheets) + 1):
SubElement(root, '{%s}Relationship' % PKG_REL_NS,
{'Id': 'rId%d' % i, 'Target': 'worksheets/sheet%s.xml' % i,
'Type': '%s/worksheet' % REL_NS})
rid = len(workbook.worksheets) + 1
SubElement(root, '{%s}Relationship' % PKG_REL_NS,
{'Id': 'rId%d' % rid, 'Target': 'sharedStrings.xml',
'Type': '%s/sharedStrings' % REL_NS})
SubElement(root, '{%s}Relationship' % PKG_REL_NS,
{'Id': 'rId%d' % (rid + 1), 'Target': 'styles.xml',
'Type': '%s/styles' % REL_NS})
SubElement(root, '{%s}Relationship' % PKG_REL_NS,
{'Id': 'rId%d' % (rid + 2), 'Target': 'theme/theme1.xml',
'Type': '%s/theme' % REL_NS})
if workbook.vba_archive:
SubElement(root, '{%s}Relationship' % PKG_REL_NS,
{'Id': 'rId%d' % (rid + 3), 'Target': 'vbaProject.bin',
'Type': 'http://schemas.microsoft.com/office/2006/relationships/vbaProject'})
return get_document_content(root) | [
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openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | openshift/installer/vendored/openshift-ansible-3.10.0-0.29.0/roles/lib_openshift/library/oc_adm_policy_user.py | python | RoleBinding.add_subject | (self, inc_subject) | return True | add a subject | add a subject | [
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"a",
"subject"
] | def add_subject(self, inc_subject):
''' add a subject '''
if self.subjects:
# pylint: disable=no-member
self.subjects.append(inc_subject)
else:
self.put(RoleBinding.subjects_path, [inc_subject])
return True | [
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ENCODE-DCC/atac-seq-pipeline | d777eb707d481156fcc21089fe9c3e6f2aec933e | src/encode_lib_genomic.py | python | get_samtools_res_param | (subcmd, nth=1, mem_gb=None) | return res_param | Make resource parameters (-@, -m) for samtools.
-@:
This means number of total/additional threads.
-m:
This means memory per thread (for samtools sort only).
It's clipped between 1 and DEFAULT_SAMTOOLS_MAX_MEM_MB_PER_THREAD MBs.
Tested with samtools 1.9.
Lower version of samtools work a bit differently.
For such lower versions, -@ is number of threads.
Run `samtools view --help` with your version and check if
it is based on total or additional number of threads.
Args:
nth:
Number of threads.
- index: total threads.
- all other sub-commands: additional threads.
mem_gb:
Total memory in GBs. | Make resource parameters (-@, -m) for samtools.
-@:
This means number of total/additional threads.
-m:
This means memory per thread (for samtools sort only).
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This means memory per thread (for samtools sort only).
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Total memory in GBs.
"""
res_param = ''
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else:
res_param += '-@ {num_additional_threads} '.format(
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if subcmd == 'sort':
if nth and mem_gb:
mem_mb_per_thread = min(
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res_param += '-m {mem}M '.format(mem=mem_mb_per_thread)
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rowliny/DiffHelper | ab3a96f58f9579d0023aed9ebd785f4edf26f8af | Tool/SitePackages/nltk/grammar.py | python | ProbabilisticDependencyGrammar.__str__ | (self) | return str | Return a verbose string representation of the ``ProbabilisticDependencyGrammar``
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samgranger/EQGRP | fa0a1e1460767b8312839dc4be922f26ecdd250b | Firewall/EXPLOITS/EXBA/scapy/utils.py | python | RawPcapReader.next | (self) | return pkt | impliment the iterator protocol on a set of packets in a pcap file | impliment the iterator protocol on a set of packets in a pcap file | [
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/lfunctions/dokchitser.py | python | Dokchitser.num_coeffs | (self, T=1) | return Integer(self._gp_call_inst('cflength', T)) | Return number of coefficients `a_n` that are needed in
order to perform most relevant `L`-function computations to
the desired precision.
EXAMPLES::
sage: E = EllipticCurve('11a')
sage: L = E.lseries().dokchitser(algorithm='gp')
sage: L.num_coeffs()
26
sage: E = EllipticCurve('5077a')
sage: L = E.lseries().dokchitser(algorithm='gp')
sage: L.num_coeffs()
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sage: L = Dokchitser(conductor=1, gammaV=[0], weight=1, eps=1, poles=[1], residues=[-1], init='1')
sage: L.num_coeffs()
4
Verify that ``num_coeffs`` works with non-real spectral
parameters, e.g. for the L-function of the level 10 Maass form
with eigenvalue 2.7341055592527126::
sage: ev = 2.7341055592527126
sage: L = Dokchitser(conductor=10, gammaV=[ev*i, -ev*i],weight=2,eps=1)
sage: L.num_coeffs()
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Return number of coefficients `a_n` that are needed in
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the desired precision.
EXAMPLES::
sage: E = EllipticCurve('11a')
sage: L = E.lseries().dokchitser(algorithm='gp')
sage: L.num_coeffs()
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sage: E = EllipticCurve('5077a')
sage: L = E.lseries().dokchitser(algorithm='gp')
sage: L.num_coeffs()
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sage: L.num_coeffs()
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sage: ev = 2.7341055592527126
sage: L = Dokchitser(conductor=10, gammaV=[ev*i, -ev*i],weight=2,eps=1)
sage: L.num_coeffs()
26
"""
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python-diamond/Diamond | 7000e16cfdf4508ed9291fc4b3800592557b2431 | src/collectors/squid/squid.py | python | SquidCollector.get_default_config_help | (self) | return config_help | [] | def get_default_config_help(self):
config_help = super(SquidCollector, self).get_default_config_help()
config_help.update({
'hosts': 'List of hosts to collect from. Format is ' +
'[nickname@]host[:port], [nickname@]host[:port], etc',
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graalvm/mx | 29c0debab406352df3af246be2f8973be5db69ae | mx.py | python | MavenRepo.getSnapshotUrl | (self, groupId, artifactId, version) | return "{0}/{1}/{2}/{3}/maven-metadata.xml".format(self.repourl, groupId.replace('.', '/'), artifactId, version) | [] | def getSnapshotUrl(self, groupId, artifactId, version):
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wistbean/learn_python3_spider | 73c873f4845f4385f097e5057407d03dd37a117b | stackoverflow/venv/lib/python3.6/site-packages/pip-19.0.3-py3.6.egg/pip/_vendor/pkg_resources/__init__.py | python | _ReqExtras.markers_pass | (self, req, extras=None) | return not req.marker or any(extra_evals) | Evaluate markers for req against each extra that
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"""
Evaluate markers for req against each extra that
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Return False if the req has a marker and fails
evaluation. Otherwise, return True.
"""
extra_evals = (
req.marker.evaluate({'extra': extra})
for extra in self.get(req, ()) + (extras or (None,))
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return not req.marker or any(extra_evals) | [
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home-assistant/core | 265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1 | homeassistant/components/hassio/__init__.py | python | async_setup | (hass: HomeAssistant, config: ConfigType) | return True | Set up the Hass.io component. | Set up the Hass.io component. | [
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"""Set up the Hass.io component."""
# Check local setup
for env in ("HASSIO", "HASSIO_TOKEN"):
if os.environ.get(env):
continue
_LOGGER.error("Missing %s environment variable", env)
if config_entries := hass.config_entries.async_entries(DOMAIN):
hass.async_create_task(
hass.config_entries.async_remove(config_entries[0].entry_id)
)
return False
async_load_websocket_api(hass)
host = os.environ["HASSIO"]
websession = async_get_clientsession(hass)
hass.data[DOMAIN] = hassio = HassIO(hass.loop, websession, host)
if not await hassio.is_connected():
_LOGGER.warning("Not connected with the supervisor / system too busy!")
store = hass.helpers.storage.Store(STORAGE_VERSION, STORAGE_KEY)
if (data := await store.async_load()) is None:
data = {}
refresh_token = None
if "hassio_user" in data:
user = await hass.auth.async_get_user(data["hassio_user"])
if user and user.refresh_tokens:
refresh_token = list(user.refresh_tokens.values())[0]
# Migrate old Hass.io users to be admin.
if not user.is_admin:
await hass.auth.async_update_user(user, group_ids=[GROUP_ID_ADMIN])
# Migrate old name
if user.name == "Hass.io":
await hass.auth.async_update_user(user, name=HASSIO_USER_NAME)
if refresh_token is None:
user = await hass.auth.async_create_system_user(
HASSIO_USER_NAME, group_ids=[GROUP_ID_ADMIN]
)
refresh_token = await hass.auth.async_create_refresh_token(user)
data["hassio_user"] = user.id
await store.async_save(data)
# This overrides the normal API call that would be forwarded
development_repo = config.get(DOMAIN, {}).get(CONF_FRONTEND_REPO)
if development_repo is not None:
hass.http.register_static_path(
"/api/hassio/app", os.path.join(development_repo, "hassio/build"), False
)
hass.http.register_view(HassIOView(host, websession))
await hass.components.panel_custom.async_register_panel(
frontend_url_path="hassio",
webcomponent_name="hassio-main",
js_url="/api/hassio/app/entrypoint.js",
embed_iframe=True,
require_admin=True,
)
await hassio.update_hass_api(config.get("http", {}), refresh_token)
last_timezone = None
async def push_config(_):
"""Push core config to Hass.io."""
nonlocal last_timezone
new_timezone = str(hass.config.time_zone)
if new_timezone == last_timezone:
return
last_timezone = new_timezone
await hassio.update_hass_timezone(new_timezone)
hass.bus.async_listen(EVENT_CORE_CONFIG_UPDATE, push_config)
await push_config(None)
async def async_service_handler(service: ServiceCall) -> None:
"""Handle service calls for Hass.io."""
api_endpoint = MAP_SERVICE_API[service.service]
data = service.data.copy()
addon = data.pop(ATTR_ADDON, None)
slug = data.pop(ATTR_SLUG, None)
payload = None
# Pass data to Hass.io API
if service.service == SERVICE_ADDON_STDIN:
payload = data[ATTR_INPUT]
elif api_endpoint.pass_data:
payload = data
# Call API
try:
await hassio.send_command(
api_endpoint.command.format(addon=addon, slug=slug),
payload=payload,
timeout=api_endpoint.timeout,
)
except HassioAPIError:
# The exceptions are logged properly in hassio.send_command
pass
for service, settings in MAP_SERVICE_API.items():
hass.services.async_register(
DOMAIN, service, async_service_handler, schema=settings.schema
)
async def update_addon_stats(slug):
"""Update single addon stats."""
stats = await hassio.get_addon_stats(slug)
return (slug, stats)
async def update_info_data(now):
"""Update last available supervisor information."""
try:
(
hass.data[DATA_INFO],
hass.data[DATA_HOST_INFO],
hass.data[DATA_STORE],
hass.data[DATA_CORE_INFO],
hass.data[DATA_SUPERVISOR_INFO],
hass.data[DATA_OS_INFO],
) = await asyncio.gather(
hassio.get_info(),
hassio.get_host_info(),
hassio.get_store(),
hassio.get_core_info(),
hassio.get_supervisor_info(),
hassio.get_os_info(),
)
addons = [
addon
for addon in hass.data[DATA_SUPERVISOR_INFO].get("addons", [])
if addon[ATTR_STATE] == ATTR_STARTED
]
stats_data = await asyncio.gather(
*[update_addon_stats(addon[ATTR_SLUG]) for addon in addons]
)
hass.data[DATA_ADDONS_STATS] = dict(stats_data)
if ADDONS_COORDINATOR in hass.data:
await hass.data[ADDONS_COORDINATOR].async_refresh()
except HassioAPIError as err:
_LOGGER.warning("Can't read Supervisor data: %s", err)
hass.helpers.event.async_track_point_in_utc_time(
update_info_data, utcnow() + HASSIO_UPDATE_INTERVAL
)
# Fetch data
await update_info_data(None)
async def async_handle_core_service(call: ServiceCall) -> None:
"""Service handler for handling core services."""
if call.service in SHUTDOWN_SERVICES and recorder.async_migration_in_progress(
hass
):
_LOGGER.error(
"The system cannot %s while a database upgrade is in progress",
call.service,
)
raise HomeAssistantError(
f"The system cannot {call.service} "
"while a database upgrade is in progress."
)
if call.service == SERVICE_HOMEASSISTANT_STOP:
await hassio.stop_homeassistant()
return
errors = await conf_util.async_check_ha_config_file(hass)
if errors:
_LOGGER.error(
"The system cannot %s because the configuration is not valid: %s",
call.service,
errors,
)
persistent_notification.async_create(
hass,
"Config error. See [the logs](/config/logs) for details.",
"Config validating",
f"{HASS_DOMAIN}.check_config",
)
raise HomeAssistantError(
f"The system cannot {call.service} "
f"because the configuration is not valid: {errors}"
)
if call.service == SERVICE_HOMEASSISTANT_RESTART:
await hassio.restart_homeassistant()
# Mock core services
for service in (
SERVICE_HOMEASSISTANT_STOP,
SERVICE_HOMEASSISTANT_RESTART,
SERVICE_CHECK_CONFIG,
):
hass.services.async_register(HASS_DOMAIN, service, async_handle_core_service)
# Init discovery Hass.io feature
async_setup_discovery_view(hass, hassio)
# Init auth Hass.io feature
async_setup_auth_view(hass, user)
# Init ingress Hass.io feature
async_setup_ingress_view(hass, host)
# Init add-on ingress panels
await async_setup_addon_panel(hass, hassio)
hass.async_create_task(
hass.config_entries.flow.async_init(DOMAIN, context={"source": "system"})
)
return True | [
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pyqt/examples | 843bb982917cecb2350b5f6d7f42c9b7fb142ec1 | src/pyqt-official/desktop/systray/systray_rc.py | python | qInitResources | () | [] | def qInitResources():
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ray-project/ray | 703c1610348615dcb8c2d141a0c46675084660f5 | python/ray/_private/services.py | python | _find_gcs_address_or_die | () | return gcs_addresses.pop() | Find one GCS address unambiguously, or raise an error.
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"""Find one GCS address unambiguously, or raise an error.
Callers outside of this module should use get_ray_address_to_use_or_die()
"""
gcs_addresses = _find_address_from_flag("--gcs-address")
if len(gcs_addresses) > 1:
raise ConnectionError(
f"Found multiple active Ray instances: {gcs_addresses}. "
"Please specify the one to connect to by setting `--address` flag "
"or `RAY_ADDRESS` environment variable.")
sys.exit(1)
elif not gcs_addresses:
raise ConnectionError(
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llSourcell/AI_Artist | 3038c06c2e389b9c919c881c9a169efe2fd7810e | lib/python2.7/site-packages/pip/_vendor/packaging/specifiers.py | python | Specifier.prereleases | (self, value) | [] | def prereleases(self, value):
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TencentCloud/tencentcloud-sdk-python | 3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2 | tencentcloud/ft/v20200304/models.py | python | QueryFaceMorphJobResponse.__init__ | (self) | r"""
:param JobStatus: 当前任务状态：排队中、处理中、处理失败或者处理完成
:type JobStatus: str
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:type JobStatusCode: int
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:type RequestId: str | r"""
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buke/GreenOdoo | 3d8c55d426fb41fdb3f2f5a1533cfe05983ba1df | runtime/python/lib/python2.7/imputil.py | python | ImportManager._reload_hook | (self, module) | Python calls this hook to reload a module. | Python calls this hook to reload a module. | [
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ckan/ckan | b3b01218ad88ed3fb914b51018abe8b07b07bff3 | ckanext/datastore/backend/postgres.py | python | _programming_error_summary | (pe) | return message.split(u') ', 1)[-1] | u'''
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n1nj4sec/pupy | a5d766ea81fdfe3bc2c38c9bdaf10e9b75af3b39 | pupy/library_patches/Crypto/Util/_raw_api.py | python | load_pycryptodome_raw_lib | (name, cdecl) | Load a shared library and return a handle to it.
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basename = '/'.join(name.split('.'))
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filename = basename + ext
return CDLL(filename)
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attempts.append("Trying '%s': %s" % (filename, str(exp)))
raise OSError("Crypto: Cannot load native module '%s': %s (%s)" % (name, ", ".join(attempts), exp)) | [
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wrobstory/sticky | 0cc9ea8ce433eecdf6292d5610dd1e4cba4874ca | sticky/c3.py | python | C3.width | (self, width) | return self | Chart width: integer | Chart width: integer | [
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hakril/PythonForWindows | 61e027a678d5b87aa64fcf8a37a6661a86236589 | windows/winobject/event_log.py | python | EvtEvent.time_created | (self) | return self.value("Event/System/TimeCreated/@SystemTime") | The creation time of the Event | The creation time of the Event | [
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cloudera/hue | 23f02102d4547c17c32bd5ea0eb24e9eadd657a4 | desktop/core/ext-py/pycryptodomex-3.9.7/lib/Cryptodome/Cipher/AES.py | python | new | (key, mode, *args, **kwargs) | return _create_cipher(sys.modules[__name__], key, mode, *args, **kwargs) | Create a new AES cipher.
:param key:
The secret key to use in the symmetric cipher.
It must be 16, 24 or 32 bytes long (respectively for *AES-128*,
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For ``MODE_SIV`` only, it doubles to 32, 48, or 64 bytes.
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The chaining mode to use for encryption or decryption.
If in doubt, use ``MODE_EAX``.
:type mode: One of the supported ``MODE_*`` constants
:Keyword Arguments:
* **iv** (*bytes*, *bytearray*, *memoryview*) --
(Only applicable for ``MODE_CBC``, ``MODE_CFB``, ``MODE_OFB``,
and ``MODE_OPENPGP`` modes).
The initialization vector to use for encryption or decryption.
For ``MODE_CBC``, ``MODE_CFB``, and ``MODE_OFB`` it must be 16 bytes long.
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If not provided, a random byte string is generated (you must then
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* **nonce** (*bytes*, *bytearray*, *memoryview*) --
(Only applicable for ``MODE_CCM``, ``MODE_EAX``, ``MODE_GCM``,
``MODE_SIV``, ``MODE_OCB``, and ``MODE_CTR``).
A value that must never be reused for any other encryption done
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For ``MODE_EAX``, ``MODE_GCM`` and ``MODE_SIV`` there are no
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For ``MODE_CCM``, its length must be in the range **[7..13]**.
Bear in mind that with CCM there is a trade-off between nonce
length and maximum message size. Recommendation: **11** bytes.
For ``MODE_OCB``, its length must be in the range **[1..15]**
(recommended: **15**).
For ``MODE_CTR``, its length must be in the range **[0..15]**
(recommended: **8**).
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then no nonce is being used, which renders the encryption
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read its value with the :attr:`nonce` attribute).
* **segment_size** (*integer*) --
(Only ``MODE_CFB``).The number of **bits** the plaintext and ciphertext
are segmented in. It must be a multiple of 8.
If not specified, it will be assumed to be 8.
* **mac_len** : (*integer*) --
(Only ``MODE_EAX``, ``MODE_GCM``, ``MODE_OCB``, ``MODE_CCM``)
Length of the authentication tag, in bytes.
It must be even and in the range **[4..16]**.
The recommended value (and the default, if not specified) is **16**.
* **msg_len** : (*integer*) --
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If not specified, ``encrypt`` must be called with the entire message.
Similarly, ``decrypt`` can only be called once.
* **assoc_len** : (*integer*) --
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If not specified, all associated data is buffered internally,
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* **use_aesni** : (*boolean*) --
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The initialization vector to use for encryption or decryption.
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A value that must never be reused for any other encryption done
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For ``MODE_EAX``, ``MODE_GCM`` and ``MODE_SIV`` there are no
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For ``MODE_CCM``, its length must be in the range **[7..13]**.
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For ``MODE_OCB``, its length must be in the range **[1..15]**
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For ``MODE_CTR``, its length must be in the range **[0..15]**
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For ``MODE_SIV``, the nonce is optional, if it is not specified,
then no nonce is being used, which renders the encryption
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If not provided, for modes other than ``MODE_SIV```, a random
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* **segment_size** (*integer*) --
(Only ``MODE_CFB``).The number of **bits** the plaintext and ciphertext
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If not specified, it will be assumed to be 8.
* **mac_len** : (*integer*) --
(Only ``MODE_EAX``, ``MODE_GCM``, ``MODE_OCB``, ``MODE_CCM``)
Length of the authentication tag, in bytes.
It must be even and in the range **[4..16]**.
The recommended value (and the default, if not specified) is **16**.
* **msg_len** : (*integer*) --
(Only ``MODE_CCM``). Length of the message to (de)cipher.
If not specified, ``encrypt`` must be called with the entire message.
Similarly, ``decrypt`` can only be called once.
* **assoc_len** : (*integer*) --
(Only ``MODE_CCM``). Length of the associated data.
If not specified, all associated data is buffered internally,
which may represent a problem for very large messages.
* **initial_value** : (*integer* or *bytes/bytearray/memoryview*) --
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The initial value for the counter. If not present, the cipher will
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The counter number is encoded in big endian mode.
* **counter** : (*object*) --
Instance of ``Cryptodome.Util.Counter``, which allows full customization
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and ``initial_value``.
* **use_aesni** : (*boolean*) --
Use Intel AES-NI hardware extensions (default: use if available).
:Return: an AES object, of the applicable mode.
"""
kwargs["add_aes_modes"] = True
return _create_cipher(sys.modules[__name__], key, mode, *args, **kwargs) | [
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microsoft/nni | 31f11f51249660930824e888af0d4e022823285c | nni/algorithms/hpo/networkmorphism_tuner/graph.py | python | Graph.extract_descriptor | (self) | return ret | Extract the the description of the Graph as an instance of NetworkDescriptor. | Extract the the description of the Graph as an instance of NetworkDescriptor. | [
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for index, u in enumerate(main_chain):
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copied_layer.weights = None
ret.add_layer(deepcopy(copied_layer))
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skip_type = None
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if is_layer(self.layer_list[temp_layer_id], "Add"):
skip_type = NetworkDescriptor.ADD_CONNECT
temp_u = temp_v
temp_v, temp_layer_id = self.adj_list[temp_v][0]
ret.add_skip_connection(
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elif index_in_main_chain[v] - index_in_main_chain[u] != 1:
skip_type = None
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skip_type = NetworkDescriptor.CONCAT_CONNECT
if is_layer(self.layer_list[layer_id], "Add"):
skip_type = NetworkDescriptor.ADD_CONNECT
ret.add_skip_connection(
index_in_main_chain[u], index_in_main_chain[v], skip_type
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/astropy-4.0-py3.7-macosx-10.9-x86_64.egg/astropy/io/votable/tree.py | python | Table.format | (self) | return self._format | [*required*] The serialization format of the table. Must be
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"""
[*required*] The serialization format of the table. Must be
one of:
'tabledata' (TABLEDATA_), 'binary' (BINARY_), 'binary2' (BINARY2_)
'fits' (FITS_).
Note that the 'fits' format, since it requires an external
file, can not be written out. Any file read in with 'fits'
format will be read out, by default, in 'tabledata' format.
See :ref:`votable-serialization`.
"""
return self._format | [
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] | https://github.com/holzschu/Carnets/blob/44effb10ddfc6aa5c8b0687582a724ba82c6b547/Library/lib/python3.7/site-packages/astropy-4.0-py3.7-macosx-10.9-x86_64.egg/astropy/io/votable/tree.py#L2227-L2241 | |
khanhnamle1994/natural-language-processing | 01d450d5ac002b0156ef4cf93a07cb508c1bcdc5 | assignment1/.env/lib/python2.7/site-packages/scipy/signal/filter_design.py | python | band_stop_obj | (wp, ind, passb, stopb, gpass, gstop, type) | return n | Band Stop Objective Function for order minimization.
Returns the non-integer order for an analog band stop filter.
Parameters
----------
wp : scalar
Edge of passband `passb`.
ind : int, {0, 1}
Index specifying which `passb` edge to vary (0 or 1).
passb : ndarray
Two element sequence of fixed passband edges.
stopb : ndarray
Two element sequence of fixed stopband edges.
gstop : float
Amount of attenuation in stopband in dB.
gpass : float
Amount of ripple in the passband in dB.
type : {'butter', 'cheby', 'ellip'}
Type of filter.
Returns
-------
n : scalar
Filter order (possibly non-integer). | Band Stop Objective Function for order minimization. | [
"Band",
"Stop",
"Objective",
"Function",
"for",
"order",
"minimization",
"."
] | def band_stop_obj(wp, ind, passb, stopb, gpass, gstop, type):
"""
Band Stop Objective Function for order minimization.
Returns the non-integer order for an analog band stop filter.
Parameters
----------
wp : scalar
Edge of passband `passb`.
ind : int, {0, 1}
Index specifying which `passb` edge to vary (0 or 1).
passb : ndarray
Two element sequence of fixed passband edges.
stopb : ndarray
Two element sequence of fixed stopband edges.
gstop : float
Amount of attenuation in stopband in dB.
gpass : float
Amount of ripple in the passband in dB.
type : {'butter', 'cheby', 'ellip'}
Type of filter.
Returns
-------
n : scalar
Filter order (possibly non-integer).
"""
passbC = passb.copy()
passbC[ind] = wp
nat = (stopb * (passbC[0] - passbC[1]) /
(stopb ** 2 - passbC[0] * passbC[1]))
nat = min(abs(nat))
if type == 'butter':
GSTOP = 10 ** (0.1 * abs(gstop))
GPASS = 10 ** (0.1 * abs(gpass))
n = (log10((GSTOP - 1.0) / (GPASS - 1.0)) / (2 * log10(nat)))
elif type == 'cheby':
GSTOP = 10 ** (0.1 * abs(gstop))
GPASS = 10 ** (0.1 * abs(gpass))
n = arccosh(sqrt((GSTOP - 1.0) / (GPASS - 1.0))) / arccosh(nat)
elif type == 'ellip':
GSTOP = 10 ** (0.1 * gstop)
GPASS = 10 ** (0.1 * gpass)
arg1 = sqrt((GPASS - 1.0) / (GSTOP - 1.0))
arg0 = 1.0 / nat
d0 = special.ellipk([arg0 ** 2, 1 - arg0 ** 2])
d1 = special.ellipk([arg1 ** 2, 1 - arg1 ** 2])
n = (d0[0] * d1[1] / (d0[1] * d1[0]))
else:
raise ValueError("Incorrect type: %s" % type)
return n | [
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PySimpleGUI/PySimpleGUI | 6c0d1fb54f493d45e90180b322fbbe70f7a5af3c | DemoPrograms/Demo_Matplotlib_Image_Elem_Spetrogram_Animated.py | python | draw_figure | (element, figure) | Draws the previously created "figure" in the supplied Image Element
:param element: an Image Element
:param figure: a Matplotlib figure
:return: The figure canvas | Draws the previously created "figure" in the supplied Image Element | [
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] | def draw_figure(element, figure):
"""
Draws the previously created "figure" in the supplied Image Element
:param element: an Image Element
:param figure: a Matplotlib figure
:return: The figure canvas
"""
plt.close('all') # erases previously drawn plots
canv = FigureCanvasAgg(figure)
buf = io.BytesIO()
canv.print_figure(buf, format='png')
if buf is not None:
buf.seek(0)
element.update(data=buf.read())
return canv
else:
return None | [
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JacquesLucke/animation_nodes | b1e3ace8dcb0a771fd882fc3ac4e490b009fa0d1 | animation_nodes/nodes/text/text_file_reader.py | python | TextFileReaderNode.create | (self) | [] | def create(self):
self.newInput("Text", "Path", "path", showFileChooser = True)
self.newInput("Text", "Encoding", "encoding", value = "ascii")
self.newOutput("Text", "Text", "text") | [
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facebookresearch/demucs | 7317db81a34349e028ec943b199c9b9cdda47a12 | demucs/wav.py | python | build_metadata | (path, sources, normalize=True, ext=EXT) | return meta | Build the metadata for `Wavset`.
Args:
path (str or Path): path to dataset.
sources (list[str]): list of sources to look for.
normalize (bool): if True, loads full track and store normalization
values based on the mixture file.
ext (str): extension of audio files (default is .wav). | Build the metadata for `Wavset`. | [
"Build",
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"metadata",
"for",
"Wavset",
"."
] | def build_metadata(path, sources, normalize=True, ext=EXT):
"""
Build the metadata for `Wavset`.
Args:
path (str or Path): path to dataset.
sources (list[str]): list of sources to look for.
normalize (bool): if True, loads full track and store normalization
values based on the mixture file.
ext (str): extension of audio files (default is .wav).
"""
meta = {}
path = Path(path)
pendings = []
from concurrent.futures import ThreadPoolExecutor
with ThreadPoolExecutor(8) as pool:
for root, folders, files in os.walk(path, followlinks=True):
root = Path(root)
if root.name.startswith('.') or folders or root == path:
continue
name = str(root.relative_to(path))
pendings.append((name, pool.submit(_track_metadata, root, sources, normalize, ext)))
# meta[name] = _track_metadata(root, sources, normalize, ext)
for name, pending in tqdm.tqdm(pendings, ncols=120):
meta[name] = pending.result()
return meta | [
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apple/ccs-calendarserver | 13c706b985fb728b9aab42dc0fef85aae21921c3 | calendarserver/tools/dashview.py | python | safeDivision | (value, total, factor=1) | return value * factor / total if total else 0 | [] | def safeDivision(value, total, factor=1):
return value * factor / total if total else 0 | [
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] | https://github.com/apple/ccs-calendarserver/blob/13c706b985fb728b9aab42dc0fef85aae21921c3/calendarserver/tools/dashview.py#L113-L114 | |||
TencentCloud/tencentcloud-sdk-python | 3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2 | tencentcloud/scf/v20180416/scf_client.py | python | ScfClient.GetAsyncEventStatus | (self, request) | 获取函数异步执行事件状态，事件状态保留 3 * 24 小时（从事件完成开始计时）。
:param request: Request instance for GetAsyncEventStatus.
:type request: :class:`tencentcloud.scf.v20180416.models.GetAsyncEventStatusRequest`
:rtype: :class:`tencentcloud.scf.v20180416.models.GetAsyncEventStatusResponse` | 获取函数异步执行事件状态，事件状态保留 3 * 24 小时（从事件完成开始计时）。 | [
"获取函数异步执行事件状态，事件状态保留",
"3",
"*",
"24",
"小时（从事件完成开始计时）。"
] | def GetAsyncEventStatus(self, request):
"""获取函数异步执行事件状态，事件状态保留 3 * 24 小时（从事件完成开始计时）。
:param request: Request instance for GetAsyncEventStatus.
:type request: :class:`tencentcloud.scf.v20180416.models.GetAsyncEventStatusRequest`
:rtype: :class:`tencentcloud.scf.v20180416.models.GetAsyncEventStatusResponse`
"""
try:
params = request._serialize()
body = self.call("GetAsyncEventStatus", params)
response = json.loads(body)
if "Error" not in response["Response"]:
model = models.GetAsyncEventStatusResponse()
model._deserialize(response["Response"])
return model
else:
code = response["Response"]["Error"]["Code"]
message = response["Response"]["Error"]["Message"]
reqid = response["Response"]["RequestId"]
raise TencentCloudSDKException(code, message, reqid)
except Exception as e:
if isinstance(e, TencentCloudSDKException):
raise
else:
raise TencentCloudSDKException(e.message, e.message) | [
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pyansys/pymapdl | c07291fc062b359abf0e92b95a92d753a95ef3d7 | ansys/mapdl/core/_commands/preproc/status.py | python | Status.areas | (self, **kwargs) | return self.run(command, **kwargs) | Specifies "Areas" as the subsequent status topic.
APDL Command: AREAS
Notes
-----
This is a status [STAT] topic command. Status topic commands are
generated by the GUI and will appear in the log file (Jobname.LOG) if
status is requested for some items under Utility Menu> List> Status.
This command will be immediately followed by a STAT command, which will
report the status for the specified topic.
If entered directly into the program, the STAT command should
immediately follow this command. | Specifies "Areas" as the subsequent status topic. | [
"Specifies",
"Areas",
"as",
"the",
"subsequent",
"status",
"topic",
"."
] | def areas(self, **kwargs):
"""Specifies "Areas" as the subsequent status topic.
APDL Command: AREAS
Notes
-----
This is a status [STAT] topic command. Status topic commands are
generated by the GUI and will appear in the log file (Jobname.LOG) if
status is requested for some items under Utility Menu> List> Status.
This command will be immediately followed by a STAT command, which will
report the status for the specified topic.
If entered directly into the program, the STAT command should
immediately follow this command.
"""
command = f"AREAS,"
return self.run(command, **kwargs) | [
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oilshell/oil | 94388e7d44a9ad879b12615f6203b38596b5a2d3 | Python-2.7.13/Lib/lib-tk/ttk.py | python | Widget.identify | (self, x, y) | return self.tk.call(self._w, "identify", x, y) | Returns the name of the element at position x, y, or the empty
string if the point does not lie within any element.
x and y are pixel coordinates relative to the widget. | Returns the name of the element at position x, y, or the empty
string if the point does not lie within any element. | [
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] | def identify(self, x, y):
"""Returns the name of the element at position x, y, or the empty
string if the point does not lie within any element.
x and y are pixel coordinates relative to the widget."""
return self.tk.call(self._w, "identify", x, y) | [
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ptone/jiffylab | c98e4ed260efe680bce2d671d92d4652dfdecd88 | webapp/app.py | python | slugify | (text, delim=u'-') | return unicode(delim.join(result)) | Generates a slightly worse ASCII-only slug. | Generates a slightly worse ASCII-only slug. | [
"Generates",
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"slightly",
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"ASCII",
"-",
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"slug",
"."
] | def slugify(text, delim=u'-'):
"""Generates a slightly worse ASCII-only slug."""
result = []
for word in _punct_re.split(text.lower()):
word = normalize('NFKD', word).encode('ascii', 'ignore')
if word:
result.append(word)
return unicode(delim.join(result)) | [
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pyrocko/pyrocko | b6baefb7540fb7fce6ed9b856ec0c413961a4320 | src/trace.py | python | Trace.mult | (self, other, interpolate=True) | Muliply with values of other trace ``(self *= other)``.
Multiply values of ``other`` trace to the values of ``self``, where it
intersects with ``other``. This method does not change the extent of
``self``. If ``interpolate`` is ``True`` (the default), the values of
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``self``. Linear interpolation is performed. In this case the sampling
rate of ``other`` must be equal to or lower than that of ``self``. If
``interpolate`` is ``False``, the sampling rates of the two traces must
match. | Muliply with values of other trace ``(self *= other)``. | [
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")",
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] | def mult(self, other, interpolate=True):
'''
Muliply with values of other trace ``(self *= other)``.
Multiply values of ``other`` trace to the values of ``self``, where it
intersects with ``other``. This method does not change the extent of
``self``. If ``interpolate`` is ``True`` (the default), the values of
``other`` to be multiplied are interpolated at sampling instants of
``self``. Linear interpolation is performed. In this case the sampling
rate of ``other`` must be equal to or lower than that of ``self``. If
``interpolate`` is ``False``, the sampling rates of the two traces must
match.
'''
if interpolate:
assert self.deltat <= other.deltat or \
same_sampling_rate(self, other)
other_xdata = other.get_xdata()
xdata = self.get_xdata()
self.ydata *= num.interp(
xdata, other_xdata, other.ydata, left=0., right=0.)
else:
assert self.deltat == other.deltat
ibeg1 = int(round((other.tmin-self.tmin)/self.deltat))
ibeg2 = int(round((self.tmin-other.tmin)/self.deltat))
iend1 = int(round((other.tmax-self.tmin)/self.deltat))+1
iend2 = int(round((self.tmax-other.tmin)/self.deltat))+1
ibeg1 = self.index_clip(ibeg1)
iend1 = self.index_clip(iend1)
ibeg2 = self.index_clip(ibeg2)
iend2 = self.index_clip(iend2)
self.ydata[ibeg1:iend1] *= other.ydata[ibeg2:iend2] | [
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hzy46/Deep-Learning-21-Examples | 15c2d9edccad090cd67b033f24a43c544e5cba3e | chapter_5/research/object_detection/core/preprocessor.py | python | random_adjust_contrast | (image, min_delta=0.8, max_delta=1.25) | Randomly adjusts contrast.
Makes sure the output image is still between 0 and 1.
Args:
image: rank 3 float32 tensor contains 1 image -> [height, width, channels]
with pixel values varying between [0, 1].
min_delta: see max_delta.
max_delta: how much to change the contrast. Contrast will change with a
value between min_delta and max_delta. This value will be
multiplied to the current contrast of the image.
Returns:
image: image which is the same shape as input image. | Randomly adjusts contrast. | [
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"."
] | def random_adjust_contrast(image, min_delta=0.8, max_delta=1.25):
"""Randomly adjusts contrast.
Makes sure the output image is still between 0 and 1.
Args:
image: rank 3 float32 tensor contains 1 image -> [height, width, channels]
with pixel values varying between [0, 1].
min_delta: see max_delta.
max_delta: how much to change the contrast. Contrast will change with a
value between min_delta and max_delta. This value will be
multiplied to the current contrast of the image.
Returns:
image: image which is the same shape as input image.
"""
with tf.name_scope('RandomAdjustContrast', values=[image]):
image = tf.image.random_contrast(image, min_delta, max_delta)
image = tf.clip_by_value(image, clip_value_min=0.0, clip_value_max=1.0)
return image | [
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DinoTools/dionaea | 4e459f1b672a5b4c1e8335c0bff1b93738019215 | modules/python/dionaea/__init__.py | python | SubTimer.cancel | (self) | Stop the timer if it hasn't finished yet. | Stop the timer if it hasn't finished yet. | [
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"""Stop the timer if it hasn't finished yet."""
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thu-ml/tianshou | a2d76d1276bef334bba537a355a5ea12f4279410 | tianshou/exploration/random.py | python | BaseNoise.__call__ | (self, size: Sequence[int]) | Generate new noise. | Generate new noise. | [
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"."
] | def __call__(self, size: Sequence[int]) -> np.ndarray:
"""Generate new noise."""
raise NotImplementedError | [
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] | https://github.com/thu-ml/tianshou/blob/a2d76d1276bef334bba537a355a5ea12f4279410/tianshou/exploration/random.py#L18-L20 | ||
tensorflow/tfx | b4a6b83269815ed12ba9df9e9154c7376fef2ea0 | tfx/types/artifact.py | python | Artifact.__getattr__ | (self, name: str) | Custom __getattr__ to allow access to artifact properties. | Custom __getattr__ to allow access to artifact properties. | [
"Custom",
"__getattr__",
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"artifact",
"properties",
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] | def __getattr__(self, name: str) -> Any:
"""Custom __getattr__ to allow access to artifact properties."""
if name == '_artifact_type':
# Prevent infinite recursion when used with copy.deepcopy().
raise AttributeError()
if name not in self._artifact_type.properties:
raise AttributeError('Artifact has no property %r.' % name)
property_mlmd_type = self._artifact_type.properties[name]
if property_mlmd_type == metadata_store_pb2.STRING:
if name not in self._artifact.properties:
# Avoid populating empty property protobuf with the [] operator.
return ''
return self._artifact.properties[name].string_value
elif property_mlmd_type == metadata_store_pb2.INT:
if name not in self._artifact.properties:
# Avoid populating empty property protobuf with the [] operator.
return 0
return self._artifact.properties[name].int_value
elif property_mlmd_type == metadata_store_pb2.DOUBLE:
if name not in self._artifact.properties:
# Avoid populating empty property protobuf with the [] operator.
return 0.0
return self._artifact.properties[name].double_value
elif property_mlmd_type == metadata_store_pb2.STRUCT:
if name not in self._artifact.properties:
# Avoid populating empty property protobuf with the [] operator.
return None
if name in self._cached_json_value_properties:
return self._cached_json_value_properties[name]
value = _decode_struct_value(self._artifact.properties[name].struct_value)
# We must cache the decoded lists or dictionaries returned here so that
# if their recursive contents are modified, the Metadata proto message
# can be updated to reflect this.
if isinstance(value, (dict, list)):
self._cached_json_value_properties[name] = value
return value
else:
raise Exception('Unknown MLMD type %r for property %r.' %
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mrJean1/PyGeodesy | 7da5ca71aa3edb7bc49e219e0b8190686e1a7965 | pygeodesy/latlonBase.py | python | LatLonBase.lat | (self, lat) | Set the latitude.
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@arg lat: New latitude (C{str[N|S]} or C{degrees}).
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'''
lat = Lat(lat) # parseDMS(lat, suffix=_NS_, clip=90)
self._update(lat != self._lat)
self._lat = lat | [
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RasaHQ/rasa | 54823b68c1297849ba7ae841a4246193cd1223a1 | rasa/nlu/featurizers/featurizer.py | python | Featurizer.get_default_config | () | return {FEATURIZER_CLASS_ALIAS: None} | Returns the component's default config. | Returns the component's default config. | [
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openhatch/oh-mainline | ce29352a034e1223141dcc2f317030bbc3359a51 | vendor/packages/Jinja2/jinja2/ext.py | python | InternationalizationExtension.parse | (self, parser) | Parse a translatable tag. | Parse a translatable tag. | [
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plural_expr = None
plural_expr_assignment = None
variables = {}
while parser.stream.current.type != 'block_end':
if variables:
parser.stream.expect('comma')
# skip colon for python compatibility
if parser.stream.skip_if('colon'):
break
name = parser.stream.expect('name')
if name.value in variables:
parser.fail('translatable variable %r defined twice.' %
name.value, name.lineno,
exc=TemplateAssertionError)
# expressions
if parser.stream.current.type == 'assign':
next(parser.stream)
variables[name.value] = var = parser.parse_expression()
else:
variables[name.value] = var = nodes.Name(name.value, 'load')
if plural_expr is None:
if isinstance(var, nodes.Call):
plural_expr = nodes.Name('_trans', 'load')
variables[name.value] = plural_expr
plural_expr_assignment = nodes.Assign(
nodes.Name('_trans', 'store'), var)
else:
plural_expr = var
num_called_num = name.value == 'num'
parser.stream.expect('block_end')
plural = plural_names = None
have_plural = False
referenced = set()
# now parse until endtrans or pluralize
singular_names, singular = self._parse_block(parser, True)
if singular_names:
referenced.update(singular_names)
if plural_expr is None:
plural_expr = nodes.Name(singular_names[0], 'load')
num_called_num = singular_names[0] == 'num'
# if we have a pluralize block, we parse that too
if parser.stream.current.test('name:pluralize'):
have_plural = True
next(parser.stream)
if parser.stream.current.type != 'block_end':
name = parser.stream.expect('name')
if name.value not in variables:
parser.fail('unknown variable %r for pluralization' %
name.value, name.lineno,
exc=TemplateAssertionError)
plural_expr = variables[name.value]
num_called_num = name.value == 'num'
parser.stream.expect('block_end')
plural_names, plural = self._parse_block(parser, False)
next(parser.stream)
referenced.update(plural_names)
else:
next(parser.stream)
# register free names as simple name expressions
for var in referenced:
if var not in variables:
variables[var] = nodes.Name(var, 'load')
if not have_plural:
plural_expr = None
elif plural_expr is None:
parser.fail('pluralize without variables', lineno)
node = self._make_node(singular, plural, variables, plural_expr,
bool(referenced),
num_called_num and have_plural)
node.set_lineno(lineno)
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fedora-infra/anitya | cc01878ac023790646a76eb4cbef45d639e2372c | anitya/db/models.py | python | Run.last_entry | (cls, session) | return query.first() | Return the last log about the cron run. | Return the last log about the cron run. | [
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huggingface/transformers | 623b4f7c63f60cce917677ee704d6c93ee960b4b | src/transformers/generation_utils.py | python | GenerationMixin._update_model_kwargs_for_generation | (
outputs: ModelOutput, model_kwargs: Dict[str, Any], is_encoder_decoder: bool = False
) | return model_kwargs | [] | def _update_model_kwargs_for_generation(
outputs: ModelOutput, model_kwargs: Dict[str, Any], is_encoder_decoder: bool = False
) -> Dict[str, Any]:
# update past
if "past_key_values" in outputs:
model_kwargs["past"] = outputs.past_key_values
elif "mems" in outputs:
model_kwargs["past"] = outputs.mems
elif "past_buckets_states" in outputs:
model_kwargs["past"] = outputs.past_buckets_states
else:
model_kwargs["past"] = None
# update token_type_ids with last value
if "token_type_ids" in model_kwargs:
token_type_ids = model_kwargs["token_type_ids"]
model_kwargs["token_type_ids"] = torch.cat([token_type_ids, token_type_ids[:, -1].unsqueeze(-1)], dim=-1)
# update attention mask
if not is_encoder_decoder:
if "attention_mask" in model_kwargs:
attention_mask = model_kwargs["attention_mask"]
model_kwargs["attention_mask"] = torch.cat(
[attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1
)
return model_kwargs | [
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oilshell/oil | 94388e7d44a9ad879b12615f6203b38596b5a2d3 | Python-2.7.13/Lib/bdb.py | python | Bdb.get_file_breaks | (self, filename) | [] | def get_file_breaks(self, filename):
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django/django | 0a17666045de6739ae1c2ac695041823d5f827f7 | django/contrib/admin/widgets.py | python | AutocompleteMixin.build_attrs | (self, base_attrs, extra_attrs=None) | return attrs | Set select2's AJAX attributes.
Attributes can be set using the html5 data attribute.
Nested attributes require a double dash as per
https://select2.org/configuration/data-attributes#nested-subkey-options | Set select2's AJAX attributes. | [
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"""
Set select2's AJAX attributes.
Attributes can be set using the html5 data attribute.
Nested attributes require a double dash as per
https://select2.org/configuration/data-attributes#nested-subkey-options
"""
attrs = super().build_attrs(base_attrs, extra_attrs=extra_attrs)
attrs.setdefault('class', '')
attrs.update({
'data-ajax--cache': 'true',
'data-ajax--delay': 250,
'data-ajax--type': 'GET',
'data-ajax--url': self.get_url(),
'data-app-label': self.field.model._meta.app_label,
'data-model-name': self.field.model._meta.model_name,
'data-field-name': self.field.name,
'data-theme': 'admin-autocomplete',
'data-allow-clear': json.dumps(not self.is_required),
'data-placeholder': '', # Allows clearing of the input.
'lang': self.i18n_name,
'class': attrs['class'] + (' ' if attrs['class'] else '') + 'admin-autocomplete',
})
return attrs | [
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odlgroup/odl | 0b088df8dc4621c68b9414c3deff9127f4c4f11d | odl/operator/pspace_ops.py | python | DiagonalOperator.derivative | (self, point) | return DiagonalOperator(*derivs,
domain=self.domain, range=self.range) | Derivative of this operator.
For example, if A and B are operators
[[A, 0],
[0, B]]
The derivative is given by:
[[A', 0],
[0, B']]
This is only well defined if each sub-operator has a derivative
Parameters
----------
point : `element-like` in ``domain``
The point in which the derivative should be taken.
Returns
-------
derivative : `DiagonalOperator`
The derivative operator
See Also
--------
ProductSpaceOperator.derivative | Derivative of this operator. | [
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] | def derivative(self, point):
"""Derivative of this operator.
For example, if A and B are operators
[[A, 0],
[0, B]]
The derivative is given by:
[[A', 0],
[0, B']]
This is only well defined if each sub-operator has a derivative
Parameters
----------
point : `element-like` in ``domain``
The point in which the derivative should be taken.
Returns
-------
derivative : `DiagonalOperator`
The derivative operator
See Also
--------
ProductSpaceOperator.derivative
"""
point = self.domain.element(point)
derivs = [op.derivative(p) for op, p in zip(self.operators, point)]
return DiagonalOperator(*derivs,
domain=self.domain, range=self.range) | [
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openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | openshift/installer/vendored/openshift-ansible-3.9.14-1/roles/lib_vendored_deps/library/oc_adm_router.py | python | DeploymentConfig.get_replicas | (self) | return self.get(DeploymentConfig.replicas_path) | return replicas setting | return replicas setting | [
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''' return replicas setting '''
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tristandeleu/pytorch-meta | d55d89ebd47f340180267106bde3e4b723f23762 | torchmeta/datasets/utils.py | python | download_file_from_google_drive | (file_id, root, filename=None, md5=None) | Download a Google Drive file from and place it in root.
Args:
file_id (str): id of file to be downloaded
root (str): Directory to place downloaded file in
filename (str, optional): Name to save the file under. If None, use the id of the file.
md5 (str, optional): MD5 checksum of the download. If None, do not check | Download a Google Drive file from and place it in root. | [
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] | def download_file_from_google_drive(file_id, root, filename=None, md5=None):
"""Download a Google Drive file from and place it in root.
Args:
file_id (str): id of file to be downloaded
root (str): Directory to place downloaded file in
filename (str, optional): Name to save the file under. If None, use the id of the file.
md5 (str, optional): MD5 checksum of the download. If None, do not check
"""
# Based on https://stackoverflow.com/questions/38511444/python-download-files-from-google-drive-using-url
import requests
url = "https://docs.google.com/uc?export=download"
root = os.path.expanduser(root)
if not filename:
filename = file_id
fpath = os.path.join(root, filename)
os.makedirs(root, exist_ok=True)
if os.path.isfile(fpath) and check_integrity(fpath, md5):
print('Using downloaded and verified file: ' + fpath)
else:
session = requests.Session()
response = session.get(url, params={'id': file_id}, stream=True)
token = _get_confirm_token(response)
if token:
params = {'id': file_id, 'confirm': token}
response = session.get(url, params=params, stream=True)
if _quota_exceeded(response):
msg = (
f"The daily quota of the file {filename} is exceeded and it "
f"can't be downloaded. This is a limitation of Google Drive "
f"and can only be overcome by trying again later."
)
raise RuntimeError(msg)
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pypr/pysph | 9cb9a859934939307c65a25cbf73e4ecc83fea4a | pysph/tools/ipy_viewer.py | python | Viewer1D._configure_plot | (self) | Set attributes for plotting. | Set attributes for plotting. | [
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self._scatters_ax = {'host': temp}
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onnx/onnx-coreml | 141fc33d7217674ea8bda36494fa8089a543a3f3 | onnx_coreml/_operators_nd.py | python | _convert_acos | (builder, node, graph, err) | convert to CoreML Acos Layer:
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'''
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https://github.com/apple/coremltools/blob/655b3be5cc0d42c3c4fa49f0f0e4a93a26b3e492/mlmodel/format/NeuralNetwork.proto#L3793
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load_input_constants(builder, node, graph, err)
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name=node.name,
input_name=node.inputs[0],
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dongrixinyu/JioNLP | 2c5b11439915891f0f24955b7de4f637f38a4b44 | jionlp/rule/extractor.py | python | Extractor.extract_email | (self, text, detail=False) | 提取文本中的 E-mail
Args:
text(str): 字符串文本
detail(bool): 是否携带 offset （E-mail 在文本中的位置信息）
Returns:
list: email列表 | 提取文本中的 E-mail | [
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] | def extract_email(self, text, detail=False):
""" 提取文本中的 E-mail
Args:
text(str): 字符串文本
detail(bool): 是否携带 offset （E-mail 在文本中的位置信息）
Returns:
list: email列表
"""
if self.email_pattern is None:
self.email_pattern = re.compile(EMAIL_PATTERN)
text = ''.join(['#', text, '#'])
results = self._extract_base(self.email_pattern, text,
with_offset=detail)
if not detail:
return results
else:
if self.email_domain_pattern is None:
self.email_domain_pattern = re.compile(EMAIL_DOMAIN_PATTERN)
detail_results = list()
for item in results:
domain_name = self.email_domain_pattern.search(
item['text']).group(1)
item.update({'domain_name': domain_name})
detail_results.append(item)
return detail_results | [
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orendv/learning_to_sample | 99e977e1c53ec0fa8b2b8a5151a56d0d088f6f78 | reconstruction/src/in_out.py | python | PointCloudDataSet.__init__ | (self, point_clouds, noise=None, labels=None, copy=True, init_shuffle=True) | Construct a DataSet.
Args:
init_shuffle, shuffle data before first epoch has been reached.
Output:
original_pclouds, labels, (None or Feed) # TODO Rename | Construct a DataSet.
Args:
init_shuffle, shuffle data before first epoch has been reached.
Output:
original_pclouds, labels, (None or Feed) # TODO Rename | [
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'''Construct a DataSet.
Args:
init_shuffle, shuffle data before first epoch has been reached.
Output:
original_pclouds, labels, (None or Feed) # TODO Rename
'''
self.num_examples = point_clouds.shape[0]
self.n_points = point_clouds.shape[1]
if labels is not None:
assert point_clouds.shape[0] == labels.shape[0], ('points.shape: %s labels.shape: %s' % (point_clouds.shape, labels.shape))
if copy:
self.labels = labels.copy()
else:
self.labels = labels
else:
self.labels = np.ones(self.num_examples, dtype=np.int8)
if noise is not None:
assert (type(noise) is np.ndarray)
if copy:
self.noisy_point_clouds = noise.copy()
else:
self.noisy_point_clouds = noise
else:
self.noisy_point_clouds = None
if copy:
self.point_clouds = point_clouds.copy()
else:
self.point_clouds = point_clouds
self.epochs_completed = 0
self._index_in_epoch = 0
if init_shuffle:
self.shuffle_data() | [
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IBM/pytorchpipe | 9cb17271666061cb19fe24197ecd5e4c8d32c5da | ptp/configuration/config_registry.py | python | ConfigRegistry.del_config_params | (self, keypath: list) | Removes an entry from the `config` parameter dict of the current :py:class:`ConfigRegistry`, \
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The entry can either be a subtree or a leaf of the `config` parameter dict.
:param keypath: list of keys to subtree / leaf in the `config` parameter dict.
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"""
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The entry can either be a subtree or a leaf of the `config` parameter dict.
:param keypath: list of keys to subtree / leaf in the `config` parameter dict.
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self.delete_subtree(self._superseding_config_params, keypath)
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python-babel/flask-babel | ec7ae9ed2e22c7aebd4e732c1c3dc6d45fe8db76 | flask_babel/__init__.py | python | format_timedelta | (datetime_or_timedelta, granularity='second',
add_direction=False, threshold=0.85) | return dates.format_timedelta(
datetime_or_timedelta,
granularity,
threshold=threshold,
add_direction=add_direction,
locale=get_locale()
) | Format the elapsed time from the given date to now or the given
timedelta.
This function is also available in the template context as filter
named `timedeltaformat`. | Format the elapsed time from the given date to now or the given
timedelta. | [
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"""Format the elapsed time from the given date to now or the given
timedelta.
This function is also available in the template context as filter
named `timedeltaformat`.
"""
if isinstance(datetime_or_timedelta, datetime):
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devbisme/KiPart | 3e5d9ae927ea27fb29998ee8d613fdc26a019243 | kipart/kipart.py | python | do_bundling | (pin_data, bundle, fuzzy_match) | Handle bundling for power pins. Unbundle everything else. | Handle bundling for power pins. Unbundle everything else. | [
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for name, pins in list(side.items()):
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del side[name] | [
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gnuradio/pybombs | 17044241bf835b93571026b112f179f2db7448a4 | pybombs/packagers/port.py | python | ExternalPort.update | (self, pkgname) | update package with 'port upgrade' | update package with 'port upgrade' | [
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update package with 'port upgrade'
"""
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therne/dmn-tensorflow | c107d6c08e29cfad219bc1b9421f1bac56b72cc3 | utils/nn.py | python | bias | (name, dim, initial_value=0.0) | return tf.get_variable(name, dims, initializer=tf.constant_initializer(initial_value)) | Initializes bias parameter.
:param name: Variable name
:param dim: Tensor size (list or int)
:param initial_value: Initial bias term
:return: Variable | Initializes bias parameter.
:param name: Variable name
:param dim: Tensor size (list or int)
:param initial_value: Initial bias term
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:param initial_value: Initial bias term
:return: Variable
"""
dims = dim if isinstance(dim, list) else [dim]
return tf.get_variable(name, dims, initializer=tf.constant_initializer(initial_value)) | [
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reviewboard/reviewboard | 7395902e4c181bcd1d633f61105012ffb1d18e1b | reviewboard/datagrids/sidebar.py | python | Sidebar.__init__ | (self, item_classes, default_view_id=None, css_classes=[]) | Initialize the sidebar.
Args:
item_classes (list of type):
The list of :py:class:`BaseSidebarItem` subclasses to include
by default in the sidebar.
default_view_id (unicode, optional):
The default "view" of the datagrid to display. This corresponds
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css_classes (list of unicode):
The list of additional CSS classes to apply to the sidebar. | Initialize the sidebar. | [
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"""Initialize the sidebar.
Args:
item_classes (list of type):
The list of :py:class:`BaseSidebarItem` subclasses to include
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default_view_id (unicode, optional):
The default "view" of the datagrid to display. This corresponds
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"""
self._item_classes = []
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self.default_view_id = default_view_id
for item_cls in item_classes:
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/networkx/algorithms/operators/all.py | python | intersection_all | (graphs) | return R | Returns a new graph that contains only the edges that exist in
all graphs.
All supplied graphs must have the same node set.
Parameters
----------
graphs : list
List of NetworkX graphs
Returns
-------
R : A new graph with the same type as the first graph in list
Raises
------
ValueError
If `graphs` is an empty list.
Notes
-----
Attributes from the graph, nodes, and edges are not copied to the new
graph. | Returns a new graph that contains only the edges that exist in
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"""Returns a new graph that contains only the edges that exist in
all graphs.
All supplied graphs must have the same node set.
Parameters
----------
graphs : list
List of NetworkX graphs
Returns
-------
R : A new graph with the same type as the first graph in list
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Attributes from the graph, nodes, and edges are not copied to the new
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"""
if not graphs:
raise ValueError('cannot apply intersection_all to an empty list')
graphs = iter(graphs)
R = next(graphs)
for H in graphs:
R = nx.intersection(R, H)
return R | [
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jkkummerfeld/text2sql-data | 2905ab815b4893d99ea061a20fb55860ecb1f92e | systems/sequence-to-sequence/bin/tools/profile.py | python | param_analysis_options | (output_dir) | return "scope", options | Options for model parameter analysis | Options for model parameter analysis | [
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"""Options for model parameter analysis
"""
options = model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS.copy()
options["select"] = ["params", "bytes"]
options["order_by"] = "params"
options["account_type_regexes"] = ["Variable"]
if output_dir:
options["dump_to_file"] = os.path.join(output_dir, "params.txt")
return "scope", options | [
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sagemath/sage | f9b2db94f675ff16963ccdefba4f1a3393b3fe0d | src/sage/modular/modform/element.py | python | Newform._atkin_lehner_eigenvalue_from_qexp | (self, Q) | return l | Return the arithmetically-normalized `W_Q`-pseudoeigenvalue of
``self``, using a formula based on `q`-expansions (Theorem 2.1 of
[AL1978]_).
INPUT:
- ``self`` -- a newform `f`
- ``Q`` -- an integer exactly dividing the level of ``self``
.. NOTE::
This method assumes that the `Q`-th coefficient in the
`q`-expansion of ``self`` is non-zero.
TESTS::
sage: f = Newforms(Gamma0(18), 4)[0]; f
q + 2*q^2 + 4*q^4 - 6*q^5 + O(q^6)
sage: f._atkin_lehner_eigenvalue_from_qexp(2)
-2
sage: f._atkin_lehner_eigenvalue_from_qexp(9)
Traceback (most recent call last):
...
ValueError: a_Q must be nonzero
An example with odd weight::
sage: f = Newforms(Gamma1(15), 3, names='a')[2]; f
q + a2*q^2 + (-a2 - 2)*q^3 - q^4 - a2*q^5 + O(q^6)
sage: f._atkin_lehner_eigenvalue_from_qexp(5)
a2 | Return the arithmetically-normalized `W_Q`-pseudoeigenvalue of
``self``, using a formula based on `q`-expansions (Theorem 2.1 of
[AL1978]_). | [
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"""
Return the arithmetically-normalized `W_Q`-pseudoeigenvalue of
``self``, using a formula based on `q`-expansions (Theorem 2.1 of
[AL1978]_).
INPUT:
- ``self`` -- a newform `f`
- ``Q`` -- an integer exactly dividing the level of ``self``
.. NOTE::
This method assumes that the `Q`-th coefficient in the
`q`-expansion of ``self`` is non-zero.
TESTS::
sage: f = Newforms(Gamma0(18), 4)[0]; f
q + 2*q^2 + 4*q^4 - 6*q^5 + O(q^6)
sage: f._atkin_lehner_eigenvalue_from_qexp(2)
-2
sage: f._atkin_lehner_eigenvalue_from_qexp(9)
Traceback (most recent call last):
...
ValueError: a_Q must be nonzero
An example with odd weight::
sage: f = Newforms(Gamma1(15), 3, names='a')[2]; f
q + a2*q^2 + (-a2 - 2)*q^3 - q^4 - a2*q^5 + O(q^6)
sage: f._atkin_lehner_eigenvalue_from_qexp(5)
a2
"""
if Q == 1:
return ZZ(1)
a_Q = self[Q]
if not a_Q:
raise ValueError("a_Q must be nonzero")
l = ZZ(1)
M = self.character().conductor()
for p, e in Q.factor():
if p.divides(M): # principal series at p
l *= (p**(self.weight() - 2) / self[p])**e
else: # special at p
l *= -self[p]
return l | [
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indygreg/python-build-standalone | 1dfe9d4186e37bd57c544b09402690e968a225be | pythonbuild/utils.py | python | add_licenses_to_extension_entry | (entry) | Add licenses keys to a ``extensions`` entry for JSON distribution info. | Add licenses keys to a ``extensions`` entry for JSON distribution info. | [
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have_licenses = False
licenses = set()
license_paths = set()
license_public_domain = None
have_local_link = False
for link in entry["links"]:
name = link["name"]
if "path_static" in link or "path_dynamic" in link:
have_local_link = True
for key, value in DOWNLOADS.items():
if name not in value.get("library_names", []):
continue
# Don't add licenses annotations if they aren't defined. This leaves
# things as "unknown" to consumers.
if "licenses" not in value:
continue
have_licenses = True
licenses |= set(value["licenses"])
license_paths.add("licenses/%s" % value["license_file"])
license_public_domain = value.get("license_public_domain", False)
if have_local_link and not have_licenses:
raise Exception(
"missing license for local library for extension entry: %s" % entry
)
if not have_licenses:
return
entry["licenses"] = sorted(licenses)
entry["license_paths"] = sorted(license_paths)
entry["license_public_domain"] = license_public_domain | [
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oilshell/oil | 94388e7d44a9ad879b12615f6203b38596b5a2d3 | Python-2.7.13/Lib/macpath.py | python | abspath | (path) | return normpath(path) | Return an absolute path. | Return an absolute path. | [
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"""Return an absolute path."""
if not isabs(path):
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cwd = os.getcwd()
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GoogleCloudPlatform/gsutil | 5be882803e76608e2fd29cf8c504ccd1fe0a7746 | gslib/commands/autoclass.py | python | AutoclassCommand.RunCommand | (self) | Command entry point for the autoclass command. | Command entry point for the autoclass command. | [
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"""Command entry point for the autoclass command."""
action_subcommand = self.args[0]
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if action_subcommand == 'get' or action_subcommand == 'set':
metrics.LogCommandParams(sub_opts=self.sub_opts)
metrics.LogCommandParams(subcommands=[action_subcommand])
return self._autoclass()
else:
raise CommandException('Invalid subcommand "%s", use get|set instead.' %
action_subcommand) | [
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... | https://github.com/GoogleCloudPlatform/gsutil/blob/5be882803e76608e2fd29cf8c504ccd1fe0a7746/gslib/commands/autoclass.py#L186-L197 | ||
mesonbuild/meson | a22d0f9a0a787df70ce79b05d0c45de90a970048 | mesonbuild/environment.py | python | Environment.get_static_lib_dir | (self) | return self.get_libdir() | Install dir for the static library | Install dir for the static library | [
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] | def get_static_lib_dir(self) -> str:
"Install dir for the static library"
return self.get_libdir() | [
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] | https://github.com/mesonbuild/meson/blob/a22d0f9a0a787df70ce79b05d0c45de90a970048/mesonbuild/environment.py#L817-L819 | |
openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | ansible/roles/lib_statuspageio/library/statuspage_incident.py | python | StatusPageIncident.__init__ | (self,
api_key,
page_id,
name=None,
scheduled=None,
unresolved=None,
org_id=None,
incident_type='realtime',
status='investigating',
update_twitter=False,
message=None,
components=None,
scheduled_for=None,
scheduled_until=None,
scheduled_remind_prior=False,
scheduled_auto_in_progress=False,
scheduled_auto_completed=False,
verbose=False) | Constructor for OCVolume | Constructor for OCVolume | [
"Constructor",
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"OCVolume"
] | def __init__(self,
api_key,
page_id,
name=None,
scheduled=None,
unresolved=None,
org_id=None,
incident_type='realtime',
status='investigating',
update_twitter=False,
message=None,
components=None,
scheduled_for=None,
scheduled_until=None,
scheduled_remind_prior=False,
scheduled_auto_in_progress=False,
scheduled_auto_completed=False,
verbose=False):
''' Constructor for OCVolume '''
super(StatusPageIncident, self).__init__(api_key, page_id, org_id)
self.name = name
self.api_key = api_key
self.page_id = page_id
self.org_id = org_id
self.scheduled = scheduled
self.unresolved = unresolved
self.verbose = verbose
self.incidents = None
self.incident_type = incident_type
self.status = status
self.update_twitter = update_twitter
self.message = message
self.components = components
self.scheduled_for = scheduled_for
self.scheduled_until = scheduled_until
self.scheduled_remind_prior = scheduled_remind_prior
self.scheduled_auto_in_progress = scheduled_auto_in_progress
self.scheduled_auto_completed = scheduled_auto_completed
if self.components == None:
self.components = {}
self._params = None
self._incidents = None | [
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eth-brownie/brownie | 754bda9f0a294b2beb86453d5eca4ff769a877c8 | brownie/network/web3.py | python | Web3.genesis_hash | (self) | return self._genesis_hash | The genesis hash of the currently active network. | The genesis hash of the currently active network. | [
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"."
] | def genesis_hash(self) -> str:
"""The genesis hash of the currently active network."""
if self.provider is None:
raise ConnectionError("web3 is not currently connected")
if self._genesis_hash is None:
self._genesis_hash = self.eth.get_block(0)["hash"].hex()[2:]
return self._genesis_hash | [
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buke/GreenOdoo | 3d8c55d426fb41fdb3f2f5a1533cfe05983ba1df | runtime/python/lib/python2.7/mailbox.py | python | Mailbox.flush | (self) | Write any pending changes to the disk. | Write any pending changes to the disk. | [
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] | def flush(self):
"""Write any pending changes to the disk."""
raise NotImplementedError('Method must be implemented by subclass') | [
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